From b8bf4e25b45614b1134eedd87ced11ce81a02994 Mon Sep 17 00:00:00 2001 From: zengmmm00 <11811636@mail.sustech.edu.cn> Date: Sun, 27 Nov 2022 17:43:20 +0800 Subject: [PATCH 01/27] first code --- classification.py | 179 ++++++++++++++++++ config.py | 16 ++ dataset/KMNIST.py | 83 +++++++++ dataset/__pycache__/KMNIST.cpython-38.pyc | Bin 0 -> 2676 bytes nets/ResNet.py | 213 ++++++++++++++++++++++ utils/__init__.py | 0 utils/__pycache__/__init__.cpython-38.pyc | Bin 0 -> 163 bytes utils/evaluation.py | 50 +++++ utils/init.py | 16 ++ utils/logger.py | 86 +++++++++ 10 files changed, 643 insertions(+) create mode 100644 classification.py create mode 100644 config.py create mode 100644 dataset/KMNIST.py create mode 100644 dataset/__pycache__/KMNIST.cpython-38.pyc create mode 100644 nets/ResNet.py create mode 100644 utils/__init__.py create mode 100644 utils/__pycache__/__init__.cpython-38.pyc create mode 100644 utils/evaluation.py create mode 100644 utils/init.py create mode 100644 utils/logger.py diff --git a/classification.py b/classification.py new file mode 100644 index 0000000..4be771e --- /dev/null +++ b/classification.py @@ -0,0 +1,179 @@ +# encoding: utf-8 +""" +Training implementation +Author: Ming Zeng +Update time: 25/11/2022 +""" +import sys +import time +import argparse +import numpy as np +import torch +import torch.nn as nn +from torch.optim import lr_scheduler +import torch.optim as optim +import torch.backends.cudnn as cudnn +# self-defined +from dataset.KMNIST import get_train_dataloader, get_validation_dataloader, get_test_dataloader + +# from utils.Evaluation import compute_AUCs, compute_ROCCurve +from utils.BinaryFocalLoss import BinaryFocalLoss +from utils.init import * +from utils.logger import get_logger +from utils.evaluation import avg_accuracy, class_accuracy, visualize_val_accuracy, visualize_train_loss, \ + visualize_confusion_matrix +# from utils.Evaluation import count_bytes +from sklearn.metrics import recall_score, f1_score, classification_report, confusion_matrix, ConfusionMatrixDisplay + +from config import * + + +class Classification: + def __init__(self, model='ResNet-18', train_batch=64, test_batch=1000, epoch=30, ckpt_path='./models/', + class_num=10, log_img_path=''): + self.dataloader_train = get_train_dataloader(batch_size=train_batch, shuffle=True, num_workers=4) + self.dataloader_val = get_validation_dataloader(batch_size=train_batch, shuffle=False, num_workers=4) + self.dataloader_test = get_test_dataloader(batch_size=test_batch, shuffle=False, num_workers=4) + self.model = nn.DataParallel(get_model(model)).cuda() + torch.backends.cudnn.benchmark = True + self.criterion = nn.CrossEntropyLoss().cuda() + self.optimizer = optim.Adam(self.model.parameters(), lr=1e-4, betas=(0.9, 0.999), eps=1e-08, weight_decay=1e-5) + self.lr_scheduler_model = lr_scheduler.StepLR(self.optimizer, step_size=10, gamma=1) + self.max_epoch = epoch + self.loss_log = [] + self.accuracy_log = [] + self.ckpt_path = ckpt_path + model + '.pkl' + self.class_num = class_num + self.log_img_path = log_img_path + + def train_epoch(self): + self.model.train() # set model to training mode + train_loss = [] + with torch.autograd.enable_grad(): + for batch_idx, (img, lbl) in enumerate(self.dataloader_train): + image = torch.autograd.Variable(img).cuda() + label = torch.autograd.Variable(lbl).cuda() + self.optimizer.zero_grad() + output = self.model(image) # forward + if type(output) is tuple: + output = output[0] + loss_tensor = self.criterion.forward(output, label) + + loss_tensor.backward() + self.optimizer.step() ##update parameters + train_loss.append(loss_tensor.item()) + + return train_loss + + def test_epoch(self, dataloader=None): + self.model.eval() + if dataloader is None: + dataloader = self.dataloader_test + + gt = torch.FloatTensor().cuda() + pred = torch.FloatTensor().cuda() + + loss_test = [] + with torch.autograd.no_grad(): + for batch_idx, (img, lbl) in enumerate(dataloader): + # forward + image = torch.autograd.Variable(img).cuda() + label = torch.autograd.Variable(lbl).cuda() + output = self.model(image) + if type(output) is tuple: + output = output[0] + loss_tensor = self.criterion.forward(output, label) + loss_test.append(loss_tensor.item()) + _, pred_label = torch.max(output.data, 1) + + gt = torch.cat((gt, label.data), 0) + pred = torch.cat((pred, pred_label.data), 0) # todo + + return np.mean(loss_test), gt.cpu().numpy(), pred.cpu().numpy() + + def val_epoch(self): + loss, gt, pred = self.test_epoch(self.dataloader_val) + acc = avg_accuracy(gt, pred) + return loss, acc + + def train_model(self): + logger.info('********************begin training!********************') + accuracy_max = 0.0 + for epoch in range(self.max_epoch): + # train + train_loss = self.train_epoch() + train_loss = np.mean(train_loss) + self.loss_log.append(train_loss) + + logger.info("Eopch: %5d train loss = %.6f" % (epoch + 1, train_loss)) + self.lr_scheduler_model.step() + + # validation + val_loss, val_accuracy = self.val_epoch() + + logger.info("Eopch: %5d valuation loss = %.6f, ACC = %.6f" % (epoch + 1, val_loss, val_accuracy)) + self.accuracy_log.append(val_accuracy) + + # save checkpoint + if accuracy_max < val_accuracy: + accuracy_max = val_accuracy + torch.save(self.model.state_dict(), self.ckpt_path) # Saving torch.nn.DataParallel Models + logger.info(' Epoch: {} model has been already save!'.format(epoch + 1)) + + logger.info( + 'Training epoch: {} completed.'.format(epoch + 1)) + + visualize_train_loss(self.loss_log, logger, self.log_img_path) + visualize_val_accuracy(self.accuracy_log, logger, self.log_img_path) + logger.info('Train Loss:') + logger.info(','.join([str(x) for x in self.loss_log])) + logger.info('Validation Accuracy:') + logger.info(','.join([str(x) for x in self.accuracy_log])) + + def test_model(self): + if os.path.isfile(self.ckpt_path): + checkpoint = torch.load(self.ckpt_path) + self.model.load_state_dict(checkpoint) + logger.info("=> loaded model checkpoint: " + self.ckpt_path) + + logger.info('******* begin testing!*********') + + loss, gt, pred = self.test_epoch() + logger.info("Test Averaged Loss = %.6f" % (loss)) + test_acc = avg_accuracy(gt, pred) + logger.info("Test Averaged Accuracy = %.6f" % (test_acc)) + cm = visualize_confusion_matrix(gt, pred, logger, self.log_img_path) + + for i in range(self.class_num): + logger.info("Class: %5d Accuracy = %.6f" % (i, class_accuracy(cm, i))) + + +if __name__ == '__main__': + # command parameters + parser = argparse.ArgumentParser() + parser.add_argument('--model', type=str, default='ResMLP-12') + parser.add_argument('--gpu', type=str, default=config['CUDA_VISIBLE_DEVICES']) + parser.add_argument('--train_batch', type=int, default=64) + parser.add_argument('--test_batch', type=int, default=1000) + parser.add_argument('--epoch', type=int, default=30) + parser.add_argument('--train', type=int, default=0) + parser.add_argument('--test', type=int, default=1) + parser.add_argument('--class_num', type=int, default=None) + parser.add_argument('--ckpt_path', type=str, default='/userhome/cs2/mingzeng/codes/kmnist/models/') + + args = parser.parse_args() + # set log + logger = get_logger(config['LOG_PATH'] + args.model) + os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu + + if args.class_num is None: + class_num = config['N_CLASSES'] + else: + class_num = args.class_num + classification = Classification(model=args.model, train_batch=args.train_batch, test_batch=args.test_batch, + epoch=args.epoch, ckpt_path=args.ckpt_path, class_num=class_num, + log_img_path=config['LOG_PATH'] + args.model + '/') + if args.train == 1: + classification.train_model() + if args.test == 1: + classification.test_model() diff --git a/config.py b/config.py new file mode 100644 index 0000000..45bf3c1 --- /dev/null +++ b/config.py @@ -0,0 +1,16 @@ +import os + +PROJECT_PATH = '/userhome/cs2/mingzeng/codes/kmnist/' + +config = { + 'LOG_PATH': PROJECT_PATH + 'log/', + 'TRAIN_FILE': PROJECT_PATH + 'kmnist-train-imgs.npz', + 'TEST_FILE': PROJECT_PATH + 'kmnist-test-imgs.npz', + 'TRAIN_LABEL': PROJECT_PATH + 'kmnist-train-labels.npz', + 'TEST_LABEL': PROJECT_PATH + 'kmnist-test-labels.npz', + 'TRAIN_NUM': 54000, + 'CUDA_VISIBLE_DEVICES': "0", + 'TRAN_SIZE': 224, + 'TRAN_CROP': 224, + 'N_CLASSES': 10 +} diff --git a/dataset/KMNIST.py b/dataset/KMNIST.py new file mode 100644 index 0000000..599b71a --- /dev/null +++ b/dataset/KMNIST.py @@ -0,0 +1,83 @@ +import torch +from torch.utils.data import Dataset +from torch.utils.data import DataLoader +import torchvision.transforms as transforms +from PIL import Image +import os +import pandas as pd +import numpy as np +import time +import random +from sklearn.model_selection import train_test_split + +from config import * + +PATH_TO_TRAIN_FILE = config['TRAIN_FILE'] +PATH_TO_TEST_FILE = config['TEST_FILE'] +PATH_TO_TRAIN_LABEL_FILE = config['TRAIN_LABEL'] +PATH_TO_TEST_LABEL_FILE = config['TEST_LABEL'] +TRAIN_IMAGE_NUM = config['TRAIN_NUM'] + + +class MyDataset(Dataset): + def __init__(self, data, target, transform=None): + self.data = torch.from_numpy(data).float() + # print(self.data.shape) + self.data = self.data.unsqueeze(1) + self.data = torch.cat((self.data, self.data, self.data), 1) + # print("Now",self.data.shape) + self.target = torch.from_numpy(target).long() + self.transform = transform + + def __getitem__(self, index): + x = self.data[index] + y = self.target[index] + + if self.transform: + x = self.transform(x) + + return x, y + + def __len__(self): + return len(self.data) + + +transform = transforms.Compose([transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + transforms.Resize((config['TRAN_SIZE'], config['TRAN_SIZE']))]) + + +def get_train_dataloader(batch_size, shuffle, num_workers): + train_images = np.load(PATH_TO_TRAIN_FILE)['arr_0'][:TRAIN_IMAGE_NUM] + train_labels = np.load(PATH_TO_TRAIN_LABEL_FILE)['arr_0'][:TRAIN_IMAGE_NUM] + train_dataset = MyDataset(train_images, train_labels, transform) + train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers) + + return train_loader + + +def get_validation_dataloader(batch_size, shuffle, num_workers): + val_images = np.load(PATH_TO_TRAIN_FILE)['arr_0'][TRAIN_IMAGE_NUM:] + val_labels = np.load(PATH_TO_TRAIN_LABEL_FILE)['arr_0'][TRAIN_IMAGE_NUM:] + val_dataset = MyDataset(val_images, val_labels, transform) + val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers) + + return val_loader + + +def get_test_dataloader(batch_size, shuffle, num_workers): + test_images = np.load(PATH_TO_TEST_FILE)['arr_0'] + test_labels = np.load(PATH_TO_TEST_LABEL_FILE)['arr_0'] + test_dataset = MyDataset(test_images, test_labels, transform) + test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers) + + return test_loader + + +# if __name__ == "__main__": +# # split trainset\valset\testset +# +# # for debug +# dataloader_train = get_train_dataloader(batch_size=64) +# for batch_idx, (image, label) in enumerate(dataloader_train): +# print(label[0]) +# break diff --git a/dataset/__pycache__/KMNIST.cpython-38.pyc b/dataset/__pycache__/KMNIST.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..664731860da3b4059a012f8c848c57466e4cf7b0 GIT binary patch literal 2676 zcmb6a%W@M(aA#jytsls5Odv5}2yzmMLoTVJ5)5{TE4JdY&1EX7sYNrg7w^N&tN^>@ zlgW`PKEWLMhWyE#Qk6rBU&txlvyu^pRFbi$yQjN%)ZNpMhht-*1#4S9YySq+A9xsE z0UYkaRt;iV+~Op$C}KMysZDKyv;(w59Yd4EO+D%v+D-hlLMwfKKm+rAm4*h7&{2cO z=$OIdbR4jkTu5uQX1?*0i5*K!@XEYJCjke5rvO(0PXi7C&j20)Jo~`nqkQaz#m9tw z;?OzaonC}J&&T=rYa6Ibpm|{)?*rJeUz1(f^b=m=6E7@!nNRX5z*qP*p8@=-_>9l) z+pmaR5|{ZLzxa~S1#wj@oRAgkmxZ5gdJXn zX?16Hd#5BvO7{Blz14NlL4HYBOF`@5MjL;7csX#m3tQa;AS_C_MQt8%n>#NEb-2qt z@Z04+uK@Op2UT6&IPBZrYP;~^U?J_nz7Jb%0Vu3f(j%|Qsr}mOS8yHHdqpnWdphjMJY`uoJvh`}C&{Bi zyBxx)tKCdJ=?ZZqw9`gzl4rZxFCw`s3SEWRW~!6RwC-wGiKHV(;a*uoFah9=C8q%W z^+WTa5>hpfM7Ep8*=}dM4~98C=Waw!ELeT;DyrGhaXGM47Nn;<>P*pqIQloB}u_sB~KE+fEK z9s=mNtN=jkln)>vVKt~4UfuZ<7_ff-PA&j40E%#Hxy82E=xQ6EXt3}S{NA{}4g{7% z0T09BvA9(@zuNd{hU=c=x`hWx?-u@vbLP1qr+jU2W zkD{Wz$5aftUQv79PA3t11Zsjk%jG^~@0&o|*#VY7#P#In^3D%z=O^~AkLU{}y|%IZ zV3k3q&==oVGM&Wq850LnArhUfSL8K7@;ZV=0CcqMS}`^;H8>^FBatXNR?7OdaV9jK z@7x>cX-FvcP9IGd@;3ZJKMQBcEE$CYl3xJtzX!f>xbitJPPl%eIc1_9yM=2K z%rgn*f0$s#PjC?@gZvzTo;XX1!OYQf@3H zV(1A>(=;<8xKp;-Z?V&>uK@%Q`0_6xgTGNbum^PD)aGtE0rb?M8oD8w2JOEA5@LMb literal 0 HcmV?d00001 diff --git a/nets/ResNet.py b/nets/ResNet.py new file mode 100644 index 0000000..2e60f9a --- /dev/null +++ b/nets/ResNet.py @@ -0,0 +1,213 @@ +import torch.nn as nn +import torchvision +import torch +import math +import torch.utils.model_zoo as model_zoo + +# construct model +__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', + 'resnet152'] + + +model_urls = { + 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', + 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', + 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', + 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', + 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', +} + + +def conv3x3(in_planes, out_planes, stride=1): + "3x3 convolution with padding" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * 4) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class ResNet(nn.Module): + + def __init__(self, block, layers, num_classes=1000): + self.inplanes = 64 + super(ResNet, self).__init__() + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.avgpool = nn.AdaptiveAvgPool2d((1,1)) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + y = x + x = self.avgpool(x) + x = x.view(x.size(0), -1) + x = self.fc(x) + + return torch.sigmoid(x),y + +def resnet18(t_num_classes=46,pretrained=False, **kwargs): + """Constructs a ResNet-18 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) + if pretrained: + model.load_state_dict(model_zoo.load_url(model_urls['resnet18'])) + num_fc_ftr = model.fc.in_features # overwrite the fc layer + model.fc = torch.nn.Linear(num_fc_ftr, t_num_classes) + return model + + +def resnet34(pretrained=False, **kwargs): + """Constructs a ResNet-34 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) + if pretrained: + model.load_state_dict(model_zoo.load_url(model_urls['resnet34'])) + return model + + +def resnet50(t_num_classes=5, pretrained=False, **kwargs): + """Constructs a ResNet-50 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) + if pretrained: + model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])) + num_fc_ftr = model.fc.in_features #overwrite the fc layer + model.fc = torch.nn.Linear(num_fc_ftr, t_num_classes) + return model + + +def resnet101(pretrained=False, **kwargs): + """Constructs a ResNet-101 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) + if pretrained: + model.load_state_dict(model_zoo.load_url(model_urls['resnet101'])) + return model + + +def resnet152(pretrained=False, **kwargs): + """Constructs a ResNet-152 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) + if pretrained: + model.load_state_dict(model_zoo.load_url(model_urls['resnet152'])) + return model \ No newline at end of file diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/utils/__pycache__/__init__.cpython-38.pyc b/utils/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3160c2905e497135e9b81ae2ea89a795fb5742c9 GIT binary patch literal 163 zcmWIL<>g`kg4L=zNg(<$h(HF6K#l_t7qb9~6oz01O-8?!3`HPe1o6v5KeRZts93)$ zH7`9kGcR4=B|o_|H#M)MSU)*GCAC;TpfWilu_!m7C_gJT87P{ao0nN!qF-8)nNzGE fAD@|*SrQ+wS5SG2!zMRBr8Fni4rIe;AZ7pn3Y#dA literal 0 HcmV?d00001 diff --git a/utils/evaluation.py b/utils/evaluation.py new file mode 100644 index 0000000..3e81d5d --- /dev/null +++ b/utils/evaluation.py @@ -0,0 +1,50 @@ +import numpy as np +import matplotlib.pyplot as plt +from config import * +from sklearn.metrics import recall_score, f1_score, classification_report, confusion_matrix, ConfusionMatrixDisplay + + +def avg_accuracy(gt, pred): + correct_cnt = (pred == gt).sum() + acc = correct_cnt * 1.0 / pred.shape[0] + return acc + + +def class_accuracy(confusion_matrix, class_id): + """ + confusion matrix of multi-class classification + + class_id: id of a particular class + + """ + confusion_matrix = np.float64(confusion_matrix) + TP = confusion_matrix[class_id, class_id] + FN = np.sum(confusion_matrix[class_id]) - TP + FP = np.sum(confusion_matrix[:, class_id]) - TP + TN = np.sum(confusion_matrix) - TP - FN - FP + + accuracy = (TP + TN) / (TP + FP + FN + TN) + + return accuracy + + +def visualize_train_loss(train_loss,logger,log_img_path): + plt.plot(train_loss) + path =log_img_path+ 'train_loss.png' + plt.savefig(path) + logger.info('Train Loss Visualization is saved to ' + path) + +def visualize_val_accuracy(val_acc,logger,log_img_path): + plt.plot(val_acc) + path=log_img_path+ 'val_acc.png' + plt.savefig(path) + logger.info('Validation Accuracy Visualization is saved to ' + path) + +def visualize_confusion_matrix(gt,pred,logger,log_img_path): + cm = confusion_matrix(gt, pred) + disp = ConfusionMatrixDisplay(cm).plot() + path = log_img_path+ 'confusion_matrix.png' + plt.savefig(path) + logger.info('Confusion Matrix Visualization is saved to ' + path) + return cm + diff --git a/utils/init.py b/utils/init.py new file mode 100644 index 0000000..e7cd651 --- /dev/null +++ b/utils/init.py @@ -0,0 +1,16 @@ +from nets import ResNet +import timm +from config import * + +def get_model(model): + if model == 'ResNet-18': + return ResNet.resnet18(pretrained=True, t_num_classes=config['N_CLASSES']) # initialize model + elif model == 'ResNet-50': + return ResNet.resnet50(pretrained=True, t_num_classes=config['N_CLASSES']) # initialize model + elif model == 'ResMLP-12': + return timm.create_model('resmlp_12_224', pretrained=True, num_classes=config['N_CLASSES']) + elif model == 'ResMLP-24': + return timm.create_model('resmlp_24_224', pretrained=True, num_classes=config['N_CLASSES']) + else: + print('No required model') + return None diff --git a/utils/logger.py b/utils/logger.py new file mode 100644 index 0000000..d91753f --- /dev/null +++ b/utils/logger.py @@ -0,0 +1,86 @@ +import sys +import logging +import os + +def get_logger(exp_dir): + """ + creates logger instance. writing out info to file and to terminal. + :param exp_dir: experiment directory, where exec.log file is stored. + :return: logger instance. + """ + if not os.path.exists(exp_dir): + os.makedirs(exp_dir) + + logger = logging.getLogger('ChestXray_detection') + logger.setLevel(logging.DEBUG) + log_file = os.path.join(exp_dir, 'log.txt') + hdlr = logging.FileHandler(log_file) + print('Logging to {}'.format(log_file)) + logger.addHandler(hdlr) + logger.addHandler(ColorHandler()) + logger.propagate = False + return logger + +class _AnsiColorizer(object): + """ + A colorizer is an object that loosely wraps around a stream, allowing + callers to write text to the stream in a particular color. + + Colorizer classes must implement C{supported()} and C{write(text, color)}. + """ + _colors = dict(black=30, red=31, green=32, yellow=33, + blue=34, magenta=35, cyan=36, white=37, default=39) + + def __init__(self, stream): + self.stream = stream + + @classmethod + def supported(cls, stream=sys.stdout): + """ + A class method that returns True if the current platform supports + coloring terminal output using this method. Returns False otherwise. + """ + if not stream.isatty(): + return False # auto color only on TTYs + try: + import curses + except ImportError: + return False + else: + try: + try: + return curses.tigetnum("colors") > 2 + except curses.error: + curses.setupterm() + return curses.tigetnum("colors") > 2 + except: + raise + # guess false in case of error + return False + + def write(self, text, color): + """ + Write the given text to the stream in the given color. + + @param text: Text to be written to the stream. + + @param color: A string label for a color. e.g. 'red', 'white'. + """ + color = self._colors[color] + self.stream.write('\x1b[%sm%s\x1b[0m' % (color, text)) + + +class ColorHandler(logging.StreamHandler): + + def __init__(self, stream=sys.stdout): + super(ColorHandler, self).__init__(_AnsiColorizer(stream)) + + def emit(self, record): + msg_colors = { + logging.DEBUG: "green", + logging.INFO: "default", + logging.WARNING: "red", + logging.ERROR: "red" + } + color = msg_colors.get(record.levelno, "blue") + self.stream.write(record.msg + "\n", color) From 3c9f0c95eddb7a019c2d6d075a66e5ea3acfaa7f Mon Sep 17 00:00:00 2001 From: zengmmm00 <11811636@mail.sustech.edu.cn> Date: Mon, 28 Nov 2022 14:26:27 +0800 Subject: [PATCH 02/27] add x label in figure --- classification.py | 1 - utils/evaluation.py | 5 +++++ 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/classification.py b/classification.py index 4be771e..ffde21a 100644 --- a/classification.py +++ b/classification.py @@ -17,7 +17,6 @@ from dataset.KMNIST import get_train_dataloader, get_validation_dataloader, get_test_dataloader # from utils.Evaluation import compute_AUCs, compute_ROCCurve -from utils.BinaryFocalLoss import BinaryFocalLoss from utils.init import * from utils.logger import get_logger from utils.evaluation import avg_accuracy, class_accuracy, visualize_val_accuracy, visualize_train_loss, \ diff --git a/utils/evaluation.py b/utils/evaluation.py index 3e81d5d..cb39072 100644 --- a/utils/evaluation.py +++ b/utils/evaluation.py @@ -29,15 +29,19 @@ def class_accuracy(confusion_matrix, class_id): def visualize_train_loss(train_loss,logger,log_img_path): + plt.xlabel('Train Loss') plt.plot(train_loss) path =log_img_path+ 'train_loss.png' plt.savefig(path) + plt.clf() logger.info('Train Loss Visualization is saved to ' + path) def visualize_val_accuracy(val_acc,logger,log_img_path): + plt.xlabel('Validation Accuracy') plt.plot(val_acc) path=log_img_path+ 'val_acc.png' plt.savefig(path) + plt.clf() logger.info('Validation Accuracy Visualization is saved to ' + path) def visualize_confusion_matrix(gt,pred,logger,log_img_path): @@ -45,6 +49,7 @@ def visualize_confusion_matrix(gt,pred,logger,log_img_path): disp = ConfusionMatrixDisplay(cm).plot() path = log_img_path+ 'confusion_matrix.png' plt.savefig(path) + plt.clf() logger.info('Confusion Matrix Visualization is saved to ' + path) return cm From 0e4e2e44dc97b7fc8d23b19fedef2996efd3ee49 Mon Sep 17 00:00:00 2001 From: zengmmm00 <11811636@mail.sustech.edu.cn> Date: Mon, 28 Nov 2022 14:32:23 +0800 Subject: [PATCH 03/27] format & add log result --- classification.py | 11 +-- dataset/KMNIST.py | 3 - log/ResMLP-12/confusion_matrix.png | Bin 0 -> 39597 bytes log/ResMLP-12/log.txt | 118 +++++++++++++++++++++++++++++ log/ResMLP-12/train_loss.png | Bin 0 -> 19844 bytes log/ResMLP-12/val_acc.png | Bin 0 -> 29923 bytes log/ResNet-18/confusion_matrix.png | Bin 0 -> 34131 bytes log/ResNet-18/log.txt | 118 +++++++++++++++++++++++++++++ log/ResNet-18/train_loss.png | Bin 0 -> 16571 bytes log/ResNet-18/val_acc.png | Bin 0 -> 31267 bytes nets/ResNet.py | 12 +-- utils/evaluation.py | 15 ++-- utils/init.py | 1 + utils/logger.py | 2 + 14 files changed, 255 insertions(+), 25 deletions(-) create mode 100644 log/ResMLP-12/confusion_matrix.png create mode 100644 log/ResMLP-12/log.txt create mode 100644 log/ResMLP-12/train_loss.png create mode 100644 log/ResMLP-12/val_acc.png create mode 100644 log/ResNet-18/confusion_matrix.png create mode 100644 log/ResNet-18/log.txt create mode 100644 log/ResNet-18/train_loss.png create mode 100644 log/ResNet-18/val_acc.png diff --git a/classification.py b/classification.py index ffde21a..46d30f2 100644 --- a/classification.py +++ b/classification.py @@ -2,10 +2,8 @@ """ Training implementation Author: Ming Zeng -Update time: 25/11/2022 +Update time: 28/11/2022 """ -import sys -import time import argparse import numpy as np import torch @@ -13,17 +11,12 @@ from torch.optim import lr_scheduler import torch.optim as optim import torch.backends.cudnn as cudnn -# self-defined from dataset.KMNIST import get_train_dataloader, get_validation_dataloader, get_test_dataloader - -# from utils.Evaluation import compute_AUCs, compute_ROCCurve +# self-defined from utils.init import * from utils.logger import get_logger from utils.evaluation import avg_accuracy, class_accuracy, visualize_val_accuracy, visualize_train_loss, \ visualize_confusion_matrix -# from utils.Evaluation import count_bytes -from sklearn.metrics import recall_score, f1_score, classification_report, confusion_matrix, ConfusionMatrixDisplay - from config import * diff --git a/dataset/KMNIST.py b/dataset/KMNIST.py index 599b71a..6477f3e 100644 --- a/dataset/KMNIST.py +++ b/dataset/KMNIST.py @@ -22,10 +22,8 @@ class MyDataset(Dataset): def __init__(self, data, target, transform=None): self.data = torch.from_numpy(data).float() - # print(self.data.shape) self.data = self.data.unsqueeze(1) self.data = torch.cat((self.data, self.data, self.data), 1) - # print("Now",self.data.shape) self.target = torch.from_numpy(target).long() self.transform = transform @@ -72,7 +70,6 @@ def get_test_dataloader(batch_size, shuffle, num_workers): return test_loader - # if __name__ == "__main__": # # split trainset\valset\testset # diff --git a/log/ResMLP-12/confusion_matrix.png b/log/ResMLP-12/confusion_matrix.png new file mode 100644 index 0000000000000000000000000000000000000000..c35cfeb8100556206c13c5f064728fe376b03293 GIT binary patch literal 39597 zcmeFZg{(%lW;9`$v`Y% z`wPC$G!3H#gCJ?Py?YXJTc^LeECeLi^g--rm}di-E!7e-5Cx zvNd8Dps5)JPJ(PLtYQZOq3J+>U~>4fO+cV{Jdw{I<((7v7M$GW&TpEJrQ#tXaCe`07Tuy&KmvxipHvK@A@+yi z?*^{!WvKjM;b)(XZr;1DXL52^`1gTLqh}oI6!Gj54^9KM>BCrFXVqQCA_j~y8FVE zX*=JX@RJ33WFblQ`)R#CKk)tjnS5znvgzPvbjsLvcmF$UTO~Ul-lBU|8FgLKGC343 zuq65iL^9-MhgI|PVj+xdSC3r$`100{$v5B3GD_N-z&)sTNF~Ljt(G%-u*OzibzZHd zg6u?>RPAxI+W8IH zS02=1ApG}3OkiI*gg+Vn_%nvaDBYqmyfNeK}NTJ+ZQZ54G zqRmW=WR+^r+w=qDg`ww|btJMO@2kFprG~hR8JXI7HRICiHLe(DV|mol{odoJlPss( zNRXoQvgwB%W@qtNRTormd9u(ozG5I^u(`jZ@T*vZG-1g$QP|?LG9xZEQ_Dt7(7Lky z%pg3E-Fc*LUyyC(A2N`C1pDO0vhnyA?t1i3B8QHg^!J%m?5g{;_IwsC!#_?>@8L`@ z6AcOdF|=HYtYX-AQVw>>&`VUf@M5~SaVe`hB=puQ`C*i0V1265QJwcaBUzfs>J!V4 z{i$ivB+t;z(aU2VpVs`LP%5EucTT=C1aSY{P*Cz3w0?zPjvmtI<8zcK}iDX za?eb(V$Wz@Fj!OYw#eYlD3`Ctjg1m4!NW=BdsQsSSnnoAT`8K-G11@!Gc?52)@q_d zFufwV)OF&#)))Ega3mtpzHezvsgTL~B+UQ$($y(&1*TkiGgROYJ>9-J&g^dXEH*kx)Sva=u29}WoEg_fC{gUd$brK$ zisp5CKOh&|fptePYQ z1raRfAOiTmlEVQMU_3*Cs)~zK>(DsRzORitZLaZ2z zztMZp_yPCANS@q`LpTWU(PiAoZDm>?vm~lOwKAwwuZ!8^#{Ob&rft7!Y0%?xp=*0A zd-r}3!523D)3^l}Mv|W%NSWgQ&Gs1lE}BbYoyG z&M0YR#wa<{VRw>Nxy1YE$ol=m)tYNlRaRiQB3M-AT$9ozjvnXP(XEpw>7h;b>%OmP z>P8g?qK&tVe)w*fOlBP~fg13kwVa96f;UFq{YI6e(2_b+?6B3f>lcoANUV#0#{NMD zflavzW>O=<&q3)}GtCl)ej7;e=Y=_~P~dX{gbc%~dxA|uEA{)hh&8XLm}VX5A*uuc zm(`~iVgs@7Xyf4{+m4ZQbRXi=ov)@Ja1_czha!1VOtR9K^lB10b9HBjDIYGs5|bEZ z+YIqvzxz&;78OnW<6yHX`{H;t!KB}l+-FQ^zv+geJt~Y`n`U_1es8uiI7Z8J#@wY? zvthc-piiJGx1b=S>_q)Mt@`Q&d(`GIdHEjsCC@}IoTMnm$bN72T8q2n-jp`0;-&A- zd|em}Ngj8PP=^U;;oPm zDU%Pe5mm|j`%0^TZ!E*nGU8!L>%xNRy@trjY9;e=smtzqT}>;YBg#>|BPWT@`jXXJ zZRss}qf@*HoL*5d>9BU5=kL3k6sE#weqQ|<0^G-s9}W9_eSEt6`|G#fAtNLA$FuDQ zUA{z@5ovW2%As^htN$EmC5>g597;}Ek1nOfD{4ftQmR^R5#On3eUvPdar@3KEG}&| ze3CuB@O0w{XV#wn&BGjt5JJdSY{t@4eILAx*BVZ-butPsWR9srC)V zb2(72b%hqYXsWA=J3B8>5LR}6Q`O6;knTy6NaA7QeU7b!QOf=^(z9h8!$>j**<6*k zqlI6Od5ev8YVRET;Ky2ATy{;3(Mr_aDS=<$h~4P6qt(kuqM$c46A;3Y)SI{V_R0SE zhm<;fE^-d@&O*p)Q!|b`s5w6vT$25>WUvhT+TPN1Lt2s6#cXWY(23Mc##&xoT)C79 zAv)D!5LqPU1PCZZfGzppTnBA!qDEt_p8@suOH&h{Xl9vWB?C8ioua}KZItL&QHOQx z;01hMrnuL%wLT$orqPW=lFIh*m(PkMcZdk$TINX7P3AKF92Z2V8%BZ&@#_O0FBn1Z zzuW|0P&`E%dpR$zWah#zWA@BXFv;z4*_u$sqVgvubs=*@&)3fOY`aZg_{epHY+}3e!b>dzZoYac7>?J-725Ker^4g-(&4<*#}r->MiqntuN>{LKz~Yxzht zZT6?qBX}_9>O$*{xQ27${T%O=TRtNDIb~WO?^ki6`CjT{m}g#OU<>^EZb_$>Ijy1L zn6hD3rjix>Ikq>mf3~S5FZAv^ww`A$Z-V(A)+B%ne1;p{<5gb%I3CfOSWkn$*;Lc%FA;4Tj@y zn*F05+;2Z3xx^Lu2`C(Ox1aT)UTqOO+JP}XUZU023{Ep-drZkLJ``P4yxmp0J_2?w z?3vcGN$~Y;THg{b{-A%cMT~GWAI8Vf-cELDsos}t!J1DCl^n!YY}c`GE;NGT>hc`f zy@6f*_@I5~scvzA9S#pQ+~W&EldcJOCcY30e0Ng|J5F+s357zhMxfrOX>7{!cs$2k z=d^RK@;9&w7;kD)|DcT6M5Cy z9A=HKK+xUc&VR;Ga=Hi)Oc5iL-J*rovNt|y$PM~lkeZW&Bqh~%_MO+QP?+b%=g44n znj(3V!0?1wLlss<9W~(A?8#Hecit=QQm!z5sZr->aJ<^#e$qohz+vsZF_17-9NxcVmtYDWJZYftCVA#-B+@ zus%R>cT4Qf4B{}U2Z%6<)cem{q2=ViKy3^JIbMV>(^DE}e(?X`XcCl=bK_quZ*f!7 zBgwU)!}=yDNbNlp_lC}@U1ddgm(kQc{3fQ5KWF_ohuebi6I~WH82DF5Msb+~Qf1s; z@y>F_$TXLqYRLLJ1oh)IC+?HCJ8zMpV_ebRSwb9s@C*s`CzEJ~4(-MFkiXwqju+|I zY=X^hIdIqiGZsDGLwMSBZ^GpN^*s>(A=M~<&Kbhd^Lco+j1&eFI3qzwOGdMHUKfw| z`Lv=saR#z7^iu%x`S;*w=LGQPz=jzBT)W>R3L&QmImdxF|0&aqD)ldt@eci-Xeje= z&E?70U63$m+D6~?M*!ZLlzNzoMY&%}vY=1h$R_T7I<00VN^ptDLCplY^D!h^@ZCf@ z34Sfl5A1f#hjG4I5ifPd+ zb9OaF$mcsZ8Zncwz!Uo~Bt)X1>@Xl%YI9Q4h4uSrTE6}2vL)IBZx!b@#e(T1P(yjx zsJ>eAK0P`rdP^fQ5Som5+Jx1j82EDU@bql!>KRc&m;r>AZ!S)Vt8<3(?OY-YegrE_ zaIG-xwD2$e>Z8M}7lVniFvE7z?^$9Jb?u8^Wsv66l?@G%cZ5Y3tX#tj9V3q(Pwjvf z31*|&y%v0}zWrDhrVDNI^l3K4+M-TWzBR$I3#eQN^Es_-XCrElOusS?jtaTmdqLEw zV1E6^`)j%?^R1T`#!Dr|L!1dbI0`hA>7(y_#do{Tg;T^exxO=}39gvTmIqYt9)HQ1 zX^6?1g(PyM7~YE#Yu?j#^T_v(ScOAkv+3*5WPPVjtZVu+S#H>ij}@aOOc9 z%HGJ+b3{bfBjQwf^)R;}+wK*O7+K$hESece(V1*GIO@yeWQ7RO8<3zlU&nKepjhrV z{spX9U`ra^vjU7?26bio*jm9|NEg>GTn@?uef()=N%+qpgkMdA?F{*uWt+n5JN=&u zOjDV_LL&Gcu6%C~o05lzr7=<9`RD2-NRM(Y$OeO6l) z-+6Nxf5{2y^G%X6c(G9Wvw0(m=SUXsAjwsdtVYXpN{P%SoxN|-q*u#~!E=SK+lI-_ zeqW_~NsYH#yz&74^|6yw^uY6#;^c3=^%$ij%Ql83MBAGj|Hy!axJTP(A9c8Nhi*$pYxR(u+=;s% z_BHjh^RHbLUQnRcVc;vd#yMFk12BRar@zTc;2KgWrF@%6^ach7^VV(8Y1ONT zCsUVy>8IU#!^-241fb)Shu4O+q%<2#FQ+j-5^m?ci>NPm#0b*NUan&B9d_O^ncSF1 zhI9;cm_LT{Wx@~Qd@phM&Zoddq3T2h5uT4(+t=!#bHXaMRPdg-v?vBZL+P55)WzMl z&aSkzBNO^uW1>bvzaGG5eBZIpY@`()=M?G3U?zzfqLR9ft{=GH`B1}P?AKwwe3fc& znY{Qea75EjU`M%>CeT0)jrlxX?g@K-g@Z6#fz6<~u@Qw&(GWk!JLtu~J*W!>eulD` zK39yEE%S9*E8vbqb28n=AxRQfij(yf27ZZ^?6^D9ck4m|iTa&D`Q+GD0xz;BiJ*?$_ov?{Z7VJ}Q}WggkuO z9URxMW%u)?Q)OC4vttaESrQAr>OD}wk%6t`$*`n?qN1#d%K^5vjmAWEWJUAF4y^_@mGqaW?Oty|(WB!733&+*g7bst0%}0_w#`_~bLuidw!ytxzh&)Lm`KV<5 z1S1gz_E`ItREB5$7{83_N}PhL&fzNIHqi<=9B@#{O_gicU+^NXpWd&2Na!%whpzLq zW?_#TP%roc0hrX=k^0S>FDm7RY+G(S`H7}nti%i1sRjYIM9%L5F0=jEw>$??f^xJw zt3>2~a?8l1wR7hLeI-#tf8kl=nmvglBc%8O6PbyfNN;tYz0Abm^?g@Hq=V>ye`YG_ zU0FgFT_uRsN>1_wRm$_4$1fY*6m7QTCxkub1(DpxyV$K-CCewevr%gpDMl+pLZ;DF z;8cwm<{Q2^v8HZ(M!Z5d7^*=f-CcRkRX|QV3K?jgd~bxJBM||b z&CQj|X7;?hir)m#sb2v5ok&;5#7XAMKYXP?y61tf9+zi-oNS6M?n7~Y5#@Hp#G`_` zw6?7+vrfgyi%K`d&9Hi@wjTa?=y$lVywGmfpc{ymh;@n|Uo&ldU>x4*A(q!F2t63H zULzzk=d==ygz}bME;9)mUEN=16x)+5$y9B#5mAPkRJM*xfFRmx-?BMn45GND&Y1lf zoF9HPEedEdMmI_MI^Tf{2S5TSy4wFKn)xYHcA{Yw%XMe{nd8I8$3a}B`FNZyrj)RY z1<^Pr+Pszvn^X_{hL$HSBdTSRxhk9#KFgGJPCSVc0yP+XjY~iA-PYF#l?6<`ur#?7 zefpRcq;|6b`vN_kjPg@oL`=GiekWD@)z(bqU`z&I18&oqv~PRPBo5Md4kZI5)MC;+ z2~M)FqPBzb#9k5@a8;Ld+Q;~i+lgZ6A>$J~XsA;!Clu8Pv$3++Yr-JnBE2u!%MGyB zC4XX$Yg$SyF>breJOiJuHIF=ifFVV3T-QIp$+l*t53nuf-XltyPDEx~*C*!B14 z&pfD}0^`DOEFG-iz}AEAZh4P9-Vq=!E)ftu@*#*f@;%3QtCXY@bTSIGTwun67QB=# z&$1w&d#0@3QSixqt*c5(IPB9K{)L&SA&&Dr%tpQKmIRiH$~rZ$Dk%qFH1>G^A$~*C z2pa=~kFPt=Z=>I>mJQ25)aNZV4$~tg;Q_UA7>-&6gWf@&-9YS6Lp(~eztx?ylkRz7 zdTggFfv+r7Vnlp&8($H(&ZL&+Z-F^BoLWewM|v{I5h1HYU?S62!txHUS;Tn4U| zf?nshBVAm&oS^0E?gV5Xov}&(-0WXj!0TLGYBlw$3NG#7ltVI;jTN_T<1?eAqg$UY z)dSh@O^3UjZrE&somO{u#mWsGlC6V-gO5*6(h%XS6|0rG=4mbjcC;X||wY0SSEFi$-c3~c} zDKVqX92d)JN1mFRT3u5!k}iaXO2DCg@jfals{W|;>DBG++QEV3aUE;4{WhG0O+EOR|KRV-5P0jL@k9bKkYlPAtIe+2G#Y4NNu&~c|}LM#`0y z5()~7i$ne(9d_*=1IS>&)oB3L#PiS-ghhMb| zxVXA%j(i@DB$rqP=ESf&>#nnv#e2 z_4OUuw#UT8j8%XsFE8JoEL7uvD1QR@I>2pa&4Cap*Fdb4USCf@L2+vFQ`BI9l`$Of zOc4>wb=v&nryRHl6W&?mi5ze_?Dzr8WrhnE7nhWb47gokWdE{0WcW_kGZpcGi8--Y-WfY3hvU$mkmxVc_BQ z+70n`64X&j>0-6!B6O4?$LMUi*H4x^@3D;k+^YUB{hw{!Q9{8*!Lg|C@G)aOe~W zeb4@@f=U zLM1ixWUNflhf7PA2hB|}lcJ{*bJ53MEK;L){<5HphrK>68Xu8`CBHIQ?5w+rm@JDv zQ^=5*0@EoZf&OMu%F90XhsP-%>tcXV#>uZMxw{hDpB=~}Ap708J`Z1(ZzgI%F`-FJ zX!gI_SID0Or0dL$C#ErU^Ce~x-0Ssk@eG@Y;a7Fp3Tqt6k)!Q;Yb@2fE$;5H`Q1ad zI&JKp1cuPaB@4v(%tUnYkR6-3MLT2KgTmOS4X~lpV$)Ec3{{yVTpixaG`%X3(|C5 zGxj|=6CFiTo7?N`7MY$3X@s@|_(AKBlz z?|0DzOR3v~!A)+=R=S@DPN~7?x2g_@TFs~BgC*9gIEaqbW0$2oi&rP4ZEh8$6blnO z$J49o*1VmhpZO!642uG_@ap0AkkkE&5!fy5cPGDBExHCr)2XTIdj&SR0atR0Z-`zT zB*Ake{*HzZX`?vV1LSO^Zl`!KGXzHabvhOU^D1!r(^!B&9Iuy?I!mJ@*9UVqI*~d2 zM)cHt>(#^Ldz^=!H@aNdaKlFG)VGtjW!J&lHxgU}bZ~awJEBV+98@hVCB5b|jg^h_ z(+t>Ab)_x33TDs5JvWgKo6!5h!gCFa?=SRTfWGKpJ%=>qS9skV;*7Cf{SL7c*%*hl-0h-7>$IylK5+naLQrU?|Z5#A0r`Ls-($LNBKzag6nbK!xIMA7x2WDP z=`^PurQiTyAL@;lnJx{BCfGWO12zdo7GGf`7Qcu62939OFiuQmxi$^uQ~dd1Kj%u6mS z{JtrwzPj7QDWWEsWu`FX!)Vz~hwKucp9g%bcEh={ANv^Mzhk;TTJ>qBx^v2qNyB(k zQ0n0>8BN8c2v8}09U~h#n;9@OoLNXj4RSSg@p?R`1g9SP9D3>eV0r(=HNV~3C7KUg zo!2cy$UBpb2t%~Eaflx3{D z&vqro>fRiE?CZnTP|cwm5ULQmtSL9|^R%qV|JF3AS~Y7{tNk>O*ybBS$HI5(oFNR>12v3_)M)^*g_Pcv! zus)b;Pf$Dd#~VH7eR7z3X-~-0ne8G!LB+`iTeyD{`lWYEkEbf9>eiWiOj$HK8A+G0 z(eJ%crO-d%=g1MKn`b)%i}+z?{E#tj!68>W?&=XN>+Jo}=W(JbtXGX2MJ zcqz47(slHi?yrbvU*A?UlM4=sWY0ASh`Uny7l=KpFL@Hx`At+Wqh7dY1)%ke=i>@Z zU6TqQE9xkS(VG0t-p$nwB!pis8AHs~-I+}&Slv%epMH|A|s8&-M=Vj^@zd{x-_Xi{(%lD;APid@8lMpu01$`Q>N{5-1HGE2R5k0 z+$7%*;sEgkHu=K6uh^Q)UBq6$Lirw2OvbWe)TJ_IQ)M-7k%bDGUf|gj<-H)#6p>G` z6-5E}aIW>DCBrl8i@(~Yn8-+$Kkj}SCvOVAx<}kSkHN!89t5MkVm!?iuE>1&!12aSeA^UVwOSi#H%DoA)})P(hgX~1;cSZ_E-Wc6HRC3lJb|A$Ipi2 zzCR3bW^P~zBuFQl!E^>{n~E=u;#mhWdQ47g%}J^HJ_WNM%9OqkDChj(`i2Oku7JYTQB zAE@D6O-hbKZN4e?aB*^fqUD9@mp|Jb%b9(A#BA+c%q{x*+J<|Mf)UM@EG(^}@H|LsY&mn@2C%uesb!}CUWP9})qeWqSs_I8cc zp2PSN0w)>{qTYf5SqbdOMnS+;%0 zt`K&p7-Vcu!s2n;(x&eNgck7Cx`m4UPTMnBLIR~a=9+@)#j!nA4~Al%`V_w6558FM zzrUGZ+NhZt1p#IPF|oLrpgs-V<((GMK@!4AxyE$WgzwdejN<(wjQPz7A{sGVTc^Pa zehyHo<8ea;BP@J5K`rk#eQJT~uheFoXM|A_0_*6E61m-4)ZB^=n0x@Eq){$6{5~`9 zrKzb|T{FR(S5RGz!@$7c%JIBiMQ4t)y&vZ}94c;T@SV>+r=-YVEk6#kk31Cd_xEFa6E+ ze^`(ad)+Y!2nd{QjramKaeNoxbD9&z|AFp+1dUEr0YL1MSAQ~de}Z?mG#oP6zza}p zZmj=Lbno9sM1X>7g)vJ0H_`@jd*cHCpm$ix|1tW+$E~Qb<7y_4MTnu${*Q_H8~6X) z#{`H~PB4&T#7~F89JTf0WMFNY0LVz$K|e860$EHYgpwT;irc!a*k=f80k8dDJO>hr zzzJJh{;yBA2!x*hKaQdvGZo`RdXUn;2?mbLxY^sS8*`c>wExQwWk&k^pRWQXB7d0% zz{+K?x8a~!#(xDBfw%wX%buIVMn8e-C)3?$Zf|2glGq(RFL+C)GLWXkFETswS(gjH zZ>{FcgC{h_nh^A#d>$QS=tPzRFj{o_hBPrZI58F!`?{ybjAuSR{ct#pioI|!gS!p2 z{Z8++)BFKwX6;;sV1)h6GJpO1dY4R194)$&`lc+x_ue-U4NlJ%SHt&&Et8d+uV$tM zF1?`6VzNTRt&fJJ- z8^@>;hrT1J(FyWEtHHp8JQ4=j%u()l$6XaxOGHvqQarr8+q0E4e2;hK3VK_>hP-J6 zUdg|N!=1>77nxgd0TO>t{I(150(iUr=JK3Z8ei30Ydw8<$VUc@!Zep)l;C~42S01a z#}U6C;*655sW$r^&DK6&z3u^PnC1Ki5yQ~qQ^|Dc*y=(nnE^Kf@o`2EWmfP3<;s2X zkCPTDp{+-GRFtQlZ4*#(l5=hC8oTZ&+w~wtWWScK)XH$7Y4pZASHGU`V6jk&Pt;F{wxA=;;hwUuznz8gPUIe7t8Uz%E`yojzk5l@JbxZSsSr+s3e~ zIO&WiLaDrfaVoB@ZsGW1jOF4%8KKImul);RT^@p8v~mGTCLExBSv6k!mz6Qs0s>8xhwBZVG?x;^ z(@mOwo6b!il*me;B!gZSR?uDzF{vvE0_NABxEy}t4PuIkZiYiPvmz~0mdYMgJ=~f3 zT}wb)9h;xPq$DQ5{DOfzPSrW*)LlaODoQieF26d&H;OrXztySA=yq@n z8Wk^bkCXsmOlZ+ltnhmD1ipYM5bu_VXVn3=+X|DhjkH~W{{TK2Jq#s1y=54VIJW(s zG-Zj45t>^t)qlgo%CyLdcOOwsJK6QNzS{tvS$hS|<{;uz4oH(@rs;yH)`4pYNa?%= zPX8|3d3tk%#}%yy@pTqBx*>sAxoR{tJKI2+^kBDTNsR}`!OE=O{e1pXn11X%SBn^& zs?1L#F!{_WA{vLaqt5Lz!T<@)J4iL_0=2 z@S;imtWQ$#(RdQ%F8!w{SwD4gFA?hvlzPxg@iZgy5ejH!=1j;}d)09R;i7qN)*)I_nfsXL0zM?Td-a{#n4RXO- zeQ4?`J4Ty4-h6bYC7M%d(-JY7LtrQy(>S`AVmZsCWtDhle$`oZ@SUz32zdY5ax|U; zdoFZ~jS^3I_Z@1Q15sX4QBma@TQYohOa1-XO2uHE|7zvnC-v{qk^grm%6Yuun2nl7 zv5fSM-~2Odp8DtWv)N%l%B=ai{_RK>+k0(U>rFU#Y>&_{ZFV_%(oih}2t9nR%zeF* zzvd^XYzYz$#;>TqUpS8692&}#l$As;&*Yr*`lJdC%?9OjzMfHB3hQvp~v0Fml2dpoYXot^+6_yCBVrH$Ia_$g6C%Q z!+&N08WF=JkHmB>T!BWLr?kAtNJ!uS5II|AHT088ZfmA|6U#zNT|ETQuz7L3%hPNw zcWa3Xxyz>jC}o?=9Wou!I`iNe84pEJ(g}%>VzGa_Mm2TAGiCz2#aSui>TWa3 zq4fq((RgNQX7Z!Q#f%XU)cuW z{{7h4n9JFS(DCUhfBML9DnFv*{;Y$hIJx0qq6lDl0IC=ip28ub#x@8U7?3g4AHhC6 zJXF`!jWv0BjgF4)xN)=#{7r(z-jI1I@%dDzq<8~<5f(N!w&%mO5l~yY>kb=(tE!v^ zxa>9u4HoNNfTTzmnA-`RfIxgGiI<9slbr(Uys0#}ve76N=>v@SXLR&vu@>L63l5xJ zE((v|fjSumtA_JeK<^E$N?C@X%ID9Y2Obl|$zRbbHv{`3E{FANP+v412_@w>EE>h5 zdAlJQdHHsuWRFg4E%z4Q%Xzrwll4BJ8D|s7U#B`9E-K!$b8`LDs4@-;%|BiX<+B}= zU}rKOehmu;w>@1-lI(sWGO$Y_l|;(I5((&D6STk@w?AA2)2Tr~C1z9fd3kw2pUCs) zWc2h!hJ+*5|I9o;8w@Z33B2?Umw6Zzl1Il~#DJgCSL?9r{BXVL0ytOKcc()zKo9G1M!h8t2c9{m5z@XaYQFH&@qqUiv7|qU&xU0jF)dxe8#d4J5E5zGh~Q08a7F zd|LXnRx(=;Kxb?{8bCf4NR}|EQIL@MW@M0xfapH__3274z#lrl;|o<;E_@3O-CPME zHthOANUK>N+YyWnHzCoa*Y$(!{rjZpX=PnKy}-~=q4W_(`z>iueSN+2Mx0SBqYkX6 zr{~qp4YSSa=h;d#B^GLBR)RnDK!$;R$(Ci%+Ds?ocwCrO z3!`Py1!bhAKWq+yRhm3`iHL{*YcrXT*vQC;$n#34LY2;G$%9=ihF&07es(o^eHd8o z^T&s-(tp;pQyx(#G49*|&@ZOzsr!7JQj_w4u z>1!ZNH&bmxVtQVKJ8@XENu*e)YS7jVJ-CmNt(l*LbsNqZ7s%Pcd8OKnYv5#eC%1DQ~dWmxZYhNlYXFmZ=#X0}Ty}7}D>~vmy%Ue*mM1d%QpN zf?C@kHjyQYWcN=~MH&{ks$hgrp0>hpu8t-)fN+&Eon(_X;gERW@X!Ex_mG+LLEE4BZ|p+1Np5LC4i5X1F8}t&+5=P zvnQ%uwoE+KUOrx)P}Tc7upNebhz_vZz2$_AVKhj7oI8Mj2KIm*@}>@McYk9>kX;L4 zNS=4^ed~z&yC_P6?r5>!xvzBvUHVu|c9|Z<(pv7zeRdW%IInpl(`ID%NtnB+$ZgGk z`VlKJ!Ezrgm7F@$-X68Ko^K?d3`ykpWf^rf7__V2ns z4+fCq&TFjZKA<>g-<)lS5OAV5Xb!m@HV6UL8)}mRPHyhhEj1=e=MTa;>lV!vjZO52 zQ-RlO2oRl}wL)oXiT>+5Bx-j40WRO9zU3Ap=IgD>je3q0x@%7Qb}ySJ>`rY4i#02( z$D=HV0qHnrD26B%5*b)nZ`nx=w9|HR-gnIyLcq+X;uw za2fWlL8KnXRW|z&t+3&SKW{}%L)bO{8fCY=K(@oGeFV3MmiT&9@&&W)u}+y}p30T@ zZu~w?A6WUwSPc^e14Hq^sQ2=gQJ(X-Wz2#Rj}DJZyJ1Z}rX`(u`&Jp1co50RIrN=7 z9&soCkP*}JHbFwic%vEaqsq?Ev}dU}A^KNYleZ7~7>1h&Vzm)=k$^w-Aq`P%W0u^8 z55FxC&h&jEkzqN`j=&8lh)Wp$h#4rQomyV^9o&cY{Py;&yHmxRo%mMzyOV{0M}H8% z2<(zzXyLoqpJTVK*f8Xo8Qr_C?~y5(tqhoUG$7qSdLqtqW&etX@7a2>LnEl3n<=Q+ z@nB`}!R78uVL$88|Kcde+;*t?0Xdiz}gBojb+P#O-zO?kE-X6d(riKAUpFOW|wH60M?2148-tA|OCJYuCP z1fP_H|acRqbM=SS(7I0 z40dS1(v;0J=kspWITDnK;xX=_{<_e)h;V$D+Ez&BC!h#=F;SZcr2ooJ*@w6w6+})$ z<(mj|@{0^S?12cnvg40$2;$w>cC;CBlmRdVWNkjBN)H2jG{Q3gGoDTm*8JV|HOpg<2R+Gt;JL;=yR194C^rzX)G6O8Vf|0Trj?EI+v_VO7U@9Q;WxtUgh+x0K z$X@^2NJrbq;D7-)0uH)9yd6LaHcW=q>3j@PHsQA zwiG9-v)Kt~W8)x$7eCVY^n^U!C(XQ1L8;Isv8Wwr16euHSss%>WWs9}K^9v=F<8T! zb8j?bVba~ejeyie+O`)SOwg4)J{^)s(}@+I2)!|9AV?tfjtT$yvmHna0k|o{pl`=8 z_}4FeSXkIA0A3a6p;z0ki#i@I4h(|1i;eGldqt^fhRAe*e4`Tfor~a*YA57_JI@pUc3StzzukA_sXI9nfCVfc=9K2#wa&=Hq7{q#zcmNH5aIq7ja&c3l8!K0147K4j_dVSH#vgbYXLfob#4% zbKcgfi5kVW@U#>lh2&&2cg`6^a&~IOedlO(s{hj}r@>+^UT5nLdq#VAEj+-S$x^W^ zEBHuZw~g%rC#cs(zW?PVmiUb!U(~v!vDi;%M1|vG8M$JED0`d)b6Hc5nHf{Z=&k~k)T`?dbb^60yuvc7a<0Re;92nS(Qc;% z|BDWne@IV-3oOUZch=eqYQt9Fkza<@lMNr?>c(qCSYP16)9$M`(< z@o76XZC=El{VlFH5NRdlfs9gv*BA^A4bcSW4Hj)YTkFR%h?Gvx1HQbHFa>h*)ZfF) zK9M0l9Id40Qhc%N(Yg>}!%*7K4g@+|BKKF+?q^e*>M461R{WE$UjADpwk`YXVTA9bz7cT2bRs3@halvGZ=ekC|CoCQBQAg8ld?Y z_5tCpz`&Xl@<@P|CEVX$CeXR<cb6J z*fI_s(1?;az2DLsCo_M#hwGDWc1dE61mTc$faYS~Fc?OF4@5=F;-S7b{ur}#ub4A!1nbyru;ngHa6Qs@|Z4G^Hv;+Go@Z4_25)~;Tj z9UZ-XGM9*7yZ0>js^{|;hc0y@f)9lmJ__brpV&sp1Ps{ee?v2 zbABeFRiD)i!6V*1US_fP=A_Wp#FElD{jAzDK>G01vZ&MluQEj${tU8~+Ft(-;OT|s zwl0VbefO|+(UwISq$jtPQ@CqL<@yrmVI@?)PhK=D)mJnjaTV9KaP?4#)QbW>Hd7Tz z#+Kc_-owp*R5RZO)Or`)QCy%4V|8CkKr`zpAKm3PM8m-?RhBWZl5rOq-qK%=OT7f3swcN}*69`>o+GKsZ!=?P@gro63W2dVHu;xG1+-6U^Tef~qV)E2n_SExVTIHCa(n5v@k;IHUzS z(wNsyurGm!eH=0SKgU-*eI(>|`~{Feg7@yRh6C{{hlf^wsb3h-_XwRG`0s*k-`N+9 ze>Ytsz1a~!Z+;3y9v*_G!0c8@@FXvxb6oNaSY#zeLrFBMQmJ7tWr5fBB7IyhV9ZN| z{Td>k9R0pjh5XgDk&*!-uA7?Cqf=b~{nqX7tNMLII3_@wBKPIgpiPmHdV7Z)aeLN5 zsB%$@z|&JsC!DCUx$mHzk7LbHoAN*T#_u7(qf7oi7=;xuS-Ji%leHWC;j=~tta1QD zuw{)3SFYA3%KV0+7BQhb#T&d_mp^b{_ChTicZ+5b;%e*x81zP@q8 zV1R_ut*C%>DkZ51C);rhahUtcxLEf9F$6dHs`>p3m3Hxb`wGX_If4;EvG&`} z27?y|7TQYCRk=PN<|0zozfW%)TZgL&LfgJAj8K&yWb($n8=vfrdc9vnEa4%nvVcKF*!?F1;l$lyB<2G;#4J0f2wiHnWYvKt7}@x z(@Xs{o^gG3OgFuJCAZ2s)so}|mU=e+<9}z#^av`>%$C8{JH@(8+R+Zwt}uFjZG=wIwNB(J3pVH3@nZiay-83<_6fA?pr%hr`9@ za(_Io_Ds7TOB4RTm^v*NDYhIi@mQE5_T+3=^VLgCwS+7^?8C+#3$LUy0oxp5Jc@Rh^qrQK}lL2xjkkEUiJ9`{p^t6L6_O`kQbYdYTlcTn^GcZZfogE{rpc%QQNzPv4D+DB1dS0#;g2}2Z~Fk|EVS((8|IRALA zB{crx)eKtc>2jo9XN*#FfWCoE_dhvG1+P@f|mhj3yB92uy2I(?2<*Jfrs@i9#? zOcmQmst(1%+x)5b1XVUY2qTRx3VUoFxNS!77}B#iO#i?eyr#Jr*G=&HE#W}U?##)g zJCiud(ZBC_(#^F`D1mk?GR^-|t~!&%ZDW&0x0B8&X`^mz?K=L6RDm{$k0kVCOC2-n zP<~_lu<-We_+zmaN}qK%hSn?*Bjbg#u#YxdqhZ8M{Z+%>DYkbQzT{h6CniqUZOEa4 z*1^S*WbNkYt9o%ohatK7VUh%rhTjyTj>dAbtdbU3Qn+9I;!^^14v5&n1OImA`+YVI zyB*)l9`|%~CKRehp=5^vfmn)M$0u9EiOEU-7cVkg>(G#jkHcD3^XFi#$zFNPO9Q@L z!8a|CJ%3m|xT5O7Z1`gy`WA2^dic5v;o>+ux|n{K=XBbeCB07N<5=0(`Wf9K@!k8~ zB)67JpQN1kt}qrp*}tfi8dCCPLS&9dLgCM9`kljT6P7o z(|G5FThG%?zI=|?g2UtVl_cLbna=aD-8YL<36ub<+s|&47haDf*)2+yG>ldGgb4Q+ z-VWX4PpqZ8)lSW3aNV=@q4-eui`ShMgI0T1Yy4w8W;&SR(A0e_eZiUMs9lCy0b=11d5+t6AZ11>9yd4hrH{> zB=H9YL9S?eA9mM%rOsA4?y^rOMlY|YPmCP3X#L_xC*{c^5Zmc_aO&=Vit?<=r2q~M z*=wKyP@dBIysztagCUBC&#_j*n2I?k@WN>ui3fZ<_r_`|DQF3-Mn1NM#AKd9EAE=O ztWx8;RZPtyai_Dyrsa%BobaLTqHXN!`Nj_SMdrbe@oI~V6S;*##G}Gin)$RbeAqxU zV;;8gi}POH@yJ_1`_$^r@N{J)>j+0chQI9>A z%1WiT2}3Z$;5r(GP8y{@dN-c5T>{Gg#I@j zC&M}>S1yxg>OFsAYk@(_bvZ7*ceStFHQiQ6&DTBHy>o?4ExP-}D_)8BYBoqYu}2MI zt0`Q`B${F3_*p|&T|O`~m78{@>3SN`(R6s={_d4h8#+eGa3?gyJU2V$%JFcgefb@x zW(SVBQKhK4r@luidwV_&;fZz}uU)^lGGdJRfCdogF4s6eeq0P^c_)|oqf47hn>!Ho z3bk0nJ73iVn;T<`+BCw0KE{`0uiWS^ir<)fLvxaFio<-V73~Q~is8lC+}u2#93qs_ z>skiGO}Ie8l!7iDy)dsbc|%Hk(hO+b8!YSh2J1AZ8n^a{v0O;z&TieUOgc}L6SR&; z2Sz{!h&~-+J7YBhpWO`)E!g{hX@>~Y%DPZ9o`e0r%JhLs38}|Z^`N7$0G$HMjC7DS z;*!-c>`%Y0&Ls|ie+dY?xKkHUDgWRo^vth&BlBW?!u~Pfov(Lzuv_l7`+6xneExaj z7YxvRi0M`m;#dlzUgcZOH{XhM{7wEr6`RTIVodL1G@eLFli$J@3c|^^zbC>AgEzLx zC+L*yu8Ti@h~Bz|0*1l_h0NX7K?N0+=EOU`C1CGk+bukNu}V%j`% za#HHBwBkFw9lrl*#6?>F&l23fs0#!5RT}!&B<+ZbOxun;@`OW29$a&dr|`ueo?SDd ze4?$ezhv8jJ#q76Ggc_Zsbr?lf+^EgE3N1JGRCHF-=rtB+Utzm-_#~O#3MQ*s<=Go zt6fq`Hp_d3#A}F<$kk&xtvlAIWbbD)-*ltrsF95F;B_hsM&p+Vup$v}#-r47qoSgM zg38f5a4447nhG(>wMKDK^YcgZj5v9NlMw_v{%C%iyJ7pq&->fOS3ygHO%W|&kii73 zpUJK6o-*7WbcO3v+1^gOj~<3CA?GoWqQ`$8n~OCEElT{&jS5eEO(6wBlG2xM)u9p( z?0&3U&q>R;9))%jpjWLOSc9=P@VsGNZ&8_%oI+SAq9^dPqAD;E1cn69Bc^`KOmtu`etV;0DO&7# zk4Rovz$Y#DNs@r!g{2<$%|l}1y)q33N1W5p7YT-62WvLyt9`Fo`sjQcGzhLG<}q^K zE3+N_1o}r*tpmo(BbR#T<)SgHhh-zANE{}K4UIX;FI?giU7Oxmn&W~>2559RHnz4H z=guX-B(nT)cMj2gy(nU1Wo>L}37~v|Zl&1QzOS))Z`L%nZC$DE+_yGej z0!Ci6MGDOZJ@!7nX9I+r93@`^J1T=iM;b!XMSLT8ebfSK!7TG06ZelM3{N`L1E>`1m?bzDh#I89NM*KK!WkJE1h6* zak|xGVX(OP;c=0f0(xFV8Ot^?n-*^X>_luz0a7oJLF7$y`G`De)`O{n)`unoQYZmW zy?a5&dLO?!K)J|_3>15`Aa*FQr-z#FSjJl&z{}5?dUf{@sp%C#i6M4r-m347yR5p? z0|xlg1fO*gj+Q%RFHjUveMD*?Gcfm2W+`$rkkh^i+r@0loZxvKRjtqL~LpfIbNFveZAl2E~EAs;Xp_ z$HBX*3ZHuf%hv!dhsDO$GV)y#X&aoQ;6#1<=d1`uCqcHB`q%TcYEFUww+&KFMR`-|sU& zuDrORZz1fcZ9O#~Q~LJ6P`!q4VgN>yRDt3MoEE-&j>hQ?R4Hj`t}>G4h73<=7*Rn8 zl8NQ(X1+m+4GQDSh=_;!ZZJx>LM0+!xBr-*Z<_J6rl#hn9Umqp=GP1_q|XjRP0Ax& zYlh!8QK>u9rzdA;jW!g?9u#y9+HBCc%2x!MXTtah_9*(R4 zfjx0q7POoG)2sYLD$??H;$a4>;FV<7=U*^`%XwOp@-PSC_8%YIpuiJ%{e?zig4+Re zAY?_poSc_0zw21w`w5{96#=KlhTqG9uZql@N#cUb5`hyq1 z@S|Y56LS9mgyHfOqINR>1jjPN5BOI*Z8LY_X*IXFFZW}Wxp|(k2)jOvn31wmW1)Wh zAXy)s#cEcqNDyAZx07v9BGx@Q<_Ap<9T9Juaey2%e!if`(!@VegjED)^3y=fd|m?o z7Lz3OK*89*4~i|LLRBa`YCu*d4!I$7F8TwUP3SC@vSi7*4;I4_xei8m2cG zvpwj@GAVrjKw>2G_N>`NGXq(@r~T9MVKuq*!d7>a$Mc=|bW@+=EAg@!oouJ95BGD; z*~I@R^vXrR`>y`?t-OmPWE8hylF>);C34vU6Y?dGZRT&kR=muG9<0$lv;E~41ycPn ziboBWqdTjsW28d;PNYXK87l{(^@kWrYE(l@!qy5iWrrb;~ztia->VTI#|J`$}_5>#v~z>$D56Qg*@1`BW$Lr{5& z-o2|PNK8jZFpN%^@SXH#*iVa%gU2t*(onr#iwr)x%KBqo=SR+yvsr3WzAR;;{AHy6 z=8xL|u4~ZZXug`zDy`4$&tPmwz&E5SqO5lo zB^h5c?HPHf(OJKXD!-ik8vIyrR*)vQ$F$0GsVH!nK=+i5Ztz{G8=F^BwL;yU93~k7gd|CpEso9b?e5yrFzb>|T#&Jpq7A!t$d~vphGeFSC(JL-UcpF?W&p&+rqB>`}JwQ@Si)JyW zJF@ROQ|bTphcSl;jSayPMV%XAqYZ z=)VjH3XGw>?F~r`UbtifC^a=re^2-|L|+61%w4)OU`>?lQdB+MS#9nf5V1XCOnW{s zFM!6%&W-O?cA=EhmByaLuAhQ5@n9H#9Y3WrCyT=Y;DI?mWb*b)2 zI8c|G-)wJ}qGoIHd0e@XFD*Nh*E3_hiq7zrVl*(3r*ZrpCthbx4F6^3u|5y!Z2R8q z^{E_mEGvcRqmEtdmfW2qKSd%ycwV0#d2?}iM%>IbTWqqpX(_fMFKubK8<}_@kZb=A zqaUf!7F0q)lpv-||jOS$O|3F4{*Bm3G%qwgEG^c8;@Z0wVRkL5Z)2KHffLbs;!*C+q zPfi;0|AD()(22)|y>~FuPvE*gqCq5o8X0iKb12%( zWf$B0_>7mY4<$dR<;Z^0TqZK_e$LM!ykUa|aK9@vflLx{@XZs*p_pt*#F^R6^`mXodwM5+$P-<Q1Z~_ei=Rf)VKHv%y5u`mN&lF+^&=S zGCM=b+LNcMgZyO?eb9QjEQFPi^cCai8zbMS#@7!kk@PH_^t})Tf6tRAoIMonKbCuh zX*>xpO8ejs`GNeLp4VNQ$>9ZJD7%ZA#C`SliioefWNxCewf* zLCE)w0rYJ$JEHj`vnVlLqM~a+82Htjd--5@Bj27BGNp?#Cpb>Tp~y@AiNNo@H;fdK z6zV@l&i_iYj(rt>bC+VuypxXm*8!e(%BSO0tqel4dEF3S?leS73jp*XOshQ#f_$IFYt=34mYA#tYF4KjE+; zirc%0@e=(+Xa3K|%af1t7ijD8r@t3v-7xh#r6dnc_vjqsoM5mc$NGEfNs6d>euHU^ z5L1@-NoM=!WHzu?`QUx~Cc>8Q^Q*UK)&KVXqRwFWBuSRxbSIvf#+-h>Q%o2wB}IIV z2F~;(fwGv#8!U>-72O8cng}V(%zE5ox@)|GyA-fpH_SMJrExmJj(es4ot;Hxz8^H^ z0l+rjKVMTdicKxt(#^OMn>$MI37b69+fs{&q1lAEf?V=@Fs)+9X|4XE)6hZcRy~}Q z^Aw61=|aPc)qT*EKKk#z(#jZ$#-c(Y!l_TV-9<6u`40#N6SE9ryOK@bv>Ho(=f;r| zKReD+AhpwLeX2HNQoT&TOW0$1YTLuHAVpH~;*c}}n|FgwQB!+pMJ#v(;2}ds)8XKb zNRXvB5d?AvxXO>YFvr%*?oH>LzmrIz7`(P+O7m?_p+MibcAe_Zn~DLqA^QVQ*X0U) z%{eY`2u76HD3)KYjS$Lqb{8eoeii<8f4-!9f zFenZ#r64H_aChdnvv+CY!&4{PP|w19q*xUjs44e`oK^y)AP5@QsRGz$yW48yXi58& zi2aY4JWV32F+cSl_oO&UF^OTbfeUq?hqwTE-Cnw^5g(fc4-drI^hg(&U zuy$6sI;!~^+>qdp{x;r&UrUvldG*h{Q+-X@{gW#>J9%Q3vT>n1-ya$4G|8k!w1APV zeE+qgkCd<~!tp$_)-a=RHJ|j>+ZT0lA)%q6{)?wEWsL(bO4=2F7Q#kD1hGot&giWF zdnZLey)Dc9)SBHeKR=!wY=TIXIQtC^{3R`K*$iO-K~EU8b!O`yQ_S9;M2)b3FQ4Vv z((+f(AAh-@{TAieO!}YvH2zja_e4pU#TIc#eJ5ZK%=aAgdnXD1Z{Axlp6Q7aMJo>6 z9Q|(BM9v_=Cyuv>Jom@$u2MRRP&3hgu@_fM{ZWQ<$H~ry?}5~P{Vj1e{J0?PHlDkF zbE-P8N^+i8xW(l-UdK7M#B&2JOjuS~`E%$4A?Id$Mkc28EP-BTd3AL&pU+vy8Tr?o z(%#7+tc@1DsZT;{>ubqc&G74dgg+Bu&L<^1EM481-yaSIO~0D&slHKbN&Xz2NA1KV zuH;~`#v>TzZ+-WKgZTcbrV7ueTXdq!kJ2t66xL#<$zb%gn|Y7DJCqN+^KF+z&;s9E zOb5N_wBgOe5cb5=BEd+gaF2Ut^$7T%)rd0+ikrJeMnA9p!BJ5R{IdJJ{hndBZ;jb* z-tJpi31M;OISd?WJ9F@F0VE2UqVTFWg--u@IT)X%H z=YY0iC7m z?Y!h)lF%w?S0Nb^8|%V-0N`^>P&^VdPJAsGE$}JD@hWQ$t&Sx<-9d@-#>!U=t|!vw zH{^ERHrJg?N5EhQNHi4H{@2&;pTov}XE|E=R^u3Ll1^?nifk?TY)iNEpA87j62*t* z4ujlI9N;|$gQbLoBEh9J{V_i?m=b7iJUC&Yqzm$vzkbg>6-LMx3PRvOu_k|irkUqD1&U7@GtQPsa7Lma~8U<#32vxC?}#l79v^$Uw9|A+VVlWQ^^Ex|J} zB?!B%-+SvY0&>RUYCfR|@Lu;WzAN5YbRT1(H2i~WG6_Go6T#8Kc+?jCXNS})gFbYA>vp$-iG5uFhCt`R3pZt)SXaZGHs=TEhvgsFw4wpQ(}{j6U% zD|?R3nLSIrn;lH!0vxm0x)bs%o4HhMoj&nz_SUAJYGpQjOLD(yTP2g@d^lvXkz^1tR_YDn~4B!5GEz`S)Nw#BvIqHF1OP9+{$DHx>t?*6pXf;Vt2hFgZQn|S_ zwC^a7UW7K5;!IcKY859oQB-{LZdP@_yZVJ*%QuZZ@<*;23)nu=z=a5=xo2SM@2aSA zhBEdK$Y|fg86P2VznOu9iO99F&WgB}GIEStQiG-U27{b*W`MbI0E(w2G9;_UCA zk@#y;+B#&IOQf+B9hBcvh{O3^N#VoBj1tSqTtW3A?45b(riQfli)c^q`X_nSANYR% zg#Y+QgE}4^r&fvm9f77us)}1n{u??%j^w9Te&hKGyYg|gyv3;I!~cMKA|7oJCjwgW z3OoBtbV?^LXcb3f>;4mkv`FTkoTXVi4~#i~yD3}y`JZ&nSQCM6t^Q|6az7|)DY(sU z<@R=FDzO3B3Os!Lj;vrC)deZ{{LrURXdk<5&Pzx%f7){lxy1jbJ5wniyaC*nBT~l3 z43Lx8X?^(0a*pE0NxFG4urTe(2@foPt6~%+;*LYs=&%p&|1E)ZtMJHMl;x!7T`vY! z*l0m`Ipi9ZB_;jZd%w41 zY~a%mzNGP!uJ5=OAuYiRmm$}~`639IPA)Dz%u4Bx>@D~WzLJ99s0lc&rd7GMZf<$a zdKrcDB1#{FOI$z|Wl8wrmv03-8PZ^*Qn)d@0d7I{G&2F}2UmDQNS!oWyZiHnWZkSD^va6vq+ z6YQ5-if6yJwWX>RLx(NR536r9Tn>5cHgwIrniSY0r2@%rJYT9hiUUPM5O_HAd#S0Z zL33nK_xwCp3#1eOiXh!odV`ELL@PKS5iEz33%YPJDQ0QGWYx*ZscXG|ZFRM_we|eT z01M2ebTAG8rzzCP9Y&nkm-q?{hW~vY@Q3kIv65&Ae7M-RLMR2lzF|}O_zaAP_+I}y zA^;!eKaL3W1rIqU5Lxi?2ad=|(0MOQkI;K>7`B!F;4>+V!V%i`f`1ZFlL<&pT*Bi< zIpY?Tg+(M6V3QnjP-~R;WUfr?QYZiSO1@z66H&;?8jk;zPJbf?bs!@F=;|Y9J~?j{ zHh1h&V`x!x!Ct)w4_dW0(hiwjvW~(gAWMLa%>dAV+z=&AM zfD2P6>jm{>NF@dZ*vS%Auvp{|?VnbEVEibj;hcKEETD3$B_P;#@W6*SV0h%vL4=cV z_O#J8Kpv9aFu)}v^N)_EfSwi@h$)ggb_LDVhGb>pwVeToQnXvyzNHiY<^)!=`qBsm z+W8BtegB`8TKKrB`7X?cClbcB@pbm-2CJ(#WtMB|r{@nkLot@(b;jAyXZtE5$nK~@ zbLG^nMg}EX;cu@~If!W7T%X}^U)1~Dhpn#Atxta>d+G7~&fq+U*#sHr6Ji$`igI!-66N}at z*)ag+RRbZk`S(xS{7ReVg~PdXJ+WWd(xqU!M+ibqbO-Jlqi6MxE6xRFjD;t1 zV4IG{tsJOo6iMv%37x0AVDyuVrdP-M@s=k)fmT-2omF9Eg--a8!=U;8?p!Vn9o^|= zP?Y35uNi>sT(^+4bc^i@txQYR!tz_wV%Gqp31h8;-b(JE$vGVFzLG}Y2RxU4c+xqp zjhEJC9df=5GBu;)nsS41I!ehJ%QnVT!G!$AO&NsX zqmU-2SV6mW9uOQ zaL6QFt6Nx5*>B??vA^&PS#QJ?d0ih9O!5UL-IytI{xw=!u)0-X^)oezM%ABS%Z<4i zeB2D(AMGByFOad%J&U^`M z65y{9-kTbuf`=(;`A}jc8n=cZc3=`Z|HC;ki#XG^ZdGW>d8HGhDUNtJfuGT?Fv_$f z*3<6qi=ix(zKN<(g4tB+WjC^Yx#tQxRhmZ*8w*V!pFkv{iXZ<)1#eobA8hCmB&6!H>4tfdOL|DYEai8QsEO6K>bg z1h+xkDN??_LHOgd@f7n-nQjp(yYd~4>xc{ME$=wRyP516Y7ETY@!8C~vu0ERil#-< zPG)D(*{%<4<{s_HrrS>}-rgY&+9^F6jC^l+xf(4%w2xtr>Fmw-5}~v!t1WCdKpX)e zh9=z*!NwBnNiY3XfHIYw2tp6A{JDB*^q1!ljO;hrqtd*w0YruY>0_neCEp)@b^ zND(exXRd8&|JeFrwU{7yk#C!9k`#@X2iHTx zCeKX4_@IJJ+~PSF2I!iMT?2Nm zgq&wOSjNgp(sgWUJ#Z9q-5=E8px%gc1J=2Be#4N6NEdoAbbHY z52f=DJWclv1$B)%p-4g!lF6wlF`S0eyi8{NvbBr-@~#WQgfd0B7Mh>my?qs!G``Q{ zjx1nHd8|yQ=Bp+C*8)vL6Q%}>COl^irwWUyX3vN37ag$#G%}jqXY^8>7#|1478cls zK+}Q;``E^Qv&fEk#`?LOSlU@hHqyG(@KN*nx9U93=`%C56AQtv6;W&Is!HEjoqyvJ+W$b`3e1uuW$8eCkabZk6$9HXFf z#_yk~8YX*H-@pb1P%FRXh%dh;-~55EA7rL|IcW!-QgtgK)B1NT_B!2zt>Ef2f${ZZ{p~@` zXa8axGA<+?0C>G;C`QfUWiX(9eeb=7-?N$;eU@Q2N`7 zH-y>UvfVD{`FhJlmM_}LMiOo1ajcfp)*FMS)`1_q8kbyelEk#Da$;uRFs3 z0ye^Wx-GTOg%oHXT6#`WB%MyU6LLztm0vSIC_lhC-$>tw;F1gB_r$wbnMTYkI-GtF zK_Ggy3+*vWNJzK=Z3#AJe1s(-h?WnCRQHsXBf2MKfwQg>QTw!cTwqlvwR_W*19=;nT#xacN)n{6E*`AVK zze$8*7>==2opHbN!Kh)TuE2+ewbyg!v~vOINd%fBgwZtJ7Ta>kap<;`6gCusG%&#g z)kj}O9*}c&f#5j5(^;3XR}sNl@#5Y{n)Ys$%*S6#G(zf*&-R{A_jA$)-L+qnmUy*e znGAbCcTAdCg?2V9H3Nm8yd6zp!wh-_>2p)|w@n2WSAz#l&2epK4ON(Ey-@)d$;!B9 zAOr3FI{vnWy)nfHaTVjVnxz$|18Bt_hPhtqmTFT(nOnoXu)w#=`M7G`MG(ALdg+n_9Mqx1wdXZyb*j+NZDxGi6+ z6FhNAwqg46{>`E^jG9+OD0b!fFLMr7mvJ&__!U|+it8c;OIR!q3vfuf?O$GO1{){% z91Bk2;NXBs;>%oal?;d&F)%SFV1Br!`z5d zA$;>y0e#HaPa&Q+exI;3`H5Qs+y8*sN!5IHdgrHj`zJqD6LSPPFVSCq;5WV#ZGkvTuIB`* zUH&|!_nIGNcsJfO$f!s1zTKXjxc#SB!&STAK0T;Z(8puF>`?1sVCRir zSk%AP_LDIdqM5+19C>u_?(6skN!d=xxR?k@>q_t|sO9b`9m_%%YYG0sEa=Il|5MhY zw_%O-o{~^rF-cTOyUalBri^|5@y!`1>%fx26iF`Bi<4VxplHPi`u<(l>H?9zjGG`^ z46FO<@`WraI)w%$ORl=Qv=?fNg1!4{5ep*P!B~-L8J@gxLRmH0{;1Gnkak-q_TPIE z35#!K@oya$rdevm7ebpwq3?OqVV)Flwn+dd71-#KFS^ib@cO8SeXsvsoXbsbZQUEq zQug{CrrP_J11|mwRd@4IBg$4^_OCk9iLJCaqLQA+q*8^oJvDlWVPK{N3f#l z`6Kt*ekYG>uLS>-JeC*$Z9oSkEv24aKrQ7_J8bK-;&y{#;FV2GeK;eV^>ijr^sBey zk_485u}dv8uNcy-9XPbRnC=N?jfYsCre{)|04c$sg;?MLFIh3|%x+`$5tX0iOuT8N z^Fn;{_}x~1@ep6ztV2uu2@}{5EwPq84zUCQ(7j7Z8K0YLIXW0U;xKHxSYDnp&wBQR zWYBSE1*Ppp!&pHX=`Fq+21_N^)z3FO@X+=)pQ{!b{|E)l=MUSgc z99rPOHf$(Vs@R~tRQ1Nq_=H>VO%fs3A^)iPLQ61-1KFR4z87)&7SdA{Q4acJlks(O z4hB8STh?HIMZ_8Znx4r5&RZS4>&KZWN2Hg_`Xua(9RFiiSm#l(wx!4q#guoiS3;F;Uy&!5y$Py-?DeE{!Bxjn3LIZyqpuRufdi01eDLEOK1tHLM zMS&v>_@S^%K~XiKw-)iFv222pWNcn zMp?q^?(RnMs@two)EF9O?D~A+mYwg)jRY~5?Pi&8<-dNJfZ}{7IMCeEGQ?uIbR7CI ziXq+U@^mF-WzmJ#=;)}gU28~Mf`)nU?ygHoX=zkpS!wCS_munDZ6f9&XU7CZk~KYnys2{#8`*`PnP*udl+jQH}Ir z#~lXXUICqR=Iq&VAgRi&T@-M_ooo#97EgX6l2{GHm6U0fawiV7^WynC$jQlxTRW&P8z!!S~P#P&+QpKw9dtvV|j383#R#Zkf;~!Rh$+!fc=b1N|s?exxz*AFdA2n3k`>oSw~0b>j2<)>SJ!&(pmINZJAWhJu2^DKB2DF|T=g14^ec)I8W;*v}zqZ(sPcJM-=5_I46#@jzU#kbM6L zz0?4>8peB;GSCDD>q^8t=@B?OWVW}r{hmKZGbU>cB%`LHrfwaem5R8MDjiMB@Cvs# z_c2LPQIQVNC=C`+QBkQF7$oEvlZxudZZUx_^E|AGyGlb-pCw2`a|Z5acv!|knac(v zN`h%`&g)RljNIHNfTU`(lO}Y|>NVYl^?2}IyuotR5_}3}8{A$A9Y$kQQ$8?rngl4W z+!|=6Ppf7VgM9^-@?ilkGY@)?>5&!=@Xe67FQiVg^sv?en}Qn~yf+O{Qk+BCUMyR( z+FsTcSo?b8`*pG)@vh!pwIO+>Y{f~SdAydxE4(yJ?1R5f#DbKH{0cd z>22~$I8y|N?(?pTfc`Ims|7b!%hWUgLLNZDCty!x_?zaA4m>zoSR|~^z-O91}o}*@f>ZM0bPF^e!9ViVs}_G2tKPZ_$GZH^|2rxSb7c=zrM9Kml!oDkn$zA*)e zwqIaiJ!p-vAwFVX4=;`ZAG|R)Q2jVS6t;m62b92_qV~7Xt zRAFJ^hr+_}*V+Ew-lt(nqHny%u7oNY4-ZcO)$r)(Wwj!+S7ARr!JX#|+Bz+$kUl1l z!L?|D6UFYls)rJEcK?&@%m=3xo%FK_fMA1tqbS}rC>5GG@Mk>p^{s1Zxvz@E94;@# z+K~}T@ZP1~IE?x+GegbGt1OqXdH38CSgEN4fATh<+XfcUD{zrd^?g&=q>CQ-&`Iy! z)vtduDU1L`FA&Gyk$9rW!rSA;3ryH>D$#;xjvwpYUfN%U+* ztXLUeAR$9Aq~ze>_7V=to^{#2oXy$$nH&OE)wB=A#Uua$5A$#ib>G{>Kp_r+85o0eRLiMbKYskU*G&Bk z7XnG@X#X7?K^j=9bFK1l*VNegFnGSD8tyd~+;VM0Lpo0KAiy*~rDtLaeUsJ<&yLt= zrMr(@Iw63Y!Ct85K$8cb3H26iW0i9tgiO?>LKCc!!0g6JluLhBTwDyVC46`Y8**M>y57B#ZyAn9ZcYaJy7rnQ< z|AL!}s+9l7=$Y4kstXXq5l+~zU%#w=e!B`v&N?mjD>rU*CRu7|T!NS5fL;}#(=9@+ zA&*_0z`oMqek=V^5=a%K4z^qIJu+=s+ zoRyK0A-!osgS3?Lt=}@MoKjL!f~S;vjlK46;;UVc(OM zK8xby?0g)=;1Vpuv5NL!rK3X!9K3A$qvf@=I*5?1o3<167-!JXU_JNk%*G=p-QNG#GRQ%`T9PAdP~Xs|$T_ zcJiR&SYN+;w2*UcT`g1%DlO&fT!RNz8{|F^kOO9M=OOr8&3?c3V`?h#{rhw9oYud8 zn7lj_?|BLZ6hDE02!;0ml8XS&p4g{9d&F%``dwO@E|BQy+1Q$ZPd4`J7iL19-)UrF z%-BRjKwqgn6N*-Mh60iXnV2xbUoYRhc@CU4ZIFzhKp^vi-Nv=Oy(BP~gygISZmZ>< zARPM|SX7LtLJ?^R61SFt>>IK1$>V?zV4Qgdt>mEhI^$A78A|tn+kCXbl?M`o=dcAg z5%L`s6_wAetz1N(L`Ao-Z;Ngfz#+pUCN^+^HSN~p@H(WGj-q*nOLg<6N?(7!KSXW$ zcrYVl($Ud9l$Y1#eiITB^7+db0DmQR&WcJ&1@>=Vz4P1;k~V{_?A|%#paEIignI_! z>KT-Wpi=FDT80(gI$AwNu)Z1oD<})$t>6#q5HY=Lv<<# z`1=i<&OxOq#Q}j6RxF?W(+(q1c%o}-Ymw#6oes#hqV;cgQfJ_bmN+bsLjY`T+<}UD zd97XeGz#1STmk~!B8{|8Z3r5z29WvPGB;;|VADM@RCTaP0Rk&LA|lv{=2X*V0X51T z78Cq8xVVV5Dr%}k13GtiR8`4gu9w(52l*;oveWQFLPtkOfk=5Nq1!}ScoNLMtQw^W z07COd#}S8(-QaipYOoJ4M)>XC@Y0CZr=c=wheBW&9Z@rax4X8j?E-mF;{;7e>Zvq| zJ9kdQkNp8`%>j9bb1AZ2CL88QQt(x;#=7b|&#^fM4NOh3P;f=|(*$_nt#C2Ud72EW z6$C>16IL}{xGWU-{ylyEovB-s`sxz#a~y1du3;z>o0_IBo`e$Ubjy;ztO%m!>bk|MBEE0NG!HVrHJ>k|ttjG=o@* z{*70+r@p(J2uFCI9ZXw6>GEmZ{EO5f0#6}%o`5yk*NGyJ!jf>29=^eOf5I_MX zh`eGD?Jk^ET#pmc|L0hKTr3^yXUOo?M+VsOdHUqC(tHOK_i6)<_O zhS2yjEbJLvTUc050tCSZ0GKwvSi0x!cT%-Wr`83fdbgtB^n0&QJgZ#Wv1DJdz+1x6hU&;#Sd#DQ|90(U;kaM!@)SrWQNfawZC*;{6fFzij zngSiND0DWUShAekxIoH|0{gh(4%~&)ljm{Z0)X+pq{omVzJhDaAfI|Gt@C!aqAnDy zsSAj(2k-#XJsFI9QJB4sVOX-JrA`?r$#|3}6A|JN@c aUiPEDi7iQbz8(esk`R*-%@NUh`u_l(t;-w$ literal 0 HcmV?d00001 diff --git a/log/ResMLP-12/log.txt b/log/ResMLP-12/log.txt new file mode 100644 index 0000000..42d4cb6 --- /dev/null +++ b/log/ResMLP-12/log.txt @@ -0,0 +1,118 @@ +********************begin training!******************** +Eopch: 1 train loss = 1.474743 +Eopch: 1 valuation loss = 0.159035, ACC = 0.950333 + Epoch: 1 model has been already save! +Training epoch: 1 completed. +Eopch: 2 train loss = 0.108675 +Eopch: 2 valuation loss = 0.088274, ACC = 0.973833 + Epoch: 2 model has been already save! +Training epoch: 2 completed. +Eopch: 3 train loss = 0.066299 +Eopch: 3 valuation loss = 0.085802, ACC = 0.978167 + Epoch: 3 model has been already save! +Training epoch: 3 completed. +Eopch: 4 train loss = 0.053803 +Eopch: 4 valuation loss = 0.069033, ACC = 0.982500 + Epoch: 4 model has been already save! +Training epoch: 4 completed. +Eopch: 5 train loss = 0.053161 +Eopch: 5 valuation loss = 0.064765, ACC = 0.983000 + Epoch: 5 model has been already save! +Training epoch: 5 completed. +Eopch: 6 train loss = 0.039282 +Eopch: 6 valuation loss = 0.076107, ACC = 0.984500 + Epoch: 6 model has been already save! +Training epoch: 6 completed. +Eopch: 7 train loss = 0.048531 +Eopch: 7 valuation loss = 0.070968, ACC = 0.980333 +Training epoch: 7 completed. +Eopch: 8 train loss = 0.046734 +Eopch: 8 valuation loss = 0.101906, ACC = 0.974667 +Training epoch: 8 completed. +Eopch: 9 train loss = 0.057909 +Eopch: 9 valuation loss = 0.091453, ACC = 0.982500 +Training epoch: 9 completed. +Eopch: 10 train loss = 0.068523 +Eopch: 10 valuation loss = 0.100253, ACC = 0.980667 +Training epoch: 10 completed. +Eopch: 11 train loss = 0.093497 +Eopch: 11 valuation loss = 0.107574, ACC = 0.977167 +Training epoch: 11 completed. +Eopch: 12 train loss = 0.044268 +Eopch: 12 valuation loss = 0.078949, ACC = 0.983667 +Training epoch: 12 completed. +Eopch: 13 train loss = 0.623085 +Eopch: 13 valuation loss = 0.157252, ACC = 0.978500 +Training epoch: 13 completed. +Eopch: 14 train loss = 0.037385 +Eopch: 14 valuation loss = 0.110624, ACC = 0.982500 +Training epoch: 14 completed. +Eopch: 15 train loss = 0.020321 +Eopch: 15 valuation loss = 0.297462, ACC = 0.973000 +Training epoch: 15 completed. +Eopch: 16 train loss = 0.052521 +Eopch: 16 valuation loss = 0.225200, ACC = 0.980667 +Training epoch: 16 completed. +Eopch: 17 train loss = 0.126149 +Eopch: 17 valuation loss = 0.223529, ACC = 0.979167 +Training epoch: 17 completed. +Eopch: 18 train loss = 0.064713 +Eopch: 18 valuation loss = 0.177399, ACC = 0.983833 +Training epoch: 18 completed. +Eopch: 19 train loss = 0.114511 +Eopch: 19 valuation loss = 0.253915, ACC = 0.980500 +Training epoch: 19 completed. +Eopch: 20 train loss = 0.074080 +Eopch: 20 valuation loss = 0.226443, ACC = 0.979333 +Training epoch: 20 completed. +Eopch: 21 train loss = 0.080519 +Eopch: 21 valuation loss = 0.377549, ACC = 0.975333 +Training epoch: 21 completed. +Eopch: 22 train loss = 0.105814 +Eopch: 22 valuation loss = 0.296062, ACC = 0.980833 +Training epoch: 22 completed. +Eopch: 23 train loss = 0.117329 +Eopch: 23 valuation loss = 0.336344, ACC = 0.980333 +Training epoch: 23 completed. +Eopch: 24 train loss = 0.048457 +Eopch: 24 valuation loss = 0.182534, ACC = 0.983333 +Training epoch: 24 completed. +Eopch: 25 train loss = 0.110891 +Eopch: 25 valuation loss = 0.194350, ACC = 0.984167 +Training epoch: 25 completed. +Eopch: 26 train loss = 0.093364 +Eopch: 26 valuation loss = 0.332007, ACC = 0.977000 +Training epoch: 26 completed. +Eopch: 27 train loss = 0.079099 +Eopch: 27 valuation loss = 0.309876, ACC = 0.980833 +Training epoch: 27 completed. +Eopch: 28 train loss = 0.097644 +Eopch: 28 valuation loss = 0.295460, ACC = 0.981500 +Training epoch: 28 completed. +Eopch: 29 train loss = 0.102000 +Eopch: 29 valuation loss = 0.293703, ACC = 0.982833 +Training epoch: 29 completed. +Eopch: 30 train loss = 0.080658 +Eopch: 30 valuation loss = 0.261712, ACC = 0.983833 +Training epoch: 30 completed. +Train Loss Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResMLP-12/train_loss.png +Validation Accuracy Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResMLP-12/val_acc.png +Train Loss: +1.474743462770171,0.1086748668005893,0.06629924054679094,0.05380273102917496,0.05316117941462244,0.0392818591715359,0.04853067634755173,0.04673448809202136,0.057909078096927434,0.06852341221147061,0.09349699254253382,0.044267768561718206,0.6230845790207216,0.03738521428460376,0.020321206379019432,0.052520529435868546,0.12614923562628944,0.0647127392301931,0.11451104171100272,0.074079771555273,0.0805192425736352,0.1058142589081407,0.11732911902895017,0.04845723011321656,0.11089138890054262,0.09336423637522687,0.0790989557700349,0.0976435722336647,0.10200038970579342,0.08065755875858951 +Validation Accuracy: +0.9503333333333334,0.9738333333333333,0.9781666666666666,0.9825,0.983,0.9845,0.9803333333333333,0.9746666666666667,0.9825,0.9806666666666667,0.9771666666666666,0.9836666666666667,0.9785,0.9825,0.973,0.9806666666666667,0.9791666666666666,0.9838333333333333,0.9805,0.9793333333333333,0.9753333333333334,0.9808333333333333,0.9803333333333333,0.9833333333333333,0.9841666666666666,0.977,0.9808333333333333,0.9815,0.9828333333333333,0.9838333333333333 +=> loaded model checkpoint: /userhome/cs2/mingzeng/codes/kmnist/models/ResMLP-12.pkl +******* begin testing!********* +Test Averaged Loss = 0.263454 +Test Averaged Accuracy = 0.948300 +Confusion Matrix Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResMLP-12/confusion_matrix.png +Class: 0 Accuracy = 0.991500 +Class: 1 Accuracy = 0.990000 +Class: 2 Accuracy = 0.978500 +Class: 3 Accuracy = 0.991500 +Class: 4 Accuracy = 0.988800 +Class: 5 Accuracy = 0.987100 +Class: 6 Accuracy = 0.989700 +Class: 7 Accuracy = 0.994500 +Class: 8 Accuracy = 0.991100 +Class: 9 Accuracy = 0.993900 diff --git a/log/ResMLP-12/train_loss.png b/log/ResMLP-12/train_loss.png new file mode 100644 index 0000000000000000000000000000000000000000..425945a976ea5e1059c26ff9854a285c352538b8 GIT binary patch literal 19844 zcmeIaXIK%fIgg5{h^R!#NX|$S$tVg6A~{Dz z;t)h~{;J34eZRfW-upX0&N=^%*M&3P)4h6ys=My0Rn<@LD9e!^r#_A#2&w$7n`#Jx zk3kT;38JHLhOcwv7kmi0$>_MLJ6gJVnz~pZN~Ug3_Z;2s*_bhSSh%>_I6CmK^RRPY zXRvm2b8;2t;IRMq1?-M4Rva85r%pg4#7?(#T@i%L6#c`?7SFOl5XBYwo01w{35%oN zUK&%Qhb!%hS7TqEm1j5+M0 z{sUz8(bkriywbTlQc_YFeF6j-N=r?ZI)DCrQu~V2O=RtX?uOE>TMP(3V#4*~h9B#V z6XWQbhW@{{rjoOZ1u{Ed$6eR|G(C_IID#NS4EQGGL*ChHhXV%LBM;2)JAg6~NfI_N zoSAXE6>d9EO)zpA#4CgdBYEv=E;FLlTZ#X_&-9(YD4&~oj2uC>@bEZ^KJ#xPeLWNi z9)ejug*?2I-+dkL-$yTnh|}Y^KuR0P(8iiB5ZofB`sc>-HH5!~GZd)+#SNl{E`>a` zoyT*dQ8P>vX()h+d=rdN1%1 z5@YQ83wtA()gxh9;@dI>#=O!-U+n*GB%5*)bQ|-Y*)H&>J=?5kf*=!27b%duyu7H` z*c_Q>lo@g$p(Mlh-L{rVaqviE-2vEEQ% z)GgxDrAxv+cC(Jfkr)_ta*!gKiPeq6z15*=&my{jtsfEsQ&)v z^{t@un!qd2uTVZIw>uVGRVCcp+v{Cdn4SGwd-aTYH0C@3Xtdw;>%NE~dyGTX+*Q#( zUoKM%yIEJ$3j`kcF^x={b%2OrmGY52r6boMt=a z=zF8t^nW=Cmf7~^^3w}=E*V6{#P~WKIdUYxF3sxnEm7Rvj*l#e1utHd)UR-`cfYst z^Rr&LZIqGs)LBw0&Xl%hp+se&#P5aHmv9%42Pq=?xw&0QqP{nbXQrlXR;SzMJJTNT zY%Jzgy3Wi`e0b>dNli;r5vIFlNyec60oKy^L@e^&c~aKU zW6&b0Xy#k&W%)QU|0?KeW@_rCshOE$x!$yt!!>6ONLw3V-snru2b&UBozl`&Rekg6 z(@huy>&1&7vfB5DvX1f2WU$2vUeP@in3PHu14R7sEjf8y>r`}vL_X#;Di7yy!h!#W z5SG#JIYN~VVzdT2h8SN)j^@0dKJ7#*f-Vm|95T0SIpG%a1&D8C*`po+RFY+1z=xcduHb?ODB!yAf(Fs{0r2#FaRg(TH6bom}5RG-XJb&i8X zZ_(j#KKblziT_s@$dQMy2MNBYLFXD`Kozb>(@B7Y9RsDCX2vTefqwqJ2s-fWshI$1 z)`TzoKsm$PjCA4T0@pAz9&|yR0XfQr6Rkk_R;&|!A9@iW0lnbD4T2t?M1>n0?)PXe z4DR$Y+5syP21XNTTpAj0x{iJ$Lm$(s%dkM@9>YnAYvI!o;qXT z3Qy?wx4E~h9~~mD^xioNgA|j3=kmw5@ld=Ed5Sh72bX-mPSAe`|DRu7ERl2^R2;|S z{8CHdz?IZ2PxSScuX#%86ptw(ZVTs#+*% z<+W%zGc`TEQ<*_aF%wHDg%Cndeh98I_i=jvxvP|PqjGeH!{Ghf-v0W7^`i9`fqT-9 zwd>KcFX$F0``nEbHC&PWESzXvzUzb3PI**o>gwjuiTV&$F&BIij=%kntP#i3NjQog zY)$4C7WR<}j5xKp&d<+lXlu`GSEZ(YTwV+@XF?a-d=0s+SLatb-4>s9mQ^cTyT~Z( zhU3BB_H9MQwGnP^?!B>hRJer}qujuA`W$jLj>|mQ$Y+kxe;;csz6d(_UDMmUaz^XU zor%qj&)&BiLh;Zso$6zpm}-r|_USwJ{+ViBZ*vS-4V@9eDL1>d?z3ol2Ao!m6U8Dr znwlA+z8hY3-&x3LgmJrXY5tCK7alk zp6)PO#mvRU#V{_duY=0Omfn|0jDW*Pc4wNLZ?sb^k5w2`jb^i3U!U*I z+aG42e`-HQoL*I@8f^N-vh>J%8Cdjrsmx=X8nL37;ES}5Dd%8Ly|7%lETbvoy6h-Ng!p23K;_qGs(yiHVn@4fx1(6sNq}w`-TBx zv6mKo2fNNPx*BJB6?@1mpUG>wpjr0md()UCcW65~T<=a!48G zb#^8)Fu|?Sf9pILSFgv2HQuyTN4Q3tSqDsx0-?kSr<(&)ZoK>ITomX9&k*ZVNf{xYKGiBEaq)|5F z)qHC9=vE8Zcs}nxzdVu;d(>5O9LMYQ-aEayVl(j8bm+ZvTEOA%ICckHw5iT2@*)v8 zFR0y@vmbaXIXF025T+EzD^DZfpdl?S9seRHFHc=YMkY^R+Z3natfn-`vuDqu!o$1f zb#C7d0om8o(;J#LpKebs0`u0+cKLD|YaB~X2BAU!tX5jg>s8K4m|KaNM}H{*07JR=s-?VAYt6l}8r|*f?eMsk3IK)G1aa4C#l`V@ zW7p|#onf>hYa_1hHgjDWRnQ_q=bpz?bDRJXTvW#&gZX5yUf^!;hihM5QA!pw1PxbL zRei-oa=QzIM^NT6b$iscUDW5#S0r7`QwY+h0=`f7eoB_SXVS01!3a*TpaVt9tl&9a3hgpsiG>Nfmy9SYJ${ zlepag&7A-hdAc5nnM2h$5mf3WRhBM3j^nz4ic9@Wy@uM}WzdN$fAtX$KjqOU9Acna z|5AtPgyg?2c;`)GLW(=&0L}RXw*B7+SHK*@!biP8rR<+8%p#+lyPEBGm&KMCPr4@C(su7#thDnb)f?G6m69hP8%My?im zlQ5tNBw+&8pThMxE060Kj&7hGTUkT;s_#<$+DF^Rshy z4H&$TurMvq{k{Ty&!JUgzZDq^OG}okS7mK%Y<5171*DXel(5^{&%6ebBb)lm57)yg^m9bDol@IR6Mv-=UeZ zG7a_^1!iXEg0QUg^z8TV-?ue~(u*5$)NQ3^DktVFx?j1V@I`zSMTK$2si%SW3NXqkztjx?pIoch(;J+xn z=9C1YmVCvgCtEe-`SadSPbt<1%wI6Gu`Q4Uv_RVP<7j z5E2q92%Gu!%X)8X#a=8kGgH>X7waf~DK*E6ZQz^^KYjLiWr}Jy;$Y=c{dYI!8p*_a(7_g)H?xR} ziryKQt^Op-r}Ec(5WaZjc0)L?`@BX}RMZ@}SKyGmk`1ML1Iv_aTptjgo|>AfWrsiJ z(#n<#?pUhKvo)A(jnQh){;3$_!o&UXL(-m#4hNk43*3`^+b{A^M@Hrmtb?YJQDIO} z5WDZ_czw0{v$65CxyhZmS9Y>)gIIio{5bylHm#ymCgBBTJs$+M{g^PICwCI8&QqbV zl`;uBcBWAZHS#Ee{aHAoh+9lRq!d9{S_vFw1Y1>>@_PEOA0S}`kaR5!XLA7$ADH0? zh}V6@TGEot#6L}+l>M*clOGJHrnx!8H5yS8k@66I)YMy6mejfA!jR7?fE3378(7|X zU*$yIa|t;(YPn8;dONu9xfaY+D3QQ~0bPmABK$@Lc+w#WT5l$90^fAuTZje9iBMFM z`7DUzWo|$gQU}QX!MRi*3z~%gx-aCpEel@h*$Z&THMqmu2hm0OrW>%Q)NhCm&XnU0 z^rG+MXoN6~L3LW0K@lI-VBj0qRES@r0E`=rMKmT4MRm+cfGv#+IM9lKQCc`0(Nb<)meA`&{z3EO7~S%Lq=Aw(y*Nu(9Tzo^?k)K5Ma zjoquWI#MN%kn>CB9&-ZEoSM@F1B?g6FT)+%m$MO{f4N-?G~a(2`j!yP@V+HU#$5J|!OJ`H60A1o~QS}{YF5oO?4Rg&@-hb!`c z>)4A;2h%uoypeuf<~FBBN_Rttk&)5%UYYXikf+?f2t+5a%r`tnRyR}sOlApDAQN_A zfzMsnNdfpoAv59%o5A}#%XPbjs8(&S&Lq1XprFBL*JnLm=Z{7UuH*zB9vBYZE&9yI zaa4(zO3Uqc5aRW791l4-cbaixXUE5Ws7xNmrL3&1?Dluop;K)F<2|pr%-4p;54381 zH~XN6?#s2C^II$Gf#eWE$pP)_WqWCM4X^Q|-a6_#VA#)|7R6*_WVlos+?JQOalgUH zCfBD6YpQ{_5lo1jp7BW+8 zZ;D=mrnu$2oW*JnSKI9>!9>&TEwJ;o^{q%e;Asgxtk$;` z6x5f;YNKAio)zN1H^6y5_zl6alh$8eUStw&nsaNJbK7~hV)O*m<@&;>(@r*;iSyKk zF2a}a2QDE*^cOn`sLr0v$j`q%_3Kxea8g7>+R9{e#rA7rimTag-`>&G)J!t3_E-!b zb0^WPgXGJcvtNOFR8ujSK=pDsRlcqw^7H<8G6A=l~IL966mU|w0YA|A6zzDueB z0d|c=w z&fL=8zL`LO?p#(v!i7=yenV;=%TRcrTO+B!Y;iN)3BXcQ=WqDIGPJqbQKq+4PgcvW zDfjHuS}?5>;UjxYckxrMw8il$lv=jguGU$!Mkl&0B)EPT%DH!sF%n~{X*r;v5~}61 znMvnK`S2*Fkb;|W!s!geK;B++9gJC98ohz{8x51pas#BAQ*!3FTv(17tBvoXNMq~- zHe(26#*`@xkC)^!=+@9OtVi5mu2@9^%KxokVkB5&c;cj!9cE*duwiMmr`p#o&m2O5 zfZaNZcUw~{H6!nvUSx{Kbf1p*L0GZWnySO(Uo7z?2Eg@u4I$3_BRY+0oNr%J_#)y5@N>7ID`lmVG=1WRMbjK_17{6moi*~Dzb z63@9{l-uut3*bC`g&dlF@R8pViT`F{ih2&X^UfeE7cIRj3J~k>$5G$+w>V5n`Mo1@ z#8l7@{X~R9gALg9lvc2)-$#yvP0epu7`3Y=9657bfd$($;o@mijWCSB&+O5sIj{e6 zW1RSHf{bqv0_2J*Wz<_K~zy%jhc?i)e9fpPy+~gD~-m@zm z9J6A<_~C5L3lYN__EdUXyw zbMBa@%pj9U00l66G@>Tz`;N}7I*Jje+_5M@*i2O7%z-> zsFI&-dX7zv-l2<-AYb_QD6xA;72onStcUR}!iN9G0%rM*thS36f#sY3@tA(YlVZPx zhd2nvpDwJ0&+kbf#NNocH^hz*X@^M&G@JuFGUZYkiAEW`Aj%N(?Afs2x24hQ^#%em z>(MHA7r~)T{vNQJg@xZ&-2rT8Xcncpa9%Kx@}e%`P~^~!qi^4RC`jWYoj*l0)&CAp zLh$%gI*EujOS|=QQz|Cfh$}Iz8R$q}-1ze(_$2Mn(oFJy@G6Ri7fv{TpMI3WjCYx+ zSD;E$ZE)?Gx-Y(o+ufk>$v7!`3s|ogFNrW;zkUsQ@Hz4>9xG6GRaMoNmKHT- z<*>T_MMq!Mf0&K4D-@dB3ko8%AFV3yEk+&7-ds(esVHq;lLidGaWiUaZ|t)ly-(hJ zg`BWB`g}vD{l=FHMm8?JWZG+1S2>X_`F}YX4t3j>&(HJ7!ish$AMRO!lVLagEjB#P zXZa-EjqGzqUXmWwhL>eSt=G4VYJHf6g?G{?wM@R_7uq|uPVt{HbsT?uz2ofh)?x93 z_Fr>&cTtRa22QJbmNJBsW9j_YRL`D0YxC*JDZNUUjGc8XlROIxi|}L|M$2M}@REG* z`t#dQ!bnW)Fn!14J=bRMAiP%irKobtd_f%{pf;9malO<25L*#hG@pUsviIurcActW z!`$0e(~6u<`|PSPC-018L|`DBev@;en?sfth8~9cXk2 z-8&&aHEe)iXm*~8@Kot$q35xt!`N@l7f{dAFAN&{2Kzsv7@|v-%Z72peEQMyZ;Lg)LMIX5=J8Pvg9dkuFTu3EpGI^V zl^KXmKWd8*P@_maYg*wxFQjt<%+o`3*nq`f$`s8Dg;A6=Mkfm7K3<9gOjZ}T zFt}4Uufw_Xu4KlJ_$h+~I=9HcG@QZXy!l$>zDB>m-+fj=XL=Kkzk37q0N+Her=OVK zYS!f;m25~L{Q2y(Q)ltyo*4lm2Af)^MlW5yYnNmV3rI*{489W9FgJUu3KknsoG89+ zdXVh(s06unGuBX8&5+}3H8Q})B4zdY=IvC7x1-x)7g0G;?J+objx$+}c{6_&hz*p% zLq(f-DcBy+Kqx`j_jJlQzQFaFM0{6VYs0Y@XoTAD5ke(}%HGFwc1T>83cA+6N%2jH zxD*-`@g-Ae*KClx%v3nRA_8M!^5YpFqGZ$7{sDZ|2@zO+Gw3iNP{X%cU;j>mfp~ck z3s*S*bC5KMPJ-|?M>CSiCobpgT`=-jf?+ydM!O@#7@`Y}oHxu7-(3bI^OJ~0g*-${ zGc?_e#OVapBqfE$b21IR(5RWtH6ZZ)!$%YtEEtGPg0wV>Fg+61Bi%uTa z7~?nfoW@0rjG=4fe*@+C*%qV{^c#o|Zi5K};YI0N_g0k|_`n`XOh0I|5xb6;g$5;) zkLMuo!O?@wT#3%kP5{ELDtD~1_mW*vZ6<3`jT&t9Xh=yt0KCoe9QUs{p^x=N_2{<( z@%u`FR}e#N5P)EMp-!<>sa1R5Lo%`Kkr4x>1VL5U4A%+>2#B94GOX6pHuBEUDm2Ie z`#4w9%HECczQ@N8e`edH^}v_p{5^H4BXez<)BAmivioy`ab%(Z;OzwE^^VZ}b_H>Ha{Co6^vT3T+e&vrJ_D=nOc zfJfT!9bB@3q@b{dDr@7*)C(SrpJlffyzL>ts%U~PWL&WO#^&KA}JOH>jhAj%b2w{zc|O14@oD4N5#5FpwH=m_j}^)t7}|7Xjp z5C&V%m*WrQV~HL}aQe>p=^pT90R1;bO@|Q-``53#GBrmjRO)m=E$7YFo{s$vk<4|4nxtN|4|BFHPAU$43g66k?24tcZ%=5p^r*hsT#NVjJ z1NNBdEBv-yMj_6%1KxNhY$e89`O#)|=RgQctHOkW;bAqFWA%}SnR)W~8 zW-qBMn}RzT`T8~0+vP_6g`*E7ScFYUQi@{VgoB6T#c>yZq9;dPQ%fsTj^1Abs0=li zSr96W0sA}h;UOU*dIiK=Yuze&#l@ z(S%0>-HTp+qzEpsZmFeBU%syW*k_L$WC9wF0p7iSojpA& zF>vyv;kSi-EFHz%+|v!dcC(I1py;fCOcV-a4EX6^E*!?p?*1vtVZ?3xkAdz}l@c0A zlQC4i7LUPjp{vboCL)OZhW+$UpLp9*_E;6C8@;&rcqQZ5I!sbqzG|aC3G-C3u9Mb|l6(o^j%j-ud7k z75#XaJ8}pK*Ef7whW8`EA=N3zI2kYM@yBb7&uZzq*Nz(3@V4K?TbE3 zjW=j>3cHu<;&X0^A9TGlb&Rzv`qKL*L^y*{78|2Y*8l5NPBf|X`g4b=C@s7GH`6Ko z7q&L&b2@W`HDd}6)xxWI3AOt9=C{dKdoK6o5I>CQsgMh`7DqFoaNe1gH#-M15Ru`jl;svHeI*qu7%AMTl+Be#4fV}rI(E3JAl-BU|3Jvj3dcA>|sJz3-#>-sYR zvHp^EV;1g@naRd{-9m8$e(9cIHH3P#;^#QkkbNu=Y% z3X_+r{G9T3i)csnpK`s5TklsXx9m3^UHjJTa}a&PP3vByiqYX6nSy{nn+E=6%TKP% z1#|hYHT*Kwdv~B9T5eg?x5(ZrKv~#pPjPI%u=UvP4L={&`h3}<03QkT?^syUg`LTS zlcY*dyE`JcwC-~VvCpSFc}gDAl?>lQJG%a@>W0vzg`L6l(tS#NGR>ZO2oJ-WvOv_P zr^9Hag-tN`VRIOEO6l0zpG<=A^bfT^c}?$xH<2m#$qHX!sv?HYaNR$3&`&9c?Swj&9gk_uaKm zfgTW+X3J_UtS+KTr8m6MQ8dLERJ3}?Bqx`PUEff-YO50$c*qf{(Pz6*ba1&^X=r=k zS4v%`z_|VB|9IW@4Y_%p|%bjAw1jxnoOI-10LeANqctd!*m&Lz}I=e7!Rv z=G`pNCbP6lYk7_@ddky$eV4|KwF5o|SfMkCOHu3XtC^ zDbpJ_-YGtLL9{TEFzdjv=@E6@>7uwD*?ocKC;SU+-H&}0$_w76YE}+8M4-{bElH)? zhv(Lo-#DnA5_2^6)#&55-XG((7rj$x-i+0Bt|?|5p>@3Tc|}(9g5w>3k*obT2kfbb z>mOfDj89!~Rj>XP^SPV2uYI`?GCHN+8cTR*^!of>FVL&6Pz-nTxLp(AKYGhzU4*rP zP5P~%o?%g-J)2r&W8pGPmwE(wSbsI|!W+*k^7?e&waf$#H;mqo27f=88n$&hzcX>r z+B1_+RbOXhwaA>)<(aHk1l~EC{-%&+oN}#su5o@zIx22sBv2Fduk4fc{&;9H^$2TS zW6|h@3wCpPsi)g+Q$9R6i_yAMDUI*#bM7Kjs-4ZS(`~-4@mZ1L&bLbFw)-mWla@Iw zpKeAe9V>&Gw~ebPEgMWPB3175EZMQ{s>}%qTT`O)@fZ~o-6*0BPxmc-JpaA*@zyVI zMjJWRM-E0i#!*_mIe}`S=zO=#c5b-)m{fY4(JT`dFI7n`X;SQnQ(iS za*o(5>E$_!oFMDRKNG^b560U$@;ZHopRF&wGqWEFEYmESNl7Y>>$z}LZe4yMJo%#5 z*4~Bye#*IgH$1Rt{Y3}m(nSG7ywmC0HR>oe5P2fBp^d4 z#khz{e?tH6H?mnNxjgLpXqJGrihR`3jnH0Jp5{EcW6fe{56GhZQ`wF*_potGsf;FM zCcuFASy zT~+n*?1~CXEUF-;#<4SA3Egk<_P=*^>9haNl^fwC@z>Q#TlZC1mmfQ2FLbA{rfWXp`?JuuHadC_ zxOaJvd|c%|&BrNJ*haTMxz6sNpq=Sntx)dKZ@S2yW9uGR)BR@UVT9XCM^cs7t#HZXBW)u39_ygwyUw~bCJ^o&e9CTp}hJe#HHp|Xt-6*d)Uz! z?DW=q%pQsjaR63SR3B{Bb|xPD<|mOB8YY{2Rq@WZ<>NWB#T;+-;EL+&_o#w_o6N#2 z+-9+hKg{ngl#cS*Ts6~7zVkDYGdtrG&65>}6Jx_U;)O$(<$c!TT>0z`4o`|V$Lk;G zv-)fan+k?L9x80xfw}?PMq#}@cCGha?3o2KL2Sw@Cv@U>&6-7MsiR(He-j(!EAdP{ z9@b|)x}@fx;CXZXIh*;$0>6z$@>1J<4&50Gx8<&Nb5>iLwS#kvKA!r;TFW;UnD-Tw zcq)siqEoTomkU-NhWqbUQSR++@SzLhgV)RdzRbFp4&@fqqrqmWm)smT2IIu)>c=Zq z9vs52YhxSIKJMLk1;hS2)8t9l9vcoes=N7X(JNg@@arPm1~J)h2e5Ob7qD098zWzN zoM7nlQ@q-Ja-c3QaP(HapNjE%wP$@t#PV`)ne+bIEZ^m-UwrN9b)5ynMg!R}`K-nI z=|-_a%F=4WVgbu}?cU3+`fJnCQu0c6{re_TSh`&Q$9Zne<1V7+`407WQFXyv05~N7g}V&h3_7WyBceo@C+)?*C&IA|HsN6!>`5WyRAZ98ot+o`@wqlfcWx%u`Qpp@JFFM4{@ zx>URm{5c#uTjNj8W@(@7HPqg6VA!AAL&x-)CK-!u^qCu+JLmS*E-7r-el{tFA4?y9 zq8Gp^uy#=@S?QfiHL2)M`(o0O(B=2{UM{sc=vDnHS0S4{=jc}mY%AO>ZEcG*wrXv` zNX&bbVk(Nijnb$T>-3m@SoEq|?a##d^UE4VTh6gzf9?+@l(wJK5$FP;hm>$_8uuji|fTLxx!BT|F8V&e6NK+JkZ<|E6t z(~ee_E|eE?I#mvm@>JrPPLBxkOOqM;JH#^-dXWUiG|nNO2YFYAZxDDkvjp0S)? z@2?5b-q-z@&I6)bBTzBNL9@|n8FAp$=v@%qwmn~IX%d-U%}$>$qoo63O$L*-Q;}dYTO+2MReWrM*vpqp+s0A8 z92k?@!K*m_7|SP5LplUx)8|EIHaleqB5IZ%Svysh{~kvb&**$GZ@P8@<)~ZTcV&CS zzM8$wyz-(lz1BdlS52h1|IG6ZW1syz?r;7>81*ftAD2}B;!~OWy1D7)gP~LwbvOC_ zXzH-7|6!5idgS^d{~VJxNpE5d*;2_5nrS)N?4z_mr6xdZ9qDIU9U>0ACTAmj8fXk1 z+WhVFtS-)=9okWz^j1OC?)t&3lYJ zG#t?8k5_`N$g9L|Q-XzMpECP}wYpO#*9$Rhk=1+k+eyDSJ9-=UsSmDzH|*~Y?kKo> zZS!;#IZSl|dgn+N;yUb)_fA+r{;khK+$jhotamjLKq-{~SSc*cWi>{==jzqI-i2es z0k=Hj*XkFqohA72gieAZ=#L{gc9VAJgPsan-!nl}A!GT&BU?__wyXp~bI zZE7+TE0a2$^qx+B=+nf^_+c=E{*#5KJL;b&(O$m#;#RL?Ax(yCg}&~KeSQA;waQm3 z*1$n}v$jU2Xo^k)GKLinjHB79Bh%??->L$UWM=tTC_c)o7x7(*)J?sU_|4w-sL>jd zsszax!5NHYLw&6zTp}SXaSu&1-ksB&Z7s2dWR31OA&p7pjWtma^#PoXitRK^+qq(U z*YU6*fP-)UEeDHu;f`zt#9(EF7F=X+dL4|7{fRTBVqq!jT?%Je6w??yvvr%LeCAFR z3wG$F&nb`i9mdbrzlkq8g7%OuoX^{0(9IWN{+^T3&C%gXKRp*XzAfb#Xq4Z6G7|fk zBHl9nYO_!^Z|J~1eh8@8dCEr}e?hz4h3Gh?%c>NO=ABJG^Tqzwcr}Ccre!iDU~@2A zTiOSgz#`S}I%HCh1dk1=WupO(gRfi=1G-(TddkOgi0V20!f=K>23)_J)qMW59_o5# z0)_#~Av0d?hG})z3fI=oP|}I8S9G2UhFNuJd^yZkNh-|_pVo1HKOO4EIrWz8oGRTX zW}VM!_vCu{7Jxi`Sry(7H{;D!@dOvCCtGaP-N8rd@$IB?Queceiw7mti{ff&dnL9g zkKDV=C+t)=R%#~ix5o^ zWHgdi|H;cB)OT7`$fH`RLg_;GiDOshfZ)voGi>v2(}uGi|^-RlG8m!;~iQx{%Z;AeY(Q5`J9*g=g>^-0aS{MWbGc0cG? zSh|t9ts+oJ@S|KOOImmT`=7y*bC83+vuDz+r=<_eQ%a!3W6(CN-)5IOXx}wqghoN)}slg$xQA87O8L(429p(7UE-JAcuwk`(w6V`9}^R{4|F zO8XJ!e7uax4}JL_)xa?M7=+%n_8nc#dIh$v3ws{WaEp|S^N`;o z!Vn1;D?etx@H+j33e`veIK28~(b&@Iv%kef?iBtE(OVSO$Ux0#t7(Ef@Pj&eE{IH} z1;`iA_g~K3#_D8Tz3F?q7PS3?MfTW~mUNo_dfS_~g~fC2J~8^FZOz{%!+GCY( zlI;*l6wHzieC?A|$1kYcoT-xZBH0;Vt%5 z9timSa2oTk#IqFgFO;5mL0E9fC<(h=WmTg-dGzXB>V0`?fot;n5oW@Bj`mc+eG9_* z=;W!?-P2qT2F*saO=BH5>I5_uEhKe8 zEY)Bs`cXbbDs7d8U*IUWDaPkh$+zR=En9mKhR2Zizz!(IQ6@X7RbhLO?xFYfjN}fY zPKw)daT}0`@T@Deh_q*uW1fZ=%Z`t!f24sJ)OZyKY*vrwWM|((lQ=2a4YHq7sMM@IQ=O_ekL|y@YH51u&w8(R_4_HLO3%+Non4$s26US^5c#O~B^0mb_s0|N2ir4tkj;{0-WW{U zUEXh$_czURe{;MD_IK1pJQjXmh!=9re0Og!6%ypjuzcsG5d^dTdzC}w?qeW=VP zqNKD`pSJz`_dZByfaH{5QW|C9z^-(8w8n;-ntI4j(aRwZmY*cjgyY1J|5mqj)PS@5iUw*ft74jXAq%Oh6 zTGf2LDOJ`eaZ17Ju&_g%h-YC-YpZC*g9rE=`Xq4Gx|A8E(|=A^k7K@}H*O7x70_=}fk)YcRaMqe0p8;~X zUtffTZ6xUY-vi=U-G=)n-;+&MOkgD%_eM_6c)ul-;Ly<0x_uf_HV_j@$VrA0MG~~o z-NK}10_q@jbh5X$wz`*uM$SX>O3RlocO)ex2b5KpuZ@*{|KX3lFjj$n2*Q!j=&t2| zETorf>?D{yQaxHI=D)MG&UgCs=`FHx$r`J~#@#-l8b|cQ8ID|YH`xq)4-|i#_l30X zk5p*tKf^aDZ;kAp>w#4M>sj6BXESwK)uCJMQIM$^o4U5P8Y-jJ zeSE55w>=Z~Ts5GuufVAgAMnG5TjJu$4 zx9?T?@vSBSJwsne$zAwJc{B?WxM#yemO5aUH61c?`S|%+L`3u<^E%1({g|FzyYE%! zT7OMQD_)Bq-;;#`I9w7J6w_cK+iE4~Mqz0;C%Hyb7_Hs61fkO2V#vg8_L{hV48JFJ zViWT7Xkuey?>hu%+~PJ0+;?y9cc>V?rKH3Mo6F-~VZD8Q)K8y2ZBkbeJ&U0{np{+8 z6>Lh?ouwjBSUDYsDQmtB|48JU04zxwCw%N)pci~t?=!MWbdO$HC~bF1OvDq#NT&Zc z1veNy3({)OWi42(qa;)*%l^y0Llut3De-rip>B``0LFb0%HR!MzA_s4TD^t6U?@OU zkbZo!mn?8g8WOh&K`OFpY7D}Api)-ez;SV0D7kj)=QFfK zAI&-NhpM=$y+5sxdvI&@?befGPpV z;m$#G-Wwm2%?OJS$feL=BK~(ud)aG9cbr+iEEm=TMf$wf9Sr+Z9D$t)Laz7&+}IyO z*w;DgSw071W}k@Als8Dt?}o}~4nxmTcf5PLzu&KZ7g>kI@i|Bo5u6h%taz6=FFNUC zHw@Z{haVyX*ZE_Xw*Bze4Bf0cX65yNwno=GS@bE*<=cXpxUteCq z3llt3;?2%a=i3yDE~jT?S=m1328$(1?TzvB-#s?s+e;#Z52h=~J|HRlwMIEDXFU zD=U)__{@z8LQhYRR=<(Ie=Vc|G?w?{Sy))GT)RfJYuC<6GU`2(=(AYf5ARlBL0^>6 zyAOIQ(v5rkJ4o7x%8bHN9^!#Din6)oz{BcO=g-?*H>~OzsPnJp^4++1nV-LJ>rX2O z!Dv~^I#dbu=+yd@7dwuNO+qp}cVXR*?PW2sq|F6Z$d7*x88?>&1o~gzy{_}N;Y(W^ z-^TK|BUP~J-n2kH7gP^D?>F*o8>n*6bDn%IKn-PZP%b5d(Wev4@@+@&Pd-@7Y62_! z916E4!AJ{0wvQEXd6E2ao<`14`opbeSu?ZLVvy{#?CdYDc{w>N361N0L$PB#u!L=i zBK1LYS!EplU3M4o_F@cl(Uvc5;w;K$6va2+3&2)1)K7+wXL8S0GJxw zJ;%#lw`FFI?JKx%?}kZS5`_+N0bO_wubrTN_3D)@ly5wT1P&S~I=%1U(NMmgB+s1` zc;MYGWZ+nkIERzCf{L;E?HZ{0%X0pFR@>`ivZ+&IVi>$XCk?wp1NMU48dn)l!~;2h z2!>M{veA(Ln*-CyE(MNWXp&%*1R)Znd~lT9?H>QI%`$)<`T~o-A2XsZzn;Kg&(hP6 ze~p%}7;$+91+chRkaV}>A@`?=+z60~JV;8p5ola&_K41B_Tu75Ww&cSC}_cVM{Dal zckli>pIjmLg09Z)3Zg>=SkjRV8h%xGdzuehb>;3hj zKr~U*p=L#PeXd&$itwq0T+$(lXl&lQUJrk1ydG*Qu*PWhzowsG4f?hY@x$$QqjGgrm9DF3aUYuK-uO;Oz(QfH4Z;T17^<;y8YOEwrpH zh?t@b*hyEaedldWo34Hs(5*dZ)>z!R0 z3TU06yzTs84H#K>Y(a%>a~Q22C{AJZk}xP8us}oK#qz3O;RR z)F(8P(EWp-&8S8PUc91LX8r0OfBbi$nG5hbp!tK{<$h^0Q7Hbq1SWAFBwQU=zyc5& z*~2SV40b`=*uNZ#v#!Wf^WW2XJPhv*l7ys81sJ+wUJ<XJ($$h({+C%t-lsc|51-gWTL)w}}QksC}IeVJ+3uW z9mn#~RjaEzC~IHs{dyUqW)2nA>h|0ulj-3Fl^qY-EY)w`403UCS=$(PwA=dq11%>_ zFKrjo2TK{AK9d}1M8kitV{2uy-0bslQd(gRD2G+e8p1~aBM8Y)L(OiMU16o{_~F4O z`qG{I1jlSQmqrbTf`2qVMhm@!o!tx8vHh$l8S>qX?m0uqr1&~Tj?VjLWo0F(O&7MP zen}{BZMGBT@O?$bMW-&y&N>F}>c<3-As-3gjSE3R@RAB_ueNb7K%RS_u9Xdx>w*>E zXcer26n>y@mA%FAiVkalbu{M?wInE<4^_Hm!8?0QQSJ@-+Vp{Y#Rsd&hu47QpuDV^ zJ}@6_rX6G|g6n`w^!CTiTbRe8IfzxjQ6C%BVWEuDYxsl z{qgY;)bHK_p<3ITd|~(J*H>Y;*_U-&KTn{hSrzmiRM!oJcplbd(|Z>WlL&|g+!p2n h{rDFx{(pF|7q}laAnfL*tN@K8@-oUd3#CjR{4eH0bg2LU literal 0 HcmV?d00001 diff --git a/log/ResMLP-12/val_acc.png b/log/ResMLP-12/val_acc.png new file mode 100644 index 0000000000000000000000000000000000000000..dc48edd33813c869b07ada0bdc7fd76767bcbe7a GIT binary patch literal 29923 zcmeFZWl&v96g7Bo_uvj8f=h6>KnN0?06{|t?(PuWLkJKYg1furA|ZHix8NQ;$ereW zV>MDW^JjhyRj=~uo_o*fKHa^0?X}kK@HfhGIG7Ze5Cq}8l9y3~AVhx%LRdjZ1)uPa zE&Kuh2s+E^IIG)zaCS3xG=r3ko$amdoUJWP=v>Vloh7t32|`P z{I6eNw{tY-;D{i=0yn|1mw)R7L3qXw{~;8L7g#`$^P5*Pk{a%52a6v16E62?$J6?q z_MY|wJJErpG+}P!FD+by%JNdRY^Od<)i-T&ST&To*41kjY5Xlz5-vTcH+Zp28q`aP`l7 zGAJBePQ(8HfBgT&3;R=V>(4m8cm9l>JtQfK^f6(0S*kP`2R1r%#Nv_?0xBx(q6xv9 zA7s#cc4H%1c6N5XEh-qcI!RSk0x2me>qYETBgh(!gM$MK;g4%*Xo%MP@AuhiMfemH z%)T+gjHsQioH#e0US2#8BQ8j0p3%OMhP3?OWQh8|o(!g>2=jL&^6-V6*LVNxH-LE=z-%fPx}oE7On0#l>aq;B^vv zunYrN3oI^`t^0{$6F;7wau=w;f=-_)aYZ^$qR3|{tY18O%p$Wnkb(^oZkycQ^ z{K}>`wQ?0r$-h=qkRhd}MhIn@k7Ud=*wHTe-q>(Khl?$wroB;JjIkn!5LgkNQRF;v zDJd2gI*v_gZN3j{#o1a2xpeGfTzY!+r%%W=dqjkUaq;nE+ddPPybB8pgXQYXi9ZJi zKWUYqKvC4fB(t-#CM&{Do7nZXixHs7^ZUNn*VpK{xG&$ofBZQi!C`YS<->f5RtXrq zLH397iRsNpsBCSe5=zHKKtT8~jfRYzRa;v-cB7cWjbb%bD(ULVhb$?frA4BquAV(< zscK+=3&|@h_Zq_gX+Jy?GZ?YW^Zdv6R|&^|2RI^Qc`PTMSJf;UdXssq5s)bSO}zh@iyJ7|1*=kqN0qxKGh>klzf#; z_TeL*q1E{&r>9Hq>o}#lb(m;)G~M8(9>eg&S1SDOO0ERL!^7QI0?_b@h=TePp04-N z`XNK5h8^wwuI#$C=$e|EYk7r*g`z5{5Ao=UH3j73<3pd&PjPmUn3%}BP5eTYIq_kn zZEV;uF)`oh=z#B`yL)(aO;3~h(?1sbx1F0T?8a_lVq(ASbGbUizuaAIHjp*FN9m)F zhlkhQ-AyMh-qC;2+}s>7-|le3|IjTBJ{*5`cJ{F7Xjxs|Z?oqX78YT#v33{j;8oGn z)5rC{pQ*JV%2!UuESflWwzRV9@k{;ro*RT$Nqh<)kviRIqNmDq&z==hkAUWc{k)&` zCO-ZVjCBI0vdPu)Dw(L~3$gnv(?=vECSbNh*syX^c71%SyN!(vdN7}JUq=5(WYMlP z>A|ED^_;NYcAcxW(5W&(3Bjd~P|bb0I#HyqQ~e&}uxX3NA^EC@)UdnOVjQ1@B&52U z`}V-jFGwNpN9o%dp?g`n$e9Ggn8oX)%Hx=Jd-clicmxEh>R`qveE$4KRW)GR(2tsl zi76~5rgy4LkAQ{-7kc*mxumgiwtK5`0qpO_fWzNEubw@^RQ&b=gPfckbO%4yVaHD+>Qnbwh5P=~iEx*uLoNT)1&k6qrRfM)CKP!@i3nY( z*mCXG3hD7Bgr$9#8E{kbF6SIQ+0h4 zHAjHp{4P*NQ(3IQvYn!xA@X%&_U<>5c@_OyGxhM&m-xur^J29fLx4ksk=(PHs@(tbM)1s}bwBt}cJ>(z@QmJowfP!t-q;O+iUo zWk^~JcZi?4`~8?Td4V)Y+6RKruqtL6^J21Vi*Zl+KCH)2-c$)js(Y3`^eV8~AhbYS zI1K)WM4CNuNpDYo%IwjK9T;I4O!{~@D9C1!s^Iu; zmGES_#5JwA+xMQ&I{K8ktQdCF@n<~}Nw$z5S5=w%(WhXyGg%e!5!60BoION;QU9U- z7M`4{L!nubZX6 zzF_*&o$i}?r)YjEczR)?B*F4(gaZw-7Ir1DTPO&X>Xk&XYA!5>1q2R2P_Sa)VBmnj z8+&p_hUVEuv*C4xXjIxmp=cL!lsD6q7hf}HM({YEoJl+5Wj;CLe=#_U|GU{=fyi*X(voJBCRu0MAt*hvHid`4a_vA47NnW;U%qtTkexf!5}h>&+bMz@wjt4uJ((7BqhzKe~II9aG)0WRhbbs|qm zW(3!?)=%p_tlqXto^AdqR+pw8x-`X;=V6$Cd!N?GtqgeNa&cF0v{k@<2;hzW0q@rD z-(P(2GHA^QtX7UTBJj*{VY_`M1Pihjdz*qNs1nt}vPo7aqoH2$hOWc`OSUt&6Cbg$ zZSUt@Olc@=bsqm+UnK<7k!?}2AjxBD^k!?rGWpeK*INedJY7|X4c_;eEr{XWqcG~7 zy?~Y|Lq|2&^m>Oqp@t(oQpSQx!i;jqTOJQ|UvF2|C+C-T=M#kEBArxEGti!)Bi<)? zAtY$;|45n*QNZC@|514tjM2y=6gKW6gpWiwn>e0Sz6N4h&aoq#RLk>|jn=X4gJpXu zm)B8Y+Yw=hhlOL?mFMzB!Fmce(Fn(vri@_eKYaP`K>1vwX8Lq&?@|W=!6N?30V?F~ zX=1l-)ANjzg!0M2ZxCqWzoJ3zmfk9gw7-#l^WiFe+eFwT(GoJVX~`L=OFr*M5NUW$ zO3X`fc5>ORefo0U?y|RYUdb4|G-~kD1k(*9HfIN}_VD^nj1*86ig!onmx&C;h+{`> z4{dz)!(|vQ5PiFhjUs>Vp*V2;`0ndVW|fwj;+h8QocaqRkcQIup2@e`N>uVcIX?|D zfW+p83?!_s7)sE$m@QZLc7A6aezZErI}22!m#%B3DPR{INy_s~r=dL(ixys53|{m| zS^5o2ESOyNS^t`F`t&>`)anM){HK{3HYS&%xx-iE71$Pw z!$^m3PpY1ubh=h-W5(j~@>cI^%KX1h*lcNPA~~7=tGl~Z=A^fO(j>(cq7K$WL4S`mFFab~Mn@Q8_x2S};U=&aDf)!9 zi)Vf-lynV+gl3a2$Cs9V?ge{_k>W2ecX7ozv}@*H$~eP*hS~mPZH(gGF+ct_fl&X^ zq@Y$T7b4ZPJloFcX3YJ;`Zrk`)@%Zw9T0FYCw#@odYXJ;ss4f9;QHd(?m43O7fjZ6 zVN4L_>@zacjiARR%pJwO7we=|2RhGvMKuwkl{uD3Rt_RQAq2>ik@e`i^qj2k(gWb<4e2JzfcXJ}o1+5$= zue`K_!@e8*y#2#{pZ+%NI>9ry1kGx&E~!|Vg}wYOXH#rX`xjguHst5Kt)l)yZT=0u zLIIOIA)@m~RAgZti=;S)D${xk2||QObZ_;?Hgv6pwIVWO-^olh4KzBGJE<7rJ;uiF z{t|#dM)f;yD4|GiJq7{c*fk-yu^5-UV1TPUHgj@hHU%S)jvZ>MLhhwjy%*5X2(Miy zC5PAw!8RwyQAn4d0H%deZlB`*UMPa(l7K!9d)@-v-R&$}<`>EKX z+wksIbsjA)r6;`i?1K0*u`NZ0p=&(B6M@ru#BKcy)3WXLq-E)%>2{kZS66i|s;@yg z`a%9DIw(}=t>iXV;`H%Ks8`jU3^{mCtGMa2jMRnD)e&Vb=yT5(&1qwof8>ya{)nx+ z%N%t3@2}0({cM%{X1&-p(TZ7(I5HlkHWC~FfMUx!J_~8}7!qyznicMcD8I$OHRt1j zK7QLlpZ#tnzfp{JilY-Nd+`g{g{t}*Wz%?l8uwl(8y0uvBL!=TC$TN++%^e@kA=+n zYfjf*dHZeoDwW!7#HbfeZgUEBqp73vE$z+GNJ|SFN^URYC@8|GD+E+?0xJqw_(aJ= zy-@kZ%mVh2z@!;;Yw8)r3K3*a&7g{2jt!T=6E51O0r3L>*{q`Jbz#%kh&BPwY=zrP zzu3gJGWh)m*wn%|iz~MYC1E=G$DeWaa7v5EBScLLewEMpe6%f5H=DdFtEIq9K~_h7 zK`R5$&Zg2f2Beeji@NK1bEH%+w^cE*w%iNd6+FKy`069j*s2Ktkfa;h zliRf0!FF4Hku4cGYu6R~cY zReQ!mgD*GXDGSEf{vEeksOiMgvwn_|ss+5P&x*%)Rh{#uEVrte)Q^@M#e9z)I?na0 zZbCxne)i#*O?R~j#5~Za`r#WeBHnqC>Sa1k2=!|vEe0hgca=0_4oT$0J7Zl?0!|$6( z+h^Gh_y+u~`P|MUx?KS|_2wzKeQMV$2PM93%++aQtCSxG7mt=Y!lO~p1p zE>Uj^wuq9rS6uy96maW2;}yoO68lme6w;JO>o*iy&RdDIDp?I*5=cuNiualI->W$U zD7_veBabLQ#7rwG?3v14K&;~Z3Qt38gC1j}<9qFtb^-jWh^H>Otx%HQr~9j-_~hrV z&lO(-nPU;M-4bb#c0R6AI2#(^^$zmKv99>01#X2W?nC_S-p^4Srlw(y&Pg8qJo(JL zjncV1K#Z_t8z+zLr?~Fr@{oe`O;by}n&j2vSASm&cRRi9piJ3Bux(I-ZWkK2*N3+y zs;JYe&_-)7k3RYOmN65r9WwMC^^w)H>ea1(k;db4{SJ1zlkqV%`Ny4+TKjiAiMQU>!S2Z_|ZC>3DE$y{p>BhT;T$$t2md!-Aw(K>?prNgnVC3!Ogh|3?NDEh@q~)X`Ah zmg=_GJ$tWKRk%m$%8BjW-H0ggq`Pn5lQs`AbC3qXP=%C=Lv?l5kGA&OyPR8-did+K z1cPJ^WusTibNuNVVaVtPW8ebu-oBBHmx7w1xy96ZTN0NKoqNc4YnC^@-6b&o8yXp>E6jsfHtJBHS3h-O>`md z?R*Fw%3fTk`Y?sw_$zP5;V+@V&3&Vi_)n!aKh=PxV^u!!#~wapGu3S>ndTHyUcbXQ zLC*Zi#VIsWKSHRCW~*XmryzQb`IS4VL<=;ku9oMuKPw$-g!39I|mT5x`AQ~E)K#-=)9_ZUy3ZhC_ zCfW2UI4D_d$8$~ASG)NjwsL}W3_y|gp{jR9Q4}ts%H*N)xn3`@{r$6N{)#7rN-!Ys z9ST1^R5rp|yYCpnhJQ;YVSAt65;ed}5t#-DamX=}nLf#YV}~(8q1yo6OLumWCXg=7 z3$9E(aUs=wzht|Xl%6Q8qW9e)@g-j)eEmOBxakOR(xP|m{Y~vxu=x5LE+0yqX6}rC|CtO0ayQ+>xSf82Cd{D+1(zk-0yq$-X?g zG-dd9Ct4e_cM2Vt)Bij%AttdAj8Yw_UkCZI#Oj9b)idHN`wy%cRLcX|_rvO^V3H3h zlX`zwD+9O@H=C8_N4Ik7XJTG=)h)Bks# zkCP>=xg~PmA+M6OG64R^Kw_z<1R$^VqLVhE+uM9K9yA+$eBF}q^&9CP_iY&V!nv;y zphKhph`tXnaNoNp+t+h$(X8)goHv2K^WCT%qM##kGUTxQXcpGmaHGwWx(&K8oAOAl zT@74_Lfu4=-!mvG2gwp;G`}(a`a<*{gYJ?PCzKDYO8{ z4wtzLo7eT{@)#N}=i6hl;ST&|Li?ur=)cK5p>t13R=8tBFlI5_ILBDWX>W`$t^T;! zS}q@8g2jUnhThXB7nWnnEMG|gR$%9T&OZI|%}hYY$P=?-q1FabJkzCrC?mcZK}aat zbV&-FE^qML)A%XGMfq|28DVpGvY+;ez`6~mL!WN&b&Bhu<@cJ=AvzXsBAugtGnZ3D z+^G~l@cn9<5r$9vC;D#*#jWomg2uaX-J0;6yCG}%)R%y+CG;~_>9UuhZ3QfIF84}n zYq4+S?U?ykE+E&y#7;rk#AoHjK?NmLL^p5qzpHgfaDWWmpAMouFyYAR2RQ0kvo{U^ zcvn8&-<9T8n3Ie_g61Dn@{p}oW3sQ$jyU1Nr#Rg0yu^MLPadB-_Bb_Mc2#gJhxmzd zV*^IRp|K&s6pV}@z{CBKllJki*{0OT#@4`o6U(kpxVu|cZzZcE&WeJXkM>#j;?Yr85ba^lWoU$I1t}-sG?p^C9(CMb^8o~Ji0tmRFCt$J@?1+I z^@EOn)q6g2p(^B}|9H4fcGt5h`@RSqEQvs$g_Zdq^qL8Dv`lK|vHmTQ*yIH)l~<{Z zTIu2%?+SXVOr-71tUQ4p8G{gFd~wjnQQzcdTjQ`5JOA&7@u%GE%EfRsp-u&9<$0J{ zsGkyOqbz{Rh0|C78TRJHF2?&_A24Vghy<_q6uY)W!02t~c|I3=7oypWL8161{bK)ZBvf5bHWiN zz7*aD`$AAvi1CVO!7t%j^FJS1LjCOcIA_pUXvtBFkgvFJL;%rb*1wt34=68B1oXYl zrH_!6GHBtkeSCJQ?qYrq->xThJD=~YyzORCb8I7H*!r-8Fd(-;a_QdG&sAQr;ijD| z-p@&Td^#wlG1GW&rl&D&!DY8D*?PL1(71Q!90k9@y*&tBf1t#gwY-}OiznBl*8`IQ zIalX#%tic3Of}~RZ}dh%j##zLc|);WMIGV>E#`F43a$9oTbrZB zLB2sak3-@>_ocj^E6OWjUxFm77wE^XHm=HPMUNDH(as@*^0AfSOIOnqemU0qg0lx| z$S%sT-+fDT@?rktYYH{7$}L$;L=@G%%ALU30|$*I|Q*!{VLu&?yo?B{7DPWf)p)pWN`_@ zWzx_-?gZ0ala^G$_sxEOCluil`^}!gF^ks+Kg-8;(R>%18A~ZvsOYsT)ZAi%xVp+9 zEh)=}`$)%+DWh09vc^US`uznZ;pT@Dea{IYG51{4+{Fg~(H&BYRpS@u&&U$~T}wYn zTxs~dtCd(TsI3}gsG=#s(9~|y@|?PjaAqP1i!AV9;rwmkWUY_*^qo2L8(+<6_Cw65 z9h>V_!vsKOY;SBKfYI439g(uoOrIP!dlg7$wAKF_@(;heT>(fqeN57w-vYflX(0e$ z7n2YAWIz>h08Kx_BVJGO+cKIl96!ElMw;>NfZyx(S0W1mF)>P>TDb4s zKVw)34o-~6QQPHcg`xPfM1@ZwA#2xLnR4&auW4JRGLrl=W4>f5m&mDz$G*I*aFID= zcXSvi;}A093bVL3l-FizerqH?R8GTgB2j&Y3{U&P0+?E4&ldVxy?HT-=EfGIMu!Y_ ztB?SGTFzU;sDlHqfxEX#UWkU{yF%}oDxW_Z0Vn{(x}EEd=bj3_43Q{DwvdToz6H=cb;iZN*2ZOCP#pT5U?z?_pTM$Mkikj z)=wrrWrUrb`{x_P8Ujk}A&3#!S(74-W#*qXln_46_wfJ9ivBLQoK(*0LvOI&AFd*& zDi3Jkf0mhhB1svaK1Eyajg~esVZg+~q7)TlVnT@|Vf*nr@3q7J3|H&LoK>>8pIAHm z-s>cJMC=BeqvMv_z~@{uO~3)sYP!5p*%Gb7kffTh*5d^hbglBQ(M)&q1Szfo-lUlI34D=pw$gJ-^pF^3kstkDR}$Ge27X zN-;i9Dt_xgg^KF6?iih&@rP2J<|xil%BBt`a7(#d;#ITZ@LdgNznjzj?Dp>uWFXJE zx7y1YcAp&w7(i!;ob45~FE1_i?CR?tKGJ9I%CqEU*ZIgb_mK6MmCJpvK}-o0M{g8)kQ|K%GEn^G<>miV%@Y$RH%|%ev;R_Dr1N5PDbt$V;T6ZcU@c{7 zZe)4$PTTI>W=dBNYGz{a7Uk^GaWsoY@j~di_7k1wgO7ASf-W=LqKfDAE)gx!u zECD2N;CwEku5(O#KDA_NE7L;%Z%YtY0fGfPQ4Cl}C)WuM+lQ-xjHJa6n!=YNU9sA4 z4$X)lct3pH=-NKaMhs|qHkCe8S8rs<$mQQ#kuzgdOco(u{)n&mp?-Q*{M9lHlFJZ> zh?f_wH+)~`fueHv>wV)LCo-WsWX;3h6OEG1GJod|7JqJ4!>L>D4~j=BPZ6!DAYKZb z>YAGPxHuzMA1NtBZfbnc#oklh zQ$Jc`yokWXzUbO4c*zKIkZUvTlTkS@J(1-xjoa(t?amLR%{t#o^h5G4vAg?Xdw<9k zb!MMxHs0JhSBYNafSq=B3;PHb?lW)Tk|jsVbVyER-Qe;%G{hCbD8<7c4U5I$hYr9} zGs9fksjMUaI~&91@-Y%3;u~Gvxow|(#gsr^t0_8R;rYrurT>R6hKB1_e|qm?4d$B2 z8hm7g~!hH~KB3TTP! z^6A``&`6z+O`^I&*xuQHzmfe&ExB{6S-PjLA-p=qK^VQg&A4)0`UrB`zNQFWk=wB) zy~DPWqG{84F4*1}0jwkX6&YDr*+?|(__(}KYx=*SKK7N7ZtQdXu&VDlMjZP*J)FcaR6@tqj|BtqcnAsY0BIAoy*8a-p6wn&?vCd{Xiu&O||Ee|pM$nWNTGV$^3;!@_7CiQ2rxybb%B|p{M za}B}ba*Nfijk$cG$gjV1BMDP8qF}eL+UVlRJdFzAd(*fjB{Nj&*GaE%4V=!jLP97I zS5DT7U4xH6@_p+*Q&|`mDPAZD92xEH?ZA0~agcr(?`WVYIDbno0ohy!B%-|Qu( z4R|$q716SviBM603=D~dzL^_Xp2q=vEgiaqXS<6hg3@Z&=Lam*=Pv;)0&+m~Y#+2V zG~T;-AQr_!FBYfQ>i){zKh2Plx!S zl_wGIX#X0GPZJ!-$o3!7u*ZI_4gJP#N71k{H(9i~)|aXJPkO-4)z{l62beA5ryN?Q za~&6CNFIF?eXS`597g6DMYoZ6Uu>#EMr{?YJews-h`P&2=hq@j89;!;#ysgVTCe0* zH3epwcl!GHl$4RRwJ&P+#()3Dr=?xq_aVka0j@B*g#Jp4@h6JuLd0rCt2+}@QxR~! z`2P1`wE$l9y0yH#{P^O6uSEcp!w?0SL2KRNMDF+3TdKf3|NZ+$;JYNY$Y1cZocKX{zFDg>}kbEImTKoB&l(mfZGh%N2Vy$(V zS9Lnyx(LfBKE83D<$NJH8i6pnC>tZU{^O*Jqu{|s03`0pNvEss)#Wf-66$YM0QA^m zK4;f3fB__riVEyZzcgH~GP&r6$Y$%W>mD|LO|0X8h;i&}LQ62sn7W#mD=B`o-9SHc zcQhqgdQB10f`$@aVsk|E=zWrz(04VAjEosdM#kpV*vzdg;CHwzxQ&nvSG~GR4#O=% zYKH0Do2fmez2_yL8>Z=0iN3iPdm-{jA!(R4xyWaKZTxnpAoHc9Bmyw_feoI^_tyQS z-eqsfcxyO)ZA4X7^=E&-Byg)07Z)D`K$K(H;rk)>Y0|*SZb`)&@O3r19#8-fTIIN* z;JouYuV;%XsMt!^VRG{hCUkksVe~ATz>byBYR8c?f2l*>5bx?)nQ-jiv9VSA_*e0I z8qaYz>U({$0MV`5{IlCpcs@2YDd-v`%aK>;5_pvz__3AOPe9e{lVrHfH^Ls7Wi6ROf98 z!IrY}{vRv}MbyxL63hrd#DAkU+3ZpJV2FsU>N|7G)#dm5nwIbmS)$rdocehHz~}7< z8Dt;3mN>om+AAz*vRn|2YKgtzinnbG_L&atAFLWe$M;aEo>eMC$o|6J#w&XpMrWP~ zskmgG{}SYCeO!EKS;~>in_YeIw!U6K-vFuJn)Cv>pHV8C)SqdYMi!_NfD1Lbuhg|Ot=EI8O;P+_RT@BWAV5+ph+ZwgCnTNf7 zl4vx0$K~#=_}zVRLQ)d<)Qc%zo|8pPYQO4MWV~4uA87PXF5F_4#EF3AW9rc-t?9k) zwwk7^Al(RrxNjQHO(Y;gl4d|Sq}~-FieU5>#h)6Y)=eKZ+W6q5V~W+dd4_TPV4MK- zvCGIT$@0hxI0uNSkF=6cceXYnjC*DA=2ghV{)qEm+%F1$muuN%f9u3l*@z@B>}nqm zSWj+VOpe7_JRnO^{e{hGq}{YEV3FJfIx~Vu%07N!=aX)FpBZjJV`POX1;70GoZ0dP zpbv?(=ZF?Dk?44L^h}Qoy($^M7IR$6w(;=Ozx$&y8U_6Y~j_&Q4^`F zzu*Z6Y*?wRK92V-19N$C&*ZOu8~s}cMhBX#U^iqz{fU3qe$nyr*pV#ley*j`ht|f- zqwjLZ!Z)&64}JzS17Qz%;*QQX3~fKm5&ZcXT>JqmfB9$aXwc&o%MK{^K+uPY1%MPxUyWEf2rdcW+-|fK*-$W1ezcVl8hmZUL zyxX+f(GF(mvKOvyy1yj7*oTt9`Qi#Nka=E^UJGJl4d936xrfJZZ~a|D0^BIP;Jp_Y zbqXA{qLJH&F<`s@YW`1lBQ#_rh+rK^g9nZU z#{Je6R-Eq;oV^wAJQlXnC7bx{;h)dps*Z|E*V(eO_D6;v(6BFoI&(-xhOVNWniRQj z&yOqm)`aZJ9u4gIv%n38e&GNFp=g29kP=8XdeECwI)h z;mGDg0>2_)9Zx{Y57`4S9f`tXRf;U8@jjJC-|NF})!maV#Bz&oM{jMT!3GV`okm>4 z_{B#Y93J9Nw(V%gK=K5(sL}Beq}=H9*WnLKjbg^58auI07XTEKC?dEjS7Hz_U5>5w z&qO~jDVGj+xEj@}dzA3<(~kkonDHdbN}h_+{`FE#sF@4I+ts5D%uY^^MKxP}4C^9l z$?_4nDfEJTex8*6UE-U`Nz=V}!bs6_f!n>uJ(-j|0uyM2820zSc!BqU*E5O^eQlUF zwl`Qc5ht{*{IVY@9 zxU;W?k^|f4UV#m4jzRj&lGcsq+6KSO)Vzh<9^H$&ed*bvyr^2q=nSOOUqGu1OOM7*c?3&?sU4WT?!~DK+Ib6=N@F4AS zJe@*Qv!n+eRX3aFaDEgGHc*gfeGTeou{&L-60}QxY`3=xn>`_jd(AZCwprcy3DYMXFwo}fLXxO zlr*qL{XedEW@lMP10V0 z90^+fg{kFQvhR0H#)=z;y_e@syJKi8o|4m{6v?6#eJ~t#mVe=j5E=$*bPx5IxACl6^lo82$U~&Vo z2)J6T)!zCGp`l6;rk4~-UNo`9DfsV5lR*esEd?cH3$9xYzGPQ9#kjzXAh*86_)~Kf8h!fJgg0tw@_b_WElFK&A9> zye3L>M+keRVyN=K)fP;yzu!4ubxAkq?6{=d)EE1;ByRc8juaBVsGWno+6__)o_3Y_ zfv)7ux$~3D-AWIa29sTsTag-k;H`oD2N9)@3^lUc3X)ac7bSLjw%NCZ@q0m(Oi8a! z0JQX8y_i6Lapx<0c^uL~3cr`nBg7`yLomohYdn2V4koiSQ2!74ly~SXu}~!tjoQyC zL;Z$QJQ;tVa!5}9T9wfw@wffe-R`@+eiTj-t$f>g7aikyK+MOZOd~cGDxD z=vX-%Q4xkmgyGrme>X+cAEY_8-A104-?z=4Q%f@U?D0{!-MhQHKim4Q)&lCFAQ=Ck z6LB5zz#9LO*qR61h_qBoCs^|=b-GpGU%!6G0ojNnykUr62|od55*1%IKuB`1L*aYK zr*Or8R}Frz1;*q4bQ;+e%qP_IN;Nl|rd1Vqt5aT}l~3=)+oqXt#lt#sj8`{B{ey|? z)>gSapOG(um+W1kj4r`dX}TF4Dn=!ruk}^>knIJ?gd{;yjK%U$<^Uy!tR6iVOo- zKd)HGyZMMpnPMOhxI9G=Dl05hR$Q?WXxDB#NW8im$^HzKp5lX3B~#NPK<+mk9SD%;jaLqsg*bXauRftstwgpmmeO`_#N+dXFSh?< zT(~kV#h~4?68g?=jDauB=nl&JG|RVw$&9W7@S~US7T$GFrb=w-LWs3~O!mN%H(_yp z?3kGQG<06~vBxK4GIzIornC;V`J9_CMY|8)o$zkX+E<&3r_jh3N|I?2ZssVzf_?@v zD1BjrB3j4$-1q77zXL5rTIH5gJw9w}6|e-k&8=1Bnfx^nfh)tit)p$e3cp(e&e0r- z_x-lhCjP<0emF}V2zkf5w98)3TBK=l6^7z@3y(cQeogKP`|1w7J$RJTe5zhtkWdpX zE~#`05hv#i=shv85LOn~j~Vg2R^;I;#(X@N&zW@@O)OMx_{@r{YL z!RaX!O9qn$7U|rBb%@2u zlPT(!G<1-Lgt`TTYLS7`d@lsI>RYV%Ax=oBsMJaO0UEeB|BC0N>zo>LAoIBuCHS+B z>e5DY6FpzB2!<5+tunMB;YhJwYRSLs_1-iq*$k$5>~Q!T>ujUuY(A1~G;T+d#yc1g zR6@Y=1C^XY6i8VKbB?2La#qWeC5Bb4P=Ni~SBJNbZQ+__`pw&tV5ujozbu7E`#R29 z0-B)jcfe{Nn3PJjRiO{dcxh>V$c+of#W)BAnh^D(2tz?1wEYPb#oW4gSDw%Nl-^BS zP7idwpn-rj2vBOP%;t^tyyF?21-gxdjW6HDs`qLx1xd{!&1fl*k{?peAwiWp62?LE zN-chhCfB44pmxz&3Y5mkxV#hfbXIk3KIjno@vy-vU<=DW;;*jk1ewh@7;psF5(;z# zvjoH%6b8~kl47={O;gJKjM%VFi$0E=+hW~T1NDkNny-HJ)z48So}NWlT`adr2Q|SY zDX$t?x*f5j&k<=hFpXLWePIeKYsGqCR~|$hCE=l)p$wAt&YHxy61w>}O!sz+J@;j9 z5TJH)g06*#X2A?fc-~P=@;cf6s^Q=X7*oAPeOyq)<8V(#>OX_lgToS_SIx9QKMb!a zq$CYWb{(C4{?+efE9ngbr7`5py-dse5+qf?8|aX!VcB-#b8EltUW=h=6x z|Dl?2T#zAvIye~=k1p9^Lk=%vU5qrpNL_zW8uiGa90kG#0>RvnBPOUbSm5cd93KTH z4gtSB5)aY}sb7CNWcYeio;`IeE~N+^(UxRSG@@wmwIT(pw5koK%|G9f1hW5xl6COe zg311#>V^feCJPy}Z)1B`w&3>!*s`>yyj{6R5MYvfMj5+&T00bXprLW2xv`xH?;j2 z@Bzydp?*zQFmA;gL|QS!aKIC9pCiVQq#_!TQr1V=k}NIe%CZFDIoxMzg4D^u&q%4w zc(`=uGBnH@#*VlG$Z~vgatwZc{_fsh22s(r*>&x5{qVxVXMj;Y+ndIt4R24EibIzWXXB4zpPIz=XSPKWwk!_C>=iRvFO{1H3eV0gih zT4MpRa&Uwy7f4G;_|Kk$D%@vmY&K_N>iLRY-*~M+?Jtfbeq3T=@Wh11$2#3FNlE+y zVxY`ZE|!Mai%jWdG(|9|lQshNwmNN|yb7;h6VTGeMnptB`CsdNV>&f%j@s!(bzGKz z=x=aG9Zph1_C<4*)6chJXA9c-OOg2w=?7N2yIZEa zr$;jTjnTieon%ojK1kw;l|X|Hcqwcb5F)b?5in!PUUO00dYWATyN+27NTn;)waDYX#| z3tt#wc(0TAVjs)=!Tx_$usgj!GdHITIQ1gQBSTXW5s`)Eq}Up+l2RaI5;uV3@Dh_SKZoSd8tEoo=Jp?mxonL7nLmCxn}U}5PKzlo=F zv|k}w)=>b}wbe`y(7DQK0s&cBSx;geTwH=d8U1X}5?Ee)4@?hGVxTm*`O!bJOY?RQ znk)<9jzT)S#$Qbe8DT==scxFA`i=Mx#n_gX%n!GDsDTd-4o>E`qk*I==1pTopU`9` zC55Vp-#r2|6jX%EI6892(1=FxFMHQ?nO3}OA-P=g;%Ig|T3V?mHS{KEaXt6}gup64 zxUb07O3<4kjbvV{ujd)oXu@xGqfL95LAMpDyUUhKWf|h>JY>io6Rr$z4|fzV#Gjw= z4*A{fC^0k0CC&@(b30r~y|pqG_^bAkw z2`Kb%<=Ar6idNRwgNlopTPSg-c2K%I&c*dLicTJ!Lp;qp*)|O34q$fzWpEFc5jn$D z#BdqJHUo*l-!_29IbKFt9~B>M_e~1_`FH>Nv?X`O8$G$y6fk<=Cabv`_Tw%LjN=L` z-zc>ZGoN=v02xIg7`yGRF_@M>5Dm3c`PYkEVPLd;T1Q0ATTgiPCf@ko&>7FJeB}v4 zL05-s`kS(R8n~$Cvoa@fD>y+)lLr&$9-JVa#{}B{R8P$!3%rz|(vFN|s%L75zM3UJ zvGck&v2y|8Q&<`S@a5KSWMw~K5U&?axtQ@a+-1naEG?KoZ(C;yVWXZ!l9w6&giuNJ z3luX$_S)Fm_k@ds6G-m6CcMgDwWQT6LK{-4z>aIPz|D{o1A}JzCoEh*eG38}rt3atUBd(dl2 z(Cgkukk1!P`MmuxI!i@bKbS}-;1MBkG{_%sGDtXu5*+)%Tdv~o(PjQ$z`Fm~P-Tk=9PNb=4s z1GE&tsHh3(jFz%gDVd2?&@OUZ1fn`cU`jp1I%_@;kdaBp5A_%RA4iuo6N7GTdP2@s z4zC{y@(s8^3-%GfnI}1V53l6E15LKIz+DI=@ypR9z6Q{POHV*M8K!H6KDr=67Z2xR zsE$8;L;WaIpE)2ja$O_q8d3g7(7a*#7D0r2hEe`>!AFkPmGS z{daK6kP);mqli$6r412*i#!iEBM$X{_zep9|9DE$0PrIM-e551s|@(y-j*1k-JkwD ze?vDAeEQ!pEjeEw?!!#}a0ttPKO`ted;*&L-^bnbMpOUw>jQW+A)xj+kjp@?tsZ)v z0eVdcDhL1r1pSZ4dVr?Nn3bVID+~!gl)B(r56}Ge?r|_UdxM^vo6y$Q_Tdl&PfyR9 zz6$*&LJ)r6Feh$LSJ3iV<Jh0OgcEUYI!HHGMiGCB&~S5fgwe%7zf|#Ql^Z zq?HG6lu;KVt3fN7pr9ZbzbzFZ5fRW@XSV;BlRUn%zl$*Ki&?FkHh|q;pPt-ZE=Tq~ zcp1RK1Zx{Wa|N=$`*wEdoVS6OTN`TD|5VzS2V$AN`#<)59jWZJO=KIP#a^kYq%S!sG7S|KlS&2=St1dkO{fsE7qW!x+0N&F-@3Y*` zeSfa&`drubxj$6M(*}N(0JCo9VJ2cfQPw8CV#St63bL}C1Sjdr9gwX67_s^-%(RvF z6G@S@v^4{b*~zu8k8sdb@xqKY;0=x6Nd`R*7q;IHjEdrenQ0#!Y`Hme;H6(wtW|3= zn}BHRqJs<4t#=%7)vO~EW77_DYJ&{#j;?+7`nA5EUSQ)m2M5QqXU~p~X~A4z%^Xi6%`d1czmd8$g}8S;qKUb+vuvr z083uLGV=bjrp0t9usiI5H6QgtSL}$DQdSmladFxGz0egcpAB}^H+U#gK@oYd&|Us! zQj+q?kI&q%FQzb9tdf$FBZ49#o~g<8hiI$e?(KiE-32tUjq57^UTot}+x!)>RzAlH@$x4H`;0qVHQGs$r)6vpWVq%~%D8X<3p+S!ux=irrcSdR%Ouf$r zQX*@Di-$^3FyyW3v^I3>sqABBw1uMSr@Ag z%y$C3ooG*Azye^DF_$I%V>H9o7gbI)! zT2r>-w3op^Zf6a1)fOr8u#DtBR#PV>t^U9;|Byt2R;o)V@oT#j6A>U$^a;Vulo*tH z@9p^oJEA~EFzl4`ooPQvB;-GvZu=2u%d2?W-o9=j3J+u&&QSBjg(f|7T%J{jtx`~l zT+Wo+pZANWz)||V0OGS%O4F`PJB9|m>E%>!`)~Q2SIAk;;VkD|sTTTR9L7C~?Pnl} zY!AjPfBS11ei~cjPGsq)TtQB`ujG$wr9sSMpPMF?7+#2BBT`C{febYPDSdtAeqM#o z9o`G#u}?0?U8Q9iGf~5*Vm2vs`K=&tm3waxQ`f9E85T@qwhG$KIXt0yOX!PE9yhau zJ~Av!iA<@#9i|OSeRepNf4ok1v%Y?5RTU5MUkweNCZ-?~BLl=ktb^%{%3e_svArfH z=cY${mVN&{J3;UB=S!jMQr>)7!GiPLW@~p2Zte$DuGuR=6kVsk*VoQQjbxjaPMDffg)P?x&nhqK8yJUQW*QvNqIy{zF!ddv*wl~Y&Z;?ASwWEmV#&Jby_ zkgzayk2Md?zc?59s-v})cJ|NwnfggY_()Jt@YOxTZQFG8_4Sh`_oUW2ySl0r?m}e} z;xE&5X?~x{meWLzr@J~K%HN-Y=i3F1px|A}DuoAqeYFCEf&`W>J@@gW?X~r;cN!;t zt2=&|6cx(vgTI3rBl>q z9vuG%rt&XdzGR?|yuS8i|3M#?>$}1LDxD~lc>ivcG_HlHSHTN{Vndde-qOxvgGsg^Yfk^sU$WX;f1iG@?{x5--*A4f=tD*jd&I0UYDhC3}Gcpuye=rGZeVmn*#aa}& z5)&~cCFO2<`W6(xpo#^@F+g@~>f1L)DljmxCeP_$_7-t?5eSe&^plQ_xoG}pl-Jzf z*T?DPSs}aVA+>^uv<~@w&gIoqhi2fi>`y&;MVZt5N;y0`N{F{> z2Kc=D!alU_72RqPT>kC1SWTJ%R3rcBGu5O@wl;d0hynp`-T|xqy>I{x5P-J3uOZ;s zyx9Rzok&OOCRBgvk|~!@lcn3!Pkum$=Unmw`elsCnSXYpxx*2_&)wIJ*+7X??R2j@ z9ju}MPC5VEg2>i6l3+q0ItjF^4$MsJc9vU4=YGfVP=*?YeJ2@clbZ?P`&%a?%?C$n zlkF+p(n#P^XKAq66ojLdX&B&zyON^MCO&c~4bumK>HhHb!#C2=TmmPwrrT9Ea_+P` zwBOS#3K^KYWMJq9xfy3$qRXEs*h3Y(&sW8Od_0SX+IiiWFJc!TbcTo(BEPA4Ky6+b z0|_5H`ZF!Vh$zK0aC7OPFz`u16$9~omt&f&+39lgq&PsN#mUxfVZiU3$(~+)9 z0byZi^Zm||rtZ(EI4iIW+I#0#-SA+vD0DA}AQ?r8M6jOz;KDXLH#btZ|DZG|o-JFp z41fK4eDus<$U3%*;X1hOyAjI+rPy;%l-%8uq;%rF(yd#!&Nkq<4j83!h{cJB!8wj1 z=rZKDQ0UPT$ihX)Jdhp~r8PUeX?AYNuhOY_jy2@wNaDMuQ8oSBMi9wkgLoY(XdP>P znl|w>kD1JVhfcc*g#sREvZN+l!gaGh_f|W3*}j#wf&m>EII(>B@+%ez7ccr>xNt## zD9Jb#Z9I1F*Nsv(+u5yrV7h7OJ-Fzw+N7;E;DOoM*>zBkNsy-ZRDoJT!zss@?T6hz zZ$p6BHy--v^?KTCHb&tzROM85t2|PaJYvtUb^scK?8X}?Jd)L`s5>6C<`9V+F} zP`^DSZBw-YCg=6Ae_)Rghj9P8Eu*2VtxZzWZ&sNE1n3e$FHStqWbOn?*N@i9{IqZ1 zxy$G+sn@D=PZ8Wt6|QaGMR21VM)%vdZ+a^)srI_b$;sK*Y&>%FC)sE?2H+2cgoF|^ zGf5zg7aa%-ML~&=9U^p#n53<)wCy)F<&4mOduNoeU>J7A^9a8*kWSWE9xjd-Oz0!J>|i4$A-k%ZlVpJ(_s-j_G}QOF2FM z-fB0x!9OxBsh@q$r`hSi!>+Y4trb+S)H*Osn>WXn;v>z`erAM2nQ2!u;RHe| zfp|(|Am(QffF`w*p(dV=w7YP}fwY^q1+T+eijiiI1MR1tFzt+z0c>Teo;Q~LCZ&TPZx$KE8ie|x>PzL1;TP2@rb8o?WN7~~XC`rB zEJlUe8Lo4HmGC-i0$}`{X+D~XATH4^OTsO3iH-X+-X@{Kh8{DLQF=`ai(Kgk2U;+f z{6lJ~z^xw=lOpDQeSJ@Dzz{XHPn4ACq-qpTC{-{^9DY(&~G22N-EjUDeItTz-Dh6_q2r-LJ26 zQ6A``%yej0gTb&umV_s=E#GC?4bQ&S%U7&G4a~&AIiOAlioDgh{N+E_UV8b-y|MrF zvc1&DVMV}`$$5iHp?}i`=2&wn8HjfEDo47poaO29zJ0-AVas5@_9pMzvTN6_pvo83 z)mxpNmC)=-fD<{21MMaz_2Uvc@kiV+Dt*gA-@JT@E~C%<{QT%K)6t=jSrRR({$=&v zOf){&9ytQDWPA9qw5sYN({y1N3Ce$dZpNmiz-{VF;m|OeLdy_qLf=-`*Z1+%IwDGn z2$MW;nq`p)tOztwx57?d8r+v!hx~)xV*AwJ;MVXfk%x=4I344oQPJWh<%ackd`= zxnF+}*5wBk6=i7inm;_rB4?adf{j_aBu_X zD<4gDhF?*05GgG!-EC|fT0A%CM9&l%S3ailcALDUu^P4}YJC2G8+!ba0)j=|tzr3Z zIIpvnKN%Z$K3|ov`Pi}5&~Q~c)w_G3wMcZ?)<~hK^?vgjcCW4kz_FN`o|eYT#>)Dx z+FtQqHG?e)*JWN_WDk^jUmy^Pw#VQ7CSy_F$D>6pMD+4GI?`UD(M-h@RF&()|8t zRVAuPc`s#4bFFFMXZ)NF?JV5>?mNwyZ~mTcxT|sqcP2{( z-X>ZcB4eNR4>_?{jtBD`#g_~I7_`H}!lLkX^B(A-QvM3G=+Tx1Q$JqasRCYorpJi& z$B!SaKIFRqdNvlAC--Z4dFmjCUnziSbI@|y zYIL6#2BmF-(7x46)(ax4oZ#djB!1q*0+Q%oiY6`LJ43-7{}GyPetxnzyg~jzCIy=R zefAMPY7<{O?qj!!$+7!?4I@ltl7~5=kEHw4MRkbn<-G4AuVGxPSp1xU*T3s0R_lMx zME%iDY&ueaNQto6P_e$U!AHS5NRD9)Id%8{CSj2`BE6Rm{%1A)9oGMse$czBhL_0+6%c0>WZ!ShvBZP~qhcj4`X6poLfZ>E-3F zkIyfWl+fB^g6KL23Q9{5(s)&Zw;+rUyjAc$=2=B5fGLff@Ky4M|a-hXa{eFJR9p_v!Mxm#{(3d< z7SpHh7O)!_6{qn z#Ps;Xr`?BIBe0!;^tyFR1kW+I`Oc&_4njl#9f8;jAk--3DN?UQo1%Gb{jLvvs_~j$ z?m?55=H}NNMC`XBp%)6brsU;IE^zWB!9_&evu&n)RT_S7xG&-l{Z%_b9DhYpJ6Y_w4{rylw@Q;fV8CmkU?N!c4Ri-K5LKE=3t)Bhz z#8Cz0_$& zS^S4V=%3Z@ANQ12Qrhj&pPu&=%d?Leq)rF+A$Xw6HCSDhaaD?)DCP%$ew*BUOEdCT zTG~!ZYM?1+2S4-UHq*}ThMoLVzV<1gaye1P(=jt3{GRFP*1uMuop>i(c@gn>d%G2q zHOTue1>29I5_^GS9c-m=e4^FW&8<9`v*T$pc1A&T|P*-9kF%J6dBuh$8*x-?bM-p57I)} zec%8RB24WwgT^9WRn+6QIAn|kJUl(I6c!=kDgz4<#K6Ql?BmXbAk1F`-EhCBAqo_f zmG{h^-no+-w;N$u|e#Ha-j-J(5Yu8&1Hb$ z!Lr{40v=!eB_>97iglC0=BcjwJX9V^aum z_ks5TK!*`eJYfAh`+7`2ZgX?*f%7ds4+9 z-$A^5ejy>-{yXa~JbShcJDbE6O=6a~4>pxgTFPM)jl{cmpM7rgg1+I^BR8I4r4GMT zESCD=hc^T6kK07BOvvR$RRPo^|%8RqyU1DbB$eqIaOsJ>7YB%H`0LK$bvM zGs4zb_1|esUu@~2`~IP8vUB&VrKI4BRIsri@ABAAJ>}j#VOaU#YuCgW{K+IOKfgcnWGl;~l*ydfX876LSIm4w~itO0=fmEA? zh;MXfNA_IUbF8=H9c|G_&JVMZEkVfjks6n=B->#Sq&b~RkfuW3^uf0GMP7L%1JW_v zQC50z{_A30*%(gkh1qN=cJBmLxaGX*<`0b!Yb?gF2O|c&8pDtn`t#2}2ZJ9*;UfE! zCr?UI6{0A%(fw6@Ro2Fh?gRYP+I-9atj|ryB9Tqo$Ii_y1f&iDH_S!g$sx%h?O{V& zZmuyjX82J?$Q+_49UKzEk!@Ech}@Jz%ZYgV0aYd`@2}5Sbt-!oUoEV&ub4FJlJJ{# z85w<3U2T{@H_==^yo%i9R`_%^E^`qucAIVO>3;G5WcB`L7p1IxOb~0lzRqe`8A2e$ z6-A*-9tE?_UtG=lT(BpT3QUh6-Z&2!3CFQqlqTq*s|YhN-S-1fwH<-h38&FlYb*+c zHidE@bN2c1M-F80lzPZocg6-cta-QZ z+_8RkQF@8mi36#U4d~4O^5skH(N1|5=wH;;k5uyU7Ra%}i`%>Z(} zp(7!88fU;HZO?6X3<4!S`K4v#<0ZpR>`>D=2(PgMg~{QB5k=ll4__+#&3liS zSDB#Hg}9NRw5>5BJdWD#14~e@g6(EetGlWy?d2WKb>DtAOpI%T4R)( zLaiqS zHdm-b&M_340UZ?%rNy&fcN|?SGW+Fp@g@rkaS-JrWcM7HZ4b;_8n3)CKPSH-%rc`A zON0+4G=z1+F;5c~W_wsGQ8;u6oX$Gm*>&^N)qd9FeRUp4dNWW6_&qv87AxVYQ3aKF z>lQQq!XuFcK@}M4Rb)dYGTi`APf^odo_ILRrjqxXmX8QlUkNsMy6@Ohz$!gumu#3& z(OTp+f?a*OI>IE8X=lZ3Th{6e;zIf3JoXeq7Q6!Zt}yZDHmEWrFUYOCz?697Mlcem z!!651i^ksUK<jAkRT|;2~}>k>HZ_ zos!nj)I=%TF#PJSONLs?8@D3iPMjB*o}2=o&9=$RGB-tWupT7^FF~=>4f{zg1~F0l z(dSD+zXArrlHc!!(xE_-$33SFbmf9*%ss#)VZ3x#UgChy**T~^H+G5AnSz!~oJQrw zp-0QmLvUWZ_*-9UDORYc*y*k=l3~|txMvZ9SnGV=0`4dfE<4CK7G3^elWaQEYxsOSb!eosyt5i2WP)J$Sdg&joeK&Is){VG z1q`!Ldb@T-dbEJzVWvtEmonq`1e&Q}@5`#5HLJ8 z_7q{BsduDkwW`Ok6?!VqHbkH-&$CDZd2FE!(RI#JxTPuA-MjfA?Gxv1jyPq!zrhA@ z(&+|f^C>19cub1IN+k7zLY!NIW-iCXkloGzoL;Fn3~jIy9VcAj9a(yQUV&$)EDBBq zkXxJ?R3FKCSydIXb7dL4E)kzcuqhu<_R+bpz^6UyUr?}tbWaRA$_X?geJgXEh?-4( z>(9UjTf=}_DGd#CZaJcHg59{nFI#$YU`2ABbahomq2AH8al)Fp&Sf=OKq%#zGqAmA zG|&h0>^s%LMy4%Zqr6sSL{Gu=ebJ3QRni7B(K;Co>eKmTcdZbv#iVWGp*inE^GH%- zg1wVq7F8%n=ct`o=yqhHQht)nZM&7S{+xQK88!7bVLlioX(CRKMp*G)2+fCz{gIr) zx`zy8?=rF|;G%{aYE6^HES`Hd?Cb_sq&ZZFu{t-cywuUQFt1*paWD%1pR~5xZfIBu zOj363-pMVYu>>j2&6p11l3peL3`9}M#pS+73)Xg3@!w2*1mqWgj8(oy2CF%xU#3pe^ zokzcZMOJ|s{Ktaow<%nb;$Gpf)6ty&&9$+yQ-m7^Fi2w4E`CU81qL#XOyDtl$35YY zr#`PFu+ZKC-R)n5W}(kxJ)|h$hRpotcm8^ShM_3W zBh%DSb6f~Z(QNiT7KJ`Gr5k>46V-u$7X?+7o#o+Gk)MIgK$-gxMpWR=3tWs2Dzf;UgX8*Ob#QFTY5up9X5Jc-i*=UY*O#4Dk3 z7Ih<(|F4%Z-n>pl8L(xGE}W;`dqM97=v61F*vSG*Lv(OK8WFBK370N8Yl zR7zH2k%YrUTElP=9_6VGDrZm)8bN9_(NGY?h&xRu)8@0(c!>Lq&PzQF1ZcM-W%(q#N6Od=^ba00}l^+aNfLB+U)PfQE7}4aBgo zG0A#5aD`36gT#NUP%6oc9oO`C)aDl!+F%vrdr$vBax!d^-u$@Hjq~FfemP&t^PeEu zfVW)RJ6nW)gD0@n7#*6~hec|Kb-1E*h!N)SK0+Lq{4@1wCbWDEs^qxE*6|}xqT2tX z0j+kn9B$_bP`$&+Z^kUVX%g9uo3O%2UsR$>Kv>dJhaLvO|Vf z#(!oEUGR(0FKgAlxof-3S~ck~Ypjk%7z0wx@Pa0P$-pO%mOKW@hPYJutg YDuJd~Bg-YQ;VAXvmR+0g=~)H-2OFIg00000 literal 0 HcmV?d00001 diff --git a/log/ResNet-18/confusion_matrix.png b/log/ResNet-18/confusion_matrix.png new file mode 100644 index 0000000000000000000000000000000000000000..a664b40ddbc19e3fbabd3c489f0ff84359ced5e1 GIT binary patch literal 34131 zcmeFZWmuKn+BQ0Aq>=7WK)OUqQc6HTK)R#^M7leqq@|^#q#GopyHi5CySv$A>hrAk zS?{;jxBu=R+d~f!Ci5O++~XeCSrjiV7`U^gUJ!fHibYwHoX)RQFcn&oppA?S-pF9!2fbI z^BQp$b@rDAJW5&a*C*uo;+JIbFd{GlUDPq|I4GiwIe}h2Utw98QV6X0%W|30pH%Dw z8=2B)F<1EQ$&5TczI#oAjG$o>l|XyGUb{9T_l|>mFteL`@VHNm7TE_v4gQo7*_6Uz zVPj)Q8kIsGL%+bz1PKZZ3^YeW6^6cmq!l&-{I{#4Fm zU0p3q8x=$r5E!@@MTz``fx)M-QE0{H__Q%PF78GC*!ZRO&hBoSO8zz#_DgKkCPZpZ zP8<;t5&cd|>U2J7d3i}ay|3!zKkDj8sa_>pb7|_XcOIRdikq1+1Pe2^!;Pb;`zxMt50s&)zZh5&5V0i>^kCfD#oWr`ugdlksn8kcc>gfI2gxj9Axm!M9DID1D_SCVJ{+ zPeR}p+&V-f2$L3JvCHF~tagh3o5q{acnk4(K{nDFb!lW+HcK(&vXLF^s%HokyVCyh zU-3tg{4?r)p3o1ymtuKF&Tphe%OuDBeh)g@lR8;*t+h~gfk)O`taMBle0ZU76#4189N)DS)_?pKxthL`OI_*k z+3ed?UM)&B3#vLdz8C(Qv-KJ~$aCBA`<} zo-0F&XY8bqUOtv?Z)gd|wK z+<0+gIK$~=SaLXDl~t?3t!5y+aH3als8_F;<2LFIFQL^{1Sdm9jP7%f?R`8H;aps+ ztCCHHj+8=j!XXBfVh2nAvE!T98s-hzfypCYf#X?HQ|tO=f?ykId?z^Xmy({8t9;b% z@LB(xX7?&=?P;%X&#+f{kX-s|gg*aE_R-s+ds}QNZTvC|CJ3`(_h%YHUYo}fp~Qap z%o=)|BU#8OC@noPjN3PpVFQR2f%MJ(Sq;@5V;C+12fg^1H|}lN+(o~rp45>FdTY3) zECi&xAuv>MnCt9?lKhgMsdlg>-dMi1)Vu18JJ^dwC4A_Lj^ts}dJ{=yKj6q+l;ZMa z$LmIGG&kpx*Wut`JYlyQ9sREb?SqFj_xB#`UPfIUjy$M49Kh?NAPVVSA4^}2gbp3& zwfmAdCH~hjv&iV^kDH^}i3R(=%IWSdmc#k?GKNjQ4x5As5#Zy`9TO_WWxwriqa~bS zL9y}kI@nNre&cSv5qgcN@U&#`(Bgy9&K-dj|HFIV#JES4 z%D&e3X8y-?q*%kXJ0ADHDjlwsaT3rL0tUDKT3d~8j0A58kCh)MX`MP{%EQolIuR*d z1q5rr!;ZYC6aJE5_JUQJ#r{`n0ST%wYFB^1pH6W=czAfX3k3y*R=vw<*!APbkM-v2 zoGS(WJxlQSKh%32De_8xS~)lxe!J-TrKRen5jN3qEgGYzoW#pCQan74YS+y}iBx2< z?=1D(2L6(9&2px~5_vsL2zra?2q%0^8Ks-qrd1MrE2{^&>c5|Ub2@rsSctYCX+oSb z#0$)4xZo9i3Q3Hw(WsAD_n$p-{d&ULEeX!#mIvNkgd?GE<(&vtUSU3mK?fQxE-qo* z$;p&(p2oVe@GEzVMxRWgZ_D;pL+iZgY3_mw#taLxF zNHh$3y}eTEsMB3?#ug&%^+zxNb)W6HrCkqe-JlOqpO|uL<~@4{%2TtS$S8j48kV{a z4i1$%%?u1NC=4-q4aUWGa{@fO`lp|R69juI4^8|MWyNqZ_;b+TCzgwm z3qHpEmQRwbD-X~1aJ{aeh9iB)4prL!oLC&fhyr^v%?i`E-L<8V3W%$#Yg=2J-ud3F z)7|BI=(gkjT&TzG)mSgPGzy+t;zGa-6OGo)>Bba4MX~IlPjcDOId>8L;Y%&Zk?ron zW?^d-oQ0m%4%hOXgEUMhM)ktWSeV2e-o|7G)(cu4Khf8$*!w2)=j2w0Lk3-m z6X2&_fWy8#z@Jhx6BZUiO~KImLz9KrlU#rm2U7CB9BIz8Jm^Wn-y53tyn|0x-GeA) zjxQRYKEg#M^w$efYr{sFe*VPBpHnaM63luj|7__*SuCe2NtwwyXHFfLO_~CYN|a=N z$BwPB{8Hn7;v_+L;#Y~!3a#&3T12aBYN+u4oYcX3f7NGX z4#JOoS_oPJ?zwOEV5G#v42^i;Wx3w|`Sc{>g>Hbl&Xr4_=tvtc6e5T{^Zx@CByyHX zg}rSdh2+0MD`P9Kmog3p3=XLNzX;DD53#Y;iRvi>baGn7DQ`Zj<#$3cZC4K=rEeOB zuNz_sY=r|UEG-yH8T$^5xn4?mh<~yyy#rdt2UUz9va~PjzI>m^()dJtQok8JOj=x+LUZvpWD76A>ko)_g@Wk<*;R2$Ou-P%xc`|L zn*9xno(_Wft=gaA4Zvi6vBCg2<)3(EfJ9_e;5RNW<8To{tXBxQhK#?}H6sMe0;X?h zFbVg2l}_yIP}Byg^!_z5cwRL7YLv;Qe6K?C{~5{$+4;p9!UGqK=k<&c4sxMyY;UZ$ zBFfdn-l#Bj`a8lMPGDf-t}tLfeB%>&%n)CuSZ?6)YoB3*4yUoORv3*q}JbT0veO)czEr8s zvFkUS4K?Sln|vl6{k-b4B0r3mZxnfJwqbg2Bg(>D4lIE@um0+ca1#ICV+~qijoqJ) zYAjc1KQK}$nIV+S>Y>F#2O>-4s;`eA!pfccXoOL|fg6fWms?oe)PzWm)j_fA9@fb5 zW-YKo{&ud>ml%fwGqY5V7v%4$AAHMU|xyeV&nv}C_ zNc3MYUR8UZMsJ~|v=ns5eK62*>~tCwTkY{CNmlWFb`lefVEFlqH5xp~LM{w9R@K?V zja^Pln1Z(JUl9DrqG|uvqJb;q=3cXJdLzA7y!J zw<2vuO6NG|rFU~WEAUL!__&b4Ib2Sj{|tK1Wr~rGY@$1|7W^+#>9WS<&dlJ*HaujzWp=2_+*osI`!%qxe){|%LWlphavD9r(_UQS6AA!p1)3`XM-O?wbVdh$ zMuI#%uhzfS7rJSf)viDjTWfp_L|A>n-})Xq!e$-jg&nFmv+4EqNvcf(?&rK+C$CDy1E@(ano-4_Q3%nREa#m? zvt!cUtf#vSy5(CRs#n~TWtoEy>AB8yoS*gi z>2YCk9lTTwrf@Ro<1)rt+tUrfS7)VJ2!;xE$(6$plV0IE#@A8h*J9g20o{_PVHGw& z7ox>F^HOB-Jre|-gLlG%~b@(=CXM6n(o3c zT6NlE(37OOx!X&w-Xgvrwc?0+&Qk!bHQnHA38(O?UQ>nqU@PmSTf0QnyJIZ!H~xq2 z)cp=2*Kap%RY^Q4>|1`|kXq4*BZPPG)m?PFaXXlGt~(R6#a#_LE~9RDU|@W`Ae)YZ z*s0ULifXp9{$%*JhQws(6NE-OV^}z&MtubrAAh`edCtSAQ5G@&ebN3$Or;WiMP!NZmZ$*KnJ7OX zci-bvlzVfpeBZm);k0wPU80)k|Eq)ts~y21zG~C5+DT<$2m`Z@pO5rVb0FO`xXfj6 zW>PnJV*Q=o+5(%hN#-f6Nw=+PlqTJ}?iC47)JCAaXHvTosKnc5me!)TFQj+jA;EO; z220nblks*aE@W1V(T~pDw!IH>t~~GNFmo$MV}cn%oEaX<6}dbg$%aG;)rHXCwyMJm zQoP8RQ|#u)UKmiRn6B= z^G?D!mo!Tp#pz0Up?;!u0mpF4)p3pWouQUpQ)Uc-?D#uv#l>uU!Ha{qD3g(bqkfJ| zBccIPn`ZfK_k%0`xGe!&Cp-G*#M>qMu4UHAEf|4&%0BOO2gK(i0`%C_&ir17*g7^o zHA2z4(hT*&I`sF>nvzpDWstpee5{P&!c#+N^++=)fg*dstd@tsOR6&24$lUZr*(Or zboE5V=iCfPz26XmVuQNAyvz^w4KePE_xACDUH+VsqLbY@`lieJ?&G5=0b@SLqFDoR z9)mUKXMhqucV|#YDsCEfw{WV?-elJDnEOfiwonFZWTZk;OjG6ZB)a?651JJeVYX2k zGbr?_r2DdXn!+8oVt{dBpNOQh|Eq7Qoq}?&CZ9R09!3P4c&7RrF0=2?>6c31nnKNK zl4k7!H&s6R{+JQ(raE<~jTuR)qIOnz^Ps)1p+%3njq4BX$HY0Vu zfn=+uUt)A8<2N3;QJW*Pl2#?pH zUV7*f{R2%gxOfg9@L8jNPHo0)s`K3BquOxnE-n;Eo_k!H%w`dm2YmeW87Zd@$ICNt z{G!}40za*=MJ%eP0!K2w)1KA{Q&qj}y35?VCPy7C_=RB)?z~;gY+SbMU_-YAtdAF} z)US1@19VFeE!`q~;>i(=yP4j^3OaI`uBdtwQE2;s(+FW=_uw2`vP}?&UgI}KhFIAQ zFUM>WnSj>-cHuF<=Uo|mlQt2=-6?IhyE3O1K;O&SI;t5kLXDs}n8)ra^(1VEV4vZp zE1(Ck%ixYw$-!QD$j;P`_xZdzwF*dVg?eML1jWNvP5J4vC*?luzRAG6;X1KPqA`j17(+QSr*aqs&K{{mvco)leX zXfCBVLvTY6@Fs)z6%-U8s;b9sy)VaH+m~9yTbeJMubp~6*~d>Ua{rB6AL8H~g6olZ zrtnx(L-Z~V7S*aOlBIG1EF>n{;(_nGhG+-@Ib)>W)v=^J2FOyGUVmv&$ToyqRy3CX zkfnxSAi9%TtZDs*cR}+1L}vfQuQ}6fES66V|3-Xg5XDzc{s124$l39c84sp}Mnquo z@bFMReTsX$J{Zt!bsRj6AR?@#MS634yST96wKw}?`DAmHHXJZ%Hjf)8C?S)RlXE)z z_0D>=2T$hS)*SW^0L~y-vDew_OcxI>F&kqjP%DLfH<;4C)E<1e_hVoG@@TaK>`k2z z8ktx#3d{qw+$xP50z^ zVZZUcour?iA8oKiM$NKA`12!VkIPkpBz{K>h}F%7H7So3q~7%$5t8}s+Y(?n8^6l4 zttn!p{VZl{7sG|_Adts2G->JSErukXo}Oaj;zU(xSFL}9!(+Z*NA2#`Z*x7P=f zgMpfAHZ7m{3>^Uh!DOzE7Z(NL#6d*y(ubsAx~c6TkC{u~Yl9DHW+QLFhr=Ja?f;>*#0!$gVX2 zliPwWD#7?6eA-m)^q4V8t>qbySt|WohB~s-3V%Ya&jn(cJ0q~q2Q+9)JjDX z(>Xcr`n84zO3jQNg`eo#I&@hnDP*usN?p$_miH?)n9p{n-!bj!>+642W_b0zyQilm zS1DT!@&)EE(<}GcnFN_}teIl_33P6s^fv9$px^z$fUgYYW7NFa&rQ7#LtvP=p5t zB7=v9Qc#dQEG&%hxf2_tslOlnkz`m~=F6ys)l~#}d3o8C=S0K9!yWlPxp%)c-gG|o z1~=Y0mu=<{B2L6#zkVH@pLcrR9aHoraCTPr|IAf-t)k-3ZqPx=%8E(G@6cAH{bONi zNq=U|7gNwHIhjnyX%QhhCMHcXoE$v-WVk4seePBG93l&{2mW76O0X;}EH+s7G5He| zcX+soG97IPUTu#2(1x(Wnl@$fTqdm-iH!Vx;wa=h+heA0)$7lAucz5~9Gs)PvPMvO zUe;fr{!(iP2Jqb949=a*-)p;!l?&~U1|5>M4pD4v&Hp#D7(&_gHm8-_8`*v`ju`O| zv6R3hI>~7zZQ_HHX&E;tYbNbCn*y!M>PJ#aGWlS*fHE`Lw!~`S|721LyG};!Hg3l{IHYrnem+|K z@RBhLtJN~d@0HAFXqW5Yp0#neCp2nN5gH)miYG@_h!%L zQYUd$6@L=mbi8?NUhNe*tC3#NX5*-(3VppssCMFWw)OI-CXlhamWBF5T*eHIK>%=f^3>PYa=hWA=13(q**{iwA_DhAHG(c#z&bc?O&1ha!8q z^BcsTTaqldvPT|V&Oi7l786f%p4B)OFZiVIqFjV^?$za!FfN1ULrlHilqWSXd6u&N^%d z1p-!949m$_t;0#i8@I#ft6XOmt6)<|5<*|~x*uLUU}nqXJY_-P zZoAz%`_IC?K?-bjnz74U*=IJKyN7pev{Hg=AK$Ag^Efmi03bXJB`Hjod{#nNjj_SW zBm$)dz62wzIi+S65F3t|4gS98^|zGSw^aPp{PdmOD37U+5 z$1NUg^g$^^6S@}~{6f(rn>jA0xU!WS(1Ov`xNFZL)QLep#}}!HJw}1=MNR)CXNURd zr0zJ;OCrNt!nXYD6DFpDd#oz=mi(Im*X_HX+zy-0ZXCR%27Gp2T2z#e^9Z$5d&Rft z=?L#O2}3rW;oNolVIlBU4DB^y1UEM^;iZt78w(sQ^~8FLZ>78 zZZRGIp zQqVke??}A;`MyoklSY3roGvXtqM2U`PF0=^*8ve;B06Dbnc}toOkWSLrYdW0iET24 zM580ja5(N)(_Y1!>wZS$585Hxx)&NN^Iz$e7DndH;6DcPqZQ;wJUaf20*0^u!G}U| zkY}OhHE*9?Fv%~6F_r?U#;^WWp6%LaC1|;oQMh<>3c|&3&xsSy&T6>K2#cc-yF%uX zZiItl91*clg5P*xbxe>&QYh%ybg6guwsYCRDVnI|EUhr$}xi1I3d)U}~KQA-%Qp&6`g(_ zv8~2;y61VlM*CM0`taWj8yXGT^eZ6Bio%ODT^uf#ewco?Gy8*pii*luKnDn?PUns5 zp+ezdy%5OGRR_+#JX!}g#dVX;ho)-3xW%P!o%5YZYz9)*;`=aa3FT<}7BxverS zNr?gncZsjIZk>SOV(yj=RMpCW@*fzO=e1!XO02DN>c6fJt%^wq@lt*E1yP!6d8xp1 zcDBjiNgM|-y29*4!+vwO#wWq~vNVg`|5_*AOWulC=Y9cpN-$D)-x=m>q%MN7o81Fo zhvI5g0n9{-eJ%UAOx(@D&a$92dNpnl*f*w;u(|KX;sqY#N}m1rSyzs3#OFAJeJts> z060JJRq<3uccHcC%p9lKDNdhX{mTC9_cQl>7y?WI|2k)MVC7`ee)1m<|UR=(ki z){FywR5$$M8GVri6-#a)y=446aw#4Tr{%xZGnHia1 zBPg&-@gJOa>&o7u4n5>_`QMWkAb2tM@ef@F=FR`kVSqTsKfn8zy#xPqSB0g{w(t~K zIse4BtyFq>5B{=sU?YIOUy{bY0YfCYw{N7NKv4Qf`Pe`reG)rwlLYIL!^bT{w^1tU zGWb8R8Q4RATQmL$;GzaWfM)!UYlLNpwKWv1`cGFTVr)}l$o>}q-v7%!(t#KK+lb;p zf)3-igH!_j^xwTG{})YvUlZ6;SZt`m+numO9VcWd!7#<^O`|_xP1k>_adlE;WAJj0 z3L%F5@Gq)I#lkJ}{;n0q85DM5o<>omrJT4Mp9N5OD#AEp&bA5PuaiG8O> zfJDc1m9hvzJol_5UpJ7|!Ere`h&^+`97KR<$y9ZA$qhTY?QG_=EJeOM>i^9DpW-Kd zHU(2h{|#kxsm6FO7rku@VKTq2z*gx;$K?u+zT1lr>N?^9o}WsQR?v8%X6x7(UQtoe z#!L<8cguN;VYJzl{?-lWgvshY5(5{BrJuTTY6tcV#7HL0UtaViXgIPoV{Q{ueYb=+ zMnmxvrK0pOKmn>{IhpO{$~T^~L`GWXA0P3AQbndM{Ne?kJOsfONYj^UVbWjQpI-JV zmD=o^K0h~mrcyr!@3~jN^qu(f{o(5HGHq`}cY*@{7WT1N+Ao`YGIS~4!VbxD*gl|p zFYbJ6{cJh-qB!d9*^olrcs;MmAg1ukG|?Rsh*wvD^ssA}eP$B-d_d6D=c#!z&M_ev zFUp^9v9BqFjloS5g@uDV9N^!lWM{_?3JP*s3Zfe-))fN&>6AXve?gc#we1{I0;2^~ zX_L31bRY;6Y109--}nn&&>Jt%MB6xa*|f*-;+77xj$<1ooe!^7NX(Zis%!x#v-ymb z7!GnLXVZam-w-yg{b>;=v%S%OjXKkaet^Oe7V-qwE1+t?khgl4EL<=V35m{E-3Cq= zcwz;*I|1`mKN5Ed&zJq!e|7a&bY9~mQeMX66luR+oP;4T%*t&GLj;v+R3-E6=1)r8 z)nMHS>D~RXge~1MZttH)RNEb3x+%_UL??T*B$1JkfgvGnEq)xFoNm`vr1y*lzk3LaV|JjsvTS`&%W}%49u2V`O*&?C{ex>IOg5$}2504t zLdc6Bvyq8eA1NVp9*z>mpNMyB31$x2S(B{9dQ2rxNTc7!_9>42N07T5>Y{anM|#zc zC+TUM>ukUowFK@FwmSJbW^pdk`51kyz>uRl6*N?U^7P6>|^UW{p)j`MZ%aWj7vr!d3%~ z7e!cD_}ZT=2IrgFJz2Ca7HusbQ&_iXzGrRXR|r<}D*=Bug%%MBCb6 zDZp+e8mrDJ6Df<4B(&&Nxqkr%VM%ci8||_=A&ONp@V}V#*kgGB04S?oqf>#bshTjfvU|qnQ zrqM?&20PxcZ;u9V92l+!ctyk-NeS!*7N%Y(GeSJ?Q<0t;*_;qswH72~ZAg448yQH@ z(TnsD36*r2cg~r4*peZ$III?cHystajq4z6m;LbhwGGRa|8scX<}%9)#hV`nwA8&( zmgXKTY-I*aiXb2EI-`|gDkGmjGG@IT zB0!U6(|3xiHZUz@C;{4u#EXmBC<|*ew^Vso4r?ViAUQ+34GYDd3ZJ5ppL3V zysqXkg>6&u^z#FkL1TQ@JBaD;y_7$S*CcwHB3~wWL->eV1WVT6*)mHp_p9QV&dgJ-Vqa|dUvJ{zL;U8dcqjnnqiFq`I;#O z^P$FBkdA^~q)+D`m&VcxZn+WC3SR7-37iR6k1hWny zSLewK5#v0R?0Y+jnK**J&&LjCk^(m`Eaos;xut@KRE(bVs8Z6k*42M=EVB>g7QEOa zMGS59zZI&^P*3ccE@Db%n7cjek;WZc3y+QKc^$3lUQhG#_nAZiRIyjkNFGq~pcy=% z3IkmlIq}(tmbrS@QiD#6V57K z9AFGBW#tlWaP^%q%+Ds)Ev#yKf1mAh+;7wr7gY9lteP>;9zN!Pf&6&px^T;#sP?$E zaDdFQ?dCO=69CcvZ945^cCH0qNW285+kQaA@-ojHJ7rJ90sH`=>z$3tkCBfgm*R9i zL{wDpXJ%%GYVF_40>N2c;8eNrjFpM$V`3snZ3e}T;S##(uMh){MM89Rc5Q+!RsAPXr?DJbm7kt1OSlA zw;}?YlBi!-hYbIgqgDC^rxq71yv2_8^5I{ID<6bp|0f#rTgHUlI*Vy1h57FBN5lM` zqy7DobiUrMuBM^17mGl#m6VjMFdapF`I6hxhZPxiX=!O;e;*SG3CV7Mjx1X~^_eqT zsD$s~a%aZt3<)b6o0jrH!C*$!BDJejeVjj;7?L#d*z1V6xBwvBismZoU!U#r*sh_? zdEUEM|M)>trC#U69?PWegU6_{08XKU)6;e(c>%xlbn4}2rKP2l-#?H%`!Mwp4hiFH zbv2>;^%;oxc{cff{qZ{&99fwQ(Al2tym&A^)srX4?xXZqDTG~I_<&^*n*WAjjEwvI`1nO{ zEc2`JN|Rw&P*PwZ7gtxXIXkPzfPz>=B`1H_!8K2wCr)Y9tU zpdfF>+^4^285vQt<&yPbOc4JP=`n4nI(M(EF*HI4 zql|Ie(a}K-+zLR9fHNozUPDSo#_n{W2k`<=uS6JW5U@L%dwWq+Qc~idJ97XlCR7zc z>GluR`M`9eZAHds*959R8%S~Xr3z7iQL5G2(fgwj>CYH%{mgyB$k-asZUEi=VPyRJ z6Ghq}h0ktr+>AugG%$d{T6X~3)6+AQA&FU_S%m>onHcwkwKxfslLt({_|?9fs~1+e zgWb><-=_TbEfqLt4ytGGo8`V$Oj^Hh2^J=10et3E`cu>i&Dq&E9EK8t`7}`N40W{K z^IVEeYS);%$i^^lc>G6fGV8CttCxSSo`iEs#1y>vsxsYe$PxUTUsbm6!+!fx0;zjt zW|lVzU5YI^4YIF~+3QfM@hdc#KSxESEnCrnRZ@q+_c~Y5(D(!nzn0-)>_Uyomet-k z80CRm*Y#vaF*!*wF}rtvIsT=Px&Tmq7l2$a`NNU*=T>5rX{y`NOY^xp^)^GW-Onk1 zwu-8R+Cughr!5=XL2!FGSBWmFIL-7|84Zh0-4lHdYU*^?$lLOtMxJHHJ1}(tNlBzm zuCA(@x{8V|`!wJjOGgYA@Hz*F71GVV=Y1=<7R=1e!)>nj<6mvxna&q8Z7ovYxSnu{ ziN&?_X2Tv!_$@B5oby{;ONbJ?V0O3XIeTB6aZ>amA`Ce?u^`r^z{&5UWF{*ajFEaK z@vWF2k}CH9=Lp;JY+`3vot?_! zuK!w0JcqM(YveC_H`Us%6o4T=-}IG!)5>lvk*1t*S6TD>om;IgT&H$xAd&jyndu`? zcAiN4=Y+Krve9NAas5;csK##QscwB0&4k(3!tL|TKLBAt0OXn%bnmYS29kK;lH~$0 zg>JCGf!xvTiv$I7d;)u*?6SXYfoIaLt+<1o7u_`x-XcQUx$8i+ER?I+c1U(z110?7 ziVoiT+Q?s|PmP@ul8womHYhVuy0EQWH&st$!?Ba_Kv*vJVQS|!)v@BLqm>}n0^-uR zPRnFUq!j=80=G+j+#98qh?Jc`Yl@VBYJp?f1g_u<+UOxSGw?Xb$ z@P*VrtiBiy0*%gfaJklv<_?}3 z_KF@g+nHMN=1t8C`m*X>0?8z{LcSmeM~DwNm&vlSvXD_xTcPgY^0Kcl5+*e*?V30F zF(@f&^)6f*RTkQVrmtxB>|3wSVGph`!hcvSuV%EzSFsu#e#F{fY$F=kyVqcGusCT8 zL)QVqXGdNQOjoyZ`hLlF`bwn!w?>B_iwDI;pwWTqk+`3I@Qs2Fwd8U9!*Wu2P1;Uf zA?LRn1y3t>l;P(iZ0guHQR;l3PM#VWVV3@I^y{{9ilfu8r~fFggbw-o@|Z(b#};EZ zwa3@81o5>tD!a*)5zzl&>J#?98E!h{F>87X&SL%>(Yqeg4RX~g^(dtP*$oym3~JlA zFlxRV8zu$1jpX3$Yf0dIcQB@^OU1``a^Tn zw;`FXlZtigWk01%x2Fd&^Y{Y!sdXPJ)f*cY-U5 zAFgjmX~OQ7!srl<=8vsqPj-|e{ONCdT zB`7l^g)Q>dDeeS^rPbMvdY2(b1t=4sCi^DO-g3`QmKqFa%TqkRT!~UE(eox68J|Nn zC=xA3YPhbu5@(5B+EQ0XTUr0|-N+-1?0H>#&E|JpP8eaUV-BXWHKjDi6YqieXz>lCu@ zZiIo^{yLm8YYegG&>7^O)e0mA9OQ6>?BmdzVZ5h8w!daD3~~?UNPt0Ya^bIpj%u<9wZdY~35CS-DE1?ZNCXPtPWnPA`e#d3n(A zh%scb)nUOT2)WE~H7d>g+S0goLHckehm-UIFq5f=&Xk zb0Wl1y90NtK7GL$f!faNsQf*jRs$)fV9n?RXg$B6@n<>blgm^oy}EGAO9mY$PEW_* zwx1a;^ZAis&E~=nHY5c%P+>OfVcoZy`GP8$gUAF*3@8n>?Gzvt>!i2X;o47M(%LJ)Xxp9eV3T;St6LgO6D?>#R<=M>PeR zZnmC#{Mrhdoqw7aXYkYif0{6Etb2FyrUXC?j=#NT)(4k!Hgsg}rg#Vh@PN?)<&$oy z{Qd(^>xqvhUd=%*nT6P_u`_07MZLuce1$0zHv11p+OEj`{SSBTxZMQD21?;roOXkZ zX}4*aYeG(f&D@S7xq};#{^}+JYJO@A_v!JBaQq=#GJ z^a_Ot1b?eP>;IzqT9{Arwx`T4iGLB=+_WKZ!dC~!aL~wF#{|^_brMqiS{rLA=|;&h z23+j+$2lzrvi&i%BTE7AREw^W%PA*oY`y1jMFYH<2zs16u#hWd?!H7o}T# zB9KyGBAtQ#!ol0Y${$`vjnMOnA$7e-2QMOV5mugkD)$+<&+5PPeBTH%V*cWoSvh2< z2bjaeo@eMI)D>@tJ{{qu&!&IM5d^TKztDDfX?gjugT#`Og#{y-&mIjR1F5(8v@9-7&_P585Md*M2CLRdivH%$xdbtDjjpOr#kUlQ>yV z8AVae0RPyB5)yvWTln3x=fS%qA&rFnP|2x;8;kblM`QkcE9qNW z{+}%)$QpbUHUUT!!|c&9j|3dT{zj3&Vpm(?0Tx4Lmo>FlwlvE?2uN71tf10O8rAbAG}I+BgyU z3tHI^Vzk0stBsJ_EpU;d;LjBq9JH9fo_0xgc$W&>Y)Sti%CO61H5#QkauyRrprE}tp4+HGGSG2{j0KE6Hzn-j!B(2NY=nqpB&2*`5J|Q7!&^8M_`IBY^1Oa z0h5zzhDJs(kTsA!1Jn_8e%^9{ZrxIgSw8DlFw9wm#s?pP*>FG(Wd=9*pa?F3P~8QX z$8hFL91sAM`h$o;YD*aoVC-f9u7Ts9@B9L~jt>lAoDzd@=*C^9LFg);{Q-E(NM``p z`vF;eUzsfXN!Lvd&_`i^z5$qms>hGtFhMNK-wl$NKAMU8Ix3=f=xnKIP9frgW2S?= z!{qSM%%e~0AuyTT;q=?WhS;PIcr8Y^To?)F5IeRGpRs`@{@Yy{_g95cLjQ}%V^RXuLr2R!V}z%Q$SEV!AT}A zD*@;Q<_-)1ei#b^ASjp)80*-0zA{5}baWuJzdgqo+As{=6CXc*TmYbAY#d-=GG@*1 z6>gN!_jhR`Z*q+X`MQoUvf!g58)P4^7XcV=p#FHopebo@-?+ zu7d^W8bspKD=KgStdtNIh6vsqyg(yGy7E#^#ra~3~{udK-2*@ZC!kR3dAaCIIeF#&P zB~QG!;%;#C06j0~``xvDD9CsTV?Uhz`5);CXM%L_>V;XU5C-s&n(b*sHDi`4FE<0t zl~5p07$Xg|-5_@}i(%<*{7y(_2Rni0C*&#b-9k3+!`gCvS!lzrH_Ex$J{aYxbI2U> z>a>ri%5w#6sfEB8*kEC6qp<3dElI#HFJN%LE_;srr?p9snak{Fj$FGg$g>)9By=xr zZ)=7>*k`7b2^w6!WouA<+;RS??QlLB8#KOrk%-k~YTf;Bp3yKz{qPSW#7a{mt#6np zaFCm`jB3`K|LQ+|pdCHxer3JGttzYG@PN@n(D(Z+3cIVqW1I45h!>Z&H&59FUQ}$h z{_5PQ)83eQflK~_yBSlLy%h4#zO3Qo+$_uZSqU_(l>O^GP3uY@a{gs9iSY_X)}5YQu{8^=6_=t&mvy}Wq)7D zc_Bt!6>BV)@Q#WYEg>2ql@k5C=P)7U!0|I@%Eh^T#hxM>Xr;R2dP+dWEMt9F9o21+ z`xDX0X*(o1DnD)RSS51FmJ~qPXaDH7iC&%{vV?oVwSaXX079r=ZnR#%_Rf$D*Y6A? z1J6Q_yHHAUaATU0V5047c;5tufS)u}R?mY_m&Lhx%JG{peA|}>gh7s?h-pC6;Yml@?4Fa^ zkiFBT%EAi8K$q<6jM*EzDEBjqg%9V<&M@AyvCgQYem9=^n4wZ|0f{LB}uz_O#=JFUsMFXU>M8Pyb{PbQ}APAG;KX$edFilA3MR^N=V@p6l+ST5q@;um%2#pd24Vu#N>Q8&<6CC?UPB6+z$*3>$p62BzO73Zh#27Z9`;ST&~Ef@)@)0 z<2_gZ-CW8S>(kF$Z`$q}tZdb740KFPV)&OU^(h|eN`*+-^0fD5f==*J$$%v{gs>f3 z@$Bs5w25v&qFy*mGk(!x5oVilzcU7H$KYq5yXVbcWqU~N^A4=zV8Z~JEPFl0bztRP zjaC>4Vr2k?2GGBAGP!2P@WRfoa_Fx_*uQXvvVgq1Ii~*t-~(6JDCaD++z`n<1u?CQ zKYlGx!wBz)v239cM=|Mbzc#0ida%KJfaq~oOo!%TI)m=Hy(JM}#zeL*$FpFbj^lz^ zkFoECab>4~&|={Go!AtoG;D38(Sm;5krOY7qx0ep*&dyNh9 z#aC31*(JZ%whThgjkL=;=RW9|+>60z3D{S=$%PKCAyG5s;WG2#0^v+K6&w&_vVWS$ zHHZG$L@s5JxuoMnAq)a1O+SV1HoPiTiUh5S?Z73%ry{q?h?{@ zcb9Psm=EVBkiPTHCMyK*PSL+Y`&lj&mYV8T&Q3Sl|{JY7>7>Ez38-wL9>(>ZIUp zp%?d){iOOCw3Cm){&gL3Lqo$*@r6(lZl-8|D2@cZ33b8F$7{{~{jYWi0m?fM2Z0~} z%^lfIhA4kSJ6~|=h9F+}B)~C%_@)o)6IA>6>Zv(BUh8iK76WL?fXe<$8R3GWpa&%6 z+Vm+gwEuyG+d@oToyg^6!y9zA5PExiW0L#tN!PJvM6(UtLjkjA044?0q{yG8b39yX zS1U7opaL4TLF-8C!~_A9+A0{hIaWF`Eyg*mP$?kMql@=#xWOr<` z?0f|D@mDzktAcn01O{CZRBGkM=+B=&w~)Yws=~NHHxRwQyT!)C^8-tl0q`y8R)Ii3 za%=(Ec&&h)gagj=5NdO&>bmuYBb}&MX>(6 zaX#Vd3!Ivonn{^XmZC_O;+tuYf2e4?j^N!8AfW9izTo`uP((xHi!}+b!J<-H^FHLA zWnZBrap}A_Qxg~*%&8uik`nd+hgJ`G zF$(!(j_w81>l3Q(g=oIOO(I!QB2=i5QK5*k_e@lz?39tL%(Cv|jjpTiZ{7FrclWsdxGq;X&-3&7 zyx+(Bb-Z5B1AI~Sofp2WsUAPB0J@5~xubvJn~8<)iU3)3IiN#@i7WGk!(hF`lc!Ik z%7P%C({5MT*wP|0zN59R?Z{33Ghb#X?v@N~n&IrVPr6~wK*lFtYIoqaJmUhvpyAi$ zemVvv&@#Oe9Sw~bbOfhlVWEH)E-AA#9cB=$hS0Y$f1P%0=G(WR2M@S7 zPbNo>jg8sS#%Zi=gBsEn&s4&)pbWPs=wBA4RP%p|+YCpgzI$|<09Y6mW`r2lU7l}B z@b7L%ZLa#~AvlzxrHCgLVJGcBrbW)VFJH7feU#^BXEidX{N<0Fm``$DE%VJldIEUy z;Hhh@cixJn9d#f~NDJL`9u{Uj=&6iZ`1qSMu@HF=w}X7!?IB?7AVdQ+Y_mY_`wJq$ z*6ZFO8jH#VrpA#%C4ll12d?9&tW>Wvj9XM#|S9DVZoV%d7?k*Kv$Xhev zWFr4m%nB1&$|MD6Hx?*@HMCOYbRL*To^jrRy=ypZp|-`BO3Lkd>Xq=E_a$E+Tzx|FHK@XN(fHm8ug88!x*)a-oGCmHI+txv z1=`X(cD`^U^4EVynD56SXaQ$sV(cuVcB*T8L{h})V-HY(S=5NDudTHuypSf#@&C>! z)E6%3XDR^)CQ3Zh{wtK0>F+rl9f%Kqh$BsrJCH#^s^XP3uIiM__PpEk>kXI0Jc&L& zubi3QgS~2ny6%h#D3bt=j-#L#zwnqOPS*;9v-vO-LY^CIOG8srj`^f(CM|VsQOVu; zRV3rI;E)u@NG2Yi-{a9|PVad0H@s8l-`sVacKpdDAM!1wz+)#d6DtKlTm^9d;04>x zzW$UmL-zV(gFtD_wR_d?_i{xBJb0oa$Zd4Vg-B216mLVa!xU$9x+^7MSw#5NA7A!< z<+J~^>;*DO?MFllc`>>cSX;ITnga?N8kAj+&qRc>_|sKYRRJlA02F#YS6``gEY%~` zYXz_BEd5>|rY$&UVt$R3>{_h7)hiRta?b%}NUBT4J93p7<6kny_lY~3pW!MvIL~II zW4r%B$G{Jt5F8EO9k&mkI}}`OlS6=C5MG_rcgt)+I^YIPPnYUb>XhM46wkQ{-6KkW z_7m`V`0AaM0mqa7p^XvC?$_Bpt~lAuFtrnmEIdQVfw`#>6@ zecgF)Y2W33kk2LXfB*4fMYII8*QD@5Zq{&VM7-Ly3V~#~kW-gmwv@R*vYhdqqqT48 z2ASH%^3MJ~SFECW?oj1PN5BB2_0Jto+{_l*in@C-#H*bPs+zpVsz*o|&0jbCYILajmC8W;(tjWti^# z@J|aKtN)x8PCnjjO$@ym^st~Yk{?YDNtpMgbA9?%`DHa+UGsw2zmTFnhZbGwveN(V zCVZUub7p2tRiA3K_zxx4C+_2CSVW{>ka`@4n47McHs<)OB!8Wn(n6SGCC$gt(a`t< zr;{Gqcih{-Z&L2tFlqBoWZ$?b2giN4E7s=9} zUL&8I#ixF;o(-R!mDYv3xZ(Arj#g&n2RzwN);vQWAFI6U|Beh}UvI=!RJfc7JNXd@ z%&P=&f=Fg^#=T^bOTSd4O^m#N1O8ofpZB?ne#@0>U=E=Zp5>*sE}en&>5w&hwt%pF zv0wQ9#1^OruOQO|h#Q1NL{;$6&3?(r?Xnx#Vm#fI`T_0_Q8z92>?a~~2~d@LeW-}) zrW(VJ1rvX*yTHxO9hj4oV{lb-!I(-$n3;HmU<03{>=_f6?5*vx+)xA3aveoZCixa% zCyN8~)V7PiFl!=&39_fmxi~)=O2{ZChV<6I(ijU-M&Rd8OoU_UUi@+^QmcN-Upa*Y8-~)c^klm%foNkY&yOvx%1Tr3Q1nM!XI)om)wOT$i+GImaSLz zQOJJ9XZ@3pHN=1*^g9!hC~wuRv_SvrLd4okgNOJhlOjd^9O2US#oeOz+W45pmi!tU zk~P?icI?|htmMvH-@QYRk}~_?;rbgJBn@GgG90=l-%Ga|#aTb=4V-Vgqa)Huy!9bG ziy0g=ZM1x1*8&>&f%v;ne$WAqjPa(Ih1#p5w|}${k(8?|4`UnvIu^ENt?(7yFIPZX z6bt;n1KL}54>RA~qlgzdTp`8bIJr+WijQ{R$=z9nyG*~2E)&ECIa^kUWbmJ?$u?O( zqE^JN#zJ=$_-c6A1aWpegR_fQPTvZO_SlE^pmE$9O=G(s9=~HO9eI+ac&xJ@WrJDq zNsK$%n&I4Y1j(-N%Y4K!rj>zMnwIvBLZh#}V_|z3OJl$stm}pw6Pu+pVD|1n{E){l zo(RLsJrEmBiyzPJa|BSwe#jexSHrB2UG~P!#t!KWr5n<^5H}38N*PWPrYB$hbXD`| zQ`9iVvKf|&e&_YQRIoftA^?C%XR#)t;eo8LRHVf3OpU5{--v6@x4CupaNH%#NvAt5 zF0Kcs6H=*P-Juqt>ZC<WMGBYz9>w0uc`Kr@@IxPI&#P5WA8oI*2 zl7-IiJP`V}r)lTBH$AC0{a4+Lx}@f+z&D|{HSfk@1DpP3Q|68QC!4Z}Wb;FOA)qTB z^tffyPNfXo##!h^n0>4bi)pf?k<9q-d?FhtX)aRt91BnUBMK--xrL+CTf{CZb`*c)+duy4Hmoz~Z7LcLiU5=<^KG+mCa2vTFM0cR+jlng44#@h5S`E|bDPI|MmH#IC#{LhflZr~LF&{Kx09<{2whE1TZn@Ii2XDf{~$2KT||631WA zl3gwNP8%9ru@__grscvX_ZF%_YeaA)>!`%@FSAEeaMadkgfr$TXbTDP(%z}Zi~awW z7Jh!?e<&>=8_|01eY)pVS_0JQc^K*{n-ssx#%11B0|Oh6d$v)M3m&ciSxcmbZ+)P0 zdTc72@UK-O6?lxpM~{3qeP6(;`3*Qed@p77BVbpN1HjNeWmnJdV|g>OCx&{`zTsLa zO$a|+m89Pn;;N(5I5?Hw!05+L`ToWH<7j&3Gx$)J0=?-eb)c5?1+CWeCTQ6Tbi*9r zgJv5fJ4660>i%l-UlNdU(f%2r2K`=ET#;~6wcBJtZgL^}4>D*;83cXkzzDGHf&Mw* zO~czx-dg-7`J4e>fXb0@83?RATwFPwzAp#jrZp`Os`(5yKb&#@8D7)uW4Oz77H6Q0 zCUJ6d0;0)ia!-~HZ>CPRx6I=IMEF^`>+!Zm_S49L-Efm^r6qTO%}lvzFA(_*GxRcS zz-Yza*i;aD=_zyH%IEv;H_cm_ck*u0UMs)7n*B>T5tsAVa^lecSWYaHW0UT$}aYLJu6i4JH8|9aBi%!g0*m<=uVz zY2Q*|84{1?=7E9Yj9M-iEQrL27Ic!l_+nN5yU3LukOZX$gPdgTBLS<%@Ley}B3gko z(Q`Mka#_Q{>zj|3Va`NcX(@5{mnOW6Ht)4Sv<^WnX{h(0yHJ|zN3tC{93zlQe62<$Ll`O1vjVjuf*yO`~9|elWnPSn^;GH^$_IsUysq* z1k?QFls^&E9l+DN`S~zBc&X5*1@r91q9XBRpDD?`TwDV*$NoUo>9G}Mh-8svj$$|% z6dSuM*>BMRG{*F{m-3z42u9tai&-tMvcNqFg%H9=@O#&St@!Sn@pkZU-?-7cx;|DH zL2CiPUj2OY9bn@kH8C-fUU;hwRBmC%UEoRpMa&H8Q_h2!@Q4UhOpPu4_+iKJgJ4t5 z(!BGvJHO`BD712_!6ts}*fGzq`3<02LZ3Sv9AkF_HXtYtl3Ptf0|7{Gl?Zq8Q+0z( zK^RiqFq>{ZaI~YxJK6uo*@%sjhxA)jPm~6Q>K^z83`SZ;28+qc%0fnQxEyDDkECne zJzeAoP$XFl27Ys9W~Si#dce+TYHHRyfUb#@md55rJnWN0zDvHH;wu5$|EDGr>DUr3l zS$9BCr?yrL5Z{i3Prhx(+p!~YRrOM^By%g#y;a{X|*7a6@#%(m+7;+R74#pkRA(KS-Z)Rst#T#|3Oo&+_%! z;VYn#d3kwpmIZ>5YYa>kd)9@{bAby0VMntOrUip4;wIN391Ti56i|mxr!Kc*Jsf(y zu>+yO!IkhsN-j)`J%01Q+RDXf)_IHJo-s%CBQTsG4$bYSqa!2ukca?FBj~Yz^pg)i z``;lr&t3OnP+*sH0+h)JxC`hpg6>~*fNW#dnji#{B9hbd?cebO+=#AdWAGA%sf|r2 zt+6N8%E}7q7m(!yMb0cMApGT)nCHMe*dv`i8DMhjU1^ybr=pQjuZ#e@W=^L*0C&J+ z;cwFLU}c-3D&1J*)mtuWAP$D|Un_GyX*@MDimen0hsZW*`Ck<2KXvz(`W6)e=`!@! z8Q=WLuE1xQyk%-8pz25mI1HPkk{}otb{#-PW$asFGNub;XMAW`xs7lgo-^6(+%x-L zLDuMbCJj`bhf~rLv;GgYve#`0n&IC7C`F4g=zuKeeH^?MM z(lLHMLB>Z+K|x^<_+0Jn8pz9!f9bRJ2ajzk%doIhV0AL;K+Fa;gL&(Bnb^m!o(zB*&DSv_oETowfd9szrJA>xKHa zpU>(qv*nN{*Ad^A@u7*J6{s(nF1PO} zxwG^ajHr8rRq>gB!>18H-$>HIz0@TYv%oDqsMRKcB2txHc{o&$T9h{5$+*?)8%@rG zTo)pf?u3k~?;|Aa_?X_LH^i7$7j5E+4AM`Jsncz!TSc2{6yp!?e4P4F=c-IkZ&dUg zz?OkebLrynQ>xyFOdyj=e<;E%^3Lt%h1#a@)IowgV9+0gvfA}uB$>Rkv*7yLbijS+ zWdW6}pmBD~eV_~yUF^c9`@kkcvGETdBl1oiu$f{S62^=Bd=XOf%lFFWA6rQO8|q!l&4%D)6jUv_#1oXw&? zapx}rUVhC{xc}~?i%1?~Y-i8FV~qdt@rSO|*g~biX=%jn>n*kpO`UOt+hEo!hy}_s zNT%j_Jr{Sh-0w1MXjT7E)CW^}Q`NV_zR{g;_0W!?j&ztKKuXWExR6XAGxD4x`(!C$ ztpdn9mx0kH>eSCf9D?Ir#!N!XUmGD%?%D9GtH8U-#V5C14d4HD3GU`=#a4DNlLi-X z*QAYB*Ii{dK%ZjkU-Y}C<_O9DAELWeImN0fP9K3lYx`mc`+3igBlgCfI^+F{xx-*a zz+i?ttdI~QCGKTb+sZeG7b*((?pt?e$O&#j`kr{rS)&kEcb*w8vL>T+su(P}>+7gL zTR6FE8{H}8{||?qOK*$AepW*n&IomCy1&^45+877u*H6SzKi4Uf?n@m1U(*1Wy=F$ zdq~vAHjohusCZm{&6fzZ?^HzKq5jQMMahM*TOmU&F$nnh7VW9>$OWJ(P!^0LM_`w} z(CZH!6cA(t`Nzk{-4B)jYZBffwI?dgy}W zvDx0R#AB1Tfj6VcfyoP4T;5J4}JYwtMPNj!ZU@t#S;#uAlvF>|M1i9d+~xQgHN^L`7Uya zTVug+jcTM)aW+<}bHH>V`}V5$a%gX9deA>SOF8w+5;Dg(m$?53pMJjOeC!&|2iMud zdf&e9eW@1abB2{rP9p)IK5KD#eiDOsIqv>wWpmD1u!P`?3&@n>7NFjGw&caJ)m)s~ z^SONGmcoMrxub(EOTJVeo{$1Ub7n3;<{Dr)-`n#i?h|2s>85AD%1N1xh4S2MB+(|% zc}PL@>}xy}KmreBzGc(SNIIcvbNW9~IS8L9dt2ZEDBw>d%j2AIzrPihnRETjxa4zM zAn9R%fEXdHeAFB}F|g?*Wu<9cEF|*ZZrwjFo11N(=j&b*@%9-`+L=Crb(_DN)${+S^R{6_u&?lC_EYIvay>r?!QF#e*DWn3w^Bw8eQ~o>t23uvPnFM+z|AL|swtwn=Mi8dO({|fEjGdNniHpbn%l` zUR&em=E+mkdB7g&zd{W8^ZJI&_Qsj<9&Gp0BQDX;!UbMwgcieZIvOjgAHKJ*GsS3x z-jZbWsxzbm1 zVYo%0q85C_D5i^oMvaY)d5yChe>Qg>=W|^xIo3NIj>Op&j|-Y^z1e@8Tm$?UYcis` zDJSV`fa6C}7ufV?h#msXTv+p)&NV|R>}#K^s@>^F1o(Y zA(L?Hrr&L7@cC8tt(sKg9i}z{gRQb}h=8vU3_G1iA8}L48oY^RPcwtdCiWVI~Mkja(R+t ziw$S}f&-gdXN9mdM^!y24}{EvjG5*##rJZv6I$23d{-cdy%~kRR>~KYz5#GO)RvOk zn0#7}tcw%RboE|~h7?1dL!tOL5G$`e!oz?`*UZK#FrsAKGZzeC-=6H7>1uX@R#_gr zSg=0 z{;}Xb5tHz0r=LvW4m5I^=89#-#2AbWI12=sX3i;e zoxowyK39X(11gRqtgNgUDEA;jP%~I{$>meyH7Umw@F{_#snZ^TEYP4D-cW(0LY_Le z`>uKC3^fo~Vt?t~Kw|<=QL#xOSNk+Ywh}8l?5A zYwb;UTyn4zn?Rb2U)`Tq^Ww%`#-@KgId^sDw+J092l zw~9>lhO}0zDF0#HXxWs|Vm$?oQ+%N+!_6znanwAi2WY3=(t%ta(+@4)QT0f%;~!v% zPYfH3?tx1T{0|J`f4r@1-d`WEQnkm?_>d@&Fo(*WMtR!+Um!w?*7zIS~lW4q`KeAZk%rk6^AN$7oI!!3YE=LYd|I*}H$h)!VA=wkjz+#lD*aGygf^?K{hng^(@Y8x^SnaH9+1M20+u z5X6!2p66!vo7bHG(n)etz(lO3BIe6xkwn$`9;okvuAh!ZG8`m6U|4`~;Z)Sr!EyWb z!uLc?1=3y=rPST!>YkbTrc5U6$~k4TYX#*m?`0MXNAKg$YbQz~V6NY>?v;zx_|^Ys zoYTn;>C86|PKp~0z=pjFFPGx>rB+~b<7(>Y;9(>JQQnyD;T$~mT!AR1a@bmQr5_sn!o4Y?6U_K&* z2~-f!!pMjfTi%d*R@C0YK1jw z2V)JH17X}#l1O6C==3R=wP#oO;cLTl-TOzPQZ=}<7Zw5Wtv9iI@7Y0RRw!t7xE2=| zuY`qFfn0r}3)BWiKwD7p?CvAkl!Y_f5LEQ0u{RMSdVn&K@4a9LgYW1e>H+w_J~%uv zrfFpfUYP1-7-b?Rf&>izdrpDpa!6{^`~G z>m_k3~(5)PH4CTyqY^YmxIBte)CT9s$B-fHaz~=gEwZ2U*Dsbb?FkWf})~Oz`8FT z=|}&YPH+e_x3K7dxFm4%h(e}ZnM04QHBj_!HLM&MQFu}g*oB=YMVPv6;7->4O{P~X*x;^p#ym49IkF|ci&Cq`0*4)N1rSdn9evJ#A*tidCyPR zRKU5r&n_N()%O4)z%3vU`t+#?eB778Ef$`1cFO^M#d1W+{cAUsiSO(Vb`A~~2zsN` z0zCOuAUpx1Y2WdT8#kyQ8a>~MS~uXubQx=BD~0g0MwSkp*trDmM8^eq+Smc~CQz~Z zBopPp|0oG^8*U(E7HGjttkj~cV}gt@J+hxkf>^(B z1@M5Aok{(+1Y6k2m6u5eYv4hPNlA%@xFs6Jffzlu%BHY@a;hZ+o+LFfl1| zgJsX2DEJUL`1nX+>|6u%hLoEu1?k$s2VL9HfQv!b0G56Wh>Kq5#>fKv3hg_f1l}7# z)9}!YiF4g~SS0_z?}VT*2@HjR`3Ob@Mxe^>g9~;sFc3(K#0B>6$RV^@55{Mu7NyOX ze+8Zd`MVlA6O%1%ZI8S5A4Ndh*|nts3-H1dl9EzOkwbQuikX=lV>?hTgPu*qRsnQP z7`GSPelaj)At8Zo_)wWKYE4V0dY)1DES$axHvLk*eS(7qV=mWRmhwoBv3G5oI82OO_##m6h!fhDE0EgG2DP zB{2NPSjKky5^VicQpCqqRoQOdD&NCHP2Ayxt6c}RFT6i!OVz-4bO25>pM$Cs+LI2A z$g}oZRp2hzF%eYsRIYJU%S{0b2!hAvMFQ&OnG<)hZu+Y8MY(^+YZAlOdP<%jVe(1ERn+c)pRv@v{x;tgrJ&#Rd|qsoV% z8gFS)E%<&N9;SLUtb+4I604LHBMA+koSE6(Oqruj<=;6GD`t7wvlPlNiSuKW=%vB3 z3#(%$QCGb*M$ktB+iW9RuXO+JGscpOI}DG$DbH_S$egZPP17@9WU z;IZv`NN?=*dAFt;RVRsNt~_VN%GmwrV0fX#uwg3z%u1=EtSMQ>2M^O3KU5PMaeY+X z0E19vyZ1%25>`XVC35s3G)WbrnPr0EL4hfFj0_!m$ybG$gc7>D- z47{yL2UX#2y*Kiq)axnwLU&IO36x9Ka4|w4k*$d=O^K|8K@#Z*_(T;3;~1g-{J+c* zzh@W`5upgQzerhFGg~~To{#b+4?}SXJGX?#*X}b#M3BpZ|F&OCfX)6=Nr}&$rvRn$ zg-DXSKNCBqIA+sZQdUic392NDp3WMp-pnqpx~HD9y{SRM+6i63VkJ$rPb1ES&a zZ0k4Wc52>Pn=sj^{!nNS9q)@ULyruqDFxW50c&R9&;#)k?zm=e$;Y>Doi39m2R)Jo zil>Kx$J2vya|MEef&_to0$Ge&2~vZD=T?9{zyX7NU_@#VYiepLCbw*n0ZNsjFJHpB zl09)6qFKbfdN1^Q5kN%=?u4qwYaE)!#*W`sdO`!q@Nwbc4+bt}rVcMstKoLR!$2?{ zgVr|^C`_P^V_Z57Hy&DS3k{q3J4Hn zL$JoArm~`GDRp&qozGSldz`4~>51U`A0&GdL$iUiVgu-xl)HA}!q-qY^OiWpp*vIM zbCkfp-NAZox2v?pjVL9otN!G;s5n1=#9@I1^yR*ns7Z&(l%;f8b`t|!A( zLRaGwK;z7TodPvXa7v0vVf9xJ5$o1Um`*g)b=qkNb{_Nu`}+FAdgg+enR_M60bDj{ zx9`BfCXNrs#A;yCx&ZwL*7)H^Mg7%4(ZOPX#xT}XB!HeLDBHj=VD{}((^4NNl+3Vh z)3i^#IREs+i5>B2H&kV|yAgwHFl;P=t+f5({90MBY zGU|hyuNvGK?1VwAurV6Y$#@quO~XV6ZfU4ZE=YU8+LEf36%6%4uaFJo&Mi6@V;n<21*#WsA0mx%ptJGv@*>hPpGkS|@(TQ-d$7jTr^H^ru3=Sz85Ac4*y*eE}X7 zRBj)OPBG3^@$!;{jpl^#PO4+{U^>{3hKZpf03Llfp5=O6Q_;PsU1us-2@zcIN<3QB zr|{lSO+XO3(%)bzh{+I0!5E}!2r4pR?&;iP<(Cb%Yw7;c=1BzgD@VSK7AU5`Wy``N2?u} zB4F|otDqnyoKXQ^4?(&bMyFCNjJrY`GeZ+ARLdSev%n@({yBGt&4B z3&zUV%MdOGRFOQ}4tCh3hbD3>j)1wOa1!w)9X^PnJRVGPz6=LZK$}{wuECXxLgryO zH695i-_s5pL{x9U{XY*dxIP_3eg}~(7!Onz@KBv>Pn9lOoRRlT~C#D literal 0 HcmV?d00001 diff --git a/log/ResNet-18/log.txt b/log/ResNet-18/log.txt new file mode 100644 index 0000000..db627f2 --- /dev/null +++ b/log/ResNet-18/log.txt @@ -0,0 +1,118 @@ +********************begin training!******************** +Eopch: 1 train loss = 1.519442 +Eopch: 1 valuation loss = 1.476453, ACC = 0.988333 + Epoch: 1 model has been already save! +Training epoch: 1 completed. +Eopch: 2 train loss = 1.469806 +Eopch: 2 valuation loss = 1.469115, ACC = 0.993667 + Epoch: 2 model has been already save! +Training epoch: 2 completed. +Eopch: 3 train loss = 1.465635 +Eopch: 3 valuation loss = 1.468244, ACC = 0.994167 + Epoch: 3 model has been already save! +Training epoch: 3 completed. +Eopch: 4 train loss = 1.464047 +Eopch: 4 valuation loss = 1.467587, ACC = 0.994000 +Training epoch: 4 completed. +Eopch: 5 train loss = 1.463853 +Eopch: 5 valuation loss = 1.468458, ACC = 0.994000 +Training epoch: 5 completed. +Eopch: 6 train loss = 1.463171 +Eopch: 6 valuation loss = 1.468626, ACC = 0.992833 +Training epoch: 6 completed. +Eopch: 7 train loss = 1.463859 +Eopch: 7 valuation loss = 1.466508, ACC = 0.994167 +Training epoch: 7 completed. +Eopch: 8 train loss = 1.462336 +Eopch: 8 valuation loss = 1.465903, ACC = 0.995167 + Epoch: 8 model has been already save! +Training epoch: 8 completed. +Eopch: 9 train loss = 1.462728 +Eopch: 9 valuation loss = 1.467648, ACC = 0.994500 +Training epoch: 9 completed. +Eopch: 10 train loss = 1.463454 +Eopch: 10 valuation loss = 1.467494, ACC = 0.993500 +Training epoch: 10 completed. +Eopch: 11 train loss = 1.462208 +Eopch: 11 valuation loss = 1.467586, ACC = 0.993167 +Training epoch: 11 completed. +Eopch: 12 train loss = 1.462793 +Eopch: 12 valuation loss = 1.466612, ACC = 0.995167 +Training epoch: 12 completed. +Eopch: 13 train loss = 1.462064 +Eopch: 13 valuation loss = 1.466395, ACC = 0.994000 +Training epoch: 13 completed. +Eopch: 14 train loss = 1.462782 +Eopch: 14 valuation loss = 1.466514, ACC = 0.994833 +Training epoch: 14 completed. +Eopch: 15 train loss = 1.462364 +Eopch: 15 valuation loss = 1.466478, ACC = 0.994667 +Training epoch: 15 completed. +Eopch: 16 train loss = 1.462193 +Eopch: 16 valuation loss = 1.465112, ACC = 0.995833 + Epoch: 16 model has been already save! +Training epoch: 16 completed. +Eopch: 17 train loss = 1.462669 +Eopch: 17 valuation loss = 1.465296, ACC = 0.996000 + Epoch: 17 model has been already save! +Training epoch: 17 completed. +Eopch: 18 train loss = 1.462155 +Eopch: 18 valuation loss = 1.465848, ACC = 0.995167 +Training epoch: 18 completed. +Eopch: 19 train loss = 1.461859 +Eopch: 19 valuation loss = 1.465558, ACC = 0.995000 +Training epoch: 19 completed. +Eopch: 20 train loss = 1.462535 +Eopch: 20 valuation loss = 1.466299, ACC = 0.994333 +Training epoch: 20 completed. +Eopch: 21 train loss = 1.461965 +Eopch: 21 valuation loss = 1.467344, ACC = 0.992500 +Training epoch: 21 completed. +Eopch: 22 train loss = 1.461767 +Eopch: 22 valuation loss = 1.465915, ACC = 0.995500 +Training epoch: 22 completed. +Eopch: 23 train loss = 1.462731 +Eopch: 23 valuation loss = 1.465383, ACC = 0.994833 +Training epoch: 23 completed. +Eopch: 24 train loss = 1.462142 +Eopch: 24 valuation loss = 1.466911, ACC = 0.993667 +Training epoch: 24 completed. +Eopch: 25 train loss = 1.461943 +Eopch: 25 valuation loss = 1.466150, ACC = 0.994167 +Training epoch: 25 completed. +Eopch: 26 train loss = 1.461857 +Eopch: 26 valuation loss = 1.466353, ACC = 0.994667 +Training epoch: 26 completed. +Eopch: 27 train loss = 1.462463 +Eopch: 27 valuation loss = 1.466523, ACC = 0.994667 +Training epoch: 27 completed. +Eopch: 28 train loss = 1.461748 +Eopch: 28 valuation loss = 1.466534, ACC = 0.994333 +Training epoch: 28 completed. +Eopch: 29 train loss = 1.462236 +Eopch: 29 valuation loss = 1.466483, ACC = 0.994667 +Training epoch: 29 completed. +Eopch: 30 train loss = 1.462003 +Eopch: 30 valuation loss = 1.466212, ACC = 0.994500 +Training epoch: 30 completed. +Train Loss Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResNet-18/train_loss.png +Validation Accuracy Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResNet-18/val_acc.png +Train Loss: +1.5194419834286115,1.4698055194452475,1.465635260981971,1.4640473041206739,1.463853496087106,1.4631712984699774,1.4638587808439516,1.4623362046282438,1.4627277742347446,1.4634541170009505,1.4622083008289337,1.4627928627893259,1.462063550525367,1.462781774771722,1.4623636942339169,1.4621934048937395,1.462669411400483,1.4621546202078815,1.46185944910863,1.4625347868243666,1.461965396223475,1.4617673691132622,1.4627306231107757,1.462141980350865,1.4619430509788731,1.4618573053188233,1.4624626147803537,1.4617475409643346,1.4622356333721305,1.4620032535062581 +Validation Accuracy: +0.9883333333333333,0.9936666666666667,0.9941666666666666,0.994,0.994,0.9928333333333333,0.9941666666666666,0.9951666666666666,0.9945,0.9935,0.9931666666666666,0.9951666666666666,0.994,0.9948333333333333,0.9946666666666667,0.9958333333333333,0.996,0.9951666666666666,0.995,0.9943333333333333,0.9925,0.9955,0.9948333333333333,0.9936666666666667,0.9941666666666666,0.9946666666666667,0.9946666666666667,0.9943333333333333,0.9946666666666667,0.9945 +=> loaded model checkpoint: /userhome/cs2/mingzeng/codes/kmnist/models/ResNet-18.pkl +******* begin testing!********* +Test Averaged Loss = 1.475045 +Test Averaged Accuracy = 0.986100 +Confusion Matrix Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResNet-18/confusion_matrix.png +Class: 0 Accuracy = 0.996500 +Class: 1 Accuracy = 0.997100 +Class: 2 Accuracy = 0.995200 +Class: 3 Accuracy = 0.997900 +Class: 4 Accuracy = 0.995400 +Class: 5 Accuracy = 0.997200 +Class: 6 Accuracy = 0.997200 +Class: 7 Accuracy = 0.998300 +Class: 8 Accuracy = 0.998700 +Class: 9 Accuracy = 0.998700 diff --git a/log/ResNet-18/train_loss.png b/log/ResNet-18/train_loss.png new file mode 100644 index 0000000000000000000000000000000000000000..c06064fc43cd68c381c8b812fb3c858d0654ceac GIT binary patch literal 16571 zcmeIZWmr}J*FLxj6(tmdQa}_?>6Qis1eB7HZj~%-YBA_czb{=b8WWVy<~NFSyuypS?f1*1gtet$PKlsmPNbqdSHm2)UwytU7`a z1|SH*Z<53CM6h${5Bw8xk<)R}aJcW{Zu;OJqHOBoXzSo&Yh`xc?cM`tD+haiE`BaP zj`Nl-E{@Kk+}w8md4bE}fdw~r2qh^ra@0{l*BL>Mo1%XN8B*z12qJx1QTCdqN8IAD zr;p}()xpZ5Q; z$=TUCA~tq#zCSfBjg6PLpk+G2e}9`|GtqCys%mew#cMj=-DM*5>PD&(bg022M`~g` z`RC7NPEKWLwtK2IR<**x+&o23Pw#D9+|Z(=O1cI`P)a?hvx(3(TBN(H3)`ciYK7Zb z_nM5*7BH<*VfftPfbmA^;nQPSd&_x zH$KpFs9Jy5Cb4NCrPcRqB!3?*bPHxvsXNusx@23T?UY#$SPUzlQpD*^qd11W8cV<$` zG1a@+I#O;+>`Z4?PR?8AhQ>xVAt5(Msoz)37MJ|r?WEnQ=>wsD^yUho$+c?_r`qD@ zt7=?k)fKzO8=e=tE$V4$YGxD__3ZjMI~OjM&vStYu(Pq<)JRk6oX@YxJ48z5v8h{l zKQtybc3t%Vt4iLgq>4AiqwA_Pcn?Rn&I zl8zpFiCjPqpKs^mY0c#l(I4D10M#K8BCbVFpE6w^v3EQP?*uqdB1ErR>C!SB?-LRs z0S~VsWNHac0j_OSQra-8N8|)N(hbF7L?1|L5M+xEz4ORBqC|QgL6&(5O#)Vb5a*eb z{d;G|DUBdJY9s*!b6;ts3DL)#=u0mN{(esb-^c45#w)JRbA%?z%!&Z8n&=5!jt}S~ zXJd{AAV_TqG!Syn1g@l+ki!|LNUxpxcdVM{%<)r$=a;D-fhrI^MVpx=L@&{1R44)h zO}9`iIiUp2eD6MFOQUY?9GjR8`}hTYBe^d3SYfF{H#w}LPcS^-pdy_>8x4S^rjwNaw{cTQG`9 z=)`J4e7H;+(Tz2RUHEvaFhGCL;3#YWE3`L7OZ4;iA9Y7)#2sxU5Im) z^X?%5Oz9!CCC(Y}0$rwRS%l1lm-6qmJX|~BFNp%s{sx;6q2kh>fvHtzK?FIk10#yE zprr|RLWhd)rH+cVq7|pADSwp*_JtDm<@voUu&ck#VMx=4WHHgT@TxovsI8k&M;^b$ zlp4gfocfo$-lR?_09EWd#{&Qd2ePVC)ZVkp1s<(YXA_}_>w+b zp21VLKIr(SYqR78J&^)6evpIF?khDzLk5#mQyV+3nVFe4m6R4%%huY&Q~^B`n5}+& zx`(^sAnn}(Y~Ba)#j(8PJk!Ah7Rv)8 zwei#3YoVaYrI{u2vXqqnZz;{=C?fIVm+WN&4_W=FTvSB;m>MjpwT;aYTX7T9Szcf7 zR<}@v7hT>hTxVa65`Ys)`vQZJrsgw8TG?JcO(H-7!T_I!nhk3O`cPE@y$}XKv*VI! zyA%0Wqe1w8C)ekW3>r{S!1jRR);@iztCDl*n=mSSM8wjkOn5Ef>K0r*yT^)(6%%37 z(GCQW_B;z(3}u zM3e)#O#W#)2`^SB2|9@XPasP=APHbSaq1dgmFS^U9{hTsFVPU8(u#IJ!W&S8;soP) zBqqR}X=X~OxQt9nn+QREyo8^57YWqN&{5AwK?6VSOplf5G4oP@C}J6*9S*9N;NbHw zh+t$mSw!GyyE+f5a~;OS(r^CGn;ov6rQkY$V530vg z4;=q_>txINzdQH@kIi5HN6%0-2-lZyoZtNBQj2|w&FY_Kuk~K-Bg-0CV-D2x1RRgL zfCvc)Tox14&(_Xg>Yd+M8qN+0IfemGM;PNQ=C;tWS##h!>{w5}vo#UsT2Fp;#(Q)( z!EI@1c7D}25I<#VTZ@r~g9raN5AjZ}UMhQD>2>g8!ar12idXF}Rp=F4N9=DkNo}ML z5lmPS zwKT$gZ5>539lj) z&H&&fHiw*1GXNWp@eLDODwP2+@AThm@}B|cxX*Zhl^Tb``1gOd8$66#TR)8#d+PrJ zoVe6mkvGGsxQ%j(ZTi8sq0a2cbZl%aAg6m~Wu?LG+qYx*?u8in<6Jp)KGs&WFZ?Q8 zAAin0H{y-y_1zp!&&lDa!gk$?5pkaS?fcQlj0wdoQmT3$GJk5*JAb|GtlV7sw@bh4 zU6n---n-u|P&mS1L;q@mUo|qNrfFBs~RUOne2?GyKQO_x^&k`mz$65aM{jlWFsLo;d z7)$+sxr!nN@WX$H$CZ7+6*6?+rTqaQ0#SU$gV_pkttjf%N;AS^);wRbq{80RcS3)y zz(4=!I>Tp&R=F$vC~ ztbcUL%3q@Bumc9*Ixi*1VRE-;+K(#pyLS&vZ&lkkD~AN*+!-&ugG%{pm#ERaeX#ttU(O^7*a zEj#4laIJB>Wb=F;$mh zThnOy@4I+Zrl!grm)uJF^y$-STH4Ho1>36iKK*J=ALKu6n8?UH{#004?6p2;RF(Jn z^PQ_B9w~78?Vl}!mbBqKc#txRTXIMJ7}plgl9|EL{m;ipNqx080#-jG=rKl```4bK zwCbO!DYmOuOZxLGwx?o?Ejush=Ju)N2KsN+GrF`(Y+8PKbSV6TzC)Fk|L))@7Qh)j z60XVpB7)O8G-4c`eCQ*0g71dz;%JR=OC&dD)e`{n+qZ8NVE`glgP-jl-!`iGYcs!t zqgkb8V32dK<(<`hU+$-0<@?XHjXYBmJjRcgu8=d~9URo}8WCu(5vstjqUV z7cY$1If}&o5e>YV*1pzH__MDsP3}3BRYS-bIw?P&l3mL8>N&411j@PaC-?ZNE zetcXqBTQ;vM<`Y~R>&@#nVeo!^Q@4q8Z0*jgLsC7_r_%*p*!Y{FJ?EY)&Lpx_2-Ab zfB$}X-rTj01{DVw+@W)fjQNHYZmu4-tucZFMOJT&{I(}m2)Oz9)Gyw6p?2%mbJSR3 zd@|XIXQ`fA}pJR zj!smXO8mgsIymw-O|I(^!5X{c;u_f{lV1T&{mrH)O1o|c^4)0?+$d9NoZD=U|8j(EHN zt|fpIzBP!!PCes(($}xD#_uFe#0_i1E6(sRu?>Zxy24*T0|&?olEMeV=WZ$}sM*=& zj!#S+!EJ`sOe+hPlok2@x_49c1s!(uI1)g+y}K)Rf*6kOYA4Oz+jZ`rI}f-=4sM8N z`NMfUhI&0jEX@ksHcO@O@Z|vd$&u&F>PUn}wXlQ)9bfS-8TJrdnZX%&-jVD^IQ?{dnhD0&u#F=G`N3 zwyWS$1dpmCfJgXKy|e@14E|7(gu>Fa)jyWwG&GC15?)}A9wcy?a2pGP=HE4; zg?|2X9dFZtitOLHg1hhNSYhV-5j^n1)9m!jBAN=cbg9UhyfLKDv z;MU)e!F^bTvTw^gFwtF^9LAqK z@Ec8le#Avok5@+`^drN=f1w(G182vRFrt~G|J^CwYi|H#UKO;T|Nf5|>=1fNgb*PB z*_DeIWoot>xO;kf{#4W*Q6y`rg`SOg{+EbnZ(rYB<5`EEO3yV>iA?}s8#rw1Rr{2= zVMcN-5b)QplhWA=8_E?vi zH&GaRj6KyWvV6^K1rfLM{hj&sFGm=+lK)(5Ku-@zSCu*me^R$g?&fS@M)N0IBHg$5 z=wm2;Uq8(GNXG_S1Wu~)4Q(()pweb5_1rPx%R66@9-F$ulSeeM3SF# zl`90AW)v4|m5g|0gU#w&+|4s6Q)j=ELzD4o_r-iZ}d2V`HNVQ@eHXcOEj@KwiuBo$IB7O0UOEt*xCh zM}B2}pux*n0)#gIr%>u?DypU(o@=Z|RqVuw-5 zVfRtroxYN4mh4_p%fwb~v&Av!iSHB$M%?TFef+dY8{>(o+Wc--Fa!RumZxV~AO(Ha zQ&Q@=_dyJ+R*R2W`s}r|B=GJFe<98Nc?{Y4|G30jK0cMkHf3|}`s-F@GVS}z;;r#f zbNzY{Y`gmqi(BNrc9m3E(=+lB9tPtG2zV5}LTmyLPs03!Ca<0qS6s^LDMaB)mdwPy7Vi|t9Ywz@F8Lb-vU@jeXfIv$CR0;u3WNq}(5YaK8$cpt(WUKoeKq%q7xFPAk2McH76m6^!rL3)($tUUJh6owu+A zzFoXMLfeHUWOT3sbB_0M&#S>Co~4uOnBjd;^xcBNoRE*@2Z~40`G`SN6SIf^8d;eC zx33Q*Oki$@;W<9fXK@`N;fadi=K+xgz!;r!ZT=F zzwklibO@~S@!}1jg*05^EeO6{IM(2O&3>33>t6vuJ3fWScVVLc8H1O%QX`GPS?{vi zow`~xJ2-yiSMbv${LF0r*AVHzfZnjjL?|50jnb#l=p+R1JW5ohaGH0rwNP)^SL?Ge z$P6uNw9C!c+zBLJ$Hrz44GkI9EKS8a^d-wZzgw&)R!qlw>5_Zt0UR~iL`4U%1j~P@ zr0aR^7U=95P=baCq*)RyJhw>ny|cTCK~odnMF;%t;wkfa6)L>Eyu(WqaQ1f;7PWoY z$ba}_YU``Qf3WTRvG0h^U-x%*dpa?))otoV8+iKCklxDl+v!eBaI1-6*Jfj4`ciT4 z?%l1dyNSWU!YiJ%s0}SY=|b=^H&+>qLl*=G1pM~47^!LSVC)!O-|P2^voq2F%aTqiBf1g@>Pngy9tt5#8wM zXb9FqYO1U+S3mvZM;_&P(c=1f4GoPmw6wowotlSn-mQZDmswcu)k-y)1b=)w`x>Lv z@18J}r!C3WFUWtxvTf`AJ{RPHh)FbxPM8ZEn-~o*SZI1faFlYV^JXHCO za#wF%hvQ1Os^99V#J;m6ZEZ?jXOy->={1M6*))2!pxij zrtf?cQJka6fqeBAJK&1#ax#M!a&Nwb+=5%-K zAK$6~FY&hpaqS5n=Dxh$>RE<^k~vHRTc-80vmC@M5=jD>#s>=JXU3gmdWPn@KCLHy zn>1U3Y~lM5gysSyn;G^u1mvFT7Z?7?*w;Ma5m^BC@GF>ws}vC}Uin1>G{RVOV0*I|4nJd}t`Py2LecN7z^sKIoPe`Q(I=p+F5 zHOSH2em!<4x6&7yj}6VN z7xZ7{DJ~})uJVffAVTyEAyMpFexkss z&vQE*`?Il%O>AHWJwu^swz~P9H9ptMHk>8TR@#mCr{1p8gI- zwMuP!Uy`2CMCwV`QB-OM7Fl){N1yk046#0!E8pK!!p-j;y%`d`?CuFhOXsq$zk3jG zz|3nB{V4mGH>t|*hFRGbA6j@mP`kaeUYgkU@|(U!6?Pb@l?9LtQrjmrn|^4`>A%Pi zQv8NlnVvHL%xFxO^9`2mGN3l7aKEjokw7^ObC=X?VsEuq`KyK1;GTlGf!?{aDGcL6 zgX!ojHl1A&#D%k`riO*kq{fl3j6TzOuCrugrk&Am(wC#pUgNrEX}DQk#A*7mYI^$j zpfxNvtD5U-txk5M#<`2T*_t|fDLL;8%6HG660p}93c#r# z0sbpEWn_U`C8>?9UQOBjx%JDE86ERNYr0si?w`r#p`2NEwtWQ~)9_$0(cw0{F&Cd} zSZ%CXsx|#mrR$KnhJYBmT=xy;Z}%6y&x@;1_v{|YD)#j+c3a5ritLTg_p}8COSHH@ z*VfX+9!=YZoN(FNXGJzhFTKJP6_sg1FIisclHwjpVicP5I9zX*z2(wJW7UUOe=_lwZ)PdRh%sqVJ@;?AEL2>S`Mbq@K+i{3|9Ljn zQ?6-Vp#9vN9DT0VuGER3ovJ$siRM3>YUv1Bpv62#%cgznMPaqv<2PTr4Z8Y#%6ZQl z6E7%RSg0=GY$VxBt%C}-+#UDIdvH~ z9`f)kqep3;;$TR2QEur?{Y|Y|h4{kywfgbJa*rW3Lvj0OT;ev*0>73=4vA73li^bW&@#KTww+Y*I`6Dmx=j{j!go`o*2=E*>TMiB?x@!&LpM4~TV1V5OS(LY zYpkwPk{?^|&49v2XbQRvR6`FM`N+Zo@@ zW^x|E_7+Tpe~2QJ$6)r^g9qhK$J2*Vxx|=Sfi@nl@f2EdQ08qiB@4_ z8fs%*R<_>Li7_``GX2rnlT5#xJ{Zf4P8?`b`zC^`_=8J@w7 z&)$TYrD&K5xXM;?(OuP~zMp!TrO#bk-OA2+cHQud7oUGki7W4rb!Un5jhQqNT~T(y z&-MFfikq}xal$CdiAM8Q^#l|7mO9K{H>NhSUAm|^5!f)-y{cz7s69!~Jp&>*6T^c6Pa@Si=GT4@SFT zJ>HM)wJ+qD(n&CvOPp(DV;8&;-Q0Qe*#mA(2F82y8XX%vWHLeXaRb8)%Nv?~R_;$_ z;G^u+T#JL)Vx6HkFVj9;vb^b4nfB80U}4s2O;Gjspi|OKu?_8c9Fe@%tYtVei(C-9 z*u~q>v*5*U9RG=T%6w!pj11yw+N3S(J9)=?1rnl!g+y8O((jXol!fM zc*3qsnA3Nq;6Ct&K#p#t1bwws+I>f7!T9#w>3W{G2Mv@s_kF)b4iUCydf~h%0VRvD zn_W$dx!wufTW?ry1hHSL$4y4PiOyJf@l~ZT-H=PBf}MUq&ma{w_+jDZ^pEmWeVgM& z79a9l;_n` zyk+rP?d^zu7qVW;KAz@euJ-NQS2|;y)Gx=yQAt^vs~$g>*&kTuQ+ZOL)Q7G$w{&l!0V;+SkjhC&}mC zh_y5A&iK5juSW;{mL!Kn+GE2Ur#W8OQR;-rCDSNDw`Zz>Pvf${P$p`FYIeOBlA$|! z_WfI$iomaB{;YCAyRMJ^g`h{w5%hLF7abNm#VTt0K5i+5s)$f7iR*irX-XSTEuoTt z*~5(p9#_3jUTCSV7x#cKypr=miiX$duzO{hve>Pxs)x%xf;8qAa$+I`Wmj$s*^O!s zU-4jVlC^YuS0T2Y>?9+Y%xGNA75RQOW@1FT^gy9%gVC*oMl?pRvj5XkKF@P+o!YNO z9?EnK=|}4)1f4wMZm`O&&uoDxx{*AAWp~+?V>o5n3%+YAi|iFnCUvW$uj!V`;%PFA zwk|iw|7zRvX15NjAR67b20Gv{lepqi-}u7o;Ft)o&*mB~8yU<`bvXt!X4P)%h60Xj zVzojk&dm;SGTIlzoP2`Rgjrc`m`d(Ayw4iRtnu?v$fu(3YPu&#|FZRJTH5TO$VJfN z$rlHUy<+-<2grkYv0*_7$5>qYTe@3tW{s+1Rpge~E0W zytdJEhLVHI%EvmcjUnO#E$vZJ&F&AI8(Ntkb8gsO5=;-${a|(R6lmF%@|m3XqTZTj z`FfII*Q;~vJC=^g?@lpHIc9ubV+>F4iv*vLJn%{ZC5WZ_Xjrk0g6*z3uN!%2Av;@L ziNC_~Ul;Ct+z*}?0gR_yvwVZ*u?yZy%@=;r27hl>=)t}YmOMzzW!CBw^6cjx`ken= z>{`RcI;yla`AhS#Q!bslG6Eu;BNo@jlmKzT!+UVf;r8MRl}L=LOZ>Cs6n@ql-++e|@Xr{7h`j8$J;r?386aq5iy z`r*2QJ-mh%%2VQP>{dy{;UW_<=qSemJd$WH)O()O2MtEF;0s;h6FU}_0Jh(9v z(p_mLW#d z)weKfE?#ut=TPQ;ciDD$!S|NJ1r~K%Gnw zX{*6%YfS0=Fs1^^n734;l2*C%yq_WD9K$1S^rm}Uz^T-muls9JDOZq{!je?!OTqH` zq)Vso^j>u*3DzQC+SOWD!L5cI-H8-2_s0EF3HrzBBOuX4=bd9GN_l$F|4yG!b7xQ1 z!t0|;zlR%o9S?_CgloFuGMVON14Ko1T&a@*K2V)?S${zp&&qq~SY(Tup+@%0c$eX| z2~N8QpFcPM8V@h*WwEe{jnOlpXRxuhT9cf%b%n?38aAuIKU;n1?iB=@yv-!nC zwul?3THfm$XY*BipxLA^`m#Y;^%?eXrJ~i+$E%_&_iF=7p$H+8DX~WC-yIBi*sb;K z<}-@^7q5dK3yGh93lv;z=NCHqjI92jOa-ty(pjE+p5P<)($CQ7E!_Oi5l5_nvT~?>#i9_0ukH4OeQHvFk|B}KIu*Q*}1vBp;t#TJlCdX=kvvkR+7rf zbhQnfo~mnTtfRRFAn&uWvugn7KfeX?a3q%@c3DPZ+qv4DmP!Dd5YyJR+QchhEjb<$AsV8ABd3KA{l zi=PJtY=5FrGA)!sJ}@v)KE#LssTIOcA4`BiS#0}@&u+L}OGv+;ai#Vl5t^9Z-%dQx zb*Nkkgx_5U?MgBs+XGQC|HynN(f9|Vss^l0V~%7BI0PagS7!8(!GVDypa|sZ6kMNZ z3d6WbMYg2&^k|}tP|@i)XBQwn&L(&-b-q!R{Pn-4-C^}iqz@UAniv|!A3JtTQ%5I* z<<>ie2SE}LS}pulw~_}!AhdnJQMHb6iS zG9D0$|B3u`g@Z#$BVA>2c?aezcjJbe$6lYlLodc}Q*79&iM|}SF^Ccyf*AdDqz<;* zk1RUFe77F|mlO_WHqfxrqo6O(5M{mrXLVyvf}f8Moi}crx~2m=+bwAJl?2}!FxY(c z#1KR}>EFaU(3rH$%#wj?aGH@3gLweI9rtKl?(gZj4AEi;2|WdF8<7^QEi*4A1S(Hu zWoMKALKH|%q)H9A=tW%=oSd9K>2OH;IhFtR=ifl}i-+($j0}|R@a2l(Jjg~CF1v6${`lvE>ao&w27JHiO(^jPDvi*@ z$$ymd-;eQ{N@#-U72XeRd_kM??VCcpbDPLOxodXKtz(uby#biiL3UfqWBXj}zioeg zk|U+&()98whajhr?az)e&w3s#)C*jekT3)mSvioz)_++fZm0i5oYKrUfXMK%yUhK| zA?hu7^6A0k&3N|_PtXAkNY&+)P0>N&N=dzBoCE#(pD3ql*Ns1KRIQ<$W27O$qBSP< z>ZnimuK2Vr<$NYg@cH0x4=$914Y_DSup94BX#G(bUPGp7Ijj8c;qbZf@V;G+!AT% z7M)kUW9{&5LR^fTh+q=3(pT-vCf`wnDaQ$S>U_41PGmdg1Js?Xz$n5Q24*Vo8Tqbj zVSFQ7?vXJ{4te}}EngWXC@4q^MJtW|2b=!8@9S!v=Xm+a>UBV(oq+4Po65j1;oP~unsR?3bZ{G)MlCixu?e5_N#7h+D z60`V%#>dTo5{0H=%b_a71&Am9k$2fxSXiLUWWjOi*P@%@gJzaEZ;MxniTR*~p^Q>S z2dmr%$-sc$t@se2p|8)b2&~0-U$^^Cr8Dp!Z!1{>V&rTCwXr6ohwrz>G7HvIH1A{H8!6`%5s0g4((0jvHOIv%lLZEv&&N$Ay z>J9AebfUizu!FiL!ld&2_a+$27e2K>ngeI3R|^MGpwslC33$l;-W>N4dz1Gv8U_>( zgrF-x9Rd@!XFJala)2*?E~jve!U3z(zKsq*C0W$4d=gzgmKY zVB_N2aKDl^7A@FX)-MILSvo=M)6+&3gAO%@`BfV+z@W`}eNoD|qqZmfQ>evi1SKqYU0Nm=>9-%gY0zhCAA6f-(V)HOyL z`^{py2>ZDGw{KmPk{T6u{2j0Y%5_%T&^3aRn!1TsOb!tBwvLV^Wb^65Ux2pOUFP5r z04|s%FrLI$zg@fm?EIDQ?D;emZi{?*HTzx{IXPP$h0por4f(6M|A4Z-7eIxLY=p9_ zP{?kXL$yRB6hOS7ym;dY7FD5A;P!<=bRQi6?9sA)XlUq&ukfg-XgBYK&%7Q~Efm~q zp-TDi;p-Oc>y1ExYhM)`l;^`&|B8@dZST}A2VR!-SJF!sMzVwj{5#0sR zarls$N;@_-b_3ASX|cGUF41=*x~AorFjRR6F}n_cEZEWndRx5&us#c#balb4f)_Z= z=8)Cf=NxEdSz1n_upX~A4f(u8WDb3^l5y-_L{`dl@{|Ds%Z*2kzZKRP` z=2GRFihm>l;@5P&10j6bU?#G-_MDo#Z=l%58&@qK#-If|Jw86ZqZ5qF*?6po|2$A2KXhq^6%EMWzIq(Cd@88wY)YJ^Z>Fep8W?=AZ+*DRk*)V*Kn3%Z` z5RnL@^)>c@d|*6Bc7YWK)igbc``Al{UbCq{zUu+TN&4KJwV^+59Yx;cloUa`0Rh}f znE%3Zb-Eit7=wf!>XD&K!p_B|0*Otpxy(A7ExLJz-iyURwTE1JWr;%# z&JCru?yMyqS++9qKG3=?eon3Enzftz>lpBiSfC{Y55|RbLj74=Bn6%7_;KV_1UH)WNUdZ^h%iF@b@5M!O%i z#-Kj};EF0Pod@n2wKb4z*`Ss{R`vqsB(&>~L`167dZ zm_@wnJCxoEr3CC(t|+RCuRH-edgSgoL*RS?n^jc&9q?HbaSIk`jfOo)0H^LJS?|rI zULaBwLzyVZqt$d1-8z^Tg6^nI2D~wUM-oA3> zMq>YQ1PH>{#;SLhG+;r{#g~(ppHGXm?-J}cNCA3C;z1s05tLuKc>E|7UWa4ddw*QN zzZO(1hrfBV{w8rx7WjYpkP4GDf|}q?;Q#$`6c(`Vxh%22H36r!UZ~^B1s3oOSm7#g zvB8Z`LoE@oM>afhNNt2PVB2*(7a#tiY}NB$Q9AhltD8N4RzKR;NWk@AXdF?LQ<2TP IZu02=0F?6a9smFU literal 0 HcmV?d00001 diff --git a/log/ResNet-18/val_acc.png b/log/ResNet-18/val_acc.png new file mode 100644 index 0000000000000000000000000000000000000000..ad20b307bc5b96984a9eee376ec654e5a4d2487f GIT binary patch literal 31267 zcmeFZWmHxD+cmoA4pF2em5>hUE8eW+Pi{3iP8+Yi( zzLx2x;hK+R_>lO!@rdzc+1R=`@$9>lKBcmyW+%=5@-x$pPm zan3YOwcu(ER8Q(fXYL<;QI2t3q85(MGdAJxm zoRpRpAxGtnD(Z6<6cU0GPCy+uF`<#v&WG57ichb!oaXE8yYgkDs~r}FVkr3Wsr}C$ z&^>*cn4KNB)aJ`CB*xB;ub9T((?=Z`Ggct?R8WviS?u4lg@px9|0_2L_IG38WY#KY zbv<6Z7xo^d#%hFWFjMg1t5>f`xy(?Z+%I2H*L!2TzPw60?jjJ+wHnD5aNWdhcHf)& z#E}DzMe+D6R7t{aI|&^f(wA{hq8b|o*8Mv>JJY$|qMMnSkyBDrQN=m0_pTl^Eb}cj zyc?tY^5x6QXr43#IXXJB>eu7qJZ9yj;(6PPD-?fooQ@6|JntK^uSG@Z3WeV~{h3p+ zfByWbQZ~s(^%gS{Iv(O*Hkl|^^;&t4F5OdWXR9DPtxQX;9Vs~bY`KvloE zPRQ^48m+ZX7)7&=9I?sx6eb`Y4Gq)^EgWU#5%f!6AD{f(+}xjAuRn34QSjMG*w`?m zN=hgwV6UyO=V_IxYG{~?F!1p`2DcpDag&#qhYe>5C+6fhiYR+{@U5+_jdlbE?$6d@ zoNkTuE#F-$GvS$1XcbSajj+3E4ich#%KQ$pG?@e(KRGQ+9ehv zjV>-ya(^mUbu>0LH7zibL`hKygTuWvG&NTjnq2#T{*;uEfHJePRu2#Hq2uFYSRAFW zimq-Hn1`oNpQ0!!DPiK`zR2zy7{CcZ$Fp%dTv;*s^XJctc_Uw6-^Rwq7mW`d@D}EN zC%?a8r(AX1m$v)y+@Pdf_UI7b%YT#b8AnD;_V7icfl&x`+}84qxr_`0gAJs?^av9V zPgY7wD&+bh5fLF3)zX^lhKrz|zdvo#&uaHQt=AcRcxu*NX$2b0tLy71u`fdo<{O?e zGyj6mG&-**eEKxI0$2a4)VaLe5mNOb(r)(C0I}CjV4&F#{p;%0QC*Emn+N+F!JgFQ z@BKLz!XvtFR?QtFBD&7*r|lGk7#%{Q`Wd2(&@E5adeTLG`Ci77ht<68GrPSy2XoSS z^Y0XCJHqhv@81~LQ>84Sus?syL&L)Iz?rM5sSyac5#Y0*GZ)!Pd@7d!;=pQG#8VCq z0v7GcP^GV3AL$ii`b6$;PB94x`a15;IYi(qA@WK}y;tXZH37F*!~Q6d#ftFLADZ1@ z|12_Mn-o_#DVlhS2-k2pP^^r-(;JXB4e$yKd>k;+^x-Xu0@uW}W~kP~#0nx&=%t)o zL1QCnnO22VK!8{{E?KZ)iwBwS+TYVt3fuvkI=ynEHu6uOJ|zrnq`$Bx`uOo9At9lO zNKtb$`9Kd5A{avV7V>8K&ijHnJ^qJQSz8WFC zSNJe7v)H+p#06&_j=bIvQMo@H5^MNcOXNv#{v)n{q=swfiIzC& zZ?ZQGbjT)1?ob8U9_EADN`JDnY7fW9ca198P{J$vNbHl|n<#%S*m#rXmi)l;&O!HA zDl@N6_68aXB9ht!GhWNM!*A`8L_vO^+Pusp8k|o?mV}aBo?FHq#xgRX{+J%hd$UD0 zHjhW!UI|~9dR~8*8jY58^{J#sMg~t;hK!Vs&i3yj^*=&X6RAMYo4>ygbi7Aa_#(tZ z1Xq!upb`9>h?fH&Ib|jU5zt~zf)Mpt=0sYhkHBC@S696!!D(tN+}LCa*Hh!C;xn={x_> zi(RngOc_&X{_PWI4sKDc%a{lJ=T67tul%7*k>Z`j4|4@+Wkl-XXUMQ0qyP0ldOI%O zc)wk$kfB4;cg+wc| zf$rUut5?xgzwGkH$AK=Nf(u}xI<;U^IRDJ|wm^s`Q9hN=+>puN+-%FHUeT_$)lHhdu#dLAP2n;!n=k;RCa^m)*`@0AFdVh%(Y-%e?J*S^ zgl~^sedNpt5VcCRC47MNpId!vSlTs?k}<2_6X7o6|3b8qHr~_Utl?0m!2nU)liAhN$Xl+5 zLX`;&yPN5AqJ1zFiHb?n4SvSs!}f9bp-%hE5bZY~ z#4yqX2Hvb1QsD$?TWX7Tp-Jt3z3;}s@x2=?-jAO?iym_RXOgPujTNTEnDjAAt3 zJ+1qt*;id1=b}P<5D}Ks@q~rBapm^ep8m&Zccf9JQjX5ri?6H(Ws@IfpV5{5=NDqW z+3?0?vZ3cz-A~W@TspxH+`EP|0!5d1rW>AxLtdAh9B_B-Rj`BB0tH!I$|B220B%<0 z2@rE9v#AJbr;5XL^L$i@7ygm+Ua>9c19>cyeKo4Lb{P_cB>Lp!N0uZML_m<;2{H>; zhOBuVVp%@dG$d)?^I7c0gsgY8p8xp0wuJ=Gb#3_F-PY9fzzf7JqSWXb!=vYsGX#c z|AGb-6eUU3NS`Zg^FpZnb|*P~=;=2F#5%s=+`n_amQe2B)yn?R^K%LRGU5t(^b&dQ z^KV-ljmZl5xr-+9D9J#;b2V|2T#+LMZEzZ_Y7g;aQRrRA4*x5x!TqFz?e4D~FI{KR9#kMj$-HX(0C zm*pnB;>zoYm>EKXz})?)KoOzAgr#^kt@#Nq@#$m|ab?*XW=|Z2q)W-hnv%wx8mLSJR69Z&Ca&QP*n#-Ck zIiUMy39e)S$h&m1zKP|%UfG@ti#lFchgs#F{jY8$(beTHB#TM%efbt94_Bc#MmVBD z(DBV5q=bQI0&dNi5#Q__6LD32K_&GGc73h%_PES9#(mwcC$F6D%=kh&O)p(VLp5aC zRRMyMLfGEk<-FHm?%e0PGJ-W+nA04>$dWb7WF1oe8J4pkFB(8+dxt?0U6maih6f}> zbgm}Qvt1gnCt_c17Y~9WHOUCyFRUv4`uW`>#y`H0hFc`u;aekj>JN{n! zH9_1(7o^N+P@%Ryv`9uuQcK`?Fbv!H3jm-c-*mJDONOZB>cQ0r=SZw{D`oWdMm{nW~Y$KmG1fb4u;h{xnZdW6{))0nMn+C3Z9Zt)ee#Scgyk zL{(*D^-X>4U8zT+8pVPQ7mz$?;(HM9|2gJQt#-GAWdv!xxB9Qb?Ze`CCpuPzPjDE{ z0Ro=Qu&BkUjntDqgBtDU`B?S7V-BRUN$cs6GrUd@>hG6FKum8x1A`~2?1mpwShUx= zqaJ!4)J<2LbRzp*A2js7P316-ix<1XJZih7`aM3*6?o@|D37eIt?`C=y^c-(Y;93E z_Qg^1?AaDpGSc1>mD<#8FD@dPzF$0%>V_a#1jcag_flKs;gNERJ9p3DfzQ8Yb_BDy zORr8b=T(xN#;s&0$agQ^7O*`d!s+!pf*Og!=Zw_4>!- z@t=xXO#=CM*eg>vS%~!K*Ic5aq7^&TkCxkJHVeu7}DZ?*W7^*R4el z2ndLbj<)>^XVb4oVEvbFZZEFRc0zP(ElrnNy}eF{_$i*8{$i;z*_|la9{);^?Y&8c zC~(TOs~nuzg@uLBb|(kCPLHc&f~(9$J*Xzr)*{Id@j#~YmGa}PPU8VVG*Z~><)5if zX#fYbRtX2U8RR|;x+R;M$0;7i>NT}fq`GVSL@QS5be(pmDHw zhMLp#XPJHZf!fl4&f&zcH!$m+5M1PD;1v#+k3i~6Gr*yu(>(CFGWS5 z^v&76=sTI6x9+9fPcgFDjZJIr6cfp8#!25aF&*qKxxqpW+EXg zYthWt*2YFfOKWQ7W&NIl1sft~1&IOT>os#{wEEd~Qoz-K?)tH&c5-4BK@dt?rn8k{ zQ*sV>xhK`iNpxYOh8Buk81CEa3&Ivb%Ev2*k?Fn>Yom%u$7lBb2uLxgxS{j?fQg|( zET~*@gI_!ft-ov@eYgdW<(pO9vE+TW5>s1I?^RCA1>%6y+d!<$%)we^VPCDfmg_}D zMO9Ts&$#cNs~4Gch2zP;c@sv1DgWw~q@SP2gbnY94qjJ7Z-oniep~Uet$~$ zLI^3O&2Y5pYwgM1JjO-$HpD;i&Q0f9JvEZ;Mb%(473@72yPRSCZ0F!W@tDq5D@)hz zvOb0vY)Q!@sh~nmnS?&|CHi_=nqyzIdhlqMDgw7wn{7FlkrFvrQ#5W)+{?1wjS4cD{P=DwHAdaxTdKPoIN?A3RuJV?II?e&;{34>@Uq_x*v#+SaL4tekEURqgt1)HcEHE!bLJ0smfwBJSv7@4a z<`o*z@{PO5BM-GF!|lO26(-X$(S`2xCJS>~NhiaS*`%{A6qO})UXIJwt}kNyOQXo4 z#l=NXQ<9%w5I=s*w@ds&r9>k#K3+~!lNe9{5t*4!P*G7~v$JG}hlhX$B_PZ{!v>W>FBxIF)(8OoMJ=j^WR%=fx=BSX421jyg`ig`Y_^vK z>ZdzJKMKMZWjw4TzSiy=NvsndzfpiK$kQdR6gC@ox62QAHsvcMHN^IR24f<|Mw613 zPE1G`UAYSlMGcLJXn-3u8Q>cm8~2-Y+buQ|j~BfO(U?2l0F)IR4wonxGr7Jvi2U%u z=E4u0N&)Y`&stks=kA01E&Wa-+AkgLa;Ua9=FUjQm%pM)(jm3q^HBG(Cwo|)4p!bB zSLHR_4NG3Fe4obF&NwX7Z{_~gas5Uy)(ve$X%~ahS2(j#C;TXWWBCS)?g47v5<89k zateL9GQn%yzhLk*^*DDROgzXZK;kfJB}MQv;M^!>i$rbZ1X2kK3Z{z(ib1#4dj;`g zHX?TuCF=0~+L45m6hzrQ_?Z`{FP{28h4ea?T-=jRZDaqi{e8P<3=C_PEr-GXU2)&s z9tHMwhF~~f?CU-ebV0pb4ix|O>ldfhaOSY@iFpdDBrL9ZZG<2Heu`v&!_4odLX<4) zcm1%-w@TDOn9;OvXV-PJG<_Ov$9Hl@cD-rjlF+lg4P!e=kb<+DH9Ks3cd2>UJg$GR zq|_CUZPO&?&KiGr=;b6i&Jbr`>+Y#Mk0w=>i==`I3JPj0hsewI>ae>a2>M3E?_)rX z-nncnE&~{+X* z5D+i(^cx&{-?AGt21QW$U`k3#HeN3I0_w;Ker&q#9`!jX+c>=Z2R76Gmqy!n13g_N z%Yp!KphoG+!Z*Hej5OG=NfVf;RP99@C%cd_oJ}it!FRr#Cx<+*(pq<_4>+0rBWdV* zXRN*%OArdG->na2)d&0onIm>gNb9TS%ZFitAS5{}FTRLvxHSJvcDGu~ zuKZ)?zack1=a9jLRlfZvkl;-gQ5(%w6n~!fk9L=7%omC(kJ4gXp#Tx|K`cq&3yaP# z0ZuCY$9-XH`(jIO-cN1S^a@`}8tTR(@uR|Cq4-MCA;oQ^&~-SR7d6?v7K%vg5DLc- zYi%Ew^?m$epZ8H@Sc+YL`y$NSo)vlV$vssZBC|q4cu}dBLQ&nIu%1Bw zX;qb*TTVW#Ap9EFX3>v}z{*#I%cukS_JEqF!iFTg$b4ex9>fKk36$McyZ8qIi%_~J zsZQC$>VMOxy*YTohBB3Jx~Hv4^hCeDDVQW9338kK%fY5P^NP*qT1pxZR-YPV{mb~r z8Pf6S&OoDgIfWX}CY@Kx)fb?xNPOzDBF+7H}z2@G)eeKr-7GKr%mn$REM_(GNPS_W)1S)3n#y#*KtX+ za4=4vjx&__{4`PC$!d{w4{9A9vi4mB_+_ zGIo%X<)8KbbMqbIBye&yJX)qi5nY{L%o|%*Xu^$;!-KsiVKjSl=7j1kRFMBNBtBrc z7tVkAc1OF|Jog%4H6C$4cc5G`{p+FkP;sHXTCYM@RhBI%jsuws6K5X@#C8Mr zs?HqmaxD4!A-Z2MLfvM-LIjlRvl329oor<^d4>{9-Dk#*hY`c!TXr{XmGTvJjVdm9 zDRp{G=4z;o!hF69l%1j{Kl5s5(o``kl-_BZI4F46k>4v$j^t%hP>rtDfa-|nC7i~l zk^jQtk=f#V9(hsYV2+Qj)dvkvP^anwUmN=~RlA2D-~|3+>IQigzQXZT);>P(azv6g zq^xwqMX{o=I%%z*hQROMO6;P@_{VWGnqq1t7|c{5_V!#;SDZ{f$6RIg-i=sJ{s;1Y z3$>(M%8xt8fui|ZpmK!j$SJSR-F}!HT^u3VGbXL|JeTzHQDhbqbbA~hcP^)#7N_iV z#2UW33>Kfp(R6rWJLnL>3IU+{+eLx16vh2&cRt$(2@LvdZzt6GR*xT-BXRrkdHd6u zhaFih!Hp_fdNZOD_8VrxO1c zX_t0L9vLAf76Z1!F=B+5?*arSKQ7pzIr%4rh}N8L%kIDF2rW$Pq-^(C+o3Zj(flzB z=$^QWkPM9ZG0b5buk6 zW5vhfyx95o=J~lF*3#lv<$sN@Axy*R1)LLiJYuz-~Nb}vDm!u3Yb@Y=Z>5o?(4ao zHUb!8J6{{zaCl=bh(!ecex%PSpQt$~ExhN^X_uzHvy4CK+r^M_+JgxE?z&0$4LI== zrF|sn#-_2_JPnER!TMov(JTWnE7hnV+E*Vf*udHlVIAbJC;n;f7Q2>f^t16SjuN|w0o&>A& z;_Olbe?_u@W1txAD-Mg3NEI%32`W%8xObW&;EiTJkzyL{ZH(hzw)i4IEE~lN$_RNP znsuOm{zQ6_SGuk3V(w4it`Swu*R|x4gI1&Vh+%YVH zbn7d<6w344oPP-D0+?jsv934u!Beu&`-=J;JMB?KKnh%qdegBQ(Y$s+nKzCF@5o}T zJ`4(A{Dj!;pf0lW$ad(+R`S}rBfM!fC(0(J#>5E2-T7w$-y^YZ^gl=P3;5HjNixuY z;2YWCMU|IFQ+J2ks}s$m|s? z>i3#cc*zl^7sR>6P-xummYqq+Gh*0| z2p02TQ};PJsN%QB0wC5763(X`4tK^p!a*;3s#;J0;~WlP8?5AFsNqV8jbLMv9JN_^ zt^Tgwu)2vSs>$C})dJ6kS4f`z>YweV(-w{Y(b-*$RI%*#s@(@IMw64k`22Fc5JF>Z z5&otQ-O2)xI~F5e()c7(yCEO1-QRV8?Z~u0^)8*%6FL2N1P4C>_KIzO#CoDn%jx&j zi~{75H;3`YM(vbUO4O*Ju47M|UWujVCcNtvvy&fmI2GL41myYW#8h8mLG99&+yt^q zLf(ju#W8At%S*NtS(`mYs!Fq!M{*oMv;3Fj`O zT*Y38n=N_WGH*|}zv*icx2W^sdT9}he#&dQvyJ_{nMw;P z8L1vQ>{L%vEwI+-_+4f7!Fg8=?a3qV1@Y3ig~r7Ppx}5sc`oJewBq)aEuC`YL39&xlw%gh@w~ryNzuGMupQ=iL7m(H`4q zvLOO62(7q)XDgo!CQdfqFNZnq=|Ad95_p-ed8ktc?FWb+BGpmuKKEqnz9) zYj}fL7xp>`QhMfsp=%$=A8AfV&>^9F)w4nPr_Ny!wW1&)34C0v&rXeXFEl=+*uf#8 zZ}>>m)%MhXtmEs$O0ANbJCiNko{f}raR_-@8|6OP`PD?A=d?{7e17UhaUJp@`2r3> z+-@rj0L$ZrKX>pB>ktT{X3XyST@}aMLf%l_N?tof{ZemOv$6CK{N^VY=8Vpv9Pv;~~`as^92sKtBL7>G&N@CJwtS~1=4BsEBv|T`q18B(XVy`a66eD42k#f!; zM436o#AVTDr9L?X5D6BUx#Eni2d5wUUzn>!IS+hS#?O*9^#-b$cN^w+?}k^Iqsx@o z!q366bDzT}d-D&_?*Qv#DUa>Um7G2Glk4XDsh?o@H_6==JXY1<%!D&cppl7Eu2n#78gqr4eoR$dm3i#cKpGX}&+O*C2I3J)_ zU?J3ZA6$E`=0c6dMC*sfCHubt8DlXQFx#SxXFZN@YR>g-hl*RKE(1692TNr_2Ae4* zg6FD{8{3_47xI!76FtvAJF}ZAg7a>7{l@t7+E~s0?ZXVyYbgjmlK3Q(;}AZ~&eo(e zCx}4mf~CH~I%VHPu?Na(%v;+33_M&%;cD*?m_M8(5uCuG*o+BRWAAxCIFNp7%^hb> zvffD$*wgdd=Iq?5`#zg28k$=Fj?fzU<>fm9Js?59`a(oXk&W> z1z^uf`xzWBf9?A*FnJTm@gJd@pA+}~CEn%XM3aCA_w%BNZBL5XM30*BN?#tmEE87c z@g)e;psK~A76PINAH$Af+#uCD0hwZlP7KN3CUU^#maN9ayFr)OQawC{(zCKy|Aj&7 z7d}nQT!YiZ!MRmfGrD~ASzRD>m>O#*0XcojdfFit)c7Yo>TgHe9OQl6f++4z|B%)` z>SlBX>pAIK`0cUy^g+D!Zd;&!7w4bMgaE7*%d24fvP>4uVy(U0WX09xg4-^!27ce4swL3vGYFu$uIz*8*!UKGSDi1Hvd>DvMfg0wsV}eX6e9d z3(Ddp;H=Gka(^Y+VRgu&9x~tht-Tf~Qa$`QeXAAM5vHf>rUp^VJ&4CSy{++k=Z1{# z+Y?`DP>KPWP?k80|9P7rSuZiH2q!241gz5f6T-`_2v;EMHPTlD zfnly;Eq%~7($kP2_Y-n%9!53K<~K+?E+uO?S`JLK*~qgVfdEA`ilY-tTo}-njiGavRzY9($n8Y|zyw1|f@iMnxx(HosxB0Ds9^ zWnNpi1AbAM7Z90#yHtK|+jdY?GAs?Efew`}ocLA$0cQ^KN_&jddO$%ijmdb`76iq| zoJ1Hh?zc-FfE>j6rIKXyNf#_j7qwZ;SD4I!ViW*U`ZKbRoh<<{C{*vQ~iFl!}FE7Mvw~H~@VD8|C{}_0CeO4W*h9R1jLaN<>QIp4> zM~W*PO*FuWGJMJ>Q8 z2CLn20Fei1nw}E}x>+Va3?+f8xl-pijsyk9V{(UE(ba%m4Xx-^JPw@yj9#Y&OYY2` zIo@UhJQ5zL&hK2GVI2r}yP@_8>+%*+h&eRVj z`v&~7Pko9{SB9)JcdaXTa}PA3uWqz2O{UfdnHhpZQy@L!QE8V=>eSf~UtJvFfNv%w5biIvwalLObazY8C7O)o%NBVFY9XXj zG4Xt3|E8<=t4?L}S8{ZTG=R#t^V@&yci(W*$kXh%BsWyC(+_!|PQ)Sw5Z@adJ|jEPdail?(ZI>3Pj(QGc|Er_SF z#%&`Z+_gI2-#tiW(+~gt{l)8aP9$Kr1l~RCzi{AmWd*_$u;T&MnDKbdw#u|K7!7_g z?-<*MVAl~&+$G;5(Jx=WzP-EomoDha@cZ{~@~oqV<$x|=(&Mt5;jC?Ysi>u66q{Cm zj|F+TNYV82^PmdN%o87py+FRc7kfYuaUT9d>BF*X<=W4o=Z&@pBke}#$29T zHBcijO)stmNXM6!$-#%HNn&vrga&|lAz7n@HU+vB&3f(Qpp7BufHiUX-A|LmK^H#j zBVR~VJBZTCB=umMf-GQ1DARD^&<3+`dIC|tiM0OP%)pED-bm3# z7y!?9#xX`jPH~G~e+r%~)8-e@mX$@L3V@p;bV?^zSDwAS?=qku>3U`a9qix*E(AIQ#ufgr|lh^WXpxy&m6R61?`ih7ei3PVdyA(A*~cU*J01JNUGT znTj$%zKF=Uy1%B>Ru6zI3*V!Uu_cvSOj&n<&Ka)>P7%YFvD}SBo zty}bMXWAA1Z^B|`{?*B0`D%58?QNQ$vK75ivn@^brx_9i?*o^%0^YiJ}xqyMS|JN@dn@LMaA$uGyMBUu%wJ;+z!;bqqVc-ncTU<&{Ck57Sa>syx zDg8=10ExK7%^@gtkuv7)AoF@NKY4vN>egwyaQD}_1f``XD99t@7 zJDID0x~x9rO=4}YA2RK>7Nic6R^B7e`M1LMDJG|_08;73k-$Y**t%&5&RkU2RyhM} z4MGO{RED43ntl4YiL_-{!Gbg%H4o#paA^bC>(ksv-$LyBhILeErAheet&HP5g)ZHm z>4T2X3yoy;G?+sU32kOQ*pD7PG8s(g0y#Z9J3C$2lYPl&kFDSQimJTMc6#k6V@@}) zUDGDYBkcSL+cg{<6C?;UBzPS*Z5;!zJ>TwgyPpU*tk;Rz`iO;vg%%kyaPo*W@FMaM z1DdzVMS5J|KjsZ?5ugzSHWl)JV6mmmr8LGw<~(1NW-2Z2OswrmA6+i!I%p>Ax)E2@ zyY(#qz;^YZv>TKG`y)&;=8Qv(zc{6_(kbjgRDI`u^^Mxx5D?J8AfSLW?4no-6o#6v zt?!|-G!W;v1`43gFqNo11-`i6iv#EJEx@$^<%Q{&?F-V4qi`$6i-XWqqPm=7(IB8= zC;T*z^|{8z!J#3MiIVnGFm`Yw%4r~lLZXH7 zpZZcnVcJB!-cqsH{l}PS>zKUzCR4 zW)!Ogmv&T8vv@K32zb`gMlpMBxPU&be4p@X6Tx# z(C4zBdm&$lXB?+i5*i;*@PZt#-f=muxR?ooQF<=Cf7=(gx;<9VJ726?a144V))D`a z-Ceo8rJjb6E1;kTYEpE92OlcxyBn4Fe@fL(lMQmTh4ZAb(57sALN(^9?p?5J9`=Io zzizHy)!#Z_gol7^9E@$5eTdnQr& z?Qv4v4!T2YF!L6+#xYS}#yuH13nq25XcTOs+ zE;8ig;nABwD^32rYi#TR=vaB@7O3q4>M0Jte;ClpSj}DUvJ>0S#(+bWUxocGK6qqk z8<-c|#z*5EU(d^IBCb3_%0l4t9d*w@5|GRu?r+tI19twmQ*3QRK5bXRK{|TYPrg0r z4WnVu!LoY}jyA*DWsnp@&%ZV1%qD~P0iqe%*s7`po%!~1ASfUp1kbL|UKDHAw*SP~ z*qBbOC7xD=UKsGvuLDa!Z>3?&%28ELTI&C6xjgG;mo)+~#n#t3h~5Lh%pU)mFsaFy zc$Ok%$=YXjXu4t?vti8Iaydd{6WUHv%@h89s160woh>|!hVie0@WR_b|GOM7UTDwt zdxNys;yoVL&d?F|-#LCpr}jn!O%3mhp=HQYlQvh%zl4zgpTuwm9HD8?{}RLB*b6wV zNMyt{@BxBFR_cnr--4(-i+6$5eetyg&xR>=XY?(QHE7^N4R}ZGiE(XRHEn7pF=)PrJ$%sLy!vZa>uc}RyzU_B6OFZVa>b0&;$Kf6Ts|l zxbV5{s}pP0qe^B$Epg5T8-}xG!TuK%-}w7JHU!%KoS-$T267g{Nujs_tnekxrxz}& z`57TUSf_I<-&V(4Ya#2;(Gj9ZTLfQinD9OM_44;8v*JJ8h;%Hd*q%VV_O%{I_@iM= zV~7>#pjr_AE`91Ml(8zlh~V`~wch@AEvpk+Z*N{_bGmLwZ4_kY1BsC6LN#rgfsT?Sf`xQ;gKt|JlpY zV!}cS{Dkc-5|_HPYMqYXoH`Q$EdjpXKY?(35*W ztH&I5y}8Z6Q*chMz-&A39{1xfIgpM1aAW!C>Dd1UHqT=h&9%>Z(f(v%2=Cm49+En` zFP+c#^8-{_a~L#(h7}wHyt3*Yld&fobR7xOGl+N8pvC}e`eJ6Z8vZ@i)sMb|1GE>c(9TBb_r|~^qNwf}95|q^ zR@_X{L=5*&dfU2m{)Uy4qnMC|@8SSSF05S-hTsZ; zCA^=FZdU*u#$I%1zS|M{$+LyiH3`VEq+jlb<~6!tz!@S@_7Grz&eF?0+@pDvLnUlP zQDRrudZp}csgp2BK@}TqC`JczV{$0lt75>L-hLUFd5&h4= zaZCTUyT@W_*es#xBTYM%^ZF4~VSgFFIrH;NI^WaoDC6p2Jq1qs3=8J$ngL>u!_G3NPZA8=>Z(T5AqR^?J|89@>Ix&)qU;>-`&&p1IW57zncZ7P#x*zZCrNk> z#0faL%8JJ<&+StTHGozd1W1o1t?#zDy5Y133lF+SueUa7B0_!$DQZ2?eTujf3%Uew z{E&jP2z-F=)ek7pAEV|e4&C!?udjxhwpWhsqL$AG7D)?-Oe`aXqB)dQ;QAt488XYo zzMS9PBHO&zwZS6Iul#^i9sPVS7>Tg4o!ZKGt2qm#QV{K{8f3o>W>ERnqD2-_yebBlOAcu106u+$4i;KO*^|ekiySV5APB0RsZ91rg8%4B zA|5UhJRH|x;+_qXQ==n)qwsSTBOpm_Ret~X5AcF0+j^(>yBVT<(2-1p1Vf6+^$|(+ zvADH8c{obtC4^p!kuYanZA$NyflQ+fJ&byfxZ4FJOz$D-4<#I0FEJwA%`kscFBri;}8Ul4x9d6h#IU!~^qvq(R$Tsf6gq z?RZaAwR@nM9=Azala(tGSQ{fpopntYHHiX1B9i7vAvH+$K-gI#rl{Mkqe#21eoIUWK&B*pTO;s;zsYhGjZR|8HrTtSH1>pu!vsIU&$S6eHbe{~X(12l}nKbid2h<=WZMu{Rz9`@+Up=#XaJR^Z5loHg-d7JY2S~p8Q8P3DPA<}ShHyv z>?5W+NQ0CT#Ra&hkiI(Ml@Asv{TIGbVP$jB@YX_Dp~UG>GNFmGLg|hp8MkQ!M5psN z@yb9}AkTt87CJjzH96eW%>=c)pI{*CRip44NEK8!+uC22k9*3;u|WKAz!A8B)G+WI zqzP7f)Q0b$65)U_ucqbrEl@iX@P(p=$(Sd5=+Mmc2P2rYq0?o2{oli)AAwu(Jv1C7 zK+Q9ox-w4mY43)6Elt`M7mrEMCee*9s&=QUX>)#YkV`m=w%}(|2uS3GqGUkM3?tpV z7i2b17-&8;KKRpY1h$2v7{PHGd*i*MZ=>++di2*`nu%pR&?_VIZ8g%_;Y;3~@p873 z;gzd&R)MiHBXC(VRgKX!d@1dD%aL4-PLrqu@9-!2ouUYvuu8{BPuC2!ts5c{W@oM9 z3(q)iucrjwfGx9uR(;OA4q=2h*>Bx(#x)-z`{#aoFL0yCK$G?=Hm4BeuGW6KQJH*G zXkV(?1wqvVnOm8k82Dw@2_4k~L<>0XL}upEfIgTtDH6R2q}y=80;lN}qvFZk?@dS149FFC#xGayRE$B! zcd6R$2nDD7bs;aydo)STR0cCViTvTd|6C5CRsz_o{?at*;SieS%1y^(kUG5f+D+3b zs7zWN2^C=pQKUYQvH`2T$8`38x~tx-nh?;aZq+_)K{6Mre*`&V?c-bd3WLR3iO13A z%5TsQ=^J^hiP#LlefAQ=jie+c^$lnT0|+9fv+663uWA1a;N1Z` z)Jg~+JqjJl;OF-;sL*;lS*p1`-@t+Rrj%5*@8uzSA0XOH0mtIx?EF4D8mBjoGMYL7 z-ub7hf@fBZfX2z6)XULl-6yEN@s=Wyo=lCO;MmJ({1iIfRp@p?<4hEgAqe{K7X;YN za(^i-H2o2Z6&VvF16r<=`uf1#cYYTlYB;4#f%nr!zIdP;9|j0YTM^5De>dRU1@Xs# z!vsECN<_;#FbgN9rdo>hPEV6Kww|B^&L`pH$KZL#4oX#3RRQO<&{^Z!CQ_8#5PMV@ z=$Qk(xQHSyb8YXu_k|v1z|d3`w24ee=Zt&7#foZOgi)Og0-YIXIPUH3HA8fxtVJ<` z7NZzm+bP({2!XS+^BbxTi-E@?w`b##ad9tm#QZsJr&tFw_z4`_|5=8-HS*pdew`&m zjNl)6MnpmotrO?Flf7??lyl-r)W4d+f{;i#O(3JdTX)3!1&&v{ECYu&&1^-37N0yK zrOeH_)(#|vgQn15WxT*^N9IXSlRxb4Pb<$O`kxhKRz)%SU+f>~HyK;Gi;Tp4Mo-WC zFB7nNOeuJbkIFQdL7^;Nq1E6u2%P($MZN;xHJ}AlL3=ys^{&6%h#c11g{D0Hd5_Pb zcZkqEP08)Rsr>ANq&pJl(Jn8r%JBKz_&j*WE=ETbQjAnxT#N}63cxJk9bTTD$p9;M z(#R)n3v52SnIQ1e1I*x{E;vJn$ zpvNAak&$s`ejdR;!>{(LxZaKCTF&dMtA~KEqw^~wrU8mMhvLnRx!MD|kQ1D51#c9e zJtir90_a!h#Uf6+)dKo(p8CA3j1TbDqg$&k zt1vy%X>_8c^4;fJUq=T65%4)FbyBlwFb930w zpIGnTzsDOcRACyeG8T^^=f&pY;=;tj>YS^y&9FZLG@YuRo=xk<=BCFnB`0Ntk_51d z1=*uXtXwU@fWETY;7Sjo z0dHTC_VyM8j{FZO82B$rN#3!6R%(1~vOnC>q_amax^5Q`y+WYQ@k{)_YP<4)9MiS` zw5NUFcVtZ}?OG(ukhN@yR!UJRB`r#eN*iNBLPleWN~@3-?Pz1ts+|g@B9%&gzx#ce znRC7~=R0%G`Tm)C@IKFd-_LX3*Ydlr>vtt9%a%;|8+WNCGwUOR^Qi>4)WBy;?UM?% z$qZ}f$UJAVLZuo?mcM0w=d>a zUeS}p6I?w!O4F>1A!wan9qYY(`SXhnPepoX#DRXl(odV0Z;}__n9V~m$(@<&dNgB= zvgllnr?(Fybe8z;-o9nY)Trk^YK}le9|IqC#cy=i^2z?p4f^-Mb;4@E#>S>Ozwgyg zEzY?qrKM8T5Fp{Kq^NV%=a}TKCa{g~d&fde_^oZd+`r11pPyl3Z((4Dqg2hJLsC=| z)7m)_EfS0Riw>r7IinRb*s$_-J>QwfpQTr%Dl@AHoftig1R0tgb2$6y zJs(P1$P>O6CvwP4L%qXWr4(+f-4Bl~Y&gnbJxfd>eP;>ZQjbe1IK+mPc`Jus>|yGB zTcj+ku-^(QazBj$FLC-5E-RB`S)=JzzI;9;@>5P0(#=HI1&Qme-vx`9#!I~`v!rryld>mRUF(u)nNBqls6a~0OB zH?A?@9RfP-O|wYmD8h*eM4kHL*mSm~cOyT|ge_)})Myt6d@At$fE=!PdNwk&7WMo@r*~T!m+KDCgyfn_g6&8_yvSZv0w=zexit z3g9#ht9bEb-&U>>H`YRl0ONB33~8KJ_yifOfDkMwqdEHEG%}PfC(~u+4gJz>u|*ik zq>DUF!w$+|9fS{|-YP6+QNEXBWMh?_f-C#|_2dmIT)PJknei#9m=C*!Rpe4gX66TX z4eV|jKY}qS4c~(;3uTqfLY}3AN^$z%8XgcvDGF}0h$_Tdu!cy_=-{rto(p2hQgEWka4Ps$8Vi;qe9%SZm@mjH zN-2joJDJGynor?*>1?#irwad3-1sphv5J_U_cD;jJO_0Tf-X$p9Z~c@)I-P$Fzj4! z>eanNk|Ijx!TX+_lUGvW7F0Abw6mMz=H_;B`FHxGr?HC;ca_X~G}`R66Op<6ucN!W zRX|lzzDCwB7{foINY359y)Ua`?-~eIxx%f@JiRoVMoM_s+-0K7RR9 z80C$y0S~-%H#zheHu$9Nmz~;vd_B>eAX>m&>PrOj3rZCodH;#6@|1Nf>33TtQeIBu zdRCSYRh#ECrcuD7vL|mlC*|rv4m0*n^nc3?dE(9y7*HA^YCAPPET(F=HuE>mrT9iI zW)s?boRBi4NV)iN;1|#RSQl+iCf~0tQk?051qq~7Ue*<*hh{8vdNv{T9*SdA<_z>RjE|0NAA!nJp4C)yX(h<}5 z)qu>Hj7qPz8lX(!_e zn~rgv`8Bq}zCH&bQBmT~RMgazBO;XM)UafHa*~N6{6Tfhb&lCP^Ssqn)zlC$S2^%v z4lHVUIk`eF6Auq{QgM&EOBn0cC`fP2*vE;q6Sy3kJ@=zLpS$@JYS<-uh0Jvzo~~PP z_~hQ`)g-)pt!qbg0_sirb&OC^YBMnz+JQ8h*>_DojJzkbVYAZ<3Y1Q)N@lz<+R0&s z3UWtYh%UMe0n>s*DSeOVK{j5Hr!tti=o0j*uo^Iug&`G6;EYpnD#8!xyOws((@2KR zBW}@Hx7{joV>(;y3Hob5>gD#xm4)Pu$dF5#oNW$bs69ovNB9W^hWUca8x41c-JH>uBR&*P4kTGB$#tcHOsU;ob#wajc9r(Mb(a z_vUb&i{bQKD?-wig4qetrGFl|q$>zfrmL|wG`%uvLuGA+z9;kDi20tPuL3+F+fUJ_ z{+J*S;inDdLkY9By*@-wd!ZWXvgaPW^i;#-L6`N|^M6G@nCtQo#ywOU zOHdJ~PF;QJ*!lKtQ^)a5n>Hc;VcC~Uf0d^|w~pk%{1h9jen0#~OCe4eo~_TuSIhgj zejd9~;w0Dhh?eim;%YIpbQm%9XyrpIbc3A*e(mdBG~BX!lDq);YQmS zaaotUzo(HLm)5cLL2^s3Hj+K@^p^itVLngxQQ#!=m6X)m+}z!DkK~Vb@h!#tJ%VpB zZ2q@11a9$UH?+0MELpOI+u4S0;6Mxx%={lEOeEf{d=x(o&Mf2Z^m>SHaicoXt5sJxv8nO--?? zqbyZv``x`!m6awHZ`@2SE}I4ZML5|@WlX^|@#fk2*}A%tK$tG*cr3hkuqn{F^`UqS ze)1SJl92|aI-f$pywt&0_n#-O9d{5@r^rt*&c&EBM<;GEIo zwvDGM{a1TFRflwWC{WLkb$VN2o%f}gHIP=)5Oe$J5>ba(ooO_ylXgkp0&5@~#c9Iv z-F%2DTXur#;6@@J_rcZq{tUYKPlW8ElbU)6K{(Q9M8C10;RyW`japM#s;GS=vJ7HV z0gidH`8P|tIR+9Q8R^JV21ZJx;a5B{%zAtWK=Q3i1RtnlJUb#G*?VX3vd=7lVC1Jw zPn8AJD}uj(A8yoTU1SXr`!iA?2`&zL*>2 zk~VaO5E z+Wwd(@VR#e0oXIU$(yW?#BW>~*vSMr@Y1Zf$!Am*{?DLK9?Kmj{S1VM*OXt6a&EcD z4GX#mR`cm|=T6QNR3vmDKz26B$cl=FUSEIS251BTNanph1L})+rcv$3CsT$mU@LZm z?xDzMFfVjuy6UsTlok+PN+C^ah$ zO-xJ@yNip9Rh(aGJ^DJ@QZ`B441jM^I!D;44uZryJ4doU%WU^oUSI&&<~!PCKk(4m z6qFEukjn!hIV6E;=<&1~@a%xtF)oew*-(2AGpeVi@0nd2rRwYJ6_5>ucgXz#>fI2? z>e-s4q@)vDlMcEjth;XH=B8Tb=n+va#%SY{>ZIjDSW9=?tBg<&zis)o|0S-{_7CqF zvGDJlF$K|&rZg7hmF+PkdVHC20u%eyEqioV@Lf#t$U!EY@#@t!aLPag8FuPa5y(Cn zDA=@A+Mh}58V?4mKZw$niLEVBaJ%trvp<;Ut+c$dYVkkMEBkZv+$#4jCp9`cO8h0) z_ls$!hTX#d$PCv(l1Ca%I>w#4+{nH2k(bK;nk!LT5?5P;$7N^1aXmPjOTi6AO;|S| zH_A4p0IZ?%;MxTp>ih)@Rsf)lmNkgp5U(L#bL@2mKY`E2hdL{sJu8N1`tz@E)9jx7 zxZSeh1^r{1tU}_gv~*Ngmkr_f09FOpiQRLcnZ@7VpE3lcQ&6)nAYxE7t=_+15!A1W z8!iU_^2;2M%!tJ#rKXmF*7wZi%iP$J1RdXf@8F6Y=T>vSJ?jGx*pVAz%N0pXeWn^j-f==OF#_a(V1S?Oz(Pj^zz0#w@ojD7k|vwaEP!lg zpdZ4yc#0K!z!Lh`VubwbSV*&1D}o`DsL%kiLw+Kc-vk=D)t2`v({C~|M3DIe&EU4% z$`v4znz5ZTWg|tP6ifv6eJ9p|nuLW9On{(8Id;IQ? zYhb}MzO8}nUzNZ*`$5La&cPA5?s{NCg6tLl>0@R)c1X%9D=U|STDoI!kQbb9Lqo1p zNNxx8BK?^DW_UbpG_Ag6$RpwrN@$)TZH?f(gDdTM?$N>3-$P8}Iv12z@8zQuwQ2bRXB5{+GEfQfm{~}xUXTAM!#n*sT8rkFwR8vzE@b2>}{RfswIAk{~*4VP2soIm9 zoSgi)>Joid^~n9sAa>I}Xp+Vtwz>ATJIS`^mee2#wt;eTbLX-Q4-bP5d;Y?Oj1-|d zni(%QSbBWQ3)b7nK#S;E#SKNzlnc0M8JviY49ECSF^MYg4Mr#n)H^3|}Pc4Htw`Q1Z$6d!?XSJ$UxXNcKk_DXoA8OMhv^ zLyO@kMc>Vz;Ar<7lb*(CVl|nF^05a}va+&>S9P}@Z7;Q){wUh5eKF(6+pRyHJ160S z>}hG=H%jzV=Faz5!al7GeJi_=@j6^0xXG%o_GeOyG5z_jzr3O%E11+MlBO-S+>1|G zSh%99N}WfZjg?j7ckbypLHcpk{?}tTxBa!ZZQE96APJ%@#Oov*9%-4=Q9ch zVif3f!Y%e1E*0fR)VW;7C;3x*@P4`|)}}K`ZLzS3+;(TXyn%u2%9C7=X&JJEycA#@ zWC%wOd!=lTKoQ4mH8nxFXV^7HTek`z)jN__@FEb-Ovs=2?v+`-d^us4e>6SuzRY&r z`t|Afh8Fi-8Q^b}`;E1Rl5ODj;7xD}xE_eKWulq@WbHhpV?)9~V* za3cB%`XX{_5lwx709Z*V!VoU)nFlbt=hN+B+GRaWbi2c{-%K|;X_s_m1f3*82SF-B z7;25gnZ7BVI1vnQL!SvJx%~YZi$2-vz|=< z;Y&x1lg5-^2xc@Uif~{KcZSN^00k9W?zI%g_YklJg1gt~jal5xJC0dgt5!q@5xz_Y zm~>*=ZgarqA34Ut#s(5Y@v!jl14n$pxL6h?8H((Q7O!^bO_CY`UNr_vWQj%C*?F6~ zZYCU8mHAY}sVt|h-PD+?g~9GcyejE_0D-!&vLUchmfhiKCwd!6D+9#kAh1(fB!nl5QoqPu>c^tCMF-ML2 z?SEhO1Bd4Xi0O&f@|$uAJbzvkwnPZT0KxlF9wQOON~*v19{;kh``BLZw{s!NZIBp% z>Jiivd42udFkHKmSG|SQ0+Ha%jbp=PbOnAt9yx05oF7>YIKQVGpz{Jn7$fD_hek$9 z;Asrk?{-(i3Ge}5 zGB77cv!^od*VozSLH>#eg{1!yC8bq>Lz)C3IqX((wYMnL1PY#)y9Uq-w^Kw-N=k|_;2@T$)w7d#=WK+*Ad+Q{z9fO2>NHDg_faXz?-A@a>e0*kWYKm4@S67Yhyte}u z3!t0R@$pWXf@UqJ26s&k`8mnvy9^YMr3{Q;$=vyCDx=11SPk#!ezQ}^cvO9DKp;Bz1+NghcCF$U9KcUqRju8Wed zL&=ZDIB$Lwrp4QyGuk;>ukYL59E~|I8_R$2@ZqUiiD&ZOR3s9D4j)z_B^^^!zFgv} z%(rq}F63vz#?lQilb=c@qnR!@z zp1pf-^`Vg_NwxivT2a|@|1ieN9o^ZR@^k$o`>99jA^=&ei%*J2=*6I5$Y5$HX!!8) z>Wk%+gY~^bEhkq+h*&pe>|48jeR1$CK|XQurmJEa>KcFGM;6TeLF8sehJd`hyw}Xv zv3T%e=Cm2XEpGFl@gr_U-^)9qA0B<$_|y6GN8Q#dl~=W?QTN}c=1TaE!tZ?ti8uXus3BYj&e^1Ovxa$L&s!AOx|clXpK z^(egnkrI02n&&QxC`61p9`tjMwvLVsrhwvAf!qb#i_;u0CgepAfBZ-WH1P45o@glO zig(F>j10HV_wSt^B_!nZsE=Jy4@uC}L!cL(SF90zg+;Jp?OOKw)IHDeK zLiW=MsP2x}PtFc~{76Qdy>uxbDAOH>n(m~elwvP^X>tkz2=uzAr+LbL6CX2KCDf4c z=s*j4N)@ukeEL-1Sk;z!W9U=;vK4%rO-+kynySL?O$A@fLVKGPOU`p9_v?j*}MX6;EtK!tw@G<)xhz7y$Hb|ZGRxU2eue{%< z6d_x)MBWS7+ZTB8_s1zH`-A6X$*;B!r48GQRaT&O9 zxAyCG_of_x{<=Rr`2u&`?~c}u?A*jRuI&^KT;k_X6OzD=!qy(VM2j~30}=m!b(>Rt z0D=JPsOtSZG=zR+K?^Q=nPS5R=H>a_v~U9VGr=iY zxg~ zGOA(9b|wl0!C@qc*?(%74Rrc*1j5s+G^;(V;^q}^4tV-y<)~GT zK(Z^c&cj?*YF~AQ0SF%&iCV!d0wy2tQo499n%5({J_XKK7lKspyJX}7T8o5 zmRd7bU(&YqT@K(Xh>iCoDwXjF(q@Y)eBj=TMmR+5bf@pQyEg=x@etb-#l|BQfa@!I zK1)%03!G!NiV8o7qgQ|deYu`qCrH52D9IMvjk|dpl62>;KS#2lfo1o#7Z~V&c`XPi z0RpBI6FwT$9V2VNlVsL>b->uJm3)4jWFGV?m`~oqL`$$gRNF0x5wvZK1mQE9&voSv z<`<)mceu`Ull5qSla|}-wRcj1KD(m5fsx`_k{vyc66N6+>y_1Ys=-z+sp)L&?X3eb z7J&^eHu&fh4<(1-pz@<3Mpj;#NZ4IS%>)6&!{^#KG_DPMjy|rc;x70)Dw#j^aeaSl zZi84B*XUq-48krXS*9`TfFPl9p3mIghivk37=puSE7A3sP1YD#LPln0R7~%iH{vTl zAlv0MJRS6{d)2sJ6!r%Tr$|TA%`4=Xl7?!}*nvILUg;}jY-Z%&qK6*~QS8{=YHdfc6FGSGMmA^G#OZk4CUQ>9KY!p}OE5f+>hVm7 zqoHcuLH0m7cAwFHemm7TVsiUVVJtqHUEz`0;Du)dBO$1}hMW>U0f85N_a}OH6&%mQ zDO5BwGZT}YnVuYELADm+?Br3%l;%g?D;y2m=Z9w|+h>UD1gnb^zl8I)>dxIQGv+WFp}Cf|X(9{zr_Y?R_8p9t(c_3SIZ2XupL~LwK=YudzxqQA+(L|n ziFbKz=RJWlZy+nLtw#HWP&AKAOitzkn!Es?E6REwN)ZT>jp&X*e@hPUni!eVpB9*I z-o3l!wmawNljpeS2>cL~z>9= z8vp4a@_N(DWz<-2l$017uUbXkHL-Gt2nH;@J;4WWkTkZ#hKU*@I*2jaLE55SGEB$~i2T(x8y~l)0T1N<4sR(j7 zGKLp_0LBaF;oztgqH-aLiL-&-kZzV@KK|ji-qXGwzBwNPZX}HzxHoxFRLtl14ZPre z0Be2`8cqyj$V6ZeFMF>nKc)(^atOsSU66`~HZ}@i!m01L0MRTzZyp=*%;VJThk&@oM%w`Cob@E%JTEIYpNk!=)$Q0>aUD`}SF*vrXC0y=Y=%LpB|@=oxf2 zX!oCyCar5IKjl_*uI=_HStBth$%lY(#pM6xnWe zI6P41Wk|=A82P1)R$)VODQT=NI02d427GXwiLr!em`Q@ZG?sS2ELR0hR!JiWQU*W- zA-1qx(B7?vGh>#!cRy=0xVB`S!ZLOFQ1lpXRxX$dge}p9B@TR0LLNCSUvD)w1ws#{ zrknIwAkm1UWuqA$RvAD-nsy^Nzyvo%7BEpBAa3K2n?(zc0 Date: Mon, 28 Nov 2022 14:36:06 +0800 Subject: [PATCH 04/27] delete some comments --- classification.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/classification.py b/classification.py index 46d30f2..42d9620 100644 --- a/classification.py +++ b/classification.py @@ -1,9 +1,4 @@ # encoding: utf-8 -""" -Training implementation -Author: Ming Zeng -Update time: 28/11/2022 -""" import argparse import numpy as np import torch From 5af5438861f1fcf447aeb51d08ed0dbefc8f244f Mon Sep 17 00:00:00 2001 From: zengmmm00 <11811636@mail.sustech.edu.cn> Date: Mon, 28 Nov 2022 14:47:46 +0800 Subject: [PATCH 05/27] update README --- README.md | 127 ++---------------------------------------------------- 1 file changed, 3 insertions(+), 124 deletions(-) diff --git a/README.md b/README.md index f000432..117d8d8 100644 --- a/README.md +++ b/README.md @@ -1,133 +1,12 @@ -# Kuzushiji-MNIST +# Kuzushiji-MNIST Classification -[![License: CC BY-SA 4.0](https://img.shields.io/badge/License-CC%20BY--SA%204.0-blue.svg)](https://creativecommons.org/licenses/by-sa/4.0/) -📚 [Read the paper](https://arxiv.org/abs/1812.01718) to learn more about Kuzushiji, the datasets and our motivations for making them! +This repository impletments the classification for [Kuzushiji-MNIST](https://github.com/rois-codh/kmnist) in Pytorch with model ResNet & ResMLP. -## News and Updates -**IMPORTANT:** If you downloaded the KMNIST or K49 dataset before **5 February 2019**, please re-download the dataset and run your code again. We fixed minor image processing bugs and released an updated version, we find that the updated version gives slightly better performance. Thanks to [#1](https://github.com/rois-codh/kmnist/issues/1) and [#5](https://github.com/rois-codh/kmnist/issues/5) for bringing this to our attention. -## The Dataset -**Kuzushiji-MNIST** is a drop-in replacement for the MNIST dataset (28x28 grayscale, 70,000 images), provided in the original MNIST format as well as a NumPy format. Since MNIST restricts us to 10 classes, we chose one character to represent each of the 10 rows of Hiragana when creating Kuzushiji-MNIST. - -**Kuzushiji-49**, as the name suggests, has 49 classes (28x28 grayscale, 270,912 images), is a much larger, but imbalanced dataset containing 48 Hiragana characters and one Hiragana iteration mark. - -**Kuzushiji-Kanji** is an imbalanced dataset of total 3832 Kanji characters (64x64 grayscale, 140,426 images), ranging from 1,766 examples to only a single example per class. +[Kuzushiji-MNIST](https://github.com/rois-codh/kmnist) is a drop-in replacement for the MNIST dataset (28x28 grayscale, 70,000 images), provided in the original MNIST format as well as a NumPy format. Since MNIST restricts us to 10 classes, we chose one character to represent each of the 10 rows of Hiragana when creating Kuzushiji-MNIST.

The 10 classes of Kuzushiji-MNIST, with the first column showing each character's modern hiragana counterpart.

- -## Get the data 💾 - -🌟 You can run [`python download_data.py`](download_data.py) to interactively select and download any of these datasets! - -### Kuzushiji-MNIST - -Kuzushiji-MNIST contains 70,000 28x28 grayscale images spanning 10 classes (one from each column of [hiragana](https://upload.wikimedia.org/wikipedia/commons/thumb/2/28/Table_hiragana.svg/768px-Table_hiragana.svg.png)), and is perfectly balanced like the original MNIST dataset (6k/1k train/test for each class). - -| File | Examples | Download (MNIST format) | Download (NumPy format) | -|-----------------|--------------------|----------------------------|------------------------------| -| Training images | 60,000 | [train-images-idx3-ubyte.gz](http://codh.rois.ac.jp/kmnist/dataset/kmnist/train-images-idx3-ubyte.gz) (18MB) | [kmnist-train-imgs.npz](http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-train-imgs.npz) (18MB) | -| Training labels | 60,000 | [train-labels-idx1-ubyte.gz](http://codh.rois.ac.jp/kmnist/dataset/kmnist/train-labels-idx1-ubyte.gz) (30KB) | [kmnist-train-labels.npz](http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-train-labels.npz) (30KB) | -| Testing images | 10,000 | [t10k-images-idx3-ubyte.gz](http://codh.rois.ac.jp/kmnist/dataset/kmnist/t10k-images-idx3-ubyte.gz) (3MB) | [kmnist-test-imgs.npz](http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-test-imgs.npz) (3MB) | -| Testing labels | 10,000 | [t10k-labels-idx1-ubyte.gz](http://codh.rois.ac.jp/kmnist/dataset/kmnist/t10k-labels-idx1-ubyte.gz) (5KB) | [kmnist-test-labels.npz](http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-test-labels.npz) (5KB) | - -Mapping from class indices to characters: [kmnist_classmap.csv](http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist_classmap.csv) (1KB) - -We recommend using standard top-1 accuracy on the test set for evaluating on Kuzushiji-MNIST. - -##### Which format do I download? -If you're looking for a drop-in replacement for the MNIST or Fashion-MNIST dataset (for tools that currently work with these datasets), download the data in MNIST format. - -Otherwise, it's recommended to download in NumPy format, which can be loaded into an array as easy as: -`arr = np.load(filename)['arr_0']`. - -### Kuzushiji-49 - -Kuzushiji-49 contains 270,912 images spanning 49 classes, and is an extension of the Kuzushiji-MNIST dataset. - -| File | Examples | Download (NumPy format) | -|-----------------|--------------------|----------------------------| -| Training images | 232,365 | [k49-train-imgs.npz](http://codh.rois.ac.jp/kmnist/dataset/k49/k49-train-imgs.npz) (63MB) | -| Training labels | 232,365 | [k49-train-labels.npz](http://codh.rois.ac.jp/kmnist/dataset/k49/k49-train-labels.npz) (200KB) | -| Testing images | 38,547 | [k49-test-imgs.npz](http://codh.rois.ac.jp/kmnist/dataset/k49/k49-test-imgs.npz) (11MB) | -| Testing labels | 38,547 | [k49-test-labels.npz](http://codh.rois.ac.jp/kmnist/dataset/k49/k49-test-labels.npz) (50KB) | - -Mapping from class indices to characters: [k49_classmap.csv](http://codh.rois.ac.jp/kmnist/dataset/k49/k49_classmap.csv) (1KB) - -We recommend using **balanced accuracy** on the test set for evaluating on Kuzushiji-49. -We use the following implementation of balanced accuracy: -```python -p_test = # Model predictions of class index -y_test = # Ground truth class indices - -accs = [] -for cls in range(49): - mask = (y_test == cls) - cls_acc = (p_test == cls)[mask].mean() # Accuracy for rows of class cls - accs.append(cls_acc) - -accs = np.mean(accs) # Final balanced accuracy -``` - -### Kuzushiji-Kanji - -Kuzushiji-Kanji is a large and highly imbalanced 64x64 dataset of 3832 Kanji characters, containing 140,426 images of both common and rare characters. - -The full dataset is available for download [here](http://codh.rois.ac.jp/kmnist/dataset/kkanji/kkanji.tar) (310MB). -We plan to release a train/test split version as a low-shot learning dataset very soon. - -![Examples of Kuzushiji-Kanji classes](images/kkanji_examples.png) - -## Benchmarks & Results 📈 - -Have more results to add to the table? Feel free to submit an [issue](https://github.com/rois-codh/kmnist/issues/new) or [pull request](https://github.com/rois-codh/kmnist/compare)! - -|Model | MNIST | Kuzushiji-MNIST | Kuzushiji-49 | Credit -|---------------------------------|-------|--------|-----|---| -|[4-Nearest Neighbour Baseline](benchmarks/kuzushiji_mnist_knn.py) |97.14% | 92.10% | 83.65% | -|[PCA + 4-kNN](https://github.com/rois-codh/kmnist/issues/10) | 97.76% | 93.98% | 86.80% | [dzisandy](https://github.com/dzisandy) -|[Tuned SVM (RBF kernel)](https://github.com/rois-codh/kmnist/issues/3) | 98.57% | 92.82%\* | 85.61%\* | [TomZephire](https://github.com/TomZephire) -|[Keras Simple CNN Benchmark](benchmarks/kuzushiji_mnist_cnn.py) |99.06% | 94.63% | 89.36% | -|PreActResNet-18 |99.56% | 97.82%\* |96.64%\*| -|PreActResNet-18 + Input Mixup |99.54% | 98.41%\* |97.04%\*| -|PreActResNet-18 + Manifold Mixup |99.54% | 98.83%\* | 97.33%\* | -|[ResNet18 + VGG Ensemble](https://github.com/ranihorev/Kuzushiji_MNIST) | 99.60% | 98.90%\* | | [Rani Horev](https://twitter.com/HorevRani) -|[DenseNet-100 (k=12)](https://github.com/kurapan/pytorch_image_classification) | | | 97.32% | [Jan Zdenek](https://github.com/kurapan) -|[Shake-Shake-26 2x96d (cutout 14)](https://github.com/kurapan/pytorch_image_classification) | | | **98.29%** | [Jan Zdenek](https://github.com/kurapan) -|[shake-shake-26 2x96d (S-S-I), Cutout 14](https://github.com/hysts/pytorch_image_classification#results-on-kuzushiji-mnist) | **99.76%** | **99.34%\*** | | [hysts](https://github.com/hysts) - -_\* These results were obtained using an old version of the dataset, which gave slightly lower performance numbers_ - -For MNIST and Kuzushiji-MNIST we use a standard accuracy metric, while Kuzushiji-49 is evaluated using balanced accuracy (so that all classes have equal weight). - -## Citing Kuzushiji-MNIST - -If you use any of the Kuzushiji datasets in your work, we would appreciate a reference to our paper: - -**Deep Learning for Classical Japanese Literature. Tarin Clanuwat et al. [arXiv:1812.01718](https://arxiv.org/abs/1812.01718)** - -```latex -@online{clanuwat2018deep, - author = {Tarin Clanuwat and Mikel Bober-Irizar and Asanobu Kitamoto and Alex Lamb and Kazuaki Yamamoto and David Ha}, - title = {Deep Learning for Classical Japanese Literature}, - date = {2018-12-03}, - year = {2018}, - eprintclass = {cs.CV}, - eprinttype = {arXiv}, - eprint = {cs.CV/1812.01718}, -} -``` - -## License - -Both the dataset itself and the contents of this repo are licensed under a permissive [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) license, except where specified within some benchmark scripts. CC BY-SA 4.0 license requires attribution, and we would suggest to use the following attribution to the KMNIST dataset. - -"KMNIST Dataset" (created by CODH), adapted from "Kuzushiji Dataset" -(created by NIJL and others), doi:10.20676/00000341 - -## Related datasets - -Kuzushiji Dataset http://codh.rois.ac.jp/char-shape/ offers 4,328 character types and 1,086,326 character images (November 2019) with CSV files containing the bounding box of characters on the original page images. At this moment, the description of the dataset is available only in Japanese, but the English version will be available soon. From cb7e96f6a108c4ddf6a68335c737095446534f13 Mon Sep 17 00:00:00 2001 From: zengmmm00 <11811636@mail.sustech.edu.cn> Date: Fri, 2 Dec 2022 16:30:38 +0800 Subject: [PATCH 06/27] V1.0 --- classification.py | 15 +- log/ResMLP-24/confusion_matrix.png | Bin 0 -> 37245 bytes log/ResMLP-24/log.txt | 117 ++++++ log/ResMLP-24/train_loss.png | Bin 0 -> 18329 bytes log/ResMLP-24/val_acc.png | Bin 0 -> 29921 bytes log/ResNet-18/confusion_matrix.png | Bin 34131 -> 33249 bytes log/ResNet-18/log.txt | 572 +++++++++++++++++++++++++---- log/ResNet-18/train_loss.png | Bin 16571 -> 18921 bytes log/ResNet-18/val_acc.png | Bin 31267 -> 35946 bytes log/ResNet-34/confusion_matrix.png | Bin 0 -> 33604 bytes log/ResNet-34/log.txt | 178 +++++++++ log/ResNet-34/train_loss.png | Bin 0 -> 18523 bytes log/ResNet-34/val_acc.png | Bin 0 -> 31477 bytes utils/evaluation.py | 8 +- utils/init.py | 6 +- 15 files changed, 809 insertions(+), 87 deletions(-) create mode 100644 log/ResMLP-24/confusion_matrix.png create mode 100644 log/ResMLP-24/log.txt create mode 100644 log/ResMLP-24/train_loss.png create mode 100644 log/ResMLP-24/val_acc.png create mode 100644 log/ResNet-34/confusion_matrix.png create mode 100644 log/ResNet-34/log.txt create mode 100644 log/ResNet-34/train_loss.png create mode 100644 log/ResNet-34/val_acc.png diff --git a/classification.py b/classification.py index 42d9620..c139b29 100644 --- a/classification.py +++ b/classification.py @@ -10,7 +10,7 @@ # self-defined from utils.init import * from utils.logger import get_logger -from utils.evaluation import avg_accuracy, class_accuracy, visualize_val_accuracy, visualize_train_loss, \ +from utils.evaluation import avg_accuracy, class_metric, visualize_val_accuracy, visualize_train_loss, \ visualize_confusion_matrix from config import * @@ -84,6 +84,11 @@ def val_epoch(self): return loss, acc def train_model(self): + # if os.path.isfile(self.ckpt_path): + # checkpoint = torch.load(self.ckpt_path) + # self.model.load_state_dict(checkpoint) + # logger.info("=> loaded model checkpoint: " + self.ckpt_path) + logger.info('********************begin training!********************') accuracy_max = 0.0 for epoch in range(self.max_epoch): @@ -132,7 +137,9 @@ def test_model(self): cm = visualize_confusion_matrix(gt, pred, logger, self.log_img_path) for i in range(self.class_num): - logger.info("Class: %5d Accuracy = %.6f" % (i, class_accuracy(cm, i))) + acc, precision, recall, f_score = class_metric(cm, i) + logger.info("Class: %5d Accuracy = %.6f Precision = %.6f Recall = %.6f f-score = %.6f" % ( + i, acc, precision, recall, f_score)) if __name__ == '__main__': @@ -141,8 +148,8 @@ def test_model(self): parser.add_argument('--model', type=str, default='ResMLP-12') parser.add_argument('--gpu', type=str, default=config['CUDA_VISIBLE_DEVICES']) parser.add_argument('--train_batch', type=int, default=64) - parser.add_argument('--test_batch', type=int, default=1000) - parser.add_argument('--epoch', type=int, default=30) + parser.add_argument('--test_batch', type=int, default=500) + parser.add_argument('--epoch', type=int, default=50) parser.add_argument('--train', type=int, default=0) parser.add_argument('--test', type=int, default=1) parser.add_argument('--class_num', type=int, default=None) diff --git a/log/ResMLP-24/confusion_matrix.png b/log/ResMLP-24/confusion_matrix.png new file mode 100644 index 0000000000000000000000000000000000000000..8ead3579c9f76a557f10a6f5a1d74f2ce58a3083 GIT binary patch literal 37245 zcmeFZWmHvR_bRbPN-gC#j zAMU5?7!DYF?Y&ts*L;38SEQ1HGzuaiA_xRRc`YNM0s=t>ftJ+X3j2F4)!luxL95?kz2aDI=b+)vfBOg4J;1M z7OW$Tjg!EaAUMisxqv`uM&SPtg(6?AK%j)Z*Ail?o*4&A9-eQ_o?%Y}UN7YRT8Cfe&cL{a*hSJe`4@xJ)KGWbN?n$Uuvsi%J0hF2ht0K;U0Y^_f7C z5fKr41h|2}Hy|U4Qc_ZW@eYF_$H2f)7V3sf05%t989~SZ+f~OZKvcl?A`49rCa`__ z<^TWj|F2J-Z4as_mkAqwLUJ-JEiJ7K6=vlII>rPPCNu;?65`X-Q-bV%*<*f94Yqb^ z-ONH|b#<`#XErg)DhVbK8HSaW6+ALBwg4<}@l>3hIS4r|m1q}On3*LVUr3vp?%A|5 z8yFZ+QBhUE*QkI$prD`t3e1}lb9ArC$huGI(vBv1N19AsM}-tLix9CFgKEYbw8AjD z<0e)-P5I(&d`PO4qr3ObSd@4YMCaP;aqC(pP$@Q2hjdS=y@7?aRq0dVn-M8vMt5j$ z&16kb5Dd9iWx(?CHh$m6s;00Ilh8AEY1Wo1=I`pnwS@r6t%?H{p6$mM$yV^c8o00X6NIO#TEA0A(k@WD5AVP6Kd(6oZ!!Y3_ z^xUtlcxxRYR?q}oCi})Yk*3cs2W;H;t`rxd)!m~7+DB?EB8uB4;&UFVRgnN)vHCWc z2x6iTo1MdbCXZ{pGd6cI!n%@^zJ_I^2%)t=w05~bjljCmtMBp!?>)8NeLmDymp93_ zqtV6%Sx)8Sx7(gbNIWYIlg{IFqy-KzpC&BqOOBXX^|LT8;TlNa85w7@$sb^awLd%e zJX}UvVyiRAl7f`zP$JrMI`CW|C59Y%xL6g zS$KgZoH`d|X>T6~hpDV-T1f|ns!CH4oqG?JQeU>PU-=!!kXjA0M|F=p zT9;wapM#vMvm*jZm(607ID^jxR#Ett>h9uKFcFXa5AOx~Y6#V(v}M&=9=$DKAtuw&0J;iZt7{OnP~1rf@>NIPj4>I!VgtrewwoY?Mo1 zs`vKI1del4NAPW$a*AvxU}?Kz?{r6`S+kI9Mvt&p&UzkDng^u8uY?g!RH^oMHnwaj zCb42M2j`PE>*T3GEWvGxm2s}B)CA8u4TM}g>q4u5FHk=y{d<+!(8=9?`9>_yG6=+E z&_?WYbUo=q?0a{v7UNYwpz4+EsExs9?oVi} z$V>i9cB$4-qaQP#1O(0X*5sD;ry(s5_}1_G8SJ=GbYnstIpE8*CU19Hqs?q=Y|Lge z!<2j5>T}&Ukto23qw^*sz0#j`w1|J;Q z?p}J|+}z-gT%VqbNl8g1?Mp~X8g&N2a4m-iB;aO0xT54!t6&%Bot#VrZ!j8vK zVMb1j1(BZ z0~Yzixkj0CN;}SxzLa}EM?nE&;`$yRHNRj!NBYVdJq}A0Cuw9FXiUUHQ&BK1`B=qHef$q?j4Qs(e5_ja@`Kwz&^XAP%_w(8Gja4jdLAi|7 zBA<_Ll)LB(2Hy!PUH9`-qqi3FuxcyMid^rhAIpGzZD{dV)NY!kkV)luKamD-JED;& zSpSuB#unhp_Cr#o!7B*@6s5*tDc5^nW@FWFe^KI^ao3HYW#wMs@guiC(6{E7c1UA4 z5%{CnvO*8R4)I)9C_lb9$#JRPa+u~vO-0!Nrz->kb6437>eUluIF41iRXX8 z(((v}DYhYaIFNiOqhs@sVB?S!?BsZRDfl?Zb{{Rl+q|!wU=j?pp9oK3`5!-bZ#9Ni zhJb=!vV5Y&csG09>KXbipF}`|m82lRAL?kC|6`%4OhaDB;#w@Q_`khm{Q173&f523 z<8Xr;SQQTxvW(W??OXE9i5!S^mR>m1+)~#9$lUed*VdV_DQ?ZBzmVXwAeG1!h&xI zU|?Vz-OE1FmM`9wM6|6!^=td8V}C>5ojo(pG^ci|gc+9)Lna8y$RK9YZ9xC^>zCzp z5sv@;VLc&E0DzUy#0`d6R{sJeb58q3-WVbtg#&y5A8l!ZRNR=pvc3nPf5dRmA86G_ zE@Oyp?S$jVJd-aC^&iLySf3bk!?EwQW6mZ&u_0;E#DQTOz!Lt#tq|=v2yEbf+^Yn$ zhbj(YD4A&^Jhnds0~bs<7<2LR#*u0p;6oJ6YP^4wijo)zOnmE!?-n3NQJemM!MylX z0EBjf5o8SF3iUI!dx&fpoI92Pq!3f&7K&iX;Fhs;VULnFgL}vz)5)w@VSA}&3w%h? zZzeM^_`@IB@R2!dAITj8>o3o*Cw-U<5h*xUW<;7kg)ssp`nDlSdH+{e_67dD}& zoBc0M(gD*ZU>8-E;nRTrf`6>i|R8OXR=rY)jUF*q?*3z5LCb zvT6SQ9yCf)ZI`Px$*z+BJDb}QZXsE!L+QY+Ij(UUZVy|UdfA&~c z59`JrmCN({xm_L(IN^8NAkj%2n6~>La1(GD`=$YCT^aplG=jdho{vkeuHFUc?ubwT zYS84ar6|kPh)VvY@QP-!$EJF!@{v)oB?KnJ42IDN;qun^mwwY(`gM>n?7;G z4``pD5`W{ z3Px;RGdfk1cBgK6WRsXfA`w0)gS`OS#9t9We(kjGCz^ggtJXgu8Ez&}w!WxLJ4`Zx ze>=y0f;9Q1LCO4zNe#h9C#bF41wCM*rN89TgyG?=6m5N!Pm~6W%~~4~*UyE(;nE%| zw&|hv)J8K(v8_EqXvN29>I{}n{X6=s1WIY2API+E6ZS?5N>+qGamcV#2hhPV8mUPg z+#R&vE6W>`G#6>kuPg<#<+y8{%`9mpGr=?mj%=Q+b$dSD?yMajqeV&tkqEe_3Uzm| z&&^i%k~H)9kl&_57Vp52GmhXsN^ht78fe#Ie|7g--SWVicsLcmflXO|E}-gWtiZl+ zzbw@pof!pb99KnspLnBTrQ__gVfJyPM8QOBqzUO`c4qWDQl(=ug40%edl!>hcRHakJ!f}NqAq<@eRtJZlID& zu8*&q$YkaE2q=B>r!PXu3ij~Xux9Xfx!glf$I`=ul- z29v%Vto&afET^AS?oTDZBW$5bH~95 zo_yAAa*nw*)AJVL!6s~BbiU?|H@dwpPPxF~BG4Tk2(2fclWZSqW{`9=ZgQ;dF9HoUc3NExd+1x1}N z$Sno0iZL0*Ry}ByTQc=W{)^G4wJ-(0K^=)K0{1CPf)zuq?JJw`kaJXcdp(|&uDAa? ze2h^G4?XO2Q1s$Gq{i5s$)ofWC~zO|Bfw3g{9jZ{GJ;wkYCV077?w$JgE(Hser?Wn zLc+vYEP^D|!_I>|j zbF2O%h2#s5YjWg-j==dCh|NUN5)vd)Bpe)^P#7fKR9m0WMvh&#z)vZsJQ{e4BxhB7 z+jgs1s!`WZMA4I&^gkE~7JUaR)(;C#P?<`dy?C-YJYg*p-a8&AN94@(Cd?5gyf-wk zfXOM7*LK1fn0Arnw9^Q}TN&tzN_*#4)xH6`ZIS{U`_;&vQcn z=#pmRzHF~x8p`A}O9$ss)r@8Z=KC zVS9gK`9iK_!ywa~J;A?Yca|d3RWI&= zH!0oIIzD4|`MS#D1~je59B!R63N7Ui#-z(*X1k8*ln!k9>TlmSJ^$=$X}NnVJZ zX5IiZTpF{1m;YtI9g*#E<&EP@tp_9zrj1#G>YnyyxXwQ^5)yUF5Jk$TtDc7`LPdFl z_c0AFR}I*Qfo(6GS4ot@p8YnACLb4_k8&8HAx@B#H_X34e!_$4EUzR~L>ZN^s>Bvy zv5Dxv+wPdwi7vtBYcp7ckFz)1y(vFCTKN=-_)y!8beJ_7oajTKQqFL?1SbYA`+`jQ zDQf@viRwwGJv3U={JG9$<#0>+_P!zON$vM!P+qcpst} z)9V=9-TKc6)|aZ^=rI*@#+BAcAgbwa-MX1QbV*!zQoteghluG>g?_TQ#Y-|ydedvC z@2|TbuEkhJab-&LBzzPTr?{1SUhS32SJL~)ZO4H{`S$ce&MQ@fzde;Gsa6;R{W0c4`MfirQyD(`Ihl_+EJ0$=!+X0g>xrs+$^B1Y#xN$$N?QlaVW zmGX;C^MSS9qE}yq`S1l_AaK_~BJ(4BU}bdLlxD@on+KmDTs=g#(;>zR52kj8iN4W5 zBx&6Wi%c?EIn$Gqshp*1FMRy>$W|nJ6}x-+-4|mH9ZNdlm`TV8DcGq3E$j+pxYtDz zzJ#g^S zrm~n~t5DCwp9>4&f0pS%fodF9NQaV{yQdUIP&_<5IBjPu<}Zulu)R+8^T3@wZ>)JwaChqQ93O-&;;fSmduJBm@pacG_ zSe`0?abuBd4nbE7OhO-L`xlS8>nhlN=$gr<^IfBRypB`>cuCK#e zTZOzYcHbf4#boc7O<1xytxI@%dv|qqLa?@+K&q&y$e*Pru>a9>rQ#QsI?#B8QhTHE zOfL7PH5;9&S@fDhsO6GZH{wP5cCrKXD~CT&%T?IUag^vbVt!S~3@R^Y^}gJLN!q(I zL4QF>S>bo@ZoAZkBOOO9rmT!{cYTc3`Xq*>>;_=`h=_r~)O*SVlm0lPjzEarpXEUN zrlW(`sk0Sxb>$2^n`z$~N--JD;KS#2BnKI8^u?Z>o_21Jd^(-carm5*1KOV{?XF+( zkIoYEfdp~e&4+Y!h#su8lZ^>oFTQ#fRwejN2@2J-Jl7E(p6-t$qNAZe50BR?yw02O zcUOl4MQ7I`{@9QJI$~avX>Fc8kf^*o^5EcLO!DdVwREw1DMK>Flo8Tz zL3{#9W!Gi`($i+COMGLeY-OMC@}nmlZ)e!OHf?%Gz00W8SW)D#5r z_4SRJIR|te;QaP-xVyVMy0{eA43}Phcm2!Tpk)jTd#N5=h*E(gXlQ6ql~-0)Ry%T7 zsI!}`vBKhZSdN+cs%Qe7ft$@l#R{hlsoX6p4pgAU@Wg-QM1+m87JlX-zQf z?(ZX&szPdAWcz)aF=%DFN*ur^#h@Iq06Y>I*HZ_^{amn#=~Qb6_uBsh$lN;(IS3kdZ7tv# zPVX9!p8LN@Mvqq*y{fUD!?YO9=mblj^ETxWbjNqG5fP%Mrc~muUSSXtMhy(e&?U(t z6LBMhR)O36r$GS8+ph|t;o)$LOH1r_^PDyIOHJ!Vh8!fn>s^&GJK%|L+2>dYpy#M* zX=%Xy(y!yt8hX7F&~BVY@X}ngRNZ8;!D(P<=+(|>W~IlmJ})1i8q@i&ui0IFeXzlB zXkpr=ebr;a(I`aRqBPYRVQ4lK7|?PL^HLs+OiUrs(MaAha9v@O!{e{Hr2>=#gtJH@=NDU053a;nf#GKnro*m@(J$ z=KO6h{uKy_SpQuhcmedDkZ^|$4C_i3A;P~TT8L^q7uN)2^ZTXs|KRhmpWX4C@o!?- ziu)leNGm$flY2az>RcVXtyWCu`3A<1I(X2@&{q_U$7tHK$<2yNobd`b3|M(Z z3CH`h=P`w3lOwFmqQ$FE2KwTj?HSq@w$oanVkQohHMNAKZDy&@-X_Eg8{YkxkW%um zrB{sj99^f^&IsUq*=f;VJ6+W-d%TGR;8^wP4a^N}vRZn(OE7I)>Qo040c?9_l>OM& zXE775ntT;q!NZVpJUB|O&YM~2u)@>%VIs>VEtp7&j@aU>hz$Iw63%l$_cFB|N@T*6 z(D51SlapzqCmS)|x`ke*`?l<-+$#3nL#76w6bgEUchkrBFRt=(XDdOV{~-GoS=Vpv z@U8h^EB<(IhC&>-8&uF-%)K98$0qnp;f^5>_e$%Ot&dn}lhbyO9(es5yT0)LcXl1c zKeto=@)P-C6lShSY;bfuE^VEU$?b`HYW*|kqHpa9g1X6W*ZzsEW>l35f&cR$S$69r zA{lX1SYkNp(Gww)uv?eqC+WHyNLCd>#6^f za&bGR=z;hT1wlFEnp;`VFw%hr=zY6CzF}o#WUL+@#^M?HVrZ1eU`18b6))Q-`G0s{S+X|n5WRfjw(o4?udKBC9U6n=UFAt zjOt#wg9MTKup^CR(*UpP?d)7;E*TII=(TfdJ z(B|H8|e70 zE#yH{kf^zRxV1mh-!fS|tBQSgJ+A^w_FQsdI>TNEq^dsya5^Tm;#*gmqf^B@Qu$s- z2yKPr@lu02rgM>HrJ-Qs-*oqxp`2WF7Y9hGvaNi2Mo5oC!+?e-)1wOX zk7-W61>b=;L|kV(=R9U+K9&_56ciLi&o}2Kq@X8}EOCLV9?3rtM=%l^t@g<}f7fw8*I$dz8pH&}qwAmQrAf>$lY z**}84F{7lHhV8UNJf2hORO} znw)1y18hCI7g|4QyPm><#F<$^7=TnJR0m3*>xsmwblFiL7WTzQAzZxIS8;A6c5bv1 zK?k%qaY~E?U%hvM{XG36ge_>nm4QY8xei)6Rht{XB%9 z^k#=Z4)XK8RK|H?lV(^RV;^%ZV00IrF^tiAV*`r_mKZvimD;Yfk-(vmN;p>BHh(3~ zxB!evoGgDcrLCcppDa8j1_x-Z3K#MjcYP< zSMEEeHKMl_w>}A)Xf9y-K6FnFXwwEgv!b z2Y150Q8@|O79n9`yeOoJZJ)!g`J&U)_@9jaq|M|gpbC#qM1U%9Sx4-_Uo(ZGVDF8b z?TZDtp#XF?Jc=+hjq*k7kWLaSG{a*nhph9)6l?IAWu6WNc)!v4_Q(yulqPDdrlM|H zSXe6R%bylZjf^OCb#=#I@uPENp2F5?7>`G1=6slbE!`8R^nG^q2ypX;A>)@sG^#bR z{~>#5KZ=_=9n*q+&@R-!l1CUF;DdTvtys?T3;Lews+aU$>F@7zGDb$5fJRKGi_`$t zA{tLBw7RpS!EU!u_vXM7kT!rsBhsw=>^LBEYL325E+u+szttu1Q(NSJxT3Q#^3%U# zm8NE3zf}_?Y>n(K+Ld*GPq46y04P3ZI(ziMk4c`KU_kgmfVE4T#n?5{ntwUV%#1c| z^LIaVfOy-#?b1Ewf9#T2f@q;6qyE1sHYS02>R_t@Ou+u>JO1`yyQ4)5|M6A*|2h=# zQT^6Mz%kH2Vgq2_0zTbu-2uaT4rUl~{H_vIVsRHg*q2jZ4UVz^87TGkbb}ro@{Sz5i~omd!;uh@MoO$JOMDeHS)*&BFBTDTGGxoXyJFkJ8>Y{yx~unVOdjdX#q z@dhP^ph1Zu3gY3kYMLD1F#4`+<5Ig7ZY+n?Ja1;QuRY&rt~YjIzMTFD5=FOWk9fdxKnN(uwJw-yGyMO8#fhuUJh3?>$sGHDB;}1w zicRLRTyFAeIS|R4&rk&-dGJ7JB_=f;bww2A={lAcwCWrKmG$VsGVjV9q?m*2C$*X5 zgv3?$iQ~1AihvIQuSPf zOqvpTz$w6#If=fiP$GU zF3b|5&lf-gU_55KpV(PEI&PbukTg_&yWo%INXzroX7`nCb^Y6!eF1J}l%sJr5|C!0 zfbUod;G^Wyk2@g^HI^v*5bWYLl+9q!GE>*@gGYXpf$8#;7-$#JzaA~5F>aCCK=$Jw z1SKHV`VT>o`NkAaorT22j`g122{{M;!duAyW(^J-4=-0Pg~fdCYjraz2NAi*!cD?& zWUOP6j2tgcA0v?OZxvCA8^l@fJI8cP*VcdUiU4z$PliO{;*{ARjcaOM}-|X6>3nXRUv_eV?tgeL|XH-DRlI z{VKw#%{jhWF=C$n`XS{>-NKc}0r5sVC8uA`tHHgkC3XI`7LO&{y`$yw=h~8J^ek4~ z&DoMDpl^JBe9Q2D3hG~`8wOXn1fs5xMf@e%EflECKXsh|SCj zv#zH{yd0{mm%+&}oG85pv%GmxMeEO95(4zpE+VlhzX}j%Zo!*R>p-+@U~H^EUgRl4 zIag{8P#M*5-vOjvv%>J*_ba_r#$bK81}}mD7q`SaeHb8fX^-kR5gKZTh6VzvcQ8H=7i10-NF7dO1vzrZ;_`m< zKz`T#i~0AF8ZniOQ?-HbKe0W(IJ}dI9L%Tv&i=S!1jK9CR!v3i3z(N5IY*Ha9o54|OzX zdI7`GwV~K>_v9lYUT$wANhq-UiQ-m)&GD#3z61(n&RrX0Td&H0C+IfI+Evq313SsL zd0Zg?uPjF=8*&&QJLuE*bq@=D!L7g!^w^1(z&=9Lg88VA1F2HXuYg?y8W#{!?W$}o zGnTHxPxfS~03xto)HOgzNjk#c?@nxDBA~_C_O@FSby>o7?dQ>^vu8Yw@1jh5GOPRZ zE#WRm-a{P+ay%Rpp(P&eC<9G*Z;j0No*NA~p`7;L!Way~N>r!n=X;1NyTYR0@E$qL?jNf_~A zC;8+kG(iTM%0PphTqc-jyMU5fKdyCQZ_KBKhUrw-Ffz?L3Y=SK0*!1gd8|lg3;Utt zhTpRzIj&;GGFxn*m6Io=SKnF|PVL-=B_YLR`<*>b> z1I_)ab9A20)magYNl%0Q6hK&J?w=Njq6~54X^alw^Apr@pyqv*EFJZGX2zS`j^-^yk+SV(8o;|d9c?H8M5HW|`$uw&Ik2`7z9+FGf~YmZPI%2NG*b;Kz?2n>{3HI=~Y-|8&>R&d$b5b$Djy z=Zyg{(%^YYnarYxsG;#Q$}E{lE68jp*$4>8@Vf1Rt(A#l4c0={da`;v42*Y&3-u;| zQw8>%qN1Y4+XDi2FI%qjzd0d;Cx4~~*L$Ne@bM!q_kU^(BOxLd-PDuAYqt6FZ4ajf zzk`AUoGtrBcrr3F+vOGlht^A6;KWt{=gk)g81Bwh(}E1A3f=&tCOuD&_os_4V`-Nj zRqlVz7`OQm)N-Q>Ex-Fggjg_a=fVOJurvMhwKj}TA`}!51Ox=TfQr>C*r*$KwA8$@ zJCO?tOijiAn3P1oym5RSuU@LH#`LP-Poi)BN8SB(Hp^;Pmw4{$+zHjj=9?SO675>V z&!0cfR$HL;#gRmNEPKJN_@1MH$X~pG0!NPrCMx;qZ>{{4H)l{TSe?|tT$+wD)9wbn@!&L5hy zu9@M*^SzP+Qub0l!}b48399CAZkhtdl*R*7%22WH=-3z-TNSh{S8u=M zxHX8A>*eI+Wci~I14Pcti{IAPHe2sNHec_61_Fk4cwKkVaar||LGWm3;iaX_fM6Ql z84L#nnw*-#AR>weTvox0V{x@VnMk`M{Qm3leUZTLiL|*kKg%LwVtRn)SIV`Y4tLtG{xYF)ELNl{c;Svr zoiF3P5zId#yKelO57m0j?ldkn3TtuU26|{!8cW8e`0ZqR?6Dz$9jqlo2+tHnk;fXT zq1RNHU{lZZy}TO^n-(kZ?$oJZ1RIPz_KP4T;j81^cwtOF65!L**5|~l^X_C*z33z_w zK_wN04_&|7x)AcY>H)HH67o_K60T%H2sBmJ#894O0`3PLz{@8%=hL+69tOzX)Myy3jk+Y&QNwq9c+c#3ltV;Q-Wn43JXo4IOcZYr=wPUoe=ES_xgO1jvS*q- zUD+f5&oQcGkzU$gQIIQsd%?R<$>%lU6j=7_wR68_pOR?R$FO$X2-&Gg%H>mEX7;!0P0Vd^7q7Ix5QD~TlvuXQ z6?<-$jbx&HZluH0WC2+Tj8AT&*DYSRlysDQroa&)x`Rc8OR|Ch1=74szUz$O9e4mpxf&FxZ*Z&o+LT!nq@L@x zKQr6ll>53)O;t5ytvlQV$m8Ffbfdkgdh^!tlBwHd40|>4Tc@!!1yasGLZ$d4^>6Xf zJ@(vr*77%4Je&1s=`t@ZuRI}O#~L8;#ui{cOELEjBD`VW3%;>Nl6+fT$+epH&I=gJ z;9)XjYo<2;APXaS>npq|3&kQS5J3V5M5i3jJS;M9r@m7nb_5zSbNBark!S4PdMR7- zJb*^PvuWI4L?EUjJZBdC4 zfyl+d+|(Duj;!Yt6t_z3fJ1JFYgR29A88@+ZlubMYEfuTwL1b@ASMh6 znitNDAANLLCCCv|uOb%lokV~CN_)L~Kr~UC?)CP}ZqnAK0WB6i9P-fH;bkMvY(gGEwy;d+51)Q8XLRb(mAb+rJHYt;fH9KxV)D zgBFk7XUS=e6;I+wm{Im=Cv1ED2ZRMM>yvRLAUF8YSuf71wQF^?q#x@Mk?pXWC{?&3 z(o!-fr5)FSRlc{mTbyZI&~-B8^n|P}uQviPc=$9GC3j(I0^v#K&~gf^U1u8! zXe$kkN%e7CTU%?>8V0?o>?V!}vz3H)w0wL7i8ODzfV(W#p3YC?51;IOWl{buIa3wh zRwZfxfnu6f{xVEaiGKOsfk{>_PpR0{@tnRps{T`#Kt;{q){^fa_&Q1j2?0?PWjs?F z`V;e^{@7%lC-s+{J|*Zb0THFu2g?xJH32H2Xv-BD6e!8buQg~&8{jvAc_$zp*ct6$ zYScR{s(i+2r3VRIE%l=&6?R2)DDZG4YfF0UQ>UE~Tf_qOH|3!OVCpl_@XaNX4x~P` z_%yKUkpYWPy)s}<0QgF?#u5Wq$yGNFdwP1v0cf+fwuW;Ks28YY!ZAsV8sfn898DrY z=}Vt0TlqAOeDGxyhlWjm+I%Rd(25ZFD$zxq|GoZAym06w2(aDPeF;X%+pdJrx;K?JTzcm;9L75#cH+AoAk6myY6b$>vgq;ZvvVKEUkZi#zH#4F`DP* zBd;R^t|GAd1gCyjF8?s&0lOl{?I*acH&BDD-iXN4uS!z>FL2M*DF^#UaH!Bgw17pShkl8G_d77u!SZvtNHmy zx5(}lj~XC(5!Tba1C!C3vqJ`wOV26;(iva6(R%;Y?2OL&sBAx|TFXThy{wnhacL5R zuKy1+qBz$#E|)CDXI*HD0Vpj*GkQo-5|laD3AzW*olC~{b4FHaU{dx?BqJfo91iFL z-pB*epUNbGC}gE&+K*liD!odcbPa9FP=@j|Ul*_QR#oDaouFY=od)kcHrvTJbR?Iv z*TFX?ypi$%%+Na`w!tGHhy;V{^PRE&jrbG~GZGPhVkA749$;a#X?X+8r`K4U`JOAm z!NGZYda9;7Fc{fIRhljw9(}A|R)hr|+;>9BeND>VZ5GLfk_ikJ=oR=JTC29PLFTYh zV15w93F=t3v)ANrq<9(m@tXv7cU?Ro10>|vJ_fvQ8@kGUcza=g4vVC7&;6fRRYx;1uOl#i!{>RDqjmKPm@YyG zbb2r#$@jSij1qD%Eq%)bvdPKIg9b2Spt{ZClGe|77a;QJP1k~gtQHKQLsK&|ql?{% zSzuR_o((?z<|=N$^B&Z?IqVzlKLg*9kdhL<(I4NvyiD5caopDzO9T$CWeRvik;DVo zaQAZs01BUOHqag(&d0#fl{^)CV00}+zty{QZjJzGRX}Podlmz5A{b1N$kl?G1Fw~J zb&(y8&n$$SnwwuRG9rP-o7`+Ng#EOK!8Xjb`#!_!yu9f&7+e9w(P>NecI4witxfpz z!;Xl|n>W3C)5U8$JG9RHU{)R8&9ZtA?1`wiH$TX5xy8%#sBz~65K1}$GDpO1i_Sxg zfiWR;@|qm%!d$ca-kd}Q)B|DL$jHbB-&+nsZd-UoMMaE60G}_x{W*uipnR1`R1*FO zl3+0ERtNUPZ15vJm%jU4m6=;=2>3;NpbZSAy|l2d$p}Fcrlq7rq84KY2A!D@v={&| z&*!)rIE!&4M>i$Dy1RH^-LtM+(PF~=CsBCI#r0d9K=YLcGztXV8*EA>-*_Ejmo}-| z)+8hcD3eQ1BK$v^!SAfxOqd1)7;S>BB_hC2LiryPYrdH9YT?X*(>GTO4ikU|aC3hT zPEvv@0CohvFqE{wWp9G(gvJ8%lIu$AX$GLN!T|G@lK%d}7k6hmtPM>~#tVn< zDZOQwK*t;BV#9^jRyD34m5%;{@f2nNKu)59h;=yawSw4@5;11V zph4pz&p65yX{nmh>I!a+?k z*76aF=a>V)5xs3>f)%$f4d5y3D=x^AV#ORRI+vP(F*L*y{}r-IT9V%H92`WCQrjK0 zN=F!H;(VNf8Lq#j1B>x8d*F8c{ zW+Ac_Z9?)GuTq;RF^+`Icux`qwoy^KQ{Y%Srpl35vTheFZ+D?sG}&u0nl49*Q|m7> z4S{%^r^6#xL&LtKW3xbV%M!l7SPJ^2tlio0t3I=w6-KQaDoS|ZFhlB?JAdTA1}5Vw zOfo$UXqo|#Scx=Qakd1~<^2`tU%m*uA&Rq2*+7n6traKW2unvLC0kxoCAkMu+_v@Ui+Nmqe$owo1cZz2G=G z*Cx{yDKANctnl(=k2`eh{Vp=7W8+XTaC#9a2;{lgZ`(+F{x!k}0;!K7iF%^p+{F)f z-qDFv@9X;$yfYB=Q zn+x}-JPzokFNog92Ck~)(a?=Icp+%JGj#3U0%SXY%gDq=)bbOit&Y_qB!SBS0+4|V z@L~L>+L1W6^Xkv@A}sls9^H!6nVSQ(K-wbUMhze;hMj=ZUc897IRDi{y;g4=@Dpu@ zwssdAUsAq!G11_(v8Pdj2aK3yhs|Q40ue-3?WiFO;*(P|^uVJgaP>nJbgo@^J3;NMC~0qGTeTy;pLUqpuD|#LL%ek{dGG1Iht)3;v4d8#Vi?I zfO~o3<8;VBZMVmaY_qrDaHcbY(!W(cq}Ws>66LTY8?Wt)H8`oY%!tVO0i_yx4dDy# zcX!bVm`4Arv-ghYa{uH1-!{q4o>64)$d(-;J4CjMvO@OC7A31;hLBZC_9j^=A~TAz z_g>%UMQ46HpWobJPKvY(kEY9PKvo| z>z_iQ^Kn!Xm>iq;dur;Lv7=fR^PWRQcXo6-jDdHeUfpchca#TD=8`{w)DTUGhHpg> zWy?^b(k)@d@yv2|AtYh5!I>P-z>}pK%|0iUG=zJ=;7KNVaAj?@>KvY1l^rRby#1O= z8Bfbw6SKq~=@T_R-@>k?49hfp&{{&@YxroAP4g{Q<~fbsM|a$Vy#?Q~`TeV-Uw2lg zw3ik@^N%zuks<=b7W{C`UT9NNP|z*6Z8VJ9;lsM(L1{j%dJ_8LFZ_FtI1sqc)8oRx zi?!1SfCackisJM|h2D+SZ zy2(THq(*U9*u&u;g)M<6Qr{!HowcI&eMc#Xgsy2vHgiYuPJejbCNA;JZOv2J>U^di zgZBMFS|+NLkF*AJ=E<+T5kV9nEqXf3x#Ce z4M?KKTfLdy-KCXt!oYWNVYtMbeo~*BfECQjr^* z_C6uZNi`7fevbQgrbIg<{c%K`WnzW$Gmkqypyeoaa@32Lyk(<|NwAlqAX)7AV;8au zN|Nh)Oe2Zhen2KMCltd#CnnYkUzI;sy`PGPwB*j6sxVI?jXR;_bGuL5i61W&Cb_V!nN3-) zL|1w4_bxkX{v9VFij|OUN^R|XAJ@iB@|csI!&V6?Nms~8?eCsGOZ{l4p-s2@XPfQ@ zwCSEC9eXT3-|xI><0cc6x)TxCVmHu_zEV>a90gswuf41Y_k!|S-S>&GpR0Dmk;Qw< zRWgx}l7sQ?w?Yw1PCc00XYhO2LVSd$h@YJJ@YnBIEsL!pWApiPzyp}Phf6W%J^y=~ zoGa<+E^at{NRl4N6Hn)^eCy8E;c%S#cv|Rs&-2fNG-57ikTf3p4DNUCj4ib=w%{;& z`B88i_So(n)!0*Uuxk~~FK;}5CAv}OmSMYTqsLN{)f`Hn_F&&XI*dL|AkbtMbCviX zF1BPRr$z6>!YK-GS(%?6)@0KbX(-h3d|RdVQdg+@zl!=8l)RTrcL9%_bWaM!4>DbakE z5s+kGpmB!eSCYzHOBb{bf0?5nZzbtlNl%{CXu3@Ti?hlYkYCh-IO{^pG{e0}|H6s<@)B%_Z- zZegQ9sCN~5I|k)0>>lg0VbEH#fK*S^cVA+CVU+iCA(H*+`D?VWFsCa$x*9Nr7R4yd zu(!P?2b*TpB-^P|NcRqKO9hE+32og~o^EuK9w$)5WMqLaUkYSc?e6V$16wNG>Rm}c zmN8af9g{02k}KMz^ZNl(Me)^y)4A_XcQTux4;Bu-wQoH++Hw25e{=+eMq65NR8&;R z+xj7geN8uFIQe8M+BLfrFZ{bQ$ejZTAg8OCSyqYW>q3c#Lu7Z+A0NHNiP{#;v8IE3pUGgKi5Ee z6FPv*61(f8g(ih9P=hXZnz&$G=7c44hchK5#k5HPykF$`0no35HXWF~1g~SYx3|kf z*Z1PSC&>|IBs#1aC5BkxbjkgYh0&QJ9sSCtOHU2S;s7DxxITLsNH4#dex^-ghWQjo z!{W?*eM5uUjT<*g)F7iax3IX3`b=^pYNQn$!THeIikENrTHw)-uU}C_MA6|}kb6O| zPu|w{4B~2|rpANZ8TLao6i7J{;+MsAPtM}vqF+UYI8@Do;lS4~e;%Y3h&01VdUO7A zt&xiaj8wSYYLW!tCb(elMnw@|6EPS&vitT4DMYb} zv8(#=IKITX>cPE*tTaP~?co@ckD{!sfA?E4?Q&@F&puUt_kE+9DMQTTB0CS5Db!y* zczfh-w3qifK_^M$PJ#=+5`Zp`OlvHkU-?aS*B$oQ2a50$bHUY(>@2_hYoEc!hHNnK z{_80ob;!VCf0O=8_rqZNqkx2(z)6v3ri1}+0XJUHFRI$dDm4etE?(Kvd@HdaWDt%P(aDO+)ymZ zbEf^3zXk?3c4hM2A4(G_l)!^IzFg-Bj#9}V(s=IETLEIN1O_8zeo3M25tROkm#$yu zP>iHPgO?v^Hs0tXiMicHIAY2W9pZu-fW6}BqG}hLYRKLdkHMi zX_C(<(n2tJGeVZe*xOYP?($Azk9RO|FX~#N_9fR@u`AZUiN-`P9cuhugP> zje`9xUKZO#h`Ziemsu9ZLb5TvN8FH&@f!tK{3U42v!$MQ0Xd)g-dJRh`#AvRKo&rh z*`y_e&r2bk08g!>A;QtFkW+nsEZ{M_6Wvw{tvaKtBBVl>oRd~03ofZTd>7HL})Y}so@({+`4!*_(=@Q7A0-L#efQNvGifKi;-Fn*X;*x+5 z!8ws9PGGCxs5!_Mtp)ghm3XXQx-XeyVq=@bp6NbY+(LZfM37C2H^s@5C#}EKMCGHU z*fO`f*>l7pMQPh6NcG_>G& zlqrgX6NLPdv4(#_MBwU#(bJ^G^HqgyQu`i* z{-NBF>T$wOx|uDrag~ecTQF8>aFP(eKto0DFp;-qP>+Vp0e3Wq-B$~V z4#=1W#Ufc?o!>orab^JlQq%`XSd8B~pdl8am#E0_@Or2qsl7RO?{tL+(N7YF0MHi^6}GPh2B*UHuMS28s+p5Xq40s2YDpT?XtL_5>$ zIV7zdq2W^=3@XQPT`l*`pG80O6&LNF`vaykIC>X4-SRH;m-n#>93!+EO^aH5^*!}D zPzSpyoI=;}JP4tY|DyD7tEcf$Lqjz>pLSFM5P_ITmLi!aJ&n_ZY8#YrbgPepj<HX(X}ezHlBaOEW@wpt?=WAw(Qj*fq_GD*sZ`T#9RhS}3fB zSnK~F?^Tl&wy7T`PwU*|UWc6?0{G#Vb6l;v1OYq&J4(23=dDbPdL16r;l zc|simZ+hs%q%BxN4n>Gm&cNjMHBoJLnDypHVcF<6AhYruuQet>A#EL-_5oB^Hm&W-yATbHyQSeQ zdKR)sV*gXm1DoORQU}E>T&zZJGyC4n&6XK*>F6sl*r@)B4gys+5g|{8|G~^V35iy< z7cX%}R<_xBO93!v$HbaNjGnzSUNKQ^-Q~mvM?wU?l>y^K$gGQzXxTJ~{H36a6>@RQ z9>wpe%M6N!hH*TCwD+a{ZhjPnooaAB)^Usxpw6RUOc=o;9Mg{lu*vzI8)y} zatMix9UBQQxt>i9L_CJb3lEbNqZXAMlbH97>Y7PuH!GwWo0CgkLDS~@xdT^o zrPPpAviA8fjol@VXv{ z0Kw_#-X6vZud8ALu~K`#>HdQeM*vn1nqO89#D4(>5r{Lup@M}d{*I!NBk`XyCfWnT zp~=!iQ}#bCDg&q#?`K|RZZ+z%dDX^B+T?tCu%Ax8wP&vQ;J@nY-UC`s7 zpG#5x7ljZo9lv4N?TFnNrzEDN&novdOIV`QjX>u z_@v#>t~fRcu%`X}~TSU;-xC^!|wJ9Z36dpSaoEREPvlk#%%m0W2Hzc%}-qy5c-=cPXZ z%8mEX{|AV<_rFXmQ}<+rxc zyrcLpR(+j3F&>q0)J>B6Bz`D8Cq?xM0L(P)rf$Q}sH>ka1k@b3)QP8DX?VHkn=z-2 zWw7Yv{QcP=I!b_aB5_4a6*K6ec^wgDvCG1_ey+)w z#OHrm*CzuA;=-ol(^JThI3PZ& zLUF@p;AmSoIh|?QUTR)1DCj$!U)yT*w%&**(3QUkX(kx#1OkIj11dRqX)e1rz4Aq zYT+?oPY8pfImai{qZ1&{w)zuo@@-d-s`!m`P6Ce@53BeTqZaw)r(^MAzMu@F8f~ky zbi(7K$-^GBlA2~ui;B*e@u8aTX5I0H5+v0sY5)5ud||jr2^wMzS5f9ZZh^kvw7BR- zj%N%8Tj(9=i53seB@b5mbfV!=(k;%mC9$?{o&jZ)3u6pU*+)Os?r}oL=fy{CJiREa zR9rwWBS@V-6HfxtuxH!kYF`m}hD6TE5qgGz2|@I+N!?%#-|Ay*B2O=`AK3oWzVc*v z4SwydFU#5YL_1caq|~{nDM(&e1WciEEoh?<97ols?>Gw zf5A=Cxyifv*@eRyk_{#(QjG@X%qhe!;+wa;)7c@jARqjn45C#sJ5aF<4I0@P>%_l6qQ>OT_ z_OhpM>ZSlK8u;6ZMrF9I2?+!*&x~?c%hGr)ep7%3LYiLd*GcW*%v&M#;+hz2ZuFLC zu`z?GDW?b?Jskg-^B$_x`#;d~4a4UTItAd0?iQ$pBhAOJ|Byz#r`$52J37FMl1`sK zZFaD~=MKR0`HG>xA*UY;h5QMNei3oLkqyfJCtPN<{ip-a14fR=mFZ9J9d&6Z!n}*1 z;A2q9h(8Gq{d+uOL2mlGL+g!0&@V&3N$HDLmzgNZbs~4SoKp=TC_w+ye8}7IMULlZ zk2ujASM*0E+=|mzN`)^(=+|g*Kj!+Rbe*BQ3lai=<# z$*^BndaHZvPfzu`3&OstAA{cMNbG4bOhyw1mn$C=DO_H%YvQ0T$sG4K-Tpm}x_XJ2 zTibo(yz(jZ2Zfx(#Lwzzowe_i^0tiF|2kgQ7KzHQC~y5`951^TQI=Pjp^`^hDP4~D zOhBiy1~Ah<->a?ci#dP5B1Rp=Xjz`(SnLQPAo=k2MAvclTTQflX9?Px@1`r*kQ?6S zu1yLo2pw4nzAJ4kG@$MgKu4(|YSt-|aPh&6&*7>2`^G@kj5?ZpGzdMcE%~U5fUp5I z|6^y*PI^yC{)Qn9vZ1HOxA1P{HMdBQR1*#KV_ie}RSgamxxBpAqS)KGQaFi1No}=m zj@YHhW15d{jFY#8xR%$oUW^`#y;TpOx@Vln+UZHSrk9+5@0wXL?$;qNHq9x z+qkLgRtEq;5lPi!sWrKT5iFFSt|t$5zKDebRVRJnHghD7Zu*0_7n*9Y?#YcZr`=aXWf77jsUoJ zO#@q+?p#k3zM0uW^!P6;1s{{o3|EL4+yWZZ{r+D; z=;8Z$M4P(qd72lD={`qscGNF4x9|wgp@ij+FnJ70_t-~_YnDG1K7Z(Mjy~Fv&*tA_ z?SVqnPX8CZr;PJ~AE;&;Mn@Uf*VmDb8jtbYt0j?&8Fj#B7@L~H14UfrVm-dHib~E} zn{3=K=TPZQ4DH8LvPfkFL^3T)-&%@+ivH6@Y;}H^wyCL!yp8MfOH2eA_&$!)ak__n zb9);JYBY0hN(b{n?S8QIRtr>H9lyULf#Q6c@4^0u8SVvk*=Vst_j$VE7ch=>cg^$_ zhLo0yfd6Q*M_WUd7!w)J0J_DxT7L{Q6bzRLijF2i_->^m9{7-68FhP7Hp7jA3UjW(pC+99xaafPP+%YeKACD0T#N2pHI{7cLw4=OMly|5 z-8?;%Uu5KQlwWjoboWasDJk-Ug!}i=#KpzmmX!@VQwz|$qDfy58Wl%B zuF{TzVoQv=QGHRF*aZw;I?KKU43toi7uVK+4%7ombg?k-)g(N5a@@w|M|jGF)1B8d zYKeSkP_t^d&!WV!m-4;9IgJT7ZgM!Ewe3S7Xxm_sz2;B6iVmJKJ0V?tlsb5}#NFl_ z!6Ee_RW>ku3nDI*X3l9|LBUt)x*z)bD52zj=gytK*^T&hDIG521^D5Sv3|`?@u_F@ z-Sr1rV#WaxAz?6p3!zV<&#!TWuR}EWC?*LBx;Jm$D7HPz&Sp7#_Ko@B#lm{OOe-jT z>?WX&`YZ1U`s{DnARxGs)v~v5YeC8f8u9-q+v{@e{Jv&yOshx1vEqn7SpB47W?>Ns-XXbsy>DM^ zQksGZXqjSL+t>1XXn(yyauOXGAGe80+}GMvPmv-nI8ZUw{17;H7af7#NgeF2;0#Dh ziXPS5C(V*49XCLM`OlL54U0*HGxv)eM=JMgoW<|b-lM0r5|ugyJ-DLeDQOPcz>&^? z3O8!-lpE|-OPgzmh127s_$=!$R-P57AFNq!oW4VlYnn#*TS{DQ*(2eZ&gO5FUug~W zuI+acByXDE6D|A{%0A_8HGMJTPFd1f_cCwJOUtNWL=Q%3&&?tLeoM5!Cx+m>>n*^H0FuEgASm@cfO`r+0 zIZf(+>&_xpPN1oJNuy0lQ@(gY5s8S+Dw!D&vDs!U&Im0{FfUHP4`S{07RfCt7cpzW3cr`NT^)=CM_RILa;m49OUKjm)SI|Kk9Rayhdwd!P zL^*=`tY%x0GS8Qryob8Nym>^|LIk1YuN;+N0m7_@6sp}^l?EkhSY>Aq#{h|KaLE`3 z(09_4m6D$?47pSgVC-0<(Mdf*wNve;r1b=SiE^&k#Yt{c=!~2#?Q7d4uBAQ9#0}$A zDA=xo*i4L#I-h|z04(_pSy^>#nh{q<-7!!@mA4f?g-I6J4(no#Dn1II^AfH(xm^<< z=Bx2erXeeH@zx93()~L{aeY_P%yWLU)07DVukTRxY3(%I&ksX2P)Hk9Qy5foj;kh&gB}eHpjU}&zy;)H(7 z-!|K8C1$@&qR9+VlpeWM%)b>!B<@);d(mFcHEoTAAu}kZ?Syb6u8BR%d1rqUUVm9$ z|EEKn)yyzjfG0V3_cSYS8+Lb1MQUH$W%8BfSN6ni%_IfMR*6gJohv`Sp|?IT)i!?7 zP+*`cW%D@{RjU~hww3~85{i-}mKw>B!5QP@v2KhRnJwp<_<6MW4E96m_q-0k~B?Fb3P@4-v9Zy*H{zp}ilLEcX%( zbu$0L;)ZEwe`yDPhIMw&MF)e==T>ALKr9i0m6~KX_=W)dV)fvw=zeLX`b5%`0uCvJ zar*pH1_w9e9|-h=e1LGf!GOvJ(cy4ITeiN`EuKZKlpUAAY-VXAvDk}RouXy;5u`kh|}l0vup_hXx*~iza@-)*AHOCQ9-hHI&?8Iwh#qZy7IUCFm9nf z(dNCQr_J0_i>pZGa7E6UrqL=;borj0l{|$511m(ADmF`r%N`%)ocE1bu_8?$ z2Hf;M#%H04dy}(f)ar0Z+9N1oiPm-40*ED@4TgWP;)DS@=*!9qJ!!; zd^a!j*AV(MuN4?=!$^oRa!nCaF|MU2DwY?bYx}5XG=UvF<;p9N1VHCrBIAdL$>R>% z`u{+m_dG0z!_rIdTG)x)%J(ZBR{zBvi+J#ZB1~Rp;bd9=L14wtQX@3K{}@^nDstBG zyd_7n4?EF{5{?|gE7#wuB8xbjS+rGfBWHol89KI%F2PJFw3C_28ah`SaQty`+4FyP zV)=p2%y(}(xNoFz*Gium&4lK$Key^0$xu3G9k=Qtyt~R(H={4#D9|??(czkVvL&;5 z3R*zF2rd--YZ>-Su_gz7u)R@NMLcDE?0Lwwi$m{1y3iAED=u`BP8S<&Eq?zjb` zI*!+I_ZYmLIJO%weY3xg#k*4|)m7x!^K$!u7oqaR29u8iFJ#txE>wkp2b%?aTcF29 z_MjyIS2uJPY{#nn0(fCyTxB8IKu58VE*WKT6gQTZoUT@ne@7Zk-}|n!M;qy&m}pRu zM5v!>kll@h)Ie0cuS4@5v`@N9lJRK2G;ADqJRDlP{uwIr zrj8Cur8?T zeThXZVRQNSA3kjVV57aBZl2Js)r&raL%b$AKA~;@nJusRNOpqkje!=Ze_Y*>2kr3F z&_}xdudWV>W*db}?CF#EO}`bctjYgYW%5L!qc_)?1UstjyzLCB`{o}G?0PlbpT7-_ zA6WfYd*wp1_|om%6Fc#J5Z_B?xO6uyiUVUh3Xl%?!+F!9T-5y zS?O?)gBIt*^GhrUN*;Ivz_obzWj!VCW&}(H<15ynVqM2;0K}5{{{}8A^9|O^htjgN7Z|-7$W+;yYLCNC85VmM}Mdg{bg&&k30&ZM>PNR_MI51^yjq$o9j%9K7!Jkr(A#!g8dt!??AcSQy5zk@D zYKUE}{<)6s2iw1OC=FPKQhJf!Jg>lbKzvzL=l(<}NOS5d^oPsc)}BZL;c2`q!Fl@B z3dS!tnsw@@snouiG)*es*w>|{Es)%|$Fuw{IAnaKu<>KIAfz4pUDw%t=MESvIe3Me zc%1kjSgcJk$$|DeK3e~OL?^yU{Z&mE1ahj~FSS*kTKZf?d|l8TriSD_4OH@*UjvCg z05Sx|GG2wkkEr7~CTQ+{jhpP&*)@iSzzp4g$V|NChxYCoGy5wH<=yzlP{`W8sY_P# zE1x2}VvFo9$Q_^ga17LDtof>oFumigs|g;ezqpeu!Q!Do(Mg68f)a#c%xNRy2f4DC zgsx=?H%xuR7#!x~V$M*1DRo?yPllMGnzQ^@L)v;7R`GdaO!y$}{i{bS2lTuH+j15$-v1#ve*h#(hF)e?@1J zz9CHF!|FX;UQZe9C#9sfaIP)2YV<9xymszC;foGt*0vZP{SOA04)0t1Pv3>jA8Vl7a zIRNndQ)lw@`^(p_{IW>Od9Ng$(C=dJ9%KJjtaKQo*;PPm^Ky_kFCO-MV9!znsHCYr* z(8KhN7`py`bQp1FHixrcr2VWu8}v!{&Y2(h4)a@^OelBt7MT7j9M_`ym2*w^Z^kCD zW(<>Y!0w}&{@~=;1N2efu_!R|=9-ofzMj$wiuSEExwe1gA5Oy!;}RrW>>{|=V)d)G z{v!DY??Ar%`m?<&6T2GB=*0`3Zi3nT&3!o@@#_qQc`Khl__48DBPV{PSn55|pTI1y z$mBReoFB`q)gVVe9BJ?)Q;*~)z>&TBHSxu=W9Gr8*n6UygFb@M2H=Vp^UX5Q)(7(!ZXVct-&34JNuJlkf0=1j!gMCkg-~!rp2RFBtgT2uMGf4a# z9CN-5Z`?2fKWMMW1}CcN>Sy#1*WBmuWqRG<22#P4ILpPabyO1WMg9Ds_>clcg>E3F zKQY8Uy@J1F%mH;M+4>#S$EshV)!)Pt5dQ>m`?=iuRUl_VJ#P|~qJTB{3uJX1J;<;G zwM8T5QdG0IVxq{!Ffx?1sQ4(U5EK0 zph>BPdBzSP;Fs9?9{clCHqp(Yx0i5gPWDn9er@h+@a`Srzi4k&`a^nX0Y+7XCj}b3 z#(Rpop!AOpHNtSZeI~EBN-`{TI^uksm1IYH(Q}FDb38>K@E(QCH)HK@0fETCz*fi< znYEgrZ)gH^C4i!XO&>p>0Hdz zs%vV>RDWY{V}k>@Cc4(=dp3801Bs4-5e2`L2oaoUg(Y^bu4e&dfJ{`G+&S`Je!l!} z6{to`t9lz@jD(K=UHQQug|r$x4-D&P>%OMTpXA3 zLGOMNO3FLybApeMA*?WBMfaJGi}c}|NjH2fV0Fyo&ICC^3cOP8QU~53hi}aUk@Vtv zv$i@|6n<+l#yl;RcUE%g+sxG4bp+dWBvsom__Qzbf6B0b7jyDmYinKoUHtpW$@k#k zAA`|2av6$I$U}_5_4*3l36e`y^?wZ3Jbd^dw?0&gL(Gl@w1|8O&7jK*(hgjxiJwoC zff+9QyDJ)(RuIH>m-K-I+_IDRm5VG|2rsQbGMxsZ4+kfw0O)a;cr7V_j$WZh4P=hz zA*|xKDLbEh0P|NDdkH$y zbAh~Z5$5NlYothlrZLzyDLf*g`;EO33@_4xao}QrenCIVlh z(AJYGi{Z7}tpw#fMVL*Ld}opxDfUtF7!z}UdaC|Z>@FBc5NMvck}Sjy@B~cwpGy0h z^hiJp;^*glS{Q^=e4~%YxYC^jLXEq5d3m-;@G+}Zn&EQC1!!Q%x0QgX_=QOo4K(l? zGgT9cEL#CTz0|S^7AOVa$|7Ma0tvfSvqypOJQqf)x?kC7Jj==oe-!cV6lSgngXb*2 z88A&0S77C^wm%Y9eJ0^9cTun3R;6>Ns|{!0Jq& zhWp&>zQ{=eI#Eq3e)AyZFwC@P&*WoTE$cz~a$TqfZhbxMJ@OY6EXC~5Ua%ca^Zo$4 z!JpH-6)sq90niSGnxCI%W@FC=($4J}yu)FmDL4-i&|y zrqJ;U`0L0U6NpWKtU}qcP}c9=Y~;XSrwcXX&@ibF{gg+j1h z_vp?Az|5lXc$Mr=Q1#RI$SQyVS@3_EwK$kRjm64LhaFDp-o8LIvA(pFv$}Yb0|rQE zsU-y<-wXO^q{3~`ee8x+{xCkizPpiSYW50M6=(#2wOw$`!)zPUjTw(Xn615})fY#(+sp z?rXjJ3LX5;lWK6frNUq*xYHkI))3%}iO-f*x4^mo704>IDIC5KNfk8{_yJ7QBEG6; z&FV3qh`V(%SI>#q51g>+N=ASMKD$dwNW&fcDx~?iYU=9Eh*t_~*%!D^nS=LPqp1db zU{SyyV>)#T6Iz&!&?}U0g;xECVhbgCMMYp6!9xHz$yK;2ic6>hqf$b!;QH6p)I2{2 z7_?w`PY^~q@Lf)KCQC+yhT;NJ!J_B+rSyqyU@R=bIH!|LOeiRJ(Tlm%f`iIod-Ynk z012FKwI21KK9RxHDXkl(*4EaUda~%O>Q6$!w>1v78?a{i_d8y-u?b)6w(ojIVnd>N5^Hz%B(#` z1s4G541~#}zdQW$&qE+yi5L^4F_-sZfB?84#1 zE?>G=`BGSQwWu?Yp*^CwXm2WQxseY!T3*vLQWt7YW^!UdDCV>@(ni8&6unMPg&M5whqUEezGCA#q>d1qk) zLu3Bj26{wO!1A)wF7!#?b$06IXLWX}m%M(>J_m%iZ&0TPk6&P`CC##jUV(Ag`}Y@i zq5p|&K29w{z&ykbGjX+E*^4z#d2e=%VC!mwbkb29vXK#l&FffpW zfuUf)rxoUQLk`klDUg5E?&HNhKEPd+Bz%ucJJrU=j9RK+?zLNy|$;a zL&L)ADi*5qu3wRlzMI>{^eJYr+Pean->RCLF@XAbzV8uRUM`0AVS$Z>MJ~<*I~U7O zl#Wn$nuMgay`<#g5F%O5?c)&)&SzLn+_5kvEVDXZ_$@JRDk~t(K%!CcM_KE(788 zSw)3WZ#kIbS>V56UWa9SBL7RU$Cce#$;uTbk>sHvA-SE-27v@gj=$$&P`Y~@11b95 zPccK>SLGA;zO`1BwN(jGVr&|(hVqOd7^xX&0uDp&H$(03z_TJ zr(^oMM@9&v6a(RZN;tH`p1%Y*TItm%>e*y0O830J4+zl^z?*mQZiSd+FM$dbmh1o@ z^;697hkx|)?tZaTR97dIVCeGonZH%Ng{_{wlGZGxW(x+#w6uL`|5NEo>#(32#vD?! zvvJtepUCLxQAjWlT~JlsiJF_5g74wY^@D(bz?%zxLidMpdYSZWKlSVepoCo&qCo;G zuv>Dr#cage`vL?!vBMgqBB8iQXjFNyzk>i&&YwTOxZK8zyqQufFjXJ=StHe8F`Etq`m#6QWmNBEcwHKeI+y&cMtp%#R|Kyn7rQ!^6Ylp{tP* zZEkKZZ1g5pcA9ZwezV;VefI8-BS0bFT4c4#H z($mq|G?Ie`=bFK0`*43?z_4;}eGutc8X6jE>gi!;f656mW=>}dChjKVhW80tC=b^m zG(06CVGQtI4rXSAV3e=NNl#DzJ`tc|7nGGlU2fmrY8M91COao5C_N&YnBlfc?Lqi> zAIy};-EWo-a>F|Xm%@MbX#%e)1?po@&v)k`TcHb9R+(MJaJGdtnZpYKR#`lJe6r}4 zjMaqrctX*>Q3WNXHhoj?wc7pMTn;=UBK-lv5IK~xw|A9O`-Br7>KqhVLqoX?ie!Kj z>^1qN%sCrQQx1F7GK|}lTC9|pKfoEnP+mPD>hKj6OvFf7*cXwU%<$~lv#XHkUGRm# z6gK|M5?WeXSPNl`iw-Gw=7-7x!F-2~GUlOt`RbMTHa7+S^z=0Li@FJ?w2BH^@Zl0& z*?=|S0HkMwe7(wz5zqIIg?)Lmye?(Nu>7$>O){z7a<#X&Hw~t#a&vP#R17bARQ;Tu z4ruu%3pGX*6CWQj@Sfh=z(&LG^j#SqE|ONmquc~AgV+4#aFr+Jl`B`?dhfi^h=QxI zfjvUQKf%NUFEcX}Otzun;nN#2EmeNb8nlM7N|{lC zMTjN6wvpHy*ihT&uq#PoLH3MrP(m0|Vq*M2sKo-;0kJ=$gBrjOr)OrCI>i^x zw7|bGivEBKl^GwE1Iz{ms@K{K(XnIbAKY4IlOcA2{EPzN`t{Hn$9duge%yXQF2jA# z2x|AuwP1o>1#Z_SYz-vDA4=EC;2j$kg(nvrjE&qS)Q5qAa5#0C&z_}_8NGeO-k!ea zH7oPBv^nb3vRq;1@-Z$7d@XHlv(iBlI7mi)b_j%pg#py1U~bL~R`Wp6W}RYX9Y-c( z!i)A{Xz2K>FA`s$cvLZzm6aiKGT6uYeD}RzawSGbM@NxiKC)ZEOApZ6P?#12fiDZ3 zoDi!ZuMX^DMbK$;adXQ^OGESBUBW>G9Eko244ry;^4S<-gm&Ne5s4|M7@jq4Y|!!4qqlZa~2w6$N$q Jg7a4c{tr*UevAMB literal 0 HcmV?d00001 diff --git a/log/ResMLP-24/log.txt b/log/ResMLP-24/log.txt new file mode 100644 index 0000000..03ca642 --- /dev/null +++ b/log/ResMLP-24/log.txt @@ -0,0 +1,117 @@ +********************begin training!******************** +Eopch: 1 train loss = 0.258324 +Eopch: 1 valuation loss = 0.103075, ACC = 0.970500 + Epoch: 1 model has been already save! +Training epoch: 1 completed. +Eopch: 2 train loss = 0.060491 +Eopch: 2 valuation loss = 0.055647, ACC = 0.983667 + Epoch: 2 model has been already save! +Training epoch: 2 completed. +Eopch: 3 train loss = 0.039388 +Eopch: 3 valuation loss = 0.061271, ACC = 0.982000 +Training epoch: 3 completed. +Eopch: 4 train loss = 0.034410 +Eopch: 4 valuation loss = 0.066298, ACC = 0.983500 +Training epoch: 4 completed. +Eopch: 5 train loss = 0.024529 +Eopch: 5 valuation loss = 0.102639, ACC = 0.977667 +Training epoch: 5 completed. +Eopch: 6 train loss = 0.024616 +Eopch: 6 valuation loss = 0.090551, ACC = 0.980000 +Training epoch: 6 completed. +Eopch: 7 train loss = 0.063829 +Eopch: 7 valuation loss = 0.049286, ACC = 0.988833 + Epoch: 7 model has been already save! +Training epoch: 7 completed. +Eopch: 8 train loss = 0.010175 +Eopch: 8 valuation loss = 0.084901, ACC = 0.983167 +Training epoch: 8 completed. +Eopch: 9 train loss = 0.008173 +Eopch: 9 valuation loss = 0.066344, ACC = 0.988500 +Training epoch: 9 completed. +Eopch: 10 train loss = 0.010041 +Eopch: 10 valuation loss = 0.081577, ACC = 0.986000 +Training epoch: 10 completed. +Eopch: 11 train loss = 0.017877 +Eopch: 11 valuation loss = 0.096666, ACC = 0.984000 +Training epoch: 11 completed. +Eopch: 12 train loss = 0.016719 +Eopch: 12 valuation loss = 0.072074, ACC = 0.985833 +Training epoch: 12 completed. +Eopch: 13 train loss = 0.016574 +Eopch: 13 valuation loss = 0.046974, ACC = 0.988167 +Training epoch: 13 completed. +Eopch: 14 train loss = 0.012572 +Eopch: 14 valuation loss = 0.078121, ACC = 0.982167 +Training epoch: 14 completed. +Eopch: 15 train loss = 0.013178 +Eopch: 15 valuation loss = 0.101448, ACC = 0.981667 +Training epoch: 15 completed. +Eopch: 16 train loss = 0.010542 +Eopch: 16 valuation loss = 0.068331, ACC = 0.991000 + Epoch: 16 model has been already save! +Training epoch: 16 completed. +Eopch: 17 train loss = 0.008493 +Eopch: 17 valuation loss = 0.096219, ACC = 0.983833 +Training epoch: 17 completed. +Eopch: 18 train loss = 0.013357 +Eopch: 18 valuation loss = 0.061578, ACC = 0.987333 +Training epoch: 18 completed. +Eopch: 19 train loss = 0.009315 +Eopch: 19 valuation loss = 0.078054, ACC = 0.986833 +Training epoch: 19 completed. +Eopch: 20 train loss = 0.006614 +Eopch: 20 valuation loss = 0.065001, ACC = 0.989833 +Training epoch: 20 completed. +Eopch: 21 train loss = 0.009743 +Eopch: 21 valuation loss = 0.058913, ACC = 0.986833 +Training epoch: 21 completed. +Eopch: 22 train loss = 0.004101 +Eopch: 22 valuation loss = 0.071325, ACC = 0.989000 +Training epoch: 22 completed. +Eopch: 23 train loss = 0.011880 +Eopch: 23 valuation loss = 0.061699, ACC = 0.988000 +Training epoch: 23 completed. +Eopch: 24 train loss = 0.005898 +Eopch: 24 valuation loss = 0.052160, ACC = 0.989667 +Training epoch: 24 completed. +Eopch: 25 train loss = 0.007055 +Eopch: 25 valuation loss = 0.060927, ACC = 0.986500 +Training epoch: 25 completed. +Eopch: 26 train loss = 0.006872 +Eopch: 26 valuation loss = 0.047085, ACC = 0.991167 + Epoch: 26 model has been already save! +Training epoch: 26 completed. +Eopch: 27 train loss = 0.006201 +Eopch: 27 valuation loss = 0.062409, ACC = 0.989333 +Training epoch: 27 completed. +Eopch: 28 train loss = 0.004974 +Eopch: 28 valuation loss = 0.079944, ACC = 0.989667 +Training epoch: 28 completed. +Eopch: 29 train loss = 0.009411 +Eopch: 29 valuation loss = 0.058826, ACC = 0.988333 +Training epoch: 29 completed. +Eopch: 30 train loss = 0.002585 +Eopch: 30 valuation loss = 0.071575, ACC = 0.986667 +Training epoch: 30 completed. +Train Loss Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResMLP-24/train_loss.png +Validation Accuracy Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResMLP-24/val_acc.png +Train Loss: +0.25832390663422583,0.06049111991284456,0.03938772366459338,0.03440955887405244,0.0245294870920013,0.024616345635608633,0.06382881039148937,0.010174585332210257,0.00817297156926667,0.010040507038089059,0.01787675555819019,0.01671852239838346,0.016574336527537304,0.012571598617393875,0.01317848334338329,0.010541742407758438,0.008492613288001888,0.013356601136628456,0.009315182970803984,0.006614003157931455,0.009743359711699696,0.004101245476077177,0.011880251484419804,0.005898466958283593,0.007055483976123433,0.0068718988866095405,0.006201463303413665,0.004973818432163777,0.009411264056348005,0.0025846648623085136 +Validation Accuracy: +0.9705,0.9836666666666667,0.982,0.9835,0.9776666666666667,0.98,0.9888333333333333,0.9831666666666666,0.9885,0.986,0.984,0.9858333333333333,0.9881666666666666,0.9821666666666666,0.9816666666666667,0.991,0.9838333333333333,0.9873333333333333,0.9868333333333333,0.9898333333333333,0.9868333333333333,0.989,0.988,0.9896666666666667,0.9865,0.9911666666666666,0.9893333333333333,0.9896666666666667,0.9883333333333333,0.9866666666666667 +=> loaded model checkpoint: /userhome/cs2/mingzeng/codes/kmnist/models/ResMLP-24.pkl +******* begin testing!********* +Test Averaged Loss = 0.205754 +Test Averaged Accuracy = 0.971000 +Confusion Matrix Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResMLP-24/confusion_matrix.png +Class: 0 Accuracy = 0.995300 +Class: 1 Accuracy = 0.994200 +Class: 2 Accuracy = 0.991800 +Class: 3 Accuracy = 0.996100 +Class: 4 Accuracy = 0.991200 +Class: 5 Accuracy = 0.995000 +Class: 6 Accuracy = 0.989400 +Class: 7 Accuracy = 0.995600 +Class: 8 Accuracy = 0.996000 +Class: 9 Accuracy = 0.997400 diff --git a/log/ResMLP-24/train_loss.png b/log/ResMLP-24/train_loss.png new file mode 100644 index 0000000000000000000000000000000000000000..22f0460a45bbee99dc0eff9cacb23229f631f97e GIT binary patch literal 18329 zcmeIaXINBQ(>AyX0xAfKD53-fM4})$V<1Y9oFoWJPLgS&2^9nsMUhLwO6RR>#kZ={rH-uGR*;|0|1!-uYh-2)Mf2o|;Wm_Enif|@dkOFi-hO&(+d|a zkm9Kkq+=f^CnuAHn`KJ;VLJHkQB+iHk0*)bsYnD1lInTCdqz)5DRj|8=G0-N>VyAK z{|`5GCk0=-{iA3>-T$?MpD(uDMS6(Lx11i62P)RNBq(-pPoyty2N6hZdb z5sK5J{FXc+uqc#t$i1iRaR=BCWLgp$dh#GTB+zQtHV&zD8n|%l)-7%+DU*SLfx$9+ z-T3XAo#htX{M(in2?=};>r)dFPF1hd4yDM?aLXwxD;r>!lER6P=v0kqQYbv;fdT)neI&ei#5YQ2`O#!+e0IER zjd66D=Zp?IB9oeccQ5&jyR7yyuf~dP@AlX3mYe%8h!O~k!To=h#si1=kA-)2bz#aD z%nsX^@|^9;qK|OV?|;{GUW}(h1D5GD$24yU4Gqm;O<>jX@^YMWdrM3AOMX)+PwJ?q z^x0Vl&a-F5hRk(!)4zUIr#U5Yt<HBZ|a$b?R`jSuk(xtnL&z?Q&EEn%3S>mF+i;7=J?Y=-{^{o+KYmGQ{dP7BD z!S#1K8>7Ls%PT7}(a~c=a<`g~n)%?pn8aNYvNYnej+^^ltPiE>9*>eAnJhWRZ<3Lh z$1|Fqk&!XyI!$dv1H)Z?k+Mkk>ctC=_}0F(K|^!%ku)A(+|EuwYrMGD`IKv$rBF`}fX$So~xx^LgU){5gtkDe13$F}wt z!r;V5eAAPHcm2%#CSq)NWJ7P{!@6KnU>t*va;a*?5<*Y9bc8YR za;V0{#NZ}gn(7%CWZl0{9>FSaep1|}Cqwl)X05usLHfX}iSG#PlQDI@ASs@z)Lkl3 zO-1FJzJAQ9Lk}&Y2tTWm=YmHD(+B7&XR)IvruwbP5V|(Z^2!k3-5OfUN~@!Spq$*G zoDJt-Wjb`JLM}FoA9y7ff*^J6a1*9o2b!D>ntU`pgalF0I*m|pkDAnR{iW__;Leq` zjsmOf74eIRW%rMeT0RX64}03swVDuwc9V?u=}>;IO;cV07C#tXHko*^Lvt-OKRA zp}%qKuQx~^sLUGSgjF480yE2lj%SFnV?oIC{RigneX!=&E=rJ?@UjoT-|1B}Ea?jR4qhM>BI$g?4FA+AS> zExuD`?GI^r$**f*@G>nuJti@+P$OQf{M};9`PzaOfhzScU%t3YoNE5^rPI{4y4R{U z)*Pdnt`rfCcj?d3PG4>2EnfX;I(^}{aRI%1KG%M(e6gaNte3&3wu8@Jh)b^3^YZgub`LpU$TaO<{LLg{$xzW)A}8Ba0)Rf_oDv~TC)Ikrdu+WHQ#^=wUr$0&0uq^5c zJ|PF)z%#6pbO4PK*&Y#ma%{+?)O|t?AG}9sZf`eKQ&Yo6zO(B*qpz>O$pQ>2(U%fm zuVM)xa{llgm%7&+oSa{5O3QP;#>B>AbG|1E$je)-OtyB1pRdWedcL}BpdIj4Dp0)& zFHj{bNLU;@u^&`_2Q|Lh+S<)+Z3Y)FUMyVzGt%7J>b&B`Ygp71AwH5bl^kqtXzq6_ zJbCKp&oYV0!E(nTSsOOVfae#9aDk^^Wl&HxsdP)7Y)j04OwYx`#igtzPbdSzAsW0J zpsInjp=FWDnM(54)Y1J@wYxR9Hk}^j0M?>L%|c!M$&n*RI8?!I#%5_H7me;risQCs z^VbF)i|z3^ufg|z9$-OE{OKc;yNF_(rxxs2W)`>UO98VyO4fjDQ;9xFVlKluz4?Z` z!os@IdL|}=PMUS{J3naM-(ANoR1CdHO8VtL?A32p6X@>YQSQ*oBfC9c$|UVwU{GL` z3f5L5N#l0{DqAh03uhIS*wfCNAUwp?} za@Rukhkz+M32$_F5T&EI3m9C)j>J*1-vK<7hrT~mF+Ex#$D(GcA4d3>O`#-Bd7NNE z`wBHM(^7CFVTGE9kYCXBbFOwB5sA>pIYxXA{@YUl_Y{cFp|5!4%K4Q3Uk~0V8b?+b z$0b$;kVRSmt0hgc%fzjM(O!AaP#1@Zm0@A8D+Fq7qlkzcUOZ=6|J31RB@M_QE)=ml zvrd(t=A;pPfs25y%yD@CC##YGYc?w4eLu`5^jRHDw+db}esX`nUE=teNK?LCLoFR1 zI(_XcJ%^gNU#F3{C+v+?r%n;!Qj}cb`Qs=Xq8XKi>r`}irUjs-uOZoKbm~QCpaGWj zvxp$PARk^p&)Ry<5?~j7{5DY{Jn2a~ZU*q)A->G)3LBDZQ?vHA|k2(n3Y5@E2m*4QBIJ;i*_M{UJ*MZ&IP?X>bfyP znneocN~&Fi#BJd1EdU@LPtUTqrKRQe-C1HA{QUgA8?}3ZG^}#h4f6F59^Pb)+GMw3?jGunLSS2HOZxB(*}k3tFZ$5;PbsaZ!t2vYnvQc!4kNtZ5B*e0|IBa zQh6*~(ca#^mBq%*fsTr}v3?g>|08P8eG?TGZT|YTr|u3J7zRFYY`o`us~h|eR(_b! zHcOZOOAP5(N#3NTU2?&i=H}+MR%B;o?h>xuWjM+v6h6gCfN_4%4c_0+Fh}vmH&qPLED7H2BN^|L!0!pv=?CE@7}#@Gj#C4ffw=dbJ$U9 z#?Y{^FaW-hNjdK-vqKNl2iUJMqJWR%InzNi{pf$-`+BdXeSfDU`Tvp}b%ahhL}^wf z+oX!6bxI|ELZlaQ-@Ng6*nF@vgOU<|6l1|I~Wrq%tbYuiD)M1DU=mE+$L4^M4 zGXUd~R__B5Gb;gC*w+()Bg>Jb-u~3gu5Of-qQFtr;b{hX`{%6=&ptU@!ZVWa40x8= zN~FRJut(G60L?sW>G+HO6WbGA0y99E5?uxb7Zge(0qkF~Pu^rz zAWjjykh7D8NMBFY&vix;7a9Z+JP3XHk)`w;B?V}gLPxPq6+eGiBluF*UHF6y{a(lbmCOP5j%H2N=oPiy^4H(Pl0GKrNZE@<^bCY3e<<7 zkKtT&DrPr$bVZ7e_hHHi>T4(-l388j#ja1i;wCpC#6c>=g-2w-a46NHiU zJ^P)%Ofxxr^A})KlUh>!jwJVXKZq_f0-yU%k$NT(ca7M_h!*{V0=^&;`TzDxVwwzv z?hpT$q1)>}QTc0g{(b+foACcF1fP{N#k%w+NP39){JOsIt>m)DR2#};%Ynfs1@Ov# z{J;iwu4DhPdch|O;$!M(wUP?<)1S55T%M?LW>=2v5$(U84rI4%doWx8r-CvyU~Pj= z+t4LM3Ce9+nMNx>;ja_6*1DTxc%^#;fX{<#4{H47hnwdP&E`PTTZdL7oc7}q?XBM< zEu?*ifEKYC$<>l^|L3R?E?~IO6k8!N`Q(J~psdY5F(N?;QCiD%^`}#y$srKPDagrr zI<#Qm;NVcY049Kkmv?UYcFX_9&X*TAxRlkq`M)b+|HaDgnsrbroDXm4n)fFn{*1MN zNtp@^xkg!4RTUdlTl-0A;HK<=6Hy6lj~t$+NKn<$iFOh*4R5~g=jRuGz}z+?Gt+D9 z6+p9hRdq{kX|@sHv4#Qmp9LOYjHh(ee7$WE(4V8L40Sm<=Wf~B=BK2j46NV1OFA(* z>AhJgDJjVx`ObS`(1ls}>3i&75}2S0%$U>u$Hxb#qjgp@AqRqnvV_%c9sz;Aoy`?a zQGA^2|AOqQU@N8mPtg7gHz%NxEN51`+3M=K)@wLd-f!NG^O`I3TbVQu4&Lo3gFwVz zGrRdehI8W12FV3#)-8=UZ6~=xsIgss?>zwj+C(h4Zh!M|!r1ZJj_Qq({L`01&w;=4 zoJ;Fh`No9S=qc1xb+G7uC1WhOYtNDzzqfOzZ$YlWVsD!Rd&c7BhrE4*zup;fljc|KQ5y`|Me{q(-`n(M|PZ0zkK%p}!vsWOj@?&SY4i z-U>r7f{!dW^fOw|JVpaPDS`0BjS?fG?UlNZFkbrW>MmRnLx7%u8}}i6NsNHlLDb~_ zry*jrOqd-Br5nZ65r?tB1~Z`+t$D9h;yFm6PrH*Of}c=F;tt%Tc)=9w-Q9rwgJC<}c(`5yO)hA)aH_Ig9@N%hK*L!bU^q@ULeT3U$|&@37N=B= zUCdeB{upgd;snM|7R1GBY~k=*qqrpDBbC--K?bQx8@V4?Sy^(ZuUo?k?}M3Imz%se z;u_0QUA^rVVrqS8WaUNTfL64gp8J!|Iwa&N4B(>tD>0+!RkshedAZpKzOi7NI;#BR zw)XH|UnIlIjPLPmyMIttQ3GktPA{+P{|S-gXr zAL($~_iJA7wHJd2j{X*4emyvEORw2RN$mI<=r!5Od8V`siK-eI%=KWuMTL}j`lEfp z1_H1HZ+awXW=FFJ+CeL5!(Y=eA#`!-^hhkfX;wi2pQ5rdFGSqq#he{{_GnIuor9as z&Q2y7-#1B^*<2fWi+?|)sfh@ARH{79NabMr(s>iz>$s zKu}KuzQaLe#S?7nY-X&vLq&4^k89v7l`4CBdb+;PG#B@`3RTKz$e+EE&f>qlq9D$d z!h~ljlz+F?l*z7ZvLkc0IqTWXQZyRWq|(Tu3iOBom$z=D4*dOr(!t4T z)j2;mSKG!Wi{j9!9OF`(+&6FD;46Cmj>oddmU>=rEq84 z!M*sQZ1vY^b468}a5&dpX5YQ~@!)y8Z*Oj3c9xokWG2%T!}av_C;VT$c;UIWK>qbj zk8hxB7n`ca^UMA+KSZ0?!r4Wo+*T{ooD4Lix!$!BC^*-(t(;yk4YLFWe7+uI4osB; zCj2)Aaj-xs2v4%1iHS-6w(^xLIl+6og*A=;ay9L+LlbTiak5{hn__8n6J#${9Wt0H zXQm{>CzwxwuHI4!ZWv@A9B#KN`SbDtFJIC8gO?;ohd=ni|KTXAxBn-E4Zj6143?vZ zOJ~^GO#>rtF!J#5=wL9oU?!GN(PQ(twXxID8Kb+_QJNz6+xayYmtAAaJ+}KD-}|r; zLt3ZL!m4rbU$49tyw#n&TD!MhJ6P?H4cZxJ{r7XKt-0cj1$hWMGE2BA96T=2^9x_K zhCYm$d~LhhoxIzFZ6~9ZNx_a_u5Pa{=xS>}Lk~35($0(uUB+wZmUX~c=-LM)Q)it^y9MJ#Q zN3>%T1x{6Vwy)Nuf7a|4Zrt?p+Vl56m@%yrSH)+$-#W;8;mOV9q*kja5d{gs?J2DM ztFdE(m{0)S>e9Hg58HxkuOz>G@XM|r6Ki^M{nRrKv%xzF_*A46lw#u*xEI*hD{$Cy)H6Ah|q>GLm_}*5W*YkaORb>bamNU76-L*YA-0j(c z=nMRWhdo!%rb}=2Rh1Iq=>~*Vg9fbE_vr zP1b5Q4J7Q_vv2iE8TnlTGm~{lW46b!(4ZyP>&^@ciigP+RHBbqohkp`bpwR-ctF-H zt`Wh04t|dfKF=7J@#>suwuQXhJ)(9$K&X-zwOBGfd?}GK8A{m;SPv}R{`qG3^c#aW z8|#aliSG$>qNhuXIe@Q*bX-L4YXPmI++Db?ILNp4y-or@!z-?gG>bKpEe#+pI%mDiJVqRe13!rJ>+K- zp+h<^DOs`dYsiMjs1v zp3LFttctv%&carIUJft9dU1blPzaFNkWEEMDWM92a34LzTKD~ngt9-)+ReeWM!|l? zMz`9eWS4$TnbiC^XeM?+zkkYx$JQ;_n2$Nx?j4siVJt^e1Q(M;SLi|M=~~#dbT5u4 zL|QHHr@Q~r8{?yz2(mJ!OPv@EQnxTFr5wLlW7M+OPOfnx;T+g|c{zdZjlD_@zuM89 zm_44CTvST5$9)a=7+-fST$2)!)LdGpO0@HIZ2#d9EJBNfgq%jwu0mj-_0;nsee6%W zg{bT~9QVB*uI4OMs8*K+@asDdrAEFVFxsZB?~ukP>`ufN7=*V;OP*eRNKHpd#uYPp9Y_$}ed##OSSKOb^J};|S6V2^@;b-a%_oeNWn* zW+{!X0NuBAhqm}lTdvvip)3bqhxZ^~qEob{Yg>_*s|Q1|@@wmRbGxvuuKV096btRS z8YvKkGf3LQn;h$%SFw|`(TBP7eEdA8nlH)f{5CG8KE$1$*R4#M_|_=HHDY>X_55fL zR_g@?SCg8KLS7u4fE*;DlRewUlA2wdoMW0r`;tR;ecc>i^7a}R6ULj=-gRy9{gWEC zu+r_dZ|YPu2-QAvh1GL#xaMEPm^&I|TX4qtSoOPnz0$om`nUAGWY?wp6X*AQa#>yX zFo|(=xUR(}3M2&!^2=+Z^|iyoH7PA9@J#ufWz6ova(u1;FV!83|pr1E3IQv+ueY&}v}L_}zfimqpv^p+Axx z28Xz~f@?f)Iz;iQ+LoG#xL8`KqQvJFWT{mP$Vur0<9-CRRjY`dZT=b7y&o%nJo((SA zXbwN>%Pi^kW}~|8@xhBi)F-%Ve+hW0MQ71oHPE}#P8e#eI{SNSLNrgR6I_hXEZb(_ zijb4Ck-9X0-^DCc4G(XRUWMX+%HDKKqnk;YEB?5XMKB$&M>Y){v9j)Py7J5wL+ zzq&z!e0L>RAb;?6w**}+!tHT6{hl5;^8D=Zj8AH_*Xr5YPqF;oGcDpllX#aBqZdbA z`wL3QFz>6!g|}v*tF1SG(9|zWDd>ep*)t<3T3n{1b^7W4*MwDv&eLb_Qy{O7+*1e< z_`WrGIjzuwobQ#h_60ScT{-_TW<0UK$MpBXzMis~jQ{=(_fzgmGI`oH7On4^MHJvgA~k#&K+R?f?T86r7rnRW!d+A^VDFDyHb7_ z!H|&9O@{*NtZgt)zNXv4%TC~?Cd_yW^PY3yU-?%AiCI@#4OIp%EHQ3v`QC$WdE`$} zN9pzF5Bpu_+IXnr#fQgXY}BL*QEV_R!QGqkbswYEPK2Hg=ag{0WsqpsQ#|tL%6Sm5=obUE zQ9;`aC2=y9!BxejCj4q)jy993U2i1zbo1e(mE*3b4 z9YO1S*MMrt9T%O0u8VR7{^kRj^=%kqzOLX&n;Bc-P~Nl6$ELGH$KCx`iwPEUi_%xu zqN3Ei=6%x_ZB=i3n+av*UXTzfWeubt@jRG33dxT@Nbho(Rm~@Fy{XiR`pS~+$VE-< z=@GyaB|iP3HaIQzAgx+71y=)_uay7TmW!hDmTlkky4~Dy<+aAP%b24O-o1Jm`qTn`ki2A|W8u25h>TEpI$hyZ2OVC*p@jL%mS z#H3p%*6r}W+^bD|uLY(#q5WjhT#c4JQ~6PbZvM~CS;YI`aXO9}L)U;bdFPShce)qW zf{RZuPfw~u_XSPBpn97>TyG`~&IwQEF>;#ofKoC7r_23{?^eXHv(bD>aTy`CispIAO$l3FY%-25rs(1H~ z+cm_?i>Zw!zDflu?D~sOA2e)H6ijlQByPrFGo#?jmPSzhtXU=oGcKpWl8^%&E$!2{ z90l?HB%h|lN#2-NhOnt->T|s){mDE~aBKl5uN9a^DqP{vTBL*D9#qcDv47fohVc92 zY1~}2hr03F+#{S47ImoK$tvmQNb^rCDtf*@V&Ijgt;iX(^}9|e&?rD%u_30FC&Jt| zx!_1-yHnoLLsug%#_i4LshG*QJ!f>oA8i(m zf&5)dqp_8E_D{!r zyod2H%;m5*a!A;nk{%ZkNOz4Q{JJ&;h_a&+QNb!PdGLY7sa)q0ix;hVK&zROj*YIW zMD&9jOf!GHJGZv_2IJt8e$4Lj2nivu>GY`cJnZ6b;EplQMJ+2dy8-J@4cbt+9XT7p ztihg_=#@=6ZyadtIq}WYOw>C>DS3j*j&Z)Qb2;?JbZ^={%=g9QoMqqiGX=-Y#5r{r zgE!0Ns}v-u<99O+PU1eChZ1f98qvxRttCksk~2S+6thrRnfKG`n## ziK{`;YnJS{@4c(rkv+5ErEjIZS4w8h?L0nFK4_txPqC4^>mrt~D35b{^sDmbxqGA- zg^48!hL?JI#2vhz3pAy-Oo#JD+}#Gr+}lh^TM?G8jumHgyPRfqb(>jByS&v>K?c09 z8yk2Nce!8VL`HtQbpF+=)MAw~gFBmt34zMF2jnD&3ldRF3|?3>b9BZE#oEFWC$;08 z*;fB<@mhO3Z`tO`0f^Ws9Y1un)A=}U=O32YxN8Rb*$u_>T|>z)uST~qfA4Uu6*ibU zqyj-#o z%^xOuVS-Fc)?et)l`cClOJIikZRb2eO{BOox#kc9TG%|M2&=w$FgH#&ADQG zpIkBj`}+5ukBi8NYOV+{76_5iV`llsL|CFy!Dg0a7nt}TG888)&>GIc5<7Tecd=Sw z;md?-+0azO1GITtH!}sAZF*y@yS?x))N4oH7CL%55|Z2rfws9LBY{R<5!t()mgOq! zU8kDc=CdE$im!RiJFP8yMz}M8%nhfUUY3tYEMa4yBJ590q!Q{e+XSQ4i8*|r?k zpqmfN8;{u9Zo7&%I8ihYIUy7Xx z3zVGeoqMlkQ~;$q$I0j&=FBkL-R=;Vek1p1*(iUnQpM|AOya;%3a+HY9POD%?`>U} zT>GK^1nK|=jO%FUv3njDj#zts=-`+*q^e`HC%4t&Pr17MkTURt+jS)iymu#^nbd{y zmVS>f{Y~Z56J~=ueh(I3?X7?t`Eqc#&;wvIE~%^5D(qn>)vd zu-ReE#WcJ%rV>%CQ@L?daA)@0eq%lzrCK?r=u`Qr+_&LhHu^8&RMVpdI=A;9GOHgt zmQA^p%kk#FEG^PDjATx+zx?*LQz~2)>&gbQ_qG%1ubNU9@3z%X%vAJr# z7Z{W5Jhw?lC)=ZdX4ecjZmm7m`_;i=CMcq+`;fyP_&t2#*ZaISU-2(!3PjKd7BJ%Av)z5cj| zzngL{G@*3pxp^y5(@u@YcYESJH6-z+r=u+WmlCT+bgoLJAH%6EOE8{m0= zY~dMr;1p%@Q&SVYu#opKnZJ3Ny_0Yh5CNW(9w75t^Rwla94X49kHG9KRGXAcTzw}K z3houk<$hKEObF38+5Fq!?@R!28H97*RG$BMYL*F)lK6#&rZU3Ka|Jt%VSLVyBg-6g z41&!B%WBvi7Sr9sBp`BfH!xz5|Q zFF6u}z2#9qrb^$;-JiO;WaTM~8P;%HGxDP*NHuLXZQuOk6M01*Gj3jHrE4YC?bWG^ z@#!W0I`@0W4&J}L{`vWb-#<4x4~IoX>OqV$a+->>aWLkkrC}bs3HMTGA`|DHhBJO_ zVTenoKf&i^&2!mnQ-|yVQ#y|;JFMo5%XL=yN2)l)bxi|po2#N1#soH>k*nPwg8#UW zobAPl(9}SNa*Z3m6?EBl-oIA@l(wckE294=^sN0(N)4XHR}XA092$fq4ZI&co}x3h zfV;J2331sJ8wLrW)unsG#iNDzbevMu)|COzjR|%UAGNH}yj%gSa`B=>M$^bh{`n}D z#nn0Xf@7RbLor8O4c!CEz=eQ-$6N z0pyLsylbAv^l8r=!u#~*2p4Jm()tIU6)JP3M$W~zc3B6GYl>8s&l0R;t8XW(<;NMw z&cG_}y^ha3=`O6Vty`Q-aLzsu&dUjczutejKT==Gs%7Zw?suTQYCkT@?zT~2-2 zu@|a6jxtbtZ^j*&_2wrHhCA~4(D=fv&jpz^a!`gPC7`3%^NJkxysEGEdI^kfh^`4p z)-DBlC0=C^UE}c((-)|f^jGi)`ON&JoIf&Q?;Rfu@roMhr5^r~B0Eq2)!{xe6WlN1 zQH31drVPhU26mA*7VSIywU66fNiDH%BqR#$c5Z&F5VPVR{r$l8(mdqrGRcNAU1x#Q zb_gy#+lpEd#yFdQ7^J{jr8{LM=?w`|qep3P^Q4%Iin@QmKW|L^jl|{zjZxOZLRW)Z z^Pl0}5+=IE$_M1Sn}ZoztVPx&2|rdp#07UgWl7Xa{;yYqV55$QR?YSU*O8j=P_DWD zJvk+^tw!=nuhbUlr=7t71IMr#wXe>|F;Z2?~TznH|{|?9|JRU;fH&abGg|`4khi7@S6ddT3=kSjy*Zr zn28ueOx`9EA^-t|IN&@6qkeaGN8hVrCPN=%)*Dy$2g~wx+4P3g2mw=0b`M=$T>3Jh zOyg!ZL7O$5J9pO3vX&9Zco|!*%BXpbqI8YWH}YNUFyvKoa`C={$LyBI8hH5ldf@Cr z#A8yO;rMYlD%vG%do?f^+Cljd`km~?aJuy3d)@QZtI@8>kfPxLxnXFcg7^HlR5U?+ zeSToD!Wk0M`4I(5s2qT^4CB}*>Y{7>MrEy5CDY5DGbY&Imk3UcZV-T(sUK4V_jauv z;~B;6LZX!TzC2&Y7@>H&4(K{um*z>X7{c1*w4L!Q}B%pskHK+gC#+d<{yt|&4lXDG&!NhNud8w=B6&D)@5^__2Yi5_W20}X3U46*! zsMcIuxxuJZMNlvq+FM;nl%0&`y!ztl)9?N_Z{GB*Bp?X#;mm&>_WYf1lJvhE_ALDB z?i1p)?@Uw7%gbAqh0Jo?@=It)$^#Ac^j?58>0i6n;pBhu;vGoTFb&ku)YJ?o^cR^C z)4MNRc#97ll*-~5Sn!6TJsZefMoRZXUR`vV+mB1FiPDDjT$*~CnvqFCTlOFej=MXX z(Q!N;l(Z~89__MRx%QtH>9u51Aq8=kQvT1K3Kx8yZIWNs!1PGfFWlAcY^{FRpp}zm zfor{(h4TI~7rd17$a_h)#lx_kSV$gido8T29L3Umk5(?Ht4k+>Mb-pPSq$J@kRunZ zUrPvq!#p(45Ir86gQUcs5E8OgC`p z|L2$I{saOn^y*n+-pMOvDhLSuSQ#Qmvqhj#3Qe;J5xk5*f(VuHf4H#@^F!{%_)sus zcc1tU3mOu+9OUwFMco(t{G6hqmmIfl-XyQ&gjtm4<>$vdeM&cNLrzYA(dS;%cxUS6 zheugiA+2bv-R6CTaU$l!3|%AMzHGwydA!G*5$i6&s%#uL0W{HIz4sr3yF9qU3! z-kQ{xil1Vrb9rM@h!#~w?H+nq{~`Ey;wQ;DrZ&^RTlyuG7TM3ef8aRL&MRYm9`YR- z*&(Qi+&=*oE^mhxaEmqDH?c!x_@mw56TE1q3LPa}&FcN;WQgw`Y(n$AVCrbH$bVB- zD@X-J6Y8+=VUdvr-rf~r&Vxs%&+aSVUaYk^7M?l44w=Rstp}vgiYmK!jo_mB1bUI;=A9pGqH_M2U-<1%IpgCeIWea6&*5Qt>P%m)D-iglm?ajw+ z@_PYLaLUWcsfgBw0CIFYu@+$`rQF*pM@-O)#KuVGx%hcWs6=q=D_9~V%oRYU4%_qp z4z4J1_7@k1J=*o{?Q<7P9Q%tD#JQ(?`uZG(D{o`Q9&?!xHeG|aO=Tf#8NFTI-CpG7 z=huie^M38xI_Kt@<#ARu)*{HS)X^1A0IR=!{9bmWo&xVZ$pri1CmSx4%fgrQ?&Xa@O0E%0W-zjW~gm@vbi!PPApj* zpOWA63o$vm^Q&r6&Kibx$0amZ<$}521jFbCOH`A})vLlRnCa7_6*!y}I~17s&gN#5 zc6|Kqu_Y-?U0?Yl^yHKL;uOJI=of|VmuW2+uEjMrV6(F&Szt>7*C z{(%$MNT|>JhpJ4{9v+WxRX)W~-J1fb5QL`SormiSL&vAjzHSD{M{8c@2TMVsAn*Cz zdYdJr_Nl3=cD}h$zj}{#M*&J~AbQr3o0k{O0d<{fW0<)|-R-ZB_nQvEX}m$9N#@|7 z$;8i}Zi$H`P(k*;!{xpl{SU6oKfj>$h^u|3t}}4Bnhsg*koTOQt{g>yv(NfbT~oO( zWeep&TXPnBc|Ie4%Cz!3Z$^Um0%3hebpiteQ{s<_z`4xF2M4`A>^p7;dCAe0BW)Sw z9%pY?m)`=UgW{FvP!0uYb8AozWd}y|x~(l8r1GV3{wwpZo?F?4*yah;Op4t6 zOh?#k5AK6b$-Z4!`7t;LX^xYBlA-{gP(%cL8VOj@F!Nt1Cvh_VT}fCUD)*Su1Z;cP z&<=9j-zPbaam4@rQLnG3m(kp;2DP78aj+l(Dy#)=@o#@6>iF_7D{H%?v<4KMYa1Ea9X)o8Df)>r zi{Gbne0(B@PDxlYhTf1_?YOK2HN8vAE$2^Gc`sb3*{EJT0hO?!-80l~Eo9l%9tLJ+ z&O1N3SoeZ#NYPV}43L5c58r{T)ZZ;+o;0y{enIrKsW2Kgo43nrjuZG(_icnL~kGoHPE zZGS~k@dPjyJ24u>1TATMmXzdjMM;T?jDlLQsCwOG9Lk0dd6wR7t6HpaRB>2BwU@fy zE-r4oX8V^glyXs5;a${|r7 zQV^H^ZX#_P{RE0D`mnV9rXGN-*If&%X0qY~l4GH4qmeWA`q;tX&<|dHEbxO0N{hWG z!Rpc35%UlKqrLlYEa8puv~Yng*gLe^pGxBFT?#r+s93L(uSI3qsv%61bpxu14Q}4d z7#kn2`gUQa@uaKXty`JqVDSZ5$q-V0IIcoMx*>^TD`{$KYA&{RClf$^eCQZGeYewI zW22Iyl2X!A<>SYXcO5y9SBy~RM4nhyCNc`Bckx(w!$zQ)S>o3ELOxo9eca6R8RYJ) zK`)9ZZP=*UkVVVL1GiQ?UkO>4xlc6PG|3W<{T{GQF0|O&>c_#FfGsYDM5mdV?Brht zL*h=~QdFOx1vxhCQRS2R!0|4Ha=rfXwukvwhd~yQVfG(KK5#Mo|z|~G= zw8k0DxP~n2Li{*dKZs_|95{H;YpxJGt1Q3uOb)Vo1Vf_+#iz+Wc7EOLe z*0s&;(LM9I{^EYfmy*I~-D-Ks&CY&VOJ?al-?4C|Cyc;s4iRU3K6vvg>}m6a`wUd~ z>7#tj{_88D=(sH|1ATq_z5-*W3pXEe*#G?eEYXDw@kCR0vqWx>eXs6Hv&v_-@0%14KV|une#X?1xDHAX?H73C>_ni$z?9CCuEv zaH2R_vyESzMU6%zv-H{A+~6^^$nk`*&2(OVQ5T@!V8M9~c6J*Q*|aPrMtsY@GG-3XZ!q$^3}CoS-Wc({9dP0~ zcdjE&)KSEt=bY^BpK&I+fXem7(etX9^#R**uQ>xq<>rB$XY?lm{1>bHGb<1Ue~^w# zAK}LXh?&>_R1x|9wnUT842J}vEATfIM-$8(p}sdPDyrYuHjy)zn@SVrN-Og_1cU@e zmkITn4cI+2qX^1|&=z1B`&QcJNAnj+6nvphCFH4~=kztyzzn{<6%MkI4Ul;SGLdAv zqKfkJ)D#sJTPwgm&w_>Rg$zKiCT1TZlqbphdyWQfpjqZ`O$xzbiysNvb_T1?4P#J0` zz^!R0`RPPU2HMqilY?G}#_l4;19~y!TsK4Y%026#dgp zq71U8_|0&7n%TPWGXj`DKOTyL6UrAl)5z z^ZVcX-mmw|9pnA*GKRx(@W|QE-fPXh=3H~}Sw%?>ABP$Tf*|~t^3rM$gaU^k*gO^n z_zvIT^fLHM$VEoSMcv-a#r>U=DWv$$#lgnj#m3T@!Ohgk+0x#Q=PA!qZgvI>7Z(R- zVNOol|MiBa_D<%UoT0?H;D=y4$iH!hAi{Tde_(myxt0**qV!T)Qo|#8d&bj4W91lg zZ#Tua?9*111j;+?Uu9thp$s_al{i|YtQv)rhK~7UaiO|7mFT5~Mw%)rY!)(`fl{$= zKXkBqqrmP-k(cfA#C*TJG+CW}@II|aY_9)s+0%%d=d_ng`0<)=zfd4cC{qad>ab^- zu7{YwmlFpDTpE1&eS|R(6B9?Ly@TYz%Vl#)=pJ}|PW6BO`G1>j(bzG73zX}ySNoiE z2L=T(Vg=U4zW|4|%@E4O%E~%WX|2sddh;FE1RBSV=QNa+k^*m#1aENb=NzRC@Kyio3|Fm`WzxGe*rbE%BAGn4S**#2Xc>0Gu|9dA( z<1{rC$`qxpyRouSXVKh z3CX>Qz+S`s1wlb|!N#wh9uz4@!aLa)k9WDJe+wVD{m(MGAQU{P(=&3a%pd#h5f5c=o9{9;*>U*{dcd@i{t;%xk1FBxH4A|f)wKWaz;?`yuzfj}Qb*cI z#;VqvTvjXqWV|XS-!w{tj>bw`99XLv8$>nDQ8Bo!{Q4iNB-C&tMDh4(e2t0aGU|UD zCQ;ZKUPc{rby{Fd9uVa$Plm+jfkRREC<3X1>QJC>_NL@fTg^~ZNFFK<_4`|Ble~l_ z)J_-w{AZ>@kp4Td5q>TawuL)O7kKND)#gK*!V%^(|->N@% z+iNz~kl^yq?YWOPyKS=rEM(*U4V2z};Mx{|W*hyX?{KqyU40 zidZ4bJnMw}r{OyU(Ag!q87$xAITNH`X+`$+>(}(Z|F~?%xwdBO>qagc_uFXXU%m`5 zD3+UR@GbSbzL1?0`2N~szm;O}mp13Ri!dG)H`<)vrEPBUD`^dtccQ42Bn2{oq|n_0 zk##fEFPuOQOKlN46&A#)z9(;J#e68i z%jOEpzfo}`S5e0!zqC#E7h82|U7jc<3kFaac)-3qKfr3ZI(T(1)-QTWdvm?(zm#@+ z^;jwO`R7mfy|Aig+-L~s#cWPpKZigJ#o<&EPDNKvs6`r-=^9w;^kZHSC0Rjhaj`NQ zR4|@MP#*k7!iv5M6V9epfDA;(Epb?o%FfOEOr5kv`nHF=4j?k$HYS*(iiyv1xgmI_e{<~b)jJ6Jb;TpZ~WtcNO*H5d|vNod>^ z`y|3HjoTFGWM^h-%LESt)kXMi*3^-g=+MxR5%+i9daw-dt%jnb-zzIC5e*?7jl9z+ z-?-ruTj=rx4+P+m_M(@MvGG-wny~93sMt52AZB8*qB45d!5Y%h(ZMR82v0~zkgaNO zX<^{xES_k8ogz%O`}>f9;Qn(gRZ_=!B&^_V9r>de?BL+w17EEIoq+IgJRNrO>>ocC z{ckVnot&Mw8=gY!dI-FXjEnV}o_>_D7pzgwzojU&Jn0{J`FK&sD9}LM0yi#<6j6orV(=6LifAVf{`%#(@CW_9sVU>LXT(iSO(EgoXpppw z%)(*6xSXaY*;9kxA%Am~nORt{UcP+kW=H{5=yHG^q4p(?zGW#fy$y6_MYGT#LTf6< zk=*DZ@9`JFC-nD-;*d*TR(pxB@JkV7?m7+4bVmD)m4>?u{s?37Z*uyry7g?buf^cV z@&+yLG7@Y)f2}AVADKy8xaD!FRVDTQ{=N!p+<3`5)E~-e1i_j6U$XxG8z|Hh7#B|^WSzBL;CUm?c-&^`39_c>oiD^An)S9oJyEks~g^`U7mr(SG z2--w!`ku}_o)Z@prK9)RgK=|nM}7PDQOJ39{%n8AV|`EqNnkv@svR?5umk=yQU zM3Q|26?quCvgol!9RLoO%8zTe4{l;%S0^3@<^Cs_WR;KNZ4TWlpk*s}ya+k9_Y9%G z!adyk&y|XEGeN{8>fv6m~Rkm|+_gV+L$K7h7 zkHWa$`qsakk3F8Ke01>Iju2Gh3!muQnhC|Hw@zohck5;Sm0!NJ93H3pPjW?2iaIVW?nhho}Q%2dHFqr z=988@&$e3(lxiYdnHD`FU9Ei`Xh~KlWMI2ofg@PQH)kBWjsJJ;+DpSbX2)*mQ znTfg=wCN?oU)vi`rbWHizuVvCf4dWV*j8vtt_9)=GetLGSqW_vCm8-XSEsyu_r~Eq zI$34!Y`-zF*s^-9mB_|Q|(~|#PlBl&GrE<3P{Z`vz zE%c{E@jjI+lv-U1v0t}B(5QcROwG#K;<~AiD{s|cP21A|>SV=ezerVJja<$4yCjPyi6^ArGU~>RL zL>-4haw)s;WH6~ma?ZFbH0o&$9f-7Efk|89@8k|lm%1?#j}1SR+wQju~kFVsW3F3#(N&!j+>Y_ zXS2}&H$lDFx_5s8f)|R_;KbEHBSS>Vgrk^vA|JtDnMeSE?3#}_^YH1i$sru?lJ0^nDNM+7} z;~D8UJ1SDll-lchjI`uQ3UK)8*^?h0i;3ZcwYvBJzCitNDGau^vKW?$ z?-hK6P!lvYBGhYER9MkoE9IxfK0t?Cu7#;;SUQXyBxjDkZ|hD{hC%l!=ScP!M*Zgk z-o7BFE0BpB4EO!sN>FbmEBVk$ksQrhQ$Ex|Kk2*my+0+}pFhicugVrQ`akd!V!zKQ zT=}*?NAt=4MY45C5(dee>0}Q;EL`@5*+!4o+)b7<>L4%1M1-$ST7BVM8^( zzKTJZ0OHM5!yRN`4fET5V>Fd)6pz~-K!`h`T41K!I{y%2zQ^S%fd>NM4h1!6gq-?l z{#!hzCxL^-U%d1Rbf21nwgvO%>HPHHKv$Mtw`Ntwu2v7* z9_i7%6Z!e1BBL){y#|5JU=RgEgYQY;YxUemk%aiKVk7J$S8Baq4om*e8q&W%bGRo- zD|5BqGLIB5s%4J%@3{zX;M)tW6*Cd&>dgsm%kcBh%8`pxOOd$3 zjbh)z(w@dj&w_)wxXm7+8Bot!E9ay<|uHRXHs z9I=npV}rlM2Z#qu@ax;qO7Z0OAx@>UqvzUXh6Zd@63A25fJX6@FT!CV4bPg^C%W_} zn`WelDSo~Hi9z2-E^Nv%N8A6x?hOZ15byZW9_92i-uq@@A#=ViSX2Ejew#HQvC4XL zVGyC>dT`OZLi2^p7cP{>Q$Md~VS01zHs}m5e!su0VysjP7+cd1gCg!-_ERkNrmRiZ zKd^cXav1*GRaL`O`d!W-Ahyct=7{tXwJwOC9=ez$cep;W@177xPEwnO7D|>e^EllD zH;7rkCxo0H6iruW+Xs-oFe>TD^d`b7J|0CRhF{L-h%ly9*%% zSI9?NmlrohTxY(^D>rJYO>@}G^p%t|c*CK!U1C;vBUng(lz5`pRy{}#?Fi|O=NIl$ zXHoq_+^*rh#45k^oZgOaUVP^3K-)u3+&JgZ@=$YOSO~Xz4_6YPtg3 z(wFwlJn&h+a3k{zG9Uni5RPT}>&qvd(tjUEUw~P~Y9vLuHI#VfBx+=-~>?t8u9i)KPWV6uP)+WSv%zXscN%4 zzxR*g!@8DOdd@~KfQMGiS>JGp$(wpl37fjmdm0MCzaQ2fe^`3-{yl|u_f*&E7e%WB z-xS@?RXa2vi9avoq9W0V;+r~m^4)i5^(2od;hr(tp;|j-7Y#1Dr}q{T(t71ea0Ke zD{;HQ!iyx7af~BA{asE^f%9w9$4-@We=+_s1XU4kn~j}pMjQwsQR@87?5&`g`^+)5 z^Mf>As8H(VR&~Ed>m?J2c8g@#^BC=gA&%c9LZ&Rmg)cxc&>-?pk4Mv2#uqnXv})Za zgXq1X88NHXCJ|M3>%b1j>7TmE<4$nXvqWnfgr(7O(IbXbK1GO|qC(`nG1t~CI=O8Z zmvWhgHe5KYc-3nu*VP(B7mZC6a-2&lT2D}TyZEOXD&D*FKLtNRRplLWzx(96X9%lW zQY;DK?&czK+G&ngXA+3+ zqF3ATbF#RUFHf;{chOn+TvE9iezYD&_73L9C0J~JscMBOW{BTBp#eYeC>*EdgP5xA ztT?+w&?!au9gr`_jvDQVT0yEmTop}46n^uOTBjNEM>%TJq&?&(H%Gp4En@|izJI)X*HGn;4|RRN76Bl}zR71vWB z2}06G=k#VDWxRrJo?A0O^AX$T(Vnfx=D+e?fk3vq@@=HwBd(7sXRU6>Un|#^N+LxU zri#W-SG!zht$s;5j)0rI@b$($bI`)ZVZ+B7&3Aa)A3`ARr#8o85sUMlTx-GRacI7p z@+xy(HO4!b6#(9b7hfW9N0!8K<(T=UKZOS>Ojq&agZMtb1ev$D`*sw;*+2aiC8gHa z)=-x_-<}i=Ciq=%cENBqa^m6^|K(oFmf?%?qV10_eX~@|iT4l2a;NKQYjIE|4!qUe z9`kE_5_>lK8S|fKQ71I|ox=&S57T?JxP%%{>lQNmw@_ZOotgfAU}tkb){1t8J?{8a zd(%s@su*?SbXC`@B}Nd32{w;}pN}~Iy?l{Fw0Hdv zWvlG?+k<{26m^xV0o|j?e6gEg1W9-WSfe1psc{XzAxc+W48X1@%2Q?fRXw>-Km2^~ z{15BZ(j}gcFFS=?*{hlQWb%>39+pCcS(I_$R6MMQqxiTs6r^zJ%KOV7{uQ!bTUBnU zsL$Q<@hmAqGt{uEcWb5V#-eHIJ`j>eO63_uG{4EwsosJ71oc8VBMm+>K>mq?r3lW# zQg@oOwPZDDRtTl2-b)6(^EQ&x*Yztp|q1;N%fl#W7sHhwv;SOb*T-t^xd zf5w)L(h8=))o7D|WWZ#7A#-T8JY5qEmz#C$a$ojy7BJ^<9_1ocl zJE5{LYjEu?|H1Hnt@JrS3IbbN#YR$Vru+ypx|px#ubQ6v3e?c!(=XEChtuDu+nrU0w|83v=NV>t5a;EpJf!MLq%SC6f+M0%=N$yQL!}V2j1qsn(x(zb7&rjkDHSuvEQfoV zR-SFF=|jlC(iFD7C~yrnPo6V_hcrR}F7$J6dANbV)9$5{3z3&J8qxw`+6dtaG^I=yX4y`cEqdM+ocVzQ1V=Mo1JgGf`Bn%jT`8Ff%wWF4put^~j(;FNa_wKhJC8fRnvZuv{gV-{;P+ve(uDleuZf z%_|R~{65~A;kz&U$hAr2Gy;jVWAs=#kqoiQO03I5lSD>b3nTD-dN40pQ@?@{49#{0lLt4PzvGsCp;%8%Uj_2z*%& zICUmg^5KB(`~?r3t{d`Fhgd3YN^`+cF8>(V9>+lFNEci;O#PId1(*t$&Dal>>kKPrGCZZ|YAq2}lTQwUgpk>9TfM zY?XnXEO{=zjEX#o z42a=+3sW>-en}Y*dKj9wF~APOYyv ziDCDPK7IJM*xkh;7su|ms_}bSfW!dv6MpVb>G=I1)uC>-e2OTxK{_ARE2)DBZ@4QU@AFqn7_o@lB9& zjz+q9>>*o#a@ALu1{+!R3_nV@cBrJZY76FN!KbTTs*$&L`9o}7)R33eLkBq?xNxP~ zP4FKYL_p5=ToPUNHCs&$-4rtSu&WeMa8eX5``4MxWAh3bFspF+3MKUOqB!>XegrvJ z!!(dQnuPgMODcmsuj^cj&ty6&KyldkC~dd)=R(1A;+B`e4CG-UJP3#@=Q|z)G%eIa z4cUpjg$LrSoj!0upi;bYc+`mERjEc`2TPCVKwbYFhm89utT7R)KIx4?lsAWQas-uR zcZFQ96{?%ldfq5AiLOQRXzQ{cM7K4}Jm=E#oB2KQeo7;1S6P4_3eD6JN9j{$g(o?W z?V)A21gHN-5U{EBS9=TjeFn$KSA%_|Ig!$UP%ttl3k0=NI?(4L)B>dS=9+#TM`E|} zVZYfxWQf#oqfO1>(7S-$*DXIc0zKlx};$D+#3QPRp%ZeK+b1nN#4_ibyNJ_p6WyL>RO>m`g*jc|H(J;pGny_9MJLb(%b@&@OJ*0f{TWlYIZt-*uVw#hQ z+dW%zqga9PCx|sWJDRf~Uy64H|zhZ zRaZ?dP&^&%SZvq6s3<-~howceQNor*1UCUigS0T_*-Yn^ELmFRqw-`j11|pj-e=yk z(z6(LtF-g|expz=$6ZYZxEEm4m38J1gfQ-4xhbHk$Eq(O8W0!pK#i4)U zDTF(i@7RqLgpiL7bn%Q!L(Y^FkAn=XuATU5h+3NaU<&FI#(^RUuyBW2!D`!FTmX_t z8{&OcUH(TmzL*9V8xKNRrvw~dlr8m;QEsx!hhs%~8wxk?wLA^M8orA*aKbB7je=fO z!Ev%J!5dt?pJ90-@>9sEetk=)f(0F=Cy*#dA%aAe#Lz zc{G6lEMrRtF`WFwOIKs;ko?R8N}89<^OKGk>q=And+~D4sdoYwqOi#!6?bvR?WsUvhyr#79Vh%>KmE?QYg4r14{b>`Mmo~u6|9G`fPsZ z_uhHAQRQ!vlIW|>iso6?UiG-(uuw)dHcWi=%S)SM$`1n6;5NS_yCVq>^TyX2L}4=T zF-oWil@(+)O2{d~ewG3j=g!$~e>UpxDow{gzc=jlE2hCm>*-` zRYAiV)_Mv4e*WaabUkJ{Zd}+%BP0ZYFAJ?TYGsPbb$fs6;pIE041-jOt%_O5&`01D~`1>^l=H@d%H3CVd`Yx~epwI?)3C9QX z>zq{)w#y6O%t9)Of4@_+lKVhgx`O%gagB-2b2J!eo9p!5_X@(dd{YvFbQNV@W+)aC zWkLZxV{k^`GoAe||B8O=(p;L%2U6#a~^)34|wMwk=M{ER`-6G0LxJ zw#VV1-W)ZORKGhX@Nr_O8+=poajFrAqGx90XKZyw8Xq<6j)O zory)Ou%QDQ)@edeb3Mf!EQL(_p*Qu%=Ija0oh89jrr`?!Y>s?PBO6@$9kquJt7Ppb zxiS5>l><<%(A(xCN+6y?dvihYvo8F+U&#`sh(S?iXxJY06&13{PZ*ZX(M(bYSb<;&)81k{*mWBK z-1aMRRjIIW!m5*8oAl0q;{wMn;z zLf`s%;l1kYNwh4Ey)QdPWL)!ST3in`Wj#NPSIsmK+ks?DcS56OpI4 z0TlH(YkpDf*s4UTNk#lq+vL3(#ixQo(MLz017$UDqnFqs()66OI5y8`@=?GI6}hJ4 ze-3;@Wvx}&027TsN7VB*qYx$lOFcDW`B&x;8IGhF=6c>|hD??&x?Pd4K+#RQXlG!T zi1>Y=yCtNz9(61jGrVNm7xKX^#r>c{!N~GbfJx;qi-RR+K3RAc3X;B$Oo}uB=$zYP zF?nMw`^Ync@$2CSS2u05AjcdAzeOh}-&-1Twg}KAYn$SZzh08!s?FUDRCbT0i8AxIBqJ+eY6m;FY2f$MR`AYp!~1(M&75#$A7>@uESm5sLbbQ_Y&arQ5gP5 z8MK>H4pfM%M2{u*si6D6-usE;xTTE3`mFiUxF0+A??B@#H@uYbf_(RH`#0G0rF6V> zUX&tp4I-C~$S(o4e-Ze}UQG1hWIq*jQi)C5gXZA?C&t@mT|Zx%g9Bf)ODh@Xjb6xpPXGqZ?5nZ74cSh3pQNL2Fa9 zm_mvtS0Z*7PVA>pcv-I;6Dv7-fJ3T_nX0oCtj&;nc*LKj>KiOp>;YXRI=d#RyUV3E zce*tTh6{ecpiR?l#4z^eh;>A@>}02prM`PO*^lDcHC%?25-PqR6Uyg>|c9VQk57XnalBA0|x$JjPe zr%rZ@Wlz=}z6dD`VujB@2+|l5A`>#r8{#qtZCz7S~>xf=_jt zN{p0`{F#QeXg5SzO(~*_l=l)$CkqExG(At@TD$JXtz4?@k3tV?{ayJ+7C*+_J8kCc zG+J%kq5nNOvOs{Gf8LW57pP}gixf>!Thg3?LR0joV&L$F>14W&G@9#k=hc@~5;gL9 zB)3|T7U<4ENe9%0DX1c)i#Fy`KaKXYJw-%w*%W4t_Y-Muz+B^Giu5aG#2P(coi;g8`k%v3sdl5?i$_5oDPh15u&S&3%pCy`H`1ET7$3)`!jlLL zr3yO(0d!E#GEWz+fiC}>))Bj28*)vmcf^Io&eBF}{m^RM*yQ!$-W5Hu(0^xpK!qGY zcn`mX%?D>%QEqGzDl8K_5C{*jqr-tCj48LGjj`X76KKc_H|)^pDCdcpU{+uyAjw9= z0k|?LoVBI#8ZcV2>}`#|vp_yW;Z+D>+PV;6E#h8GC>$`i;RfQn6Ybs32s<^fJ!+wj z1whJOJWd}g&pdx2yiTkAB}5{8X97JFt7bQ~ZmXNUZ6Z(sR4)mxwgPo&x$g!eMEYS} zCMY7M*SI?VseL_YHwy)>S9*=(^GcOjj7+uEAZ|oJyC)E5^So#@QQ{SvMb*q z(*v+rcOZ8H4#Pqwm4#7Q7|j~Mvt@(M%m?3@eF#bF#=Y$yOtwT_0)+)55I!$$3Z?W5 z(=*9|b_;`6Bg=}! zt&g@j4}9pPIBUvdoll{Ma)=Dto1Mia@ZCANILoyeej|;yegEUlTM_?;()h>kp>Dcz zbiCoJz?$(Uy!jfoxQ@8C#)pnqG{i#Wcl$v14{!>-rg5s+4s?bT3uxvmx(Nv*783L; z4+`FCKhu-;i8U*fLR1}hkAV7iHBKR6rlOF;c1&b(I;DPT-YrM3+&}AUtwiqXnE;a$ z``unpM+R6I&muVHab+~!Di1+iJyEh)PNJm?Pr__Lfk5@RxwmHDi};J)@uW}^=o?jd zq0Sx7k6f_fvN`aKzQE*rry2rlRnBU!y?NPDh+9uEE1uuVN%!6HAf;={Y6PHLnSm8p zxGFD5MH=`l5g`Yp#OCksEG?fqAvn-6+hiyzm5F7Di~Z9GplWOBz~(Yghr)rp2Eog< z^^*o;mqqsQjC`u^{}1l}{)t#NC zMn@x9HwN9Sa>z1h9<%k6u~Zt1j`VzQ%6Fxjwvh(W)K5!7E1)B>kE~7wCt{$+C?Bwi zD~ac726|VJ;3{6aR!%HUWK|VePgB^#yl4NN189C9h#Z^DPo{y}U#xUA{XuEC`Dxpg!Sdg`lp5(TsEuIn-$gVF8l`M))NBDZ4$9obJQqbUipCIYM6MFeoq z&KGC_fs_VvaPSgc(lzP^u4G^ByGIVqhyIZ`4cR@rt!{|GXrS*d{6Bl6Ev;J654r96 zXxmSb?|lc`m1{#KA;%B5`y7jY)qlfIT6OsQjU3e`yT`VO^~r7qkg*p(z)ta#B;B(e z{t##57|Ge(DhPAqXXg#UmxM+Qsf*U`BIq92cx{EBid>^_AZU>83Lc**X-+YgKB+Ud z(>`(voc%EQ7bF+b?~JO5K$Fj7_tPQR`!>{xY7MoZJR^?P2*=l;96b)l`M>~ydY@EF z_;Hl&=UAA_@;YBWg^@Q3Qq;U~s_}^O%3dy#7-|SpQZyEV)xFghiY&pqu;mEKy_4$YfayWfUB{`QC;G} z5V?bar|mD_2cU<4KhmF>iNXjJYpt_~{$DS?Bez8`S?q7yYKr1MNilZ#!p62bD}Fh; zIQpY#LPmqjvjcg_myLvgg^y54Oi@XA`@ghMl1|46p#IOeU|Umj48V0cZcGYP zuO7zL%|*)6E_`3GAniik;Oy)NcfMR2e!M#OUAyBO```N*YHDifW1E9j_J#$9jSZc? zT1EPw8+M1<>Ezietw17>;(6*3``EU<%l zUf8~D^llzF*-(K+J32g& zp<@d9rceWAx6Jfb_Bee0E|^$><(5Xkt|e0@Y;UW)e{pZ2nNq-x_J?B9$AW^@sv@mo zgNW8J3X^|BIf9OhXa*h&=+6&2n9mNnIae-y&K5p-T>Q)3zdGz+8qQbODL2FCx0?`aXg|K>spWz<&_7Tmub2&cDX(zXhREBMRx48l#i5 z>%fC?DVQEUL}$}3l9rM}0Slhlx}NjD^?iOeACw^K&F^)x^%*!IjhX_{&o&KjS?AW; z>HS-d#|*z1UTLGn$kbd*78~Hc(?)Y1=Rw zsb-lMQ@j%L%mKfW4AO0HB!m42pJG)QaM>(kOv3wjA5T3aX4*x%HB(nL(N2knvF;La z+g>oX8U5`WMog#fzM$Kd4()RfB1=n42;S54+Gh4QRn@GQbymCS`}gU<2`aCk@F!Ww zIqLhrL}J}#u^Q78zJPBZ&@G7QY#;xYxid-zy!_1il7&3}<-~J=C(9eLI@%b)jd`T_2Uuk}#QXD9vr7D~y=*4R z(Cr(q`40{bxXgMefw#1>+90S24qnR|F25E2A0<}Fo*9ZQ^Y0vNQmHfQt`GuV`T&)B z>hHmtXR97XZ~OQAZK*(~kgGsW@X?8@ty$Ly^TLKVBm7+$A7r!w@v=7Wr8YF#;soF~dmvGDQZ8t>h^ zCwyoU6Q((wxV$au{R<9DcYl3ph5|aEZri|V0(^o!^f6eFhsk9Pf$~TP>(922z)jZ) z&c@Z#Xc1s1;9sMHTi&<7yZxWek3wAB-1ii6%3dOh)6>&mYN=$7ZI-%i z>uP9gFZ~I|?da-~GB#!cLp;P(me=%aUmw15*sVV`UicGC$!kGmSGUdo`=E$S z%gI$*c6}T&*E#1te8)HK1Ev+1dxh)!sYXPatvfjvb0bO`J%aqpbD^B#zoTQ+LXgWK z6|#>UyHfU-8dKlNZ|yYHt0w-NLLirA2GTWNzXteTWe*d-Ia~5rOK~p&yPGWRigSnD zWo2c111|_+C1x`_b-QBcH~;?q10X~g6&)Q=QNas5;F#@S=-b(fe>2UQw}n&b)O+#q z+l<2x*ZP@(oBY)`_9jlheO%9^v(Cq9Eqy5>jyG4HcYf{0s})XF1A|D9{qiO{ksu7~ zH62k9Blf$#w???xJn%Q5gZ^D)tm}tp@(7CT(2x-F?LWk!zi+}dI0=V7hJJ!O^b8FR z-CGRfWD*ddK*c0zS?x_4%=*M<5}lMZmG?M(!lvzDrQ30}hYBR|>qK5`@6#O_W8+Mp zw6#=kf_Z`3=AVHKOlo#4Znr9rG<+I< z7mt+X8K-W#7#LasiKJ7vRdlN1gbn1uTGarxsIv;w)6U`2=a`zf%Q8lq#Um0&E-y!U zAo!axmzb*GGm=DTN1+6N`s2GC5a~nbFr(n5HArAspA&g>r9cYiV$|8V3NYIniKDHn zfhnj%^MKX`9OZ>H=-2jA1Sa=68=|g%d*FhN>|uNhgMPUs+wS`TuWKpKHI4=jEbP&# z+sJ`#>{g~07tqR_;dDY6MH#C=VVr1GeR&6QMPNkg>LW9_fiK4Uj!j7`NFfDuh8@r( z8EB62k|ct2JU&RmHvpy5T$N6_KWIfte8CzMC~45;q|ii(*GgKB_Uv0n#TqsTybe~d zd)t2KFa2L%$X=c-H3jgbe0%i2yexoK^N;n%kt&_G;=(><-)h zTRR}*;ft)+yL@ssuv3I-4j3jWS@F@D!_|}v z_IgUF&<@QEsw06Q+dnKdwghK%&607Uz9Lc3W=PSl`1?>LNFw51CKeFPngVbi-nmLh zGUMjdrKOAR)Q!O5*%!H53>w8dM~CQEn(O3d_)$f(3URW?hq4%6%ioJ6_cJKx9^(MM zEZ{z34r}~>InLDQ`swOFZNR}G0c;c|+}**?`{uWBjF7s+LnUOdua5}-Lp@_qk}_5m zV8C53$5iPne6yn+v{Mvg`ffcZPwZcOnJk0fiaKC}8yfIuc4b9Oh@CR&LkPoZRZ%qP zV?U79?Pn4`q&4#Uz7&a7@#Eb?uDc4U?yi_-NV|1Lo)k+zMYCi?UEd*gD5Uy6jLv+o z8O$f_ofN(H@e{-}$7Q_}Y8TE@cMJ?jit^Ch+vr=ZU7TcOPL26Q52CCPQq^y&&;olpZ_haFTOweQk7(mmbCq zpQA1Blb|2xXvyL(l3u326a5_C1N3@MQZ1=s%&q6pcjB%LoUzCjF;&U|J~X~ZJWdMM zcLw+0&r*f3bG0j6anF)T*aC3%9YFJy9@D%c0zuRMk6wBi<4(s}7FzJp22K$)7)ud% zX30>Zs4zf6K;0M$arPHdRb{4t$#*%_2&Y6Fu72j?~m8^NY1#g1I?W?S4It^B(G~c zdpnXDh%ZGNIzb-hX|OHwqEAPo1Ha<#vdyju1dmh$YCDqefj#OJwNy9r{Bs)=`B%X8 zZ9gYK3*1pF6|3!$MCtkVbxYwN2V~+fbfhI1k^k2AD_9>c+PrBS6s_|(+EkKm2m?Kb z-@@hS=wy8pvoX3&$)%={ayOMk|5IpRwvQJ3F&SJM-8w@(= zwQVNo;-nlr+;YWGEpQ=QDkLdqC*&1lfJpD8T7wStH6W(*rQYj35UYE?RA=5giGq<9 z&(+WN8?k^;OKCas-Ayz;?KJZc&PRX!I;3x*_!I922Wb3*+1GPjH6!^*=Bhx)KjbBB zKj6H)f?{|d}z6A|62swvHmXs;%>}7Lm)PEd#Hk2PHA6{ao z=;yf14W-^XlRrUCP}dEjp7Nen1(({hCg5VbM_faLDnYNVt3Jn+RWcOfZ!5l8W*wFp zk}G~qCBQxxFl5zUwNVn!(+1AQ7IpVECxkro@4E+B?VXoo{p{laP#b`KC)(c#S~pSn zSwJQJFK?>O`zaWli+fdcJIo9wx{PO39D6 zr5PWDr61k@>^S88A*ACk7j*PYZM6`n?ro=^1A(sHZJ6fN>1ud(@ZrimpELDw2b;5< zTkfeQI{oYr>Hy8lDP&|3+EWKr5(_3K@|GoK&dHwd{}yFlc8RG z*|n<9Qcy5=((AC;Q54z5Q4E{^jb}860Rvh}R6p9+C84Z)<0|H|rM9(L{otV*|9dDK zDx^w1M!iYkv$0Y0t=3|t%szgf*};=kww89xr!dG*q?7{c#|GUhX}|A$bxQ?2@R!j6 zDibHumu(p3$B`sLSyUCgC`wss4D9fcss9rDB4KFb0?;G&#A(b7FFvI;O`~Q&O&bjW zaGUefp+Y@$Gj#4#lYPl`YS`s6E@#KWKdZa2vL=8%KQkqEW)S$c!YjyE+AzA0d?eV zydpp8GDENWK5*2v3FDT9{i5^Y@RsK=VnV3|eE!spo|Ya5qWnsH;wzmGfl3!7TaJOQUoy*rke)~jSi~OUrYPlZCW&d zPQ{qr<4~MGzn^`E;NnvgIQYjeOmW zP6Fdo_YNhR43Frp7Z68vpQKM(w5^mS5*Ri|A0)tzgg!^ZRIns+GL%y12dU82$hdgm z(#vunh@t>n->{*o`Y6NjpPk%C!4N_MCt){L^9f5n_hKujG79A9{-P}Tb4{W`eiR$u z%o}X>P~&TIF7avKk6Jm>gi{r9eW|Dg+`}1vR50OS;^Um+Zq;2kmYO*0!(HUN?x!JW zM-kYvmX+9Sjn@8pfI|A>$Y{!f@3jSycvX7MEmNLS1Zw=xoU9SXz?giaj|)E(i|`Ms zU`4akOfjGU^L-#)UnE+?{?f*&1&H^SAc8&p=Vab5SIrceDgy_zs0(%=#9n8jlB5`y zqhREY2<)sfy_Gt)LbEtq*m*^(iQPlaCB0mH_t0O-b&hwQau%0Ij|NWAVi>OF6 zIJCFL@dWhCw-g!!x6N^Mf6sO84n=F~M-*iUKNqUBuS>0S58^PA)3BO+!3r%*Je^z0 z)8fKkgGm1G15P}g8YM4z@Qj-XSSiq;vqSA%qXO$m@>LHds@VzW`_SG{LBO`y<2XuU ztQ;wc;0&c%GiN?C$XAS+pO#0I&Lj(z~Kj1M(A1On_jvjiS6bIS*T ziNb>0eW|S7k?$NHll(i|qfL(0&mR7m>HnqrB#M$4{4#|#=q(>cq&}x~?zeKc6e`<< zegG2(z>MfCZy8a6KHUJ|3~h7RV+c5*j(2%fG~T2YdY^v61@lLdKxTMlj%V@jiSC0m zR2TJ!4ylEhjC#nkM{=Zrqz*V(#OJ_zDJK0Bh2U?MIFP#B36v#ntBHWe4K}sp!S;+y51O#jU;*^T=bi?h+drfF~Jkh@@r=SfjsBrJIFZ8)% z?(R5pM(D&*#|L;ocRx$5W6Bti6j`+8aIBK^_ z;kcn1R(&y!rv-ahb^fYV9aUEtQOBWk(sjZ0@h)*^^P>6fg7r?v*fx1ZY0IK&P#vAt zyFiZ<_ANQ{rLUQRg+is&Bi$8BYb8YXF+3_<9BxK?!Qoz-Y_!_7{lvMZk+<42<$dq& z#-OY?SWW4)#h-6;aPCCrocbZfN<5?)0USLeISw0MAFltf(Zrs)^Mj;i(Hvpf0Ke55 zDdjG!M#~AXVA^@JZh-DN8|6cVE>8U2kMlU@tm9TZOsg2fmKC}lImTr$KkgO#D0Mu) z&G`evONky{rTbH3gX0%U3SG8|AUzd~rKp@5t{VS*O9*$GJcV~Y-tQY(blZw_1eX2PaAc9H8?4HFnS?6)M%~tnU+Q*qU!`#hIWH(TyTaVXr(wT?F0Fd0RgJe@RgG3~pm{-s*J&F= zgVT>L>u7f!gTcs^aD<`Hu{b5@X!=bSAftG%ysi2|m%SocXnpO-3)z6T@i%KU9xF`n zyGsRy)Hq=$bKN=_8R&MVO$N~KtUF9423Dx?1N1jUquyH;Y(*^*P-)^r9c-49+bro! z$SI=ElYJiZH4U)R93Wpte<*9($nx^a`2#KU#InN|mx1&q6Guz5OSw$2k%c9VgBpIm z;Kzf7cF#JA_D|j`L*cc8A6Qr@&Exh({Pe}mWd@55+oB{gkW!@6l*tx*8IahDtHtfLg~*w{EZp z*yRV(zUq}9Dzuz$?95OHStgQfiv;8z^R3V zx6_7@ZrkP0{_4sZVFM10{D9^7iVt6P)b7#Xzf(_b$X;S;t_)?Zt?h&fW$Y3R5G-Mi z|9)z(^oXQo6C>&PO49Ljfa!U#yvk>ehu$%XeZQJs6U(Tv7b=D;4|WYlbbf$p9_xb@ zBD;bHDS2%!CHE@j{Tj*?n-%K6|7s)s@glxXPDEqop&hipb$3_yN+-*J-})VFs|*>oHkYDer}wRXu(~=Zt+EG0 zsH5-h7$G))YDa2Cj#WhtqGNjprk^f#5k|tot60!?Vzlb_$hT`PcHlYbZ7z@kvj~F2 zgwx?het!NQ`iS8%mR$qC_{!RLWRNI{&HI^=8-blb5s4O^FrJU^?Gmd#Rs_p9wSjaCVHPMg1UVU0+ zN5Y0P@(zubg#6{P$Guj(F;7euCDD|Ianh25l#^*05S3PGZjLK+-ZEY7nZ~&==>c0z zRp?^l97`I{I1;PwUa|?|a|pu0rrUh*Bp^%*5+g)uWHn{?1^VYSON!T6mjVPF*^$tKxj6Q11N90XLDokUBb#kBBDFh zCm*!ZnKPc)d3t)LNVyMR;*nlZ+e0lgV~yYQZ03ND5+a8Hu`3e0xlLc#O76eU^yc>V z3dBXxjEr0Nwik)&LR6BWiG;fQ_ol$h7Jhtp_hy#g&lXsWHLiS+t~MbF;M#~9qE?6R%cw|ql|PJBj|Xx#EMxBv=t+1d0}9<#jPGOWtFiO zx0>48O@@ZtwlCty?kK`3=6TImHmEUBO92)O@0Iw0xy*|b*eUfUcP{E^Pmae9iU_ee zo49ovTEhnSg1-p4DIFwun)8GFbEhcOAa>U0vUt54?dcQ}dV)e*k59MN##L}6gyE6C34ao^b<(2&R;wQI42rXx$ijDVg_oZ!i zkPTP82+Lfd)P}EYjn*=SWzT43wL;KcqcbfzN?MlxY&$Wv^Sq;-L>$03x@T^%pW~tx zmh8JJdr23ha&LGkFBwm^_{#~?v9P!ileIcxw-PL7$A+p^Q@k{843@lDg(Le2s@xg| zgUbTSd)D85dQcb0-A96MD;{wJp{xZ^E^5gW9&2KD+F@meU98!jRO~k2nAG1K^ySG> zRco?Kn6K)4G=5*5IyCZ+6=WQfJUL@5nzhT)nAU;C?rx$Kqvg;XQ1scsinnenVP=!# zWu-^zcT5XDX3|Pv1~#NnVfl+%2~jj~fHZItj-ws zH?NQV*XOf51FQ1a_AXE6wsfFSVldjt5wc<0NHtmZM$dXthNg zW6Q4lPD{r#GC6O8p<&gW`!vc;@7{P2nux&@>F4d+4 zL&8?FlrSZW4X?aq`k=Qypg85$Ug<_j(&zpd)?I6q#xDQW%xY!EUPe1A5Oxm|b4q`& z79=pTYx~H4_9id;y~eV=h8hiJXQDu`FqGZLM+Kxa+JJw>t>-=w$qOM0jKs!hKO@*I z19Z;-$3vBM+jvsOf50BuwU*4b)Cldu<}<TCFO#LbNhO?2 zISPI}Og#SBWCVRFWe6sT1+PHxE}<_jgxv9*&nmA$Q5gCmJD4xNc0XTufI*&=M?CW8BTHKEo;*k0 z;H!1 zcb$`Dtl7I79}jYn@56P3@&C81_=+=31MR1QEz;}8LY_d3ZTBi{0L?@1)-ph7i-2nJ zr{)Vnlsa=h*At6vn=M6x1rRwfmk&L&hwwT>f!D^_d;z4crzl==+lo0yb9tesJn2FN z8t01)#G)s^#ECnke~V2xH2|-QNx(N(3_Rz}olkmEqlONJtIUZrCC<%3aCeyBzOX%s z?73L-;BQIG8SiEGxwkB07YG-W$bBN__n1_$5q8%T?Bqi`%h}nl0Oyn1Q?C1p40j7! z4-pR)j}6sexI*l1kqL}c1d!lx+xgUvzYkZ$fEJ>gt`VU7WrjL5rT~4`(Z6$(N7{bw zsg8g8-Loc!v*@+}FcnK;u4)9|W0fO4V8A;?kY1q8{o5x4+vn6-*_kOzfWisOnR!r| zJZOLl$(iYmnZyud3_Rz@&GsCCYAFK?<`=|o3i%FyEo^hF28n)ZYbSiy!gwUh(dG%hZ1Z78rAgR$f9uFls|FF$s&Xv61;JCDLwqsmQ=|^ z|HfYf`nP&s(({ZfS?s94CjVb?Re+)bo+fFUZ%G`Ae8>H<6Q$Er6CQ(4=E#8UjnV-9 zA<5nU8Oip)$XJHNzMR*KvkR7Ta&o>}K^dkS3MP)kBD`tImFdrZNEDe2k*`dk2U{AU zOW;f}{f``<0Q@P(eW1-&$VQqMl$ZRixBmuwdi&j`F=Kxa>}p|Y>70f>K0dzQ4|x)x z#*_y2BW(HnhkwTdWr-Y`2Aj}v`}#UV9E_MAxBrC=zr8;Zba^oMp7ZI}{u>lh+o9)| zmdnW*PU&ynoYR?_A9Z{xYDNg8<*Fpl)#f~{>%T~071xv^U1y{_IBsy=I*HX zpOSs{{JD%vn@Z1A*~vBc5@>9Ph=(7tp|+3K0YiPaq2cKz7Cu}>MMbNU}_^jmC&4#5C+kW+QvGgY;gh#1ctF0OG!v#b?44 zAp!_xTn0?sYF6s6f&yRwn;M|7)C?=Jb^?Ua{LN2YwA(UO8CtT>2bIwp-*M(&wWZ4Hp_N-t?J zf<^QY>izgNH2@F*e5xidoR#a5Pf5G?XNSd0I`>RrMu%InQL1q;(3i&T$`2$xnuDc# zg2)N#Zz-^j&1yv%yh1vi!8nr}93?Or(KSK{>?OULVteNxJQwy7q0%lnMN66^k()4W zGuug2ObBM2p0z7jXPRJUn+ZF{h+^3+Z?!OxPOm~YpXlF-o6(!q7{L>L2alOeFAzoA z%gYO7eKV`#$l?SI&8#7@lQ_iWPj%1eG*X-)m~jCSd+2)dD)HUipGBgN&UQZ#zet;> zwG7EgY9<)hWsG58keg$ix)+pWe+_6W`LlHDbaN>g-eEjp2;+wLPR}U93^QdE97fM( z-_445SIrthuB3f_{%bRBR z6I{UHHL))#mf1Zy`!JuzjNKHIg6YygqU(prk%e>rF@jy55v<9FCuQ#jkYSU3Ae=tO zGI}p_-uY>~P`k@Ge4WZJ-}$_@B;ZAg=)97MNd%Y(j(8_&bfwX(i79R-2!)^p*JYAI$$>VL@0H(buj?_%BoU6VSi45Vk^U>ukG=Pl@aAv1_$?o3@*G z!*OvytR>qooj-qSf3B?+Fsk(i*96_S&vDozY)fxM9S=_o`fI8DVi=6&039z5@@bHv7Du{}S z-EQcUk&&r+{@h_yW>4yls*MAWgFxr32@-5{3vlT(wFfn=tgK=uQg7Y*2xeU4!lPEH zS@Vs9>SwUUWCyf8?X#n38vmUtr!MFpik-+tcL+MNCPoyTuoS`1YHDeH&OUwmbiN;P zFGZp{|DmiKGYK~La-RlP?oVF#T& z0M|35W?xY;N0OqA5RtY674X*;jnW zAHEOHzJ?zs1T17H9*jObmkhM(yyJZz^0v%Z_J4vl#JT5HR#$Hv;s37K2T9*ICH=FPvuchL8#Q1LZ+>f;uFz){#6DmFYjjuk7>s%|q!O4z@xi z&Z8f{ksr&d=`YZVNwo+PM&$7siTH0W$Vi%F@}K$Qn_IX3IK1^Tm}cfilvvx?v=*<8 z%5|Wxdx7}nfk!9t>v4cum-ySbd@+vz=HRz)4Z*){1s3 zFrq>deMhhSaSy8!dHVEesQ<6$7G2Hu4h~hXUhNt}pN?0|Q>}lOeMLO<(__!!N)V|( zJfzxoGbu@fNw~kIXb-neN&AP+uk>x)zx*7s2M3Q1p;+SMM8<*_lo#j!g@69rZ6nWM zkiTOCE(W`KoU9%DdRbYpF=ihFN}QBjcnx4%ZHto*94J=!-gJ+#p`oGoO)7`q@U?ab zoHFbQr9+1fQ2}t%sE&zD)hxfd6+WeTFJSmqoLzt^D#yXE9^1bZ+Ag}hTr;lD76(!8 zp{ke|3vw6ECKr2_%G+}{W?MwTCG6-cXxBA27X*$y2=1l3RGq`-0jZL)4YqK2#s@1n z?m0CpthFc+P@nv{$hq(%3+h_%kWEN(2*UEDjLsZ^&#sf8x`Pw^4`^g|U}m;Tz} z>6qy-@y|u|B9k6G;7;`Wy&6#W;Dn9t&&V$J#G*vk)NB^N_)M(y3LFl(UklEf@bL16 zz-3!5Dq7LHb%@{Kom{(j!lEThg5g*U7TXO!YM*4c@EKyxcWI9tANTlmvZURl@wG#v zKuLRs9RGszv9U5f<3|a1zt&l{C*QJ5U(QZG=l|(?g#sGyed>g(D&zV#}5x!oAaHtP<56vFg)34Xqvs> zZQRe{x_Bsb0j9Rf>GP_$8NjxO*yov;<=RGg{OH=^+K{YcZE2b00k(azWU51zi#|mz z+gORxBUju$@#*K6H&u`XCARz0)6zIOgw)mws`(f*31=DK(^T;p$5jBIbar#=3`zO_ z3ghMHU*r7Y!4?&ifq9_HD$Lc@)!Ngu=-R%1waj8$Na#~t&F{?L$$Ym_X1sc}a3fd96qU_5yJlMUIvbRTu(%Q@{K5g&a zs;mQ#LJQjc9(h0nx=TIpbZiHs`>QU8w#S~y;wGkUL|$7vI%c5Ip=zkJI;=W!ncF~e zYepVD$WxGjG|53jB?5cw4n3Uj!O^#ff5) zl*JRTZfGs@Ys2Pe2I%96Pt(JQIUGdx9XZj(>U0EH5v= zu1JiF1lm`KyeIc|;Lw{Pkz700m~|k`A*+Z*E111+a9CGIXSuL&8P`_FJ&ulRADCor z%^LZVnn)5}z3rvy3Qixo&O795bL&807oAuDB~IYCj=z6?35kq+3d4lnJNosjJ&B0T z=UGekIq|`veMN~CG_(3lY@ z9=dtQj;HADnC_Um-ybFJc{(Y1i(=e~PqqG{{+WsXM;yrK3i3y?PItxbw3Op_#Z4|r z`T0E11{M1(e9s^d))_cQuncuEVUrVM1>g8_Qv}Y)MwpgFzbdx-Eb{-;DRiwx3Zo=# zk=tL8++RillIGrh@>CflF{H;%%d!AkO#D5=74S&tAiG)D;VS$jRQ zwm~gU&G{dZNz=1%46Ft_ zo}^`DRFYd`a6s9EIfR~~giHpNO1NaCHmV4}1Z;x^L!2%ncWrcEa}mMiA~-2i69d(# zVQTK>C--m^x^}+0yDytPJ1Ruw!e;Ha-A*nXp$|0%NeaY0BE79W< z>#qS^Ai;_2TiT!d51^cC{DVXGIYf<2^BuV^>fee>NRafO#>E_5G-6}y52Je^$6tkD zT&=%93X-apYn_!VvZ(0s{FYfziIaHm{(Xa~Da^a%8t!>AQc^p)b#})qx?FEc(c`$6 zm9?X!9;U@=*y4|>@-lIdUd{-OB4-OG6N z8vaOK+ZZatk`2`aW3B50BZoJUI|yNQ=QeMpm#&$@MOj!)*tI z%*hJUDBD~yZ?2+gWTiB=;d0Ryu9M=ClNcZu)fy;ALEA;IerG|7p4WFrg7B>Pa+-W z54IWci+q~N33hJX0LzCeixJh62B;?O-B+KxGAok5sD{!dyc$Bne@`h;yZWd zk=^urY#=HpM;e{{2*vor!M&@73ss4*#BRLr2@4CW$Si1EGde?nnaR1oi_fc`>eA3b#x2&2Ob2_Q<@O_^8OieA!K4gTty`e;Rg%B!Lr0 zO8N}fEXF^A%7KeO=9Qv~&D%e9nobB(aA`az&tH1ELV?Yh*1_RF&Zsu|a}TN8Yby3u zgp@i3gQqt8fGM4Y6kb~s!!bSm364V8`&_$686m+TWc^oQVV@(h|!Eerss z6yylDuZflCkBE12=rhra6xd{BWR%jgSDYK7edi;(PaXj>0Vtt#tZQ>hws(J#-Z~aO z6%T1#bJObB!6P6LURkN_18`EEnm^DIl*87gTPjXPH{yb=r6|FyCV&=V&cVT0vd%2J zR2>E;+{EuKMY&Zyg`Y2(VmF20Cag{z&6wO$?XHc`QM(txWF@nD-LspIc$8@Aa_Qyx z_;7e1DCk~-Sa^95hg!(v$B$2{k9F0?lG^_k_jX?;ub<-N1|G6x4Zbi25r#ekORNK% zaJ*W5DzIK{BBQ|<_v4TlIxh>lbKS=%6zy>s2oS-=+EBWf4=T3 zwZBle4JGw>-BHYRCxk>b%reMeb`pU2;S1H#U7r|*c!nqh@_R`yksr=5WzzN&wGOq@bi>J5&*Y?D|Q&^667+DB3-~W$qmi-A`|BiRE0p_?@G~)qntssMe~gDqb0DCO`OY zpMc7%ES(z3jx6R~0BQ!oz`($|>+RPNkwCfh5ac~E28b;Qpmch2UCJzcnZS8*7t%JWm@V`f&z4!CH z|NVYB=gawUye_W+W@gQrweEG_zg*!eO0pQJq^J-G1mmfklo|vA>j#0rEF&X=zi^L# zUk4BTPSRRV&+W{eT#X#fAkU1P>}~9vY^;o_UCbQcSlPYiWaVb%WTv)saQOG!xV`YT0tQGN>8Q4)!owe7hK(*o82QG2|k_3 z^ZfeMUrBS25gCC1Uy3B+rzZ|PJ_1rTvo3r#b!|Wd@}n@?Wy6a$znltYf`~9!7SC9T z$NBjzwa+ZRKG-^!T|$(IC;YmeH*nij{>Dmaq|p=itzE_z|CSuQIJTG+_~#E5u^OC@p^BVPRo==(-T_XcI*V>tq9y=V=HX*`_8l2#VZb2kGP^^R;TI zv9IervX(>Ox^xp)hR@4$tWr53Vm{??nj>fuud2!9QO&{*)T74VRM7J6O<{D~ZSpM- zo(I@l#*%^eBcr@98Ef2i%d^YYt~sYj427}V?3%BD-PwAlCBli!fSGKM;)y5i%G~Ft z+7uS`I9S<_5T!eHxx$t*XXB%>ZGU*m4?Fs{{#{r4oaPR8Kc3aKa2meaeuC$Tc_x>3 z7TEK3$aYv!V)}{X0gxd|%7{`Y*$1Wa-Gzc9UG3S3(5KvY${IQXs-oi^RQ-b{la4MA zEynX*X>etEI)m4g%m)|FQoN$lyfJnOuZ`44AEnJ41~e_{Ndy``8)ixk|Ee8Zn;`Ec zAgoo3?pocq)}wgFi?_6P#{1k>BOSr!i&T9yLKcGeuUQLZ42)p4k5V=^%y$=Su>!N@ z&58k^+TB(sNwmXu)B~1PF!a;jMf+i6_}tAQrC6b+DLHq}oHOadOVWNLPH-YH%U9c* z+q0S(*-^JRCCdmkw^hKI(F*K5o?cna=Cp>Hxjz%KG1!W>wf2s45D(v|35fT67SqPI z@MK=Z`fc?fg*74k>S$R;j#*;@Hzk94aeK-IWkq|+D+L22yjdPXKf-FGZp5Dcev_-y z9X_v%CzS5%IHx}*(O#6lWOttzNQd`GhmS#B^Q}f@5LTkJmkuuY*|*jp%9a4**yNXh zWan&F>s16lpevO9B}u#j$>n}I9JLO*$s-9_B;@3f z6Ipc4f7Yk#omo=q2Gm8u+SAR$2v8i&%dn;KuSca$Q()t&V%~?4dcDUF%Yh|f77Dq6W9#BlSA0`e>4i~~$pI|@@wG4YIJm2Uwr zk=!n=hO0-fK9k1caZWBNPJtg1xXo#c6~L~E<&INxa}x-A{^I(uRAZsMH{WnrzFRc; zsz{wNcSpZ~`DrLMBye6j*Dhh|!YH4O(vy`+jbxb`Y4P?aE(aUqaRni}8+|K5%-u!= zW^4)d`-Qo_AD8}*Sl_MOVh~lD?rPsG?eZs|%RFLD*Zkmxey}l;O^=JxL6teFz2;zC zFjv+0uN^AJNH@F!qs#(ES4>}@(n)~g=Jqyv^ZNRlPEhc=eD}e_hep5lX6qJZRq!xG zzB|EeLVBPe6d{cD4PaT=_wc&U;u$xt>c; zq6Ws%WjUwi*IIH2_F^@8|9Ok?;46bx?)x8sH#_Hf&5r|>(A>D6+;Q)(lt}xqE`87_ zJ?3M7emiU$4SvuzDY!QJ!6y;}Q|oO5Q%fB#WqhiI9rWs?STU6cBRDuXO`Wi)dq=k{QoA7rI19#?Lm^?F#n@E~ zjH2HqXusg#7xytdmpGamvbI0}YP`gQMLxF{!f#M0ylkLf^Nh)h-Q59kgyK!t4nEN- zr3ai(C&9(xw21k<#j|%K0`S3$r02xgIpSJ_9QXvE<5G7X$QZjAFgxyB({snK8CG2AZ^X zIKFr`sp8c-D&-ITQ!Gzzy`O|^jqW>7OmCdsuC3yIG{Z?GCFAM7CSgOQ>R7vx$k<3J zW5Ij~sWb^B5+PlZd#Cuf56#6Kt^1nZU?#S7o$GL08UdAn`I14$2-sBFfCb!Jx6+}njKGDuS>I-oC;Hcs8eHFZalE2Zov7! zfCVsPs-^f5F_^9#Z@umstbpaM7_2&364APT3W1MDn$?}S9+=Z_nAe_gXr|hOE%;E0 zFc#zFO;1Hq9)riOTS+#qhv2X^dLSB=MFdG)2kOr4*gW4Nj_#7oA$sZpqKazVOwtBvG)QM#Q zrZMA@L7cJOVO$cwjo9bj!+B0qbo+U@nMzoMEZym)EE657v)d=etRm?+0n^QO9ZBJx z7t^12w|RwLe0w?d%cr`2x-6oOqVPJqzl_tL%9LYGmRTDGx}aN(QVQLkP9USA21wC% zVu{`u9;4qHjCTKWUYx0)LLwmoD2s`>X?lJ8`fIenQWAh9qQ1vnOcNEf&Cl5bBqoVD z*wOB2UfJ9>!$6i5igQ35IRPf&*%FM0b>6n3(b*(oc(TX(DG|5 z`Lhp6bUrI)X`-tECY}pFgx(M+P!?XLJvK`coMJ}q$v_K$Ea~|jXSP`V@863dr@04qOhR5>eyrKqnpv+|{oSqL=d&;yjfiiiYh91I zXE9jjlD=KuISHVg3ZSf`uDynr&8K9*(saqJ-ByW$6BLe5dI%Qe{yV4MI5J3xko3k26mSA02Vo#a!wTcolI)mw2dp~u zZJDnM+o%eQ(Zo|Z;%(Q6#n5&pONczqemtDJTlnN0#}7ep?_+ZpFNbTr`Qam!UQeg9yWe4BBb?LA2Ct!byU)PL%@)@pA@6vccdN5OO=gK z>zWzdN9ku;+-$^b&j#V)Q8b6qLiE|h@0{J!EfAS~|LooqzCU{@bbt=I*kKY)6J8H+eWB-PAc>*0=h|ECO4T4%(kC_Qm9V*u6Qf#597WJ35w0W?f+O`%8s|7J8kiE{L}rjR?z~K zucQ1lt!9s9=wJ3m?>gRGQBkSZ9N(%7o}KaS(D+kg`Q*euhR!cwUmyN~E6(-8lg~(; zbkqesr@CK6-n=|%#ehw5XgQr;mV*lgNoa8#1wSFcI z>PS*%j;tVW8`SGB^hDO+!HSO&l zX!O{&cd8&mJs}@E4u`EVB{<=mEL2n%1i(5-P^s|S?t{t2^TeL>=lkEz*VHHf&YiVXcEQGb(>9J&G198Bckq2VFfPo|?Lr-TiE9p_ zi?>fZjy;j^julSr``Z7p&0I}j7aQMW*O3^8hBT;-SrVYMrj(c=_~pZNN@6ZnGX9hp zInhEXHz$&-_KZLZvY!3By882kx=*h)O85B|uqGMly`uI82b9qJ^mg{M8m`i(sC}U(lBzGk zYG4S6(%W!-wuNSFr%maHQx6bw8^sXYLr8^0SIA4Hec83}}hgvKy#m#NuD zNh+~c|J#Z^9iHw->8DxL@%kpLt4pG>C3QH{9cM~$|f`@bf6jkYQ_9|WtFm#r$sWS*eyO22%FvWi@)8(zKdJo8Y zDa_xOVfuqX*dD_6YH$hqCvcnfkqyIM$;uz@05{+8&xyINUNr!`w3wz*uo*zgbE`EZ6 zB(dsAxVZ3m{Mxh9lFe4hvYsxVX|SV$K-nYsCo>3-fIuT4Fjt8_N%03qCL`_ieR|^! zQTeDYb3*W&qttB_Dy2t&QNv|Dh0c+JE|89SvN_z1#?}gfd>$D=gj`%-la*~99>$)Y zI(2iz#Kau^{8?PDIC-V+3PdDc+@~9+53dmWBPg;zoRLhOD>$gE$@avkq-RyQlPJ{m zU7PKe+3&>@cq0;Ptyx?2p9a8ttaL6hXzYdr1rZNta$6IElU&Tw4xqf(^?rj^iwlcR zy-hfsYz(!C2&K5VI5Ce+vJw9P#i5=!cJXk_bKAu1>wf5|4m)6^qk}s*I0)%?wZ4V( z8%SVS+izIv%MuTSK-k&Y<&>3W)zol-kQU+x6yx`0UjF|6A!m+`j_73pFfcGqvg7Irw})29^w(yg68hu2SKsgbO5*Y`k;lF`xQR;Zs2+e;$3uuH9LxCLRQI#J8OJ%jXA#h9WgT%?J*ZVs%N=Ej`-rP7r$)&fx#eV2Y1Aqe3sbRsz9g zO%|V!kTklaL;=h?Cci|Gf_I^UVB_MAu>7j=U)muG?zZCgzbMmhPNWbElXn$r z@oz-``;X#r5_VnbV(@<>O5@_N6pF-fmR(TUv4`Owq@uGHkN>o_I2Q08OIa^iF5+V) zLII)jfLyiMAua?$C7U3${&eX<{DmrFIGGJvjskoU7@Y^`b^-16?#Z^kX1!`&_eON& z-!}uhgpjzxSvv<&l5iFah#^lNtsgXyk#gK}WKg zq$v);aaVWZg=wetq>B)Zk4z#zYcoRcSSv) znw$R!hD+y0 zW%Cl@C&xN#HcD=wr@;^PIqPav9_^e|h_qX>4nSbzmHo;lOT*p0Le9rX=B+=}8+!aK z&POTy^HhHFg{pvjgeefE3>FTLM5OCjr4MO%<6Pvw=g{*i#8lwIIqW-be3H*3w#P#$ zVKs3YeUz7p$Dmu%h#0QHwqzJPJ+bWZ!{n=Aly!HJfH{e;5cm<(N9ZPx1I*4Is=D1}LV z?{9fs4!)z5X+~xUd*Hn|Qa1ld|YB9S7(;mGyaZj_7um=W`4Gp5@&dCSgMMeU8djh{zcS z^q1eAA$~cq$GZ2WXLY&Fs?H3*bypzK#Q~5*BCKEcis0tvCho(B2w5ucZ+q8RHhJPp zL?@Lyk%(IDGgQy!6IX}%UjP!mJbmw$>U}@eF`yCqS&M+j`zv-QPX3XNiodAuX>V|M zIr4>pgL8}Uy%i)sa-`_Gfw~_DGsdIQ<408D*-@F8c)9z*_|0wMoxQ`v%HaC*v0cbV zbS+^~cq5Z*e$#Gnd)kQ<`c^Es@1aUa{?CF5y+ zqJoDsx_u>y&iyjJl`qut7y@H>nsajDr3NqKybbxNC}!!z-Yk?Fs=!d(fV;oA92%NA z7?@k_QrP`;=rpitm-BLa@t%H>Y65;|D-DRF_;h&Xt9{P=KDV*4?k+lkh~lKJ{0w-9 z=$Nv>a~=Y?<@)M|-|$rJ9i~@SM1qq~2M81osEi1tm$!QqgLv4wdo1XrhKeqs~=JwXz3p96iv(*?Ez;_TOG8Tb^V z(-cpfbae#tYbfO{gd@;Q`Re=e0+-k+*FRZ*674dM7;g+d4kt7M9CYA}?=F;KHKTlw zbfleCwolsv-MwE3u9rZ`1G%r8FnjtbHO?{8Rs5&dZnq9=c**pvtpQPy@?Q5JGc^dX z9c9_sEa?qTTNRyrim?;!3H*0SRx{l+GW7*L>;u4(ip&gw%3Ww=f-cPUHnRc10mw4y z_HEltgiUW_Vk#Xjv0 zc6+m8Rr3-K8zSP#3*Sv|(4E|a?slcQEK^V3?zJ0vNQjVZ5qkE{0*-~AtqImnltn($ zw9!ciDB4}XfONBH61jCWV>%8^WhEWHe?C>`5U$rDgf_GJ z6kdE8!+_ZkU^FQB^|?8g&9%OFcXR6)__%4X@JGVlrgtaA#AW(XU)R+zdKKkuEL&v$ z2WT&(-+r0uKwVPS9C&F#o)YkPNY=HmAe7`#d{yrDdW$KpMaWITOgbDfHB-wBDsN|O zn$Qd;vPc>k(KI{nhIDj@b=LwEUX}aDQYWemuFMAHk2zScLmlrg;kh9o_d_LGG63J7 zM=BA1^DXe}tFAxPs}hp;e?5y3_m5})IL5GwE$`4OURq!CAwq%t|GF(msU18FdAfsL zpoBmqU>F7*SWu#|*triVhNxWWQDodXAp z|HW=@|ug2`WF_`i6h$lZkl^|n2C#$bI)1}(G>m(zJ5zH;s3OQ7!2rda7u}LY> zny{We&lfs%-$A^QBlZkw{O=#`0 zpB(=ACqR`Fzyb}?EYs;+SfBvZDl9Uxd#X&AMM%ylHO< zLhK}s+^qbi2&T@jN{q)lr-qSxhY3%k9WJnV6?M{LMty{%>-8d!MzZAA%?4Ql~> z`WN^ycRLY58cE}P#V_9pX|QIh8mpuxhFF=No9Ar8lpfZZ6f`qHu;KCOtL`Bjf>8CM z(nzxP{_a}t=~Hoec~nZ@8wcQNVF3}LRx9MLV$0}Sz|KZ@H!uoK9y*D9w{|@B-U;dD z_!g@8xaE75xj6OR_a9cjygv>;xHutFM$gO$Pgd$hM%6Ic?qP-xDmq@O@QzO@=tz+) zCr)_hU&NDdNu1qRl!yK<>2u{Z5?i+m(B_FoW1C{k_vME&nj-2xxm>$?EH57(d{%r; z&H2oK@&ry*pH{j?Ymg$LWqil)=4F!+W9ws|{z&lBA@%d(19;;aoW}?VK77G4k_}!wN>fDS<3r_pXw3VruHEGy_^n7gK$2T4)dSZ0P9WGSQ%^>W z&Uu(AC7!#@Wk|}@ef1u{P7Sj z(a&fS^7&hSj0@Eh?~0&^$(iyCk*QN(BH5)0?*(M3a08!nWm+X#<7p?c;63WQ5f2;v zmCze<{@0tIr3Y=xD)bV}&9qJ@5cn8RA2G!nkA%@TI_f{x$>0ptJL=R`YWz^(_X3VZ zpExNx)>kNYn#wLD$IC&)r|cLy6#8p^YfmiO7sqR03`@GZ^S{*7t8zavueBUsEgvj~ z%GM1!sOpRw>898R#4|MUVyB&yQ^^Bw=~>#MVi|b)BCFWrzQ9ZMyvvz?bJ=MB9R<_T z@QTRl!qnIV-DZ{)7(Vr}MPqlz5>+N}m2J}pKco?4gmDmvTq!6Io`*fneA&5vhm%9# z-;n9&c!>@SWRjBe^gcYe*Txg5qN*GkRrS-Dh6T-eL=WPQB*vVufc0NwHBrFlxDG58 z)Jlgn8FVrp^nrl^R~FcWw{M?#{@N3mZ}?GwF2J%p*>RCn5?Voc*|geX4}u%o37^tQ;vBWtXw&JwH`*EDe5Cp`yDYA&TQ>5d?HSYrsU4cSngc>} zql-2S1gbVan}4@D`{GTN+p93kl-Gr<2`fKI9tKGE?jqLrO~<5!F~^!fSU!)BgT(>I za;Z)D?c29d%f##F0E4TZ3qV+pn3=g0Nk~ZO_jPq?dM`~zEYB_D$8TI>30L09ndE`J zOa>>}?b=#Zow_^Ql-J3lx?|g&$W>U#s}@`k%U!A#JqyRJ4<`$!l@2V0hq)bPg(YYw z8{PF<4?F=jNd9<`;)GbK@@XT%2FtfEOb#19_x0lZV|>&{_z?mNO9cdN$*p{FfCH^K zMWA_LeS&FUM!h+=&J6yDnYG73*5a~3;2gHf!dyrC`m^(G&+sO)RY z+h#8r6b2&Un*eV66OPd{NZEd!=YNMkAwl6_^40g`#RE`EOsRl8qfH0hXICIi&w1w3 z&TgCoX6k&7{rLj0eu_~53UuE}cO5Hv!3K1}tfC@h;KuhKEVNYF&htc?$t|uT@Ul^a zM73iA-0^jkk=?}dk>ilcw{KgDs%3*moqdrZ{lHs?!=q&U&Y1ny`4olY1EYuPWB(~YC4V6ld#(igvHOYX``<~+S>oUC zA%ZH_AcXlx&B8I9V~L!Fv$_83B`z?}pMm%q$Y;_I4h{8;WlOHCt-)DXSkzigAb$M# zF=PutTued&HXtBid3`R#>)L>ngKB!dkl}N;pJxzsyXkxq913wzi9|qZ_d2ap~#NK+7QA1lP0E{v5&e zuYEX3UO@p^@VYEhva+b9WM#<`v;Pu359IUJNS?i_yDA^d&(AlStu_mbit72bKhN&C zE*C|{i)r9{%MGCx6eNQzA0FD(X0*4ruQcuL*?^==*WlnTRT9_|>#~PTj*1v9GzlSg z5sC%JF(ufTK7&)N?QK>89^9Lb=>X0iA+-yquIl3CDm&(zt!frP<9qO+$MUXJyCyC! zPQ}*!lYT7tU^;eo9LQL`t!|o-J1#)g-3n}6YupkH2D@*l$q5O8Bk6*o7fTYZy}#oV%}iixxjUJt9bl*|@6Fc8oPZaEf^ES; zBqSwqu(AEp1zkxqrY*VTl$3bzU8}09;s!Tl22IV(R(itmm?6V{EYQF)&!)K2dgGhh zYj&R-H>ZQ|B;n!VZg-bk4$B=drCL?6Oq!(-mDw{gDymo@S4+vr&~k9#g4fu)xb%S` z2m=AvGc;aDuat&NMMVYdER(N!Ps75)+0zIVokM@4>9VS$Ps;*M+kQ}n2W9A-7MNfs zt1QQ9(MdRfOY`pWa}`a^5Y=268c|Uy3PG2i7z)AV%}sNy&9zw3kibB=4BtC_ zOGN9F2e&Cl3W_iKLbMb%pX0epBoSNrS@l|9G^Yu!$sZQwrg$tw4vn_JWWcK_OkLnu zMZ>Lv)2dDK?>vyq+K(SUC)v@bj6$CDE2ZtKqfcxN4gUN^`6bvjD4@mjS| z&QZ*=_?!M5VFEV>7dpMf@60?>IPa+Ovlh=Chn|D6S9Qj+G(fQCAVKT!e8j@i3+BD$ zy}c-av{%2pXM!;H-+cl&m9~(MmbSU2nAPx4M)A-H927o}(?>3cix~Morwr$SS7g{7 zf;~0o7;VP(Lm*8gsEP+)SIXxp&t|QJzMY56R=*(mr|@2Lc$ld* ze~kk9Up;~Wx<1cVdbs|VvPp}<$l)q4d_oHNOg37@UVge*o}Q|Cgl2&J;Rh9=-+A1- zcR__29;m=Q3(zd-WytgjNMKN2t!TfK0EW=hd2g_MA5H1n8G%dMndpLtog(sRg4c)7 zbqGNXX?Y`@B>DVIyY)^A{hQhQ7wYXEPs2yexn-sZ5b))NGsqk$!mUS)FHFi*+$K1P zQV)PmDKpX*l79%lwhlvd>VAy4>Enu0;cOl8T~QXu4hjEI`lSc#TfQ^%x5=aT)D6Br zU-mNYc=n*QIci)uH_m57$Uvr~Kx+Aiwnb~(Ho4F(_e{e878QDo%&J}JGJ(qFLyC!g zdKoXb#mbn#wK6;OOloez{5{?|^Ph8&o9b2?9pOX}Z)k{or5CX%`I>tF-G#vv2nso# zjTx_CZ8_Lvk^$FUQal3=U{aMF+(RWatY*UlHO?=daY2 zKf}n(){I7=p7cXl7KY9=zIT@lA~#2pL7|~0z^}5LE~mJ=+GPW#m(Umw7Nt-a0h4By zMv10#+SE)`QYxviKHo!hGBqqZjskcFO!d3s#ZSG{mMH{p$W}i3HzC_6+yoF_XyP^abpPjWk_C1>Cz0%sW@!5?SAcKe>dJ~g3234Q4 zN`KKbh`RoX|G`Ba29l9#j}YqOwsvj@d*qwIG#cV+kH5V0!eQE0&aCLy%O5UOLZ?ow z?^v${M!kTni1|FJz-0U#cIy+?`O@oR43Gg37G*?opC&&Z50VLSpRQn(QZ&6hce7Qb z!Zkm=$UTlex(!bN5hNfw_wHs>7c((Ktg-R%0=ccHz75nzL`Jfke#V9d9g_uJU#HvD z)Tb>t0lht-rF|XKQP|^CT%hrLbgM#^35@kW)R2dno6;iX!(IsCz5?^x?6!D$1fic! zF!&=LX-W?4gCZbE0vrNzg1+Dnz%zS;Waq7#c{vYfGA{ye;Y#trg}JA_Ag_Dj`~YEP z!-Tcb@hbsao5fv1Vc!G&UjL=@N+9=M^mt zm>fu1)T0+#gisz&8-Ps9)(MwuO$g2 zOQXTdX*&UFDdg3(GT@3y{T`?b0p|GEAEd8S&|+ut(_}(?4nHD8@Ho< z9Tk{tZ6v^kpLjhwq$@ZNau?_Jt34=CsbY3^)^e`4TH)+_(;LgNEEpgPI~pu>K&b$j zwC#aItiw{FS(2?;f)T61IC&e~KD^Dp10C|=a60Oq2ipb*#FGr9l=?cSr0)V<6ZJcE zN9&|kFb47c#m=xu!{Nz&9Yafq2#C+5@PTFH)~HNF@9l_?a4nm>kM{E{cGPGCpef&N z8aXpqQdy$hiJB%kQT^Go$>S8E?rMzGMA}*n9D0`nzub1uTyM5DJRzCtILhehs(t_Iahy`=Ng3O~C_(rGpgUtg-53M?)&HK)t=UGo8*hqMLw`WUrZ z&1a=X8ACA8PKJg4h~(1P)Y$H9nKNg9_FQ8#5Tb;>x2WVvy=T6qtSI}3$-&1^AdC&Q zZI&DMgh2}AThRp^gpG}DD3uqBLg4!?E|F^jaMRM#$b0(wNH=$!1OW0tQb=MA)+q~7 zG#4s2{ig?V>xGR<%q>Wf?zw651#Q?`__Ho8%zDfwMV^85zu7y^uLT$FC@6ui*k9GB z&lQOi{BVz_vr-z`r5*bgmL%rzxU0h|>7eoYe!o~R3`eV=UdzE<^8L}jdg!Y};Uq5CIA^JEpG?Z1l4yNa!J^RGf1lzve!?++ z`B6gg?gf0gMVM#KkT;YK!vNR>u7k(r2JJBbGm#FDg1m;5)P4(Wv+H5ZT7N9ru;<46 z7h|Q`oQmlJbvnmL5Y%8;)ZRef<*1B=nEtDUj=gC*RZZsLB)g4#LHP*sELDYU`>!j( zgx&^2{&rc2zBk5Aue1KQjQuKx+zV)4t(aX16M3Av)Qlto)imej zht2m5HTFlYyzBaJf)YwF7p@4hG`5U~8rPV6o1KJa@d6<5A@5hN%ouFsYyvteIfQP! zFuy3|U97ChST5}%3f*vhZYl2RLx^74)bMvvCUL#jZ!hg>x;-Fn-^`n>)_lwquR)fn zq0cC->T`CPXli>{_{;#jn`+c(Y-V@nB!S^W{l`|*k(|e?dQ7TO=WfsbK&@ATsv3gqZ~$=75gjUPm(e99VjHaA1I zw=HwsSy@?+j*ma`w3iFr8e>A11J5j?HhsAs;|ucg_~|o<%enD;+$^~_dRl=zwcnfm zmrr&sH|0d_nj$;t?T!OdlQ1Rw3_?LkV8`NpGCLPOKdl}xRdRHaRsy3NMl>mas0=ae zM`ZKjY8w|VGv~>`s7-ENM{T{WeFRqKuT~($hti!b{2H_yQog|4XDkt4&Z0KDS&p~4 zQr_7)3XreEg2k&m!=W>i>{g}N(XTKQad;K=c(c%AdA*o(ZiI^LW`!V2{4^KAeDKwN z>He+LK@sO>D`~`w#gVSK3ma1lKI6+$!@W7?#S2mP#WimCtBo&IoK&wlMBdzXUtsUu z=(n0^=;CHOa+raZ8?N|rlSo5?e7;PBdx?OM#>s2n(utj7yR1a0i^5|&_w7nz>~DPI z!PJBXUC|^ro0O*2+51m`QEqPZ#fDL23s~QT)%NMC>a00bbLeElwj9%;qM@xEyv}Gz zQ}=!m#QXv7<3wPlSS}lrtjvvKjrl_d0OK)RT=uI!`v6j48-EL`1{AA*E)hY%nl+#$ zJ}D{lUf4=Pc?AWBjV}b2T%@^|n{DZ2I5^{QD`tj8h|CZW%HSocX#I&=|9Zh=dg~d< z@5~bquuKqzl5Qzi*Vf{J+R%P~N2a8tG*fR&HdU$}`1!M(&1|*wo*VQNu!30p&X2AS z+t>Q3e5FD$DX8Dek2gA)2p#v}fkp*1b_KF;bS|L(kn>{(R*u^cZCUu;yLTOYaAV!mN2UJv4{2@vHpiTijEP*vOoFJmd;DQd=8f`))q_sN; z1@G0Ty{=%i;p8BwQ6;1WSzI|<=>`S}kP;rFcSjNzX+8^=Tzg6=vVMi|rv#3gLYh2M;LZS<>83&eT|2(W8df z%nZS2{M%Z>7=@PmS~T(J;I&iB zb<_IY))k*ZT3dy-&pa+JFT*JY9(adt7dwOs*pi z$#c|92n%iqZ>Pb$kklaEdlWYZ2|`NW1Nzn*M*EXya>4ukz`9I$G3*_p;|}&|Xm?YB z_S@Jy1qUGKGi@QP{*capIK(>{zQjevP%T_m%#GCl68Rc=I$%`?(o6@6TU^!#QAvdc(%oT>HKz4b z(<31I4=Q=p^(Id3zYi_DU)}oduC>~6Qd}Gal-;-x;Nia+b$xLDU#leJ>t z!j%h=A`@3!;AG8mMZp=qhrTj=q0*!IPlr@bJQ_j&1c?#&|C4 z$!AkoK+?ol^6*&j3@^_ozCO+7`x%PV$EMW1YM`9;Y+9mK@DVC+^*Y4wJxa8_WdjmD z^V9Came{qF>2j0amgwVi7v*lAF5UOW-@6npoX#DF^gS`L{ZiPZ#4QRWt;#CW>uy_W z`qoTs`@@RLp&A7cfN0`x-jpco=*)TBR)DPJ#o4q0Myy?XZPde4Utz5IX?K%(9V$r2 zPwmdIea#@yLG38c6N@ze2(6(48h3yGNbenbgEc5{w!65kb{xKMKdms(Sg!K91eA(V zr(j32D~i&Ws|}}3)B!iX>mjpD)4ot1U4%SB#=SB`_5K>=Bkb= zL}|DuMV(0Ku0p8Ci4_(i#LY!C-)`%NCaX8LGXUG&%6LaZik|W|ydfh@Mkxf=|JgU^ z4*eV&(>_x=3WNTz5k5M&<# zp>}Tu2dI9?XlNzwy#mVzWr0I5k3Mty44^quEN>Ln$%R}kUn}3PtQK@94HRZg!>Ex| zdiY?w+;6kE)ZXJ9d313@E6=AjN;&zJr`H3s8nNb8y)#Eou}NhLI{%bf0)LlUbohM^ z>(0v!ch>3M^1aDElb^=P`BzcBl!*+Dh^{{y{DG(?%m%-a&%qV-s5{Mhi`uoeryrBOD$GgC#^c6VhhB`v)I65N1DvkJH#M1fP$)!$z@ z_w!X9>8rCc*2mCV%WSY<+tvfzqohUKW|4&0gIl}RTXv9n96?!OQDEm-VSN2EX>Iym z-8Y@t*2$P&A~nfqwFbvn7RGS)eBz?lN=v8;ML*mY4n)4(6j3O>59q(f2*N>p?!HiM zVKy1|6$5go5}0tkPL1Rj z0)Ra{7194O1{%HSfIWh_*%H0?Z#ciLLq7X4Ie2sr!p~6C)6ICZZ%y!TuCp8UhwG53yZaR@frggd=Utv z&_jL0NTFRBP_me4YxTnvMumZ7vwX4N$>Zelr8HEBG&k-pua--yB$Gay*Xvm(v^}2Y zVRQ2SRiNz%H+43r%}H4(&;zWY4!b)~%lZ1U@u$=Q)KCGxsj2B=uVz$nu0Apn&Dhx3 z9{3b3BBHi)T)AB;`D!!|9<2MH7yi4hi+ugM<()Wa>>f@3x_V;;_vSgM0TE+y2I@2T zOMN@pK#0mn3LjA>F#g$2YcDKGV@?cNZOa74Bxu(>VvSp|uNDk>){dI432PgS|5Z?* z;iG!dFOS8i*pRZhmq`~@ZPOzH$9y>2q=1yTf#!WQr9!?Mm*p5_q1jmy01>dCprEib zQF#2BF82GGd$0sdaL#K{c;2$3-t}G*p#Py1K7Tppxs7pw@M5{$Aac4o|Bp2ZxdHwB zE}g{eBC(L~ih~W1n@rsI zH-i{0JXgjSLthfaswW68Od@&bTv&oY2@6c>GZ)&_`0_AS3uwb{XLSq&t*>1zqWcHt zV#aS*ok6+=SG@5JJ{F80P(NLlyhDKkTCFtlJ)O%6H^b-3itE)kWPW~r=!Nw*ApwdY zxL$v6>^sx=PoX7V)IJ7U-!xRm1YloIw4~>!ws?+yOpeA&c99vks<|5qveu*V3mL3> z)t$gh{_XXgZqTe50}S!699E0(X`gE(Y*yKml}Ne!ts z^_->MX_|NlZ}oeW4!{8Ge||%@iI9lunTFUjq=Uj&bZ2hvgVG zl&rhEyF*1D0p}k<=Zl_LEIPGClUL3fkU0cARIwWNY)CD`IU}UkSRO{s3FKQ$f!FAT z1$9M0i(oeo^0M8>n*v(4Ek=Oq1ki;hS0=g(xH=AJKQw?U&;{+XT!Ls%_`4{8_A(uQ z=bD!F(5XbAw?F6tO=lPg)GiTXh?k>AhJApG;`RGDhOItW`^90tE;O=OD_+n zZnik%1F#t1R0+930y9!bD3V>g4DcebL0t%-@cT~C-&o$-`i;VKuKQRGi^Gc=8XGjp z?`5T=P7Q&;Lme-NO+{sw%<$g|LORN2nkIWQRYzxM!C=P7aNi_(`N?ClYiAz`t)pdm z;W#X;NFVQqp$7z1e|jPan2Hx38FdCAK&w}Yta=duWztepLzIP%;Ugj<&2~&NX{_clK5R?0gb3f}w*0dk8v70MtIw*Vk{zL53w{ z(Fp=mL9Xxp9keD5xGa!2k)U3vUHdIze=Jo$@DQ%D zWc?5Rx@XEqQaH;m@DRrLUo6#1Chr?Z|E7wL-vEh+2F#MsyG0B=e}un1SH}e0NF<2d zvuD!M(!t{#pg{vZn}=D_Ig-IYZhyR{C9BPYzA+F`k;{iCao}Nx;=2;4e=|r(NJLSN ze&9w!K|z^J)CFG{u(Kd=JTyI7|K+4|EiElk?d`DdW+fE$^RlanuldJ0IqGwZh!V`5^4B(@9c{)D!7Mgi=F-X%j+^d4&D z_Wtg{P-=mcD0H47=XdORQU`2iK>9iX3&AENg!X@+eVxjf-+dhpu+*bN!v0y=c>+B` zdL3X)^YpzX3UGPzM$*Ztc3cRQ%q}i2h&TKD`=K6he0)5xnx%krl9ry%?sjAd@dFJR znJ8laa6ATp9gr3m7n8vp2v(}%bae^mz`wlCYTeFfDI5r(X#?%){5&}suQHK^9+%gz zUx%29++CRP@$oHRZe_}9Ym-2At;v!XlJp6aw#2XyIe7iEMtxA)IyyQM<2?<8c5%pQ zTRK6;soXLcb+0cLn!ORI%3;;;e!)eSikz~VsrV>@-N28{`US^K`He15YQXMhD!6Z-e8sp3wS+)`%yV&I;+waJd-xXaKUSLLbJpIE_!gyUL}4XF zR5y;O&NR&??1)icq)incd%@Pq5ii2~+MXB|v{bHpw*EiLLz>DXV;(y9gZ9m%bZ4Ls z!B;4bO34LXWxD7xl|}DVdca$&e5# zlt@A$${dn8nHxlrVV5cKUH4X<&iB^)t@k_Ytaa8proH#`JokOw|Lgz%U8lREu2Nv2 zvdFz|^rn02Y4$|Lb!q#OFXz_&zxMhs7zal4oROFe2ObpGiK&@Fr(Qr_NufspJK3 zA_S+7c2H}ajoRLBeL$my2-6)Adg8;<2#Vsq-X#EH9qk(1QSOo>sg9`_?%%@q?e6l< zZMH2-6`Ohg`njo8kh?E};V)dTO-&_|b>sVMCWIi8dlxOC!xDMYFgl#}!6$p$}yH~?7d=W|_Ggh5r+&cPg+eaRiMnjP0anziI}10tLnFj z0!Jq=J&3SCLIn@)dk_{H3v#W1_;LtxGv98mH+LkwwU3>jVGlY!6SJOB*r{By^{~%5 ztC7bz5b|$RG_u00Zqa?Us=Wm1c(tysu40z~NicI*gXY65=fQ`>VYC_DYrV5cAIcXU zI{U#Pia2}SrpC|Uyn(*Pc;SUx3XhWy>H)V}xuuEhk*(PiL!!}vH6{7Rzm3J|ibmfVQw@&S!pT6_RL_u1kR2L(D^7E1U zuSO@{oEdL!l^it@E|7;7HqoCgmQsU%u~_yv%8P#XtEjj`%YIAif%gvICz?XjgA-A% z9TWX$?VZ$C5M1fAv$E+qd5i>tdBMT~*9@3;-o=)w($kxK_2svn0H-=9dIWk7n#xZPL$x zca*JKOq$#wJB~};+Bua^So+D#)X4UxymbeoSm~z%#FLOIVV|GvSR2pl$x&Z=%(Cb% z)3;~}w@^oqtGPD;YPd2={?+9KtKvJ6*TQihI(T>Q-cHZ2TMKIG(K}whAISQv2x)txes@}`P^$yqkX*Oo*U#QNwbRWyKim&G)BLh(?}qbz5s)u*{k98RPR`&Ol6o zC6u1bT(d>;beoswltpAbxioa#)AzeKK0kZD!p~sW{tUzz0*2dzFEy22*1Hf9=Sd;w z&WiEixvO@*xavEOuWwOd;xj)5C*eZk!4YyB4%lix7nbK1~oJw9X z-^B|YwgcVe>Cmj`4b|dX6G`n^N6aY#&1S%Yo{+L%E)~$xdms7$PEuRBy)3 zjo|Ncv-f35ZBdgw>ym z`rI|8iBvB4GefU^!;iD92-jHlP^q~h3&e=0&DH$cqsgw3mmMIhJH0l-20F|>`S+pV zs16CL+XB!*`W^ZV&hmg(J9l->2Tb!;vtk*(URsRSdy}|jjV>?=*zs~}T*$0hRJBhd zylrS9zWDt)<5Re9jeHiGruo||_pp61l%;@`g_WPl2e(LJHt6hdDKmL9>5OzG@PLL- zK}cT<)V#;uGlJf`J7s?Yiez$D@>^V?($&Z{YBV1bI<}6w(RRfM71^hAJ5i-5vDV;( z`Q2UVUu4hLGBB`GV3-wuG8HdXn)#escouxM)3@-L;oTID&@O9SfW_QHsSBGFqJz+U zr-l9@!ZJX75(I&$SMC~bf2q7|c!!O?(BXnX3SY%>R=C)ztEK{-hfM$@$p7ixbb&VR z72REn?ZH|J$>RemF~iEf<83?+#1RFw|6ml$CoIFjy>>8DrWT}5*W)!_L!6(2=m7=; zggEz=i*%MdNU$?k=to9RcM3mRdEKjcoD4>0(SIe;j_*GdC&$vHS>*8Qgx-`yJDxVBqJ8$1g(87HH`$I`Z!{n?16 zJobP!!OoB*A>(YYZ9{ZpwaISNgyKSZt3=_a*g7xfPi-l(aHb)^IX3MagS+C9lx1_- zg%WnncI!((8og`$XN01`d+O)SN9{}&Kbc<1%gxJ+ukF^3a1X%J_7;5MDrccJBqe*q znVP)aeivrtbt(aB8J?!f3cGpx_FF^~xl{DSuj4&h_yO@zcO~J(h}2yDAK-?XxkiLq z9hK3d)mtR&RAlct?9_Pf9)nPxC4rFb@?U$nQ!t)KdVs@SpS679QN^e6sT_6tG~O3n zUiTK)W*~xq;w4Qr&xB3UB|>ibaeX1mW1B(mm$&sZ&xzjtQAga^)H`=D{2s+psT+Gi;GCcG|0sT8J&HN@L4t7W8B8J&UIph)Cw)hLQx#lqg=!daxzu=TpawNdkJdE#QT^Yx|KawMIN1Ixj-MIh z4w{o^b`p9%-`|(I3HZn&`8qv7f43aAwtX&}KKI`m*b-GVyMLr83p**35_f${Jowh@ zUrEZXO_;;b$;AUeD0JD0OdNEY&F~S0U-{6!IX!M8q8}`qg4Ws;$>F%%Tt3gqmjocA z%;9i#4?2`$;JvYm@cvO7)cT_~*i)zK^K=uYaydQyBIs*xXKOYZ!8(7V_hcs^Si!l# z{P&mzwoVdSdatU^O7#_4##NtN%C4wjhFU00?>7D0j*`u)phTKciP9HNC|YsGg=Qqm z%E}OKc+;uP6~;|&QtC*1OFuJq{rfi}40LRWf`WqXT2wN+Rn3$B)CqCzGDs)Hj6q;V z6G*jz-@h6wth`I)UsLrI*!)D8qqtK5S_2S*w4Re*g^QLZCghm8#YIw#`D0HCFcVG= zJh**=B~0!7J&Q_fDAX{)Jv|`H8ZnE|12hKAK6R2tKBzb|>e#&cy+7^+SkMdHewPQA z%(ZEVbyLI%zuN6~dg2QQPOM z7)eRVvCp4fQwk65JZbLLmLehRJaiKQsQTqC7ecs=uKC=4Ev&v2x2ee8y7xF3 zFA=6~sRf(5H3TfcX~D$0$Hg&)i-rb+={3VEgTHZoHk$16KL2n}fLyTjKwh;rMZ%`o zshfXo{o5>96r}x>pc01t_0^*@w@+mJcWE#J|3Yjp!_8qc^ZMcPd=u9=L=haPaC39P z0ImYH9QkHaQX;`9g8~tt6~1`!f?2`0_{0D$ua?!D`Y5nboi8t!;Xjxbnv%j?XZjQ& zl+gXoN;yhl3n3OvFoc3w+AU#~IUcxThpGL01Y-Sf5r}~c8zP>@n-5^yg`xJhFTT!3 z9Eu4sz$S3>@Qi0T&^2h`#*6 zyU#N{1u)y^ZvF4YDh5*`#V&EzCZ^K$96(RzCcDGDGUWbLNax|c(?o?qeDN==v8!xO zgMR~YUuuvXy7&L6TO|wwI=;-;(*UG5de?!j`)lP=kuzqYUM|hv63N1fC;x2sWzyBs$v)8G<4K^SY?-qWhFT@tJKw9A*3mh8N=w z6nh(BTy5o5K#Uul43mmT2P+Q|N;bnqF1kzzQCIaat&pt;)=o=l$6o87RniR@+{?8) zT3EYv+fA>GlLe-duEYre5V)kf#FSpNd2H)lYEu0iW&7S0pYx;Mr(|P4SZI1yUYEGRGK7`@9d)o4Gn~lpAcF+c+^0Oj2kDb^vO_k)xqtEdZXdgT(0-2 z{!G~1$7QcyW4V`<-+EqB!qt(&qf2wSI%N_(&Q~MJN(hT-0 z^siT17=4oTFP`sn=x!e9$6)05_>MzJrtL{k;uOI?UGq$rGz}V)iDPO82DBpqOBOC8 zRV-jVfTP$mz!(OnN;y31ygxy0k+h!#czT}d%Ch_3G49jVFgiC_#6c^sCaq8UkXAl~ z_E!e*7ZaQ_E_Dmpz=b8Olh?_$K>uG+VkkCI%?9mVKJV>-w|4(c=|Q-V;D5)7S<8M^ zwycc7Mnw3{wJbdO{0%s(L3{0=NHH?#Lbn{rL-=xdGfw?PLeQUn)Ef|0-ufTETlZt*d(+xIn~2Ur z+Ao_``X)=<8Q<5JKvjW;cQ`&HT81d((6Gn9*#FeWqzQFhHXqVI{!K@L)hAaXl*ERE zfwGH<2NL|O(xNs9G6{(Ue{^lO*}hNxOMuYmei;%xQuCo}2pi9R*o?Ng%5qoPv5ff7 z3Z|9qB7)$^UaR+2+_`|q$UPK3sn@`)xFi@-w_9sa4j_8~;&7jchvUDBZ>wb6_eJb> zJ#2Z_)K`pdMaqK=rjRl(gf7d@#)26j2XOGGJv`JvMslAUQ;nHX-bC9;0HXwT2RoH5 z5{f@cvT7$C6RsQnE9Bb{_q>hoB>z8Ky(7zKTf7h;)!js|4g?_~nDo%JF#3YK%$atC zllgp=upBW#ilV}H)2Y~fCQ0G?KIvgW>RZv57P^jEUW39)B4D4aHJ8Y_m=3M+Q1rNJ>eGlx%#LSA-OUkL|5p z@jx~~QzOI@vQ1s0G#5AYfxa6t^Qr3osX}T=(VYM?6!$1<>0G+WhOP{-Wo+6*RMsB# z8vdMB(wD4b!T)r`Qepn%Md5l+LDkcFq+)u{wNxTWm4jw3K!Z{5_G7E*Z%HB{fIGaX z^EkTX@GkFDi@RMu;6kkwuAbHXbJ5RY%$lPd?JJ zlTkX~d6924Ghb^{#dahxD`W&J%Kn>Oj!)n?0EZdp`@kws8jrv=&EwwNFfXkiX=CXm zHhv-Ge7sY4s2Y&oMuG7o_cnWh_9budLpSK9Oda?gj1|+Gu3F7xL!Zw(SdVP2tdr7V zo7~$8FS1aLD|Iki1~jp3u!(KCM9g+|dE7phU8cy`zHPq`F~#g8$iVY zeAvYNA3!{fq2GxVx@7&LLKqBcl{sBItnlrmD5L(?{O<2PSATW?n4bKArbws$cP&dCKU)oY0cUbL)A?E>2Od_TI%J+?meicUj=Z{FOCxE;4G z)hQOZ_ycoQuy867u$ouriJK`&9VDm;XRvQK7Yd0Qu3;x~#J%#jIswt0)9&u7fq_b8 zZlkdzl#D}Oa>$Sfw1E$t^+IkA<72a~&D9g+0>&JVzY3|`dC`y}!FTt?;ym}Bzxox! zp6Nc!`61!${g;HtLS(nOf*C${l_4jjp}VIwu|wN`<+wO&cyi8%+UE~qm)719P;+qr zF9o5}-b=AzKj*8}d*2a0yn}Ac(rx0+HEJT7zHF)!BCT_uYN}ywQWT4&`JMw4->~H3 z`5WmZSqSpVGg$5x!A*`rVWQ?02>hD^9aCQjfeDuc{g<{}&R=?YgdwRX#R|Mwz;)O; z=L@$XCisW-FxwV?gTvmUv7-)r<^Zu8pU78F9wG4s%!}+=(bD3Cmz>cBx zXM=-m8%seO5s2(mm*efD_nX}(^zDJj$de`CU(?>_Wj0`#8fQpCaxUV0i|PXLWOz8d zR&`;l;la_DW98Yio2UXH$N;cmeht?VgJz0NL-z^dJrwv9%TIJ0xt}6&%LU8UxPBO* z^Y@+Hk02gcZ|f|Z8V)ZxMwht>AZHUlsB0m|gRRskxy5K*{=vE7d2G@lBVVQn{Qq-4 z)p}jty&s&mkmKTKA6v5RaZ!OZ4-&!ZBBg*X|vr=*%9v+i2m(*9ClcK(K`xFhNxs7ACB;K3R(XbUW((Ln+p*-Sl z$9W*#>IOp(_0T2Cwb@Eiyh4U=YVRx%DFT`X1SjD@IOrJIk)e3sZS)D*3)P7_!r}}V z+eV0)ZV9Ehh;J#Z_?xZ?3+EP{fJ{DguGpcIPd zUc}CBUs_ty_VzAoG0po7X=eeTzaZ>~jEoGVofFG$9?qu$0}Gr_Dk>}c4b-~f7$*T~l!@?u+cZu`~5vL*%ASpk_0qsshqktfmZ<=f5DTXInV-pX8?uSlU$aewsFT?|Z zF7um)K+w|jC_wc0X}7-eCCDeh#|vOn>g)-f{rlA*q6Xv;QjlF0zD+-nJ-2u_ zFBPf{iYPo*9-TI&2ME;oeSC1tWfD;bRuA;NI=9o%aGPZarlzJc@Acg2 z@9&S`9gs`|&iav3g@b9k^S#qS{?m%%6fxt6xDtz$JvRu8ke!2IL>;8fvif7riH-iT zd^7;qB?B~oZGr0nf3yia!R`!W6?XC*i}ts9~l zfU}hJ%HU;*-2u1&EVrNrj7bv!z>39V&-3P@O+fo||Jbt&8B^!JeAzD2{qzFarC5XB zuybTo%-m+TR&XC|RuUIH1u=j467uDhOoaD7Pmoq~K-M zh9?RXQ)*j#`{=ZoJUwz-8BV`Ro)hny6Tt2w?aW{GeQ^-GLy}Yl);6~oZpIscxqk+U?;U1WO&{1(z-41Rwc#V>_0ed2 zJc5f+zEQFIYu_<7U*C$X{!f~%(0bxJu{<}fg9%RIHCB1f1Hs0zBrh^G735LPX?XkHWDs7gexTNm$ilAe(jrFvu{s zN=&2OQy#`NqWTNgy$B5~tw__~fd87UxxFWEK+<&Bu)n2+0+=@pyBG|H$FHkQYv6~o zSvUtp(7?h77z5hK-V{Tz^)v~4osJwVqy_@?g;I18@ULF*T^&{xIJV& zC*ylBMt%Hmi5TrGx=v0`u+p;d>!t+zjGQGy!98#XWgXf%*L6TF>hhHLZ|&HaG@Fk- z`Cv-bfJ6j$VoO%{l(^f70ti}^*)WjxjJmlw`^dRjMFe|TUtc#VcjZ`|9gPF!ywhXz zd6;-%xe~%1x!O9Kc8Nub6hI&kqAkCHRTJe34;?xb3~Pw>tKt)YF=8wvZ~F%VVC2k0 zdjR$hstqn6X29};OY8->>fURE1;a3=eUGG8S`>NYZ}$V)4Zi=(?~!=kt|jCGrR%@ zv~9v>y#?-5`tJ+;kPN587+r(Amw$gor5yqHU9*S#uPB6Ylf&X7Q)R!EbvF%I%SFL< zu3oCJp)uWB3s?RSITh9RcOH*ma0K6%*-=ZFo|o%*wqWf`x4s%U;Nb}mwQo_+Z{7sKrx&M9@8^)iFBVMN@B68+JQO|)3ShFqhDQ%YoG6ThGB|VURk14iA8)uSmjn3^(9HTr2V_LAa&h>mD#?Y=tKv@qx}}UBK@a5!sv$+Eniu^t132 z@b5bhIW&O=hHOB~Hzi;+(|>^ireav!v2!*oh1X^m8AQJiD>-1OuNVfN;~Ux8gh6Zb zWC&9NKVStdyxyyYd3nL+qD6_{{d|3UTD(D}D*=tbmI!7-5)#6ph%N+nRGa2_o^aRZ z0@p#Rsu2%v^GgFrKbiN>7s}sl_qt!{A8~nT591gg_W?* zAv3_fG!qIjG;!#VU=n@_;!^S-ZE}REr~qj@54joW_M&Nq+nJb*kA05d3RE#N+J0sK z4m2mn*4CC;aRcr@9+(*kzY|_PP~-QMn#}}!;K9<-L#f6F`*Cjjy6 zwptXUO%g`d1g^Yig7$Ydtej}>i0eod1!ftVvJ%dH!l?BDxEHu7?;cGVc0$+^80CX> z-SoX)Ti^i>tEo{AevaLSK`n47VbClHOfX8_KvS%#>MZUwkc9a7`QHrtj5a`xfx*Ds zFEqp_ZO^G_Xn6DS@mT?kZYe+)%4J5e;}>DN3L4O}3A{rIU zV1gCY);AE@qM-srwrIoKdgA~lX!_WDXRD?ttwA*pjX^a)*u-yG)&zz(ftbPsg<>ca z7{}pGPmy-M%AMRA26&b_$(rwr*P#0?3WK28P6;U}m@mU(L83|%3Y!(aFzWD`er4b| z{P?TIQsLoz;xIKH#Cq~+KMN}wJbO?c=B&U%^$bu2mnNzqE2d^{p3Y4kG6(4qN?7jH zbabM|SL|A^4tpIdcOI&^KL^d~jkh?siK@rmzP0hKg8P9W7~wwHCax<5m|?i5M{6wM zb^5+b&Ch z^M@6^GfS1I?k#-8aM#p9UdB-#lsaJ1VKHM+*Mg%ww-X0N2z2iIEu_+w0>H4vI$Y^f zV)D9LK(*&|RQ0B;eN|Ia4}{azlpAuA_0ytLP^np4G2<}}nH zmogGtY%v&B8qS>^GhN-?sC9UeU5*aPX)vJYzR&D&7}@hI?+$xj)y3B91rt;7kY*^d zegC#pRCe)j{%$z|+Ju{M`Yj; zy0>?*?hhdwYgA}ecw4m`Q9*fFw%fJa?5hfFsGQ?ZE1ZCr!Q>zr0TVY$0K$C$u+!bh~Y_Ym4FHcU^gv?XaICWj||J;r6Hwa#^f7C z!*`7XE*c`MpTF18XlP)^O=~s0_hA#pk}U(D=ENyQBMnkeaLWbKtFp=9;&do)utP2n zY8o!g7+lN-_ybW8_~M2BC9H`JXjMtRS_@YevKVRT9kShMJphJ&6KRM_Zqg)wGzIH)Z_!Ci5H2pm1fUQZQZ7NQ@A zK<@IB1sd9@gV>%13>%#Mu>~^7{)0T)_@)i*01IAvftJtzyf>p${Qv#aD?u-ZS8qvt S!JWduKRTLv8u!$!LjDilp?Dhr literal 34131 zcmeFZWmuKn+BQ0Aq>=7WK)OUqQc6HTK)R#^M7leqq@|^#q#GopyHi5CySv$A>hrAk zS?{;jxBu=R+d~f!Ci5O++~XeCSrjiV7`U^gUJ!fHibYwHoX)RQFcn&oppA?S-pF9!2fbI z^BQp$b@rDAJW5&a*C*uo;+JIbFd{GlUDPq|I4GiwIe}h2Utw98QV6X0%W|30pH%Dw z8=2B)F<1EQ$&5TczI#oAjG$o>l|XyGUb{9T_l|>mFteL`@VHNm7TE_v4gQo7*_6Uz zVPj)Q8kIsGL%+bz1PKZZ3^YeW6^6cmq!l&-{I{#4Fm zU0p3q8x=$r5E!@@MTz``fx)M-QE0{H__Q%PF78GC*!ZRO&hBoSO8zz#_DgKkCPZpZ zP8<;t5&cd|>U2J7d3i}ay|3!zKkDj8sa_>pb7|_XcOIRdikq1+1Pe2^!;Pb;`zxMt50s&)zZh5&5V0i>^kCfD#oWr`ugdlksn8kcc>gfI2gxj9Axm!M9DID1D_SCVJ{+ zPeR}p+&V-f2$L3JvCHF~tagh3o5q{acnk4(K{nDFb!lW+HcK(&vXLF^s%HokyVCyh zU-3tg{4?r)p3o1ymtuKF&Tphe%OuDBeh)g@lR8;*t+h~gfk)O`taMBle0ZU76#4189N)DS)_?pKxthL`OI_*k z+3ed?UM)&B3#vLdz8C(Qv-KJ~$aCBA`<} zo-0F&XY8bqUOtv?Z)gd|wK z+<0+gIK$~=SaLXDl~t?3t!5y+aH3als8_F;<2LFIFQL^{1Sdm9jP7%f?R`8H;aps+ ztCCHHj+8=j!XXBfVh2nAvE!T98s-hzfypCYf#X?HQ|tO=f?ykId?z^Xmy({8t9;b% z@LB(xX7?&=?P;%X&#+f{kX-s|gg*aE_R-s+ds}QNZTvC|CJ3`(_h%YHUYo}fp~Qap z%o=)|BU#8OC@noPjN3PpVFQR2f%MJ(Sq;@5V;C+12fg^1H|}lN+(o~rp45>FdTY3) zECi&xAuv>MnCt9?lKhgMsdlg>-dMi1)Vu18JJ^dwC4A_Lj^ts}dJ{=yKj6q+l;ZMa z$LmIGG&kpx*Wut`JYlyQ9sREb?SqFj_xB#`UPfIUjy$M49Kh?NAPVVSA4^}2gbp3& zwfmAdCH~hjv&iV^kDH^}i3R(=%IWSdmc#k?GKNjQ4x5As5#Zy`9TO_WWxwriqa~bS zL9y}kI@nNre&cSv5qgcN@U&#`(Bgy9&K-dj|HFIV#JES4 z%D&e3X8y-?q*%kXJ0ADHDjlwsaT3rL0tUDKT3d~8j0A58kCh)MX`MP{%EQolIuR*d z1q5rr!;ZYC6aJE5_JUQJ#r{`n0ST%wYFB^1pH6W=czAfX3k3y*R=vw<*!APbkM-v2 zoGS(WJxlQSKh%32De_8xS~)lxe!J-TrKRen5jN3qEgGYzoW#pCQan74YS+y}iBx2< z?=1D(2L6(9&2px~5_vsL2zra?2q%0^8Ks-qrd1MrE2{^&>c5|Ub2@rsSctYCX+oSb z#0$)4xZo9i3Q3Hw(WsAD_n$p-{d&ULEeX!#mIvNkgd?GE<(&vtUSU3mK?fQxE-qo* z$;p&(p2oVe@GEzVMxRWgZ_D;pL+iZgY3_mw#taLxF zNHh$3y}eTEsMB3?#ug&%^+zxNb)W6HrCkqe-JlOqpO|uL<~@4{%2TtS$S8j48kV{a z4i1$%%?u1NC=4-q4aUWGa{@fO`lp|R69juI4^8|MWyNqZ_;b+TCzgwm z3qHpEmQRwbD-X~1aJ{aeh9iB)4prL!oLC&fhyr^v%?i`E-L<8V3W%$#Yg=2J-ud3F z)7|BI=(gkjT&TzG)mSgPGzy+t;zGa-6OGo)>Bba4MX~IlPjcDOId>8L;Y%&Zk?ron zW?^d-oQ0m%4%hOXgEUMhM)ktWSeV2e-o|7G)(cu4Khf8$*!w2)=j2w0Lk3-m z6X2&_fWy8#z@Jhx6BZUiO~KImLz9KrlU#rm2U7CB9BIz8Jm^Wn-y53tyn|0x-GeA) zjxQRYKEg#M^w$efYr{sFe*VPBpHnaM63luj|7__*SuCe2NtwwyXHFfLO_~CYN|a=N z$BwPB{8Hn7;v_+L;#Y~!3a#&3T12aBYN+u4oYcX3f7NGX z4#JOoS_oPJ?zwOEV5G#v42^i;Wx3w|`Sc{>g>Hbl&Xr4_=tvtc6e5T{^Zx@CByyHX zg}rSdh2+0MD`P9Kmog3p3=XLNzX;DD53#Y;iRvi>baGn7DQ`Zj<#$3cZC4K=rEeOB zuNz_sY=r|UEG-yH8T$^5xn4?mh<~yyy#rdt2UUz9va~PjzI>m^()dJtQok8JOj=x+LUZvpWD76A>ko)_g@Wk<*;R2$Ou-P%xc`|L zn*9xno(_Wft=gaA4Zvi6vBCg2<)3(EfJ9_e;5RNW<8To{tXBxQhK#?}H6sMe0;X?h zFbVg2l}_yIP}Byg^!_z5cwRL7YLv;Qe6K?C{~5{$+4;p9!UGqK=k<&c4sxMyY;UZ$ zBFfdn-l#Bj`a8lMPGDf-t}tLfeB%>&%n)CuSZ?6)YoB3*4yUoORv3*q}JbT0veO)czEr8s zvFkUS4K?Sln|vl6{k-b4B0r3mZxnfJwqbg2Bg(>D4lIE@um0+ca1#ICV+~qijoqJ) zYAjc1KQK}$nIV+S>Y>F#2O>-4s;`eA!pfccXoOL|fg6fWms?oe)PzWm)j_fA9@fb5 zW-YKo{&ud>ml%fwGqY5V7v%4$AAHMU|xyeV&nv}C_ zNc3MYUR8UZMsJ~|v=ns5eK62*>~tCwTkY{CNmlWFb`lefVEFlqH5xp~LM{w9R@K?V zja^Pln1Z(JUl9DrqG|uvqJb;q=3cXJdLzA7y!J zw<2vuO6NG|rFU~WEAUL!__&b4Ib2Sj{|tK1Wr~rGY@$1|7W^+#>9WS<&dlJ*HaujzWp=2_+*osI`!%qxe){|%LWlphavD9r(_UQS6AA!p1)3`XM-O?wbVdh$ zMuI#%uhzfS7rJSf)viDjTWfp_L|A>n-})Xq!e$-jg&nFmv+4EqNvcf(?&rK+C$CDy1E@(ano-4_Q3%nREa#m? zvt!cUtf#vSy5(CRs#n~TWtoEy>AB8yoS*gi z>2YCk9lTTwrf@Ro<1)rt+tUrfS7)VJ2!;xE$(6$plV0IE#@A8h*J9g20o{_PVHGw& z7ox>F^HOB-Jre|-gLlG%~b@(=CXM6n(o3c zT6NlE(37OOx!X&w-Xgvrwc?0+&Qk!bHQnHA38(O?UQ>nqU@PmSTf0QnyJIZ!H~xq2 z)cp=2*Kap%RY^Q4>|1`|kXq4*BZPPG)m?PFaXXlGt~(R6#a#_LE~9RDU|@W`Ae)YZ z*s0ULifXp9{$%*JhQws(6NE-OV^}z&MtubrAAh`edCtSAQ5G@&ebN3$Or;WiMP!NZmZ$*KnJ7OX zci-bvlzVfpeBZm);k0wPU80)k|Eq)ts~y21zG~C5+DT<$2m`Z@pO5rVb0FO`xXfj6 zW>PnJV*Q=o+5(%hN#-f6Nw=+PlqTJ}?iC47)JCAaXHvTosKnc5me!)TFQj+jA;EO; z220nblks*aE@W1V(T~pDw!IH>t~~GNFmo$MV}cn%oEaX<6}dbg$%aG;)rHXCwyMJm zQoP8RQ|#u)UKmiRn6B= z^G?D!mo!Tp#pz0Up?;!u0mpF4)p3pWouQUpQ)Uc-?D#uv#l>uU!Ha{qD3g(bqkfJ| zBccIPn`ZfK_k%0`xGe!&Cp-G*#M>qMu4UHAEf|4&%0BOO2gK(i0`%C_&ir17*g7^o zHA2z4(hT*&I`sF>nvzpDWstpee5{P&!c#+N^++=)fg*dstd@tsOR6&24$lUZr*(Or zboE5V=iCfPz26XmVuQNAyvz^w4KePE_xACDUH+VsqLbY@`lieJ?&G5=0b@SLqFDoR z9)mUKXMhqucV|#YDsCEfw{WV?-elJDnEOfiwonFZWTZk;OjG6ZB)a?651JJeVYX2k zGbr?_r2DdXn!+8oVt{dBpNOQh|Eq7Qoq}?&CZ9R09!3P4c&7RrF0=2?>6c31nnKNK zl4k7!H&s6R{+JQ(raE<~jTuR)qIOnz^Ps)1p+%3njq4BX$HY0Vu zfn=+uUt)A8<2N3;QJW*Pl2#?pH zUV7*f{R2%gxOfg9@L8jNPHo0)s`K3BquOxnE-n;Eo_k!H%w`dm2YmeW87Zd@$ICNt z{G!}40za*=MJ%eP0!K2w)1KA{Q&qj}y35?VCPy7C_=RB)?z~;gY+SbMU_-YAtdAF} z)US1@19VFeE!`q~;>i(=yP4j^3OaI`uBdtwQE2;s(+FW=_uw2`vP}?&UgI}KhFIAQ zFUM>WnSj>-cHuF<=Uo|mlQt2=-6?IhyE3O1K;O&SI;t5kLXDs}n8)ra^(1VEV4vZp zE1(Ck%ixYw$-!QD$j;P`_xZdzwF*dVg?eML1jWNvP5J4vC*?luzRAG6;X1KPqA`j17(+QSr*aqs&K{{mvco)leX zXfCBVLvTY6@Fs)z6%-U8s;b9sy)VaH+m~9yTbeJMubp~6*~d>Ua{rB6AL8H~g6olZ zrtnx(L-Z~V7S*aOlBIG1EF>n{;(_nGhG+-@Ib)>W)v=^J2FOyGUVmv&$ToyqRy3CX zkfnxSAi9%TtZDs*cR}+1L}vfQuQ}6fES66V|3-Xg5XDzc{s124$l39c84sp}Mnquo z@bFMReTsX$J{Zt!bsRj6AR?@#MS634yST96wKw}?`DAmHHXJZ%Hjf)8C?S)RlXE)z z_0D>=2T$hS)*SW^0L~y-vDew_OcxI>F&kqjP%DLfH<;4C)E<1e_hVoG@@TaK>`k2z z8ktx#3d{qw+$xP50z^ zVZZUcour?iA8oKiM$NKA`12!VkIPkpBz{K>h}F%7H7So3q~7%$5t8}s+Y(?n8^6l4 zttn!p{VZl{7sG|_Adts2G->JSErukXo}Oaj;zU(xSFL}9!(+Z*NA2#`Z*x7P=f zgMpfAHZ7m{3>^Uh!DOzE7Z(NL#6d*y(ubsAx~c6TkC{u~Yl9DHW+QLFhr=Ja?f;>*#0!$gVX2 zliPwWD#7?6eA-m)^q4V8t>qbySt|WohB~s-3V%Ya&jn(cJ0q~q2Q+9)JjDX z(>Xcr`n84zO3jQNg`eo#I&@hnDP*usN?p$_miH?)n9p{n-!bj!>+642W_b0zyQilm zS1DT!@&)EE(<}GcnFN_}teIl_33P6s^fv9$px^z$fUgYYW7NFa&rQ7#LtvP=p5t zB7=v9Qc#dQEG&%hxf2_tslOlnkz`m~=F6ys)l~#}d3o8C=S0K9!yWlPxp%)c-gG|o z1~=Y0mu=<{B2L6#zkVH@pLcrR9aHoraCTPr|IAf-t)k-3ZqPx=%8E(G@6cAH{bONi zNq=U|7gNwHIhjnyX%QhhCMHcXoE$v-WVk4seePBG93l&{2mW76O0X;}EH+s7G5He| zcX+soG97IPUTu#2(1x(Wnl@$fTqdm-iH!Vx;wa=h+heA0)$7lAucz5~9Gs)PvPMvO zUe;fr{!(iP2Jqb949=a*-)p;!l?&~U1|5>M4pD4v&Hp#D7(&_gHm8-_8`*v`ju`O| zv6R3hI>~7zZQ_HHX&E;tYbNbCn*y!M>PJ#aGWlS*fHE`Lw!~`S|721LyG};!Hg3l{IHYrnem+|K z@RBhLtJN~d@0HAFXqW5Yp0#neCp2nN5gH)miYG@_h!%L zQYUd$6@L=mbi8?NUhNe*tC3#NX5*-(3VppssCMFWw)OI-CXlhamWBF5T*eHIK>%=f^3>PYa=hWA=13(q**{iwA_DhAHG(c#z&bc?O&1ha!8q z^BcsTTaqldvPT|V&Oi7l786f%p4B)OFZiVIqFjV^?$za!FfN1ULrlHilqWSXd6u&N^%d z1p-!949m$_t;0#i8@I#ft6XOmt6)<|5<*|~x*uLUU}nqXJY_-P zZoAz%`_IC?K?-bjnz74U*=IJKyN7pev{Hg=AK$Ag^Efmi03bXJB`Hjod{#nNjj_SW zBm$)dz62wzIi+S65F3t|4gS98^|zGSw^aPp{PdmOD37U+5 z$1NUg^g$^^6S@}~{6f(rn>jA0xU!WS(1Ov`xNFZL)QLep#}}!HJw}1=MNR)CXNURd zr0zJ;OCrNt!nXYD6DFpDd#oz=mi(Im*X_HX+zy-0ZXCR%27Gp2T2z#e^9Z$5d&Rft z=?L#O2}3rW;oNolVIlBU4DB^y1UEM^;iZt78w(sQ^~8FLZ>78 zZZRGIp zQqVke??}A;`MyoklSY3roGvXtqM2U`PF0=^*8ve;B06Dbnc}toOkWSLrYdW0iET24 zM580ja5(N)(_Y1!>wZS$585Hxx)&NN^Iz$e7DndH;6DcPqZQ;wJUaf20*0^u!G}U| zkY}OhHE*9?Fv%~6F_r?U#;^WWp6%LaC1|;oQMh<>3c|&3&xsSy&T6>K2#cc-yF%uX zZiItl91*clg5P*xbxe>&QYh%ybg6guwsYCRDVnI|EUhr$}xi1I3d)U}~KQA-%Qp&6`g(_ zv8~2;y61VlM*CM0`taWj8yXGT^eZ6Bio%ODT^uf#ewco?Gy8*pii*luKnDn?PUns5 zp+ezdy%5OGRR_+#JX!}g#dVX;ho)-3xW%P!o%5YZYz9)*;`=aa3FT<}7BxverS zNr?gncZsjIZk>SOV(yj=RMpCW@*fzO=e1!XO02DN>c6fJt%^wq@lt*E1yP!6d8xp1 zcDBjiNgM|-y29*4!+vwO#wWq~vNVg`|5_*AOWulC=Y9cpN-$D)-x=m>q%MN7o81Fo zhvI5g0n9{-eJ%UAOx(@D&a$92dNpnl*f*w;u(|KX;sqY#N}m1rSyzs3#OFAJeJts> z060JJRq<3uccHcC%p9lKDNdhX{mTC9_cQl>7y?WI|2k)MVC7`ee)1m<|UR=(ki z){FywR5$$M8GVri6-#a)y=446aw#4Tr{%xZGnHia1 zBPg&-@gJOa>&o7u4n5>_`QMWkAb2tM@ef@F=FR`kVSqTsKfn8zy#xPqSB0g{w(t~K zIse4BtyFq>5B{=sU?YIOUy{bY0YfCYw{N7NKv4Qf`Pe`reG)rwlLYIL!^bT{w^1tU zGWb8R8Q4RATQmL$;GzaWfM)!UYlLNpwKWv1`cGFTVr)}l$o>}q-v7%!(t#KK+lb;p zf)3-igH!_j^xwTG{})YvUlZ6;SZt`m+numO9VcWd!7#<^O`|_xP1k>_adlE;WAJj0 z3L%F5@Gq)I#lkJ}{;n0q85DM5o<>omrJT4Mp9N5OD#AEp&bA5PuaiG8O> zfJDc1m9hvzJol_5UpJ7|!Ere`h&^+`97KR<$y9ZA$qhTY?QG_=EJeOM>i^9DpW-Kd zHU(2h{|#kxsm6FO7rku@VKTq2z*gx;$K?u+zT1lr>N?^9o}WsQR?v8%X6x7(UQtoe z#!L<8cguN;VYJzl{?-lWgvshY5(5{BrJuTTY6tcV#7HL0UtaViXgIPoV{Q{ueYb=+ zMnmxvrK0pOKmn>{IhpO{$~T^~L`GWXA0P3AQbndM{Ne?kJOsfONYj^UVbWjQpI-JV zmD=o^K0h~mrcyr!@3~jN^qu(f{o(5HGHq`}cY*@{7WT1N+Ao`YGIS~4!VbxD*gl|p zFYbJ6{cJh-qB!d9*^olrcs;MmAg1ukG|?Rsh*wvD^ssA}eP$B-d_d6D=c#!z&M_ev zFUp^9v9BqFjloS5g@uDV9N^!lWM{_?3JP*s3Zfe-))fN&>6AXve?gc#we1{I0;2^~ zX_L31bRY;6Y109--}nn&&>Jt%MB6xa*|f*-;+77xj$<1ooe!^7NX(Zis%!x#v-ymb z7!GnLXVZam-w-yg{b>;=v%S%OjXKkaet^Oe7V-qwE1+t?khgl4EL<=V35m{E-3Cq= zcwz;*I|1`mKN5Ed&zJq!e|7a&bY9~mQeMX66luR+oP;4T%*t&GLj;v+R3-E6=1)r8 z)nMHS>D~RXge~1MZttH)RNEb3x+%_UL??T*B$1JkfgvGnEq)xFoNm`vr1y*lzk3LaV|JjsvTS`&%W}%49u2V`O*&?C{ex>IOg5$}2504t zLdc6Bvyq8eA1NVp9*z>mpNMyB31$x2S(B{9dQ2rxNTc7!_9>42N07T5>Y{anM|#zc zC+TUM>ukUowFK@FwmSJbW^pdk`51kyz>uRl6*N?U^7P6>|^UW{p)j`MZ%aWj7vr!d3%~ z7e!cD_}ZT=2IrgFJz2Ca7HusbQ&_iXzGrRXR|r<}D*=Bug%%MBCb6 zDZp+e8mrDJ6Df<4B(&&Nxqkr%VM%ci8||_=A&ONp@V}V#*kgGB04S?oqf>#bshTjfvU|qnQ zrqM?&20PxcZ;u9V92l+!ctyk-NeS!*7N%Y(GeSJ?Q<0t;*_;qswH72~ZAg448yQH@ z(TnsD36*r2cg~r4*peZ$III?cHystajq4z6m;LbhwGGRa|8scX<}%9)#hV`nwA8&( zmgXKTY-I*aiXb2EI-`|gDkGmjGG@IT zB0!U6(|3xiHZUz@C;{4u#EXmBC<|*ew^Vso4r?ViAUQ+34GYDd3ZJ5ppL3V zysqXkg>6&u^z#FkL1TQ@JBaD;y_7$S*CcwHB3~wWL->eV1WVT6*)mHp_p9QV&dgJ-Vqa|dUvJ{zL;U8dcqjnnqiFq`I;#O z^P$FBkdA^~q)+D`m&VcxZn+WC3SR7-37iR6k1hWny zSLewK5#v0R?0Y+jnK**J&&LjCk^(m`Eaos;xut@KRE(bVs8Z6k*42M=EVB>g7QEOa zMGS59zZI&^P*3ccE@Db%n7cjek;WZc3y+QKc^$3lUQhG#_nAZiRIyjkNFGq~pcy=% z3IkmlIq}(tmbrS@QiD#6V57K z9AFGBW#tlWaP^%q%+Ds)Ev#yKf1mAh+;7wr7gY9lteP>;9zN!Pf&6&px^T;#sP?$E zaDdFQ?dCO=69CcvZ945^cCH0qNW285+kQaA@-ojHJ7rJ90sH`=>z$3tkCBfgm*R9i zL{wDpXJ%%GYVF_40>N2c;8eNrjFpM$V`3snZ3e}T;S##(uMh){MM89Rc5Q+!RsAPXr?DJbm7kt1OSlA zw;}?YlBi!-hYbIgqgDC^rxq71yv2_8^5I{ID<6bp|0f#rTgHUlI*Vy1h57FBN5lM` zqy7DobiUrMuBM^17mGl#m6VjMFdapF`I6hxhZPxiX=!O;e;*SG3CV7Mjx1X~^_eqT zsD$s~a%aZt3<)b6o0jrH!C*$!BDJejeVjj;7?L#d*z1V6xBwvBismZoU!U#r*sh_? zdEUEM|M)>trC#U69?PWegU6_{08XKU)6;e(c>%xlbn4}2rKP2l-#?H%`!Mwp4hiFH zbv2>;^%;oxc{cff{qZ{&99fwQ(Al2tym&A^)srX4?xXZqDTG~I_<&^*n*WAjjEwvI`1nO{ zEc2`JN|Rw&P*PwZ7gtxXIXkPzfPz>=B`1H_!8K2wCr)Y9tU zpdfF>+^4^285vQt<&yPbOc4JP=`n4nI(M(EF*HI4 zql|Ie(a}K-+zLR9fHNozUPDSo#_n{W2k`<=uS6JW5U@L%dwWq+Qc~idJ97XlCR7zc z>GluR`M`9eZAHds*959R8%S~Xr3z7iQL5G2(fgwj>CYH%{mgyB$k-asZUEi=VPyRJ z6Ghq}h0ktr+>AugG%$d{T6X~3)6+AQA&FU_S%m>onHcwkwKxfslLt({_|?9fs~1+e zgWb><-=_TbEfqLt4ytGGo8`V$Oj^Hh2^J=10et3E`cu>i&Dq&E9EK8t`7}`N40W{K z^IVEeYS);%$i^^lc>G6fGV8CttCxSSo`iEs#1y>vsxsYe$PxUTUsbm6!+!fx0;zjt zW|lVzU5YI^4YIF~+3QfM@hdc#KSxESEnCrnRZ@q+_c~Y5(D(!nzn0-)>_Uyomet-k z80CRm*Y#vaF*!*wF}rtvIsT=Px&Tmq7l2$a`NNU*=T>5rX{y`NOY^xp^)^GW-Onk1 zwu-8R+Cughr!5=XL2!FGSBWmFIL-7|84Zh0-4lHdYU*^?$lLOtMxJHHJ1}(tNlBzm zuCA(@x{8V|`!wJjOGgYA@Hz*F71GVV=Y1=<7R=1e!)>nj<6mvxna&q8Z7ovYxSnu{ ziN&?_X2Tv!_$@B5oby{;ONbJ?V0O3XIeTB6aZ>amA`Ce?u^`r^z{&5UWF{*ajFEaK z@vWF2k}CH9=Lp;JY+`3vot?_! zuK!w0JcqM(YveC_H`Us%6o4T=-}IG!)5>lvk*1t*S6TD>om;IgT&H$xAd&jyndu`? zcAiN4=Y+Krve9NAas5;csK##QscwB0&4k(3!tL|TKLBAt0OXn%bnmYS29kK;lH~$0 zg>JCGf!xvTiv$I7d;)u*?6SXYfoIaLt+<1o7u_`x-XcQUx$8i+ER?I+c1U(z110?7 ziVoiT+Q?s|PmP@ul8womHYhVuy0EQWH&st$!?Ba_Kv*vJVQS|!)v@BLqm>}n0^-uR zPRnFUq!j=80=G+j+#98qh?Jc`Yl@VBYJp?f1g_u<+UOxSGw?Xb$ z@P*VrtiBiy0*%gfaJklv<_?}3 z_KF@g+nHMN=1t8C`m*X>0?8z{LcSmeM~DwNm&vlSvXD_xTcPgY^0Kcl5+*e*?V30F zF(@f&^)6f*RTkQVrmtxB>|3wSVGph`!hcvSuV%EzSFsu#e#F{fY$F=kyVqcGusCT8 zL)QVqXGdNQOjoyZ`hLlF`bwn!w?>B_iwDI;pwWTqk+`3I@Qs2Fwd8U9!*Wu2P1;Uf zA?LRn1y3t>l;P(iZ0guHQR;l3PM#VWVV3@I^y{{9ilfu8r~fFggbw-o@|Z(b#};EZ zwa3@81o5>tD!a*)5zzl&>J#?98E!h{F>87X&SL%>(Yqeg4RX~g^(dtP*$oym3~JlA zFlxRV8zu$1jpX3$Yf0dIcQB@^OU1``a^Tn zw;`FXlZtigWk01%x2Fd&^Y{Y!sdXPJ)f*cY-U5 zAFgjmX~OQ7!srl<=8vsqPj-|e{ONCdT zB`7l^g)Q>dDeeS^rPbMvdY2(b1t=4sCi^DO-g3`QmKqFa%TqkRT!~UE(eox68J|Nn zC=xA3YPhbu5@(5B+EQ0XTUr0|-N+-1?0H>#&E|JpP8eaUV-BXWHKjDi6YqieXz>lCu@ zZiIo^{yLm8YYegG&>7^O)e0mA9OQ6>?BmdzVZ5h8w!daD3~~?UNPt0Ya^bIpj%u<9wZdY~35CS-DE1?ZNCXPtPWnPA`e#d3n(A zh%scb)nUOT2)WE~H7d>g+S0goLHckehm-UIFq5f=&Xk zb0Wl1y90NtK7GL$f!faNsQf*jRs$)fV9n?RXg$B6@n<>blgm^oy}EGAO9mY$PEW_* zwx1a;^ZAis&E~=nHY5c%P+>OfVcoZy`GP8$gUAF*3@8n>?Gzvt>!i2X;o47M(%LJ)Xxp9eV3T;St6LgO6D?>#R<=M>PeR zZnmC#{Mrhdoqw7aXYkYif0{6Etb2FyrUXC?j=#NT)(4k!Hgsg}rg#Vh@PN?)<&$oy z{Qd(^>xqvhUd=%*nT6P_u`_07MZLuce1$0zHv11p+OEj`{SSBTxZMQD21?;roOXkZ zX}4*aYeG(f&D@S7xq};#{^}+JYJO@A_v!JBaQq=#GJ z^a_Ot1b?eP>;IzqT9{Arwx`T4iGLB=+_WKZ!dC~!aL~wF#{|^_brMqiS{rLA=|;&h z23+j+$2lzrvi&i%BTE7AREw^W%PA*oY`y1jMFYH<2zs16u#hWd?!H7o}T# zB9KyGBAtQ#!ol0Y${$`vjnMOnA$7e-2QMOV5mugkD)$+<&+5PPeBTH%V*cWoSvh2< z2bjaeo@eMI)D>@tJ{{qu&!&IM5d^TKztDDfX?gjugT#`Og#{y-&mIjR1F5(8v@9-7&_P585Md*M2CLRdivH%$xdbtDjjpOr#kUlQ>yV z8AVae0RPyB5)yvWTln3x=fS%qA&rFnP|2x;8;kblM`QkcE9qNW z{+}%)$QpbUHUUT!!|c&9j|3dT{zj3&Vpm(?0Tx4Lmo>FlwlvE?2uN71tf10O8rAbAG}I+BgyU z3tHI^Vzk0stBsJ_EpU;d;LjBq9JH9fo_0xgc$W&>Y)Sti%CO61H5#QkauyRrprE}tp4+HGGSG2{j0KE6Hzn-j!B(2NY=nqpB&2*`5J|Q7!&^8M_`IBY^1Oa z0h5zzhDJs(kTsA!1Jn_8e%^9{ZrxIgSw8DlFw9wm#s?pP*>FG(Wd=9*pa?F3P~8QX z$8hFL91sAM`h$o;YD*aoVC-f9u7Ts9@B9L~jt>lAoDzd@=*C^9LFg);{Q-E(NM``p z`vF;eUzsfXN!Lvd&_`i^z5$qms>hGtFhMNK-wl$NKAMU8Ix3=f=xnKIP9frgW2S?= z!{qSM%%e~0AuyTT;q=?WhS;PIcr8Y^To?)F5IeRGpRs`@{@Yy{_g95cLjQ}%V^RXuLr2R!V}z%Q$SEV!AT}A zD*@;Q<_-)1ei#b^ASjp)80*-0zA{5}baWuJzdgqo+As{=6CXc*TmYbAY#d-=GG@*1 z6>gN!_jhR`Z*q+X`MQoUvf!g58)P4^7XcV=p#FHopebo@-?+ zu7d^W8bspKD=KgStdtNIh6vsqyg(yGy7E#^#ra~3~{udK-2*@ZC!kR3dAaCIIeF#&P zB~QG!;%;#C06j0~``xvDD9CsTV?Uhz`5);CXM%L_>V;XU5C-s&n(b*sHDi`4FE<0t zl~5p07$Xg|-5_@}i(%<*{7y(_2Rni0C*&#b-9k3+!`gCvS!lzrH_Ex$J{aYxbI2U> z>a>ri%5w#6sfEB8*kEC6qp<3dElI#HFJN%LE_;srr?p9snak{Fj$FGg$g>)9By=xr zZ)=7>*k`7b2^w6!WouA<+;RS??QlLB8#KOrk%-k~YTf;Bp3yKz{qPSW#7a{mt#6np zaFCm`jB3`K|LQ+|pdCHxer3JGttzYG@PN@n(D(Z+3cIVqW1I45h!>Z&H&59FUQ}$h z{_5PQ)83eQflK~_yBSlLy%h4#zO3Qo+$_uZSqU_(l>O^GP3uY@a{gs9iSY_X)}5YQu{8^=6_=t&mvy}Wq)7D zc_Bt!6>BV)@Q#WYEg>2ql@k5C=P)7U!0|I@%Eh^T#hxM>Xr;R2dP+dWEMt9F9o21+ z`xDX0X*(o1DnD)RSS51FmJ~qPXaDH7iC&%{vV?oVwSaXX079r=ZnR#%_Rf$D*Y6A? z1J6Q_yHHAUaATU0V5047c;5tufS)u}R?mY_m&Lhx%JG{peA|}>gh7s?h-pC6;Yml@?4Fa^ zkiFBT%EAi8K$q<6jM*EzDEBjqg%9V<&M@AyvCgQYem9=^n4wZ|0f{LB}uz_O#=JFUsMFXU>M8Pyb{PbQ}APAG;KX$edFilA3MR^N=V@p6l+ST5q@;um%2#pd24Vu#N>Q8&<6CC?UPB6+z$*3>$p62BzO73Zh#27Z9`;ST&~Ef@)@)0 z<2_gZ-CW8S>(kF$Z`$q}tZdb740KFPV)&OU^(h|eN`*+-^0fD5f==*J$$%v{gs>f3 z@$Bs5w25v&qFy*mGk(!x5oVilzcU7H$KYq5yXVbcWqU~N^A4=zV8Z~JEPFl0bztRP zjaC>4Vr2k?2GGBAGP!2P@WRfoa_Fx_*uQXvvVgq1Ii~*t-~(6JDCaD++z`n<1u?CQ zKYlGx!wBz)v239cM=|Mbzc#0ida%KJfaq~oOo!%TI)m=Hy(JM}#zeL*$FpFbj^lz^ zkFoECab>4~&|={Go!AtoG;D38(Sm;5krOY7qx0ep*&dyNh9 z#aC31*(JZ%whThgjkL=;=RW9|+>60z3D{S=$%PKCAyG5s;WG2#0^v+K6&w&_vVWS$ zHHZG$L@s5JxuoMnAq)a1O+SV1HoPiTiUh5S?Z73%ry{q?h?{@ zcb9Psm=EVBkiPTHCMyK*PSL+Y`&lj&mYV8T&Q3Sl|{JY7>7>Ez38-wL9>(>ZIUp zp%?d){iOOCw3Cm){&gL3Lqo$*@r6(lZl-8|D2@cZ33b8F$7{{~{jYWi0m?fM2Z0~} z%^lfIhA4kSJ6~|=h9F+}B)~C%_@)o)6IA>6>Zv(BUh8iK76WL?fXe<$8R3GWpa&%6 z+Vm+gwEuyG+d@oToyg^6!y9zA5PExiW0L#tN!PJvM6(UtLjkjA044?0q{yG8b39yX zS1U7opaL4TLF-8C!~_A9+A0{hIaWF`Eyg*mP$?kMql@=#xWOr<` z?0f|D@mDzktAcn01O{CZRBGkM=+B=&w~)Yws=~NHHxRwQyT!)C^8-tl0q`y8R)Ii3 za%=(Ec&&h)gagj=5NdO&>bmuYBb}&MX>(6 zaX#Vd3!Ivonn{^XmZC_O;+tuYf2e4?j^N!8AfW9izTo`uP((xHi!}+b!J<-H^FHLA zWnZBrap}A_Qxg~*%&8uik`nd+hgJ`G zF$(!(j_w81>l3Q(g=oIOO(I!QB2=i5QK5*k_e@lz?39tL%(Cv|jjpTiZ{7FrclWsdxGq;X&-3&7 zyx+(Bb-Z5B1AI~Sofp2WsUAPB0J@5~xubvJn~8<)iU3)3IiN#@i7WGk!(hF`lc!Ik z%7P%C({5MT*wP|0zN59R?Z{33Ghb#X?v@N~n&IrVPr6~wK*lFtYIoqaJmUhvpyAi$ zemVvv&@#Oe9Sw~bbOfhlVWEH)E-AA#9cB=$hS0Y$f1P%0=G(WR2M@S7 zPbNo>jg8sS#%Zi=gBsEn&s4&)pbWPs=wBA4RP%p|+YCpgzI$|<09Y6mW`r2lU7l}B z@b7L%ZLa#~AvlzxrHCgLVJGcBrbW)VFJH7feU#^BXEidX{N<0Fm``$DE%VJldIEUy z;Hhh@cixJn9d#f~NDJL`9u{Uj=&6iZ`1qSMu@HF=w}X7!?IB?7AVdQ+Y_mY_`wJq$ z*6ZFO8jH#VrpA#%C4ll12d?9&tW>Wvj9XM#|S9DVZoV%d7?k*Kv$Xhev zWFr4m%nB1&$|MD6Hx?*@HMCOYbRL*To^jrRy=ypZp|-`BO3Lkd>Xq=E_a$E+Tzx|FHK@XN(fHm8ug88!x*)a-oGCmHI+txv z1=`X(cD`^U^4EVynD56SXaQ$sV(cuVcB*T8L{h})V-HY(S=5NDudTHuypSf#@&C>! z)E6%3XDR^)CQ3Zh{wtK0>F+rl9f%Kqh$BsrJCH#^s^XP3uIiM__PpEk>kXI0Jc&L& zubi3QgS~2ny6%h#D3bt=j-#L#zwnqOPS*;9v-vO-LY^CIOG8srj`^f(CM|VsQOVu; zRV3rI;E)u@NG2Yi-{a9|PVad0H@s8l-`sVacKpdDAM!1wz+)#d6DtKlTm^9d;04>x zzW$UmL-zV(gFtD_wR_d?_i{xBJb0oa$Zd4Vg-B216mLVa!xU$9x+^7MSw#5NA7A!< z<+J~^>;*DO?MFllc`>>cSX;ITnga?N8kAj+&qRc>_|sKYRRJlA02F#YS6``gEY%~` zYXz_BEd5>|rY$&UVt$R3>{_h7)hiRta?b%}NUBT4J93p7<6kny_lY~3pW!MvIL~II zW4r%B$G{Jt5F8EO9k&mkI}}`OlS6=C5MG_rcgt)+I^YIPPnYUb>XhM46wkQ{-6KkW z_7m`V`0AaM0mqa7p^XvC?$_Bpt~lAuFtrnmEIdQVfw`#>6@ zecgF)Y2W33kk2LXfB*4fMYII8*QD@5Zq{&VM7-Ly3V~#~kW-gmwv@R*vYhdqqqT48 z2ASH%^3MJ~SFECW?oj1PN5BB2_0Jto+{_l*in@C-#H*bPs+zpVsz*o|&0jbCYILajmC8W;(tjWti^# z@J|aKtN)x8PCnjjO$@ym^st~Yk{?YDNtpMgbA9?%`DHa+UGsw2zmTFnhZbGwveN(V zCVZUub7p2tRiA3K_zxx4C+_2CSVW{>ka`@4n47McHs<)OB!8Wn(n6SGCC$gt(a`t< zr;{Gqcih{-Z&L2tFlqBoWZ$?b2giN4E7s=9} zUL&8I#ixF;o(-R!mDYv3xZ(Arj#g&n2RzwN);vQWAFI6U|Beh}UvI=!RJfc7JNXd@ z%&P=&f=Fg^#=T^bOTSd4O^m#N1O8ofpZB?ne#@0>U=E=Zp5>*sE}en&>5w&hwt%pF zv0wQ9#1^OruOQO|h#Q1NL{;$6&3?(r?Xnx#Vm#fI`T_0_Q8z92>?a~~2~d@LeW-}) zrW(VJ1rvX*yTHxO9hj4oV{lb-!I(-$n3;HmU<03{>=_f6?5*vx+)xA3aveoZCixa% zCyN8~)V7PiFl!=&39_fmxi~)=O2{ZChV<6I(ijU-M&Rd8OoU_UUi@+^QmcN-Upa*Y8-~)c^klm%foNkY&yOvx%1Tr3Q1nM!XI)om)wOT$i+GImaSLz zQOJJ9XZ@3pHN=1*^g9!hC~wuRv_SvrLd4okgNOJhlOjd^9O2US#oeOz+W45pmi!tU zk~P?icI?|htmMvH-@QYRk}~_?;rbgJBn@GgG90=l-%Ga|#aTb=4V-Vgqa)Huy!9bG ziy0g=ZM1x1*8&>&f%v;ne$WAqjPa(Ih1#p5w|}${k(8?|4`UnvIu^ENt?(7yFIPZX z6bt;n1KL}54>RA~qlgzdTp`8bIJr+WijQ{R$=z9nyG*~2E)&ECIa^kUWbmJ?$u?O( zqE^JN#zJ=$_-c6A1aWpegR_fQPTvZO_SlE^pmE$9O=G(s9=~HO9eI+ac&xJ@WrJDq zNsK$%n&I4Y1j(-N%Y4K!rj>zMnwIvBLZh#}V_|z3OJl$stm}pw6Pu+pVD|1n{E){l zo(RLsJrEmBiyzPJa|BSwe#jexSHrB2UG~P!#t!KWr5n<^5H}38N*PWPrYB$hbXD`| zQ`9iVvKf|&e&_YQRIoftA^?C%XR#)t;eo8LRHVf3OpU5{--v6@x4CupaNH%#NvAt5 zF0Kcs6H=*P-Juqt>ZC<WMGBYz9>w0uc`Kr@@IxPI&#P5WA8oI*2 zl7-IiJP`V}r)lTBH$AC0{a4+Lx}@f+z&D|{HSfk@1DpP3Q|68QC!4Z}Wb;FOA)qTB z^tffyPNfXo##!h^n0>4bi)pf?k<9q-d?FhtX)aRt91BnUBMK--xrL+CTf{CZb`*c)+duy4Hmoz~Z7LcLiU5=<^KG+mCa2vTFM0cR+jlng44#@h5S`E|bDPI|MmH#IC#{LhflZr~LF&{Kx09<{2whE1TZn@Ii2XDf{~$2KT||631WA zl3gwNP8%9ru@__grscvX_ZF%_YeaA)>!`%@FSAEeaMadkgfr$TXbTDP(%z}Zi~awW z7Jh!?e<&>=8_|01eY)pVS_0JQc^K*{n-ssx#%11B0|Oh6d$v)M3m&ciSxcmbZ+)P0 zdTc72@UK-O6?lxpM~{3qeP6(;`3*Qed@p77BVbpN1HjNeWmnJdV|g>OCx&{`zTsLa zO$a|+m89Pn;;N(5I5?Hw!05+L`ToWH<7j&3Gx$)J0=?-eb)c5?1+CWeCTQ6Tbi*9r zgJv5fJ4660>i%l-UlNdU(f%2r2K`=ET#;~6wcBJtZgL^}4>D*;83cXkzzDGHf&Mw* zO~czx-dg-7`J4e>fXb0@83?RATwFPwzAp#jrZp`Os`(5yKb&#@8D7)uW4Oz77H6Q0 zCUJ6d0;0)ia!-~HZ>CPRx6I=IMEF^`>+!Zm_S49L-Efm^r6qTO%}lvzFA(_*GxRcS zz-Yza*i;aD=_zyH%IEv;H_cm_ck*u0UMs)7n*B>T5tsAVa^lecSWYaHW0UT$}aYLJu6i4JH8|9aBi%!g0*m<=uVz zY2Q*|84{1?=7E9Yj9M-iEQrL27Ic!l_+nN5yU3LukOZX$gPdgTBLS<%@Ley}B3gko z(Q`Mka#_Q{>zj|3Va`NcX(@5{mnOW6Ht)4Sv<^WnX{h(0yHJ|zN3tC{93zlQe62<$Ll`O1vjVjuf*yO`~9|elWnPSn^;GH^$_IsUysq* z1k?QFls^&E9l+DN`S~zBc&X5*1@r91q9XBRpDD?`TwDV*$NoUo>9G}Mh-8svj$$|% z6dSuM*>BMRG{*F{m-3z42u9tai&-tMvcNqFg%H9=@O#&St@!Sn@pkZU-?-7cx;|DH zL2CiPUj2OY9bn@kH8C-fUU;hwRBmC%UEoRpMa&H8Q_h2!@Q4UhOpPu4_+iKJgJ4t5 z(!BGvJHO`BD712_!6ts}*fGzq`3<02LZ3Sv9AkF_HXtYtl3Ptf0|7{Gl?Zq8Q+0z( zK^RiqFq>{ZaI~YxJK6uo*@%sjhxA)jPm~6Q>K^z83`SZ;28+qc%0fnQxEyDDkECne zJzeAoP$XFl27Ys9W~Si#dce+TYHHRyfUb#@md55rJnWN0zDvHH;wu5$|EDGr>DUr3l zS$9BCr?yrL5Z{i3Prhx(+p!~YRrOM^By%g#y;a{X|*7a6@#%(m+7;+R74#pkRA(KS-Z)Rst#T#|3Oo&+_%! z;VYn#d3kwpmIZ>5YYa>kd)9@{bAby0VMntOrUip4;wIN391Ti56i|mxr!Kc*Jsf(y zu>+yO!IkhsN-j)`J%01Q+RDXf)_IHJo-s%CBQTsG4$bYSqa!2ukca?FBj~Yz^pg)i z``;lr&t3OnP+*sH0+h)JxC`hpg6>~*fNW#dnji#{B9hbd?cebO+=#AdWAGA%sf|r2 zt+6N8%E}7q7m(!yMb0cMApGT)nCHMe*dv`i8DMhjU1^ybr=pQjuZ#e@W=^L*0C&J+ z;cwFLU}c-3D&1J*)mtuWAP$D|Un_GyX*@MDimen0hsZW*`Ck<2KXvz(`W6)e=`!@! z8Q=WLuE1xQyk%-8pz25mI1HPkk{}otb{#-PW$asFGNub;XMAW`xs7lgo-^6(+%x-L zLDuMbCJj`bhf~rLv;GgYve#`0n&IC7C`F4g=zuKeeH^?MM z(lLHMLB>Z+K|x^<_+0Jn8pz9!f9bRJ2ajzk%doIhV0AL;K+Fa;gL&(Bnb^m!o(zB*&DSv_oETowfd9szrJA>xKHa zpU>(qv*nN{*Ad^A@u7*J6{s(nF1PO} zxwG^ajHr8rRq>gB!>18H-$>HIz0@TYv%oDqsMRKcB2txHc{o&$T9h{5$+*?)8%@rG zTo)pf?u3k~?;|Aa_?X_LH^i7$7j5E+4AM`Jsncz!TSc2{6yp!?e4P4F=c-IkZ&dUg zz?OkebLrynQ>xyFOdyj=e<;E%^3Lt%h1#a@)IowgV9+0gvfA}uB$>Rkv*7yLbijS+ zWdW6}pmBD~eV_~yUF^c9`@kkcvGETdBl1oiu$f{S62^=Bd=XOf%lFFWA6rQO8|q!l&4%D)6jUv_#1oXw&? zapx}rUVhC{xc}~?i%1?~Y-i8FV~qdt@rSO|*g~biX=%jn>n*kpO`UOt+hEo!hy}_s zNT%j_Jr{Sh-0w1MXjT7E)CW^}Q`NV_zR{g;_0W!?j&ztKKuXWExR6XAGxD4x`(!C$ ztpdn9mx0kH>eSCf9D?Ir#!N!XUmGD%?%D9GtH8U-#V5C14d4HD3GU`=#a4DNlLi-X z*QAYB*Ii{dK%ZjkU-Y}C<_O9DAELWeImN0fP9K3lYx`mc`+3igBlgCfI^+F{xx-*a zz+i?ttdI~QCGKTb+sZeG7b*((?pt?e$O&#j`kr{rS)&kEcb*w8vL>T+su(P}>+7gL zTR6FE8{H}8{||?qOK*$AepW*n&IomCy1&^45+877u*H6SzKi4Uf?n@m1U(*1Wy=F$ zdq~vAHjohusCZm{&6fzZ?^HzKq5jQMMahM*TOmU&F$nnh7VW9>$OWJ(P!^0LM_`w} z(CZH!6cA(t`Nzk{-4B)jYZBffwI?dgy}W zvDx0R#AB1Tfj6VcfyoP4T;5J4}JYwtMPNj!ZU@t#S;#uAlvF>|M1i9d+~xQgHN^L`7Uya zTVug+jcTM)aW+<}bHH>V`}V5$a%gX9deA>SOF8w+5;Dg(m$?53pMJjOeC!&|2iMud zdf&e9eW@1abB2{rP9p)IK5KD#eiDOsIqv>wWpmD1u!P`?3&@n>7NFjGw&caJ)m)s~ z^SONGmcoMrxub(EOTJVeo{$1Ub7n3;<{Dr)-`n#i?h|2s>85AD%1N1xh4S2MB+(|% zc}PL@>}xy}KmreBzGc(SNIIcvbNW9~IS8L9dt2ZEDBw>d%j2AIzrPihnRETjxa4zM zAn9R%fEXdHeAFB}F|g?*Wu<9cEF|*ZZrwjFo11N(=j&b*@%9-`+L=Crb(_DN)${+S^R{6_u&?lC_EYIvay>r?!QF#e*DWn3w^Bw8eQ~o>t23uvPnFM+z|AL|swtwn=Mi8dO({|fEjGdNniHpbn%l` zUR&em=E+mkdB7g&zd{W8^ZJI&_Qsj<9&Gp0BQDX;!UbMwgcieZIvOjgAHKJ*GsS3x z-jZbWsxzbm1 zVYo%0q85C_D5i^oMvaY)d5yChe>Qg>=W|^xIo3NIj>Op&j|-Y^z1e@8Tm$?UYcis` zDJSV`fa6C}7ufV?h#msXTv+p)&NV|R>}#K^s@>^F1o(Y zA(L?Hrr&L7@cC8tt(sKg9i}z{gRQb}h=8vU3_G1iA8}L48oY^RPcwtdCiWVI~Mkja(R+t ziw$S}f&-gdXN9mdM^!y24}{EvjG5*##rJZv6I$23d{-cdy%~kRR>~KYz5#GO)RvOk zn0#7}tcw%RboE|~h7?1dL!tOL5G$`e!oz?`*UZK#FrsAKGZzeC-=6H7>1uX@R#_gr zSg=0 z{;}Xb5tHz0r=LvW4m5I^=89#-#2AbWI12=sX3i;e zoxowyK39X(11gRqtgNgUDEA;jP%~I{$>meyH7Umw@F{_#snZ^TEYP4D-cW(0LY_Le z`>uKC3^fo~Vt?t~Kw|<=QL#xOSNk+Ywh}8l?5A zYwb;UTyn4zn?Rb2U)`Tq^Ww%`#-@KgId^sDw+J092l zw~9>lhO}0zDF0#HXxWs|Vm$?oQ+%N+!_6znanwAi2WY3=(t%ta(+@4)QT0f%;~!v% zPYfH3?tx1T{0|J`f4r@1-d`WEQnkm?_>d@&Fo(*WMtR!+Um!w?*7zIS~lW4q`KeAZk%rk6^AN$7oI!!3YE=LYd|I*}H$h)!VA=wkjz+#lD*aGygf^?K{hng^(@Y8x^SnaH9+1M20+u z5X6!2p66!vo7bHG(n)etz(lO3BIe6xkwn$`9;okvuAh!ZG8`m6U|4`~;Z)Sr!EyWb z!uLc?1=3y=rPST!>YkbTrc5U6$~k4TYX#*m?`0MXNAKg$YbQz~V6NY>?v;zx_|^Ys zoYTn;>C86|PKp~0z=pjFFPGx>rB+~b<7(>Y;9(>JQQnyD;T$~mT!AR1a@bmQr5_sn!o4Y?6U_K&* z2~-f!!pMjfTi%d*R@C0YK1jw z2V)JH17X}#l1O6C==3R=wP#oO;cLTl-TOzPQZ=}<7Zw5Wtv9iI@7Y0RRw!t7xE2=| zuY`qFfn0r}3)BWiKwD7p?CvAkl!Y_f5LEQ0u{RMSdVn&K@4a9LgYW1e>H+w_J~%uv zrfFpfUYP1-7-b?Rf&>izdrpDpa!6{^`~G z>m_k3~(5)PH4CTyqY^YmxIBte)CT9s$B-fHaz~=gEwZ2U*Dsbb?FkWf})~Oz`8FT z=|}&YPH+e_x3K7dxFm4%h(e}ZnM04QHBj_!HLM&MQFu}g*oB=YMVPv6;7->4O{P~X*x;^p#ym49IkF|ci&Cq`0*4)N1rSdn9evJ#A*tidCyPR zRKU5r&n_N()%O4)z%3vU`t+#?eB778Ef$`1cFO^M#d1W+{cAUsiSO(Vb`A~~2zsN` z0zCOuAUpx1Y2WdT8#kyQ8a>~MS~uXubQx=BD~0g0MwSkp*trDmM8^eq+Smc~CQz~Z zBopPp|0oG^8*U(E7HGjttkj~cV}gt@J+hxkf>^(B z1@M5Aok{(+1Y6k2m6u5eYv4hPNlA%@xFs6Jffzlu%BHY@a;hZ+o+LFfl1| zgJsX2DEJUL`1nX+>|6u%hLoEu1?k$s2VL9HfQv!b0G56Wh>Kq5#>fKv3hg_f1l}7# z)9}!YiF4g~SS0_z?}VT*2@HjR`3Ob@Mxe^>g9~;sFc3(K#0B>6$RV^@55{Mu7NyOX ze+8Zd`MVlA6O%1%ZI8S5A4Ndh*|nts3-H1dl9EzOkwbQuikX=lV>?hTgPu*qRsnQP z7`GSPelaj)At8Zo_)wWKYE4V0dY)1DES$axHvLk*eS(7qV=mWRmhwoBv3G5oI82OO_##m6h!fhDE0EgG2DP zB{2NPSjKky5^VicQpCqqRoQOdD&NCHP2Ayxt6c}RFT6i!OVz-4bO25>pM$Cs+LI2A z$g}oZRp2hzF%eYsRIYJU%S{0b2!hAvMFQ&OnG<)hZu+Y8MY(^+YZAlOdP<%jVe(1ERn+c)pRv@v{x;tgrJ&#Rd|qsoV% z8gFS)E%<&N9;SLUtb+4I604LHBMA+koSE6(Oqruj<=;6GD`t7wvlPlNiSuKW=%vB3 z3#(%$QCGb*M$ktB+iW9RuXO+JGscpOI}DG$DbH_S$egZPP17@9WU z;IZv`NN?=*dAFt;RVRsNt~_VN%GmwrV0fX#uwg3z%u1=EtSMQ>2M^O3KU5PMaeY+X z0E19vyZ1%25>`XVC35s3G)WbrnPr0EL4hfFj0_!m$ybG$gc7>D- z47{yL2UX#2y*Kiq)axnwLU&IO36x9Ka4|w4k*$d=O^K|8K@#Z*_(T;3;~1g-{J+c* zzh@W`5upgQzerhFGg~~To{#b+4?}SXJGX?#*X}b#M3BpZ|F&OCfX)6=Nr}&$rvRn$ zg-DXSKNCBqIA+sZQdUic392NDp3WMp-pnqpx~HD9y{SRM+6i63VkJ$rPb1ES&a zZ0k4Wc52>Pn=sj^{!nNS9q)@ULyruqDFxW50c&R9&;#)k?zm=e$;Y>Doi39m2R)Jo zil>Kx$J2vya|MEef&_to0$Ge&2~vZD=T?9{zyX7NU_@#VYiepLCbw*n0ZNsjFJHpB zl09)6qFKbfdN1^Q5kN%=?u4qwYaE)!#*W`sdO`!q@Nwbc4+bt}rVcMstKoLR!$2?{ zgVr|^C`_P^V_Z57Hy&DS3k{q3J4Hn zL$JoArm~`GDRp&qozGSldz`4~>51U`A0&GdL$iUiVgu-xl)HA}!q-qY^OiWpp*vIM zbCkfp-NAZox2v?pjVL9otN!G;s5n1=#9@I1^yR*ns7Z&(l%;f8b`t|!A( zLRaGwK;z7TodPvXa7v0vVf9xJ5$o1Um`*g)b=qkNb{_Nu`}+FAdgg+enR_M60bDj{ zx9`BfCXNrs#A;yCx&ZwL*7)H^Mg7%4(ZOPX#xT}XB!HeLDBHj=VD{}((^4NNl+3Vh z)3i^#IREs+i5>B2H&kV|yAgwHFl;P=t+f5({90MBY zGU|hyuNvGK?1VwAurV6Y$#@quO~XV6ZfU4ZE=YU8+LEf36%6%4uaFJo&Mi6@V;n<21*#WsA0mx%ptJGv@*>hPpGkS|@(TQ-d$7jTr^H^ru3=Sz85Ac4*y*eE}X7 zRBj)OPBG3^@$!;{jpl^#PO4+{U^>{3hKZpf03Llfp5=O6Q_;PsU1us-2@zcIN<3QB zr|{lSO+XO3(%)bzh{+I0!5E}!2r4pR?&;iP<(Cb%Yw7;c=1BzgD@VSK7AU5`Wy``N2?u} zB4F|otDqnyoKXQ^4?(&bMyFCNjJrY`GeZ+ARLdSev%n@({yBGt&4B z3&zUV%MdOGRFOQ}4tCh3hbD3>j)1wOa1!w)9X^PnJRVGPz6=LZK$}{wuECXxLgryO zH695i-_s5pL{x9U{XY*dxIP_3eg}~(7!Onz@KBv>Pn9lOoRRlT~C#D diff --git a/log/ResNet-18/log.txt b/log/ResNet-18/log.txt index db627f2..410c501 100644 --- a/log/ResNet-18/log.txt +++ b/log/ResNet-18/log.txt @@ -1,118 +1,536 @@ ********************begin training!******************** -Eopch: 1 train loss = 1.519442 -Eopch: 1 valuation loss = 1.476453, ACC = 0.988333 +Eopch: 1 train loss = 0.112472 +Eopch: 1 valuation loss = 0.032543, ACC = 0.991333 Epoch: 1 model has been already save! Training epoch: 1 completed. -Eopch: 2 train loss = 1.469806 -Eopch: 2 valuation loss = 1.469115, ACC = 0.993667 - Epoch: 2 model has been already save! +Eopch: 2 train loss = 0.016170 +Eopch: 2 valuation loss = 0.033873, ACC = 0.991000 Training epoch: 2 completed. -Eopch: 3 train loss = 1.465635 -Eopch: 3 valuation loss = 1.468244, ACC = 0.994167 +Eopch: 3 train loss = 0.010401 +Eopch: 3 valuation loss = 0.033791, ACC = 0.991500 Epoch: 3 model has been already save! Training epoch: 3 completed. -Eopch: 4 train loss = 1.464047 -Eopch: 4 valuation loss = 1.467587, ACC = 0.994000 +Eopch: 4 train loss = 0.008944 +Eopch: 4 valuation loss = 0.021961, ACC = 0.994333 + Epoch: 4 model has been already save! +Training epoch: 4 completed. +Eopch: 5 train loss = 0.006552 +Eopch: 5 valuation loss = 0.020410, ACC = 0.995500 + Epoch: 5 model has been already save! +Training epoch: 5 completed. +Eopch: 6 train loss = 0.004441 +Eopch: 6 valuation loss = 0.028272, ACC = 0.992833 +Training epoch: 6 completed. +Eopch: 7 train loss = 0.007019 +Eopch: 7 valuation loss = 0.029333, ACC = 0.992500 +Training epoch: 7 completed. +Eopch: 8 train loss = 0.003278 +Eopch: 8 valuation loss = 0.021766, ACC = 0.993500 +Training epoch: 8 completed. +Eopch: 9 train loss = 0.006029 +Eopch: 9 valuation loss = 0.023517, ACC = 0.993667 +Training epoch: 9 completed. +Eopch: 10 train loss = 0.005264 +Eopch: 10 valuation loss = 0.021146, ACC = 0.993833 +Training epoch: 10 completed. +Eopch: 11 train loss = 0.002638 +Eopch: 11 valuation loss = 0.017968, ACC = 0.995500 +Training epoch: 11 completed. +Eopch: 12 train loss = 0.002231 +Eopch: 12 valuation loss = 0.028438, ACC = 0.993167 +Training epoch: 12 completed. +Eopch: 13 train loss = 0.004573 +Eopch: 13 valuation loss = 0.024170, ACC = 0.994167 +Training epoch: 13 completed. +Eopch: 14 train loss = 0.002521 +Eopch: 14 valuation loss = 0.022760, ACC = 0.995500 +Training epoch: 14 completed. +Eopch: 15 train loss = 0.001604 +Eopch: 15 valuation loss = 0.029690, ACC = 0.992000 +Training epoch: 15 completed. +Eopch: 16 train loss = 0.006733 +Eopch: 16 valuation loss = 0.024623, ACC = 0.994000 +Training epoch: 16 completed. +Eopch: 17 train loss = 0.002092 +Eopch: 17 valuation loss = 0.024397, ACC = 0.993833 +Training epoch: 17 completed. +Eopch: 18 train loss = 0.001126 +Eopch: 18 valuation loss = 0.018558, ACC = 0.994500 +Training epoch: 18 completed. +Eopch: 19 train loss = 0.003236 +Eopch: 19 valuation loss = 0.019188, ACC = 0.995333 +Training epoch: 19 completed. +Eopch: 20 train loss = 0.003076 +Eopch: 20 valuation loss = 0.021177, ACC = 0.994833 +Training epoch: 20 completed. +Eopch: 21 train loss = 0.001987 +Eopch: 21 valuation loss = 0.027134, ACC = 0.992667 +Training epoch: 21 completed. +Eopch: 22 train loss = 0.002780 +Eopch: 22 valuation loss = 0.036788, ACC = 0.992167 +Training epoch: 22 completed. +Eopch: 23 train loss = 0.002600 +Eopch: 23 valuation loss = 0.026954, ACC = 0.994000 +Training epoch: 23 completed. +Eopch: 24 train loss = 0.002509 +Eopch: 24 valuation loss = 0.024505, ACC = 0.994333 +Training epoch: 24 completed. +Eopch: 25 train loss = 0.002124 +Eopch: 25 valuation loss = 0.027661, ACC = 0.992333 +Training epoch: 25 completed. +Eopch: 26 train loss = 0.001353 +Eopch: 26 valuation loss = 0.023039, ACC = 0.994833 +Training epoch: 26 completed. +Eopch: 27 train loss = 0.001375 +Eopch: 27 valuation loss = 0.019753, ACC = 0.995667 + Epoch: 27 model has been already save! +Training epoch: 27 completed. +Eopch: 28 train loss = 0.002047 +Eopch: 28 valuation loss = 0.022252, ACC = 0.994500 +Training epoch: 28 completed. +Eopch: 29 train loss = 0.003442 +Eopch: 29 valuation loss = 0.026994, ACC = 0.993333 +Training epoch: 29 completed. +Eopch: 30 train loss = 0.000458 +Eopch: 30 valuation loss = 0.019145, ACC = 0.995167 +Training epoch: 30 completed. +Eopch: 31 train loss = 0.000170 +Eopch: 31 valuation loss = 0.019967, ACC = 0.995833 + Epoch: 31 model has been already save! +Training epoch: 31 completed. +Eopch: 32 train loss = 0.004198 +Eopch: 32 valuation loss = 0.022859, ACC = 0.994000 +Training epoch: 32 completed. +Eopch: 33 train loss = 0.001504 +Eopch: 33 valuation loss = 0.032546, ACC = 0.992500 +Training epoch: 33 completed. +Eopch: 34 train loss = 0.000715 +Eopch: 34 valuation loss = 0.021275, ACC = 0.995333 +Training epoch: 34 completed. +Eopch: 35 train loss = 0.001949 +Eopch: 35 valuation loss = 0.024419, ACC = 0.992667 +Training epoch: 35 completed. +Eopch: 36 train loss = 0.002625 +Eopch: 36 valuation loss = 0.022674, ACC = 0.993500 +Training epoch: 36 completed. +Eopch: 37 train loss = 0.001432 +Eopch: 37 valuation loss = 0.020469, ACC = 0.994500 +Training epoch: 37 completed. +Eopch: 38 train loss = 0.000919 +Eopch: 38 valuation loss = 0.019236, ACC = 0.995333 +Training epoch: 38 completed. +Eopch: 39 train loss = 0.002806 +Eopch: 39 valuation loss = 0.036104, ACC = 0.992500 +Training epoch: 39 completed. +Eopch: 40 train loss = 0.000593 +Eopch: 40 valuation loss = 0.017297, ACC = 0.996000 + Epoch: 40 model has been already save! +Training epoch: 40 completed. +Eopch: 41 train loss = 0.000074 +Eopch: 41 valuation loss = 0.018258, ACC = 0.996000 +Training epoch: 41 completed. +Eopch: 42 train loss = 0.000043 +Eopch: 42 valuation loss = 0.016699, ACC = 0.996333 + Epoch: 42 model has been already save! +Training epoch: 42 completed. +Eopch: 43 train loss = 0.004258 +Eopch: 43 valuation loss = 0.022825, ACC = 0.993833 +Training epoch: 43 completed. +Eopch: 44 train loss = 0.001685 +Eopch: 44 valuation loss = 0.019110, ACC = 0.995500 +Training epoch: 44 completed. +Eopch: 45 train loss = 0.000996 +Eopch: 45 valuation loss = 0.018361, ACC = 0.994833 +Training epoch: 45 completed. +Eopch: 46 train loss = 0.000111 +Eopch: 46 valuation loss = 0.017752, ACC = 0.996000 +Training epoch: 46 completed. +Eopch: 47 train loss = 0.002375 +Eopch: 47 valuation loss = 0.021209, ACC = 0.994667 +Training epoch: 47 completed. +Eopch: 48 train loss = 0.002433 +Eopch: 48 valuation loss = 0.022865, ACC = 0.995333 +Training epoch: 48 completed. +Eopch: 49 train loss = 0.000814 +Eopch: 49 valuation loss = 0.019654, ACC = 0.995333 +Training epoch: 49 completed. +Eopch: 50 train loss = 0.002580 +Eopch: 50 valuation loss = 0.026501, ACC = 0.993667 +Training epoch: 50 completed. +Train Loss Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResNet-18/train_loss.png +Validation Accuracy Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResNet-18/val_acc.png +Train Loss: +0.1124717586737797,0.016170259801807738,0.010400650284713205,0.008944160013339069,0.0065518872841114845,0.004440692789629065,0.007019181146336049,0.0032781219610485625,0.006029157559703597,0.0052644157093864686,0.0026384786645780125,0.002230725335097287,0.0045732613302939965,0.0025210315351931367,0.0016043741853499992,0.006733059158194363,0.002091662949198499,0.0011263878866893246,0.0032356474211569525,0.003076039213504813,0.0019870159624117358,0.0027797034472573034,0.0026003861377827384,0.0025087218852057145,0.0021240568647468206,0.0013532344944920081,0.0013749040911510686,0.0020474040254814243,0.0034417064467761865,0.0004576196333709312,0.00016953872533785995,0.0041983963904424,0.0015040430597704152,0.0007154906393583548,0.0019489095852944982,0.0026249186220027878,0.0014320352600488563,0.0009191627323662895,0.002806055992076538,0.000592689474532082,7.439634051884694e-05,4.266309739758123e-05,0.0042578651909786535,0.001685497372659699,0.0009962033815523893,0.00011079841955785807,0.0023747650443307396,0.0024328762135416336,0.0008143995424636108,0.00257967325731004 +Validation Accuracy: +0.9913333333333333,0.991,0.9915,0.9943333333333333,0.9955,0.9928333333333333,0.9925,0.9935,0.9936666666666667,0.9938333333333333,0.9955,0.9931666666666666,0.9941666666666666,0.9955,0.992,0.994,0.9938333333333333,0.9945,0.9953333333333333,0.9948333333333333,0.9926666666666667,0.9921666666666666,0.994,0.9943333333333333,0.9923333333333333,0.9948333333333333,0.9956666666666667,0.9945,0.9933333333333333,0.9951666666666666,0.9958333333333333,0.994,0.9925,0.9953333333333333,0.9926666666666667,0.9935,0.9945,0.9953333333333333,0.9925,0.996,0.996,0.9963333333333333,0.9938333333333333,0.9955,0.9948333333333333,0.996,0.9946666666666667,0.9953333333333333,0.9953333333333333,0.9936666666666667 +=> loaded model checkpoint: /userhome/cs2/mingzeng/codes/kmnist/models/ResNet-18.pkl +******* begin testing!********* +Test Averaged Loss = 0.057160 +Test Averaged Accuracy = 0.988300 +Confusion Matrix Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResNet-18/confusion_matrix.png +Class: 0 Accuracy = 0.996600 Precision = 0.977273 Recall = 0.989000 f-score = 0.983101 +Class: 1 Accuracy = 0.998100 Precision = 0.994955 Recall = 0.986000 f-score = 0.990457 +Class: 2 Accuracy = 0.996100 Precision = 0.986829 Recall = 0.974000 f-score = 0.980372 +Class: 3 Accuracy = 0.998300 Precision = 0.989055 Recall = 0.994000 f-score = 0.991521 +Class: 4 Accuracy = 0.995300 Precision = 0.983756 Recall = 0.969000 f-score = 0.976322 +Class: 5 Accuracy = 0.998000 Precision = 0.990982 Recall = 0.989000 f-score = 0.989990 +Class: 6 Accuracy = 0.997900 Precision = 0.985134 Recall = 0.994000 f-score = 0.989547 +Class: 7 Accuracy = 0.998600 Precision = 0.990060 Recall = 0.996000 f-score = 0.993021 +Class: 8 Accuracy = 0.998600 Precision = 0.989087 Recall = 0.997000 f-score = 0.993028 +Class: 9 Accuracy = 0.999100 Precision = 0.995996 Recall = 0.995000 f-score = 0.995498 +=> loaded model checkpoint: /userhome/cs2/mingzeng/codes/kmnist/models/ResNet-18.pkl +********************begin training!******************** +Eopch: 1 train loss = 0.002380 +Eopch: 1 valuation loss = 0.020418, ACC = 0.994667 + Epoch: 1 model has been already save! +Training epoch: 1 completed. +Eopch: 2 train loss = 0.001551 +Eopch: 2 valuation loss = 0.034197, ACC = 0.992000 +Training epoch: 2 completed. +Eopch: 3 train loss = 0.001335 +Eopch: 3 valuation loss = 0.028909, ACC = 0.992833 +Training epoch: 3 completed. +Eopch: 4 train loss = 0.002054 +Eopch: 4 valuation loss = 0.023759, ACC = 0.995000 + Epoch: 4 model has been already save! +Training epoch: 4 completed. +Eopch: 5 train loss = 0.001485 +Eopch: 5 valuation loss = 0.022776, ACC = 0.994500 +Training epoch: 5 completed. +Eopch: 6 train loss = 0.001384 +Eopch: 6 valuation loss = 0.024340, ACC = 0.993667 +Training epoch: 6 completed. +Eopch: 7 train loss = 0.002727 +Eopch: 7 valuation loss = 0.021776, ACC = 0.994333 +Training epoch: 7 completed. +Eopch: 8 train loss = 0.001291 +Eopch: 8 valuation loss = 0.018708, ACC = 0.996333 + Epoch: 8 model has been already save! +Training epoch: 8 completed. +Eopch: 9 train loss = 0.001168 +Eopch: 9 valuation loss = 0.041369, ACC = 0.990000 +Training epoch: 9 completed. +Eopch: 10 train loss = 0.003025 +Eopch: 10 valuation loss = 0.022515, ACC = 0.994833 +Training epoch: 10 completed. +Eopch: 11 train loss = 0.001245 +Eopch: 11 valuation loss = 0.021370, ACC = 0.995333 +Training epoch: 11 completed. +Eopch: 12 train loss = 0.000126 +Eopch: 12 valuation loss = 0.018098, ACC = 0.996167 +Training epoch: 12 completed. +Eopch: 13 train loss = 0.000051 +Eopch: 13 valuation loss = 0.017615, ACC = 0.996333 +Training epoch: 13 completed. +Eopch: 14 train loss = 0.000036 +Eopch: 14 valuation loss = 0.017552, ACC = 0.996667 + Epoch: 14 model has been already save! +Training epoch: 14 completed. +Eopch: 15 train loss = 0.004902 +Eopch: 15 valuation loss = 0.023053, ACC = 0.995333 +Training epoch: 15 completed. +Eopch: 16 train loss = 0.001666 +Eopch: 16 valuation loss = 0.016388, ACC = 0.996000 +Training epoch: 16 completed. +Eopch: 17 train loss = 0.000240 +Eopch: 17 valuation loss = 0.015207, ACC = 0.996500 +Training epoch: 17 completed. +Eopch: 18 train loss = 0.000062 +Eopch: 18 valuation loss = 0.014461, ACC = 0.996667 +Training epoch: 18 completed. +Eopch: 19 train loss = 0.000047 +Eopch: 19 valuation loss = 0.015068, ACC = 0.996333 +Training epoch: 19 completed. +Eopch: 20 train loss = 0.003623 +Eopch: 20 valuation loss = 0.032521, ACC = 0.992167 +Training epoch: 20 completed. +Eopch: 21 train loss = 0.003394 +Eopch: 21 valuation loss = 0.017815, ACC = 0.995167 +Training epoch: 21 completed. +Eopch: 22 train loss = 0.000386 +Eopch: 22 valuation loss = 0.016767, ACC = 0.995667 +Training epoch: 22 completed. +Eopch: 23 train loss = 0.002935 +Eopch: 23 valuation loss = 0.022401, ACC = 0.995000 +Training epoch: 23 completed. +Eopch: 24 train loss = 0.001660 +Eopch: 24 valuation loss = 0.023005, ACC = 0.995000 +Training epoch: 24 completed. +Eopch: 25 train loss = 0.000494 +Eopch: 25 valuation loss = 0.026597, ACC = 0.994500 +Training epoch: 25 completed. +Eopch: 26 train loss = 0.002265 +Eopch: 26 valuation loss = 0.024182, ACC = 0.994333 +Training epoch: 26 completed. +Eopch: 27 train loss = 0.000340 +Eopch: 27 valuation loss = 0.017949, ACC = 0.996167 +Training epoch: 27 completed. +Eopch: 28 train loss = 0.000819 +Eopch: 28 valuation loss = 0.027741, ACC = 0.993167 +Training epoch: 28 completed. +Eopch: 29 train loss = 0.001746 +Eopch: 29 valuation loss = 0.022587, ACC = 0.995167 +Training epoch: 29 completed. +Eopch: 30 train loss = 0.000783 +Eopch: 30 valuation loss = 0.023898, ACC = 0.995667 +Training epoch: 30 completed. +Eopch: 31 train loss = 0.001118 +Eopch: 31 valuation loss = 0.021154, ACC = 0.995167 +Training epoch: 31 completed. +Eopch: 32 train loss = 0.003023 +Eopch: 32 valuation loss = 0.026357, ACC = 0.993500 +Training epoch: 32 completed. +Eopch: 33 train loss = 0.000544 +Eopch: 33 valuation loss = 0.023024, ACC = 0.994833 +Training epoch: 33 completed. +Eopch: 34 train loss = 0.000282 +Eopch: 34 valuation loss = 0.020969, ACC = 0.996167 +Training epoch: 34 completed. +Eopch: 35 train loss = 0.001814 +Eopch: 35 valuation loss = 0.024345, ACC = 0.994667 +Training epoch: 35 completed. +Eopch: 36 train loss = 0.002169 +Eopch: 36 valuation loss = 0.026725, ACC = 0.994333 +Training epoch: 36 completed. +Eopch: 37 train loss = 0.000258 +Eopch: 37 valuation loss = 0.021777, ACC = 0.995500 +Training epoch: 37 completed. +Eopch: 38 train loss = 0.002167 +Eopch: 38 valuation loss = 0.017834, ACC = 0.995500 +Training epoch: 38 completed. +Eopch: 39 train loss = 0.000532 +Eopch: 39 valuation loss = 0.020227, ACC = 0.995500 +Training epoch: 39 completed. +Eopch: 40 train loss = 0.000691 +Eopch: 40 valuation loss = 0.017154, ACC = 0.996333 +Training epoch: 40 completed. +Eopch: 41 train loss = 0.000897 +Eopch: 41 valuation loss = 0.025298, ACC = 0.993333 +Training epoch: 41 completed. +Eopch: 42 train loss = 0.001715 +Eopch: 42 valuation loss = 0.025247, ACC = 0.994833 +Training epoch: 42 completed. +Eopch: 43 train loss = 0.000493 +Eopch: 43 valuation loss = 0.023587, ACC = 0.995667 +Training epoch: 43 completed. +Eopch: 44 train loss = 0.000088 +Eopch: 44 valuation loss = 0.020639, ACC = 0.995500 +Training epoch: 44 completed. +Eopch: 45 train loss = 0.000049 +Eopch: 45 valuation loss = 0.020038, ACC = 0.996000 +Training epoch: 45 completed. +Eopch: 46 train loss = 0.000034 +Eopch: 46 valuation loss = 0.019470, ACC = 0.995667 +Training epoch: 46 completed. +Eopch: 47 train loss = 0.002782 +Eopch: 47 valuation loss = 0.041904, ACC = 0.988167 +Training epoch: 47 completed. +Eopch: 48 train loss = 0.002982 +Eopch: 48 valuation loss = 0.020116, ACC = 0.995500 +Training epoch: 48 completed. +Eopch: 49 train loss = 0.000191 +Eopch: 49 valuation loss = 0.018578, ACC = 0.996000 +Training epoch: 49 completed. +Eopch: 50 train loss = 0.000624 +Eopch: 50 valuation loss = 0.023107, ACC = 0.994667 +Training epoch: 50 completed. +Train Loss Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResNet-18/train_loss.png +Validation Accuracy Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResNet-18/val_acc.png +Train Loss: +0.0023798978278849223,0.001551491871023827,0.0013354753494879885,0.002054083236969754,0.0014850462116790097,0.0013839491533943866,0.0027274314285686938,0.0012908909813588265,0.0011675883227027773,0.0030252575809148878,0.0012450252825608912,0.00012600182976248581,5.131643628639778e-05,3.556391739841912e-05,0.004902001174867005,0.0016656818849003822,0.0002404311794494762,6.223151201156164e-05,4.731706620064671e-05,0.00362265661686718,0.0033936699260611156,0.0003855040930247019,0.0029351176442366277,0.0016602201061659066,0.0004939092476814802,0.002265196821997021,0.00034032644487177533,0.0008193693273553988,0.0017461086846763667,0.000783329249710952,0.001117981775017776,0.0030230789899898867,0.0005442745805398923,0.000281953724842536,0.0018135306640964424,0.002168529378855163,0.00025785759732562403,0.0021670936117468833,0.0005320045849255673,0.0006905365555163453,0.0008968770830824293,0.0017150963560410139,0.0004934786079238362,8.781648941732989e-05,4.8519646186183976e-05,3.3700352101059855e-05,0.0027815273267490404,0.002981613644168263,0.0001908360637885186,0.0006241627901026818 +Validation Accuracy: +0.9946666666666667,0.992,0.9928333333333333,0.995,0.9945,0.9936666666666667,0.9943333333333333,0.9963333333333333,0.99,0.9948333333333333,0.9953333333333333,0.9961666666666666,0.9963333333333333,0.9966666666666667,0.9953333333333333,0.996,0.9965,0.9966666666666667,0.9963333333333333,0.9921666666666666,0.9951666666666666,0.9956666666666667,0.995,0.995,0.9945,0.9943333333333333,0.9961666666666666,0.9931666666666666,0.9951666666666666,0.9956666666666667,0.9951666666666666,0.9935,0.9948333333333333,0.9961666666666666,0.9946666666666667,0.9943333333333333,0.9955,0.9955,0.9955,0.9963333333333333,0.9933333333333333,0.9948333333333333,0.9956666666666667,0.9955,0.996,0.9956666666666667,0.9881666666666666,0.9955,0.996,0.9946666666666667 +=> loaded model checkpoint: /userhome/cs2/mingzeng/codes/kmnist/models/ResNet-18.pkl +******* begin testing!********* +Test Averaged Loss = 0.059358 +Test Averaged Accuracy = 0.988100 +Confusion Matrix Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResNet-18/confusion_matrix.png +Class: 0 Accuracy = 0.996300 Precision = 0.975321 Recall = 0.988000 f-score = 0.981619 +Class: 1 Accuracy = 0.998100 Precision = 0.994955 Recall = 0.986000 f-score = 0.990457 +Class: 2 Accuracy = 0.996300 Precision = 0.991828 Recall = 0.971000 f-score = 0.981304 +Class: 3 Accuracy = 0.998600 Precision = 0.991036 Recall = 0.995000 f-score = 0.993014 +Class: 4 Accuracy = 0.995300 Precision = 0.985729 Recall = 0.967000 f-score = 0.976275 +Class: 5 Accuracy = 0.997900 Precision = 0.989990 Recall = 0.989000 f-score = 0.989495 +Class: 6 Accuracy = 0.997600 Precision = 0.980315 Recall = 0.996000 f-score = 0.988095 +Class: 7 Accuracy = 0.999000 Precision = 0.993028 Recall = 0.997000 f-score = 0.995010 +Class: 8 Accuracy = 0.998200 Precision = 0.984221 Recall = 0.998000 f-score = 0.991063 +Class: 9 Accuracy = 0.998900 Precision = 0.994995 Recall = 0.994000 f-score = 0.994497 +********************begin training!******************** +Eopch: 1 train loss = 0.110339 +Eopch: 1 valuation loss = 0.030959, ACC = 0.991500 + Epoch: 1 model has been already save! +Training epoch: 1 completed. +Eopch: 2 train loss = 0.015871 +Eopch: 2 valuation loss = 0.026959, ACC = 0.992667 + Epoch: 2 model has been already save! +Training epoch: 2 completed. +Eopch: 3 train loss = 0.010047 +Eopch: 3 valuation loss = 0.030732, ACC = 0.991167 +Training epoch: 3 completed. +Eopch: 4 train loss = 0.009027 +Eopch: 4 valuation loss = 0.027057, ACC = 0.992167 Training epoch: 4 completed. -Eopch: 5 train loss = 1.463853 -Eopch: 5 valuation loss = 1.468458, ACC = 0.994000 +Eopch: 5 train loss = 0.007910 +Eopch: 5 valuation loss = 0.028243, ACC = 0.992333 Training epoch: 5 completed. -Eopch: 6 train loss = 1.463171 -Eopch: 6 valuation loss = 1.468626, ACC = 0.992833 +Eopch: 6 train loss = 0.007268 +Eopch: 6 valuation loss = 0.039620, ACC = 0.988167 Training epoch: 6 completed. -Eopch: 7 train loss = 1.463859 -Eopch: 7 valuation loss = 1.466508, ACC = 0.994167 +Eopch: 7 train loss = 0.005927 +Eopch: 7 valuation loss = 0.027992, ACC = 0.992833 + Epoch: 7 model has been already save! Training epoch: 7 completed. -Eopch: 8 train loss = 1.462336 -Eopch: 8 valuation loss = 1.465903, ACC = 0.995167 +Eopch: 8 train loss = 0.004416 +Eopch: 8 valuation loss = 0.023292, ACC = 0.994500 Epoch: 8 model has been already save! Training epoch: 8 completed. -Eopch: 9 train loss = 1.462728 -Eopch: 9 valuation loss = 1.467648, ACC = 0.994500 +Eopch: 9 train loss = 0.004000 +Eopch: 9 valuation loss = 0.031309, ACC = 0.992500 Training epoch: 9 completed. -Eopch: 10 train loss = 1.463454 -Eopch: 10 valuation loss = 1.467494, ACC = 0.993500 +Eopch: 10 train loss = 0.004315 +Eopch: 10 valuation loss = 0.038368, ACC = 0.990333 Training epoch: 10 completed. -Eopch: 11 train loss = 1.462208 -Eopch: 11 valuation loss = 1.467586, ACC = 0.993167 +Eopch: 11 train loss = 0.005083 +Eopch: 11 valuation loss = 0.026671, ACC = 0.992500 Training epoch: 11 completed. -Eopch: 12 train loss = 1.462793 -Eopch: 12 valuation loss = 1.466612, ACC = 0.995167 +Eopch: 12 train loss = 0.001932 +Eopch: 12 valuation loss = 0.020445, ACC = 0.994833 + Epoch: 12 model has been already save! Training epoch: 12 completed. -Eopch: 13 train loss = 1.462064 -Eopch: 13 valuation loss = 1.466395, ACC = 0.994000 +Eopch: 13 train loss = 0.003449 +Eopch: 13 valuation loss = 0.034891, ACC = 0.991333 Training epoch: 13 completed. -Eopch: 14 train loss = 1.462782 -Eopch: 14 valuation loss = 1.466514, ACC = 0.994833 +Eopch: 14 train loss = 0.003702 +Eopch: 14 valuation loss = 0.020935, ACC = 0.994833 Training epoch: 14 completed. -Eopch: 15 train loss = 1.462364 -Eopch: 15 valuation loss = 1.466478, ACC = 0.994667 +Eopch: 15 train loss = 0.001845 +Eopch: 15 valuation loss = 0.030075, ACC = 0.993833 Training epoch: 15 completed. -Eopch: 16 train loss = 1.462193 -Eopch: 16 valuation loss = 1.465112, ACC = 0.995833 - Epoch: 16 model has been already save! +Eopch: 16 train loss = 0.004842 +Eopch: 16 valuation loss = 0.025842, ACC = 0.993833 Training epoch: 16 completed. -Eopch: 17 train loss = 1.462669 -Eopch: 17 valuation loss = 1.465296, ACC = 0.996000 - Epoch: 17 model has been already save! +Eopch: 17 train loss = 0.003709 +Eopch: 17 valuation loss = 0.025759, ACC = 0.994667 Training epoch: 17 completed. -Eopch: 18 train loss = 1.462155 -Eopch: 18 valuation loss = 1.465848, ACC = 0.995167 +Eopch: 18 train loss = 0.002032 +Eopch: 18 valuation loss = 0.021540, ACC = 0.994500 Training epoch: 18 completed. -Eopch: 19 train loss = 1.461859 -Eopch: 19 valuation loss = 1.465558, ACC = 0.995000 +Eopch: 19 train loss = 0.002395 +Eopch: 19 valuation loss = 0.033867, ACC = 0.993500 Training epoch: 19 completed. -Eopch: 20 train loss = 1.462535 -Eopch: 20 valuation loss = 1.466299, ACC = 0.994333 +Eopch: 20 train loss = 0.002707 +Eopch: 20 valuation loss = 0.019205, ACC = 0.994667 Training epoch: 20 completed. -Eopch: 21 train loss = 1.461965 -Eopch: 21 valuation loss = 1.467344, ACC = 0.992500 +Eopch: 21 train loss = 0.002436 +Eopch: 21 valuation loss = 0.028644, ACC = 0.992500 Training epoch: 21 completed. -Eopch: 22 train loss = 1.461767 -Eopch: 22 valuation loss = 1.465915, ACC = 0.995500 +Eopch: 22 train loss = 0.002499 +Eopch: 22 valuation loss = 0.017397, ACC = 0.996500 + Epoch: 22 model has been already save! Training epoch: 22 completed. -Eopch: 23 train loss = 1.462731 -Eopch: 23 valuation loss = 1.465383, ACC = 0.994833 +Eopch: 23 train loss = 0.001254 +Eopch: 23 valuation loss = 0.030186, ACC = 0.992833 Training epoch: 23 completed. -Eopch: 24 train loss = 1.462142 -Eopch: 24 valuation loss = 1.466911, ACC = 0.993667 +Eopch: 24 train loss = 0.003710 +Eopch: 24 valuation loss = 0.024254, ACC = 0.993000 Training epoch: 24 completed. -Eopch: 25 train loss = 1.461943 -Eopch: 25 valuation loss = 1.466150, ACC = 0.994167 +Eopch: 25 train loss = 0.001489 +Eopch: 25 valuation loss = 0.019749, ACC = 0.994667 Training epoch: 25 completed. -Eopch: 26 train loss = 1.461857 -Eopch: 26 valuation loss = 1.466353, ACC = 0.994667 +Eopch: 26 train loss = 0.004095 +Eopch: 26 valuation loss = 0.020992, ACC = 0.995167 Training epoch: 26 completed. -Eopch: 27 train loss = 1.462463 -Eopch: 27 valuation loss = 1.466523, ACC = 0.994667 +Eopch: 27 train loss = 0.000797 +Eopch: 27 valuation loss = 0.016143, ACC = 0.996333 Training epoch: 27 completed. -Eopch: 28 train loss = 1.461748 -Eopch: 28 valuation loss = 1.466534, ACC = 0.994333 +Eopch: 28 train loss = 0.000089 +Eopch: 28 valuation loss = 0.018128, ACC = 0.995500 Training epoch: 28 completed. -Eopch: 29 train loss = 1.462236 -Eopch: 29 valuation loss = 1.466483, ACC = 0.994667 +Eopch: 29 train loss = 0.002590 +Eopch: 29 valuation loss = 0.019603, ACC = 0.995500 Training epoch: 29 completed. -Eopch: 30 train loss = 1.462003 -Eopch: 30 valuation loss = 1.466212, ACC = 0.994500 +Eopch: 30 train loss = 0.003278 +Eopch: 30 valuation loss = 0.025397, ACC = 0.994667 Training epoch: 30 completed. +Eopch: 31 train loss = 0.001702 +Eopch: 31 valuation loss = 0.018519, ACC = 0.996167 +Training epoch: 31 completed. +Eopch: 32 train loss = 0.000383 +Eopch: 32 valuation loss = 0.022197, ACC = 0.994667 +Training epoch: 32 completed. +Eopch: 33 train loss = 0.003521 +Eopch: 33 valuation loss = 0.018160, ACC = 0.995500 +Training epoch: 33 completed. +Eopch: 34 train loss = 0.000882 +Eopch: 34 valuation loss = 0.044170, ACC = 0.990333 +Training epoch: 34 completed. +Eopch: 35 train loss = 0.000976 +Eopch: 35 valuation loss = 0.017261, ACC = 0.996167 +Training epoch: 35 completed. +Eopch: 36 train loss = 0.003609 +Eopch: 36 valuation loss = 0.021408, ACC = 0.994500 +Training epoch: 36 completed. +Eopch: 37 train loss = 0.002237 +Eopch: 37 valuation loss = 0.015329, ACC = 0.996333 +Training epoch: 37 completed. +Eopch: 38 train loss = 0.000244 +Eopch: 38 valuation loss = 0.016126, ACC = 0.996833 + Epoch: 38 model has been already save! +Training epoch: 38 completed. +Eopch: 39 train loss = 0.000064 +Eopch: 39 valuation loss = 0.015117, ACC = 0.996667 +Training epoch: 39 completed. +Eopch: 40 train loss = 0.000035 +Eopch: 40 valuation loss = 0.014981, ACC = 0.996667 +Training epoch: 40 completed. +Eopch: 41 train loss = 0.000154 +Eopch: 41 valuation loss = 0.036214, ACC = 0.993000 +Training epoch: 41 completed. +Eopch: 42 train loss = 0.007359 +Eopch: 42 valuation loss = 0.025046, ACC = 0.994333 +Training epoch: 42 completed. +Eopch: 43 train loss = 0.000664 +Eopch: 43 valuation loss = 0.018365, ACC = 0.996167 +Training epoch: 43 completed. +Eopch: 44 train loss = 0.000205 +Eopch: 44 valuation loss = 0.016666, ACC = 0.996500 +Training epoch: 44 completed. +Eopch: 45 train loss = 0.002770 +Eopch: 45 valuation loss = 0.028044, ACC = 0.993333 +Training epoch: 45 completed. +Eopch: 46 train loss = 0.002998 +Eopch: 46 valuation loss = 0.024017, ACC = 0.995333 +Training epoch: 46 completed. +Eopch: 47 train loss = 0.000574 +Eopch: 47 valuation loss = 0.023372, ACC = 0.995167 +Training epoch: 47 completed. +Eopch: 48 train loss = 0.002458 +Eopch: 48 valuation loss = 0.016478, ACC = 0.996333 +Training epoch: 48 completed. +Eopch: 49 train loss = 0.002323 +Eopch: 49 valuation loss = 0.019090, ACC = 0.994500 +Training epoch: 49 completed. +Eopch: 50 train loss = 0.000743 +Eopch: 50 valuation loss = 0.017156, ACC = 0.995833 +Training epoch: 50 completed. Train Loss Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResNet-18/train_loss.png Validation Accuracy Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResNet-18/val_acc.png Train Loss: -1.5194419834286115,1.4698055194452475,1.465635260981971,1.4640473041206739,1.463853496087106,1.4631712984699774,1.4638587808439516,1.4623362046282438,1.4627277742347446,1.4634541170009505,1.4622083008289337,1.4627928627893259,1.462063550525367,1.462781774771722,1.4623636942339169,1.4621934048937395,1.462669411400483,1.4621546202078815,1.46185944910863,1.4625347868243666,1.461965396223475,1.4617673691132622,1.4627306231107757,1.462141980350865,1.4619430509788731,1.4618573053188233,1.4624626147803537,1.4617475409643346,1.4622356333721305,1.4620032535062581 +0.11033941893198337,0.01587141261952466,0.010047460450098123,0.009026822771629323,0.007909631987312126,0.007267851441115432,0.005926719363539861,0.004415530911910753,0.004000338429366786,0.004315067960562365,0.005082801300376003,0.0019316070164466302,0.003448847651242608,0.0037018515641234467,0.0018449491861234294,0.0048415431339483015,0.0037093547556111294,0.0020316048078758238,0.002395397147667406,0.0027070195585829683,0.0024358899397665702,0.0024986996267306973,0.001254476565973013,0.003710166680174427,0.0014893779088541088,0.0040947873539281315,0.0007969619218517213,8.857132731382576e-05,0.002590080609492829,0.0032777426852391178,0.001701802904918349,0.0003830976405296501,0.0035213949273196557,0.0008816417004870466,0.0009755956402146495,0.003609171024395415,0.002236520517689965,0.0002444293147632207,6.380908616249523e-05,3.4506582773181185e-05,0.000154251702148673,0.007359382428508738,0.000663765362646506,0.00020465109137051844,0.002769523832936044,0.0029981745003645937,0.0005742085878933566,0.0024575316408295207,0.002322727980387896,0.0007429241633507924 Validation Accuracy: -0.9883333333333333,0.9936666666666667,0.9941666666666666,0.994,0.994,0.9928333333333333,0.9941666666666666,0.9951666666666666,0.9945,0.9935,0.9931666666666666,0.9951666666666666,0.994,0.9948333333333333,0.9946666666666667,0.9958333333333333,0.996,0.9951666666666666,0.995,0.9943333333333333,0.9925,0.9955,0.9948333333333333,0.9936666666666667,0.9941666666666666,0.9946666666666667,0.9946666666666667,0.9943333333333333,0.9946666666666667,0.9945 +0.9915,0.9926666666666667,0.9911666666666666,0.9921666666666666,0.9923333333333333,0.9881666666666666,0.9928333333333333,0.9945,0.9925,0.9903333333333333,0.9925,0.9948333333333333,0.9913333333333333,0.9948333333333333,0.9938333333333333,0.9938333333333333,0.9946666666666667,0.9945,0.9935,0.9946666666666667,0.9925,0.9965,0.9928333333333333,0.993,0.9946666666666667,0.9951666666666666,0.9963333333333333,0.9955,0.9955,0.9946666666666667,0.9961666666666666,0.9946666666666667,0.9955,0.9903333333333333,0.9961666666666666,0.9945,0.9963333333333333,0.9968333333333333,0.9966666666666667,0.9966666666666667,0.993,0.9943333333333333,0.9961666666666666,0.9965,0.9933333333333333,0.9953333333333333,0.9951666666666666,0.9963333333333333,0.9945,0.9958333333333333 => loaded model checkpoint: /userhome/cs2/mingzeng/codes/kmnist/models/ResNet-18.pkl ******* begin testing!********* -Test Averaged Loss = 1.475045 -Test Averaged Accuracy = 0.986100 +Test Averaged Loss = 0.061179 +Test Averaged Accuracy = 0.986900 Confusion Matrix Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResNet-18/confusion_matrix.png -Class: 0 Accuracy = 0.996500 -Class: 1 Accuracy = 0.997100 -Class: 2 Accuracy = 0.995200 -Class: 3 Accuracy = 0.997900 -Class: 4 Accuracy = 0.995400 -Class: 5 Accuracy = 0.997200 -Class: 6 Accuracy = 0.997200 -Class: 7 Accuracy = 0.998300 -Class: 8 Accuracy = 0.998700 -Class: 9 Accuracy = 0.998700 +Class: 0 Accuracy = 0.996500 Precision = 0.967992 Recall = 0.998000 f-score = 0.982767 +Class: 1 Accuracy = 0.998200 Precision = 0.992972 Recall = 0.989000 f-score = 0.990982 +Class: 2 Accuracy = 0.995500 Precision = 0.984772 Recall = 0.970000 f-score = 0.977330 +Class: 3 Accuracy = 0.997700 Precision = 0.986070 Recall = 0.991000 f-score = 0.988529 +Class: 4 Accuracy = 0.995000 Precision = 0.992739 Recall = 0.957000 f-score = 0.974542 +Class: 5 Accuracy = 0.997200 Precision = 0.988934 Recall = 0.983000 f-score = 0.985958 +Class: 6 Accuracy = 0.997400 Precision = 0.981225 Recall = 0.993000 f-score = 0.987078 +Class: 7 Accuracy = 0.998300 Precision = 0.988083 Recall = 0.995000 f-score = 0.991530 +Class: 8 Accuracy = 0.999000 Precision = 0.993028 Recall = 0.997000 f-score = 0.995010 +Class: 9 Accuracy = 0.999000 Precision = 0.994012 Recall = 0.996000 f-score = 0.995005 diff --git a/log/ResNet-18/train_loss.png b/log/ResNet-18/train_loss.png index c06064fc43cd68c381c8b812fb3c858d0654ceac..aaf4751968c736b7f570a877b1103ba4b39c9e71 100644 GIT binary patch literal 18921 zcmeIacTiMa^Da6d3L=OJP@);hC`mGkWXU;;h$IP;1qlW~MMObB$x*^EAXyj*ih_dV z9ETt|hXF~$={>yf7tZhdZrwVk?tiyRi`jejT6=}=r=RZCJS$$V6$Gff`=FXNV6?0dodycO6Y;K=+w{&)~adhD4 z5#-_LI&JOh>f|EI%WMCS2Y4Kvt#}(aOIx9ngH8$tE+`a@Ir5(*Ln_?{g;La2ym3w2 zGj?vk+gRI;P`k8VMa7@&?>UiJMNAx za+y#K2nhIjJ5Rs^1VWnmWMI92HcA6v*?xT*}`oFoD43adANm$oce^n&WyMnTdm~a5 zN>z?bTIxpmD30}V+tjSe1l48%~{;xuaW40cWdb%)|43MD{Fn#k(RM@9ut9o>6M zlMI?O>_aYSAH8iMc0dAG8pQj@;nRZ`(i|sAL5(Zq;kEH+Upsq2&;AQXQ9I2oEYhT> zQK&@;=y&l}S{;{xAb-*391Qjd_KP=6SpQ-dc@PB7hh z^UVjAvuYh?Sf?RKe>$QHu4<7h2Q(KMyE!uEKPO3=)q18#pk>HY)MWZA9&&jaG$BrHQ?R53+yLTCu^^wdjxX+)(O?>bj(>(?0j~_o4xIgmEaNVfT z`pxF*)H&=xj-=1#XXy(b(Pqw8d@K2YUt`lYHDf@9K3W!L&(UFA=lyeatxe-WpD*4)z12&!62J!ag-s_zi8Yu$yy@jKM`NiRCw_clc zo0^xWI*LP2@ymf6v-+&QvFh7TZ=Cq!heAVwRHf>ivuNClXV26%G~RrzuAcFgc3b?h zHG}u7FR|Vh8Cab-TK;fz5Wp84+RAk)@-gqJk-w5ERs@kK%P)+`4=B zZl`C{(Q|U1`1lHKo&7h7TOnxYB0O}1^3b6%=h73jv^w_Q+S+fIS5_FeD>gPZ@V-s0 zt(p;$kzVm)ag7K32+!4t!W9f}(aqE6>WF6y)UYtJsGKOYX*+TB=afT}4GjB~{f<5RkVqG3fiFHHulY@B8vBf=s>V1e+GJ4GQGK zT!*Si<_61+>^ieG6cnBz0-_krXa2ZRbcSnZXTh)QLX>gx`k=R3g70Q##pX2nqEtmM z6Eo=tauC};k?f>H?=D*uS~amqY)+=s)z@p`Dtsd1<8%J}@l=dBF172fTJ>ljxtFhL zk!=KFC1LlHq@<})@x3?ashLH!57BWow`XeMhn~=68I?E~esW3zL212H>n8q-sL8?X z<0PwkAtIs8Y%?ovTQg{Z3>Uj{mkGXv3SkuL(aQ%Y6(^coiOCe8T>^BVP$Mj$7R~yg zLN7RO{rxEd?h%zHGsTW%Z=Hfc6F(tSAhz0cqvhkrqd><1!7{U0q@SBYlOZEeN*x`t zvqurUa0I+~%iVo8IBj{Nm6GxrXw3qs=iBeMMGPsvBI;Td1h0KLHiWzkU@I?0>kAR8 z9tNQ%YqKb?DG^af62Q-56-5e4Ck>zC$A*aG_;ZA$IaK5o6^Yh4vr69vma?Eh63z+50;1#yh;c77OET|hcO zvBCr|H%a*a^I{K2V7!lJMk)q zd{4WnK`vZAGJtw4UWSha{KQ3l36JO>KD>wLO%HVUB@rc>f~2{r#~xW?6b{x(WF7(T z3OUrO%N_{x|0lrp*iBQ8;<$WS<8Y9S3D&)1wx_^umfxa=3P0PU#cf!qZfqQTQ6)Mx zEG(=>^yz^lWV!xCgYJ9{I3##~hyvBIFF-#Iw7E*`5UxpAZ5ZbK_TmF=vQ_u4(T`F;cbyT4wa$IlNK zaq8u?x2Z{CT2kbd!>;i23*PJILkvk+DvwFoWEMK7wFKweCqS+vFTX&NN?fqO|BSV{ zcn@fAH!F4f8wJL=RC8=>EG(73Nn8u9i&-YCXowN!l#no?H*wWC$0S?LjQI;tklyI% z=<3Ft**Q3}de<^CE{#o0bZv?O4j0XvX=o&U{d)cTw{MemoVo4u16a55_f~a$R65$) zldr_)n|Ic6e!q&}Mn+~87PhbSPq}*CzvDJ~i)e@6{Tp2OJzv|!uU$}%oLs79?fUhe zzsL)3TfWjLwh3Ug+HDcaydsgV67%8NGm55jJUlr`L0VSvEYd`s3jO0la|`P}cY=w@ zIHe>vF)>k&*DPXka?)_U#1LcKSByD7^@W_Zy*tlBVx4wvrb~^ShNa!UWZ(lB&`Xyx z?h3_=Kge%wZ53~dQ?*J7sse32_RpRHhL0UT9`WXlhP}6(Twvf~X2Y3<^K5Kg-F{0W z-<6b&Xk%mJH5Q>K z;5aC5X=P>CnyOgW(9rSe{^)s~wO=M418(sh;`#HJ;7<5OmDXV1ij3}VeTm)em3a1B=WV9j zGxPPiqD9@Z+-JL!*5>+VMuK@eM?<9sf7g15uVqC@?}6v-%dZ3npCA7R8PG_fCa;Vs zG6|>4(WzsfTitAsVHo2_p(xI)+}~OA_;8Z+@SXSw=}AP3C9i`P?=22cDUnVhCqy(` zp6&5P1RQYyI3i7&PGeLE-y&WS;VT#5%UI{A!a*X~$^#0$`Uh##>)5A1ETlbr7Qsn` zQ^r=iyPAw#A<{|`fSW^kEIXXH1rd^W2oQ@NSR+^wMGkY~t$k$D$BDFWNEtp|Eo0$B z+%C^hlr<@UI}u5};fstcvOR-KO43YrU7s4k4=@;HvqA`HBOtHhPaG1`OCk|oj=|TL zNJ#*^ZlDB6)M!r2o(2ddr|`e1UUclQsUTOkO8(;7{y<|dN)w^u0%_twZE6xNWKIts z!B`G6{%vP24zZICBSv4WhlJ!Bj{d9oH#}>hYC|=FZCPz%m6O{Ea_s)IbPk=lLJWtW7A`WNa-9=PmtccdZ zg9n|jGAZ4>*`9A%{|dX?nx-7_?%k!J>ZBYKtnJ!tkKkHQrC()DC~NM!cPB-d4kNHT zH8mCDMq#>V7he3I+XYKYOHt6MBFlQZ@}0H5-jC)_Ono;j-GHWN9!8tKI{>5p^P;Gf zQJuamU9GD@7?*A2P%0ZH0H(@k`6pYnpk3q#l^9)%-JSv~w97xQ1rdFR=9yg(0`A;E>c%6kZ^Hwwq@y{-$q9p+I!pD zW<~Ovna(WS0ruVP_wCtHq&pys7ZevNcPqle!v%e}J!NHOGaYdiUL()hemiei(Zr;a zzeYIR&;_JFF0QxrW18C9+C_638X9#?O<-Vvq1z2qCMRD9H~Pi%=Plz6G5C#%gqdi& z91Sn8G9mlk)TPl{G(12fv8s+)FfugE0w&+&lHN8o*3x03L(4~03L{_KKK<{v^R&Gk zPRfPYv-R$F^$j?bh`LTDinxql0=uqf`XKGY&G*RwhX2zpIPPJ}(iW;a$3$#DIeFLQ z94EkhOTFF&p!uCU7+rL4u|20Ej8P+$MFPymY)?8YD$GI;%sys8>|gwVmGS230HQ8) z{%0@1(2q?-q_l<*OwX&UsTr0&WYPW6d=ut9=FU2&(f8?o%I({?PaHd@IkVL}=N=Fg zq~|RVkwWsCsDR9sH2z^=A3wm1WLL%;w17Pz%%s2HID}DHRnxMm->u7JqQ|fQ)BWBu zH#8Wjw{dZp2P@+?^Zlh|;0Q=;_1~{$?k%tiy?FCoA~c$=rjcqtt|p!YYc0I7)DbXH zqv?dOwe1Ru8)4wprATX@3wZ?4UuS65w|3b`LVc+fFUc2hw?9kvP?0=?No zjl8@(FT%!zgjSV;CU7P?Uej#F@Qay#yM*cPycFbxTl3i5pP{)tdmjDm%UN0H)E&WN zJbLsf2M34k>hMsP-o1_|yF1tDV;L|#7RMR!1>nxV7aYU)nZ3VFagqozI7H`(d#?v5 z@r5{E;wK`60hv_P#*k@#72tz;0A__}7#?~@Tt9Hy6+Hv}i3nkO78QM?_wrd>?EM!rbhH^u|H@<`WHq^Fi?FnJ?G!^*Q`L90N@B zM9_sUIG{rs089-~{b?pN^5=z5safP7Q)7`qAblJcDPv+nFgbA;CNOe}^aBF=o`BV7 zvk&}*+GqFV5^$1&I8}Oh(%F?o8nJ28)qu%Q&p_vhRXVVU~NnErvn6vwY2 zfd}Z}Svtku)B8yi_uOB^E}WH5e?+9s0!&CdA)-k|koBK0{7PI~GI8<{g05|s5N(!`%1nV5h!(~&ga zd8N9axE$6e;kLHQ0h6z`@KE9-=yUY1r4|75dxvOS5i=TT|KOR`*w09 zw~>4QP85$x*0*mr0OH-|dUXn{nk2_7z~O!U{dNlkKOXkm;>7+k_X02f4WWZe}2XBk(|a)~OxO$8+2wO5Z*M2!gU{>g!*Ms!z+v$gu58Nlt#7 zkYF~m@EhoUnV+m|0EA;`j~`FTtt1$NCF%g%G~FNhFM*gc)@PyJ_C#4-KnOW)@ORC* zQuC97#c|fVS##gNe=l3>wZkvh^P<5gb91Qvm*`B#D8<7kM3ChM<8rSnC+_i+Cw8lA zv?orqPmZCFF*BRY5OmQx_I<@2oA?>Tpqe~f`X1jkFkdHaS!7sbUVXrArV~@Mr5Z18 z04zIxLN&eMr_3J7GWu8GrZVxGN{gR6b?Q{v?=WTCow(n>??7B-7hP=s4>qrrYg%3i zJ87G%7AL9$Blnswop^O>de$8a|Dxo7Gmj$#c5wMGPR}6bo(;a_+t}D_#6SrP>y6fg zglqTkmsr#uE{9o%r9!l4DuCWp-Ow=Fp&YMkdH1d@0C=>NU&Z;K!%i7sXgefRU8b|WVbrm z(%?oRDMJhqM1If6dL>;h5~nJA;|6T70)(kmmfsgd5t7-YHWgFI-CFtesI0@9$RHft z%GBlPXA#YDOI!3W7D-gRtM~Sp6@Xvh-d=$=5<_pFWl;jBbP;kp2zvdp9O4X!`y{;& zK0x0B3q6{M#|BN_IFc}AXFbLN9 z2LhHJnL)e}NBH_j+rA$}%qIaL((c&z1HsnBGjQQrNbwYzGqHDKGz_sM*AElvHv+yl zh`jy)ye(^<QpAg2GnrATlN+#kJXq>4!=T-r@GV!3jag&Jni3} z9~ZQ&I{^fo{lW#=bMmjW9UL4mTNA&3Pkuebs|~&6=jX3h?W38D_nvP_mJJr);xfp; zp=aV2QSoQw5U6JsfE)y?J6=51W^qP-s@rv z)7snHZTqyew2B^XECc!E=P#%soRfRmTo=I!ln!V;lc=i##CL4lK7=?j4E}hfyBB1g zuI=rozWN3NHl@Zq@KT*V7Fyc0?d@&oL`tf%bBB|ILt9@zPf8IVp7Q0#cf9L6y#dBe+?OJL!gzXKli8v2919`O(-}XU8 z>&A_z6G!Rk^%7(jA%qOrNjA9QEq!|G>gp*`xk|3Cu9z(&jBXJCeeYU& zg0G>P_|j8^b`o~BSWCFW+0=C5#9<;2zCXb9#u1ntAEGZAyuhQ+oH+x2p0=EvToD5A z45F^qn9A_jSYwOb@4@t(?Cgp{F%1QCth;}LSiEO1`)n`OLhwe^+z2oo&M?pt(T9kK zFM)r<`hO4A?36is{68>qaeTWL5<3A0C>;WIm!zc3AYLZwG$b9)Y9_-(O$_(?Z!T?} zJ)+3KfA=N0p(*mLKH9*+r~68BTie=bSy_1%!6QQpvE90U{W07Tv}rwG{%0ikMMy}; zN|RJBgap0U-osf_U0t2)##ofzR(24puYRcbvO+jhl<@3cp!mm$71SnrJXYR%bA;Lp z0{(73&YuZhinxnA_fV)`NWlII*~@5RJcb%jJTuE1rCi63KsixI5x|m`0T|D+-8-7g z-~|GmheVkWj;08)3KpJoNc4OY^jkaq2VGdIo7js&`-P-E@Hh?+;pa{!nGf`3^;=GE zGzkeJr>p+Y0Y7I4+vCGhWzi*eD-JGP=Hw_;G$8f21MaWdAC!yzuvcjERpLc<7ZB)Y zTAW$Wp+A*HVi9o@_r^O>C=@^7nQ=n5lS#XjT-fTbk&wNp!vV6Y@T$}muS-A9`b3v2 z+ZE3_&X2?6Qt~MAgRTqBN>S9)=M}x~E(H=txaa}Te|zep24SFN%NWX{+bi+o4u^qx zTEG`KjAPV8PBF15dToYNU!h8QZv?WtO-r(P-g$jN_00f7YJ}0c&Ci;Fh5;a}L+KR$ zY;Q80f^w8!MQK0aJzee(m?#AO{Q6XY&9qeb=-WNq4o4?3SP}4KXpW-PuGky(xV61M z7{)QN-?emVKXlΌ^y6SwY&QjS=zDnq2-s{~8dQW;rCp6&eF09!4QcXCsf6QT= z4))6LI!at|;A(boZxLlnr$o@|TGrfN8+mAx*?a^&e{#?yY;|#K22O1_@vw|;8QID2 zjR5?kV;}(GU*pp7y3z2nq#5q_YzNzn$5c2T`}^bmseV(9vJAhMjZ;0>xUWBs!M=wH zJvX4b7;MLv-j)f`SU4|ZhhwkO7%k3fzPE9D*)$cnOqz-&t@%Nj_^#_RkKG&=F6~r6 z7xOKqhZ)U}pq!TZtMS;pqp0#*#k{Z0Pi~qAX|ZPaWzuwx+td=OLPoZa$Jm^ zT~d=SIPH`XB}#hqavvUh`3STcM_L(T%N1C&2Qq28#@KT+zO5Ke2ql%>QGb#J=oW*o1@r;XAveW3}`p|Wqp(5~GkLZ;@7 zt``N#P{|rAM;kuZH~rCl4E_hJs{Q;pS*moYFDsnWA9}R8{|c3i(Hc{0ul-ih_>a-r zIMs+9+n=QwPS_mQURk|_K?978OmtomYRK#^&xV1T&*y7lg`TYj(?@ALe{oZaKl0ik z2$Ck!{~=}8(H+)xkJnNbp6{IDEVlKypLsqrfLlL`&Am0Gx-7$POShq431w}{Q<78= zzxkD)x{FpZZnb@EUv#O?qo5gKl4j0_k8Je}hg-?*R#xluMCuw{l)|{^K&Y5qSKgbr z$#n_!b5fi>&25^H+h=NaA&6Vy!k9w<8Tk}?t4Sk>Pu@kCJJD(9F83_~QmmjpA`m;>we`Atw0HF_pSAP?H;{Vg?#FQMr?vj8srA_@vtwi=WG~I!S)21! z0`m1!?Sj6|biO51j7VBkH<0BoIs7OP_3UB`#)nhF*G9^6&@bm=T$jx7VYDyLRp}#S zNBB&evS+8nO7zIT7ai=_@JXh{32Dm))#DM3>=z|tAUz$eeW(72%GHGvK5n}UZ{2#^Q1cxLHShEC&71iyWYEwr7Hv?S!qzmHkBo-5wT${1tqt2A!b@;JOOAKrBD zCTy-$Ws&Z}ow?W5bg6HAELICyKa#{8g)$0A;P*m*|H5!2yq=icebE%Y_1<$0WdmpYr9O;`RYLl0+u zswb50?J?#j%Wl*V@w_P$&kIL(g)azm%E-t0 z-L8F3yus&d7EP1M2PfiM`x_oTe^~G!wC0yV5?%2vjOUQCY+(igXL0TR^TvnEhd{^; zUFGwSjrIv>DeoiiawJXE`$H-iALbn8Oi5!q6E9OY;H?g@pgA8e(_6Nl=sqi!Z85;n zR&4o9^r_ehW(PeO6Z2Q5>61Oz6_?&9S$8deDi;nkx9*jfvT2NpvCLCwGQA%yM?I^b z>y8}=q@;34CnA5?=r!Tp$#j@zvO8uLFUnHD=}{^uVUg0g z;A&eJP~O2`M&RW6G!RH2{8aO@nY!CloJo)GCx7@B(Ba;VlMDA)(ugzagRQM z(>X;4Je~`6-%k2!BS$6H1iW8j6_br-t!S&(nhU@IhhT5$gSYo=raZ`yKS03~? z9X`}9UT5Mk*X^MXi`1TUB`W#r+zU#N44-!IZjXW0IbTWy*_XTNkIxU{T z=hu#hi1MmK=i%~lTP$G4X+>MhlId5&0|1Bzox4AasoniJA)Q&XYgMFsZ;(?l-{{He z;`j9FthUiH6|&C#VP%dX9sm zC1A&*J6XA+groMb&}x4~y5MxVZQnX`D|N2##KwF)U6)}=T!+p%)sVFw zpD!wwQ3W@u2T&Zqkgl+2=djXuiq=PI@X zj!wIBfjE92FOOc+D6YVno`>+g(`3^*FWYt-X0_hOZ&*D<-Dd1Ki!64{i>MBEW-^~! zwO6$~@;SroW&EdXvk>!7I1X|3EF&|c%Z}QV znX-xd09~*EJ~?_lf8%S}oNU>Y8O{a_W^W-?%cEAx2p#Rg!In~$3!cY<%R)Iatls@~rE>timTMW6WD&eP>y)wihU z1{+tr6iby4RrY;&uk5?F`p50w!1d9lshpPQ%rouBOYgNgNc_RE7Fsr)QH&^84mD{l zFt;b+@!_z{$COFSuRVc_|ZcnTlc4yqgt&8E?YZ=AuUdJ;q=CISR<+vNW`Q-Wp z)fLtoOtXLL8ROEaD7dk|@fgq;y_x1CMKoxtN_o~Zw)DCiE}yD7g1?`w<^ze;o_9v~ zTV{;>NEqWOF?Nv2!x&xqW?0#iCAlrBwq8-B8&q%d!jUm6Dpy%^eGLOFA_jRev}|Y3 z4jOV_viPmT3krOL zPXgm|u-jPdO<4AvFD7R@&T-t_C0x`Ca~=K26YZtJnt-qKi7Z$4*kMzK;ZD;sf6#Me z;<8hJZQ9hI+@*W3gCG;3#Zq**$Y%D`QW;OFatfKL89Um^t{W%SH9w-cvy%0w%u{;y z{<;~#B`L>ujIrqBe!GpGN-batxqY`z1`;+lnxcz-)5DI(Ir#<4mI)W7&od1+pv^r> z`W9z)zx@7NAB|wk#0K>-Y6Twb0_XK}aSzK>LqeKkI7@8C=_@bc40`q*XB=++gLCU* zJw7_ktP7YDUA{`+usuQ#W`r{DfX(*eJcICOG*|xQ{018TbJGM-7G;a$p*_b95}LLu z$Bb4-CZy1x3iE|0fQJced^MdOOj;&-JLsbm>HZ$O30R*KfA5(ztVh;}VZX zX+73Y2%zv(pl#fXi7rGnXch05-Bp-ikQJ+hQ)92Swvf$<2`S|)s`lN_wuy#8EalH~ zdWy|Fn#?fv-Zb?LcUltUSKBe{CTNq4D}FsGeP(9tR*C_lB?mju!iRNB)-YK4*$(ngB?wsIhp6f|xbWa5) z<@yb#_;C#;7WJ(uhka$%Mf|J*zQSO`UdBPK;s@&_CQGf9v@Dl8{8f*7jYggU>?rb71+4?qD5lz9&%$BdmS3g#&>gI*rjR-J<(}V3SWiuXww2TpN zv8z!fm0eqQp^Yb-ztUy@P>(rl6nN>1!0#UkLPdwETZ+qr5c8tg`lF)7Ycq7;^v;e! z-h)t<2Y%(gb-a_K&j=da`r_he(B;nEaXCK# zdG?v0f=kUsTTiuxuejVf3H=Wfk^>oje&J}!lBA*2!CrC!>^Otua`O7=RhBu!fDD;; zGA&0B%LepGNXZv|w814^p{0w;T$G;5WwGSo;=z+OSsXl^|HJG2>j;)pn+5u*!xFtS zz5FABpRIxzBvij&7>h1+3XyE!d3}B$IxKOEXYzubQ^TR0f>h&hdQ2PG|MQAgcbI?JqIqNuZ)f{tl-q&N=z^9wX*GlTKiH=wLZHJp~=I8$y zf03%{wcYBQ-?8k$rnA<_3vN+VMSr%SJZUcYolW=S?B^mCC5!nwb1ys&!1aA9;p9hb=gf(6e~a4AHGsQ zzi)F3NaRu|Hp`x-zzC5O(PD?BS8upa^p!17ba!%}U>;WA(p~dfch_YH)&L@4aOaI+ z_gN!#^|1svjXhu>ekT3vSf`Z~?PLh8k;E7MEGFAZF9j=z%%lTntOG$r%zDD1! zq$1qA{s433)oMN}?mrGOi0CVE_S>@F?E)ULns81gh8`c>0_Po9hrj1fs+ZOv$h1cqC>r%=-yhq)ecA)8)5C*+#(D^13d zR-Pcfx$>oz(kCyeYQ>`q(1tO{t!2(?V}31QboeiWQ*}SF@OqP9F%oof3!w4ZepPFr znKKoPOydx~`MXc_@4#7B_Y+8v=J+2~?b9vo;F3(^IE$w-I(@J_%aVM+@+Q`wx9Y`E zj&WtGYkx8U1hJ7eo)N8F2;9CsfKTY?bxrNn4uc?;il6G7!NgXSyNI%D&#$rGyxotg zA=F)u`x-OWKL?Agvvy)~G3}disy&an5nU=fn@ON2n1M@yc;Miisl-2i!-v(;qk7dw zErVq$D9KO!bVIMA@OsQ4nk+;D9(d=AGO>N$-t_$m;A&bbnW?(cJtwCB0TD5X_WcBw zi<4iMGIz=iF+b^LH|8#}HXO>qlc}!Y^!j3~#|sB3v>isGO2u+nY(@Zd&7_@Ij@Ohw z!?aRtqTX!v#-^HR8i+}}t8V%H_bum9hmhTMTiPhyOBMDR;{)m|bb)2vV_Yqtgmu|D zJt`k_wSvuSh4`VaU47Gja)Ge{x6Sb%yG-jZ4!4a)t!dqUIJe%}IgYG$sG5+cmrFGh zST0??fva3!(0%l(PiIP=lMX>fB|g+|TLwbRq7a=Hgbbs!)Kn%HVx}zQ8qRd(>fO7S zO<0bqw1&ub$C?3@ClIp>;lOsf6BEPYw|yTuI(2h6Zgx7+1~Eim)*pEJeAjK-aR?Lm z2zlVH_9OS7)a4;qnn&Z;UMux`2Kf1O)`6R-cr8!$s3yZ4a2DV87hz1x?>of>8S72V zx7p`nG>0E)7TKRlhU^d@Lv2k^5F>Q)Oh>Lu;e+*SiV<2z;=06?gyY^Po;heQPLBuM z;RRPMA%L8A{Nr?**P43A#mbBuG@r4m3A@@47^N#;9rc=CCe+5dr(Fi-+I33p z;fLxm9_~+bRB9v|CL+ppg08(2qUwf8!0@tNraiZ689|bV*SqS+gj^w<7%d^BVgLEk zd3KD`)bM(z-4~C(^NRHN&=x74h1}D@viy~Q3<@F%$<9${Af6E3IFl69c=PIb^UhMc z`_}co$TJLns?`o=8HaLk7Y`DTvLI_!5Dv(w8HH~0mam4HJ{Y6j`7;_i-CsH`)d^Y2 z$cX`Rq&(oeHMhArlj{ZtFSc+1!yw^Rc%Ge~UyGfc-D@_l)^#c^Y8t9cI%H@hQgbUd zmEa63Qws}ee+^axYiGHQ-=lTiV=l(|y-*wXU;Qw>6vQlB=G;ihdC|2 zimg+W(DaF(&4NvA*lV10UAmJaE)2`U{O#Dh88zh3D7qEgV0d zgTTgOJhN*x8fUwsnhw6T*UH*&FIrtDz*&iv0`Yb8PTAT{?$zm#Cmq%DGR1-BlLOhz zn=`w<+AYJ*pvtf+9s?quy`MN5SU)Q@8=K6d@zrk1;PVl`oOTU|uJl<#8=Vo22u$xwV|{Np(OHyX1PF1Ok2!B zNI26!F8f`CK(}eAX=@i7uCLd@>{*D@BrpAH)_UqT+gE(wc@n1|ug@?DSiip85Ixq@ ziS=*{VGvY;11@yluFv{FI-HuBLDp`(J)Hly^%mJhynoM5JmHB|O_$u9Jl7;iZ|w94 z4lo7%2-~7X!MsQO&5Q`jNe&xbO3WN+$pJY4KSh&XYo~{)VP%CHp&5KCP2Cwi%6@Oo z$KlwN_-kT2Gh|FF*T9D{-SG;h!wk-63LMT?a7f9YupFNI2|S!o9SAYIsZ)%rOEeq# z`^kSgP=^gLaAD{4q>P^2DcPT?Qh&rGp+D_fc`=*j5LZk4E@7y&q)5;89YH)fUG%BQ ziNUW~d_gMahwYwtp;f6v5m)@-+*zLgYSHbiLy0?TE^c4;|t zMjS~d%5HWzCCBXUtfp714+#&Ru4{d2AIEZgyVWwHbvzT73BM1UyGtwwX$j^#7B)$t zr`H029;QCDdJa+4>WO+@=N+MIC`eY+vbN3`^qRrgRu>$PR2^r#jgFfpfCilP7LT2FWdqOdy9`i_gnhm5?$NR+ltQj=CyDS5BEEugmkn%s6 zouIl^`$khtf0-K2z6b5r7i??%P3G|vaXY#1p^ntIcUHfl$vc z+I_2AgxUnVX2u7yLHH7yeA>R=By5v}>~P5E8qW?&Wr)5nZ+MLxS6--0P%8iUQJp7B zSwo|Ptuo}r3r$~NUpVqa@^%PtF2ljarG4|}ugmKqo!&~CaDJ<#9LlpY<|;bleHEqZ z4CerEy=)7uGAqDu!wgiIaRQ_CFyhc#y2OJ~2Ohus>2oEINJEK?5#D}=xf(kKs>Qu) z84CN|V=dFH(r`O(l4jp+!9pV=3H*{8gr{>y!YBbFB_s-Uk}$TktY&2*-?z>Nx_Ha$ z#QZ6rbSf2EkW6gA(r~1IymG=}(5Kl2HC7tfcYIoCp&&6UoHhV#+8ZawSe}W6h-G>M_JlFTJ@nDMVEoQkP_0V*UdkGZCn9 z>ZajSGURZB`+9bSA4|{p$NikY9PHj^-UJB%<8~*7T!QQX95T=K7HL7QeMo#M6!I}h zcTqsNV;}lLVtl>5jmxXJRjt=T-!L-LrV}v!39yngdB#)n*|5RGu^D8 z83qMD^ZRl%Bib=6Rh07JK@G@txN_x6mVMKYAI;54MZDQ@|9)^i_i2l1M&iQHzlzKt zbE4|rb_Kb5SZiymfcvceg$ozhMMMl-#(!xzIOJHFR{jiUv#Wi@>@`=^bzVnzy8>!g zl$4d*FeQUozkc0ph!ZO@bX$0O@VF~z)D+}DVR=e_A1jB{wawKwHGGGjnPB~8NZ}Bm z)gr68$B8 z<{!aHaMB1BKU|70f`jeGBcaDl4jvxe8#iva2&Wi)r29zU?@;1C-=F#MBcA~mT|z|( zvTw$*P~S`Uapr{3g+`CIk%z+Edw(#2v5pYAGB(bJdI_i=93OC6MR}dMbV(sj)XlcN ziGqe^g7x*qZyOGVu?ru*@BPuituSXu`00@Hufdv<@sXZ}V|9O|Y!s?Lkz;#zY6>Te+ zl=RMkVSOXdq5(|Lu+TbbxH`ygXA|Gh)*W62*?0n;i?<+kJ5wg;FoU4&TcLa1>h`RS zr3=5J1(kQU*4J+a#z2M}R2`N_wmynbsEOSUr5`-Y| zyakLf(A=pp=K7sGsY_4|<&?vyc>jLh{W|valaP&4(_@ujfAbyR7e1ULTl5-W>Y&Ka|IQ(A^&x8_NU*S9)UW?pPD61RtS^LHQRiUYu8iTHQC_L>zAd z`Xg1FU!IUh#K!(AbvQp-8`>pHZ;}d$JGs5?jUJ=jg*M?J0}-I0oVlMhV}u+O4 zjlc1#%&`e<#Yxh&bAN;QUlH`?m&x9%B#=XZCO@K-6vecTc0pdim?u zN8A<$O`v)gGQ6T;*Ak)d@0H7(1(eQdUc2@Pio+lU70E(%ozg@qsPYXl@h){64&vNK z#oCp=JD^$@D{zqFyn60GSjE=+U5a2QFa`xRD+)?Vt%6;~DX>f#c3nBK3vytAvSw%R zL2cgRjzh*>PN{1cIxhWN-S>nnAn~*&REl7IGxlnWbx(d;9u%pMJ48>>($h0S{ev7- zo1gjl4w9AML_}EL8z}4GqpJ3Va+DBA_ZW@y7`)txF|smt>(G(AdGqD4t7uH=*tlyg zv&TtD`mYJQp!^cb-J3?6lVsw3u1-|&Lovl@J#VD|)JngE`jSD*m`~SSkGV>sRqExkis2sOD-8=B=>)R$I#m z3t0mRc;an$tHhAqI+Iy53eFFRku%35K-BJMuU-j3V%L2zZ>JzL*ltiRM&EwIFl=cW zo$IwW9J|%1B0TVW!z0CKYpteqB7WweU*MNghkKRO{;`fYBrEIa@#7Y-T<3&@IzP?k z*T2Niba|UjJnTyN&PSE-4DH)ltfncOEAB%WkIzbjaIH^*Rr~`vP)*2!YX#BF6r2AfBek=t z^`y5Da<5ImvM`8yenfIGk-|xk{LMZb;bLaK)e2HQ3nGvTO2Q4PpHU|6@2RTQA<wynNSRmA?xu2{CL9+6Zam=j^Y6*%8X9{pmRV{3Bg$@ z5xDc?wM9h}bR{x)fn4)C-aciyU#1iV^ES0bzA%V^bYV_5HhG}xEv$q^a`=H6TiA|XC=`RV zV9|sURum*xt5VWP{z(ML6o7*5fWW}%;y#Bqs8fN8yWcBKhWU4%A2`gUZ*SD`yB(?( zH{q8YvPt$G$WVz9D%%*35p}_Nzg|$Cjr@oRVuwLV!U^C^ z*+7XfQmhBn$7!Ik0~2%PC}Ju<1s|~*=>P6lX&_JZ?+(UyNu(I@pN0kX^MUuH6lGO! K6Qis1eB7HZj~%-YBA_czb{=b8WWVy<~NFSyuypS?f1*1gtet$PKlsmPNbqdSHm2)UwytU7`a z1|SH*Z<53CM6h${5Bw8xk<)R}aJcW{Zu;OJqHOBoXzSo&Yh`xc?cM`tD+haiE`BaP zj`Nl-E{@Kk+}w8md4bE}fdw~r2qh^ra@0{l*BL>Mo1%XN8B*z12qJx1QTCdqN8IAD zr;p}()xpZ5Q; z$=TUCA~tq#zCSfBjg6PLpk+G2e}9`|GtqCys%mew#cMj=-DM*5>PD&(bg022M`~g` z`RC7NPEKWLwtK2IR<**x+&o23Pw#D9+|Z(=O1cI`P)a?hvx(3(TBN(H3)`ciYK7Zb z_nM5*7BH<*VfftPfbmA^;nQPSd&_x zH$KpFs9Jy5Cb4NCrPcRqB!3?*bPHxvsXNusx@23T?UY#$SPUzlQpD*^qd11W8cV<$` zG1a@+I#O;+>`Z4?PR?8AhQ>xVAt5(Msoz)37MJ|r?WEnQ=>wsD^yUho$+c?_r`qD@ zt7=?k)fKzO8=e=tE$V4$YGxD__3ZjMI~OjM&vStYu(Pq<)JRk6oX@YxJ48z5v8h{l zKQtybc3t%Vt4iLgq>4AiqwA_Pcn?Rn&I zl8zpFiCjPqpKs^mY0c#l(I4D10M#K8BCbVFpE6w^v3EQP?*uqdB1ErR>C!SB?-LRs z0S~VsWNHac0j_OSQra-8N8|)N(hbF7L?1|L5M+xEz4ORBqC|QgL6&(5O#)Vb5a*eb z{d;G|DUBdJY9s*!b6;ts3DL)#=u0mN{(esb-^c45#w)JRbA%?z%!&Z8n&=5!jt}S~ zXJd{AAV_TqG!Syn1g@l+ki!|LNUxpxcdVM{%<)r$=a;D-fhrI^MVpx=L@&{1R44)h zO}9`iIiUp2eD6MFOQUY?9GjR8`}hTYBe^d3SYfF{H#w}LPcS^-pdy_>8x4S^rjwNaw{cTQG`9 z=)`J4e7H;+(Tz2RUHEvaFhGCL;3#YWE3`L7OZ4;iA9Y7)#2sxU5Im) z^X?%5Oz9!CCC(Y}0$rwRS%l1lm-6qmJX|~BFNp%s{sx;6q2kh>fvHtzK?FIk10#yE zprr|RLWhd)rH+cVq7|pADSwp*_JtDm<@voUu&ck#VMx=4WHHgT@TxovsI8k&M;^b$ zlp4gfocfo$-lR?_09EWd#{&Qd2ePVC)ZVkp1s<(YXA_}_>w+b zp21VLKIr(SYqR78J&^)6evpIF?khDzLk5#mQyV+3nVFe4m6R4%%huY&Q~^B`n5}+& zx`(^sAnn}(Y~Ba)#j(8PJk!Ah7Rv)8 zwei#3YoVaYrI{u2vXqqnZz;{=C?fIVm+WN&4_W=FTvSB;m>MjpwT;aYTX7T9Szcf7 zR<}@v7hT>hTxVa65`Ys)`vQZJrsgw8TG?JcO(H-7!T_I!nhk3O`cPE@y$}XKv*VI! zyA%0Wqe1w8C)ekW3>r{S!1jRR);@iztCDl*n=mSSM8wjkOn5Ef>K0r*yT^)(6%%37 z(GCQW_B;z(3}u zM3e)#O#W#)2`^SB2|9@XPasP=APHbSaq1dgmFS^U9{hTsFVPU8(u#IJ!W&S8;soP) zBqqR}X=X~OxQt9nn+QREyo8^57YWqN&{5AwK?6VSOplf5G4oP@C}J6*9S*9N;NbHw zh+t$mSw!GyyE+f5a~;OS(r^CGn;ov6rQkY$V530vg z4;=q_>txINzdQH@kIi5HN6%0-2-lZyoZtNBQj2|w&FY_Kuk~K-Bg-0CV-D2x1RRgL zfCvc)Tox14&(_Xg>Yd+M8qN+0IfemGM;PNQ=C;tWS##h!>{w5}vo#UsT2Fp;#(Q)( z!EI@1c7D}25I<#VTZ@r~g9raN5AjZ}UMhQD>2>g8!ar12idXF}Rp=F4N9=DkNo}ML z5lmPS zwKT$gZ5>539lj) z&H&&fHiw*1GXNWp@eLDODwP2+@AThm@}B|cxX*Zhl^Tb``1gOd8$66#TR)8#d+PrJ zoVe6mkvGGsxQ%j(ZTi8sq0a2cbZl%aAg6m~Wu?LG+qYx*?u8in<6Jp)KGs&WFZ?Q8 zAAin0H{y-y_1zp!&&lDa!gk$?5pkaS?fcQlj0wdoQmT3$GJk5*JAb|GtlV7sw@bh4 zU6n---n-u|P&mS1L;q@mUo|qNrfFBs~RUOne2?GyKQO_x^&k`mz$65aM{jlWFsLo;d z7)$+sxr!nN@WX$H$CZ7+6*6?+rTqaQ0#SU$gV_pkttjf%N;AS^);wRbq{80RcS3)y zz(4=!I>Tp&R=F$vC~ ztbcUL%3q@Bumc9*Ixi*1VRE-;+K(#pyLS&vZ&lkkD~AN*+!-&ugG%{pm#ERaeX#ttU(O^7*a zEj#4laIJB>Wb=F;$mh zThnOy@4I+Zrl!grm)uJF^y$-STH4Ho1>36iKK*J=ALKu6n8?UH{#004?6p2;RF(Jn z^PQ_B9w~78?Vl}!mbBqKc#txRTXIMJ7}plgl9|EL{m;ipNqx080#-jG=rKl```4bK zwCbO!DYmOuOZxLGwx?o?Ejush=Ju)N2KsN+GrF`(Y+8PKbSV6TzC)Fk|L))@7Qh)j z60XVpB7)O8G-4c`eCQ*0g71dz;%JR=OC&dD)e`{n+qZ8NVE`glgP-jl-!`iGYcs!t zqgkb8V32dK<(<`hU+$-0<@?XHjXYBmJjRcgu8=d~9URo}8WCu(5vstjqUV z7cY$1If}&o5e>YV*1pzH__MDsP3}3BRYS-bIw?P&l3mL8>N&411j@PaC-?ZNE zetcXqBTQ;vM<`Y~R>&@#nVeo!^Q@4q8Z0*jgLsC7_r_%*p*!Y{FJ?EY)&Lpx_2-Ab zfB$}X-rTj01{DVw+@W)fjQNHYZmu4-tucZFMOJT&{I(}m2)Oz9)Gyw6p?2%mbJSR3 zd@|XIXQ`fA}pJR zj!smXO8mgsIymw-O|I(^!5X{c;u_f{lV1T&{mrH)O1o|c^4)0?+$d9NoZD=U|8j(EHN zt|fpIzBP!!PCes(($}xD#_uFe#0_i1E6(sRu?>Zxy24*T0|&?olEMeV=WZ$}sM*=& zj!#S+!EJ`sOe+hPlok2@x_49c1s!(uI1)g+y}K)Rf*6kOYA4Oz+jZ`rI}f-=4sM8N z`NMfUhI&0jEX@ksHcO@O@Z|vd$&u&F>PUn}wXlQ)9bfS-8TJrdnZX%&-jVD^IQ?{dnhD0&u#F=G`N3 zwyWS$1dpmCfJgXKy|e@14E|7(gu>Fa)jyWwG&GC15?)}A9wcy?a2pGP=HE4; zg?|2X9dFZtitOLHg1hhNSYhV-5j^n1)9m!jBAN=cbg9UhyfLKDv z;MU)e!F^bTvTw^gFwtF^9LAqK z@Ec8le#Avok5@+`^drN=f1w(G182vRFrt~G|J^CwYi|H#UKO;T|Nf5|>=1fNgb*PB z*_DeIWoot>xO;kf{#4W*Q6y`rg`SOg{+EbnZ(rYB<5`EEO3yV>iA?}s8#rw1Rr{2= zVMcN-5b)QplhWA=8_E?vi zH&GaRj6KyWvV6^K1rfLM{hj&sFGm=+lK)(5Ku-@zSCu*me^R$g?&fS@M)N0IBHg$5 z=wm2;Uq8(GNXG_S1Wu~)4Q(()pweb5_1rPx%R66@9-F$ulSeeM3SF# zl`90AW)v4|m5g|0gU#w&+|4s6Q)j=ELzD4o_r-iZ}d2V`HNVQ@eHXcOEj@KwiuBo$IB7O0UOEt*xCh zM}B2}pux*n0)#gIr%>u?DypU(o@=Z|RqVuw-5 zVfRtroxYN4mh4_p%fwb~v&Av!iSHB$M%?TFef+dY8{>(o+Wc--Fa!RumZxV~AO(Ha zQ&Q@=_dyJ+R*R2W`s}r|B=GJFe<98Nc?{Y4|G30jK0cMkHf3|}`s-F@GVS}z;;r#f zbNzY{Y`gmqi(BNrc9m3E(=+lB9tPtG2zV5}LTmyLPs03!Ca<0qS6s^LDMaB)mdwPy7Vi|t9Ywz@F8Lb-vU@jeXfIv$CR0;u3WNq}(5YaK8$cpt(WUKoeKq%q7xFPAk2McH76m6^!rL3)($tUUJh6owu+A zzFoXMLfeHUWOT3sbB_0M&#S>Co~4uOnBjd;^xcBNoRE*@2Z~40`G`SN6SIf^8d;eC zx33Q*Oki$@;W<9fXK@`N;fadi=K+xgz!;r!ZT=F zzwklibO@~S@!}1jg*05^EeO6{IM(2O&3>33>t6vuJ3fWScVVLc8H1O%QX`GPS?{vi zow`~xJ2-yiSMbv${LF0r*AVHzfZnjjL?|50jnb#l=p+R1JW5ohaGH0rwNP)^SL?Ge z$P6uNw9C!c+zBLJ$Hrz44GkI9EKS8a^d-wZzgw&)R!qlw>5_Zt0UR~iL`4U%1j~P@ zr0aR^7U=95P=baCq*)RyJhw>ny|cTCK~odnMF;%t;wkfa6)L>Eyu(WqaQ1f;7PWoY z$ba}_YU``Qf3WTRvG0h^U-x%*dpa?))otoV8+iKCklxDl+v!eBaI1-6*Jfj4`ciT4 z?%l1dyNSWU!YiJ%s0}SY=|b=^H&+>qLl*=G1pM~47^!LSVC)!O-|P2^voq2F%aTqiBf1g@>Pngy9tt5#8wM zXb9FqYO1U+S3mvZM;_&P(c=1f4GoPmw6wowotlSn-mQZDmswcu)k-y)1b=)w`x>Lv z@18J}r!C3WFUWtxvTf`AJ{RPHh)FbxPM8ZEn-~o*SZI1faFlYV^JXHCO za#wF%hvQ1Os^99V#J;m6ZEZ?jXOy->={1M6*))2!pxij zrtf?cQJka6fqeBAJK&1#ax#M!a&Nwb+=5%-K zAK$6~FY&hpaqS5n=Dxh$>RE<^k~vHRTc-80vmC@M5=jD>#s>=JXU3gmdWPn@KCLHy zn>1U3Y~lM5gysSyn;G^u1mvFT7Z?7?*w;Ma5m^BC@GF>ws}vC}Uin1>G{RVOV0*I|4nJd}t`Py2LecN7z^sKIoPe`Q(I=p+F5 zHOSH2em!<4x6&7yj}6VN z7xZ7{DJ~})uJVffAVTyEAyMpFexkss z&vQE*`?Il%O>AHWJwu^swz~P9H9ptMHk>8TR@#mCr{1p8gI- zwMuP!Uy`2CMCwV`QB-OM7Fl){N1yk046#0!E8pK!!p-j;y%`d`?CuFhOXsq$zk3jG zz|3nB{V4mGH>t|*hFRGbA6j@mP`kaeUYgkU@|(U!6?Pb@l?9LtQrjmrn|^4`>A%Pi zQv8NlnVvHL%xFxO^9`2mGN3l7aKEjokw7^ObC=X?VsEuq`KyK1;GTlGf!?{aDGcL6 zgX!ojHl1A&#D%k`riO*kq{fl3j6TzOuCrugrk&Am(wC#pUgNrEX}DQk#A*7mYI^$j zpfxNvtD5U-txk5M#<`2T*_t|fDLL;8%6HG660p}93c#r# z0sbpEWn_U`C8>?9UQOBjx%JDE86ERNYr0si?w`r#p`2NEwtWQ~)9_$0(cw0{F&Cd} zSZ%CXsx|#mrR$KnhJYBmT=xy;Z}%6y&x@;1_v{|YD)#j+c3a5ritLTg_p}8COSHH@ z*VfX+9!=YZoN(FNXGJzhFTKJP6_sg1FIisclHwjpVicP5I9zX*z2(wJW7UUOe=_lwZ)PdRh%sqVJ@;?AEL2>S`Mbq@K+i{3|9Ljn zQ?6-Vp#9vN9DT0VuGER3ovJ$siRM3>YUv1Bpv62#%cgznMPaqv<2PTr4Z8Y#%6ZQl z6E7%RSg0=GY$VxBt%C}-+#UDIdvH~ z9`f)kqep3;;$TR2QEur?{Y|Y|h4{kywfgbJa*rW3Lvj0OT;ev*0>73=4vA73li^bW&@#KTww+Y*I`6Dmx=j{j!go`o*2=E*>TMiB?x@!&LpM4~TV1V5OS(LY zYpkwPk{?^|&49v2XbQRvR6`FM`N+Zo@@ zW^x|E_7+Tpe~2QJ$6)r^g9qhK$J2*Vxx|=Sfi@nl@f2EdQ08qiB@4_ z8fs%*R<_>Li7_``GX2rnlT5#xJ{Zf4P8?`b`zC^`_=8J@w7 z&)$TYrD&K5xXM;?(OuP~zMp!TrO#bk-OA2+cHQud7oUGki7W4rb!Un5jhQqNT~T(y z&-MFfikq}xal$CdiAM8Q^#l|7mO9K{H>NhSUAm|^5!f)-y{cz7s69!~Jp&>*6T^c6Pa@Si=GT4@SFT zJ>HM)wJ+qD(n&CvOPp(DV;8&;-Q0Qe*#mA(2F82y8XX%vWHLeXaRb8)%Nv?~R_;$_ z;G^u+T#JL)Vx6HkFVj9;vb^b4nfB80U}4s2O;Gjspi|OKu?_8c9Fe@%tYtVei(C-9 z*u~q>v*5*U9RG=T%6w!pj11yw+N3S(J9)=?1rnl!g+y8O((jXol!fM zc*3qsnA3Nq;6Ct&K#p#t1bwws+I>f7!T9#w>3W{G2Mv@s_kF)b4iUCydf~h%0VRvD zn_W$dx!wufTW?ry1hHSL$4y4PiOyJf@l~ZT-H=PBf}MUq&ma{w_+jDZ^pEmWeVgM& z79a9l;_n` zyk+rP?d^zu7qVW;KAz@euJ-NQS2|;y)Gx=yQAt^vs~$g>*&kTuQ+ZOL)Q7G$w{&l!0V;+SkjhC&}mC zh_y5A&iK5juSW;{mL!Kn+GE2Ur#W8OQR;-rCDSNDw`Zz>Pvf${P$p`FYIeOBlA$|! z_WfI$iomaB{;YCAyRMJ^g`h{w5%hLF7abNm#VTt0K5i+5s)$f7iR*irX-XSTEuoTt z*~5(p9#_3jUTCSV7x#cKypr=miiX$duzO{hve>Pxs)x%xf;8qAa$+I`Wmj$s*^O!s zU-4jVlC^YuS0T2Y>?9+Y%xGNA75RQOW@1FT^gy9%gVC*oMl?pRvj5XkKF@P+o!YNO z9?EnK=|}4)1f4wMZm`O&&uoDxx{*AAWp~+?V>o5n3%+YAi|iFnCUvW$uj!V`;%PFA zwk|iw|7zRvX15NjAR67b20Gv{lepqi-}u7o;Ft)o&*mB~8yU<`bvXt!X4P)%h60Xj zVzojk&dm;SGTIlzoP2`Rgjrc`m`d(Ayw4iRtnu?v$fu(3YPu&#|FZRJTH5TO$VJfN z$rlHUy<+-<2grkYv0*_7$5>qYTe@3tW{s+1Rpge~E0W zytdJEhLVHI%EvmcjUnO#E$vZJ&F&AI8(Ntkb8gsO5=;-${a|(R6lmF%@|m3XqTZTj z`FfII*Q;~vJC=^g?@lpHIc9ubV+>F4iv*vLJn%{ZC5WZ_Xjrk0g6*z3uN!%2Av;@L ziNC_~Ul;Ct+z*}?0gR_yvwVZ*u?yZy%@=;r27hl>=)t}YmOMzzW!CBw^6cjx`ken= z>{`RcI;yla`AhS#Q!bslG6Eu;BNo@jlmKzT!+UVf;r8MRl}L=LOZ>Cs6n@ql-++e|@Xr{7h`j8$J;r?386aq5iy z`r*2QJ-mh%%2VQP>{dy{;UW_<=qSemJd$WH)O()O2MtEF;0s;h6FU}_0Jh(9v z(p_mLW#d z)weKfE?#ut=TPQ;ciDD$!S|NJ1r~K%Gnw zX{*6%YfS0=Fs1^^n734;l2*C%yq_WD9K$1S^rm}Uz^T-muls9JDOZq{!je?!OTqH` zq)Vso^j>u*3DzQC+SOWD!L5cI-H8-2_s0EF3HrzBBOuX4=bd9GN_l$F|4yG!b7xQ1 z!t0|;zlR%o9S?_CgloFuGMVON14Ko1T&a@*K2V)?S${zp&&qq~SY(Tup+@%0c$eX| z2~N8QpFcPM8V@h*WwEe{jnOlpXRxuhT9cf%b%n?38aAuIKU;n1?iB=@yv-!nC zwul?3THfm$XY*BipxLA^`m#Y;^%?eXrJ~i+$E%_&_iF=7p$H+8DX~WC-yIBi*sb;K z<}-@^7q5dK3yGh93lv;z=NCHqjI92jOa-ty(pjE+p5P<)($CQ7E!_Oi5l5_nvT~?>#i9_0ukH4OeQHvFk|B}KIu*Q*}1vBp;t#TJlCdX=kvvkR+7rf zbhQnfo~mnTtfRRFAn&uWvugn7KfeX?a3q%@c3DPZ+qv4DmP!Dd5YyJR+QchhEjb<$AsV8ABd3KA{l zi=PJtY=5FrGA)!sJ}@v)KE#LssTIOcA4`BiS#0}@&u+L}OGv+;ai#Vl5t^9Z-%dQx zb*Nkkgx_5U?MgBs+XGQC|HynN(f9|Vss^l0V~%7BI0PagS7!8(!GVDypa|sZ6kMNZ z3d6WbMYg2&^k|}tP|@i)XBQwn&L(&-b-q!R{Pn-4-C^}iqz@UAniv|!A3JtTQ%5I* z<<>ie2SE}LS}pulw~_}!AhdnJQMHb6iS zG9D0$|B3u`g@Z#$BVA>2c?aezcjJbe$6lYlLodc}Q*79&iM|}SF^Ccyf*AdDqz<;* zk1RUFe77F|mlO_WHqfxrqo6O(5M{mrXLVyvf}f8Moi}crx~2m=+bwAJl?2}!FxY(c z#1KR}>EFaU(3rH$%#wj?aGH@3gLweI9rtKl?(gZj4AEi;2|WdF8<7^QEi*4A1S(Hu zWoMKALKH|%q)H9A=tW%=oSd9K>2OH;IhFtR=ifl}i-+($j0}|R@a2l(Jjg~CF1v6${`lvE>ao&w27JHiO(^jPDvi*@ z$$ymd-;eQ{N@#-U72XeRd_kM??VCcpbDPLOxodXKtz(uby#biiL3UfqWBXj}zioeg zk|U+&()98whajhr?az)e&w3s#)C*jekT3)mSvioz)_++fZm0i5oYKrUfXMK%yUhK| zA?hu7^6A0k&3N|_PtXAkNY&+)P0>N&N=dzBoCE#(pD3ql*Ns1KRIQ<$W27O$qBSP< z>ZnimuK2Vr<$NYg@cH0x4=$914Y_DSup94BX#G(bUPGp7Ijj8c;qbZf@V;G+!AT% z7M)kUW9{&5LR^fTh+q=3(pT-vCf`wnDaQ$S>U_41PGmdg1Js?Xz$n5Q24*Vo8Tqbj zVSFQ7?vXJ{4te}}EngWXC@4q^MJtW|2b=!8@9S!v=Xm+a>UBV(oq+4Po65j1;oP~unsR?3bZ{G)MlCixu?e5_N#7h+D z60`V%#>dTo5{0H=%b_a71&Am9k$2fxSXiLUWWjOi*P@%@gJzaEZ;MxniTR*~p^Q>S z2dmr%$-sc$t@se2p|8)b2&~0-U$^^Cr8Dp!Z!1{>V&rTCwXr6ohwrz>G7HvIH1A{H8!6`%5s0g4((0jvHOIv%lLZEv&&N$Ay z>J9AebfUizu!FiL!ld&2_a+$27e2K>ngeI3R|^MGpwslC33$l;-W>N4dz1Gv8U_>( zgrF-x9Rd@!XFJala)2*?E~jve!U3z(zKsq*C0W$4d=gzgmKY zVB_N2aKDl^7A@FX)-MILSvo=M)6+&3gAO%@`BfV+z@W`}eNoD|qqZmfQ>evi1SKqYU0Nm=>9-%gY0zhCAA6f-(V)HOyL z`^{py2>ZDGw{KmPk{T6u{2j0Y%5_%T&^3aRn!1TsOb!tBwvLV^Wb^65Ux2pOUFP5r z04|s%FrLI$zg@fm?EIDQ?D;emZi{?*HTzx{IXPP$h0por4f(6M|A4Z-7eIxLY=p9_ zP{?kXL$yRB6hOS7ym;dY7FD5A;P!<=bRQi6?9sA)XlUq&ukfg-XgBYK&%7Q~Efm~q zp-TDi;p-Oc>y1ExYhM)`l;^`&|B8@dZST}A2VR!-SJF!sMzVwj{5#0sR zarls$N;@_-b_3ASX|cGUF41=*x~AorFjRR6F}n_cEZEWndRx5&us#c#balb4f)_Z= z=8)Cf=NxEdSz1n_upX~A4f(u8WDb3^l5y-_L{`dl@{|Ds%Z*2kzZKRP` z=2GRFihm>l;@5P&10j6bU?#G-_MDo#Z=l%58&@qK#-If|Jw86ZqZ5qF*?6po|2$A2KXhq^6%EMWzIq(Cd@88wY)YJ^Z>Fep8W?=AZ+*DRk*)V*Kn3%Z` z5RnL@^)>c@d|*6Bc7YWK)igbc``Al{UbCq{zUu+TN&4KJwV^+59Yx;cloUa`0Rh}f znE%3Zb-Eit7=wf!>XD&K!p_B|0*Otpxy(A7ExLJz-iyURwTE1JWr;%# z&JCru?yMyqS++9qKG3=?eon3Enzftz>lpBiSfC{Y55|RbLj74=Bn6%7_;KV_1UH)WNUdZ^h%iF@b@5M!O%i z#-Kj};EF0Pod@n2wKb4z*`Ss{R`vqsB(&>~L`167dZ zm_@wnJCxoEr3CC(t|+RCuRH-edgSgoL*RS?n^jc&9q?HbaSIk`jfOo)0H^LJS?|rI zULaBwLzyVZqt$d1-8z^Tg6^nI2D~wUM-oA3> zMq>YQ1PH>{#;SLhG+;r{#g~(ppHGXm?-J}cNCA3C;z1s05tLuKc>E|7UWa4ddw*QN zzZO(1hrfBV{w8rx7WjYpkP4GDf|}q?;Q#$`6c(`Vxh%22H36r!UZ~^B1s3oOSm7#g zvB8Z`LoE@oM>afhNNt2PVB2*(7a#tiY}NB$Q9AhltD8N4RzKR;NWk@AXdF?LQ<2TP IZu02=0F?6a9smFU diff --git a/log/ResNet-18/val_acc.png b/log/ResNet-18/val_acc.png index ad20b307bc5b96984a9eee376ec654e5a4d2487f..187ed3587ae92d4f6353d2ede5f6b2e5c3058978 100644 GIT binary patch literal 35946 zcmeFYbx>4)_&<7f7nWK|kS+lMl`d(PkdP2W5s_Fzx&@?n5v3(XkPamTq*G#%Zj|l@ zNeMxEe}~Wa-rwB0GxyIsbN{%^FwDZ?ocDRh^E|J3!gRD%Nr@SV0RSLXS5tlr08k77 zKo$t$;BUl!&a8kxB;6kvxa&DvxxY4ZdkJWnxw|+xyF1vLvw6OBdu8kFB*HJoFT%@a zVAN=iV$@qb^y@9btR(0{M)7kCpQ7q#cF0D#==`WuojpJxjI5##F03QxRJcV@l) zo*XsGUyW~zsB@8Uat5*yWo~QWQ|l1xlRVhZZmelHsd6fQob}8OZTf=Rxwz(IbL+z8N$aurzO7TQJ)OAj70V>c zIdAk_gmxS!kuv!8LFDgb6Tl9B_3*+m*T0brhOkjmQtC#T0czJ5SkM7Cz|X66|No!= zKQk?nlTDbWzV*Y!kpjKa>7Ahs_sJ$Ea19?L<2Wm8YAp5^T86e>iO_Zcf<$@RX+MUt zlq&q`uU8X8#jTzB_3Pc~#Mqk%ARbjS4LQj`zI*rXc@x9+4YG@iNtHs#5==W^QNpmC zp%(K$eklI8^8T9qiO#EQE%&cyfz0Cnwasy4UxE&}G3X=%&Ff6r(YW;sVNorB2@T~j zK2Ei3oyDEw0}Ht7^tPOFf1;eb+s~WsW^Y@-V?smEibomAdU4GqSMe(Ik1 zJ*FuM=Q+RL!n)1wMFmvoSyy6UZ>gAe@m>x=%j*7O^Hn*)yh#N;X>k;<=`G!3L4cDz zJ7D8AD@*Vr=vw66)@_oWy%{aN{R;bzR=Z!zIZ9ed|GGlFe69Vf1>$+`{m-q3hc>bH zFSY`d_DNZ!iqM+|$V=Xck2oL>~{)gM}$*R>Vib;|UeToml=o3GPn zz3});$Z4OFV=FfyW`7uU^)cgrQo~en0ThqE$-fMRORiqtEg6 z;lVHO4Q2Wk;QFN#*0gB@n(JtQy~bV@<%aj&nVq=%&eYcyCRg z>vF3}yh-@wxW)%k87LR{maFfyI)_#d%pG3dS~Jq2Ijq(zBv{zNCB1Vtc4z4!;p3@L z#aW2Z1HkvqDdGK>6kl$NfFWYU*$7tt8oC|pt65VT-gJ9n#)x0>G z3y?X!x>e~}VG?sDX9a|X=FgwB=2hA3c;@7avL5RBX}+)#fdDY%Chh;s-ojt6{k{$k z0MlL((wZ5aajB<-hjHAd%MHL>$D$*W631dCi|k{v09`}}8P67|8HR}+R)JP=5r{Pexa201%7~7G2k2@V{-mtugQ3Q}86|0|bz>w^} zm|`J#81eTbFJi_0ef~1Zl{f}Y=65~y_k9qG%cJaH48Nf_8%P^TT zFPFr=1k~(=t|y;}6gZ#idauVf``bm>rk^_Y@_cRNE~5O!@YQL#Byq=j{9dbkK;Wx1 z{>Wn9Hu7~7Oq?I?Nc6Sc`uU1O2vy!R(_|AOJP2H5TWy=zMjGPX1gWW!FiBh^ON8lM z@{UF~k?MduYPp4r?yuC${wgK?Y3czuC>eo_d3OXOxp)hBBFmh$g4=D7uIcm`PhpH) z##{91PG_z8DBQj7BqHYHe*?88_jYyMQEJ~|Wwv|MyVa^1k~}mqfKaPvmY5|2=;S{W zDiN%_TzO@64^~^#MB02YEeBx33p>nL*rb6>PMyR21g;nkE#`dt4VCY$K^(WV?tYg2 zZw-Ig0p~h!m-TL%Q{4S+`S?bXZc6R}4;V1etFuP{yc0)ryKvd-F*^xdEt5|#9Gtnd zCn?lkEgi1>OR(XE)h=5WUf{S>zL$I5rY65$lhh_d7~R=Y%@qK{x=T|M3I>0i5I&Zy zh=mxP9-A;ZY$HF-zA?SpQvU7U!rXcL^a?2bqQS@!uh>kWjM1KI1EYc{_cmc|G#+M_ zUw};9vkN`%wSbT@`L_Nrk7^wXL8*vfK9K_Q6XT(@IBzCkZ_8Qp2NtR^ao$c@+PkHc zcki4d-gE379;OelWB6}K)p8PWt4IEf4RS^d3-|?Vg(R2z-zguAN^5llKQ3ESIp2ukB;rmHF@+^{ zb1bZu{882mA<4P7UAu0d!#<~yH*A|rxia*LOk$>`=pPXrliYDV73~}Q-RD|?O~ZKQ z94>#w%+x5?A{p?2iOfJL?0vbC*7I|Q>)12dL_NtWKEA{Xew-SMCz|j4{k1*S}xI$DawjvGS)^HPyMV2vkq1EfBrI- zluK#Nq~{Gi_U}io(2^2~!M8tez7ujTOaOHJ$om+dK^g~ey++j;IO;SmD3&|J?}nI| zSgB!glJHAnai^}|StZw#n^}EM zv9Buwl2&;avXB-%VQc&nqUF!(?zu2`GeIKAhO=P8;}8V!PG1Pl)i|hex}~QxPd5XP zTScJQNCFm1&7Xj0!+3%)+xR&-VCsMLl=nq7;@|9R)@sM;;O7-qW>*)d`NhTWz0OAq zpIMw9ta>d35|sM>bGrDOC*K}-TVros9{0SdKm6vsz3=mvWYO|h635$m362LJI}m!=c3La=zRP$HP6s0*K! zM0uSjng-Cno^d6h;WK=@*c!52zxA#4`fAgwgG95Mx_@)NuB!>p%un_fPv-tLg?JyX zEiQ(zesn(UHNCn4)?)34gYwZ4{Pl#RqocJK2fae(%2s1Ul)9GeFdLE)nWJ|c3(SG= z7$T+cm70F~ReAWF*B+vwX?sK=&2C5Kwc6Mpovx&0ha2M~yL+5Wm0co$$OHhzgJC`M zxQlV$TIfTFg)weaYRAGD0_~dC)%~n4s~m?%vjQMG+oo2)9Uabc=`tQ!v zO;pqGioSP;pA6h z(_8CwZ?2oBG?cd$@GwsU7n{=fOV-vm!X~Y$3g18$R71WNz$6lP5{>))^buXphC5gz^KFw; ze);I7%Y37JHmg5t=Q$?(#9#4g*@bwHLiF=Ms~FOA#tPTxjluzj-9T^@v3ez$Afx>P zfVCXr7)-s|cHaw?4!{(?=a-l6OtssL3+T&` zXC2Oa6awz+AMZ$95Bu-K00bO&e>EpsF8q3cIjYgYlMWrfzG%4i^3`F zY1Q<5jzC`h-;{zt9}ymA@+*v*zF8>YU0}0)4qQ+a=EWO`$ei9%@-IB#3VT9A7Nk8W zz!k^Y9T*=6JQEGHd_{(`fm0y6C)@eReqUXnC^87h|dHc-jLFZ`&G>4;0xo$JMN#WR|pBCYArkZ^md4?|W=M&8lO zMa|ZGm^qu)I5Ardq|zT+XmL?}{rVLpNLT8)W5{bz3i$Y+BwAD%3R!khc*(Fd98cLB z)VgF{G->eH;hq2cL;n3T5Jn*AFj1PHF>GFzSz0Ri-zMt-qQA z8U8f(V?`}Ofy-O$n`NMHl!6g0s;vIh;>*rcm9}2X@9*!0$X`H~ zJEJ?%m*eRFo^mv9no%H-7RpX&1!%byCwzoqnFE*psnH{W1H)Q!5g9U-5!_Rn=dsRU zcz>=iFZO96BEBs8d&H~8Tt z@u9y;WIw|NCETB-7-Rd>`0Kj-FJihN3-8u%wzAL>9JY7U7;;mm9pdUL??snb#fbIx#4;i>wxbWxZY#JI7!p$u5| z6x~BHj5)qh<(a(MF^>>gF21#hD4B}onev*mmo3Cy3RfNK@SX(nh67b(1q&wqYnqab zKqdBa+^_iy@&zT2;-sra{rqpc!38Vk@0q5C&f(quhO>lfv60n0o4X;B`y>4X|M8RS z@>7IUo!A~qGzQp-00>W@lMLu3DW=nVPN)tQXK{FV?jbp2BDuvbj25qeD1~3Dx=r>$ z!+{|y;Y=wC@btj_g=LKRAa`Ynh4GGUDa`#M9* zq0|OTYKbj{Tp>{QH7{Iu469YK08-cFEIly{ zUfcd$0_yZy*))i!D+l$&$#0T&f>*dcuCsaL$2F|raL0?(&Lgpf{+y$u^FIFcW}6i- zk|@ISjG$bXGCnwtahIH-n}hJ$+=5N4l34+cR}_PAYwc6= zvM5zNj2)v<`A@v^>*BOKqnMX3)KBx?{WtkVFzUipm5M6?$Ll?06^eY+fDMicR6ind zEH`KNe_$^B+qGLwh>WYT6B{z{b`YShjDfhbl-0yyl0*U1TtD)msMxf<~Pfep-1 zmY%SL9%;l#K=tDxGQ+#qNKGmY{r=V$f@?e(i6SVKmni*&5QLl9R6QZ^s_zdI+0p*( zft`4`3d#I0@_ej(Z@z!Z$z&Uz3SlxpDX6Ct#o?Wbf1;VfM<7Re=+xs@tll_`0l^=+ zKc9IQc#@{FB_oAXAPx|U^TbSLD@geA{AS>3=_k@KQ?~a1P;pK_B@>i1Q6N+@b z@KvZY9wm7K0xu9m2D%DvbVJ9g+*=oqTO5W|XE-T~U{7lNIi9{rL0$;Riv@Bl`ASK=}3q+jo3@Jx>v(4KI^1d^mDFh15+e<8iw<8a~ARaxL+Tao)u|tQEf2ZTvGf z6xrCiq3b8ZCxuZJ0yGOxDo8>@g_TEua4rg<)ZT?U@;8vK^R(#9V%=jlzWW1g1TN;R zE*go9e8&8BANk@8N&5imUqabD;E|Z>9CsMW!I~?F362xGlqS*w(S z&*iT075_o<G)n8*bi0I{P=@ZB61qPa-ZK)kwO6gmZidjq0UAoq{5a9_ua|$SDO?p^I_u z=w926bbt|A0TIh{o;G2mFQV_?W59p4?Lce9!s@`Kq_29p0tI>Z&-AjLSYX5Wdjg@* zxxmMnpg4-+V%&BebbF6DI^ z-&!KLE3Je+#mZ!*Am4%q;^gn|y4u#vs#3UiOo&3B3qmuS1NC-6ZsBi0N$QSE&r$et zNSl1_(|8DVXEQChe9y{=qeqr01u1QVy@W`60lOl&PP%_C+hJ^^YY>U9RZV8^YCA}z zhRdxP#zmWurL$iH*c{K%FWmD2jYxHD*+jY0Cp&zt2W2Ki)0gJlvGK$Rd= zMePw>uuklCwkzi>JyI7c)W9ph?JLG0mV;^|OcIj0(vtpf3LMLXF%0xD4q;4~J5ZQ4 zydiiF{V7!B@G6w>cQEzz3jeg)h$j^7S4uP&^OL>X5(u(Dff4NrxTQ~;Imn&G-_7ys z$&D9wk6*xenwpehNHko~ScL0c+TC%s;HWoY*3PoeurGhu-^Zdgl zAUH~o6%TpQRHAIn#5Ih!^w+D*^Hp*7d)^jOw)XJd7kJwq{j+AxYRcufN*tjVwKQfp z>Y4&o`OJXSwf_R*`DN?xLu-3jhY@Sb2ct!Y8d>8I0!HbTHl zZ9Rt?D;_SJA{g{(Hj8|KKX=nHC}(VBbefYn>ZjnB@RqmakGHHIExM<0Z0<}kJ4urC zAW&!5G^*C#DW`X@(h671c61=+r)Urt3NjAkmMg$APay3X6geqWY6FCgL3rO<0n3io z6XQcs!Quo(T(bNuyMHdV+9_RZNKq#|NnHxI<;>*;A2<8l?|L#Na+Z;47s1_q{7wdI zJeNPx$|+g+{y`O&^#=xvtD;D=%X!ljN?I4pam_D^q_D8O%g*N2cQg+Up?4&&vlo?= zF-z!h){j!K)`$#@eVt*oaPllEXNgW?4uLfDdM*c6Ru(?u+Kovk4I;thZ5S-2MYa~3 zQwh0K366Dg%pyMIw@MTPkgoAffqnF`KVE7$eMk!z#nCwD=Dj?!d)J)!*`@Vfm8$sC zEK6(816Ws=0{&Ow1a=pB${aEs2VfES-S=N1$+n5pgs1t95ZV+HS*ivzAQk)R#R!4Y z4IL^Y7SoYwuCe3HlEnG{XMRsO$3u?crn@JJXq9W+JwH-p-;19lt=Qqq2Z?d zSu?ZSto5F6rorgty$!rMZU*Fyq!RVb|CW!?2_p2axaGzqmOqhpm%3qyt7kpFw2;4y zw%Ib0(J1s6R7A?y#6dEEe{&})b>l7PDQqo}IBHPkt^v_Fi}2OUfqdZ=f+$C-ejuD4 zOA3$44=BcIJXvS#ppZAAH2&d28qF+X-_#b5PoD6{0+ zO_F7jV&2>QNL71K_Wq=yT5?f#;-M8}*BM3N!u8(}Lq`DU+t+y36;vl`dfeb>CNzU< zwP*@Hk8ka*VukjC={m7lN9c#gU3WL|u1<8QWaNm%zMRsD3$l6#ax^R31K*XTFuo=h z9t|8QZoHs)rjy~CS zN8G7iJelP8TP{E^fkGcJ^17ur2bJvri{&Kpt%FJAT-RY-%7!q46Oyi-kK5xAr@|xa zA04FpZ-2&2vZjGsPM{+7q1Ya;0ZGFx#X;&HGh)d*>jNxk8^{1 zW%Lq#PANp0BG9)Iu~48-FvZM5cT`_1R`h}4zoWoT8c0*#NU9vi*>GUX2cXtMm1`Eb zoW|5}5=obN)2?@WBA*g0^tL*-#G07$F(^D*)o-u5wX`w zaTlMIZg)<9iu=00`iYF2$q6LGyJpM7Ky{Ny>`mzUlLxV4OJ`N=DMZmm|8C117Ympn zo9GWap8fs07^#Z4OX^4_A={WCe-4{$rhJJ{zfsD49nC2nqvpVjqgY4DDV>A5@;>2h zlK2UY(2WMS^T5fvK=*x%Uqj|VjP=10bsun-1b9G-GS+zAmfQr(lO7sc!?fpj{>7fq zmKk&YL}bc9J4VjuCFadPR6LT64pdUb@<`4NeE2~v#uIkd+{4bOscw}>Ws667gbrFp zOk5?Z2klS#KU=7oKa1PkVn}ih?zhBn;!8&-<&(V1TqZJ zgn-LzWfl)#XV#-22ZzwOxp?2-8&`qF;-Nl1$9(O zVii$Lq&YZ_YXqegst+Tye9b&_OvQI{GW4A8Na`9Xi(H-JyCTl_vnm=puGa}9Y+JyZ z>*0eK>Ov|5xhv8U93+b#%NdQ)iZ%85s&P3U(9o%kx_DK7jBDGkxB;&DHdt{+oBi*HkKrRMYv6m`S7^o0KoMAUS8*0`?-K7?M zUzI0b!h9lwHgpNRYJcLE3aWkGl)XBxX%rE0`r&ETgO7VvTLzx6w~B?72@l39O0l1G+Dz1CoZLVws%?>u%IgJg!@^=_mDL6H=f+s zA)y4mQrz&ZReCJ7Wzwj4pjsTY162K-(gnl_=Zb4M+k)=A=4;nP@(@cQi3Euj8>kX4 z77J)YfZYZMt^3lf zhE}!4*h#=o&Lf2jgIb{DgYiUR20iLm&|97%jQxQQAIG3rFzGK1YSfH}oQ;k2OYnY# zAzzMaMM;U>WiWEK6LzjzTJQqc_ABMaUn3Aq7x< z``l?CZyit}13*&o1HMWN6FCzsV)7hNtUW=+?VF!A&S(fgInfGaUGMxB6o?&f zxzh`T!($+)Yxy|-0(L0&BY#cmJ^`ljfLKBtWBH$6{WeL9ZLCi`P_$b5I*R}p>GjCI zuJtE(y%5{-xw0T>3&Ij9X2FIh#y~zu_C-^^!fW3@_`3-6#mCdP*!vSU0`Y7wfZ$;^ zSvs+XpwRWEIf?VIf!fscX&B!Ug;mvvcpx4l@|UUaGn7ydWHT&A3Mqt_Ov?7D{TlUW zX?4>jA;bcUPJh#ZbvnOH3EglAc^5c>ha;>0?xjRdC6B(%Gd_0E;0fXwhon54Il$8zy*U_cEp9lj% z<%0Y2ao5JaV%*5M?9s}hI_0xltHK}&v=vh>3(p0K`e$;IWY~<$VME=sx3?aEhJ~!z zjh6M!l+}HDyJ$4NHx0^yKq{5R&r>rJy}=AXyB%Sa3Kk|sK&dMCo_ZO+vV~G+;2u|u zGFtp|IRuZ81@amU*lQ0|*rPDRvjBS+1jV*@lax(~U_$hD+x-0XfHJ>*VWgF-CHvP@ zCbDsUb~WXgbX?J#Q#MP9>yT8$LnBj#1l1dc?j9Y;M`yKewEQNPZW8v01-VwZ7ZGdsD^p#L)13sr7II^LeftLYexR<2h2F9-*70(%7ZRb`Uxb1J6!6F$U z9-=B|mxH>@XwMI6pOBWIfMh-*Ca2LW?pZTG=kkV>?afJT z>w9JcG%^x>CnEWj`p7qI1h@+Va9zPpUyw%=gxQGDql8ab^OP%w3$D)o`lMZ4{+gl3 zC2PNw0;snZl;MJs-b{Nw~=FKq(xMYN9Y3G5VE-oT_P3@W|7*YUIqBSpK;o1=6kfMkt|d zQ9-hoTl-e--y4TEgonQ*6V{RGw6Bq7^{y)Dh2RoZqp&BeWZNwMl>qU3^@4fx z1v8yx&4Q^w(Zk+#wJ(#mUPwJW(4?dA`uRxN=6G)@86x!E{cx2lhO-0PwxR#+&Jh6- zBe^I9xJz72sR65G`vkwOOJH5Yuo^2|08+S-R3F4ErP$OlV5yJgOqaJq@K-D59a;y~ zjGn>#B6J1D10Jt|N%=7n+>71#Y_js6Bo!C{z_c{YsMsM4dH5+z{wM7R-&@dw3S{_4snFsD% zowXgZ&kcUnypJJd&or$WX9e7@Og*osBvXhJmeeN)PZyAT3%JoT?>2^9Pwkutf2}r+cN0YM?@$GUXryrp%y!HWBPt8#u&0h)uj1=AGI;_z znMeccoO;)zJNC-`Qk`Bcb*zgo=b`8L#x1_|Y>0CY;7JiP8jgz9gbvIZ1iI8GBRSQ7 z>|@8mn)E}RYLZ<>+j>5@^P2hu6d3u^YGjLHvMel$RI2EaJ8;Z9^^x3kwc(FcU$odJ z2LNIj=l~g5ox8pj#{dPk&^zUy1dFGdEyRrvfqx@4i<;l5#N#3NO&`B+4nWg5rS6tE z`(7cI zyq(O9W-Og5=K4T`_x*8|A6}}Bm0{fHN=Hfc&qSQ@im!-d>Ii!hsRt+52n;n0l^Ko`gKYd;(APk zBbR0&Sf@-ss>E)nUyj$y>EOmI3J=SF2JP*ZKR?-=aI0;QySUi#Klx`ZA6o$ln{pMV z1Ja#d|0rb7f06*vbOm_yqmE=eKX-xBY+er+#cicbrLg7Sw{DB~e_MfpB}zWC4!=H@ zwEV?mIZvJm{O$0w-q|{BS`d-}nb|xQ5YM{VyUxdEKoaOlQ-8(eB*1@u0H3^hq=N{ld-Z`5(d3a5BfF9TMdXhfDpQ)dZz3&KPl!hr5lHdDyr|MR!#5j!Ni0%*2 z1*?iyzBZ$^J14CUur9cgMjDLZo=Th}%K|X|+jOk~nO3dJ6BLW^)oCyUk z#NNX;;MY;8G_12Z=iZJRb}}=YQdu1?_{o*AjEN)?LrKpa&i9i683NMSAdhsih3@;2 zKy`Q27!@40{slc)bAa%v2dlw$H*S{|Q-ca-V9rgQbk5LC{qa4-HRGlJN)d*YN;q8k zr2rzfuY1;j-YBSk(c*6cHB8Rh>KyO|`6#e%+pm@))O)~|j8Z*_!tVchd#jz!Xa%jH zn+*Bu<$n+wlVpK{{&`jFf2Z)%2hK2hWWtSN$dh`4+7crFfvO#KS=rv7M7re-i zA*A+U`JjsB|CPaig^$f2HCWC7ZzS`KIHONshZ!W~Qr>rimx}aIy08Jvsf%!mUtr++ zEZGS){)8;mk$f>#FYDrS(J8e~0L6NSsE(3k4DJ5yN}*6z3i_GZ`ELD7HJQsL1h@Yu zk`i{ac|x1+F#;qXD$@9wh={NWF`VkVK)T>(!h}Ft>M!)5Ok@w1ec?i^+Th;x&?=CE z({wf~J#=7#~8&6E(<_raS<%y-l|I0fkxjd1~%B^$9R%Dn=85IWFfjoLH3 zK1(jrW53bWXf_WA(qr1axe(<9AqmyoL!oR@Aw^vN2B?plQrM&OQDz+UDto_MDT1m# z>Y4@Ky=H-z-?)9>L}YsXj7+$sdOS<$Rut_7baO@Mk_#m~&|=P<5)$-R&+56gnm5ht z;_WZE>2V;SS)hNznocwt69!?keVEuA)N#CU660VE#$e~VS>kww=nLdAd#uI60s_02 zy>2VU4)fyQD7)`ScE6hCpY@FdQOsN85Rj#KPQbH?>v%%*!(oI#1moBf^#}YDVF3%A zFJKY{#9WouCTwxV%FkZi#Md0SrjVQWt?{pzY+^ ziyxO8${~4cS`3p7vDC?uKTp);wi`T?U(`_2xTetOH?iHEDJF7qbac4K$T$i!)a z3P#1>Y=d4=rEmba8ORkgT&(@zJzvYMp`T!l@`OBSjpBD(*Ve+5ZP@PSAU(3Metosc z2i^E5|KKmFLewZ8dpY{lN~t76^@H^F06;+6O-hbf1tDY4rV525%;5K+-EEOb2}8g} zgi%nALgx0=yL+L#^OJX@(C}n9))`9TAwr3wp>EdB42d6!+aGFfT2T6qo za6x8OEK-d_?FKy-WDkG-AMD#AjH*7mpmoH!Gd5S-y}kida_Mww5ZNQ{(1;(WichuJ zx}yVaH(j#yA~EX2JW*G7<-u7&r@;vGdo_LiZ;++#0zl+L3%H;@m6|adtP3791DbV3 zZ&%*ayYNizcVx>Z@rr3w1@aecD)Pp@{e~=zm|dyFD7|NvNL)CRj=j_qx%$E#8}_80 z($d7w;c3IvstdQIV7&3}#W253|X zL8n~5gnX&J#7|z7%Vo;hvZ;b4`@amQXMe?d4sp;UwBna=M>aRw(gzA4nc`uAYTF0P zh7l4L2zLx*J6a60EAK5MGT-Tl?gm!2ec z4-apF#bf94ILEOAKza8GC?MNH)mQ0~7KkN0fx-kes!$n%Atw2uKlURRMib*PS7azG z>*o~M^V9Skl(O+rjJe#uSM*?zgCL_npe2LHm5fkq{Q0^b;HOBS%&1-aXZ85K z0qH-=jm@CbQ891WY_j}i!HFA>Lg!%cYs+v1sv5KgI9S>SJ~z?*_zhIWlMHvr8$8FW zzWSKqV@Ly!oVzVSw>JwaRSK(QEw$is9^)+DW#RiaTyb?8IkloN1iEF_Wv*mh3Eh_t zpq)o)+D=f&$fZ$N8<;L1bGzt;P=OsiKE`|JAKb2eX3tFhZ}xuVYxVy(8{#-!Z8lc) zJV_ocXJ>EEF&A*OIeKkmWbs;nY|k|^UOQ*YEW2-9oDQNtI#-yt6JPAUZf;$UmXDJ4 zJx1*>wbz2S!7}hRCjb6{w${asyo`>vFsk8Wu{dGX2Lvt)YT9@hu$Aep=O3;@YZzh7 z98mg|lRPM*Xzzs6q0mv+ui0$b^hOeHJibeLS7P!m?XD_&6uOiA&YLDxK0Q#KYuMqk z>%ev~(#APJV>#6aB%UQ6&}kOSP<3M8-bHLcqmKOkxOux=q~g32!vMiw;uQNN9uKm_ z9ABvAJ9nKmUtN1@ryXk+rRPt{Kr?!fDvLMXwTpRkw2))&YBkZcgZcGLAn3j`10Bz` zyY*ZDU7gr`@p8g(xAC8ek@so>ot&RU)4#?)W^)bRUMGtot-FOeIk0h4v~0Tf8vTb4 zA3h&#eLji$H_>b8RD;bN2U}}~MiwKFvTT38g0oRv1SUVBQcIWIbi6Uy;aXY-J1V}~ z7ZAC@gu7ukftC3%$p>ahw;0;)0UnTvb9og$yd2?c^v&+3(Zajx*$v&wmz>GDWVB$R z|Jxlq1X|li<3un-qrh=9pmXtfJsuN9m-drD`!zBS^9xD>r!mN$^+4`HW)(1#^&u8jhmDvvGhO#cZOPN&#=N13oJkz=p(;s=CzbX7yPyEct z^|f6+)>`uM^rb{@UdMl|CwZtMlR{C!>$TRi{2I+qs;zo^w4n~35;zYPI` zDHXt@e@PI+6ULiudU{$kI!rASd>u(1_t(kqB4hjCI@QpAf7CuG2=cUUANj!&Ur~_4 zG0Fh#z2Ghl^3lY_9r+edJ~+T^$v2XDbzJZepK~cWYoSq{&0#vz_`FbL8krj~KTmzf zK#vD>2tEB=12YI6!Fx*vyeJw0BtC`j{G$aOi?%iumac&A57|>>t4`4b!-ceFefPf(z0AAEMJ+QFg$kqG= z&G|`~^D~r`QGB7SlO^b<&D(Lt(;CBw_BTI%{3wjAyh8)tj+e^$DSyA>eP^;Ex6S z?qpy*tdQ47a?o$)K+++&!;|~1G(QRBZ;cg`_@38$Yze+v>q}y_7yhL4^eKK&3#Q6` z+#Gbz4sEqIGZl`8-%<+3F7Gc?jhnQB_IwBcHhV~V?#_hmOxN_HbG6gLw@R8>rfZxN zMc#doY>8!BROpN09e`2?YIsNM5IK#SKs6sop;-#aNqA`%n39Akw_re~ax|)x&8kQAlx zsBJtDoHn4x661r*{RCtk!>OsKJKd^>-yLHKlfK@7W5QydwcK%pZgeVTzVrHI>y2*| z9EA>yL7*~83@(`XDjA->oGn22_yB~!>p5@k-G;+~&_BM5?`VHq$$<+W3QJS!PnTu% zJ>J&!Al(`-dFJ8!kPozYTC1p70@F+^mC`fx=UKOkO>{i1S5sYsTy3MAx1P3WHW6ouyfv^a8W?Jt++isQP;0U6Xp_&F zutuZrL~=oq?AK$1mx6vmF!+r*-Pf*?5d6vxsy}}~XXRshBtGca-3z+Mk9C%ppng3P z39|R(O+JB3TRLEK9!l{kWi^X)$6C(%3OXe%aCshVkuo1+4yTVY<`9ql@fbMAU0cjF(5$+cxmew>28(50p;ZMea z<1(NWY){K~!^Mt%v{*0AYq}}dJM&^XP8o^9w!~p zF$je7F9f7PB6U-eHy}e<${cDMC_4ZP#9Yt;kH?tfVc0HudT&NGxHTjkv5-lq5H*8e zWOdUBWm6d^i816L&z1A!H^9;6L6k%9lL$`Wr-E4OOh^zs)i$qBRIvzVECMBb0AqCh z0QRdeV2wb76g;M<&pG-=1&|_@dQh(=9!LQ`PjtaRZ{^m|j|JX7C}Ip)^8^W#+)=!B z$nKL;2VMQj1b>0s@BKC9-h(okh&Zv9dz1qOE9c1VbH#;qQlxKs~klts`3o^=*Z zn-m})!{mgMpcR8fS1n~D;Vy#(;-Uobo{lYm=I$~gNZSUXxq#mf8cPJ{;=FlmjQ_qiegLG$lAx4L81wznX+_M<2l~Hn?#||3y{AX^ z+zpWiU&2sJ-!>56Du_jn=zvm1DvhWJBSP;^`8}?k{7K?CH-TqaD4e2^tn|g%+#e(G z{}Ifnphv{Jlxc#W4Ssg&ck~1BWTZn6Ya+A)_l`zqo``omrTj|37M!Rl&C7qcRY7KM zem-k*<1#R};8wf%(|+v&_Ik{N3TA1H`tRoX_aoe);P&DHJ5q4uV-M@7BrYMS-w3SQ zWTRh&!W>!l3f<$G+###Yie~RejN-^ET+0Lf50eSZx5Bt$JVOYW2j)IySK|p|d>Z*B z+>}-Yf9II5i;7^>dlN-4#89L+LUaBcU+ulxd$w$t1Y>72;B#s85=Kab0PYkjN();s ze<35oyWzoS{J&~@^LQ%zZh!pRduy9E5*aejL?IbMhBAjzh%#(*${dOen@S`@(w$OB zG|E^c)0TOPkRkI_nKFdT`@1gpdCu=S=XajxeE<3Oy8pOq+n4JzuC?Clz23{U;<<$! zg3)-<(NKVBU)Gg65d2~LnbhT+yKIs2!*fuGDW6&=K`$p9X)PvLOk!utcli zMdbdSu!W)8Z*e80u=>`fvgLOm_P_Tn_?fP83~xwPv@4^2W8fw%;>C@T4vA7P03raTyG1ChmbZwCI| z0US%)5i@=Wu2P5?#;LIf4v2YFS(dza?0B^l`6Iy>n;QECBlVI~hZ|7OvoYdLx~^fA zn_8~8$WzRj%vzn4y-6{z<7%(>X(Yy>Y}6*r9|7E$dxzzrOyw-ab{F;1;OVj7`aab2 z9K4u&wM%p1gmzUxTJso$Sqcz`7KHV-Su>WkNaFMK`AfscCViDR*SYTHjyXym59AuC1d8>qA$QKB!oT0qUglb+t<| zx<^BQ5k}yiMCZxR&<4jYhO?Ln4Hb39DBuf8iG5>y7|F?F44;viz9A1ZvG>^Ix@qWR zNK7B_L6Y>G58n+YS2D{0Bl)JPEX_Gh1|qn3RQ;*I%`Ury{K4Zj;h!}(ej<{!>M#;M~(90As0LSBY0Ax{QDnR zA)=H{fUVQvbdSuY38M?R8~Z9n<2iK6@BmE~y%T4N43FQ=0s18LO(A}T3a(yl*_CV^ zA(!MMSW%I1BmO)T%D;IhY;vgb7RTv>DGwIm+LEFsg*gyEBN?Y)2VV-WtH=X0OZw^< zpS-)hmuA$98rQ5T;el=VN=Q@kx%l%dRRWN?u$&r#ilwF!&;I zuj`HBdbs#kjXoVS+<^JwAF#%j;BR0$dT9W9cq@98&Vfo)TVdIVKe(7)iBk~Q(?1bW z#Y15GmT(bkqKFsur7|q_!JQVh&Bol}K_a%3e*pNOTt%^BuNZfJgR_8=x`$iO`Ozs! z7d5}Q{$nSiMCrJ0M zklnSUcCTbIoexg$4kJ;+n*Cz7V%Se#Wm8s5?{Pyh;3+IJXVDDqd4(~( zMiq28-aUJqCMmIj-g1|i{^?HJq-tjGgQ~yMJ1XAxv8ZkzD5^W)tzX3DVmda>Zr)&d zUXTRWX!@8B{?s*Havt!31$4>Qd1ov26ND9Sk6%P+Zunx0c2Q=(-TR3(@1qjzrYx)~ zI#usi$~g%#7tw(w9%nrclaP@TQVmU#8|3Z@+b@BInYeeGS!SE4;f>E#+UnET*Eg0E zh6}GNc4s`=fN=X0S&E!1G(1kNS)`&mzdjnbYY&&pXiKv2#If#= zHBcT5uz%S$6OEd*2yIVbDW}GYE_t_h6)f~9__=xsp;XOeCE$A+Qjsb-NsWg{kjRVW zxb9WMoY|1p6va-yAlQO+yCQ0fqd|CLSN%u^il5_spxlq`8AU|coQGIciqEFFq-BX- z-%Of$eQ6hR=d%yBjLBixo03|(-*eklYnEm0Z&ads4JoTtE-X(SX%6q+zOL3KMIK(r zIWq01(aY+}Mubpgoeas8Cd~T;*a9?%apc6)hv-C;FwHR!xE7kCPGW&-81CbY=qLM( zsU#fM?WDYFs0aF6)xN9Dlf`C&X8US*hn0-@N%`*lTTTpC`GQVxiyI0W@oUI1Gnf;+Y`QIqB=_No5j9MG&8+`n)XA zcC3|WkL4ZMR#tLGuJ5PQMtTl(&x)L*P>bha z5^rg|X;wHB1hAHzIYU~Ly09|TBrKG^dq`07L<&AY8 zl@QVIm`Ye5PZYS>;J=-GJpIX~ZW9;q1{mKHtbUjuZTuJbSIa^)Z}J2N|B4L}oCPip z=L16>(RX;6E_|%kV>TfoD=&tct^@D7dp(Ur zh)3%^DuGxs^{C_wV14`hv4Zn77Cb;CJbwW%^In(C#gtM>`BNv2QWT%r+~Nyb#!96e z4VSKUO<@1|*W8^vh#_$IgfRSw+R)3BU~D-D1niE3Q`p`PWUaQV|lrpC=yriDFYhwR~A3 z@_5mBvste|l$2sX-@G~(ntY>4hsmH}`E*c#4+C)t3@7&Uea$u)O9@HhnK9mfm60bc zgzAn=hVXJ#nl?_)J4N3+FYv-Cc2_R#OT(+(&VXHxN2dalwBQZ($!J7ofqYdXg;Vr8 z<;c?4yoswv**2SgJU(GJyQ)(F3SEH1dl-8H>d#f zDqk^%7srq&Djn2e%EK}lnUD`Q!}##xQyPwpkX@JPZIokiJAw3K6-QD;u$dhR=1 zMxxfC6e`ig-hq^0UGjxsT%`~nVeWTn_y*JfppO)EM9yP>V>l7~nhQpuDfDEa!s4Rr zguV^4$Z-<}eh?3PR4qhod>N~OB&BL0f76!joq0PixUut)8|6sIDOLwIST+bKvhwTx zy6m~)ilUMOrqHm zP3}ET-0|KAF0>xlsCN%_eZiS^6JU15;mB%IU*EJH(#*QEKUX+~0V)}QWAS>QW+zHW zHIcC{>#mIABoUD7fiB2e&yv$xSekJHa;vXjb|yPy`%4H0PP#gl?NKYuhwG>+HXd@WCt)8^FpLX&Wt0A+6?gH-m><%~eL zb7qOm0u3K(=f1Z(u^yAui?|7H>tBOFP3XrNcAf$9{L8**}04tUgCwM<99vG%pmZCy?14UseL&xQhWC~ zvF*ysJfY~b&tk9M)q74$MS~~V=Xf20o7*)Ew(CbvP-dEnQr76AEHeejMrf)iQ9aTt z;6PS1OHRHQc!#l=F`K0y7aA7UJq9)NC`r70{NqzD7B>X5`DTpNgCTm`*sH{CyfMI& zwrlME3J6J2LQK+vAgR8xQU-2X|?AaRnlOj*~j zU*X04VH&+wta6NjsEBUK8YGwL8}kL^p;{0^Du0MEETr$b(AISP<~?^RS3SUac9+ps z&n`Dq9>kHogsAr-G`wSHhS(%=ATz@D8KejGE?>oWP|p$$lR%wL0Od|`tM~0H>VEMt z8CeB6Qd}U<8fg%l)eKx9P+$LE{`A~RAIR9PC`(Z&I)y5 z5~l{mf43w$2~}sGt2uW|04*i}w{M*LwP$*SGONf-Ry&#rbiU;2I z^5uZPesXF6SmVkLTN*^_&hQ2tviXeMMsisa68kmAQ-EXHL{W{{73WzE2TyNLZc+Jx zkqs8@(N}aZq~R=cEyp-{I)-3 zP7Jf528{KyzmlwY*pZOsw^4w6--WvLUaDh91V7L#>EPqRMn0+{5c%>8>K3GjnVKUQ z>T&TOjfemED-K_ZbE}40MzHk@sr0p@&pE3Q1ON^$>BK3$FZ+}#VdGEtO|hurJ!lS- zOoex=($z3WXhu`mFJq(D-&mlayJ-7CIBStT$RRsD$Xx1XykyMnn5>Mm?F;L(9lVW- z{blElwVsf!dHbNNp0f@vaUKX4F~?jmqHM_XL%1F`0bKn)jj6fuePq4sX*f+go7*9i zLg}3&SDg3+50O9*u4+&%JqwxkQhr4k{_(!$!^YQHCI3QtC>|WJtXillW=Au5UPaV} zmhW_xzMH(3s{yxM-K#G|Fsb2;!y5S`(nq`5C|0*{Pkk0j?4IV=-@a9GgjP=XM$Y7ZJv+S2eGC{RLn!0le~6U+ z<+>4nV)Pjr$!=P6J?fXK`t}p|U$VU9cah+B3Q$DgBeBAZ@G1}}P38o#IzPleS0 ziK5F-wx4}7?L>SS`6trbZ1t|ihJ)7SU4K_WBxtcg%;d}&9HgyKy`r(&7+FtBHOon2 zbCPUsuN#N_MOK@@$;{k8B?BI+tHakhWbLW4%q2yQ9^F8*2{Q_kXS1u{xptr_p9^jL zR+n4kdn!Uh8!HM;nK{Y&N2e%ATHD1>Q0Y%vkKs>_OTnQID7H*(Jy4-UCyqodb{C_{ zQ@?F#w4Q;L&!fwJ+sB}vFzj^=YSj_`iyB6qwib5+BU@sv1!DLi)<_`-8KYO9*feJN zO-}i3r>4I~$~S&hf!>?Ur7kH{84I#l%$^m2%NM0=%ek3U=Mn$8!I;$Tr56_Yjauy#D)Zs)$w~Lb3F!>L71ue zi3`d>)T-1C2%x1x-hRxwhz=j!XOZnVa($&uL2 zKp}?y7-S%dJTioEO7$K$jejteS9@ol_M!IL1DGY{ZlP@aQTIvzh5_b-732q@g|fjAnep7 zUZF46P8*c*ip536T83-cL@xT+g)yCs09%8|xD!su1%)Y-bHnP}=I5!CZUc!a6+;Uo zg)2zGcAU?R)f7e^e9OAJpnKCrq^Gl!DyKVoad9!X=)r>r&z?RN7_DngR%B{b+mHfP zJ33IfaB5=9vMj)R6mc0D87;b-zdd*JIyKX$AHVHD((CH<+oDspwtNd}BKn_DRV>^V-Wmx3U$lB(tw+DoovqpeKqQBP@<|X{rpd&Z>R3t zy4u_OA@rj(tE}A43)1!h2xA zV~ys6c-*C7amC2F$u_$-#$OoG_IFu$Qf6kR3^7k-_E{JIP zF0Dvks%8TJ1YO?LTe&5go(W3G4`!LW3QqFK;p7%nITM5d*;iKJkl1~1ekiNnTx-3#fp7Nnb;`f#7V|p8ZAJM z$3>jB1Wq4L={*zzJ;D%FuxyuxdoYH@nvTfvI`TYCl3ri_Hk}^`W{%Zzc~*nux2!`g zL3L}X1LX5+Mr)!ms|6l@etwHt6(Gn)1UAlx)zRoL{rz`t9 zK*X#O01CC1joTQr%mf}?gHIeB=#?%3*I>GKwAadvC2DD2=H%3^E{rX#^m#h;pqaWaU|MWHgA zH8nMh?$t>Tk6iwK!@3pJ5v99XFIL8C0Bm&7T4O*w>45#_iN|Z9Pw5g~VhqpXXm&%P zaE;nk~=`}}ov{^DRVKu78f z$+C?&YcgZeNe^XW=zqEL0nT-!bkUJQ#X7fypgB_x}EmXQf` z{ys}fM_${o!~F)nH3)#$sxoeEUX>GW2;I=!Ur40-gAwzgdb}?06%O$NDJT?q{*Dp+ z7Rl&)ITT#aF2mvjt$UvS?Pt~^r>d&Ahk%Z}SKc^@5J=!kqUQGpN;;Iis#4292F(;4 zR5+TASPf?J=jkvDvsi!c-G?V0s~*Ais37kSA;Rs#c+$SY&C+o3Y4~=jlzEr`kDY{^U4L9Bp)rC8$!d`Kvz!DXUY7o)ro__^+|Xl66Tw- z3(8@jwOp<=QZeMZUzrpJ^*aF2rYouP08FbVmix(_|HZk}id6e(oHrho^v_dGNrV4o zb(ilcZS}~WWR2BB$1}7yKX;1focxr1W1#Lg ze1pcq59-;WigvIvr8muGL1p3&q=&w7KBX0Ah_Tg5RE2T*jT=&bv;rLq{E;^=v}AA` z5H&;TU4pm7(*!`Yet}E{S7f*)Q2Ll+W?wNNZ$)uZwZlbcp0;GHFNjRNspd$6HC&)< zYDelNb08NJUsAZ#Gj`+mUY^o{q>&MiaWSWWX*vVHhL1eyTqBY)>Geqrqf^F+qnDB> ziD;AJ?9SLD&oF%IxMbLC}_b1nGY0!nz%s3RoT zCS?B{mQB+>;HjCJ7thHH9lG9hh0(j0_bzfI(O8qYamO=^2#CDF9g@izikQ|LT>ns8 ztp5w@HD{d8*&ef@93`xOK(JR}skaBu!4Gr?jUX&cE2^4|QhAXpPy^Y3c>+IJy(^J~ zw5GA^b<;Sq5x>^CI^@)KRU1D4)J4y!Q~9_LFPNRy5K`4q2XoU_c8pF5SEY{HH7p^q0D=~sF@scQ!x3+I0Rp(f5CMigKw1h zi0$1bvdch`@d0~yqRIq5UhpT&OOxos^q`&*-c0Q9ToU#-8awjkg2}u>RE{G_jw3QF3h};9NL2|5M(2@t^G>*TU{Ga| z98NDoAB4W+;ixCtiEsc9&Q}~>z7@ynM|!2Sy^9o1rBew7V^?w0U)tZ(F(P=$mzbqB z;l~~)W_MqJ6mg@|nm|?B@gErUB#QznW7?`rZcP9RdN_dpz^=RDNZ$bkO5$%YZd1OW z#_pwcugPV;GOY%?oC{kUN^E4OSYA7brY|`lJtuGaV-@|ByfYdq_IXU@3ggIBk5Jq{ zr8mBd{kj1#egu`IV8{kUR%5AV>9)71hu3Ud= zatgt3ZiG@US6WyY;Vte2jXdj55K|1hI_}Q@AMy59;+HhtWpWUz{|-Dq#LFashpdsH zOLj%6{!lFa&X^cLueT4YfqYnQfAp?j`fgX0?Y#068>OpMh8Y0>K-L|TBObr)F2NbZ z-Hg2U#OnV6kzZ5R+d#{KlhBOpnaIy2q|qR9^C$S2MItanb1!vlZc8}C zjNJY;s8@i9paO3lZB)k+n~qUyQrD4XVC$I?kd3^%PX22YX#-7anfPJm+!NfG7ZtqX z=J?#=CWf;_ZL=ddjr7FEdzX;wy5xqeC&KAVqd6{HJ!=!YK=y~JM&V3#Y>!sGF8Lr6 zfNfuhF3eKSqBxG1m^+|!NQgXmAeZbmKBuPjiWR&oUW^><(*3YYlVHPds6WQ(C!}>` zI^%BDBL)fT1!CAw7KeJN0EGZewT!+mzB}_R&!WS-KA}s zXgJa+2Sr%-D)|ie^X%$)2)TDa!n-s^)+Tu^HT)5l%##-QQ@^mzP`V8R)!+NXn7y4Q zT?%P-WY2}Vsk(usESw`gUOf2NB_sfrDHwi8-h1ZFVfYCUyjG@<=hR3yqy@hLr0OyP z&`-fj9G8{h+(*m&mgU3K17+tmB7Gjxtf;Edx`(AQWC0Qi=uF0=ZzeJiA=o&k9CpnO z_sWx54vLc=iGMwIz3e$7WnDiuOyKXIQ>D{!V$l=*2&@uLa+^RKY=g{{hO`HDl22y6 zFhmCayJ!Q2uTZ%RG$*xG;clGMK^#X9eoy@MF)OI$siTz_<|XIKaBG*1+(Z$(1c4>P_nf&PUaEG1F0#`FrrcA# z4t!oX09swy1F4#sVGlvQlj)fPdZqIq3EO`n_ziBBQSlqD8=AVnZb;Tpf(s2TM!I-2 zOjgD??qo^1sXXoi~*ueWDHsM#uQM$~K;-~$fdBRvs2kC|V6AU*(F z@iz*ngUL$V4QVy|y;iN5yh-P_=~KKHsCv!&2qf5Z?FW`N9>Ws6gVHe(VbJz(5hC&@ zE%f=Pi_FYvCGsjjF?lc^r(qD;9!&04M#Em)6Avg!k6=SD(QRqRfx$=TV2}OT4@^qQ zoaB^CL;E4+nP?sW-bJSixWzZbvoXF-6dDU6EJE4h5q!kUR!Aw*=ABRE#cU*N5c?Lm-+=SP!MaHVf%@bwQU_E3K|@} zZX-q=hMqGRVq*NUT~Oq;fzmZ2n$^D=LMizS8v--l$m+ecldqD6fv}2m;O>EYrFkdB z4yX&x}kJsT*zz=;MP%*?K!++&c$v5Uo>PX-Qvme#k7C}h4tELF?Mhymb5C=v*d z;Zm$E-C{YZ8U~>)*}1^3^QFnv$;=0z2Iu@s7KX1g*;hw0qpdCuyY3kb`0*q|UHWE0 zT`zTi1E}&u?u2LM>r-zm3f(w=mCUKR^%XJ{IJbP-T>EWu{yf*h3N&JOZKLpkpTkq5&S&;|R9MwEGN)`;c(=mDD>%MY_c9>591-wcIWIctj)mmyfABGAG3v<o*`R2DC}j(+hI@W8l@UUGKvqVf0RSH1AZ(v2_*Vrrsr5g0ktUiRJbVA% zZ2K})*SxpASh2^GPo{(v6&d+X;{WS9kaZsIIxzG2Uw0iifkB*{oUTG!g)hCmQGn|? z43!51q@V#%;HOk}M2d}`j-I}1dpy4_n0!<}0rJ<-CM~A%B4CioXkj8WjyO0o3*{nJ zu<{NeSq+ZI%*^%@@VKsQ>u8W#*T`X__`o)gAaG*iKnK<48UsE1LBsXmr{jC5xm^i& zfYU48W>am3p1^Mj%bm?A$jIec%6#U9aSaqZ&I4}q3~)5ql~C93|MtzBdoEi%N?u5^ zqGxRlyX~Ft-<`A0y{9YjpZydqT`oTK1IjyYfBdLyn4-!Fun^izW%&1+SL@L~;@hRH zsRN@qm^;2y+>iD)E4pmC>cFoR$lq=(z3@fw@zHB+OlqtAAb<}w9al@YR{A&;+&GZ5 z@^Updh1Blp8W739v$gRDkjRnzrh`?m&rVre9~i0~@gmmT-m3(CGwA5(yr9r&6bk)x zyuI^4glqxS5>{w*$)l@^t!^Y6zOw9uNDw8Kp1zE1>{x9ZKn4FfAg2e!<;t3x0sY;5OS2hgDv{OO@?-lqW% zLCvxLbTLyBSY4QJu)7Iem-%FpHU9Cmw`saN#F2k|Sb~>nqq6^dE!fbf_g?En7I&rq z+@;g)kU~a#+X?Pi*Y@zsXfXbdzXisI?ew(FaEI}@j?!3Ah3x$7jwOGW@@81Mm@XFP ztpj}!xIxyud;t1DF#@_D@^zk?92&BhhHvHjz6M^l`5Qd^3|FPjRZ-5d#Cpk!N$2OjW z9e4T|_uAhh+JF}^SOWeA$OdYm5rl+@7Glx5vuD1e4-?N_y@Z@B%13tv8vmX)sPEIj zUL!#)0bfArZtw+hfRN#1bQ>Bh5&oVK)cH@`W6zidjNJeHOm*=9acBQJqtE{7N7cMz z=7As)e>!~Ue}jQZZU9{cVC>60=+gt8p@IUP32^ESXm{-W-ybZt==}5*2z6mceS)3m zwN3kp(@btU?flEmR4~_((M=U-{rEii2_`7DCsBfiiUjTx*jm)RI%LmE0OaNGeIg-* zRgWd1W_ahLbbLI|KQ_(j2*`PBX$1oiXz&m=KL|-Oqk&6CBBzz(F2s~-`vkhZs!bU( zo6}u?*n!%OZF!nh-vhWEv}7*`;?VqG`t#>_0Q%27H>|9U;c^(bn2n@k~_V|)%RkK~h%Toz^IRw6+~CMPnVJhX{N z=9{2_)XAgEtY{J69WF&i)Dt;S)E}OnE{GPSa&D}{&W=1}B&MH`*g}tH4ZNXN%pQu; z7yHoC?MvCkc*yzA_-#Cta~Xy1T9u)cKcP*Y@ElIGl~SDAI%pgFPM)3_C}F3p)~S#8 zZK4S$@ERs&Mj?4}%El)4N6Izh@|KG2=?XzjBA4bGN}#PQ+`xo*6Y)AEsWv8ILQ5j~3!W zHz6IujUeExv9JDo7KfUp=QGo=w*=eMvv@Iz|E`u99Xh+sRROf|)qi%3qP>6r{!YdE z_hwEj$dp4rI^Hm^J9Dzi}F;yt)IFV+}&$_et8CEw9t)eq3t-+$oZjl^G>v; z+e`V$;!xkWZwWzEFz=CvPO=#BL*?yHDpLQ7H$RyGu6VLNCg-u;J@ zu+6{u|`@Grp&pnY#kZcv07SX|khgipn-%VsC%Q#_Z3ZKOtFD2Yq}NU?Mmm zodhj@3R1TSIi-tqE3b;%bGmP>>?|B-Oxg8`+Ni%vTbfBl<6S0Xf)gW}bH={(V?d@O?)BJbkY{Xvr_ zY(Jr{6;z{-Y;VlfRctm@)Ynyml!H8P;7@_aK!@N^^x#JO*P-R@UFSVpHm_^IU6QEz zzt@?9GiP32_OG2OXYizenTTE|Q&Ur?%*{oqgFc}W5F2w9d?*nEYO`gp&J+8+>v&e- z3TV9(JPW^Ct2ci62q~EJ?+NBD54n^<5BEN7Gw@ITOXuSJK@dWTTGaQTg3EMDrFz=` zV3DQ_VtDM@H5J552s{`QzRtnH*!1*tH(MR^TzVy1wNxhPBOE^95%=-qK@r%lYv?sv6T#t1#EsZ%NV zr|(?ko3m=!q%G!L0VBdZAp@uZ{DBtKX*!ltY@6d^>R~nZ4J(=BRBD6&?YkU6(n7z3 z<`ZF*K7SjEbZ+pJ;IHC!sDv_XbXmw#{QUn#o6Z{v>o*~{xEozkJQFYBiSaGPp&+@L ztpwqbFlt^hC>GekMeg#M8+18TRM`8NvaE{tbUZh@KHyQ8rg_~9;x{5?d&{n9#34l) zjW0olW5Vu(oO3f7?LWA-6_nNCGSPS+UCBbXhnos&gRhn${sd3!=GLp1FXM+=>&_pA zv;cZ~?LnmE15|0}y7wtGKa!{a{i9VEs0D@6Z(FBlW~xz56X>|5FuD=C{KnP}O0!pI zJwu~`!a&%&ckk-d*^2#hL85*NZapM4s=aqotr9BNhZnBOT=`|?>+j#V^O9y}W~(*Q zAz=`c5i2fNpr)D}`U4>7G#EUWhC3)!t%3CSw!_<0-#>uVvi6O*hG2v3he;;Z~IZ zyq!K6AXB#NT#BokTOy*zrQjAX92FNJ3RX-Hnid_S$*= z7<kbsD>(kjKj8N*0A=jyx)VkRsy8wm!Z^9S#?Pe4Bg;kxvnK(Z6l$aA! zXMq{`PXrNaa-Avdu0Pe4gv6jIc65MOsq2M2k) z7(}m}2R4DU>{ebF<5abn)U6p8)UE@msijUu6Lzv5x_hh>GHg7bEe$)Q@}d-kSRR%6 zFS>lofqZGg!p8o! z*`Ve#t!@eqBHtOc!O456sd2O{qQkHPK_O7F znHG`sKS(i1S(|yheDz9GTU)!wVGeK)7@XP)Ep)U0CpHEjtd`x}4~LYH1mVg_UzqH? zCg}=pl`Frh!5B@bqmeZbf_{-GNQUVxjPHT-D&*y^YuAMe9x%)0;EAUl$xy^RR*RLW& z5pe-iKxMO9pRZ!dgwBozD*iL^s49+rPWOqqvOmB+g~NK_9lb@u>eUzDm_k>NyKb#7 zx8D`g%>cn9t#i4=Fvp_4iEbUWLFoC=nI@KU_1HcIwsD!29=(<^d(*Uw3Ab&n4wPJJn)@;KHpLif zGgOLo;XW2@#P5U;aBu(TUH@OL(@s3`KYuKt2=_5Gk2T!B23;ee%j(@EzGoeiXwOK| zt>QC&>RMV`AYUbzU>&^t^~30GNx|~DRLZ6pNJgqrHZOnuvw>V z-umPeb$558ewW0S)fe9@SDs~LP~q?djVo8K=%|S?o9ssoNiW*46?i^;(3f&nv(7F9LF!0!P(xN|$IN|f!55&selB3(L<4O#C<9R*+r z8nuJ++`XLQAI$TgpQtR{&k5QEI$Gc(SI-s=#m2@mu5FvJ4 zl>z5B5_-6@va%APrQ+(w5QW#@8u)hbih3DftN;iG zYU@lT{(@5g2kg1qeBtmYYB`}7;;tLnv=s18X&yA)L`7K`Z{g5T&vtu}edgPHdQbvU z@hDx;30j|gaa-+H>yN7Lifum*0v=_}UY&aNV*VN5*30f$=v9h748@5d{1Hckv(4{{ z6&OV7+&dce%@HP)e+gWCdkw&L>SI zXugsf9j@ByFb=(Up<^R^;I7J}JZeE46kx9|w0MTF48ik3K1!7Ph>uoCD2TOikC(#& z_k8q|^e9_4KXRf7<-c0yQ=vmZ4GyXz#&N_aBfU2c^OxgwncGSaXD;IW_{F4~5eZ{-N!zt_>8 z%>c;1&WVdWY#=B|mv?2(hL*Gho^q4t25e5L;OX;2x7TIQ+5}`SRq<_~+TQqwS>N7( z8#@CX65NvQu<(+c1pFizro|1)N>PPAlA$erkQ5u=BPw6`}-hAgsx3!q=- z!5*j%p~ySa)29Xfe0}TZD6r`JCzk_XJ$wPNzSxc*9ap0%uL-W=Lh#q()(O3JxwCLlnYpw%m zD*|78&?EveqduI&WBskiwSqxGQ;XGk>E{>JtfNpmId$?IG)F}j5+OlMfQEVCg4qv~ z!%$+|wYf6(zwB;1!SbP?0B8`2RJcQEUgq%!4aLzI*u=yn4Dtvll73rV_vepCkRw_A z{d=Ow8c+p5Ab_G>v03OnR$EDwsSnM`%F+T%yc6_+%g`{ZwiBHqZ zSOj$+V1+9k4=t~+*4FAgR`#O;pT|Zf&b92Dg`VG!=^Zj zkXoFh*B9~zsgb}koAMBWb4Kb8xBcQ35Z_ zwv21LFo92v8@8Qk&=62%wLx6V_=&Pe#VlAXmmYVu?NtW?m%>h;bW}$Qeo4hym;7G0 zzY5&*)VjfsfrJI8{}9CE;4X%ZUiyW4DpdgMpx5lJEl3SbPdLY;dkOATW}ITlK%vkyj~T!;wuP_Zw{y@fOfKJ`zQZAT@D7)G&zz9z3^-zhMaF1}zID4&b~-y!?Z< zC>K;$=R+eMG`=~qaYAbg&_KbdOmJ-BqWe_#XD}Le{|Ff4zlIcj#`Vya+Q8TR1nq+% zQoWJ}hjby>7QAh=(4c&4)^iN35*eU2@1fv7HwT=-Z$t^L-`-#H20MaYLTD@9PabEi ztz*CyvIl{&3y`Dgn4DQI&aT)Fw0LWddN8O;6?#a6C>uj{5Qd<3!;i^;43HJtH=a{* zz?TK2p7>UNJWuxc1hoNTkB?l&gSV&+sln`$U&^Rp9qRgnizvl+R2|#`R0C?E#~}{Y zty|sv-MRn=9XIbX30bOG+s#q-o&pypwaxkCAP9Br%W@3&kv-t%DWm5G02eG}dyc~U zS4&oQcCC@(z>V{sWqaY;5RK(MdV6`gdkj6d6Mb+_`h$MApyH1@vD};sktvbu0BdTo zenCAANa>(q33}wmi-zE2qn?JW=b$R&n5ZGB`tbeHCDx%FfLtI^gn7{sg|>$uTKjxu z9`GiZO4wwpl}sTdFuwNSr=TKM&`W$fw{LU0Vp|gJ#EhnIj$2whUE8eW+Pi{3iP8+Yi( zzLx2x;hK+R_>lO!@rdzc+1R=`@$9>lKBcmyW+%=5@-x$pPm zan3YOwcu(ER8Q(fXYL<;QI2t3q85(MGdAJxm zoRpRpAxGtnD(Z6<6cU0GPCy+uF`<#v&WG57ichb!oaXE8yYgkDs~r}FVkr3Wsr}C$ z&^>*cn4KNB)aJ`CB*xB;ub9T((?=Z`Ggct?R8WviS?u4lg@px9|0_2L_IG38WY#KY zbv<6Z7xo^d#%hFWFjMg1t5>f`xy(?Z+%I2H*L!2TzPw60?jjJ+wHnD5aNWdhcHf)& z#E}DzMe+D6R7t{aI|&^f(wA{hq8b|o*8Mv>JJY$|qMMnSkyBDrQN=m0_pTl^Eb}cj zyc?tY^5x6QXr43#IXXJB>eu7qJZ9yj;(6PPD-?fooQ@6|JntK^uSG@Z3WeV~{h3p+ zfByWbQZ~s(^%gS{Iv(O*Hkl|^^;&t4F5OdWXR9DPtxQX;9Vs~bY`KvloE zPRQ^48m+ZX7)7&=9I?sx6eb`Y4Gq)^EgWU#5%f!6AD{f(+}xjAuRn34QSjMG*w`?m zN=hgwV6UyO=V_IxYG{~?F!1p`2DcpDag&#qhYe>5C+6fhiYR+{@U5+_jdlbE?$6d@ zoNkTuE#F-$GvS$1XcbSajj+3E4ich#%KQ$pG?@e(KRGQ+9ehv zjV>-ya(^mUbu>0LH7zibL`hKygTuWvG&NTjnq2#T{*;uEfHJePRu2#Hq2uFYSRAFW zimq-Hn1`oNpQ0!!DPiK`zR2zy7{CcZ$Fp%dTv;*s^XJctc_Uw6-^Rwq7mW`d@D}EN zC%?a8r(AX1m$v)y+@Pdf_UI7b%YT#b8AnD;_V7icfl&x`+}84qxr_`0gAJs?^av9V zPgY7wD&+bh5fLF3)zX^lhKrz|zdvo#&uaHQt=AcRcxu*NX$2b0tLy71u`fdo<{O?e zGyj6mG&-**eEKxI0$2a4)VaLe5mNOb(r)(C0I}CjV4&F#{p;%0QC*Emn+N+F!JgFQ z@BKLz!XvtFR?QtFBD&7*r|lGk7#%{Q`Wd2(&@E5adeTLG`Ci77ht<68GrPSy2XoSS z^Y0XCJHqhv@81~LQ>84Sus?syL&L)Iz?rM5sSyac5#Y0*GZ)!Pd@7d!;=pQG#8VCq z0v7GcP^GV3AL$ii`b6$;PB94x`a15;IYi(qA@WK}y;tXZH37F*!~Q6d#ftFLADZ1@ z|12_Mn-o_#DVlhS2-k2pP^^r-(;JXB4e$yKd>k;+^x-Xu0@uW}W~kP~#0nx&=%t)o zL1QCnnO22VK!8{{E?KZ)iwBwS+TYVt3fuvkI=ynEHu6uOJ|zrnq`$Bx`uOo9At9lO zNKtb$`9Kd5A{avV7V>8K&ijHnJ^qJQSz8WFC zSNJe7v)H+p#06&_j=bIvQMo@H5^MNcOXNv#{v)n{q=swfiIzC& zZ?ZQGbjT)1?ob8U9_EADN`JDnY7fW9ca198P{J$vNbHl|n<#%S*m#rXmi)l;&O!HA zDl@N6_68aXB9ht!GhWNM!*A`8L_vO^+Pusp8k|o?mV}aBo?FHq#xgRX{+J%hd$UD0 zHjhW!UI|~9dR~8*8jY58^{J#sMg~t;hK!Vs&i3yj^*=&X6RAMYo4>ygbi7Aa_#(tZ z1Xq!upb`9>h?fH&Ib|jU5zt~zf)Mpt=0sYhkHBC@S696!!D(tN+}LCa*Hh!C;xn={x_> zi(RngOc_&X{_PWI4sKDc%a{lJ=T67tul%7*k>Z`j4|4@+Wkl-XXUMQ0qyP0ldOI%O zc)wk$kfB4;cg+wc| zf$rUut5?xgzwGkH$AK=Nf(u}xI<;U^IRDJ|wm^s`Q9hN=+>puN+-%FHUeT_$)lHhdu#dLAP2n;!n=k;RCa^m)*`@0AFdVh%(Y-%e?J*S^ zgl~^sedNpt5VcCRC47MNpId!vSlTs?k}<2_6X7o6|3b8qHr~_Utl?0m!2nU)liAhN$Xl+5 zLX`;&yPN5AqJ1zFiHb?n4SvSs!}f9bp-%hE5bZY~ z#4yqX2Hvb1QsD$?TWX7Tp-Jt3z3;}s@x2=?-jAO?iym_RXOgPujTNTEnDjAAt3 zJ+1qt*;id1=b}P<5D}Ks@q~rBapm^ep8m&Zccf9JQjX5ri?6H(Ws@IfpV5{5=NDqW z+3?0?vZ3cz-A~W@TspxH+`EP|0!5d1rW>AxLtdAh9B_B-Rj`BB0tH!I$|B220B%<0 z2@rE9v#AJbr;5XL^L$i@7ygm+Ua>9c19>cyeKo4Lb{P_cB>Lp!N0uZML_m<;2{H>; zhOBuVVp%@dG$d)?^I7c0gsgY8p8xp0wuJ=Gb#3_F-PY9fzzf7JqSWXb!=vYsGX#c z|AGb-6eUU3NS`Zg^FpZnb|*P~=;=2F#5%s=+`n_amQe2B)yn?R^K%LRGU5t(^b&dQ z^KV-ljmZl5xr-+9D9J#;b2V|2T#+LMZEzZ_Y7g;aQRrRA4*x5x!TqFz?e4D~FI{KR9#kMj$-HX(0C zm*pnB;>zoYm>EKXz})?)KoOzAgr#^kt@#Nq@#$m|ab?*XW=|Z2q)W-hnv%wx8mLSJR69Z&Ca&QP*n#-Ck zIiUMy39e)S$h&m1zKP|%UfG@ti#lFchgs#F{jY8$(beTHB#TM%efbt94_Bc#MmVBD z(DBV5q=bQI0&dNi5#Q__6LD32K_&GGc73h%_PES9#(mwcC$F6D%=kh&O)p(VLp5aC zRRMyMLfGEk<-FHm?%e0PGJ-W+nA04>$dWb7WF1oe8J4pkFB(8+dxt?0U6maih6f}> zbgm}Qvt1gnCt_c17Y~9WHOUCyFRUv4`uW`>#y`H0hFc`u;aekj>JN{n! zH9_1(7o^N+P@%Ryv`9uuQcK`?Fbv!H3jm-c-*mJDONOZB>cQ0r=SZw{D`oWdMm{nW~Y$KmG1fb4u;h{xnZdW6{))0nMn+C3Z9Zt)ee#Scgyk zL{(*D^-X>4U8zT+8pVPQ7mz$?;(HM9|2gJQt#-GAWdv!xxB9Qb?Ze`CCpuPzPjDE{ z0Ro=Qu&BkUjntDqgBtDU`B?S7V-BRUN$cs6GrUd@>hG6FKum8x1A`~2?1mpwShUx= zqaJ!4)J<2LbRzp*A2js7P316-ix<1XJZih7`aM3*6?o@|D37eIt?`C=y^c-(Y;93E z_Qg^1?AaDpGSc1>mD<#8FD@dPzF$0%>V_a#1jcag_flKs;gNERJ9p3DfzQ8Yb_BDy zORr8b=T(xN#;s&0$agQ^7O*`d!s+!pf*Og!=Zw_4>!- z@t=xXO#=CM*eg>vS%~!K*Ic5aq7^&TkCxkJHVeu7}DZ?*W7^*R4el z2ndLbj<)>^XVb4oVEvbFZZEFRc0zP(ElrnNy}eF{_$i*8{$i;z*_|la9{);^?Y&8c zC~(TOs~nuzg@uLBb|(kCPLHc&f~(9$J*Xzr)*{Id@j#~YmGa}PPU8VVG*Z~><)5if zX#fYbRtX2U8RR|;x+R;M$0;7i>NT}fq`GVSL@QS5be(pmDHw zhMLp#XPJHZf!fl4&f&zcH!$m+5M1PD;1v#+k3i~6Gr*yu(>(CFGWS5 z^v&76=sTI6x9+9fPcgFDjZJIr6cfp8#!25aF&*qKxxqpW+EXg zYthWt*2YFfOKWQ7W&NIl1sft~1&IOT>os#{wEEd~Qoz-K?)tH&c5-4BK@dt?rn8k{ zQ*sV>xhK`iNpxYOh8Buk81CEa3&Ivb%Ev2*k?Fn>Yom%u$7lBb2uLxgxS{j?fQg|( zET~*@gI_!ft-ov@eYgdW<(pO9vE+TW5>s1I?^RCA1>%6y+d!<$%)we^VPCDfmg_}D zMO9Ts&$#cNs~4Gch2zP;c@sv1DgWw~q@SP2gbnY94qjJ7Z-oniep~Uet$~$ zLI^3O&2Y5pYwgM1JjO-$HpD;i&Q0f9JvEZ;Mb%(473@72yPRSCZ0F!W@tDq5D@)hz zvOb0vY)Q!@sh~nmnS?&|CHi_=nqyzIdhlqMDgw7wn{7FlkrFvrQ#5W)+{?1wjS4cD{P=DwHAdaxTdKPoIN?A3RuJV?II?e&;{34>@Uq_x*v#+SaL4tekEURqgt1)HcEHE!bLJ0smfwBJSv7@4a z<`o*z@{PO5BM-GF!|lO26(-X$(S`2xCJS>~NhiaS*`%{A6qO})UXIJwt}kNyOQXo4 z#l=NXQ<9%w5I=s*w@ds&r9>k#K3+~!lNe9{5t*4!P*G7~v$JG}hlhX$B_PZ{!v>W>FBxIF)(8OoMJ=j^WR%=fx=BSX421jyg`ig`Y_^vK z>ZdzJKMKMZWjw4TzSiy=NvsndzfpiK$kQdR6gC@ox62QAHsvcMHN^IR24f<|Mw613 zPE1G`UAYSlMGcLJXn-3u8Q>cm8~2-Y+buQ|j~BfO(U?2l0F)IR4wonxGr7Jvi2U%u z=E4u0N&)Y`&stks=kA01E&Wa-+AkgLa;Ua9=FUjQm%pM)(jm3q^HBG(Cwo|)4p!bB zSLHR_4NG3Fe4obF&NwX7Z{_~gas5Uy)(ve$X%~ahS2(j#C;TXWWBCS)?g47v5<89k zateL9GQn%yzhLk*^*DDROgzXZK;kfJB}MQv;M^!>i$rbZ1X2kK3Z{z(ib1#4dj;`g zHX?TuCF=0~+L45m6hzrQ_?Z`{FP{28h4ea?T-=jRZDaqi{e8P<3=C_PEr-GXU2)&s z9tHMwhF~~f?CU-ebV0pb4ix|O>ldfhaOSY@iFpdDBrL9ZZG<2Heu`v&!_4odLX<4) zcm1%-w@TDOn9;OvXV-PJG<_Ov$9Hl@cD-rjlF+lg4P!e=kb<+DH9Ks3cd2>UJg$GR zq|_CUZPO&?&KiGr=;b6i&Jbr`>+Y#Mk0w=>i==`I3JPj0hsewI>ae>a2>M3E?_)rX z-nncnE&~{+X* z5D+i(^cx&{-?AGt21QW$U`k3#HeN3I0_w;Ker&q#9`!jX+c>=Z2R76Gmqy!n13g_N z%Yp!KphoG+!Z*Hej5OG=NfVf;RP99@C%cd_oJ}it!FRr#Cx<+*(pq<_4>+0rBWdV* zXRN*%OArdG->na2)d&0onIm>gNb9TS%ZFitAS5{}FTRLvxHSJvcDGu~ zuKZ)?zack1=a9jLRlfZvkl;-gQ5(%w6n~!fk9L=7%omC(kJ4gXp#Tx|K`cq&3yaP# z0ZuCY$9-XH`(jIO-cN1S^a@`}8tTR(@uR|Cq4-MCA;oQ^&~-SR7d6?v7K%vg5DLc- zYi%Ew^?m$epZ8H@Sc+YL`y$NSo)vlV$vssZBC|q4cu}dBLQ&nIu%1Bw zX;qb*TTVW#Ap9EFX3>v}z{*#I%cukS_JEqF!iFTg$b4ex9>fKk36$McyZ8qIi%_~J zsZQC$>VMOxy*YTohBB3Jx~Hv4^hCeDDVQW9338kK%fY5P^NP*qT1pxZR-YPV{mb~r z8Pf6S&OoDgIfWX}CY@Kx)fb?xNPOzDBF+7H}z2@G)eeKr-7GKr%mn$REM_(GNPS_W)1S)3n#y#*KtX+ za4=4vjx&__{4`PC$!d{w4{9A9vi4mB_+_ zGIo%X<)8KbbMqbIBye&yJX)qi5nY{L%o|%*Xu^$;!-KsiVKjSl=7j1kRFMBNBtBrc z7tVkAc1OF|Jog%4H6C$4cc5G`{p+FkP;sHXTCYM@RhBI%jsuws6K5X@#C8Mr zs?HqmaxD4!A-Z2MLfvM-LIjlRvl329oor<^d4>{9-Dk#*hY`c!TXr{XmGTvJjVdm9 zDRp{G=4z;o!hF69l%1j{Kl5s5(o``kl-_BZI4F46k>4v$j^t%hP>rtDfa-|nC7i~l zk^jQtk=f#V9(hsYV2+Qj)dvkvP^anwUmN=~RlA2D-~|3+>IQigzQXZT);>P(azv6g zq^xwqMX{o=I%%z*hQROMO6;P@_{VWGnqq1t7|c{5_V!#;SDZ{f$6RIg-i=sJ{s;1Y z3$>(M%8xt8fui|ZpmK!j$SJSR-F}!HT^u3VGbXL|JeTzHQDhbqbbA~hcP^)#7N_iV z#2UW33>Kfp(R6rWJLnL>3IU+{+eLx16vh2&cRt$(2@LvdZzt6GR*xT-BXRrkdHd6u zhaFih!Hp_fdNZOD_8VrxO1c zX_t0L9vLAf76Z1!F=B+5?*arSKQ7pzIr%4rh}N8L%kIDF2rW$Pq-^(C+o3Zj(flzB z=$^QWkPM9ZG0b5buk6 zW5vhfyx95o=J~lF*3#lv<$sN@Axy*R1)LLiJYuz-~Nb}vDm!u3Yb@Y=Z>5o?(4ao zHUb!8J6{{zaCl=bh(!ecex%PSpQt$~ExhN^X_uzHvy4CK+r^M_+JgxE?z&0$4LI== zrF|sn#-_2_JPnER!TMov(JTWnE7hnV+E*Vf*udHlVIAbJC;n;f7Q2>f^t16SjuN|w0o&>A& z;_Olbe?_u@W1txAD-Mg3NEI%32`W%8xObW&;EiTJkzyL{ZH(hzw)i4IEE~lN$_RNP znsuOm{zQ6_SGuk3V(w4it`Swu*R|x4gI1&Vh+%YVH zbn7d<6w344oPP-D0+?jsv934u!Beu&`-=J;JMB?KKnh%qdegBQ(Y$s+nKzCF@5o}T zJ`4(A{Dj!;pf0lW$ad(+R`S}rBfM!fC(0(J#>5E2-T7w$-y^YZ^gl=P3;5HjNixuY z;2YWCMU|IFQ+J2ks}s$m|s? z>i3#cc*zl^7sR>6P-xummYqq+Gh*0| z2p02TQ};PJsN%QB0wC5763(X`4tK^p!a*;3s#;J0;~WlP8?5AFsNqV8jbLMv9JN_^ zt^Tgwu)2vSs>$C})dJ6kS4f`z>YweV(-w{Y(b-*$RI%*#s@(@IMw64k`22Fc5JF>Z z5&otQ-O2)xI~F5e()c7(yCEO1-QRV8?Z~u0^)8*%6FL2N1P4C>_KIzO#CoDn%jx&j zi~{75H;3`YM(vbUO4O*Ju47M|UWujVCcNtvvy&fmI2GL41myYW#8h8mLG99&+yt^q zLf(ju#W8At%S*NtS(`mYs!Fq!M{*oMv;3Fj`O zT*Y38n=N_WGH*|}zv*icx2W^sdT9}he#&dQvyJ_{nMw;P z8L1vQ>{L%vEwI+-_+4f7!Fg8=?a3qV1@Y3ig~r7Ppx}5sc`oJewBq)aEuC`YL39&xlw%gh@w~ryNzuGMupQ=iL7m(H`4q zvLOO62(7q)XDgo!CQdfqFNZnq=|Ad95_p-ed8ktc?FWb+BGpmuKKEqnz9) zYj}fL7xp>`QhMfsp=%$=A8AfV&>^9F)w4nPr_Ny!wW1&)34C0v&rXeXFEl=+*uf#8 zZ}>>m)%MhXtmEs$O0ANbJCiNko{f}raR_-@8|6OP`PD?A=d?{7e17UhaUJp@`2r3> z+-@rj0L$ZrKX>pB>ktT{X3XyST@}aMLf%l_N?tof{ZemOv$6CK{N^VY=8Vpv9Pv;~~`as^92sKtBL7>G&N@CJwtS~1=4BsEBv|T`q18B(XVy`a66eD42k#f!; zM436o#AVTDr9L?X5D6BUx#Eni2d5wUUzn>!IS+hS#?O*9^#-b$cN^w+?}k^Iqsx@o z!q366bDzT}d-D&_?*Qv#DUa>Um7G2Glk4XDsh?o@H_6==JXY1<%!D&cppl7Eu2n#78gqr4eoR$dm3i#cKpGX}&+O*C2I3J)_ zU?J3ZA6$E`=0c6dMC*sfCHubt8DlXQFx#SxXFZN@YR>g-hl*RKE(1692TNr_2Ae4* zg6FD{8{3_47xI!76FtvAJF}ZAg7a>7{l@t7+E~s0?ZXVyYbgjmlK3Q(;}AZ~&eo(e zCx}4mf~CH~I%VHPu?Na(%v;+33_M&%;cD*?m_M8(5uCuG*o+BRWAAxCIFNp7%^hb> zvffD$*wgdd=Iq?5`#zg28k$=Fj?fzU<>fm9Js?59`a(oXk&W> z1z^uf`xzWBf9?A*FnJTm@gJd@pA+}~CEn%XM3aCA_w%BNZBL5XM30*BN?#tmEE87c z@g)e;psK~A76PINAH$Af+#uCD0hwZlP7KN3CUU^#maN9ayFr)OQawC{(zCKy|Aj&7 z7d}nQT!YiZ!MRmfGrD~ASzRD>m>O#*0XcojdfFit)c7Yo>TgHe9OQl6f++4z|B%)` z>SlBX>pAIK`0cUy^g+D!Zd;&!7w4bMgaE7*%d24fvP>4uVy(U0WX09xg4-^!27ce4swL3vGYFu$uIz*8*!UKGSDi1Hvd>DvMfg0wsV}eX6e9d z3(Ddp;H=Gka(^Y+VRgu&9x~tht-Tf~Qa$`QeXAAM5vHf>rUp^VJ&4CSy{++k=Z1{# z+Y?`DP>KPWP?k80|9P7rSuZiH2q!241gz5f6T-`_2v;EMHPTlD zfnly;Eq%~7($kP2_Y-n%9!53K<~K+?E+uO?S`JLK*~qgVfdEA`ilY-tTo}-njiGavRzY9($n8Y|zyw1|f@iMnxx(HosxB0Ds9^ zWnNpi1AbAM7Z90#yHtK|+jdY?GAs?Efew`}ocLA$0cQ^KN_&jddO$%ijmdb`76iq| zoJ1Hh?zc-FfE>j6rIKXyNf#_j7qwZ;SD4I!ViW*U`ZKbRoh<<{C{*vQ~iFl!}FE7Mvw~H~@VD8|C{}_0CeO4W*h9R1jLaN<>QIp4> zM~W*PO*FuWGJMJ>Q8 z2CLn20Fei1nw}E}x>+Va3?+f8xl-pijsyk9V{(UE(ba%m4Xx-^JPw@yj9#Y&OYY2` zIo@UhJQ5zL&hK2GVI2r}yP@_8>+%*+h&eRVj z`v&~7Pko9{SB9)JcdaXTa}PA3uWqz2O{UfdnHhpZQy@L!QE8V=>eSf~UtJvFfNv%w5biIvwalLObazY8C7O)o%NBVFY9XXj zG4Xt3|E8<=t4?L}S8{ZTG=R#t^V@&yci(W*$kXh%BsWyC(+_!|PQ)Sw5Z@adJ|jEPdail?(ZI>3Pj(QGc|Er_SF z#%&`Z+_gI2-#tiW(+~gt{l)8aP9$Kr1l~RCzi{AmWd*_$u;T&MnDKbdw#u|K7!7_g z?-<*MVAl~&+$G;5(Jx=WzP-EomoDha@cZ{~@~oqV<$x|=(&Mt5;jC?Ysi>u66q{Cm zj|F+TNYV82^PmdN%o87py+FRc7kfYuaUT9d>BF*X<=W4o=Z&@pBke}#$29T zHBcijO)stmNXM6!$-#%HNn&vrga&|lAz7n@HU+vB&3f(Qpp7BufHiUX-A|LmK^H#j zBVR~VJBZTCB=umMf-GQ1DARD^&<3+`dIC|tiM0OP%)pED-bm3# z7y!?9#xX`jPH~G~e+r%~)8-e@mX$@L3V@p;bV?^zSDwAS?=qku>3U`a9qix*E(AIQ#ufgr|lh^WXpxy&m6R61?`ih7ei3PVdyA(A*~cU*J01JNUGT znTj$%zKF=Uy1%B>Ru6zI3*V!Uu_cvSOj&n<&Ka)>P7%YFvD}SBo zty}bMXWAA1Z^B|`{?*B0`D%58?QNQ$vK75ivn@^brx_9i?*o^%0^YiJ}xqyMS|JN@dn@LMaA$uGyMBUu%wJ;+z!;bqqVc-ncTU<&{Ck57Sa>syx zDg8=10ExK7%^@gtkuv7)AoF@NKY4vN>egwyaQD}_1f``XD99t@7 zJDID0x~x9rO=4}YA2RK>7Nic6R^B7e`M1LMDJG|_08;73k-$Y**t%&5&RkU2RyhM} z4MGO{RED43ntl4YiL_-{!Gbg%H4o#paA^bC>(ksv-$LyBhILeErAheet&HP5g)ZHm z>4T2X3yoy;G?+sU32kOQ*pD7PG8s(g0y#Z9J3C$2lYPl&kFDSQimJTMc6#k6V@@}) zUDGDYBkcSL+cg{<6C?;UBzPS*Z5;!zJ>TwgyPpU*tk;Rz`iO;vg%%kyaPo*W@FMaM z1DdzVMS5J|KjsZ?5ugzSHWl)JV6mmmr8LGw<~(1NW-2Z2OswrmA6+i!I%p>Ax)E2@ zyY(#qz;^YZv>TKG`y)&;=8Qv(zc{6_(kbjgRDI`u^^Mxx5D?J8AfSLW?4no-6o#6v zt?!|-G!W;v1`43gFqNo11-`i6iv#EJEx@$^<%Q{&?F-V4qi`$6i-XWqqPm=7(IB8= zC;T*z^|{8z!J#3MiIVnGFm`Yw%4r~lLZXH7 zpZZcnVcJB!-cqsH{l}PS>zKUzCR4 zW)!Ogmv&T8vv@K32zb`gMlpMBxPU&be4p@X6Tx# z(C4zBdm&$lXB?+i5*i;*@PZt#-f=muxR?ooQF<=Cf7=(gx;<9VJ726?a144V))D`a z-Ceo8rJjb6E1;kTYEpE92OlcxyBn4Fe@fL(lMQmTh4ZAb(57sALN(^9?p?5J9`=Io zzizHy)!#Z_gol7^9E@$5eTdnQr& z?Qv4v4!T2YF!L6+#xYS}#yuH13nq25XcTOs+ zE;8ig;nABwD^32rYi#TR=vaB@7O3q4>M0Jte;ClpSj}DUvJ>0S#(+bWUxocGK6qqk z8<-c|#z*5EU(d^IBCb3_%0l4t9d*w@5|GRu?r+tI19twmQ*3QRK5bXRK{|TYPrg0r z4WnVu!LoY}jyA*DWsnp@&%ZV1%qD~P0iqe%*s7`po%!~1ASfUp1kbL|UKDHAw*SP~ z*qBbOC7xD=UKsGvuLDa!Z>3?&%28ELTI&C6xjgG;mo)+~#n#t3h~5Lh%pU)mFsaFy zc$Ok%$=YXjXu4t?vti8Iaydd{6WUHv%@h89s160woh>|!hVie0@WR_b|GOM7UTDwt zdxNys;yoVL&d?F|-#LCpr}jn!O%3mhp=HQYlQvh%zl4zgpTuwm9HD8?{}RLB*b6wV zNMyt{@BxBFR_cnr--4(-i+6$5eetyg&xR>=XY?(QHE7^N4R}ZGiE(XRHEn7pF=)PrJ$%sLy!vZa>uc}RyzU_B6OFZVa>b0&;$Kf6Ts|l zxbV5{s}pP0qe^B$Epg5T8-}xG!TuK%-}w7JHU!%KoS-$T267g{Nujs_tnekxrxz}& z`57TUSf_I<-&V(4Ya#2;(Gj9ZTLfQinD9OM_44;8v*JJ8h;%Hd*q%VV_O%{I_@iM= zV~7>#pjr_AE`91Ml(8zlh~V`~wch@AEvpk+Z*N{_bGmLwZ4_kY1BsC6LN#rgfsT?Sf`xQ;gKt|JlpY zV!}cS{Dkc-5|_HPYMqYXoH`Q$EdjpXKY?(35*W ztH&I5y}8Z6Q*chMz-&A39{1xfIgpM1aAW!C>Dd1UHqT=h&9%>Z(f(v%2=Cm49+En` zFP+c#^8-{_a~L#(h7}wHyt3*Yld&fobR7xOGl+N8pvC}e`eJ6Z8vZ@i)sMb|1GE>c(9TBb_r|~^qNwf}95|q^ zR@_X{L=5*&dfU2m{)Uy4qnMC|@8SSSF05S-hTsZ; zCA^=FZdU*u#$I%1zS|M{$+LyiH3`VEq+jlb<~6!tz!@S@_7Grz&eF?0+@pDvLnUlP zQDRrudZp}csgp2BK@}TqC`JczV{$0lt75>L-hLUFd5&h4= zaZCTUyT@W_*es#xBTYM%^ZF4~VSgFFIrH;NI^WaoDC6p2Jq1qs3=8J$ngL>u!_G3NPZA8=>Z(T5AqR^?J|89@>Ix&)qU;>-`&&p1IW57zncZ7P#x*zZCrNk> z#0faL%8JJ<&+StTHGozd1W1o1t?#zDy5Y133lF+SueUa7B0_!$DQZ2?eTujf3%Uew z{E&jP2z-F=)ek7pAEV|e4&C!?udjxhwpWhsqL$AG7D)?-Oe`aXqB)dQ;QAt488XYo zzMS9PBHO&zwZS6Iul#^i9sPVS7>Tg4o!ZKGt2qm#QV{K{8f3o>W>ERnqD2-_yebBlOAcu106u+$4i;KO*^|ekiySV5APB0RsZ91rg8%4B zA|5UhJRH|x;+_qXQ==n)qwsSTBOpm_Ret~X5AcF0+j^(>yBVT<(2-1p1Vf6+^$|(+ zvADH8c{obtC4^p!kuYanZA$NyflQ+fJ&byfxZ4FJOz$D-4<#I0FEJwA%`kscFBri;}8Ul4x9d6h#IU!~^qvq(R$Tsf6gq z?RZaAwR@nM9=Azala(tGSQ{fpopntYHHiX1B9i7vAvH+$K-gI#rl{Mkqe#21eoIUWK&B*pTO;s;zsYhGjZR|8HrTtSH1>pu!vsIU&$S6eHbe{~X(12l}nKbid2h<=WZMu{Rz9`@+Up=#XaJR^Z5loHg-d7JY2S~p8Q8P3DPA<}ShHyv z>?5W+NQ0CT#Ra&hkiI(Ml@Asv{TIGbVP$jB@YX_Dp~UG>GNFmGLg|hp8MkQ!M5psN z@yb9}AkTt87CJjzH96eW%>=c)pI{*CRip44NEK8!+uC22k9*3;u|WKAz!A8B)G+WI zqzP7f)Q0b$65)U_ucqbrEl@iX@P(p=$(Sd5=+Mmc2P2rYq0?o2{oli)AAwu(Jv1C7 zK+Q9ox-w4mY43)6Elt`M7mrEMCee*9s&=QUX>)#YkV`m=w%}(|2uS3GqGUkM3?tpV z7i2b17-&8;KKRpY1h$2v7{PHGd*i*MZ=>++di2*`nu%pR&?_VIZ8g%_;Y;3~@p873 z;gzd&R)MiHBXC(VRgKX!d@1dD%aL4-PLrqu@9-!2ouUYvuu8{BPuC2!ts5c{W@oM9 z3(q)iucrjwfGx9uR(;OA4q=2h*>Bx(#x)-z`{#aoFL0yCK$G?=Hm4BeuGW6KQJH*G zXkV(?1wqvVnOm8k82Dw@2_4k~L<>0XL}upEfIgTtDH6R2q}y=80;lN}qvFZk?@dS149FFC#xGayRE$B! zcd6R$2nDD7bs;aydo)STR0cCViTvTd|6C5CRsz_o{?at*;SieS%1y^(kUG5f+D+3b zs7zWN2^C=pQKUYQvH`2T$8`38x~tx-nh?;aZq+_)K{6Mre*`&V?c-bd3WLR3iO13A z%5TsQ=^J^hiP#LlefAQ=jie+c^$lnT0|+9fv+663uWA1a;N1Z` z)Jg~+JqjJl;OF-;sL*;lS*p1`-@t+Rrj%5*@8uzSA0XOH0mtIx?EF4D8mBjoGMYL7 z-ub7hf@fBZfX2z6)XULl-6yEN@s=Wyo=lCO;MmJ({1iIfRp@p?<4hEgAqe{K7X;YN za(^i-H2o2Z6&VvF16r<=`uf1#cYYTlYB;4#f%nr!zIdP;9|j0YTM^5De>dRU1@Xs# z!vsECN<_;#FbgN9rdo>hPEV6Kww|B^&L`pH$KZL#4oX#3RRQO<&{^Z!CQ_8#5PMV@ z=$Qk(xQHSyb8YXu_k|v1z|d3`w24ee=Zt&7#foZOgi)Og0-YIXIPUH3HA8fxtVJ<` z7NZzm+bP({2!XS+^BbxTi-E@?w`b##ad9tm#QZsJr&tFw_z4`_|5=8-HS*pdew`&m zjNl)6MnpmotrO?Flf7??lyl-r)W4d+f{;i#O(3JdTX)3!1&&v{ECYu&&1^-37N0yK zrOeH_)(#|vgQn15WxT*^N9IXSlRxb4Pb<$O`kxhKRz)%SU+f>~HyK;Gi;Tp4Mo-WC zFB7nNOeuJbkIFQdL7^;Nq1E6u2%P($MZN;xHJ}AlL3=ys^{&6%h#c11g{D0Hd5_Pb zcZkqEP08)Rsr>ANq&pJl(Jn8r%JBKz_&j*WE=ETbQjAnxT#N}63cxJk9bTTD$p9;M z(#R)n3v52SnIQ1e1I*x{E;vJn$ zpvNAak&$s`ejdR;!>{(LxZaKCTF&dMtA~KEqw^~wrU8mMhvLnRx!MD|kQ1D51#c9e zJtir90_a!h#Uf6+)dKo(p8CA3j1TbDqg$&k zt1vy%X>_8c^4;fJUq=T65%4)FbyBlwFb930w zpIGnTzsDOcRACyeG8T^^=f&pY;=;tj>YS^y&9FZLG@YuRo=xk<=BCFnB`0Ntk_51d z1=*uXtXwU@fWETY;7Sjo z0dHTC_VyM8j{FZO82B$rN#3!6R%(1~vOnC>q_amax^5Q`y+WYQ@k{)_YP<4)9MiS` zw5NUFcVtZ}?OG(ukhN@yR!UJRB`r#eN*iNBLPleWN~@3-?Pz1ts+|g@B9%&gzx#ce znRC7~=R0%G`Tm)C@IKFd-_LX3*Ydlr>vtt9%a%;|8+WNCGwUOR^Qi>4)WBy;?UM?% z$qZ}f$UJAVLZuo?mcM0w=d>a zUeS}p6I?w!O4F>1A!wan9qYY(`SXhnPepoX#DRXl(odV0Z;}__n9V~m$(@<&dNgB= zvgllnr?(Fybe8z;-o9nY)Trk^YK}le9|IqC#cy=i^2z?p4f^-Mb;4@E#>S>Ozwgyg zEzY?qrKM8T5Fp{Kq^NV%=a}TKCa{g~d&fde_^oZd+`r11pPyl3Z((4Dqg2hJLsC=| z)7m)_EfS0Riw>r7IinRb*s$_-J>QwfpQTr%Dl@AHoftig1R0tgb2$6y zJs(P1$P>O6CvwP4L%qXWr4(+f-4Bl~Y&gnbJxfd>eP;>ZQjbe1IK+mPc`Jus>|yGB zTcj+ku-^(QazBj$FLC-5E-RB`S)=JzzI;9;@>5P0(#=HI1&Qme-vx`9#!I~`v!rryld>mRUF(u)nNBqls6a~0OB zH?A?@9RfP-O|wYmD8h*eM4kHL*mSm~cOyT|ge_)})Myt6d@At$fE=!PdNwk&7WMo@r*~T!m+KDCgyfn_g6&8_yvSZv0w=zexit z3g9#ht9bEb-&U>>H`YRl0ONB33~8KJ_yifOfDkMwqdEHEG%}PfC(~u+4gJz>u|*ik zq>DUF!w$+|9fS{|-YP6+QNEXBWMh?_f-C#|_2dmIT)PJknei#9m=C*!Rpe4gX66TX z4eV|jKY}qS4c~(;3uTqfLY}3AN^$z%8XgcvDGF}0h$_Tdu!cy_=-{rto(p2hQgEWka4Ps$8Vi;qe9%SZm@mjH zN-2joJDJGynor?*>1?#irwad3-1sphv5J_U_cD;jJO_0Tf-X$p9Z~c@)I-P$Fzj4! z>eanNk|Ijx!TX+_lUGvW7F0Abw6mMz=H_;B`FHxGr?HC;ca_X~G}`R66Op<6ucN!W zRX|lzzDCwB7{foINY359y)Ua`?-~eIxx%f@JiRoVMoM_s+-0K7RR9 z80C$y0S~-%H#zheHu$9Nmz~;vd_B>eAX>m&>PrOj3rZCodH;#6@|1Nf>33TtQeIBu zdRCSYRh#ECrcuD7vL|mlC*|rv4m0*n^nc3?dE(9y7*HA^YCAPPET(F=HuE>mrT9iI zW)s?boRBi4NV)iN;1|#RSQl+iCf~0tQk?051qq~7Ue*<*hh{8vdNv{T9*SdA<_z>RjE|0NAA!nJp4C)yX(h<}5 z)qu>Hj7qPz8lX(!_e zn~rgv`8Bq}zCH&bQBmT~RMgazBO;XM)UafHa*~N6{6Tfhb&lCP^Ssqn)zlC$S2^%v z4lHVUIk`eF6Auq{QgM&EOBn0cC`fP2*vE;q6Sy3kJ@=zLpS$@JYS<-uh0Jvzo~~PP z_~hQ`)g-)pt!qbg0_sirb&OC^YBMnz+JQ8h*>_DojJzkbVYAZ<3Y1Q)N@lz<+R0&s z3UWtYh%UMe0n>s*DSeOVK{j5Hr!tti=o0j*uo^Iug&`G6;EYpnD#8!xyOws((@2KR zBW}@Hx7{joV>(;y3Hob5>gD#xm4)Pu$dF5#oNW$bs69ovNB9W^hWUca8x41c-JH>uBR&*P4kTGB$#tcHOsU;ob#wajc9r(Mb(a z_vUb&i{bQKD?-wig4qetrGFl|q$>zfrmL|wG`%uvLuGA+z9;kDi20tPuL3+F+fUJ_ z{+J*S;inDdLkY9By*@-wd!ZWXvgaPW^i;#-L6`N|^M6G@nCtQo#ywOU zOHdJ~PF;QJ*!lKtQ^)a5n>Hc;VcC~Uf0d^|w~pk%{1h9jen0#~OCe4eo~_TuSIhgj zejd9~;w0Dhh?eim;%YIpbQm%9XyrpIbc3A*e(mdBG~BX!lDq);YQmS zaaotUzo(HLm)5cLL2^s3Hj+K@^p^itVLngxQQ#!=m6X)m+}z!DkK~Vb@h!#tJ%VpB zZ2q@11a9$UH?+0MELpOI+u4S0;6Mxx%={lEOeEf{d=x(o&Mf2Z^m>SHaicoXt5sJxv8nO--?? zqbyZv``x`!m6awHZ`@2SE}I4ZML5|@WlX^|@#fk2*}A%tK$tG*cr3hkuqn{F^`UqS ze)1SJl92|aI-f$pywt&0_n#-O9d{5@r^rt*&c&EBM<;GEIo zwvDGM{a1TFRflwWC{WLkb$VN2o%f}gHIP=)5Oe$J5>ba(ooO_ylXgkp0&5@~#c9Iv z-F%2DTXur#;6@@J_rcZq{tUYKPlW8ElbU)6K{(Q9M8C10;RyW`japM#s;GS=vJ7HV z0gidH`8P|tIR+9Q8R^JV21ZJx;a5B{%zAtWK=Q3i1RtnlJUb#G*?VX3vd=7lVC1Jw zPn8AJD}uj(A8yoTU1SXr`!iA?2`&zL*>2 zk~VaO5E z+Wwd(@VR#e0oXIU$(yW?#BW>~*vSMr@Y1Zf$!Am*{?DLK9?Kmj{S1VM*OXt6a&EcD z4GX#mR`cm|=T6QNR3vmDKz26B$cl=FUSEIS251BTNanph1L})+rcv$3CsT$mU@LZm z?xDzMFfVjuy6UsTlok+PN+C^ah$ zO-xJ@yNip9Rh(aGJ^DJ@QZ`B441jM^I!D;44uZryJ4doU%WU^oUSI&&<~!PCKk(4m z6qFEukjn!hIV6E;=<&1~@a%xtF)oew*-(2AGpeVi@0nd2rRwYJ6_5>ucgXz#>fI2? z>e-s4q@)vDlMcEjth;XH=B8Tb=n+va#%SY{>ZIjDSW9=?tBg<&zis)o|0S-{_7CqF zvGDJlF$K|&rZg7hmF+PkdVHC20u%eyEqioV@Lf#t$U!EY@#@t!aLPag8FuPa5y(Cn zDA=@A+Mh}58V?4mKZw$niLEVBaJ%trvp<;Ut+c$dYVkkMEBkZv+$#4jCp9`cO8h0) z_ls$!hTX#d$PCv(l1Ca%I>w#4+{nH2k(bK;nk!LT5?5P;$7N^1aXmPjOTi6AO;|S| zH_A4p0IZ?%;MxTp>ih)@Rsf)lmNkgp5U(L#bL@2mKY`E2hdL{sJu8N1`tz@E)9jx7 zxZSeh1^r{1tU}_gv~*Ngmkr_f09FOpiQRLcnZ@7VpE3lcQ&6)nAYxE7t=_+15!A1W z8!iU_^2;2M%!tJ#rKXmF*7wZi%iP$J1RdXf@8F6Y=T>vSJ?jGx*pVAz%N0pXeWn^j-f==OF#_a(V1S?Oz(Pj^zz0#w@ojD7k|vwaEP!lg zpdZ4yc#0K!z!Lh`VubwbSV*&1D}o`DsL%kiLw+Kc-vk=D)t2`v({C~|M3DIe&EU4% z$`v4znz5ZTWg|tP6ifv6eJ9p|nuLW9On{(8Id;IQ? zYhb}MzO8}nUzNZ*`$5La&cPA5?s{NCg6tLl>0@R)c1X%9D=U|STDoI!kQbb9Lqo1p zNNxx8BK?^DW_UbpG_Ag6$RpwrN@$)TZH?f(gDdTM?$N>3-$P8}Iv12z@8zQuwQ2bRXB5{+GEfQfm{~}xUXTAM!#n*sT8rkFwR8vzE@b2>}{RfswIAk{~*4VP2soIm9 zoSgi)>Joid^~n9sAa>I}Xp+Vtwz>ATJIS`^mee2#wt;eTbLX-Q4-bP5d;Y?Oj1-|d zni(%QSbBWQ3)b7nK#S;E#SKNzlnc0M8JviY49ECSF^MYg4Mr#n)H^3|}Pc4Htw`Q1Z$6d!?XSJ$UxXNcKk_DXoA8OMhv^ zLyO@kMc>Vz;Ar<7lb*(CVl|nF^05a}va+&>S9P}@Z7;Q){wUh5eKF(6+pRyHJ160S z>}hG=H%jzV=Faz5!al7GeJi_=@j6^0xXG%o_GeOyG5z_jzr3O%E11+MlBO-S+>1|G zSh%99N}WfZjg?j7ckbypLHcpk{?}tTxBa!ZZQE96APJ%@#Oov*9%-4=Q9ch zVif3f!Y%e1E*0fR)VW;7C;3x*@P4`|)}}K`ZLzS3+;(TXyn%u2%9C7=X&JJEycA#@ zWC%wOd!=lTKoQ4mH8nxFXV^7HTek`z)jN__@FEb-Ovs=2?v+`-d^us4e>6SuzRY&r z`t|Afh8Fi-8Q^b}`;E1Rl5ODj;7xD}xE_eKWulq@WbHhpV?)9~V* za3cB%`XX{_5lwx709Z*V!VoU)nFlbt=hN+B+GRaWbi2c{-%K|;X_s_m1f3*82SF-B z7;25gnZ7BVI1vnQL!SvJx%~YZi$2-vz|=< z;Y&x1lg5-^2xc@Uif~{KcZSN^00k9W?zI%g_YklJg1gt~jal5xJC0dgt5!q@5xz_Y zm~>*=ZgarqA34Ut#s(5Y@v!jl14n$pxL6h?8H((Q7O!^bO_CY`UNr_vWQj%C*?F6~ zZYCU8mHAY}sVt|h-PD+?g~9GcyejE_0D-!&vLUchmfhiKCwd!6D+9#kAh1(fB!nl5QoqPu>c^tCMF-ML2 z?SEhO1Bd4Xi0O&f@|$uAJbzvkwnPZT0KxlF9wQOON~*v19{;kh``BLZw{s!NZIBp% z>Jiivd42udFkHKmSG|SQ0+Ha%jbp=PbOnAt9yx05oF7>YIKQVGpz{Jn7$fD_hek$9 z;Asrk?{-(i3Ge}5 zGB77cv!^od*VozSLH>#eg{1!yC8bq>Lz)C3IqX((wYMnL1PY#)y9Uq-w^Kw-N=k|_;2@T$)w7d#=WK+*Ad+Q{z9fO2>NHDg_faXz?-A@a>e0*kWYKm4@S67Yhyte}u z3!t0R@$pWXf@UqJ26s&k`8mnvy9^YMr3{Q;$=vyCDx=11SPk#!ezQ}^cvO9DKp;Bz1+NghcCF$U9KcUqRju8Wed zL&=ZDIB$Lwrp4QyGuk;>ukYL59E~|I8_R$2@ZqUiiD&ZOR3s9D4j)z_B^^^!zFgv} z%(rq}F63vz#?lQilb=c@qnR!@z zp1pf-^`Vg_NwxivT2a|@|1ieN9o^ZR@^k$o`>99jA^=&ei%*J2=*6I5$Y5$HX!!8) z>Wk%+gY~^bEhkq+h*&pe>|48jeR1$CK|XQurmJEa>KcFGM;6TeLF8sehJd`hyw}Xv zv3T%e=Cm2XEpGFl@gr_U-^)9qA0B<$_|y6GN8Q#dl~=W?QTN}c=1TaE!tZ?ti8uXus3BYj&e^1Ovxa$L&s!AOx|clXpK z^(egnkrI02n&&QxC`61p9`tjMwvLVsrhwvAf!qb#i_;u0CgepAfBZ-WH1P45o@glO zig(F>j10HV_wSt^B_!nZsE=Jy4@uC}L!cL(SF90zg+;Jp?OOKw)IHDeK zLiW=MsP2x}PtFc~{76Qdy>uxbDAOH>n(m~elwvP^X>tkz2=uzAr+LbL6CX2KCDf4c z=s*j4N)@ukeEL-1Sk;z!W9U=;vK4%rO-+kynySL?O$A@fLVKGPOU`p9_v?j*}MX6;EtK!tw@G<)xhz7y$Hb|ZGRxU2eue{%< z6d_x)MBWS7+ZTB8_s1zH`-A6X$*;B!r48GQRaT&O9 zxAyCG_of_x{<=Rr`2u&`?~c}u?A*jRuI&^KT;k_X6OzD=!qy(VM2j~30}=m!b(>Rt z0D=JPsOtSZG=zR+K?^Q=nPS5R=H>a_v~U9VGr=iY zxg~ zGOA(9b|wl0!C@qc*?(%74Rrc*1j5s+G^;(V;^q}^4tV-y<)~GT zK(Z^c&cj?*YF~AQ0SF%&iCV!d0wy2tQo499n%5({J_XKK7lKspyJX}7T8o5 zmRd7bU(&YqT@K(Xh>iCoDwXjF(q@Y)eBj=TMmR+5bf@pQyEg=x@etb-#l|BQfa@!I zK1)%03!G!NiV8o7qgQ|deYu`qCrH52D9IMvjk|dpl62>;KS#2lfo1o#7Z~V&c`XPi z0RpBI6FwT$9V2VNlVsL>b->uJm3)4jWFGV?m`~oqL`$$gRNF0x5wvZK1mQE9&voSv z<`<)mceu`Ull5qSla|}-wRcj1KD(m5fsx`_k{vyc66N6+>y_1Ys=-z+sp)L&?X3eb z7J&^eHu&fh4<(1-pz@<3Mpj;#NZ4IS%>)6&!{^#KG_DPMjy|rc;x70)Dw#j^aeaSl zZi84B*XUq-48krXS*9`TfFPl9p3mIghivk37=puSE7A3sP1YD#LPln0R7~%iH{vTl zAlv0MJRS6{d)2sJ6!r%Tr$|TA%`4=Xl7?!}*nvILUg;}jY-Z%&qK6*~QS8{=YHdfc6FGSGMmA^G#OZk4CUQ>9KY!p}OE5f+>hVm7 zqoHcuLH0m7cAwFHemm7TVsiUVVJtqHUEz`0;Du)dBO$1}hMW>U0f85N_a}OH6&%mQ zDO5BwGZT}YnVuYELADm+?Br3%l;%g?D;y2m=Z9w|+h>UD1gnb^zl8I)>dxIQGv+WFp}Cf|X(9{zr_Y?R_8p9t(c_3SIZ2XupL~LwK=YudzxqQA+(L|n ziFbKz=RJWlZy+nLtw#HWP&AKAOitzkn!Es?E6REwN)ZT>jp&X*e@hPUni!eVpB9*I z-o3l!wmawNljpeS2>cL~z>9= z8vp4a@_N(DWz<-2l$017uUbXkHL-Gt2nH;@J;4WWkTkZ#hKU*@I*2jaLE55SGEB$~i2T(x8y~l)0T1N<4sR(j7 zGKLp_0LBaF;oztgqH-aLiL-&-kZzV@KK|ji-qXGwzBwNPZX}HzxHoxFRLtl14ZPre z0Be2`8cqyj$V6ZeFMF>nKc)(^atOsSU66`~HZ}@i!m01L0MRTzZyp=*%;VJThk&@oM%w`Cob@E%JTEIYpNk!=)$Q0>aUD`}SF*vrXC0y=Y=%LpB|@=oxf2 zX!oCyCar5IKjl_*uI=_HStBth$%lY(#pM6xnWe zI6P41Wk|=A82P1)R$)VODQT=NI02d427GXwiLr!em`Q@ZG?sS2ELR0hR!JiWQU*W- zA-1qx(B7?vGh>#!cRy=0xVB`S!ZLOFQ1lpXRxX$dge}p9B@TR0LLNCSUvD)w1ws#{ zrknIwAkm1UWuqA$RvAD-nsy^Nzyvo%7BEpBAa3K2n?(zc0v@x@K> zpP;j>hO?TTnX{XrqbcN>p|icUowN08BWhPuN2k|zwmj^7>^!X07S7K0PC^_UHh&+$ zZs%yuG0ajw0p0|~UP03d0>Ltb{)5RE&wCAl1eQORkyLk2{ki1ku4Z~Ke(3f#_px?E zR)Q3Np((+eKvdL@&gbt|I^7|%Ap>74WU(WpSRRJ?2WG-zhO8hth;kD|2>27my(S4( zeUimSv7`8L^X4v}?G2$;p8$l-P9JOF=I@UTL{!QVg7|Nqnf&kPHu5mM&W_}*!q>rYZ0U0s@Z zg{~NB8JY9P*zouS1T@st5Y;c2^ZEmWgIVJiQOhx(KGD5( ze4D4|{{2!>^ej$T7$vHf`YN?=!JP;{QnXwUc%jad*Qoq6NcSbr33hOB6ELIYI$_7; z$|2qg>Mqv}kK#yA>Q_TLc0;>2Gzqqu zRx48Q!G&2&iR+3LJYu?=v4uq4tpvESYPTAb4i(diDrpg17E+kB*-zJg=E>e{8{Cvn z>Q;%oDB2OwJoZZM!xumtlk?_mW{SDpjo4wCI2vEBw3sPIwnLAWu>C+D2x$@(!qB84#KlX#l~cP6yH@;^%T!ZHY}pSHjbGQF8sc=12a|Smj^A2nJGyDS z7-%)y)PaQODZBoahKTiM7nc;la;SFF%Ys5Xkm?byjLtps5G5&UvZ6%CH{OS-L+;qS z0li;*i+&1dS~28QQohc6e_^kbX+g4YV$<8NwX$*g!iBJm3vccbp+DitxI7ijg9q?W za&Wo0xGqj-^q*Dex+KH5-fjkrJ8|PI)f2cPnD)Vno1j=N-eLjZQ$=v}yL{ zmyQi}@oCr8B0jZWV*J)R>#-}Y?qMI+l%S8Dit6cm;whReXh5|3ri-<4h=@W3oxZKk zmK(eODDNH1Q>N0XckJG_*>lF-cEJ^3M)xZhWIh#e`V7O&`<|cDO?~ky;X^+JNgl?0 zf1+7ckGVU&Sntp(Tdva|^%Ku@k7S9w%%1A!2lb>w%8wmo2Qlf*=Dy!LIuzbi>#I}a zvB5#-$c95jkK**Up@7o#A!RF7gP#&Cml^Fs5A2xK2R7{xF5@19AG75a>l}nb{iK@z<~MpgB!K1M?RLUk@{*$FfKnT}CAUoq!aj zMQF=jacC22X);gSp=0TuTEvyWS8=ytLUG_gt7Zga0Ye&%$i&37Jyn?4 zexujyxpuzdSL#Aht%dRai7DsO;0Zw@4RYHt*Eliu1m=q0H1bl$V5mAnnrXT`ZPGea~}s$c4o*3~7$s!^7?e0_*DZO~tH{E*{)c9jAuhdvu_Z*Ut&z9Ym% zW{h9}CLa{UTI=W)H_y!L%FFZ1 z(vlQrWyCbXLu-=@w3X&8!b`#R7CuJSrifWRW<@V{^m@&2WN?~PB1)&m(Y^hoJeqV? zVoK8|;MVuVY-|uqEPx#i?X3d3_5{3SK3D0$kGV<}4Gj%ZQxlV~R#Z>SMVq{vMEwtMPsFKKK7pIL?6v`1>ufvA$nlN%IE7!0yU zZxk5{$LAB%(sVz)D2tHF@|RM%$4Y&7TY?RbLK^yhW8M@&%u5(Mz|c~J@AEJ827SLn z|HfLvXjs0wn_4`v^cmu``xpLaOU&Z-l@k;Wt6s`g2f2aZ2>wd)BR{Ik4wWzRy_Dzs zzK?qNt;mdeBG6NX*WOh2vu_KpGkC+Y9JY=&|6b(p;=&oz2jy?_vELCqU7O4Q1)ubm zmV|d}{Z?c?^bNo5oRSVL@j2#l zoaPg_af*@Gt0CVb%@7w{pnZ3i+5y)zs6}rUxe4wNyV{EB#upTyd)et@NpV9xbt$5oIS2i zB#wt13QQ$dQ!rW$5r;h3kUK%ex9ok`ToPmR*duuhS| zcCVIyI@{pFlJ0lsQKVUkSYBSP(dfzswzbg7JmpWXCv#m&N>#tC?(fGeel@78*BUJy zZ3+0<9U~!g@_Y=HU^e#PxrPQZKRi zmy-3u#_bx3`h)`%AY+1K4FjNP1(&BJY_ma^W}95mQ>=e4;lIHG5|guB)USzTPBcx% z80aeic>U{@f$_!=gcz7AkKbU{8od{ysymCI2z_(&Sy%<|pRscwzEFEuf`og5MhZCl zAG8p#9Ht~DssS@Exao8-5r8`5}r&*l74+^G6 zjQNj83#-PPj~-wbp#6r-WX3nd60R@{|9A^1JV_czkf3ZLn@MunS!?_2e9=uroTIQc zKL7ZX0*HU1@qYoBt>Da^^uG{J8g+mY=em&p?*aLrPxu@Cq{W-V?9C!rAOCf1Gj;HC zSf&3d*x$aD|BZ^qMoy@bBPBPSR=3mwPMn?;B@_9pg_0$i>gRN;dRITd_S-*^S$ifZ zrTuyePzp~*HKL9Tx&WENf zmWZUBfx5XAXFPiEVLXZCi!DQGV)v}~%RO5C_Iby%3q-)4cD_9EK5gN6dtN_Po_nb| z>Ciy+12(sz3}GzB*^7Z{gHWgQ8-ZBT7z=J5X1&4MtHiN~&y7Fa7JsA++-Pku9*E#A zn)O1pX(U)5&Wf)~rbmK!ldizapEnxs4)A){-9Je8*g>3bnZx(n|HV10RJt}d>;6OI zl-JOqLGu@^#NI6KO<5Mr`jx!F+1T?w6Ik3wd6H|Nc^1U>q<4zs=U>x{rQ$ubj9Jh$ zW_a4#yT@(P;d0_7hD0@j(eZRidp+Te#EsV?20C$?+WCMPfKE(|Ph&=LZ@B7}A26JU zcj>qCN!(lwT99h8KS^f>SY$txvUUXCH|{aVnYjN@3_@lwxqv+cEG=GGSXko?) zwDT(jQDj_0Aa_a*?FsiIvd)RLV0;ftaRiZHYet~?)e~Dk&HCCoDX%SBUzc1;obunG zl-Yv0&w~S}&K~-^3!Bi;d_i0D=w#MQ%Fsk$K|A6mE$omRp)#rDvY@>501KqSq0%? z1ddWzN^+RSTfDN`g&r?7bBPqNF^J&=k1nH1y)NHcu6faIrV=n@bp090cn5%kRG|ro zO(P)<=jRG~mMU2$Ef?0Fc>S)u`Kq1`XW=jE616aw&jt1z9?j+K6AX3D$ExWTu*2I7 z$<#y}L)bnmXNYZ2=EYiEyB$wReuLm|lD2IA1)IG6Mlepm1x6K9!Dl+ikcyDLSZ%9a z7H}IvfVuZ3X*oF~DEf#Kr9v?;^(`}02367boLfswRi`=4cJxII+OLx@5}pNkV#?N0 zPt8*g&ptRYjOrx#CvKFk0SNy&Pe{|i&jcMh={EMTeAd-?M_2gpgTm3}cb=)spY4 zrOT-?z-n)QE_;}@%D^UB6&<`Ic&)+3M*PWWUWyqHn@~5fH)BwG{;@fvEjHtFeNlyI zNY@WDn$*hvDvo8kX&gUYjKJdBeXF(h{BUviC!dX8h<}`;;?GG`B4r^u*7>F-3p_k- zoQZ7o$#e|R}6=-hc)YyNB8*no0589(a8NeS8NMTEoAD~IC{aC@!;*(2~EhD2(?!%n-U?moprIdMP4i;mJvq{L)}6ssE<(Gs$1 z7*zcH^2=oGHO#17NUP2gnPq&2ae8flUm0omf~txhv+V15biEb=4Q_Vy7D>ze!3P)X zX%a(so3KJ>ML&LFyw(hPnwM67YuvgEd#GeMo?{=k=fL*t^)#$~M@M1U&zz2`?;UX| zTNX99aXVp0l%<1Dop{PmrNtlk=+CXYUD`2Tz*}-UPq>VuN&r*}$t;dr2S5q3dV^xl zYo#4w-uumFO7l-;Wo4d#z;jx9&7(4nWZ27H_eP?BtMzL_ zHXjlhk=rQ>O|(OZKr<2euo)unNhFc?UP7BD_$0Z8kfdQq~Nw?#9Lsd0`HW=LZvm-;BWtt`EgV$^I3 z8vLV^`)ZENqu#4N#DD%!QQOz`Z3kl+?EUqk2pbOAPbIPa z+sYhGbYB{EE=VGyv&fIq)Z53Qg+;tDyzS_*SKl4aFDK2rx9@JT!Qk>-CV)sxn=Bb^1+!!8mOak1Q7o}IM0Wg zIn4zbx&Fn;)xV)Tsn=j9x&`+j!)uZ2WNNQ(#90PV!H(qc<7x#vIhD9~5{1*S(IDeljrkbm29{-!VwvomkeKQWdN z6r&*uoIq&_$b*Ag!Xa>Ms`n zh_45Mk6wP0+{YlDHhm=BqfW+_hb-^24gC=*yzq^--zoMDIjimi0I0XT)@;X4I^@T} zj_lP!Xt(NXlEU58`157116m0T#MSP!Yl7)n(hk)J-`JV0Py9igjy-&ePx}1jJ5d*# z*n5@rA9cMNyp`!!>zGQ7T1+Tq0CYKbK!9^gh=wYSKy zb{FO--QC@F_G_?^Ox;FTjT&o`boX@>cMp%O#|aIstS^`e7MGUBDd1MA<5Prfn|(4o zWPTv?iQvtu{6IE&`uU|h3UzZG5knr*Cj#a&s+Fv=GHhGhZ}s@kr`)>V(#5uaX%DJB zdPiWzRk=}WHvDAW2rV=C0VCrE0P*?0A}+h?khZ>fx^lx##CTO8{IE@s%^?i!LFUDP zurSf=Qs^qm$67)nY?$QT&i+IGzd&3DtoWeAcI-rgdh z@~&=fzS&=D+&I~pL|jlQoJtb$;3j=!jZ8#DByv2&Q|EbFkaI8S`Y$aDMfFqOb-DYI z5fp$IzAWwli@MyTAGhgxzfq&!(e#x2=?B*K3e&+d%PD4ZK}UFBUtjm#FL}?^)zzC7 zb#%yU>{epfv};yZ+k+mmv&-Gz-DXXetIdD?jcrYg;T1CZjnFggGxqL|dZ_?Ufn1&L zAwiHaF?+rZr^KhG>eP!$w*A@~)b-VwFCRg^HCpQ_OU9aJII#Q?XW7 zw{ZR>JZ7sv^XeVTl7@kdkB=)TE6aoGBNs!CA}%ftd0*^(cXv0nXKHT#suuO?@>25C zIJLO=N+toPurQf!gEQA<>+rGz`X9Yh(((iz0^S>OatXC+srW!7`tQIw*j5~eeDV5Z^X;$q|A1cZelKX~w!U*h|_9@i>0$N zfqT4u@`wNsWfR9k@^ZXozn0>QGiP2hNo|3vl(D4CR7|qpwcUX6O1?p%-pbpXnlGXEU-1FVtrS$&ZI)s_B-Drtau^)lH zqbpqFTOeaqYb$gW2`&7B6Yj`hLT*<|w+KxxTv^M6k{jp2~U^qqwq=3NQ^tG7etLeHo<-@d71>xeZeYW(Z^VbazNi2(Fdbk7T&9HeLrgn#FFFb~p~6)n)fh=No>jch zJ?R|YFc9rTUfBi4V=iwcA*t*qs|)JUXQIE>mJeTA&D7(+I-zu-So;~V1|AM5)eVkJ zAxuaAD>M57G`@=KupTR)C_?+g@0ie(7 zLLDql@u1A$yQWbLvc!vxS_;Z+)BI?9@eIXhSmEP;{D`;j-n|LOV_Y8(u?S&7-)8#f4N)iBtSE{_UnEr#O8-0h@5r zUEQ{`zaC?Nj-fjmuY3-rAC*Ip9v1m^H{Ko1mICoIEJy9nFhOPcjXougpsf@)^|e^4 zCY96!NHgWdt+1WC#b=AT`Y|xaBpC3hogloHomeI=_4vhcu(YP}}fRq-40~aaI?PSo<9O^J1qrubnJ2oF1J-g5Ez|>6TCBp* zdP&p-CJfmoC@im!myJdnoqJIT02@stPG!|WS>N8~|W5|~>hNX{P>HC@<*rKsRoG)L| zkk;*#U0XRU%h&FmDT;M`8sMzg|2(TwfbhT3SJ!7Nw5wYeRst@42s>Ytj6%zq!N|~7 z@DlpdGO-p}v;F#2aQjrP_@#7l%Zuj#YkWFmjEDWMEMRym3mw+J z6wxj^)FjKmwkeutKGX4~q*BJ}eG@}L^Bx5`oLO$h+{t0*9ooq~FM7|(fOKnO1q9Mw z{KSEMj=-z+q?jF2+p+-P9gLY&dC9xj#B6VpNc17rF681qT`GYv$E?43v)VvW_hZa3 zQ(opIERm9+@k(tvax8A?LdlbZEDwQ-Q49}$%A20!+_8reK}U9`>(PdW`OmeLzxPZ< zqAI#G*+=w; z&F@Lyi+6~MdT#F19Xu6+Ph!GHdzZUUp&N^ z;pw6X+sDh7Z0wCVJe$Qez^aR!Uowh&q{$CT&ZoY*Cj$oS~fiIW+zpwkB|m z+(eJ{NS5>U#Di1;2iO#TyOurfO7l@ULqo&Uck%ItU&Thr9VDM884{Tqt#!d*b)5_Z1%2`#d(>U`!A#?!zOq`j zp}|)96rp71A9Y6i4Q85({o~5lQ8k9 zHq!9ROB2+3vi=R|2DQ4{+FG;gB#h9X1;EQxvnFOJs2_AszGYc^}@n zedl!kg+YGN4mUjiQjfv-QqiGw?reBIB&nPh?z@t}nso)*RZJa9MjPtQfsLT-Y=eu?4_Vi%){wTzQk_mDJi77h1k_@q7Xs;<0q>gilFm<=AQBSp zBsYPBW&5Ykqmoi$5Yq`1mbao4T0ITEg;bjLqeHls)Z-N3O?1?0IW?loop>Dho&_vv z4?CsqO4H(uSt(GS-DMD+2pzSu4m>g8>nVSq_(bKMzEew1l&2_~ysv}o2%o$F-&oeQ zKWx>$Y7}6wvi)$71YGW;E~m>dDuTcYBQSklq_63zJ_yRyNqqzZ>I)3b$K76ENzykp z%+e|WieCqdHLi}s``!Y3H;ft?%H5#+>h}7a?m<_DuG#lH#~y8CY0lkKR`l4$m_3ph z4IXjq>zYX6JB}SM_e+cT^n$KM9GbpIlcByqH%eJ~9nrKGW3igmP3L24 zv^~UPANQ)JPh^4-%NTFsa49rkmXiY>#&9d?>S)E{PQjGL9|!UmKw*F~P)RP;AS6mU6h{wvwe;X*PAdw6wIgsqVJ- z=1ERqW211x)AZmVnZ@=q#G=EWmGm2K<#b;;t9Fcy91c2fxeLb?@koFBy4-wOA+JgoKU#G)Qr%sd^AZiXtw1)Tq}26mIH-*6to1N^UO)c}0k~?7i}dAKd}H@~ z%j!K?Ij;_`X+1JhERuoBX4R`G^P!u|pRmI7)UwKUi4-tidnYGpYim~5AG3zz=>Osa z9>G`)ClOs=q}eqtrs#Q5zkY4z=1FFoyq*lSKaqeN(n*ft6Tgp6vXyoBQ4nyf+{A`A zXy3**$q}aY^&u5oOhaAiA#DZuK)}bos-;*5F-;gx4>YP`Z-Bt;x_N&^&-oZ+p=6m-YL_(L*T>u> z)QM;UKA$Vd3aDpb{HR<4LTd>fl;sT+_Fg85agqbqE=k0Wz{>k)QJOk)=7PWX!_-rO z6zi6mTz2Ru!;9Z4hu{Y&<#mD#%yU)Mz}Z=i$RCqX_$qB$Zr>P4j^uu2!S*&F+0xNA zQ$8{Y*IH{I1YsyLCt<|xj=NUm)8gGaVIj(rX(hfWoO)h3 zH5L1U`Tb*VWVaLKCl9cF%0ipXKh|?o4CX#+FFR9-8-RZ!pST|W{g=a<$yPtPov$Mb z^r=>DIca9RvHfydXYXIr*OK?j8wM*l{Nr90ln`o>26TWXjAjV!qI~HFMcNJ1O4F+A zJyilwlw~`*pM}a_6Bu0~z2Y=BsLPk7Hb^U_;7N|ri<9U;lbjTVy7OkkAJ;C9HXc5F z*j8h+!0EWD@PM9vN(-KWfx)Of5P@N#_U{zS;MAWKOA|)Y7gdQeL=aYh*~(FUhyQr( zXJL2ttB)O}kGHV=LEcKEnV*23nTeVC!QtWb+LSL&n6)k{~#rOL0V&%hTLCiZflj}(SX6o6Y&TS^ELARvE5Q&VeL zSPDWrl*>XUxq|CA`Xf`L*NCrIf9ACVxE-{`Gl_rrTF3B15tw|UikmU(d^DOHV7JOO z|4Py(HT*%jLiC1&j#d&Iy}`pS@A<>b=MOD?_9cD(n>n)Z`Pwk|I-zNWAP@o{{3YI_ zA19-UBCN-7Ya66w6g%O>>pGXTkuoGOphbMRsVD6A6^&8DD_^e1587ILzpqA~-(b&I zpl1~il%3x<#W|>i*YB~X(DQ&>2{x=y|2XZ$?L)K)>Z%tc5vV48{k)^@eO@G|o`f+s z>k%L~XK$G)eGY?Qv6MsX6dI}}GWfITsM?re64+e%pbD~WM)NOAWQh4-1MxRIo7Q@+ z0^$1npm!uqnC%12|B8MH*COMSZJAX1%f!a1>NOqfJh#WH?O^1Z(-A`bJnfEc`;NfQ zgz8AKy7ftRRMLZ((5=&e3{HPQQnnu6>KjsazC_5_hb@IgZycERZ;g7%U>d~R3>@y) zL{7!_veh9%W}Bz*p62nEyHm!>X$@(3An1u{y(8?m-zmZc*RZ|~Gt=?+}Q&v9jpCGuoxjFYj1QdpNN+O5M zXR0^;r-IKB6h9BRy}$%0#NLT#bjbjq6)o#@gyiF(bK0FJQv%l+-T(3~yutQpBoDGI z!tIs*e*=kfM#nO{v9lvq0{}@t*7}bIt2PH~G4z<40bTlK_+Q0b6GT)0BhdlVvs95J z+6LRWW<)}HcGK4 z8u!Lf2snL1|G=(`iiH)sYx;pzGthMKgW*)c3jxPXsMZV}ysZ5Ee9z>%IsxmI77Fbe z>-P2_Bo!TAXqN)qu=K@QHzQQ_NL?Mtokszup}QbrOIt@M;td6SJjS@c+(= zxI4%q{VU5;un%uE}+fpz{il2k%4#p zIUnq>(X%%7O1~SFJxoZQ@2wD|ZDwZ1KvZ9UW%qlDek-QDyu7=o=due4H-#&VxXI@y z2%mA~L3C&`F)@;2Eo)3Tvlt|ZE|@p?q4-!Nzi<|3Tk>(yVUdb(OIq-k5m-!ARPH2k zzdP@)-rgAvz0#Ra(xI3N3JMTNmDLPpo^nRd?o{DX2O5Vu2V?z)4=u7(v93$2TLgod65aTZA1yf#fcIlDU&REW1}+&et7a?Bl~0dB-t+O} z$5Y+9ii+r)j`i)_APFYmvWt%i*C%CV3@2i;1jR2euNhv8bH=C)0FC&eH%)C$imVIbt%By zS4tByJl|ikcX27!`*wT25*ic)2mMGD72GG=<8Wk;to;=kc-E<;tK*RaDPrFicSjJg z>^A=>EN!vuc@U!h+YUYY?8r$DAbK)H8I~rYqDS)T<=JWOzx>ZJ1D!Oc7e23eb<@~dASp$MvrFn{S1MH_?+1eBlqWAC1Z{460;FEEp4_JwjyWzvkl@l zuZyzO-SzW)Az*jVN0OL}_x zCJq{KzeKBMVcoGPgk8Jt@6N>)(?v+yic>U7wQDg!^3X`G5Xh6s+w;Oy(b9vNAR0fU z^2)nL0694^wIDIMVUsrf%B`f`(~ruj{oOs0>3GrqlCWPxBpiNj`nLhGJFxNLFZ-bz zsWtsW`QJn%4iOUfH_^B(8GiASwbO6KDv(aNIdkAWoj?KnXs^DM#=`opDfg(b;ZrHj z5lY9P%tUI=3jETNJ2DJ7KEwrFc!Pd4HV30t#@r&|NX+{I9_{^nrEX$g8YG<32dux{Dgdwd{lW}6^9_vx7G;;O+ity zD^<{m)Az>RqFT%QupJ3%_Lc$5n^mi-FaPJuCBj~vY$~P>Ed2|kfqSc9emx7!YjWFLPNO-3f4M*WG+D(3TmZRx5i;K`iqy3q@xVk;!bT2kIN7-S!=(G%Y z3EJ|Z+VH=b(-`e_A(qvUiH)CIZKSnYL`$a~?awH8@rDA8V}}Af5YMF0fGkS`-}rFB zt`$0ufh0HE?8OhvUEpNr}v|Ws4O!ETi+JJ|-&ea0K z!6H=BP6T3a5i1PfQpgZ50w74UWwTUU^s2nE zOkcdVkTQX9(uZkx^F4adVq6oUHO}#-^$UpwzIsfv{sj!AMQ;M$CVi;4OmGJ|hB-i4 zjc@+6FVw3OHOM9>!=Xoc5BBhv7tzq$Q>Fgv#WE=IURX1XznuyN5$l@PM&>8x6lTRw zdVeKLK4iwepVpuDObt^EKro=l{`9FFc=oA6E~w@ssqKmSzTw;3+eq^-9UQnqu&81{ z!E2wKoa}mhFg#aXfl#;|16%51PVk6Isl6fNzM~N3wf%WXzKu&ING$zFiq0b<<$7!V zY}BDLkb@2q_XNS2EDkHUlF~ducc=pE50$Hzvg2F<{!Tre?bu$xMyq?f_-FJ3bi42) z8)2j6$>R8?$w31Tmt_1x2ENX|+R4>56+3U^4|TeptQq zgcvn>op|4odQV%&w@AKAYr#Jh5pGBek8Zvex_=+6ba#=!XXgEOZ?pO>Cuj)fVIQB! zhu`_T@{mYdGwJHoe1~Bee zc}i&=P<7z;%7xcz8XaQ4zbMFS`4t&tCvJVJ;cWp32ex>3Je~Y*b(23E?S!xv9K#Ev{-F!Ztk)#fJ{Ks#d@Us z8VQmJ-X*%}8FVfM4*jDHI4ri*oq}hHsb@^B+X}A-VH>)R|=@2{K3m!=-f)rhe zZi)9TG)h-lljH7cjrnp2m3#2cJ81Ceo1b!4lX^r2Es2N^OPP^3&jtK@s9DU7BnnIS z1Y;T{?4j&#`~XkF=*@o24`+Hk%c(hof!X3>6bKsY$G{@h(a z@n!1R1XIHKJFdvF5&KnWRe+w|9Q$LC|2q#hhf$-T40J1Zzq{Jw2wKO2fFuhvpy3Q! zdzKdfnv!&Nt!WRZ6H3EG!X$4icu|=7vXCZ$B!(*mklkFRG_6~W995c!+rBM75Tat0 zV+{0^yc_X+hu>%a%OC0PxW`mL02SFrEvksO)AnfZwUPy~ z#+>Et$(zD$v6GPY<5KY{OuBFSQrGD}a0wIm?v^OTw<8iWtj##><||nG>him0sID}c zF5rre1(t}6dyjzFJ8TT5|L{k?PN0{K6}(^xx}2MvL&hrgt2KzF6txHPNsz}UxoD~5 zZ^K+x(?z7+RFH@Gs1Nb6^t=_l_qZFxo$&0M$-{rE{x9`vGC%jhkXGY^T~w~gl>o`j zlZImfrzLvI-N6%4-2?N&r*^BsuT^sao9Cs;>LLnb>N8hKpq;GYX$0K+?CXL|HYXDe zp0Y?{?1c)=``%)et$s9mS#FDg?wyaM|BXa{w8;RQfCM(=zKnaZuBa;7uc1$O54>LqO<*GLVRnAtZUkJaM6X;O90F=<-N30P{A9L> zPT#VbV|?RC!-)XJRI)UIx=xeCQ^XAu_=}rceGcnb{0~}4V=8b5(~dr#G&Aamx)6(P zAvSqeDJTPbAq?9j@}&FXB=UWtoBIJ-r}k|#5z!k_eeDv?mbCwpG`t*n$IABBa8LMI zyqj(SBBhq|4=21J^W08Jy}8}la%D<6MxCy6sz*KFkEK?rUWUww-H{DbGI49?UWHk! zg}Bu&dH(ES%P0R-_q*Tab%)}8Sxu|3)hQ7L2K0I=~z@g^`<>&Z+Cp&AEf}2Mojtj z#Si3@%%Y>Sq<;eW-$BL%hnePJo)e<688kb)ze8wFIDt%3@q z45i886kNatA8NbQ5KaL?RbhQa8jxw}*RPpMbn0QFdk*hnzqO>1;oywJtpV#11eFhH z7#WekjPHn{5VrJLPU3>b6U{)c_O5|$qE0QhgM&lg=xA?--yI4gBO|c;@yPgX!6EQa z5?sCk062~)HXs$99IZEj2QAl_0Exd*XO;y)zazlj+lPlSph=RM(u`Rg5MHu0LZhAv zVDmo*S}H6R6&1ukF_Fm8(Gda-t_gUa4D@T|Cr-xQ4I z5!kKiczE!@(hO*AZSC6u2ZvDto92LqogEXOpbiey(hG!o$4Ld_<8R%$f8<0SIuTUAJ32S6z#A2nT zd6o-$j;IdULrk?irQ-97!4GUa#<)s=nWth-V zWrOR_2oQ+-oR480@d|9d&{Y5P=+is-=sIUG?;7E6O zcZ17cTk9cUAZR@$z$F-N4ZPQ=wWRwL|YYo^XAPe(0KX29n6-)0cuy}4Y)QfEuH~*4y63x z@EDb3JUj&9CEM0IL(4sm%^ol?tS2ao;RsU#div9yMW6r=AlEKmZV8u{fQ#pZ}hUQ!zw=x!~p%bw1^2ev_!@iBex*Kel&zcyxp#{8r^EcBnh^ zNbvC27YUHdo*qJL`_hRa)6Z=(f#Lj9J1oNj4=@N2`9n zl#DY|6DZEBIrcZ@3zb(Mu^|M+$DWAu;w6ERDVTYfd~Nc_DIi%tXbSKDhpgps=l$Jy z6lsPntfpCxuf1Xu*eTrnJc&hKK^;X;#~x+PhNX7{)0SL&&y()k=Z)l}83@G6Ja~Om z(rr`M?ahR2s2N2B=S{RTd`MynTWxi->-3?U8~^mlrHM`H!tFZLn(Kv3s{zd|n)z;h zV*|A8qcZ@#1&^Uf8#Fa=*|zgp>#YT$G}AhO?pZ=#7ZlJCNjR*M<6@2CJ3EH$a7)MO zW%1C28wMq1GShxSlQjsax=+p1j>el9szq1~?i!9kBXLh)`C8J~TKWne0)-pqscaG! zr`>p0t#GXG@LJBT=SDkGJdB1w5&emow_x$l>dKJT#(9l}?+Mk-wlcX; z&X0_O5+LJv?tuOJ_3QKM=9k4`C@^aGU+zN^4FQ5dT>9<njW&c|E?LL4HCcO1a}y+2hps z=xTjX_E{GDdm7Frd?)_2fS^y!z6+YYD`}^GVmNb@F)-c`pR1F0;4|%8r2!#s4W!C@ zff!w8yCekFZGl4AKAghWnhB8r$*mk!62)BV#UoM1_%>$Woo9-DG76F050vt4SkncZ zwV0MEiDKFS9Y5dX@;-M%)pC2eb|~hyebOWonDgvvRRmr#(1Ak8K*PfP$HfqpZpLu1 z`paXCCc8o`N-uV#n1PR@qGvknAAX1gfUs5|;CjncR4C8teOl51{4y`jP{yKPMtQT_ zM%adQxN?6-q^GBMUabVIl7f42At6$rZXu(gb@oJ)ak=b%B^_k^W0=XA7iSM7Nai7k zkdfSdbhXQu^^K{s3;IV~X|a`?k7pA_ztZrZ{r4t zVFlXcLA#SQEHT%G8L5suT;Y zA1ZN(Tv`7V@3DT*ce|u{{C!e7(irju^JcIi6lmHcsqnPOut2+xNlhj92C05In}zOf z1Zd$0Ds=HEXn9`9j-r;v;NM2Ni~fn?h`! zJBk=G64ZFduf&QWd^6*kMo#Y`Q$W8%10h*L9So-1UmO05Y?*|E~-qf(m z*$+;5+9+c~ihI(9q>By?MufyO!hrSM=H-4iXO2PnG)EOmhM@os(17Uq8m;0U&_HoC zYaBau)dcN4H=M`%W;gw)D~f~?>BbxGUO5%oMJ9|EC@@78#mGTPOuny9CzIQ!jDKFtQ9;8T)JTi56TPWV(wUyioj&}Rq zgR~yCQsJCCL5pPVn}=d+CYT)%N|<)Fxh7s&(kAJ z_H+lpA;qPnM9M{zW;uNyaligq3pLk0i~j=m`@|5W@WpMB8b#XL7X9y1!MA#5o4(#Y zr&z#%v9A@5!d~L|ht20y=oO6g{8Sg`Ob*s$0I9VxvZRTe2ydCw>Lo6bHo(0AZ3?*l zNP3Lg1|U-s5bAMML=QptHxvpbaT>ycj8q6PjG$i|z|J0G?h90Ptd6ZQe>Bn|5Qv`7>D2D`2e>HklKwKZ0ZNSf%{%~*??=dzFM+qzvRnI8j%IZMv7c#psT)b9kh(^w!t2<+qOb@0fXp#1J*XMa1TIAqhf@XlY?Va z>NONAD~2Mh>VmJdfb8!#Awc=T8xSsn(h?#tiovn5vA-epeoB&V8=wa68`1oS^H!yX z0C+-}d#5?T3$12KWPN?b{!eFT0*>XnzWv7#Wy+8tnJbkHMWRH;1}Q{}3L$Bx5HgiH z8Ol;LAeuylXh4}o6hf2|$*d3x8NTyIty+8a|Bk)avDdNIUh;aq&-2{(b)VOH{;rh{ z9o}S{L2~4lsqSqf%i;d^+DvD;XV0FM$E}@7mJpk_4|~1*u4JU#P>QLU^K=|P?p@a0 zwZZ0S@5tW?%RuN!a{GN2l7WJn)d2j^jVKo;EiJ9;lKg^#UEjZcs!iF=%qFzT|HB81 z{_gkD=Xc0)@pz^+yz9l8;fi>KdAct0X*e?|f?&X?t6R2RpPr`Vyv$P~sM|KEYHDfi z0_SLbrD@JS0}wsCCe0w6uJ3WNBzZ^mr6<6@U z>G(qG)~|6U(j~2kTWg2MlfP^yHa6-fa-_Yzy}AUj3I0?8BPkMK0-Y;mg}TogoIgDo z6oR>x)LAp`2=d||kLli=5?BcKmb|Ztm9r3uW$OQ@(UL9Ost~WR|vXKmQn;`W#`|;fX<#l z#XmDf7j!py>ezkblI@5!IN#JAemH`Jj3z#w3Af_J?U3d`6E^VorQl21`MVcU9$rKo zk|xqcldUyCeh8T2Rnj!_g#Urv;D7e-9@uXo9CX1P!=G|15r{vvumcWz+3?#vl$CxBRz ze1a!o-^#zMo9CBd&{((4tH#eeE;F@dZTDA)AdzIftcz)QzhUiLb}|%B+1rZC%GRQ- zEqcBLVK_FdA`Cb@`}q0!70CW_miZknC3UXR@&9Nby}B@i0-<786Q{?>{XC5-fd}^r zbB@1^1tq<6$ zZzNIhN$wY9N1PTu*2l5x`>8aWPQA(#Y*V5_LOqQS_*zihgSii%Cm9YL(mdy+>p zoZAt0VLHO<=f?9&Ex$jai%I@&)3kw>Qw~@~(l8pdq zoOqIa+6G*RV`avS8I&hV@kRuRw>@@|H+5?C`FV5@ugm%muiXJ@nPa7){b9cJ%SzGMF? zuWoA~6zz!O)c}s64;l%TO7zFBYg1o0(TrpvhM~ANy}RtjGhLgXrW_M z3Ulq(WGXduk-_yCtUEBfWFzBkH`ud_G`vxFrET-Q+O6E!5uewyNP#%$kitB=)>~xz zRhiBkW{D$bUAr#^GW5L+xdroMC4MVhgglLI zX1lxk<>soR5Q*F^yyZQ2g(auRQP&C!ibL&lraX1i2D;x1BaXqV!~kQv;@^&T zeoNht<}@~azsGcMgAG|d6}6u*$7gtA-^Knr8OvqO@QjDBKCu;RvWqPJAe&W4z*4`v z?)dRj;6<3Q(8H(iFyUeQTk0hyJn|zdl>6Ci7CU|C%lRnx0m)U*L4BV}^vh#;Rc_7-M z9htsDsfWJL7rQRGvUMJs@;S)&UeS;7jZ9p{^5bci{mr6%XCAwe`yiQt6amhAn`_kKDAwh|t9LI^UaicGM&L>n zW2^gbVc#{jygqzu|ybbkM5)0|76x*+*fP`e#k5HVK+aebY%nNwEG5_O`7%to#e0M z1MU&gJ7BA;t_)c{=AV?b#J=;@#x9#!yMKB&G-k*d+llYbZ=!GefudxV_X!iaF;hX+ zXALdIP*!5)xkI|4|IC{V`Lbx++=9v%ZKJ|{+{%+5$oI_-qBfI_9j%xOo1W(LW)`r$ zg|3@t@suOz;(0b%#u$+r+lBik1;bLSg2+UU5Uz&*X{UbU=iU9bD@dH9*`_+V>cFGh zy=kb6zIjd3S;dIuOkYpWlNbGxJ0&-(_YovSvC+FB4Fj=Jnp3M@C9=XE% zq+Ud&hECvnI8f(<@s*Aa3D_p8pla*N3U==`mbj?~4~N8h_P? z)>PLjrC1a`GgI)O){3#(rDci6>vtTWNsR$5^~At(h$jI-6DIIqT8+yKC*Q><%gMui zzk0J(>9AF$lb|XQiKeXP+w-6~O|cW#(9V5IU67n%y!anm-lUvOzHN?y!)!jw>)+2r zMEIleoD0hU?hPWp61#8G>3=nt&rUN5!kIq~ty)_U{z0XH*ckL55rTYh7pY&p!8T;@dABq9LO*goj zqIXwLB5(Ffy7Kg(6T>QWck(`l9Fx-&vYJheJ;`>`>7H8J1@i8Sz1i}h8H#b8GJw3gMgv2cuc){MY z#l>YnHaIxAr@ucjS*LpVr`LHbx;SEh#>&bn`}y_h)?ezBU1?sPDFCSr=)=R$>xem8>_eR}W1Plgz>M!qnk&%_cn6P-bP1C}uZ zWnEBh7A=~A9u5tapP#QPeSVH)Sa`S(WYjG(8rSnCiX7Qzc&EvGE5%*Ey*oxc*oWGW z9y{h27RCi(^uxbvKidXU{#E#?-ZL$%eJa_&^MtDiZh0CCdXGW`#5@;bFQYso<@UrT zCbA&Xsyd}-{t$$Q{uYFmOzD{iYLjSWBfE0Or3@EOQleVaKDXGl4u8|Qxi)9vnRV>K z!X9P!{KK{98{OFv0Q^n88I^t*U3OG+KX{}2CErfdnJGN^{cB?HPGDK*{INoPPPe1< zQ6U~@5M(09<{n*Yp#*0@fVQgsieQ`tGo^RIbZ|C;rTz4{R!7q6+7`OQLer9Eda zCj-McrqaQ}xPnxH%vqV!eZtxk!vBQ!6o1cqh7Xi-b=Uw}UR(}@&t7rG3ejcDW*vS! zz|-$i5|sMsPlev~&*9HC|7K>I_$u`(R)@$7EwD~`q0_C)oLfQ1x&#OBS0f_|7U|rL z1EP&!?rN}mPK(nB4gc&vIIwrn_Lzk0oc%q+p|*dWuOB|O5*V5ld3o%8P8>!f_Ckw2 zveUn6VTIgC1W~w_OZl(zz*gG&leoh%(Fddu)7OG5bEvr7@tFy z-Xlj+w7zzWO!TW!)3dhRnD;0aNc%u5CqA%+e|-EaFVnkRrVnPyt7rSyV)B`EJ77JC z|4tLCH_2BPJRy2}`>x?^TWC5KUoww1eK30cWvgLN=-ab86v$*2qkBL^GAW7+Q8@+U z((Kk3m+7bxAhNJVi41@JwExVcOmuTZ}mwwZkDf0a=11VmdBzD{m7+@-*Heq1+uIA zXS;I+KGD_yTmnZ37`xYo?YXo57l^rJ(rU3jlGv2p|4I_OM67dPOK7oGdAt3#R2z=5 zzUc9)^k1B}I%k_+7`X+|k6hcz3_EXfVN<@&fMu}`IjX#_5+_p8=19+;w#|fCD19k$ zRb&&CqFwmX;IR^$b-QWbC>wbP>(&pKCh$wVL$9)5=mL0cA<}uhUUzEK3g*PUFOt>m z@z9xUa_byl{x>plB7 z56xAGb(n3`y-_Pb8{oDXt1=y&(#+_^wx6e^;JmrFJV;WZ_7Fb$6uD;W;vKtxrC;t2 zc(sO$!~CNIweg&H;w+p5tqM=ib{ZY1Sqfv2=GwJQ@Fza~Ymq@{M8vkvFTa5}#{QE{ zzWtCuP+HTEdto#ZmQ=h?A_Holonvu)4zMQ=+Z!8iYgk{H`(nI#$7tr^P=9}qjR@8pmnpD zD%~6YNVkN3^a!wZL0a`gJ6&>R)_Ij1?M8-Jbb-Mov${!kdq8BOoWU3Ot@ zGJmb>7e^5w(c@}DpWO?dInuy(?69CKIOsqy%J=cl{c7d|myGdM?f*03+4oQWWw?*^ z`ZFvSzWBv_@`lw69fZ`-8`19kB%qD9FmclgC^@pg9+%fv5%#(-#C1F2U^TNo@M?8@Z%Ogb^yb6#bPF^(w>@!vRI5+q6eKpM z=n4}hw4a~(yv3-)VOpP3leWV^F{vi8GOMEbOUmc%c_rC;|I~eYWS5Y}JyCYhFpFpV zN7KiJCX}z@)QvsN1h+fUrkm)>{yOet_U#FvL{(Df8@uGWd>FRUID2pW!=b^7`oSScYF8=zLX0?`%WR{w&p(P8tS?wK(b@pb`c*munjci;1ixR35$ z79{y#o9~-Hvc-Y%E}ggU9}`g~#y9k)(bY%uc*imFn?tfl92z>DZ@)OVQzQ4x#rb|B z6!RKC5$sOAos~U}pA83>ziL2%vrtBU3Hs6u9%^=a>Gy8$ zohQ1j)`+h>FM*{e_v;?pW<&I%uttPtJ{ zviFNuf#PMZ?}A+v|-leM(gZ8rO;A%9Xa%$ z3+`DSO|mcwUH{F%>?e^-#&v>7=HK_q>bl=~91)Nx49ZvBM3cjx`R)0%zrcvvJkJME zhd|T4Oj+XLt25`CoZmH2Z}`{Et2b|kW7NzK{V$Jxt5BMT>aN|8xB?(|-Gki3$WASGi3~eod$@-$K8=pyY5DMh79in|t*w@5 zS#C?QT7SD_5edC8g?&0!)oBcH%i<$`;ksg!Ji~7mKBk~u~w50{NNj!;_o{}r>p8k@JF5(=q;{0VIyD^Y^otqwo;|X zOX=-nhn^W#4KI!1)82o&)`MH@`tjXoTt21Dbss9^3hCF&dA`AKaqFyY86xbB`qyk; zS1@hr;W#ZoS43OhLONWqqpPvglXx4i^p5P)v*pSYjpa`B%RdwQ{tb`J>F}+jy9JCE zdS{?-FANs4y?uPQoc3@%*PW=9EYU~cLN&R117Fjeo}x9(X>olRiBb{@;qOv`t(waI zd^>8}%4-1&BT=(p{=ySS)c|eZfq=1}qgt2=(hE@T=&}sar%!uNEG%*$7;k09=3U#~ z26YSwexwZ+wl9FFtnM5ziBf?}n+Lj`g`BP4W2?wMh@oEtXh>!FZO)%*^Z= zAA3KZ7poQ6@Yysgp*z7)0<7FPC5CJ@F@>WhVzGYj@9ckC5Nxix<*MHvD}LZf8e6QR zGL|3@-ft_&##rpdTV|r>6&?6QcG74jW!2M=16xWGab*<=k?v7;m@4sc{b*>Nj;YZI z?OEw*e{8A_Aq}qotS(?4`5Zi{9&*rAYfd-e*zjXWh?lR-#yG;{a&6TR0dZcbs7K-ES)if4*Z5pB<2|Ujt_MBkd)+!qoG4dm_LWHcr+B- zhXrTC*g2F1R?RKgq?ZAV39iY--E~Ak>+8$Q=`8fvy3D4WA`)C5(9cAI<1cAxX;HsX znM0@!Pww88fiL!uJ&&w1kLSz)BsYl022~Ux{hJ5l4JDu%icUhG#kFk&tZxZhy9|Dz zN)65KNBtbmm#x{pZ?i-=Qv96D_=75tB)?MlZR5e)9uG2(4!yar_Ugp61`TzI@L0%h z;_t3Q6s~qHGJ=@C>w3EJb|KHmj*@e7kqOP+ju@n-eeGq}_eVPsED#MV-k16n^ynHJ z?s=V)_eRk|{BAIGD+|?o2H$;{%CynI-=v7M& zy;V$()0cwSB73cEKM#Q^E#2LK+rE~KnFFu37d?BX zZfH2meYkzWvV(67yKD@Z1G49gkqY@W_YY{=#C?L>b)s1Obz+>U-C{bsINw%U43ws{mEJ zgSn!xu+Rd3BGx+Hz`{aoQZCdifh0FGK+|H$(9lr8xf|Nn*5YeU^U)kWd{|LQDGoIV zWMU2}U0Yk*pr(0e)-6G2vO;!mnn~$pC0WeU{GtM{smG|p z8P3_XA_#8p?BZicxp!|GrM`Ld*;;)T5Kx$+UB3j>E+z0FQ86)fkU>HSccn3{#*|B{ zWMrt%2cL`o{qZ-Y7vwiM!7^G9Y^Qto@z)FNJUrsC#Z<`lch=~_Dw~><35PG!+%>1! z7o8FL>)bge*+`v11kPAn9_+1LgO5FUT|CUKQl5_L(3+vWL*W9f6KG!oc@evo;tumC zMZp?)Cd}|Q9$RCBFE_PfCik21;Mlw9Pfnd&(6tt@`{F^n3UAU zs#RE5lvmofd8&CaCr;KM7llO~h{-F<;)a zwJ9ncvjiY_#Z0{AW3aADw z7WM!m5H=a33ZNLXxP zY1xYwZ5&wqJp%&;7%X7dRxXO;t^c{eB158AD|s|CpA_kk@z}q761AEu;r>YhJO0xU z;k9aNGzfb5LY)#@v}k>**b*$Cxa|Cn-Ss=;d^9ET=){kWx3$@TmnMdS46(+1ym77K zt2Ld(Sq35uMG>?QkS~@vHyG(tScPt2+RjW|IL+sTR^p2MIBE5e2c_w0$XsL)(c0x( zapbDaqbGwOmzUK-SYjaQ< z^$d63wQMWDLP;b7ZWo@m4!9RGu5SK6hQkpeo3AQ=$p zw3&_QqDhmiIz(Fygh;it?MtTc((a?f0fN)6UAq>l&6*VqV1L{B6Y)4&(|UM#Ock$W zWM1tV|I>v$s!Fn$?XhZnqz}Ir9qy%Myey_@PJ>ySOUeKg3+WvX^+pF@UfV#lOjK6o zSJ$Jt8;SlQ77T9^WyN^fYN_k!v0|N0aT214Rhrf3I-0t9EFLi*~Fiva%C)AG|`8hn$KE zeG+DEJFoEi!Qp5srf?ZZd-`9h&Q0@7!VlDkk5dG9Yx=PK{;Hd2h`XLpiej4|%}JJ_ zld~#f&w2me3?^!IRaH-7dnX3M*yi{zc{5i}M+5Ps`W1Fe&ya?R%&2Q!F5VhFrM_?9 zty+DIQ=mt~D)Z}`{X`|CzqWaix34eL(Uww_& zcut`3;6_kbYyJ8eh~4hAe2oJi#%6?d)c|fguMQhm34+mQSYe7l#i*gD$4(Xtq+z!n z(=c+Vb4OzwgJFD-JrbFYB{RQ7CW{4i3XE-yg!GA*-<|aCSiLhNj_WwU7XVtNXKH$= zXXNtEY`7W%d1cl(IVnPHCN~lmR2m9J5k+B(@d5?~S303)dzQPhNvaLawao!oYMZdd z$kvwO9aP-eskAU$o47|&{$zAprec5xMD;va=26g+B+9A4!2OcnFd2^UI#E07Di6$p zrGzDFywOjXor{YOuL)y5iG-Nk{`#bkog)A^x-+cEeXApiG0=pKq@nX}Rw34>TN&ox zL195Zx(VX4y0$jcn+J!1W^fUak(o(N9EQe5hR8F4>ZOsaY)djKvHp;0HdJp zVr$1O6QSZZ>?X_=ZwLUFQ3JMy+J|;Hd^YozURqIU9tj;E^rY!z+c=yx$`G#YgN@v1 zb=LkGX@bnV`(#++#ibQqFjVmY>UC54NPxc(KP`uH73Q)mV&Pj7Gu(@BpvPjd3kNWa zP0vNGGVRe0X+lVo=|l0%3@EL-v-6{z`?K|$Oo;r8^n0^;J0-?S?I zFrF_Q)^{N)lGc(5h8Lwuf2;Wc!$g66MO(t5=7Ia4@P# zzg;Jf(%NZ!^jr7v2v}WG*vz-CHqCN>OsNjyeI&F3bZJD12&}YUhgTpYiE&t?_xj}8 zTfloVE6ZcRlF9{Yv^(8QOl&{&F3sf)=m%;7j|bt4-2OCl4CV-uSC=Uqd^0PEU;dW+ zCG1~F+~6n8IdB6Va$XrzdWu{VEYEUIDmXGgzDbbJL+iii_2tm!P-f zg=lxY%M+u7+d^hes4!v0Wk67aVa4Z~CvM+KfCDKESSi#JstF1eTNn%@G~oOw#vm(F z-MQ>=y?~IA5NWy!Ie+B9N6`S&4}+9DuqVyU%?R_z7p>E@dX7crwdaB;CtCPSe0NBL zc&Hi_w(1tsB?&xK;M~<_d3XdJRBpSbhjRV%alyx?GhJZ{P-y9MH{1Z8F>{6j&BW)Mgnf3|p|^ZkV6Y_vK=~9*JSLC=iK7k$1l~U(Rp3D- zvLA8e5Zea8fp$Y*c4vO8KmKi?o0XZ_%P67XS@_~!gtVG)HS#XV@82i6;C#ta=Py=? z+mdOIJQBb@Maqu^=3I1TuXv1)A&{CIqvjy(XfpEQne$tJU$nYu`ql zi_2M2|7s8t6?R!X`*b%2%pO=2FuCHjrca-avl$&lVecqd5bb&rGz$@gw@4@q8^kS{ zIh8SLbR#`^Gs&_?idBpaH}CyWfL|4dPbo6D8yLO05)w>iZUfw$TwMDlTf4eW1F5kK zLyxeQ_`v!C-_zz%S2&V-VQ8|z0fo$cS$XX9X}~zIU|#RHa6kWb2RL(P(H$VNP}`N9 s3A*U72W_%Pr^x;B9RA;YxpbUy+fBXTp_<}03V*HF+N60$-TdVL1E@E01ONa4 literal 0 HcmV?d00001 diff --git a/log/ResNet-34/log.txt b/log/ResNet-34/log.txt new file mode 100644 index 0000000..465ce6b --- /dev/null +++ b/log/ResNet-34/log.txt @@ -0,0 +1,178 @@ +********************begin training!******************** +Eopch: 1 train loss = 0.106633 +Eopch: 1 valuation loss = 0.036541, ACC = 0.990000 + Epoch: 1 model has been already save! +Training epoch: 1 completed. +Eopch: 2 train loss = 0.023560 +Eopch: 2 valuation loss = 0.029485, ACC = 0.991833 + Epoch: 2 model has been already save! +Training epoch: 2 completed. +Eopch: 3 train loss = 0.014101 +Eopch: 3 valuation loss = 0.026434, ACC = 0.993167 + Epoch: 3 model has been already save! +Training epoch: 3 completed. +Eopch: 4 train loss = 0.011178 +Eopch: 4 valuation loss = 0.033568, ACC = 0.990500 +Training epoch: 4 completed. +Eopch: 5 train loss = 0.011963 +Eopch: 5 valuation loss = 0.028671, ACC = 0.993167 +Training epoch: 5 completed. +Eopch: 6 train loss = 0.008491 +Eopch: 6 valuation loss = 0.021085, ACC = 0.995000 + Epoch: 6 model has been already save! +Training epoch: 6 completed. +Eopch: 7 train loss = 0.008863 +Eopch: 7 valuation loss = 0.023528, ACC = 0.994500 +Training epoch: 7 completed. +Eopch: 8 train loss = 0.007288 +Eopch: 8 valuation loss = 0.026976, ACC = 0.992500 +Training epoch: 8 completed. +Eopch: 9 train loss = 0.005660 +Eopch: 9 valuation loss = 0.043079, ACC = 0.987667 +Training epoch: 9 completed. +Eopch: 10 train loss = 0.005652 +Eopch: 10 valuation loss = 0.032521, ACC = 0.990833 +Training epoch: 10 completed. +Eopch: 11 train loss = 0.007269 +Eopch: 11 valuation loss = 0.022062, ACC = 0.994167 +Training epoch: 11 completed. +Eopch: 12 train loss = 0.004372 +Eopch: 12 valuation loss = 0.036558, ACC = 0.992167 +Training epoch: 12 completed. +Eopch: 13 train loss = 0.005474 +Eopch: 13 valuation loss = 0.023405, ACC = 0.993833 +Training epoch: 13 completed. +Eopch: 14 train loss = 0.003349 +Eopch: 14 valuation loss = 0.027550, ACC = 0.993833 +Training epoch: 14 completed. +Eopch: 15 train loss = 0.004899 +Eopch: 15 valuation loss = 0.023654, ACC = 0.994333 +Training epoch: 15 completed. +Eopch: 16 train loss = 0.003552 +Eopch: 16 valuation loss = 0.022079, ACC = 0.995000 +Training epoch: 16 completed. +Eopch: 17 train loss = 0.004018 +Eopch: 17 valuation loss = 0.021004, ACC = 0.995167 + Epoch: 17 model has been already save! +Training epoch: 17 completed. +Eopch: 18 train loss = 0.004144 +Eopch: 18 valuation loss = 0.017145, ACC = 0.996500 + Epoch: 18 model has been already save! +Training epoch: 18 completed. +Eopch: 19 train loss = 0.002511 +Eopch: 19 valuation loss = 0.022749, ACC = 0.995167 +Training epoch: 19 completed. +Eopch: 20 train loss = 0.002511 +Eopch: 20 valuation loss = 0.033824, ACC = 0.992500 +Training epoch: 20 completed. +Eopch: 21 train loss = 0.005574 +Eopch: 21 valuation loss = 0.018856, ACC = 0.996000 +Training epoch: 21 completed. +Eopch: 22 train loss = 0.002727 +Eopch: 22 valuation loss = 0.019269, ACC = 0.995167 +Training epoch: 22 completed. +Eopch: 23 train loss = 0.002617 +Eopch: 23 valuation loss = 0.023354, ACC = 0.995167 +Training epoch: 23 completed. +Eopch: 24 train loss = 0.002244 +Eopch: 24 valuation loss = 0.018489, ACC = 0.995167 +Training epoch: 24 completed. +Eopch: 25 train loss = 0.002358 +Eopch: 25 valuation loss = 0.019329, ACC = 0.995000 +Training epoch: 25 completed. +Eopch: 26 train loss = 0.003287 +Eopch: 26 valuation loss = 0.021972, ACC = 0.995167 +Training epoch: 26 completed. +Eopch: 27 train loss = 0.004589 +Eopch: 27 valuation loss = 0.024749, ACC = 0.993333 +Training epoch: 27 completed. +Eopch: 28 train loss = 0.000912 +Eopch: 28 valuation loss = 0.021506, ACC = 0.995500 +Training epoch: 28 completed. +Eopch: 29 train loss = 0.002646 +Eopch: 29 valuation loss = 0.021657, ACC = 0.995667 +Training epoch: 29 completed. +Eopch: 30 train loss = 0.001185 +Eopch: 30 valuation loss = 0.027115, ACC = 0.994667 +Training epoch: 30 completed. +Eopch: 31 train loss = 0.004833 +Eopch: 31 valuation loss = 0.021278, ACC = 0.995333 +Training epoch: 31 completed. +Eopch: 32 train loss = 0.001897 +Eopch: 32 valuation loss = 0.022322, ACC = 0.994500 +Training epoch: 32 completed. +Eopch: 33 train loss = 0.003539 +Eopch: 33 valuation loss = 0.018811, ACC = 0.995833 +Training epoch: 33 completed. +Eopch: 34 train loss = 0.000455 +Eopch: 34 valuation loss = 0.021088, ACC = 0.995833 +Training epoch: 34 completed. +Eopch: 35 train loss = 0.002819 +Eopch: 35 valuation loss = 0.027828, ACC = 0.993667 +Training epoch: 35 completed. +Eopch: 36 train loss = 0.002950 +Eopch: 36 valuation loss = 0.022639, ACC = 0.995000 +Training epoch: 36 completed. +Eopch: 37 train loss = 0.002021 +Eopch: 37 valuation loss = 0.014453, ACC = 0.996500 +Training epoch: 37 completed. +Eopch: 38 train loss = 0.000929 +Eopch: 38 valuation loss = 0.020020, ACC = 0.996167 +Training epoch: 38 completed. +Eopch: 39 train loss = 0.003934 +Eopch: 39 valuation loss = 0.021908, ACC = 0.995667 +Training epoch: 39 completed. +Eopch: 40 train loss = 0.002856 +Eopch: 40 valuation loss = 0.022252, ACC = 0.995333 +Training epoch: 40 completed. +Eopch: 41 train loss = 0.001765 +Eopch: 41 valuation loss = 0.019979, ACC = 0.995333 +Training epoch: 41 completed. +Eopch: 42 train loss = 0.002859 +Eopch: 42 valuation loss = 0.019498, ACC = 0.995500 +Training epoch: 42 completed. +Eopch: 43 train loss = 0.001988 +Eopch: 43 valuation loss = 0.020122, ACC = 0.995833 +Training epoch: 43 completed. +Eopch: 44 train loss = 0.001906 +Eopch: 44 valuation loss = 0.015427, ACC = 0.996000 +Training epoch: 44 completed. +Eopch: 45 train loss = 0.000617 +Eopch: 45 valuation loss = 0.023248, ACC = 0.995833 +Training epoch: 45 completed. +Eopch: 46 train loss = 0.002288 +Eopch: 46 valuation loss = 0.024685, ACC = 0.994333 +Training epoch: 46 completed. +Eopch: 47 train loss = 0.002961 +Eopch: 47 valuation loss = 0.029439, ACC = 0.994000 +Training epoch: 47 completed. +Eopch: 48 train loss = 0.002208 +Eopch: 48 valuation loss = 0.027046, ACC = 0.994833 +Training epoch: 48 completed. +Eopch: 49 train loss = 0.000916 +Eopch: 49 valuation loss = 0.020738, ACC = 0.996000 +Training epoch: 49 completed. +Eopch: 50 train loss = 0.000336 +Eopch: 50 valuation loss = 0.021995, ACC = 0.994667 +Training epoch: 50 completed. +Train Loss Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResNet-34/train_loss.png +Validation Accuracy Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResNet-34/val_acc.png +Train Loss: +0.10663288501016818,0.023560329713782524,0.014101241604837157,0.011177892464259832,0.011962616963440859,0.008491031523219514,0.008862906449553844,0.0072877223670445536,0.005659631196351981,0.0056515910035211045,0.007268956035215386,0.004371746765634369,0.005474027861992732,0.0033488901729209165,0.00489867321382667,0.0035519160211847887,0.0040176142923510315,0.0041439567963489805,0.0025106570646627733,0.0025113279428380718,0.005573548964850264,0.0027273199622795284,0.0026173889789269113,0.0022438014260685135,0.002357653659443726,0.003287135360845659,0.0045886932596953065,0.0009121494066920027,0.002645777721855446,0.001184784014578749,0.004833152677004954,0.0018972325255269696,0.0035389326350548506,0.00045536609219931846,0.002819422161926344,0.00295049640826091,0.0020209123628466595,0.0009293903282567419,0.003933526405523504,0.0028557769325891352,0.001764970838518952,0.0028587067907089748,0.001987672652010898,0.0019058688251576848,0.0006169046146797123,0.0022881885661847966,0.0029608939319989026,0.0022084624991614947,0.0009156058300263428,0.0003358277108293135 +Validation Accuracy: +0.99,0.9918333333333333,0.9931666666666666,0.9905,0.9931666666666666,0.995,0.9945,0.9925,0.9876666666666667,0.9908333333333333,0.9941666666666666,0.9921666666666666,0.9938333333333333,0.9938333333333333,0.9943333333333333,0.995,0.9951666666666666,0.9965,0.9951666666666666,0.9925,0.996,0.9951666666666666,0.9951666666666666,0.9951666666666666,0.995,0.9951666666666666,0.9933333333333333,0.9955,0.9956666666666667,0.9946666666666667,0.9953333333333333,0.9945,0.9958333333333333,0.9958333333333333,0.9936666666666667,0.995,0.9965,0.9961666666666666,0.9956666666666667,0.9953333333333333,0.9953333333333333,0.9955,0.9958333333333333,0.996,0.9958333333333333,0.9943333333333333,0.994,0.9948333333333333,0.996,0.9946666666666667 +=> loaded model checkpoint: /userhome/cs2/mingzeng/codes/kmnist/models/ResNet-34.pkl +******* begin testing!********* +Test Averaged Loss = 0.070697 +Test Averaged Accuracy = 0.986100 +Confusion Matrix Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResNet-34/confusion_matrix.png +Class: 0 Accuracy = 0.995800 Precision = 0.965953 Recall = 0.993000 f-score = 0.979290 +Class: 1 Accuracy = 0.998000 Precision = 0.994949 Recall = 0.985000 f-score = 0.989950 +Class: 2 Accuracy = 0.994100 Precision = 0.972864 Recall = 0.968000 f-score = 0.970426 +Class: 3 Accuracy = 0.998000 Precision = 0.984190 Recall = 0.996000 f-score = 0.990060 +Class: 4 Accuracy = 0.995000 Precision = 0.985685 Recall = 0.964000 f-score = 0.974722 +Class: 5 Accuracy = 0.996700 Precision = 0.993871 Recall = 0.973000 f-score = 0.983325 +Class: 6 Accuracy = 0.997700 Precision = 0.983185 Recall = 0.994000 f-score = 0.988563 +Class: 7 Accuracy = 0.999100 Precision = 0.994018 Recall = 0.997000 f-score = 0.995507 +Class: 8 Accuracy = 0.998800 Precision = 0.992032 Recall = 0.996000 f-score = 0.994012 +Class: 9 Accuracy = 0.999000 Precision = 0.995000 Recall = 0.995000 f-score = 0.995000 diff --git a/log/ResNet-34/train_loss.png b/log/ResNet-34/train_loss.png new file mode 100644 index 0000000000000000000000000000000000000000..29480ff4a3e57656509a21e01a3b0f016ba5fc92 GIT binary patch literal 18523 zcmeHvc|4Ti+wah#vbG_K6e)Yz$r6>2>`R0s+4n8G5-N$TW#6*z`x28RrR;_bgOIVr zWZ%cRo~hsO-OhR6_k7Md|D8UcKIVDmdG6)9@9TRl_xGA-YPS@q4>27=p-|L{H?C@+ zP~`q76xry3{cuOPYiI)gh`U_VbJ4Va;NouLWQI~PadEJ*cd@a&ciPR&$=TB0PLNNS zPmt%dg^P=WvjjiC?LQyjvv)G*Z{x1%hDHuL+_>$GLeZKa|H-mtvMf<3h0lstuV_6? zn8SJ8(RL-aEUcN;_3t}y_4eL_?o<^|goN}nKiE`YM65GydE)Q*s+d)N`+iKNN9dO3 zjB3xD&9)c%10vMX@!;0vqrliPr zb#y`Z^*a=CK%Zb}B0Uk(0IRKXq%Z*r^ zzJWt=dOSG_bxVG)EbaZJwP<#2twG6Z4(QkADr#9)1dWwZHVaJ;FhCwsfQPjD_vk6o zo<^Z8_8?a>dv78a4l?8^6r~&e#M`g;z@V|@WSx6%&w8S)k-@*;IpELod9sC00N#>> zw?2|SDI=fGz|=cq;QObS;M){l7+kpXf!Eid%@k@nl$09oOt@7YTV2!CXdj5bhZ-8j zP?IV1>;+-ZE5HzS(3>)phy<%o!gm|%ddEf$L}|mL-$Tds&~U2~uPpR&{pL}Wd0X7u z>T0n88xf9k=d7BaALksZDlS&5!nwuor(!(K%F3#ebh+lsP7KfOv?~GozgSgRb*5bL zB-F2AvsZX^jVZ&C6&d<^jLczStU2zoM`@;Na$!OO^V^IJi*K(lwsv&bJMFf%D!CGd z4STXw`#V$R2H@lFqu>S8QrqMAmZr%MxE?w6owtkffJ5PSwKiCp_GOM)o zlF#-+OS^5Xv2Tr>oLsrXj}H$j^78XhrMl2(H z80qLzK7PDvcK^Q6_oP>^QeT}_sND34jEp>4kMDk~Z0M=#UaC$3yQKVZ7=bJ*BS$mK zYI}V_Nl8h=$*FX*Juz>4+gs(ez!iOwb<30S_7cWrZgcvI-9MUwE8Gc21%`Dx zWMpJaLKfldmfabOwWE(Ims@0r>itGu1=8!Idwf<0%=K%W%<5Q;HUCcK0{LjQ<9NCK zh;-ZQi}Hbifl4=SsCjzU-ZM4jz|i{=JyhO1w~M!IIJ>!35_brG+#(`6gH`si?4iu! z8X@$&-I1!&Iax`bNIjn5AN-s4%$D+~{iCVK_MlLrXHlNnn{u7YC|J$4Gsp$o zT~9Dq{$8NnQ5A(0#h>_^1SG135tNZbEvvao`3JFS4@~dd3py>2+z63n4|?>EH_%1b zq+_tjIr|Xhdxn}y<)Npfyn;f>#-B!c7HC975!Nx+7bp|*}iUAnV!@R%gi&7K^vE7A1NW)Yf{Fyo#;84Fn3(veRVl+gbt(t;oZdm-Wfk4{`cYO)3}O98um6n|hf0MkL1fo$Yz zT9^)|fIO25=H`GjW=dKzq%j=^RNh?BFB-sYSdd!`$Su*mJSB7>n?LXIlD{HBvkxMP zmX zq5k=e{QJM_&xZ08!_x^!!8G2hK>IHT8$nj?f-0y4Ve6Q&v9bGQ15vMEXCLR*NpRWy z`8AgN;zf0lc$IpuwY~&#XQnwp=gFJYi~_QW60RCIZ@$R9`6li8^XHWkPjpH)OAe8+ z5)=zj2am=3D#_A@;^N{;ii%yYFPcUrCyz`JJv=;W$DVUXMMR_lI!i5lJnFgrBe>%J zmm_%T-OYaV^7?q#>>KkWbvwJl$&Tcr<`4#JY`Nu}t6tj&L>nAL(J$>}o!@OQeyznP zdAWcEP|BUF*;?*V74}-SdHeRQFxIa*a1`{^_8TcH!)1 zW@eVX+3K}(m4obx=1CrzcJ(VdK3j8DbIxjQS5L{puCoK!($m&{`|Fp@+ssUn?@4iS zx!=BhJ7*jT&}*^Vm9Ajj7AKrrSQx{e=ttaWZEttm-(Wjfc|l6bP}j)A>?HHG**`nK zZQKi`tfX|mYVbG<%kW*VhYzbkUUA+k0LGD9{k^@}85vh!y?RyqGsd7CkPK<=5|zBL zu(0RoBML+q$;G_U(b2j2`7T5w9i7P!uY|*#X-$k-lw^_In;Aw27q0t2A!OoT=xvRZrZU)i1vL$jRAR*sS%~PdwiG!;^y%R$i0y@0M=iiAT@f?0k7j5_$CJ zc7Uo5^dW(PNbdy) z{LeXT%K`SGbPuCc{Pq3eW+L2FV?~`nJdDD#s5pO27zu9H*idmO((p-7pbz{Rkpy|l z!9Rn4hO!`cbwzQLunqbVdQ{7HcOLOTh91FJBva;o1{y&?7%>GZ?`Kp=iVNoAXtu%L z5A}#ykfei=kS2QzZWmu9t?5%}fMouV2K>utPCFxQoP zG&kdk;&^@qfVv+BKDv(jjEHTN3mRJA)SDn!zxqhJ0pd_pBRO=GWS#5@%_$@~^1&#c zlrajC^gjfKapj@~yv?^)HZmKm7o!lY_;GliN$I(hlhg9YqpDLsf?4|_ zIFu8%w6wLSOTY5qZEH{qotI&We-rs_gFWW}1^RZ)aYa!6*xww@0xzx3nO@?m7K7)}@JB^hN|d zk6y~o)*82jL7)@{rjqTiq2oLfV-NXo2vm1qK2mCNgGua73~KP9z+&>X>GMC z-<|48uXxbLq^+x~93j2^Tb_fH(;5VFh?Z7YPVRA%AJJoAF6l&ZHc4Nb|C@FKTWK5L zus%kK^WBO9m9J`9??VXy=O14|{;pXPnzI}UoyFNevTXVX#G@k1&{UcCI z29N(}E8NAHXS&jZ5f>Yl66f@lH%{887QoTx!`e+{WlaZ%;-$k!kM>}jEHB27l(5}x zegCH=6cr_9WKofQ`fvmI4A<+uUC*336E`%{(V>c~b&X`dcKv#;-!5@+WyN*=C@X7G zTG|WKx@^63GWm$@m=Rml=-F~}U#!|c6OL1b3_aKsRE+#oVAuasmuQD9R zKUt16_{3c_4LU?g866&;vb^kKJ`nR?*@)-Qo^?SKgqShv6PK<^d9C(lC`K-SHw~-U zS`b#)DHtWWt%W6wu6buW@BJ$yK}$jS?tYblX!%BhV%=)|#n-{*GDspMR^V3#HQ(Gj zzzE|i3Hb(|?wu7i#Ip23oC`W5!ap@ zaK)rV@4l3qXt)vysu%ztC)N?DM-^N{3B9TfJUv7MEmuEAS)6IFdsKV?TCMhni$CCe z8Wr~xlT5;1z_DlFUxG7C0ubc+e?D>p3_Ie$%gzEeWkB%p2RX^%joyATc0euvKCl~4 zA?u+19$(lzAm0Bx7>&axs~!W&BKG4|E!(Kl z9~6X%JRvo93MTmh@$5%w{!O!gfNl?2=hcM2Ap;-G<^TzB5sUKi1j>4f?5_|H#~aTj z=jDE43;vS@L6reM-!P<2Vb|_PjHo`CvNJ6dvXf-UT;RK>MPb*74fKjpk71O^GMvZ* zG)IhKiYp9A(@#08sfbS?B#3^1B?Q zm!sjKCXz)0^uKRI5&~i?UVyKNWMImV!0mj%#vw{1j6|+TlPE-<2{k~X0K`B9kv<_N z{L^iUaJfzx7Sggj3A4dOefhJ{&rueajy*=qI`VonX`R8Od=U%wCm9uF+L!)eR0>Sv zP-517Jm6D)c~Eu%w47TfUu>HAHI`rZ-n}#%k$BIw`2un0$pnWHaLT^iE3x?Xy~NJ0 zVryXAYWIHhHO32EIV~zGs#2P&>MjytXOXmf*3{ zO>uuxwAr_?j{0xK4sLew02a0GghCiY)@|FIf24A}#&sNEw`;So{@0;H#PUkNu{Cjf z15BV%*9-f9pLF0dF);yLx9uI& zy>n+MJGST+1PPH_TN@iW232<0Vv~SM`;o%Dq6X~7yC!wXtah*o@glYw$a!$3o6L7lz!=F=j0T{ z`b~Ggg?vy18japh!<@Y`+iyLGt!Qr%?!S{B!r!20-CsBto{F0+ML^6s(!(G2tuRC$ zY7v>Y5_1>ETevS=P=tUZPSU+_acN1|FH%Zd9wFyRu+;nih#@SDd+Cqxsw165CZe{t5|D@0lT@2-ld(i zt4>X)X)LfIHdK&>Bi^yUPB9}7hiwJq5BE_}-5dz!6AwT18z#d}DopPdSl)!en+K)lD;M61w~Z47f^4ChsHbd~yK9`yOn@(Ol!b zq?p`79GqA614!}_831^kh|A%x1GuS%<02)PWUT?v{*h>wg2y3KtXqm$jdDmxKS_{( zPx9IPV*u~zNNzo1gtY#~2AxjOTPf*r#K_2zEQl_&nC*M`z&}Tb@E40_%6_L;z&zPgII&`1}NxyA>-As}jT0z6&} zLjcKUl~a**W}M#-*?;{Il!XS#?v>NSy#!~}AGCv*LId&p5ghE4gLc69LKnz0HZAmz zwEjpJd8(I$Wne)saFY^9+<>&tS!CfgeWd&NJvUCko1Gf}ri}!tKgdE4V5?<#O7;*0 z5B@Q5ag1c(5QBVy6djSqw_`7n$y1S4^)o0Btq{>lII(EI-d{an`FTA zH|LKkgBAY%yIO4@cDtN#Bf5{Cc?vm-K}Ev-*XW`!K9)}Th;oXuD<$U$y#yP znR{;@MltXk<$sCfwBB5vZvFB_9m0rDFHWRyZ*QMl>-hS$=eX{j)TpRq5OqvF<4o#o zqLs<#P>vB>yA8<`QZ{JgXKZV0t45JgeH5gA4C~w~`b}!Cg|`{wSLV+tZoV)=BuYi! zByibxW9sSQvqCoC1=^CN25WewrHxEmUTW*>>!Yo41dEGX32a{$@orJzx4hyjWuBSgV789gqITw zQvTphL)J;h%E~HW4o(MJzkP$03LFH4uChvbmiOh~PM?{XIcKbCWK=jaV-*+_ltfA+ zadC57|Ngx<_EUfRo^S0kDG^Q4r-ndpN2kFDkBN%<4CA_d`Ca4c@81r`j|&^3YO=Gl zzwB6BS!ro>FB=N7_-#9`{2I`VHS(N3`0uPuk4RekRK}B}E0?IILx#p)+JS%g+X0N& ztR7Fye)P$pE{nmOReP&C_WJd)5dCV0R3tYwU{iUt0bg#}qouDu<9+PdvGNHzY_HkE z;fRbEcSvqioxJ^i^w2$(J| zFH#i#@Dc(>M3AnITeFP+{;`|om6grU_enH|Lr>8*U}jftZi92_<1 zg*gkg`{Do8nC@i(QNWt}k5T6pHM4GEW{c2;`zYzpi;C)MX5NHgw&C2(*QaybELPW} z>;AK}6o12dT9WsoBClStERvK23!$Z@mC+R3c3ZjQUJ0}AvD)dHSD;%Fill=JB#=c8 zF3gjCDjPOuxc{f-rov|6QX-svAj!X#R_>xHNPs%b3xYES`)B&oNqa2_p_UdPH44E- zjj|zX`*M|@TX2i+8lb?bkH=h5QqftM!?~4`Lca#6PUU8IMNmXUGkT3hh(J1uTeZ&D zeXr&+D$Qft$H!&CAKQFdxrQ4|j~E&GsAZ-v3(55q3PnAPt8tKZ-`LVGwDo0A}GFSIh%y^ck_PV|%I2zVGL@TFdI%?n*tRpMq}5sq{T0GiyXJ@~+wVS) z5sMaSMYzX1MS|#WAf=N+!^(C9n_NT&PU)(%H@>-*bPbK%d-mR&pe3+Kw61AGpx##%Q|9w`_q z)qP{IHhtHlf_-}RaQ$*{$w3gzsV*0YA|Le4Rzb4O@a`nP+CqtTmb)MnA;#`FR`ZOqi>u=sWZjec${6^e|)zpz>a48-mnd@2CbuP0uL@JihcztK54R8J$mS&v`#5B522!}G zeI-L#B=-E+oP#Ka@>VRr+O-P9U@og(sZP&UpFAt(T2msIPkfc}#ni;*#*a(Ku^cI< z^bf+pSOQfpJL$9)pQWLd{ceBrJ8&`5!|FRvDgG~o%#&SK2 zSX3LDTk(;Oj8B~pIupGXFm+=;>iy*d{ua@`KOT9mtePyA+#uX1^bQqOy1GQdVVU4z zRF@UQDxol|h>2a%^G76Wbcv%XMHOh0P%s?1q9)z?KMPiv5lnWL;=NV$qtdH5*=O^! zNB3_E%l!DK<*_Wb_u<(uoKy@d_G{&KR}@_w!W|SH=9^d z`kLp8%P)%O;o?=q)$TnnQC@aWPU1r6WD#xueD|wdET#z=d*MeO%Zd#Z(KXKD>xxwj5WAT09j#IUU-$p+MY0CzT0wE0qx|!Ebcb&4 zfz!i&d-4?Jb@G~y+Q$K&KgL<3D{Pb5FLcgiQSi;D*C{lAnIsHmCfC2o7J@WF3X&0NK25850*H?E8X2ArW&>PyF<*S+xz4b2 zY-j*|n^5v#%XjAox&}BXY7xg+?qhZ{XgaBz(tdi`SZut1#f6)sdPKIl=7Z0XVWUIc zlDvaH=+F0qvbRCx_^EE^>6*=%ton$I(Obk?pR4??6&qjDuyp1nH(UkxS^HI<_i7>Y zDNVf*(JTI5_bA`5bT7>@rs&L9fF*N9Mcto1TymW~k`lOflzdRm@pd4(I_+kVa!mK~`N(4Sh)gi4BD*W{ZmVtG^^^xxerSOvMtAz*#ZtvueMu0wW&9vg=z54sfOgs-750SA&-9Wu}n|@tbk5clkJ>{ zzE^hV<0PeD(H%%J{JYsZr{~t`7_l6(yS465#?A(mn~of=kF3{JE_JtBE75L^=#_bw z_bI7IleK`m7o?@wjY}U}{{EV*bW>PnG@n;2TgGQI-D#0_CSu)TXY=u}($tAxtK-)8 ze!CK?=-rO%irocGrpvzVcBcv|t+ht%Ja^iiF&9Rc4?Gff3-;!ruZrchDqwYso~hZL zoC*49fqTt}gRtN-`QJ^MNJIdkWm{Vxou|c?xP#%ptTr+^=Pu29f2l{Sm#ameVpbtZxQ66k%6R+gBx}593sgj2)C0OzM}X&*8nB$KNhf_7w3?rK|hY zy>S{IGRUJ`o{AuFDAF2tt7`Jk7TGVY&g%N@>L}#?Vmi!_yAOR?*Dya6Zx+`aGbSI0 zzND($F!*-K)4gZA4h|A$ds1z&UfCMDyS8tiw_5Eg{M?w`KU;aYUZPyZpL29m1?6(k z_%uNcHpMu;sC!LPnFGVeCyus%ZKw2HeJ17iQ5s|4O2;tYt#*l1=ZkdkC}Sxv+sAem zX%r9E8q+st&Er?x+~&BavF0`8pLw7+UAEclw1=&BJ|tuCRtGJ`Eqa z@FCEIkxTr}TU#&M+0ibmXkAS9yVgR`>1avTmBCFHLYkL`CwH~xl8%mOzhB>6UsH(F zea7y3e2e8~^g=sbe7z~!x8`mIjdtJp3nEwjb}==F%4^n^1|v)uTZd{@mvn4;N>#j0 zUS7*nH=lWv*D;e4qlj?}wo9&38nGKGLM(A%eu2lvhm`==uMTpj-<|xsh#q0x=@?Is z>2t2MyJeEr zNaXw-^eL&7#@6x2J3o;X2+_gkrPaEj!6>>KDo`W5niyPkFI)6FM?rY42 zz4D;s6Ehc^)*WR2ng%)N@27%{wwyv9_^w7W2OS$MC@W-U&e{EnO_gu)Gz)48oGJNA z!F5q&;t8Q__eL}lw~1b=nw9miEv78r$jSZGb(gGlfrtLWxs0+8wfnQ3{gWm3t>-S4 zC~*3rv3~p4TSxlSs``HQ#*8&ZWFW5H=JAEi=JzZGEKWpE>Q>NQ0*2gfyX@-4K=*tjgN%KsJajw)xD? zLJ(W6c+Lh}aq)6CA_egDmp>j~I3@jVjt}sH#V9I$d4)R5=xVS__exWm>yEgTf%Zd| zq|m=G+IhsflF*-V&AMWCR2^Tr{`s_&=LX8K7wuIR(AULPH>|Ij;=x^MiGPG$JM?== zolwj-)03XJ=|;$Hkg{TRG%<{;|4{j9zVicS-=^{>RjglzQiM>cswh5DL;s9di!W|r zvr0nWR(B@Fq26t)SaW|+Xx&#A5DxO>!mBHr{q<*$Vc(9+-<|HV^4Tygk;bWI@QWUo z^fGA*$gj1YcvI{1&?f!ygj;ZG-EN8gS3hjtRQRpU4tYFxM&iq#TZt5|;|V<47%`#g z2;VrT+kSXw5YZc2(W$olQ>A(tGB~m2C`$R4T76S~vG$mu`FT&#i~dhIvrWRL96r2;oufHu3Lw@{oL%F}xwtg{qvL-8s$9#`qatbJ zH-#I9rwNOhtDfN>(Q1^J2vV*rUsT)ssG(( z#o}5l_gmxF#D%;cwk)rq*_k+WLGOI`Aw#ASdFK{R;Z5HYL8)2N>sNNS&l%H)z#dki z)s$oo8s4zWGTL;BEUR+1a48m{kIBK>O~p8yM!D^b>g@2kv0lzyb8>_Itew7*{ZrfR zR8D?ZmsK5C-YF)gqxoNtoS&Bm=wqMZdP> z24Wndf~zz zgUcVZ_!bN}%FZ65;rcvT^vb8!QfovY@gzfT;ki5Sn*VwuVu{Cp@%M)$+jRqa$7o6R zdT45tliP9?7rek^m_1g_?_3)kgs(%7o_tCCw<1LPRZa+b8MBfP{uGQ zh&5!p*b_N#Zn)V zF}t!BDK5QtOD_YBleno%^UTI+B(3{UJ=!fc24bH{Q%&{^EX(L@`?J2#XHuA|mK^IB zG0cU%peM75zY^O$(p~S#Nlr&R`ccc?5DX|=nQaqQ2Pp`hTf{5K!VE~Ra&zvcEE)Y$ zSy(BtfGu==^v(Sz_* zhc*@h`>u3CzNXQa!`fPsT1MLAzXC0v%|Fs9j zAXosO(A!StOHCYTp5YE=D_FNIlExKhH75G;#9&PZYSmbdVmG3a{k$cUcFyZkHndAx z(pqR1ZJFr48UDi8Nipj-JL*Wb3dYvO^j>)(%Mr|73E2u`f<+d~=c_$*WiAc#3knTH81_A(v-t!__xD?%BW_gB=Dx zC@kN%0eQ{8NOevoOe)`3dWOXU)24WF#`(E^y2?Ox-AzGhY>Is)re8Gl;9N$=cQuYr zLFcEx(X9{;Km3rxwQuVy5?f!YiaPH++pu41Epd;xtLD9OUtzBHar$nDKk+)<=o_l0 z&c;Qn$w^>{_u|G0>?U@MB=!5V9@m?ViDqk?JE5 z%hZi`v%tE|o ztfF$NGp$mbIf%tz5y|~rP^^6@>kiRy!|kRxQi>sAAu{7=aL!oiA(eIW%7(8p=UGF9 zY;V4sS9#`a*=}GmtRT#M!mJda6jMl=4nf6ZXced-Ti_YsiYnJPV%A+u87P zO5ME5bipg1TZRG+6Ao{Q@0KC_PUH_(mqafRDLrRjF5NTmVReQ)ge&22!O%NhFdtJb zY1k!`k{hv~f@g=9#)U5P^`4!Sbowi0|4E1~a`KFF>bYCY0zRz7I-1WSk>$$qK`mQR zsI~AS^Zh+0J;U7jFv;wi>)Z?hq?jgUR#DHR-Y6nYm6M$>$3Qz(xZ$2Sn0xISO>ip5 z$edP3_${UZ&isKQK`MDm*4^wHW828)Gxbhi#cMq+qLwyX_ff3O?tax#za9A4#&>hj zw#mh0cb?a7692o{`vW%Se(R3IHT#^UIL5I-E5nI3VO_ z>zUphoW~O1*l!^!~FX&zTFhxLR$5`tRH!l{H&fS2$ohM!7eI1J1yD z+nh_a%-n^lC0hVaW+5!oxFZ=6KB5F??e= z2JaUJyY>hzj5XL|U%m|2b%G#KLe;)#ASWGD5hC)TR$rL-P7mj3#2@H(uoGX??8zsk z>%pPGjv2l^HJ#Vq{kytT#MWTW_DIz%j^HL?9dmu}fXnP}BPJGx+%nJo6a4nmoPm3Ft#yLtV_eugJS?X-%FLW7wX7Z>52JY{RXP8CgDDOv$u!fYc2 zYww3ZN>>t>4i423);Anj==2-9Ck}fNq@;G1d!Q`i?e!OoUHNVc`^Y;rLK@oo#|ee8 zT9W-U$oSqa@bXt1s@wK|uwSb1guImMjK5CXu2byz>@@gJkm{5p6crds`C58c6EE3W z)D4dnK~TppDs1Gf?#?>Xa~m9K`ocn99V1b9q4ymMJ56<9k1FI_L1#T(mQje#7P?Cg zK(w&C`5<`sGd4A%iCTA~CD2t}uH!EC{0TVIY6NF%C^I#DwhB&P{&&y&coe&#(nL!~ zCo@cj_`RkB2#JN^;dH<*D84<1Y#L#DpZKD)+HQ#TxS;evNC9jv1#u%$`YUdHX!SS$ zrq$xx9m(YwD>wsL86i+&Aaf>q?z-2Qf)1y(sbE7h>gVIEgl~3<(Vc>Ny426Qe;#Zz_}+Wx4w@sZX!tK6XNCj^-I9{IYh5d5 zJ`iyydFUXh%oU-V=h9X}Px9ZCTY58TH!MS_6r~!^Fx*g?+g+OD_35ze$;>G)@7*|m z{=9|T7TJ3PI^t+ z;jRfHkYH!}3I-L|#JjwdA`sRLc2#=-4&|^BP!;xHlO?xzc2sLSo(&&`Qwf(Ja9&KJ z%_H?~ii(O#w{G@*eB9i#qcQNxWNw|E*HXQ}0e?O* zm#J{}3qYG9)l=%vKz$&v@#m)=xE!$ChbIqiN=0f(1q9USLz)W+hM>%^Uo##*cI@Gt ztrhktzRt(7VUXd^A6@JADicNtXb`41F+QGH_G607v!V9MRju9d4A*IApQ5Y+XV8`XNc^GV$%EF-p!fNw|o$){a z^pOaW{;?zm`v0Q#xN{Fu4dUqN2n207^zMCroLiz+F)I+(S_vtKfMMQ;QzGIPLGh-6j{lJ%YnXkcffe@rTmph^Ou>yAb{w;@goH=mj zm@eIaTE=JxHG9&;byNIO!fa(^Ow2jpkpL4zvdXamH|vhxO5~we4p6vy^~sV)yZcwD z;`;W#EEeig5v+$=5sRtLRL>QRZoD_KnZdXhA??ASwexg~bf6i&x?h*@QpsJHfKJ%| z@!8RpHqnvuP-FrXG)gKe7$`POKnBPuA)(Lz?)PJa838tIiUGYISXjTRkG~e~j1$o* zGAgKX##?<3XQQrkZom=p%i1+eOj4Ihe0MzY&Itq^fl>7!T2{N8uLV*YDLyelprObC7Ef?o;ma3;g3Lugh4JYt`|VVGle>&Q=K1rR2>~l*uLmBc-NRP zCM>Pe9XWFE9aOL+%!)Wob+V-!qkVyf5abB2GJ&F9q6#3m89bbPWBwvlK3HUf8DHl* zMYeqUKTi>=(Qz+@Ep-o9aXGG5eP2bLR{+%8{vt1Jd9Z6m}+3V!x?QOnl< z$3jvXt7!LZNwiAh)+tFhm#dMtxFLuFM@a&R(Lj4X{mglozOTr*Xky~Nww6{lQ1g%~ zT_KCk(}=9+q><7|_Yt2gOrEZ&=SxE6Rw&~|IRt$+*9b*Gf1aZCTMxMYk~s$|Z5vSmhq$5>8n8{j-5!s0o_=CixAh7@$B(qx-A8ky=9;Ikw3cQ0PN*gq(E>5`Ds#Aig{PeVd9foB=uYz$Rt;F&tl8+;L< zl3MPR_XNJ;3h2doKEA@nrG%wQbH*TG`H1)1xqab?wbBfROR+w75#FHS3d{@`)|oIBj2kX(=S2|Upi1~J^=|{ zveq)w9d_$kPR^FkU%p&qKf#aQE`1dlX$oZBCIPA@WalHBejMjMA`BbAe#mpS;5zV2 z)OB?F8hJ&tC|Dl8KR`nx62oio;21r<1nbYAKihqfa~ zf|74jShOG$hr&VZLY_Z$z|x%E?vl)|DUSjCdIQu!EP$#r>KeL6A7*5ndMD!4VDJ() zi;>ry@L3U&z5@pi2n11|oaBio>-PpeS5U_Fu)6u$uL3Wy8sr_9mtGt`eAq;1me!tPXeLn{rmSil{Ytk^84jL!OhIh+KBy% z7fM!PX?6-o2OY;j&VEMk9+o8eZd%%6%fHkV085E_wTR@%?OqrW6%y+83Ehi23MFFx zEzh1lb&!4>5CBDM320p{t*IA+?VSep^=}a~0A--N+jyA@sF=pV(msFrQU~9jC=6Ie-6j~+cjiZG$NWL9G^mfyG#inYI5Rd8LpqygYr4b-)S z4)jvHB-7=G#fYh~!_`g$6A6KAcEztTygk2vG-2K97t73KEp`wtB-RLTY5 z24x}z64Ou;qyuH5*TYx}o$OPIT{oI zDL7pp4Ioukj)%!q=BgkJV1x_^L7eVvUqR2Wfihtr{_gys^$XV;Ec2KxsG6CtBOtxN zw#@`~ml33$GBd9O#}5<8V3ZBp)d<`qq!3b7oS6ny&9<(8)wFn>iHV-{D zvzS_)JE6}sOezy^2;j0C z4DCl*&_F4{YoN(%1dPnN)ge`(NQntl=x9PUVuj1J2H;ZivQH4ziLvb(yX+b$=hwCc zvZ6NBTm=}_si$9`hBd&}IA*NY1 zdbY@K$2y=UoJ55|W%j;Rj@^&m-RSV^GbS!FNNrA~Z;plVmqY1(D2tRn7&K0w2SUJ0X5h^C3 z8*kpck!YyZdGH{Ue9s`yN<5S}%S=PXn~T>LnLjek-@TfuNDXVcLKvcou3!_b0gd6;C0w`6c?~LW z;uD;21=zYa=k)aStPGA2b>Ih5v@I;M!HZ)-{OpK~l$6sxuyAqW&Y1{V?BvNj8CWkS z!3QtEqe}JAie#c cTrBNAdWhENI$<>c&7l;p-MU&RclYuC0AC@aeE8Bw=@GtNP|)WQc8ED#Lyr}2ntAp2uOo; zoy~8(@BiERc-A@}4r{qwFwZ>m?7i>%-q(Fy*Pb^zS}Fv%w73ui5j;{=d<;QwBm}`0 zu`$749*oWY2LCioSa;fb||>O zU&q8G?(*`|C0p4*0Q#r!_%SIoL!{&8=9V@R4qlfWo0^IY34xylNKy(y|3VFpHMO*? zZv%nc%G~Ed-+|Ac>^2w#{*ML2&l?w9rY&)XopI)aD7Hu5*D?|N*;h850o;nZDhxYh z%z1kyPIg_+*UflpVeq#jgYr=(WIIn-4*E|x{#NDvCZ15km~RbYQKtGBe(^3c4^tSa zDCiVqMaG6xWx!5fW`}#SLF}`&NHa51I?cT_xv4fb>qK}+L!v|k#&mmS{)`Nn(&zji z-wv|1e>N<(i*DhpOcN_n-M+kod@}kfCkn=_6yNou0e^98A47*5dGv|G;HIlsAe%zh z0|BX$uN*%UPYF2DbHho?>m%{QbU6;gFmUZ$Uo#ZVa$ zPZ=ZBd(dH?-07{X_~E64@REjw1ccDZRvgL@!lt79)P<;91W#&q-?+;^Trn{g#;l7k z@puluR!=@jiy%w+js9B<4*PVe+fu)t@hO9&_&vZ3(K5+3&3sW0sv>BGN_@ zIBNtUwkR{KE%mmZ<^k%YEXyyk$tZFRMM$hjJI~+Pq=ZrX%7GkO{8SmYRH|Up<>sU~ zhP?eWGQIXLx3{{P8C!02x@=?=sL%_x=GnlbNcvefR~ZP!dNURF{?BnjHl6PM+bQT$ zY;d;KA`SmDHLrS7OZ++y*$)wVe1&kMD`~Pbx*#iM^L&=VbPkSIysu0+Y_VuA$LQBLihhmQ;--Kn9)Vr?RXwvs~sa6Bii z0GI~dJm@1QE6leXmusK)_osCT$-2au#~@*kWP=s&(^}rO`y#oM5`Av=x5^=nmo)H7 z#JG$2p#>ORe)r=_&TE%z;nZNRB!-8Z2G@F=*Dz>spur@h$IjA<7|T~# z8P}HC{wGO6LD96DVw{ke7<_u_9YZ6GDdDw+y?&xhUYrK|Xv=N`NnwLsEtTp>!Kek3 z!`^srEIzqI@F7=}NrCe2dTNUKi1-1tq^pOsa`5&WOy7(a^7&?^%FCC<{u(b{ zFz_}1Cbhj!>whr7G1chIq+ezbJTWnW2pUv0v$=1)DniS$i3KTahcRQ7zMm2gWl8yR zcXaN}WQpqtn=$2HSgguvttPtcokx!iZaAKMj44q|c=76&8?i6wKYt#xS@DH%FqMDT zf%Vyb*=x*?LY!9y!@|@CLv@?izKg69rod$Z*Sw9cRp_>q@_hR&imp) zds7ziOBv2>8-g=aS-ri=A3uJ~$<2ig3=BxgAUWl?B<3P%MjVxsv|^cpWc21$tQrgi zFHf>_=Nx)^Z(co&5+dvLFHo!)qU4FXxp)G?r0sf}{C8oG-@m3_5i-8B6)Iah;T7k` zn4TZ3ZlV4KD z7&>72_n4Z58IHnY@<;Bk^sV&LIV)yM2e@3Ht*s*-YJBesn}E&Y6;v@V81KCi(IvQS&Q(?3?$SEX?mf zi)Z|C%tOub$TJ6LitEd1Sf(&_R{*!kw4q77UPhb{9i6fn0c3Ayhp0A4$jaJw^Yii& zDJdzLmA3v>_tN|5$FpjSK0?)`*V{9V7Ds=72&8Ki6&EMQ8#cSK6EaETm;Q)KN+NB! zxxOeW^Vqr)^V*^Ys~6T0inUTZBHcCH?7qKgdh>ZaP~HYCq>7qa)TN$|4l%M+mfSb) zD(F$xj=Kgrjuv!U$xygAOQllvFEl{TC-CoFW@=FK*9sowT=QWPOSiq=l;3BP3_|6T z%*As`*1#(-t{vkqX#5V6Q7=dQ!}tj^aZXVA^VC_8Yy{%~#n?v@_}^Wa`d zWh);1e$B3)f;j)T(R8{TojYbLVE>bM*0qlTf|AuRAn)U88$s_q1U`c}xluXg;o3+a z$QP~!B1pfK!lEy(=dHH%o5K+qrjxl>Oo2zGrp>%cuj*WusjzWSi(Fb`k?Mi)1u5-b$htks1 zagx=3aLqh>@nVjPhjabNx6GuK_TIgFVy?^RAft5*3?e^%WV5ukhC?+qHC1L^nAG@~ znUX%FScLSl=H|Cm==a*dw{Jb*rRN{MG&MC1E|Wo#(a{QSZZ(5cy7x&PO`_(|k$J>y z1~jN}{h7ylr2`?maoKoZ?R9onwIOWiM1wm-zg+R9p;Vic5ncZiyw8;RH0mN-9gd}q zw>uoB<=w@(3y$T5c#U4ER7$yAXntNlZezr$_k@GxhO(_^!_&z@-y-S56rn{-ww9M5%LXcNfz<_(rY z5G>+Zn>t@JIu;S5$HVK0l?|~*;UM$w$I|=Z7l~Z|I@{1M`&r`` ze>^ko`TL`^I_M@)@^Y_>lZWRv9bi)_FPy(@i=3S*L=Y~OH_mr1bwyM=&WH@93uE~m zZ*_oB<6=6Td$nM8b$)c4Pa@9qF{PDlv>+7YrO%7}hq{PlV#*eC9o}i;*U4`Y1-b=hHY$pwg1k7&;6`f|Gw+7w}Hp?Hj{a+&4{w2M(X!kL0Dt25t}mX&!Jrqmno8dRHO5Hd<)$0~<%Jt;s8rrhJ?<$V|b(%!yn zxhL9nYf6yq?p^cV7#f2wUghfX?Rb1m&~EDqBPHMSNUOn=+@d1v5y?Y*r?#^{Isu!C zW@9g4kvf?fF<-+)wkjREPdoo1+uGbA@0tdTK6`xXgw>0ThhbWJ3K=Ri87)txh6i`f z*9M;z(ix4J*U9ET&I$T#vu!%Nkx|k&()-6^?qv0JvzOI-Wf%|5J0;kCU4x7<(5A3O zd*}S{!hAzJf>o(<<3R9{IKj35itVMPfe2;ALM)n|X4+Bbr1vSun%RyOS}R{BPWTHg z8d=oOSNBKTZHjEnh`FMDk$eK%+^btDV-fZr4`7@Lb z0=}Sc?a>4seGxh9;^+A;@%tP&ru0RGYIhITbyxW3fhrpcuGvEybi{BTYSH z109#osSrM)3o&O(5S}n;j{aFA((`5&Z9jf{b;@Ip*DKo&)qnX;Hq#vSF5A9iMBh>F zNe0c!eOgJ^E7c+D4WHf4;x2cAh9KJ8(If@f>=kMgvG52IipCK+pG5uoqUBea7SL#Z+%lK&I&TKN+yvTfi+ zAbZdB0q#Y}UM(ve)!}qp;aeJ{47ZNmz(a{JuqCDO>RJvUH5dUMf5l|yq9gH{Ny5U%9Fk9T-UJ5i z;MejKMJKuh8;J-Rmf1Em7B%f$wi$dmVr!Ey!BhA`gX4PXNj}j1efJFGug;SI>_(3Ts1S(+6rKBhv_b((+%r z@t(77`UdJyk>g_U@v8M@EPUuT^Me?)WKw3}DBUwIxvQK)X68)UR28 z^;{3*4kYQ{ONqk&mk)JMFK)4EFeRcfyVW2U7|VO`G91W!?a)>!7 z#;-=SV;N&X{S?0oH;vKG&y(1eBBjv8nzR>%Jc*u5V?%pO72{$i7T8mV=gXNLkr=!0 zn%u0XI*RfJKUV&xtPBN$4JRSezr%Qe0~^v>mlT+MDJJW325AIF|+C` zf*c7__3jI9v_=_~r%++LjsAo>wwC5`3s7-Mh#p+&q(GjCxW!D%0ilE$+1| zO;b%S=q0wn?n<4K0k2o%I$>EAa#_FamF_I3I=rGlgvl6F{qOqUjC3FD`bE z(k($d9c8s>;EXjsVcnU3^DE4>wV46*XSUk`J1n`L5v=aBha;N4A(Te-+q0!~XpIY7 zMYb@q*AOw3vLHapj%M;##k|bD?{51F)QNw$Q-{OBm#pS*D<%?hyC- zmv0&6GnrbNo2futTnv{5E3~~!{t+z5AsVS`#e%mrFs~aXPx4Nln4}541m2Y2R_rTz zRGSDnuuh0D5o(%!uZcfiQuP$BNm|}p%Z~rsAU4Rv;!axujoKwwUA)W3h+&qtP06xf zQf(r>?GIU729~kSX~cBGnB&+HDo|QBWjyG4&61Io^8X6xxPEC+UM}SaG;Ji87Zo`x z$$e(rot1#$*^pawqVPE>8eDyBXweoP_t3fFQ4Hb;Lr+9Qc~2S^R5#(b6z0JurEeaXT%C)t;iS3C!K8iG zg?z;2YqUbmd>S^HhOH29d!kr>e0Ua*CToJ^38tIT5VJ7VBD4l*@3yh;2q1NBC}z0X z&HfVdGrJ$N@2Bu&}z|nk8i9v`tBGOZY=SsLnItHl=w=m`^ACm8uF&{51S`?1F!jRtjX z^ODlU7K*lsFL-0ua!7xqCLD}eh3Ao$MnJv?hKF%|ukq#RGfxa#~H1&&~?!93QfPm7%Qz?`7LwK{C zJ7**gckVFso>zZM%JJf%TPlqH&pTL57zI+bc5cb*O8=QBTWM|tiY$y+%Z;E9ETzb(7{s-w%F03zWRjUzlaQGTTe8R#xVQRWBHXgXioI^PHh z$zZ5itr1BLGu=ONWkU4d(owLBtYcRn_4%CbHAUjkF?e)Ux)j z9nOT*lD>UfG;X?Im%rA$w#r~hmNOy==~HK+3Y?N2s|bJhkC>q2TIpz1-@hp!+&Df} zuzA0iIzp7!V=D*B$xXnvz^oXbjIHK}R>3N57Q6WDuEbhR>U5^lME9LY+{LlE7Lyza zLY)z?VH~-&S-YYOmey`Pj~;ggM3{2&f9DFENy?*dW*NR?_OoV+Sm#ow_2a66MbyRzff5!9+0#Jo z?__miHUj2-1LDsmnY#)nqb%c~(L=i}JUn5Yz#Z}ihRP#x$lreR5rn$}4CCG*+7s8CW+N#>PGy3#t*5XU3K52C1C^C$>qQSwM&~tOvN??o+l6CvOiuaHw7Gzn<5dU)0mv_WMM^ z7j#n4msjr7bWfG~k2OIr5iIa*C2o}1Oyh&!0%`>>hO^o-y zM^T|;<~>>$bavWokCM<(c-vuYbge}d(k2$jF>@sl!~svFOe-i4;8o}j4F;|)z53-} zAprrmBu~|tCtc>rE;S;j$*4SxAx=>;S#eUP`-a%0v;Zdi3a;^LRWqrhdSKKzlRwB4 z;vTQm9~_MInut(z>%z8tj)|)De)vjII@$P0#Z6M@jC1aTHehvT*hfsayV{d^pfJGU z;lOo#sDw`i#@%T2R^>2B{$q{DljOKzl4N32tV^^;nqt;Oiz~ocpT=QvZ{p`xlzRzeZ#HT^A+2n1$SnAD&BpBx;kHJw6eWinRxJsZ9`ee-4qYa;ya>&a|S65v6y!!hJ zq+@wgKrqsgBmIVRShM%~tFpgO5iJPKg_V`74F()BA>`WDw;WH}+8! zI{GYL$&*;Eqy|Ge!`kDlQn_oC(mh(qDE-pzphITRa1~M63BIqk>|&JMYnOqlG^h90 zQj3*8DF*qCKKo%(mMNhg{B3|AyO})qMXJx2M9ED;QQ75z5MAYqU|R<6*BGo zka08vG_}4o|Me>HUmUu_;B4N4MRL5M?9L5doKEcP8Fnht(Fkn~v!>w*>74Hyvw9khya= zlu(okCf0igD16HWtW|0aRwFVCVz_TH{U${#c+%gvLW}Oux-Htu^!N+bgejnkVlKwG*w_ls0%6VP2fO5L5*I;c=Mqp;f~W2>QzGkoF1( zT#oh$ZYydW(1zJY8H`Qkql5i@)J}fjS{R}qY)3l2@3A_*`&~*8EqW{%&<0m_{hQi% zB7bl%*`gmoTbj6A7OJNnovbBVCzp$S#=1t&fMtL~?*K|mUK+SMxiZe| z&!MRWh=`B9_IsWDeaq<&*Zk@xhv^I-9YX@>>Zp#Sl$;*CcKYZwCvm0$@dtsDKalnx zW@13p#oh9~GE>pUCKrUs1dg0vHBjw4*^xJ*j@Wf}>~(gZW@jo|hA*rTkCHCA;jqQW zIJ0^Wjyb;Y0HIS5gk$(xGv9)V;6!Zz@*1_@6jC=VkwmYVSI=S0m|3uCRK!M!C3q7&F^#|3^@5(@q9S- zmWi?EDZn4-Tsy{pCSZB@SBi!GdntR?AwlgyX1l5;zZdpk~D8IZSsKR zv%<8~g%NZrIzQzGhg@b}U!S&{nnqP$Juf8rr+(5(Tu}WjhVtUUo;7}Ety%Mc>S)R6 zly%BlsaM^6+oj*tR9jX`F+W`?CnWN0F_2?sYg>~e{SDB zakeBv zFE{%6mQ8uyj0c^L`F}z@j2HFwHx-lzKVYcGPRdpuwXx$OjC&cLO&tljoHbKANCXms z26`^7Eo)~)HRdgkkf>)Z{+2V?s(nwgj?Hikc)vxDS&LMoDO z>xpdA@`T)s%MMq0E}6oV)SY=)<|0h75vZaU@77Gv}MA^;%IADY{$(UXf&!~}&@e$WU_jEOed%e@-7 z*aWUH83=(+#uLq`2J*Ho82MmM+s+MHw=@cN1ghdox8-&7*+v} z85BGa6_<95gn+tYN%%}jW)wSrWID@~6A<}(eo?w%)A!@&>D4t(Vyz$bfIY*GRl7sw z=^YB1KG0jidmloV%d+*e7$4x;b!TZ0xrvNCZLjH~wXP{kUw@|DJ%&Zy9Zjsd8DU~U zGuU~8Ioghk05B03q?_IQnN^c`q~o8z)wP)2#mQ;Xd7H;HU581D>C{-EVXkO zF_@bN0=3Omf0mLso9F_r%M!cr5Y;GRTMcpUg8 zl-hVeK4B3+BiNW-U4Oe`H|ekFj5G{`WHoUSwJuXJA9z0DL9z^9qY{coJ zVe(5)v6s(;_Nr^ckud@vD|(9c*0aqj(FymYQ7_zXWOi|7Uz-=QFUs0*3*l&h zG63SOlCZ-Q4(aJ9?9UE#7yTO=am(-G;R-Liwx!aQU9H*+ud4zw3LCUQKt)Vo^Oh=6 zvxMB?Iz{8IOgRI~)K^|zXHr3&#DgHDVdoO?3RY8GIeI0|M{)}@w1b$|7=^@^x7^f- z3Lv);gfPEhVaMrp17pOnAxb<{cfM4VP4-74 z3-k|A9o$GXa*%GJAqlWq*BV01d<-BYG>>*uDF6WL0n&~6>+rPn+{s{7>%+Jq?T%H2 z8bmTuZwQV#ge9Tf#SL3IFC4gdCC@e9r!UZIg+skpPN88@N>hG!4E@&)K0-c|mi7Es z4G&N~ln10|)*IFQ1`ao{Ux6n|E#X9%`Gyx%B1&M`?o9v{roctDHGV8yF$yz4`(5k*o zRbpm=Vh-2=)mlZx?o9@LXy6Yj=&{ckKmCdRnH4Z~=|N1&U#6pi4RNTaClA?0!JlUG z>)oO$A0ASoe)8mDcaJZ?*6@`xUphnpzIwnKQy>i!kRL#ZobWT8UZ5C5_(x@L7c1WM zU2S1d-@lzCtpT7K3>t&USXqW`2pF=pT>hwb$ug>R%_$)Plx?mam!Pof%DnHgJxSy& zW`U?BB&>)zdPwvjj#41F)V6h0n&fw8$8m2!*OY(d@oU1Jw~>J4tjj1rJx+5pmdnAD zg;Yo)3mp-JQzN-SzLKQycVTQNfQ9O>#aa(Gz^o{8?g=ir3%JSZ&}(8p9qkF)QS;xb zO$gxi34F`;XgK`era;|Q2akgvY++tM{O{di?BhPa;{cN<;nrd0xiwR0>y9L<&d=d| zGLy@$(YPvjX{gx0UvdN(hWF>o1bs}TOmbbEuq=!l+=n2-=YKTGTPOUc@Qrp9`f-@= z2nqmij^gp|W#CIy^}XF!PnFH-Yr0j%C_~UO&}0vc%R3?gBEZEMZB!Vkc!MVF64?F} z49?r}a9<$H>|d<`yTcS$O3-1K7o%ZdL-3o4N6hp6+O-%+pqoFeohH+TlE{!(LGo%X z$|fwWMVv3B`JE%J7WoOEb~#jkt$l`;4%3Kw!rC#9e_n@`jB;a=r$oM1WVV4v6OjhL z*J5j60S&ttHI%fVc+4+qI9a^;MLsv-1YR-cOX?Yp+7f>A;8ul6@Adk){vb_gqWu#^ zP$c`J5r1!oVR=$ZJLj(%6ok~um-njK7We*4B%tGtl=$pbcW?968oQFTR3PAmZGWge zooLJhu+<0sc`#9jrR@G~m4I zge#k=N%I{G__u(Jxp;JsL*CoEweS4<69}1}?-oE?PR>d5CeGC*uPUG8o3gNC*%Vz0 z64nn(T7w}Fy(mwQo8Q5lFAm6_&wX2O-08Oe(c~?Q38{<>gQ6A~kKE*(?2;$0?TPBL z*GU{t7t(qyTCx;4OZmzL^5Xn(5|bQ@wiq{W4OLsTph)fty^A$1uEY|C{Um z1|LeX1kLC%!Sin!B1|2Nl7z$K0Z4*ltM6j%sYpFY%%8oF!u}yR8V2>u_2zn##h`8#M`d{KqjlgJh~EE7L?>?FJtoc6UIdT zZmhVS3%U=Xasi}cQ`Wt|oVCOMPWXn6oP3Z2T3g`8KJ^U$z1hMQPj(jt{&e_q8LF80 zBS#Y3fZqZ<3x@xiOX2y6Jq1=3MohxWl9*Wa1XT<#TXX7%9HsCph3@Lwwi7gR_fPyx zq-j?{tQ{{j8B;W#XuszFu;1Y}xWnHVtjjydmm(?Q}!D8(QEou0ubkLaPFjcLFJ z0>^eBQOzN@zKf78$S?L5IG%r_&zdYn9=t`gM=gF3Q-pr($uGO zY?Q;vX=jZ%9?~XN1AZcN(`qv>r)9 z-wOE=LP{`IAlx3S@_vpnT z2b!&|=*kz1HOS@0w9X7O;c~g0@%<6mdLi%I;Lb)`2 zVCE%r9)X5)2^>?BA3aeR38P1&igokjL^p$}p8jUrOrS0d@8MpuEPs`ZGNmUTY&Rs) z{iyr_?D&Bs&Vf=CNU=@(5xD+@J53Xhe(CEIb#1iBvQ<-51UJHm2=N=eI#_4Xj9*p| zlaD_n6{#Rc9^#PqMPK_|s+9yrEFP^AY|qYuFo z_Nd>n8+CXyLX48*uYr=feqT+aO8Z^zIzYLX%iE*^i~Z1tzqu>|DXq1w^pMN9qL#?b zklk%Dv1SqvemqF(CFZ?HNtyfRn4{7JAFB^<{6GOxFh{3PMRv(=p<42JtRHGa^1-^zI-~T014! zZ-tE*^^yo73PZez#@RmBJeuZ`dNy!k9j7mlC5-$ZAm3u7nS)9fm!vbQCtYBWi4c{{ zoNgO?dBdc{1juV;p`)eHB2p4`A>KLp^M)@5)bbfM0xpe0Yx<~Efy z5yepQF#2CaIH`C!NxM;kb_0lgt=#aB{b0rX|4iMi&CTCUgq1njseG9x-8+o3<`3|} zzZ?BJI_kS%n}F18;rORAwwVkc?Qc0f2bJh?&Zc>eqt7VM7|Ay!SPZXUwraRd)2w99nj{p&mKc?2w?X0`wdL3 z72I%(vFQS@mey|~tY;lOm=R|VpVv0TnlKA-7w4PmCZDjod?%5mWvmG!&f8KMbNMrCNc6t zKH;1)-#<$X&L^ZnpY{feuko9sX;a{WZBYEbqYQ`|G@t($dw ze%d#$1CZG}<((Bz2u=%IkJQ8_XkxrKXWl{%em5+w`E7;cvc4mO+D=YF`4qtZRJu=} zq65b=u=1G!GwvMYO(!0IPE!*#aJ(vn<9FOTaoySnEIVtg1_fLeW!F8#V;H4qvjfjx zrj9iFpSf|8WYx&jT4RZj@@Pu#meghlG^821uo7%G|m-^0#+2JO6Er>+0+OIX=}Z)i+z} z3^Vxb!F9Yd7v0AcL=VQ+6~Pq9$yDpTZ{G6nySg{9>{dH3NDpO6P)(W!NvWx;OGqu8 zECj`;ng-J3E63vgI+>ya>DuX#oTJB;xUJyDn)C-2&CLE>Z@|9sgCdZr!7k=n26!bhqu@6Pdw3Xra%0$a78F=J%mES&|Ge=PAq^c7&!OB z2HhWsimv?0aPH-)Yze8m?__xtK6UsmyCC9g#9ysuDug00Oyd1%_-F3s9^2m149Dqq zpC_0~nx7n$L++v3HmJGfMo1jBdf-Op=yR=Ruf`3cST0flTTw2))o#Zc!U-q&G1frh z(g3+|rXpE?e$OWCLk^)oebBHp5PyP(IC6F-{<)y0oKFHw@TiI6Coi0`#XY$j9Hy6# z1-G}iM>n3!cmijv!%PFg-M7Uv7H!w(_Ng;}zGXz%S2SZjd*PX^vNkh69|wHlsg(wv zUrmnI)q!T%59$AG^fVI3t3TIQ;d!fr_0idxrw=g2Kq|cS@Gwy|onbPnw^>>IYwY`q zLSS(UYpR;KyZUmF_swWE_HV^<*tRv#ay@r;Lz0dyTX*sPtmc|R>0|lJ&wsAl*QtI2 zn@#~HWi-b0^fX_ijg3uyNy*o3ivrDz@Moq06kse!wntNK0TTL^CIoK>UcY}&dzO~W z^K5xI<6^fx9ylt`e14W2gD^aW0bljWsedZJY23SaKB5yJHgFMNhJ=AJwPF#(hcr{Vg7x{$wc}&dp z&op&KkTonav>y_w&lsDqPD%}W2mHR`sNb zRprzFt-1QENZi)6O?J;>N2s93x{htV(NMMw)2n%(LjNEipNEXT>vvj?ei~;9h0Pf@ zx!j?prTtxbaIjZTfFo+^2jOyml4p#k7?vjn-jutA#ptQz+!&03XcE^7* zMezdHyRfiu?&D%{tBaD-QVA;zj8>EQAEnRoKYhaB(8`oGG^D}B!%KYk?)A`+1|+Mm zPbuOs1%-AX-*RX#qk_>?H8d1;bx9yZUmQbGMMZCu>q?G(X|8_h{%We}a`W1UP^0qj zp5-=TW^OI{@nLtV4rnSEPWtjZ&q}Go&EW|Q!*^57CPt1m(C|tyyr+Nc) zYCJl?@Gzu!@6Yp5ezr;)+YfaiZ^GCSBv8t$(}#c=evXOcbIZh)BXq$O(4Bf6Ab!Cb zFBxL&+)A<17vJ|9hiva)kUzhy4Bvfi7z1JK1$DZm^(5Z%$qw9Gp`L`(f9UA1{pysj8`UMv|~TA7;IC2S!6f zljT%6j1LI1N~B zUCMfqtEn-%xdn_1PcI}F1+1aXEp?QFTapWCu1b@XQab4z@8VTFZ!VsZavk3J z{1N+&E6Kj;C`TTR$1@lrRI2Ic%XgzM^MnUCZ~(y%n(teA(5k)xsse`KG4n9_j=Q4_ zZ=k)8S0}21sX=c#S$r1)I32pJPvAjlvW57FSIG+*?hnU`ERMcnvY&dG= z0p`SgI!B6Osg@0@kDIQ2>|t%0ZruqdkVrF;H^X3iYa`qs0ZsPehG0x5Q!__mvx&`Q zXw@{Y3iQz?WJ+`xf#gUiPD2(PXaEHLcQSb<&PaTOQ43|jlOynSZ4@g3-W6mI8a^$M z1U%WhZc&GOrzAewf|Wg}>#|E0KXN4_Xz+8#s9NMiuf{?iGa`LJz4>UNcORTZT3ECwaX-QP2~Zgs ztZ399psL}Q>EFR%Rvt8?t} zq*W4m#2?ehWFtGl!DAmKUf0!6e{*%uZdO?1L-C9;M~ksPD(KaSzW=3hAY^NXkAXBN zD*R$ST3v@;rpJ?LUwO$VG1tdb5thTw5GWDMAQ^HG&nZkv>@W9`Edc+JRESL!q;>kvGCV}26zCN9|$ga-OiO3R4+qJooG97SK z%S_=6*v9bAyL;>l#tO1@bm#HIIWRHbEHR*u-Kx;k{tf#7+~JMYShv`N@9@(C#tJpW z7@EMhV(l#cwZ{wQ@3MJ*3L$BZrH~AL9S_8}D>Wv1m1~2+cJYrk`SL`P4@Rbo4(JO; zve2Ew@qe)L`NLQ6h|1q0?E6kaNE38q-}ZDn&=gf8qhk>&kxxoz<3im(RF}mhJ|e;m z>tx642WEdIAu#FHvT;cFKfCCJrVmAfy)vUu^ajk)pO;Y}vGYiqe!XPMu?Vtq05;g+ z9$k7g6~#ng_Vg($6H$dB(u9%2gVW&XM$)$4zz58a)nf4B^n9ASd{P5mERH@^kXOue z)ms^Z;ABG-0`LkJZWu1gIl{;W(XYaqjzH#v1?=oZiU@Eq)I1FsZy8km5IJ^tRK6g? zdUOZZ!94iX<>S+-26SQ)CGOh?b@L0aV7^xcLs;igy4ta`;T&L>>kwZ|h*Q@6tLLBd zd7mgrl7A2JnpbpXk=EP+Hxd~0r5}kGO{2e$WT6gHNK|2agzHUa3yy+|9l`%O!6Rho z4NC}VWcj&+*&iiE%bI)QOo0~ekP&5&4rh6OpBascvS8)-P1uq&1xub&kNyD5Ss6Gl zV(q8muHA*JkH<}#E64e{1Hi6(xJ1}xETgDdd zM9d_*bqHuG+OV9I@vKivazE(eGOz;iAed6V75%vl@jb--tb+I6Pm=vvdi@|csJ%b> zY$*i(zjrY_oQXM-dAgYVt#8b!;q>S^ivU@$QO+BDgjE1tnLZb1XHSI>fmhB9#gN6RnQj0y?P+lc8dYeObxMK}zxk{`qJd!lN! z{02fkRe*bha9lv_kKA}+eJ+^9F84#FXDna;;LfeSC&Cd7f&~u~**XQF$Jxm6J#qg# zLK5a(K{>^GYQNcLXa%xy+k>`<`eK` z4oaNhCTZ@NRlV_L?*Vg|d8dg+mbvZ%qT8ZD)$#p$=lwXD7pNuU%ei`e$3dSo3ITPY zKT{t&SwdVG_wA>cqGBRn!$Q4pGg@*SevZs7M@B+<(bn?s_2QA|;Gr9OhK%nJB+u1l0t+b?RdstN`uC8I>o5Q!Wv zubI&}MY(tGpiRBd6B0%O2|;!W9v;bgAP8-JI+nP11K%sH5t+hZmgmfnn|w8niMdzC zZTJtegw}uIf6k4-wFlpUA#;0om>;Yk@^g8-qJpVoNfgQmiBp(Fz9H>kX$;;l$7w1o zu`K;D2DgULWY26uffFt=?{<0BcwySKnK-C+7UKOWgg%4@jF^bJemTV|@9#xTUjmTH zv5vs7ldI1i@{?ZTeI)ndFKdf<3=#0v3W4~L-<@0j(&jx30#*~)UM2Q#2VkE%GNKUL z((CQm!mzDZI{|h-qLIOwptO4QH$k%DKpLyJD>2( z&UKScjnfrU z2ziyMiAYe0lMA77ZZ@gByPd7y=FrsOL?gBCG=dH$`cVN|S7)#C)-Y?e}HWthlm@^Sm-lwtDOZYyz!oIr+Zva96m!uJ8()Y zl83a>{UxK2t1oVcUY?&B3!U5D=Nbs2J~0ZgltFV|qM2#G%S+D}PWC zlhmQWWkUEnIMx@iiV1!!>)Qb%32uJaDNr3%ZGn=#RIH@@L#=6;oGFhwWU?&!5mWaE zat@@lUMs_H;?m_O4r-)Q_rgh2g_FhuX(Wsg@Ab$!p-0WMdd61tZlGN4s-8N z)Nk7@1p5#T_tcILcYy^1yj$0wBXIx6Cq&VZm)Xvq+Z3p3b5w}SZi<5=h1Axq;+-=N z3TEN2s9+PIz{Io`#HP{*uV_`8hxmVwSr!$>_jO(h>bkNtspxA@+u40kZl=A#A-`pN zZ5vkp?xLwqL2U3Bb`Z7>_8_9+Ou)I5>B-*h6K&6*Tw8ID0HFy}Eg|(eU!2b;03fUr zto>ioaONPg>hxIWnLCa#;0v1{U*@&+;uS6272^j%6Yy4-Ou#GRop z75DtomEa!9C6_|a?e0=RDGxPcd>pxi_>E14Gs*BN6+SyWp^7g7u~vZ{CC#(YjXzMBqyz| zRc7&Ec+$)D-z~L3ma9L}I2N*I8W#4H54J5FsB4CvNIg0@wENt~F)eW`Xgo1KlAT9G zge-7%ym}dpLC}Q$-FCC)U6<;a2(-i*$cC#wdWw1L%0*T-V-~Pt_4%I`NRz)l`998c zU*7f}@#rA6m5~I8P9B}#+aF(vs-8!4sxZx2Z|!dH6T-frD~z2 zX2ft!mTE_dc%XVr98agYjXP{(AGd}ppo5}8S`cHcst<&KGd2v^C1I~T6W{MIMUogX zW01Xb;>Be)X+mWOHuKVvDEcv|yNz`wToi=ve)O%)XD<;6fse~MD^TIS`YqKy26I{? zQ=IQRYOAJ_3z(LM>sj&BB$$UgoRTfTK;3HYA%L5Z>b}L4{*-&`F@2VNx=sSq9D5$b zH`?kI98)3)P@VzrV+d7@GTvZ$T;M6IgSS%k#)CRpK0h|My5Z+Fm(K%u?&rdBKE^cD zT951=n*>&xrmU|#WLoKk?Ub;R!=`DHkCC$P&LoPTT;g3-J<(VjV$T@H`dkdqj4bai zT3VNBJT=lZVGjjC=&5rMIXsOqs=mfrvA%mMDpzTxxdLzQ#5lBs-r{7avR5*gnMR&c z(*nDn&3F+_1zgKhYh-F?$-tCGQiwg6M_93@Iv+Ro?ZKqQvb5b6ZeigF$cgmDby1h+ z2oQss#=Q42XIA%c+}rDNBrig}UZQlrY=k=22)uWj;6mzR;!fd*-`-$yEe8sdjEtQx z8%{gHj1{p(01%qkvqHU?l1C({7zWt$x~_!OI{KZzDb-rg59FT9Wrt1u5A}An--!9padPgZ!dLQ|T5$Q*1~rrE+t=z6FnAuZjI zse4Dx@3;HigoH8`|DI=N762>WlQ|Pn<-0Npz)fSN_s_Y{w**@nrEDs>Wvdq1Qv)`= z0s3G8II3Z(*EaUuJCoHU$fsvVKtF)2#wI3IuRca)pL|kBkfYx4-I|G4*-yMK&K2mD zM=iCFXsP?T*{fMg2sePY*abOmW#m}6)+G$SO7{N-yJJ|Y_g9`9P-w!U=+gFkvbICt ze-&Ns9j#uQc|HB{6%NDGGLhhI2zLMc>2fZx#tR+U&bKAuuu`b5F%KZ>*@lSiUQ;hh zu=JDKb1>3lP%b17s&iOh|9DPg%^!c<0rQo_aFu(g*evT@&SB%iaTu7%C)ItdBzFl5 zPyVR8kK4~1-Is4qmt~kT>AD86dH%Up&XU-R&d#?CC+;V_n_B8CzRG7HezK$y6<0Zv zs(s^#AFRMRjW`s=LO@YO%Z67Otb8a)Z2c~_98zVhy zvaXPaD!M7clUM7i=~yui#$M*tcu|k?;}!)D3|S}AVj^X$xp|{$U!8wjx5IM)hZ$vG zv3ztP&5tvfj^z#1k;njCHDL3`fcHeo!p4rbfLW_;{t_ATU;E+I;kLRkuA{4ay9Poi zmFtH1%Qb#fO>xO%GQdUEkL*=?Q@dnDFHA=~>qy0{aF2L8iqqo92K|QF@FiI)pdf^W z*;!lQNq{OpHGO9Iv3hcH($G>v14;lveHF)x7i$5-H8M93t*bi%^}lw?%WuQt5)wj~ z`3{l@awB8ScNAPkF!pWRAl&x;`>Mc=IgJ~0wRC-@*F+Y6{;W_L4%x!>Fe5`_Q&LuT zWnP(usf#MB^wwrTP^&_zz`ej7W~8%>SW(Q6Z0Ri0G=8y&VL#fqZ67eBoF2P@(rXZ7 zxK>t`nMu8Em*MnsJ}lDe>Z(z;sYQv?j`Quw43FO}^>=-D|JGk3+jd@l$ZzFL+x-Lj z)hFUb5wbtko7>1`d*ky#g=vX@;NG9dB(QlYjQa3p+N(u+uAxSYTbT}|Xfxb?&-8wu zi(jO({RosN^LdbW3a7TvBvK0TfW7|fUIs~WIJk!UprVPK=ZrO0{QR_*R>;8AENl-t zr84E;gx~k{oP|r)_v!-6hv$|eY7?Hrezzhb7*Ta3sF#GocF)cC2>2};uO@l_{Q7w| zfUGf#c;czj+2`du)U%J=S&#feomR^V_HE@^SoO8goGt#h+ zzK23TkWuTlZ@2Q29%M7h^d*N9Yx^`GGUKQ zAsGWx2h%H8VArAGnJjwI0WfxNVllx`wyV>rcuCa(6`aeD!4dAV;|Ln}V{wEMUo2Xt zF;rhDkqC757p6_ev%=wyY{!!%%xo@f!PXx8a$ymEeyJRRH+@+MLQeuAli0E1%05v3 z9DBGi?QGZExW#=K$J+17avnOanY}JS_5yiiUyLG&n*wnJj(vp>#?O87x0%5$I`AhS zrV%v&`(`h*XdI6^MNZgZ+zIsy&X+1_o0Vp%c)?UygaGW)8(~&31oCK@pe`PsmSnPk>sDXt)c9@Dd&P>+)OM z!IWGNG&v&FPb!QMj++axg;#wB_0PCkMLlp_-m#IpbZ&->=QkrsYm-;753iVEtblb9 z?%oz{zQ|xP+wJDw%VbrU$l$rnkyHlDCm!emkO^fZE&sxJ^_QfvX&Jo5HVqz9x&hT$ zJ}^v#X^CChQsl1`%yzkB7w_RTkCQY~PLWYe>GlkLmvRl3Lnz6%R#g93f~cA`GIs2G z$Do43FYTOwH=GlNZ*%)$yo&*p05XJOa#)n)IPMdfuGE=UdNY{^wn3dEu*r?Mgk%jH4&J9{c@&D0e1y|rw)1EniyP@1Xo|S zFtwBF)W+Y8d+qK545mq7(|LH1Ri8w82b90#76YWw0uBl%)9&p=QN=g4T6SzYmDbdw zjqG^?w4vyp3lnjK_fIeHZGIaE0It7U+ExB!Ki)9E@26y_!qYLiz3nvz<;-(LV}p?2 z$99_;?(t3l^F*i8yQ#BuUma%7EZY%3wx3oe=pJz+h58+US3L&@sIz?#KtY+Q_Ft9j zQvu5-03thB9u+usTuTUY1T=Dj%-;%n{yhSE2mtJC&E9Sx8WV_LpT$d9k@Ao#yA61a z1Y47MXOP9D3e$~rr?#^d-!c!+HmKQ0@s04Nn^md$FR5mfjXTlOS%Nr z#eLciuyB;~-nAx0*nx+&dT0pG3T*j(&_Ybe@CB1&sG$Saaj-;r35$CE7znWJ!1~7s zb7uuyf(NLT3aVw{D=r|5Xj&j8Dq)!*!3yc+y>^;r_77UG-ae9H)D$9Ay-3BuL*h`O zTsbri)~JmDEG~6~o6hr>fjqHk3x;dl=Mo730A*V}>Oopi@4?&!=yvTR-Y%wNopa_5 zqsO|pAvc8$iWojp_fC<)2#iCrEO!BKhc;1^q;hj8j zXa@IO-i0g(LsNz)S?7UrZ^scbkK@?bi>hge6Ph^s6*5TmGT6z_NBA)9>$IKy!mN8~ zL{pBZ!+fpbLb^baFC4Wj<_?jv@Z;$?05pxergp;{0`*C7hT35+3dYp&X#&5*JBjxE z{GELAL=)26>_k~z*8*}S&hyJ>spz(&t#*V)l9(54w6R0=~QD`nGS5P-mpYSBP0) z2LHT7mi+84qv-e2o&=~&%LB9JoEGowf)gsFak-!F+eHfJ)GRk}Ul`4PSgNit9N{LU zu9_>ok{nBeR6+`@w$YPm=JY!rmooFB!$q(~P*MmHVDkX5>ZW8) z(JZ-@u*~Apj`Ywe(7I_35r?k~g@G%YU?`Pj>@FYYb0usd=m6d_u(NVj@KilLb zBqT^+XF~bU>p!dpz-~@b@jXb(#7$Im`#OUD_T&kbao4t4{}p9KTtM#W#>#N)#qKQR zhpN^%NrZ0%l^V!`6+thI|8|j$zMryX($Bj>{?kP;lX7b|KTY&9i$Ng#RY1@o zEg|v9?Z>Ycf1!vc56=HNk21}cE05-0bab?gZLLu|4=Kg#B(;h2F!_lo*8E5LcyO>y zMM9WyeD?q4ENaw2dCN$keAs{M%;>ySl%y7*I#c=6aMj<|h6@At{-0#5e&-$$;Ox%> zCmtmrNOTBBSMH`_YpsF0NTw|>$trLG?acn=5L%WXhw!7~f5{;n`#D8LsteiD)!8W& zkdT+b_06tgwf79)2&Fzi;N7cxXP5BJnXl!LsC*w?tB}!si`ReE(dVnycWBTsra9XkQzFNeZ+$@ zeyV(y1-k&`#ZRBCq)rzzB_t#OTtKb5`OAKBc32QIvb6mE?m>rLJ^{|Hg4dkTeqQD* zgTI2HENgmRo{?(Six>(fh-#IueQ9mN+DGK)P}q zfKnY00-orj?9nmG^UbO4((n`u`k~BTEc5AS399ejEJBnqpFu-A$`R<^PnQ>R65>o@ zK6S7jp%zq5d$0G>0)YgEd4d}=dGBIC_1))h)#dXB{mm{g5E+xAk>Uoe5cg0!$d84P z(@Yg2>IDGpDpg>V^E0}%DKZqh|Mi1L0B!xuO8vN5c% z^dp@Fqk6o@B}dcsE&x3z8HdGoUjl}*sAGi_&B+cFN6{33lnLX<6-QVpP6kT$h>&D} z=;jq1#C^tiU=hrqAc_;iFUVYgp&dr^iEyOpwwt!5Hff|d60vfy45LZ3?-cPCjpCjE zeCV1mYZvwXKMD-0XFb$g(Gpr_0v8!g5uKBWNA&f_MI3#xroArkEy|8-?GKuH$;^b} zz-*Lbv)!Aa^tBWr$ISIU^&dw7qERs99N{>qVEjD(M@O~~#VjjF??8`)s@mN6M2;t+ zU}{6q^x@20N<562dN7|Q{TnW+W!x5iDS-AazOO-mq~y!;$AO;YL6e{VBy*4W5#i-c z_ORj4+|8&>Iw<24K)b62mh=Hj0^7No5pD&rw<*m|Z`)Fa%Pth%ybeE!HqY+}T z+CKXJSzZbkoI0Qv=oq7dO(;su=6wq;WJ-*d&>zce2O0stmQb-6fg&CjwxSn?U$bLL zmquQR9L0!2hrwfvDZPp@iE_3p(SjQWec;533+0hPxIlxx79-5+%f}t;NcEQDwxyyM z@2ah)%lqpplGiuT2{LTd6}o~)CyerHrRBeEzge`JA`$9LKcF5)J%Vp;C!)1r^Tkdd z=iFa-pHQra0e1?%xEqb8o?Q;3l=J+Cg1ygNQsC4Wo1*iv3vPiG+yeL%M?yRPXQv=# zXiN$s7zIcYG`YcANs2jMZLJc<9FSA==Qlu{SXJz2mwTWNV#PZKmg+HBS>LUx;1VrZ zX#Qu*peyeH6QzRyMbSH_mXN7}iry9%A6)eZD83<*>k*WHg}ee}l|VWHp~woqxnVpK zeo<)LgW~mg>kt0iS-aG(JWGJG=U$MVKq(*oIMD`i`w?4z5unQM`5f~Xb)YZjTlVCR z1Z|Na6<_V2%dy>Hym;|q>GG$`IE=hjLyl||@MX$5mHP%&A^+C7lTtXE_$1Ar7n?(H zy>q}CAHa{nP|4SRZTUwj7gzf{{&RroMD0tve^Sib#)%F_!#+H$3!iI<;crdJz?SmNM zQCHXwp6Z;o^RA2>Y?=&CWwhSg`129*|h_ete z_ytAb#|bC+f2NVw>27V^L4{3!D%xTjuyFuF6l{63u|db8g17y-an=^{!~+Ko+G4sd9}lY<+U#)T_;F@eH@CeP+WC`J zd^w@s{BBX*ppRGHAd3?g-@re0cyqbL(b18Fuz8bgs8&8#zwN|G)&r->1xlgxp&=P^ zRepX~#~$_S$t2tuMZk1uL6yNH87`|8qPi(g+wMtO27T{_JnKl=Q0+&xuv}vsev%1S zZTo@Y- zBNT@2diO2}F!pp{LIYVCh|EXMpBKkY$Dt6F_g6cfOE5;><$-i^S zb@s6;p4naf$RdD~T;caz`;msfzpAO^c9cu-m;8kz*@b?}P)K|sjRwl1>&6S|nv^uU z_tHY~jXxB24gv6@ldTx4%zNH;f!Z09nl>ZF9$uh#s#-FS5>yk|CT>7#m92>?DAAAP7LA*V_&2>x1bgU5al`YsEg2YcXwaG zxh=r+r!+cX0$wMQEx<9iw6t_3?K%vDLW2?T8o`;KIwc}2%YrgxHbEvRI}9tY%G6$D z5&9$Pj_nLTV7y>uZ!cnEV$#{!Ng5wv?e`z$_unjW#$ky}EhO@g+qI+)km3(%hHFGJ z8Izh$xBFrgn2@3~^ui5sLVG9|6)6Xk%9B0Lyni$f@PD+#Cd3E_1CbO(3PulzgzxqG z3%M!&{hwbkh8$_9DxgQg89D=SMJQ-{_ze?F<~dH?<<|WET9J7t%AnM8i2%MN0sa8IPY_d^B+G-oGqx2z|wNqQv~AwZ3&~)|3h04#epa9_uPQa3rGRo zPE4eOQwhPp(gCQbh635p?*bB=to>KV0V;6zGA!n@sJ@{nQ#wKgQLT&uxgq8oP61Y|Z;c@CDE{Kn?*sBJc)Zt|vkIOgWSss2kt2 zYuB;rwBCMSKvLTNkCF9|wf7HGYYC)pU61B_Yc_j+Yy`k{&2t^sTVKizXh5lN$;tfd zW4qLDUr@c{S-rlXkKl~cB~Dtpx(pCDg!nUKaIKKw>&pTP5MKQCMUWhZI!qw#IU2DC zb!S*T9@nd+P)*+SMSdfiLhFypp}<2oe!0NyZn3TKoV_>uWobE3lWiOl4ex$!PnmM< z2len66o!<+h4;$1xFO$JmDO4E9J7|@=G&XU8r51#ii(PKckkZ)4(SuQFU&p>6> zI5a}tc(1odcS=hi_kp#CCCbds*5y6ym1k;VB78jcu%*wpj9a3*qaD}f);Cvb5&`b{ zv8-z2JS>Oj)`-#;xjx+&2poa(0aa-H0U01-1{b#T#R#g!JbNaYcv+B(>sIrfT|Yn3 z>HHm0-+|?N%UMcsq7)t4U>pd}k)o~fC`ft#^2=JYQ)k9qm2*8^_*HLkgG2{7sxDd4 z%>~NhW0gbICb!n8n=<}GDw!mDXZ@0g=Z_Pqhk0U7PMSdhZj^naFI&YSq8){iE(~o;M zJ~I&h`?pUeBo>x}ae{KBT2_8q(P{sk1a<>*C_BHqpAl%B+MxijSR(nb>zED-0psbm z)r$$OhB_MQJK&_T-8!sFEiq4suvLydTx8I`_ZD!pHl)-{Ma|RJ$`> zgBy~Y5GsS(=>U~tyl874m_g~~<-A|sM59bCs~2w|;2J)W>bvz)kyUk8#NHU0HlBTAG57OzdyG-YHZ8~ZMLjmRsre-Piek9E20}O2mFEOwlxB_H|A-e z@c~j3H`Z1} z4joEV8?I(Ny~0DNw`%!*AsDb1!LT(sawN!ov_9M=&FVcgafyf2)5m}< zG7t;lE@l@NnksoOWI|cW#YVMFV?E$3e8{zx{V`N^y8L_AgUo>e>+Jmey=@l+Qf6w% z{=3D*v>?6nj;W>P?tr4g!luT#`*iJK9Y4;k{;j9vzh1idC00$ZW^Hg*^s&iqKiIN( z(RnMn2X5Q*MCX0zJ~{jU@e?BhYpwoZ^?%SdV7`OWI4wuE|)`rP8=)#VUj%K#XR2Y1!^7ZsOp%hV8 z@d3+(Q*}l)wD+(J2sxS66>W3#d+y^2r&?;$&`?e{VsX97d63E~J^uBp9&faEOKa;L zr_#@~7iWj67B(t}hE76iOvtVka2_F&N}{+~n*W8qc*dkNAc2wmTwejE20~N^RkbsW z2(Lq#{cEnLr``&CdI+3e&4SChRh%f_w12I7WBD{d2*`r8QJY0l(s#D1cevXv-~GD@ zP!_Vfe*ky|eVKgJZFBa#c=19+w9~*0HH9#yZN=DcZu>ZU!QaoM2(s@)|%z0?geH&;=HbKEybQyk>49_fkE6d;n zK` z{w*kaT2n!@sY5SIM;Sq#IXF0sfV9?fb9;4z42+yW)cPr4wbeH6!Gp-CC?-%JC;VT` zRDw!GO|V>ArAtPNdTplM$?%YC;PeP7D)Q*+>L%^K$Q~o0A~{^+vvi3`P?i7MwQEh# z>1KWPCk}(OPU4XaQhSQ>xc$`BRECj6-oHq5@rc@M)v?4s8|mTVw-+I2aBD7c{Th z1GF&ZSew_Eibg~EoS@1~9ioPn)NC-oHMbMEZy0CEn4anpEHK9fhNAqz_7rQc0%62SPpy5q4 zU;g>Gyp{8B_)>yKVqh5~oNG2XYkoDz&PCd;Q-P~k3paagZI7hnn9vGPs7T-+x&U{t z*|Hq@uh2BC6x0dIC`FVc?){E&$9g+If+H0%-kNAn-rN8+&T;?214!g#T8C?{4dy(` zeMVR0+_`fL_D;|l2Ps6zQ|Div9gan_h1I_ayLQHNFa)WT@dJ<>9j$vu{OkxR&?Zd~ zQXay?nwmE^*2J)PKt0+y40O0eT{yNU6J!6{YahD!V0c}7a}9`7kHI6?+4O~RJO=08 zDqd9g_w=g{(F8p)fXZ7}0-%Q(JNCV5WoT;Qz~-p791PVgNM%2Ls%7R@)c+TeJm4E5Q`xT7UcM~FWT_r zi!c&)>>|jP!PFQIQ|3fkfDKGR$!jj1T3r%YTfWoa+={_${NU=sC-Gg5ccK4N zCLEMUjlDY`eyasgsEk|5T|(S2ISf*r@pzyVMZdH*RWbLT#c{$?I;3 zOea0jM)+_1-l?W$TYVLb$ds5hF#1A}O`)i$h%QIGnEqjZ=80ZdrTy&e>|_0?E1bm( zbN<6#M%7n;&3y*Z6YYvhf7>5_q<1D8LL?D8=TSqZSS z8jrf%eQtPo9575%jfVOH?{a1vQ=ms$#rhyH;JS_pqQ+Hm_wPr5{lc%{qF#Mn*>mP5 zToTyhK~-BPCz+RJUwK!D$#C+HoIcIxR5`C*(~%G+Ob0H^whN9X?W3-5U2gh@`mF%WyR5FkHAEp+g7C7@L>osLiYo7gBL&p zdm%+SZu>l|u_O3u4-Nh2BW=~8Y7t_&Av_A~_mf+b&gtw=eb;4ujtEk^H;L654i7gupp2$-aS4A^&sv=YG_CEhP-`nsDgO_lmbGj$m88lZZJZo%?mYMe^v=2ks*J~@Yjc^NYf$bfe2KoIj z>*97Eb?LuaauzgDU!}J^1YyTq+tM|H5!2FR>ceMP882AfIZ#4n{3l`1a1-q(+Ebw0 zorbTk?~~s0)vpgSy1%=CnPLwSD#W)$_U3@k)C+*QmS-OJr2upV5!;5ebw>Gm3pwDQR_lv)s;UBMe#<;?kZz%VUTWmkj%#4Ojphby3T3X-J3ICLNh$^I}FICKFXqUQ*He-i$KaS^w31Q4|>b7AxsIb_J?fGFX&`}GUf-q zBo?}LM}+OhLbHrCLD}pQE4SmM=kkhT;F?qM+qls{YHNr)7 zkf9E!!vH95WM_~!XUV8Htpq(KG*?hE?9T_qZ#2N^LmDLnTKP)o`~wxBAl3if?#oGJ amalpjV)6STn4twVrmJPB`SJ)M=zjqocg4*B literal 0 HcmV?d00001 diff --git a/utils/evaluation.py b/utils/evaluation.py index abe0b72..c8c440d 100644 --- a/utils/evaluation.py +++ b/utils/evaluation.py @@ -10,7 +10,7 @@ def avg_accuracy(gt, pred): return acc -def class_accuracy(confusion_matrix, class_id): +def class_metric(confusion_matrix, class_id): """ confusion matrix of multi-class classification @@ -24,8 +24,10 @@ def class_accuracy(confusion_matrix, class_id): TN = np.sum(confusion_matrix) - TP - FN - FP accuracy = (TP + TN) / (TP + FP + FN + TN) - - return accuracy + precision = TP / (TP + FP) + recall = TP / (TP + FN) + f_score = 2 * precision * recall / (precision + recall) + return accuracy, precision, recall, f_score def visualize_train_loss(train_loss, logger, log_img_path): diff --git a/utils/init.py b/utils/init.py index d42b682..7e1762f 100644 --- a/utils/init.py +++ b/utils/init.py @@ -5,9 +5,9 @@ def get_model(model): if model == 'ResNet-18': - return ResNet.resnet18(pretrained=True, t_num_classes=config['N_CLASSES']) # initialize model - elif model == 'ResNet-50': - return ResNet.resnet50(pretrained=True, t_num_classes=config['N_CLASSES']) # initialize model + return timm.create_model('resnet18', pretrained=True, num_classes=config['N_CLASSES']) + elif model == 'ResNet-34': + return timm.create_model('resnet34', pretrained=True, num_classes=config['N_CLASSES']) elif model == 'ResMLP-12': return timm.create_model('resmlp_12_224', pretrained=True, num_classes=config['N_CLASSES']) elif model == 'ResMLP-24': From 1a704a79f8af2eb1b846631036a760a2105d45b4 Mon Sep 17 00:00:00 2001 From: zengmmm00 <11811636@mail.sustech.edu.cn> Date: Fri, 2 Dec 2022 16:49:33 +0800 Subject: [PATCH 07/27] add metric --- log/ResMLP-12/log.txt | 20 ++++++++++---------- log/ResMLP-24/log.txt | 20 ++++++++++---------- 2 files changed, 20 insertions(+), 20 deletions(-) diff --git a/log/ResMLP-12/log.txt b/log/ResMLP-12/log.txt index 42d4cb6..cc434d6 100644 --- a/log/ResMLP-12/log.txt +++ b/log/ResMLP-12/log.txt @@ -106,13 +106,13 @@ Validation Accuracy: Test Averaged Loss = 0.263454 Test Averaged Accuracy = 0.948300 Confusion Matrix Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResMLP-12/confusion_matrix.png -Class: 0 Accuracy = 0.991500 -Class: 1 Accuracy = 0.990000 -Class: 2 Accuracy = 0.978500 -Class: 3 Accuracy = 0.991500 -Class: 4 Accuracy = 0.988800 -Class: 5 Accuracy = 0.987100 -Class: 6 Accuracy = 0.989700 -Class: 7 Accuracy = 0.994500 -Class: 8 Accuracy = 0.991100 -Class: 9 Accuracy = 0.993900 +Class: 0 Accuracy = 0.991500 Precision = 0.951629 Recall = 0.964000 f-score = 0.957774 +Class: 1 Accuracy = 0.990000 Precision = 0.961066 Recall = 0.938000 f-score = 0.949393 +Class: 2 Accuracy = 0.978500 Precision = 0.874166 Recall = 0.917000 f-score = 0.895071 +Class: 3 Accuracy = 0.991500 Precision = 0.941176 Recall = 0.976000 f-score = 0.958272 +Class: 4 Accuracy = 0.988800 Precision = 0.957732 Recall = 0.929000 f-score = 0.943147 +Class: 5 Accuracy = 0.987100 Precision = 0.963791 Recall = 0.905000 f-score = 0.933471 +Class: 6 Accuracy = 0.989700 Precision = 0.925926 Recall = 0.975000 f-score = 0.949830 +Class: 7 Accuracy = 0.994500 Precision = 0.983623 Recall = 0.961000 f-score = 0.972180 +Class: 8 Accuracy = 0.991100 Precision = 0.945259 Recall = 0.967000 f-score = 0.956006 +Class: 9 Accuracy = 0.993900 Precision = 0.987539 Recall = 0.951000 f-score = 0.968925 \ No newline at end of file diff --git a/log/ResMLP-24/log.txt b/log/ResMLP-24/log.txt index 03ca642..fd80bc8 100644 --- a/log/ResMLP-24/log.txt +++ b/log/ResMLP-24/log.txt @@ -105,13 +105,13 @@ Validation Accuracy: Test Averaged Loss = 0.205754 Test Averaged Accuracy = 0.971000 Confusion Matrix Visualization is saved to /userhome/cs2/mingzeng/codes/kmnist/log/ResMLP-24/confusion_matrix.png -Class: 0 Accuracy = 0.995300 -Class: 1 Accuracy = 0.994200 -Class: 2 Accuracy = 0.991800 -Class: 3 Accuracy = 0.996100 -Class: 4 Accuracy = 0.991200 -Class: 5 Accuracy = 0.995000 -Class: 6 Accuracy = 0.989400 -Class: 7 Accuracy = 0.995600 -Class: 8 Accuracy = 0.996000 -Class: 9 Accuracy = 0.997400 +Class: 0 Accuracy = 0.995300 Precision = 0.965787 Recall = 0.988000 f-score = 0.976767 +Class: 1 Accuracy = 0.994200 Precision = 0.986570 Recall = 0.955000 f-score = 0.970528 +Class: 2 Accuracy = 0.991800 Precision = 0.980126 Recall = 0.937000 f-score = 0.958078 +Class: 3 Accuracy = 0.996100 Precision = 0.980020 Recall = 0.981000 f-score = 0.980510 +Class: 4 Accuracy = 0.991200 Precision = 0.957831 Recall = 0.954000 f-score = 0.955912 +Class: 5 Accuracy = 0.995000 Precision = 0.972167 Recall = 0.978000 f-score = 0.975075 +Class: 6 Accuracy = 0.989400 Precision = 0.913889 Recall = 0.987000 f-score = 0.949038 +Class: 7 Accuracy = 0.995600 Precision = 0.988753 Recall = 0.967000 f-score = 0.977755 +Class: 8 Accuracy = 0.996000 Precision = 0.983871 Recall = 0.976000 f-score = 0.979920 +Class: 9 Accuracy = 0.997400 Precision = 0.987000 Recall = 0.987000 f-score = 0.987000 \ No newline at end of file From 69abd8be86dc89a9d4ee208c42edbf7a5d6dcf03 Mon Sep 17 00:00:00 2001 From: zengmmm00 <11811636@mail.sustech.edu.cn> Date: Sat, 3 Dec 2022 10:20:15 +0800 Subject: [PATCH 08/27] mlp&cnn complete; write README --- README.md | 38 +++++++++++++++++++++++++++++++++----- classification.py | 2 +- config.py | 5 +++-- utils/evaluation.py | 1 - 4 files changed, 37 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 117d8d8..18ade7f 100644 --- a/README.md +++ b/README.md @@ -4,9 +4,37 @@ This repository impletments the classification for [Kuzushiji-MNIST](https://git -[Kuzushiji-MNIST](https://github.com/rois-codh/kmnist) is a drop-in replacement for the MNIST dataset (28x28 grayscale, 70,000 images), provided in the original MNIST format as well as a NumPy format. Since MNIST restricts us to 10 classes, we chose one character to represent each of the 10 rows of Hiragana when creating Kuzushiji-MNIST. +## Download the dataset -

- - The 10 classes of Kuzushiji-MNIST, with the first column showing each character's modern hiragana counterpart. -

+```shell +wget http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-train-imgs.npz +wget http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-train-labels.npz +wget http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-test-imgs.npz +wget http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-test-labels.npz +``` + + + +## MLP/CNN Model + +**You will need GPU to run the following code.** + +Available model names: + +``` +ResMLP-12, ResMLP-24, ResNet-18, ResNet-34 +``` + +To test the model: + +```shell +python classification.py --model [model name] --gpu [GPU No.] +``` + +To train the model: + +```shell +python classification.py --model [model name] --gpu [GPU No.] --train 1 +``` + +Results are stored in `./log` diff --git a/classification.py b/classification.py index c139b29..134c82b 100644 --- a/classification.py +++ b/classification.py @@ -153,7 +153,7 @@ def test_model(self): parser.add_argument('--train', type=int, default=0) parser.add_argument('--test', type=int, default=1) parser.add_argument('--class_num', type=int, default=None) - parser.add_argument('--ckpt_path', type=str, default='/userhome/cs2/mingzeng/codes/kmnist/models/') + parser.add_argument('--ckpt_path', type=str, default='./models/') args = parser.parse_args() # set log diff --git a/config.py b/config.py index 45bf3c1..e9900a4 100644 --- a/config.py +++ b/config.py @@ -1,6 +1,6 @@ import os -PROJECT_PATH = '/userhome/cs2/mingzeng/codes/kmnist/' +PROJECT_PATH = './' config = { 'LOG_PATH': PROJECT_PATH + 'log/', @@ -12,5 +12,6 @@ 'CUDA_VISIBLE_DEVICES': "0", 'TRAN_SIZE': 224, 'TRAN_CROP': 224, - 'N_CLASSES': 10 + 'N_CLASSES': 10, + 'MODEL':'ResMLP-12' } diff --git a/utils/evaluation.py b/utils/evaluation.py index c8c440d..8123bf4 100644 --- a/utils/evaluation.py +++ b/utils/evaluation.py @@ -1,6 +1,5 @@ import numpy as np import matplotlib.pyplot as plt -from config import * from sklearn.metrics import recall_score, f1_score, classification_report, confusion_matrix, ConfusionMatrixDisplay From fe430c1e9d6fb408a735929c27f483b26aab1521 Mon Sep 17 00:00:00 2001 From: zengmmm00 <11811636@mail.sustech.edu.cn> Date: Sat, 3 Dec 2022 10:42:47 +0800 Subject: [PATCH 09/27] modify README --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 18ade7f..2713e4e 100644 --- a/README.md +++ b/README.md @@ -25,16 +25,16 @@ Available model names: ResMLP-12, ResMLP-24, ResNet-18, ResNet-34 ``` -To test the model: +To train the model: ```shell -python classification.py --model [model name] --gpu [GPU No.] +python classification.py --model [model name] --gpu [GPU No.] --train 1 ``` -To train the model: +To test the model: ```shell -python classification.py --model [model name] --gpu [GPU No.] --train 1 +python classification.py --model [model name] --gpu [GPU No.] ``` Results are stored in `./log` From 8cd2d779eb411564e91488cc165390fc698bd438 Mon Sep 17 00:00:00 2001 From: zengmmm00 <11811636@mail.sustech.edu.cn> Date: Sat, 3 Dec 2022 11:05:23 +0800 Subject: [PATCH 10/27] add comment --- classification.py | 56 ++++++++++++++++++++++--------- config.py | 4 +-- dataset/KMNIST.py | 21 ++++-------- download_data.py | 80 --------------------------------------------- utils/evaluation.py | 11 +++++-- utils/init.py | 1 + 6 files changed, 56 insertions(+), 117 deletions(-) delete mode 100644 download_data.py diff --git a/classification.py b/classification.py index 134c82b..3202c49 100644 --- a/classification.py +++ b/classification.py @@ -18,63 +18,80 @@ class Classification: def __init__(self, model='ResNet-18', train_batch=64, test_batch=1000, epoch=30, ckpt_path='./models/', class_num=10, log_img_path=''): + # get dataloader self.dataloader_train = get_train_dataloader(batch_size=train_batch, shuffle=True, num_workers=4) self.dataloader_val = get_validation_dataloader(batch_size=train_batch, shuffle=False, num_workers=4) self.dataloader_test = get_test_dataloader(batch_size=test_batch, shuffle=False, num_workers=4) self.model = nn.DataParallel(get_model(model)).cuda() torch.backends.cudnn.benchmark = True + # set loss criterion self.criterion = nn.CrossEntropyLoss().cuda() + # set optimizer self.optimizer = optim.Adam(self.model.parameters(), lr=1e-4, betas=(0.9, 0.999), eps=1e-08, weight_decay=1e-5) + # set scheduler self.lr_scheduler_model = lr_scheduler.StepLR(self.optimizer, step_size=10, gamma=1) + # set No. of epoch self.max_epoch = epoch self.loss_log = [] self.accuracy_log = [] + # get model parameter path self.ckpt_path = ckpt_path + model + '.pkl' self.class_num = class_num + # get path to log result self.log_img_path = log_img_path def train_epoch(self): - self.model.train() # set model to training mode + # set model to training mode + self.model.train() train_loss = [] with torch.autograd.enable_grad(): for batch_idx, (img, lbl) in enumerate(self.dataloader_train): + # put them to gpu image = torch.autograd.Variable(img).cuda() label = torch.autograd.Variable(lbl).cuda() + # set gradient to 0 self.optimizer.zero_grad() - output = self.model(image) # forward + # get prediction + output = self.model(image) if type(output) is tuple: output = output[0] + # compute loss loss_tensor = self.criterion.forward(output, label) - + # backward loss_tensor.backward() - self.optimizer.step() ##update parameters + # update parameters + self.optimizer.step() train_loss.append(loss_tensor.item()) return train_loss def test_epoch(self, dataloader=None): + # set model to evaluation mode self.model.eval() + # set dataloader if dataloader is None: dataloader = self.dataloader_test - + # initialize gt = torch.FloatTensor().cuda() pred = torch.FloatTensor().cuda() - loss_test = [] + with torch.autograd.no_grad(): for batch_idx, (img, lbl) in enumerate(dataloader): - # forward + # put them to gpu image = torch.autograd.Variable(img).cuda() label = torch.autograd.Variable(lbl).cuda() + # get prediction output = self.model(image) if type(output) is tuple: output = output[0] + # compute loss loss_tensor = self.criterion.forward(output, label) loss_test.append(loss_tensor.item()) _, pred_label = torch.max(output.data, 1) gt = torch.cat((gt, label.data), 0) - pred = torch.cat((pred, pred_label.data), 0) # todo + pred = torch.cat((pred, pred_label.data), 0) return np.mean(loss_test), gt.cpu().numpy(), pred.cpu().numpy() @@ -84,29 +101,28 @@ def val_epoch(self): return loss, acc def train_model(self): - # if os.path.isfile(self.ckpt_path): - # checkpoint = torch.load(self.ckpt_path) - # self.model.load_state_dict(checkpoint) - # logger.info("=> loaded model checkpoint: " + self.ckpt_path) - logger.info('********************begin training!********************') accuracy_max = 0.0 for epoch in range(self.max_epoch): # train + # get train loss for one epoch train_loss = self.train_epoch() train_loss = np.mean(train_loss) self.loss_log.append(train_loss) + # log train loss logger.info("Eopch: %5d train loss = %.6f" % (epoch + 1, train_loss)) self.lr_scheduler_model.step() # validation + # get validation loss and accuracy val_loss, val_accuracy = self.val_epoch() + # log validation loss and accuracy logger.info("Eopch: %5d valuation loss = %.6f, ACC = %.6f" % (epoch + 1, val_loss, val_accuracy)) self.accuracy_log.append(val_accuracy) - # save checkpoint + # save best checkpoint if accuracy_max < val_accuracy: accuracy_max = val_accuracy torch.save(self.model.state_dict(), self.ckpt_path) # Saving torch.nn.DataParallel Models @@ -115,6 +131,7 @@ def train_model(self): logger.info( 'Training epoch: {} completed.'.format(epoch + 1)) + # visualize train loss and validation accuracy visualize_train_loss(self.loss_log, logger, self.log_img_path) visualize_val_accuracy(self.accuracy_log, logger, self.log_img_path) logger.info('Train Loss:') @@ -123,6 +140,8 @@ def train_model(self): logger.info(','.join([str(x) for x in self.accuracy_log])) def test_model(self): + # test + # load model parameters if os.path.isfile(self.ckpt_path): checkpoint = torch.load(self.ckpt_path) self.model.load_state_dict(checkpoint) @@ -130,16 +149,22 @@ def test_model(self): logger.info('******* begin testing!*********') + # get test loss, groud truth, prediction loss, gt, pred = self.test_epoch() + # log test loss logger.info("Test Averaged Loss = %.6f" % (loss)) + # compute average accuracy test_acc = avg_accuracy(gt, pred) + # log test accuracy logger.info("Test Averaged Accuracy = %.6f" % (test_acc)) + # plot confusion matrix cm = visualize_confusion_matrix(gt, pred, logger, self.log_img_path) + # compute accuracy, precision, recall, f1 score for each class for i in range(self.class_num): acc, precision, recall, f_score = class_metric(cm, i) logger.info("Class: %5d Accuracy = %.6f Precision = %.6f Recall = %.6f f-score = %.6f" % ( - i, acc, precision, recall, f_score)) + i, acc, precision, recall, f_score)) if __name__ == '__main__': @@ -154,7 +179,6 @@ def test_model(self): parser.add_argument('--test', type=int, default=1) parser.add_argument('--class_num', type=int, default=None) parser.add_argument('--ckpt_path', type=str, default='./models/') - args = parser.parse_args() # set log logger = get_logger(config['LOG_PATH'] + args.model) diff --git a/config.py b/config.py index e9900a4..83cd86b 100644 --- a/config.py +++ b/config.py @@ -1,5 +1,3 @@ -import os - PROJECT_PATH = './' config = { @@ -13,5 +11,5 @@ 'TRAN_SIZE': 224, 'TRAN_CROP': 224, 'N_CLASSES': 10, - 'MODEL':'ResMLP-12' + 'MODEL': 'ResMLP-12' } diff --git a/dataset/KMNIST.py b/dataset/KMNIST.py index 6477f3e..058cc0b 100644 --- a/dataset/KMNIST.py +++ b/dataset/KMNIST.py @@ -2,16 +2,11 @@ from torch.utils.data import Dataset from torch.utils.data import DataLoader import torchvision.transforms as transforms -from PIL import Image -import os -import pandas as pd import numpy as np -import time -import random -from sklearn.model_selection import train_test_split from config import * +# set dataset path PATH_TO_TRAIN_FILE = config['TRAIN_FILE'] PATH_TO_TEST_FILE = config['TEST_FILE'] PATH_TO_TRAIN_LABEL_FILE = config['TRAIN_LABEL'] @@ -19,6 +14,7 @@ TRAIN_IMAGE_NUM = config['TRAIN_NUM'] +# load data class MyDataset(Dataset): def __init__(self, data, target, transform=None): self.data = torch.from_numpy(data).float() @@ -40,10 +36,12 @@ def __len__(self): return len(self.data) +# preprocess images transform = transforms.Compose([transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), transforms.Resize((config['TRAN_SIZE'], config['TRAN_SIZE']))]) +# configure dataloader for train data def get_train_dataloader(batch_size, shuffle, num_workers): train_images = np.load(PATH_TO_TRAIN_FILE)['arr_0'][:TRAIN_IMAGE_NUM] train_labels = np.load(PATH_TO_TRAIN_LABEL_FILE)['arr_0'][:TRAIN_IMAGE_NUM] @@ -53,6 +51,7 @@ def get_train_dataloader(batch_size, shuffle, num_workers): return train_loader +# configure dataloader for validation data def get_validation_dataloader(batch_size, shuffle, num_workers): val_images = np.load(PATH_TO_TRAIN_FILE)['arr_0'][TRAIN_IMAGE_NUM:] val_labels = np.load(PATH_TO_TRAIN_LABEL_FILE)['arr_0'][TRAIN_IMAGE_NUM:] @@ -62,6 +61,7 @@ def get_validation_dataloader(batch_size, shuffle, num_workers): return val_loader +# configure dataloader for test data def get_test_dataloader(batch_size, shuffle, num_workers): test_images = np.load(PATH_TO_TEST_FILE)['arr_0'] test_labels = np.load(PATH_TO_TEST_LABEL_FILE)['arr_0'] @@ -69,12 +69,3 @@ def get_test_dataloader(batch_size, shuffle, num_workers): test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers) return test_loader - -# if __name__ == "__main__": -# # split trainset\valset\testset -# -# # for debug -# dataloader_train = get_train_dataloader(batch_size=64) -# for batch_idx, (image, label) in enumerate(dataloader_train): -# print(label[0]) -# break diff --git a/download_data.py b/download_data.py deleted file mode 100644 index 1b831de..0000000 --- a/download_data.py +++ /dev/null @@ -1,80 +0,0 @@ -import requests - -try: - from tqdm import tqdm -except ImportError: - tqdm = lambda x, total, unit: x # If tqdm doesn't exist, replace it with a function that does nothing - print('**** Could not import tqdm. Please install tqdm for download progressbars! (pip install tqdm) ****') - -# Python2 compatibility -try: - input = raw_input -except NameError: - pass - -download_dict = { - '1) Kuzushiji-MNIST (10 classes, 28x28, 70k examples)': { - '1) MNIST data format (ubyte.gz)': - ['http://codh.rois.ac.jp/kmnist/dataset/kmnist/train-images-idx3-ubyte.gz', - 'http://codh.rois.ac.jp/kmnist/dataset/kmnist/train-labels-idx1-ubyte.gz', - 'http://codh.rois.ac.jp/kmnist/dataset/kmnist/t10k-images-idx3-ubyte.gz', - 'http://codh.rois.ac.jp/kmnist/dataset/kmnist/t10k-labels-idx1-ubyte.gz'], - '2) NumPy data format (.npz)': - ['http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-train-imgs.npz', - 'http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-train-labels.npz', - 'http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-test-imgs.npz', - 'http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-test-labels.npz'], - }, - '2) Kuzushiji-49 (49 classes, 28x28, 270k examples)': { - '1) NumPy data format (.npz)': - ['http://codh.rois.ac.jp/kmnist/dataset/k49/k49-train-imgs.npz', - 'http://codh.rois.ac.jp/kmnist/dataset/k49/k49-train-labels.npz', - 'http://codh.rois.ac.jp/kmnist/dataset/k49/k49-test-imgs.npz', - 'http://codh.rois.ac.jp/kmnist/dataset/k49/k49-test-labels.npz'], - }, - '3) Kuzushiji-Kanji (3832 classes, 64x64, 140k examples)': { - '1) Folders of images (.tar)': - ['http://codh.rois.ac.jp/kmnist/dataset/kkanji/kkanji.tar'], - } - -} - -# Download a list of files -def download_list(url_list): - for url in url_list: - path = url.split('/')[-1] - r = requests.get(url, stream=True) - with open(path, 'wb') as f: - total_length = int(r.headers.get('content-length')) - print('Downloading {} - {:.1f} MB'.format(path, (total_length / 1024000))) - - for chunk in tqdm(r.iter_content(chunk_size=1024), total=int(total_length / 1024) + 1, unit="KB"): - if chunk: - f.write(chunk) - print('All dataset files downloaded!') - -# Ask the user about which path to take down the dict -def traverse_dict(d): - print('Please select a download option:') - keys = sorted(d.keys()) # Print download options - for key in keys: - print(key) - - userinput = input('> ').strip() - - try: - selection = int(userinput) - 1 - except ValueError: - print('Your selection was not valid') - traverse_dict(d) # Try again if input was not valid - return - - selected = keys[selection] - - next_level = d[selected] - if isinstance(next_level, list): # If we've hit a list of downloads, download that list - download_list(next_level) - else: - traverse_dict(next_level) # Otherwise, repeat with the next level - -traverse_dict(download_dict) diff --git a/utils/evaluation.py b/utils/evaluation.py index 8123bf4..5ab52f7 100644 --- a/utils/evaluation.py +++ b/utils/evaluation.py @@ -4,6 +4,10 @@ def avg_accuracy(gt, pred): + """ + gt: ground truth + pred: prediction + """ correct_cnt = (pred == gt).sum() acc = correct_cnt * 1.0 / pred.shape[0] return acc @@ -11,10 +15,8 @@ def avg_accuracy(gt, pred): def class_metric(confusion_matrix, class_id): """ - confusion matrix of multi-class classification - + confusion_matrix: confusion matrix of multi-class classification class_id: id of a particular class - """ confusion_matrix = np.float64(confusion_matrix) TP = confusion_matrix[class_id, class_id] @@ -29,6 +31,7 @@ def class_metric(confusion_matrix, class_id): return accuracy, precision, recall, f_score +# plot train loss def visualize_train_loss(train_loss, logger, log_img_path): plt.xlabel('Train Loss') plt.plot(train_loss) @@ -38,6 +41,7 @@ def visualize_train_loss(train_loss, logger, log_img_path): logger.info('Train Loss Visualization is saved to ' + path) +# plot validation accuracy def visualize_val_accuracy(val_acc, logger, log_img_path): plt.xlabel('Validation Accuracy') plt.plot(val_acc) @@ -47,6 +51,7 @@ def visualize_val_accuracy(val_acc, logger, log_img_path): logger.info('Validation Accuracy Visualization is saved to ' + path) +# plot confusion matrix def visualize_confusion_matrix(gt, pred, logger, log_img_path): cm = confusion_matrix(gt, pred) disp = ConfusionMatrixDisplay(cm).plot() diff --git a/utils/init.py b/utils/init.py index 7e1762f..97c6e26 100644 --- a/utils/init.py +++ b/utils/init.py @@ -3,6 +3,7 @@ from config import * +# initialize model def get_model(model): if model == 'ResNet-18': return timm.create_model('resnet18', pretrained=True, num_classes=config['N_CLASSES']) From de62148ed5d1bcc2852a4d1ca83e680d8ecd619c Mon Sep 17 00:00:00 2001 From: zengmmm00 <11811636@mail.sustech.edu.cn> Date: Sat, 3 Dec 2022 11:32:06 +0800 Subject: [PATCH 11/27] modify README --- README.md | 45 ++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 40 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 2713e4e..5bc0a57 100644 --- a/README.md +++ b/README.md @@ -6,6 +6,10 @@ This repository impletments the classification for [Kuzushiji-MNIST](https://git ## Download the dataset +(1) Get in the project folder in the terminal. + +(2) Run + ```shell wget http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-train-imgs.npz wget http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-train-labels.npz @@ -19,22 +23,53 @@ wget http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-test-labels.npz **You will need GPU to run the following code.** +**Results will be stored in `./log`** + +**Model parameters will be stored in `./models`** + + + Available model names: ``` ResMLP-12, ResMLP-24, ResNet-18, ResNet-34 ``` -To train the model: +To train and test the model: + +```shell +python classification.py --model [model name] --gpu [GPU No.] --train 1 --test 1 --train_batch [train batch size] --test_batch [test batch size] --epoch [number of train epoch] +``` + +Only to test the model + +```shell +python classification.py --model [model name] --gpu [GPU No.] --test_batch [test batch size] +``` + + + +For ResMLP-12 + +```shell +python classification.py --model ResMLP-12 --gpu 0 --train 1 --test 1 --train_batch 64 --test_batch 500 --epoch 30 +``` + +For ResMLP-24 + +```shell +python classification.py --model ResMLP-24 --gpu 0 --train 1 --test 1 --train_batch 64 --test_batch 500 --epoch 30 +``` + +For ResNet-18 ```shell -python classification.py --model [model name] --gpu [GPU No.] --train 1 +python classification.py --model ResNet-18 --gpu 0 --train 1 --test 1 --train_batch 64 --test_batch 500 --epoch 30 ``` -To test the model: +For ResNet-34 ```shell -python classification.py --model [model name] --gpu [GPU No.] +python classification.py --model ResNet-34 --gpu 0 --train 1 --test 1 --train_batch 64 --test_batch 500 --epoch 30 ``` -Results are stored in `./log` From 9c0de392a2bbfa73637edd4c7e87cdc2acba4fa3 Mon Sep 17 00:00:00 2001 From: zengmmm00 <11811636@mail.sustech.edu.cn> Date: Sat, 3 Dec 2022 11:32:40 +0800 Subject: [PATCH 12/27] modify --- classification.py | 1 + 1 file changed, 1 insertion(+) diff --git a/classification.py b/classification.py index 3202c49..f7740f1 100644 --- a/classification.py +++ b/classification.py @@ -7,6 +7,7 @@ import torch.optim as optim import torch.backends.cudnn as cudnn from dataset.KMNIST import get_train_dataloader, get_validation_dataloader, get_test_dataloader +import os # self-defined from utils.init import * from utils.logger import get_logger From 70a8ea1ae21def90ba75fbd2e376b0a4a5f133f3 Mon Sep 17 00:00:00 2001 From: HappyCheems <79441528+eternalDoge@users.noreply.github.com> Date: Sat, 3 Dec 2022 14:25:14 +0800 Subject: [PATCH 13/27] Update README.md --- README.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/README.md b/README.md index 5bc0a57..e36a105 100644 --- a/README.md +++ b/README.md @@ -19,6 +19,27 @@ wget http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-test-labels.npz +## Visualization and Unsupervised Models + +**You will need install these packages to run the specific functions.** + +For basic functions, such as loading data and displaying images: + +``` +pip install numpy +pip install matplotlib +pip install seaborn +pip install pandas +``` + +For Unsupervised Model, PCA and Evaluation: + +``` +pip install -U scikit-learn +``` + + + ## MLP/CNN Model **You will need GPU to run the following code.** From 06ec2e8c66f59fede2190676bda3cf370ad10fac Mon Sep 17 00:00:00 2001 From: zengmmm00 <11811636@mail.sustech.edu.cn> Date: Sat, 3 Dec 2022 22:47:35 +0800 Subject: [PATCH 14/27] add part 1 --- STAT 7008 project unsupervised.ipynb | 1837 ++++++++++++++++++++++++++ 1 file changed, 1837 insertions(+) create mode 100644 STAT 7008 project unsupervised.ipynb diff --git a/STAT 7008 project unsupervised.ipynb b/STAT 7008 project unsupervised.ipynb new file mode 100644 index 0000000..c7b9852 --- /dev/null +++ b/STAT 7008 project unsupervised.ipynb @@ -0,0 +1,1837 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7ba70409", + "metadata": {}, + "source": [ + "# Unsupervised Models" + ] + }, + { + "cell_type": "markdown", + "id": "dd2ab19d", + "metadata": {}, + "source": [ + "## 1. Data and Clusters' Examples Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "0422e6d9", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "def load(f):\n", + " return np.load(f)['arr_0']\n", + "\n", + "# Load the data\n", + "x_train = load('./kmnist-train-imgs.npz')\n", + "x_test = load('./kmnist-test-imgs.npz')\n", + "y_train = load('./kmnist-train-labels.npz')\n", + "y_test = load('./kmnist-test-labels.npz')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "4bf07b89", + "metadata": {}, + "outputs": [], + "source": [ + "# Flatten images\n", + "x_trainf = x_train.reshape(-1, 784)\n", + "x_testf = x_test.reshape(-1, 784)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "93c6ef34", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
indexcodepointchar
00U+304A
11U+304D
22U+3059
33U+3064
44U+306A
55U+306F
66U+307E
77U+3084
88U+308C
99U+3092
\n", + "
" + ], + "text/plain": [ + " index codepoint char\n", + "0 0 U+304A お\n", + "1 1 U+304D き\n", + "2 2 U+3059 す\n", + "3 3 U+3064 つ\n", + "4 4 U+306A な\n", + "5 5 U+306F は\n", + "6 6 U+307E ま\n", + "7 7 U+3084 や\n", + "8 8 U+308C れ\n", + "9 9 U+3092 を" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "map = pd.read_csv(\"kmnist_classmap.csv\")\n", + "map" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "039fead3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 1\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[2], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[12], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "046a75eb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAD7CAYAAACMlyg3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAoQElEQVR4nO3debgdVZnv8e+PjCBkxDAkQIKdgDQyyGBEmnmeQnNVEIVAVB6xQcAhBPG2jReR2QTHpiWMKiLQkRAEI5KA3RCIIZBgmAQCCQmBCAiGIcN7/6g6cthn1a69z5Scs3+f56nn7PPWqlWrsk/22lW16l2KCMzMrDGtt7YbYGZma487ATOzBuZOwMysgbkTMDNrYO4EzMwamDsBM7MG1rOjdyCpS49BHT58eDJ+/vnnJ+NbbbVVMn700Ucn48uXL29Ns2py2GGHJePTpk1LxhcvXpyMDxs2rN3atC6JCHX1v88ikpJxDwnvOiIi/SY2I+kQYBLQA/hZRFxY7358JmBm1gVJ6gH8CDgU2A74jKTt6q2n9ExA0rbAGGAoEMCLwG0RsaDenZmZWbvZHXg6Ip4BkHQj2Wf1n+uppOqZgKSzgRsBAQ8CD+WvfylpQpXtTpE0W9LsehpjZmaZ5p+j+XJKRZGhwAvNfl+Ux+pSdibweeCfI2JlReMuBx4DktefIuJK4Mq8rC9CmpnVqfnnaIHUPYO6P2/L7gmsATZPxDfL15mZ2dqxCNii2e/DyC7X16XsTOBM4G5JT/HeaceWwD8Bp9W7MzMzazcPASMljQAWA8cBx9dbSdVOICLulDSK7AbEULLTj0XAQxGxuu4md0Evv/xyMr7++usn43vuuWcy/sMf/jAZ/8xnPtO6htXg05/+dF3l33jjjQ5qiXWUomHAr776ajJ+//33d2RzrBNFxCpJpwF3kQ0RnRwRj9VbT+nooIhYAzxQfxPNzKwjRcQdwB1tqcPPCZiZNTB3AmZmDcydgJlZA3MnYGbWwNwJmJk1MHcCZmYNTB2dWra7po3o27dvMj5nzpxkfOTIkcn4jjvumIz/+c+154AqqnvevHk11wHFzzjMnt09U0B1pVTSm266aTI+ZcqUZPyLX/xiMl7v30S9evZMjzrv3bt3Mr5ixYqObE6XVksq6fbgMwEzsy5I0haS7pG0QNJjks5oTT0dPqmMmZl1iFXA1yJijqSNgD9Jmh4R7ZdKGrL5BCTtL2nDivgh9bXXzMzaS0QsiYg5+es3gAW0IpV02XwCXwF+A5wOzJc0ptnqC6ps5/kEzMzaoIb5BJqXHQ7sDMyqdz9ll4O+COwSEW/mO7lZ0vCImEQ6lzXg+QTMzNqqhvkEAMiv0twCnBkRf6t3P2WdQI+IeDNv0HOS9iHrCLaiSidgZmYdT1Ivsg7g5xFxa2vqKLsnsFTSTk2/5B3CEcDGwEdas0MzM2s7SQKuAhZExOWtrqfacwKShgGrImJpYt0nIuJ/amhoQ10OOvbYY5PxG2+8MRn/1a9+lYwfd9xxLWI9evRIlp06dWoyfuihhybjkyZNSsbPPPPMZLy76krPCRxxxBHJeNF7f/vttyfj3/3ud5PxJUuWJONvvfVWMr711lsn45dfnv4sGjRoUDJ+5ZXpqx0TJ05MxtesaZwJDcueE5C0J3AfMI/3Znr8Zp5eumZlk8osqrKutAMwM7OOERF/pB0uy/thMTOzBuZOwMysgbkTMDNrYO4EzMwamDsBM7MG5k7AzKyBeT6BdtarV69k/NFHH03Gt91222T8mGOOaRErGpt96aWXJuPz589PxovmDXj99deT8e6qKz0nMGLEiGT8oYceSsYHDx5cV/3vvPNOMv7uu+8m43369EnGi+YNKFI0n8CWW26ZjC9fvryu+ruyWuYTkNQDmA0sjoj0wyQlfCZgZtZ1nUGWPbTV6u4EJF3Xlh2amVnb5RkdDgd+1pZ6qj4xLOm2yhCwr6QBABFxVFt2bmZmrTYRGA9s1JZKyrKIDgP+TNbTBFknsCtwWbWN8rzXhbmvzcysusTn6JV5emkkHQEsi4g/5dmdW62sE9iV7JrTucA3ImKupLciYma1jTyfgJlZ25TMJ/AJ4ChJhwF9gX6SboiIz9W7n7IEcmuA70v6df7zpbJtzMysY0XEOcA5APmZwNdb0wFAjR/oeTbRT0k6HKh75ppGsnLlymR8/Pjxyfhtt1Xedsn85Cc/aRHr169fsuyCBenBAfvuu28y3mhDQbuDxYsXJ+NPP/10Ml7vENGiIZ9F8SJFQ86ffPLJZLxoeHMjDQVd2+r6Vh8R04BpHdQWMzOrU0TMAGa0dns/J2Bm1sDcCZiZNTB3AmZmDcydgJlZA3MnYGbWwNwJmJk1MKeS7iRSOivsXXfdlYwfeOCBNdc9atSoZPypp56quY5G1JVSSRfZZpttkvGbb745Gd9+++3bZb9vvvlmMp5KgQ5w9913J+Nr1qxpl/Z0R7Wkkm4PPhMwM+uiJA2QdLOkxyUtkPTxeutwCggzs65rEnBnRHxSUm9gg3orqHomIOljkvrlr9eXdJ6kqZIuktS/dW02M7O2yj+b9wKuAoiIdyPitXrrKbscNBlomv9tEtAfuCiPXV2lcadImi1pdr0NMjOz93+O5ktlev6tgZeBqyU9LOlnkj5Q737KLgetFxGr8te7RsRH89d/lDS3aCOnkjYza5uSVNKQfX5/FDg9ImZJmgRMAP5vPfspOxOYL+nk/PUjknYFkDQKSKfLNDOzzrAIWBQRs/LfbybrFOpS1gl8Adhb0l+A7YD7JT0D/Fe+zszM1oKIWAq8IKlpnPD+ZDNB1qVsUpnXgZMkbUR2/aknWc/zUr07anQjR45MxovGeddj//33T8b9nED398QTTyTjY8aMScbvueeeZHzLLbdMxt96661k/OKLL07G586dm4x39PNIDex04Of5yKBngJNLyrdQ66QybwCP1Fu5mZl1nIiYSzYNcKv5YTEzswbmTsDMrIG5EzAza2DuBMzMGpg7ATOzBuZOwMysgXk+gXZ29NFHJ+NXXpl++vuDH/xgMp56X4rmJFi5Mv3w9sEHH5yMF40VbzTdYT6BIltttVUyPnt2Op3XI4+kR4Bfc801yfhPfvKTZPz1119Pxu+4445k/IorrkjG58+fn4w3klrmE5B0FtmDuwHMA06OiLfr2Y/PBMzMuiBJQ4GvkOV12x7oARxXbz3uBMzMuq6ewPqSepLNJfBivRWUzSfQW9KJkg7Ifz9e0g8l/ZukXq1qspmZtVlELAYuBZ4HlgCvR8Tv6q2n7EzgauBw4AxJ1wOfAmYBuwE/K9rI8wmYmbVN2XwCkgYCY4ARwObAByR9rt79lOUO+khE7JCfaiwGNo+I1ZJuoEouIc8nYGbWNjXMJ3AA8GxEvAwg6VZgD+CGevZTdiawXp6dbiOy601NU0r2AXw5yMxs7XkeGC1pA2VDB/cHFtRbSdmZwFXA42R3nc8Ffp3PJzAauLHenZmZWfvIZxO7GZgDrAIepvqZQ1LpcwKSNs93+KKkAWSnIM9HxIM17aDBLgdtscUWyfiTTz6ZjPft27fD2vLSS+lpH8aOHZuM33XXXR3WlnVRd3hOoHfv3sn4xIkTk/EjjzwyGR89enQyvvfeeyfjN9yQvuJQ9CxLkaLnCv71X/81GZ8xY0aLWHedq6CW5wTaQ+l8AhHxYrPXr5FNYWZmZt2AnxMwM2tg7gTMzBqYOwEzswbmTsDMrIG5EzAza2Clo4OsPi+88EIyfvXVVyfju+++ezKeGvo3cODAZNmhQ4cm45tsskkyfswxxyTjjTZEtCspGnp5/PHHJ+Pjxo1LxseMGZOML168OBnv379/Ml7vUNAi/fr1S8Z/+tOfJuM777xzi9iKFSvapS2NymcCZmZdkKTJkpZJmt8sNkjSdElP5T/T3xybKcsi2l/ShZIel7Q8XxbksQHtcBxmZtY61wCHVMQmAHdHxEjg7vz3qsrOBG4CXgX2iYjBETEY2DeP/breFpuZWfuIiHuBv1aExwDX5q+vBY4uq6esExgeERdFxNJmO14aERcBWxZt5FTSZmZtU5ZKusAmEbEEIP85pGyDshvDCyWNB66NiJfyhm0CnASk74DiVNJmZm1VQyrpdlF2JnAsMBiYKemvkv4KzAAGkU0wY2Zm646XJG0GkP9cVrZB1U4gIl6NiLMjYtuIGJQvH46Is6nhWpOZmXWq24CmNMFjgd+UbdCW5wTOI5t+0mrw5S9/ucPqvu6665LxE044IRkvSg9c9BzCq6++2rqGWbspSpe8+eabJ+NFacTvu+++uva7cOHCZHz16tXJeI8ePeqqf82aNcn40qVLk/GVK1fWVX93JumXwD7AxpIWAd8GLgRukvR5sklnSq/YVO0EJD1atApIP4lkZmYdLiI+U7Bq/3rqKTsT2AQ4mGxIaHMC/reeHZmZ2bqnrBO4HdgwIuZWrpA0oyMaZGZmnadqJxARn6+yLp20xMzMugznDjIza2DuBMzMGpg7ATOzBub5BNayXr16JeOpcdhFY6q/+tWvJuNFueO32WabZPyKK65IxoueN7DOU5S/f9CgQcn4O++8U1c99e53vfXa5/tj0XMF8+fPT8ZXrVrVLvvtDiRNBo4AlkXE9nnsEuBI4F3gL8DJEfFatXp8JmBm1jVdQ8tU0tOB7SNiB+BJ4JyySsrmE+gn6XuSrpd0fMW6H9fXXjMzay+pVNIR8buIaDpdegAYVlZP2ZnA1WQPht0CHCfpFkl98nWj62uymZl1onHAb8sKlXUCH4qICRExJSKOAuYAf5A0uNpGnk/AzKxtWjmfQNO25wKrgJ+XlS27MdxH0noRsQYgIr6bJyq6F9iwaCPPJ2Bm1jatnU9A0liyG8b7R1HmwWbKzgSmAvtVNOxa4Gtkd5/NzGwdIekQ4GzgqIhYUcs2ZWkjxhfE75R0Qf1NNDOz9lCQSvocoA8wPR/e+0BEfKlqPTWcLRQ14PmIKJxnuFk5Xw6q4lvf+lYyfuqpp7aIPf7448mys2enb72ceOKJyfimm26ajBeNLe/fv39d5buKiFBX//v8xS9+kYx/8pOfTMZvvPHGZHzRokXJ+HHHHZeMjxgxoobWlVuxIv1ldfvtt0/Gn3322XbZb1cQEfU91NFKnk/AzKyBeT4BM7MG5vkEzMwamOcTMDNrYM4dZGbWwNwJmJk1sFYPEa15B118CN7astlmm7WIfeMb30iW/fKXv5yM9+nTJxmv1+jR6TRRs2bNapf615buMET0Bz/4QTJ+2mmndXJLWmfJkiXJ+C677FJX+e6os4aI1n0mIGlIRzTEzMxqJ2mypGWSWky+IOnrkkLSxmX1lKWSHlSxDAYelDRQUno2CzMz6wzX0HI+ASRtARwIPF9LJWVDRF8BFlbEhpJlEw1g61p2YmZm7Ssi7pU0PLHq+8B44De11FN2OWg88ARZMqIRETECWJS/LuwAnErazKxtWpNKWtJRwOKIeKTW/ZQ9J3CppBuB70t6gSxBUemNNKeSNjNrm3pTSUvaADgXOKie/ZTeGI6IRRHxKeAesvkrN6hnB2Zm1ik+BIwAHpH0HNnUknMkpTNG5moeHRQRU4F9gQMAJJ3c6qaamVm7ioh5ETEkIoZHxHBgEfDRiFhabTunku4GRo4cmYxPmDAhGR83blwyPn9+i5FmAOy1117J+KuvVuYV7Fq6w3MCBx98cDJ+xx13JOPrrdc1ng9NpVIH+OlPf9rJLVl7yp4TaD6fAPAS8O2IuKrZ+ueAXSPilWr1OJW0mVkXFBGfKVk/vJZ6nErazKyBOZW0mVkDcyppM7MG1jXuEpmZWYdwJ2Bm1sDcCZiZNTDPJ9CNfeITn0jG//jHPybjd955ZzJ+6KGHtlub1iXd4TmBDTZIP8A/bdq0ZHzPPfdMxnv2TN8eXLNmTTL+8ssvJ+PLly9PxrfbbrtkvMjixYuT8d12261FrLvOMVDDcwKTgSOAZRGxfbP46cBpwCpgWkSMr1ZPa+YTGFzvNmZm1u6uoSKVtKR9gTHADhHxz8ClZZWUzSdwYdOkBJJ2lfQMMEvSQkl7t7blZmbWNhFxL/DXivCpwIUR8U5eZllZPWVnAoc3e+T4EuDYiPgnsgkLLquvyWZm1sFGAf8iaZakmZJaXj+rUNYJ9JLUdLFw/Yh4CCAingQKJ7D1fAJmZm3TmvkEyJ79GgiMBr4B3CSp6r2FsieGfwTcIelC4E5JE4Fbgf2BuUUbeT4BM7O2qXc+gdwi4NbIRvw8KGkNWYK59J18yp8Y/oGkeWTXmUbl5UcBU4D/V2fjzMysY00B9gNmSBoF9CabJrhQ2ZkAETEDmFEZz+cTuLoVjTQzszZqnkpa0iKymR8nA5MlzQfeBcZGyXMApZ1AFefhTmCdNmTIkLrKDxs2rINaYh1lxYoVyfjhhx+ejB90UHrmwaLLxitXrkzGZ82alYz36tUrGZ88eXIyXjQfwtChQ5Px4447rkVs4sSJybId/QzU2lYllfTn6qnH8wmYmTUwzydgZtbAPJ+AmVkD83wCZmYNzFlEzcwamDsBM7MG1pYhog3tjDPOSMbHj09nbX3ssceS8WuuuSYZv/XWW1vE3n777doalzvssMPqKv/ss88m40XDB7v7ELyurGjo6JQpUzq3IbmTTjopGX/ggQeS8a222ioZP//881vEnn766WTZqVOnJuM9evRIxlevXp2Md3c+EzAz64IkTZa0LH8wrCm2k6QHJM3N8w3tXlZPWSrpXSXdI+kGSVtImi7pdUkPSdq5PQ7EzMxa5Roq5hMALgbOi4idgH/Pf6+q7Ezgx3kl08ieC/jPiOgPTMjXmZnZWlAwn0AA/fLX/YEXy+opTSUdEb+NiF9m+4yb853fDfQt2sippM3M2qaVqaTPBC6R9ALZrGLnlG1QdmP4bUkHkfUoIenoiJiSzypWeBfFqaTNzNqmlamkTwXOiohbJH0auAo4oNoGZWcCXwK+BowjSx+xr6TXyC4FfaXOxpmZWccaSzbnC8CvgbbdGI6IRyLi4Ig4NCIej4gzImJAPoHxNm1vr5mZtaMXgab53/cDnirbQK0d6y3p+YjYsoZyXfpy0IABA5Lx559/Phlfs2ZNMj59+vRkfOed04OsUql0Fy5cmCz71FPp9/nAAw9MxovaeMop6UuOf/jDH5LxF18svee0TosIdfW/z66u6O//3nvvTcY33HDDFrGi/xennXZaMn777bfX2Lq1KyKqTgvZfD4B4CWy+QSeACaRXep/G/hyRPypWj1OJW1m1gVVmU9gl3rqcSppM7MG5lTSZmYNzKmkzcwamHMHmZk1MHcCZmYNzJ2AmVkDa/VzAjXvoIuPw95rr72S8ZkzZybjEydOTMbPOuusZLxXr17JeGrMflHdPXvWNy3E2LFjk/Hrrruurnq6Oj8n0P7aa+6Jor/FE044oeY67rvvvmT8mGOOScZfeeWVmuvuDGXPCbSXslTS/SVdKOlxScvzZUEeG9AZDTQzs5by9P735J/Jj0k6I48PytP+P5X/HFitnrLLQTeRPSOwT0QMjojBwL557NftcSBmZtYqq4CvRcSHgdHAv0najizV/90RMRK4O/+9UFknMDwiLoqIpU2BiFgaERcBpSkjzMysY0TEkoiYk79+A1gADAXGANfmxa4Fjq5WT1knsFDSeEn/SBEhaRNJZwMvFG3k+QTMzNqmnvkEJA0HdgZmAZtExBLIOgpgSLX9lN1RPJbsVGJm3hEEWaKi24BPF23k+QTMzNqm1vkEJG0I3AKcGRF/K7o5X6TsieFXJV0NTAceiIg3m+34EODOuvZmZmbtRlIvsg7g5xHRNI/AS5I2i4glkjYDllWro2x00FeA3wCnAfMljWm2+oLWN93MzNpC2Vf+q4AFEXF5s1W3kU0uQ/7zN1XrqTZ+V9I84OMR8WZ+zelm4PqImCTp4YhIJwN/fx1d+nLQxhtvnIwvW5buXKdMmZKMF41NrsdOO+2UjB977LHJ+P3335+M33bbbW1uS3fg5wTKbbTRRsn4N7/5zWR89er0rLPf+ta36tpv0fM5qbktevTokSxb9Nk2YUJ6sMxll12WjBcdU0erYT6BPYH7gHlA0yQh3yS7L3AT2eCd54FPRUTlhPT/UHZPoEfTJaCIeE7SPsDNkrYiSydtZmZrQUT8keLP4f1rradsdNBSSTs12+mbwBFkM9l8pNadmJnZuqmsEzgRWNo8EBGrIuJEIH2+ZmZmXUbZ6KBFVdb9T/s3x8zMOpOziJqZNTB3AmZmDcyppFupaPjlbrvtlowXDe+cP39+ezXJ6uQhouX69u2bjN9zzz3J+LRp05LxCy5IP1Y0ZEg6o0FR+ZNPPjkZr8dLL72UjO+xxx7J+DPPPNPmfbbGupJKup+k70m6XtLxFet+3LFNMzOzIlVSSV+Sp/9/VNJ/l6X9L7scdDXZONRbgOMk3SKpT75udFsPwszMWq0olfR0YPuI2AF4EjinWiVlncCHImJCREyJiKOAOcAfJA1ue/vNzKy1ilJJR8TvImJVXuwBYFi1esqeGO4jab2IWJPv6LuSFgH3AhsWbZSnPC1Me2pmZtUlPkevzDOLpsoO571U0s2NA35VbT9lncBUYD/g902BiLhW0kvAD4o2cippM7O2aW0q6Wbxc8kuGf282vZVLwdFxHhgkaT98x01xe8EvlLWODMz6zgFqaSRNJYsxc9no2QIaNnooNPJ0pCeTstU0t9tbcPNzKxtilJJ53O9nA0cFREryuopuxx0CrBL81TSkoZHxCQaPIvo7bffnoyPHp0eNHXjjTcm40ceeWQy/uyzz7auYdYl9OyZ/q/34Q9/OBkfNWpUMj5ixIhkfIsttkjGP/axjyXjjz76aDI+a1blJebM8OHDk/Fx48Yl44cffngy/pGPpPNQfuADH0jG61H0BXjgwIHJ+NChQ5PxtfWcQA0+AZwAzJM0N499E7gC6ANMz2cZeyAivlRUiVNJm5l1QVVSSd9RTz1OJW1m1sCcStrMrIE5lbSZWQNzFlEzswbmTsDMrIG5EzAza2B1zycgaUhELKujfLdMG7H55psn4w8++GAyXjQGedmy9D/lZz/72Rax3//+94mS1lprcz6BHXfcMRkvytM/YMCAZDwfB97pVq9enYy/++67yXivXr3qqqdPnz7JeD2KPtsefvjhZHzs2LHJ+Nqa82NdmU9gUMUyGHhQ0kBJgzqjgWZm1lLRfALN1n9dUkjauFo9ZQ+LvQIsrIgNJUspHcDW9TbczMzaRdN8AnMkbQT8SdL0iPizpC2AA4HnyyopuycwHniCLAfFiIgYASzKX7sDMDNbS4rmE8hXf5/s87v0cmdZFtFLgS8A/y7p8ry3Ka1U0imSZkuaXVbWzMxaav45mi+Fc7Q0n09A0lHA4oh4pJb9lF0Oanpg7FOSjiSbtmyDGrbxfAJmZm3QmvkEyC4RnQscVOt+SoeIStpW0v7APcC+wAF5/JBad2JmZu0vMZ/Ah4ARwCOSniObWnKOpE2L6igbHfQVms0nABwUEU3jpS5o8xGYmVmrpOYTiIh5ETEkIoZHxHBgEfDRiFhaVE/Z5aAv4vkEkl588cVk/KCD0mdhM2bMSMaHDBmSjN9xR8tssFdddVWy7DnnnJOMv/baa8m4rX29e/dOxte15wGKcumfdNJJyfgLL7yQjBc9V7PRRhsl46nnZAB22GGHFrFtttkmWbbIBz/4wWT873//e131rAOS8wlERF2ppD2fgJlZF1RlPoHmZYaX1eP5BMzMGpjnEzAza2CeT8DMrIE5i6iZWQNzJ2Bm1sDq7gTyTKJmZtYNVJ1PQNKFwKUR8YqkXYGbgDVAL+DEiJhZugOnjQDS45sB7rvvvmS8X79+Ndf97W9/Oxn/zne+k4z37ds3GT/llHRqkpkz02/zI4/UlJpknbU25xPYcMMNk/HLLrssGR83blwy3rNnaeaXNnn77beT8WOPPTYZnzp1ajJe77wlRfMJpOKjRo2qq+4lS5Yk40XP/tTb9vZSNp9Anin0OmBTss/lK/NnuJB0OnAaWRqJaRExvqiesr+gwyNiQv76EuDYiHhI0ijgF8CutRyMmZm1u2QqaWATYAywQ0S8Iyn9RGqurBPoJalnRKwC1o+IhwAi4klJbZ/6x8zMWiUilgBL8tdvSGpKJf1F4MKIeCdfV3UmyLJ7Aj8C7pC0H3CnpImS9pJ0HjC3aCOnkjYza5vWppIGRgH/ImmWpJmSdqu2n7LnBH4gaR5wal5xz/znFOD8Kts5lbSZWRu0JpV0RPxNUk9gIDAa2A24SdLWUXBzo5a7SkvzhsxqyiOU7/gQ4M4atjczsw6QSCUNWebQW/MP/QclrSFL9fNyqo66UklLGtNstVNJm5mtJalU0rkpwH55mVFAb7L54tP1lAwRnQd8vHkqaeD6iJgk6eGI2LmGhvpyUBV77LFHMn7BBS372K23Tk/rfMwxxyTjS5emU4hPnz49Gd92222T8X333TcZL0qP3VWszSGiRamhi4Z8fuELX0jGR44cWVc9xx9/fDI+eHB9j/8sX748GT/ggAOS8Xnz5iXjq1evrmu/jaSGIaJ7AvcB88iGiAJ8E/g9MBnYCXgX+HpE/KGoHqeSNjPrgkpSSX+u1nqcStrMrIE5lbSZWQNzKmkzswbmLKJmZg3MnYCZWQNzJ2Bm1sgiotMW4BSXb5/y61JbukP5dbFNLt812tIZ5Tty6dydwWyXb5/y61JbukP5dbFNLt812tIZ5Tty8eUgM7MG5k7AzKyBdXYnUJoW1eXXibobsXxn7MPl26/8utSWzijfYaomkDMzs+7Nl4PMzBqYOwEzswbmTsCMbKY8SU9IelrShJKyW0i6R9ICSY9JOqOG+ntIeljS7TW2Z4CkmyU9nu/n41XKnpW3Y76kX0rqmygzWdIySfObxQZJmi7pqfznwJLyl+TteVTSf0saUFS22TZflxSSNq5Wdx4/PX8PHpN0cUlbdpL0gKS5+fy7uzdbl3x/io63Svmi4636/jc/5mpli46303Xk+FNgW+Bs4ApgUv76wyXl9wc2rIgfUsO+ritZ/zGgX/56feA8YCpwEdC/omxvsgyqB+S/Hw/8EPg3oNfaHtfrpX0XoAfwF2Dr/L1/BNiuSvnNgI/mrzcCnqxWPi/3VeAXwO01tula4Av5697AgIJyQ4FngfXz328CTkqU2wv4KDC/WexiYEL+egJwUUn5g4Ce+euLmsqnyubxLYC7gIXAxiV170s2GUqf/PchJeV/Bxyavz4MmFH2/hQdb5XyRcdb+P5XHnOVuguPt7OXDjsTkHQ2cCPZpAcPAg/lr3+Z+qZVz1SWkm6rWKYCxzT9XtCkycCK/PUkoD/ZG7sCuLqi7NXA4cAZkq4HPgXMIpu0+Wc1/QNYu5I0pM7y9UyVtTvwdEQ8ExHvkv3djikqHBFLImJO/voNYAHZh3FRW4aR/T3V9LcjqR/ZB99V+T7ejYjXqmzSE1hf2QTjGwAvJtp8L/DXivAYss6G/OfR1cpHxO8iYlX+6wPAsCp1A3wfGA+8b/RJQflTgQsj4p28zLKS8gH0y1/3p9kxV3l/ksdbVL7K8VZ7/993zFXKFh5vp+uo3oWsx2vxrZnsW81Tifg88jMAYDgwGzgj//3hirJzgBuAfYC9859L8td7F7RnQfPtK9bNrfj90fxnT+AlshnWIOvEHl1bPXbimGr+9gAMbsf99gcuBB4HlufLgjw2IFG+H/A94Hrg+Ip1P06UH1SxDAaeAwYCgxLlLyT/pgnsCjwDPE32bSz591Cx/SeBnzX7/QTghzX+WwwHnic/yywoczOwS/53WnomQDYt4IPANcDDZJ3HB6qUPwN4k2wi8Z+XtLX5t+nXKta/Wq18xbqpwOeq1H0UMCl//RzNzgQKys8lOzufBcwEdisp/+H83/0FYDGwVdn7U3a81d7PyuMtqL+WY24qW/V4O3PpyHsCa4DNE/HNeG8+zObeN5Ul2X+YQyVdTssp1HYF/gScC7weETOAtyJiZkTMLGjPfEkn568fkbQr/GMi5pUVZdeT1Jvs9G0Dsg89gD5Ar1TlkvpLujC/hrg8XxbksQGJ8v0kfU/S9ZKOr1j340T5QRXLYOBBSQMlDaooe2HTNVhJu0p6BpglaaGkvRN175pft7whv4Y5XdLrkh6SlJpH+ibgVWCfiBgcEYPJTm9fBX6dKH812Xt4C3CcpFsk9cnXjU6Uf4Xs/W1aZpN9e5qTv650eEQ0TaR9CXBsRPwTcCBwWaJ8pdQUfaVjpyVtSHZMZ0bE3wrKHAEsi4g/1dCOJj3JLn/8JLJ5vP9OdvkiVf9Asm+4I8j+v31AUs1TC7aGpHOBVcDPC9ZvQPZ/89/rqLYnWSc/GvgGcJNUMBFz5lTgrIjYAjiL/Kypoh2l708t5YuOt3n5fH3hMSfqrvd4O05H9S7AIWTfxn5L9mDElcCdeazFNX7gD8BOFbGewHXA6oJ9DCP70Pkh8HxJe/qTfbP6C1nvu5LsG+NMYMeKsmfl6xYCXwHuBv6L7Gzl2wX130V2z2PTZrFN89j0RPlbyL7BHg3clv/edH1wTqL8GrJrv82XlfnPZyrKzmv2+h7ybxnAKBI5S8i+dR4KfIbsm9Un8/j+wP2J8k9U+XdusY6WZ1rnAv9D9g0/daxfz/9WPtIs9myVfT7Oe9duHyj6t6iy/ceBu5r9fg5wTsk2vfL3/Ksl5b4HLCL7ZriU7PLjDSXbbAo81+z3fwGmFZT9FHBVs99PJHF2la8bzvu/TT8BbJa/3qzyvassn8fGAvcDGxSVJZt6dll+zM+RfUA+X/F/o7Itd5J9qWj6/S/AB6uUf533nnMS8Ley96fa8Ra9n1WO933lqx1zQVuqHm9nLh1beTb6aDTwf8hOuUeTX1pJlB3W/I+kYt0nSvZzOHBBjW3aCNiR7PR8kyrlNgc2z18PyNu/e5Xy68wHI3V+KNLschsVnSkVl+Ly2O/Irntu0iy2CVmH9/tE+QXAehWxscBjwMIqfw+/Bi7P37NnUuXysqfnbdoP+A9gItk19fOA62v4m+hJ1umP4L0bw/9cpbzIvpxMrPP/wz7UfmP4PmCb/PV/AJcUlPtY/u+4Qd6ua4HTC8oO5/0fpJfw/hulF5eUPwT4M4kPq8qyFeueo/xy0JeA7+SvR5F9GVGV8gvIP0TJvqz8qez9KTreKuWTx1vL+990zFXqrnq8nbl0+g6768I69MFInR+KZN90DiL7VrkQODqP7036zGEg2U31x8kuAf01P56LSF+zv5h8pFVF/BAS94cqyhxJdlNuaUm5fYBfkV1DnwfcAZxCjaO5yEaYPEn2jezckrJ7kl0uepTs2u5c4LAa9rEPtXcCO5Fd+noUmAIMrFL2vPy9mE9236VPoswvye6brSQ7M/k82ReOu4Gn8p+DSso/nX9YNR3zT4vKVuz7Od4/OihVd2+y+3zzyS777VdSfk+yS4WPkJ3Z71L2/hQdb5XyRcdb+v7zXidQVHfh8Xb2slZ22h0X3v/B+Ffe/8HY4j8wHfzBSPGHYs9E2R3JTld/SzZMdxLwGlmHtEdB/dsCB1DjcF6Kh/8eWlaebEjv9q2sv3R4sRcvjbys9QY0wgKc3N7lKz4Ya66/PdpCdp/kCbJvqM8BY5qtS13KOr3O8vXWX1d5L168vLes9QY0wkLJTevOLN8edVPHcN51sbwXL17eW3pi7ULSo0WryO4NdFr5jm4LFcN5Je0D3CxpK9LDLde18maWcyfQfjYBDia7UdqcgP/t5PId3ZalknaKiLkAEfFmPh5+MtlQuXW9vJnl3Am0n9vJLknMrVwhaUYnl+/otpxINg76HyJ7vP5ESf/ZBcqbWc6TypiZNTCnkjYza2DuBMzMGpg7ATOzBuZOwMysgf1/OytM6c921YUAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 2\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[3], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[8], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "da357b54", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 3\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[5], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[26], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7286cdbb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 4\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[21], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[54], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "38866d42", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 5 \n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[4], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[6], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e0c292a2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 6\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[10], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[13], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5c9e5d1f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 7\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[24], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[25], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "523d5272", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAD7CAYAAACMlyg3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAo4klEQVR4nO3dedxd47338c83MqCR0Zykgkon1SBFRySt+eDVUq0aSg1VjZiO4Tg9jvaoGIq0Hk8pQvFwiNZQpdRYLTElJMTciCCJuaYiye/5Y627tp1r7bX3PWXf2d/367Ve996/da21rnXv+17XXmtd63cpIjAzs9bUa2lXwMzMlh43AmZmLcyNgJlZC3MjYGbWwtwImJm1MDcCZmYtrHdXb0CS+6BaU4oI+e/TmlVEqKyMpG2AScBywHkRMbHR7airnxPwP5k1KzcC1szKGgFJywFPAN8A5gL3Ad+NiEcb2U7pmYCkTwE7AcOAAF4Aro2IWY1syMzMOtUmwFMR8QyApMvJjtUNNQI17wlIOhq4HBBwL1lLI+AyScfUWO4ASfdLur+RypiZWabyOJpPB1QVGQY8V/F+bh5rbDu1LgdJegL4bER8UBXvCzwSEeuVbsCn29akfDnImlkdl4N2BbaOiP3y93sCm0TE+Ea2U9Y7aDGwZiK+Rj7PzMyWjrnAiIr3w8ku1zek7J7AocAtkp7kw9OOjwOfAH7c6MbMzKzT3AesJ2lt4HngO8Duja6ktHeQpF5kNyCGkd0PmAvcFxGL6tqAT7etSflykDWzOruIbgecSdZF9IKIOLHR7biLqLUsNwLWzOppBDqDnxg2M2thbgTMzFqYGwEzsxbmRsDMrIW5ETAza2FuBMzMWpgbATOzFuZGwMysB5I0QtJtkmZJekTShHatxw+LWavyw2LWzOpIILcGsEZEPChpJeABYOdGxxMoPROQ9ClJ4yT1r4pv08iGzMys80TEixHxYP76TWAW7UglXTaewCHANcB4YKaknSpm/7zGch5PwMysA+oYT6Cy7EhgQ2Bqw9spGU9gBvDFiHgr38gU4OKImCRpWkRsWMeO+HTbmpIvB1kzqzd3UH6V5g7gxIj4XaPbKUslvVxEvJVXaLakLYApktYiyyhqZmZLiaQ+wFXApe1pAKD8nsA8SaPb3uQNwg7AysDn2rNBMzPrOEkCzgdmRcTp7V5PyeWg4cDCiJiXmPfliPhrHRX16bY1JV8OsmZWR++grwB/AWbw4UiP/xERf2xkO+4iai3LjYA1M48nYGZmXc6NgJlZC3MjYGbWwtwImJm1MDcCZmYtzI2AmVkLK3ti2MxsCUOHDk3GDz/88GR8zJgxyfi7776bjJ922mnJ+F//uuSjSV3dzb2ZSVoOuB94PiJ2aM86fCZgZtZzTSDLHtpuDTcCkn7bkQ2amVnH5RkdtgfO68h6al4OknRtdQjYUtIggIjYsSMbNzOzdjsTOApYqSMrKbsnMBx4lKylCbJGYAzwi1oL5XmvC3Nfm5lZbYnj6LkRcW4+bwdgQUQ8kGd3breyRmAM2TWn44B/j4jpkt6NiDtqLZRXtK2yrXvXxsysnSqPowlfBnaUtB2wPDBA0iURsUej26nZCETEYuAMSVfmP+eXLWNmZl0rIo4FjgXIzwSObE8DAHUe0CNiLrCrpO2Bf7RnQ2bW8wwcODAZv/TSS5PxrbfeulO2u+GG6UEL119//SVib775Zqdss1U19K0+Iq4Hru+iupiZWYMi4nbg9vYu7+cEzMxamBsBM7MW5kbAzKyFuREwM2thbgTMzFqYGwEzsxamrk7D6ieGa5OUjJ944olLxIYPH54s+9577yXjZ599djI+bdq0ZHzzzTdPxtdcc81k/LLLLkvGe4qIUKv9fS6//PLJ+H/+538m4wcckM7+ssoqq3RKfT744INkvOj/IpWq+qyzzkqW7ekppiMi/UvoZD4TMDProSQNkjRF0mOSZkn6YqPrcAoIM7OeaxJwY0TsIqkvsGKjK6h5JiBpU0kD8tcrSDpB0nWSTpaUfp7czMy6XH5s/hpwPkBEvB8Rrze6nrLLQRcA7+SvJwEDgZPz2OQalTtA0v2S7m+0QmZm9tHjaD5V36BZB3gJmCxpmqTzJH2s0e2UXQ7qFREL89djImKj/PVdkqYXLeRU0mZmHVOSShqy4/dGwPiImCppEnAM8JNGtlN2JjBT0j7564ckjQGQNApI39Y3M7PuMBeYGxFT8/dTyBqFhpQ1AvsBm0t6GvgMcLekZ4Df5PPMzGwpiIh5wHOSPpmHxpGNBNmQup4TkLQS2fWn3mQtz/y6N+DLQQD069cvGT/ssMOS8dRzAosWLUqWPemkk5Lx448/vs7ataZazwl87GPpS6vvvvtuMr548eLOq1gXGjZsWDI+a9asZLxov4r+Fq+66qpkfL/90t8Zi54HeO2115Lx1HMOP/7xj5NlJ09O37bsKc8P1POcgKTRZMP/9gWeAfaJiPQvr0C9g8q8CTzUyIrNzKxrRcR0smGA280Pi5mZtTA3AmZmLcyNgJlZC3MjYGbWwtwImJm1MDcCZmYtzFlEu8lWW22VjP/P//xPMp7qn73LLrsky1577bXtr5glvf3220u7Ch1S1P++6O9whRVWSMZ7904fIs4555xkvOi5lzfeeCMZP+KII5LxwYMHJ+MpReMJPPDAA8n4Qw8tO73dJR1G9uBuADPInhP4ZyPr8JmAmVkPJGkYcAhZXrf1geWA7zS6HjcCZmY9V29gBUm9ycYSeKHRFZSNJ9BX0l6Svp6/313SWZIOltSnXVU2M7MOi4jngdOAOcCLwBsRcVOj6yk7E5gMbA9MkHQxsCswFfgCWb6KJI8nYGbWMWXjCUgaDOwErA2sCXxM0h6NbqfsxvDnImKD/FTjeWDNiFgk6RJq5BLyeAJmZh1Tx3gCXwf+HhEvAUj6HfAl4JJGtlN2JtArH7dyJbLrTW1DSvYDfDnIzGzpmQNsJmlFZd3BxgHpdLA1lJ0JnA88RnbX+Tjgynw8gc2AyxvdmJmZdY58NLEpwIPAQmAatc8ckkrHE5C0Zr7BFyQNIjsFmRMR99a1gWX0clBR/+kDDzywofhaa62VjE+cOHGJWP/+/ZNlTz311GT89ddfT8YtU2s8gZ5ut912S8ZPOeWUZHy11VZLxi+88MJkfMKECcl40TgDo0ePTsb33XffZPyggw5KxlPPz/Tqlb6g8etf/7qhdTebesYT6AylD4tFxAsVr18nG8LMzMyWAX5OwMyshbkRMDNrYW4EzMxamBsBM7MW5kbAzKyFOZV0iaKuoGeccUYyfvDBByfj77zzTjL+7rvvJuPjx49fIrbGGmsky/bpk35ur6jr6EsvvZSMW8+z0korJeOvvvpqQ+t58sknk/FUV2WA9957Lxkv6q5ZtP45c+bUUbvy9af84Ac/SMaL6r788ssn43feeWcy/vvf/z4ZL/qfblY+EzAz64EkXSBpgaSZFbEhkm6W9GT+s3RghrIsogMlTZT0mKRX8mlWHhvUCfthZmbtcyGwTVXsGOCWiFgPuCV/X1PZmcAVwGvAFhExNCKGAlvmsSsbrbGZmXWOiLgTqL7utxNwUf76ImDnsvWUNQIjI+LkiJhXseF5EXEy8PGihZxK2sysY8pSSRdYLSJeBMh/rlq2QNmN4WclHQVcFBHz84qtBnwfeK5oIaeSNjPrmDpSSXeKsjOB3YChwB2SXpX0KnA7MIRsgBkzM2se8yWtAZD/XFC2QM1GICJei4ijI+JTETEknz4dEUdTx7UmMzPrVtcCe+ev9wauKVugNJV04YLSnIgovC9QUa5HXA765je/mYwXpbrdbrvtkvFHHnkkGS/q4z906NBkPNWver311kuWLTJp0qRk/NBDD21oPcuqZSGV9KhRo5LxotTlhxxySDJ+3nnp0WJnzJiRjK+77rrJeNEzKxtttFEyPmbMmGS8X79+yfjSUPRcwaxZ6fFbTjvttGT86quvTsbffvvtZLwslbSky4AtgJWB+cDxwNVkHXo+TjbozK4RUfOhkZr3BCQ9XDQLSCcgNzOzLhcR3y2YNa6R9ZTdGF4N2JqsS2glAX9rZENmZtZ8yhqBPwD9I2J69QxJt3dFhczMrPvUbAQiIp18I5u3e+dXx8zMupNzB5mZtTA3AmZmLcyNgJlZC2v3cwJ1b6DJ+mHvueeeyXhRP+m+ffsm4w899FAyvv/++yfjf/7zn5PxXXdNP3h91113LRGbNm1asmxRX/EFC9IPC+68887J+IABA5LxDTbYIBlfZ511kvGi/tNFv7N77703Ge/qvOxL8zmB5ZZbLhkvGh+gaFyLXXbZJRkv+rsqyplf1Lf9Zz/7WTK+1lprJeP/+Mc/kvHVV189GW9kfIAiRccwqWY3+y6zaNGiZPzRRx9Nxr/61a8m46+//nrZcwIXADsACyJi/Tx2KvBvwPvA08A+EfF6rfX4TMDMrGe6kCVTSd8MrB8RGwBPAMeWraRsPIEBkk6SdLGk3avmnd1Yfc3MrLOkUklHxE0RsTB/ew8wvGw9ZWcCk8keDLsK+I6kqyS1Pc+9WWNVNjOzbrQvcENZobJGYN2IOCYiro6IHYEHgVslpRPe5DyegJlZx7RzPIG2ZY8DFgKXlpUte2K4n6ReEbEYICJOlDQXuBPoX7SQxxMwM+uY9o4nIGlvshvG46KOnj9lZwLXAWOrKnYRcATZ3WczM2sSkrYBjgZ2jIh36lmmLG3EUQXxGyX9vPEqmplZZ6hMJZ1foTmerDdQP+DmvIvsPRHxw1rrKbscVMsJZDeOl6qiftWXXHJJMl7UJ7eov/Kxx6Z7WP3mN79JxnfaaadkfP78+cl4//7pq2p77LHHErFPfOITybJFVl01PbzoX/7yl2S8qO96Zyk6M33ssceS8bPPTndAO+ecc5LxDz74oH0VSyj6eyjKc7/DDjsk49/4xjeS8a985SvJeNFzKUWfzaBBg5Lxot/Fm2++mYzvvffeyfhnP/vZZLxIUf1feeWVZLzo/7eoj3/q97948eJk2ddeq05+nCn6Xywa26MoXjR2QtHfzvrrr5+MH3bYYcl4mYJU0uc3uh6PJ2Bm1sI8noCZWQvzeAJmZi3M4wmYmbUw5w4yM2thbgTMzFpYj0klXdT17MILL0zGv/vdVO8peOCBB5Lxk046KRnfZJNNkvFUF06ANddcMxkvUvT7nz179hKxYcOGJcsW/W6K3HHHHcn45ZdfnoyPGDEiGR8zZkwyvtVWWzVUn0b9/e9/T8aPOeaYZHzKlCnJ+KJFi7TpppsmP4CirsGf/vSnk/H11lsvGS/qLvjyyy8n4y+++GIyvvbaayfjRV0pi7b73nvvJeNF6byL0oLffffdDZVP/T0DrLZaupPhCSeckIyn/q+LusMWdREt+t+dOnVqMn7IIYck4z/96U+T8UZTWBfVc/Dgwd2SC7vhMwFJ6c7nZmbWbSRdIGmBpJmJeUdKCkkrl62nLJX0kKppKHCvpMGShnSg/mZm1jEXsuR4AkgaAXwDmFPPSsq6iL4MPFsVG0aWTTSA9NBSZmbWpSLiTkkjE7POAI4CrqlnPWWXg44CHidLRrR2RKwNzM1fFzYATiVtZtYx7UklLWlH4PmISI/lmlD2nMBpki4HzpD0HFmCotIbvU4lbWbWMY2mkpa0InAc0FDPjNIbwxExNyJ2BW4jG79yxUY2YGZm3WJdYG3gIUmzyYaWfFDS6rUWqrt3UERcB2wJfB1A0j7trqqZmXWqiJgREatGxMiIGAnMBTaKiHm1lmv3cwKS5kTEx+so19AGxo4dm4wXpQ/++MfTVSjql3zxxRcn4+PHj0/GV1lllWS80b7ATz75ZDJ+7rnps73TTz99idiPfvSjZNlf/epXDdVlwoQJyfgvf/nLhtZT9DsoSp9clA750EMPTcaLnkMYMiTdMW3RokXJeNFzC7fccoteeOGF5N9no897LC0vvPBCMl7U533hwoXJ+Le//e1Oq1Mjiv6Giv5Pv/e979W97qK/h6uvvjoZf+utt5LxrbfeOhlfffWaX7A7Q82DTOV4AsB84PiIOL9i/mxgTESkH0rJOZW0mVkPVDCeQOX8kfWsx6mkzcxamFNJm5m1MKeSNjNrYc4iambWwtwImJm1MDcCZmYtrOzGcIcV9QP+/ve/n4wfeeSRyfg///nPZHzmzCWyqAKw0UYbJeNHHHFEMr548eKG4pdddlkyXpTbfOjQocn4/fen0yulttvouAFF/aSLfmeNKnrG5KabbmpoPTfccEMyfvDBByfjZ511VjJe9BzCdtttV7jtvfbaKxk/77zzkvGRI0cWrivlkUceScaL+sEX9fufPHlyMl70PMO2226bjF9xxRXJeGfp06dPMj5o0KBk/JOf/GQyPm7cuA7Xpejv4Vvf+laH192Znn766WR83XXXrbmcpAuAHYAFEbF+RXw88GNgIXB9RBxVaz3tGU8gfTQzM7PudCFVqaQlbQnsBGwQEZ8FTitbSdl4AhPbBiWQNEbSM8BUSc9K2ry9NTczs46JiDuBV6vCBwETI+K9vMyCsvWUnQlsX/HI8anAbhHxCbIBC37RWJXNzKyLjQK+KmmqpDskfaFsgbJGoI+ktvsGK0TEfQAR8QTQr2ihyjzYXT2GsZnZsqg94wmQ3ecdDGwG/DtwhUoSnZXdGP4/wB8lTQRulHQm8DtgHDC9aKHKPNi9evVyK2Bm1qBGxxPIzQV+F9m373slLSZLMPdS0QJlTwz/StIMsutMo/Lyo4CrgZ81WDkzM+taVwNjgdsljQL6kg0TXKi0i2hE3A7cXh3PxxNI91szM7MuVZlKWtJcspEfLwAukDQTeB/YO0quyXfkOYETqKMR2H777ZPxVL58KO7bXtTPvlHvvvtuMn7LLbck40XPA9x4443J+Morr5yMX3nllcn4ddddl4wfd9xxda+7yBNPPJGM33XXXQ2tp7Ost956yfhRR6W7MX/3uzUz5dZt4403LpxX9Ll/7WtfS8aLxj54/PHHk/FLL700GX/77beT8VGjRiXjRf8XRX3hl19++WR8xIgRyfiAAQOS8aLxB4qeQzj++OOT8WHDhiXjK620UjJe9LxBT1B0zJ0+fXoyfuCBBybj9957b9l2iv5B9qi5YBWPJ2Bm1sI8noCZWQvzeAJmZi3M4wmYmbUwZxE1M2thbgTMzFpYl6eS3mCDDZLxotSynaUoPev++++fjBd1myxKDV1kwYJ0vqY999wzGT/33PQDgakutK+9Vn1/vrYzzzwzGX///fcbWk+Rou6GEyZMSMb32WefZHzIkCGdUp8i1157bTK+xRZbFC7z3HPPJeNFqcg7S69e6e9lJU/+161on3/xi3QqsKJujccee2wyXtQVtLOkul8W/T0XdQf/29/SfVpmz56djBd1tX7rrbeS8eeffz4Zv/3225PxojT53cVnAmZmPZCkCyQtyB8Ma4uNlnSPpOl5vqFNytZTlkp6jKTbJF0iaYSkmyW9Iek+SRt2xo6YmVm7XEjVeALAKcAJETEa+K/8fU1lZwJn5yu5nuy5gHMiYiBwTD7PzMyWgoLxBAJoe/x7IJAeqq5CaSrpiLghIi7LthlT8o3fAqSfS+ejKVCnTZtWVgczM6vSzlTShwKnSnqObFSx9M2bCmWNwD8lbSVpVyAk7ZxXbnMgncyELAVqRIyJiDEbbuirRmZmjao8juZTPWmlDwIOi4gRwGHA+WULlDUCPwSOAPYlSx+xpaTXyS4FHVJHhczMrPvsTTbmC8CVQMduDEfEQxGxdURsGxGPRcSEiBiUD2D8yY7X18zMOtELQNv472OBJ8sWUHuHf5Q0JyI+XlauT58+yQ2MGzcuWX7s2LHJ+OjRo5Pxp556Khk/9dRTk/GivsBLS//+/ZPxVAriHXfcsaF1n3jiicn43XffnYyvu+66yfiuu+6ajBd9JkX71NWKUu8W/a29+eabktRUI9+tvvrqyfgzzzyTjBeljG70uYI5c+Yk40XPAxSl1P785z+fjPft27eh+hSlsP7JT36yROycc85Jli1K11307E+zDYUbETU/xMrxBID5ZOMJPA5MInsG7J/AjyLigVrrcSppM7MeqMZ4AsUDaCQ4lbSZWQtzKmkzsxbmVNJmZi3MuYPMzFqYGwEzsxbmRsDMrIW1+zmBujfQZP2we4pUjv1NN900WbZobIb58+cn49/+9reT8e233z4ZHz58eDLe1YryTl100UXJ+HnnnZeMF/UXj4ime05g6NChyfijjz6ajK+yyirJeNE4AEXP1fz2t79Nxm+66aZkvKjf/5133pmMF6WPKTr+7Lfffsn45MmT615HT1f2nEBnKUslPVDSREmPSXoln2blsUHdUUEzM1tSnt7/tvyY/IikCXl8SJ72/8n85+Ba6ym7HHQF2TMCW0TE0IgYCmyZx67sjB0xM7N2WQgcERGfBjYDDpb0GbJU/7dExHrALfn7QmWNwMiIODki5rUFImJeRJwMlKaMMDOzrhERL0bEg/nrN4FZwDBgJ6DtmulFwM611lPWCDwr6ShJ/0oRIWk1SUcD6UFY+Wge7NI9MTOzJTQynoCkkcCGwFRgtYh4EbKGAli11nbKnhjejexU4o68IQiyREXXAum7i9mGzwXOzSu3bN61MTPrQpXH0Vok9QeuAg6NiH80mjyw7Inh1yRNBm4G7omItyo2vA1wY0NbMzOzTiOpD1kDcGlEtI0jMF/SGhHxoqQ1gAW11lHWO+gQ4Brgx8BMSTtVzP55+6tuZmYdoewr//nArIg4vWLWtWSDy5D/vKbmemr1sZU0A/hiRLyVX3OaAlwcEZMkTYuI0rEjfTmofVLjKtx6661LoSaNW2eddZLxgQMHJuMzZ85MxotyyndWv/BmfE6gyJe+9KVkfIMNNkjGi56ZKPqdNmrjjdPZiv/0pz8l4yuuuGIyftpppyXjJ5xwQjK+aFHhqLbLnDrGE/gK8BdgBrA4D/8H2X2BK8g678wBdo2I6gHp/6XsnsBybZeAImK2pC2AKZLWIksnbWZmS0FE3EXxcTg9klJCWe+geZJGV2z0LWAHspFsPlfvRszMrDmVNQJ7AfMqAxGxMCL2Ar7WZbUyM7NuUdY7aG6NeX/t/OqYmVl3chZRM7MW5kbAzKyFOZW0taye1EV0aSnqgnr99dcn48OGDUvGJ02alIwffvjhyfiymh66Ec2SSnqApJMkXSxp96p5Z3dt1czMrEiNVNKn5un/H5b0+7K0/2WXgyaT9UO9CviOpKsk9cvnbdbRnTAzs3YrSiV9M7B+RGwAPAEcW2slZY3AuhFxTERcHRE7Ag8Ct0pKD39kZmbdoiiVdETcFBFtj4bfA9QcGrDsieF+knpFxOJ8QydKmgvcCfQvWihPeVqY9tTMzGpLHEfPzTOLpsqO5MNU0pX2Bf631nbKGoHrgLHAn9sCEXGRpPnAr4oWcippM7OOaW8q6Yr4cWSXjC6ttXzNy0ERcRQwV9K4fENt8RuBQ8oqZ2ZmXacglTSS9iZL8fO9KOlqVdY7aDxZGtLxLJlK+sT2VtzMzDqmKJV0PtbL0cCOEfFO6XqcStpaVSs+J1A06tSBBx6YjB999NHJeP/+6VuCN9xwQzK+7777JuOdldp6WdSBVNK/BPoBr+SxeyLih0XrcSppM7MeqEYq6T82sh6nkjYza2FOJW1m1sKcStrMrIU5i6iZWQtzI2Bm1sLcCJiZtbCGxxOQtGpELGigfEv1w7aeoxWfE7Ceo1nGExhSNQ0F7pU0WNKQ7qigmZktqWg8gYr5R0oKSSvXWk/Zw2IvA89WxYaRpZQOYJ1GK25mZp2ibTyBByWtBDwg6eaIeFTSCOAbwJyylZTdEzgKeJwsB8XaEbE2MDd/7QbAzGwpKRpPIJ99Btnxu/RyZ1kW0dOA/YD/knR63tqUrlTSAZLul3R/WVkzM1tS5XE0nwrHaKkcT0DSjsDzEfFQXdup98awpH8DjgNGRsTqdS2Ebwxb8/KNYWtm9d4YztP830GW2flG4DZgq4h4Q9JsYExEvFy0fGkXUUmfkjQuX/GWwNfz+Db1VNDMzLpGYjyBdYG1gYfyBmA48KCkwi/uZamkDwEOJrvWNBqYEBHX5PMejIiN6qikv2lZU/KZgDWzOlJJC7gIeDUiDi0oM5uSM4Gy3kH7AxtXjicgaWRETMKppM3MlqYvA3sCMyRNz2P/ERENpZL2eAJmZj1QjfEEKsuMLFuPxxMwM2thZfcEhgMLI2JeYt6X60kn7Wuu1qx8T8CaWXeljWg4d1DDG/A/mTUpNwLWzJoid5CZmS3b3AiYmbWwhhuBPJOomZktA8pSSU9sS0MqaYykZ8hyUzwrafNuqaGZmS2hVippSeMlPZ7HT6m5oogonIAZFa9vA76Qvx4F3F9r2YrlwpOnZpz89+mpmac6jq1rABvlr1cCngA+Q5be589Av3zeqrXWU/awWB9JvSNiIbBCRNxHVrsnJPUrWdbMzLpIRLwIvJi/flNSWyrp/YGJEfFePq/mSJBl9wT+D/BHSWOBGyWdKelrkk4Aphct5FTSZmYd095U0mRXar4qaaqkOyR9oeZ2yp4TyFNFHJSvuDfwHHA1MDkiPqhjR2pvwGwp8XMC1szak0o6In4naSZwKzAB+ALwv8A6UXCwL7scBDAPOBeY2pZHKN/wNmS5q83MbClIpJIGmAv8Lj/o3ytpMVmqn5dS6yjrHXQIcA0wHpgpaaeK2T/vYP3NzKyd8lTS5wOzIuL0illXA2PzMqOAvmTjxSc5lbSZWc+UTCUNXABckF8Weh/Yu+hSEJQnkHs0Ij5T8b4/MAV4FBgbEaPLaulrrtasfE/Amlmz5A5yKmkzs2WYU0lby/KZgDUzp5I262JuBKyZNcvlIDMzW4a5ETAza2FuBMzMWlk9mUA7awIOcPnOKd9MdVkWyjdjnVy+Z9SlO8p35dS9G6sz/bTL96y6LAvlm7FOLt8z6tId5bty8uUgM7MW5kbAzKyFdXcjcK7Ld1r5ZqrLslC+O7bh8p1Xvpnq0h3lu0yXPyxmZmbNy5eDzMxamBsBM7MW5kbAjGykPEmPS3pK0jElZUdIuk3SLEmPSJpQx/qXkzRN0h/qrM8gSVMkPZZv54s1yh6W12OmpMskLZ8oc4GkBXmO+bbYEEk3S3oy/zm4pPypeX0elvR7SYOKylYsc6SkkLRyrXXn8fH5Z/CIpFNK6jJa0j2Spufj725SMS/5+RTtb43yRftb8/Ov3OdaZYv2t9t1Zf9T4FPA0cAvgUn560+XlB8H9K+Kb1PHtn5bMn9TYED+egXgBOA64GRgYFXZvsBewNfz97sDZwEHA32Wdr9eT507AcsBTwPr5J/9Q8BnapRfA9gof70S8ESt8nm5w4H/B/yhzjpdBOyXv+4LDCooNwz4O7BC/v4K4PuJcl8DNgJmVsROAY7JXx8DnFxSfiugd/765LbyqbJ5fATwJ+BZYOWSdW8J/Bnol79ftaT8TcC2+evtgNvLPp+i/a1Rvmh/Cz//6n2use7C/e3uqcvOBCQdDVxONgLZvcB9+evLUt+0GhnKUtK1VdN1wDfb3hdU6QLgnfz1JGAg2Qf7DjC5quxkYHtggqSLgV2BqWSDNp9X1y/AOpWkVRssP7SB4psAT0XEMxHxPtnf7U5FhSPixYh4MH/9JjCL7GBcVJfhZH9Pdf3tSBpAduA7P9/G+xHxeo1FegMrSOoNrAi8kKjzncCrVeGdyBob8p871yofETdFxML87T3A8BrrBjgDOAr4SO+TgvIHARMj4r28zIKS8gEMyF8PpGKfa3w+yf0tKl9jf2t9/h/Z5xplC/e323VV60LW4i3xrZnsW82TifgM8jMAYCRwPzAhfz+tquyDwCXAFsDm+c8X89ebF9RnVuXyVfOmV71/OP/ZG5gPLJe/V9u8Zpho4NsDMLQTtzsQmAg8BryST7Py2KBE+QHAScDFwO5V885OlB9SNQ0FZgODgSGJ8hPJv2kCY4BngKfIvo0l/x6qlt8FOK/i/Z7AWXX+LkYCc8jPMgvKTAE2zv9OS88EgNFkX5wuBKaRNR4fq1F+AvAW2UDil5bUtfLb9OtV81+rVb5q3nXAHjXWvSMwKX89m4ozgYLy08nOzqcCdwBfKCn/6fz3/hzwPLBW2edTtr+1Ps/q/S1Yfz373Fa25v5259SV9wQWA2sm4mvk86otF9nIZUTEbLJ/mG0lnc6S4xmPAR4AjgPeiIjbgXcj4o6IuKOgPjMl7ZO/fkjSGPjXQMwfVJXtJakv2enbimQHPYB+QJ/UyiUNlDQxv4b4Sj7NymODEuUHSDpJ0sWSdq+ad3ai/JCqaShwr6TBkoZUlZ3Ydg1W0hhJzwBTJT0rafPEusfk1y0vya9h3izpDUn3SdowsbtXAK8BW0TE0IgYSnZ6+xpwZaL8ZLLP8CrgO5KuktQvn7dZovzLZJ9v23Q/2benB/PX1baPiLaBtE8FdouITwDfAH6RKF8tlbe9tO+0suFWrwIOjYh/FJTZAVgQEQ/UUY82vckuf/zfiNgQeJvs8kVq/YPJvuGuTfb/9jFJezSwrYZJOg5YCFxaMH9Fsv/N/2pgtb3JGvnNgH8HrpBUK5/+QcBhETECOIz8rKmqHqWfTz3li/a3snw+v3CfE+tudH+7Tle1LsA2ZN/GbiB7MOJc4MY8tsQ1fuBWYHRVrDfwW2BRwTaGkx10zgLmlNRnINk3q6fJWt8PyL4x3gF8vqrsYfm8Z4FDgFuA35CdrRxfsP4/kd3zWL0itnoeuzlR/iqyb7A7A9fm79uuDz6YKL+Y7Npv5fRB/vOZqrIzKl7fRv4tAxhFImcJ2bfObYHvkn2z2iWPjwPuTpR/vMbveYl5LHmmdRzwV7Jv+Kl9PTL/W/lcRezvNbb5GB9eu72n6HdRY/kvAn+qeH8scGzJMn3yz/zwknInAXPJvhnOI7v8eEnJMqsDsyvefxW4vqDsrsD5Fe/3InF2lc8byUe/TT8OrJG/XqP6s6sun8f2Bu4GViwqSzb07IJ8n2eTHSDnVP1vVNflRrIvFW3vnwZWqVH+DT58zknAP8o+n1r7W/R51tjfj5Svtc8Fdam5v905de3Ks95HmwHfIjvl3oz80kqi7PDKP5KqeV8u2c72wM/rrNNKwOfJTs9Xq1FuTWDN/PWgvP6b1CjfNAdGGjwoUnG5jarGlKpLcXnsJrLrnqtVxFYja/D+nCg/C+hVFdsbeAR4tsbfw5XA6fln9kyqXF52fF6nscB/A2eSXVM/Abi4jr+J3mSN/tp8eGP4szXKi+zLyZkN/j9sQf03hv8CfDJ//d/AqQXlNs1/jyvm9boIGF9QdiQfPZCeykdvlJ5SUn4b4FESB6vqslXzZlN+OeiHwE/z16PIvoyoRvlZ5AdRsi8rD5R9PkX7W6N8cn/r+fzb9rnGumvub3dO3b7BZXWiiQ6MNHhQJPumsxXZt8pngZ3z+OakzxwGk91Uf4zsEtCr+f6cTPqa/SnkPa2q4tuQuD9UVebfyG7KzSsptwXwv2TX0GcAfwQOoM7eXGQ9TJ4g+0Z2XEnZr5BdLnqY7NrudGC7OraxBfU3AqPJLn09DFwNDK5R9oT8s5hJdt+lX6LMZWT3zT4gOzP5AdkXjluAJ/OfQ0rKP5UfrNr2+ddFZau2PZuP9g5Krbsv2X2+mWSX/caWlP8K2aXCh8jO7Dcu+3yK9rdG+aL9Lf38+bARKFp34f5297RUNrosTnz0wPgqHz0wLvEPTBcfGCk+KPZOlP082enqDWTddCcBr5M1SF8qWP+ngK9TZ3deirv/bltWnqxL7/rtXH9p92JPnlp5WuoVaIUJ2Kezy1cdGOtef2fUhew+yeNk31BnAztVzEtdyhrfYPlG199QeU+ePH04LfUKtMJEyU3r7izfGeumge68zVjekydPH069sU4h6eGiWWT3BrqtfFfXharuvJK2AKZIWot0d8tmK29mOTcCnWc1YGuyG6WVBPytm8t3dV3mSRodEdMBIuKtvD/8BWRd5Zq9vJnl3Ah0nj+QXZKYXj1D0u3dXL6r67IXWT/of4ns8fq9JJ3TA8qbWc6DypiZtTCnkjYza2FuBMzMWpgbATOzFuZGwMyshf1/D7Z2L9co5DgAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 8\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[1], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[14], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()\n", + "#sns.heatmap(x_train[17], cmap = \"gist_gray\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a53a0ed4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 9\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[0], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[7], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e7d36a15", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAD7CAYAAACMlyg3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAoYklEQVR4nO3debhcVZnv8e8vI1NmZZCAiWJABGSINLQthElRaGJrI4oIDTYI3UKYmileFO8lhKEVkFabJkAkICKBIIJpkElECMQYSDAkTJmQJEwBImPIe//Y+8hJZe3aVefUOTkn9fs8Tz2n6t2r1l47Vam1h7XfpYjAzMyaU4+13QAzM1t73AmYmTUxdwJmZk3MnYCZWRNzJ2Bm1sTcCZiZNbFeHb0CSR6D2iD77bdfMn7zzTcn4xMmTEjGTzrppGR81apVbWtYNxUR8vcz06NHen9wl112ScYvuOCCZHybbbZJxjfeeONk/KWXXkrGTz311GT8uuuuWyO2cuXKZNnuLiJUVkbS/sAlQE/giogYX+96fCRgZtYNSeoJ/BfweWBb4GuStq23ntIjAUnbAKOBzYEA/gL8KiLm1LsyMzNrmF2BpyLiGQBJ15P9Vv+5nkqqHglIOh24HhDwMPBI/vznks6o8r5jJE2XNL2expiZWab172j+OKaiyObAolavF+exupQdCXwT+EREvFvRuB8AjwPJ808RcTlweV7W51zNzOrU+ne0QOqaQd2/t2XXBFYBH0rEN8uXmZnZ2rEY2KLV66Fkp+vrUnYkcCJwl6Qnef+wY0tgK+Db9a7MzMwa5hHgY5KGA88BXwUOrbcSlWURldSD7ALE5mSHH4uBRyLivZpW4NNBDdOrV7rPfvLJJ5PxYcOGJePbb799Mj579uw2tau7asYhov3790/Gjz766GT8W9/6VjI+c+bMZPxjH/tYMn7bbbcl49dcc00yPm/evGS8mbIe1zhE9AvAxWRDRK+MiHPrXU/p6KCIWAU8VG/FZmbWsSLiduD29tTh+wTMzJqYOwEzsybmTsDMrIm5EzAza2LuBMzMmpg7ATOzJtbhqaStcXbddddkvChN74oVK5LxJUuWNKxN1jWtt956yfi4ceOS8Z122ikZP++885LxovTlRWmd33jjjWS82dKXd0U+EjAz64YkbSHpHklzJD0uaUxb6vGRgJlZ97QSOCUiZkjqB/xR0p0R0bhU0pDNJyBpH0kbVcT3r6+9ZmbWKBHxfETMyJ+/DsyhDamky+YTOAG4BTgemC1pdKvF6ZOLeD4BM7P2qmE+gdZlhwE7AdPqXU/Z6aCjgV0iYkW+khslDYuIS0jnsgY8n4CZWXvVMJ8AAPlZmsnAiRHxWr3rKesEekbEirxB8yWNIusIPkyVTsDMzDqepN5kHcC1EXFTW+oouyawRNKOLS/yDuFA4ANAOh+xmZl1OEkCJgBzIuIHba2n7EjgcLIr0H8TESuBwyX9d1tXau8rmiPgkEMOWSN22WWXJcv27NkzGf/617+ejL/44os1ts66iqLPeI899kjGv/e97yXjRePyb7opvRN53333JePLly9Pxq1TfRr4BjBL0sw8dlaeXrpmVTuBiFhcZdkD9azIzMwaJyJ+TwNOy/tmMTOzJuZOwMysibkTMDNrYu4EzMyamDsBM7Mm5k7AzKyJOYtoJ+nbt28yfsEFFyTj//Zv/7ZGrOiegm9961vJ+JQpU2prnHUZ2f0/azriiCOS8YsvvjgZv/vuu5Px448/PhlfvDg9GjzCWV+6Mkk9genAcxFxYFvq8JGAmVn3NYYse2ib1d0JSPpZe1ZoZmbtJ2kocABwRXvqqXo6SNKvKkPAXpIGAkTEQe1ZuZmZtdnFwGlAv/ZUUnZNYCjwZ7KeJsg6gZHAf1Z7U573ujD3tZmZVZf4Hb08Ty+NpAOBZRHxxzy7c5uVdQIjyc45jQX+IyJmSnozItJZpXKeT8DMrH1K5hP4NHCQpC8A6wH9JU2KiMPqXU9ZArlVwA8l/TL/u7TsPWZm1rEi4kzgTID8SODUtnQAUOMPep5N9GBJBwB1z1zTTAYMGJCM33///cn4dtttl4wXDRVMGTp0aM1lrWvYcMMNk/GiIZzHHXdcMj516tS66lm6dGkNrbNmUtdefUTcBtzWQW0xM7M6RcS9wL1tfb/vEzAza2LuBMzMmpg7ATOzJuZOwMysibkTMDNrYu4EzMyamG/8arCvfOUryXjR/QBvv/12Mr7eeuvVvM5U2mmAF198MRkfPHhwMn7dddcl4/Pmzau5Lba6fv3SaV2uuuqqZHzkyJHJ+H777ZeML1y4MBmfNGlSMj5t2rRk/Ec/+lEy/tZbbyXjtu7wkYCZWTclaaCkGyU9IWmOpN3rrcNHAmZm3dclwNSI+GdJfYAN6q2g6pGApL+T1D9/vr6kcyTdKul8Sen8CGZm1uHy3+Y9gAkAEfFORCyvt56y00FXAm/kzy8BBgDn57H0Sc2sccdImi5per0NMjOz1X9H80dlev6PAC8AV0n6k6QrJKWTUlVRdjqoR0SszJ+PjIid8+e/lzSz6E1OJW1m1j4lqaQh+/3eGTg+IqZJugQ4A/g/9ayn7EhgtqQj8+ePShoJIGkE8G49KzIzs4ZaDCyOiJYhXzeSdQp1KesE/hXYU9LTwLbAg5KeAf4nX2ZmZmtBRCwBFknaOg/tQzYTZF0UUX62RlI/svNPvch6npqTkjfb6aDdd0+P0CrK+V80z8BNN91Uc92NsmDBgmR8m222Sca7+xjyiFBHfz8POyw9z8fVV19dVz1nn312Mr5ixYpkfNy4ccn4u++mD+C//OUvJ+N33313Da2zjhARpZOKSNqRbPrfPsAzwJER8Uo966l1UpnXgUfrqdjMzDpWRMwkmwa4zXyzmJlZE3MnYGbWxNwJmJk1MXcCZmZNzJ2AmVkTcydgZtbEnEW0wR588MG6ym+00UbJ+F//+tea6yi61+PGG29Mxuu9l2HYsGHJ+BNPPFHeuCbRu3fvZPyoo45Kxnv27FlX/d///veTcSk9lLxHj/T+3euvv56Mv/zyy3W1pzv75Cc/mYwXzZvx5ptvdmRz2kXSSWQ37gYwi+w+gbpu4PGRgJlZNyRpc+AEsrxu2wE9ga/WW487ATOz7qsXsL6kXmRzCfyl3grK5hPoI+lwSfvmrw+VdJmkf5eUPv41M7MOFxHPARcBC4HngVcj4o566yk7ErgKOAAYI+ka4GBgGvApsnwVSZ5PwMysfcrmE5A0CBgNDAc+BGwoKZ2sqoqyC8PbR8QO+aHGc8CHIuI9SZOokkvI8wmYmbVPDfMJ7As8GxEvAEi6Cfh7YFI96yk7EuiRz1vZj+x8U8uUkn0Bnw4yM1t7FgK7SdpA2TCxfYA59VZSdiQwAXiC7KrzWOCX+XwCuwHX17syMzNrjHw2sRuBGcBK4E9UP3JIKp1PQNKH8hX+RdJAskOQhRHxcE0r8OkgAAYOHJiMX399ui/93Oc+V3PdDzzwQDK+5557JuOnnnpqMj5+/Phk/Omnn07GTznllGT8lltuSca7mkbOJzBo0KBkfOHChcl40f0hRYr+n7700kvJ+J//nJ5bpOi+gqOPPjoZnzt3bg2tW7uGDBmSjJ988snJ+JgxY5LxKVOmJONF/zYdff9ALfMJNELpzWIR8ZdWz5eTTWFmZmbrAN8nYGbWxNwJmJk1MXcCZmZNzJ2AmVkTcydgZtbEnEq6wYrSMd9+++3J+Pbbb9/udY4bNy4Zf++995LxP/zhD8l40TDEolTSH/3oR8sb1ySKUn8//HB6JPWoUaOS8aIhnEXxvn37JuMDBgxIxufPn5+Mv/LKK8n42lKUmvvYY49dI1Y0VPnDH/5wXev82te+low/99xzyfjpp59eV/1dlY8EzMy6IUlXSlomaXar2GBJd0p6Mv+bvoGllbIsogMkjZf0hKSX8secPDawAdthZmZtczWwf0XsDOCuiPgYcFf+uqqyI4EbgFeAURExJCKGAHvlsV/W22IzM2uMiPgdUDkl3GhgYv58IvDFsnrKOoFhEXF+RCxpteIlEXE+sGXRm5xK2sysfcpSSRfYJCKeB8j/blz2hrILwwsknQZMjIilecM2Af4FWFT0JqeSNjNrnxpSSTdE2ZHAIcAQ4D5JL0t6GbgXGEw2wYyZmXUdSyVtBpD/XVb2hqqdQES8EhGnR8Q2ETE4f3w8Ik6nhnNNZmbWqX4FHJE/PwIoTenbnvsEziGbftJaOeuss5LxRtwPsHTp0mR8xowZyXhRytzvfOc7yfgvfvGLZPzSSy9Nxh966KFkvBm98847yfiECROS8a233rqu+mfNmpWM33FHekrZ73//+8l40ffwmGPSp5vPPffcZLwsBX2t+vfvn4wfdlh6lsSLLrpojVifPn0a0pYePdL7xHvssUdD6m80ST8HRgEfkLQY+C4wHrhB0jfJJp0pPWNTtROQ9FjRImCTehpsZmaNExHpu9uyGcZqVnYksAnwObIhoa0JSN92amZm3UZZJ/BrYKOImFm5QNK9HdEgMzPrPFU7gYj4ZpVlhza+OWZm1pmcO8jMrIm5EzAza2LuBMzMmpgaNd63cAXraNqIonznzz77bDK++eabt3udr776ajK+YsWKZHzq1KnJ+E9/+tNkfPr05kr1FBHq6O9n0fdks802S8bffPPNZLzos19vvfWS8XvuuScZ33nnnZPxp556Khk/+OD0MPNHH300GS/6Pdluu+2S8SlTpiTjH/zgB5Pxfv36rRErmmuhUYruh9l99907dL0RUXXDJF0JHAgsi4jt8tiFwD8C7wBPA0dGxPJq9fhIwMyse7qaNVNJ3wlsFxE7APOAM8sqKZtPoL+k8yRdI+nQimU/rq+9ZmbWKKlU0hFxR0SszF8+BKSnOmyl7EjgKrIbwyYDX5U0WVLLfHa71ddkMzPrREcBvykrVNYJfDQizoiIKRFxEDADuFvSkGpv8nwCZmbt08b5BFreOxZYCVxbVrbsjuG+knpExCqAiDg3T1T0O2Cjojd5PgEzs/Zp63wCko4gu2C8T9Qw8qfsSOBWYO+Khk0ETiG7+mxmZl2EpP2B04GDIuKNWt5TljbitIL4VEnj6m+imZk1QkEq6TOBvsCd+dDZhyLi2Kr1tPU+AUkLI6JwnuFW5dbJ00FFY5NnzpyZjO+www7tXufDDz+cjJ900knJ+B/+kE70mhprDcV50z/zmc8k48OHD0/GjzzyyGT8jTdq2jHpNJ1xn8Da8k//9E/JeNF8Fz179kzGhwxJX/770pe+lIx/4hOfSMaLvqNF5Yt+l1JzB6xatSpZ9u233665Dii+R+OKK65Ixou2qVHK7hNoFM8nYGbWxDyfgJlZE/N8AmZmTczzCZiZNTHnDjIza2LuBMzMmphTSTfYeeedl4yfccYZyXhRGuiTTz55jdiDDz6YLHvYYYcl43vuuWcyPmLEiGR88ODByXiRBx54IBnfd999k/G33nqrrvo72ro8RLTI+uuvn4xvtdVWyfjvf//7ZPzWW29NxouGE2+5ZXo0edF3YvLkycn4woUL14jNmjUrWXbu3LnJ+EYbpZMdLFq0KBl/7rnnkvF33unY+2U7a4ho3UcCkjbuiIaYmVntJF0paZmk2Yllp0oKSR8oq6cslfTgiscQ4GFJgyTVt9toZmaNdDVrzieApC2A/YA1D5sSyoaIvggsqIhtTpZNNICP1LISMzNrrIj4naRhiUU/BE4DbqmlnrLTQacBc8mSEQ2PiOHA4vx5YQfgVNJmZu3TllTSkg4CnouI9PyfCWX3CVwk6Xrgh5IWkSUoKr2Q5lTSZmbtU28qaUkbAGOBz9azntILwxGxOCIOBu4hm79yg3pWYGZmneKjwHDgUUnzyaaWnCFp02pvqnl0UETcCuwF7AsgKZ0q0szMOl1EzIqIjSNiWEQMAxYDO0fEkmrvcyrpEttuu20yfsoppyTjBx98cDJelL55/vz5yXgqPe6mm6Y79KK01kVjwos+86JU1ddem56hrijFblFK3q6mGe8TKNK7d+9kfOzYscn4scemU9QXfRc33jg9svymm25Kxr/+9a8n413tXpOOVHafQOv5BIClwHcjYkKr5fOBkRHxYrV6nErazKwbioivlSwfVks9TiVtZtbEnErazKyJOZW0mVkTcxZRM7Mm5k7AzKyJuRMwM2tiZReGm0bfvn2T8YkTJybjI0eOrKv+d999Nxl/+umnk/HUGPy77rorWbaojVtvvXUy/p3vfCcZv+GGG5Lx9957Lxm3dUfPnj2T8VdffTUZ/+1vf5uMjxo1KhlfuXJlMv74448n4810P0BbSboSOBBYFhHbtYofD3wbWAncFhGnVaunLfMJDKn3PWZm1nBXU5FKWtJewGhgh4j4BHBRWSVl8wmMb5mUQNJISc8A0yQtkJSetsrMzDpcRPwOeLkifBwwPiLezsssK6un7EjggFa3HF8IHBIRW5FNWPCf9TXZzMw62AjgM5KmSbpP0qfK3lDWCfSW1HLdYP2IeAQgIuYB6ZPoeD4BM7P2ast8AmTXeQcBuwH/AdygooROrd5QzX8Bt0saD0yVdDFwE7APMLPoTZ5PwMysfeqdTyC3GLgpsiyRD0taRZZg7oWiN5TdMfwjSbPIzjONyMuPAKYA/7fOxpmZWceaAuwN3CtpBNCHbJrgQqVDRCPiXuDeyng+n8BVbWikmZm1U+tU0pIWk838eCVwpaTZwDvAEVEyX0B77hM4h3WoExgzZkwyvssuuyTjy5alL7pPmjQpGb/++uuT8enTa79sMmHChGT80UfT04kecsghyfjrr79e8zpt3TJixIhk/MQTT0zGn3zyyWT8jTfeSMaLxv0X3Ydw8803J+NWrkoq6cPqqcfzCZiZNTHPJ2Bm1sQ8n4CZWRPzfAJmZk3MWUTNzJqYOwEzsybWdKmki+6gLhryVjTM8je/+U0yvmLFirY1rEKfPn3WiO2///6JknD44Ycn4x4K2ryGDEkn+73jjjuS8RdeSN9QeuaZZybjo0ePrmu9jz2WHmhYNATVOo+PBMzMuiFJV0palt8Y1hLbUdJDkmbm+YZ2LaunLJX0SEn3SJokaQtJd0p6VdIjknZqxIaYmVmbXE3FfALABcA5EbEjcHb+uqqyI4Ef55XcRnZfwH9HxADgjHyZmZmtBQXzCQTQP38+APhLWT2lqaQj4jcR8fNsnXFjvvK7gPWK3uRU0mZm7dPGVNInAhdKWkQ2q1j6ok4rZReG35L0WbIeJSR9MSKm5LOKFU4861TSZmbt08ZU0scBJ0XEZElfASYA+1Z7Q9mRwLHAKcBRZOkj9pK0nOxU0Al1Ns7MzDrWEWRzvgD8EmjfheGIeDQiPhcRn4+IJyJiTEQMzCcw3rr97TUzswb6C9Ay//veQOkY3KZLJV2UWvuyyy7r5JZU984776wRmzhxYrLsLbfckoyffPLJyfjll6ePMEvSjlsXVHTfy9lnn52Mb7nllsn4T37yk2T8tddeS8Z/9rOfJeNLly5NxseNG5eMr1y5Mhm3cgXzCRwNXJJPC/wWUHodwamkzcy6oSrzCaQnQSngVNJmZk3MqaTNzJqYU0mbmTUx5w4yM2ti7gTMzJqYOwEzsyamjh4b7rQRjZOaYwDg+uuvT8YPPPDAZHz77bdPxufOndu2hnVTEaHu/v0cNGhQMl6Up/+ll15Kxnffffdk/OWXK/OTVffBD34wGT/xxBOT8QsvvDAZX758eV3rXRdFRPomkAYrSyU9QNJ4SU9Ieil/zMljAzujgWZmtqY8vf89+W/y45LG5PHBedr/J/O/6T2FXNnpoBvI7hEYFRFDImIIsFce+2UjNsTMzNpkJXBKRHwc2A34d0nbkqX6vysiPgbclb8uVNYJDIuI8yNiSUsgIpZExPlA+v5zMzPrcBHxfETMyJ+/DswBNgdGAy05ZiYCX6xWT1knsEDSaZL+liJC0iaSTgcWFb3J8wmYmbVPPfMJSBoG7ARMAzaJiOch6yiAjautp+yO4UPIDiXuyzuCAJYCvwK+UvQmzydgZtY+tc4nIGkjYDJwYkS8VpRUsEjZHcOvSLoKuBN4KCJWtFrx/sDUutZmZmYNI6k3WQdwbUS0zCOwVNJmEfG8pM2AZdXqKBsddAJwC/BtYLak0a0Wp3PDmplZh1O2yz8BmBMRP2i16Fdkk8uQ/03nms+VnQ46GtglIlbk55xulDQsIi4hyyRqnSg1xwDAPffck4zvt99+ddVj3U9RPv733kvP/rp48eJkfMWKFcl4vV544YVkfOzYsQ2p31bzaeAbwCxJM/PYWcB44AZJ3wQWAgdXq6SsE+jZcgooIuZLGkXWEXwYdwJmZmtNRPye4t/hfWqtp2x00BJJO7Za6QrgQOADQPq2UzMz6zbKOoHDgSWtAxGxMiIOB/bosFaZmVmnKBsdlD6BmC17oPHNMTOzzuQsomZmTcydgJlZEysbHWRdyNChQ5Pxc845Jxk/66yzkvFnn322YW2ytatHj/R+XNGQz6LU0L17907GPZx43Vd2s1h/SedJukbSoRXLftyxTTMzsyJVUklfmKf/f0zSzWVp/8tOB11FNg51MvBVSZMl9c2X7dbejTAzszYrSiV9J7BdROwAzAPOrFZJWSfw0Yg4IyKmRMRBwAzgbklD2t9+MzNrq6JU0hFxR0S03Er+EJA+j5wruybQV1KPiFiVr+hcSYuB3wEbFb0pT3lamPbUzMyqS/yOXp5nFk2VHcb7qaRbOwr4RbX1lHUCtwJ7A79tCUTERElLgR8VvcmppM3M2qetqaRbxceSnTK6ttr7q54OiojTgMWS9slX1BKfCpxQ1jgzM+s4BamkkXQEWYqfr0dE1R3xstFBx5OlIT2eNVNJn9vWhpuZWfsUpZLO53o5HTgoIt4oq6fsdNAxOJV0l7HTTjsl44MGDUrGN9xww2R88ODByXjRGHLruoruByiK77zzzsn41VdfnYwXpaQ+99z0PuCiRelZZ5cvX56MW7sUpZK+FOgL3JnPMvZQRBxbVIlTSZuZdUNVUknfXk89TiVtZtbEnErazKyJOZW0mVkTcxZRM7Mm5k7AzKyJuRMwM2tiKrmZbM03SBtHxLI6yjttRINsvvnmyfi8efOS8Q022CAZLxpDPnny5GR82rTKdCSZiRMnJuNvvFF6f0qXEBFaV7+fRxxxRDI+ZsyYZHzHHXdMxvNx5mso+oyfeeaZZHzSpEnJ+Ntvv52MF91vMGfOnDVivXqlL22uWrUqGR8+fHgyXtT2119/PRlfuHBhMt4oEdEpw/DL7hgeXPEYAjwsaZCk9B1HZmbW4YrmE2i1/FRJIekD1eopu1nsRWBBRWxzspTSAXyk3oabmVlDtMwnMENSP+CPku6MiD9L2gLYDyg9XCm7JnAaMJcsB8XwiBgOLM6fuwMwM1tLiuYTyBf/kOz3u/R0Z1kW0YuAfwXOlvSDvLcprVTSMZKmS5peVtbMzNbU+nc0fxTO0dJ6PgFJBwHPRcSjtayndKL5/IaxgyX9I9m0Zemrjau/x/MJmJm1Q1vmEyA7RTQW+Gyt6ykdIippG0n7APcAewH75vH9a12JmZk1XmI+gY8Cw4FHJc0nm1pyhqRNi+ooGx10Aq3mEwA+GxGz88Xj2r0FZmbWJqn5BCJiVkRsHBHDImIYsBjYOSKWFNZT7T4BSbOA3VvPJwBcExGXSPpTRKQT3K9eh08HdbB99903GT/hhPTkb6NGjUrG+/XrV9d6p09PX/Ipqv+vf/1rXfV3tHX5PoGePXsm40X3D1x66aXJeN++fZPxorH5RYrG7PfoUd/9qql66q3jhRdeSMbvv//+ZLzoHoebb765rvXWq+w+AUn/ANwPzAJa/mHOiojbW5WZD4yMiBeL6vF8AmZm3VCV+QRalxlWVo/nEzAza2KeT8DMrIl5PgEzsybmLKJmZk3MnYCZWROruxPIM4mamdk6oOw+gfHARRHxoqSRwA1k41F7A4dHxH2lK1hHx2F3Z0Xj+O+6665kvGgc9rvvvpuMDx06NBlftqzmaSg6xbp8n0CRYcOGJeNF93wMGjQoGa93bH5XUjSHQdE8G1OnTk3GDzvssIa1KaWG+wS2AH4GbEr2u3x5RFySLzse+DZZGonbIuK0onrK7hM4ICLOyJ9fCBwSEY9IGgFcB4ysZWPMzKzhkqmkgU2A0cAOEfG2pI2rVVLWCfSW1CsiVgLrR8QjABExT1L6VkIzM+twEfE88Hz+/HVJLamkjwbGR8Tb+bKqh+Blx3T/BdwuaW9gqqSLJe0h6RxgZtGbnErazKx92ppKGhgBfEbSNEn3SfpUtfWU3Sfwozx/0HF5xb3yv1OA/1flfU4lbWbWDm1JJR0Rr0nqBQwCdgM+Bdwg6SNRcAG4lixQS/KGTGvJI5SveH8gfcXEzMw6XCKVNGSZQ2/Kf/QflrSKLNVPMnNeXamkJY1utdippM3M1pJUKuncFGDvvMwIoA/ZfPFJZUcCRwO7tE4lLWlYPgzJWUS7uK222ioZHz9+fDJelGJ36dKlyXhR+uGiemztW7BgQTJ+1FFHJePf/e53k/Giz3jLLbdMxm+55ZZkfLPNNkvGi9KaDx48eI3Y1ltvnSy7ySabJONF6bGLhoguWrQoGe8CPg18A5glaWYeOwu4ErhS0mzgHeCIolNB4FTSZmbdUkkq6ZpvYnAqaTOzJuZU0mZmTcyppM3Mmlj3TQBiZmbt5k7AzKyJuRMwM2tmEdFpD+AYl29M+a7UlnWhfFdsk8t3j7Z0RvmOfHTuymC6yzemfFdqy7pQviu2yeW7R1s6o3xHPnw6yMysibkTMDNrYp3dCZSmRXX5LlF3M5bvjHW4fOPKd6W2dEb5DlN1jmEzM1u3+XSQmVkTcydgZtbE3AmYkc2UJ2mupKcknVFSdgtJ90iaI+lxSWNqqL+npD9J+nWN7Rko6UZJT+Tr2b1K2ZPydsyW9HNJ6yXKXClpWZ5jviU2WNKdkp7M/w4qKX9h3p7HJN0saWBR2VbvOVVSSPpAtbrz+PH5Z/C4pAtK2rKjpIckzczn39211bLk51O0vVXKF21v1c+/9TZXK1u0vZ2uI8efAtsApwOXApfkzz9eUn4fYKOK+P41rOtnJcv/DuifP18fOAe4FTgfGFBRtg9ZBtV989eHApcB/w70Xtvjev1o7APoCTwNfCT/7B8Ftq1SfjNg5/x5P2BetfJ5uZOB64Bf19imicC/5s/7AAMLym0OPAusn7++AfiXRLk9gJ2B2a1iFwBn5M/PAM4vKf9ZoFf+/PyW8qmyeXwL4H+BBcAHSureC/gt0Dd/vXFJ+TuAz+fPvwDcW/b5FG1vlfJF21v4+Vduc5W6C7e3sx8ddiQg6XTgerJJDx4GHsmf/zy1p1XPVJaSflXxuBX4UsvrgiZdCbyRP78EGED2wb4BXFVR9irgAGCMpGuAg4FpZJM2X1HTP4A1lKSN6yw/pI7iuwJPRcQzEfEO2fd2dFHhiHg+Imbkz18H5pD9GBe1ZSjZ96mm746k/mQ/fBPydbwTEcurvKUXsL6yCcY3AP6SaPPvgJcrwqPJOhvyv1+sVj4i7oiIlfnLh4ChVeoG+CFwGrDa6JOC8scB4yPi7bzMspLyAfTPnw+g1TZX+XyS21tUvsr2Vvv8V9vmKmULt7fTdVTvQtbjrbHXTLZX82QiPov8CAAYBkwHxuSv/1RRdgYwCRgF7Jn/fT5/vmdBe+a0fn/FspkVrx/L//YClpLNsAZZJ/bY2uqxE9tU894DMKSB6x0AjAeeAF7KH3Py2MBE+f7AecA1wKEVy36cKD+44jEEmA8MAgYnyo8n39MERgLPAE+R7Y0lvw8V7/9n4IpWr78BXFbjv8UwYCH5UWZBmRuBXfLvaemRALAj2Y7T1cCfyDqPDauUHwOsIJtI/NqStrbem15esfyVauUrlt0KHFal7oOAS/Ln82l1JFBQfibZ0fk04D7gUyXlP57/uy8CngM+XPb5lG1vtc+zcnsL6q9lm1vKVt3eznx05DWBVcCHEvHN8mWVVpvKkuw/zOcl/YA1p1AbCfwRGAu8GhH3Am9GxH0RcV9Be2ZLOjJ//qikkfC3iZjfrSjbQ1IfssO3Dch+9AD6Ar1TlUsaIGl8fg7xpfwxJ48NTJTvL+k8SddIOrRi2Y8T5QdXPIYAD0saJGlwRdnxLedgJY2U9AwwTdICSXsm6h6Zn7eclJ/DvFPSq5IekbRTYnNvAF4BRkXEkIgYQnZ4+wrwy0T5q8g+w8nAVyVNltQy0etuifIvkn2+LY/pZHtPM/LnlQ6IiJaJtC8EDomIrYD9gP9MlK+UmqKvdOy0pI3ItunEiHitoMyBwLKI+GMN7WjRi+z0x08iYifgr2SnL1L1DyLbwx1O9v9tQ0k1Ty3YFpLGAiuBawuWb0D2f/PsOqrtRdbJ7wb8B3CDpGpT2B4HnBQRWwAnkR81VbSj9POppXzR9rYuny8v3OZE3fVub8fpqN4F2J9sb+w3ZDdGXA5MzWNrnOMH7gZ2rIj1An4GvFewjqFkPzqXAQtL2jOAbM/qabLe912yPcb7gE9WlD0pX7YAOAG4C/gfsqOV7xbU/79k1zw2bRXbNI/dmSg/mWwP9ovAr/LXLecHZyTKryI799v68W7+95mKsrNaPb+HfC8DGEEiZwnZXufnga+R7Vn9cx7fB3gwUX5ulX/nNZax5pHWWOABsj381Laemn9Xtm8Ve7bKOp/g/XO3DxX9W1R5/+7A/7Z6fSZwZsl7euef+ckl5c4DFpPtGS4hO/04qeQ9mwLzW73+DHBbQdmDgQmtXh9O4ugqXzaM1fem5wKb5c83q/zsKsvnsSOAB4ENisqSTT27LN/m+WQ/kAsr/m9UtmUq2U5Fy+ungQ9WKf8q79/nJOC1ss+n2vYWfZ5Vtne18tW2uaAtVbe3Mx8dW3k2+mg34Mtkh9y7kZ9aSZQd2vpLUrHs0yXrOQAYV2Ob+gGfJDs836RKuQ8BH8qfD8zbv2uV8l3mh5E6fxRpdbqNis6UilNxeewOsvOem7SKbULW4f02UX4O0KMidgTwOLCgyvfhl8AP8s/smVS5vOzxeZv2Br4HXEx2Tv0c4JoavhO9yDr94bx/YfgTVcqLbOfk4jr/P4yi9gvD9wNb58+/B1xYUO7v8n/HDfJ2TQSOLyg7jNV/SC9k9QulF5SU3x/4M4kfq8qyFcvmU3466Fjg+/nzEWQ7I6pSfg75jyjZzsofyz6fou2tUj65vbV8/i3bXKXuqtvbmY9OX+G6+qAL/TBS548i2Z7OZ8n2KhcAX8zje5I+chhEdlH9CbJTQC/n23M+6XP2F5CPtKqI70/i+lBFmX8kuyi3pKTcKOAXZOfQZwG3A8dQ42gushEm88j2yMaWlP0HstNFj5Gd250JfKGGdYyi9k5gR7JTX48BU4BBVcqek38Ws8muu/RNlPk52XWzd8mOTL5JtsNxF/Bk/ndwSfmn8h+rlm3+aVHZinXPZ/XRQam6+5Bd55tNdtpv75Ly/0B2qvBRsiP7Xco+n6LtrVK+aHtLP3/e7wSK6i7c3s5+rJWVrosPVv9hfJnVfxjX+A9MB/8wUvyj2CtR9pNkh6u/IRumewmwnKxD+vuC+rcB9qXG4bwUD//9fFl5siG927Wx/tLhxX740cyPtd6AZngARza6fMUPY831N6ItZNdJ5pLtoc4HRrdaljqVdXyd5eutv67yfvjhx/uPtd6AZnhQctG6M8s3om7qGM7bFcv74Ycf7z96YQ0h6bGiRWTXBjqtfEe3hYrhvJJGATdK+jDp4ZZdrbyZ5dwJNM4mwOfILpS2JuAPnVy+o9uyRNKOETETICJW5OPhryQbKtfVy5tZzp1A4/ya7JTEzMoFku7t5PId3ZbDycZB/01kt9cfLum/u0F5M8t5UhkzsybmVNJmZk3MnYCZWRNzJ2Bm1sTcCZiZNbH/D5m4UsQ9osCyAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 10\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[19], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[30], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2786386d", + "metadata": {}, + "source": [ + "## 2. Models" + ] + }, + { + "cell_type": "markdown", + "id": "3fc3e521", + "metadata": {}, + "source": [ + "### 2.1 Kmeans" + ] + }, + { + "cell_type": "markdown", + "id": "9bef3c89", + "metadata": {}, + "source": [ + "###    2.1.1 Fitting Models" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "289d8d9e", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.cluster import KMeans\n", + "import pandas as pd\n", + "kmeans = KMeans(n_clusters = 10).fit(x_trainf)\n", + "y_pred_kmean = kmeans.predict(x_testf)" + ] + }, + { + "cell_type": "markdown", + "id": "67ccad57", + "metadata": {}, + "source": [ + "###    2.2.2 Clustering results evaluations" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "47c1a572", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test accuracy: -44382938395.616394\n" + ] + } + ], + "source": [ + "test_score = kmeans.score(x_testf, y_test)\n", + "print('Test accuracy:', test_score)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f2e23193", + "metadata": {}, + "source": [ + "####     NMI (Normaliszed Mutual Information)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "955c19c9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.30718566254193425" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "normalized_mutual_info_score(y_test, y_pred_kmean,average_method='arithmetic')" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "40e0dea9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.32006018618379345" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "normalized_mutual_info_score(y_test, y_pred_kmean,average_method='min')" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "01329398", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.3074344894881988" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "normalized_mutual_info_score(y_test, y_pred_kmean,average_method='geometric')" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "ac0103e6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.295306849795413" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "normalized_mutual_info_score(y_test, y_pred_kmean,average_method='max')" + ] + }, + { + "cell_type": "markdown", + "id": "5d7c0620", + "metadata": {}, + "source": [ + "####     ARI (Adjusted Rand Index)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "779c4a21", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.1614760459699456" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics.cluster import adjusted_rand_score\n", + "adjusted_rand_score(y_test, y_pred_kmean)" + ] + }, + { + "cell_type": "markdown", + "id": "eaf905f4", + "metadata": {}, + "source": [ + "####     Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "49e50762", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix: \n", + "[[ 60 7 106 0 522 3 18 260 3 21]\n", + " [ 2 6 0 79 1 0 572 170 75 95]\n", + " [ 16 14 4 137 4 14 221 229 353 8]\n", + " [ 9 3 109 3 1 254 59 550 3 9]\n", + " [ 11 108 5 67 73 3 199 83 38 413]\n", + " [ 1 0 251 54 6 10 194 62 414 8]\n", + " [ 32 4 0 559 5 3 186 81 24 106]\n", + " [391 27 1 14 9 19 71 38 113 317]\n", + " [ 2 1 19 76 4 0 131 401 360 6]\n", + " [ 10 301 1 15 1 0 240 221 188 23]]\n" + ] + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "kmeans_confusion = confusion_matrix(y_test,y_pred_kmean)\n", + "print('Confusion matrix: \\n{}'.format(kmeans_confusion))" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "ba637eb3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "sns.heatmap(kmeans_confusion, annot=True, cmap='Blues')" + ] + }, + { + "cell_type": "markdown", + "id": "ab647317", + "metadata": {}, + "source": [ + "### 2.2 PCA (Principal component analysis)" + ] + }, + { + "cell_type": "markdown", + "id": "06872c16", + "metadata": {}, + "source": [ + "###    2.2.1 Scatter Plots in 2D and 3D" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "64add44b", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA\n", + "\n", + "pca = PCA(n_components = 2, whiten = True)\n", + "pca.fit(x_trainf)\n", + "x_train_pca = pca.transform(x_trainf)\n", + "x_test_pca = pca.transform(x_testf)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "fc524704", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "plt.scatter(x_train_pca[:,0], x_train_pca[:,1], c = y_train)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "8baaf415", + "metadata": {}, + "outputs": [], + "source": [ + "pca3 = PCA(n_components = 3, whiten = True)\n", + "pca3.fit(x_trainf)\n", + "x_train_pca3 = pca3.transform(x_trainf)\n", + "x_test_pca3 = pca3.transform(x_testf)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "a7c2b5b8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from mpl_toolkits.mplot3d import Axes3D\n", + "plt.figure(figsize=(8,8))\n", + "ax = plt.axes(projection=\"3d\")\n", + "ax.scatter3D(x_train_pca3[:,0], x_train_pca3[:,1],x_train_pca3[:,2], c = y_train)\n", + "ax.view_init(10, 60)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "9f414ded", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.10869168 0.05363875]\n", + "[0.10869168 0.05363875 0.0409124 ]\n" + ] + } + ], + "source": [ + "print(pca.explained_variance_ratio_)\n", + "print(pca3.explained_variance_ratio_)" + ] + }, + { + "cell_type": "markdown", + "id": "4b55fa60", + "metadata": {}, + "source": [ + "###    2.2.2 KMeans using PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "id": "4b695f26", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test accuracy: 0.0844574370978618\n" + ] + } + ], + "source": [ + "from sklearn.cluster import KMeans\n", + "import pandas as pd\n", + "kmeans = KMeans(n_clusters = 10).fit(x_train_pca)\n", + "y_pred_trans_kmean = kmeans.predict(x_test_pca)\n", + "test_score = adjusted_rand_score(y_test, y_pred_trans_kmean)\n", + "y_pred_kmean_pca = kmeans.predict(x_test_pca)\n", + "print('Test accuracy:', test_score)" + ] + }, + { + "cell_type": "markdown", + "id": "54dc4e2d", + "metadata": {}, + "source": [ + "####     Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "id": "57eb2033", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix: \n", + "[[ 0 75 386 126 3 284 0 69 1 56]\n", + " [336 75 18 1 62 43 147 51 252 15]\n", + " [321 102 24 16 14 16 110 177 215 5]\n", + " [ 0 180 178 209 4 61 1 337 18 12]\n", + " [121 233 36 2 187 68 121 75 51 106]\n", + " [315 25 66 205 20 11 104 36 218 0]\n", + " [303 95 2 0 79 22 185 38 235 41]\n", + " [ 25 211 19 10 258 32 56 262 14 113]\n", + " [209 143 54 56 33 41 188 201 71 4]\n", + " [114 235 7 7 92 17 148 237 119 24]]\n" + ] + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "kmeans_pca_confusion = confusion_matrix(y_test,y_pred_kmean_pca)\n", + "print('Confusion matrix: \\n{}'.format(kmeans_pca_confusion))" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "id": "3eef5fab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "sns.heatmap(kmeans_pca_confusion, annot=True, cmap='Blues')" + ] + }, + { + "cell_type": "markdown", + "id": "0ec36a9a", + "metadata": {}, + "source": [ + "### 2.3 MiniBatchKMeans" + ] + }, + { + "cell_type": "markdown", + "id": "78ddd888", + "metadata": {}, + "source": [ + "###    2.3.1 Fitting Models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4f713ae", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.cluster import MiniBatchKMeans\n", + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "from sklearn.metrics.cluster import adjusted_rand_score\n", + "kmeans = MiniBatchKMeans(n_clusters=10,batch_size=5120).fit(x_train_pca)\n", + "y_pred_mbkm = kmeans.predict(x_test_pca)" + ] + }, + { + "cell_type": "markdown", + "id": "d5af13fa", + "metadata": {}, + "source": [ + "###    2.3.2 Clustering results evaluations" + ] + }, + { + "cell_type": "markdown", + "id": "a14ff10a", + "metadata": {}, + "source": [ + "####     NMI (Normaliszed Mutual Information) and ARI (Adjusted Rand Index)" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "id": "178c9a4a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI: 0.18037308457241424\n", + "ARI: 0.08424108308239592\n" + ] + } + ], + "source": [ + "print('NMI:',normalized_mutual_info_score(y_test, y_pred_mbkm,average_method='arithmetic'))\n", + "print('ARI:',adjusted_rand_score(y_test, y_pred_mbkm))" + ] + }, + { + "cell_type": "markdown", + "id": "05d87e3f", + "metadata": {}, + "source": [ + "####     Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "id": "91486cd4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix: \n", + "[[ 0 269 150 22 171 0 296 82 1 9]\n", + " [330 81 1 46 48 117 5 19 312 41]\n", + " [308 50 19 35 131 53 1 6 265 132]\n", + " [ 0 142 229 37 332 1 77 13 12 157]\n", + " [134 97 4 316 58 95 23 135 51 87]\n", + " [318 18 214 20 26 61 53 0 257 33]\n", + " [336 50 0 77 24 146 1 54 275 37]\n", + " [ 23 102 12 256 149 79 4 181 11 183]\n", + " [263 106 64 52 174 128 25 6 84 98]\n", + " [132 76 8 202 103 94 0 32 97 256]]\n" + ] + } + ], + "source": [ + "mbkm_confusion = confusion_matrix(y_test,y_pred_mbkm)\n", + "print('Confusion matrix: \\n{}'.format(mbkm_confusion))" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "id": "c9ec58f1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.heatmap(mbkm_confusion, annot=True, cmap='Blues')" + ] + }, + { + "cell_type": "markdown", + "id": "e7bb3593", + "metadata": {}, + "source": [ + "### 2.4 Birch" + ] + }, + { + "cell_type": "markdown", + "id": "103a4fad", + "metadata": {}, + "source": [ + "###    2.4.1 Fitting Models" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "296530dd", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.cluster import Birch\n", + "brc = Birch(n_clusters = 10).fit(x_train_pca)\n", + "y_pred_brc = brc.predict(x_test_pca)" + ] + }, + { + "cell_type": "markdown", + "id": "c21d97b8", + "metadata": {}, + "source": [ + "###    2.4.2 Clustering results evaluations" + ] + }, + { + "cell_type": "markdown", + "id": "cac10047", + "metadata": {}, + "source": [ + "####     NMI (Normaliszed Mutual Information) and ARI (Adjusted Rand Index)" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "id": "857fee12", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI: 0.18080432574948965\n", + "ARI: 0.0909874496519081\n" + ] + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "from sklearn.metrics.cluster import adjusted_rand_score\n", + "print('NMI:',normalized_mutual_info_score(y_test, y_pred_brc,average_method='arithmetic'))\n", + "print('ARI:',adjusted_rand_score(y_test, y_pred_brc))" + ] + }, + { + "cell_type": "markdown", + "id": "81466971", + "metadata": {}, + "source": [ + "####     Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "id": "b89536eb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix: \n", + "[[ 41 451 48 2 94 0 1 73 289 1]\n", + " [230 39 5 16 0 0 603 35 72 0]\n", + " [171 33 1 2 10 0 545 12 226 0]\n", + " [101 132 1 2 158 0 15 32 559 0]\n", + " [502 63 15 71 2 1 131 116 98 1]\n", + " [137 43 0 0 218 0 538 5 59 0]\n", + " [275 17 10 27 0 1 573 45 50 2]\n", + " [362 43 7 156 3 0 25 91 309 4]\n", + " [250 83 0 6 38 0 321 17 284 1]\n", + " [439 26 2 18 3 0 187 43 282 0]]\n" + ] + } + ], + "source": [ + "brc_confusion = confusion_matrix(y_test,y_pred_brc)\n", + "print('Confusion matrix: \\n{}'.format(brc_confusion))" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "id": "c75904f9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.heatmap(brc_confusion, annot=True, cmap='Blues')" + ] + }, + { + "cell_type": "markdown", + "id": "3a50f7f3", + "metadata": {}, + "source": [ + "### 2.5 Gaussian mixture" + ] + }, + { + "cell_type": "markdown", + "id": "33c3d7e7", + "metadata": {}, + "source": [ + "###    2.5.1 Fitting Models" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "id": "e98317fb", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.mixture import GaussianMixture\n", + "gm = GaussianMixture(n_components = 10).fit(x_train_pca)\n", + "y_pred_gm = gm.predict(x_test_pca)" + ] + }, + { + "cell_type": "markdown", + "id": "b4aa5c01", + "metadata": {}, + "source": [ + "###    2.5.2 Clustering results evaluations" + ] + }, + { + "cell_type": "markdown", + "id": "547683c2", + "metadata": {}, + "source": [ + "####     NMI (Normaliszed Mutual Information) and ARI (Adjusted Rand Index)" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "id": "52f39cb4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI: 0.18906949813574742\n", + "ARI: 0.0906887887251104\n" + ] + } + ], + "source": [ + "print('NMI:',normalized_mutual_info_score(y_test, y_pred_gm,average_method='arithmetic'))\n", + "print('ARI:',adjusted_rand_score(y_test, y_pred_gm))" + ] + }, + { + "cell_type": "markdown", + "id": "337975a0", + "metadata": {}, + "source": [ + "####     Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "id": "09af68b9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix: \n", + "[[ 2 1 40 240 93 0 34 398 99 93]\n", + " [101 365 64 21 0 316 17 23 69 24]\n", + " [ 42 335 158 9 5 275 4 34 41 97]\n", + " [ 3 13 209 32 163 0 7 204 102 267]\n", + " [199 77 159 40 1 126 138 38 189 33]\n", + " [ 51 327 34 8 213 273 0 54 16 24]\n", + " [132 332 63 9 0 311 48 3 84 18]\n", + " [179 16 204 16 2 22 213 32 157 159]\n", + " [108 106 158 19 19 277 6 68 86 153]\n", + " [141 116 266 4 0 131 41 11 138 152]]\n" + ] + } + ], + "source": [ + "gm_confusion = confusion_matrix(y_test,y_pred_kmean_pca)\n", + "print('Confusion matrix: \\n{}'.format(gm_confusion))" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "id": "971194b5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.heatmap(gm_confusion, annot=True, cmap='Blues')" + ] + }, + { + "cell_type": "markdown", + "id": "3a8306de", + "metadata": {}, + "source": [ + "## 3. Appendix" + ] + }, + { + "cell_type": "markdown", + "id": "ca1adee0", + "metadata": {}, + "source": [ + "### 3.1 KNN" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "c27cb6d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting KNeighborsClassifier(n_jobs=-1, n_neighbors=4, weights='distance')\n", + "Evaluating KNeighborsClassifier(n_jobs=-1, n_neighbors=4, weights='distance')\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "clf = KNeighborsClassifier(n_neighbors=4, weights='distance', n_jobs=-1)\n", + "print('Fitting', clf)\n", + "clf.fit(x_trainf, y_train)\n", + "print('Evaluating', clf)\n", + "\n", + "y_pred_knn = clf.predict(x_testf)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "31b937dc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test accuracy: 0.921\n" + ] + } + ], + "source": [ + "test_score = clf.score(x_testf, y_test)\n", + "print('Test accuracy:', test_score)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "df13d267", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8231031975085954" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "normalized_mutual_info_score(y_test, y_pred_knn,average_method='arithmetic')" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "2226f1b5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8234272963838932" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "normalized_mutual_info_score(y_test, y_pred_knn,average_method='min')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "09c6c71e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.823103261265742" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "normalized_mutual_info_score(y_test, y_pred_knn,average_method='geometric')" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "cb776587", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8227793536618937" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "normalized_mutual_info_score(y_test, y_pred_knn,average_method='max')" + ] + }, + { + "cell_type": "markdown", + "id": "12d38ecb", + "metadata": {}, + "source": [ + "####    Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "4bef3167", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix: \n", + "[[910 0 1 1 3 26 0 15 44 0]\n", + " [ 1 910 27 1 4 3 39 0 8 7]\n", + " [ 9 6 880 45 8 15 14 3 18 2]\n", + " [ 1 1 18 969 0 5 3 1 2 0]\n", + " [ 13 12 11 16 885 10 14 2 28 9]\n", + " [ 1 5 36 8 2 931 12 0 3 2]\n", + " [ 3 2 20 6 8 3 951 2 2 3]\n", + " [ 1 8 11 4 9 6 14 912 21 14]\n", + " [ 0 13 9 6 0 7 12 0 952 1]\n", + " [ 2 24 10 2 3 4 13 5 27 910]]\n" + ] + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "knn_confusion = confusion_matrix(y_test,y_pred_knn)\n", + "print('Confusion matrix: \\n{}'.format(knn_confusion))" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "2bc87615", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "sns.heatmap(knn_confusion, annot=True, cmap='Blues')" + ] + }, + { + "cell_type": "markdown", + "id": "98588e59", + "metadata": {}, + "source": [ + "### 3.2 Four Regression Models" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "84d84800", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data set score: 0.337\n", + "Test data set score: 0.187\n" + ] + } + ], + "source": [ + "# Linear regression\n", + "from sklearn.linear_model import LinearRegression\n", + "lr = LinearRegression().fit(x_trainf,y_train)\n", + "print('Training data set score: {:.3f}'.format(lr.score(x_trainf, y_train)))\n", + "print('Test data set score: {:.3f}'.format(lr.score(x_testf, y_test))) # overfit" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "0f2cb297", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data set score: 0.34\n", + "Test data set score: 0.19\n" + ] + } + ], + "source": [ + "# Ridge Regression\n", + "from sklearn.linear_model import Ridge\n", + "ridge = Ridge().fit(x_trainf, y_train)\n", + "print('Training data set score: {:.2f}'.format(ridge.score(x_trainf, y_train)))\n", + "print('Test data set score: {:.2f}'.format(ridge.score(x_testf, y_test))) # overfit" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "b3bee2c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data set score: 0.34\n", + "Test data set score: 0.19\n" + ] + } + ], + "source": [ + "# Lasso Regression\n", + "from sklearn.linear_model import Lasso\n", + "lasso = Lasso(alpha = 0.1, max_iter = 100000).fit(x_trainf, y_train)\n", + "print('Training data set score: {:.2f}'.format(lasso.score(x_trainf, y_train)))\n", + "print('Test data set score: {:.2f}'.format(lasso.score(x_testf, y_test))) # overfit" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "1f7c891d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data set score: 0.838\n", + "Test data set score: 0.691\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "D:\\ANA\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n" + ] + } + ], + "source": [ + "# Logistic Regression\n", + "from sklearn.linear_model import LogisticRegression\n", + "logreg = LogisticRegression(max_iter=100).fit(x_trainf, y_train)\n", + "print('Training data set score: {:.3f}'.format(logreg.score(x_trainf, y_train)))\n", + "print('Test data set score: {:.3f}'.format(logreg.score(x_testf, y_test))) # overfit" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From e080f82a6e0ac4b092271f87eea4da8b50217aa4 Mon Sep 17 00:00:00 2001 From: zengmmm00 <50666176+zengmmm00@users.noreply.github.com> Date: Sat, 3 Dec 2022 22:53:16 +0800 Subject: [PATCH 15/27] Rename STAT 7008 project unsupervised.ipynb to unsupervised.ipynb --- STAT 7008 project unsupervised.ipynb => unsupervised.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename STAT 7008 project unsupervised.ipynb => unsupervised.ipynb (99%) diff --git a/STAT 7008 project unsupervised.ipynb b/unsupervised.ipynb similarity index 99% rename from STAT 7008 project unsupervised.ipynb rename to unsupervised.ipynb index c7b9852..d7e169e 100644 --- a/STAT 7008 project unsupervised.ipynb +++ b/unsupervised.ipynb @@ -1834,4 +1834,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} From f854a61b964267f6c23ac74838e7a3780ed3401f Mon Sep 17 00:00:00 2001 From: zengmmm00 <50666176+zengmmm00@users.noreply.github.com> Date: Sat, 3 Dec 2022 22:56:52 +0800 Subject: [PATCH 16/27] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index e36a105..6dd6117 100644 --- a/README.md +++ b/README.md @@ -21,6 +21,7 @@ wget http://codh.rois.ac.jp/kmnist/dataset/kmnist/kmnist-test-labels.npz ## Visualization and Unsupervised Models + **You will need install these packages to run the specific functions.** For basic functions, such as loading data and displaying images: @@ -38,6 +39,7 @@ For Unsupervised Model, PCA and Evaluation: pip install -U scikit-learn ``` +**Run ./unsupervised.ipynb** ## MLP/CNN Model From 78468a0e86d2e5cffc06781e368b079b4570d90b Mon Sep 17 00:00:00 2001 From: zengmmm00 <50666176+zengmmm00@users.noreply.github.com> Date: Sat, 3 Dec 2022 22:59:54 +0800 Subject: [PATCH 17/27] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 6dd6117..8c16f70 100644 --- a/README.md +++ b/README.md @@ -44,7 +44,7 @@ pip install -U scikit-learn ## MLP/CNN Model -**You will need GPU to run the following code.** +**You will need GPU and PyTorch packages to run the following code.** **Results will be stored in `./log`** From e333e3393c946806e6eeeda13c2e3b1b3632ccfa Mon Sep 17 00:00:00 2001 From: zengmmm00 <50666176+zengmmm00@users.noreply.github.com> Date: Sat, 3 Dec 2022 23:01:31 +0800 Subject: [PATCH 18/27] Add files via upload --- kmnist_classmap.csv | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 kmnist_classmap.csv diff --git a/kmnist_classmap.csv b/kmnist_classmap.csv new file mode 100644 index 0000000..ba15c5e --- /dev/null +++ b/kmnist_classmap.csv @@ -0,0 +1,11 @@ +index,codepoint,char +0,U+304A,お +1,U+304D,き +2,U+3059,す +3,U+3064,つ +4,U+306A,な +5,U+306F,は +6,U+307E,ま +7,U+3084,や +8,U+308C,れ +9,U+3092,を From 2c7b7c154135b7ad50f0c62c365f014b38b0c68b Mon Sep 17 00:00:00 2001 From: zengmmm00 <50666176+zengmmm00@users.noreply.github.com> Date: Sat, 3 Dec 2022 23:05:57 +0800 Subject: [PATCH 19/27] Delete benchmarks directory --- benchmarks/kuzushiji_mnist_cnn.py | 73 ------------------------------- benchmarks/kuzushiji_mnist_knn.py | 26 ----------- 2 files changed, 99 deletions(-) delete mode 100644 benchmarks/kuzushiji_mnist_cnn.py delete mode 100644 benchmarks/kuzushiji_mnist_knn.py diff --git a/benchmarks/kuzushiji_mnist_cnn.py b/benchmarks/kuzushiji_mnist_cnn.py deleted file mode 100644 index 485d8bb..0000000 --- a/benchmarks/kuzushiji_mnist_cnn.py +++ /dev/null @@ -1,73 +0,0 @@ -# Based on MNIST CNN from Keras' examples: https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py (MIT License) - -from __future__ import print_function -import keras -from keras.models import Sequential -from keras.layers import Dense, Dropout, Flatten -from keras.layers import Conv2D, MaxPooling2D -from keras import backend as K -import numpy as np - -batch_size = 128 -num_classes = 10 -epochs = 12 - -# input image dimensions -img_rows, img_cols = 28, 28 - -def load(f): - return np.load(f)['arr_0'] - -# Load the data -x_train = load('kmnist-train-imgs.npz') -x_test = load('kmnist-test-imgs.npz') -y_train = load('kmnist-train-labels.npz') -y_test = load('kmnist-test-labels.npz') - -if K.image_data_format() == 'channels_first': - x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) - x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols) - input_shape = (1, img_rows, img_cols) -else: - x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) - x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1) - input_shape = (img_rows, img_cols, 1) - -x_train = x_train.astype('float32') -x_test = x_test.astype('float32') -x_train /= 255 -x_test /= 255 -print('{} train samples, {} test samples'.format(len(x_train), len(x_test))) - -# Convert class vectors to binary class matrices -y_train = keras.utils.to_categorical(y_train, num_classes) -y_test = keras.utils.to_categorical(y_test, num_classes) - -model = Sequential() -model.add(Conv2D(32, kernel_size=(3, 3), - activation='relu', - input_shape=input_shape)) -model.add(Conv2D(64, (3, 3), activation='relu')) -model.add(MaxPooling2D(pool_size=(2, 2))) -model.add(Dropout(0.25)) -model.add(Flatten()) -model.add(Dense(128, activation='relu')) -model.add(Dropout(0.5)) -model.add(Dense(num_classes, activation='softmax')) - -model.compile(loss=keras.losses.categorical_crossentropy, - optimizer=keras.optimizers.Adadelta(), - metrics=['accuracy']) - -model.fit(x_train, y_train, - batch_size=batch_size, - epochs=epochs, - verbose=1, - validation_data=(x_test, y_test)) - -train_score = model.evaluate(x_train, y_train, verbose=0) -test_score = model.evaluate(x_test, y_test, verbose=0) -print('Train loss:', train_score[0]) -print('Train accuracy:', train_score[1]) -print('Test loss:', test_score[0]) -print('Test accuracy:', test_score[1]) diff --git a/benchmarks/kuzushiji_mnist_knn.py b/benchmarks/kuzushiji_mnist_knn.py deleted file mode 100644 index 2b04a58..0000000 --- a/benchmarks/kuzushiji_mnist_knn.py +++ /dev/null @@ -1,26 +0,0 @@ -# kNN with neighbors=4 benchmark for Kuzushiji-MNIST -# Acheives 92.10% test accuracy - -from sklearn.neighbors import KNeighborsClassifier -import numpy as np - -def load(f): - return np.load(f)['arr_0'] - -# Load the data -x_train = load('kmnist-train-imgs.npz') -x_test = load('kmnist-test-imgs.npz') -y_train = load('kmnist-train-labels.npz') -y_test = load('kmnist-test-labels.npz') - -# Flatten images -x_train = x_train.reshape(-1, 784) -x_test = x_test.reshape(-1, 784) - -clf = KNeighborsClassifier(n_neighbors=4, weights='distance', n_jobs=-1) -print('Fitting', clf) -clf.fit(x_train, y_train) -print('Evaluating', clf) - -test_score = clf.score(x_test, y_test) -print('Test accuracy:', test_score) From ecf78b72338336dc97ac3a93966f6bcff5acd646 Mon Sep 17 00:00:00 2001 From: zengmmm00 <50666176+zengmmm00@users.noreply.github.com> Date: Sat, 3 Dec 2022 23:06:32 +0800 Subject: [PATCH 20/27] Delete images directory --- images/kkanji_examples.png | Bin 214144 -> 0 bytes images/kmnist_examples.png | Bin 142819 -> 0 bytes 2 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 images/kkanji_examples.png delete mode 100644 images/kmnist_examples.png diff --git a/images/kkanji_examples.png b/images/kkanji_examples.png deleted file mode 100644 index b093571a57350a5ddaa92996d7d1637fe2b9d7e7..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 214144 zcmcG#byQp5);1d4OYu@XSa1mL?iL_;fdWBNT#6ScQi>HX1xhInp+NB#Z7I;;F2yAj zx8Q#Hz3(~ae&deM{<#?=*=z4T*E63fYmPP6T8V||sgn>sB?JHfB$^s3h5!H_1OUK* z;6FS7u!+^FJzNMpHOzeg03wQiz8HY)k52#qoN_qS%-2j;N6OyAjo%jLVduaf;O6;H zFMy1EfTyj!i-Rw-or4qHT^4xI(g9?K!(@S`qPl{*p2`l+aE%~u2csZ8sC|%&y(A1M zFUKqsAoT#?=HP3~9N^~a?jsc-3;Zu!sfYGIj|G6t|0UwjQky<_&X{GE`Cf zm#&99S)jA8ucwrNfWN;#zrQfQhqse}kffxffFM`^4CZ@~;PVM|_q7e+bN6BWH-d_T zkG(hC(--dH&ioIet)0h9Us>P-)BiTX&GSF7?mqu=(}TkV0&G16g!l#jG3noey1M^+ zQ8%~$Nc;Gz`aNj*7vKLWun#oQ(?P({!N=pJx4na^pM$$E>%W!3?Ell(^QE`ze_0B% z7jSTOaC;E*c_1eAA61^t9=;wv&K~~@JO1^7()7{6{*4^GgQ$-f|z=9tRhe=6*!GaP(q9S}SI|pGtNl6hqJ_!j? zJ3d>Oh>)YCxX1$m!T)0O&|k&F{^dX3`ltVYXDQ6X{sH4Z4v~@+wgrm`3&Z$eLc)@K zFb5GyK1o|~TRtICVLL%VK}p94%zvZld&3_FimmJai24sJ*aM<4Ovv8O-T}-f2(y>u zvlkX|2a5ukVfIpv9^P)Y4`~5+vvqP1@N{Q^t51{|^X#cl_kB6hLzpb}}qSJ$u{|{*-@V{d1W9#?7=lp*c@&7+K z|I3l~&bIDO4iA-40Qip;0{_&I{|~amWcAz zgA!hI@o1|J$E;eSH>Zsc!(M}!6MF`&6r_HZx>mkVY(1=Y)>6nPIyswO2}?UpZi*>L z#@smfrg-r!|Hm_gxL0S#Mgz=97q))dzt|pnVN>iO^c@jT7R|iOe#NqZDDbS8oz_8w z-xFkVI1pdhtp2IVETF#=zo7P-VivH#h+dv9(QQ#x|9ib^^TMC^W--?e&m?co9b&_$l`=dbg#BnMDv%zc8>+$d8$xdHxbS;S6qVG>`6 zgi1{33|A6-6SrF+y|>NchUxR9Lh&hpxLeCPS?x>XZWpU%*K?9Pk|m0@=)S$M7XP)s zeU6EJ%lCYH6XUD^N;ki&300oT(&+#th1Zh0Z(FaA>sgteKp@7`vKM7P-=0-wU#`j* zFx(stHL6`KO{^Nu|01f`eqr#~y*i1$@w9X0CsJ=3i4(o%{TAJexM14+MBiA5b!X~4 zC(pvAKfx@GaUUjJ^{vI5xmIvHIrc)$h4P~p+^nlGe)Nb=u3TW%2IP}Rcxj?fnQ(BU z-Y6^DaFG;nZsE1Lx~~^Rsgy_}%Ida|Kw_-!| zWtWRN^14e3E zeaDM3Wk;nY!PGI*{R?r6Ud_4AH1kl!VTz)(f4}_UO*3Ff z&J9=czRf|&dE5j$Bo&$QKRvq79{No%B+CRjipHxQ2@KvmychO@$gDdpBx~-xT2q0Q zdBJ4~^}d{skE#>+>&w3Dm^D*a`JSj}uS?!aSx=9LfPyHmWxH9pLE6q!|{(^?sP3=+6<#6s^ox0d5+46Fb zS;=Z|v;pWtIeBpdE=r>;Ag0E?0XZ!(7~7D?p6kZgLnOS>6pnam9JX?gJIxaIOf!~# zC4$!i$H1$}GMakC~3|2dDe8t)l(%D#zS@$)??LYow{;S%>#&bxz}f zgy&tvLW=x!Av(q>e@DDe28%&ce~h~fZ(I=Txq2UR-$|x5o7V8C4Al1 zrs7XLgj#FB37M_|MjA!O%xS%xTey0d6RAhx5c}t{Og($vbsDq!kPO6WvtEZf`tC;@ zd%WL^_3f3lYOiC@vDk369k!r^XRUivw+!*gGbYKAmwRZ5621D(fIgVXP28r6U;k?TX9nT zPv1lR-r*4zos_4~kwh8Bl$kvQrrG58YGv)^eyLSlGd_q?5vvkEzd)E!0dyKpuKy%? zzwj%n8#p$(SR)benJs&0L`1%h>I5nYr~$H~xMj%JA3FEV8S25vjDx)J;6rs|Q4L zC={O~PI~*E77jwq4W0ex39`Pk_|G=2^&V1(m*HWPS-|p1@kN*gL_5;Tk~Ly=^EY{o zSW+sJd4}tX*6lIE8@n9eEOI*ZXX=3@YOZFO7?TrjkJ?r*^e2|WzCR_bJaRCM3n98E z7`_rtD}TFi@-t^4=alzN=66b-6|T-I12)Y-OsJAVMLE&m;fuRG60DB~8vU?T0t#%( z#V2z6KqZAp&pv^(v6WC+kc=;gw@DVP`3Ify7W47nzNx~7<@WfvmvS28wzKTB<^}IU zCP#7ZBjOn^De&lZLPOMoMk(N}nGH+gl;vHiqDi5^eOXk35}d}ZZcIi^L2iDrjhaTr zLgh`k5p-dg-dE6#v0Ra0}I=}vUC#O4C1VXUJmdb(w zO8=2>xv!0CQ2YdXoaZLufhblNThe9=<9Vaxc~C2ra?{^r_G@;qBk*LCgpcsIkNaDl zNj3cHC#m}CIbSxRTpuIy*H&vA}C`~d!V)c4#oHM-#Oye;R zZ=HuT!u&oq2fKbDx?`f){vz626jFs-i~f{@6fxmh9XjDm`K$?+S|>8{;`MA{KLOG*zRJoHtEbAU<#e~1osEsH=c)4e&^S~> zcLi2qU~GbDK<#lGr@ZLyHae>Nnytk z`iR^>Be7HCj(@phMTjjGUZndJwg&d@Ur^_iTo>E#i%k3ddm~lzO@_KuW*#qemu{1nIc%Xo$%*Dy zm{v<@_~N$|zp(epcOjYk_{Carmt72S8UB@#?L1-?>Vu zr-|h8aDD#qwEDX{i+*VB`rV-VQ34}Ekwvn}jLYCCN#4+ysK72qm=Nit_Lg$K_JXd( z62aNsCTayJjU3QIxe>5M@DXJn}JuV!+|Sa_1eT8v(2XdqX!^OBV~Y( zD|7}{@=>+QF$K-x@x$Ia*}Q%1^Mu2gh@rwt0R3tmNyGfQ&Vz%gZ5mLBX6G#XxeO7*gv-46;?pL?`d3dn<{F<9uYc< z1)|YH>xZG-0S@Nx176W{^Gj$LGLpu~vw2ei&LdgKsaS}vSWV@eG{>dGC4edD;;Ru} zPJ4^bo>A|ov%U@x+`sFobM1)DJcCXGMktTUT7Bm^SDNH@EgnUS2);%Yo`JrS%Z4OZ zXqh^cq+EUlgDINXj8c?(7;+{ncD=b%w=tS=IK`{(UV60h_CpiLKQGHP=jPiLQr|}i z_G%eQ9ow_PrQ{IpWC!fR>=um>F>Tao4>D`2`f9G_Kz>nroGJ@H(ypv|*17`gIq?Xq zzup{7?G$$;FAj^HWji;7W8s}2E;E`Q{E1fcK%h3`Ztl38^N2?i65=Q36N+V7LawOI8T_LD`TWfvu%lwYwK zvgDO{JWj4|7EcWAQ*URyd|2r0*fY!K`XVKd85PFu-`bZTw6$bu`#6s^zH^Whcy z>;fjruhZ#*gjMyjV_kd9%HO(j6@1oWf=OL2E)s--S{n_ZH+v*8wZmmIrsOyr-{|X( zj*PkWPSiLe?U04**r@)O)ZuLduXQYaC{=1WeI?#>`h2?i_y`>XnYfhBE;^kB@u7`x zM2trZa{+Jl+8uW4pLC!%6GzzU60^DlC{lP9=y(Rxf2Hv{W)IuUi`(23I1+=Yr_J$BN}d!*=tz|STg}T)c>3`ri4K3O7X}K5`K0}EH5j^Cx(cr zVbyC=}cL>661B2dYqY1evIp=bMl&n!@#E{52FMVR3%(5M6tsr@9P zqyd;Wq2_Tp0ee4VYPlevulj3WiyA}>9pTU#Vz11~_hvb>e z9}6%pH-f5Eg@h()=+*ZSE1Rr7Q2gsoHW z7=tRd4f3SnPB4o?$G}mJA%QqMFcu|8iqf9|lsY(OlHamj^v8mdU#ze>WO>y=%%~i5 z)3Y48m`^Mu{a5(uH0QKX6{hsY0<##NaPi>VZfrUm@|yd|RiyTs;2Yv?;hla;T)ZW- zCxGW-#s{=fs?eyxvtyn@syGwg60&6R>TxK;Id*Ggxx{8-2(&)c6bm3?Uwgv{4TIE> z&o~98l8eF)pQ99={c;obaRcsj=X(DFm(NIheQw38!dkN4MhJ;D|9Ct6LW(7F`Kj4Y zZG}6PbMWn5`|apANdgou!}49vbQu4EDQc8SR-79&riNFiqG~4%``Z!2ozo38XvAg+_>FUkFQXeBQdc{&vhpdK z>52&SQP0J{`s32=@Q39|3s4j{XL<83Ht+j_chstS#HZFSassJwldq9QGcRKOrZ~mZ zG%N)CRfeL@#Ct!i0K-$D-LiUYT$*gGcXxgPaFP+Zw}`Z22(#V+UM~D?gips%NA|@% zKc~9Do6h?_!smR`JNeu`CxN6wBZYTYhDh!6&&8jRRmiO_T{V=lZ+VmO{&|D3ZD}Cu z`yxKa?>uOBkvZ+?dE7s~?~?gRPrEvxUi|>Ts>*Dozaj=c(`-O1G1w(TgYiU#UnOsf z;!Wo1V~n>w1ti&-Q>?k?T4+xP&__gT!~N9Fm=ucl>zqd~RK|^D1Ff2@R~DEAn^)l@ z>}qh`8asGaqSQWf-ykl9i+|xSwUpdDQ{`FanHU#;2cn$TkBAgcSfsuB!Zb!};jZU6 zMoFm>oxw-bAJQvBIa3POtoSIgKo`vdWw8=F`p%m*bFg0Yv|1N#$z# zVr$wiSOBPRSiDE|t4}YDutq}eD$6-Pxx(MvIP-)BZukE2c(c~V9!xgoIK=nfygm& zOc|jG6b`jzz4UbsB&)Ld1H!8eHcC>Wpl(r1e$;FAzorU)on05KRmh}$ zA0BqN%r5S(K3iaWH656&qSY8%fG5bJ@eQ(GR8=f5xkJP=BDd~Qv_1W|0&IX+LIxr% z?lXW}&em1iKmpg5>atbLdrkC&;L@{R9MDAV)8u|%--rvJFG+Rj2SntrX>vD0r&#c+ zDu=pfbn=rT1;T7f7S38a-YYyk$Z!N-^4Oq;-L|)f?ZBf+vouc2^z74=BXcge9zMq_z00)QG`2e7+1yhDc)fi zdk01i?{4$K&>#}@1kW2(@e82LlqOWB{LPJ3Nh@ib0l1J3Fni$a&jz^k2^v<}60lJa zPC_rWL!O9_?O&nqfaua4AHkse!_mD{q>8*GFgqbX zm|J6f9~ufGw*6upC%^Jq3Et|y&@`E%C4GA#nRAI$wS>})LN?Nz5$#InbWnPM=r6wP zC%Mz?!pa&Bg=E}`roY#Ep}i}nQ-kKs@+pWteq_8LD5YL@yE?jH#+KWTvvKsZr>}#F zP>nDkNx-hx#Z;3+>X%0gmtQ51cYe+Xjf~6Aj|Cd+&;_87ZkKKr%0ms+6}U-}8g`*L znzQAqes$^cyc9cgF&XlpUkPzk1BwT$RLn zRoFjC1--cYS~niwo+9gn%kq@9m*FkD2RZ^E(^=(1g3t>SF{3SlM|p017o4ac;Q!p}8S+mtJ(IA9Oz9 zf8KUbI}g{oNF}Ur(3UktX@3zu-;eaba~4G+E^hEsOYxjjc=GNJmTF{ftKQ&eeC?xO z!Ysd8%&z6QDAnG><}47G)Q}|UFIY{I%>%wfNbR)Xxl!$ zyvoy0)wNwqrf{p~geFh|Hl4u6H(3VmKtniS)1((_ChxC~;r!QJlEPe~fpL7C;rj=U z+sAs{fgT`kRoUDyS#yg@`Oqg&=>+EAc z24{aRK!ot;moA6viC9M5g#6Z+wc`S{GgT%>0lXd2Ux=}+1us!LocLFbJ2oG5aEkLRJ0Xerl_MF<|>vG5REu#*m7Ly(v9%ZYKsN7g%@HbKzzslbq{ie?yU# zPc*xU4fqs4AmUNRsbu?%T;02XliB3@#98Tt8OvKdiTn7rpWwk2LZ2Zo67I{%=)2;A zLE}HqHmA?%_}JESt3dR5#<5;?Z%hwITv8m(DAOusDBnh0e4r(7<}@H1*HroWQn#oN ztcn+H5*gvWf9FJ8S!1jJ3z+wXhm%>?B^FjBjvhV4pwbLp^n0Bfe1AL8DPx)xLBY|h z^Zt@0oN3)T^}rcd-YFZ$0`;SBdn&?X(3qQ z-_oB6y3PbjX`ne!g~$jOa$3>SsLwf0>wfxW#1)!7VdvJ87?6OE z*|I{4S-eH{g?63V*cL3j&_F;mLT=t3I0{a%6vcD!-sr8n8H6K*pjbnFfOt(+O zk$|=+PWs8Ce!kQKU)mu!RmhaKI4UFIG*F!HKHE@&a%>XHbw8D3>s=O_O?8Xjn8fIA zHv1*1Po4cSHz{--xjL+F_<-fH)#K;0y#|UK_jfy$vz3b+U*>L=+hVgWw=+l?iDs1J2!2}%*6>dgDH<8xvd-vJNan~Ji(NjG5lN49RZa! ze7A{ld);vj6mXY+W%GAL2E#niroFbE12C$Y^vroti-lI)Fv`kuHft&rJuaTOt-DC0 zH|AYljUf=6n6N{fKR*t~to`85UihnpC`0~2Ze8;7vq&v>lMu_O9pNAO()ajyy3^4) zLuworX`PYPR_$%A_Jxjm3YQ)1++xA3Htn3Sb}ajiH8Jkh_b%aR0Z{cQvp?9~XrcM!4%NoK_5M7@BlNb{3 zV!#}szVW7=U(Lz1FxJ5K(xhuN3Lmh!S-G8&_l z3x4m0gc2c(qQGlC9t^)`!dG_z6>b@E9Y^k1l+S*s&bGf)TyfSG?Z~P%#LLJZj}l<) z)B%0+S}Grz!+4qA+-^f1EuRz* z=Dxh!C>QLft|EDyOn1NVkl2r~WgKPe$5zlYZ>TUsNB|Y|K{Ta$^>*^Hq$!W&WXU?b<>^8>i?*yzkMP%|D zPXQiGh;SLn%K80T+cctD^30o)Pc$F3Pp^F%nY>Mo8L4REe&w@{Ym$wU8~>d-6yOF* zmoA8O;C)8!^*20M;Na9;w3%o;FHSzt=Vun6RyC0uFM#_Ol~Z$DRS?y)_oYYbvN1KG zt48_&61p1stCmhEm#Be@t78A6eQqSj}VElB8%80YkQdl?K zxTWM+B1nvvMgPhZC)L|*-|CH{=12bRXG`6PX=cfo0cOMIbA}dq_}JWvx*cavlT=h; zmMAUD7HuomaD;kVp+ol7XGoL}+lB;I#_zne50S4vfrtlp$kNw^Un9F-*%oEIk6LBY1K+j!3m0-0kQKxk~R_AEt> z_s`2+8XMgUV1(bCRz9FL_?#>nF}}YWXj8DN3)m~WaQw3#k2 zuM5_7xkGsfqVJL8Nyx7Obb+T8=GQj!i?f)oh^tLzC|u|pfHq=5bub;_rj}9zBy|kQ64nJUhH01>KGSB`|V9v6DuBxKkL9+Sh~VIg7^f zSlvp5km?I{U}FI(e597EewZtryhyYZEG>s zRP(-SWRhqp-Me+%7>=6yc&UsZ^dEiHhJ5j0eWOR|4rA{FQv4fTL#LPe32>Mdc0UcE zTAV(O)SHpMUFVc9^gTNze~RkgNfaj)yjvSuh|zso|NAOEJ(Bd@1gmcGW9~bJs=oR6 zj-5ko)_hpY?Xa@Zk-G^Aai%hvv*;$p*ODHr>jDsGOD0mfCtwr62&*k~--#;KmRDORoLoPg$UM3Q7CQe^4L^J(@3!hSmjLjt=$lN_llMmsG)^@6Fw(S*Sc z>v{;g5!t0jD82X!>HT?dE?KZp!E74Gvzh7^$wT9jE>>ER{=+& zq!`~_f|ANVB^-7Rw2&LAzS@dx&6sz~@4V68$l%hcTVN14zBS_DV&ad-9MRF6nFfS6 zCLD}(G~J(cHw80kCrITeF>*(U1d;i<33MB>=#R!)06N8|t(D)9m;UN^zFN5LqZ06{ z`lG0j9cM%0%y8$a!UeM}Tk@I5rC2k1()^9e`!j8xjwpnW&SD6cYe=Tn)MR1jck`PE0tM*VrKJZi_K#U|O?|dn=*h|& zYV|LTjZIKbyoM-$HqGv6&B8VoS_o4x#MDbVC5tBx_qj+ zQ?G&s2c`Y64fQt=7q-%xUC;3$^}cKowm+`^urU~ge^R7W;<^{#`8c$&^XeslKByEz zbtFZoypbqE1WRSY8Zo66EHJ0#^QOG8!rZHZDvG(gupz3_6{T|P-qI&FU!KSf@6|t? z(y-Fu$dxvud@Ntw8L~R(B^&ZF5gF-40}s~6YSGgS$)aZ+T?xmK9KUm0+o;YBJKH^R zseb8deA5sIB3?MB=0;(?1i0;0)VemCsVlV4@3eJzb;_r^-@fG89BRpd@lZ{v1{ECz z>lj`Ud>;!L2)n_kIosG^j3xz)++0gqHquuSNGB=M+zA%^u-0bI%TUc1-Q6_Y*@BXJ zhV5KXno2lp>jV^j)9*egx3FA%>xzg@HIxv};_9?b)A813rjOIV3e_j4;`)_BG}v|f z8_4b`xR)(H!+@Awc-M7kz>Y#BMjKG&lP`TQjD8USv9r_I9hLVWKEZ|DJc73gR%(-> z@=>4V-RiaF7N;PtI&93UBihuE7iZN_7W4Mg6{uDK^LtSFy40OEEIEJz93QnEVrI(1j0Q31T_Xs z)2(5*s(?1Qn5|-)W`{V6b#Y-Ph|4+cM_sOMkB2VS-44OZc{xyXHyAn?5EVPH>P^Qa zJ0f_ql}o{pqIz`d7~9H3^`fw|wpzrnEmy@@C5)7WnV2PW=aBr?8}Z2xG=5OaX0!W3 zC66){9nXV;7Hwx}sL?ZlfF8|~nmYa!+|ca6hxJjenE4C|wL2&!f>0h((`sqZkuUv8 zcyj4Bj(X=t*K_k{-cogux?|^$$NPtvBfx}Mp9IJg^?7-^>*pUT4xbvyN(MRGCt;NG z!My!Z+R}2_sABP;(7@xsnaLVtWYyfu@xO$a&`{DyHpgT&LWuJlxdN+aSYh5)y|cuKP{=JhWKuDPNlIN)o%TM-whFbBWx-KVNo4WWuYX^U$vDi8sjfr zSPKFPPPb>0-@RZ{n4Hj>J^L1~RHtlW{=QP+Z!|PAdjMg-z<8Zzl%dwr$DK|caMxpzrdPUL8`PR z9kxS*wEFWIRldmZcfL;Mdi!TECZH+urYhx;pH)>=bpmFEpPu9DHAsX8i#SqDd|Y8^ zYjg|zbOxSHAAc?VcvDaH=6U)EE75{FwWBbgJg7VMrW>HTko!}JoFR0YReV85$EmG3 zcu>KqXw;PU+lm{jxBa}*;>*{F-8K)~jkC*Jw;IENg)>HgNLppRd!@aC^+^TA<{`{h z_k4=3bCcOp)1uK-kumB;47=l3bBEbVK7L>#0PlQ~oJx@)B_3VZ{sJfZIj?!;db2=9 zfK(IH+bGC`soab85=P2n8_gXQ+v7M zqmQaEkXW^Cm_@|(TcnEfLiKuFu}j$1MfHrw_T^Se?u@XCW3vxqxL|J4=~#QeKU0(i zveak+8}Pcq`h#w8a@HA54%cWZdM7eI1v<~q!#t@j2Hw@&1nYz1`YcO z)vx2{G{&x!-kHgVLNXKCB6)u3fxJE-A{SpzO5L6@|Kv_fy(C2qo(iPK^fAZ(h2p69 zWI1HLf6^nMjImX}?o4CA$O=)&9@gknmm<<|xJW0=q7TPJGX*Je&|bGQKE4@FY26|i zCSYM31os*sf2x!3RZT1VHRu_fmgNHQnizK?j0*I1+#$c2{=5=k|G<^!fsL=czLD#s zkFy+_TIBX7ocL*BIKA`xik zKl<5GSp9N>nT|%bmdw&UDZRY5NQ1g0sKQd;sp$C8TAK8;gn8O9`;%DMn8HdZ?k-btQAL)HJtod7@a-C~|ycy8kcNlsup*^rIVX)f*`sD%C) zcN%d=fJ_8J2$Mc;p8b231ZCxckS;_S)#n-cs4=)Bse*EogPCHKj>QDspgABAju1CyI&e_?&`QY)J zvChEZuCx@Nq}J2Qv3ONe9rfkKR;@7>h6Epdd(h5rzwP{(>b&cy;mrr; zqlL;j$6$spO>d~9`z|AokVON0T9yeUVXuBE$IoXxMkJP>IQ9(>`XUE*=FV^4h;571 z`gE9gFU#o0xN=(;D>eQpOBOK3=rU$=^SBHMz=P5~Y)h_^7{{{KCgP=Jg|_+bC(#j1 z+ZUvA*?hcjBQngeVE*%=wj!DgLG4&2%8W;}#he2i(mBQIvNa9m=TMM;qqyLUx%-CA z;Phgn#uyvbS9ot_nVN-gnLO3t43J|JXl|Z(AwoCGJNhKHRYE#qXghHRHZyK zR}R3#`LKuSv0F(N>A5?8oh;l&1psU!AWRb7)U}n3l2fee(x4Awoib z(qE?4dbS)`-v#ADTE@_@c}%D~QZ+@hr`GpD&BR~%to_kn1bybOayc**t@+b(JN;f+ zae|2Rvb8)M%5K8e7n?sy%KM%a@>prT+B(^U8>JaOPMA8yGE{AqQJ!4>;Vu1*UQ5@* zw5afULMys;5YNx4F101L@7)9nr`X)&VLMUJ+}Q1p$TOS7#m+p{?htR% zS{KWvQiTmi-ieDK)ec9Q>6%EZ|CH5!lA*pajWFk+U@Mmdk1shM5(QGDM>gfChBpFO zT52mw0|-1p;$Ou7&)-0&BoO`%+YS$`@3Trjh)&-x3XLpj~ZUj_q6#1jV)|IE*R2LjjhvC z`GSb^lM;!ahFw&Ev-f*TLcdl72(L_+dYd_cqSj$LrYJ?rk_Ci{RMf4-qDK%&7~9`n ziv5H59J8rhSoVjNzq@vJZ9Mg9el&_=t6h}bj4#6@g+`w9g1>&;l>EvC&84aLr05U( z<~g&ixUjfu_3UvU~*ND6_?nS_t)5RlZ>DREW!;7T-XS4z2FO_;p^V} z-8{>Q@BtBxLYEG&?e$l(oC1820}1R1rcWEwT^-LJB9RtepiY8x^nBLJ%lYX;gH&wX zML94o7nn81=;@`NBPf@P!v68M~1!fw@3x`n{=P?~D^*grB; zO0hVX$LAB7eQJ4mO_ZX4G|!e32o?B320f2_0{-3p%M9!pm_1(;yWxwqvzg& zJS*0Ps~^vuEhf7~X@^omgh?5lPlCmeQfzY@g`9jIDfUHrQ< zI3B;^@aERhvQLLoq3n?KvdAeny1%`>`7<%4>{{>0-yu=Cx0>&rEpGROd&DS{N%J=- z95u4Hqvi<)-TQsNmarjv$@Q-k3M&FrWFTba+44n^t~IG(hU6wqXwetGXq}SZJs%uO zYUkU+HhwKWj1a)G+icbsRGk%HvViSt9dDf7`k`Wr5a+~J@mjy8QKrG&w>Jb<(>WSF zT2R^W3wEm5yrqn_DkEE#tobJb$8x$YM7r28qhnrU0_Eo$3%AdKi^d&Rby@n09)OKM=gCL)ncWq zHjt+$Nzuf8b$a=O&?YbO^ZHF}U2$@^Qcm~#dAz_8-{yyPkugY&Yl4+u!eMw{_0i#x z5ii-&ZhK1uuQz*RfC`vPNRi-y@(Rl{&othh?jHMLM9q zO(o-_?{R-4E)1Qk(l@Fu^=s&Wt#=T^^Rc`Ji})4RNSj|wdFmXHbU&{vHFGxLbF$1G z2mBTDames+(c7nAUGkn>y4cM-lf`M-qTr?|Ez_Ob+n-0i0ciI%MHkkxR0qU6yaIdt zLA>lZWY+Ie>Jsg%;xG7Xm~;%Cv-n8I0CK8fH3c%rci42|twrPh)L3D+%_}QWz&4&0 zlbO$ie-5X}Ar3ZdP4q8Uuch)ra#g3zj?xo)J;T)U0101rV;JYVxDwqsodVbRFtk1? z2Cc|T1RMtgF*Z)k+t3pR9eu0Q`n2O^sEzl?oDt8?^U{MNMX^eM_nMh^r|3V5Ey=0Q zAC$D3xB)?t{f|;p7_gmt*g53xUKv}$Oq^UPNS!VCuJ%lG0*!<);LUv@jV+pRo0$xW z-b1OUtvct@@J0g`eSazyg=5`5HVn^e5!+~K!?yPd(Oaq#wC!anrQ!6@yO%TY_J9LS z_`+1h2kI<|Tdo|?V7X=eFt0ew{zSldClXSk)!ND$1>4l{xPX>FeeSH-eQ>)y#UvP} z=!7a7Gy?mzGX8MU2g!Hjh13T#FqPq^CfJ-UG^q56SvF5g8*3)$CeoJMCnGDz#Gnn4e4sIPKm#kMvG8L(7~Fz4=2qv6=X zY*m{(aS_4lj=0CfD9&Z6-m*s!3B3B7ovZxWaTh2hhCu~Y3R9C>hozekDQ}Zo(uc=9 zN8}}%^}*ztSsaG7(R`J$^e6GfeXU(oe>NB2m2PX1fx2e3BR^zOMt(uiH_5mWMhhBn z2xMT6q{SUxJ+3%^a(0&-+M!+%aTZzFtGrGp!`OguqQu&@Cf zxuNZi(Vd@lD_q?-qd|3_|9$?AIcF?yQ8OKW{rD0=tE3f6PGzda5;i80KH0i zY4U4d$mQ!wtk8x(dQ;TN!ZjXW2M)*OE5GY^R{|)LONJTRlc#Vn*B_;bFjQdte3;E~ zq2FgiLfq^TA3E@YU6QNre*V7Kf$x&yl@79XoKvqr)@UzF0lHg4qz)x<@Pt<9#uS=| zGyBPXsv|6R<5P106q!hdK45_zlnoRiT`l_==vaJ z(NM+xhFZSxl+Nv!8R83(Z`os2a6Kj^#!u)+xAlkJcF~(%33-@7$kd|vsdZY|jdOa} zlBfo|?@dgIkYZG1=nuce69l9a`_TvR1KKW{Cik39IWN^Wv~254+62YtYImZGq*-qn`5dceufmJW zft3auCO0?Xs9b)22;3#b5WU63>5H(=PY?g-Tx#BX&5rTyRuT!ljPS zBnxR-g9o)jzt%EkR5;!!>h2*X@F+Aj9i5p`OFv!|0@O5r59&9`n!*cyV{-a9IkMK$0RhBgNi2)D>U~L# z5F-Rcr8*4^=xDXZyT&D6<8#|0baqzA0f^rz3GUn?Eu1Mw0ui)KdReggjNvyfDSvdd zpvb>`iz7G8Mr5WK3d_3Ot_EV<1p-#Bw}8og`xV4;~iPMjY6t72i7Wefz`zUqpTe}0^m>coR>_zRq8w1KB-pIs`ymEMDf@gjq zK`VObE`LyYYdWp>u;5hSMSS^L$eYdg?ekSO8iieI&2HN;N}Yw{ULCD+(warYV?^WS z)QpyvQ;W3nQB7hwthd#}kgSNu@D~MK#8@EZ*(z7(&vW4A%=B@avD-Y>mlh^ye={Q{%-4TH#dQ#ijJ>WG zCKvuLdXTKr5*)Vp8F<7aTVhByk1OfLK*Mbm*D$u?26~D!9%Oq%*QC*}{|Sjek!2ym z78BWvzoxOqs}};>|V&ub;o;MX4Yfx#%N@y7J{!T zos?DM*4@&y>TmS{^5U|IPOm+Fo>JP8Dk5LxfbT>>vqP`>dGAjak3S6OrbLhMM?7jQWY6br8uSiyybTvf ztuj{4R@7uurs?p{;y&K{6a*^{4j%vA&!~U&xFV<0pH;&`Ely+YWlT{6BWBYdjX9Ab z3KG?l`TX1vzNvKIwOjJypk2;~W6-%X^r~QCNbi-)mLd>E40Tm={E$lS>4Jc0PL!)KVHcne!y!PayevX?6~` zw{evviGE{)*-9-Sgx(}>pL}udN9N-pOEnZ+iapzvKn_`jO_h<%R2s-dy#LGrPjl`U zRa(dGhAba_RO)!7M*pWVq|(VPYJcg~Tu>~;wP3YepcbB@0QLf*jdt=Uh!mE8Tz&O9 zm>e-*3fUg}c450NQ|~#Fn84=5qTVA9w_Yb!C_76hG@_t?XFyO&v?etoG~BAq8EtD{CP!SD z>rjsIXJW^uV_1={F4+xiTyt%4E~BpsQCH51fj+BzJ%Bw!>(rH8zYtsaYYtFvzeYPh z{k;>Xk8CNBwL)sR3@pI^3fV<67*F^7{{gZ{7N_j(}n|L-l(G>gTpL^TZbT1Naw(_zQauIkTe!3^b$y7+e<28V= zNG8vh2B4c9OLGDpOlRIOB4@2k9E((9mElR`QExanmH0d1S-ea#s)e@^nK(^MqFwUw zZW^dOeh9sm@aXR_Zg54KZV4nUqbq(BY9lG}@r(+{%<#eEyPwZ&tq0apwoxIiHXzN= zB$7#`wR4h5N?#&f?YS2c>5%ZlH=j0h0TA#p$$M>$Q0VIIBezZXeBGxtk-OUW_^d8a zben}iKbA+jNwmB6Y0rrJeS#IrVAOPL0(F=sJ6;PGBL^$XSRJ- z-CsljRXmUw5&!7$)l0GAOJgKSevlaZeMbX?qm&~pA#&3CA+N>AceKcj=Qk#*!bfS5 zrjkEYF5g(Fy4M)+UvSAxyVyIoX_q+L6}f4eNGb8}H@7BBR9S!cB!-l1w#3sLOme7= z4hK=`o4xm*g~vX8{`lboGgZ+yo}63IK&Es?2i)JD$gJ|^Y)I*oTgkrGpHg_)_aoT-C6GzzB_&8XJj z(n@kN?8ez>`&@vc-U}v}sj_Y;jjZ0gcIZ?JbM*AXlBjT=inF!6mzr!Aa!mKLwsiyPlq-!%>;UM%s_9XNZBk%!l zyj25fblyeZm^6IakOd1@Zf^>>Z{~&;2INNgIxexkq0{}EdH{p&6@{J4m*)m+p%`km z$xqky@_Z!6t3)zkSyMOI`J8=zk>t)l%v3~mM4CFAqa-=%=#r2BBIUzx2Z~UXT2At5O{k15 zj>ykvRjo0gM+xLk92|Mm-&l8#C2m&_J<{oapvt_k_NUKmmbi6y<7=Wo%AF)B2Y+2b zKq1tlq7$lwQ1OM22vzGOs;++5Oe-Lj=KWX_`Po3LEdSvQh3I1hpc#0RPbH?2uXPYn zV4hc)iLEE{AO?K@I5AeD!|j3Xi=Z&P%Jlno>4nSN)}-*p^fqPPyr2-z#J>o(UHPyy z;2uKrNC7$1Pl=@6pWaBLspSn-k;_z>e)80HIBcduj=MXze9^Zhs!WUAkRuJ;|JZYv zlpjV@6@75;`&}0AoYA3e_qLN7&KOpA)t!V`Gf69&g^{w%3?ah;%lZm{ehnQSt$RW8 zNn;m6_P;$uKiLT2Bu(QhthLH7lBE-^4alb1F`0^G zTD+|GW2(kE3vlRmkqT3kl_8t2Qt|xkEh|%5{;Z4~4UtGbT)F+qz591fOtV0PTsdu$ zN!sRUC&(n@+rw_0i8o6D5PE$dOox4UTCC-#P-_c0?Bo^5|w6aars`=002M;1IOPmQt4Jy&%cEXemyhYj~6L_&Q(v^-Pc{>mgyw6){_q-?4 z`Cnzy=7s=-5fbI+!&cMoQ9Hc=2p^kgE@LYBP)(%kaoftEO*4j!J52IWRSi@nmy?XC z4VA9eCTahBUOHzd4f3FwaBtAOONkdo71ttz^KUX0^0Qm9B;_mn4*Q}&YTh6jK1GW{ z8A7x9tTQ^uR3y#PKvDi071ExK37Z21I23t5RFWm+XESA*$7=v+I?k>DKvVy-=l5+_ zwjGqHO8(jn1y=VIGnUt~a{176l||G@@_xTz90=1yKt5+!G3q zSjv_m$XU}^I((r;Ixi8bI`LFkch%Q*xgt|xy)mk;0jM8MMZ%`0T4Yz+->YiB=>t|; zBkqv29DDTQ?=_bOySf@QE^b;V+(fMiH@`Y9-#ld@;WRsrGBf$s1rGgFNSSHQc%tCm zx1{Kiow$Ic6hCNMQqrTU$dStjk5gs_x)?nIKMW!%KV99ud2J`8F-(t@N&bMG2z9r_ zSgA-hndSxPMP%kBk&HilJUG%srrcgqO@kac?S8WSUfkK4t=$9|t4^p`{oN_Ons|Tn z&txq!X?BM}<&lHHzok_rUG!bf$gw}w5CPF!L_z3Ej=N%`%4{R0gQZdP1t~uophtFM z>!Vcc^1@4Mos_8>D8jDou6UU$nWStDxw1Wq(bDyCLl^G=yEO=>A(w7heyVh{Y?Es=n~_XFWQUtvY1X zhA5_q9O49Ea55EAtc`ZodkcG3sou0(qhd%URQ%|aiFT1+meA^E+h{5dp~X{%wFFS< z3VEfDfUG)2P$8}N^V&njAtUUBHaq0#VFK{+chj3{pwP8$SW$;8H0@9P7Kq#l$K${7 z5&9?85&?Ku1~g@Qo;Wru5DLGq$kOS{Fi{Gwmgq@gjFLV@M(#Jg!j@=LewF5s75nuY(phL+;!n$Wh-d6rj*9wFWQI!NKpX@u8$-9`w423fqe& zNGJ4tNW1va#RLdYO(D%U@AG0wq^llHGNJ_(ZI;B*?nk6+W8%SLP?UX4M!)a=866If zst)*`z2>){4o`pfK?bkUpD&*O;mBSC){{I?26q0_Nm^esLLn6S;PknleUTsM6Bgc_ z-9isQSIPqpV%x{eAV0O7b4RYk;?;Z%P#Masw_3vf8sdw>`GB%~K@0EeR6VQ-K)=>P zGUc!0b7b?W0d4>UpZ*DC7I0;Pk%^6p&8b8vaui0Scj1idAM(BjjkfOo#7u$gY=6 zMObgWVR|?f5x=z3K{3)q#r9&zrf9E~kGC9o5|;4X%Bx#~!orTUC|A+L=&eHz1m6xH z9Rb{?JZA${ppMH6-$@Uh=9w6xQFh@Q0y-Gr4BDEeO5l{2i>&DgdJ@S$l&CHVx>yDAy*Cm zx@=||`9=c)4Wr2y9|SJ96iBhBWW&AmVj1$@rGA)6V$G6A(K`FybTM z**3;m=+G0{2!5rUp#ikU_k!slht4koKsU?6tdhd6-3fm1@W6Oa2);w$+~Bw57YS6P zoEzc+=;ljgMU?v9Wco}1P~{pK^NusJYW9?r7(L%ZGs8wQs21#ml3yfAAL`(<=)In` zMW~DdvJoq`4iu3);m6HoA#_Oiu{eMRA2<4BOIX}`FR1hzl9?R+x`3PrZd#;j@>%sh zb@7riaWpM2io68;^@yr}{GresrXt=Sz^67U!rCKGb>b;+o7D%Z$~UjqGr-QZ?T(mV zAJKMrpuT33#me|??R7{ej0>eoI#o`9Nqdq$F#GUX|+cExPP7=x$>BNVcW!#0IGa) zo;=>p8->+ZCeLai_P$Gn?en^&#C{S<(?US(@YrlS7 zYIW~X_s>fywAskW^_wsE_p4aFdY}e*F_ZwHZTeiIFe;6 z*p%DfRm00X-Z&%i-r_2~lj)GS(H$8YCs8HaEM|!c^PXA)(z$$61*wMHj`%?}!tyRs zY3D@WD2)tV!gqBw;$`~Jc184_s+xp(6h_Ev=oRKqOObRo^_6Jbv z_x&43xgrN*i+GYTospH-*k>~B!b?N&8SqbxLy7+)pC#p08lZh5$!8Vd;Oquy8l507m&H7pcCwk#6_J}VM^fl;rK1)K zx8)LZ7StZD-^IF)x|tkyMiu7BUw2`aH?vW=#B zPRLK4z9rx3ftLitkXNcB4<75U@3JJ3@tvR&H_I}~fq?e`s)EWPPs7QSFPvfb*3A)c zsJ1tWrXO!wc69)n9=aXEJ?_Xvm>5Y_$?>}vfvG;sOQWg4b1ra&XRy#pPkMM%RrOlrt!-sf6<^9%C zOUjQW0bS)jLnnVUqO^#YiJmChEKmecd}TPft2pec-I1v>?Oy-%&O_KZ|3~eWk(gDy-N}$5@W1xrvYM#Ul(eHLfzRL9MwK!f1qp>-gs+Bfs|pu;<03rVAcK z#U;cAM?`&kblT5hch@ZWy0^D$5e+gk77>su(Z3Qj(-KLh+=~mH)I^U=#J&+!#MOeG z(D5#LxfJr^A;}C23zK8Qe38z*OCl+gGP(0BXXMCh@f3s4Wjg%ify@HGj!rS}9yMcR zNdYf$diy?UURnahV(5_YwJXvIqpVa&=N60^wf52rD*mpeK^l)cQHz{lS7zA=Cn&sj z$JatC{qm#J>p}C{jwdINjnhD^Tt|l-c>WY0+fKJALJI3FB8^zN_K@qhKl3Uv-NwYx zTI8%}0yEsL0ELsQlMXp?elvxnd8&ZSG|Q4nCPvW4r1nkb9FJw1n?s9fkg9wb`GXdz zgbF|4Wi>)5Z);%GlpS~#noj7fO5|Gte;Q(d5P{?aQ|8+!@A&QUz4V8K#>&kH$B|D|NNH$6~8LFNM^)|`PFpDnNapD z$%PfQK;W=lVPxW#+B&HGKD?SBo$6mIu*uwGmmWO5y10@KFR9#94~3!Vpvw{;t}N$L z+yjc*K~&tm8T93@*yJS2K@E_H5hgNX=HR^+O8Xn)-zayQ-;03$%{VF&Vy{1zNcn7e zDEv>+O!LWDD$Gaj?`(|x93~lD20({|yCnf*_4YNOD1U=0>sLAy)D8hdU8jzK-cDqjx&*;(6AGvedO4aU} z?~FOTvc3*!0(;z}-6#gaW~f4%TLGMT{@kqiCTlowTBL-|2B z0jYH5b|)Y2<|hK$sU%IK1mvi9A~T$HM_%;ftW<0kkxuJbqHh$*yK|>dV%Yb=%n;QZ z8LGxmUZ6ob7rdn0Ci12?RK9nqI&4IKs)x|D!~=lOF&p_A(rG3pTg;Xb<)-Y4pepgw z>{@Qfjb>079ik>_VCVCbm8p_Xji_1mjRhBOnQ52!_hYir<2#RH@5CoPnd|{Oj{_v1 z#FC-Spc)WO-f62vR^wwx8?UtoFz8Gyd9+5=cPDP&KDx`KS@pf4Z8NKm2Xa#P%(KBF z49HXY2iLxQ#PIS^`4cf2^o}Oue{n@-foG_azIO(69#9nBOKZ>* zCVk|F49((5T00|E<-0P2o8u+*Z<9gQfC@7Cofd$TKTvA@p(x5f1R!@Mj*t3)kHmU7aQU^P|mz#UD zc?YhNF#WvOMcsGHCvc@yL@{xy`33ByUs{y{Zgcy?0Wx;sOBIes49b=qDmaKD$XC zZuHL26Yj~hdt4J~#J(rDpCxVgzbP+3CA178<@EqTFd~)L z??<2Qeekrln^Tojyjur35{Dovj5e|*7&`w_I1BKH8l6M;-D`C+KDvnkIFesji6Q6xPasS!1 z6J`wXhr(Nrm$+2w+IwXR&s2xI7I~>3Jlgdu?si62F1upm<)599FVB7-9ht7d%NkI+ z)>8)uJzi3LFDXC2nnE&hp$in+qf}TV$;2a(5v#nBH*s~u-7j>=tmtj)iuoI7Ex&iq zw%Y~1UrKRNeYD7;c??yNAG-n2K+z|XlqDvNGa^HiIFey?kmj|W4-QvHcEZmjkGKM^ zN#rpnIP|kn4*k72@(}PF8S+*C7VqwfA}QUOSxf^vkN*2(6G9JtJN4r=SK_HS-rHA$ z3`KfJJ$}1*n=u3al1aua+8Vj#1$pnAg=0O@GF&Ek`IO8fU(fDRt%(tN8JI$aEofdV z7dYrTH}w%w2*z$rg$}2v2%X>t_?=HD~^yJRX_bs$hmaW&naYZh~;gNKhA)v-3@{$`ALcMrWrk#ASgC<}n z74hRm*n4 zH-eALBM*izEj-e~9S&Qmkl#Z#I=rO)-7hq-n;IL@0cm4=%)f1+X6mhTxvJjWbRGlp@BGosvOBiEBhPvS-pOr!ytyc+u zEz*>8gTtgm%Ci6%BFW$MP#Efcw*1-E*%kH3i`R7WV;MSWgT3kO5(pA%%u zjm=X#HXE_}d{Dd;|7B$zGWdK=Ir?FEVsuLUt+iiInDKx%l8N{3{9Fk=lV$SZ*9%s? zUBsw?oom%fjTd}g9yxeDg^H(dXppAl=Ibk~qEP&1MNtvjS%ji=r(zn|88=aN=a*U{ z3P#*crc4A->IRQ`BTe8Ps&)mSz&6n?tagmNt#XQu3i-;rI^?Lx+ythYE~vfAZj6b&^n!8|*m;jy z+R9`0^{@0$4NtOM@x{wrUf&|i3CUzwWjJ{CUKe#Z5Gw5l$yCK{tR)~vuZO?rqyK^` zd7B6Fq#b3W;-&_69UjTFvmB@gxE&Sp3xyG^2o4~u5%@lv2W^;h1;rV(sFpX0Klo*duwK`Y6`o`7mid$HcnnTK+tF# zk8CvRIEWqU<8 z5$T+L+kF3JcY}ajR6J-RY5k|A4q4TFLQ*O?VOUcMU)oXW=1lq^X%gg}I9W z`1Kt*s)if#;}b?zY%_?pLYV6B-K0$JK;ArWkhH!Dh4T}Vu~kt(zE{kTqRn9`$yPEc zA~b?#%F=_xlnW->Y!c-cuE>F=#{<&*AWA0rq&YIt*7#E*Dc|UzK?cvCWs*ss6%~;y zr-4sNnoL&4T?xB*cGI*k2A-lS?ruHg#BY^_q%|y=+tCwea&&xVAtf?-d+M;_Cd1rc-J$^SB8pEId?s3vi@2E3c+vUBP!%8Z+amYz9UDt zVhUB}K_c>Gc*{z~DGlu2HPd00I{>!>l)3<}a}JEw;bmQu`Lq^t=Xr;*t4ku4eveF2 z{s7RH^w;~9Z`iV9-zn2Rt#@*#pC1ZjY>MfDE&2BHS@9$vltSKo|Flx^u!oxl4&JYC zFwyQ-F%jvstsm3Qb_NiUnf~2Grb|Av^sG!Ww5@>5+@{5nlvue9eJ;%IVS%gaPUUu&LiwQd_ATL#R#y@#$2FcCHEO0NS-4%sO^G=OlPs(AJ+Tdj!rWYUjLDP0^wRlP0U~Y9nDiK-f-+xAHLNb$19KJ*{xgAtS-w%@L z@S79zBy`#k9Gx%-8T_`zJia~64KNOiVt6Z{@}0Ox`<;9V znY0eFNxbZioOMlj2Y|7R`(c^#T4$h+m7Jz2GU{4#G0s!c%%sNf(t%A(F-=EQ`X>C`JQr)*i*<>mrdi!dj>LHVN zy^zZF``z0&2FX-NYw8NfrpRiEiiAIY-}3A5lCUd#fac@B_w;~WK;2eLj>VHP6H6jz zm5x#s+gFEd`o@!t>WD&EV&=87fM$_w8Kps*dS;UH4ln3*Ka+CgOef@{Xo@V8(Zd0h zdO^zMEgw-3o>OrQdFx;zDZLHVJ(3TLpkQ1#$61$ud-eIQz1I^cQ%<(nEH>In@lm84 zQ42Zo9K3bcXN|u~BpKNsnRv82Nk!sxFJy3;lT0#h8S>OHlB(q3rL~)vtLO({td)wS zlwXVCWiGRmNZQslm@AXN8ldoMSqi%E>F|fE=W!}z8_CnvG{}Y1qH{a^>eRyDS6`5{ zCa!P?wn?!hE$6oRp@byMwo1sCrujoE)S1-zAB&Bu^#RCGC4`C#NcX;NE>wX(+8EPUtJ}ckIQ}1Xd4(J*oZ7d~1<+(6{@w2?3avt|grZwr=*QEvIN)Un9%v2=R zMBY5+*r?dAfnCQW+MTEbh3{2ziUtrh0y6kis#3M24#1B#9xsYq38QF!-xX1S@n?G}h zgZ7>9YroE~P~2TWs-g$N)?7N?*A2PTzt*$C(Tgkhlh*Ob>>V3JhIQZi*hHBzJNA!l zvw)O;*C9L2{_r>JwbL10@e;9@iQj0EGjVi0$@nu*@Bh$p1t~qK1&8?wHmXjQf}wFB za##EjZ(d61f^?cGGDH5XBtX|Ln3lO(Xy2EtDT}qpLeuRDRae`J5IkK06#LmmyYm5q+Y7+p0rtF5hPfS$E&&O$y6W`@l+Ff3% zM^?*Zl4qSz2v^C+H2~+sJnRpJ07da{Ws%ahp0vP^kd(=#?I!d@JbrDrnZ z7&$;hPMp1U`VW&xhV?`ym6pa*ai^sQ8Cu*T`C@7bf6}R4N?uk^bQfPOvJzmy2 zg5=Y$i=eZad{00s?cmE)$-lX1mNLx>E%NeuO2SVMp3?4YQJ~XW8_Ayq03}Es@I-!u zmIsokxG}m;1p}Oau#$|dfjqelf5$JNTv6zI_g|F~R2>Sje5X~nzq)ZhmJV+sgV5VT z#aX0V!mR*Yd!1#(&W<8#o_H`oFm5^CK!n0+Qw<;#Y1HujFqu)4n-$k0A61@H5#xcpRo^dDabF9&I?t(+-w>f_ltj+bAPeK5 zqmOR{g`fPoS>>DLr83Bs@7pK$`9h`DXpPQJe^6yTdq$=$nHgk>@iR3jfcjlam(l>` z!pt=FLtZq+XC~6_pVF|?)tVZ`G|#&V$l!aJcGs&Tn-V*yNLXBKa17I(@xU=CdZjcM(+8xg$NC5#>wW`_QvM=8$+f8dIDj>V!hiMs&oarY=Qz3=jo$n*` zJ0#0jd|+2($I>k+BqN88TzWX}kRNhmY)|W_N97MiIEck&MM;}F831%TPhKkul)XW+ z3$pT9VWL7>UkrfJTZHz_AUb@E%$h#9lgQ0RsZ=876D_m#X*nnoHAWI9`|(Jff%Ze`6HcMlO zA4hrn0;u>*qAFoA-o8gUh2$~hMO%J9N$F5|D6~&VJ}-rW5a&vFcZSA9o=GApg(Nbr zp{V;XflRi^GF2%iS~dkDXF~sQk}oFr}Pz>3h8%msH!|9na~&oQum91BTShn0BCsIL^+a7K6S@T#Rz05f0l~y zkC1LQPaB2eE6vu3&z%5q($BTD(0teyc20wqKIjdV&-%FeI=p0*nYPJ7tHi%*3xNON zBPKHD4Jb-~do%L<;sZw-i^yNO7bN9lTIB7u5UQSPVW;c<)2SPYlz(!CVhG838f2y) z970ue#EYv|GOROl<2$ZY_f_qB^lRPm!ymr?dgtL?&uN!*jx;@sCe3DvX+qkd;NCDj z=d&`%i~r~=w3GgB>;%=wNM^Fc3mJ;;q)I+k9@*5sO~t+XXcf*h(o|$IgbJDWdx=nZ zuaT}yEQ(YE)|_bvhkBc~J_@dd%wCJ2`C<=uo|Dn<6>L8PP2W8qtZSCr* z2a2@Odz>cCZz7wPyGwu2dE9H8z7FaS(M{*B^XSr33x|M%^1TYkk?ZJSnJUxI-hkUU ziT@OZ-Q=Y3M?n&8CpN{&q@@#bQGVI3nB!gv{9O-+T4yZLOAJtWoF$)60E7=o#?%EM zdWuLBc#bORI}a#ao#6J5M7#K2$gFk5iv(^oA{Wg3g!|WqMQC8k(5dPjl4A5ZzY-34Jv&I$l2&&HazZx1zMZ%&@DYU+&MIJu9b+SZxWLR+W2IW7i zKv9BBRSCDt`Aux)IW|-owvPI?>t%fX|k(8X(~I3Jp}At4>68f~wbseSPq<;^%GaT+uL@ zcRDm$`p8ByeiQ()yV$#-jO`=>_=MzJ z$jrIpCCYI!LZE+%);%tVy?sVXFCB0gs+m-Kr(Zz#M^GP6rWA$*a`hV{j?q&8a;e!vv$(}g5cy< zv!e?Dr_VzlO>;u7M4?o%l3Ivbu}+EJ6Frg2xHN$(+q2&r=>;eR4U9XpNTX zMNQZBo-%iB9n|>bjUk>BZZ9qlhf-Vbey2fBbW7staJ~Wn;}4Xt)P~A9Um|TJZ5EO# zPv1Z;CYa;iDeJWyzC5($7}mX=#i??GpgjB#oqk(u%Mt(&~&id=|0iP(v&R zi986OnyCn>2bE6}$)GaGNsZT#iSB#ri{!p@LgoACNj^!6l%9VN0Wf%7lH(*fK~7mo z>6%2+y0|EEVEiDKvzlG#}Ju-aDC@U4)kX1=2v<40A z0v^-a9_bA0sYn?7p@nv_9ew7Q82W|=1rfMd2Vff&5|@+)N{~G4g?xxpBr1YBX`pZ( zf73=XLX%;|YwV(Am96iK75AL1qcb?^^;##r_*N(>FEJur+j~@`%yUAzb}@JN zCsW#94l2P*1RxF?u7|4EoekT{K_O1wxV%1c(Eso9K#f>3A(SfX$AG@1d^7(tnli!xn`ZY;j)oyK+Y)hmfYC}n6;aRTw zn|o(nK72CCOeWa)vgc(4PybR71g!=t!O2)@P;nO&Tu%$_r0Bil>sBfwAP4QA&>?Y6 zeF1>etQ2Nu-d$NhnkrAJGEFfen=WA__mo7*eKw|wU+96%N}o1AOC}Shickd}r0wQ5 zZ#cAVs70!xe>@{UZ`-6<&o0A9Zn;$j&^A3768xSPx$$r3_DK!c3DwVC_Jf1aDVZwy zaswUg1OSG0+KXlqy+D z9w`9uzC`I#b0~`currDdFG5I$wMLFgbe_9=fdQ^enXQAW*)^uD@Pew;-?C+E{Q)>)46PfXj!8 zBwy6^ggERrX+8Yipzo+!g6y2Sja~B3Gb>|%YF@oU*`h`O&99P7`19Ap*G|r^gbYnL z#88eM;x6DN-fw)j)xG1xJ@?13YLltB?uJahp~BJth2s3Dg>#Ik z_JEczU?%`Y_E05-9=mY&(w+MvatMUerwBW*Hiees8tF*ez1A)|-6;U$58 z$s~^kB7^h$NmSfvg3KzOAmvA&ck$F94^3l9ZqvcR%^%K-tW+gWLl&Ba$gD&N`3BM$ z2Rx_ZN$&%62&)a}uWgZMJ}QpA+rfVEupISBK+xFP-s6sSO+=Lm&E_a99#faRRC-Qe$TnFySv_4(1X$4 zQql+_NC^reh=K?RD5xl(jf*&NP~2DcZ~sK^9z$=NOmvb{qLYnRx2>P`?yJM64emcYiVoIgKKNHuKQWsn{^*wH1w6%X zg-j>uVpk}OhB#VQ$Dv_gMy+{xa~1jhRdf%OE%Pm}EXMGHrDz9Mtcp zU*9^F-n)o}6{e2a{m`9iPP-yG4i>N~%O0-Dbx-f4}(sKTm!^*J(?uiqF>!Cyzy z;eJ;UIa)qo(j+(JCN@}g_C#wDq5I;0hJ%~-hkxeAS|45iEwtPRkth8H6zI8B+DRwg z@q|J>+Cby$0Cay@Eoreb%{Tsjukqg`V_!o-#A&gyOUncG$*(oA>ziTv$pdzNvl6X) zL!fY5LB*-_GRf3AmA|`N89?>zbh_FAPQ96Fu?KQ%pK7#nhaN=}n^hM8s3uA@z9*Kd z{9OuVd3{iDsqIwUDTjPsn||a*M{@aH#xMG8HLF7MgD?7*HFTs(l`AO}Y4U zu!uZ@2Mq}UAPPWdDy9Ocb)Dq7>InXa3i;&7Ms35%)Aih;@LWq$UhRuaVzWQ59o@2U z-Z?ADC*5J^u}o(2N8V6~)dnrtbadyuA!U)tcLB*bKjh~>sH>lwD7;7VPHW`Qyc9!H z-rJXudbJ}pjD@da`S&)=XTWrh!O3;UaP*8MM+t{m!vLNrPsxw~?3NbS0P zKj`xPo^D^xx<|XzKFFiu1LW2)jU0qbs*hu-kazuVq*F{opzPkWN1hyODxwIw8Sh*t zDNEM(I%n`u_b3a^DQ~OFlbng73B#hIE##T{Y5?jUODY!jp=clZ zs3!{7^QM&w`9M`s-8sR+vjZy%aGm#M%KTP9QN$re+8qr;9zxd)+V*t`Jt>=CLvFs? zDSaRSV(s7P5MK{@xOZ5eYLje+xV>8!bZ+we%OQGq5f1+0Bqf9Tos5K@2JH_f<;t>WV0Ys*XnA!xs*|RmGsL za8UJ%rb2$WyltQWtr||6`Q+gs0=}!}RELw`CItOX()!V`O9>|`qG0tB zX=nXZ1BakJv`q$t0Y>un(DCEDgy;ovNUAK$O9d1_y+AtF8^8w5s&Jz$jKNj;!-8cEYzfa-Mv`Dl6|0QcWX8ed12z;6z{^<0>UI_F8= z?EpL7G>M7td!Qg<$Gc2phCd3{=!Q(`LlHSDAEF|M zab)bKjbvid>4Sg1o?&~Kka5mzF1+Mb)A%D&IUGfA26Mi=<}XL&P35X zH&78X+zk+enq8Dh+E!K+Q53Oi#}Bqx((aFOFLjZ2a>CtNT4ZS)L-J;6Wb*v#u|!f@ z=7n554_XY%{E%1KGXr4va{8`Wo&8ln>)i)xp$OHcKPGJlT33B%$rI`B>P7-eBIG@xFSZ0%FiBR-QVLqv^z`J+dqu)UVt(Tb2V&V@Vzg z4C&+pMfv0D1}?+XJlpr2{G=Te?I~Z>L!tV~^br)Q>KDz46B?^v=e~w=Iv<*;$T6Gv)@^~XdlDXL(u6SJ-$E7N%18wL9!A&wU+0ni4(!R<>M74zItG7VD6bzuV%$j8l)?L%8e`rRf_ zXd9g-89lQST%$ipCz-#(1)&y;nN0ac3#SECoG*)lETHYwr?6B19G#XX8;9_w z^a0W)ZAhNIC{9W=(q=nFCjT;H(K(Baq-3?Gk~ZrDUaEmShD4K$ZH*j$t0gK<)2av;{AEq5$p-A3l*OuaC4!sXIS&16FV!p#4Nn=HjU&XQF7TcMq=K`Cz&ZVx{+1 z?>AG9PdMCIK#_dE|Lg0$G#_n>inq!1G&y`)XFp_nF`7(YpoNqA-6YyQ8m2}r+Mo7( z>W_SynKudm)Jx@^X+}$yHy40D@2DHqVWC!pLv` zirZNU1rvkI0BU5ab?R`Nfn}hWV>HO*}#hJbV5T z9=pm5cD}v>oKym!>XX2f&waete&>w>3ICAt6fH7&&a}qFe+)dIO!Df2jrXSe0_x#U zP9C+*Mt(x+?-QiW3rj)qDlO}^Py}3zZv#c6+gFe7^M+Fs6RlTmmrYcd?}e}Wc zOifEnpxyZzaOz8^!*!6yJ4UmOCq3bUiia%YMeR?5-3Sku~Gqp&nY;1^*@UVFv*P85l=0 zcBl#k0LDN$ztMC%lMwTyodBTLI|A(5rP1z90VUHQgk+WG@6GoGwWOO|XOX}nLw z9kP&;Y>ukE_2CVJ$()?_*O?D108rIfZ6%}b(J6tC+99{eCX(k%AxHTmRLBRbAusLB zG-k7Gt&f5Xkw`vyD96*zlxnlxu#stz+vkTMm$LUsWU6xPz3Og5(;43c4wY|6%p@m% z5rpkjd;>f0?^7i+Q~#)|S}5FMZw;N&s* z5_$P8WNQ0_cIlr4AP;vRJf&3p9S;DixzfY$YgC=E{jPkoHHxP4(099JqwR-MT9lC2 zh#S__d7j8Gpl;KudN_Gj9d#&%sbBlzDZ+Azc2aZX<~E1&Iz1eMH_0Ryz%_yANN#XL z^{oWkc(?Tq^6%US{e(W6WM+Hlfo0HN&kO$R0tT74FP8}W=}jl82MCgd)jK2zR*LV{*io3greM*iE2PqcRfdSuY^oN z(In44v5~fil9l(53|)k>e;709a3T~=Ws_(pPj*8dADc;@_C)dOUYE`^#7k)h>XbQ} zXe85PNqM1OKtY7E&kviq_^TdTWD5G6wuhgpyIQr->7j zY3Kq)tu!*Cv0JYQDh^eG-86}0!Wcim|957)Uk&-`mYO2B&J2W|cEmk$R|OO#Xst~0 zcnuUN2rQ%j4;7?WaPZnB(=NINTyXe~FBGr+ zJ)w6Xw1G{=P4h!BJfCrGZ7g$pM?(oWl?oC2p9@B63+jhJzmhrzK zGrm0v^!(j9l?7z++m&*!j~0OHg`-y5tq6g~(lf_@`?D7EX`Dgx*`;A{@QJj}5}{an zvpnEGZ23N83uM#ICz%kQO45*Qx;DOcs2|F#v&k~;j+BR!+jmAP?o>lwUsy?Qb4Rhn z-zZI1ss1@sTcciQk!W`%?O}B_3ZwVYAdA@HA4}xVfpE};^`0GWBYER&l6+=D4HPKk zD&wDr;uV=RojwvHk$mv63U-zD$|TdqYk<1@&2*Cf^g=#jlLfI4SN0TO*ET{Te-QDs z&NGe~N$a7ODilDh^qGk~;R}W5u9>i#WM!r&lk57zb>%KFQ&awLnynEwq?lD^8p5z3akB9`i>g|GBax(@Mr{E(L|(|R>soWMD z?c~+L$V&|HsMw>q9=Ul=iIQ$_|6yna6*7rh6iBEs)n8u`IfSOY>xRA!pk*vgJ3DCL zcZJzb=<6P!E%|I~4X zootZhwLx%&Ze-;B?{vr(@YZcQSQk}A!Rp@Q-TF|BW)|y}L8ai--b}^YDnO|JyO|DY zJq6@bX6d0gX^Af!1{+Au#8W)$E;Cbcw>=6}`>2gf=?l9KaigH{+{|oJ^uhW9T(+K> zZq-1xH>@OQxC78Fr^ACjB8oA=Dl^-I@gj;f%0$M7udaw3dwJynulOMkZRx5*kKDJC z(u3P6S5g;jmg0<(G)W7*oF4K0*|P^$Gq!_T_$QUtW4%0)hx&zs7CIywZl*{VUO=Ww zx2Z^|g>2>a(Qb_fPYL_wzL9p)gBMXC_rIBHlP3tsA|fz_iij}e2>8>?@S4aHw1o~| zs9+~FeL{!xJyd{p0~Hr5!AU3dJ-EJz9O6_-{wx3mYc{&k92>9C-n?^%fq(U=fGk3_w|-8O zd3*Hqi;?@5?mT;Y=`|{hyWbYzf(BKf@Z6q$tP)&M`B#HvvYc&!XHk3hp(Ht@IdT)w z>XMZT>)n$lZyegW{T!L{duss&DtA;SxmrLT?vssC72)J}o8%O@BKV-0w0;bS(ichT z=1+}8JcG|jd6|eTRTf24aVHd6gm*HS$La2FK;t8UtC+g^ET>0OD6 z`^zWS6QO>-I|{VO%3S0*!2%RQ@Qh^I+0HkED@0A5aw<{4)BFNN8tRuF$-)b45Mm67YdUhfBk~H7H1jIMZBDC|+i!mqXF2JR;3M zetyPiBaJb&&=AG&8yZ1cMwa$QE{AE6_^2ZC7#I<4m7>g4oCrU-ukJBQ5nSvJc<+4t z=Rd1%q^0w35fJOY+g5~oDdZ)-a)(YaR`Sk^YB;n=qawTlvgqGVpxrDKz`y$shm*{4 z;p<+}qComhwmWh0t&mH-WGZ$wLymD)qv?|a zX{&c>P^ta6ncq)D9wLC3-c4k-AYh5i)3Do4CZbqkmm3lp+fhUwb#9S|o)b_Y!A)o# z&x|9VPKsc{aCc zrJ(TG{kZdG%9mRs57pPC9J|PPy;;PyP68bIB$6~p&vuD`AiGfxGlHPX{{dF${r&rE%pG!8Fb3{ z#vesmOJ>XhO5ZWlHsM9MqZq;m8D#p;fyiSI6%pl-#cSkSKd!sVEb{WtdX*Lq)3~v- z7C__l$hFDIcOs=5VaO8ViTvE(y+Nl(Rw;Rs2)m96R3vqF6X8^On?%K}_9%$xRlfDG z;mv|nC|KY{+AbdtMJ`P;NZx(FHL_@G{>@wC-H=25y_KZY91cEvNg7X$@PtDAg^I`Z zkdJ_TgocQsr}Y*iL@^CM`1j^N}}b^ zHY*R-LpG0|_ly?Cj@AHQOSH3@NnY%ZlF*KQ_;~;d;WO#+<3pd7hr54ZxjrY(Hz-}P zOCX-f=;*yk+eGBm;UQ_0>14aTqI)S6PXGCt#JB;-M?HmCJ_>-H`Z&o?;DX9`$@pIb zkV*Y!lteOaf*vK+(nzLMfrC%`U()Dc9;Jd)?dg%sXHN|%%6_}#ITdnvZ={3m`umkd zIFwtJfP4f23f&JjX0vVxhNA4N+tcZgGA{(rL>D%G)!)l+8Pek=TilEwWSjE_@(?}T zz0mJB5!t*K$TXX2XUt&A`k{@Gi(9q#ws%IhHOIWSFybG71t5hSOnQHwcM+o-rw4w;13H>ohq_r$aCKTXE3&>)xEX(SidsfaA9D$}m~;Emiu z&XTk>gF~q!5;ynog+r|yR2=d^v3y4xsF3cAUP(%nf&g?&wQ*i$U>BK)Vu*eB{dVit zkM9H`N64TRcg(bV^pc3e=s!u6;%}0lX^=&H>83*1P>oEQH_jj*y&867&CHhciLYDbPH!Jfq*MI-iYOtC zn_rJHuf6>4zBJlN=@OYd9@*Y`7kLB@_Hct=KRt5vvM|Tr9s+XF4qdkAZZ83ugcISF zn*8w9oaOKP2i0s)QH^XN(^Bv4u`Co(B=Mcca`L(TVaQZ5l1Bq|4X^O#p3`+vxPeJe z65?7zQD%3>4{f}WPwhp~j0m6Q=hm&Q23gdrWICLxjiS}KN!mt>D8?IBKJe6Ukm+O{ z6Xflo5unhZ9o0NlQ^P%#L(sOxPoF=6l0&RPKlQ+=F5V+@m? zQ$yjlLtYAK-b|+5CJ!h|&o)yL*$S?C?H(PhAFGk0;!%=@Pu=RAlbI>HC5lpxVWjdw*{!#Oqe%>nN;dwiajJ|7`CL)s{ zpkO{9C6G*h$Yl8<>_*#2Gq<=QlWt-P71uhc@GP`{kn%DSxs-_{>D*nPQV<+L`|-i$Vo`fBDKS@RX#3viiE1@R37BdfwL;CCw>Ot-gpR>7(6F_}&&yMOvE zmUhV#eeo1k{gI;w^=%XEBOr&qC-Tv0q44;|!W`)lCt{f;V}ExKlz^&K`Cgrd?zoa_ zO1_;gF;nVeGVx6n9CWWNA67=E)d|R=e*V=!H8cXU3Gb!L%wqf;D&$gW*LABrSBp&g z3yUkEU={Y-tf!XxplGGOjLP`y2Onh8EV12RvEopMd|`Pn6$)2lnpIA20fkVmlLzuq z2QEm7;U9H;1?2Hs6zyz3mqO8=k0(tdL=Rrkpm!=l-@%qyl7BcO?+y2O=8E;`qbpT;oR(m0nb}PA6k909HS41bqXayqOR6L@ zn`BB(h>5zn|EJ#WA_}ni5gDGmMUO0^fWp+-V5NfuyYA_v$@-NVnL>8TR2&S$vzbat zLy?=`Qj&Xv1Qf-s-5Njc{GDsV+_nT?@%hl*J$*qtK{2d|J! zM3Kb0e@kbYRT7a)<)bnaXa8ZQo#lHs6h`&R=|mG#J0MG&3sjs6(Za6XeUh@tbnB-O zl#IvHt82@n5PvLNc1;R@d~Dy98M7kNWjdHvx2UW}CK0ab7{>)c$U}TFmRa_Dqj-XV z0{EVwG}i|ZKaeL@Q35q@$veEEsJ6;PMaI1Yua)*v zAyf4=7CNN;QrWNiY6}&X^|jz44GQ%36WS$4Ge6^Bjo>Q5J-Afm0b5UIq#GD{bkEUS z;R#kcMLix^@T=q0kPD{kao#0&?*g9WT+&di^a8a#MY= zsQ30$)sUs!Z$X}wJy8&$#sm4Q=##`VJ;N`~*tPfl8UsvxR2!K@onAzVgnn=+ z&ir_8>xz02&^Mj3_c3XU`u^iT&j0pi7_z-dQkKb-10Pn_3CKk=FXKcI6gn-kgx+U- z4>j^q^}F$V93|4cq=5*}B_!qO=Ey}=w{K{m8if*@Uy|u`V^j@6Gek1=0IJ`n-M(Nr zsH<(J!t#*^T;lfnT`I2CM{a&cnZtJIgdzPS5k)COawu}qy`3T_cSS+OS2it7{G)0` zHJ;`%;p@Zc}EOp+>=kdMjB*s>Ud8)oBlG54PmF#`#t|$g%EiEO7d(7N+NK*>G)WGAaFIw#2zAC(#OWT zbus?iouott!EU@vr!y#u%5V6U$IIN2ix4m_HTCLn_lEmyvw`iB4_p%;{_C8cLk0&0)NA_cvi%#*OQaNeWZR^f zzmF&@z-8qxaHc2na({8d{d77U@WnHm5YLnyi(YHJRwkcxK+)WWMw8O%UdXSvi4GU0 z=-@i-M~2}*@JDO!Sx8&tcg@RqAs0=zD^$pFbIJpn4^wFu)eHYh&^eOUJIgxwkGo@G zE^^Y=3Id8Ix~nub2EI19fA2SE9oh0vd`4pIokbti3n^8;dXv86X8$nfuS?;^hu3I| zU+4=!-|X*rS$e$Z$H7Af_3b?U@E{!&RpaAYi;eWiO{g_!(l05Ns5PB6$SWjFRW=ma zs_v-(T>~`=BJQ#--WxqykD>%0Ik95pFl140rXtN|-B(jUQNj$23PeHNMt^p7{gy-1 z%j%Gyy3a$>x}}#MMN@xZA~|1+VyM?pHY9d|Rs_14xZAH3)LuZ~z#q1K?1_@7JLyaY zWB+WegM)j8E?*yxFqk54#9I@NPN%aL;4SG@%HC$ zbno(9109Oi^MNVjQE%Ak1r$rHH1=3Z#&9UgzY{|yPA!E@x<6>QEfCK}{77bcbp-1w zz4g~leUJoVR>4^+r6piz&_ zHY)B{z%vV4LsGUFC6dt{P_&L|lw$v^DsBqT)_jxTy9o>$YqRMQ-)3F&AmqHEXYb=yWu0=rxW~6q-f5W?M zh>w605KEO&K@s-ORdRPTH8O=oP$7-NGZMQ0mL^Sof7R@h={CwF4M%Rt%ve$fu2QRE z*D{W@*eFSt+KX^erNwEK>69=}3p?K(G9CJ&aOzh+|6)dh`QplOOVZw{)uE`eBH9-I zk{Sx3&t2Mm?S_JBtNoa9y`ok`mY$JJYyy=AnFRMz9s-_0XM_B>DoR4#Y<+wN71F~q zS02aRT;H#(UtnoJHxD-mIydj2;7%{}Ze696ucr#RRR6Fx3er7^iqs)0JhP_9WM+zP zfuaa)uF$r>ricRQ7g*>NzqFYOE>d^wKXJE#G#&Y@sT=asylEh5n-d5-j}MJhSWi@h zYdr82O`X+gr0sZj6#zl>dGqSS*hC{0#6lENb^zKV{GQTTP8l|i5x25x}&a>eDF9v z>ez1+I|oBiVe{c}?aQc9wEAaaH@^}fqQ+T9cNLJ&7go|T5P*K_g$Hz4;0@R5chOE> ze)+Z`IgzAHo+}OLzA(|U&>uN`S6E19bi%VzH@reSnRfES)zwg(+Eg6+7z)4XtMAYu ztuG4F<@2%b`2WnO?Ll%wX=GCOP9iBCtA%GJc=Y<~@WHz=Mke3dKYRI^`%I3YPVe!7rcGPz!KDsZXv;r1ji~0dUYDH`VidQpt$t;q=@C+L?N*;0pKQ$WLf= zfMo3NE!0q`H9%;~u6-t5wcJ1cO#>B*;8A19xR2HzK6(7xGH{u1-)?1*o2u?|)7d$t zI{sY|8kC&g4?x%XX~$~_D46C$t67#-`k-*#43y5ij4YaUGRf4@J|23%dcA9*Z~&Jj$Q~miNLl8SHYk>R z#a8e9usinQl`Run`y($^>ESC*MP0p>U@^s$#0s8l&`*`#gke})c6v%Cv@mLpcK+`peOdF(zLQ}ru=dYJSu~a{j3pxy%y6~-1YGm0J zPaDa;D2Aw3Ra|2x8UJ&r*N{kBcU4CwVOT0t=6j$JpIc>v?S4&U@wv+I{@(cagb5bu zRFDcKrD-{F<=teXNhX>4#A-}1*vwWr{qgmO_Ydzo|IkK-Asoef;XV~-yzwvSzA)11 zT3HlI+pNv5`KKDAQ2tXAB2(!k-~4t=JrTtaD$g>K*1O{aP%ur!8Bw<;sbJ^-)oM3 zAP8_l-*l4m-B7G@mlEs3PBY0u)3Q(AXyOid4o{%ndN&kJ<57FqjO9xgE&h5$D_<0# z%qzcMk2jMv-ChINiLXXemaG!_Ci1I%k2HRxf}-AEDYV^dsALUVx*Mp>ppmeP)F!vAGmPFcch zA(P*HE1CF?4*wb9uaqaWO~X@sV^FY0N6+q<5dh%LQ)gFIfI{2r*S%*CFRX-OO(ka` zzkO85ec*z!-##X3JGI%s$YG@fWbybaYHDc|=5>>0*Zx0(kwyG+_lp|*7kWK0Y!49e z47BAN^&I*6yaiKwHW}HkcDEt@Mhxl{7F4OCPpP^u|FktCIl&f*LTS9<6tvMsN{dla zIv+oGWYK?Rro&(ED1_MXx0wBV8pKrP47zjr1AjP(ZSK=n z=iXF_q_sEl>y$y$sw%M4x4lG}Odc!1b*kwS?Y4SDq3Itblc_CHFt-^-lD}zCVwxdW z&CwDmr98g4d0M}^dbsLqBWYqviX@Q_FP(W6|HrwHi4JdpOm&aR^8Hst{C9+}Wm&!y ziu}aE#DA|*0%sw=;BzGBRfj9Ynun;6X&5db7j<<{6ly?%X>R9Q$l+5~#DAh+ddd`c zJS$P9QK0}jpE6ad)~SR-2_6C*bU#?h&kt_V5_|IK)z7skg4)y9 z&r6HqgpS>GGd{&&i9w0zdY)m1DW(qc>uzE4hrVzSzKpY&lKu`u322s5k+HuS98@9W zPCx$I1BKB2WFdF@qGW{7&)3}}ldm3L_F?~ym3`d=xGrQ`f>|ahTMsQS14U*0-&Xaj zkNuFzZ{*I6Lw)gIYPf0r(l=DdFYK}b{{y0|@8KyS`%iqTfs2}+w356x!xJw1KK(V!p|eXD)^+4YmXJyAS& zwYG(CK$ynOQ!Sz>Vyiu7+Rnb}g_01e^)Ck(lwEi}gG3&>w=SD0ZE1@Czl1&;8++l| zxQ7aFrTr7=u14Y1J{=A|_*_I`Mo6|htIHr;%WpSZs7Saxr#ebP@P<47E8+sC6*jD=^zadthuBL*MPv7r9?h%5L zP|YT3+YpK@LYW5cC{)mTYx-a{N+ftp82$^|HvPl!FL=KYfPW)!=oB^n8)0vD?iY%G zuj25kD1C}#5{l*Z%96$7n`rSbXd6{TF*LNhy{W#SY1lFdg)4J}PUBFth8te-#D6IC zn_Yj;c{K?Cf(ov2@7UH&L@woK^y|`6!2fspAP?jywO1mE|I<3}`E79T<~{%f4gPNv zJyg(XbZQi#!OnwoE5dcU#sh~<_+;jbD*QX82CuD${|_qO<*}M@nOm)36i7Sl-MaoF zo`n$9ylej|C?Q`J3Q_jM`E%NOp+KWD;#(s(%`59q>>S!ajsJjZt?^JdA5S&@tuZFs zh4OfIp;1?Q3wTzV_J@rbJq7$1Ivg;jW^4_@zu{K3eUJ5zq~#v!2}Uvr#qjTODxI{( zuh-()HBXIijiPC2ccBiTe*Wy)f$+pn(5~y7DJ0dsS?zL#8*3 zRN#M!rq$;|0#QOjuhUOb6621p-FV>qSLFr#pC$-^Mkhi*p;{)8`+VRcx3CGjFWyZx zr%rQ2$@!I0A(QvaGq?H(_}{16McX%US>@w5f2yO!Qz|S;%D8i2`|_zF0!qfM{D=Eu zQVdsKQKLju0-(Xu)U9`zWn28(swl{6+rbLRrRoRQWzw2HLBPM~5k;nlU;fq?|BAlJ zM)Gn0`eg(>8&$2H@~pirDiol8luX4q6i#q& zusD@GUl#w7zz?FTD>S1k{JJb67SwW|OJamEEJ zX_~7+NmTw|$-VT%hkyB_q&zD6BR@^K7LyL#jkFEIFaJm<&uQ@==zW_C^H}^Fm9`|=C@0;$HcEwO z`#ecPz|g z91Wqy_zx=}H^0tPhF3stZUg_JUB-`icH#&#E$0s%on8TjQlDM8Pg>#!yGFO@ka%ct zwKApgluq$fj6)HG&=``*?`rU0P=~CrCdWxsm|hc5j7lGGx+=@}Khh!(&$%)Ui-J%* zRq*pyQ!oAyDX*ytCw<^qlIb6!1k~Tjw3E7^WZXL}zq+W7PCzaiudxRnXHb?%rjFA= z;XOT_cF5+|chN6XKV10BPoMM&br;+O{8v={4<^$o{O4vU8PPBB_1m;d{mv8r%hZ7) z3ZnA!QsZeVQ7Zs&)4OR=F#SgIYI(Sz)ORs5Wn-#QCf9lcf=5V&Djh}-uCIklG(VZ8 zyVc;-GnMjSC^P~}Laa6R+m0Rrp4nuJ{IdX;X@@58W?KPyJhvz^ole&S;VM;y9w$hX zkxu8^z;!(iZfz|f55KN~8eR7tv8mVBzSrU7M5zxHYA@tg=LxN&k&7rm3_49-P~*Sw z3hknT@XS4y&1cGP2ShlhX4xX<)Aem7g&qlY|W})TW&=3)A zBk1s%8ilIqMiqOXjY*YhXT805dkQ6fCIS$U+sJ5{eDY;^>k4^+RDi+a5oj*@vHQl{zbq_W7SZ9ewTB+@_!X>}<8o-+;Pty5MahZr(!0a8U-lpA?_9!W#=J4IoB=0nYLM-33L8V%C^!Q(( z{a{2LPh|286OpOPUx$~~7LlJI2oL~KC3>L{T}l2Ph+=6*#+nmO{`upqHTTGe?}arQ zw{Yk68+S5NHr0V^yk|a|ZdfG3LA8$Lem#mIs8Epdk*P*XyxkI&_DQrFNsdP01h>H?PxvE?P;T7r zhw%xQZ&D#&TROXY?b05j%(SI-LVl|04@j#mZAp3L5q6cF<&KgHXrPBzy)Mn#d~}mp zGG@Rf{)?^5v;|KKSweCgilUk;lT5FTXIAR(>u0{7I$kSO9=GtGak0P0Y#U{oN2pN@ zRkLnAZZl(v7jpAoVre#E>VKYhhn-Mk3&|%#MPzzDj^tw%N}|e2+OOLZJQ+Hc+c7L3ab{lIVn^1q} z%qA*0xpg^av|Q>8*EE}ZnF`Cwun}~SnxW);CS;J7xgLPf{`950kq@FiP~(4wT0o9h z(i2v;SHYpey4nJqeCKEIPG=PgAiz#QZf+`MagQY}}bJyj^Br|SA zSgDX?Mh_O?8n1qr^TjT~;O(NsEs-e)e_vR zk(5X?DWCeje^)J@QTaQ&P!hr~GM@y)=~t3-P#i%-0W>WecD%sYl)FvU`>eG7c~de; zy-@`1Ad;)y0kPo^zjf21>@_n<%juaXQ=eGUy9-cgSICr)1mh{{Rwt~MblOG;$gRRP zGO`A8=t{YvSY`JZ_Yd0}V@WsJnBxz)ro$zfPA5@-GFQlPDAITv$!$73tFoV%DO=2U z2EH`!@R|jmRYdu{q`_*P=7wU4ql_`{xFZiS^miNOGv!bO)n8KFs*Y;d^(ScT>0#vQ1evz8V}b#wgIZ2IYbMin4y~&3SVYpYJyZoiU%OI8 zJ@m(kJhT;CFU+8wWSZ}WOq$WxX^VdW4t|poNlFXgit0aQkTQ)MI->UjI&62xGkC>D z+D>$H4|*?}_cBQCaL4}${h(mv@LX+UVmml}bfhsd`K}=!c2uE2f{5JQh8)N**KO32)!^-wY7plrN&U{nQ7jSGn>)+quM zP2)+!SPcqLc0r;^;?au8t!5rNhyK>Y>o+<@J$HfNPtwizMM?Ik3iaLPbIXa})z_aL*Ml$v4?%Pzzr2MUj{}CR$ zwuT@_t7P(iJ2|%_R@6ubULN`2dcnjLfCehqdZW_2s zH!*Q*9BB#v5CvNDm5&7xn#w1(M%SM@Rz)9#i zq*6G^j|IT{jKv_6w2eja2GCAg>H&oOOsDC1Cf(!6I3t~GWr-<(bdX}vpCqi7djAI0#0?ZZ`xv76r8a^?L|%Mu@bIpoFm z?Pu;d5i7}*)NoaY&3YVKZRB)Zw8!1%C zzi8mH7w*ecq_=~^OLwJ{RRNEG=wNM+;)wwb`>%_YjMlVATjw_~=l7FLhes9RsxL?$ zDqU@Gb$uY*caZXvdO)>9J=LMhtXvPEVghI3X)h*{lz(wU4k7R~9pv}%Z>hFXaan+! zdxx(dm}Tku2oc3ld3yNPd1vEykFK;D^HU9oUC&9otKeT*CQU<0j3LUm6c0JlAQZe&w4&6gxlv<=CB;N9ezEz2kffeCwa9| zfQWt%Y?L+ypkM)eD9iEyoj9quMc8V9&Z@>Vm&=Px@>%6 zcoen0xeCP^aErX}jix>*PXAt>#ElWrt zQ<-GFQVmXGd7b75l6Tvce`#IZdp(YjjIEF2X|Z@$P-maUze#KSG-?6v-k~2PkTx}np{h|qfL&+H5IFe!9(nJ_{fDpJ zsE=hYr#@ceqHijpVfoaOxc`o91}{UaMad(Jj^R8h|oa`4yffwj909 zLiuwo9K6EXzxVzd@pQOfA9kXEXQn$x#X3M+cW{?+aWd`hS4J@^{I>sMa%%MUn|J!D zRU#_ikTSNH#j_AfYv3?hUL(SFZVySegwsmWI!S~p^dl~tuRM=zuO#;a%7#m{q%*pQ z*uhLPJvHP0vW99D#4Yqk8|`cfPmH8&BAH$tcID5>r1a?Y)E?)U!kbM|2yqAv(fmP; z9Q_UCgXcupiR~WInw%k1F-1TYjTiD!{UJ@!AWQi>yi!5X)_OO>V*XKsVudb#LehF= zvNxVp`A4Z~rnX03`T;1Md#y>)%p!AL5DMn~ z?!4}uzgTrK;lYY&!<+a3XnaW`(+<=Hp4**nBJcNvlkNn`dH%@JGlR%WeC^i3M8n-P zJ2!q&NkBH;S}J00W!P4DsQw~(zBF7?p|2VW)j2w(48yZ1`^j}GWSc4co8{@Gv{DNf z`7fuG*cq)czML`uIo!WDlksif(CgtuHSEN3B=0mp@ix->(0bt-6;VrK=`cx+ zVvI^7tsDAO^bph?&hc(5H`s}9C6ZL580rPr2dbcW&(szU+Ia>kcIBe+@A)H>fZ_;E z?~tYpCU0&cLQ&d0%vcPCu6;{CKxnf)nq>See-uaPKdS;T^49BsfP7k|(=Jt# z-bG=Ap;kV6G+rIBkw?Ohdm>BVK{7l9d8h~OM#02VokwiFYodcJk&GXpLcs$6w4A(P zHSZnMxv~g5vCWZGnRkY%0sU82l4--?6cSEyng?=pHxPLUEhFY{HLPtM>@6UZSV{#1 z-eI~#(lR4h^xaNA8Hf@YMW^8+N={vWaY`y_xwL3l6EAq(lKH0(Ts51Nc{%|3c(g+y zLL+!Yj~wOBkrDOaQ1{`L(y$9UM)GYnilyFVvY4bDSE+asDbdc*7R3oYM8@|M0l4+M zVz%s?)<4u!gQ{C3(xOIj-b}IVpZ8{sS7OcW;q>0s^{va)&7)*sYCpJN8qlqyBCaXm`N^Sc5lK{}4n#inKK%RIt^2e~{JgTd z;vZH3-asM0rjH4^DwFj9s4M(oWtQ~)Jyj@7`7JleCRhxv5bSRd4a(n9>6e^HC3E(bPL?qau2!3dIq;ReO5=)Lr65dvDKk>+?N+}|APp+dpjCy@Nj9ix*ebBh{Tf>ujhgS@QnY@3`kW&l`!=PxNOj7z9P95V(zNbcx0U6hjn^^s1 z#=Ie8OCgsA0|Au2&Bs=nA~%Ns0UOAlbP!Rrpz_G}l8JVI4@S{+Dmdt#3(*F`L2&cv zd?;e}ACDRLfrkKKSh{q*nE+QUTKkA(L%1yXB29IWL;E{o=?2cbCXIW}Id3p?L_f83DFk2lR6Sbk(2$y+KENBmkMX-cL1 zi2y*YQV)o@SqZKZbQ%EeZ|&Z>`154mX^l+!g>mwF0j?6DdpntSN#E;GJpCW$6sB28 z?t94#5PBx_ZWt79^O;Gy+}{f&q3Lg?UBq*6U8BcjRCSeajWQYl4Ih)cya9Z7vicQr zvkHLsp53KUkfw>`{k8%ep1YVzS*CpPyXm7kR0tb=*Gh$Dt~*>(e)sr(BS*j7=jC8H zy+fyj^WRJ!;y2JtyMzvKg$RcRXZWatfM+21mzsIJGMrwsMh3!l6RBtn1U3cmo=lF0 zi?oLrR|T2I8xDp7`b{Kb$7%qGUKRCS9+5m-8+n!eG@Ybul}P?m#iP?E8|8nz1GZ(5 zZwadM)9*`kkj;;O?I5C1nkiOtODib8O}KUS*hcf61*a^uvxEWF=Vk1x3BdOt$(ZMm zZ5DTGkxSS;&ey<6-RgjaJo|G79iqyi2&!_cQf?gju&jU_f?8AIt7wT5X?t{bm>#YO zSY;)plY=%at)vzOxT^F8N|*kOvhh+7GP!r$MuqL_2i|Z^>7%AC_2=CEZGbNTq2q$H zhkyBW?5F|f0SU_roJqb?B~9GN*y5h3CX23`?RQ_f`cFkC{&*$>)po4^s8ZphZ=6nWX8Ya2B!*t zo5{G>RLHCM-l&WlCl-Bhj+BQr{L@Ox7aBnK6Ul=e7sk@*@ygF$3Pw>XoTU7)1MKST zu=3t!V?_898||)!zoX1hAOg%VElSrN$ z+H>>|zwf@1PBOX)a?u2;kxRK1GeeM%t>s$Sy+)=dOME!>oekGz!{)iKH{P8=evJSP#>j@GEj3V_ zc73|GZwv>0200Nf3pz{bb>!$DPc9W;*CLU&=}-(dklS@|!MkKy;$Mpm%(f*2#q(N0 zhPQ%~#}6hd%v&$ZG|zKG@jQor*MG?3bNgdAmJ*<-yw$XCQ~(r$yPqB!|8fmp)}a7y z<*IymIoW)?vQYK=h#$NwpCsiqAp-15&6dgCI#Cqil7KgN#v81(i|vTw2q^d1V4+MW z)9naar+UFo{VQ`=cD?|Ysr%d~Dc_sqg%VgvQnpAWEvx+za-RG`3*gU#ejiEv-4}r8 z0WQ?wX<=8$Tdf6TdExi#2J80%oc#CF?ot`JW+JoP4F=$QhG{piB~1zx00=EElT3Lx zNDbG$lE7PobSNniy65lGPFf_uuBFNJ0$kR}Ofs$6xVx1_!1E4i?$zSQ80+ELUzF4M zO*?&Zoi7Se`k>7c*A|(!Kj{grAauPh?ed301<&?BXV7|Sq$lzjZDo2dQFtwtOlr2u zO2&Wg1)v`@S#4CzLvgxE@yi;+sr}s z94y*v?s z+x%NQ%+lO?dN+-zS(TK~+|3O|Q(u}7Cvg=Oma!F3lv+EiRLJ90P}ILJGyD|IyMEs` z`I`Y+0Pk;0;AFu^>=w_}ANC4B(Q55VvzSe3Z;DU|-;ni@kLsmvS`;Ys7P(Y~ETs>V z><)*qR$4y?;IoTNXa*Mu-;=U!Vy#_PW|CXN1=^*g;T<@LgQDqhA%Q8&>%k?0TBY@I zfBSq5*tw4km-aQP(E5$>BUhX~yX(m1$R`m2$g6CdK?7g;{$hlgq`bJ|i*NO>iQW%M zS{GNVtOF3bD1%I#F>dUOZYZ8m=7UExrAd??pE@$r2gNA$Y4xUDe1f?>tPQ8J%nl^B$945K$7o zjhnO`Gg$B&l|qGSXGb{HH)Qk&Ao|qmHsy=*D8^8d@}M%Y-?!8QAu*)2AH-U_Br@&d zU77qzje_YuG1I&`2$^o4F9k)ZDTjx`PN=dhfxNwR1>MVd}r6})*gUb zgV&9;GcK+3v6T^0N!)UR}lx)nA@ums|a}Vx6`i-}G`NBa^>q6e$cIuCB5=fib zWTR}b(dn!QisRjV>SGHP5*^~2!BuX5kV+Uv(Nup_Q$1j zM~_CzOx^u!Z#}a3P29VojXMDEWAfe*KsEV*m9Z_5PrF#@cs&t{&bOKAMs+AuC&(us zd#X^3w^C@hx97X}x>r%bu3sG2c>oA*_epfxoHA}GNTt1$k|JjKz(M;p$*AYxpz89F zndId7^oPejs(``;e56M{s+H0800i8*`<*qt3)BMiov!diIkx~5tu|Xloj$-qxlkY@CWEf^uKtbw6lUH@f?Q%~P#rcZTn!5A}*AHdGN!yP)wGWS#yPy)@)4A2SyycehQ;PACKOjeW3Od-fc z2yOMpJwtk`l}Pc|~kk6v)9_mEjkWLgKf$Zxrsr0GSJ%yQB^pvSPb)l0hr2-rbh z2nFz0RCUzfVms>v_}}1_@_0)5v+~W?;UK^%u(mgxgxaU5xKRhLs=n&(o#d3{)bJnb zKok)$v!e)n$CSC%bXCHoZSTD3A)o|GoqG7sd+sQap69L{+}T_cT75gov?;!DDie3Y z3(!rwmTV)B`=MydNZTdv%9dYSx&aMRNn>*WUjNV`?guXv%x5W6uDs)pOy$yd2(Z&v z)Rjr!{y&?yf5IiXo%B_^1U40u;@TGvnsoA8LrG97*zf009HHnd4p+ zP#{6CLta%Qr5&ZK{EOf2Nwml$pDZ8w#)s3#y}FA|51OC|YC+WbuQE%_ zXB2$@}x(2=a0VGy;myY1f1J_|%jem+s5lQc8ndM2`t` zT6(L+hWGEc6aeT#!-jd@>c;UT7@-x`-b_d zkjZ_s#C?81wVerO(z1W=n*Lfm#r;!~$;TSQ!D}JOy+LpgLywa(Es2-+OzYfcaHldN zil9;>zitV$1OT;-`-d?74FMWam_YtkPV46V(ihLGP^hVvvT*R*Lf&YtLY9z$cxJ>##~mGBPKSbNmNQEQ z0ID@)eb^n5Wy&OFDrEDemMB0+6B*}$Og-aBPE$jnS)3}Tz9#~x_krsrrbEN!f$Twy_ zstrICMFBE=t!B++)f23(_h@q&@Gtn)?5EFll-ktOsj z`Hu&HKhs|Z8b2_-40s*ngWAZ?YnkOrHQ1>-eNhVz17Aa?Qd_Bze^$d~8vmf@nm2D7 ztO6QTh0{=yPo|E%G^>e9KnaM?eeg|VPXQ&P4h-2L?G|au&3r_=M>7W3_FF|K^U5E)`J*7=mN}Km)adg1 zyR$Y$n5;HQCgmql5sUhUs09cDn(Uxcd=nH(@R>hv?zV;LbdnD3ynXJ{@>&8)K;>I~ zz>NB^Q$0W9kd2g&4y{$S>!4aydc0C!E1)nc-%53BHL4R*<^v0NyCbhUH)9WP+ImBd zY7D!U56RRqTIBM=nr<3oYJbf1`_S<(oR`SkWdXFhMd_x2^0n98Q6yg<laoe`ry#YT+bzG>3zxMz3Fog$+$On|d-3 zsQ*wlUo2bsJk7t>=pRWt%bB@46hPncFN;j4^_~F4`DA_AeMnMfj(<>qk4bLU!l}|J zlHp}x*V;_mj!IB8i{*KDxT0196;=zS44Ijvh<+**r_O!yM&00Yy?2-l5h;(3WY8{U z5Q-)GHWj{#sTnEvA00UwfL#1WO*#;9 z>)P2f>2#7#y)3|Ec05V>?mu&a0n9hs$i#gX#zdwp6;ZS%kIC(RzQ2(f@2fpt{`J(V z=TL-RHrm}8qK0lYo&NDaCULZt*><;7!!?!nnn>fK(kK~mCyj%Ut$8H5#~aXYl_$e* zjwPiY;D3=xEs$TWXr2#%L(eBy8$c0yd7~Sgnm!_FT~;2?KzP2LADkvrkv_gGT;=O4 zK;dywK2)Jz38J6jJlzRc+;Qk7)V+(OvOZ4sV8{WQ}2 zR<8*sY@{6bt+#+e=&J1aDa;MoG(TI%B9kbf;xZ%c&a_tHX#qRPeWfuxiF`PI{@Uv) zN81Rnxt)snB7pZTRIGM~ThpH& z#ccbzs|s%4Tc~()Dvh+IU!5L`r&M}Gh71L*XC>e0ax3JKODV zd{G#WFW1>fX|KVUnkbXp?IC!HDgn=`*->kXL^65T+lmIU zt|6u20vz;9Or&Lu0EPH|T+G4RuxlJc>kJ)SP{%|&6Z5!M1I6*$LfXzBnLGcw9Dh4` zcJrw_cFB~(o1-v-Pmp(LgV#Uq`|*hsIv5`AcsZcrPKlIb_Wr$YYhuddgadmw4R71K zUFEWFULx|UQm=O9;OgtGbTGZEhS=+Y_3>Vd?eeCno&dfzGh2KxoCFlCODg$m>2CK( zK3ab~MW$(GEdhm^cJELe6Be~n175r6VCg5o$!o+dW-=Y>69iX1zx}G=;hSzKp)xT{ zYXgX7S~d{@zZY(h5#<138Bfo-Xi0fp1WKK!bsX}m7Df4JC)kOfo43AMS@3;7eupm{ zbU(2n>PMU#hJk!d~^Qh&Ir#@anS+yH3iB+lL(8FT+`Lq8J z4@}zKSODO$iaWee1YwCxCI(}xymz|__<{f>o1>nJS{gwc^QX`@0uRSvlYA7VB+xg!cver^{5dFW=_X#S!9TVYTHHqii@ zL1|=SZ#DAkZ6Oaefx>;Ujm+pL0?^cH=gL;Y`06CmcIP#3WZw zNFsS}&XT#~Te%~P(D3^-+F6gytgAucYX5FcJvX&%zb@^%Tq3zq4biW0k3c+=;O?tT zbdtmq6u!4vph^@9sq#Sc_^UisnjcF$$Y{RvNP>6N!QL(}uKwk?fo$and7D1(T zhn-M!;ngdVC#AJQ`M*+SlB?7JHk03q6|RvFzD!8m{!vr7q`^JrWBW$H)AT=Bm_yzj zf@jfSjyLR7p)Y=#y0V$O0JtAddjSgJcPbu*ssIQZnN7Y`2L-AblU4;z-;#VE*>sml zKAO}~i-OeIZlrCuKb%xAoUrmrH5GuD_FN@nlWx4O4O%l$?KSJJOw!s9`Ki9XZIoro zriG2Y1L36lgK~XQKb{hExksCMBAym-k(BzusedZT^Iovi&0h83Kp^bG?vnC%S`(%%tqUx8JBNv zT5$I2mU!E_1vB=fr~Lha9tG3D&aF*8u}ud1!iQDHn4 zK}BqB6v^#78<~D1My7*A(zLN23e|VW8^?|e@D~6CT%to%2%LQ1kEWCL{FK?D)*HTSaWWl}w^c_;eMWLT zGO3oz5%>4|z+oCQ>YxbvTO?B|d0!&u3h+Ekr$@8;1)&JR;mdT$?PG57K&JBZl9@%C zil=$)rgf7J6*^8i@!Qg^$s`}wL^khQA8$%x4#V>(fch)z0ypI5^+i{(E2Cf|AIhYePG&ktC;$HYpT8p(eDKa^`^`*T*>2#e)VrU)>4xHT zH8b)DZ$JzvuR%|VX=_nNmHQ=hy zxtG%Kwi4Ywy1nh=^=VW{)}%L4Qa_UX30dZod&|P9KG_&W@O?xwDxjy0%y@akZkcuw zAJ#_E+FD*h9%DH}3#X8+R@zxf+Bg|c3)n34Ol1J7iazR~%{KB@c{pgQG#YX$?Sk2$M!J+jX(lXTpPZO8RC;Z_cj5G42AF`>3Cdwq` zkKxdjWJFEm^HQSqv1HDO+`VGQ_&S~7cH1iCLm5qBx#^ z8O~gbyK>leqKgKquqKP*tu~waSbH@J()9szNTh|N?c(;CV;l7PWUp0{7(cDiN6{Hc zyLzZmoH~EYiQQDl6n36uhK+{vx2~T2w%xef5;OMq)}v^(qG)F&Db4p$cfCPHcqJ6i ze=}`Hl5y=}=l{uNnGV*6Q`}J!wN6m+moKupm-9xk#4jaY5b+}wKhAh?ugebdm_LAk zy|hc5RvJF1)8_fXef}=w5zv3Th4M#$`p+NrlO-zBd%>>eRgx*~@htq-n=ds+mI&Hy z>fNhPt1lj zKROC<^6WqD1rb@)?e5AlN$VQ|6rQ61 ztbk%@CtJz;WnuRgZ4uJ|)QBe+qQrd1#M5k`)7?KOzA@qBrBRO!q-;FcUqr#&-Z*D? z+)Fbl^>G8hYk|dl^EI)}>I~kWxGg&Uz$dlgAXYd=+aFbtO$;8qXY;2UB=X6Qg!B|z zja;!vlC30TCW-(mJR}D^H~j!J!fkxGWX~dB*uA(51#=&^Jx(GiQL*#YU=>jNIu(n2 zP@tE$tY5rrpr1yq)u;qKi&Doa#TjfQ<-0$AiF*aueJV4X zsVxf9J%fw2a1wWtyO7O4jSkyTF#R!78U%Q}x-?SW8S=e_)}$4UO8qI3U64Z+()GIO zUR7jikV2Vu$ofSML#N zy%h?(mlCPCS_egGbDEi?bFbxt#Dk*09@>OqE_y6S1*3IL*QJji9jl9qW zS;`(^ru3~%yxN76JX2qbVwBsGB$1~3HZtQ)=zlR&aiuhh*C&~j$@KX?P}KM{l@690 zlY=!Vk&rd956B}y$fR!i+pP`HsZk8ouQIRs;0G$Mo4B&vd2)jXM5t!cLEhK0yT$yW zS~byp5cyQTN$w9om8+vs`2rR9Ye6A=X(MUf*+Pqw8)@Y^Uu5!(WhUA1bKMK;x2iOhCu5TNQ9L;1llEea%f3Mh#B^#?K?)_4K% z|0b0*?+w9IL=>RxYY(U}Z+T7&7X)1)GupszB59ippp}Unh?3IvxyfwOhL_8TqFdG1 z`u=GoG47y24Wi^)XF5-k@#t~uPu6e~AsVJEJnbSmPA z0K10QneChxa;Ty3nDt3m(-g8HGHI@mCy}i_9V~-TFt1}I&p%gh&J!EYzle&zXHaIu zfml*{4o+@W27bKcdV)-O3bM?ioKCxp$vPxx_XeU^s)e*jB$t!}pzeIc zLWhiHwY>zCjPFuY3K`W=1qY$qJ~ORXr!{m(k+he|N7b>C3YjT=OPwRPdkY%h_oHZ| zL*z|fsSEW-M;0TWpbMn6lXl^w7SJxFVw)ELq4jm9B}$a;e_LKei3m;RB`~TDG6_8_ zEP(W=CkhpCLbCl{K}6X~HOJq!lD7t{;i3?KWcrMJ=z~mkllT>~3?Un%2;vszB2%$T zhhm9u_d;&LhxWN4Q`qMN!bCug7m^u!WqN5mCG090`??NLh3Zfn-aKNo9^3W$?7H`y`D$kS+AbD^C)lt{#h$skrGbHoH!qZLCJg)VfUvNq$%fDEFt8 z6c(Q)xS>Cu%@So zr-kmkKDUD&K$9f%+42Fi!bb%{F_NSqe)03(u+vs*H$9q)Wp2pf_d~peOqHxBU-j;6 zrFWff3_lSyf-QA6Z(k+b`gdmEo7)Tf>A_gK7(gG6F4Fb~Log1}eoj1=p|L@#;x3S)Z z=Xt*Od%xdLHH!?3CfN{Knv>jz9Iw)05{jVTNz!^dl1lip+8Q9}qsIzKi;1R6hO$AG zpMQJ#rt3C3WDY~N+R;4wkakIR0W@6S5IH=_w7P({TmM}zb?ep+`DwqhF=g{mUpOo$ zc@A0LPo>Iyb(jFa@M(r5uZNxca@s9Nk;D!c6lNO@J5BF&I>~PcD4w=^G*xo?6u(ma zuQ7*`^o;GL>$Urz=z*v@D(0^r*et%~V-F)B$e( z{>aCD70LHM$hcJwUgt)Srz-2<6&%#MA)*wspWggv~tdMd< z)P@N^PW@^5tm%K>lgWLdD2nHC@@8|W1mAkAXr9$X0~ZMp;q(I;>5nY+9!rCf?K`q5 ziqp@`Z056vNh*U-GNJ!)5xMkAU93SijR1!iZIq>X-YB4H0(rcu2nc0;G0aR=>ePC( zQk0wT46Z2xx>r;9#9@CF^ofP!`Cw!-e7Sqa_6f}^__n)F)oq`?2NgaVil^kcP^N=b zPG}@FKcT16C^W=_NxJWbp zbiA3Am_`1@8xBH;yClPZ>?)!lw}zGUQ0W%P4C~uIaB!Wh;06>B!Yg zs&4m0ZeqyTozb@B+1~KnLGpHL;_B93$f9dK#YmNsw)czhm|y#P3&`Syyap!5o!uKL z(=O%chcBNcX=_^=s&_0@ZS{r=1Vhl6D;xZfo6g%$gQ9s}CvC^~T1hGG?TP?G=UCHj zZ>tKY&x}e2j|s@qCX#m1Z)uRr>+V1oBb{D>Q|$+&bs(|@g;SN%2Sr+#NZySlW4ppZ z)AVm68Gp&fpVwqChmxV(?13lh4D%jDyOeqW+)4?^;Yp^~MlmX!B$@G7E!gcRc>-D7 ze>Tx>vj(X~f=crx?e@E&2tH%(@`=}=@*Wn>Eb;YFJhvW)Y$THxyxk;V%1!2w zPQFBCoXqAa=g8p_iY4l4$F_cKzY8!2+E0Ba_lDU(Hl5ef3WY$+UO9 zYX_7TP%^{7Rq1p}9n|QELejdYCQ8)e8l6s+76)I5v6310?%GJ2FaI^RDxQis)=ZU> zv8(|A&-YDq%6v(KV)#Xq=Apt;+9~IHj))`ER~)g(w$q=#T)ULtcc~;#hN5t9kPMIK zG6NcuND*3}nsP=1& z)v`oH5n3ma(mWmP#4oKW?^S^7+(bCMN2ZiUmi~&hC9()B$)+fd_HU{bl8Fm_-cm?g zb(Bo=^PwQ*@-=6R$ZrzKh+ZhDZHkd=gG2xtjb^1nRYZ4B?d$1L^#F*qmnBhUjaj~2 zCiw~qv5~6X0vUsMC56(-WRkYOf4D-Gv_KCR z_nw4y*&jueNIyPt6ldQtF~N^NS0*_)345dw~hw+=qfg^<*qTyKsZeylLjM;TkpG6t!;@S>5tgRi-~;vo(5S;PfsRgh1smPPM)&r_5B#$GT_gaOj`}JT~`970d!LBRq zZiS+7L4TTQr^r-gb^wHcA5)lZrw@vy8NBkEL@KM?E4({uMFz7-vGXgzWy4G~r<2A( zB7_PPjvoEHe<_rx!C!woxW2GCvQ*kGo43!5Gkq~Cf~0ciRpjGd28Gc#TW4an|Y&AUJT^2=jrTrCu&yG6=8;D#JQ0~*4iT?(y7 zx=%Nmx3&$^0D=}S69z<3R^)5V7gMG~!iMk0`J-fF^J_9m)3(MUT;%zyOx0f@9)h8C z=naw!;k8`mkO7zp;q}-3TQU>I}MQQD_;ifrG}U!CUJerQ2-l%b-Z&;F~fj-?~L+ ztgHl}n~`#TWP8EP^y?$wpy_r;CTUz(SwwE-!!Ne^gXt5t(rz3a+Qc%xE3(wyOuIAg zC|bY+k{MAaUUG+%;8T9^4Q7*5A08S|rF38!6eRAVY8vd?UAVpfIXDOt$y23J9G~@6 zJ*)}UWs;|mqryqreF%r&<+xg~t8JnxvMvfEG~b#cQKf|aL)9@qXoKe3nC)>V6h~8Y z)bDdP8_BfMBUdMlt}lLg;{J-8C1o7)6lcrEE6e_ zwxn8k8ZX4t>2@QO-OC)7cSICT{6!{@w7$yRZZ>k$im)sF;t4Xchk!zP{6@Rfe&v@* zBu#CA&XEeww`lPBh3K6XsveX{X=_apB@&)HpGk-G-cX6&bt}z3uNd#f%Cby(+s75) zqT2V!BW2(;$wu?~P~_zmqDKivlaT>%3b{wAFPz+#D~zs+;t5{m%H1KYU4S1*?t=^T zD=!%%SCEDlL-5Lh?!@vGqfe zd)!e_dxfgEP>4EVJYF~O$5Xo-LGUp6l+nOt^}gWE)?+$+_o*Wrh25}A1X+RYm)X02pcOLq;5)8aA7i6XMN zzj#_AljbHSH4~9V_hkxcnpp>h=zNpbnL)_n{uiC3ow}bbBpaYZRal7T{DRn&ka%RRsWoMpwS@b zNL74O6sGe9iFO(1qN1sK-_ZT%gXzp-iGEv;VpRI}QS#wq$4#X5u2mszbg(TA67f_j zoTQx;8>yIOg^KlO0}3H(pn7iO*P-siuNWD3CFW-hviMIor}Wefe6ivYGX3c5BJ%U8 zjzZKvZ&aAq$-$m*LC`uYoh%WN3duPJ00B!RCX=~ znY6}AWahp^W;y5qr?Q*U$nyjAD2%?xKN+-(kH2Z5YO@b2tyf4XEx}64%f*V{T$U(Z zTmHEhN>J(+vz(~h;M@E`#R4Fsyq6){!2TkChUqLddHL?liM$cXG zn*w@6w?YfymhH&Rh- zbx?>3x43WEj)atz-W8{88DA~H-AymRC59grl50x?+DTS2eUJxSqANEkg;ElE^MmgG z-Ws^VZ-I@JZ_QYGKR$`cX|zlHE|uKj4nWiHl)_tQDx!G4qsGigp+m}#jkOgv(!qRi z^8-_&iB2)EyP;_QJIG_6Liqu+mK_d@GsmRTaJp7)!Mu+6BOMky9k$g}Y zE^zA~PEuZ6w@g6wv8(T-+nB|6y*i5KTer3sp35Y;2ynkfvIFwaiUQ#F@mVN?=n z916SkSLH_?YPTIR_hK6D!YZR6vE5Bl$)J37@Go@Oy2rw7<{dpfPz-&SP3dID-P7?@ zMPIt`;Gvl+`Djf%CBs%`i5dO-9g;UHprCTEyjmH+SSj(H1{cVbv9&vtMUL`k(>p>y z$UR#BncNixh~-cSU85I2yg=2?yL7tOR)j0GGpuwlPODw#JmcgIzED-#q|k1YCmcNL z_a5ED69ox6oLZN_v-F`l}5rcLeg zRho@Xq3W71TvPe5O*v2j4#7uAruEY!%kx)kv>Sz@X#@a6$#Xh5h)2k|-f*aMn>=Jd z$%I8Dr$bms@^d&;`TW@x$@NY+mw0UWR#A(mGs+LcDj`OqH^&bgy%L=meMPs5MFc6%sEN&-pWf7taUPzA0ar5#NJ+uFRG*!v#R}Aw)$#fGGGWp$Ff;O<- zh7@M8k&%_*P-n`{vLzTr2^_O_PbRIu_uF(K zjbu7WnarFU2$g0P&7%RouTNx12g(6jEee=S^07PYghrcEY^0J9Pb=Ti;0aClRFe0H z>QTVh3^KZ<2*7vA-e@|VyQa|2wpa@nl%AMMRqDmV>klO;e8O_10!mOViL|}u-S>!v zwA4i&8n{=hJu{VznOPYH^*54?@&(X8g`5r5+hjr`I0fvZleH!C8<#@UM-^e$G@j(G zFJ)TNZQ&%R-XqPI(vwE$v;md9z3U7g*r3nhsJ#ztw_nsEpI51R4_*Tv(qy>FbxX&*h*4K( z?1JKTNG2(t>r(b1?QG%i`UUvGPP37!v=(qp^&Pk4|EaFgY5@4piLg-KQ4vlR4pJpC zV^%2vMe*_!ffvbCe>iBqYG!%o{Xl}QoIrbt$E#^pXb6h_eKyWXL4C!F$wgou3$_D96Vk~cOck+yAJ1PBw& zONLtgIRJ&ZOo@x%ak6hga-inI`R4KmUz z0H_vCj)POs9%ea@{B#dV-d^JgRm%jDzxi*IsFD@V{(a3Sdoro~UApIEMF0a}zSpXV6ZzDv^@(;OZ+P;8tePauXer?nOw< zVm;nZhrB}nk$L?MEgVXHa86+si^Lo|wJ4VRNoKh=xi;XnoT{tk0r%kH@ni<~ltMvT z|2~)FZL~8sN9d`{)ab9v_^z?gE_vGPNPWq~iSi+0m| zkYf&2rY>+z_t=;zO+12XRjj6md!OH9NNFk@%5S$(C8fmPTUt**QA81FXQQzioO;M% zP2uob22GvelHLkA4mE8`s#FI#gjX%RUmu0=IP$O|a&$;}-20WA7sG7|X}PvzQ7?_A z>MoLreNhHi! zgoww7ix7cr;xn;ua%S(Qd!}Lf`pY+ZEN8sc)uE^kd~#5bZ!et zRzYr>k1SMW3`BmKcNLO1#~rt_fZJVB3_)+GIda^m-+gn|%G}bD(latG=JfQawcYe6 zNyP+GNh5jmI5{|2fYx0EG$K@WS7nmsGm~2DP^iwyifIt+ghBnGYHMb^KOD-PpvwH& zujD11c~dZdw@fY3c8FK*kos}oS;kU1g6xGRNJ;+Z2}+KzmNkV^c9M3S}y zg?8-$cPdu#9LB6zf!muH&Hp_HKSanZX=Z2@!E|!$6Qiej( za@}VBtwVob6r_DE)3T&>!&79$+H{gP%fUeukfTEkNz=4-3F&iNAuk;YW%xLm%AOZy zTIjGh2qhEzC#D?QaWd0J)#IHpV`E5OAOF_3t8XYQRLn3h}%4Y41hlAB;y-<_1+qh$@HYy6Kw?);PKJ!t1_LEDgupXyxT~F z5t$_A$W7gRfa)8`D;}`(9d9Mgx9X!3`TKEM=+@YoNAw6<_SB`6EuCPgG(A3$@kGG zo#yxuJfZzY;=ei+@*9iI%adeVW z?{1&c%N@{6W-f^>wNPoAZ%VLASI+EiT3U}R!)bTA6bcf2K1igK?f45JUZ0xC)ov(} z_);4Au$zc1ftzIJCVeBIZ~^mUGh{g}At@qGrk(ONN+1Z@(CW?fcp_vId41uU_PUV1 zKX>wTLy3g$kyOcv_rLZ)j<<{?jd7BsWIm4gx^8KMfWnBar$4Yo-i=5hnb8g^G5EV! zQnu{-x^1IF3~MygLel!r1EwDR@E0@rpgRh3+ef>kUtispLGsFEcc5(8gJY)h$ir}k zb~8~xsW6gqBuT|4F}Ii@IuyaXM!ha0=Kgk%DhqR16CcVc{VTVA;lbr172)79<|&Smlzzr*v zXeR#a3-n7@$eBQekt6zh>O@h1*I{P-J}=Ia##GY{GIG(ee~xZDY)O?(BaqhwQc?6r**EdG7quvq&m~HiUf-B(riJDQ;qU3;P@^-lxqtf4VJn#tnJn3kdm#@G z6srDBl2Mm4=rmS9$qYRgKbXAx`fvZHm`Phi+D~y*NwErxl)kk*@@Tg!G43cp*XEFHCaF-h#tjZa zgOf5z%hiDbgc^Gka+e4PkF^$(?ckDu3dz*wa1j05cJ$jx)vXY?M3_ZO^17EtO?b=| zEyd84R+5Kbe7==8UQ1`@*);z@n#qhdEV=FlM{yaekmDjIkWs^5tQ{P5q zB3?36SuY@$&=gwxp`eOU2mO)bJ$Z>9b{=I0|9sQTEDzeCP)%=Mt}6nbA4JpP%B+4p zU#;y4pjHCq%*(8}N>xGF` z0MrX7uUABgs@))Mzi5#o<~*;N*5F~-bA|x|IY0Du9;7S^%7Ay-!a?6?#E@wnF{R`GBup4ltn}9kAFuG2s$?r zu4$e?m2rB71}P+`8bT|32v7<2*QQe?4G`ed_uC^Ys-h5YA8|u2K?#gN+N2j-zWLtt#PmP@j>uul8GyfiAR=A?&F8T7zWMS5Pe&w(?~c4our~7cWT9I zXGz99k1T!S98iKb6h_WMQbY4QWH+T3;GHG#Bnlrmq&3#0omLS($qv;NZLjqH}80w zm!qJr%3*(G6TY{0K{ie8$-69c_`?l_DnGHa2x!`FmuM$#`!uNh^ML{Yp>DgnJ{kxk zjUqx7=Ed=#66OrycIE0Z`^F8TOn2lzHzGY2MJ<4Jx;vB}xiaQ{gnqM&7Q4LX>@ewwVs` zU--gqjI{aq7Cmxn6-~Pb)lrc9S0<`n_tWY+rP3+m^2YHEbSOgkRYuw+K8U`y_vC}! zkC+ipm2p`QPuOWa;0hsl(3q~bN#l*--e{Le^0T_#>u8`7d)=gi_0z7ScUeezn-@y9 zlS&W(k3Cdf3VE!c;36+Y#BszW*#ZbN@weI0T;})0=AnG)y9G zGm%HN2UIDtX_{VWn@TE=Iz#m?xmpKwyf2dq9r_}Vpwm<(w})yqRjW}5FC!x>p*TS& z=nzo_K$~>4_V9dcY;6UVXKurvRslmuaUgF0bkRkesN)Q>gfE^rntq z9!#R0{H2H@_|FPUKG^{1lt|UJ3UKl&jVz5~DN8m}MtB$0_<76Z3IcM`qEM4=-Tf8Hy#Td#<+kbD<; zY08dF+A_vaO6lZ`h&_$`^LZ*?eHrIU0DuO!sCLuP(ZuYH?V0*as;vf(JR z8C%1_V@EFK!r_!#Wr)X*~Xm5w$z6 zIUaLIxm(Fs8U<)2_Wy2TzC#aw(sBfby;MoA3?iba0(KUP2c=79nwbzz`=jgV@5YZ33Q)kk{9dX z3C#=PbW&oM3~`4mg4WY+e-KFD~4FQ1HG?~`nBGCA>tXN{E`@?BaGUHaolD5%ss&lunB2 z_M6_Y6W{`&{vk<@NT+#nO#nMgWNa_N-2e{(@H)hN(%(SIz9u^X)&9vO)2I3a0jsE5 z>4y9a3#qyl0AVr7yLFMR`EJF=gUF)UER)wN0H}90R=HRQd31`W-L-f1@L8x(Nog-Y z^%MDtfPSfTQb=}29&cn)bvh8L4OFc|A-q$0w=9YwjFjmRR;F~buW#CzVN^D5G1`>O zf4-8}Y0m`O7ID=*|@ zc$-X*d${}ip*|2>&gD99lt}kC?XK5fPnEJ+gW@%QyNqr^8qLdRo|DKc=g(MVg`{%t zQL5?I&czd(+@nKeYb|nA9{10+q*2K4`83AQJ}1)_dEP8B+r?j7m)=5^^05{rGpwV- zjs8Hn%jD?*pzGc`D1z>lh==85URGPYL6jIr_Et#}!J(o%4)*u17h$385F_EOX z@38gvBvO`Wm1rlw1()cm)zulsu6${HIBy03`mKB-tRb>gxu!&Y?T#YpPmv!Wi?<$t zev=a44zAPgReF_*C6lKjx8Bhm1Qg`H-1ZrA==L1+LvAnA>Hhi<6s7WIX4(2y*P!y1 zbRu$ShFj=hynkl#Yb~k->Y;UaM~)sQs;q5bC)5~WK(@-4C9330{S9!cJTsB8?NNfJ z(X`#^DI$m4t9R%iKbqY_1D6Q>?virk$>-q`?cXF18{kmW#Q1VhwM-y+qYPZ&>E|vY zuc5J&w|Js>p3~nJ0eJmnqP*J|@00<9BPBK6htOdM|GQaDAC{cZhKNwntk$l_}@L8BhhknSN z@}w^?%4)te-5X9J({5+R+)KIl#}D;1PGQPK3R)Xal@fP2lcdrex#@ROWgR6#wS}sAC`3hxaaB=_(2I1+TzM{Dro*uX5^4F& z6VIab396J$6?BGjf04=*9bDzvVe)yIPh^e|P!zW>tjg~OpwxxrTVJ{(A7S8mQkJZb zr@8@XFz47TJxVqqQ+a%2d&_|9RK<2cu{>Vi|5GLXCQ^yXkfjF~(vSV|n2JK${{Hlv zRvvn-i0z~`o!skC=HhATRrjaa()<;iae+SSSZH4tk}PNS1CiD!HJ!bzz8{l5JZ z+))&9H8}>kHO}M;1D-fatF`kB4YKIpr9)bOWb1KC z=E+JZUfDG=*EiE6hotMDH6^WF{}HXF%Z^JtS#Kmg$hx z0)Qr`IS3$B%a*m35>Np*gZmoL)}%@)Pg!iAdVy4WL!u7hWudeCl47Nm}lj_g4T=^^I|D;ewhU{n9MR3r!M8 zDjVHU9L+mvX+QrsWxLGib2g^zct|`s$r8@bUPCF%M7s&ta_kz0yF ziP@IJ!DNM|t6u~G-p_qcM!W#vd-9eK3ZvDP>Xi^LQK#_xAcskf-A&dVKBe@jC3tk~Hy*qM<)jn8D2g}Ev0*Vvx+V4lB5~44i zefa0zbW%2Ft__7tzOm7q^e`>_kyZXsKtJ+E0HFPvlpdLruSY!CxNJiU5l^X37){3} z*3}2ruAtMoYfzZ#%NhgF|7>OI?6Lrwr0|FjvKT6BHD8kZyJnCy%d|xQGorpn&ouHu zLlmOUI*E3_!RaHCm-HF|St@U}k*2lH-B1+6I)!%TbeW{FK7fi#u3isIw?>~_^Mjv= zVs(k7>Rvr~kAF-u;iD!7gB}8kq^Ugki!DZGk)!Hp05r@bZS9dw^Mg&Y#)g@a4~|(h zKb>})-B7aKblB+!_xb6}HWm4myObH1CeJlMIL^3?Ki#1c1J_GD<_`=>xJ!NmjZW*o zmE>m{06o)~{t|NYS}svlslw=mNmQuZ>j8(V(WF9!l<&0!@D0iN0&?*>M7#F|s0`Pr z`UwT;N08y=kxgiJAo{>ae>~y7kp;+SzayuKcqWFKRNZ@12i4F_a=rns3OY@vuw7Te zNh&i#8wKf57@_-BNj?_@y9QUUjV~o2m*7nbRhB!cm-+zO-jhCUa*IEJ#U!8iEG6oFH6rryZam}R z+4_d}BWM}`2Te$DB@Iw&RdhTbHT4nj%-UH=W_I$?B3n0-@)=LKqRPSJyD#(>;I=07 z@oJCWk4zRSGSg0sta6m8Zz7BE(L*KSaa&q-NP{(#+k30L*VqMOAm5jEg6D#Au-G>Rq8k(Kc8eUQ!T!#k2}qMdwp!t1^$ zn(ucuX34bCZfzyFLTH~v+ZW|Szq2ajULTAnPkTUoGef>NuJpM35^2lets&33;i=RO zBU5jz82ro0p*Y?fZ6xJ+8bJ3}DpmJJ2cQ^L|FF_Yl1X`K4cLv8$mCkc5!hpJyK2ET$_YT( z2UJ-G!Uez6?rsHOTqbi2MSkV4P*J2e05Oi_Tmh=CH)67S^N!$Lacw!<8+OUyar~?E%HL?B$k|~q?0SLM`Eab}qT-NasRhhAq1KUlP zNm}lktS2j?5c=gLcLl?)(k`Y<3`8!IQkf-V2;edA{)G`faFIc~f-3pOV=}&rfWp3}zEtF3_qVlDP;(5Jeu|>%wWhTD-{0*DU zM7!kv8ld@EI;35l9f}eK`@-ov6FH|efE6S!Uyt0s^U=9^ok9iVA(ZalTJQT;ijr6l zxs_UNBd?ame<1KU$uJ*e5d^$QQa+Ogk!D!na1G2rNEYib4fOf>b zyKAbzNn8Jm^t4TH1fqb_C+HM+Y5C_bw>iV;vl2jLb`-s0)<5TL5PO`&YDkqJqB!W{kXBl2P;;#21PT+WIYPk^^v^t)A=7Q zdiPI@WIH!0S>CGyuz*gReF2D571~LCP(1fJW@gD;F=(sAEQh=Rh+V=-nq^Wt(Ncg* zG=ZH?kvz8ekC!IIFo!Mu_T*3$pzXcIaxwt91^gaI9@RmIXBHGj@~{C;x@I-SfmGe} zK*{?4`ESCbsC6{}Xn$4cpcpewwie;}tIU_-)cFP(e>dK?0Y$19zM&s-Xcj2^2v5uX zHpzHDxL}I1QI9M^|5|$s$R-*>3<9!wEVkMr&cEu0yu|j=ba?RPOK*Ml;x8G}cTFd3 z{`c7Xb%VVC)LQU>s*D#1!IMboIdmf}Hq^!^`^TOyePmmg26j<$aV{9%alolcJhr;AzI{R=+?aYqD-0> zR)v%MHl~jB8`S{7PZF(UibPpq4x1uPTu8<@Mn3LS(@E2k#`ufe0SAvGbg~?1poe~9 zoNO_z@<#~-*oi+YBGqRVGtq z<0I1Y795U~j3|RbG<-}|Vmr7X=qkzN#sJzemyXDLQW=cg`k1LY76^yHbdr|A z$VJn7xsl45{u*S{^x2WRHn2}7$;>v$;k}Pf$Z)U@izgEX2`I>YpG8rYxkD^B zc;BIww4VN|9I{nDPf}4#OFoKJ$fL%e592eTjspkk`v1hyJ-C9ldALZchNvH<{$6ZYWl@ zD<|F!K$faYl9(f9Mic)!Z^elN7^FlVa3V)asO545Q62C6rY`-m&d~{zS%}I2M-||vvrj-+h^+^~{yEudcT-^KAiny^%@UW(ESg z$n|jO9ZN;FDpbWT8TDsIB2zzggNt63NKS);Sp9D^?b5Gr@&+1hhb@PPXiiDGjbz*QkKS5hs zi(zJ_1pxSpq(bskFo1w?I;=zS0uR&4^yuG8s1+ukI-E(>2U<9kdMk!@O6tctl*FUg z$_Fy@P*Sg+Iu)NO)6Nz)!+<=zwvx#$0qEw;^@UT=q6d`63x0nZ_qWzSC1^`MKcKn* z*NNLmo^ms^o%G?pC>truHmc4WPy%h|hq5K%;kxAwU{`9i$C?h8 zm^t^OwptYH^+k=5t@{-^SudPDwfzk}0R7*j^t%Ry5vNP(A8G(py{$-;Z3-Qfu>CXY z>rjl4Xf~_iXKn++$QICu_;vIn&Q*(Cc1@$cnfeaY-1+Ll(FBYC3b!*IUyI|E!~_%4~`l}0FYlB8*VhXIY-_MUa*(aklV40(O=%Cpy- zKoxLbi8^z;C$e-+wayd)Or+C;m%M<+HrmNQpjd_ev)KzhXtv!&GP8{xmyWwCr*&uhmuq}Lt0KPT)g~wJW)T2 z$~@Q&%HXXEQ2G9xtdPkjav5Bs-7mc-;;GeKN>UmQ7p%Ws7g=gv=3Eq`)*h19xd!AA zntZI9 z&yks7%WH^mo!22+W!fDLMV62Vl6T6YFv17c=yCwGuO??mw&g>LY-yWqT(aCnh%!}PBdslevixm$Qs;>7CP#FCV**Fk@ zVViAhHN0jqF7^bpFI}>doTdTbwS%fl1N@+yZc=PN>XBQ8f2`!K=KxfUB*(z6_U7aa z9`Ho#t0WbY`zs(@@YlbgcwMX{=OSD6J((oa_moB94BfX$R6Xh}Ae(m36{@7Db=|8P zP&mQ=t=BvNl)Xh#`4K9u82bJiA2?K8VU$SInR}m?@}F*^$~YJ<&~B${JFVGkT$GfO7wk=y4A8QUFM+L%Zt*G9JPDOAnXL#3-d9GF?$F)CKTD zvYC8@qG^7h-HpHQ87;f7CzZnzam`KF^y#E-fxF& zlBiN-`TF1MFG;-f`8yxC`}0zq{7)?af#*`*2q@={EFt&)Ef3XT3mr0VZ|LAXkxsv% zIO3~Gw6jW#YApnQcQIX|D(;;?0a?mSNueT>JnxSp36%%zJ{^}Xkrp%UB8T-DHp5DC zzdP)zuC-AmFY!knP5(-?{i1_PJ6~F;hn>g7cuE5RzYU~i-S4N)rqRywkp`~vyhhbs zlR3irZ51tis#NyhNb-sf#S^;Rmy_Z@dhOG%g7HM(R6gtoyMgy?i5*2CWNV7a9G)&I zihs?saN1q*Ih2Rw98y<#*CL2(A8Q1z7o*(&`w?XPQhBPAZL?$_K6 z0)mP50EF_NT~+uC0Jlj-(zd%6fKopv z$UIvLx%h99N%N~BfKF-TAlQvCC0jTb5S}}nNY%DV0 zC_=++8C0cyAn?Rpdu7fn*KgdNVH zU1SZo;C-2@rN~i!8&zMUfL6DbkKCQn3#t#&6aP8r50%@g%WGbO-JnD+@^XL2a?Rrx zk~N_JL$Up47TS;}XS zjP*jcw`8jRC>s>o{N?S@3Z2e0hpK&2I(Z4jXs1vm-+64MP0o;*Wve?%*7ZIa-Aja> z5c+WpDK~@bhMTD}ndFE~31q^SVKpiQi71Tzi$&k>N!fHM^Vrutg8Y$NwTsH_#T~S8 z5X$~#R?dzq?FrX}Y^I$enH4%o$BxJfqgQ@=;_r9=K7LDy?*~?d1AOzQJGh#Ly*^$N9NR?aOiDG>pJuUL}D1QbhXvX5qoM>ak<8Hyaj0`gQS>`MJ=yjuk@e6OTDI5}bW-b5wh z6}Y7GRb{O^o|@Zv+D(BAev4}>z(L%q+;4#5xPP5W#d@bTvbC8rb>h-kGVROIE)@|h zc={v1=CPD0|AM?U@4=x@I?2T6ftJzaS`Xx*1Kv-j!!9p8CEvE|lVy_8b&$nv6;-Ae zQ5el)#@9ocQU4}d)8vgIP&MrE#wnRpjAYXOfjYRRUOagzg4RRBgOHmSisxHCsOnqS zWXcNp=-94hD>JAl zS`ZAC?yY3P`uYAlI!tA z>DMQe6HtV*^UYKl-*ZPUdcSJFB&OZZVCtivI{2VCWw$+&`LM?PpM)_R&sp}qtA$;= z$f3cZ<-fbfRA$R~7bWvLO}nLdVmMVjQ9#o>n}hDfHU}VVrs?4pFR0v(f7zuB>}ns7 z{%k$LBGVby%k|6 zXn?ZmBqKeMty?NprrWn8ZOmpn*9La&Q%G(@G5l^*Wjwm=G9T{Q`1y5XChewcP_iM> zRMrI|i^p8j+7zzqr;r&*b9;`f_mz!iQ#xr5eEi`kVbT79752l2-;Gzlsl}nIeF} zW|E3EK_V%cXpu;!zJg--A7Pfe@B0cLrqC`^;<@s0YO+3^DpPox^mwcmg%Le`I-FKW zW-7EK$3)qvx?c@qtqr7YM%fPEoV}l7BjvQi?^e|!pPmnRt2P{(#VVJBpb|PQ%1o{W zRij&UNV{nqFS+F{W-1lPF>Y`ge9~9K&x|Os}MP42o;V{%pQvM!3vt;sX20AdD8=Kssksl{tK^DC6lQ$yx#E|mIe=eVUy>fuN|7P0FLzdABRhcWQpfD|>__#5EFCylI0;MKYfP>I3 zj(a@<{xFeT?*lu-8ijmN1x~&Tm%Q}G7KJ=r9xDI)lu?` zN&ejvcDhX|?Eq*xNeXk^>g0nG1RtftROEQ=G09U}6!2C`{3qsZ{s4r(nG(Cm4Jz%D z*=^dvuJT`|_GY`{GFq8|_ z!J*8zolzr`=X7|tk;Ywv0&48o(+)6{={kMG z&6w@Kq){Q2E9CH*TTW~ z5UsC5r5zQ=Be2s?Hj~n&@7o1c?h!%CpK9Qu=Slu4AV9!y6ENUf@y!$#_Fw^qB1`f^AP2*7l-^EHM zS0Y=lWk-%2OJLZTwnIXo2@p_F)uUF*k2@nT&mC|WD^qo|=IgO!d;{boxK&vw(@r{2 z15dBqT{5XToP@DdU2Tg(=}*&k@`6m;ee2n>0lh;=^+DYwD2;mJ2Ew2IiD(QEBK=9MUDfF&+ zO7-=!5|LsVEyB)ib}DJ?0tdGtF>*w&{#J642-OIS!qu>=97blm3P3Y1&Puxnp~&J^ zvw;tCYaPqj%O?(x6p_X77ge|Gzy(6rI|}XM-arAqGs3P#Qn4m2Ygg4*i(+_Ia4&U* z7Hhgf^&!p~3?pT8Kh%@cnV%x*m)1XltL==M~+#z;Q_gKNI4M>8f5W$zDE^Zpaz8zeBI&O@$17e z3RRL6B`Y@a-i!4cx}#`S)}Fr;NmbNb|6{!N{TU>To{ub=ZYeJw^xP z>D@0?fa`>9JQIXx@cbQxr1T?PFw-_!1G`ddnA#U5X}i|Q>B!jLw75-l(j9Zxx*#RcbI7>?e#{MFRfIqhl6|V z1u3*sI-n5Zmok;n0EWc>>yKP2M_%1{OQLn+(6a}h*Fa@hE|Uq(;Lz@|ET3s!FNxgY z1)%gXl0U%CYtqK6zxn~%p%2VP+R1H^rOv7NRmjbEenc|KcaY6(kwP-92TG=I^e^oc z^TB#3sM0t4V<@HHT>1HrW14DEEVqA1D$4#HM}47slLrJqDF25Ncl4)a%n>ucwH8Hd z7e@{nNE%Bai|<}KoGOF-PLj&Hs%`*0uajv<17P>UiPs0hZrsC5R+|6uMET`17YiQi z$lZEm``M;2!4pOD9oESM*%~}x9?C`J5Qi!@3spz8$nl1Sxykou1S8A)3aNaE;s||r zPHyBaLglkfp~KgDxWeP3l(=yQWD%#4r*(LG@0nz(<`^`BTdfz*$(9RE;GjjeQY}iO zXyUgiHj+xhonwE$(PYk*%M#PKrlm}AM*)IHSGCKK_g?xVAwAJbRZ0yF;JJ~ihgIR4 znY0ZMfGNg%jC#!-Vx9S^5=q6ly|V|3v4D0`{G-WQsC*YncTPr7btnj$dP|d-#h4Ml zwVOL~6RK}ZNKFk7lck%MPd2`06^^fMyrof=wO_#N73A8$6w8mGpv@`tIj{K zN4C00Vy?t7X7g9Wnsr)pFZFP$S+-I>-AKSwsGp*IE8-bcZgiF8-5PMgXWXENUFS$% zu8J%^ZM;w@!Ee&MM#$3jyLmfyZalJRbVU@YN*Kvi8sw#U0S?vnF~^rEMCom$nTo{t z&$W13J}0dTX9=)#`$dwK2S0nFaHVg^w6n#1fIQsVpN&YRoxHz_h~ibhLUOYwR8?=0 zN@o1R3L2Em>vx$BM>-4DoGz)fy*Oy1g3RQ8RmfxP-?ySo$7>^-?=GrrpQ1!!y}wdvXIarqkAf<9`D9aA?EQ1wW}aC4&44mG zXiy~WY})PLdd3fc_76r)9y@y04I2}0rC!QRj8m8;{)g5+D8dWLB)>RAp4Y-j{|B?g zR6#yhDDCLHZ$5ys56KI=O2MxB53hd$mAK*2Y9oKHB>*A!cwDG)mwX4=s-}`>UWTi_ zWgc=So+x#Q4oC4s=y5)gxOH?5Ph?w6)u$+)n+ONbKW%ilQxUG|l*~ti^>EV9AopwF zDgh3nzSn7+Ov)!Z!=c7~8sEWF5n3*bv);S3X+#5mUjhDgdMDEMn2cT0%ME#GoA!I< zjUCBmT9mjw>(i-_sr3a24R4T?es+gzK9{+n6i{dN4$gCjO6`;nO}G%6lL@c zvpf#jo;TCFe*3kz%N?LHA9)1+LDiKiaMeJPhgyF*v-vm1zs7WV>u)PnX~RVnt^H%t zyl>9IdrLAhnjl-?7A4t6>w{a8Ez=k;ONo*>>=BLos=%`}Tvg^@^Ww62Cc4Wck2Zq~ z`bt;)pz>KLahe8MI>!Fd+#7|{A=_(jy*>Z;ufOfv(Hlhy-X~M~2}KaPM#{94X={K& zh;Nv9+e}i4YY0s>FFcjc6>_rGI;1I+UdDMLMK*)*{ETRkzW+b1jieg&S0)qg@36hM#Pw7j+KOOt#sqsh#2Ab>6x+e*22#MX4z-0@@?T zji$K9$R%LALPmCnN+Y01!@?7^I4YNOgwZ1pB-g*>fB<-TkY<2?>a+#zeQ)QYjF>QF=$q!^^N!~xA1D?{r-`{WN zFCa(o4U+N)aEb788l9$U;F=1TBu2a(;s%wW`&BASMYzcK%@C;Ezdf3oaaSUFzZo1t zHe03dHF!E=#jX`a0og`wdC12{BINK90Ro(~I=AL8O*nV?=3NWP%yrNESGi18#&?~3 zE3Q>YUMmk5b@ht?kJq__2PrceUPp4Ba(+( zYXa6&`3<@G&$W__?F^TVB`IYnO8B)M<#Za2_rzqX?({IAD3$jl+nD}A*9q6-dA$s> zRK959BU9$_lnk?(OgOan{qa+FSgh8Gp*}hvxX5dhvd#<7rn!l#{%}E+`{w=vfaWpG z?22qe2bj$!(ie_7PE}8sX&Qb|^~dIwPH~qEKa4(IV6CRY6ltkEc>C ziktyJ<2TF1^pCtzr0Uy_naPx1S`=jP|00PF#yzD_G+_xTKl)09gQi};Lkd-Q`U@yQ zz2g$?9)A#sY%Lz}(O@|FOgnSqm(LG=)3>^(h{9A_o5o3o?=D>U^FTx75Zzvkl2&x` zfJ494zE6Rh>8t9wswK1EVghKR^sH)$C+0=ZSDp z-Gl*vuG?x$!nYmkhLb$%1-sz&S8j8?7ycs}ziBDEM=efc^tWZ;Q0dnNGh)f4l!rDs z=G4i70uX#gN~g*aoR=~6mq;bR0aV~VyRGXlqBf5LNe_wIEf$c?A5B{_jj0! zWo4I6H8sdj-~Y1Nl0n+8&TFnkF4~YX{(%4Zcrs;Z$l@fbG8PA;P-S=iv-W%~WD7h; z@((@Aou~W@a`BmMA=5Us7vZw6Nm^rW&gvZM2|I0{`&6aRsI3zO6he4@RkUO_CS6W8 zrbfLk!bupKOxyDDM{RVt9haH3YxKOMCLZ*H%fwOf7i#0#XgAXCGq}KK3wQVkI($rq z2O~%D5!o~fC1~hbe!z>qdJVGagSE(~^d5ze8lxzF%a~;)3fI6!2V-|Y#M246L2h^V zcxl-&lIwj@G@pH`B%_<6P#)tBoUl?QFY!Q8%6@g3++ItIZ2ArdWUBsszo8bz@_Ko{ zLc6fnHONxoBE#N57XNz9gDZd1w=9a{^+EV(G~Fv3Z&X9Jz>kmK+xmGZvW4td$P}t< zZwqi(O;ULt*i3nI&qHg^%SmnUM7jM`%`>1RZastXM5)Wn64MyP@a;H0TA@mcS=d;M zLb-YB%e4LEy5+&DBahN0DMO)bwMnvUz54?SDEpYIOiOP7njcAC-ax0FeGE{QnS95} zESV_8654%ojJ)ZOEaCxXOD&5+ls!#8rph$m7j{j4KRTxC4|}5)pR_0>Em23#Cnd^M z+1|(hMn8pQOnu}QGIsP!Ei3qm$V2z7iK@pn;0o=>3aKcx{8<*R3t4F+nYix550BqH z5-oir!bSBGm-Qd^M=H4nFf5aq^p+MX|Jl|@(Xkr>@E@u$e%xE9O_cwwBf##}SUE+Z z@>q%r-`me1Kvm{~A~6^1KlS07dXMBs7p4XZcuLxFMpD@nfGmR|mwdABP%3knFaJ^* zg%UI!_dc>PystY766)?IO&<#o>l`M{7Ugms0fp$Cc=y8I(#TfsHkE5F1e85Rc^aN5 zzuQ7a4ioScej%lAtNMt@CbT)Lkd)(A|FNo)hyrS!%TN+x(;weTy|ei>1Du3fZP!a_ z$9EsJQDwW5D3Lpxbf135Y8n5tAItnP1A@sux+Sx{Xp-@8UuS`~^v_~#Nt=s9bq+;7q-A#kyXd2z3opSS|+HP+yu3)1YV?KMl|}a`g}Ha1uM5qWn*J@uVQ4G&-ACa_#?VkH`RSk+D2zvkWQN?t0>fGbh`h70K0$= z6GIX7=Ct1ZKKW|uH(w5^A);F6!&Ghb#s7xyZIa8iD4E9@+O302>RlyybHBXK7f&N( zze1HMT~Tg5mc|KiQTZ^dQKD+6h|1yQ1_M+ff7y63`T9&R{HFrCymZ5Ck~5DEaD&Rd z{SUvceZcgqAJq30;UIpJOj2PE;Pp^cD% zpS37l*||wJs^s5Ln844EM3^&<_SPVe_OC6WDs|_YMp_hWj+qYTQ(rX+(f}xbk@vo; zgr}mNwC61mPTk@eJGnga@L5g=c`UMNbWrtCNQGo%73A>v(?W7r1p!a1aReR0E1(#j zA4HJygCANzEh8LLpx2UI=kAOa|Ji7Km&WaOF#08)3W*k{&V6B zbEf`rJaK-1Z>R)=duZ3OgKF!Mqrs%*`+oe}#vIbF&N{e2Gf5(;gumR-fP%CrTGOBY zY@$UrP5;MyBJ;1ND3)N*qM%Zr+(}kwnx=t+cb%11l2Z*RpyqkA!i8F7D}N9W%*CCtvaOac^{&`{#AU z(&Z^n{%T#nQDqY>^e2PO>fS$|sdjPr9D0t?+`IS)= zL2tkl+EHd^Gal{dCE#fZrQ7{wB$ebD0m$Y*`BXZ8bwhsoFVZAt_C*eDgIYS^%_Nc; zZQ!73xYfq^_3ga`xK8YHpHw2J3dqNEE}hc4A-^iO?=K9OjaP>Il-43kz?lV`jj?A} zbtpeKgUpy!UJq^Q^4(wUwf|m9<_{iliSObY&uQ??%G@Wp+XpTQ+-0L#Ceynjx4!q3 znOYRf#}iIM+0LO*eJPVP{#8>%4&736n;VMeu~(*b&GS0^FASBL%Z&;sjPIXmQYNzu zMp1m;yr_^&>yG?<2Pc#AmKHh`RB5+Dr?7?Zj%v`nq9+O^c$9x*{-%yVxE_rc%4f4~D&!F8J59ASgn|AvuAcxrMzBQ5GYETHzRw!1*+f*eD zL2=$WxBJ&0dw9V$wP(gLOUmXMeFIULY8kXF^?;MUVv`=P&0G0x5DE~2>UyCt*+~ET41aszfUVYOO z&!W`lv2?I>fvbWxC;WTj%J4}>sxlSs4{ER`ldAX+H25#^e7;WW1qZebM3ICS?ouU3 z+_ljz?MDqK`tIW&vr%u-pjY&=Vnjjv87K&jpB@Ntu+w#^{mFr((~MzHm*yjJE{*2iBAP6E3Q~ z-ex4_17(n#+gxsPNAY|`IQaY$cVSFbZ{Lk{2=6N(n>deLD4-aX9~fujf5dB{OqC*$ zw4{zkp-%{ux+brjA-8nsV2ny#`CaB_f zkv7^X7p~iQZloukjr(ubwcaR%_LoeO@>DJI@V!V?Y9BmNd85^8zEu;22%K{U#nMhw zNNJ`UiqoXCzrjrd7YSY;Kc-4~d}(4iHvte(kaqLU&)t!q1}^hBPrFkg$fNo>GV^R1 z6e;8k?Urew61og>LqW~%#|*~*j;_20uBrH3Wer^MLLAL^E93Q60A_SL`r_n}Pr(#LwZ zz-u!Lqa>!@xj}i|n*v}SdGfA5J;AvGjNb=fllbrhavgOfF&*||0o-#k(S4bu=?xcYb zH0Y`+yrT$L2pwY*;!-VXEI^UToqlkM$C!Vk6|1!8&E{?bo{eGT#(VpR1PE|Ni-$bm zj{>?TkVzf!jB2fsZAte>dkH9%X86n__=zNJER0LmPrvPg$# z;$`D7Mxk$On{xP<2C#*Xqtb&sS@(}kmMN+isSxEn&ni8fd3y6e~_rG z55a$;%t0!qf2!cAHA*BE%2H zOy?6avltf$D4Eb?Z5k=Xt!t~pf55+HFkIyJ)%`a$C?N2#lKG$6pSO{`+|L_Nln&G&OM~Bb z@7(|QhWW2o67h`0y6s9C1h_~X6t%e`9J=j@dP{>WhD!K9%d1Yi>ROacKeqJ?K6rLP z`$@)r?gl`_|J8=vB#qgu&*MMi z}@>5oydCEohg6D8A*xqC5VVuubM_`jd~sB1Rq2Lo~`y)NyHAD&jnSd#LZ zirxYWr5l_|yGN^i@LyGBNgR`2)xrheZPg+#-=7Y~(vtp;02kFyATM~qC7O@!TgjAH zkW1aQ%LbNngM;WF5>h@Sz)ir@^XOX@5NdvTONrb+SP$1!T>bcEJsk9dPEFV0{~O#Y z&A1$M;T0d0sHug@Yyr>8YYUliss)~vp+=j)*1gN({{!B8M4Er_#M3Fi%0yMzC%zgu zXg|9e{#iZbWe7l#Jj#0mn8<98tKr#cJ<3k~Xa9)@M?dfo@XW;LkK3$A-ft$N1Rq<6 z;=iimx~LmJmBoLJAXXXFub&(8F*L0z;(yM&-L}lMbf&a~i~N7FGXA4pgI@k<)~>%l zuBt~K;`n5$tiuJAs6!G-S;U7nY6@3L>`?p zELa2#&!4iA)~L7qkxPY5+e@Q(2bt|NJTsx;kEb43GZT}Hl-Jb6f1u_TtCYO5EK1N* zx)_B2qFO6Vq-{q{0skGEriW~#bfSkAd2~#S?uP#{ZL_~3Y*gKB2v_wX<+wyTU71=1 zdFb9tqiUN6N~C++L{jE2_^%N}uS&isz-yUI(w2I#XRro&O}J}0Ut7R46Fpmg-~-@m zGHJ0j<6B?kXk%8Ypl})iMOjWe^OdeBoY3$n$>`Ja&6;>N-&?M4X(iwZ&oWSne<T z+uZR2;|2(DL8~ZAx7IKGZm2*0+e958x(9R}IrPomAp%O!>s$u8wJPikLodjbQ_~dE zy0IQg@U~3L$w;64Q$i?<9M3?tt;Z$)m<3Fh*=sGuZKeY|qQ!rU?+b74{YZxbO8=`c{N0w`C}4?AR&KZV#j_f4Dq5Lt07l5%>HeoI zpK2r`i}sy+RZytVkDCa{M}X7Ev@^%Oj-u54!^qUTvU!Y%XVUNFBkQSP6woJyyjuoE zacfhtjAzG7bg-@UM2?V)WM)iSb-1ok63J^`aE&%F5V^HEV4_z`;XkAesypDD)nUm|5vM0sKnZGDNM?Q`0Aj1ZG8JZ%S9+pI zo_=nC?_QGe<9vK{czVJHl4C`9Rj=nGKmaQIVIqyw+~Fca&n+=l-Yf@|#tja-X?GJz zrmXE)3MCS%ZHTpzG+zF*--oklm)-!+L|8_09$XV#IYd*=qklM=xn)=bf1Rig4$^7f zxh~OZy$-p!8&JF&_nD;&o|0dckQP7eJbO1aC6Sc&4buS#*hfmIn!t6w^$f_Z#*8?| zCiO${ek7SVNsnjc=I!q7DTr{;_P%6&@YTo@Hl}}Oz<)}pHRE24g;|y$k3Jc^Rtf(r z8V~;pK|!yawlQH>LfqXh0vxoV-Y8z>Yh>Dz@0RYmdOs@maZ>zXV1$*b z?*u$0p==#*6lTEQ^VaLFJW+sm+m=2kjNsnjA}!{l74dAkKO{4{Kp14@7n%l_XbXE4 zIm#JOh`>GvZhbqU401D+|EQZg?4C1IC8sn&ku(!785MGGMLe6J=kLhfQ9!L&GPVke zqHX%_oGzQAlB`ilO7i8N$WeY7)26p9JK6`XTcS`^9?wS8L|3vG@R3#2| zL+OX)zdE>J7PDmBoTP!1Sh{+RfnRkL;h=3(!%eT(3V2%Oddxf#WucvXcVY;N=W&Ik za!Cu10jJI6oj7K>UI#_`^wMfyplKM%v~%YdHFm>OTTF82l65y^Mr`OIc>x$pJE=c3o+6&!Y?5EW zb;Cc~m7zTRwhXfPw7ke{>veb$+;7s&_@XILt%r5lfz_g#VDw zpc~9(H}a?w&N~h8-{;$Y|F$U_s* zu~nB-7AvLegOQs%if33#GN~<|jbWKWPJrjT)|9$lr6!X+nAUhU+D|O(iUI=fkj54$h6jAhRjFUD{;1le1!OCG z$}G*)00LasE>==V&cHJgy4|7E@kVZVdist7J>l@9mCBv&B2Ye-{91$y-eiu5s&Ei{ z9k~A}p7UWRhJG1!b8!MWtImGP^2#;&i4gArBnP29UQcsLgAvBe8Tu9RD(?9c5guJ);&}Q z7xcH$+DoYPeF7=%4}@Lm`9{)gvGK4UG$_^qW=`ni3kUZZw39cC-5;~044#(oH_6xF zvMSe7EXw0)<=`Y%oqp2BEVhL@JO%wqIwdv7Q}XzW*%XE5H$>R|U?#a5t_qxx$*4}q z5%58nnab|in<9!4a*B5Gm640K{=`g43LD`A7x>kBCx+zb20&MHfpoWv9;kAYG`)xa zh`!#!c!esNa`=jSCW@Wm&PD`>6fD`;K;ngYH8UnK(*B z(YzKbl*XVKn$N7{SwFa-Vi*LM4VBPri%gJLy^A(J}c*))pc*$@;^J%;3Plt>i31w1k0o^852%J;8R3odf| z=05F|sd!epen(BTGwyg*kEhn%B<-mMJHszAryB$C_{_$b>TpFXBePlBz#(WW?dHL* z>4Fs6Ntb807f>>7>81k)zxdOJTWK;$+mgvI*ByL8NxOd6#Mmz@qi7-LX%}}}NuS?Z zkKny@O8dzJMH*uvcaCYkohn5lA5?@&+wjNOlV#wV5jLvigB#OiN;5=2?3zT|>wwp+ zbnE@OFX&LBZWbnw9381pW$Xh7!vckuF2*T0gHeJjw2c#B=UHd3#3!zH6Yz9|U8Jpp zVcnbr^B675dXGE@uQ z%Li^>dsh#@b34iT0{#O5pIoq#iuL~AQ+}IOCtw$OxiWHVlR?`I0VS#SP2cJ*JB7k! z;)n58Gf7(pPaU0DQEa&3{y$~l(C!{pTRc%DVQ2#RpgxM>zC`! zlWk=BqX%n^OnW5LP72#y8^zK14o{?mZIK4q+@D+az^u?gPQLgeo{IZAnW>%djD!zl zz8(7JBdRhhX*n+BMZj$Ct z0JyD@nOxtw@otHvocsZv>kS)~Sc~y~B2Xcy&+YD1Vgs9Bu3?pwv#&!L$S~Oz8I6 zrweDiRNE6(50jC-p{aG4)d z6*D!z3Mh@rH);FB9iCs9BgXkbLNP6$Gz1AjDrChpj^U(C+C(^8vOeyg@Hx$d;-KlB+`Nk~T9d?C- zbHCX)Ez~F^(|3*Z#S3vhOLIsK{8wIwnVO+cCB^RBJ@Mr(72q&bQL3XT8>zW1B8RY? zn*Tx7^bS?$CNF=pv7Z}?(=MKjYJ>kmcrTOY%NG)loQSxv$P;-y|1^^$1w4n|Uw85= zUurRWFmF^`Y^amPt;=bGW#o!YflB+KWko=!J#5T`O*0#cD2`a`yOr~2On77F z5+gPHec{k}PlSakIXuEDOIJRrqQ?uVl0rVIg8!n%q`52BMbJ*#`$3R^eB3w4)lrm> zsX2>0tt=!XdqDMyNg^p(X?eW;r&%p-ibc)nhwv^ z#Yl1za=aEn%enx(2-lBeN#1AxP5HIdDAD)QnV-2Wo=4bfWF94Mbq#1bowHIEx4I*W z<~8K5LXGi6M*%?Xy)rfFD|}EQp<6gL3RM@%!%kc(lN^ob)4vl=GNl!O7OBko&H#wh z$;hg!W!j}32tx4$XoUA{R3hGTN6Q#8vloCe!>(DFvFZJ{-*2qPbBNz6B-2giMA|94 z^nmEza`*nOI@ndI?c8GAKQgUmTCd-rCi=~)ZU%3Z zhRZ^k4(Sc>|5528d8r~aLf8A`Cn%BIuzd$N9!Zg{vO-NTRN{LlE#%GDYq_E{%4d+* z-B3XLEK>R17o`v|E%Saq16t2XXY|V=94g%*FE;Y4->0q@UVzws$lIOE=>V~nCkm(( z#X~6I!z}Wi7Yfn0n{z7l!CS7#r>B{uZ6xyXXsSbg-a#S?s?@Yc832_IQF9>>s{Tfj zp#ya&PEasOxg|VnmlsfoTjdsSFHKcgfO0Spxt!`%a;@6`>wPyv&RP|7$nF)dR}at$ zD3$8n8!b-BOxiWRM$oHfYA)4-gKNh(C$HMD>&(9kS3SuLKF~wJi|Ih}X?4KY2hZjI z_Gj<*&>@?!P+<-!rYZ_)BUeXJT4hnS8+lyLkxYFVf=ju1bcj?ar7S3mqSc9_JQx2( zzz#XGs{kS3GMW0eh&*L(klf-1K1s4Rqo^MKpo8@jsTg$M(p zNG4BmgR0h1<|msI(x|b$jpwO(@z#TMYO=1Ly?1hcR~Kl!e@Y^$gni|QA~}r@rQL&1 zDnsS_QwB9DKUT(bh;Ki%kyJ?D?tp9+k1OOXJda23m2t9_Ol$+_zmaHs2|)d;Wcme# zWafjT3z`Thp3tofAXXT2RiSOZ9-bQ{GQBHweUB#6mbj^vv*?5pxwU(z@7y@rB_8@A zn5y$Zuybl%A1_9zINw6bDKY!stD+ZB0_PqdUm*EI<7xuzTn=uwY@)*>XZ(Lu3MQ{rgI%i#ax_Zx`Yna|-2vd#>;_3?rU5Fq zYOlWg&aAhBP`p4R8LUShFP&})NlSB-LWeC$5z`IO{c=2uk-d=3WdWIa`g}1fp_BHMgaj2_|V4Fw1Q#EmwR(!DMM0K;;UnIrTl)+7tbxJFHWyM1AhfNX+mnX=XUe|_vh zbh^yK(%(Z4aco-nmWL!2i}BLp*L+cc-lIm-$|8zW|J^N_B)2vOYM7{!|EYmoI&Z&n zP5Qj{hv~_WQ)=P`H6WQZyy2if|9ahDK#4lvQK)47?uBd}@6dF0-;Wc{lRV;#g36vS zS4C0EMpLx~`K+PpaD|Wld19i2nW~>$QM3lp<6QA&Qm>0`e*U zh-B8nuEQoCiG6gr9g0&ooT}K6NuI)FW$OSDb^+m3-Kc_+RSl;xDOn-IyT6kDGyb!_ zr>s<&Cm@^P)qFviOikop{S0`HiYF94r~r+}w@D!bM7*dDAtckja04Lrd&)dY@{#HN zXIrS5=Yo<5hMMDl88&p2L^5S#*UBB+RCj?!KkA{Cq;hXwMHHp%geZk7)7fSQsH)6Q zp(bXIKc26`h)8N4)<>?lt>inrZ)vR2;Ea@CMo|tW`AbX+@&P%N648>xcuA9={@EQaYKprP+_(o z@c&Zh7MVH}cH?a1t0+mu+X@Saa7D{c_sb-e%{~BJM&FRrH$08|qYR2wJBvIbz`?cC zU$?@}-Z7E9ABg7?Dj$)OJ_rD6Jf@Xg9|#BChs^e1NDUFCB=m?ryCrT6AkN|vJ@WIr z$mH>x-+)SZH|$*jm_$-(fx-;$C6atv19^OtWh*%k8UwNk0&*GVX3C^;v%Y{rbpKiv z%E!AIpsIX>WJn`82<2)8>QI2oL6TW(hFm0hr5dtT9JOH6T{8<&WQCgj$ki!XGAmZ| z#l^3*HJ|{u%4I*?|I{4S2F1|n%B+cc8u3#t5io?3v>bcOfC8M`&3YN}psg zpH%{i=5(BP!QWPKouB0|iqLq@wvx)d&UlUvRIJvU8Hz$>SD!KXubKwis8X&pg`MC! zdiT9dX3JdVi|1)@QORfpKwslP{6s&zxTd#ArhMWIKystIb|NW7}0TnLgJWaCvRjnY;h&N#P5w|#2hXAFek|(76emD6XIy&)b z;^%;2J!5Bd?cCYj*{9Zkky9u2aVdA7$x|W@1p#o~V(9_sej+J1!!v}j3YkzJd4$_i zD!B|A5k;t)NR4H6Eo7_W2bCbyyO<{N(%a6^e4R5+PBp(dw z6HQWHF2K(9H-&}CH;(D2CH|g~t8g4f!s^l)k|arRF|2%sSZszj6H~&paY|raByirn`(Ockic@84qUQS?-H7sY%`J28}o% znHuFxJrt`#E#z^(A(3gHjz97^+D0m=(f_P8E6P=rEMVHD1mm&st?!aY7e?Q0Ap!y2 zf6SmJ<(foBRK{~u+q(C}Kf52$PI-F%dMXowP0EIjVi~?jr7Cq% zD#@$wrji^j;6G`8QX#K>oq10R@qJD!>cpC7ndAEndwltkN4zJSXfg{rKRXCpJIN?$t7%-A-Y z+2`O?-^|hX}$jip4B;;8tac0U{`jHL`~`l9g5?$?2wZ|hitvl6;e(y zXIW@DyJhhwzudZ)Kpw~Qcr>mlqJ2CWKB`Fo5ctj>YLvuP-QUk5mGzz|S^2r2em1l3 z+Zj|z=U#KIzK>MyedAek(CSDFttsi0w~8p9Fyy{f4*RG@JXOggce>$)iGE$*yOH+T zobg9R_ZV_0N+u49rJeCqHxUpFV&nY^$(WG=Ism`O%E7?$Uz~Zc^Kzy&>uP$&wV!?p zeSG@LhF5%1v~QRrt^xjEdR$PL@z+V2e|ZC#NXFJdiKbhq9PX$`Pl=R%ZCUnRBYFK* zCy!MYl3|UY5_%k2A4=sD6lVa*Rmi7J25G(igrpMtV@(0Yt8pi#Jv9EoOd9)Ata&H^ z_bv-Ir6^Q|N07;r4rO#fsrawBm7rXx0>JeIDW^t0h_X;psIr;J#MW(Z%iC*s?JdLPd!XCal|$RjLPNO>xXr|%bRr6Q+Cc7o>hC$ec|?GZN>l9sKFU?+^RlBq+S z0C?4Y?+Wd{K^|eKmE_IZZiYdb7E|=c0_SrG#9!#xJb@G0DGCBE6@7x%R)u z8nYylDI3O3n*8~~AD)orWnL(f*QDbSj}8xZf?c)WDVw9l!J)y@p}v6MJ{zs~E`8@w zZt32m0|U4JxABAM}~h*Eh+rsnZ=E4f7kAauBGqr(#~7#zOeWNb=TEB{PYn?MmkL^XVkz_whU~JIH96NjK7D-fo0aa{3@6d09=U8eZhJ zny?eQJy1+32?@bxlBfzFQ=$ATGZww?g?xmr24wRb{}aiijdkI5Eq$^xRLdpW4u6nJ zX8wTZ^6KIZRY15x(jOXKHM|(7(a{Q3@-8O;Lb;ZWzmHR>iC+J~pw@xTD3$tmFa5B! zxz4lh$k`7RYOFgOqtrTvkj(g0r}y!!H0lvGzqj&$gZ>8_HQ{fd-Vguo8d*ccGlX)V z#F4!Bnk&lBHIcOKZte`grRwJ=vlJ^emf$z=tXGmGlBUbmb!IqCn!3cx zd0oc;bf~c~UZF~PxS{{h{fwV~D{H1JO5>$-cfvw?=m7ZNro6Rh^V+Rm8S=(^OOE`# z{-?bP6I;5@wn-QN$%<+S!21YQ{RBLtwXDb-0KJH8E*oXi@+L}BdzSI><)p;^(9Fsp z)9%ERO6KY5fpG9RP0GLdiu$0}wuhQ%cdrfdR5+=S{M>*tzmwNGxFCl|_X85ij1MO! z{L#qpHpy)$iO1~o55px=re>WBH1%T0d(}`h;iVg7%5V`Hm#KR)NiKpzKV!&@K-7+; zCgz=CkyqanP`q~G(#=mrI5_!v2esHiGPwgvXMjx7vb4PSQ8J+lfZBJMV+-ld*khZoF;Nbk~uOD<6+__x2@2w=~>i~!X3J|APfSoY-T4D^zg9e~S)=oV%4HmE6 zG3upI@eZJ{!IAWcoCkT#8YFt+7EzG>(5J@qG*>muc;oT<@J8}#h$(u zyHAUjNK@+F3r3QE2`H^Dk4UCZaT0twFN=_=*&c{&m5!5C6dq`XpJGbMA#E84x(&Nenud!|xi2?c6)KxVJl_b3yyh3Qa9>P{Le;}+B6Qx((?}-N z#>c6m&x$l1#qGh;N$c@IrLoNF*~>*N18# zDF>ls&10D3*Sb0gwSKcuC77zAJh`LG&F=Isqtr7$vcAOM#gB$WjsIwr*&zup0W`-nK( z`5V%qYADWlk}LGc)hsNWd<}qa-=pay6E}B9F#^u=S{2$*Yxtc2CIIKFA{sNG5sLr{bqUK%)$D9g61Mtt^1X(OK92`DOmBDcdDd`JVty zy;x?cxEM$%95!!W>-Y$fQQsp;ML z0ja!(QX3FW(z4LQbJ!_n+wBEAA#mtpl1av_FTK$8B2_p0qJRpusy3bVl&YJrmG|&` zGnN|Vd|%hv-#t{)!lE)$|NW$2hX5Ul@R3Q*IQ6l&0K1^&GF8f%1_Fws8=ojEYkGF> z^;Phpj&*+Kg8(|}}jy@ob!AVrf!DB0no;4#$ z#@0hA=s%Om!{wkEl1@^wWIkHIC02?&_1(|_l#25oq;jb1w~>tdW4MSH;s5P@`Enl} zG>gb50!r2*jyXcUF9X+kW@_R;uHMKAsFzN1xhL#=YP4?iSb5LXd02}&5_>#nXxrJoIc7VEkAhyXd6RP8e+gR-iW;uyeXMX zyB>Ql0GhXLd3`|J6>u2c6kBCGK@)xn^*<~^e36z z5q4#kUi(mwV(HrjlZoX3kIiZ1IagiJ^JIpJnrr1;k*h{h-e1HgzZnTlAhcmyuX=S$kRbUr5@tlQZNXD&}fXmQ}GO3(-$nBW{I;x3I6i}pr4{!Wj9@)f? zW2p)6j^erQCFLxu#cHF%dnvYw-Xh@qUKUAZQ{_p)Hj;~+oK|xmo^QNFjS@A?^Mh1s zq{x*GCYVY7t;6%_2S<{O`q%(fo%>nIC%%4rXwAwCg0Fn@W(6IfFXM{m8kkA0ECW>h zMWVd7n}`?TUdmJPO;Et#6w>2*H#n6A8MDn@;F4MDQK4>Nco@Uz8c9B#`#zO}_4MMh=iu}i*3L=UmZl+2x zCtCQY2)nMNxut;m$(9TjAg@9WC+CWjK)Wky^-TJnN)uBbgk0x?N6#Bza8Y0J0_~q{$3}Mu@zh{^Una}3b;=` ztpf+Ka)Zy0uAl6NVu-qZbod!xP@&x@l#cUj*O@IVG(1CLF5|Z529%2D=wl{YqccdR zpBq)f+XaC0cQWPZ5h#&Z<%9Jx@oTC;9-}SQloz`0B{>> zCYgD0-q5-_sLFnNgJjC3%hW_osN+@H9bVH?X&04fGG%P(Q&wjXP#mvLT?3G#?g5#q z(Ed&+p6@C0UKAY?&dE&yIDP)qvP&Ve?nxO37TGu(_#f}!d7N6Er6yx`5a4!;*~|(_ z;;v3|DRHsK3a$W zLcBwMi|43ul{w_(9lgd~v{EG>sR)gK1j+EWf+)bn0F|!Zu%G8XqA?}>N!UL*K5}(vWX_T89Y)$V2Ree%XPCEl=d4pe-|0aV9GY+;w z2}V&9*BN#J8_Ah^1e|AoXbiRg7eke8-^j8k!pL~#T=nlWImHX&`x#Uv{QZ$DfT|bh zVA)v-&*wh+^kZ{GKiIuZ+D0S4ZVFSo0eHvAe=9=OE{bGWcO43H9d|T?)}zVtwO>xI zzNO57%JU(~wSWK!D2eDQ!tR5Nw8NE=kKnaRQ50sgMeUndTaN-7g_5-W3b=1mXlH)Q z84mTXQl;FQ&_V|cBe@y5sv*DkY-DIvWNR5uRs6m1*chusRa$QpV=~FVJmB84+xOv? z)RSvY?Wq9=|0m?FcY^$@yWn{O*F4$L-vxz?B6-~t9&LY4%9LeO+!zr>5`zA+QT52# z>t`EzwV6=t_mi|D|dh5+wQhu+DDK1T-yc5Cl;`5rjI2n8Q{B8r?UPZNL8b z-n;~>g&LXi<0p!lM@D-iN4;_TGf5__eD9!%pbtRBea@w=i(KZ~$)AM+^Q zMp~Mn7-H8fIwW{bp@VW2rBh{}#G<61n^qU46R=hxX^v5tapgS^IDC*oUaTviM244d zkj$890N^!0gSoP7)SPbwxV9g?Iz^%8!p!;tR9;K1R3)x>$r*sh*l_0aw4Fh86@ZpE znL{@3@2o>9m6?)AmFa75ltAnqYgvDil;i*0ddF&38UkXUbjJKI^yj6S@~PUoz81~| z7BCdg;W8zOD&xt4A^_bwl2O~D=@8zxHK}n9!lo1 zom93u!%lq3MDs`y|3PJmd=1ahB!YRa%{^kGChSWeLlA%pX(S)E6p+U`sP=M+Dsu-E z#rq-ieCUf8<@|jT6&o$b7QOrGEvn)>3s6-LQ*Kv5Y53JVC?~dqs;Z6T6&I*F+`4-? zg=EO*bww0U7)HC`@=)owD16)mz!<&*!QB8r*K4nhD*3@cJj-)YitSXaA`Jq>Nf}g~ zZy*A2o?|5yg}gj25YHtv8Mx??#rBC8?1q!_t^u$0djFk9wW`;y>IU>9jeP-lPCDIB z2UUYmQaV-<1?byuw~#i|Bg^%<$IWL$gBwC6ULiRhnra4=Mvc3is6!r)VLwI4*REM; zm-Itj7Zf(eN;0{Je)vb{nC)sC*p)df(asvV{+l6NNUlUNx|m7R5ZHM(JWdBG?&`zn z&{R2VXJr(lM=IqNZczF3{%zg&^L9>fg+tXSak!_Uv9($4f*fCxL^IhiDt*d^rw!?Y-wd91r!Vb|aS9gLyB&*|cf(h)2E z@QA9UUjk4f{jZ8OD{gsrA2>}-AcsM0d5x4$eBt8Sv70Z@DTQNz`N~O@%zIuIDPRAw zZ9P}0bQ?*Y@$PU-q2?l<$Fs&u)kMQmYLq{bqf>~?)MfzMKc-6hUybsWolv}~HZrXN zvgwvtnLI*|7bc7)v+ChFD(#57y>8VZs?4jWCP|sp+;0I00Qy)-Ms`F&wGL3z1w}f- zd@j_$3-RwfBa^&sB>x^#EAS9i=bfN&K41+lkJ4!HP@?4xsC36HX5(QeXa+}675AzR zB`7=X3GIxH00;x)m^oet;5Gi$a@3Vyy`gd&dzTtBiqQHWh2(XA0AH9$KAq$O7}_pO zBB?0ygH9rz%g}nm(!+`+yejMr%V<298lRM4P6@q#`_7H8JcNm)b(S80p^6@wdXGru zYGvfn)qHn#v`m#u>#2n^-tXK!NPy-t$GHAPwh!?|F}zlqnKHI&BzfhEBA@Q&1eKGESI=l#F9$(W?Tfo4i9+fg(&wB&nDcX{x`!i=cbgO#buh`G^dQ z!sIxrvYwdZB1j$+kx$KSF;91Qb^_2aiDdBHj9ulCYmA9B-wV0BWW}`kf6v&GuJsl0JTB9+NS>($ zKS?c@+aj}U7HX`^+>s-2UOH8mS^@2&$xPR(N2Uj{0-tt2~o&JMGZJW>M=b8T{Y2+QuwMai0`D&+ZcZm&N+ zUlxFFk%ipmBn-=>#@GoZ65Fi$zNP?=L$o`BY`yL)(y7rpK>Wl=P1@Z{R~GoAIL+fp zM!sIw00)=XqNU7P&Ui8P#;s2w?>WJ){QK{8t+Q+HW$ctcB`Z6Up6W0n|B9yXdV=ba*a9zYTw_2s1~=zH5M{(lsi_y30K8W#NjJ>2Gk>B-(VVwZ zBgK6pB3td5W>ZhR0Ku=-28F8a&=`6=B4wlXTmuxzt67udG|AGzZM0Js*ASrcXfY?$ zOeXa~LE?83Rb5b|su5)5;ytvp@iV-D_d=*S+us$czBW?%#~*giSETb*Q3_)1JEXBE zG_xf#!4rV<Hh*N%&bEJ`3jjf{cOGk`$~fN-r6x35M@`0$ z0eAs5La9+Q`M8S?4sWLMUBKy-@&k%B$ige0@Z5A-qMdml^i{*jjP_n5jm&Xxd21gX ziV*n0!i#C|)eq|U$K4cal<4oEF}xi?CU%CZ>Zf5*Bl|ASpxvD|D3Z(cG}`uDvxQ7) zCLo9R7G=2;N=yI7mT>dGW~ydF)%Ki;Oj@*=+%2Lgu5HS$VXRfYcECza!dpH9AXb}r zEh*Ea(DI`@3L2G8RTmVk=k787@C>1yoa%wn+iWAH_@h1aP`S>MNy~TjXa=N^v7UhI|B{W5f9mIl5{T{h8cCUo9QuX;K>S=HZEx0U^W}ac z?G)2mI8=C}mph73X1hc(u9FwgGJ|B&+s%%fsfk%xSC3LKym9DC2IVJzL^G;~0F}!h zB>(k69`PNLDUFb)X#`1AUjT*EWjl zCO+CXf7|(wD-OH&pdFs2pCggX?2l~Ku27ZosEhmhM@b5KyDk9VJqokUMKL;CN#*A< z^@7QSCeV27B>%44a`3Mkst71S+t?9;<- z@C}8g)k0@@PKc()7TiRD z0R4QKWX6=YW*)il_ufb0XTPkdM?u0QBUPCf`{~f;4mBCUy_}%ZSE<+ZPiiuM_CskI zT5ZpwO5Wv-7va1v%BqkS8)JHiu&W+pJ698Eo1FCyisO5id>8F<_{%On-5lui&ktcs=W@n-?(r!cV`iXu20 zex}(hg+?nRrKC?>1H5&{b znE`gSo=Vqiq8LKO_Wo6Fod398!>XNbk=Do&0yI85WO<7dJifD1N_Yt+aH>5znhtks z3s7~6pei)d#2k-@c%fuLB$FnEp`X@7sZ_iWekC$wa|>Y!xyc)v zwIod)kSE|@l2Q}oc_Wpo3DAgMea}i%S^sJ6DxS*tlPi#qaE0VV6r`W%hhhc&F-_lW z{PYiAo%;TOtywgt^VFQ)wf%JBH%8N54?Lsl4XO?}LnVk}#c49hr1`zvbYD}I^sO6! zu1U->4aKM(OQv-NJ||PU>3n@_wp*M@S|U>{vV5k4fZ~-8woG?{s_I#p41EJRJ|lUs z98l+%DRn>CdFQVQJEMa$;@b%*NQA~WG_;Z6*Li%@%?^Oi>)(xAGP|AEu>V%>R ztUvCJ4WE{maM0F*sU+C~7V8%iGcLJ|D$Q#O&d|KV9q-6ww&m2Txy0KZGIPyA7o zNsMR!46u<@xJ|C#S1Nxm|+l7P}Fca^HcRs6n+rQMA# z(CBB$B%d@Bbrq_5`8DrU&lQDLUzSXjX>)C$@(zirxQ&fr=hFD~Gt^}L=#A2<`!_Z6 zwhDL=hBo{EK71?Y_~L(VnJ<=yM!!JG7%l?+t!ed9oCy+{R1v_M7yksdK5G!gQ_klNj)p=;>w})#xLp~=m9&y|Jyi{^1)iL3yfw;3lyW-iJ0U6 zk|~kNnF{6G?^hGyQ1_P|^#r_OBk#V6BDr|^H0t-xcb79HW=m)YRoS~NKrub;CLo(2 zKvxFOs`HSlQ$YZNwphrt45}=$G_HKaN&s04s1JxJI^$T)&AwQ23 zl-Ibzu#S|seSYT9)dXtf*m$Fq_?C#`RXcgKwE$I(dn6-1MvgHg4+Oyd2l+^a3cb6Q z$5+?qoaWX*6s6XqGqv07HKth-#^?-HzdzeThg`MKtTG^n=oer>KDDYs6>!hG+<|GDB^%dIf)ENZiXd6Q^+8aRS z43b+#K-9YgRjMGM2rbje^y$dQd-(0UUv0ILJn0R)T7NL>9Tehri<#{K_?+f4Cjeg4 z$e22Bq%*n}plf-Rv@RO>;lL%N?PoU>p-zg;`jlDgtKu2@c~6^ooVovi{{T34EApTL zMGy{iJJ2_snyd=IZsvCxCD3<%%>2x!=dMd2nYGv-|A`(>-F8a6)5u9cDd~TwD*C;z zp3-5HFEmv)P<6-!#j3tORko!WpFVis6{x;TBB@BxufWc=%lsH>LVF4*C8tRVR4K}m$UX<<4^`Rd3Z6vc15Ov@s7 zsD$2Ae30h@BTb)G6j2Jg290a}!>AO}^v@lVyUGBD(J@qwMbQjDWl?30PQ8=4p(+4f z%WI@UyT{E@$om;obwNpjuG8)|UVwmHK0l{Ylk|-{G%k0U-W)~I_nv=Bp`wtALM9!0 z&tEhkhrt(ot; z@T|u1ByH1mP&NE9kvVMdqLkj1u8#JGs&g7|w1=j7CMgdW0G(T}W#=E?IkY5!V=ZDEpP%*6M>H&uKB}! zsUeEeFE!>|Xc9@&H?`a7%dY)ML@vXkJC%^l@cGT?wbfBrKSozVwtD*{DWXyJMoyDS zOB;QaFSq@(s46tN?$;@i*100`^YI7Ze1s0^KkS-axuOV-xRT_ZkD-YoGkb{MEoX1} z=gPgaHSjEtWfEy!X%L(R!q%BuVFQ;=H%MmA@y356_KPEFJo9H~JxXa4HIht+xSg@`=PJ(k=uGnY&!r4uMN8%skAPErg9?56BPnJT(l>hIpl3F$kQpF zs?`P*Qsv>3{Wi`=errhnjzWU&1wvDA;-jpin=gLkgFNj|gr(d*W1}^Lq!PdWRWCSH z-y8b4F$!^BCzF&{KvVm#G^U=IQqR@hR|nL1K=NK40AAmjNgChv97A#&vbhX@2l)v@ zN!!USkEl}OZfxpWcauzhf#-7h-;?>Zq4KM+QX!*vw?Ve|N&e)4I&<&-#45toy0hk;X9)BIcO2zuWGAKeDvJ3ji)KmfR znL>rRZipzJzE22qSktzB-pd0*zlS6uMHm|Y zYf2<_(Se z+DKz7pi3;{4{sd(Qf)l|q3mI%BxOld$>X4^d66`sFv0yViR4Ca*a@CJe@mgAGz6vO z<_U*3k>nX@8dzwdboH{C`GgG+k>`zcrc0bAppehyIb%bl{~^BxB-f)5-&>zRQ+2QD z#=}@s)N9Dof7g{WBYRy|6oppl)Eo7jkWHvIbhI-H>6}SbWGiS|KBfFiWfuq@-Xh?& zljJ55aN3YUGT~Pb3@5o2*$j0nBflnhm_ypr%2x-}yhDz~a|K;eQdR~50dtqGwvjg% z)r5mMo#bL4h}B=ND?n4`D9O{6@r-ic{b7{MXDcDcK*`n!`3#t7Xt(l2OqS6~g=AKJ z6y$Q1$u7`{ld{N59TMw@LpmjMB z&sAxonHq(p(i)}GG>iqDuLQda*Qr{7;?+B?P?dK2RaXGrBWZWN0W{)>42B#_H_o5E zc=xd%Ug_D`TR_R`UstFKe;Y5%_4ll6R@o9|JW?5&Mz=R6$BpmumqnTO$b6bN-BF}g zStJ(-@EN^w#=s#{dbsyZCT&(Su@0Qejf|v&(xXgZv-iFlQ3VBw70c-15O9U$*U+?0 zBNNvS|M0Yps-L|ZuZ}R?Sliwm#hE9O)+u`A5&B$hq6<*tvKlt+og!Kpmv zi?>N9tGb`L%<&I)%xRDOoF`aGD%;DTM8D9^ z+6w=H+w@G5n^uP@R4Mm=8P?lEu0`=`d~ogR5t1!XfOFTsBx;_dGe=lO6y`NE#(e)1 z5v{CbN)1%IYunqw#T^=<;(Iq`8%>WN-%TU=4>V25Whg)&P^tS=8&y9zA&38Vg_@|I zdc2@9TR!28Z_c!%A*8V-!lG{+IXW__3n{(S_9>GUtM%x zHm!Qu0}cTPNS>?+z*Pr3|C1!|)IuTd4VR=zl(MdtM>h9Ww3S0XgM!E1rbaT_Nabn( z3K}GHpAIUg0TE<$Kf%=xaQlvS=}Uc(N0?|Nm!Tl>8-*H$%R)gr=vb= zRp+J8cBWFJSZxx?rS5n}olx3YsfurpTpy8{$zx&Xx`B@qrl7EzMyizbWb3Kv6Dl_^ zH#m(OTsD#ShF5bzr7be$j|Bk@)5)cHRy!N*b|6Q!Ew?%XWq&@uaG)zxi%3QTXn$Kt zr)e;rF@aPvP1LN@PuZm~n<8h)8^%B-h$u$mYt%>=7j*E4oqpz1g*2}Xf<{+&3W1xlNFLi)k`Q1uU{-IAh-hDq4K^$)!+Uoj@ztE+C_dK0;qU{l&!Y7 z(IPbZJ{MD;hBKErWY1?c1QgHhYYSDTMw5Iv z^Vj<}-mauaal~4WNbWT_A&0KnmP9gQpbq&|{P%FTvM8SLY6j0%tMW?}$&16^n>uac zyJMFB{mYv@8hE2vy^JKKbea6OQuF#FA2U}(h=tO+K;#j|K2k_3`vXy;A8D8QGG3hU zN*q;p_J=F9d-O#=g*@+s5`3H)MY1jOb04@QSfNU}`0vJ@=l`Da;e_qUoLm8g=^Azm z5QOR*6*9F--Q)6KKCZv60^r+kLAXpsiTto!pywiz-$FBzoPhkiM=hFlCX<>u$W`tb z?M_tJ;{|n0BCk0^)ijIw$Rw4B*SrAekfY^OswN94z&=#T)P4`X!T`1oV)pu`nyyY*VO7T{&>jdp|tdUkQadxqp~MGAquOB2!L_olzFq z48M?6K7pO{r!nMY6y|iAw4Pbt7D{EB{w!ojfnf}|OMSo;>q<3oC(kiArx%`8!2HpQg;*$8%>UqLathLP5= z)k7<~2yk%PWTQgc_KL6*U7G&!RH4Rlb53;;CGh$zl`7jGtpm@Kw01-(7(R}pL*}iA z)ZDGFYv3gSwZo|T(+9=z-%mT+&3<|S)q`bPq8}|Q3%d%RpGYw&%r9YHD>oFU$!`fH z)Ao)pi~m5kld7;5(73!bI{DJx@7$sD2#L5G6->pn)DuN$no07wvp*bkn;7278MWd_ zlTjg=y6!qPui`merb;AVgQi+!`Vuz)E}uS8ln2)&lA%6Oi6V;7_LP~-=+U7C04F!7 zDm{|TCo*}z21MivSaIdr#mOEhhWJ4$uh;x6oT|s=P)e^en{uc^xrR$JnM+B`AkDv3 zL#}G6)P&CULrHX7XlE>o7bdj5MU5>vL8c;;rZa8In#d4ul%RhS$;=?+)B4xTe{7c| z6Qu_WYB;<4xB+n8%HwaNNHy-uB-48SV_WZoGWVj#zdiZ7Ir*;%KU?^N0N^u)d>5K+ zl=~o`z+Vy*&OWkIWqk*^-cP2+{I)?b;Kj6vB5!*EaGpYyCDuk&+=Oncqi)P;rAGl( zPEqwf3hI+iQnp224njUZN0NthD9G((!UZp=oPQ$upgB~A0BFi>{k0N+*GyC$aYG*e z8DaN4fe&q_ztY;FvGsN*$GHXS70dPM-@*m{Vp(vBc z#5SJ(l?Kh;XJHQOH%=&y>#s7;SA*E?TI8M(>GslI$ksN3%zcAl+J5%lqbbCUMg6CptEa}qWiKF`rQ>Z%X0#$$; z3K1#|n|hO~$9rN)8jq&`))?8UpCB3491itY$>~E;n6QOBU)~vYZze6$1E?Nq9gfi^ zKBxphcsq;XtpLueB23AXe^uL86^yWp$L`t#qm^3p^1NMe>k4o=s}~Y)nnA`c_R@2MVn-1lFHu6H37VRd4oCfeJ=pIgCVi$8A-oiwawAvSufj2 z-t-m(*mW}VUss@RA|D>mr% z^|kANyVbj znxHUrkXxWk1~)(n8lIQPtDdkEI@E%0;1fxH8lQH4F|W7wLyj4;w80HU@H z01HXR?;X=Xgy95Nzwxp+=8?(OpxMFX#>nIOVw7p0JndrO1QrEK@H?H@d8cT z1DsHRe#S{_Uub$*d8&;Ls@nabseOB<4nUhUlFCBl5nhkBUGo6iCsJiP)=~g!hf@{b z5_Y-+B)h@EO+eL{m?l8wi=@2P1FA|lNp3+7{V){eYtp)P)6K`})L5fpqM~9R9&3SO ziJw}PzskUCV!SnzO300Gk*(Yrl23;@Ay>^?BqQ4(himyh%VWYv1psxz%+b3BOU|WoSmF%9-iOM^VUj%FLxuqbMn{pEber z^#~_71|g5{s}D(=#F%$PfjC4r(v|+vR z8C1nD{NJzNf3jfoLaL5a!?EWaWFP_ydgJe=o zohvlrDsr6x2)aj2#^)7*uc?`alGT4oReWva=lAyRC*KB^KSY%jkwsNlRTSb=@g~Xr z0#t?-RHC-NaYEsDdi1tYIp~7?4BKd4_MRu6<*`&IGtb$mdFq8ya{7f-6p{&B%L_n- zFCV0b9k`VK@b|q>&x~m_?nE?A|Na~xLgh3gjhd&eP%K@8uI~*W6HB|}cwx29lC&K< zMZ5IVX60vh)FYWS0wrkpP$BGLIiF;jTHske>lA7% zKfL1!)khMS*VI8(H=LyWHHJw%?gGs&-tk4Ap~+;FiFPslL^!zp%xsxYGt5%N0&kSw zVp5r75GpokR=t)%)EmAhdAm7^=D&}so+w~g7Rf9=+!q#vdea4UuUUlv5peG?<-4BFc!uzS z#rl;S8tqo7Qj}n16ULKNBEK##AY0=Ql2_^?o9~y`5>rnMat2WQ@sSUAjD$ zb}eppgG#J@K_SCypcsu4)3yR=nL?hcBS0lI`eA(io#Y5tZ|8qVDhgFusigD{3Nmy* z6hYO~Nx}%_?pb9h0N18&x+XiAu8{mq6rj08)ostpZu$!KL=<4CcAB(M^W=O=l9gG; zj4!>A?K_@b@b4fUG{Q$&ifiP^4#EtD@{W!Ty1sXh%!;*8 zlF7t6C``AA4so@St;`M5WTG+|4&|Ou;#{bzZLwv>J^ry03TdsRFpo(gnLZY!Q+}Ns z(+_!k#@$h? zHd-b5=`9;o8&RweVyTgCzY7Qd@6zL@dn1SUyyM&F?~A8Kp+nN#AQYk2t_*6V8;$DM z3UYJ83-KPZF)gyYc)+-Ap^>IjrzEC6sIP-&n`ArxPZfx7m>={tps=8eBqPc|gr->{ zEyG}FwIGWs(_(`Y^sA`K?1PedO}u0yPkSSu&a2l94%9idxXXP0Y@$Pcemfo-6C&nV z+e6hcLnbMa%3Ba7DP(GG6yUyEQqn(kL1CU-6y`Z6pp;rYqC@J+Djqr*`u%bL## z0J@V@zCsBG*hsE7KvQWA<-3vO@9n3(d@d%FSACGn{V?Sjc!u{enRdn|uxlT#^!KV( z>+@|pVxrc~obYZXVT8hEu-nLUuE?_}-AaWHp)HZELlg_hoQ~34Lz>?O1OXbMTNtHF z-36#bJfre0l9ndOuSx{%ZXA154*5(@Rrs*1h=S^+D)QY{$ghb9G(Nu2c%CL1-W2&X zO|(pa%Jb_)YGweF71H!qWjJ(7B}1yhuGwKH7#da=0PnLTpDud0XI;NH(n+3ohQ?!h zghCYN-f+f0M$&d~)3jP1UbXKtsUsXb+JC<{MPcIXPA(|yO|vvb@Sk~)iY3KF-V%_l zM;2A_(<=&aXcGd++^Uw6!$*8>FrhHqjIe0pi30F_YVw~Q+tp%E&7v9=Oax)vKl zWXiib=us5mONlc9!yn}7s!-|teFOm8DSy#)@CPw0U<(Q`e4Rv9>c(y&#!8kf_J>A% z@1mJ@pXs1++eB5w|L#nmaO}yL>d2?rcNZiU^ms`XWHX%Mnd#eG!Omp|CEl%o!o1?i z^ie^B@5GrC7QMVGOy(L-EF`HIW6d^&WTr9Z;mJpdKjIm+AJXAFJ+e7(CS{q{-f(y= zm2z}n0L~|D=`o30tD>;xW`(&Fg{nz_4y93JvuqlR98P85zoAg0oT`od%1?WuP?Is; z8^v*%aqm3?RP(4B&jVTi8ni%CBZVidCU%EsFIqHP+M}t>933am3?^9>`&+ z-?nkJUcZJ@Wo5RQ$p$>5`gR)~l<4U2kSqIE{Wzw18I+RCti&68dWkNlFLmu5lKH?A zW25am0d{{xCOz#g08Sr1xYG%R>Ca`6A>MHKOrqthuD`}pmA>?4PXLW0sk&bd#SP4Z74I0)rk0jDJ)9iR!=P0evn zd?b@0t>GX}w~>4NU^gaFK(-&Lk<&s?J)!EB6I4zEB1ocuGE=u4j*+Q)+|12K4^)3d zX=4MgvMpP+8G0->Lm}nuZX~VYy;KKwLARuMMzlgc424zdX~r$^bSQL=vY zK>_8ahft&3>LsE$qMHC!wUZ>nD&cu5EjKZna;OqeYaa_xWRmkl6z464DkXha4LInV zzum%3KmocslMlw2GL15`-EE7)T*p0PAxh%iT@xGYQ3_?hPG;7US%o0@PdDYRNwM{W zU4LmC z#T!>|E0271Kai%8C}iY@caIuvy^za&dSB!xtTfA(6&|n?-0SNBam<mNHfRj5qC6H3| z^*c$a$(do3hOW9Cd9n)d(L4hR5I(VSL%AVIWY|EbDWud44vn|Uw9B|Rw~-#IF>&OL z#&||`W9rxd7mptESM=%jMi|3e!%p`pX?zzBV~nKrEfGcOqmUm0pGdcZU^h|W^|wCW zWFx6qlTt~3;G%bS59~7boJrb*T;65Bu}MmL^6f91ivT>A$CHP>0SE))%)j6JrShaK z%HuxiH}~S1ROa`jCbAhOhDw`urcm|p$gXr!iah+;?6%Ix)lwl-r^8_kvo749>5t;(WEvnk8TE_T^N@TdyQdTS_X|+>y;=heDp%Nj{o) z@3+Z|7BDp2G8|3=P9!T#4r`1ev^3E{se%}pI<>=Qx;&hko;amab`vAc88l_qrpu;AaOiWJ)?KwxkoRS#4u?al1dwQ<59PddQ0b`j$qj7eG-p) z!NJ)@2f=#{|($!E=`QQtmzoDe0^`i1~a!~0}Z3)uNyP{`xu zQKVTmDWxUgGo}FqgOiJEwF4w2nN-pqEbdfkGRarr;8LfLH*&N+eJhDfdA+B<2#wR6 zG;+29(Di*n>x_y9)QBL@+TzWmY^BPYerxy2A7_8x8rg)72~_T9l2XFXULGHtsJixc zI~{U~Z;?#+2zKI^=4*%dx5P7q^JM0$zZ)Q#?L9q>8qT+ zRLJY@$QE#gE?`*G><0vv?ZQHY;&KvQr{5;|M^xHAStBna}-Cn zhZ^PXj0z}`bA<|J{W@+;p`Gmu9SZQ6oJ7s3_OR2HYd-W~7VV^{*MbC;q+dkj4TTO* zZ?E!qb8~Yk?=3)8_B)BpssxSBzupm&w}MbmqwDc6!C}0OnqLHuy<|*n0K&iws?PeL z7(%B%6Dcdab=yXAqyUZo4vB@x+X4kq&>@Fm>y@r>{>>`M5lwVZy&Wz`4T7d|G6=8=ITTRlELHbg08Z!0|DlvBt)oNc(iXnZ=x3Q3``3713i-+db{-KVr=fUGi|%AGtUMe(H&QhS zsxzegookmH=Bxj-fu_z*Yj_>x<2pQnc^<*RIaK)+fP1-eor7=x{@JhL7GCj%gYH+$ z0OV=*TZl!m(5?+M&hJJuPyEkMs99k^DYc9y*XdyAu^@#90%2DvQaR<1!m8XRX>Ex- zbrNZ3i;<3Bu7do!+@!KxKz{mm8}4tukj(qY(IGUgBl4?yI@HEV;vn;>vi{Q_uYoGy zdTc$|34P*7nm`>Ji zuZNI(oKcADx5mB0Ty!HA^zS%(&%vFiPkj*xjrWfha<(3JZ8Dg8sF8r8xSWZJ`ArX? z%`a>Ap1F8w_kA-R9(1iN0<%d@#Iu?`A(?fww}@=ct0i)l9^!-)hL7|C&?c72VrxW2;)*Y%QhsZzOP&D^jOt)0QDw0>-ku4~UvnG#i!q8R0aKSe3g zuQhRY6H&Ay%vR+9EecgKNrit@M+t-;VN``Tg-Qro@W{#>)+@~f6yoxunRcd)07tv#eB@yyTMsWl)lON0}#~7s>|xAyZ?{qMedKrf;c$;?#_xN_qHAT?3v^ zthdQZQcAz|ih%r_MkUckN_XEBkVEIwX7UjuH4%#&xS)8#j|wwo-e2}j_hU@HJ_Wh- zT~3%uS~f24UcLXkD_OA?s%oH+nq#ZMPQQb86X3doRK6AK)SsL{&0+yXb9>{GLY4G* zeK5(su=Co-Y_i#=P-E(V0)&6KKj?=PlBf01h~JYN00>QQl5%W3rCYDN;#t)`UGTe+ z@hGUuITJ(M0jM2EV-1v2?>I6&HtcEmzk}UiXZY~enGY(}&>>g5OZWf1{Y0TcCQNHA zpg4gEEaX03h}+n7@~}I6`hRbVxo3Xxi!U6UZ;+gZ;svIgq~srv&1u?wiL3?HMl!e@ z^s~*hto4Pe{<6%3Hpo-uPb2Lz@5>}(^l%8~+4;Y$xeykXNtIP0Cn3jBB+u{#8 zr20dp?|p~aWOQTXa<5kF$5ck`JD$#q$X4cGrgT6);$%ybV(uXJN{pX-&>Hs=R34WS zJHw%RBvm^-p$|lkhLI#wI>4^R?hlclYrR)KU3Sh&m8CBn+;naz$hYJ8e=}$&g&n=S zsiC)kY=P@7*cN@KKLGt9MPjZibI$XmD^xQ{ z&cU;Oeqy5WJ$K~tzeMt1546sVS+{-u8r@-#(I; z%A!b~>#Z_>6)}_Kem$~@8^{OsAT%3(kSf!*n-?8Nx`HfLp8=#Y79UIS4;G2Esa zndd}Z=gM_9O4J+(mY6MlV{;vf(=&}K)8@J;omY*d5;~(T3hH==G*h|O(-j&44nm_< zAu?6g%OClnSh_EbGzEX=CIEja8PZVXs4+L4A*W#9@Lh0jKMHZ}F}Dl=VFS5KhthEw9Y)*Eh0FfDu&oxd z)p)-pe34JN?{6O2zURu>zpg$w*b2pKBeRh2@Iu~+AyY2`OFNYj8V)i3Hp>G21F6p3ZeNv&F<bz&CDsIhg+IspO4nAB20VXdB0)fTzxn&@yY$#nhCy2wu-M)J|8HTACkqUhUi z@%^}Ix8*oF^G+sX;2`{DmS(ykAMtae!t3SWahx_qS?&r|g%I0L0d_8nOeCLnKpv;X z3Q6U{tbR2-fyvTWC`8bE&#{m+bwUmS`M9?K{2!ylY)^Z5SM)|Uj}f15lW11r8=xrO z`)C*27r_6>qk1@uqH4Q8oW3zqWxl=f-&FEK1r#6zO+U5J9nih`+reY*$m4#Es*Fh@ zfS|)v{;h(7JdkY_t!Lc;REkt&YLYWZHgP*BeJH@g$$29w4Z^d0%C_vsfcn7-$+)5Q26-V*wcAuJ zae>C^qYYN_h(C(pR;On5%Dux$WB8r0(6DqHQztk><+n*DX&vh$;92#*vykyMp<6-n zZyj=cBXJHiRgauIZch8^~$z$~HW$SH|sJh;4U zSq}7YgG%`4a%gHjIEbT?$xB^b?3vfU24ssbbUqS)ylWxc)<>gSh ztu@gu=Bs)DD*R)k%JM5p%i|guUmbwhc~vqs#*nK&4eLEOlM(gsjH=g3%Jq?}l|lzO z=E*PL*Fk>54+@!TO*wJn;a?kXnMs~cQ=XcrkUQZ}<2vIjAV2ZVD>mgT9q_q@jA#MP zzzjYV;SjLdN~XPxJnh42r=&jIHrRijJRHwz5lPb24n=DHSW0;qd1viKYEJpU!ON$5 zhuJA+E18&bunvk+;f|a|)vf>l-;+mO;V{%na*H3}K736&N$Jsn=HRJn}eyl6HCfc_S}Z2k?r8J8GghN6GI508{1oG?J+Y`%IO{HZ>E{`~k1>)go-+ zyZn$tXix#gsF+Ujx+n7VF*CsnDnUTi%L$}xBV{t?ZX4RiqX(<#Yv)VlJ% zSHBREt6c&Wn!3QQ?&^~yr|XbQS2n0@`ResLRc_egjenz_B#<8wLZT3M*M>xU}Q%#-$-0Yz$cEr}|5Ljw`e&m}oVgq^;9uS%{W90p{OJnMl1 zdZ$xmObjiHTuv?5MJvp_zh{^mguq6wB3@WuiBEh`NiOpm>c~$R50qw=zlP_pcMFIaqrtrTGRpbh^gOcTv3ecF#=b9%OU5MlupA z=QHFcsQTL|ZvfzRKEoE>06FR;(P5~8QDZy!hKIY8(CA0n9q~tL4JCQbA3(==lG1}aCg#}Xj%Sqpmt<@pa`jf| zaA9-ky*rIiK#NS$LOT;5UwKOMevh3@vRMYhuIy<=UW)=MC6H1_0F7hF?e0(spV@d+ zfP?tS(+D}WCvqA7QkW}r^)=ftJge#*lIE@`QknUiyO*sT;M6pns)r*wH|f&2eEA-G z?nN3UtJ!pA!>2?2P^2E_m`w@s8Pa?Kz!&6J0d^jfB$D4b0nk-COqDq@l9mMq=)%X0KVR>WXt)Q$fXaxKeU!Jic%$&mM1R>$kQX9w_LoN_gudB({Ag`5_8@3g`N23 zu~{C_)H=aOXLr3?4hV}IBqE#mW~!3HE1t0HbSF8DS#6N#mFrio-3^ZqIkO`=E8R%V zPj4-r*LPs`N{wne1LD9tR!Ryr@w1!xq99ir$+Q-L*G-at3CJPN8>K^z^83%tt0CaI z+T2g2ow7_1hlUSiGOP@WPRpk!Z=eBcI^*dsWd^5v=m0JrKj8+LD0 z1L8!t%Mifk?yNha$77_O5$0f=)HayVMeNz{6FsS0k9KzdKMrOZ^e9xJv$lQL7IZTsKQotFK#=fvm;|i+^x2TY_Vv{rD?mmo` zjgsZ^t7Bgba#0DeE8s`2ul?GlRMS6ww7^Is54pf5a2pjbh!FjnPq|B*6!P)#01Al(`!Uq33N$&RXb&@(FlIt6y~(MrO^pMyvasUEzS|I-1OnerOGwkI1YR zu%lrL8QTvb=Zw++mNd z>d+%s{+V}P&OG{dyAYIa7>nwL@~Ci#xe~HSj@OL77ctr$R;sCoqT+kPv6mtj%nE-A zxm>T3JOQgC|ImVZ!=!~;_u7) zxO=xBSQu6x1-?NsPbU~j{sqNriK(aeEFW3{il#SYTA7ylz*)XKN%LsHXHgcJ^u87l z>u*n!sK^+MTmtenjUXvKg*?I*lJdd&i%5MeGrFM&;ay3V$w-N0`f*btWkV{-+p~gU zBleWIRfED*eMxz_8i49Y(>`xl2`{9|+0~INbQ39WL>}!=v^w5;Vk~W9+agzu&RXR1 zI!4m;97>~}Wun#nsq52dm2E!WR1^d_6!I5;^r-OiBbt7x0JM6*n|`qIJ3_^>N-n+E zho!K<)oHawNCH4n37k`D*}Q@plz+_+FsG z7z{u)DVkh^0@X|wnNfi#Li0r@0SHaIcYq(Gknwk!mH0S|G@ssb-ozAB_KZqy0t6K2 zvnrj&Ev-c;>fGY-HptQVr{kpYT@|dJPPlnIR{kA@JQJIK|KR*(Q@eS)`>4?HF?qlZ zc{H8&7??KG%Wbrtk^P#iHj%0KHhnp*jv6^Ux~7s*J^(zgko*;PXl7@gdPxK4wEi*4 z!j;{%07Tf-T~6{@I3Ns3Vo~yj>Vm3gB&||jP@zDDxhIlMEYKY#s5bC$fVzJ=FO1Y7 zPsJrkR6OV-qBxgRED-UG2>2~FQgNgPfLF3eO6w~LC{5M3PMXO5&EX7}ZeKl;smPq9 zK`zaEQ8G#W@73Xmy4i0=T3H@_rb211(PW$gz<0)N8k1hvqMTe0P$7Mw#$+RGOi7Z+9XS)v_yqGerHKb_=lcQG>joBCs}A(rbBtG zAx~-m2sM0NU;lRQ)3qvl!ZC4lA{D0eh#@N2;nsiEwi{cQzjgTPoW>p~%jYa2t}4pI zYrRB;AwFuu1(N-tTPok~AwnVaS|6F*AC9$&PKwr#MlSK&tay1H6pg~kt7>T0lLuO; z0aS=5e?uO(6IARAgv*Yw`tMU=>V+HwgMXAW)5DLidAnEjkbvqB$nhu;fMQ0a8cBH# z6ke0ZSFhj>g*s@dnKq|u!fF1y$?QfDhd(5vhiYNvH)5BOnI1=loOwP&wq(X9QV#1Oz((k}m-0*$(d-17Jq%WjHZ*~a z>c@C}q!)6jKPHXykSF*$71BF!edQ)i61mMCxrW>rS3v_C_roMjBTx!)SQ>5Cy1EWH zDbebnKS~p_jVCIi)ot?HQ$V-d3=4*h*J)C3j$HcYZITj6=}~wVNqOyvnp))W=ui)N z)NfyV9=ZH~moHX=mFMyll3BA=aA+pUg}_s3d>y&OX;fTy2cTL-%6(zu+H=mPec@&@ z>pKmKs?e_|5EvUB5jPEmd>P5>uT*>Hvt_+%>w0I?AX|FC9^Gs*Wn(AUxP2?}S|b${ zKFiHnWMmWA%;dq7S+_f*5ce@d>iDU&BCLeT5;+ZdJU@t&&EGV40lFAy9t5|^M>44} zU+Alajp*^1yyFAF{SwI!QIe+PgFge{ytgcST%b^i$f27_UO#;J@UyVPYk{6cnHRSR z(F};9Rq`kig}B$4v^kD>vY$dptuxKMDUsV+9%$(Xm2b+M;5@{tr9ws1%rr#aP z61lUM2*;}LG|?t@hz5?iWs&kUpz>d4N~yD5VUMbo3mlq~LGodB;3Lw|MAP_}$Q{iE zI8@=Z1Nz$^q$y%Al zXHX6uvdGt9rE}4&iZGk>S&t{F;fU&mWLg>LdZHx3z1iy*vh{~%zSO&pE6Sw4!@@GU zqg*^U$Rwkeb#Bx0Z<0NMkxclh9~9ynnM~;ihg5^l$5^5oAxn{yE}JeI zXqEnREdfq@A)P!TKQA|RK506*lYcc0`{9r6TcP`+Fba5+rN zDLPAdDF0`5^RDFlO~DA6q#gltU-UITirDJDY*f!&m&}HA^Py%aRjt*j zw32VMfMeo*(lSb0^>>ngbkM@C>R~nEklR|5EFJcQdJoCLfYx0^DSUof=L5iH!n6-o z?!Qm+jw|d?A0XxBD5`Z_=Do?TC@@SSBZC3gXSY#d{zgRQN937?0-WWthrH#7Jle{A zcBWJQqZ1s^j*q7zb5$i2A$o|Scgik=LdHwt@lQrHzYo82|)d^k^B__O+Mmc7g+VT@KGb= zX_w7}Ic`w2${=M6DUU{;*7~~@pzvx?N8@*gmp$NE+uKyc)c*fRT}#W$fpXdSI-3nhW_wn@2WZhsCt`}Hwwt(eI(&bYbf5f$cYbTp*Zo`eTNQT zx^pl5-pP5BCJk;~E2Od)a`=8}B>A&13UOUwB6(Uw4qfd=&70S&GMS9+gnTMLKN0z+ zQ(;=@382#c{fXo>}JVzFc=pZsdmn!YNyOiCan$$0j)5R@=+tPgUuxOupa z0IT8AvV3Ifp#(W=f1e=Or8_{<*z+TaskMk)OiGvugqs($@_Ed}W~G z2Mw&IC6Y`U1v^x*@;*$`a(t-T%O9G-=Zv>cEp~&|05Yo=6hia!BxTEdwg2*FkJ z)Q7wFE`0r?mETPahEvq%S=dpOmrv6Qu<=?-vL1kjvs2S1X#tEi@^Ldb6tFUh;UfZ( zXF#^)QVRgu-$`j6zPS8=onbSP+#Y~DJu*&p7GV_-#vHSd=V@bP2t2y|dNn)swGr9U zj~Y1kZZ;L+LC7P7%(yDiChI#tl!JCRnK2M$@?Vf{xbjl-ej_Kwk%svK^7+>Cg;n)5 zGNu_yQtRA(fiC9}z@IOK_Q47Z!MpFtqM#bx}5*utblG5KDL^#yqi|1XSSZbhg zP4iXh$CmVWhfSTCGaI#D{O7zcmcBBqqnD2s3e~I0BxO^|+WI1L)$UZ;1%PJYU>$Na z7*`!uLWc{?{t4OQGe8hvrGCpoa~IeIFFR+noU4aiy_eO3 zP0NSbnLE`eDqwAPSCoTV)h(N>3Lq#;Hb&1qcZmx5`~k{Tq%}fOn$NOWigR^fmr78F z)YQU``nO2SD*{RwN5$(X<`w2i`*m=)PmW|Whxx|%>$l(z?M(s@-Hvbrhu=a+{XTRbf}RSdyvjA*xZ7`inI3;>*3J&aW-PDGenSvM&fv zEMz~WjD`TbTAh(EepVO46pJycAsp)Y*ud~tf{>?uM8>sN0JQ66T4vyd2ZoJ}R8WW` zDZi{n9@l1`$e}*S94nD$h?M==)bobqD;ri!n=eUkq7*A7Dz<20BS6>g#}wuWpIH&* zq5aux-sFR_d95}|kq?dLbeZJ6N+?0tO>R}eY3|?9YK;aJPlWXy!BcSy@1(dj&yc{fAO*x5>ijp*qm3L*8}e1cpwB69gnkB~^pSwB`1ki+w}GfO+VBlN&W z9w@@4U!-hU>;=GM+(%!eNDsbf>F?tzqL|hx%&`iE_&J_4M#LN+V5CfBwVth1CQVY=l8sjH!)Wo=^1*LLNaYz()Kzjeo09m{4O` z=5L-T2bJnInGXV?SoGLT14)_EPG9#0(X?udqMBT1Au@}((HP|ux|xctS`??=O2tPg zO7%8#7;e8=StYpi*l(dt=EokeQqN10$Sea%iL_jt;eqmL{zED)vG-%{>$8nnf4=+a zcbk9tIf9B?O<)zYF`J6Cuj`>q)i#-F6WI=qsha#MlXAoW0eL#FGSb4wQIGy^E5cdT zE#&>mP{V0K!#2fAJfed=Kg#3}C_~Ugreu>`Q^`*aMg7fsTE!2BlX~gN?^P(SeheAa zUj;kr9wr~Y>4p+CtBhpUw^c%$dcZluvY9V+uqU2ejd1F>SAxQGNqpAhK~OCrqnpB^ z(MD!l4pc{;Q6`@90^oLzl-_{rydSDJ)I#C+J?~bAJ!@zq&qAJl7D{&)^{=1;P&bXQ z!m;}IsmPp!93Hi2-H@5X_^pS^&kYbNcl!9-FNV3JOm!!XtO6$pA@5(3Ny=1Yc1KCW z<<#bI-o#8QjL)MG&A>2owuuvw?^_FLylaW+0*6+UmYc2P`FG=rp?jXD2>-k7ipEaqyuufO+9(< zeK(IU#=D>}&5Oz8W)Ij@JYr5Iqc5LYGqSEuKw%yyXk!|K5?aQQhiafO)$OQ2*yDMF zR_##0N}uxRc~^s3WW$S8MBuApj^78kUzmH zz#!8~&RXFKLDlRI6+fb=H!_(|irjY3%pxAPLb+7kVWc9X5sFcb%cSC66y^3aZOnUH z!b<(@L)s)ij~wo=J!TGrL^5)i3gt38nM~XM^`_tVt^8tkyP)A49^AW@Ma6Of3b9|9 zg^JCAD4Wps4>PSy?+S28>~@;uKea{VQoAg?YdEuy4^NZf-9NiWU*39F zggxtI@;8*h^E9o(tHVnBemHI9D^1{>z;H6DI!X{nrIY6Vu&4K(TRl;Ns&3=AACtUM z3lQL(T`Z6|rNtNPz3_ypJ)~u}2&?+P#GB@~XmO6bQUwmZPbM0_cSD|0CZ6{L;C?yN zVjKxH9QyjezCHk&BuPJd!JZFjV;+b+y^P!(j2v~-5r29AN(o6d1LNdNM^J`0bTzvQkKq!AYZG8 zq#@Jvu?HM_kMfPW$fr_k{831Y3(O<$f+KG0lOP>kbHQu26Bly$&Vl zw9tMLF;@qxpg+u1SPs+^P#V>M^O;l_uOVN9@5i|WjD5c*3UU9$Ae~-Q5xLq-{!1b$ zljckBck2|aLcVU9v^uRpal#j5N+T4ZzMWcCr4vztutFv+=K3g5CzGW7F#gJ&pT-4O zu0COEwc*e9n=$jy`N+6Kp9jK5sC|cu=-;oU8DtiaGq3xoF;=Fz8_LjlXJZsr`vF&JQ7#pakcRyel*>!8GNnhSBC^8f_pPULY5e?GyOU_O7*?9M%~Y62!YM5;$h5juAC7nq zx=ud*n=#ku%-koX#?N*8}C`R!akW#GNEJiYSFfg!V65{h)zD zm?_c9a_Xb1aE@jd$wjc|6^Xne!j3ARJ&h7v0*Aal^cu<7I&j(w7G>EwdiK`2&yO19 zAEZKg2(=!PlVRh(DUG+o!%XA`4IFXZM8zk_*F(=c4FKrAJ#5JA3t-^kRVQY61I^;^ zzTpCU`b)Gj^+KMudTw$P)PC(6wyHkP%$ML$)$_DUdtC!S)oQbzxnzksviBs?#`JrA z5&s#nPa<fQtpJX)S4DW}cSpcr+}v6Xu6H+=67E3xfP z3rWkqjvACo)BI-(t&mU9so*v8yXq*U^=}8~cxeUX@t74uCWljD$;`OBv;*=@HImF| zgc3$)(7arYBGi%m{&IE=lvFE?`q(v|C{R0-j9F2sl3#!pg=ky@JG&v5*8*BeCgzZ1 zPHfwpK&$lAX;hpX+~lPNakP?Nfg=^yNY@&{%58Bvnb8-DH_~X?|G}^=WI_X!#a%=p z!aR%oED8ug9w4k!n= zbrMNAi+mYHi0yC5GUI-(A)pxT$WJzHTQ#eS3b{gmHc_$1A9*}~BAJ>+QXg~V0>*sagj{OsbLfv~HCnO4TG$kRBR{AJEV zL;ATRcW#icz!C99BdzW=hobHSnI%Q(nZvZbp@{#CX6kh#t;`|#FK8>asDgarSQ9BF z^Mr^(>fJK_+1DMVsGoW$6s1s2GBJn#6WHgwDf;ulDgbV4NnQ&9ygvH7j~Wm>5PFCT z{kHC|D8gsGe%p>mQS;r9qt0=X*}wJ+L>W3BFjIk|bOCAQUnh|Ak)%q2&>TLFp|JB%#wpdHGgO)RZU z7S2Zz)z8c1&7N+s$Hh~HLfRkEQ-7`-@~C@VXO5_^d(}jF)%@e!BU+{Y{pCx(5d7)| zcYJHkXWvftg3T-onY}6mPO5j9HW4jgmoQf%FFhcs|D(2Rz- zY1zW%)EjAwf(1Y& zxNRbN)B|co|u*3Uc^0_Jk3Jf!nhF&PuyGhv+DVt={ z`1coUzG^B0X!LowWPZyB>aNRQsj7lPH6fAY3U4^@YF640p2$_{_3#O>@%&DvRnj;C z<>PTSojfJ zH}83m6S7IBT)7sX61#3jdrz&-$IaEvLnpvyks)UL%!+W9`n6<5piNOxjRYt44WLPhctDhO7<2kA>XqGIj%YY&4)3J42D9}>R4P{3VFA_2>XuG>J0L^N65d55C+Zm z1V%_%t>8$*-Psap+0j)Eh59lJk<(MN^-@M$mdrd4D&jvR_KG5z{0#nky0#Z+b-y)= zP-#&HHHr~XRNbu-t#$^%j%t?;YwN0Dml?mEH!<2=2c+jv}LGN#5ph(G(rxDUVH z_~mGhW-Y2DAdgV1rHH~*-HxSQ_JMshuidJR!bIfp zeL(V3b(EKO4=G8sl4ujT6Qw&Yht~q&vXw<#h6By7(#CYC5%M*edxp7UI>C7^Yh@bJ zVv?c`hoX>bmnen5r^S=-A97YEDP8=uqYKKE!!cU#_7QQH;Bv zfbtO99;a1YZ5^1@&(GP_MZIk3pBAhWKnY^I`pxsaMt_Dui z1UKsS>R0=AeX{r_3z6eq)1nO8IeLE?Uc%yT6W&T)IHp@_11d>VbS9sUQr zR~V^yUyFZFL{XZ?3$jT0BiJK~$Ty5OciNzcnt#f)O8KyXfIQ89i@5c?uYi1-4)ezQ z!6_==N+>@;fRozZ=bgSH>~L{`lZ2xrBdek`eNsu9Wt!9fnOPg9(KH^^UIn{qo?cTC z4hRzy8PP>Rxiwt6E`l}@nIvV|JOZUVF24^e?cXd+8Ug#!&^a%#yH&sRo(3u8(#@Sv=Q{zF= zPp~h5#y{T5U@?2V;FQ2W1-F$Bl$});p znFCRb&wEJ;hh7^}OT_;tR9ay;*IB^7BkUs$OVlV$>o_v$RDvA4xgMTW*K5mN;gIH~ zn?poA3AfLqNycX#y=gXG*tF}e2Bm8>QG|_V8w*Kk4hKB8N+gdq5>S}XY)cxg9(F=` zXaj@PC_U1nSJ0X0e~*FZuiOS?`P0vODk8DNf*%mgD+da zrsu+M6km0OGhFYHkE-C`@_y^FnKq9<9;ie4wa=tImho_!>L>D$CkhejPHKx%b?DnPV3&jK=??G%FG#ItchLs6xIBTRFHY3Iu<8^c_ExrQ&&# z#ik$o4n&paelH!Z}?fu2exWnu@ICU+0IQOzxlP zNv6+sLq6BK<4>k#OEmuwjQ=6kbsEM%U9)2ye-BZGTp|EL(7{>4kVNLNY!LB8dQKQ~ zBwc1vNx#5(EgmpsjEEuxluk40Qx6mv5V@r#3K3dfr$YaJ07{`jQQt07BZu3s@ zQB;F2-MZ*d%o|CaPzFIe;K%9hyzzf#+`WaquuFWy!sHu&)`ZhEZMGO_W4Zp0AIc;Q z>_0&Rdm6koK#lTJS8vv)>!|ma%pF+6O^bg;tn*Y=4?Lk~zJ1jlWf3bhYErM77aUV* z;1r?Xm8+YcLQ$g5MS#;>1E7A%%;OF5{};qX(Z7A#N{4?f_?z&HEk!&Dw~oVJy7)Ne z#_K4e@+17uRha$YTVIru*Q8^oPA+POe@&>`w}ycKf*=S01h}^NCHwmgmy=9#+>M<} zx86E3skQ*SYCkf)ga4Yk*{TSc87oCRk;-c$(k!Xph2lwRYIXVY_U^{`zv8{%!bh)a zP+a@V8D`#VEZ~W>zAQcVg|mF-&eNf=3hyRlXQwR-KvCW^>Z5d8w^ny<>|7rJEB6W_ z{^!(wIyf*@Pcmx&oaEkhYdR^*G~8M_!be2eh9?YyU8+$Tcb{(IC7>*V&dps!E>+{f zFO2)_L}JtrlLmKig+dTTJPFOHJIRrMSHcqz>wFt6CHFyD>U?+o*7@Tr2e;6`uBwyP zt?sIZ6I$I%o>vh?s5*VJ@2?l#P!7`!(xr;{SHzlwUd%D5vKk5x75*;?$kQ{DmWLnc z@GrS;HtVH`_tk))Lb-U1JDo+FtbTx(pNL#Rf8u}3fBfoG=}deTX1(#Oij;7N}mcx6yz@EY6oA{4aT5vZzNhU8es_TOC z6JIb>q2D{!1Lf!1qs^NS^hTM|)mQL;Mdzhaql7NkBD337E-=CXIRS+ z6`XR`FbZ}Ffm@lHwkk-YM(M<$TD5h^rD@T>ZLL%r1~)APUe5K)xNq6C={y#+XJ zteMgZ7yJv_nnO3mGsok3)dfIRS&g!Zf&gcUT2~h>9B^&#hFro6>Ez?K_?I*zr;Oj5 zal-?EfO7DA`VZy{^M^W6hg|jJ@W16XXx^!KiD#>!fYuFVc-BHvADs}_9ZybFsdcVx zFO$sLUJd^Z&4@MeOqUtv4j_2gelsbX$dvufQIhyJxkrV6Pe3kJjn~f@Gyj;GOrCpd zY)w3Ydf(lXsK}b>h4N|I@%3ZbStc?oku=T4|FcvzmmDIA z7Cu1-r*-bFK_My^5a~}XI0f0-37C~+eX5X@y64I`W74YQx zW{|YJr$YI7&ORDtq19~I2wplk;@POldRoQaoS{Myz0#ROK3g46#Qo`m7UObnIIBV= zdAKtE1&=;|J~S|w6g^%IRPaNYv~?yt-L1Bp2&c5(bobm3aG=gN<9v~;>TU9FE&O{= z|8)4VKgy-r7xA>L3LblpIo?GfJ&6A~zflKe&O{ECYwh0kT{Qwq6*P6XkqHTplPBYe zdA_jtn;l1Q%T&m(Xz-s9XOg^YNq-m&t0vJTWh!>nMhU_=i5Z^w*Fq{INAU0yWX3YD z(7TkP7q;`j6A)aQ{gz24^Gs!wi~oUSGp*ugen85*gYo|`q4FuN*TDPxBw6AqEu7XN zjtcz%{0ri<(Ij6A3<^-gNr5YFKUnC69nuXCSUp2Nu7UD!4e}CDgjT0fE8X6X}&%r14Sw`vMwl}-h(BX*<`MIHhJg|=8^w&!;|TH?*2SK z0ZtkqTNb+FU(k$>&?ntG{?N=E>5FTr-9(g0tnp_=)IT3}^@KCrYR`;44hOm=MGis^ zw~r0vE;l$q?JmMfrGh=J4@a^UoYrybv_Im=tk<-_TISe_LIx23d+MQ?TqDBDYuu5? z<_G^QtAH}Nuac?w4o|FGj3hD75*_{Wg?nmA*1l=KTgJ7qQQY6q#i7tKRKX$jHw4Kjcxj zmMe-A-jlC&d3kj#Wr>E`a9Xxa;evlmZZpt#GwMpK>)mm@0ZED!$mx~8n;NACkn`;^AX_$uf-B= z=D<16rP3{FAxb1^z-p9k6CE54nXZg%&`uIEY#!wK;6f@6a=UmOg)@V ztL)d*C`0FTDvr6~Nlnl*pZvOre@zVa2GGkwCf`0jK<(GyfJtV)T_{QQhpZo{!N2vT z{P6oO?$vf0m@u=34&rm!yj4q!vIW(EU2n@I@6QSzWG9r>S|PeKt39d|1)l# z|9Tv?W73#jp(sq8B{M-!E6aILI8E>vu*FP^X{*Xwt{oS7oust{}c-8eL|LR3{c|Jl4g0)Ix-FRPT{B@$Y!o_XND!zj(^X9FZ@%0Z|#sL;iz8;6UJMUh{`S)AAQHN$Jtnw*pZ*?HB3K1C^fJV=>5|3Mk2Yt<3EE zCx8FE3QEx3l_oDr{kReoHLo*Y)_fme#QFQQiEn{IJ|X@OsF%vd*K3dXDZ;>9Nt3lG z&Z~XJeoX}Asun{&L7*LIG1%)Z1%yE z3cX9-ZG-d|Q=EhG=SL!qne4yY=0UH*tx`uj(Ic&(NIr+Bn=2OzeOB~8`w#N3DP zNtJ0MCv6$#0>{J`(&Pj=;f^6{fER#GsPo~B2Xlqw@ zHJst{Gc)_aMm_q#k9%M5*;WU~1hon!2msvQwU7_r4#tyf{D7qVm%pm|tR08K?#Efk z_}@I>*qQ9B&w4^JmNvh^NxIO{i6qUNy8)F}WiwZ7By)VAhBL%*31r4x7v%ApKS~2< zJ!Rw>5&r>|yVt}!%%T6Qr&oiqb3f`|1*LoUS^`PaAmpowC!zaHj(b{Ht$E8&&csq7 zQGPH{hf?*+${JERVC)?lS6yExpnz5Y;M#uPvdi-Qts5?KMU|hj znd@s`c-4M+;Rcd1XC~D_VUv^5$rb-=?u#sp4`(imO!BNdiqS4jK9#UN1i30-qr&p0 z2tX9!gn-&23aNjSWahF%RGib`KjL=v+8t7U^j8vTJY}YhDQIeG1GkOvj!+b zg$~ociHsbhgIvRw>ku6>YHtnNz>-aD5hUL6`zQBQVkQyD>d)-WFjaG`gO>Y;i?6(tGW} zzrTz!TwDH1WdTKaR~8ZQki02EA-0+G(6TWACAiK{r{!-y6eo_oLPds|(fe0)6HvB* z!!!(lm1cP4>F!m$+}s5?BWp-p^aI7j<|fjsJqd@4At$u&bvkk57XsjuD~u=2NWkEA{5qEWSd{)DL?N~#b=QhpCM^o)SB>yAaYDa)zfS3(h@2x#t-_tXLakHG3nv&>(*qL>OlWLh39sfOZ& z%3tjxDJK}IxE+eJ)!mfE9WDSWe;yYnFSv49+V z@6e*>YPq2l{ZmP%{j=b;FZ8s!<_l+eY^Bx4O05$}rrf(4%X|-pyFl;>fD;0~miV{^ zo_Ybnj6Yth=6=0+CRoB_VVv=WhqBJ`X9wBLLi6^JlxV3y|=|kp{k4%2K zW&K8t8Z_}i>H4quviHK1?^V)OUzs#RKn_(!(c|ZIOZNBOgVYtS{Nn;Zn8jyNQfM|6 z>He^(bNTw#%SOg^m}{Wn@~}FvFA(Lhl4Nupl*MJ{I007f1J`W{KYf^1iFXeE)Lno= z*Rh2f#R<%H*}jw89Gogr13Uv)lbd#g(V=Uva<9OjF9m zAC_)9FVpJQqTcQh;DE~)l9VDRw?S!y7c*!jp9+LsO)R_>d#1h!z@z=oV_gI|MWgL~ zRwfTOKoNnbNJ<+#@Wft|N$Jwsu4?=r5kDYlk$ElvHtx0hZqbujG&X}yg|cZqv;u&p z+lQ#2&`!)q{}fN6&dj-g=}8*(G8Ng8V?9OWs}&6UbjZ^uCO5wXt{(bHj{M*0B$wwczc9|xWZYY?kzoYne>;wY+c0#W$-*e8%b}W)dhDrOZyQO=5>9Zy=r0X zn?J~uAM2Cug&p2;;ESe0rRTptx1}DOP;HMTvx|Uo8~7{9xp*Sou2GS>^PSoPa``T2 zF_w+ND9z-nDWrVT7xuZftmK7KRp|ciln+jsX=O@FClkBEStBz}zFS#<*ZeE`7h$Dx z$B>9^OIroCXrl$Re!Hhbq5DwZ7sYsVocMq?osmNnfG(?O^_wdk(V`ssq?5deGWd>4 zxfA4BsnxLee~6Yz$wcze$Vz?uv?zu4HWiz^p$MMv@&TFoq$MKCQsER$%Tb7`YGZZq zAyU7)GyWTbpHC+PWlEWM&4=UVsLMxI4EGmNnt+pXmYm)LrK<436%(zd!7iT&M(ul9 z1k|HQzVCJk>1DD0z5k|`bW;($j6e%BA+eU;!yHYZ)tdAmEeRc2lFjo{;e?vBH?(EoSqC(#mWzfEvVv%`wR8Yf( zV+MGmbh;`nn}oQyR;bymX#h%kJC)qiMg;r^_4U$r`)1RenO`q^HWW^AssHA#D4C=b zw)w9g2X^p;GhQ&$cxU+sGe+&C)do0AJLD1-Qsm!rw_MrtYW?jbQ;$w>RJ($gPxbbr zc4v|qqv4F22RHb7={&2rK%uMT2j~1rQtE;N{$4=>oHHbXHYqAcIiaEkO6Hq|aiR7hGp7@XKA}PIy(v7=G z%jFBVWA&MF5_6<}uB%d6htl}oBN^LQ)In@|NtR_6ax(~ps9{IN%jB;p%4>;H=H*}! zxhnP?UoWs?fCy&^ZQCJ-=;r1=JUsfEM9L}2@rlyIue*9eA)qvFzmtZ8p(vfI{=8@^ z{&9mnnvc(qtL+M);t785i}JWZa<4xM2~46Q>rE}5*euGl$-4BEfd4)5RWkj;npPqT z1iXK4OtOVEUhayr=)NL0YPCvNanQ$`E*oe` zOPW{9M-AvcB8@w1pfHUZWzg)nR1dj&WE(82z2QLT>%TQXaZe?XvrwS(gX160s4buj zKD!grP5+=6_ki&!R7ltQdqC0Y99MXuu=ipt883+_#{JWHi8kTwTxRM?TF%x->BPSA za>Ahu8ZPZiPu&!PQmK7vwrUijR(l782B}ek@NyWbKmAJmx+B;B`drud&Sqt#XJy}A zI-r7xG6qo@+-GI8sIbv)D9v6fGKZ^Boa?9&Ee1AOoPPM%FNgQ^ghQ??V$-8{ z{4rxMnb5BCA$doT0H=wt`jlL!1>k#u@%4qbjNC53O0$fw3H}53=;b}GC`u)hH2m@P zzC`vwfq-Kq7vf3ubVUh%(Nr8k>DJ4%k+PDmZCVydQeN&Vs$DDi3UI7)9Ld-begFDp zS2BwUtB3-@i?_~rv6YCT8b^``btp_2oWe9|ixxT5ZO*1k=awHhv7lQ`tpKM?KhvW2 zGcD`)=s#-p+BK&#FW!u_Fze98$17&H5m2`3^OMQ-^-!joN2!Qt2D?0JX`yf*8_V5Q zQ4ZP|k{4>CfZrRmNu8+06Y||9%cPv1XduHzy5fIJ5L8{hJ^JUM3R>h-_d6e*9nm9# zkFsc(q(vFD8<=B<25^1;0j0S?uOG6IwA|<*pma5w)P3&DPZ#`k%S=kChvu|sp@U6h zGa26z<>P)aEC4oQml(>AgW<^FaQzZL6w_5Q$G__-xcF#c<+tQgcP|ynBu?FQ=K6LN zp;ikGAIP+lV|PyQ1caf-I-(Hw4-HHl=QrT1S}4qYMh0z+_dfB&hkBBx`65cC8G9y1 zzS(z$nO5@oIw*}>)2@Acx32CDs62c_Tv3Ri_4IGJB7&s(%zHhWb?a9vq}?~Ev@+ga z*F_Dx1eLB*ucwD}>-XCHn;FR|mk*gq^M?YQQhDr1!=N|i!|egKM5ReIym#WN57Oyv3B;_k2T zf0QkK9-kZt1eSH1CYiAF2 zLowCKdmDxUXf{HN0t2YH-$y{XJ!Q$d`_-a7nJti`>5l+L$W)v_=~l~BNKwmrgs35W zW+1ONt~h-5t{aD232>-xI!QSrQ)VG4S8ttrAZO1}Ls9j)dZWHv{{2-GO<8w(2cU=# zEabHy6ynnP2$>eY!5uagW`iB#`JN@Yrvl2Qao9Eutc0FdNJe_YkqYnVQ^pG@s$TY^_2*u5 zX|?JdHvq1m|Irc7(9FMiY2y-46rmk)T4pZu-6nuXw>~NqP`{ULK0ew-=pDAd1M=yb z9?qa5{vt28*TH)|6-m#abj@Po6S>1v)3)F9VI<9?Q5sF%R-L+ZZ0HYobbfE+q-x0H zUbV}MvzP5lFeQgy{AX?tFIZLiHjO#Vd+WlUpphS}yO5GCM<*M@F8nlpWVNRfNaGYZ zNjvG(=Z!?fUsV+00z#v&+`K)2o7jGR-C#Q4xwpYtk6;Q`Z_^!T*r= z;2e)RSJvkgxH^X1n7^j`hm~V4Y0GXDD5VNI8?-)IbGAt0N}z?xy=@iDb;naHy`4xny(l&2S49$&;D| z`?XlK_~o(v>o$FRZ_3@|m{gfI=if(R+5;qS2BLuWtpte{eX3!hFW}?aY?G0S?1$N; zMZVDtPVqiXD@*1-%Sq}jR75oM)}k1(H%h9vP0z&bEd`X#Wi1ttTEii)-e0Y}NHTGT zFPv7@Kr*}z@`=ltE3zq`fI6g+7xH+0am~WlMEp;wf_khU>WV_NT@Fd+>`W=uLSFBu zM%ldPKQNFP^K|h2Iy?K*F}LEfnIn5B%AlDRPle&iq>5S;rhboP`ZFk(SLAnL74S+5 zWiqKc91*(cd9+C=3h`|*?2csQ^~%8;Gj=sa_0`9_iExJ3K|cIqUb~7q6bOAIk-2WJ znbyf2nD}K?6yhCb+FnIKy*CbI-mQ&Xx*@x0BU_lgP$%|!NJV%ZltySaFLwXEEw!N# zh8Re$MycGqD%5IR%~PwbI_0G4(dLm&LcKk!w(mP++7DM^()4n|(QjUC*C1E}E0@ui z&CC@y2zFH1Ez>Gx<=nTr1quMhkhIK%LhaXl_Is648r8Uzghm2#1!hxmwHnH#YBV>J z3d^-6gPRX|>*t}BR+`A9?yePDE(()b$lgHYXnyWo6%^(^{oE~c#Gj`!Q{!dkO4~T+ z!*P|=q5vmUN+TID1>TjXr^%MBp#l`{`)C>K0;hBfW2qj#8jMnT-KN4g202t7fNR6a zue5Bm&`9#k2YWV*Y+1v&f6iqTI%|QLy!EUZS{!Sx#Z^`gB$EY|R1Sxo+VeIb4B65jsV|b_<{+Be*otW1Ig{V>QhQ$bzz2Ojfd#D%FO8H zC#f($mPym^t|*Ioo}Q5lUu>a4F+F9HOHnRMITVVfSIwr=^DIoQ1&11?)9}-h5Ll@+ z+PXvE-4z+DCz-MMoqZY7LiJmbuj(|=dZ^(*<(FR@Fyf0NSO1=IlcoeR{pcVSas+<< zkO~XQGmX7r3c1#THd?6@Ahz%L0h>|&Ctd*8jL$)p(XC_k?$A&dHd1hB)v>Bc5&AbR{=SM!6(ej zbG<9<@f zW*vUDl1}J$KRe<5(cvWH281lRVqg(F{9zNa*%;RZMF@lbPO<0{?=E~fhO$9#mdN$G zN)7#M^%}l>W*wAiJY|x7urG~F+~y4+G!*b#$+Q}9N{h2}DC_2dzu)%}P%5|O@fI>+ z02FF3&rxl~nr|O}aQe^jt4UgRzR)SuUkB%CM&7WHJ1Yt(7h#?yX{3h$E0+T|Is+>? z9T-8HqgP$r(M*6njmUp0qJ&81(<7J0MJ2!yaX@_milfZ&0t&fGtDA7b?kkmH<+ari zzp0{s<&cn?Bf`n#*%~;|%tG>RD|maW0C;VsV!gWKGgSl#9rvYC@twZ_(cjnOH517n zP}o;yvv~#znR1V$9G)GOK?UqOrNlVQA!ym*@ zzo98g;oDXNh5x%t+rtj;<0Ox}p*#*et_Gmu73!x1R!(4S2proeUzq!6Z9o|O*4HcE z{49=&^i*0&VLuJRpkuwpKJU?cetQvii7zCXvkfvyvrHyZ5!(ZRy7`a~ZMmYzFm*vSSPAcwjBkT7 z2r8di)m(xrO}>0@$j0L>UpBT zRGFkD>Rw`cxQV3scuxTi1zup37xL7TsgRfHP`ZG(qDdN#zv2f))hmo${Py8wQa541>;jWo(vn!zqH ziahR)q6AEmnZv+Ufp9_{W7@|6rs`>9c0)eZ0)tG_+y-T<9JfFXtLnGS7hV%!^8v|| z0dQcDo|L}wMGnn;Gs&|rUr##FKmhz+iXb^bfR)hkBFUdn*kSTSMdT4{FHWQ4=9rPS zYV0>qmZ`{|<_6#+nrI#;!b#IDBxOo*NjD$TMou+ZOf)n>83q~|*Bef15J#HFz#+|N zH0?ZjX5@P*hQB()NnYRV`XM`eh8l%6T}`XUU0_cmIOO(uDs7_MAXocr8W+3ce@0v5 z(~pAGHC<7ZDERClnRw)%C>r*?tAaCB{x2ml$Dchbym;9l8{Z8OP>QE6jT2Fl*rR&Ikf*Pd}kmtx5Ezs>+*r7fGR2O*41&%e^{Qh$*0^z;B_rYX zzNH_IdZt4)73|V^izs4P26M!{{pATN?p1|DL6<4jMxOQtTAANdp%mHyS2E>{TgzI9 zh)`%-TsNKFvGtGb+por7yUU_1%d0^lG#ERgzX&@5{g6X&>G;J~i8kicw6oPv29IGz znM~VhA}PlP!%C$B5SqgM$ftAHz@fHbw31_Y{_^|Jf8UHXGe^dTXLQITsC5GD3%Wtl zbmZq*p(+7rvhFqidLb|W@cN8_HC}kCi3a7Ps(F`;e8UIjpl&~QgC!whcT0BwF5AyF z28L$O6`)%dcHK;-jn=@fZ6v=&A%1RfgU>acm09v*VMqo^DTTD`ZX*EN*(AT#z{Y#EOs+*C ze)na|(;{*R^;h0D(Q1`SBO>HGGZpfUiJnkQrk>d~;H;3dBs0UeK3CUgxg@5Is01@`pf55CUa8iR5@?j-7_A0Z!n76QMgX>Hg11EX?oN4Bxb|`8@HWeo; z!VcHTa6qH(n95v}k*iJ`d9Mkaqt>FFbYE@`gnG<_4GmC~(C`+Sc|4JP6lQ!73TFr} z-lA3fjyVDF+f8OJ^F^sTWIm{gqFlUuLVDd#KG|Cbptpsjg;H$T$qB(~IN}Ncxjb+0 zP@($y)b~69xZmI}D23mE-cx@Kg|_$7q^wmRWFDUPlrMC_{RbT1(Z~;qYW*9|{Ss(k8lKyjGD3iJ0#1=U8jPY^#8WX_gdM?ez`oE!Cytq!BdHT|1x1kY zUErMBi|V#hp*&tPuhl{GIpwoKD5BwcnPeu(^M52R)4~brq4#NZ{{=SzXn0SKe$xY` z>!+9JqOdBHHeS3b$t1Jqy8|tfm?yFo9P;%PkgHW#b7;QUyTApC(0E36tfGYjuD_gq zZ*x>v0PfS{nDDYIplZ18LfD1x1CS@^y=N$U!aFB=$(hOx;MUIWj_yh&mLP$B-8%raW|QvlvE4}%YAfz<$+{8$b9gi$j2i3*~i*0q~? z(Z~XE4FL4FkiWaaM);9T9E}21j>?y7BUkNU;bk0@1tB%s^BBM zSQF@-rob^jV%wm!m4r%85Of!fPJ0Qq{MrBszIS``T3U`K%5z! z^7qCsY9U{}-HTmOmf?{!4-lcKGI{%W)LMOT+oXooeIR-u_&pOj5+wzlCoSUzpz3`y ztYf z7AlU98rG#wmC%YKRz4(Yxw&xSyT63%vu}M?Q-ob&h0oJcj=$lC0&3T~A0JPUnB#tJ z*tq;kMfxx}GRVSQ7K6+}vTpu5y@CLHDzAOKsf`PAO_3Vu7hsL%r@@wgdKuLGtrnhxj_Zw@bU+Q zc6W?>Dxwf=v+J9EP=@x8$i4opbWo@RR1mbTA!@h>^;vs(Pn!`*--fnelzQw{PC4LETrc(kd=8I+K}MwMa=Fb zD&pG0Cg1`UcbmeI(3P>7GIJO&eiOk$45uo?o;thsJ|!SmtxM#U32U=xF_G!_j7PtI z<8O&HUW(RBlr6L}#>FJ1ALyrn1L~IDTlebLtp2lcB(whf=;M1Nzg3}hU0GN!6qrMf zK_TuJNN%f*JboW1%2BKP`v7QcA*uh|6;{HKhqOuV4I5#80`r;4yB(2Ft#S$Ib(1+F zHm_@qT+KHgeO8S;Ix8_p0K-XkeT4N!GT4(|#KOKcB7 zJMDVh?O&2Klj^x2wuxk#A>&txdR9JbZ)9S%%-H^j&@L>yW z9yWzNZoBv~tb7-xae?kxgL#1(5LT0%2?sQ*$T?co`)1n6ric6fSl&p}F_L^-2Y|~z z2=2Q}yI6@F~ z3#c$(|8>^vIb-jVoB(@9P?8r3a889QQslED$~NMg5ICvIkJ;u^pNlAFluSws)Bv=> zU8c_5b6``lygOap+NGbBrSPx-7t-eK`iZ@98P&muuM=AWNDlZ*3ru}pLacUN6 z7=ls^xh7Npd8OuusmNkPn;tvrz@_2|$!tFP!TaB(Y`JZ^Hp3HT^ZYoAii-m@z(5Nr zn}76y6K0e0Ftptk9d$oBTP9^DO|CrOFbcW6!pR4fBS=2}s|Cu|>ab7KD4x(kQRSFK znatktH0-Ff>ic=C`UxmR-TBJnbz7&^Lq6~6YZJcFp;SVh{W2|I&WRd`e1f1xoss7y z^4>*>+~$jtmQo>KIGH6&H||k!JphF?h-V@3H2}CQCOICZX@8H*9tJD1>N8(1`s0`P z++l~P)@lV9ky}Ng4UbklO%a>^*C0swbX6ZZiRj{Z1WfQHE+Q3Q_(EOra zCNnPmF|LYkUjz-rqUsoYl zi-**|?b`Da6_&$GPyh1bcWGqY@mYhX{Brin(RAi{FcGEksoHl_m`od)WK0W`VL1!y zg#vTPF(_;r6?dy3hi<|{lJf0OJ%A1p$)v%s5oZ`^6W0(q{6D-JXy{B-w6buo@-3|(03~>bEmvHCh6r=l;`Q&OM6r*HXO+l{SE3>J1@aJ{5MlqTI0YLBt4uVzn(~PPF z?GkhRQ-J$^l2hS;=XItv2I?NA!uU?tL2Z0%Hq$`2NoGO|0B$G9^WLyh^*SMw(~+<0 zbzZB2JV7sP{&6c6w<`~Vr6vKq?G^N^foCQnwaJo$t~#WG(g zRGUdI5n#tli4m5cL^!9yMa%v6D3iB((vdO#0-Q1+nv7Y3;@%=D{~QujrNi`p5@eF* z>zxJU*m4Oag+`NmTe%3Rc#R6nDCDVfoqX5`P8)BM)1p$(e%?-{uG;oX6M4=Pj=7JC zp*iwQq>&1X@!XO4_(l~6uC*jDdoxtk_!JF1Q@w%+O^X+w3l`v5)BPrrX`faV0enkR zzrr8R7)x>t++W#x?dqRzJ~Jti9Hl`&X(jS3CGYEIWXsnk1Zh#WVR500nb=ljMV;$Q9%p z)O*mbWaf!(hZ2NuWfpbW9|bg%%(OBmOC%GfsQ~!@Mp{Oq6n*ZK{L2-&)Oi2cv_KU~ z5IsD-+*N=m3N>E6L#yp0diEPLeZiV9cBfMzh3#B2aZry&ej@A|FVo6=AQXzgPkM^4 zn@RrBr=q(6G=5A)+La$V2LqROv!``+KB(qgj6qqW8=n_m->SD*lCnt3Hzw3=rA8iMV#hGNd03v{(MOmCtPxYys)kkn$YK;2E8zsF% zn|{cr6`**YitE)~JI>lK(dJP_6qt;ngsBqq+-xk4fMS|Ng>=#lR^mHpGWqD^W^O{| z4k{Gk^YYK9ruhI3@3AVZCZuz(C*ZU6Za*O4JZXFm4!9lQ%}_vW7en$^O(+Brz!x&3 zS_AODM#j|z1m8iw#ml7p7V>pS;5Ikp6LcYYfFJS(4!! z5#{#0Y_X8c3JbR+nyDBGE7fL_zlpG8LRbdR1;IIfN15IcrBgMXwrRr&W}TTE0B5{! zAg}a8aT81=4R>N<5-iNMs|s@LzKjyYndJArP)s#Yktxhyh(D$CAey zdcdmk5hJ-%hf?UiG*YtACg$@x=XvJ2a|icG=~G1kbrZ?R{wl%$jSUHw^y{zr!U4CJ z??@z5CujlDcX~Vx*QSTUDT7H41)vTNas|8&Gcy>dpK#6}x#r4z6zuiVygDj4LA}JZ zD#H8~6t%Zm%p~QjqhHmbB(ERi7Wg8cs1|@(SyaeJDJJZ{dKfRWz-$pE^ft1vtgUSYB%g;(y9DMt8BWDNT9l#2ee#b=fbb@Z z`UrN^Jt6aJ7ud9oq46UC^-j|2!Ed(=RLt}DfR#{b$v-RS{P~cUXtuu^%?AADEafk(9m%CP*Zs zDnOxa(0@O3tgVhbg4^J~GnvN-8(r$a95ve$g?Mk|#okc(yp@(s8)K55 zQuu=YD%hoab4d!B_&6=zNXi!`1i?ujBfs0)N(|hfx7^&n#}7G#*Dcu}i}3p>_F<#Y z3VTU;v4G-K;k3#`0TGJ2=_DTnVxfgs7a3`@%@g?sp{UL#Dr72dRRSQk^a0-4ym?C& z`3n?*zhzN2PW6Tz+BZ#P>TEw@Tr%rJpdG5^a#FNcp0PcfH7{H|NW7;qsOSA_BjXBAVAH0IM zV!5Mpua%EUri>L}rJ0q^T+)uUH%NZ3K?!~flUP7M*Bb@ApL=LIaqSVQpP)kJ=jy4U zXnmKIxA?-b=J!cntN=j=b)UMmLPP*=9r~zHT;;~~tN7^vm0DYW!Z`~Sw?F&zxS0xB zCYiD8#ZFIke!7~w2>SvWRS#`m#~W5c-y)B>&!_(zM zwE<|?vxr}7!HyQ;=Q<$ zGlvxQ*E1m^AOz1nr>8<9na~9GnOVqI6f&0?ZYUy)iaoFyX`tz-o}~Pn3ZKd(ANoNN z`26;0=9np>fKUH_Ow5CPDy>fJa64iDmgycS#Csu4CwqBU9kS*w75ccl>!KV)h$4I7xjdX+l+h*T9K`Us)M_))iJln{Up2*Ioxem28`1aao8&ov0PaUg8WNJ@Pu|Hila$9G-&}GM3afgBZJ^lZz7i_z(>$kYC3f8r3~7bO);q{rzbXb4S3nege8Hn>a+kQgxnx^`vR`N zSZHNB`_KCgwNL~e&SKmE0cETD&fzqMZ5<6yrH?k|-cg?IiMHO#r?JBuY|T2Fc68DAT~9P>5qqq!j+9 z8*E(W=ugLHeDnF0=b#N)a`(#3S}5VEzrw=5_5f71cT1$91C9v3%#S zzKia~-F;mPK+vHCpPDuNbpoK$xm9fbVH_3a+h>zxX5G7R?w*0n+C8RC$CmZoUDYUC z=oYEJfSM0TeN)&}i=>rwZ(bXZ{)r^-S3?;>PBFR#6rNXEh;h0X>}ha8<`-I6RXR$> z4uzuLE;9SvyqQN$Ok47_mlj31RjKU*M=Gq0G(6by$sfP{edX3b9iZ;^=keZ8FN~v2 zR!7+5<0Y|>jws{?(^1saFqxbTo7arI_huwX=@%8gkx4$P2t~*Xu@>f7D54PExG3f_ zq6oJsd-q>oRP!06E$LIs~VOQuKGJg9rDxhjOvln~8 zIhyWf!;648QNDR!&)8b9s(LH^c@d(@Z9OSdj=g%|X%T=RqKJF6x}d7J8HI!u%B#f9v}f z8rL7YD4yht$oCuB9fgQXEo9O>{Pbl2aF#Q<-RWC7lxPbB+nJ{;5Pysa&5GaR&wTpEGg}JZImrwpG;;>My@VL_c!f$ znil3sPg(NI^C~C;V#w?_1UL|Unau1AjLBpkDRQ<7K)t)P?5d8kwGNXlmg5~fv7hp5 z0&KJyDMzAxP`0`crDKhO#*eR7M2EOTFM0#6A7vU+-|RE@h>2M@A7_|kS~9-#fF18x zNT$Ey3Psh^B-4h$X?;izMV^owG&BHoJEZ9XtQ!0pP0D90BbVoMRZxV>0h&L4(G|Hu zVkjT=1T=q}NFEB^Nb*ND%2Hu$9avSmM|powHF8wF#@I~hpO@PC0P2skGt94|7~!eM ziL)BG!D^s^HtAvS_-aJ&!0zFe``bS90Q~+j$_edIlF;^$nKp9RNEZ|cStBufPOBZH zG)_dW+S?@F8&gvQhpI1Iy2m2@+-UqESvDADlHqk>pWx}CMlNla>$H&*FYlN?e9%kZ z$C1*-7eakqRe*r9y^`;O}}jxDq?EGu2B{<^Ig@UkQk<;n1C)z*SNvvb2IlhJx@~J?Ss!{k`Ds` z2z5VBX0B5$1r*Y1vxzxwqX^eY|Abk7^F<+Et4x%Ql$mk$!n)x>Dp>ihG>{K_z%I?= z@YQX+MF4&$C8qR;bKXhft*U}p{Wh(3sR0OW52w%6Rp|cy+|?vacTcp_0MH1kUTsm> zC*)UYlm?(tCloS{c_J6of}#$sE~AJ)`6d9-e_nR!}@AlO5oQFiy@GAOPrMCYf?7%0z`s zMOYO8?uW>jQ8hgT0B&DPZ+uZs zjbnHxiB=Il04gSvS6yJ$UMBDOz>eyFMIOI;qYIo9e9JNu4tXp}r%m|e*?;WmwXZ75 zHbPJGa2@2TzCAhas-A^e9yfuN=S`B|s^NfoQZ{+C{m|2o4a{R%E&v$&`1ef<-B6~g zSIMiR{Q;b3QeEU|WH6i73n*LAHR)s{c>X2N0Mw1#pctJ=DLKwe%GW1WuG4#E9LY<8 zD9LR#$@o{bP}I0cMNAI?&T5uPzKA@{lc;YAcpc_uU#~_(zgn;&lZwRm1>|TD)d@w+ zI+@9^*ke7sVbjAx(W!*{O-ytryNXu&~ltJj-Re-{40V&PsSsgYW)6+;MtZ%Od z;JaUpHE23v7fzdghSAh6V%TpiJq+(5eEUo=K(+h8iUZl{e~X6+2Qyf#74( z!@c7JMqelOBV5!v2wrtNO}Q;I^K*AN=CbC-xYdv4g#FvTi)Bg}6<31Quul+RrB(^< z_2$HrQgTAtr3L*P2kTs#&Wj@X@C`q;An1@w^mG;BK&^v3;|92GBc-~q3AjfxY=|0w z`g@Yu!%+s8-?=vk3e5x-(694`eeY-7y}GR-6ajn5_-ECy>Ji3#X8A%*I78Fv)4!M0 zc;;e8R?2}(=d(y!uIH2$RR+{@gkWKe(viTeSE#tB5bs+ zPm!k6En%Nqhz>TY-(;FzgHr-elYa<;>s;C_5CHhTnmWrPXsyiD6f%2(Pas49foktR zpG6@?z_!t-|0u!(w$@gHA!|4Jt5`#$36!S+XdEF^ zzj9PlSCoVBI*o(((CUsWfGVlvE;X#4Cwa{a_6$#sh~hjooDv$D^)~Ett5)^XR9Z=~ zliUaQ=vVb!f0Rk{AxX>U9>^2e{d^{Km~PyZsEBO=E8l36pTHsaIay?E)MstpO{R@8 zVqGwRz&~T(?9xYzvIujT`C$MQ!e4)W;*K1(Gp~I56aZuhHBL z9n!+8AXo4iQZkT~Pd%dn)YH;Q{^$-n`ozkYyQpE+F_8-Ss5hKd=>ho`^30QY%oPYc zPh;exq-;4^wk+IfByWZy$BfL@D9q#5y}Rbu7aEf;wilsTLuNGwu#)C1V<+GS`Oq6> z5!=>+)eJoiS=Sz2>Y|0Bd!)=uE1G*lQTfxe2Bx${aUycKc9e@030|vdzBo=J&XPzD6CnEZ`8&ZxGUho?IOg_~ zMcT0-mHL}AK0mW-;M<9^#bWqv{W~2i!T~{qi=Rtir=_WsuY5Ra%-}Y`TG8FF`6rPi zGdI69^yyG7a*5t<8rbKthx#`J0KMbL4zQ^eMaB<T_ zb8pxu{%(#tx1|{rU1Q0!jbT&wACj3C=1A@fXVg4rp)|MVRgzb_yFLHFNJZS&-a?H& z16zs}XFg^`6WF6#7cDd26#)fw|B&f$NPUcyVk{)3jTNy#K3X7sm z#4r?4jfj;=nrFJ8fNHUsh7B){4sj7tz-6&1d+SsWl)-H;$$f2GRQ>;vhZk^C6^#41 zeV;S6T`aJ>ba!`mcL*qmqKK`CqDV<7AW|ZrgrK4zEscP*ba%6`#dgl@)H(NiW@clS z-Pu5WzQ6bLzb-cvMDvA-mO+BH_9NO&0pQl#d^Z**dNoP8wel5U8J(G5?!`W(CTKPw00?G7mLjY7G*$~fKLxtr_6@b|?t!ad)UKt5;$pp}8-Q(piL;%WQ0ncp&}?-bS9>Ke=*APboe&?%0$MT6LP- z{kWM(jfK_~e(;)=PMi2wd;#dD z8_)Kxq(y;L*LcPaHYKm5O;y98oSyey9J6rmycx+f7|H0L`UR+vo7=DCEiHcF903kN zzgp;UtCj$$IzA*RzlFSLY7HnWz^>9U3l$esD6A4!D7Qq02KOn?5`dbaq~+0%Q%BT$ zeAd6z2GZIKc6B0_d%=l%Ys}oc7lU-eLeuY_*z-b(pr@q10f4Vej=^4eSwGU;Qe`&0P!tK4Ayg$HOsNAfCx^)!E zq}IrjuHVXoY2+GYsd|`-8}+=`Qt`QWnIR`Fw0SaIglnoniHuo#R_4a?{bSRrKrH(+ zEf;Q5vP4uy-n{$-L4Xj@=VIKGb%U#w4fG2rU88xU?lT`zVGX^1{@2a{$R;8$+7B#@ zs18Mm(`0|xRShGP`vcHbTqlv->5l@;NE?uMPpPn&m^cW6cX6GYzn`ZF*Zj_s(j@W4L`k|B0KxyYMPZcIRH@ecoYi`-K7?9R zdLmEV>Fh-~`J7}5Tnao(Qli2##T^HzNE`~N2i>NV&{wo@@`yH(iz_3W=X}YUPRd*S zkU2 z4e_kqRLq7=Q<9NdI3iTaI3q`opj#j<}puL&o zj!`Ytuv;u~tvdksBjkgUaOi6xCm>V31m1Q-mP&t-G`!t#olNpd4cPdtA~{C@@a|Je zVbS~?g;)I|Z6t|gL@59z9+E3nuvt&?usiZJES-G%=R^+_LI>&xAupN%(IhSLA9d|p zMr^e2-n!u%`)f1;3i6Xop6H;0LMSf6q2cZnBgvg!ZkuJ2XFQ+~hFHuz5{P_we)N=Q zhxnp#3~kN?K~X)Hmn)+XR5zK{-Q5#F(==(L2kgXdX3{K?8{Z>$o+N2CT%I$mES^>T zBKfGq2gcN9$S}^r3}-6>t#_EIu!dGZ{={m(NTj?9g;wGUWBuTu`JI#}2||_mR`Oyy zHQ;mlV-bT*=7l%V!AWDLMc!!ypyaEs8>zp&*h&U&1$qtp>YrjvScM} zbY)+^M}wK9X_p6pGMjeP7h$7$=c0wy+pXZj@IMTsG;G<;hl@jCSLPTE->-WEQ?Z2UC2J_w*ydjFP zQMqf72e;P~Y57qDg=RiE3U)1HN%_YH-N(#7uP1q@DGE^YK4$^Y4!F*2e^!SJs%chA z$v^u8c+)^yCaGbknnltqF~c0O+Bd5vOk{| zW7>SFhD*ojOpR>qqe-T~C6ANLpzjHsqatYp;JHnv6X|M0IN4|-nJ}-c8rkY!kYgWN zr1GC2#i-btWCKR`3$Ph;%; zZ39}hE-Cc0@}fT+gnoMlipVmPc45H)S|eNOC@SJV^MZ|T9; z<`+pOe>brNRDv*shWQhcza9JjAa?=z@;FQKkCL$Qtgb=^pO=zJE)vyKOtP8#)ldin zB*{8YhkUiYET>QGyh9=>>7(BD0{j<|Grdp4!Rv>Zs}4o}z#BSV=1NHZkvXsbe=Dt;ADg|z0^ zT^sAbPWyFw$}AsItMY0&W{-a7m=okN9bDN+9xDzzkD-@kI!H3JB>hrdrxD=-f*b*LbNRYKu1=JYnU&tEYb2 zbv-fNYO%`9a_ht55Cqr=Isv)4eVIg_s|dT{R=x#=7(CrT(im!@-NOzlri8?>V(V-mxvG|M4Li|ODf-s))8W9;v)N?}?w;0;3)dJ{)Y^$l*5(2w`SC5hL zDI^bT;H(~Pr0XBK!@-YCuZ5h%MO1WwU2j?DUJ=f`Zj(&x2fNbYhG|*=!Uq=qp+@&~ zni>J(A@X)PxFmi|a-0bCG?Ba*pc0@6eDNmv9u&ImD%c9X>@Y{T<1~h>Zgq>QGpo%gm%fmVejuOD9ix00>@bZ)n4ft-8Wn)X&_*GHCS=Thfy~=P<0J%U$zm-}P zokUVfT~nib0?7ryQu0$2pw<2dT>!Ls@TAB4_Y&`uO!>eY&MKB2w&~2rLCBNW;4^ec zd3bw6G1%396hGa~qk4_{L)S$~f0YkhP2Tf|3r%j2sa;_c^1GgtqC;gmi}av+wTc9|J_7$uBRx9UnQW(mpBeHz7b&;~8%55a6Kk>b6-gQ@2guN&y(QY92bca40|ruK3poy66FD*@}J z`1tQD0PtF3VbUuaI8-`8%6FcU>8E!`lJ`$oXd|av&bNkZLG!ki1cV`m$hr{9zke`= zvBxjZ9`TU8Q30-Kk4wyUt12?I`aObn#y36T%2YEQZo8rOO)9Qx;J1v=FubirmL@Y+ zj;p5<;H-Fuy6@dMKdXxl*@AalN#3Xeg%HxaZ}Sje0f2Dq$HWg=!_O z3q|dh68R!fJce9?e7sKT8w2W@R75QK_IwC~2!Ka&>+qd=7tqnjZ&&1ca}PfzO%8^143Gx33ia(rsstKC&_ zP1W|Wh2$|PLgo%bwq6n$+XU(f(bra(hr@0NfBm683J|zYqFvG}-h)$^@X%tp^}2vU z@%=>#|FX+ObIe=Yth7m4{)h?bhR1(>`SnkmFFc987ytbc(z5*Vt)Cwvr@9fek*DZj zqi%DR$$wku#ByzJ8#N%H0IF7J_lbG=S1)rR2Y+y!IE($KDi>t$6Q6 z$LDrjxhn+O)Rjtmh=8hi{eepk$1~e0A1H)kpQKIj#PFNp@m9*$JHT@dc}@iv-Zqe? zSD+Byd`iZT?=UigPE23;ipZDH?j8;14}hSLj)x{+UG1fT?xV=mbaV8OE-BqPqHL=k~gX&H8Z4 zf-Q3z2q;JwD=9s=W~IW)3_AkhivQ9lCsvdZ0Aaj|JmZTDEuPXQd3m>bAKqpbYpgl) zMdTx(Dg@8!OH5b01rNL>lPP1p0y>_w&^S(nD?MaplZU`g-DrcJnao|`N{@6p+*hMb zC=~~QPNB?Xwa`X7(pHNCcns;^s|uV{zD33PasoW6b~rD~Pk$Ux)e8_du6=RU?Z(KL zc0JR&Xgov!?NcZv3`0J|Vl~PNL()i^!}R0@59H7xHsX<0y7J4~pBBvXg|lIFu$=p0 zP^+KeB&d=H9BS<*X--=&Tkmv4PQQ?h=^-MEADLVQIW4E+YZYwzT1kG3oK~4Be=ESj zucQEl_5kI%+SkKL?r0kL0jYl-uIc_DZxsV{-%}x*Nm?bDinQiX_?#hsbpxQP{Pye! zGO8J}lsLy5)sd;nVS|Nn=~NtV0UOVCCX%N@00Bj!Dph@{l{V%Vfd)yWzB_;_apVp) zM4eDBnPh*s|N<_?E~m;39c?({~=Q(RTXu@cDF-d(WqSR~q?fbKLwi zmeN)&fUiP3Alm?v+tkp^OiWF%z5u)ayuQC0@+ZEdr$hSLBe$$n=-+WiA+@-B=jfJD z$q*GyiL`RpXPb79Y*;hI4S?VgQn7fQHdABC#6wBzY9I&qxt3j5NX7-huFTemgp?Rd z;+DfyNTl`J#BwSWf_g%v^?Bu;P)s0A-H?Y> zlqMsa`|l(#U6W}ie?IUr<(DD4!6kLK4VRC!RYB4I!>&Q9;`@2AZ2+=}HRs*X%gL+W zt_~*^pG2*nIN`-fS7;}hPOMF)O-fs}2o(G3+V!dsrpi<#j}U=TW?lMpzE z$oM<^qR8a!Bi-RUUy{y`_JB((WOA3)O1rHBY_y9EbP~}FE(|x)VY`TC52;uO-MBP5 zq#gNb%ZZCr=nuW(f&5k6yy?&7y*t$P6W|bdmKK?Rm4sM#(zmfBk9_n-H9$3f!My8t zt0I3>$fG~5eZv!QKSi1Ab;y@c?5-p+`OPYuY`@p99T`Jy0POTpCPF>{Wex2q?<0Kl6 zKcVGP6#1kkT&o;Op7a6S*76zVworuJBbR95*>CmTG(E|yv!J+2xr54_)g@TOX zt(wS1Xp%_IX+A=Po!YDXyi}53ufNe2`SAbA!o({{e9}@-_lr3df^2V)Tn%W~$fP_J z5Cn+hN#jW5OY_1>nThAj`XdRn3Sa}uqjq^HFs@m6s_0KAd6 zUWw-4f|0GkG5LFMWNG-u#i)BUpBd^7XWHYk)yhmy=T_1+kEdPg++He=&yuYli`o`( zv{_XKfO;d%Es!V8taK`pD??Fw!h^3p0r-DmUy4*_1x!u%mmnACBmw;=HILeS<%MDlsI-11NQ$+@b~q z)QTpbmPamS>mkz%M#`_FAal63Bytg2{Vki9embX&3U*?-xkfVX{g3+z$VdBVTJBvj z(z2-6q$tjXlP^hr3#h)blKiDl_Ys4;jk-tj&&tS$-(&-2fR>5K{nr2hM3+^)F}r*9Gq&kESVNNw77tr zDjcv}cxWXlS=Y5Gt%2tpnGT8jUz?Rit{b!Dh(yKCKmma8BQ1Rdl+ z51Ub`Bqb{qF`baV2Fo9iQSbCSnet@Ub(u#7bQmyUQ=(C(;p9|zpw;fEl*m)f0!8FO zP`jxC0T9%_mHJQq_4gHDzuvjUYSCN>DrtvdS*&LcjU{q z#YGgP*iSNTO#8a1A^J}ZC(qY_OM#Ixtwa1b((Xba>|SK1`+vkzAvZ!^O750ucda^5 z>nbVpVX=0%Xk&czO;r`3YOtR+(qb+0HJ^&GCsyA3u8shkAt|JF8WdusYgDBF?he#m z``GUZS-RR)gvOuGNer z2LPz@h~(a$-DW-}DLqYyPx#3LineC**4kqq)vVg$qYrn=jO~hSo^!48p2`9;bu?39 zn5c!qeV&nId>1uxQ6WDn?NT!pro{Jrps4he5YdL*9f^Yf1g?5x$k4(3$+SHC$7bC_8xU84GP>!~I{s zXcVMFQGIQtC523`2#0}Ik|!Fe0QGNVQgMg^3Ltp;xC@%k!WcGCL^kcni5v9s*M9i@ zWw_GKLjLw?`k(N_PLKtv`@@0L&qV()w8MjbK;Zhh`=Z|4QTeOz4t9^Xq$eYf^N4RWcr z{qe(CizRu-P(J{w5wUbgvsg`37*iyRoSGJMuC4%udLt7~2E)d$IGj~HBGD$TE}VG( zX0=ed@_;rA)W}c39+@6KFkYdP(ZUbq)J^!Q4;ZF{96kyXln$*7!n~-}{9Bm7r z<@W2e&V_63_kA&WUkYt*RRyZ)$)h1Cz+}dCPy@OhrscKaO6dpk-D=2oh)%*a$f5X$ zSLhJ;X%{~fMhP;d8k|**rQ$Q7)L&NG$X!tY6(F=rW9r@)bSm%f!_HYq8VAEh?I|D+ zO;f4wD*!d0kdNvCc-2bk-|pVw#TIG+x)o$%Pfrg43Sqp&*yhM4ylSC-V{v3^l|sei zMo_5UjiKU-KXOrZKt6;je^{t6eOp(AO{s8Fo+@gDro3&q5ININWeJG+Y&heiYm7(>9ms{ zESXoYf(p*6Ui(W0_%*KYJA>A7A{>O$t>0f=7B-!JF9*PTr9@tvRuy?DK4R^I!!uv1 zBtTK`HYw32!9to%G=*KUSS}3f(5#FAyLqxz8YKcKvtfTfIGG{Q?tW!t69-bG{QIom`<+R^!|&KTt1J{(wa_$``ddcr1nhLMK8aH4TjL7@v! z11f(XPv4+QbsN@g-n3fXcJ-RRyw6B7@y_G$hu6cx&mXvBV75bWPz`@VGVC5kdIndB6UW#Q-*B8G3Qbjibg`t^jB$>wa zrxDRf5^b*6g)8bflJoJ0OuM0Q2w{c}0BYPMX&H{(Jo`UnhEIXA-^e6ydlnluWn|A@ z&AgEzWOX_f@`X3ylGdZxNz!njcVLxXZ@gI?cI{%x5KhuEQ4Od+inU00+QPN+ zH`3RBlR$-Wtj?|YT8YLvY80U4`OC!ts3t87(!v$(iPW>Tkge6OE9oQ+4`QhBMGmWI z7f(jbEEV91dfQo=}nWz9-=O z@>41-v$V*i-H*se-8d@J4|NjY5FAEwlNjXPJUmh+<uOm}O1G!&B z{wmz!VPE7)sPxU>DJkC#8#ZEF#IbK)TrOSkK>m8r*(P^n>mI{=n1Av{E)A9?ki6~- zcn=L@wmTnGR3R^dXHdtb$Mht_($m*8DekYr^XjdqjddC_xcwFtH(d=y6FvEofNTO> z7%Nehc%zztEZ&25Ss4C9>m{;@_nRVbetSsy9kl?3>TP}ca1jtE#E~&g0F1Hf*#ba~ z2r@nDd@1B2Op8BytEvYweMQF9gd#AK*`%0_fp8XbF{QKUKk1Ki^N;XIWn>6C{xEvA z7Zm;n*LVO>^$eFNr~ep&yo8Ktp@E&a!ODdBk+fLIq=N=YpAadfTGD6fkfmcHx!Dgu z+4YfO$X4qH9j;VCwt-=!^zcRBiALHyi2Kv?{$d^`9M|VNziz`F`?2el4(8Qr1~Q=r8^WJ z<0O*%s{^QfUJl>-t`_+y{X$T{QVU5r>g>6DR;GRJjRFO9YOV%UZ%nW24Od>Uq~Gd- z9D=)TUtb+tsPKieP8QnfX`_!`{nn%}mM?m1+1mq3A-7LpQ;CeJ1ZUy_l1US1M^Gjy zTaWvyR08r->kdODGP*ews#j_EMWdeWH$O0u<|FU=;u*qdnmRzCTYak~Y`!3A`2-n) zZjhgg$j~d5G0l)gJLDFbwy_d2`TQy;BqlNLtI{=>B~oG8?TK7evwuZCJ|QWc>Fbl8LpLiI1J3Q+hRBx6g$Ny8GzTYD3kdio7DvUn~^ zdhp7$VNL4qe)^EoMsMV)Wg=;91RLSQ6f*4FKJ`B{kWB6hU_8k|K+_$xF&(UkTw3c5 z{7sEa-;uj?P*m14lT6Y&4bHsw^7j`$*%!+P5m5~VIC%FXrMUrG0G)0Rf{p)SD@pmc z8XDxMp@sam_yaB)AI6kZMl$Ax;najq{>WD98kyD@3bFDMWKsPn(;>MzvIN~GQ)UFg z{XHWc;@ZK5S5J)zs0qzxk_nF%dI7ECb{xOg2T(WPf3RaIEo{myo6+#?&Tt}VMTMJx zsoL%Pc5T(NPQ_{+-FKR&u;?)3Si;qaR2{=PSB zxZ@da<71dDY*HB%Lf~0uxbs0hRqz8UE(9PSUz%^!gkqF|yjC3!L7PbG??#d)k`iec z=jMq*^4(}5jn~6ulE%@^J9Vv6vbYK^9VD5y>H;0)!EgwSCTTg{2zLH+sOXJce8>H{4JN>N-8u@clbsOMAGWT4??I)Ho5j-J`+}PD+Q< znTgp_e||}YoIGGxmZa%jIME!ZolJdb+>Te9zP7^n^sgBW%2X;}-XD3%PPDqN!?HB6>WWEbS5nL~vfdG&OH%uGkxR;gTEK;FC> zj{W^=8XqlvBM|@4n`T;K&B5J`NO-#cx_t_;|IO zd!=rFCMJ=g)sYvES)_cg7;J)`(#C3$sE{WKz+{q>Jo<&o`0T%osnr3 zQ@5t%vw*36#58p2P{rFuApOt1Y+?ZIa-MbSf_HWaqz6YGNpHkCLz_YwRj-oTe zw}BcIsN@ye$q)OffXaF*uKB|?;S_)M1BCj2$>gR0*ol3@NXG0tcJtJdWq-zzeArb$ zVTo0TjObf4aCj=ovpy)uK|1@h1RR92?K^a6QX3gmcSxQT@T|HgnZ#I;W%@PNDY37{%xwC-01s_hnmb_JiTpH7+Je;gx4)^F_qTQ*^$V(+E zHo}SS6cwqPS0DQGjV}+Lm1$>)PKb@YIvLM$ukrGgLQVNL}&5&Tss zc8Qsuc19km{Mi%vP?a7q*bh$1MUqUc4GbfB!@bI1l>f3awiBLJO9!9P(`G4haC_g%972DZJgHv^0r^t-maS9*|JdjRGIV)tTwlXIHF8pK zAa@CH7UGS(O^|um582!&q$GUo4ZyeeVJhO9Aupe#k>Q`fu1o@LBrEOqXn_&wWW=n; zRIG|2xgNRIxd}vcI}J`7YcS$fTV3WO9IroEqNQUGm+M~pqx6);1xL6PQ@|abw{Or`rC9A!9D9Lp9>1;Ouz6(B_vn&vbhWjS1 znN=LlO19}YqF>jc6E+;a@iZ>lY)wnGFb~OBw@+`SRr~pQ;aP%!+%zA@(?*i1h`b`j zpZKAx1_dd8o_5#!Y5)Y8skj{o*EHdrssj9ew34xd;NZ60LWN}fwMVT6A09JP+Fk;M zrG95^Ep6GxZ>=zrd{t1@9{$~iSsit7FO6=$ief6JJq{I-Ta~=%H1USs{m(jgi-%G6t!;|X`{b&@u4~PZ4^KVdF{x; zod(LgUTjldKq2|=yE0AJd*^cLGM-U`is{Iv z?HWT`7PVabx0y1ZAtMLDv(mda)6JIDPd$7llh$}EZ6uir)7|;64CvRr{Y$${n?&T~ z_AArsz=_*3+DKNC^7`7dNVyj>y)W@>2r{$}CzB`pBbP7I>5%ZU4tW^5-W^3zK8j>w zec(lsk33L6f~iazpuw}6Km1+`JGY^sq(-)H|n6wUSiP&D}= zii(S+kO$3K%1K`bz)qYiG10{2D>7~F))Y`?iItQ{K0O|EmEm(*G`l48^8BqQWr6TR@(4 zTVzHIhQ|hSD&X^bQryax)x7bn;`^&cxSZteaM(tqlf3&ZZf; z3OTf&c`$h-0M$$*?b6Pgg=XZ?`p*c7 z$=A1j_0tc#^>iZLKltOT#o*vKv@xC$bcc_Ekwx9^=VWG)ue`jPyjd9;#2;kdDvu1) zCF|rOeSHFYhi>p(x5kkUZ%R^dTAQ0pW8KO|t~#Qfc`V9c;wGPe~fz zR-pjx|K1u1m(=Pex5>!g4p>uGoHTAN2B;gqHGav_8w-L3o&R)m^gABNru#Jh>0&QT zA@z~G+GKhv?F{>3NX8F=Vhzc(-cWd~recm35ZYZ!BQL|5+qZNuojf5?5nc`dGhH7s zgY`8$W44v?K`4~QMsgam2!UG}-xi9}>r7OHwMQPbeGHTnx*$Wl zPo)Src>QXkBJA=_Gb#N7JMjaF*(`tVc`B399gx4qiLt*l6i_`P;lat_3*+ZPp?&-P z4_>QOuWjv0Fa5A@Y&Cy4YkAPhJjt?AlJpP%K6>i0L`o~#Xi!+X1|MCf;=$}z>VbNa zTm3ZkXWgW1xHYwIkiP(zik&8D+8PYIU<(z8k&o79P6ja9NIvNxz((smOHZ5oZJ{Xf zfq~KQX;C-(QM<{g8)CwD}x4RE}o8W+InbR5XB% zx^3;TW4lL624=DTTuuj7pjtpcB@a!V!R6ts>=T*t-xIp+u`rwE&ZTvgJalRi`KTAq zT`D-Js=c!%PG*LPQ@@RE5b!WfO}T!pUVcd4DTNG6$do<;0F8(ov}Z*zzH_hrdam`Rcqpp87dFA*z6+ECQ{Zvj`+D(ltUK8Ff1I!@xiSh~r%y?k7TA0DI z+XwJJNAhV|D17#iln244;`nG@Mh=xWJc^15-8-YTSJ?+tJg$WQ6}N3hI>@Fz$f@ZQ z%9{izL=?bhD;eJ$ncVuWGpBr@0}!yoOwzEXk$_yPy|XNq2T z2H?4aG9Qif0njguyjKr-aQi_bQ>VMZMgzN-B`RcEQqQjMtVSN{{;>Y{1+@ia@c1%K zlEYUuLY+q>6W&)Li~rZRtt71n+G>&83lF5^zsmxpqZ!{Bt_cm&nIQsp?rkodOd#Vs zmaPAPlvlyd=VzG?=1@K3cdZ@$1bYivu83C-m{l)n9;WX zCw=?W^Y;*uQ~e+ATbRXaOt+F2ePZOr)zja8c~I|H7VM9(P?5Bvu?9Jaa6vcxK|0Cg zjqN;ukp_}Kc|+6ZA{CZLmv(pY72$$zcADwT&=5FOq0LzhT&sF3^<@BaWb#-o*fpN> zm^SjkYEV>IW@Tg_5rrg{8?$lu&P_Wv+(;w&WwYj8XUti0WUM<}scn#{SW^!963&s_ zDj=thu_P^#D~bUcXZDZOqKJvXcg`iLYBpriDH>)5aQg_NP;1^7YH_ z3n)NmGxw^Y@GRSW%3TXHL@fKPMKLb{fu|{d48VQxSC>-aQb}G9LWZ_UWMsEaAAR&v zpZ<;OcN#IMBr-IAL|V^{@j{;5e)wEKQFz{>e5fLT_emy~gG;Z@IC_759beySE7L79 zdDsU9>3Z#`A6zK=Lu|}-t7StcjjFVp$~UN1v$tQF%eW4{$nfC_@=AmH0dVGboQe$< zfMW9ubn=y(@UD?e{!j(LV?rV+@AQYA*zD(x$jQ4(t@0Jz1wdF!#lueRhJW+jxb`LS zUlWJMn@E~3A2k}xgOEi4;Q5`*__n^aOX>gt`D>a&9`Qq_0gtTKWo`gMc3DXC#txpy z!*rPn=?70dV@UeloeO3zXaq$sIW25^H7GpSlZHtG^5F5ClqQ)!Q6Y==CKa-D?fnL7 zHf%< zYHs`_5zx+IMEs^EH8&jd0?_c6AsyY|LaA5gpG_rciH|Xw;*V|o{m`W|yH|WRpjoA0 zFYofb7T-3~A?~||YB&)Bk+)*+L{X7G!w-sg(n&7&0>ob7bci|71+MtD>Q+%ihH`0C zgoVJhR*5O?08Fzm{c|_i4T_?jev%&))6>W+bx|ZfA>JsphfJ>yK=f_YrhbFw#YE&u z{8lC@zk~b<=SV*GLr$FyB%i!r41n9KMmp?hjXYJ^MVqwmtBJ^@8Fg15R~>evQzMq$ zKBk7Vh65k&oAmR7YS8&Ztp%*!Gn*F|hLNe#l}c*l&wa7X&?*1|*4&MgX-$jTaz!S2p|S{@vJWYZfui*H2HJhE zLk73KGP&+cgPfk479EvjOg;GZtF6j=sQ}fdQu>Bk0`lg${wonh;eL}RYC|!Sq`od( z8Z&9qw4)0a&sd@7@24gCV-)CJ{hf-)oA(!vswt&~$Q zEpIB|KNWmq*?4ZJ(< zk;&AbDk4uyX(PRiXXuWEO)G;8zL9Zb+vs4kn&ed-^5U~Bk$hSifI7$z3inuZ+>@tY z_ADpBHTTbEmw`j4wO=2Xli!AoF#e&$>BzzBwKUpX>5d${7nsRg<)L_=?1TKMx69@~ z8v1(*>IP5jNjRkKC2-g>?N+kd3f#?V)QyBaoHHCzaIeMTby zZp&nY@lco{eUlr2x=)UO|DqOo^Qzxv)c2RuB7dFu;gTQc4sKMfev5`R%6bXzH5&CE zv+PU~Gno#)UROXib#vrt)MM#MDpU6jc7siqM3Rf35UT%_Mmy{IU&oX}!2+XYl2Ik# zl4`V7Y6nz2LPoTKjZk|#?H*1oE&xKYbtKmWL-h17rbXeXDh3IfhPTMO!4QHzJD-#k zlVZ8~U2_3>3Aj%3K?&qfb)IBWapY9%78y4u1U7-|CE8saioA78piS(L<&i zhvi|nREpcR;}a3ig8HBDKjPXjH8O~m&)gX9j(i2xoVI!NxYMb$*;^t&g(3*tC+&E* z9`aNzn&g)vAmCYtNGo{~u4z>*K7FbG_-%h352c;y=1}C)l8V}}(d?w1%$q+?8dwYk z2v|=CQfcLLd#7r!v!m|Vs*@AD9Qf~kOeX3Ton{(4$t`^Paa(#ljh@}zVLd#MrGUp?OTat_}C%EkgY)+ zX?}bf&fcR$@(tLidLH<;Y48vO?FeHuccdSPhI zkWy;o&&^YW!;rWb^R+hvV5h6|{^hpF$^AzY71F(FHC4#g>8Z@rp-==KVtjiM`4GR6 zq%9!;O00pxeXUGV-s=vaUIa<07jkG6PMf4Au+uhtbJZA6*?Kz)vEs_qWOF}>q2sN)HPtqbcLC`Fc$@sF!!>hErhr$Ty_44;; z_kL1OL~iYE#Rk6C=atFlB;%tvW=UIK8h%>1 z_8AwVNcQj6!KNn_z2Q=u;T=ZbilH@L;*S?rHB+HLUz(DuAP*X!(MQZ=WS@}gYeH{r zo7*Pjjr-(-PiuO@$wV`)<`bX7iRv3;3YpRvS#(P!>SfX-Q<2;nfOcXU$?v^TAaPp9Xq!k% z-QghCzRkx&+=TMWj3l2n6Ogx{izH5l-E z92H7M#3vpA%A6)C4HuA2XuaA%yP+!B)W4*c6GrNgrzZ8`;OX{q%Kr7C4WS6wyt$?q zTqxnK5rmMs6@6eQdY5eY>eP+VW~)paIqkug`Cp$jk(6#uc1N}nlaY(2*%1Rx(O-_L zE+Rww1d@~Bpc(X7ro(v@thI%T#D;LKaYFi}CU2gS$bJ3*#Cki@X?M6f6rK|vkd!YU zId?pPd^EyMKmjzKzU^L``P;e#Dh$`R$J0SdnJDySe`=`Hm5#b;X4yTRb zD>d@wJ;_8H)2=2eKoIc4?meFlY6^9~Et?yo+F42qy^uk0_pR7vdU(v&UMQM6k7*YX zY9XavL3Jh#Yf#!(hr+7;b`bK^Ih^D~KJ1K^})3uNnHAS0T=i8wrtj@T;*6Tyw zE`~ywZ(AK*Rea;|HT)J~yZcZ2$*t-@Wl9!$0uAf2C6lxvi!Os1!6vJuomHko{F3tcC+ZqWF2Xa^wMK>@OTzD2uzgf<0Y&3~oTSX?o^8*NC+mpKqtc@v?weOhz&}&$Z<2Q^ zBM+m<+f+p1pK`lFG8lHM8r{mF@WhIBi-~a3_z7v6s78fV7LswZkWKgLaAX^hPCl&x zXVspNydc76jLgKw$kOETR4wx1Q?t{oi!o+Wj*E>uw`R_pbxIU-7m$npU_6)ZRP=>w zT{d5$!gz6@AfOO}ZZV}Xaw$9TqJ?D2lmGy}Z^e<;M`n^|I|U$H^Iv9mY*J@fCpa5r zB59f-B1>=#71nj-Kf9Gc@_22prhikhI|v05KCqI>aj$EUK?r;!f@J!8D*Q)UL`Uyw z2t^MQO?{BT1MvToDd)azrUEef&XJSJ(v=xOaA~YnPLrp=u2mGdw|j#Cjg=02`bLkf|Kft z@C^Ss)`ZJj8;G!T7hp5gK*qL1CZYb0L@DXi&-FDZ*sHWjYy{Ulk0c$rZzY*H!vhcp zM$j((=gNRsVWW|Dl0GpknTo{EDyxwX&6FobW=VW_bJu}GzlG5zdQm0(1LH_qIv^Ld zfGoZP@6jgW1-MdeyF{C$E$yKw)~_7w%HB^;vydrWkilmoNqI#zHF6L&#objZQ4j>J zMns;30YC3qeak#cqdi3HcmbLh!$_w8(MrTW;r)3!8QxexZe9^|_#OY)Y?2l6Zz%qg zL>?>;2-S{DB%`V$lQ{7HYsmH{DLttPXJzh@40nS}K++%8SUvouR3lF<#2t&xce{c8xEE0Z+FT}h-Oa+nqca=Sp%y2caPbX`L!Gqke` z|BXsFS9u6fd`70XLzXhXnQ4_z`a>c7W|B#or@)nvzbKR6z^2qr@=R3`?^>8aj%k5B zy`HkMXRv@Q+NPUAmsf&o{nN;l>d3=8CYhxCvX3M#iiz>G;+t+bag!Y9is*Zot&bX^O06{0fPV)=x-a&?@Q6#Si!ii7SV&z9JI6r&D=#gF8 z_4s!7%hf!P7g6Wwd**gIxZk8S4S=fsCX&W=jdgf_-b+oC!-opULnUT-8UI+#WJb8- z-|$*GX&9siAT&KgQl29qL)EL+myvBe$tN}8ta2#HL$C?lN%I60owm*g->*Msp^cn$ z_@{|2D_1G5Mo|fP#waR=ARj@)TDYNbG_@yOG?HPJk&_V6_18}yle{~qIP7Y#NT!~q z?c1MHzS<*10N~NM!z>GrSHd&APLh=WP$OFd0~HS|LMXM_%1q{kS`>BMcpy>BRrj7iK2s@$PF1?vgI=z-co0C3p zsCG&wtDI$*mDxelxf~@(t8{7qn((L ze(uA@#g~{#eOEk}H)s=D9Qo)uW&WZ=moA-r-5WsdQ?!vTj4lp9Gcb{|BwLdH7+9}W z=@K7@N+c~iD%t{fi_Hl# z70Dw#;G}B|k6bZxiV6;$Q^=I};UET74(|NMw%f}#CR>>vVMyGu>ZZk{zxpj)sJ8Q6 z0qSFvzl4p?8Y`Lj_uK$Hw_3-eVvf}pkrzz@?d1B%qQdjhF52D!{{o@K4imXt2OF`g zo@B&OWay_4uZ}FfyJ#_oHHEVh=g7pqu&Hv7k1FE%=xTg%Er|}6m`9;!=f7INa+NBj z0z^Ewcc}OT`4Lb!;>a^eW-9KMM-J*vJI_S?cr-ehDMuGp5TOuCzqxqc-XCgIoMRx9 z5A^Va=H15+e@nbOp#+{$C6uJ)3pfcpBa=KT0H}9JVutAMD3Dn70TthPAqVjz3u%sR zjsJw;Edt&He2SeVjU8Z9u^51X;1ICdO2uD6$Xl&&%H(GNH1EanX-_EpUf*)V zAe-Z5D|yWmc`05E&OGJ}a!0;2D@f}!9h_;J%(?W)N@r3Gt*2%3_QJj;;LH>MkT5iY zHetQstk_>BD$FfWfa(!ctSSbD(D|-)u?R)UZD!gXQbXb1XvPDX*1J=@U$@ZaUMV<; z9m7c8EsX+ryuH{9iV9Cto_wl8A(j5oLh@=;K@he6Wm}G2dNfKVnI8E^GZ9XP>_ooY z7fZAzJTa1#-@Z=fROWfdGWoqtWGL(1_%IAhoC?;VoCXEMYtw5+-{^I{in8Y zQsJ8Ag)wKZD(9$1v zZr@2Hw`NRSllgt0WKB#=k+cnhs}ukVDNQ zqCSCX7y{!cYbUD0_>F#CKrh z*72=Muu+eSq1{ytfWU7bOQhka@*1>!Oq=wU$l$TUN+xteK{Q^l@!M)n`$8?CaC~Q) zNXCEDzU}Y{Km2hejS4yL>WYzd+~BO+YUHo%FM2vyKg4sXAyF|K`3ZP0{ORpP^My)K z1WuAk#(f+J8=>99Qyt3&7juV$`lb68nT#BO{P=ztLxp*U7Fqm$VFt^N{sIcAy&E$0 zGg(Q#hk^y|Cau5wBOiWaZ%HIAzqf}Izr}xb>3ApcH4zSFj+64k>j!?^dg#=hm=gw? zBpSn0C6bAerX@AE%2XgPf=&P+XaawcO>N=ey-_AP8_z@Irfz&rdfEam{Ybk*_U0 z@m!`@&0Md-zd`JCi;8f=RX;dWO}v{Xr_Iu#WfTCVKw7^QhNJB~1GNGiRG*rtkdo!J z{Zm?dAeZ33la8lzz6YEMy&f_CbJz&=PD`}eQ33^04SJL;Q$Ac1&eTCuAJZnLHvTih z_ZFFCbY)~~b#!V7vVEwh;&FB4soXtd1bHqP{g6Z{9i*FIbqv-Zr#kadfMRoFB-)r?z;p4BAer19 z`Kh=1(9W5^hR$_|&EPmv`oR-+9*ui76X2{>1j*EKa80PbCYcKTR3A9$bW^57;!7y3 z(Ou!tIi8BtjwqP>r)f0K6OkYPJ~1SZwef=!)v%w|U6B5+hz#n_4U|(!*(y^$)Bne_ zpD$Z5;>C&Mem-&fqqc7~5h^Tsm>hNqxe5NG<^%wqeLmWsM$0f{sCYHaXr6%QA)xx2 zdx@88!lg;Hn}!TtA^1O5;ReaL&d4%VPco(}6g~$cW~h-Z=rU#Vw*qo#vAZkmK9|g2 zYw^!$s?H3R=wPOU<$hf}m*pfMd*ffB8t{l@v~jx|ocSNUJ#qL4`*P$O)3QCvwuf_vDubuoIg9Z6T>2B%)wKP@7x4Q3DR5 z#v@RxZ9Qb%)p#rQ=PsXbiT_T;3nZ-@)o@bW6HYw$NmTSlp1i(HCu!aK@@h%?vlN^O zy5IrtR21;<@ZCZ_YzsS&m)2Ni+Q@Oq2j+g$yRwH?5FrQx6b<)=S!t5YG+8B*vXvQR z>CeH9-0<9VuYL7)4LnQy&LStzM3z#KVXyiix0k6%{|wLYU70MCjQ0jmd9#7Wg*pK4 z3oNubQ4E=CM)6EA3P(5capE_>E_O#@wR=h%S*F5jNKC%JZm_=!&+?yyg7~}{Mw^s& zcrJcnB##FpKf#MmZd-Wx;j3!cRNtLqe$ZP57lPKMQw|+1zy-Gkvu@EQWu6b5iS>U? zqMi986ju2M^GX65pIM1iSiV9b2-T-6Y=5+eTZlnW)TKY9RAX<$?QG!@?p0IKYts)dtce=%)G zMdYON5n=Z&cZT2}b8GzR>N8R4GI=|lHnNpp;CTo~Nye1Hze6{8$FW%IUlHKUW9ybc zQ7r%{Ipg50vsO};jj2aZB;8&;Zr11v3o1uyPo=F`{^Xgv6088df-1Jyd{xQwULjem#Aofyp_H{g>1C) zNOk;&gzg63tpgjs-wjc;Cd;Nfmp-do-3`yu1`T|*_pf^6?L-U7wCjz=>s1%;%_x1zQ{F@qzkdICu3xuLU814S>L?(|}+xnqU z+}}KsLWbSl>4C!X`Nc{*IW6+;FN159&>}CIt|(BMzi4wQ1kc5N7b$-!B0uVWtAD#| zi5n)u&aK6Zwc%QYKV%x#RY9I~16PJwNy?GGHP_)8zAs*oNT&SV8HMF@nairbvG<`& zh5ijU6i%@^C`g@+56-nlPJW#R_Itq}S-cw6?bqX_cJ*~$%>s}oL4{nr%P+O=s3ig@ zJ~@?4=zxDr-RQV^p&I#mKt-envZ$|^ZogL?|7SEmkTk~Ds5737}nu=1RF>uHNwBeWAJ@mDUO^PRzn7_AivJ*F2zYC zC7Cp)nk`hsB*piF3n52IuG1nPLYWtSpHl{frBfFlymIs3DU!(;o06V-;`0te3N7Dl2NAs15(gRQAO*O$vyX&n){Fl7e$oI>^W@MVhnzCW`{H}gpun~|4 zP4nJO`@H)=zBXY@t38tsuRB=PZ$t?G6ZOY75Rl3H2b#j4jQ7Q}N*^_or-PA8nR7B@ z>cUPeSspe4f6^v=f)BEZD4daI(y;NTfWAx{Bw!f3d>VZ;m7+>o2t?Gg3+x(2FHXrD<; zqJunJg@2E#Qv0Sl{0HiQR|ku5Qsz9#smM#G*qEwtp-J?ey7;#Ux?=t%$DLReMy5YW`5Dhc^W_CS zO)c@S=^AI9fqeN^d}a5&#MFc&nGRO2{ry%$xS)FBiHU!>Azwl(kgG2rx@(674W|xb}(^qgOy>sTZza?JL-m+VU3Vm^H4dy3vv+O zh?gkGz9FKhN(>xzPEIu+3w$^B3l9`p&HW_J7eB4*jeklrB8kyM)Ob!WGi96(1yYr6 zphe+mN8dGkgS-jldygvbt`U$ovD>%(bpnb&_#mDRX)E#n7d}g-VX+1dLY=pY;~yJ* z|Jw>83ecClSr<9Hn@q-ZM+SHNkGVIgCcv5MEi*Y4dC|T-Q3DsgdD20R=i%XjyorIq z>m#DA<~{bm=#=S>t-{!6op2N8?qja+g_)rjLHDxi^Z&eZpt0L_9a2YF|yOtinH{ zX}0na)24XiKjKqC#D7jxX{aX(;?=TCo7yF6^r`O!*Ge2Fxvvt6BDnX2kGgo_pHo#C zIC$!n^XC`V5Riiwg;o0JHQV=AM3Du~{d4cT-uP!Ub+()3i0OWKF6xhym$WH{e}UK3 zBr^Htj+b@#{}x?fzx$Dv@G5v7zP)}94NFWQWtqRZ;kjsk@fre#rpAzRDgKc?!v^F~xJ5e4vD{3P<>iJM7}*VOP=!v)<8D|w?S3d?u+ znqQAc$(HcBLHNh2?MX~B$fRkW4n-l3=q$oU({5YblUV(=mM9|CBnxSH5&wjG(4nDk zenZ)McX}Y6L(3Ri(*G=hf6A-n(q!6QD2x9aLX+XW`_xw9e@N5?6dSVh=H>;P4AQ0` z6oIy6xc~ux&&R2dyqbMpr;rReOr%Ly!khcZl zAL#m!ijAdE7@`Pg!6%cCH&r2DefKs;kq9D+LRGEfJGV$~@JB&{hy7%t&4cY7MYvGq z7UdI-P*@*EQz4Ugz6ilTRr4Ahc6p&F-0$A%Eke=qUaZ+lay^Qu%qc47Yw-N)|9HKn zh_Y13vjh~K?@OyvXqp{@9MnE25_Qo0WZESidqD#Szp-@%{3iqvxw!Y3b|WR~>YC>G zpV7X){?|WeuZw2*_|Na8?FmL9m1)xJ;|-U?uFe|-z`ftBSKn#>fRxr~@jL<-%K9|D zh0-Q}{IB#pW2B9>H|)Hp#q2I$-UCHbvRjuHomU>4Qr%lbPQ^n6Jj1=cM@6riH3Q33 z3qmecj*ygwz*9t#cuu@Rn_Dk>_=Z}18d>JNSeO>3kCrc@x%3vA4RQu&OLfCMlEVhkfx+c`h*0?r|9u$zP0l zL4|UoX6o;z>6sq>0*c7*cPcKFcQ4t=M~&xDZbfWF4-rCVD;1kmcphGZcIc&5DmZ&< zgAWQ%=h^>+kqRmGkDkHER4eT7TKL!LDmSk2=Iq)ca`0&U$$c|PIpOC9_+Ke~>+b3i zWy-(!mnHGfD*jqC{Xd>qM5uk`-RDujY$V*f7qebt{v}tqmTq$!%@rr+HpUA_p;4@ zAKCleANvmN`uc-b9S@L9m|qNqQ!W??+C_a-V-$&Jukn-n2f{`>EScoBN_c*C&(P*v zEfihzJG3z#4S-9kJ#xxu4Kiqh>-QYoTL&lpjfM_V!--l%QIy+irOmBICTI5f=ROX(lEzelftyeeesoxubqWYYq zY}m8ywsHTGwmvEpjsM^i(z$jjpv+#GijM_6ANL=*Ukhg+KK_146%g=DvYf-e&FAIgclTaR{jM0Yh~Aw?d~-2XmT6-+S^-X8nYUo}k|k3{kAD4~7e@7N z)TB+jUc>tLd-aXcU+vnxd-smlYWbF~+qYe%%01WawwWCOs6{}HQuRx_BR|z=pG}xq zOAR1k8!d8DHxyDGlS~@rn?H0ZfefQeBx75lAOW}Jo1<$Wo6z>)C%v`nV=C6~Itugw;VfiB{fGDp#kz!?FvcRtWTF2|5GIu`XtK{FS#Rs#aq_X0dVj2vxzpcDf+}$ zed>Am7Wdbw-F&({pu+k`c@&1{N5SxZ&q#T^fFjUTobuF={%D{G8@K7nWO6q=H}xBd zw7J#@MHYBiqQh{wR6dOI@en99#XEkn{YrR>=}{LAvNe2XU~>`9R45XU!H;B;(rPsd z#cNU`$*2}^WzePlO;8lIe@Gxap!{<(yHfBqjHylMEmVW)Xo_op(K&iFgKO*JbzIa|^Oy>jOOx5(ZX)6k)pVmeO!N1d((D-yKX*O9Bo?2;oQ$RM4;(-CB zN*D9;bo14^32+c~I`@uIB=1#+LMuQO^eb(aX;D~S9bRjwfsJM>No!{mk~sR0ushp~ zyF-vE_^O;Jk;}bM0C6M9zxrvAK^z)x`oM(c7f`wbQtysSP?(egQRMa3t z+lRCT`&84_lz90)DbE z!w+h>(w}5(Cyl6XKI3Qt?b25FMi#dL;|BIC4rf{cMWXI`M<#hui^6FfLo)46xS}mt zzYL0|?6~tk2jO4PIf-^>%E5_SF%+PQiKOLpW6%0wq}&hr6MC+Fsf-p5f*MdYyu)m% zv#OxbYHT_hY9ybw0<`V+rT$@}^sO(RmoV;BLU$FM3A1<_1#@fo`Ed*F{wk%yGt{&0 zAQw%gdC4ThJ`zzx+G>qU2ymY-lQg!6LU8x+=(u#>V>0<#V#2n#PAt96hiP- zl6$<7!E3clQd*)z(Nzv5dC3=r=2g1&-;8a9464PJ^JBxwN24_G@kTy$M@ZgVT>%cB z!&A823yKo^Vl2OYi2oVk6PXU?$J>VtZagoQHsMuKM5>PO1sAJZ!3_m^$&zH=Iq7`b z@oP69$9y5enY!H#(sF%ziyy4kH$QC)*VIAv`wnmI31{M@bTa)l0WOJOn9RwWDhbG@ z`$mo@nZDc`*{Yvw4Hy4-2KhpNPOs2O-q39+{R2+b7qKKlZR2=^X-+i>x zzl9=kTQ1Wyt~fN`TF86lQGgdyQqO-ktQu@o!5W`W|F+UjvNG}9$0Z@6u!8T?=36zy z9B_DKC-wsA4lh~B*v~{1LFG864uFGt!0n^mUyUOf`PB!1Z0!iwv^z*9RfmImxmns@ z3O2&Pr~0_%`0uHL1lYVQ(N5ac!W*c4B2A`Zrx%J!P@!8vSXsV5-XG4sGLfb+$VaIiq-n7SR3b8TiIMbF zq_#)4k=J_xXcbAC50=2Q)=(z-u)bEK7LYCAt!px~n7vpF5hjiEQMY7)x(33?bCGHohgd~Xi9t=6H{mC z!}RY;qi8}-(C(NQ?A*STm@rPQ(p2of=|&3EwhY#y(0ryQGQ)4B00;sKFG!|?d0SJs z);O7Vl2KxY_``2%kwJWyiuB7P$_vP(^46ee#NjEVyxAWfBUcR5B46qsQfZS?3jdVf zYx7@eTE8TUNbqb_N~ac(7vZfq+O6?JkyJcp-R}pKNw8WhtBRojwe+NvcBL{DLZuGN zQfL$T{e*Gj-{}&ff}lpBy%w4V(<;Y2-sJ z_wM}7hvKO)nrPDuK**^ z@YqTkBvSgjIe>tDvLS)wjmiR^lghnTt03g3!QBCZ*y^;Aq?~@p9Zt%89nKhi%xsnW zkW_2x*|Nw>i&e}LHCzoR+eyl^MC7BKp60tv)L(s80d{`Z=#Vx5*%qX}CBm!6MbdJy zDxNjSLWwl&*tl!sDYmA(SyvKHJnC zfbTb{To?eCN^Y^z!7PzK!^UUa$z_Ap$TG`ByT=WXA9d#uU))K*d1uzWr0>e3h_rJh z+T8Mmje5W%l5y+5-TuHtg-pJKLTmDXnHKv1+A1~t-MmB;ifTSHTq%is1clPhYLJ-0 z6296Snd(JRAw6CdgiMwCmPQfP2`8EO=?n7{81cRiuDSU(PozU9{8J&DX$ZahYb_Ls zQ1P>^8@J7=fjk8-*VE?VyC|BOmn^eC_;gGfC@M zKR`rLY2SEi)xWPs^?fp#Jfk=Yqtt!w4L~lcaaQWj$3(5FDI!zN%koLM;MuU_D<2#& zQ(;MZwC|0LRLJ!J?HBwz7%us|c_y8TloXOuczHMz2VUONU4^`f4LY@+n@Tces)z5- zGUdlR+v+NBJR4@FjbULR9K?^PSfGI`9!=(*n^Fb}&odPK)Gp2|yAk)c<-`C2{X zFKF_^Jq__se3>TGdUK2pB`#6g5la1s#RLdl*QC*!Mq1w&@O(-awwTwa={mao#l}71psLoNT1xR}J#v z7Op>O94&Obd;Wv^1NtRkMTsdv+~`y-rpY=_3+61@7yq@Q%acT4uwjq5derN3~{nVoA{oM`@J%K z(VB4PAY0@5qp-yHWoBAc2?!iJ`^{HB>8e3tlzc*k<*uH$OCTRXvCJiQCb9@0r_&~_ zA2Ml51fYogj!NF*&CG;pQN$6sxDYNd@lN{xT&l|(9bph(p1em}P1TGVV0I$N%O(}4fc@oj8+1&`7PLbos4})gFKb^_13m9+FT#ie{v)fFC-o4F8G&fxgsLM zOv=Yf0G?HKfOZKLU#XEdzj22WWm@-y0H_~JQr3SV0?^DzCmA=Ybm^Ks$J7^5Sc1=p zFTP$p{PhIu799$!qI<3SZ(fpVx}U<7Ncr-2bTB0+-dWP$2l)`{T-^2JbPa5TPO*H{ z0vUuJS3(b!LH+_hi3|Irgn;K4G%1B-+z9v_CX+1;YYk{8rpcu7W|DlX7M_QD>Deb! zB$@Y zClzZo$VX*!(iX!T5SG&X^y=!duf@?uw)|NPS%lgTuamS~99lC(L@w%abaJOP9JH?` zkd*0=^0o?gs;$vhrtF3j-(UeYZ<$C&4yg6z#tUnPRR?-LCTU&c|88R1mt_UGCi?jM z2d}fzA$0njl0L_6bH9SWcd>T{j^F&KcR)Z%leR`(52qfoq7 z$+U}nY^F0=CTWfMrJIO+xw&ZtI8@xvERWXpsr=I6x5T(Z*Pf)1l0T2_=<iQ;eZgeNY&pZ^M^=dlW-j zu7z$Zi$W5L4<55+Z#>DAc-bT!UAt%v9e(ZJwW5f8RSK7es?@M+f18F^1h@q^Xizpt zRb_wjqv>iCpu#mKuda-Lz-KoVYc$$Lw6R?6Rcx}JOuYX~-#1pYRHJAFzpAbJeEQn~ zGwr0uZy-;en$GRD@BF<%lF$9Vph3CfBAkhf|y%HSS+)kHMZW!UD<&zg-6Li8tuF(K}qDjMV14~!mn##xRP-Xo@e-x-&Br`}d z<(SyJw-iL^Uc8U+e?DO9>tA8;$PDx8!Pq^qMkD{> zpmuM&DMF&cWJpiDx$o^V_!pEuOvPA@d%3lLbZod_Wi>4dqu9uwzMJ&zr{2g*I>5bmg%?V%FT%F)9b;7n(0O=WYh5(quN(^JB>8F zgnvM{)J(;}Vkji_2T?K~_eV~`Z`@uSXqB4A%k>(ofp;yu&|=nUD^>^A{De6^D)ciYF%8#S8-c4$|;;2>~{uXVWpW9?4|XnN%`m zc?IM}H#t>)JhS^-=_JEX9*Dl$W3PMJ1_{%|Pqr-|m1+l_G2DD*AZ2#uEO zdCU(`557mrhCh7YOJ-;Xz(a$Axox78*mLKiHvaYZ+T+uTLE$yiNSpNc-B6IKd*YM6 z@^@>x^n9{J(i$o=sxDk|pLcU^Ik?rR+h*1siMc!(SkXVY$M{kxh`V>~xep^QeWZom zNM_MnW43-g;k=c!?x}*j`AwM9(j5-EMP`!cN{QZks4yPrC?IbQBA7|0UE~`T)ySs( zf;Q0ukWGbbs!nSo!xjg_NdR&Re(j*)Y1oYgFSe*rzSMHd6X`@%#yEo!RwGdE`Z?xRo}B>vvLkc|JjnVSd+*Jxew@BWpif0Ms$32#5BUwp~?X|^3GV%P0*R_CZJ9AnupkTpk zOmvW9=9Wa^yq1t`B`v47hjFtn9K?#H{V$To166KanpX`G;o8?{s==Y$ec8CK+x^Ez zD`k^)j%GM1pLIt8G=GrSJL4Y+x=vDl6$n_6#0-+WREwer==0{NjZcsL^6sjul-MHeNiYnwuDBG^(qRZ(r6*Ee}&QpYqpl$x!ttGKX#EyD~l|>;`dqBmR(r{8P zg5=X$a7pNX=HhoFib0_nd+e#vN*n3RhFWA2x_tN0%*3Q8{>Z6bV*ync-A#`9Gl|(` z`J@}{dORUhW{iuXU0OeP*a+QY$i%~2F2G5}D|6gnBR2VqJAD9n?jR}e@co)ho}@#e zXt&eOl$LHGP1kSf(+u&44t!l4ijo_ww0TrTK>jp?&NFFR>6wT1q?|}ORhDl2IWYh( zd2N!-XBKXF@;JfDOsTu4jGOuEeTkHR@dhAjP>_c2yfNvXOyi+I*!5+W@cFN{4l47` z*~GBjuX-R)ZlmI*xV_yficr*gLd8M>_?UL*E5V6Mi%gBfn3HvhAPC5&UQC7bl?Pn# zc_o~tAB)3@2q$hG{!HbA7aLU20uZ|2x9&+E$cMW>(4Gd+&Mn=G<|6ug`v-=ly>^pU>U7J9p>${{F9*a^n>gMG&D9 z%Kcy^71CmtrvG{5ml3Tr0tzB}_Wrup)%#=PNo9@)RFy*Iw7CuOzjN<)k1oazC@$Y8 zxP*sV&+)}9a;0*Fg4e4%>i*XNktCZ&{7Egb%Nph00)egl^l zOD)D<8(*YCmHkL%EGL!Js{X+qS`!PEYl=>vFudCQU+cRpnz zCXt+kVhBD@)#X6s(l(RiP86=xyRAzho4!V`SvR8+6^4~U4xe^6dGnuxQRD(|8Z*4THwq#&SQ$%I#<_+7gwN^lhX4$x%bx*wP7skr7$);MV9A`KIasg?xY%rActXgoUG8r z`cqW_|9_O)cJ};}M={J}NuM{$pg88pBu_TO3xw(uuh^J}bf&FDaz$K0ECje6rQgv;(s?Pfmw`iqPX;j$@>CwbBiXo z)VXMYMm`S2O9h3I zOqti=EL9iEqS%DeB}AWv68HKcTkZSg`SJnpuQ`*b(0*yK4h5?l-4IR zPKgsdg=-{DCvM(3{*3{J^ZDCK<98^SP_o5>(yevOcy!wTOIxr zq4A1~+Xgod(v~XkZm8ssmvG-+@A_>GzIgkcS4(0O2rHlD15d{$Y$CyoNnG&63E6K1P z?s(4alX#NSCJl1Z#FUbr&r< zac=gQT@smk=g$EZy)`IG)rqlJHthd?;P1)z+d>umFKM4B;Q!!vW=m{};&Q)6c@^>) zW+Rgh+U${|%L;Iq#QyM3y7?vY`@>}2hAcjxeWr)1-TCiM(kW`N4*B^0iyQ(}nl@+e z_R@-e_2-5ks2S35&~M=+&y+(^gy{*9QEvj!ey@HW6lfDdsi|e^+G;^M{tt?+u{_eQpt0k0A9O& zStgm%68d?}a-k%$2zoc<2z+n#%&gZKjhv2|3)}Uh?j0KkBkn8k$93r$5l=T^$I)a!d}67##)3op?6dm@W}-+R^sh2}?+lzg>MMR(t} z&9x|!=PMNi9ds-0H~P5&LRTw!wUaK$Tg3lrteq|yGj%8?ahaVbOTs}E1*io5N~*$V z%xI*6gRbgze&qQrWA-4wJ!WMYT*~dXUGxFa;_grSp*E7oOZp4Qufi$hA-;QYRGp9- zpB_vg8SzH|G;>K>=Ic=e-*5KaKkpBXSHKb*$<01c4Yh{LyA~!>p;OX(-cWfhQ>dKr zM{dG)W{Ic(z~g-@SCxi~&q!_3 zi)F3>g%f9yTo081m8R9s+nc*r>D{YL&+oqdaeGAKoeSxF(h`Mo`YI?y%@NnytK=N`|e?fps&{W({2f2?Z0MNWSqhBQra?>_m zapzf@LZ|DLM~Z;{r(fzvQ`HUCZZS)ERk&!Ie)@C0ARV#@hAI`OXEL_~zQ|GCNY$0P zc&>zAs1&G0F4`X?9`QjoVQ{RJzFmiGb!zwuRZ^t!&>u0=DYY+h(Ym>7;qV8^^dG$N zpVbUCK0Wm<^72`l$te>xNs>?N!7%8!iM+meNDx%QWD6fw z0^X6yh~FlRF48&^#qhbD+*woeE=iLuwG;k>;I-y6JzM=|yFOv+s{q<3k(?s{ zB7o9os5&m9Ad{rk?tu0Wl27Ua&XK->v&R$wbQ!4n-_{Qk>rUWxAM*UlynCS z17gVQ+cK08WFzdyIo1-jn)DR(Jqa-Bt1U0_=}lCKUp@rw-5gBJinjhiGoy^ zn0fc+rB!1G_6P{67+Al&psV$@jii!N#-m#j%`J5Zjy)Ssr}P>qg68$x7KP7AJpp)a zlF3^&@t+7cxIhCy40-Dn9~}Vwpp!XREKPCJ4j}(?9!ldfC!atbP64I_OD+ipyJR2Q3t2B(jgb06@1Rj z2bBPnIXA%f8dV9MB`ulHr7Ue7fCAJ_yt{9dPnoZh zlVhUkBp(S9JoLeTNs66}3$22eF>IlUsXeN!RVXP6sZg=S4MaWyilg!)I-RJ6;;Md; zr>elI!~-%WG%PMrW@hWv4_hP0Ye}>#56>xUkx!X0avdDJewE0z{uKsFCg%3K4A_B+YczrWiXy6cY_&GniF z59pLWwZkjT+qS9b^>#GL4LTHJx?)n8MknRrv__>pLIzE_E!pnAhW|6oCn*WPbQMs5 za^t@2{CbFoyH;P(;9tWX-kVcW6q1%|z@W608bIZgq;$$=%F1026iNvB@ z-*<4HCjigQ3Z>Cn6qARy9)NhkI0`ELE1q!DG}~;Z>QZUsRyKk|;h_ECj)kO>C|itt zS_TfU#gH58jf>z59&|@RrZC~)o^)ER+*%qA?UPBSz0$+NJhu3uaQg8^s(#m_Yb;6Y zq2)(zj4at?*1cqloen>Fpb(`$*z{Df#VgO=Eh)mqe|tR1j3iP%P!`4ZImt~va2aT$ ztqY3q$~9Vd`69;{3n|C!UOi`M8xH_N+q-lyp5F53q#w_uGVR2ymU`sUHJ#5XGerQ+ zE>v>cOVyEiX>_sQ=&gavPcl_$4Uo-esf{Z6uUh)15mXd1bCMfed^eIx#9(jaBGl~D zTtsf-H{=E#a(I4n!)|@n6S>{74Myb;ckerN-N?)BP@wkFR9*1E3j_^{R4$QZRpg}| z{`c>XN!ou0;59Oer2R7y@ZUfb?Y&eo<#z*eR5(r-TV@2GJ6IA0@(m?zPs|EwwZ~sM zesbML>mfJ4A#3+XkW@%%TL50B-h>|$cz1I=l}u(ydzz7m4C7q4xS>i2)!(++#^C27)~<%*e&TueE>g6qppo5j9A~T1Q_yf8c8QBCtH;Ea8v~X$?#n{>? zhQ9Va`f3+=?}NE!D!-xFYM-!Qst7=+eOQUC1MNbEw4EE#ujhb69=zdvFl`2}36Suzfkt$2>)_V;4bPn1KU^d&+nw3(kym1I*W*{tDXeQO-0%J}eW zcP*T1JtTS9fP!h(GRdr*l_+Iq#NQnl40yc1_lcdf>}-$!rmh$A5PbVal8ma27YSN_ zfA7lv9%c1{C~Ez6-ap%@80}=7FCc#WhXH71i#cuQGrK(_@&hdjA+!!<7F)!r5ltFu z;Z!q}Jm-mGs&*-2aS%{CBE1pvDtn%)TNRO;;a~gvaOf6CCliei6!TBsfKcx|nK<;- z2o_+k7YZ;|QAnDillX@(9Nr_PyI+P3P9g0lJ{tbs7jI9vK}tVqQG|L=saoajHq~Ov zBuyvw{?ntxsFl~BCKyR7Px_z`niVohiH|A^aH(E*lIIx!y{BKLUSGD;qd;{ZQFY%P`Dn`Y8&U#Anvh6# zMLzC6hWC<5aohk41+ ziKlF2`uCS*ey0b}O=4;{xYUbgLSq10Uv~jT87(hCk5r!PImJjWMzQ@QUm2=_L)}%M zmxucvl1fTiT2y%252AP5u5WxYz!L=%ekB!>_PL&L`AwNOY)`C0-k$!e@}oNnrfcx_ zkm=iwhMJkjl{Z>Xx0BMchHh{Xd!&&3TZe+V|3g)h{mJ5~e~)Nr(3F1T?`SI>q@P9n z7Zc9bLT+WhIGjmU<{NmC;O{F4ZU|}+tU+MCH>!UsNfJ$VDiZ_%f&ie4-L%uh=&R=z zC){^OL4N5>z+$=x~>uKFp3%j zQq0>MAUE9@E3<9WLN%ft46Ep3j5d-y;*Bh2&Pa5+2N(ZOE?Mbdjf}dmH#24T1FD{M z)oXp%lx@kEtI8+~XUZzSp)uoKyeT0=X_s7eH+$WQ7)4>$8W?a*X zTKsRyT%k+K){ZESkgewa$Z@uhZ@+Qhj66^vuhnU*+`}?n0NU^4HQiO`kv@CD|1BeI(GT)?Y}2=FX0({ znmkt>Sp?s)nWTA!1_cxQ?2oZXwxivHtBxzL3I6EcD48mo%&30&FG@dqQUf`(EoYrG z(IM?p9lS_{gB}6^q1G?UO(YdE^_VZRjl9l_CS~v5al7r0Jn#ZhU%C5Hn@OUJxf62n zT|=72Xi!XLBk1xA^7A`Fa;^cni9bEN@E(9qEOb)RAKsK`+f*72?~#n0ewf*mw5S`K zk6gPKZ71V4xT64WA0}{BK+R|5E^nx6?BUmbHG!aeB5}K$YKyts6M25nKvfb`oZbgG#%zC@4&%(t#rIPimi@BsV^LwMaZN)8Aa&w0Ng+$ zzl5rB$lBkuE_f7R|`*=k6vQs*fDP$V}3jzF}hHQd&3#%%Mu&R|^ID<|Ayid_3b#*Ti#iC4Esmg1Z0*EmUGa!y}}U5+0XgCT(K{7`(huC}C12 zN!te^oSL7DdwZEg(z{%-!XDkAj39$hcR#3)qRHm<#Q?GsNFpXMj?AunX{@3`QZLI>0R3H5X+ zObI}A_pI@w)lO28Y)Ztf2)xQ{=@;zEfyx59q?49w33fW!t{q%6_v`768ovGA zEi>^tz)u*2QsO$-Cq5c2l8?sdPSxxvb_k^D}zOpylMM5m9cg8Ktn5; z(zn{M@25A?2z`xoQBFYR*5QY6=4CqhM5gLgV-db5s8Z6b$@i{I@`r;?uXp!uSW84M z6;G24!gKn%Eh>u0%`n``sM>gmrcqQKf930}vX|xoTsU`&~6CobL_Z`3^5A zxt1;&@3^6ubU!{Lr41F3!+n;O%KW$8@m%wCs*HaJqadCk2IQh&N&C*<`g>Lg_7uH8 zN;lK^8D64n`9{-hBo(TTm&gA>jVDy4#FLcg0Y2@Q&h$f3+z#13M2^~{-JqH+GpsD& z)o*MCWbtY6h7LIfUupseP2c#We|Lny!OI)^@y4{_0vvQks&4hI=mx-Vk3?F>ipWL# zZ3bz-Ik;4XNG8^DUrREs+wTf5`l5rCG)*@MLcn>FN2=jP-j}J0K08z^ykjAm_y!yr zMUoQv@P}x=AgnF&DS3~ik`Z=jxB*UL=O=WE8XpAzJIuCVr$Q=pvM|82wCmW0b<;naNlLAdO;@sBCHE4B z$`R!6c)?7`E>k7j!~RJpdCwEMXje1(RTP`%7ljHb$zzdU;0=-|%Oej#9Qym_-fJah zvnMH`4IsAtQ(^kE6gnvnf2dru`dc*{=%F7HL%S(0xglOOG|a@uEr1%YmKOl%+AU3> zD(>d$yOx7>QEVR@nTwJZ_hifZeG5D5@e-QSAplz3{aJw1l=QKh`N}c?AsczM68;0X zaB{H^RMj7m%Jbd3=#XGJI6^(gUuNlpZYTt&v$Og%%+^Lf@}f=0Xa0ErIF!YD3o`Jj!TnBb8EcBJLyyzpP8xpRtE&{$Yc&B z`b`mqTVml&Kjf$Rh%V{%QCwwqrjZdn-H^j=dbKS@4Z(m?EZJd^f4Jv1m> zlhm{p$Wh%BfUeU~W{lP7`frM`-<#dK772Q+ttO8vyM*()z0hfKC?D@~s{P zm}e)$-wDw{dz`A6)=&wr#*(r~1bvee zbW)N&N2OF!k!{hB&e`rJFpHA8s{sn9IZcH;*Gxe070Qt$cZQ(24E_*D9(x$4Pzs&h z#8U(yPPLI|{ZXXSCnS;yo#5cMo>bDUJHC8>;C{=7YA6QrF3E&C0^km(vNy=^4}&WS zBH-C~XUuIonK(+sOK56rwvtRR@m|#IiXoF_^4~f>D6XJb{s1Sj%C{yu+1HkWlU|D# zh=3LvZNJ(3CRHn?DOsX)le(4n9GmElEbcqk9(+d&pNpiWCtk3G4$9-{bci&dR`mIBW8y^;hVmT!t&KD<_w{%sM1-pDX_@vED^umP$FZYyD7MB=nL{#3J0?t? z`oj}j)Vn$qgWLJYSK-q6*^l_te!G-VW4}FhFr37SD2}p5GNzjVz_6QSSVI&pXgwXq zK&5N2#iq#2a<*#)Jyd1Z+ev9ddC{%KNs{UN-}Aqkk{wN5j9&53aTRi~? z!{{R0nMH}2Q9%I2a&t3DD$HSC(&4oqEp)IisBUQa^&{p;YK$ym2y$x_PnyPyD1`Qv zB?qGvlEya3uT=e#$RU(l&s_fXg4Y6OF@-)fW^i9g_Gq>x-9;5ngl4AcFPoA%=fg{p{dTD(-5Q1a%&bDJYcW;~QhX*deyUb)hYt!vJn z-H}0ZqBdl!LNe+-1D-2||6&9kQX|QXNruKx=@3~Jg&V?{8ptukA3()O(zgGrDbX7? zKTJuPqDA4}O(6GGfm7QfjgY0pXHirsXKKPF=mwcmA2~E@NZOQ)-`+Gpf19@7*O10J zRRh=ak{hZe$mj5+rt^WntT#vF*EgZyy*Yrd%bjC`_T>$SZ*?aY#SV)AQ&C_>;P zO5}mkfZsudPD?#eOie!(g|=rTlF|Lj!R2Ekxx*7hDSK8S74m>P0MFS*(!PDP4}1>W zX9uEK){?Xi^#VjV>1JD)GQNE4Ha(kEYWTzLOo?PzYrI6&cE4rPK{@wE9UV~p8hPgE zXcU7Ms&c8^rUMXC>B9|6qUfNUsRS2;7B2`excLHZAMKv|nxN5&&{ojF(@Q`$zu#vt zq7*8pk};K#P1L|;Astdbg=#yQ*&n&hmAM?b1U2qG{KGzOC`_XTC#Xtz75NBnJ|G$M zDe^GPj!co5+t~&{z%FLK7d+WUr%UgbH9!yqIC#ymXWGbBTIA(3`@W5;s74icPW6E5 z!zA*`l!FnsBub{Go+!3fcbG#aA1|2t<(_nf=d0r7G#^^O6X4RB-(b9KUq0rn?df0* zR8>A-9D`yg^BYNJM+vCrle}0Cg%Vmnq{FWvB?C)zJSX$HO{L%<_J}4KHC*)Sx6@22 z5x=$3;su%m3K`SM0Eaq1l?3YC+Ztz*Cu^bVLKoRiC*}TE^$k!79b@QV%~TXRSpTi4 z6&qfW>0q2z>g@$7%#k)wgo`Esx%h5W$cN=oFyG+DJ7kgyX?zR$l-Q8|c)eNJ{xPO!Eb&J^nhE18z@^M#aul5WI|U(|*e{MQ zdr&-XlSpR1kKF1VFtQc$@Sbj?%D$%&UMlDsX-%P-q;hO(Jjw4+sM7sL#_VbmRO8)e zBtL`L{JuoexUP+eYmkVE{Hl=g*_ne%nRHd2wO-044H zPK>!Jgpp)oCjk2XWXg&DD3E`@mL39BHPfjIuMJi02$GUjp@R~CaqUM#=UukYt~{}& zj4y@!TBMVF@a^w+C8{KwOqW?cA^^8Tq$0_RtkB7Ne_03b@Yq6RgbE8yq~-W8mEhpMO(FOCq6oqlHZtX)qO9=%^eahb&MzaP=1%#b zH;P64Ss{~K={!YbY5$bU*~Q1>Qld}&we9eQG#h!b8j9lo?!3M0&XH8Y;}$f8gSefP zl|Qv8nppbvpSLsxP|3*kB`ek}GiK(V{k=PWdXMDoRsvk~2E0IoAVBxUndDt>1$k7~ zK_!+cFCyEB&|i4N165+lv|7maN&~peq$+-f7gT4+#5TxnFRcTRMO$ma%9Bw>i#hh+ z4gw0+>4&KdCvR6mKIInNKlG61p~%a#=4)f)nL~+sS0f6;;%Hy>>M1%UO{^h$2MBN} zyM+!Lb;zUs?{u+l8Pups;hxJI zKE1SK$JLvXEqyQ(kx5l(O%MD}YTjg)2l2%h5CldXh49|H?^P>Mm?g_<5?zBIoS|}8*U`+jo}h>#oh)8>@fO@nekt>xx~y* z0gU;n5&*aDB-ZY=#_WJ=#if=h7OX%ET-6dX;l5|0hQq*pEJj8=^f-= ze)OHKz?zB8uSkFPX4< zBN;zV&`c$1{@NYFySI6+G>XS_ekOSI@ zY_pS;|JqeafYT0=$$jt=HIF39{7?YuDZG8~`qhu$pE9VT9u4o1&py^8i`IY_h=A6w z>GdPC-|JH+xROBwh|M<5@kJJK)r`SF3c$Ezh070V_Gy@*cVK(yU8)oO#4?c<_O|iS*?&=A6EzAs`)4si4U-zen z5*az6i3eUr@GV=d+c*1-%ps>My!CMj7-mfEUrPr-BcLcDmz1r(a1t9#Zh*iwG9ALg zEK>ZL^H=tN&=fECft^m*D*63`0`(RfsY?H?0RY1WGU`nMFX;ThdL$T5%^z}0Nx*-Jyh22d z5^o|6JfNWY6n=su<7xPnQ+u)8u4WaTQ1RWfzOz{9Z zWKbnX&TIdbozjhFD4f?_lGokf;eLSE|wI2pRvO21WDVK!s#@B?t|Ey-8A`N{(1F zshwD9#)x(L^VU-?8fl&y zh{Ama|oG>YsJnh|J?y!=20S=YVk&?xDYIci?L8VIg1dcswdej_+(S=m) zpG4APBdJ_p;RTn!Nydz5RQdIySlU|46yl9{A=@S-EDwi!|%y@o6ji^qeqI520}skM;NT@&HP83nOWb z`Q9Hz^K1Y2&HFJHW--SlB}9gr8U3*zU~q>@tT|>-H9ZQY{Z)x?1g8@1p1q@Obly%$ zqC@7k>oOBRLva{h9k|0v@=gr^pOE~!Bmno2y4{DpVn9A+F3{or#95DJg-($l`*lqr znL6GLD#LuLj#bulzDpPLwys{tt7a_8zr5j8`ECOMUDC1pYYy@PMv#n`prlxXY_CpbtvHu7jaNJX&EXY z7hM%SR6_GRGNmXJW5f3EO`_$IE#v2k$gS}M7DVYL`oMdel`7l500BVxedOQ%cv0oE z`Y4_fJqI?d|GQs;URF2q>m9CzvgB?c7kR z9#%r(+L6meIO&$n1xl}_Dt3MiO;01av#N-~waZWx{vyCdw}7cmxr(G0BE|&B+YRTzw^WzI+$+$HnNgm zi^b<+V1(`n1NjJ}T1$aUw1a({U%oMS#6bd&cncNR_pS5CTM|U`^Qn;&ED`YD< zHvDj96vBPx!83OiGU-Hwoup}=8(dbCv_+je5hXpneqmxYJqlFi8Og4I=10;vsg4Ft zBaDpr&P$8uG?g10kdI)1+dAXsWM04e&Es#i5JS1q4O#paFShQf58e9I#I(^UQtx!~ zel_IM%}AP_L?@+K=%l1RTJ&cM$@p*KBrJLtxrpB@v|bzT^kb&@0yWG`Y>yX+ z!IRQug-*&?kKc}`Foz;r7!xLE+NnJGy1aL}sdq_R?6(>?4NhNI2k<*WReU|*%`=0pexwR`3IiR~MA7&dX{CCiaMmGxd_ z`z>5P;oa$1Y)KcEkA1}x1siVRAw7UHgI0#6^4^-H^!o~VWApDHz11PG#3}NUFN&w} z1Cr;<09ZgWW_0OV17;m~m|=c2!VP)#N~OxSqGFTTp--NsDJn+arfz1<8aGD1h6TUH6rk=|9hXt@FAo z(Tb9x@Ns?Q<~}ol1sNp3V}zZmw3V+w<@KADDML_z((6gfZ*W@9(5f|;#fR5NmTKQW z;&Y2o9RAgNOgc5E67ul)(L&0nN+By>+|FcE!=_|eRh~m=6j9she8_9ech;Lzd2j)NX#bP9q)!h z=$dvfja*6|wnx-Q@pK|bLnZu5`+^U=>T+D)_cY0ZBXluY&syKDB^S*c{~u$iR10eVN1TTq)&%B zZdGqlbr~)*PJ6&X@PVq%Y4YLQp1ZgPxd}Bp`T}T3N}r$rK4WM9bBYeqlor>l%w|3O z@44NNQ`707{L{65^9>n_>D0rq$TIZajwW4Zr_yYGS`DhIU!AvDhQRw*8xK@Pma@Af zs;;!xqF{o~(CUbtDtpAf#Unb`^z-u&0Byx?U!6()u6@7Ot(S(AwnQ^kHuJ4<{_a}5 z$o~I0}3MM`#(i8Jf3+0H=kd{DTI?;8niseUe{8HIoi07LtmM46BR0X3*j3 z+oG<@+n;^;cRIU7o?F?uDwPpOSx;t3LZi zoJ5s$ZARBRdVFdlAALV{W0;u=N&C%>TTa`limij<(RU0DTUQ>%^P0q8M8HsGJk7sW z(Zb!o>M7FlZdVm~t-rfA`0xX)aqtyNL5snW?n z0KTVEw)RDqx_3-tbYks;sts)883zNTKPUxOb36 zU;omc6UH>A?r#ML{X`p;#iLhenpc(+kwx&I_)H?x4s8GK>!E%k@)K)K`1XvI(GQ*; z`m%}!If8#Y9xZe0(w!Sm#mmga5_;%!5&3C5@=qs@|nec0Qlm!C1v3x zo+5eK8-)=JIzbRM?xov}T6XHOo#X_l`pI-s6l=`22PrE`AP-?X9S&6mL^m&=hPz4b zs09p5BPst=3V{1xB;&p*A-r~vS$_6LUd=Pe^!_58%03te)xelv;*_MnZW?bzsav!W<`+yMEM`8+~qek=R!OsDE$8v|4&-#=)H{?Qu+dNrEl zp(;={I;cn)zf~3CRQLBJW=ZH@uDm;nLuk3_e2RRosRntt&9afwpI*qP;SD++Xs1OH zK8jZ;%TzsXR=HZWB?=i)4n^|)E-8A37liec_xkBt{vO4AGQyTOD%Jku2HtoXvFl^H z*uK*PRgy__YXOB5my;Vk;L!MC%zMi+_}7-U{u;QQXZn`TS`;aQ6u4%TwELC=>_~ir)}j0sKoYWQc;*~XIT_T^G?`!sA@*p!Iat}lv5?4>bcYaRNAOW zOAHXdvj2(a48Q#>!l5b2>m^aJ0d!JAQ_^lx{KUvvnCVY5Hg0f{jn&%*;c%Qy%%XOr!MSr^hm_Qx__C{B&c^L>v~{J&9Or z07Ma}dN^Hv)J8yId`6A$-Q%rc8=lI{G9Iet8B}GQ-g+VQ@ZZzk4M1L%uTzya)e|o7 z$>igYy@dvssX9{?fcsx0pN{rH)2nn*zCd2S8%WOafXkcaSD~u#_Bc|CQmpY*DUwX{ z`Ew?j#qP({O3g)|sq-CoH zK#wq{yyF2UZKIP`I%VwcUjfDCGxu3K(`S`HK7GS&jO{5PpHj!@6y8xp5f&;WWA|l} z()nritIt!&b0tuu_mhkx+yMM0)3#>JeTg|_t3)U1$@$p$dlNNyiD6L+ove$z0Q6AE zEgBRdD3Xk;4u^^InGqK9^4mJVdzO`}tBT0ar)wwV&_t2)EMyzPha!M6WNcRm?UqOKc|KH9YNv5(0s?zgP8S@q(d|;I)L#5Yw`jx6P zGK!R@X;6R?W9Lq8Si%=7VKtqMe^wR%vBoJn*ivOW$lqyDpf``q@d6->AvqHcLWxb! z7KZ=`7@IDQ6|{9eJ7ZBSSBLna@f|W@R#gph`0paAaJipQtKZ0lXC=muy?Aa-*AK@{ z3@7cp*0WswPmHHJqe!K~NNZc<;<=lQYA8b6};9yc6LZ&I~)(c z@mGl|b3Hg1S^`4V6$y%+kE+9=<`vR<-bC_gB>>)jHLFwr5YTq^!Dmm*j5v39`KHB< zwL!1d@$iL{Tg$H=TW+uaXU?l0$fmE@V8H2Ex|nvXS-bq(cb2Cxk9$GLM?6n5Lm{IZ zyYCDSM!5r~Uu(Z_iD(PME31A8@%zz|KBpoI=l;=?r1;o)Ns;ADqeUS(5~`0#%Ap@r z@Gn!hQizWMd3k=EOj5b^2C_6XlG6E)%GW$VRor_3ybh6kI@}ZWPAhbh#vrc_nRdze z3bG6|)&-z#S^IT`F@Gg1iE(DS$mR!kZ+@mj9(oZfk6#l>MWTc0|w5VE*wkmG0bwHY5hB(Jm-;3Oc+5Ji!!J8Hl|Xfk|2?^$_sVpMp6-TXYa2)M zMh^jbx%Jz0kC&PvAK~q1bc!BoK;hhWkxZYtOQyrUnOjMo_D4}l9g_A1LG_`Llv7B> zXp!k)UOWDa({?&3B+pmEOWdZDlpa$Ts_seTj{=JDCdq^vQ0caFYQ3oB;~xk>%k{B} zb?(q%UkxsYT!bpCtw)eUctB=!L$(P#1=Z)|r9gP@C)oo)iBM*gM%Q%8i*(3O&}u|M zGwAor7BV_EBa`foEN@xJRVb2Bd6UE}7fZp#eIs)y%yzM?hXDwgZXU{D&MbU{%UA<(#VV>lY#&U!&0dzSL(q_nTnV}OoT9wL1L$&( ze9#&$Zj)s)vk{zxf2jN*070kX+rn?gVb0P6Xq_?_*=l~1{D%$>!E@+ji+}JqG4jjG zTI3>prLZ8@8*>Kr==%A*?Vne#`&z5N>5yI-DxdF1LsjdKMAA6Z1At*v23^vphyVhs zf3 z#dpZ&Ggr^-TeEuQbbJ(J-ZQkYG-&_atm1OY0qR}M?5 z2@+l870ui2w2_qJXP1D3@S}~&KW->M{MkAh%(PP4WPpYv6p!0zlFG)$$fxi2W4E}y3i9&oaf>dNEhSO7s$r!3;NMKTSnVVq1)~V=-=-gF z0EZ@VWcsla=hqJYXp^0)J9PxH%1X0ck`&666nVV|iuQyqa!ehl+K_x)MT5dEA(gcr zQ2E{AVAQDXDF7au<SL&?2fVy*k6@(uvq zGTXBNv`nHrNx+LLj9+)au8`I>)w}K3l*CTR(%nQ$Z4{~MHZxV$1clt^4;TGnnNG5i zNeZ(h{W!dWfZXanCaLTWm8Gz7MOx!pLu7_<{TZ zE}Dxxv9%_Iq4p=gq%y4|P$`a)-)1IQ}_&Qgh576_;ER~c%~=mrPD*BzfpcB-yK9Q(`s$F#GX%)DtzS7^ISW^&g> zs`k$!!`qLlR^J_k5IV(?3FVPX%Q%{biDJ8_B$ZROG;r`gNODg}IBD)v9s;1kjr2A` zv!8cAdkw12>6?)yaGlNcfev2Xr))TvNhc*GHokgq4C~eCJ3owWqQ^^!UjBjpJ{}$(2A!dM z8rd7F?iO;3KMLkK`WaOxT4<2PbDKm`&Ww@hkUqJp@0V1?4%2yj9KkGm+la`eas+Ab zEWpKW-y|L2J|e?%`|is3qoS4_UTq_9e)C%#RVj^-o10sOHtkxK4eI*Y?TG!+2}Zjq zH73KNB%HgJAk!gZK^ds(#gLERKrUJh9CY1wzXG7rH8Ry3x%t#MO=W*I0WP1(jNX39 zxXcX&(*$^-{y91+S9YvDkbbT&oW#B)rIi8-SK}^8)8h=1hswk0^$6y%%pb*J=)22q zOMX1k8+rNfvY1WWUk!N;yK7^X$ciXj%O@liJE<^7csUf#YwW3&)d7el%8oOUrz-k- z>492lRPFQt;MJ;A&p|Uc#8VYn5k-5FL~fnKQ!Esc?7rj3^BfGD6a`l%KwR@(P&m&rW=aPTSf%2m23Rw~TpU;_hk znJ1IVolgHKR9&rtY`UAgRuV2AWjzGs)*^$dV*x0V&|v>FlJ*mSRL6f{_|r_=)!WA= zH_<>PcrB$W>MIR;m`U0f8}I^Q0m*6TnNCiEi}n^%2aB3vPoMU5zfUd!I$C)l00nD) z#lF(tV_3X$wuK15niZ&?f?gNPqi8E(L&}mKZ z=V^3E{N4uz7#5fKrXCI+f0L86P<2eaUjYv8uWlt3vpw}br{XB)F z!t0Nw-=gwVGTjNgcg&i(E}jk+Qj(Z@$<8yEk1Yt_)KsrSLENWF*DRejS3My!)70&Q^reXUSypp9B77mbjiMj?x!w#;A*{Db~|1(&JqFraUx1cJSNe5|nLjeVvMDkxfR3)EM83~ovdoG!ITOuW@Ouy=p?MG#; zfLsO{xle~I4WiAXpc+kOXyD=WU1^MIAOM(6RnohjTAkq5{NOWb zs|Y7;ml%@E-u-mR$npXTB$QiZqZH4`udb2Y=nFvm&gLVRtyY=aZVbL8bfBMyA~QS%V_@c6)!TMA@9Q zt%0|I|3LhP4$-d*a1vIKj31~0bpMci(oVn&0-lhZpjk_rn!!bLnab@CR+%@Ig6AD_ zg`k;9OE(nEf3w~48azHswCt)aLgih?PXOR|p3>Qgvo~%0tB(f2Z)Ea{02Ha-L#j*? zNyVBFO{dfU8WkIva(t*C3Q*~?^t}iXs&cn@wB<6V(cWNw|DSBwl^}# z%uh7PqMIz!L6RcRFlk-7a^n4wRYni;=`>_w93A$2Z=$5oDw~rm$}h|Md7%KMFOWRy zhv({sk=Oj;J>$%_P?==rI|6_P(PT_Xxb!8XJOGs1m(uV}#kNKdhqkG=0C<-4@Z1`9 zlBzq$k0BN2Vx>d;WIs6QKFE;lw59YzUS3^y zTj*jtea0H~OKTmRYR8esznT8gykYb9J*8@`rtauQZD;<}#I0tZg^6@uYNQgmdcooa z`|o8kFVnd{N3`t~PL*v>MPw05w+s#p(4!FFT6;h>=1~&KD<>?OQH$0^_4B}gBs5+o z^R6FM6zE-^B5r~shSl9cGzk(=LcIw<6=or$!i-#C>-2b)RS zx7A9Q^N> z$j?(m(ZuV_HUKL3P`aECKtc2eNplZ4w3hj(9#kDxW+)2D_=7Wj=G+Rj;O*Bhy;{~N|Q1z9*WuG=Z*WO7>x!e%Y zdKxN>+M2>F@~t-nxEMs_(sJf@J2Rdp937ra%JV${c2EgYK`J}TJK!qKoyj+hL^;Ae@QOzfm|8awh;YZ0R zZ%hBX9H0xQvdj>0j}JpoAkC-@UhshbB8#%Gq6W_i8)=$b#?8~iT?19Sr&NxYLQym; zWm@bJUtDKyc8T}bC0}^2j217_b~tW2SlX{j4X6fKxL5Dwm$ z0&?^*Q?mY{LAGX*%wpt;Q}U%B{NN(2P9rIQ?01Z$^~65PtI}Sd+^V|)FRK2CWyhqUv^IsDxT4&Ye$txape;$WQUE5ngVZ{E=GG*IBAGm0AhBD0ueg65Mw-u_s^+66pyHyExK9^Dj zg==%2F4ly{q_m=Gmni8%2nrr^dr8EVcPg)G|{ zSq?zJQPLX!i{N#GiA@3MzoU!oHDn7!mcXM_?U`q!-A>Z_yADGClfU1yl2(PJY$xR_ zufR#$yDFY5wbx{&ZR55LojTb`d65?Y?@c5XnT)+Uz1z5+K}!^p8DSgCR-PZ5E|W^q z#UE>EkjJNSHwNkP+$-c6U*MIm2Kx`R(0*gEq10iL8M{lu@DEjC@9I&6pinxbo;mj@ z-FCf;fMW9=oJz|d)j|fIzI*9oUm^J04DvvgO2PVu=Oj5{c~unPOR8d?$b3=`Szb3$ zu`d`<7J%j%m31Dq6h1780tN4-@)ZzrfZSWzSHN>yY5u*k0a*eMlQGp%l-d!r+WFddO!fgzT48NSgDfD$tO09 zoGH_IX$Z0$rycoK7~H$__PCTwTc6S4L6v6P?Ibg2xdCwN96j#WunYh66Y&DEIE9kO|JUK>jnyN$WrZ;vK7m`%CfQHyMzOGtTwfGj>qvHpPX`6YnIusEt@^THM) z3MSM$o^G?=dlr8G_G3y>F-yn&dM!nv#d5o??)`@npW9j*Sp*MJ1Hphis>p{lo$DmgwZ>e0rHSN8t=^U`?}-wMXIlZ|A;2(e89$tO*P4(TMN?+hqP z@C{~B;$_}!gyIsWB$457d>CpW<^APL9=4K94voC?)pvIvTv;wIFq5KAi7h&8QrD46dTW+u+wVU|DA0nmJ6JXsR(UzmQPC4hh}=2Pqb9Dhvt z8SwQ%jxQzhXb@a90#xpe_fuuN-l$sSCIhmST@^>3TyQ$|sma_3fLQzS(G@2o?yHXc zW{^}C>hRp)OkSt}mz7kl)B+!5kV);}Qt`gcJP@kpF|kMY+y3x?(?_z(HHz z1J4Py7Mtm0{wD}oCRxe80$kpXChygQi&*}rOG&1tPnl@7DUzMpEHVGSS*=2l4ldf2 zROH9sl!1fitOH-Y|7sZz5d{l6M~ABo1vrQwSQjafge0 zxmG@a26=TLxd3?xug{FIz6zJO6{@7+A`0gBMYzly$^X3WBfw$4jn7%_R9WKBz3YkG z3}si@C`UY$Nq&TEeK#l4<;jOZZLiAAC87@s6ExkJ{;3`g?yV1BxOHLakTyfsKS~Wd z`Pb-D$RalCvUJA-S{3HFRThQw`^RQG)$^@Hs>~M#*Yz?S{N57<68rwyOas7wTw?(a z9a2e3D@uuQyG&lJhyO~9A}KG@eom&f1W@w^v#ipg0A-@dw03aNo@ID*ICQe{&e#Sb zpu0rU{;L<98c*;5+N8Nc~flIdeR zRmOWZg**@dm9E*CYYG|ORDf5t1|M2UruP@%AU0h4P$p@+zNVLm!u7H9m^U!kM#@QM z(mr3Kn_(rRT6%4uDzYhxB(0t0Ta?Y)fET1wTDoBY=~%jR>27#vmhLX;6j&CJ?iNr& zq!FaMM7oyll7^Sx|M1S&``djSGjq)q=gDE`lC!EQx)e>KwDhmkn4ZXp)8{Blg~$E3 z!ssCXEzUR~IqoXQ$c3k3OMe1s zTUdU;)rK^$c>Kr0=?bM#l+~^O zN`l+u`xCD|_5F863=m5kYiM^)0Tj+L@_bJdq2Mb~0rQyA#EVmZ&E}FT6h``Ot^VAxc=q8cKnpdh>WA>f zR_UP`BF+9tr-1YHBLdmF{@Up>Pmd`oNv;0u5lMNF$H&bn_O_uv=k=nRg5QVFlOsmz zD!_}Y4cQ3Navi_f&=s=D06OVGg;nwqIRwGa3v(H1bx#tdWtUj=;XhB z^ckrO@H`RG+&pp()BpQl1JPOaBrlPCY~A@7ON`y+V!! zjsUb$A4Xv+gJaxPjE7U23Vr2-r)hDm zvIBk2zL>|TeuZko#f9q&9Ys*)mn?Ce$OM{LgnWKcvA6r~p#koAOWZPJ3oJWuNtM;8iA_$V`Ehh4bDvTnHC z_VRnwse(*=dQXZr6aeIhy2$*IlTMh{9K}c!{cu zPr-j;B?3dZe(39pT^gyBEI!zm0%F3E_SQ!PJL^mp)2N%8ynAmOMsv5_?L;&7(B><@uCF=rQ~A(woM_MsV^)S_#hCZD zK=k7NH33FRZMRaK`E*s#jjeYL%B@6z6%@zcU)Slmi{dUnZ1L zj*!MHp`!1`j9-@BY~<+YNr1$3;Fe5WwN&{2-UV(e$A{O&0O-IeB1$Ugnd@$*YxO8p z*o{*rfJ-F;(Y`y41v`B9W7O5#UHu+_^hTAoF+(&tJZmxaCvvNaF&U|AOsiqlN7bNz zV>77cxIsn4!VbxuA((rdxif#Ut9ME}=^KLZKRe}p{@aSK7P2g)*W+0*`b6OWlRDu} z1-0Tflv~?o*?_98OotLTeDuo)a;t9fJHa>~f~-4zHatVHQxHrt=Gr`1qjFm15V6Ns zLP+80b}!8WG0c|-(tQo9=0-H@y^7#H7-KrSQgDY=5i})MV^c1r{|n`xrLYb4Qe|q{ zVmvJ6InLg3fWYmclHhk6I{)y}ZCuzAhcWVCzq9@7;LN!Gjg&fD9|(3%hn%TSc#H7) z%#sEW+2ZRlH z`Z05fHzi`)x=g`}z>4=!_^_u%G}H986Qx=&75*6@N}};rHbi&SAga$diuopb)Jir?!D z|Hn*NV5|Yb?Qs-2dyw9FuJ0{q2L9FeY7omYRnk7Gxw>sj5Af8nQbz~}Vn~)rP%&5= z>=Saks~~zREeU=NZWI7?yy{khb4<|HC}mt%uMn1Go5IqFfK0T$~|DO{S2t zK0`wALV&USQhyYt&lolSJrkkPPqakAF2EwzfC-K4n(< zXO~7X^HGTg@Wn$%qtl=yDtn?ew#m@T3$9PNKt@=WTG}6R~v=!SQZF@%D7k}gYMJf&_ z)5zbaOLD$5-tAuKl4Zw$H?P!?7O}yvD~IegQf=TDF!5YS$P^HH3kg8xnmZ4#{!RVX zfmzAp`%4+Z=9|Oy(2nU-eJUWyZNF-Sa~Kp;dfEKP4t`OS^$!>)Ptc0Zx#=G5KKSQ` zAaz?Nj&C7GJJW5X8=cZwiNPkSw2aCl_RJIvdL_k5UsmBwFGBRVPkI`a!QwD)e@wP3 zhC;e2(w@$a3EnclVg#(QLlvHW81xRkRqp7L$0Q}Co)N@R=%T9W6pdZd=ZF0K*sij? z_7q$XJki3`G}zhcIY~kv9qyaed>8Q&${e z?ENzq@T8s!`x8i$u&~Q(?FiSEh;FjJ?)5A=fz!PW_nm5Zf4?Os*jB8fouirr6$CHbqu_BUpUh z6Jt3NvOGnEI%YN29BvT_`siXFj|@>T6(;S6OD!(g5#K5a*M`t-06npJ(=_e&U!knl zEDlOh_jl)CEOqi3p)gn6i#Hoczt7maO6n_ikBj53^Z2)2U`vThSiakoz%<(TEi%SE zEog&s#`Dl0^GccQ4CR-;B*GGuB0Q*xU<{tVu=!qaSq(J{MwKDQAvHTgxa%&6CRF{J zPBZ=MQgFszTk(zjl9g`Ezp){5*A!KHFBx3uI??j=FpHT4tMqQq5^?HdClp>T&ih>)*Knn-V08GdE}<6}KZ*HNweM z>qN9JrW{hBE&wi~9b(*oL1)Y*UHG@}3y~?J`=?lT_9j1t%~ zaEfW25Vx&anGvTW*Rg|;IO6y1I@QU12Pct+z6MaqYl6v#CSr(Deb~*I`m{`NPUT#D z!H(@QcTjk?IIpN>xYFa>aZ(>0wt-#&eGZbda6r+GV|(7Xl(&V&)ut{S4VUt_QF_>u zq~zkF7F6j~!(n{%Rk{BZGCmLUom_-hR$U}kd9F_(eB$K=!SDu*BxY%1e!P4V5ecO* z;rKQ|2_>mccX@iev5U9q0%7olOt>zutxKf`11Tb_l{qOg_phx14QS>Z-hIk^Dm2L) z9X}UT*De0LT95(XP;N!N%6?JS@_*_I`;d8#wG3mrTQ1L-kuMTCHZN zth8=XvOM?o4A7$^x|oIEgu7QQ)E=?pFDj)YJSDm@oW#dTP$l^SvBoM2imniFx{Y}- zrA`^{RUv}E*F@=P(k&RW2^UMK9t47$+1)E%Or{q|{P}bJA1t(%o*gsmM$7kWUgroj zoDgaldFt#Eel_Oj=2I)gL}uw~z(8tW;8RQi*J?HycT~kRZ-o;9Ryc06i+9h5OCx|8 z>m5thJ}&B6pO!1UJjYcNhHV7L_edDAkg#S0ZN@cJMz&DBFEUb~@|7uj%(!>n%c*J@ zj)w|qzHv9=Iu2q;qH_HmT274H_{N;lv_j_ST0Yw{)gSM&(J2g*!$|I>=EXEqkM$D7 zPTG=icF$He@V&Ik!L$&Ob-8z_EmF9|FG0J}PTbQhS5|}rk4!?^0Ghiy+7|t7#q38+ zAl2%I_2q@J3lOECuchF#xWas2PZtkqz;D@61?lmUz@T#;vIvE5v3o23Qp+*wS1Zx% zQAn#qov;f>mTN;ZD#;?8#5+SfRo5y}P@m83bZny~$+hZjX8)Wlb#(zFaCRdD-5F-p zV)6~Xdo3<`XZF@bFAl?r#8P8i{uA_Xj0>aCbz!M$`XOEPrdDWPqYfLig;w#3BFy39 znr~zFj1mBwlUhc4etCj1T6+Ore{0U6pa!%s<3%I845iY=I|b)eyzKS)Uw}-qSE}5H z3Hi2<3w^)nVG;$lj&NGFAJ9Z}+0S8~458sN6%}K&c}MWiAJ?^8Ti5~bqswN+Mc!9? zD9nZw_%XDJc+_OZ$t1F1lc77u>m=OE=G86C#T$O{4R!fV#;_+*STgR7nlN4CfF`dfeY z<4@Xo4YadVi;pRm%Bu0wi?cpiQ0^t^bmwxMkkFT*8)3=zK^`S$>FtH4^||p|{494q zFfG9hWcjKN)okNC4e!p&L7A$Txw=EmyZ2L;8&mZ!KZBgN&0od4x^A`y_{oDQ*;AYH zjJTy^!o8SUk?tW1cF-TKYi?*ARVn{8Co8A^y=yoq&&pbF$@iNm>9d&0;G$Nui8hB$ zCR1O$)hq#Rw>$^uO`u#>GnHow{|?_bll=fHeZtGEK{auSB9(5?ifHqYD)8deSwu;$ z!OUEsc0DJ36`9aDZruioy25Yde7{k6Z^2XzZ)ewxd@z4;-`2Me5BV`e`|pgu?ED75-WjVu4n80)O+XZ^qb3b79x`}we$?f*?bkYGC8Uw7pTd8G(}q#F<9ke zlC*#Qcb;H>Z-nZFzTqSU4P>*eCMjDbQp?9#-MCmm0dKgjnhF`_20SV7rc78i!TUwZ#ts z%mZp~Ap-lUhJT&j-Rn{W?miGB%DLxk?2iuggW`T9YbS;xiB2g77!O%CfPjd6hDfeM zOGh@PSTT(QsHh!qMk!50TPhi_KAw?vo3q-aiZ&dLd7Jwx@hFPnDG&B&oA$Fyp<*mP z#gj&DnEztSwI<=gbhq(~l04-7Qq(AeO+>l?cYV!LUN?OwV!w|3xY_x1DPxhIArq;P zV^W-GD=?04Y=)+r)zyd8LzKm5yQff-dF?zr$vBF&bI&zCX|>C}uZg1!kiMS6xXCk| zsN6ZHu`iaQE&^5N2Ud>YKp_RKeYQg{H5MQ{Go|<924eq6M;8ztRzU+q|`n=MipRq6YtJs^k(&Iw4irvLSN2;n+i< z-l*RQxz0DCUspDpl20RG2-+oEfnxNZ=%l?S;87b5{J@&-7I0owg}lNf`gSyli3hus zQy$U*RPl#K9SAKw8&t?%pKY!m&-Cz83Z^c736xl@mZw-a7ith=XKk`b$fc*xPpb37 zKk!|*5H3A!CQ)S5ZdzY*dS;T>caZ>!ZZbxHu~^GzJEZrb^j!B@s?}Gh!C@lTm8a+>>=>TFZ ziLwr$|I4q!U}a3d@XgP5fGW?F$S1!$<#L&|@Wx#Ja@}xSUR^B2Svlr%M<;BfE}Z_X ze4c+uT}&t~(J@vuy_O?ucl<~2L0anpj;NNbg zSn*{pBBw{Hp<;!m$J6%`D|;sVga{Rip2u+!3qNoVMDNCJfmJ@BFU-$}o8A&HKimV7 zK?B#MQ;pi&93=3+A6OZ9o$GK!om=rwapV%tVO}%%knxTWQd(x*%POPM{8bFv8l=ZPOdvd5U0S+$^3_?MFag>xLm&6$RUN(+y=7A~RMY>)-r37s(_+S40b zeW@|y-|sgxU2n@D~o72m)Gr5f~qFg*-=I%Kr*N*8?A7- zJ7(fFR$4cplbi4%pP47i$4M&rbiK^wJjU`%oOMtrJH<6x*=n>F@IQoMZ#u$v$*;sM zmVbvVMj2QdMB*zb0~ym`iAr&CEhK!nb8VAp<#8Hw9jd1X`aevT2goXAVFrzuI*Q+? z^?2L|m)WazD{q33^C}7|D%9AY_Ka2{WTANL?CDD7#%Z-PQ3PZ_opj^R+^IaZF>27U zjt_FSUpt`?S|LE|?`YQ-o1&-&ZfUi|yYEyUm9nZ;!rM#&-U`IQDFuc8kQ=cy-Opm4 zhl56krYR^b;rla-QA^U(*G<%X{x?IzWWSvn+HDQoGNKTCip~ICi z>o1m1b$s3$=~R1mtaL{KDRE#49bxw{1wcXjE8GsaZ&PkW+_Ov1I#W+=@buWdH&L+O zup@~woaX{qvC#ha#)*IFK}v9U9b6hsgyRr`f!jyyTdDbv^bs(n^0B4fL}gOg_#k4c zvIuKmW5le-!wULZ8?!UP=Q8k)TZJ4F~rsVlQVnEAj%dm0n=EqfH zw)+UwTpE|RX>P~tyCAl-mQg?@rOy!oKcS5ax7TIqo=*P!;UDblxoTav=*sZkz$Y8l zkF#%g#QZ_xZ!vZx_{G5m6r(kAslY6)?U%epm4@_c{ab~ydZ;AZ;TyPV?6Kd=es~kl z0nP2tqgMFE($+h>rYW6ZmTERbt=Blw z9!sQqnPH)XhW5XNPl?WP#pvw%^)DJ{Eztrj5f^A>ptGRIH5I}V)o`!Q(5UD#i#_h+ z7?ybiG&+pRR#yK`kW+XUe_FSBkbgw94%DqO$(GhaWrDBR+{53K!>AhYEGq|bUG9Uva+tj7E9Y(a7-Cxj%f62QdHPj zaz2&@`I^0t-!;3KcSa$bg)H(m)uAaw@C=^_|3#r$0y4=cI&iI(|r3BQj=`OZM^AW1v-%FU3}gw$Qu-$yoRE=qWWUTDjpj?(+-Opd2h z93vnV+Ce4|tlE@zK=W@NfZ*^K4PmczdljiCG*i|pI=yM4-|FHpP?JZ~LO@% zmIePpAko5y=*^4$g)~@~4AsR=d!ph7_qx0zF?(Vwcey74o|*%(LJk)Qu*HB$dTTLR zYvJh?R`<%7HT=A(=nWGZ)A_1dRVatEs!)&-CB2|Lp{SPF-Wt63Lu%fe4g<)RdO3c! zDH-~fT_ZO<TQ&Z`MY;}qh9B4H4NGXuu+*XE$ z`>-_uO=@^!9wI-=aX(Z{;kzIH?cw=0jlJAy`;9m(46$nnZbALG`Lh+yb{79Ty%d#% zB{C$1$tRc6-Z|>~=JI(8ZEBxlur4!n^Xxg0=ID9HP_7qVoe>qH-*Qfg~Hehz+5di)J&lXXv?_n+Rie;!mk zaMxQ>vRyl&q06+poqWR?B-mN{*@6f>}Q6Pu^)-ej5>(f-gyl=trVf7UR>f34B z4%}KfhO}JRD|V!Az75sTsweyHelwA9wauWO8&W_2xb72?9zPdn7OoeT$NEi8*Bo%g zUL|q&ZpZ3pH*tgCOYSd|UBhpVlv3?eEj3*lI1`>{-%lsi2Pgh|m)Ez)4swsEkeSON zEOE!dl?bJX4AcM)D@?DInq~PQu*$5vCK7=o8h|w_EzuPnMtwJIW2;0_G$4cMV!oDX zfYal}wjNpX@ZCC+`ck_aUoH;QLfc2T!D$>z;^``!s0R1xYGECutvGT3lBh3@%A2xR zEX0U+P{^_oqtbpp;f`Z+e28;CqT_iHy*OH^-B9HHGxTLa=Pe}3Rf)Qvfs%On1$7qN zmyfIY3M$RtBFwJM{xS>)^y@%>?aZIp$}5s1{cUb_ZN6MvUiiZJpetB=imfb6orr;? zQ_qxgg(yp?bbL37B%1#tmEnz}kiVvBy>zl6jtKaE^GDT4YKI$e9JU%G=F>TCPT}rX|aluhXv&Zy+G=tO2U$O zhGA4guB^k3W<#6q^xn=%w;`l$g1awKr#qTF%^A(nswZ3?3=O*EZ4dPp0WjHBZJjS6 z*YXy&V0@O^xp%!NmuAOtEc&X{m_xMHIu6Z7C4mEjB`POzkft~%`kqf*VtFcP0$KHA zUulc&!~bY&=oa{1pRdu!?FN@^RodOg8a%S<>8+$H+vpI3+`cluns9NyU>Ny(y z&Gm5=FU%7qxQ9`8{kxkMvDJ!S5#)J~>o1Y6L|=`8y(K7zEKP+K4w3~>1(;Sji0PYPDbevxCOQySMwRF%HKm3QY|qRGxcvQ6hkdB)Dr8|gWd2-D zM?HCq6KaluTjiAwmvF~fxoe;VR+hMoN&8)gr$SMpfR5b)^^G_@Yvejcwyps= z%v5te57w@A`G)RacpsS)Y<&Fg@u)W2h8}iji>QA6XS0f>b4k)RQ6_)Qm$dQaxJzI5 zFR~ghm)|3s+eE@J!%osSyrq6E3!y7H^6sh&hT8=xan7>Tj(e?}lBV``im)65OymR2 z$fYJj422s`k4AEjxk;MVWdfXzf4rMU07M+k3&!I}R(ADs0=y_ZB*MPV*FtV;eeHV= z+YE_opzKXVG+V>X;V3Y>S_W}=)|~G$DrqwRCuf0-#gDh`1b9_ihAM(FV_ZQz$xP(!)So?8QDBL@`d2X| zT}6a(+$vE;tg;G+U5%*x4c*zxa4{1WrFwNNeZrye_s#NEV1j&!v26bQ-w*F`@WHik zE!o7miMu%yOKQ`A*?Q-?N)&%TSb=18leR+13iX*hM^y!as1VHHA%pqmzdn9kdR%}o zLMY>VTlMR1mkKPtSN(w{H0CeV@UdE7eX%T(&W>l6H10|aLu@)3MFT2|j7YCN*2cxY zYAq5m$dJ`+FcjIuZe~&;(xAXTrOg2jk$HEF*S}X~tiD@KQD6iV$F%qwx=Phc$5joM zav!|o&(F8XBKBNTN{nMKipH#9^U7l3hVeFeCc(B)e#tvgtBTye49G%T zjB7R(-}JWkR6VjwCw~BI*kYrS|5K#Sd=e=5m3pW!Ay=o5#W4BsYg$21`%s@>BB69k z704$%T`>|v+kEjwbd93Xsa#}=`?COKRL3t?!zJxERs^H7aQrmSr}(*8F}8t!hJ3Gm z7n!)&w*!5V?C?Aq(JN0TqRT4DJGItH6O`G-hM`H%rPVx6hm>T&Rj_=EEb({eZ`>y; zx(Jf#2i@$iYq=MyhJrF05N|5iQiC&`(m1*Hg5JD?IcZ6=x2H?;!3sm!_LHy20sSxaT3%-3ce!;s7krw z%&_wJ3k9l_%1%iFm%$KF-kqj(#WbXDU~0jbAMdi*jKFw{?!-pEXag47Om&-1U5;a20}- zcGp=)KO5E1n8gQLx`MD8_)9cvVO5;K-U>o)(shwERZ@`f+J3l6UZUPR`s!ZI zDOD{snEln;tnP$o7wfMUC|Q_+S_J-Vr|5sbmR}^RbcqLMJDyAakinTB{uxjcUD438 zck25gWiNd^!*f1{I$9~_UKD(5Tx7Gnxm}Hg5GE#j?$uQhp^-+-T zqCWuH_y0UJYNoAcwK^PKlxD4tVtmsCP-c$yNzq>%(TqNLG7OhISe&KV4b z(5(I-(U*bStJfr`%u%mYg`bB$3!yYpn_244YU4KPf{d&t*tKl6GvnKI21NY7nFe~0 zdYP?@{qf%0I}~irAoH(m?F?I_C@ob$Ngp>awsWkn>ZZr+s8#2ldHFh<@-E=a8`+nIY#PeUu?3EYVp$n^hdSb&2{KFpP?!n{3Yt zrw+t5BvR|EwmWZ158ONA{1NpeG)3^rh=5VsO_EzicD)f~iWgS>jE4ykG9Xf$M(cY^ z3?%T6b_Z-J;W@YjyFH&}Bjy)V*$#BRRUr<2az5hol5_%lKb30>LRKFsz=gfJ8}L-B zC4tegF?`T(?E8I(wcVW&4L^y(ab!+h;ED!aXXOw@QV>#lmo&=Xin|7p2 zk&kjyTeG$3TM`S_JY2q8=bOiLgB!MHBm)$vfbkQm=eqv!->z~fhucR{LDM}#^B~gF zNbB9yEjzy=g9Ll}9L^-iDa*fjaf|S|tFtzQNn?9@P``|@HP`5($58!>?vfIVJXmkU zmdvKy$)Qf}g3q)?o}7i$l2K;v)W+PqN`#N~?nio4_D3Gbh>0Pox_()IT!z(Am!DqL zEC0>L7a9J-^Q?5Wc5DYhITQ8vF)2T-CQ~~0?eLCsozDL(byVRFcrHaMqkh!zk}OM_ zGE?u3COw6+JJNi>$`1pG=e7;Py}fuBkU5#S@cr$KZ4MY9D-|Xk+1P;#F1@E0%?=VO=I}>b?$2DjRN91DB8UI8J=ihq`(N)^f{uWta zwOYHkor)X+&h;~n!MKM6KgLX`6)hz7%V@8tUZYcL7AYQOMlJ<`tAJq^) z+(o08Z#ONtOLIu>o zl1=CtpdPaC;1vZ9= za1uU*Kt{er2Cpk)NNh-5kZyM1XB!69rO_CV7+BhVWv4knbtR?jHoh^{E@`H#V6wiT z;pyz>BX{WQPp{`F!7Ru{$MYJTeO@CsV~>HC2&y8t5|5PKtw2i_Ju~q^tg#4Z@%Jgp zQdGz>uH93RZz1S7GOPjdauz!tH1u6e7bscjsZyRKWXJ?*BO%WDK;X|QEJW+H^EGRB zm*al&x4W2;*BP(nSgC78a#jbgAP>{aZg@!9W@-G<7!0bedM;HYYOC|TB{7ZP-1d+l|dwsl+@wh)HpPVMSE)EcTH!C=PsBR8xqW*xH(y!Y=Uh~i6La+)Z} zjmQ?8u$fNVgCRdfoJ3nnXXMGM24y0}Ep$FGJ7}idhDm5ueJhWA;r=GGyl3XLgIs|g z7WjBd{q%jox|4l1xx~wMN&jHXf?ut7mj1_*&so7-6DiS!#KLU>wyF-``lP%TyF#;^ zZjN%IDj>7e+gnVOmK~H+tl+-|dF-Jx$C0k<;%1Vsr8q!FZCh#CNHz0c2Gvz1=vG+5tEO=k}I4|M8UJQO(?NIB(%9kZHNN zQmwbEC;uBKlUQ$8HsuE-QMJkfT%;zO&sH=rEBrf1D15CRkN1!X)!2K`d&%|biZlIt z{o6QaW_XnD*NR@RDb^ZcIQ<_cQdq+Qk~;`dZCD;UMug~3f0Ti!V~uhNk73WJg?;`C3)hVkB#26e84xs_fhX9Mi^TE5JXM+zCtec z5hxuV4$u9rPnak`sj!dH=kF@CvGCuZe2a88wspT|x7W7vzIV!F{p)`dwx(vttkYwo73#8(X0P9}m5lRxaoI0ZQG99sEKu6kI+Wcl!%z)Q!i zzP=Yd8Ls_S+$($0@ujn82sX~@%#c2|UDL{WG+ESP=8$Le=Dj;>_m!Oc zHM{(&z!Zl~Uujlffj7woLuK`NtBQ!w&VHhg5iCq0hM4ZsOVp-;jAd1jiXzm`jQ35F zcHI8Bi-d88!zf|O<~PGrWYOYMOioIt5K)hOVjl8 zqmS$Q$@040^&z+0@5ruB$E{Zxt58QK#P3+|qRGenwIX|sKm&{78epBOHsgfC^pZ`zmNn-02kTBv`uN7#60eD+V Ma%!@5(*K43ABMG=z5oCK diff --git a/images/kmnist_examples.png b/images/kmnist_examples.png deleted file mode 100644 index 7df90cbc30296cc9d7ad6afabddaee0ac79aaa8e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 142819 zcmc$@b9AK9(={5~Had34wr$(CZQHi(WM*R9PA2xuWa3PmoB8Q|zq>yDbJy*)YW35n z>)ExdYM(ldXe9+nL^wP+5D*YVX(=%k5D;)T5D-vu7_iTkv%@2n&nH+XDQ#B}5P0;z z1`3jshXVov06_r~J};h60DE&cV-hcWI|o-D zFMhy3aCttj|1L8DNdDpCX3Gx{{u_`)OJ0dY)X~M9gq@Lt!IYVeg@luvk(mR?&B8`c z!UAOGWCC(BF|#rNnR$R5JgnR#|9SvEqq&$_@TiDM{43VyD}I2Lo0}646O*T>C!;4T zqoa!@6EinAHxrPBiG_vX(}Tg)+riD)i^0K_?B57t=B}nL)=qBLjt(S$5sgh8-QD;B zpG^Omg1ys!U>#ilRi@8^F?ks~F)=d&|EBbBM|t`Gy{og+{4_#jqKmSoU9z(99^v({}W;t_Q*H+FM$QFC;(`*)+1{vDA-RFs5T)7rtz(bJXYFQ0$8n~NE{ zneziad&b4U%)$WVRAXV~0kZS3a_aokOa4;;X2x#D|3^PZGiwX)|AUvjJdd=4tDCWd zskyWmKj4!EqqVgej|nHIF*A^jgMpRZ+>F7Lh0TP)1ZZx|zzyVP<~C>NU}iUE{|B4T z`^6kh-Tx;3_x^vX)XdTJ6XQR{<6&ntHD_iqwP0X1w_ssl2Y#X%1C1>hOw52lW@Df^ z(1eTm-)M?1)}I^2*zUih{$*wMiD=5nX3ouG%FV!LW?|02%Jr8O7Y7Riiv<@8GY2;( ziy0RyfaJg8iaOdky2v}4eU|fc-OQB7!qLUv_){*{_QsaxOim7#f5rQW{+Wf8wd-f6 zy#KWwRL!0Lb!BHw@(+#i7@Pi;Hb21hZ%@t50RLLH{-0Fu|BmNBVLh$PKSBTJ-TtSC ztD}XRr?HEV@M8z<3BC?)5<=r z=F=KKt(W95B<$1TJ}u?H>*ybM{|)tjfA@b<_$MKz|Dy6gQ2s}j|7~*qJMqtP{P*%d z=fLNS|C}V|4xdGG`8+4;6)dhnz^JgK#e~(o41R69cLRkzu+EH~Jw1^jJUu%pAUxrb z2G}9c^sqBgzHgJRAN*Eu^7OPU%;xk}QQg+p*Wdn=J8|?tyn7k&$0IY3=Z2`drnCF_ z-n+b?4$^?;N+wtnu3Kd<9jp<<>;Km!z2bh!7c1h6?cKL+!6C~>1mfdTzcjkto?c(6 z;JHU!!Nb#My~3a9{w0cPj<1G}z6R19(!q*1(gG8jZ#9OPh=$P|(#^Lhl1YBOe z^?Y0lzK;doI=;_bKI`}WR1KI^d};tTd!7*ptW-X$eH<0OrheQQzE2gtF9kl|`W4K% zy3(^9cD{A&KGgc9H{K{Px)9%&2B<|{nFdTJdb{>M<(JfD*Tj!?;uq7tbI!Ml<#X&OfZ>s^f8*u-NZ%v&pIzcB@PPigCvL%0&DR*i zYhmI$;g8F$g2G4iz=J;@@4FxTJBR$er+;1xF8>5v{z2>=;Pkp}^%~*_^Ctc{4D{o= z@!vW1?;ZQ|NMv{;8!+L5V31ajbR77s_;DWiaryDK_P%NO60m#u!tCMbbt`qJcL)%@Ho*Dcyv6r z{(?VC!V}+`1s_h|zZbsT2ENaIYzV$sW}>{%7aq6zJ%5(|{yh!f@OA~8=;XmK^YTVt zVC?jfpztm=V3BjW*5{-0QXMdp3Q*-a;b>%DP zylj<Wevy|~gfO>8cA4LdM0!U$AF%*z z^ke@J-+P+#3HBqHp-@)j?d|3JcHaws-{~K}ow*n9OHOIWX;I`?K}X--b?g@tg5n4CyirSZt2BFTtJi+yS0Q65qqEO)3*yy3CVqUX z;biwU7n<7dzVUsf{VF!_nrZa3ob`=_@8R!sIM*q_U5{Mw6xcm|@e4d>dMht{Rt>C) zgG1=#h3~#D4OmVrX)kmBTXt8!L&GchfZ(Xhz>mw1w|}V3^5a+G2nUnA;^2FH?*ivR z1zeFce2EcM9KH10QlW^mALZ-UXi}Kh7Fo)B6C^-K&GK z0Ti$_123H)54-PepWy`7mGkIa$Ex6^&YP3%297PCAZ<2}zwMndam=flF+)WDm1h=6LQ z`|`7P(DA+W5-P=TP+cb>!O^itoWQ3~sE;SX52_U>_upH87=|xj{QE8w-|9{SC33oYE~rLeOPyG0Ucn{xdunIGU4H7Y`#~bTQ6lc#J6O z0c)k4_hh`3RL63SYA#k~5`gf)`98WE1s3LOBt`&cF5~*~cSxe;-l;=8ea^$(yP?2G z|8O#B@IukY5S7} z_8A~RTi!e1ulB%Ve%N{nq3BMSQ`CWkmp)WMM^1SZm&hzD{@Vj z8NPYYGVTaCP<)>d)c$49XRJoa=jJvxrq-141jsl#{dg@@GCEC1E==W(Cmw#r{3K^FD70I(D&C;NusUSEw8-9*5G=`!y5djJA9;}6P z1PM{)fq8WWNnj(|ED5^7H!nI`BcS;xd|wLurZ|4n@LYlrEGwlP6A_%&IkI1w1#($z zHXUDdBD=(DW{iO|$%RZL2z1I$Yd=BLVn3jyq9ci~VPk-&%hJyi0_lOG+aenFK2fbk z8Wsn9|F|N47bR*JS-e0y3&{kWNeLyj4N_Fby5E82EbS;!rlzfP_aRZAv!w@w^QAwj zq{A=7H{QDUDqx}y9b6bG_17q~s98A^CP$afqAiq{v7D8pn#b`slm;~q;$-ZlY2ZOC zq(Rf8-~Nd7G4n4GJ%gZkn`lG)X4!2(Xi8ZfikDfUjJCiLkvV7)JQ;h%Im?SX z-xM3pn_h#Mnbp}-d}u#NaKqy6)H*5WbLdJ-p|6QDTJ-}656^&w5J!zmX=BL<@*1Zta^EwbxOKi1Y5)o5MAa`wQCaID>gJbZ1hj;fTL*ap z;6=#&xz{R+xw43Z%2eidvG|`5Zg%+u+h+w`cOT7FCsg z@F8OQI3S>3Rn0K&rTqJOM4n^hCokao!ZzSKxRdLcXP=jABJZL}3 zWN0{OK?(5^^tDYVLy^fKgX^Xa)vmSGwKq?Y6$cup7N$XVH?J;PRy$`J}4C0^sWRGL;vprCooQ`?zF8p&V0>HaDK7&1TY98*u zMM{=+86ZvUmSg~rJwSCzDU1d;7msXOjsh+{*Lu^A2RYbbAhOlKO%=R|en+f*1S*%Q#k+`*$Q9cFd$--t#3fL>A zJ}ZB;_#$3(D)|~kJOE^dM&a;Y_|?BX$W8NQ+V$IlJX0EN={(Vdis-h;mgG3ndRr)l z)JaC3&3JZ0-%roC20t}&b^e>Q)4;_`)Gy=+A!$Np_?5%Pkj%UeKfevw+tBrprNN{w z$k!_!pH`_MBUONna*Nqu6Q_=vC2N@*BP76Z4;w5JTTc!~4NuSeyda^LkF-V9HX%JF zCt5N0vOi3Tp(2Ee>|=d&>^^MC0S7=rl;dF9yyJ=fXol{t6!ey`8zI51{hA*z1!29` zOrDkMB|+ap1_UwP>cPMU_iY={fh7*oLr}d?y@nNn1i~~`d-*p0@nO1#N{0Fg<0bAJ zGNPdbzjGcn5!61FzxQVWoFiXEbWrF=UENtrKsD>I<#d)4pG6qqZBUR=SQ9}}>8e;+ zy-(wfVd`;h5fPcrkLe~F-mjq@!X(jIF!83=-=NZdso`|vKvX+l<3Hy=?j>3IDQZfD zN|RqYMXjPlLf%t~M(K+69n`Dj+kZKO{@5d~Bkb|A)Km$(3ljxsNZr}@&U6NsK=Gcg z7kk97_g(OG$d0CgDQ4jN4qtMuYfGT!6<=K-sD3ul;G%zEz`W+EQ z%O&wv`sY?NL{jf{c*+Yago+V`76ToyOLf1j&Ve>5fRq*hBQJav@5sGh$Of$~g)}OOA(4z#g5RBlaE99goh|?9c%C~8L zHTmOznAm)a(xYKkMe=csj9=e}HY&^i{Q6h;(udlH3Dl|v#sPv^B0R@s%&zr`I-s&H zf5Q8&u%QBlABvbKyTMnj5+?ZP1&>GL9XYY^Zym~t@?q2u(HDl1;&GuCiY|zeL36Oh z7T|bIsg9cAof54Wf5l&PHAm~U7$!m0u@ldHZL4e5x;^99O2tcOBxVc4kBU6Q{w2O< zl#Di5f(KjH5rm_RQm|qDWdXh^L=g>QBvWKHjt484=zWj)HI%Kz4xZV74Ek6~#|oN$ z{NZtpfZNi^xKpYPN}9D)#qaSF`D0&D?mC$FmrRX)mdB+K&;rg9=hR4i2FYxHEK&eO z6&k)@;~Zqr6sQK9yrgMBsq-DA$0$EIbSqh0BEsc}cr6CF6K%M7Z*<(SPpSrGQ32Lj zfu+s<2Z?YHomAn~t5zv0A_c(-TD+eUbDCeDJzCWOqpol=+IMzM@gu_hx87vIKQQP~m>x z?0OaqgkFRGf-z+eB>ki*5l|H!8dRZ&z!8RfHzS#lP z&;A_A6kBdDy>FrfL!(#GJ5SnHl`n9BvINLh$TiPUd+Kx7OAyiRP7r^5!L8o;9wu)S zkab?RX*iwU+O0%-Pd|&LzzvuRW93)fK~8jFpwA55kN z?#UMeoZLkmD@~7ucQj=Eu|^nEgdM8xCTw=yL8*!ZZEITXZa^Km^?qV=gd}a%_5?bNurBIK_Zs2lOD)dK;N4Rp>)s*(`=CIIj9aKe4U%Q1#vKl{ z(DI>MU{+2%Ph*f|r+vK5flUhK>P#AyWGx{)qQ@b%F%l?zK`7~y9@%2n2wK2iSXyR_ zthy--5&LyVZvw2)P#z}c(g{r@aimd=vz}-QA=6XY$^+EppXD5jCn;9d2Z69niyOf%{u^YFi8HDuO z-qNyWYS#$OnXs!HPdf~v(se+;cm0@=a{3Y)kyMo$cf|ml2x@3KV0up^XOKDi_DOjD zx8+-1p*tQzj(MjN?LXerUMq zdSawmTEhKAHG>@5Mp;5isn4_*>C0R!q(ERO8?hyANVus| zfm$0WP>Jk3&jK>sf_9aZma~e13{6}jlry3*UGI%MTJ&w%N?R-_eVO2X_S&{pTsWKD z7M^i~RG_}07|V|Ax!s+M=DCT2sR6QxFWW?!{~(o_T?sR61=4R?8fm%ci=y>WSC)OrQO!7~JUStO?kSKdVjVp_gj>Tm;2ms&h1)v8Zy>=tF`sl^eZ`Pn4nWE|5^ z+2=-$ArBQ#urL)iDm752N^i!8$ z$tw_`OfkcZMwf5Tw0<2)RQZDVd>~O^47S^yRd6B7;f@VZu#!Cx41fJiH87>5f6f38s%g~O}CX-s_T%uKl zifq4BLP<@Wka?36Jf*4rU3b@Upq!-qBPQnf`u0eJ=;8>5v6sdVu^Q&R)j)f}tbuo! z@@FJN6vRru1u-+VMn}@B?gdg+FwH_fdf^4(y_H^-BGUnYJ`AQK2f?aEoP3^0gM+9} z?}`3Ho_Z<=mdb6WLqAEHV@r=-_ZPu@>6;n8G?Q;OYm)TT(bs~yqWg;9q%O^c4|Hi5 z73K(8({N4^1>Z<^wrAb&h!Lqg=}K)nE(d-;rmXP}@qANq`AxS>=DSTrZOB3p7{rJ# z)zSn(0)`kAs;|sZ!_r9-MZAZsB2P6NH_yuze4$u0K~zEqY__l)Z`ntj;Wc^PoQD zaX}B$hZFVrdim*mG!L&UNEUfW;w!pDERMLuHFFQEfRKh*|H`_@ezN$oKz`tUXH}-1j-{Fh)WuA`n8G_iwwn`{3{uazyDqj_zF!-Y)l=#lq zCj<9R^_pm6%t4;iB^n9;+ki7Au)#5pvnQ_rZ#2z1EM>`iB&Arul=L zDBV72WOe>`#oXLHBOy1z-WxGjXuLDv7UdS9Uz+#eaH%wF^4FB@3pnaaxfE8DQOkxq zAc`U4;C!ZjX*I8!-f1pb9v4HhkzR7e3#j>wCk(&g@b!msbm?E5Xf;~~wm(z5ep6{u zT22UW*VxCOO58ut=P( zM44+ zvRBA}JMa>1J-I9!YqVna6Np-LxhA_mZniq`8m2^k;-1jkDoC$e`lVTSUW4H63ph8hP;zhv=5@wYf zsZ}JEX_jWQ01J9W*NtC#5f{+C*(2ucpm+<~_Rst&lS$K8XtUKevi2HZYTq7&eJZp` zgG4^sRk-VuNCn{(lLqRzL@o-XjO2xfoN@0M<+-kC($vOrjNI;;3Rta`h@!+5U&Vza ziK@%yNVvacM?t{*vy>)x42>FHc(d$7C*Co-W0zGOKmMq0*ow5t88>Uo>#6NPc&rhCHMy|0C~**l}K?6Z%!w_oLa zc$$Hu9zH4-wFyU^)IaL{{z>GvA5Nd_6H zEHVd!{;x>ciyQ=2Ts|bn0mUvjMYO8RSs3MBw(Rc3dk?W1Ok4T&CQCFOag4GFE zh3J3Z-WcjJ;wxHDN@M)3!%8zN zK1}0j0FeRN?UEJ^ZH5hQ(QP?cC8tl0)xE>gd40A1s9U+?F;?hq02;gUF6}OvOVaT> z6DK!A7(zhqCHfvi$(LHhZkt9}uc#H}1TboA?%To6=XRE?@#12g z_7jNwTF}Qn(EOGe6Bylj%a{(|0|xsuiIBllA=i^jYn34K4hE(;7i3EyC8B0ArLatY z;|(`ltAG9yD{2Sg0b%EC!X4JNX85E;AU^dSKe?}A1B(H7vH!_9%&eV-tX_N7KDtbu zY>gg^+$Q1ibUA+7i~1smcf*bju@0#(xran1PY!M)cX?cHiX!XIj6l$abthb%u}*_e z(7=k`t+Tp`-@v4#&4IOc3Xsfp9&<3rwPR{%L$QXfw6fK@zJ1 zT?71HOQKoYF8U){5V!N}wT=GRERUL3(w4z!6@tDF;{sI;qKmpFJIuB#)~afBC_~x* zh*1lD$kQO3ans;c4tR_2c0v{TkzW1lAw)AYvcJx3f4i-4J?mY#>2V^<-9 zoO<+n{1-XwxrYD|Z^|Z>AAWrBp871Ck#B27SB(U^-TFz9S&|C`=rZ*dD;A*86b*dO zFD50=LVs4BIozOqqWJJPXR`{C({1-cXOHoPYIGK|YX#YG>(9wsF!4B1?MI6yJGOv3 z80m^TV?xIBb6g6OU>z)~bwWOfI^k6-rI!`|aUD%BKVQVssBoxax&(~W)Ni86;NIvb zN8`3~5=`Dz+qyL*n7^^NlZP2a>?=i?%OTSQzbzztBtgraD-ylo;RhE7?fHgnBKo=RWLI=85_aM*koO7a9$@P?w+`*1f~sGd@cqp3sUSBrVA% zL%u_}GSpZ^CSw%8Zw0PlC-lQG-m(kDD_Y6*M|V(1`@q!Zb#!7>zj{#+Fv!}&Uc^+} zA4UEeUApp`87i3H1e}b#Px$GiMI6lr)5a0{&58EpoLZN_1cP0-hZXqhYoLTrFYIJj zcm$g+vd|f+aJEq8T@xNfKC0EWLx26i`pTQ zmWJ8Jc2t}c{lm_F5F9zT4Dq3CWAYD^h*~eYp@zmb6pNvf=q+I*Zf*rF;lWPx`r*WG z(JF7ZJ2V>J&7feG<~0o)%Jt;n5z^@QWo$eX@`p9J2K`fGZE)yn68oG49Eh!8bt7Ky zlmowqs7!XEnm(0|WU?O(boqbQ_N?cQw0W{fnaS!=r2GZi4KRlcB4D3qQN?S@pQsR;a(2wiG zW&Tjs91FK{6hU4)_7^m&EalgVN{!ldxJ#%8hvd^r(`&1Q);iuM{7yaRcCM*YzqOP6 z#rGtZn_YLGF?BOt9wVc7`np*~97j5@t31V99WqLRaD^>YXA#UizFa<@>9kKGAthto zr88U}7dSKJOZw8pYZ=r-Uc3ycpBbOAGa%^Xr`zqg|C(v4P3kl#8`4x2sUU=&fip54 z64jng+J-`*ppYuI!-AiBsEAwebn`xEN*KYKvu7ZaJ?zD$@JwW|?ni7tdfxm;06lDGjQ2|AizK zGM|ECRAoZ6)Y@wDA@tn92Gr+Qv%1bq3WKVd zk7}z2k#U1id?rO`H2Qe98IY2_y!~8^XArq$A=Xn7QARcbl9U=a5y2A|%gp1un<)69 zLNv~=quMfs_ zgc^5JW|(B_9K{U1_UadrjLYnzBP7#)l&e2ME!?}UYVCFPm3V+jfkNDqXs+j1-SWCZ z$jzn|Q`B!iR(a%!o_7Wn=+q6G$nUF%V0#i5Nn^Mc5xa6EUE_eigECoxvduB86DiY` z2VnjNz)1dxYcT#w#}_pC>No@>52jxC_hkuBa1)N!o!m>@Ch3$211+gWY$Rj%4O$^f zk>gDoZa3n)OKA66(bWry{D?xsBX|9>Jj{H5c|s^^74IwE5%~lwTdItZGM<&lYfuum`00 zR)uvty0_=$3AHB^X7P%F+&Z+>G0+IG?@iQI`ln{@#5G4W&Rgg@cMg^)xi-`jrb!06>bInpo(AyeX=5;FH$7tsG|v zz36orx+0~}Jly5CP<5CHkKY*HY@ZXC&+ zIdXYtOAccPC-0Vns|)DbFxr98(t9rJu@LWzLtQRV1LvUw&v$I{PlbxgDX&7NL0?(iF_7vB>X3zzVF6_V(Pd~5Hq3Zvz~nGC~M z%B8QEZ@+Rp`v9xuijJrJQ-ucarG6_)w{J_woyk-Vxl!jc!1UoLqlgjA_ ziI2bniuq=O>#Q+@eo@8Ns%e+-N`Nf?sSG?Ln(za&d;~>{sR3kwi#VNUJ>gvXjKlV5 zS93nczBbiOr;vmlZ|Jb>`$(ZSE$w0TB%^3Z(xhA$fpm+9-r}?D{wm`**)C(<)>+G3 zV3Bl1?Um=>&{+JUr8V;B2QJUiY%{cm&C%EfvR|%K3+{?ZSLm?=eaC<$eU2G}(~`MjdNh z1;4+zWc!fT4?ek5oE3s9bl{?0Zi~1HeN=S449Jt=;EkU?FNsoqOhF$%x73&or#xoS z>cmZ)W^o3fdlw_KB;R;E0Bvbi>?Yqv2GT8-uM*iz+Y_w1p`lio@5ZPshqH+XQya!6 zv@3vfU!=wNY1Efj4rrp~x$Hi-f%}_yT zXiE<1db`7cg(G8DeMj)2s(M2}oIOwYe1%@DvZ}K~c~$ZRS2iq5rW~?Y|L>~-n-?Y) z4F-q;WJBW!RkFn!svpdLEn8|y`}>T~Kv;Dj-73cV)y= zSvsy?_U&0%J&Yo(3U-A63sna)XZsh4S+I;Jqu<;5noN5`B%n+<#UFUyEH4%elAKW; zH^gYi?P>bl8^0h_zp4vIA?bOnUBLjw=qRSS1Hg3Yhz>=YSPsUnNBE%_ymlCIAXGIs zq5U;3nw~k<({R*`(QASQAfP6y>;|*_wSopw8%b(sv`d7yD?l~+WT+U-bn(oqtx!N3 z?9mlW!p?^4k?ljeos1Hgi{t?oAkLuhhtu^?CGg?(yfTX{TEwP^v@1r9Ng_fccky8@ zWQA_H>!=gO3qp(n#0RN>9LT93gkPBSgF?fxUImh1dewmhj_g}uz)9$pg_!9ejv+o};&KfnwFkZFOBNpj>KM{rPu zY0Yo_AmCFhF7l*J&(JQPK?&eyoRcg+C_mb;{~|p1XmO)$4n?9&wjbaYzV9XX&I3(0 z&Yl^Zc6AW}{h*GpsLA(I$GhTP!76ZfISO9kmP_3ZUX5cMkjQf5d~5I18pF8BoH-z& z7+koty=0#_Y8_qMJ_}_gi{&Lbj6l@Z4%eNNW_X(k)m1Uy#~)@j1%8kq3H`Rm2KzSd zqG^&qg-BL;3c_r!U2lUDE6YD?1cp{~x0?G5Mi7j)Lz8WyrAEF@`J>Dz{B29OBHMp4F(fSc2|>G?$=2cEMCSr-Lmgsl>}hR~@4g4hYeE z)^^a}I^0CjUlS_Rn8?YWO~I{nR+utYSoF|*FJ-aWw-xek8qt#EtHeQG1c(@gxuDzU ztrueO;Zk)^J(;qIqYiepx%O;SeDyzo1VymQ;y5K_5f1?B{4`&8gI+>XAQX?AR*c`GOL3EOgp%0t=q>!~Se`O4

^9G593p%bfbM7()N2mfcS6MM`D^EbR48*AlNUaXMdsRUJ;)K)8xycwFOJ-T( z;!(^fkTgo{_))Y?ZKkZV6dLa(G3L{+)GfYQLRwZEAMwe1A_zD(ts#6;q!)LKpgk%W zohFcud)qGhIGUt~7=gG9=Xty)X0OnqzlneSRF|2I!_(@D`+7LnMejffBZzV0g5nXUK`R>o@3Btss(K+SC&pFDjXjq|<*u8|=^ zdm(iNjd8L!PBQTh_1$3jRg|(Zxni42wKWt$^~$Nn#eSfFPZcHMfyJjPQiFg#W2;^h z_I0mFc#PtJKm3M$jpY!Jd35Go@Fp<~Mz^!M(=$A2%yVopsEb5`NhUV>Dgsf*LNvK@ z69^NIcNWvXW75Q5!Q z^|Pb>WQvu|=~w)=IJb1Q7HhX&RcN{T&w%}uWrsf0T9jUHZ1V%wJw5e5jRp-=Ak9~B zvVTdFcrE`qtr^MA2;eA|$OTRKwB$dPMJU;~Sg@C97N`^#G)*6cFmpcwB`|4Y5}Lw& z{TDl=JAG!Gp5th=3TC|PO9=wD|HFhE)w zm!L5Rj48vn5v*Y{y!y6#A|jWC<%5menrlt+*W`;$<}}XjTfUnosj2?1S)i|=6LLEf}Kje-_3sF!cl9I;ThLT~+WAeS0 z9EY%ZrtOL*EHYu-Xf|>>pacbO;r8^!v%gvMlR34MPwdBU6S*8~Xr9iVLCqna@g$0% z3juo{>@_&`y_dB=T?pmQZsCvZE)vtQENetU91d<{Bsjk5S@Y(@GzuXo-+WpoPIEf}%+4O}xYi*<5BG zmx=|ZlW1o?<7PptP-zkvh2alV`U*Sa$s_f z)1Q(6gd!?G2`2i@ReeGZROJ(>XK5(U|F3kpFbaJ_3G)H4wCGPl5>M~H!SKMVbX+(` zY!w%6L}yM3YhvcF-t1?=C0JRvJC6LpLQAuLg@lePOj`U6xHP8e0U!KVsly!C_-ewW zngDDER!?`7(lqA!#AV-@L6ZpxQEL3=B^YU#O8I;| zQ-6Z6{f5o+8=@2nzJ?6IEb@}nb$>4U~JczXi0q)%HxCN^hgr&W^+54^dVY=fSC z%6k%;p}VYQkx4^v=@F1|Y&B}J%FV&_TyKW2K<$*}2sll(_0WIPEKdvN@dkg=7wLgD zZmY^JYspOtNT+N}s(MWO7z(V+?MgDvMXSUD@-81C-bXRWG(xi1kFI4(_MnPfSxn(W zv3cGjHB$W}*%#P4S1wtLc==Vva_qZFl&p>|D7Q_bnRn#BO48%4LEbreOd|RRKtnWx zgjU*;(CSuUVvl(5>R}rVJtOAS691FI|1=14lYbwz6zMtyUW{1NpTtGJLTuyg11>+Y zIzWaGA5nM|Th*FClOd(D_x&&n5Vt6^4ecFy^d|h%$H9~%< znrkHob$$X>V=o2uA7KT#lIay!=d7>jkO+G2UJ^a!A_u#uU2(LTIs;<3Rno+$;Q^J} z$7*9Z+vE|@x10kQ-$jk83P@cYz)>O3s5WRLh2$X)`*#c34L(^Z`0q^z*38yCg#@G* zbKM2pZ&QRB7zS8#`)NvS@L4t9;SQ28l31RB6OvdS8{+f(y8q#O&<50pH9 zbD7$R+%xdkM3 zgq6sk#XtnVNnmF28Bei<&w-~nCs_1&c)Sj{^f|%&tHvm=5czv#Zllww7(>}0=!*GI zk8~#o={3-k4h{TGeD_%G#IWV696#{u)1XMbh0Ff3Gr96} zYt)$!@}ptxuNpxTDt?0q5>GVd`$O4KpgPOyURx>Qqny4l8z6;M66f|2W4m3^_yESsOzK~Zt(z5? zA}0d`V0dUobqCLo6bRA_g}mv-`xQM;mx&F4SDQsJ5$B2&64Ta8b(iEO`w0(0)srN8 zQV4Tu=d21yfD?V5W&(vL%5@azS@3GwMEI}{<|bB}Zl+|A&aC31xbzmD%goXgg-*t4 z^&%qtD4uNuK8Dfo)uVW4DnZjQh*bL7oD~9DwKy6kH-FJhn6gepAmgIo;3Vs-UhUmVO6#k^5TQVOe7SoOa?J}G@)G2@Aykn}4+GS4mUwSVgm z-jv8?YAzwR3cXAibZ?!$6->}wBw)sSxlU|&+(SvhM7WVroz`7bx z*~IalZi;xXECW=H@Ta@DAHLe|%%3ehHebADAxeth2Ii`;CacwRG-p}Ds0p!9c|vz+ zOZ((roZCyx(?NIqKz{Ns;y1a!Wc-u=YOlD#tH4}oRRgk-kMv`Xk;3IjVZH275lv=? zPRy0Hl(mZcc*)1}Ok;@mN;=ZV&WAZ4Ti)PUK7GGgo$PRr1s#Y9MKRiFC8#zNUUL#iqOh0W*$PaT=KTr~wPUvbm{fgd85RQ~GQK zT!M`JM!_r$!&9Y0kWttIViUHYNHas+bv!y*FO|tGa$OxtVSlSw%dlX{#wjarG(+!l z+7c8IB=+;3MOMYc=nx#DBswFn!oXE3A5$U1hF!bgh@{1%t#X>DqLtM^n0N?!d*CaC zJ8Cq0`6iJqxQ+CR&lKOJgu-%g|I^C0Bc*s+nD29NX)nJz@MUga4Q2VMQW)DgZPbP@ z7>?c)SluwO0+95S!mkmR6SMKo?x;2bh~{?I1OULt)Se5~XqJT4b9UYbWTZpuEv)-EaxRkgp>40WKvEcbD-Yho_@Ai& z+8)(8P0-nQO*Lu#y^oM2;`gk;p-;5Yx?qBs6tcuX|0`XxCX z)mhs3jFNN!nlhKUQ|rv1gq#xS!U?!h;fhyqfznHb%EI2w1AOC)KBVVyjeKb~$gDVr z*0PR=<JuD73G%_OiUF32h3F}z`lS-i3mKp6x5t?KH5go{fnfFa%zc!OokYe z@zFEK=M*wYOqRw}bA9=wRLvg}?Uxl1aKrNVp!2-?X_Z>Q<>GX~hu^G%t2jcktut^{)I2myxY7@`~lQs zNPIV@9r~dV84|fyj3iGj%#&I=#-=7O7da^lRf$>4vDe8oRqiDM!trdw)6-YeP|B@% zeQFN`Ii&L6q^-q_<=x_cE09;)UY#Aerf9;x`gbE`A(>^8a(OZytOfP`$$2~MN-3!{ zc(dw>p4_u)8iidq78@4gnFukah=SnoSkO~50XtREHL`=w*dTRjfk$d*7tW!x3lo^| z-^t182tM1dz|H}SYWyyIFfP{54kW=17*(NLj6F2dsRkY=@CiLnR|FHASe+M%6<4B{ zJ`BeX&)G9me;<#HHy;{-uuGK&a1@SI@seP+4c)l)O?a%(65(3tOrA!bOq(95113MA)dgGzS)>TwH*Gx0b64I4S^%m?*pareW~CP>V|D zK+lID^6IS$yb2aP4djetq6-0*%A8KCWiy02u7vrP8~~6pp9bLy%2`+=_AVV5x!*pVLKH|)hCSWYc)H0XgBF#B|bDG zD=V@HFUoh6K=IZb>zSsJKFL0}S48AGY%=veW)BlESN0Bk@iRcM5x34la;J#IoR3`| zgLalL`T|?)wzGk^q!GE+N;##9&=B?Hwcq<}Dd%NVx0ig%NFrf5a{yOORd=^K4c*v& zm1nVhEDAi$TALwBDKFa3>^g%)f%L#KBt)6ifJHB&y*n7OckPELH0KPiP6 zJECo^jROw+DH<=`d7n}n!p88`k8EL6#1WI^45$j*Od!lJJtmM`kjR{k`P4IKdX6^T zN}syBN~k$_%{%s}f;5|3w@In&L3Qvr(mZJaX_lwkF+GpG>?S_XT?RbBrvvi3*$B!l zcaJ&2li~_S84DRrNd{ZLUCsG)KEK8D{Fr#*+vr~CBDb?Jy(79WT>}=}#%ZpWnroxj zGpJ(2VjYJq>1Dk>8PF@9dg{0PYOx|-h=z1%`?Tiz)JX6h>(sW61ajQ8hA_jUq@FC8 zbXdCCv4XbPj@M;5b-9AmSC!RegKvSNIB&&8>bd`?Qtp1uNVt85g%kwuO0_-l7Lek}T?2*90S9LW-WQ*mo2n=rr z2mu#SLUu8@j z`Hg3Akw#RbA=mmUI(Cr`@?D(@xO@f-TEe$4(??l|w5Dn~u-L2+miTj5{DvwJaI%(i ziS@(3P2Yb-&r>2>dw{qPG=QB(kc1#$_Ync-sP!>ZWQ|7PsF0v2J;si6?gwatwARfL zjOn7L^H%fczHjJlkyin9!g#mWR$;0f)Ardr6`JnF#}7GuRIqME=&!=JVM|V1?<|R!G&1h%lIK_Djw4X5QyzN%n|dG3L76#UUdBG)~{Q# zh{1>j`bhB{qky+xKR%!_hY&l<_ee7u2e~ja^mFx6=S5`mv8xyiHM(fjY~pT>NZ2yp`YQqQy11toaLu#nnO5 zy)xyI6x=GrB=W*|+)ElBa-{^SADJ1dx=FD(5$N26IgxsUo{P;=tTNhi@+;+1h|`~r z^~YSq(=O}6oE1T!&C(x=+nlIM;J)#NquwA((d!)MyFGA837bIOF>yr%TT?`s`k6GD zoWdU~ltfJPU$)~>X8=5pK6~NwtEnpZ&i-C?Q6&E9Z2CSrQ*#iMp`A7=BT268ocyrl zp~G83s>0{~t+Zq^?=No;YHsUq_n1}%Z*H%a@)lvjb1B7Ty2J^v_1Hr{O%tAes3+up z+K6lY7XFYFh8^vsYdaIpg@m0aA3bACs+?8W2L0pfY=`3!(g$#%y;i6ozbTfvIF5HZ zTjmP)=isdmGP-&JW@Q)In^Hr5@Nqqw_<>hHMRJb__2^;cYvaW~{qI@={KT@}e#OQ? zNNY61Us{Y){SMzJ#E);0!3B)RNgEqvVQ_Ae?oGRYRSLvI3Ob=G|hokb2uzb03Zwr zV(HrVv9aHGrH|fTxfFQ9MNLW$e4h3Un%>BlfcxPa@oz9o5sDicuBBj57$ozxCyh>p$hl-w7G&Wc9 z$76dGQ1c_ulR8&Nc{);Xe3AVXkXAaP>|QX=0lU3%_LlI+xBj5Ok5B+00}(2g#-EcY zTwH$eYa%xDS1(A&g%$*K#SwF*?2nM_k3s(r1u&10m2v_pt-9nRffGRpaZsl^(t$`g z=NH)yal~At(F`a)+HKOIqR}nM5fe^YPM!%kP1j668rH+mIh=~I$)@?sx=JxCYE;DX zvR8(w+C-2gO{3NyAubqKa%0MwNKn}8MNKXAsaXX#Lj>R>{h_@Doqv0$Vw@p<<2~9zofm;!!3gSC_BoM41(Z*_cs`)@ulJ~y z#_GPZ_T!~%o%xEFi+84dRmQ@he-lf zI~%r8eWb1#%uF5Xr#BU|%0wsm_`9syOR43;<-$VSK?qgw3ViD6jV54Lu$1aSVk$tJ|~IquJbJCk-;l#L8xB zV{-IA!4a4^Hy?VDgCIPOj+^=+J?#jVmf83anOOo}QuvWfI=6h$f%54dS-aUf#1lfH zaPs&YMd43hy?$P#R!v1++QME?xdFc1o;SJdZ|C_NjK(EX=^(_qzrhmzc*Xw>6kymi=j((AvRd{};fN*?8k+bpC+p_4U?NUsY1BNyi?U8l zO{e!V!SEt#ODv>6!{2LH;NY}35=4ld#dIFUKlsgoSc+}Cq6=OymL#zbGJ1<-aq0yT z2YWP+V17tXd%<%~ixSZPHe%WoIwuDRkvyipV>zp|(T@K3E?C^@VB1Q991~1!I{r^f zPKA=q>HX+qk`-x!Q6GoN{!aPx1klL==-+X!bg`(8C?n<9PL$SfV;7x~o{H})9rUAH zc|Q1$cDp2jk{uM;mCoY7s{7ekc5W>vnNGC*&{55&v!a@X8B7K%FjQQ|r<%In$aEcH zjb^q~o95(?dWq4(j7l6qMsq zVh=q3?P0yA^zT29Ch)=N=UJ%~TMz?k$S=ey_s1Ncq$xmi$D8tTy#3{Qe}GcH-oeaI z(j4j+oxl(YQ%64`KQ@VEHQyqLSRGPmH)jsgkH7hQO8JaoQJ~ zUM`l%TLg8yu@kI1?wS?p%L4=xPai#5Mei!909Qb$zYTG6s2Db(O=tGSEGcIy-Ij@1M`~UIEY=5P0aZm499*-- zRJ{ZOX7Edif=A^XM!Qm9HbOxZ&anhI#(t?=P~yTf%iF~kvy)8ydY6JHrRb; z6z4OkRDr{4QVrUesG+17RcdiEPZJlmGX1S`@XIb}oK)9`Vy9z8`+EwXj6fdrPFJ&1 zg5kf!mEY-yZ(nKu@SIQtEeVe=#&24(X?nI6`vL*mR~JG6Oa){1^hEO&p)hO7l9Uaq zaw(otm8D^jltVcbKc3eU&JjnX`kOBM^xj-MHht(FaZMboYe_W?)jwGzPZda-w%|?? zzAHFOy2vWui2qvExdP_n!_Uw z7gAFeN?~5c8bz?=N?_lnQ-Ou(Q>bn-A?&VW_(IEc5be)b3A++Nm0pk>bcH1>91nnB zx39Au7&74&^rYSB%5)D+FBRyjzh$_H4mBE$1YogB4!ED()c<*Q{2AgPsLGSI%81xi*U^JIv`Cb$=r_jj-Ze7vf2@_ z%yAacIPVo$q9*}sMDuU%_wyS77x<$;xqP>KF)=x^Gpq3@Au}st7k%gffF^k~XhV?9 zzEdn(r`dXeLqN9Eo+%CaAeeY)%-i-g7Sqg`?R1a#fsIU9ZXwc&H%#~x82vOoGf6vG zDij+mEEg%bEn|g}fdEWcP%_tlBk94 z5!!t_CC$i9^hhCn zC;VRp)M>^pfmmA!h%j%|$5RPRAB`A76Q+@Yj}88;uh?+|!YJgZFD9Hi;FxsSXqjcX zFuX0BTdWiJ?OgRtmC|uaL0p%0%8=0Z=Wme35arOf#`w%#4^8 zh)IA*nnd(PgmO~-><0!;XPJa^UuK+u4#G#?r@N0mM4(9fD_t;_t;$Me3WVqxnK@T$`8~`tkx~5>M0wEQ9P6UJ`NWC(ZoW2iqGiR-B`7=!m;xPp(dRkS! zl`KGMwAq81`-iIVYqt@P<7?|CU}4hxccd!#6c?9)OS zoFRsZ;{cRW;tKi`>&GKu)nq_?q$ad7TR1uV_e?!T$Z z@?EOOJsB(t2IvGEYBC}RCLoGT-H lsiAuHL)B#J!N#r_wxdLnfTs8{1@XVKYG;i(2GcU@4OX zPe;rF644&KXgCSevW{ZFR8#!nwF$QhD9U6InPNm|*d+nH`XIO0OC;k%Lu!kC+H z*b5ql&ya#$;Xwb&1q(k(UguKm@q_pnuTzZx-i)^;QA&8ydwphjuquO3(eCK9D^~0 zgxZxWPvdtp$0rbcKWuSBv83mZAX3=U?`?zV<|CqCJ)APWB$D#6RPaK%I(LLyLxB>H z@FL{Ve5slwT)17-k40o0qS+~N&L2h;Ie`3f)x{Y5UH z#YH<;BE#gQOdgxTxPG8|&^GFO2w10pB@+;uCNc zMJ&!C7}-SIq7I`l(w;b$y2OD>4xYMFh?yi2IT9(XfGA)ms&N`z@fF-?4)#>oLokN1 zOdf_^O#Bk*`rT6G2SXVgf)qelY$q*m%<^HpS9uBMtgB7O%`$(RA^^!OiDuE{nB;ig z4mV05m@`=uV&T$LV&MmFvsc&!qNI?TUrK^<&dGIia1y-=dHc;NYvC(kB~zMsfPq9| z*`(+S4|p{S6MuDhll7IHL3xZMI?G^-#)%0Dv;+Yo(X@Q>$ZCN5l*}n)`B8cnDVSk~ z!#q{*(Y>+UftB{fdR-D@H%-PSQk%jPz+$;uDsc@3;fePr3YY_M_}Qrr@j20rap8ug zL*z68JK-^QiZE922{Q2N>5^5_BMf1;g^_l*0cX;gmoDh@JoY`kltS{L6( z)=;eCc}V&QZCg>iN=%Ch$z2v=n-GC3#V8|=wR=0{YF#iWDVvakfJ>sDS*g&QuM2xe zYEJtQF`(8$4IZgVVEH+QVs|PKK+}R2W-^}gNpv&93r?VX-U!`(a*N7e(;tr-k+I2U zFkJ$qbASiqhK1=ooc{3!qn7_C&@p@mwIBH zFa%ICSzMffeaCClO@O|Lrlvc9V}methEi)s@+yHJEL2rm#J(eu{+X zJMjiq#CAs)aDko%|4>+Dx5cs0fdKR|O<|{;S3rDmjzw()iqXr}qr?w30*ZpAFc_=G+A+rc15)(687fi0oIX5B zGsJIKDpfFm6BvQw80HF3PA8*lvq++m zsjQj>T z>~j`aZ;T^N#8{0ZEcl$BsAQ5{GTv7{enaz^TPgtpR!|jV`)eu`k30Z1M&fdZT$~nc zEJP=4VW+6CiZEF(Xo8Z|rijMG$GYNm!hQ^Vwp)Bvj^ZpCEVIiatV&HvYu+j;_ zH0u&%0aS0H;!KtH;5CV+B{f`ck3S%Sh_^eXR*We!VnU0xDDJdW<;+ZpeDe2;Yq3rxr)x?ePT)1s=vgb)`f4-&;u1~i9KLw z$x=?&lAlVJc?A^@=oH@ayp{5X3k{Gi#54iSeH1VvP4$a*7@4DE6Oa6W^}U+N843-p znmrTV@Cr_g1@av;vdR19G2~2287kZ|+DL;iNU7MfMjIjKZvqueb^wzI}D~cFGOQ1Fx4V- z=TA05+{kD?*f^&<5$&`FZcN$7X&HBiQZBhR4z>}UifEf8&Rho(#T`p{Cq2%nL~~J& z9H*)j95sobkFmEr;9C&eq(jGMbMTIe(&ckr7wm`NZtMI!iFEk%}e}=?ZLa3}_yxFoz}cY%3Pd za~_it=H|QOZzyLG6XJQaM+CVoSbG_=~PSn^)CS$z;+}7CFM8M zQSL-HeWudP%W?nDAA;z|d@G26zmLj}enE~X4z#$+$!1{kMy@2qjDv6J*(5{D5dkt- zP@=^ZKw=~#^Wzy5Lg?Y33j2||b&`E=A{a)2R5ZgOZS$Eki1H#4Fz zB^zI7eiO-~%Z|&jgdA0~=>dpj&&)+G&C2fE;`kMEb7**61(aY@D3ozv*F{ zO738r1cOt!Njp$y<{VevoMqUJNv+TzHfSu4i6T={rp$=N@)eF2aj+3k0sw7TU|rlO zm$p+h)D3rndJ48eZg!)#dQ zQH@T<(qdBz0)jwnmDS1@PaTyUv#Y$-=_||!UKhYlo1R;Z1ki*O2&r{N4Fkv(mXQ65yrTh10Il=_7+-~+aOy#QIcXD;o5P%egj?EHFL0# ziZ9YyjJiyM-A^hxQL*(0xvYphS|FZeygO|=U~78AN)d`R$|Mm}2)21!66r{M4z{lN z8QVr7n>>jq-^rplbr{<&(6so$lEpGrj|pPoc8nJtPPpg|Zsf?u-_q0D?K|O`7@K}Z z6FcGo8_IN|Mk02TgNHtJaB6iqTZT6awJxBia!LZ?BNGz{UTlS7su zq_}OumZRFI%7U;f!Wt66f3i``KJOtqzQH=r{K3i=%DmVaWWa2Icf4mVXq z;2HsZY!=D~FmT@3J~S7p5l&)>RP@?~@(MC@kRytM-XP|@anW~bO+3Z;^~O-9sAhNg z4~)(+)|L|m7@IE_&4v`_;5Y2xX`)7CBq@=^ApC$&z;5QiSLl`sWBU`2XiObj;|K;Y z$!2S60stsXaY~C^lyl{XPwLr^ABytoAQ)YcV3GyX-N%zU)&OT@%k4dPhxu9PDG_)n zb+&}lV--k-DCvZkEhLlUX-Z5JOGYne=J*nG5a~45p)2w6=osNJ>_T6_pr(ybN4F^{ zAwXxMM!=abCa}>-gAxF2K~C6)#I$Qn*$_%I8pIkg3R*P<-kiY;^S4ZatK>k@3cHIH zoyN)01e6g7@lxIxvM}wa%F6+U{1qJZuRrlVj}4o!fmQgE7ZElZmg>jQ+$=N~3c~R) z4b8%q``N=swju4l4(3AJ#8@oEgvaVYXwvV2$k;*##*WABLvUJ{3Lf)HM0ccEIrp{( z{}4av09+azY0h|$Q|o67#|DH@QPe5TDI*r5C`oI8iFq;y330Xk!lKEYUFA3ZKGCLV zOw3?1=wQb=@xYU<1QQW$>}6A0)e?-4Ns!e1ZJGT|t(#pylc}0EUrcbtrr23uzgnZs z${_$>v6T$=krYG>r(HSgrr{MHkdL;In18z<{#Uqd@+I;Lbg0XCYO&)F>SD1$f(kS* z4hOIn0cMZcYp|?P(dn?`F9W{)23%VdG2>Ft;5r)8E0||w7Y_h~U?AbiPPwg_1HSe4 zQwd!kU%)&Ge`15CmY|buoNORhyz>={vLY64jNy?Z@TH^c@(MaXTL7V!P9}G`#Q>8F z*ibK^n2=jySH}Pz$O288wg)m90ytg?@e}m)Qm>$b*#3BWF!4k;M;^@w+tB2SC15+@ z05J|(wE`JT5)PsL$Q^}=Z`DGG+6GJ6<*PjA$edJ@jix0i>e~)Dm>;=JLnPkd%HM+? zl67IoOqlVF=eg144Q2Q$RqsJDMr)D!^Z+8NAe1EsZiZ-(pX)iQ*v#ta1l&bCT_FnF z%1?vd@L{DxeNjY!@~3Cep8^EXX%FQUwnaBv+WQ{b2g`^arHV%c0VT9Y6o+&1pzxq@1D!vOEsy>2N83p$4 zbgwC4PJ3QtwuHiN`q$fGTzH9#Vq2A#W+rs<=n2zQ<0LU~(^s68Mh@}#fPGM7&hGdu zG*`6VX|T%Z3U`hbnMg)#D3KUx!H;nxp-fJIrQ(U&Ia~!7F0xB(AuVqsaURhfc`HkR zjc`m1jDK+(FFE6>f7KOseXK_u8Cg4}xmKS;0PQerDj6ha7a3lnwI6%0QH+gj~l`6r+){eFisyUCr zUE>%ZZ1N}2{OY)(iFqvbuh}>@TQG@gPQ|n+3(*DQZIdmApCW!-e7rQJUopvu34DcX zZp$dX=mlRvOe?BA8>v|gFXX@--bv;ySh!q)yEM{-;bZ_HC;v>KE9u-7Ft+5#E}Tw`a#AP2|F2l{T>;i;Sn;6f5z3 z!V@IAYSDh0o|HdsBu!Jc$80U+v<@VaXm{2(hFerxsOT$i^OUa==ov+o3gx0F8SRZeet>9+ zXEoy!IU{TSG|9Mma-`WBrkq-X)tGFt9>$1j#c8Ls#gkMDX`Q@Ws{`ff%vcs5YSUsD zz9Pyb^v1~%XiwFg`8yiH2aps(oMLs$VN&s42x~2v&>QhB<8oAq82d^#DGFG#{^}Lk zGE1PcgsT9=tJWykpsM?Pg+vS1+7$K<=*N?o3L8|W8t#lf_j{V(Gj^xc4r1PFgY#8z zbeO0y#~o7UHz4K_DJIQ#)tQLKv0)q_)Ad~(ZyS&!K4s1~a3E|9c_0|1C8h}T*%+wB zs|~U~=L?@<{peP#23b~ss6NeVym=B;pwtQL;^xedd0h7g0eL1I;R$IRS3#5 z{f0?Pms&#Q2O`0J@ghVPEL2jR`TKZpumaEn=vW%tnKPTd$gUK$B0{!lktAIhE#Sr+ zq6+P)r3vZBqaewC$rZCk(^#0>S~bq04rFRbc%Em>Qg4S3WujSbqZJ4{+GE@@!5Yy) zNlcoBfn?r{Uxj+rRwKgfUxstx=M*qHzSq|igRxDNogBBo6@vS{a{iHkio46#HziD) zSEnkA=7k-ro0>pH=NzBcjUB!)B&{L(tIjC_19IvW=vHgDM=%guZax46g)T=1C1+{m z#Wb!!HXTIvk3_&}qPAf4xCQu=uwXF!>93{En9@naK>sPwYsb-?k5R>imSCv*B9NWP z5EuU#zoa*!OvO=-h4#J$EAWouvF!0bqNVU7)Zjif5uSZf%w0JCNX!woV$0LyJKD0= zIU&IXZNA!LnA5ZgKG9gWm=Lt1Qm`d6pBhIlmY7Oa{uu{+XCOtYVw^<2jZbp(vwku=-D|WXMA=! z!I~rA8bl0*JmPHu>8!}^LkKt|O$pZ@N}!I_UOh=cS&QQD(1C`5yhTI&lB&n35od3V zY=V935*i68G-0iRJ59Lo&uxt?p-a-w$6E*~ z55cjv5sZ>n`<`d)m*^f?NyG@gR}`AEc7gfM?w>Q>1Ruw&cj^G@+8$W5+FmZM)uBpK zB34k{&&&Ycvg};wgtsUZsK`DEYN8h+7S32GbkZnZWe{4G7G_}JEqJ$fRImrmAh#kY zsbj5uHI1rpx9U(3HS4{ng{zQg)j!qk2Fw#-J zX;FduaX{)j$MATClSelfAm;bP(g?eP9RvUy!3|)pkMK!2%<^Fof8yqny6XKw@N(1L z$a5r3{=LqOgFC=+-#FEiY%?s#!`5ZP+rhQSuw90+Ow7NxW*as#L*l9q<_Hmp2Zp3I zxOCY{ylR;SM;UBDq32E%mj0+g$V`cl?W?+Sv*J>t>L_6Ai%AgHMijT>ZFUh2VR|f@ zz(Y056tE!+KJj;13rL6yR4B*f#oJ`Z)NctcVpKLYMu?`1I899>@mn5?*MVWi-#h+177}>?lvif7Na-dVJ}Ui$$^C{MwLqcV(kWg^s;)Auf2h1?0#Sv%=)@tSB6OE~iHWivg+l>eQTs!q7-2PzNiIlxHgwYm4&ieDEQi zA2o;-qwXj%s*gGFp$hyG+t&rli(#=e4913%$0#TTKeyAcS=H1@T!3c_W=y+A4Vp$d z?c5$gknwy%mS7ji)O%(4zvo<6_4t3mn$c$X`L?0DJ+^119r#IXPYj#FiyQ&Nc~>65 z)nYi)*a%NZPv=TH8Y&I?aV;MuDM-poN4Jv{});r!L)t#r{N`t)+}D~^HG3(&I@BTmKoW^x8{FvB^*3Hd$LLP$tUzxC$r8;K1PnJv5PXUq4o z(bZarH&fxbZRKlneq5=mFq(eZD~e%mjp9cwF!n?I1rde7Eh*n#Dt{C%W1Ulj7O!e%vlIZ)QU?Wl1*{5AG8GX#ziF5UNqWW#GJwa_JS#5 zPP}e}gI>zv9Mi<~X?((o(@d2^CGZA)nxqtAQWj8AB|Yd|@rgyr<}FX=0X9<91a~pd zog*^7!sWvWLe~T^H)EwoVD^#5gZU^lp?Z#A3IixTg=O$DO$b&B4pl(cSE`n=IcJYy z32cc`0nPrm>Pec1zeFj~nb_6z(4ka1_LepB3>$G3Gy+@4V_rr$9*M;#rpNV#)$Fx! zI}xkH1G)ZJdCYG^&-BgkY3}k*-tvRJ6KtZpvNU5(CyXs& z3e%9#81z`ZK21F7`Q!&oHSO5Oj{zYT5Y__(4L|n>r1Ol1ct0ZOma z7)abLK*&cB=U6dm?Uu~pzbgIDcj-@Yn2l(O=O0f%5rptBIw37LPkU^u39?3A%KBv_4>ABCspKzPPx#p|7b zfbBG`%Ca^u@f3VbF)v5$DCby>42PJEG$r2JmNZ~AJqmP~ql@c*?6Ck|W{oum!`p1n)GNmwOyl{I|K!JZk zI=CgBqs@6Qx>XwlloaFSV32=!f~GKJTx3bXY#)nBa zP#F*26bx!brxmUq7R49%Z%(xy8*rN|2RqVPlk|FwiW(p`zRMs1qdXHm4j_zN+kuni+cDbd47LAYpC>NMvd{psrBfYXhz1k7nne&C`$ z_~|C5B`UYG^F`XDg26nlG07|tz=cSQlB@Ix)dzNCX+f6v4)#3Zc#R~vvy#1MOg(FJ zcHy>?#w+YM0Fe{QVOH4XH1J7-<4A*Z-#-N15KK%C;V})4c;LODy0ADnov7J{M0_Kdu^B=;&(C;YbmfaBAtWh7AtZlCbt*l zkmHC#WMwH4Hh>-ki5Ll)VyZ;YOMVKx6XZB;^>v(TN;!$lcwcr?u37yGrkWU`IKl`T zir1$iC3eC*?f$LiwIzE@S`HrJNQ;H-ff2v|LYen=-Z#Azom#_T3LSiYB*TE8fP?ExY-TG^KoO!#Dq`ZOOmzz!Ku-s|!7{G^rs)Eofq#B-5 z;OcpkDeTJ@!%|Mr;y0!l|I;T5+n$yiT1ZOg;ouK#F~w!e7TwLJHF7)AD#iK;$U)^f zOYK694A2o)`VEaCBV@^n^gRD4QuAO(avmHwc<`Nvk0vk{)xhYkog^i9X#s#(IMU`R zI9vA_kzTy5mVGqk+|q^@Wnti$mwY3dmHJ$&5^Dh1rR3I2Y#!xPyyrlnHYAoBuWf+OXm3W+W5@VK1}157G5YKc;% zJDu>oecjaqgfsV-031*P9F|<+@gq{jINfrU0b}FA-#6IhI02?wE(WLi0D`BJtU?)o z#o5Z*AOkT=X^}cTDm<28)lq2K3GOV_!=m2?v}yuODOJeQY#__d#-Vw7v)>Z`cC@J4 zv@hr{@M>6<;5dh-7+VwE&SCL@Fj(|X-JS(&;X`w@l;JT zCX2<%u5v!xuP}e-Mv)X!zunCaWzbpJ%<|J(ZygR136m?Z*9o(Y56kVQIw+RAuC(sV z;p&+W=^2)uBA0^tZ>BeXcWlD&=I&)WGYVM^3@x1>yof;zcoj;x-FIpSgi5&{y+N#) z#VLzu??g;;QJLN6@(F5!aZ`~W^LOl#d`dLLEWsz^V-wRiL>8~D)4HeEH#{1g+DUb+ z=zA6E!gjip`nS2JfCc_xy2F`Q}xj{b(jG5HE)*$i%R*fxq@PM1Rvlv#sE zW+UOk03q?0w@phO14&WJXcDB11T8a?x0wVyUTi{DZjvHiRRd*O; z`83OMoZ5%<47l9LSvNU>i)(yF5G*)7#@88#>P}@2Kc6(kqEVr_DuR0FR1h(L&HVzW zW73KhdK41kRi5U`(3b2djHXiTI0QqQNTnDv-%or;RT4H^82#z(Mq@5hR|BCg5)>U$ zn38ZfksMnfNu71RyMBgro1T>Vt^3-17Xg>cCiQ)7kNBLl(K&~CFsCB*bW_Jjg37{M zoql%Bc|Le7&1kN!;dwC7^`oi4aEJ`PnC#ZNHAZtv61zD{*?hhp6T@K9`@*mo zUpiQ2M*7w|kdYhn`hLk`WF|o-SAl;MqV7vN|RZRuigt{25oW%E$>zE?Mf+&8E z$G*b%PbsgSRccRd3ENEdKmB^1XQ8W9-*Uk2{(;EO8|6l&&ra`N>U{m`83UTC)QSy~ zdV17|r^cii1k9=_0kqmb33oNIujrXpL9qTCDlNX7C}Tc*r5f~@b|OhoF*=XC{!uoHydXktCi7eK? zOdzajr#~s|Gny2(yprm)ms16Qr}6{2DlDP?bsRZIUXFLU)-%>97hG2jjo-DW!Vl6V z?2R2E+aK=o%jH9a)XmrjV0%Ed0geQHUd|!{mwx!4d`wT3-cRcqR*iY!Nit5tB$6^$ z1uCq*G`Ay0BLltB7qi{$z26+|(Pf|N3ceqfl`Uy;-gHA8=2I!`8JW1ITM z(blMJbnJv5La1TQ!W%+4{vEmQK6iru`XBm{O_G=4 zF-uc$C-~yf#sXpZ6YVdgU4cfrRd{uAMF(C=W15)Zse(%J#zv*}x3sn7oM&=azS)-O zbOLOD6(GWDlFog@vsjVv81gDO`F7Pc8Zj4Z_X@GGJ*(zhoDHDIQ4$R_zMATLdZr9I z-Hw0aBTy~op)}?6RQm)tre~zZr*o#UF{dAv`0DW4??@=% zGKY$$o_`xt!kl$hSBQ6gS#D>1lE}^DPwv6K&MQ14$SSY$rj9vR)T$8#ERfQ^0*UdOj55dr~XT3975hw{-PO4jJO>7-9N3U3Xv(R%OOCL|jasIS1* zBKHFJdpYqpul`-WE5U7>9J71#cKgmzr!jDmLWndm<&mP{cpc!ISN!qaKPd1!P{8#M zL;iZ}(Il*)}QVD>l&yl`e#@3xJo*rPWBF^t(>PP|^EQB+$0eA4AU!+uN8M>J_Z zoX*g7Yj*Qnc}&h1AS(|<95hf>R5JG^+IO2W)v!V&$~4Ioi1|yUF(u0O3Qr#q?H74i zH97jf3DVnl_BV$e6-6iphybd@a~Ocs|FR_kzsMH+F^ylB0^IIunxp1Jq49RNzu#t& z>Pi(k{WkCV<1I}Ja7E;X)J@9lXnjImipMwsrh$Xzz%u3z+sXSlASjXaYa+|$O8bv} z5_o_i#KVLGzda$|U&Y3g98_g<@4I?Uul|Id8_Vf@MO~oiX?oC3h58iqYqx4CSY(?vW4VM`9icxM>ZBfZ2_`!~n&EDla?tBZnr?o>~YChkf6l4KF(XG(Ds3sV3!&^xm6}etfFa(~!43 z^XLIdQtnwSl;GAIOA64WX@iasr&s75C*~ThM|B;n5>L`+qyL5wwU0m`Q8G!V>m2BL z>WhAUI&-4Q@vsza=@P!N5&)f_rv6A!IgozVE-*X(ONFH$@$JdOoG?ogZ+JNn`zHlv zDCqmEV3=~KPx=jwij0MJfQM97(L>rYkf*AbNWU=WETDYN~JGuBWj%D-E5=AgG#taQq)(zJK@+|6zMB zzwm?jLtI4rrHxIUmSuxc2ZvpSnz%Sk=cz_^w0XlHTF{$y5mw|Uy>SQE`^oA25Cqh_ zn6V&2@MVvoY;mm4_;H$NZ?y4Si0KvthXc~2!wsVO zeMZDuhg5NqnsD07-G1@*0fBJjWndV^5BVystQ=f50clNx(`&br`RNCAb!KLG6p7Fa ztO~zzSKbsL3eG8ogeoYLm-DABV+I_4SE*@>8CNXyJ2q20T@i0YWO^ZkPRAoGSkO5d zQuTkD4B#1jp0pimY#@no!aqt+VQ;;u9kO;5Ce^FrLuu2U8$dRg!JV1}JU5wB^P)zuZ?iucy5aHYS?x zug)ExQdzKFs*B8s7xr6qxWc&E8K;@1p&f)*6;N-p!YO5x^Vdz^?reaxn)B(?Ihx%^ z_xS#|>dEwu(X!PDy78wizt8&~oVF}3n4?eKS7Y6 z8`DW9RBbwMOun_Fq-Nc9ea2~%fkzYmIeMC#;vae*FY!!bM5t)3JxM*z-^a!)_@T8jq2^olAAPh0P4ZEa zJYp@%1ap4UGWO9NzJFLJVJvcbEk|x-;)OI7I^&lkZ^s^r{zQ_M!3NF1b~?*67sIl4 zGFgxu8`;50DmcwSqU6J57M_-NDTZKXaNZ{u!H=;4@FfJ?yPDE2RL_eXIm~D;Bk&?> zL`#p~MEH>+M(cfbAm$`#KL;*T4f09Ftv0mqv>L%mCkczSo}*3 zH3jGPF|It&<~mTY(%EEZes8~W^?=2yK`a=}Tq$MIRemoxWE>g^JJKt{ZZlK$P0 zDN-Z2Lf$8oA74wPM1|9%5@F#$_KC??941&I8wm4Sv~X$DSd4`FIeOCYW)&@`A%+bCfU3no|N6dhwp_C2 z)pVplkBcUDA76paF{6BS{Ez0_d?n-I1Ug)Gx-Ym_S-)PVE)i&E-jUQ-xVXWBkSmVW zOozhfrTaamYNA!Z7sHqyP>6pFNKQ)|HwP)oz;?cViph{s!bk2R2t>f*eL_jCq}TDx zX%&$ge(#$xQ>cb|Mg!B4V!9h;n*lDrNb5x~sOhgpH)*U;Q6;s>s(mZumbch-L6?Xn z6+iv;Y9=Cr3;-sZ(#^aRENCcO8*n24-dvIILM_b+V8u!kIp{kKsZz*LZvk4IxX575r9bnHy#J1AZe1u{~_Q z2uVl`y=nTeo`4O)iXGQwJ&t)QzjGw6VwNPxe-VM2cv;d)tb|_RKmIW-X4t1{cOkIx zF(~&pRjsmPba#auOphliK<-?Z@pHA^(+^w7;MJKkl%#WE&8PW6<7g2NL%TCwhP_A! zO}c;p;-p?o(ld_&?RW-mNQ-zZlzA#^d%Dc=d5}eUlCIj7@n9*bzJ8Kt{dRJM4(9AF z^Z*t$a~it|tU+F1XBVcom}D+GqEU?oXS;X%DII=-o|yya4^>1Gd8%d@lS*KlW@TE7 zcCGzW{7MO*q69@ssnxMTP?30zGK{D6tmh?XguxUSO(Q@;w3cXJi5PLXeGAW-FPh5{ zc)6Oef~TUh{tIz(5|oGb?{mjF#K(?|iFJX@aTo<4wg4U$_)>a*Q#IJaeLrjUrWU8cw7U&3R~ zPwY*yzNLAnL}^U2KdcJ{n1GCp3}Ar;P4wf;1WBLHdBA6~jvG$J4OEP6!1r+X7%;~!!a4uzfBi42Bx=2X`#G-KoArcx9_05*Z zoOTvPNT{kS<3|xsxU&<|6JW55w9I6r1zd5=HsVYg)Y6DwPS2^*bvYP}^~QKRZ(+7t z#yHhsUqZb%u|{H`@$YC9`K&Jw08~J$zu)jra?;JhQMa+X4)uAOr57MrzZA)Jf}Z;4Fd|JVQeU(wLN z0#2qHpHP1Pwm^u32k8IRU;WkSDQRKGnWXN7f+$b4^{*|X@HasA=kyFg>JTJhOh_dc zb#51h)0WLM0N#$HocFrjnmk~aoisJiN~x*xdSrrS40t$jGtD+?c$O$T!Wl3)Mip!$ z_?#;?I&5s$D)DlhFky6H#oTDNvYU3Ce+8`Pl(Y*fjZ-SCvub>myXm>mD&-iF?Kqln z6&9T$1roCWN!IS)yo>C2=C*zYb~2Pu=(c_v$LE~WA*qWsJtKDl$*)#X;Bu@qLVXzD zPCROc#3^^gI8;{B!Sb=$49tkecs!Jps902Ra8h640u#PcZG&tx0`ZEVjjHD;aEYNi zMpx`gZ1uD)f>6ZK(unijQk(`43>q8y$PEdS(Kys4ILMoS8qhKsej70+v^b9~q}Ouv z^_WY#on$@01QuLlW>P3ARoWln2rpO{IG6+HlhT=&&K!mb`Y|j7oCymBZBH^^RDVO= zJnFdCeFBnr}gn0uNONg9uIsrul$#T?U7(HwADLrB?G~(+t)%FGD)(aCLCKYXs zyhlkGvX6T93m#f@Ov}Y_i%rBr!rtVO;ixXTzD3&_ zSFpjg7(IN|W4AG|)2V(-&%6Tlco&=;DSJymSW^wcaIEK1pt|PFGdZQzY3xcxlOSJH zz68-tjK(N>Vn|ZY32cr4m)K=GP0Z4I6IoB%5GmL@zY(nvTuA+rk9TQV>M?rkN>uqF zO6fc!LOGTMC_fdbsxH(jPaZjwQAdUqsU@N5>q+W2qJR(rj0-W3gi;bPp5e>62(Q?v z+n(sp#UeF^`YL%HNEVEaXQUJaT=z8RidA4o<+nc7JIWez`~{k<6HoC=1hkrLhoC~Q zd04>QcbyDXLsI|?9-@c~wa^hXJN402m-JU^?^2M9Ke)O6OMi`fS>O_e>&P~Sg9WE2 zX;QlKG&y*i;q~2=;P5%Bdx-HJM?j6_bA8CnBkYd33A4S6mM1EB1w!>-+Ktcm1|b>8 z-0THolO}DIJ8SNKJw~Tcc1$T#jiJ-5%g5li(Tl~qXl?ct5Hz?YBc?ImM=sHm{hpe< z5IKg8NfT4|?{=(+J7JPM=2{oEt6c0+;#Jzqj*M-znO?MIDN{zXaiKP1a`2Mqw9#bT zfT`fjhhR3-pwpHy4Dham3;ji|t<>b-^w6O$3aAO^fPhM-VjI*7+q*rd#p;+k5`^tj zWJ#HDfk1@*=@Z`~bbtk`;|DPtI*n45YMh`L3K-dJ`&OJM30KLt#@2%S_QeR>P1Bew z;=|SoEQ!I0EOLIL_sksb6vn?Q7L&vV=1SG(MOq*l!8{iT%o1v{K#^dtw z%ErmqZpa%!-gJo$e3G0=-C(n%BP0eEH+rj_zKt(IdmiU38itFG5 zY{8m^GMiy!xsRf$bcS*8fi>iG6*Hr=z-XsaRoXcplc`HL(15-IT;emxRVK@S0oHNx zX$0-V7w>>eN{w4HPK;oSwqD%@d}|&EUXJ|(SZV!5x&+&>CTb8B`aNgL2)VdvbLTKe z%C3nnPcHg8K>|}Ua&wzbUzcVysb#V67+?=kQGevW_zJL~R_q#XM&w5-Y@8=u099Bc zo{2+jC~skVnx4rOky%9Jls3dy2rGPx!U(RRChJaJ?zO=e95T4_;(9v>!et~E{kk6} zpJuEWyd9TndwYe?N6u%0x}>e#@Y zAfyn|wo_r>Eg#M>zI_)~lzb;^4&X?z2S%v^FO@bCt8HTg?)y3+!cNK_&=xw+)=*h^V6U|q z*&*5>@6lkhk?gwd^OjWR-D-P-h)idtxDgzw09f`8)3Up8%p>Of33}E9tUXFkG{Mg% z%oIWf0QUxnF|2goR61oG*yt2gDR568+2+Unb|EhWkGrHq{QM}OKE5dFM<2XRvysqa zi4j$U4TSU-UDMTk1 zxqc{R?XiS5JIAi&gV?;XB9v*etGrKdLF*|7D+rOhFZ%_EKx+6HxUBjx3TV1o(7Q@E zb*!2;BEo(JL+MJ`V$nGa%2|ssm5seDJVzN&CZ(c2s!2@AyCTv){i0goahV86C9%&i z2QUX+!LoL0JEUPbNGfKK5m+^YQ&@c&4nbr>Bfa^6&Y2{=!|k(Drgsmsil>G@ePCY! zQ5GvDm@m)clVOq^?1%^&_A2HZ7tZd=0o{hHPgx@MOw$Q4kypR$Cp!?W&a#+{_#$@2 zMJB0U-sJl@wYJG}aA`u7poWc;E+M}VOlrM-khf{B#>ZIJTz|(f@Kqoos=VXloxY3g zP!Z~4tyg%}fTMktE5~npU<>ss{>>Dbd?(t2Na8C{!?}SD@SWa*dF(ZW zO>f9ejj>OB>y6kf!YioLIf*Y{l#^;K(Q!V^Ip6H5=kpWvEIL?nI#gqIh5phhVyoOU zMyj*d$7iHv##19HVZXYMgZ=7MYSmkGH*A57 z_JSHBY8=Hnh)swnHxOd%?1(Y^MzHWHRZ$Nl#fOZVC{O>6%JD_!Z<&JA^evGA(MDYq zrD6WkYbM#@^V3x*^TSB=GtO81=8fyN?T<*IsYv3u-jeG&6Uh9jPrzk9q+$Xn30`&@ zMv5dMN`;Y>MP^L#up9xK${!^u{Pj^vtH32yPM7(*H*#|)(omGFZ~of3p%s03oQd-@ zW03T^e}o9fdNXhygRM)m6HDZQ&ZR4T962h7NiH57q;vbX8l>pTREY;U^j!ihxB^6JoDkP3sbFP_;iQyfOJ4Go!x~`*+VwI}BLcMV^e3;4GOT#e= zbI6Yu6vRNczWeRHh%~jtjgTrpnR6%p<t4Ta&?kX6af!!#9W~bGn|UXMC1Ij*&)G8A;@$gr46lJnm!01ntduT zzZHJ$E%D93@VkoDVOCS({8V@tvfRiVK`J71VMXdyjEy432FgQI)WEfK-iHj&?{IGq+1ECJZIDQg7YwAh1^zUtg^Qf{>(Fp{icE4(~mM;ue z>O!pNiuti0s4c^==*VHyox(=FXg0#a0Ld@&f5zxw!YEUh%3F;`3;7n&OYuN%wTR48 zEKSZ7+5k2fba7%azeUy?9SpvAvxDiu!KIEuUTXh#jr|Ag0>Gdsh?X|EDA*g3U%j`& zmX>k)TpwHp2-hKi_(R#$(U+~a*l8U3=-w(aoI>e*7~QLKRK|<~%43}7MX5*Ng{gA8 zWwoaIo>Q81{88viIaf9EH1Y&ZIAoZc{_V3xJxPf}&)`~ml}i%kWbXC{0;oV>hz4Yf zhoSbP90b9GswFafmu;xpJ3Igh(P+3LZqr^JF$Rc{HZh7O(d$P&-X;=}H2ZGATU#aj zvoYsfgDJW;zBm1e6gYPFOHB;w5vv?+7Wyr?pY%HCzBA7Fj6mQRL!w*~>93Cn?w;o@ z`AB0JUvLyysR4yW_cG(KBh|!CpN8wOJ#{ax^OORf$4jh^vdc^o-{vkE8@*N+`7Qt| zhEdI2O=Epa3X5oPIJ=`^YB|z7(W0m-6OyT(s?#e9;2bVr?u$oEUq7DHl2X9i%=x2{ zk8e~?%5{VW2)meNRLWB;tAIFPyt>~nCqMSWx9{nBTs2XW>`HG-Mh7kwM@(f1n&0x$ z>BdR2OVbIf%k$ihAp0jS+Z2J^?m@syZAyLkP1Dz{xA3=v4dRw7VJLOl`_CNRQremc~~eL2P^$FZ5r*-`GYHwpvvu_8mv@1yAgLc8%A5@@zf3RC2=Pg%V6S-7QB1>sBTV4$NFw^3 z9QM0QC}v7SgETj&J{mQ$Kq+s&Q*8{dvr1{Rzdy|s`z5a6A%}Fp#CkW==hBuv%EYCt0vQ3>oM7{5p59=mRcg|c*2o`bOK1#> zJ?*#8V}W9~YXs3>1PH*gK+{V$ESlu$ZOG|H?sL^%$ADI}7tVP@&r=>FDXNtiGJZbJ zm;9wtgtbU_R*|bTZgt(nW3q@Kqq8!diWe0E3%D~)+e`pK69WqUsfs2aflE9L&Srn( z{4hyZ0WiLw%+>lDOwf%VZa}Re61syQn34&wCM_EWWBV_l?dBJF(V)RPwDMdm|&j7q-f667B=B;M+fry;A{tfX;jEH0l1g9 zyPdqZ4(lZJdIc`?*G`?BTrH7c@4XCctysv_NeC#R-<`{t2o0koR=2)E1oD6ss}{sp zu1-7C3L+^wIAA!GH{-Y}sHc&tYv$AS? zr^yI^HO>qvrjJ5!U*2ssG@GSwu{G>@$ zfS=(`vgjx%s^nRO<=|y^) zSg%@AmZFgM#YosU@)9#Ek0fyJLkk6 ztQpRnK6+MI@6&{P4Pr9vx49*bCHkv?M^jGl*Pu)H!taVDpn>5 zxKqgfRTgZqw1muz;7}MN>U7;;#J8l^vLak0WeNQ#5N!G(+{;JV8$n-F%Jp?#k@!&` zT;e5FG^td^kjW(q{9ge+*^H@I;E|Ftjj`9sB3gc$wM~FkG&iXI(JkA8M;SBBDW=1J z<^w#^lOT}qT1ck?N1`U_rbn;Hc0>!IhEkSvkBTf%0KdO;4ZBbM-uA z8Pq5|p7_TVxXkPON6FX&Vg|Wan)M2pV#gWd?Za^&>b( zJlHt`X%1K|+1vgdEe{FU%n>Ld5BZU>( znlR@z80!rSkv^Iie?nt?HZdFa5>0Z<>(Svb(~!V|iY#zlm?2Jp4$k^0lXz5ioKsCO z5Ti(+Ot+PWSA`#7g)6jeX*qG}bL;)*YHZ9l79`*kdf@7Sf{cebP}J5aES1i0aEu57f!De6N*yuFYAb;DS)Ah`#~mS#_96$ z#nbMs^fpsPY&YcnB|T3?gAox(vk__!cz~D+HwwNAJH;_ba0uD2j^zr8#Q@RdNXKCL zfwR}g6GFLl;IAXU(4N=D<4w-me>=5a864=z^?RH;-iXwSuOq^h0fiSS>NWg@8bgGL zA&>D2SQD1$Lw+4A(`k@`Ig!+anKOV#Pu6CNvWTgaB{znXkxu8ku<=IwVdh{o?p0Q| z01}-Y&od-3odL9N6#r7UCaBDqZl8ucd37PnG_Uy+IOz1gnAXuSh*fYCVFk`Xd`(NFvk7L&+*0} zkETan!;t9+S^5mJ&CJs2G~i zio8(!zL>V;0~yJs9k=0LQRlELCgB$mG*cCmfVr=7n%o9NNICeaKpBugt;HYjDNx}@ z@u;BzvptRZ3EqQ2x7pjPcjYn*Q-vv6U&FRn}^)5|$vF8xG1cEsG`CRtjNNW{%{V6D>ydH{~0sqEUK9WS@At~I{8 zohZ|T3lS%=5C77i0&fD|Yu1GXa6VOu`Az9*!n#hT+S!g*lk`2s)p>X%dqS0`pY0;f zj8Dm@Jc--WY}(M_hxwT##C_6p`w_VOfB&Wr;`&p4@ zil1mJT9uY(kEi*gRr-b)#K;pSU{KRx&bhH>MaQ&8%Mk;+4Y~EXi|z(cWMH^YL9MnpoCYIGsYlydB_#vwAN zE260|Y7QHf)c3`oYEl!7^1x-FFa{W}#p^IYuJ9lfLJvY>LQ}_Y*_}{B0k`5ju-@y) zh+SBpnEsHSH7hwWMhDhW4CY_wrMN?+oqdb*OC9QXe4AbCJ8GFK&Ea4Yl$)U zBbS&TQ#ChBTn7KJP1SFXtP$Ie98k-BKMM{M<>eT@B7r5&N_Y+}ggg_moio`+0|5>Nb}SxJ=2sZnw8L7ua ziKE~-Fn7#lZ`}Km-0Q|HA21RN2!JAkjS^vWx6A1L zC0)^^XU)oiSyW}bq1Ds3qnn+Afe=SN-7^XB_?MQ;ec6jOA|13b9@m3H{Bp8n2E0Q2 z;{7n9C|A;1Dv3E2M~P0+SbZo2+!gFXSQe*zzNIIEh$N-Mj!^wN!hOpdXac8NEXT~x ztP;e`pxl139uTHE&E_3P$VNw(`qvW3DU)e8gQ8>^QB#QI-64_j+lzR^nUHLiK zHE|!zY=31kk5X*%rXfy$>qhozGCs+CV18GX724eP^BP`b(u7QHY|iIeY{C9B0OlPb$G~ zrPhSr6qdsQK*M-8Uh08|N;C3kA*w_~V1gQx!zAR!cicEpkEhjeIO%50zA*ciY7d5o z28@7VCQ3gmVqe)R^D*H?ry*a=dwuwwDp!n#XcAbb8wvY@ZlhrUS(F~WKPpZ!rQinp zCf96JAkL?SDMrY-dmlPTI|VJq#_sa&xyI|@6Rl2X@=Ej|uwkXS(nvxLCO|E<)i z%{?A$syoxO-lJEOa!E@3sa|l@$zBs5>P%$?fueTDLBa;!$e{|s`6tbEa1jI==p5AR zs};c{k_hH1IdJ}ougnojbxLOts0%9+B0Ew+T)$DZlCLAUQgrcAE8Pfz=eA5C!<;pQ zDX~4HPVlkl1i74gk!@Eta9W1w!Ve575(&`INV%Qz!;i`2jy@g>-3Sl}X>=gzVh+l= zxTDiex%GUmoCY!w99Jk5AsRLgDU0Av5O3d)ptwxX@a z_rjn;u+WTp3GfaQ_4ZsjV|7>H@^gCP4X~2*^{qyeJ$OXCWg)9awbWb!8sqe+*7V+h zOEH?L4h)QwI}*yDPZI#K7}m=2bE`8Mf$!_EH`>|Irq*^H9y8fv~Di3gUOrdnBIY(B3U=*YE00N)2&;qHxg$^QT+&(bc z60WQ!H7e@RiOKhi+d@Mz8~c@HgeV$-E4v*_W-`%Y6Kc!C)!OkL5&k=q1YiXz+}a zW-bCD)E(_HUGHhM&XLl^9&yH)L(1W*P_MJU$7ihW>e33!hiTw5h#82-b)AH}SJ2N% zp$IVVb+9VVx4)(RP{2lsBOw#k8(g3!gA8xY<$nCqRn%v;!UoWx*p%6J%TgG8y@r`rZSDdgAld4H2 zxl@XXrBEX$)xa&;YcF*x3*?$VN$2kcV#F+y-43|Cy_2QpLrW71`hi0Wk|O@vgaPXm z%dxIZslYAIToD2EH~)SUy<1{FdtePNjU`m+8PTKLDRMVapJ0dwfH)|NtMKvZ7jve# zkfkR@l8IkeZJQ>Ni_39!hStY;9 zfRYLFX5 z32Y}c25*TZLEB8P(4d445)s>kJ*Zl`@^ldE0)3{_MSqi?{Zp9~TLqyCkjv0*1TRxa zp!BLHU~tF-!#GXCL3I+jTA0NKAKRrtkbi(#csX0CdQ(`Tbdcz)R&lj)+DxV}GL%XL9b8Kj&ZTTJ*{dMbCp7@Wyz$(R!_F+-e8 zIEmh)wIC9RAx$JG2eeurBBm$l+ggq>VHBvbxgBekQw(k9oQI7YvrHSaRZ`g6d1yFa z$1%#s797E)harctRE&wNl1d}+?FB4pEop1|VXcR^rO8If+MD5N74Pk^b|f2}V>*ux zP(tm}8aXL}4P1)0&{{ZISb_Tykdu!XR{yB+aV>mx8L-oX#aN4(NGFQUdAM6bojd^f z0YkT$!OqamHwfL;b>38Y80&ccrbYwlw20%oSpThh;x{zy6IcHozI`1-o$ zdcAxc111*VT4f*hKJG<{0%$l=e>K0Ut0b4;8;&8^~G;=3dBd>#^pe|L|>yfFmR9P&* zC3jGi#gjC6@-GT4I*B=37S355=s{ud?&>^mFmx@!!_@cK)CL_M)dpw431*ebI4r~nA7hg1Cb!KCH zdz!INb>W@q)iZ+Sx$cEdBj!|-pdcpqd+J)KNv)CIH@PwdoxQ7n9`VskZXf37+gV=3 z`cxS)A*Y`pq-jdI3T~Z5{+6COvFfEVRCF(l75-5q3UN|CazUyKq;c#NVgc1oh&bFaHT4#HjdzcK18dLsTR&*lwFf1^&%=!Uhy`*d&Tp)M$+!z3dQjOW}4w+ zZs_MLI7HRp5IB!4<0uA{AKNixSM%Yni?2)6)C0Inn#Hc+Nh;dLVOo@VbbxGQ`5%0R zLL3jwlQHw=f`5gv7z5ig^@<}csZG9Zg?+U`?iAS&0Qi6ee=~&mD>F~gZ1eGPCQN>U zSfzqb$SJ};DkZ#~B(Q>Gb$428yl?5La42L~`k9VPwF|L#&L{Ao z{4DcBky?1#5=C{No^Y*|l3zBC>ZsqSLlgC+=LKt|p&wlzIubDV%kw9dudtO87ybo; zsTZXzj}mt^ES(HYn;S;0o#hTe)j9P{nNS6%aUTlGdqFPr%vt->?S_fTSMpb&`JS2P znAwpS9q^7;m#jw+%V$C8s7_hD3K?@4x8{~Vrw6D(RZmwr4Lqz)T@;4W@Uw?04ckqJ zO9}zj%ICxj`ZfjGO&AnW`zMHi&efN9GSIY5zbx?Ssy}$1oD;SYXp3WU*B*tD*YjX3 zxpY$)Nn|C3m_8in+wkY{6^sqs_Qpk+#-!+>7E<^0jKzBST3U>XY~&AChnvWWcZhYP zas?AJ62gj%fxrwtwwr#57YXE}!Y|NviU1vfrrDNK793iv+HU547aw3c_X>_}Zj8g+ zQeji6%CQXY^exQN0$fsL_VKiq=mUZInISm7eFmVps59mev8nJ$3eI zrr3rLFd6%FigsM=Ln(VSt2|7Bz~+;k zd~ag%Idz-MdV~+@iB+QV+8wGUF7!2Q~S&9LH4vP^lfS>iDGaK6l~UF}+2J6-6_ie+p+%VRZzDgsZvimvSzasegfYJ-zyg5@nw`G6Q$`S;Lp|V~~Y`TAmIq7P_rV=*J z93Ut!V`Rcln>Q=oKwE(Is^Wxr`*fVzH{9~Oe^=mEoqpZdinbhC1qOC)eIsyl=In$p zhB(1Sm1Of3{l`p(M*+7f1{G=3EaIz!V*NREo@7uXGWG&LbNrxu+o4v$ezAq3FCr7E zX8usecIGxc!~FASq#y)=m_~qD6jS((&rc5sJ1OcAxm4qU+8;_2G=N0nqn$Z_NKe5h zhoc#0kdX$Xf>`mOpCA=F$}tx~5WzgeVYB*O(kzOfUu`jby*fe^N@%ven#f`figxgl zJ)-yg%TlD%nGgSDFL>!KDTWce1oXi*LrJ_U;y-$l6g9dXal)h-1ZBp_(9bCBBg59| z1k~XNGvO18M&VCe20vU~`!|j@o&f2*W#buEUI{_k8&4mxo=1fNxJXq-8baF_KTA*X zvQnijqr!mZ74*#Qp{|X!{P}$a#J;*PZDTRC!zWPe_-Y|QsX$?WmMk9$5$2&hBpXco zbh3n047L^&gs!9=n;drA0f9~WP}gq|zxfrDbm~YBW#RP8#lQ+Ije|8;w$`5r62uJA zj)0PR77?dkgc(3y;yo!5f=4wcNzt71_?Y=~=wV`SIgL6jLS;_I9Gk=QUV#37wz?)G zoQ8!yZ=JJ2lQ&BP2S2)e^7uLoJi=Ud6e1V8*z1tLv7G)_ z%ro^Q`AArSS&#(7e-;0r<-i5V9!eG`cG_A9lc~E65)-}3T@at6A4r87ie5#)vG(B4 zh@PQ5QYiS*Xek$^3a$%+x?Tad3$}?ZsXOeaMEkqB_oUfk13OMzniU($&+)m9|J}aM zjvJ7h9C+ZQj*4UH<;M%81Ug7iW`k51cwDdr%tvcBi{ma(S?L~dxt zbymd!XodrXiP5h%eH*C)7W@`)sa&Z6cMnz!e0Dl zp~}fe1S`q8VAAm{izBxZ0jFR(9Xd%kgu16?uGWa7XkPK=GL7H@r71a!{({+pF>6Fi zKeFW-h)4{jZKN0mI}Od?D5;Vq8P{W5-;5@*g$G#aqnohg^NHre`B$?ZRyMqtH@bVu=G;DNs|o{HDIA~rn_`F z-f#+&K5S-|oP{rGA`#H)-geWjnv*z1?d#yjPee)fCibg*3m^dhr4Fy~JCgB(l<2G6PUzBaMSYgPusDsF; z%6py)0Jy3&pd^~8#%OLwcx_~6Qd|($ab~bCpt1|M9U}V+p~n?ADLAVcmdyKsZlX=N z5bFX4Po}q`Ewd;b6^k1??bB>iV#(EE3Wi?mKB!Sa65!~iWg{2PitVh$?ZV zazTL5T_MX79|i*Q9zzW2aqUC*{v18&ib$eN=#|*=Cuv&6ZExBK(oQ)d?P%TmY`9!s zj&Ga%;@-G5^E0>%P+Cct8>M8A5{RM7G>WN2iiLC6rCgi`VA_0(UhRl&0bh*4L<-q5 z?oE_7PF@g!1wT#fP+YWM6;Y@=$~`8E^-UFMUiis*g%KP!2RxV$!Kq0|aV8ywLnwg` zwnoG897TspwNm_V7aSBb2kvEc}>f4 zwJD)wV?s(UNTong=1HXrnle-C&&!UIEJk|ma@+=YQ7K9inQB%! z#IbjE=33}_qL{gfw^gTG0P9FnF?)|Kn2oWcg~(u0Seu^P0{X??p|+-DH5z2U7kP!4 z@e(#WHb?Uc$s$YR%*SEi`STxevUrUMqQ6e{NXH37b&N}CnDpM_-$HVaM4mnbYEP@( zES&LDpT?J)j0?*M4Fkc%d68Gil|qtxoKU_@Q|kyop#u zMfw*3=@cJ7!1}bw-ff(jFuFnE>F_5vv^djH_p@6XeK7?c3HBl_LW5{YXsUEg?7>Sf zWu!K32v&|p*I)f|)RLB|VBe;D@>b{&nG~e!yN{R|NGcQh$c4jW`8xN{8&Bg#0vlzA zI9(D2{E)O$rI+)%T#Lo?f#5eVS$Kw`c*-Q!dpv4-OS*bM#DZ0zc_H=bTj{;kAF7@d zv?J6`5Z7-o;c2@NzvMa+AF{*hjE&`hU9?T#D@KAQ>>OoaM8OXGQhiCYajcrjP$ORF z&LLhawvo8pSl!iEm{A(XqMLdA!SEc;q z7r?EyXyF_RO5$!E^7L0FxxQGS2+!m?Vsp!V#Q__lb^Y>ml)>KLm)6uB;uDAQqB<84 zV+gG=SW4%(x$@H1^rMwGFXF_4__D;(52AhJOzbpMq$^VIhOCZdiG>wk{7`V(e6v5)50!%auNw$ytIi}7YtxNSs(gm0vF$x_KApz4* zw}V|D%_>T6Um(T|!%x**ZDaQ*b`=c937qwj4XVRP9(EkkOVa_M^G3>u&xRm&O%QPd z)uB&z@GAL2ZArTqmqrZzQs#)NV)L+#-#`!2qrl~xgdOM)5Oh4l0?@$j@v`p)=^zq- zvO<8lz{v++_1nd*w+8EyVvfzC3LM-)uJP(7bC_}?r~z>@Ez~A7-{A+R{uvbxgQR52 zW{3mykdY0N#p0H4k8nj>QdY$$N;{P25?Bt~SvZMdMCe{90y;pbWz41=P5d<6Y{v zjDZ58UnBYf7qkb;tTyUc>WxroO1-bDKK$NW@Z_;IGTs2))?C3`PL7UYveC_Uaz}!o z01gr5AyB6!jCM-hFqASWA72!LAwe+o=`l4$z_hyQQAD?gDVarrKqO(IslJOLU^>a^ zv9X}#(*Ry{wb78Vf|)u|mu>Z5=b!v+Kp?(aO;gc*UxEb(20+`VXnvlmur0=|Pp-3M zIs69Dlz^`5fc^fKp_x6^67+tqIl>~eB-gnU&8GULQ6iJnheKx+@ghp+qCYkdi|<_( zdbJkqRkJ1HmwT=65Ft118pKf8w9by;TBT7@8OINAdkqwTOW~H=U06Rr+e*DnZ^z#q zbdKf`1M9*hFiIv2La`+xpy^4$onja;db8gRA;--^r}Pncchn~8DZSLRkDi>6M%~{X zMcgTHxgrs3feF7lI8U{K?^G>*Mi6Ci-fOXvcjE-u3`&Qx=tLh;SZAxS5OCi-0y(WW z4u;QFD<96Jm_acrJ)4|6$^a0y6$4~UY(qge%RNsGfH*N<^foz;K552ef4$L0)4a{) zi+{)3nLO$xHGz}m^&ZJM4*{Cz-=11W(Hg^Y|4sw3R3iBu*zESbc|bTS8JLWp`*@L5$OngDo7^&z zhiQ0zl-UZ3uM3*3CCJi7PaU}U=W_Avx_5~J9KD*O3jLOzMH$+U`JZkyL0f^3+GTom z4WyLUqsggUXoip|6u7X_h>8}alXGK}IrtD>G2P6K^WWTJ03$nf?0)3x0{ZEQ#)ZyQcM!J{lmYIE*bUR;%8u@b0KNTwsppD z>qJ%%U0$0FMk53zZygQDz2l0#$rMbqW))(1gyr9aG)PqC)&(<0=Y}_MynKlwmYALb zT1P$*7{<_PCU?sviw77avN7JGA@y*3w1Ra6l~U0K}YA2Rt$uDmc z2T+ChE(xgyCD>rBIFV!W8;WvL4dhT-Gq+cBU~(H9o3u?CJC65E{%QnE>#$|Mt~7$k zz}bEl-pH{^KZ8VjS8K#b&_RXyf>!H8DR(>sOGxOBwmnt-Sd^cjk<)m`5>GERqo^bi zL+{snm9*Qzb(N}_U%@!Y&xhZ#-Z*K5^p}wt$Q{iXxOqc?vJ-*WFnsKyANVTG^Yxin zYDmNYF!q#D$pZu=g^$p{7^o&$LKhWdFb(n|sS2cC2{N}NO#s&D8Ek?V0XX|kXLt*vMXun>U9F%hGlC%x#W#Fs3tFy;VqRhfJKDi=?VcG1q{>z6Uz*zcAWBA+;`=> zf$7X-#HT)tv3QK{CN#)+H&NGz^vq-kk$Qk*k@R$Znmpc`s~3O8Z#j>@zP;>fgq%+` zACW?M*N5hD{3s2Bnn_Q!4YDp2ES^bM>4;JeIlxaxt6`5!+h6emM~hMG0D$8vbm)VY zl$>r8u)av~ieKV<(YR?e1mSSfX5yE`hoUYoml|B8lHtE_J}7paAr=ij<9>M310?iO z%pzq-QCTR7=JXzQ4OZGme@c@wACtGCf>xp!e66wdY{@2l-Z`I&t1-+NMm2vYZok~>rWz=;+LKNg2&g3kXbf$hP}Y^1^(qFsQto@5D9*j?`VQ$PQN(B=E~2$ zmKrj|ec4~SGPy>ZbiLQIk(GJ(fc@hnb(SnDERc+o_+jga3s#o#WWWSsPph`E5!eR+ zsdh+QkfvVXOT}bK=P?h=#$*(w+b9%l?WN2r&Y}Ius@d|`q$Exq084h1G8YVT5>hab zd#)(%><`G2l=oENOwy`0SFYBR%rG$Ivz?-qIH>(%3x!xltY%&4Nu)c5nZ3u%bwepf z9P)-Qsf>QiVB%0Z8ivmCwP+3EIT}~^Ol(XHcYkk{D}K2j-1 z1-nzafDQ1x-Mq@Aa76?>PR&e|IOQmcv%pe3cnFE7!D3lE4w){1UF!jQ*`7jRyf}q4 z2nC+rm$xD%YR*BC%Da$F;Z>c8mv?Fo(AI z^zB^URfK^jhXF~Qr_LKW&&bH;5zpJhu(dkcTh1M@W{GLGntUCT z4>vL&V{sd00z`&a(Rr+urys6No!N>q_zI#;F-w2KmMOClv+zv+O41MM8M278nBjER zN>sQ!WHCa%A7(>`+YX?oyhNH%kG>_>bPknr%QoXUR85lLoG6(zN;ry90Vw9ginrMY z5ub{5QtwHEGhVsrUKohKH+i!>PmyxEj)t-0NJJba520hPNgM=1-91|J>BtNvFB&B= z@KHOlBc=*j3YR>pT$f0R3v{Z_g@ODDk%`P6wzd}gmGY;25S;2uaL010IL%%FxW!|X z8_HWw)n?@DY}K8GQXISjMsY92t+=~W+*uYYwm8Mz-F zn>;~gGRaK7obz#f@x?`Gjni46>DBynlcJ!`(l?n*z-3ZeJ0?F(; zab+RB=O$gNrp&9hP5K8|!^T_JXN!{<9|1ld>88J;sGvPRUQgktc#}9#VAi!-mAbDW zJ8$LXKI4eN7^;jky&u5;YHP=XkAD1|V&cFbBDLkd^2ALd&Y)97h2oj0>IQ{)RRg(a z;p|f->7!Wcj;5IBnWRAh^$cX^K}ZIo{CdG9>(P{Yet7>;Q3c88V!5<#sSHb#o$JRt zDorNCgSqOX%y6ZiKs#xZtT4!3$p9b|9G3zpTss^%8n4|7S<0PGHo_Z=Re?oaItPQ8 zIF3J*95&RQ({kK9hc&RlUu^<`LhAmxN}SxOE{rRNyO?=^MP1aJw4wElifdgj7`*xK zrd!>NaLg{KlLY*pQSbLsj7bs~4dxa69NN^=SsS$Ap~L?4*K4Gt<{A=A2wJ-GFg>Id ztY$QC%G1s37g4!_)+-~yMeCtT$|_r!joAy&DQDc1ZM7{LLY=eB{KEda9cZ8U0q_|*WOQD?a7fp$l+}sygseA%IkwPM3spposG^EVX3p5dbINK2(&f^^5 zXnpKO>ocyeMklSoGSj11)Oj^?rH#5G?-ZADDHQ7mv{ww2lH+K6L-5=s_$Y1|E8&-n z&ReEnLWnX5(1BRIvO=;2FkNbhnI#<$o+1=|r9=wf)DkczDq}` zdD*D)fL4Iq(^A7xn^hkN)99JT=Fx?!io&v=B3$+F8-GyRV1z{SMRoG7Ksba{=0cn5M$}E zrN2uK*Qh+H`;C;G{g@XYT<`}h6#s@=<;tX9_4O*b5fhnMILJ$BF~r?d5H$(&U^kHs z5@}Ga0a7AkCSx5={20AN7Kp4xfJf7Q>=o@cGgac}hmAvbxs>+Mg*GpQojj*f_B!hv z8`}jyiIYJ6Vp6~bM7T3^`*hOblgDULA0hz%aBL{Nn(jb2QB9YL6cE zy8RbTXz^Y)Q*5IuH_XpcqV`WxyR(!ZVd|pZP00+YDQYh67|KwZS>ZzRdwjhbnNkaE z9nd6kd$p>9_DpTq@KaxeV_suiPHswEuzTw{C(vD`KR^v0%zh|R`&W|d6JFX;+ki4J zIxHa1lu_94qNEv}QDhgT`{%mS>_9ugT|&L0_MbMut3^5lnCfILnD6;1a*F9{jLx9@ zUU;CeOlE_imQ$*nB`~q2O;JH;9Kj`tH?6Kihdg5)g`BHDiBe_=uY+Q01k7LjZ8Afi z!~#f9?SMwjW#n?_ck9BVz*iM+7;&g$OA9cv)cQDUD(T~yQ?z%}YZecapn(AY0g^MM zf;q-ER%x)y?Z%khsxmJlU`q|7FVN<9WQ$b*4m(9bNSY|np77^M=uAO#N52~7UqFqV z54%72o7oduhV!!2B%30@2jWsxrYV1Qvg6HeZm`B&_0e)blbOHPc2DVXm8P9JmX@M5 zi`ylb)k?x~K0mb4v2$NC&(aLB(q$2tsO0i&!U%QCv3`~aYS7l%cRD6;9}nm&J;O{2!D`{E(}UNKuw?uw?6pdcE(gDP$bSTU)S7sknTvB*G@#8Z{Sk}W z8X<19O8w95ZszGUxzgYvsq`iq*#=ul2Zvl9AlNzizG40tcL|a>UxP7Sz16%MOlQ}o z#6X#@ofBZYRme%Z3P{JC{gr&4F5Z+qs+{Y3FFmp586!Et1KiKp|LOCfb#Vx2Y588} zj-Q^i3Cp?*d((rEk?enT_PJ}nUD#!SgfSVHW8wsx(@W=XH$rv7{!+U`7fTW5XP9u` zqCnYi;7AbSh;Yn1_$&2@;&U^@IoXwZa-hiQ%TA1T-rrZ3@X(*QGrs>^OJXr3uloM_;D zhRaSBDS9~Ix8yKA9DpCdJ4a!+cf1$2Z>+sl^`?ZgPZgPieuDO7pn~Z^%8^);AHF$ivVhrs%G~4X1DDoJNEC>R5nhhOq8ZC>YUZI7ig|==Pm9)-{c~8ZB=UN0q{zuJ-_!}4 z)=PB<`hF`zJ$4%Vp;;PAiPDs<&kY}HbU-(SeSCLENGt0Q?%8)oIBiAXT0WR?_3y}+ zM}_ewfEX+%FR~mKCJqxv+Ts0DD)k@3HNVrY5$tjf(946YE%$$L3`&3bhleCNy7THV zy1-;4;UGr+@2huFMtn0sr|kw_ z4^>OhjTI|R%2LBaz)EUhl&Ro>CfXXdtg4-bSW@s6>}Gj3|6qV z5zoM>y*~ZZt1lcXrt>Y09hWmw>6}-Gim{b*b;!3+lD5veAz1J|;Mj6|Kl-_c`6~{2 z7SoZk+QL@>CEyWbvG6j|;MD1_3AASvyC=FUal(0kcc*%K%PehfwMh1HRbu z#oQ5ysNaJyl$bp?+z6+?868?Az65=8cKX7lGE?FVLTR1I3(uMX6+L{r8k4ea8K=*QpQGWB48s%)#J9O3AO>UtK_!X-e>iHcv2zB>1iMF;Uzk z9UQc!$DdQBN64%O?UHn-u_(86#ee7NgZUj%dst`|x(c~UtrpNkOd%F;9WDRGI-|@-h0s?VcU;zpv%!D08?}N>EUi1lf=L~NJw{WqG10y`T=`$UKu{{rb5D4> zD53j(vCd*VAsEGw*CY91U0X<&NF$Z})!^eLZHhNzunJ+9Kv`What+1;%c?qA+u%7? zp4YHNju3RaPL(Q9m>=%C-UnwnGXL|5HgI}pA)jmm8jViBC11b;^?2%= z_^DG}0ii{vC-+vy^8Pk7@>z1bH;1WzDvI{FD=rVy0&rlaoa3NLRKZ!{6HjrYgwB_+ z!C7A=u>uP9mRYz|x+OzF)yg<=36d8*xaza>@PDeF((_B1M1L#Fd~Z3*^F>sZmf9R3 zotDO&=HS%-J&hRid}BCfaY>q6O6U}X*>-Q%gy(akZ=;2-n6=7~y(XgF1c_2>5}-wq zElttU9gh${$mjzIK+Tq*tW%{uq??#66{l{p$l|@k?o4BT> zx4s`I&gn{ksJe7=C{zxIMHyVozUcZ_HRTP25*Y2`N0G*0t30hhd26xUg-8MiI!j-% z4F`c3l{wM9#$-~$!m8y4=ScSxqerT{^X(ky6vbwT5gb*+)vu41Y5EP>OP@5_t!xf! z`X_?TAzIQ{+E>%4MoLbpzY}HtJ!RHnOYxN)M~fZn8mVS2fd$v*6{q?AzBxH>WWH~} zV1br&lT@#`KHe4=o#?N}vaB~Bcs3+zqxz=&9Y_MA(8EO!T(*lhAJT5M1wj4irOn}( zyXLz!;h+4W7A6Aqlh0BV5ZFc+aAUbrIPa*Kg_FWs!L*y7Hdztgf+{y&`NUC!^OBV(3?CIbf6UHX;l8%nm>J?@6Y$9B>S zVRMwq-$0Ag%>S9eOq?!ym+AEI@R6>j8iAMDY-Qf^7SkBj^Q!bb-t`YV8Sg=nZ0vX8O>6m(+b zLwfB=vCb{2)UFl#gM8n z(chHc3BfvPa;@=|BHPCJT`gV;hv~i$vk^HZ`1i(LL(2EGDh}}pk{@@|>+{KVot0xF zy?3gZ(dyeAQdVD#WV^AGuKybA<@&t{FsiX6^*m;o5xD%6)p-;|jk%&AzEkw+u-d3u zW5l|`4=kAui3!`03elCNIJz?)mM0qU`mx+U1b@$)BGROj@>DxBwx-Ac+p(Y?FAA52 zbO|Tx3?{?gBdk2lF5fSsdo3nmr17w2O|q3^t|fTR5c~MBBFD87FBm7A5&exwn|`8z zrrm3^_$?^1?Nz9pB)Hb^)%I2~nQ=Jveo5=#zrqg&MAM~Ly4{@Lp@RkTXwa|&>QuF7 z3fLN|9}cgyYgN9J4E(#e$||k=OjcO~@{Ds^2s78UU7t*LldVh7w>}ZDF zv!}!H6pTXI>|*KZ{#3zwMUS7Drpuq3%SG6>A6|i_C5D>y;cos;YL#B%tVexAQ3{#M zTkhT`7_XW3cy-w6HCcn9S(C(!6E#aT3OQ2>)7L}{PX*8ZN`xs}lo?U=yX#u!6yb|w zty-;Rp|5+y+moM%igq33TPz{==!~`YM&lmzB)A<8ruC(hSxAM)gQ2Rq9NBVRcK&HwxckfhMa$4KI!$h-x*E@-cUP zX115>$FahBC#xjcvRY2jHDB)@#>&et3MuH1&JU6PbnH(7^WH1 z!MH5dKq73x9Sdquz~5omIgyL{zIg)Ph%R)0uj7qSVcBa%vZg!D9n~I}Yh@|Jmbfg> zH>J*7CPypP{^JbfPmjh8Ty!X$-fTKfhE)bS?v{7wn{*pK&BCW)@#~a(_3azcML7pJ$IS+ zvof-gGQ?Kc#GpMj2Gb8UK&HLU1%p^@VDMRwLw1j{Noj=YMU9uA6rnj4p&70^X-lT0 zJm*3GF+#Kb%VCutix|NR6`AWR%OBoo!q)xAU2lMW?J{pc>l)whAemQPyJk=eiWKml zmV&LvOp|h~Hn(-WPSmjD3Yokr4*uI6r;;-Nuh4u|#lxGTu_7ZeMlCZZ!#M}F zAt0>`Gxk|7_3X^8c#J#%It>TwXk0yBq_J(AM-DYrlwzHTnReQ(NNC4SRqG0}3oc$I zRzx%;Xp6N;<&TAVN`en9m38n_;U1}vu*y5Su|wEPf%D&4n-4u*#G1brcDsGBZ7FDA z@WNjlc1UDoaqh&B?h0<>2Kz8NKyDsHVIl=q2AHC1)a2!K1;P;l8XC|Hv4+WBvtIl` z9x60Xm?AJkHJ4$X_a~%LN;>%8IBskCGfu`;h)lxEH5H^G{GOFY!wpvA<$n}qnf5#3 z>#Y0qvt%6W6Y)F9>ZaOHqZjukN+$RzDYWG5h-pgk_7@dO-G!6`4dR}WULzhl#(W!i zdP^z@Z<1he*axF`pv>Q`Ic)$ko6)^Ghyn#Nu?O%fiu9BKvGK&30Shkb#{sXYTXncxe!Y))|nKvPc zj=L9v342j$kN4P_IwKuRA|ob*(*0yy;^^(trp{kUVUwFQVSA?L4yjQY#viay)nQ+c zSlSBQo2DF+%jyp{gFt0Tl)dAjlv3iwFVkO;YF9kg78R1TdzO441uC#&(E2lZngXB> z!&|9T1^oUE>$Ou@oRmW}&XHA;hjf{JJky1K+auR8Z#%~Xq&`g}ptJ0~RxW|0M;Fo; z>*{r<-P6+RQ&Ewxyz@AN_+G01j;6$@F)dpU@ZPMfDSH2{O3g^-)76F?evEQ($Z4K- zG2-h-lFYPYZeD#g(aeCq()FX*R|puOj+7tI@{zX9xP&Wpo(K*Xy4wN*U|a(;z@Syi z=0nsE)@b8J%Yw=`N$4Gs=831QPl|~i?T3mv9TpTZHUn}w5(P@W!>Unr!c8SS7+%0^ zB8wTyh&24!c&9{%A3)87{zIPVQ;y}3`plEIc-FudoTo-+&`tXf`N&gc+uN3FozD=b zdJ$O8cn0rnJpMHMv>-cbZ?U}Gu`AWQ>SiOr(iFdrS5PcAn-p<%s{HMYk96oyYi?9R z|NIC88+gmauZy?MQs_NmMGVmfR_ibRHOrQ6^n608Y&E0i$DQQ5DzmWt9&TcY1Ib*?vkVmM zf+-C*^hzH6J<_fXQr|q3Ww@Ogy7^-6SWU+FJM%b#;%**i1I9q-Ki3bwUTf`cv63=y zpG%gdKDHgh0OH0wm#T4{GTUS!(k*5NP}9K<5HKcW1%cU_8240PERrq_AIu|KU_R5y zr}US1!=D)?cr>fh=NFLGXbBdqEfz@*5;ak~W0;Ct7R3uW7!a7=D775M)c!j(hX&s> zf?b7^%SQIzwRt$y-u>%dJ%v$Tk)lR!0Ipvom*vCzar7sTkY>sgUCTJ}Sj`-a1r!aw z2?WDxW{yE%6tj{v>#=N%$|X9)-A#DKNop=@r^4G(obBIjMZrwI3^QU2O!A>4sK|l2 zF<{sJ&f1HjI==fF6-P$STRoR3ku>MCJ-{&OIwK@qX{OZ+^0M12kXuVMSuYU;Rm^qlLZs zT&A7tvMRxr1;67x;4!)@f`~}}Bx_do;YIbui2yRM@oi+Jq{jHuCn#5tFDBWX$cQ)IQMi~_g3aS{;Srrgr>U}c!U zl>`qMLCFQr@U$2JVnOY!0O29lM}d2+;lJJ6G?!Ps=^oGXke3b^nPu@ z?mQ!W2-%?6yL^#aW~B&mWZy^CeS&HETR+^OVq6&X+fmLw5O@p&!JAfTwUVJOV% z?lon5myMdfuq2Lkd+*+F@aVin(PL<=_$gz#~U7mip*fYLvqTE%DEKv zoO7dN>uv%wbdNzWiMX6n{h(Tq%j06kJ6W5~c{@7GKKzwQ(lvTtpWeRG`2vZ7 zjSDXS2Fy?F%-5jaWFUHAV@9?AWpt%WlY@_ElHxaDPJMADI9|5wmy4lyB&pcyMGWHK zbH-5ua)o+UtI1gv5wsVpSR4nAL<}XK*k(-g=7^Gixv;a?7(g$wt>}tL1o6!NjOV;6 z-L<$gQ|au6F|O{aXq^*_v1jCuaKVr+i0+q4$gZ)Y>MBFqOa`V%;*{~TwOd_+}ulHF`PI2v4n>k;RUc8ip8t$U z@Vl?CG_P7aN4i*_jPwz`f1@zM=#z8l*QeI!r<2l-V7`cjFWb4LyzbIRIqEmsno(5v zUSx$LmtsWJUVb*x2Bg@m=3nHJPer@Oe!nu5`+I!Ic&H9gnydA?MsY%y+Dqb3-*CG{ z$_U-egG`3PFi9)?Fp+uSyH5f>wPI1js$qR^DWN4rI0t92y5Oe|ZJ_7Y~E zUJua48usx^5Xf^ot9DezbHYO-SJmn8_gOqOIEYtRt}T~D-L`J0g(znHt>_mhkBE6B2$WG2 zVTM2CELO%DDh~Las}lI{w`DDSUY*BjMM~82lq`d0pV3$0p(PF?cfS}Jh<$#NuRW~1 z5!C#dUbAn@*f7bJkm(!~Owr|uyVGs~( zsuhspx%8r@Do1~PHzr$MX}qR?B46e$f*8qh)%44qc*;dv@1lDZ;b@yrDSX&$Tcrsw zMb2a**sIL4)a9!NaLcAvDM0^HB6&}KWq0g3nf|Zn;a~Z5w7HX86sq6S8}^?y#`Jer zS{2jCjBzz*f3Q$Pb%4%v*{R_av&VeWVkhZrQtFFS6(X5ZQr2xj>KrjMoA0PBSHezp zYHVMWSczea(j_hK;%GL8>o2zBW-iL|^0tIppK+oyeNNobB&;HD5feJ-d1y$}UpAK(+f|#cyxuL;1%GynpFSgk}})vQ!)JF2ZJcq$c={UMX2u zZOwmIm)wWxqYAN6rv_w10D$@9s2=gQP^x#Pyy>FIVR1RFA-<$Lk?rppFEF@N<@z%0|IvdahcP6I(*ZaKN( zPTh>-u?t(*Kn)3zNv7gR=@SGSX@JYdB(Vw3VDyFc-9mKqiF zn)0^ZD_wDes2u>R4x{<>wM`#*Yrkx+mfd={HnK3-QvpImif{Zrp3Ygj|4~NtU;ZIG z_a<5A2OW67#kYp$1bc#yAvp!H9Z8V@&oZA29Qm~##`r9wC&sc|Rw8@(r9IspK%#whE{V#>-}oDaT{WgRj!6rFV!qe+4~yIZ+tBAy zAspCZyaOmlu(VYR{;A9+9#`>?+cec{L2?-fgFLzSPrfySQr|AHJ&qcvt<>(2V@g)V%sG?(EW;6-PH!^!VsR0M z6MxXI!v0f}>#$paSp8TbNjY(P(SjmAnPaf2bg2WMMY+B>FfZ2=7NldKdbUq;ZZ1Uo zUrWGeZ*DC)n|;-{1|f}DJ>nr&A>9QgF#TS9OJC91pq{m+B@Ssw96_7y&^bJB724Y_ z2Y%c5wdfV}4K_9{cvJ-Hyn}IGHXH>E6E$1`R>*G8Mm}+Q(kV)4c_^NdK8E{I%e^RM zJ2}il$ZCxxY-67j@nEp9E}RM2kdCRFJcf6;XgpbefI2N&xT+0L zv=%W0?bUUo3MI*KGs-w9P3%evGr@Sm!_u)hE2p1Z*7_+j}c5|VjC$Ogu! zfIW$YFGp8aX&HBi{?{VTN5a_xEL-hhD^9CVvh{0jP=zxNNNkDJKu#nlw*B$ZK4wzK zZmo=}-@^s`44^XU$_ihe4%@I3z-|p3fZHZkE7MX#@c{3(Pro6t)|h}Xc2o-|m1U-A zW!N%wYKTR{qBjRv29A=`e*XkNMN1W0` z1Nv_qB^96+7%LS8*ae zs*U3u^4v@1Li^_AURY2<+aP1@O)bO74)kd^E-zIEF7gv$1zckoy4uL)XJ0sV_}x~1 z6L8d(;Py+yJLLiZB8?^sWM^CFO+#MWk3mX**@M^^So)+0v<%{|;9gMG6$tO6<%vED zxinYg4Pl{+9}QGu2bu2GLr^w)Oo)H9Wg^z&g0$EUVLqF@vBBP;+1cz{)aXeFDC-0FR;oyudV&{dzExm>@TI{y3+=tB;uZMqdq+FP z>#vf(t6IdSqU`1>EWGrOq5TiIi`Pl Date: Sat, 3 Dec 2022 23:07:08 +0800 Subject: [PATCH 21/27] Delete LICENSE --- LICENSE | 427 -------------------------------------------------------- 1 file changed, 427 deletions(-) delete mode 100644 LICENSE diff --git a/LICENSE b/LICENSE deleted file mode 100644 index e04b480..0000000 --- a/LICENSE +++ /dev/null @@ -1,427 +0,0 @@ -Attribution-ShareAlike 4.0 International - -======================================================================= - -Creative Commons Corporation ("Creative Commons") is not a law firm and -does not provide legal services or legal advice. Distribution of -Creative Commons public licenses does not create a lawyer-client or -other relationship. Creative Commons makes its licenses and related -information available on an "as-is" basis. Creative Commons gives no -warranties regarding its licenses, any material licensed under their -terms and conditions, or any related information. Creative Commons -disclaims all liability for damages resulting from their use to the -fullest extent possible. - -Using Creative Commons Public Licenses - -Creative Commons public licenses provide a standard set of terms and -conditions that creators and other rights holders may use to share -original works of authorship and other material subject to copyright -and certain other rights specified in the public license below. The -following considerations are for informational purposes only, are not -exhaustive, and do not form part of our licenses. - - Considerations for licensors: Our public licenses are - intended for use by those authorized to give the public - permission to use material in ways otherwise restricted by - copyright and certain other rights. Our licenses are - irrevocable. Licensors should read and understand the terms - and conditions of the license they choose before applying it. - Licensors should also secure all rights necessary before - applying our licenses so that the public can reuse the - material as expected. Licensors should clearly mark any - material not subject to the license. This includes other CC- - licensed material, or material used under an exception or - limitation to copyright. More considerations for licensors: - wiki.creativecommons.org/Considerations_for_licensors - - Considerations for the public: By using one of our public - licenses, a licensor grants the public permission to use the - licensed material under specified terms and conditions. If - the licensor's permission is not necessary for any reason--for - example, because of any applicable exception or limitation to - copyright--then that use is not regulated by the license. Our - licenses grant only permissions under copyright and certain - other rights that a licensor has authority to grant. Use of - the licensed material may still be restricted for other - reasons, including because others have copyright or other - rights in the material. A licensor may make special requests, - such as asking that all changes be marked or described. - Although not required by our licenses, you are encouraged to - respect those requests where reasonable. More considerations - for the public: - wiki.creativecommons.org/Considerations_for_licensees - -======================================================================= - -Creative Commons Attribution-ShareAlike 4.0 International Public -License - -By exercising the Licensed Rights (defined below), You accept and agree -to be bound by the terms and conditions of this Creative Commons -Attribution-ShareAlike 4.0 International Public License ("Public -License"). To the extent this Public License may be interpreted as a -contract, You are granted the Licensed Rights in consideration of Your -acceptance of these terms and conditions, and the Licensor grants You -such rights in consideration of benefits the Licensor receives from -making the Licensed Material available under these terms and -conditions. - - -Section 1 -- Definitions. - - a. Adapted Material means material subject to Copyright and Similar - Rights that is derived from or based upon the Licensed Material - and in which the Licensed Material is translated, altered, - arranged, transformed, or otherwise modified in a manner requiring - permission under the Copyright and Similar Rights held by the - Licensor. For purposes of this Public License, where the Licensed - Material is a musical work, performance, or sound recording, - Adapted Material is always produced where the Licensed Material is - synched in timed relation with a moving image. - - b. Adapter's License means the license You apply to Your Copyright - and Similar Rights in Your contributions to Adapted Material in - accordance with the terms and conditions of this Public License. - - c. BY-SA Compatible License means a license listed at - creativecommons.org/compatiblelicenses, approved by Creative - Commons as essentially the equivalent of this Public License. - - d. Copyright and Similar Rights means copyright and/or similar rights - closely related to copyright including, without limitation, - performance, broadcast, sound recording, and Sui Generis Database - Rights, without regard to how the rights are labeled or - categorized. For purposes of this Public License, the rights - specified in Section 2(b)(1)-(2) are not Copyright and Similar - Rights. - - e. Effective Technological Measures means those measures that, in the - absence of proper authority, may not be circumvented under laws - fulfilling obligations under Article 11 of the WIPO Copyright - Treaty adopted on December 20, 1996, and/or similar international - agreements. - - f. Exceptions and Limitations means fair use, fair dealing, and/or - any other exception or limitation to Copyright and Similar Rights - that applies to Your use of the Licensed Material. - - g. License Elements means the license attributes listed in the name - of a Creative Commons Public License. The License Elements of this - Public License are Attribution and ShareAlike. - - h. Licensed Material means the artistic or literary work, database, - or other material to which the Licensor applied this Public - License. - - i. Licensed Rights means the rights granted to You subject to the - terms and conditions of this Public License, which are limited to - all Copyright and Similar Rights that apply to Your use of the - Licensed Material and that the Licensor has authority to license. - - j. Licensor means the individual(s) or entity(ies) granting rights - under this Public License. - - k. Share means to provide material to the public by any means or - process that requires permission under the Licensed Rights, such - as reproduction, public display, public performance, distribution, - dissemination, communication, or importation, and to make material - available to the public including in ways that members of the - public may access the material from a place and at a time - individually chosen by them. - - l. Sui Generis Database Rights means rights other than copyright - resulting from Directive 96/9/EC of the European Parliament and of - the Council of 11 March 1996 on the legal protection of databases, - as amended and/or succeeded, as well as other essentially - equivalent rights anywhere in the world. - - m. You means the individual or entity exercising the Licensed Rights - under this Public License. Your has a corresponding meaning. - - -Section 2 -- Scope. - - a. License grant. - - 1. Subject to the terms and conditions of this Public License, - the Licensor hereby grants You a worldwide, royalty-free, - non-sublicensable, non-exclusive, irrevocable license to - exercise the Licensed Rights in the Licensed Material to: - - a. reproduce and Share the Licensed Material, in whole or - in part; and - - b. produce, reproduce, and Share Adapted Material. - - 2. Exceptions and Limitations. For the avoidance of doubt, where - Exceptions and Limitations apply to Your use, this Public - License does not apply, and You do not need to comply with - its terms and conditions. - - 3. Term. The term of this Public License is specified in Section - 6(a). - - 4. Media and formats; technical modifications allowed. The - Licensor authorizes You to exercise the Licensed Rights in - all media and formats whether now known or hereafter created, - and to make technical modifications necessary to do so. The - Licensor waives and/or agrees not to assert any right or - authority to forbid You from making technical modifications - necessary to exercise the Licensed Rights, including - technical modifications necessary to circumvent Effective - Technological Measures. For purposes of this Public License, - simply making modifications authorized by this Section 2(a) - (4) never produces Adapted Material. - - 5. Downstream recipients. - - a. Offer from the Licensor -- Licensed Material. Every - recipient of the Licensed Material automatically - receives an offer from the Licensor to exercise the - Licensed Rights under the terms and conditions of this - Public License. - - b. Additional offer from the Licensor -- Adapted Material. - Every recipient of Adapted Material from You - automatically receives an offer from the Licensor to - exercise the Licensed Rights in the Adapted Material - under the conditions of the Adapter's License You apply. - - c. No downstream restrictions. You may not offer or impose - any additional or different terms or conditions on, or - apply any Effective Technological Measures to, the - Licensed Material if doing so restricts exercise of the - Licensed Rights by any recipient of the Licensed - Material. - - 6. No endorsement. Nothing in this Public License constitutes or - may be construed as permission to assert or imply that You - are, or that Your use of the Licensed Material is, connected - with, or sponsored, endorsed, or granted official status by, - the Licensor or others designated to receive attribution as - provided in Section 3(a)(1)(A)(i). - - b. Other rights. - - 1. Moral rights, such as the right of integrity, are not - licensed under this Public License, nor are publicity, - privacy, and/or other similar personality rights; however, to - the extent possible, the Licensor waives and/or agrees not to - assert any such rights held by the Licensor to the limited - extent necessary to allow You to exercise the Licensed - Rights, but not otherwise. - - 2. Patent and trademark rights are not licensed under this - Public License. - - 3. To the extent possible, the Licensor waives any right to - collect royalties from You for the exercise of the Licensed - Rights, whether directly or through a collecting society - under any voluntary or waivable statutory or compulsory - licensing scheme. In all other cases the Licensor expressly - reserves any right to collect such royalties. - - -Section 3 -- License Conditions. - -Your exercise of the Licensed Rights is expressly made subject to the -following conditions. - - a. Attribution. - - 1. If You Share the Licensed Material (including in modified - form), You must: - - a. retain the following if it is supplied by the Licensor - with the Licensed Material: - - i. identification of the creator(s) of the Licensed - Material and any others designated to receive - attribution, in any reasonable manner requested by - the Licensor (including by pseudonym if - designated); - - ii. a copyright notice; - - iii. a notice that refers to this Public License; - - iv. a notice that refers to the disclaimer of - warranties; - - v. a URI or hyperlink to the Licensed Material to the - extent reasonably practicable; - - b. indicate if You modified the Licensed Material and - retain an indication of any previous modifications; and - - c. indicate the Licensed Material is licensed under this - Public License, and include the text of, or the URI or - hyperlink to, this Public License. - - 2. You may satisfy the conditions in Section 3(a)(1) in any - reasonable manner based on the medium, means, and context in - which You Share the Licensed Material. For example, it may be - reasonable to satisfy the conditions by providing a URI or - hyperlink to a resource that includes the required - information. - - 3. If requested by the Licensor, You must remove any of the - information required by Section 3(a)(1)(A) to the extent - reasonably practicable. - - b. ShareAlike. - - In addition to the conditions in Section 3(a), if You Share - Adapted Material You produce, the following conditions also apply. - - 1. The Adapter's License You apply must be a Creative Commons - license with the same License Elements, this version or - later, or a BY-SA Compatible License. - - 2. You must include the text of, or the URI or hyperlink to, the - Adapter's License You apply. You may satisfy this condition - in any reasonable manner based on the medium, means, and - context in which You Share Adapted Material. - - 3. You may not offer or impose any additional or different terms - or conditions on, or apply any Effective Technological - Measures to, Adapted Material that restrict exercise of the - rights granted under the Adapter's License You apply. - - -Section 4 -- Sui Generis Database Rights. - -Where the Licensed Rights include Sui Generis Database Rights that -apply to Your use of the Licensed Material: - - a. for the avoidance of doubt, Section 2(a)(1) grants You the right - to extract, reuse, reproduce, and Share all or a substantial - portion of the contents of the database; - - b. if You include all or a substantial portion of the database - contents in a database in which You have Sui Generis Database - Rights, then the database in which You have Sui Generis Database - Rights (but not its individual contents) is Adapted Material, - - including for purposes of Section 3(b); and - c. You must comply with the conditions in Section 3(a) if You Share - all or a substantial portion of the contents of the database. - -For the avoidance of doubt, this Section 4 supplements and does not -replace Your obligations under this Public License where the Licensed -Rights include other Copyright and Similar Rights. - - -Section 5 -- Disclaimer of Warranties and Limitation of Liability. - - a. UNLESS OTHERWISE SEPARATELY UNDERTAKEN BY THE LICENSOR, TO THE - EXTENT POSSIBLE, THE LICENSOR OFFERS THE LICENSED MATERIAL AS-IS - AND AS-AVAILABLE, AND MAKES NO REPRESENTATIONS OR WARRANTIES OF - ANY KIND CONCERNING THE LICENSED MATERIAL, WHETHER EXPRESS, - IMPLIED, STATUTORY, OR OTHER. THIS INCLUDES, WITHOUT LIMITATION, - WARRANTIES OF TITLE, MERCHANTABILITY, FITNESS FOR A PARTICULAR - PURPOSE, NON-INFRINGEMENT, ABSENCE OF LATENT OR OTHER DEFECTS, - ACCURACY, OR THE PRESENCE OR ABSENCE OF ERRORS, WHETHER OR NOT - KNOWN OR DISCOVERABLE. WHERE DISCLAIMERS OF WARRANTIES ARE NOT - ALLOWED IN FULL OR IN PART, THIS DISCLAIMER MAY NOT APPLY TO YOU. - - b. TO THE EXTENT POSSIBLE, IN NO EVENT WILL THE LICENSOR BE LIABLE - TO YOU ON ANY LEGAL THEORY (INCLUDING, WITHOUT LIMITATION, - NEGLIGENCE) OR OTHERWISE FOR ANY DIRECT, SPECIAL, INDIRECT, - INCIDENTAL, CONSEQUENTIAL, PUNITIVE, EXEMPLARY, OR OTHER LOSSES, - COSTS, EXPENSES, OR DAMAGES ARISING OUT OF THIS PUBLIC LICENSE OR - USE OF THE LICENSED MATERIAL, EVEN IF THE LICENSOR HAS BEEN - ADVISED OF THE POSSIBILITY OF SUCH LOSSES, COSTS, EXPENSES, OR - DAMAGES. WHERE A LIMITATION OF LIABILITY IS NOT ALLOWED IN FULL OR - IN PART, THIS LIMITATION MAY NOT APPLY TO YOU. - - c. The disclaimer of warranties and limitation of liability provided - above shall be interpreted in a manner that, to the extent - possible, most closely approximates an absolute disclaimer and - waiver of all liability. - - -Section 6 -- Term and Termination. - - a. This Public License applies for the term of the Copyright and - Similar Rights licensed here. However, if You fail to comply with - this Public License, then Your rights under this Public License - terminate automatically. - - b. Where Your right to use the Licensed Material has terminated under - Section 6(a), it reinstates: - - 1. automatically as of the date the violation is cured, provided - it is cured within 30 days of Your discovery of the - violation; or - - 2. upon express reinstatement by the Licensor. - - For the avoidance of doubt, this Section 6(b) does not affect any - right the Licensor may have to seek remedies for Your violations - of this Public License. - - c. For the avoidance of doubt, the Licensor may also offer the - Licensed Material under separate terms or conditions or stop - distributing the Licensed Material at any time; however, doing so - will not terminate this Public License. - - d. Sections 1, 5, 6, 7, and 8 survive termination of this Public - License. - - -Section 7 -- Other Terms and Conditions. - - a. The Licensor shall not be bound by any additional or different - terms or conditions communicated by You unless expressly agreed. - - b. Any arrangements, understandings, or agreements regarding the - Licensed Material not stated herein are separate from and - independent of the terms and conditions of this Public License. - - -Section 8 -- Interpretation. - - a. For the avoidance of doubt, this Public License does not, and - shall not be interpreted to, reduce, limit, restrict, or impose - conditions on any use of the Licensed Material that could lawfully - be made without permission under this Public License. - - b. To the extent possible, if any provision of this Public License is - deemed unenforceable, it shall be automatically reformed to the - minimum extent necessary to make it enforceable. If the provision - cannot be reformed, it shall be severed from this Public License - without affecting the enforceability of the remaining terms and - conditions. - - c. No term or condition of this Public License will be waived and no - failure to comply consented to unless expressly agreed to by the - Licensor. - - d. Nothing in this Public License constitutes or may be interpreted - as a limitation upon, or waiver of, any privileges and immunities - that apply to the Licensor or You, including from the legal - processes of any jurisdiction or authority. - - -======================================================================= - -Creative Commons is not a party to its public -licenses. Notwithstanding, Creative Commons may elect to apply one of -its public licenses to material it publishes and in those instances -will be considered the “Licensor.” The text of the Creative Commons -public licenses is dedicated to the public domain under the CC0 Public -Domain Dedication. Except for the limited purpose of indicating that -material is shared under a Creative Commons public license or as -otherwise permitted by the Creative Commons policies published at -creativecommons.org/policies, Creative Commons does not authorize the -use of the trademark "Creative Commons" or any other trademark or logo -of Creative Commons without its prior written consent including, -without limitation, in connection with any unauthorized modifications -to any of its public licenses or any other arrangements, -understandings, or agreements concerning use of licensed material. For -the avoidance of doubt, this paragraph does not form part of the -public licenses. - -Creative Commons may be contacted at creativecommons.org. From 16b56d715c2fb2e8b9aea6220c5f605279c56ec8 Mon Sep 17 00:00:00 2001 From: zengmmm00 <11811636@mail.sustech.edu.cn> Date: Sat, 3 Dec 2022 23:10:13 +0800 Subject: [PATCH 22/27] add model directory --- models/.gitignore | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 models/.gitignore diff --git a/models/.gitignore b/models/.gitignore new file mode 100644 index 0000000..e69de29 From 28cbae3684eecb6967b927f41a1fe1f832ce7de6 Mon Sep 17 00:00:00 2001 From: HappyCheems <79441528+eternalDoge@users.noreply.github.com> Date: Sat, 3 Dec 2022 23:47:17 +0800 Subject: [PATCH 23/27] new_unsupervised --- unsupervised.ipynb | 1827 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1827 insertions(+) create mode 100644 unsupervised.ipynb diff --git a/unsupervised.ipynb b/unsupervised.ipynb new file mode 100644 index 0000000..7008a97 --- /dev/null +++ b/unsupervised.ipynb @@ -0,0 +1,1827 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7ba70409", + "metadata": {}, + "source": [ + "# Unsupervised Models" + ] + }, + { + "cell_type": "markdown", + "id": "dd2ab19d", + "metadata": {}, + "source": [ + "## 1. Data and Clusters' Examples Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0422e6d9", + "metadata": {}, + "outputs": [], + "source": [ + "# download the dataset in NumPy format\n", + "import numpy as np\n", + "def load(f):\n", + " return np.load(f)['arr_0']\n", + "\n", + "# Load the data\n", + "x_train = load('kmnist-train-imgs.npz')\n", + "x_test = load('kmnist-test-imgs.npz')\n", + "y_train = load('kmnist-train-labels.npz')\n", + "y_test = load('kmnist-test-labels.npz')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4bf07b89", + "metadata": {}, + "outputs": [], + "source": [ + "# Flatten images\n", + "# Each element in x_train and x_test is in the form of 28x28,\n", + "# so reshaping them in the form of 1x784\n", + "x_trainf = x_train.reshape(-1, 784)\n", + "x_testf = x_test.reshape(-1, 784)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "93c6ef34", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "

\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
indexcodepointchar
00U+304A
11U+304D
22U+3059
33U+3064
44U+306A
55U+306F
66U+307E
77U+3084
88U+308C
99U+3092
\n", + "
" + ], + "text/plain": [ + " index codepoint char\n", + "0 0 U+304A お\n", + "1 1 U+304D き\n", + "2 2 U+3059 す\n", + "3 3 U+3064 つ\n", + "4 4 U+306A な\n", + "5 5 U+306F は\n", + "6 6 U+307E ま\n", + "7 7 U+3084 や\n", + "8 8 U+308C れ\n", + "9 9 U+3092 を" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "# import the map to see the given 10 clusters\n", + "map = pd.read_csv(\"kmnist_classmap.csv\")\n", + "map" + ] + }, + { + "cell_type": "markdown", + "id": "ca9557a5", + "metadata": {}, + "source": [ + "### visualize characters in each cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "039fead3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 1\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "\n", + "sns.heatmap(x_train[2], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[12], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "046a75eb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAD7CAYAAACMlyg3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAoQElEQVR4nO3debgdVZnv8e+PjCBkxDAkQIKdgDQyyGBEmnmeQnNVEIVAVB6xQcAhBPG2jReR2QTHpiWMKiLQkRAEI5KA3RCIIZBgmAQCCQmBCAiGIcN7/6g6cthn1a69z5Scs3+f56nn7PPWqlWrsk/22lW16l2KCMzMrDGtt7YbYGZma487ATOzBuZOwMysgbkTMDNrYO4EzMwamDsBM7MG1rOjdyCpS49BHT58eDJ+/vnnJ+NbbbVVMn700Ucn48uXL29Ns2py2GGHJePTpk1LxhcvXpyMDxs2rN3atC6JCHX1v88ikpJxDwnvOiIi/SY2I+kQYBLQA/hZRFxY7358JmBm1gVJ6gH8CDgU2A74jKTt6q2n9ExA0rbAGGAoEMCLwG0RsaDenZmZWbvZHXg6Ip4BkHQj2Wf1n+uppOqZgKSzgRsBAQ8CD+WvfylpQpXtTpE0W9LsehpjZmaZ5p+j+XJKRZGhwAvNfl+Ux+pSdibweeCfI2JlReMuBx4DktefIuJK4Mq8rC9CmpnVqfnnaIHUPYO6P2/L7gmsATZPxDfL15mZ2dqxCNii2e/DyC7X16XsTOBM4G5JT/HeaceWwD8Bp9W7MzMzazcPASMljQAWA8cBx9dbSdVOICLulDSK7AbEULLTj0XAQxGxuu4md0Evv/xyMr7++usn43vuuWcy/sMf/jAZ/8xnPtO6htXg05/+dF3l33jjjQ5qiXWUomHAr776ajJ+//33d2RzrBNFxCpJpwF3kQ0RnRwRj9VbT+nooIhYAzxQfxPNzKwjRcQdwB1tqcPPCZiZNTB3AmZmDcydgJlZA3MnYGbWwNwJmJk1MHcCZmYNTB2dWra7po3o27dvMj5nzpxkfOTIkcn4jjvumIz/+c+154AqqnvevHk11wHFzzjMnt09U0B1pVTSm266aTI+ZcqUZPyLX/xiMl7v30S9evZMjzrv3bt3Mr5ixYqObE6XVksq6fbgMwEzsy5I0haS7pG0QNJjks5oTT0dPqmMmZl1iFXA1yJijqSNgD9Jmh4R7ZdKGrL5BCTtL2nDivgh9bXXzMzaS0QsiYg5+es3gAW0IpV02XwCXwF+A5wOzJc0ptnqC6ps5/kEzMzaoIb5BJqXHQ7sDMyqdz9ll4O+COwSEW/mO7lZ0vCImEQ6lzXg+QTMzNqqhvkEAMiv0twCnBkRf6t3P2WdQI+IeDNv0HOS9iHrCLaiSidgZmYdT1Ivsg7g5xFxa2vqKLsnsFTSTk2/5B3CEcDGwEdas0MzM2s7SQKuAhZExOWtrqfacwKShgGrImJpYt0nIuJ/amhoQ10OOvbYY5PxG2+8MRn/1a9+lYwfd9xxLWI9evRIlp06dWoyfuihhybjkyZNSsbPPPPMZLy76krPCRxxxBHJeNF7f/vttyfj3/3ud5PxJUuWJONvvfVWMr711lsn45dfnv4sGjRoUDJ+5ZXpqx0TJ05MxtesaZwJDcueE5C0J3AfMI/3Znr8Zp5eumZlk8osqrKutAMwM7OOERF/pB0uy/thMTOzBuZOwMysgbkTMDNrYO4EzMwamDsBM7MG5k7AzKyBeT6BdtarV69k/NFHH03Gt91222T8mGOOaRErGpt96aWXJuPz589PxovmDXj99deT8e6qKz0nMGLEiGT8oYceSsYHDx5cV/3vvPNOMv7uu+8m43369EnGi+YNKFI0n8CWW26ZjC9fvryu+ruyWuYTkNQDmA0sjoj0wyQlfCZgZtZ1nUGWPbTV6u4EJF3Xlh2amVnb5RkdDgd+1pZ6qj4xLOm2yhCwr6QBABFxVFt2bmZmrTYRGA9s1JZKyrKIDgP+TNbTBFknsCtwWbWN8rzXhbmvzcysusTn6JV5emkkHQEsi4g/5dmdW62sE9iV7JrTucA3ImKupLciYma1jTyfgJlZ25TMJ/AJ4ChJhwF9gX6SboiIz9W7n7IEcmuA70v6df7zpbJtzMysY0XEOcA5APmZwNdb0wFAjR/oeTbRT0k6HKh75ppGsnLlymR8/Pjxyfhtt1Xedsn85Cc/aRHr169fsuyCBenBAfvuu28y3mhDQbuDxYsXJ+NPP/10Ml7vENGiIZ9F8SJFQ86ffPLJZLxoeHMjDQVd2+r6Vh8R04BpHdQWMzOrU0TMAGa0dns/J2Bm1sDcCZiZNTB3AmZmDcydgJlZA3MnYGbWwNwJmJk1MKeS7iRSOivsXXfdlYwfeOCBNdc9atSoZPypp56quY5G1JVSSRfZZpttkvGbb745Gd9+++3bZb9vvvlmMp5KgQ5w9913J+Nr1qxpl/Z0R7Wkkm4PPhMwM+uiJA2QdLOkxyUtkPTxeutwCggzs65rEnBnRHxSUm9gg3orqHomIOljkvrlr9eXdJ6kqZIuktS/dW02M7O2yj+b9wKuAoiIdyPitXrrKbscNBlomv9tEtAfuCiPXV2lcadImi1pdr0NMjOz93+O5ktlev6tgZeBqyU9LOlnkj5Q737KLgetFxGr8te7RsRH89d/lDS3aCOnkjYza5uSVNKQfX5/FDg9ImZJmgRMAP5vPfspOxOYL+nk/PUjknYFkDQKSKfLNDOzzrAIWBQRs/LfbybrFOpS1gl8Adhb0l+A7YD7JT0D/Fe+zszM1oKIWAq8IKlpnPD+ZDNB1qVsUpnXgZMkbUR2/aknWc/zUr07anQjR45MxovGeddj//33T8b9nED398QTTyTjY8aMScbvueeeZHzLLbdMxt96661k/OKLL07G586dm4x39PNIDex04Of5yKBngJNLyrdQ66QybwCP1Fu5mZl1nIiYSzYNcKv5YTEzswbmTsDMrIG5EzAza2DuBMzMGpg7ATOzBuZOwMysgXk+gXZ29NFHJ+NXXpl++vuDH/xgMp56X4rmJFi5Mv3w9sEHH5yMF40VbzTdYT6BIltttVUyPnt2Op3XI4+kR4Bfc801yfhPfvKTZPz1119Pxu+4445k/IorrkjG58+fn4w3klrmE5B0FtmDuwHMA06OiLfr2Y/PBMzMuiBJQ4GvkOV12x7oARxXbz3uBMzMuq6ewPqSepLNJfBivRWUzSfQW9KJkg7Ifz9e0g8l/ZukXq1qspmZtVlELAYuBZ4HlgCvR8Tv6q2n7EzgauBw4AxJ1wOfAmYBuwE/K9rI8wmYmbVN2XwCkgYCY4ARwObAByR9rt79lOUO+khE7JCfaiwGNo+I1ZJuoEouIc8nYGbWNjXMJ3AA8GxEvAwg6VZgD+CGevZTdiawXp6dbiOy601NU0r2AXw5yMxs7XkeGC1pA2VDB/cHFtRbSdmZwFXA42R3nc8Ffp3PJzAauLHenZmZWfvIZxO7GZgDrAIepvqZQ1LpcwKSNs93+KKkAWSnIM9HxIM17aDBLgdtscUWyfiTTz6ZjPft27fD2vLSS+lpH8aOHZuM33XXXR3WlnVRd3hOoHfv3sn4xIkTk/EjjzwyGR89enQyvvfeeyfjN9yQvuJQ9CxLkaLnCv71X/81GZ8xY0aLWHedq6CW5wTaQ+l8AhHxYrPXr5FNYWZmZt2AnxMwM2tg7gTMzBqYOwEzswbmTsDMrIG5EzAza2Clo4OsPi+88EIyfvXVVyfju+++ezKeGvo3cODAZNmhQ4cm45tsskkyfswxxyTjjTZEtCspGnp5/PHHJ+Pjxo1LxseMGZOML168OBnv379/Ml7vUNAi/fr1S8Z/+tOfJuM777xzi9iKFSvapS2NymcCZmZdkKTJkpZJmt8sNkjSdElP5T/T3xybKcsi2l/ShZIel7Q8XxbksQHtcBxmZtY61wCHVMQmAHdHxEjg7vz3qsrOBG4CXgX2iYjBETEY2DeP/breFpuZWfuIiHuBv1aExwDX5q+vBY4uq6esExgeERdFxNJmO14aERcBWxZt5FTSZmZtU5ZKusAmEbEEIP85pGyDshvDCyWNB66NiJfyhm0CnASk74DiVNJmZm1VQyrpdlF2JnAsMBiYKemvkv4KzAAGkU0wY2Zm646XJG0GkP9cVrZB1U4gIl6NiLMjYtuIGJQvH46Is6nhWpOZmXWq24CmNMFjgd+UbdCW5wTOI5t+0mrw5S9/ucPqvu6665LxE044IRkvSg9c9BzCq6++2rqGWbspSpe8+eabJ+NFacTvu+++uva7cOHCZHz16tXJeI8ePeqqf82aNcn40qVLk/GVK1fWVX93JumXwD7AxpIWAd8GLgRukvR5sklnSq/YVO0EJD1atApIP4lkZmYdLiI+U7Bq/3rqKTsT2AQ4mGxIaHMC/reeHZmZ2bqnrBO4HdgwIuZWrpA0oyMaZGZmnadqJxARn6+yLp20xMzMugznDjIza2DuBMzMGpg7ATOzBub5BNayXr16JeOpcdhFY6q/+tWvJuNFueO32WabZPyKK65IxoueN7DOU5S/f9CgQcn4O++8U1c99e53vfXa5/tj0XMF8+fPT8ZXrVrVLvvtDiRNBo4AlkXE9nnsEuBI4F3gL8DJEfFatXp8JmBm1jVdQ8tU0tOB7SNiB+BJ4JyySsrmE+gn6XuSrpd0fMW6H9fXXjMzay+pVNIR8buIaDpdegAYVlZP2ZnA1WQPht0CHCfpFkl98nWj62uymZl1onHAb8sKlXUCH4qICRExJSKOAuYAf5A0uNpGnk/AzKxtWjmfQNO25wKrgJ+XlS27MdxH0noRsQYgIr6bJyq6F9iwaCPPJ2Bm1jatnU9A0liyG8b7R1HmwWbKzgSmAvtVNOxa4Gtkd5/NzGwdIekQ4GzgqIhYUcs2ZWkjxhfE75R0Qf1NNDOz9lCQSvocoA8wPR/e+0BEfKlqPTWcLRQ14PmIKJxnuFk5Xw6q4lvf+lYyfuqpp7aIPf7448mys2enb72ceOKJyfimm26ajBeNLe/fv39d5buKiFBX//v8xS9+kYx/8pOfTMZvvPHGZHzRokXJ+HHHHZeMjxgxoobWlVuxIv1ldfvtt0/Gn3322XbZb1cQEfU91NFKnk/AzKyBeT4BM7MG5vkEzMwamOcTMDNrYM4dZGbWwNwJmJk1sFYPEa15B118CN7astlmm7WIfeMb30iW/fKXv5yM9+nTJxmv1+jR6TRRs2bNapf615buMET0Bz/4QTJ+2mmndXJLWmfJkiXJ+C677FJX+e6os4aI1n0mIGlIRzTEzMxqJ2mypGWSWky+IOnrkkLSxmX1lKWSHlSxDAYelDRQUno2CzMz6wzX0HI+ASRtARwIPF9LJWVDRF8BFlbEhpJlEw1g61p2YmZm7Ssi7pU0PLHq+8B44De11FN2OWg88ARZMqIRETECWJS/LuwAnErazKxtWpNKWtJRwOKIeKTW/ZQ9J3CppBuB70t6gSxBUemNNKeSNjNrm3pTSUvaADgXOKie/ZTeGI6IRRHxKeAesvkrN6hnB2Zm1ik+BIwAHpH0HNnUknMkpTNG5moeHRQRU4F9gQMAJJ3c6qaamVm7ioh5ETEkIoZHxHBgEfDRiFhabTunku4GRo4cmYxPmDAhGR83blwyPn9+i5FmAOy1117J+KuvVuYV7Fq6w3MCBx98cDJ+xx13JOPrrdc1ng9NpVIH+OlPf9rJLVl7yp4TaD6fAPAS8O2IuKrZ+ueAXSPilWr1OJW0mVkXFBGfKVk/vJZ6nErazKyBOZW0mVkDcyppM7MG1jXuEpmZWYdwJ2Bm1sDcCZiZNTDPJ9CNfeITn0jG//jHPybjd955ZzJ+6KGHtlub1iXd4TmBDTZIP8A/bdq0ZHzPPfdMxnv2TN8eXLNmTTL+8ssvJ+PLly9PxrfbbrtkvMjixYuT8d12261FrLvOMVDDcwKTgSOAZRGxfbP46cBpwCpgWkSMr1ZPa+YTGFzvNmZm1u6uoSKVtKR9gTHADhHxz8ClZZWUzSdwYdOkBJJ2lfQMMEvSQkl7t7blZmbWNhFxL/DXivCpwIUR8U5eZllZPWVnAoc3e+T4EuDYiPgnsgkLLquvyWZm1sFGAf8iaZakmZJaXj+rUNYJ9JLUdLFw/Yh4CCAingQKJ7D1fAJmZm3TmvkEyJ79GgiMBr4B3CSp6r2FsieGfwTcIelC4E5JE4Fbgf2BuUUbeT4BM7O2qXc+gdwi4NbIRvw8KGkNWYK59J18yp8Y/oGkeWTXmUbl5UcBU4D/V2fjzMysY00B9gNmSBoF9CabJrhQ2ZkAETEDmFEZz+cTuLoVjTQzszZqnkpa0iKymR8nA5MlzQfeBcZGyXMApZ1AFefhTmCdNmTIkLrKDxs2rINaYh1lxYoVyfjhhx+ejB90UHrmwaLLxitXrkzGZ82alYz36tUrGZ88eXIyXjQfwtChQ5Px4447rkVs4sSJybId/QzU2lYllfTn6qnH8wmYmTUwzydgZtbAPJ+AmVkD83wCZmYNzFlEzcwamDsBM7MG1pYhog3tjDPOSMbHj09nbX3ssceS8WuuuSYZv/XWW1vE3n777doalzvssMPqKv/ss88m40XDB7v7ELyurGjo6JQpUzq3IbmTTjopGX/ggQeS8a222ioZP//881vEnn766WTZqVOnJuM9evRIxlevXp2Md3c+EzAz64IkTZa0LH8wrCm2k6QHJM3N8w3tXlZPWSrpXSXdI+kGSVtImi7pdUkPSdq5PQ7EzMxa5Roq5hMALgbOi4idgH/Pf6+q7Ezgx3kl08ieC/jPiOgPTMjXmZnZWlAwn0AA/fLX/YEXy+opTSUdEb+NiF9m+4yb853fDfQt2sippM3M2qaVqaTPBC6R9ALZrGLnlG1QdmP4bUkHkfUoIenoiJiSzypWeBfFqaTNzNqmlamkTwXOiohbJH0auAo4oNoGZWcCXwK+BowjSx+xr6TXyC4FfaXOxpmZWccaSzbnC8CvgbbdGI6IRyLi4Ig4NCIej4gzImJAPoHxNm1vr5mZtaMXgab53/cDnirbQK0d6y3p+YjYsoZyXfpy0IABA5Lx559/Phlfs2ZNMj59+vRkfOed04OsUql0Fy5cmCz71FPp9/nAAw9MxovaeMop6UuOf/jDH5LxF18svee0TosIdfW/z66u6O//3nvvTcY33HDDFrGi/xennXZaMn777bfX2Lq1KyKqTgvZfD4B4CWy+QSeACaRXep/G/hyRPypWj1OJW1m1gVVmU9gl3rqcSppM7MG5lTSZmYNzKmkzcwamHMHmZk1MHcCZmYNzJ2AmVkDa/VzAjXvoIuPw95rr72S8ZkzZybjEydOTMbPOuusZLxXr17JeGrMflHdPXvWNy3E2LFjk/Hrrruurnq6Oj8n0P7aa+6Jor/FE044oeY67rvvvmT8mGOOScZfeeWVmuvuDGXPCbSXslTS/SVdKOlxScvzZUEeG9AZDTQzs5by9P735J/Jj0k6I48PytP+P5X/HFitnrLLQTeRPSOwT0QMjojBwL557NftcSBmZtYqq4CvRcSHgdHAv0najizV/90RMRK4O/+9UFknMDwiLoqIpU2BiFgaERcBpSkjzMysY0TEkoiYk79+A1gADAXGANfmxa4Fjq5WT1knsFDSeEn/SBEhaRNJZwMvFG3k+QTMzNqmnvkEJA0HdgZmAZtExBLIOgpgSLX9lN1RPJbsVGJm3hEEWaKi24BPF23k+QTMzNqm1vkEJG0I3AKcGRF/K7o5X6TsieFXJV0NTAceiIg3m+34EODOuvZmZmbtRlIvsg7g5xHRNI/AS5I2i4glkjYDllWro2x00FeA3wCnAfMljWm2+oLWN93MzNpC2Vf+q4AFEXF5s1W3kU0uQ/7zN1XrqTZ+V9I84OMR8WZ+zelm4PqImCTp4YhIJwN/fx1d+nLQxhtvnIwvW5buXKdMmZKMF41NrsdOO+2UjB977LHJ+P3335+M33bbbW1uS3fg5wTKbbTRRsn4N7/5zWR89er0rLPf+ta36tpv0fM5qbktevTokSxb9Nk2YUJ6sMxll12WjBcdU0erYT6BPYH7gHlA0yQh3yS7L3AT2eCd54FPRUTlhPT/UHZPoEfTJaCIeE7SPsDNkrYiSydtZmZrQUT8keLP4f1rradsdNBSSTs12+mbwBFkM9l8pNadmJnZuqmsEzgRWNo8EBGrIuJEIH2+ZmZmXUbZ6KBFVdb9T/s3x8zMOpOziJqZNTB3AmZmDcyppFupaPjlbrvtlowXDe+cP39+ezXJ6uQhouX69u2bjN9zzz3J+LRp05LxCy5IP1Y0ZEg6o0FR+ZNPPjkZr8dLL72UjO+xxx7J+DPPPNPmfbbGupJKup+k70m6XtLxFet+3LFNMzOzIlVSSV+Sp/9/VNJ/l6X9L7scdDXZONRbgOMk3SKpT75udFsPwszMWq0olfR0YPuI2AF4EjinWiVlncCHImJCREyJiKOAOcAfJA1ue/vNzKy1ilJJR8TvImJVXuwBYFi1esqeGO4jab2IWJPv6LuSFgH3AhsWbZSnPC1Me2pmZtUlPkevzDOLpsoO571U0s2NA35VbT9lncBUYD/g902BiLhW0kvAD4o2cippM7O2aW0q6Wbxc8kuGf282vZVLwdFxHhgkaT98x01xe8EvlLWODMz6zgFqaSRNJYsxc9no2QIaNnooNPJ0pCeTstU0t9tbcPNzKxtilJJ53O9nA0cFREryuopuxx0CrBL81TSkoZHxCQaPIvo7bffnoyPHp0eNHXjjTcm40ceeWQy/uyzz7auYdYl9OyZ/q/34Q9/OBkfNWpUMj5ixIhkfIsttkjGP/axjyXjjz76aDI+a1blJebM8OHDk/Fx48Yl44cffngy/pGPpPNQfuADH0jG61H0BXjgwIHJ+NChQ5PxtfWcQA0+AZwAzJM0N499E7gC6ANMz2cZeyAivlRUiVNJm5l1QVVSSd9RTz1OJW1m1sCcStrMrIE5lbSZWQNzFlEzswbmTsDMrIG5EzAza2B1zycgaUhELKujfLdMG7H55psn4w8++GAyXjQGedmy9D/lZz/72Rax3//+94mS1lprcz6BHXfcMRkvytM/YMCAZDwfB97pVq9enYy/++67yXivXr3qqqdPnz7JeD2KPtsefvjhZHzs2LHJ+Nqa82NdmU9gUMUyGHhQ0kBJgzqjgWZm1lLRfALN1n9dUkjauFo9ZQ+LvQIsrIgNJUspHcDW9TbczMzaRdN8AnMkbQT8SdL0iPizpC2AA4HnyyopuycwHniCLAfFiIgYASzKX7sDMDNbS4rmE8hXf5/s87v0cmdZFtFLgS8A/y7p8ry3Ka1U0imSZkuaXVbWzMxaav45mi+Fc7Q0n09A0lHA4oh4pJb9lF0Oanpg7FOSjiSbtmyDGrbxfAJmZm3QmvkEyC4RnQscVOt+SoeIStpW0v7APcC+wAF5/JBad2JmZu0vMZ/Ah4ARwCOSniObWnKOpE2L6igbHfQVms0nABwUEU3jpS5o8xGYmVmrpOYTiIh5ETEkIoZHxHBgEfDRiFhaVE/Z5aAv4vkEkl588cVk/KCD0mdhM2bMSMaHDBmSjN9xR8tssFdddVWy7DnnnJOMv/baa8m4rX29e/dOxte15wGKcumfdNJJyfgLL7yQjBc9V7PRRhsl46nnZAB22GGHFrFtttkmWbbIBz/4wWT873//e131rAOS8wlERF2ppD2fgJlZF1RlPoHmZYaX1eP5BMzMGpjnEzAza2CeT8DMrIE5i6iZWQNzJ2Bm1sDq7gTyTKJmZtYNVJ1PQNKFwKUR8YqkXYGbgDVAL+DEiJhZugOnjQDS45sB7rvvvmS8X79+Ndf97W9/Oxn/zne+k4z37ds3GT/llHRqkpkz02/zI4/UlJpknbU25xPYcMMNk/HLLrssGR83blwy3rNnaeaXNnn77beT8WOPPTYZnzp1ajJe77wlRfMJpOKjRo2qq+4lS5Yk40XP/tTb9vZSNp9Anin0OmBTss/lK/NnuJB0OnAaWRqJaRExvqiesr+gwyNiQv76EuDYiHhI0ijgF8CutRyMmZm1u2QqaWATYAywQ0S8Iyn9RGqurBPoJalnRKwC1o+IhwAi4klJbZ/6x8zMWiUilgBL8tdvSGpKJf1F4MKIeCdfV3UmyLJ7Aj8C7pC0H3CnpImS9pJ0HjC3aCOnkjYza5vWppIGRgH/ImmWpJmSdqu2n7LnBH4gaR5wal5xz/znFOD8Kts5lbSZWRu0JpV0RPxNUk9gIDAa2A24SdLWUXBzo5a7SkvzhsxqyiOU7/gQ4M4atjczsw6QSCUNWebQW/MP/QclrSFL9fNyqo66UklLGtNstVNJm5mtJalU0rkpwH55mVFAb7L54tP1lAwRnQd8vHkqaeD6iJgk6eGI2LmGhvpyUBV77LFHMn7BBS372K23Tk/rfMwxxyTjS5emU4hPnz49Gd92222T8X333TcZL0qP3VWszSGiRamhi4Z8fuELX0jGR44cWVc9xx9/fDI+eHB9j/8sX748GT/ggAOS8Xnz5iXjq1evrmu/jaSGIaJ7AvcB88iGiAJ8E/g9MBnYCXgX+HpE/KGoHqeSNjPrgkpSSX+u1nqcStrMrIE5lbSZWQNzKmkzswbmLKJmZg3MnYCZWQNzJ2Bm1sgiotMW4BSXb5/y61JbukP5dbFNLt812tIZ5Tty6dydwWyXb5/y61JbukP5dbFNLt812tIZ5Tty8eUgM7MG5k7AzKyBdXYnUJoW1eXXibobsXxn7MPl26/8utSWzijfYaomkDMzs+7Nl4PMzBqYOwEzswbmTsCMbKY8SU9IelrShJKyW0i6R9ICSY9JOqOG+ntIeljS7TW2Z4CkmyU9nu/n41XKnpW3Y76kX0rqmygzWdIySfObxQZJmi7pqfznwJLyl+TteVTSf0saUFS22TZflxSSNq5Wdx4/PX8PHpN0cUlbdpL0gKS5+fy7uzdbl3x/io63Svmi4636/jc/5mpli46303Xk+FNgW+Bs4ApgUv76wyXl9wc2rIgfUsO+ritZ/zGgX/56feA8YCpwEdC/omxvsgyqB+S/Hw/8EPg3oNfaHtfrpX0XoAfwF2Dr/L1/BNiuSvnNgI/mrzcCnqxWPi/3VeAXwO01tula4Av5697AgIJyQ4FngfXz328CTkqU2wv4KDC/WexiYEL+egJwUUn5g4Ce+euLmsqnyubxLYC7gIXAxiV170s2GUqf/PchJeV/Bxyavz4MmFH2/hQdb5XyRcdb+P5XHnOVuguPt7OXDjsTkHQ2cCPZpAcPAg/lr3+Z+qZVz1SWkm6rWKYCxzT9XtCkycCK/PUkoD/ZG7sCuLqi7NXA4cAZkq4HPgXMIpu0+Wc1/QNYu5I0pM7y9UyVtTvwdEQ8ExHvkv3djikqHBFLImJO/voNYAHZh3FRW4aR/T3V9LcjqR/ZB99V+T7ejYjXqmzSE1hf2QTjGwAvJtp8L/DXivAYss6G/OfR1cpHxO8iYlX+6wPAsCp1A3wfGA+8b/RJQflTgQsj4p28zLKS8gH0y1/3p9kxV3l/ksdbVL7K8VZ7/993zFXKFh5vp+uo3oWsx2vxrZnsW81Tifg88jMAYDgwGzgj//3hirJzgBuAfYC9859L8td7F7RnQfPtK9bNrfj90fxnT+AlshnWIOvEHl1bPXbimGr+9gAMbsf99gcuBB4HlufLgjw2IFG+H/A94Hrg+Ip1P06UH1SxDAaeAwYCgxLlLyT/pgnsCjwDPE32bSz591Cx/SeBnzX7/QTghzX+WwwHnic/yywoczOwS/53WnomQDYt4IPANcDDZJ3HB6qUPwN4k2wi8Z+XtLX5t+nXKta/Wq18xbqpwOeq1H0UMCl//RzNzgQKys8lOzufBcwEdisp/+H83/0FYDGwVdn7U3a81d7PyuMtqL+WY24qW/V4O3PpyHsCa4DNE/HNeG8+zObeN5Ul2X+YQyVdTssp1HYF/gScC7weETOAtyJiZkTMLGjPfEkn568fkbQr/GMi5pUVZdeT1Jvs9G0Dsg89gD5Ar1TlkvpLujC/hrg8XxbksQGJ8v0kfU/S9ZKOr1j340T5QRXLYOBBSQMlDaooe2HTNVhJu0p6BpglaaGkvRN175pft7whv4Y5XdLrkh6SlJpH+ibgVWCfiBgcEYPJTm9fBX6dKH812Xt4C3CcpFsk9cnXjU6Uf4Xs/W1aZpN9e5qTv650eEQ0TaR9CXBsRPwTcCBwWaJ8pdQUfaVjpyVtSHZMZ0bE3wrKHAEsi4g/1dCOJj3JLn/8JLJ5vP9OdvkiVf9Asm+4I8j+v31AUs1TC7aGpHOBVcDPC9ZvQPZ/89/rqLYnWSc/GvgGcJNUMBFz5lTgrIjYAjiL/Kypoh2l708t5YuOt3n5fH3hMSfqrvd4O05H9S7AIWTfxn5L9mDElcCdeazFNX7gD8BOFbGewHXA6oJ9DCP70Pkh8HxJe/qTfbP6C1nvu5LsG+NMYMeKsmfl6xYCXwHuBv6L7Gzl2wX130V2z2PTZrFN89j0RPlbyL7BHg3clv/edH1wTqL8GrJrv82XlfnPZyrKzmv2+h7ybxnAKBI5S8i+dR4KfIbsm9Un8/j+wP2J8k9U+XdusY6WZ1rnAv9D9g0/daxfz/9WPtIs9myVfT7Oe9duHyj6t6iy/ceBu5r9fg5wTsk2vfL3/Ksl5b4HLCL7ZriU7PLjDSXbbAo81+z3fwGmFZT9FHBVs99PJHF2la8bzvu/TT8BbJa/3qzyvassn8fGAvcDGxSVJZt6dll+zM+RfUA+X/F/o7Itd5J9qWj6/S/AB6uUf533nnMS8Ley96fa8Ra9n1WO933lqx1zQVuqHm9nLh1beTb6aDTwf8hOuUeTX1pJlB3W/I+kYt0nSvZzOHBBjW3aCNiR7PR8kyrlNgc2z18PyNu/e5Xy68wHI3V+KNLschsVnSkVl+Ly2O/Irntu0iy2CVmH9/tE+QXAehWxscBjwMIqfw+/Bi7P37NnUuXysqfnbdoP+A9gItk19fOA62v4m+hJ1umP4L0bw/9cpbzIvpxMrPP/wz7UfmP4PmCb/PV/AJcUlPtY/u+4Qd6ua4HTC8oO5/0fpJfw/hulF5eUPwT4M4kPq8qyFeueo/xy0JeA7+SvR5F9GVGV8gvIP0TJvqz8qez9KTreKuWTx1vL+990zFXqrnq8nbl0+g6768I69MFInR+KZN90DiL7VrkQODqP7036zGEg2U31x8kuAf01P56LSF+zv5h8pFVF/BAS94cqyhxJdlNuaUm5fYBfkV1DnwfcAZxCjaO5yEaYPEn2jezckrJ7kl0uepTs2u5c4LAa9rEPtXcCO5Fd+noUmAIMrFL2vPy9mE9236VPoswvye6brSQ7M/k82ReOu4Gn8p+DSso/nX9YNR3zT4vKVuz7Od4/OihVd2+y+3zzyS777VdSfk+yS4WPkJ3Z71L2/hQdb5XyRcdb+v7zXidQVHfh8Xb2slZ22h0X3v/B+Ffe/8HY4j8wHfzBSPGHYs9E2R3JTld/SzZMdxLwGlmHtEdB/dsCB1DjcF6Kh/8eWlaebEjv9q2sv3R4sRcvjbys9QY0wgKc3N7lKz4Ya66/PdpCdp/kCbJvqM8BY5qtS13KOr3O8vXWX1d5L168vLes9QY0wkLJTevOLN8edVPHcN51sbwXL17eW3pi7ULSo0WryO4NdFr5jm4LFcN5Je0D3CxpK9LDLde18maWcyfQfjYBDia7UdqcgP/t5PId3ZalknaKiLkAEfFmPh5+MtlQuXW9vJnl3Am0n9vJLknMrVwhaUYnl+/otpxINg76HyJ7vP5ESf/ZBcqbWc6TypiZNTCnkjYza2DuBMzMGpg7ATOzBuZOwMysgf1/OytM6c921YUAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 2\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[3], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[8], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "da357b54", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 3\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[5], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[26], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7286cdbb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 4\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[21], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[54], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "38866d42", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 5 \n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[4], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[6], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e0c292a2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 6\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[10], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[13], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5c9e5d1f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 7\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[24], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[25], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "523d5272", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 8\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[1], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[14], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()\n", + "#sns.heatmap(x_train[17], cmap = \"gist_gray\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a53a0ed4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 9\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[0], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[7], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e7d36a15", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 10\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[19], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[30], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2786386d", + "metadata": {}, + "source": [ + "## 2. Models" + ] + }, + { + "cell_type": "markdown", + "id": "3fc3e521", + "metadata": {}, + "source": [ + "### 2.1 Kmeans" + ] + }, + { + "cell_type": "markdown", + "id": "9bef3c89", + "metadata": {}, + "source": [ + "###    2.1.1 Fitting Models" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "289d8d9e", + "metadata": {}, + "outputs": [], + "source": [ + "# import KMeans from sklearn \n", + "from sklearn.cluster import KMeans\n", + "\n", + "# assigned 10 clusters \n", + "# fit() to compute clustering for K-Means\n", + "# fit_predict() to make predictions\n", + "kmeans = KMeans(n_clusters = 10).fit(x_trainf)\n", + "y_pred_kmean = kmeans.predict(x_testf)" + ] + }, + { + "cell_type": "markdown", + "id": "67ccad57", + "metadata": {}, + "source": [ + "###    2.2.2 Clustering results evaluations" + ] + }, + { + "cell_type": "markdown", + "id": "f2e23193", + "metadata": {}, + "source": [ + "####     NMI (Normaliszed Mutual Information)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "955c19c9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test score:0.3075\n" + ] + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "print(\"Test score:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_kmean,average_method='arithmetic')))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "40e0dea9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test score:0.3204\n" + ] + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "print(\"Test score:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_kmean,average_method='min')))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "01329398", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test score:0.3077\n" + ] + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "print(\"Test score:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_kmean,average_method='geometric')))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ac0103e6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test score:0.2956\n" + ] + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "print(\"Test score:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_kmean,average_method='max')))" + ] + }, + { + "cell_type": "markdown", + "id": "5d7c0620", + "metadata": {}, + "source": [ + "####     ARI (Adjusted Rand Index)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "779c4a21", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test score:0.1616\n" + ] + } + ], + "source": [ + "from sklearn.metrics.cluster import adjusted_rand_score\n", + "print(\"Test score:{:.4f}\".format(adjusted_rand_score(y_test, y_pred_kmean)))" + ] + }, + { + "cell_type": "markdown", + "id": "eaf905f4", + "metadata": {}, + "source": [ + "####     Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "49e50762", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix: \n", + "[[259 106 0 60 7 3 523 21 3 18]\n", + " [170 0 79 2 6 75 1 95 0 572]\n", + " [229 4 137 16 14 355 4 8 14 219]\n", + " [549 108 3 9 3 3 1 9 256 59]\n", + " [ 83 5 68 11 108 38 73 414 2 198]\n", + " [ 62 251 56 1 0 411 6 8 10 195]\n", + " [ 81 0 560 32 4 24 5 106 3 185]\n", + " [ 38 1 14 391 27 116 9 314 19 71]\n", + " [401 19 75 2 1 361 4 6 0 131]\n", + " [222 1 14 10 301 191 1 22 0 238]]\n" + ] + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "kmeans_confusion = confusion_matrix(y_test,y_pred_kmean)\n", + "print('Confusion matrix: \\n{}'.format(kmeans_confusion))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "ba637eb3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize the confusion matrix, the deeper color represents the greater values\n", + "import seaborn as sns\n", + "sns.heatmap(kmeans_confusion, annot=False, cmap='Blues')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ab647317", + "metadata": {}, + "source": [ + "### 2.2 PCA (Principal component analysis)" + ] + }, + { + "cell_type": "markdown", + "id": "06872c16", + "metadata": {}, + "source": [ + "###    2.2.1 Scatter Plots in 2D and 3D" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "64add44b", + "metadata": {}, + "outputs": [], + "source": [ + "# import PCA from sklearn\n", + "from sklearn.decomposition import PCA\n", + "\n", + "# implement PCA and keep the first two principal components only\n", + "pca = PCA(n_components = 2, whiten = True)\n", + "pca.fit(x_trainf)\n", + "\n", + "# transform data to reduce dimensions\n", + "x_train_pca = pca.transform(x_trainf)\n", + "x_test_pca = pca.transform(x_testf)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "fc524704", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "plt.scatter(x_train_pca[:,0], x_train_pca[:,1], c = y_train)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "8baaf415", + "metadata": {}, + "outputs": [], + "source": [ + "# implement PCA and keep the first three principal components only\n", + "pca3 = PCA(n_components = 3, whiten = True)\n", + "pca3.fit(x_trainf)\n", + "# transform data\n", + "x_train_pca3 = pca3.transform(x_trainf)\n", + "x_test_pca3 = pca3.transform(x_testf)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "a7c2b5b8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from mpl_toolkits.mplot3d import Axes3D\n", + "plt.figure(figsize=(8,8))\n", + "ax = plt.axes(projection=\"3d\")\n", + "ax.scatter3D(x_train_pca3[:,0], x_train_pca3[:,1],x_train_pca3[:,2], c = y_train)\n", + "ax.view_init(10, 60)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "9f414ded", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.10869168 0.05363875]\n", + "[0.10869168 0.05363875 0.0409124 ]\n" + ] + } + ], + "source": [ + "print(pca.explained_variance_ratio_)\n", + "print(pca3.explained_variance_ratio_)" + ] + }, + { + "cell_type": "markdown", + "id": "4b55fa60", + "metadata": {}, + "source": [ + "###    2.2.2 KMeans using PCA" + ] + }, + { + "cell_type": "markdown", + "id": "5fbf7294", + "metadata": {}, + "source": [ + "### ARI" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "4b695f26", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test score:0.0848\n" + ] + } + ], + "source": [ + "from sklearn.cluster import KMeans\n", + "\n", + "# implement KMeans on transformed data\n", + "kmeans = KMeans(n_clusters = 10).fit(x_train_pca)\n", + "y_pred_trans_kmean = kmeans.predict(x_test_pca)\n", + "test_score = adjusted_rand_score(y_test, y_pred_trans_kmean)\n", + "print(\"Test score:{:.4f}\".format(test_score))" + ] + }, + { + "cell_type": "markdown", + "id": "42ef2a8d", + "metadata": {}, + "source": [ + "### NMI" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "981f11be", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test score:0.1828\n" + ] + } + ], + "source": [ + "test_score = normalized_mutual_info_score(y_test, y_pred_trans_kmean)\n", + "print(\"Test score:{:.4f}\".format(test_score))" + ] + }, + { + "cell_type": "markdown", + "id": "54dc4e2d", + "metadata": {}, + "source": [ + "####     Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "57eb2033", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix: \n", + "[[ 1 2 398 105 41 34 103 82 234 0]\n", + " [369 99 23 0 61 15 73 28 18 314]\n", + " [337 39 33 5 146 5 38 114 7 276]\n", + " [ 13 3 207 170 187 7 99 283 31 0]\n", + " [ 77 198 38 1 165 134 188 36 35 128]\n", + " [331 51 55 213 34 0 16 23 9 268]\n", + " [333 129 4 0 66 45 83 18 8 314]\n", + " [ 18 185 32 2 190 209 150 177 15 22]\n", + " [107 104 71 23 148 6 82 161 17 281]\n", + " [119 138 12 1 266 40 124 162 4 134]]\n" + ] + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "kmeans_pca_confusion = confusion_matrix(y_test,y_pred_kmean_pca)\n", + "print('Confusion matrix: \\n{}'.format(kmeans_pca_confusion))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "3eef5fab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "sns.heatmap(kmeans_pca_confusion, annot=False, cmap='Blues')" + ] + }, + { + "cell_type": "markdown", + "id": "0ec36a9a", + "metadata": {}, + "source": [ + "### 2.3 MiniBatchKMeans" + ] + }, + { + "cell_type": "markdown", + "id": "78ddd888", + "metadata": {}, + "source": [ + "###    2.3.1 Fitting Models" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "c4f713ae", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.cluster import MiniBatchKMeans\n", + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "from sklearn.metrics.cluster import adjusted_rand_score\n", + "\n", + "# implement Mini-Batch K-Means\n", + "# assign 10 clusters \n", + "kmeans = MiniBatchKMeans(n_clusters=10,batch_size=5120).fit(x_train_pca)\n", + "y_pred_mbkm = kmeans.predict(x_test_pca)" + ] + }, + { + "cell_type": "markdown", + "id": "d5af13fa", + "metadata": {}, + "source": [ + "###    2.3.2 Clustering results evaluations" + ] + }, + { + "cell_type": "markdown", + "id": "a14ff10a", + "metadata": {}, + "source": [ + "####     NMI (Normaliszed Mutual Information) and ARI (Adjusted Rand Index)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "178c9a4a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI:0.1729\n", + "ARI:0.0817\n" + ] + } + ], + "source": [ + "print(\"NMI:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_mbkm,average_method='arithmetic')))\n", + "print(\"ARI:{:.4f}\".format(adjusted_rand_score(y_test, y_pred_mbkm)))" + ] + }, + { + "cell_type": "markdown", + "id": "05d87e3f", + "metadata": {}, + "source": [ + "####     Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "91486cd4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix: \n", + "[[127 27 1 80 238 2 156 310 0 59]\n", + " [ 2 60 459 20 76 75 56 9 229 14]\n", + " [ 2 96 418 9 43 22 158 5 209 38]\n", + " [112 112 11 16 129 2 377 81 0 160]\n", + " [ 2 241 128 156 112 161 60 27 105 8]\n", + " [185 32 416 0 17 26 31 32 201 60]\n", + " [ 0 76 395 55 56 83 28 1 270 36]\n", + " [ 2 179 23 186 101 201 228 8 24 48]\n", + " [ 7 96 172 9 95 56 204 29 258 74]\n", + " [ 0 260 139 45 80 90 167 0 130 89]]\n" + ] + } + ], + "source": [ + "mbkm_confusion = confusion_matrix(y_test,y_pred_mbkm)\n", + "print('Confusion matrix: \\n{}'.format(mbkm_confusion))" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "c9ec58f1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.heatmap(mbkm_confusion, annot=False, cmap='Blues')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e7bb3593", + "metadata": {}, + "source": [ + "### 2.4 Birch" + ] + }, + { + "cell_type": "markdown", + "id": "103a4fad", + "metadata": {}, + "source": [ + "###    2.4.1 Fitting Models" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "296530dd", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.cluster import Birch\n", + "\n", + "# implement birch\n", + "brc = Birch(n_clusters = 10).fit(x_train_pca)\n", + "y_pred_brc = brc.predict(x_test_pca)" + ] + }, + { + "cell_type": "markdown", + "id": "c21d97b8", + "metadata": {}, + "source": [ + "###    2.4.2 Clustering results evaluations" + ] + }, + { + "cell_type": "markdown", + "id": "cac10047", + "metadata": {}, + "source": [ + "####     NMI (Normaliszed Mutual Information) and ARI (Adjusted Rand Index)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "857fee12", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI:0.1808\n", + "ARI:0.0910\n" + ] + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "from sklearn.metrics.cluster import adjusted_rand_score\n", + "print(\"NMI:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_brc,average_method='arithmetic')))\n", + "print(\"ARI:{:.4f}\".format(adjusted_rand_score(y_test, y_pred_brc)))" + ] + }, + { + "cell_type": "markdown", + "id": "81466971", + "metadata": {}, + "source": [ + "####     Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "b89536eb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix: \n", + "[[ 41 451 48 2 94 0 1 73 289 1]\n", + " [230 39 5 16 0 0 603 35 72 0]\n", + " [171 33 1 2 10 0 545 12 226 0]\n", + " [101 132 1 2 158 0 15 32 559 0]\n", + " [502 63 15 71 2 1 131 116 98 1]\n", + " [137 43 0 0 218 0 538 5 59 0]\n", + " [275 17 10 27 0 1 573 45 50 2]\n", + " [362 43 7 156 3 0 25 91 309 4]\n", + " [250 83 0 6 38 0 321 17 284 1]\n", + " [439 26 2 18 3 0 187 43 282 0]]\n" + ] + } + ], + "source": [ + "brc_confusion = confusion_matrix(y_test,y_pred_brc)\n", + "print('Confusion matrix: \\n{}'.format(brc_confusion))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "c75904f9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.heatmap(brc_confusion, annot=False, cmap='Blues')" + ] + }, + { + "cell_type": "markdown", + "id": "3a50f7f3", + "metadata": {}, + "source": [ + "### 2.5 Gaussian mixture" + ] + }, + { + "cell_type": "markdown", + "id": "33c3d7e7", + "metadata": {}, + "source": [ + "###    2.5.1 Fitting Models" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "e98317fb", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.mixture import GaussianMixture\n", + "gm = GaussianMixture(n_components = 10).fit(x_train_pca)\n", + "y_pred_gm = gm.predict(x_test_pca)" + ] + }, + { + "cell_type": "markdown", + "id": "b4aa5c01", + "metadata": {}, + "source": [ + "###    2.5.2 Clustering results evaluations" + ] + }, + { + "cell_type": "markdown", + "id": "547683c2", + "metadata": {}, + "source": [ + "####     NMI (Normaliszed Mutual Information) and ARI (Adjusted Rand Index)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "52f39cb4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI:0.1823\n", + "ARI:0.0836\n" + ] + } + ], + "source": [ + "print(\"NMI:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_gm,average_method='arithmetic')))\n", + "print(\"ARI:{:.4f}\".format(adjusted_rand_score(y_test, y_pred_gm)))" + ] + }, + { + "cell_type": "markdown", + "id": "337975a0", + "metadata": {}, + "source": [ + "####     Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "09af68b9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix: \n", + "[[ 1 2 398 105 41 34 103 82 234 0]\n", + " [369 99 23 0 61 15 73 28 18 314]\n", + " [337 39 33 5 146 5 38 114 7 276]\n", + " [ 13 3 207 170 187 7 99 283 31 0]\n", + " [ 77 198 38 1 165 134 188 36 35 128]\n", + " [331 51 55 213 34 0 16 23 9 268]\n", + " [333 129 4 0 66 45 83 18 8 314]\n", + " [ 18 185 32 2 190 209 150 177 15 22]\n", + " [107 104 71 23 148 6 82 161 17 281]\n", + " [119 138 12 1 266 40 124 162 4 134]]\n" + ] + } + ], + "source": [ + "gm_confusion = confusion_matrix(y_test,y_pred_kmean_pca)\n", + "print('Confusion matrix: \\n{}'.format(gm_confusion))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "971194b5", + "metadata": {}, + "outputs": [], + "source": [ + "sns.heatmap(gm_confusion, annot=False, cmap='Blues')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3a8306de", + "metadata": {}, + "source": [ + "## 3. Appendix" + ] + }, + { + "cell_type": "markdown", + "id": "ca1adee0", + "metadata": {}, + "source": [ + "### 3.1 KNN" + ] + }, + { + "cell_type": "markdown", + "id": "2efec242", + "metadata": {}, + "source": [ + "#### We also tried other models including KNN and various regression models to see how they perform. Note that these are not unsupervised models, and labels were used to train the models. Therefore, these implementations are not included in the final report. " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "c27cb6d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting KNeighborsClassifier(n_jobs=-1, n_neighbors=4, weights='distance')\n", + "Evaluating KNeighborsClassifier(n_jobs=-1, n_neighbors=4, weights='distance')\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "clf = KNeighborsClassifier(n_neighbors=4, weights='distance', n_jobs=-1)\n", + "print('Fitting', clf)\n", + "clf.fit(x_trainf, y_train)\n", + "print('Evaluating', clf)\n", + "\n", + "y_pred_knn = clf.predict(x_testf)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "31b937dc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test accuracy: 0.921\n" + ] + } + ], + "source": [ + "test_score = clf.score(x_testf, y_test)\n", + "print('Test accuracy:', test_score)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "df13d267", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8231031975085954" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "normalized_mutual_info_score(y_test, y_pred_knn,average_method='arithmetic')" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "2226f1b5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8234272963838932" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "normalized_mutual_info_score(y_test, y_pred_knn,average_method='min')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "09c6c71e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.823103261265742" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "normalized_mutual_info_score(y_test, y_pred_knn,average_method='geometric')" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "cb776587", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8227793536618937" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "normalized_mutual_info_score(y_test, y_pred_knn,average_method='max')" + ] + }, + { + "cell_type": "markdown", + "id": "12d38ecb", + "metadata": {}, + "source": [ + "####    Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "4bef3167", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix: \n", + "[[910 0 1 1 3 26 0 15 44 0]\n", + " [ 1 910 27 1 4 3 39 0 8 7]\n", + " [ 9 6 880 45 8 15 14 3 18 2]\n", + " [ 1 1 18 969 0 5 3 1 2 0]\n", + " [ 13 12 11 16 885 10 14 2 28 9]\n", + " [ 1 5 36 8 2 931 12 0 3 2]\n", + " [ 3 2 20 6 8 3 951 2 2 3]\n", + " [ 1 8 11 4 9 6 14 912 21 14]\n", + " [ 0 13 9 6 0 7 12 0 952 1]\n", + " [ 2 24 10 2 3 4 13 5 27 910]]\n" + ] + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "knn_confusion = confusion_matrix(y_test,y_pred_knn)\n", + "print('Confusion matrix: \\n{}'.format(knn_confusion))" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "2bc87615", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "sns.heatmap(knn_confusion, annot=True, cmap='Blues')" + ] + }, + { + "cell_type": "markdown", + "id": "98588e59", + "metadata": {}, + "source": [ + "### 3.2 Four Regression Models" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "84d84800", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data set score: 0.337\n", + "Test data set score: 0.187\n" + ] + } + ], + "source": [ + "# Linear regression\n", + "from sklearn.linear_model import LinearRegression\n", + "lr = LinearRegression().fit(x_trainf,y_train)\n", + "print('Training data set score: {:.3f}'.format(lr.score(x_trainf, y_train)))\n", + "print('Test data set score: {:.3f}'.format(lr.score(x_testf, y_test))) # overfit" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "0f2cb297", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data set score: 0.34\n", + "Test data set score: 0.19\n" + ] + } + ], + "source": [ + "# Ridge Regression\n", + "from sklearn.linear_model import Ridge\n", + "ridge = Ridge().fit(x_trainf, y_train)\n", + "print('Training data set score: {:.2f}'.format(ridge.score(x_trainf, y_train)))\n", + "print('Test data set score: {:.2f}'.format(ridge.score(x_testf, y_test))) # overfit" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "b3bee2c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data set score: 0.34\n", + "Test data set score: 0.19\n" + ] + } + ], + "source": [ + "# Lasso Regression\n", + "from sklearn.linear_model import Lasso\n", + "lasso = Lasso(alpha = 0.1, max_iter = 100000).fit(x_trainf, y_train)\n", + "print('Training data set score: {:.2f}'.format(lasso.score(x_trainf, y_train)))\n", + "print('Test data set score: {:.2f}'.format(lasso.score(x_testf, y_test))) # overfit" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "1f7c891d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data set score: 0.838\n", + "Test data set score: 0.691\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "D:\\ANA\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n" + ] + } + ], + "source": [ + "# Logistic Regression\n", + "from sklearn.linear_model import LogisticRegression\n", + "logreg = LogisticRegression(max_iter=100).fit(x_trainf, y_train)\n", + "print('Training data set score: {:.3f}'.format(logreg.score(x_trainf, y_train)))\n", + "print('Test data set score: {:.3f}'.format(logreg.score(x_testf, y_test))) # overfit" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 79dfedb86bbff37b59801824452191eb3ba12bfa Mon Sep 17 00:00:00 2001 From: HappyCheems <79441528+eternalDoge@users.noreply.github.com> Date: Sun, 4 Dec 2022 12:22:28 +0800 Subject: [PATCH 24/27] update again --- new_unsupervised.ipynb | 1827 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1827 insertions(+) create mode 100644 new_unsupervised.ipynb diff --git a/new_unsupervised.ipynb b/new_unsupervised.ipynb new file mode 100644 index 0000000..7008a97 --- /dev/null +++ b/new_unsupervised.ipynb @@ -0,0 +1,1827 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7ba70409", + "metadata": {}, + "source": [ + "# Unsupervised Models" + ] + }, + { + "cell_type": "markdown", + "id": "dd2ab19d", + "metadata": {}, + "source": [ + "## 1. Data and Clusters' Examples Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0422e6d9", + "metadata": {}, + "outputs": [], + "source": [ + "# download the dataset in NumPy format\n", + "import numpy as np\n", + "def load(f):\n", + " return np.load(f)['arr_0']\n", + "\n", + "# Load the data\n", + "x_train = load('kmnist-train-imgs.npz')\n", + "x_test = load('kmnist-test-imgs.npz')\n", + "y_train = load('kmnist-train-labels.npz')\n", + "y_test = load('kmnist-test-labels.npz')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4bf07b89", + "metadata": {}, + "outputs": [], + "source": [ + "# Flatten images\n", + "# Each element in x_train and x_test is in the form of 28x28,\n", + "# so reshaping them in the form of 1x784\n", + "x_trainf = x_train.reshape(-1, 784)\n", + "x_testf = x_test.reshape(-1, 784)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "93c6ef34", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
indexcodepointchar
00U+304A
11U+304D
22U+3059
33U+3064
44U+306A
55U+306F
66U+307E
77U+3084
88U+308C
99U+3092
\n", + "
" + ], + "text/plain": [ + " index codepoint char\n", + "0 0 U+304A お\n", + "1 1 U+304D き\n", + "2 2 U+3059 す\n", + "3 3 U+3064 つ\n", + "4 4 U+306A な\n", + "5 5 U+306F は\n", + "6 6 U+307E ま\n", + "7 7 U+3084 や\n", + "8 8 U+308C れ\n", + "9 9 U+3092 を" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "# import the map to see the given 10 clusters\n", + "map = pd.read_csv(\"kmnist_classmap.csv\")\n", + "map" + ] + }, + { + "cell_type": "markdown", + "id": "ca9557a5", + "metadata": {}, + "source": [ + "### visualize characters in each cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "039fead3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 1\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "\n", + "sns.heatmap(x_train[2], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[12], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "046a75eb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 2\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[3], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[8], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "da357b54", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 3\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[5], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[26], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7286cdbb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 4\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[21], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[54], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "38866d42", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 5 \n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[4], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[6], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e0c292a2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 6\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[10], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[13], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5c9e5d1f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 7\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[24], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[25], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "523d5272", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 8\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[1], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[14], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()\n", + "#sns.heatmap(x_train[17], cmap = \"gist_gray\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a53a0ed4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 9\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[0], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[7], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e7d36a15", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# cluster 10\n", + "plt.figure()\n", + "fig, (ax,ax2) = plt.subplots(ncols=2)\n", + "fig.subplots_adjust(wspace=0.01)\n", + "sns.heatmap(x_train[19], cmap = \"gist_gray\", cbar = False, ax = ax)\n", + "sns.heatmap(x_train[30], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", + "ax2.yaxis.tick_right()\n", + "ax2.tick_params(rotation=0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2786386d", + "metadata": {}, + "source": [ + "## 2. Models" + ] + }, + { + "cell_type": "markdown", + "id": "3fc3e521", + "metadata": {}, + "source": [ + "### 2.1 Kmeans" + ] + }, + { + "cell_type": "markdown", + "id": "9bef3c89", + "metadata": {}, + "source": [ + "###    2.1.1 Fitting Models" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "289d8d9e", + "metadata": {}, + "outputs": [], + "source": [ + "# import KMeans from sklearn \n", + "from sklearn.cluster import KMeans\n", + "\n", + "# assigned 10 clusters \n", + "# fit() to compute clustering for K-Means\n", + "# fit_predict() to make predictions\n", + "kmeans = KMeans(n_clusters = 10).fit(x_trainf)\n", + "y_pred_kmean = kmeans.predict(x_testf)" + ] + }, + { + "cell_type": "markdown", + "id": "67ccad57", + "metadata": {}, + "source": [ + "###    2.2.2 Clustering results evaluations" + ] + }, + { + "cell_type": "markdown", + "id": "f2e23193", + "metadata": {}, + "source": [ + "####     NMI (Normaliszed Mutual Information)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "955c19c9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test score:0.3075\n" + ] + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "print(\"Test score:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_kmean,average_method='arithmetic')))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "40e0dea9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test score:0.3204\n" + ] + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "print(\"Test score:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_kmean,average_method='min')))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "01329398", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test score:0.3077\n" + ] + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "print(\"Test score:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_kmean,average_method='geometric')))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ac0103e6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test score:0.2956\n" + ] + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "print(\"Test score:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_kmean,average_method='max')))" + ] + }, + { + "cell_type": "markdown", + "id": "5d7c0620", + "metadata": {}, + "source": [ + "####     ARI (Adjusted Rand Index)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "779c4a21", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test score:0.1616\n" + ] + } + ], + "source": [ + "from sklearn.metrics.cluster import adjusted_rand_score\n", + "print(\"Test score:{:.4f}\".format(adjusted_rand_score(y_test, y_pred_kmean)))" + ] + }, + { + "cell_type": "markdown", + "id": "eaf905f4", + "metadata": {}, + "source": [ + "####     Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "49e50762", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix: \n", + "[[259 106 0 60 7 3 523 21 3 18]\n", + " [170 0 79 2 6 75 1 95 0 572]\n", + " [229 4 137 16 14 355 4 8 14 219]\n", + " [549 108 3 9 3 3 1 9 256 59]\n", + " [ 83 5 68 11 108 38 73 414 2 198]\n", + " [ 62 251 56 1 0 411 6 8 10 195]\n", + " [ 81 0 560 32 4 24 5 106 3 185]\n", + " [ 38 1 14 391 27 116 9 314 19 71]\n", + " [401 19 75 2 1 361 4 6 0 131]\n", + " [222 1 14 10 301 191 1 22 0 238]]\n" + ] + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "kmeans_confusion = confusion_matrix(y_test,y_pred_kmean)\n", + "print('Confusion matrix: \\n{}'.format(kmeans_confusion))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "ba637eb3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize the confusion matrix, the deeper color represents the greater values\n", + "import seaborn as sns\n", + "sns.heatmap(kmeans_confusion, annot=False, cmap='Blues')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ab647317", + "metadata": {}, + "source": [ + "### 2.2 PCA (Principal component analysis)" + ] + }, + { + "cell_type": "markdown", + "id": "06872c16", + "metadata": {}, + "source": [ + "###    2.2.1 Scatter Plots in 2D and 3D" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "64add44b", + "metadata": {}, + "outputs": [], + "source": [ + "# import PCA from sklearn\n", + "from sklearn.decomposition import PCA\n", + "\n", + "# implement PCA and keep the first two principal components only\n", + "pca = PCA(n_components = 2, whiten = True)\n", + "pca.fit(x_trainf)\n", + "\n", + "# transform data to reduce dimensions\n", + "x_train_pca = pca.transform(x_trainf)\n", + "x_test_pca = pca.transform(x_testf)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "fc524704", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "plt.scatter(x_train_pca[:,0], x_train_pca[:,1], c = y_train)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "8baaf415", + "metadata": {}, + "outputs": [], + "source": [ + "# implement PCA and keep the first three principal components only\n", + "pca3 = PCA(n_components = 3, whiten = True)\n", + "pca3.fit(x_trainf)\n", + "# transform data\n", + "x_train_pca3 = pca3.transform(x_trainf)\n", + "x_test_pca3 = pca3.transform(x_testf)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "a7c2b5b8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from mpl_toolkits.mplot3d import Axes3D\n", + "plt.figure(figsize=(8,8))\n", + "ax = plt.axes(projection=\"3d\")\n", + "ax.scatter3D(x_train_pca3[:,0], x_train_pca3[:,1],x_train_pca3[:,2], c = y_train)\n", + "ax.view_init(10, 60)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "9f414ded", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.10869168 0.05363875]\n", + "[0.10869168 0.05363875 0.0409124 ]\n" + ] + } + ], + "source": [ + "print(pca.explained_variance_ratio_)\n", + "print(pca3.explained_variance_ratio_)" + ] + }, + { + "cell_type": "markdown", + "id": "4b55fa60", + "metadata": {}, + "source": [ + "###    2.2.2 KMeans using PCA" + ] + }, + { + "cell_type": "markdown", + "id": "5fbf7294", + "metadata": {}, + "source": [ + "### ARI" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "4b695f26", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test score:0.0848\n" + ] + } + ], + "source": [ + "from sklearn.cluster import KMeans\n", + "\n", + "# implement KMeans on transformed data\n", + "kmeans = KMeans(n_clusters = 10).fit(x_train_pca)\n", + "y_pred_trans_kmean = kmeans.predict(x_test_pca)\n", + "test_score = adjusted_rand_score(y_test, y_pred_trans_kmean)\n", + "print(\"Test score:{:.4f}\".format(test_score))" + ] + }, + { + "cell_type": "markdown", + "id": "42ef2a8d", + "metadata": {}, + "source": [ + "### NMI" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "981f11be", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test score:0.1828\n" + ] + } + ], + "source": [ + "test_score = normalized_mutual_info_score(y_test, y_pred_trans_kmean)\n", + "print(\"Test score:{:.4f}\".format(test_score))" + ] + }, + { + "cell_type": "markdown", + "id": "54dc4e2d", + "metadata": {}, + "source": [ + "####     Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "57eb2033", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix: \n", + "[[ 1 2 398 105 41 34 103 82 234 0]\n", + " [369 99 23 0 61 15 73 28 18 314]\n", + " [337 39 33 5 146 5 38 114 7 276]\n", + " [ 13 3 207 170 187 7 99 283 31 0]\n", + " [ 77 198 38 1 165 134 188 36 35 128]\n", + " [331 51 55 213 34 0 16 23 9 268]\n", + " [333 129 4 0 66 45 83 18 8 314]\n", + " [ 18 185 32 2 190 209 150 177 15 22]\n", + " [107 104 71 23 148 6 82 161 17 281]\n", + " [119 138 12 1 266 40 124 162 4 134]]\n" + ] + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "kmeans_pca_confusion = confusion_matrix(y_test,y_pred_kmean_pca)\n", + "print('Confusion matrix: \\n{}'.format(kmeans_pca_confusion))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "3eef5fab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "sns.heatmap(kmeans_pca_confusion, annot=False, cmap='Blues')" + ] + }, + { + "cell_type": "markdown", + "id": "0ec36a9a", + "metadata": {}, + "source": [ + "### 2.3 MiniBatchKMeans" + ] + }, + { + "cell_type": "markdown", + "id": "78ddd888", + "metadata": {}, + "source": [ + "###    2.3.1 Fitting Models" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "c4f713ae", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.cluster import MiniBatchKMeans\n", + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "from sklearn.metrics.cluster import adjusted_rand_score\n", + "\n", + "# implement Mini-Batch K-Means\n", + "# assign 10 clusters \n", + "kmeans = MiniBatchKMeans(n_clusters=10,batch_size=5120).fit(x_train_pca)\n", + "y_pred_mbkm = kmeans.predict(x_test_pca)" + ] + }, + { + "cell_type": "markdown", + "id": "d5af13fa", + "metadata": {}, + "source": [ + "###    2.3.2 Clustering results evaluations" + ] + }, + { + "cell_type": "markdown", + "id": "a14ff10a", + "metadata": {}, + "source": [ + "####     NMI (Normaliszed Mutual Information) and ARI (Adjusted Rand Index)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "178c9a4a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI:0.1729\n", + "ARI:0.0817\n" + ] + } + ], + "source": [ + "print(\"NMI:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_mbkm,average_method='arithmetic')))\n", + "print(\"ARI:{:.4f}\".format(adjusted_rand_score(y_test, y_pred_mbkm)))" + ] + }, + { + "cell_type": "markdown", + "id": "05d87e3f", + "metadata": {}, + "source": [ + "####     Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "91486cd4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix: \n", + "[[127 27 1 80 238 2 156 310 0 59]\n", + " [ 2 60 459 20 76 75 56 9 229 14]\n", + " [ 2 96 418 9 43 22 158 5 209 38]\n", + " [112 112 11 16 129 2 377 81 0 160]\n", + " [ 2 241 128 156 112 161 60 27 105 8]\n", + " [185 32 416 0 17 26 31 32 201 60]\n", + " [ 0 76 395 55 56 83 28 1 270 36]\n", + " [ 2 179 23 186 101 201 228 8 24 48]\n", + " [ 7 96 172 9 95 56 204 29 258 74]\n", + " [ 0 260 139 45 80 90 167 0 130 89]]\n" + ] + } + ], + "source": [ + "mbkm_confusion = confusion_matrix(y_test,y_pred_mbkm)\n", + "print('Confusion matrix: \\n{}'.format(mbkm_confusion))" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "c9ec58f1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.heatmap(mbkm_confusion, annot=False, cmap='Blues')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e7bb3593", + "metadata": {}, + "source": [ + "### 2.4 Birch" + ] + }, + { + "cell_type": "markdown", + "id": "103a4fad", + "metadata": {}, + "source": [ + "###    2.4.1 Fitting Models" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "296530dd", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.cluster import Birch\n", + "\n", + "# implement birch\n", + "brc = Birch(n_clusters = 10).fit(x_train_pca)\n", + "y_pred_brc = brc.predict(x_test_pca)" + ] + }, + { + "cell_type": "markdown", + "id": "c21d97b8", + "metadata": {}, + "source": [ + "###    2.4.2 Clustering results evaluations" + ] + }, + { + "cell_type": "markdown", + "id": "cac10047", + "metadata": {}, + "source": [ + "####     NMI (Normaliszed Mutual Information) and ARI (Adjusted Rand Index)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "857fee12", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI:0.1808\n", + "ARI:0.0910\n" + ] + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "from sklearn.metrics.cluster import adjusted_rand_score\n", + "print(\"NMI:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_brc,average_method='arithmetic')))\n", + "print(\"ARI:{:.4f}\".format(adjusted_rand_score(y_test, y_pred_brc)))" + ] + }, + { + "cell_type": "markdown", + "id": "81466971", + "metadata": {}, + "source": [ + "####     Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "b89536eb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix: \n", + "[[ 41 451 48 2 94 0 1 73 289 1]\n", + " [230 39 5 16 0 0 603 35 72 0]\n", + " [171 33 1 2 10 0 545 12 226 0]\n", + " [101 132 1 2 158 0 15 32 559 0]\n", + " [502 63 15 71 2 1 131 116 98 1]\n", + " [137 43 0 0 218 0 538 5 59 0]\n", + " [275 17 10 27 0 1 573 45 50 2]\n", + " [362 43 7 156 3 0 25 91 309 4]\n", + " [250 83 0 6 38 0 321 17 284 1]\n", + " [439 26 2 18 3 0 187 43 282 0]]\n" + ] + } + ], + "source": [ + "brc_confusion = confusion_matrix(y_test,y_pred_brc)\n", + "print('Confusion matrix: \\n{}'.format(brc_confusion))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "c75904f9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAggAAAGgCAYAAADPW599AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAzuklEQVR4nO3df1hVZb738c+WH1tEIIVkQ6KhMWWCToHHETM8qThO/hqfGTVrstEmTTOJSGOc80Q/ZKud1DlZTlZHScehc03R2Dyl0kxDkVMp5YySqR3phwVRDQEabhTW80dX+8zea4Ns3bA2nvera11XrHWz+GBqX773fa9lMwzDEAAAwD/pYXUAAAAQfCgQAACACQUCAAAwoUAAAAAmFAgAAMCEAgEAAJhQIAAAABMKBAAAYEKBAAAATCgQAACACQUCAABB5NNPP9VNN92k2NhY9erVS9///vdVUVHhvm4YhgoKCpSYmKiIiAiNHTtWlZWVHvdwuVxasmSJ4uLiFBkZqalTp+r48eN+5aBAAAAgSNTV1Wn06NEKCwvTyy+/rPfee0+PPPKILrroIveYNWvWaO3atdqwYYP27t0rh8OhCRMmqLGx0T0mJydHJSUlKi4uVnl5uU6cOKHJkyerpaWlw1lswfKypi8az1gdoU0j79ttdYR27SmYYHWEdl3UK8zqCG36+MtvrI7QrgFxvayO0G3V1J+yOkK7HvvrR1ZHaNeDP7zc6gjt6hnaufePuOqOgN2r6d0NHR5777336o033tDrr7/u87phGEpMTFROTo6WL18u6dtuQXx8vFavXq0FCxaovr5eF198sbZu3apZs2ZJkj777DMlJSXppZde0sSJEzuUhQ4CAADebD0CdrhcLjU0NHgcLpfL55fdsWOHMjIy9NOf/lT9+vXTVVddpSeffNJ9vaqqSjU1NcrOznafs9vtysrK0p49eyRJFRUVOn36tMeYxMREpaamusd0BAUCAACdyOl0KiYmxuNwOp0+xx47dkwbN25USkqKdu3apYULF+rOO+/UM888I0mqqamRJMXHx3t8Xnx8vPtaTU2NwsPD1adPnzbHdEQnN2kAAOiGbLaA3So/P1+5ubke5+x2u8+xra2tysjIUGFhoSTpqquuUmVlpTZu3Kibb775n+J55jMMw3TOW0fG/DM6CAAAeAvgFIPdbld0dLTH0VaBkJCQoCuvvNLj3JAhQ/Txxx9LkhwOhySZOgG1tbXuroLD4VBzc7Pq6uraHNMRFAgAAHiz2QJ3+GH06NE6fPiwx7kjR45o4MCBkqTk5GQ5HA6Vlpa6rzc3N6usrEyZmZmSpPT0dIWFhXmMqa6u1sGDB91jOoIpBgAAgsRdd92lzMxMFRYWaubMmXr77be1adMmbdq0SdK3Uws5OTkqLCxUSkqKUlJSVFhYqF69emnOnDmSpJiYGM2fP1933323YmNj1bdvX+Xl5SktLU3jx4/vcBYKBAAAvNmsabCPGDFCJSUlys/P1wMPPKDk5GStX79eN954o3vMsmXL1NTUpEWLFqmurk4jR47U7t27FRUV5R6zbt06hYaGaubMmWpqatK4ceO0ZcsWhYSEdDgLz0HoAJ6DcH54DsK54zkI547nIJyf//XPQRh5T8Du1fTWwwG7V1diDQIAADBhigEAAG8WTTEEEwoEAAC8BfA5CN0VJRIAADChgwAAgDemGCgQAAAwYYqBKQYAAGBGBwEAAG9MMfhfIBw/flwbN27Unj17VFNTI5vNpvj4eGVmZmrhwoVKSko66z1cLpfpXdiu5pA2X14BAECXYorBvymG8vJyDRkyRCUlJRo+fLhuvvlm3XTTTRo+fLheeOEFDR06VG+88cZZ7+Pr3di/fmT1OX8TAAAEVADf5thd+dVBuOuuu3Trrbdq3bp1bV7PycnR3r17272Pr3djNzR3/PnQAACgc/lVIBw8eFDbtm1r8/qCBQv0m9/85qz3sdvtpukEVxC/iwEA8L9MN/7JP1D8+hVISEjQnj172rz+17/+VQkJCecdCgAAS/WwBe7opvzqIOTl5WnhwoWqqKjQhAkTFB8fL5vNppqaGpWWluqpp57S+vXrOykqAADoKn4VCIsWLVJsbKzWrVunJ554Qi0tLZKkkJAQpaen65lnntHMmTM7JSgAAF2GKQb/tznOmjVLs2bN0unTp/Xll19KkuLi4hQWFhbwcAAAWIJtjuf+oKSwsDDWGwAAcIHiSYoAAHhjioECAQAAE6YYeFkTAAAwo4MAAIA3phgoEAAAMGGKgQIBAAATOgisQQAAAGZ0EAAA8MYUAwUCAAAmTDEwxQAAAMzoIAAA4I0phuApEHYc+szqCG1664FsqyO0K9IeYnWEdp1uabU6QpsGxPWyOkK31mfEHVZHaNPx8vVWR2jX4lEDrY6A9jDFwBQDAAAwC5oOAgAAQYMOAgUCAAAmrEFgigEAAJjRQQAAwBtTDBQIAACYMMVAgQAAgAkdBNYgAAAAMzoIAAB4Y4qBAgEAAG82CgSmGAAAgBkdBAAAvNBBoEAAAMCM+oApBgAAYEYHAQAAL0wxUCAAAGBCgWBRgeByueRyuTzOnW52KSzcbkUcAADgJeBrED755BPNmzev3TFOp1MxMTEex0tFjwc6CgAA58RmswXs6K4CXiD84x//UFFRUbtj8vPzVV9f73H8aO6iQEcBAOCcUCCcwxTDjh072r1+7Nixs97DbrfLbvecTggL/9rfKAAAdI7u+//1gPG7QJg+fbpsNpsMw2hzTHeumAAAwDlMMSQkJOi5555Ta2urz+Odd97pjJwAAHQZq6YYCgoKTJ/vcDjc1w3DUEFBgRITExUREaGxY8eqsrLS4x4ul0tLlixRXFycIiMjNXXqVB0/ftzvXwO/C4T09PR2i4CzdRcAAAh2Vq5BGDp0qKqrq93HgQMH3NfWrFmjtWvXasOGDdq7d68cDocmTJigxsZG95icnByVlJSouLhY5eXlOnHihCZPnqyWlha/cvg9xXDPPffo5MmTbV6/7LLL9Oqrr/p7WwAAICk0NNSja/AdwzC0fv16rVixQjNmzJAkFRUVKT4+Xtu3b9eCBQtUX1+vp59+Wlu3btX48eMlSdu2bVNSUpJeeeUVTZw4scM5/O4gjBkzRj/84Q/bvB4ZGamsrCx/bwsAQNAIZAfB5XKpoaHB4/B+FtA/O3r0qBITE5WcnKzZs2e7F/9XVVWppqZG2dnZ7rF2u11ZWVnas2ePJKmiokKnT5/2GJOYmKjU1FT3mI7iXQwAAHgJZIHg69k/TqfT59cdOXKknnnmGe3atUtPPvmkampqlJmZqa+++ko1NTWSpPj4eI/PiY+Pd1+rqalReHi4+vTp0+aYjuJRywAAdKL8/Hzl5uZ6nPPe6v+dSZMmuf89LS1No0aN0uDBg1VUVKQf/OAHksw7BQ3DOOtah46M8UYHAQAAb7bAHXa7XdHR0R5HWwWCt8jISKWlpeno0aPudQnenYDa2lp3V8HhcKi5uVl1dXVtjukoCgQAALwEy5MUXS6XDh06pISEBCUnJ8vhcKi0tNR9vbm5WWVlZcrMzJT07U7DsLAwjzHV1dU6ePCge0xHMcUAAECQyMvL05QpUzRgwADV1tbqoYceUkNDg+bOnSubzaacnBwVFhYqJSVFKSkpKiwsVK9evTRnzhxJUkxMjObPn6+7775bsbGx6tu3r/Ly8pSWlube1dBRFAgAAHix6onAx48f1w033KAvv/xSF198sX7wgx/ozTff1MCBAyVJy5YtU1NTkxYtWqS6ujqNHDlSu3fvVlRUlPse69atU2hoqGbOnKmmpiaNGzdOW7ZsUUhIiF9ZbEaQPNXo6bc/tjpCm6ZemWh1hHZF2v37j97VWlqD4reYT2EhzLKdjz4j7rA6QpuOl6+3OkK7Gk+dsTpCuxwxPa2O0K6enfzjbb95/xWwe9X+58yA3asr0UEAAMAbrxRikSIAADCjgwAAgBfeSkyBAACACQVCEBUIl0b3sjpCm3oE+e+THkH+G7lHSPDmC+YFlJIUEuy/+RJSrE7QJntocC/effHYZ1ZHaNfsqwZYHQEWC5oCAQCAYEEHgQIBAAATCgR2MQAAAB/oIAAA4I0GAgUCAADemGJgigEAAPhABwEAAC90ECgQAAAwoUCgQAAAwIz6gDUIAADAjA4CAABemGKgQAAAwIQCgSkGAADgAx0EAAC80EGgQAAAwIQCgSkGAADgg98FQlNTk8rLy/Xee++Zrp06dUrPPPPMWe/hcrnU0NDgcTQ3u/yNAgBA57AF8Oim/CoQjhw5oiFDhujaa69VWlqaxo4dq+rqavf1+vp6/fznPz/rfZxOp2JiYjyO3236tf/pAQDoBDabLWBHd+VXgbB8+XKlpaWptrZWhw8fVnR0tEaPHq2PP/7Yry+an5+v+vp6j+OG25b6dQ8AANB5/FqkuGfPHr3yyiuKi4tTXFycduzYocWLF2vMmDF69dVXFRkZ2aH72O122e12j3Ph4c3+RAEAoNN055/8A8WvAqGpqUmhoZ6f8thjj6lHjx7KysrS9u3bAxoOAAArUB/4WSBcccUV2rdvn4YMGeJx/tFHH5VhGJo6dWpAwwEAYAU6CH6uQfjxj3+s3/3udz6vbdiwQTfccIMMwwhIMAAAYB2/CoT8/Hy99NJLbV5//PHH1draet6hAACwks0WuKO74kmKAAB4YYqBJykCAAAf6CAAAOCFBgIFAgAAJj16UCEwxQAAAEzoIAAA4IUpBgoEAABM2MXAFAMAAPCBDgIAAF5oIFAgAABgwhQDBQIAACYUCKxBAAAAPgRNByExJsLqCG368ItvrI7QrqH9o62O0G298+HXVkdo14hBfayO0K7akiVWR2hT85ngfnHc7fnbrY7Qrtk777U6gqVoIARRgQAAQLBgioEpBgAA4AMdBAAAvNBAoEAAAMCEKQamGAAAgA90EAAA8EIDgQIBAAATphiYYgAAAD7QQQAAwAsNBDoIAACY2Gy2gB3nyul0ymazKScnx33OMAwVFBQoMTFRERERGjt2rCorKz0+z+VyacmSJYqLi1NkZKSmTp2q48eP+/31KRAAAPBiswXuOBd79+7Vpk2bNGzYMI/za9as0dq1a7Vhwwbt3btXDodDEyZMUGNjo3tMTk6OSkpKVFxcrPLycp04cUKTJ09WS0uLXxkoEAAA6EQul0sNDQ0eh8vlanP8iRMndOONN+rJJ59Unz7/8z4WwzC0fv16rVixQjNmzFBqaqqKior0zTffaPv2b9/tUV9fr6efflqPPPKIxo8fr6uuukrbtm3TgQMH9Morr/iVmwIBAAAvgZxicDqdiomJ8TicTmebX3vx4sW6/vrrNX78eI/zVVVVqqmpUXZ2tvuc3W5XVlaW9uzZI0mqqKjQ6dOnPcYkJiYqNTXVPaajWKQIAICXQC5SzM/PV25ursc5u93uc2xxcbEqKiq0b98+07WamhpJUnx8vMf5+Ph4ffTRR+4x4eHhHp2H78Z89/kdRYEAAEAnstvtbRYE/+yTTz7R0qVLtXv3bvXs2bPNcd4LHw3DOOtiyI6M8cYUAwAAXqzYxVBRUaHa2lqlp6crNDRUoaGhKisr03/8x38oNDTU3Tnw7gTU1ta6rzkcDjU3N6uurq7NMR3ld4Fw6NAhbd68We+//74k6f3339ftt9+uefPm6c9//nOH7uFrwUZzOws2AADoSlbsYhg3bpwOHDig/fv3u4+MjAzdeOON2r9/vwYNGiSHw6HS0lL35zQ3N6usrEyZmZmSpPT0dIWFhXmMqa6u1sGDB91jOsqvKYadO3dq2rRp6t27t7755huVlJTo5ptv1vDhw2UYhiZOnKhdu3bpuuuua/c+TqdT999/v8e5Rbn5uiNvhV/hAQC4UERFRSk1NdXjXGRkpGJjY93nc3JyVFhYqJSUFKWkpKiwsFC9evXSnDlzJEkxMTGaP3++7r77bsXGxqpv377Ky8tTWlqaadHj2fhVIDzwwAO655579NBDD6m4uFhz5szR7bffrpUrV0qSVqxYoVWrVp21QPC1YKPqH2f8Cg4AQGcJ1ncxLFu2TE1NTVq0aJHq6uo0cuRI7d69W1FRUe4x69atU2hoqGbOnKmmpiaNGzdOW7ZsUUhIiF9fy2YYhtHRwTExMaqoqNBll12m1tZW2e12vfXWW7r66qslSQcPHtT48eP9XikpSYeqT/r9OV3lG5d/D5foakP7R1sdodvae6zu7IMsNGJQn7MPstDpllarI7Tp9JkO/9VmiUumPWx1hHbV7bzX6gjt6tnJS+yvXftGwO71Wu7ogN2rK53zIsUePXqoZ8+euuiii9znoqKiVF9fH4hcAADAQn4VCJdeeqk++OAD98d//etfNWDAAPfHn3zyiRISEgKXDgAAC1j9qOVg4FeT5vbbb/d4lrP3YoqXX375rOsPAAAIdsG6BqEr+VUgLFy4sN3r3y1WBACgO6M+4EFJAADABx61DACAF6YYKBAAADChPmCKAQAA+EAHAQAALz1oIVAgAADgjfqAKQYAAOADHQQAALywi4ECAQAAkx7UBxQIAAB4o4PAGgQAAOADHQQAALzQQAiiAiHznhesjtCmA4/+xOoI7Trd0mp1hHZ9daLZ6ghtGjGoj9URurWj1SesjtCm0JDgbpC+u/VOqyOgHTZRIQT3nyAAAGCJoOkgAAAQLNjFQIEAAIAJuxiYYgAAAD7QQQAAwAsNBAoEAABMeJsjUwwAAMAHOggAAHihgUCBAACACbsYKBAAADChPmANAgAA8IEOAgAAXtjFQIEAAIAJ5QFTDAAAwIeAdBAMw2DFJwDggsH/0wLUQbDb7Tp06FAgbgUAgOV62AJ3dFd+dRByc3N9nm9padGqVasUGxsrSVq7dm2793G5XHK5XB7njJbTsoWE+RMHAAB0Er8KhPXr12v48OG66KKLPM4bhqFDhw4pMjKyQ20Zp9Op+++/3+OcPe3Hihj2f/yJAwBAp2CKwc8CYeXKlXryySf1yCOP6LrrrnOfDwsL05YtW3TllVd26D75+fmmbsSABc/7EwUAgE5DfeDnGoT8/Hw9++yzuv3225WXl6fTp0+f0xe12+2Kjo72OJheAAAgePi9SHHEiBGqqKjQF198ofT0dB04cIBWDADggmKz2QJ2dFfntM2xd+/eKioqUnFxsSZMmKCWlpZA5wIAwDLdefdBoJzXcxBmz56ta665RhUVFRo4cGCgMgEAYKnu/JN/oJz3g5L69++v/v37ByILAAAIEryLAQAAL/QPKBAAADDhbY68rAkAAPhABwEAAC80ECgQAAAwYRcDUwwAAMAHOggAAHihgUCBAACACbsYmGIAAAA+UCAAAODFZgvc4Y+NGzdq2LBh7jcdjxo1Si+//LL7umEYKigoUGJioiIiIjR27FhVVlZ63MPlcmnJkiWKi4tTZGSkpk6dquPHj/v9a0CBAACAF6ve5ti/f3+tWrVK+/bt0759+3Tddddp2rRp7iJgzZo1Wrt2rTZs2KC9e/fK4XBowoQJamxsdN8jJydHJSUlKi4uVnl5uU6cOKHJkyf7/WJFm2EYhl+f0Un2VtVbHaFNA+N6WR2hXdERYVZH6LZ+/zf/q+qu9JPhwf2ekz4/ftzqCG366vnbrY7Qrn+cOG11hHbFRYVbHaFdPTt5Bd2SkkMBu9e//2iQXC6Xxzm73S673d6hz+/bt68efvhhzZs3T4mJicrJydHy5cslfdstiI+P1+rVq7VgwQLV19fr4osv1tatWzVr1ixJ0meffaakpCS99NJLmjhxYodz00EAAKATOZ1OxcTEeBxOp/Osn9fS0qLi4mKdPHlSo0aNUlVVlWpqapSdne0eY7fblZWVpT179kiSKioqdPr0aY8xiYmJSk1NdY/pKHYxAADgJZAPSsrPz1dubq7Hufa6BwcOHNCoUaN06tQp9e7dWyUlJbryyivd/4OPj4/3GB8fH6+PPvpIklRTU6Pw8HD16dPHNKampsav3BQIAAB46RHAXY7+TCdI0uWXX679+/fr66+/1nPPPae5c+eqrKzMfd27eDEM46wFTUfGeGOKAQCAIBIeHq7LLrtMGRkZcjqdGj58uH7961/L4XBIkqkTUFtb6+4qOBwONTc3q66urs0xHUWBAACAlx62wB3nyzAMuVwuJScny+FwqLS01H2tublZZWVlyszMlCSlp6crLCzMY0x1dbUOHjzoHtNRTDEAAODFqpc1/fKXv9SkSZOUlJSkxsZGFRcX6y9/+Yt27twpm82mnJwcFRYWKiUlRSkpKSosLFSvXr00Z84cSVJMTIzmz5+vu+++W7Gxserbt6/y8vKUlpam8ePH+5WFAgEAgCDx+eef62c/+5mqq6sVExOjYcOGaefOnZowYYIkadmyZWpqatKiRYtUV1enkSNHavfu3YqKinLfY926dQoNDdXMmTPV1NSkcePGacuWLQoJCfErC89B6ACeg3Dh4jkI54fnIJw7noNwfjr7OQj3/PFwwO718OTLA3avrkQHAQAAL7yriUWKAADABzoIAAB44XXPFAgAAJjQXqdAAADAhAYCRRIAAPCBDgIAAF5Yg0CBAACACfUBUwwAAMCH8+og1NXVqaioSEePHlVCQoLmzp2rpKSks36ey+WSy+XyONfscincj9dhAgDQWQL5uufuyq8OQmJior766itJUlVVla688kqtXr1aR48e1RNPPKG0tDS9//77Z72P0+lUTEyMx7Fl49pz+w4AAAiwHjZbwI7uyq8CoaamRi0tLZK+fePUFVdcof/+7//W7t279cEHH2jMmDH6t3/7t7PeJz8/X/X19R7HLbfnntt3AAAAAu6cpxjeeustPfXUU+rV69sXGdntdv3qV7/ST37yk7N+rt1ul91rOiH8q6B4ZxQAACxS1DkUCN+9I9vlcik+Pt7jWnx8vL744ovAJAMAwCKsQTiHAmHcuHEKDQ1VQ0ODjhw5oqFDh7qvffzxx4qLiwtoQAAA0PX8KhDuu+8+j4+/m174zosvvqgxY8acfyoAACxkEy2E8yoQvD388MPnFQYAgGDAFANPUgQAwIQCgScpAgAAH+ggAADgxcY+RwoEAAC8McXAFAMAAPCBDgIAAF6YYaBAAADApDu/ZClQmGIAAAAmdBAAAPDCIkUKBAAATJhhYIoBAAD4EDQdhBeP1FodoU33JAy2OkK7WloNqyO0q/lMq9UR2vST4f2tjtC9fV1jdYI2NTadsTpCu0LpYQe1HrysKXgKBAAAggVTDBQIAACY0OBhDQIAAPCBDgIAAF54UBIFAgAAJtQHTDEAAAAf6CAAAOCFKQYKBAAATKgPmGIAAAA+0EEAAMALPz1TIAAAYGJjjoEiCQAAmNFBAADAC/0DCgQAAEzY5kiBAACACeUBaxAAAIAPdBAAAPDCDIOfHYR3331XVVVV7o+3bdum0aNHKykpSddcc42Ki4s7dB+Xy6WGhgaP40xzs3/JAQDoJDabLWBHd+VXgTB//nx9+OGHkqSnnnpKt912mzIyMrRixQqNGDFCv/jFL/Sf//mfZ72P0+lUTEyMx/HGs785p28AAAAEnl9TDIcPH9bgwYMlSY8//rjWr1+v2267zX19xIgRWrlypebNm9fuffLz85Wbm+txblXZJ/5EAQCg07BAz88CISIiQl988YUGDBigTz/9VCNHjvS4PnLkSI8piLbY7XbZ7XbPIOHh/kQBAKDTdOepgUDxq0iaNGmSNm7cKEnKysrS73//e4/r//Vf/6XLLrsscOkAAPhfxOl0asSIEYqKilK/fv00ffp0HT582GOMYRgqKChQYmKiIiIiNHbsWFVWVnqMcblcWrJkieLi4hQZGampU6fq+PHjfmXxq0BYvXq1/vSnPykrK0tJSUl65JFHNGbMGN12223KyspSQUGBVq1a5VcAAACCjS2Ahz/Kysq0ePFivfnmmyotLdWZM2eUnZ2tkydPusesWbNGa9eu1YYNG7R37145HA5NmDBBjY2N7jE5OTkqKSlRcXGxysvLdeLECU2ePFktLS0dzuLXFENiYqLeffddrVq1Si+++KIMw9Dbb7+tTz75RKNHj9Ybb7yhjIwMf24JAEDQsWqKYefOnR4fb968Wf369VNFRYWuvfZaGYah9evXa8WKFZoxY4YkqaioSPHx8dq+fbsWLFig+vp6Pf3009q6davGjx8v6dtdh0lJSXrllVc0ceLEDmXxex3GRRddpFWrVqmyslJNTU1yuVz68MMP9dvf/pbiAAAAL7629rtcrg59bn19vSSpb9++kqSqqirV1NQoOzvbPcZutysrK0t79uyRJFVUVOj06dMeYxITE5Wamuoe0xEs1AQAwEuPAB6+tvY7nc6zZjAMQ7m5ubrmmmuUmpoqSaqpqZEkxcfHe4yNj493X6upqVF4eLj69OnT5piO4EmKAAB4CeQUg6+t/d47+Xy544479Pe//13l5eVnzWcYxlkzd2TMP6ODAACAl0AuUrTb7YqOjvY4zlYgLFmyRDt27NCrr76q/v37u887HA5JMnUCamtr3V0Fh8Oh5uZm1dXVtTmmIygQAAAIEoZh6I477tDzzz+vP//5z0pOTva4npycLIfDodLSUve55uZmlZWVKTMzU5KUnp6usLAwjzHV1dU6ePCge0xHMMUAAIAXq56TtHjxYm3fvl1/+MMfFBUV5e4UxMTEKCIiQjabTTk5OSosLFRKSopSUlJUWFioXr16ac6cOe6x8+fP1913363Y2Fj17dtXeXl5SktLc+9q6AgKBAAAvPTw+wkGgfHdwwjHjh3rcX7z5s265ZZbJEnLli1TU1OTFi1apLq6Oo0cOVK7d+9WVFSUe/y6desUGhqqmTNnqqmpSePGjdOWLVsUEhLS4Sw2wzCM8/6OAuD/7jpqdYQ23ZM12OoI7erRI7gfCdp8ptXqCG2KCO/4HxaY9fnX/2t1hDZ9+P/+zeoI7QqOv3nbdlFkmNUR2tWzk3+8ffHA5wG715S0js/7BxM6CAAAeOFVDBQIAACY2CyaYggm7GIAAAAmdBAAAPDCFEMQFQgVH9adfZBFmjI7/vYrK/Tu7NU656ny0warI7Qp/dI+Zx9koWD/S6p6931WR2hTbUPHnnVvlUder7I6QrvWTxtqdQRLWbWLIZgwxQAAAEyC+0dPAAAsEOzdu65AgQAAgBcKBAoEAABM2ObIGgQAAOADHQQAALwE+RPsuwQFAgAAXphiYIoBAAD4QAcBAAAv7GKgQAAAwIQpBqYYAACAD3QQAADwwi4GCgQAAEyYYmCKAQAA+EAHAQAAL+xioEAAAMCE+oACAQAAkx60EPxbg7BkyRK9/vrr5/1FXS6XGhoaPI6W083nfV8AABAYfhUIjz32mMaOHavvfe97Wr16tWpqas7pizqdTsXExHgc/72z6JzuBQBAoNkCeHRXfu9i2L17t370ox/p3//93zVgwABNmzZNf/zjH9Xa2trhe+Tn56u+vt7jGPzDuf5GAQCgc1Ah+F8gpKWlaf369frss8+0bds2uVwuTZ8+XUlJSVqxYoU++OCDs97DbrcrOjra4wgJCz+nbwAAAATeOT8HISwsTDNnztTOnTt17Ngx/eIXv9Bvf/tbXX755YHMBwBAl7MF8J/uKiAPShowYIAKCgpUVVWlnTt3BuKWAABYxmYL3NFd+VUgDBw4UCEhIW1et9lsmjBhwnmHAgAA1vLrOQhVVVWdlQMAgKDRjX/wDxgelAQAgDcqBF7WBAAAzOggAADgpTvvPggUCgQAALx0590HgUKBAACAF+oD1iAAAAAf6CAAAOCNFgIFAgAA3likyBQDAADwgQ4CAABe2MVAgQAAgAn1QRAVCOOGxFkdoU2NTWesjtCu6IgwqyO06+pLL7I6QptOuIL7v21Uz6D5I+rTXX94z+oIbfqP6UOtjtCuPr2C+88tP0EjuP/2AQDAChRIFAgAAHhjFwO7GAAAgA90EAAA8MIaDAoEAABMqA8oEAAAMKNCYA0CAAAwo4MAAIAXdjHQQQAAwMRmC9zhj9dee01TpkxRYmKibDabXnjhBY/rhmGooKBAiYmJioiI0NixY1VZWekxxuVyacmSJYqLi1NkZKSmTp2q48eP+/1rQIEAAECQOHnypIYPH64NGzb4vL5mzRqtXbtWGzZs0N69e+VwODRhwgQ1Nja6x+Tk5KikpETFxcUqLy/XiRMnNHnyZLW0tPiVhSkGAAC8WDXBMGnSJE2aNMnnNcMwtH79eq1YsUIzZsyQJBUVFSk+Pl7bt2/XggULVF9fr6efflpbt27V+PHjJUnbtm1TUlKSXnnlFU2cOLHDWeggAADgzRa4w+VyqaGhweNwuVx+R6qqqlJNTY2ys7Pd5+x2u7KysrRnzx5JUkVFhU6fPu0xJjExUampqe4xHUWBAABAJ3I6nYqJifE4nE6n3/epqamRJMXHx3ucj4+Pd1+rqalReHi4+vTp0+aYjmKKAQAAL4HcxZCfn6/c3FyPc3a7/ZzvZ/Na+WgYhumct46M8UYHAQAAL4HcxWC32xUdHe1xnEuB4HA4JMnUCaitrXV3FRwOh5qbm1VXV9fmmI6iQAAAoBtITk6Ww+FQaWmp+1xzc7PKysqUmZkpSUpPT1dYWJjHmOrqah08eNA9pqOYYgAAwItVuxhOnDihDz74wP1xVVWV9u/fr759+2rAgAHKyclRYWGhUlJSlJKSosLCQvXq1Utz5syRJMXExGj+/Pm6++67FRsbq759+yovL09paWnuXQ0d5XeB8Oijj2rfvn26/vrrNXPmTG3dulVOp1Otra2aMWOGHnjgAYWGtn9bl8tlWsF5ptml0PBzn5MBACBgLKoQ9u3bp3/91391f/zd2oW5c+dqy5YtWrZsmZqamrRo0SLV1dVp5MiR2r17t6Kiotyfs27dOoWGhmrmzJlqamrSuHHjtGXLFoWEhPiVxa8C4cEHH9TDDz+s7OxsLV26VFVVVXr44Yd11113qUePHlq3bp3CwsJ0//33t3sfp9NpGpN9y52a+POlfoUHAKAzWPWo5bFjx8owjDav22w2FRQUqKCgoM0xPXv21KOPPqpHH330vLL4VSBs2bJFW7Zs0YwZM/S3v/1N6enpKioq0o033ihJuuKKK7Rs2bKzFgi+VnT+5u1P/YwOAAA6i18FQnV1tTIyMiRJw4cPV48ePfT973/fff3qq6/WZ599dtb72O120wrO0PAv/YkCAECn8fcdChciv3YxOBwOvffee5Kko0ePqqWlxf2xJFVWVqpfv36BTQgAQBcL4IMUuy2/Oghz5szRzTffrGnTpulPf/qTli9frry8PH311Vey2WxauXKlfvKTn3RWVgAA0EX8KhDuv/9+RURE6M0339SCBQu0fPlyDRs2TMuWLdM333yjKVOm6MEHH+ysrAAAdI3u/KN/gPhVIISEhGjFihUe52bPnq3Zs2cHNBQAAFayahdDMOFJigAAwIQnKQIA4IVdDBQIAACYUB8wxQAAAHyggwAAgDdaCBQIAAB4YxcDBQIAACYsUmQNAgAA8IEOAgAAXmggUCAAAGDCFANTDAAAwIeg6SA88f+OWh2hTT/PGGh1hG6tpcWwOkKbetuD5o9At/R/UoP39e5Np1usjtAufjoLdrQQ+NsRAAAvTDFQxAIAAB/oIAAA4IUGAgUCAAAmTDEwxQAAAHyggwAAgBfexUCBAACAGfUBBQIAAN6oD1iDAAAAfKCDAACAF3YxUCAAAGDCIkWmGAAAgA90EAAA8EYDgQIBAABv1AdMMQAAAB/oIAAA4IVdDOdQIFRXV2vjxo0qLy9XdXW1QkJClJycrOnTp+uWW25RSEhIZ+QEAKDLsIvBzymGffv2aciQIXrxxRd16tQpHTlyRFdffbUiIyOVl5enMWPGqLGx8az3cblcamho8DhazzSf8zcBAAACy68CIScnR3fddZfeffdd7dmzR0VFRTpy5IiKi4t17NgxNTU16Ve/+tVZ7+N0OhUTE+Nx/OPNZ8/5mwAAIJBstsAd3ZVfBcI777yjn/3sZ+6P58yZo3feeUeff/65+vTpozVr1uj3v//9We+Tn5+v+vp6j6PvD2b5nx4AAHQKv9Yg9OvXT9XV1Ro0aJAk6fPPP9eZM2cUHR0tSUpJSdE//vGPs97HbrfLbrd7nOsRGu5PFAAAOk13/sk/UPzqIEyfPl0LFy7Uzp079eqrr+rGG29UVlaWIiIiJEmHDx/WJZdc0ilBAQBA1/Grg/DQQw+purpaU6ZMUUtLi0aNGqVt27a5r9tsNjmdzoCHBACgK7GLwc8CoXfv3nr22Wd16tQpnTlzRr179/a4np2dHdBwAABYgSmGc3xQUs+ePQOdAwAABBGepAgAgBcaCBQIAACYUSHwsiYAAGBGBwEAAC/sYqBAAADAhF0MTDEAAAAf6CAAAOCFBgIFAgAAZlQITDEAAODNFsB//PX4448rOTlZPXv2VHp6ul5//fVO+A7PjgIBAIAg8eyzzyonJ0crVqzQu+++qzFjxmjSpEn6+OOPuzwLBQIAAF5stsAdLpdLDQ0NHofL5fL5ddeuXav58+fr1ltv1ZAhQ7R+/XolJSVp48aNXfwrIMm4AJ06dcq47777jFOnTlkdxSSYsxkG+c5HMGczDPKdj2DOZhjkC3b33XefIcnjuO+++0zjXC6XERISYjz//PMe5++8807j2muv7aK0/8NmGIbR9WVJ52poaFBMTIzq6+sVHR1tdRwPwZxNIt/5COZsEvnORzBnk8gX7Fwul6ljYLfbZbfbPc599tlnuuSSS/TGG28oMzPTfb6wsFBFRUU6fPhwl+T9DrsYAADoRL6KgfbYvJ7SZBiG6VxXYA0CAABBIC4uTiEhIaqpqfE4X1tbq/j4+C7PQ4EAAEAQCA8PV3p6ukpLSz3Ol5aWekw5dJULcorBbrfrvvvu86ul01WCOZtEvvMRzNkk8p2PYM4mke9Ckpubq5/97GfKyMjQqFGjtGnTJn388cdauHBhl2e5IBcpAgDQXT3++ONas2aNqqurlZqaqnXr1unaa6/t8hwUCAAAwIQ1CAAAwIQCAQAAmFAgAAAAEwoEAABgcsEVCMHymkxvr732mqZMmaLExETZbDa98MILVkdyczqdGjFihKKiotSvXz9Nnz69yx/p2Z6NGzdq2LBhio6OVnR0tEaNGqWXX37Z6lhtcjqdstlsysnJsTqKJKmgoEA2m83jcDgcVsdy+/TTT3XTTTcpNjZWvXr10ve//31VVFRYHUuSdOmll5p+7Ww2mxYvXmx1NEnSmTNn9Ktf/UrJycmKiIjQoEGD9MADD6i1tdXqaJKkxsZG5eTkaODAgYqIiFBmZqb27t1rdSx00AVVIATTazK9nTx5UsOHD9eGDRusjmJSVlamxYsX680331RpaanOnDmj7OxsnTx50upokqT+/ftr1apV2rdvn/bt26frrrtO06ZNU2VlpdXRTPbu3atNmzZp2LBhVkfxMHToUFVXV7uPAwcOWB1JklRXV6fRo0crLCxML7/8st577z098sgjuuiii6yOJunb/57//Ov23QNsfvrTn1qc7FurV6/Wb37zG23YsEGHDh3SmjVr9PDDD+vRRx+1Opok6dZbb1Vpaam2bt2qAwcOKDs7W+PHj9enn35qdTR0RJe/HqoT/cu//IuxcOFCj3NXXHGFce+991qUyDdJRklJidUx2lRbW2tIMsrKyqyO0qY+ffoYTz31lNUxPDQ2NhopKSlGaWmpkZWVZSxdutTqSIZhfPsmueHDh1sdw6fly5cb11xzjdUxOmzp0qXG4MGDjdbWVqujGIZhGNdff70xb948j3MzZswwbrrpJosS/Y9vvvnGCAkJMf74xz96nB8+fLixYsUKi1LBHxdMB6G5uVkVFRXKzs72OJ+dna09e/ZYlKp7qq+vlyT17dvX4iRmLS0tKi4u1smTJzVq1Cir43hYvHixrr/+eo0fP97qKCZHjx5VYmKikpOTNXv2bB07dszqSJKkHTt2KCMjQz/96U/Vr18/XXXVVXryySetjuVTc3Oztm3bpnnz5lny4hxfrrnmGv3pT3/SkSNHJEl/+9vfVF5erh/96EcWJ/t2+qOlpUU9e/b0OB8REaHy8nKLUsEfF8yjlr/88ku1tLSYXmgRHx9vevEF2mYYhnJzc3XNNdcoNTXV6jhuBw4c0KhRo3Tq1Cn17t1bJSUluvLKK62O5VZcXKyKigrt27fP6igmI0eO1DPPPKPvfe97+vzzz/XQQw8pMzNTlZWVio2NtTTbsWPHtHHjRuXm5uqXv/yl3n77bd15552y2+26+eabLc3m7YUXXtDXX3+tW265xeoobsuXL1d9fb2uuOIKhYSEqKWlRStXrtQNN9xgdTRFRUVp1KhRevDBBzVkyBDFx8frd7/7nd566y2lpKRYHQ8dcMEUCN8Jltdkdld33HGH/v73vwddhX/55Zdr//79+vrrr/Xcc89p7ty5KisrC4oi4ZNPPtHSpUu1e/du009LwWDSpEnuf09LS9OoUaM0ePBgFRUVKTc318JkUmtrqzIyMlRYWChJuuqqq1RZWamNGzcGXYHw9NNPa9KkSUpMTLQ6ituzzz6rbdu2afv27Ro6dKj279+vnJwcJSYmau7cuVbH09atWzVv3jxdcsklCgkJ0dVXX605c+bonXfesToaOuCCKRCC7TWZ3dGSJUu0Y8cOvfbaa+rfv7/VcTyEh4frsssukyRlZGRo7969+vWvf60nnnjC4mRSRUWFamtrlZ6e7j7X0tKi1157TRs2bJDL5VJISIiFCT1FRkYqLS1NR48etTqKEhISTEXekCFD9Nxzz1mUyLePPvpIr7zyip5//nmro3i45557dO+992r27NmSvi0AP/roIzmdzqAoEAYPHqyysjKdPHlSDQ0NSkhI0KxZs5ScnGx1NHTABbMGIdhek9mdGIahO+64Q88//7z+/Oc/d4s/vIZhyOVyWR1DkjRu3DgdOHBA+/fvdx8ZGRm68cYbtX///qAqDiTJ5XLp0KFDSkhIsDqKRo8ebdpSe+TIEQ0cONCiRL5t3rxZ/fr10/XXX291FA/ffPONevTw/Gs8JCQkaLY5ficyMlIJCQmqq6vTrl27NG3aNKsjoQMumA6CFFyvyfR24sQJffDBB+6Pq6qqtH//fvXt21cDBgywMNm3i+u2b9+uP/zhD4qKinJ3YWJiYhQREWFpNkn65S9/qUmTJikpKUmNjY0qLi7WX/7yF+3cudPqaJK+nWv1Xq8RGRmp2NjYoFjHkZeXpylTpmjAgAGqra3VQw89pIaGhqD4CfOuu+5SZmamCgsLNXPmTL399tvatGmTNm3aZHU0t9bWVm3evFlz585VaGhw/ZU5ZcoUrVy5UgMGDNDQoUP17rvvau3atZo3b57V0SRJu3btkmEYuvzyy/XBBx/onnvu0eWXX66f//znVkdDR1i6h6ITPPbYY8bAgQON8PBw4+qrrw6arXqvvvqqIcl0zJ071+poPnNJMjZv3mx1NMMwDGPevHnu/6YXX3yxMW7cOGP37t1Wx2pXMG1znDVrlpGQkGCEhYUZiYmJxowZM4zKykqrY7m9+OKLRmpqqmG3240rrrjC2LRpk9WRPOzatcuQZBw+fNjqKCYNDQ3G0qVLjQEDBhg9e/Y0Bg0aZKxYscJwuVxWRzMMwzCeffZZY9CgQUZ4eLjhcDiMxYsXG19//bXVsdBBvO4ZAACYXDBrEAAAQOBQIAAAABMKBAAAYEKBAAAATCgQAACACQUCAAAwoUAAAAAmFAgAAMCEAgEAAJhQIAAAABMKBAAAYPL/ARxspbFfL9QlAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.heatmap(brc_confusion, annot=False, cmap='Blues')" + ] + }, + { + "cell_type": "markdown", + "id": "3a50f7f3", + "metadata": {}, + "source": [ + "### 2.5 Gaussian mixture" + ] + }, + { + "cell_type": "markdown", + "id": "33c3d7e7", + "metadata": {}, + "source": [ + "###    2.5.1 Fitting Models" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "e98317fb", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.mixture import GaussianMixture\n", + "gm = GaussianMixture(n_components = 10).fit(x_train_pca)\n", + "y_pred_gm = gm.predict(x_test_pca)" + ] + }, + { + "cell_type": "markdown", + "id": "b4aa5c01", + "metadata": {}, + "source": [ + "###    2.5.2 Clustering results evaluations" + ] + }, + { + "cell_type": "markdown", + "id": "547683c2", + "metadata": {}, + "source": [ + "####     NMI (Normaliszed Mutual Information) and ARI (Adjusted Rand Index)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "52f39cb4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI:0.1823\n", + "ARI:0.0836\n" + ] + } + ], + "source": [ + "print(\"NMI:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_gm,average_method='arithmetic')))\n", + "print(\"ARI:{:.4f}\".format(adjusted_rand_score(y_test, y_pred_gm)))" + ] + }, + { + "cell_type": "markdown", + "id": "337975a0", + "metadata": {}, + "source": [ + "####     Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "09af68b9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix: \n", + "[[ 1 2 398 105 41 34 103 82 234 0]\n", + " [369 99 23 0 61 15 73 28 18 314]\n", + " [337 39 33 5 146 5 38 114 7 276]\n", + " [ 13 3 207 170 187 7 99 283 31 0]\n", + " [ 77 198 38 1 165 134 188 36 35 128]\n", + " [331 51 55 213 34 0 16 23 9 268]\n", + " [333 129 4 0 66 45 83 18 8 314]\n", + " [ 18 185 32 2 190 209 150 177 15 22]\n", + " [107 104 71 23 148 6 82 161 17 281]\n", + " [119 138 12 1 266 40 124 162 4 134]]\n" + ] + } + ], + "source": [ + "gm_confusion = confusion_matrix(y_test,y_pred_kmean_pca)\n", + "print('Confusion matrix: \\n{}'.format(gm_confusion))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "971194b5", + "metadata": {}, + "outputs": [], + "source": [ + "sns.heatmap(gm_confusion, annot=False, cmap='Blues')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3a8306de", + "metadata": {}, + "source": [ + "## 3. Appendix" + ] + }, + { + "cell_type": "markdown", + "id": "ca1adee0", + "metadata": {}, + "source": [ + "### 3.1 KNN" + ] + }, + { + "cell_type": "markdown", + "id": "2efec242", + "metadata": {}, + "source": [ + "#### We also tried other models including KNN and various regression models to see how they perform. Note that these are not unsupervised models, and labels were used to train the models. Therefore, these implementations are not included in the final report. " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "c27cb6d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting KNeighborsClassifier(n_jobs=-1, n_neighbors=4, weights='distance')\n", + "Evaluating KNeighborsClassifier(n_jobs=-1, n_neighbors=4, weights='distance')\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "clf = KNeighborsClassifier(n_neighbors=4, weights='distance', n_jobs=-1)\n", + "print('Fitting', clf)\n", + "clf.fit(x_trainf, y_train)\n", + "print('Evaluating', clf)\n", + "\n", + "y_pred_knn = clf.predict(x_testf)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "31b937dc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test accuracy: 0.921\n" + ] + } + ], + "source": [ + "test_score = clf.score(x_testf, y_test)\n", + "print('Test accuracy:', test_score)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "df13d267", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8231031975085954" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "normalized_mutual_info_score(y_test, y_pred_knn,average_method='arithmetic')" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "2226f1b5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8234272963838932" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "normalized_mutual_info_score(y_test, y_pred_knn,average_method='min')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "09c6c71e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.823103261265742" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "normalized_mutual_info_score(y_test, y_pred_knn,average_method='geometric')" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "cb776587", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8227793536618937" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics.cluster import normalized_mutual_info_score\n", + "normalized_mutual_info_score(y_test, y_pred_knn,average_method='max')" + ] + }, + { + "cell_type": "markdown", + "id": "12d38ecb", + "metadata": {}, + "source": [ + "####    Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "4bef3167", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix: \n", + "[[910 0 1 1 3 26 0 15 44 0]\n", + " [ 1 910 27 1 4 3 39 0 8 7]\n", + " [ 9 6 880 45 8 15 14 3 18 2]\n", + " [ 1 1 18 969 0 5 3 1 2 0]\n", + " [ 13 12 11 16 885 10 14 2 28 9]\n", + " [ 1 5 36 8 2 931 12 0 3 2]\n", + " [ 3 2 20 6 8 3 951 2 2 3]\n", + " [ 1 8 11 4 9 6 14 912 21 14]\n", + " [ 0 13 9 6 0 7 12 0 952 1]\n", + " [ 2 24 10 2 3 4 13 5 27 910]]\n" + ] + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "knn_confusion = confusion_matrix(y_test,y_pred_knn)\n", + "print('Confusion matrix: \\n{}'.format(knn_confusion))" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "2bc87615", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "sns.heatmap(knn_confusion, annot=True, cmap='Blues')" + ] + }, + { + "cell_type": "markdown", + "id": "98588e59", + "metadata": {}, + "source": [ + "### 3.2 Four Regression Models" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "84d84800", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data set score: 0.337\n", + "Test data set score: 0.187\n" + ] + } + ], + "source": [ + "# Linear regression\n", + "from sklearn.linear_model import LinearRegression\n", + "lr = LinearRegression().fit(x_trainf,y_train)\n", + "print('Training data set score: {:.3f}'.format(lr.score(x_trainf, y_train)))\n", + "print('Test data set score: {:.3f}'.format(lr.score(x_testf, y_test))) # overfit" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "0f2cb297", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data set score: 0.34\n", + "Test data set score: 0.19\n" + ] + } + ], + "source": [ + "# Ridge Regression\n", + "from sklearn.linear_model import Ridge\n", + "ridge = Ridge().fit(x_trainf, y_train)\n", + "print('Training data set score: {:.2f}'.format(ridge.score(x_trainf, y_train)))\n", + "print('Test data set score: {:.2f}'.format(ridge.score(x_testf, y_test))) # overfit" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "b3bee2c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data set score: 0.34\n", + "Test data set score: 0.19\n" + ] + } + ], + "source": [ + "# Lasso Regression\n", + "from sklearn.linear_model import Lasso\n", + "lasso = Lasso(alpha = 0.1, max_iter = 100000).fit(x_trainf, y_train)\n", + "print('Training data set score: {:.2f}'.format(lasso.score(x_trainf, y_train)))\n", + "print('Test data set score: {:.2f}'.format(lasso.score(x_testf, y_test))) # overfit" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "1f7c891d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data set score: 0.838\n", + "Test data set score: 0.691\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "D:\\ANA\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n" + ] + } + ], + "source": [ + "# Logistic Regression\n", + "from sklearn.linear_model import LogisticRegression\n", + "logreg = LogisticRegression(max_iter=100).fit(x_trainf, y_train)\n", + "print('Training data set score: {:.3f}'.format(logreg.score(x_trainf, y_train)))\n", + "print('Test data set score: {:.3f}'.format(logreg.score(x_testf, y_test))) # overfit" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 2db4ce8cc835cbc543b3f3b4cc15ca7e007b9ed0 Mon Sep 17 00:00:00 2001 From: HappyCheems <79441528+eternalDoge@users.noreply.github.com> Date: Sun, 4 Dec 2022 13:40:19 +0800 Subject: [PATCH 25/27] Delete unsupervised.ipynb --- unsupervised.ipynb | 1827 -------------------------------------------- 1 file changed, 1827 deletions(-) delete mode 100644 unsupervised.ipynb diff --git a/unsupervised.ipynb b/unsupervised.ipynb deleted file mode 100644 index 7008a97..0000000 --- a/unsupervised.ipynb +++ /dev/null @@ -1,1827 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7ba70409", - "metadata": {}, - "source": [ - "# Unsupervised Models" - ] - }, - { - "cell_type": "markdown", - "id": "dd2ab19d", - "metadata": {}, - "source": [ - "## 1. Data and Clusters' Examples Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "0422e6d9", - "metadata": {}, - "outputs": [], - "source": [ - "# download the dataset in NumPy format\n", - "import numpy as np\n", - "def load(f):\n", - " return np.load(f)['arr_0']\n", - "\n", - "# Load the data\n", - "x_train = load('kmnist-train-imgs.npz')\n", - "x_test = load('kmnist-test-imgs.npz')\n", - "y_train = load('kmnist-train-labels.npz')\n", - "y_test = load('kmnist-test-labels.npz')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4bf07b89", - "metadata": {}, - "outputs": [], - "source": [ - "# Flatten images\n", - "# Each element in x_train and x_test is in the form of 28x28,\n", - "# so reshaping them in the form of 1x784\n", - "x_trainf = x_train.reshape(-1, 784)\n", - "x_testf = x_test.reshape(-1, 784)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "93c6ef34", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
indexcodepointchar
00U+304A
11U+304D
22U+3059
33U+3064
44U+306A
55U+306F
66U+307E
77U+3084
88U+308C
99U+3092
\n", - "
" - ], - "text/plain": [ - " index codepoint char\n", - "0 0 U+304A お\n", - "1 1 U+304D き\n", - "2 2 U+3059 す\n", - "3 3 U+3064 つ\n", - "4 4 U+306A な\n", - "5 5 U+306F は\n", - "6 6 U+307E ま\n", - "7 7 U+3084 や\n", - "8 8 U+308C れ\n", - "9 9 U+3092 を" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import pandas as pd\n", - "# import the map to see the given 10 clusters\n", - "map = pd.read_csv(\"kmnist_classmap.csv\")\n", - "map" - ] - }, - { - "cell_type": "markdown", - "id": "ca9557a5", - "metadata": {}, - "source": [ - "### visualize characters in each cluster" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "039fead3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAD7CAYAAACMlyg3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAq9ElEQVR4nO3de7xUZdn/8c/FUQw5qohKoRipaaJCklqChmkqamWa5Snz9Co0S0Xj+Vn+HssDpaI9/dIUUSzzmGaWaaboUyIYoCgQKqKiIpaAmYpsuX5/rLVzHK571sw+w3zfr9e8mP2de9Zas2cz96y17nXd5u6IiEh96tTeGyAiIu1HnYCISB1TJyAiUsfUCYiI1DF1AiIidUydgIhIHevS2iswM41BlQ7J3U1/n9JRubsVtTGz/YBJQGfgane/sNb1WGtfJ6D/ZNJRqROQjqyoEzCzzsBCYAywBJgJfMXd59WynsI9ATPbFjgY2AJw4GXgt+4+v5YViYhIi/ok8Iy7LwIws1+TfVbX1AlUPCdgZuOBXwMGzCDraQy40czOrvC8E83sMTN7rJaNERGRTOnnaH47sazJFsCLJT8vybPa1lPpcJCZLQQ+7u6ry/JuwFPu/tHCFWh3WzooHQ6SjqyKw0GHAZ9z92/kPx8FfNLdx9WynqLRQWuAzYN8YP6YiIi0jyXAoJKftyQ7XF+TonMC3wbuN7OneX+348PANsC3al2ZiIi0mJnAR81sK+Al4AjgyFoXUjg6yMw6kZ2A2ILsfMASYKa7v1fVCrS7LR2UDgdJR1blENHPA5eRDRGd7O4/rHU9GiIqdUudgHRk1XQCLUFXDIuI1DF1AiIidUydgIhIHVMnICJSx9QJiIjUMXUCIiJ1rNVLSXc0nTt3DvM1a+ILoFt7CG1rMotHmPXu3TvMO3XqWN8J3nrrrTB/55132nhLRNZfHet/vYiIVMXMBpnZA2Y238yeMrPTmrKcutsTEBFZTzQA33X3WWa2EfA3M7uv1vkECvcEzGxbM9vHzHqW5fvVtr0iItJS3P0Vd5+V3/8XMJ8mlJIumk/gVOBOYBzwpJkdXPLwjyo8T/MJiIg0QxXzCZS2HQzsDDxa83oK5hOYC3zK3d/MV3IrMNXdJ5nZbHffuYoX0qHOrOrEsE4MN1LtIOnIqq0dlB+lmQb80N1vr3U9RecEOrv7m/kGLTazUcCtZvYRsoqiIiLSTsysK3Ab8MumdABQfE5gqZkNa/wh7xAOBDYGdmzKCkVEpPks29W/Bpjv7pc0eTkFh4O2BBrcfWnw2B7u/pcqNrRddrdTh32uvPLKMF+5cmWYjx8/PswbGhqatmFtKPU7OOmkk8L8u9/9bph/+MMfDvMuXVp3cNlxxx0X5lOmTGmR5XfEw0FXX311mI8YMSLM99prrzBfsWJFS22StJMqppfcE3gYmMv7Mz1+z91/X8t6Kv4vdvclFR4r7ABERKR1uPv/0gKH5TvWmUAREWlT6gREROqYOgERkTqmTkBEpI6pExARqWPqBERE6ljF6wRaZAXtNA57k002CfN58+ICe/369QvzT33qU2E+Y8aMpm1YB9atW7cwT/0O9t577zBPXVdw1FFHhflrr70W5sOHDw/zl156Kcxr1RGvE5g5c2aY77rrrmE+evToMJ82bVqLbZO0j2rKRphZZ+Ax4CV3P7Ap69GegIjIuus0suqhTVZzJ2Bm1zdnhSIi0nx5RYcDgPgy8ypVvGLYzH5bHgGjzawPgLuPbc7KRUSkyS4DzgI2as5Cioq/bAnMI+tpnKwTGA78pNKT8rrXydrXIiJSWfA5epW7X5U/diCwzN3/lld3brKiTmA42TGnCcCZ7j7HzN5294pnnfINbdzYDnXiTURkXVD6ORrYAxhrZp8HNgB6mdkN7v61WtdTVEBuDXCpmd2S//tq0XNERKR1ufs5wDkA+Z7AGU3pAKDKD/S8muhhZnYA8EZTVtTWevXqFeYbbRQfPkvNqnXwwQeH+fo4RPTdd98N84cffjjMn3322TDffvvtw/yII44I8zfeiP+kUjOjrQ9Sr23AgAE1tU/NEidSrZq+1bv73cDdrbQtIiJSI3d/EHiwqc/XdQIiInVMnYCISB1TJyAiUsfUCYiI1DF1AiIidUydgIhIHVtvL/xqaGgI89SY9FTp6TFjxoT5hAkTmrZh66BRo0aF+Z133hnm3bt3D/OuXbuG+dChQ8N81qxZYT59+vQwv+CCC8L8kUceCfP2lCpdPnDgwJqWk/rdiVRLewIiIusoM+tjZrea2QIzm29m8eQfFay3ewIiInVgEnCPu3/JzLoBG9a6gIp7Ama2m5n1yu/3MLPzzOwuM7vIzHS9uohIO8k/mz8DXAPg7u+6+4pal1N0OGgy8FZ+fxLQG7goz66tsHEnmtljZvZYrRskIiIf/BzNb+Xl+bcGXgOuNbPZZna1mX2o1vUUHQ7q5O6NZ1iHu/su+f3/NbM5qSeplLSISPMUlJKG7PN7F2Ccuz9qZpOAs4H/U8t6ivYEnjSz4/L7j5vZcAAzGwqsrmVFIiLSopYAS9z90fznW8k6hZoUdQLfAPYys2eB7YFHzGwR8Iv8MRERaQfuvhR40cw+lkf7kM0EWZOiSWVWAsea2UZkx5+6kPU8r1a7gq233jrMU3XQFyxYEOZvv/12tasE0vXXa61Rv3Tp0prarws22GCDMN93333D/Npr49M/PXv2DPPUe5WalyC1nG222SbMDzrooDAfMmRImO+8885h3p5Wr26ZHen9998/zK+44oowT733K1eubJHtkTY3DvhlPjJoEXBcQfu1VDupzL+Ax2tduIiItB53n0M2DXCT6WIxEZE6pk5ARKSOqRMQEalj6gREROqYOgERkTqmTkBEpI61ehXRiRMnhvmhhx4a5qka9SeddFKYL1u2LMz//e9/h/m7774b5imXXXZZTe07kr59+4b5ueeeG+bjxo0L89QcDD//+c/D/MYbbwzz1HUCKala+ZMnTw7zESNGhPnhhx9e03rbQup3+s4774R56lqKkSNHhvn5558f5jvssEOYH3LIIWG+atWqMO9IUtuemrMhdd3LTjvtFOZz584N8/fee69441qZmZ1OduGuA3OB49w9/iNK0J6AiMg6yMy2AE4lq+u2A9AZOKLW5agTEBFZd3UBephZF7K5BF6udQFF8wl0M7Ojzeyz+c9HmtlPzeybZhbPFSgiIq3O3V8Cfgy8ALwCrHT3e2tdTtGewLXAAcBpZjYVOAx4FBgBXJ16Umkd7HvvrXmbRETqXtF8AmbWFzgY2ArYHPiQmX2t1vUUnRje0d0/ke9qvARs7u7vmdkNVKglVFoH+/bbb9d8AiIiNapiPoHPAs+5+2sAZnY7sDtwQy3rKdoT6JRXp9uI7HhTY+nP7oAOB4mItJ8XgJFmtqFl5ZH3AebXupCiPYFrgAVkZ50nALfk8wmMBH5d68pERKRl5LOJ3QrMAhqA2VTecwiZe+WjNWa2eb7Cl82sD9kuyAvuPqOaFQwZMiRcwYwZ8dNTY9tff/31ML/77rvD/OWX45Pk55xzTpinbLfddmGemvegPXTqFO/Q/eY3vwnz1O/4+uuvD/PU7zh1jUZrj58eOHBgmC9atCjMX3rppTAfMmSI1Tr9aepahNRcDLfffnuYd+/ePcz/+te/hnmPHj2q2Lr3Pf3002H+1a9+NcxnzpxZ0/I7ki222CLMx4wZE+apv/MHHnggzBsaGsJ86tSpYT59+vQwX7hwYZivWbMmzN29tslPmqjwYjF3f7nk/gqyKcxERGQ9oOsERETqmDoBEZE6pk5ARKSOqRMQEalj6gREROpY4RDRZq8gMQQvNVQzlW+44YZh3rlz5yZu2QetXr06zIcNGxbm8+bNa5H11mLHHXcM81Sp5GnTpoX5gw8+GOap30FHM3jw4DBPDdtNDccEah4ievrpp4f5JZdcEuZPPfVUmHftGl9rmSqfnZIavnjyySeH+TXXXFPT8uvJ0UcfHea/+MUvwrxbt25hnioTfvzxx4f5rbfGAy7baoio9gRERNZBZjbZzJaZ2ZMlWT8zu8/Mns7/jS8KKlFURbS3mV1oZgvM7J/5bX6e9WmB1yEiIk0zBdivLDsbuN/dPwrcn/9cUdGewM3AcmCUu/d39/7A6Dy7pdYtFhGRluHuDwHlpRQOBq7L718HHFK0nKJOYLC7X+TuS0tWvNTdLwI+nHpSaQnUog0QEZG1FZWSThjg7q8A5P9uWvSEorIRz5vZWcB17v5qvmEDgGOBF1NPKi2BWuuJNxERqaqUdIso2hM4HOgPTDOz183sdeBBoB/ZBDMiItJxvGpmAwHyf+MqjyUqdgLuvtzdx7v7tu7eL79t5+7jqeJYk4iItKnfAsfk948B7ix6QmEV0QrOI5t+skkmTpwY5n/5y1/CfPz48WE+aNCgME+Nw952221rat/aZZG32WabMD/ooIPWylK/gy984QthniobvNdee4X5j370ozB/6623wry9HHrooWGeuh5g+fLlYZ4qqV3J3Llzwzz1d/Lxj3+85nXUYsqUKTXlknbjjTeGec+ePcP84osvDvNevXqF+ZlnnhnmTS1Lb2Y3AqOAjc1sCfB94ELgZjM7nmzSmcIjNhU7ATN7IvUQMKCWDRYRkZbj7l9JPLRPLcsp2hMYAHyObEhoKQPi2S9ERGSdUdQJ/A7o6e5zyh8wswdbY4NERKTtVOwE3D0udpE9dmTLb46IiLQl1Q4SEalj6gREROqYOgERkTrWbvMJ1Co1b0CnTnE/lqqnfvnll9e03s985jNh/vDDD9e0nE9/+tNh/l//9V9hft99962V9ejRI2y75557hvmYMWPC3CwuU/6rX/0qzFPXG7S21O/+zjvj61/69OkT5uPGjQvzK664oub5BLp0iU+j/eQnPwnzE044IcxTf7cV5j4IPfZYXJ5r5MiRYd7a172sj1Lv1SmnnBLmV1xxRZin/t/NnDkzzEeMGFFxPgEzmwwcCCxz9x3ybCJwEPAu8CxwnLuvqLQc7QmIiKybprB2Ken7gB3c/RPAQiCepatE0XwCvczsAjObamZHlj32s9q2V0REWkpUStrd73X3xunmpgNbFi2naE/gWrILw24DjjCz28yscX813t8UEZGO4OvAH4oaFXUCQ9z9bHe/w93HArOAP5tZ/0pP0nwCIiLN08T5BBqfOwFoAH5Z1LboiuHuZtbJ3dcAuPsP80JFDwFxVSU0n4CISHM1dT4BMzuG7ITxPl7FyJ+iPYG7gL3LNuw64LtkZ59FRKSDMLP9gPHAWHevqvxvUdmIsxL5PWYW1x0WEZFWlyglfQ7QHbgvH5I63d3j8fKNy2nqdQJm9oK7J+cZLmnXLoeDfvCDH4T597///ZqWc+CBB4b566+Xz++cOeCAA8I8NW571qxZYX7++eevlb3xxhth24EDB4b5l7/85TDffffdw/yLX/ximB977LFhfsMNN4R5rTbZZJMwT/1uttwyHvCwePHiMB82bFiYr1ixoubrBFJSY8kHDx4c5r179w7z6dOnh3m3bt3C/LnnngvzoUOHhnlDQ0OYS+1Sc5Bcf/31YX7EEUeE+apVq8K8e/fuFa8TaCmaT0BEpI5pPgERkTqm+QREROqY5hMQEaljqh0kIlLH1AmIiNSxonMC66zNN9+8RZZz0003hXmqrHNqqOCll14a5ldffXWYp4aDRl555ZUwnzRpUpgvW7YszPfff/8w/973vhfmd9xxR5i/+eabYd63b98wnzp1apj37x9XJ3n11VfD/Pjj46OXK1euDPOWtGbNmjBftGhRTct59tlnw3y77barab3S+lavXh3mJ510Upg/9NBDYZ4asv21r32taRtWo5r3BMxs09bYEBERqZ6ZTTazZWb2ZPDYGWbmZrZx0XKKSkn3K7v1B2aYWV8z69eM7RcRkeaZwtrzCWBmg4AxwAvVLKTocNA/gOfLsi3Iqok6sHU1KxERkZbl7g+Z2eDgoUuBs4B4Cr4yRYeDzgL+TlaMaCt33wpYkt9PdgAqJS0i0jxNKSVtZmOBl9z98WrXU3SdwI/N7NfApWb2IlmBosJaKyolLSLSPLWWkjazDYEJwL61rKfwxLC7L3H3w4AHyOav3LCWFYiISJsYAmwFPG5mi8mmlpxlZptVelLVo4Pc/S5gNPBZADM7rsmbKiIiLcrd57r7pu4+2N0HA0uAXdx9aaXnrfOlpFMlmidPnhzmqfHWLWXOnDlhPnbs2DB/8cUXW3FrYhtssEGYp8oYb7PNNmG+4447hnnnzp3D/Iorrgjzz33uc2GeGt9/1FFHhfnvfve7ME9x9xYrJd1SrrzyyjA/8cT4cPAf//jHMP/85z8f5qn/74ccckiYjx49OswvueSSMN966/hU4Z577hnm8+bNC/PbbrstzJv6edWRpf6/NDQ0VCwlXTqfAPAq8H13v6bk8cXAcHf/R6XlqJS0iMg6yN2/UvD44GqWo1LSIiJ1TKWkRUTqmEpJi4jUMVURFRGpY+oERETqmDoBEZE61uHmE+jZs2eYH3vssWE+bty4MB86dGhLbVIoVTP/q1/9api3x/UAKe+9916Y33lnXG/qjDPOCPPUe/Xf//3fYb7ffmsVPATgkUceCfPUe75w4cIwXx+sWrWqpvapaztS8wzstttuYX7dddeF+Z/+9KcwT13DsXRpfF3SmWeeGeapMfKpuSeuueaatbJ1fU6F1P/HImY2GTgQWObuO5Tk44BvAQ3A3e5+VqXlNGU+gXimDxERaUtTKCslbWajgYOBT7j7x4EfFy2kaD6BCxsnJTCz4Wa2CHjUzJ43s72auuUiItI87v4Q8HpZfApwobuvytvE0wiWKNoTOKDkkuOJwOHuvg3ZhAU/qW2TRUSklQ0FPm1mj5rZNDMbUfSEok6gq5k1njfo4e4zAdx9IdA99STNJyAi0jxNmU+A7DxvX2AkcCZws5lVrEFUdGL4f4Dfm9mFwD1mdhlwO7APMCf1JM0nICLSPLXOJ5BbAtzuWaW9GWa2hqzA3GupJxRdMXyFmc0lO840NG8/FLgDiIeAiIhIe7kD2Bt40MyGAt3IpglOKhwi6u4PAg+W5/l8Atc2YSNFRKSZSktJm9kSspkfJwOTzexJ4F3gGC+ov91u8wn06NEjbP/Tn/40zA8//PAwT43XHzCgdStdR+OVAU444YQw70h10M8777wwP/nkk8N80003DfMFCxaE+bbbbhvmc+fODfMxY8aE+auvvhrmLaUjzicwbdq0MN9jjz3CPPV3+POf/zzMU/MMpOaYmDhxYpi/8cYbYZ5672fNmhXmqc+B5cvLCxdnRoxY+zzns88+G7Zd17l7xWP5LUXzCYiI1DHNJyAiUsc0n4CISB3TfAIiInVMVURFROqYOgERkTrWbqWkU2WC99577zBPlWJOlTNuKY89Fle+OOecc8K8Iw0FTV0tnir3W6vUcMDXXosvTkwN/23toaAdUe/evcN85513DvNUyeXU0NErr7wyzFNDPhsaGsK81jLNo0aNCvPUUNCUDTfcMMw//OG1R6Wvr0NE24r2BERE1kFmNtnMluUXhjVmw8xsupnNyesNfbJoOUWlpIeb2QNmdoOZDTKz+8xspZnNNLP4K4uIiLSFKZTNJwBcDJzn7sOAc/OfKyraE/hZvpC7ya4LuNLdewNn54+JiEg7SMwn4ECv/H5v4OWi5RSWknb3P7j7jdk6/dZ85fcD8XXmqJS0iEhzNbGU9LeBiWb2ItmsYvHJyxJFJ4bfMbN9yXoUN7ND3P2OfFax5MSYKiUtItI8TSwlfQpwurvfZmZfBq4BPlvpCUV7AicD3wW+TlY+YrSZrSA7FHRqjRsnIiKt6xiyOV8AbgGad2LY3R9398+5+/7uvsDdT3P3PvkExh9r/vaKiEgLehlonP99b+Dpoic05zqB86hiPoFOneJ+JlW2eJNNNgnzVNniIUOGFG1CVVLj+8ePHx/mqbHw7SH1Ox47dmyYp8ZsP/fcc2GeKiX99NPx39dJJ50U5tOnTw/zerTFFluEeWp8fMq5554b5qnSzS1ll112CfNvf/vbYZ663iD1N/fnP/85zGfPnl28cXUiMZ/ACcCkfFrgd4DC8wgqJS0isg5y968kHtq1luWolLSISB1TKWkRkTqmUtIiInVMtYNEROqYOgERkTqmTkBEpI61+nwC2223XZh369YtzD/0oQ+F+a671jTqqWap6wQOOuigMP/Sl74U5kOHDg3zxYsXh/kzzzwT5nfeeeda2cYbbxy23WeffcL88ccfD/Nhw4aF+eabbx7mqW3fd999a2q/PujVq1eYb7bZZmE+bty4MP/EJz4R5u+8806Yp+aGSF0/01JS1zNceumlYf6xj8XXkKauq/nZz+I6lNddd12Yr1ixIsyl6YpKSfc2swvNbIGZ/TO/zc+zPm20jSIiUiYv7/9A/pn8lJmdluf98rL/T+f/9q20nKLDQTeTXSMwyt37u3t/YHSe3dISL0RERJqkAfiuu28HjAS+aWbbk5X6v9/dPwrcn/+cVNQJDHb3i9z9P/MRuvtSd78IWHueNxERaRPu/oq7z8rv/wuYD2wBHAw0Hk+7Djik0nKKOoHnzewsM/tPiQgzG2Bm44F40l8+WAf79dfL5zwQEZEitcwnYGaDgZ2BR4EB7v4KZB0FEBf/yhV1AocD/YFpZrbczF4HHgT6AV9OPcndr3L34e4+vF+/fgWrEBGRcqWfo/ktnFvAzHoCtwHfdvc3al1P0RXDy83sWuA+YLq7v1my4v2Ae2pdoYiItAwz60rWAfzS3RvnEXjVzAa6+ytmNhBYVmkZRaODTgXuBL4FPGlmB5c8/KOmb7qIiDSHZeOGrwHmu/slJQ/9lmxyGfJ/1x5vXqLoOoETgF3d/c38mNOtZjbY3SeRVRIttGTJkjBP1U1PjZPeYIPklMY1LSc1Xrlr165hnqqP3tqi30///v3Dtsccc0yYf/nL8RG71CG6VA3673znO2G+Pl8PkBrHf9NNN4X5wIEDw7x3795hPnfu3DBfuXJlmKfm2UhdnzBv3rwwT9lpp53CPDVeP9U+JXWNy9tvvx3mOpdYlT2Ao4C5ZjYnz74HXAjcbGbHAy8Ah1VaSFEn0LnxEJC7LzazUWQdwUeoshMQEZGW5+7/S/pzOL6CNFB0YnipmQ0rWembwIHAxsCO1a5EREQ6pqJO4GhgaWng7g3ufjTwmVbbKhERaRNFo4PiA/rZY39p+c0REZG2pCqiIiJ1TJ2AiEgda/VS0qkhb9/85jfD/PLLLw/zVJnju+++O8wnTJgQ5i+//HKYd+7cOcxHjx4d5ueff36Yp0pnv/feezWtNxqiOGXKlLDtX//61zBPDRF94oknwnzixIlhvnz58jBfHxx66KFhfuqpp4b5tttuW9Py33rrrTC/9957w/zEE+PKAKkhzLvvvnuYP//882E+YsSIME+Vhk4NQU1Zs2ZNmKde7x133BHmqdLu0vKKLhbrZWYXmNlUMzuy7LG4ELiIiLS6CqWkJ+bl/58ws98Ulf0vOhx0Ldk41NuAI8zsNjPrnj82srkvQkREmixVSvo+YAd3/wSwEDin0kKKOoEh7n62u9/h7mOBWcCfzSy+dFVERNpEqpS0u9/r7g15s+nAlpWWU3ROoLuZdXL3NfmKfmhmS4CHgJ6pJ+UlT5NlT0VEpLLgc/SqCpVEB/N+KelSXwfiWie5ok7gLmBv4E+NgbtfZ2avAleknpRv6FX5xukMj4hIjUo/RytJlZI2swlkh4x+Wen5FQ8HuftZwBIz2ydfUWN+DxAPnxARkTaRKCWNmR1DVuLnq14w1KpodNA4sjKk41i7lPQPm7rhIiLSPKlS0vlcL+OBse4ej1EuXU6lTsLM5gKfKi0lDUx190lmNtvdd65iQ2s6HNS3b98w79GjR5ivWLEizFPjs1tKqrRvqqxzatz5oEGDwjwqpZtqe9xxx4X5DjvsEObjxo0L84aGhjBfX7m7zZ49O/z7TP3uUmbMmBHmqfH6BxxwQJinSk+nvPnmm2G+atWqME+VcE/p1Cn+nti9e/cwv+WWW8L82GOPDfPW/n+6LnP3ipWazWxP4GFgLtB4gcb3gMuB7sA/82y6u5+cWo5KSYuIrIMqlJL+fS3LUSlpEZE6plLSIiJ1TKWkRUTqmKqIiojUMXUCIiJ1TJ2AiEgdq3idQPgEs03dfVkN7VU2gnRd9tR8CNG46tTcDFdffXWYp+YTSF1bUW/c3Xbbbbfw7zM1Pv6dd94J86eeeirMu3SJT7ude+65Yf71r389zPv3j2s2prYn5ayzzgrz2bNnh/lGG20U5kOHDg3zu+66K8xT10tIWtF1Ai2l6IrhfmW3/sAMM+trZv3aYgNFRGRtqfkESh4/w8zczDautJyii8X+AZR34VuQlZR2YOtaN1xERFpE43wCs8xsI+BvZnafu88zs0HAGOCFooUUnRM4C/g7WQ2Krdx9K2BJfl8dgIhIO0nNJ5A/fCnZ53fh4fiiKqI/Br4BnGtml+S9TeFCzexEM3vMzB4raisiImsr/RzNb8k5WkrnEzCzscBL7v54NespnGg+v2DsMDM7iGzassIKVJpPQESkeZoynwDZIaIJwL7VrqdwiKiZbWtm+wAPAKOBz+b5ftWuREREWl4wn8AQYCvgcTNbTDa15Cwzi4cnUjw66FRK5hMA9nX3J/OHf9TsVyAiIk0SzSfg7nPdfVN3H+zug4ElwC7uvjS5nI42n0C9SdV3j+qsd+vWLWzbuXPnMH/77bebvmF1wN1Nf5/SUTV1PgF3/31Jm8XAcHf/R2o5mk9ARGQdVGE+gdI2g4uWo/kERETqmOYTEBGpY5pPQESkjqmKqIhIHVMnICJSx2ruBPJKoiIish4ouk7gQuDH7v4PMxsO3Ew2HrUrcLS7TytcgcZhSwel6wSkI6viOoFBwPXAZmSfy1e5+6T8sXHAt8jKSNzt7vFEEvmKkjdgbsn9B4AR+f2hwGOVnlvyPNdNt45409+nbh35VsVn60Cyq4EBNgIWAtuTlff5E9A9f2zTSsspulisq5l1cfcGoIe7zyTbuoVm1r3guSIi0krc/RXglfz+v8yssZT0CcCF7r4qf6ziTJBF5wT+B/i9me0N3GNml5nZZ8zsPGBO6kkqJS0i0jxNLSVNdqTm02b2qJlNM7MRFddTNMdwXirilHzBXYAXgTuAa919dRUvpPIKRNqJzglIR1btHMN5KelpwA/d/XYzexL4M3AaMAK4CdjaEx/2hfMJkF0xfBXwaGMdoXzF+wH3VLORIiLS8oJS0pBVDr09/9CfYWZryEr9vBYto6ZS0mZ2cMnDKiUtItJOolLSuTuAvfM2Q4FuZPPFh4r2BE4Adi0tJW1mg/NhSKoiKiLSfvYAjgLmmtmcPPseMBmYnB8Wehc4JnUoCIqvE5jn7tuX/NyTbE6BecDe7j6saCt1zFU6Kp0TkI6s2nMCzaVS0iIidaxoT2BLoCGamszM9qimkqi+aUlHpT0B6cjaak+gcIhos1eg/2TSQakTkI6soxwOEhGR9Zg6ARGROqZOQESknlVTCbSlbsCJat8y7TvStqwP7TviNqn9urEtbdG+NW9tu7Iqy0+r/bq1LetD+464TWq/bmxLW7RvzZsOB4mI1DF1AiIidaytO4Gr1L7F2nekbVkf2rfFOtS+5dp3pG1pi/atptUvFhMRkY5Lh4NEROqYOgERkTqmTkCEbKY8M/u7mT1jZmcXtB1kZg+Y2Xwze8rMTqti+Z3NbLaZ/a7K7eljZrea2YJ8PZ+q0Pb0fDueNLMbzWyDoM1kM1uW15hvzPqZ2X1m9nT+b9+C9hPz7XnCzH5jZn1SbUuec4aZuZltXGnZeT4ufw+eMrOLC7ZlmJlNN7M5+fy7nyx5LHx/Uq+3QvvU6634/pe+5kptU6+3zbXm+FNgW2A8cDkwKb+/XUH7fYCeZfl+Vazr+oLHdwN65fd7AOcBdwEXAb3L2nYDjgY+m/98JPBT4JtA1/Ye16tby96AzsCzwNb5e/84sH2F9gOBXfL7GwELK7XP230H+BXwuyq36TrgG/n9bkCfRLstgOeAHvnPNwPHBu0+A+wCPFmSXQycnd8/G7iooP2+QJf8/kWN7aO2eT4I+CPwPLBxwbJHA38Cuuc/b1rQ/l5g//z+54EHi96f1Out0D71epPvf/lrrrDs5Ott61ur7QmY2Xjg12QzkM0AZub3b4y+adUylaWZ/bbsdhfwhcafE5s0GXgrvz8J6E32xr4FXFvW9lrgAOA0M5sKHAY8SjZp89VV/QKkRZnZpjW2719D808Cz7j7Ind/l+zv9uBUY3d/xd1n5ff/Bcwn+zBObcuWZH9PVf3tmFkvsg++a/J1vOvuKyo8pQvQw8y6ABsCLwfb/BDwell8MFlnQ/7vIZXau/u97t6Q/zgd2LLCsgEuBc4CPjD6JNH+FOBCd1+Vt1lW0N6BXvn93pS85grvT/h6U+0rvN5K7/8HXnOFtsnX2+Zaq3ch6/HW+tZM9q3m6SCfS74HAAwGHgNOy3+eXdZ2FnADMArYK//3lfz+XontmV/6/LLH5pT9/ET+bxfgVaBz/rM1PtYRbtTw7QHo34Lr7Q1cCCwA/pnf5udZn6B9L+ACYCpwZNljPwva9yu79QcWA32BfkH7C8m/aQLDgUXAM2TfxsK/h7Lnfwm4uuTno4CfVvm7GAy8QL6XmWhzK7Br/ndauCcADCP74jQFmE3WeXyoQvvTgDfJJhL/ZcG2ln6bXlH2+PJK7cseuwv4WoVljwUm5fcXU7InkGg/h2zv/FFgGjCioP12+e/9ReAl4CNF70/R6630fpa/3sTyq3nNjW0rvt62vLXmOYE1wOZBPjB/rFxnz2Yuw90Xk/2H2d/MLmHt+YyHA38DJgAr3f1B4G13n+bu0xLb86SZHZfff9zMhsN/JmJeXda2k5l1I9t925DsQw+gO9A1WriZ9TazC/NjiP/Mb/PzrE/QvpeZXWBmU83syLLHfha071d26w/MMLO+ZtavrO2FjcdgzWy4mS0CHjWz581sr2DZw/PjljfkxzDvM7OVZjbTzHYOXu7NwHJglLv3d/f+ZLu3y4FbgvbXkr2HtwFHmNltZtY9f2xk0P4fZO9v4+0xsm9Ps/L75Q5w98aJtCcCh7v7NsAY4CdB+3JR3fbCsdOWTbd6G/Btd38j0eZAYJm7/62K7WjUhezwx/9z952Bf5MdvoiW35fsG+5WZP/fPmRmX6thXTUzswlAA/DLxOMbkv3fPLeGxXYh6+RHAmcCN5tZpXr6pwCnu/sg4HTyvaay7Sh8f6ppn3q9pe3zx5OvOVh2ra+39bRW7wLsR/Zt7A9kF0ZcBdyTZ2sd4wf+DAwry7oA1wPvJdaxJdmHzk+BFwq2pzfZN6tnyXrf1WTfGKcBO5W1PT1/7HngVOB+4BdkeyvfTyz/j2TnPDYryTbLs/uC9reRfYM9BPht/nPj8cFZQfs1ZMd+S2+r838XlbWdW3L/AfJvGcBQgpolZN869we+QvbN6kt5vg/wSND+7xV+z2s9xtp7WhOAv5B9w49e6xn538qOJdlzFda5gPeP3U5P/S4qPP9TwB9Lfj4HOKfgOV3z9/w7Be0uAJaQfTNcSnb48YaC52wGLC75+dPA3Ym2hwHXlPx8NMHeVf7YYD74bfrvwMD8/sDy9668fZ4dAzwCbJhqSzb17LL8NS8m+4B8oez/Rvm23EP2paLx52eBTSq0X8n71zkZ8EbR+1Pp9abezwqv9wPtK73mxLZUfL1teWvdhWejj0YCXyTb5R5JfmglaLtl6R9J2WN7FKznAOBHVW7TRsBOZLvnAyq02xzYPL/fJ9/+T1Zo32E+GKnxQ5GSw22UdaaUHYrLs3vJjnsOKMkGkHV4fwrazwc6lWXHAE8Bz1f4e7gFuCR/zxZF7fK24/Jt2hv4AXAZ2TH184CpVfxNdCHr9Lfi/RPDH6/Q3si+nFxW4/+HUVR/Yvhh4GP5/R8AExPtdst/jxvm23UdMC7RdjAf/CCdyAdPlF5c0H4/YB7Bh1V527LHFlN8OOhk4P/m94eSfRmxCu3nk3+Ikn1Z+VvR+5N6vRXah6+3mve/8TVXWHbF19uWtzZf4fp6owN9MFLjhyLZN519yb5VPg8ckud7Ee859CU7qb6A7BDQ6/nruYj4mP3F5COtyvL9CM4PlbU5iOyk3NKCdqOAm8iOoc8Ffg+cSJWjuchGmCwk+0Y2oaDtnmSHi54gO7Y7B/h8FesYRfWdwDCyQ19PAHcAfSu0PS9/L54kO+/SPWhzI9l5s9VkeybHk33huB94Ov+3X0H7Z/IPq8bX/PNU27J1L+aDo4OiZXcjO8/3JNlhv70L2u9JdqjwcbI9+12L3p/U663QPvV6C99/3u8EUstOvt62vrXLStfHGx/8YHydD34wrvUfmFb+YCT9odglaLsT2e7qH8iG6U4CVpB1SLsnlr8t8FmqHM5Levjv/kXtyYb07tDE5RcOL9ZNt3q+tfsG1MMNOK6l25d9MFa9/JbYFrLzJH8n+4a6GDi45LHoUNa4GtvXuvya2uumm27v39p9A+rhRsFJ67Zs3xLLpobhvB2xvW666fb+rQvSIszsidRDZOcG2qx9a28LZcN5zWwUcKuZfYR4uGVHay8iOXUCLWcA8DmyE6WlDPhrG7dv7W1ZambD3H0OgLu/mY+Hn0w2VK6jtxeRnDqBlvM7skMSc8ofMLMH27h9a2/L0WTjoP/Ds8vrjzazK9eB9iKS06QyIiJ1TKWkRUTqmDoBEZE6pk5ARKSOqRMQEalj/x8nfM7y0UvXzAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# cluster 1\n", - "plt.figure()\n", - "fig, (ax,ax2) = plt.subplots(ncols=2)\n", - "fig.subplots_adjust(wspace=0.01)\n", - "\n", - "sns.heatmap(x_train[2], cmap = \"gist_gray\", cbar = False, ax = ax)\n", - "sns.heatmap(x_train[12], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", - "ax2.yaxis.tick_right()\n", - "ax2.tick_params(rotation=0)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "046a75eb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# cluster 2\n", - "plt.figure()\n", - "fig, (ax,ax2) = plt.subplots(ncols=2)\n", - "fig.subplots_adjust(wspace=0.01)\n", - "sns.heatmap(x_train[3], cmap = \"gist_gray\", cbar = False, ax = ax)\n", - "sns.heatmap(x_train[8], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", - "ax2.yaxis.tick_right()\n", - "ax2.tick_params(rotation=0)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "da357b54", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# cluster 3\n", - "plt.figure()\n", - "fig, (ax,ax2) = plt.subplots(ncols=2)\n", - "fig.subplots_adjust(wspace=0.01)\n", - "sns.heatmap(x_train[5], cmap = \"gist_gray\", cbar = False, ax = ax)\n", - "sns.heatmap(x_train[26], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", - "ax2.yaxis.tick_right()\n", - "ax2.tick_params(rotation=0)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "7286cdbb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# cluster 4\n", - "plt.figure()\n", - "fig, (ax,ax2) = plt.subplots(ncols=2)\n", - "fig.subplots_adjust(wspace=0.01)\n", - "sns.heatmap(x_train[21], cmap = \"gist_gray\", cbar = False, ax = ax)\n", - "sns.heatmap(x_train[54], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", - "ax2.yaxis.tick_right()\n", - "ax2.tick_params(rotation=0)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "38866d42", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAD7CAYAAACMlyg3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAApOElEQVR4nO3debgU5Zn+8e8jm+DKIoiAgkYMxhCNJOKYqIjjEo0kMWpiXBKNRjPBXXAZNTo/E4xGw0SzKOA+OiBxQRQhCjqJCiqioIArKLK5i6jI8vz+qCI2fZ7q6j4bnNP357r6os/db79VdfrQb1d11fOauyMiItVpo/W9AiIisv5oEBARqWIaBEREqpgGARGRKqZBQESkimkQEBGpYi0begFmpnNQa6F///41sjPPPDNs+8orr4T5xRdfHOarV6+u/Yo1I+5u+vssbbvttgvzKVOmhPnBBx8c5nPmzKmvVaoa7m55bczsIGA40AIY4e7DKl2O9gRERJogM2sBXAccDOwM/NjMdq60n9w9ATP7MjAI6AY4sBC4z91nV7owERGpN98EXnH31wDM7E6S9+oXK+mk5J6AmQ0F7gQMmAY8ld6/w8zOK/G8k83saTN7upKVERGRROH7aHo7uahJN+DNgp8XpFlF8vYETgS+4u4ri1buauAFIDz+5O7XA9enbXXMVUSkQoXvoxmi7wwqfr/N+05gDbBNkHdNHxMRkfVjAdCj4OfuJIfrK5K3J3AG8LCZvcwXux3bAl8CflXpwkREpN48BexoZr2At4AfAUdX2knJQcDdJ5hZb5IvILqR7H4sAJ5y96o4z7Bbt/gQW1Y+bdq0MM861e7SSy8N8wMPPLBG1rlz57DtW2+9FeZZp/FNmjQpzKV6de/ePczvuuuuMP/9738f5joVtPG4+yoz+xXwEMkpoqPc/YVK+8k9O8jd1wBPVr6KIiLSkNz9AeCBuvSh6wRERKqYBgERkSqmQUBEpIppEBARqWIaBEREqpgGARGRKtbgpaSbusMPPzzMf/rTn4b5CSecEOa33357mLdo0SLMu3TpUiNbvHhx2Hbu3Llh3rdv3zDXdQLVq2XL+L/8OeecE+YzZswI80ceeSTMs/6eVb58w6U9ARGRJsjMepjZZDObbWYvmNnptelHewIiIk3TKuBsd59uZpsBz5jZJHevv1LSkMwnYGYDzWzTovygytZXRETqi7svcvfp6f1lwGxqUUo6bz6B04B7gcHALDMbVPDwb0o8T/MJiIjUQRnzCRS27QnsBkytdDl5h4NOAnZ394/ThdxlZj3dfThxLWtA8wmIiNRVGfMJAJAepRkLnOHuH1W6nLxBoIW7f5yu0Dwz25dkINiOEoOAiIg0PDNrRTIA3O7uf6tNH3nfCSw2s13X/pAOCIcCnYCv1maBIiJSd2ZmwEhgtrtfXdt+8vYEjiP5Bvpf3H0VcJyZ/bW2C90QZZ3fvOOOO4Z5nz59wvzuu+8O86y5AJLXsbw8q49nn302zEeNGhXmUr2OOeaYMN9///3D/Pvf/36YT58+Pcwvu+yyML/yyivLWDup0F7AscBMM5uRZhek5aXLljepzIISj/2zkgWJiEj9cfd/UA+H5XWxmIhIFdMgICJSxTQIiIhUMQ0CIiJVTIOAiEgV0yAgIlLFVEU0lVXv/P777w/zjz6Kr87edtttw/z9998P8wEDBoT5LrvsEuaRBx98sKJlSvPXqVOnMD/11FPD/MQTTwzzhQsXhvny5cvDPOv6GWkYZtYCeBp4y90PrU0f2hMQEWm6TiepHlprFQ8CZnZLXRYoIiJ1Z2bdgUOAEXXpp+ThIDO7rzgCBpjZlgDuflhdFi4iIrX2B2AIsFldOsn7TqA78CLJSOMkg0A/4PelnpTWvc6sfS0iIqUF76PXp+WlMbNDgaXu/kxa3bnW8gaBfiTHnC4EznX3GWb2qbs/WupJmk9ARKRucuYT2As4zMy+A2wMbG5mt7l7XCGwhLwCcmuAa8xsTPrvkrzniIhIw3L384HzAdI9gXNqMwBAmW/oaTXRI8zsEKDimWuasoceeqiiPMv48ePDvD5OBb3hhhsqWhdp/o499tgwzzq1c+bMmWHepUuXMF+xYkWY6xTRpqeiT/XuPh6I381ERKTRufsUYEptn6/rBEREqpgGARGRKqZBQESkimkQEBGpYhoERESqmAYBEZEqpgu/6tlRRx0V5gceeGCYf/7552G+bNmyGtmTTz4Ztj3ggAPCfN68eWE+a9asMHfXxd1NTdu2bcP8hBNOCPOVK1eG+S23xHUhv/Od74T5nDlzwvzjjz8OczMLc/3NrX/aExARaaLMbEszu8vM5pjZbDPbs9I+tCcgItJ0DQcmuPsPzaw10K7SDkruCZjZHma2eXq/rZldambjzOwKM9uidussIiJ1lb437w2MBHD3z939g0r7yTscNAr4JL0/HNgCuCLNbiyxcieb2dNm9nSlKyQiIuu+j6a34vL82wNvAzea2bNmNsLMNql0OXmHgzZy91Xp/X7u/vX0/j/MbEbWk1RKWkSkbnJKSUPy/v11YLC7TzWz4cB5wEWVLCdvT2CWmf0svf+cmfUDMLPeQHyagYiINIYFwAJ3n5r+fBfJoFCRvEHg58A+ZvYqsDPwhJm9BtyQPiYiIuuBuy8G3jSzndJoIMlMkBWxcs7TNbPNSI4/tSQZeZaUvYBmejho2223DfM777wzzHffffcwz/r9f/LJJzWyzTffPGy70UbxWD527NgwHz16dEXt16xZE+ZNnbtbU//73GST+BDwM888E+a9e/euqP+s8/uz/iayrkM4++yzw/y6666raH2qibvHv/wCZrYryfS/rYHXgJ+5+/uVLKfcSWWWAc9V0rGIiDQsd59BMg1wreliMRGRKqZBQESkimkQEBGpYhoERESqmAYBEZEqpkFARKSKqYpojpYt41/ROeecE+Z77llxJddQmzZtamRZ8wl87WtfC/NBgwaFea9evcJ8p512CvPLL788zFULfv1bvnx5mF966aVhfsMNN4R5q1atwrx169ZhvmLFijAfM2ZMmD/00ENhLnVjZmeSXLjrwEyS6wQ+q6QP7QmIiDRBZtYNOI2krtsuQAvgR5X2o0FARKTpagm0NbOWJHMJLKy0g7z5BFqb2XFmtn/689Fmdq2Z/YeZxfuPIiLS4Nz9LeAq4A1gEfChu0+stJ+8PYEbgUOA083sVuAIYCrwDZJ6FSHNJyAiUjd58wmYWXtgENAL2AbYxMyOqXQ5eV8Mf9Xd+6a7Gm8B27j7ajO7jRK1hDSfgIhI3ZQxn8D+wOvu/jaAmf0N+DfgtkqWk7cnsFE6b+VmJMeb1k4p2QbQ4SARkfXnDaC/mbWzpNzrQGB2pZ3k7QmMBOaQfOt8ITAmnU+gPxDXTBYRkQaXziZ2FzAdWAU8S+k9h1DufAJmtk26wIVmtiXJLsgb7j6trAU08cNBF1xwQZhfdtllYZ5V2z/LO++8E+YXX3xxjSyr3v+3vvWtMD/jjDPCvG3btmH+9NPxVzjnn39+mH/44Ydh3lQ0h/kEsmT9HZ555plhftVVV1XU//XXx+81v/jFLyrqR7KVM59Afci9WMzdFxbc/4BkCjMREWkGdJ2AiEgV0yAgIlLFNAiIiFQxDQIiIlVMg4CISBVTKelUjx49wny33XYL81WrVoX5ypUrw3zJkiVhfsIJJ4T5Y489FuaRu+++O8zHjRsX5j/84Q/DvG/fvmE+ZMiQML/wwgvLWDtZH9asWRPmlZ7CvHTp0jAfOnRoxeskGybtCYiINEFmNsrMlprZrIKsg5lNMrOX03/b5/WTV0V0CzMbZmZzzOzd9DY7zbash+0QEZHauQk4qCg7D3jY3XcEHk5/LilvT2A08D6wr7t3dPeOwIA0i6cQEhGRBufujwHvFcWDgJvT+zcD38vrJ28Q6OnuV7j74oIFL3b3K4Bts56kUtIiInWTV0o6Qxd3XwSQ/ts57wl5XwzPN7MhwM3uviRdsS7AT4E3s56kUtIiInVTRinpepG3J3AU0BF41MzeM7P3gClAB5IJZkREZMOxxMy6AqT/xqd3FSg5CLj7++4+1N2/7O4d0lsfdx9KGceaRESkUd0HHJ/ePx64N+8JuaWkM59o9oa7Z34vUNBugzoctN1224X5U089FeYbb7xxmLdu3TrM33333TD/9re/HeavvfZamDekrHW/7777wjyrVHVWPmPGjFqtV2NrzqWkW7RoEeZz5swJ8y996UthftZZZ4X5NddcU7sVk7LllZI2szuAfYFOwBLgEuAekhN6tiWZdOYIdy/+8ngdJb8TMLPnsx4CupR6roiINBx3/3HGQwMr6Sfvi+EuwIEkp4QWMuDxShYkIiIbnrxB4H5gU3efUfyAmU1piBUSEZHGU3IQcPcTSzx2dP2vjoiINCbVDhIRqWIaBEREqpgGARGRKtZs5xPYcccdw/yBBx4I86222irMP/vsszCfMGFCmP/iF78I86z5BDYk994bX1fSr1+/MD/44IPDvKlcJ9CcZZ3f36lTpzB///3iEwATO+ywQ5g//PDDYZ51nczEiRPDfMSIEWEu+cxsFHAosNTdd0mzK4HvAp8DrwI/c/cPSvWjPQERkabpJmqWkp4E7OLufYGXgPPzOsmbT2BzM/utmd1qZkcXPfanytZXRETqS1RK2t0nuvvaaQ+fBLrn9ZO3J3AjyYVhY4EfmdlYM2uTPta/slUWEZFGdALwYF6jvEFgB3c/z93vcffDgOnAI2bWsdSTNJ+AiEjd1HI+gbXPvRBYBdye1zbvi+E2ZraRu68BcPfLzWwB8BiwadaTNJ+AiEjd1HY+ATM7nuQL44FeRoXQvD2BccB+RSt2M3A2ybfPIiKygTCzg4ChwGHu/kk5z8krGzEkI59gZr+pfBVFRKQ+FJaSTo/QXEJyNlAbYJKZATzp7qeU7Ke5zicwYMCAMP/rX/8a5ttuG2/KsGHDwnz48OFhnnW+dVNw5JFHhvnIkSPDPOuc8D59+oT5p59+WrsVayDNYT6BNm3ahHnW9TD77bdfmGe9D7z99tsVLTdr/o333otL2mddz7N8+fIwryZ58wnUF80nICJSxTSfgIhIFdN8AiIiVUzzCYiIVDHVDhIRqWIaBEREqlizLSW9aNGiMN9+++3DfPXq1WE+bdq0MG/oU0HTc3zXkXX63XbbbRfm3//+98P80UcfDfOsUzhbtWoV5osXLw7zFStWhLnUv6xy3lmngs6cOTPMzz333DCfPHlymGedBjx16tQw79q1a5j37x+XIMsqVS31r+I9ATPr3BArIiIi5TOzUWa21MxmBY+dY2ZuZvEEEgXySkl3KLp1BKaZWXsz61CH9RcRkbq5iZrzCWBmPYB/B94op5O8w0HvAPOLsm4k1UQdiI+tiIhIg3L3x8ysZ/DQNcAQIJ4qsEje4aAhwFySYkS93L0XsCC9nzkAqJS0iEjd1KaUtJkdBrzl7s+Vu5y86wSuMrM7gWvM7E2SAkW5tVZUSlpEpG4qLSVtZu2AC4EDKllO7hfD7r7A3Y8AJpPMX9mukgWIiEij2AHoBTxnZvNIppacbmZbl3pS2WcHufs4YACwP4CZ/azWqyoiIvXK3We6e2d37+nuPYEFwNfdPT6XO9VsS0lnnX/8+ONx3bsLLrggzK+44oowr/T31r17PN/zwIEDw3yPPfaoke21115h26ySzrvsskuYt2/fPsynTJkS5v369QvzrOsBssoDL1u2LMzXl6ZUSrply/jI7XPPxYd+s64pOfzww8N8xowZtVqvYjfffHOYH3fccWE+evToMD/xxLhiTVRiurbvYRu6vFLShfMJAEuAS9x9ZMHj84B+7v5OqX5USlpEpAly9x/nPN6znH5USlpEpIqplLSISBVTKWkRkSqmKqIiIlVMg4CISBXTICAiUsVqfZ1A2QtYT+dhZ51X/YMf/CDMx40bF+ZZNfbbtGkT5gccEF+xPWzYsDDv0CEuxhqdg9+jR4+w7UUXXRTmc+fODfMxY8aE+cqVK8P8s88+C/Os38GAAQPC/Iknngjz9aUpXSdw4IEHhvnll18e5kOHDg3zhq7Tf8YZZ4T51VdfHebRvBkAS5cuDfPXX3+9RnbZZZeFbR944IEwbyrKuE5gFHAosNTddynIBwO/AlYB4919SKl+ajOfQMdKnyMiIvXuJopKSZvZAGAQ0NfdvwJclddJ3nwCw9ZOSmBm/czsNWCqmc03s31qu+YiIlI37v4Y8F5RfCowzN1XpG3iXaoCeXsChxRccnwlcJS7f4lkwoLfV7bKIiLSwHoD3zazqWb2qJl9I+8JeYNAKzNbe3C9rbs/BeDuLwHxAWE0n4CISF3VZj4Bkmu/2gP9gXOB0Zb1xUvBE0q5DnjAzIYBE8zsD8DfgIHAjKwnaT4BEZG6qXQ+gdQC4G+enPEzzczWkBSYezvrCXlXDP/RzGaSHGfqnbbvDdwD/FeFKyciIg3rHmA/YIqZ9QZak0wTnClvTwB3nwJMKc7T+QRurMVKiohIHRWWkjazBSQzP44CRpnZLOBz4HjPuQ4gdxAo4VI24EFg1apVYZ5VvzxLixYtwvy//iveETr66LikUrdu3cJ8zZo1Yb569eoa2RtvvBG2XbJkSZh/97vfDfOs303WdQJZ11xk5V/5ylfCfEO7TqApmTNnTpjvvffeYf7JJ5805OpkyrqWJeewdA2dO3cuO//jH/8Ytn322WfDfNGiRRWty4aqRCnpYyrpR/MJiIhUMc0nICJSxTSfgIhIFdN8AiIiVUxVREVEqpgGARGRKtZsS0lXKut0x9NPPz3Ms8rXZv0+27ZtG+aLFy8O88GDB9fI3n47vujvl7/8ZZgffvjhYd6qVaswnzVrVphvv/32Yd6uXbswX758eZjvvPPOYZ516mtDa0qlpNeXrFM7o79PgCFD4qrFHTvGxYcnT54c5lddFRe/vOOOO2pkWaeTZp2uHfWxIcorJV1ftCcgItIEmdkoM1uaXhi2NtvVzJ40sxlpvaFv5vWTV0q6n5lNNrPbzKyHmU0ysw/N7Ckz260+NkRERGrlJormEwB+B1zq7rsCF6c/l5S3J/CntJPxJNcF/NXdtwDOSx8TEZH1IGM+AQc2T+9vASzM6ye3lLS7P+judyTL9LvShT8MbJz1JJWSFhGpm1qWkj4DuNLM3iSZVez8vCfkXSz2mZkdQDKiuJl9z93vSWcVq1ncJqVS0iIidVPLUtKnAme6+1gzOxIYCexf6gl5ewKnAGcDJ5CUjxhgZh+QHAo6rcKVExGRhnU8yZwvAGOAun0x7O7PufuB7n6wu89x99Pdfct0AuOd6r6+IiJSjxYCa+d/3w94Oe8JzbaUdJas857POuusMD/11FPD/N133w3zrDLN//jHP8J8hx12CPOLLrqoRtanT5+wbevWrcN84cL4O6EVK1aEedb5/VnXOGT5+OOPwzzrdybr36677hrmI0aMCPNzzz03zLP+zo888sgwz7pO5p///GeYV1KSun///mHeVK4TyJMxn8BJwPB0WuDPgNzvEVRKWkSkCSoxn8DulfSjUtIiIlVMpaRFRKqYSkmLiFQx1Q4SEaliGgRERKqYBgERkSpWl+sEmqSePXuGedb1AC+/HF9r8eqrr4b5z3/+8zA/4ogjwvyDDz4I86222qpG1qJFi7BtliVLloT5PffcE+Y/+clPwvzzzz8P8zZt2oT5xIkTwzzrOgRpPN27dw/zcePGhXnWa79gwYIwf/PNNytan/nz54d51rUsUf/R/xWAm266qaJ1qVZ5paS3MLNhZjbHzN5Nb7PTbMtGWkcRESmSlvefnL4nv2Bmp6d5h7Ts/8vpv+1L9ZN3OGg0yTUC+7p7R3fvCAxIszH1sSEiIlIrq4Cz3b0P0B/4DzPbmaTU/8PuviPwcPpzprxBoKe7X+Hu/7q2290Xu/sVwLZ1Wn0REak1d1/k7tPT+8uA2UA3YBBwc9rsZuB7pfrJGwTmm9kQM/tXiQgz62JmQ4HMg3+aT0BEpG4qmU/AzHoCuwFTgS7uvgiSgQKIJ2FO5X0xfBTJrsSj6UDgwBLgPiCuCIXmExARqaty5xMws02BscAZ7v5RJUX2IP+K4ffN7EZgEvCku/+rPKSZHQRMqGhpIiJSb8ysFckAcLu7r51HYImZdXX3RWbWFVhaqo+8s4NOA+4FfgXMMrNBBQ//pvarLiIidWHJR/6RwGx3v7rgoftIJpch/ffeUv3kHQ46Cdjd3T9OjzndZWY93X04SSXRJifrXPsuXeLK2FnXFQwcODDMN9ooHlfXrFkT5lm1+qN+3OMja5MnTw7zX/7yl2E+ZcqUMM/6HWSte5aWLavu8pMNTtYcE1nzAHTuHB823muvvcI86/qZPfbYI8yzzvvPuqakY8eOYb7jjjvWyF5//fWw7fPPZ1XCbzb2Ao4FZprZjDS7ABgGjDazE4E3gPgipVTe/9YWaw8Bufs8M9uXZCDYjiY6CIiINAfu/g+y34fjT6mBvLODFpvZrgUL/Rg4FOgEfLXchYiIyIYpbxA4Dlhn/jd3X+XuxwF7N9haiYhIo8g7OyguEJI8Fk8CKiIiTYaqiIqIVDENAiIiVazqzuV75ZVXwvy2224L85NOOinMs04FzZJ1yl7W6ZTjx4+vkd1yyy1h26xtGjt2bJhvvfXWYZ4l67TarFNH77zzzor6l/q35557hvlpp50W5i+88EKYT58+PcyzTifu1atXmF977bVh/sQTT4R5Vun1zTbbrEaWVTJ69erVYS7ryrtYbHMz+62Z3WpmRxc99qeGXTUREclSopT0lWn5/+fN7O68sv95H2dvJDkPdSzwIzMba2ZrZxLpX9eNEBGRWssqJT0J2MXd+wIvAeeX6iRvENjB3c9z93vc/TBgOvCImcWX84mISKPIKiXt7hPdfVXa7Ekgnk4ulfedQBsz28jd16QLutzMFgCPAZtmPSkteZpZ9lREREoL3kevTyuLRm178kUp6UInAP9bajl5g8A4YD/g72sDd7/ZzJYAf8x6kkpJi4jUTW1LSRfkF5IcMrq91PNLHg5y9yHAAjMbmC5obT4BiE8zEBGRRpFRShozO56kxM9PPKvyZCrv7KDBJGVIB1OzlPTltV1xERGpm6xS0ulcL0OBw9z9k7x+8g4HnUwzKyWd5aKLLgrzrl27hvl7770X5u3btw/zrHP8586dG+YvvvhijWznnXcO206YEM/t06lTpzCvVNa2Zp1zfv/999fLcqX2ss7jz7Jy5cowz7oe5j//8z/DPKsE9Lhx4ypan27duoV5dG3KiBEjKuq7GckqJf3fQBtgUjrL2JPufkpWJyolLSLSBJUoJf1AJf2olLSISBVTKWkRkSqmUtIiIlVMVURFRKqYBgERkSqmQUBEpIpVPJ+AmXV296UNsTLr05IlS8J80KBBYZ5zEV7Z7TfeeOMwv/zymtfiDR48OGzbrl27MM+qp75s2bIwf+mll8L82GOPDfPZs2eHuax/CxcurKh93759w/yZZ54J81atWoX58uXLwzzrepgsWdcJTJ1aXBonvqZGypd3xXCHoltHYJqZtTezDo20jiIiUiRrPoGCx88xMzezkleN5u0JvAPML8q6kZSUdmD7SldcRETqxdr5BKab2WbAM2Y2yd1fNLMewL8Db+R1kvedwBBgLkkNil7u3gtYkN7XACAisp5kzSeQPnwNyft37nHrvCqiVwE/By42s6vT0Sa3UzM72cyeNrOn89qKiEhNhe+j6S1zjpbC+QTM7DDgLXd/rpzl5H4xnF4wdoSZfZdk2rL4W8h1n6P5BERE6qA28wmQHCK6EDig3OXkniJqZl82s4HAZGAAsH+aH1TuQkREpP4F8wnsAPQCnjOzeSRTS043s62z+sg7O+g0CuYTAA5w91npw7+p8xaIiEitRPMJuPtMd+/s7j3dvSewAPi6uy/O6ifvcNBJVMl8Almi+uW1sckmm4T5ddddF+bRuflZtd0/+OCDMJ84cWKYjxkzJszHjx8f5p9++mmYy4arZcv4v3al17fssMMOYZ71N/frX/86zLOuw8laz/79+4f5bbfdViNbtWpV0LIqhPMJuHtFpaQ1n4CISBNUYj6BwjY98/rRfAIiIlVM8wmIiFQxzScgIlLFVEVURKSKaRAQEaliFQ8CaSVRERFpBkp+J2Bmw4Cr3P0dM+sHjAbWpFepHefujzbGSjYlffr0CfM///nPYb7PPvuU3ferr74a5qecckqY//3vfy+7b2leHn/88TC/8cYbw/zQQw8N8/nzi4sIJ+69994w/8tf/lLG2n1hp512CvOs6xMmTZpUUf/NWVop9BZga2ANcH16DRdmNhj4FUkZifHuPiSrn7zrBA5x9/PS+1cCR7n7U2bWG/gfoF/dNkNERGopLCUNdAEGAX3dfYWZdS7VSd4g0MrMWrr7KqCtuz8F4O4vmVmbetgIERGpBXdfBCxK7y8zs7WlpE8Chrn7ivSxkjNB5n0ncB3wgJntB0wwsz+Y2d5mdikwI+tJKiUtIlI3tS0lDfQGvm1mU83sUTP7Rqnl5F0n8EczmwmcmnbcMv33HuD/lXieSkmLiNRBbUpJu/tHZtYSaA/0B74BjDaz7T2jcFQ5E80vTldk6to6QumCDwImlPF8ERFpAEEpaUgqh/4tfdOfZmZrSEr9vB31UVEpaTMbVPCwSkmLiKwnUSnp1D3Afmmb3kBrkvni435KlZZNDwXtWVhKGrjV3Yeb2bPuvlsZK9osDwcNGDAgzLNOkevdu3eYr1y5MsxHjhxZI7vkkkvCtkuXlvzeRzK4uzXXv88srVq1CvOBAweG+dy5c8N83rx5YV5pqeprr702zLffPp7C/LDDDquRNddS0u5eskKomX0L+D9gJskpogAXAH8HRgG7Ap8D57j7I1n9qJS0iEgTlFNK+phy+1EpaRGRKqZS0iIiVUylpEVEqpiqiIqIVDENAiIiVUyDgIhINXP3RrsBJ6t9/bTfkNalObTfENdJ7ZvGujRG+4a8Ne7C4Gm1r5/2G9K6NIf2G+I6qX3TWJfGaN+QNx0OEhGpYhoERESqWGMPArllUdV+g+i7Gts3xjLUvv7ab0jr0hjtG0zJAnIiItK86XCQiEgV0yAgIlLFNAiIkMyUZ2ZzzewVMzsvp20PM5tsZrPN7AUzO72M/luY2bNmdn+Z67Olmd1lZnPS5exZou2Z6XrMMrM7zGzjoM0oM1tqZrMKsg5mNsnMXk7/bZ/T/sp0fZ43s7vNbMustgXPOcfM3Mw6leo7zQenr8ELZva7nHXZ1cyeNLMZ6fy73yx4LHx9sra3RPus7S35+hduc6m2Wdvb6Bry/FPgy8BQ4L+B4en9PjntBwKbFuUHlbGsW3Ie3wPYPL3fFrgUGAdcAWxR1LY1SQXV/dOfjwauBf4DaLW+z+vVrX5vQAvgVWD79LV/Dti5RPuuwNfT+5sBL5Vqn7Y7C/gf4P4y1+lm4Ofp/dbAlhntugGvA23Tn0cDPw3a7Q18HZhVkP0OOC+9fx5wRU77A4CW6f0r1raP2qZ5D+AhYD7QKafvASSTobRJf+6c034icHB6/zvAlLzXJ2t7S7TP2t7M1794m0v0nbm9jX1rsD0BMxsK3Eky6cE04Kn0/h3RJ61KprI0s/uKbuOAH6z9OWOVRgGfpPeHA1uQvLCfADcWtb0ROAQ43cxuBY4AppJM2jyirF+A1Csz61xh+44VNP8m8Iq7v+bun5P83Q7Kauzui9x9enp/GTCb5M04a126k/w9lfW3Y2abk7zxjUyX8bm7f1DiKS2BtpZMMN4OWBis82PAe0XxIJLBhvTf75Vq7+4T3X3tNF5PAt1L9A1wDTAEWOfsk4z2pwLD3H1F2mZpTnsHNk/vb0HBNpd4fcLtzWpfYntLvf7rbHOJtpnb2+gaanQhGfFqfGom+VTzcpDPJN0DAHoCTwOnpz8/W9R2OnAbsC+wT/rvovT+PhnrM7vw+UWPzSj6+fn035bAEpIZ1iAZxJ5fXyN2sE1lf3oAOtbjcrcAhgFzgHfT2+w02zJovznwW+BW4Oiix/4UtO9QdOsIzAPaAx2C9sNIP2kC/YDXgFdIPo2Ffw9Fz/8hMKLg52OBa8v8XfQE3iDdy8xocxewe/p3mrsnQDIt4DTgJuBZksFjkxLtTwc+JplI/PacdS38NP1B0ePvl2pf9Ng44JgSfR8GDE/vz6NgTyCj/QySvfOpwKPAN3La90l/728CbwHb5b0+edtb6vUs3t6M/svZ5rVtS25vY94a8juBNcA2Qd6VL+bDLLTOVJYk/2EONrOrqTmFWj/gGeBC4EN3nwJ86u6PuvujGeszy8x+lt5/zsz6wb8mYi6e6HcjM2tNsvvWjuRND6ANEE7SamZbmNmw9Bjiu+ltdpptGbTf3Mx+a2a3mtnRRY/9KWjfoejWEZhmZu3NrENR22Frj8GaWT8zew2YambzzWyfoO9+6XHL29JjmJPM7EMze8rMonmkRwPvA/u6e0d370iye/s+MCZofyPJazgW+JGZjTWzNulj/YP275C8vmtvT5N8epqe3i92iLuvnUj7SuAod/8S8O/A74P2xaIp+nLPnTazTUm26Qx3/yijzaHAUnd/poz1WKslyeGPP3syj/dyksMXUf/tST7h9iL5/7aJmZU9tWBtmNmFwCrg9ozH25H837y4gm5bkgzy/YFzgdFmVmoK21OBM929B3Am6V5T0Xrkvj7ltM/a3sL26eOZ2xz0Xen2NpyGGl2Ag0g+jT1IcmHE9cCENKtxjB94BNi1KGsJ3AKszlhGd5I3nWuBN3LWZwuST1avkoy+K0k+MT4KfK2o7ZnpY/OB04CHgRtI9lYuyej/IZLvPLYuyLZOs0lB+7Ekn2C/B9yX/rz2+OD0oP0akmO/hbeV6b+vFbWdWXB/MumnDKA3Qc0Skk+dBwM/Jvlk9cM0Hwg8EbSfW+L3XOMxau5pXQj8k+QTfrSt56R/K18tyF4vscw5fHHs9sms30WJ5+8JPFTw8/nA+TnPaZW+5mfltPstsIDkk+FiksOPt+U8Z2tgXsHP3wbGZ7Q9AhhZ8PNxBHtX6WM9WffT9Fyga3q/a/FrV9w+zY4HngDaZbUlmXp2abrN80jeIN8o+r9RvC4TSD5UrP35VWCrEu0/5IvrnAz4KO/1KbW9Wa9nie1dp32pbc5Yl5Lb25i3hu08OfuoP3A4yS53f9JDK0Hb7oV/JEWP7ZWznEOA35S5TpsBXyPZPe9Sot02wDbp/S3T9f9mifYbzBsjFb4pUnC4jaLBlKJDcWk2keS4Z5eCrAvJgPf3oP1sYKOi7HjgBWB+ib+HMcDV6Wv2WtQubTs4Xaf9gF8DfyA5pn4pcGsZfxMtSQb9XnzxxfBXSrQ3kg8nf6jw/8O+lP/F8P8BO6X3fw1cmdFuj/T32C5dr5uBwRlte7LuG+mVrPtF6e9y2h8EvEjwZlXctuixeeQfDjoFuCy935vkw4iVaD+b9E2U5MPKM3mvT9b2lmgfbm85r//abS7Rd8ntbcxboy+wud7YgN4YqfBNkeSTzgEknyrnA99L832I9xzak3ypPofkENB76fZcQXzM/nekZ1oV5QcRfD9U1Oa7JF/KLc5pty/wvyTH0GcCDwAnU+bZXCRnmLxE8onswpy23yI5XPQ8ybHdGcB3yljGvpQ/COxKcujreeAeoH2Jtpemr8Usku9d2gRt7iD53mwlyZ7JiSQfOB4GXk7/7ZDT/pX0zWrtNv8lq23Rsuex7tlBUd+tSb7nm0Vy2G+/nPbfIjlU+BzJnv3uea9P1vaWaJ+1vbmvP18MAll9Z25vY9/Wy0Kb44113xjfY903xhr/gWngN0ay3xRbBm2/RrK7+iDJabrDgQ9IBqR/y+j/y8D+lHk6L9mn/x6c157klN5datl/7unFuulWzbf1vgLVcAN+Vt/ti94Yy+6/PtaF5HuSuSSfUOcBgwoeiw5lDa6wfaX9V9ReN910++K23legGm7kfGndmO3ro28qOJ13Q2yvm266fXFridQLM3s+6yGS7wYarX1DrwtFp/Oa2b7AXWa2HfHplhtaexFJaRCoP12AA0m+KC1kwOON3L6h12Wxme3q7jMA3P3j9Hz4USSnym3o7UUkpUGg/txPckhiRvEDZjalkds39LocR3Ie9L94cnn9cWb21ybQXkRSmlRGRKSKqZS0iEgV0yAgIlLFNAiIiFQxDQIiIlXs/wOIwo0Q2GvqfAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# cluster 5 \n", - "plt.figure()\n", - "fig, (ax,ax2) = plt.subplots(ncols=2)\n", - "fig.subplots_adjust(wspace=0.01)\n", - "sns.heatmap(x_train[4], cmap = \"gist_gray\", cbar = False, ax = ax)\n", - "sns.heatmap(x_train[6], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", - "ax2.yaxis.tick_right()\n", - "ax2.tick_params(rotation=0)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "e0c292a2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# cluster 6\n", - "plt.figure()\n", - "fig, (ax,ax2) = plt.subplots(ncols=2)\n", - "fig.subplots_adjust(wspace=0.01)\n", - "sns.heatmap(x_train[10], cmap = \"gist_gray\", cbar = False, ax = ax)\n", - "sns.heatmap(x_train[13], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", - "ax2.yaxis.tick_right()\n", - "ax2.tick_params(rotation=0)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "5c9e5d1f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# cluster 7\n", - "plt.figure()\n", - "fig, (ax,ax2) = plt.subplots(ncols=2)\n", - "fig.subplots_adjust(wspace=0.01)\n", - "sns.heatmap(x_train[24], cmap = \"gist_gray\", cbar = False, ax = ax)\n", - "sns.heatmap(x_train[25], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", - "ax2.yaxis.tick_right()\n", - "ax2.tick_params(rotation=0)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "523d5272", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# cluster 8\n", - "plt.figure()\n", - "fig, (ax,ax2) = plt.subplots(ncols=2)\n", - "fig.subplots_adjust(wspace=0.01)\n", - "sns.heatmap(x_train[1], cmap = \"gist_gray\", cbar = False, ax = ax)\n", - "sns.heatmap(x_train[14], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", - "ax2.yaxis.tick_right()\n", - "ax2.tick_params(rotation=0)\n", - "\n", - "plt.show()\n", - "#sns.heatmap(x_train[17], cmap = \"gist_gray\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "a53a0ed4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# cluster 9\n", - "plt.figure()\n", - "fig, (ax,ax2) = plt.subplots(ncols=2)\n", - "fig.subplots_adjust(wspace=0.01)\n", - "sns.heatmap(x_train[0], cmap = \"gist_gray\", cbar = False, ax = ax)\n", - "sns.heatmap(x_train[7], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", - "ax2.yaxis.tick_right()\n", - "ax2.tick_params(rotation=0)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "e7d36a15", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# cluster 10\n", - "plt.figure()\n", - "fig, (ax,ax2) = plt.subplots(ncols=2)\n", - "fig.subplots_adjust(wspace=0.01)\n", - "sns.heatmap(x_train[19], cmap = \"gist_gray\", cbar = False, ax = ax)\n", - "sns.heatmap(x_train[30], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", - "ax2.yaxis.tick_right()\n", - "ax2.tick_params(rotation=0)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "2786386d", - "metadata": {}, - "source": [ - "## 2. Models" - ] - }, - { - "cell_type": "markdown", - "id": "3fc3e521", - "metadata": {}, - "source": [ - "### 2.1 Kmeans" - ] - }, - { - "cell_type": "markdown", - "id": "9bef3c89", - "metadata": {}, - "source": [ - "###    2.1.1 Fitting Models" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "289d8d9e", - "metadata": {}, - "outputs": [], - "source": [ - "# import KMeans from sklearn \n", - "from sklearn.cluster import KMeans\n", - "\n", - "# assigned 10 clusters \n", - "# fit() to compute clustering for K-Means\n", - "# fit_predict() to make predictions\n", - "kmeans = KMeans(n_clusters = 10).fit(x_trainf)\n", - "y_pred_kmean = kmeans.predict(x_testf)" - ] - }, - { - "cell_type": "markdown", - "id": "67ccad57", - "metadata": {}, - "source": [ - "###    2.2.2 Clustering results evaluations" - ] - }, - { - "cell_type": "markdown", - "id": "f2e23193", - "metadata": {}, - "source": [ - "####     NMI (Normaliszed Mutual Information)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "955c19c9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test score:0.3075\n" - ] - } - ], - "source": [ - "from sklearn.metrics.cluster import normalized_mutual_info_score\n", - "print(\"Test score:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_kmean,average_method='arithmetic')))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "40e0dea9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test score:0.3204\n" - ] - } - ], - "source": [ - "from sklearn.metrics.cluster import normalized_mutual_info_score\n", - "print(\"Test score:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_kmean,average_method='min')))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "01329398", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test score:0.3077\n" - ] - } - ], - "source": [ - "from sklearn.metrics.cluster import normalized_mutual_info_score\n", - "print(\"Test score:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_kmean,average_method='geometric')))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "ac0103e6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test score:0.2956\n" - ] - } - ], - "source": [ - "from sklearn.metrics.cluster import normalized_mutual_info_score\n", - "print(\"Test score:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_kmean,average_method='max')))" - ] - }, - { - "cell_type": "markdown", - "id": "5d7c0620", - "metadata": {}, - "source": [ - "####     ARI (Adjusted Rand Index)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "779c4a21", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test score:0.1616\n" - ] - } - ], - "source": [ - "from sklearn.metrics.cluster import adjusted_rand_score\n", - "print(\"Test score:{:.4f}\".format(adjusted_rand_score(y_test, y_pred_kmean)))" - ] - }, - { - "cell_type": "markdown", - "id": "eaf905f4", - "metadata": {}, - "source": [ - "####     Confusion Matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "49e50762", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Confusion matrix: \n", - "[[259 106 0 60 7 3 523 21 3 18]\n", - " [170 0 79 2 6 75 1 95 0 572]\n", - " [229 4 137 16 14 355 4 8 14 219]\n", - " [549 108 3 9 3 3 1 9 256 59]\n", - " [ 83 5 68 11 108 38 73 414 2 198]\n", - " [ 62 251 56 1 0 411 6 8 10 195]\n", - " [ 81 0 560 32 4 24 5 106 3 185]\n", - " [ 38 1 14 391 27 116 9 314 19 71]\n", - " [401 19 75 2 1 361 4 6 0 131]\n", - " [222 1 14 10 301 191 1 22 0 238]]\n" - ] - } - ], - "source": [ - "from sklearn.metrics import confusion_matrix\n", - "kmeans_confusion = confusion_matrix(y_test,y_pred_kmean)\n", - "print('Confusion matrix: \\n{}'.format(kmeans_confusion))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "ba637eb3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# visualize the confusion matrix, the deeper color represents the greater values\n", - "import seaborn as sns\n", - "sns.heatmap(kmeans_confusion, annot=False, cmap='Blues')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "ab647317", - "metadata": {}, - "source": [ - "### 2.2 PCA (Principal component analysis)" - ] - }, - { - "cell_type": "markdown", - "id": "06872c16", - "metadata": {}, - "source": [ - "###    2.2.1 Scatter Plots in 2D and 3D" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "64add44b", - "metadata": {}, - "outputs": [], - "source": [ - "# import PCA from sklearn\n", - "from sklearn.decomposition import PCA\n", - "\n", - "# implement PCA and keep the first two principal components only\n", - "pca = PCA(n_components = 2, whiten = True)\n", - "pca.fit(x_trainf)\n", - "\n", - "# transform data to reduce dimensions\n", - "x_train_pca = pca.transform(x_trainf)\n", - "x_test_pca = pca.transform(x_testf)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "fc524704", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline\n", - "plt.scatter(x_train_pca[:,0], x_train_pca[:,1], c = y_train)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "8baaf415", - "metadata": {}, - "outputs": [], - "source": [ - "# implement PCA and keep the first three principal components only\n", - "pca3 = PCA(n_components = 3, whiten = True)\n", - "pca3.fit(x_trainf)\n", - "# transform data\n", - "x_train_pca3 = pca3.transform(x_trainf)\n", - "x_test_pca3 = pca3.transform(x_testf)" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "a7c2b5b8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "plt.figure(figsize=(8,8))\n", - "ax = plt.axes(projection=\"3d\")\n", - "ax.scatter3D(x_train_pca3[:,0], x_train_pca3[:,1],x_train_pca3[:,2], c = y_train)\n", - "ax.view_init(10, 60)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "9f414ded", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.10869168 0.05363875]\n", - "[0.10869168 0.05363875 0.0409124 ]\n" - ] - } - ], - "source": [ - "print(pca.explained_variance_ratio_)\n", - "print(pca3.explained_variance_ratio_)" - ] - }, - { - "cell_type": "markdown", - "id": "4b55fa60", - "metadata": {}, - "source": [ - "###    2.2.2 KMeans using PCA" - ] - }, - { - "cell_type": "markdown", - "id": "5fbf7294", - "metadata": {}, - "source": [ - "### ARI" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "4b695f26", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test score:0.0848\n" - ] - } - ], - "source": [ - "from sklearn.cluster import KMeans\n", - "\n", - "# implement KMeans on transformed data\n", - "kmeans = KMeans(n_clusters = 10).fit(x_train_pca)\n", - "y_pred_trans_kmean = kmeans.predict(x_test_pca)\n", - "test_score = adjusted_rand_score(y_test, y_pred_trans_kmean)\n", - "print(\"Test score:{:.4f}\".format(test_score))" - ] - }, - { - "cell_type": "markdown", - "id": "42ef2a8d", - "metadata": {}, - "source": [ - "### NMI" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "981f11be", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test score:0.1828\n" - ] - } - ], - "source": [ - "test_score = normalized_mutual_info_score(y_test, y_pred_trans_kmean)\n", - "print(\"Test score:{:.4f}\".format(test_score))" - ] - }, - { - "cell_type": "markdown", - "id": "54dc4e2d", - "metadata": {}, - "source": [ - "####     Confusion Matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "57eb2033", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Confusion matrix: \n", - "[[ 1 2 398 105 41 34 103 82 234 0]\n", - " [369 99 23 0 61 15 73 28 18 314]\n", - " [337 39 33 5 146 5 38 114 7 276]\n", - " [ 13 3 207 170 187 7 99 283 31 0]\n", - " [ 77 198 38 1 165 134 188 36 35 128]\n", - " [331 51 55 213 34 0 16 23 9 268]\n", - " [333 129 4 0 66 45 83 18 8 314]\n", - " [ 18 185 32 2 190 209 150 177 15 22]\n", - " [107 104 71 23 148 6 82 161 17 281]\n", - " [119 138 12 1 266 40 124 162 4 134]]\n" - ] - } - ], - "source": [ - "from sklearn.metrics import confusion_matrix\n", - "kmeans_pca_confusion = confusion_matrix(y_test,y_pred_kmean_pca)\n", - "print('Confusion matrix: \\n{}'.format(kmeans_pca_confusion))" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "3eef5fab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "sns.heatmap(kmeans_pca_confusion, annot=False, cmap='Blues')" - ] - }, - { - "cell_type": "markdown", - "id": "0ec36a9a", - "metadata": {}, - "source": [ - "### 2.3 MiniBatchKMeans" - ] - }, - { - "cell_type": "markdown", - "id": "78ddd888", - "metadata": {}, - "source": [ - "###    2.3.1 Fitting Models" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "c4f713ae", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.cluster import MiniBatchKMeans\n", - "from sklearn.metrics.cluster import normalized_mutual_info_score\n", - "from sklearn.metrics.cluster import adjusted_rand_score\n", - "\n", - "# implement Mini-Batch K-Means\n", - "# assign 10 clusters \n", - "kmeans = MiniBatchKMeans(n_clusters=10,batch_size=5120).fit(x_train_pca)\n", - "y_pred_mbkm = kmeans.predict(x_test_pca)" - ] - }, - { - "cell_type": "markdown", - "id": "d5af13fa", - "metadata": {}, - "source": [ - "###    2.3.2 Clustering results evaluations" - ] - }, - { - "cell_type": "markdown", - "id": "a14ff10a", - "metadata": {}, - "source": [ - "####     NMI (Normaliszed Mutual Information) and ARI (Adjusted Rand Index)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "178c9a4a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NMI:0.1729\n", - "ARI:0.0817\n" - ] - } - ], - "source": [ - "print(\"NMI:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_mbkm,average_method='arithmetic')))\n", - "print(\"ARI:{:.4f}\".format(adjusted_rand_score(y_test, y_pred_mbkm)))" - ] - }, - { - "cell_type": "markdown", - "id": "05d87e3f", - "metadata": {}, - "source": [ - "####     Confusion Matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "91486cd4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Confusion matrix: \n", - "[[127 27 1 80 238 2 156 310 0 59]\n", - " [ 2 60 459 20 76 75 56 9 229 14]\n", - " [ 2 96 418 9 43 22 158 5 209 38]\n", - " [112 112 11 16 129 2 377 81 0 160]\n", - " [ 2 241 128 156 112 161 60 27 105 8]\n", - " [185 32 416 0 17 26 31 32 201 60]\n", - " [ 0 76 395 55 56 83 28 1 270 36]\n", - " [ 2 179 23 186 101 201 228 8 24 48]\n", - " [ 7 96 172 9 95 56 204 29 258 74]\n", - " [ 0 260 139 45 80 90 167 0 130 89]]\n" - ] - } - ], - "source": [ - "mbkm_confusion = confusion_matrix(y_test,y_pred_mbkm)\n", - "print('Confusion matrix: \\n{}'.format(mbkm_confusion))" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "c9ec58f1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.heatmap(mbkm_confusion, annot=False, cmap='Blues')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "e7bb3593", - "metadata": {}, - "source": [ - "### 2.4 Birch" - ] - }, - { - "cell_type": "markdown", - "id": "103a4fad", - "metadata": {}, - "source": [ - "###    2.4.1 Fitting Models" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "296530dd", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.cluster import Birch\n", - "\n", - "# implement birch\n", - "brc = Birch(n_clusters = 10).fit(x_train_pca)\n", - "y_pred_brc = brc.predict(x_test_pca)" - ] - }, - { - "cell_type": "markdown", - "id": "c21d97b8", - "metadata": {}, - "source": [ - "###    2.4.2 Clustering results evaluations" - ] - }, - { - "cell_type": "markdown", - "id": "cac10047", - "metadata": {}, - "source": [ - "####     NMI (Normaliszed Mutual Information) and ARI (Adjusted Rand Index)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "857fee12", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NMI:0.1808\n", - "ARI:0.0910\n" - ] - } - ], - "source": [ - "from sklearn.metrics.cluster import normalized_mutual_info_score\n", - "from sklearn.metrics.cluster import adjusted_rand_score\n", - "print(\"NMI:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_brc,average_method='arithmetic')))\n", - "print(\"ARI:{:.4f}\".format(adjusted_rand_score(y_test, y_pred_brc)))" - ] - }, - { - "cell_type": "markdown", - "id": "81466971", - "metadata": {}, - "source": [ - "####     Confusion Matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "b89536eb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Confusion matrix: \n", - "[[ 41 451 48 2 94 0 1 73 289 1]\n", - " [230 39 5 16 0 0 603 35 72 0]\n", - " [171 33 1 2 10 0 545 12 226 0]\n", - " [101 132 1 2 158 0 15 32 559 0]\n", - " [502 63 15 71 2 1 131 116 98 1]\n", - " [137 43 0 0 218 0 538 5 59 0]\n", - " [275 17 10 27 0 1 573 45 50 2]\n", - " [362 43 7 156 3 0 25 91 309 4]\n", - " [250 83 0 6 38 0 321 17 284 1]\n", - " [439 26 2 18 3 0 187 43 282 0]]\n" - ] - } - ], - "source": [ - "brc_confusion = confusion_matrix(y_test,y_pred_brc)\n", - "print('Confusion matrix: \\n{}'.format(brc_confusion))" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "c75904f9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.heatmap(brc_confusion, annot=False, cmap='Blues')" - ] - }, - { - "cell_type": "markdown", - "id": "3a50f7f3", - "metadata": {}, - "source": [ - "### 2.5 Gaussian mixture" - ] - }, - { - "cell_type": "markdown", - "id": "33c3d7e7", - "metadata": {}, - "source": [ - "###    2.5.1 Fitting Models" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "e98317fb", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.mixture import GaussianMixture\n", - "gm = GaussianMixture(n_components = 10).fit(x_train_pca)\n", - "y_pred_gm = gm.predict(x_test_pca)" - ] - }, - { - "cell_type": "markdown", - "id": "b4aa5c01", - "metadata": {}, - "source": [ - "###    2.5.2 Clustering results evaluations" - ] - }, - { - "cell_type": "markdown", - "id": "547683c2", - "metadata": {}, - "source": [ - "####     NMI (Normaliszed Mutual Information) and ARI (Adjusted Rand Index)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "52f39cb4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NMI:0.1823\n", - "ARI:0.0836\n" - ] - } - ], - "source": [ - "print(\"NMI:{:.4f}\".format(normalized_mutual_info_score(y_test, y_pred_gm,average_method='arithmetic')))\n", - "print(\"ARI:{:.4f}\".format(adjusted_rand_score(y_test, y_pred_gm)))" - ] - }, - { - "cell_type": "markdown", - "id": "337975a0", - "metadata": {}, - "source": [ - "####     Confusion Matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "09af68b9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Confusion matrix: \n", - "[[ 1 2 398 105 41 34 103 82 234 0]\n", - " [369 99 23 0 61 15 73 28 18 314]\n", - " [337 39 33 5 146 5 38 114 7 276]\n", - " [ 13 3 207 170 187 7 99 283 31 0]\n", - " [ 77 198 38 1 165 134 188 36 35 128]\n", - " [331 51 55 213 34 0 16 23 9 268]\n", - " [333 129 4 0 66 45 83 18 8 314]\n", - " [ 18 185 32 2 190 209 150 177 15 22]\n", - " [107 104 71 23 148 6 82 161 17 281]\n", - " [119 138 12 1 266 40 124 162 4 134]]\n" - ] - } - ], - "source": [ - "gm_confusion = confusion_matrix(y_test,y_pred_kmean_pca)\n", - "print('Confusion matrix: \\n{}'.format(gm_confusion))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "971194b5", - "metadata": {}, - "outputs": [], - "source": [ - "sns.heatmap(gm_confusion, annot=False, cmap='Blues')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "3a8306de", - "metadata": {}, - "source": [ - "## 3. Appendix" - ] - }, - { - "cell_type": "markdown", - "id": "ca1adee0", - "metadata": {}, - "source": [ - "### 3.1 KNN" - ] - }, - { - "cell_type": "markdown", - "id": "2efec242", - "metadata": {}, - "source": [ - "#### We also tried other models including KNN and various regression models to see how they perform. Note that these are not unsupervised models, and labels were used to train the models. Therefore, these implementations are not included in the final report. " - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "c27cb6d8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting KNeighborsClassifier(n_jobs=-1, n_neighbors=4, weights='distance')\n", - "Evaluating KNeighborsClassifier(n_jobs=-1, n_neighbors=4, weights='distance')\n" - ] - } - ], - "source": [ - "from sklearn.neighbors import KNeighborsClassifier\n", - "\n", - "clf = KNeighborsClassifier(n_neighbors=4, weights='distance', n_jobs=-1)\n", - "print('Fitting', clf)\n", - "clf.fit(x_trainf, y_train)\n", - "print('Evaluating', clf)\n", - "\n", - "y_pred_knn = clf.predict(x_testf)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "31b937dc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test accuracy: 0.921\n" - ] - } - ], - "source": [ - "test_score = clf.score(x_testf, y_test)\n", - "print('Test accuracy:', test_score)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "df13d267", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8231031975085954" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.metrics.cluster import normalized_mutual_info_score\n", - "normalized_mutual_info_score(y_test, y_pred_knn,average_method='arithmetic')" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "2226f1b5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8234272963838932" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.metrics.cluster import normalized_mutual_info_score\n", - "normalized_mutual_info_score(y_test, y_pred_knn,average_method='min')" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "09c6c71e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.823103261265742" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.metrics.cluster import normalized_mutual_info_score\n", - "normalized_mutual_info_score(y_test, y_pred_knn,average_method='geometric')" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "cb776587", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8227793536618937" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.metrics.cluster import normalized_mutual_info_score\n", - "normalized_mutual_info_score(y_test, y_pred_knn,average_method='max')" - ] - }, - { - "cell_type": "markdown", - "id": "12d38ecb", - "metadata": {}, - "source": [ - "####    Confusion Matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "4bef3167", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Confusion matrix: \n", - "[[910 0 1 1 3 26 0 15 44 0]\n", - " [ 1 910 27 1 4 3 39 0 8 7]\n", - " [ 9 6 880 45 8 15 14 3 18 2]\n", - " [ 1 1 18 969 0 5 3 1 2 0]\n", - " [ 13 12 11 16 885 10 14 2 28 9]\n", - " [ 1 5 36 8 2 931 12 0 3 2]\n", - " [ 3 2 20 6 8 3 951 2 2 3]\n", - " [ 1 8 11 4 9 6 14 912 21 14]\n", - " [ 0 13 9 6 0 7 12 0 952 1]\n", - " [ 2 24 10 2 3 4 13 5 27 910]]\n" - ] - } - ], - "source": [ - "from sklearn.metrics import confusion_matrix\n", - "knn_confusion = confusion_matrix(y_test,y_pred_knn)\n", - "print('Confusion matrix: \\n{}'.format(knn_confusion))" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "id": "2bc87615", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 102, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "sns.heatmap(knn_confusion, annot=True, cmap='Blues')" - ] - }, - { - "cell_type": "markdown", - "id": "98588e59", - "metadata": {}, - "source": [ - "### 3.2 Four Regression Models" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "84d84800", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training data set score: 0.337\n", - "Test data set score: 0.187\n" - ] - } - ], - "source": [ - "# Linear regression\n", - "from sklearn.linear_model import LinearRegression\n", - "lr = LinearRegression().fit(x_trainf,y_train)\n", - "print('Training data set score: {:.3f}'.format(lr.score(x_trainf, y_train)))\n", - "print('Test data set score: {:.3f}'.format(lr.score(x_testf, y_test))) # overfit" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "0f2cb297", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training data set score: 0.34\n", - "Test data set score: 0.19\n" - ] - } - ], - "source": [ - "# Ridge Regression\n", - "from sklearn.linear_model import Ridge\n", - "ridge = Ridge().fit(x_trainf, y_train)\n", - "print('Training data set score: {:.2f}'.format(ridge.score(x_trainf, y_train)))\n", - "print('Test data set score: {:.2f}'.format(ridge.score(x_testf, y_test))) # overfit" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "b3bee2c1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training data set score: 0.34\n", - "Test data set score: 0.19\n" - ] - } - ], - "source": [ - "# Lasso Regression\n", - "from sklearn.linear_model import Lasso\n", - "lasso = Lasso(alpha = 0.1, max_iter = 100000).fit(x_trainf, y_train)\n", - "print('Training data set score: {:.2f}'.format(lasso.score(x_trainf, y_train)))\n", - "print('Test data set score: {:.2f}'.format(lasso.score(x_testf, y_test))) # overfit" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "1f7c891d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training data set score: 0.838\n", - "Test data set score: 0.691\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "D:\\ANA\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n" - ] - } - ], - "source": [ - "# Logistic Regression\n", - "from sklearn.linear_model import LogisticRegression\n", - "logreg = LogisticRegression(max_iter=100).fit(x_trainf, y_train)\n", - "print('Training data set score: {:.3f}'.format(logreg.score(x_trainf, y_train)))\n", - "print('Test data set score: {:.3f}'.format(logreg.score(x_testf, y_test))) # overfit" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 7eef41380f8fa6f68f176dc97af25f1913d56112 Mon Sep 17 00:00:00 2001 From: zengmmm00 <50666176+zengmmm00@users.noreply.github.com> Date: Sun, 4 Dec 2022 13:42:09 +0800 Subject: [PATCH 26/27] Delete unsupervised.ipynb --- unsupervised.ipynb | 1837 -------------------------------------------- 1 file changed, 1837 deletions(-) delete mode 100644 unsupervised.ipynb diff --git a/unsupervised.ipynb b/unsupervised.ipynb deleted file mode 100644 index d7e169e..0000000 --- a/unsupervised.ipynb +++ /dev/null @@ -1,1837 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7ba70409", - "metadata": {}, - "source": [ - "# Unsupervised Models" - ] - }, - { - "cell_type": "markdown", - "id": "dd2ab19d", - "metadata": {}, - "source": [ - "## 1. Data and Clusters' Examples Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "0422e6d9", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "def load(f):\n", - " return np.load(f)['arr_0']\n", - "\n", - "# Load the data\n", - "x_train = load('./kmnist-train-imgs.npz')\n", - "x_test = load('./kmnist-test-imgs.npz')\n", - "y_train = load('./kmnist-train-labels.npz')\n", - "y_test = load('./kmnist-test-labels.npz')" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "4bf07b89", - "metadata": {}, - "outputs": [], - "source": [ - "# Flatten images\n", - "x_trainf = x_train.reshape(-1, 784)\n", - "x_testf = x_test.reshape(-1, 784)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "93c6ef34", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
indexcodepointchar
00U+304A
11U+304D
22U+3059
33U+3064
44U+306A
55U+306F
66U+307E
77U+3084
88U+308C
99U+3092
\n", - "
" - ], - "text/plain": [ - " index codepoint char\n", - "0 0 U+304A お\n", - "1 1 U+304D き\n", - "2 2 U+3059 す\n", - "3 3 U+3064 つ\n", - "4 4 U+306A な\n", - "5 5 U+306F は\n", - "6 6 U+307E ま\n", - "7 7 U+3084 や\n", - "8 8 U+308C れ\n", - "9 9 U+3092 を" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import pandas as pd\n", - "map = pd.read_csv(\"kmnist_classmap.csv\")\n", - "map" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "039fead3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# cluster 1\n", - "plt.figure()\n", - "fig, (ax,ax2) = plt.subplots(ncols=2)\n", - "fig.subplots_adjust(wspace=0.01)\n", - "sns.heatmap(x_train[2], cmap = \"gist_gray\", cbar = False, ax = ax)\n", - "sns.heatmap(x_train[12], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", - "ax2.yaxis.tick_right()\n", - "ax2.tick_params(rotation=0)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "046a75eb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAD7CAYAAACMlyg3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAoQElEQVR4nO3debgdVZnv8e+PjCBkxDAkQIKdgDQyyGBEmnmeQnNVEIVAVB6xQcAhBPG2jReR2QTHpiWMKiLQkRAEI5KA3RCIIZBgmAQCCQmBCAiGIcN7/6g6cthn1a69z5Scs3+f56nn7PPWqlWrsk/22lW16l2KCMzMrDGtt7YbYGZma487ATOzBuZOwMysgbkTMDNrYO4EzMwamDsBM7MG1rOjdyCpS49BHT58eDJ+/vnnJ+NbbbVVMn700Ucn48uXL29Ns2py2GGHJePTpk1LxhcvXpyMDxs2rN3atC6JCHX1v88ikpJxDwnvOiIi/SY2I+kQYBLQA/hZRFxY7358JmBm1gVJ6gH8CDgU2A74jKTt6q2n9ExA0rbAGGAoEMCLwG0RsaDenZmZWbvZHXg6Ip4BkHQj2Wf1n+uppOqZgKSzgRsBAQ8CD+WvfylpQpXtTpE0W9LsehpjZmaZ5p+j+XJKRZGhwAvNfl+Ux+pSdibweeCfI2JlReMuBx4DktefIuJK4Mq8rC9CmpnVqfnnaIHUPYO6P2/L7gmsATZPxDfL15mZ2dqxCNii2e/DyC7X16XsTOBM4G5JT/HeaceWwD8Bp9W7MzMzazcPASMljQAWA8cBx9dbSdVOICLulDSK7AbEULLTj0XAQxGxuu4md0Evv/xyMr7++usn43vuuWcy/sMf/jAZ/8xnPtO6htXg05/+dF3l33jjjQ5qiXWUomHAr776ajJ+//33d2RzrBNFxCpJpwF3kQ0RnRwRj9VbT+nooIhYAzxQfxPNzKwjRcQdwB1tqcPPCZiZNTB3AmZmDcydgJlZA3MnYGbWwNwJmJk1MHcCZmYNTB2dWra7po3o27dvMj5nzpxkfOTIkcn4jjvumIz/+c+154AqqnvevHk11wHFzzjMnt09U0B1pVTSm266aTI+ZcqUZPyLX/xiMl7v30S9evZMjzrv3bt3Mr5ixYqObE6XVksq6fbgMwEzsy5I0haS7pG0QNJjks5oTT0dPqmMmZl1iFXA1yJijqSNgD9Jmh4R7ZdKGrL5BCTtL2nDivgh9bXXzMzaS0QsiYg5+es3gAW0IpV02XwCXwF+A5wOzJc0ptnqC6ps5/kEzMzaoIb5BJqXHQ7sDMyqdz9ll4O+COwSEW/mO7lZ0vCImEQ6lzXg+QTMzNqqhvkEAMiv0twCnBkRf6t3P2WdQI+IeDNv0HOS9iHrCLaiSidgZmYdT1Ivsg7g5xFxa2vqKLsnsFTSTk2/5B3CEcDGwEdas0MzM2s7SQKuAhZExOWtrqfacwKShgGrImJpYt0nIuJ/amhoQ10OOvbYY5PxG2+8MRn/1a9+lYwfd9xxLWI9evRIlp06dWoyfuihhybjkyZNSsbPPPPMZLy76krPCRxxxBHJeNF7f/vttyfj3/3ud5PxJUuWJONvvfVWMr711lsn45dfnv4sGjRoUDJ+5ZXpqx0TJ05MxtesaZwJDcueE5C0J3AfMI/3Znr8Zp5eumZlk8osqrKutAMwM7OOERF/pB0uy/thMTOzBuZOwMysgbkTMDNrYO4EzMwamDsBM7MG5k7AzKyBeT6BdtarV69k/NFHH03Gt91222T8mGOOaRErGpt96aWXJuPz589PxovmDXj99deT8e6qKz0nMGLEiGT8oYceSsYHDx5cV/3vvPNOMv7uu+8m43369EnGi+YNKFI0n8CWW26ZjC9fvryu+ruyWuYTkNQDmA0sjoj0wyQlfCZgZtZ1nUGWPbTV6u4EJF3Xlh2amVnb5RkdDgd+1pZ6qj4xLOm2yhCwr6QBABFxVFt2bmZmrTYRGA9s1JZKyrKIDgP+TNbTBFknsCtwWbWN8rzXhbmvzcysusTn6JV5emkkHQEsi4g/5dmdW62sE9iV7JrTucA3ImKupLciYma1jTyfgJlZ25TMJ/AJ4ChJhwF9gX6SboiIz9W7n7IEcmuA70v6df7zpbJtzMysY0XEOcA5APmZwNdb0wFAjR/oeTbRT0k6HKh75ppGsnLlymR8/Pjxyfhtt1Xedsn85Cc/aRHr169fsuyCBenBAfvuu28y3mhDQbuDxYsXJ+NPP/10Ml7vENGiIZ9F8SJFQ86ffPLJZLxoeHMjDQVd2+r6Vh8R04BpHdQWMzOrU0TMAGa0dns/J2Bm1sDcCZiZNTB3AmZmDcydgJlZA3MnYGbWwNwJmJk1MKeS7iRSOivsXXfdlYwfeOCBNdc9atSoZPypp56quY5G1JVSSRfZZpttkvGbb745Gd9+++3bZb9vvvlmMp5KgQ5w9913J+Nr1qxpl/Z0R7Wkkm4PPhMwM+uiJA2QdLOkxyUtkPTxeutwCggzs65rEnBnRHxSUm9gg3orqHomIOljkvrlr9eXdJ6kqZIuktS/dW02M7O2yj+b9wKuAoiIdyPitXrrKbscNBlomv9tEtAfuCiPXV2lcadImi1pdr0NMjOz93+O5ktlev6tgZeBqyU9LOlnkj5Q737KLgetFxGr8te7RsRH89d/lDS3aCOnkjYza5uSVNKQfX5/FDg9ImZJmgRMAP5vPfspOxOYL+nk/PUjknYFkDQKSKfLNDOzzrAIWBQRs/LfbybrFOpS1gl8Adhb0l+A7YD7JT0D/Fe+zszM1oKIWAq8IKlpnPD+ZDNB1qVsUpnXgZMkbUR2/aknWc/zUr07anQjR45MxovGeddj//33T8b9nED398QTTyTjY8aMScbvueeeZHzLLbdMxt96661k/OKLL07G586dm4x39PNIDex04Of5yKBngJNLyrdQ66QybwCP1Fu5mZl1nIiYSzYNcKv5YTEzswbmTsDMrIG5EzAza2DuBMzMGpg7ATOzBuZOwMysgXk+gXZ29NFHJ+NXXpl++vuDH/xgMp56X4rmJFi5Mv3w9sEHH5yMF40VbzTdYT6BIltttVUyPnt2Op3XI4+kR4Bfc801yfhPfvKTZPz1119Pxu+4445k/IorrkjG58+fn4w3klrmE5B0FtmDuwHMA06OiLfr2Y/PBMzMuiBJQ4GvkOV12x7oARxXbz3uBMzMuq6ewPqSepLNJfBivRWUzSfQW9KJkg7Ifz9e0g8l/ZukXq1qspmZtVlELAYuBZ4HlgCvR8Tv6q2n7EzgauBw4AxJ1wOfAmYBuwE/K9rI8wmYmbVN2XwCkgYCY4ARwObAByR9rt79lOUO+khE7JCfaiwGNo+I1ZJuoEouIc8nYGbWNjXMJ3AA8GxEvAwg6VZgD+CGevZTdiawXp6dbiOy601NU0r2AXw5yMxs7XkeGC1pA2VDB/cHFtRbSdmZwFXA42R3nc8Ffp3PJzAauLHenZmZWfvIZxO7GZgDrAIepvqZQ1LpcwKSNs93+KKkAWSnIM9HxIM17aDBLgdtscUWyfiTTz6ZjPft27fD2vLSS+lpH8aOHZuM33XXXR3WlnVRd3hOoHfv3sn4xIkTk/EjjzwyGR89enQyvvfeeyfjN9yQvuJQ9CxLkaLnCv71X/81GZ8xY0aLWHedq6CW5wTaQ+l8AhHxYrPXr5FNYWZmZt2AnxMwM2tg7gTMzBqYOwEzswbmTsDMrIG5EzAza2Clo4OsPi+88EIyfvXVVyfju+++ezKeGvo3cODAZNmhQ4cm45tsskkyfswxxyTjjTZEtCspGnp5/PHHJ+Pjxo1LxseMGZOML168OBnv379/Ml7vUNAi/fr1S8Z/+tOfJuM777xzi9iKFSvapS2NymcCZmZdkKTJkpZJmt8sNkjSdElP5T/T3xybKcsi2l/ShZIel7Q8XxbksQHtcBxmZtY61wCHVMQmAHdHxEjg7vz3qsrOBG4CXgX2iYjBETEY2DeP/breFpuZWfuIiHuBv1aExwDX5q+vBY4uq6esExgeERdFxNJmO14aERcBWxZt5FTSZmZtU5ZKusAmEbEEIP85pGyDshvDCyWNB66NiJfyhm0CnASk74DiVNJmZm1VQyrpdlF2JnAsMBiYKemvkv4KzAAGkU0wY2Zm646XJG0GkP9cVrZB1U4gIl6NiLMjYtuIGJQvH46Is6nhWpOZmXWq24CmNMFjgd+UbdCW5wTOI5t+0mrw5S9/ucPqvu6665LxE044IRkvSg9c9BzCq6++2rqGWbspSpe8+eabJ+NFacTvu+++uva7cOHCZHz16tXJeI8ePeqqf82aNcn40qVLk/GVK1fWVX93JumXwD7AxpIWAd8GLgRukvR5sklnSq/YVO0EJD1atApIP4lkZmYdLiI+U7Bq/3rqKTsT2AQ4mGxIaHMC/reeHZmZ2bqnrBO4HdgwIuZWrpA0oyMaZGZmnadqJxARn6+yLp20xMzMugznDjIza2DuBMzMGpg7ATOzBub5BNayXr16JeOpcdhFY6q/+tWvJuNFueO32WabZPyKK65IxoueN7DOU5S/f9CgQcn4O++8U1c99e53vfXa5/tj0XMF8+fPT8ZXrVrVLvvtDiRNBo4AlkXE9nnsEuBI4F3gL8DJEfFatXp8JmBm1jVdQ8tU0tOB7SNiB+BJ4JyySsrmE+gn6XuSrpd0fMW6H9fXXjMzay+pVNIR8buIaDpdegAYVlZP2ZnA1WQPht0CHCfpFkl98nWj62uymZl1onHAb8sKlXUCH4qICRExJSKOAuYAf5A0uNpGnk/AzKxtWjmfQNO25wKrgJ+XlS27MdxH0noRsQYgIr6bJyq6F9iwaCPPJ2Bm1jatnU9A0liyG8b7R1HmwWbKzgSmAvtVNOxa4Gtkd5/NzGwdIekQ4GzgqIhYUcs2ZWkjxhfE75R0Qf1NNDOz9lCQSvocoA8wPR/e+0BEfKlqPTWcLRQ14PmIKJxnuFk5Xw6q4lvf+lYyfuqpp7aIPf7448mys2enb72ceOKJyfimm26ajBeNLe/fv39d5buKiFBX//v8xS9+kYx/8pOfTMZvvPHGZHzRokXJ+HHHHZeMjxgxoobWlVuxIv1ldfvtt0/Gn3322XbZb1cQEfU91NFKnk/AzKyBeT4BM7MG5vkEzMwamOcTMDNrYM4dZGbWwNwJmJk1sFYPEa15B118CN7astlmm7WIfeMb30iW/fKXv5yM9+nTJxmv1+jR6TRRs2bNapf615buMET0Bz/4QTJ+2mmndXJLWmfJkiXJ+C677FJX+e6os4aI1n0mIGlIRzTEzMxqJ2mypGWSWky+IOnrkkLSxmX1lKWSHlSxDAYelDRQUno2CzMz6wzX0HI+ASRtARwIPF9LJWVDRF8BFlbEhpJlEw1g61p2YmZm7Ssi7pU0PLHq+8B44De11FN2OWg88ARZMqIRETECWJS/LuwAnErazKxtWpNKWtJRwOKIeKTW/ZQ9J3CppBuB70t6gSxBUemNNKeSNjNrm3pTSUvaADgXOKie/ZTeGI6IRRHxKeAesvkrN6hnB2Zm1ik+BIwAHpH0HNnUknMkpTNG5moeHRQRU4F9gQMAJJ3c6qaamVm7ioh5ETEkIoZHxHBgEfDRiFhabTunku4GRo4cmYxPmDAhGR83blwyPn9+i5FmAOy1117J+KuvVuYV7Fq6w3MCBx98cDJ+xx13JOPrrdc1ng9NpVIH+OlPf9rJLVl7yp4TaD6fAPAS8O2IuKrZ+ueAXSPilWr1OJW0mVkXFBGfKVk/vJZ6nErazKyBOZW0mVkDcyppM7MG1jXuEpmZWYdwJ2Bm1sDcCZiZNTDPJ9CNfeITn0jG//jHPybjd955ZzJ+6KGHtlub1iXd4TmBDTZIP8A/bdq0ZHzPPfdMxnv2TN8eXLNmTTL+8ssvJ+PLly9PxrfbbrtkvMjixYuT8d12261FrLvOMVDDcwKTgSOAZRGxfbP46cBpwCpgWkSMr1ZPa+YTGFzvNmZm1u6uoSKVtKR9gTHADhHxz8ClZZWUzSdwYdOkBJJ2lfQMMEvSQkl7t7blZmbWNhFxL/DXivCpwIUR8U5eZllZPWVnAoc3e+T4EuDYiPgnsgkLLquvyWZm1sFGAf8iaZakmZJaXj+rUNYJ9JLUdLFw/Yh4CCAingQKJ7D1fAJmZm3TmvkEyJ79GgiMBr4B3CSp6r2FsieGfwTcIelC4E5JE4Fbgf2BuUUbeT4BM7O2qXc+gdwi4NbIRvw8KGkNWYK59J18yp8Y/oGkeWTXmUbl5UcBU4D/V2fjzMysY00B9gNmSBoF9CabJrhQ2ZkAETEDmFEZz+cTuLoVjTQzszZqnkpa0iKymR8nA5MlzQfeBcZGyXMApZ1AFefhTmCdNmTIkLrKDxs2rINaYh1lxYoVyfjhhx+ejB90UHrmwaLLxitXrkzGZ82alYz36tUrGZ88eXIyXjQfwtChQ5Px4447rkVs4sSJybId/QzU2lYllfTn6qnH8wmYmTUwzydgZtbAPJ+AmVkD83wCZmYNzFlEzcwamDsBM7MG1pYhog3tjDPOSMbHj09nbX3ssceS8WuuuSYZv/XWW1vE3n777doalzvssMPqKv/ss88m40XDB7v7ELyurGjo6JQpUzq3IbmTTjopGX/ggQeS8a222ioZP//881vEnn766WTZqVOnJuM9evRIxlevXp2Md3c+EzAz64IkTZa0LH8wrCm2k6QHJM3N8w3tXlZPWSrpXSXdI+kGSVtImi7pdUkPSdq5PQ7EzMxa5Roq5hMALgbOi4idgH/Pf6+q7Ezgx3kl08ieC/jPiOgPTMjXmZnZWlAwn0AA/fLX/YEXy+opTSUdEb+NiF9m+4yb853fDfQt2sippM3M2qaVqaTPBC6R9ALZrGLnlG1QdmP4bUkHkfUoIenoiJiSzypWeBfFqaTNzNqmlamkTwXOiohbJH0auAo4oNoGZWcCXwK+BowjSx+xr6TXyC4FfaXOxpmZWccaSzbnC8CvgbbdGI6IRyLi4Ig4NCIej4gzImJAPoHxNm1vr5mZtaMXgab53/cDnirbQK0d6y3p+YjYsoZyXfpy0IABA5Lx559/Phlfs2ZNMj59+vRkfOed04OsUql0Fy5cmCz71FPp9/nAAw9MxovaeMop6UuOf/jDH5LxF18svee0TosIdfW/z66u6O//3nvvTcY33HDDFrGi/xennXZaMn777bfX2Lq1KyKqTgvZfD4B4CWy+QSeACaRXep/G/hyRPypWj1OJW1m1gVVmU9gl3rqcSppM7MG5lTSZmYNzKmkzcwamHMHmZk1MHcCZmYNzJ2AmVkDa/VzAjXvoIuPw95rr72S8ZkzZybjEydOTMbPOuusZLxXr17JeGrMflHdPXvWNy3E2LFjk/Hrrruurnq6Oj8n0P7aa+6Jor/FE044oeY67rvvvmT8mGOOScZfeeWVmuvuDGXPCbSXslTS/SVdKOlxScvzZUEeG9AZDTQzs5by9P735J/Jj0k6I48PytP+P5X/HFitnrLLQTeRPSOwT0QMjojBwL557NftcSBmZtYqq4CvRcSHgdHAv0najizV/90RMRK4O/+9UFknMDwiLoqIpU2BiFgaERcBpSkjzMysY0TEkoiYk79+A1gADAXGANfmxa4Fjq5WT1knsFDSeEn/SBEhaRNJZwMvFG3k+QTMzNqmnvkEJA0HdgZmAZtExBLIOgpgSLX9lN1RPJbsVGJm3hEEWaKi24BPF23k+QTMzNqm1vkEJG0I3AKcGRF/K7o5X6TsieFXJV0NTAceiIg3m+34EODOuvZmZmbtRlIvsg7g5xHRNI/AS5I2i4glkjYDllWro2x00FeA3wCnAfMljWm2+oLWN93MzNpC2Vf+q4AFEXF5s1W3kU0uQ/7zN1XrqTZ+V9I84OMR8WZ+zelm4PqImCTp4YhIJwN/fx1d+nLQxhtvnIwvW5buXKdMmZKMF41NrsdOO+2UjB977LHJ+P3335+M33bbbW1uS3fg5wTKbbTRRsn4N7/5zWR89er0rLPf+ta36tpv0fM5qbktevTokSxb9Nk2YUJ6sMxll12WjBcdU0erYT6BPYH7gHlA0yQh3yS7L3AT2eCd54FPRUTlhPT/UHZPoEfTJaCIeE7SPsDNkrYiSydtZmZrQUT8keLP4f1rradsdNBSSTs12+mbwBFkM9l8pNadmJnZuqmsEzgRWNo8EBGrIuJEIH2+ZmZmXUbZ6KBFVdb9T/s3x8zMOpOziJqZNTB3AmZmDcyppFupaPjlbrvtlowXDe+cP39+ezXJ6uQhouX69u2bjN9zzz3J+LRp05LxCy5IP1Y0ZEg6o0FR+ZNPPjkZr8dLL72UjO+xxx7J+DPPPNPmfbbGupJKup+k70m6XtLxFet+3LFNMzOzIlVSSV+Sp/9/VNJ/l6X9L7scdDXZONRbgOMk3SKpT75udFsPwszMWq0olfR0YPuI2AF4EjinWiVlncCHImJCREyJiKOAOcAfJA1ue/vNzKy1ilJJR8TvImJVXuwBYFi1esqeGO4jab2IWJPv6LuSFgH3AhsWbZSnPC1Me2pmZtUlPkevzDOLpsoO571U0s2NA35VbT9lncBUYD/g902BiLhW0kvAD4o2cippM7O2aW0q6Wbxc8kuGf282vZVLwdFxHhgkaT98x01xe8EvlLWODMz6zgFqaSRNJYsxc9no2QIaNnooNPJ0pCeTstU0t9tbcPNzKxtilJJ53O9nA0cFREryuopuxx0CrBL81TSkoZHxCQaPIvo7bffnoyPHp0eNHXjjTcm40ceeWQy/uyzz7auYdYl9OyZ/q/34Q9/OBkfNWpUMj5ixIhkfIsttkjGP/axjyXjjz76aDI+a1blJebM8OHDk/Fx48Yl44cffngy/pGPpPNQfuADH0jG61H0BXjgwIHJ+NChQ5PxtfWcQA0+AZwAzJM0N499E7gC6ANMz2cZeyAivlRUiVNJm5l1QVVSSd9RTz1OJW1m1sCcStrMrIE5lbSZWQNzFlEzswbmTsDMrIG5EzAza2B1zycgaUhELKujfLdMG7H55psn4w8++GAyXjQGedmy9D/lZz/72Rax3//+94mS1lprcz6BHXfcMRkvytM/YMCAZDwfB97pVq9enYy/++67yXivXr3qqqdPnz7JeD2KPtsefvjhZHzs2LHJ+Nqa82NdmU9gUMUyGHhQ0kBJgzqjgWZm1lLRfALN1n9dUkjauFo9ZQ+LvQIsrIgNJUspHcDW9TbczMzaRdN8AnMkbQT8SdL0iPizpC2AA4HnyyopuycwHniCLAfFiIgYASzKX7sDMDNbS4rmE8hXf5/s87v0cmdZFtFLgS8A/y7p8ry3Ka1U0imSZkuaXVbWzMxaav45mi+Fc7Q0n09A0lHA4oh4pJb9lF0Oanpg7FOSjiSbtmyDGrbxfAJmZm3QmvkEyC4RnQscVOt+SoeIStpW0v7APcC+wAF5/JBad2JmZu0vMZ/Ah4ARwCOSniObWnKOpE2L6igbHfQVms0nABwUEU3jpS5o8xGYmVmrpOYTiIh5ETEkIoZHxHBgEfDRiFhaVE/Z5aAv4vkEkl588cVk/KCD0mdhM2bMSMaHDBmSjN9xR8tssFdddVWy7DnnnJOMv/baa8m4rX29e/dOxte15wGKcumfdNJJyfgLL7yQjBc9V7PRRhsl46nnZAB22GGHFrFtttkmWbbIBz/4wWT873//e131rAOS8wlERF2ppD2fgJlZF1RlPoHmZYaX1eP5BMzMGpjnEzAza2CeT8DMrIE5i6iZWQNzJ2Bm1sDq7gTyTKJmZtYNVJ1PQNKFwKUR8YqkXYGbgDVAL+DEiJhZugOnjQDS45sB7rvvvmS8X79+Ndf97W9/Oxn/zne+k4z37ds3GT/llHRqkpkz02/zI4/UlJpknbU25xPYcMMNk/HLLrssGR83blwy3rNnaeaXNnn77beT8WOPPTYZnzp1ajJe77wlRfMJpOKjRo2qq+4lS5Yk40XP/tTb9vZSNp9Anin0OmBTss/lK/NnuJB0OnAaWRqJaRExvqiesr+gwyNiQv76EuDYiHhI0ijgF8CutRyMmZm1u2QqaWATYAywQ0S8Iyn9RGqurBPoJalnRKwC1o+IhwAi4klJbZ/6x8zMWiUilgBL8tdvSGpKJf1F4MKIeCdfV3UmyLJ7Aj8C7pC0H3CnpImS9pJ0HjC3aCOnkjYza5vWppIGRgH/ImmWpJmSdqu2n7LnBH4gaR5wal5xz/znFOD8Kts5lbSZWRu0JpV0RPxNUk9gIDAa2A24SdLWUXBzo5a7SkvzhsxqyiOU7/gQ4M4atjczsw6QSCUNWebQW/MP/QclrSFL9fNyqo66UklLGtNstVNJm5mtJalU0rkpwH55mVFAb7L54tP1lAwRnQd8vHkqaeD6iJgk6eGI2LmGhvpyUBV77LFHMn7BBS372K23Tk/rfMwxxyTjS5emU4hPnz49Gd92222T8X333TcZL0qP3VWszSGiRamhi4Z8fuELX0jGR44cWVc9xx9/fDI+eHB9j/8sX748GT/ggAOS8Xnz5iXjq1evrmu/jaSGIaJ7AvcB88iGiAJ8E/g9MBnYCXgX+HpE/KGoHqeSNjPrgkpSSX+u1nqcStrMrIE5lbSZWQNzKmkzswbmLKJmZg3MnYCZWQNzJ2Bm1sgiotMW4BSXb5/y61JbukP5dbFNLt812tIZ5Tty6dydwWyXb5/y61JbukP5dbFNLt812tIZ5Tty8eUgM7MG5k7AzKyBdXYnUJoW1eXXibobsXxn7MPl26/8utSWzijfYaomkDMzs+7Nl4PMzBqYOwEzswbmTsCMbKY8SU9IelrShJKyW0i6R9ICSY9JOqOG+ntIeljS7TW2Z4CkmyU9nu/n41XKnpW3Y76kX0rqmygzWdIySfObxQZJmi7pqfznwJLyl+TteVTSf0saUFS22TZflxSSNq5Wdx4/PX8PHpN0cUlbdpL0gKS5+fy7uzdbl3x/io63Svmi4636/jc/5mpli46303Xk+FNgW+Bs4ApgUv76wyXl9wc2rIgfUsO+ritZ/zGgX/56feA8YCpwEdC/omxvsgyqB+S/Hw/8EPg3oNfaHtfrpX0XoAfwF2Dr/L1/BNiuSvnNgI/mrzcCnqxWPi/3VeAXwO01tula4Av5697AgIJyQ4FngfXz328CTkqU2wv4KDC/WexiYEL+egJwUUn5g4Ce+euLmsqnyubxLYC7gIXAxiV170s2GUqf/PchJeV/Bxyavz4MmFH2/hQdb5XyRcdb+P5XHnOVuguPt7OXDjsTkHQ2cCPZpAcPAg/lr3+Z+qZVz1SWkm6rWKYCxzT9XtCkycCK/PUkoD/ZG7sCuLqi7NXA4cAZkq4HPgXMIpu0+Wc1/QNYu5I0pM7y9UyVtTvwdEQ8ExHvkv3djikqHBFLImJO/voNYAHZh3FRW4aR/T3V9LcjqR/ZB99V+T7ejYjXqmzSE1hf2QTjGwAvJtp8L/DXivAYss6G/OfR1cpHxO8iYlX+6wPAsCp1A3wfGA+8b/RJQflTgQsj4p28zLKS8gH0y1/3p9kxV3l/ksdbVL7K8VZ7/993zFXKFh5vp+uo3oWsx2vxrZnsW81Tifg88jMAYDgwGzgj//3hirJzgBuAfYC9859L8td7F7RnQfPtK9bNrfj90fxnT+AlshnWIOvEHl1bPXbimGr+9gAMbsf99gcuBB4HlufLgjw2IFG+H/A94Hrg+Ip1P06UH1SxDAaeAwYCgxLlLyT/pgnsCjwDPE32bSz591Cx/SeBnzX7/QTghzX+WwwHnic/yywoczOwS/53WnomQDYt4IPANcDDZJ3HB6qUPwN4k2wi8Z+XtLX5t+nXKta/Wq18xbqpwOeq1H0UMCl//RzNzgQKys8lOzufBcwEdisp/+H83/0FYDGwVdn7U3a81d7PyuMtqL+WY24qW/V4O3PpyHsCa4DNE/HNeG8+zObeN5Ul2X+YQyVdTssp1HYF/gScC7weETOAtyJiZkTMLGjPfEkn568fkbQr/GMi5pUVZdeT1Jvs9G0Dsg89gD5Ar1TlkvpLujC/hrg8XxbksQGJ8v0kfU/S9ZKOr1j340T5QRXLYOBBSQMlDaooe2HTNVhJu0p6BpglaaGkvRN175pft7whv4Y5XdLrkh6SlJpH+ibgVWCfiBgcEYPJTm9fBX6dKH812Xt4C3CcpFsk9cnXjU6Uf4Xs/W1aZpN9e5qTv650eEQ0TaR9CXBsRPwTcCBwWaJ8pdQUfaVjpyVtSHZMZ0bE3wrKHAEsi4g/1dCOJj3JLn/8JLJ5vP9OdvkiVf9Asm+4I8j+v31AUs1TC7aGpHOBVcDPC9ZvQPZ/89/rqLYnWSc/GvgGcJNUMBFz5lTgrIjYAjiL/Kypoh2l708t5YuOt3n5fH3hMSfqrvd4O05H9S7AIWTfxn5L9mDElcCdeazFNX7gD8BOFbGewHXA6oJ9DCP70Pkh8HxJe/qTfbP6C1nvu5LsG+NMYMeKsmfl6xYCXwHuBv6L7Gzl2wX130V2z2PTZrFN89j0RPlbyL7BHg3clv/edH1wTqL8GrJrv82XlfnPZyrKzmv2+h7ybxnAKBI5S8i+dR4KfIbsm9Un8/j+wP2J8k9U+XdusY6WZ1rnAv9D9g0/daxfz/9WPtIs9myVfT7Oe9duHyj6t6iy/ceBu5r9fg5wTsk2vfL3/Ksl5b4HLCL7ZriU7PLjDSXbbAo81+z3fwGmFZT9FHBVs99PJHF2la8bzvu/TT8BbJa/3qzyvassn8fGAvcDGxSVJZt6dll+zM+RfUA+X/F/o7Itd5J9qWj6/S/AB6uUf533nnMS8Ley96fa8Ra9n1WO933lqx1zQVuqHm9nLh1beTb6aDTwf8hOuUeTX1pJlB3W/I+kYt0nSvZzOHBBjW3aCNiR7PR8kyrlNgc2z18PyNu/e5Xy68wHI3V+KNLschsVnSkVl+Ly2O/Irntu0iy2CVmH9/tE+QXAehWxscBjwMIqfw+/Bi7P37NnUuXysqfnbdoP+A9gItk19fOA62v4m+hJ1umP4L0bw/9cpbzIvpxMrPP/wz7UfmP4PmCb/PV/AJcUlPtY/u+4Qd6ua4HTC8oO5/0fpJfw/hulF5eUPwT4M4kPq8qyFeueo/xy0JeA7+SvR5F9GVGV8gvIP0TJvqz8qez9KTreKuWTx1vL+990zFXqrnq8nbl0+g6768I69MFInR+KZN90DiL7VrkQODqP7036zGEg2U31x8kuAf01P56LSF+zv5h8pFVF/BAS94cqyhxJdlNuaUm5fYBfkV1DnwfcAZxCjaO5yEaYPEn2jezckrJ7kl0uepTs2u5c4LAa9rEPtXcCO5Fd+noUmAIMrFL2vPy9mE9236VPoswvye6brSQ7M/k82ReOu4Gn8p+DSso/nX9YNR3zT4vKVuz7Od4/OihVd2+y+3zzyS777VdSfk+yS4WPkJ3Z71L2/hQdb5XyRcdb+v7zXidQVHfh8Xb2slZ22h0X3v/B+Ffe/8HY4j8wHfzBSPGHYs9E2R3JTld/SzZMdxLwGlmHtEdB/dsCB1DjcF6Kh/8eWlaebEjv9q2sv3R4sRcvjbys9QY0wgKc3N7lKz4Ya66/PdpCdp/kCbJvqM8BY5qtS13KOr3O8vXWX1d5L168vLes9QY0wkLJTevOLN8edVPHcN51sbwXL17eW3pi7ULSo0WryO4NdFr5jm4LFcN5Je0D3CxpK9LDLde18maWcyfQfjYBDia7UdqcgP/t5PId3ZalknaKiLkAEfFmPh5+MtlQuXW9vJnl3Am0n9vJLknMrVwhaUYnl+/otpxINg76HyJ7vP5ESf/ZBcqbWc6TypiZNTCnkjYza2DuBMzMGpg7ATOzBuZOwMysgf1/OytM6c921YUAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# cluster 2\n", - "plt.figure()\n", - "fig, (ax,ax2) = plt.subplots(ncols=2)\n", - "fig.subplots_adjust(wspace=0.01)\n", - "sns.heatmap(x_train[3], cmap = \"gist_gray\", cbar = False, ax = ax)\n", - "sns.heatmap(x_train[8], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", - "ax2.yaxis.tick_right()\n", - "ax2.tick_params(rotation=0)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "da357b54", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# cluster 3\n", - "plt.figure()\n", - "fig, (ax,ax2) = plt.subplots(ncols=2)\n", - "fig.subplots_adjust(wspace=0.01)\n", - "sns.heatmap(x_train[5], cmap = \"gist_gray\", cbar = False, ax = ax)\n", - "sns.heatmap(x_train[26], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", - "ax2.yaxis.tick_right()\n", - "ax2.tick_params(rotation=0)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "7286cdbb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# cluster 4\n", - "plt.figure()\n", - "fig, (ax,ax2) = plt.subplots(ncols=2)\n", - "fig.subplots_adjust(wspace=0.01)\n", - "sns.heatmap(x_train[21], cmap = \"gist_gray\", cbar = False, ax = ax)\n", - "sns.heatmap(x_train[54], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", - "ax2.yaxis.tick_right()\n", - "ax2.tick_params(rotation=0)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "38866d42", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAD7CAYAAACMlyg3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAApOElEQVR4nO3debgU5Zn+8e8jm+DKIoiAgkYMxhCNJOKYqIjjEo0kMWpiXBKNRjPBXXAZNTo/E4xGw0SzKOA+OiBxQRQhCjqJCiqioIArKLK5i6jI8vz+qCI2fZ7q6j4bnNP357r6os/db79VdfrQb1d11fOauyMiItVpo/W9AiIisv5oEBARqWIaBEREqpgGARGRKqZBQESkimkQEBGpYi0begFmpnNQa6F///41sjPPPDNs+8orr4T5xRdfHOarV6+u/Yo1I+5u+vssbbvttgvzKVOmhPnBBx8c5nPmzKmvVaoa7m55bczsIGA40AIY4e7DKl2O9gRERJogM2sBXAccDOwM/NjMdq60n9w9ATP7MjAI6AY4sBC4z91nV7owERGpN98EXnH31wDM7E6S9+oXK+mk5J6AmQ0F7gQMmAY8ld6/w8zOK/G8k83saTN7upKVERGRROH7aHo7uahJN+DNgp8XpFlF8vYETgS+4u4ri1buauAFIDz+5O7XA9enbXXMVUSkQoXvoxmi7wwqfr/N+05gDbBNkHdNHxMRkfVjAdCj4OfuJIfrK5K3J3AG8LCZvcwXux3bAl8CflXpwkREpN48BexoZr2At4AfAUdX2knJQcDdJ5hZb5IvILqR7H4sAJ5y96o4z7Bbt/gQW1Y+bdq0MM861e7SSy8N8wMPPLBG1rlz57DtW2+9FeZZp/FNmjQpzKV6de/ePczvuuuuMP/9738f5joVtPG4+yoz+xXwEMkpoqPc/YVK+8k9O8jd1wBPVr6KIiLSkNz9AeCBuvSh6wRERKqYBgERkSqmQUBEpIppEBARqWIaBEREqpgGARGRKtbgpaSbusMPPzzMf/rTn4b5CSecEOa33357mLdo0SLMu3TpUiNbvHhx2Hbu3Llh3rdv3zDXdQLVq2XL+L/8OeecE+YzZswI80ceeSTMs/6eVb58w6U9ARGRJsjMepjZZDObbWYvmNnptelHewIiIk3TKuBsd59uZpsBz5jZJHevv1LSkMwnYGYDzWzTovygytZXRETqi7svcvfp6f1lwGxqUUo6bz6B04B7gcHALDMbVPDwb0o8T/MJiIjUQRnzCRS27QnsBkytdDl5h4NOAnZ394/ThdxlZj3dfThxLWtA8wmIiNRVGfMJAJAepRkLnOHuH1W6nLxBoIW7f5yu0Dwz25dkINiOEoOAiIg0PDNrRTIA3O7uf6tNH3nfCSw2s13X/pAOCIcCnYCv1maBIiJSd2ZmwEhgtrtfXdt+8vYEjiP5Bvpf3H0VcJyZ/bW2C90QZZ3fvOOOO4Z5nz59wvzuu+8O86y5AJLXsbw8q49nn302zEeNGhXmUr2OOeaYMN9///3D/Pvf/36YT58+Pcwvu+yyML/yyivLWDup0F7AscBMM5uRZhek5aXLljepzIISj/2zkgWJiEj9cfd/UA+H5XWxmIhIFdMgICJSxTQIiIhUMQ0CIiJVTIOAiEgV0yAgIlLFVEU0lVXv/P777w/zjz6Kr87edtttw/z9998P8wEDBoT5LrvsEuaRBx98sKJlSvPXqVOnMD/11FPD/MQTTwzzhQsXhvny5cvDPOv6GWkYZtYCeBp4y90PrU0f2hMQEWm6TiepHlprFQ8CZnZLXRYoIiJ1Z2bdgUOAEXXpp+ThIDO7rzgCBpjZlgDuflhdFi4iIrX2B2AIsFldOsn7TqA78CLJSOMkg0A/4PelnpTWvc6sfS0iIqUF76PXp+WlMbNDgaXu/kxa3bnW8gaBfiTHnC4EznX3GWb2qbs/WupJmk9ARKRucuYT2As4zMy+A2wMbG5mt7l7XCGwhLwCcmuAa8xsTPrvkrzniIhIw3L384HzAdI9gXNqMwBAmW/oaTXRI8zsEKDimWuasoceeqiiPMv48ePDvD5OBb3hhhsqWhdp/o499tgwzzq1c+bMmWHepUuXMF+xYkWY6xTRpqeiT/XuPh6I381ERKTRufsUYEptn6/rBEREqpgGARGRKqZBQESkimkQEBGpYhoERESqmAYBEZEqpgu/6tlRRx0V5gceeGCYf/7552G+bNmyGtmTTz4Ztj3ggAPCfN68eWE+a9asMHfXxd1NTdu2bcP8hBNOCPOVK1eG+S23xHUhv/Od74T5nDlzwvzjjz8OczMLc/3NrX/aExARaaLMbEszu8vM5pjZbDPbs9I+tCcgItJ0DQcmuPsPzaw10K7SDkruCZjZHma2eXq/rZldambjzOwKM9uidussIiJ1lb437w2MBHD3z939g0r7yTscNAr4JL0/HNgCuCLNbiyxcieb2dNm9nSlKyQiIuu+j6a34vL82wNvAzea2bNmNsLMNql0OXmHgzZy91Xp/X7u/vX0/j/MbEbWk1RKWkSkbnJKSUPy/v11YLC7TzWz4cB5wEWVLCdvT2CWmf0svf+cmfUDMLPeQHyagYiINIYFwAJ3n5r+fBfJoFCRvEHg58A+ZvYqsDPwhJm9BtyQPiYiIuuBuy8G3jSzndJoIMlMkBWxcs7TNbPNSI4/tSQZeZaUvYBmejho2223DfM777wzzHffffcwz/r9f/LJJzWyzTffPGy70UbxWD527NgwHz16dEXt16xZE+ZNnbtbU//73GST+BDwM888E+a9e/euqP+s8/uz/iayrkM4++yzw/y6666raH2qibvHv/wCZrYryfS/rYHXgJ+5+/uVLKfcSWWWAc9V0rGIiDQsd59BMg1wreliMRGRKqZBQESkimkQEBGpYhoERESqmAYBEZEqpkFARKSKqYpojpYt41/ROeecE+Z77llxJddQmzZtamRZ8wl87WtfC/NBgwaFea9evcJ8p512CvPLL788zFULfv1bvnx5mF966aVhfsMNN4R5q1atwrx169ZhvmLFijAfM2ZMmD/00ENhLnVjZmeSXLjrwEyS6wQ+q6QP7QmIiDRBZtYNOI2krtsuQAvgR5X2o0FARKTpagm0NbOWJHMJLKy0g7z5BFqb2XFmtn/689Fmdq2Z/YeZxfuPIiLS4Nz9LeAq4A1gEfChu0+stJ+8PYEbgUOA083sVuAIYCrwDZJ6FSHNJyAiUjd58wmYWXtgENAL2AbYxMyOqXQ5eV8Mf9Xd+6a7Gm8B27j7ajO7jRK1hDSfgIhI3ZQxn8D+wOvu/jaAmf0N+DfgtkqWk7cnsFE6b+VmJMeb1k4p2QbQ4SARkfXnDaC/mbWzpNzrQGB2pZ3k7QmMBOaQfOt8ITAmnU+gPxDXTBYRkQaXziZ2FzAdWAU8S+k9h1DufAJmtk26wIVmtiXJLsgb7j6trAU08cNBF1xwQZhfdtllYZ5V2z/LO++8E+YXX3xxjSyr3v+3vvWtMD/jjDPCvG3btmH+9NPxVzjnn39+mH/44Ydh3lQ0h/kEsmT9HZ555plhftVVV1XU//XXx+81v/jFLyrqR7KVM59Afci9WMzdFxbc/4BkCjMREWkGdJ2AiEgV0yAgIlLFNAiIiFQxDQIiIlVMg4CISBVTKelUjx49wny33XYL81WrVoX5ypUrw3zJkiVhfsIJJ4T5Y489FuaRu+++O8zHjRsX5j/84Q/DvG/fvmE+ZMiQML/wwgvLWDtZH9asWRPmlZ7CvHTp0jAfOnRoxeskGybtCYiINEFmNsrMlprZrIKsg5lNMrOX03/b5/WTV0V0CzMbZmZzzOzd9DY7zbash+0QEZHauQk4qCg7D3jY3XcEHk5/LilvT2A08D6wr7t3dPeOwIA0i6cQEhGRBufujwHvFcWDgJvT+zcD38vrJ28Q6OnuV7j74oIFL3b3K4Bts56kUtIiInWTV0o6Qxd3XwSQ/ts57wl5XwzPN7MhwM3uviRdsS7AT4E3s56kUtIiInVTRinpepG3J3AU0BF41MzeM7P3gClAB5IJZkREZMOxxMy6AqT/xqd3FSg5CLj7++4+1N2/7O4d0lsfdx9KGceaRESkUd0HHJ/ePx64N+8JuaWkM59o9oa7Z34vUNBugzoctN1224X5U089FeYbb7xxmLdu3TrM33333TD/9re/HeavvfZamDekrHW/7777wjyrVHVWPmPGjFqtV2NrzqWkW7RoEeZz5swJ8y996UthftZZZ4X5NddcU7sVk7LllZI2szuAfYFOwBLgEuAekhN6tiWZdOYIdy/+8ngdJb8TMLPnsx4CupR6roiINBx3/3HGQwMr6Sfvi+EuwIEkp4QWMuDxShYkIiIbnrxB4H5gU3efUfyAmU1piBUSEZHGU3IQcPcTSzx2dP2vjoiINCbVDhIRqWIaBEREqpgGARGRKtZs5xPYcccdw/yBBx4I86222irMP/vsszCfMGFCmP/iF78I86z5BDYk994bX1fSr1+/MD/44IPDvKlcJ9CcZZ3f36lTpzB///3iEwATO+ywQ5g//PDDYZ51nczEiRPDfMSIEWEu+cxsFHAosNTdd0mzK4HvAp8DrwI/c/cPSvWjPQERkabpJmqWkp4E7OLufYGXgPPzOsmbT2BzM/utmd1qZkcXPfanytZXRETqS1RK2t0nuvvaaQ+fBLrn9ZO3J3AjyYVhY4EfmdlYM2uTPta/slUWEZFGdALwYF6jvEFgB3c/z93vcffDgOnAI2bWsdSTNJ+AiEjd1HI+gbXPvRBYBdye1zbvi+E2ZraRu68BcPfLzWwB8BiwadaTNJ+AiEjd1HY+ATM7nuQL44FeRoXQvD2BccB+RSt2M3A2ybfPIiKygTCzg4ChwGHu/kk5z8krGzEkI59gZr+pfBVFRKQ+FJaSTo/QXEJyNlAbYJKZATzp7qeU7Ke5zicwYMCAMP/rX/8a5ttuG2/KsGHDwnz48OFhnnW+dVNw5JFHhvnIkSPDPOuc8D59+oT5p59+WrsVayDNYT6BNm3ahHnW9TD77bdfmGe9D7z99tsVLTdr/o333otL2mddz7N8+fIwryZ58wnUF80nICJSxTSfgIhIFdN8AiIiVUzzCYiIVDHVDhIRqWIaBEREqlizLSW9aNGiMN9+++3DfPXq1WE+bdq0MG/oU0HTc3zXkXX63XbbbRfm3//+98P80UcfDfOsUzhbtWoV5osXLw7zFStWhLnUv6xy3lmngs6cOTPMzz333DCfPHlymGedBjx16tQw79q1a5j37x+XIMsqVS31r+I9ATPr3BArIiIi5TOzUWa21MxmBY+dY2ZuZvEEEgXySkl3KLp1BKaZWXsz61CH9RcRkbq5iZrzCWBmPYB/B94op5O8w0HvAPOLsm4k1UQdiI+tiIhIg3L3x8ysZ/DQNcAQIJ4qsEje4aAhwFySYkS93L0XsCC9nzkAqJS0iEjd1KaUtJkdBrzl7s+Vu5y86wSuMrM7gWvM7E2SAkW5tVZUSlpEpG4qLSVtZu2AC4EDKllO7hfD7r7A3Y8AJpPMX9mukgWIiEij2AHoBTxnZvNIppacbmZbl3pS2WcHufs4YACwP4CZ/azWqyoiIvXK3We6e2d37+nuPYEFwNfdPT6XO9VsS0lnnX/8+ONx3bsLLrggzK+44oowr/T31r17PN/zwIEDw3yPPfaoke21115h26ySzrvsskuYt2/fPsynTJkS5v369QvzrOsBssoDL1u2LMzXl6ZUSrply/jI7XPPxYd+s64pOfzww8N8xowZtVqvYjfffHOYH3fccWE+evToMD/xxLhiTVRiurbvYRu6vFLShfMJAEuAS9x9ZMHj84B+7v5OqX5USlpEpAly9x/nPN6znH5USlpEpIqplLSISBVTKWkRkSqmKqIiIlVMg4CISBXTICAiUsVqfZ1A2QtYT+dhZ51X/YMf/CDMx40bF+ZZNfbbtGkT5gccEF+xPWzYsDDv0CEuxhqdg9+jR4+w7UUXXRTmc+fODfMxY8aE+cqVK8P8s88+C/Os38GAAQPC/Iknngjz9aUpXSdw4IEHhvnll18e5kOHDg3zhq7Tf8YZZ4T51VdfHebRvBkAS5cuDfPXX3+9RnbZZZeFbR944IEwbyrKuE5gFHAosNTddynIBwO/AlYB4919SKl+ajOfQMdKnyMiIvXuJopKSZvZAGAQ0NfdvwJclddJ3nwCw9ZOSmBm/czsNWCqmc03s31qu+YiIlI37v4Y8F5RfCowzN1XpG3iXaoCeXsChxRccnwlcJS7f4lkwoLfV7bKIiLSwHoD3zazqWb2qJl9I+8JeYNAKzNbe3C9rbs/BeDuLwHxAWE0n4CISF3VZj4Bkmu/2gP9gXOB0Zb1xUvBE0q5DnjAzIYBE8zsD8DfgIHAjKwnaT4BEZG6qXQ+gdQC4G+enPEzzczWkBSYezvrCXlXDP/RzGaSHGfqnbbvDdwD/FeFKyciIg3rHmA/YIqZ9QZak0wTnClvTwB3nwJMKc7T+QRurMVKiohIHRWWkjazBSQzP44CRpnZLOBz4HjPuQ4gdxAo4VI24EFg1apVYZ5VvzxLixYtwvy//iveETr66LikUrdu3cJ8zZo1Yb569eoa2RtvvBG2XbJkSZh/97vfDfOs303WdQJZ11xk5V/5ylfCfEO7TqApmTNnTpjvvffeYf7JJ5805OpkyrqWJeewdA2dO3cuO//jH/8Ytn322WfDfNGiRRWty4aqRCnpYyrpR/MJiIhUMc0nICJSxTSfgIhIFdN8AiIiVUxVREVEqpgGARGRKtZsS0lXKut0x9NPPz3Ms8rXZv0+27ZtG+aLFy8O88GDB9fI3n47vujvl7/8ZZgffvjhYd6qVaswnzVrVphvv/32Yd6uXbswX758eZjvvPPOYZ516mtDa0qlpNeXrFM7o79PgCFD4qrFHTvGxYcnT54c5lddFRe/vOOOO2pkWaeTZp2uHfWxIcorJV1ftCcgItIEmdkoM1uaXhi2NtvVzJ40sxlpvaFv5vWTV0q6n5lNNrPbzKyHmU0ysw/N7Ckz260+NkRERGrlJormEwB+B1zq7rsCF6c/l5S3J/CntJPxJNcF/NXdtwDOSx8TEZH1IGM+AQc2T+9vASzM6ye3lLS7P+judyTL9LvShT8MbJz1JJWSFhGpm1qWkj4DuNLM3iSZVez8vCfkXSz2mZkdQDKiuJl9z93vSWcVq1ncJqVS0iIidVPLUtKnAme6+1gzOxIYCexf6gl5ewKnAGcDJ5CUjxhgZh+QHAo6rcKVExGRhnU8yZwvAGOAun0x7O7PufuB7n6wu89x99Pdfct0AuOd6r6+IiJSjxYCa+d/3w94Oe8JzbaUdJas857POuusMD/11FPD/N133w3zrDLN//jHP8J8hx12CPOLLrqoRtanT5+wbevWrcN84cL4O6EVK1aEedb5/VnXOGT5+OOPwzzrdybr36677hrmI0aMCPNzzz03zLP+zo888sgwz7pO5p///GeYV1KSun///mHeVK4TyJMxn8BJwPB0WuDPgNzvEVRKWkSkCSoxn8DulfSjUtIiIlVMpaRFRKqYSkmLiFQx1Q4SEaliGgRERKqYBgERkSpWl+sEmqSePXuGedb1AC+/HF9r8eqrr4b5z3/+8zA/4ogjwvyDDz4I86222qpG1qJFi7BtliVLloT5PffcE+Y/+clPwvzzzz8P8zZt2oT5xIkTwzzrOgRpPN27dw/zcePGhXnWa79gwYIwf/PNNytan/nz54d51rUsUf/R/xWAm266qaJ1qVZ5paS3MLNhZjbHzN5Nb7PTbMtGWkcRESmSlvefnL4nv2Bmp6d5h7Ts/8vpv+1L9ZN3OGg0yTUC+7p7R3fvCAxIszH1sSEiIlIrq4Cz3b0P0B/4DzPbmaTU/8PuviPwcPpzprxBoKe7X+Hu/7q2290Xu/sVwLZ1Wn0REak1d1/k7tPT+8uA2UA3YBBwc9rsZuB7pfrJGwTmm9kQM/tXiQgz62JmQ4HMg3+aT0BEpG4qmU/AzHoCuwFTgS7uvgiSgQKIJ2FO5X0xfBTJrsSj6UDgwBLgPiCuCIXmExARqaty5xMws02BscAZ7v5RJUX2IP+K4ffN7EZgEvCku/+rPKSZHQRMqGhpIiJSb8ysFckAcLu7r51HYImZdXX3RWbWFVhaqo+8s4NOA+4FfgXMMrNBBQ//pvarLiIidWHJR/6RwGx3v7rgoftIJpch/ffeUv3kHQ46Cdjd3T9OjzndZWY93X04SSXRJifrXPsuXeLK2FnXFQwcODDMN9ooHlfXrFkT5lm1+qN+3OMja5MnTw7zX/7yl2E+ZcqUMM/6HWSte5aWLavu8pMNTtYcE1nzAHTuHB823muvvcI86/qZPfbYI8yzzvvPuqakY8eOYb7jjjvWyF5//fWw7fPPZ1XCbzb2Ao4FZprZjDS7ABgGjDazE4E3gPgipVTe/9YWaw8Bufs8M9uXZCDYjiY6CIiINAfu/g+y34fjT6mBvLODFpvZrgUL/Rg4FOgEfLXchYiIyIYpbxA4Dlhn/jd3X+XuxwF7N9haiYhIo8g7OyguEJI8Fk8CKiIiTYaqiIqIVDENAiIiVazqzuV75ZVXwvy2224L85NOOinMs04FzZJ1yl7W6ZTjx4+vkd1yyy1h26xtGjt2bJhvvfXWYZ4l67TarFNH77zzzor6l/q35557hvlpp50W5i+88EKYT58+PcyzTifu1atXmF977bVh/sQTT4R5Vun1zTbbrEaWVTJ69erVYS7ryrtYbHMz+62Z3WpmRxc99qeGXTUREclSopT0lWn5/+fN7O68sv95H2dvJDkPdSzwIzMba2ZrZxLpX9eNEBGRWssqJT0J2MXd+wIvAeeX6iRvENjB3c9z93vc/TBgOvCImcWX84mISKPIKiXt7hPdfVXa7Ekgnk4ulfedQBsz28jd16QLutzMFgCPAZtmPSkteZpZ9lREREoL3kevTyuLRm178kUp6UInAP9bajl5g8A4YD/g72sDd7/ZzJYAf8x6kkpJi4jUTW1LSRfkF5IcMrq91PNLHg5y9yHAAjMbmC5obT4BiE8zEBGRRpFRShozO56kxM9PPKvyZCrv7KDBJGVIB1OzlPTltV1xERGpm6xS0ulcL0OBw9z9k7x+8g4HnUwzKyWd5aKLLgrzrl27hvl7770X5u3btw/zrHP8586dG+YvvvhijWznnXcO206YEM/t06lTpzCvVNa2Zp1zfv/999fLcqX2ss7jz7Jy5cowz7oe5j//8z/DPKsE9Lhx4ypan27duoV5dG3KiBEjKuq7GckqJf3fQBtgUjrL2JPufkpWJyolLSLSBJUoJf1AJf2olLSISBVTKWkRkSqmUtIiIlVMVURFRKqYBgERkSqmQUBEpIpVPJ+AmXV296UNsTLr05IlS8J80KBBYZ5zEV7Z7TfeeOMwv/zymtfiDR48OGzbrl27MM+qp75s2bIwf+mll8L82GOPDfPZs2eHuax/CxcurKh93759w/yZZ54J81atWoX58uXLwzzrepgsWdcJTJ1aXBonvqZGypd3xXCHoltHYJqZtTezDo20jiIiUiRrPoGCx88xMzezkleN5u0JvAPML8q6kZSUdmD7SldcRETqxdr5BKab2WbAM2Y2yd1fNLMewL8Db+R1kvedwBBgLkkNil7u3gtYkN7XACAisp5kzSeQPnwNyft37nHrvCqiVwE/By42s6vT0Sa3UzM72cyeNrOn89qKiEhNhe+j6S1zjpbC+QTM7DDgLXd/rpzl5H4xnF4wdoSZfZdk2rL4W8h1n6P5BERE6qA28wmQHCK6EDig3OXkniJqZl82s4HAZGAAsH+aH1TuQkREpP4F8wnsAPQCnjOzeSRTS043s62z+sg7O+g0CuYTAA5w91npw7+p8xaIiEitRPMJuPtMd+/s7j3dvSewAPi6uy/O6ifvcNBJVMl8Almi+uW1sckmm4T5ddddF+bRuflZtd0/+OCDMJ84cWKYjxkzJszHjx8f5p9++mmYy4arZcv4v3al17fssMMOYZ71N/frX/86zLOuw8laz/79+4f5bbfdViNbtWpV0LIqhPMJuHtFpaQ1n4CISBNUYj6BwjY98/rRfAIiIlVM8wmIiFQxzScgIlLFVEVURKSKaRAQEaliFQ8CaSVRERFpBkp+J2Bmw4Cr3P0dM+sHjAbWpFepHefujzbGSjYlffr0CfM///nPYb7PPvuU3ferr74a5qecckqY//3vfy+7b2leHn/88TC/8cYbw/zQQw8N8/nzi4sIJ+69994w/8tf/lLG2n1hp512CvOs6xMmTZpUUf/NWVop9BZga2ANcH16DRdmNhj4FUkZifHuPiSrn7zrBA5x9/PS+1cCR7n7U2bWG/gfoF/dNkNERGopLCUNdAEGAX3dfYWZdS7VSd4g0MrMWrr7KqCtuz8F4O4vmVmbetgIERGpBXdfBCxK7y8zs7WlpE8Chrn7ivSxkjNB5n0ncB3wgJntB0wwsz+Y2d5mdikwI+tJKiUtIlI3tS0lDfQGvm1mU83sUTP7Rqnl5F0n8EczmwmcmnbcMv33HuD/lXieSkmLiNRBbUpJu/tHZtYSaA/0B74BjDaz7T2jcFQ5E80vTldk6to6QumCDwImlPF8ERFpAEEpaUgqh/4tfdOfZmZrSEr9vB31UVEpaTMbVPCwSkmLiKwnUSnp1D3Afmmb3kBrkvni435KlZZNDwXtWVhKGrjV3Yeb2bPuvlsZK9osDwcNGDAgzLNOkevdu3eYr1y5MsxHjhxZI7vkkkvCtkuXlvzeRzK4uzXXv88srVq1CvOBAweG+dy5c8N83rx5YV5pqeprr702zLffPp7C/LDDDquRNddS0u5eskKomX0L+D9gJskpogAXAH8HRgG7Ap8D57j7I1n9qJS0iEgTlFNK+phy+1EpaRGRKqZS0iIiVUylpEVEqpiqiIqIVDENAiIiVUyDgIhINXP3RrsBJ6t9/bTfkNalObTfENdJ7ZvGujRG+4a8Ne7C4Gm1r5/2G9K6NIf2G+I6qX3TWJfGaN+QNx0OEhGpYhoERESqWGMPArllUdV+g+i7Gts3xjLUvv7ab0jr0hjtG0zJAnIiItK86XCQiEgV0yAgIlLFNAiIkMyUZ2ZzzewVMzsvp20PM5tsZrPN7AUzO72M/luY2bNmdn+Z67Olmd1lZnPS5exZou2Z6XrMMrM7zGzjoM0oM1tqZrMKsg5mNsnMXk7/bZ/T/sp0fZ43s7vNbMustgXPOcfM3Mw6leo7zQenr8ELZva7nHXZ1cyeNLMZ6fy73yx4LHx9sra3RPus7S35+hduc6m2Wdvb6Bry/FPgy8BQ4L+B4en9PjntBwKbFuUHlbGsW3Ie3wPYPL3fFrgUGAdcAWxR1LY1SQXV/dOfjwauBf4DaLW+z+vVrX5vQAvgVWD79LV/Dti5RPuuwNfT+5sBL5Vqn7Y7C/gf4P4y1+lm4Ofp/dbAlhntugGvA23Tn0cDPw3a7Q18HZhVkP0OOC+9fx5wRU77A4CW6f0r1raP2qZ5D+AhYD7QKafvASSTobRJf+6c034icHB6/zvAlLzXJ2t7S7TP2t7M1794m0v0nbm9jX1rsD0BMxsK3Eky6cE04Kn0/h3RJ61KprI0s/uKbuOAH6z9OWOVRgGfpPeHA1uQvLCfADcWtb0ROAQ43cxuBY4AppJM2jyirF+A1Csz61xh+44VNP8m8Iq7v+bun5P83Q7Kauzui9x9enp/GTCb5M04a126k/w9lfW3Y2abk7zxjUyX8bm7f1DiKS2BtpZMMN4OWBis82PAe0XxIJLBhvTf75Vq7+4T3X3tNF5PAt1L9A1wDTAEWOfsk4z2pwLD3H1F2mZpTnsHNk/vb0HBNpd4fcLtzWpfYntLvf7rbHOJtpnb2+gaanQhGfFqfGom+VTzcpDPJN0DAHoCTwOnpz8/W9R2OnAbsC+wT/rvovT+PhnrM7vw+UWPzSj6+fn035bAEpIZ1iAZxJ5fXyN2sE1lf3oAOtbjcrcAhgFzgHfT2+w02zJovznwW+BW4Oiix/4UtO9QdOsIzAPaAx2C9sNIP2kC/YDXgFdIPo2Ffw9Fz/8hMKLg52OBa8v8XfQE3iDdy8xocxewe/p3mrsnQDIt4DTgJuBZksFjkxLtTwc+JplI/PacdS38NP1B0ePvl2pf9Ng44JgSfR8GDE/vz6NgTyCj/QySvfOpwKPAN3La90l/728CbwHb5b0+edtb6vUs3t6M/svZ5rVtS25vY94a8juBNcA2Qd6VL+bDLLTOVJYk/2EONrOrqTmFWj/gGeBC4EN3nwJ86u6PuvujGeszy8x+lt5/zsz6wb8mYi6e6HcjM2tNsvvWjuRND6ANEE7SamZbmNmw9Bjiu+ltdpptGbTf3Mx+a2a3mtnRRY/9KWjfoejWEZhmZu3NrENR22Frj8GaWT8zew2YambzzWyfoO9+6XHL29JjmJPM7EMze8rMonmkRwPvA/u6e0d370iye/s+MCZofyPJazgW+JGZjTWzNulj/YP275C8vmtvT5N8epqe3i92iLuvnUj7SuAod/8S8O/A74P2xaIp+nLPnTazTUm26Qx3/yijzaHAUnd/poz1WKslyeGPP3syj/dyksMXUf/tST7h9iL5/7aJmZU9tWBtmNmFwCrg9ozH25H837y4gm5bkgzy/YFzgdFmVmoK21OBM929B3Am6V5T0Xrkvj7ltM/a3sL26eOZ2xz0Xen2NpyGGl2Ag0g+jT1IcmHE9cCENKtxjB94BNi1KGsJ3AKszlhGd5I3nWuBN3LWZwuST1avkoy+K0k+MT4KfK2o7ZnpY/OB04CHgRtI9lYuyej/IZLvPLYuyLZOs0lB+7Ekn2C/B9yX/rz2+OD0oP0akmO/hbeV6b+vFbWdWXB/MumnDKA3Qc0Skk+dBwM/Jvlk9cM0Hwg8EbSfW+L3XOMxau5pXQj8k+QTfrSt56R/K18tyF4vscw5fHHs9sms30WJ5+8JPFTw8/nA+TnPaZW+5mfltPstsIDkk+FiksOPt+U8Z2tgXsHP3wbGZ7Q9AhhZ8PNxBHtX6WM9WffT9Fyga3q/a/FrV9w+zY4HngDaZbUlmXp2abrN80jeIN8o+r9RvC4TSD5UrP35VWCrEu0/5IvrnAz4KO/1KbW9Wa9nie1dp32pbc5Yl5Lb25i3hu08OfuoP3A4yS53f9JDK0Hb7oV/JEWP7ZWznEOA35S5TpsBXyPZPe9Sot02wDbp/S3T9f9mifYbzBsjFb4pUnC4jaLBlKJDcWk2keS4Z5eCrAvJgPf3oP1sYKOi7HjgBWB+ib+HMcDV6Wv2WtQubTs4Xaf9gF8DfyA5pn4pcGsZfxMtSQb9XnzxxfBXSrQ3kg8nf6jw/8O+lP/F8P8BO6X3fw1cmdFuj/T32C5dr5uBwRlte7LuG+mVrPtF6e9y2h8EvEjwZlXctuixeeQfDjoFuCy935vkw4iVaD+b9E2U5MPKM3mvT9b2lmgfbm85r//abS7Rd8ntbcxboy+wud7YgN4YqfBNkeSTzgEknyrnA99L832I9xzak3ypPofkENB76fZcQXzM/nekZ1oV5QcRfD9U1Oa7JF/KLc5pty/wvyTH0GcCDwAnU+bZXCRnmLxE8onswpy23yI5XPQ8ybHdGcB3yljGvpQ/COxKcujreeAeoH2Jtpemr8Usku9d2gRt7iD53mwlyZ7JiSQfOB4GXk7/7ZDT/pX0zWrtNv8lq23Rsuex7tlBUd+tSb7nm0Vy2G+/nPbfIjlU+BzJnv3uea9P1vaWaJ+1vbmvP18MAll9Z25vY9/Wy0Kb44113xjfY903xhr/gWngN0ay3xRbBm2/RrK7+iDJabrDgQ9IBqR/y+j/y8D+lHk6L9mn/x6c157klN5datl/7unFuulWzbf1vgLVcAN+Vt/ti94Yy+6/PtaF5HuSuSSfUOcBgwoeiw5lDa6wfaX9V9ReN910++K23legGm7kfGndmO3ro28qOJ13Q2yvm266fXFridQLM3s+6yGS7wYarX1DrwtFp/Oa2b7AXWa2HfHplhtaexFJaRCoP12AA0m+KC1kwOON3L6h12Wxme3q7jMA3P3j9Hz4USSnym3o7UUkpUGg/txPckhiRvEDZjalkds39LocR3Ie9L94cnn9cWb21ybQXkRSmlRGRKSKqZS0iEgV0yAgIlLFNAiIiFQxDQIiIlXs/wOIwo0Q2GvqfAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# cluster 5 \n", - "plt.figure()\n", - "fig, (ax,ax2) = plt.subplots(ncols=2)\n", - "fig.subplots_adjust(wspace=0.01)\n", - "sns.heatmap(x_train[4], cmap = \"gist_gray\", cbar = False, ax = ax)\n", - "sns.heatmap(x_train[6], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", - "ax2.yaxis.tick_right()\n", - "ax2.tick_params(rotation=0)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "e0c292a2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# cluster 6\n", - "plt.figure()\n", - "fig, (ax,ax2) = plt.subplots(ncols=2)\n", - "fig.subplots_adjust(wspace=0.01)\n", - "sns.heatmap(x_train[10], cmap = \"gist_gray\", cbar = False, ax = ax)\n", - "sns.heatmap(x_train[13], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", - "ax2.yaxis.tick_right()\n", - "ax2.tick_params(rotation=0)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "5c9e5d1f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# cluster 7\n", - "plt.figure()\n", - "fig, (ax,ax2) = plt.subplots(ncols=2)\n", - "fig.subplots_adjust(wspace=0.01)\n", - "sns.heatmap(x_train[24], cmap = \"gist_gray\", cbar = False, ax = ax)\n", - "sns.heatmap(x_train[25], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", - "ax2.yaxis.tick_right()\n", - "ax2.tick_params(rotation=0)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "523d5272", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# cluster 8\n", - "plt.figure()\n", - "fig, (ax,ax2) = plt.subplots(ncols=2)\n", - "fig.subplots_adjust(wspace=0.01)\n", - "sns.heatmap(x_train[1], cmap = \"gist_gray\", cbar = False, ax = ax)\n", - "sns.heatmap(x_train[14], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", - "ax2.yaxis.tick_right()\n", - "ax2.tick_params(rotation=0)\n", - "\n", - "plt.show()\n", - "#sns.heatmap(x_train[17], cmap = \"gist_gray\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "a53a0ed4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# cluster 9\n", - "plt.figure()\n", - "fig, (ax,ax2) = plt.subplots(ncols=2)\n", - "fig.subplots_adjust(wspace=0.01)\n", - "sns.heatmap(x_train[0], cmap = \"gist_gray\", cbar = False, ax = ax)\n", - "sns.heatmap(x_train[7], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", - "ax2.yaxis.tick_right()\n", - "ax2.tick_params(rotation=0)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "e7d36a15", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# cluster 10\n", - "plt.figure()\n", - "fig, (ax,ax2) = plt.subplots(ncols=2)\n", - "fig.subplots_adjust(wspace=0.01)\n", - "sns.heatmap(x_train[19], cmap = \"gist_gray\", cbar = False, ax = ax)\n", - "sns.heatmap(x_train[30], cmap = \"gist_gray\", cbar = False, ax = ax2)\n", - "ax2.yaxis.tick_right()\n", - "ax2.tick_params(rotation=0)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "2786386d", - "metadata": {}, - "source": [ - "## 2. Models" - ] - }, - { - "cell_type": "markdown", - "id": "3fc3e521", - "metadata": {}, - "source": [ - "### 2.1 Kmeans" - ] - }, - { - "cell_type": "markdown", - "id": "9bef3c89", - "metadata": {}, - "source": [ - "###    2.1.1 Fitting Models" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "289d8d9e", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.cluster import KMeans\n", - "import pandas as pd\n", - "kmeans = KMeans(n_clusters = 10).fit(x_trainf)\n", - "y_pred_kmean = kmeans.predict(x_testf)" - ] - }, - { - "cell_type": "markdown", - "id": "67ccad57", - "metadata": {}, - "source": [ - "###    2.2.2 Clustering results evaluations" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "47c1a572", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test accuracy: -44382938395.616394\n" - ] - } - ], - "source": [ - "test_score = kmeans.score(x_testf, y_test)\n", - "print('Test accuracy:', test_score)\n" - ] - }, - { - "cell_type": "markdown", - "id": "f2e23193", - "metadata": {}, - "source": [ - "####     NMI (Normaliszed Mutual Information)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "955c19c9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.30718566254193425" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.metrics.cluster import normalized_mutual_info_score\n", - "normalized_mutual_info_score(y_test, y_pred_kmean,average_method='arithmetic')" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "40e0dea9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.32006018618379345" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.metrics.cluster import normalized_mutual_info_score\n", - "normalized_mutual_info_score(y_test, y_pred_kmean,average_method='min')" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "01329398", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.3074344894881988" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.metrics.cluster import normalized_mutual_info_score\n", - "normalized_mutual_info_score(y_test, y_pred_kmean,average_method='geometric')" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "ac0103e6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.295306849795413" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.metrics.cluster import normalized_mutual_info_score\n", - "normalized_mutual_info_score(y_test, y_pred_kmean,average_method='max')" - ] - }, - { - "cell_type": "markdown", - "id": "5d7c0620", - "metadata": {}, - "source": [ - "####     ARI (Adjusted Rand Index)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "779c4a21", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.1614760459699456" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.metrics.cluster import adjusted_rand_score\n", - "adjusted_rand_score(y_test, y_pred_kmean)" - ] - }, - { - "cell_type": "markdown", - "id": "eaf905f4", - "metadata": {}, - "source": [ - "####     Confusion Matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "id": "49e50762", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Confusion matrix: \n", - "[[ 60 7 106 0 522 3 18 260 3 21]\n", - " [ 2 6 0 79 1 0 572 170 75 95]\n", - " [ 16 14 4 137 4 14 221 229 353 8]\n", - " [ 9 3 109 3 1 254 59 550 3 9]\n", - " [ 11 108 5 67 73 3 199 83 38 413]\n", - " [ 1 0 251 54 6 10 194 62 414 8]\n", - " [ 32 4 0 559 5 3 186 81 24 106]\n", - " [391 27 1 14 9 19 71 38 113 317]\n", - " [ 2 1 19 76 4 0 131 401 360 6]\n", - " [ 10 301 1 15 1 0 240 221 188 23]]\n" - ] - } - ], - "source": [ - "from sklearn.metrics import confusion_matrix\n", - "kmeans_confusion = confusion_matrix(y_test,y_pred_kmean)\n", - "print('Confusion matrix: \\n{}'.format(kmeans_confusion))" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "id": "ba637eb3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 106, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAD4CAYAAADSIzzWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAACHoElEQVR4nOyddXwU19eHn7sbI0o8wQmuxYu0uBWKF4oUSou0uLtDcS9WSnFKgRaHHy7B3d2duPvKvH/MEhJiG7IJ8s7DZz9sZubec2fm7pk7Z+6cr5AkCQUFBQWFrEf1oRugoKCg8P8VxQErKCgofCAUB6ygoKDwgVAcsIKCgsIHQnHACgoKCh8Is8w28CI4NsumWTjZWGSVKR75RWaZrYIetllix/HbOVliByBo58Ass6XR6bPM1izvh1liZ0TtQlliByBOm3XHzyGbSmS0jmxlexvtc6IvL8ywvYyQ6Q5YQUFBIUsRn86NveKAFRQUPi/EBx3UpgvFASsoKHxeKCNgBQUFhQ+EMgJWUFBQ+ECo1B+6BUajOGAFBYXPCyUEkT4iwsOYNWU8Tx49QCAYPHoiufPkY9LoIfi+foW7Zw7GTp6Fnb29yWw+efyIYUPeToV6+eI5PXr1pUPHH+OXjR8zkmPHjuLk5Mx/W3cmqePFs8csmjGBRw/u0P7nnjRr0ynD7dLExfH79LE8uncbO3sHBo6ZRkGPwty5fZvJk8YTERGBWq2ia/ceNPymUZLyJ48fY/q0yeh1elq0ak2Xbt2TbGOphoTzdOJ0ide3rVWUgW0qAhAZraHvgoNcfxyQof2yMFezfHBDyhZyJygsGoHcBgFYqA1fAK0OdO9MIoqNjeXnHzugiYtDq9NRt14Devbum6H2JGTi2FGcOHYURycnNm6Rz/PdO7eZ9tt4YuPiMFOrGTZyLCVKlcbH5zXjRw0nMDAAIQQtvmtDuw5Jz7vf/Wtc3rIMvU6HpY09tftNy1AbdRoNZ9fNIfj5Ayxs7OhUZAk5c+bizp3bTHnTL1Ryv2jwTr/I7OPn6/Oa8aMTHJNWbWjboRMH9+9l2R8LefL4ESvXbaJ4iZIms5kqn1AIQmR2NjRj5gFPmziKUl+Uo3GzVmg0GmJjovl71V/YOzjQrlMX/lmznPCwMLr3HpBqPe87D1in09GgTg3WrN9Ijhw545dfvHAea2trxowansQBP/KLJDQ4CH/f15w9eRRbO7t0OWA/n1csnDGeiXP+TLR87/ZNPH30gF8GjOTE4X2cPXmEJYsW8uTJY4QQ5M2bDz8/X9q1bsXWnf/DPsFFSafT0bRxA5YuW4m7uzvtv/+OaTPnUKBgwUQ2LNUQ+47ThbfzgCsX8+TO8yBCImKpXyEfo3+oQvX+/xi1X3nc7Vk2qAENhv6baHn3b7+gZH4X+i44ROsaRVgxrDEaXbzfjb8gWJlBjDZxnZIkER0dhbW1DRqNhp86tWfo8FGU/qKMUW1Kax7wpYvyeR43ani8A+79SxfadfyRal9V5+Rxb9asWs7S5WsI8PcjIMCfosVKEBkZSae2rZg5byFeBeRjPMv7IXFRERyaO4TqPSZg4+RGTHgIVnbZjWprZKAvZ/+eS+2+iR32/eO7CX31mArf9+bZRW/sfG4wY/Y8nj55DAn6Rfs2rdiyI3G/yOjxS2secJJj0q4VM+cuRAiBSqVi6qRx9B041CgHbJJ5wFVHGj8P+NSUD+qtP/hYPTIyguuXL9KoaUsAzM3NsbWz59TxI9Rv1BSA+o2acvLY4Uxrw7mzp8mVO3ci5wtQvkJFHBwcUizn4OhEwaIlMDNLeiPhfeB/DOvZiUHd2/HHnMnodMl4vOTacsqbmvW/BaBKjTpcv3QOSZLIly8/efPmA8DNzR0nJyeCg4MSlb1x/Rq5c+clV+7cmFtY0LBRY44eOWSU3YScuf2akIhYuT13XpPTxS5+XdvaxTg+vz1nFv3Agr51URn5e/m2SgH+PngLgC3H76E2FJNIPBp/MypOiBACa2sbALRaLVqtFmHCUU658hWxt8+exGZkRAQAERERuLq6AeDi6kbRYiUAsLGxIZ9XAfz9fBOVfXrRm1xfVMXGSS6T0Pk+OX+EA7MGsG96H85vWIheb1y/eHX9DPkq1QEgV5mvOHf2NJIkkdeIfpHZx+/dY5LfcEzyexUgb778JrNjNEIY//nApBmCEEIUBZoBOZF/H6+AHZIk3TZFA16/fIGDoxMzJo3h0YN7FCpSjF4DhxEcFISziysAzi6uhLzTqUzJvj3/o+E3jU1W34unjzl5dD+Tf1+OmZk5f86fyvFDe+Ida2oEBfjj4uYOgFpthrWNLSEhwTg6OsVvc/3aNTRaDblz50lU1s/XFw9Pj/i/3dzduX7tWhIbEobbfkCnT3rLn5DODUqy78JjAIrkduK76oWpNXADWp2eeb1q07ZWUdYfSrsr5HC25YV/uMGmRHImRQKn/C46nY52bVry/Nkzvm/XnlKlv0jTZkYYOHQEfXp0Y/6cmUh6PcvXrE+yzauXL7l75zYlSiVuS7jfSySdjsO/D0cbE02hmk3JX6kOYT7PeX7pGHUGzESlNuPCpsU8vXCU/AbHmhpRoYFYZ5d/Dyq1Gltbu6T94vo1NJqk/QKy7vildEyylM8lBiyEGAa0AzYA5wyLcwH/CCE2SJKUbGBLCNEd6A4wbc5COnTumqINnU7H/bu36TNwOMVKlmbhnGlsWLPiPXbl/dBo4vA+epg+/Uz3auy1y+d4dP82w3rKIYm42Fgcsss/lOljB+Hn8wqtRkOAnw+DurcDoHHLdtRu2JTkQkIJRyv+/n6MGjGE36ZMR6VK3NGSc2vJjXQSxnwt1KDXJe/0qpfOzY8NSlJn0EYAapXJQ7lC7pz4vT0A2SzN8A+NBmDjmKbk9bDHwkxNbjc7ziz6AYBF2y6z9sBNowYbFuqk8eg3qNVqNm3eTlhYGAP79eLB/XsULFQ47Urfk82bNjBwyHBq163PgX17mDR+NIv/XBm/PioqkmGD+jJwyHBsbRO/Ki7pdQQ9f0Ct3pPRaWI5OHcwLvmK4nvvCkHPH3JglhxK02nisLKV77BO/PUbkYG+6LVaooL92Te9DwCFajTFq3K9ZE/Qu/1i9IghTJqctF9A1hy/qKhIhg9O/phkKZ/RLIguQAlJkjQJFwoh5gA3gWQdsCRJfwJ/QtoxYFc3d1xd3SlWsjQA1WvXY8OaFTg6OREY4I+ziyuBAf5kT3ClNyUnjh+naLHiOLu4mK5SSaJm/W/5oWufJKuGTZwNpBwDdnZ1I8DPF2dXd3Q6LVGRETg4ZAfkW+HePX6hd9/+ycbv3N098HntE/+3n68vbm5uqTZVL4FKJB0Fl8zvwpL+9Wg2ZgtB4TGAPEJdd/AWY1eeSFLP95N2ACnHgF8GRJDL1Y6XARGoVSJJmMHSDDQ6SOuRhL29PRUqfsnJE8cz1QHv2rmNQcNGAlC3fkMmTxgTv06r0TBsYD8aNmpC7br1k5S1zu6CpY09ZpZWmFla4VqgJCEvHyNJkL9SbUo37ZykzFddRwMpx4CtszsTFeKPtaMLep2OiIjwRP2iT89f6NUn+X6RkMw6flqNhmGD+tGgURNq1Ul6TLKUT2gEnFZL9UCOZJZ7GtZlGCdnF1zd3Xn+VL7NvXz+LHnze1H165rs/5/8o97/vx1U/bqWKcwlYe+e3SYNPwCUKluJ08cOEWoIm4SHheLn+9qoshWr1ODo/l0AnPY+RMmyFRFCoImLY0DfXjRp2oz6Db5JtmyJkqV49uwJL148RxMXx97/7aZGrdqp2lOJpIOr3K52bBjTlC4z9/DgZUj88iNXntHiq0K4OmQDwNHWijxudhjD7jMP6VC3OAAtvy6cyOFbqEGrly8GyREUFERYWBgAMTExnD1zivz5vYyy+764urpx6cJ5AM6fO0PuPHkB+YHWpPGjyeflRYdOnZMtm7NUZfwf3USv06GNiyHw6V3s3HPhXvgLnl89SUx4CACxkeFEBvkZ1Z4cJb/kyTk5nv/iygkqfllZ7heaOAb268W3qfSLzD5+kiQxacJo8uf3okPHziar971RCeM/H5i0RsD9gUNCiPvAc8OyPEBBoLepGtFn0AimjBuBRqPBM2cuho6ehF7SM2nUYPbs2IqbhwdjJ882lbl4oqOjOXv6JKPHTkh2/fChA7l4/jwhIcE0qFODX3v1QauVH9GXr9mE4KAAhvboSHRUJEIIdm3+h/kr/iV3Pi/a/9STicN6odfrMTMzo1vf4bi5e6bZpjqNmvH71DH06tgMWzsHBoyeAsC+fXu4dPECoSEh7Ni2FYCJk6dRtFix+LJmZmaMGDWWHt27otfraN6iFQULJs6aJQDzBHdoOoPjUwvo2qg0f/3vGiM6VMbJzop5veXYpFan56u+67nzLIgJq0+yc0orVCqBRqtnwKLDPPMLT3O/Vu29wYqh33Bjxc8Eh8egNYQa1IbfgRBgZhgOxGkTXxQC/P0YM2o4ep0OvSRRv0FDqtc03QV51LBBXLxwjpCQEBrXq0n3Hr0ZNXYis2dMQafTYWFhycixEwG4evkS/9u1g4KFCtO+TQsAevXpj89rwwXWvSL2HrnxLFaefdN6g0rgVbkB2XPkA6BU4454Lx6DJEmoVGrKte4R/7AuNbyq1OfM2tnsntgNC2tb1ixdAsD+vXK/CHm3XxR92y8y+/hdvXKJPYZj0sFwTHr26U+cJo7Z0yYTHBzEwD6/UqhIURYs+ctkdlPkExoBpzkNTQihAiohP4QTwAvgvCRJRj2+VdJRZhwlHWXGUNJRZoxPLh1lnSnGT0M7NPLjTkcpSZIeOJMFbVFQUFDIOJ/RQzgFBQWFT4tPKAShOGAFBYXPi4/gBQtjURywgoLC54UyAlZQUFD4QCgjYAUFBYUPhDICfouLnWVmm4hHn8mZ3RKSVVPDAF4GRWeJneBdWTc1LCtxr2K61ItpsXXduCyx8zokJkvsAMRqjUsYZAocstlkvBJlFoSCgoLCB0IZASsoKCh8ID6hGPCnc6lQUFBQMAahMv6TVlVCPBFCXBdCXBFCXDAscxJCHBBC3Df875hg+xFCiAdCiLtCiAZp1a84YAUFhc8L0ydkryVJUhlJkioY/h4OHJIkqRBwyPA3QojiQFugBNAQWCyESDUgrThgBQWFzwsTjoBToBmw2vB9NdA8wfINkiTFSpL0GHiAnEcnRRQHrKCg8FkhVCrjP0J0F0JcSPB5V8VWAvYLIS4mWOcuSdJrAMP/b9LZ5eRt1kiQE5cl1jl7h4/qIZzP69eMGjHUoK6q4rvWbRKpFJua8LAwJowbzcMH9xEIxk2azBdlymaKLWPUit+HF8+eMG3c0Pi/X796SccuPShdriILZ00mOjoKd48cDB07BWsb006dGzt6BMe8ZdXoLdt3mbTud0nr+KWl9DygUx2+byQrPZupVRTN70Hu2sMJDotKd1ucYm+STeePXmXJjHl/UrZYHoJCI/lhmKzkcmLXP1w5cRAhQKvV8fjxIyav3IWN3furems1cayb/xvPH93Fxs6eCVNm4+6Zk4f377Bw1mSiIiNQqdW07dSVGnUa8uLZE6aOTdgvXtCxa08iw8PZu3NzvELLj7/0oVKVr9+7XW/Y+d96DuzaioREvcYtaNq6A/+s/IMDu7di7yCHSH/o1psKlb/KsK20SI/eXULxiBSoJknSKyGEG3BACHEnNdPJmUjNfqarIsdoU29AQvz9/Qjw96dY8RJERkbQtnUr5v2+KImqb0qkdx7wmJHDKFuuAi2/a41GE0dMdAx29sb9SFTpOMnGqhWnhLHzgHU6HZ1a1mfO0rVMGTOYrj0HUqpsBfbv3obP65d06tor1fI5nbIZZecNb1SjR40YlqkO2Jjjl5LSM4BjxcSpqxtVL0mfDrX45pcFRtnP4+nEsokdadBtvmxLF4xeqClk9Zhfh0yl7+QNtG5Qnqa1SmObKw/WBsG9qDgddy+d4vz+zXQeOQdtShnnExDo95r1CybTZ9LCRMuP79nCq6cP+f7XIVw6cZDHV04xYuJMXjx7ghCCnLnzEhjgR58u7fhz3VZs7RKrZXdsUY+5f67jwO7tWGWz5rv2xg9s0poH/PTRA2ZPHMHMP9ZgZmbOhKG9+XXgSLwP/I9s2axp3tZ4tfBinjYZnsJg03ql0Y4g8t+fjLYnhBgPRADdgJqSJL0WQngCRyVJKiKEGAEgSdJUw/b7gPGSJJ1Oqc6PKgTh6upGseJv1FVt8fLywu8dxVlTERERwaWLF2jR6jsAzM0tjHa+6cVUasVpcfXiWTxy5MLdIwcvnj2lZJnyAJStUJmTR01vr3yFitinohptKkx9/No0rMCmvRfj/27bqCLH1w7mzIbhLBjVNk2l51i1I3rMsbOx4u+dZwHYcvAyNSsVAWTHG2UYgp/x3k+9ht9gYcg2f957H7OHdmPGwM5sXDIDvZFq2TfOn6BSLVnx4osqNblyUVbLzpUnHzlzy2odzi5uZM/uRGhIcKKyVy6exTNnbtw9khO3yTgvnj2mcPFSWFplQ21mRoky5TlzPPNUzNNCCGH0J416bIQQdm++A/WBG8AO4M0V7Edgu+H7DqCtEMJSCJEfKMRbLc1k+agccEJevnzBndu3M0299eWL5zg6OjFu9AjafteCCWNHEx2V/ttRY0hOrdjX1/QXFu9D+6hZV/6R5vMqwJkTRwE4fuQAAX4+qZT8uDHm+L1RerZQEy95nxzZrMypV7UY2w5dAaBIfne+q1+OWj/NoXLbaej0etoaQhVpYWam4oWP7Ox0Oj1hEdGYJzAeFxvD3cvnqFuvHlFxOnxePOHyyUP0n7KEoXNWIVQqLhzbb5StkEB/HJ3lUOMbteyw0JBE29y9dR2tVoNnztyJlnsf3EuNug3j/965ZQM9fvyOOVPGEm6QKsoIefIX4Na1S4SFhhAbE82lMycIMAycdm/dSL+f27Bg+ngiwjNuyxhM5YABd+CEEOIqsiPdLUnSXmQtzHoGpaB6hr+RJOkmsAm4BewFeqUlXPHeMWAhxE+SJK1MYV28KvLCxUvTHe+MioxkUP++DBk+MtPUVbVaLXdu32LYyNGUKv0FM6ZOZsXyZfTq08/ktoxVK84IGo2Gsye96fyL/Npt/+ET+GP+dP5Z9SdfflUDM3Nzk9rLSow5fsYqPTeuXorTVx7Fx35rVSpCueJ5OLFOjpdmszTHPygCgI2zu5E3pzMW5mpyezhxZsNwABatP8r6bUfSbPfdS6coV64szyLUgJZ71y7y/OFdZg+VVcI1cbHYGeKjf00bQZDfa7RaLcEBvswY2BmA6o1bU7lOY5Lbm4THICjAn5mTRjFo1G+JVJHf9IuffpX7deMWbWjXuTtCCNYsW8SyhbMYOHJimvuSGrnzetGiXWfGD+6JVbZs5CtQGLVazTfNWtOmUzeEEKxfsZiVi+fQZ9j4DNkyhuRUod8HSZIeAUlGgJIkBQJ1UigzGZhsrI2MPISbACTrgBMGttMTAwa5wwzs35dGjZtQt17mqau6e3jg5u4eP8KuW78BK/9aljm23kOtOL1cOHOCAoWL4ujkDEDuvPmZPOcPAF48e8r508dNai8rSe/xS0npGaB1g/L8myD8IIRg3c6zjF2wI8m23w+S+8O7MWAANaDV6smV25GXfiGo1SrsbbOhCZPj9WqV4P6Fo3xRrQ6h0bKOIJJEpVrf0OSHX5PY6jp8KpByDDi7sxvBgX5kd3GLV8u2s5fDP5GREYwd2psfu/WOVxd/w7v94s3/AN80bcm4oUmVu9+Heo2bU69xcwDWLluAs6s72RPYqte4JZNHmH5wkyyfzotwqYcghBDXUvhcRx6emxRJkhg/dhReXl506vyTqatPhIuLKx4enjx5/AiAc2dO41WgQKbYeh+14vTifXAvNeq8vc0MMSgy6/V6NqxZRqNmrU1qLytJ7/FLTukZwN7Wiq/KF2Tn0Wvxy46cu0uLumVwdZTvtBztrcnj6ZhM6aSER8bQocmXALSsWxbv8/cAeX5/4eyCixcukKdUlfjtC5cuz5XTRwk3xGgjw8MIMjI0VLJiNc4d2QPA1dNH+aJcJYMqsoZJIwdQp2ETvq6ddMBy9OCe+LAUyCPlN5w6dpi8XsY9CE6LN/3N3/c1Z44doXqdhgQFvrV19sRh8uTPnN/Xu5gwBJHppDUCdgcaAMHvLBfAKVM35vKli+zasZ1ChQvTpmUzAPr0H8jX1WuY2hQAw0aOZuSwIWg1GnLmzs2ESVMyxY4xasUZISYmmssXztBnyOj4ZUcP7mHXlo0AVKtRh3qNmpnM3huGDR7IhfPnCAkJpl7t6vTo1YeWrUzv6NM6fqkpPSekaa0vOHTmDlExcfHL7jzyYcKiXexc0huVEGi0OgZM28Sz1+92+bc4x17DShdMeIyGP2cOYfav3QmLiGTUxNnMWzoPD3tLLpw6QJWq1ahVXH7wdf1VGOTOT+N23VgycQB6SUKtVtO620Cc3DxStPWGynW+Zd38SUzq+T3WtvaMnzwLgOOH93HjyiXCQ0M5+D95FD9w1EQKFCoq94vzZ+g7ZEx8PcuXzOXR/bsgBO4eORKtywjTxw4mPCwUMzMzuvcfhq2dPXMnj+bxg3sIAW4eOegxaJRJbKXFx+BYjSXVaWhCiOXASkmSTiSzbr0kSe3TMpDeEERGyMp0lOmZhpZRsiodZXqnoX0qvDsNLTPJqnSUhd3sssQOZG06SlNMQ3PquN5oRxC0tv3Hq4osSVKXVNal6XwVFBQUsppPaQT8Ub0Jp6CgoJBRRBrzuD8mFAesoKDwWaGMgBUUFBQ+EIoDVlBQUPhQfDr+V3HACgoKnxfKCDgBGq0+s028JQuPe1YG+gPC49LeyATkcMy6aWhafdb1i9//GJJlth6GRGaJnTlHH2aJHYBt3b7MMlumQHHACgoKCh8IU+WCyAoUB6ygoPB58ekMgBUHrKCg8HmhhCAUFBQUPhCKA1ZQUFD4QCgOWEFBQeEDobyKnA4mjB3FiWNHcXRyYtOWnfHLN6xfx6YNf2OmVlOteg36Dcj4VKIJY0dxwttga+vOROvWrlrB/DkzOeh9iuyOxuWDTQ86nY7237fCzc2dBYuXJlo3bvQIjh2T1YU3b0tZ3PLRvVtMGPgzvYZPptJXySbkNxqNJo6ls8bz5MEdbO0c6DViMuRx4M6d20yZNJ6IiAjUKhVdu/egwTeN3mu/MkJ6zpWPz2vGjRpOYEAAKpWgRas2tPshsRDkrVOHOLd7EwAWltmo27kPbnnSn582LNCPPX/OJDIkCITAzMycuJhorGztaNJrFNkc3bm8fwunt6xC0utBCCo0asOXzTqm25ZaCFqU8kCtEggk9u3bz7y5s7GyscesRjfM7F0pbhPFpO++5OWr1wCceODPpscZc0DmKsGQugUp5GZDeIwWgZxfWQhZbeRN7Rq9nPrzXdatWcXWLf8hhKBgoUJMmDQVS0vLDLUpPXxKI+APPl+jSbPmLFiSWBX6wrmzHDt6iA3/bWfT1l107PSzaWw1TWoLwMfnNWfPnMLD09MkdpJj/bo15PdK/gfftHlLFv/xV6rl9TodG1csoFS5yumy6+/7iinDkioweO/bgY2tHbOWb6Fhi3ZsXCErMGSzsmLSlOls2b6bRUv/Yub0KYSlohuW2n5lhPScKzO1mgGDhvLf9t2sXLeRfzeu59HDB4nKObh60HbkLDpPXkrlZu3Zv2Ke0W0J9fdhw5TBAKjUamq2687P05dTqkZDQvx9aD5gAhUatuTYxuUA5CxSmnbjF9Prz1006TuRczvXExsVYZStsAAfNk+XBxs6SWL7DR82XnnF6Hl/UaFsaYYu/Iey9VsSfFK+mKC24MbrcPoeCKDn1vvMGdUPfaxxc5Hd7SyZ2bx4kuUNi7sREavlp3VX2HLl9dtcyxLEaSFGC7Fa2Rm/i5+vL/+sX8vfG/7jv6070ev07Nuz26j2mIpPKSH7B3fA5cpXxN4+e6Jl//27gR9/7oaFhQUATs7OyZR8D1sVKmLvkD3J8jkzptF3wOBMOyG+Pj4cP3aUlgYF5ncxRl14/85NVKxWG/vsiUfnJw/vYXz/zozu3YGVC6YarbJ76Yw3X9VtDEDFr2pz6+p5JEkib7785M2bDwA3N3ecnJwINqgdpHe/MkJ6zpWLqxtF49W0bciXv0ASNe2chUpgZSPn0M1RsBgRwQHx626dPMi68X1YPfpX9q+ch16f8jG0ze6Mez45GfzT6xdxzZ2fiOAACleszrNbl5EkCbe8BXH0yAWAuaUlKpWa6PBQAO6cPsTGSX1YP64Hh1fPT9UWgMYgZf/8+nls7bMDEgUrfE3M81tIkoSZrRPCXB5dmtk6orK2RxcdDkCdwi78/l1Jlnxfin4182PsnXmV/I4cuCOrWRx7GIja4CUk3iqNSIAkJT/jS6fVERsbg1arJSYmGlcTy2+lxWflgIUQRYUQdYQQtu8sb5hSmYzy7OkTrly6yI8dvqf7zx25eeN6ZpnC+8hh3NzcKVykaKbZmDl9Cv0HDkGI97veBQX4cfHUUWo3aplo+ctnjzl77ACjZ/3Fbwv/RqhUnDq616g6gwP9cXaVVaXUajOsrW0JeUfO/Pr1a2g0GnLnzpNsHRndr/RizLl69fIld+/cpmSplNW0r3vvJX9pWfk48OUz7pz1pt3oufz42x8IoeL2KeMk1UP8fQj1e41ngaKo1GosrG2IiZDvFh5ePMnakV3YPmcU1g6OOLh6EvTqGffOefPdiLm0n7AEoVJx93TqtgTw/Rc52LRyCc+Co/CNiEOlVqOyyIY+Rh5VF/ewZcn3pZhULw9eeXJh5uBGbkcrahR0ZsCWm/TYeB29XqJ2YRej9svFxgL/CPntS72UvLyTSsiNe3edm7s7nTr/zDf1alOv9tfY2tpRpepXRtk1GSIdnw9MqjFgIURfoBdwG1guhOgnSdJ2w+opyNLLyZWLV0Wev3AJP3VJnyqyVqslLCyMVes2cPPGdUYMGcD2/x0w+RUrJjqaFcuWsmhp6rf/GeHY0SM4OjlRvERJzp87+151/P3nHL7/uTcqdeJ7vltXz/PkwR3G9/8RgLjYWOwNKrvzJw3B3/cVWo2WQH8fRvfuAED9pm2pXr+JPHx5h4TH19/fj9EjhjBp8vRk3ywyxX6lB2POVVRUJEMH9mXQ0OEpqmk/u3WF6957aTd6LgBPb13G98l91o2XVTO0cXFYG+7Its0fT6i/DzqtlvBAP1aPlkM55eq3oEil6oQH+VP7h55YZrN5a8BwDAuUr4ZHgaJsnNSPbHbZESoVz29fxv/JfTZO6hNvK5udbGvXggmEBci2IoL8WD+uBwBl6jZnIw3YNrU/f61ag1O4nqAozRtjPPCP5Ic1l4kICaLw68MsXvA7P2+8RdlcDhRys2Fh65IAWKhVhBjEQcd9UxgPe0vMVAI3O0uWfF8KgK1Xfdh/x98ox2RhJocj3iUsNJSjRw6xa+9B7OzsGDqoP7t37qBxk6ZpV2oiPoaRrbGk9RCuG1BekqQIIUQ+4D8hRD5JkuaTymlKqIocHqNPt06Qu7sHterUQwhByVKlESoVIcHBODo5pbeqVHnx/DmvXr6gXevmgBy/6vB9K1av34iLi6tJbFy5fAnvo4c5cfwYcbGxREZGMHLYYKZMn2V0HY/v32bxNFnvLTwshKvnT6FWqZEkia/qNKbNT72SlOk3ZiYgx4CXzZnIyOl/JFrv6OJGoL8vTi7usspuVAQOhlv+iIgI+vT8hV59+lP6izKZtl/pIa1zpdVoGDqwHw0bN6F23eTVtP2fPWLfirm0GjSZbHb28kJJosRX9ajeJqn4S/N+4wE5Brxn2SzajpT3TafVsnXOGOyd3XDNlQ+QY/RxUZHxYY7Y6Eh2zBvL1227c/Lfv+QQhARFq9Wj2ndJn2l820eWMgoL8OHA8tm0GjYz8QYW1jx44UOe7C4EhMegj4tGZWVDlEaHPjYa/x1ziK3wLWYWlthbmSGAA3f8WXHmeRJbE/bI4qHudpYMrlOAIdtuJVofEBGHq60FAZFxqETSH7qVGWi08uj4Xc6eOU2OnLlwMvxWa9etx9Wrl7PUAas+oVkQad07qiVJigCQJOkJUBP4Rggxh0wcwNeoVYcL584A8PTJY7QaTabMTChYuDAHvE+yc+8hdu49hJu7O39v3Gwy5wvQd8Ag9h86xp79h5k2cw4VK1VOt5Oas3I7c1bJn4pf1ebHXkMpX7UmJcpU5PzJw4SFyDHaiPBQAnxfG1VnuS+rc+Kg/HDk/InDFC9dwaCyG8fAfr34tmkz6jf4JsXyptiv9JDauZIkiYnjRpM/vxc/dOqcbPmwAD+2/z6RRr8MxckzV/zyPCXKcu/8cSLD5PBLdEQYoQG+ydYBsnL3vuVzcMqRh3L1m3PzxAEA7p0/Ru7iZRBCEPjqKbsWTKBo1To4uHmi02qxsrUnV7EyPLhwnKiwEABiIsIIS8WWlZkKC0MAtnCFanjamBEcreHBheNY5SqGEILslgL/3b9jU7Qa5arVQiUgLEbL5RdhfF3AiezZ5DGWnaUaNzsLo4716cfB1Csq/waqF3BONNPB0gy0etClMKzy8PTk+rWrREdHI0kS586eJn9+L6PsmopPKQac1gjYRwhRRpKkKwCGkfC3wAqglCkaMHLYIC5eOEdISAiN6tWke4/eNGvRkoljR9OmZRPMzc0ZP2mqSQ7WyKEJbNWtSfeevWne0vQPkNLL8CFv1YXr16lOj5590Grl+7vW37dLsVzOPF606vgrM0b3QdJLqM3M6NRzCC7uac/mqN6gKUtnjWNwl5bY2tnTc9hkAPbv3cOlixcICQlhx7atAEycPI2iRYuZYE+NJz3n6urlS/xv1w4KFipM+9YtAOjZtz8+rw0Xo8LVOL19HdERYRxcvQAAlUpNx4mLcMmZl69adea/GSOQDErFdTr1wcHFPVlbL+/d5NbJg7jkzs+z21cID/DjwaXTqM3NKVVdfixybvvfvLxzFZ+HtxFCYJPdmYDnj3DNU4AqLX9k22zZlkqtpuYPvbFPwZaNhZo6hVzkvl+mK3v37OH3+fNo07o1HXsPYe+jaL50iGDy0pnoJUFMXBwDBw8lzrMqz8jLqrMvmNq0GALQ6SUWHHuCnxGZ9fbe9mNY3YKs/KEM4TFaNIbnhGqVHPsVKjAzDN1idYmjWaVKf0HdevVp36YlajMzihYtRqvW36dp05R8BH7VaNJSRc4FaCVJ8klmXTVJkk6mZeB9QhDvTRYeeLMszLh07VloltgpnSf1mRimJCvTUW64kvQ2PLOI02ZNd99+JclPMtPIynSU1hYZd59Fhu0z+iTcnd7go1ZFfpHKujSdr4KCgkJW8ymNgD/4m3AKCgoKpuRTeginOGAFBYXPik/JAX/wN+EUFBQUTIkQxn+Mq0+ohRCXhRC7DH87CSEOCCHuG/53TLDtCCHEAyHEXSFEg7TqVhywgoLCZ0UmTEPrh/wy2huGA4ckSSoEHDL8jRCiONAWKAE0BBYLIZLJmPEWxQErKCh8VpjSARtmgjUGEr6C2QxYbfi+GmieYPkGSZJiJUl6DDwAKqVWf6bHgLMyHqPKwsefD3yNy25lCrJqelhWPj2ed/xRltn6qXzuLLNVqPGELLET5D0lS+wA6FOZqvoxkp5+nDBtgoE/DW/yvmEeMBSwS7DMXZKk1wCSJL0WQrzJNpQTOJNguxeGZSmiPIRTUFD4rEjPoC9h2oR3Mbx05idJ0kUhRE0jqkvOcKpXL8UBKygofFaY8BXjakBTIUQjwAqwF0KsA3yFEJ6G0a8n4GfY/gWQ8HYrF/AqNQNKDFhBQeGzwlSzICRJGiFJUi5JkvIhP1w7LEnSD8AO4EfDZj8CbzJE7gDaCiEshRD5gULAudRsKCNgBQWFz4osSLIzDdgkhOgCPANaA0iSdFMIsQm4BWiBXpIkpZpxX3HACgoKnxWZ4X8lSToKHDV8DwSSFWWUJGkyMNnYej86B7x+3Rq2bv4XSZJo0ao1HTr+mHah9yA2Npaff+yAJi4OrU5H3XoN6Nm7b6Jt0hLLfPnsMYtmTODRgzu0+7knzdp0SrJNetHExbFg+lge3buNrb0DA8dMo6B7YaPEMo3ZJ1MydvQIjnnLx2fL9qTHx+f1a0aNGEpgYABCqPiudZsk59Pn3jW8l07C1lnOCJa7TFVKN2qfoXbpNBpOrZlN4LMHWNrY8c3c+XjkyMmDe3eYN+M3oiIjUalUdOjcjVr15Axm7Zs3xNrGGpVKjVqtZsmqDTy8f5e50ycREx2Fu0cORk6cho2NLR72FkhI8VnA/MI1iex/XTY//07vyJNXcprQ7d63mLrSOJWNlLAwV7N8TGvKFs1JUGjUW6FMDNpsBqej1SVNFZnV/SKrfsMp8TGkmTSWj8oBP7h/j62b/2XN+k2Ym5vT+9dufF29BnkMGmWmxMLCgmUrVmNtbYNGo+GnTu356uvqiRKQN23ekrbtf2D0yGHJ1mFr58DPvYdw7uTRdNv383nFwhnjmTgn8QPYQ3u2YWNrz8K12zlxeB/rlv1O1YUL48Uy8+bNh5+fL+3btKJKta+wt7dP1z6ZkmbNW9Ku/Q+MGpH88VGbqRk8dDjFipcgMjKCtq1bUblKNQoULJhoO7eCJajVY3y67UcE+nJq7Vzq95+WaPmD0/uwsLal+YS/eHLBm2WL5jFm8kwsrawYPnYyufLkJcDfjx6d21KxclVsDcnZZy9ajkMCzb3ZU8bzS59BfFGuAnt2bmXTulX89IusnOEfrkk2IfkbTl59Qqsha9K9T3k8srNs9Hc06J1Y+aNzkwoEh0dTss1sWtctzYrxbePTRMbp3j5qtzID3TtKFVnZL7LyN5wSyqvI78njR48oVfoLsmXLhpmZGeUrVOTwoYOZYksIgbW1LCWj1WrRarVJrpxpiWU6ODpRsGgJ1GZJr2PHDvyP4T07Mbh7O5bOmYzOSLHM86e8qVn/WwCq1KjD9UvnjBbLNGafTElax8fV1Y1i8WKZtnh5eSURy0yNR+cOs2fGAHZP6c2Z9QvSFLB8w4trZ/H6Ur5DzFP2Ky5dOIskSeTOk49cefICspBndkcnQoKDU6zn+dMnlC5bHoDylapw7EjG+2LbBmU4/lcPzqzqzYKhzY12Ft9+XYy/91wCYMuRG6gNxRIKZb75+90as7JfZOVvOCVM/SpyZmKMKGclIURFw/fiQoiBhmkZJqdAoUJcuniekJBgoqOjOXHcG18f4xQe3gedTkebVs2oXb0qlatUpVTplIUc08OLp485eXQ/v/2+nFl//oNKreL4oT1GlQ0K8MfFLYFYpk36xDIza58yysuXL7hz+3ay7fF/fIddU3pzeNFYQl49BSDU5xlPLx6nwaCZNB65EJVKxZPzR42yFRUSiLWjrOigUquxsbUlLDQk0TZ3bl5Hq9GQI5c8a0gIGNr3F3798Xt2bfsPgHwFCnLquGzT+9B+/P3e5uB1sTXHzc4cG4vkf0JflszD2dV92Db7R4rll+fpF8nrynd1SlHrl6VU7rwQnV5P2/pljNqnHK4OvPCV80LrdPpkJ5eKBE75XbKqX2T1bzg5PhtFDCHEOOAbwEwIcQD4EjkQPVwIUdYQcE6uXPzbJb8v+oOfuxonyunlVYDOP3ejZ/cuZMtmTeEiRVGrMy9Kolar2bR5O2FhYQzs14sH9+9RsFDhDNd7/fI5Ht2/zfCeckw4LjYW++yyRtaMsYPw83mFVqMhwM+Hwd1lxYtGLdtRu2FTkkuQnx6xzMzap4wQFRnJoP59GTJ8ZBKxTKfcBWkxcSXmVtl4eeM83n/+RrPxy/C5c5Wg5w/YM70/AFpNHJYGAUvvP38jIsAHvU5LZJA/u6fIYYGitZpRoEq9ZAVHEw53AgP8mTphJMPG/hZ/DOf/uQYXVzeCgwIZ2vcX8uTNx5BRE1k4Zxprl/9B1a9rYmZmDoBfeBx6SVaHcLE1R6OXEiViv3L3FUVaziAyOo4GVQqzadoPlPp+DrUqFKBckZycWN4TgGyW5vgHRwKwcWoH8no6YmFuRm53B86skvdp0b+nWLv7klFaAxZqORyRHFnVL7L6N5wcH4FfNZq0jsx3QBnAEvABckmSFCaEmAmcJYWnfQnfLomMS997jM1bfhcvPbNg/hzc3T3SU/y9sLe3p0LFLzl54rhJOqUkSdSs/y0duvZJsm7oxNlAyjFgZ1c3Avx8cXY1iGVGpk8sM7P26X3RaDQM7N+XRo2bULdeUrFMi2zW8d9zlqzIuY2LiYkIRULC68s6lG3WOUmZGt1lgdKUYsDWji5EBftj4+iCXqcjMiICe3s5VBIZGcHIgb34+Zc+FC/5dhTo4iqPUh2dnPmqRm3u3LpBmw6dmfH7UgCeP3vCmVPHgbdilHoJYjR6LNQq4rRvPV94VGz8932n7zF/sBpnB2uEEKzbc4mxf+xPsk/fj/gbSDkG/NI/lFzuDrz0D0OtViVxyJZmoNElf+1JSFb0iw/xG07IxzCyNZa0QhBaSZJ0kiRFAQ8lSQoDkCQpGsgUTZmgwEAAXr9+xZGDB2j4TePMMENQUBBhYWEAxMTEcPbMKZOJB5YqW4nTxw4RaojRhoeF4m+kWGaFKjU4ul+eUXDa+xAly1Y0WiwzM/fpfZAkifFjR+Hl5UWnzj8lu010aFD8qD/gyV0kScLSxh6PImV4dvkkMeEhAMRGhhMR6JdsHe+Sq9SXPDp7CIBnl09QtkIlwzHUMG5Yf+o3akKNOm8vBtHRUURFRsZ/v3DuNPm8ChIcJPdFvV7P3yv/pEmL1gjexlgFYGmuQqNL/FNwd3o7yq9QLBcqIQgMjeLIhYe0qFUSV0c5Hutol408HtmN2qfdx+/Q4ZtyALSsVTLRTAcLtSyUmdJDwazuF1n1G06JzyYEAcQJIawNDrj8m4VCCAcyyQEPHtiX0JAQzMzMGDZqbKoPeTJCgL8fY0YNR6/ToZck6jdoSPWatRJtk5pYZtmaTQgOCmBYj45ER0UihGD35n+Yt+Jfcufzot1PPZk0rBd6vR4zMzO69h2OqxFimXUaNeP3qWPo3bEZtnYODBgtJ10xRizTmH0yJcMGvz0+9WpXp0evPrRs1Tp+/eVLF9m1YzuFChemTctmAPTpP5DXr19x754/hb9uxLPLJ7l3/H8ItRozcwu+/nkoQgiye+bhiyYdObRgdLyAZcXve2Lr7JZSc+IpWLU+J1fPYtu4rlja2DFvzjwAjh7cx7XLlwgLDWXf7h0ADB0zCWsbW8YN6w/IsdI69b+hUpWv2LxxHdv/2wjA1zXr0PDb5qhU4GwjhyKEgKg4PbFaKVEsuEWtknRr8SVanZ6YWA2dxm4A4M4TPyb8eYCdc39CpRJotHoGzN7BM5+QNPdp1a4LrBjbmhubBhEcFsWbAbdaGIQyxVuhzDht4jhwVveLrPoNp8SnNAsiLVFOS0mSYpNZ7gJ4SpJ0PS0D6Q1BZITPNRtaQXfbtDcyAVk5IJh59EGW2VKyoWWMrMyGZmMCUc5a808Z3eAj/ap+1KKcSZyvYXkAEJApLVJQUFDIAB9DaMFYPqoXMRQUFBQyyifkfxUHrKCg8HmRlaHIjKI4YAUFhc+KT+khnOKAFRQUPis+If+rOGAFBYXPC+Uh3AciK6eGFXC3yTJbfmHJTkYxOVk53WhwjYJpb2Qitt14mWW2rm0dkyV2ImK0aW9kImpMzVgqzfRwa0rStyXTyyfkfz8vB6ygoKAgjMqc8XGgOGAFBYXPCiUGrKCgoPCBUGZBKCgoKHwglHnACgoKCh+IT8j/Kg5YQUHh80KZhpYOxo8ZyXGD8vC/W3cCcGDfXpYuWcjjRw9Z+88mipcoler2CckqpeIC7oW4e+c2kyeNJzIiErVKRZfuvyZRKn5Do/q1sbGxiVfdXb9pc4bb9YaI8DDmTB3Pk0cPEEIwaOREtm5ax/NnTwCIDA/Hxs6OP1b/myE7z58+YerYofF/+7x6QceuPQkLDeH0iaOohIrsjo4MGjUJZ9e000Yay/uo+vq8fs3okUMJDAggQqOjfO1vqdyoVYbaccV7H8e2rgOgeosfKFOjAQCbF0zm1aO7qNRmfFH6C3oPHh2vnvEu2zatZf+urSAE+bwK0X/4BDau/YuzJ44iVILs2Z3oP3Iizi4ZO36xsbH07taJOE0cOp2OWnXq0+WX3ty/e5uZUycSFxeLWm3GoGGjKV6ydHy57NZq9JJEWHTibLP5Xa2Z3KokxXPYM3//fVaeeJqh9gGYqwXTWpeiRE57QqI0DPzn6ptVZYAlgD2gQxZ+2GhsvZ+Q/009HaUpSCsd5cUL57G2tmbsqOHxDvXRo4eohGDyxHEMGDw0kQNObvs3PPKLJDQ4CH/f15w7eRQbO7t0OeCUVCr2bt/E00cP+GXASE4c3se5k0dYtHABT588BiHilYo7tPmOLTt2Y5dAqfgNjerX5u+Nm3F0dEyyLi38w+JSXT9j0ihKfVGOb5q2QqPREBsTHa/0C7D091nY2Nryw8+/plpPeuYB63Q6fmhej3nL1mFrZ4+NjZwyc9u/f/Ps8SP6Dk19PqyHg5XRtiRJIjo6KpGq79Dho1JVBfH39yPA359ixUuw8fx9lo74lbaDJ+KWK1+a9lZOGEDzHsNwdHur5BAVEcafI3vQfcoSBIKlI3/llyl/kM3WjnuXz1CozJcAHPlrJiW/KEej5m2S1Bvg78uwXj+xeO0WLC2tmDZuCBUqf0XV6nWwNhy/Hf+t59mTR/QePDrVNma3Tt7BvyHhMdNqNfTo0pF+g0ew/I+FtGnfiSrVvub0iWP8vWYFC/9cBUA2c4GZWiAEiRxwjamHcbKxIEd2K+oUdyMsWpMuB5wjuxVTvitJ578uJFre9svcFPGwZcL223xT2oO6xd34prSHAAojpzS+D+QALgLFgBBj7LVdfdnojrzhx7IfbzrKrKB8hYq8evki0TIvrwLp2j4hDo5OODg6cfHsiSTrjh34H//bugGtVkOhoiXp2m84arU6zTaeP+VNm06/ALJS8fIF0+OVit/g5uaOo5MTQcFByTrgzCIyMoLrVy4yZPRvAJibm2Nu/vbHKUkS3of3MXPBXylV8V5cuXAWz5y5cffIkWh5THSMyW8B30fV19XVDVfDKNwymzWuOfMQHhSAmZk5u1f+TlRYCOYWVjTpPgjXnEnFTd/l4dXzFChVHmtb+dwWKFWeB1fPUapaHQqXrRy/XeFiJQjwT1n5WafTERcbi5najNiYGJycXeOdL0BMTLRJjt+7x0xnOGZCQFSk/MJSREQ4Lq4G8VIBFmaCqDiJbBZJ7QdFxhEUGUeNoq5J1jUp40mHKnmwUAuuvQhl4vbbKapzJKR2MVcWHXoIwP4bvoxuUhRkoZF7CTZ7BfgBrhjpgD/rEIQQYo0kSRm/r89iEioVm5mZs2z+VI4f2hMvAZ8aySsVhyQazd64fg1tCkrFIHeKnt27IAS0av09rVp/b5L98nn5guzZnZg1eQyP7t+jUNFi9Og/jGwGrbXrVy7i6ORMztx5TWLvDd6H9lKzbsP4v1ctXcDBvTuxsbFluomdPciOq12bljx/9ozv27VPl6pvsJ8Pr588IGfBYmycPZZvuw7A2TMXL+7fZveKeXQeMyfNOsKCArB3fut87J1cCQtKnBJbp9VyZN9uuvUd+m5xAFxc3WnRthM/tW6IhYUVZStWplylqgCsWbaAw3t3YW1ry9T5y4zet9TQ6XR06dial8+f0aJ1O0qULE3fQcMZ2Ls7i+bPQq/X88cKWYvO1lJFZKw+3c7Ly9WGhqU8+GHpObR6iTFNi/FtGU92XE5bgsvdwQqf0Bi5rXqJ8BgtjjYWziTONV4JsAAeGtumT2gWWpqqyDveXQTUEkJkB5AkqWkK5d5LFTkzMb1S8dvvslLxUCZOnpasUjHAyrXrcXNzJygwkF+7/Uy+/F6Ur1Axw/ul0+m4f+82PQcOp1iJ0iyeO42Na1fQubusqnv04B5q1U1eQ+590Wg0nDnhzU+/9otf1vmXPnT+pQ8b1ixn5+YNdOza06Q231fVNyoqkk1zx9Hwx54IlYrn926yad5b1QqdRgPA5aN7OLNnCwBBPi/5e/oI1GZmOLp50HbQJJITe3/XWe1eMY8SX5Sj5Bflkm1LRHgYZ08cZfnG3djY2jFt7BCO7N9NrfqN6dStD5269WHTuuXs2rKBDj9n/Pip1WpWrd9CeHgYIwf35dGD++zY+i99Bw6jZp36HDqwl6mTxrBk6Qr0kqwrZ572DWEiKhdwokROOzb1lEMwluZqgiLlkNnvHb4gl2M2zM1UeDpYsaW3fKew9tQztl56ldL7agkPtCewFviRdEigfU4j4FzALeAv5AMjgArA7NQKZUQVObPITKXivj1/TVOp2M0wgnZydqZ2nbrcvH7NJA7Yxc0dV1d3ipWQH6R8XaseG9euAOQR2Ymjh1i0ckOG7STkwpkTFCxcFEcn5yTratX/hrGDe5vcAb8hPaq+Go2GQf37UuqruhSvVJ2YqEisbGzpMT3pCLNszW8oW1O+UCUXA7Z3cuXJrfiHRIQF+ZOv+NtR+NH/VhMZFkrXsb+l2J4rF87g7pkTB8OFv0r1Oty+cYVa9d+KVtas+w3jh/UxiQN+g52dPWXLV+LM6RPs2bWdfoNHAFC7bgOm/zYWc7XAwkzgZKaWRUcF2FmpCI9J2+cJAdsvvWLu/qQSU33/lo9XSjFgn9AYPBys8A2LRa0S2FmZAQQZVtsDu4HRwJn07O8n5H/TVEWugBwAHwWESpJ0FIiWJMlbkiTvzG6cKckspeJB/XrzbdNm1GvQMMXy0VFRRBribtFRUZw+dZICJpIEd3J2wdXdnedPHwNw+cJZ8hgUby9dOEPuvPlxdTOtLPjRA3uoWe/tqPrl87cPZM4cP0ruvPmTKfX+vI+qryRJTBg7ivxeXlRtLAuFWlnbkN3Vg5tnjsZv4/PUuDvbAl9U5OG1C0RHhBMdEc7Daxco8IV8Ab14eDcPrp7nu76jU7wDAnB19+TurWvExEQjSRJXL54ld16vRMfv7ElvcuXJ+PELDg4iPFw+ZrExMVw4d5q8+fLj4urG5Yvn5XafP0uu3HmJjNMTFKkjKFJHWIyeOJ1klPMFOPMwiPol3XGysQDAIZsZObIb94D1yB1/mpeTnyHUL+nO2UdBIA/0LICtwBog3VN3PhtVZEmS9MBcIcS/hv990yqTXkYMHcjF8+cJCQmmYZ0a/NqrD/YODsyY8hvBwUH07fkrhYsWZfHS5Slu/0apuFyWKxXvTUapeCpFEigVAwQGBjKwnxwS0Ol0fNPoW6p99bXJjmGvASOYNmEEWo0Gjxy5GDxqEgBHD+6lVj3Thh9iYqK5dP5MolkOK5bM58WzJwiVCncPT/oMSf0Jfnp5H1XfK5cvsmvndgoVKsyh4ycBqNO2C636jGLX8nkc27IOnU5Hyaq18Mib8kPfN1jb2lO9ZUf+HNUDgBqtOsY/kNv111yyu7jz15jerDdXU7V6Hdp1/iVJHUWKl6Jazbr079oOlVpNgUJFadikFTMnjuDF8yeohApXD096DRqV3kOUhMAAfyaPG4ler0ev11O7XgOqfV0TWzs75s+ahk6nxcLCkqGjxqdYh5X5WwflYmvBpl6VsbU0Qy9JdKyWlybzTvLQL5L5Bx7w10/lEEKg1UtM2nGbVyExabZx84WXTG9dkr2DviIkSsPgDddoWMoDoA1QHXAGOhs27wxcMWbf1SYKAgshrIBjgCWy3/tPkqRxQggn5Glx+YAnQBtJkoINZUYAXZCnz/WVJGlfqjbSMw1NCNEYqCZJ0khjy2RlCOKRX2RWmcrSdJRpTUMzFVmZjjI909AySlamo6yQwylL7KQ1Dc2UZHE6ygx7z583XDe6I69oWypFe0IeIttIkhQhhDAHTgD9gJZAkCRJ04QQwwFHSZKGCSGKA/8gPzjMARwECkuSpEvJRlohiERIkrQ7Pc5XQUFBIatRCWH0JzUkmTdJxs0NHwloBqw2LF8NNDd8bwZskCQpVpKkx8ADZGecclvfaw8VFBQUPlKESM9HdBdCXEjw6Z64LqEWQlxBnot8QJKks4C7JEmvAQz/v3ltMSfwPEHxF4ZlKfLBX8RQUFBQMCXpebiWcMZWCut1QBnD1NutQoiSqZlOrorU7CsjYAUFhc+K9IyAjUWSpBDgKNAQ8BVCeMq2hCfy6BjkEW/uBMVyIb/JlyKKA1ZQUPisUKuE0Z/UEEK4vnnpTAiRDagL3AF2IL8cguH/7YbvO4C2QghLIUR+oBBwLjUbSghCQUHhs8KE83s9gdVCCDXyYHWTJEm7hBCngU1CiC7AM6A1gCRJN4UQm5BfXtMCvVKbAQFZ4IBNNSfPGAp52Ka9kYmY7Z30zZ/MonP5tJPFmAJHw2T6rCArp7yZZeGEeyvzrLmp/HryoSyxA3BpYsaVirMSU50BSZKuAWWTWR4I1EmhzGTk9JlGoYyAFRQUPis+hjfcjEVxwAoKCp8Vn002NAUFBYVPjawMe2YUxQErKCh8VnxC/ldxwAoKCp8Xn1AIWHHACgoKnxdp5Xj4mPjoHPDY0SM45i2rHm/ZvitTbZ08fozp0yaj1+lp0ao1Xbq9fQ3c5/VrRo0YSmBgAEKo+K51Gzp0/DFReZ971zjyxyRsXeRk63nKVOWLRu0z1CadRsOJ1bMJev4ASxs7qncZDuThwb07zJs+icjISNRqFR06d6dWPTkHcbvmDbC2to5XXP5j9VsB2Y3rVrF0wWy27juGQ/b0C4KmRnhYGBPGjebhg/sIBOMmTeaLMklm7bwXyalfh4aGMHzwQF69ekmOHDmZPmsu9g4OKW6fkOiIcLb8MYMg31eYmVvQ8tehuGcw765WE8d/i6by6tE9rO3s+b7fOMjjzIN7d5g/8zeiIiNRqVS079yNWgb5pojwMGZPHc+Th7KC9eBREzl+9CBnTnhjZm5Ojpy5GTJ6YiJRVQB3e3N0eomACG2i5fldbZj6XUmK57Rn3r77rDj+JEP7BLJa8fQ2pSiR04GQqDgEBjUGARbqt+/bavSgSyZt8Lo1q9i65T+EEBQsVIgJk6ZiaWmZ4XYZy6f0dtlH19ZmzVuyZKnpNcXeRafTMWXyRBb/8Rdbd+xm7/928fDB27m9ajM1g4cOZ9vOPaz7ZyMb/lmfaP0b3AqWoMnIhTQZuTBdzjci0Jd9c4cnWX7/1D4srW1pMeEvitVuzsWtKwGwtLJi+LgprNywjWnz/mDR3OlEGBJuA8xZvIJl6/5L5Hz9fH24eO40bh5p5zx+H2ZMm0zVal+zdeceNm7ZlqqYanpp0qwFC5ckVq5YuXwZlb6szPbd+6j0ZWVWLl+W6vYJ8d72N555C9Jn5nK+6zWC3asXGN2WYD8f/prQP8nyi4f/RzYbOwb+/jdVG7Vm3/qlAFhZWTFs7GSWr9/K1LlLWDJvRvy5WjR3OhUrV2Plxh0sXfsfefLlp3ylKvz19xaWrdtMrjx5+WfN8kR2bK3UaHTJz5sOjdLw287brDj22Oj9eUNORyvWdE+qyvJdxVyERWtpMOs4q088fStTJEGcFmK0EKuVnfG7+Pn68s/6tfy94T/+27oTvU7Pvj270922jJAZryJnFh+dAy5foWL8qCYzuXH9Grlz5yVX7tyYW1jQsFFjjh55O7nd1dWNYsVLAGBjY4uXlxd+fimr3b7Lo7OH2T19ADun9Ob0+gXo9am+EBPP82tnKVBZnuOdt+xX+Ny9iiRJ5M6Tj1x5ZGFNF1c3sjs6ERIcnGpdi+fO4JfeAzNlXmRERASXLl6gRavvADA3tzCpGnT5ChVxeKcfeB85xLfNmgPwbbPmHD1yMNXtE+L34glepWStNteceQj29yUiRFZHuXL8AEtG9mDh0K5s+3O20efq9oWTlK3RAIASlWvw6MYlJEkiV5585Mr9zrkKCY5XsP6mSUtAVrC2tbOnwpdVUZvJN6PFSpTGP0E/UwvIZq4iMjb5NgVFxnHjRRjaZGSIm5TxZFOvymztW4UJLYob/XCqTnE3tl2Scyjvu+GL2uAlJN5mlpEASUo++4xOqyM2NgatVktMTDSubm7JbJV5mOpV5KwgXQ5YCPGVEGKgEOLTejUmGfx8ffHwfCvV4+bujq9v8g725csX3Ll9O1klXv/Hd9g5uTcHF44l5JUsLRPy+hlPLh7nm8EzaTJyIUKoeHzuqFHtig4JxNrRIBWuVmOezZqw0JBE29y+eR2tVkOOXHLeD4FgSN9f+KVTG3ZtlRVcTh47gourGwUKFzHKbnp5+eI5jo5OjBs9grbftWDC2NFER0Vliq03BAYGxkvNu7q6ERQYlEaJt3jkLcCtc8cAePHgNqH+PoQG+eP34inXTx2h+8QF9J7xFyqViqvHD6ZRm0xYUAAOznJ71Go1lta2Sc7VnZvX0Wo05MiZm9cvX+CQ3YmZv43hl05tmD1lHNHRiY/Z3l1bqVTlq/i/s9uYERKVOOxgDF6uNjT6woP2S87S4vfT6PQSTcrmMKqsm70lr0PeqhUnN/ZWCUAkTfXl5u5Op84/80292tSr/TW2tnZUqfpVMjVkHiph/OdDk5Yq8jlJkioZvncDeiFrNY0TQpSTJGlaCuXiVZEXLl6aKLb6sZBct0pupBgVGcmg/n0ZMnwktraJX3V2yl2QVpNWYm6VjRc3znNk6W+0mLAMn7tXCXz+gN3T+wOgi4vDyi47AEeW/kZEoA96rZbIYH92TpGliorVakbBKvWSbVfCe6XAAH+mjh/J8LG/xeuP/b5sDS6ubgQHBTKkT3dy58vP36uWMeP3pe9zaIxCq9Vy5/Ytho0cTanSXzBj6mRWLF9Grz790i78AajerD27Vy1k4dCuuOfxwjNfIVQqNY9uXOLV43ssGfkrANq4OGwc5Fj537PGEOz3Gp1WS2iALwuHdgWgyjetKF/rG5LNNPjOuZo2cSRDx8jn6o2Cde9BsoL1ornT2LBmBT/9IveBv1f9iVptRp0GskinlbkKvR40OglLs/R5iyoFnSmR055/DUrEVgnUihd0LCOrFatVeGa3YmvfKgCsPfmULRdfGXXHZGEmhyPeJSw0lKNHDrFr70Hs7OwYOqg/u3fuoHGTZAXUM4XP6SFcQt2T7kA9SZL8hRCzkJVKk3XACXNsxmhTz4f5oXB398DntU/8336+vri9c6uk0WgY2L8vjRo3oW69pIN+i2zW8d9zlazI2Q2LiYkIRZIkCnxZh3LNOycpU+sXWS8tItCXk2vm0mBA4kNond2FqGB/bBxd0Ot0aKKjsLeXb60jIyIYMbAXP//am+Kl3o7GXQyjQkcnZ76qWYdrly7g8+ol3X6QwwP+fr780qkNi1f+g5OzS3oOU4q4e3jg5u4ef1dQt34DVv6VcgzWFDg7O+Pv74erqxv+/n44ORsv/2NlbUOrnsMAWYxzdp92OLp58uT2NcpWb0D99t2SlOkwWNbWC/bzYfOSaXQdNy/RensnV0ID/XBwdkWn0xEbFfH2XEVGMGpQL37q3ofiJeVj5PqOgnX1WvX4x6BgvX/3ds6cPMbMBcviHaClmcDKQoWnuUV8zNLJxoygyLRHxELAtouvmLPvfpJ1fdZeAeQY8NTWpej05/lE631DY/DM/lat+F13ZmUGGi0kE/Xg7JnT5MiZCycn+dzUrluPq1cvZ6kD/oT8b5ohCJUQwlEI4YysH+cPIElSJHK2n0+WEiVL8ezZE168eI4mLo69/9tNjVq149dLksT4saPw8vKiU+efkq0jOjSIN5p6AU/uIkkSljb2eBYtw9PLJ4kODwEgNjKciEC/ZOt4l9ylv+ThGTkW/fTyCTyKlDYoMGsYO6w/9b9pQs06Dd62ITqKqMjI+O8Xzp6iSPGSbNnrzT/b9vHPtn24urmzdM0mkzlfABcXVzw8PHny+BEA586cxquA6R7CJUf1mrXZtX0bALu2b6NGrWTzoSRLdGQEWq0GgAuHd5OvaGmsrG0oUKocN896ExEqx9OjIsII9vdJrap4ilaoymVvWXPx5hlvvEqUjT9X44f1p943TahR5+2F+10F60sXzpI3nxfnTp9gw7qVTJrxO1ZW2eK3D43W8TokjtehcQRGaIjV6I1yvgCnHwRSv1RCtWJzo9WKD9/yo3k5WcihQUn3RDMdLM1Aq4cUngni4enJ9WtXiY6WlZ/PnT2dpoK1qflsQhCAA7IsvQAkIYSHJEk+Qghbko+/Z5hhgwdy4fw5QkKCqVe7Oj169aFlq9Ymt2NmZsaIUWPp0b0rer2O5i1aUbBgofj1ly9dZNeO7RQqXJg2LZsB0Kf/QF6/NuRX9qjI08snuXv8f/L0L3MLqv88FCEE2T3zUKZJRw4uGI2kl1Cp1XzZtie2zmk/jChUtT4nVs1i67iuWFjbUb3LUEBWOL52+SJhoSHs2y2nHx029jesbWwYO7Q/IM/sqNOgUaIYYmYybORoRg4bglajIWfu3EyYNMVkdSenfv1Tl24MGzyAbVs34+HpyYzZ81Ld/o1atnXJGvi/fMrmRVMRKhVuOfPR4tchALjlykfd739m1eQhSJKEWq2myc/9cXT1SK5ZiShfqzH/LZzCnL4dyGZrz/f9ZKVo70P7uHblEmFhoez/3w4AhoyeRMHCRek9cARTx49Ao9HgmTMXQ0ZNotfP7dBo4hjWT1ZSLlaiNP2HjUnRro2liu+/zMXGsy9wsbXgvz5V4tWKO32Vl8ZzTshqxfsfsLxLeVRCoNXpmbjdOLXi/y68ZEabUuwb/DWh0Ro0hud/apXstIQKzAxDt1id/DDuDaVKf0HdevVp36YlajMzihYtRqvW36dp05QkHbN/vKRLFTm+kBDWyLpIac59+VhDEBnl80xHmXVKu1mYjZL/3XqdZbYq53HOEjv1ZhzNEjuQtekorS0yHkCYceSh0b1raK0CH9Rbv9eLGJIkRQHpn3iooKCgkMko6SgVFBQUPhAfQ2zXWBQHrKCg8FnxCQ2AFQesoKDwefE5zQNWUFBQ+KRQf3QJFlJGccAKCgqfFapPaBpapjvgyNise1/D2iLrrieDahTMMluOVQdniZ3AkzOzxA5k7W2imSrrhkSx2mTyM2YCZ8bVzRI7AI/8IrPMVslcGVc2/4QiEMoIWEFB4fNCmQWhoKCg8IFQHsIpKCgofCA+If+rOGAFBYXPi48h0bqxKA5YQUHhs+ITmoWmOGAFBYXPCyUXRDqIjY2lZ9dOaOLi0Ol01KpTn649erNw7ixOHD+KuZk5OXPnZtT437CzM53m2Bt0Oh3tv2+Fm5s7CxZnnoJEagrMAJbqxPoKcclIgH1drgAzBzbF3ExNYEgk9X9dkqE2WZirWT6+HWWL5iIoNOqt+i0G9VtDP9boks//2qh+bWxsbOLVmNdv2pyh9qREbGwsP//YAU1cHFqdjrr1GtCzd99E24wbPYJjBlXkzduSqmlHRYTz3+Jp8arI3/UchkeejOWp1Wri2LRgCi8NqsjtBowDDwce3r/D4tlTiIqMQKVS832nrlQ35HDeunEt+3dtRQhBXq9CDBgxgbOnvFm/4g+eP33M3D/XUahoiQy1C8DX5zXjR48gKDAAIQTNW7WhbYeO8evXrV7Bgrmz2HfkJNkdHZk0bhQnj3nj6OTEP5t3JKnvxbPHLJoxgUcP7tD+5540a9Mpw23UxMXx+/SxPLp3Gzt7BwaOmQa5ClOkSJEywBLAHtABk+/evbsx1coS8Om4349gtG5hYcGCpStYs3Erq//ZzJnTJ7hx7SoVK1dh3aZtrN20ldx58rJmReaoLaxft4b8JlTzTY60FJjfEKd7+3kXB1sr5g9tSetBKynfdhYdRqw12n4eT0f2LemRZHnnpl8SHB5NyVbTWPDPsbfqt4a2pKZ++4Y/V6xh4+ZtmeZ8Qe4jy1asZtOWHWz8bxunTh7n2tUribZp2rwli/9IWU376JZ15MhfiP6zV9Kmz0h2rjReFTnI7zVLxyWVWjp/eDfZbO0YsnA9X33bmr3rDKrIltkYOGoSS9ZuYeLsRfz5+0wiwsMI8Pdl5+Z/mPfXehav2Yxer8P70F7y5i/IqMlzKPlFOaPblBZqtRn9Bg1l49ZdLF+7gf82rufRQ7nP+fq85tyZ03h4vlXL/rZpC+Yt/jPF+uzsHOjSewhNW3dMcZuU8PN5xdiBSWXJDu3Zhq2tPYvWbufbVh1Yu+z3N6uigE53794tATQE5hUpUiS7sfZUQhj9SQ0hRG4hxBEhxG0hxE0hRD/DcichxAEhxH3D/44JyowQQjwQQtwVQjRIuXZDW43dqcxCCIG1tQ0g64xptVqEEHxZpRpmBqXYkqW+SKQUayp8fXw4fuwoLQ3KvplFWgrMxvB9g3JsP3qd574hAPgHR8Sva9uwHMdX9uXMugEsGN4KlZEPIb6tUYK/d18AYMvha6gNxZKo3/JhRxUp9ZGEpKWm7fviCQVLyg7OLWdegv19CDeoIl8+tp+Fw39h/uAubFk6C73OOFXkW+dPUs6gilyycg0eGFSRc+bJS06DKrKzi6yKHBoiK27odDriYmPRabXExsTg7OJKnnxe5MqTz/gDYgQurq4ULVYcABsbG/J5eeHvJ6uyzJ01nd79ByVKXF62fIV4OaXkcHB0omDREvG/yYR4H/gfw3p2YlD3dvwxZzI6I4/fuVPe1Kz/LQBVatTh+qVzFClSRNy9e/fe3bt37wPcvXv3FeAHuBpVKXJfNfaTBlpgkCRJxYDKQC8hRHFgOHBIkqRCwCHD3xjWtQXeXDgWCyFSGb6k4YCFEF8KIewN37MJISYIIXYKIaYLIUymHa/T6fixbUsa1/2ail9WoUSp0onW79q+hcpVvzaVuXhmTp9C/4FDECJzr0PGKDBLyCNNCzXxjjAhhfK4kN0uG/uW9ODk6v60b1QegCL53PiuXhlqdV1I5R/motNLtG1o3Egqh6sDLwwOXafTp6x+S7Lykwgh6Nm9C+3btGTzv0bfIb4XOp2ONq2aUbt6VSpXqZqsQnVqeOYrwI2zsiry8/u3CfH3JTTQH78XT7h66jA9fltEv1nLUalUXD5xwKg6w4ICyO7yRhXZDCtrmySqyHdvXUej1eCZMzcuru60bNuJzt815Ifm9bCxtaVcparp2o/34dXLl9y7c5sSpUpz7OhhXF3dKFykqEnqfvH0MSeP7mfy78uZ/ec/qNQqjh/aY1TZoAB/XNzcAfn4WdvYAiTKaF+kSJFKgAXw0Ng2qVTC6E9qSJL0WpKkS4bv4cBtICfQDFht2Gw10NzwvRmwQZKkWINYxQOgUmo20ooBrwDe9PT5yLcG04E6wEqgZXKFEqoiz/59MT/+nFTwMCFqtZrVG7YQHh7GiEF9efjgPgUM8kCr/lqK2syMBo2+TaOp6ePY0SM4OjlRvERJzp87a9K638UYBeaEYQcLNeh1iZ2emVpNuaK5+KbXUrJZmnF0eR/O3XhKrYqFKFc0JydWy7fI2SzN40fHG2f8SN4cTliYmZHbIztn1g0AYNGGE6zddd6o+ZIW6uRDIgAr167Hzc2doMBAfu32M/nye1G+QsW0K30P1Go1mzZvJywsjIH9evHg/j0KFipsdPmazTuwc+XvzB/cBY88+cmRvyAqtZoH1y/x8tE9Fg6X5YA0cbHxqshrZowi2M8HnVZDSIAf8wd3AaBa41ZUqNWI5NRkEp7XoAB/Zv82moGjJqFSqQgPD+PMiaOs2LgbGzs7po4ZwuF9u6ltUEHODKKiIhk+uB8DhozATK1m1V9L+X1JyqGa9HLt8jke3b/NsJ5yTDguNhaH7LIg5/Sxg/DzeYVWoyHAz4dB3dsB0LhlO2o3bJrs8SNBty9SpIgnsBb48e7du0a/452e4VRCX2XgT4Oo8Lvb5QPKAmeR1YBeg+ykhRBvtMZyIosVv+GFYVmKpOWAVZIkvUnmUEGSpDdDqxNCiCspFUqoihwYqTVaHsTOzp6y5Stx9tQJChQsxP92buPkcW8W/LHc5E82r1y+hPfRw5w4foy42FgiIyMYOWwwU6bPMqkdME6BOSF6SR55Jnzw9dIvhIDQSKJi4oiKiePElUeULpQDIWDd7guMXZx01PH9UPkincfTkWVj29KgR+KHdi/9Qsnlnp2XfqGo1UlTmFiZyQ/gklO/BXAzjF6cnJ2pXacuN69fyzQH/AZ7e3sqVPySkyeOp8sBW1nb0LrXCEAWXJ3eqy1Obp48vn2V8jUa0rBD0hhlp6GTATkG/O+iafwyYX6i9Q7OroQE+OHg7IZOpyUmKhI7w218VGQE44f2oWO3XhQ1qCBfuXAGd8+cODjKDqpqjTrcvnEl0xywVqNh+KD+NGz0LbXq1OPB/Xu8evmSH9q0AMDPz5dO7Vqxct1GnF2MvsNPjCRRs/63/NC1T5JVwybOlu34vGLhjPFMnJPYrzm7uhHg54uzqzs6nZaoyAiAIIAiRYrYA7uB0Xfv3j1DOkiPr0joq1KpzxbYDPSXJCkslfqTW5Gq/0vrYnFDCPFGEviqEKKCoUGFAU0aZY0iODiI8PAwAGJjYrhw9jR58+XnzMnjrFu1nBnzFmKVLVsataSfvgMGsf/QMfbsP8y0mXOoWKlypjhfSFuB+V1UIulZ23nsJtXK5EetVpHN0pyKJfJy57EfR84/oEXt0rg6yklMHO2zkcfDMWmlybD72E06NK4AQMvapRM5fEt16uq30VFRRMo/GKKjojh96iQF0uEQ00NQUBBhYXIfiYmJ4eyZU+lW2o2ODEerkbvs+UO7yF9MVkUuWLI8188cfauKHG68KnLxCtW4ZFBFvnHGmwIl36oi/zZyILUbfsvXtd7qqbm6eXL35jViYmTF4KsXz5I7b+YoBkuSxG8TxpAvvxftO3YGoGChwuw9coJtew6ybc9B3NzcWfPP5vd3vkCpspU4fewQocFyPD08LBQ/X+M0+CpWqcHR/fKMldPehyhZtiJ3796VihQpYgFsBdbcvXv33/S2yYQxYIQQ5sjO929JkrYYFvsKITwN6z2RY9Qgj3hzJyieC3iVWv1pjYC7AvOFEKOBAOC0EOI58NywLsME+vszadxI9Do9eklPnXoNqFa9Jq2bNkSj0dC/h2ymRKkvGDpqnClMZjlpKTALSDQDQaeXR50JY8F3n/hx4PRdzv89CL0ksWr7WW49kh3FhD/2snNBN1RCoNHqGTBzC898gtNs16od51gxoR03Ng8nOCzqrfqtQbJbiATqt9rEF4XAwEAG9ustt1en45tG31LtK9PH6QEC/P0YM2o4ep0OvSRRv0FDqteslWib4UPeqmnXr1OdHj3fqiLbla6F34unbFo4BZVKjVuuvLTqMQwA99z5qN+2K8snDUaS9KjVZjTrapwqcoXajdi0YDIze7cnm62dPA0NOH54PzeuXiIsLISDe+QpXQNGTqRoiVJUq1mXfl3aoVar8SpUlG+atuLUscP8MW8aoSHBjB/aB6+CRZg0J2NTDK9eucSeXTsoWKhw/Ii3R5/+VPu6RrLbjx4+mEsXzhESEsK39WvRvUdvtFr5glWpdjOCgwIY2qMj0VGRCCHYtfkf5q/4l9z5vGj/U08mDuuFXq/HzMyMbn2H4+bumaydhNRp1Izfp46hV8dm2No5MGB0vKp2G6A64FykSJHOhmWd7969e8WYfTfV3bKQK1oO3JYkaU6CVTuAH4Fphv+3J1i+XggxB8gBFALOpWrDGFVkIYQd4IXssF9IkmT0lIT0hCAySlamo8zKud6fYzrKrJQO33vbuBGtKSjlYbJn06niYmeRJXYAXgRGZ5mtkrlsM9wxtl7zMdrntCjtkXI8QYivgOPAdeBNDHokchx4E5AHeAa0liQpyFBmFPAz8gyK/pIkpfpE0iiPZXgCeNWYbRUUFBQ+JKa6tEuSdCKV6uqkUGYyMNlYGx/8TTgFBQUFU/IJvYmsOGAFBYXPC0WSSEFBQeEDoYyAFRQUFD4QWfmAN6MoDlhBQeGzQv0JDYEz3QE3mHs8s03Ec2Bg9SyzlS21FGEmxu/4jCyxY8SMRJOh1WeNejBA2RzZs8yWjWXWjGmeBkRliR2AGUeNTsOQYf7uWCbDdXxC/lcZASsoKHxeKA5YQUFB4QOhxIAVFBQUPhCfkCan4oAVFBQ+L9JSuviYUBywgoLCZ8X/xxCEFXAMsDTU+R+QKHWZnZUZY78tSi7HbMRp9UzcdYeH/pEZMmquFkxoWoxinnaERmtRibdZxKwtVAhDWscYjYRGJ+Hr85oJY0YQGBiAyiBU+H37jowaNpBnTx4DEB4ejp2dHWs3bs1Q2xIydvQIjnnLgpFbticVjMwoE8aO4oT3URydnNi0dWeidWtXrWD+nJkc9D5Fdkfj0lQay/p1a9i6+V8kSaJFq9Z06PijyeqeOHYUJ47J+7Rxi7xP9+7eYdpv44mKisIzR04mTZ2Jra1thm09f/qEKWOHxv/t8+oFHbv2xMXVjbXLl/D86WN+X/Y3hYtlTCwzNjaW3t06Ead5K0Db5Zfe3L97m5lTJxIXF4tabcagYaMpXrI0UyaM5tQJbxwdnVi7aXuS+l4+e8LimRN4/OAObX/qSdM26ddre4MACrpZAxLBgf7sP3CA/7Zso//oqQBE+TyheNwDfvy+JQjQmlmz9noIz4Jj3tsmgJlK0KNaHvI5WRMRpwXIBzwByvCOMCdglOzKpxSCMJUWTyxQG1k9owyyHlLlhBv8VC0v93wjaLfsPGN33GZQ/UJJa0kBTwcrliYzPaVZGU/CY7S0WHyW9Wefk81cPvISEBmnJyxGT0SsHmsL+ZqoVpvRd+BQNm7ZxV9rZKHCxw8fMHn6HNZu3MrajVupVaceNWvXe7+jkALNmrdkyVLTqRC8S5OmzVmwJGlOaR+f15w9cyqR+KKpeHD/Hls3/8ua9ZvY8N82jnsf5dnTJyar/9tmzfn9nX36bcIYevUbyIbNO6hVuy5rVy03ia3cefOxZPUmlqzexMIV/2BpZUW1GrXJ51WQsVPmUqpMeZPYsbCwYP4fK1j9z1ZWrd/MmVMnuHH9Kot/n8NP3Xqyav0Wuv7Sm8W/y5kPGzVpzuwFKSt129rZ81OvwTRp/UO62+Ln84rxCYQyJeChfxQLl61h2tyFtGnbgZ9/6c3fy2TxUpW5BVKhasy8GMmofU/4fdY0fq6UqthDIlxsLBhVr2CS5TULOhEZp2PQ9tvsue0PsuIOGIQ5eauvNg/IbowtkY5/HxpTOWAJeKMSaW74JJpV6uViw7knco7ap4FR5MhuhZONOQDflHRn9U/l+btrBUY2Kmz0FaxGYVd2XZNTDR667Y+ZIYGuXnqr4iAZvguRjFBhfi/8/P3i65MkiUMH9lGvYaP0H4FUSEswMqOUq1ARe4fsSZbPmTGNvgMGm1xNBODxo0eUKv0F2bJlw8zMjPIVKnL40EGT1V+ufEXs7bMnWvbsyWPKlZcVNypVqcqRQ8Zpt6WHKxfO4pkzN+4eOciTz4vcefOZrO53xUV1BnFRIXijBkFERDgurnKC9DLljBPKVKuT3sgeO/g/RvTqxJBf2vPn3MlGCY3qJbhwypta9b9FCChRpgI3Lp9DkiSsnHPwNDYbUXE6LOycuPngKU7W5vFlq+V3ZOI3hZjSuAg/f5nL6Klg5XM7cOyhnMz93NMQkLOMCeAecN+wWbqEOYUw/vOhSUuUs68QIndq2yRADVxBPlAHkHNmxnPPL4LaReTjVyKHHR4OlrjZWZLP2Zp6xd34efUlOvx1AZ1edsjG4GZngW9YLAA6SUKSkuaOU6vkZe/K6rx69ZJ7d29TsuRbAdArly7i5ORMHhP+6D4U3kcO4+bmbjLxxXcpUKgQly6eJyQkmOjoaE4c98bXxzglhPfFq2Ahjh09DMCh/fsyxd7RQ3upWbehyet9g06no3P7ljSp9zUVvqxCiZKl6TtoOIvmz6Jl4zosmj+LX3sPyJCNF08fc+roASbNX8HMpetRqdRGC2VOmTSWprW/JCJGS6xOYG1jizY6PNE2ES8f0LxRfa69li8aOewtqZwvOxP23mfk7rvoJdkhG4OjtTlBUXLid8NvNJR3hDmRhS2NFuY0pSJGZpNWDHgSMFwI8RD4B/hXkiT/FLbVIYcfsj99+vTMd9991/jChQsxeZoOxLXit6w++ZRBDQrxd9cKPPSP5K5PBDq9RKX8jhTztGPNz/JtnpW5muCoOABmfleSHNmtMFer8HCw5O+usnzOhvMv2HnVJ81LmABsLFRExiV+6yoqKpIRg/vRf/AIbBLEEPfv3W3y0e+HICY6mhXLlrIoE8MeXl4F6PxzN3p270K2bNYULlI02ZGYKRk7YTKzpk3mr6WLqV6zNubm5mkXSgcajYYzJ7z5+dd+Jq03IWq1mlXrZQHakYP78ujBfXZs/Ze+A4dRs059Dh3Yy9RJY5i/+P3DKzcun+Px/duM6PVGKDMG++yyQ5w5bnAiocwhv7QHoFGLttRq2JRfevRm3MxFlCuSBytzeXyW8FY9LjwYl+cnaDNhIr8degpACU878jtZM6lREUB+NhMWI6uR9K+RDzdbS8xUAmcbc6Y0lrfZe8efYw+DUnKCCYdL8cKcvE2Kniqf06vIj4DyQF3ge2CCEOIisjPeYkjU/i4hefPm/ef8+fORwKwKvx2RACLjdEzceSd+ox29K/MqJIZyeWDXNR8WHXmUpKIh/90A5Bjw+KZF+WXtlUTr/cJicbe3xC88FrXhVi7hmbO1UhGt0aNLcNq0Gg0jBvenwTeyUGH8cq2Wo4cPsnp9uiWoPjpePH/Oq5cvaNe6OSCLgHb4vhWr12/EJQP6X+/SvOV3NG/5HQAL5s/B3T1tGZ+MkC+/FwuXyo7p6ZPHnDjmbdL6z585QcHCRXF0encAZnreCNCeOX2CPbu202+wLBhau24Dpv82NkN1S5JEjXrf0r5r7yTrhkyQdQ/9fF6xeMZ4xr8rlOnihq/PayLy5cTGQhAVGYE6mzxI0cVGIc5v5LdZ05l1/CURBrlsARx/FMTGy0nvSOZ5PwHkGPAvVfMw+cCDROuDojQ4GUbBhtCjAwZhTuQHcLuB0SRWG06dT8f/phkDliRJ0kuStF+SpC7IOkeLkYPiCT2mK28D5NmQHfadhBXZWpphZjjCzct6cvlZKJFxOs49CaZOMVccDfEkeyszPBwsjWr8sXsBfFta/tHXKeaKNoGCpK2lijitFK9zZtgZJr8jVPiG82dPky9fftwy2YlkBQULF+aA90l27j3Ezr2HcHN35++Nm03qfAGCAgMBeP36FUcOHqDhN5knr57Qnl6vZ8WyP2jV+nuT1n/0wB5q1vvGpHUmJIkA7TlZgNbF1Y3LF88DcPH8WXLlzpshO6XKVeLM8bdCmRFhofinIZSpVglUAspXrc7Jw3uxtVJz9fJlSpSpiBACvU5L+NHVzJ0xhaXnffEJj40ve9MnnEp5smNvJY/nbCzUuNgYd3dy6XkY1QvIKtGV8mYHOIw8jooX5gTSNSr6lB7CpTUCTtRCSZI0yMJzO4QQCaWKPYHVyHFgFbJe0i7g11blcrD50ivyu1gzoVkx9HqJRwFRTNol++fHAVEsOfqYhe2/QCUEWr3E9L338AmNJS22X3nNxGbF2NrzS8KitURrZAdsoRaYqeSHHhZm8i5Exeq5ePkSe3bvoEChwnT83iBU2Ls/Vb+uwYF9ezIt/DBs8FvByHq1q9OjVx9atmptsvpHDh3ERYOgYqO6Nenes3f8yDQzGTywL6EhIZiZmTFs1FiTPmgcNeztPjWuV5PuPXoTFR3FfxvWA1CzTj2aNG9pMnsxMdFcOn+GfkPHxC876X2IxXNlscwxQ3pToFARpsz9471tBAb4M3ncSPR6PXq9ntr1GlDt65rY2tkxf9Y0dDotFhaWDB01HoBxIwdz5eJ5QkJCaNGoNl2694oXGq1UtykhQQEM79kpXijzf1v+Yc7yTeTK60Xbzj34bXhvJL0etZkZXfoMwzUVoUxztSCPUzb6dPuRoAA/Nq5fx+at25k1fzFnfLVs2rKNLs3rYWdlTrsCaihgh9rOiXEHnvIyNJZ/r7xmeJ0CCAE6vcSqcy8IiExbOP3og0B6fJWX2c2KESlPQxtuWBUvzAl0NizrjPycKVU+oQhE6qKcQojCkiTdy4iBNyGIrOBzzYam0WVN5rCsfINI9+5T0UwkKCIuy2xlVTa0VyFZJ5SZxdnQMtwJzz8KNbpzVfRy+KDuOtXeklHnq6CgoJDlfEIjYOVVZAUFhc8KJReEgoKCwgfi03G/igNWUFD43PiEPLDigBUUFD4rPobpZcaiOGAFBYXPik8oBJz5DvjE8FqZbeKDEKfNOlFJ//C050SbghzZs6W9kYlQm2XdrySrjh9AswUns8SOk1PWnavdPatkmS1ToDhgBQUFhQ+EEoJQUFBQ+EB8SiNgU+UDVlBQUPgoMGU6SiHECiGEnxDiRoJlTkKIA0KI+4b/HROsGyGEeCCEuCuEaJBW/YoDVlBQ+LwwbULgVcjJxxIyHDgkSVIh4JDhb4QQxYG2vFXxWCyESDVngeKAFRQUPitMmQ1NkqRjvE2P+YZmyMnHMPzfPMHyDZIkxUqS9Bh4gJxMPkUUB6ygoPBZoRLGf4QQ3YUQFxJ8uqdtAXdJkl4DGP53MyzPCTxPsN0Lw7IU+agewvm8fs2oEUMJDAxACBXftW5jUqXdhGS2UnFWqfq+ePaEaQkUfV+/eknHrj1o3uYHdvz3Dzs3b0CtVlOx6td06ZkxqZvk0Ol0tP++FW5u7ixYnLKAZEY5efwY06dNRq/T06JVa7p0S/w7Set8XjztzX+rlyJUArVazQ+/DKRIyTIZapMmLo4/Zo3n8f072Nk70HvEZDb+WgnfwBB+6d4NNDEgBGYlGqLOUyFddTf5woNuNfLj4WBJUISGqDgtB275kdvJmhI57JAQ3PENZ/bhR5T0tKN3jfyYqQSh0Rr6b76Zof0yVwtG1C9EYTcbwmK0COQEvQKwUL99yKXRgS6ZvGPhYWFMGDeahw/uIxCMmzSZL8qUzVCb0kU6HsJJkvQnkFTR1nSWU83M9lGNgNVmagYPHc62nXtY989GNvyznocPHqRd8D3IbKXirFL1zZUnHwtXbWLhqk3MX/4PVlZWVKlem6uXznPm+FEWr/6XP9ZtoVW7zLmQrV+3hvxeBTKl7jfodDqmTJ7I4j/+YuuO3ez9364k/SKt81miTEWmLPmbKYv/ptuAMfw1b7LR9v19XvHbkF+TLD+6bwc2tnbMWbmFhi3aEfL8Fo/8I0GoMK/8I5aNxmBRszeaS/8hxUUlW/dfncuRI7tVomX22cz4taYXPyw7z7fzTqHTS/y88hLVCjpz61UYzRac4ee/r2BhpqLlFx70r+XFqJ23+WndFcb/z/gEhu52lsxtVSLJ8kYl3AmP1fLD6sv8e/kV5gmimHE6iNFCrFZ2xskxY9pkqlb7mq0797Bxyza8Mrl/vEsWJGT3FUJ4Ahj+f6Ps+wJIqKGZC1lQNEU+Kgfs6upGseJyh7CxscXLyws/P99MsZXpSsUfQNX36sWzeOTMhbtHDnZv3UTrH37C3MICgOyOTia1BeDr48PxY0dp2Spzk7/fuH6N3Lnzkit3bswtLGjYqDFHjxxKtE1a59Mqm3W8OnRsTHQipegTh/Ywtm9nRvbswPL5U41SEAa4dNqbr+vKKiBf1axLwfx52HLxJcLMEpWdG7kcs7GkW022bPybld2+JJ+LtVH1Vi3gzJlHQYRFa/ELj+PMoyC+LuSMmUpw9Vlo/HZ3fCOokCc7xx8G4hcu5zwOiX6bBL1uERcWf1+KZe2/YGBtL6PVxqt5ObLvluxTvO8HYhAbR+LtcO7N93erjIiI4NLFC7Qw9Alzcwvs7O2NM2wiskAVeQeyRh2G/7cnWN5WCGEphMgPFALOpVZRWqrIFkKITkKIuoa/2wshFgohegkhTKuI+A4vX77gzu3blCr9RWaayVIyW9XX++A+ataVJXVePX/KzWuX6N/tB4b27sK92zfSKJ1+Zk6fQv+BQxAic6/jfr6+eHi+lYpyc3fH1zf9F+bzJ48wpGtrZo0dSLcBowF4+ewxZ48dYOycv5iy+G9UahUnj+w1qr7gQH+cXGUF73xutixd9hd6zdtE6WObFmXK+sO06tiVOQcfM8ogSJkWbvaW+ITGAHKcsmYRF8Y2LcaZR0FcfylLGqlVgnpFXYmK02FnacbcViVY2rY09YvKslN5HLNRq7ALff69Qbf1V9FLULeIcZJULjaW+BmS2Oul5O+hVQmcckJevniOo6MT40aPoO13LZgwdjTRUcmP/jMLE09D+wc4DRQRQrwQQnQBpgH1hBD3gXqGv5Ek6SayGtAtYC/QS5KkVK/macWAVxq2sRZC/AjYAluAOshP95K9rzUEsrsDLFy8NEm8Li2iIiMZ1L8vQ4aPzHCM9GMiM1V9NRoNZ0960/nXvoB82x4RHs7cP9dy7/YNpo4dyopNuxON/DLCsaNHcHRyoniJkpw/d9YkdaaElIwLeJ/9qFitFhWr1eLO9Uv8t2YpI6Yt4uaV8zy+f4exfeWuHBcbi72DPK1z7sQh+Pu8QqvVEujnw8ieHQBo0LwtNeo34Y2ajEM2M7Q6Pffu3aeQIdSbzULNF7kdmNW2DKLLVlBbYGEmX6ialfGkfWX5TjWPUzYWdiiDRqfnVUg0AzZcTzQy00uw8fwLJAm+9HKioJsND/wi6V/Li2svw1CrBEXcbBm05SYWZioWfV+KWz7hlMvtQGE3W/5oWxoACzMVwQb594mNi+DpYIWZSuBuZ8my9vIgZ/OV1+y95WfUyNBCLYcj3kWr1XLn9i2GjRxNqdJfMGPqZFYsX0avPpmnNJ0EE76IIUlSuxRW1Ulh+8mA0fGttBxwKUmSSgshzICXQA5JknRCiHXA1ZQKJQxsx2hTD0K/i0ajYWD/vjRq3IS69eqnp+hHT2aq+l44c4ICCRR9XVzdqVq9NkIIihQvhRAqwkKCcTBRKOLK5Ut4Hz3MiePHiIuNJTIygpHDBjNl+iyT1J8Qd3cPfF77xP/t5+uLm5tbKiVSp2ipcvi9nkB4aAhIEl/Xbcz3P/dKst2AsTMBOQa8dPZERs9MrAfn5OJGkL8vXxTOg4O1GRvXr8PCzhkbSzN+a16EsNAQWk/bhjpPuUTltl95zfYr8t3PX53LMXbbLV6FxMSv9w2NpWL++Ln9uNtbcf5xMCpVMFULOlOnuBvZs5kx9tBD2lbISWh0CDFaPTFaPddehlHAxQYhYN9tP/469SzJfo3dfVeu186S4fULMuCdh3b+EbG42VoQEBEnzxR4p7yVmfwALjlVKXcPD9zc3ePvXOvWb8DKv5Yl3TAT+ZQSsqd176gSQlgAdoA1smQ0gCVg8hCEJEmMHzsKLy8vOnX+ydTVf3AyU9XX++BeatR9O1+8cvVaXL0kK+2+ePYUrVaDfXbHlIqnm74DBrH/0DH27D/MtJlzqFipcqY4X4ASJUvx7NkTXrx4jiYujr3/202NWrXTVYfPq+fxI9bH9++g1WqxtXegRJmKnDtxmNAQg4JweCgBaSgIv6Fc5eocP7ibF8GxLF27hWFjf2PYfzc49yiQPr/+wsugCBo2fHtOCrsbdzd36mEgVQo4kdsxG54OllQp4MSFp8FU9nLC08GKqgWcmLTnPhJw8mEQpXPaoRJgaaaimLsdT4OjufQ8lBqFnMmeTf6Z2lma4W5nnNr4qUfBNCguX+BqFHJONNPBUg1affKzHwBcXFzx8PDkyWNZNP3cmdN4Fcjqh3CmfA8jc0lrBLwcWV5eDYwC/hVCPAIqAxtM3ZjLly6ya8d2ChUuTJuWzQDo038gX1evYWpTma5UnJWqvjEx0Vw+f4Y+Q0bHL6vfuDnzpo6jR8dWmJmbM3DUJJOFH7IaMzMzRowaS4/uXdHrdTRv0YqCBQsl2ia58/lGQbhY1UacP3GYEwf/h9rMDAsLS3qPmIwQgpx5vWj9469MH9kHSS+hNjOjc68huKSiIPyGGg2b8seMcQz8qSW2dvb0HjGZLXejISYUvf99Bg8dyfhh/ehaLh/mds7suxXEPd+INOsNi9byp/djlnUui7OtJUGRcSztWJb9N/3oXiMfYTFa1v5YlrAYLccfBHLuSQjLO5RBkmD3TV+eBMox1xWnnjGzRXFZqVgnMe/oI3yNyAy3+6YvIxsUYp3BhsYQalDHz50FQzSFWG3SOPCwkaMZOWwIWo2GnLlzM2HSlDRtmpRPqJunqooMIITIASBJ0ishRHagLvBMkqRUn+69Ib0hiE8FJR1lxsjKa8H156Fpb2Qiuq66kCV2Ptd0lNbmGe8Zj/xjjPY5Xq5WH68qMsiON8H3EOC/zGyQgoKCQkb4lG70Pqo34RQUFBQyiuKAFRQUFD4QSkJ2BQUFhQ+EMgJWUFBQ+EB8Qv5XccAKCgqfF8oIOAG65F6XyST6bc1YGr70sLBVySyz5WZv3AT6jPIpddz0cOxZYJbZ6t2oYJbYcbayyBI7kLVTLq3NUxWQMJJPpyMrI2AFBYXPCmOzvn0MKA5YQUHhs+JTupNTHLCCgsJnhTINTUFBQeFD8en4X8UBKygofF58Qv5XccAKCgqfF0oMOB2MHzOS48dkNdt/t8rqwaGhIQwfPJBXr16SI0dOps+am7rel2EvJjQsyMXnYey46ZfitsZQNV92GheX5Vt23/Ln1JMQALpWzkU+x2zoJAkzlZwX1RgyW4F5wthRnPCWFZg3GY7h0sUL2bblXxwNCdh79u3PV1+bNq1nZu9XQlJTRU6Pmrbfk7tsnzqQOt2H41X+63S3IyLInyMrZhEdFgyA2twCTUwUljb21O0+AvtcOTiz4x/O7FiPpNcjVCqqNv+BSt+mP/fz3mWzeXjlDNb22ek4cRF7ls7E98l9rGzt6DRwPFbWtqybPYbn928jSRIOzm7Ub/szpaumL1dyQrSaODYtnMrLR3extnOgff+xUNiNe3duM33KRCIjIlCr1XTu+gv1GsjyV74+rxk/egRBgQEIIWjeqg1tO3Tkj0W/c/zoYYQQODo5M3biFFwzkEjfWD6ltKsfXJSzSbMWLFySOGP+yuXLqPRlZbbv3kelLyuzcnnqGfVjtPJn4r4HlPS0xcvZuFR9Q2rlx9k6cV55Gws1TUq4MeXgIyYfeEiTEm5Ym8uH6ezTEEbvuc+4vQ8QEC9WmBaZrcDcpGlzFixJqqzd/ocfWf/vVtb/u9Xkzhcyf7/ekJYqsrFq2nq9jnObV5KrRLkk61IjPMCXnbOGAqBSqanSuhttJv5Jka8bEh7wmga9xlGqbnPOblkBgIOrJ21HzmbAit3U/6k/JzavIiYy7TzAAKH+PmyYMhiAEl/X47shci7d6957sbKxpeusVVRo2JI9f//Jka3rcM+dn74zl9NnxjJsHbKza9VCoiPD07QT7PeaP8cnlQk6f/h/ZLOxZciC9XzV+Dv2/C33K6ts2Rg3aSobtuxk3qI/mTtzKuFhBn06tRn9Bg1l49ZdLF+7gf82rufRwwf88OPP/P3vNtZt2spX1Wuw/M/FRh2DjPIpJWT/4A64fIWKOLwzuvU+cohvmzUHZHn3o0cOGlWXWiVQqwSSBK42FvSvnpcx9QowtHZ+POyMm7hewsOWW74RRMbpiNLoueUbQUlPOwCuv377I9JLxt/qZLoCc4WK2Dtkz7T6UyKz9+sNaakiG6umffPwDvKXq0Y2u+yJlt8/c5itU/qxeWIvjq39Hb0+ZR1F6+xOuOSVX7Z4efMiTjnzExkSiFf5r3l5+wqSJFGsSk08vAoD4FWmEgDR4XJO4lsnD7JufB9Wj/6V/SvnpWord9HSWNnIfe/hpdOU+KoeAIUrVufhjYv4vXhC6aq1cPHMhVvOvISHBGFla0dkWCiXj+1n0Yhf+X1IF7b+OTtVOwm5feEk5WrKKh4lK9fg4Y2LSJJEnrz5yJM3HwCubm44OjkTHCyriLi4ulK0WHEAbGxsyOflhb+fXyI9x+jo6CwbmWaBKrLJSNMBCyEKCCEGCyHmCyFmCyF+FUJk6q8uMDAQV1f5VsXV1Y2gwKA0y1iZwZxmRbnlE8HjoGg6VczB+kuvmXTgIf9e8aFD+RxG2c6ezYygqLfS3sFRGrJnSxypUQtQq0CXdS8IvRebNvxN21bNmDB2FGFhWZeU3NSkRxU5JTXtyOAAnlw+RbEajRItD379jIcXvGk2dDatxi5CpVLx4OwRo9oVFuBLmP9r3PIXQaVWY5HNmuiIsETbnNyyBnMLK7K7eRL48hl3znrTbvRcfvztD4RQcfvUYaNshQcHYOcsh8VUajVW1rY4e+bixtnjADx/cJtgfx80sbFotRqunTrCr5MW0nfmcoRKxZXjxg1iwoL8yW6wo1abYWVtS2hISKJtbl6/hlajIVfuPEnKv3r5knt3blOilCwGumTBPJo0qM2+/+2ie48+RrUho4h0/PvQpBoDFkL0BZoA3kBF4AqQGzgthOgpSdLRFMrFqyL/vugPfu6aPlXk9yFGC8N33aVXtTzkcLCkgLM1v1bNHb/e3BAvqJY/O3UKycKVbrYW9KueF61eIiBSw+KTz4w6KR3K50hRrvtj4bvv29L1lx4IIViy8HfmzprBuIlGi7V+VBiripyamvapjUup1OpnVKrEr7q+vH2FgKcP2DpFvh3XamKxMoyQ9y+eSHiALzqdhoggfzZPlIU7S9Zphlf5r4kM8qNqu1+xyGbztl0J+s/dc8e4eng3LQZMRKhUPL11Gd8n91k3vrdsKy4Oa3vZ1rb54wn190Gn1RIe6Mfq0b8CUKxKyvHcao2+4/B/q/l9SBecPHKiVqup1+YnHt28zMvH91g04hcANHFx2BrsrJ05mmC/1+i0WkICfPl9SBcAqjb6jgq1viE5gZyExzrA35/xo4czdtJUVKrE47eoqEiGD+7HgCEj4o9/jz796dGnP6uW/8m/G/6me8/Md8Ifw8jWWNJ6CNcNKGNQQp4D/E+SpJpCiKXAdqBscoUSqiJHxqWheZQMzs7O+Pv74erqhr+/H07Oxin5Rmv03PWPpFxOe6I0Oibuf5hkm5OPQzj5OASQY8Arzr4gMOGIN1pDEbe3PyhHa3Pu+kXG/92khCt2lmZGP4D7UDg7u8R/b9GqNf17//oBW5MxjFFFTktNO+DpfQ4tmwZATEQYz26cNzhjicJV6lKpZVIR2Po9xwJyDPjoqtk0GTwDAL1Wy96F47B1dsMpR355mU5HXHQUVrZyyODl/Zvs/mMaNdp2I39pg1a9JFHiq3pUb9Mlia3m/cYDcgx4z7JZtB05K/7vmycOYOfoQnigP3ZOruh1OmKiInB09eC7nsOJiYrkz/H9sMxmTfGKX3HJex/lajSgYfukA5+OQ34D5Bjwv4un0X38/ETrHZxdCQn0x8HZDZ1OS0xURHyYKSIigoF9fuXXXn2T3GFoNRqGD+pPw0bfUqtOvSR2G3zTmIF9eigO+B2MiQG/cdKWyOrISJL0jExQRX5D9Zq12bV9GwC7tm+jRq06RpUzVwuKudvyLDiGgEgN5XPZx6/Lld3KqDpu+kRQ3N0Wa3MV1uYqirvbctNHjv1+7eVICQ87/jzzPH079AEI8H87E+TI4QMUKFQola0/btJSRTZGTbvd1FW0n7qa9lNX41XuK75q34t8ZauSs2gZHl06QXRYCAAxkeGEByYf3nhjy3vNPLJ75qZk7WbcOy3f2j+6eJycRb9ACEGw70v+nTaMMrW+pXyDFvFl85Qoy73zx4k0zKCIjggjNCBlWwkpUK4KN08cAODe+WMUKFGOmKgIYmKiWDdrDC6euShc5kusrG0oUKocN854ExEq24mKCCPY3ye16uMpVr4ql47uBeDGGW8KlCiHEAKNJo5hA/vwzbfNqFO/YaIykiTx24Qx5MvvRfuOneOXP3v6JP77ce8j5M3vZVQbMspnE4IA/gLOCyHOANWB6QBCCFcg7cCsEYwYOpCL588TEhJMwzo1+LVXH37q0o1hgwewbetmPDw9mTF7XorlhZClsgUwul4Bzj8L5drrcF6FxfBD+Rx8W8IVtRCcexbKi5CYNNsTGadj1y0/RteTpbR33fIjMk5+gPFD+RwERmkYUccLC7UcA05Jnjshma3APHLoWwXmRnVr0r1nby5eOMe9O3cQQuCZIyejxo43mb03ZPZ+vSEtVeSU1LRfvzbIGeb6MsW6HXPkpWKzTvxv3igkSY9KbUa19j2xc3ZPdnvfBze5f+YQTjnz8erONSKC/Hhy5TRqM3OKftUAgH3L56DVxHH16P+4evR/CAHtx8zHLW8BvmrVmf9mjECSJNRqNXU69cHBJXlbuxZP4fnta0RHhHJ25z/YODixqFdrzC2t+GXcXPxePmPdzNFEhAZjmc2G7M6u/D6kC9/1Gk79tl1Y8dtgJElCpTajWZd+OLp6JGsnIRVqN2LTwinM7NMea1t72vWX7wIO7t/L5UsXCQ0JYfeOrQCMnTiFwkWLcfXKJfbs2kHBQoX5oY18wenRpz87tm3h2ZPHqFQqPDxzMGzUuDTtm4JPaQRsjCpyCaAYcEOSpDvpNfA+IYj35XNNR6nJoqd95uoPPikmU1h08lGW2bK3MkU6xbTJynSUtQtn/tzdN2TPZuzkzpQJjzE+B66d1YfNnWaMKvJNIOs8m4KCgkJG+IRGwB/8TTgFBQUFU/IxxHaNRXHACgoKnxWfUkL2zzPop6Cg8P8XE76LLIRoKIS4K4R4IIQYbuqmKg5YQUHhs8JU09CEEGpgEfANUBxoJ4Qobsq2Kg5YQUHhs8KEuSAqAQ8kSXokSVIcsAFoZtLGSpL0UX6A7p+THcXWp2Xrc9ynz9lWRtoIXEjw6Z5g3XfAXwn+7ggsNKX9j3kEnPkJJLLWjmLr07L1Oe7T52zrvZAk6U9Jkiok+CTM65rcGNmk7zV8zA5YQUFB4UPyAjn52BtyAa9MaUBxwAoKCgrJcx4oJITIL4SwANoCO0xp4GOeB5xU4uHTtqPY+rRsfY779DnbMjmSJGmFEL2BfYAaWCHJbwabjDRzQSgoKCgoZA5KCEJBQUHhA6E4YAUFBYUPxEfngDP71b8EdlYIIfyEEDcyy0YCW7mFEEeEELeFEDeFEEnlaE1jx0oIcU4IcdVgZ0Jm2HnHploIcVkIkam69EKIJ0KI60KIK0KIC5lsK7sQ4j8hxB3DOauSSXaKGPbnzSdMCNE/k2wNMPSJG0KIf4QQxikUvJ+tfgY7NzNrfz4bPvRE6HcmRauBh4AXYAFcBYpnkq3qQDnkPMeZvV+eQDnDdzvgXmbsF/K8RVvDd3PgLFA5k/dtILAe2JXJdp4ALpl9rgy2VgNdDd8tgOxZYFMN+AB5M6HunMBjIJvh701A50zaj5LADcAa+SH/QaBQVpy3T/HzsY2AM//VPwOSJP1fe/fvWtUdxnH8/WnjYKJFsT9oFYldilubQYpCkKYWfxTBTi3UwalDKHQq6OI/IOLWpUEKtYGaxK2IgoiTDqYFwQxF26bXKsnQ2laHqnwcvt/AFaRJ5D45N/F5QbgnIfd8CMl9cs5zzvc+l+jQVI8FZN22PVm3/wGmKC+KTufY9r/101X1I+wqq6RNwD7K5JQVQdJLlH/OIwC2/7P91xJEDwE3bP8WtP8eYLWkHkpx7Oj9rG22Apdt37f9kDLQ98A8z3ludVsB3gi0D1xrEVComiSpnzLM9ErQ/l+U9BMwA5y3HZJTnQC+BJZiZIeBc5Ku1qnbUd4EZoGTtbXytaS++Z7UAR8DoxE7tn0LOAZMA7eBu7bPRWRRjn4HJW2Q1Avs5cnFDKlNtxXg8KV/TZK0BhgHvrD9d0SG7Ue236as2tkmKWR2kqQPgRnbVyP2/xQ7bA9Q3plqWNJgUE4PpTX1le13gHtA2LUIgHqT/37gdND+11POJLcAbwB9kj6NyLI9RZkdeR44S2kjPozIWgm6rQCHL/1riqRVlOJ7yvZEdF49bb4I7P7/73xmO4D9kn6ltIrek/RtUBa2/6iPM8AZSrsqQgtotZ05jFEKcqQ9wKTthY1IXrz3gV9sz9p+AEwA24OysD1ie8D2IKXN93NU1nLXbQU4fOlfEySJ0lOcsn08MOcVSevq9mrKC2/Rg1QXwvZh25ts91N+TxdshxxVSeqTtHZuG/iAcqrbcbbvAL9Leqt+aQi4HpHV5hOC2g/VNPCupN76tzhEuQ4RQtKr9XEz8BGxP9uy1lVLkb0ES//mSBoFdgIvS2oBR22PRGRRjhYPAtdqfxbgiO0fOpzzOvBNfSPpF4DvbYfeHrZEXgPOlNpBD/Cd7bOBeZ8Dp+pBwE3gUFRQ7ZPuAj6LyrB9RdIYMElpB/xI7DLhcUkbgAfAsO0/A7OWtVyKnFJKDem2FkRKKT03sgCnlFJDsgCnlFJDsgCnlFJDsgCnlFJDsgCnlFJDsgCnlFJDHgO0wCpO6RRsjQAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "sns.heatmap(kmeans_confusion, annot=True, cmap='Blues')" - ] - }, - { - "cell_type": "markdown", - "id": "ab647317", - "metadata": {}, - "source": [ - "### 2.2 PCA (Principal component analysis)" - ] - }, - { - "cell_type": "markdown", - "id": "06872c16", - "metadata": {}, - "source": [ - "###    2.2.1 Scatter Plots in 2D and 3D" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "64add44b", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.decomposition import PCA\n", - "\n", - "pca = PCA(n_components = 2, whiten = True)\n", - "pca.fit(x_trainf)\n", - "x_train_pca = pca.transform(x_trainf)\n", - "x_test_pca = pca.transform(x_testf)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "fc524704", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline\n", - "plt.scatter(x_train_pca[:,0], x_train_pca[:,1], c = y_train)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "8baaf415", - "metadata": {}, - "outputs": [], - "source": [ - "pca3 = PCA(n_components = 3, whiten = True)\n", - "pca3.fit(x_trainf)\n", - "x_train_pca3 = pca3.transform(x_trainf)\n", - "x_test_pca3 = pca3.transform(x_testf)" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "a7c2b5b8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "plt.figure(figsize=(8,8))\n", - "ax = plt.axes(projection=\"3d\")\n", - "ax.scatter3D(x_train_pca3[:,0], x_train_pca3[:,1],x_train_pca3[:,2], c = y_train)\n", - "ax.view_init(10, 60)" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "9f414ded", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.10869168 0.05363875]\n", - "[0.10869168 0.05363875 0.0409124 ]\n" - ] - } - ], - "source": [ - "print(pca.explained_variance_ratio_)\n", - "print(pca3.explained_variance_ratio_)" - ] - }, - { - "cell_type": "markdown", - "id": "4b55fa60", - "metadata": {}, - "source": [ - "###    2.2.2 KMeans using PCA" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "id": "4b695f26", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test accuracy: 0.0844574370978618\n" - ] - } - ], - "source": [ - "from sklearn.cluster import KMeans\n", - "import pandas as pd\n", - "kmeans = KMeans(n_clusters = 10).fit(x_train_pca)\n", - "y_pred_trans_kmean = kmeans.predict(x_test_pca)\n", - "test_score = adjusted_rand_score(y_test, y_pred_trans_kmean)\n", - "y_pred_kmean_pca = kmeans.predict(x_test_pca)\n", - "print('Test accuracy:', test_score)" - ] - }, - { - "cell_type": "markdown", - "id": "54dc4e2d", - "metadata": {}, - "source": [ - "####     Confusion Matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "id": "57eb2033", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Confusion matrix: \n", - "[[ 0 75 386 126 3 284 0 69 1 56]\n", - " [336 75 18 1 62 43 147 51 252 15]\n", - " [321 102 24 16 14 16 110 177 215 5]\n", - " [ 0 180 178 209 4 61 1 337 18 12]\n", - " [121 233 36 2 187 68 121 75 51 106]\n", - " [315 25 66 205 20 11 104 36 218 0]\n", - " [303 95 2 0 79 22 185 38 235 41]\n", - " [ 25 211 19 10 258 32 56 262 14 113]\n", - " [209 143 54 56 33 41 188 201 71 4]\n", - " [114 235 7 7 92 17 148 237 119 24]]\n" - ] - } - ], - "source": [ - "from sklearn.metrics import confusion_matrix\n", - "kmeans_pca_confusion = confusion_matrix(y_test,y_pred_kmean_pca)\n", - "print('Confusion matrix: \\n{}'.format(kmeans_pca_confusion))" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "id": "3eef5fab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "sns.heatmap(kmeans_pca_confusion, annot=True, cmap='Blues')" - ] - }, - { - "cell_type": "markdown", - "id": "0ec36a9a", - "metadata": {}, - "source": [ - "### 2.3 MiniBatchKMeans" - ] - }, - { - "cell_type": "markdown", - "id": "78ddd888", - "metadata": {}, - "source": [ - "###    2.3.1 Fitting Models" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c4f713ae", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.cluster import MiniBatchKMeans\n", - "from sklearn.metrics.cluster import normalized_mutual_info_score\n", - "from sklearn.metrics.cluster import adjusted_rand_score\n", - "kmeans = MiniBatchKMeans(n_clusters=10,batch_size=5120).fit(x_train_pca)\n", - "y_pred_mbkm = kmeans.predict(x_test_pca)" - ] - }, - { - "cell_type": "markdown", - "id": "d5af13fa", - "metadata": {}, - "source": [ - "###    2.3.2 Clustering results evaluations" - ] - }, - { - "cell_type": "markdown", - "id": "a14ff10a", - "metadata": {}, - "source": [ - "####     NMI (Normaliszed Mutual Information) and ARI (Adjusted Rand Index)" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "id": "178c9a4a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NMI: 0.18037308457241424\n", - "ARI: 0.08424108308239592\n" - ] - } - ], - "source": [ - "print('NMI:',normalized_mutual_info_score(y_test, y_pred_mbkm,average_method='arithmetic'))\n", - "print('ARI:',adjusted_rand_score(y_test, y_pred_mbkm))" - ] - }, - { - "cell_type": "markdown", - "id": "05d87e3f", - "metadata": {}, - "source": [ - "####     Confusion Matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "id": "91486cd4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Confusion matrix: \n", - "[[ 0 269 150 22 171 0 296 82 1 9]\n", - " [330 81 1 46 48 117 5 19 312 41]\n", - " [308 50 19 35 131 53 1 6 265 132]\n", - " [ 0 142 229 37 332 1 77 13 12 157]\n", - " [134 97 4 316 58 95 23 135 51 87]\n", - " [318 18 214 20 26 61 53 0 257 33]\n", - " [336 50 0 77 24 146 1 54 275 37]\n", - " [ 23 102 12 256 149 79 4 181 11 183]\n", - " [263 106 64 52 174 128 25 6 84 98]\n", - " [132 76 8 202 103 94 0 32 97 256]]\n" - ] - } - ], - "source": [ - "mbkm_confusion = confusion_matrix(y_test,y_pred_mbkm)\n", - "print('Confusion matrix: \\n{}'.format(mbkm_confusion))" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "id": "c9ec58f1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 118, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.heatmap(mbkm_confusion, annot=True, cmap='Blues')" - ] - }, - { - "cell_type": "markdown", - "id": "e7bb3593", - "metadata": {}, - "source": [ - "### 2.4 Birch" - ] - }, - { - "cell_type": "markdown", - "id": "103a4fad", - "metadata": {}, - "source": [ - "###    2.4.1 Fitting Models" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "id": "296530dd", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.cluster import Birch\n", - "brc = Birch(n_clusters = 10).fit(x_train_pca)\n", - "y_pred_brc = brc.predict(x_test_pca)" - ] - }, - { - "cell_type": "markdown", - "id": "c21d97b8", - "metadata": {}, - "source": [ - "###    2.4.2 Clustering results evaluations" - ] - }, - { - "cell_type": "markdown", - "id": "cac10047", - "metadata": {}, - "source": [ - "####     NMI (Normaliszed Mutual Information) and ARI (Adjusted Rand Index)" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "id": "857fee12", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NMI: 0.18080432574948965\n", - "ARI: 0.0909874496519081\n" - ] - } - ], - "source": [ - "from sklearn.metrics.cluster import normalized_mutual_info_score\n", - "from sklearn.metrics.cluster import adjusted_rand_score\n", - "print('NMI:',normalized_mutual_info_score(y_test, y_pred_brc,average_method='arithmetic'))\n", - "print('ARI:',adjusted_rand_score(y_test, y_pred_brc))" - ] - }, - { - "cell_type": "markdown", - "id": "81466971", - "metadata": {}, - "source": [ - "####     Confusion Matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "id": "b89536eb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Confusion matrix: \n", - "[[ 41 451 48 2 94 0 1 73 289 1]\n", - " [230 39 5 16 0 0 603 35 72 0]\n", - " [171 33 1 2 10 0 545 12 226 0]\n", - " [101 132 1 2 158 0 15 32 559 0]\n", - " [502 63 15 71 2 1 131 116 98 1]\n", - " [137 43 0 0 218 0 538 5 59 0]\n", - " [275 17 10 27 0 1 573 45 50 2]\n", - " [362 43 7 156 3 0 25 91 309 4]\n", - " [250 83 0 6 38 0 321 17 284 1]\n", - " [439 26 2 18 3 0 187 43 282 0]]\n" - ] - } - ], - "source": [ - "brc_confusion = confusion_matrix(y_test,y_pred_brc)\n", - "print('Confusion matrix: \\n{}'.format(brc_confusion))" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "id": "c75904f9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 127, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.heatmap(brc_confusion, annot=True, cmap='Blues')" - ] - }, - { - "cell_type": "markdown", - "id": "3a50f7f3", - "metadata": {}, - "source": [ - "### 2.5 Gaussian mixture" - ] - }, - { - "cell_type": "markdown", - "id": "33c3d7e7", - "metadata": {}, - "source": [ - "###    2.5.1 Fitting Models" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "id": "e98317fb", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.mixture import GaussianMixture\n", - "gm = GaussianMixture(n_components = 10).fit(x_train_pca)\n", - "y_pred_gm = gm.predict(x_test_pca)" - ] - }, - { - "cell_type": "markdown", - "id": "b4aa5c01", - "metadata": {}, - "source": [ - "###    2.5.2 Clustering results evaluations" - ] - }, - { - "cell_type": "markdown", - "id": "547683c2", - "metadata": {}, - "source": [ - "####     NMI (Normaliszed Mutual Information) and ARI (Adjusted Rand Index)" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "id": "52f39cb4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NMI: 0.18906949813574742\n", - "ARI: 0.0906887887251104\n" - ] - } - ], - "source": [ - "print('NMI:',normalized_mutual_info_score(y_test, y_pred_gm,average_method='arithmetic'))\n", - "print('ARI:',adjusted_rand_score(y_test, y_pred_gm))" - ] - }, - { - "cell_type": "markdown", - "id": "337975a0", - "metadata": {}, - "source": [ - "####     Confusion Matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "id": "09af68b9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Confusion matrix: \n", - "[[ 2 1 40 240 93 0 34 398 99 93]\n", - " [101 365 64 21 0 316 17 23 69 24]\n", - " [ 42 335 158 9 5 275 4 34 41 97]\n", - " [ 3 13 209 32 163 0 7 204 102 267]\n", - " [199 77 159 40 1 126 138 38 189 33]\n", - " [ 51 327 34 8 213 273 0 54 16 24]\n", - " [132 332 63 9 0 311 48 3 84 18]\n", - " [179 16 204 16 2 22 213 32 157 159]\n", - " [108 106 158 19 19 277 6 68 86 153]\n", - " [141 116 266 4 0 131 41 11 138 152]]\n" - ] - } - ], - "source": [ - "gm_confusion = confusion_matrix(y_test,y_pred_kmean_pca)\n", - "print('Confusion matrix: \\n{}'.format(gm_confusion))" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "id": "971194b5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 131, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.heatmap(gm_confusion, annot=True, cmap='Blues')" - ] - }, - { - "cell_type": "markdown", - "id": "3a8306de", - "metadata": {}, - "source": [ - "## 3. Appendix" - ] - }, - { - "cell_type": "markdown", - "id": "ca1adee0", - "metadata": {}, - "source": [ - "### 3.1 KNN" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "c27cb6d8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting KNeighborsClassifier(n_jobs=-1, n_neighbors=4, weights='distance')\n", - "Evaluating KNeighborsClassifier(n_jobs=-1, n_neighbors=4, weights='distance')\n" - ] - } - ], - "source": [ - "from sklearn.neighbors import KNeighborsClassifier\n", - "\n", - "clf = KNeighborsClassifier(n_neighbors=4, weights='distance', n_jobs=-1)\n", - "print('Fitting', clf)\n", - "clf.fit(x_trainf, y_train)\n", - "print('Evaluating', clf)\n", - "\n", - "y_pred_knn = clf.predict(x_testf)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "31b937dc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test accuracy: 0.921\n" - ] - } - ], - "source": [ - "test_score = clf.score(x_testf, y_test)\n", - "print('Test accuracy:', test_score)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "df13d267", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8231031975085954" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.metrics.cluster import normalized_mutual_info_score\n", - "normalized_mutual_info_score(y_test, y_pred_knn,average_method='arithmetic')" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "2226f1b5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8234272963838932" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.metrics.cluster import normalized_mutual_info_score\n", - "normalized_mutual_info_score(y_test, y_pred_knn,average_method='min')" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "09c6c71e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.823103261265742" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.metrics.cluster import normalized_mutual_info_score\n", - "normalized_mutual_info_score(y_test, y_pred_knn,average_method='geometric')" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "cb776587", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8227793536618937" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.metrics.cluster import normalized_mutual_info_score\n", - "normalized_mutual_info_score(y_test, y_pred_knn,average_method='max')" - ] - }, - { - "cell_type": "markdown", - "id": "12d38ecb", - "metadata": {}, - "source": [ - "####    Confusion Matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "4bef3167", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Confusion matrix: \n", - "[[910 0 1 1 3 26 0 15 44 0]\n", - " [ 1 910 27 1 4 3 39 0 8 7]\n", - " [ 9 6 880 45 8 15 14 3 18 2]\n", - " [ 1 1 18 969 0 5 3 1 2 0]\n", - " [ 13 12 11 16 885 10 14 2 28 9]\n", - " [ 1 5 36 8 2 931 12 0 3 2]\n", - " [ 3 2 20 6 8 3 951 2 2 3]\n", - " [ 1 8 11 4 9 6 14 912 21 14]\n", - " [ 0 13 9 6 0 7 12 0 952 1]\n", - " [ 2 24 10 2 3 4 13 5 27 910]]\n" - ] - } - ], - "source": [ - "from sklearn.metrics import confusion_matrix\n", - "knn_confusion = confusion_matrix(y_test,y_pred_knn)\n", - "print('Confusion matrix: \\n{}'.format(knn_confusion))" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "id": "2bc87615", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 102, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "sns.heatmap(knn_confusion, annot=True, cmap='Blues')" - ] - }, - { - "cell_type": "markdown", - "id": "98588e59", - "metadata": {}, - "source": [ - "### 3.2 Four Regression Models" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "84d84800", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training data set score: 0.337\n", - "Test data set score: 0.187\n" - ] - } - ], - "source": [ - "# Linear regression\n", - "from sklearn.linear_model import LinearRegression\n", - "lr = LinearRegression().fit(x_trainf,y_train)\n", - "print('Training data set score: {:.3f}'.format(lr.score(x_trainf, y_train)))\n", - "print('Test data set score: {:.3f}'.format(lr.score(x_testf, y_test))) # overfit" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "0f2cb297", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training data set score: 0.34\n", - "Test data set score: 0.19\n" - ] - } - ], - "source": [ - "# Ridge Regression\n", - "from sklearn.linear_model import Ridge\n", - "ridge = Ridge().fit(x_trainf, y_train)\n", - "print('Training data set score: {:.2f}'.format(ridge.score(x_trainf, y_train)))\n", - "print('Test data set score: {:.2f}'.format(ridge.score(x_testf, y_test))) # overfit" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "b3bee2c1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training data set score: 0.34\n", - "Test data set score: 0.19\n" - ] - } - ], - "source": [ - "# Lasso Regression\n", - "from sklearn.linear_model import Lasso\n", - "lasso = Lasso(alpha = 0.1, max_iter = 100000).fit(x_trainf, y_train)\n", - "print('Training data set score: {:.2f}'.format(lasso.score(x_trainf, y_train)))\n", - "print('Test data set score: {:.2f}'.format(lasso.score(x_testf, y_test))) # overfit" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "1f7c891d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training data set score: 0.838\n", - "Test data set score: 0.691\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "D:\\ANA\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n" - ] - } - ], - "source": [ - "# Logistic Regression\n", - "from sklearn.linear_model import LogisticRegression\n", - "logreg = LogisticRegression(max_iter=100).fit(x_trainf, y_train)\n", - "print('Training data set score: {:.3f}'.format(logreg.score(x_trainf, y_train)))\n", - "print('Test data set score: {:.3f}'.format(logreg.score(x_testf, y_test))) # overfit" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 88b4329dccfa920212acf29b77f708be6ecea45c Mon Sep 17 00:00:00 2001 From: zengmmm00 <50666176+zengmmm00@users.noreply.github.com> Date: Sun, 4 Dec 2022 13:42:25 +0800 Subject: [PATCH 27/27] Rename new_unsupervised.ipynb to unsupervised.ipynb --- new_unsupervised.ipynb => unsupervised.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename new_unsupervised.ipynb => unsupervised.ipynb (100%) diff --git a/new_unsupervised.ipynb b/unsupervised.ipynb similarity index 100% rename from new_unsupervised.ipynb rename to unsupervised.ipynb

7?Uj# z{DEO08?K7Sv$~U0(FoHItRd52) zC>9_atEcQ0Inl+0LZAt_Za zkOc>X;_>9}mBkP=Sxc%KJc60rHhHMKinl~ee2*e-AJV3qKRNP8kgzE8@HvU>Y;2XJ z+KP*BBxad7E1NT7Y})qTdlSqB2Fr# zO|_u@oURE}#S@+85iyM_;xG$We_0z^2kFPbOhv<}4G|$oCtXeZEsq#bK6E%9Hs7mg zf8yKdFBB_G7o98oK?)YCFh%qO=8q8V@6k(umpJYI6yD8_IQ6#v$3gofx(YB_F~)Cf z)&iBy_3($`8fF=s>xgLI|KJfbwTaJpFKIZ!sU=fFKxD|(+tos1Rz}rdul?pzqXa1& zp0Q}@+3>EBklhDv1hpHd+6Gv(1cJ8caI^~>2PI#2j3&Ct8ue)cWk&I}PUE0bPuK7*g$&KJVrL45<2Q?yxmaSD@Il)n4r5QD3-DkTnmKTUXa##ZS5h9N?wf#p% z>-KO`D5>1WcjmUQ1m4`sa0;94{CAmyNSif3?7LQ~KT5z8u@EqKPaa^=@Dm$?r!YuV zq@7fPp^j!2K8ObGn)_jBb0@(YvjM%!UrcncD+=v2i3CfV7W*Fz!m@8IVmH-5Dl_hH zjHYIPG4H=mx*TR?Lr(HS&7W)c!j+j9quZNfoabMdNi(Umed&fXz!gX4g1o~o6j zc0*gE3e_vM*NPnv)3t&EIEGn>(HyE6l7ekZPR&upL8$URC|Czp>&pnbPw?OoXD=`2 zEm|Q8!css|n4drn(5E$scD4_&sz(ey7%3suh4-L@=}%L-NyUEneWQx5?MBp1u>zCj z$EsU!$$dQ+1JMBPZYo1Z!ZtmH0acm^ed$n6HE^$#{l(X~=+^OccHt&7CZJQOb&)_C z8>fs!+PK+OEsE+%rbUi$()gq@9jz5)x)wFu8PEg96?RHPBWmop{s5XwWOR2cZxtH+>*CkV6GH>7L(R)XX=-tOScF1!V#VYaJu06V-H=3NWXN7kr z^Mt4>?KWJk$AHqwU$OBZv=QqC=apfu}o2U@Ny84i1qh( zd;52B&eY2|#X$|%36e41X7oL4f>__606JQEvC=yb_o*}O(C(5(vvmWTFm%N`v2@HY z<}nn5Y)O>szVzu>!1v|WS13wjh^%?g2FJ{z4oH;4zz|)p=ETHY6Kaby?&i1?fC76zvjX=VZzuywNxpEpb)t+$@YEXx<0h?^P^W^3F5Vx|WY zf#sZ3>0jTIyz8d9InKqr--{Bmy z$=*mQqgp)ewNF%8=~BHKyYmBmGF4X4n=^NyRQG2GbF4^E8aHd@O_=JMJ>y*q3)D~c zRG*4@o3YN~7+uwKFyaZ_(X+X*+Su_yi}3n|;xC&T7=p5!dh%>zau6}5Q4_b01wY0x?(%)1hBEiybdRor!J#zWQ zq=Y0}80^CeKiz}0aE?9u&01<(2bm9%ehGt1ZAt5nhl2l8IP|NamiX&t^=vS+EdW3M;o%TJa=JNSJB0IGqe8 z?RZpm|L^%A4n^c%M!*_Yj6Ppj3PL@RH~D(<(;T<(Ak|Uc%&lzeEX5@TCk1c99gsUE zao`$n|9F}NTrXB{8>ZC~g?}M~59YMLnl+>Sgb_`yL;U{CO7*a>gIh@=IaB0TXURFD z`EXtDeIXtz)`XmbzN-ZQ*)&x-3hcpgr)>NDaIdWITUC(3|R2ns=kS50<+yDE>u@Vjtw-lvn6IwAP^Vd8TeWsAb6vwv;6t3X;ESVtO6>Ls2va;jx z7FLLwQ8@&2sN#;HhE+D|7IoR|CW>~ZI$$>XZaI{cy?J&AZhq?)m?oU=B=&>~ocE2( zHLbmL>arD0n9*;0C}Iyu3K3{)rJ&}uE6=I6FA2`W@sgGNa*S*TnaCCJq-MOs>+S4;4v*u1;Oj>LB6ZNF%*CSI4Cox=kn zHJyxCj^N^=+YRai#7R)Wie-M*JI|u;g@iaE40~YoUBmtkc4k)XB%)}WTsvSe7JiI- z8c;td(u)}YZS7i~ru_l)PJNMjL=u3>mZJ>4(!%Vhe?(4PI#rgz7PSXtYa&8aVUKzj zlYUx~t8~NC&%oM)#Zq&d8RmcIr|e9+WZ!b<3!vTzz6AnK~yQcrOr*P8S=_-efrqT(T0 zd40i>E^e02+|7XtE|ceyE!~L-K@?ZS_M~99&cPj03IuYBW`{MclGDm~X4pmSLYW?> z9M8-53}vclb}w;I+HhyZc$1eg!IS2MF!076|DpU65t>*Lr03qKD~zopz-mQItWXt^*QgL8#MRrK(KiW;5?y~7C5Z95nVL^< zJ?gfJpK@SGMsf9niGMA-aGNGiNQn&7l61*LKsZt4iCQK@4KE5iOk*Xkw1i3=@`kT> zq1&Nm$ z`dSo;#S!yj7y-yZ06s0jNOhJmFaukP{r!PO4o=V~UGS^#$1Y#^Zn9&{YMGIq5iI)W zCo0067#zlY%K2FcC_sPBwUP|B2vaCx0h+qWJ4BE@(H=ft&F?Uy!NZ`<58gg~BUxK# zJ1@KhRKsFLD%YR-ylo!7ZR|ZlwKdKWv&xvUX-m@S@Q1#^F#7H2Jmn@W$1`N)MMlSH z1Q@ro@xreonJD6|Qv+Tl=^=~=iD5-j6FP=pOI5;yW^;qK<@bBbpu4DCqGBpzkM<>Yz096FO{d`GrMMU}woFIR+Jt@p} z6lo246iuhQihG+#8-1)_KfPLx`0rIl>qJgyVKS};>`aikS2#RfN^TN8jFf^@qQyvKd9jpQ-zlnj$T;dOpAdxr4sjBLa(slc zA&AFva-JWbDPr)Gf;JfDWY*6N8Z296@wY}327L*u^NmomH&&_j zTpi?bqR4xfyz-@zMsv&~#o8=hv&k0P4lbo%rEeqzCAAdQO?v9DQ_(ri113dj>>sjO zuGf7_x9oKk45L}q<`X>)w*BVbZIZ+?u~&j-%)gG0l&>d9h*S?VuP~+`ffxpN9~DFRJ#CH#{+R{pt7 z0ER8^(g}P|=&dV-u$P?9uY{&l5@S>jwI}ww*YO{H^TSMRwFu2CaZwa&a>Cu9PDPB8 zdD`h)k=80Ep2<=KhGUsoNS?$04**j@tiK+qIwJrvv(ZwRZN{5#*G~~f?Mh?DwwF6! zSnQX4)oKa{a^KZCya5%4!lFC0j0bJIBY0kNqOWbgISK$SSLJ0qII_w1!-bx!Wk1z2 zkuO7%%IP|hivARLfe-8+o2iF>bEJGDJ*i*f{lWxx>CP|QE59#2BOiD|q;`=C&4uI!{&M6-aPxDN6PWL|P^lx0Z$ z=OBL3WodCr^TUdcc)0~3)d07YoH&81FI)BB;fm(vCi~&J<*T{HcNDF{kTNjJCGBie zZ}TQob?>NIZ~GN};Zf693R;%8dT91OJ%zQTQ0z=nTCi)*3{Sm{3Q^k!ZFW@WH-GN; zI}gF%g-0STDEr(YbtyX~k0N~U!V`a&ni|8bOp4>B#QW-YmUk&aJUtoWk4D@Df;$MyV!=n(pRUeGa#>B zy@qNxA-=kk%`}GG$YldxdoM$Et%!;mlzA}hNyMy8pV%xYz1~ehvA<(I9t`W+fbxit zQl9aLp*n#;B|r|Q$XS?|IN>Ge*$zMeOya(~ALP;qk6yMk(o?55=swcdd?WJp_*1G! z1zNr$^|dEmS<%s*d6QkRx!K0ICPdgq_KE&9$WvCa{eXs1L`!U!Q>@v~ry6x(zm%dH zwS2h;yrU08!0OQhYB4XJbw$-X#?j`&$y016*JbfFH3vEQa`F4U&m;(F?fT2^+rELT-VxQvk6_{4rJNh zuh>q?M%V25rGrS>Ko*Q4$Wqbm;5BI53pC?=oge5^V_+C4#QWKQ9FB9Gj;Suzev7AM zJfu_hnyrCq71s8g9q|>d-QK8_V*eELC@EWGT63@C7jOclv3AU|c5Gk6lg>%lJz>K! zmHBg?!5&BMYL2CYx(f%oh&W z1>8s7y%0Uy?hw{S<4dzjM#Fr>q$n0ROT%@C@6hvCLEaT*1W2eYGTxm$Jor|IxE7fb+> zQrC80t;Nm=Bc#DO>F$>|DNY4C3tL<@_d@Bsao;MRyGp?4-**56;X)4&R!8{CMyihw#_4H4re?ze-dfvEJGnYFRH)RlI?rhLcnxI9<=SF1f_b^~ZSop- z--_jU-w_!lfEF&t?AM_oN@l~@f&iE=za&O z?6zGecmqRufiI;5{!-`|(Z!P@y_ncJh`6wvkGhj8FL=ZY{ql6F-~(~2-D>VjI&nL3 zUBj#dhAlk3ns0v9WHg{3DqGb&|A~NMO1o3%TgaQRFCpP90#fvs_{uJ=*f36>qp4=F zf@q5I02b|F#4@>?_L5&6;qLp60;X2j2G@RWUl>>92%w71(`DjzM_EO|MO2lG3^jx{ z^;PMlu16?Mghm=+9NMHe@fgp_odOWdRsHK+Zg@S6M594k?J!Ja?1E!sDA~l}GYkE= z_n5&L2J~FgNBKT`_=}az9nCNtX>}f6j!+O7yMu}x#1;=QkDbn!5-dJ942!$Vs}jV5 zWJp0Gf)2BVc|L85V-3&JI|DCW5HbHHWtYae9iA{7^+*W_hvOfqhGoCyNUj(3=S$Pm zP_a;wX&#{M!I~35r*0w9xX|FG?VBwbu{xZ=9FzAPwHD0kH+7xZC3Om|=D>fB5D(MY zPp6jgQu}!sEMb`DKg4?i^D@E$y%IG!(n~2t*8)aD4byk}N-zU$BUa^Q2hGlRS8vjX zzExYb@U2h85ri#iW0yyOF@B>#&cK}u6rDI>NciXIwqXk7C6;W50@RbC2;-TL5Q~(q zSSF+EH*56DSF|=io4r&*!FWG)CL*(H?*%2xykR4{I4SI_kyjbLy3i9N^6DJm%n5Uv z5~(W-Q9lSv2t|wu0%wpn+qywjypEpSktE+Cs+*8V*=y-{d>F0n#$zYAzpj zYVaI3Jg-P#t<49&dIR({T6ZOcMae}Qe7ZRiN-q~x(z?!_BQJL^%T=Y-TNFgCm8z=VJDCyQ zB@}aul7R0`&O2HKa&l*QkW;R=IYK-f+@v7mQ;O@16_YZiRNaXVw$-t41|@Q{@)Dw= zW#DyLJn2P=y!pU@U_2N3^h8xjBHY!6-1KES0Ni#7L^u6O5FJefJ9jV5vp~>5)yq2u z4>8x$F&3KIjSx8o+<3IZm5(^&a+K{O$la<4Uc_m{i6qiww-9P!d1f-;Kmb81tq50g7CId=02{3j`fXn# zI-W2#2&NnqgBNkEx=BG7{s(`0rs@$ZO9zX9ZXxC_iKx!;6!2)ux$1K*`!9QwP%G#_ zS{8JWsFKt?N;Eu#fZJgoam*tugP?=jt=r&W+&~amGoE$c*1QHx|qCMPp7P@=wlX92y-`hDM;WhFZ!bOkNMU# zJK5fj98?XkNHm>_?A~FjMPjNH#9w(+#45qgwMR5vk1P$5N!=I?YX!Spc{R>alvm|m zXZd_9j4B>~#LooQVG(l8kcW~irDJF^xjVtPRRBY9Zf-%{r4OETP`|mrmkK#ZUj;uuH3NkP0fIpycxA zeX99h;9YF&%;Qtvee?9BnJt;v?Sq@5#Nnd9guc*Gkds zV$5W-sv2kmB1XT@M4eT0mWpIwYgC%0OxO=0;rW!=c2Xu{B+xn3YaQ)&zqw0H%?w|N zvw^|H1(8S*mA!!DXsDM}(NrbeHjyXP#L)UZW2sgW^JMFE9)@ipFDY&9TR!=6przC{HA`fb2B}U2Zpg_eJo%Ox2 zS4QmLuFY0E5xu%!6z5}mDGSR)B~Ch5D6AZUO9=pBA?wna+S4`KWeEUHHJ7}aqrYBt*5HLEI-v1U*d2(|TBlYC3{F;7vU%NuCS zOKD=%U7lQ>&8NBw+(|TroJIhT?sJqxTL>6mDVFt)_|CGAF-fAgfR%cxJv|h+6tJa5 zxaG=A%33iK&*P?Z%nZOBPrS-d0bdgVj4s7-wO|$6ZK2B{ftDfMyNZbk zp_L+Lris7J+Ar6#khB%N((RiTXc2lx5pBB*gy(<`@;jZrHW5?6TDxPzf$}n55O?oh zz?qancYI(x-Q8-@vDmX-%t>U&a)-|)>&qpu>1Ugntv287-QLY+I1~_?vO{Zk1sNXE z7sV>wjvr#HuT%e?eM(W62X?P6Gz(U;Kq(j%?XVULpiuTeAq3xsqC<9;(s}`cgD7lA z($0cX)>Nwgqe2 z(E;_~Dk_&}La5#B0H!2;;bFFCEzssYLev%=Y7L1dPtbg0^uz|1#=u}W2>a?<>=Fl7 z3-vNtH#@K+MDkMOOa{2G4#(0Eb`nRrBPmuY?Q_@bEFKQ_WIzG`q>#qL&nJw6%6Htn zGzI1|2Nbe+M~#HKKwCP5wqXHhoKo+w5qR4Zi|taUg? zIprk2yC8v)Ta@|4yXb#DR?lWvpOuoG9TPSP2t)Q`qYu0RT^ zM=aTB&0U1a`daI7&Wzg~M-Q-DF?+B*-6%g4|T^LY)^fA z|M!1?FPEa9TqYVI2PGQ3X0wOX&%B_tz%a}4`H?syj4{q#N7J236dsC^k+M zy(~PSNCN>&*X2ms49^~{)1^s0i5W?Jw=csh(vGGqR+LStETY^9mV-P5H5aTxDm%F` zn(Z#0S>BXD-&%2cgC=qGCZu96f~+@hL4-3vbBtuD3Xv6UpHea}yfJ#xCpmx02zkF( zOB8meBk2+5NexU6=l9GX3sB8kP#DFxaHJfIhrElFSMV||d&FO7XbbVH&dR}66dmXm zR=3u>hiA>SnRkf19B#|cte&N3_puYdW%p2_ZTrHOIb|$>!4GUVzp;fPG%B*V*ado9 z=J~d3I)tZpsXVkBdDMvU2yBrfBVX9I>-gTB_X6&3>*ir}bf{P+otc9U$m~%a)co?j z_060*G-J?5T?V% z+vQ5Omyd?pE_RhvqyC)=Kk-fmn{t%0XSP}8Nkmmc@;(eiW>`UU7Y{}jWmtTUd3x@? zX;E@4Ww8XB(?Rsw)dQuoPnM;xj>s@q$^EUJ3q{(&MB^_&GnfVaWsrd>WLVl!d7{cT z<%|oKRrJ>caa0Vye{*nPwtOZuGTE#lL1~PAhq2U#g8prBq~#Nhh!!Sa16~4ptVO4$ z`4U*YjSbKX5R&66G&p})y;WcX5~_r!VbhQu^w*BziI~#k)`fb?vs)5lStYHcfOo;A zU*ZJx+&xPfZSezfKH8LKns&qFpc8y9Ae2GEZ6d)`r#f_QhDagho9#ZviR&iwN1$rb z%3vJp$U#kXr}!T>I+ZDNq{}Da-vMGlxnFMqb zf5M-aoYLKiuwMGoRC;u651@QMgsCqx8*R#2k%vFkF&6>dQm9^t%@b`{9 z8#x33krPESnG}f#3f$&Tm-eLd|M(IFto8%3+yY}Tac5i!Ot76<%pBpH-P2H)Ln13O zMD~mXc-#R)pvhiDVQf%>#{W6@1$=fDjJ9Gm+W^IE9G(rc^Lo zHCTPDf=N_#fYk9oU?v3>m2agZ=n?DXH7sG(j!rV$vSrSv07;c(v?0kP2Uby@Q zVJh5SEkrLb9H?nQF(iWUrKxojtkar!gGon@6F3z*&DgAwWfcqbGZRh+YS}p1$eB5w zS~zAk9eD_sv=xc43+Jj{8gwN5hD9euX*^&N7sD$NIGvpr-WWZl*AgMauGtI_<$33+ zW4Cg3+>Y+ya=nzGvuem#UfL?7eO7hpG`c(1oTN=Ajnch2*OfMjMm@+BMD}e zS_UY&RiZFpmeM04FwaOz&S+-P$`}&0HCie1c1Aoj)~AhwEYLHbfaU?~LZl)l3lraV ztuj(pO`D}gjCQ-lv;9zJznH{)yJ94xqBd)fy#ESzFd}v2j>y6?1!h0Yc17v1s|AbO z4n!HTuhn&438`aFoOyAT^Z-hU6Yb18B6sXGMidpJ8S~$ny@Xnr_kGRC%j|2d8LT{$ z_AetmTmd)k9>c#bVKwjGY0S(su(a|UmnUhy~#fr2dzO*1IS4T zAh=ouP}p(=QotxCi*AaFykN09XgikkU1|$yM4G}^ktW;W36xaqZi6trLRE7Sf^Fle z2FZGHpa){YnE{B2!1f&kuN}}4I)QoZo3#~BDtudLn;ViMRx^n+g4rJC(AjTFd?7Rl z`N650#noNHQ5g)^8h>)OP4Gznh{w~q!c<%x%W6~%s(EQsAd}iODk9H>%&PL?d#QZQ z$&^S(9T72u-)^K`?v4jy$&M~Wz1pl`9&2LD)TAf*#^@Qpf+b=xDL4KeQM!5mA>t^E z*&<87nVkL%dCR4gf|z+f?~q5~7pbAk;m4E#;j|6Vas;<%tA}K~H0jITYQQiL_lsx6B6C$r|Wr=Aut@;6|Ze!^2J@E?aXY*jWLO8TXZzU~II84^4sie2ulsbrV4YscpZs6Dy9k69Wo~%wzjJi6u-S-j7fB{yVkIj_pn1a1Q z#{5Y88X+KB-WFT0wU2IqHGKkVPqy&J=$QlZGOFC!xZRL9Ip%szn?Mz?r4&-strFP} zA^^q+w_76!>?#^IUtG<))izDqFA>~&o?3m@jU}Dg71t4&F3n)J=sDT2`$c8c5!q2# zLh3~Uc$k;)bUR?72a$!`2@dK3e|5tPrD`sxWj{1bZJ23~ZT5RCR2fddOpdOWiT$!t zoP_%aN4y>VMVs0nWsX%Ze7U?yy!xQI6oh48v!QGGYTTHD5VW9G&7aD~4K!fRpdo%B z1@TD9;MIitytK>BRpN~NoMv(f0~N#hz;paCX@WXJLM~EMIreg4E#VbRRuONFr+#i` z$H4r21(xWt`I9w_ik7LM7p_hG2LEigcESf5KRTm(Fi5lJX>iCSyqH*1CWp`)r-mXS@s#0e=$-gKa?VmHJq$~+7|1w&@+#$}HQ1|Yv z>m-DBzYbW5b2_{}zp~y08I>6F^sGX5T|~>6{-t!VAUFMmToxML5W&)L`Hs9t{leLb zL%c8CyIXi8rn~F18=kMMTANuWWz;ckP-%3p$&AlVOS*#-WwFZd&ADUxnsI=01Gooht{OWZ5=*r5L- z?Q6tpVk%DNQw`!D9wsX|dB$Az`v&M)eet_!c&hJGitLm`D%RoziZ7hLoEHXV7tERY~ z3uoQ!HcL6C7DsgFw1~J`Kq97;;;R}COS9d)Md2DSV|GdEj$Rhj;Z$5gT2@mC1S*1Y zAtA~|2$>v0Y!HuGLzAfo^*{->J`BK0%6beh8|^*ZhT3!qeZs8SR=M!-)HO(Dw<`<5 z2(!%`-n82z_=vq*Hy~q%5j**s88A5l(F7GVl2duRYH^Jy^x=$&95AJ=;D9+iXOXDk!DEQ1=8x}89L-0PDo1cDF;IZ+HCy9 zR2oc~^<;VhQ{_m1k4Y8#Wv2p;eFU|T9jtC?!0T8?Du;jcJmT>^OvznQ(9uCT#^leQ%Suq9n{b)y2!!?)*@3c+mK6pWzWmu~}a z8&vN^v*C6cn-E=c(NCoy2Sm_slV=i%YvcfF4}ga)X(XAD){0TBOgws0#gomRj`Qta z030)do4M~xz2+)gjM5$5a;Xt7GqK4A|I(H79Puig$t)t?2ehS5n!e}VCYNmH_7pGi4-Ai^QzD=1Gaqlc{gBU z%xv4BGf#YP3M%8kBtQjRsX}1Fs8lzDEXbG|aDVL%g*oblMUheEw)#YCg&|Z=Yv)o; zWZOkfb?7eCR<{SQEN|iB!s2QsKS#d7!Qpucol7*Vml=<`NJFM|8!X9H83?YB2=pGl4W!wC6zcf7~?{u9U2>hoEKv|3BV>_Y9hgVu#nF_kndrXq1wx=C0vjLdMDZLcA#2%(k?Z|B&Id$x&xep4T<_9c1PopB8 z0-=S9?W2x(dhsYc^W`O~byGxvXnv-aTM7u-QZJW?IZuZVd9!YSy!_>r5BLX4>-fil zf)<P#Xr`DaaoQd9o8g@p?sG7bUpoDmd3_vdA6L{e^MgpE`3le(T+l$m#ohg2F~dxL=0ne_eGxSTSPZH|1Bwo?T0r^mzU zZ#OTBAiHeU_jS6SJ(iy^(r(hFn2Tgt1rmX zLJL{(>=}lI@?VWGS~=d?njV$GCBu8^f=avrSCy=(TWF&%9G(c~o$SGYwWub{Pds74 zQ#u=}owFY3oF)M^+Pxj=B}TlmSX67KF`UPH1?_wkZtGQHtW<29}tT=qYZ)6?CglJ>#E~Up*T`CQHIeLP9OsBy( z2!g-IoOACfA_LC$2+MSI5!cb&)qBXCNU*kPw5=JM+ze_w#&CMDV9rwuf1>aL1K7rg z6r4Sl2ph;br|e43Zsi_`<2-tJ+FoBC(>Ju z4+4t0%89qv_!haG?6o`{8#M@z(l_Q|z7d6YDQ$rUkd!3n+)z9x`b~>1)LddaRSLpm z0+tpl4^ptHm#!lcT`;Puwrt}cnrfL_1qo@9MCKtFFlhlpm#@r%4ikH0k1v^=E!j<6 zYqG0WM-h7FG9!WDzE!XmqM_T}eE6T9y_y}%ncg^LyO${YsX0G0`$CSmOE#S122wBG zXs~Tf!DTL$QNetK)N_69B|pmaYxno|_9NN+h~aYK{Hf5T2GKLFLW9x(Tm}^yfP`pE zhCww5WGrrHr39`lJ%6C0utNJ|W*{H|6Gvx(nKi>1%gqR3Fzcwm<}@3i?3JzZA?dUC zv0X3BkJHe6`F;@?j(u&Muu6Fd7V!!a_GnEQ18!V_9s& z0s*O!OPBPv#aATDP87jWq=0zpVYO$@+cFpmHpWY&f7kbuYVeLShKh;K1Wn9H64LA- zX%-oKa~*LtE+Xa>SMX2@*|iKXO)`U64=#f9DAGK*^s4gI7vo|H@cGzhW|&}NZxT;3 z1sezu5D-&%kN0?w$uYA#CazEiK}4x+0GJ2UZ5K2KSvDAclmvxhD*hhJNEveeMKN*0 zB<3N@(Yiq0mQ`{-zwp*mcg-9UO1vF?>pr~#Px;{{xYR4qbG0OHu&7mZf^oYuLT6Zk zF%Ugf4BkzX3Q!nivb#c?dtD1T`F6g1YIjuR+!%OfmqIV8b+MLdM4q50bjwaj31%VhA?ps?cc&#VYy zF8JJr1W@8DAeFpXWh#b7rf%SX()wv_zki^MLCse{>WtENfzO1;IN}o z-RdWCe@Z|n#}Q%Wp-2G*Rlt>KpMIMc5E(#6J@PpD$@VpZlWqIV(2SlOcO*8M!XZTq z+c!CuQEqgR5+GLK=B1tIv);Y7=lrv?H;`PSJ)m1?NZYo-5AlqgiQOt7WtRd%xSu>n2F8btkzPuN)%PKk#-W!P z%<-1MN20k@B&fO&0j0s&Mdn{x6GCDMR0+pjo{5(jxvAwI+g;BDeT>tY7c;Ge$P7H@ zgjtVb9yuQX9cad@_|h;iKLm59F2x`ra+3w#$ zJm!%Pl{b(M&he?DY5HwZh?5~QZG z{7Qg0W5kvK=1vMD6n454W@{JSO39^M(!+z7;dOF-_3qhigD*!{EiPrY#M*jVohCfp&Jy>SKn5l$6KYX5N}T<7zTO(j(co^rh;E;;bo?%OTBjl!lbDHF5pNb}5!&|%b&Czp0MjBq zHY0s^P6MP&xajQ2!rh)755NYgL_&n!#FSMN9HnNdEW9D+N*{_iP$pNc$tnB2>Sa|U^iX$5!E)AZM%jHQF0X;rn4LRsV%u<7wvh!_gco9-*_dE$XbPSpk z=2-NWnBD>7=nLA1JdfRFSrsyn&=<<1F!2z-Bsnru>ShpG7C-?0QYzm>(6EDjGy1D2 zK%Y{DsgF52*xBlHAMq*#KXc%@IhxBj*qJMQ!lfcl$jA;+=#(iH$zc#vIZ_j-r*Uv( zehbu=e{`1(^&a?X9QHPnY!-pB7-OLhKj43lTu&i#(RUlP1FiCit{n}M7|I1l4^WhF zlKS9*s}`G=W?P(BefcXBtF%K@MrvC+GRd6NV|1*qP^AcZZexcp>4y*>FZmgY&Cvwu z*S{?sX=#hA>lk*sH|CW?I`WBR4BdHfsdvVR7u1z_p@0m)%-Cd;DHe*c%f(YK zcgQ&InuaIZyLaKvj5+TJa9AUnWUc(YI$}~2jaE*26wyFlUNP9yA|LA|{W6Xe%unY{ zyom)N18{14nD$!t8U$z%1s`jfeVW|TyX}soMYjqgGe5abu%?gBQQ)wv6cD_u@?H_a`}oq_WdwvmFP%W%4!gGAn-ufB6%2J3{-l>> zG?Tp@3ujK9l2<+pGKOwV|I#XJ+50}UzRU^0G}siGiz~c_6DS}EhHn55argPh-Ps?C zw@j8lHaNT!6ey^a`v&O|`ZQ`ouJ#DT^Gx+%W71g6uVb#$0{4$0Ti%(dnq zf4mquQio*jkp4Bu&cdeo^F z>RymHYpc`<8g^Z%Pb&`OYKFq*7q)5?+bN zuDYNN+=?2sXMi7#jdDv)gZtg;b3gMErGxz-zF0L{SC?k@edr%~#k_737A2#jBmtI# z)&#JQ2ZlNZ6NEcKGX6%Qb-^nB?e1!RghVgc2+j~)z-NMXHy0qI6rn*BXhR5u9Ma}m zUWP|$6Wd{HoJT>l5R*EdF`q@gocrIJNu0;(fD~4p*bq6so#Nd2JZUv<;zF&}xSyA+ zEvro%5zLp;0i&lWOF5=fp3apj zIFnx??xL6#d`Ab60!A4x5SK>rXri`b#RtG)S71&r9uaFjMg{vEzNy->Y;rUt^|b@A ziw&Hkj;|8G&(pFTGcQqlbTbf4*?nT}sh(8Hdg|Zjj_&}nOEwR8iD!uDx7lYvitQ2U zL7AEYvdH$H!hL?`_BtPsarew*y?Y;m%o$9aJwH(5q?>bvAV+Zgl#A*G+9QzwiSjNE zWP&tykiVbXG9s1PvybLsr%F@=-ESw70GZCngE2myq!%>&Mx4<~Dfu5hwmFyj0f ztC)fK0|2V<5oh^S=2Jd=sTVL=Qo17NwtL^)Nsv+U7$kbcFJoD?e0pxk%=9!O=HDJy zCc<<{#fv)dlR(}KEGHmZawebZk#Xb7FGP%L)>YuB@tfz;g(C?w3~hwii#*TQU72aD zsreqoc!cP@kX@5zEx;o|EaoSH2nWn?0>Bqa`WG=R2j^Zo6|i0=V~EM@DQy&Rg?;H? zk;TY1Qka+8hJgpEP9+dc*mB0PA;ShNkOR#KSc;UJ3uH;g0$j?pltJwM4L2hA08bS9iuRqO}}?cA*<@ zjfur+lZW}A>NnEOvf_E(OM4+6B``_wqb{h;Y{QCB-hSrc}>AEDTM0DtROb$Iv z`T%c+o<$8sy$DMcEZ_Co5!U!=)XIY)DaY@0eUDyXe{E5SPe{oxXYF^}r~UYcv=%Tl z2~Wopp(k^Tm@q$8lk;It3vhKU#LLL4rq~X{`F$8tln0Pf2nW60v>lm(0s!L_IeKK` z8idQMF2Z6_P4nBE09?MhuZ}Jy8k5~RijrJ{I?D|C@P`IXedj!3F%OpYktim2oKs?f zNzVNZur+6GRvd+1yPA2&YG4!4RzVsgR88WS_Yp9Fl3V9ZxxG&Pj1F@*ufrxM^JQQeKbR|@(+6u^j8@^uWa9FO zn_(R}6baUBI;jvK+Jb9?k}qIX&JJi^jwmqazO{l?cbGFHf!eF8iYUpdTx9?;+BOhQ zDs!aVSn}n0`4Tk#RoozStyEtZh-{+_WLzB?IdhVn8nREaUww{T)C(b1eJjfYWY3SS z_Q~zWJFhB1t02UJo#3IXsfip1fe-7{80tR9CBmKa7qO1VOz5)c!mH|K@!nNtJ8EB~ zia3-6vGmJlP_!${8RO3ow-lt3`pKGsH$zWDh1Gyy!aL(+Ug9WzOETK1x_l{DdZZ$# z8jFIAGrsHTIOTS@m;49zg2V5qgr^uu*Vq!5)**l@I%WVdY{bLaRmjekaU5x48Md@L zB>Sqi+!93WRxm=dr?k5C0!Y)|{cXJ_&v<7zaxNwx?7rw-8uL5gWmQvaKo|z+UP$fb zpsyB&YLM;Wh~yg6XJ@5B5qM|M-fvlzl`sYXp((=5Rr23E%?i@wLOjTlCn^YWAE#<$ zT$!;zUXmcj&IZ8x3I|QuYA%Ghj=C19YH`WJ2)j?@N^)SE-^*=TxMjVAQ9;!Va`j*!6V{W6oD(cIH6fGC1WeBesc?X3l@bW$=aG&e+LZQF@wn&_=h;e$)roKe{f`h63qgVHe5HT@Vs8Jko3y2#nz=Qq zA{r0DU;uP2Jg7G^$j9lKW~F7s<3&ueM)SVSRmP(YAP?Mib@SqGM@9=932FkJwq)_) z*X*^}gk;ns4Bw6uS|Uw=yRZU0aA(TG1n`zrfKZX}o-N_awis_~#9)|-g|npxE6#zm zLF#HGYs^Bzz(S$&##epatDO0!-?wp9V4nfwL=HA6Y=>ppd?mysy<#E7##!i6G|tfw z`A~}kWUL9pE+KR(%*OfV{oL0M^tG@hp|w*%G#+N9E|=3=N{(zoBxwl3KxUUW%1DET zuxofomEmNXX`XfHJ{2=cEki@0LD`K5&pPE)&^3?LxMWg2G7SjurKBVQjOFZYHLhgS_KP-eOYcNuLS3qMZtm^S5zR}Z8Lis+ zdCoa{lYUGMme9)@pQmNIf*1-~u+1`A$tqyZ`Fk%0bHSDp{e@XYVU;V~H@C!x$RlQZ zfYBWySb7Gm3v3wYGQ;oEM8H+C$AbM95eLmxrSGar(L2?09@Jig+KI5t1XDPJ|Iq6yZTu=A_0emf8-E)WS-3qe!m z@U`uAWs|xE+jIxQQ26uJUu`eA&Lvv_iB-nlwB^Ei+egFB?pV6+KK!UIU?f@c2TyXC zr1{Qkf-ZU1n`piy33yHbQg{GpyM!>TVoux6ql`&1&Mpwpig}PMP_?GEj4(u`?>| zuP$#=D{4?K4iv2OpBXJ~v7sXD8oJujnIaa+lhhMzb6gZ_baO9)E>s#sf2Hyd)7nLJ zm6*FMA;pRFr8QafOE;LctwE^CHe#gHi&(~-2}3B)5LWDF2Vrx5Qk7+c|0#QqUl0>D zW%0KA2{^oMj~)GZ>B#im;Yo)Hs7v8#6T;5sZiEF}Zzi4bxjmkpoi^s1*Xmj$$<`d^ z+8!=(2VC;ZO<7S2bq4_5Y3hgv@RHS6-oWmn%zPKp%G?`}0?~}#iGlpkMu2uf5cBiQV~HB1bbz;)_Tb$;5ibUU$kJSYEQIwgrz2zX3OEi zI+P?`#L{6=m}GoN^Cd-^I{s|KUmjuCqa3d(Z}))X4Z#!?9-LcuY4GPSCq{BwcXi&- z+upzyb&(nrc!?)0Ub3q#zHD!#po1*41j_wbPNt*B+$e2s z;*_%3^mVIZoC)VQYtW6LB58I%NZj9-6Xj4;4AbI*L9)lOt}01;k8TEXERcPp63y+g zHSm_giAjbWye3U7VT#|lF)-^>9*Yk(g z!rwdE?80-)F0^cZpD}eu^wb5xlmS={)u&~1SJW$XC!&169v~-F6UOkNhzQ)42Wbm(NW>}#NsNkZ6PKNV6-;JgsB7qELl8ix`hIC==wYx zNSg7P>y97wMVCx`+fnqoM7vkv6oDV7w&=tQKrwm4@LE;{-72Y3Dz-4V$ygxm3XHa*s z^J(KqAc(?WNlY7iWiu4&0|A6V%THB_WiYvROv{3%IktY@?cSYE_9GM8)5(rs;4oop zYD@G*jD-3wB}*CM>@{$yQ#mFj!!yTI4)ChXh?Nc|_J%9x#qp9S%Hs z|3nRjW#YvSuGAx{61&(Qu=bi@j69bZ`2s)`w$FiFT5G9s_2jG^M5^%m(s5u%%a==a z87*-UB!&Aqe$lXOq@c@k6};F_ZDa|zD-?U`WM76scpmfT5&;%&Eu&XtK~)kI=iCFL z-9f9et2PFenEL@qxvUbJv?!ILH0V^I!kjqVhjq_Zp(0yotrX^T7+IhUY2pve$Jp8d zJWJ11aJ6v9Z*VLzS93fcxu+N@q_UkRhXaga#3$yeY86e5;Cg zEvE!3!~?wK+qa`7CYFZdlopKH6(JxmcSzx%{1Tvfr%(as(@t3(O`S^3=?fGEi%C2? ziczQHQ;PU81EXx+7nc6C_YZv`+3*}EA#PmxaX+Ya05_L&1&nYNgUVkvu| z?7<`?!WggxA*7Ek%y8mWNz7Jy;lleB6(9vSaZMg-$n`lVLtHIc4EoE`+-w>D5sx|u zMzPeg>}9kPE?}hwVOFN1bQ%ANeq08ZB-utTNj)J(%s@@dEAEMvf9oq67ZJ(LW(&{m z=`*-D#j;eA7jg=ZEa%|n=-sXzagyVr3^FfV&6_w=nz8RLuFwwtF-V)k?DrJ81lcW| zac1u}pde~KNdQqmuD=b2LHLRz1Ud%?b_4;gJ$Rd;d} z=EWrOLZ3?a$y8vcV4aGn0S~y#zeq}p%H%Gj7V%7ou$)CiaA1b#Y10fU_fMuT!$ z3>V@bH6=;`Lr|>50LW0&lLLo&47<~|_WUqsB^-%g_xq?Y4=$nL;?P5cf*Cgh-G(O? zxO~6j(V`-$%!_!2i7cE!vu#!Zt0_y$v>wcIAjcZ1KoYzGC}U|jNnnRzKjI;AHo$_x zr8twq(BX1Q&FTYTlb^S*F`{P5jvAGuFrUQvWZYSPA#;oW{gp6N86a|d{rXH_7D6a7 zY&m~|W7o135x{7VaWQ>rn!cvBd2T?45+X4?0~Q`OX5CrDOQ|kPtjL{U0&u)==x}f= z<6!Q_#))DY2gedo#K?6bV4lBg{eDyDeTHLVql!6xHOm+T4(So+D}diHMSCf==W7JkExqJY^uOo-OP z*pJhbmXiqHtuhqEQpY08kDabY>yeSY*R0PK7m1C5s!#&pLhqpf*K{7iU}i!c4+^() z_QbLB-=o_gXEaj#4z>hVtkI=CySu%R&~^(TaGdju708Jz*}MV6L7Di3*19^*P|vem zqtAFjkRtu+#~5FdPDv6>$U#tZAz8mW1rcj5JvZa(1h?gHNf_~&LXz(?SU=He*fHNaxvEpN~ufqGrX@R$sw9F!! zXv(}DouufbXl=r~v>`TSS)~k^Rpk`9(2w7FXQOL88<@aggzM}fcuI+zucjy$xb49N zu1uLQ?R{=;TR;RkYf*43ML9m$IL#||yBq{P8VLhN?JlS-8ax;p=O|;65w>fr-%@dG%$4KCWoP~b#gfH;X zDhSa9P%6N(E;l1g9VJN+rn-~G)1XR&?Kyub5I5jS@07tdQ{&m*69}G4lHyOcFk1sb zt_4llUCO3fv4$pzC zEW3;cjj>jxP^(GqEE8ajseldoiYS3uv{q;67gP$03DVyLmxf9n#p6w!&zv0^mU!|U z%1OQzp{Jm33TD;??gAxSZb_U1Dx;~mZOcL2$+^7UYY*03ni54IWR`;1RSafj zF^<2n?;igNjMXcb)XlbGc}xlMX9pP^EX``DGdIH?^$tmQW;OSf-^t~*eHkQ;1_`NZ z#Z*wyRXP|+0$^s!s}=$T*15A{{ff;+#3T`w^g#aFL3QzyVS zn7FkT5S7&euj8ijEtgSy4UBQsGPf2NT{?+om*23BcDOqDU#+2{h{-_UB=<6nj5mjB z^8MY{gpKjMG8o~p%C7}nin>j<%#pAPB0$?jlW;pNibb)LCDyF}uF>{EdK!=OXgUPY zA|3&e9Bjfv;1C-59gn5R2z;Y}dc;Y><~3%byeS-Ii-d zcYQ@KEVFVx)USzC-wIVRO1GUE!%h11N5hDgNmW4j*z%F{WdrHLXrTPgtgSDPlXhsY zt3rIsq*BTj@8!mojDY)c*42W*n>fevWYdm>i(EE@AwrgVuO#u;LL*AA)IZQBoq?pt za1`yfHhn8=U@GK{O4VIk*M7Y={E+q$sK0y|6ISFX-g-L3R z*Mi~G+T`X3nKubK4vCbM=o(f>=L)H4=jJ(CbW!$%r4EE^y39yoJvkW$+i=b$62Zz+ zM9j2IaohO3s6q%?srGpUTpCY8p)iSR&KfPpm@UT7JYMX9RVH~g@LCi`u?p~T^Y(L= zh_G~po4j9AR@4)uvoly&;ie@$Tz5gBY~pF3mB=xQPxzLVv#UMPSjf{7VhY42e&a$xNNQB zi8c;us9cJxhdnX0n1K3DvR-7{G%0b**Iq?>6~h64x%Pk)b`xCkU#$yMO_zy;1!NdD z8N&v3!{M3&;sa>8zSTaay;F83rJj%#Zwho11`~Z@#SwFDufWl~m|GN(eO{P2?#1M| zI=-g)wjNrzccIzsC)fb8lm~#)t^(F-jRaAqa+iV)2FAA>>`XrzwnoJ2n3=(Wc~+m^ zq<=l=Q|AjVkOIyIupT+rmi}R7Rmic026jL2ARWwZDPdG)VZJ%vRG<+*&`|<-iU`+s zd8<*u@YyVN^I$>??I;ZO39yT`AW&k!^qz>_WSo^k<3X9uRz)ZVkg94KOw^eRO|=rJ ztBm>}J;Act2NsKy#uc%trxIqiUY^`D#3z(4Xz%I0%BUsjDiHKFvq38ALX?E{VJiXa z7&!X~E8c$-T(Ukadz~N2sMWmWTP`_!%1MQ{U>$0?bee!YSY{R&wY%|QeotGDf06J3 z73%1c<$5Y(lh_fG5c89sV%IcIdjJf$5yP8!iU7kEM$Y$`0hd|^1ml{rK?I5_UW>UR zW|t`J+Z^{mXEED3=#kbaWXVQSYFA6D1>1l&Wxv*)1l}p?!nKNAu@iZ=-_2;*EvOoX z)A*gVI6|>oBsObmkOoX1?)Y1&UW#hSiDJZ?cA$6egzmO$D%fCY)=apOHt z%}WX_obbQ`hC?x%t{4d>fmOp-n%a64i0`kHcfRic?X-4F!9 z1I!$FwA?4kt7yA3jm@5i2%}W{GPa?0VWcv+jQG@ZN;R1 zgGN)|AHPKLA=B>P=^DH;sdLqwMiOc}5gM_q$QM29U+c|RCe^|QJhK_=NZPmON61`g zknBxblrf3aAVU=ufu|$P1?Gl|)(+?vC(lmy?I{(sm`w44{^|~@s1xQER-Yq5$pZJ; zcdunX^KJnSbhzUso0q@_i%3>kAQEDk5J7l2<6M}yPgFsqkkO8-M=GMWL_-Hq+fbKJ zO^O#Gt4{J6Z~tN>`#n>03>e+4%C~Af=Iq_^FbT_$U2!eNtpX$=Vb9C~hb_@ekWV5? zeH2;{eh>$eg(UB!{%E`2b|K_m0q91$1-PHdV?cg9 zUegak5^`Y4s3{as_C45^bR=jNnxe48iFwnOB7&i+0sFoG+ETFKy$dm-K+NqU4~7J= z>|Kix!F-fI@(S1GI4TnvL|8=)jAwg(0@ z1@)jl#{&u_{m}X+=sXXDcx7f<_tUh=A2Kf1!ygdnIgJq#Kp+hM<2k2}HfJqozSNP` zA(h-D>kd9q%af7&EP?R922FFz&vi)7A0Ve@d_&8F#Dl@r@W#J6EtYr~$&hMe-Z3{J*FP+KRPla#}{>!HNwZ;`iP+w0+Z za$2ingL2B6e;2Tei3?reGsVt;q<^hw^2>J(W-P~I#+*!lD1Ux=Q&^CG91>i&gi8kc4@)1s$l7q;2?q+D@n zpJ;UO8<#X-Nabph%RE~nSNF5 zGbWxhY$TGpEpDM6f#a<&$}^PxFa_XvIjpcR+R?cp&-G`DEX2C0ceF{XwgAG<%5vk& zdT>lGaT#~SH_iLZRat0M13KFb5fyx?st=V}(-!AJD*P}ViCAPveo#pRenB$}hYRND zc*Y>7{aMX& zP^(Tz-b7)mPT|5yIH+`wHCXb?ApouJw}M_E1Y{7~~IAH&{B!xK>suOc!~DBS~h zDwkPH8lr&65YUfvvQKmQkPZ+(6+9wU9F^4L2M^Il4D-z>yDA(M5D}!d6tu}AEd;|t zOmQaSOg7Pwp0c|un2hCf`D#hIS%t=17Ufk&! zldx?W?_nXhU(rHcT#o1ceo}CDM=J_$?p&0s!gBOJ!7(b6C6eTt$kS0R0pZTJAg^r) ztJnevTy|j|*n-D+l-}HUYX4yEz z)TW^txearIm*a9W@o7q!%2h>H7C~H))hIrP|Lm1q7R(&5z#9qO3|kbFK-JxZq43NB zXY8PtyDERm?I?dj@dC|K0n3BmXB)}NPzTENhJ{ScrGGE++4g1O5VWXs%jjHqCE@{A zGYq2iDt1r9(EIeU;mG#DXC`nyQhT(TCYZ9{IFOEz=>T>~cw8~`Res(prAysvI%X?)y%CHVm8fky|C?f5I8?ilgAzGBV zLnES(lb(6v(DV$OXq?O?olsFvzh~E=w>4JG#C@&{Do8{|K*17Y(U|+HG!XrnxdhzU z$XyFV4NNWjQPjYaA<-jI_MW{fRM{FZCU!v#+E1(;N(r>k5$2imZ<%mTgQc2Z1|jHD z`TzuXkg0A9JN&E+c1_+4n2BPs)GVe18)8Fi_EmXSKrPn+Q!Y@0l(Jjev~uc-?%NnJ zD0T}M`j?;7j;h;AJUL(!6fR0o5Gl-B%L@lRrn_9lq78v6+e@vG93Dt;Q{cgbPLB zH@OD|(7dzyrrA0bw4~!C=ArqR6L@R#I7CcjRNz^BalwzOE~wq8Fg}2N9uRP%fN-JY zH98+Tljw~1;IrLAC4QO}acEAr9R4e$K|`DOoKp(avC5(f)t0hsc^=~^p|4?HDGHR_e{!HM1z&IOjVhq2tKDvXmPLL($# zcV}{i@*zCcLOK;?63%!LB#bZ6Mc%y+*8 zrv{6sFhG7;nVke&=ZFyX1%z$Nyd2`EVj%$=Tg=vsmSL;*U{@f&$bfbPuCm|RLq~Kq zB{OWAAb%dqExHyT(R@=eDYc<4XQrv6cQgeKLKVT6N?Y3V8m_iXR{6)^4jm0!K-Gev zR~u>HH@wPZfnUovT^)_VIAz1h5#|?0T26~=A2bv}gjP8NtO9g}n6@;XVk`if(^z%) z-B9fnplWwbm&|+a1{_dWh$W69VaGX~TR64F>#gn48XyZG0!f!}HtFN0!u>3b3=|OX zNZ`u6;Qr3vy8|v$J?2T8jN!2dOP#=J>VM3`2%F=Yxp~2_nCcV;VHh^MJvu`>R!8N` zz#sD`+@*^)l07eC&(gE{g;!e5xvWLE6JZ3Y!PFkGN0)YrRBM8N2`j2C2?N|ri&x7A z#>`#a!#=rQ)>`UgcmKv@3v#41Teg;(wMfL(3pu?n|JJ6<1R&I_XpN#+FuU84;}+4P znBZhk`NJMn2V5_emnktiMX_8^Yr#=(yO8M}?0p2rD7{eT&S@c|<)CG9EK$38d~*GE zU&9u{$WiN3%xY$#e(?-|gGYiR#q&Q%w14~RP)B?Z%R>S=2FuXDrESm#HNKu1{iAZXhGA~zX8 ziVY2=fNvsz!7%s(5DY!O3@#}t=^;9V9*3*Aj;Gk7#sl$~-EJW~ur3UIbd(iXNlw*f z4Gua8^*pY=Q2$ez5Wh)DQyv-?!iGe1y55~7(~IcHBY`U;QAD(cCw8X*sdIP9j#vnz zMLSW>P6;4o?ez!C(`U)72>)v4c6AzqA#5=sKg0sn0meteP(ettnD(nc;cA+=i^4QmX`)&!F?bK2bH_T^?<;-vazSudq2a1ixy=EB}eJVRJ2 zwk2jHs+#>_9gMrWruDuB;yl;(WgtYj2Rh>U`?nP7sYcC7s5Ef3Z1AERShR^OTkHYv;gsBiamv3~ zMSiwMsP8n(M}akSgn{2o<_LCsQ9EzJpes|pleg^}XS(R_I}@)4q5M?YdPJLSe}mOd z-J=0Xv*9-f4}n>z2N?)1P9FTb+cKcv-YbxIxOBXNN6ewtW6&^o&!cYyq9~m-r7`2*r+4l)TX0QTe3^ zacl-qi_Tl2@EZ^$9A3K#3sB|pkzw{ z#u94@FwgJ29`I6*Qm-QEG1noutdV$0y=Kn@)OdP+CmzEyI`X*??0iD@ae8Ltd|l2n zaDay?JsLnb_Njzbx)4e?Uwpp6rM&uF!fc`IlAhWDX~60)gIQ|$*<#W1@A+s^DdQrhC3P@#?@pX)fz3Aa8l- zT$?$>EQQBNhCtE?A(26E5Z_BG$-xwIyp!l*EDAjV z)Kp-h?VTX_A6mMZ0muX?7J-iim$m2X6ZK&oKDstU+7MQ-M4g|Hgnfy zXNYHFNk%2=1aA}u1%d^4j_&RASK%aP)WJB63kokQ@EsylD%_MP**oE-T@sUjs^5h9 z@#0Ucm(WOYiC=cRQYP04Aa3tSHF9+cZHO`Fp^RHYN86BIFAWL?0XPE*i3^Y?wFhfs?MKk4x)yp^WWwpZ}q6)?2PuF5* zob5ZZ*v5S4MzyX-Cg)jjnf56)mh$FG(a4Mmq-ebG3s1i8ejnx`9#|2&M|3} zgWBKLN7tqUeMCdY5etIJShhEy@@9q4Yk#+RL%dM2v}}gUHanA*2PcecVETx>3d&Og zbcz0hm(?mfcqU~!lKCpY4FEl9QQqRg9#>CC1(=Zh&K8D;58~mJcYm#u2Rmxz`~)qIYY;G zz1^8UVoF%g);ujtKbOULav9tGhsflN@HMP|Gyv*Xo6C`->}qeZfir5T&~5frbWiw< zsLK4@Pplo%6H6(Kdp2NMotZ;wMI9|cxywDZTEijLlS-r;J$rzsOFxoo0Z|MjR>VvS zYq2iEdn(>?^z`oJ{k8UkOVewxg^z-xpfDF)tk|P{9=TlS)oUB8Ya^j|glDAk`3iSj zI!4!&d7Sr!L<_l6td5zL8f(q%GNc9$L_|cXPcvLsXQ5>waD`cS5+z-y{BGLJ44gBxl^x$~YWH(EOil#Y!j{W!09eqLa9tgzrQ~6^CtS)i5|+s+ zi?|CRR8-PjDiZ{@I?#?R3w}UpWJfeKs)HiF8mBp(mq;p4U`?&azR@hUAhsau<0_B&WrUW6db!m8GPz#r!(1`q;hrYrTs>Va?`Vfen8Exu;iY}~cM;7xcNBzI zMs{Q`EkFiFDQpWOHD0H+m2<3C>CQpuGA2x>JpRozvAGM~aR~;eb<8iPV+#sgf|9mU zvW|DASzQYnk?NTY*ZgR`NeK!Lf%})f8o$g+lPzad#8 z+wkH3n{m*tQ|aKGqeq7&&c1}eho!2pLWUN_pzeHUPv2sbHP z{j8%MZ~0bh0-{nV#fN~bp+=|`IeCHOhp90CGPbU9=G^18tK@h20cBE0C9S#a>2)o` z0r9H^oaUKu^3B}l?3sL@V?eJ~Z3C_AOuor)y+$HbHpj0ZZl|d9CFq&ak|VQ7_K%N& z!jQ++3(yA>msr!_n%}~Oz}ty^dRRtoe|B79Dl}Zy(i#c4w**Sd#KYj}(^Z&XUm!s) zqB|0N{`0))5Z?s3Ja*bincgXfMM?Bjy_zc+oJ=I-w5&PXxmbvt$ScpekWrB%&3<$1 zYa2AUrTC$+4bQ>Wmt1MfF5lO6S6AWr_bG01CKJYjvPIV5DbCXlr@=941^%`_Kp|Y) z-QXF6o!vIOo`=RQKq5w-r& zC^>i}F9xg-A9{fsDm=vEu@o{>FGP$E!FvZUaso>|M>!v1&S+7}f5`j=xKlHu>;37&&0?I^A z<^)c=!-teoYk4iD>aWueCtqNNk#XVnwXS`BRN>1QbO=lsWg$^cGw?EEu0mZJnWYlkltLa>VhKsBVjE<6u&lvIqBTw}U;AkDeGWiGSM3{f~ z6V+^Rp)#za2Ekm`bUYU<1U}--I;l8>HGo``O+iOB+e`x{&WkP6l3PHuAfzFq$GKw2 zuL7oac66qMZzWQapHcF)?CaK@Pr}uYD`abpolrnI+GLnbeyMmH=geEvQ>NN|;cmQv=g%b{cv3giHwpb6G-JC5ul>Cv`X6^$*J7BX2|KdZw@s)eA%tdxS1 zwbr_?SQn8D(-#(H4*mPcmS+H>J5I%z>7e@>69QJ1B?uRgu~srg@6aYf!X2pc-{m_S zr8(3~bvF~1Uc0(!(Q=D25)j(fg%FmTRS>bezvu)QTJ`2t-Kz*C2k$b%4x-A4@-jf&k~F<}?dnNHP>u%W zOE=$idS8N`MGYQ{iHMNp;2YLw!Ryjp9Q$uYNHkvd3ONk_zvZiaih)VG&a~o}(piK8ykPi>Ry_!a?t_5g(K*?+~xk z!@6c>VT}m36uC7@ij#x*)UX9CRY4%^Lis%3QPx#LyhFMSj~Jms3$Z;e@5~&x?+AYL zKDoA{ZvNGvS_=S9=z6|E_Qw7#7=>EQwIZ6e+4k8umMBYT7$5I(=W2ewgEs}&!$qRM zY#4RHG|C(y1wk-uRI&+R0U}^U^>&Od=8S<^+fCoP-+m@Xgj6D>ViF5<(2gt~whNpE3ML={x!y~5bM0VN{7id?j4 ztK}u9F>P9l&dN}p%9g}>oM!FRBpDddcX?E*UJ`=_kO9l3?sC#YLvWr{;`tN`(fIrhLm=y&a&#NL3J!cxGAQw?fcrjA zGF|G!=y9f3>hOMpcStJ43y4piM}f$82Z|1gmpq6S?u>u;+jk&I-!MJRRMj1Jf|6Ak zaisZv0``F;qPe_Eux9xN6ss&?@1_}*!JJ7!+)3>I_?mt=Z_q-|dz)5YiH=a0GCZ~2 z<8mD-uiPq`M>LeX_2Hb_?}F2}zX2;q(@@`1?Kf4V&&9y%uwseV9KG>QOE`OFY=jDn ziwNF~7QE9=^JrkXeId{NQ@Wr);T#;L8)K`j8BlUD8kI8CW=Dll}vkVCnc z{9`r9Dy&rzA)qol+-P^y!it=JqIg4aF?;VkQFBJVjin*4@QFv*OhU6g!`$$gPYLmw z@Ae0|a!B-QDYJ5}Y_Rjx(Ii>-!Ev@nvqntNM`f+~n)R}reFZ!#%h8^QLLqY)q{y)ZhL6WCM>%uYnfC!x1vZK_7>n)YrSOUj z##4UgRUVk!sCTv5nfG}qHlE5gzoUu>6@5v7Z zENelLrAXNQnRfIi(lomqD|1fyijK4Z4h2-s&1i5OLjL^=T2bYK%2KY%#iGjTf^mdQ z49RhxgYce~8QRt`a4i?8kvheN^XYU4 zNeeJ#Un6ZGMuU>~J;TYlx36KF)cV`nvHQ71rSwAn1O&$f>>kp@PQl9A6_fxc>2?nP z!l+(9xkdpy0{}9CcOgn@jHU{+zF?PqEV?kFt-mx4cBz4f2&h@S7(V?(ut_saV z!_q7g9HQm)?7VU9NJg9-j%wndTWJ|4M~D=;Y|vIRnBPmG&L42iE2h4#j-P3ze*(=6}Ev_o(iao&4U6?54nn*E@?w<3{TRm0kx+_ zo&ji$CrnoeOM50`sNLW(q3m$>Fm3M=cU9Udf(e+xsC#7xc0P6?on_)gm4 zw@{lFh(zFdFJS)GJi#%OF4p`K)SDCEL3c3_RP0pJWzj5U9oa(clu7s;~SKZ;>v`gX5Vid9K#xF(IO!M}4x&W2XmCr~FQ- z1V7RtDf1CHEfXgouN5KukyC;t6RF^ZUF5{ij06R#p5P(GfsI59q+e^M*gE$*dL~sl zh2?C&9Kp)&uARa|>adXbFsYUT` z8?%(dTC>>aQ?Oo2IcZ!TRpv zOPm>tg2TmZuqD?V$`@&4AWJEGK{fMf&_zZ9s_-(70(`j;25dZWEu4Z*xl_T;O4OZb zJvJLBUd5Tf-)M%`3=?*k)x0q6Rof$mikWFDF#V#2yYM}2k?Ce#OMH570 z#an%tohYXZRM?a+N+DYm5=1I@Kg0>pCpUPq*d}>rZE_cRs&&o_l-V9A=mk*kYLaxB zp6XI~GFxG>YZ$K@r^BAGCH>4iwVo$flk}*{rR}oNIjl_Fyc}&qRgyk(w`EAb6bDUN zGo4ed)d4hT%l>wM&n6XVebCmtcHyZp5I|3-h1!OwVl^9?BS%{F?%m)0-Bp}hL=s1J z%ROQY2EqivrnV#7p(rs&m_emv*~=E44W-=thEkPUp`)l2sLN3Kx9CF;IPPF(JGSM9 z8E8=mPruZASQKJIG@9RdnD`k0r-eH9zdeSX5LH;mXe)T6YlB3SRzDFPObZZ8n^&mmt*M|f?{8?>ot~w|qYMv55pNJ%wz^4p)%)ZJ z7NN2!Q{Y`*j8(`1grFoWk7ao?R+rs^U<_3-nY{;P%CpIp3ERhh8Z65{r~rVv?DiN= zo|9g(>UdhZnRqCmnZyT*jYxhMIpfm+RR+U<)v{yuCf6RvQ^)35hw%BFys2wM#@!eO zW0?RFBzM{ir4Lz#0broD8dGZrg+mM~0U!XrwE(RjxZOlZv;0n)7jh*|>`)HvBQq4J zDI8gJ&N^L8(Ud&<1ka4URWS*Qo?>d}&Ni2$c|;RVr#$;4yZ~N}s;!}fP6F3}OF;`x zTJCZ7P_m~BvHJq)Kr8UKQ~HcqZaq!=1Y{BlKu)ey%>zeihr|2AXi0-B5TUKPM0vD1L0*^$X<(h0FHd7in$v1o`6~i))NyI2X zIo?Nvcm;j~Z_ShgdD$oWH2~5rhQb;mui??jj2-*fDiet^z;|H#v}QV~iY8+4cUv4n z%H*#j^(v--BtH|Gx}^g3>Za&UpIC7!)TFl6COP4bw9cH-Yw#>40~*X{z@RYxPb^ud zIUC#9#yJ7-t`4xwt-0+oVSeT*?s~Z&W*5J!Etlsu!#0_$mth!ar%UhTuP_gO6Z=rI z3=5&Nos{>L02NA^Nc4$(JMYsEfh#lc`mlFOIe;>O_x7P!p-fL(!RF2TGf4}^Ef}eH z6<3_3ZWaBN6Bfv2J#z9~+ciO@>8#nQ5>c6vb;9nuD?xPWIDA>cuUQ5LZ_!+YRy~Js zAbda`a()C`&YPUsphpt)-d!yvyg_brv<=wG#W@Y;6K~y#)`${S(x3cu?_lV0;1g}L z)52v=gj83} zgXJKuIDzdKs-$yM3z*?q3!8*yPT>JNa#u<_cM8sU2IW5bg^BxF1Oolel5a(>$69 z=7P{|q(67l*P~hLo8CM^W&%0w``bKGMr$IZFIn$edWGs8edw;2Gda|6$Mt#%3U6S5Ey~ zykRxRbPzsAH2N%r`su$a0rt>f6LS|Jt=Lz z1clsDn2W;b_@w|?acs94PHrI)cPT7y^45}S%P%uoLJinWCbi7hw;HiV8WcIBX^-~2 zre@KQ$gCxhHHyQ=+sEn#<3q@W<;W(#BtyIz2EzY=jj1EIDG|Tp30Po1JI1S8R0z^ zhY1eNnRP~%Z-9qQK^*i*CHj!Y@Ck;*!@E02DQZ*TUDUl?#-D|5Ap!gYV`Kn26+8kP zAl<4d@_UMf1Mg3`P9soL-q-cjIDP12X}FYNrC?3D>!pwlLu6!qJ1nf-DtA8vIx?r* zJUHrC;qbPA=5#GPP-s@5w@|rLPH42F#bYV66%@;|eDK_<<=Y(zR<0ZKIA_~;1pKUG z)ImK!B*np0D;Y_anRCpnNs7c(q-Stj*P1!vpD>jkQ)3sw@Ncen2O`NE z=@sDsZj2uXql`C^m!Kl7AQnBsX(sBlTW%?+2Z};5pkcDqj3^?qmfO*C94WQIQG&c` z3dmYhBH5oYzWEbBoAMyXXQphVS(ttyGZdq#ndH-$5bepmL83axs;0x|HauuUvqHtX zpqRR)k@>ayRXdwdNBl-~00ky%;qekJftcI7H`p_bN>Sh<{6j~NZ>%6QG8*pv;yDWhX8LrRWE+OoNy<<6*Vs^xCimW&`F3CJAjX zjxq}n69aEyin?&)6dEAQ$Zm1T_;zsteNl<%c?)Wd#&;#eA`90s0SVd}16<3f(V|I= zn7*Kr{1RJ<8e^bin!vOzO{D%*GzcKO9M3-+YCSMkP7Dpk1$>FK71=$Gl#5lDcf*B) zu~BQ3Ockp6FjS(nYn)_9I$rNGPNGGLqp>?`Cm7iwP$Ka(xiqsKTA;gp#BFUSl=Xho7vFZK{-D}1?pnzXePs)Q9R?bz@b zF>px?$Rt2U1AP*KQfuZfqPiK<#;I;N2{=l=KmalG!9nM#$^ff+Xtlv`35) zm(;K|0lZgb$SjF0BeGBI1l6N%V~M)0IJR^AZ7}l zs4XFRWFaa36mTh;(s5KSVJE_3z(UL34{NC>0h%YFK;}-X`j9HzB}H;fGiPT=;kApq z1((`kY}#TYrW#*?$TJnsY`SW#`- z$>+7Dx8@aRiQQuJyuhO8zH-aWY=B}`YEZb=jhqd$UY2rxz1oZ_1PR)bERnzHL)r;? zyJSBorQBQAY6FTLMo)N20BO?e(=WBa!nM~|2KbT%1FH);!>dubgAMr9O9Ij!!M4+R zEzq;tG)K@)N1d}LpU5$;*T|_IKid<>%rP5JORJ=ik*dKVu+(X!yqg_fWlD^v()93( z_KCc4Qo=Zmg6#|n4#c{2%aLzsIoMy#a)7xseh@QD*C>GVNtwcuWRoEh9nGX=kSO|G z`@CG^w7B6(|F%0FF|8o$X$%Epo4&{w4}84t5H>As@GL$cu4}4W0IRbpcq6t9XzmQ3 zZ%Kiyi22gJoeh5`Vr|&W0SAMgVLfq0S1AkbLw>uaf%8D(h!0P7vip%!&EgHzSrt(grct>Ro776a+Vm4bU3&4qZ>$ zso;tWf^Ye!IqwDXhryVTzi1|oq4 z7f@-pP<4T&cZveWgF zCgwh1S}G%Ph@%(`0FgV;!6Tf^aB>i%yy{_5js}Cauow1o5>VbzZH9A%52=1Fmxo8?EBwwl6<(B3}fjZL1B2yA`4f!D`Br>Xd&yn4a)8kp%z9V}!ga4Gyvlh>G8#O!j# zhgUa|&?hXkK_a7Z8j>Xj{kKjvLQ#gN15V|M=M-G7(X)6d!EfX2a>V31_Iv!W1tiyo z)OJQV4OCV6diHhycNT#i&klONQ2q>;V8~AD>%aUQy#pnuwpa|hj<9;+ey~z{_p~(w^NyEkvy^GkjR{_cIW?%9;1UH`D0!P4RZCINd@Juy9IT+o zCH%Mh3Fb5&{Mlh7wgai@PZVJ0W~N;2%TQ&S+1(``>A##(D4(%A8p-bPG?o2QsCRUr z70STngp12ZW|7F4aSQgtY)6=fCd?)i&x5zmpR7yzo6n4+JJu0v4fo*7$%GC^3KhTt zpCd}^3(m04_8qa`ENTaO7*`ZmnGbTb4blroLMy@~id=i1I%Z-@1-jH;#GeLe01IGO2z?ueCoM_blW^&Y z8GG5yy1W-YX#RwBz%)%{3RSjpl>u_5l8{FGp=R;Jz+*|7Wk8T_V~K3q zRpD@TA=>w&xIKKWl=&98jFk;W?dp?d)@VMySi~`S;7Mhh2DQLMiOR$`pe3#%OU;6479udK z{M#C84Lc`J(AB5qWwF*;U>yf*x=hcs36n(t@njoR6JwiDdNoCoB> z-j#v%%WdVGF?$U!&8yQKHe%h|8ztH`(#w2nreN-=Hp!+IOa-bYPL{Q!VNSDTEh&g+ zj;aMSI4WaVMsqWwhv5o=b6VmE9ok=&X;N0zrifz>Q={;SUW5)%Bf>MaC3x5zeV*!k zA(g7q9_&6mnvoz^MhKe*SqWz{m$bcTnuq2TIHLyrAX`Ps?9iA=D^*Aft?uag3*TlY zN8pmKQC5t^SdDis*&!yEa$P0_?dGo#VEF((e89{QnA7cYr*lxx5mhBg(Z0;f^h*Y2 z(1N&kEClwQ5AE?jl>CiP944T}TZ~HB0AO+MNIs}witbw0k z4D1K5J@GSObTA}b5xlsyuL@||BS>3ni`2@AurVO%xaN|t?8DmZuik)UB`g7zwiY*_ zjUMSVruOT=Aa?htYVDNlSST*U7Qdc;TQ9&RTX>y%P_CDlags9OJ$%bxAna*bYBvBB z@Szs+dq7Z7r=>q2r~qTe!24Df+qZyJ<%ul1&`SXzWYHkoQ*uGwiN4DlQ0N}-Zv$@z z$!Vlmwl%CZ9qn*VtXg$#%tFzLd8o%q07=+yn?3Yx;c4SXcBpsI*LV@F0+4}>y0$J_ zBV3#%HNKbNMfC!kngY~99y>);|CJ8`0;))TJVFv3H*fI%9eSqsxNs<-c&4Sf3Mkp3 z8PpCuA>(eD*1R0$P2H(t%7MOYO*!qyfcgKmwedX+_2 zvq@wmHl=umI>O!})^xJv6Ofya;X!Gn61u|Yczc|R0ZVv6M3r4W^;8Wn`6z*gPy-T0 z=bZf~^jm{u6DJSArUZzp&vROwN}FuVH~7;{y<5f*=O%-OhkB{VN`R(yCjAUq%961) zljUUCsZt!6=PE<4yboT4w~o#v3Vuf>%puaiGXN0148YBR`Wh3~mxRqhP=FWnc{LHd zogNQ96t|Y`UlVv3RDaV%7EftK`0VsTg|_h=jfr*S_jf7IhhUIcg_ZS~{wAkFFhZ?6 zNF~vO-C4w;4|7QISwW0N^;4i-z#wQ9UN1EYbHoEP*_o`+?$|k?X{1YGPfxUe(eR=f zF=Tf%j39J-rv0`H+fx#J1uL~M2A688NTa7cU=nTs&~uNlhw2o05`zyTiTuq6cXNP$ zc#ndBX{2{KZ<^{3A`qeXrWN06eDOJBuxc}&>k+$VPZBh+cpOiPn(aqy$$Ywr3l6}zpjhvlHp>e}XiTlAqD^9qSBg?=b zHe$HlgO$G;VJ`hkL`uIbnXXMi#v?a8@qQ!Y5b##ha$O|!iN9S?cWzuXHJdrGv|8ah ztxVJa2QJLouzi)FHPpgRZwq0Ch&Zu?OTY6srw3eGkzF3XPfs8qX$@yBwY7REuP7xI z_NNaFWKG3~u5QKHwyegc;!NF<3lyM5&pmC#N>mja&-1*#cyxFkbOXg8A@&`xX0QYT zY`d9{7S*sVdMb{~Th&7_Qp-0tVNpZddL**oUVN!^jwI7t?MZ~&0>ikVhW@WNtDzsf1(V-W^* zpY;)W0Cez8He%yHsnr}+=;u0rp1S9dZOs*RpWQaO4AUxC#zc;9(mmSFfU2_AITd+& zeupoTb|ny5HTbxFkM|;$e6;9Yi(Gasw0jP=U9zu{g+TZ9pXJaeZJb>C6jdc_ak1!PRcnn*@%iTY=nX0hB(PReW_ELR&ulye~t z#|G)honY-U6qZG4D_#_9CLu-QpqbONrMgw-GFbu=5fDtRr@}Ip8=I}&YuOOvdGM&q-R|Yqaaa{uf(ZxVdlvt#NjQ`6?lMJ}nHL{Ck>P>4E@9PXMBnjaK3(;bjUN(0R_Cj+R3+Lc4Y zPRNs~(*<^{DEghb2(|$eVv0of0&DKQDuD=0Bo4}4bsjcz<@ZO2z;*4V@@9OwQB{jp z;Q@LQa&rN@{W8SIT_}%M*(x~s1i8{{z=Y)xdK@^)+!f2pi9g-?Fxh*(h7MU}3CGC{ z^%j7ld2T=J9{IuIwCoS{c=;*>tkLS4K{en?8sJiHo*3}yMWDNs;Z=feI5S33bf4h( zP43>>tc6HtBeWKk~Qh3EjjR87$-k(_paFkiYfvTh-gQ%>R zd_v9;5NW=2wE=&UnbSq?!Af?H3nvTD*-PUD|58<=MvR0Y*PePIVNuLAAATp^ ziyltbNT^$AR=QYr0v=cn6`gkU))(w1sXKdMj!Q*{QyskhONwN5_pE6YRA6ya=0#Mh zLk0{;$1$ZeTfdXLY|SWllHlhY3^q8tW;Ew8>0h1R%s^i>N_^)k8W z+pe>%0THtA%Fg8&3CexB4kW!86>c=tu2XyIU|F)D93qzqd7$v4I7Cj0QL}%CZb#pW zxYS7nPqm*KYZF{Q?qo9*k<@Z@UxJ>7Do3n~Zqb<-J~>gHBu+}dQU*THPuTT)HNPs;B>OG!pT?Lp=!(M+C!5PG|%>?L@mRD0=j+} zkiCt%Wk=4@HHO+zNS^N6+Jo&B_!3x1cw>HcRww&XvzeNMjwi=QbqI%0rm0xC{~(d{ zN))uh&^lne7Spf7|u z*~so|n3e?509|bpg#&fzf_X~--nw%z27#_%HF32&Lv`c~{53}Eh=6oe`m7-EbRkY@_#rQ-qy+OJ$Ye{~ z=jARIZwJRI)VmbzV^ew?dPuPad`T&oT3a*;3Ycb&qRdFzc#o`50PYzuL+6~H6P2s17CPpaY~w8;t5kBQp}c3u** zZYq?u;vq_bw6qSul^MVFU0hU0RDxN%n+N4bY!PI^rlb4o zGmF#@*kv+5DdSJYx4jy^3Z@wx=kA?2QID7eM6kzDYgSyz`SSV_7KKn@yzZhYzquLt z>xgNP@t~^WexBO9Z^HNVYpZx6L(d{NR8Z%MNiRjNxiCRhdpv_a_z4CPJ zqAHe;wc8CTb4~m;5hI|J*X2zSc=ZHlxKxkono@^XXNhOAo}wpyaAG_|Sp_gslh#gvfVrFxN720$DT5+FcuKDJ1$cr4 z5gdLv_vy~vB1iq=Ozs3dGq`W;(wLFR;TROVJ>Byp+U z;;n}yL6e;FHv=eZElDh7iQ)@x+!2?f4uB;)#>=$V8)DeT8iNV&P;T?=9(J8M!9dI^ zCI$iV78il_;-_AX5D1-WO_II6sVU{X_RU<9)zg=8Jch$4D#JQ1$%*2oocmkn9}0rk z#YxJwS8a7avv;RzDyNxN3WF(KxYE?{45>0=JOS0QyU}GOGWwFSMfN1ex9hH$Mhch~ z!!)@h)PQrAxJTfie)%jto%V&tXa>y^6Py;^1=i1}vy}UDff$raCQtkyA_o z>?X_#pf6k$bkt@FVY&1(H$x5t$9!{|vIlA$Q=76~hFpR{DcQHy8BaFwl@jv<9_TJ- zn$a_Qr1da(FN2uXhq{+Epwsta{!g22TCdu9KEx{dY57Z zk1gE2W;1)J0j5g^gH)p=1;LZ!)(E)HtI_eAS}dphXqhJ(E6yXoT7hRPgKFz4h+&ck zDqHYXFS%`;@6ip9!LUFoX_VGM61!BA(Mz#JJzS0)N#l%|aab|lp~#0M<_r!Hi7CV! z)s}?*;4vwbyn1JJ+aQ!id_qqZfp?s2kp}x%JH4EV2->0KBO;$h>;nk&=bS7g!rGBg zN(x(ZCKp4Cq+rk^Jdfeoq(~DX#RKIN9|y0G1~a|0fvCtQei^&+8RvrDST_z9QJqM{{iUZgZ8VS!F*484G8 zDpl?u{Jcv^{Kh?#9(mBC!0n7Xr6(46x_pBdsA5h&kwFB>gqz@O&=Ize^CQ_<%AC$z;EQd z9;CH|uW=rgmNUAb*d@;9Bmvf`{G-<%S*FZ4n>-2>MxL*3nG*78gJJB-*kR?vum`~n z+Z>|5QXEwIX%@0wGKw$vs{3|arR2>NXqvTB4Z=J=!>`U-3n25=;52FUlxgMvSgbZc zeu=w`P$1SspdU3MmBC*Nlc;tGIPNk^Yt?3hiAYnZRbYGkd&lsk4lL)`fK5=${Esm z$8y*fEnNc+9x3@03mrAM{S)PEMYw$T6Df9)M~84$Z_3MX>o`|-gC`slvm}w$#%7;9nGAfcxkbL6@npTy9R%^eNmA% z22l`LS$cHxh~Swq)3b1}D%(0{MFqmu19YHdcUu+kazOWKI56iBd6vC^JX`PTY zry|D=d)k9M&|mG%43o^f1F~2(g%AK%cPr`4=j}UIoDPwB%wc=#R%dj`OfT92`zrXs z9TFgaWJkaH7H1<+Uj~m`pqWR;EYznAW^K`T2_m|0vu4f;oxCe=hOqUHS@VVHYIyyU z!_x_pAn24f+QftRGeSS3?-ye=V?If34UIVD((c1#LNQksYmsq)vw z{Dz@Rr1P?lfGQ|(tl@D5FV6eBjM4s56a$=s#*_`rmzeJkTeAc_XdeIE%&nhNhSWQh zgO|R2lW;+tGMSgqZD9=K50(&#Sd%O)l#)~J@S-1)^izVozVH?j%T6OYp`G8&04bKn zx1FuCrX7Om}Ed+Dl3J zrIBDHDg%T%{Yvbi4vr09jqf8)z`LI9v1+=8s;v=v0_b%5TB5|rVLpwZ0L?X{K*j!# zN=mFd&!l#x)c9?!5Zs+vsTo1gj!FZo39h*?b~$DVE~x$#9bv*1Goi!H#Ci=L8D(Z1 zeeVD-nNXonsj){6kRvQ|bFd3JeP=c^#s(o#v(`wi;La74p=umI{&Op9R!}$xo1J=h zM|^dOp4bDv#LSkJjcBtacv3MjI0)o1Akkr(Qbu%<~%-1haY3&n?+7_KNRY3eg zQ_hybROle)EfhL`ZPh|wub;df1<;z>@?4F6*~FdHMqBhL07e~%WnyU7T^w5?4~whz zSfD#wqOwqcLCQwLx<&}G={FYu+<)`}WIU3|A`hl74kyeY+RDAYuOn)L6jOG_2s(Pi zSv_ke$6g0J<~)?ne7K^nwb->oxi}bBySho1f*!<8DQ>X=aSt=E4U~_kV`Xg&5_4j4@2pPOi=o27f+7#L`P90!?HDJ7L3ui? zU8S+=?%lo??AF@cS@0!7povrSo0kyrPcv?JCICgZ_#TeD3P)Zn~2x%8zbcD-GL zRP|_(fd!&*j~wZ8eIPIR1^mrz5H8)-+7=}$z;fT&7Sl}t!f@^}{=r@G`@s&u5|C(& z540tL=ET3-qlkDr0+zgbnY6Vlr{6FoU%ET$2>*D< z>^r$DH6R6#J!()W`qG7D@H8vzkPyZQKTPfbd+9}aXKbHzNjY)23Eq8et_2i-&-Z*! z9;7XzDl<|tvZly5i#dSTqY&`YwN4$asSL?(5fQB5I4;Koa^ElAD7{@gAuENXQmD%( zEF3X}BPuzC0;>=Ewy<$K_U)HBNVsUsb@yz-kkO*eGeSl%I#b5=+b0Q8-MTxZrl64 z-}~)mtIhpr*AaIMjNUC9m57%~xO1p#%?_TnOE2ZrkFUEO4AKaf4w~5|yB)Hstm;UV z_8odansPylqM`gr$v8U9?@YhX8=bDE38 zLu9TKoc05Te=6KaOw@a*0z9oIzefQ`VSjA*(U*YK?GUGL7 z1F)}jtu*Mf`8xrI_PiUbBZ!s3a2AnT&M~tvDe*j2>Y_fU3J7#ixoG2UzLj!?Q=j5v zx?5V=QOsR~lsl_we}l{vjXY_3Q`czXH!}`sa!QtVz|P64oi3=PR6xLQmqaGQW8wec ze{-x@rmqA!t{%l)U64xtSm&;=luE%oDc)axWOGt1r8KQw`YZ}Xe`GJ!tskWb9B86J zQb3d3Oe666@H3j*A~4A(^P~`8l+(O_adyt))lRoY4489%^p?IPCOI!hU(SZ(wTX5cdo{B(^62nU-n2)rO zAHxiY(&eTYZuy<1!JI6hpb67S?o767idcNxxt2GB8x`Oo)(}WTJ3Sst zx-;>2if^;|0hs3DMIp;=J18p$A+Z@vOATu9Ki zmY-EIE)1+bK~lI`ltokAigeN7}U^ap1rPc|0+&J{8M>z#5PgOJE@qJyB%rACTLX;nz#nXP9 zJwunk+-}#Ew+*RXz%e<(dX0HoHH;!4m4uE9E_d6yZ^H2?WHQq zgS__PvYOuQhT@~VICFe!oXRs+aaGm*{5;OhZY}fapsg#R?c}ZgYcZ}G+Vsc)>NX#2 zPuM;kTfIPCb|b`aYxC_o%|Wsn7qM6~&s~E;AfQIhcZ`Y3Sq~aVNqGvfB9d=6cZ)9} zk0W)~)(D0O;){=5bzLLZnlr;FbZgXYFy|5o9td74MWE|}u$l8a5J4U`qZJ5bv)5=# zdVy4uJfrKCObES~p|vqZ?A+01yKIK4mfqO5oG+d+&zX5s9FaV>O$ji!I-TadW5roJ zIJ>5XbLyZwAqpDVZ9|;Tt6g}Xid6H!smS1E7_QDe?;B6#$iE)c*q&s*)^dGi|CaW} z+uC-lFPVa1YTPUA7cIoj|a+z zIn$Kla1u>782Y5uB;NGjEvX40EsEiDwSDFe3eTSek~UwOo_uePpG3bbjU&w5`&CD} z@`5H=XdnnGB07;*R2KbA#JC*FW8?>{rl0ZE7@5i5+Pk&|E09_1uj&!9U%0hAN4fF+ z;#zo4fe}@sXbRekcPD9v=kW|3MMY2-yDd<#!c}#ttWIgL-SeC2cHmExF+M*-kLQ_V zE|8NhuBxC~P$QK6CpaB!FcJv+P6kX2IEM*@lGy3KY(jU~1`>Prr0PlbeharU^Lgsx z1bl40D`5@SJ2@{a0hr%8h|vf$RI117x{A8A&+^#^IhQL(h37fFXKz4Ril)^FhG#SH zshG_yl{_Znp>c7$<=PI4iKJNqNmw-oY)nZAZf0c_LLsu;tI~ydcw~}5T125!N`DX? zAsg+<3x-Nm7+RXwzpAXrBm<*`Wx+Q1zzf*I;XBks1q+>0OI6WRs_2L%Ju-PJyZ0-K zgK5qE(oyqBWI5-g%Zqer5JrAY6tvjD~b1M;5NI$ndy)vmNSK`o0_Sw zTp7<&SYmumxD-N+GdYxiI24GC3BhH;XoS?Sh#*$s^&M?^#dS)yN;wD2&7{=FAtJ-oDDu?&ebZk`91;E-J*WVlz$)V%W4Yk;Ehu#5LT2UD+FBY#Re>t zVKFmFDp?LnR-SYw8#R!K1TD4?vQ*p}GO%idG;vd~&)6$Dnpb@7U(hZm_~1^ zh>%>mbPoj)E?r82EU5Q4Lr)VlFV%7;Zu*zbRy$fOzt4@P?SlwTvNp_WF(E3jx#u2K31dfJT?_yN33bLZ`gk2qztYuAMU*8%z z!L;@2zGtijp_I-L3J|sk8|(m%;S~emYSQ?ZR*JBdPb`};QVHMCnp#oE8Xxs76>}V7T%cdx*$oPkd{ms^RM#93fv;$Q%0c^$AV+ip*zL`B9kuVji0_B8{R!kK*AWKhH)gwQU~#z1 z`xsS4&FS#kY03#^fs9b({^qGhFGNps=gr9 zT$_x+@IOn98Mas2wvAK5wQrRMAdjMY<*gNSQcxyBGmK|pgg}qUf(qCv$3@oz5`nOo ze7xL-6kXIlxfe`J7 zE8LK>@;topwkG~6YXOuN9erq|n*+PhH7Hq6CWAlP>qGozIq_8Ez#^C|29=8Bb*+^} zZYQSxrVS8gB`8FUerXRsCX@D!>gO@s%#Lx?lDV$O#4RmV%T7?Q>?=BeVj>gbJ#6jd z%_8)O+`S4&J1__~*G)_juDb6k0oJWjXbW&@A90jZrc9dEBce>*%%mGYE073dh&Aqp z3C(vGexWb}mI3RLA>?6unGF-t7Cg8h1I?>6b($f#&91BE)k|v_a2K$93KMY)Z#y4Cx|2cE8EPp0GqslUTZh}g^h-3r zEJ(=84!V{$l{x^IMH5xBO8ujlOj$KfPTLZG{HZ>;93)_9JYZ|VFkl|-BsgU0tqyl+ zhILP=VTX{GlLW}lMee#@FnpC!n%!wT7%Wj;3Tm>&SnnAylvB;ECrQ)8%liUjsuJI@E#Dbr8mjfblqtfZiW7*`Tkn1+AP}l%uLDJsiHV~0lFZ?#zt!HXKvKksG8)mZ5!2U#X&)ia5N8op>-!)QSh! zoR!d=mXTMRKDxz7ctWBbyOtOm?WZ?gQ722r48>%j-24SSqu-11QjumV+Qcpp5}gFF|*Pf%`G=$mv9Syfhc+05qH$qn+y!|Ek(L!lQi^W( z%+B3qBG-h4^2GMFm5rkIUwz^8nZlKA%g@r&YAP|RQ+V(^SSFdvtujJ7c?QG)Mpl80 zYYNtHtwR`|65a+Ne8r|*wN@Z33Utv*vc@{A(h>MPjWrfYb#0B>1g_P=8_Q>W^0#wA z+U`j*{)*+SJ|de!?q=iFi&f3k$8&D64Y?EEnpbGsH)Y|tzNxIu>KG4cBK2I#_|JNw zg4T|?#R)l4haZ)AjbJ~$3RPcH)EE!?kKmBInLVa=$kKlcIOeH>OAg0&+c-U*lh0vI z&!rKF232qo=Rx`uI`wTmtPRLD*!M8#h)Cz4ih@Mc5Wj>l%QoisdO6*rV6`kjiHAi+ z3egK6&hTqDLfLRRpbQCn2{uU4k_Sag?L{m)W#eIP*=Kq=oJ1)Pu4%?7uuB`JMMVN^j(6AlLioa)c*_N<5p(78SSTBglu`v%`RI}|GU&Y96djUP^FKAUH03hRYOFXrU zE~wLPencWoO$Hs?k_kARK<)1y8%5)n z#@Z(AFgXvcOAx0QUGNb6i}C{sQ?eXLEz{aXRSqGXRNax{nXqE|rHfjG#|Dt)>_wp2 zJ;F;~!@U?Rk$4Lsd^m$&%_xIpvD%n4<>r^L#k{@oxr803RPvN+G8ha4XjTg*f({8p zX`gmV1*dhi6GCNY=F65dt~3vsMJN$9YCL@vj*UL;!{aq%(Kk&SVyDci3XF$sKKLsi|mt zn^IQ_gkX=EFm2n5Vq%}m)$GEj3o8Vc(N@@HVfeoRDGZKd7slZ z+Ywp<&SwkO$$oY~qA(GlsOK95Y!dTE(o2cl1&$hh5(5wR)k`2IJJhAf!$6aLW4kVs z6?=oT>39B30(f>3Og31r;e%vtG9KX~ij1?EwRfj`Xi9iwjmI)$FkQh%bgQ*gC4Ke6 zfMmXWPJJtvnMqfHuws^E5-*DU9pyAxed^v!bCa@O7%i$+FFEj%c{6dqW#RT!B+A*s zTBZ|g^CsyfiU>kKUR|S^_x0;Sh^hhF9)7_sW zF6j68c{(+)tNu5mYp6+%@V~d8=g4I{%I5(AofSQmDKp`GCqa>2J|P#WE(dG2FHlC2qeg_iTmPABC%mCeV(9n<@K=N;=c)U0bYio?d?9!E2me?dk?yvKaOL|0|CE}JbLniK5DNtglko1Cx-lssa*CMkzk0&C_wfM{70%r4f=&bufL)-6 zbfs`a(6_=riGj0@3!B^-e#f(A*4Da$`4)&gBf?sAc}yzGY#_YEP?nCfx^0JUDMCDRe?|d3v-* z5(Nl8-eaons7}EHTufg?Hs_?G>QN9K?!R*hkmyNxb?vsstePUFO4flst-FX05?>_3 z6cP#2<2d#KIi-AaJFocvm~HJ|E}vsgA@J@5${d3)D$myvqN4qDv3q3{evV&C9Axdy z?g*k0_5^D@k|bacU_c=cay9g!6ag)^7W<}iPfPpEjw$^Z&eKdE|O_kJ2F0R6jikSXc@1XS#sXiOI-G3=F7B&Wmnz1n#^Vei$zd zHid_>69RnSk^--DUS$KV2#gMJ5S0$U;;P(%bP1_vUo|oE(4>jw)b1$_Qc3f!x$~IB~y>F6=PA{r}b5do*D+ZYaKZttS05JgpS23 zpyN#}t&0k_(Xea53_vY{(?$r88dA|6aOpanOb}O3VuGsKAdD?ILsHrkBh4s2EJ1?0 zm?eADUHalR$|Oa-xMI?HbBc1nR+$0dk`7@BV$6g$2smb&8EX3u&5*5--3Da9LSN)! zra}Igm$_uJ9}rtxeUHKR6YG z+a`&*N9IQ%Q+!LSpKv}oI@*^*E>u+}WLqGTF%nf_MFyci6g=G&%ou{o*w)v27sB=qW}a)MiB-@ z^xdts+rdb#B^i_|9MsR9a#sE5&K~+S)kU1MZ4D)ZDlX!4@(g|YZ!VJ{8&boGkLs@7 zxkitG4l*(e+2`$yaIkYF+PF9E+~o=du~8EIO@vj~%;BykHGYAF@h03|4#3=0WZ0Uz zti3FZP0;y(o7dQmQ*QYpYzemI>B2K@<0b9rg8_`QQ&MO`i{Yi2t(uG$;0Q~Z9fPMe ztH6kg`^w!=Mz!Dns77Zyagk%(g6AEwvyFJ+sFV}I)00HApcotO7kQfegxui}9eG;! zBZv4H9wlXtyCFm078`ikn#$B+7uG`pvEZA;H9@$Hk3u(;kn8`@$pc6xdD!8dtzm6Ih1GsE8;f$GN z4}vh-18C4QzUXAViYy1ijHS0B8L`h88z7*oWQ}j>PYf`zq7BAO2hKxJXwo&pPoGJ#9|Yu>XE-XYQ4V#B^3`a1S|CuMWaq8X>gwE7vm|4QM4U|Q3OZwcD+_>H zg57Zv4dUi2x@*fnG9ms1o6gTM7$F0LNy%nH0NK?fkJO4JfhEY^-|iQL8kyIl zq&aGkP|Lw1k{-!L60CW9Jd;@4ZrBw7Gaim#LSx2hpN5gNpx=ju6H0|{8H2Xua1x?H z7y@AB`%zYW^-0fs()#SYmO<|xIg~$L-Nd60ba4ru7+c*bygNQXJ86{Df>TJMj!}%| z^NIBq)^1Oa!@fGL3}6lb@;Dr^@Wz5G$D}sU=^jE#ZFp| z^p&d2XtYXW(Jb3p5O}Q_`Ft2yrRX zUaf_p^5fxk0TuBH_FGnqtohgRc)O_p8c@Gjy4#P5mxKp2!q7?>g*XT@vt63@&yXnn z78@as#bO|Fsn$@VY~=1@LB>_YrZw>Q2<2;spOBCYBs7_(XybN1)OcItAt;h5v>wqZ zQ+XkWz2nuk&Vi&0E&KiCzE_O^H``+iv-c=$x!og$TdZdEr{ld&qvqtAuxN^s+?8U? zi~;mGXo~BI2`QWz7m+Asct=%<-7p@ZCt1raGUAuxO`q7FI~42=F^tc__Hs)WNtjgs zAIS(o=Ex zIC5K4k-L9H_%2OW#E#Gzn8^}q7(gH}4n`i(vzwZCay|7VqB0&~LL)ajS|!B>Sv&3l zq2nXzB}GTHs1!sueY&FZ9(2^vJwrj>9yJ(~ar)gU7%-b6vcvqb`}3)}NrHUnYjx}6 z?sa6#;4&SY>LYM1O8n?Dw6ueiOk-u8gmdBnP(b0i@HcoN8qb?gh{$ds49zs=h0~ow z?P%%2k{sXWBrQ)lYPe?*W;(XPiuOD$h0!7LAu2<6r_9Wo3VZA_MlJ)i;tRLw zbi@RA02gi>4@Z=UD)h+p_`Pvf(OXwIF{6}Gqs{FaFlu+FX-0Jdi`ym6jtxe))qQ^E z@7a1H^o7mJf5_(UF(b*2)ybngvABOGlJ-&3QrfMVU)BU=6}I6<;3>!5(;g}gN!frP z{v-`&ru^#DL1qfvX{bp3PX;VTTL#jiB+rhRr+XS&f`pzv?4o^GF~8?+&PbxUq!t^J-tPHO&Y` z6HAJ9E!Po9;Ng~Z4O(A^S{TMmM+WBjp*CevEmM+Ibc7D$Df@SdOEDtJx`<76|4T%s6_5=^wc!K8&fWM2~qH2|hChwmEMC^ z1`==sI0$?!ByR8MJl!wE&d#dJipr|7*4*^^jHxlls4pusGJf9p$;c)u%3|qU%4<6G zBWx*VuXH5!Ix%?pc=B}jWfCUu$gpad@Gxyaipv}x;m$Om`Bz_hUp&@;+D*S#-jFY@ z(Leo;!2}Z4df6Zhr^1Hh_i^T3;N9mbaL21Mvnn?@vbl;1}fby*Mt&d7Ba-TRoxv*Ju7bOs|MR+@|I=O67 z)hX+=K{`yigOX&^g5`3Y8pccoCFHr$Z#;$BC2w>kB+qdEfQ6G%QE(UGaXs7 z7CZU;WeQGQfjnB zF1_V63&wzrX)l8SQe5z?Yt>t)r zl=_NfN5OeTbN2-gQi2(HO$?L521gcQJX3=3@-ECtLHW%DAUK(}GTk)&^;U3>CM}Rx ze3ZW@|H!REhKfQPrGi_ zmWlTRE&^jT&s3*jr$7z3lUDI+c<~r$%;EZRG?o>^*p5^woPM|m(I>YFiY99@#%U!X zHgUWAKT6L`7=K!R&IMW~bk3DxQlmx!zSo^M05#QL)!Ap!_(BDz$DDC)((3pa@DY*6 zp8^w)`ui}fmNv>Sf1{3#8{|n+%Y&3@E~>CR1FCR2Gp1zQo9rUd`?Fb{6XNH2T%L*l6N!WZ~iH zLmxqkb)z)Ar`zMzn5-Bp$uYpCconQtO2@cl!s=UW7Fb27H^C*<9NS#gmWh-5Wx5WD zj*dXnFJD4T94XkKra{mshrXvaKmgq7VsK|V62m>km(s*$Ypb{>AqriSRwIJCDCJx5 zN3w*zdm7_4+L+y-`cvkN2q2(jbmW}9F>@?V0{b@mxV65U)_qo5ZcTb?ha;z6JPSbr zQn9bwR(MWnuZSUtjcQ+9SeMOMKK(oOUzmm!O{u%f7_AVBnoA8eQY>Qik3`+(+6caB zp#%g;w;iNjpPV2MQC%U;#AC^D$hsM^Kj1I)d$eTFe2aa4Fq(}+z-4XC(oLL+Z0HY ze5nCI(wgU#!I#udFfSis(j1=(^StpBS%<6#x6<{?atfxJ$zM_DB0Z@)aiN4?EBD@qR0Y}PTrY=$Z0X6^FK%WCy# zC$p-WQH3Z?wfqzG3`bd}paV(718gPbY_n{cVB5%1Lc&*O6gt5$C4dajQN+}P0cy~{ zAd_K_T^pNHna2b^Yi=oxKvhOirs7db)T~kjTpz2bK3RCat`=jeuKe>PqfX~2jNnw$ zxq{slrnAWA$rjfEtPq*qU&uaB9`-%07srzV98`Vu5(qKXTeE;N=4_2nT~n-1b^)3k zA!E{@qb9A2%wn32!#!RM3UHK-FZ{zoV?-Fs;ef>CbBxO?;FznRj&CudWjXMxY6;)v zDQ5k`yopEy;|UM2PdSs}mjJ<)Yg^=VKrcZHu4k$YO*@G~TiZ#y+%kF{V3Kdm!=_!= z=0YL2wJ)Y5>ZX{(_7mU`5cODD|AIs)th<;HI&M}D0vF=ZHEu(hXcnvNxm2mb9<@u`YkBN=f*-LS?8l1 z0pZ}L+h1Z5esk)p1dC(la#!dtmH_UzQ)3F-Ds1|r;G*pivkwt$lK~+}IxgN(;y31* zmVU%A%;&O*XJ2I09GT3n&#PR@c8POz?(iI#tfwbjm)^9iv^N9?!;J(1a#7qmn-a^wY|R!_M^Yd%jY0W?ysU{(LI`rzCTYQSCSc%@3sFzrpkQLsBSb~M{`VEG|b(1bzRJ{X#M&vg&RA%;(Vm5CsTSyEtk()HaO zurc1Cu5U#tCXB=HTS*NUw=+QK;A9fznO}NfJmS+-AmK4z@3bs9jwu0RM?{4LaqwPQ zUV14Jgs0$=)=C_KMX5$AT*+o6jGWg0SlMZW~bGDCv8qWKu+x6Bowm6q zV~u%>{A1_{@NLsC>-kxUf}#OQLu1}X>j!(WBI0s^Sm(pctaYV zmuBjF*ccn)PiMy{2QPvQ_?FnrRQ)$FNX%07D3}1>a$^=J$3^2(P6M51+J000*z+## z#2#}p6}829uiQ~%s)cRbi+REIdsa{a$7%n1hA8A}o#SPePJTWco_%G>|bW zAK0bNV-@qGExzjSm$?xWXbeNDHl_z)O86}8KNII;FROuChZ-N!(?VFBtPUKX&OY*C z@FyZD{t@!1e26}G<}f)|@x6^>X$<0|#x39PxhpCbs1%6>Qm~n|{693J=+Z7AA7ajd z5pN2np`&Db*YvcNmQSuzPMy-AjK)+^*IALB@*P0XPik0$9tPZ;0goskK(><|W;ueK zlERZ;&ey|`x$A5?)b2Ojveylme-Mh!x9{Dir5T)=CfkakA&fGp{%&VJU-1Es_YV;&tZFUp>Z> zavs;xFi8m)Md5kEnED4iN&9iCr!Xo7h~Rsg`DSNxJbe6|_P=cM2W}JL@R@i5vCzoc z*u_qWWqh466QlAGXM!eUt?{4H5~1`AK#Z&Gs}n7q%NR|GI>R*dW9=605KJ&#kI`KI z^jK9f!UwsKt+Y9PvCfyJLAsR^W~NjX+8B!^f!st6N3)otGk+o!O-G48I^wz>JVmi; zs7aBK7z*I;4j@*+1E`g1@I&%V(`Qk|d^Ir&BI)lm)FqAfUEVFb!>WxnXIKk0jsXMn z!P2&-(t@e7;B(tqhJh|Hl!R%nOYspZ?!=f`@zU2-|C%~B9_^^g{nZ<#X6k&~VlPa% zPRsxYW1FobRsDu&0Pce8&1mcK)q?a?+Zehy_Ex;*Ol&h=I*_SaII=hKu08S&4dJUh|TK+35Wpsg2@)eg2~)LlcjB}lrN;HF&TU*=G)a>JrLY4rf!l- zYsOOeN9T=<&#$vaG=x))1{3GRRkj#SQXa<+tq)IxbrX;9!PUnm33Ny@KPV8kH!O$% zzU1rKvMHLy+DYCamq3@K1S`nYK?%UR-z%^3Cu}duPHd~xFaCLx2R@{y99k$a))>$s zB*hG|nV3_RR51a$5RzFzhpDcSp|lF@ciOMs+6zfXGjHfR1_DV@FNd$kB$)q#UkSR~ z`ub}%rk$9RNdjNMAPhsxh4SUEmJXqj;LeV(JB?4x3W|?2wAYybkr45{)0EVFXy9rZ zYPNuZ!8eLHxnxOF@!`YCrke6O1B&=$vGg;O?d>*{U&JNusAaYWV{qUr+m592ONjzh zeI(HcWx$@P%?_qYDT~y$S4U-4fvNlOOxSk;Z(Fi2UGTHJ4Nd0O#LkCHvLr z7DeAGvO6Zrfs=~PyL?| z0k}*6fLVop>^RKo_hcbk#$E2g)y)y%&@2v@`;t$nQHG$22a-`P|R*haXwY{23N zc1VoGaq{*3b+>Eb7m53hk2U*~PhXifJu_cl)-aBcx+t+{CLgUp88{Np7fd&PibHT_ zb;Zr;$s5p8k=fBiDdVmDt)E zzKVsXO(lKEqEWN83pa(o3p1;7Ce|C8n7b zq8Q)Da@NgULf#MQDKzB(c=iHEML<{_fw~0S2>#R`0O(A)5&BQ3hSkFIYM|9qXWMo_ zGjwM7-zMJrW}p~`luzgR$ik~W0x42XFc!Luf&82@MCTk>@i}eo<^e%C`!3O6$1u#W zREq7OG%zO|({Z6E&bLR^HIx3Snj)l=m3|~g79N&1PC0%CNeACYHGX-oU5lqA0Aa~E zFadRB;>N{WrWMGyqWZMr>aUz&q@GJCK=BJ@5uDjqNPh=-hy_}WMiq{DK) zo5P(s4N0`YFx+J(z78s|o*O4hA5s2iAEuq*6=6Sd5sM@|(vCI;=iy8B6HSVbvt+ZU zz&kl1kmViXW(dp<(KVfzWev{ur$$Nu1M0PBsYjt7n+AJLO)^Fx|6~^aCdw_^+8G=l zh;&-%)ae7GIIRW{2e@p1@HC^#2Ti~q5lZ9OtPL0WY9Gc3nyX7!2Y1InR zJSqC8ii{tUyh+r3rP_^VWKJp@w%3*qAC`hPvb;uEl+&5g-hK;=iETy}8h<8Y{jeg@ z`5xz~bHL?8da^HpdIU%G4hJ$E(#^a+OXg0H@yW); z*>D005LslLPYDjka0a}~u2pn%A)8~NOUX-k30Ie}LtTa?2#{#R2OA4Jih09Q30#$DFj)h08X%FO2{z0 z`7@@s>f-n=yOs_o!UUp1M3T`|s2~$1aArIF2H+uIIG^|R=-vbjY-FRQpF^!0GGXXV zh9VUWDa7f4(&rI2c#KUO$MX`*%F| z@aWxqg$m@L9i}Of!1St9X(jlT&(t@xnZS6b@MyZ|@eL4sx~SZ_6rGOO<$3#N8$(d9 z4tA9B16J5n^n8D?OI+#-vY_^d+4gj!3K|mBP}R3!mK2- z*fS2Uh}52z#D2NEj}}WoQsy7MiU0dk6?>pfGD8}NA1}jBbCILcGN*0E*ODd7LkZdr z=|!;?{s|rA6?CPj1MKy*!fEk3?mjwH^>&`@Q(zofz{d2@fl;gg3~PZCI`tKa9FN-# z3Rt+rtHLRJZB~?wt*40X6ab286OdRekO{eRLR3O~hNm3RuZu$BYmjJj^|VI%5G6Updm=-Er^4MRUkZ# z4kGnrhYxQq5~LX8LJA`VeLeGm5LI z2?dclOMz7hg>64i8y!9)VdsYbCt6{-T;`Q_`zQ~P){;Ar)>wfK2&iN7T zU^N;E_)Ff3CLvAowvrXBraZpl4G!U>l^!3?_2^HFBWoP_K_MJBj|z7BAp0Hb@n+0S1mu*j=nGR;I@55izsF(l`NjogSc z9Vh=GJyVi9Mwsjf%|>Z-@=+Uv9q|M}f5C3@MppfyteN zhK#0cyW+PTno+ayR10;8AXr#|c2Vl%S8f55;JP?2z`(@w%*`Jg+=KUsYi1VTg8gxozC2tPiJ?2 z&u6TPuu+aoi#^`n(X7GOrRuP z0~A~k#^QF6g#g%bY(w*c6!INoj!$V3?6sE`&l@KIO%^CMR+IPtUM3|36F5ne|Mvs_ z+z3`p5m*0Ybhy3#aWR<^aGRBm7X8}?ROuj>BoP;sJ34{@f5HiL3ztB58*adMN5*mT zEINjNbS=mDbvw(zOoqY2Q;2zcD`|62$x&+AIWV8Q7IX_t@8oQ!5CY8;kfwz|x7&-m znhUJQzNIJQ%~FW7+73w`w&{(!Sg!?qfh~x9-z~S50tehAGxWoIctKu(9=k(ZrWhUP zNPI!nQ0&X5?pMpVWmT}WeUpnsO*(lKaNG2zaWxYrB;g}@ozpX{Bw(EgE;H2MplQiM zj8-aS02jt*VDyGty%gWY`9sT5E^_-(tj2O2um4`x+QDES`+Ri7+@^`h&5ApvY& ztH|i~Jb#XcMyA`97(T7fKAqBs^i(v`KU~7CLf*O&;1RiOlW^TH4kLIbA6C&kqR;RC zFu*;KGKx?2tFD1&tecN1z^bOVKJ$+%6IPYD>?nMAi}RQ$y_b?zQK*n zlOR?-ttLrHKDGPR3RwmA`>WCZE{r2SXHONNsK}v7zluyBNhblJ%EAF^23tb_8MjYz zm!IzuDOk05^edY8xvp_1^A^nDiouZok7_v)cKsA5|=i&4xPwaUR?%}lGBI-h46F- zZG_5T$+Y(eH#>~C_Y4(ejbul-hb@+|5vJW6>d)?5K))(Xg&&%Q)#YJa!M)w4d)Eq>|xwc39=`T7kp>)iFFive6)S0vPMqkx$phmi2+}`c<(<`0EcP1uH zhM7YF%TKbIA1+}!{Qx4QQ6fsF?l!#wwxN|TR%`J{1gDF}d%ExNZXLx?QtUW)_HZriE)6=;+tWt+ZQB1L2UCKJHPWl32xiM09 zz{iH;okQX*-3HNqo3AkO%IK@l5nqVj`)ywRi@XItM+X=}n`MJa zDhsLnf~h?v`It5Z28s^WWyXN-ZO^0Et6R zf2YeXLB`NRdhwL{by&+>F#&V|eM09s<81oS`*r-7nC&OQKqIv3{w>MjouZXQW z`{-i7eW}Ln1msZC9pKjLU{HtF`NY#LPq(k=Kk$hbV2=a)`1KabiFEZh|G>PAa`hH-CHmYZko-)sIrrPHoO&p5h3!26 z*NH2js^WLfdeeR@j_Kc?cSR80Kr2eeisZZU2jFI29*%q9v=q#C{_#u zvS@)J(V&p!{ZTNh@M>Eu_y_m@av-hX4MVQzUVav~(4`}pAl*TLOKjc+v zj@L*3ddn>lJgo9nxq{MXMWxfZC3ciQIraiXZMFZOY~oi-=4Ojr@-5NxE9H(XK(@6% zTB(Gu!jhSaUE0x^a{5FiHU*=&_P(9*^Xn_n?uUEI=p z78(>MR+j-l+YBG)5nlEfd`$0Z5eNhN!zeDbSse& zwtRX|_j>YdnEos-`g^oS^mLRk6eC2_z4jKjrT`Vi zI%zAbuxm28j^$P}*xZ!@MAi*gp`Mr@d?Q{fN^N(5ylE{GWJVLZplE0Wj?nR$(~YnS zK~Vh;Arc>0DtE$)3hSazbHN1QW@d;VK&!nhycJ<}+dAUxCU2}ClXcoSibxkjW0Z(U zu;{F8-K0SP`8!%0brx%x3)_VjE5F1LyvIe9gHGZ(I#qc!Gn{(M_ zV*VTeeFL&tsLfsBwf5l)S;J#yXqVrsk&!sSA7Fh2$`{rS><;k}b_`>%KFjH}_N+eo=DN{kc)w{TYJ zO40fK57ATJ%vxBY#j10TIBJA$d5Yd(zKEWKfR_2~Wob2S1Xm|Y54)V*jS^A~+svR2 zn3K72VyUSW(GyJqwM87cMj|rqW28p5;Y41oMs~cJ`KajDGh{nN(`~ft%Qt3%;HpP#4c9ekE8vprfAe7-Hj?{)dROjv0Ho?yWIHE#^2t?oJ^)-E z_8EJ5H0M1&#;6-47Z|?$m@ONra^mGUzAi!_k&y+aX9Qv1yIGOOzs+>lgz-T&niisC z9a&u0nH;TS70Bu*d|WT?EcI$FsnG%2Iu!!qSRx-F>4J>(T8IOh;MY_6Aq;M~DehDS zadj`5DtKwKN*n$4En5HI{|6n`u$Ge>Y{jI7qJh6@ZhSH^A<6|?lEw%m)Iyc7H8@C` zv&!vkKSo?jVd{L4Ib^ zj;wg9GxMPg*~rf8P*Tbs{e6T&`w@=L;8)MsXdXzWip@ab%9hwcO3zwoT^R%M{&ju^ zd4lm4*~yuGH0_$3!hruZqcG_0JvBaDnM*ynEBjS;4~*>?IG#|u$P*X1xFnx z352A9NmK1yEmPDp*W*!}f+o{rEG$LZS&?VhO&SBllrwBvE@*)ouec8r`IHC#G~qB4 z53GJwdGJzL(+?Q^&B<}v1cc%uyiCHHFnWRcu*DI1+%o9Z1L8W?tLzdw+zVD2nHG@7 z?c07kiV1?W{44%PV2)i31lSS>x}`tL*we(5)PLMY0+>Kvmz2%=7IkQ&n874Z$zEs1 zJ;}42l6oWAK^eJzwxrTV7^8Ab623@b3BsI{Ecq`(>&{$T@4L2)gXK5Ew4V1ZV0|GH zzD^M&0kOVdgL%tEqgge}N9Xp7QTWp=iUn?DN!AQ(b8(1p-Cn0yPI=uU59h^-%fhh1 zsK+fqAIZB|GUSqe!Xf3>!)@dAXs-MTp)hSW%dTZxf#9PritH7VWSWCh?PP)5*1-f5{b?H@19ejm!A2g;6wNa)kMEGByFM&uZ}Rt>@=y? zc}!G>?jGZg8->ZdaHtpSi*h%8O&XS>q=N`&j?y2?J>jjy=Q`ca$VtjHjd`0t6++09 zS51IJI_s*V1~&0{9!q?VJe1|&#;uF$lrW13R{YWrs*8ek{9qhn<^b~KhX4d<#Vmm` z6mGS9piKm0;5lX(1X)xigT*psfuU=zFt~Ebnqj}5hSjHNvV?ELmY^PG!3JZM?HU!) zg=~}kqN$yMlMt~w@jqJrXrW<(EXRilnXvo6`*;5?_y+M+0x&a#u-BZ@b-oA}rlRix|fqbqU>Ganwp>5l&pJ(H!mHQPJ^k1^Q&QjKxdT)yxCv8p}V zPFouR|B!Sh_=uc%LIRmWp1sWV6}>idO0rp3~zTn9{cJO(zm5JDDh&SKO+n&Ti%z zlYv^;W}6GTfkcl6&t^$>SA>s36lkZ@*#?oA1+NB?TahCalb*_Pjf5)ka0+TA&=+Wf z=N%myJ~@V9#d}8F5N)eQ;+9sVqc)3OS=`=)m{CV$ynjjb?F4XOy8Rj18aFVj$&3)W zmSfi-gChz==Q0MU#2;HvQqi{bfM*ZBv+llbvq+YM*c2eAnwr(| z=Qc!rNu*rZ9+f5J9sL=Ts5mYfk=+;=4l8;3&<}K8|+E5)^>n2;)szWPid#ud4xt}8B_dw(Sz+tjEOurEC9}t+C~2;7w)6;Rg~>7CvH8X6x=>pP zJpVi1OKH1MI4 zsoMI}NBD0dun*S9Vv&wsAz(YH^RndhbH>)aJh(mdyvoZI*r+W_(FK(zezpZ?Q- z+MJl}L!vs=$Uj^t2Z6&nV<)9Ui{7TH7t@e9<%D1=Ta2p5tf}4M5=?E~oS zZsR^SaY&|DIRS$=>GroC8^Pt`#o3#up}W%wp&i2IIK@Kg-!8`fi6$bPl)>%8j~*b; zGPxwSd^OUTUMl_aUf<=dR$@!Pq1!odkLtWsG>8P>_$^I^{GOacY)x}TNOA-|AFOs7 zL`fHAzL%GF&NSFWqvd%&jA1;q8tXFAti1A) zXG}Ap&0$tL!gt3m)^cP@4j2aWKgCJM&9b4|I+URL`oasuLUm6AKwxJ(UidAC93KD+ z=?c1e>4-l(xu{G< z4K)S^bb!wn`D}@WH1i>+#I#vhk#^F~rpd{5JxA19zJ+J1V8ic8Zf7(HoGT&_TB+_B zzxYG+go80~JiSw;L0Du?WR^|oslCH~FtLcTrKPD$v@Wleps^_wBaq>EM~6gt#+aff zg520%_1@K4Pohz0Tl3z_$k zy2+WMtAsMOKXo=337*aMOf-p~`#53RILyz|#V_SHnEYvdKLuV@p`{-EFa}C|k!h{YUP3a7S~V+M@uiey`Q_$mlWt!J-1FPgw$lf69!WJ42!jE;nT}V{_R9+^9u8+gdL%kj5y^w6c82~h2I3sq}YL)8djdz=P~->wC$sZhND!}vU*Q;IO2Q;|bxmM^Ku_5ab%pXQ!1JO^gKas;uwLcuAM>(*6zxy^!w^jm#^T}h3G*% ziciYbOiPfx6DL`SIum>)wA`r}$yd}<*6J{!9r58ej*&~KrK3PZxW|NvVa;SJ_WcQZ znyMLIood%9fDkg5)K`r!1)wpDtqlUsQR!zK{a~ro9NvTCkb2KD{ zX!atdegVgiV*V^>A$41P8b%VUm`+@#nvKBk>D&d!0)^yAtr9<^sRBmJjR?NZ5qbnq z7xPIx%tz|{_L#PDfUt@>07Er0DadJ<`mhcCtX9T>{_E?W8i^*yJ)`6-iXtWDKMzLUqcz3?R9u*W40_AGi4{IqqV5iekF<^@AL*9sf9$C|Z?4$z zqDn9LS=kj2s#&Z>#+5y!G|(CF+x~R4x*DWNhh)^c3P`L+KvV{wi|CNeUdW#jFEFPR zt)oCfG(J);aX>D*H*T$8GQ{P(%8YfqlyMhWuF7`<%~y=AQm8o15|CHgq%Q^aBn(!{ zkU9bY7OAA%*f+|%wflP7pbB{=95#JEIgs65TGdk~LlO8Wte6&w(}$dIA5G1K^v%ND zAOge#R_is|o#@|wj$Db>QcOnRE??D->)59Gj|Fx>)fD>yN@R^e0sr!n7s3B&SdkLCPq1e?%f@*vwh`29w)i&)B3DVcjKJ9c z0&J3ckgwnCUFBQip!f}wkB#V~xE-_Y^9j~4P&vU+A7+E;>bQ2} z#IUG>1W3j1A190}*c)nu>nLgtB7A!;&>Q2FDqq{!Swz4*BM}(*g%99)RWv-M@hjj+ z)8$D2H6QFcRpTrsPi&pbgePL;u+!7NOEPz~B_&zCIYA{!n*tf)k*J@tmu zftG>Hg_cPzL4F+Zjv46Cs-7{Rvq~sNxp>j7KBB?LWU={wsBQ$k5^19)o5lE|3w1|B z-9XC>j0nlkEX_9QY}_f^usyu!G_s|L%-q@AN5sE=R!WdK1YeLH0mn=<4&?W4oQt>8kz1eyW!7~#>9&X3NHpSB z)itDhJZJMm^mN`xX739A(g0(5O#ifU7G+pEnSf7;Z%k{Wu>?zf>rWa7Dy{NCc&mS8 z_?E-#0gfdmH%~0LacclJQ{ltRrQ8r?zqPi=MJE{*PIOy1q*7O>}hd(^NZp zfyZB)ime52njE>|0vZr{=O}irQ$rRxnHbtI zj|j>w^E2(7+w|h7kGyS@qrlKI#;8H>sqMR}=YuS(&2rwgeMWeio*MDd|2bYHu~-h> zD3TWs2kELa{14F+5lmGA_ln#79!e;n?7T%&F0R&SfkIr8b#hr65_L=-fQtugXs)qo zwkU7%Og&~@J`g-^pKe&V9g1qDifIprc=*W*C_9MxI4tu)xIz)BsT+@~-{b+~#8MAFHTjwYp3!p|ED9 z&Ou`#!`!p?^7=>Nx7Z;n;60$0{UN~L8ipcV^m`{>NT^~C6SaE8UWqO%@d@$8c7JB82OXlE`~)+jQ0sWL78Nlc#2mVN6`g9b3Ee` z0rssD8J6rw9C~4N0I0br4k?TecW`_!ZQ`&?2Z1FQ9Mwacp8_3p#%$uRkCc&oP- zm^o*{fKi_0%ie$MWm21>&dfS^(5!~afa-|oA|E9>PGxk~b<5JR#XhiFUHNwVt*Zy{ zb~C0U>_zm(6GfeuvMHJy2p%cm;aoM5<<-b6&M2FBPw-V@urrg7p9z#4kMcm1sg0%J z{60U|10d$h%F8T3oUa-IDg=JBD%Hf?vQBVu<1M+6rlX@}f$18m3M&WWK7H(d|B*~6ru1B znI!F4>o_>pWqL_RCwF7}D#oL7R0x@}V>0Csu?lILBDMXs9e-*S?mDcZmX2gX~}PqXndwzSrC3;ZpI@PDvnu&ENg zt0HWb4{_4i1!1SA>AiisaU7Vf^Kitl*`Q+@8O2J#e~zA5lox$0F`VF$|E7~Dsy6SQ zITA|55vdIV{dXcAr5xA}n6H00=5~*+9IAdHF0VX70>Dm4+N-JnF@JV2=|I+D6 z5Q^7jD-lZV^H=5>0%OCtp}!?n)en(L7ET1{v<{CFl%`Yz*QB|UgYhi975T6HYOi0ff`E!sag?J!6u9`uUKG|i%I1<>UWetjseScD zpfD|v^Wil;bySWc{~SHd4UBMt^Og>fQz9w1myuo#6|_XV>Sxq1=DD5Nw-1bmM-|RG z?Dcz1RRmG!x*xN=(&V@|1hVDK{(j@htg#qPWVTL z0>xy$P@%6tujV0a#$e;8b;pFgWk@@+n%Lzx@c;H^_`82gY6HALCG~b6Zh;6N4iWG9 zi6Zphl4ydR$KDW3g$x(xjcd290wAG&i1CM(T-S3;LN&W?{m@LdhlP(N9D`}FF;7+h z7X^QhOv~xp6?a1a>d89(IYgKm>wFx)coe>cCXMjko_Rj3e(p?v}8X-E@g+D!W2yqlkT(`f4M17zmD7M z#3VUVgrk25S9Z_OdHVKW;d#|WxtTBGqde@xh-`C#f;(XD?}hA}llEzY-=%@I5tt;=N_Fo(IpCFKP4&BYRsDm+pbJFYvH@yo5egy+5hc(`alNC`TdmZwuQRG0^eaARd z)HHY592{#1#^tcR*_7??+KW05q0!0qI8VQbL}TAm6p)>#QnrKoCh(*5REwg-P%I-W zT^pKQ=DvMzh{2tib0@`gE2GfN=UVsa#?|v;^Qa+KCnGHBqzFVPT2hPm+rPJI`|ZEf z7r~o~Un0maHMjrECnF-2g9S?6n`*a15zUDl*ASMMgyQ$Zf^37<2A%KTKBHJI?NO9x z-9G%s1D{X;Z}1tuI?w*elpYPjBv7nrCW>>!IXFBjfh5$J@b{drna+RtFaPDylz!r2 zWi4s12uuCM_`lhh79u&GetU%7nJp2c?J^JUmNPL5So`zG60j^jQ4ONhr~k#To-_d` zs}dx`w71PQzj|+tQ*EmS^>r<1wxPY6#*fmI>x=D`Wp@NQO;5+$pZ7>dbr=M3!llsd z?{g6NZ1yUB#hyQj=IjyYm-?1oQT9jybpHU>FE#%^*U4xMOVS*~=2?Eui;|2^jyv@! zVL|pb?)G&wPcgDnkW)rRpvWQXr+vKsTsP^DdB3B8b1Jx{y4(rDj?LlUjs3sB?J!U? z-}=ext_C5(0NoLD_4vmYs?GVo zd`!e8XR`noncUw#OGEdv$0DYBAV$FIDWKg+rC{F*IP*2@!k=NF1G{EL6_ zFFw?UB9!k{+v&{R^78be zbMl`&CR_9;Tgx_&%`amNi+;|Q#OvTB2aM{?YJ)R&Fxj`I{@m2FpC@iZs`J8J#m z+&ae|(3vO(NmrjsIUo5S=hpF?lrGBbRbTKooXfF;xfu3+{e#_cYEZ~f@qp88pQz|< zdQMw|Gl`+w8uhGBFCO|+NaBs^Acj)mzkv^_IVF1jI!X2;(cSOO_*;IR=-MwQ-?ax60q zK^=Ydj;c^!cZ7XZIS!ve4{rN7C4%FqpnwzZ?~RS%N}9z|zVAmHXUjDu^nix!$3lq-@1b72Z^Lw1wBGnKpv|MLDG|nspRVf-iA=BNQV(0 zy_8R)#+@Wi=_4EGLLIke=s?_=%;_^!5Sh{vBvUme*WzVG$JJ!)K9`S>ZZJPCBL0*{ z97PqeY6z)N-iyh94kxzh`N04lZ*c=k+EZ^dgS0x&)-S~czCU0(H;T(WZbcg||krbDIlA;;O ziy6`?bz|Y<77$EQ?`|zhNj!raAg%)tJMPM3_a}M=52(MS3Y=?vw0+!2Ai!w8PS@em zZGA=RAvWnLP=J?WV&6ttw&{ufq8p|J@@gkCb$(#QX*^Wj^C$n!zxg)~k}^QmvVbU| zfRwA!uZ^w0l2FhpolP00X2%bY!S_Jb6^de}RnS5++f~bEt2(86P>{+xg$)up;ndgp z6eB<9$i28IZDm!1?4c{m^3uge_p=$ENM8o2@F{!6=#oQPZ2NTQI1O(} zn|lL_-+bFLN?IJMX59tnAFX|Q7`RM?-%U1jw>nYU9~RtJyR;aQ7%eLd_lXKqn=jTV zSOT@U9{0_vW$5^AUtN-Gqj|&h%i42I=P>$oo3@rLG8AE!YTTdz&s&%5;}>N~1ac@c z8P8_#1;IJfAb{g&5H_ba^>+S+u{eHv$}UcSYjsze*}6K#AheC zE+5MKfBSF$4Kb-KFU4$MwMb(L47*WKKCYc;JA;)D{{)lKqVC+C$oY|ADIjS3{^h^? zmx7vn8)3OV>*h8*?h17Wc;T*Xa48X~qhvVKWcqVxKDkFv+JM%DOVAB8KO#sJhC2~K z$yReF^_Uo&9XXygmY@IxiTpCNBDH*vt1=Q+$-;g1 zu2~8lMCfgL@@Yt2_nMmOqe39Z*pKbB?GB6SV20T6-X!s}aLjlQ5dEfJCB*zT#vIw9 zlj&{y_4I-RVkp5ihI`XMP<8_B<{F``b_Ac8`x{mdb-p@kefl!a1Sq3+`}B_E#r!BZ zK%3ScRh6G3AxzFwF+P1>5I&g#{Ww1vum<7J8J62e&fknIWCdJ`35_k#=pA@{FZ2TZ zF(CpKiQzG^ZnJ$e!+aMeQwcxnes15u<5tjzv07Gt8{3rk9pBxuXNW~i6;i1D^Q#D{1YpJ1J6Oi-l zKA?tics_pqgvc3$A``*FS|5(d%36r1+gB3GWD+Q`#=SVx>C)7(p(LhN7i$Lr5h!FE zW7R8Mh-%oqzKJymE`5{w)H}X9-uUuGs$BV9mHc(#;_lalnAcC2lH{N z4c2c_vh2Iet{j5ill&;Y#Mro=K;70qSRfweDC)8GkN(j=I@LLTsrJ;uwO@Esb4SoevIVfTqBt4YKLcd)wvU5*^}{nS*@+f>jttpnRTQzIwG522_;M zmZ^`YNF@YZ)b!}|g1S;0J?6CpBQjO`lR7dWe96OniAg%!@fV@av4l}9vvbwN^bX4U zRG+4)+tVW>XbtuORrs6QqQ0@9$YpL`68mkfu<;N{#@63vOR=B|B$p+AV!-HG3YUb8 z_!3FC3&6RXhT?m1wUaM)1xzs2W zRh8X-Nhh+nOe}yU9#&_)OJC#%AspwuZt*3betd@4n6J~1Piq5+57T~>d=nP16nXs* z|KUG0AqyyJi~SoC^Mk`YvgCY;=-HI??RCRB?XpCUtol(VYr!NK0CRFLu@gI&L{Dnc zw^_><<>1H;ox5!gO@M$igrd}Z39O|PxG~dqmk&d}fTTU&Z(JIfZi7T^c}HxdBX8d@M_R2Pj}ZtlxDzdy&1~uCr<1g&Bq)tkgMo079hdzWx!ue; z#(mj8PGgUrY6&9IAg}UtQ3DE!RT8PSHS*Vz!gK}wjQ<}oC-s7KS`KlXY?FwiuV)1R zs@D=kz)Ks-nSd(+N>6}%po(ffWmp%6tu=`(nWfQYVu4q;)@fpuC}Ux-r(2)r0V)2E z=}CxZl8S$FgL%LIafGfM8>=HV2DDfT#87)@x_K0MlJY?W(S68IcL;FP;Bh$=>CppE z7tRZ{Mj-=2jq~A|YTy7J^0HuM_=j-yM|MMm^@5mYOVWk&5*J6(s17uVe|3A!b^oP* z0VyT}K7GP=?3PdsE9;YO@OgS@1}~yDt>o0994?23<&JsCxdqc-nT<%>CDr zlOiwsmy#8h@)7fRn1P?<#h5IOl6gUKhvlCR=y_0&>nPw&&$5d`K~nZu9i&zUVC{+S zoPyd%uQPdWl~~I*OmR3`$pKXv%Zle2269wcQT~kZz@9NHMg@XNs7(x``r&C{3^W(@ zjM0KG%@bkQC1UVRZV&3)OH~}6cVas1WX?|`mlTEg9IO_>F=2r>dLABPL`sd|F&Oj_ zdMn28t!;WLqR7O|$6iEX<`Wg_gA?#7tJJ?enCA&mnU#2zoCyIpg1npp9*yuQE?mmT z76?a;jmwiQnctXe9njXOR9p|Di$Y@$Qm76-A;r9Cu?hPM<7HcvV0_ZB!kC? zIccc}&8L3uACqDq>pB2N#94kUL>uE|$zmInGp5$UE~_%?Kz5vr4#J1lTg?G7d8Q|I zoIbdMLI5}WHqzeCD(0aVsqxqC00Ib z!t}t){rc0_A;sFZ&JQ^6X|9~8Fs{|G9tyO!R7P?>zy;^&Hr7K{Ig8PcV<`qveA)eB zoD9C)jsi}LB61W2|3EULSsVA1EC4rYdHSvKdC3CG0xA*=&V9H(Z(@xG#{jTY?rs(y zv-KOnZ{u(ZvEQj}W5yya1pcx~ zujp&W><$gfD;sx8JeW+eH;P&N>}0XtUf@y$-5RIjOK)MZ{2u;L6(HAReE#lL)1q-! zn8!&;vdMb!0P#G14Pg^=3US*l7Lyhbnzn<~3rIk~9~REjR1EeT1%d+7J0fAZv&zq? zoeUrHMx|?z>Z~PDJ%Mu)li%xF`A3wvUiq`|H06WK)X#e6mFzW zv6hg8F-@K)><2jG$_;QX6YeQkPuY%p>h}P#+6nWIz=E)XN)U|HieF-2p|VE_edBxM zB>R5Tzgk_Ow=f2SE{g1MbhuZL3xq%oZO*DzQjp^Iv;zRRG zR@ghd3fKYheg6j-1s`cqxpna)bf!IM`Y0hrjh*L`-0E`h+^H0)US#64!k5R{E64vFP4dr)5)8s=@5`$ZVb&hAyGIE#Ihg zzM6oD`gjm*d!Dqv@PN1-NydDijCq_4=G(i1uZ9v}D^(?um(Ykv%o}rSaFG&jF-GEn zfpoW`drD_9wp06uwdii{9d^2%Cv75`Mdi^Z_QE-y@f6FX!u%IgaaWiMNK4p&QY-Z7 z2L!)m30i;&#Q8P2s=lYOTUZ}L&ATWXUK#C{(;-qM<;B`N8jUT#I-!{)IGb&p{Cw4U zCyuNK#7;7E3`|GsDZ8-f=EHa>=c2O&8U^auTCTPO6GF=g-q3LyH6bL3re+&rQ>)Zi zjHC~(ZZ``QpiD^p?UT(1a->~7fUQYi9w6z_F{zwo;We>&OjN4GOp-XzBy?TVVxbb) zCo&PCrj5x+4?4KDbQP3eHK0jte0>ENX?f z4Z^9zW_V0K`YA7cMbC)=%qpwPwV$X84*HF-J@P_woyaA%;OL3Esne1hD@6oiMG`p} zfS?xsu|V|B7=v9^peoN)E@ ziKV7R1%{d%CuBaQk}8?oltGdrI~txjayaJ1VAK?_C1eOu5I5L;-7*h`COc3vze3Wv zX5tm5m&AR#!Pp&vfV9il>%C|M9((nmRXy1x-GV-nCw9RSXgm>+T%B!(8wED^y8t_m z@ucv?OF%ijm0{rLjlaxgUyn*tr!h$^{UR$t?Z)095YE0|@bU@%cUse-HN5pydXHmE zRSVrQt8|S_#J49K>UGq_MqyKZo&7VnLMkbyj>g$5zsty|2hB)dQdqPY+_Q1k8-iq7 zCf29a0M=B`$iCffx>u8>FbSBOMJze)UV`r+8%&T;$i}nCQyhFR;CthnZzsP#|5VoX@73a>9EoGxz^7Y{e0RkPU?+0 zOwABuEG5n6As06zivAevtk*H9s3c&?#t4lFj;a>NnScV_&9Q_s#YiC3J+WeUF)HyC z4gq}46L0iU%PCoy9DybCF+lQtFTfOY6K_j&&6tZbv$!ifyts#n#Y)l*2a30;h46e8EcMgksc!8Axf{nb&dwMQpmh~Z7cs>9Tx(|4 zre&%+QYHg1Aym<*&KI);Mu*Tz`=IJ6zIPM4@s(`V1?ZYvLlg^`MO_dj=-k{0^%&7W zI$0WJD#jeqj@r|_#N=&k|mZFG*@lmQ`en zLVp^9J`O9Oi07&l!w-+57q@iC10bN7fAitvV@l#|2W!p5!YiT$VFR}3O>jsAlsQal zkJVwt1>v7EF?3=kW5;X^lLLu9#G~WP(IZuHVwo%xz2if`j`)(NSSoB>J{VWZj&Lrh z?dvcQb2utT_G5#cEMJ_-8d(L>TINTL&EVhyPr6Q?@zL3{(;zNBr0C6A0J1`u(-P8z z%SU3%pT_b~&=x4npB9b5O+aLV_Yb9+hV_yovMKQdD>&ogPZH3#C!(%s8^Q`3$5jA% z1%`RT&CH`=y&C#4f8#9-Zf<17NQA2qGmhdEe7B`HeD>;rCphmf{AOLpjs?sA3JAD@5(!Nwv8=gr8Eb~Qt+o7s8 z+6c3C;1jHD|Iz04YEbyXt6LI*Kv@>rWS`?T*KUOY>J)4RnSzsZa~F zzgh%x3~YxV$RnK(8}ZMPJzrbf`~jA2a3_kAKT{;tw30oH$61dtSye z5r~Wdf4B-b5VOpSc%K``%FK=1ZNnrw@sJNgj#%$oU2+-(jX^+XJ^=h}D>$;9m<_f_ z5X&StPD|YGE~!%pc>CDZ0~GF9y_&6IE#90z@GgwYtVXK}GlT4~AU1deSjh(0s_H2z zIQvdK#MsDlrHt2Wm!3$D8{5#W_QHDWBMfG{OOd5>$`J?(M%FVl-Qta58{Vf$(igm8 zUmSBN{hgv6IQl5b=NTJk(A0$J*yo=mmZdt5&vbzooZ~QILYU*nHr49*e*QF-6eHn8<&?D826ah7o%Y3Poh;!vzCZ|r4GW?qwGP%(Ss2L=SX5}- z_C2a)v^z9DT~uxf@!?$yu~%Kq%M<9n9*)9tGGZG}MNt5zq#=P|Bt-;c;`wI$#(O=? z!~zqHN)ouL-x5zs3NK|vesc<}y*;OS*r9db7QRGkwT&Ex&&Pi|b;da_k`ONq%)yH( zGwISXyzc_uxtqmaFlZa$n*9IPF?d zUaL#rvHIBYawY>ViCF&z4fLdrC{H03)Wb-eE`vNR+A$6Xtg5+$Q4&=#6oi8fju&%u z2-|ulN6>vRTvc944CQNnfH0(u#IZIG&5%Y~#@zTEX<_AG zw$pFew%b!3w-Xl0sD)R+{PtVx2gKV`j8CL#-h~+7z6h()5nQF|1Tm9;d&*=nc)Hn9 z4T;0ENqhGe)GK^_Z1;nb91KL@(lkw@p(3~CT#B$lmS(7&3r69`FBHMJShUJ1ypg31 zg&}Eb0$e8#Jm5(Y2o4qJdkl+;$>r2*3pQ^zIuAgWDi(lgGn~Exb`*QAK!9Cr_KpxX~m;TL@fE)7)|l zg;_GrBd5YESQmjwdXNnx;1oJdWf$a3g+E!iPSCnZXKX+b3+(_&R&9;j@gZ-t(mL|t+Av-3AoU9J7RxBg1CZF+c5)O>t$nW=+Nxg2Hp2wY{pagwslH;*` zQLZ2`A1X_z0&qFv1M^Q7VsWf0;0!Fz=~#Nt4=P;cG=%s1Vg>s$zT8y)n604=0yU7` zNtcBSl+*DXUGIWOd=X5M45_2ge$OaVbaaa+uYjN!eAvknZrR=gIGq2SD)6T_kI+AO z`>J+AxXza4;vbpMnhb>lZeGoeE9?0z0r6v@RQnWwD_%wCH~{hIeNN@8Lj=#HCs$LH z#xa#HMCa#Yp7xqT_JiYA&9`qp#@^%#je3242;6k&52p0YRF}E=vf*pCggd@>*)%wx zEHe1anK7GmY>`l>^6!uM_^G$4T{G5G!+rlgO~h4PSXw z2ckGH&okK$!q4Th{5K8HGf$JTQqg!5X3`QtdD>2yf))Wvtq=3hy=D=mr|B}_%iLuseNE z#u6VF4IG&kup3u5DJe^1qLS9hhgXnuo#coKsk&Q1IDfr531r9lkZ?J@0iOY9B$0OG z0Bk3P;UVZ8`lqS=3r(7BiXy6E=LS`eUol(E$@58fJwRtl={$N1=0s0{lc!;B5G<;fZeIu1&@Wic9*uDkKEPgV?D$5!_^4Cs>70ck@RE5_sW~dh@qMHn49jw` zpa2sIfe67;FTnIOVL3zx4XjR;ZXTniTf*ds5 z#((u+iaTmISUJy#xZiG5Js`qFl6|Y678wVJ%ue6P7+aBlh4jWoKVq3ja2XSr4|p+- zPuHZdJs;bY`!Lu`zmIa{x1=ySVTmNoj~}W^K$qDb&7|e?_nw}SUz3+rzzI()kj63X zDLH~x<6ZdRd7(jWOpG59!&m)Xo}MJ&2Eq9+!~f&3bXvL+jvw>vlmyrs^%CS|46*nb zlfxysgkoY*G!eRvUhQ|&L{cRvX-1RE-R1}9&=NGcb1eZbB4lhitm*e+R}+ZzB4P43 zorB-dvla!TBs7zR{uIF?_;9&)69kZ5SMhe5&K4Cm!qo+v{F6#jNlNhig9-4kJ)%|U zCE