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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 44, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"import theano\n", | |
"import theano.tensor as T\n", | |
"import lasagne\n", | |
"import numpy\n", | |
"from PIL import Image\n", | |
"import os\n", | |
"import numpy as np\n", | |
"np.random.seed(123)\n", | |
"import matplotlib.pyplot as plt\n", | |
"import matplotlib.cm as cm\n", | |
"import time\n", | |
"import zipfile\n", | |
"\n", | |
"DIM = 512\n", | |
"CLASSES = 7\n", | |
"num_epochs=500" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 49, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Downloading dental challenge dataset...\n", | |
"WARNING: timestamping does nothing in combination with -O. See the manual\n", | |
"for details.\n", | |
"\n", | |
"--2016-07-29 22:22:21-- https://ndownloader.figshare.com/files/3697980?private_link=fcfa13522aaf8a60815d\n", | |
"Resolving ndownloader.figshare.com (ndownloader.figshare.com)... 52.18.117.215, 54.77.7.44, 52.30.170.244, ...\n", | |
"Connecting to ndownloader.figshare.com (ndownloader.figshare.com)|52.18.117.215|:443... connected.\n", | |
"HTTP request sent, awaiting response... 200 OK\n", | |
"Length: 1277280529 (1.2G) [binary/octet-stream]\n", | |
"Saving to: ‘challenge.zip’\n", | |
"\n", | |
"100%[====================================>] 1,277,280,529 16.8MB/s in 74s \n", | |
"\n", | |
"2016-07-29 22:23:36 (16.5 MB/s) - ‘challenge.zip’ saved [1277280529/1277280529]\n", | |
"\n", | |
"Unzipping dataset...\n" | |
] | |
} | |
], | |
"source": [ | |
"print 'Downloading dental challenge dataset...'\n", | |
"!wget -N https://ndownloader.figshare.com/files/3697980?private_link=fcfa13522aaf8a60815d -O challenge.zip \n", | |
"print 'Unzipping dataset...'\n", | |
"zip_ref = zipfile.ZipFile(\"challenge.zip\", 'r')\n", | |
"zip_ref.extractall(\"challenge\")\n", | |
"zip_ref.close()\n", | |
" \n", | |
"\n", | |
"def load_images(path,listing):\n", | |
" xs = []\n", | |
" counter=0\n", | |
" for imageFilePath in listing:\n", | |
" image = Image.open(path+imageFilePath+\".bmp\")\n", | |
" image = image.convert('L')\n", | |
" im = image.resize((DIM,DIM)) \n", | |
" pixels = list(im.getdata())\n", | |
" xs.append(pixels)\n", | |
" counter=counter+1\n", | |
" \n", | |
" x = (np.concatenate(xs)-np.mean(xs))/np.float32(255)\n", | |
" x = x.reshape((counter, DIM, DIM))\n", | |
" return x\n", | |
"\n", | |
"def load_manual_segmentations(path):\n", | |
" names = []\n", | |
" y = []\n", | |
" counter=0\n", | |
" imageListing = os.listdir(path) \n", | |
" for imageFilePath in imageListing:\n", | |
" names.append(imageFilePath)\n", | |
" ys = []\n", | |
" counter2=0 \n", | |
" segmentations = os.listdir(path+imageFilePath)\n", | |
" for segmentationPath in segmentations:\n", | |
" if segmentationPath.endswith(\".bmp\"):\n", | |
" image = Image.open(path+'/'+imageFilePath+'/'+segmentationPath)\n", | |
" image = image.convert('L')\n", | |
" im = image.resize((DIM,DIM)) \n", | |
" pixels = list(im.getdata())\n", | |
" ys.append(pixels)\n", | |
" counter2=counter2+1\n", | |
" localY = np.concatenate(ys)/np.float32(255)\n", | |
" localY = localY.reshape((counter2, DIM, DIM))\n", | |
" y.append(localY)\n", | |
" counter=counter+1\n", | |
"\n", | |
" globalY = np.concatenate(y)/np.float32(255)\n", | |
" globalY = globalY.reshape((counter, CLASSES, DIM, DIM))\n", | |
" return globalY, names\n", | |
"\n", | |
"# Custom softmax function to support fully convolutional networks\n", | |
"def softmax(x):\n", | |
" e_x = theano.tensor.exp(x - x.max(axis=1, keepdims=True))\n", | |
" return e_x / e_x.sum(axis=1, keepdims=True)\n", | |
"\n", | |
"def iterate_minibatches(inputs, targets, batchsize, shuffle=False):\n", | |
" assert len(inputs) == len(targets)\n", | |
" if shuffle:\n", | |
" indices = np.arange(len(inputs))\n", | |
" np.random.shuffle(indices)\n", | |
" for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):\n", | |
" if shuffle:\n", | |
" excerpt = indices[start_idx:start_idx + batchsize]\n", | |
" else:\n", | |
" excerpt = slice(start_idx, start_idx + batchsize)\n", | |
" yield inputs[excerpt], targets[excerpt]\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Network output shape: (None, 7, 512, 512)\n" | |
] | |
} | |
], | |
"source": [ | |
"\n", | |
"def build_model(input_width, input_height):\n", | |
" padding = 'valid'\n", | |
" inLayer = lasagne.layers.InputLayer(shape=(None, 1, input_width, input_height),)\n", | |
"\n", | |
" # Build the uNet\n", | |
" # Downward arm - convolution followed by maxpooling, two levels\n", | |
" down1 = net = lasagne.layers.Conv2DLayer(inLayer,num_filters=10,filter_size=(5,5),pad=padding)\n", | |
" net = lasagne.layers.MaxPool2DLayer(net,pool_size=(2,2))\n", | |
"\n", | |
" down2 = net = lasagne.layers.Conv2DLayer(net,num_filters=20,filter_size=(5,5),pad=padding)\n", | |
" net = lasagne.layers.MaxPool2DLayer(net,pool_size=(2,2))\n", | |
"\n", | |
" # Now back up - upscale -> concate with skip connections -> (de)convolve\n", | |
" net = lasagne.layers.Upscale2DLayer(net,2)\n", | |
" net = lasagne.layers.concat([net,down2],axis=1)\n", | |
" net = lasagne.layers.Deconv2DLayer(net,num_filters=20,filter_size=(5,5),crop=padding)\n", | |
"\n", | |
" net = lasagne.layers.Upscale2DLayer(net,2)\n", | |
" net = lasagne.layers.concat([net,down1],axis=1)\n", | |
" net = lasagne.layers.Deconv2DLayer(net,num_filters=10,filter_size=(5,5),crop=padding)\n", | |
"\n", | |
" # Softmax output layer - change num_filters if you want more classes\n", | |
" net = lasagne.layers.Conv2DLayer(net, num_filters=CLASSES, filter_size = (1,1),\n", | |
" pad=padding, nonlinearity=softmax)\n", | |
" \n", | |
" return net\n", | |
"\n", | |
"\n", | |
"model = build_model(DIM, DIM)\n", | |
"model_params = lasagne.layers.get_all_params(model, trainable=True)\n", | |
"print \"Network output shape: \", model.output_shape\n", | |
"\n", | |
"#output = lasagne.layers.get_output(net)\n", | |
"#outputImage = output.eval()\n", | |
"\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 69, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"input_var = T.tensor4('inputs')\n", | |
"target_var = T.tensor4('targets')\n", | |
"\n", | |
"# Create a loss expression for training, i.e., a scalar objective we want\n", | |
"# to minimize (for our multi-class problem, it is the cross-entropy loss):\n", | |
"prediction = lasagne.layers.get_output(model,input_var,deterministic=False)\n", | |
"loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)\n", | |
"loss = loss.mean()\n", | |
"# We could add some weight decay as well here, see lasagne.regularization.\n", | |
"\n", | |
"# Create update expressions for training, i.e., how to modify the\n", | |
"# parameters at each training step. Here, we'll use Stochastic Gradient\n", | |
"# Descent (SGD) with Nesterov momentum, but Lasagne offers plenty more.\n", | |
"params = lasagne.layers.get_all_params(model, trainable=True)\n", | |
"updates = lasagne.updates.nesterov_momentum(loss, params, learning_rate=0.01, momentum=0.9)\n", | |
"\n", | |
"# Create a loss expression for validation/testing. The crucial difference\n", | |
"# here is that we do a deterministic forward pass through the network,\n", | |
"# disabling dropout layers.\n", | |
"test_prediction = lasagne.layers.get_output(model,input_var, deterministic=True)\n", | |
"test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,target_var)\n", | |
"test_loss = test_loss.mean()\n", | |
"# As a bonus, also create an expression for the classification accuracy:\n", | |
"test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), T.argmax(target_var, axis=1)),dtype=theano.config.floatX)\n", | |
"\n", | |
"# Compile a function performing a training step on a mini-batch (by giving\n", | |
"# the updates dictionary) and returning the corresponding training loss:\n", | |
"train_fn = theano.function([input_var, target_var], loss, updates=updates)\n", | |
"\n", | |
"# Compile a second function computing the validation loss and accuracy:\n", | |
"val_fn = theano.function([input_var, target_var], [test_loss, test_acc, test_prediction])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 60, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"#Load manual ground truth\n", | |
"#Y_train, train_names = load_manual_segmentations(\"challenge/Challenge2/GroundTruthData/Training/\") \n", | |
"#Y_val, val_names = load_manual_segmentations(\"challenge/Challenge2/GroundTruthData/Test1/\") \n", | |
"#Y_test, test_names = load_manual_segmentations(\"challenge/Challenge2/GroundTruthData/Test2/\") \n", | |
"\n", | |
"#Load images\n", | |
"#X_train = load_images(\"challenge/Challenge2/RawImage/TrainingData/\", train_names)\n", | |
"#X_val = load_images(\"challenge/Challenge2/RawImage/Test1Data/\", val_names)\n", | |
"#X_test = load_images(\"challenge/Challenge2/RawImage/Test2Data/\", test_names)\n", | |
"\n", | |
"\n", | |
"#Load manual ground truth\n", | |
"y, names = load_manual_segmentations(\"challenge/Challenge2/GroundTruthData/Training/\") \n", | |
"\n", | |
"#Load images\n", | |
"x = load_images(\"challenge/Challenge2/RawImage/TrainingData/\", names)\n", | |
"\n", | |
"#Do some randomization\n", | |
"ids = np.random.permutation(len(x))\n", | |
"x = x[ids]\n", | |
"X_train = x[0:len(x)-29]\n", | |
"X_val = x[len(x)-29:len(x)]\n", | |
"X_test = x[len(x)-29:len(x)]\n", | |
"\n", | |
"y = y[ids]\n", | |
"Y_train = y[0:len(y)-29]\n", | |
"Y_val = y[len(y)-29:len(y)]\n", | |
"Y_test = y[len(y):len(y)]\n", | |
"\n", | |
"#Reshape for convolutions\n", | |
"X_train = X_train.reshape((X_train.shape[0], 1, DIM, DIM))\n", | |
"X_val = X_val.reshape((X_val.shape[0], 1, DIM, DIM))\n", | |
"X_test = X_test.reshape((X_test.shape[0], 1, DIM, DIM))\n", | |
"\n", | |
"Y_train = Y_train.reshape((Y_train.shape[0], CLASSES, DIM,DIM))\n", | |
"Y_val = Y_val.reshape((Y_val.shape[0] , CLASSES, DIM ,DIM))\n", | |
"Y_test = Y_test.reshape((Y_test.shape[0] ,CLASSES, DIM ,DIM))\n", | |
"\n", | |
"X_train=X_train.astype('float32')\n", | |
"X_val=X_val.astype('float32')\n", | |
"X_test=X_test.astype('float32')\n", | |
"\n", | |
"Y_train=Y_train.astype('float32')\n", | |
"Y_val=Y_val.astype('float32')\n", | |
"Y_test=Y_test.astype('float32')\n", | |
"\n", | |
"#Sanity check\n", | |
"plt.figure(figsize=(8,8))\n", | |
"plt.imshow(X_train[0].reshape(DIM, DIM), cmap = cm.Greys_r, interpolation='none')\n", | |
"plt.axis('off')\n", | |
"plt.show()\n", | |
"\n", | |
"plt.figure(figsize=(8,8))\n", | |
"plt.imshow(Y_train[0][3].reshape(DIM, DIM), cmap = cm.Greys_r, interpolation='none')\n", | |
"plt.axis('off')\n", | |
"plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 71, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Starting training...\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 1 of 500 took 26.948s\n", | |
" training loss:\t\t0.294690\n", | |
" validation loss:\t\t0.301313\n", | |
" validation accuracy:\t\t12.52 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 2 of 500 took 26.631s\n", | |
" training loss:\t\t0.290656\n", | |
" validation loss:\t\t0.297979\n", | |
" validation accuracy:\t\t11.74 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 3 of 500 took 26.731s\n", | |
" training loss:\t\t0.287239\n", | |
" validation loss:\t\t0.277915\n", | |
" validation accuracy:\t\t10.77 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 4 of 500 took 26.673s\n", | |
" training loss:\t\t0.287879\n", | |
" validation loss:\t\t0.284979\n", | |
" validation accuracy:\t\t13.41 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 5 of 500 took 27.678s\n", | |
" training loss:\t\t0.295710\n", | |
" validation loss:\t\t0.296894\n", | |
" validation accuracy:\t\t11.64 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 6 of 500 took 26.981s\n", | |
" training loss:\t\t0.294418\n", | |
" validation loss:\t\t0.277862\n", | |
" validation accuracy:\t\t10.25 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 7 of 500 took 27.361s\n", | |
" training loss:\t\t0.283648\n", | |
" validation loss:\t\t0.279195\n", | |
" validation accuracy:\t\t9.94 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 8 of 500 took 27.814s\n", | |
" training loss:\t\t0.288428\n", | |
" validation loss:\t\t0.296586\n", | |
" validation accuracy:\t\t11.61 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 9 of 500 took 28.012s\n", | |
" training loss:\t\t0.283594\n", | |
" validation loss:\t\t0.291638\n", | |
" validation accuracy:\t\t11.44 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 10 of 500 took 26.971s\n", | |
" training loss:\t\t0.283551\n", | |
" validation loss:\t\t0.294822\n", | |
" validation accuracy:\t\t9.13 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 11 of 500 took 26.774s\n", | |
" training loss:\t\t0.288020\n", | |
" validation loss:\t\t0.292131\n", | |
" validation accuracy:\t\t10.57 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 12 of 500 took 27.014s\n", | |
" training loss:\t\t0.287456\n", | |
" validation loss:\t\t0.279458\n", | |
" validation accuracy:\t\t10.37 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 13 of 500 took 27.039s\n", | |
" training loss:\t\t0.288217\n", | |
" validation loss:\t\t0.296714\n", | |
" validation accuracy:\t\t10.23 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 14 of 500 took 27.040s\n", | |
" training loss:\t\t0.290051\n", | |
" validation loss:\t\t0.281298\n", | |
" validation accuracy:\t\t10.18 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 15 of 500 took 27.015s\n", | |
" training loss:\t\t0.290029\n", | |
" validation loss:\t\t0.288426\n", | |
" validation accuracy:\t\t10.41 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 16 of 500 took 26.777s\n", | |
" training loss:\t\t0.293446\n", | |
" validation loss:\t\t0.289795\n", | |
" validation accuracy:\t\t11.03 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 17 of 500 took 26.800s\n", | |
" training loss:\t\t0.293948\n", | |
" validation loss:\t\t0.288069\n", | |
" validation accuracy:\t\t10.19 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 18 of 500 took 26.951s\n", | |
" training loss:\t\t0.287021\n", | |
" validation loss:\t\t0.290236\n", | |
" validation accuracy:\t\t10.85 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 19 of 500 took 28.666s\n", | |
" training loss:\t\t0.289443\n", | |
" validation loss:\t\t0.277581\n", | |
" validation accuracy:\t\t9.48 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 20 of 500 took 28.318s\n", | |
" training loss:\t\t0.289757\n", | |
" validation loss:\t\t0.286948\n", | |
" validation accuracy:\t\t11.71 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 21 of 500 took 27.044s\n", | |
" training loss:\t\t0.286358\n", | |
" validation loss:\t\t0.281015\n", | |
" validation accuracy:\t\t7.25 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 22 of 500 took 27.168s\n", | |
" training loss:\t\t0.289647\n", | |
" validation loss:\t\t0.300890\n", | |
" validation accuracy:\t\t10.54 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 23 of 500 took 26.945s\n", | |
" training loss:\t\t0.294976\n", | |
" validation loss:\t\t0.288441\n", | |
" validation accuracy:\t\t10.95 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 24 of 500 took 26.958s\n", | |
" training loss:\t\t0.286222\n", | |
" validation loss:\t\t0.280810\n", | |
" validation accuracy:\t\t9.39 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 25 of 500 took 26.929s\n", | |
" training loss:\t\t0.286172\n", | |
" validation loss:\t\t0.288337\n", | |
" validation accuracy:\t\t9.47 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 26 of 500 took 26.811s\n", | |
" training loss:\t\t0.294870\n", | |
" validation loss:\t\t0.293409\n", | |
" validation accuracy:\t\t11.81 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 27 of 500 took 27.065s\n", | |
" training loss:\t\t0.286074\n", | |
" validation loss:\t\t0.290459\n", | |
" validation accuracy:\t\t9.92 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 28 of 500 took 27.021s\n", | |
" training loss:\t\t0.286022\n", | |
" validation loss:\t\t0.283801\n", | |
" validation accuracy:\t\t8.74 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 29 of 500 took 27.108s\n", | |
" training loss:\t\t0.287319\n", | |
" validation loss:\t\t0.279614\n", | |
" validation accuracy:\t\t9.69 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 30 of 500 took 27.093s\n", | |
" training loss:\t\t0.283292\n", | |
" validation loss:\t\t0.296472\n", | |
" validation accuracy:\t\t12.59 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 31 of 500 took 27.021s\n", | |
" training loss:\t\t0.285875\n", | |
" validation loss:\t\t0.281774\n", | |
" validation accuracy:\t\t8.04 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 32 of 500 took 27.182s\n", | |
" training loss:\t\t0.294631\n", | |
" validation loss:\t\t0.279003\n", | |
" validation accuracy:\t\t11.50 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 33 of 500 took 27.071s\n", | |
" training loss:\t\t0.283157\n", | |
" validation loss:\t\t0.272481\n", | |
" validation accuracy:\t\t7.75 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 34 of 500 took 27.140s\n", | |
" training loss:\t\t0.289165\n", | |
" validation loss:\t\t0.287169\n", | |
" validation accuracy:\t\t8.98 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 35 of 500 took 26.824s\n", | |
" training loss:\t\t0.293205\n", | |
" validation loss:\t\t0.277177\n", | |
" validation accuracy:\t\t9.91 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 36 of 500 took 26.972s\n", | |
" training loss:\t\t0.286947\n", | |
" validation loss:\t\t0.293511\n", | |
" validation accuracy:\t\t9.82 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 37 of 500 took 26.854s\n", | |
" training loss:\t\t0.293100\n", | |
" validation loss:\t\t0.293742\n", | |
" validation accuracy:\t\t9.85 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 38 of 500 took 26.901s\n", | |
" training loss:\t\t0.289015\n", | |
" validation loss:\t\t0.279478\n", | |
" validation accuracy:\t\t10.17 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 39 of 500 took 26.807s\n", | |
" training loss:\t\t0.286779\n", | |
" validation loss:\t\t0.281855\n", | |
" validation accuracy:\t\t11.40 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 40 of 500 took 27.000s\n", | |
" training loss:\t\t0.282830\n", | |
" validation loss:\t\t0.280517\n", | |
" validation accuracy:\t\t11.35 %\n", | |
"(7, 512, 512)\n", | |
"(7, 512, 512)\n", | |
"train_batches 1\n", | |
"Epoch 41 of 500 took 26.898s\n", | |
" training loss:\t\t0.292882\n", | |
" validation loss:\t\t0.291724\n", | |
" validation accuracy:\t\t11.28 %\n", | |
"(7, 512, 512)\n" | |
] | |
}, | |
{ | |
"ename": "KeyboardInterrupt", | |
"evalue": "", | |
"output_type": "error", | |
"traceback": [ | |
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", | |
"\u001b[0;32m<ipython-input-71-1edd8e229f72>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterate_minibatches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0macc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_prediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mval_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 21\u001b[0m \u001b[0mval_err\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mval_acc\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0macc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
"\u001b[0;32m/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 860\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 861\u001b[0m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 862\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0moutput_subset\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 863\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_subset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_subset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 864\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
"\u001b[0;31mKeyboardInterrupt\u001b[0m: " | |
] | |
} | |
], | |
"source": [ | |
"# Finally, launch the training loop.\n", | |
"print(\"Starting training...\")\n", | |
"# We iterate over epochs:\n", | |
"for epoch in range(num_epochs):\n", | |
" # In each epoch, we do a full pass over the training data:\n", | |
" train_err = 0\n", | |
" train_batches = 0\n", | |
" start_time = time.time()\n", | |
" for batch in iterate_minibatches(X_train, Y_train,10, shuffle=True):\n", | |
" inputs, targets = batch\n", | |
" train_err += train_fn(inputs, targets)\n", | |
" train_batches += 1\n", | |
"\n", | |
" # And a full pass over the validation data:\n", | |
" val_err = 0\n", | |
" val_acc = 0\n", | |
" val_batches = 0\n", | |
" for batch in iterate_minibatches(X_val, Y_val, 10, shuffle=True):\n", | |
" inputs, targets = batch\n", | |
" err, acc, test_prediction = val_fn(inputs, targets)\n", | |
" val_err += err\n", | |
" val_acc += acc\n", | |
" val_batches += 1\n", | |
"\n", | |
"\n", | |
"\n", | |
" print \"train_batches \",train_batches\n", | |
" # Then we print the results for this epoch:\n", | |
" print(\"Epoch {} of {} took {:.3f}s\".format(epoch + 1, num_epochs, time.time() - start_time))\n", | |
" print(\" training loss:\\t\\t{:.6f}\".format(train_err / train_batches))\n", | |
" print(\" validation loss:\\t\\t{:.6f}\".format(val_err / val_batches)) \n", | |
" print(\" validation accuracy:\\t\\t{:.2f} %\".format(val_acc / val_batches * 100))\n", | |
"\n", | |
"# After training, we compute and print the test error:\n", | |
"test_err = 0\n", | |
"test_acc = 0\n", | |
"test_batches = 0\n", | |
"for batch in iterate_minibatches(X_test, Y_test, 10, shuffle=False):\n", | |
" inputs, targets = batch\n", | |
" targets = targets.reshape(7,10,DIM,DIM)\n", | |
" err, acc = val_fn(inputs, targets)\n", | |
" test_err += err\n", | |
" test_acc += acc\n", | |
" test_batches += 1\n", | |
"print(\"Final results:\")\n", | |
"print(\" test loss:\\t\\t\\t{:.6f}\".format(test_err / test_batches))\n", | |
"print(\" test accuracy:\\t\\t{:.2f} %\".format(test_acc / test_batches * 100))\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 91, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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5zzzE9y1F7M2LZ0DGokRePd41TUM8xhZ4dCugaZaNEwHmFc97FsNGrnOoXxwl2aUhcwEg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ekSdgmXDNU5kyRVWfVNEuJI6BnnyXHZgVWdm8xhF0WyDK2XfmQijPiPBAqsT8cr6dGtgtBmHeFy\nZIPfzgdrGc6+1LZusVnrWjkyDtPyUtlH9t+UfKrNWnouPXc6jQyPgmvlSc4z1dNNaDfx+WyeTgy3\nu5LOITExBZeS9NutNPCeVZ1TsGXdda4m4cRp6vkO3FQvkg8FjfQ8r55jP9LBJR0TcXNpOsfmrulu\ncz2ntlNp+J/OBFTq50AwAaMjT6m92u/UvSvbOabu+dPulpSzfQUYR2yOlJ/aoLo7cX1C+5iCj/YJ\nfUL1my5X6wtcdkQ0+aukY+d7qj72yeVyefeXhx8tO6BNfvMj9Otu2yW5GxBPFDzCYM4y2DP1ccJ3\n35VegcvV2bF6B8YqHQDvjDOVxWtpgqd2KDHo0rNPnPPUCHItz0DpMPU3QVfvLfNtYYlIabm7vqT+\nqQ9UGP1qlFFL0xrtOuenfcdrzh5ZnyOOLm1HOvib4E3wYH9Uerdi4cAmtXPnDCc+wgHwNCDobJ7H\nicRyHq31Y48DiTE3LzpioOfZNh0n+pv6ducobiNgInlnpPOTZ/NO6z1a1hHA/zRL0xPpGtNNkAR+\nrozkgKeSHMURFl7pObmOiLuvl5yxTk5OvB2AlK5ar0rX96zDAUZJnWcUxlf+aVq2Sc/xHhmXXyd2\nUGWTPNCOunHUx74qbenHd0anNrIu1S1Fu+l2gkvrgDmRnGT/R89TJmnc3E/k7RrRMXB9s/M3SQ8H\nkjvArjS8fZPEjVMRIz1PX/Dly5d3JF37gcB+7S5pyq3G79Y24OSor1/rkwAxRQ2aDjEZqTPYxDSd\nsXJy6TkeU+h49VuXkty9tl2ElNrbCXcxunZWeWliu9/sFz1f5+p5SVd/AUpNaleOptV+Y+SsS+mu\n312E4BwmIw61Dz524vqHjs0BYWqjs3EFQwVSTZ/u5TrHSJKSSIlGMJrOjZParYuiXD+587ztkPo2\njaO2OQGQe0woCUlhB4wOhM84YIojWGmceex+d+V3aZzdqj7OF2j0m97y5ojeRJ8j+u/KOAPGR4hA\nd9soyV2BOEVs6ojqmnP6TpxT3oFNipp2ZauuRwbaOVhKumfLcyxTN2S564m9sw9S37MfuramcdK2\nu81cDpQo/BcaBQwtU4GFwKhgknaz7uzNtUvLTUIbUN3pyNgP7pg6OlKjafjoCoFYlz71vdTa1wRF\n3QhYOvBRNLVDtwSd5mKan2yXEiFHZjkm7j63gp8DfYrOPfdolrZvco12qnro40zuupOd3nW8I0Q8\n5jxWEOaytCMTjqh2fd618whQHkn7s+QuQExDco8/TIB34iQnHb5jvrzWle/qo7E7FquOQ8vhxL5c\n8l85rvX2kZyaDOVEKz8nVtpxzLpU16rLAYASho4EOVbtyuUSV/Xjly9f3rRNN7DUOb3uQL7qe35+\nfgegaZOR6rBzIA4ktRyNRgmYk3vCrK9rZ10jyL68vLy2390SUdAuvesRrJRWVzH0293Pr3wEm7Xe\n27+2yYFAIiTJ+epmP12iZZ/q7x1RIPGgpCcbNL0DXu1bHbvd5jb2gZvXVRf9nRtHnSe1KUs3Fj49\nPa3ffvstbthy5J7tPyJTouzyTOs7opcS0mSXlE+1NJ0a2znzriwHrBMddp1GlrbWD4ekoEZnwR2P\ndIpTPRQodLMRAUQnG+8JHpGkX3fMaKT6Rx29Opu13r5sQyMuB0QKxh1jp+7OASXWzmd1Nb2W7853\nfaxj7trGtjgQocN0oKF9R+LLyFfv63GMuLS41o/7gPrcrPbjRHT8OZ7dZi2WoQDa+ZBkJ6UDj2nD\napeuHbvfyfZcIFD5HDFw5GoypxNZ2G1EI5l3vq2+Hx4e3j3v7m5fpGNX/0fKEXs9Is5+O7krEKdl\nQ4qLaJLs0nQd41iaY5NqiIlx1m8FYH3MoM7Xsh+ZOevW33QaypTdbtGvX7++izIr3/S+lrardOwA\nQiNydbTMwwmojL+upzo0UtDyWD7tazfpk3PsWHwifQqEdKAESTo2/mY/uTakuqkrx6PS6v39SuP+\nLlFJhNapryLlSletYOj/KdOW3fi5+e+Wnd1vN9bOFtySdhJH2tz19DvpNyFeLp+e63wfx92NqZuj\nHJfK514Sw7/b7KQbjzOS5gHr+Bmi5GMinyIids7WSQfIXaN3QJ/yuPRpAnRsW40+sU4VdW6cOCzf\n6eN2itJIXfTh2t3p2/WRAqS2heRF63eAqh+KiwxUV+bV8vk4FMtIbSSwk5CQqNFJkcCp41d9jjiy\npLuzuxJ3T1x1UdtIduT6PxGjy+Xyps/dnFSgcO3kPJgSEv7egVX3W/Vw9bjyXBrtI7cPxF17eHj/\nzO6O1E705CZSHYO6BcGVJxUSYyWenez82E46fz+VSdqjuu2InJO7b9bSSZUcbtcRiTGntEeE0YPq\n6SRN4h27dYCwA2xuMHL3YjoikSYBrzvysJtAu4hB0yVH7M67TV0O3DryUTJxaHRkjPAoqrdGlvrP\nSARhByxu7BmpODvkOReJk3jxfrG7v+/mGElVXa9VlpeXl/Xt27fXc1pm3Y+ulRq3l0CdO891Nkl9\nHDnr+mx3/kg62jH3GKhdu7xrvQdsbQ83bjlitiPSeo02lkisI3gaGeuy9K6PtMyJz0iSfMYtZEq6\nkpBoO7nb/xGnDmcHHmGelG7S0aEk4QaJpKfTJbVTHRj1rPS6eUjPJzBJoFaSHHj3e8I4tW+6MVUd\n1WkXiUjRnP52kQPZdwJJ6sF2pHYmctORJrbPkRtXx5SdOzve5WH5SYfqZ72lwvZ39k0n7Zy9IzVu\n3DqAc7rvCLPL1xG3RHZ248V6FGxTvyaZjm+l41zqbMqN327uPDw8vAF/9p8Ccv2e+AYed5LGpSvn\nLMAn6caFxGpy++9uEbFzDildYrc87kSdR3JIExblJuiUKenE18njootOR6dTp29yOq69GnVqmprg\n+hiLtsm1NfVBfdMpJeKw1tt7lgp0PJf6oL5LX/cvTM4pkfjwPcJ6TZ2hblRh1K5jkYDN2YMDFncu\nER9nW1yWrPQVzTw+Pr7btEab5RjW8fPz8xuypeVX5Kx/gMHNh13bKJ3znebryFVn45wvKf1a/q13\nR0iV5u1A1BHGTi/qVmlTmQq29XHnOn3OguOkv6Y++Yg4YjmRyVL13e8ROyfhwIrpj9bhBsZNICcF\nRMznynH1snw1pNRO3UilDJS7slM73e5TB8qcbJqfjoVOlXV3/ZjGsSZvlU+d9NjdPytyoH2jfcGl\nQM2nbdF03GhROmifEPB0+XktvzTt7CA5zBJ9J7XrE+eUCkj1/rTrV62XREEdKvtDd1azL2o8Hh8f\n3zz6ouXUhq0q4/Hx8R2As23uuxN9hIRt5a5wN4/qW+dvcsSqjz5zzbnHtMkn6abLCcFUvd247nzF\nWu+f5685VX+qUiTp5eX3N76pXdcu6efn5/X09LSenp7ePLqkdV0DkK69R/Pu+mEnJNFOH+cfd2B8\ndyBODkHPMX1yZtfqQaNJ9aaBSxFOWopKE6dE7ynt8ib2mtrAdvKYoNn1eTd2Tq+kZ+r7tOu66qtl\nfOqhBCqNWSIPBDhndyyHx+6+fQKVZB9cgeA4uXLUmZPguDY5p115CiQ1n27gcXZL8NJyk0NW0qTt\n75wkHzPieRfhuWP2SZVJ8tL5Br2+c+y87ua4PtJXZetjWkyXCHpH2FQSQPEetLPnInzusaVb+Oak\n4zRthxG7sUp+MpVHW5xu3Lr740vOuB3zL3EOL/3mta7TOxB2uqXfdc6xaerpJjqFE4ETjuyMRscN\nHl37u3Q6Jq7M6bi4iTwhYe7FDg8PD2+WTTuyQxJFx6v5O7LnyIwjKumb9XfkjvW4+cAo3fVD5Uu7\nmTviu7MLFb3VoLZa9ekjegp0T09Pr/nSC1i4WUjHnnq5vQQTx7w719kx6/sIScSO4KySCFJd00iY\nt144592GLF0l4/3hW4LwNXINLhxJc43cDYiL4U3S6feU3aRIpJMd891dqzI6htzp6IChnjFmhOEe\nP+k21yTQ0X7l8hTbtWN3HXOu87qsybroaFz0l0AuAUbqbweAiWSx7Mqry4dcFk+3E1w9mi9FVOk+\nLPu6IhPqkIR2pfrzuXTqqaKbczSNRuWuTWxP6nMFCb3miJVe71aVNJ8jU45waR8wXdXnwKyOtS+o\nqxt3HUPukqY4UuVICful0tQtg/I7dW5CWpR8fSQQT0nhEZn4dP12+RzZPSJ3X5pOoo73o9kIy5kM\nSrrmDNT9TkSEE4mvOUxOI+ngACTpls7VeS3LLQvuSBIjJTpJOj+K3jfXtrn2EQg0TVfPxJZ2Y1D9\no/efuyhZ9e2u6acbJ6cTJQGZlkPSlMqp9pIwrrXe3L/nRjcCEm0p7QlgO9l+B+wEck3vHDHPd/W5\nqLvqrna4Y02Tymb70/V0nPKrTeo46G899/z8/GbpmTujee0jQLjkI8regfFOl2t1+lRAfLYzjsrZ\nOnakQB1mmhh8vICOM5GPDuD0u44TyDJaONMXql+9pEEZuAODyvP4+GgjDBeNajpuXnH3/zgpFBAS\naE/ICPO6PnPOXx996/Kka1pfitYpulTIaEbfaMU21jIvN3dVdKRgTFvT8hkNabTlIohqo+sPrppM\n+iDp1j2ruys/lantIGi7PGkzopNUhvMLXLnQsSUhXOvtSk4KBoo0MNJ194z1vnABcoqM01inPmD7\nfwbAsy71Q05f56dcuZ18GiBOBj7NOx0g55AnUUQCDjdI+u1YfHIgbDNfhMDyd0tuyZG48w4cmT61\nwaUh6DuHlfTWerQsgpJ71Eo/GoG5nb+pPQRpHZtuPDsA58Ym1y+pP3YApnp3OrB92jdMr3V09yCp\nZypTnXYdK0gocdDd1kUQdPNZalfpS1KkJIJjsOv/JB3RTuRJiUWabx2ZdITUrZJVP9NO0q0Ut7ub\nZSvYKtBqG6pc6rPz4w6cJz75o2Tno46U0/1W+TRAvJYHI+dcKEdB2DmrxH7ShEllpzInjFnbryCi\nL4bQtHQmTtJzi8yzO6b+3Gin4MiJzklJQHNp6psO1Dk6glOaxARiB7iqD6+V3izPATOXdNknDlAT\nQUvRjnPSdX94rbcRqgKSRkjaDx24VZ+lnc3sc3XGZRP1SJMuaZaOpfvj4+PrvcrqxzQf2Yc81v7R\nNusmI7ax8ulbxnS83FzWb13+VhDjY1z0AfQVCmhKVvSb9TggVLDkPgN+63vAtayKdOuRJP2std48\ntsSl6c4v6jkeT0j7R0mq8xpddvjxKYDYgcIENDoHekYHl5+GkXTvGJwDzdRmdT6uHgVl5ktt7/qE\nji3lo9PX9rg+S2UpYDuiQyeq9bhnm+kI0xi5tnbt5rVuEqqzY3sYmdTx7rZAssfU1nJ+XH5093g1\nAuKyZrLlctTsCx1rrduBmBPaBe2Ac9uRr04nbWeSjgC6dNRT6+HmNGejPF7rrT24Jd00Rqyf45ke\nndQ8+q2rHwq2Hfjrc8S7e8ST+cr09wDjNO86XVKbJpj0aYD4mrxT8HUdOZ0oOx0nQO5Alk4j3U8k\nACcdFEC4McQBGHV27XCAzMiAbXX1sDzmTZGMgjDLSRFKaqMbn9S+RBSYthxwApGJfaoT79rhRHUu\nObN7M9mWIyMTguLKpa2V3iQznSPvQH2iD+87M5/aXqXXF6us9WMTpaYnmdb/eK583FHtCFz1RwdW\nmk51dfar5SSSlWz95eXlzf1flqO6duN2jThi/DMlkcFbyt2AeLoD0DU6DfgtjGASHe5EjXL3+ITW\nmxwWJzrz6TePK38yJjp8B66c0Jq3Plw+75g4HZ27frm8vc+lL5ZIbeQ378uy3bsJ5RxYt9lOZQIW\n19iwOife+2M06jZZ6W8+1uLSplsclG6Djvad6sQNYrVEzbqSXSUf4eaLlqW/aT9qm3z+Wetwew1U\n17V+/A2pEjanG/tJy2af6xyhnbo57crQsaBN6u2Cy+VH1Kv1VBq3OYu+4hqZkNFbyoT4TuTI7vG7\n/enDRKaD6CbIJI8aCyeUlnWNMdGRTCMJd33nhLg0mBzRWu8BIUWxrj/cbzf5FCVjcMEAACAASURB\nVMTJ2JXwsE7XR44EuAlKJ0QHc8T2dtGDE0dkjjqmI2BMx5c23kztjjutOU5OBxd5pXHS+7NTonPE\nD7h27lahtK7Swc0BXi8grHNVl9b58PDw+tpLp5eT5IvYL+mj6bv+SzahAOv+3UzTaDoHPLcA4p8J\nwmu9x4Zd2onsArK7ALEqr0tSO/aqS0QqtxgoF6U4Ftnlpc5kx1wSq986edPSdAn/R1eNJkWYHSAn\n/UvcxHIAle5dq56lIx0GndgERBNxcCCpkdSuHzrAvlzePjPq0jowqjZ2AENyknRg+3nMc+mbOiRy\nsdM5RVruPB/fSc/XpvaQZJxxkgRrtjndE9VvtWHdHMc+4lyuzWl6jeOe5hvr4Jx3RHUaVLg+cOCu\n3/qHJvqMsQPtXb23AOtbln0ET1IgsNax20N3v0fsNjWkicbJ0TmPSWdO02l9XVnpuAC2A9nkiDqQ\nucaAO+fdycRZa3vrHCeGbgRhGpbNsZ7q6SKuM6RtSsjWev+eYxKTVI4jMHo+EYNEYqf9lUCU7aZu\nXcS4kwnRmOZ1fZX6mt+qt7vH6gDIRcJ6nhvh6lMgpcvcSkAcmCcySv10aZ9zTctKRIbzsHTlsrMT\n6pP8VTfWtwTknZ8+U95aWbd0Xm1qIncF4oeH969N7DpRI2JOwDOT+qhMIghNW+l1UrqyCFy7Pqi6\ndwM9ja7YnglrTs6a+Qks9YiEPsKix2UTyQEdceLJoTq9UluYNxEudYp0dC7KcZJAuI4d+XRgqXbH\n8hPZ0U+yA9q+61ud03XsVrLYP2wHVxBc30zIZD0Gpdec3mqDCkaVRu/t6pJt/ROReyRJyXXtcaiP\nRsZK3uhj2NecM0k0bxpHtSd3v/fl5fd/Wqr+UbLCf1tyK11VF3W4xldzDrMdrp23kJ2fo6hubpWF\n8mkiYgqdmZ53g8GBTgPTOcGdYbOejg1pGt6vVePUZ/emIJ90qzIngE49UvpUxm4Z3IGROodqc+0s\n5T0nbYcuXWs5yTYmY+ScswO7nRNJIM82OxBh9JIIENM6XSu/G5fUNkcUtA4XrXW2stb7fQqufaxP\nndXR3d6OnDlfkPqWkR/1Wev9I2hVhwMeJRW02xLqwr5y/VN6kBw4oa7UkbfISIpruV3z835xgTDf\npOXq2+l3Vpzd3qLcrr4puDu/0eW962at6bOGdV2Pu4YlJ9Wl4XlO8FQn03Fy6n3g+pTBkiUfaWNy\nymv5jQHJCU4AoKtby1fSwbSVRiMUlXpZgO7M1GMHMjtiVd8JVKnflJQwXQKqHcg6wpDKS21z7ejq\nSXWkejvQZnn63ZFB7Y9k/5xLbk5O2p7SVHm6DKv1cSe69o/bpFQftqvK0PmfyuScdbo4osB6OT/V\nL7BOzaOEpK6TKJMY6v8OlyS/QUk++Fays9VKMwXX5DvUPx3ZKa1y979BnERvboJ2MmVkKa1zXJP6\ndJLogGh+NX5u1kpMXical8Ccbl1UsTPAxKa1/lSuA2IVErAC5nJUjDbc5o+Hhx9/+0ZmzzbwWPVw\nY8O2pPa7TWWOGCVx9uX63TnVVF5qz46o8Heyi65+RzhqPN0tFwVeR2p0vDVaLNtzO5Cnov3JKFjn\nLm+ZaF79gwMlkImMsC++fPnySkiVmHa+kEvnBEXOg2RjDw8Pr88D6x9ykGQ4oqNzgASERGk3B/Xa\nETCcSlfuVLdJHdeWUXK3XdNlmPrbpWOnMW3nqDpG1AkHMKWnHmTwrNdFqYlBO6el5ZSD2t0n3jni\nrq06ufQ8d207HVN5pbc6qZeXl/X4+Pj6v7TK3HWScyPU5fL2fdKuXS6SoqNn3nR+R4BSH6uQbLhr\n+rtLQwfpHA/bPhFGN1rOBJBVkmN29tKRpt2c78bD2eBabx/BKXDiMu1a69UudcPV5XJ5syz79PT0\nrq/r+WElmi8vP96pzX5wfaYAWXWvtSzw6zfnhSOP+n/OrKdEN6WpDvomLefrjoDrLt01QH0rkE+2\nyfP08dMNW3ePiEscyOp5nltrBpC7a6msLjLYAVdyGgVCyTCS3owe1AFPwJh6p+gpOXaXTzffaNsc\neGi6SutIlkYLGnmyfaqHRkl00FWmI0mONJVObgmcvwns1M2l0/S0dSUXO7LpbNbZXqWd5D9KzLRs\nfivYsQz320ma807XqaNVvRRsHbDrcYHa4+Pjm6VstS13Hzg9CVG/FaTXev+f0ykIqTwk4zsy4vqK\nxLTrN12Sd++VVrkF+J0tK80fd/1aPWk7zu4n9n63e8QKImqMSVyHuQ6dMHenj6urGyxeU5btHA/B\nob7rHHUmq6SjLsBgXcrkNbrsoibeO0q667c6E36cI9cxLlZdEUPJr7/+ur58+fLKtAvwed9K9a78\n+qII7U9ODJ0wCXDcbwcEbJ9+0wHvNtYoQDjyoOkTidC8LqpyoOxsPDnmFMGppHmnG/C6OZrarRum\nSrSsdJvCjb8CSm1M4os5uDSt9qQ7pTUa1nQ1P3Wel70/Pz+/6pve381+qLLcpjLOS7V77sNxfc16\n9bjK4b8vVR8oIb8l+F4jR31/p/ct29Tpddel6eQseG6teed2INwB6pTVUb80kE4PF4FMjECBRZ0h\n+1HBjve1qEOn385RamTPe17qqN3jZqm9CuLu+eMUFWv7NUrRPtS0jKA7e0k2Qbvc2WmKOhwwaltJ\n4Hjt6Bh2tj45p2WS3PHbEQ/qQjJEINu1yYFN0rvSKxDr0qou02pakh7eE+U8cySJYEq75jgTUF2b\nk9Aeu3Fg+9w+jfqd/gaRfe2Cis8k9N+3AOEOA6byqV5x6aJCptcoWtMw3dQYUjo3UKljnTF2zpp5\nHflwjtQRmKS/WwLvDI9AMZ1IdDacwGyX6keHrRETnT2j3UrPMuu8AzzaSkf20jh0Tt7pQodd144S\ngCmgMs1uPF0UNLVXNwdL+OywOnQlW3qNqz5sK/ugxlmXZt0+Avfhci7BnHsVWJbTScsjKe3EERw9\nrnY9PDy8voNbV746oKcowLv5lPqqIn/3XmltL/vhFkJ7uKbszmZTWk3v/MYk/04+xXPEaWJPge9s\nGtaZnLLqMh2II0yrK4sTWx1IIiMO0HmddUycuh7TQVBX11Z1lA5E6QjSfbBO390E07LYV7sIUp1Y\nun+tkkDb1a1lHmnPpK4Sd2++c9qqF4mE6ub6bHq7w+lcfVzlqw5qQ7STkponnAcEEN2VzyVsFxGz\nrVVXZ/+ubRRdYUr9wtUeBdJKsyPomjfdQnNL+XqeIJ3am9p/BkSPkJlby47YTGWn392AWJ3aWh5A\nduDgouEzea4RDpQrsyaG/gsLJ17nHDnZGOHpBGNZThetJzkNgqLTgYy92HrqIzoILbscapVDtt5J\npaVD0/wcFweg3MntrrljV1YCLgK5E70+sVmm5zmm68bdtUGdfgEYo1qVukYw7GyvI5N1jiDPuZBA\ns9pR94O5RL3W2//h1Tarj6p7onWNzyC7trg+dvs23J4FzkG9J5v2PlBSGQTUzh6rrbxvrnq6tqex\nv0ZSOdPyb6HHDjfoD3a3E9b6BBHxWj14USaNSrKLpqZlJElsmcbpnEgqQ0GrgIqTiCTAlet0d5Nm\nR2QqLQmFiwwqj7ueNl0poOik58YzF1m7x8McoHMVRh0tow4CnAKRnnf92927dJFmOk477VN61qHl\nvLy8vHu0pXRz88MRvg7snSTQ381DAinH0u02do5f7UjHW/+wIZEQzq8jvsq11R2n+T8BWP6eAI0S\nEBIOEuVKw0e3SBxSu/X4ViB4TTmuv4/03UfJ3YDYOV7nyFWmrFPTMx0nqmPfXRkunU4YVxadmZtg\n/O0AgECok4HtSARAf+t3OiYz3zkcx4J34F4TXB9BItjW72QnBGaNspm20mtZaWmyzuvLD9guFx06\nYKRtuOVBpledKG7JOBFV7Q+nf32zT1M7XL+lfA7k3MYgFQdIBH7dYKWEkOWmOcs+oG11oOv8Ror+\nOn/miECawyW8/878Xb1qq9VvHQmruenuC0/aOL1e9d2inGk9yT7S+J6tayJ3A+IaWC7bTTt6EpGk\nfOz8ziEkY95Nbh3sNKGSA6pzZPrVX/VqzG/fvr15xVw9FsFyqB/rd2TFOTMFRL4ZyO2a1vL0nL4Z\nS/tYy6HO6ti/fv1q/w7TtdMt4ZHNV70q6vC5lOnG3j1e46KGI5Nb2z4heu6PFbgcrR+1rURYeU+d\n6dmP3ITlHHy1yRErtR/uG2B71vrxpw4EL9d3XJJ1JIL5OKdJ0KrOIpHJL7Ac1zecF9qHWjfboVJ9\npP3KtA5seb5+698d1m+mdbbw2YX6Ozw4CsJp/Cdy96XpbjLUMRs4AcJKV98JDLW8rhNp2F06N6id\ncJI6/Si1TE12W86fzrNz4BPRPqx73QXIpY/qqn3F6N5Fo6qzpuGyrDrptd6++cftxGV73b0t1dWR\nAN2pT3vsotaj5NKVWW3r2uVEQbGze3fdkZ+ubrax6t7dRiKoVlm8v1wRoNvQpIBWonWncevAZ623\noF3nCcDOJ3R96Zbx0zy4XC5vXmyTVia0jOQ7jvggd54k5KPkXkB+qzZdo/9dnyPurq+1nyypTB53\nOqR8SSdKx56OAnKql1EMmbPbUer0ZvtStJZWBlQHdeJHmSDLUUe01tt3CbsoSe+Tq+j9T82j47tb\nNnU6Vr0daXT91kUK7pwjkC6fpmMaB75Mp787u+bOcGcv+s2+dLuZtT95rurUvkzztNPd3d9l2zpR\nm1bdCETTstnGKov7PdgefZ3lpD0uYOHYs5604rGWJzFuDP5IkXAnR0hzyl/i9pd0ctc3a621fxif\notFJyucMciqTqJcyAVznxJwRp4mWDF4jRkYS6kDdJE6G59qj+qZIi4SGjD1FDyndw8PDm/f1TvrZ\nRUv6W20nkaikpzotOtfUj9OIxLXBRdtHyKaTjhQ4gJ+QLM3HCM71UbIfgm4HOiSC7jZAkdbSS887\n2yjpgNyBtJbhxluBrdtp3tlQ+s06SB7cd9XH8pK9u+udT/rMcmY+HhUdbz3X9dXdl6adpAlIQ6Yx\nOSd1pu7JhDhSDiO7jk26a0mf6hONArl8p8C8039Cguq7Pq5dXX+4MWO0VlJLcwTj7n6cE63HgYTT\ns4uaCQKaz9Wd6mIZLKdb2k31dY8WpWX+pJfqvYvEtB1uI5ESQ27MU91VV9qMRuhqc50d6BJ110b6\nDupc9XN1wNVNQuGucxx2ZJU6O9LRkQumnfoy1w9/ZJn20c+WuwOxc3ipoxwIq5yNEihnIkf3u/RI\nINzp7dJeLu//sUofI1KH4nZGno3QCLgKwmzrTriRRNvCSIh1auQzIRU7YlD6sK0uOiKA02F30RPr\n3I2BjhX7JpE7yi760m+e1/YS7Drnznazj6i35q0xdhEsdUhluLYl0sX+ZUTpNua51RSWxz7Y9VWS\nBPRduWmcUhk7kpWWrFW/e8jRoEjlSDByizZO93fcHYhLUlTQpd8xerJMPd+lTxHQVPeUxzkQV1/S\nr64xGqhztZuYDstJR2R2k93JpE6md8RCr2kkxGUyAhOX5V1dnRBoCdBu41IHwuwTd95JN/Z8nKvA\nq467qI9lc3nYgT3vcbll+VTXlKTRnl0ektJEJJinqzfVx7ms/bFrc2rfWu83JLpyJ3bRAW1HIHcg\nrTbs7l87vzCd67eQo/7lVnXuyPytdLkrEF8ul3d/X0fHoMJdlgnAnKFpBLfrvCMTr/Tk4w50HG5Z\nkI6P9STHQBDTNLWcWzqp4Wq0kSYm7wmp7vWoiIuGqWNych1gccXj69evr3U+Pj6+PkahkZMCqEbN\n6TEuXapkxJgiSEpnH2xTV4ZzcARL2lH1i94m0DITGOv4aRuYRvVnfZfLj0dnuj5wNqvidvRzY5hK\nWmZ3/6dLouZ0U6Kh4KNjMbl9ode1v1QXzoX0VEPNZzfvE8HpAgqm4TikYEP9CturbfzZcus6JyA6\nDSh25SefqPIpIuLkiM7kd9eu6dBJ3aqD06WLFJIeXQSgg5oAhZtmGHXsHIx+6/K3c+g0MhIF9oUD\nXq2ftkDAdbKzgd34do+J0BE5p9sBahIHUs7JK5lba04YNC/H3xFK5qm63H1SOnWns2uXAp+rm7bE\nPQ9uLrnxZXtcfu1/kjU3h+lQXX7OMdcP+tG+6Jw6dXdpEnizHdqepGtqG/vjHjIB0Z1M9O/q6Wz2\njNzt8SX9XssDkMtzdtOAOhb3MohJlJNEJ1ZawtTjFK110YVzJq4vClD02cuq091fTQ6rMzT+TmPV\ntSvpod/cGJOc+27vwEQSmenqr3O7iej6eEccHx5+XwX48uXHs9p8ZpvlJ2fsCGIXdSUpvWoVq1sK\n59juxojRFvclMB/tpyOGWgd1dXsWlGzsbDu1oyNkNS+rruQfOv1Zh5s/iTDsfq/lVy128/5nya3q\nvYZMTIF8Wv6n+hvEI3KmE49EElUH5Sz76cgHj7tz7rqListhuk0v3LxVZek51ddFVGyL/naOP0UH\nWnc32TXKT7K7rsJnsPW8I1PJ6Vce/u9yyutE263pa2legbhz8Bwf6toRCid00qprtTsRY21LsnlX\nJvWbAHEXqTkCq3p0faFvcHO2zXPUqSOwrm9dPaqraxNBlHmmY00bdPX+X5Nki3p9Suq685Pg8e5L\n00ecAn8nMC6mexYwnWHToXX1a1lndZgwz3S+Hvlh9LJ7/tddK+CePqo0IRS739qO0uHbt2/vxqU+\nR1ZJFDwScE8jkvqeOE21lc42tV06hmv9AOIqix89rzonMGK91EHbw0gpSZozLo37duU5XTkWXZ0T\n8ssy1d5Z906/jnxpf+reBleW80OuDa5NO6Bmm7SMyXPUO9/3GcWN5c73dWXtgPyI7787ECdDulYm\nnXArY6JDnMoEyN1EUAehG3oul/d/cqAOuHOMrm4HwqzPMchUptsIlsCN7XcRhwOeWlLU56upQ4m7\n57p77IdAs4t4d6SDbar+1o1q2vdTJu501fO7/EnYVvdYV5enc17JPlNZOv7OAbo5oHo4vdhvukzd\n+akp6VZCU2Xr5knOYYKos6cUEesxgbmL9NwcTv37RwNjFfYpieFOnL0qwTrSN3f/96Uk086gJOe+\nO3dGpvlT5KLfUyalE+Xh4eEVcFx/aVp3X5GPozij1GMto4u6knT6EWCZT4GI97rVaa6VwfTa/QXT\n445l6zkF2YeHhzdL0V+/fl3fvn17B8RVftpQ5MpXvVyk5MSNi15zdbgob623/e7GTdunqzl1zb3c\no5MEzEkHPkXQ6c5zWqeri9e13Mvl8uYpB9UnlTWJfqmnex66yq88miZFxDtC9UcUErA6PivT8afc\nbbOWRi47ljYt88z1xIon5RK4dIMH8/GRGp1wkyinA6qqW3eYsi8rOtQ28qUfdVy/1UHuyAInunPg\n7C9XlrOJ0qcc9Pfv39e3b9/ePEqjAO2W+kgkOiGQsy/TRq4UedU3216gU5/Hx8fXf9aqjVpMX+2o\n+9LabufY3bPH7HcSIqera4cjQLoKUWXV9e6fe0oUhDlfEqlx6dx4dBEeyUUiOa4/XD+qEDx1fhIU\n3WOOej2VTz2mL5LQNPoHKmv58fojiwsuOO4p/ZE6zsjdl6bTbkrHmiu92/XsJs5RYL1G0sTdpZ+w\nMOeIukhrJ8qYudRc153jdo7MMXTVmcY/0Z19mYiB7gRXvV2E7vSdSJqMbqPSDsTqWMusthTwkvSk\nqIhpCEIOTJi2A0Tm0br1HAlKAjQlEzp/WW8iNmkcOCc6EHM6s1z2j9qZIyPatq49TEfiqhsonST/\n5mysykx5C6j1BUAsq7Ohny1nAHFa1tTuP1qvu++advcNabxqJJPlxYkBKaBPJrnTP9VBJ0nW7sp2\nOhxlaHQIdLgTsrDTYSepXu0TglEqI7Wr+rMAms9K673U1J40dgTY1EdatwNO5/zcOFSkr/eGSYIS\ncOyiOCedzU6u7+wh6e6cO/vB1a8kbAoKE5t1Nul8jxImBeNuXu30SsDo2pjmrCN7jlBoGoKzrqjU\nd6Xr+ntX963l1mVP9e6unyEoXZ67R8SddEx4Ld+wrrGcVJOyOoad8hKEed6V7XR1TJtlqvANU0lv\nBTE9P4kuVJxBd86JfdYRAcfIdfWklnBrs0uK/rR9SXQpmvfVunYnSUQyjYm2l/VoP3Q2oee4HD3R\nlzbQbQzb2S2BuMrlCowrS+3StW1CJp0+u/OOPOg8uVwubx5nSjIhmB2Y7h7BIxHQ3wxqHPmh79O6\naadT//qRIPxZJc2rs31xdyCeGB3T7yZDVw+jmTqX6itJEUEHwhQ64zrXpVXH5lg7d/52b4ZKLypw\nzkrrdaDBtmi9fFQqsfwJi69jfYe2Ose1frzWs8pwz/QSHFWOPF+e2uzaoOk06tG09fny5ct6fHx8\nt9M79X3p4sBDI5qOxLroztWteRJA78BZv/V56EQCtX3JBid1VznUh4TGbWas80qCauWCxCj1ZWd3\n9Dv1r1S7l4no/OOtmY488ZaO6uV8E/uik4+Iij860t7JhOTdSu4OxHQanHglZPo6EaZAOunYxAJd\nfbt6NJ17dvRIZKW/ddLzfIomUpu68tkeXnP6u41OXfqJVD46nXIsDuDq20WSqutRqbK7e3CJULBN\nbjna1ZX0cA5Y+2UXPbryWZbq24E650bSbWoDHaHdAXPSyZU/qT/d6kjgpXlTf1Z+/V7r7WNNbhyn\nc5G6JYLbRcL0rc5+UttuLfcG5amc9XF3BWIHrpRkQF2ZXKJxEU8a1M6B0RjSJGS71DkmHTp9Ogc4\nfZtU6r9uItU5tykqTUx3LQHVhJjo+LulN+rO47IHrbekW0WgMIpw96aT4yvdNV/ZxLdv3978iQNB\ny0WLHUgpoWV5nVPtiENK2wHrhNC66ykyTcfJ1hwwdQSpA50EhgSvHelwbXd9e2THcyIGjKqTbeqt\nnZ2v3QH/rYFy4h9TvrOAqPIzwf/u/740WUZb68eSIyeqc4DJKaY8rq4OaFiuOhj9KNPkTlGCNa+R\nVfN30ttFMUyfXiTAPJrX7eqkQ5mwYxIH12c8TsShAKz+menbt29rrbWenp7eOBneRy5wrt9uqU7H\niCDKslybtQ4+o6mPLLlHw1heAhw9p4TDlcM6HFA7gHB1u1UXN6ZOry9fvqzn52e7vOvqZl+kOtI1\n2uyUTFZe6p/mqo6vE5eP/oNzT2/HVD7ep1Y/ovUo8a821Nyr+aG2yX0WiZAlP/2zAGsnUxCepJvg\nkstzRu6+NL3We4feSWKLXdT1kYbDyCw5O53QKYJ1ed1kV2FUV8udrn4HrKw/AYy+aMGBJAFUz9Wx\nboTioy7sU9eXzvm6dPpIBvNRr/rtNvBpH6f+136YOgAFebdSou1z5KazdQI0dWX+IxFial83lwgU\n7Gs3ppXG6eV0dLrvZOITkiPm/36z3m6eOvKg6XVu6PVK7+ol0WE+ll/XOCdVl0QMPwvgJplGwj8r\n2p3q8ymAeCLTBhFMksNe6/2S6y5KTsbp0qkeuqHKRfTaxlRHTRxuznKS7ken6GA3+VhfR5ycc0pL\nunVdwZ467YDerZKobg6IKy8dnnslptqPc2jVJkY3eqz9y8eVFKgcQXB1sx3UYxKV6Tl12A58HLGb\nAKQjUu6YbdyR6BRBp/PuGs+lCDD5jV0+zc9zvLZrF693EXpnM5qfL81h/ckn/F+Rabuubf80/6cC\n4qmz0aWZjqUTCHn+7GYd53QTUKuj7SahXqcQhFw6XTbVcx241u5VFw1Wul0ZE7CocqgXHSjrSs5A\nd65q2XXu8fHxFaSrHO37tLmr01ujTbZHpRtX/dZNWikNy5iwePdYnutT56Q7gpXapL8d8NLmOX/W\nOv+vaN180bQdCLvI39WXQDIBnpbpyLd7asORoe4xS/ofpuvGPhGINJ872zt77R7yM3Xp/Cbl7pu1\nknBZZTK5mK5+631JXu8ehyLIcsI450WHz2vOiUycHvvCPSI0dWgJyNd6/+jRLl/HyHftmZSnS2aa\nj1GkEgolEXzhhousWT+dR2pLF3WUbei9ePfRfMne2W+8TlvryuiuO+c9dbATB6d9m/I6HZykDZic\nsw5M0phRdI4n8p1kR1AT8dXffC3trq46duCrx3o/2IHyxEcdmeufSZxtfQaycPeI+CgbXmu+TE1x\nTmxXhxugBMJM75wuI4QqL21Ccw7a9dl09/RuQqVHJtwET5OR+ROwJ92cw1awLSfF3dDTiM4BkZad\n8ifywDQObFx7Ur5OXCSXNt4xTx0TWArIGY0lnZhuJ25c3H1QpmE73fXOJl0Z3dxP9bK/XPu41HuU\npHRpd2RP9XUg7PpZdSRJ/b8sU2K5O39GOtu7OxDvpGNnE2ar4ML83aNTbvJr1LgD4poU+g86el4d\nWbeRo9qlOrtHPJwkQE1t7sB2F0k4Z6/nXZuo66RNmr42zfAebf3We7AKUm4VhMSI7aBOBCxudtHb\nBJWmlqO/ffv2+mYwvabluvZSD9oQycSu/7SN3SN+dPAkhsmGSZ60LhK1Cbl1xDYBNfV1pLEjFKqv\n5tNy0oqLA7lKX99OP01X575///66Qcv9yUnVqd+s15Eupk8yJVqfGcDP6jbpn13+aRl3B+LuPu2Z\njphELCntxAlSdm/BcWU5YNY8POd06u4dOaejTrHTV52MKzfV6dqZyu/IlRIOdRopLYlJtTH1D/Mq\nmLIPtG4+3uHaWHn5oa7pBR5abrJTdaru7UiuzVVGioxS/6SyVBSAdexcva7/OvtK56qs3Vx37e0A\nkOOfdHB6uzlHPRUI08qTI2fUabrRqo5387Qj5xNxZXxmYHZyxPY+Qu4OxCWdIXXpGa1dawTJ6aW6\n65v1qkHS6WuaLk9Kl/R293fTN4+rHY4AJKDaTdyuTufsUt28zke01BFOHYnrFz4KVpIccwd8k3rZ\n5u4NYUfatAPlSrsbg8p3RKeJnR4luzsh2Om3XnegnwCaeVJ+N4ecD+K5pn2ZcAAAIABJREFUehxQ\ny1Nf8fDw8OY6yaEje52/Otq/Z0G58n4WuVaXn0Uq7vZ/xN25DiwmZTmpSEnzuShhwox2TLRztqwr\ngXOa0Gvl5wY1v9M/AaKbdDt2303UCTlyAJt01XLdI0Z1je+jVuB2jtXpo+XpOLu3c01sj0DPZ4dJ\nIKh/OWT3/uxJvZQEJonEdOU4+91d2+mqelC/bj51uvJaNx+S3ZIgd0SReq7141+2NBrmY4063mor\nuiStewHSBj03jtO56wjNESA6Mjd+hkz0n9rNUXG+OMmniYhVuog4GdN04nfl0Qj1vFtKotF3jmEy\nCVKeqiu1jW3fOUlNc4TI0Ek5HescAcv1nbufmUDbkRU953aor/XjDyE0D+twfVfl7BzWVNI+hQQ6\nLj93kCd9tI271YspQerSKOHRttYYV73Pz8+v+dxSf+fECZ4OTCfjOGlT0snNdd4n3u334L6FOtZV\nHn3zWgFu7YUoIK8/iFDC+P37dztHqUfXZ5pmGhEn4Ndy7gnMnY/+LPKpgNhFhwS7jvkl49eyWGZy\nBlNG6Ca4TlIyZU2jurjJQP2dk3EviWc5yQnRWbGvmK6bnAT/5NBYr7aN+RR83HOXmv/r16/r+fn5\ntT/L0dc5vivb1a96qnPtHJIjZny1pqtHz/HY9R1fwuCeQaVOaQxdGgeuyYl2Y9y1r+6N8+1QCia7\naNrZ7A4w3BzvAFf7ofMTblwSKOmKBsmhblZb6wep5H9Tu9Uv18YpgO7kGtBKwL/Lcyvdk+wI2UfU\nVdLVeRcg7hhU+qZMJ6GWP3F6ji06MHUsi+m6+lK5yfknoFegSo6rrnf94fJ04hyX6pecBPNMJ0Y5\nMAqjMEciEllyfapg0NnIhJwxPZcgtezSW5fRj7xohSDriKRe5zve2R6NaqdOi23blaH6MF2al46w\n0Y7ORmDOHjkGTNNt+FNdElCyz9hGXc6uyLjy81Wbqb5dWz9CJgHMZIyO6JnS8vytQDi1gasik8dK\nP0VEnKLSab7U+fpdx26Cu006CXyVAeuOaW4e4mB05IOTMQGXDrwuY02MOpW5exOT5iVIOcNPj1a5\ntM45sX7u3nZ9UecL6NwSLpcCtSzVr8ZVlxxdv9M29D4u2/z4+LgeHx/f7VHQneHqQLW+Ih9uB62W\nof2r/ar3FzUNSY0jK2ks+Mw6+9g9nuTGgO2usimJFJFIsO92BCx967zs/kiDzp1jpHopKamNWu5R\nxOoDHf9v376t79+/vwLxWuv1j05eXl7W8/Pzen5+fhOZu7fKUXfnM24JzkcJUecjj+RPZOxWAMzy\n1B/ubCvJXTdrkQEnQKUT7CbIUSbowIEdXEtEZeT6cbqmZypd/WmAJgSC1xMjdnV0/Z4cmWvPTqas\nN+nYjade4xJhXecYpkdrHDBM2knwS8+YE6zqfOoL1ZVkT/O62xG7sXfXE2jxXNcvCrQOrFT/Slv3\nNdlPtGMXNbO9Oi+pc5WfgEkljcHk1bpOL5dHd0brUrXqyWfRXRkF8kXId3IUHLtyVCZzddfvt5Kk\nh7OhW9V3bXl3f8Wle9g/TXSNVCp/B6qsi8c7EuCcjk729PJ1/u7KpmOZDGi31ME2dSDM3ykq7SaZ\nc9LpTx2Sjk6nyqtL71pe6icHwprfRS2JUBEgnJPX606Huk6QYnTIOh1ga7kV0Trg280J1UvbxTYx\nTQnnH9vBVYw0Z6hT6t+UXusnOZ7YK8tZy/8rmas7/WY/6YarGjd9lpybt5R0VT4lk3W9NmtVnWfe\nm1/9MQHwnST7Sb9/lqSx+ihQPiuf6h4xnYljgbt/ljk6iZi/c/C65Jle5LGr24EvGbvTS/PoM4gO\nPFM0MNXVOSv93uVLEYsru9NJiZduYNG8ru/UKTuHzbbpeO5elqC/E/gR3NgfjtgVgUkvcdDfXdk7\nfdknSZKNTsCOwmiPeZVMkACl786uO3vbieat3clarxufWiauNO6ecjc/6UvqnNb/7du3NxsyX15e\nXt/aV+krsubz6GwXhT5mYh/perL9nyX0AzyX5BpAvkUbP8U94rXeGnByAGv5v9HT7zP18rcOoC4z\nu8hhWi8nojvujKGbWN2GLQfsyYmzfROHliZcIluVNkUmFOekpqSrhBHnZGI6/bp+6/TRZUjNz37o\nQMiVWdcZgeouXLbZjX/XB10aAhQjYrdiVPOcdr+rI6Vx5Gsqri9oG90K3K5sjXI1EmZfkYSx3wnW\nl8vFjn8dT4B0p7u2dwrOzP8z5ahPYL4z6VJdlXaySavkUwDxdAI5EEzG0THuxOScU9Q8jJ4qHb/J\n7PW66r0bXBqXS1+Tb/f+2F0fuWMH4k4vVybbu0tT59341Hl328Ll0TS1SaocIOvVsdRjHe8JcKWV\nmrS7WdtTUY+Cddr4VGVUOv0rxdKbz+iqjbA9jmAybyIg2obqXxJOBQ2WrWXuQJ+S9HWSSOIuckr9\nlcqvvtclaSVHDoj1HjFXe5RQsV/5bDwJg2vPTna+dALG10SWt5DkP/R6yne2PidHQHitTwLEUznK\nSnaMpSt3rf0jMYw4Ko+WmSaFi/Lo6BJhUNlNtF0fTCeqTkLXjh1ZSJIA+4wTKcfGfOoEXX2OWBGE\nO/JAsKG4R5boQEk03H1jbSsBW/srgbc6draVL4PQfLRP/eZboBwQax+nD+vjGNVxd53C/kskUctk\nmp3e+rx4SvPw8PCOED4+/u566zz7z5Gryl/2ovZcu+NJJp0cmVdn5B5gfMs27fyZpkvC20w7ufuu\naQrZF/Mw3bV6OAfjHINOuHqUIDHlLgKgOKNNzFvTcuNal0/b1unlHJzelzpCDNSBpyiIdXZ5CBbU\nqc7VzlYu1xGEuYKg33S0dV4jHdZdjpCftCxdeZ2+CsTa/m5O8PrOERIsJ3NAz+vccYRBl8u1v3gP\nk+OkL2XR/NSZ7Ug21vWXtoN5NWpVHWq8+OhitauOdfwKeLky43bBUz+tV+skmSv70heHpLnkJM3b\nRKxd2T8bfCmdXlNyMJ0/t5a7bdY6Gron2Rmaq6cbFD5iUXWs9WPyTTZq0SCcMTAa0LrSOS2PS5Bd\nZEFn2pGhkgIhOhbXDtVzQgZ2fde1Q9PoN5fmNNJI9891QwwByYFL5zD104k6+rVWuzRd4pYiubOa\ntuDsT69Rp0rj+kP11Tbo25/0P6I1KmckrhugGAF2hHEKLBOg1jJTHXqez5S7ZePqn+fn5zdERFcN\nain627dvr32T2l5zvI5Vn3qTHG1C8+oYHA1cjqa/p3C8JwHDrpxbyqTcP9TS9LWSJrwOHh+XKUkR\nVKUlw2eaXQSq59LA7QDUPeqT6koT1L3go9OXejlAqG86XVdOiXOmqewS3ifjsinrcvfU9HpHCFTc\n/WFGXHrPl9EWIxztI126rvKc83ZOnEJSQRtmhMd8TgeSAp5nRFvkh2SWRIEEy7XPkY0jzpRzgHOr\n2xTaraiU1PKz2uLXr1/XL7/8Yv0Fx8SRQQJvXdMVnzMgmkjbUZlGnreUna47nT5C3yOromvdEYi7\n596OGgEn5dTZs4wubTkERqGTcrrzrM9FgLs2Jd0TqeCE3U08B4paTzpmPal9erxjsyRTLtKiHrso\nlY6fjpnfBAm1DSfOoVY93bPoa2ViVIDWyVFbdwDEOp2zr3bwbV16q4B2zGitrnGJ1enLfuRxIm4u\nj6Z3ZLX6WYGzotzqK9ZLoqTHdWsjrdRRaPu1OkXyoUveaZyOAOxZUP/ZIOzqTuN7TZmu3E7+EPeI\nk+wivpSnO58iBneuOk7Zrp5Tx1v5u/KTdFGMOqsuYta0SVR3gsG0rzvg3bW503+XnqDtlgJdRFTO\nvxxTIjBalgKiA+Qqe2KTLl+yE52sbnMWx0/PJXsuZ800jDidHZRwA5nemnCRsNNdAUKj2jpX/yaU\nCIm2U8e805t94YS2U+foL6ofOJaM8nmPe623JKKEm7ImJMHZjBtD2kx93PPbqX+Ogjfn3VHA/kiZ\n2oimvYW4PSrTOj4NEDsHmDpyN+jM546dgZLtsm73OINLp3poWypt5wgTCOvEV+ekUuU4NuYiKerU\n3ftmlMTdyZOJyInOdibQpz1U5Ms2Vt/Uiw40SnDl1bGOq26KcaCaJDkrgop+1M4U6NZ6+w5x7WuW\n4ezZtZkgwnzJprU8BZHHx8c37VO7VdB8eXlZj4+P7/5xqURXbarvtZ387caim++JuHEOafTLfmG7\nXJlaty5J1/ef/vSnd+Os9507vXlN60rEfffKyyOEeiK3BLRbyWfUKcndgHi3hs5oR8VNIpd2yobc\nZHATliCcHGECqA48kxC0JsDVEYO13i6nlXT3hil0PGmM6ESpX6rDRQBJv8fHRzsmujlG29iNh3PM\neq1j/jsbSUDCZcZu807qo52TdvkcEVSnXqL3jh3w8lhFVxlqnPQ/iav8OldpGTE70OU8oN5M78SR\nLZeXfabtqrz6XDdJsyNa1NvNa/a92qaSRS23yEQqO7WTMsnn+ur/J7llmz9NRLzW8fsRmsdNmm6S\naVpGSmqEZfDfv39/t+TEpU/HaF3bpiDnriX2zHxc9lRxS2cqk0iQ/e7OU+dUFstJ0Vkd6299gUIH\nfHp/vx5Bq4+OsTpnrZ/2pDvK61sdpHPAzua0fOrsoi1KRwAdGHPJd60fj8ak5TSSm3TvS8lSpdO+\neHp6eu2jTn/ap96PTfpRkv2RaCloal/tVlLYN5VX74lXnoqQqV+yc9WFdbs+2tlF1zc8536ndI7M\nfbQkgvDR9Xd16rxNc2On393/9MF1bAKqIze/tY4q0xl+crxr/Zj4fG441eFkZ6RksF3+Stu1Memk\n/ay7Yl2b3Hi4vtG3B7lJ6RxgIhNJl5S3xG2QIghSDyVW+nw4X8zgyq0y2IcuSlFd9OPuK3758uV1\nuTf1D/XoxDl3d151rGNHah8e3v6DlZbr/iea9Thyy/rro+TGOTmOuepT49A5bD1foKfjcLlc3rXJ\n2ZCbY/Vd/VLHulNabSX1G6UjZc7Gql7aYiJrU+nAm2V+FDhq+WeCNy3jVrIra9cfn+6FHiWd4gRV\nOo5pORM9Fai1Hv2cKddNYmccbvLdgolyM8eO9ev3bsenAxLty52kPtjZhNvIpE7PAe5a600k7N5M\n5NqWALsDGAfEBLlubkztm79ZH8vmpiS3XO4ie20L6616FKjrWPtb66g+4G5/t0HK2Uj1hbv/qvnd\nXHb3wR05qfx1XskCQU/7zO1nWOv9a1UpjjhVWjdOSiImc/uWYNTpe2u5tp6p75z6qyqre5Sxk7tH\nxCU7drXWrJGOKU07k7sMK3JKDLbbGUdm7sBXJ4guDTpWmRggJ7/TjX2wux/McelYbmqTuz6dNGfJ\nDfXQx5m4IUsd2BGS4GRia+l8ciiq05SIpDS0Lebj87JdWanujkS6+ci2rfV+edjp5MqfCkmU2rTO\n1dp1r8Bf+diPCdwrPR20uzXgfleZDuDrmiMblUbJiOvPNG+nZE/11vy3EG2z6nWLOpIfm+T5SLkb\nEDsDUuFAdB2RHIHWNWFQmkYNzDkCBzApUkyMWq/z2OmaQNeJTvxyIHS4O6de9RCMeT9Rz/E+ISWR\nLzfpXL84h6EvM+Akro9bemZUq+ndeTpVgrmCup5PwusTh8m+S5FYeoac9WuaIi7uUSLuNzgTkaRH\naqij6uD6Z+IXqi4dR85vrV/byetOB1emAnGVR3DmeNVucRU37pxb2g7OI0ryr05uDXxnJY37NG9H\n3M4SuIm44GxS36eJiNfyLLNrRAI0NVA6uwR2nCzqyHcv8TgibFtyvjsGmsomeHEz0TVGyPwaYei3\n1k3D7Ji3q3N3nv9YU9901jWOvDf8/Pz8btWjI0o7Hat+vW+pOrlzqT+co019Uul0Gdj1S9I31c/7\nwuzvTm/qV99KDitNvfrxcrm8vm9aiYHrP9cPtEle177RzWdp6Z395mxBz2tZKrpfQce17LDKTCTY\nRfHa14kcpk2Cyfem3z9Tki4T3zXxm86PHfWLEznSh59i17TrmK7zGaF1YOZAnQ7V1cUoSqWbqOoI\nXKSVdE590k0cvUe0ixCSA0nXuvTVPsfS2TZ9GcQ14kDw4eHhdRlRzzuCUCCsY5r+qKHKcMfuESim\noeyASb/rOBHGrn+0rK6enY0oqHRg6/Ql4HGFpEDV7UpmebyPqqCj/alzS/MynQNHEg0HxI7spflJ\nIHbAOhnXnRPXfI7YOOIyJZJJn+SLP1qO+I6Jb03tuMZHXevf7ro0vVa+J6WGXqA4KbOLpjon4CIh\nPt5CHVU/lleiS2yJ8RLUWZdjb6k86pj00nMJ8FOeSpNAiU7CgY1KcvQ70Cpdygl10VB6hjiBcJKd\n45kSo2oHI6VE2BSYWEZ9sx9ZLueHs3ddhmbZrvyOULjd0Wt5gCYhVrvRe/pug6FKlVN/iODGnUSD\nwHm5vN01Xfaj940rXddXbPt0j0ul7+axptP6qj36fL0j7E52tpp0db7xjKQ27/SYyLW6XSMTfT9N\nRKzHDlBS9OWkjNHdj3KOt67rbz4XyrTJ8aR7cqwzseMEzB2Y0TFNDa5zaEzj2rNj8xMysCMBXVsU\ntNIOcPcI2vfv398sf+oYO9s40vbS2RFNXlNS6OxA8/KeaYqqJtEW6ynhIzvUz+32Zl0dKVWgUMDv\ndqArgLnd7TWGnC98FIx6Pjz8+I/gh4f3f1PJcSq9O3KowF6iPkvvBWt79X42+03r7oIR51/00ayz\nt6bOyNF6pjY7LTPN3Y9q/2Se7eSujy9RHGt3aVweLVuNl/mqfDrfSqMbV9SRO70JkinSplPeRSqu\nnq4fOueX+toBRWK9O8BNear/dmCT9HSEzOlQZVREt9b7P3TnRi0HugkMStwGopSW5zVq565ctoXt\nna4Gud/XOAmC7Y5oTurgvKxzSjgIHARCBa0iYASytPSttlj3pR0hqjpVBy3T7QGo81wi1mO1VfaN\nE0fEOhtVfXTJWtvz0XIWhI/kOdIOF/TcQo8Slt09sZLk00TER53bLepIjjcBtYrT1zkqOhhXH+/v\ncAJP6ta0UzCmc+giQdd3LoLd1ZsmUVcvoxN+OyLECGJSDuvV/N2O847IaJqJLR+ZBwTK9GjMGWFf\nuWhY66G97sgNSVLtHk75HFFlvztyxXQKaG7su3vXuuqy1lvip3n0saKzc5k6uDlNEkIiV+NWH9WF\nQcDOXtj/t7Cva9JOdGDbEs5MdDnT5ikZXuvO75ruoq8dgzkjbuKWJJB2enQT64w+TrdUl9sNW9d3\neRNwOiNLLHIHvmxfOQqOd0cW3Lin9jEtI6ru45YDOWm137tHajqdHDB8//799T7e5eI33nEnbDqu\n+uo+puu3ndBJuz5gO5mfOiUQZv/V769fv74Zv0SOuJRb5daYqh6ubQpS3GWeIuPST8+lnemOqGif\nUGeK9rvrAzef2WZNn+bwWf96rd87Ih0ekExo+kn+I3IWhN1qoJO7/h+xc+pHHMeEEXWRjzsmo+6M\n+BbMsMQ5wjqvdSvbdXppPm3D1AiPMMbUdq2PAJYcjNaT0tBG2C6+sYnPDLt7jWTMu/ZNgUl1SxuG\nHECXfkxX5wsAHPBRpnNqNyccyKR69NzOPlzba6nYAbYCrd5q0I1UjkhxzhCME9hqWp5zy+vsPx2b\n2jzm7G6yiYvinj8uolL5Oz82lV1+R/xvKYlQ3bqOXbk7Xz/xHZ18in9fKtFJpC8kUEeanOau7LXe\ndyaXkLQefdSFDsgx+q49daxveuISmOqR7j+xbJ5P9xK7yeIc164d9e0mOxmobqTp2pNIWbqmOlCc\nMyp9UqQ8neC7KMY5W9quGzfnRHU86jdt42gkwDHuiCrL6wigs5FUDsXpSNLm+sQtC+s4KxFyQKwR\nsQNp9kna6+D6ifU+Pj6+I4KM3jmfJnVeLpd3rw1VslbH3SNV9X0GTHYAlezwSLlTG5uSzSPXOuna\ntPNxlE9/jzilcYNTokt0yUjS5K8JcpTRVL3d7m7H9LUtTr+uX7qIY9puluMmwKReOkkHNvxf3enk\nTIBcDjfdl3dkodO/uz7ZMNXlVYJXzrF+r/X+j0UIPPXNjV71raSyZOckOBZJdP+CaysJdIGDjo0S\na7e8nOZE9ZX+LaKzNd760LmTlpv5fm/ONwVptdVu/uoxd4brN/Wa2Gk3xxJRYn4tZyc7uzgiR8Fu\nB7TOF3RlnQVbrcvNm6ns+vFTALGTmtTdBpQpY2OejtFWmqORkitjd41pOJGODvgE4KlHmmzO6TjH\nvSMWCXhdH5xl5+maOj8FrK6cVJZbCnS67khQfRScNV9yxpfL2x39dMgFzgpy7l+Rqiw3RnrNzRM3\nZtoGPqrDOcQXT/BeMK8TFN1Gq7qm/atpkn1Vfek59wS6KtSX6RPpVRvQfK6PdTxIeJKunEeTtvxR\nZRdwsD+v7YNriEmX91O8a3oidFJH8zrwcBOPzzSWA9zp65z+Tifq535PDWhCMHZ6MO+OZU7HYheZ\nTaUjRokYnJk4rh63CYi/6QDdR/Ppf1zreDswIfAmm1EnnWxnMr6OFFJ/JRSJPFFXLq3XOY2YqRuB\nOhGEHRFlhLy7BZRucbEszlVN0xH98hXTZ4Tdeb54hP3xfwF8Ox+4I763ljTWSaYraXeNiF9eXt68\nAUa/dfJ27G/HiNxvOogqg5t9JuCWIpiuzVzqI+PtjIttoC4pvUs37TuXp2unY+HcFDPRk9d3pGE3\nXhPC8FGMNz1KlSJNTed+JzDd/ea15OCPOPA0Jzn/3G0EgmJHgpQkc844SQ6cu9+7OaUrDGeeDy1h\nufqolkszscWHh4d25aPsRSP/3by+pdySBBwhXJr+2jm91mw+pOvu6SAnd1+aPhvh7jqnM2qdgMmh\nTaM8LePIwHcRXHIMTseUV9PvosSJU0ttmIJ/khRJdXXuyiehq+iTZU9JXafrVGduEtM63RMEzpYm\nRI0ElsumrNtFrUl4z5ftOQLq3bi7drrI4qhzZERcIOV05PK5a1eqr/M1yd/UrQ8CzpQUadlu9eZa\ngnpUzoJw8tXTMjtf0vnKe8tdgDjdUymZAMLEGe+cQ4rEJ0Cwc8hdGj5XS2dY310EyfqqznSNxxMS\nw8iDDpx95QD9CAHobMFNIoJYNyapL6YkpFs+3PWtRr612aqime5ZZvcsbemadljzrU4JjJ3u1DcB\nuj42pMurWhb3dhTo7QAhbXRM41NluD9mSMcKyJP5NUnndErzJKXXfnf+yemm39ou3QxX+pN03MLP\n3lqOBgMUFwk7X3xmPKdp0/zv5K4RcWek6Vpn1GqYaRNFMu7K45aMKn8CsBTpJP3WeuvcnHN0zynu\nHOgRoa4pamGejnHyXmEBASORVI8jJl36XfuU0NRmn07/o8LxmLJstc+yOT5GU+lc5OzsIPUZ54Ij\nDWpvSX8F3xI6mI6Q8DjVw4ibbXRCIOO1aqN7l7Z+p3lf34lIdsI+KzDkc88EYc4d9qfbdc5+4G2/\nDpiOCPvrFmB9S8DnmN3SV+7qm+jl5O5L0yXOkRGMb2VIrgxXT6frmfIdsBHgJ848Od9JRJnSJifT\nlenKpagj1D0BSR/nvB24JNBJk2+6acLpMLW1FNmofrRpBTj29+R91DsbYFTroi220V2vY6fHRLpH\nzOpbx8gBCPVx4JPa35EC124nOzLs5sVa73fcJ/JS+rvVBE1LcuYIBvuiytUXphyRWwIlyzyqyxHC\nq3VN83VlfUQ/rPWJXujRdVKxQ02b0us5Oh+mmXRqqmMXWSVQT46tqy+lrfPJAezyOxDcRVhO10q7\ni3rcG6GcThPG7cpxTnsKFgS1BD4EPT1OYN+Bo5KUXT2OaNSxRpJVPqPcLjJ2eroXTrj667vS6/O5\niZCouLISqXK2oHM8tZ3ifMDO5hNZ6dq01vsnKdIjU/ydVpBoLwT5yZxNZOtnSiJYO9kFHCRrzr7P\nAOtH9tGniYidsznDmNyAOibpnOnOaZez3Q3IEZBJYNz1Q9cv6pg78NVydsRgB3aprZOx0POT8U5t\n2jmgdI1kYkcInd1Mytd0Whcj4fTyDM0/IYJMQzDm40IsJ9mqlun+9GDyyA/7zZHsRLQI7o5op3u6\nu3nLededP+KTWEZ9asVDxz49q+4AxY15fXNDGvvpqP4TOQroO3J2FPgcCE/rn5Stut1a8qz5iZKM\nm5PUpVGhwbp6XB3uPKO7pOfEIbrIwEWMLOMMsLjrzrm5TycpgtjpMC3X6dnV4caH1ycTxo1Lqifp\n0I1lXeff7U3ylSSCRJ2nZR15cxwBiVGnvtAjRaGMAB2YTvRQsp7E7a3oACjZmrPzne8p0Ze1uPcS\nJNKkJD/ZBu1e+2Tnw5wf6tLt5MhcOSsTX5B0SKT/WvmIdn4KIF5rvTPWlMZ9d2mmD8qnCX7EUerA\nTwdr4lymsus3rXOSz107mlbblu4BuvTudY+urgSibkx2khzL0Yk3GcvE+M/YwWTeTByam19dhDoV\nBSRHrJ1dJOmA4rOAR/dKUAKt7miuNJ29J1+xCzIm8zb16Uf0kavH/d4R7u7aLfU+avf6etOJfJql\naUpyVLs816btIrAuMkmOjizbMfvacKETQtuvk1DrmrTBTSTXt24yOh12feEkvXNY26P96PqT7XZ6\nVZ9Oo2HNc43soupd3clhu531LFvvy1Ze17eu7yp/d+9+d195LR8Jp3KcLfBRLM3fkVsFMf1WXZI9\nsxz3LHflT31J0fy6zFxj6WxWHzWqP29wPmVq04l0H4l0UzlH59YthWPndDmCF9eC9BFCN6nr0wKx\nyi2cpXPset49LrHrwASQTKN1uTL58vkOKJ3D7JzN5NnXKkOBjOcc4GndFdm4Za+65t4AxD6k43AT\nMOnPvuAjbK7P2OdaZ7otQh27c1+/fl1fv359w5B39kApnaYbHJMdUr8S99aoOtc9cqY6ccyd/RbI\ndK8L3c3zBCbuUUXnLLXPXNto/6kcp7+WwXwknEV++GhT6p/dC02Y/W1KAAAgAElEQVR07nJ/yOPj\n43p+fn49z+eJXZ+6Pr4WvChHgq2k3611YvlHsSCVs5NPAcTqXLtI61ZMzDmiHYi6azvZGVoHFPr7\niFHsoiDH9Fm3y9+10eVfy/+7jOuDSttFACkadP+ypQ6pytLvVB77hqJ9xg1BLqpy9XQvsyknuXOC\n6nRTO7prbM9a3tETJFKbKKkOkh2Xvit759QSKUk27ggI03XldwQxAae2PY3z9LWITpdUR5135IBl\nTMjQWf0mcgSgP1rOEoUjj0uu9UmA2A1sN1En+VXShDsjO7ZM2Tl8Lj+mMnicItoujas/RZYuEnWO\nZycJ+Gmou9286T3FztF27wembom48JgvelES4CJVjTzYdxMQUyLRSSJpiVi59jJ9qqOTaXRe/eHq\nrj52ESvLTu9OTjbbjQFfUnKG8DMf38S2IxcOFPWlHY5YpbmrwjJTfanMW4kD/F36Hak9Wv9HkIFd\nf6UAhHIXIN51cHKISci4zw7YpMO0jqkjc/nd8Vo5euwiRJe+M3zWv4sEd9dc+yricPWnPOq4uuXS\no8SqQNE9GrLLV6IOzN2b1DclObtIEYmKAxYnadwJBGzLjvBR3Dxyfd8BQBdlUXeXZlpfkp0vOPJU\nxrSeSUSpjy6R1Fd+t8riItqOUCuhq01zWu7Oz9xKjvrjW0fEHxVhp3J1rk3qvsuuaQXO3b2PjvHu\nIgv96OME7voE8FOnHjFcl3+3m5i/SVScQ2P0liSVu6uzK8v16ZmIS8dM71dqtNHZgC7zsg8UGPlx\nbao8db/369ev6/Hx8fUe8Ldv3+LLY3YrCUci0539O8c+dQa7CCtt3KrfnX6T/nb9xKX6awj2ZJ5O\n+8rl29Xvzrm+UJ/V1cEVg+TPHImkbZwFql2+CQnX7y7PR4DpRwH0UfkUS9NH5Fq2xvuWXblTI+qu\n7Rz7kXKnrNulcdF0isaPGqerRx02HSyjcBUu6ZWk3ba621yX8bgbuORI1EL9iwzwkZO0TFr5U2Tt\n8ug5XaZ3febGamdfDuy0bJZZ17TPdRy6PQ5JaANVPp3ymVtKBJ7ULpePeVR2kaK7pv2kdlljXO8/\nf3x8jO+4d1K2x6XvjgC6Mrprk/lxRN/dtZ8FiJN6ro3ez7Tl0wDxJGpyyzA7Z6bXOnDQbz3PST01\nQGWozjFMBys5kE6PlGcK5C7dLgpWvZwOLCf1RRe9u2sKEsmR7zZOdI/76L1gOjpd4iOxcfcHu2+t\nW4G/e/Ul+yC9JUvr4LUOqJKTnO7ET+DGcdGXXpx1zB3ZS/N6tzrBstPcORIckOwqqXPEq7P5rn0T\nou586S6v1vFHlR3pOlPetSsDn+IesZ5LRj41kE64Q3cSERMwyOK5Yagrz9XnHAh/sw8c2HU6uMiA\nuqR2TtrSnXdOWPtV+6+LYJKxd/d9U7Tmyu4mUzlKXZJea71GNNWG79+/v3t5ha7AlIPlJq/ujUrq\nlElYEsAdASXXVv1mPyrpSWlYD0mOlsN6mVbbVB8uybr+SQQxXXdtqnzUaefAE4iqnU3yr/VjFYYR\n845Ia361PT12+aYyaQPL3oHRJM0tJBGOs8HRxF/u5NNExCV0RLy21n6S87zmZXmTDTy76POsOIez\nm6hsO8EzTbAuGpq0icz5Gkn5O0DsnCzTMH+KijkhXbnpESg66HK8BcY6LmrTDrx4v7u7B0573jmv\nSb/tCJ1bWeiAzdnvjlA7++pAg34iAWgnzqF2eqVzRxyxswPn8wikWo+md/6C5VGf9LjmrSSN12cC\n42vEjVW3w38idwNidTprnQe05Hwp6sB4b3EaLVR+l2438XeRKME4lZEcGXVxeZ2uTm+CTOXXlYQj\n48V2qjNJ49dFOtVXmm8X1TI6JolhH9WxRvBut7S7T5peWKHlqP5uVzDnhfbTDnjPgNJO0nJ0t5Pd\nLavXsZ5zkWOyB0rX1iPAOgWKiXT9neabfvhiHJ0Lrjz9rvZ0uim4cyxuJWkM7wG0zvZ26T5SByd3\nj4h1I8Nae4UnDuUWHTp1XkdAvMp1x6lcTZeijARY7loqw51zabt+mUbxDlg7orCrZ+d4zo5NAkGn\nk97n1DYq+Orytu4C53/ETvo4PYqU+mtafipDy1FJL8TgSlOynWQbChja54wQnQ5n534a/6Rvkh0I\n7+y7znc6TP2lljfZL3GEsHW+pZu3PxuMfzbwn5FP86cPKkeXdlIeHfijgOnKSVEJnQkjLqeHMw7n\n1Dqn6M51dbvyEjFgGxl5pnqcLtSD5/X3rt3UL01+5xzSI0ydXmyT+13Aww00l8v7fzrqnDSjoEk/\nUFLUNNGhq8/lV3vtwJAAe7RuV159n5nPk/rq+m4FQvVROfJmpV3fuDmmx2lneVpBmPjO7nyX9uz4\nJt2OlrGTbkxvQeDOyt0j4rUyk3LOdufIeI7HU2BzwLBj8135rj6y/y5dRyR2kc4R59odd7rt+itF\nA67fHONPoH82OtByOgBkBLYDhAJf1ufK0HZ149YRpIlMHWpqXyKd7m1o6S1V3fzQMXb3hOsad693\noHKkj1J9yZ9M5sck+mTZfIFHmvf0i90u9lsROh2jBGJH7OwaOVLXRI9blHVtGZ8CiNfykZH7zeMz\nBub+fKDK2oFEisKScXRAMwWOjhHrpDxiDMmhuwh2opv+dqBbdU7y8prahnPs7h3PdZ5RW7p/SyFg\n6rKyvt+a5SmAazn81DVtD22ijo/YSmrHpL1JurwKAvpvQ2u9/6P7nU05MqR9wV3ojmjfQs72deVd\n6z1J2dl+tUff0KbpOvBzoJ+eoU/l7Mb/Whu6ldA/HwXljkjcWscj8mmAWIXP++4mLo9TugQ8iQTo\nMR0aI7UdELroq9M3RV464XTzFB/NorN3joAOPznK1C7mmzDM0jdFiKkc55y0vVrW/2PvzXocS5Ls\nYGNEkIy9MitzqlU1KgnSCNAf0P9/0rMgQGp0SxrNjNCq7p7KrtxiZTCC/B4S5+bhiWPmfhmRzaz5\nZADBu/hibm5ui7u5X1WGDjLDCOVlQVool/fAPjw8xP39vd1iUp3ohbVht81qrDJwxyG6Ml37M1oo\n3lm5ii+esxLuVQCOb9EvLl01LZsZxtmYcHRw+ceAU5KKg449HU/gETVqlNfdqYFqzLixXSl7x49O\nXvTyquO7sQ6Eu26l7zFoesrLZOmvNmqaQYkL5ugRpJWVp/AUS7en7N58ioda+25wOAMAn5Xj9+4Y\nQsfwKqjUsOhtW2a8uHLdveLTi1elXCC4ek5mUmHPBg2XhWcItIqIuL+/HxQx01oVa6/x5QwobWum\nYDLPXKc6GS9+5hSp0imDsfzijNiszZkScWMoM16d4OwFh1sP6Na0SvY479WNwzH4uPHRmg1yecaU\nPwYyhTyWzlXZPcYkQ49+2EZ/9LTnq1DEDjKLlRm05/i7XobVay1jG1DBWuGY3beEYiaUezyFTEBV\nA7UXN1WsTpm6NFm9GWT5VCE6r8RdO6OQPVhWxHiv+4b5neLU4gMup2XNV2X1CrOWYG/1t5Y1RumN\n4Sfwjhqa1fnXvXhH5LR9Co2rfddVnXxfjQl+52ZeFD/2qnkstoywDP8Kn7HwVMWelfdc6b407EwR\njx00mZCqynfMmFmIz9EhPQwd8bT1iExgVgqtwqtHuGZlVWU4+rc84Qo/5xEhf4V3D70qgEIHzzHv\n8bQ00vJ7Z/W78hlPJ4xbAr1qd2XsKA1c2WOUa0tpufe6fZFxV0XL71UJcdyHo0EPri1l4ozJ3jGf\n5XF8pbMAlecf0VbCuM74qEcetcbbcyjh54KWQfnc9T6l7YCdbV+qBkclcFvTDW6g8LOqDi03E6oK\nPcotw4lx0wMQ9H3W5goqS9O9c8/GDlpXB66r/mkZRBlurgw+ajJi84MNrh0tQ0+FI+p4eHjY+I6u\nKgjeX+y2MrWUWq9Qyd5VvPmUvuwBR0elUzbOsmdupqdnZizD76n5sjKqiOlWvZXB0lMG89VYw3tX\noAZJj9zN8vKzDMaOq6dAT7lf3dS0sxb1GeBLEY5xcde9eZ5SbzYYXR086EAvXeusyuX3LaU8ZmBn\nwiDzwrQdrgzX1jEwdksJe8TsGa/X60EJ6zos1ppVsatwGGMUKk4tYZV5akqzMd6QKztLO8aTzvhO\np5zZQ16v10OEMZ9HnfHvWOOl14Nqld3K38KD/8c6Bb1jpOXtVu+eUwY/RXb2yLaW8b1L2KkidoEM\nlcJVpczvK8Guz3uh12t39/x82052A6lVLw/eLGDJ0bGqQ9ucKZBK8LNwZcXlPMnJ5POWJG5P5T06\nRRfxeBtJ5o0rj2EteDKZbFwjH3vCzlthhV0JmExQOn6ulHBPH2fl9kDLw8oM5iof/zQNeIDv0Y/V\nRzIc6E6CDDJFl/E5tzuDMcav45VsnFZKeRvjKnvWA70yrpLf/P4pOD2HI5SV+yWV9VfnEUdsfnGG\nrV587aalnHqhJSQraHkhGdO1yqiet8AptYjNNbSW0Bhj9asy7cUR/9UhJmyEOKFc1al4OSWsZWTK\ngN8j/cPDQ9zd3cVisYjlcjlEr3OZrIR1eprrqTzbHqGEPLrH1v1rHi63x3NSaBmfTH9nlKgi1rxM\nN1amPd5qZqAoqJJjhV/V4YwOPM8MCNxnClcVsvKkvkdZWbCa4uV40I0JLaPFF2OUMOrtlRe9fFkZ\nJr15M/hSyp1hZ2vEPQpBBVk1OCqlmimRHms6g20s0VaHZuf2RuTf4u0pV9O0vBFN5/JX5WoZWcSy\nWz/NcKvuAVV/tviN88ID5n9cr1arWC6Xw75h1Kt4KE166Ofaooq64jGNMaja6/DMrrV/8LylECsD\nQ8tzz7lu/aG9mULjsivl7ehUBXFWciYDN3YZn55+y/iowqfqC8cDuFd6PcUT1PocbtWz54AxcrKn\nnC8BX7VHzFbtcrm03su2UzSZtblLyLZitL5OUwlUZ/FrWfivBnRmmVaGgmsbymIvKfMs+H2Fg+Z1\n+GUWOTwWTrO3txcHBwcb09HA/e7uLu7v74c9xBke7Plkilj/2ZNGnVxuj3B09Gx5Fa7sloHl0rQ8\nejxjRadLJ+v1+tEMAz/npQb+glrm+fL6MeOe0aEF7K1z/VxfBY4m2QlkuG7JOMdHWtY2cu4pCjjD\nsSqb02UzB0+tv8WzDJWcfW7YqSJ2EawqnHUdORvsvYPI5X0uaHXsNlCtoytU71kRIk2PEgawwnUH\nEDDo/t1qqq9SEhl+TxGkVXrwF5SwToeu1+vBG84UsQN4cXx8YQtXVUQ6RrSv1ZhotXesgBlLWza4\nsn7GO6eEePyjHx4eHja+WKVGXYZnZpD08F6rjQ6U38f0tyvb1eP6z9HAtfmvDZlBlrX3uXHcVs7/\ntZRwxFfiEfN0iBMyEY9Pn3FKuQU9yqz1zqXLyqy8UWcUqMffi0M17cP1uGMwxxgP6j1yfZlHwqdS\njVEEmXWf0ZqVNVvTTFOmlXplqtCQhj137BleLpd2/yvXzfW6aVgHrh8rAdXqN1ee0oOvM4XZU0+r\nDysFmPVrxVcOHH1aRmNl1FV0aMVcuHXhqmwn2yonIxu7TgkzHz2387ENVMaSPvsSzo2Dsd4xP38O\nHHeyRtxiBqeQWp6vKvHnxKeCShnzIGh1cg9+mWXZytvjcSrwWhynH7P9p1WnXlc/B5mwGuvlMO+4\nr/9EfKJHa1ralYe8LlJc87i8FfQI197xMFZQj/EWMq8+S8t9znuxdV+24tszE8DPlLfctfKoOzyj\n6q8q7iPDkWng1sGzMjivw7GqcwxsI2d34YmPqbtnxuJL1Av4KjziiHoNTCOlq0Gc3atg1AHimLfH\nYFAPPhNovd44oLVnmHHI3nEaFf5Kg0yxuCk2zturmNkz6RGeXG62hBERw4cT1IvQ8pXmbkvLZDKJ\n6XT6yNtBgBZHSbsobq6LlS68aFby4Gngr/nUq28pW+Cj5Wk6x6sKFY9VhjDnzTxvrsN9nYjpqsF8\nq9UqDg4ONsrt8R6Z7yojH9d6z1PnAN7ZwfWrXBm7fMHftWb+U6Oe2+TOlkc7dU87QPu0kkkVTSvo\n5bW/lufrZiZ68zlwNHUfcOmBnSlid+BExDgr4jk6UAeeE14ZE7Mg2JZZXZ0Rfp2placqmwcm/+Nd\nNQWowlKDmJAmO+jepXH3KtBUKFefr1R6tAwY9ar0/GIoNwhiCEj9wlIGaDd7c5zPHbRSGW9cbgbZ\nF2Aq5dnyIscIEwVn6ER85o+WgmDFosrUGcEtqIx9rY/vK56toNdYbe2I6GljjyGi5WqaFq85XDLv\nvhf/VtvGyPa/hjJ38Bx17mRquoehWtM3Wb7nBOep8LuWQBhrVDC4KbDnAhaEEW3a8fGQznBShZnV\n5+rmKXDGw+V5rj5Wz0H7kHkQ3iyCtDgfrpG28u5V6VTrxuz5MGQGR8tbdtcZ9Bp8znNUHFvCFVvD\n1Khr4eRwcP/sSbuy2MusvOJtIqF7IcOhp9wMd1dGi08UMiOl5bi4PK10zwVfwqvPgA3Cp+IWseOp\n6bEWUtaxvYIoYtzH4TNPhd9VdTqLvsrbM11e4e2sYk3LHhruHQ48Y6Hebjb9UhkPVdS0e69TVlpn\n5m1VikPTYCCt1+sNb1vxfnh4GJRxJvgZV+dZ8dRjRP7hdsYra497l52h7fJWXpFL2+vh6PjUPCzE\nOa/SnvuEp4V5Ojjz8hjnFlTjH+94nLgZBx1r3G63fME8oUYcG2u9Hr7WrfhnwZXa1m2cnCxfVX6v\nofcvDVo0/irWiMcID4bMIncdn3mcLaVW4dSayuktayw8ZRons54zhRjRH3DC7yqBqFPdrXIU2IPV\n4Cq2VLV9LQMl4rOCRHAWFDCvC/fwhvN++eMQakw4elWG1zZKmKHl2bT6d1vhjbqc0eLKzIyusbze\nmlLO+IHxy8rIvCM1arUNKpuwnzjzXnufafmcrsVL2b32U6+xU9X13HKxt/7nqvc58d+JIq6YJ7vP\nrD/ndfYqyOpdJhCd18Lri04BMJ6ujpZA21ZhuXZkFml2LrVLx/fr9ToVOE+xditcce0GdWaNO08W\nipzzYD14MplsbFfK1oad95oZOOr1KC699HL9nY0Vzefy9ArnMYInSw+FhnorBdEy2MbyFwe09eLr\nytD9+K202SxTRaMMMu+yZWjyM83HEf0ZVHLEGVZVO8Z4/c8Nfy2nCNAbT7Azj3jM/sCIx5bxtsLD\nTYH2girhTPi5AeEEtl67ALYeZh0jJNmiBWR7fbP8Wd1jI6kj2tNbLc8qK9O9zzxOBl7Pvbu7GxQz\np1dPo1KsoA9PTSMPnyDF+ZxQ07a1oDLO3DhyBoy7/pIehRqFiqvLz4aOC4BzwAeDoF71nh1erWcK\nVZAicGTjzEVMc7qeMcG85/qUy+hRmhn0eNG9ef+aUPGvjr+esp4Ldho1nTHKWGWg7ypGxfSPplMh\nmuGhSrjlEWW4Oy/bwRgLswLFzSkk/ndCslUPK2S39pwZQdXAQN2ImnaeI7fN0TMT+EoTXg9erVax\nWCwGPmXPwZXnDDN+hi1Quo2LBaejUaakma78vgUqgDMaVfm5vm2UFGjAR0SqMeeOes3wYIVWbXXj\ncieTycZ6fcabWd4ewxs04jZpECIMPVbCSKNLGQ4vNap6x6jiqO34lwy98nYsuLy9TslXsUaswAzF\ninMMVMyVeaaVMHPesMvvrls4KmSd16OwKlBBAA/YKRBXZ6bg3PPsWMsWZJZ8ZrhAcDlhpdcVcBmY\nkq4im7X8yghjRcFRvFynE4rOM+L+qWaVxhptvYbWGB5o4eLKV/7UMqpxzcFcru5t9nj2GjgMmZGO\ncRERdm+yS9vy3lq46w/PvxT8mpX5GNwzveL6tQe+yqjpzNLGe8dIPYIk4vEBFcqcKvS5bOeJPSdT\nb6u8MlCh5ZQwv3d59HkFSotsqvqpNHM80uPlOWUBxYt7rAmrgqlmC7Q8rZMVMXs+vXi79/DQq0j7\nHkGt15Ui6zUwGTLDStvCZWbTxkzLntmPLA2gZchso4QVHE+wgcdLIaowuYys7Ax3BRdo+CUV8jaw\njTzdto+eo28djF1WBHxVHnHPlEE1NdMzxTJW2WXK6inTOdtY2M5rGlPHtgenVLRVoZEJRVf3GOHS\nmo3YFtgDc55wddCCA+cVTyafA8JQB378fe2sbDainJJ0cQWVstS6sv8MpwpHBy0vTPlKA6laQYTM\ng62oZk3D19mygONd5r/M8GoZ7TpDwjyne+xdXTo7kkFlqHytoPKuB8Yo1i+lhBnUqGzV91UEa7Wm\nYSJya3xby7Wng1XpVPdPKTuivYe4F5zCzJRwr/frjB1Om3lTCpkRNKb/nPfem5+FGq/D4R/rwxxD\nsO2AZW8MZbHARcAQcMoied0119E7Q9RSrBV/V/1f4ax1VGlcnQBeQsnyZoYyv8v2zOuzzFB39Tv6\nO7qpwkUcgnrH+M/2mW+jmJ5qTO0CtpEHEXkshcJTx3YPn0eMOAJ4K0yeGcYotMzKV6+pt85eb1oF\nwTZecaZ8eyPIlQGcUMCvZcmPwRt183/mnerzHuG5DR6unOy9w41htVptfMzBefpZvS0aKF7r9Xqj\nHn7vftq+rN2u7SyIXb+1cNX2j5kKzZRAL/S2mXF3CpvfjdmpoemzcZPNCGV8DyOMlS+n17Fb0cH1\niRtTjqcc/rsGHiNj8eqV4613vdCDIxzMHvgqFHHEuOCJ1vMsTYsomZLNBjfXN4Z5OH9PR/W0mfGI\n8JZYj3LkclqKgMvs8VqyerJ3ej12+qn1jr1UFo7alrHGHbdN86NO1Nc6u5o9JW2X6x9HI1XILV5V\nIe8MBsVR0/bwjqu39/1kMkm9DYePTvkq6DR1Vb/SwNFUaa3R0cpzPU6HMxArJ0bpkPXp/x/hKco4\nM1zZ6Btbx84/+hDhp9hYoPC7bQlYCSu13lmA9pTHzzILVe9d/p6Tm3o8vh5vWHGoBHiGt/NGWHHw\nj2mT/Wd4cH09Bpd7zmus6/Unr3QymQxnSN/f3w+4Z55si/eQFpH+2iYI4OVyGZPJJA4ODobzq9Fn\nmKJ2PJ/RiT0r/nKUMwwcOIGPZy16AE/3z+kz75DLyHDg/I6X0bZs+5Ibk7qeXHmhyvstXmAaQfHy\nVDQUMtKpMcbBg2o8qCLO6udlD36uyvg5vMMvBV+zseB0lnvfC19FsJZTCD0KrKX0eurLnrupjkwZ\nKT6ZUuthrG2Yryq7stwqhegEaiZktb5qX2Yv3pwvU/4uLbcPaVkg4Z49lZaH2AsZnmpQcvQ04+Q8\nsYxGjge3jdisjJsxxlkPfyj0tM8pH21rZeDqs55ZKNdup9zUGGUDTJ0N9srZ0MG9M1oyA6caA0q7\nVlT2U6BXrj133l2D0wvufS/sfPtSJViRpnqXCZFMGLQWz7+khZgZGI4ZWxaV8zAhLKr6Wvg5zwTv\nFDcniLksbQdb6r1tZiGXGUA9bdFr5RUWdC1LtwecQuO2a+AW2lgZR66NWb0VrVrep5aTGWjuv4Vz\nC/8snfJTFUmt7cq8yCrKmtO26MflsXGnSxCqEB1OGS4MWb9nffYl4Sn17FIJVzM1u4CdKmLuCAih\nMcK2B5w1rNOGmjaz6scOmAonFQBqPeO/JfQUr7HWWY/H467VQtcyWIEC1yoilduqz59iXLg6dL2w\n8gizZxm0PDM2KDBdqTsI3DhQ46dlqGUGVYUzXzMPAsfWvuWs3VV9ys8tD9B5fIxjj5GhPNij1Hu2\nParCRVAeggHVK82MrjHGGKBlPDKdnFf8a/ZOt4W/lhLumYHZWbAWM5l6xnzvlFbE+FNyWhGQvZ2i\nTJxN9ThGzyzZSjll/67ObfcKbwPaf1xnptx7jJpMsLcUsBPQ+M9+Y6fsxnp6qji5HPacImJj3bAH\nFy0zO34xw31boctGjMJYJczPqueOjjxu9FmlMB3NsjoZuEz3nnkKMQc4HEaj8VUJq4E4hicd7q69\n25T3/+CvBzsP1qoClJxAbykbvndWfOtUo0wx9yqEnjb05HGer+ZXXCvlNnZ60nkpWofzXJAG3olu\nz4jID0/Au6w+xT2jjxPa2sbnFkiV8QE8WRDe399veHAueJHLUbz1nj3Cqq/ddZaecRp7CE5FZ+23\nFo9jXTZTkjrmK7wctKKkkQY05iN3mf9ZtmSeJ5ftxjjyZDSqjFnlm0zZZ2Vs6xVvM6b+nwe+CTs9\n0IMVa0sBRozr6NZWhDHezXMxDLeFBVummMa29ykGAkOlBPE+M0zc7Ia2Q70LfpbNLmRCOKvDeR2a\nx8FTZxGcIuSylQe43RzZzeU5/LSsMePIPav4TQU3GxA9fKR1VPRhw7kyFDK5oJHTjmda08yZR8x9\nxEtcwBveMEdHOzrwMzZWuf7M8G1BRd8vDRUPfU2Kd5c0ymAninh/f3844o8/Bcf7N/f39+Pg4GDD\nytePP/CAanW0DnAdAE6ZtKZyVEBVadnoYJzwP3bqEPjii0SMt8MzYz5WcpnCcx4v/6uHisMQ1GNm\n4cXWuXqsjAd/oYfrdwLKKXwISaaDCkmHQw/0en3OA1mvP59rHRExnU4jIgYeX61Wj7YyObqrYq+8\n4ozHe9rDfcMKaMwhGVk/ZThUipv7DTzi+I0NB5QD/NUYzsaPq5ej3pmXeSpat8xxfrRVty7p+8oQ\ny2jkeJr3L2e0z+p8Dhgr374kPs+phCsHgetq1bkTRczE1SCLliBk5sum8ypQJgU+/K/Pq/tKCGu5\naCvut/3IgzNceqDH41Fh2RuZqm1mYaIWPrc/YvMbwFyetjeDrF9a3oS29a854PEOe4phpLBSUTwZ\nnBLmMeH2E7sx0mNwVm3JArhagonb5QyArO90TDk+cWOj4qHe2ArGU41oVnTOm9U+4G8icx/08qAz\nXvValf+Y8nuhVyY+tdyvFaq2jmnDzqOmeQq5+oSUMjcLcxcF7aJR1TNqCYxMMGTXml/fj50+HgNO\nebUGhCpRV2Z1z880fzW9qNZ8hpsrl/HOnmdeRI+xNqZ/es8jTTUAACAASURBVASO4zUV6BxpyzNE\njnaubGf8tBR5Rr9twB2k4ZSs65vMYMsMc1eOzgSooZdBRgPHJ1yf9hf6kbcrtZReFmyq9Os1Erm+\nzOh4boPzSyrLlnz42uCpeO5EESNQBR4AQJWWPot43EFZ0A/ATcXozzFu5jUrVAyTedo9CsFZvBl9\nehRwpqAqBqraBHyy7TeMr1tCgODkaye0Kx5x+ODebQfbBbQMEBiROGUL0/psyFRGB1+rh6zQO43s\n1usr5cR4uHQtQ6ECHkOZcbFerzdoBzzAm1VEOfOwM2pcfRzhDqXsjixV+dEz1lwbW/1Q0Qp0+BJj\noGWwPAV+LR7xc8FOFDFPnx0cHDyaWuEfA3uUKuSdEGjtWdX02SCsQJm/10PEAMnw661Tn/WUocpA\nBWQmSFsKPxM2LCB5OprzOMGX9U9P+6u2t+6fIgSqvGokQHBHfBbubIAcHBw8Msha3mHl/XP+zPOq\ndhVkPIXnHFnMdSkuvQo5S+ee83hSGYD6NW5Bd1MANBBLjX49rhJ189S0zmhgvZghi6xuKTg1gDSf\ncza+pEJ2OD5HWbtQxtvW+1Rcd6KIMQAgbBiYyZxS42dj9s0+hbhf2qPKLH2HS68nzdeuTBYAmVDW\n+jLF6NK5tcNMEagAzYSOM860fE7bEnAVfZ4LHL5qvMGjc4Yp51FDqVV2RDxK7xS3U+haXg+NquDH\nFmifOx5ws2TOOHM/167JZDJ8EzqTJcojmeJstduNjwwyfHXMZuOacdep8h4jqGUgVTii3qfCrjzi\nL92uDHaiiKfTaVxcXFjBnFnsFfDUp7NoWic6terqZYptPDSk7WknyhwbpOW8X4aemYGWQOvxXuGV\nuKnPzOuJyL8kpXldX2b/FTyHNd5Dc/zDs9JpTfXE8FxnV7KyAdWUdMvrZRzcuGJ8K6/X0TOrq/L0\nMgB+CILisYIytQ0646B4sCep9/zvvE1XX+8YHyOHqvQuJqYXj13DrjxihRYO/D7bsdDTjp0o4kzA\n6LRKSylnwk4HdKaEW8JBhX3lHTq8WPlk7RijgCeTyTCDoPi0PJusjc4b4DIzGjlm0/apVwYPhPuX\nFblTRGOMpIwevbRw5TwFMhyUb1erz1/ngUfs+lHHDfOFbtXi/G7KVoWzmyZ1npRrm/YRb9tR4eTS\nZ8DlZwpfZYQa3crb/B5f4NL2qMeq0/WsiLOIf571q84y4HzOCHP/2mbG3xkRes51C7IxkRlKVd4x\nculfCmwjU3Z6oEe2ZtHyvgCqNHs7OkvrlLATIPy8qqO678GN/7PtGJknou1yz3u2Tukgcn2TDTT3\n3OVnoa2R8yzUnCCoDIlt4LmtcGe8cd9x3/CYQBrdDqPKzxmrTlBrxL4zxlxgo5ariroS0NnY0fdj\njKweucDP3ZQzB/9x/WqYRNRH6VZjvOJL16eZAsscjcrI47Y4753L7+2XHiXM5Wq+bZVwr9H2JaDq\n38rIz55XcmqnR1xW23nGWNDbBCFUHocyqcPRKe3q2uWrcOH8amVrmh4LNRvwGWQKlH+t/JyP6cnH\nBDqFogKeD7fgtK226vPs/qlQDcqqLuRhr0oPgsh4v8WrSu8Wbi2PqTK+nDKuFIwru+qv1vMK4PWq\nAdMCpHfR9y0DikH3Cmv52X0LL5dey3dKsJKllRzqpf1zjq2v2YNWOd8yNluw06np3veOiSuhkTFp\ny8JxaStlrOCUcLae95TtNdrxOjB5bcoJZraQIWxY0fUIhTECDel1G5Pi3uOhOwPNCQpWBFx2ZaH3\ntmlMvkqI8bv1+tPU/P39/XCiHE+dZji3xgD6OvPssnXEqnzF20FrfbTHmNJnLbmg5Yz1WKqy2djX\nD3S4MvXMdDYqtV2cP2ujvlcDKOMN7d+xMNYD7lXavYbHLmGMceTux9B7pwd6AJxlmBGhR1hs28HZ\noOJyezyGVv0aXdpSCqp8xngVPZ4G3qmH06uQK8+Ny47YPGPcrWf24Kr1O8+shedzQq/hx+/cPW+J\nQSDRev04OKvXc1FF4vpIFbD2e+Zlob4M3CEfFQ3c+15F6uhS8VOLF9x7ppP+NG/PaXRarnvn0nI9\nLAs0nTtYRGcuWn1QOS4Zvv+S4DkM81767OyIS0BLUWSeKQuvSgk8Fc8ej5gHLadzA7Ll9WUCo3Uq\nV2ag8PPsZDHnjUJROiPJRQe6Qeu8AT4bW5UxK+hM2LagB6feAfYUwdPrdTFeKN8ZQZmx6nBjIa1K\nlstpGYSVZd8S0tkySi/wmHaGll636nIR3ln+bBy1cFVQQyjLWxnNrq+VJtnsgS559DgsvR7tGHBl\nPpeSGyP7v5Se6KmzBTv/+tL+/v7GQQAsmPf39wfBrYejR8TgLVSeVQtUAAJaSo/rc4P+ORgEgww0\nUIWsAoqFDb/HQMzqVIEEekfEMFVaeeMoRw0XJ2BYketAAp586IczcCrByXgoTpVX1Nv3LR5znlRv\n3ojPa5p7e3vD2EBe7metLzMwMsXbMtrctVMIWgdAPwzhlGmGv0vDRqBrm7aPy3EGiabLDH1Hh2xv\nbk9wlusnbTfawfECCpkC1jpXq09fhKrqd2Vrmb0KxfH3GJlcyc1MCeNdq56M9mONBM1TyUVAa5bk\n+cJNRwC24CC03w2yiE3Lnpm1snYr6Okorrs3by+TVus1Tji5ejKhUSlItoj5vjrZR/HsGWDOmNHB\n06Ij+ICnsLc9M7g37XPCmEHsaKPeC99rH2bKs+fH9Vb4Z/kUqnfZ1ii93pYPW9DC3aV3OLo+YJ6N\n2BS4GGMZPll7sz7AdcsDd7h+KWgZf9vAtvj21psZ571ljNEjEX1Hy+7srOmIzx4trlvKRy0gJ9Qy\nizsD9ZzcrxeewoDOq6nW2CJyYabP3T5Cvs48LYB6pjx9rOVkbdNymb68B5Y9ZuRr7XXm8lyd3MYW\nZOU4QdhbnhvsKsjZG8K/w0frzRR7Dy6ZkB4jCNUoRP5sLLuylfcdXhkPZXhU/VTRrKJHa1lJ8dHj\nXF19WmfWdjeulW48hlgZa7m9cq13zPTkqbz3bSDr5zE4b9O+LwU7UcQPDw8xnU4HArJHCwbGtJz7\nHq1CNiWF6x5wytil0TLHCj9XXlaHKuWWR+ws7MwbcgPerQnzsoEqgNagawk5Pn/alcNesVOs3PYx\nkOHn0vC90tkZTz2Q8QO3yR3CgWf8iUPNVymsyqvKPLTKo8rGiNIKCkK38jhezspu0Vjb1CP4e/qN\naYb/zEgYI2sUr6p8l5/50ClpHes98JyKKRsnlbIcM44qg+4pOD8HqNPSAzuNmsY2ACgAFrr8448G\nYFC3CD5mUDgFo9ecvqqvInxPpHDmSTgjoRKEuO6xxp33oN4J95Frd8vKRnt525QTxnxdfTS98ngU\nf0ev7PAQ96/0GmOBO6Gd0YnT8vog59H+4nwtXDLl1DIUW8KzpZAB2DteRYG7snpmzbiclvGb0d8Z\nLPqfGSzIW3nNjpddn/A/x8609oPr+EAEfo9yf27I+uG5lJ2rr4KMR6vynmLst+pW2Mka8Ww2G6an\n2TOO+Pylnsnkc7CWU84Rz2vBuLJVyDy1Q8Zs1+EArd726iBs1cHP9bg+vuZ0+lNQheGuNb0aYVl5\nrp36c/l7+kz7fex53gq9nk2Wr2c9uCrP0UjLdHi6e0BlQKhSrZShO8GrKq+qP+OX7NnY/gQeqtDG\n9ElF06ytGbTOH3Bj+Usq4d6yMyOwFyoHAP8tA8s9H6ugt4Ge8nY2Nc3R0hH5dJVGCfPPCV53VF/2\noYEe6FVmY8EprCpt5f1lUClNJzB7BAnSV3k4r15n6VVYat86z7BHcDHevYJc02aGk8NT6eTqcrTX\nfnDGBdLoASyu/Kxtrj7tU8UhA7zXzwtW/bJery3u/L6CSqi2jJMxnjAbL/ysB8cvDZmzAHzRH9nM\nIctPLk+vs7wtQ6vXeMr6pqeOrGwHPbLC1a/6aFtQejvY2T7i/f39eHh4iLu7u5jNZjGdTje2tSBd\nRAxrxZyfCaRTN9XXliJyIeMEsAp+xzwwGPgZrjM8WgxW/Tgdl8MDTy1iFeiufYo/ewO45/aoAHbt\nd7Ry7Z9MPn/QQoWIM9D0ntM5WmZCqeIDF2yTgba/VzFlfQEa8Hvn0Wqd/O/A7QFXJaOKyJ0Ox2W0\nDA6ljTOUVfgqHZ1x4eia0cCNHU6nZTEPOqWs5XJ+J3+c4eMMINee1gwCy5n1+nNwJu5bssbxg/ah\nw6ulXHog68NtlV/Gk46n3LWD58CnBTuZmo74PBhvbm4iYtP7VUGI904Q48cHRTBoJHAv4zyHFTS2\nHG53pYABmQfjpqbYaNF6snV5DHAty+3pbtFBBTw/y9quW0J6+i8T3I6n+DlfMx5MM52d4X/FoXqm\ntMho5Dwy7o8sEr7nnp+pgtG63XYq1z7O5/oq6zst36VtGRmubRndM3w4P09Fu2WaDFz7qz7J8M94\noVonzvCtDHn+1/IyGn1pcMZ0BWNxrNqb1bWt3mDZUcHOgrXQMLW42bO8v79PT5RC41xEL66rxqsS\n4H+GlnddAePEwi5rD+qrPD3FWQc4783OFB3qYU83w4UFL5eRCRRN65RNxuyZoZUJ0KpM16ZKiTol\nzPVz9L7SXHF0nkImXMYINsVRg7l6PJSMlqosIh7PNDncK5oybVrCFPW49E5Icn1Z/2f1cjuzcZSB\ne5fxXqX0MxwdTll9jqfUEOAyHK0qXuX+y6CH554CPY5MRoeeclq0eM62VW3ZiSKGElgsFhvKgRWA\nps2sOV3/7RVG/M/lKbSCI1p1aBl4p0IsU0IZbsg/mUweWeDOcFBFrPR0sw9apq4/ubzI3ytEHB4q\nKJ3gzMp0PNILPUaCM8ycAtZrTqfXGb6ZFwWDi9NorEXVFidsMg+Iy3aGbSbo3VisDErUgzpgUHKf\nurIymmaKj8vKFKYeoJIZ0W7fvRPsmeHj8HPQ25fMJy1ZNUaBbmM0tqDHOHtO6KVhRRdXhiuzd8vS\nRp7ROZ4BVqvVsB64v78/fG0GRylCyCANIBPCah1neZyVXZX3FHADsGVtueP7VBhlSjsiNg7ucFG3\nEf686WrwOmGo5bvo6h5gw8AZRZmXlZWVKTKFShhmZQOy2ZHnMgaUzpWHA2hZ/5UAqhSvmxbP8qnB\npG3QZxlkSl95APi4afoeJeT+Vfm6cpXXnTGgeGxjEGpdVZvcOM62LfZcV8+eC75U2VX/94z3sQZC\nD69ly6YMO/sM4v39/fAD8XS6FF+hcV5ixGPhN8bKdNDrQWgeFRo8AMfWn7WV8asEnXvu2uWEamXt\noz9gKKHdWTAP7rlvKjy4/UqLrNzeQaNpnYLvBQ3geg5vQZUv7rM1wR6LfRuDEuVmJ6wBp/X6c+Rz\nT/uZDxgqw9nxVWWQ8ZhwsxEunT7P1oUzftNx7visRyb1GA8ZMA7Av3c9u8Lpa4GxinFbyOqoeMlB\n1t9fpSLGIN7b2xsUMQteWBDcqGpKkMERrifdWCFdCbptFHA2ZZwJq0wZu7SKZyuiUuvHD32A7WdY\no+ToXhc9zoyYCTOnRJwyfoqCydqo6Xp4QLfetaL0tTwV3hVO6oFVuFeD3tWZlTPmnSpwxhEGtirA\niq/VyMlwyHjK5a0Mt14PMiurMoBa4zdL46AyPrJfhXdP/cqfvUppGxno2tYzFlFXJlt66s6MRXVw\nWmOPy+SzICLqKeudbV+azWaxt7cX8/l8+MIPBi2Qx5Q1d4oGCmXKRKd5ObglIiee63jHwExcFTyA\nyhrmjudDTPiZw0fbDMXotvtouzIF6Kx4jaRlvCI2Dzhgw0nLcvRqDeysT7ntLSHtcHZBZ5rPeUAZ\nrdxMiPaBU3xaJufnfnZr8spnmEGCYYR/Tov/LDpZ6a2GmsMfaXhbIcaX9mlGEy7PvUfbslkBNX7c\ntij1rHnccNuVNq58bacGy7F8GQtOqWsQKs5ecDMFwB+nFVayp/d59i4zTlrPWvVkBkxLCWuaythr\nldPCU3F1abkvIj73W1XuzqamIz59/MExLzMbBOZyuWyWq4NcOyLb36e/nnpQHuerOiUrg68zHJwy\n0J/b45jVB3CKS9uH/nF7hlVY8UyHlp0pngxaXkWmjKtysrL1nfZDj5BR3tKBnfGHo4u+42vdJcBK\nVvO0BJhTvijDvXMKOcNTBWlvfzEgT7Zu3Joh02ArTefiHJS33V5ynZbnMnsF/1hQucDKl43xMVut\nevDNjMYxMCZfZqQxLhUfZTJNjcFtcWzRYr3+vPOHT4Zswc6mpvf29mI6ncbx8fFgOUDZshJ2lqoC\nK8LM+3CCUAUtM3vvYQ49lmcmhNkrc3hpec7TyNqk1y3FqEKIBWDmzXI+9ggypbutQNb8Y/O5tj4F\nlKccz7Dh4ow/d437zBir3qP+HmOy6oNe7yhTwlm5FV4oo9ojqwYHAAaQ44/KKB3DC26cOtkx1uPM\nPCs1rrWcbFxl69rPAb1G8zbvNW2liHvqcnTaRuZUZffwUHYOhoOd7iOeTqcR8fiEJlxnSirzPDSd\nI9ZTGKrVnrHCjYWqOzQiEyqgkf56cHT3Omgr4yLzErX/XKBRZlC4a1VyvX2j/e7eO5xaadSoqfLx\nljtV2JnFX1nqWp+rGwrJHR/p6K3laqBkS+lqO3Gf1c1QedZKI06HKVduR09kcUa7HiPfGeJYRtM6\nlFdb+GR8oOCMMowx0J+9Yf2YSYXDGMjG1bbGNZebPXfv3JhRfsrePQW0HK6nMoJbzlzEDteIIz5P\nQR8cHGx8GpGVEQ5SODg4eBTYFeE7IIOKID2MxGmyg0YcHg6vajA6RcR5WNnhh+eV4lIBpmVmuLl3\nzjLUKUwo4x5FpMD9nBkMVfvc816l7nB0glrpnQmDSsE478bhw/WzMcpeuMOxUr5V+7N7x0NIo0rZ\n9R3KULwc77s6FZ+KZhmvMO+7LVrsZTtQZc596rYgurzKB87wZONIA38Y+Ct2WXyAq5Pp1ILMUHft\n6IVW2pacyOih46ySv1xWj9HIuCgd8MOU9Hw+H963pqd39tEHMO3Dw8NwgtZisYjZbLbxxaUI7wEy\ntCziiDxSWBm2surxr16HE67VmhvnQZ3ckU5Q6TWvD2VtcjDWOmwZN47RgZd+S5qF1hgcK1q0jB6l\nZTZ4s/rGAvPRNttIFDIPBMDLAWr84D3POLlylF/5fWVA6DWn5fI0aKrqs4xHlMddezKDKWsrBzWx\nF5mdOMd1MB+1eKrCy9FVlYiLz4jYlAFuz3MrQIhp4wygCqo29ebvfZ4p5MogGIPHmHY4A52BdcR0\nOh0MVI57crATRcwR07DmZrPZ0LDlcjkMXBdl27JSeoQt59FtF2rdOWuV/1EvA3sqGYOPOYHFKWNH\ngx4ljuuMAVWYV94e11tBhRenyfJUQmJsfzuF1GsYZErd4ZdNzbPX1bL03cDPZmMgtPWZlpO1raX0\ntUzGSct3uxYygTrG8MlwzPjD8XxVjuKUyZwWvzgFq++0XtTBUdlsOHC0tMawqNGXybIKtlGqLcXJ\n7XLPe8rM6tAyWaZlbXH0UN1R0SzjdTWGue/Ozs7i48eP5VfHdhasdXt7G7/88ssQrIW9qThpS7dh\nsJDRAcVTOEpItSC1TFxH+D2wlcDUtMAHdTGodcptYgXKTOE6na1fTNVzHSi7mrJ0VhwzIQtRFaiO\n0TPmdhG+2bYsxSujayVIFTLF7vqzR5C36s14BAOQeYJp4/hCBSkLZPUuGS/1gB1PcZ1uXTgzHluK\niJ+x8siMPRWAShuu0/FDS3BqvTqLxMpL404yHs8cAGeUYiy1eEaVNEfdOmXDbcGUtK4LK15ZWyoD\nt4Url8F1tejSU0/Pcy6bZ6CUxx3fVgZmr8HAPASnEXFPZ2dncX19Hff39xHxyfnEtYOdBWtB2c7n\n87i7u4ujo6NH09IHBwdxcHAQy+WytLJU8WaCXqHyZlqQMbgTXhVjOSVcdXymCJUp2bhoTZFmSl9x\n7Rm4leXv8OUyVeDoOqPm78G7Z8A5wevawrxVeR/83Bk4YwWg4lRZ/C59Vpcu17SMHVeW60uUDaMn\nYtOg6wleqYQorqs+y8rKlnR6ynFKcyw4vs/apmNY8XNrw1xWZuR+aVCl6651TI3Bz8k7Ll95ZuxY\n6U3LdbAMYEPq+Pg4FotFREQZRLcTRXxzczOsC3/48CHW63XM5/PBc0BU4nq93rAiehSFggoZZ5lV\n5fI0YjWVrHXrVhb1wnTql3FpDSC15rN2tfBUIdBiWCc41IBwOLDCzASf4pMZKZq/pYR1fc0p4aq9\njD+X26J3653Sjct0RlmvZwHeGLPs0RJWlSeyjYDP2pAZo9r3mqaHpzLjRfvS8ari6MAZkg6nXmHP\nhkaWxxkULk3rutWWCkf8V/yq6atrd8/PW06Ha1uvob6NYYU6GY+Dg4NYrVYxn89jPp/HZPLJqcQn\nf205o2t+BpjNZkOk9P7+ftzd3Q3rxiqINCKYowczBaIMkg2QjLkVerZLOVCBiOkLVcIZnm4wu0Hn\nFHxGn4rpx7St8h71eWVYZApRLWf3vqVMW2VWeauyNA8LTP5pZLpeK4xRahn/ZGPEGRWZ4KrwUr6s\naN8reLNnvdBqq05ZOq+49dO2K85ZHn7v8vCzarxyueoJ8xjJtq99CR5U6FXCKit66+yVOQ6fp8g2\nLs/xNf+wZHh4eBgRnw/1wC+DnU1Ng5keHh7i+Pg47u7uhu1LQHo6nW6s/aKhvHaCHzN1ZR2qhZn9\nV2WMscgBbm209wAGbhOvBzlB6GjCuDkl4trfwkfzZ/jwc7f2p+WokM/6U4Ue94WulWcGTdYmfdZD\nGxXWFR9luGeGWFZOr3BBHc5AcNPTPfTSsjNcej1zxtG9c2kzcFuS+Hm2dS9rb0ux8H1LFjjeaBmS\nnJ6NCHeUZRUcmvG8tiujR4/B+xxGltbVGqtj2qt6otIXWXlZeyB7sE6MsjEG9GuCDDs74hLIHh0d\nxeHhYSyXy2EaWvfG7u3tbXxKSpkt6/xtrWzXMRpZjWtVHC1rivEdq4RhDWd1qDecKbunQE+bdf9w\nldYZAhkdWwPOCTjdRqblaTkOP8ad/zPQfq3qzZR8jweh+MDwcOCUcNV2F7zEeKkhrP2XWf8tY0bL\nV8GpNMkMSzZceRknMzQ4vwZY9fR7r3J19aoxxmW5sYV+1HZltBgDzgDJ6KVGbybPWkqyqjPDMRtf\n2exlJnd6DKYe3DktvpGwv78fh4eHMZ/P4/LysmzTThSxWuTr9Tru7u6GPcUgAh8QoINeieEszbEW\nrlOwmoeBO8GV6aKwkU8FjrtW4VExjxP+meCtLEUWOmMGcjaYMrpVBkPWtqy8lnHEadye2sxIytqX\ntTfDXWnp+MbV3cKpZfj1PNN3mVGj+SscXbqeerWftf9a5VQGk5MZmXLX/mrV7dqTPa8UjVMwWL5j\ngDfsPPwe43KswlacuI/dUliPMu2tT+ut0mB8Y98u1687bpRPGN+qD5UveHyrEXx/fx+TyWTjMKoM\ndrZ9aT6fx2Kx2HDlcdb0ZDLZsPh0QZw9Zv7PBq8qMmYi4MPPGXr2ZVYEVgblutGe7DQetw81awfP\nGOB99X1Sx3RuIOtJPT3eQSXcMEA0eM0pTEzlIJ96AFlQEpeFZQzU1zKoGAc1FDJjLlMYTDNej9Q1\nSifIlC9cmTr4USZ/otK12Rl11TY319eVIOavcVXCMzMsx44xVxZoobNr3H414LN69JkeOqJj0pXn\nynVbtJxCA/A33HlmzAWGVoaj65fMkM5o4Izcio5V/2ndPQqc+dY97zE+XF1V/Y4eutx2cHAQL1++\njNVqFdPpNC4uLuLh4SGur6+/vqhpEGE6ncZ0Oh0sB4R7r9frYZ14vV7HbDaL5XL5aL9wNcjdvQo9\nFiY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9vY3JZBJ/93d/F2/fvh12/bB+ymCnihidha8q4UPK+/v7cXJyshFxhkbx2gkHMag1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\njep5o8dMzgHLVaTbY7XV0vAtQU+OD9uphRT+14Du8/9H9Y38zO/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oMWVL29LvR6WT6XXb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\nFFNMMcUUU0wx/Szp/wBjogPlqpGuYQAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f146b601310>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"#Sanity check\n", | |
"%matplotlib inline\n", | |
"plt.figure(figsize=(8,8))\n", | |
"plt.imshow(test_prediction[0][4].reshape(DIM, DIM), cmap = cm.Greys_r, interpolation='none')\n", | |
"plt.axis('off')\n", | |
"plt.show()\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 82, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"0.208396158137\n" | |
] | |
} | |
], | |
"source": [ | |
"print np.mean(test_prediction[0][4])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 92, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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MSjaqXY7QrOP694yAByJYW4Pdc+7Zw3/j7N+XWnMW1J6AgICAgICAHcTpSXqk\nlkd6wZwysGKoPjPr0mm+CHzB4lzaFoADcE4Gz8eRnotwZT0XLLOweIpeMgbjQr3Sm1GXRORkPhMp\np+pojGkxU0WnJOo0MHDHdERE0uOqKgLrUtqsNZSVMxiIYhdYWmuIogqDnRonGBVouifvmyEJSJNJ\nj3yj7xqCljjCUXnSMaFWNPTlZDR78GjXsNQpOyYtiT3RyXqT6ZiTtJgSN1F6rHVmDVUZQanqhuRV\nxKku8iObVJFX1R5eU9KoNf8R1fTejeizUc0wXJlzRfYjasc2ndrWvoXt0+oxS2qbrnPayn1N15+V\n6lVIlly7KDrQTP1qO96JE5y4ton5wlaZkbqfkhhPzELad97ZQvTbDXChaSKht5P5nTXr2HlDcShi\nMliAFeP8QhJgOYLxDJywsGoc2bkNlwIncyfjCwgICAgICAjYIZxepEdSoSTYK4Alb5G75mt0Po2z\npy7v8RsdcgrP9+Pqdy5yS//wMnt2P8icWSOhYELKmL5XTWLvzpZTEhFjSFSqj5AXo6SSLsJTEnWm\npHWltVlrqMrIER8f6DqqpFOLrCdCdhMJkJS7CRmj9T4MszqdTDZtN93sSClrpGbFQFY6wpOURHFJ\nkuZTshPH5fS1DSE8eZ44lUk345xeNM2+MTrdDSgmKZMyo4qjhvKwFdpzIsF6QcKYHsONAfZUr1Z5\nxtSpbbphaNVapheFIxXSm0jGK7U88r6tXElKnCY7Qjq7jAegSZZQ28l8WWrCo1We7aZoUz+hgv5g\nTOr7IOm0za7fWdtBTx4EpOTMxOvs3QWnTMXGgV3wXJyN9Xdxas+khLnYjfcOfGqdn695mimOAQEB\nAQEBAQFPME4v0iNBotSZAKymMDSuH8hJXLH0qhRfnAP7M3gebvk+4Fkw98xj7Jl9kF3mFAn5VCGR\noM6pPKlXVCylXy/ERgLDaBrN1uuk/qYiIsI2GmdW5dapbFFUYSLb+Bz788uzd318qIN8lyGVOOJW\n9pgMe7UTmbaJFrIoCoOkZmmXMAk+DU7hiStMXGH8+Kw1zoLa1tcsJgmbbpeQO2u6baqj1meNAqoi\nZjzJyAfpNP3q4SKmJPO9eSakrDHLcGUWlg0s41KwVmkqPZJ2pgmPVa9R4wS1m512tYPNBgUuQ89B\n7oPcC4Hu5aPnop3eJvMmqXNjmve6OQnN9DlNclOIZyZk8bjxlbgQugQ3l3cmdWpCctrk0mDpMcEa\nQ7UQsXFzUaXvAAAgAElEQVRoDsq0Jj2ngEnk6qhuw6WW3oFrjDpHrTyZ+rhb+yAGBAQEBAQEBDz2\nOL1ID9TqwAy+50cO92buifEXgU8CHAcWIV10dTui8DwLZp9xgj1zx5g3q2RMXLqZb/Ypi/VPvCsi\nChJfr1NhpySkrnFwD81LNTzji73LhoPb1PXNp3+JW5ugqqKpa5uJ7PQ8EmJG2Ok4BKI65aSUxORk\nDNcH2LWBC4RlviSFqu3Upu2j64EqRcD6pXKkzFhsFVFZQ+m3M1j3amorbqBhx01pmqqGLs4X6LQ6\nd3HYPGUy6jHu94iNMDWXxvZQDV6dVldMU9uGxQzVaq/uyyMNSSXFTRSTLrVHz40oPJLCJmPWtTza\naEC/lyai0qNGiIrUDUmfIp3W1p4jUXiknkc3JdXEsstOWxQqv6T9CYmpe09thiM8Dat1pQTpzxEV\nA0aUccLimcdZzvfAiRT+x4/vVlOTvH8HnoOr/zkL1zMoBrxluz7fTqEoIDn9/vULCAjoQnBuCwgI\neAzwvXXQfLwhAaIBZi30LNyfuYDpU8A/AKMxkEF/l1N3nuuXZ8PggpPsmjvGgllmwNBX7lSIWUDk\nP8uTbZcWlTEhoyCdpqvp1J+4FTC2bayhJifi2FaW2wfsTuUppuTJxdqVP059S3SFT0HMRjlgtOz7\nz+jAWNesaLWnXUwvwfY0Pct6cmKoypiqjCiLmKpyvX7Gwx6TcUZVuPVCdHTj1aryRgpQ/5p0mpV2\njJPvpLC9giJPyKukUcW0FeGRXj4x1ZTQlsSMGLB8fAmOR04NPAmcoFZ8ZK6EmGi1R6Bd5rS5Q/v/\nWv0XIwRF9wJaoe4PJGl2G2oZUhMwUXFk0SpRVxPVriBdEzFZPGEzUTW1Aq8U0aj8r9uRfjFIj6f3\nQL6rTTTqerU+QxZ7yywcOOn6Xr0ceBFwpj/vKT8xt1unAt0DHPXXXbDp72kn8Dd/A2kK//APOz2S\ngICAgICAgCcKp9+zTqlHSY0b3QRXNP0/wMkCF1WdAQdwpOcHgAsgO3eVXYsnWDQr9JhMn2Drp8ri\nUlXrLMa7oSVTAiIpZ3V9Qx31tlPPdGNNDWO6n2LbykxT3LRjlpCxhFyRH6coFSR+jCnjcR877G3u\nN9NelEOaOzE12RH1B6/yxBVElqqMMD61rShjZ1WNpTS+R5EtnRlEq17JKgK0iUJr1USPRQLzKqLK\nEyaTHuVgSOqlAlF6JPiuLb2jqXIn93RCyno1Q7nWd+lsK2qR9DYhDkL8ugigEMM2iWjbUlcdr6LE\ntOuGxHJd5kbOIaRP17kYamVIyJSQWZ3C1nabQ41R35oY10tpqmJKIqWZ/l61AmTVmubvXy5T9rHM\nsEHZj1k/ME856Ltmpbfj5vkoMNmAagDH/fozqEkdzb+dncAb3lC/Doc7NoyAgICAgICAJxCnCekx\nzno5wuX+S1A6BvaW8KnY9QHBAHtgF9N0Np4B/QtX2b3nAXaZE2Tkm+ylgWn9TkQ5DabldUIG1DQo\n9Y/WI9/bxMWZUeNYUKs9OkDU/XnaBMFEliiqSE2uxiNkS2qFDMYH9eIKNyFjw86wsTzrgnkhhl3E\nRwfabQghKoHEQJFAFWGNBWtE9AEsZRFhEmdPXRYJUeIc3NLMz42uXSoeIoDVDmUtPmi9uiTJh6ax\nw2bzgoKYjJweE8ZkrDPH8skleDB2Afcxv6zQNDHQZAeaBEHmTLuzyTZt0iZGG/JZVBlRktoqW05t\ndlBfVE1iNFmRcQgB0gQqwtu3t8ai0brvkkbpTikKpVYSY/9d0zK97pXkSA5efxQCmlAwYzbYf/YR\nTs4uMVrbDT+MU7KWgeHAKWWl//wArmlpyfQBw06ntwFEp5fOHRAQEBAQEPA4YkdJT8McIK0gih3p\nKYDZEu6M4a7Y1fLcb4GTMNjr+vAcBg4C55Qs7T3Grugks2xMiUS7dkBS1doVDjXx6XmVJVMGA7Uy\n1GUwIOvK1jRGUeWUE2M3EZ8orhpBn9AvCfuNOq6zw3YpeKNJn2p1pk570hbR7foUiSe7gjpdg4KB\nKq730fuVMbYqsHHlFqASPSCy2MhMSdz0PJp0dfEgXQNjcLVAecJ42GPU65NmuUpJrNUejdiTRBnq\nmB758pxLZzuFK/c6hQvApSZG0saE+Mj1Vo0D16+inEgzUk16ZDhCSERJatdVtUmOvjdCYoT8TJUv\ndS6xp5b7oomUJmIJtZmCVvgSmsYZONMNwybeqVIqLbal8lRTdUiO41ItYwrmWKOajxj1d7u/ydtx\niuwwgvUJfCtz5GcG17vnAEzI/N/TVrZ2TxwC6QkICAgICHj6YEdJT+Ppc1zBIIKBdcHfPbEL3r4G\nfN5tTbzX1Q58H3A+cDYMzlxmNl6nx7ipuLRSzyJvTq1tep2aEvm0qVglvW02FXjoa/GvHUQHHBGK\nooo40oYK2iVOP/A30wqknJQJPdZXZutAXrBVkK37wEjKlqROVdTOeLp/DDTTvRIDNgFb+l5Dxrm8\nFZYqqoiilqFBF+mSY3VNo2xfRtgyoiiThsOerutxqX4llmhKEAGGDFgeL2KPxY7sPIgjP2vUqkhX\n49H2fEWt90J42u53el9NOoWEFq31cjyd5ifHaDvFaSMCqdXS5gXtAH27gN3fd1vViZ3VJqqzGbZF\ncOpTNUmQUz9dD59BNMTsG2EP9515wR04dSePoW/hiHGE57vAWTCekp4Kkp0lHoH0BAQEBAQEPH2w\nw0qPQ0QFo9SlI1lTB4X/BnwFGHlrsKUUzsWpPIeBAwXzC6sMGG5K43HHr/vuSBAtZdtCLHRxtxgb\nSHpc3BGtt4+vr0Uc26KoIoqrKSGQpqQmcul1OhStU9yKTccXlWc9n6VYnZkWg29SduR91FrEjaxU\nrwYXUCfTk+iLq/et/L2wiQvC0xJbFRTW1YqQuHqeqopcY1SdNtaoHVLoqj0poRr2GA8mFP2ExNQ+\nz835rSNUg+s5MyFl9eSCIzz349LaTuHmSa5L1CetmrTJmR57F0nR49Vzr5UeISn6e7kHmuTI+fU8\n6O3lve7R03blg1rdaRMo6u2rwiWjaWVMPwxo1+0w3aZ5wNJva6gatT0xJSkTdp15nJPFPuxFGZwN\nfBt4sHTNSo2Be3EObvthVA6wsXHUatI+8xOLQHoCAp4ksDY4uAUEBDxq7HBNjwv5E0rM7Aj7QL8u\nho5xwdN/uu2Y84Tn2TjXqAMw2L3CjNmYOqFJOltJ3FBSKjb/Y9lFXgpiDBkTT0x0als7SGw3dWzE\nxUIG9Pm8lbWkztVXbxvP1kVryklcI1I7YPXUPKzGLsBuKw113lG9lGxOn5J6Hhl220RLqzNCToQs\nxP4ENsFWEUURE/nanmqS1GlkQjD0ObQrmhzf0iQjJqYYZRTzCTZuzlvdKNbNccqYCEtOygqLVPfP\nOBXhKHVamx6LXHsX+ZF5E+iGpBpV670QHE129KJT0bTKllErb9oOW8YwVu+141xbmeoaW5uw5k6F\nq2xEZNzNrztC1ZpmG1vV2lR+YiL/vfz8UgoW4hXKM2OWLzgTXopTej6feWe7Cu6NnHX1Gqzlcwzj\nPmP6O+4dGWKogICAgICApw92PL1tWsMRVdgY95TeAF/FKT1jXNrPIZxxwXm4p8l7CwZzQxKTq1IG\nFwFK+pOQFolipSu9oB30WV+vMCGbEhEJuuWYdS1CMlWKjH8KDi7QjJPNRgruemsjYJ3aJjbaQtSc\n6pQyIWN9Mkux7FPbdPqUJj6CtpogjmHtAN+29jWtV0mLk/SuFKfmEE1/MdU0rS3abBIgr5K2FXW8\nCvxYylHGZJIxGAynVFDmR6qrIkpSclIK1phjZW3BGRYcwxXLi8ojZEfXL3XZVMt32xkX6PQ2Xbcj\nixCfrpoe3Z9HvretRc+JkNSKureQTnPbKkjXY5brnMB4vc9kMfM9kCRxTRsZbM06dJ8e+V06/ij3\nBXIiUnJX29MzLA/2wuHEmYzch1N4VnEk6DvAXTDeGFD2/Y+oALuDDtZB6QkICAgICHj6YOdIj2VK\nGCoiqlHP9TY54b8/jqvN2APM40jPRTjScwZkC+v0ItdtvvLRnig80LTFrY2rW25qPryu1OfKp005\nMtLdq0fSfyR9Tt7bykyVjGldj3EqjzGWZFqkX2FbuV+SQlSQTFWeoR2wsToDo6iZ4iSvoiIICmqi\nIsRGjAWEmOiC+Ta0UtSuyxFrtzKGJHJkB6CM6qC/UvvRcQyog3pN3mKgcP2N2qpcnYIoZg+Vd22b\nYf3YolN45Lei09pkDjTxaytRcs1acTGt9/VAavUF6tQ2re7oGhytwMi+QoB07ZR8Lz8Hq7bvIlHT\nmis1fk2Y5L6PwE4S8jIliya+kWttA/5w0GXJ3u71Y6hIKBgwor93hdHB3XAxjuQ8CKxGEJUwiuEY\njI/NsLx7kTE9SCHPN5/3iUJQegICAgICAp4+2DnSU7lAvyBlgxkYx3UweAJXo5EDc8A5uBqeC4BD\nYPaMmZkfkpg64ndmBHWKm3Zv6+o70iREOgquvOKje/c0VR55Yi49Y6bHiWoTgzjxFtS+zsfV83g1\niGRK+OoWkJI2lJCTMaLP2mSOXAwMpBdMuzhfw6hFAumuWhbZRuZbKxntIFzHx1NC4XcqTV10L8Sn\nbB1LH0MH52LlbPx1Termp+17I2qDzBXAajUPx1L3OzmKUxS0pbO+Jv2q32sSod3QZF+x2BaLau38\npt3gNOGRe9QOqCWtTaerybnahLFszalOzWurd1qdy9T7CbCSMBn3GKTD6W+2WSdlGr99ncLZ3k6/\n6gcLzsC6wGCZn19jPDePPTt1zorfwqlvRe5Un3Niqm9nHHnmWawzC2ZnSU9QegICAgICAp4+2BnS\n4wPwHu4J9JjMqTw9nKJzO+5J8QyuFuAAjvTsB/aXDJZWybLxtJZG6EPTZar5dFp/do1Lm81L2yqQ\nGBvoBqLt7ZppanZ6bZU1xP6zmBg0+tqocck7Z9Ick5MyJmNse4yHPfeEXOyLoRl4t1ODdEqWVmi2\nIksSQLcVDtkP6sBcCufHfn3uTzDx63R6WxfxkPGK0iGkR4L+iUsNrLslRVNDCVHUJNgeMeDU8d21\neYGQwi5lScajea1pbSuLjFlsq7XRgfBrXRckJKernkf3JGpyareNEBS5B0IGoVaUNLlCbd+2/oZa\n9ZP+OOvAEEbrfeZmYqpoM+GRV/23oc093OE3p2nq4xis/zuxDKIhi+cc49TorNrJ7X5gxVtXT4AH\nYZV5l2Y6gF6P+jf1BCOQnoCAgICAgKcPdua/fR+Ax5QMGDJgBEuVW38M91R4DRcY7gOeybSWJ9u1\nxmBmRKSif+sJQ+E77XQFd9J5R6+DZlBXV9YkU11Bmjm2e8bo/bQ5AYaGi5u8GtNUm8Qdrq7xcUYK\nOalTevI+441+rV600VUL0U7jKtVSqe90Ib4umNfpVLLfhGYal6R1ySKkR5au9itCctq1RDIef9xq\nklLapFONkDtTEjO2PfKTc3AEuAen8mzX9kXPlVZUtMOdrj/SpEiueUyT2Giypxf9vTiwyVyJ5biu\nB5LxdJEnXQOk50xDX7fMpahnq1Cs95iUqVdCm+Yd8uvTaOuPpf+Vaujv3RBSDJY51ljMljFzE2cp\n/33AGTgr+iRy9+nb8MBkv0tvqyBN2TGE9LanF+666y6iKOItb3nLw9r+z//8z4miiBtvvPFxHlnA\nQyL8sQYEBDwG2LlnnbEL8i2GHmOMzV0q2zG/pHhlB6f+nAlmaczs/DpZNCZRaWcaErR1pa/VQZ9t\npErpfa11S+mNCsR+QKhQMqVEZWM/gRAecCltxri0N+eg1YSMU8ha6Wt5JmSMRz3sKNtclL8ddCpU\n1/t2qpQmRLp2RILuXH2WwF1qTXJcDY3YaAsKmiQLP/as4zq0ajIGWyQURdIIzkXh6TEmpmBEn1PF\nIjxgXFrbih9XW01pKztyPk142vbU2mRBK2S6VqZNRoSk5Gq9nF9S/2TuhABNWosmmvo4cj79M2+7\nzyWt74RobeDuzXrKZNJDukN1Ef+HgqhvJZubxQopmnir9xk2mNm9Artxaan7/BiHwEngATixsp8R\nfdeIeAfVlqD0PD1hHmYAbYyZLo8E73jHO4iiiH/91399RPsHBAQEBDy22Jn0Np/Kk/scnx5j7Ime\ne2p/N64AehZnYnAQFzydXTJYXCNLJ2Tk/um/i7brQM5RkZRiqhBAneom6yIqxOcNaJgZAFRVRBRV\nVKYO9Eqfr9TVu2fqwJaULkWr2hxN6fQ4PQ4Zn1QPVUSMix6TUeZMArZ64i8KRdseWSDru9Lc2pAi\neald0c1NdQG9VjMMtQIkAX09pU3jBEkVk7QxSSXT4y/BloaijKnSZmqbkJ6KiBExyyd2OcIjiqCc\nu53y1U4B7Lp2sY6Wscl2cptlznKaxhHtFDd9n2QMmrjIueQ7Xc+ka57ExECUpa76LW3WIMfV49IW\n2COcHfhsQkLhf/uRH3pMssmkw9BFieRvSF67HjjIw4HBzJD1+QoOR8585Igf3xpwOyyfXGJt7xyk\nUG1Vn/YEIJCegO3w6le/mhe/+MWceeaZOz2UgICAgIDHADtDenzQPKLPGrOsMu9IziquDmAd93kv\nrqZnCXpLK8zMbjBg2CikltSyuq7HToMvDccRCiwpEZYSsZtWKT/WOYjZypOoVs8YVxtkOomPIIor\nbOXUImO8wmSa45RzSwApqtLEGzLnk4RykjqjABk8NAPgmM0pXbqGRCsa7e+6hq+NB9qBuJwvpk5T\nk2OJ0rEVoRAI0RGSIedTBMUWhrKK/XCiKTE0fu4KYlareYqjc75WhDrFTJ9vK6LQdm3Tkb12PxMC\no+dO19y063q06iPfoz5r+2o5rlaXNInpsr6WY4kzn2xraM6xjMFQK3EjyEcppY2n1tWO9EhqmiM+\nNamp0wm3KpFyp2qmeloME1JSCrJowuCskwztbrjDwDdw5iSngAfAHk25+xnnwjmQDDoO/gQhkJ6A\n7bCwsMDCwsKjPo61Xf84BgQEBAQ80diZ//Z9wGd85DspUxe8nsAFsrM484LzcUrPvglzC+sMoqHX\nRAoyJp3kY3Mqm5CjmihtVZwtzmuS4tauZYh91U/7XNPjGjttTCrfRXFFFFVT97Gu84ppQkFKblPy\nSQpF3AzShXToCDRpfW7X7xStdbCZELTVCJ1ipZ3GpK5FUrS0m1zpP0ttUHvcQnZETUlpqir4YxWG\nqowRUwpR82TOLRHL+aJTDr6L+620a4S2QpvwaMMCGZt2rBPio62ju+ypdb2PzJ/U9OjaHkl160oj\n1Cly49Z2euxdcwv1b6Ds2H4M5TilKJOG+qlRKWqpp6vqqOdp7lMbu0tvqYrI1fYsrGAWh/D9wAuA\nBeq/+7WK+zmzSfh2AIH0PH1x11138brXvY69e/cyGAx4wQtewMc//vHGNlvV9HzjG9/g9a9/PYcO\nHaLf77N//34uvvhirrnmGorC/UNw6NAhfuu3fguAl770pURRNF00jhw5wlvf+lYOHTpEr9dj//79\n/NRP/RRf/epXO8e9vLzM1VdfzTnnnMNgMODZz342f/AHf8Add9zRWa/05je/mSiKuPPOO3nve9/L\nc57zHGZmZnjpS18KQJ7n/NEf/RFXXnkl5513Hv1+nz179nDFFVfwiU98onMMhw4d4vDhw6yvr3PN\nNddw8OBBZmZmeN7znsdNN90EQFEUXH/99TzjGc9gMBhw4YUX8r73ve/h3JqAgICAxw07o/SUwAgy\nclfEL4qKqAEzOJVnL7AIyWBMFo3pM2wUtbddpzSx0ZCmpM4wwL2XEM/4/QriqTIDvjYnYWqQkNHM\nXJKGjboRahS5paxcSlwclXW/HjXO0rtdFd7jzWAZ02NCRpEnFEJ6upzadKAoKWM68G8Hxnq9bNel\nEGlFR6saJU1CoI/fPpc+rg7INbkQgqHPAVOiNiWeam7FIWxIn9UHl5zKc5yaLOhjNNsfNVP/tLqk\n0/javXzEqS5X67sc4doqT6mO0TZt0HbgOo1QK0rQJEKSIij3Weas/Vcbt/aXV4MnoxGFdbRbzNI1\n2r2R6ro4wNec1fdiM+RvL/eGBgOGLLDC8uwCw7Nm4BDub/k+f02TggfZ52r4dpB4hNropyfuuusu\nXvSiF3HBBRdw1VVXcfz4cT74wQ/ykz/5k/zLv/wLL3nJSxrb65qeb3zjG7zoRS8ijmN+4id+gsOH\nD7OyssK3vvUt3v/+93PDDTeQJAnXXHMNN910E5/5zGd485vfzKFDhzaN48477+TSSy/lyJEjvOxl\nL+ONb3wjd999Nx/60If4+Mc/zkc+8hF+/Md/fLr9aDTi8ssv55ZbbuH5z38+b3rTmzh16hQ33HDD\ntG5oq/qjt73tbXz2s5/lFa94Ba94xSuIY/ePxvHjx7n66qu55JJLePnLX86+ffu47777+NjHPsaV\nV17JBz7wAX7mZ35m03zkec4VV1zByZMnefWrX814POZv/uZveM1rXsM//dM/8b73vY+vfOUrXHnl\nlfR6Pf7u7/6OX/qlX2Lfvn289rWvfSS3LSAgIOBRY2dIjw/mRvTJSUmZuJS2O3AF2Ltxrk/nA2eU\nzC6u02NCjwlQ1xPoFLZSRX5SgyNqQV3T0+3IZluBoDF2ajUt20khOEBC6eNXHW3WCpImOm3S42Ji\n44+X+BoLXwxeZQw3+s3UtjaREBIi33WZCOggWdZv1a9mKwUI6vQpmeY2WWorINr1TO+f+aXvl5Ta\nvWyixx5NjSAkvc0pes61bbVcoLqv7+q+jlPX8sgYZV40MdSmBjInUWvR16NT+ERJ02SqTTq1IQSt\nudLzpf/StKmEfI5an7VNthxXH6OgqZaVbP4tRHhTg5gyT7Dpw2cYXYYF7jLqvzlJjdPfy34ZE6LI\nuuvYByzimgyvgf3/Mo7+xBlsnJsyM7dzjXoC6Xl64uabb+ad73wnv/7rvz5d94Y3vIEf+7Ef493v\nfvcm0qNx4403Mh6P+ehHP8orX/nKxnfLy8sMBi5f821vexsnT56ckp7LLrts07F+4Rd+gSNHjnDD\nDTfw9re/fbr+F3/xF7nsssu46qqr+M53vsPs7CwA7373u7nlllt4/etfz1/91V9Nt7/uuut4/vOf\nv+0133LLLXzta1/jvPPOa6zfvXs3d999N2effXZj/crKCpdccgnXXnstb3zjG+n3+9PvrLXcd999\nXHzxxXzmM58h9RaMb3rTm7jsssv46Z/+aZ71rGdx6623TtMDr7nmGi666CJ+53d+55GRHmvDH2xA\nQMCjxs48ZzXAhEZAywlcDcICcDYuUNoNg92n6GVjMsaKehTTRSAEqCuFp264KKYDdkqI6m22zo3S\n5EqCPP30Wy4JHGHStTyunqeYki9dSyHEZ2q3XUVgjV/YTDK2SgXSxEOUh7bjmKRptZ3ctKNb1zm7\n1neRLdlWF9f3/JJSk54MiOzWvzy/vzMyqEjJMVQUJKzl884B7BS1iYJA88+ytb79f6WuV9J1O2JY\nINcn6XxaadOkpn0/2ulqclz93VakUys7bfc5nTonEJWoaxxyXvW7yEfZJgL+SFCovz75rKEfKmS9\niUtVPRP3AGOA+z2cgmoUsTGT1f2KdgAhve3piUOHDvFrv/ZrjXU/+qM/ysGDB/mP//iPh3UMTQIE\ni4uLD9vp7d577+VTn/oU5513Htdee23juxe/+MW8/vWv58SJE/z93//9dP2NN95IHMe8613vamx/\nzjnncPXVV297vmuvvXYT4QHIsmwT4QFXz/SWt7yFkydPds6JMYb3vOc9U8IDcOmll3Lo0CFWV1f5\n3d/93UY91OHDh/nhH/5hbr311lDjFBAQsGPYmf/2fZy0xClyEqoickGsW+lSYc4A9kyYW1hjjrVp\nXUfcisrjVnSpG4hqh7T2e4mEawe17v+sulSlGh21QdY0lubWrlC/rBImeY8xGTlpTX6KmLJs52fR\nHVzLonvmSJCua020zbRe2vUl+jzt87UC6OkY5LztWhcNUXr6QM/CYAz9vG562lI0bOXSqUTlEeOK\nIQOW793janmWO8a4FYQIate4tsLT7pWjew5JnU17DvXx26Szq+5Hn6tNfOR+iJ21VuweTs1S+77o\n/cfAOuTDHqWta3S6fu/2Ef5zoIlPScyEHgBpmmPmh+4BxiFcfd5BYAHMyYhkuM01PQEIpOfpiec9\n73md5OTgwYOcPHly231f97rXEccxr3rVq7jqqqv4i7/4C7797W9/z2O45ZZbAPiRH/mRaaqZxuWX\nXw7A1772NcApL3fccQcHDhzg3HPP3bT9JZdcsu35XvjCF2753a233sqb3/xmzj//fGZmZqa1R7/y\nK78CwH333bdpn6WlJQ4fPrxpvRCoiy++eNN3Bw4cIM9z7r///m3HGhAQEPB4YefS2xKYrdY5IzpK\nagr39B6cKtDDBUZJSTJ9ruz0kLbFrgu4HImRVoqa4AhE4cmnhdfN//QSSirj6npMrGp7pg5rMTFF\nK8DbPH1dSo9Lh/PrIkuZi1V2PUZrjSM9ReysqrsC6eaFdxMRmY422vvrdWJXrdPE5FXOX3bs1wVt\nSy2Q4NK4eyrUlKibaNZpg3Ua4PFiDxxNXFpbW+XpQrveSHiutuFuKy1Gbdeew+0IR9fnru2l3kfX\nDWnS1yYu7euJW9u1f376Huq6qxzKUeJcCbcJ9CvFBtsPFx4K8neReOV2TMaAIXOLq6yOMjgrdsR3\nAfc76FlHfnewOWkgPU9PLC0tda5PkoTqITzUX/CCF/DZz36WG264gQ9/+MP85V/+JQDPetaz+M3f\n/E1e97rXPawxLC8vA3DWWWd1fi822adOuf8YV1ZWADjjjDM6t99qfft4bXzxi1/k8ssvp6oqXvay\nl/GqV72KhYUFoijilltu4aMf/Sjj8XjTfouLi53HSxL3j9L8/PyW3+X5zqW0BgQEPL2xM6QH4ATM\nsTfnlD8AACAASURBVOZi4/XYBWyzuNS2s4D9FXOLq2TkWzYiFUiKnMH6Yu1yWlytSYsQna0cqWKK\naWBprSGmaBCTkmQaDHYRHtlPv9fmCFOlp4hdH6DWOKLIn0ubGAh0OpNWedrEp8tnWIJgiWllW00A\n9FB04N+2Z5axwOYgvUfTuKBPbV7QB3oTksztlGcpbCR1Qb/uLwOb6nlWji+63jy6lqcLXeYKmszp\nZqQCmQeZY01+uqD74nSpLPrcUBPRkibxmqjztglYm2jqc3aNpw1d0zMEJgl5mWKT7otqqzwVMdE2\nf3PQ/HuoT1vX9qRMmJtZZbzYY3LBonNxOwrsge+mZ1EsDyCabD7wE4RQIhDwSPBDP/RDfOxjHyPP\nc7785S/ziU98gve+97284Q1vYN++fbzsZS97yGMIadhK9Thy5EhjO0kVO3r0aOf2W60XbJV2d/31\n1zMajbj55ps31R29613v4qMf/ei2xw0ICAh4MmFnnnX64G4lWmDAkFPz8y4424Urdl6CaG5C2su9\n0rOdyuMgpEi20a5qbejv2hbXEuhpsrIVtLnBlGuo/aSep+0qFyclJnqI43epK11Lm/iIVXJbKYJa\nVdApVfI6UZ8lnUunebWPuVUALjU0PWqy0wdiS9zLiZMCE1WYpGg6uiVA4hq81pbk7iTr1SzFsTlH\neNYexvk1pAFoG1px0SQQmsYA+vu2oUMXwdkKbae6rnqerY7TJjVdCo9e33W/8jrdsiu1bRMBp/SG\nIAklSeP9VoS/Hk5MTkbGhBmG9PpjZ2SwC9gP5HB2/7vcOTjPNSDeIQSlJ+DRIE1TXvziF/POd76T\nP/zDPwRokARJWyvLzQ8PxHjgc5/7XOf3n/70pxvbLSwscPjwYe69916+853vbNr+c5/73CO6httv\nv509e/Z0Gi185jOfeUTHDAgICDhdsXN9evY59zZDRf9Y7oKiAc65bQH6i+vMxBvTp/1tMgNsIkHA\ntIi69M5s0kukPnXlX+tvBVI3NCUnHYSpK+DTznHiPga16lMbKfjPUUUcu2tqKElFjC1iKHxQuh3x\n0b1yxmp93vrue1mE9LTdwOS4bUVjOwKkU+cSoFeQpAVJUpIk6qIitQ3eOY+KARv0mDCix4nJHkd4\njuHqeUb17g3SpNd1QWp6BG3iIdevUwpl2aquZzuy0yZ1Gpp06fftvkld59guDU6uUyA9lAqoyrjx\nWxV01fK4hrnqd93aT/4OtiNBBhgwdAQ/wdXpXQR8P5RJzIPZns0k9QlEID0B3yu+8IUvMBqNNq0X\nxUac1gD27HGMvoukHDhwgCuuuII777yT97znPY3vvvSlL/HXf/3X7N69m1e/+tXT9VdddRVVVTWc\n3gDuueeeTcd4uDh8+DDHjx/nm9/8ZmP9n/7pn/LJT37yER3zcUGQZQMCAh4D7Ex6WwYMoWfH5Cbl\naHmmC87OBObB7C0ZzAwbbm0CCdrarlHQJEFaxXG20BER1mdRVZ7yuGNIDZCkyBmlzGiSJe/FcU1j\nan8tT9N9nx85jri3xZEc2zmTuR5BbsNN6o+kWXXZEUOt4OjdtGX1dmKSDrbdJDUDUElT00qRPnfS\nei/7tAN9T2hMUhAnBXFSYgucg1tiITN108qsIo6dMiZueiP6rB6fdzVfazTJVxt6TDJ3EZvT1PQ1\n69Q2uT5d42TVglqv56I9hodC25L64eyja3q6oMci2+rxF1AVmw+gHQUfLUrlaCh/uQkFcVy68eyv\nYCPCnDdhwaxw3OzZ0T49gfQEtPFQzmK/93u/x6c//Wl+5Ed+hEOHDjE3N8ett97KJz7xCXbv3s3P\n/dzPTbe9/PLLiaKIt7/97Xzzm99k165dGGO47rrrAPjjP/5jLrnkEn71V3+VT37yk1x88cXcc889\nfOhDHyJJEv7sz/6sQaKuvfZabrrpJv72b/+W2267jSuuuILl5WU+9KEPcdlll3HTTTdtan76ULj6\n6qv553/+Zy699FJe+9rXsrCwwJe//GU+//nP85rXvIYPf/jD39Px4KHnMCAgIGCnsDOkZwiU0Dcj\nJvSwEU7pmQPOqRjsO8VcvEbmize6ush3QRdTi7qjU9mkKalEj9K3R84hjU/bSo8O5NoQu2mLcZbT\nQFHExErNEDIl44jQnX+K6RhsZfSB8QPTF7j1Ivt0BedttO2q3cCaVsmSFibkqf1de5y66SjU9TMZ\nkFgir/DESUlVRURJSZlVUMUuFW4CUS9n0B+ReTKYk3Kq2EV1dMbVgqywmWh09a5pz4durto2NGhD\n21nreyCLEEGt3mw3112Qudb1VW1o8tZFYNvnbPcP0mRZlJ4ipm46Kps+VoSnVn6k2e+EjITCkfnF\nEWZ/Cgslg32uKHueFXjgMTn9I0J4eBygYYxp1L60PwO89a1vZffu3XzpS1/ic5/7HEVRcPDgQd76\n1rfyy7/8yxw8eHC67UUXXcSNN97I7//+7/P+97+f0WjUID2HDx/my1/+Mtdffz3/+I//yM0338zi\n4iJXXnkl11133SYHtH6/z6c//Wl+4zd+gw9/+MO85z3v4fzzz+e6667j0ksv5aabbmrYRG91DRov\nf/nL+djHPsb111/PBz/4QZIk4YUvfCE333wz3/72t/nIRz7SOU8Pdw6/l7EEBAQEPN7YkX+B7Cux\n/J9wx6vO4v/m5/ijE29j/XO7YBGiQzm7zjnK7vg4PSZkTEhUZCgxa5fSIxDFp247aqfJbMU0Wa7u\n+FPXLMQNslR30qm76gh0PY8bZcbqZJ4iT8jzhF5/QpK6p9w932MIP24niBT0GWKJyEnYsLOsD2dZ\nW57Drg9gzWy2oy5an8WOussqWhOQiKZaoV3E2mYGEsxr8gLNgNqo9Yl67eHqd2b96zzOrWsppze/\nTn9mRJyUFJOE9bVZymEG4wziCipIl9Y5Y+8RllhmgRWWWeT2Excy/soSfB24HZfitka3mYFuFqrT\n9ETt0YpUe65kvXaf0+9lXnqtuYVu4iIkRBMTuQfCM5LWq4YmZXJ+Oa+ee73otDdpCNvD2cBfCNmz\n19hzxgPK2O3xeubhFNKMnB4jTrGLk8d2UQ4zZvob7Nt3hAu4gz/gGn7wzbdjbuz8d8g+Xk+MJe56\n/vPhK195XE4REPCE4gMf+AA///M/z5/8yZ/wsz/7szs9nMcexsg/Ep3/VjyhYwkICDidsS2v2TnL\n6gkkIxj3eyT9sevlMVPR373MXLzGgJFXR6yvz2n24Nn+8IbIb2sxxL4oW0PWRf5xe/uJd50et/nf\n07biI+cBQ+FTiMoiwkTOApsIn15XN0b17Ug936htrqfD1MNtX3K7AaZO+ZKguj1sCZjbaVBCDLSN\n84Q64C9ax5BzaCQ0U68Mzoo4ca8mzUnSgiiqnI13ZInjkiopsWWF6Y0x1jCYWWeONWZYx2BZreYZ\nn5x1qW3SkFQc0JLWOGT9VoRGxq1TBvW+kfpOp7tpsqSvr412Q9Q24ZH99L5a7dHru+yy26mMXWqb\n/qxtsb1JRZVHLv3yYZh0TIdYRbWrYGs90PmdDK7ylXMZE+K4pCgS5hdOscgyeznG7MmxU3d3CCG9\nLeDJhvvuu29TM9G7776b3/7t3yZNU175ylfu0MgCAgICTn/sDOnx6kQWDRnRJzKVtze2ZFlOxtin\nmhWNdDNRZR6qh4gjGHUkaDFTwiQqUEkyreWpq35qSAqapKO1XeBqK2z3bUmEPJgui8QV61tnZlAQ\nN4wY5NiiQsnnBnRwKzbL7ZQ2aT6pm4rqeFb1x5lu025GqtPhdPPOiqbNs2zTVZQv59DkR0hTYp0j\nW1wSxRVxVFKaGIxLeSurnCgtSZKCNC7o+5atEzJWi3lYT515gSha7XOiztflsCzEQr/var6Kum69\nrZ5PPQ9ybm08oFPnIvV5uzTDrvqrNoGi47Nck95P9xeSMVZMSXFZxJRVTBRXPs1za1TV/8/eu+RI\nknTZmZ88VM38EZGZfzXJBfQOOCDAFXDWIMA55wVwH9wGAc45IbmWBjggOGmwAZJVf2ZGuJupyqMH\nIlflqpi6R2RlZDizXU6V/Wampg9RUfWMe/Tce67FmEzOLW1TLNi1LftLpEhUpJWZiZUPP/4KwA+n\nn5lZyRhOboH/89Vh/KEYpGfgz4Z/9a/+FSEE/uk//af8+OOP/Lf/9t/4T//pP3G5XPi3//bfvtiP\nZ2BgYGDgrUhPDYg/zQ+l2Hle4R7snJjnpdbSlOgtYnGkzZVNkGp9TsJtJEiUFI3ellorPkI4ijJU\n9qrX7clJv19xiBOVJwZHimJT6jb1Jjq/7VOS6oREtf0WcmBdImrFQQfkcFzP0xOCPgVNKww6tS2q\nz7Jcp7npHjqa/AiOvktzWSFHU8RNxcCgBMd5G4sxGTevzKeF8/nC4/yJBz7jWfmFDzx/ui+E55mi\nVhwdW87rJR4sKkmv8BzVKOlam9T91n/+knudzN+svr82fj3e/r1XdXT6ob7O+2Kdcl9MlLm7QF4t\nMbtq5NGIDDTlZoNpNWZ976ndcLO56UclEHMPT8CaxOnuyk/8Pf+I/8GZZ/zD5fWeS38wBukZ+LPh\nX//rf82///f/nv/wH/4DP//8Mx8+fOCf//N/zr/5N/+Gf/kv/+VbD++PQ86jCG9gYOB34+3S2wxc\nPj9y//DE5AKcV+wU8CZsNKK3kIbWPyTXFLa+gaKoOvr7UTNSITMl/S3VI5bH8yVDLG/r9DhKs9ua\nMmYJFCFW++o0WZyV8cs5tX3tpibavblA2fk+sFaOXDdBMrRULzmQNiPQSobUv+j+NLqnz5GaIPt3\ndR1dXyLHmmjpbS7hXAJDvRIllc+5RI4W6xLTtHKar5y5MLESao3T9de7Qno+sycpegL5wjJN5l4i\nSK9tpyFphKIqSY+kViTToBWxmX09zmv/dstteGSiIPs92qaHVvM2cmZJqRAfZ/b9qKxNG/Hp1Zwv\noSdMzpV9S53cHc888JnVTxgyJy5kLA/hM/z61Yf55hikZ+DPhr/927/lb//2b996GAMDAwN/SrwN\n6THAEzw/TCzMfOQX/t/zgj8tN05pPalJ3XJNGoTcaMVHfheS0siKuLUVYrM5qFVCpdWdPrVNVB67\ni/pvg79GgMQeO2xjEiVJk6eoLYU1+SgT0tKodCB7pREgDVE2+tQprfLI/vTp6ZQuCcAlvU7QKwt9\nYb0oRHPGTKGSnFgP35SB+bxgbeJ0uvLA59LThcSFRz6nB/KvcwmKJYUvq+NoHFlqy5zJ70epb/32\nkr4nl1XOW6ftvdS3p4fMRe6W6XRFOE417E0k9DX8klOcNnEwdYyO2oPIEqPDT23AKdstDVPwWwjP\nEbTyE7EYMnc8c/FnJlZ+5Gcijl/zjzz+P//zdx3r92A8OB4YGBgYGHg/eBvS8xlwxcHME7jjmbuH\n56qwtAj8SMUR6LS2tn7aiqdRpKUnPkYxConrd710FNlpxKeQI+14lWrqnUCn+BiTiaGs64LDuUjE\n4tU5tNS4Zh2co4Vo9s0qJSA+SqeyvOxkBsfBfjx46VoeCZh1PYpOsxKlR36XSUR9rmTJuYTz8WZu\njCnpfMZmnImcuDCzkHA8c8fn9QF+NU1N0YrKS6RHeux8zRN8TTBeUsmgEUQ9n+Kip6+FJlaaFGrS\n2BNQnUbXp6bJsV8yReCFZTLGqqay0AwpatPbnIwao+Fbm6SlZDeSC7DimVmZWJlZuOOZez7zxN2b\nWlYPpWdgYGBgYOD94G1ITw2yLpyZWHjkE6e7KynaG+Vmr7K0aLRXX2R9qf/JHbloe8hop7aituxV\nov3+IkJPbGUisr3U9AgknQ0gRUdOhhg8KQZyNjiTVAXQnoglSuoRwe0bh+pUNtT3habUwJ4IiZrQ\nB+Gyv94MQX6TNDBdq6ObdLYJ3ffEEXVH18aoO6sUzsv5FmXBuYifApNduTPPPPDExErCcuXE9dNd\nIcefaYpKr5roMQpJU4RrR1b0XOo5EjLXN//U++/VMU145Bi92hRpaW1Sj6QVo77WCG7VrCOl5zVo\ngibvszr3WNTENFlSrjT/K/absylplxTCat3rJghQatpk7IGJM1cspSHvxMqvfOTEM/zfX3FefxCG\n0jMwMDAwMPB+8DakpwbLV2bO9Qn/5FaSsTvjgFxd0VoDT6NqYo4DLzE+aPGkufFm09+NKvpwRJVW\np1WeVN9zfYDe1j+Cdr0y5K3YO5nWDFWTnYAnZse6eFh9Iz0SvMqpfqk56TaA+t47ecn+dHqcJgFH\nls8v3SFCdASikFhKPY8FfMD6kt6WssWYVOaiFPhgTWIy66b4CcEM2ReVbOG4iaiMqe93o+fqJTIh\nEOKmydI+W7GtB43ISG+k3ghCH7evodJ1VHJjWvWuyalurCrnJsSyJ2ba8lrS8uS8RL3T9tX1XHIy\nZGdvzQv0aWdT1jswLojB7ep+pBGvLNekKEZHchbd12pm4Z4npkvm09tltw2lZ2BgYGBg4B3h7UjP\nWdzMEjMLZy5Eaze7alMpz0t1Na8hYmtNT2tMCs3k4EgFEsKjU+RE6UkvEBxpdpqw5KwiKJUKJna+\nOTeDBV3Pk+pxQvQlCE0Hj58laNVkJan3HkdPsLUCIAHwkQmCfJdUMUmt6lPItMKjU7h0mptLm6pl\nTCbWc8yp1Hx4G5hYNxvvUvjuWdNEXFwbhz5HfUydkifjlnn5WnyNgqJd8qRJrD6ONp7ozRb6Oh05\nplHryxxrh7reZluW9QqULO+JcVavoxS6V6CVndfW2YahbKuP6oHE0r28l7TUMxeyNb+lZdA3xyA9\nAwN/EgxZdmBg4Bvg7dLbZon7EufaiNRur1vXNoG09wRe7deTMPX/2j4aDerT3fa1Pnub69ajJ3Qy\ngCZTu6fm1e43p32Pk6JCmY1QlXE6Viau1xPxcmpBq05f08pPO8HbAF9Ui16x6APfo+30b1r56E0A\nRFHoA0Zd/6NslI3JJSjOqaT7ra6k+vm4za1cR5nzhN3skneKkv6sl2klxHbrHKk8L0Gn58l5C3FZ\n2StPMn99fZWeF+3Wpl3b9HHkWq3dOEXV6UnUSzhSxPr75ZXtRZWU/jy/BTkZYuolsrLc2Ix0prIk\nPAEDfOQXfF5ZXzOY+IMx4qiBgYGBgYH3g7chPTVF6AO/Uixsr5y4staGodKYFFqBv9mIkM5a8pWg\nlL4jLeWtrBW3bfNGNHrFSDQlOgWoubO1FDsNSdWJeJKkqonlry3bWZewNpWmnC7ityQfcagrqW0l\nnavUAN08vY/qXaez9alTso02FeihLaq1CtCTBOpvE82iWce0vfLQ1wFVGB9xPmxzE4JnWSZ8NTYQ\nPc1VDWAjmhJ0iyLiKbUpmrzpnjo6va2vLdLE5UvoVRmpnYrsndqOCI9AxiM1OYoA3rjgvXR83StI\nzucoQO//env16QtKRp/CtpH4l9zbsiF2JgVHEBdCYzM5GpKT1LZWG3fmmXO+cvl6AfebYyg9AwMD\nAwMD7wdvQ3oAJvjMA77m4kgw1BqGuhoIS7qZPYxdGxlKdUtbt5PfW0qZ1PrYSlMkvUz2UVLZzKZA\n5fpb7qJHIVut/idvKVvYQnRiNlgfcU4CfHneHeo+WrrPEmeC1PNIcL3SPuueOkeERwfJsA/8RelI\n3ba6AF8HnqIuzGp57x72mkOaKE1qn1LTtC4T63UmxViI4cyW2rYzdpCLLOeh7Z/1vvUYNBGrzW+3\nmp3fConpRfFZaOTniGz2x9c2130NTm96oAmNmC/oFEEOtunnXl97vUwrb9022k79q6Gsx78Gotal\nbGs9W0ltO3Hlnmc8gdMbEo9BegYGBgYGBt4P3ob01OAxqejOEbeaG0kAa0X/8n60q9tTaIQpE3Cb\nrbSQHGAjQBpFWMhIL6BSYzLVeh+PU85tUs+TMaxMpGRJ0an9lAJvcSnzphTrFzMES2AiYbnmmRhc\nKdxf7b5/zkJRxeSlg+6juhJJPYtqWT7YTn9X9UfbNqLwyP7kmvXEqg/w5eS3fe/rPsLqSYsnJ8P5\nrqUqFh2gDKZPP9wIzJ0al1ZYNAHpzRUER2w5d79pZUQbIEhamyaMR9Cql65x0qSmJy+9SicpaPlg\nPy9Bbv+XxqWvjZpWSb88RGYznNCqm07hFPOCr0FKluREdS04ceF0DZweKPf2G2CQnoGBgYGBgfeD\nt1N6TvDE3aaWQEtDM1VlETWlubc1AlQ+72t2NHQfn1jT2pKK+mIXScYtta7oL6lW3/T1ReLcJkX3\niebIBrUHjS1Hci7ifMTblak6lMmxY03qWtPMcp3Iq2tBtZCdlUZ4ZFlvZCAEQJsLaItrndKmiY5W\newSSjtXbWKfuu5gZ0H2f98vyZWY9zWAWcrKE6wSXGe6vLRVwIzqQ6nzG5EAX0s80xzQ5Tn/emoT1\nsfyREnK0jkY/x0eER5s59DgiWfrVH1OnCAoR1Wl6fa2SqF8avWIn19O078bkYg2v3Nduh65q0YzB\nuViamMquXyA8so2YGuyHZutfSqnrOXPFXGn3zBtgkJ6BgYGBgYH3g7dTemwhMBL8J1qPHqn2MLUg\n40jN0WYAfXqUfC5paHELrDVasG1q6pwU0TcSpmuAshqHjHlbHp2qY0i4WsvjfMS6tNXymFpnFGpw\nv+SJFG1VeXxxBrsAz8CvwCf2pEdeRwF4n/alFRptVS2ESBMbURZ08XxmT5B6c4SjY/eK0lLmJecT\n62WG5xMEs7mDpWRJVq69DFf1PhLVSQL8M2WOVvYKh6Sy6SD2NSGiV7f0cn2+ovS8NN+9aUGvmOn5\n7FMKNaxa76Wxa1XtJXzJzOArMtm0+YZsL/e2QH8XgmNsSfE8IjwxOJIzOFqa58y13N/Tl8f0R2EY\nGQwM/EmQ8/iDHRgY+N14m2edEqiCoh1JJbaVYR2RnbYLe/NZtk0Hp9WTnt6xrZlHx43ovNSH57Vj\nGcrTdOdrWpuTtDZxgStnu+I3lSeuDhZbAuxnSkNOndYmgX6v1gi0rbNWJ3pra/msSVA6+K5T5I6s\nrl86jqTkqWOm1RfCs3oIph7HkqLdXYPCCywLEyG7sr7uHaTrXvSl1NOv5+Y1AqTnQUMIibjn6Zoq\nXY9zlH72UhpaT0R6IinQvXt69Ircl1zcZBtZ92geO4jq81rvnsPDJYuxeff9eFhtuSUys8ITcPpN\nh/umGDHUwMDAwMDA+8HbKD0WuC9NCieWWiuz751z5LTWQ1LjdFNR2V4rPntnt34fvq6zf5otJKxH\nUXyk/L6Ql7B6gjz9NnlzbNMGBnL8VMcW8ZujWbrOTd0RhedXCtmRvjALe9OBvlC/D6KPerSk7r3H\nS405xQFNN8/U69g6PqkfuVCe4D9BNueynpCjCJwccZ0IyYGl1l5NLEylT0+Y941GJVUrqO/aZU7e\ne9OHI2VFz5EoMWK+YLttXzItgL1JgN6f1EHp7fScR7WehiYkvlvuujH0xO8lyLXRDnK53sO1kSiU\nBqLGvpzu9iUcKUHWJZJyetNqKsAkpOcl9fA7YKS3DQwMDAwMvB+8XU0PbAXs4pYmxgBN6bmNiI6I\n0J7g7Ot8JF2txH77bY8UoS/91itMCbdr5GhMBlPVHgJ+c21rx97Gmg05WQjutn5Hvq/q4BIYS8qX\nViZ0Hc9R+lZfS2K4VQNegmwbu/VkuVwmmQavxiipdto4IBlydORsFSnMZCxXqpOdnJO+DJICFtRn\nbfzwkoLTQ0iEGAdo6Oaev6WHjK6n0mqZVsaEZEX1WTu16XPS0KRFvstfriZ9+rM2aNjIVLk3JVNE\nzAqAl00N/oGQv4kY3K4GSGryLKkQ/TfEID0DAwMDAwPvB29HehJKL0m4WoFz1FNnT2L2T4xlmaSi\naVKi09OkUF4bGpgtUvxywNf68jhCHW3AEUJReWLwWBe3p+XWNvtt3YOmjMWQsmW5zoTLDJ9tUXc+\nUwLBK7dqgNQ+yLILTaHQKkqfMlUmYl8IL9PSqxWiIGjHr9StU06g7UeOO6tl0Gy379T2pq4XSk0P\nma3eqWziWDgRl6nMgU6z08cXlUtqbvQrqPcj8mO75VotEzKiz0MI20uKRH/76OtmuL2Oev3Ufe9T\n0CTlTRzsPDtTgg2rGrvMizY/6NZP0WJ9/M2pbL8HMTqya86JE+vXEdRvjPS6eDwwMDAwMDDw/1O8\nqdKTFLmRFDchNYWc7IOypgjsU+F2PV4U+hS3tp9ypNdqho4gxKeluPlmNw2k6LZmnDkbnJFkttaD\nyBPVcTO52lzvgmw4drXST/flsyhCAh1I9zbKWpnRU9VbGkuQrZf1ShLqs+nWfVLfdQCu+u3kWFIO\nNVkNTMTsitLTO81plUePR15X9uRPq1Oo/RwF2Vot09dBB8iiLhn13XOcqtYfoydCfRqinuOeXHlu\nCU8PrQz1/F1fG98O/D0Jj25+Kn+jllTu2zckPfk7H3tgYGBgYGDg7fA2pKem3JReNaYOJBKYKH1y\n9gGZBEpa0RHEA+VHthFC1StFBbdBn1aXXkpvKwl5JTEvxn1qG7A5kwn61LZYKVcMRSVidXuTAulJ\no5/wC0ThCBQnM+rnJ27T4LzaXr73jm1HAfaRYYCQAamrCep7X1vTEyMd1J/qNisYk7AmbyYPEcfK\nREyOLOl+izqOPj8Zg04JPCI+Or1M3za5e9e1N3qbnkD0CpmcmxCgoH4TkvQSjhQHTVAOeuvsFB/B\nS/Ou7a7VNsbUJr7SOHSzmc6HzmvArtZn69vzW5CLupRd+ZsUO/obZfI7IKr7YJCegYE/CYbryMDA\nwDfA2xkZZAh4tDFAa0yaWZWXrfTUcTU4bjbS1Pfb/yAeEaTesa2HVpGEfElALttn1BPrDCG4XVAY\ng8fXGgZ9PuJL50isFItfsgGTm3JzptXbTPW7KD4S1EqA/wmQxo5nbpt2ntinQmludkQEemXoS5Bj\nidqh07Ku9fOFptJoI4RKep0tyYIy7xHHNc0Q7LEqo93TtMKjDR9Ct25/nr0TWn8+WuF5zVFN7tH4\nagAAIABJREFU9qVJjhBLWS6ETY9Hu+iJAnZkNa7PWdCn+Ek6mx5Pv53Mu2en9PRIyZCSK2mZppGb\n1FlYO5eIldQ7p+rUXiFD4vC2EX65wd4gvW2QnoGBgYGBgfeJtyE9NQgWMiBPf+UJcMbsHNmKClTU\nFUfYgiZJMXO7yK/Uhni1vU6Lk2NY4i7lDZTBAM1MQdtSl6ainpWJhbn15jGZGB1ksO42sJS6JaMi\n8ZwNxiawuREcSX2aKGrPx9wC6U+mBPZnWgH4hfL7ieaYJld0Zt/XRQfvEvj3tSPS+FMrPEeF9b26\n4A/Wl1oZIURCpk7lHK1PzO6KR/oXOVZ8SRW8uqbyaIh6o+28j1LuROnR56rfj1LMjmyh9fp6/rSi\n86WSMCE4mhzCvqmqdpCDW3LzEmTb3lFPQ+beU+41CgmxNh2muH0p7S0qFVN/dtWt8DVoExJHaCTw\nO2KQnoGBgYGBgfeJt2tOel9IhqR+FRUnVzIiBc9xMw4oKFFKyShyNLvpY2vpXtHZnKpqlU2//Cil\nTZplCuHZjAxiefVIyr5Xj0srPY7I7Bfi2bGcl5LOFU0jKg/AXWD+4dOWcnQ5fYDLVBQeUToeaQ0e\nT7Qg/aj4XUMUBZ3pp1UeCfihpWn1qpAO/NtktSBbqyDaVa0SEmMb1ZV5jtSePq+lPQmh0e/9S6tB\nfYqbVoBkfzLG/pi9o9rRX4tOeTsigQKt8Mg6+vtL28k4+rop2e5I3dHbyH0wgXG5kHKdKviNIATI\n+56pNtz8fb2BqcAgPQMDAwMDA+8Tb0N6auZa6c/ShlBS0tiUm6LsNGcvWVbe/e57qAH0EQESiKKg\nm6HKcTX6nj/xgAwBOyteqP1JfMSYvOtRIvv3m0td4MSVeHLM5ytLsGQ7g83Yuwvn+2dO54Xz9FzS\n4bLHucTnnx/hcm5k5kxRhB7ZmxtoO+Qj0hO6d212IN+1wtPXqsg62joZ9sfXJEDUEEUQ/Hmp/neR\ngGfFs8SZdZn2bmRCVKSGR146ne2iftfkR2+7XSRuScVLSolse3TuPcHp1R/5XVtHi5IGTaWbabVc\nGrpmSuqzetc2OW6f2ohaV5RAucb/wF48X4sQ3KuqjzYq+d6pbbA3MhhObgMDAwMDA+8Hb6f0pH26\ni+T7r8xKFYn1N7eluO1341QK263BgeseJev0Ofl+RJLkuJLmdnRsDf1027qIdQlv1zq6Eo0e9RFy\nJnI6L5UkFQu202nhdL5yZ5+ZWbAkJuOJD8Ua+3o5QTAlaHYUVWilpTkdPcXvVYy+6L4nPYKj+LgP\n+PVLW14LtOmBBLlzmScxMcgYAhNrnopSphUb1Oer2t+RwhO63/vUttfmpF+mzQRCt+wl8qNd4HrX\nu75WStfx6H1p4qTVpXiwzmtpbbAnPmewPm7K4R/p3hajfVHx2fXZGkrPwMDAwMDAwHfC27m3Wfko\nKkhkoRERowiJpLl5pfqU2p5GRKS2R9SeoupYTF1HegJp9N97++tjt7i6P3cb1G0GBiZjTFYJce3Y\nJaWvLDtxxZwTp7PH1Or+mYUTV85c8axbgf9iZp79HVcfwE9N6ZFgWBqZvqRyiIGAnPKRQtMH3PvJ\nuXUv6991/UibsAJROe6Bc2SerzgihsTKVF7rRL5O+3Fq4iMKjyg7+tUvE+LzEunp0ZMgPXdHCo6G\nkJG+vqcnltr9TluS6/oevU+dpifbHv3FHo1JpzeK0nMAsVsHbup8rIskncJp8uF9f4QQ3I74iNmH\n/L1b8jHx/IMxSM/AwJ8Q0lF5YGBg4HfgbUgPbIFaUqqLRiM/VRVRxgQ9ZLlRvx81BNXfDemmrkfW\nseheOjJct1GYMp6wKQG667x1CefiRtDKWFIlNQ1CprxypvOELfVNxi9KkSMynxeezwvpPJWUtjPN\nue3CnhDIu65t+ZJF8Gv9Zl4q2NdBvX5BIx46AD+Du185+YXSm0kMI4qNN8He1uHI+WiFh+4z3CpA\nPUnRQXavbB05pwleIjr6d6lj0t/ls+4xpI0l5LtWgvplet/6GK9BFDdtXX4GO60bqcnJ3opfnfqT\n+pq1bDaSJP2oXhxCZ+iR08HNkyAP0jMwMDAwMDDwHfB2pMeLSYDdtJAjhFr6LyTBHaSa9WoMQMJt\npggv1fmY6qoG1CoDq0hGwFQTA0lxK8vdjoA5F4huP43V6gBoznECcXITK2zPSqp1RpLO1itQ5bcr\nzt3jfCSdV7bCKElnE1eznynubtrGWppA9oShDOg2rU0H55LS9VrQLes69S5Bt61DfQR+An4Ed3/F\nmtIcVuY34ktqmzYa0EqHjOu1z+GV7Xr06WW/N/juj9GnwAUaCenXtRS3uqN99nMuClB/nNCtp1Pb\ntKtfxe9Nb9MK0REBkhQ3UXzyUS3R9+uPqsbVPg/SMzAwMDAw8H7wdqQnyZuQDSEfkprWjAsSrtbp\ntzS3oIhQ26W9IQwvQdaz5Ko2vdS75/ibNlhodTvlDHQtkShHMj45Rzm+EChJeZN0NllX0vUWTvtA\n9VzZzhnsaQED6TLD3QS/Ap8pys/KPvAOalmPXmEw3efX7JlFyZEAWxOgM6X26BF4DJzuLjhbUv4S\nlhVfiI+cnxy3TxV7rYZHrxfZqy66X85LeE3peQlfU1+j5xNKqpksl9fC7dwL+rEfuc9psqdJkhgl\nnIApb+YCmrB8C8TgD4lPCF+Y9Deo6dHmBYP0DAwMDAwMvB+8XU0P1Cf8rqoeLcgX5CoXFCLg0T15\n+tj7JbVHtJ6j38s6bf+t3iCSuig516h/ZrlJs+vVnJa6dlvLo1u1eOU6IPbda70k4mx35VRqepjK\n0/JswEfIBusj0+nK6W7B+UgMjs/nR+L9Hfxii531M4WI6JofCYxfq+9JNOMAUW2E9PS1JTqt7Z5G\nfCTovqe4zP0l4h+e8T7eOuZlV5SeZPZKT+C28ag+rqSPHRk49GTot97tX5PSJ2Pqa6j69Ld+2VVt\nL8SHF7bpSdBrJE5+k+a027WoBgbxdXlFFJmczWZ6EIPDmEzO5saxcDvsC8QH9vU98vdWv4yanoGB\ngYGBgYHvgrcjPSqY1Olqra4mvdA3pxEf3ZRUE5VmjnAbhLXanqbu9AQn3TxaFyKT0Tbabfkx6dKN\nV8sY806JsuRNwRIXM0mjS8wEHEt9j/gSsIoVsM1gMtMcOJ2vJaXOR/wU+DQFFvsBTrYoPs+UK/1M\nMzToSYR2K5PT9jTCoZ3MpKhe94ERRefMvt7oRCE+E/gfn3j4+BnvWvqf9OfJmHJ+We0P9q5nenx9\nYb8mQP1l12YI+o7vb69e7bll1rfudPoYWnXqiYncbjIWmVsZt5A2+V3GcaT06PEI9Ji0oYSob7XG\npk8zy8lgbNvpRooMW/NdvZ0QoKMmvK8RnzJ0U3f9dmxjkJ6BgYGBgYH3ibchPTV1SRp/gqSxFYLg\nOhOCHkJ8tOIi9TipKkfNfa0FZ9qqOmEq6WgKU0s5C2o9t+lFosaIHXZPgIw6ksApQpPUGlapT0cp\neUIGrszE6InBcb3MpXlncOASKTvC6gg12HQk7t0TfIQnF7l+vidPJziZ9sRfiE9/5XWzUoGs1yst\nugGpqApCch6AH+rrMZbeQ+cVYzIPHz8zT0uzTcYiVuVkMNmUQDTSGq7K8TTh6OtaYB/ki6GB/k1D\n9gWtGSvqO93+9e9HCstrqXayvtQ3yWdJMbyyn3edjqgtsuWcztySMa3C6Vqee8ocnsoxjGlNRDWh\nOVRbXiEEGUOMbnt4YJWjmzTsNbBbrqGV0e9NPDTpGX16Bgb+JBjObQMDA98Ab2pZLcREt5y/VUxy\nJTi+W6rb1O+3E/JkO8LT7KwbzEG0H/E75Ug+N1VnP1bdFFVbXsu2tjrFWdKmaqVKobQ7ndT3yLws\nzNs5htWzXuei9JgMywQ+sFxPGJuxDwnvyj7u/BM8ln8nLiaT3QmylYkqqW6JFiiLAiENTXU9zIlC\nQKRhqG4MKilUDzSF5wfg/wA+XPF3S3GymwLTaWWernjiRv6SuobGZIxL4Ko1qaRm3VOI2jNFMVrZ\ngviNUIg6JK++1kYH1to9bX8L7VP7esWnJ0c9euUsd+89cZO5lPFqQwm9H+3+Bk0ROtHOW46jVSRp\nXFvfzRRJyWLNrdrzm5HlrV7HaJvyI+lxJpfPRshNO+am4NovO4l/awylZ2BgYGBg4H3ibUhPDbal\npqevkdEkoocQkD4lbb/zAiE/GakMynXftm5v1DrteL3KJE5w8n401pfHY3Yudaam7eWtB1BTpbYg\nUqW1rcvE9TKzXE6lAH11cJ1KetsykbLhOTjCOhEePX4KeLtiTGKeF3IuHCXHcyMToh70gb28Enu3\nNgnUr+yDdtX4ciM8f8m4n564e3zCz6GkspmIM3FzrJP5TjTXPmcidgpEd7BfqUfSZEzXG6GWa+KT\nuFWFhNhp1aWv3RECkbrtUOuJInVUa6Qtt2W+ZX3dq0entsk4jmzDLXtjB02g5HPffFYrb1NpTCr4\nmroesZgWMiPbGCjktFtfftfkJ0ZJhYubwlROSdX0fGcMI4OBgYGBgYH3ibdzb0Oc2wyxEoK+IWhR\nam4JkaSxHRGjoqqkjUjlg0f0puosjQa99OS7pu9s49n335E0N5AY2221SM0UoRQwmd2jeKPqkVrK\nXa8SgWFdJtZlJl5mCL48PQ91Ow9EB8kSTObZ3OGnwHxy266tS7gpEM4BmJqi05OeNjktIBfFQyss\nmnxAIyYfgQ8Z9+MT5/tnzncXvF2ZCNssA82pDUfAszJv18O5SLhbyKdTU0HE7lqmTsYgPYmgkY+T\n+ix3tjY4EMVGSITl1g0Nvo70vObW1u9LyI58ln48K428ZI7HAo2gibrVGz30pK7WULW0w7ylmgk5\nKaYYkJPdjm9M3lINBalzYMtADm5Horb9HWCv8OzTUd+C9AylZ2BgYGBg4H3i7UiPa0qP6DBCeI6U\nHm1aoImKVoma6rJ/qlwCbLerFbIqrU2srsV4oNlMl3BcannkGCBW1WWb0AVz+iU1SnLMvctb2p1n\nX/cDGefrmLOBYPdB7qXslQlyPrEETzgtrMuE8wFjMjE6UrQYF8nOwayCzT7olIBQ1BwdFD5y2w9n\nogTVP2bM48L5w2fODxdO7srJXJE6KWn2KgpXzpZkMgsTC3PVeyKn+ysxONb7U1MxRLk51XfdfPXz\nNk3tXassl+53TXh0XY+sk7r1j2DYH+8laAMCISfS70iTL63WiKOeFmI0+RRCF9V+NLRCJo55MzBF\nrIuk6BoJSS2tcHNsO5S9jpFF+XGJHO2m/hwZHeRsNrtsbcX+FhikZ2BgYGBg4H3i7dLbHIRqQ91M\nDAr6Zp69i9tLAZM2RiiHMTWlLNd3UYJKpNlqfPIWoMv+DaAbpsZKhpw6duzIla5RCfWcUiVhhry5\nyVkSvpojyDEb4ZM6oMjMytUl/BRYXSpW1dLA01ACWsFqIVhStOTzQk5mCzZzMuWJvksw1zFK4Cwk\nQYae1XftzKZJkLyfM9ytuPPC6e7C/eMzd+YZXzWcnigm5jLfppDLwMSVExMLE4HJr5zuL4TLiTx5\nmF2xcpYUt48UhecThfBMatzlAI0cSHCrlZ5e5ZHLJ3Mg2xzdXn2amk77eym9recPvaKj15Nx981i\nhTz1BG2lpcMF2r0w09LaTpR6nrkQ4BRtSUV7qX9OlkF+GRtRCs24QP+m63xSdDtXt51KOyyrBwYG\nBgYGBr4D3ob0bPGO2Sk12l2tW/WL0PuJVYMRGiXKTakXNzUYL9Gm1JmY3bqp/hZr3Yn0zpH6m2Zw\noMcu1ssRy4W7jexAsc8WRWPaeg1J1xJRn8SuOzPVFL7ZL6xuYrq7sppciEvwcDX7NC4JqJMlP58I\nnApJMrmkwIVqd21zCYyD2VtP68C/Jzxn4BTAJdxpLe5rNjOfF5wPnPzC7BZmrkwEfJfSJjVNN715\ncDzXeZpYmO2COWfiR0dYPeFcIvm8+jL+q4VnA39PacAqwb0U7AsJEuIgfYkkuJV6Hq3ASM3Ma8G3\n/ivRZgGSKtgTH1FtevT2018yR9B1O/K931ZqrxJtHiTl8K5uY1MjKVXh4Qt1PQA7e/R6b5Vx3E6W\nVnvglvho9HVx3xOD9AwM/AmR83BwGxgY+N14c/c2eUVFEHSfntd69miI85lWV8JWH1TIiGel2FSH\n+rBfTAVaupyuq5H6IUFvLa3re4QIFd5QjrcKCUoeZyOewMSKqXlXYmctyX0ydiFjMwvBeOJ9IQFu\nCsRsSjW2t7CYooBIMH2ug1nrXCWzL9Q3VEct2nIpgpe0NVEPxFBgAuaEfbgyn67Mp2I/bW1i8ive\nrJxYmFjrud1Gkrp+alN9clUdsJT/T5y4Ym0iPjjW60Q8rWWotsxKCJ6weNaPD/CLK+f7C4XE9T18\nDM2lrv+3UkiOVlyE7O1Lr14nJtooQZMrbVWtoZWyl+p3eqMF+ezU73Jsba8tqpO2D5eUQFPUlhRt\nUfySha9xcNtS4brlwRUiZPck56jWR4hPzkV5xLTaN/j+ttHDyGBgYGBgYOB94k2VnmIU1hSA3kGt\nKSC5qiwF0pOnKTCtTkc7g2UMAUsmbESo+LY5MiuegNQASWAudT+GffPQ3cAPUEiW24hXqA5zMTrC\n4kvtiotM0wqGWsYfanJfVGYJJfUt1r3FSgryR8NymbmYTHSR7B1MHhbXzAUCYI2q2VETPSWY1hLI\nrlOzhpb0L0nd6gPou4B/uHD38MR8WpjsWtzYaqXUVMc51R31DVnb9djXcmjHr2A8wXg8gZlrqfe6\nt6Rca61MIZ+57vfXD488/fyB9fwAfzUtXU0rOVLbolPdhOjIZ5kn7aRm1HJRgbR6pNcVYijE82uV\nI13H0+/fUpuJduvLsfQtmNQ2ktb2SFG/tsakcaunIZtCeNIXCE+yO0JzvE4ddLeeVny0iUEWlahT\nc7/3w9vRp2dgYGBgYOB94s2MDIp5lKY2t9C/aI81WymBpKbta3GK6UBSFTZynBWPr6Qm4FX6VdNb\nAp7WM0dUCiFLt2k5MgZBqnQm4omxkp7gizpPJhjPZTozsdY9FvVnJpM3RavVGc21Nsa4zOWh9ORZ\nLjPXp3MbhATJQnwMhdREFVGaUnTupkCawvbEP9uaOqZVDQucE8yB6f7C+f7C3d0TZ3Pd1DKnCJvu\nNdTbj8e6U1F5Jta6zJf+QjZhTbt+hWBei9Zl7Eau5FgJy3y6cv+Pn/mf/m9YT48lfU/IykIjHqt6\niUmAEB9d/yPbipvb/ibcp5np/j/aRlorRzqYPqrr0YRHyIm26pbvGkeEBxrBmilkR9LaJNXNR3Iy\nxODqNRf1Rimn2TTykmoaZPDg+2KlDsncNLTNlViZngx1ytLWkPYNSc9QegYGBgYGBt4P3s69Le+N\nB16yjT4KpKGoL6VGZx9caXUoq/2K+rOqlDrtlSZJcXofelymalJl3f3xmktZvtmeDCmV4v0Qyvka\nm0nOsjBxYsFSTLYtFlMlAxmPkIWZpRzrDNNcVJVrMoWwUIPPuR7Xh5J+FFUak0tgihucn1dyssXZ\nbQqkxRfiEOuTe59w84Kba1PR08JsFqTeKNVoXc5XO+mJyhNqnZQ4tskcbXNvEjm7NkemXRNPMZ4Q\nwlNIVqwq2LqpYekny6d55XP8m6pg0VLOpK+QuL1JM1AhOtoNTae7SVDc9/Lpb8GgttXfNbnSt6as\np4nN1L3PtMarol5pciPbCXy33T2N9Mz1tfXNoao8dk94BNGxNb4V4hNduR/kHjpSf45S3ZLBqEPs\nUtw0yzHfX20ZpGdgYGBgYOB94u2UHtdS24plgL9RUrSCcJQCp9eBlt7WeuKYGwJTNAoxDPBbShtV\nIep1J91fR49P16cUtUPOJtZ0uSQ7wLkiJxhTCsJzMoTsy5NuD9ak6nZmaKpI2oL9ZqVdUr3O7oL5\nkHA+cn06FzcuSUmyudTAmEwKpeDE2BLM+ingfCzjMXUcwREnV9Y1ZVvrUknFmwPTvDLZRZL1tvNP\nWFamem36p/h2SyXUqW2STuiIWJOIzm2kUVLbTiw7EiVEU45tgJmiOGFgelxZ/9HEkj+W1D4J9meK\ny9szRf25UsiFtt3WKWtCUkTtkb8MUXOE9AgRERFEbLF7S2ndhkmraEJyTuol45XvR0qPRp9q52mE\n51Htc0rl2mdDSrY6/70irWTTfpf36Pa/A7hO8Xwh1W3bTEwUvqaO6A/GID0DAwMDAwPvE2/anFTQ\njAT26WxH6s9tA88CUUbMloomtKGpPKk+4Q7Jk5zd0oVKIF6Mrcu2zdFNmotq6P47uiePjE8UEe9K\n5Jt92V5qWGINJHOqa/tS4B24Mlctquw7bSqJ2F0nApbER584f7hwvT9zXWZi8Nv5+WklZ0MMnpwM\n1haCNM0r1lbyYOocZ4ipbGtM3l7WJLwLW0qaTuMTK3BoZET3WRLFpxEVobVhm6dmHc62H7+rbcq7\nfbfrXwLrQo4slsjy08ynKfA0f4T7qRGIv6M4vYnLGeoW0+lonmYXDfs0Nkkd1I1BdZ8jISzaeKBv\n/rozhaCpM1J3dER8BNrwQCtLsK/h+aG+PurxpqbciOr3JWXlNb8QIS3BV4KtdpYqu7P171Ns0g1b\nY9RyOq81A/7jMYwMBgb+hBjObQMDA98Ab0Z6km/NSVtPmxItWiL5IPqSp/5HgVMzKNv33CkBusHW\nYHxr1mkywYmSIL1G9gUZLwVo2k2ur0nK9Zx2JmC1kNz5SM6FhBiTybaoRSF5FjfVCp+0WVrnjkiV\nOqB2XE/EuAvz3dKOmSzORmJ2hOgJqy9GAD4y+bUaAuwbrma37NQloXGeVtOR6hwW8lMUMpmHvkGs\nnn9t0y2kp6+D0usJ6ZTZFNLXXPaq4kXmxBVH4GrO2A8lferZf4AwNTVG0tsmdaMI0elrbbTLne5P\nNNWXpTnliZojbmk6/WymubkJAdEKlOxLTAvENOKkttdOclHtS6ffzRSF50cK4Xms34WnmwzJlEai\n61Rsyr8EnU6XKSTmaDNJkdsRH1vTC2MzLqh/BNIj6C0JDwylZ2BgYGBg4L3iTWt6eue2Vn9jEc8v\nCY517Y6mGcXFraVBFcK0t0cudSg1ZSo4cjIkZ1nNjLOhBtV7i2ptny372A9f9xgydSwRX4lJIQUZ\nHLseKdKtfnPUAlKyrHkG05znZpaNfGhb7UlVJS212aeYLDgiyVa1xTgWPzP7RcXyqWZqxU1N0nMq\nKouYPUi/nVBJTvl8ez1cnXOZl/5C69492qZb1KFmI1GWST2QkKveUtyptDlL5sQFQ2a9L8zm+fNP\n8GRKLx8hGdpaWtfr9PbRmvQIGRGCoWtqjNrHQlFbPgEfgCda/VBPbGb2f3WZRqp0apukrwnp2Qwq\n6n7PlNqdh3rMnyjER9+Suaotwe+VLY2XlB25vNE04tNfWqkPOlJ9rEorreuM9LaBgYGBgYGBt8Kb\nkh6B7uMC++aFL6WzCXQDUL3N1gekBtU6upPgy5ikgulGBrQbXCumfzkvKKmIUJQSCe4TBlNnOaaq\nKNUUMjmGMeXsQ/ZczHlLm7NE7mrRiFaumq22hP6JYsRdCJsjsTJt8yrzoCFEo//uu/qmVl9jN+tu\n7bknhFPIl4yxkcW0IzXye09M+2uoz07/VowO4rZfgHue8ERWP8EDXD/ek57O8FcKMXiikQVtzS1s\nUBQc1Het3JxpZMXRiJRuiPqJ0jPoV+AzhQiJUKYVIFGOYJ8qtxkPsHeIWyn1SEsd01WN6ZGi8vxE\nSWuTcV7rutmUtLZgW81RDzm+rju6WcfU33d/tOp320wQ5HtVfKAZG2RllS3ubV9Mt/vGGKRnYGBg\nYGDgfeJtSI9YCkMNh4/79OhA+sjBDfapZho6mNYqQU6GlMxGunKuGo4JuxHoQnxgUzhEHZF3Pa4+\nzUveJbXO2lt3OGCrp4nBseQZaxPP7g5HIFjPmSszy+58hQxIDVBATA9kHbOpUU3FaURC/yZzFJmQ\nOpmI20jOylRrdGT8cvR2fTQp1MvkXKW3EvpaVNLTVDO3mzfpBtQrUnJNdP2QfDcmY3zcu6TpVDap\n3zHbDvc1PBL8S9+bO0rK2B3NMECWnylKi6WQks/AzxTyc6kvbaGtx6OPbdTvQjyElF3UcjFjMBSS\n8wD8hUJ6HuuYTQZflRZbHdg04ekttFHL988G9utlmuqTTO3zpH+vX4T4iOtb/b7r2SNk9w1IxyA9\nAwMDAwMD7xNv15zUNIVEmnk2ty+3U3ugBdJHBKdXYUrQftu41JhS0I+h1LeYUOPLkjbma6NNnXIn\nBGGvNrTAW9cECSGRupnS86eYUe9TwOx2fhmLc3Fr1rmZHOSSChfvPIu78MgnZhamSn5kPKGe59RF\nkNPWltWpFLlCQ9w2jlt3O7ERL6RDmq56LGkjfknVQL1EOsXJTlLRTBdt6/nTyo+e84xhYcZWMueI\nrMzAsjnBacUnYbE2Vrc6GvERQrGyJzhlINSNm+GApKHd0YwChPz0aWU/JsyUycEUZeYXC/+Lovg8\nUdzjVnUcUZF662qZQqnHEYe5k1pPOPWZksr2I/A3dRz3NHXHUS2nq8qj3eQE/TMEIVq9GnTTH0hc\n3TriIzU80ttH22L7SFY1PZuq+wbNQZelfR7NSQcGBgYGBt4P3tCy2hCYtiBbVAhB7972WgH0EUky\nNTRPinB4EwkuYW1ismsN5P2WLgaw1joSgQTtJTgv/XGm+v6louy+zqX1uBHSUv43rJ51mbg8n2sq\nUCaujhwtT78+cL5/Znmc+Oh+4WNtZmpUFKtVG11rVFLhxCIiseIxlVBoMqeJoWwndU6yPG8zuj8/\nfT77uquW1qZrePa1W/uxyn5avY+MQ8hWU3jkugFb7Y/MqZsC4S7B2RbScEcjHiuNcAT1LuYG4rAm\nxEZsoB9opKcuNx8C04+fubt7IsXiDni9nIh/uSP/D1/S636lKDTSvwdaGttUj3OiKT4ZPlFgAAAg\nAElEQVTi+ibvz7QUvYe6zqmOSYjPA3AOsPqm8LgIi385rU0vezW1jZfVH7hVjo6amlbF53+Hmp7P\nn9vnofQMDPxJkPNwcBsYGPjdeBvSYwvpEZNpncbUer6YbpMWULfkKkmBa7UmblMe0havSapawjL7\nKwB3PANU5cRUpzJJCWvTUpyMy/5P2E1x0OltfV2KUZ+BLU2Mui/5PWMIyXN5OnN5uiMH1SASIHjy\nxfH865mwTuS/WLyNfOBXTpTzKONtdS8yj0ImtRLiERvsQjiFsMRa5a9TCvMWhQvxsIjRg5DMRGsu\nK8TGKTLW9z3qFaE+zU/OR/Y51VoqvV1L7zMYtbz1fKpX8ZTId7aRl5WWIib1Nrp2RwwFhCR9oBAL\nSSMTAvQBeFyYHi7cPz7x4fQrdzxt5Oz64cSvHz/wy+OPpP9xVyyzP6ljyrSe60unzsHeuCDS0uSk\ntkcUrAea4uMpREcTkOiKW5s4v2nn9SPiIil/tltHqz89OYr1g8v7bXriE211dGtk+60wSM/AwMDA\nwMD7xJumt0HreZOSJVut7Miqt/16JCh3NeDv07TK70J0jAr6w6Y8tN4zLQhr5fYtOGtBWnET065n\nzZY5Ebvj6/d9itxe5bguJ5bLiXyZ4OJrUFsD2GS2wHdNH/iZjPubsBkfuI0uxlo6sleW+pQ8p6qO\nmi10q1cSNUeMG8SxToilGAikekyxGxf4bl50MtOurkpdTyFLOqVQGx4YNVatHImCpdeXmqbptLLe\nPxMfPhTS8kwhCTL9nkJC1vpZ2LGjpbT9QCM+H6hGAQnzsDI/PHO6u3I+XTjV3kpyHTwBe0qEHx2f\ngyPHuZyoEBdoNUNihnCfYU6l8D+bkhqWKalpAVhM2VarM6c63seFXcPRUqBWCIkQFiE+PaTRak+4\n6i5u1B/5vU950+RnI0tdjc8LKs/3Jh6D9AwMDAwMDLxPvBnpSVMJo0UtEMXiVuFp9Tll033Q3Gpm\n9gn6sly2l+BbbKWlfsdTam9KWltrNCq9ZAK+vAdP9Jca5rd0uHa8qM7D7Zb1KEqL4xpOfP75kfh3\nD+Up/mZLrLaRwvsnWD/f8fx4z8P5iYWZE1elsKSd3qRNAoSkvfSUXchKROyjG9HQaWb6JcrPXsVq\nyo4mPn2aYk9s+nlsx7W77SVVUTc9FXXJ4SsxW/F+wk+ReE5wZwtx0elt4m4mQT/185lGcD6qzw/A\nOeIenpnPV+4fn7m3T8ws3PHMfVV6Wh1TZL33hB8mLouHbAtRENIjdUOS3nYO4CP+tO6c/aTOK0VX\nlttEim7L9Mi59H6Kqy9qiqmkQwiPpO29FNzrJqt9WZYmOGWyG+SSH5EfXw9Wa9M2xSdaUmoPFBK2\nKL6D9AwMDAwMDAx8B7xdc1Ja0bw0DXXWqRofKXFoSoKkTsVu2D3hEUhttiWqYN0IjdlqbCSIzzRX\ns7jRm0J4lstMmizu1CyTSxnIrb029ZhJkZ+CrIiVK/UfT6eiAgjp0QOXVKZECZj/18Tzwx3X84l7\nnuo8tmhVdJ6EP0wl0z1/enKyHyfbb0I09LKvRe+gJ+pQb57QE8jWT6ik0un0R3F1k0jeVbMET6hN\nXa/kycADrI8n0npX5ldqdlYK2RCjACEinn0K28f6+SFvhOd0XphPCydb3PROXLcGrpp8OiInFk7n\nK9fzSl5PjWCIk5vUFRlqzUtx8bM2QSU+zkWmOeFcNeCoN8VynbAuE1YH2RTSow0MMI2YfM3lSuwV\nH42X0t7kt5749DU+SvFJ1dxg5/T3nTPdBukZGBgYGBh4n3gb0pNaP5vy1W5Po3UPmqZeHEOCaIGu\nSWmW0yVALgQnMSE9dMozZyjOcWKq8FJQH4Jjuc7kbIjnss0DT8SdoUAbR/ludkX8hkSuBOuynHn6\n+RE++duaD6c+S5E7lDS3nz/w6+MTH86/kjFb0F3IYju+npsjQ4hG9FpKnJ57bVLwNehrb3TTUfkd\nbq+ZHlOfFihpg7ZSnh6aEFvilrKYMcTJMz9cuKwTXH2r1XmiEMyVpoRI750H9mTnYcWeVqZpZT4t\nzOeF2V45c+GOZxyRmWWnPkl/JkdknhbceSE8z3De8jkLxFVuyuAS1kWcq0wlG6wr9tvzaWWaV05c\nt2Nc55mIZ7nOXC8z1keS1IPBXuH5LYG9VnA0dM1P/1tk/18RrfbAreLDnqin70w8np7a50F6BgYG\nBgYG3g/ezMiAtHf8ytkQkyPaFlV9TcGzTn/SqVwSXO9VCraAdG+a0CABWbOibuvE1fHp50dicOSH\n8nT+nuctxUrX6/T7M7XOSH5fV1/reGiqg+xCAmPtKuYp6/7V8OnxA0/nn7njaSM9+7HHWpuzV260\nHXVJTQsbSRCC9iW8RISO0v1kLPo3rdLpFDZJJ9Rj1gYNe9wu80RirdmaWLjHkD+Y0vPIfCQ/e3gw\nZU7FHEDmV+p87imk5y5h7i9M5wXvI/NpwfnIbK+cql22IKgxayXLErE24VwkTAmCazUy+rZTU74p\nPfpMs9kc/WSetnTEcFe2u9Zup7puRmp5fgtkWr9Efo5bZjWEjvi8hDewjB5Kz8DAnxDDuW1gYOAb\n4O2Unph2SoAxGWcjrX+NqWXpr+O3pFtBc16zVeXRcaFOtRKlKWSIoRU2pMvE5+DKsh9LEP9Ai6Sa\nyiIOc80eQUrdn9Mdz788widXlAdpYqnT23RgqQf5GdJfH3j+x3cs5sSJZRuzpPmV3jx9qlo+JCya\ndIiBwd7VTYbweqQrvx+5uOljlXE2dznYW1ebqs21z+Ik1/bXpzfqxrFCQDyRZA35ofQ7WlfPejkV\nh7zLBKtpaV0WOGU4F2XmdC4kx7mI85HJLkwEJha8UneO5kYIj6eYGjgfwEawrqhJiUa01OXIqTwJ\nyMZUspRIyeB8JU9EJpaqftXz9ZHr5dSRHfPlWp4v4bXtpFZIX4L+O9wSn+BLjZI+6a8XEr8ZNOkZ\nfXoGBgYGBgbeD96spkejdxn7VtCpVJpguaoI6OhOzAWEoEgzzhgdy2Vmvc6kWNPwLicuweF8JD26\nmhwXbpQVAHFDkwB/xbOsM2lRKk8ffEnQKhASJGrPJ/j1l0f+5of/takjE+tWRi8mDmItLdbRE8tG\nGHT6mjQ4dQfqkJ7L1yAKR18DdATt1KZT1MqpZtwBaduPZa+0lOPvb2VLYmYl22fsY2JZZxYfS23W\nvJKiIiomMZ8XpnnFz4FpWrfjiEtcaV6r09nCprhosiVKzObm5iNmCuR1qu4DepAZ5gBV3Sl1O2Bd\nVGNrSYuaXF05cb3MipDTCE+ZkD8WX0N8pIFpRYqN6JYFf+D4XsBQegYGBgYGBt4n3s7IwLTHvP+Q\nvh1HtSHQUuYkqNeqQk+ohCQsnEqImsvTaGtrXUj2xKrqpNXDdS5PsAHCVJzXosX+EDlz5a6mujWF\nojjHiUNdwnIJZz7/8gC/+FuFp51cQ6jfLxR3MYBPcPl0z68/fNgK6iN258Km51anibkXFBNJcVso\nFss67a2f97Ktu1nWanHCNgd7lPVEN2kG1K3BaaikB2Bm2ZZrw4ji1XYb1cv5yDjueGZmYWYhTJ51\nmggf3JZmmJIl56IETXbZkRWdatc8/Bq5uT32Ps2tjKMoRdYlog8QaxqazTXXss5PqhVnyTLN9ZxN\nxvs2f/rOlbmPwZdanuD3K/0elUfjJeVR//4a8cl8farbd8IgPQMDAwMDA+8T/1soPX0qlN2Cx7QL\n4EFqN46H/VojzCNotzZRdawtwSfWE6PbP0mX1B4J7j6duUBJRXqUQP1ax2g39zYZT8SxrCfi5dQU\nHrFOXtTAZP+9qcGVUpAfyrE/XR/5cPq1BuWhpT3R7Lrb0M1mLgB7wiP1NBr9/JVyjv06vUojEAJQ\n+Fqgb1LaH0entgnJOrGwMO+UKoEmPEfkt/8u95cjsjI1Imf1Os2zr/xULBSkJkn3RJL9C/nSDWGF\n8MxcmZlxLjKfr1yzIZncCvuF8Ii64yPOh6IMmcx0WsnJ4GzzESxqT7lu2725U3q4JdDfCi/V8nyN\n4tM7ugmG0jMwMDAwMDDwnfBmRgY2NjcvYGu4qfFyoBt2Qbt++t73otGpVOLOZg/IkfREycmUJ/M1\nqIxR7U+aMOrA8nLi2aWShvQgC6/1NJttdcDzFO55+uWB/Mtcanl00Ceno4NGIVnlJPffP8HTp3ue\nTvfMXXG9KCOt1kXqYpRbXgepOdJzpwnnFmxvqXC30bUE/X0vHtl/UZImSjediYW5Upqm9Mgxwubk\nFrdxCJk7UnlkmSdyZUZH2b5uW+qXWuNa00Xdurnqvtap2EWfKjP1r5y7tuSeCMx2Id6V63AFUriN\n9I3NGJt3JgbGZIzLG3HtU9uWy7xXeaL5NurOt0BPfGqaW863Rh/fG8O9bWBgYGBg4H3ibUhP4KaI\nOXcd23Vge5zKdKwySFpW3xRTIIFusWeWNDizkRPruianLhHXXOyAXVbpbXWFiyHlOz7FkirFA2Bu\niVigKEdpmZrKowvp5bCOVo8hrVna4Fuz0k+wfrrj6S/3PJpPnDBbgFzUk5eVrsxtaqEoQZLe1vrh\n7CEEZWVSqlHAsA9mW0qYY2JhQnoiNXVna1KpxqLd5YoC5ZW6Uib9ynyYZqZrbMq4fJ176QflW2rY\nRgi1IYGnmDkUWqzT85wikb773LZvpG9mJeBL6uHktvs7rNPtBQH8tOJrKpy15dVc9vbHiNERROHx\noRAfndom9tLfKqh/San50jbazjrWRqtv4V6gMJSegYE/IaQj88DAwMDvwNtZVsMuuD1Cc9BtT/p7\n9MGvpHAdFcvLcYwKwF+Ke0T52ZSe4Fqh+M2D/tJQRxQf85B3Dl8JyxJmnj7dkZ+mVscjpAdulZ4+\niJXfZN0L8NeJTz888uGHXzmZyzYfLei3OxIhdg66yesROdRqxWuubTqdq1W92I08OBIzC3e03kC5\njks3gBUS4YmbUienr1PT2JbdEh6ddtaTZEnf0z2INPnR9V/y3hspyDlpRUlwRIBSpU4zSymxmVWv\npGywNpNSebc2bSltzsXNAa5kN7Z7fkt0C44YfLkngy/3pbao1sT5W+Clmp7thLglRfqeVc7vqdaY\nfckN8I/CID0DAwMDAwPvE29W02NC3gWXm3PVjbSxt5KGFpQfKTkvNcHUNsy52jwnbLUZSDgTSa6r\nY8ktWKdTgAi0Ggpf1krpjs+xOqKdPSd33ZSlGB1x9UUp0ilrqM86UO2JVaDV9Ag+w+XvfuCvj894\nv3LH86GFdZuD/Xz1DUWFJMVKCPX86Zoe2cvMUlWbEwvzdj3WZSKGO6bTSnCtQaqQClFgyhjKWHui\noq+XThNso2no09LkPHrkSnN7pUurTqIUgiex4rB4WqPVeGjQUBAqkRNL9ImVUvVV0wJnNpMMQyZG\ntzUkdb7RwMIx9qpVG7/YVQfWzeXNQ/oD09u+tN8vPYCtio9Wcy3pu1tW5zzS2wYGBgYGBt4r3qxP\nD+y75BTzgNvAXNSYferUy2YGAgnQU63KKApCcQyIW0VHeZe6jeRbfU9UlsY5G4i2qTySPiRPsbcC\nckO+nLg8rSXF7T5u53a9nEjXeW9RLcXhcmpa7REHLl2cLgTL0xzdni2f/vqB099cyKZ0vhT3NDFU\nkCBdDAHkvPfznGvNTN7qX0DHu62rpiVwqkpWxHFlJia3qWNh9YTgSMlg7jOLlWf7pTunqEKFJHiO\n3N569WUP/ZurV7v1dzKlQ85GZspU2+1+CPX3VsfUaooyrbao6S1afNsH7poYyrlEtb/iQJc30rf6\nqSmPzmNN3KWxCWkXktWInOPCmRCLo2CKrg1FlB4Ny7dTe75FVknWH6tdugX7HYnP8/Oe6Iw+PQMD\nAwMDA+8Hb+fellttSaYaCBBvit91MCtIh0pPCVelAB0kdamlMFla/xq9TwkuSx+bEoinaEnJkKJr\nPV3kcBJQWpqzGnWZN4TnM8+5PM13PpKiZbnMJR0pqX2IQ5sO/BKF0CRaTY+8Z2Ct693VY/8M6/TI\nz9MKHw3GFKtmRzjsgdOj1DW19D9ba1L2dg9ltnR/GrkuCxNrmglrmbMQCvlZl5loE8aAvS9KWiao\neh7lRLal2cUtxc3UI8m4dLpiIzJFVZEaH0dU11Luj0jA78izKEEJx1ptpMWq3FmzS3vs7z9T09Xa\nOPezJOdS+Gwjc2K/bYnMLb9zO4+W5ml2xKvcEmK/Uc02ssEo0wNsBmf2qZDfM/39yLGtRyoNWPPB\nnH0v6NQ2GErPwMDAwMDAe8Kb1fRk3574SxAr/Wx6tJoeeQJ/2z+m7Xr/+LYv1Gd7l346pbGnpDmV\nMLn068nZkJNpLllaodH1NTs3N8B5ok1cns9YF8nJFgOD1e5Jz8Se8AiRMupzGfy+lkhUprn+9snw\nzE/E4Mk/GbI1nLngNrbUVB4NU/We8pI0szZHUtkj18cTVHPOyJVTacu6epbrRAx+s/3OyZCNYV09\nbpkxp0wxCcj1dPqC/nL8zfShXm2jrnmZIrcjLY7AHZdOcZF0tUKcVqaq7nhWPCvz1qMHlHNfvebS\nUFPMGYp6VBQhWym3hpC3lrKXtjFLfZKjNDa1VdpL6nz2xg0OsztXt/U1ujydK6Gc9sYffU3Pt8a3\nUnpyo3RvAUlts7aoPIP0DAwMDAwMvB+8ndJjUKGeLeltBzgqEtdoxfQlkOpTobQdcnMba4GmEB9R\nOEIlPFA6yMdYe6EEs09DO0o9g6LSXA2YiZBptUCLLwYGK025kf0lbusmHI30yHF6cuUpNT5PwGpY\n4gf+ChvxmVirvbLoOdrWukAfttVOlaWWtM1/C9/L3hKGCydSstXwoTZyXT2x1jUlHyEbrqYSL+9x\nLhaS0V1vYyzZJjJJkZ1iPS2KSZkKvxFUwcq0rSP6VKLV6ojCk9S9lqIFU62hTcaK7ThsakTLMBQl\nRltS+xsSqcmOqbpVm7N9nZXbthFFKtcEwH390jbujqQd4lsbGLRBvIzfeEx54PAW+C//pbz/k38C\n//2/D9IzMPCnwXBuGxgY+AZ48+akm1tXOlZvjhqU6mCzFXq3+od96lvbrz2omZA9zaxc67opWeLq\nSqApDSCFePTpQ5rsrKh4zpLjqaQe2QyL3dfqQCM+jj2hMWq5pMDp40fYhBJD4QZXYDFc80d+Npn8\no+HOPnPiwsxaNQMxFBDbOFOJntnS/0AC7ZbmljD4qojd8UzC8swdEU9YPevqWS8zOZnWLNMlyIYY\nLb6mZRmrzCiCK4RDuZdJjZdW5/reQdJctHjDFaWnKYULE+tWr6Wd2WQfiaroYErKFUXdMSaDLSRI\nDAb0sT2lWWk/vh5CeDT51uYaoRIh7SSozSNCNXmIOFY8KVvWdSYGx7JM5GQJ13rxdZ8e+GMIz9eY\nFPwmI7a3Yxr/8T+W93/xL+Df/btBegYGBgYGBt4T3rSm52vQ9ykpm5rNaUvbJkvg3gLcPZHSaVxN\n5zHqey3wzxCTJQVHXpUzlvTUudYdasVHk6ELjZgYU14rzapa9nU0J634RE/CrXW1V/twwJlyDAfL\n6ZGneSXfF8Un4VRNSapkIW6l8qKa6PnY11alWum0Iu5oV2aWZWZdJtZlJl9myAaCBZ8gOvCBHC3X\nZIgnxylftz5IQoJsrsG/yWD9TsVYyTgbN4UqY1hTcYmTdDSYSbPbiI3ulWPItUdPryo1omVM3lQf\na9NOgaq6kCJV9qZOardfRbY10do7D9qqLpqNeIMomaZeXq/IuyWG0pcnrjXtUoiltqvu8a0Cel3g\n9BKkDu1L+6EpW6/1kfqj8J//c3n/v/6vQXoGBgYGBgbeG96G9DQjsG5xVglJL6MYHvhumVgKm43c\nWBWEStBe1s3b03gJlGMNccmGGFypwwkOUleHIwGecgsG9X2lkJKFptTI77FuH9S+kpoPrQBBS2nT\n9thS6/OJ1q/HU9Sec9lfvnM83z1gXcLfrZU4hi2QLmTA7whiXydVUrn8No8TKyeWTWXZVJ5lIi5+\nb9Kw2lIXk2xRfOq5heD2Vy2zpZVZm7AmErOvNTYO64oJRDBlq5wN67XVs4TgWkNPFzHcMbFwqgRP\nE4+mGrU0MWPAmgSOLbXNuT2DSFUfkzlxlQC2edqzV63y9GYbTXWy9QrsyXes9UwrExFbDCJCUdLE\nUKOoab4STNPul55Ef4tsEMOLf6s7fA1/qfe01DJ96W/8W2Nd4b/+13LN/9k/q0MapGdgYGBgYODd\n4G1IT4Zk7C6lSoLOEjSam1hr/xTdbYFiX0fBLshsaAbGCSmCF8tiCeIjjphL/USMQnh0wTiNpIiB\ngTygFyIk6T6ZW8VGglOt2giJ6QPHPiATUqQbmgoRMpTaHiFkv0C6O3OdV/wUyK4oGQnDzLpTu7Qt\n8nF6ofSLCVgSKxMX7oippFutT2e4TIV8JTUGbyB5cKVGJ5pcjA7mdbvW3kdYS4+anA0xlxqqWO2u\nC5NrSMkSlkqAggObSfNSFJtzVk1vxU5a/OjcRmy1FblYSRuT6QkKtPus1ULt+0Ud1YtplzlBI9XF\nlCDimFk3Uwg5tiZoay4pbWEpjUgLEa/pg9m0+zJsg9nje9pV/waCJWTnezu4/fWv5f2nn8DX/+oN\n0jMwMDAwMPB+8HbNSXMLMg25pT1htnQ2XTwvfVlKIblOYbNb4LjteyuXNkh/FFf9u3Rvla2wnWbE\nnJJjXabq2OZK+hA08wFRaDSEyIjNtAScfZG3VneEHBi1vSZF0s9HglptZpDUb3IFr7S+QRfgYkjB\ns14nkrek2ZKMJVSnOkusnmDrNgtCAlvPnIDZHNKeMWSunIqj2HUmBg/LBBdTzBSk9kjGlCn9jfBl\nPmsDWmNF/SmqmvMBYzIheGJVNXIoLnJbKpeQvtW3OZkzayr02LqEPYkKM28pbqKubEreAWQtI59y\ntcRONe3PFhMLrZY18mx3ao8QSK0GCZEv6XETl/XMYiMnd62Obo24CwlPyVbyV95ztK1eiu7eOMK3\nCui/ldnato+3YRp/93fl/S9/ab2BRp+egYGBgYGB94M3S2/L5rbgG1rOv1HvZZO8c+xq67eCcH2A\n/qm9tMNsheOO1qiy1FCE7FnXqQSbwe2fpsvuk3rvP6eDdfQMH/1eZIn9MiE4/fp6P1IftNTvd/VY\nsS67Qvh04mITbgqE4Fj9xORXjM3MZgFaY1bxd2uF+AZLrqfQDCASlqf1nutlJl5meDKFZOl6JRnH\nVM9vsdWi25GmOiE+lT4+VKMHgODIF1fS4/ScC3rlbDFkTqzQanSmTDJhq2FqpgATIUykYEnZlvod\na7d0NmsKsTHkZlcuVtbWYKZMJLAqq21970rqZG+RnqEm3E2seeaXXz+yPhWDi+m0cLq7cJ6uOBMQ\nJ8OQpqJ4xaLy5GQL6YG9ccEfja81KJBat6/AayYQfyQ06REjqKH0DAz8SZDzcHAbGBj43Xgb0pOK\n0tOev6ctxQjYvR+RHV2YLk/Vi81w2khNWW/v3CXLmgJgNg0oZkdMjrCWeh56C+3eMronOkJcrPpd\n0th0bYQmMFrdgaYmwf7pura07tUe+X2hEQ91gBgdqdbCXGxplmpd5O7hQvaigkW8Us9ArJ8zMwsn\nrpxYWJlYKGlX6+UEn+aqKtWXVqUSRX0SpUD6Ek2VAFHes1zaUNe/qn3p1EGZW3n5+kqWnO64xJoS\n9whmbi5+WzPUVNLDgiINllQIjemItsmtR1O9D9Z1wk7lfp2UKURLnEuKGKZ6Sl6R64klzoTVEZ7u\nYLHEc2C5nEg/fOJ8viBOcs09sHzOyezvh/CFf/y/VWrb1zizHaVmvgCZZ6i383f0MhikZ2BgYGBg\n4H3jzZQek3ONZ0OhHzZXg4J9AKrVHk1eRInQKUyOvjDfbultbR+t5kKaSkovliAF41Izkc2t2tDX\nUNiDz9KK5Chg1PvTJEnXAGn1RwJ8eZ/Zp7cJhPRcKMRhBa6e7GtwlwwkS/QRM0WszfjHwMlckRqn\nvbVzmV9fK56KgUEJ4q+XmfzUEZ7PNIVK1zTpVL+JUnuk7bZljlId8zMlVU4TKNmfzIunmDbcq/kz\nntWcudhcjQ3KWSUsayrqnbigkQ02RZwzm4MbpqlFMThSlNouuxGjYCeSW2tKW6PPZfj7xqRsw3cs\neebpesf1+Ux4PsOThc+QnzzxwfMpWdJHi5/KSUtNU9bEOx4Q8R763vu90NfmJfwW0mKpVuF2u9fK\nnP+OMf4GDNIzMDAwMDDwvvF27m1IrYPrfmqBZP9d6h1a/5iSeiVqj9ShBKYtbU3vB/ZNK6ERqZRt\n7RVEqyMpB72tnegbkpYd71Ucy/7pvD73PlVOamHU3GzraFVDbKq92l5UH6s+rxQi8gvwyTfy4YGz\nJ8+eq4/M54U4lZmTGhWZb/lcSE9JCwx4rvlUVJ4n18jOc31daTVNotysdWyOQtiE/Mg5SCqc9DkS\ntUfS21obpqaWeQrh+aDO2VlyOHO1Geci5r70ABLVZjOnoNQU5WywttbSZLEpd7u0thBc3TZDNhib\nCa6lADoiEwu+kmoh16HO2pUTf59+4u///ifWT3dw9bCqdMBrmZvkZ67zqZK1Wu9EM1gwJpNtlQRf\nIz5H9Wb/UHxNatvXEpZau2Zc2v1tf88Mt0F6BgYGBgYG3jfezr3Ntv4w0NzbfttupOvMHlKTopWi\nFsjfrivHlyDZ+FhSilwCZ6nlGvv0KvmuVR7YExm4DR5luRCml3r29ORHqxxiaqZVJ0kpW2nkQUiC\nVltUL6Cc9rOhe9JIEO/4/9h7l11Jki07bNnDPeKczHrch9iUoCYkSBwIEMBBA5qRIMBP4IQ/wBH/\nhUN+Bb+AEw4JkIOeUIAmgkip1br9uvfWzTx5ItztocG2ZbbdwuM8KrMyuvrYAqI8wsMf5uZxsvby\ntffaAXMxPzhjxrKWnjxUeM4QZeZRnTegqTbN+EzGTdXKYTuXTP2L5bhs9MqXvmhgmJsAACAASURB\nVNYZUsOk78tUrulxxqMV5YbKSU4G6+oRo6/XbG1CCA5TqSfKiallBjkXwpOs9BMy8n0qRMnN7ddF\ntdBXNVHsvR9xxIf8DX7/+++x/u69qDucG53+ZwF4h/U4Y5pXHPy5ji+VtDbjhKQhXWEiem6+BOkh\nMX+KlLzmXw4+5DBNzf3aIOn51a8G6RkYGBgYGHiLuHFz0suo41pAtE1tszXYNGi9dkJxHmMNxYoJ\nYriWarpbVEpR35sGQO0Zk3xEzlasl+fydF7XkwDNnY19d2RwzUaaKUI8hUEzHuiD/b3L1sEnz6kJ\nV19nxLF8woYI1GPNqGl3Kfhar6LnnHNFNzdf1YvSAec0Ax+d9AmiwvNQzskUu0fIfJH08BombIkj\n1SsSOg6DvY4iGonT6+4AvEdz0/NohM45JDvj7BJyPm/VnB3EquZIbyCUw/A9l8ZkpJL2FopSyJqe\nO5yKrbX8rs444Lf5V/irv/ojrL95D/weTfUC5H4fyrU8lPEfZ4S7M+bjIr8/WBgjqpUQryzTY3Kx\nSzeNdNM+/UuBv92n8BLzgispcqy9Q/p6xGMoPQMDAwMDA28bNyM9NrFNYa4BtnzaqjNEb3LQtrU1\nvU2DNsUarajc1f318SXITAhWVJ/sYomAvdSQrPVALfVKB/C67oQBOdACPxIcBpVBfa+Hr1PbGMz3\nxekZtRlpVUOoHnxS5+exWAt017aVJ++pBu8kjx5hQ3wsEgIcFsxYTgcZN0nNqZzvAY2YPJRxfFRz\nBjRywuuf0IgQvyNpXHFZp8RUtseyXh+XKXMegPEIdkbOEDtsm2vKGkGDB2sTUukNpIlRTqaZBWWD\ntHrAG5weD/BTwOQliVJs1C0SZiQYBEz4gG/w1z/8GutfvQP+Us2NDrLpcpfL9ZwtwuMBZx9qw1Xn\nDEIWO24xVig/Apox9OlsXyq1Tf9Gf+w27vp39W/7KxoyDdIzMPAzxnBuGxgY+AK4ndIDul/R2Bel\nJgd4qhKb2zI1Tru1RXgYZKylnidmj9kstb9Kr+y4EtwDgDWxNsl0PkiAbDJgE+AKm+GwWIdC4kJF\nhXUzfWqb6ZY65au3YSb6fj/XaiyY0kTjhIBGChgUO4iywO/Lca1NdU50s1dbNhCnMmEtKzzO6YC4\n+laT8gghNlrVYU0O12t1g3VOvBYSHqa9cT3T5KgkaXc4gy0B5fwcse0PZGZEA6TJKtIgKWxMZWTt\nVmZPJpuafbbJLf3PyE1KxbXuk83I7w2SF1XnhGM5rcEJR/z24Zc4/dn3wG8M8DflWvjTZnofSast\n9+MTkO4nrMsMY89w7Gmk0z71//d9FrVHqzy9icHnBPUviTGubdP/q2IA0DDitef4Qtjr0zNIz8DA\nwMDAwNvBTUkPI36LJM5ZVc0R9E5YADbqDYlPhIUpxfgoR1wxSWG6U2YFRbvguVnhoxuWOh/hfUQM\nSZ7wZwAuAtG3NDOmbJGMUIHgbFLt6dPhrk0Bn5rLxWxJDutZdEod0FzSJjTFh+fuTQCogPCzhbib\nmXyhkJH+kJC2vkYey3lGfjg0MqPrenTzVH5mSldvw62vgYH6qq6Rx/6EVi/EY3I/kk5brl+THs6b\nm6QuZwobJ7RsMnKEuPNtmn6yEWohuyW1DfwN+IQcHZZPRymVOjpMfsHBHgCIGcYfHr7D419+D/yV\nAX5bxq6tyPvlsZz2DODkEOcJ0UXkUo9kbYZ1ETFcY73dnGrjhx+Ll/yrcG04e/u68ndtu2TSV/T3\n+Vz82Z/JUtf0jOakAwMDAwMDbwc3Iz0m5dIfhs5X26IEEiBX3Nl0uKQNENhlBmiF+GccsMZJaiHK\npq2RpMgrE8KmQalGTXMqVsZSR5GAbJvaMOGS/PROY1yv3dZ0sKjT0hik8jisweGQdUrcoRzzAY1I\nMLWMZgCPZT0VFI7jIC97XOBs2E0hbKeU6qdc5uh8moGz2VpVaxWGaWl6yaJ9TcoY7HJMoVwrlS+S\nno9oihF7/qD7vFdjVQmkAd555Mlvz68JQk8ODP9Tfi80hwiF9B7E/fvx4YhHL0TIHxaE0yxNVX/r\npYbnL9DIoe4xpNW+BPkd0bluEdWpckTTHgKIrXZ5+XDZpLQnlHvX9hI89y9Cr1rq9XumHU79plQv\nLov81QjPb34D/Of/DNzdAf/oHzWyM5SegYGBgYGBt4ObKj1N0YmbGEr35pHvU7VVRjEraNs2sF6H\nPXdgtIojBgdGnVkIlYGDh4dFtAkxupaGo8mPi+WzGilVHs3X+vQ2XcPSB6Z9g1E9Mbqup79LJFUH\nXKY06calGVtCcIAoC4cM5wMmG2o9jykamCkDodojHMVgwSRW1VrNYZ1N7x6mVSaSMI5RC0tUzmjF\nzZoe1vFoJem8cwzW8vBFMnhAU8hOaKl9JB1N7GtLkjB+l9T+rC/S6Y1HA3gPeCC4uaUj/lBen3BZ\nAwZsTS94Ho5hBRDFNY4NSa1NQBBzjezEQQ7BFQJk9onN5wTzz7m27Sk8e+lsxUGvh8HXl1f+3b+T\n5T/9p8DxCDyWBwKD9AwMDAwMDLwd3Iz05Nx6nXjV1R6QIJupZ71rG4Nxoqk+BhEWK2as8MjZwE+s\n5ZHv7aaAJtdvCF1zsHmyrlOdgOtuWTpLSpOdXt3RTUd11/s9cqC30coIa1vQjSWpfXnuI4B3EMez\n94D5ZsHh7gRnImxR0mxhTxkODqEaHBgkBBzwmO6RT1MjIZqQPIWeBGmrbipXJD1UgXhsrfKEnWN0\n6lUlPQHAt2gqCpuZkiT26Ydcp38Ofc0Sx6DPC3UOpvex3onbahJMUkgFT6/TaYHavCAXQwWb1W+i\nvMlG6np6/NhamWu9pZ46bv8viH+aSWweG3wl0vEf/oMs/9k/K2MYRgYDAwMDAwNvDrdLb8tCQHQZ\nPYkPa0kAURx0OhvNDsQ1izU9rtamRFisaa4F66LmSHtNNo+0yPU4HhGLVpWKxTEKAbIuIWVVYEO7\nYELPIN3c9lSd3UlQ2zFNjuqAdm1jQL6XQkS1o7cu5rZ3EMLzLYT0HDPmd484zucy3zL3tGCWobRa\nJ7ks2RLBXSo8mnCRDOjgeM/hSytcWvVgUT8JQIAQCKoomhCyD9EESfM7oqUcsmkqDRJmCOG5Q3N5\ns+o9Cao+/gOADxDS84CWwkcSx/nt/4I04TyU95Oah17l4z2uqYkWcXVIycJaaVYag4OzBtEA1kck\nXd+j68x+Slz77W2+v8Ii6t9SM8gA8NVIz6dPsvzVr8pwBukZGPh5IX9Fq8eBgYG/s7hpepuvyWgR\nHgHXGhdqgkO7aao+qR7BIZVeMjE46W5f+p2IHTNKYA9AqUqApNdJWB+RjYHzETY4RNoc1yEZqRMp\nbwFs04H2gmANo77XbmY6zUofTxsYXMNRvc9ogfwBEuy/B/AdgO/l5b4/4e7dIyazYsJayaLdRPPN\n8tvxffZC9livw5qd/voIkgltq0y1hqlnJDxM7SLRY0rZNXJFkOBQFULZ7wEtlY8GB8crL6bXcZx0\novsASVN7wJb09OO4UDrQCFhGS60Dtuof7/+KRt6oEDG9zaL26AEAZyNi7JiHy/tqD9Wnlwb2ulGs\nxtVanf58TxAemyrxuQXW8iBhmsqQBukZGBgYGBh4c7id0hNzDbRZME/CE2Hhq3LTVBwNq4iQxrLO\nCKuvlr8kN2azL9c1yiQxd5K6+9LPxZZjpFCiQV9YSDDboI8paE8FUUz7YVNJWjUDjQTogPq5J/ck\nCKzbYbCtDRUOaGlt7wC8jzi8/4TDdMaMc7WjboeMiPBV6XGKGALquP17rTRwLHobbb/N9D7gUu0h\nKdKpbE+Bc3ZCU5iCen+AkIl7taTydYdL1zfafZPw/ICW2vaApwkYr/2Imka4ab76FCHWKW5Bfn+h\nGCf4KSCsXgSh6LYqj8aPUXxst9w75ksIz7WUNhdvSnaIUOZkkJ6BgYGBgYG3i9s1J43AhICDlMjD\nK/Wlr7WJ5RG5XisBesIB52pWEOEurH1jNT7IcOoIrA3yWOHgN2qSblK5gV7vuyfsNDXQn7fuDNq5\n4dJeuQ34En3zUga3/d3T65jS9R7ALwF8nzF994D7+wcczamqPJoANmtvuRhaVUdYOBPaOTS8el0j\nPiRnewF5T4BeSngIXUvDnkkkhJr8kAC+g5Cf92iKDEmrruP5PcSM4NQdX1tza/Ca36O5+lFx0/VZ\nhDac4PVX84dixAHpFRTjFVaiXdz4+9PK4UvUnmuEZ+9fhtf8a+Eub2Jzo/u6bKNXekafnoGBgYGB\ngbeH25AeK3XZtqg7zSmsKT19j55Yk9DakLUClMr3GRIkzsdFyIvZmiGYSqxocZCKkUKAhYczoapE\nMUjgWXfnU2vfLYHLRpGApPwYSHCajeyfpm1Pn+1FtuBVEx2e/6kn+X0fH63y3AE4BszHBQe3wEnr\n1s3uLVtPDCYCHCZYzFgAADNW4P0KHKdGFHjOI1rAbSDKiP6+R2/gpQkPa1xYy/RMe5pa/8M56AN/\nmhZQhblXrzu01Dam7T1CVB32BqJpg1Z5SCT0FGoTBd1DicSTIMngtgmX5Aei9kTTbniKrpHu6gT3\ngj/fHxPY90Toqfuwp/LQAGQHupnw1wJJjy/TNfr0DAwMDAwMvD3chvSUII/9eWibDFzaVWs0n7fS\nSLT02gGk1mdJc1V6YnCwM93gbI3ZcqmWZ+G+VQ5ysRzPeWkG6XyAKVOUANXEcgc+S73PRXF3iYwZ\nBNrSnyQZec+DeyNBNGtbnlM7fPf+jG3tiIcE9fcAjoA7LjjeneAL4aGpA5FUpNvUMXnvETHbM6Z3\nJ6zHqZEpQAgB64pYg8OaHJIjrQAFXJI3/Z7XrdP3dC2U3t53n/vUOO22ZiDqF1Pa5vLiZWuLak10\neoWnd6LjGGiNzXFxDrTNNR34SHJ0Wp9K9TNeiLd2E0QuaZf8Db6E8GgTDI29+p29VLfnzDiupbXZ\nF7KJr9Sn51pNz8DAwMDAwMDbwc2UHgDVPUyrOn0g3vrs7INpaVoB8r4xhlxc2/bAp87NOa40SnUR\nzkcEBpjXgiTfyS7GXdoImwy4JOuT2e5nS5F3MsB5bmpRP9wna4XU+zO2qgad3Q4R02GBs6JoXZsP\noFmAk1xy2wkB8/GM9fiNHJPqjlZ0DCTYJ9k5ogX+2rwgqeVT10nlaM8soFe8SEh6MkXVxaA1PaWj\nmw7+ub8mOzqVbc92W4/Hq/d901aun9BUJWBLfDoCUN0HXUIITurLentw/Tu7ZqHe3+prhgXXCMhu\nX54nfpD938QO6t/4V1JartX0AMMUamDgZ4HxRzowMPAFcDulp0Of9vKU4gOI1bR2covJYTnNzXIa\nqOlte+diHxrZ38AVdcMhlhQ3B++jPOSPqrHKU0Gdi/IEvqa/lW2jbaqOD0J0bK5GCTkZ5BjBZpcX\nNSO9K9r1SSnjQFM0DgCmBOcjnBGPO51KqAkn1Z5GNm05nLjr3b17xMP3AfjomyJFhYnkhOl5JBh0\nedsjDCQAJEBaWaBKoY0GNPE4qeNduyWxW/ZzqlUeZSTwZBrbU6Dao8lOX4uloYkGyVkAcqB0J3Uw\nxmTkbGBdsau+pvLs1e/oup6987+mpucpW2rgRYSHSLzZX4H49EqPxiA9AwMDAwMDbwO3U3oSalob\nC+p71zAppC/mAjuH8cVtLMAhhha9WVfITLKI1payDdWLBy3tjSluttT2xJLqFmyqtT0pRiCbbYqb\nUpM2aW/XAj9uUwiPm4P0YbHShyWECDgnaW576NWNPrWLYGobv7eArkvSxHKP8GiIp54tJR0Rd/YR\n9rggHb2kzgGiXnAc7JNDVeOIpqzQurnaMqttSXx4LSRQHlsyxfMENKUJ3fLaL1oTgtS9svr+tUYK\n+vy8Fu1URxKY1GePdv0alXgZxOCQrUFwV1TOPZXnGh+5JpTupbh9pZSz+vf4Fc7X1/QAQnRyHmYG\nAwMDAwMDbwU3rulJG8WFkP4w/TrdoJQpbW1djK66rvHJuGyr63piVZT4mcG+RcSMpRItb1ckX5SO\nEOvx9KiqpfW166zpcbnVOfgIYzOcD5hnicasS8jZiHHCWRMo7BMdPLFONwj1AFyG8RHGpKrxaIKj\n32tClMA2sRYotVcHnPDuu4/48Pt7ITNUlHolhwX8j9iSE4IER19fwpbYaJtnTeKYLvecqMB9dINX\nqk5aydEmAl8qANYW5H2TVW3ZvYcAIFjkaJGLOhiDR85GzAyI3j3wNbhW0/MUAfnMlDaN+ju7UU0P\nMEjPwMDAwMDAW8PNLKsRUcyQJVomkbHqMbtF2lUgGnJxbWOUDzgfdy2n2Q0IYGZT2qTQNetmZWxQ\nTAicDxKrRitd5dXhrUtwZbuYXE3TStFKupt23bIJpqS1eS91Q85FrOuEGByiD6L2sOA9qiWx1zdF\nKxy6aJ81MWXRp/s1wpcuyI9+6e/ujw/48P4MnA7NQIGNREl2Zmwd3g7YJxRcp1UeDe7PY9nu+q+R\nH60MRbQ6KabRabXpS0OTvwVtbtjQlXU9PUj0yvZ59UjJItiEnAyM7SbwxxKe3kqdeOrP7LlTRbdr\nUb2Bj9vU02cO+SVxjfQAg/QMDAwMDAy8FdwuvS2gaDWpqDEt+nCl5uQa4Uk15a0RmWtRlA702a9H\njsHUtlTIlqgZszpuMEJAvLcADAwWxOgqyeFpU1nnEJGSRS6GBSk7SYPLABLresQooQaABrA2iVOX\nLtxnfMignSfbC0B1LQ3fdwYAenpSqWqifbeu5elNIyT5z5TTRHgjDU7Pq5N8oQmi+pB8rdiSHV3T\n09ec8Ho0+dDjJpd1aEG56V56uE6tI9HRxhC6/udJwqMH+QpyoVPkaIagiQ/rkOjgpsFr5BwGj2zW\nMhoD85LonMqWNorQxFKR4IprCo+B/F6fu/yXqDydm5v6q/0q6I0MgEF6BgYGBgYG3hpup/RYVLc0\nIMMjbpSXp8Iii4ilFJNUYtRtHoMDDBCdhSsEZxtvm+3+ELKVkOEREOBwwIKzAaJ38KV/j25+GqOF\nd2lT3yPEx6C0t5HCcwNxcCswJsPY8jIZttQPGReRXQasaUGqxjXCQxyxDfpJAkwjehEeHhEJBrab\n7z7NsEeEhUHG4bhgvVuKGZ1vZIuEhYoMr4HKFQkIt+tJj4GQqN7VrFe3tP2zdmfTqX3cbk+AeDJO\n/4womLuy0Sn7/tDQYYFc3959jGg1UScAiwGsQ4qFRE8QIwPg0sig70ukrcufwnMpbV+CmZA8AbA2\nP/sb+ymwp/SwQeno1TMw8DPAcBwZGBj4ArhxTc+2wehrGhc6pOreBqDU6mekZFsA2MWvqUTJouyE\nWtejz+8QkGEKMWhpbs5FxFJTwZqhZnRg4ZyQNmTA2nINK8dhxKEtb9OUrE2bOiTrE+JxBcLcCIFO\nb+vfX4NWQoCrwasmPAa59OdJu4rPZj8jKXrJR2Dyouho8qEDadut53ttWhDV9y/lHAxg+/Q//YvW\napl2aNsNdK+d+Jq8dgVUV5jedoY0O31AaRLbjbEnHdpUwWRYl5DMDjNxpS8Uj/Ea84UrNtn1uMDn\nEx6XcK1B6dfGNSMDYCg9AwMDAwMDbwW3IT06+NjU1LSmmK8hQJvtMiqRqMvuOEzrasvLc/GJtEOE\nN6t879DqdwoBkjqfBOtTPX9K4hiXs2kGCMHCGKk5cj7U9DbnIpIR0mRdRDQZmLP07mFND4dG96+I\nbVBKfkKFZOcJfgpi9BBN86uL5fq0NXhWdT4aAVMlQsZk+GlFOE9S4M4IUpMskhi+nFr2PILr9K/x\nmlW03obKTujWJTSiw2Npw4KfEtr6mrbdJ4jic4YQIapRuleRTv+rNVIWYZlg2ai0KlhB1B4Sn54c\nX8NTyg7VoteQnWupbV3d2z6+HtsYNT0DAwMDAwMDt0tvA2pNjQ68ST40CQk7qkMsewZIChCATZpP\nb2YQ4eARkFWNSovRNdFqZgauRNO+RIretN5AXBjD1J0WUTtEhNU3whMd4FGaTcbae4UEytoEN0VM\nYUVwM7KLwOyE+Cy4VED0XdOXyXSyLjUuB4eULFK0SNZduLSR9NG0QEgRjSUSAnxNjTtjRgwO61n1\nLpIDbaGd15x6kbBpo4Y60LLsSZ0+pn7Pmp0+cGVT0T2jg5+a+JC80LntVJZnCPGhycNOz5hK1GqP\nIFODcusibLTIziDrprk98dHYlspdJyK6985LiY8TG/cLNcex4dI+m9B/a18Lg/QMDAwMDAwM3C69\nLQB7TTL3giGtxNBpzCIj9ASp2zUnI2YCpo98Wc9jNg++TaFE2xQ3V1PgMky1uTZGxuScpM31DmhM\nfbMuIQa5tljS4LyPsLbRDAMgWYvpEBDCWVyZswG8uwxcgUtXtz31h8FrSYEKwYmt92RqZyRfKA7N\nClZ4rEXR8SVqp4ebzJdFjB7r6kuTTCcuYiQYGlrZ2VN9MrZKB80bEpp6s9eUVNcD9b9eKiU8ZwR7\nfLZjXCU9Wk7bW/8CaBKmrbFZz3NWnzXxI6nVPYMS5N4Fh2SA5K3UgKW8HWFPfDS0uYVu/qr33TM3\neAn2UtdekdL2Na0MhpHBwMDAwMDAwG1IT82GyrVzDNEHQ1EpM/r7FojvqEDRblLboqrd4dlyjfZy\nrWlpxCpfqD09bJk6qiIca71E62CmXNPgACFCBlK7Y12E7lOULQAPHA7igLAEB3grak+fmqXdzUhu\n+L63JM4AgkPOFjkDKVskQ7rZXhEOK2asmOp8kMRJxtWEAI/lPOP8cIf88e6yRoaERruuaVOF3pyB\nnxns97xDp1xpS249Bxp93U5//BcFuJ8RjPMc2sWNas8nAO/Q+vWkbh9CN2pVPz1rE2KysDZJLRUg\nSkt0Tw9506R2B70V9ktwzZ76CuExxcRD17N9zZLkofQMDAwMDAwM3C69LQE0h7bQwdA2CunrbXrX\ntYxiEgDlRKZS21KyyFZt23n30rpanyvBlP+KqxxBe2soxYf9hQDUbRPJk4mwXvr4sL7H2gRrhHr5\nktZnS6SerYXzET5ExOMiRzOTqCl7ds8a2uZ6O4FAlBTAuHqEg8dk1mrq0AhOIT/ZwZo2H6awjhUT\nPi33ePx4j/zpIEE8x6AbfZKgkPBoq2kqPY1vNnKkiZ0mP9xWX9uErXKk54TmEX2Q/7VdujTx0U1R\nF2zreoB984eKQvKNNLTNSep8ZKX6EWhjg5fA5tc1BzX56V48/vI7cShsE1/r2Dg5X4n5DCODgYGf\nOYZz28DAwBfATS2riafijmu9ehoBEfWCCKXeIWcjik9qig/rV9ImnG/uZTqFri3bOk2Cguoy1K7D\ngB1wULdLiMbVf7SpHNmiJAGNXFickSZRqWKyyNGK/XUsQS4DZTayrIG/msFoWiE/h2EMEC1CcKWu\np5FAzrHUR/ni4JbLWCURLhfSc3o4Iv5wBzxY4CO2qVx7NT0kNNscwpbe9pS5gCZxmtzwvVevdgMu\nncxIPr6GkUH/16SVJ75IDlc0Ysj7ZLEdJx8MmAznEkJwMDaJc14y0vuJpgYvjQn4W3kJ4bElXe25\nlLVdwpMuG6qiM8j4yqRnKD0DAwMDAwNvFzdObwP6hLY9Veel6M0LcjZSwJ8tVI9S0MxAaldiTfHq\nB2irZpOh648aucnFGqHVxfB7vnflGzlqq4+hgQP743A8k1mQJ4M5WuRksawemCNg3bb+RaNXO5Ja\nFkUlZ4MYPEKQuh59vZLaNmHJE9Y4wfgEj2ZsEOBwzgecH+6AD1bsl9mQlOfUyx5Xm1+W97puSdeZ\n6O95HE14ZrUNe/JYNELBMWnL6p8aek5IPmlsoNWfCU3J0opZTwiDR54KOTYqSLdlo/QKuca/IMIn\nybEvnKyrCs8LzvUV7kfOQCxD1EoP+/QM0jMwMDAwMPA2cBvSUwJRV/rgPNewkBbLGnRR0zUzRj2R\nzqX+QU7X6MYekdLnT5XeWBikstySFaa1sR6HxgC5rNEpevrYphuJJlJ6W2szcJTUvBgcoi23STVG\nlfSk1F7Jtpe+Rgb90SHFKCluR4doHFZ4JBiccMQ5H7CsB6yrh7MRsMtmTtYwAQ8lre0BQiz2jAR6\ngsGYuDcy4NjaSVpNUpuwVt/CY7D5KV+z+k4TID0Nulj/FkEuSQwNDUh+eG902p/evoNzsSqZAOQ3\nkNDUHp+3BLHWC5n2/tpzBJKdlyg79fyXg7ym8NTv9Q34CveCJgbebzNk+H40Jx0YGBgYGHgbuA3p\n6SyYNanQ0OlmEZdPtJ9SgnKWI9LOWnSZVNWLfl9d2yJjktak3JOGC7YcQY+9LZuldSpRLPUSKjpU\nfXQdEZWerMmXNUhHSXVbbUKwWayKk63BpvFRerhAmrKmxQO51ADVA6GoIEKIMsTRLhoHgwkrZnzK\n9zgvB5weD0jJwrmEu+MjMkxJefMIi29F+Y9lecSWgDCw7lUVpp2RxCS1dN1n4FLpmdU2PfEBmupD\nF7kZW2vo3tzha0OTQao+Wt3K3etKIJ7SExdB4tunEhqInHG1P09uaWyvQafwkOi8SOEhvgLp2avn\nAUZ628DAwMDAwFvD7SyrFVqK15YNpRK1pSsRa6336QKX6tyWbE1xy06O46DTz7bqz1MkSnew2Ysf\nY0lkE5tro/bra4RQ32uit/veA/adGCGczwkxtNvlfIBXgWcMDks2yGHazkdNERMjhRRFPTpPB6xm\nQs4Gj6c7PD7cIa0eMAnmXas3ipC+PilaIToLWsNNHp9ERLuS6Rocp7bhpGhFiIpHj73ifr30V15A\nawx6hJC1uawjAfmp0f+UtNLC7/o54XYb4lJ+oSbDWsCYK4yIpOclY9HHfi3Z4bn6Q9lU6+f+NmGv\nngcYpGdgYGBgYOCt4XbpbQVaCWFqmG5GmgohQlVenkcqwV9tDpossjXIxiLCwSEgwsLCFUolBOS5\n+Eec3rYRs+hHzc5AtgFsqddpJgU7gWL9LmLPSKF8kMDdACmGek3Ox9oLJsoNZQAAIABJREFUCADC\n4hFWj2gSkO1l6hjaU/gULaJ3WMOElC0eH44In45AsDCHFdbKfKyY4KsdWjFIoPtYgJAKEg2tXmjb\naNYV6QC/h3Z4u/bdNegGqHSOo9sbic8nNOvsXmX5EqDyRGhicw19CRlreTh2risb8L7H+NyEvAJP\nubHtwaZdi2tbfocvUXm+Zn8eYJCegYG/E8h5OLgNDAx8Nm5qWe1KsE+QKGj7abqr6ShRGwXE6C4M\nDKA+x+DgfETyjbDsaTU8olUpalzP/Xr7aipUMxIWTKpUxdTvuK3eR1+z7gPkkGCwVPJHMmZshruL\nUlfDbW2oNUsAkIKF9RHRpZYyJoMpF5KRYwuc83lGKE1G4+MROHt56p9bj54VEyZM7UC9s5huYWTV\nOpKiizGgBfdQ6/RnXXfyVHyvf7m9mkTBcIUQnkc022jewhVflvhwrByHHov++brutUeWtKtbUWJy\nNkXtUYPW9T3hmT/l52p2TJn4i+/yLtH5sdhLY/0psdeYFBikZ2BgYGBg4K3hdqSnQBMYGgFkABPW\n+rlHs1gu9TNPOFhltY+AUa+2shZq4wop4XoAF8qOHkOrxWlqlJzBVeWGZgeM5q06h4YjwYFRNUSt\nnihiRfSu20dIYoRDnD1Oj0eUQbdgm+YAtXDbIkaHEAzW0wHp0xFYmtRiTIZ1zU2O1+d82gboAc1W\nmsqEdipjfU3vJqdvjHZq4xh7QqSnSqfO8fyA1O14tWSND5UppuX11tpfOtVt76+JhIwvva43MCD0\nnBRim5KBc/npup66fxYVJ9n2AOApVcfkosa9fjJeVcODTun5Cg9uh9IzMDAwMDAwANyS9GQuzIWG\nw2J/EotrvXr6Yz0VROVkAEcnONpVh+02MNUwoU9J21N5tImBpLnJOtbD0OWtKjbIoBsct90aIUC9\nF4LkEWCQEWFret+mEetTF820Mtee1qdokdOMnAzSpwNwss3i+S7DHpbNcTOkEes0L9s0MoJTmNHI\nDtDUFu2gxu2z2iap77acbkuCelVJg2YNHsABzfr5G0g9D9UeEh/WEz1jHPDFoFUdbcLgsVV7aOwA\ntHtX7ptzaeNU+PT5ym/1JdbTTFnr1dJnYEwWs5BkYF6QcUdnxa/t3jaMDAYGBgYGBgaAW/bpsahU\nIJY2mNQ3msNZROoi4dh93uvNs4c9s4I+1WbrEGdAlzbXRcUkYXosNC+wiJWcaIc2nT4n38n5+mNr\na2wez8LDwmFCqKlvvI5QmopeQK8yAEwCskFcvSgAqwM+ulbcfwBwWHA4NtJDcwaLBG/W5oTWx94Z\nQi6o8uhUMk0uCPJHTZB6FUgv9XXsXR8J1gyVFgYZ7zsA36LZbLMmaU9h+THop16rU2bnpclOdVcr\n2/eEss5bflLNlHGESyODvTQ2l8TJzxQizG1eYWggfXiu2Yvsw7q0sZSXla84wI/ENaVn9OkZGBgY\nGBh4W7idkYEBUm3daSqRaCTA7NbeOMSadgU0S+p66J3gMCd5Ip2shTOxIz5bkFDwufQ1O+0e4tpm\nSzZZuPie10VSpy2y7c7xNbnxWHfHQLJYv6uKSQaYBsVAkwFuhny3OiEdTLW6z5jvT7AuISaHaG1V\nrADAmwjcZWAyW6cxYKv2yMC2tT89tMJDt7L+Rugp1MrQXqTNNDXdlJTGBTPECOKIZmGtDQ1+Cic3\nGiro2hyOqa/x0fv0Sg8AuATrEqxN2x49e3hO1THld2GykJ8XwnR1Ra9VaKoq9Eo16UvguZqe0adn\nYGBgYGDgbeCmNT2tvWdr6KlraGhs0Ks9gJCCjZtbCWKsi5un4nanOzwrgmQ3bSrAmhxTv3tpmBZL\nNNvHg7Yz3LaKRLmd6JFExxWJhLwgawJSzBbEdEHmIEaHuLpCKLMQn6wK01OJtJOVAnimepEIzAG2\npEXRIIGuegAw2wXm3Qn5cLd1bNM1OzIR13EtWObtjdiqHrr8iudDea8JF8eh35PMaeJzwGUtDQnY\n5zzx79Wd3pyA42DdUU1dK9vwfV9vlAGUFLJdwmBemJbG38CPIDpG7fM5lOVC5QG+anrbqOkZGPgZ\nYzi3DQwMfAHcTumxQE8SdH2LNjfQn589tAoCxe3qMtCjEQIQNvUxUZGvNrKXRUUOqRIfvT8pDjOw\nrLqWbSpfqqrX9vwkSo1NGORKfNqF68GwpkN9Z7IQHgafuq/Nuwh3d4YxpUVqskjWgbSPatLh3Qmn\n6W6b3hbRCEpWr2vNN7eTtiVJPG5AIwv6e018otqGZMdBTAsmXBIZo46hXdVIOF6r+Oi/HK3oaOts\nqj3aZMGpZW/jvefkZjKMkz49xnRBOlPVrhEidNu+AMamV5sTPH/MpkQOI4OBgYGBgYGBW+B2Sk+t\n6VGqTLGwpoua9NJhk8w2VAZONf1qL3gyuSgXeUN+XNWXYqE+sZgKMC7fHqyvuXkKz23bD5OqT9/L\npzduaOpPrDU2up4owiFGhxRdC4BZswG0VLdsSgpUlqhvBnDMcPePmI9neB9hfapzFctseUR4BNy9\ne8TJ/6JdjFZWtHsb09t0A1JNKnQPWhIAojcXYNNTvb1GxCUZ4jpNfPpbQyWI5OkpkMzoc+rvCNdt\ny/cOMtcTLo/1FF4SkJP4aALk4usIRSFUwKUq+rnQtTzs//Q1MYwMBgYGBgYGBoBbGhm4rRFAIzLb\ndJq9mISGAwkWzkeE4JtCU/qQWJtgDMRqucCZFq2SaqQSge+VZftXP/5v5gSs73lOofKqienetiRE\nJHh7TU4zDEJ0MC4hmwzwmrOkJ2XWPZXAFnMAfARWD3tcMB8XmSfTUts4H6kkGTpE3PlH/O47AL9B\nC9z31BSqL1yna3JIanSti07tMmh2083pe+v61oMGCl59BrZEpzdg0EoPewf1t/saQXHde62a9Z+1\nnTbf9+ltWrHagag8Ga4oeAukIWhijU9PfF5BeL64stP90aZk67i3m30dtjGUnoGBgYGBgQHgxpbV\nW4JzGX08F49UoqSCKpcNco5w+gmzS3A2ls4+ol/4YgLd+uhgmy72AuwRFV6TRWp9hJTxwt5ROAaa\nNDxn000CFIoSQxKYgy2F72U+WNNDEwObxbrbRlgf4e4WTNOCaQ7SwDXZWnSekkWwDh4ObCLrzQoc\nVwDTlmAwnesa+B1T0BIa6dBT3uS2Rni4D1PQemLAz0xtO6IF/Xp7pouRjCxlHzYp3TMWeN3PoY1D\nn+dQxnRX3uvUtj69jft3wyCkES/gfETsTQ2YvvaSNLYyz9Z/eRcH/k2w39Pe969JV/1cjOakAwMD\nAwMDA8CtSE8JeuOObxlVj3gl6Nfq0IYoqSfM9cm4j0J4XIQxqTxgFxMDrZiknXE8l6rmOlmgt9Jm\n8pwO7nSPII6fvXxorGAVaXrp0/AsxRLwhxUxOLgumE0pVzJD9y03RXgvZIfqzrb+yVR3vQQHh4QZ\nC6b3j1j9pDfTk7IlKnuEg45x3P7aL1ArO6zx2dumJ1/XttW9crTZgDYR0DVEL5l6373X6s4EITk0\nUdBKUG92wHmhCqSPZYTUW5fgctw4uJG0pOdc3RR+CqKjwZq6vVo6oCmsXwtD6RkYGBgYGBgAbkV6\narbVZfizF+jvubfJYXbSZkyGo81vITzOBExYMUGWrFFhfQ/RKz3a5KBHLApIT3b0vrw6biOpYqI3\nsZbJl3HJtutmHq7ZVG8/+zY/hewBqGqPMVlifZsK6ZH307xWcvRsDxgIyQtweP/dA373/bfAR0gw\nfyUlqyol2sxAkxRgG9wTodtHL/U2Grlbp4mQNlCggYFuCGpxnfi8BB6XhIfqzntIj6D35XVEq+3R\n5+U4ep4Qy31UAbpzCbG7futjqefaH+JPTXSIqkBpNzuFXQe3nxijpmdg4O8Ach4ObgMDA5+Nm1pW\nX3via5GKK3EzM6CRAYNv4DoZAiQAMybDmVBIjqS1TVgvVBpgP7XtOaVlj/AEtL4519J4ciE/cg0W\nWbm2AWvVV3avq1wHk/R64mddgvMtKvZe1AFrM1IysFaMCozdb3jJJ/U5G4TkEK2DL6TMI+LeP+B3\n3wP4a0gQf+oO0BOY3mqaSs9TqV26judKAL0BHeSuETBdU7TX60evpwnCU/Bq6dGUHBIdvr4D8A0k\ntW1S29vupfsG8XodAC9pibyfNBvYg92pm/kx0D15Xos9lfFJ/C1oTjr69AwMDAwMDLwN3C69DZek\nojXwtJXw7MEjSjF3Cfidj4jRbeoIqPD4Sg/CpqanV3ma5fT+2HqQOO0RHxm739TlxEplzGYdlzrl\nDQgbN7enUMmgT7V+ARCyAwB+khQ21uw4FzHZBRYZKyQSZOPI3u7b2XRxD2azwP7qAenP3rVAn8SH\nwbsO3LW6Q8Kjg39g+yv0uFSEgP1Yn+5xxDXio/dlnc2KpqxocqbH2kPX+fSEh0rOOwjZ+b6s45Lf\n90YHT11nUS03l1xS2Xbren4krBW3RGuTNPF9gfKnQaLz6vF8BcIx0tsGBgYGBgYGgBsrPQHTVfMA\nuqBdIz5EXyytLXLpekaCQze2PaXntWB6Wz/mHntKj+7Hk2AQMG3S4WY8XVOkSZKG9xE5N8JDMCjV\n6UWafO093c/ZVPKmaaJBxne//gG/+/4d8Hu0wF2nkLG+qicOnJ6kPl9TfPQlMEWN67R6pEHFZ+/c\ne71yaGsNNMLD8zxnzKDrdI5ohOd7COnRRIdW1Rqci9CtY+2RhzSYBWq6Zk8qfizxsTaJaYVVfaz4\nIMJmmCzW1Sk+T360svMqlecrYRgZDAwMDAwMDAA3VnoiHE64w4J50xNGSucvlR4G35ooGWxdoqxN\nG4Vnwgqr0tpcKfRwxSiAasxWDIjt2Ip8aKLR1/NEuGpRnYsfm7bFpsqj+w2xtifD4oxDaVbKfkKX\nas/eOXke5yQANiYjRotpDjAmY57OsIYdkVqdkOzXXOLYy0g/5U/JwtmoSJaQyHv/SayrtUPZeec+\nU3nR1tR6CVz+AlnIr1WVnoj06XJcp/vu0DCADm4WrdYmYNtDqF4wWorZNfSpbSQ2VHeY0sb1dG3j\na8Z+WtfWQ0IWJbWNqZpAIbTZIMam+PTQREg/BMjJwBQHv34/bVtd1VIfkaK9SHW7ds6XkJ6XqJdf\nEkPpGRgYGBgYGABubGSQipNZM5P2lXA8p/AAQk6iSmcjGNiL1XKqqgkpSI9Q3Ml6kLD0zUBlfK4S\nHUK7tXEfqk1yHKEZKCRI9rFISQLLZI5wVtzl9Fxcq0GK2h3OlGBUOdgxWPbl/DRmILnqr2mv0Dwm\nh2BdvZYZC2Ys8L/+gPD+m0Y2WIPTJm+rvFB90bf1WnobsE1V0yrPtbodom8i2qe2kfSwhgaQ9Dyr\njt2TIY0+rc1DFB4qO3do6s6Eptow5U8rW9pAgePVDm/X8IQhAMlQ3bRsG4OrZOa5vjwp2WZ8YQt1\nf6bW5zUqT/09W8D+xLXJw8hgYGBgYGBgALgl6TlKQf+CGecSSmNH3dG9Dvfc0mj1zP42si5VZWeb\nnEXNpakdBFO3evKjn0z3xgShi07ZxrONrY1X9+3pU95YT7Muk6gBagoOOO+k0bmqGF3r50PnugOW\nSryoPEmcfcVEolN72vkcDjiVNMGAb77/gN99/w3wUDai0pMhvyptLrDXx2dv2HpImigAjcDoVLq+\nBqe/pN49zqFZSOtUOfbtIRHSKW96PFwynY3K0ffq9R5Cdvi9NjDQdUwa/TqmuLlU3fZeCuekvq1v\nCPoaUsLzXZDg/GUc2OrfcMZPbmYwlJ6Bgb8DGM5tAwMDXwC3IT0lAFwwFwc20WNIMEgKti1gQnEr\nSyUm7NJz0OLbZvd82QUI3TY6Ve6p/iGawLR0r2ZJLWqOgy1LvR8ApfSgfC4JZ+XJeYoWORmE4PHo\n7+AsCVsAG5dSndFErI65u1RrmwMc1apcFJ5UZ3EfJD4t2DbFbtvDlf1nfwb+aAU+TmJfHSCKiW4W\nShe2hG36GXAZ7PYpbZpPBgiBgNof6jvur/fRS5Ku/idhIGRNO6gx9Y0kids5NDtqD1F0tMLzHZrK\no+2rOS7dk4jn622zg1pfndpaj6W9up499ITni+Ez4o5dsvQV4pg//3NZfvttPx5ZDtIzMDAwMDDw\nNnAb0mOAMBk84g6xpLgtmDGVPjVGPZ7PXRTLOpxUjA56kCDMOJdqmVSVHtORHiEQjYgwnYzEQBMr\n7dbG2p09MwFTDKkJVtI0ArJtVgqgBrQpWsTVISwe7r0QHo9Wn8Qx8bzsNBSjuyg6l1j/9Y1ON2YH\nTHOq1y1jn7DiHo/45o9+hw8//L1GFthjR6dpkUTo9K5+KJqYzN16EgY6xPWkZQ+8LSQPTL3ThgJU\nfU5oZG0pr4gtuTJoZIcqD4lOX7ujlR3dEJWETFtW678+9giqqlCGmSKcT/W6U7LNpMNkWBcRw029\nSF4E62KtGTPl7/Glv8fPxb//97L8x/94u36QnoGBgYGBgbeFmxkZ5IiacNb68bTif6BXXrJ616pz\nSEa4bSM4jQAROrVNu6ddDm9LeABtL32Zj+MqWWoGCFw+5+iWkt0Ui2dI/cXpdIQ7BhywVCVJKzy2\nu+4MI8XkLtbjsIbnxwSYVHsa8TEbEwmPFd/e/QEfv/sF8l9PTQUh+p43THcjgtqu9qXBVunQNTlH\nNELVNx2lisTjAZeNP3XK3QFb9YV1PmeIlbVWfEhUepc2Xb/zXm2jCZwmev1YdLoc54efLYpz2/Y3\nr2+jsTQbCH/ric8tmpICwIcPwH/8j4BzwD/5J9vv2KdnkJ6BgYGBgYG3gdtESwEwSTSQlogmusw1\n7KWe6WCexIOuZ71KI2liXRAJVKXHdySHKWEaVDuYeHYNdEoTw4DWWJXH1deSkxHSU4LaFB1idDif\nD7Au4TAt8FXTYc3S9kl5iramPeVsYEwu8XauaW2vBZ/M62sXtzg5z4QVMxbc//oHPPzFr7f1MCzO\n1zU9VFv4nW4YyqL/GS3on7FVjR6wVVGuBat75IefqbhwbKy9oULFa4gQ8sOfBM97VC+mt9G0gOeg\noqOVK6389E1JgUbgnFpfek6RxBqTa70OP9dLLs1Lee/p7madOL31PZi+FqQJrqqJU2O2yNfv4RfC\nn/6pWFb/yZ9cT28bzUkHBgYGBgbeBm6W3oaMGkSTRLQUrstIpFcsNKnheluc2kgOmuKy78wGiNJD\ngwLTkSIqPlrluEZ49Pj43iNhhd9VWiKcKCnZ1qA0JVFrJM1N9jt/N2PGjAnLhXJVx6oC2hgtvI9I\nTwS5LyVBOkgV/mER4MFmrwDw/t0HPLz7FXAyrfaGagntn3sjg4yt0qFTvmrQj2063AFCQg5oBEVv\np00HTPe+N0TouXXvOqfd4ngMkiQqWu/U50ntq89tuxdJDb/juJKaj3L9Zl7FfMDgxaRF7pcphKmQ\nJSta5k3UoE7hydnU+emVq58Cf/3XsvzjP94Z2khvGxgYGBgYeFO4DemJgI2tLkY7kOWixzA9rSWk\n6RS2VGNSh1AIC6rCowkIFR6v3MsIOcee4cGlSqIJT9+vh2PLJUIXlWh7Pl6LXL5DzB7rMgnBiZr4\nCOGByTifDjjd3+EwLViwYi41T1qBSnCbYzgXEYLDlAysaySMYyWZ0+tfijb/whJmLLg3jzDfL8gP\nh0Z2eAN5k0hKeuIDNDVEKyIkCNoIgUqQtnde8XxdT29sQJWIZErzV02y9hQAmhMc0FLaeoLFc/vu\n9RSpA5pCVrY3NsG6+CrnNkGr+dGon4uCp0kQjRKMTcipZ4QvB2t3YvCi8qgxGJM7peenl1j+5m9k\n+ctfXn43SM/AwM8IOWM4uA0MDHwubkN6HJBdIz00lF4xVSUBJT1MxyS2FPHrlp8Ag3ezUXna+m1w\nr22nXxLvMB2taUj7NTr6XGywCqAoIqY0Im11R3XfbGoAyvoeAEB0SMuEx093OHx3xowzVky7Sk+M\nDjlbpCjkD+WJf1SpdCRwrW+QB6naa9PfYvF4TrDwCLj/7gMefj8DZ9PS2bjUk0zio5uOGjRCM2Fb\n9K+bheraGu3mptHs+54afKvnIUhGNAnh8ZLatu/RQ4Wn7x2kDQw00eE16poenp/j9gCmBDeHqzbT\n3guxfQ2MTUjRSepbR2yoIlmTkQoBuoY9S/N6jkJ4mG6n4Vwqf7Nfj2X89reyHKRnYGBgYGBg4Gak\nZz2IN9kJR5xwwDkeEd2KA2bluCbB057N8uWT4nxR7E+dhYREB/fSl6cdh6RAkwTuQw+1BdMuQWBq\nnpAKIRq+KE48pqnjU1FWRlVoYnCtJgMQ4mKAZZlxWg84TgcsWOAQq8tdvc7aONIi520tDmuTerIm\natmV4PUJIsSUPxKeGQvev/+IT99+h3yeWuraCVvCoxWNdqKmimzqWdCIBtUjj5YGphWfPqWM65jG\nltWrhyY7G6vo7lgcK1UeurT1ttrAVqnqj6nJ24RGqtbu+qcEaxO8j5s6Hj0259KFmvMUETI15c3U\nfjsZQC6NcY3JVQW6MHrPBjnZquSgIz3WJRjTjBV6eK8NPnKtu3sl134VYmyk51e/uvx+kJ6BgYGB\ngYG3hZ+4NeAVOFELMgxWTDjjiPPpgMfTPRbMCPCbgv9rDTipvDDdjPtcC9i3is9l41GdFtdUKFtT\n8Kj4xCtcsSdV7Ty5jtQXCuVMLIXqEuBal4BkkIKToDLKK50nPD7c4xPucS6NXHm9NF9IqdVHiDGC\nQwiSQrd37Xq8r1V5qLFxaZBxbz5h/vajkBFaO8/qRYIwowX3WrnpG3j2iohWTuZuG6ouPK8mFrY7\nnyZTcjGcnG26WZ9m149Pu7PpZqYa2rWN16iPpRUtfV4HWB/g/L7Sc7VxKIRc+CeakOYkvaDIAXkM\n62KtAUJJQ9MvpqrV7W2q45DvdqzjbYYxQs7alPz0DCNn4F/9K+AXvwD+03+SdUPpGRgYGBgYGLiN\n0jMD5pDxiDssmPEJ93j44T3cFHE8nDCZtZCE1mOHVsls0Mn3PRiI6zqW/YBfiAlNE7hfhqlKhlhO\nSx3PCn9hYuAQNs1N2fsHmDBhrQoPyQnrhJiiJrUUnW11Ki8AcBHZJqzLhGWdsUwHTAhYd1Lc6his\nXKvU+ThE7zbzQdWnn5PtXNJZz9b6JP1YXhMloYAr7u8fcb7/DljsZUoblyQBdG/bIxXspcNGpntK\nzl5qWNzZj9P0VGDbkw6NPu1ME7meh/dpbFq54XWR+HAd5yGq/acEuAg/RVjTGtLW/jw213t1Lc3s\nGuxO09K9dc/tW+2yn6g3elEt0k+g9Pzbfwv8m38j79mjZ5CegYGBgYGBgduQnhLofcA3iHA444Ac\nHMI64XQ+4ng81XSw3rWtNvSsSkqzce6bcOpeNs89ZdZpaE3z2bq29YpThoV2htOKUyopbu0aUAnV\nhFXGa0XpiTYhmXJsW57DJyOKz+qRksFynrFMM2ac4UoxiSZcMTrZrfTVSeWp/p761QwiNJHRaW00\n3W55WbzOAAeLhFBqq0R7WnE3P+L3xwX5eGy2zxGtQN/uvKeC0ytBOm0sozUs7W+hjqsXNPMEbZzw\nVEyviYq+tdrtTStSWoHSY9D79rU8WtHR35Og6XmwAFyEmyStzZlUFZmcDZyPiMHB2FwtyvfgfURK\n5tWk6EvhKbWpx5e2M6C6o7GX3jb69AwMDAwMDLwt3K6r4VnIwYIZS56B8wyYjOU84Xyc4RAqidA9\neLiOQbhuOCrr9+tRNGFhwP+cNXaolUVb17aq1NRzUxNJlfAYda5Qppn9djIMDjgjWofVTKWfSa7B\nLUyJhJMFSmPQ5TTj0+FeAuLOsIHIqaUDMujltbZEwFjngqXlxLbOpxGlPctvzj23nLDi8P4Rp9Ms\n46aJAY0DeIuobmhiATQyQQLE7bS9dVKvXpnhOvYGsmiKD80PdICryZZON9PGCToNb0bryaO5BJUs\nTXLQvdfEyamXvo4yHjMF+ClI/UzZwNpUnf1IfPrUN5JcwtqMnDOcS682PfgcXCM8VanSN6E3uvgC\neHi4XPeU0jP69AwM/AwwnNsGBga+AG5DeiZg+hRqXchjuCvNJw3CMiHECcFNOCPigKU4n9maGqZr\nWpgyRujP/GeyL+SnufSeqxsDfelJMyGWc+vtZJlqrQ4hpgeu1CvZ4jQnpf+MpGcsAIADzkjGIsy+\nEB05blz9JnhFNkAQ57flPOM8HeARcMB5Mx6NFCwwATGUPkDG1G1ZnZSxIsFi3alDkmthyl+6moWk\nSaBHwDStOB8X5PUoxfm6kJ+mAkYtGfzr2p9edenJy7UaGpT1JDx0j+PFaCtqbaBAEnONtHA7XUPE\ncy3dtr1yxPP0x7TlWAEttc0DmDKMS/A+wNtGrLeFSKjER8PahJi67VxCSqYun+v1Y55xbnsKz6k7\njUD/tPj48XLdSG8bGBgYGBgYuFlz0vQgLmAPeCdpOCWIjMuEZZkx350xldQyh2ZTnbsAkERI1/Jw\nW9YDcZ22qt4zMaCDW0TryROU0iP7Nnc3uqjp48cScduSnsd9QonuDRJmrMWswWB1vtgvz0BeYZAR\nlgmZherRASYjRYsQHJZ1xmE6g852BuLytSAjZYdclIcYvAS72YorFxJ8Sa1raXy2XjPHSe0qqe+0\nktanEHIMDgGHuzNOjwvW1QPBS0DP1DSdfgZsFQ+mtWkSgrJcywtqX5Ka/rWgEaMVTWXSPYI0sdFp\nZfwc1ba00u5JGQnPHknSaWzuyjb6s97eZ/hJDAysSosEUI0ESEqMzVtyjEKGSpojYUuNl3MZMdqi\nAJka7GuSYwyKKYF8LwqTLccR4qRpS1LnD8FdJT5UMi/wEzCgPaVnuLcNDAwMDAwM3Myy+nBuqV7L\naa5P5nMQ57GQJwQjlIM2yUwiIxJMpUDNejqW5daJTXuzXboSt5ofpqe1lDZ/ofSw4oWfbcnFMjCY\nynhJDkiiAGCFh8OEGSvm0ufmiDOsy8gHA+sSUjawyV70UgGEyKw2EhsuAAAgAElEQVTLhGWaSqpc\nswe2Pkq/n2SAEhAzKM1l/sQ5rtld95bV2szBqM9i0NBMHzZjghfihYjJLJiPZ6znCZh9ayAa0ZSN\nPsVMu6HpehkdXx/QFKLe/U2THn5m2hhVn70GpHukBGoMtKg+4rJxKmuV9Dmvlc9oYtNDX6MF4JPU\n8thQ51pIp/z2aQ4Qo9tVdjaH3lFtSHh4nJwNYjT1O2slFU4c10y1yiZBsKw3UxfAuqFrxgXG5Ep4\nWHdXHwL8BFl3PemZZ+Dubm9cshykZ2BgYGBg4G3gNqSnnFUsqz3WZQYeIfHU4pCCk/41vjUvdZXM\naPe2XNWc1q7UVeJDNItlU+tbWp36ZYraihkrJizFHjqVrXT/IJ4XQCUfQgBafQ/HKipLO18saWMH\nnCA9fQLggJM5IgYnzmvsfJmMKGOrhzFrPU+bygjnY4urGcwlixi8NC617dpJ2MSKwJcqnpYOuGKq\nxFKrYx77T/F57WLQEHA4LDjPK8I0A6sTwmAgfXtINBhokuhoq2u+MoRM6EamDkKiSKCees0QNWZF\nqy3qrKEvCBHHxBd78jC9DRCyc1bnIMFiKltfq8R99HmiWq8c6owTRcS5Vh8m6ZwWBhEJkubI3j2s\n69H1PK64rOWddDZtOw2gWlFvLp89dUyuxf7XIETpaSfBrcV2+w3KAa5zxR+LnvT8yZ/slwMM0jMw\nMDAwMPC2cDPL6vgt08wy8uqkpucIIJYC/OTAzjYZ68YZ7RLNHa2lbW1Tz2Sr8lS6q+XhkulpYj7g\nKuFJReXQts+6qSnhVB6VTpnTiol2TRP3s6WSNmMz8rGYEQSPuExFWTBAtIhFBcvZIBrXUtz6ni3R\nAm5L5EjaWAcVy5h6U4QJK1blVHetj09LiWsOdg4RzkZM84rgI+DdNmWMp6Ey4tWy9toRy2ZkAzhX\nrh0gB6wKDgmUR0trYx0RiQ+w7+DWp7Xp1DcaHMwA7tAID6eBJIppdwbbv6LefQ64JEGsLeJ8WAA+\nw/iAeV7gzboh44186v82WJsAi02dD62tSXy2aWx5Nz1Of/9ja3s2l5lMnWMeL5VkyARJw/vSnKMn\nPf/iX+xvN0jPwMDAwMDA28JtSM8KnH8544QjHvAO8YcD8AfIE/TvDVLcuotF2C6ujHXJ7u58jgxs\nnd10aluP3g5bSI8vqW2+uq4xaqW6Mylft2vQ2oqum2GNUdOPcklfKkGhswjew/qA6AOwTEX18IB+\nsu/a8Ta9VmIjhTE4hNUjTb2JQ9oQN9qDMxWPxLFP++vnTbduzZwTKw0y3XFBjBZIvrmwkSRQ7XEQ\nosP0MZeBQ3EHoLlD9oA3wDs08tSrOprwsN6G31FNAfb78GiVpic8dVxl+6zek6hpgsVtsnpPhYk2\n2jyHHtcEYA6YjgucC/W30erHchGUSnobfFV7NHqDA2O3v+8exu30r0qFfJOkxB+vxeiUtxQtzvmA\nYCacccAJYm1uvwPw5z/6FBcg6fnn/xw4nYB/+S/3txukZ2DgZ4Sch4PbwMDAZ+M2pMcC9x8XPOA9\n/hC/Bc5W0ttKsJpiUTTmloRGMBAnAWGA2JqXbnv7aDe2Hq1+RdfzWEm5w4RYKIlucOoQavBZiQrc\nhgBpgiXNSVewz09fD0S7a4eIGQtWTJjnBeHgEddJaiZiuU2xpKwFh+RsOW+UWovu/wcpWqkRihap\nKEMyPyRj27lsFgaCCUshQG5zPX3PI+5jKwGKpYYjC0lzvqktDPyp9JBUTAB8BqZVVAiXgFxqkqYA\npGnbpDTvvPq0Oau21WltvQ227/Y5QogY7amZ1kYC1ZMa1int/f/YdNsDl252RV2y84LpsGDy7fcF\nkGzun6BXZFIUS+va6PaVsC4hW7Pp8/Q5SMnC2UbEYnTIvpF13AH45rNOcQGSnn/9r4F/8A+ubzf6\n9AwMDAwMDLwt3Ib0PAD5E/A/4v+CjyvwA4BPkGDzEcirQwxSeu9KXQxrY1jnoFWe56BTtXRgr9PO\npMSfZMcXEtXoAYkJ7Q1k/xLMqYIN1haZQoRag9RYCYNWWVx9eh9L/L9iNkJ6Hh92KrBRFJyDw1zT\n5FIlUG0gopjFaLGGGWE6VcVLk0YG1W08ocy1lJtTjGB3IODS6ptgqpxzUZqumizpat420gBcmhfM\nGfABZg6YZmW0YJMYMphclK4imfQKilHHJAGiFTS35XpurxUbLtmLh6THQ9IEkwGcaaYMXh0HuEyf\n6+t6eA46zHFdVboC/BxwmMWVj/eozfc+rE0btceWlEbrtgoLgGomUPvlbGp74uU5HJCyFWJF8qM2\n0grQUwQrJ1Nrj3IyOGNGhhGl5wDg+6u7/iiQ9Lx79/R2o0/PwMDAwMDA28LNSI/5L8D/8L/9F3zj\nP0owyNSnCCA6xCj9a5JpNTB0RSMYvDdjg2302Wpo2uc9ikSiEgvpWTAhZi9Pql2s+1pV9K/31WNj\nzU7rYcP0O6vOs3V3k7FnxJIK5xGkoN1HJJtEBQGAZJGz2AOTftERS5pZqgA0y6hj8EjBIk2skWrk\ncTuPcl2hHJNjs8riOhbVR9SrRv70cQwyrEvNYplf9kIbicEEwAfARzgnqXoSkBalLsuGySVg9cDs\ngNVc9saJasnz6TQ6DZIf7qvtqO8A3OdWW0QitXogW0nB7EnT0y1qBJwI3VzVAfAJZg443J0w+6Ua\nRtBwg0TVFesJzvO1WqsevBcAjQxSmfpU7xepfW/tHowDJlFsevWIKuKLFaXcHjpkAAtm4B5NSftC\neC3pGUrPwMDAwMDA28DN+vTgPfAtfoCzUQJJBpMrgEeHcJwRVg87JxWWQSk9oQaIUo9CV7MWkGuF\nh6SE++hmpGup4dGObZodUcHQtTxisGAqkfAqva1ZbJvq6KbNCwidKiZKUoDBLHVDdsU8L1hPc+tT\nk6w42xkgBF/d7TbQVtcl/anNHfWgNpd6XLSq5udqsFAITywUyxViRhe7F6FvKsraFpth5gDnA/y8\nYppCVSWszULwdCPOYFpaGV3UZDL3DQs0evWFKW9UdyYAdxGYA+wcKuFJoTT7yZMoPj14Hl0bpNf1\n10/CNWWY4xnH94+4Pz5ixqrIZjPA6O/xXr+qOhR3SUSbw14jOiTdPYnXhHiCxWoignOITpgilaUY\n3EZRAlpKZQ8SJGTUlNEVk5CefTHzR2FdgWWR1LXD4eltB+kZGBgYGBh4W7gN6TkC+A74S/yRNM9k\n4fkC4KO80t0strs1AOzsd6ELu11RIXTZhwH7gegwLymSAbUtyUsszlIxuU0hdksJ25Ib3bSUx9mD\nVU/wWTu0rRUSguFLepkzYkVtXZL0rmxKypqD9QkxOJz9jCPOMj7t4GaTmAAEh7Q6xGALwbFoz/Mv\nx6uVM+1qp9MKdWrcXmphdbVzO2TI4tLi2UuhjPVigKCDZl5TzkaUlpMX62u+AlrqGrAlPdt2Mu38\nWuFhShvT2Y4R5rjAemkQKqldFsEAiT8ubWzQQxMdfU6uYw1PbcKa4OaA490J3khypcGWjO4hvYDw\n2EIvnKKr2uijkZ6m7vR1cCRdrtJbDzhUu+wNGQV2CY8xGc5JnZGxoigmWMxYhPT84eplvhpa5Xmu\n5nmQnoGBgYGBgbeF25CeewAB+D2+k9z+j2i1Dh8gn+8tPj3cw00R0blaa0JohaEF8Nyub7rZivgT\ntnU9DOaWovIseca6iD9yTgZuZqAfFMnyNZTUT8upJml7bQaXQFN5+qfrhK73cQhwU4SbApKLUtMC\nAEFo1+JnuEIUAMCVNKZsUBzcDBANcnCIwSNkt63JqGcy0M1XOXckRCRBHLMvdUp9epsmQBIMGxiX\nLmlRbxxgE5yPmOcVfg6tcWYyCMkjBo98moXwfISQnTOa+qWd03qe1fIat7bUvnvNqITHTSvmwyoB\nfJYA39iENc9IMQB+utxfEy9t+OfQXNp6IuQTzHHB8f4R99MnTBvJajufPbRyqImO7plk0NwNqSDp\n3yK3uwbt5BfgYOERkbBg2hAfAK2uiMSwnIKpdUmpj3Rue8A7uXf/9eoQXo2XprYBg/QMDPysMJzb\nBgYGvgBuQ3ocgD8A/wD/D47m1ArEHyEB7QOA1SCcZyznCfFeiMleg0ymXLFxI+sfgEZoWPsS0VzW\nqHDQtIDLnE19gu2nZkTgFOHR0P16eMxrwaRR5Mip9wDKuJuSYpFrjUvtWxOdkJ/gsXhRgvx7uR72\n75E0LwvYXMwDpL9PTgbRuU0qHgNbnbbG9X1qoFa5NOHhsu+jFIMraWEKtJSuPXoyYEXlMXbbtCUG\nhxAdwmkGgmupbLFbamQ0hzV+1uQD6jvdo8cBME1tmuYVzkfkbOCiKBoGwCk4wBdDhb43jx4Dj9n/\ndWkDBx9gfcTd3SOmkljJ+d6DXm+r7YUegk5hE0MMh4ADll3Cs4emhfIBwuXv2HPiXCM7zsVKEKnq\nUPVxTuZxCTOsFTL/3+Cv8B1+EHXtC9b0DNIzMDAwMDAwcA23Iz2zkJL/Lvw5/vcZQngMJN3lWwAf\ngfzugOV4QLh3NcVmn/j4WmQPtKfkWpEBUFSYdgSaCiw4FJXngOU8I0YL5xJgZEhsICpDD/W4GkxJ\nIkHQ2KYO2fLwv6knHLMoQxEGHtUtzkesPiKdD1LPAgA+I51mLD7icDzD+QjnO/UosvbFFQIxIbhG\nTAIcFkwImOARETAhd4Swfw9gU0el5zaW+7PpiRQtEGyzbaZVNQmBTzA+SFqbTWKZnA3i6nA+zzh/\nvAdOM/DRtLovTXT22iRp1UVvR1WGlzOjpblllPRBCAErJgzORUTj6risS0g+AsZvlR59Hm2rzb48\nmsdYCNE7rvj2F3/AnTlVEsp0MqKv79HY6xGl7w3NJuSU1PPi1dQ4OV/r20RiRRIOUKzKqExFER8Y\nVOWnusgVkrMkKbA5n2csdwf8Bn8fH/ANzotD/PDCmrAXYJCegYGBgYGBgWu4nZHBBHyDj3jnPwrJ\nyZDUtgMq6cF3But5hvS4keAowG1ICCAB94K5kohGetyGnJCQaIe1tTxnD3AI0W+seJ27TOPaAwnA\nHuHhOhmbKTG5LfF/UgFlrkYLoa7X+VloykUQuSCyyN8AMZQ6kGiaPXR55dD6HgU4TFjLGKYyd1M1\n6dbXs3fb+u9JdlbIfTrj0MbVw9cDlM8R1idYm6p5AQnPcjoI4TmZLdnpl3zPRqE96YnlfLoxKCDp\nlFSeAoDVIS0TVpcwzyucld5FcGIaYUyWVMPgpPcQr4fn1Z953r2/LgolPuA4nTDV3/K2nkbjWp0Y\n0T8IYNNYvid6pUffY1MkKpIppnTqPk20wZgBODipJiuH6BulAlKLFY38TkNwuH/3iANOmLEIDQtA\n+t2Tl/YqDNIzMDAwMDAwcA23IT1nACfggBO+xw/y+VNZ/wHAbwH8PUg/n/eivriDaBIHnDe1C+fi\ntibmXc3tqg/wtAvZigkJpgb9AQ5LPmBdJoTgNqYAr3Eou1arwwCUNUXbGhpJI9oLdr2ReheYLClu\nJ327DFJwCMHDTd34Ilo612IANyMsE9b7eWOkQKMHV1qRspaIc7tVrrZF8TpgltRAecUoxgkxMBUP\nraaFhfwA4DOMj3Be3NqsLeYMhfDkD3fAp9IbRytFQGtwqvHUZ/2eRgYBUh/EhqQAMg6IPuJ8mmFM\nhp/Ebty5iOStpBqa3Oyt9/56dEodz0com+q7+xOOOKn0yFxocTMW6H8Trig1Qk8aqdn7ffI4/Ftw\nXUocSbq4F04whXA7NMtyW/46mlJpynEizjjggAURUejydQFJ0iuzOL+ZCfiEezziHsdDxP/7cH2/\n1+I1pGc0Jx0YGBgYGHhbuA3pAYAH4L/F/4c7fAL+GGJd+wgJGhdImtuvAQSD5TxjPiybQLulV/lC\nZOaNUQDrVFjjw6fldFxbMSHDiDKRHcLqsa4yHcZIjxxnY/V0Iy5rKS6fpG/777Sg9in0vVck3Cw1\nQi6C/SE3qkZQBKOqQWYnvctgXSbE7BCNB4rjW+tNlMozfl9IXqhpUH3tzp7KU4kjJsToENapus3V\nMesx6TQzSBpUTgZhLcYFH++AB3OZzobuGGHnfZvQ7Tn5vbaO1opYIT7p4xGLTTXdjsYKzqWWauhy\naZSKy78gKjy9wkQ4wMwRx+mEOzyCvXh0n6droDnBc2j3h/czbWrGuI3c6akSo4iICSt03ZdV+1gE\nxGKpfsC53PODOq8vl9iUqwiPGKXJ6fl0gD+uWDDj9/ge+L+BH1ov2s/Ghw+yfI3SM5qTDgwMDAwM\nvA3cjvT8QoLuv4+/wC//+7/Ab+/+qFlWv4fqw2IQgsOyzPBzwD0+wat6kgUzFsw4Ywb7kNAILsKX\n2gQJJC1ySWVr5gVrnBBWj/NpRk7SbNFacZ3qewHtgUqNxn6KkrjGyRP9FmnxSX0zFbBXAt8d0hQ8\nlvMBfir2wcFJsH2GBPIMuiMQTzPO8YDoW8pfQum74mTuDliQYTFhrRyBgfNewM2g94wZCw6S3nae\nsZ4n4HSQ1DQSHtbzWEiz1XmtxgGAqAHr4hE+3m1T2npzgj2Co9c7NMIBtS1JUOrW88dSraUtor/D\n2WZYmzEfF6X85VLXU1zcjhC16Br0z8Ch9gKa3z/i4E51Plvdzr5c0pMh7jfjXNQWSUFjjymmLLbt\nEyaIJbZDKslvHgkWZxyw5knMGmzG5NZCak64w0mlZrK+7Vx+E/LLB0h25pKa2fcUyjgcF/zwF79E\n/psZ/8f/+r/gf/b/p/TpeQT+p28g6u4XwJ/+qSz/4T98ftuR3jYw8DNCzsPBbWBg4LNxG9JT1Jw/\n4DusmOB9kNroKOvxCcAPEAJ0BlAc1eK8rdHh0+pzqSfJiLDwNcOoJw+hpPIkmJrOlpPBuk6IpW4D\nkKf6erC9iYG8vyRDewSoB2t7mGKmC8XlbCqtSQdkWf2DX9UeSXE7nw5SixRcS/1ircqhzOnqsJxn\nnP2MA07NnY3W0JOoX4eiAjGA1eluGlplY6pUTA5x9WUcpo2FBIOubUCxqg41rW1dPZZPd60XD/fT\nig2JTf+r1Q2aYrfkezYvtWh9oXg83u73EHJycojmDo8AYrSY5sayNv2QrqlQ2rpZ/386AHDiEjcX\nxzbOnf6tWpXuqNPTdD+pGcum3uxczAX4ICCm0j8qy0Bm5+GKSsO6thUea57kd1FIv/MB0xSQ7sT2\nXGqOUlX4+t9BeyjQ/jZJrKgw5WTw/7f3brGSZed932+vfamqc0739HVmODfehqRIkZIlkbQZRQll\nCIJEy7EQ32TkQUACOw9yZAMxkgiO7RiOH3x5kINYepAAWRYM8CEPQRDbsmSItAiHEm2JlCmKnOF9\n7jN97z7nVNW+rJWHb317fbX7dPfpy0yfZq8/UKg6u/betdbaVd3ff/+/7/9VizWdm7Hfb/FH1Qf4\nCJ+DfShvkhZ3u/jUp+T54x+/9b6Z9GRkZGRkZDxcuD+kp2W8Q75mxt5Mc4viYz/uE+/2d6uGqlab\nZca7yS3NWEDfto24RpUFMzYtepVItNQSqAchAO26jhbVUmytrl0UUDfi6VaSCJDe4T6oyB+0gNyP\ntRSK1H8njMFt6seTLJ+1MaosxWZfnQ2oXfMcWM3GljX0blNZUThgHV3cqGPlTiQt8Q6/Oq9ZpKaV\nN4aSnzUzlnsLur05rGq5fjpOaxMdXdtK05On70tjXMCmynMYTBUVfShx0tewmd6m0P1WjPU4gYre\nzSAIcSiK6EZW9XSV30xZu5H6ZBUnu3slWqMjEMwkD1L4LNmxjWEX7EdDAFV2RHFbtQvWq4ahF1OO\nEIro7tdT1UJotHeOkB0xnghdKZboRWBVetqdhvXOjJ1ql5KBhvWopKrys0nWW5z556SK416tF+zv\nbdG/ug37cO38Cd44/jbK4wPsQ38j4nib+PVfh89+Vmp1fuiHbr2/1vTk9LaMjIyMjIyHA/eH9Dig\ngxVzGlpOlZfZe99p+CoSKO7Gh/btWTe05m57ZYjCmoZ+kODduRBv6F8faWpw3nYNw1COgSFAv46p\nQE6K64viRt1SgOhqJa+K8bV+xtRAIdVDHC6C1zS36/ZWhUGNAfT1qgBm8r6mBGp6m5xwDMq7VUO7\n09CVtZSph1LS2wao6xLv1NXORfrlTV1+MjBINTwNPbGxaysBdFjOklJjxzFaPAeo+lRbEmuAwtSp\nbWpep3VAwWzXy6z7amrbQWqPwgbZSg61gaiSnwHYBsqGIaY8lnVPXXv5zgzuegVHn3W7Kj12jADG\nqc42spW/Q5xesfE9SkYEsu+M9ajIddQs2eJqe5xrV48x7G4RVi42by2gDAwlUAeKWUsR0wkJBb6t\nIkEtUv+kWIq1GuJ34zg0dTuqPOqcqORHx1TFFEhVotRYpKwGmtmabrUDL8HwyJyX6qeE9Jw5PK+9\nFX7qp+T5J34Cjh+/9f6V9vq9R6QrIyMjIyMj42jj/tX0zKCnYskcFoMEyjNE5ekRwnMVCd56h+9L\nBl9SuWEsnl/HOpKurem6SpSKIlCUm0rPmH4VSrx3rFfRIcw78IUEsQB1HxuCJhezYoyyr6dBU4Jj\nVSF7RxxEnbJ3yMMBd/X7OE6Id/2nEaEG9NrMtUeC876QfB2r8ljSE9MJfVfR+3JsUuq9IwQIIaam\nORcL6oX4TOeUvNvKWMPjJEnLz1ivG/yq3lRNdDyqvCjUnroXtzcJvmNam85N09GIx+olqM15VEmx\nNtEFm+51xrp7rOmZvqfH2s8M+lQyFJI65oogtVNTJQ3ztyVlICROzQ16oHcx5UxgzTcs5NvrN9Id\nlW7uIOrLkgX7bHFxeZJrr58hXCjhMrKOuu5VIYpgWRBmc0IdBxlM+qHWNWkLngYYKrp+h6veMd9a\nsr2dCH6ivXYejKSoHxPvqlirFZsivQLhgsP92cAFzsDTMD+N3OC4CyyXcOWKvP7kJw93TB2/R909\nNFLIyMjIyMjIOLq4f81JncZlniIECbT0jnuDBELnERK0ByymJCAW/g/l6PpFKKibblRLNKDsqMeU\ntqGXupPQl0J2fHwAlGJg4Ao/KdzfJDwHFZzbeh8bwDo25YZUFL4Z5Gpwq2YGDr/5serKpgH3GiGJ\nHUkJsTU0lvQUsi30jsFXo1Vx8NHIIBSSClXFXj+jQXFacL2jr+YFLTUtMyGgMT1tVHn04Un2zXYZ\nQoH3DjxCePZnmwqPjt9aXFsU5rz2Wb/Na3NcwSbxscqOGiu0Zoxzc1xFdGmr8dVAB6mPk11rJaPJ\n7yCNUdUpc02GPpFbJTSbDWyL0fCiiH97xFFtwZIFy1gR1HC5PcHV18/ASyW8TiI9UbVhFh9VnNss\nzsnWNlkXvFncbwu45hjYYtmVlKWnbjpw0MSEysBgviPJHU5rhnpfUdU9wRfpGl2AZr+lpoM1FPeg\npufiRXl+/HFYLA53TBP7q7btzffLyMjIyMjI+M7A/SE9FTFAlFqFRb2Ube8GnkeCtn0koL8MnIKw\n19C1NcuFpMSpC5v0hSnHmpy+rSU9rVyPBdUBCeq7dc16OZcGk20lNTDTOpYiUFW9OJvdoHZHicxB\nSXD6XhFDVQkCLQNhDAotqRARoozER+6UE9holgokwqOBdMtmYK1KiZIGVT96oI3KihpxD5EAhoJ2\n1dDNxOOrIIwF7JsfHVMEI+FZ07Bst9jf22LYW6TUOlUaPNc3JAXoS7wvIDhYlYkkLdkkE5bI2Nd2\n0XSbbU6qltFpyTdJj67L1GBBx6yKkqbkhYJQNQz9AH0FnUtztfVHOubBnFeVKZ3TumBoq6jqbSo+\naiutBLgcCZG8P0dsrj2OJQsucoorr56Fb5TwAnAJcUKz6oUSnRliC68EyLFJMHV9F/H4Nr4ODh/m\n7Nc9i50lzGBw1ZheB5umHoGCmo6SHu8c566cpXvhmKSuRnOSk80l9tmStbkH6WWXYoPTkycPf0xW\nejIyHiBk57aMjIx7gPtDeuK/Xzvsss2eBPhnga8Ax0h3zveAC8AjwKKIaUFFJDzlmIIF4AdJvnHl\ngCtLgnNQSNDug/SAadcNftUI4dEAuEjjIbpXbTh0AcRUtanCYwmNqDOp1sGTzAus5bAGiJtqkE0h\nc+O5ATFnAFGlNLC2Rf4dKf1Lg35v/q7M6yA1NB0VAUffCVkMQ0lf+rGPTzCpfWq4oGNLxgWNpLWt\nGrplA0tDXtZmfI5NpWUoIMSvXRvHr8cogWhJhEaXxqbIlRDNyjZJkcbhdm2sajQ1MVDFB9J3Tkni\ntLloVYrVmBJLvQYHXRM9Zsqpgxzbt2rxfZDZhR9VytT3SQY7Y0WFNAu9xjGuXTtGeKWCV4GXEdJj\n0wqLuE4Vkga5QIhGQ/rOQPqelHENW0Tp0fVdFvTVFnuDY9hxNE0nzVqdN2YXUutWRzWqpuMaxwi9\ng8uF/LafA94NfubYYn+TtN4FMunJyMjIyMjIuBXuX3rblgR6Da0Ed3q3fY+UnnMNqeuJ25Z7C9Zb\nzVjUHShGpQfA9yXduhGXrbKGKhBwdG3Dcn/OsJrButwMhmG8yx06J65X3oFLltj1RNGxlry2dkfT\n0oq4j9YVqSI0rQFSKDWyytGo+PTRVS0Um+qFvtb0Nq310aDVBveaurUqGLqKIVQMhRMDga6G3tGX\nA+t2RjeroyuYJFjZwNzj6KNTW+tnrJczVntbhL1FMp/QtDZNH1MSpo+WlHKmqpCSHR2/HquKS40E\n7A0Jo4IU4vyLzVQ6Pc4qOppuZq9/aki0qfzY1DlV0sZ15HqiNnWb0z5Jer2UzK1gWFashxltORsJ\nsXVC020pFTLQMDBnTaBglx0utqfYf+EUfA1Red5ASI8lZJCUnT2S0lOb+UEilFU8Xs+hYx+AosT7\nBSsHfVtTVrJDVQ3S26r0VHWPK/dZsuAqx7lw8SztuWOSpvoy8HXgCei2ClbMjeJ7d8ikJyMjIyMj\nI+NWuH/ubZW4t431K9vA0wjR6UkB6zXgFeAs9CtRF2auHerJP8oAACAASURBVBWHEOtRiAXmvgh0\nbUPd9LiyipbUJf16Bss6OYppwKvBn8ki0/SiPnqYaU2Fi5YIqvAoibF9VjxudJfTO+D2YT9DCZEt\nVvek9DMA72Otx6hGjSdIysTUwMCmtund9KhK+EFqLTxOyGKsawp9Rd9V9LNyI/VOR6e2yW2sJVHl\nLLRVIgGq3OhnV+a1XlMlQppCpfblGmzrmD3Xp2UtBjFBcJ6i9FBIzxuAMJT4uoZ1lUjIzHwubJIf\nHZNVd5TcQFI+NBVMvys6diV4/eQByWBB10Kd3PT8XcVqNWe5vaCmi3xqk/RY2/OKPvbl6VixYJcd\nrpw/CS8WUsfzGnBuso4KJTjzOGat2YneAiO51JsOTXxfibO9MVA4vJvRVRVD01FWw+iAWLhA4TyD\nc3RFxcrP8UNJ+JYTsvNKXJ93wpPVy+ywm1TAu0QmPRkZGRkZGRm3wv1zb2ukz07LTAjDEiE+x5Gg\nUYvRLyKB2wpY1yz3FjTH5I5319XSa6cvhfTEviRD6fHeiTX1smG9vyDszlKdEGy6bGnw5WL617rG\nlZ6iCaOipMG/p4hbNm9RT923gLi/G+/cWwJhHbn0vRCjYyUY3stDlaxRdbABtnzQZmobbKoYeue+\nhWHV0A0VwYsjnvT2EUml76pYT2Q7CImS1dKwZs6aOW3bSE+edQPXaqnV2GOj/5JZmFRjY80KlCTp\nY6pQaGA+R0jPVk+xaMeeM6Xz8fUwrs+yWtA3DRQNlMVmupleb5syZ4mjjnUd13lFIj1KkqxCpSYA\nSjJ0DpBIj+0XpISuA5YF+1d3uDxr2al2aVgzRKOCVAuWMGPFjJZAwZIFF9rT+BcX8C0kte0CiYQp\nEVPoHPZJKs+cVLOkxEe/W03cT8+j89LrtK4ITcVQzRgqn0hTIzmJvinZ391iuLaA10r4JvA7wBfj\ndYwK2AVOw22QlJtBSc+pU4c/JpOejIyMjIyMhwv3j/Q4RuWgoZUAaA+p7XkXkq6zFx/78e/HHf3p\n2GDTO3wvKVpE5zZcECe2IM5k0pzR4dtaAnttmGnvXitKuVvtB0fb1rER5UBwxXWpRgdBt/eUG0qP\nreeZqj2qEKkRtE2HCxQb1sbjYdYFTWsxlCCOdTOk9Cpbt9GDX1fRQhhJbVtr2lxBt5rR9TWhUtlL\nxqKmEeoYpsYRYdWkoF+JgDW869lUT2JNy6hItOY4e6yqb0p6GihmHc28pap6mnkr1uJ4ymJgqMvx\nM4d5y6pa4FlIyhtsKhYdSdXTdbIKEHEsDcl6W8dta6fUWt06oHXmc6zSNDULCOD3Z+xd26Y80eMK\n0fc8jjqme9p6nllUeQZK9sI2V8+fgBcRwnOO0SBgQ3myKBGSs47zakkmDUp2lJzWZh996Nxac+wM\nqFwip6Vj6CqWvbgBcqmE/wh8GTExOAd8P3ASHuc1HuUNOe4e1Cere9vtKD3q3pZJT0ZGRkZGxsOB\n+0d6IgKFOEEVSGB1Fsn/nyGpbQ0SzF0B1tCtZqyW4ivcrhrCUCS1Qu9Yh4K+L+namnY5JyxrCVLt\nXXCb1qP9GsuB4B3duqFuOnzvoGEkLQKVDW4Mj1gOQ7J6tnbEKXVOeuLY7QolFkNfpX5CqoTYIF7t\nlvVQrYmxzm2qtHRAJy52gQJaU98EhE56IVkbZZmDo6VhnwVdW7NezRjWDeyWEmzvc739sXVbsy5h\nnlQPYwNpJRMlcu1VcaiApqeadcxma5p5S12IO1gVrRV89D2uFr2kSzrPynl6tsX1xxI/VWsw2zQd\nUGt4bH2RM8dYxzNVrnQett+NklFIZEJd93S+u461P84153HHA2XRj2YZFWK7rmS4YU1PxYoFl/pT\n+FcXifBcJn23b0R6rKW5Ek1Ne5uSHiU0eo2sCrdGFFlNO5zH/ebAuqG/HJnEFcS44HeAP0DUHuI1\nPSMW8h4nxx7UQPY2cTfpbdmyOiPjAUAI2cEtIyPjrnF/SE8MyKWvR0wNmweYFRJg7SDF2Vo83iKB\n1MsQHm9YrxrC4Oi7irCaCeHRgDre9B/6SpSeVQP7xfX1Dnr3uycG2FIj4tsanB9d07SKZ3PwSdHR\nJDC7nxIeH9PUAKro7Kb1OsmeuhhplCo/Y8+evoxqVZEUHEseVEmZNtu0zUuVr2mNSVfQrxtCgayb\nqaEJg2MY3JjO52I6X0/NmkYMEHrpi8Sq2ezJs2KTjCmhVAJhlSolCza1Ta+dplipcUENVANV3VM1\nPWUhDTC1QWbFMK67OpsV21DVPdd8wcCWKD7WOEHNHxS6tkp46riPprb5uE0d4nTNLCGwhEMJ3IxE\nLNYIOVDC1AFLx+rSMZqoYjnE2COlPno1ZqenZsmCyxdPCOF5HVE/r3Fr0mPNG1TNUZXHkh69aaCE\nRtW3geToth3fm5v9duKzpi1+A/j3iMLzTWA1QF3CcXBPyZehIMhv+h4oLbmmJyMjIyMjI+NWuH+k\nJwbBA46ajqLpCL6RAPMxpE6hIxka7CL1PbslXTMn9JWoH6syEYHYU8X3JSEgCsk61gtpPYl1EtOa\nBk1bGmL/nmrA+wIfUgPJgTLeideb/tfX8FhFKCWs6XGpqkcNEqxhgI9hvI+OcZsndtfbLWtwqx+p\ntSoa3Grqlk0Zi0Qj9BVBg3YN0h0wOIah2jBlkMwu6cvTdg3r1Qy/P09ubVZBs45fJTcmalblURKh\nxynZ0UelZgWBshwijQwj4SmjliZWzymtsGo6/ImC3VDg2y35vIVZmyWbtT6afqZjtKTHmf10PvZh\n65P02tjUQkhEQw0E5nHu1OxvbYuKVYjXt9Je6eYjToVrZpz3Z+jf2BHSc56U+rlvPt82YFUiaRUs\nrfGZkh7b4FXJnFV49klNS7fiWm7FbXvx2D1Eefp6fHy5hVX8ER8v4RGojq9oEdc6zoG/z0pPJj0Z\nGRkZGRkPB+57epuPAV3oXXL7Ok3qE2LvsF8GXoLQzITwuJACaIUDukrMDVonwdqSdIffunNpIDum\nMBWifoQS/GbzUBmr3A4vY55UYVhIUnn8+HdHg6cYg3Mfa2REQbHObXVUgETpGUI5NiUNg4Ou3CQy\nGmzb1BybSmYd6nSOTTourMukwtgC/V5ME1RDcXgChVhUh0YamK6azYaitvbDkjJNVwxmu5KGsSje\n7KMKg9aU6Os6gOmbVBmFpyT1RdJGsHpNKnrC3FGe8VxuK0LbpM9UhUJNC5Q8qvqja6aKkCVxdv3t\no538rfbPlnxos1I1RojXrF8s2J3v0BxraYouljMlNQvE6fDa8rgQnteQ34LW8aiBhK0fsuofZn6q\n+Gk621Tl0bQ3Y7HNio36KhbxsU0iPh5Jt3sJMS34KrD/GrCE4lnZ9wy0+1vssiMprVcla+VucSc1\nPZn0ZGRkZGRkPFy4r6RHTZ8Hvc28kCd2gWeQO8da19MgPXvOAceLeMe8uL6nCEBwcp4VSYnQAFsD\nUBVT9K43hag8se4jBEt5io1+NYIeF7f3lPH+vEBrYLRuh2hoIPxKHOE6GjrqMZVMzij39n2QFLKu\njc50Q5nSjKapbLZwHlLqlCU0VtXpETJog3glKB3068boJzKmloZ1N2O9mhGWs6Qu2JQqqzDoeMa1\nJaXhKZlYm2NtAb2SHVUiYGwWq807y9HlTIv9Nxu91rRKH6nrjuFsyTV/ijDUmwqYvnYk0qaXWXsF\n6VoqaTiI8FjFR9dAXezmJGKn16QnEYcBKEuW7gQXQkH/SMkWS7bYZ86KgQpP4Ep4hGuvnpR6t9eR\nnjxWabOpaDeygVY1UBvyao3T1LZa1R69Lvtsqm+a3rZF6p3UIr/NbyP1PKsL8cO24UwJb9f5lhzj\nKse4lj7/LvGNb8jz299++GMy6cnIyMjIyHi4cF9Jj7bjHHDRfQ0JetfAGcS+eo8UmBZIwOeAt7EZ\nUFoLag36VojKY/u/WCcxzHnbwrh0RdWmSGYEQlhSbx5JWdOCfzduCxTicBb7CBXxQ1XVEZ4hpKgb\nG8jokAp67T3kpaYH7w5OEbNKj72bb13ENKicBuuquFglKAb4oS8ZQklflBQE1sxYe1F5hlUD++56\n4wIdi60z0rS7aX2JEo4pCYXUhFRrYQo26qu0uN9FdSc56jGurEbR4oImqXD9VsVwtmSvPyWEWOed\nchXT2tnvku2zo+jNs517N9mmJK+Nr/fZJEcnSORO65YqpcnJxW9ArsPusAMXKqnjOc+mgYS9Fvaa\nTqFzxazv9PXU1a0yjyY+q+KzQyJIVxFHua8Dw6X4Rwmckae3xf2LwIw1C5aSannAMG8Hly/DG2/A\n1hY8+eThj8vubRkZGRkZGQ8X7ivpUUtnADp3fVPE70aCq5dIPVS0bmCbzZoVG8hrsL9kM41KY2Jb\nRzBVIG4AF00I1LigG29xJ4c2qSspWdPgjTIk2xOB6qhYMacNco6yEBeygGPwYhbgvZOapN44rE3T\nlqxFtXUos+5tGrRbhaM322yQXoFvK9quoW8qPKWoPKs5q90F7B1gXjB1MLOYimNpwdJ8bPG8BtVj\nrVWAwuOcxzmhjVr5ZFPZJMaXa6Ppg7qvw7PNHn7b0Z+uWHNciI8G9VOSYEmNNiPlgPf0WWtfbHqZ\nXX9NEVOioA5oDjgW99kD6pq98jghFCybBXXRsqpnBBx9qLh47oz8Dl5FDAD2zedqXdQ0pdGaDsL1\nKg9sElRNfdNaJmsdrql52jOrQVRY/V1eQNLuhkvAl+Ibj0HxOLwHSVmdQbHVcopL1HT3ROV5/nl5\nfs97wE3nexNkpScj4wFCdm7LyMi4B7ivpEcD14CT+hy1nX6ElKZ1PO6sKVFaAO8QNUj6agpsPYUG\nY6r06HvWqldf22Nj0XoR+/SANhAtEBowjHfhIREe7WfTUdEyM6qEZ8BRxHv4WpS+HBa0KyE9VdPT\n1C2Dl8aofV/RdzW+q6ArNu/g2/oY6xRm5wDX21pvpLexaaOsTTsDhL6QBq00DDhWw0LS2vabZF5g\nCc+NXMMOcvbWgNubfbV4fkZSeTQQj/sXTohPE9vZblo6T3O5bKqbNoNdURSB/pGKS8HRtcfkxLZn\nkBIxS3Rs+ptdU1VwbqTy6PtK4DtSHZEaNfRIetgO8h0vIVyasXehZm/uoYBzswG6kmLe41+bC6k4\nT7Kptk1SlWxBIro3SnPTdTd27eM2dUuE9K/DmvSbsqqPOuJdQggQL5sBLoBTcKaAdyK/1Rq2Tl1j\nmz3pzTVAuNkYD4HnnpPn973v9o7LltUZGRkZGRkPF+67kQGYmgwNOjXt5ypS23MRsb7dQoI0jak6\nhCBpk8OapGZo4KlKhG63d7D1M7WmRd8vpFGpImXNuVg4PzDEUvoQk6j6+HrJgjbMcMUQ40KHw42p\nbh21mAKsa1bLOUXhCaGgKALBC+HwXhzo6MtETHR8ShhUTVDSYomPVYB0rtaVzO47mL8HoCvEpW0h\njUpX+3Paa1uwVyYCqYTH1pNYhc7ecbd1I0owbXE/GGvqybUBinLAuUBV96NpgduI1JWUpg+VtDC1\nCheiOmPNvFyy2JrTH5sThiYpgS1JfdEgXNe35Hqiaa+BJeO9OZcqONfiM8j39zTynYX06yvjWl4C\nrjqopSYtbFVwDMKxSrjEG8hvYo/kSGgJl45bYc0ZmLxv52VhlQ/r/GZd3tSZTtdu1caJfhsp7DkG\nbMPWSXiWlKp6Gk7tXBTCo+t5l/ltX/yiPL/3vbd3XFZ6MjIyMjIyHi4cEdIToIpRsK1D0bvLZ4Gv\nAa8gtRA+vr6E2FvvIHfS1fFNCcK00F4DOL1jrYGfBqmQ0r5Cahg6UI0uWiG6sQ1x6bSvzkApts5h\nRj9U1FUyqbbEqBuE8LSrGUNf4gpH5wLOSQQ6DE4aknYl0nSVTbJia5Lse7BJhjDvTYN0uz7aGNQE\nwH5wrFZz1suGdjmH3VoUHk0v1HWdmhnYgNoSK5suZZUUmzKlKVR2fkWAgpje5qnpNpQ2q/JYYwOL\ngsCcFT0VC1Z0iz3a4w2roZTmrLoOmi6m3xNVQSyJsGs+/X5prY7aSF9A0tD2gGAkmBcreNTsdxF4\nDuEMX41/z4AngA8A3wWcJPXmuRSPVUI/VXksbqaiHMYq2v4m9DcDm+5u9HFwryCMrEZyT98h83wH\nonBVwA5su73Rme5u8YUvwM//vLz+2Mdu79hMejIyMjIyMh4uHAnSM+JGzSGfQVJ7/ojkiqWBnioE\n6ralsZSmwWlQqnftazZ7s1hbYZMq54eSoSvpZyVllCSqeHLto5NIkZgW9FT0Q8XQlZRlSVcU0WkM\n1n5G30m9Trtu6NuKMDi8C9BVtNE0oe9qhnUN6xpWxSah0KDb3t2f3um3RgXTde3Msxbu2xqhAFDQ\nLmcMfUW/nMF+JYRnj6Qw6GNay6JjsKqPXiNrmczktVWG9LoY/lIUgaIIaPPWMqYMKrTYX0nP1GlP\na8dmrCVFbr6mW6wYFluwLoQ0KwFUUji12dY1soRH56yKyy5CSq7E57ZFmI1hH8MCXp2LcccS+V57\nhOy8GM+j9TLqZHc27neRRD41LW/Nmw9dgw2eolLqVWSy+3HQO8Ap4DEhPG9Dfp8L4IRYiR/U4+pO\n8G/+jZCWv/gX4cd//PaOzaQnIyMjIyPj4cLRIj2QVB5rK70GPozEVH9EKq62qsVpNusRNAVLA1Ml\nPEpurDuXEgWtUwCCL+iHinaYQVkwY72h/AyxRqcfCU9J52uxmfaFWF4XovB0Q8N62dD3FUMvS16U\ngcJLSlsoJJ2tKD1DW4l1thKe6Tys+YC1fbb1NLaupzTHaWG9Bnq6fprWFdPbwqqhHwbYq2XNNaVK\nUwX3SSRo+vmWnKlqp+uvSo669Lm45jZlytbN9InYlG4Y3fHUrMBCa6yA0dDANn/VqzRnxbxZ0y0a\nlvM5LEoRJnQt1PrZEhxrEKDOc7r+SnhU5bmEpKKFi4jcowVTMxJbOgmXF8lCWlPgPKJW1og6og0/\nayRr7JxZcx3DfYGSuYtIzt0lhJXtAFtQvxueAp5G+E+DpLdtQ0V3z0jPG2/I8w/8wO3XOWf3toyM\njIyMjIcLR4/0QCIhWsNzNW5/muQSpUqDqjZzUg2C1ui05ly2hkQVIWv/PHHyCoMoMgUBZlCWPS3N\nRnqV9ufR7jFDXzL0jhAKuramqntxa2tr2nXD0FUEL+8Dcrd/cPiuwleD/N2V0vxTazZs6tRU2bHk\nxwbmCltjY4vsjWHDhsubHrMur29A2rPZ5FWfrQmAdS2zznr6maMNNUzcugV2HAVQeVw5UFbibqe1\nPENMKVQSpGluSny0vkfVIGsz7vCUbqCsB4pZT1jEtdamm9q01K6hEsapeURnHpoid42Yzqb5clr1\nr2wmskxdT722Pl6oBalOTb+nWhukrm362XefITbB1M/dbp+6OOwixXU6sBrJw3sWzjpRZ7eR+ZyJ\nb1Xp2twLnDsnz48+evvHZqUnI+MBQgjZwS0jI+OucTRJD0hAp80QtXfPo0hs9TISUGlTSU0J0vqR\nimTjq2rDnE0bX89m5pEG4kqaiGpPX+FKT1vOqBhY08SanjKaZPmxPsGVnjL2lCmrAQJjOtvQ1fjB\nweCg9NJ/xxcQlR+GUnoFaRC8Ns+W/FiiY5UfTcmaOnLZOhoN4NX9TgNnS/hsDx11alOyo2rGnnk9\nrSmxdtSYc+pYVKWzN/ttyps1VtBtSGphSl+Latn49ya5USgJUoJUMlDTMmPNupmxmrX0bQXbZXJC\ns3VKuvaWzCnptNdEla+r8TiukArFjiEyx7E0GV2Xy7o2uqhzuLAjzWM9IqKAfC8vsmlTfR3hsXO/\nneDAHtcfsE2/IMrSliTfeFWzLiDM5ingSSE870JUHiVxW0DFSFAPqr+6XajSk0lPRkZGRkZGxq1w\n9EiPpjnZu/4xYOI88H5E6fkGEkd6JP7SVKFjpBvqtpZHA3ENqtek1CttELnR80eUG4pAWZUMQ8m6\nbOgpx9oeGe4Qa3rUbjr27HEeHxztqqFf1/h1I8QGwA+J5KhjnKoHVkmZpq8d9ND9pv17LJEpzWsb\n1/ZmXxvUW6c3S3jUAly32eBfr5WeQ6+f1lvp2PQ6Qar10fFYlcdArcMtiSki5dys35GFrBjGuisf\nU+HUaU/PoFbY42cpWdbUst3J2Kbrrt+vlXle6c5dHIsSnu3Nu5T6q+tgk91GxnVtDsPxRA4bJINM\na3g20tqmFmiHITxh8tp+8afER9myWgXuIurOCiF3HVK4cxyKZ8Vo5AnEYOQkQny2ScYh9xBKes6e\nvf1js2V1RkZGRkbGw4WjR3pgs4mo1n3UiHvVKSSWXCOx16Okwu8BicMWSIClRggNKQDXGU+L7a2j\nWyQhvi+p6n5UbIayog81Q9HFGpFEfoahJISCqhro+5L1uqFva4Y2mhJ0LtYqhUR4NHVM52yDauvO\nZa2JNciGzQAcNomiIvbfGeNWa199UIsbJT1rNmtWbGy+h8S+WqKiY7FjsuqIHYsSH63b0h6vg3lM\nTAyc87EOpBiVNlXXlPQMlLFLUo/ah6sGVxCo6MbUqpKBquxwLoDzUJap7GYe52d/GQcpbNZG3DZr\nHS9QGU92DHBJRbQ1VeMXVnM1lYEWsAxwsZD9a5KqpsoTsElSLHSBD4LNY7RfgO4G2z1J1rtG9NVG\nVJ4lqVjrHdBU0lD4caS8ZwchPlvcc8IDWenJyMjIyMjIODyOJumxtTctEjTtI2YFS+BHkFjyN5Eg\nUHv2XCAF6AuSs5udpa05sXUwM7OPY6y36WP6WUNLS8PQl5RVTTNrGehjytuM/b0F6+UcwPTYKWHt\nUnE8QFWkHie2zkbHNnUR0yDbFq8flO52s6J2JVG2HmV6jKpBB+1j1SdVedaTc/STY626o4qJPish\ntette8CYeL0sJa1NLMGbA62OB8pR2Rkor+vbA0J81P0t4Oj7WpS83m3G+FpzdBBn0PlZEcQqcdcN\nzZxkVBSW5g/LYq0stg+hhYsz2XUed9NatTuG9TJX6I/BbrcESL88euG1Xkl/SCcRe7mn4O3ypPbU\nnGbzd3UPEcK9UXoy6cnIyMjIyHg4cDRJD6Q0N0gmBJqCdgH4CHKz+YtImxCdyRIJEo8jxGdBal6q\ndTw2QHdxf6s09EgwHAmKplUNMXVN060A9oct2lXDejnHrxroKuiLRDJUUdLPV1KgRgW6TXsSKeE5\nyLkNNomIVR+mWUrEc/Zm3ayKpDfwie+r853W+qjdt63lsc9T4mTVD9iMq3VuqropudFrNjVhGNPc\nAs6sdR81HrEQX4+phf2ou8kJr++p5MaUuFHtcWKQ0GsT2mmGmHWUm/bnsWs5VbbGiVsLNkgMSZvs\nKMvbju/r64LRScEHuGoUQdvjaMNNwUKlPZ2I3V/Hoc/TnEiVPW1x1xJRdnZJTgqa2vYowmx+EBa1\npLU9gxCeE0gtz5uQ1gZw9aoQlmPHYLG4/eMz6cnIyMjIyHi4cHRJj0LVD9i0sV4CP4jcSf4MEkMu\nSRbAGsPpzXOND2s2FRYNwjUFzBKJopLT9IP03qmuVxnWqxndqoHd+eax0/45sFnvooXzg5mTxqvT\n2hF1E5sSn2lNzTS9TeuVDlJv7Nisu5sGgaubPGyt0XQ8N1KdNH1wmjl1ULyt5xwK6Rc0VLhSEtWE\n9Gx+wEBFG/PkHH5MeXN4fOzho9qKqkaDjzVbvkikxdYmWdxKSTsQWtszkFifnsySjRkiW1pmYNkg\nkgoJNyE8vTmuJ3X2nU5G9ztI3bEFb5rnuE8q4tIvrPpyP4Ywm/8cztRS1vN25HkH8TV4k1QeuLvU\nNsiW1RkZDxSyc1tGRsY9wNEmPcZJDZBgao4YF/SI4vNe4Hng86TGow4JyndJSseM5PCmSocWipdx\n/wVJRQEoCwg1VBWeNb4vcdXA/u5WSmFb1cnlzJIdVV8gBfPq9quPnkQGtLYlsFlHYy2h4fq0M6ta\nTQP3KYnSsViHMo3Hra20GhjskhqT6ms97iD1ySo9mPMFrreoti56toxEA/tY/1SW/bhhQAwlKGFg\noI1RtVV7yrjYqvKoxXhpTA86aoahxPeVOKXpWk95wJTjTp3lIP2Cxlow3aDuFPpFWJmDVC6qGVn3\nDkmJHBdmFd+f5mcq4ZkORlluMK/te/rZN0tn86QUvCsklza1qAtIStvjwJ+EnRo+CLwPITwnSWZ1\nbyLpef11eb6T1DbISk9GRkZGRsbDhqNNeiDdvLZB9uNI/HURCTbfi2TgfIlUk2H798xJ9T1TJaRA\ngjMlERqMK9maIelq/RzKkISKvthM99KeKzdSPfQGupIujYG15kjJ15RATN26pmRn+pgac1nSo8RM\nY3CNqYu4Pi1JoJiqOgel29n5WcJnMdYymTFh1sLCjjUAITZ5DQXt0IzphUNfMlQlZSl0pxsaAkWs\ntxooy0FITal0yONxkfQEIUh9SejLeG05mBzqmKzlNmafyjyrEcKqjoupC6cOBnoiJR36RYuLvTtP\nxHckHuqwYQudpoTHRu3K+q3EZ3FQ7qEaKNgePJb46OJo3uUMUXmegaKW3+KTwDsQonMiPr/J/7K8\n9JI8P/nknR2fSU9GRkZGRsbDhaNPehR2pI8g5Q+7wEtI09JriPLzapCUpats3uzWFC41m6oQMjQj\nxaKK3uyrgW4HUKT3lOyo+jFVXLrJuexru62J44DrSc+NyJMSEJu2d1B6ltpVQyIZWlKiPYrmcT32\n475axrFPKuXYI7VnsUqPnZNNs7Mxtf38g/6289P0Q83OqnuKWHPjnCeUhfQ6Mhio6PuKdt1QlsO4\nr30feqS1aUUIBd47vHebn69mDdN1v1UDUCU8OubR91pJwmWSzKUXAFLNjxlAO4sLs0ciGFukgjC4\nMeFJMz6Y8Cg0je2g3EJNX7tGquG5SvrSVAjL+T4onoQPICrPOxCFRx3btnnT8eKL8vzMM3d2fLas\nzsjIyMjIeLjwYJAeHaW9C79Aaqgr4MtIPcEbyJ37l93xtAAAFwBJREFUc/EO+TqSH21wOpB6/qjy\nM6oKJMc0W8SuLVc0U0ljVJvqNa3f0XS2g5QeezMdUgG/jV9tBpOt55k+9HOHyWslO+pKZ3sAFfF5\nTKPCqBTx/V1SepslOrcyMLDbIRXe22+ZTWXT/TWeti528Tl4UXD6tiYE6PuSooCyT2ykXTf0XUVZ\nDczmsmCu9LTrBld6hqIUotM7UY4o6NqK0LpNcmOfb8QZDsKUbDInsW4mi9NxfXW/Ghto7Ywep/sc\nVLB1UD2PnlsZ+40Y27SwSsenLm3aM0iZ8jWEyZwF3g3FU/BYIT4G70SUnTlCfJTHQfrdmD5UBSF6\n6E390m8PSnqefvrOjs9KT0ZGRkZGxsOFB4P0KKwpAYhb1By5Id0jPUI8sFvC8goMO7Au0/u7CFna\nRuoO5ubcNvWsQgJ8VV5sLxa9Ca+x6TT2tITHxqnTUgolA55NwtOTgkRbEmLPq2lmSjamZETd4PR8\n2ndnn6Ty6Px0TBoza2aTJTxTm2r9PKs06Ti1dkmVNVuvBJtZWnou3UfXyNRyDUNFuwY/xN47vcNV\nXqzEY0DdrWqIqW99X+LKFFD7QVSdoZeUNu8dznn6tpbvhqpaNo3P2olb3qCv+wO22fmFmuSjrg89\nmS6OSkSqXF0hSY4OYefbbBKfKdu1sCltthBOP3MqX+nn6sVVKW8vLog+fJzL25DCnffCE4WoO9+N\nqK5qT61NSKd9oQxX66jHNMNxWneAF16Q50x6MjIyMjIyMg6DB4v0QIoBNUitkMDribjtu5B47uuP\nwCsDrIP03NlDbr6fIDmmbZNMqbQJpBb1a08UG5zb1DKrdExdx/S9aXNOJu8dpChMyZPdZkmNJRs6\nrqlttZK2dZzbfpyfEjedbxPXRktC1LRrms62MuebKl+aYmdh0wOtXbc2XtWsL43/1XBiXPNGKIOS\nmEHMI67TL3on9VahEEO8vqKsevwQic7gpD4oPgPQ1rAq0vw0xp8qWtaietpfaarwVPbFDslRQ3Oo\ndGf1VJ/FfbcRuUS/iFvm+HGSk0HBJlsuJs9TD3Dtaqpqju6jKXe2iOxqXAzNvTyFpLW9Bx7ZEkXn\nA3HIx+Lw56SbEqowruMUjB18S0NBYEYLPbg7JD13q/Rk97aMjAcIIWQHt4yMjLvGg0d6IAWhGlC/\nDYnP1qTWKI8AJ0v4coA23gVXgqM3xXdIN7k1AO9I5GFqz2z7SB6UtsZku9afT5vcQ2rIidnfEqAp\ngZmqDFMbaSUdGkSqC5sSkhWpdGM5OZcVCPR9jYOVoEyd5CzZmfYJsnPzZruOT8djjQCUfOlnqslC\nb2UiNlUEi7KAupalrga6oobBQV/Js9o+62daxWsfUbbUoGyq9lhnPPuwmWj6/7ESTbR5TENqslPG\nvxs2L75uC7J5UaS0RC2nGb90NhcTrr+I+qXTmiL94qoF32VzDkiFacpQdHGUib4LuZNwHE5uy82F\nZxFeplPSbL5r8XAVuZRj6XdvDy5+8CRX3CMEChigu500QoO7rekp478dw5DjqYyMjIyMjIcBDybp\n0bQsrWVRtedxJHbcI908nxfwnwroPAQnAf0FJL7TLCStQ9hDgjnHZuqYVVS0xuYgFQauL7240fht\nkKU33KeGCva80791XPqwFtk6Dq3f0UJ7XS8VHrRH0T6b4oESwYMIn00vPKjGR1Oa1DDCtn/R/kQ6\nd2syoX+r4qPHKhm1/TZHYmGg7s99DUWcsFXEdG3VrU5r9pXo6MMSSSU+U0Vrus0qPqOHQIF8udQt\nQi+6ITs6z/HYIjXU1e3W6fo6u+obNWXSBVcGuUtSb66aMWndkTLzq8gPQAf3KCKNPgplI5v0BsMi\nrtdr8bmPh5wEop30+FvS4Z2HK195jK9/4N38IR/ke9//PPNH4vBuA8slnDsnKWqPPXZ7xyoK4ch0\nnTya5tbHZGRkZGRkZDy4eDBJD6TgF/P8BEJ2AmJnrQF4W8LXBljuQtiCpZPAzMX316T+kGO/FZKK\nc1CQq7hZKhvmMxRW3bBE6SDyoO9ZWLJlewNN08tsMK0pRlrbruYMmsbn2Lwzb+tblCTpWthUL7sm\nU2tna8Gtx2gAb1UgW+qitTz6/pp0be123c9ef02bq3UHs046fkh1Sy2iTFwjlbVYlWdqUmFT+25E\nfnQs2gA36CDL9J623VEjDev8pvbq1sTtKkmZGy/eVNaDVJSl+61JLmxqw3ctvrdNUnP2zPFV3O8M\nYon4fuCdcLYUW+qnEFJzJo6zifPQGwz7iJviDvL7egIxFzlDVLBg/varHOcqK+ZyrjsgG1/6kjy/\n5z3g3M33vRky6cnIyMjIyHh48OCSnmlTSC1ZqJESBE3v0pizLOErW7BaAzNJedoFvo3UkJ9AgrcZ\nmwqFJRk21pzGnPbvKdGxyoa9Ia/nteee1gjdCDYQV2KiaXTWqlobr+o4dF7OPHR/NVXYZ9MgwaZx\n2bKSgwQHzLZpCpwtO7Hn0deaAqgkbWpyVt3k2T4wa2HXtiAF2WvgEnLtr5GcmVUcUdMHnbtmi00b\n0FoCaAnhdE2mjXYtwdNWPKWZsypTHWz6oNsv4UG5Ybq4UxOFNr6nhGZmJqgpb5qrFpCinSdgyyWz\nAk1TeyOuj7YjeolE1ABeift+FflNXY6nXsDV//AoX/wvPsSPut+A4xDOAN88YBo3wRe+IM9/7I/d\n3nFTWNvq7bfAZjsjIyMjIyPj/uHBJT0Kq1hoj5fHkDvPDXAeuVP+fqBy8MIC3vDQr+B8KQ0WX43H\nnkGCuxkp9Qs2i/At6YHrnYGtc5q+rzf8rauZwsakB9UL3QhWGTqIOOjz2D+GFA9DUhise5yeQ8mO\n1tXbYN5+1s2Imd1PyaeOS8mpBs36ULIwIATEkhjM33p3XwmDTYvTz7EEUpUtPdbHOV4lkR5VedZs\nKmiryXlsyt/0cRAZVlhPAduYVUmnbd6q10Bt0TdY10HSmv0AJpNQhwZd9LNI2hoI038Z+cKrs8UW\n4tK2C9uN/B6uIulsKgS9G3gRITvvR4jNHun6fQuxsn4S+EPgGRKh/BF448rbeO7ke7lUHGPnZS0E\nOjzuNenpOvAevvUteNe7Dj+GRx+FJ564uzFkZGRkZGRkvDW4P6QnZt9ss8cW+yxYUe4sGfqduzsn\npJhwC4nlTiAB2xtIvPd+4NsOvjWHcx6WA/hSgjW1ad6Kj+OkFB4NZq2aA+lufWG2W2XHZjhVZn8l\nJUp2lAxpLbpuO8gRTZ/Lyb42pU3TrBakVCo77tJst9uUoARSPU3LZkytn3MzUqa9kZTcWDXDrpVu\n1zEoKVBDCa3ZwmxXtUbXU69La85pa76UFFvCOpi/bX/Og9bbGkrotuncddtB7+lYbVmPNVmDlOKn\naYiKAlIBmvpBK2rSBdMFVImsmDxmiIrzCKMFdVOCOxnVT13YAqoZPDKDD8VdX0WakC7jR51D1NRH\nkN9XiahmFWJdfQJRe7q4316c2ykZ/vuO/RHb7HOiu8ZwBwYCn/+8PN8t6dGUtraFv/pX4Rd/EX71\nV+Gf/TP4yEfgH/yDg4/78pfhwx+W9LovfWkzxe655+DXfg1+9meFFGVkZNwDZKeRjIyMe4D7Q3pi\nfr9j4BSXeIYXeOP0o3TH61seeiN4nMSvXiLLYagJvsARuHLhOP2rc8KXSrmr/zjwYeBFB18EXh/g\nUinx4gK5I60ro13mNRC1aor22bHKw9SRTNUHfa80x1ljgWnx/EGpUvb8tvWKTW2zCs8cIW+qWqli\nZcdiVSjr9mZTw/Rvq54cVN+EmZ8SHkt+rCKjvXm0KH5Bak2jhfw6D10/HXdhPseu6fT/RV1fre1S\ngqQlL3Z8CzZrmKwCZ+udbJrbQQqPvQ7TkhslfZZk2u+J1vQ4RKlUI4DLJayPQ7dtPoTJB9gJr+IJ\nlMGfjhM9CdtPgN+Gp8qohjaw18hhqnKeja8fj/N8No71DGICQjztY0im3FXkuu3Hjz2JEKaLJPvz\nGngnPPbHX+Tx6jVOcoliH77yCreF55+Hz35WVJrv//7bO3aKZ56BV17ZtL3+6Z+W5099Cv7cnxNi\n1fewiGZ8IcA//sfi+vaVr8Cv/zp84hPwm78J//bfCml6/XX45V+Gb3wDtrau/9yMjIyMjIyMtx73\nh/TEu/IlA6e5wHfzJVblnGW5uOWhtz61RL4tNT0Ve+ywc+wYF8+e4erWadh1kvKmd/B/AvhiCV9G\naguWJDteDf4WJIKgaVK23sQqORXXB+PV5DiFEgl1S9OCei210IDdwqob0/QqSCRJyYUWnFvFwqo+\nqooc1IPmIHJzUEqXQsmIJThTwmPXRFW0LVIBvyp0U9MCO26rFE1VIovp36r46LMaC8xINT+qTNma\npWltUj95DzbJ340UMB2rfm/0tf1u6Nx1rirA7Bewrm58bt1uVSs/MLKO+WloA3ywEqLydkTJ1LY9\nuwi5OU0iwG9D3NmeIH0XzyHHa/1bH9/fQX4vHXIthwDvKaSeZ1vGs/19F/gvj/8WP8xv8RhvwKvw\neE1yEzwE/v7fF+Lx0z8Np08f/riD8Df/JvzpP33j9z/6UTh1Sl7/zM+IwvPv/p04xyl+6ZdgNoMf\n/dHNY19/HX7jN+Anf1L+/vSnP83HP/7xuxvwEcJ32nzgO29O32nzycjIyLhb3B/S8xLw2/DB7W/w\n7LtfYLfZ4evHniZs9C65O/RUDDjWzOldxf6xBde+7zgrZrzKE9Hg7TQ9Fd/w7+K13Se49O1TvPzt\nZ+BqmVKVtN7lGGm1VBloBqgCZdNTVB5XecrGU1SeshpwzlMWnsIFnBuggNI07BlwBO8YfEk/lAyr\nin5V0e3XhHUFK7cZzNq0KOvuZtUnawBgVRUXYifICRMIhTz6Ip1n6tZmP8MqTPZzYZPwucnfGthP\nyWHjYTbgGk85G3CVhzLgykBRBHGe9gVFFcZ5B82JMsqOvJ/mFgYHHvzg8H36XvnBEYaC4AtC66B3\n4uZnm5LqHA8illbxsqRnWks17cuk452m9ul2JT/Worvn4L5BB8GTrJ89QmSKEjgG7hh8CMrvWvP4\n977Es099hWe3v8ojXMXheTE8zf6wxXur56noKQgMOC5xikDBGc4zZ8Ue25QMlAyc5RwNLVc5zoo5\n/UdL9tjhCV7hDOc5z2k6GlbM2WWbd/BtznKOj/b/gWPnl+z4Xfg9uHzI5qDXrsHf+lvwz/+5qDw/\n93OHO+5m+FN/Cv7RP5L0tr/wF+D3fx/+8l+WdLX1WqyxL16Uff/e30vHnTgBH/oQfOYz8C//Jfzu\n7x58/k9/OpOeBwnfaXP6TptPRkZGxt3ivpCe4RyUnwdOwfxLPfNwmTPHL3PPOI+mcEEypIp1Ot3Z\ngvVOw6qes5zVrKoZr7m38cbxR9n90DZ7H9rhm7yTi5zmed5DoGDBkjOcZ8aagoDDEyio6WIQ2FPi\nx79rOhx+DBDLSMFKBgIFgQKPw1MwjO+UdNSsaWiZsY6PngqPo8ATcJQx6pVRyDmULPr4XOAj5Rso\nCJRxL0sKlHzpWHrKjbH0lPG9MqYOFnGbG/cB6ONzyRBjd3lX559GOOAI4zaHp6IfH/LpfTzu+sje\nEfCT/DWPw03YRUEY1b4hMgkda1p3tzHnjpouKoM+zs/uN8QVHKgI8Xy6JnoN7JgOIu+6/kW8PnZe\naY38OH6PE7Uy1EKKfYX37ro10M+sGFivFxSVZ7a14lo4Rnt+wSl3iZ3jV3i6foGP8jl+mE/xJC/x\n2NXzlM5T7XuqVaA7CWHXUfaBMCug8NAWVHXAr8F3BeUi4Atwu+B6xARiAeEEFGsIFRQtcFW2qRLk\na3BLKF5HlKLXEPXndXj6caRm6Cb42teEPKhV9V/7a4c3HLgZigL+xt9Ifz/7rCg/wwC//dvy3k/9\nFPzTfyr7/u2/DT/yI1LLUxRS1/N7vwevvgrvex/8yq/AX/krku72D/8h/JN/Ap/8JPzYj0mK3Oc+\nB9/zPTCf3/3YMzIyMjIyMm4P94X07F2E2R/C/nNyYzuUMD/GwXfH7wLey13bEMDNYn/FRaCerdk5\ntZa0IQfvqV4WkjQDHoWhdnTPlDT0fLN5hva7a1ZnG7bYZ1U37FxZg4cieNwQKEKg8B43PiRsLXzA\n6XvBj7UlAUdw8ZkCHBLuFg5fOPqqZCgr+rrCFwXBuRsSQh+P13C68LKILhKcRHaEbBUhQAiJHBQQ\nChcFH4d38nmeAl9EouDk2N65cYzBBvrO4bwE7EUIuDCMny9rYQP+ELdFcha8rFE4yOcZQ2qKFPA7\nnbvb2E8JpUJfB5B1HP+W4wYn5x10TkWBLyKBLBy+0BEXGyRHoWQqXYvDsfYpUQMhixZDJJ2hKKCE\noSyvIzxKBEMkcP2sosRzbHfJmeUFtsI+y28vePrSC5x8/jLV84ihh6ZPmtqjpgCcj6YYIaYABghQ\nOii9vNai/WHSpNeVsSesh9DHb1wl5MB5ERMLVbpa6Hq4+jw8p41Mb4L9ffjmN4VY/N2/C3/+zx9i\nke8QWrvziU/IA+Cv/3UhKtNePn/pLwnpAfg7fwc+9jH44hdhtYJf+AXY3ZU0t1/9VdnnX/wL+PrX\n7w1hy8jIyMjIyLg9ZEuUjIyMo4gvAN97vweRkZFxZPAHwF16NmZkZGRkZGRkZGRkZGRkZGRkZGRk\nZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkRPwY8BXgq8D/fIN9/o/4/h8A3/cWjetOcav5fBfw\nWcSI/H98C8d1p7jVfP4b5Lr8J+DfA9/z1g3tjnCr+fwZZD6fB34P+JNv3dDuCIf5/QB8BLEp+a/f\nikHdBW41n48jvpGfj4//9S0bWUZGRkZGRkbGHaIEvga8AzFy/ALw/sk+nwD+VXz9x4HfeasGdwc4\nzHzOAh8G/neOPuk5zHw+BjwSX/8YD/712TavPxT3P6o4zHx0v98C/l/gz75Vg7sDHGY+Hwf+n7d0\nVBkZGd8xuHfdQDMyMjJuDx9FgpxvIW2AP4ncabf4r4Bo+szvAieAx96i8d0uDjOfc8B/JLU9Pso4\nzHw+i9x5B7k+T71Vg7sDHGY+e+b1DnD+LRnZneEw8wH4H4D/C/nuHWUcdj7ZdTYjI+OOkElPRkbG\n/cKTwIvm75fitlvtc1QD68PM50HC7c7nvyOpckcRh53PTwJfBv418LNvwbjuFIf9/fwZ4Bfj39c3\nQzs6OMx8AvCfISmI/wr4wFsztIyMjO8E3JfmpBkZGRkcPgCb3tk9qoHbUR3XneJ25vPDwH8L/OCb\nNJZ7gcPO5/+Ojx8Cfg1435s2orvDYebz88D/wtga+0irJIeZz+8DTwP7wI8j1+m9b+agMjIyvnOQ\nSU9GRsb9wstIAKN4Grm7e7N9norbjiIOM58HCYedz/cAv4TU9Fx6C8Z1p7jd6/MZ5P/I08CFN3Fc\nd4rDzOcHkDQxgDMIUeg4mnUxh5nPNfP6XwO/AJwCLr65Q8vIyMjIyMjIuHNUwNeRwuWGWxsZ/AmO\ndqH8Yeaj+N84+kYGh5nPM0gdxp94S0d2ZzjMfN5NUkO+P+5/VHE73zeAX+Fou7cdZj6Pka7PR5H6\nn4yMjIyMjIyMI48fB55DAuefi9v++/hQ/J/x/T9AAtGjjFvN53GkbuEKooq8gBTMH1Xcaj6/jKgg\naiH8ubd6gLeJW83nfwL+EJnLZxCr56OMw/x+FEed9MCt5/MzyPX5AvD/8WCQ7YyMjIyMjIyMjIyM\njIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyM\njIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMu8H/D7qZ\ng7rgwjatAAAAAElFTkSuQmCC\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f146ac03490>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"img = test_prediction[0][5].reshape(DIM, DIM)\n", | |
"hist, bin_edges = np.histogram(img, bins=60)\n", | |
"bin_centers = 0.5*(bin_edges[:-1] + bin_edges[1:])\n", | |
"\n", | |
"binary_img = img == np.argmax(img)\n", | |
"\n", | |
"plt.figure(figsize=(11,4))\n", | |
"\n", | |
"plt.subplot(131)\n", | |
"plt.imshow(img)\n", | |
"plt.axis('off')\n", | |
"plt.subplot(132)\n", | |
"plt.plot(bin_centers, hist, lw=2)\n", | |
"plt.axvline(0.5, color='r', ls='--', lw=2)\n", | |
"plt.text(0.57, 0.8, 'histogram', fontsize=20, transform = plt.gca().transAxes)\n", | |
"plt.yticks([])\n", | |
"plt.subplot(133)\n", | |
"plt.imshow(binary_img, cmap=plt.cm.gray, interpolation='nearest')\n", | |
"plt.axis('off')\n", | |
"\n", | |
"plt.subplots_adjust(wspace=0.02, hspace=0.3, top=1, bottom=0.1, left=0, right=1)\n", | |
"plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 2", | |
"language": "python", | |
"name": "python2" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 2 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython2", | |
"version": "2.7.6" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 0 | |
} |
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