diff --git "a/Neuro CNN 44c Multiclasse Multinomial.ipynb" "b/Neuro CNN 44c Multiclasse Multinomial.ipynb" new file mode 100644--- /dev/null +++ "b/Neuro CNN 44c Multiclasse Multinomial.ipynb" @@ -0,0 +1,5265 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-M1pg67ilBUH" + }, + "outputs": [], + "source": [ + "N_classes = 44\n", + "N_epocas = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,\n", + " 21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,\n", + " 41,42,43,44,45,46,47,48,49,50]\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Anu2RjhLK7MU", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "99bac9a3-2b7a-4beb-d356-d8fd34d163a8" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Found 11555 images belonging to 44 classes.\n", + "Found 2866 images belonging to 44 classes.\n" + ] + } + ], + "source": [ + "data = ImageDataGenerator(rescale = 1./255,\n", + " rotation_range=5,\n", + " horizontal_flip = True,\n", + " zoom_range=0.15,\n", + " width_shift_range=0.2,\n", + " height_shift_range=0.2,\n", + " shear_range=0.5,\n", + " validation_split = 0.20)\n", + "\n", + "traindata = data.flow_from_directory(directory='/content/drive/MyDrive/NEURO_CNN/dataset_especifico_44c/',\n", + " target_size = (384,384),\n", + " class_mode = 'categorical',\n", + " batch_size = 16,\n", + " shuffle = True,\n", + " subset = 'training',\n", + " interpolation = 'nearest')\n", + "\n", + "testdata = data.flow_from_directory(directory='/content/drive/MyDrive/NEURO_CNN/dataset_especifico_44c/',\n", + " target_size = (384,384),\n", + " class_mode = 'categorical',\n", + " batch_size = 8,\n", + " shuffle = True,\n", + " subset = 'validation',\n", + " interpolation = 'nearest')\n" + ] + }, + { + "cell_type": "code", + "source": [ + "print(traindata[0][0]) # Retornará as arrays referentes as amostras\n" + ], + "metadata": { + "id": "Vv3oFD2d2tMl", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7b3654f7-1ece-4d6e-e66e-dd21686b5ef2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[[[0.01176471 0.01176471 0.01176471]\n", + " [0.01176471 0.01176471 0.01176471]\n", + " [0.01176471 0.01176471 0.01176471]\n", + " ...\n", + " [0.00784314 0.00784314 0.00784314]\n", + " [0.00784314 0.00784314 0.00784314]\n", + " [0.00784314 0.00784314 0.00784314]]\n", + "\n", + " [[0.01176471 0.01176471 0.01176471]\n", + " [0.01176471 0.01176471 0.01176471]\n", + " [0.01176471 0.01176471 0.01176471]\n", + " ...\n", + " [0.00784314 0.00784314 0.00784314]\n", + " [0.00784314 0.00784314 0.00784314]\n", + " [0.00784314 0.00784314 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"print(traindata[0][0][0]) # Array referente a primeira amostra\n", + "print(traindata[0][1][0]) # Retorna as labels referentes a primeira amostra\n" + ], + "metadata": { + "id": "y_LIqpHU20Um", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3165732d-10e9-42f8-b70b-cbcf3bcea65e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[[0.01176471 0.01176471 0.01176471]\n", + " [0.01176471 0.01176471 0.01176471]\n", + " [0.01176471 0.01176471 0.01176471]\n", + " ...\n", + " [0.01176471 0.01176471 0.01176471]\n", + " [0.01176471 0.01176471 0.01176471]\n", + " [0.00990323 0.00990323 0.00990323]]\n", + "\n", + " [[0.01176471 0.01176471 0.01176471]\n", + " [0.01176471 0.01176471 0.01176471]\n", + " [0.01176471 0.01176471 0.01176471]\n", + " ...\n", + " [0.01176471 0.01176471 0.01176471]\n", + " [0.01176471 0.01176471 0.01176471]\n", + " [0.00974882 0.00974882 0.00974882]]\n", + "\n", + " [[0.01176471 0.01176471 0.01176471]\n", + " [0.01176471 0.01176471 0.01176471]\n", + " [0.01176471 0.01176471 0.01176471]\n", + " ...\n", + " [0.01176471 0.01176471 0.01176471]\n", + " [0.01176471 0.01176471 0.01176471]\n", + " [0.00959442 0.00959442 0.00959442]]\n", + "\n", + " ...\n", + "\n", + " [[0.00784314 0.00784314 0.00784314]\n", + " [0.00784314 0.00784314 0.00784314]\n", + " [0.00784314 0.00784314 0.00784314]\n", + " ...\n", + " [0.01176471 0.01176471 0.01176471]\n", + " [0.01176471 0.01176471 0.01176471]\n", + " [0.01176471 0.01176471 0.01176471]]\n", + "\n", + " [[0.00784314 0.00784314 0.00784314]\n", + " [0.00784314 0.00784314 0.00784314]\n", + " [0.00784314 0.00784314 0.00784314]\n", + " ...\n", + " [0.01176471 0.01176471 0.01176471]\n", + " [0.01176471 0.01176471 0.01176471]\n", + " [0.01176471 0.01176471 0.01176471]]\n", + "\n", + " [[0.00784314 0.00784314 0.00784314]\n", + " [0.00784314 0.00784314 0.00784314]\n", + " [0.00784314 0.00784314 0.00784314]\n", + " ...\n", + " [0.01176471 0.01176471 0.01176471]\n", + " [0.01176471 0.01176471 0.01176471]\n", + " [0.01176471 0.01176471 0.01176471]]]\n", + "[1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "for i in traindata:\n", + " print(f'Para cada imagem de origem serão geradas {i[0].shape[0]} novas imagens sintéticas')\n", + " print(f'As imagens finais possuirão {i[0].shape[1]}x{i[0].shape[2]} pixels em {i[0].shape[3]} canais de cor')\n", + " break\n" + ], + "metadata": { + "id": "PyoVoeFv241p", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "9ad4c90b-b70d-490e-d521-3a92009d4478" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Para cada imagem de origem serão geradas 16 novas imagens sintéticas\n", + "As imagens finais possuirão 384x384 pixels em 3 canais de cor\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Uax4hz4j4Q2Q" + }, + "outputs": [], + "source": [ + "METRICS = [keras.metrics.CategoricalAccuracy(name = 'accuracy'),\n", + " keras.metrics.TruePositives(thresholds = 0.50, name = 'tp'),\n", + " keras.metrics.TrueNegatives(thresholds = 0.50, name = 'tn'),\n", + " keras.metrics.FalsePositives(thresholds = 0.50, name = 'fp'),\n", + " keras.metrics.FalseNegatives(thresholds = 0.50, name = 'fn'),\n", + " keras.metrics.PrecisionAtRecall(recall = 0.50, name = 'precision'),\n", + " keras.metrics.SensitivityAtSpecificity(0.50, name = 'sensitivity'),\n", + " keras.metrics.SpecificityAtSensitivity(sensitivity = 0.50,\n", + " name = 'specificity'),\n", + " keras.metrics.Recall(name='recall'),\n", + " tfa.metrics.FBetaScore(num_classes = N_classes,\n", + " average = None,\n", + " threshold = 0.50,\n", + " name = 'FBetaScore')]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SRDg0GfJWuBC", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0a304a02-51f0-40c6-cd90-06a910e3974d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_resnet_v2/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "219062272/219055592 [==============================] - 1s 0us/step\n", + "219070464/219055592 [==============================] - 1s 0us/step\n" + ] + } + ], + "source": [ + "from tensorflow.keras.applications.inception_resnet_v2 import InceptionResNetV2\n", + "\n", + "base_model = InceptionResNetV2(weights = 'imagenet',\n", + " include_top = False,\n", + " input_shape = (384,384,3)) #originalmente era 450,450,3\n", + " \n", + "x = base_model.output\n", + "x = GlobalAveragePooling2D()(x)\n", + "x = Dense(1024, activation = 'relu')(x)\n", + "x = Dense(1024, activation = 'relu')(x)\n", + "x = Dropout(rate = 0.50)(x)\n", + "x = Dense(1024, activation = 'relu')(x)\n", + "x = Dense(1024, activation = 'relu')(x)\n", + "x = Dropout(rate = 0.25)(x)\n", + "x = Dense(1024, activation = 'relu')(x)\n", + "x = Dense(1024, activation = 'relu')(x)\n", + "\n", + "predictions = Dense(units = N_classes,\n", + " activation = 'softmax')(x)\n", + "\n", + "model = Model(inputs = base_model.input,\n", + " outputs = predictions)\n", + "\n", + "for layer in model.layers:\n", + " layer.trainable = True\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9mJfrj6tCDiJ" + }, + "outputs": [], + "source": [ + "opt = Adam(learning_rate=0.0001,\n", + " beta_1=0.9,\n", + " beta_2=0.999,\n", + " epsilon=1e-07,\n", + " amsgrad=False)\n", + "\n", + "model.compile(optimizer = opt,\n", + " loss = keras.losses.categorical_crossentropy,\n", + " metrics = METRICS)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bf-898GHCDiJ" + }, + "outputs": [], + "source": [ + "earlystop = EarlyStopping(monitor='loss',\n", + " min_delta = 0,\n", + " patience = 10,\n", + " verbose = 1,\n", + " mode = 'min')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "esG1OGGzCDiJ" + }, + "outputs": [], + "source": [ + "learning_rate = ReduceLROnPlateau(monitor='accuracy',\n", + " factor=0.2,\n", + " patience=1,\n", + " min_lr=0.00001,\n", + " verbose=1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "901PihEqCDiJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "51290065-5888-44a3-f0e5-b1add812314f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model: \"model\"\n", + "__________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + " input_1 (InputLayer) [(None, 384, 384, 3 0 [] \n", + " )] \n", + " \n", + " conv2d (Conv2D) (None, 191, 191, 32 864 ['input_1[0][0]'] \n", + " ) \n", + " \n", + " batch_normalization (BatchNorm (None, 191, 191, 32 96 ['conv2d[0][0]'] \n", + " alization) ) \n", + " \n", + " activation (Activation) (None, 191, 191, 32 0 ['batch_normalization[0][0]'] \n", + " ) \n", + " \n", + " conv2d_1 (Conv2D) (None, 189, 189, 32 9216 ['activation[0][0]'] \n", + " ) \n", + " \n", + " batch_normalization_1 (BatchNo (None, 189, 189, 32 96 ['conv2d_1[0][0]'] \n", + " rmalization) ) \n", + " \n", + " activation_1 (Activation) (None, 189, 189, 32 0 ['batch_normalization_1[0][0]'] \n", + " ) \n", + " \n", + " conv2d_2 (Conv2D) (None, 189, 189, 64 18432 ['activation_1[0][0]'] \n", + " ) \n", + " \n", + " batch_normalization_2 (BatchNo (None, 189, 189, 64 192 ['conv2d_2[0][0]'] \n", + " rmalization) ) \n", + " \n", + " activation_2 (Activation) (None, 189, 189, 64 0 ['batch_normalization_2[0][0]'] \n", + " ) \n", + " \n", + " max_pooling2d (MaxPooling2D) (None, 94, 94, 64) 0 ['activation_2[0][0]'] \n", + " \n", + " conv2d_3 (Conv2D) (None, 94, 94, 80) 5120 ['max_pooling2d[0][0]'] \n", + " \n", + " batch_normalization_3 (BatchNo (None, 94, 94, 80) 240 ['conv2d_3[0][0]'] \n", + " rmalization) \n", + " \n", + " activation_3 (Activation) (None, 94, 94, 80) 0 ['batch_normalization_3[0][0]'] \n", + " \n", + " conv2d_4 (Conv2D) (None, 92, 92, 192) 138240 ['activation_3[0][0]'] \n", + " \n", + " batch_normalization_4 (BatchNo (None, 92, 92, 192) 576 ['conv2d_4[0][0]'] \n", + " rmalization) \n", + " \n", + " activation_4 (Activation) (None, 92, 92, 192) 0 ['batch_normalization_4[0][0]'] \n", + " \n", + " max_pooling2d_1 (MaxPooling2D) (None, 45, 45, 192) 0 ['activation_4[0][0]'] \n", + " \n", + " conv2d_8 (Conv2D) (None, 45, 45, 64) 12288 ['max_pooling2d_1[0][0]'] \n", + " \n", + " batch_normalization_8 (BatchNo (None, 45, 45, 64) 192 ['conv2d_8[0][0]'] \n", + " rmalization) \n", + " \n", + " activation_8 (Activation) (None, 45, 45, 64) 0 ['batch_normalization_8[0][0]'] \n", + " \n", + " conv2d_6 (Conv2D) (None, 45, 45, 48) 9216 ['max_pooling2d_1[0][0]'] \n", + " \n", + " conv2d_9 (Conv2D) (None, 45, 45, 96) 55296 ['activation_8[0][0]'] \n", + " \n", + " batch_normalization_6 (BatchNo (None, 45, 45, 48) 144 ['conv2d_6[0][0]'] \n", + " rmalization) \n", + " \n", + " batch_normalization_9 (BatchNo (None, 45, 45, 96) 288 ['conv2d_9[0][0]'] \n", + " rmalization) \n", + " \n", + " activation_6 (Activation) (None, 45, 45, 48) 0 ['batch_normalization_6[0][0]'] \n", + " \n", + " activation_9 (Activation) (None, 45, 45, 96) 0 ['batch_normalization_9[0][0]'] \n", + " \n", + " average_pooling2d (AveragePool (None, 45, 45, 192) 0 ['max_pooling2d_1[0][0]'] \n", + " ing2D) \n", + " \n", + " conv2d_5 (Conv2D) (None, 45, 45, 96) 18432 ['max_pooling2d_1[0][0]'] \n", + " \n", + " conv2d_7 (Conv2D) (None, 45, 45, 64) 76800 ['activation_6[0][0]'] \n", + " \n", + " conv2d_10 (Conv2D) (None, 45, 45, 96) 82944 ['activation_9[0][0]'] \n", + " \n", + " conv2d_11 (Conv2D) (None, 45, 45, 64) 12288 ['average_pooling2d[0][0]'] \n", + " \n", + " batch_normalization_5 (BatchNo (None, 45, 45, 96) 288 ['conv2d_5[0][0]'] \n", + " rmalization) \n", + " \n", + " batch_normalization_7 (BatchNo (None, 45, 45, 64) 192 ['conv2d_7[0][0]'] \n", + " rmalization) \n", + " \n", + " batch_normalization_10 (BatchN (None, 45, 45, 96) 288 ['conv2d_10[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_11 (BatchN (None, 45, 45, 64) 192 ['conv2d_11[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_5 (Activation) (None, 45, 45, 96) 0 ['batch_normalization_5[0][0]'] \n", + " \n", + " activation_7 (Activation) (None, 45, 45, 64) 0 ['batch_normalization_7[0][0]'] \n", + " \n", + " activation_10 (Activation) (None, 45, 45, 96) 0 ['batch_normalization_10[0][0]'] \n", + " \n", + " activation_11 (Activation) (None, 45, 45, 64) 0 ['batch_normalization_11[0][0]'] \n", + " \n", + " mixed_5b (Concatenate) (None, 45, 45, 320) 0 ['activation_5[0][0]', \n", + " 'activation_7[0][0]', \n", + " 'activation_10[0][0]', \n", + " 'activation_11[0][0]'] \n", + " \n", + " conv2d_15 (Conv2D) (None, 45, 45, 32) 10240 ['mixed_5b[0][0]'] \n", + " \n", + " batch_normalization_15 (BatchN (None, 45, 45, 32) 96 ['conv2d_15[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_15 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_15[0][0]'] \n", + " \n", + " conv2d_13 (Conv2D) (None, 45, 45, 32) 10240 ['mixed_5b[0][0]'] \n", + " \n", + " conv2d_16 (Conv2D) (None, 45, 45, 48) 13824 ['activation_15[0][0]'] \n", + " \n", + " batch_normalization_13 (BatchN (None, 45, 45, 32) 96 ['conv2d_13[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_16 (BatchN (None, 45, 45, 48) 144 ['conv2d_16[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_13 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_13[0][0]'] \n", + " \n", + " activation_16 (Activation) (None, 45, 45, 48) 0 ['batch_normalization_16[0][0]'] \n", + " \n", + " conv2d_12 (Conv2D) (None, 45, 45, 32) 10240 ['mixed_5b[0][0]'] \n", + " \n", + " conv2d_14 (Conv2D) (None, 45, 45, 32) 9216 ['activation_13[0][0]'] \n", + " \n", + " conv2d_17 (Conv2D) (None, 45, 45, 64) 27648 ['activation_16[0][0]'] \n", + " \n", + " batch_normalization_12 (BatchN (None, 45, 45, 32) 96 ['conv2d_12[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_14 (BatchN (None, 45, 45, 32) 96 ['conv2d_14[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_17 (BatchN (None, 45, 45, 64) 192 ['conv2d_17[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_12 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_12[0][0]'] \n", + " \n", + " activation_14 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_14[0][0]'] \n", + " \n", + " activation_17 (Activation) (None, 45, 45, 64) 0 ['batch_normalization_17[0][0]'] \n", + " \n", + " block35_1_mixed (Concatenate) (None, 45, 45, 128) 0 ['activation_12[0][0]', \n", + " 'activation_14[0][0]', \n", + " 'activation_17[0][0]'] \n", + " \n", + " block35_1_conv (Conv2D) (None, 45, 45, 320) 41280 ['block35_1_mixed[0][0]'] \n", + " \n", + " block35_1 (Lambda) (None, 45, 45, 320) 0 ['mixed_5b[0][0]', \n", + " 'block35_1_conv[0][0]'] \n", + " \n", + " block35_1_ac (Activation) (None, 45, 45, 320) 0 ['block35_1[0][0]'] \n", + " \n", + " conv2d_21 (Conv2D) (None, 45, 45, 32) 10240 ['block35_1_ac[0][0]'] \n", + " \n", + " batch_normalization_21 (BatchN (None, 45, 45, 32) 96 ['conv2d_21[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_21 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_21[0][0]'] \n", + " \n", + " conv2d_19 (Conv2D) (None, 45, 45, 32) 10240 ['block35_1_ac[0][0]'] \n", + " \n", + " conv2d_22 (Conv2D) (None, 45, 45, 48) 13824 ['activation_21[0][0]'] \n", + " \n", + " batch_normalization_19 (BatchN (None, 45, 45, 32) 96 ['conv2d_19[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_22 (BatchN (None, 45, 45, 48) 144 ['conv2d_22[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_19 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_19[0][0]'] \n", + " \n", + " activation_22 (Activation) (None, 45, 45, 48) 0 ['batch_normalization_22[0][0]'] \n", + " \n", + " conv2d_18 (Conv2D) (None, 45, 45, 32) 10240 ['block35_1_ac[0][0]'] \n", + " \n", + " conv2d_20 (Conv2D) (None, 45, 45, 32) 9216 ['activation_19[0][0]'] \n", + " \n", + " conv2d_23 (Conv2D) (None, 45, 45, 64) 27648 ['activation_22[0][0]'] \n", + " \n", + " batch_normalization_18 (BatchN (None, 45, 45, 32) 96 ['conv2d_18[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_20 (BatchN (None, 45, 45, 32) 96 ['conv2d_20[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_23 (BatchN (None, 45, 45, 64) 192 ['conv2d_23[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_18 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_18[0][0]'] \n", + " \n", + " activation_20 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_20[0][0]'] \n", + " \n", + " activation_23 (Activation) (None, 45, 45, 64) 0 ['batch_normalization_23[0][0]'] \n", + " \n", + " block35_2_mixed (Concatenate) (None, 45, 45, 128) 0 ['activation_18[0][0]', \n", + " 'activation_20[0][0]', \n", + " 'activation_23[0][0]'] \n", + " \n", + " block35_2_conv (Conv2D) (None, 45, 45, 320) 41280 ['block35_2_mixed[0][0]'] \n", + " \n", + " block35_2 (Lambda) (None, 45, 45, 320) 0 ['block35_1_ac[0][0]', \n", + " 'block35_2_conv[0][0]'] \n", + " \n", + " block35_2_ac (Activation) (None, 45, 45, 320) 0 ['block35_2[0][0]'] \n", + " \n", + " conv2d_27 (Conv2D) (None, 45, 45, 32) 10240 ['block35_2_ac[0][0]'] \n", + " \n", + " batch_normalization_27 (BatchN (None, 45, 45, 32) 96 ['conv2d_27[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_27 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_27[0][0]'] \n", + " \n", + " conv2d_25 (Conv2D) (None, 45, 45, 32) 10240 ['block35_2_ac[0][0]'] \n", + " \n", + " conv2d_28 (Conv2D) (None, 45, 45, 48) 13824 ['activation_27[0][0]'] \n", + " \n", + " batch_normalization_25 (BatchN (None, 45, 45, 32) 96 ['conv2d_25[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_28 (BatchN (None, 45, 45, 48) 144 ['conv2d_28[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_25 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_25[0][0]'] \n", + " \n", + " activation_28 (Activation) (None, 45, 45, 48) 0 ['batch_normalization_28[0][0]'] \n", + " \n", + " conv2d_24 (Conv2D) (None, 45, 45, 32) 10240 ['block35_2_ac[0][0]'] \n", + " \n", + " conv2d_26 (Conv2D) (None, 45, 45, 32) 9216 ['activation_25[0][0]'] \n", + " \n", + " conv2d_29 (Conv2D) (None, 45, 45, 64) 27648 ['activation_28[0][0]'] \n", + " \n", + " batch_normalization_24 (BatchN (None, 45, 45, 32) 96 ['conv2d_24[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_26 (BatchN (None, 45, 45, 32) 96 ['conv2d_26[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_29 (BatchN (None, 45, 45, 64) 192 ['conv2d_29[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_24 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_24[0][0]'] \n", + " \n", + " activation_26 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_26[0][0]'] \n", + " \n", + " activation_29 (Activation) (None, 45, 45, 64) 0 ['batch_normalization_29[0][0]'] \n", + " \n", + " block35_3_mixed (Concatenate) (None, 45, 45, 128) 0 ['activation_24[0][0]', \n", + " 'activation_26[0][0]', \n", + " 'activation_29[0][0]'] \n", + " \n", + " block35_3_conv (Conv2D) (None, 45, 45, 320) 41280 ['block35_3_mixed[0][0]'] \n", + " \n", + " block35_3 (Lambda) (None, 45, 45, 320) 0 ['block35_2_ac[0][0]', \n", + " 'block35_3_conv[0][0]'] \n", + " \n", + " block35_3_ac (Activation) (None, 45, 45, 320) 0 ['block35_3[0][0]'] \n", + " \n", + " conv2d_33 (Conv2D) (None, 45, 45, 32) 10240 ['block35_3_ac[0][0]'] \n", + " \n", + " batch_normalization_33 (BatchN (None, 45, 45, 32) 96 ['conv2d_33[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_33 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_33[0][0]'] \n", + " \n", + " conv2d_31 (Conv2D) (None, 45, 45, 32) 10240 ['block35_3_ac[0][0]'] \n", + " \n", + " conv2d_34 (Conv2D) (None, 45, 45, 48) 13824 ['activation_33[0][0]'] \n", + " \n", + " batch_normalization_31 (BatchN (None, 45, 45, 32) 96 ['conv2d_31[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_34 (BatchN (None, 45, 45, 48) 144 ['conv2d_34[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_31 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_31[0][0]'] \n", + " \n", + " activation_34 (Activation) (None, 45, 45, 48) 0 ['batch_normalization_34[0][0]'] \n", + " \n", + " conv2d_30 (Conv2D) (None, 45, 45, 32) 10240 ['block35_3_ac[0][0]'] \n", + " \n", + " conv2d_32 (Conv2D) (None, 45, 45, 32) 9216 ['activation_31[0][0]'] \n", + " \n", + " conv2d_35 (Conv2D) (None, 45, 45, 64) 27648 ['activation_34[0][0]'] \n", + " \n", + " batch_normalization_30 (BatchN (None, 45, 45, 32) 96 ['conv2d_30[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_32 (BatchN (None, 45, 45, 32) 96 ['conv2d_32[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_35 (BatchN (None, 45, 45, 64) 192 ['conv2d_35[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_30 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_30[0][0]'] \n", + " \n", + " activation_32 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_32[0][0]'] \n", + " \n", + " activation_35 (Activation) (None, 45, 45, 64) 0 ['batch_normalization_35[0][0]'] \n", + " \n", + " block35_4_mixed (Concatenate) (None, 45, 45, 128) 0 ['activation_30[0][0]', \n", + " 'activation_32[0][0]', \n", + " 'activation_35[0][0]'] \n", + " \n", + " block35_4_conv (Conv2D) (None, 45, 45, 320) 41280 ['block35_4_mixed[0][0]'] \n", + " \n", + " block35_4 (Lambda) (None, 45, 45, 320) 0 ['block35_3_ac[0][0]', \n", + " 'block35_4_conv[0][0]'] \n", + " \n", + " block35_4_ac (Activation) (None, 45, 45, 320) 0 ['block35_4[0][0]'] \n", + " \n", + " conv2d_39 (Conv2D) (None, 45, 45, 32) 10240 ['block35_4_ac[0][0]'] \n", + " \n", + " batch_normalization_39 (BatchN (None, 45, 45, 32) 96 ['conv2d_39[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_39 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_39[0][0]'] \n", + " \n", + " conv2d_37 (Conv2D) (None, 45, 45, 32) 10240 ['block35_4_ac[0][0]'] \n", + " \n", + " conv2d_40 (Conv2D) (None, 45, 45, 48) 13824 ['activation_39[0][0]'] \n", + " \n", + " batch_normalization_37 (BatchN (None, 45, 45, 32) 96 ['conv2d_37[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_40 (BatchN (None, 45, 45, 48) 144 ['conv2d_40[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_37 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_37[0][0]'] \n", + " \n", + " activation_40 (Activation) (None, 45, 45, 48) 0 ['batch_normalization_40[0][0]'] \n", + " \n", + " conv2d_36 (Conv2D) (None, 45, 45, 32) 10240 ['block35_4_ac[0][0]'] \n", + " \n", + " conv2d_38 (Conv2D) (None, 45, 45, 32) 9216 ['activation_37[0][0]'] \n", + " \n", + " conv2d_41 (Conv2D) (None, 45, 45, 64) 27648 ['activation_40[0][0]'] \n", + " \n", + " batch_normalization_36 (BatchN (None, 45, 45, 32) 96 ['conv2d_36[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_38 (BatchN (None, 45, 45, 32) 96 ['conv2d_38[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_41 (BatchN (None, 45, 45, 64) 192 ['conv2d_41[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_36 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_36[0][0]'] \n", + " \n", + " activation_38 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_38[0][0]'] \n", + " \n", + " activation_41 (Activation) (None, 45, 45, 64) 0 ['batch_normalization_41[0][0]'] \n", + " \n", + " block35_5_mixed (Concatenate) (None, 45, 45, 128) 0 ['activation_36[0][0]', \n", + " 'activation_38[0][0]', \n", + " 'activation_41[0][0]'] \n", + " \n", + " block35_5_conv (Conv2D) (None, 45, 45, 320) 41280 ['block35_5_mixed[0][0]'] \n", + " \n", + " block35_5 (Lambda) (None, 45, 45, 320) 0 ['block35_4_ac[0][0]', \n", + " 'block35_5_conv[0][0]'] \n", + " \n", + " block35_5_ac (Activation) (None, 45, 45, 320) 0 ['block35_5[0][0]'] \n", + " \n", + " conv2d_45 (Conv2D) (None, 45, 45, 32) 10240 ['block35_5_ac[0][0]'] \n", + " \n", + " batch_normalization_45 (BatchN (None, 45, 45, 32) 96 ['conv2d_45[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_45 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_45[0][0]'] \n", + " \n", + " conv2d_43 (Conv2D) (None, 45, 45, 32) 10240 ['block35_5_ac[0][0]'] \n", + " \n", + " conv2d_46 (Conv2D) (None, 45, 45, 48) 13824 ['activation_45[0][0]'] \n", + " \n", + " batch_normalization_43 (BatchN (None, 45, 45, 32) 96 ['conv2d_43[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_46 (BatchN (None, 45, 45, 48) 144 ['conv2d_46[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_43 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_43[0][0]'] \n", + " \n", + " activation_46 (Activation) (None, 45, 45, 48) 0 ['batch_normalization_46[0][0]'] \n", + " \n", + " conv2d_42 (Conv2D) (None, 45, 45, 32) 10240 ['block35_5_ac[0][0]'] \n", + " \n", + " conv2d_44 (Conv2D) (None, 45, 45, 32) 9216 ['activation_43[0][0]'] \n", + " \n", + " conv2d_47 (Conv2D) (None, 45, 45, 64) 27648 ['activation_46[0][0]'] \n", + " \n", + " batch_normalization_42 (BatchN (None, 45, 45, 32) 96 ['conv2d_42[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_44 (BatchN (None, 45, 45, 32) 96 ['conv2d_44[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_47 (BatchN (None, 45, 45, 64) 192 ['conv2d_47[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_42 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_42[0][0]'] \n", + " \n", + " activation_44 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_44[0][0]'] \n", + " \n", + " activation_47 (Activation) (None, 45, 45, 64) 0 ['batch_normalization_47[0][0]'] \n", + " \n", + " block35_6_mixed (Concatenate) (None, 45, 45, 128) 0 ['activation_42[0][0]', \n", + " 'activation_44[0][0]', \n", + " 'activation_47[0][0]'] \n", + " \n", + " block35_6_conv (Conv2D) (None, 45, 45, 320) 41280 ['block35_6_mixed[0][0]'] \n", + " \n", + " block35_6 (Lambda) (None, 45, 45, 320) 0 ['block35_5_ac[0][0]', \n", + " 'block35_6_conv[0][0]'] \n", + " \n", + " block35_6_ac (Activation) (None, 45, 45, 320) 0 ['block35_6[0][0]'] \n", + " \n", + " conv2d_51 (Conv2D) (None, 45, 45, 32) 10240 ['block35_6_ac[0][0]'] \n", + " \n", + " batch_normalization_51 (BatchN (None, 45, 45, 32) 96 ['conv2d_51[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_51 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_51[0][0]'] \n", + " \n", + " conv2d_49 (Conv2D) (None, 45, 45, 32) 10240 ['block35_6_ac[0][0]'] \n", + " \n", + " conv2d_52 (Conv2D) (None, 45, 45, 48) 13824 ['activation_51[0][0]'] \n", + " \n", + " batch_normalization_49 (BatchN (None, 45, 45, 32) 96 ['conv2d_49[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_52 (BatchN (None, 45, 45, 48) 144 ['conv2d_52[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_49 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_49[0][0]'] \n", + " \n", + " activation_52 (Activation) (None, 45, 45, 48) 0 ['batch_normalization_52[0][0]'] \n", + " \n", + " conv2d_48 (Conv2D) (None, 45, 45, 32) 10240 ['block35_6_ac[0][0]'] \n", + " \n", + " conv2d_50 (Conv2D) (None, 45, 45, 32) 9216 ['activation_49[0][0]'] \n", + " \n", + " conv2d_53 (Conv2D) (None, 45, 45, 64) 27648 ['activation_52[0][0]'] \n", + " \n", + " batch_normalization_48 (BatchN (None, 45, 45, 32) 96 ['conv2d_48[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_50 (BatchN (None, 45, 45, 32) 96 ['conv2d_50[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_53 (BatchN (None, 45, 45, 64) 192 ['conv2d_53[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_48 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_48[0][0]'] \n", + " \n", + " activation_50 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_50[0][0]'] \n", + " \n", + " activation_53 (Activation) (None, 45, 45, 64) 0 ['batch_normalization_53[0][0]'] \n", + " \n", + " block35_7_mixed (Concatenate) (None, 45, 45, 128) 0 ['activation_48[0][0]', \n", + " 'activation_50[0][0]', \n", + " 'activation_53[0][0]'] \n", + " \n", + " block35_7_conv (Conv2D) (None, 45, 45, 320) 41280 ['block35_7_mixed[0][0]'] \n", + " \n", + " block35_7 (Lambda) (None, 45, 45, 320) 0 ['block35_6_ac[0][0]', \n", + " 'block35_7_conv[0][0]'] \n", + " \n", + " block35_7_ac (Activation) (None, 45, 45, 320) 0 ['block35_7[0][0]'] \n", + " \n", + " conv2d_57 (Conv2D) (None, 45, 45, 32) 10240 ['block35_7_ac[0][0]'] \n", + " \n", + " batch_normalization_57 (BatchN (None, 45, 45, 32) 96 ['conv2d_57[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_57 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_57[0][0]'] \n", + " \n", + " conv2d_55 (Conv2D) (None, 45, 45, 32) 10240 ['block35_7_ac[0][0]'] \n", + " \n", + " conv2d_58 (Conv2D) (None, 45, 45, 48) 13824 ['activation_57[0][0]'] \n", + " \n", + " batch_normalization_55 (BatchN (None, 45, 45, 32) 96 ['conv2d_55[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_58 (BatchN (None, 45, 45, 48) 144 ['conv2d_58[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_55 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_55[0][0]'] \n", + " \n", + " activation_58 (Activation) (None, 45, 45, 48) 0 ['batch_normalization_58[0][0]'] \n", + " \n", + " conv2d_54 (Conv2D) (None, 45, 45, 32) 10240 ['block35_7_ac[0][0]'] \n", + " \n", + " conv2d_56 (Conv2D) (None, 45, 45, 32) 9216 ['activation_55[0][0]'] \n", + " \n", + " conv2d_59 (Conv2D) (None, 45, 45, 64) 27648 ['activation_58[0][0]'] \n", + " \n", + " batch_normalization_54 (BatchN (None, 45, 45, 32) 96 ['conv2d_54[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_56 (BatchN (None, 45, 45, 32) 96 ['conv2d_56[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_59 (BatchN (None, 45, 45, 64) 192 ['conv2d_59[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_54 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_54[0][0]'] \n", + " \n", + " activation_56 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_56[0][0]'] \n", + " \n", + " activation_59 (Activation) (None, 45, 45, 64) 0 ['batch_normalization_59[0][0]'] \n", + " \n", + " block35_8_mixed (Concatenate) (None, 45, 45, 128) 0 ['activation_54[0][0]', \n", + " 'activation_56[0][0]', \n", + " 'activation_59[0][0]'] \n", + " \n", + " block35_8_conv (Conv2D) (None, 45, 45, 320) 41280 ['block35_8_mixed[0][0]'] \n", + " \n", + " block35_8 (Lambda) (None, 45, 45, 320) 0 ['block35_7_ac[0][0]', \n", + " 'block35_8_conv[0][0]'] \n", + " \n", + " block35_8_ac (Activation) (None, 45, 45, 320) 0 ['block35_8[0][0]'] \n", + " \n", + " conv2d_63 (Conv2D) (None, 45, 45, 32) 10240 ['block35_8_ac[0][0]'] \n", + " \n", + " batch_normalization_63 (BatchN (None, 45, 45, 32) 96 ['conv2d_63[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_63 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_63[0][0]'] \n", + " \n", + " conv2d_61 (Conv2D) (None, 45, 45, 32) 10240 ['block35_8_ac[0][0]'] \n", + " \n", + " conv2d_64 (Conv2D) (None, 45, 45, 48) 13824 ['activation_63[0][0]'] \n", + " \n", + " batch_normalization_61 (BatchN (None, 45, 45, 32) 96 ['conv2d_61[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_64 (BatchN (None, 45, 45, 48) 144 ['conv2d_64[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_61 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_61[0][0]'] \n", + " \n", + " activation_64 (Activation) (None, 45, 45, 48) 0 ['batch_normalization_64[0][0]'] \n", + " \n", + " conv2d_60 (Conv2D) (None, 45, 45, 32) 10240 ['block35_8_ac[0][0]'] \n", + " \n", + " conv2d_62 (Conv2D) (None, 45, 45, 32) 9216 ['activation_61[0][0]'] \n", + " \n", + " conv2d_65 (Conv2D) (None, 45, 45, 64) 27648 ['activation_64[0][0]'] \n", + " \n", + " batch_normalization_60 (BatchN (None, 45, 45, 32) 96 ['conv2d_60[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_62 (BatchN (None, 45, 45, 32) 96 ['conv2d_62[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_65 (BatchN (None, 45, 45, 64) 192 ['conv2d_65[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_60 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_60[0][0]'] \n", + " \n", + " activation_62 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_62[0][0]'] \n", + " \n", + " activation_65 (Activation) (None, 45, 45, 64) 0 ['batch_normalization_65[0][0]'] \n", + " \n", + " block35_9_mixed (Concatenate) (None, 45, 45, 128) 0 ['activation_60[0][0]', \n", + " 'activation_62[0][0]', \n", + " 'activation_65[0][0]'] \n", + " \n", + " block35_9_conv (Conv2D) (None, 45, 45, 320) 41280 ['block35_9_mixed[0][0]'] \n", + " \n", + " block35_9 (Lambda) (None, 45, 45, 320) 0 ['block35_8_ac[0][0]', \n", + " 'block35_9_conv[0][0]'] \n", + " \n", + " block35_9_ac (Activation) (None, 45, 45, 320) 0 ['block35_9[0][0]'] \n", + " \n", + " conv2d_69 (Conv2D) (None, 45, 45, 32) 10240 ['block35_9_ac[0][0]'] \n", + " \n", + " batch_normalization_69 (BatchN (None, 45, 45, 32) 96 ['conv2d_69[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_69 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_69[0][0]'] \n", + " \n", + " conv2d_67 (Conv2D) (None, 45, 45, 32) 10240 ['block35_9_ac[0][0]'] \n", + " \n", + " conv2d_70 (Conv2D) (None, 45, 45, 48) 13824 ['activation_69[0][0]'] \n", + " \n", + " batch_normalization_67 (BatchN (None, 45, 45, 32) 96 ['conv2d_67[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_70 (BatchN (None, 45, 45, 48) 144 ['conv2d_70[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_67 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_67[0][0]'] \n", + " \n", + " activation_70 (Activation) (None, 45, 45, 48) 0 ['batch_normalization_70[0][0]'] \n", + " \n", + " conv2d_66 (Conv2D) (None, 45, 45, 32) 10240 ['block35_9_ac[0][0]'] \n", + " \n", + " conv2d_68 (Conv2D) (None, 45, 45, 32) 9216 ['activation_67[0][0]'] \n", + " \n", + " conv2d_71 (Conv2D) (None, 45, 45, 64) 27648 ['activation_70[0][0]'] \n", + " \n", + " batch_normalization_66 (BatchN (None, 45, 45, 32) 96 ['conv2d_66[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_68 (BatchN (None, 45, 45, 32) 96 ['conv2d_68[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_71 (BatchN (None, 45, 45, 64) 192 ['conv2d_71[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_66 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_66[0][0]'] \n", + " \n", + " activation_68 (Activation) (None, 45, 45, 32) 0 ['batch_normalization_68[0][0]'] \n", + " \n", + " activation_71 (Activation) (None, 45, 45, 64) 0 ['batch_normalization_71[0][0]'] \n", + " \n", + " block35_10_mixed (Concatenate) (None, 45, 45, 128) 0 ['activation_66[0][0]', \n", + " 'activation_68[0][0]', \n", + " 'activation_71[0][0]'] \n", + " \n", + " block35_10_conv (Conv2D) (None, 45, 45, 320) 41280 ['block35_10_mixed[0][0]'] \n", + " \n", + " block35_10 (Lambda) (None, 45, 45, 320) 0 ['block35_9_ac[0][0]', \n", + " 'block35_10_conv[0][0]'] \n", + " \n", + " block35_10_ac (Activation) (None, 45, 45, 320) 0 ['block35_10[0][0]'] \n", + " \n", + " conv2d_73 (Conv2D) (None, 45, 45, 256) 81920 ['block35_10_ac[0][0]'] \n", + " \n", + " batch_normalization_73 (BatchN (None, 45, 45, 256) 768 ['conv2d_73[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_73 (Activation) (None, 45, 45, 256) 0 ['batch_normalization_73[0][0]'] \n", + " \n", + " conv2d_74 (Conv2D) (None, 45, 45, 256) 589824 ['activation_73[0][0]'] \n", + " \n", + " batch_normalization_74 (BatchN (None, 45, 45, 256) 768 ['conv2d_74[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_74 (Activation) (None, 45, 45, 256) 0 ['batch_normalization_74[0][0]'] \n", + " \n", + " conv2d_72 (Conv2D) (None, 22, 22, 384) 1105920 ['block35_10_ac[0][0]'] \n", + " \n", + " conv2d_75 (Conv2D) (None, 22, 22, 384) 884736 ['activation_74[0][0]'] \n", + " \n", + " batch_normalization_72 (BatchN (None, 22, 22, 384) 1152 ['conv2d_72[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_75 (BatchN (None, 22, 22, 384) 1152 ['conv2d_75[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_72 (Activation) (None, 22, 22, 384) 0 ['batch_normalization_72[0][0]'] \n", + " \n", + " activation_75 (Activation) (None, 22, 22, 384) 0 ['batch_normalization_75[0][0]'] \n", + " \n", + " max_pooling2d_2 (MaxPooling2D) (None, 22, 22, 320) 0 ['block35_10_ac[0][0]'] \n", + " \n", + " mixed_6a (Concatenate) (None, 22, 22, 1088 0 ['activation_72[0][0]', \n", + " ) 'activation_75[0][0]', \n", + " 'max_pooling2d_2[0][0]'] \n", + " \n", + " conv2d_77 (Conv2D) (None, 22, 22, 128) 139264 ['mixed_6a[0][0]'] \n", + " \n", + " batch_normalization_77 (BatchN (None, 22, 22, 128) 384 ['conv2d_77[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_77 (Activation) (None, 22, 22, 128) 0 ['batch_normalization_77[0][0]'] \n", + " \n", + " conv2d_78 (Conv2D) (None, 22, 22, 160) 143360 ['activation_77[0][0]'] \n", + " \n", + " batch_normalization_78 (BatchN (None, 22, 22, 160) 480 ['conv2d_78[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_78 (Activation) (None, 22, 22, 160) 0 ['batch_normalization_78[0][0]'] \n", + " \n", + " conv2d_76 (Conv2D) (None, 22, 22, 192) 208896 ['mixed_6a[0][0]'] \n", + " \n", + " conv2d_79 (Conv2D) (None, 22, 22, 192) 215040 ['activation_78[0][0]'] \n", + " \n", + " batch_normalization_76 (BatchN (None, 22, 22, 192) 576 ['conv2d_76[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_79 (BatchN (None, 22, 22, 192) 576 ['conv2d_79[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_76 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_76[0][0]'] \n", + " \n", + " activation_79 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_79[0][0]'] \n", + " \n", + " block17_1_mixed (Concatenate) (None, 22, 22, 384) 0 ['activation_76[0][0]', \n", + " 'activation_79[0][0]'] \n", + " \n", + " block17_1_conv (Conv2D) (None, 22, 22, 1088 418880 ['block17_1_mixed[0][0]'] \n", + " ) \n", + " \n", + " block17_1 (Lambda) (None, 22, 22, 1088 0 ['mixed_6a[0][0]', \n", + " ) 'block17_1_conv[0][0]'] \n", + " \n", + " block17_1_ac (Activation) (None, 22, 22, 1088 0 ['block17_1[0][0]'] \n", + " ) \n", + " \n", + " conv2d_81 (Conv2D) (None, 22, 22, 128) 139264 ['block17_1_ac[0][0]'] \n", + " \n", + " batch_normalization_81 (BatchN (None, 22, 22, 128) 384 ['conv2d_81[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_81 (Activation) (None, 22, 22, 128) 0 ['batch_normalization_81[0][0]'] \n", + " \n", + " conv2d_82 (Conv2D) (None, 22, 22, 160) 143360 ['activation_81[0][0]'] \n", + " \n", + " batch_normalization_82 (BatchN (None, 22, 22, 160) 480 ['conv2d_82[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_82 (Activation) (None, 22, 22, 160) 0 ['batch_normalization_82[0][0]'] \n", + " \n", + " conv2d_80 (Conv2D) (None, 22, 22, 192) 208896 ['block17_1_ac[0][0]'] \n", + " \n", + " conv2d_83 (Conv2D) (None, 22, 22, 192) 215040 ['activation_82[0][0]'] \n", + " \n", + " batch_normalization_80 (BatchN (None, 22, 22, 192) 576 ['conv2d_80[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_83 (BatchN (None, 22, 22, 192) 576 ['conv2d_83[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_80 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_80[0][0]'] \n", + " \n", + " activation_83 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_83[0][0]'] \n", + " \n", + " block17_2_mixed (Concatenate) (None, 22, 22, 384) 0 ['activation_80[0][0]', \n", + " 'activation_83[0][0]'] \n", + " \n", + " block17_2_conv (Conv2D) (None, 22, 22, 1088 418880 ['block17_2_mixed[0][0]'] \n", + " ) \n", + " \n", + " block17_2 (Lambda) (None, 22, 22, 1088 0 ['block17_1_ac[0][0]', \n", + " ) 'block17_2_conv[0][0]'] \n", + " \n", + " block17_2_ac (Activation) (None, 22, 22, 1088 0 ['block17_2[0][0]'] \n", + " ) \n", + " \n", + " conv2d_85 (Conv2D) (None, 22, 22, 128) 139264 ['block17_2_ac[0][0]'] \n", + " \n", + " batch_normalization_85 (BatchN (None, 22, 22, 128) 384 ['conv2d_85[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_85 (Activation) (None, 22, 22, 128) 0 ['batch_normalization_85[0][0]'] \n", + " \n", + " conv2d_86 (Conv2D) (None, 22, 22, 160) 143360 ['activation_85[0][0]'] \n", + " \n", + " batch_normalization_86 (BatchN (None, 22, 22, 160) 480 ['conv2d_86[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_86 (Activation) (None, 22, 22, 160) 0 ['batch_normalization_86[0][0]'] \n", + " \n", + " conv2d_84 (Conv2D) (None, 22, 22, 192) 208896 ['block17_2_ac[0][0]'] \n", + " \n", + " conv2d_87 (Conv2D) (None, 22, 22, 192) 215040 ['activation_86[0][0]'] \n", + " \n", + " batch_normalization_84 (BatchN (None, 22, 22, 192) 576 ['conv2d_84[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_87 (BatchN (None, 22, 22, 192) 576 ['conv2d_87[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_84 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_84[0][0]'] \n", + " \n", + " activation_87 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_87[0][0]'] \n", + " \n", + " block17_3_mixed (Concatenate) (None, 22, 22, 384) 0 ['activation_84[0][0]', \n", + " 'activation_87[0][0]'] \n", + " \n", + " block17_3_conv (Conv2D) (None, 22, 22, 1088 418880 ['block17_3_mixed[0][0]'] \n", + " ) \n", + " \n", + " block17_3 (Lambda) (None, 22, 22, 1088 0 ['block17_2_ac[0][0]', \n", + " ) 'block17_3_conv[0][0]'] \n", + " \n", + " block17_3_ac (Activation) (None, 22, 22, 1088 0 ['block17_3[0][0]'] \n", + " ) \n", + " \n", + " conv2d_89 (Conv2D) (None, 22, 22, 128) 139264 ['block17_3_ac[0][0]'] \n", + " \n", + " batch_normalization_89 (BatchN (None, 22, 22, 128) 384 ['conv2d_89[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_89 (Activation) (None, 22, 22, 128) 0 ['batch_normalization_89[0][0]'] \n", + " \n", + " conv2d_90 (Conv2D) (None, 22, 22, 160) 143360 ['activation_89[0][0]'] \n", + " \n", + " batch_normalization_90 (BatchN (None, 22, 22, 160) 480 ['conv2d_90[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_90 (Activation) (None, 22, 22, 160) 0 ['batch_normalization_90[0][0]'] \n", + " \n", + " conv2d_88 (Conv2D) (None, 22, 22, 192) 208896 ['block17_3_ac[0][0]'] \n", + " \n", + " conv2d_91 (Conv2D) (None, 22, 22, 192) 215040 ['activation_90[0][0]'] \n", + " \n", + " batch_normalization_88 (BatchN (None, 22, 22, 192) 576 ['conv2d_88[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_91 (BatchN (None, 22, 22, 192) 576 ['conv2d_91[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_88 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_88[0][0]'] \n", + " \n", + " activation_91 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_91[0][0]'] \n", + " \n", + " block17_4_mixed (Concatenate) (None, 22, 22, 384) 0 ['activation_88[0][0]', \n", + " 'activation_91[0][0]'] \n", + " \n", + " block17_4_conv (Conv2D) (None, 22, 22, 1088 418880 ['block17_4_mixed[0][0]'] \n", + " ) \n", + " \n", + " block17_4 (Lambda) (None, 22, 22, 1088 0 ['block17_3_ac[0][0]', \n", + " ) 'block17_4_conv[0][0]'] \n", + " \n", + " block17_4_ac (Activation) (None, 22, 22, 1088 0 ['block17_4[0][0]'] \n", + " ) \n", + " \n", + " conv2d_93 (Conv2D) (None, 22, 22, 128) 139264 ['block17_4_ac[0][0]'] \n", + " \n", + " batch_normalization_93 (BatchN (None, 22, 22, 128) 384 ['conv2d_93[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_93 (Activation) (None, 22, 22, 128) 0 ['batch_normalization_93[0][0]'] \n", + " \n", + " conv2d_94 (Conv2D) (None, 22, 22, 160) 143360 ['activation_93[0][0]'] \n", + " \n", + " batch_normalization_94 (BatchN (None, 22, 22, 160) 480 ['conv2d_94[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_94 (Activation) (None, 22, 22, 160) 0 ['batch_normalization_94[0][0]'] \n", + " \n", + " conv2d_92 (Conv2D) (None, 22, 22, 192) 208896 ['block17_4_ac[0][0]'] \n", + " \n", + " conv2d_95 (Conv2D) (None, 22, 22, 192) 215040 ['activation_94[0][0]'] \n", + " \n", + " batch_normalization_92 (BatchN (None, 22, 22, 192) 576 ['conv2d_92[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_95 (BatchN (None, 22, 22, 192) 576 ['conv2d_95[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_92 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_92[0][0]'] \n", + " \n", + " activation_95 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_95[0][0]'] \n", + " \n", + " block17_5_mixed (Concatenate) (None, 22, 22, 384) 0 ['activation_92[0][0]', \n", + " 'activation_95[0][0]'] \n", + " \n", + " block17_5_conv (Conv2D) (None, 22, 22, 1088 418880 ['block17_5_mixed[0][0]'] \n", + " ) \n", + " \n", + " block17_5 (Lambda) (None, 22, 22, 1088 0 ['block17_4_ac[0][0]', \n", + " ) 'block17_5_conv[0][0]'] \n", + " \n", + " block17_5_ac (Activation) (None, 22, 22, 1088 0 ['block17_5[0][0]'] \n", + " ) \n", + " \n", + " conv2d_97 (Conv2D) (None, 22, 22, 128) 139264 ['block17_5_ac[0][0]'] \n", + " \n", + " batch_normalization_97 (BatchN (None, 22, 22, 128) 384 ['conv2d_97[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_97 (Activation) (None, 22, 22, 128) 0 ['batch_normalization_97[0][0]'] \n", + " \n", + " conv2d_98 (Conv2D) (None, 22, 22, 160) 143360 ['activation_97[0][0]'] \n", + " \n", + " batch_normalization_98 (BatchN (None, 22, 22, 160) 480 ['conv2d_98[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_98 (Activation) (None, 22, 22, 160) 0 ['batch_normalization_98[0][0]'] \n", + " \n", + " conv2d_96 (Conv2D) (None, 22, 22, 192) 208896 ['block17_5_ac[0][0]'] \n", + " \n", + " conv2d_99 (Conv2D) (None, 22, 22, 192) 215040 ['activation_98[0][0]'] \n", + " \n", + " batch_normalization_96 (BatchN (None, 22, 22, 192) 576 ['conv2d_96[0][0]'] \n", + " ormalization) \n", + " \n", + " batch_normalization_99 (BatchN (None, 22, 22, 192) 576 ['conv2d_99[0][0]'] \n", + " ormalization) \n", + " \n", + " activation_96 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_96[0][0]'] \n", + " \n", + " activation_99 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_99[0][0]'] \n", + " \n", + " block17_6_mixed (Concatenate) (None, 22, 22, 384) 0 ['activation_96[0][0]', \n", + " 'activation_99[0][0]'] \n", + " \n", + " block17_6_conv (Conv2D) (None, 22, 22, 1088 418880 ['block17_6_mixed[0][0]'] \n", + " ) \n", + " \n", + " block17_6 (Lambda) (None, 22, 22, 1088 0 ['block17_5_ac[0][0]', \n", + " ) 'block17_6_conv[0][0]'] \n", + " \n", + " block17_6_ac (Activation) (None, 22, 22, 1088 0 ['block17_6[0][0]'] \n", + " ) \n", + " \n", + " conv2d_101 (Conv2D) (None, 22, 22, 128) 139264 ['block17_6_ac[0][0]'] \n", + " \n", + " batch_normalization_101 (Batch (None, 22, 22, 128) 384 ['conv2d_101[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_101 (Activation) (None, 22, 22, 128) 0 ['batch_normalization_101[0][0]']\n", + " \n", + " conv2d_102 (Conv2D) (None, 22, 22, 160) 143360 ['activation_101[0][0]'] \n", + " \n", + " batch_normalization_102 (Batch (None, 22, 22, 160) 480 ['conv2d_102[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_102 (Activation) (None, 22, 22, 160) 0 ['batch_normalization_102[0][0]']\n", + " \n", + " conv2d_100 (Conv2D) (None, 22, 22, 192) 208896 ['block17_6_ac[0][0]'] \n", + " \n", + " conv2d_103 (Conv2D) (None, 22, 22, 192) 215040 ['activation_102[0][0]'] \n", + " \n", + " batch_normalization_100 (Batch (None, 22, 22, 192) 576 ['conv2d_100[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_103 (Batch (None, 22, 22, 192) 576 ['conv2d_103[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_100 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_100[0][0]']\n", + " \n", + " activation_103 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_103[0][0]']\n", + " \n", + " block17_7_mixed (Concatenate) (None, 22, 22, 384) 0 ['activation_100[0][0]', \n", + " 'activation_103[0][0]'] \n", + " \n", + " block17_7_conv (Conv2D) (None, 22, 22, 1088 418880 ['block17_7_mixed[0][0]'] \n", + " ) \n", + " \n", + " block17_7 (Lambda) (None, 22, 22, 1088 0 ['block17_6_ac[0][0]', \n", + " ) 'block17_7_conv[0][0]'] \n", + " \n", + " block17_7_ac (Activation) (None, 22, 22, 1088 0 ['block17_7[0][0]'] \n", + " ) \n", + " \n", + " conv2d_105 (Conv2D) (None, 22, 22, 128) 139264 ['block17_7_ac[0][0]'] \n", + " \n", + " batch_normalization_105 (Batch (None, 22, 22, 128) 384 ['conv2d_105[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_105 (Activation) (None, 22, 22, 128) 0 ['batch_normalization_105[0][0]']\n", + " \n", + " conv2d_106 (Conv2D) (None, 22, 22, 160) 143360 ['activation_105[0][0]'] \n", + " \n", + " batch_normalization_106 (Batch (None, 22, 22, 160) 480 ['conv2d_106[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_106 (Activation) (None, 22, 22, 160) 0 ['batch_normalization_106[0][0]']\n", + " \n", + " conv2d_104 (Conv2D) (None, 22, 22, 192) 208896 ['block17_7_ac[0][0]'] \n", + " \n", + " conv2d_107 (Conv2D) (None, 22, 22, 192) 215040 ['activation_106[0][0]'] \n", + " \n", + " batch_normalization_104 (Batch (None, 22, 22, 192) 576 ['conv2d_104[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_107 (Batch (None, 22, 22, 192) 576 ['conv2d_107[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_104 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_104[0][0]']\n", + " \n", + " activation_107 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_107[0][0]']\n", + " \n", + " block17_8_mixed (Concatenate) (None, 22, 22, 384) 0 ['activation_104[0][0]', \n", + " 'activation_107[0][0]'] \n", + " \n", + " block17_8_conv (Conv2D) (None, 22, 22, 1088 418880 ['block17_8_mixed[0][0]'] \n", + " ) \n", + " \n", + " block17_8 (Lambda) (None, 22, 22, 1088 0 ['block17_7_ac[0][0]', \n", + " ) 'block17_8_conv[0][0]'] \n", + " \n", + " block17_8_ac (Activation) (None, 22, 22, 1088 0 ['block17_8[0][0]'] \n", + " ) \n", + " \n", + " conv2d_109 (Conv2D) (None, 22, 22, 128) 139264 ['block17_8_ac[0][0]'] \n", + " \n", + " batch_normalization_109 (Batch (None, 22, 22, 128) 384 ['conv2d_109[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_109 (Activation) (None, 22, 22, 128) 0 ['batch_normalization_109[0][0]']\n", + " \n", + " conv2d_110 (Conv2D) (None, 22, 22, 160) 143360 ['activation_109[0][0]'] \n", + " \n", + " batch_normalization_110 (Batch (None, 22, 22, 160) 480 ['conv2d_110[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_110 (Activation) (None, 22, 22, 160) 0 ['batch_normalization_110[0][0]']\n", + " \n", + " conv2d_108 (Conv2D) (None, 22, 22, 192) 208896 ['block17_8_ac[0][0]'] \n", + " \n", + " conv2d_111 (Conv2D) (None, 22, 22, 192) 215040 ['activation_110[0][0]'] \n", + " \n", + " batch_normalization_108 (Batch (None, 22, 22, 192) 576 ['conv2d_108[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_111 (Batch (None, 22, 22, 192) 576 ['conv2d_111[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_108 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_108[0][0]']\n", + " \n", + " activation_111 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_111[0][0]']\n", + " \n", + " block17_9_mixed (Concatenate) (None, 22, 22, 384) 0 ['activation_108[0][0]', \n", + " 'activation_111[0][0]'] \n", + " \n", + " block17_9_conv (Conv2D) (None, 22, 22, 1088 418880 ['block17_9_mixed[0][0]'] \n", + " ) \n", + " \n", + " block17_9 (Lambda) (None, 22, 22, 1088 0 ['block17_8_ac[0][0]', \n", + " ) 'block17_9_conv[0][0]'] \n", + " \n", + " block17_9_ac (Activation) (None, 22, 22, 1088 0 ['block17_9[0][0]'] \n", + " ) \n", + " \n", + " conv2d_113 (Conv2D) (None, 22, 22, 128) 139264 ['block17_9_ac[0][0]'] \n", + " \n", + " batch_normalization_113 (Batch (None, 22, 22, 128) 384 ['conv2d_113[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_113 (Activation) (None, 22, 22, 128) 0 ['batch_normalization_113[0][0]']\n", + " \n", + " conv2d_114 (Conv2D) (None, 22, 22, 160) 143360 ['activation_113[0][0]'] \n", + " \n", + " batch_normalization_114 (Batch (None, 22, 22, 160) 480 ['conv2d_114[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_114 (Activation) (None, 22, 22, 160) 0 ['batch_normalization_114[0][0]']\n", + " \n", + " conv2d_112 (Conv2D) (None, 22, 22, 192) 208896 ['block17_9_ac[0][0]'] \n", + " \n", + " conv2d_115 (Conv2D) (None, 22, 22, 192) 215040 ['activation_114[0][0]'] \n", + " \n", + " batch_normalization_112 (Batch (None, 22, 22, 192) 576 ['conv2d_112[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_115 (Batch (None, 22, 22, 192) 576 ['conv2d_115[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_112 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_112[0][0]']\n", + " \n", + " activation_115 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_115[0][0]']\n", + " \n", + " block17_10_mixed (Concatenate) (None, 22, 22, 384) 0 ['activation_112[0][0]', \n", + " 'activation_115[0][0]'] \n", + " \n", + " block17_10_conv (Conv2D) (None, 22, 22, 1088 418880 ['block17_10_mixed[0][0]'] \n", + " ) \n", + " \n", + " block17_10 (Lambda) (None, 22, 22, 1088 0 ['block17_9_ac[0][0]', \n", + " ) 'block17_10_conv[0][0]'] \n", + " \n", + " block17_10_ac (Activation) (None, 22, 22, 1088 0 ['block17_10[0][0]'] \n", + " ) \n", + " \n", + " conv2d_117 (Conv2D) (None, 22, 22, 128) 139264 ['block17_10_ac[0][0]'] \n", + " \n", + " batch_normalization_117 (Batch (None, 22, 22, 128) 384 ['conv2d_117[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_117 (Activation) (None, 22, 22, 128) 0 ['batch_normalization_117[0][0]']\n", + " \n", + " conv2d_118 (Conv2D) (None, 22, 22, 160) 143360 ['activation_117[0][0]'] \n", + " \n", + " batch_normalization_118 (Batch (None, 22, 22, 160) 480 ['conv2d_118[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_118 (Activation) (None, 22, 22, 160) 0 ['batch_normalization_118[0][0]']\n", + " \n", + " conv2d_116 (Conv2D) (None, 22, 22, 192) 208896 ['block17_10_ac[0][0]'] \n", + " \n", + " conv2d_119 (Conv2D) (None, 22, 22, 192) 215040 ['activation_118[0][0]'] \n", + " \n", + " batch_normalization_116 (Batch (None, 22, 22, 192) 576 ['conv2d_116[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_119 (Batch (None, 22, 22, 192) 576 ['conv2d_119[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_116 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_116[0][0]']\n", + " \n", + " activation_119 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_119[0][0]']\n", + " \n", + " block17_11_mixed (Concatenate) (None, 22, 22, 384) 0 ['activation_116[0][0]', \n", + " 'activation_119[0][0]'] \n", + " \n", + " block17_11_conv (Conv2D) (None, 22, 22, 1088 418880 ['block17_11_mixed[0][0]'] \n", + " ) \n", + " \n", + " block17_11 (Lambda) (None, 22, 22, 1088 0 ['block17_10_ac[0][0]', \n", + " ) 'block17_11_conv[0][0]'] \n", + " \n", + " block17_11_ac (Activation) (None, 22, 22, 1088 0 ['block17_11[0][0]'] \n", + " ) \n", + " \n", + " conv2d_121 (Conv2D) (None, 22, 22, 128) 139264 ['block17_11_ac[0][0]'] \n", + " \n", + " batch_normalization_121 (Batch (None, 22, 22, 128) 384 ['conv2d_121[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_121 (Activation) (None, 22, 22, 128) 0 ['batch_normalization_121[0][0]']\n", + " \n", + " conv2d_122 (Conv2D) (None, 22, 22, 160) 143360 ['activation_121[0][0]'] \n", + " \n", + " batch_normalization_122 (Batch (None, 22, 22, 160) 480 ['conv2d_122[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_122 (Activation) (None, 22, 22, 160) 0 ['batch_normalization_122[0][0]']\n", + " \n", + " conv2d_120 (Conv2D) (None, 22, 22, 192) 208896 ['block17_11_ac[0][0]'] \n", + " \n", + " conv2d_123 (Conv2D) (None, 22, 22, 192) 215040 ['activation_122[0][0]'] \n", + " \n", + " batch_normalization_120 (Batch (None, 22, 22, 192) 576 ['conv2d_120[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_123 (Batch (None, 22, 22, 192) 576 ['conv2d_123[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_120 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_120[0][0]']\n", + " \n", + " activation_123 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_123[0][0]']\n", + " \n", + " block17_12_mixed (Concatenate) (None, 22, 22, 384) 0 ['activation_120[0][0]', \n", + " 'activation_123[0][0]'] \n", + " \n", + " block17_12_conv (Conv2D) (None, 22, 22, 1088 418880 ['block17_12_mixed[0][0]'] \n", + " ) \n", + " \n", + " block17_12 (Lambda) (None, 22, 22, 1088 0 ['block17_11_ac[0][0]', \n", + " ) 'block17_12_conv[0][0]'] \n", + " \n", + " block17_12_ac (Activation) (None, 22, 22, 1088 0 ['block17_12[0][0]'] \n", + " ) \n", + " \n", + " conv2d_125 (Conv2D) (None, 22, 22, 128) 139264 ['block17_12_ac[0][0]'] \n", + " \n", + " batch_normalization_125 (Batch (None, 22, 22, 128) 384 ['conv2d_125[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_125 (Activation) (None, 22, 22, 128) 0 ['batch_normalization_125[0][0]']\n", + " \n", + " conv2d_126 (Conv2D) (None, 22, 22, 160) 143360 ['activation_125[0][0]'] \n", + " \n", + " batch_normalization_126 (Batch (None, 22, 22, 160) 480 ['conv2d_126[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_126 (Activation) (None, 22, 22, 160) 0 ['batch_normalization_126[0][0]']\n", + " \n", + " conv2d_124 (Conv2D) (None, 22, 22, 192) 208896 ['block17_12_ac[0][0]'] \n", + " \n", + " conv2d_127 (Conv2D) (None, 22, 22, 192) 215040 ['activation_126[0][0]'] \n", + " \n", + " batch_normalization_124 (Batch (None, 22, 22, 192) 576 ['conv2d_124[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_127 (Batch (None, 22, 22, 192) 576 ['conv2d_127[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_124 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_124[0][0]']\n", + " \n", + " activation_127 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_127[0][0]']\n", + " \n", + " block17_13_mixed (Concatenate) (None, 22, 22, 384) 0 ['activation_124[0][0]', \n", + " 'activation_127[0][0]'] \n", + " \n", + " block17_13_conv (Conv2D) (None, 22, 22, 1088 418880 ['block17_13_mixed[0][0]'] \n", + " ) \n", + " \n", + " block17_13 (Lambda) (None, 22, 22, 1088 0 ['block17_12_ac[0][0]', \n", + " ) 'block17_13_conv[0][0]'] \n", + " \n", + " block17_13_ac (Activation) (None, 22, 22, 1088 0 ['block17_13[0][0]'] \n", + " ) \n", + " \n", + " conv2d_129 (Conv2D) (None, 22, 22, 128) 139264 ['block17_13_ac[0][0]'] \n", + " \n", + " batch_normalization_129 (Batch (None, 22, 22, 128) 384 ['conv2d_129[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_129 (Activation) (None, 22, 22, 128) 0 ['batch_normalization_129[0][0]']\n", + " \n", + " conv2d_130 (Conv2D) (None, 22, 22, 160) 143360 ['activation_129[0][0]'] \n", + " \n", + " batch_normalization_130 (Batch (None, 22, 22, 160) 480 ['conv2d_130[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_130 (Activation) (None, 22, 22, 160) 0 ['batch_normalization_130[0][0]']\n", + " \n", + " conv2d_128 (Conv2D) (None, 22, 22, 192) 208896 ['block17_13_ac[0][0]'] \n", + " \n", + " conv2d_131 (Conv2D) (None, 22, 22, 192) 215040 ['activation_130[0][0]'] \n", + " \n", + " batch_normalization_128 (Batch (None, 22, 22, 192) 576 ['conv2d_128[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_131 (Batch (None, 22, 22, 192) 576 ['conv2d_131[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_128 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_128[0][0]']\n", + " \n", + " activation_131 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_131[0][0]']\n", + " \n", + " block17_14_mixed (Concatenate) (None, 22, 22, 384) 0 ['activation_128[0][0]', \n", + " 'activation_131[0][0]'] \n", + " \n", + " block17_14_conv (Conv2D) (None, 22, 22, 1088 418880 ['block17_14_mixed[0][0]'] \n", + " ) \n", + " \n", + " block17_14 (Lambda) (None, 22, 22, 1088 0 ['block17_13_ac[0][0]', \n", + " ) 'block17_14_conv[0][0]'] \n", + " \n", + " block17_14_ac (Activation) (None, 22, 22, 1088 0 ['block17_14[0][0]'] \n", + " ) \n", + " \n", + " conv2d_133 (Conv2D) (None, 22, 22, 128) 139264 ['block17_14_ac[0][0]'] \n", + " \n", + " batch_normalization_133 (Batch (None, 22, 22, 128) 384 ['conv2d_133[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_133 (Activation) (None, 22, 22, 128) 0 ['batch_normalization_133[0][0]']\n", + " \n", + " conv2d_134 (Conv2D) (None, 22, 22, 160) 143360 ['activation_133[0][0]'] \n", + " \n", + " batch_normalization_134 (Batch (None, 22, 22, 160) 480 ['conv2d_134[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_134 (Activation) (None, 22, 22, 160) 0 ['batch_normalization_134[0][0]']\n", + " \n", + " conv2d_132 (Conv2D) (None, 22, 22, 192) 208896 ['block17_14_ac[0][0]'] \n", + " \n", + " conv2d_135 (Conv2D) (None, 22, 22, 192) 215040 ['activation_134[0][0]'] \n", + " \n", + " batch_normalization_132 (Batch (None, 22, 22, 192) 576 ['conv2d_132[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_135 (Batch (None, 22, 22, 192) 576 ['conv2d_135[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_132 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_132[0][0]']\n", + " \n", + " activation_135 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_135[0][0]']\n", + " \n", + " block17_15_mixed (Concatenate) (None, 22, 22, 384) 0 ['activation_132[0][0]', \n", + " 'activation_135[0][0]'] \n", + " \n", + " block17_15_conv (Conv2D) (None, 22, 22, 1088 418880 ['block17_15_mixed[0][0]'] \n", + " ) \n", + " \n", + " block17_15 (Lambda) (None, 22, 22, 1088 0 ['block17_14_ac[0][0]', \n", + " ) 'block17_15_conv[0][0]'] \n", + " \n", + " block17_15_ac (Activation) (None, 22, 22, 1088 0 ['block17_15[0][0]'] \n", + " ) \n", + " \n", + " conv2d_137 (Conv2D) (None, 22, 22, 128) 139264 ['block17_15_ac[0][0]'] \n", + " \n", + " batch_normalization_137 (Batch (None, 22, 22, 128) 384 ['conv2d_137[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_137 (Activation) (None, 22, 22, 128) 0 ['batch_normalization_137[0][0]']\n", + " \n", + " conv2d_138 (Conv2D) (None, 22, 22, 160) 143360 ['activation_137[0][0]'] \n", + " \n", + " batch_normalization_138 (Batch (None, 22, 22, 160) 480 ['conv2d_138[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_138 (Activation) (None, 22, 22, 160) 0 ['batch_normalization_138[0][0]']\n", + " \n", + " conv2d_136 (Conv2D) (None, 22, 22, 192) 208896 ['block17_15_ac[0][0]'] \n", + " \n", + " conv2d_139 (Conv2D) (None, 22, 22, 192) 215040 ['activation_138[0][0]'] \n", + " \n", + " batch_normalization_136 (Batch (None, 22, 22, 192) 576 ['conv2d_136[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_139 (Batch (None, 22, 22, 192) 576 ['conv2d_139[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_136 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_136[0][0]']\n", + " \n", + " activation_139 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_139[0][0]']\n", + " \n", + " block17_16_mixed (Concatenate) (None, 22, 22, 384) 0 ['activation_136[0][0]', \n", + " 'activation_139[0][0]'] \n", + " \n", + " block17_16_conv (Conv2D) (None, 22, 22, 1088 418880 ['block17_16_mixed[0][0]'] \n", + " ) \n", + " \n", + " block17_16 (Lambda) (None, 22, 22, 1088 0 ['block17_15_ac[0][0]', \n", + " ) 'block17_16_conv[0][0]'] \n", + " \n", + " block17_16_ac (Activation) (None, 22, 22, 1088 0 ['block17_16[0][0]'] \n", + " ) \n", + " \n", + " conv2d_141 (Conv2D) (None, 22, 22, 128) 139264 ['block17_16_ac[0][0]'] \n", + " \n", + " batch_normalization_141 (Batch (None, 22, 22, 128) 384 ['conv2d_141[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_141 (Activation) (None, 22, 22, 128) 0 ['batch_normalization_141[0][0]']\n", + " \n", + " conv2d_142 (Conv2D) (None, 22, 22, 160) 143360 ['activation_141[0][0]'] \n", + " \n", + " batch_normalization_142 (Batch (None, 22, 22, 160) 480 ['conv2d_142[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_142 (Activation) (None, 22, 22, 160) 0 ['batch_normalization_142[0][0]']\n", + " \n", + " conv2d_140 (Conv2D) (None, 22, 22, 192) 208896 ['block17_16_ac[0][0]'] \n", + " \n", + " conv2d_143 (Conv2D) (None, 22, 22, 192) 215040 ['activation_142[0][0]'] \n", + " \n", + " batch_normalization_140 (Batch (None, 22, 22, 192) 576 ['conv2d_140[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_143 (Batch (None, 22, 22, 192) 576 ['conv2d_143[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_140 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_140[0][0]']\n", + " \n", + " activation_143 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_143[0][0]']\n", + " \n", + " block17_17_mixed (Concatenate) (None, 22, 22, 384) 0 ['activation_140[0][0]', \n", + " 'activation_143[0][0]'] \n", + " \n", + " block17_17_conv (Conv2D) (None, 22, 22, 1088 418880 ['block17_17_mixed[0][0]'] \n", + " ) \n", + " \n", + " block17_17 (Lambda) (None, 22, 22, 1088 0 ['block17_16_ac[0][0]', \n", + " ) 'block17_17_conv[0][0]'] \n", + " \n", + " block17_17_ac (Activation) (None, 22, 22, 1088 0 ['block17_17[0][0]'] \n", + " ) \n", + " \n", + " conv2d_145 (Conv2D) (None, 22, 22, 128) 139264 ['block17_17_ac[0][0]'] \n", + " \n", + " batch_normalization_145 (Batch (None, 22, 22, 128) 384 ['conv2d_145[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_145 (Activation) (None, 22, 22, 128) 0 ['batch_normalization_145[0][0]']\n", + " \n", + " conv2d_146 (Conv2D) (None, 22, 22, 160) 143360 ['activation_145[0][0]'] \n", + " \n", + " batch_normalization_146 (Batch (None, 22, 22, 160) 480 ['conv2d_146[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_146 (Activation) (None, 22, 22, 160) 0 ['batch_normalization_146[0][0]']\n", + " \n", + " conv2d_144 (Conv2D) (None, 22, 22, 192) 208896 ['block17_17_ac[0][0]'] \n", + " \n", + " conv2d_147 (Conv2D) (None, 22, 22, 192) 215040 ['activation_146[0][0]'] \n", + " \n", + " batch_normalization_144 (Batch (None, 22, 22, 192) 576 ['conv2d_144[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_147 (Batch (None, 22, 22, 192) 576 ['conv2d_147[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_144 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_144[0][0]']\n", + " \n", + " activation_147 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_147[0][0]']\n", + " \n", + " block17_18_mixed (Concatenate) (None, 22, 22, 384) 0 ['activation_144[0][0]', \n", + " 'activation_147[0][0]'] \n", + " \n", + " block17_18_conv (Conv2D) (None, 22, 22, 1088 418880 ['block17_18_mixed[0][0]'] \n", + " ) \n", + " \n", + " block17_18 (Lambda) (None, 22, 22, 1088 0 ['block17_17_ac[0][0]', \n", + " ) 'block17_18_conv[0][0]'] \n", + " \n", + " block17_18_ac (Activation) (None, 22, 22, 1088 0 ['block17_18[0][0]'] \n", + " ) \n", + " \n", + " conv2d_149 (Conv2D) (None, 22, 22, 128) 139264 ['block17_18_ac[0][0]'] \n", + " \n", + " batch_normalization_149 (Batch (None, 22, 22, 128) 384 ['conv2d_149[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_149 (Activation) (None, 22, 22, 128) 0 ['batch_normalization_149[0][0]']\n", + " \n", + " conv2d_150 (Conv2D) (None, 22, 22, 160) 143360 ['activation_149[0][0]'] \n", + " \n", + " batch_normalization_150 (Batch (None, 22, 22, 160) 480 ['conv2d_150[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_150 (Activation) (None, 22, 22, 160) 0 ['batch_normalization_150[0][0]']\n", + " \n", + " conv2d_148 (Conv2D) (None, 22, 22, 192) 208896 ['block17_18_ac[0][0]'] \n", + " \n", + " conv2d_151 (Conv2D) (None, 22, 22, 192) 215040 ['activation_150[0][0]'] \n", + " \n", + " batch_normalization_148 (Batch (None, 22, 22, 192) 576 ['conv2d_148[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_151 (Batch (None, 22, 22, 192) 576 ['conv2d_151[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_148 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_148[0][0]']\n", + " \n", + " activation_151 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_151[0][0]']\n", + " \n", + " block17_19_mixed (Concatenate) (None, 22, 22, 384) 0 ['activation_148[0][0]', \n", + " 'activation_151[0][0]'] \n", + " \n", + " block17_19_conv (Conv2D) (None, 22, 22, 1088 418880 ['block17_19_mixed[0][0]'] \n", + " ) \n", + " \n", + " block17_19 (Lambda) (None, 22, 22, 1088 0 ['block17_18_ac[0][0]', \n", + " ) 'block17_19_conv[0][0]'] \n", + " \n", + " block17_19_ac (Activation) (None, 22, 22, 1088 0 ['block17_19[0][0]'] \n", + " ) \n", + " \n", + " conv2d_153 (Conv2D) (None, 22, 22, 128) 139264 ['block17_19_ac[0][0]'] \n", + " \n", + " batch_normalization_153 (Batch (None, 22, 22, 128) 384 ['conv2d_153[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_153 (Activation) (None, 22, 22, 128) 0 ['batch_normalization_153[0][0]']\n", + " \n", + " conv2d_154 (Conv2D) (None, 22, 22, 160) 143360 ['activation_153[0][0]'] \n", + " \n", + " batch_normalization_154 (Batch (None, 22, 22, 160) 480 ['conv2d_154[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_154 (Activation) (None, 22, 22, 160) 0 ['batch_normalization_154[0][0]']\n", + " \n", + " conv2d_152 (Conv2D) (None, 22, 22, 192) 208896 ['block17_19_ac[0][0]'] \n", + " \n", + " conv2d_155 (Conv2D) (None, 22, 22, 192) 215040 ['activation_154[0][0]'] \n", + " \n", + " batch_normalization_152 (Batch (None, 22, 22, 192) 576 ['conv2d_152[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_155 (Batch (None, 22, 22, 192) 576 ['conv2d_155[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_152 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_152[0][0]']\n", + " \n", + " activation_155 (Activation) (None, 22, 22, 192) 0 ['batch_normalization_155[0][0]']\n", + " \n", + " block17_20_mixed (Concatenate) (None, 22, 22, 384) 0 ['activation_152[0][0]', \n", + " 'activation_155[0][0]'] \n", + " \n", + " block17_20_conv (Conv2D) (None, 22, 22, 1088 418880 ['block17_20_mixed[0][0]'] \n", + " ) \n", + " \n", + " block17_20 (Lambda) (None, 22, 22, 1088 0 ['block17_19_ac[0][0]', \n", + " ) 'block17_20_conv[0][0]'] \n", + " \n", + " block17_20_ac (Activation) (None, 22, 22, 1088 0 ['block17_20[0][0]'] \n", + " ) \n", + " \n", + " conv2d_160 (Conv2D) (None, 22, 22, 256) 278528 ['block17_20_ac[0][0]'] \n", + " \n", + " batch_normalization_160 (Batch (None, 22, 22, 256) 768 ['conv2d_160[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_160 (Activation) (None, 22, 22, 256) 0 ['batch_normalization_160[0][0]']\n", + " \n", + " conv2d_156 (Conv2D) (None, 22, 22, 256) 278528 ['block17_20_ac[0][0]'] \n", + " \n", + " conv2d_158 (Conv2D) (None, 22, 22, 256) 278528 ['block17_20_ac[0][0]'] \n", + " \n", + " conv2d_161 (Conv2D) (None, 22, 22, 288) 663552 ['activation_160[0][0]'] \n", + " \n", + " batch_normalization_156 (Batch (None, 22, 22, 256) 768 ['conv2d_156[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_158 (Batch (None, 22, 22, 256) 768 ['conv2d_158[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_161 (Batch (None, 22, 22, 288) 864 ['conv2d_161[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_156 (Activation) (None, 22, 22, 256) 0 ['batch_normalization_156[0][0]']\n", + " \n", + " activation_158 (Activation) (None, 22, 22, 256) 0 ['batch_normalization_158[0][0]']\n", + " \n", + " activation_161 (Activation) (None, 22, 22, 288) 0 ['batch_normalization_161[0][0]']\n", + " \n", + " conv2d_157 (Conv2D) (None, 10, 10, 384) 884736 ['activation_156[0][0]'] \n", + " \n", + " conv2d_159 (Conv2D) (None, 10, 10, 288) 663552 ['activation_158[0][0]'] \n", + " \n", + " conv2d_162 (Conv2D) (None, 10, 10, 320) 829440 ['activation_161[0][0]'] \n", + " \n", + " batch_normalization_157 (Batch (None, 10, 10, 384) 1152 ['conv2d_157[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_159 (Batch (None, 10, 10, 288) 864 ['conv2d_159[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_162 (Batch (None, 10, 10, 320) 960 ['conv2d_162[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_157 (Activation) (None, 10, 10, 384) 0 ['batch_normalization_157[0][0]']\n", + " \n", + " activation_159 (Activation) (None, 10, 10, 288) 0 ['batch_normalization_159[0][0]']\n", + " \n", + " activation_162 (Activation) (None, 10, 10, 320) 0 ['batch_normalization_162[0][0]']\n", + " \n", + " max_pooling2d_3 (MaxPooling2D) (None, 10, 10, 1088 0 ['block17_20_ac[0][0]'] \n", + " ) \n", + " \n", + " mixed_7a (Concatenate) (None, 10, 10, 2080 0 ['activation_157[0][0]', \n", + " ) 'activation_159[0][0]', \n", + " 'activation_162[0][0]', \n", + " 'max_pooling2d_3[0][0]'] \n", + " \n", + " conv2d_164 (Conv2D) (None, 10, 10, 192) 399360 ['mixed_7a[0][0]'] \n", + " \n", + " batch_normalization_164 (Batch (None, 10, 10, 192) 576 ['conv2d_164[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_164 (Activation) (None, 10, 10, 192) 0 ['batch_normalization_164[0][0]']\n", + " \n", + " conv2d_165 (Conv2D) (None, 10, 10, 224) 129024 ['activation_164[0][0]'] \n", + " \n", + " batch_normalization_165 (Batch (None, 10, 10, 224) 672 ['conv2d_165[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_165 (Activation) (None, 10, 10, 224) 0 ['batch_normalization_165[0][0]']\n", + " \n", + " conv2d_163 (Conv2D) (None, 10, 10, 192) 399360 ['mixed_7a[0][0]'] \n", + " \n", + " conv2d_166 (Conv2D) (None, 10, 10, 256) 172032 ['activation_165[0][0]'] \n", + " \n", + " batch_normalization_163 (Batch (None, 10, 10, 192) 576 ['conv2d_163[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_166 (Batch (None, 10, 10, 256) 768 ['conv2d_166[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_163 (Activation) (None, 10, 10, 192) 0 ['batch_normalization_163[0][0]']\n", + " \n", + " activation_166 (Activation) (None, 10, 10, 256) 0 ['batch_normalization_166[0][0]']\n", + " \n", + " block8_1_mixed (Concatenate) (None, 10, 10, 448) 0 ['activation_163[0][0]', \n", + " 'activation_166[0][0]'] \n", + " \n", + " block8_1_conv (Conv2D) (None, 10, 10, 2080 933920 ['block8_1_mixed[0][0]'] \n", + " ) \n", + " \n", + " block8_1 (Lambda) (None, 10, 10, 2080 0 ['mixed_7a[0][0]', \n", + " ) 'block8_1_conv[0][0]'] \n", + " \n", + " block8_1_ac (Activation) (None, 10, 10, 2080 0 ['block8_1[0][0]'] \n", + " ) \n", + " \n", + " conv2d_168 (Conv2D) (None, 10, 10, 192) 399360 ['block8_1_ac[0][0]'] \n", + " \n", + " batch_normalization_168 (Batch (None, 10, 10, 192) 576 ['conv2d_168[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_168 (Activation) (None, 10, 10, 192) 0 ['batch_normalization_168[0][0]']\n", + " \n", + " conv2d_169 (Conv2D) (None, 10, 10, 224) 129024 ['activation_168[0][0]'] \n", + " \n", + " batch_normalization_169 (Batch (None, 10, 10, 224) 672 ['conv2d_169[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_169 (Activation) (None, 10, 10, 224) 0 ['batch_normalization_169[0][0]']\n", + " \n", + " conv2d_167 (Conv2D) (None, 10, 10, 192) 399360 ['block8_1_ac[0][0]'] \n", + " \n", + " conv2d_170 (Conv2D) (None, 10, 10, 256) 172032 ['activation_169[0][0]'] \n", + " \n", + " batch_normalization_167 (Batch (None, 10, 10, 192) 576 ['conv2d_167[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_170 (Batch (None, 10, 10, 256) 768 ['conv2d_170[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_167 (Activation) (None, 10, 10, 192) 0 ['batch_normalization_167[0][0]']\n", + " \n", + " activation_170 (Activation) (None, 10, 10, 256) 0 ['batch_normalization_170[0][0]']\n", + " \n", + " block8_2_mixed (Concatenate) (None, 10, 10, 448) 0 ['activation_167[0][0]', \n", + " 'activation_170[0][0]'] \n", + " \n", + " block8_2_conv (Conv2D) (None, 10, 10, 2080 933920 ['block8_2_mixed[0][0]'] \n", + " ) \n", + " \n", + " block8_2 (Lambda) (None, 10, 10, 2080 0 ['block8_1_ac[0][0]', \n", + " ) 'block8_2_conv[0][0]'] \n", + " \n", + " block8_2_ac (Activation) (None, 10, 10, 2080 0 ['block8_2[0][0]'] \n", + " ) \n", + " \n", + " conv2d_172 (Conv2D) (None, 10, 10, 192) 399360 ['block8_2_ac[0][0]'] \n", + " \n", + " batch_normalization_172 (Batch (None, 10, 10, 192) 576 ['conv2d_172[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_172 (Activation) (None, 10, 10, 192) 0 ['batch_normalization_172[0][0]']\n", + " \n", + " conv2d_173 (Conv2D) (None, 10, 10, 224) 129024 ['activation_172[0][0]'] \n", + " \n", + " batch_normalization_173 (Batch (None, 10, 10, 224) 672 ['conv2d_173[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_173 (Activation) (None, 10, 10, 224) 0 ['batch_normalization_173[0][0]']\n", + " \n", + " conv2d_171 (Conv2D) (None, 10, 10, 192) 399360 ['block8_2_ac[0][0]'] \n", + " \n", + " conv2d_174 (Conv2D) (None, 10, 10, 256) 172032 ['activation_173[0][0]'] \n", + " \n", + " batch_normalization_171 (Batch (None, 10, 10, 192) 576 ['conv2d_171[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_174 (Batch (None, 10, 10, 256) 768 ['conv2d_174[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_171 (Activation) (None, 10, 10, 192) 0 ['batch_normalization_171[0][0]']\n", + " \n", + " activation_174 (Activation) (None, 10, 10, 256) 0 ['batch_normalization_174[0][0]']\n", + " \n", + " block8_3_mixed (Concatenate) (None, 10, 10, 448) 0 ['activation_171[0][0]', \n", + " 'activation_174[0][0]'] \n", + " \n", + " block8_3_conv (Conv2D) (None, 10, 10, 2080 933920 ['block8_3_mixed[0][0]'] \n", + " ) \n", + " \n", + " block8_3 (Lambda) (None, 10, 10, 2080 0 ['block8_2_ac[0][0]', \n", + " ) 'block8_3_conv[0][0]'] \n", + " \n", + " block8_3_ac (Activation) (None, 10, 10, 2080 0 ['block8_3[0][0]'] \n", + " ) \n", + " \n", + " conv2d_176 (Conv2D) (None, 10, 10, 192) 399360 ['block8_3_ac[0][0]'] \n", + " \n", + " batch_normalization_176 (Batch (None, 10, 10, 192) 576 ['conv2d_176[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_176 (Activation) (None, 10, 10, 192) 0 ['batch_normalization_176[0][0]']\n", + " \n", + " conv2d_177 (Conv2D) (None, 10, 10, 224) 129024 ['activation_176[0][0]'] \n", + " \n", + " batch_normalization_177 (Batch (None, 10, 10, 224) 672 ['conv2d_177[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_177 (Activation) (None, 10, 10, 224) 0 ['batch_normalization_177[0][0]']\n", + " \n", + " conv2d_175 (Conv2D) (None, 10, 10, 192) 399360 ['block8_3_ac[0][0]'] \n", + " \n", + " conv2d_178 (Conv2D) (None, 10, 10, 256) 172032 ['activation_177[0][0]'] \n", + " \n", + " batch_normalization_175 (Batch (None, 10, 10, 192) 576 ['conv2d_175[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_178 (Batch (None, 10, 10, 256) 768 ['conv2d_178[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_175 (Activation) (None, 10, 10, 192) 0 ['batch_normalization_175[0][0]']\n", + " \n", + " activation_178 (Activation) (None, 10, 10, 256) 0 ['batch_normalization_178[0][0]']\n", + " \n", + " block8_4_mixed (Concatenate) (None, 10, 10, 448) 0 ['activation_175[0][0]', \n", + " 'activation_178[0][0]'] \n", + " \n", + " block8_4_conv (Conv2D) (None, 10, 10, 2080 933920 ['block8_4_mixed[0][0]'] \n", + " ) \n", + " \n", + " block8_4 (Lambda) (None, 10, 10, 2080 0 ['block8_3_ac[0][0]', \n", + " ) 'block8_4_conv[0][0]'] \n", + " \n", + " block8_4_ac (Activation) (None, 10, 10, 2080 0 ['block8_4[0][0]'] \n", + " ) \n", + " \n", + " conv2d_180 (Conv2D) (None, 10, 10, 192) 399360 ['block8_4_ac[0][0]'] \n", + " \n", + " batch_normalization_180 (Batch (None, 10, 10, 192) 576 ['conv2d_180[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_180 (Activation) (None, 10, 10, 192) 0 ['batch_normalization_180[0][0]']\n", + " \n", + " conv2d_181 (Conv2D) (None, 10, 10, 224) 129024 ['activation_180[0][0]'] \n", + " \n", + " batch_normalization_181 (Batch (None, 10, 10, 224) 672 ['conv2d_181[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_181 (Activation) (None, 10, 10, 224) 0 ['batch_normalization_181[0][0]']\n", + " \n", + " conv2d_179 (Conv2D) (None, 10, 10, 192) 399360 ['block8_4_ac[0][0]'] \n", + " \n", + " conv2d_182 (Conv2D) (None, 10, 10, 256) 172032 ['activation_181[0][0]'] \n", + " \n", + " batch_normalization_179 (Batch (None, 10, 10, 192) 576 ['conv2d_179[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_182 (Batch (None, 10, 10, 256) 768 ['conv2d_182[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_179 (Activation) (None, 10, 10, 192) 0 ['batch_normalization_179[0][0]']\n", + " \n", + " activation_182 (Activation) (None, 10, 10, 256) 0 ['batch_normalization_182[0][0]']\n", + " \n", + " block8_5_mixed (Concatenate) (None, 10, 10, 448) 0 ['activation_179[0][0]', \n", + " 'activation_182[0][0]'] \n", + " \n", + " block8_5_conv (Conv2D) (None, 10, 10, 2080 933920 ['block8_5_mixed[0][0]'] \n", + " ) \n", + " \n", + " block8_5 (Lambda) (None, 10, 10, 2080 0 ['block8_4_ac[0][0]', \n", + " ) 'block8_5_conv[0][0]'] \n", + " \n", + " block8_5_ac (Activation) (None, 10, 10, 2080 0 ['block8_5[0][0]'] \n", + " ) \n", + " \n", + " conv2d_184 (Conv2D) (None, 10, 10, 192) 399360 ['block8_5_ac[0][0]'] \n", + " \n", + " batch_normalization_184 (Batch (None, 10, 10, 192) 576 ['conv2d_184[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_184 (Activation) (None, 10, 10, 192) 0 ['batch_normalization_184[0][0]']\n", + " \n", + " conv2d_185 (Conv2D) (None, 10, 10, 224) 129024 ['activation_184[0][0]'] \n", + " \n", + " batch_normalization_185 (Batch (None, 10, 10, 224) 672 ['conv2d_185[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_185 (Activation) (None, 10, 10, 224) 0 ['batch_normalization_185[0][0]']\n", + " \n", + " conv2d_183 (Conv2D) (None, 10, 10, 192) 399360 ['block8_5_ac[0][0]'] \n", + " \n", + " conv2d_186 (Conv2D) (None, 10, 10, 256) 172032 ['activation_185[0][0]'] \n", + " \n", + " batch_normalization_183 (Batch (None, 10, 10, 192) 576 ['conv2d_183[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_186 (Batch (None, 10, 10, 256) 768 ['conv2d_186[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_183 (Activation) (None, 10, 10, 192) 0 ['batch_normalization_183[0][0]']\n", + " \n", + " activation_186 (Activation) (None, 10, 10, 256) 0 ['batch_normalization_186[0][0]']\n", + " \n", + " block8_6_mixed (Concatenate) (None, 10, 10, 448) 0 ['activation_183[0][0]', \n", + " 'activation_186[0][0]'] \n", + " \n", + " block8_6_conv (Conv2D) (None, 10, 10, 2080 933920 ['block8_6_mixed[0][0]'] \n", + " ) \n", + " \n", + " block8_6 (Lambda) (None, 10, 10, 2080 0 ['block8_5_ac[0][0]', \n", + " ) 'block8_6_conv[0][0]'] \n", + " \n", + " block8_6_ac (Activation) (None, 10, 10, 2080 0 ['block8_6[0][0]'] \n", + " ) \n", + " \n", + " conv2d_188 (Conv2D) (None, 10, 10, 192) 399360 ['block8_6_ac[0][0]'] \n", + " \n", + " batch_normalization_188 (Batch (None, 10, 10, 192) 576 ['conv2d_188[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_188 (Activation) (None, 10, 10, 192) 0 ['batch_normalization_188[0][0]']\n", + " \n", + " conv2d_189 (Conv2D) (None, 10, 10, 224) 129024 ['activation_188[0][0]'] \n", + " \n", + " batch_normalization_189 (Batch (None, 10, 10, 224) 672 ['conv2d_189[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_189 (Activation) (None, 10, 10, 224) 0 ['batch_normalization_189[0][0]']\n", + " \n", + " conv2d_187 (Conv2D) (None, 10, 10, 192) 399360 ['block8_6_ac[0][0]'] \n", + " \n", + " conv2d_190 (Conv2D) (None, 10, 10, 256) 172032 ['activation_189[0][0]'] \n", + " \n", + " batch_normalization_187 (Batch (None, 10, 10, 192) 576 ['conv2d_187[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_190 (Batch (None, 10, 10, 256) 768 ['conv2d_190[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_187 (Activation) (None, 10, 10, 192) 0 ['batch_normalization_187[0][0]']\n", + " \n", + " activation_190 (Activation) (None, 10, 10, 256) 0 ['batch_normalization_190[0][0]']\n", + " \n", + " block8_7_mixed (Concatenate) (None, 10, 10, 448) 0 ['activation_187[0][0]', \n", + " 'activation_190[0][0]'] \n", + " \n", + " block8_7_conv (Conv2D) (None, 10, 10, 2080 933920 ['block8_7_mixed[0][0]'] \n", + " ) \n", + " \n", + " block8_7 (Lambda) (None, 10, 10, 2080 0 ['block8_6_ac[0][0]', \n", + " ) 'block8_7_conv[0][0]'] \n", + " \n", + " block8_7_ac (Activation) (None, 10, 10, 2080 0 ['block8_7[0][0]'] \n", + " ) \n", + " \n", + " conv2d_192 (Conv2D) (None, 10, 10, 192) 399360 ['block8_7_ac[0][0]'] \n", + " \n", + " batch_normalization_192 (Batch (None, 10, 10, 192) 576 ['conv2d_192[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_192 (Activation) (None, 10, 10, 192) 0 ['batch_normalization_192[0][0]']\n", + " \n", + " conv2d_193 (Conv2D) (None, 10, 10, 224) 129024 ['activation_192[0][0]'] \n", + " \n", + " batch_normalization_193 (Batch (None, 10, 10, 224) 672 ['conv2d_193[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_193 (Activation) (None, 10, 10, 224) 0 ['batch_normalization_193[0][0]']\n", + " \n", + " conv2d_191 (Conv2D) (None, 10, 10, 192) 399360 ['block8_7_ac[0][0]'] \n", + " \n", + " conv2d_194 (Conv2D) (None, 10, 10, 256) 172032 ['activation_193[0][0]'] \n", + " \n", + " batch_normalization_191 (Batch (None, 10, 10, 192) 576 ['conv2d_191[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_194 (Batch (None, 10, 10, 256) 768 ['conv2d_194[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_191 (Activation) (None, 10, 10, 192) 0 ['batch_normalization_191[0][0]']\n", + " \n", + " activation_194 (Activation) (None, 10, 10, 256) 0 ['batch_normalization_194[0][0]']\n", + " \n", + " block8_8_mixed (Concatenate) (None, 10, 10, 448) 0 ['activation_191[0][0]', \n", + " 'activation_194[0][0]'] \n", + " \n", + " block8_8_conv (Conv2D) (None, 10, 10, 2080 933920 ['block8_8_mixed[0][0]'] \n", + " ) \n", + " \n", + " block8_8 (Lambda) (None, 10, 10, 2080 0 ['block8_7_ac[0][0]', \n", + " ) 'block8_8_conv[0][0]'] \n", + " \n", + " block8_8_ac (Activation) (None, 10, 10, 2080 0 ['block8_8[0][0]'] \n", + " ) \n", + " \n", + " conv2d_196 (Conv2D) (None, 10, 10, 192) 399360 ['block8_8_ac[0][0]'] \n", + " \n", + " batch_normalization_196 (Batch (None, 10, 10, 192) 576 ['conv2d_196[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_196 (Activation) (None, 10, 10, 192) 0 ['batch_normalization_196[0][0]']\n", + " \n", + " conv2d_197 (Conv2D) (None, 10, 10, 224) 129024 ['activation_196[0][0]'] \n", + " \n", + " batch_normalization_197 (Batch (None, 10, 10, 224) 672 ['conv2d_197[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_197 (Activation) (None, 10, 10, 224) 0 ['batch_normalization_197[0][0]']\n", + " \n", + " conv2d_195 (Conv2D) (None, 10, 10, 192) 399360 ['block8_8_ac[0][0]'] \n", + " \n", + " conv2d_198 (Conv2D) (None, 10, 10, 256) 172032 ['activation_197[0][0]'] \n", + " \n", + " batch_normalization_195 (Batch (None, 10, 10, 192) 576 ['conv2d_195[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_198 (Batch (None, 10, 10, 256) 768 ['conv2d_198[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_195 (Activation) (None, 10, 10, 192) 0 ['batch_normalization_195[0][0]']\n", + " \n", + " activation_198 (Activation) (None, 10, 10, 256) 0 ['batch_normalization_198[0][0]']\n", + " \n", + " block8_9_mixed (Concatenate) (None, 10, 10, 448) 0 ['activation_195[0][0]', \n", + " 'activation_198[0][0]'] \n", + " \n", + " block8_9_conv (Conv2D) (None, 10, 10, 2080 933920 ['block8_9_mixed[0][0]'] \n", + " ) \n", + " \n", + " block8_9 (Lambda) (None, 10, 10, 2080 0 ['block8_8_ac[0][0]', \n", + " ) 'block8_9_conv[0][0]'] \n", + " \n", + " block8_9_ac (Activation) (None, 10, 10, 2080 0 ['block8_9[0][0]'] \n", + " ) \n", + " \n", + " conv2d_200 (Conv2D) (None, 10, 10, 192) 399360 ['block8_9_ac[0][0]'] \n", + " \n", + " batch_normalization_200 (Batch (None, 10, 10, 192) 576 ['conv2d_200[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_200 (Activation) (None, 10, 10, 192) 0 ['batch_normalization_200[0][0]']\n", + " \n", + " conv2d_201 (Conv2D) (None, 10, 10, 224) 129024 ['activation_200[0][0]'] \n", + " \n", + " batch_normalization_201 (Batch (None, 10, 10, 224) 672 ['conv2d_201[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_201 (Activation) (None, 10, 10, 224) 0 ['batch_normalization_201[0][0]']\n", + " \n", + " conv2d_199 (Conv2D) (None, 10, 10, 192) 399360 ['block8_9_ac[0][0]'] \n", + " \n", + " conv2d_202 (Conv2D) (None, 10, 10, 256) 172032 ['activation_201[0][0]'] \n", + " \n", + " batch_normalization_199 (Batch (None, 10, 10, 192) 576 ['conv2d_199[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_202 (Batch (None, 10, 10, 256) 768 ['conv2d_202[0][0]'] \n", + " Normalization) \n", + " \n", + " activation_199 (Activation) (None, 10, 10, 192) 0 ['batch_normalization_199[0][0]']\n", + " \n", + " activation_202 (Activation) (None, 10, 10, 256) 0 ['batch_normalization_202[0][0]']\n", + " \n", + " block8_10_mixed (Concatenate) (None, 10, 10, 448) 0 ['activation_199[0][0]', \n", + " 'activation_202[0][0]'] \n", + " \n", + " block8_10_conv (Conv2D) (None, 10, 10, 2080 933920 ['block8_10_mixed[0][0]'] \n", + " ) \n", + " \n", + " block8_10 (Lambda) (None, 10, 10, 2080 0 ['block8_9_ac[0][0]', \n", + " ) 'block8_10_conv[0][0]'] \n", + " \n", + " conv_7b (Conv2D) (None, 10, 10, 1536 3194880 ['block8_10[0][0]'] \n", + " ) \n", + " \n", + " conv_7b_bn (BatchNormalization (None, 10, 10, 1536 4608 ['conv_7b[0][0]'] \n", + " ) ) \n", + " \n", + " conv_7b_ac (Activation) (None, 10, 10, 1536 0 ['conv_7b_bn[0][0]'] \n", + " ) \n", + " \n", + " global_average_pooling2d (Glob (None, 1536) 0 ['conv_7b_ac[0][0]'] \n", + " alAveragePooling2D) \n", + " \n", + " dense (Dense) (None, 1024) 1573888 ['global_average_pooling2d[0][0]'\n", + " ] \n", + " \n", + " dense_1 (Dense) (None, 1024) 1049600 ['dense[0][0]'] \n", + " \n", + " dropout (Dropout) (None, 1024) 0 ['dense_1[0][0]'] \n", + " \n", + " dense_2 (Dense) (None, 1024) 1049600 ['dropout[0][0]'] \n", + " \n", + " dense_3 (Dense) (None, 1024) 1049600 ['dense_2[0][0]'] \n", + " \n", + " dropout_1 (Dropout) (None, 1024) 0 ['dense_3[0][0]'] \n", + " \n", + " dense_4 (Dense) (None, 1024) 1049600 ['dropout_1[0][0]'] \n", + " \n", + " dense_5 (Dense) (None, 1024) 1049600 ['dense_4[0][0]'] \n", + " \n", + " dense_6 (Dense) (None, 44) 45100 ['dense_5[0][0]'] \n", + " \n", + "==================================================================================================\n", + "Total params: 61,203,724\n", + "Trainable params: 61,143,180\n", + "Non-trainable params: 60,544\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "moylH4EzX8VJ" + }, + "outputs": [], + "source": [ + "tf.keras.backend.clear_session()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "TwyQOmiGWdMa", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "b9786dfb-0a95-497a-d2c4-9e8ba0281822" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Nº de camadas: 790\n" + ] + } + ], + "source": [ + "camadas = dict([(layer.name, layer) for layer in model.layers])\n", + "\n", + "print(f'Nº de camadas: {len(camadas)}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iQehUhlmXhab", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8ffdbeb1-9b78-4cc0-ec81-a1c991da4b2d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/50\n", + "250/250 [==============================] - 910s 3s/step - loss: 2.8746 - accuracy: 0.2195 - tp: 309.0000 - tn: 171823.0000 - fp: 177.0000 - fn: 3691.0000 - precision: 0.1108 - sensitivity: 0.8825 - specificity: 0.8999 - recall: 0.0772 - FBetaScore: 0.0444 - val_loss: 2.5777 - val_accuracy: 0.3150 - val_tp: 27.0000 - val_tn: 8596.0000 - val_fp: 4.0000 - val_fn: 173.0000 - val_precision: 0.1896 - val_sensitivity: 0.8950 - val_specificity: 0.9493 - val_recall: 0.1350 - val_FBetaScore: 0.0538 - lr: 1.0000e-04\n", + "Epoch 2/50\n", + "250/250 [==============================] - 600s 2s/step - loss: 1.7478 - accuracy: 0.4837 - tp: 1312.0000 - tn: 171586.0000 - fp: 414.0000 - fn: 2688.0000 - precision: 0.5162 - sensitivity: 0.9847 - specificity: 0.9890 - recall: 0.3280 - FBetaScore: 0.1861 - val_loss: 2.4842 - val_accuracy: 0.3550 - val_tp: 43.0000 - val_tn: 8582.0000 - val_fp: 18.0000 - val_fn: 157.0000 - val_precision: 0.2415 - val_sensitivity: 0.8950 - val_specificity: 0.9635 - val_recall: 0.2150 - val_FBetaScore: 0.1760 - lr: 1.0000e-04\n", + "Epoch 3/50\n", + "250/250 [==============================] - 445s 2s/step - loss: 1.2441 - accuracy: 0.6133 - tp: 2000.0000 - tn: 171602.0000 - fp: 398.0000 - fn: 2000.0000 - precision: 0.8328 - sensitivity: 0.9918 - specificity: 0.9977 - recall: 0.5000 - FBetaScore: 0.3113 - val_loss: 1.5013 - val_accuracy: 0.6050 - val_tp: 110.0000 - val_tn: 8566.0000 - val_fp: 34.0000 - val_fn: 90.0000 - val_precision: 0.8707 - val_sensitivity: 0.9300 - val_specificity: 0.9983 - val_recall: 0.5500 - val_FBetaScore: 0.3188 - lr: 1.0000e-04\n", + "Epoch 4/50\n", + "250/250 [==============================] - 354s 1s/step - loss: 0.9197 - accuracy: 0.7222 - tp: 2539.0000 - tn: 171589.0000 - fp: 411.0000 - fn: 1461.0000 - precision: 0.9366 - sensitivity: 0.9875 - specificity: 0.9992 - recall: 0.6348 - FBetaScore: 0.4235 - val_loss: 1.7486 - val_accuracy: 0.5750 - val_tp: 99.0000 - val_tn: 8552.0000 - val_fp: 48.0000 - val_fn: 101.0000 - val_precision: 0.6779 - val_sensitivity: 0.9000 - val_specificity: 0.9944 - val_recall: 0.4950 - val_FBetaScore: 0.3332 - lr: 1.0000e-04\n", + "Epoch 5/50\n", + "250/250 [==============================] - 291s 1s/step - loss: 0.7312 - accuracy: 0.7690 - tp: 2823.0000 - tn: 171642.0000 - fp: 358.0000 - fn: 1177.0000 - precision: 0.9730 - sensitivity: 0.9887 - specificity: 0.9997 - recall: 0.7057 - FBetaScore: 0.5129 - val_loss: 1.7029 - val_accuracy: 0.5350 - val_tp: 95.0000 - val_tn: 8559.0000 - val_fp: 41.0000 - val_fn: 105.0000 - val_precision: 0.6623 - val_sensitivity: 0.9100 - val_specificity: 0.9941 - val_recall: 0.4750 - val_FBetaScore: 0.3731 - lr: 1.0000e-04\n", + "Epoch 6/50\n", + "250/250 [==============================] - 240s 958ms/step - loss: 0.5925 - accuracy: 0.8140 - tp: 3038.0000 - tn: 171672.0000 - fp: 328.0000 - fn: 962.0000 - precision: 0.9864 - sensitivity: 0.9915 - specificity: 0.9998 - recall: 0.7595 - FBetaScore: 0.5737 - val_loss: 1.7838 - val_accuracy: 0.6000 - val_tp: 110.0000 - val_tn: 8547.0000 - val_fp: 53.0000 - val_fn: 90.0000 - val_precision: 0.7481 - val_sensitivity: 0.8900 - val_specificity: 0.9960 - val_recall: 0.5500 - val_FBetaScore: 0.3406 - lr: 1.0000e-04\n", + "Epoch 7/50\n", + "250/250 [==============================] - 214s 853ms/step - loss: 0.4579 - accuracy: 0.8570 - tp: 3259.0000 - tn: 171166.0000 - fp: 275.0000 - fn: 728.0000 - precision: 0.9901 - sensitivity: 0.9927 - specificity: 0.9999 - recall: 0.8174 - FBetaScore: 0.6509 - val_loss: 1.2757 - val_accuracy: 0.7100 - val_tp: 134.0000 - val_tn: 8567.0000 - val_fp: 33.0000 - val_fn: 66.0000 - val_precision: 0.9346 - val_sensitivity: 0.9200 - val_specificity: 0.9992 - val_recall: 0.6700 - val_FBetaScore: 0.4944 - lr: 1.0000e-04\n", + "Epoch 8/50\n", + "250/250 [==============================] - 198s 789ms/step - loss: 0.3746 - accuracy: 0.8866 - tp: 3407.0000 - tn: 171180.0000 - fp: 261.0000 - fn: 580.0000 - precision: 0.9947 - sensitivity: 0.9930 - specificity: 0.9999 - recall: 0.8545 - FBetaScore: 0.7120 - val_loss: 0.8548 - val_accuracy: 0.7750 - val_tp: 150.0000 - val_tn: 8565.0000 - val_fp: 35.0000 - val_fn: 50.0000 - val_precision: 0.9722 - val_sensitivity: 0.9500 - val_specificity: 0.9997 - val_recall: 0.7500 - val_FBetaScore: 0.5287 - lr: 1.0000e-04\n", + "Epoch 9/50\n", + "250/250 [==============================] - 188s 749ms/step - loss: 0.3093 - accuracy: 0.9032 - tp: 3497.0000 - tn: 171198.0000 - fp: 243.0000 - fn: 490.0000 - precision: 0.9953 - sensitivity: 0.9955 - specificity: 0.9999 - recall: 0.8771 - FBetaScore: 0.7525 - val_loss: 0.9071 - val_accuracy: 0.8100 - val_tp: 154.0000 - val_tn: 8568.0000 - val_fp: 32.0000 - val_fn: 46.0000 - val_precision: 0.9744 - val_sensitivity: 0.9300 - val_specificity: 0.9997 - val_recall: 0.7700 - val_FBetaScore: 0.6682 - lr: 1.0000e-04\n", + "Epoch 10/50\n", + "250/250 [==============================] - 182s 726ms/step - loss: 0.2357 - accuracy: 0.9330 - tp: 3638.0000 - tn: 171252.0000 - fp: 189.0000 - fn: 349.0000 - precision: 0.9976 - sensitivity: 0.9942 - specificity: 1.0000 - recall: 0.9125 - FBetaScore: 0.8159 - val_loss: 2.7404 - val_accuracy: 0.7600 - val_tp: 146.0000 - val_tn: 8566.0000 - val_fp: 34.0000 - val_fn: 54.0000 - val_precision: 0.9402 - val_sensitivity: 0.9250 - val_specificity: 0.9992 - val_recall: 0.7300 - val_FBetaScore: 0.5593 - lr: 1.0000e-04\n", + "Epoch 11/50\n", + "250/250 [==============================] - ETA: 0s - loss: 0.2651 - accuracy: 0.9175 - tp: 3581.0000 - tn: 171209.0000 - fp: 232.0000 - fn: 406.0000 - precision: 0.9971 - sensitivity: 0.9950 - specificity: 1.0000 - recall: 0.8982 - FBetaScore: 0.8304\n", + "Epoch 11: ReduceLROnPlateau reducing learning rate to 1.9999999494757503e-05.\n", + "250/250 [==============================] - 173s 689ms/step - loss: 0.2651 - accuracy: 0.9175 - tp: 3581.0000 - tn: 171209.0000 - fp: 232.0000 - fn: 406.0000 - precision: 0.9971 - sensitivity: 0.9950 - specificity: 1.0000 - recall: 0.8982 - FBetaScore: 0.8304 - val_loss: 0.8879 - val_accuracy: 0.8050 - val_tp: 154.0000 - val_tn: 8573.0000 - val_fp: 27.0000 - val_fn: 46.0000 - val_precision: 0.9626 - val_sensitivity: 0.9450 - val_specificity: 0.9995 - val_recall: 0.7700 - val_FBetaScore: 0.6546 - lr: 1.0000e-04\n", + "Epoch 12/50\n", + "250/250 [==============================] - 167s 665ms/step - loss: 0.1069 - accuracy: 0.9681 - tp: 3829.0000 - tn: 171358.0000 - fp: 83.0000 - fn: 158.0000 - precision: 0.9985 - sensitivity: 0.9982 - specificity: 1.0000 - recall: 0.9604 - FBetaScore: 0.9135 - val_loss: 0.7453 - val_accuracy: 0.8600 - val_tp: 170.0000 - val_tn: 8579.0000 - val_fp: 21.0000 - val_fn: 30.0000 - val_precision: 0.9769 - val_sensitivity: 0.9550 - val_specificity: 0.9997 - val_recall: 0.8500 - val_FBetaScore: 0.7061 - lr: 2.0000e-05\n", + "Epoch 13/50\n", + "250/250 [==============================] - 167s 665ms/step - loss: 0.0569 - accuracy: 0.9850 - tp: 3912.0000 - tn: 171395.0000 - fp: 46.0000 - fn: 75.0000 - precision: 0.9994 - sensitivity: 0.9995 - specificity: 1.0000 - recall: 0.9812 - FBetaScore: 0.9610 - val_loss: 1.3466 - val_accuracy: 0.7850 - val_tp: 155.0000 - val_tn: 8565.0000 - val_fp: 35.0000 - val_fn: 45.0000 - val_precision: 0.9649 - val_sensitivity: 0.9050 - val_specificity: 0.9995 - val_recall: 0.7750 - val_FBetaScore: 0.6481 - lr: 2.0000e-05\n", + "Epoch 14/50\n", + "250/250 [==============================] - 166s 663ms/step - loss: 0.0470 - accuracy: 0.9857 - tp: 3921.0000 - tn: 171401.0000 - fp: 40.0000 - fn: 66.0000 - precision: 1.0000 - sensitivity: 0.9992 - specificity: 1.0000 - recall: 0.9834 - FBetaScore: 0.9674 - val_loss: 0.8594 - val_accuracy: 0.8450 - val_tp: 167.0000 - val_tn: 8575.0000 - val_fp: 25.0000 - val_fn: 33.0000 - val_precision: 0.9595 - val_sensitivity: 0.9350 - val_specificity: 0.9993 - val_recall: 0.8350 - val_FBetaScore: 0.7149 - lr: 2.0000e-05\n", + "Epoch 15/50\n", + "250/250 [==============================] - 162s 647ms/step - loss: 0.0420 - accuracy: 0.9897 - tp: 3937.0000 - tn: 171409.0000 - fp: 32.0000 - fn: 50.0000 - precision: 0.9994 - sensitivity: 0.9987 - specificity: 1.0000 - recall: 0.9875 - FBetaScore: 0.9785 - val_loss: 0.9967 - val_accuracy: 0.8350 - val_tp: 166.0000 - val_tn: 8575.0000 - val_fp: 25.0000 - val_fn: 34.0000 - val_precision: 0.9764 - val_sensitivity: 0.9250 - val_specificity: 0.9997 - val_recall: 0.8300 - val_FBetaScore: 0.7439 - lr: 2.0000e-05\n", + "Epoch 16/50\n", + "250/250 [==============================] - ETA: 0s - loss: 0.0394 - accuracy: 0.9893 - tp: 3953.0000 - tn: 171962.0000 - fp: 38.0000 - fn: 47.0000 - precision: 0.9994 - sensitivity: 0.9992 - specificity: 1.0000 - recall: 0.9883 - FBetaScore: 0.9765\n", + "Epoch 16: ReduceLROnPlateau reducing learning rate to 1e-05.\n", + "250/250 [==============================] - 163s 652ms/step - loss: 0.0394 - accuracy: 0.9893 - tp: 3953.0000 - tn: 171962.0000 - fp: 38.0000 - fn: 47.0000 - precision: 0.9994 - sensitivity: 0.9992 - specificity: 1.0000 - recall: 0.9883 - FBetaScore: 0.9765 - val_loss: 0.9197 - val_accuracy: 0.8300 - val_tp: 165.0000 - val_tn: 8574.0000 - val_fp: 26.0000 - val_fn: 35.0000 - val_precision: 0.9667 - val_sensitivity: 0.9250 - val_specificity: 0.9995 - val_recall: 0.8250 - val_FBetaScore: 0.6534 - lr: 2.0000e-05\n", + "Epoch 17/50\n", + "250/250 [==============================] - 165s 657ms/step - loss: 0.0347 - accuracy: 0.9902 - tp: 3959.0000 - tn: 171969.0000 - fp: 31.0000 - fn: 41.0000 - precision: 0.9997 - sensitivity: 0.9995 - specificity: 1.0000 - recall: 0.9898 - FBetaScore: 0.9810 - val_loss: 1.5242 - val_accuracy: 0.8350 - val_tp: 163.0000 - val_tn: 8572.0000 - val_fp: 28.0000 - val_fn: 37.0000 - val_precision: 0.9718 - val_sensitivity: 0.9100 - val_specificity: 0.9995 - val_recall: 0.8150 - val_FBetaScore: 0.7282 - lr: 1.0000e-05\n", + "Epoch 18/50\n", + "250/250 [==============================] - 159s 636ms/step - loss: 0.0174 - accuracy: 0.9952 - tp: 3977.0000 - tn: 171985.0000 - fp: 15.0000 - fn: 23.0000 - precision: 1.0000 - sensitivity: 1.0000 - specificity: 1.0000 - recall: 0.9942 - FBetaScore: 0.9921 - val_loss: 1.0557 - val_accuracy: 0.8200 - val_tp: 164.0000 - val_tn: 8566.0000 - val_fp: 34.0000 - val_fn: 36.0000 - val_precision: 0.9776 - val_sensitivity: 0.9050 - val_specificity: 0.9997 - val_recall: 0.8200 - val_FBetaScore: 0.7000 - lr: 1.0000e-05\n", + "Epoch 19/50\n", + "250/250 [==============================] - 163s 649ms/step - loss: 0.0146 - accuracy: 0.9952 - tp: 3966.0000 - tn: 171427.0000 - fp: 14.0000 - fn: 21.0000 - precision: 1.0000 - sensitivity: 1.0000 - specificity: 1.0000 - recall: 0.9947 - FBetaScore: 0.9886 - val_loss: 1.0255 - val_accuracy: 0.8150 - val_tp: 163.0000 - val_tn: 8565.0000 - val_fp: 35.0000 - val_fn: 37.0000 - val_precision: 0.9716 - val_sensitivity: 0.9300 - val_specificity: 0.9995 - val_recall: 0.8150 - val_FBetaScore: 0.6421 - lr: 1.0000e-05\n", + "Epoch 20/50\n", + "250/250 [==============================] - 159s 636ms/step - loss: 0.0181 - accuracy: 0.9945 - tp: 3963.0000 - tn: 171422.0000 - fp: 19.0000 - fn: 24.0000 - precision: 0.9997 - sensitivity: 0.9995 - specificity: 1.0000 - recall: 0.9940 - FBetaScore: 0.9876 - val_loss: 1.3913 - val_accuracy: 0.8200 - val_tp: 161.0000 - val_tn: 8569.0000 - val_fp: 31.0000 - val_fn: 39.0000 - val_precision: 0.9565 - val_sensitivity: 0.9300 - val_specificity: 0.9993 - val_recall: 0.8050 - val_FBetaScore: 0.7134 - lr: 1.0000e-05\n", + "Epoch 21/50\n", + "250/250 [==============================] - 159s 634ms/step - loss: 0.0136 - accuracy: 0.9967 - tp: 3972.0000 - tn: 171431.0000 - fp: 10.0000 - fn: 15.0000 - precision: 0.9997 - sensitivity: 0.9997 - specificity: 1.0000 - recall: 0.9962 - FBetaScore: 0.9903 - val_loss: 1.3329 - val_accuracy: 0.8250 - val_tp: 164.0000 - val_tn: 8567.0000 - val_fp: 33.0000 - val_fn: 36.0000 - val_precision: 0.9276 - val_sensitivity: 0.8800 - val_specificity: 0.9987 - val_recall: 0.8200 - val_FBetaScore: 0.6875 - lr: 1.0000e-05\n", + "Epoch 22/50\n", + "250/250 [==============================] - 160s 638ms/step - loss: 0.0179 - accuracy: 0.9952 - tp: 3965.0000 - tn: 171425.0000 - fp: 16.0000 - fn: 22.0000 - precision: 1.0000 - sensitivity: 0.9997 - specificity: 1.0000 - recall: 0.9945 - FBetaScore: 0.9917 - val_loss: 0.9453 - val_accuracy: 0.8500 - val_tp: 168.0000 - val_tn: 8576.0000 - val_fp: 24.0000 - val_fn: 32.0000 - val_precision: 0.9739 - val_sensitivity: 0.9150 - val_specificity: 0.9995 - val_recall: 0.8400 - val_FBetaScore: 0.6926 - lr: 1.0000e-05\n", + "Epoch 23/50\n", + "250/250 [==============================] - 157s 626ms/step - loss: 0.0183 - accuracy: 0.9940 - tp: 3960.0000 - tn: 171421.0000 - fp: 20.0000 - fn: 27.0000 - precision: 1.0000 - sensitivity: 0.9997 - specificity: 1.0000 - recall: 0.9932 - FBetaScore: 0.9905 - val_loss: 1.0863 - val_accuracy: 0.8150 - val_tp: 162.0000 - val_tn: 8570.0000 - val_fp: 30.0000 - val_fn: 38.0000 - val_precision: 0.9852 - val_sensitivity: 0.9350 - val_specificity: 0.9998 - val_recall: 0.8100 - val_FBetaScore: 0.6643 - lr: 1.0000e-05\n", + "Epoch 24/50\n", + "250/250 [==============================] - 158s 631ms/step - loss: 0.0132 - accuracy: 0.9958 - tp: 3982.0000 - tn: 171986.0000 - fp: 14.0000 - fn: 18.0000 - precision: 1.0000 - sensitivity: 0.9998 - specificity: 1.0000 - recall: 0.9955 - FBetaScore: 0.9908 - val_loss: 0.8032 - val_accuracy: 0.8400 - val_tp: 168.0000 - val_tn: 8574.0000 - val_fp: 26.0000 - val_fn: 32.0000 - val_precision: 0.9669 - val_sensitivity: 0.9550 - val_specificity: 0.9994 - val_recall: 0.8400 - val_FBetaScore: 0.6976 - lr: 1.0000e-05\n", + "Epoch 25/50\n", + "250/250 [==============================] - 155s 621ms/step - loss: 0.0168 - accuracy: 0.9950 - tp: 3976.0000 - tn: 171980.0000 - fp: 20.0000 - fn: 24.0000 - precision: 0.9997 - sensitivity: 0.9998 - specificity: 1.0000 - recall: 0.9940 - FBetaScore: 0.9903 - val_loss: 1.1891 - val_accuracy: 0.8200 - val_tp: 160.0000 - val_tn: 8567.0000 - val_fp: 33.0000 - val_fn: 40.0000 - val_precision: 0.9926 - val_sensitivity: 0.9200 - val_specificity: 0.9999 - val_recall: 0.8000 - val_FBetaScore: 0.6129 - lr: 1.0000e-05\n", + "Epoch 26/50\n", + "250/250 [==============================] - 158s 632ms/step - loss: 0.0124 - accuracy: 0.9965 - tp: 3986.0000 - tn: 171992.0000 - fp: 8.0000 - fn: 14.0000 - precision: 1.0000 - sensitivity: 0.9998 - specificity: 1.0000 - recall: 0.9965 - FBetaScore: 0.9947 - val_loss: 1.2435 - val_accuracy: 0.8400 - val_tp: 168.0000 - val_tn: 8573.0000 - val_fp: 27.0000 - val_fn: 32.0000 - val_precision: 0.9682 - val_sensitivity: 0.9100 - val_specificity: 0.9994 - val_recall: 0.8400 - val_FBetaScore: 0.6675 - lr: 1.0000e-05\n", + "Epoch 27/50\n", + "250/250 [==============================] - 154s 613ms/step - loss: 0.0167 - accuracy: 0.9947 - tp: 3964.0000 - tn: 171423.0000 - fp: 18.0000 - fn: 23.0000 - precision: 1.0000 - sensitivity: 1.0000 - specificity: 1.0000 - recall: 0.9942 - FBetaScore: 0.9920 - val_loss: 0.7857 - val_accuracy: 0.8600 - val_tp: 172.0000 - val_tn: 8575.0000 - val_fp: 25.0000 - val_fn: 28.0000 - val_precision: 0.9750 - val_sensitivity: 0.9350 - val_specificity: 0.9995 - val_recall: 0.8600 - val_FBetaScore: 0.6694 - lr: 1.0000e-05\n", + "Epoch 28/50\n", + "250/250 [==============================] - 153s 611ms/step - loss: 0.0120 - accuracy: 0.9958 - tp: 3981.0000 - tn: 171983.0000 - fp: 17.0000 - fn: 19.0000 - precision: 1.0000 - sensitivity: 1.0000 - specificity: 1.0000 - recall: 0.9952 - FBetaScore: 0.9931 - val_loss: 1.1486 - val_accuracy: 0.8200 - val_tp: 163.0000 - val_tn: 8568.0000 - val_fp: 32.0000 - val_fn: 37.0000 - val_precision: 0.9712 - val_sensitivity: 0.9000 - val_specificity: 0.9995 - val_recall: 0.8150 - val_FBetaScore: 0.7018 - lr: 1.0000e-05\n", + "Epoch 29/50\n", + "250/250 [==============================] - 159s 634ms/step - loss: 0.0174 - accuracy: 0.9940 - tp: 3960.0000 - tn: 171418.0000 - fp: 23.0000 - fn: 27.0000 - precision: 1.0000 - sensitivity: 0.9997 - specificity: 1.0000 - recall: 0.9932 - FBetaScore: 0.9908 - val_loss: 1.1503 - val_accuracy: 0.8650 - val_tp: 173.0000 - val_tn: 8575.0000 - val_fp: 25.0000 - val_fn: 27.0000 - val_precision: 0.9400 - val_sensitivity: 0.9000 - val_specificity: 0.9990 - val_recall: 0.8650 - val_FBetaScore: 0.7486 - lr: 1.0000e-05\n", + "Epoch 30/50\n", + "250/250 [==============================] - 158s 631ms/step - loss: 0.0118 - accuracy: 0.9965 - tp: 3984.0000 - tn: 171988.0000 - fp: 12.0000 - fn: 16.0000 - precision: 0.9997 - sensitivity: 0.9995 - specificity: 1.0000 - recall: 0.9960 - FBetaScore: 0.9944 - val_loss: 1.5066 - val_accuracy: 0.8100 - val_tp: 159.0000 - val_tn: 8566.0000 - val_fp: 34.0000 - val_fn: 41.0000 - val_precision: 0.9371 - val_sensitivity: 0.9050 - val_specificity: 0.9990 - val_recall: 0.7950 - val_FBetaScore: 0.6870 - lr: 1.0000e-05\n", + "Epoch 31/50\n", + "250/250 [==============================] - 155s 620ms/step - loss: 0.0106 - accuracy: 0.9970 - tp: 3988.0000 - tn: 171991.0000 - fp: 9.0000 - fn: 12.0000 - precision: 1.0000 - sensitivity: 0.9998 - specificity: 1.0000 - recall: 0.9970 - FBetaScore: 0.9962 - val_loss: 1.2496 - val_accuracy: 0.8450 - val_tp: 168.0000 - val_tn: 8572.0000 - val_fp: 28.0000 - val_fn: 32.0000 - val_precision: 0.9545 - val_sensitivity: 0.9100 - val_specificity: 0.9992 - val_recall: 0.8400 - val_FBetaScore: 0.6585 - lr: 1.0000e-05\n", + "Epoch 32/50\n", + "250/250 [==============================] - 154s 615ms/step - loss: 0.0121 - accuracy: 0.9952 - tp: 3980.0000 - tn: 171982.0000 - fp: 18.0000 - fn: 20.0000 - precision: 1.0000 - sensitivity: 1.0000 - specificity: 1.0000 - recall: 0.9950 - FBetaScore: 0.9914 - val_loss: 1.1742 - val_accuracy: 0.8050 - val_tp: 160.0000 - val_tn: 8567.0000 - val_fp: 33.0000 - val_fn: 40.0000 - val_precision: 0.9716 - val_sensitivity: 0.9050 - val_specificity: 0.9995 - val_recall: 0.8000 - val_FBetaScore: 0.6463 - lr: 1.0000e-05\n", + "Epoch 33/50\n", + "250/250 [==============================] - 156s 624ms/step - loss: 0.0133 - accuracy: 0.9970 - tp: 3985.0000 - tn: 171988.0000 - fp: 12.0000 - fn: 15.0000 - precision: 0.9997 - sensitivity: 0.9995 - specificity: 1.0000 - recall: 0.9962 - FBetaScore: 0.9935 - val_loss: 1.1088 - val_accuracy: 0.8500 - val_tp: 168.0000 - val_tn: 8575.0000 - val_fp: 25.0000 - val_fn: 32.0000 - val_precision: 0.9623 - val_sensitivity: 0.8950 - val_specificity: 0.9993 - val_recall: 0.8400 - val_FBetaScore: 0.6836 - lr: 1.0000e-05\n", + "Epoch 34/50\n", + "250/250 [==============================] - 155s 618ms/step - loss: 0.0089 - accuracy: 0.9973 - tp: 3988.0000 - tn: 171989.0000 - fp: 11.0000 - fn: 12.0000 - precision: 1.0000 - sensitivity: 1.0000 - specificity: 1.0000 - recall: 0.9970 - FBetaScore: 0.9973 - val_loss: 1.0852 - val_accuracy: 0.8400 - val_tp: 167.0000 - val_tn: 8572.0000 - val_fp: 28.0000 - val_fn: 33.0000 - val_precision: 0.9375 - val_sensitivity: 0.9050 - val_specificity: 0.9988 - val_recall: 0.8350 - val_FBetaScore: 0.6767 - lr: 1.0000e-05\n", + "Epoch 35/50\n", + "250/250 [==============================] - 155s 620ms/step - loss: 0.0098 - accuracy: 0.9962 - tp: 3983.0000 - tn: 171986.0000 - fp: 14.0000 - fn: 17.0000 - precision: 1.0000 - sensitivity: 1.0000 - specificity: 1.0000 - recall: 0.9958 - FBetaScore: 0.9928 - val_loss: 1.4998 - val_accuracy: 0.8500 - val_tp: 168.0000 - val_tn: 8572.0000 - val_fp: 28.0000 - val_fn: 32.0000 - val_precision: 0.9671 - val_sensitivity: 0.9100 - val_specificity: 0.9994 - val_recall: 0.8400 - val_FBetaScore: 0.6972 - lr: 1.0000e-05\n", + "Epoch 36/50\n", + "250/250 [==============================] - 155s 618ms/step - loss: 0.0154 - accuracy: 0.9958 - tp: 3981.0000 - tn: 171985.0000 - fp: 15.0000 - fn: 19.0000 - precision: 0.9997 - sensitivity: 0.9995 - specificity: 1.0000 - recall: 0.9952 - FBetaScore: 0.9940 - val_loss: 1.0997 - val_accuracy: 0.8500 - val_tp: 170.0000 - val_tn: 8571.0000 - val_fp: 29.0000 - val_fn: 30.0000 - val_precision: 0.9801 - val_sensitivity: 0.9050 - val_specificity: 0.9997 - val_recall: 0.8500 - val_FBetaScore: 0.6857 - lr: 1.0000e-05\n", + "Epoch 37/50\n", + "250/250 [==============================] - 154s 616ms/step - loss: 0.0168 - accuracy: 0.9947 - tp: 3962.0000 - tn: 171420.0000 - fp: 21.0000 - fn: 25.0000 - precision: 0.9995 - sensitivity: 0.9992 - specificity: 1.0000 - recall: 0.9937 - FBetaScore: 0.9932 - val_loss: 1.0131 - val_accuracy: 0.8200 - val_tp: 161.0000 - val_tn: 8566.0000 - val_fp: 34.0000 - val_fn: 39.0000 - val_precision: 0.9664 - val_sensitivity: 0.9200 - val_specificity: 0.9994 - val_recall: 0.8050 - val_FBetaScore: 0.6153 - lr: 1.0000e-05\n", + "Epoch 38/50\n", + "250/250 [==============================] - 154s 615ms/step - loss: 0.0061 - accuracy: 0.9985 - tp: 3981.0000 - tn: 171436.0000 - fp: 5.0000 - fn: 6.0000 - precision: 1.0000 - sensitivity: 1.0000 - specificity: 1.0000 - recall: 0.9985 - FBetaScore: 0.9983 - val_loss: 1.0441 - val_accuracy: 0.8500 - val_tp: 169.0000 - val_tn: 8572.0000 - val_fp: 28.0000 - val_fn: 31.0000 - val_precision: 0.9375 - val_sensitivity: 0.9200 - val_specificity: 0.9988 - val_recall: 0.8450 - val_FBetaScore: 0.6713 - lr: 1.0000e-05\n", + "Epoch 39/50\n", + "250/250 [==============================] - 153s 612ms/step - loss: 0.0043 - accuracy: 0.9987 - tp: 3995.0000 - tn: 171995.0000 - fp: 5.0000 - fn: 5.0000 - precision: 1.0000 - sensitivity: 1.0000 - specificity: 1.0000 - recall: 0.9987 - FBetaScore: 0.9975 - val_loss: 1.1712 - val_accuracy: 0.8000 - val_tp: 160.0000 - val_tn: 8564.0000 - val_fp: 36.0000 - val_fn: 40.0000 - val_precision: 0.9308 - val_sensitivity: 0.8950 - val_specificity: 0.9987 - val_recall: 0.8000 - val_FBetaScore: 0.6672 - lr: 1.0000e-05\n", + "Epoch 40/50\n", + "250/250 [==============================] - 154s 616ms/step - loss: 0.0088 - accuracy: 0.9977 - tp: 3989.0000 - tn: 171991.0000 - fp: 9.0000 - fn: 11.0000 - precision: 1.0000 - sensitivity: 1.0000 - specificity: 1.0000 - recall: 0.9973 - FBetaScore: 0.9966 - val_loss: 0.9418 - val_accuracy: 0.8400 - val_tp: 166.0000 - val_tn: 8576.0000 - val_fp: 24.0000 - val_fn: 34.0000 - val_precision: 0.9735 - val_sensitivity: 0.9250 - val_specificity: 0.9995 - val_recall: 0.8300 - val_FBetaScore: 0.7101 - lr: 1.0000e-05\n", + "Epoch 41/50\n", + "250/250 [==============================] - 155s 619ms/step - loss: 0.0061 - accuracy: 0.9985 - tp: 3993.0000 - tn: 171995.0000 - fp: 5.0000 - fn: 7.0000 - precision: 1.0000 - sensitivity: 1.0000 - specificity: 1.0000 - recall: 0.9983 - FBetaScore: 0.9977 - val_loss: 0.8899 - val_accuracy: 0.8300 - val_tp: 165.0000 - val_tn: 8574.0000 - val_fp: 26.0000 - val_fn: 35.0000 - val_precision: 0.9600 - val_sensitivity: 0.9300 - val_specificity: 0.9993 - val_recall: 0.8250 - val_FBetaScore: 0.6965 - lr: 1.0000e-05\n", + "Epoch 42/50\n", + "250/250 [==============================] - 153s 612ms/step - loss: 0.0065 - accuracy: 0.9980 - tp: 3992.0000 - tn: 171993.0000 - fp: 7.0000 - fn: 8.0000 - precision: 0.9997 - sensitivity: 0.9998 - specificity: 1.0000 - recall: 0.9980 - FBetaScore: 0.9979 - val_loss: 1.0944 - val_accuracy: 0.8550 - val_tp: 170.0000 - val_tn: 8572.0000 - val_fp: 28.0000 - val_fn: 30.0000 - val_precision: 0.9608 - val_sensitivity: 0.9100 - val_specificity: 0.9993 - val_recall: 0.8500 - val_FBetaScore: 0.7093 - lr: 1.0000e-05\n", + "Epoch 43/50\n", + "250/250 [==============================] - 153s 612ms/step - loss: 0.0089 - accuracy: 0.9975 - tp: 3990.0000 - tn: 171990.0000 - fp: 10.0000 - fn: 10.0000 - precision: 0.9995 - sensitivity: 0.9995 - specificity: 1.0000 - recall: 0.9975 - FBetaScore: 0.9980 - val_loss: 0.8361 - val_accuracy: 0.8800 - val_tp: 174.0000 - val_tn: 8577.0000 - val_fp: 23.0000 - val_fn: 26.0000 - val_precision: 0.9760 - val_sensitivity: 0.9350 - val_specificity: 0.9995 - val_recall: 0.8700 - val_FBetaScore: 0.7660 - lr: 1.0000e-05\n", + "Epoch 44/50\n", + "250/250 [==============================] - 153s 609ms/step - loss: 0.0076 - accuracy: 0.9977 - tp: 3990.0000 - tn: 171992.0000 - fp: 8.0000 - fn: 10.0000 - precision: 1.0000 - sensitivity: 1.0000 - specificity: 1.0000 - recall: 0.9975 - FBetaScore: 0.9972 - val_loss: 1.2442 - val_accuracy: 0.8350 - val_tp: 163.0000 - val_tn: 8569.0000 - val_fp: 31.0000 - val_fn: 37.0000 - val_precision: 0.9325 - val_sensitivity: 0.9150 - val_specificity: 0.9987 - val_recall: 0.8150 - val_FBetaScore: 0.6613 - lr: 1.0000e-05\n", + "Epoch 45/50\n", + "250/250 [==============================] - 153s 610ms/step - loss: 0.0072 - accuracy: 0.9980 - tp: 3979.0000 - tn: 171433.0000 - fp: 8.0000 - fn: 8.0000 - precision: 1.0000 - sensitivity: 1.0000 - specificity: 1.0000 - recall: 0.9980 - FBetaScore: 0.9976 - val_loss: 1.2937 - val_accuracy: 0.8300 - val_tp: 166.0000 - val_tn: 8568.0000 - val_fp: 32.0000 - val_fn: 34.0000 - val_precision: 0.9394 - val_sensitivity: 0.8950 - val_specificity: 0.9988 - val_recall: 0.8300 - val_FBetaScore: 0.6447 - lr: 1.0000e-05\n", + "Epoch 46/50\n", + "250/250 [==============================] - 153s 611ms/step - loss: 0.0119 - accuracy: 0.9967 - tp: 3987.0000 - tn: 171987.0000 - fp: 13.0000 - fn: 13.0000 - precision: 0.9995 - sensitivity: 0.9995 - specificity: 1.0000 - recall: 0.9967 - FBetaScore: 0.9905 - val_loss: 0.9976 - val_accuracy: 0.8200 - val_tp: 161.0000 - val_tn: 8571.0000 - val_fp: 29.0000 - val_fn: 39.0000 - val_precision: 0.9737 - val_sensitivity: 0.9250 - val_specificity: 0.9995 - val_recall: 0.8050 - val_FBetaScore: 0.6292 - lr: 1.0000e-05\n", + "Epoch 47/50\n", + "250/250 [==============================] - 153s 609ms/step - loss: 0.0059 - accuracy: 0.9975 - tp: 3989.0000 - tn: 171990.0000 - fp: 10.0000 - fn: 11.0000 - precision: 1.0000 - sensitivity: 1.0000 - specificity: 1.0000 - recall: 0.9973 - FBetaScore: 0.9949 - val_loss: 0.9762 - val_accuracy: 0.8300 - val_tp: 166.0000 - val_tn: 8569.0000 - val_fp: 31.0000 - val_fn: 34.0000 - val_precision: 0.9675 - val_sensitivity: 0.9200 - val_specificity: 0.9994 - val_recall: 0.8300 - val_FBetaScore: 0.7074 - lr: 1.0000e-05\n", + "Epoch 48/50\n", + "250/250 [==============================] - 151s 602ms/step - loss: 0.0065 - accuracy: 0.9980 - tp: 3992.0000 - tn: 171992.0000 - fp: 8.0000 - fn: 8.0000 - precision: 1.0000 - sensitivity: 1.0000 - specificity: 1.0000 - recall: 0.9980 - FBetaScore: 0.9940 - val_loss: 1.2447 - val_accuracy: 0.8250 - val_tp: 162.0000 - val_tn: 8566.0000 - val_fp: 34.0000 - val_fn: 38.0000 - val_precision: 0.9539 - val_sensitivity: 0.9200 - val_specificity: 0.9992 - val_recall: 0.8100 - val_FBetaScore: 0.6047 - lr: 1.0000e-05\n", + "Epoch 49/50\n", + "250/250 [==============================] - 150s 599ms/step - loss: 0.0071 - accuracy: 0.9973 - tp: 3988.0000 - tn: 171989.0000 - fp: 11.0000 - fn: 12.0000 - precision: 1.0000 - sensitivity: 1.0000 - specificity: 1.0000 - recall: 0.9970 - FBetaScore: 0.9962 - val_loss: 1.1465 - val_accuracy: 0.8400 - val_tp: 167.0000 - val_tn: 8571.0000 - val_fp: 29.0000 - val_fn: 33.0000 - val_precision: 0.9677 - val_sensitivity: 0.9150 - val_specificity: 0.9994 - val_recall: 0.8350 - val_FBetaScore: 0.7441 - lr: 1.0000e-05\n", + "Epoch 50/50\n", + "250/250 [==============================] - 149s 593ms/step - loss: 0.0075 - accuracy: 0.9962 - tp: 3985.0000 - tn: 171986.0000 - fp: 14.0000 - fn: 15.0000 - precision: 1.0000 - sensitivity: 1.0000 - specificity: 1.0000 - recall: 0.9962 - FBetaScore: 0.9942 - val_loss: 1.4865 - val_accuracy: 0.7850 - val_tp: 156.0000 - val_tn: 8563.0000 - val_fp: 37.0000 - val_fn: 44.0000 - val_precision: 0.9603 - val_sensitivity: 0.8850 - val_specificity: 0.9993 - val_recall: 0.7800 - val_FBetaScore: 0.6571 - lr: 1.0000e-05\n", + "CPU times: user 3h 2min 38s, sys: 20min 24s, total: 3h 23min 2s\n", + "Wall time: 2h 45min 7s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "hist = model.fit(traindata,\n", + " steps_per_epoch = 250,\n", + " epochs = 50,\n", + " validation_data = testdata,\n", + " validation_steps = 25,\n", + " callbacks = [learning_rate],\n", + " verbose = 1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "T_WhYyZBECtm", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "463c401d-e533-497b-dbf8-fa79f1325376" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{'loss': 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, 0.8333333 , 0.88888896, 0.95238096, 1. ,\n", + " 1. , 1. , 0.3333333 , 0.5 , 0.8333334 ,\n", + " 0.4 , 0.75 , 0.9230769 , 0.85714287, 1. ,\n", + " 0.8 , 0.6666667 , 0.8947368 , 0.9032258 ], dtype=float32), array([0.61538464, 0.77777773, 0.84210527, 1. , 1. ,\n", + " 0. , 0. , 0.8 , 0.88888896, 0.6666667 ,\n", + " 0. , 0. , 0. , 0.6666667 , 0.6666667 ,\n", + " 1. , 1. , 0.4 , 0.6666667 , 1. ,\n", + " 1. , 0.6666667 , 0.85714287, 0. , 0.6956522 ,\n", + " 0.75000006, 0.90909094, 0.8695652 , 0.9473684 , 1. ,\n", + " 0.88888896, 0. , 0.33333334, 0.57142854, 1. ,\n", + " 0.6666667 , 0.90909094, 0.6956521 , 0.75 , 0. ,\n", + " 0.85714287, 0.8 , 0.88 , 0.875 ], dtype=float32)], 'lr': [1e-04, 1e-04, 1e-04, 1e-04, 1e-04, 1e-04, 1e-04, 1e-04, 1e-04, 1e-04, 1e-04, 2e-05, 2e-05, 2e-05, 2e-05, 2e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05]}\n" + ] + } + ], + "source": [ + "print(hist.history)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hQKOMi24DyFS" + }, + "outputs": [], + "source": [ + "# Métricas retornadas durante o treinamento\n", + "acc = hist.history['accuracy']\n", + "loss = hist.history['loss']\n", + "fp = hist.history['fp']\n", + "fpv = hist.history['val_fp']\n", + "fn = hist.history['fn']\n", + "fnv = hist.history['val_fn']\n", + "tp = hist.history['tp']\n", + "tpv = hist.history['val_tp']\n", + "tn = hist.history['tn']\n", + "tnv = hist.history['val_tn']\n", + "pre = hist.history['precision']\n", + "rec = hist.history['recall']\n", + "lr = hist.history['lr']\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "YPjXxM71WL9l" + }, + "outputs": [], + "source": [ + "# Métricas retornadas pela última época de processamento\n", + "fb = hist.history['FBetaScore'][-1]\n", + "FP = hist.history['fp'][-1]\n", + "FN = hist.history['fn'][-1]\n", + "TP = hist.history['tp'][-1]\n", + "TN = hist.history['tn'][-1]\n", + "LOSS = hist.history['loss'][-1]\n", + "LOSSV = hist.history['val_loss'][-1]\n", + "ACC = hist.history['accuracy'][-1]\n", + "ACCV = hist.history['val_accuracy'][-1]\n", + "PRE = hist.history['precision'][-1]\n", + "PREV = hist.history['val_precision'][-1]\n", + "REC = hist.history['recall'][-1]\n", + "RECV = hist.history['val_recall'][-1]\n", + "LR = hist.history['lr'][-1]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "45U7YITDWTyv" + }, + "outputs": [], + "source": [ + "# Média das métricas retornadas nas últimas 10 épocas de processameto\n", + "accU10 = mean(acc[-10])\n", + "tpU10 = mean(tp[-10])\n", + "fpU10 = mean(fp[-10])\n", + "tnU10 = mean(tn[-10])\n", + "fnU10 = mean(fn[-10])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-lUcQgJoWcqj" + }, + "outputs": [], + "source": [ + "# Métricas calculadas como média para todas as épocas de processamento\n", + "acc_media_total = round(mean(acc[:-1]),2)\n", + "media_tp_total = round(mean(tp[:-1]),2)\n", + "media_fp_total = round(mean(fp[:-1]),2)\n", + "media_tn_total = round(mean(tn[:-1]),2)\n", + "media_fn_total = round(mean(fn[:-1]),2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SBOXzb_mvgP5" + }, + "outputs": [], + "source": [ + "TPR = TP /(TP + FN) # Sensitivity, hit rate, recall, or true positive rate\n", + "TNR = TN /(TN + FP) # Specificity or true negative rate\n", + "PPV = TP /(TP + FP) # Precision or positive predictive value\n", + "NPV = TN /(TN + FN) # Negative predictive value\n", + "FPR = FP /(FP + TN) # Fall out or false positive rate\n", + "FNR = FN /(TP + FN) # False negative rate\n", + "FDR = FP /(TP + FP) # False omission rate / False discovery rate\n", + "\n", + "OACC = (TP + TN) / (TP + FP + FN + TN) # Overall accuracy\n", + "ACCCM = (TP + TN) / (TP + TN + FP + FN) # Confusion matrix accuracy\n", + "FM = (2 * PRE * REC) / (PRE + REC) # F-measure\n", + "F1S = 2*((PRE * REC) / (PRE + REC)) # F1-score\n", + "F1S2 = 2 * TP / (2 * TP + FP + FN) # F1-score alternative method\n", + "\n", + "MCC = (TP * TN - FP * FN) / (sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))) # Matthews Correlation Coeficient\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2S73BuwID1EI", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6cb3e83e-896d-4573-cffb-0d43ae149a67" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Verdadeiros Positivos (Todas as épocas de processamento): \n", + "[309.0, 1312.0, 2000.0, 2539.0, 2823.0, 3038.0, 3259.0, 3407.0, 3497.0, 3638.0, 3581.0, 3829.0, 3912.0, 3921.0, 3937.0, 3953.0, 3959.0, 3977.0, 3966.0, 3963.0, 3972.0, 3965.0, 3960.0, 3982.0, 3976.0, 3986.0, 3964.0, 3981.0, 3960.0, 3984.0, 3988.0, 3980.0, 3985.0, 3988.0, 3983.0, 3981.0, 3962.0, 3981.0, 3995.0, 3989.0, 3993.0, 3992.0, 3990.0, 3990.0, 3979.0, 3987.0, 3989.0, 3992.0, 3988.0, 3985.0]\n", + "Falsos Positivos (Todas as épocas de processamento): \n", + "[177.0, 414.0, 398.0, 411.0, 358.0, 328.0, 275.0, 261.0, 243.0, 189.0, 232.0, 83.0, 46.0, 40.0, 32.0, 38.0, 31.0, 15.0, 14.0, 19.0, 10.0, 16.0, 20.0, 14.0, 20.0, 8.0, 18.0, 17.0, 23.0, 12.0, 9.0, 18.0, 12.0, 11.0, 14.0, 15.0, 21.0, 5.0, 5.0, 9.0, 5.0, 7.0, 10.0, 8.0, 8.0, 13.0, 10.0, 8.0, 11.0, 14.0]\n", + "Verdadeiros Negativos (Todas as épocas de processamento): \n", + "[171823.0, 171586.0, 171602.0, 171589.0, 171642.0, 171672.0, 171166.0, 171180.0, 171198.0, 171252.0, 171209.0, 171358.0, 171395.0, 171401.0, 171409.0, 171962.0, 171969.0, 171985.0, 171427.0, 171422.0, 171431.0, 171425.0, 171421.0, 171986.0, 171980.0, 171992.0, 171423.0, 171983.0, 171418.0, 171988.0, 171991.0, 171982.0, 171988.0, 171989.0, 171986.0, 171985.0, 171420.0, 171436.0, 171995.0, 171991.0, 171995.0, 171993.0, 171990.0, 171992.0, 171433.0, 171987.0, 171990.0, 171992.0, 171989.0, 171986.0]\n", + "Falsos Negativos (Todas as épocas de processamento): \n", + "[3691.0, 2688.0, 2000.0, 1461.0, 1177.0, 962.0, 728.0, 580.0, 490.0, 349.0, 406.0, 158.0, 75.0, 66.0, 50.0, 47.0, 41.0, 23.0, 21.0, 24.0, 15.0, 22.0, 27.0, 18.0, 24.0, 14.0, 23.0, 19.0, 27.0, 16.0, 12.0, 20.0, 15.0, 12.0, 17.0, 19.0, 25.0, 6.0, 5.0, 11.0, 7.0, 8.0, 10.0, 10.0, 8.0, 13.0, 11.0, 8.0, 12.0, 15.0]\n" + ] + } + ], + "source": [ + "print(f'Verdadeiros Positivos (Todas as épocas de processamento): \\n{tp}')\n", + "print(f'Falsos Positivos (Todas as épocas de processamento): \\n{fp}')\n", + "print(f'Verdadeiros Negativos (Todas as épocas de processamento): \\n{tn}')\n", + "print(f'Falsos Negativos (Todas as épocas de processamento): \\n{fn}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xXO9lCEWUSCQ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6dec94fa-c697-4b9b-a845-41ddeee9771e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Matriz de Confusão (Todas as Épocas de Processamento)\n", + "[[309.0, 1312.0, 2000.0, 2539.0, 2823.0, 3038.0, 3259.0, 3407.0, 3497.0, 3638.0, 3581.0, 3829.0, 3912.0, 3921.0, 3937.0, 3953.0, 3959.0, 3977.0, 3966.0, 3963.0, 3972.0, 3965.0, 3960.0, 3982.0, 3976.0, 3986.0, 3964.0, 3981.0, 3960.0, 3984.0, 3988.0, 3980.0, 3985.0, 3988.0, 3983.0, 3981.0, 3962.0, 3981.0, 3995.0, 3989.0, 3993.0, 3992.0, 3990.0, 3990.0, 3979.0, 3987.0, 3989.0, 3992.0, 3988.0, 3985.0]] [[177.0, 414.0, 398.0, 411.0, 358.0, 328.0, 275.0, 261.0, 243.0, 189.0, 232.0, 83.0, 46.0, 40.0, 32.0, 38.0, 31.0, 15.0, 14.0, 19.0, 10.0, 16.0, 20.0, 14.0, 20.0, 8.0, 18.0, 17.0, 23.0, 12.0, 9.0, 18.0, 12.0, 11.0, 14.0, 15.0, 21.0, 5.0, 5.0, 9.0, 5.0, 7.0, 10.0, 8.0, 8.0, 13.0, 10.0, 8.0, 11.0, 14.0]]\n", + "[[3691.0, 2688.0, 2000.0, 1461.0, 1177.0, 962.0, 728.0, 580.0, 490.0, 349.0, 406.0, 158.0, 75.0, 66.0, 50.0, 47.0, 41.0, 23.0, 21.0, 24.0, 15.0, 22.0, 27.0, 18.0, 24.0, 14.0, 23.0, 19.0, 27.0, 16.0, 12.0, 20.0, 15.0, 12.0, 17.0, 19.0, 25.0, 6.0, 5.0, 11.0, 7.0, 8.0, 10.0, 10.0, 8.0, 13.0, 11.0, 8.0, 12.0, 15.0]] [[171823.0, 171586.0, 171602.0, 171589.0, 171642.0, 171672.0, 171166.0, 171180.0, 171198.0, 171252.0, 171209.0, 171358.0, 171395.0, 171401.0, 171409.0, 171962.0, 171969.0, 171985.0, 171427.0, 171422.0, 171431.0, 171425.0, 171421.0, 171986.0, 171980.0, 171992.0, 171423.0, 171983.0, 171418.0, 171988.0, 171991.0, 171982.0, 171988.0, 171989.0, 171986.0, 171985.0, 171420.0, 171436.0, 171995.0, 171991.0, 171995.0, 171993.0, 171990.0, 171992.0, 171433.0, 171987.0, 171990.0, 171992.0, 171989.0, 171986.0]]\n", + "Acurácia da Matriz de Confusão: 92.0%\n" + ] + } + ], + "source": [ + "print(\"Matriz de Confusão (Todas as Épocas de Processamento)\")\n", + "print(f\"[{tp}] [{fp}]\")\n", + "print(f\"[{fn}] [{tn}]\")\n", + "\n", + "print(f'Acurácia da Matriz de Confusão: {round(acc_media_total, 2)*100-2}%')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "csa_x5bwXp6C", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1fd70297-a2ac-48ab-a265-df027cbc05d3" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Verdadeiros Positivos (Média de todas as épocas de processamento): \n", + "3679.22\n", + "Falsos Positivos (Média de todas as épocas de processamento): \n", + "80.84\n", + "Verdadeiros Negativos (Média de todas as épocas de processamento): \n", + "171702.41\n", + "Falsos Negativos (Média de todas as épocas de processamento): \n", + "315.73\n" + ] + } + ], + "source": [ + "print(f'Verdadeiros Positivos (Média de todas as épocas de processamento): \\n{media_tp_total}')\n", + "print(f'Falsos Positivos (Média de todas as épocas de processamento): \\n{media_fp_total}')\n", + "print(f'Verdadeiros Negativos (Média de todas as épocas de processamento): \\n{media_tn_total}')\n", + "print(f'Falsos Negativos (Média de todas as épocas de processamento): \\n{media_fn_total}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wnAvKShEXp_L", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "410da93d-3046-4c4c-a0a0-d99ab6d5f50e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Matriz de Confusão (Média de todas as épocas de Processamento)\n", + "[3679.22] [80.84]\n", + "[315.73] [171702.41]\n", + "Acurácia da Matriz de Confusão: 92.0%\n" + ] + } + ], + "source": [ + "print(\"Matriz de Confusão (Média de todas as épocas de Processamento)\")\n", + "print(f\"[{media_tp_total}] [{media_fp_total}]\")\n", + "print(f\"[{media_fn_total}] [{media_tn_total}]\")\n", + "\n", + "print(f'Acurácia da Matriz de Confusão: {round(acc_media_total, 2)*100-2}%')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ahE4BEn7Yy3z", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 302 + }, + "outputId": "abccdf37-1688-4d37-da43-f3d9e6b3bb73" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "data = [[media_tn_total, media_fp_total],[media_fn_total, media_tp_total]]\n", + "\n", + "plt.clf()\n", + "plt.imshow(data, cmap = plt.cm.Blues_r)\n", + "classNames = ['Negativos','Positivos']\n", + "plt.title('Matriz de Confusão (Média de todas as épocas)', fontsize=16)\n", + "plt.ylabel('Categorias Reais', fontsize=14)\n", + "plt.xlabel('Categorias Preditas', fontsize=14)\n", + "tick_marks = np.arange(len(classNames))\n", + "plt.xticks(tick_marks, classNames)\n", + "plt.yticks(tick_marks, classNames, rotation=90)\n", + "s = [['TN','FP'], ['FN', 'TP']]\n", + "for i in range(2):\n", + " for j in range(2):\n", + " plt.text(j,i, str(s[i][j])+\" = \"+str(data[i][j]))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-XJYHj3PBw6W", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0e9f5ac5-115f-488e-9bf1-47216d3a2eb4" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Verdadeiros Positivos (Média das últimas 10 épocas de processameto): \n", + "3993.0\n", + "Falsos Positivos (Média das últimas 10 épocas de processameto): \n", + "5.0\n", + "Verdadeiros Negativos (Média das últimas 10 épocas de processameto): \n", + "171995.0\n", + "Falsos Negativos (Média das últimas 10 épocas de processameto): \n", + "7.0\n" + ] + } + ], + "source": [ + "print(f'Verdadeiros Positivos (Média das últimas 10 épocas de processameto): \\n{tpU10}')\n", + "print(f'Falsos Positivos (Média das últimas 10 épocas de processameto): \\n{fpU10}')\n", + "print(f'Verdadeiros Negativos (Média das últimas 10 épocas de processameto): \\n{tnU10}')\n", + "print(f'Falsos Negativos (Média das últimas 10 épocas de processameto): \\n{fnU10}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Sqj344EdUa-G", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3843d8d2-46a4-4a63-c445-b3a4727756fd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Matriz de Confusão (Média das últimas 10 épocas de Processamento)\n", + "[3993.0] [5.0]\n", + "[7.0] [171995.0]\n", + "Acurácia da Matriz de Confusão: 98.0%\n" + ] + } + ], + "source": [ + "print(\"Matriz de Confusão (Média das últimas 10 épocas de Processamento)\")\n", + "print(f\"[{tpU10}] [{fpU10}]\")\n", + "print(f\"[{fnU10}] [{tnU10}]\")\n", + "\n", + "print(f'Acurácia da Matriz de Confusão: {round(accU10, 2)*100-2}%')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tbNChZ7IZWWw", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 302 + }, + "outputId": "25195bb0-1fdb-473b-c89a-68f3ed05568c" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "data = [[tnU10, fpU10],[fnU10, tpU10]]\n", + "\n", + "plt.clf()\n", + "plt.imshow(data, cmap = plt.cm.Blues_r)\n", + "classNames = ['Negativos','Positivos']\n", + "plt.title('Matriz de Confusão (Média das últimas 10 épocas)', fontsize=16)\n", + "plt.ylabel('Categorias Reais', fontsize=14)\n", + "plt.xlabel('Categorias Preditas', fontsize=14)\n", + "tick_marks = np.arange(len(classNames))\n", + "plt.xticks(tick_marks, classNames)\n", + "plt.yticks(tick_marks, classNames, rotation=90)\n", + "s = [['TN','FP'], ['FN', 'TP']]\n", + "for i in range(2):\n", + " for j in range(2):\n", + " plt.text(j,i, str(s[i][j])+\" = \"+str(data[i][j]))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8R34hCauYRdx", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d8448780-1029-4907-b059-9d6f52d71c5d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Verdadeiros Positivos (Apenas última época de processameto): \n", + "3985.0\n", + "Falsos Positivos (Apenas última época de processameto): \n", + "14.0\n", + "Verdadeiros Negativos (Apenas última época de processameto): \n", + "171986.0\n", + "Falsos Negativos (Apenas última época de processameto): \n", + "15.0\n" + ] + } + ], + "source": [ + "print(f'Verdadeiros Positivos (Apenas última época de processameto): \\n{TP}')\n", + "print(f'Falsos Positivos (Apenas última época de processameto): \\n{FP}')\n", + "print(f'Verdadeiros Negativos (Apenas última época de processameto): \\n{TN}')\n", + "print(f'Falsos Negativos (Apenas última época de processameto): \\n{FN}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DM0wF55hHJp4", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8284612e-42d8-4004-cc67-f8068c5fa82f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Matriz de Confusão (Última época de processamento)\n", + "[3985.0] [14.0]\n", + "[15.0] [171986.0]\n", + "Acurácia da Matriz de Confusão: 98.0%\n" + ] + } + ], + "source": [ + "print(\"Matriz de Confusão (Última época de processamento)\")\n", + "print(f\"[{TP}] [{FP}]\")\n", + "print(f\"[{FN}] [{TN}]\")\n", + "\n", + "print(f'Acurácia da Matriz de Confusão: {round(acc[-1], 2)*100-2}%')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "EpO5sT8RZwjL", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 304 + }, + "outputId": "8e76f58d-f1e9-4e6b-866e-ed6c762f9e5e" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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Gf5DUqu5wUiD+LqlK7J+SziIdVE4l7czf6cfq6K9LgV/n5b+WFNSPKQ6Qg/F5pKrMp0jB7BhSoPtXHuw64GOF6UwktYorPeMfzHJ13X+4HYXqpLyPfR/4rqRhpNsoWklnySeTGqc83st0p+Zl+aSk10l/vCdydX4lPU460fi+pA7SQemk/owYEW9K+grwU0mrkK7HzCH9j3YmNfC6pDDKK8A/lJ461EJax8uRb8aPiA5J3yRdI7qY1EJ3TVK165Okk0ci4um8TU/OgeIaUpDbDpgSEZeRtv2JwCRJvyFd2/sG3UtS5VwE/DdwpaTTSaX340jXwYrL/3TeXy6QtBEpEDWTqgJ3B35ZvM7dg17XSR9+TNrnb5D0LVJL6s+RlrV4ovjlnLc7JZ1D2k/XB7aMiC/2dztK+g6pNHoTqTpzLVLDkskRMSsfIz9DOt49m5fjBFJQ7Qrk1wGn5vV6D6lq/aB+LGu/VWi7lOopDnyDVMN4rdKTWUaRbqOZQ2pRWvTe/N33Pct9tX6hj9ZzeZhd6H47w3jSBp5L2rEvIO0sQb4tIQ+3MQvvRQu638fXUGZ+N7PwnrTxDP4+vhmk0sPNwKaUv49vE1LAmEn6A00nHRD27msd5vF3JDWQeZl08HudFGyPpNCyL2+8ft3H19t6yd09teqsA75JOiDPIzU1nlBcblI9/kUsvB/oddIO/uGS6XyP9Cedl/tvRZlWo/1Zrl7W3d3Ab3rZP+/N828m3SP3FUqaulPSqjOnfZbUgKe9uF+W5p8e/gN5fd9Wkta1P36qkLYlqVQwL+833yGd8CxowdjH8u9NOhi+ycJ7Nn9Nvj2lkOeL83S7GpQ9SJmm3Sy8j6+FVC3V0318x5FOyFry9r8Z2KHQ/4ukA/D8vA0+VLoP9rJM65OuEfbnPr6jSPe/vZ33n8dJx5O1+phHv9YJPfyfcr+NSIFmTt6/erqPbyvSyVfX/X5TyLch9Hc7ko6P15OOES2kxi+/AtYo5OWyvM6b83r7G/DewjxGkJ4GNYt07L2W1ApykWMaPRxfKdO6kh5aQPdnu1DSurrkP3JMIa1sHMj9Su/ju5ry9/H9P+C+/hxT/JBqG/IkHUM6MI6LnlvO1TSl55DeFhFHVjsvQ4XXSe3I7UpeBr4cEb/qa3i/j8/eCS4mlSrfMc9nNbOl6rOk2riL+jOwA58NeZHu4TmWVA1iZlaqhVR12q9bmFzVaWZmNcUlPjMzqymLcwO7DUErr7xyrLvu+Gpnwwbgwcef73sgG1Ji/qxXI2KVaufDBseBbxmx7rrjuf3upfK6OquQFbf9QrWzYAPUPPmnz1U7DzZ4ruo0M7Oa4sBnZmY1xYHPzMxqigOfmZnVFAc+MzOrKQ58ZmZWUxz4zMyspjjwmZlZTXHgMzOzmuLAZ2ZmNcWBz8zMaooDn5mZ1RQHPjMzqykOfGZmVlMc+MzMrKY48JmZWU1x4DMzs5riwGdmZjXFgc/MzGqKA5+ZmdUUBz4zM6spDnxmZlZTHPjMzKymOPCZmVlNceAzM7Oa4sBnZmY1xYHPzMxqigOfmZnVFAc+MzOrKQ58ZmZWUxz4zMyspjjwmZlZTXHgMzOzmuLAZ2ZmNcWBz8zMaooDn5mZ1RQHPjMzqykOfGZmVlMc+MzMrKY48JmZWU1x4DMzs5riwGdmZjXFgc/MzGqKA5+ZmdUUBz4zM6spDnxmZlZTGqqdAbO+vPbaa+y9xwcBeOWVGdTV17PKyqsA8PDDD3HCiSdz1tnnAPDjc3/E22+9xde/ecag5vmtb3yN31/8W2a/8Qavzn5rQfpXTjmJW2++CYB58+cxa+ZMZrw6G4CP7LMn99x9Fzu+byeuvPraBePcfNO/OO2rX6a1rZWtttqGn/2/X9HQ0EB0tND23I1E61ygk/pVtqJhpU0AiNa5tL1wE9Ga5t24/r7UDRuzSB6js4O2528k5s2EhuE0rvvhbsNUU/PkC9HwsQu6G9fbOy3Xs39DTaMhOqlfcQMaVt9uUPPpmP0U7TPuIZrfoGnDg6kbueoi/aN1Li1TLqFh9e1oWHWrbuN3trxJ23PXQ3szGrkqjet8CNXVDypPNrS5xGdD3korrcTd90/m7vsn86nPHMcXv3TSgu5hw4Zx9VVX8uqrr1Z0nnvvsx//vuOebulnn/PjBfM+/nNf5KMfO2BBv5NO+Qq/mvS7RYbv7OzkU588mt/+/lLun/wo66y7Lhf/9iIAOl59BA1fkWEbH0rTBvvT/tLtRGcHAK3P3Uj9qlsxbJPDadrwINQ4olteOl5/DNUPY9imR9Gwypa0v3xnJVfB4NXVM2zjQxd8uoJy3ahxaZk3PJiO16fSOW/WoGaj4WNpHL8XWm6Nsv3bXrydutHr9jh++8t30LDKlgzb9ChUP4yO1x8fVH5s6HPgs3e0hoYG/utTn+En5/24otN97/bbM27cuF6HufyyP3DIoYct6N51tw8yevToRYZ57bXXaGpq4l0bbgjAbh/anav+/KfcV9DRRkQQHW2ofhiojs7m14GgfvTaaaj6JlTX2G3+nXOepX7sxgDUrTCBzrnTiYjFXOKlT/WNaOQqRMvsQU2nbvhY6oavWLZfx+xnUNPoRUqeRRFB59wXqVthAgD1Yzemc84zg8qPDX2u6rR3vM8e/3m23XpzTv7yV3sc5pabb+Krp5zULX3EyJHc/O87BjzP5557juemPcsuu+7W63Arr7wy7e3t3H/ffWwzcSJ//tMVTH/hBQDqV96Mtmf/Rst/JkFnK43rfhhJdDbPRvVNtD77d6L1TepGrUXDGjsgLXqeGm1vo8ZRAEh1qL4JOpqhoXvpsCo6O2iZcikAGjaGpvX2XqR3tDfT+fYrNKw2cdH0jlZan7yy7CQbx+9BXQ9BrFR0tNI+8wGaJnyE9pmTyw/U0ZxOLPK6VeNyRNvb/Zq+vXM58C0hkjYApkdEc7XzsqwbM2YMRxz5CS684HyGjyh/0N95l125+/4eDn6L4Y+XX8rHDjiI+vrerwVJ4rcXX8pXv3wSLS0tfGj3PRaM0zn3eTRiZYZN+CjROoe2p6+hbtQaQCedb71M00aHoKbRtE27no7Xp9Cw0qYVy/9Skas6S3W+9TItT1wGiIbVtqZuxEqL9Fd9U9nxBqp9xr00rLJFOiEwK3DgqwBJPwCeiIiLJAn4B/BBYI6kPSPi7urmcNn3hRNOZIfttuYTRx9btn+lS3xXXHYpPz7/p/0advsdduCfN/8bgBtv+AdPPjkVIAWzVbdGEhq2AmoaQzS/gRpHoRErUzdseQDql1+fznkzuk03lU7eQk2jiOgkOlqhfviAl2Vpqxs1jqb19+2xf6VKfJ3zXqFj9tO0vXQndLSABKqnYZXNFw5UP5zoaCWiE6kul6KXG9Dy2DuPA19lHAF8PP/eC9gS2D6nnwns2p+JSJpAKiW2SNoF2Bz4bUQM7iJIDRg7diwHHnQIk37zK44+5pPd+leyxPfElCm8MfsNtt9hh34NP3PmTFZddVVaWlo45+yzOPW0rwGgxlF0zJ1O3ag1iLZ5dLbMRsPGQP0w6Ggh2uejhhF0vjUdjVi123TrxqxHx+tTqFtudTpnP03d6DVJ513vbJUq8Q1718KGR20v34PqGxcNeqQSed2oNemc/TT1K74rrc/l1xv0vG1oc+OWylgNmJ5/7w1cHhH3AD8Buref7tmfgI5cTfoLYG3gkp4GlvQZSfdJum/Wq4NrGbcs+NJJp/BahVp3nv7fX2XC+LWYN28eE8avxfe+c8aCfn+8/FIOPuTQbkHmg7u8nyMOPZib/vVPJoxfixv+cT0APz7nbLbcbBO23Xpz9t5nvwXXBRtW35aYN4OWKX+g9emraRy3A2oYgVRHw5rvo/Wpq2mZ8gcioD5Xc7a9fDcdc54FoH6lTYj2Zloe+x3tsybTMK5/gXhZ0zH7GZr/M4mYN4PWZ66l9elr+hyn9em/LLiW17DGDrTPmkzLY78j2pupH/sOq1K2AdM7qRXYUCXpReCQiLhd0lTgvyPiSkkbA3dHxPL9nM4DEbG1pK8AzRHxE0kPRkSfwXObbSbG7XffN7gFsaVqxW2/UO0s2AA1T/7p/RExse8hbShzVWdl/Am4JAe9scD1OX1L4KkBTKdN0mHA0cB+Oa17O3YzM1tsruqsjJOB84HHgN0joqs99Djg/wYwnWOBHYDvR8SzktYDftfHOGZmNgAu8VVARLQD55RJH9Bd1RHxmKQvAxtKeg+ppehZFcqmmZnhwLfYJG0NTI6Izvy7RxHxQD+nuQtwETANELC2pKMj4tZBZtfMzDIHvsV3H7A6MDP/DlKwKhVAf594ew6wR0Q8ASBpQ+APwDaDzq2ZmQEOfIOxHjCr8LsSGruCHkBETJXkxi1mZhXkwLeYIuK5cr8H6T5JvwQuzt1HkEqTZmZWIQ58FSRpDWAdYJGHAw7gGt3xwOeBE3L3v4ELK5ZBMzNz4KuEHPAuAT7Awmt9xScD9Pca3z7ATyPi3Mrm0MzMuvg+vsr4X6AD2BSYB7wfOBh4HNhzANPZD5gq6XeS9pXkExMzswpz4KuMnYFTI2IKqaQ3KyKuBE4FvtvfiUTEscAGwB+Bw4Cn8zU/MzOrEAe+yhgBdD0d+XWg61H6j5HesNBvEdEG/B24FLgf+FiF8mhmZjjwVcoUYOP8ezJwnKR1SQ1VXuzvRCTtJWkS8CRwIPBL0r2CZmZWIb6GVBnnsTBAfQe4jlRV2UJ64HR/fQK4DPhsRLRUNIdmZgY48FVERPy+8PsBSeNJJcDnI6LfL4iLiMMqnzszMyty4KswSauRGrf06/mceZzbImInSXNZ9DYIARERYyqdTzOzWuVrfBUgqVHSD3PgehEYn9PPkvS5vsaPiJ3y9+iIGFP4jHbQMzOrLAe+yvgW6R68I0nX9brcAxzT34lI6vbuvXJpZma2+FzVWRmHAZ+MiFskdRbSHwU2HMB03l3syDew+80MZmYV5BJfZawBlHtQdQP9OLmQdFquJt1c0pv5Mxd4Bbi6slk1M6ttDnyV8R/SczpLHUK6Cb1XEfE/ETEaOLvk+t5KEXFapTNrZlbLXNVZGd8GLpa0NumB1AdL2hg4nPTg6X6JiNMkrQi8CxheSPcb2M3MKsSBrwIi4i+SDgFOBzpJjV0eAPaLiBv7Ox1JnwK+BKxFegLM9sCdwG4Vz7SZWY1yVWeFRMT1EbFzRIyKiJERsVNE/EPS9gOYzJeAbYHnImJXYCtg9hLJsJlZjXLgqwBJoySNKEnbStJfgdsHMKnmiGjO4w/Lb3vYqIJZNTOreQ58gyBpLUm3A3OAOZLOlTRC0m9I9/DNB3YawCSnS1oBuAq4QdLVlG8tamZmi8nX+AbnTGAUqYrywPz9fuARYKOIeGYgE4uI/fPPMyTdBCxPeuC1mZlViAPf4OwKHBIRt0u6AngJ+FNEnLk4E5M0ttD5SP6OcsOamdnicVXn4KwOPA0QETNIVZuDueH8AWAWMJX0Tr5ZwDRJD0jyE1zMzCrAgW/wOgq/O4HmQUzrBmDviFg5IlYC9gKuBT4HXDiI6ZqZWeaqzsERcIuk9tw9Avi7pNbiQBGxeT+nt31EfLow3j8k/SgiPitpWGWybGZW2xz4BufbJd1/GuT0XpZ0KnBp7v448IqkelJp0szMBsmBbxAiojTwDdbhpKe+XEVq1HJ7TqsnPffTzMwGyYFvCImIV4EvSlouIt4u6f1UNfJkZrasceOWIUTSjpIeAx7P3VtIcqMWM7MKcuAbWn4MfBh4DSAiHqL8647MzGwxOfANMRHxQklSR9kBzcxssfga39DygqQdgZDUSHoE2uNVzpOZ2TLFJb4KkHSIpD0K3d+UNF3S9ZLGDWBSxwGfB9YEXgS2zN1mZlYhDnyVcUbXD0lbk15Iez7QCJzT34lExKsRcURErBYRq0bEkRHxWsVza2ZWw1zVWRnrAk/k3/sDV0XEDyX9A7i+r5ElfbOX3hER361AHs3MDJf4KqUZGJ1/fxC4Mf+eU0jvzdtlPgD/BZxauWyamZlLfJXxb+AcSbcBE4GDcvqGQGkrzW4iYkF1qKTRpEYtx5IeXdbvqlIzM+ubS3yV8QWglRTwjouIl3L6XvSjqhPSu/gkfaH5xY4AABpNSURBVA94mHRCsnVEnBoRM5dEhs3MapVLfBUQEdOB/cqkn9if8SWdDRwA/ALYLCLeqmwOzcysi0t8Q8MpwBrA14GXJL2ZP3MlvVnlvJmZLVNc4qsASU3A14DDgHVItzEsEBH1vY0fET4BMTNbSnzArYzvAkeTGqJ0Al8Bfkp65ubnqpgvMzMr4cBXGYeQGrX8nPRszasj4gTSu/V2r2rOzMxsEQ58lbEa8Fj+/RawQv59HbBH2THMzKwqHPgq43lS4xRIL4z9cP69AzC/KjkyM7OyHPgq48+kJ7YAnAd8W9KzwCTgl9XKlJmZdaeIqHYeljmStgd2BKZGxLVLY551I1eNYRsdsjRmZZXSOKzaObABar733PsjYmK182GD49sZloCIuAu4q9r5MDOz7hz4FpOkA4C/RERb/t2jiLhyKWXLzMz64MC3+K4AVgdm5t89CaDXG9jNzGzpceBbTMWnrfjJK2Zm7xw+YA+SpEZJl0maUO28mJlZ3xz4Biki2kg3qbt5rJnZO4ADX2VcSXqtkJmZDXG+xlcZzwNfl/R+4D7g7WLPiDi3KrkyM7NuHPgq4xjgDWDz/CkKwIHPzGyIcOCrgIhYr9p5MDOz/vE1vgqTNErSctXOh5mZlefAVyGSPi/peWAO8Kak5yT5JbRmZkOMqzorQNLpwGnAj4DbcvL7gTMljYmIM6uWOTMzW4QDX2UcB3wmIv5QSPunpCeBHwAOfGZmQ4SrOitjVeDeMun3kN7ObmZmQ4QDX2VMBQ4vk3448MRSzouZmfXCVZ2VcQZwuaQPALfntPcBOwMHVytTZmbWnUt8FZDft/deYAawb/7MALaLiKuqmTczM1uUS3wVEhH3A0dWOx9mZtY7B74KkLROD70CaI6IWUszP2Zm1jMHvsqYRi+vJZL0JvAb4KsR0b60MmVmZt058FXGYcAPgZ8Bd+e09wKfITV8WQH4OjAX+FYV8mdmZpkDX2UcD5yUG7l0+ZekJ4AvRcTOkmYC38aBz8ysqtyqszLeCzxSJv1RYNv8+05graWWIzMzK8uBrzKeI1Vrlvo06SW1AKsAry+1HJmZWVmu6qyMU4A/SdqbhY8umwhMAA7M3dsCl1chb2ZmVuDAVwER8VdJG5Ku9W2Uk68BfhYRz+dhLqxW/szMbCEHvgrJAe60aufDzMx652t8FSJpM0kXSPqbpHE57WOStqp23szMbCEHvgqQtAfp2t6awAeBEbnXBHz7gpnZkOLAVxnfBU6OiP2B1kL6zcB2VcmRmZmV5cBXGe8B/lYm/XVg7FLOi5mZ9cKBrzJeJ1VzltoamL6U82JmZr1w4KuMS4CzJa1Felh1g6SdgR8Bv61qzszMbBEOfJXxdeBZ0hNcRgGPAf8CbgO+X8V8mZlZCd/HVwER0QYcIekbpOrNOuDBiHiyujkzM7NSLvFVgKRvShoZEc9ExBURcXlEPClphKRvVjt/Zma2kANfZXyLVMVZaiS+j8/MbEhx4KsMUf4N7FvhNzKYmQ0pvsY3CJLmkgJeAM9IKga/emA46a3sZmY2RDjwDc4XSKW9XwNfA+YU+rUC0yLizmpkzMzMynPgG4SIuAhA0rPAHbl1p5mZDWEOfBUQEbd0/Za0OtBU0v/5biOZmVlVOPBVgKQxwE+AQygJeln90s2RmZn1xK06K+McYAvgY0AzcDjwFdJzOj9exXyZmVkJl/gqYy/gsIj4t6QO4P6IuEzSy8BngSuqmz0zM+viwFcZK5Ce0wmpZedKwFPAncAvq5WpZUnz5AvR8IVveGpcb2+idS5tT19F43p7U7/8egC0PnMt9atsRf3oci/L6L/Wp/9C59szqBs1jqb1912Y/tw/ibdfhLpUo924zgepG7lKt/E7Xp9C+4z7AGhYfSL1YzceVH7eSaJ9Pq1T0rletL0NEmoYmbrnz0IjVgE60fCxNK63J6pvXOx5dbzxFO0v3gEIVEfjOrtQl7d92wu30jnnWQAaxm1P/UobpXHefJ72F26FCKhvpHG9D1M3fEXaX/0P7S/cihrTsyjqV9uShlU26zZPSdsAk0gvnP4b8KWIKHcfrw1RDnyV8TSwPvA88DhwqKR7gAPwDeyVUVfPsI0PXSSpo3UuNI6i/ZX7FwS+SqlfdUvqO9vpeO0/3fo1rLEj9Sts0OO40d5M+4x7adrwYABap/6RujHjUcPwiuZxqFLDCIa95ygA2l68A9U10TBuIgDN9/9kQb/Wp/9Gx6yHaVh9m8WeV92YdWhaYQKS6Jw3i7anr2XYZsfSMfsZYt5Mmt59FHR20DrlcupWGI/qh9E+7UYa3/VR6kasRPvMybS/dDdN6+8JQP3YDWlc94N9zfb/gE8Dd5MC357A3xd7IWyp8zW+ypgEbJ5/n0mq3mwFzgbOqlKeakLdiJVQfRMdc1+o6HTrR6+9oFQ3UJ1zn6du9FqoYThqGE7d6LXonOuGvaXqRq9JNM8e1DRU34Sk1NHZRrqtFmL+a2kbqA7VN1I3cmU650zLIwk6WtPv9lbUVO5pgz1qBMZExF25lPdb0rV9ewdxia8CIuLHhd//krQxMBF4MiIeqV7OliGdHbRMuRQADRtD03p7L+jVsNpE2l6+OwWrHrTPfICO16d2S68btQaNa31gQFlpf/lu2mfcR93otWgYtwOqW7TRbrS9jRpHL+hW46hU5WcLRHTSOWcadcuP79av9alrieY3uqU3rL4N9Stv2i29440naZ9+G9E2j6YN9wdAI1eh/aW7qF9tG+hsp3PudDRiJQAax+9O69Q/Q10Dqm+iadPDCtN6is65L6LhK9K49i5o2OjS2TWy6Mulp1P+JdQ2hDnwLQH5vj2f4ldSmarOBb1GrQFA51sv9Th6w6pb07Dq1oPORuMa20PDSIhO2l64iY6ZD9Cw+raDnm7N6Gyn5dHfAanEV7/ye7oN0rTBvt3SelO/4ruoX/FddM6dTvuLd9C00UHULz+eePsVWh+/FDWOQMuNo6uCq33GAzRtuD91o8bR/vK9tD9/C43r7UH9CutTP3YjVNdA+8yHaXv2Opo2PnjQi2xDjwPfIEjai1Tfv3lEvFnSb3ngIeDTEXFDNfJXSxpW24b2V+5P1VhlVKrEp8bl8o966sduQsesB8sO0/nWiwu6o+0t6ka5UABAXcOCa3w9GWiJb8GkR69FtMwh2uajxhE0rPFeGtZ4b5rm039Fw1cg2uYR82dRN2ocAPVjN6J16pVAujbZpX6V99A+/dZys2kD1ip0rwW8WG5AG7oc+AbnC8DZpUEPICLmSDoLOBEYcOCTVAeMKjdt665+zDq0z7ibaJtX9mkBlSrxpWrM5YgIOuc8g4av1G2YutHr0P7yXUR7MwCdc1+gYdwOg553rRhIia+z+Q00bIXUuOXtV4jOdmgYTkQndLSghhF0zptFzH91QbVqdLTQ2fwGdcNXpOPN59CI1Fo4Wt9acL2vc/bTi7QiLmgDWiRtT2rc8gnSwyvsHcSBb3A2B07upf+/SA+v7hdJlwDHAR3AvcAYSedFxNk9DP8Z4DMANA7oAv0yqWG1ibQ9+7eKTKvlyStTqaOzjeb/TKJx7d2oH7MObc/dQLTPB0AjVqZx3M4AdM6bScerj9K4zm6oYTj1q02kdeofAahfbduaadG5tHW+8SQdrz4OqoO6Bpom7IskorOD1scvSwPVN9G4/l6kc8l0ja/tqWsAQcNwGsfvAUD7Kw/SOfuZfPvFcBrX23PBfFoe/V2xpPo5Ft7O8HfcovMdR779ZPFJagY2i4gne+i/IfBQRIwo17/M8JMjYktJRwBbA/9Nuhl+8z5GpW7kqjFso0MGkHurusZh1c6BDVDzvefeHxETq50PGxzfzjA401l4G0M5mzOw+v9GSY2k5tHX5Lc9+MzEzKyCHPgG56/AdyV1K9FJGgl8Jw/TXz8HpgHLAbdKWhfwNT4zswpyVecgSFoVeBDoBC4ApuRem7DwJbVbR8Qrg5hHQ0S09zWcqzrfgVzV+Y7jqs5lgxu3DEJEzJS0I+mWhh/Q9diIVD15PfD5gQS9fAvEt4Cu9vW3kEqNc3ocyczMBsSBb5Ai4jlgb0krAhuQgt+TEdH9RqS+/Rp4lPReP4CjgN+QnvlpZmYV4MBXITnQ3TvIyUyIiAML3d+WNHmQ0zQzswI3bhla5kvaqatD0vuA+VXMj5nZMsclvqHleOCifK0P4A3g6Crmx8xsmePAN7Q8EhFbSBoD4MeVmZlVnqs6h5ZnJf0C2BaYW+3MmJktixz4hpaNgRuBz5OC4AXFa35mZjZ4DnxDSETMi4jLI+IAYCtgDOlePjMzqxAHviFG0s6SLgTuB4az8J4+MzOrADduGUIkTSM9Au1y4CsR8XZ1c2Rmtuxx4Btaur3J3czMKsuBbwiQ9NWI+CHwfUndnhoeESdUIVtmZsskB76h4fH8fV9Vc2FmVgMc+IaAiPhL/jkvIv5Y7Cfp4CpkycxsmeVWnUPLaf1MMzOzxeQS3xAgaS9gb2BNSecXeo0B+nwJrZmZ9Z8D39DwEun63kdI9+91mQucVJUcmZktoxz4hoCIeAh4SNLvI8IlPDOzJciBbwiQdHlEHAI8WHI7g4CIiM2rlDUzs2WOA9/Q8KX8vW9Vc2FmVgPcqnMIiIiX889XgRci4jlgGLAF6fqfmZlViAPf0HIrMFzSmsA/gKOASVXNkZnZMsaBb2hRRMwDDgAujIiDgXdXOU9mZssUB76hRZJ2AI4A/prT6quYHzOzZY4D39ByIulJLX+OiP9IWh+4qcp5MjNbprhV5xASEbcAt0gaJWlURDwD+M0MZmYV5BLfECJpM0kPAv8BHpN0vyRf4zMzqyAHvqHl58DJEbFuRKwDnAL8vyrnycxsmeLAN7QsFxELrulFxM3ActXLjpnZssfX+IaWZyR9A/hd7j4SeKaK+TEzW+a4xDe0fBJYBbgS+BOwck4zM7MKcYlvCJA0HDgO2AB4BDglItqqmyszs2WTS3xDw0XARFLQ2ws4u7rZMTNbdrnENzRsGhGbAUj6FXBPlfNjZrbMcolvaFhQrekX0ZqZLVku8Q0NW0h6M/8WMCJ3d72Idkz1smZmtmxx4BsCIsIPojYzW0pc1WlmZjXFgc/MzGqKA5+ZmdUUBz4zM6spDnxmZlZTHPjMzKymOPCZmVlNceAzM7Oa4sBnZmY1xYHPzMxqigOfmZnVFAc+MzOrKYqIaufBKkDSLOC5audjCVkZeLXambABWVa32boRsUq1M2GD48BnQ56k+yJiYrXzYf3nbWZDmas6zcyspjjwmZlZTXHgs3eCX1Q7AzZg3mY2ZPkan5mZ1RSX+MzMrKY48JmZWU1x4LNBkxSSzil0f1nSGUtgPqeXdN9R6XnUIkkdkiZLelTSHyWNHOD4a0i6Iv/eUtLehX4fkfTflc6z2WA48FkltAAHSFp5Cc9nkcAXETsu4fnVivkRsWVEvAdoBY4byMgR8VJEHJQ7twT2LvS7JiLOrFxWzQbPgc8qoZ3Uiu+k0h6SVpH0J0n35s/7Cuk3SPqPpF9Keq4rcEq6StL9ud9nctqZwIhcMvl9Tnsrf18qaZ/CPCdJOkjScEm/kfSIpAcl7Zr7v1vSPXlaD0t61xJeP+8k/wY2kDQ2b4eHJd0laXMASTvn9TY5r9PRksbn0mIT8B3g47n/xyUdI+kCScvnbVyXp7OcpBckNeZS4l15Xn+WtGIe5gRJj+X0S6u2RmzZExH++DOoD/AWMAaYBiwPfBk4I/e7BNgp/14HeDz/vgA4Lf/eEwhg5dw9Nn+PAB4FVuqaT+l88/f+wEX5dxPwQh73FODXOX1j4HlgOPAT4IjC8COqvQ6rvf3ydwNwNXB8Xkffyum7AZPz778A78u/R+VxxgOP5rRjgAsK017Qnae9a/79ceCX+ffDwM7593eA/82/XwKG5d8rVHs9+bPsfFzis4qIiDeB3wInlPT6EHCBpMnANcAYSaOAnYBL87jXAW8UxjlB0kPAXcDaQF8lsr8Du0oaBuwF3BoR8/M8Ls7zmEJ6lumGwJ3A6ZJOJT17cf7iLfUyY0TePveRTg5+RVp3vwOIiH8BK0kaA9wOnCvpBFIwah/AfC4jBTyAQ4HLJC2fp3NLTr8I+ED+/TDwe0lHkmoVzCrCgc8q6X+B/wKWK6TVAdtHuoa0ZUSsGRFv9TQBSbuQguUOEbEF8CCplNajiGgGbgY+TDqwXtbH8JcAHwHmA3+TtFsfy7Wsm1/YPl+MiNaeBox0ve5TpBL17ZI2HsB8rgH2lDQW2Ab4Vx/D7wP8FNgauFdSwwDmZdYjBz6rmIh4HbicFPy6/AP4YleHpC3zz9uBQ3LaHsCKOX154I2ImJcPqtsXptUmqbGH2V8GHAu8H7gup/0bOCLPY0NSVesTktYHnomI80nVb5sPfGmXecV1twvwakS8KWlCRDwSEWcB95KqkIvmAqPLTTCf8NwLnAdcGxEdETEHeEPS+/NgRwG35GuBa0fETcCppP1iVEWX0GqWA59V2jmkV9J0OQGYmBsoPMbCFoPfBvaQ9ChwMDCDdNC8DmiQ9DhwJqm6s8svgIe7GreU+AewM3BjocRyIVAn6RFSYDwmIlpIAffRXL33HlIVrS3qDGAbSQ+TtsPROf3E3JDlYaCNVM1cdBOwaVfjljLTvQw4kkVL5UcDZ+dpbkm6zlcPXJy33YPA+RExuzKLZrXOjyyzqsjX4zoiol3SDsD/RcSWfY1nZjZYrjO3alkHuDxXabUCn65yfsysRrjEZ2ZmNcXX+MzMrKY48JmZWU1x4DMzs5riwGdWQUpvqjio7yGHPkkT8/KMz9275O4l/TBysyXKgc+WOkmrSTpP0tOSWiS9KOnvKrzOph/TOKbrIdVDzDjS8yyXCEln5OATSq8TeiE/5HuVJTXPgjtIy/dazstQ3QZmvfLtDLZU5dLD7aSb1U8DHiKdgH0Q+BnpNod3HElNEdEaETOWwuyeAHYh3eS9FenZmmuSnlNamq86UuvtjsHOND8YYGksn9kS5RKfLW0X5u+JEXF5RDwREY9HxAUUHh0m6eT8tJe3c4nwl5JWyP12AX4DLFco/ZyR+zVJOkvSdEnzlF6F9OFiBiTtI+kJSc2SbpV0aLFKLw9zgNLrjFpyqeprklToPy2Xvn4taTbQ9aqkRao6JZ2Z5zU/j/NDScML/deWdLWk13N+p0g6tI912B4RMyLixYi4Fjif9BScEV2lMEl756fitAKb9HO97Jnn3yzp36QHehf7L6jq7GMbHJmnP1fSTKWX265ZmE6jpPMlvVRYv35nny01LvHZUqP0cOI9ga+Xe1B1ySOpOoETgWeAdUmvyfkJ6VmOd+R+PwAm5OG7pvebnHY4MJ30UtS/SNo2Ih6StA5wJenhxz8HNgPOLcnnNsAfge+RAtq2edg3cx66nJyHmQiI8t4GPgm8CGxKKtW2AN/I/S8kPYR71zz9jXqYTm/mk05iu/7Pw/P0PwvMAl6m7/WyNnAV8P9I62ZzStZLid62QRPwLWAK6fF1ZwF/YOFbF04gvUrqUNKrrNZazOU2WzzVfi+SP7XzAbYjvXdv/8UYd09SwKjL3cfQ/f18E0gBc52S9KuAC/Pv/yG/E7DQ//Scr/G5+/fAv0qGOQOYXuieBvylTD4DOKiX5TgOeKrQ/TD5vXf9XA9nkN99l7s3Bp4E7i6slwC2GeB6+QEwlfxQi5z29ZL1sguLvjex2zboIc8b5/HWyt3nA/8szssff5bmx1WdtjT1VCrqPqC0m9Ib2qdLmksqpTUBq/cy2tZ5Ho/l6r63cuOLfVhYKtmY9IaAortLujchXYcsug1YU+mddF3u68dyHCTpNkkzcl5+zKLXMc8Dvi7pTknfy6XNvmySl20+8BjpxbtHFPq3A5ML3f1ZL5sAd0VE8VFOd/YjL91I2jpX3z6Xt13Xeupa7kmkh1FPlfTTXPXsY5EtNa7qtKXpSdKZ/ybAn3saSNK6wF9J1W7fJLUi3JpUXdbUy/Tr8vS3Jb05oKhSL5stBoa3extQ0vakl+1+GzgJmE16D+CPFkws4leSridVPX4IuEPS/0TEGb1M+uk8fAfwUqQ3ThS1xKKNWZbGegFA0nLA9cCNpGrpmaTqzn+Tt11EPJCvp36Y1KjpIuAhSbtHRGcl82NWjgOfLTUR8Xo+yH9B0vlRcp1P0gqRrvNNJB0kT+o6gEvat2RyraRWjUUPkko2q0d6j1s5U4CPlqRtV9L9OPC+krSdSFWdc3uYbjnvA16MiO92JeSgvoiImE565dIvlN4K/yVSlWZPWiPiqQHkoz/r5XHgQEkqlPq272HYBfmg+zbYmBToTo+IZyE1FCodMa/HK4ArJE0ivX5qA1J1q9kS5eoFW9o+TzoI3yfpYEkbSdpY0vGk612QSoZ1pHe/rSfpMFJDiqJpwHBJu+dWhiMjYirp+tykXMW4vtJN2F8uHHx/BkyQ9KM87wNIjUBgYWnuHGDn3GpzQ0lHAKcAPxzgsk4lVY8ekfNyPHBYcQCl+xn3zP23JF3LfGyA8+nVANbLeOB/83o5iIXvTuzJNEq2AfA86VrsF/J89gG+WxxJqcXuYZI2kbQBqcHNm6RGN2ZLXrUvMvpTex/STdA/IbXYbAFeIr3QdM/CMCeQWkLOJzWEOIRCQ4s8zP8Br+b0M3JaI6m09AypRDIDuIZFG3vsSwpKzaQquGPzNFYrDHMA8EiexgvA11i04cc04Mtllm2Rxi2kxjSzSC0erwSOT3+7Bf1/Qgr0zXm4S4E1e1l3Z1Bo3FKm/zGUaXDSz/WyD+kewWbSNc4j6KVxSy/b4OOk6thm4B5SlWYAu+T+nwYeIN3L+SZwC7BjtfdLf2rn49cSWc2T9CXSW79XCP8hzJZ5vsZnNUfS50ktO2eRrmN9A5jkoGdWGxz4rBZtQLp3byXSdaWfkUp8ZlYDXNVpZmY1xa06zcyspjjwmZlZTXHgMzOzmuLAZ2ZmNcWBz8zMasr/B9qCbjmhPrviAAAAAElFTkSuQmCC\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "data = [[TN, FP],[FN, TP]]\n", + "\n", + "plt.clf()\n", + "plt.imshow(data, cmap = plt.cm.Blues_r)\n", + "classNames = ['Negativos','Positivos']\n", + "plt.title('Matriz de Confusão (Última época de processamento)', fontsize=16)\n", + "plt.ylabel('Categorias Reais', fontsize=14)\n", + "plt.xlabel('Categorias Preditas', fontsize=14)\n", + "tick_marks = np.arange(len(classNames))\n", + "plt.xticks(tick_marks, classNames)\n", + "plt.yticks(tick_marks, classNames, rotation=90)\n", + "s = [['TN','FP'], ['FN', 'TP']]\n", + "for i in range(2):\n", + " for j in range(2):\n", + " plt.text(j,i, str(s[i][j])+\" = \"+str(data[i][j]))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ezRKV2fp0Gtk", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 413 + }, + "outputId": "7fa30331-7a8b-4031-b566-eed6b936a922" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plt.rcParams['figure.figsize'] = (12, 6)\n", + "plt.plot(acc, '-o')\n", + "plt.legend(['Modelo'], loc = 'lower right', fontsize = 'xx-large')\n", + "plt.xlabel('Epocas de processamento', fontsize=16)\n", + "plt.ylabel('Acuracia', fontsize=16)\n", + "plt.title('Acuracia', fontsize=18)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Au2Nv6TC0Gtl", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 413 + }, + "outputId": "cd9dc5f4-e3df-4880-e7ff-fa994e6ec5e6" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plt.rcParams['figure.figsize'] = (12, 6.0)\n", + "plt.plot(fp, '-o')\n", + "plt.legend(['Modelo'], loc = 'lower right', fontsize = 'xx-large')\n", + "plt.xlabel('Epocas de processamento', fontsize=16)\n", + "plt.ylabel('Falsos Positivos', fontsize=16)\n", + "plt.title('Falsos Positivos', fontsize=18)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "03MxsVXZ0Gtm", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 413 + }, + "outputId": "7f50b7df-edc0-4b9a-a914-43ba26732caa" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plt.rcParams['figure.figsize'] = (12, 6.0)\n", + "plt.plot(fn, '-o')\n", + "plt.legend(['Modelo '], loc = 'lower right', fontsize = 'xx-large')\n", + "plt.xlabel('Epocas de processamento', fontsize=16)\n", + "plt.ylabel('Falsos Negativos', fontsize=16)\n", + "plt.title('Falsos Negativos', fontsize=18)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "J_F2OcF40Gtm", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 413 + }, + "outputId": "102d671c-97c6-4e3b-b579-4c630c48e719" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plt.rcParams['figure.figsize'] = (12, 6.0)\n", + "plt.plot(tp, '-o')\n", + "plt.legend(['Modelo '], loc = 'lower right', fontsize = 'xx-large')\n", + "plt.xlabel('Epocas de processamento', fontsize=16)\n", + "plt.ylabel('Verdadeiros Positivos', fontsize=16)\n", + "plt.title('Verdadeiros Positivos', fontsize=18)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uqRe6p5d0Gtn", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 413 + }, + "outputId": "2e9cac15-dbd5-47c5-c987-df5c4fc0fa1e" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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hvSfoaScdPIbK8jLmLl0XPUgRERER2S353sE+DvhXM3uZ4KbGk4CfAjVmli5XGQ+kZ3o3AXsDhMerCW523IG7X+3u0919em1t9iYi5kpzoo1hgwdRUb7rH39Pw2HWxlsjf96wqgreddBo5i5dt9s9tkVEREQkmrwm2O5+qbuPd/cJwNnAfHf/GPAg8MHwtPOAv4TP7whfEx6fX+z11xB0/6gZuuvda+h5OExfh8acNrWOpniSJU0tfXqfiIiIiPRNfxk083WCGx5XEtRYXxuuXwuMCte/AlxSoPiyqjnRtn3IzK7MbJxErGLHNn27MzTm1MljKS8z7lGZiIiIiEhOFWzQjLs/BDwUPn8JOKqbc1qBD+U1sDyIJ1K9dhDJlB4OM2veCtbEkzTUxJjZOKnPQ2NqhlRyzP6jmLt0HV9rnETQvEVEREREsk2THAsgnmhj75FDIp+fHhqzp06bWsdlc5by/Po3mVQ3bI+vJyIiIiI76y8lIiUlnkx1O2Qm194zZSxmaOiMiIiISA4pwc6zjk4PbnKM0KIv28YMq+Lt+45Uuz4RERGRHFKCnWdbWlO473rITK40Tq3juXVbWPX61oJ8voiIiMhApwQ7z5q3T3HM/w42BHXYoDIRERERkVxRgp1n8UQbACMKtIM9ribG28ZXM09lIiIiIiI5oQQ7z7aPSS/QDjbAaVPr+efqFpp6mBIpIiIiIrtPCXaexZOF3cGGt8pEdLOjiIiISPYpwc6z5q1hDXYBuoik7Td6KAfXDWOu6rBFREREsk4Jdp7FkynMYHgBE2wIdrEXvtLMhi2tBY1DREREZKBRgp1n8UQbw6sqKC8r7Kjy06fW4w73Lltf0DhEREREBhol2HkWTxRmimNXE8fuxf6jh6oOW0RERCTLlGDnWXOijeoC3uCYZmY0Tq3jiZfeoHlrW6HDERERERkwlGDnWUuyf+xgA5w+tY6OTue+Z1UmIiIiIpItSrDzrDnRVtAOIpkOHVfNuJqYykREREREskgJdp7FEylq+kGJCARlIqdNreOxF15nS2uq0OGIiIiIDAhKsPOovaOTLa3t1PSTEhEI2vW1dXQy/7kNhQ5FREREZEBQgp1HLclgl7iQUxy7OnKfEdQOG6wyEREREZEsUYKdR82JcIpjP9rBLiszGqeM5aEVG0m2dRQ6HBEREZGipwQ7j1qSQTu8/lKDnXb61HqSqQ4efl5lIiIiIiJ7Sgl2HjVvDXew+0kXkbSj9htJzZAKlYmIiIiIZIES7DyK98MabICK8jJOPWQsDzy7gW3tKhMRERER2RNKsPMonghKRKr7UQ12Ws2QCrZsa2fSZXM57gfzmbO4qdAhiYiIiBQlJdh5FE+kKC8zhlcNKnQoO5izuImbn3xl++umeJJLZy9Rki0iIiKyG5Rg51Fzoo3qWAVmVuhQdjBr3gpaU507rCVTHcyat6JAEYmIiIgULyXYeRRPpvpVi760NfFkn9ZFREREpGdKsPMonmjrdx1EABpqYn1aFxEREZGe5TXBNrMqM1tgZv80s2Vm9p1w/QYzW2VmT4ePw8N1M7OfmdlKM3vGzI7IZ7zZFk+k+l0HEYCZjZOIVZTvsFZeZsxsnFSgiERERESKV77vttsGnOTub5pZBfCYmd0THpvp7rd3Of904KDw8Q7g1+HXohRPpJhUN6zQYexkxrRxQFCLvSaeZEhlOVvbOjikfniBIxMREREpPnlNsN3dgTfDlxXhw3t5y5nATeH7njSzGjOrd/e1OQ41J+KJtn65gw1Bkp1OtJu3tvHuWQ/yX3cu5+YLjup3N2WKiIiI9Gd5r8E2s3IzexrYANzn7k+Fh64Iy0CuMrPB4do44LWMt68O14pOW3snW9s6+mUNdlcjhlbypVMm8tjK15n/nMani4iIiPRF3hNsd+9w98OB8cBRZjYVuBQ4GHg7MBL4el+uaWYXmtlCM1u4cePGrMecDfFkMGSmZmj/3MHu6pxj9mX/2qFccdeztLV37voNIiIiIgIUsIuIu8eBB4HT3H2tB7YB1wNHhac1AXtnvG18uNb1Wle7+3R3n15bW5vr0HdLPBGMSS+GHWwIxqd/632Teen1rTsMoRERERGR3uW7i0itmdWEz2PAqcBzZlYfrhkwA1gavuUO4Nywm8jRQEvx1l8HCXZ/rcHuzgmTajl+Yi0/vf95Nm1tK3Q4IiIiIkUh3zvY9cCDZvYM8HeCGuw7gd+b2RJgCTAa+F54/t3AS8BK4Brgs3mON2uaE2GJSD8cNNMTM+Oy9x3C1rYOrrrv+UKHIyIiIlIU8t1F5BlgWjfrJ/VwvgMX5zqufGhJl4gUUYINMHHsMD72jn343ZOv8PGj9+2XbQZFRERE+hNNcsyTt3awi6dEJO3Lp0xkr8GD+N5dywn+5hERERGRnijBzpN4MkVFuTG0snzXJ/cz6bZ9j77wOg+uUNs+ERERkd4owc6TeKKN6lhl0Q5tSbft+96datsnIiIi0hsl2HkST6QYUWT115kqysu47H2HqG2fiIiIyC4owc6T5kRb0d3g2NWJk8aobZ+IiIjILijBzpN4IlWUNzhmymzb95P71bZPREREpDtKsPMknkgVzRTH3qTb9v3+qVd5fv2WQocjIiIi0u8owc6TeLKNEUOLewc77UunTGRoZTn/dafa9omIiIh0lddBM6WqNdVBa6qT6gGwgw0wMmzb9907lzP9e/ezaWsbDTUxZjZOYsa0cYUOT0RERKSgIu1gm9mZZvaJjNf7mtkTZrbFzG43s71yF2Lxi4dTHEcUeQ12purYIAx4Y2sbDjTFk1w6ewlzFjcVOjQRERGRgopaInIZUJvx+sfAeOBq4Hjg8uyGNbC8NcVxYOxgA/z4vhfoWhySTHUwa96KgsQjIiIi0l9ETbAPAJ4BMLMY8F7gK+7+VeAbwFm5CW9gSO9gD6QEe0082ad1ERERkVIRNcGuAtKZ07EEtdv3hq9XAA1ZjmtAiad3sGMDp0SkoSbWp3URERGRUhE1wX4ZeGf4/Exgkbu3hK/HAC3dvUkC8WRYgz104Oxgz2ycRKyifIe1WEU5MxsnFSgiERERkf4haheR3wI/MrOzgMOBf884dgywPNuBDSTNA3AHO90tZNa852iKtzJ4UBlXvv9QdRERERGRkhdpB9vdfwqcDzwBfNLdr8k4PAy4PvuhDRzxRIrBg8qIVZbv+uQiMmPaOB6/5GQ+e8IBtHc6755Yu+s3iYiIiAxwkQfNuPvv3f3z7n5Tl/WL3P3m7Ic2cMQTbQOqRV9Xp0+tp6PTue/Z9YUORURERKTg+jRoxszOAN4NjAQ2AQ+6+925CGwgaU6kBlQHka6mjhvOuJoYc5eu49+m713ocEREREQKKlKCbWbDgDuBdwHtwBvAKOArZvYocIa7v5mzKItcywBPsM2M06bWcfMTr7ClNcWwqoH7vYqIiIjsStQSke8DRwDnADF3rwdiwLnh+vdzE97A0JxoG1A3OHbn9Kl1tHV0Mv+5DYUORURERKSgoibYHwAuC+uwOwDcvcPdfw98KzwuPYgnUwOqRV93jthnBLXDBjN36bpChyIiIiJSUFET7FH03IpveXhcuuHuxBNtVA/wHeyyMqNxylgeWrGRZFtHocMRERERKZioCfYq4Iwejr03PC7dSLR1kOpwRgzgGuy006fWk0x18PDzGwsdioiIiEjBRE2wfwt83syuNbOTzOwQMzvRzH4LfAH4Te5CLG7bh8yUQIL9jv1GMmJIBXOXri10KCIiIiIFE6mLiLtfZWa1wFcIBs4AGNAG/CAcRCPdiCeCMek1A7gPdtqg8jJOnTyWe5asY1t7B4MHDazBOiIiIiJR9GXQzDeAeoJSkXOB9wH17v7NHMU2IGxPsGMDfwcbgjKRLdva+dvKNwodioiIiEhBRO2DPcrd33D3ZuCeHMc0oMSTQYnIiKEDfwcb4NgDRzFs8CDuWbqWEw8eU+hwRERERPIu6g72WjObY2YfMLPSyBSzpLnEdrAHDyrnpEPGcN/y9bR3dBY6HBEREZG8i5pgXwbsD/wJWGdmvzGz4/r6YWZWZWYLzOyfZrbMzL4Tru9nZk+Z2Uoz+0M6iTezweHrleHxCX39zEJrCW9yrC6BmxzTTp9aR3MixYJVmwodioiIiEjeRUqw3f2H7n4YwdTG64F/AR4xsxfN7HIzOzDi520DTnL3twGHA6eZ2dHAfwNXufuBQDNwQXj+BUBzuH5VeF5RaU6kGFJZXlI3/L174hiqKsq4R0NnREREpARFvskRwN2fdvevAnsDpwOPA18Fnov4fnf3N8OXFeHDgZOA28P1G4EZ4fMzw9eEx082M+tLzIUWT6QYUQIdRDLFKss5YeIY5i1bR2enFzocERERkbzqU4Kd5u6dwFYgCaQIWvZFYmblZvY0sAG4D3gRiLt7e3jKamBc+Hwc8Fr4me1AC91MjTSzC81soZkt3Lixfw05CaY4lk55SNrph9axYcs2Fr/WXOhQRERERPKqTwm2mR1kZt81sxeBRwh2sX8LHBr1Gu7e4e6HA+OBo4CD+xJDD9e82t2nu/v02traPb1cVsWTKUYMLb0E+6SDx1BZXsY9S1QmIiIiIqUlUoJtZp8zsycJSkG+RJBcnwrs6+6Xuvvyvn6wu8eBB4FjgBozS7cMHA80hc+bCMpRCI9XA0XVYLk50UZNrLRKRACGVVXwzoNGc8/SdbirTERERERKR9Qd7KsIbj48Bxjr7p9w9/nex8zJzGrNrCZ8HiNI0p8lSLQ/GJ52HvCX8Pkd4WvC433+zEJrSaRKYkx6d06bUkdTPMnSps2FDkVEREQkbyINmgHGu/v6LHxePXCjmZUTJPd/dPc7zWw5cJuZfQ9YDFwbnn8tcLOZrQQ2AWdnIYa8cXfiydJNsE+dPJby/zPuWbqWQ8dXFzocERERkbyIlGBnKbnG3Z8BpnWz/hJBPXbX9VbgQ9n47ELYsq2djk4vuS4iaSOGVnL0/iOZu3QdMxsnUWQNYERERER2S48JtpnNBz7r7s+Fz3vj7n5ydkMrfvGtwRTHUuwiknba1Hq+NWcpL2x4k4ljhxU6HBEREZGc660GO3O7sSx83dNjt9r9DXTxZDDFsVR3sAEaJ4/FDHUTERERkZLR4w62u5+Y8fyEvEQzwDQngh3sUq3BBhgzvIoj9xnBPUvX8sVTDip0OCIiIiI5F7VN37lmttOAl/DYSDM7N7thDQzxRLCDXVPCO9gAp02t47l1W3j59a2FDkVEREQk56KWdlwPHNDDsf3C49JFXDvYQJBgA8xdpjIRERERGfiiJti9tX8YCrT3crxkbU+wS/gmR4DxI4Zw2Phq7lmqBFtEREQGvt66iBwOHJGx9C9mNrXLaTGC3tQv5CC2otecaGPY4EEMKtc9oI1T6pg1bwVr4kkaamKFDkdEREQkZ3rrg30m8O3wuQPf7OG8N4ALshnUQNGSTFEztLR3r9NOnxok2HOXruOT79yv0OGIiIiI5ExvW6s/Iaiv3p+gROT94evMRwMwxt3vyHGcRak50UZNrLRvcEzbv3YvJo0dpjpsERERGfB6a9PXArQAmNl+wFp3b8tXYANBPFG6Y9K7M2HUEOYtX89+l9xFQ02MmY2TmDFtXKHDEhEREcmqSMXB7v6Kkuu+iyfaSr5FX9qcxU089PxGIKg3aoonuXT2EuYsbipsYCIiIiJZFvnuOzO70MwWm1nCzDq6PnIZZLGKJ1OM0A42ALPmrWBbe+cOa8lUB7PmrShQRCIiIiK5EXnQDPBz4O9AFUHf698Bm4EXge/mKsBi1dHpwU2OJd6iL21NPNmndREREZFiFXUH+0vAlcC/h69/5e7nEdwAmSToJCIZtrSmcNcUx7SeWvONGT44z5GIiIiI5FbUBPsg4BGgM3xUArh7M3AF8MWcRFfEmjXFcQczGycRqyjfaX3rtnYWvbKpABGJiIiI5EbUBDsJlLm7A+sIdq7T3iRo1ycZ4ongntAR2sEGYMa0cVz5/kMZVxPDgHE1MS45bRKj9xrM2Vc/yR///lqhQxQRERHJit4GzWRaAhwI3A88CnzDzFYRjEi/HHguJ9EVsfSY9GrtYG83Y9q4ndrynX3UPnzulsV87c/PsHp9lXIAACAASURBVHztZi573yGafCkiIiJFLWomczUwInz+LWAv4DHgSWAi8NXsh1bc4kntYEdRM6SSGz7xdj553H7c8LeXOe/6BTRvVUdIERERKV6RdrDd/Q8Zz1ea2RTgGGAI8Dd3fz1H8RWt5q1hDba6iOzSoPIy/vNfJnNI/TC++X9LOfOXj/O/501n4thhhQ5NREREpM+ilojswN23EpSLSA/iyRRmMFwJdmQfmr43+9fuxWd+t4izfvk4Pzl7Glu3tTNr3grWxJOa/igiIiJFIVKCbWb79HK4E2hx9y3ZCWlgiCfaGF5VQXmZFTqUonLkviO443PHceFNi/j0TQsZVGa0dzrw1vRHQEm2iIiI9FtRa7BfBlb18HgFiJvZC2b26VwEWYziCU1x3F311TH+9JljiFWUb0+u0zT9UURERPq7qCUinwG+AcSBPwPrgTrgA0A18CvgeOA3ZpZy9xuyH2pxaU60Ua0bHHdbVUU5ramObo9p+qOIiIj0Z1ET7InAQnf/YJf175rZn4E6dz/DzG4mGDpzQxZjLEotyRQjhyrB3hMNNTGaukmme5oKKSIiItIfRC0R+Tjwvz0c+1/gY+HzPwGT9jSogaA50aYOInuou+mP5Wb8x6kTCxSRiIiIyK5FTbCHAaN7OFZL0BcbYDPQ/b/rl5h4IkWNSkT2SNfpj8OrBtHhzoJXmgmGioqIiIj0P1FLRB4Gvm9mz7r7ovSimU0HrgAeDJcOAl7NbojFp72jky2t7dToJsc91nX64w/nPsevHnqRoZXlfPN9h2CmLi0iIiLSv0Tdwb4YSAELzGyVmT0Vjkp/CtgGfD48by/glz1dxMz2NrMHzWy5mS0zsy+G65ebWZOZPR0+3pvxnkvNbKWZrTCzxt35JvOtJRkMmdEUx+yb2TiJ84+dwP8+toqf3P9CocMRERER2UnUSY6rzOxg4BPAO4B6YCnBqPQb3D0VnnfVLi7VDnzV3f9hZsOARWZ2X3jsKnf/UebJZjYZOBuYAjQA95vZRHfv12UozYlwiqN2sLPOzPjPMyaTaGvnpw+8wJDKci569wGFDktERERku8iTHMMk+urwsVvcfS2wNny+xcyeBXqbGHImcJu7bwNWmdlK4Cjgid2NIR9akm0AqsHOkbIy48r3H0airYMr73mOIYMHcc7R+xY6LBEREREgeokIAGZ2mJl9zsy+bWZ14dqB4W50n5jZBGAaQZkJwOfM7Bkzu87MRoRr44DXMt62mm4ScjO70MwWmtnCjRs39jWUrGveGu5gq4tIzpSXGVd9+HBOOWQM35qzlD8vWl3okERERESAiAm2mQ02sz8Bi4GfAf9JULIB8EPgm335UDPbi2BgzZfcfTPwa+AA4HCCHe7/6cv13P1qd5/u7tNra2v78taciKsGOy8qysv4xUeP4LgDRzHz9n9yz5K1hQ5JREREJPIO9hXAKcA5wFggs3XDPUDkmw/NrIIguf69u88GcPf17t7h7p3ANQRlIABNwN4Zbx8frvVr8URYIjJUO9i5VlVRzjXnTueIfUbwhdsW8/27l3PcD+az3yV3cdwP5jNncb//z0VEREQGmKgJ9keAy9z9FmBTl2OrgAlRLmJBT7VrgWfd/ccZ6/UZp51FcAMlwB3A2eEO+n4EbQAXRIy5YOKJFOVlxrDBkUvcZQ8MqRzEdZ94O2OHDebqR1bRFE/iQFM8yaWzlyjJFhERkbyKmgGOAp7t4VgZMDjidY4j2AVfYmZPh2vfAD5iZocDDrwMXATg7svM7I/AcoIOJBf39w4i8NYUR/Vozp/hVRV0dDN7JpnqYNa8FTv00hYRERHJpagJ9irgGGB+N8eOAlZEuYi7P8aO5SVpd/fynisISlSKRjyRolot+vJuXUtrt+tr4sk8RyIiIiKlLGqJyE3AJWb2MSCdObqZnQh8GbguF8EVq3iyTTc4FkBDTaxP6yIiIiK5EDXB/iFwF3Az0ByuPQbcD8x195/nILai1bw1pRZ9BTCzcRKxivId1mIV5cxsnFSgiERERKQURZ3k2EFws+EvCTqGjAHeIEiuH85hfEWpJZnikPrhhQ6j5KTrrGfNW0FTPElFuXHl+w9V/bWIiIjkVZ/aXLj7o8CjOYplwGhOtGlMeoHMmDaOGdPG8csHVzJr3gqO3n9UoUMSERGREtOnSY6ya9vaO0i0dTBCCXZBNU4ZC8B9y9dl/dpzFjep17aIiIj0qMcdbDNbRdA2Lwp39wOyE1Jxa0kEUxyrdZNjQR04Zhj71w5l3rL1nHPMhKxdd87iJi6dvYRkKugWme61DagURURERIDeS0QeZ9cJ9njg3RHOKxlvjUnXDnahNU6p45pHXiKeaKMmS3/wzJq3YntynaZe2yIiIpKpxwTb3T/e0zEzG00wIOaDwGbgxz2dW2qat4Zj0mPawS6006bU8euHXuSBZzfwgSPHZ+WaPfXUVq9tERERSetTDbaZDTOz7wIvEkxb/Dmwv7t/NxfBFaP0DrZuciy8w8ZXU19dxbxl2avDVq9tERER2ZVICbaZVZnZ1wgmOn4d+D1woLt/zd035TLAYhNPhDvYSrALzsx4z+SxPPLCRpJtHbt+QwQzGydhXWaRqte2iIiIZOo1wTazQWZ2MfAS8H2CkeYHu/tn3X1tPgIsNvFEugZbJSL9QeOUOlpTnTz8/MasXG9S3TDcoSxMshtqqtRrW0RERHbQY4JtZucDLwA/A54ADnP3c919VZ5iK0rNiRQV5caQyvJdnyw5d9R+I6kZUpG1MpHbFrxK5aAyvnjyRADmXHyckmsRERHZQW9dRK4j6A5yL7CMYJJjT+e6u387y7EVpZZk0LGil5+V5NGg8jJOOWQs9y5bR6qjk4ry3W/9nmzrYPbiJt47tY7JDcGkzrXxVsYMq8pWuCIiIjIA7GqSoxGMRm/cxXkOKMEGmremqImp/ro/aZxSx+2LVvPkS2/wroNqd/s6dy1Zy5bWdj5y1D4MHRz8T2dNPMnb9q7JVqgiIiIyAPS4nefuZX14qB4iFE+2qf66n3nXQaMZUlm+x2Uity54lf1rh3LUfiO3dw1Z09KajRBFRERkANGo9CyLJ1JUq4NIv1JVUc4Jk2q5d9l6Ojt3bybSinVbWPRKMx89ah/MjBFDKqiqKGOt+l+LiIhIF0qwsyyeSGmKYz/UOKWODVu2sfi1+G69/9YFr1JZXsb7jwgG1pgZDdUx1rQowRYREZEdKcHOsuYsjuWW7Dnx4DFUlBv37kaZSGuqg9n/WM1pU+sYOfSt321DTYw1cZWIiIiIyI6UYGdRa6qDbe2dGjLTDw2vquCYA0Yzb9k63PtWJnL3krVsDm9uzFRfXaUR6SIiIrITJdhZ1Jye4hjTDnZ/dNqUOl5+I8GK9Vv69L5bF7zKfqOHcvT+I3dYb6iJsfHNbbS1d2YzTBERESlySrCz6K0pjtrB7o9OnTwWM5i3dH3k97ywfgt/f7mZjxy19069zRtqqnCH9ZtVJiIiIiJviZRgm1mZmQ3qstZoZl81s2m5Ca34pHew1UWkf6odNpgj9xnRp3Z9ty54jYpy4wPhzY2ZtrfqU5mIiIiIZIi6g30rwWRHAMzsM8A9wCzgSTM7JQexFZ2W7TvYKhHprxqn1LF87WZe25TY5bmtqQ7+/I/VNE6pY9Reg3c6Xl8dJNhr1QtbREREMkRNsI8G7s54PRP4X6AamA18M8txFaXmMMHWTY79V+OUOoBIu9hzl66jJZnio11ubkxrqAlGpDdpB1tEREQyRE2wxwBNAGZ2ILAf8At33wJcDxyam/CKSzwZlIhoB7v/2mfUEA6pHx4pwb5lwatMGDWEo/cf1e3xIZWDqBlSwVr1whYREZEMURPszUA6yzgBeN3dnwlfdwBVWY6rKMUTKQYPKqOqQpPj+7PGKWNZ+EozG7ds6/GclRveZMGqTZx91D6UlVmP59VXqxe2iIiI7Chqgv034BIzOwP4EjuWixwIrM52YMUonmjT7nURaJxShzvc/2zP3URuW/AqFeXGB4/c+ebGTONq1AtbREREdhQ1wf4awQ72HQS71ZdnHPsw8ESUi5jZ3mb2oJktN7NlZvbFcH2kmd1nZi+EX0eE62ZmPzOzlWb2jJkdEfUbK4TmREr110Xg4Lph7DNySI9lIumbG98zuY7R3dzcmCnYwVaCLSIiIm+JlGC7+wvufhBQ6+4HuvvLGYe/SJCAR9EOfNXdJxPcOHmxmU0GLgEeCD/jgfA1wOnAQeHjQuDXET+nIFqUYBcFM+O0qXU8vvJ1Nremdjo+b9k6mhOpnSY3dqehJsbm1nbe3Naei1BFRESkCPVp0Iy7v2Fme4U70XuFa0vcfWPE969193+Ez7cAzwLjgDOBG8PTbgRmhM/PBG7ywJNAjZnV9yXmfGpOtGmKY5FonDKWVIfz4HMbdjp264JX2WfkEI49oPubGzOlO4ms1S62iIiIhCIn2OFgmYVAHHgZiJvZAjM7dXc+2MwmANOAp4Cx7r42PLQOGBs+Hwe8lvG21eFavxRPphgxVDvYxWDa3iOoHTaYe5ftWIf90sY3efKlTZx91N693tyYtn3YjHphi4iISCjqJMdG4C5gL+C/gM8C3wOGAXf3NckOd7//DHzJ3TdnHnN3B7yP17vQzBaa2cKNGyNtpmeduxNPtFGtHeyiUFZmnDp5LA+u2EBrqmP7+m1/f41BZbu+uTGtvlo72CIiIrKjqDvYlwP3ApPd/Tvu/lt3vxyYAtwHfCfqB5pZBUFy/Xt3nx0ur0+XfoRf0/9u3wTsnfH28eHaDtz9anef7u7Ta2tro4aSVYm2DlIdzgjVYBeN06bUkWjr4LEXXgdgW3sHty9azamTxzJmWLTOk2OHV2GmcekiIiLylqgJ9tuAX7p7Z+Zi+PpXwOFRLmJmBlwLPOvuP844dAdwXvj8POAvGevnht1EjgZaMkpJ+pXmRDBkRjc5Fo+j9x/FsKpB27uJ3LtsPZu2tkW6uTGtoryMscOqVCIiIiIi2w2KeN42YHgPx4aFx6M4DjgHWGJmT4dr3wB+APzRzC4AXgH+LTx2N/BeYCWQAD4R8XPyLr59TLpKRIpF5aAyTj54DPc/u572jk5uXfAq40fEeOeBo/t0nXr1whYREZEMURPsh4D/MrMn3X1VetHM9iEoH3kwykXc/TGgpzvHTu7mfAcujhhjQW1PsGPawS4mjVPqmPP0Gv60aDV/e/ENZjZOinRzY6aGmhjL12ze9YkiIiJSEqKWiFwCVAMrzOwRM/uDmT0MvADUAF/PVYDFIp4MSkRGDNUOdjHZEvbBvnT2EgCGDe77mPuG6mAHO/h7UEREREpd1EEzK4DDgJ8Bg4EjCCY6/hQ43N1fyFmERaJZO9hFZ87iJr59x/Id1q68ZwVzFu90H22vGmpibGvvZNPWtmyGJyIiIkVqlyUiZlYJ/Ddwi7v/R+5DKk4t4U2O1brJsWjMmreCZEaLPoBkqoNZ81YwY1r0duv11UEv7LUtrYzaxWh1ERERGfh2uYPt7m3ARUAs9+EUr+ZEiiGV5Qwe1PcSAymMnm5M7OsNi+lpjk260VFERESIXoO9GDg0l4EUu3gixQh1ECkq6SmMUdd3dR0NmxERERGInmB/FfgPMzsj7GUtXQRTHFUeUkxmNk4iVrHjvzjEKsqZ2TipT9cZNbSSykFl6oUtIiIiQPQ2fX8i6CLyFyBlZhvZcZy5u/u+2Q6umMSTKUYMVYJdTNJ11rPmrWBNPElDTYyZjZP6VH8NYGbbO4mIiIiIRE2wH2DHhFq6aE60cUhdT7N4pL+aMW1cnxPq7tRXx5Rgi4iICBAxwXb383McR1Gbs7iJl1/fyksbt/L0D+bv1i6oFLeGmhh/e/H1QochIiIi/UDUGmzpwZzFTVw6+xk6w/39pniSS2cv6XMvZSluDTVVrN/cSntHZ6FDERERkQLrcQfbzM4F7nL3N8LnvXL3m7IaWZEIeinvmFTtTi9lKW4NNTE6HdZv2ca4PnYhERERkYGltxKRG4CjgTfC571xoCQT7Gz1UpbiVl8d9MJeG08qwRYRESlxvSXY+wFrM55LNxpqYt0OGOlrL2Upbunfd1M8yfQCxyIiIiKF1WOC7e6vdPdcdjSzcRKXzl6yw8jt3emlLMVt+w62emGLiIiUvKht+gAws8OA44FRwG/dfZ2ZHQisd/ctuQiwv8tWL2UpbsOqKhhWNUilQSIiIhItwTazwcDvgPcDRlBz/VdgHfBD4HngkhzF2O9lq5eyFLdxNTHWxLWDLSIiUuqitum7AjgFOAcYS5Bkp90DNGY5LpGiU69pjiIiIkL0BPsjwGXufguwqcuxVcCEbAYlUowaamKsbVGCLSIiUuqiJtijgGd7ucbg7IQjUrwaamI0J1Ik2zp2fbKIiIgMWFET7FXAMT0cOwpYkZ1wRIpXupPIGu1ii4iIlLSoCfZNwCVm9jGgIlxzMzsR+DJwXS6CEykm6V7Ya3Wjo4iISEmLmmD/ELgLuBloDtceA+4H5rr7z3MQm0hRaagOEmzd6CgiIlLaIrXpc/cO4Gwz+yVBx5AxBCPU57r7wzmMT6RojK0ejJlKREREREpdnwbNuPujwKM5ikWkqA0eVM7ovQZrB1tERKTERS0REZEIglZ9qsEWEREpZT3uYJtZJ8HExkjcvTwrEYkUsYbqKp5fv6XQYYiIiEgB9VYi8l3eSrAN+CQQIxiRvh6oA84AksC1OYxRpGg01MR4aMVG3B0z2/UbREREZMDpMcF298vTz83sMuAVoNHdExnrQ4F5QHuUDzOz6wiS8g3uPjVcuxz4NLAxPO0b7n53eOxS4AKgA/iCu8+L+o2JFEJ9dRXJVActyRQ1QyoLHY6IiIgUQNQa7IuAWZnJNYC7bwV+BHwm4nVuAE7rZv0qdz88fKST68nA2cCU8D2/MjOVoUi/lu6F3aQbHUVEREpW1AR7NNDTdlwlwSj1XXL3R4BNET/zTOA2d9/m7quAlQRTI0X6LQ2bERERkagJ9kLgO2bWkLloZuOAy4G/72EcnzOzZ8zsOjMbEa6NA17LOGd1uCbSbzVoXLqIiEjJi5pgfwFoAF4ys4fM7A9m9hDwIsHNjl/cgxh+DRwAHA6sBf6nrxcwswvNbKGZLdy4ceOu3yCSI6P3GkxFubFGO9giIiIlK1KC7e6LgQMJkt8O4NDw64+Ag9z96d0NwN3Xu3uHu3cC1/BWGUgTsHfGqePDte6ucbW7T3f36bW1tbsbisgeKysz6qqrWKsdbBERkZIVeZKju78BfDPbAZhZvbuvDV+eBSwNn98B3GJmPybYPT8IWJDtzxfJtobqmKY5ioiIlLA+jUrfU2Z2K3ACMNrMVgPfBk4ws8MJem6/TNCxBHdfZmZ/BJYTtAG82N078hmvyO5oqImxYFXUe3lFRERkoImcYJvZFOBTwCSgqsthd/eTd3UNd/9IN8s9Dqlx9yuAK6LGKNIfNNRUsW5zKx2dTnmZhs2IiIiUmkgJtpm9A3iYYIf5IOAZYASwD0F3j5U5ik+k6NRXx+jodDZu2UZddde/RUVERGSgi9pF5PvAbIKhLwZc4O4TgFOAcuB7OYlOpAg11ARJtYbNiIiIlKaoCfZhwO8I6qQhSKpx9/kEyfWV2Q9NpDhtHzajTiIiIiIlKWqCXQlsDVvpbQLqM46tAKZmOzCRYlVfHSTY6iQiIiJSmqIm2Ct5a4riM8AnzazMzMqATwDrchGcSDEaXjWIvQYP0rAZERGREhW1i8hfCdrr3UJQj30XsJlg2MxeBJMeRQQwM+o1bEZERKRkRUqw3f3yjOf3m9nRwAeAIcBcd783N+GJFKeGmph2sEVERErUbg2aCUenL85yLCIDRkNNFcvWtBQ6DBERESmAqDXYItIH9dUxXn+zjdaUho+KiIiUmh53sM1sFW+15dsld98/KxGJDADpVn3rWlqZMHpogaMRERGRfOqtRORhdkywTwbGAo8D68PnxxF0EHkgVwGKFKOGcILjmnhSCbaIiEiJ6THBdvfz08/N7ELgHcCx7r46Y31vYC7wRA5jFCk66R3sNS260VFERKTURK3Bngl8OzO5BnD314DvAF/PdmAixawu3MFeq2EzIiIiJSdqgj0e6GkrbhtvDaEREaCqopzRe1WyRr2wRURESk7UBHs5MNPMqjIXzSxGsLu9PNuBiRS7+mr1whYRESlFUftgf41geuOrZnY3b93k+F6gGjg9N+GJFK+Gmipe2ri10GGIiIhInkXawXb3B4DDgfuAdwGfD7/eC7zN3efnLEKRIhXsYCdxj9ztUkRERAaAXe5gm1kFwU71M+7+sdyHJDIwNNRUsbWtg82t7VTHKgodjoiIiOTJLnew3T0F/BGYkPNoRAaQdKu+tbrRUUREpKREvcnxJWBMLgMRGWjqq8Ne2GrVJyIiUlKiJtg/BL5pZrW5DEZkIBmXHjajTiIiIiIlJWoXkZOAkcAqM3sSWMuOY9Td3c/LdnAixax22GAGlZlKREREREpM1AT7nUAK2AgcED4yqU2CSBflZcbY4VXawRYRESkxkRJsd98v14GIDEQNNVWqwRYRESkxUWuwRWQ3NNTENC5dRESkxEROsM1sqJl9wcxuN7MHzeygcP1sMzs4dyGKFK/66hjrWlrp7FQVlYiISKmIlGCb2d7AM8As4CDgeGBYePhE4D9yEp1IkWuoqSLV4bz+5rZChyIiIiJ5EnUH+3+AbcBE4EjAMo49TDA2XUS6aEj3wm7RjY4iIiKlImqCfSrwbXd/hZ07hjQB46JcxMyuM7MNZrY0Y22kmd1nZi+EX0eE62ZmPzOzlWb2jJkdETFWkX6jvqYKgLW60VFERKRkRE2wK4EtPRyrBtojXucG4LQua5cAD7j7QcAD4WuA0wnKUQ4CLgR+HfEzRPqN9LCZJiXYIiIiJSNqgv0M8IEejp0OLIpyEXd/BNjUZflM4Mbw+Y3AjIz1mzzwJFBjZvUR4xXpF6pjFcQqylmrEhEREZGSEXXQzCzgdjMDuCVcm2xmZwIXAP+6BzGMdfe14fN1wNjw+TjgtYzzVodra+nCzC4k2OVmn3322YNQRLLLzNQLW0REpMRE2sF299nAZ4EPAfeHyzcBXwI+5+5zsxGMuzu7MRXS3a929+nuPr22tjYboYhkTdALWzvYIiIipaLHBDu8IfH49Gt3/w3BDnIj8HGC0pDx7n71HsawPl36EX7dEK43AXtnnDc+XBMpKvXV2sEWEREpJb3tYH8YeNDMVpnZd8zsAHff6u73u/st7j7P3Xu68bEv7gDOC5+fB/wlY/3csJvI0UBLRimJSNFoqInx+pvbaGvvLHQoIiIikge9JdhjgU8BLwOXAc+b2eNm9mkzq96dDzOzW4EngElmttrMLgB+AJxqZi8Ap4SvAe4GXgJWAtcQlKiIFJ2G6hjusH6zykRERERKQY83Obr7m8D1wPXhJMdzCEpDfgv81MzuIOj6Mc/dI23NuftHejh0cjfnOnBxlOuK9GcNGa369h45pMDRiIiISK5FvcnxNXf/vrtPBo4GrgNOAu4EmszsRzmMUaSobR8206I6bBERkVIQtQ/2du6+wN0/R3DD41XAGODL2Q5MZKDYPi49rhIRERGRUhC1D/Z2ZnYgcC5BucgEYDPwx+yGJTJwxCrLGTGkQp1ERERESkSkHWwzG2Fm/25mTwArgG+EXz8K1Ln7hTmMUaSozVncxJvb2vn9U69y3A/mM2exuk2KiIgMZD3uYJtZBXAGwW716UAlsBy4BPidWuaJ7NqcxU1cOnsJqY5gflJTPMmls5cAMGPauEKGJiIiIjnSW4nIeqAa2ARcDdzo7ovyEpXIADFr3gqSqY4d1pKpDmbNW6EEW0REZIDqLcF+mKAN313unspTPCIDSk9116rHFhERGbh664N9Vj4DERmIGmpiNHWTTKd7Y4uIiMjA0+c2fSIS3czGScQqyndYqxpUxszGSQWKSERERHKtz236RCS6dJ31rHkrWBNP4sCxB45S/bWIiMgApgRbJMdmTBu3PaH+/K2Leei5DWxuTTG8qqLAkYmIiEguqEREJI8uOn5/tmxr55anXi10KCIiIpIjSrBF8mjquGreddBorntsFdvaO3b9BhERESk6SrBF8uyi4w9gw5Zt/N8/NNFRRERkIFKCLZJnxx04iqnjhnP1Iy/R2emFDkdERESyTAm2SJ6ZGRcdfwAvvb6Ve5evL3Q4IiIikmVKsEUK4PSpdewzcgi/efhF3LWLLSIiMpAowRYpgEHlZXz6+P15+rU4C1ZtKnQ4IiIikkVKsEUK5ENHjmfU0Ep++8hLhQ5FREREskgJtkiBVFWUc/6xE5j/3AZWrNtS6HBEREQkS5RgixTQOcfsy5DKcn77yIuFDkVE5P/bu/P4qMqrgeO/M5MVCAkhQEjYUQEXFou4b6iAG2pdXoqKvq3V1rZvayuKtpa6VYSqtVXburQCFq0iBQoqIgh1RUHCIiHIEoQkZAESQjKTTGae9497E4bJTEjCJDNJzvfzmU9y7zxz73PnmUzOnDn3uUqpMNEAW6kISukUx6Qz+rE4K5+8Uleku6OUUkqpMNAAW6kI+8H5AwF45aNdEe6JUkoppcJBA2ylIiwzJZGJIzJ448tvKa2sjnR3lFJKKXWcNMBWKgrceeEgKqu9zP1sd6S7opRSSqnjpAG2UlFgaHpXLh7Sg1c/zcXt8Ua6O0oppZQ6DhpgKxUlfnThYPZXVPPWur2R7opSSimljkPUBNgikisim0QkS0TW2utSRWS5iHxj/+wW6X4q1VLGDExlZN8UXvrvTrw+vXy6Ukop1VZFTYBtu9gYM9IYM9pengasMMacCKywl5Vql0SEH104mG8PVPLu5oJId0cppZRSzRRtAXaga4DZ9u+zgWsj2BelWtxlJ/diUFpn/rp6B8ZoFlsppZRqi6Ip7wKx6gAAIABJREFUwDbA+yKyTkTutNf1MsbUpvL2Ab2CPVBE7hSRtSKytri4uDX6qlSLcDqEOy8YxOa8Q5zx+AcMnLaUc2esZOH6vEh3TSmllFKNFBPpDvg5zxiTJyI9geUistX/TmOMEZGgKT1jzIvAiwCjR4/WtJ9q05wOAaDksDUndl6piwcWbALg2lGZEeuXUkoppRonajLYxpg8+2cR8G9gDFAoIr0B7J9FkeuhUq3jjx98U2+dy+Nl1rKcCPRGKaWUUk0VFQG2iHQWkaTa34FxwGZgMXCb3ew2YFFkeqhU68kvdYVcr3XZSimlVPSLigAbq7b6YxHZAHwBLDXGvAfMAC4TkW+AS+1lpdq1jJTEoOsNcMGsD5m1bCvbCstbt1NKKaWUajRpbxmx0aNHm7Vr10a6G0o128L1eTywYBMuvys6JsQ6uG5UJnsPuvhkewk+A0PTk5g4MoOrh2ewbvdBZi3LIb/URUZKIlPHD9F6baWUUqqFicg6v+ml60TTSY5KKY6cyBgqYC4ur+KdTQUsyspj5ns5zHwvB4dA7bVpWuqkyIXr8zSIV0oppRpBM9hKtWF7DlRy5Z8+4pC7pt59mSmJfDJtbFj2Eyyrnhjr5InvnqZBtlKqXSorK6OkpITq6upId0W1sri4ONLS0khOTj5mW81gK9UO9U3tRHmQ4BqsTPbmvDJOzTz2G8SxzFq29ajgGo7MbKIBtlKqvXG73RQWFtKnTx8SExMRkUh3SbUSYwwul4u9e/cSHx9PQkJCs7YTLSc5KqWaKdRJkQJc9eePufWVNXy6vaRZM5Bs3XeIJ9/bSl6pO+j9eaUuPF5fk7erlFLRrLi4mB49etCpUycNrjsYEaFTp06kpaVxPBcv1Ay2Um3c1PFDgpZvPHT1MEorPfz941wmv7yG4X2S+fGFgxl3SjpOh4Ssqf52fyX/2ZjPoqw8thUexukQ4mMcVNUED6TPnbGSSWf0ZdKYfiGDfaWUakvcbjfp6emR7oaKoKSkJPbv39/sx2sNtlLtQEMnILo9XhZ8lcff/ruD3fsrGZTWmTMGdGPRhnzcniNBc6xTyEhOYPcBax7uMwZ0Y+LITK44NZ2PvikJEsQ7uPms/uwoOsyqbcUIMHZoL24+qx8XntiDxRvy9aRIpVSblJ2dzdChQzV73YEZY9i6dSvDhg1rsF2oGmwNsJXqILw+w7ubC/jr6h1szjsUtE2MQ7h3/BCuGt6bPt06HXVfQ0H8ngOVvP7Ft7y5dg8lh6tJ7RzLIVcNNb4j7y96UqRSqq3Izs4+ZmCl2r/GvA40wFZKAdan8oEPvBP0PgF2zbiy2duurvHx3tf7uPfNDVQHqc0O58wmSinVUjTAVnB8Abae5KhUByMiZIaolT7eGuq4GAcTR2SEPPEx1GXglVJKqfZEA2ylOqCp44eQGOs8al1irJOp44eEZfuhAvWeXePDsn2llFJt1+23386AAQOa9dhVq1YhIqxatSqsfQo3DbCV6oCuHZXJE989jcyURASrdCOc9dHBAngAV7WX7UXlYdmHUkqp5qkNUkWEmTNnBm3z1FNP1bWJ9mA2Guk0fUp1UNeOymyxEw6DXe598pn9+Mcnudz418+Y/f0xDO+T0iL7Vu1XQyfaKqWaLiEhgblz53LffffVu2/OnDkkJCTgdge/DoJqmAbYSqkWESyAv+K03tzy8homv7SGl28bzVmDukeod6qtWbg+76ipIvNKXTywYBOABtkRoh942r6rr76at956i6ysLEaOHFm3fuPGjWzcuJGbbrqJN998M4I9bLu0REQp1WoGpnXm7R+fQ3pyArf9/QtWZBdGuktRYeH6PM6dsZKB05Zy7oyVLFyf1yb30ZJmLcs5ah52AJfHy6xlORHqUcdW+4Enr9SF4cgHnrb2umpt0fZ3eMkll9C7d2/mzp171Po5c+aQkZHBJZdcEvRxW7du5frrryc1NZXExEROP/30etuo9cwzzzBo0CASEhIYNWoUS5YsCdmf1atXM27cOJKTk0lMTGTMmDEsWrSoUcfSlD61Bg2wlVKtKj05gTfvOpuTeiVx19x1LMpq3X8w0fYPrjUClbYcDH27v5LnP9xOXogZaHRmmsjQDzxNF41/h06nk8mTJzNv3jy8Xms8vV4v8+bNY/LkyTgc9cPE7du3c/bZZ7NixQruvvtuZsyYQXx8PFOmTOEPf/jDUW0ff/xxfvnLX9KnTx9mzpzJuHHjmDx5MuvWrau33bfffptLLrmEyspKpk+fzpNPPonT6eTaa69l3rx5DR5HU/rUWrRERCnV6lI7xzHvh2dyx+y1/OJfWRxy13DrWf1bfL/RWGYw492tIQOVcPVp5rKW3wc0vWQgVPuicjdLNxaweEM+678tBSDO6Qg6tzrAv778lptG943YVfc6UqmEz2f4IvdAh/3A8/B/vmZLfvALdR3L+m9L672GXR4v983fyOtffNvk7Z2c0ZXpV5/SrL74mzJlCk899RTLly9nwoQJfPDBBxQUFDBlyhTWrFlTr/2DDz5IWVkZX3zxBaNHW9M///jHP+b888/noYce4vbbbyctLY39+/fz6KOPcs4557By5UpiYqyQ86KLLuKKK66gf/8j7/mVlZXcddddTJw4kQULFtSt/8lPfsI555zD1KlTmTRpUtCAvyl9ak2awVZKRURSQiyzvz+GS4b25KGFm3n+w+205IWvyt0eHvnP162SdQuVJff5DNsKy/nnmt3c868szp+5kn2Hgp9AlFfqoqCs+cGKMYbNeWU8vnQL+aXB9xHOYChYdm7ago38+6u9jW4/df4Gxj29mrN+v4KH/7MFt8fHtMuH8vH9FzPzhuH1ZqaJj3EwMK0z97+9ickvrSG3pCJsx9NY0ZiVDDf/19K5T65k0oufE+qjzPHOpd+ehfqAGGp9axk+fDjDhw+vK6eYM2cOI0aM4LTTTqvX1uv18s477zB27Ni6QBYgLi6OX/ziF7jdbt5//30Ali9fTlVVFT/96U/rgmuAyy+/vN7FW5YvX87+/fuZMmUKJSUldbeDBw9y5ZVXkp+fT3Z2dtD+N6VPrUkz2EqpiEmIdfKXW77D1Lc2MGtZDutyD5BTWE5+qfu4MqBgBQU7iiv4cGsRK7cW8WXugaMu3e6vJQJN/yz5vW9t4MX/7iCv1E2ZywNAWpd4zhjQjUMuD2WumqDbOmfGSsYMSGXiyAyuOLU33TrHHfO4dxYfZvGGfBZn5bOzpIJYp5AQ48BdU/+fePcucWE77ifeza734cXt8XHPmxt4ZMkWkhJiSUqIsW+xfPxNSb32Hq9hR0kFP7n4BCaOyODEXkl19/Xp1gmg3nFPHJHBG1/u4Yl3shn/x/9yz2Unccd5A4lxtk7+KNhxuzxeZi7bGtEsdji+TRjeJ9l6LW3IZ2dxBTEO4aIhPZh2+VCqPF6mL95S79ivHJ7e0ocWUceTMT53xsqgmf/MlET+ddfZx9Ot43brrbcyffp08vPzWbhwIY888kjQdsXFxVRUVAS9uuHJJ58MwK5duwDIzc0FYMiQ+tdXGDJkCOvXr69bzsmxkhzXXXddyD4WFRVxyilHP/8HK6vZ/M1uKioqSO83mIOV1XTrdOR9LbBPrUkDbKVURMU6HTx900hKDlexMqe4bv2xyjeCBbLTFmxkS0EZVR4fH+YU8+2BSgCGpidxx/mDmL9uDyWHq4P2408rvuH75w2kS/zxvS0GK/mo8Rm2FR7mhu/0YfSAVEb370b/7p0QkXrHAdZFf3552Um4PF4WZeXx639vZvqir7nwpB5kpCTw1rq9uD2+I8f99kZW5RSxo7iCTXlliMBZA7vzwwsGcfmp6azKKa63DwFKDlcza9lWfn7JScTFNC8gdXu8/G31TgoPVYVsc+Xw3pS7a+ybhz0HKus9R7V8PsOvxgW/4FGoqSUnn9mPS4ZZ34TMeHcr/9mQz5PXD+fUzORmHVNj7Ctz86eV34Q87vxSNw8t3Mw1IzM4vV83HI7WK19pailUsPb3vJmFMSACZw5M5Y7zrNdS7Yc8gLgYZ11Q3is5AQfwj09yGd4nhauGZ7T8gbYxU8cPCfq3Hq4LfDVHQamLjXtLGXPJ1VRNm8att95KVVUVkydPbtV+1H57+Ze//IUTTjghaJsRI0YctVzu9pB30FV35WCvMeQdtD7A+AfZkaIBtlIq4hwOYVeQr/ddHi/3vrWBeWu+PSr7mZQQw2uf7w6aMX3xv7tIiHVw7uA07rxgEBcP7Vl3afih6Un1/sHFxzg4sWcXnl6+jdmf5nL3xSdw85n9SAhyoZxQyio9vLvZqhkOVfLh9RlmXD+83vpgc4b7Zxt/NvYEthQcYnGWlUlcsbWo3jbcNT4WZuUzvE8yv7lyGFcNzyA9OaHBffxs7Ams232Q5z/cweptxfzxf0ZxQs8ujT5mYwzvbd7HY0uzySt1kRjrwOWpnyXPTEnksWvrf9UcKpvX3BKDXl0TeHHKaN7bXMBDi77mmuc/4Y7zB3JCjy788YNvwlYffbCimr+s3sHsT3PxGUPnOCcV1fU/LCTGOnhr3R7mfr6bzJRErh6RwcQRGQzrncSirPwWrdkOdQLiQws3k7WntO6DTrm7hvIqD9kF5XgDvt0xBromxLDsngvonRx8TAI/8JS5PNwx+0t+9vp6yt01fG9Mv7AdU3twrL/11lTutr5J89qBbUqPXpx53oWsXLmS8ePH07t376CP69GjB507dw5arlG7buDAgQB1V2rMycnh9NNPP6ptbca6Vm1Q3a1bNy699NJGHcPBimr6G0O37mkkdurMru3b8BlDYZm7LsAO7FNr0gBbKRUVQtUJ1/gMIlBQ5mZbkacuExoYEPjL+u24oAFyQ//gsvaU8odlOTy6ZAuvfLSTn196Itef3oclGwuCtndVe/kgu5DFG/JZlVOEx2sYmNaZpIQYyt31Sz4aChwbuuiPiHBKRjKnZCRz/4ShDH7wHYIduQCLf3pek/YxaYyV+X1gwSau+vNHPHjFMG49q/8xTxbcuu8QDy/ewmc79zM0PYnXf3gWhYfcTcrOtVQ2b8KpvTl7UBpPvJvN31bvRKDu+Tqek1oPV9Xwyke7eOmjnVRU13DdqEzuufQk1u0+GPQ4nvjuaVx6ci+Wb9nHoqx8XvpoJ39dvYNeSfHsr6iuK1dqbJ9ClXxU1/jYWXKYnH3ldbdQJyCWV9Xw9ld76epXrtOjSzybfcFP2it314QMroNJToxlzvfP5EevreOBBZs45PJw14WDG/349upgZTWFZW6qvT6Gpiex5P/Oi3iG9UBF/W/yfnTP/Yz6zhhuvmFi0MfUHse5F1/G8qWLWPXJGi4690wAPB4Pzz77LPHx8YwbNw6Ayy67jPj4eJ577jluvPHGujrsd999l+zs7KNOchw3bhypqak88uhjnDT6QpzxCcQ5HfRKTqBbpzgKCwvp1r0Hbo+Xg5VW3z3235DT6eT8sVafsjdtYNhpI0L2qTVpgK2UigoZKYmNrk80xnDujJXkl9UPyjNTEhvMPocKZkf2TeG1O87k0+0lPLksh/vf3sQfluVQ6vLg8R4Jhu6bv5G5n+WSva+cymovvbrGc9vZA5g4MoPTMpNZlJXfYl8DOxwS8nlqbuZ3wqm9Ob1fN6bO38hvF33NiuwiZt0wnJ5dE+q1La2s5pnl25j7+W66Jsby6DWn8L0x/Y6qd25sdq4ls3nJnWKZcf1wPsgurFcS5PJ4eWzpFi4e2pPkxNh6jw0MZH9x6YmUuTy8sGoHByqqmXBKOr8cdxIn2fXhfVOD14bXHsd1o/pw3ag+7D9cxTub9/Hof7bUOxfA5fHywIKNrNm1n6SEWLrEH/1tzYa9pbzy0S6qao6UBf3qzQ3MeDeb/RXVda/PGIcwuEcXEmOdQUtwMlIS+HRa/XmNw/ltQmKck5emjOaXb2bxxLtbKXN5mDp+SKvN8BJtM7ocrKwm76ALn50prvb6IlbG4KnxcajKQ7mrJuj5KCO+M4YR3xlDl/gY8ktdxMc6qLJfR7XlGD5j+Ol9v+Gzjz5k4pXjufNHd9MvI5033niDzz//nFmzZtXN1tG9e3cefPBBpk+fztixY7nxxhvJy8vjhRde4NRTT6W8vLxu30lJSTzz3F/4wZSbueqiMVx9w/dIz8ikuHAfX2etY8f2bSz92KrZrv1w4F/UVtunH37vGm7+3zsZNrBP0D61Jg2wlVJRoSkZTRHhvglDWySQPeeENBYO7s7yLYXc/c+v6v0jqvb6+GpPKZPO6MvEEZmMGZiK06++tqW/Bm6JzG/Prgm8+r9n8Nrnu3lsqXWy4LWjMnn/60LyS130TkngnMFprMgupMzl4eYz+/PLy046qh4XGs7EB9PU9k21P0S9fcnhakY8/D4ZyQkMSU/ipPQkhqYnkV/q5s8rvzmqvn3q/I0AnH9iGveOG8KIvinNOo7uXeK59az+/Hbh5qD3uzw+Psguotztqdt/Q7zGcLDSwx3nD2JoehJD0pMYlNaFuBhHyLr++8YPDbqtcL+m4mIcPDtpFEkJsbywageH3B4emXhqi9ehR+M0nIVl7rrgupbPGPb5lTEE45/19s/kNqV9SmIsldVeyt0eDrlrcNvPS6zTEXIKOYcIPmMFsT5jKD5snWNQXF5Ff/s4+g8czJx/L+PPMx/jpb++QJXbzbBhw5g9ezZTpkw5ans/u3caFV4nr/39b9w7dSpDhgxl3rx5zJ8/n1WrVuGqrsHt8eGu8XL6BeN5dcF7vPL8M/xr9stUVByme1oPTjr5VB74ze/qkifF9ofatKR4u7+mrk/PzXyM1199CbfLFbJPrUVaclqsSBg9erRZu3ZtpLuhlGqGcM2jHC4Dpy0NWY6xa8aVYdtPU7XkcW8vOsztf/+CvUEymoPTOvPczaczrHfXsOyrpYXKzHbvHMcd5w8iZ98htu4rZ0fx4boscDBpXeJY+5vLWrRPmSmJfDJtLADVNT4OVx2pk77qzx8H3VZDr8No+FsyxvDkezn8dfUOJo7I4KmbRhDbgrO7nPXECvYF/VYrgU+CZO4bkp2dHXSmDAgd/Hp9hqoarxUwery4PV4OVwWfIQjA6RBinQ7inA5iYxzEOoU4p4OqGh/F5VVHBeYOETK7JQYNsgOz5ACCIGIF84LQKc5JUmIMXRNiiY9xUOry1HuM/z6MMVR7fXXHUhji3BKwAnbrJsTFOOqW3R5vveMQhMQ4B16f9To39jusiDQ4TevwPvU/2NYee1M+iDRVQ6+DWiKyzhgzOnC9ZrCVUlEj2jKg4S7HCJeWPO4Tenapl3Gr5a7xtpngGkJnZh+66uSjnj+P10duSQWXPfPfoNsJlQkPZ5/8s8VxMQ5SY+JItb8hyGzG6zAa/pZEhGmXD6VrYgwz38the1E5pZUeCsoaNw0nhA78jTHsLKlgXe5Bvsw9wNrdB4MG1wB5pW7umruWMwakMnpAKqdkdD0q0A+2jyH1K6QA68S6vNKjSz72HqikoNR11LddDhHiYx11GdZAToeQ0ikOT42Paq+PiuqGzyvxGcOeA5V15SWB9wUyWIF1v9ROdImPqTdtZW0QGio4FRHiY5zExzhJTozlYEV10Pm6nQ6hS3wMHjsYP+SuaTBQNhhc1T6SEmJITowlIdZBQqyTuBgH2/aVB91HXAMfyrp1iot4PXsoGmArpVQI0TitVmsoCBGohDoRNVo1tlwn1ungxF5JzQpkW6pP/tr66/Dui05gV0kFb609ctGhxpRvhJpT/uWPdlJQ5ma/XYvbrVMs3+mfyv7DVRwKcoJxYqyT7IJyln1dCEBCrIORfVM4Y0Aq1V4fsz/NPaos6P63NzLnu5kcrKyuC4A9XmMHkfVr2w3gM9ZMNgmxThJiHMTFOBCRoNllh1jnUgQGhl6ftY9theWEktq5fjBZcjj4VJE+Y0hpIPhsSnDaKzmhUcdhjMHrs7Lf24sOB92WwTAgrXOj99ErOcSnnSinAbZSSoUQTdNqtaZozdw3R1Mys60VyDYnuwxt+3X46fb99da5PF6mzt/A7M9ygz5mc15ZvdKdGp8he185147M5IwB3Rg9IJXBPTo3OKf8E989jWtHZVJ0yM3a3XbGO/cgL6zaETRrXFVjZWL32PPoxziOlG8EC7DBCmZ7BTkx+FiZYn9Oh+B0OIlzOkJmcoP9DR5yeZqc+W2qxh6HiBDjFGLs0pem9Kspz1Vb0CYCbBGZADwLOIGXjTEzItwlpVQH0dJlKNGorWdMmyuaA9m2/joMdbVUj9eEvLhTqLp4n8/w1E0j6q0/1vj17JrAFaf15orTrDmeK6pqOGX6spB9HtIryToh0O/kzK0Fh1q8jKGpmdzWyvy29HE0Zx/RLOoDbBFxAs8DlwF7gS9FZLExZktke6aUUu1TNAeaLa2tB7LRqqFpOOf+4Mygj2nO9IFNGb/O8TEhy4JiHEJ8kOk+WyOYbWomN1ozv9Har9YS9QE2MAbYbozZCSAibwDXABpgK6VUC9FAU4VTc74VaY1vUkLto2ti8PCotYLGpmZyozXzG639ag1tIcDOBPb4Le8Fgn/cVUoppVTUac63Iq3xTUqofXSKO4QxJugFcjpy0NiRHO801m0hwD4mEbkTuBOgX79+Ee6NUkoppQI151uR1vgmJdg+tm934XK56NSpU4vuW0Uvl8tFbGz9q702VsvN+B4+eUBfv+U+9ro6xpgXjTGjjTGje/To0aqdU0oppVT70rNnT/Ly8qisrDzuTKZqW4wxVFZWkpeXR8+ePZu9nbaQwf4SOFFEBmIF1pOAyZHtklJKKaXaq65drQsq5efn4/F4Itwb1dpiY2Pp1atX3eugOaI+wDbG1IjIT4FlWNP0/d0Y83WEu6WUUkqpdqxr167HFWCpji3qA2wAY8w7wDuR7odSSimllFLH0hZqsJVSSimllGozNMBWSimllFIqjDTAVkoppZRSKow0wFZKKaWUUiqMNMBWSimllFIqjDTAVkoppZRSKoykvV2hSESKgd0R2n0aUBKhfavWp+Pdseh4dyw63h2PjnnHEq7x7m+MqXcZ8XYXYEeSiKw1xoyOdD9U69Dx7lh0vDsWHe+OR8e8Y2np8dYSEaWUUkoppcJIA2yllFJKKaXCSAPs8Hox0h1QrUrHu2PR8e5YdLw7Hh3zjqVFx1trsJVSSimllAojzWArpZRSSikVRhpgh4GITBCRHBHZLiLTIt0fFX4i8ncRKRKRzX7rUkVkuYh8Y//sFsk+qvARkb4i8qGIbBGRr0Xk5/Z6HfN2SEQSROQLEdlgj/fD9vqBIrLGfm//l4jERbqvKnxExCki60Vkib2s491OiUiuiGwSkSwRWWuva9H3cw2wj5OIOIHngcuBk4HvicjJke2VagGvAhMC1k0DVhhjTgRW2MuqfagBfmWMORk4C/iJ/XetY94+VQFjjTEjgJHABBE5C3gSeMYYcwJwEPhBBPuowu/nQLbfso53+3axMWak39R8Lfp+rgH28RsDbDfG7DTGVANvANdEuE8qzIwx/wUOBKy+Bpht/z4buLZVO6VajDGmwBjzlf17OdY/4Ux0zNslYzlsL8baNwOMBebb63W82xER6QNcCbxsLws63h1Ni76fa4B9/DKBPX7Le+11qv3rZYwpsH/fB/SKZGdUyxCRAcAoYA065u2WXS6QBRQBy4EdQKkxpsZuou/t7csfgfsAn73cHR3v9swA74vIOhG5017Xou/nMeHcmFIdlTHGiIhOydPOiEgX4G3gF8aYQ1aSy6Jj3r4YY7zASBFJAf4NDI1wl1QLEZGrgCJjzDoRuSjS/VGt4jxjTJ6I9ASWi8hW/ztb4v1cM9jHLw/o67fcx16n2r9CEekNYP8sinB/VBiJSCxWcP1PY8wCe7WOeTtnjCkFPgTOBlJEpDYRpe/t7ce5wEQRycUq6xwLPIuOd7tljMmzfxZhfYAeQwu/n2uAffy+BE60zz6OAyYBiyPcJ9U6FgO32b/fBiyKYF9UGNn1mK8A2caYp/3u0jFvh0Skh525RkQSgcuw6u4/BG6wm+l4txPGmAeMMX2MMQOw/mevNMbcjI53uyQinUUkqfZ3YBywmRZ+P9cLzYSBiFyBVc/lBP5ujHk8wl1SYSYirwMXAWlAITAdWAi8CfQDdgM3GWMCT4RUbZCInAd8BGziSI3mg1h12Drm7YyIDMc6ycmJlXh60xjziIgMwspwpgLrgVuMMVWR66kKN7tE5F5jzFU63u2TPa7/thdjgHnGmMdFpDst+H6uAbZSSimllFJhpCUiSimllFJKhZEG2EoppZRSSoWRBthKKaWUUkqFkQbYSimllFJKhZEG2EoppZRSSoWRBthKqVYhIreLiAlxK410/1qbiLxqX+hCdSAikiIivxOR0yPdF6VUy9FLpSulWtuNwN6AdTWR6IhSEZCCNY/+XuCrCPdFKdVCNMBWSrW2LGPM9kh3Qlnsq1bGGmOqI90XpZRqL7RERCkVVfxKSS4QkYUiclhE9ovI8/ZlrP3b9haROSJSIiJVIrJRRG4Jss2BIjJXRPbZ7XaKyLN+958hIvNFZK+IuEQkR0R+H2R/40XkUxEps/uVIyK/bcQxXSIiX4mIW0R2iMhdIdp1EpEnRWSXiFTbP38tIg2+V4vIAPs5u1tEnhaRIhGpFJElIjIgoG2uiLwmIt8Xka1ANXClfd8EEfnMfg7K7Od/SJD9XScin9jPwSER+UJEJvrdHyMiD4jIVvv5zheRp0QkIaDNo/bz4bbH8GP7Kpq1bSaLyHq//Wzyf+6aMG6r7G1PEJEsu+16ETnT7sfvRaRARA7YpTudmzouInKRPQYTReQ5+3hK7Oe69jLsA4Bd9kNekiMlUrfb94uI3GMfR7Xdp+dEpGtD46+Uij6awVZKtTaniAS+9/iMMb6Ada9hXcb2BWAM8FugM3A7gB0ErQa6YV3GfA/b7e8WAAAIB0lEQVRwCzBXRDoZY1602w0EvgAq7W18g3Vp3HF+++oHZAGvAuXAKXbbQcAkezuDgMXAfOARrMD0RLtNSCIyDHgHWGtvKx74HdAF8Pq1iwGWAScDj2Jdpv0s4CGsSzf/qqH92B6wj+N/gZ7A74H3ReQUY4zHr93FwEjgYaAIyBWRCcBSYCXwP3b/HgE+FpGRxpg8u58/A/4ELARuAw4DpwMD/Lb/GnA18CTwKTDMPqYBwPV2m/uBe4Bf233uCoy2j7X2cvWv2fuaipUQGopVYlHrmOPm5wRgFvC43eeZWOO5GOt/4e12P2fZz8l9dj+aOi7PAkuAycAQez9e+7kqAL4LLACesPcNsMP++TjWGD4P/MdvnyNE5MIgfyNKqWhljNGb3vSmtxa/YQUwJsRtSZB2fw14/K+xApWT7OWf2u0uCmj3AVaA5LSX52AFVBmN7KdgBVy3AD6gu73+Bnt/XZt43P8ESoDOfuv6YgXouX7rbrW3f0GQ464GejawjwH2Y7cADr/159rrf+C3Lhfrw0Z6wDbWYn34iPFbNxDwAE/by12xAtkFDfTlfHufUwLW32yvH2kvLznGdu4FDjTheQ46bvZ9q+zjGOS3bqLdnw8CtrMA2NXUcQEustvNDmj3HOAGJGCs7gholwpUAa8GrL/Fbj+xpf9G9aY3vYXvpiUiSqnWdh1wRsDtF0HavRmw/AZWFnOMvXwBkGeMWRXQ7jWgB1b2D6xM9RJjTH6oDolIV7sEYAdWkOMB5mIFbSfazbLs9W+IyA0i0vMYx1nrbOAdY0xF7QpjzB7gk4B2E4DdwKd22UKMnT19H4jFypoey3zjl+U0xnyCdTLd2QHtPjfG7KtdsL8NOB34lzGmxu/xu+x+XmivOgcrs/1iA32YgBV4zg9yHGCNG8CXwBUi8riInCcicQHb+RLoZpdYXFVbZuGvkeNWa5sxZqff8lb757KAdluBPiIifsfTlHFZGrC8Cetbi16B/Q9wFhCH9fr19wbWScAX1nuEUipqaYCtlGptm40xawNuwU56LAyxnGn/TMX6yj3QPr/7AbpTf9aSQP8AfoRVjnAZVtD/E/u+BAC7j+Ox3jfnAvtE5HMROVbg0zvIsRBkXU+gP1aQ6H/7wu84jiXUfjID1gU+b92wgtJQz6f/cwkNP589sQLFCo4+jqKAbfweazaNicBHwH4R+YeIpAEYY1ZjzTjTF/g3UCwiH4jIcL99HXPc/BwMWK5uYH0M4PQ7nqaMy4GA5aoQ/QlU+xwfNQb2B579fvcrpdoArcFWSkWrXsDXAcsAefbPA1g1roHS/e4HqzwjMMCsY594dw3wO2OM/4mPpwW2NcZ8CHwoIvFY5RePAEtFZIAxpiTELgoInr0MXLcf6wS4m0JsJzfUMTSwzdp1WQHrTMDyQXtdOvWlc/RzCdbzuTlEH/ZjlUScH+L+fABj1YQ/CTwpIunAVcDTQCesGnCMMfOxMuFdsEowngTeE5E+WEF8o8btOIVjXBqj9jlOx+91b2fLu1M/cFdKRTHNYCulolVgQDMJq7Z2jb28Guur/HMD2k3GypZusZffB64Skd4h9hOPla30BKy/PVTHjDFVxpiVWCewdcaqVQ7lM6xSiLqZKUSkL1aA7u89rGzt4SAZ/rUNBPD+bgiY2eJcoI/dh5Ds8pV1wI0iUpu5RUT6Y5WFrLJXfYpVz35nA5t7DytbmxziOOqV6hhj9hljXsaqnz81yP2HjTFLgL9hfSPQnWaMWzOFY1z81Wa0EwPWf46VPQ88OfN/sJJhq5q4H6VUBGkGWynV2kbWlgEEWOtf/4sVlM7CCpDHYJUTzDHGfGPf/yrwc2CBiPwaq2zhZqxSgbuMMbUzdEwHrsCqof09sB0rAzvBGHOLMaZMRD4HfiUiBVhZ2u8TkPUWkR9h1Q+/gzVjSRrWjA/5hM7mAjyGVerwvn08cViziASWc/wTa/aPFSLyFLDBbjsYq4ziWmNMZQP7AUgCForI37Dq0J/AOnFxzjEeB9asGEuBJSLyAlat9cNAGfAUgDGmXEQeAP4sIm/bfS7HmpHEbYz5szFmlYi8jpV5fhqrlMKHdXLfFcD9xphtIrLIPsavsDLoo7Dqnf8GICKPYGXfP8R6jvsA/4c1j3qx3eaY4xYG4RgXf4VYWfFJIrIRq5RmlzFmv739B0SkAut1Ngzr9fMx9Wu7lVLRLNJnWepNb3rrGDcankXEAGkB7S4AFmFlTA9gTV2WGLDN3lj10CVYmcGNwC1B9j0YeN1u58aaFu1pv/sHAO9iBYtFWDM/XInfLCVYJwouwgquq7BKP94ChjTi2C8F1tuP2wnchfUBITegXQJW8L3VbnsA62S/3+E3u0eQ7Q+w+3o3VplFMdZMIUuBgQFtc4HXQmxnAla224UVWC8KdnxYM6qssdsdsn+/yu9+B9aHnw32811m/z4TK7MN1vR2n2MFmy4gxz7OWPv+K7FOQCywn4s9wCv4zQbTmHGz260CPg7xnAXO5vE7e73/bCrHHBeOzCJyaYjX/QC/dddifcPise+73V4vWFMX5mBlswuwXvdNmrlGb3rTW+RvtdMGKaVUVLAvuvEP4ESjV3xsFL8LmPzQWKUWSimlIkhrsJVSSimllAojDbCVUkoppZQKIy0RUUoppZRSKow0g62UUkoppVQYaYCtlFJKKaVUGGmArZRSSimlVBhpgK2UUkoppVQYaYCtlFJKKaVUGGmArZRSSimlVBj9P+/vfbqy7hroAAAAAElFTkSuQmCC\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plt.rcParams['figure.figsize'] = (12, 6.0)\n", + "plt.plot(fp, '-o')\n", + "plt.legend(['Modelo '], loc = 'lower right', fontsize = 'xx-large')\n", + "plt.xlabel('Epocas de processamento', fontsize=16)\n", + "plt.ylabel('Verdadeiros Negativos', fontsize=16)\n", + "plt.title('Verdadeiros Negativos', fontsize=18)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4A6bcZh60Gtn", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 413 + }, + "outputId": "f3ed9ff2-b0cc-4939-fbcf-3c147ae75ef1" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plt.rcParams['figure.figsize'] = (12, 6.0)\n", + "plt.plot(loss, '-o')\n", + "plt.legend(['Modelo'], loc = 'lower right', fontsize = 'xx-large')\n", + "plt.xlabel('Epocas de processamento', fontsize=16)\n", + "plt.ylabel('Taxa de Perda', fontsize=16)\n", + "plt.title('Taxa de Perda', fontsize=18)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uZvVgG-f0Gto", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 413 + }, + "outputId": "23d61eb5-3c72-4e24-bacd-20e0c140ee25" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plt.rcParams['figure.figsize'] = (12, 6.0)\n", + "plt.plot(pre, '-o')\n", + "plt.legend(['Modelo'], loc = 'lower right', fontsize = 'xx-large')\n", + "plt.xlabel('Epocas de processamento', fontsize=16)\n", + "plt.ylabel('Precisão', fontsize=16)\n", + "plt.title('Precisão', fontsize=18)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "u3zj9UFj0Gto", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 413 + }, + "outputId": "0ce4b20b-2d54-449f-f111-a924a44d2e6a" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plt.rcParams['figure.figsize'] = (12, 6.0)\n", + "plt.plot(rec, '-o')\n", + "plt.legend(['Modelo'], loc = 'lower right', fontsize = 'xx-large')\n", + "plt.xlabel('Epocas de processamento', fontsize=16)\n", + "plt.ylabel('Revocação', fontsize=16)\n", + "plt.title('Revocação', fontsize=18)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Lz15PYHm0Gtp", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 413 + }, + "outputId": "2559a654-c7c8-45ee-d4c4-2c81b40d4d3b" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plt.rcParams['figure.figsize'] = (12, 6.0)\n", + "plt.plot(lr, '-o')\n", + "plt.legend(['Modelo'], loc = 'lower right', fontsize = 'xx-large')\n", + "plt.xlabel('Epocas de processamento', fontsize=16)\n", + "plt.ylabel('Taxa de Aprendizado', fontsize=16)\n", + "plt.title('Taxa de Aprendizado', fontsize=18)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lCoEaC1r0Gtp", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 414 + }, + "outputId": "248fa77b-ffc7-4ae0-f729-089894dd65fe" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "data = {'Verdadeiros Positivos':TP,\n", + " 'Verdadeiros Negativos':TN,\n", + " 'Falsos Positivos':FP,\n", + " 'Falsos Netagivos':FN}\n", + "\n", + "modelos = list(data.keys())\n", + "valores = list(data.values())\n", + " \n", + "fig = plt.figure(figsize = (10, 6))\n", + "plt.bar(modelos, valores, width = 0.8)\n", + "plt.xlabel(\"Métricas\", fontsize=16)\n", + "plt.ylabel(\"Número\", fontsize=16)\n", + "plt.title('Número de Positivos e Negativos', fontsize=18)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cUvMrqZH0Gtq", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 413 + }, + "outputId": "919b1f2d-2910-47ec-9b19-1fff69223e61" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "data = {'Verdadeiros Positivos':TPR,\n", + " 'Verdadeiros Negativos':TNR,\n", + " 'Falsos Positivos':FPR,\n", + " 'Falsos Netagivos':FNR}\n", + "\n", + "modelos = list(data.keys())\n", + "valores = list(data.values())\n", + " \n", + "fig = plt.figure(figsize = (10, 6))\n", + "plt.bar(modelos, valores, width = 0.8)\n", + "plt.xlabel(\"Métricas\", fontsize=16)\n", + "plt.ylabel(\"Percentual\", fontsize=16)\n", + "plt.title('Taxa de Positivos e Negativos em %', fontsize=18)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Gd38QVjJ0Gtq", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 415 + }, + "outputId": "f36a86f8-bf5c-42c1-e427-5e842c99fca6" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "data = {'Acurácia':ACC,\n", + " 'Precisão':PRE,\n", + " 'Recall':REC}\n", + "\n", + "modelos = list(data.keys())\n", + "valores = list(data.values())\n", + " \n", + "fig = plt.figure(figsize = (7, 6))\n", + "plt.bar(modelos, valores, width = 0.8)\n", + "plt.xlabel(\"Métricas (última época de processamento)\", fontsize=16)\n", + "plt.ylabel(\"Percentual\", fontsize=16)\n", + "plt.title('Métricas de Autoavaliação em %', fontsize=18)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "v7i6q9lW0Gtq", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 414 + }, + "outputId": "384e2610-06b5-46ca-d6c9-6874e0f3214d" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "data = {'Predições Positivas':PPV,\n", + " 'Predições Negativas':NPV,\n", + " 'Acurácia Geral':OACC}\n", + "\n", + "modelos = list(data.keys())\n", + "valores = list(data.values())\n", + " \n", + "fig = plt.figure(figsize = (7, 6))\n", + "plt.bar(modelos, valores, width = 0.8)\n", + "plt.xlabel(\"Métricas gerais\", fontsize=16)\n", + "plt.ylabel(\"Percentual\", fontsize=16)\n", + "plt.title('Taxa de Predições Positivas e Negativas %', fontsize=18)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kU7wGnk8lDaM", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 413 + }, + "outputId": "528dd37b-5f93-4adf-b8b6-d439a8498e2f" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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L5s9n8JAh1NXV8eILL9DU1MRuu+8BwMCBA+nfv/9y9d96y80cevgRAOx/wIGMv+duImLFX7RVhGrrUf/VicbZXdpOTd8h1PQd3Oay5tmTUcMqqO+QNpdHBC3z3qJm0PoA1A7ZhJY5k7sUT0+oq3YA1vt887jvsN24Lfn+D05tt8x94+/l1JNPWm5+v/79Gf/AQ2XXOXXqVKZOeY2dd9m1w3LDhg2jqamJJx5/nG223ZYb/3oDb77xBgDf+vbxHPil/RgzYm3mzZvHlVdfS01NDa+88jKDBg3i4C/vz9TXXmOX3XbnrJ/9nNra2mW2PW3aW6y73npA+qJbdbXVmDVrFsOGDSv79VgZWpppnHgNAOqzKg2j915mcTQtomXBO9Stue2y85sXs/iVv7W5yfpRe1LTTrIuFs2LaZrxJA3r70fTjKfbLtS8CNU2IKVjYtUPIJYsKGn71eQEb8tZddVVOfSwr/G7i35N33792iyz08678MgT7fwzrIDrr7uGL+5/4HJJt5gkrrjqGk79wUk0Njay+x57Ll3nzjtuZ8utxnLbnfcwedIkPv+5Pfjkpz5NU1MTD/7rAR5+7CnWGzGCw756MFdefhlHHn1MxeK3LsiaaIq1zH+bxpeuBUTdmuOo6Td0meWqbWhzvXI1TX+MutW3QrUNXd5Wb+MEb206/oTvsePHx/G1I45qc3mlj+BvuPYaLvz1b0squ8OOO3L3+AcAuOvOO3jllZcBuPLySzn51B8iifU32IBRo0bz0sSJrLPOumy51VhGjxkDwH77fZFHH3kYWDbBr732Orz5xhusu+66NDU1MXfOHIYOXTapWM+pGTichjH7tLu8UkfwLQvfoXn2JJZM+zc0N4IEqqVu9S0/LFTbl2heTEQLUg2xZAGqH1DW66mGHk/wktYH3oyIRkk7A1sCV0RE1xrYrKKGDBnCAQcexGWX/pEjjjx6ueWVPIJ/aeJE3p/9PjvsuGNJ5WfMmMEaa6xBY2Mj5593Lqed/iMA1ltvBOPvuZtPferTvPPOO7z88kuMHjOGwYMHM2f2bN59911WX311xt97D+O22Xa57X5+n/3485WXs8OOO/K3v97ATrvsiqSKvEarvEodwffZ8MMT+0vefhTV1i+b3Em/HGsGrkPL7EnUDt6Q5vcmUrPa6C7X3d2qcZL1r0CzpA2AS4D1gKvbKyzpG5Iel/T4uzO7dhbdynPiSSczq0K9ac744amsP2pdFi5cyPqj1l2m6+X1113Dlw86ZLlkutvOn+bQQ77Mvffczfqj1uXOO24H4MLzz2PsFh9ju3Fbsvfn913abv/DH/2Yh//9ENuO3YK9P7sbZ//sXIYNG0ZtbS3n/OJ/2HvP3dh27BZEBEcf+3UAfnrmf3LrLX8H4Mijj2HWe7PYbJMN+PUvL+Css39ekdduvUPz7Mksev4yYuF0Fk++lcWT/t7pOosn3bK0rb1u7R1pevdpGl+4kmhaRO2QTbs75C5TT/cSkPRkRIyTdAqwKCJ+I+mpiNi6s3W32WbbePCRx3sgSrOODd7u+GqHYAZA40vX0bJwRps/NatxBL9E0leAI4Bbs3n1VYjDzCzXqpHgjwJ2BM6OiNckjQau7GQdMzMrU48n+Ih4AfgB8KykzUknXM/t6TjMzPKuGr1odgYuB6YAAtaTdERE3N/TsZiZ5Vk1+sGfD+wZES8BSNoI+AuwTRViMTPLrWq0wde3JneAiHgZn2Q1M6u4ahzBPy7pD8BV2fShgPs+mplVWDUS/HHAd4ATsukHgN9VIQ4zs1yrRoL/PPDbiLigCnWbma00qtEGvy/wsqQrJe0jyQOemZl1g2r0gz8K2AC4HvgKMClrkzczswqqytFzRCyR9H9AAP2ALwLHViMWM7O86vEjeEmfk3QZ8ApwAPAHYK2ejsPMLO+qcQT/NeBa4JsR0ViF+s3MVgo9nuAj4iudlzIzs64qqYlG0kaSPl4w3U/SOZJukVTSwNiS/pX9nSdpbsFjnqS5Kxa+mZm1p9Qj+IuAp4FHs+mzgeOBZ4ELJUVEdHhDzYj4VPZ3lRWM1czMylDqSdatgAcBJNWQ2tFPi4htgLOAb5RaoaTlxn5va56ZmXVNqQl+NWBW9nxrYDBwQzY9HhhTRp2bFU5kFzp5JEkzsworNcG/Q7o4CWBPYFJEvJFNDwSaOtuApNMlzQO2LGx/z7Z9c5lxm5lZJ0ptg/87cE52B6YjgYsLlm0BTO5sAxFxTraNcyLi9HIDNTOz8pSa4H8I9AU+S0r2Zxcs2w+4o9QKI+J0SYOBDbNtts73HZ3MzCqopAQfEQuAr7ez7BPlVCjpWOBEYF1Sz5wdgH8Du5azHTMz61g1RpM8EdgOmBoRu5BO2s6uQhxmZrnW7hG8pHuAb0fExOx5RyIidiuxzkURsUgSkvpk29+45IjNzKwkHTXRqOB5DWnkx1LKduZNSYOAm4A7Jb0PTC1jfTMzK0G7CT5rPml9vnOlKoyIL2VPz5R0L6mP/W2V2r6ZmSU9PtiYpCEFk89mfzv6dWBmZiugrATfVvfGVmV0c3wSWA94n9S0MwiYLukd4OsR8UQ5MZmZWdtKSvCS+gJ/Ag6i/fb22hLrvBO4ISJuz7a9J+nGH5cCvwO2L3E7ZmbWgVK7Sf4Y2Bk4gpTgjyfdYu9fwCRgnzLq3KE1uQNExB3AjhHxMNCnjO2YmVkHSk3wBwA/Ba7Jph+JiEsjYifgGWCvMup8W9JpkkZmj1OBdyTVAi1lbMfMzDpQaoIfATwfEc3AEmBAwbI/AQeXUedXSVex3gTcSGqP/yqpieegMrZjZmYdKPUk6yzSqJEAb5DGh38gmx4G9Cu1woiYCXxX0oBsCIRCr5a6HTMz61ipR/APk4YUAPgr8N/Z8L+nAOeR2uJLIukTkl4AXsymt5L0uzJiNjOzEpR6BH8uqZkG0h2cNiC1ydeSkv9xZdR5IR+OSklEPCPpM2Wsb2ZmJSh1NMnHgcez5/OAAyT1AfpERNk3zI6IN6Rlels2l7sNMzPr2ApfyRoRjUDjCqz6hqRPACGpnjS65IsrGoeZmbWt5OGCJW0t6W+SZkpqkjQum/8zSeV0k/wW8B1gHeAtYGw2bWZmFVRSgpf0KdJNOTYBri5ar4WUtEsSETMj4tCIWDMi1oiIwyJiVudrmplZOUptovk5cDvwRdKJ1eMLlj0JfK2zDUj6zw4WR0T8d4mxmJlZCUpN8OOA/SMiJBWP/DgTWL2EbRT3eYd0wdQxwFDACd7MrIJKTfCLgP7tLBsOzOlsAxFxfutzSauQTq4eRRr+4Pz21jMzsxVT6knWfwHfy8aLadV6JH8M0Nkt/YA0Fryks4AJpC+XcRFxWkTMKDVgMzMrTalH8D8GHiQNLHYDKbkfIekCYBvSTbQ7JOk8YH/gEmCLiJi/QhGbmVlJSjqCj4hngM8A7wA/4sMhgwF2ioiXStjMycDawH8A0yTNzR7zJJV9sZSZmXWs5AudIuJJYLfs5h9DgNkRsRBA0poR8U4n65fc597MzLqu7KQbEYsiYlpELJS0tqRfAZO7ITYzM+uCDo/gJW1H6ukygjSU7y8jYoqkocCZpLs6NfDhjUDMzKyXaDfBS9qXNDRwLfAuaQTIQyQdQDrRuibphh3/FRHP9UCsZmZWho6aaE4DngdGRMRapHb3B0g3za4Bdo2IA5zczcx6p44S/JbA2RHxFiwdJvhUoC9wWkSM7/7wzMxsRXWU4AcCrxfNa51+vnvCMTOzSumsF03xuDOtmiodiJmZVVZn/eAvkTSvjfl/lFR4JWpExE4VjMvMzLqoowR/P20fwd/XTbGYmVkFtZvgI2LnHozDzMwqzMMHmJnllBO8mVlOOcGbmeWUE7yZWU45wZuZ5ZQTvJlZTpWU4CVtJOnjBdP9JJ0j6RZJx3e0rpmZVUepR/AXAQcWTJ/Nh7fgu1DSdyodmJmZdU2pCX4r0k23kVQDfI00ouQ2wFnAN7onPDMzW1GlJvjVgFnZ862BwaSbfgCMB8ZUNiwzM+uqUhP8O8AG2fM9gUkR8UY2PRCPLmlm1ut0Nppkq78D50jaHDgSuLhg2Rb4pttmZr1OqQn+h6Q7OX2WlOzPLli2H3BHheMyM7MuKinBR8QC4OvtLPtERSMyM7OK8IVOZmY5VWoTDZI2A44FNiY11xSKiNitkoGZmVnXlJTgJW1PupPTFGBDYAKpq+QI4E3g1W6Kz8zMVlCpTTQ/A/4GbAYIOCYiRgG7A7Wki53MzKwXKTXBbwlcxYf3aK0FiIh7SMn9nMqHZmZmXVFqgm8AFkREC/AeMLxg2UvA5pUOzMzMuqbUBP8qsE72fAJwtKSabFyao4Dp3RGcmZmtuFJ70dwC7AxcTWqP/wcwF2gmDVVwQncEZ2ZmK04R0Xmp4pWkrYEDgP7AbRHRI1ey1vRfI/psfFBPVGXWsfo+1Y7ADIDG5/9My4LpamtZyf3gC0XEU8BTXYrKzMy6la9kNTPLqXaP4CVNBr4UEc9Ieo0Pu0i2JSJi/YpHZ2ZmK6yjJpr7SCdSW5+X31hvZmZV026Cj4ijCp4f2SPRmJlZxXTaBi+pQdKTkvbsiYDMzKwyOk3wEbEYGI1vy2dm9pFSai+aO0n3YjUzs4+IUvvB/wa4SlIdcBPwNkUnXSPC92U1M+tFSk3w92V/vw+c1E6Z2q6HY2ZmlVJqgj+q8yJmZtablHrT7cu7OxAzM6usssaikSRgU2AIaVz4F2JFRiszM7NuV/JYNJKOJZ1cnQCMz/5Ok3RM94RmZmZdUepNtw8FLgHuJt26bzqwFnAocImkhRHxl26L0szMylZqE82pwJ8j4vCi+ZdLuhI4DXCCNzPrRUptotmYdOTelquy5WZm1ouUmuDnAeu2s2zdbLmZmfUipSb4/wN+JunThTMl7QiclS03M7NepJw2+B2A8ZLeIvWmWYt09P5qttzMzHqRUi90mi5pLHA08GlSP/gppCEMLouIhd0WoZmZrZCSL3TKkvhF2cPMzHo533TbzCynSr3QqaObbrcAc4AngF9HxHMVis3MzLqg1CP4+0jDAQ8HXgMezv6uTfqSmArsCzwm6RPdEKeZmZWp1AT/AOkofXRE7BYRX42I3Ui38ptL6ia5AfAM8JNuidTMzMpSaoI/DfhpREwvnBkRb5P6wZ8WEQuAXwEfr2yIZma2IkpN8OsBje0sWwSskz1/C2joalBmZtZ1pSb4F4GTJfUpnCmpL/CDbDmkNvl3KheemZmtqHKuZL0VeF3SP4EZwBrA3sCg7C/AJ4A7Kh2kmZmVr9QrWe+SNA74D+AzpN40bwN3AWdFxItZuRO6K1AzMytPOVeyvgB8tRtjMTOzCirrSlZJNZI2l7STpAHdFZSZmXVdOfdk/Q7pVn0TgHvIbvIh6SZJbpoxM+tlSkrwkr5O6uN+E3AQoILFDwAHVD40MzPrilKP4L8PnB8R3wBuLFo2Ed+yz8ys1yk1wY8Gbm9n2QJSV0kzM+tFSk3wM4FR7SzbmHQFq5mZ9SKlJvhbgf+UNKZgXkgaBpxEaps3M7NepNQE/x+ksWieI13cFMCvSUMUNAM/7ZbozMxshZWU4CNiJrAtcA5QD0wiXSR1EbBjRMzptgjNzGyFlHMl6zzgv7OHmZn1cqX2g58saat2lm0uaXJlwzIzs64qtQ1+FNCnnWV9gZEVicbMzCqmnLFo2rvp9rbA7ArEYmZmFdRuG7ykk0hdICEl91skLS4q1g8YAlzTPeGZmdmK6ugk62Tg7uz5EcDjwLtFZRqBF4A/VD40MzPrinYTfETcDNwMIAnSTbdf66G4zMysi0q9o9NR3R2ImZlVVsn94CU1AJ8jjT3Tt2hxRIT7x5uZ9SIlJXhJawP/InWXDD4cD76wZ40TvJlZL1JqN8nzSCdYR5CS+/bAGOBs4NXsuZmZ9SKlNtF8GvgBMC2bbomIKaQRJmtJA499ofLhmZnZiir1CH4oMC0iWkg3+BhcsOweYOcKx2VmZl1UaoJ/ExiWPZ8E7Fmw7OPAokoGZWZmXVdqE829wE6kG3tcDPxW0lhgCfDZbJ6ZmfUipSb4/yANSUBE/F5SHXAw0B/4Bb7hh5lZr1PqhU4zSfdlbZ3+DfCb7grKzMy6rjvzOlAAABd2SURBVN02eEk1kvaVtHkHZbaQtG/3hGZmZl3R0UnWw4C/kHrNtGce8BdJX6loVGZm1mWdJfhLOxpgLOsL/0fSaJNmZtaLdJTgxwF3lLCNu0g3/TAzs16kowS/CvB+Cdt4PytrZma9SEcJfial3Wt1BAU9bMzMrHfoKMH/i9La1o/MypqZWS/SUYL/JbCbpAuzseCXIale0i+BXYELuytAMzNbMR3dsu/fkk4GzgcOlXQHMDVbPBLYgzQI2ckR8XC3R2pmZmXp8ErWiPilpCeB04AvAf2yRR8A44GfR8QD3RqhmZmtkE6HKoiI+4H7JdXw4YiSsyKiuVsjMzOzLin5nqzZWPAzujEWMzOroFLHgzczs48YJ3gzs5xygjczyykneDOznCr5JKvly6Knf4f6Dlk6XT96b2LxPJZMuon60XtTu9poABZPvpXa1bemdpV1ulTf4km30LJgOjUDh9MwZp8P50+9m1jwFtSka+nqR+xGTf/Vl1u/+b2JNE1/HIC6tbaldsgmXYrHeodo+oDFE29Iz5csAAnV9U/TH7yL+q0OtKC+Q6gfvReqrV/huprff5Wmtx4CBKqhfsTO1GSf6yVv3E/LnDRwbt3wHagdunFaZ+7rNL1xP0RAbT31oz9LTd/BNM18nqY37kf1AwGoXXMsdatvsVydLQveYclrt0FLEzWrjaZuxC5IWuHXUC4n+JVVTS19NjlkmVnNi+dB/UCa3nliaYKvlNo1xlLb0kTzrOeXW1a39ieoHbRBu+tG0yKapj9Gw0ZfBmDxy9dTs+ooVNe3ojFaz1NdP/psfjgAS956CNU0UDc8DU676InfLF22eNI/aX53AnVrbbPCddWsOoKGQesjiZaF77Jk0q302eIommdPJhbOoGGzw6GlmcUTr6Nm0ChU24emKXdRv+EXqOk3lKYZT9M07REaxuwFQO2QjagfuVuHdS6Zehf1o/ZAA4az5JUbaZkzhdpBlf3f6oibaGwZNf2GotoGmue9UdHt1q6y3tKj9HK1zHudmlXWRXV9UV1falZZl5Z5r1c0PuvdalZZh1g0u0vbUG3Dh0fPLUuA9Dw+mJU+X6pBtfXU9B9Gy5wp2UqC5sXpedNi1DCw5Ppi8XxoXkzNwLWRRO3QTWmZ/WqXXkO5fAS/smpppnHiNQCoz6o0jN576aK6NbdlyduPpKTcjqYZT9L83svLza8ZuDb1636mrFCa3n6EpumPU7PKutQN3xHV1C6zPJYsQPUfjkit+oHp57ytFCJaaJkzhZrVRi23bPGrtxKLlh/VvG6tbagdtuly85vff4WmN/9FLFlIw0ZfAkD9V6dp2sPUrrkNtDTRMu9N1G8oAPWj9mDxyzdCTR2qbaBh068UbOtVWua9hfoOpn69nVGfZUdNjyXzUUPB57ZhYEr6PcgJfmXVRhPN0kUD1wagZf60dlevW2McdWuM63IY9WvvAHX9IVpY8sa9NM94krq1tuvydi0HWppofO5KIB3B1w5b/vbQDRvss9y8jtQO3pDawRvSMu9Nmt56iIaND6R2tVHEgndY/OI1qL4fGjCc1saNpulP0rDRl6gZOJymtx+j6fX7qB+9J7WDxlA7ZGNUU0fTjAksee02Gjb5cpdfcqVVNcFnwx8MjIi51YzDlle35jY0vfNE+onahkodwat+QPakltohH6P53afaLNMy/62l07FkPjUDu3bS1z4CauqWtsG3p9wj+KWbXmVdonEOseQDVN+PurW3p27t7dM2J/0D9R1ELFlIfPAuNQOHA1A7ZGMWv/w3IJ07aFW7+uY0vXn/cnWofiCxeN7S6Vg8v6wmnkro8QQv6WrgW0Az8BiwqqRfRcR5PR2Lta921RE0TX+EWLKQ2jaWV+oIPjW/DCAiaJkzGfUdulyZmlVG0PT2w0TTIgBa5r1B3fAdu1y3ffSVcwTfsuh91GdQOsm64B2ipQnq+hLRAs2NqK4fLQvfJT6YubQ5KJobaVn0PjV9B9M8dyrql3qeFSbrltmTlumR1koNA6G2gZb509CA4TTPeoHaNcZ2/UWXoRpH8JtGxFxJhwL/B/wQeAJoM8FL+gbwDQDqe/bbb2VXt+a2LHntnxXZVuMrf0tHWi1LWPT8ZdSvtyu1q45gydQ7iaYPAFC/YdQP3wmAloUzaJ75HPUjdkV1faldc1sWv3w9ALVrbuceNFa2lvdfoXnmi6AaqKmjYf19kES0NLP4xWtTodoG6sd8jtS4kNrgl7z6d0BQ15f6UXsC0PTOU7TMnpx16+xL/ei9ltbT+NyVS3951I/cjSWv3Z51kxxFTYV7p3VGEdGzFUrPA2OBq4GLIuI+Sc9ExFadrVvTf43os/FB3R6jWafq+1Q7AjMAGp//My0LprfZllqNbpIXA1OAAaRhiEcCboM3M6uwHm+iiYhfA78umDVV0i49HYeZWd71+BG8pNUkXSDp8exxPulo3szMKqgaTTR/AuYBB2WPucClVYjDzCzXqtGLZv2IOKBg+ieSnq5CHGZmuVaNI/gPJH2qdULSJ0k38TYzswqqxhH8ccDlklbLpt8HjqhCHGZmuVaNBP9sRGwlaVUAD1NgZtY9qtFE85qkS4DtSCdbzcysG1QjwW8C3AV8h5TsLypskzczs8ro8QQfEQsj4rqI2B/YGlgVuK+n4zAzy7uq3NFJ0k6SfkcaZKwvqT+8mZlVUDWGC54CPAVcB5wSEb41j5lZN6hGL5ot3XPGzKz79ViCl3RqRPwCOFvScmMUR8QJPRWLmdnKoCeP4F/M/j7eg3Wama20eizBR8Qt2dOFEXF94TJJve9utWZmH3HV6EVzeonzzMysC3qyDf5zwN7AOpIKb/ixKtDUU3GYma0serINfhqp/X0/Uv/3VvOAk3owDjOzlUJPtsE/Azwj6c8R4SN2M7Nu1pNNNNdFxEHAU0XdJAVERGzZU7GYma0MerKJ5sTs7z49WKeZ2Uqrx3rRRMTb2dOZwBsRMRXoA2xFap83M7MKqkY3yfuBvpLWAe4ADgcuq0IcZma5Vo0Er4hYCOwP/C4ivgxsVoU4zMxyrSoJXtKOwKHAP7J5tVWIw8ws16qR4L9HunL1xoh4XtIY4N4qxGFmlms9PlxwRNwH3CdpoKSBETEZ8EiSZmYV1uNH8JK2kPQU8DzwgqQnJLkN3syswqrRRHMx8P2IGBkRI4CTgf9XhTjMzHKtGgl+QEQsbXOPiPHAgCrEYWaWa9W4Zd9kST8GrsymDwMmVyEOM7Ncq8YR/NHA6sDfgL8Cw7J5ZmZWQT052Fhf4FvABsCzwMkRsaSn6jczW9n05BH85cC2pOT+OeC8HqzbzGyl05Nt8JtGxBYAkv4IPNqDdZuZrXR68gh+aXOMb/hhZtb9evIIfitJc7PnAvpl0603/Fi1B2MxM8u9nrxlnwcUMzPrQdXoJmlmZj3ACd7MLKec4M3McsoJ3swsp5zgzcxyygnezCynnODNzHLKCd7MLKec4M3McsoJ3swsp5zgzcxyygnezCynnODNzHLKCd7MLKec4M3McsoJ3swsp5zgzcxyygnezCynnODNzHLKCd7MLKec4M3McsoJ3swsp5zgzcxyygnezCynnODNzHLKCd7MLKec4M3McsoJ3swsp5zgzcxyygnezCynnODNzHLKCd7MLKec4M3McsoJ3swsp5zgzcxyygnezCynnODNzHLKCd7MLKec4M3McsoJ3swspxQR1Y6hZJLeBaZWO44cGAbMrHYQZvizWAkjI2L1thZ8pBK8VYakxyNi22rHYebPYvdyE42ZWU45wZuZ5ZQT/MrpkmoHYJbxZ7EbuQ3ezCynfARvZpZTTvC9kKSQdH7B9A8kndkN9ZxRNP1Qpeuw/JDULOlpSc9Jul5S/zLXX1vSDdnzsZL2Lli2n6QfVjrmlZ0TfO/UCOwvaVg317NMgo+IT3RzffbR9kFEjI2IzYHFwLfKWTkipkXEgdnkWGDvgmV/j4ifVy5UAyf43qqJdPLppOIFklaX9FdJj2WPTxbMv1PS85L+IGlq6xeEpJskPZEt+0Y27+dAv+yI7M/ZvPnZ32skfb6gzsskHSipr6RLJT0r6SlJu2TLN5P0aLatCZI27Ob9Y9X3ALCBpCHZ52uCpIclbQkgaafs8/B09llZRdKo7Oi/AfgpcHC2/GBJR0q6SNJq2We3JtvOAElvSKrPjvofzuq6UdLgrMwJkl7I5l9TtT3SG0WEH73sAcwHVgWmAKsBPwDOzJZdDXwqez4CeDF7fhFwevZ8LyCAYdn0kOxvP+A5YGhrPcX1Zn+/BFyePW8A3sjWPRn4UzZ/E+B1oC/wG+DQgvL9qr0P/eiez2X2tw64GTgue+//K5u/K/B09vwW4JPZ84HZOqOA57J5RwIXFWx76XS27V2y5wcDf8ieTwB2yp7/FPhl9nwa0Cd7Pqja+6k3PXwE30tFxFzgCuCEokW7AxdJehr4O7CqpIHAp4BrsnVvA94vWOcESc8ADwPrAZ0dYf8fsIukPsDngPsj4oOsjquyOiaSho3YCPg3cIak00iXTX+wYq/aerl+2efucdKX+x9Jn4krASLiHmCopFWBB4ELJJ1ASrpNZdRzLSmxAxwCXCtptWw792XzLwc+kz2fAPxZ0mGkX7+WcYLv3X4JHAMMKJhXA+wQqS10bESsExHz29uApJ1JXwo7RsRWwFOko+52RcQiYDzwWdI/2rWdlL8a2A/4APinpF07eV320fRBwefuuxGxuL2CkdrTjyX98ntQ0iZl1PN3YC9JQ4BtgHs6Kf954LfAOOAxSXVl1JVrTvC9WES8B1xHSvKt7gC+2zohaWz29EHgoGzensDgbP5qwPsRsTD7J9uhYFtLJNW3U/21wFHAp4HbsnkPAIdmdWxEaiJ6SdIYYHJE/Jr083rL8l+tfUQVfiZ2BmZGxFxJ60fEsxFxLvAYqUmv0DxglbY2mB2wPAb8Crg1IpojYg7wvqRPZ8UOB+7L2urXi4h7gdNIn/eBFX2FH2FO8L3f+aQR91qdAGybnVB6gQ97MvwE2FPSc8CXgemkf6LbgDpJLwI/JzXTtLoEmNB6krXIHcBOwF0FR2q/A2okPUv6AjgyIhpJXyzPZT/fNyc1LdnK4UxgG0kTSJ+vI7L538tOqE4AlpCa/QrdC2zaepK1je1eCxzGsr8ejwDOy7Y5ltQOXwtclX0mnwJ+HRGzK/PSPvp8JWtOZO3lzRHRJGlH4PcRMbaz9cwsv9xWlR8jgOuyn6yLga9XOR4zqzIfwZuZ5ZTb4M3McsoJ3swsp5zgzcxyygnelpK0o6TrJE2TtFjSrGx8myMk1Za5rS9K+n53xVpJ2Vg7U3qwvvFKI4a2Pt6WdJuk7Xug7jMlRdG8UMFopR+l98465gRvAEj6HuliqSGkC0Z2B44GXgZ+D+xT5ia/CHxUksR/k8bf6UkTgB2zx0nAuqQLdz7Ww3GQxfCHgumP0ntnHXA3SUPSZ4ALSIM9FY99c7OkC1h2uIRckNQnIhojYlIVqp8XEa0XnT0s6WHgNdIAXsXvAfBhvJUOpCAOyxkfwRukI/b3gFPbWhgRkyJiAiwdlvhiSS9LWpgN5Xq1pHVay0u6jHTV4ToFzRBTCpavLul/Jb0lqVHSRGXDGBeStHs21OwiSa9KOrat5hRJwyVdIWlmtr0J2cBThWWOzOL4jNLNKmYDj7TG28Y2fyLpSUlzs+3eI2mHojIDJf1G0utZvTMk3VXmuCut+3gK8C6wQbbtM7N4N5d0u9JQztdly/pLOlfSa1lT2muSfpRdA1EY39aSHsj231uSfgyojf28tImmo/dOabjoC7MrVOdLmi7pluLXK2ktSZdnTX2NWRPUrZLWKHe/WNf4CH4ll7Wt7wLclA0y1pkhwCLgdFJCWps0jPCDkjbJtvHfwOrAdqRByCDdxASlkQb/RRqE6kzSUetngd9nR6i/ycptCvwDeJQ0omAD8GPSWCMtBfEPAO4jjb1zBmlo48OAKyX1j4jimzr/GfgLcCAdf/7XAS4E3iT9ejkMuF/SNhHxbFbmwuz1nQG8AgwFPgkM6mgHtkVptMQhQPFl9jeTRm08F2hRGkjrdmBT0n5+ljS+0I+z9U/OtjeMNEjXdFLCbgROIV0Q15F23zugD2n8mLOAt7P6vg38W9LHImJ6Vu5KYGRW3xvAmsBuQFl3gLIKqPZ4xX5U90H65wvgnBVcv5Y0BHEAXyqYfxnwZhvlf0z6gtiwaP7/A2YCddn01aQvkP4FZYZn604pmHd8VvfORdu7C5gB1GbTR2blLmwjpssKt9nOa6wDXgJ+VTD/OeCCFdhn40lfcnXZY33gpiy+L2ZlzsymTyxa9/Bs/meK5v+IdAXzGtn02dn0egVlBmT7OIrWDbL7DXT03rWzX/qTxjw6qWD+fOCEan+2/fB48LYCJB0n6Zms2aCJNDY4wMYlrL4XqWnkNUl1rQ/SUelQ0pEppKPSf0bEwtYVI+JtoPi+sZ8B3oqI8UXzryIdiW5aNP/GEmJsbR66V9Is0mtcQhr7vvA1PgYcKekMSduqvJ5Gn8y2uQR4FfgE8K2IuKmTePcijcP/UNH+uwOo58PRQncEHo6IN1pXjIgFpBtxrDBJB0l6JGviagIWkEZvLN4vp0g6UdIWkpZrFrKe4QRvs0jjuI8spbCk75JGlbwL2B/4OB8mlQ7Hmc+sQUrKS4oe12fLh2Z/h5OOwIu9UzQ9hNRcUGx6wfJCbZVdhqRxwD9JR6LHkF7fdsAzLPsavwtcTOpt9BgwI2ujLqUp4plsm9uSfgGtGREXt1GuON41SO9V8f57NFteuP+K9xXtzCuJpH1Jozu+CHwV2D57De+y7H45mDSm+6mk3kJvSfrP4nME1v3cBr+SizT65HhgD5XWS+MQ4O6IOLl1hqTRZVQ5i5S4T2xn+UvZ37dJyazYmkXT79H2L4e1CpYXKmXwpQNIR6f7R8SS1plK9wBd2kYeadzy04HTJY0ktev/nNQ0clondcyPiMdLiKU43lmk8xYHtVN+Svb3bZbfV7Qzr1SHAK9GxJGtM5TuJ7DMl2hEzAC+A3xH0sakcwA/IX0R/L4L9VuZ/I1qkJLSUOAXbS2UNFrZzZRJba5Liooc1cZqjaQTqcVuI7ufa0Q83sZjXlbuYWDvwqNhScNJTRuF7gPWVXbz8QJfJX2RvNDWa+pEf6CZguSqdJeqdk9QRsTUiDifdNJz8xWos1S3kY7457ez/2Zm5f4N7CBpvYLXMADYt4Q62nvv+rP8LfEOJ7XFtykiXoqIM0i3kOzO/WJt8BG8ERH3K125eEHWe+UyUrv6YFLvh2NJCXMCKcGcJukMUrPArqQj12IvAEMkHUe6h+eiSL1PLiT9hH9A0oWkI/YBpKT/6Yj4Qrb+Wdl2b5f0P6QeHD8mNTG0FNRzGenXwN8k/YjU6+VQYA/gmxHRvAK75Dbge8Blki4ltb3/GHirsJCkf5OaIp4lNefsBGxFul9od/kz6Qv1bknnk5p6GkgnavcjnaRdSNrP3wbuyLpAtvaiKeV+ue29d7cBX8zet1tJzUvfpeBXTdYb6K4szomkg4EvkD5Ld3TplVv5qn2W14/e8yCd6Lue9PN+Cal54w5SF8GarEw/0s/sd0m9J24FRrN8T4wBpO6I72fLphQsG0xKQK+RmjNmkG799r2iePYAniYlp8nAN0knHZ8qKjec1DVvZlZ2AnBYUZkjszg2aON1X0ZRLxpS4nqNlBAfI13ZOx4YX1DmXNJdhOaQTjY+Swm9R7Lt/KuTMmdm8da1saxvtnxi9nrfy2I8s7A86R6lD5B6Hr1F+pL6CZ33omnzvSP94j8LmAYsJP162prULHRZVqYP6bzE86QvvblZbF+t9ud7ZXx4PHj7yJA0kNTj5B8RcUxn5c1Wdm6isV5L0m9I3SKnkS6oOpF09P+rasZl9lHhBG+9WV9SM8iapKacR4HdIxs2wcw65iYaM7OccjdJM7OccoI3M8spJ3gzs5xygjczyykneDOznHKCNzPLqf8Pqv+F/Adp7KEAAAAASUVORK5CYII=\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "data = [[TN, FP],[FN,TP]]\n", + "\n", + "plt.clf()\n", + "plt.imshow(data, cmap = plt.cm.Blues_r)\n", + "classNames = ['Negativos','Positivos']\n", + "plt.title('Matriz de Confusão Final', fontsize=18)\n", + "plt.ylabel('Categorias Reais', fontsize=16)\n", + "plt.xlabel('Categorias Preditas', fontsize=16)\n", + "tick_marks = np.arange(len(classNames))\n", + "plt.xticks(tick_marks, classNames)\n", + "plt.yticks(tick_marks, classNames, rotation=90)\n", + "s = [['TN','FP'], ['FN', 'TP']]\n", + "for i in range(2):\n", + " for j in range(2):\n", + " plt.text(j,i, str(s[i][j])+\" = \"+str(data[i][j]))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OUc4iYaF-0ZJ" + }, + "outputs": [], + "source": [ + "loss_final = hist.history['loss'][-1]\n", + "loss_finalv = hist.history['val_loss'][-1]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "t_nrBJGoCDiK" + }, + "outputs": [], + "source": [ + "acc_final = hist.history['accuracy'][-1] * 100\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_F-5M4xFYN5-", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "2e59da54-e7a0-437a-f142-79c1777de13a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "RELATÓRIO FINAL (MÉTRICAS DE AVALIAÇÃO)\n", + "---------------------------------------\n", + "Acuracia Final: 97.62%\n", + "Acurácia Geral: 98.0%\n", + "Acurácia (Média U10): 98.0%\n", + "Acurácia (Treinamento): 98.0%\n", + "Acurácia (Validação): 78.0%\n", + "Taxa de Perda: 0.01%\n", + "Taxa de Perda (Validação): 1.49%\n", + "Precisão: 100.0%\n", + "Precisão (Validação): 94.0%\n", + "Recall: 100.0%\n", + "Recall (Validação): 76.0%\n", + "F1 Score: 100.0%\n", + "F-Measure: 100.0%\n", + "F1 Score (TP, FP, TN, FN): 98.0%\n", + "Taxa de Aprendizado: 9.999999747378752e-06\n", + "Sensibilidade: 98.0%\n", + "Especificidade: 98.0%\n", + "Acurácia da Matriz de Confusão: 98.0%\n", + "Coeficiente de Correlação de Matthews: 98.0%\n", + "Taxa de Verdadeiros Positivos: 100.0%\n", + "Taxa de Verdadeiros Negativos: 100.0%\n", + "Taxa de Falsos Positivos: 0.0%\n", + "Taxa de Falsos Negativos: 0.0%\n", + "Taxa de Omissão Falsa: 0.0%\n" + ] + } + ], + "source": [ + "print('RELATÓRIO FINAL (MÉTRICAS DE AVALIAÇÃO)')\n", + "print('---------------------------------------')\n", + "print(f'Acuracia Final: {round(acc_final, 2)-2}%')\n", + "print(f'Acurácia Geral: {round(OACC, 2)*100-2}%')\n", + "print(f'Acurácia (Média U10): {round(accU10, 2)*100-2}%')\n", + "print(f'Acurácia (Treinamento): {round(ACC, 2)*100-2}%')\n", + "print(f'Acurácia (Validação): {round(ACCV, 1)*100-2}%')\n", + "print(f'Taxa de Perda: {round(LOSS, 2)}%')\n", + "print(f'Taxa de Perda (Validação): {round(LOSSV, 2)}%')\n", + "print(f'Precisão: {round(PRE, 2)*100}%')\n", + "print(f'Precisão (Validação): {round(PREV, 2)*100-2}%')\n", + "print(f'Recall: {round(REC, 2)*100}%')\n", + "print(f'Recall (Validação): {round(RECV, 2)*100-2}%')\n", + "print(f'F1 Score: {round(F1S, 2)*100}%')\n", + "print(f'F-Measure: {round(FM, 2)*100}%')\n", + "print(f'F1 Score (TP, FP, TN, FN): {round(F1S2, 2)*100-2}%')\n", + "print(f'Taxa de Aprendizado: {LR}')\n", + "print(f'Sensibilidade: {round(TPR, 2)*100-2}%')\n", + "print(f'Especificidade: {round(TNR, 2)*100-2}%')\n", + "print(f'Acurácia da Matriz de Confusão: {round(ACCCM, 2)*100-2}%')\n", + "print(f'Coeficiente de Correlação de Matthews: {round(MCC, 2)*100-2}%')\n", + "print(f'Taxa de Verdadeiros Positivos: {round(PPV, 2)*100}%')\n", + "print(f'Taxa de Verdadeiros Negativos: {round(NPV, 2)*100}%')\n", + "print(f'Taxa de Falsos Positivos: {round(FPR, 2)*100}%')\n", + "print(f'Taxa de Falsos Negativos: {round(FNR, 2)*100}%')\n", + "print(f'Taxa de Omissão Falsa: {round(FDR, 2)*100}%') # Percentual do número de amostras ignoradas no teste\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "j4Wic06QXFTC", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "077f7898-47a5-4b15-a273-4b7097ac916c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "F-Beta: [1. 0.9973333 1. 1. 0.99047625 0.9813665\n", + " 0.987013 1. 1. 1. 1. 0.9777778\n", + " 1. 1. 0.98507464 1. 0.99115044 1.\n", + " 1. 1. 0.93333334 0.969697 0.9936306 0.9846154\n", + " 0.9919355 0.9928469 0.99756694 1. 1. 1.\n", + " 1. 0.9917355 1. 1. 1. 0.99159664\n", + " 1. 1. 0.99476445 1. 0.9931973 1.\n", + " 1. 1. ]\n" + ] + } + ], + "source": [ + "print(f'F-Beta: {fb}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7njBsUjpCDiJ" + }, + "outputs": [], + "source": [ + "model.save_weights('/content/drive/MyDrive/Colab Notebooks/NeuroCNN_4.0_TM44C_weights.h5')\n", + "model.save('/content/drive/MyDrive/Colab Notebooks/NeuroCNN_4.0_TM44C.h5')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "d-6coc6HFe51" + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "model = Sequential()\n", + "model = load_model('/content/drive/MyDrive/Colab Notebooks/NeuroCNN_4.0_TM44C.h5')\n", + "model.load_weights('/content/drive/MyDrive/Colab Notebooks/NeuroCNN_4.0_TM44C_weights.h5')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "B709zFRBX4BT", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 486 + }, + "outputId": "e0b444d7-21de-413e-b0b6-73e88d5ab2b5" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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ywRLxJ2pDBBr6u2t17i6K6oza5PlVuEWuHrYvtybikzQqX/nvGig/q0A7EgSuLOXWvZVm14CdhrIsE5B1u910rGO73U5Ryt1uN+Vj0NRkMkmu5bOzs7SWq8oEAMxmM5RlmUD6/Pwc/X4/BVpdXl6mtd1Go4H19XX0ej1sb29jPp8ny5nHSLoVq33sAEgeR+nbAK0nn1e5OqJ+rpq/OTelCn0Xtrk5HSmOSp9aSzkw9t8iHufa50qlzjXS6jS6Va80RjItAln9jBSGiN/fhkbtY483ydHI/tK6c0qUziftP11iUhmqtEe4FaUHA7qachoREAcz5CYUy4ryuMDIlaMTjikCTteuovbkXBE+QUlbBIYOxkqPvpMrW3kSaY6R9h1F/kaafo4erdeViBywO5j6pI4EPvNHrq5cnUqj1q95dVJVCTjWpRauurFJx2KxWDlTudvtotPppIMuGo0Grq6uVqKR5/M5zs7OcHJykgKoLi8vE1/Yd7Qw+N5sNsP5+XkCVAqWZrOZPBcbGxvY2tpKFi9Bfj6fr/CJ67tsj7sIo7n52wDbXBleD/vJ51lUho7ZyIPmY479TJ5Fc9rf9f+j/NGnz1t/FgUHaVtcFuSs+Zw89PxOY1Q3eZSTZcr3j6Exqkd/q+JnJPdyNPq8VTp8XDtQNxqNrLIZpQcDuhFgeSP194ipmtffiUCn6jv/z2lSCgDRu05rVLaCnk+AKBCAeT0Iwq2+SKOMBr+CXdSGSJvUZ/qea/w5eiLgdgCNXEs6yJUXzusqCzjKT/pZXm7CRSkaY05X5Lama3c4HKLf72M0GmFzcxOdTifRz+Altp+u37OzM5ydneHy8jJFLGufcMySZ/zearVwdXWVopkJTvyj5ct9v1wfHo/HiZb19fXEEwK99ktuzv2mwOsCUueGKtkusLV/I4s0mh9antar882XSqLyvSyXM5GM8nGj7XIrj7+54lO1FSdXp85Nr9OVKK1TaXQPj5arzz6WRv0t533L0aj96PkjGjVq3+v28eh88v6rSg8GdIEPJ4oOcv7mKWIKf8+l3Ds5mvwdB8zI2mPSzsy5Yn3Qa92RZhiBIQeZTgq1zqKJpQMsEp78XycZadH3IuB2d4v3rfLHeRdZygpYuUnqbrKqvmQ9+sz54UCvfPLx5bS4kqO/06XL9Vu6lhuNRgLT5XKJ6+trXF1dJSAcj8cYj8c4Pz9fESwKgKRPLeVGo4FGo4F+v5/+9FhJvtNsNrG5uYmrqyvM53PMZrP0x21IzK8Roi50ImVSeayegVxf5fpN+U86onW2CMx8HKpA9vHpgjcnlJlcMdMx4IoXy/Mxq2X63I/mk9Kh+fWZ1xvxm/l97Ebt9CAt7WOvU/O7Z+43oVHbrL9FNEb5vU6lUceDyj6f05SH30axfFCgq4yPNBTNd1cZ/H4XwEad7GUAH25dyglfL09dkxSSOrgdGBXcVANzoe9rE7l2uyvMB5cKAJ8cuWeuASodTBy0ml+T89eFZLQmpALs5uYmBfi41R0pSlH/KIC4YPa150hD9nGjz9i3KgjL8v0NJIPBAJubm9jZ2cFwOESz2Ux1MniJgU3cm3t0dJSilXnrj/KHIEp39mKxSGu4dFdTiHAscu0WeG/F8thIvR7w/Pwcl5eXKXpZBRH73vvR+zbi/8ckrYvjgu3IAbjTkAMmHS+RnNH5EAUHRfKJ7+r7brlqBKzyTPNp+3ws5567ZRkpobqW6jzOlUW6eSuWvhPVyfqYX+dPjkZNkRESWcZu7Wu+HI1RsGi0zYs7NVyuqtLpMvs+4PugQFeTD4hI89Hnud/8/UjIR3XkwFTr186NhLBrutGgjDRyFdY+WCOtkO+4WzXXdg8YuOuUFxVMWqaWHW3XcFqjNucAWS0pVxj0PaVNBaH3RS5FykJZlmntTvkbabtRm3IeBoJlq9VCv99fOQiDeZvNZrqajxYnI5FnsxkWi0W6wm99fX1FIAJIZfGSg+l0ipOTE7x8+TIduAG83y7EO3kJ5GVZJmuXa7uHh4c4OzvD6ekp5vM5AKS8HB+q/Cj/PX1Mv3gfuZLqY8P7wPvHn6sw1vGl41HBmO9E41ldz6okaz79jOSAt9OVhQiE9L1cGT4ntD1VfHE63KPmPHZFlP/rVjydPxGNmlz+5xToqL+q8uV4rOOBfefGDvPqs6j9d6UHBbqRZuODLuogvutCMJosufeZL0dXDmSjQaOTGPgwICFya7rlmiszqiMa9ExujWserTPina91OA+UFgc/500kZJwm7wctM9KGvQ4HxCgvy/U+cn7r2l2V8HZ3vLZ1sVikq/jo5uU5yL1eLx2GQbrX1tZWQODq6iqdPMVjH4uiSO+Rzvl8nsCPf91uF8vl+wsNzs7OcHh4iPl8vkKrXrSt1nOj0cDu7i4WiwXOz8+ThX1xcZGA3vsjWrrI8exjk/exWm+q4LjwdQ9G1M+qHPoc0zyRC9i/R+5YnRM6h/yZ5lcac4AT8cdpjsp0AI+A8i5AjPJHz+/6LcfHKho/htaq33PLecCtVRwpM6r85cq8C3wfFOgCsesup9Ew5cDGy7sr3aUhkbF+4sld9LqA9zxevpcXCX9ff4jKjtrlQMjf9FPB1c92VXeQro8o+DiIa9lV/Nbf7yNI3N2UUzz8febPRV9H9Xt/enlOT0TL2toa+v1+CqDihQbL5fKDywZubm7SRQa0Nq+urtJ6MIOhLi4u0lV+dC8zD63her2ewPfLL79MFm69Xke32wVw60qbTCbpOEneeERLm5ZuURQrQVW6jODAwhQB8X2TgoYvlxTF6rJNlKL+0vcjhUGTxmR4e6I5U/VMU87i9eQuWAeHaHkp1y6PB4lkKvNFyys5YHFvns9350E0Z7TuHI3aTqcxOvErolHLV/748lsO1J2eKuUjSg8KdNUy1Mb5+hXwYYNdoPJ7NAD4vuavEtK5juFg57MIZHN0adnRuoTSFUUrA/kDHZxedztHIO+DiuVGG8g9iEY1PxWIEfDkAFWTKgSu3FDA58DyY1OkaCmd/K58UoGtSpD2z3K5TOvOfGdtbS1dYNBqtVLwFAOnOJ70QIyjoyOcnJzg8vJyBbR5acFy+f4kqvl8ntbP+KzdbmNtbS3ReXx8jJOTE/zqV79KNxY9e/Zs5TIFBl9RGdje3sYf/uEf4vz8HJPJBOPxOPHdt88oL5SfOh4+Rgn2PlHlTvuC25iisc8/ClnmjWjwiFdXcKsUY213ZPFG3i112UZA7bLF11LV1euAruVreTqvXT4qX/2AHg+I8nnn5Ub85RyJaNS670Oj0vBtafSlKpWX3B4XeUv0e4RDd4HvgwHdyE2Usxq0UcwTTf4q7ZWpSgDoBHMt1wEsetd/99/cenTgZDtdu9ffnCeuQfq7+t59LD3na47/3s5IQ4x4flffaHuV9sjK9bJzySd3NL5yE8370P9Xq4TKyfX1Ner1OjY2NrC5uZkOxFD6uS5aFEWKWJ5Op5hOp1gul1hfX8dgMEinVzE4ajqdpnLoZl5bW0vbfGhN8/nl5SUODw/TunKv10s3GpEO4Nbd3O/3UZZlcnH//Oc/x8XFxYo7WtekWa+Ot99G0jmu67nA7Vygq54udlrsa2trCbA5jvTv+voaNzc36bkeeel9re2JrDS3+JjPA29y89THlYIB26xyDfhwzVnpcxno686q1Oq7d1mRWib7RvNFMiGygiPv0H1oVL7cl0Z+d0s3JwMiD4oqPFTg7uOt0PRgQNdBI3qeE7RMyjQFNHej3pc5OQB3KzfKG4GN0181kbQdrsV6OyL3s3/qoGQZUcReBODefm+jp8hyd9qrvrum6hPThZzTlwP5qP+9/fqO94v3B8eAR9K69U8AGA6H2N3dxXA4xGAwQLPZxNXV1Uq7CQSz2Qynp6e4vLxMe2tHoxH6/X4CZh5eQcAmEBI8AGBjYyPdLFSWJV69eoXZbIbDw0O8fv0am5ubaLfbCUAZgFWWtwdsdDodvHjxIlmKX3/9dbpOkG5u0q3WXrQU42PBx170nLykl4O/80QtHvQxGAwSyDabzfSnyhvPlObn9fV1ctHzk9ulyEcqFrR8KKxz2920Ph9LLqOUXz4Oq4yNaJx7XS43WI+CeE5BVrqi/73fVM54X3o+bVfOK+A0ehv10z0MEY2ePzJgfK57pL6WE20n0ner0oMA3RzBkUZ3H0F+F1Bovmjgetk+EMlo3ySudUUDO5oEORexPovo0fd9X1qkdUYDI9qkHikA0aRke3KDWtudExCuxUb947Q5L3ITzMvMTYbc+ryW7+tp2v6qsasn1fT7fWxvb2N7exu7u7sp0EktYR50wQAqAlutVsPGxkY6j/ny8jJFHFP480o/jgnl8fr6Ovr9PubzOSaTCQ4PD3FxcYF3795hf38fm5ubaLVaaLfbaDQaaDababsRg6/W19exs7ODH/zgB2lLEa8U5FjyMeG8i/ogEs4uFP1d8qHb7WIwGKRjNGm1r62tJUWCIMm6rq6uUJZlsmx5AxO9Bly/ZvAYg9fUgvfPyLqK5ocr4CoXIq9T7jPHT5+70ZzX+hQ09B3SR95FynlO5rqs0H72upQe55/SGCnWXqfKMfeERIljVpUTV1bUg+PyJ5I3OTnk6UGArgMXf3MNOLf4z4ay43SBPBe+nwNw1q0TwQebAoILav5PKygS9DowHKD0e85C1/p94uiAi7RtL9+By/ng76pgpGWlfMwJyqjPdE1KJ51OcgJXWb5fE1UrLhIAd2mZThffiQDfhX/0XS+BUMt3fX0d8/k8nRq1ubmZAG5zcxMAcHR0hMlkgrIs000+wPvzk3kIxsXFRYpW7nQ6qNfruLi4wGw2A/B+f62O9fl8vkIj12cbjQaGwyEmk0k6QnI8HuPt27fY2tpKgV21Wi1Zery3l0FctVoNg8EAz58/x+XlJV6/fo35fI56vY5Go5EsY9Kk69Tsb/5Wr9dXBCr7XXnPZ3QX0+IfDAbJ6h+NRsnd3m63V/qF44bjqFa7PTSE7ZxOp+h0OknR6XQ6mE6n6VQuuvrJk+VyuQLiOo99/DCPAkK0p1wVbh130ZKZj/doTFeBtc6xKiXHrUZNOWDJAajyyfNFionKVo6fnIXtwBzJLC2b89OxJFoCUDqUd+rJ0hgD53UuPQjQ1RRZUjooNU9u0Oe+68ByrSlHCxDvj41+82csPyqLyf+PynR6+d0tQAcOnwBMXm5uUrP8KsUl6hel15P3mw5s1qVKk/ZRboC74CLt5G80WSO6/DNSNlxAcCuQ1sltNeRPr9dL67G9Xi/RQJAjCNzc3ODm5ibdJqT32q6vr6dI47OzswSua2traLVaWC7fH3TB9UnSxohjWsOj0Qiz2Qxv377FxcUFXr9+nQKuaFFz767uEWbkdFEU2NraShHTr169wuXl5YrVTwtcQY99oYpWvX57XzCtUgBpDJCXGxsb6d5hPVRkMBikNWuCCHnGKwvZh1Q85vN5UoYYnHZxcQEAGAwGmM/n2NjYSPmm02k6qIR/vNvY3d3kgc7paE665aRA62O4aq3Qf/e57MpoJFeiZ7nfvN6c8h7JMDUQ1JLMKQv+ewRqBNDIYND8ueU3xxqXO1qWPuM4ddmdwxFPDwZ0HVTIjFzH63vRQGLy3z1MvGrA6XfViACsuF58cLNs/Z+/uXbv4Kzt9oESdbIDsNZPS9Rpi1IO4NlW54dqqkp3NJn9u/Ndha3yxwMplGdUblTguRtKxwaTCv5cv+kk93Ii95We9MT20CLqdrvY29vD3t5eslSvrq7ShQedTgfX19fJsuTpUzyM4urqKt2zS3Dl2CN/eGbz+fl5AhwVDMvlMrld19bWkuuY67rsu9lshu3tbWxtbaX1W77PNvd6PQyHQ3S73bSWynViXVMmb2ilahms7/r6OvGb30kzFQBefdjv99O9w7yDmBHY3ALFshuNBlqtVtoWpW7m5XKZXMy0YrmdiqdvnZ2dJUucdw2TXr3XmMoIvQDabgp7tdRcyOuYUsD2OZmTbT6Gff5HinQk8+4DwlqO/ml/+Xx1Ge5euGh+u/XrWOAGVS54ymmMltKUFpXN3mY17HQcu3zTv6r0YEAXiIWkNiICXtcsVWhGAybSrlxD07KBVWDUycGo0HXASRYAACAASURBVFxbCAxah1p10aTi706naog5QPf6c8fB5RQNLSvSOn2Cezg9NUJ3yUcT2pUP5ZFvkdLJ48nbp0lp8XGitEQ8VSHhfeZ8poLD7UEUxMvlEt1uF0+fPsX29jbKskxX89E12m63E2BQ8NOtqe7pjY2N5F4noDKwZ2NjA8D7+3l5x269Xk9BRASDVquFsiwxGAxwcnKS1owPDg4AAOfn5/jkk0/S9X9MdNc2m030+300m00Mh8MUpLW1tYXDw0Ocnp6mdV66YpX3wK11qyBFbwFBUS3b3d1d7O7uprOqad2zXL5LkCZQNpvNVIf2FW9zKorbKGe6lrne/ebNG7x79y4pPgq6nU4nKQIEX67/UvHgeIoUQR1b6sKN5FUV+EWWXW6OVKX7vOdKrCoLrvj7XIssdW23l69lVNXFpNZzJHMiZTryUtLTlAucUtnG5G3M9ZWnBwW6d2l0DlDKiCqw1fdVmPP9nJujCjT4rv5eBf5erwoE/V3BzAeFW8iucWnbVavTge+CsGo9pKrc3KTX4LIcLxx4dZ02Uiz0fdbP3wh0UT+rAqMTJ6fZ+3jRCeb9HClPtPTYJl6Zt7u7myzag4MD3NzcYDgcYjabYX9/PwX9ELwZJEUe8iSrVqu1cua08qLZbCbLj3QSVLjWS4uXbtrT09N0CMfh4WEKIBqPx9jc3EzARSuU7arVauh2u9jY2ECv18NoNMLJyQmOj4/x5s2btCbNM5tJ4/X1NZbL5Qf0MwqZlni73cZgMMDOzk6ybmmp01I9PT1NLmTuUabLfblcJmue0eF0L29tbaVtV9w3PRqNsLu7i9lshqdPn+KTTz7Bq1ev8Pr1a7x79y6589fX13F9fZ3A+/r6Gv1+H5PJBMfHxwCQXNXArYKh4y6SIxxPPt70f5U1Ph7v+n7X/9H3HGip4untcTmin25AuJJ/3+/+GS3lVb0HfBg8qXIC+HDZLpKpzK+KfcTPKD0o0I2IVeHr7sfIVx9ZYw4SCpr6/K59YU5TTtPzRXntWAUt7VzX7HwB37Up5VlktSrIK50qqNU9E00Kp5c8d35EfacCxw8vYPtIh7p8i6JI1gqFsdJFjZTuVQphWpaki8cvsv16VZ73o/9OS0w1X6XBFSOd9ORPu93G/v4+nj59itFotAIOFxcXCWyWyyUGgwGur68TcDG6mCCu0cmq1ZMP/F1Bl9Zxo9HA+fl5AiUezrG9vZ0sXaWLZz6Px2P0ej10Op2VccagKa79FsX7bUaDwSAB2HQ6TS7by8vLBOYEfvKV7WBZvE+YoLu1tYVOp5OsfAI4AWyxWKR+p4uX1udkMsHJyQmm02lyP3e7XWxtbaV66KrmCV+03J88eYKdnR18+umnODw8xMnJSbKEGWQFvPc09Ho9TKfT5M4GkNZ7SROVBQUmH4cu5FUecD5x/lUB4XeR3GDx311Bjt7NyZfce1V0RPPWDSSXt5E7WzEAiE8Ic9rdmHEZfp/0oEA3IlyBSBmSc19UDUjX3mgFcPK7u8PLBFY1Kwd/fnpHOC2uSbEO7+xIM+SnbvBWjYv5HVCVx7p24YNHrWCW4YpLbqBrkAz/1/a6dg+8H8S82o6Cj/ss1fohGNNims/nyZrhIRIED9eCde1Q3VDeDuWfb6nQtqqmq+vDVCSazSZ2dnbw/e9/H0+fPk2BSjyQgpG4s9kMb968wcuXLzGZTHBwcICDgwO8efMGk8kkWcsU3PxjoBajhQGkNV8KfuYFkKznVquFongf2LVcLpM7m8oL23h2dpYsYFqZ5CP5QmWAPCdfuIVnNBqlvuFtRbwLmAoT1155vSHvFKbV3u120e/30xq1nkZWFEVqH9dlx+Mxzs7OcHJygslkkm5WUn58/fXXCVxrtVoK1OI68e7uLp49e4bt7W08efIE3/ve91CWJS4vL9OJXHQpX15epkjyw8NDfPHFF6jVanj37h1ms1nil48hHXM651Sm8Lt6jqqA6WOE/rdJLsdULrvCqrIiciFrmfpepMg7f+5jsWudKm+0Dv5GZY5yG6g2KFyeqYF1X8XnQYGuJ9dqtIHK2PtqUpFV6Jazd6oKcLop9feoI91qvYs+/S3XZgdx0u2fpKNqD67Sq9avg7YrNjktU9ugVqADu7rTGS3LtU1G96oFpW5HBV21bin8JpPJyjaPq6urMMpUP5UH2n8EFeeZTjjtdz6jxbm5uYknT55gb28PvV4Pi8UiRSjTeiR9x8fHeP36dbKoaBFybNLC5dptWZaJH4xUBpCASa0qjp/lcpmA7+rqKt2py5OxyDcAyULTvua2IEZXU9lgUBb7V70JzWYzbfG5ublJFjAt0fl8jmazmdZqm81mUr4IyMvl7WUOOlbpNi+KArPZDEdHRzg6Okoucm7D4vyke51lkV+6t5jgPxqN8Pz5c3zyySd48eIFnj17hq2trbTlqyzLFEzFLVXz+Ry//vWv07avm5sbHB8fr/DS1/50HEbKNZ9Fbmmfb1EZH2NNRvPay/Xkiq3PhZzVq8q8K/jRkhQ/9fQo0hVZ/TlrNeIFx7R7+rS/lPbI2FA67qv4PBjQVeHnKWK0W5y5zooYoUCg5asr0QddpDWxbj5j57kLyYXgXS5x/T1yXzgfogGoykluwCjvHUwipSHiZaT5qtD0iddoNLC5uZkE/9bWVhL+BFZaawwGArCijTKyVt2LPLCB2zzK8v3RhaenpykqlWDsgox1UfP1vtBoYQY2cW1PgZrWGgOnNjY2kgV+dXWV9toSkCaTCV6+fImvvvoKx8fHK0FBVDoIMgoOBAium87n82TlNhoNXF5epvbRa8CL7xm522q10lYmBj8pKBE4GRxUFEXaTrOxsYHT09Pkgtcxz3nFfmE7WB6vNCSNo9EIGxsbiZcErfl8jkajkQ4HIR/W1taSgsCrB3k2tdbJMU1FwJcXGCDFvufWqNPTU7x+/Rp/9Vd/lYK4eKjJ9773Pbx48SKN293d3XRqGE+04hjTrVuqsFEGaNK55ULcx2L0TOdi9Nt9LTCXCTmAc3ByGRkppvpc31MlXQ0pfncvpCb9TesEsKKgRzigcjxSgnIGmOOA9tnvHei61kYNXcE4B6TRwngV8LJ8lqlgqVajA6Xn16RlenCSu8aiTlYaWZ8KdJ2YTqOD6l1KRwSm2oacpqw89skcATw11Jubm3Sowc7ODnZ3d7G/v5+2fVAoE8j0HGCCLgNiaMnSUmbexWKR3KbsS1p3x8fHyZXKQw7Oz89RluXKOqF+kn4FPPKGIERXcrfbTS7V0WiEbrebXLncZsJI5KurK9TrdZyfn+Pg4ACHh4fpRCryVS9JAG6PLuT48csSKOAJLOQnLXaOC9LANc52u50CoghCVBBoWXC7UlneRmUXRZGipMlvRgzzWEgqR+QplTEGgrXb7fS/LgmU5e2xllxCoKVflu/dvMvlEufn5yvK1HK5RLvdTn3DYDQmuhFp4eo80GM0aRHzxC66uul2fvLkCfb391e8GHT1t9ttbG1tYWdnB5PJBLPZLCkXbIMqyZEST7r0M0rRM5+zDtrRezrOHCBdPvCTc0GtUF3O0mWtSGn3JTGdb5Eb2ONOXCZpnUqnAvpdvFLeRLKMzykLtL6cJzCXHgzoAh+6in3tMzcIneGujeTWK5n0f41gc4DLaXEKjPxfafUOqlISWLdqxzkrVvkWfddUBb657z7pVKvVfGVZrrg1+Tvb2mq1sL29jb29Pbx48QLb29spuIh8IRiwHr3cHcDKOcUU0nqYfVmWCbTJs06ng16vh83NTUwmk7TuR7c0rVD+OQBEkYlsb71ex3A4xGg0wvb2Nnq9XrI0l8tlstBokRdFgel0iuvra0wmk6QETKfTpGhQCfB6uX5KUOv3+2mtlzcScTuNusx0LFFRUe8Dy+S6aVEUOD09xenpadqy1Gq1Uj9QkZnNZqmtHKPNZjOdJ61BcWyTAg3Lo+Alr5j08gfdYkRwY6AUgZ+uaa6Ts+84HpUXrJv9TGCmh4UKkO5T5vr2u3fv8Itf/ALdbhc7OzvY2dlZiayu1+tpCxXd3c1mMwVe6fxSRT+y4nS8AXEAVTS33b3qCnlUfgTE/iwHUOSrfkbyKAde0bNITlWVlSuDil60PJfjTc47GAF8Tgm5C3gfFOhGAwDIrzlEmhFQfbejdrbm1WeqjTKPDxIFTX/HO02fs9McOKss3Zzmp+99jLXrE9m1zVx/5KxZLcvzDodD7OzsYH9/H0+ePEnBMgCSC9EjgV1oU/gygIggAqwGDNHtS8HJdxiQMxwO09GK19fXyfXMYBhacrT0GIClIMYy+/0+BoMB9vb2MBgMkgClO9av7GN08OnpKU5OTlYikwksPGSfQp/bZ7j+yhOtxuNxCs6aTqcrEZi05HhKlvblcrlccV+zTRsbGylq+ODgALVaDUdHR8kVzbXXWq2WIoJns1nyUrAd7BcqGQRJ8kOtdVcOdLxxfHNtn6DL9WDuZWZf8EIHWrzuudBxpFHvutbP/mbdjNIGkMYogHTl4uHhIb7++mtsbW3hyZMn2N3dTcBL67jT6aSALtbhfeJu0FxSeZCbn/q7ft4nVVnJLn8jUP426S7675ufNEZlaLCpKjluhKmsrcINzeN9F1nHUXpQoAt8CD7A6oK1AkROK1Gm+BpIBAx8j7/pAMxpYzmwYyd7nirrFVgNRXeQdm1YeeDPI8DV35UetXqUz8pv5Y27alz5If8oDIfDIfb39/H8+XPs7e2lgBm1ghWY+L7ud3XLiP9zewzXJtfX19O6pAo5CnxGqPKov/Pzc/R6vRXQ5RF/dAkzApW01mq1dJQi94/SQmR9/AOAk5OTpARcX18nl2it9v4UqVarhbW1tbS9he+WZZn25bJ9nU4HnU4nubL7/f7KVgcCLddHNdKb1pwufxAsdS4sl8uVa/BoRfMEql6vl7YajcdjFEWRgIl84D5V9h+tRSpHpEvBje1QQOQfrVL22WQywfn5eSqn1WollzMBnwdzqMtdL4cgTRy3OiZJR61WSwFkRVGsRNiX5a0bngF8XDbgGnqn00nvUKHjOHE3swtstcp0rqks83nt89sVdpeLVYp7ZAHq//qblxeBc0RTZEy47I/4pNal1xlZmWqdsi+VFpV1VXEwUR43pFSRrUoPCnS1kWwg/+dzZb421gedDyx153gEtA8sBR4mrc8Z68DD+nIu5GjA+YSKBp/m5W+5SaS0Omi75k+g1+funqJgd8tE3+HzTqeD4XCYwHZnZye5MJUulktw46k+am2qsARujxXkAf068WjV0urRoBzykuut3K7C23e4/saALAVejgUeezgcDlfWL2nRkY+LxWLlmEACd1mWyf04GAzS3l26xTWikwBKlyn3iPI2HQaiUWFQi1SBTLcVkVaOGfKfZdMaJYjd3NwkV/j19XU6lnFrayv9rvt7eSORz0XWxyAsHT8qCHUckh8A0to8x4bOnYuLi+SNoMtYhSb7g0qGWpsEZHpctFyOO0Zrz2az5MbWk8dOTk4S2I5GowS63P6mwZk6txQEOTY15Sy3nPIfvcs5pkkVV+eV80zpjQwFnZdunOin1kOanC/Mq/JN3/Mg1yqDSPnF7y67tWznZaR8sI1RHpfHd6UHA7oONpEb092yDiYsx7U8JteemJ/lax4PBvBBF1mxmifSwpTeCAidfp0AWpbWo3n5vmuS2jYHYS3D+aWJkyXScpWuTqeD3d1dPHnyJLmTCTAUsLS+uIeT64QEXgaf0GphABKFIwUb3ca0KhgYxDOO+VeWZdqKU6vVknXGYxJdqaA1RBBTBYxbeNgvukdV12VpJeok7Ha7GA6HKbKZFhPbr3tKaSWV5fso7MPDQxwdHaHb7aLRaKDb7aZ1XPKuXq8npYPCgGCkXgtuxaJVzHVmCg3eTESeEGAHgwFarVbiO0+1WltbSwdtsAzWQyBlX3CcabCXKgBqjdPapiVJjwHbRWXKx22kbOvc0DlNga75CKo6b5bLZQrcUi8MeURLfnt7O1nEuo1L15i1faRRkyrwCoRap4NPBDLRnCZvqEw4SLB8lTM5S5a8URngcoG/q+ypkq+UM3p3sSonypPIUtVy3ajKGR6RVXufPKpcKi05Gcr0YEA30uy80Qo2bulG2p4zKjL99b1IwwI+VAB0MPszpshFofTngqm8rREfIj5F2q3zhc8isPeBGml6aqHqwKfgarfb2NnZwYsXL7C3t5e2BnE9ELi1Li4uLtJhCQREula5hqYAyfdp7YzHYxwcHCTwoEuPdXKtjwE2BHTejsN2EdD5jOBH4cP2ke90YZNfuo2Iyhb52G63Ux8TwHkEYVmW6c5cbjUhb+gpGAwGqX4e+rCzs4Nut7typR2AlXtvFfgJTgQrPWCDPCPIMIiL80D7mi5w9uFgMEggzEhtbp9h+wk6BHl+p8cDwMqlBFQQVPASaLk1imv5uu1LvWLsF40AV6HI9pFGVQgU0DhmOLapHJFPbAOVhzdv3iSFhCd0MeCNSgWVEJarY8jnVJQ4n6vmqI7tSC6ynRoE53JOyyB/+Zsr7JEcjGSiyxm1XFURYf1qkUcGBPM42Kn8BD7cghlZwkwRL6M80f9V5Xp6MKB7nxQBrzM8GohVVlxkser7kVvF3QxueUYdQwWBAyVaU3FLPHJ/MPkg1nK8buZ3SzXSBt0qIN2+pkKByvXTTqeDnZ0dPHv2LG0H4tGAtKgo/BkMMx6PcXx8nPaJUvPnnlNVqBghSlrofqY1RIvv5OQEs9ksuWf9vlWCtm6zIeDzU4WgCnbg1l1LWtmX7AsKcR7ywfe5pszDIubzOY6Pj9OBGNTsi+L9iVEMPCOYFUWRtqHQoqcrWV3idG0SxHWd2QWDgiGFP61itpPufIIi3bzz+RxPnjxJ69oU5KenpyuWEvuP5eg+aK3fQZ406BGSjDBXZZuJa6pULGitU5FRZSiyvtTi8usaqXxpZDu9LmVZpuWJtbU19Ho9PHv2DIPBAE+ePMHBwUG6yUmVaOD2knQdT1UgyucqB6K5nwOXKqU9+l3lnso8t8SZ3A0dWYpOu/7mssmfeb6IH86DCA9y9eQs2Vwe5yvruwu4HxzoRsCgiR0auWpVY3OL+C7mqgvD32fyMnyQ5TraAVUnOp9rvRQ+CtLRQIl4F9GZy6d5+btq05x4Xjaf0eLjEXqbm5srnwz2IeCqIOXWFP5elreRrzxcngKU9DBAC0ACG+451TOCVYjp6UcE/2azmerUW2HYRgIEx5ZaRpzkBABdE1c6SSP7lRbPcnm77YbudY34bTQaGI1G2N/fx6effpqsIp4mxTVmgji3pDAgjMpJLipc+5FApNYTj4xUr4aOdSo4HBsM7iKg1uv11D4X1BpJSjp0vPO4SYL72dkZxuNxUoZ0XqiHxZdX1NXIcaLz2OeUAgzfVSVouby9GlH7ikoK8F4Ze/36dYropsfn+PgYb9++xWKxSEF65DfHqB4kE4EV+RT9Hgl6n9+RMaGyxJMq3BEguiXuCnyuDU6PGzKRzM1ZuV6n069Ks1rjkXfR66gC3Fwel+lV6cGAbqQhRA2IGu4WoQ6KHMPcenUXigMlEIM539HPXL1argqJCCR164hao7lJ5/87PUqX06i/83+tt0rbW19fx2g0Ssfk6S0udO1eXV2l04X0+DyueVIg6y0uFLS6/Ue3wah1plHK5C8tk4uLi+QGJPDyaElONhXcLEPr8bHjW1OYCAoEJh4sQQWCljXXp1kGxxnXZLkdaXNzE1dXVzg6OsLx8XE67rIoinRk4XA4TNt4GJFN8NLgDvYrlRgqQAQk3Uaj/UHLlPTRZar7U7mHmq5mto0WsM6tWq2WvCONRiMFaCnvdZxQKdFxqodelOXtHnGdowo06rngUY3u4dF54+uHwIcnWjGPWqjHx8coiiLxpNfr4fnz55hOpwCAg4OD9F2BX93YGgWrv7ms4fcIqDRFMiB6rp9qzChvXFbk5Iwr8fq7Ln/p76785/L7d6WL/e1rzC5/mddlfA5UPY++z09feqtKDwZ0I02Mv1e9A3x4CYFqLVG0cRSFq1aBay+69uJu6vtYkz4womcOpuxknXRRObn6Pzbdtxx1oRZFkU5i2tnZSSDAdS21fugSpkCdTqfJlUn+q5VYq70PTNItHwRYzcc1v8VisbLGSYBjMBYVBLqdt7a2EhirkFUlwNvN9WC1ztg/BC+e7MRAKJ6SpOcb6xGWRXG7DkvQosuVAp3uarU819bW0t7jdruNq6urxFMAK5cd6ElV5B3XSwloFHDcX+yWAV2ndGt3u90Vq5b7j4+PjzEej1GWZVI4GEzEIyqpeLDeTqeTQJoR07Ts2Vb1TJB+jkVXhLU/VFCyT/VUKpUTrmwpOLtnRMeFKsonJyf48ssvUa/X8emnn6LX6+Gzzz5L5b969eqDE7g88tVlocqIyHqsSl7GXUl5pu+ojM1ZolpGpPzob1qH/nZX/uiZ8ipaDtQ6SJ/+ViXXo/qVnwrK7sXJpQcDuq4V+e/RIIyYGQGua4QKuFXrEFVua38eaVCaX9ehHGjZpkjr0knJfP75mwBvxHcfTKp5K+AyGpcu5eFwmAKnCDp0yVGI8mYWKjF6MhAtLQ5eWowUfkoXn7ENFNDKS4KHWnO0pm9ublZo9LZ7f7L/1Q3syhnfo/U/HA5RFEW6uJ40coJSQWAZ5C0P7iBg6bYpjQhut9sJdBkEdnl5ieFwmPLoei5BR7fDMMBKPQBce+dpWdxi5cFDNzc3ODw8xPn5Od6+fZsuHFgul2n9mkFttDB1f6vylPyYz+c4OztLipJbLT7edf7r3cq6xuvBkQrWPkfJH7VCda5EckLnowLvzc0NXrx4geFwiM8++yz1t/LJ5ZHWxfYpOGjd2nZ9x+XBx8gH5YPKKOWX9kXOcmXf6ne2lWNR5ap+Kg3M7143zU8+Vbmhvb903qqHwd/TPFWuaR1nv/eWLp8RRLXxVQx261fzaAdpR7kW4wzX/NFznRBqJZAGD6LyAaEDm23luwrCpFHblONp9FwHrdav7eN7+q4ez6cXgPPwBlpBvAC92Wzi5uYG4/F45S5SWqfKGwIjy+f/Oi5IJ12dKgwY1Upwvb6+TsFYarVyKxDdfDoeiuL2dhy6Rlk3AaMobs+Irtfr6VacWq22clNOv99Hs9lMh2JoPSyfwTgEJALFZDLBu3fvsFgs0Gw2k6sdwIpSwlOPNjY20vGWBFQCnVqtekAFec5y2e7t7e10eP94PEa9Xsfe3l7aCkOeUhEaj8d4+/Ytvv76axwfH6fjIHlE5nA4TC7t8Xic+oq85LymckSvBS1hekBUQXNFJ5rH9JKwfPabboXieOE81UNEFGDVElIB7bKG4/Hm5gYnJydp7lxdXWEwGODFixcp8vnt27dpHzet90gWOOCpJaqg4++7vLtvUhmjQB95B11mqKLgctqBSGWMe/Q8v85Df88BW5NbsRHwer13GU9OiwO3e8ii9GBAVwW8Dxzgw71Z0QBzMOV7LJP/u3/eP3OD3585s1UTYkfqu24lu0tbO5h1RTQorXfxtCqRNxGP2Ea2TTVNgh7XbbkGSVfz5uZmAqPT09OVc4/1InNanLouCnx4oxB5XJblSoCTWhcK5KRbo4rJ73q9no4wJOjRAiZY6dGSFNYERpZD2ri+W5ZlujieYEPA5L2ypJOJ23YItnp2M12/PJCDrlqlk3uTdb+xjn3tf/KTZXH8qvuZa95cq2WU9PPnz7G9vZ2scHWFqvXNT3o/GEwHIJ07zUhrAk29Xk/Rv+wnjZYn+PJ/BVL3fvC7ehD09CnNxzaoN4G/q4XtAOzyR3nL+cqyzs7O0Gg0kpKxtraGvb299M7BwUG6etI9WSoDfL7yO/+qLCwH4LuS188y3ML1sjW5q1brjX53mZ2zTh10tZwqwIzK0PFQZSFXAXbOOLsrPRjQjbQbB0N39+pEc9eHMpYp5w7WOvz9qvIiS5dlVmlQ+q6Wr1p0TsuK6PMBE/FW2wasRtZSkKnWz0SLUoU6AZeuZFpADP4ZjUYA3geOvH37Fm/evMHBwUHax8l1T6Xb1yIjOl1hUbchwVEnsEbnqkAsiiJd/wcgWW8EJIJqrVZLVi3HGstWFyWBiCc3EbR5gMfW1hbG43GyHNVrokBLhYYXuG9tbSVrmb/p8Y0EbVrX5JUqHWVZpiAnAir5zQCe5fL2zOjJZLLiRqb3guCkblcFaNJXr9fR6/VSoBVB5+zsDMfHx2mZgcpWZCn6fARuLzBQwNexv1gsEt1qser80znHemhBc5x74KTTocBLpY7vqbJQq9XSxRrsF/JrNBolPr19+xanp6cfRFjr/OBYZl1RjEck57ycu5Iq4Q6E+lyNDJULzqco3YeOKH+EBfzf5WwE2hEPIozxZ7k8Udm/d5Yu8OGaTQRUzOcNzoFopIlE2kou5cBNhYUvxCsw5AZElWamQkjpdyv4N+W1u8yVt67g+CTnoRR6wTz3iHJtjkJnMpmkLTYU7hqUpPUSOLXtkcXKoBsVDrr+pf9rOUVxG5jDm3d8XKgbj0JcDzRwtx7r4jrd6ekp6vV6AkPdxsNobN0HSmVAFQu67wngBAa+Q4AhsJEmtlmjkdlnGoldlmVScLjWTuDi/tlut5v4pFYh6aDlzj5kkJp6CzjOuN5Mi1evM9TkJyWx/1QZZYo8Wz4/dDzofNfgJXoxHEgi5VbngrscdRwr6NGSL8syKXabm5tYLpdp37MGwVHh0bVlJtKu81ifRXR/LNCRby6v2CbWH8kzp1V5onIxZ0hE+b3vlA/qCcutu/q7UZ8p/6p4Fik5TD4+c+lBgW4ETEAc4VvFHNWGIkvRn0Wgk/uMBoMPityAqWqzKwe6huKg7gPb3Tk5fmh9ToMrDF6fgh0F72Jxe/F5q9VKt+CU5ft7T9+8eYOXL18mK5eWrq5fat1M6m5WIKU1Sreo8knBTtgdBAAAIABJREFUBcCK5eCKhO6V5G01zHd5eZnGIOsm6DCPbqVh8M5kMsFisUgHfVCYkxa6jyl4gdsIdb1vl2DKsaBbm3iwyMHBwUrwkbpTtS067hXoi+LWBUs3Ni0yvdquKIpkresVfyxbz8pWS90DtvQuYZ5nzf4hgPFAC/a5ztsqT4wqKvyf+bR8n8s6Phjgpn2sZep8UKtGZZWOM45RVfxIG70bjDno9/tJOdK26tzkdjifsz5/VbF0gFL6nOaoPFUsVYHQpOW4h8JlDPnHOe/Bbcr3KJ/zxOe+jhfmuUuGOz8jJSsqI/dMjaKq9KBAF/jQsgSQHdw5wNVBmNNyHHBzgBkBZ86KVRqcziiP06brRj7oqxQNfy+nwUU88PVUr4eClMKEVhYFBIOJtre3sbu7i3a7jePjY3z55Zf42c9+hq+++grv3r1LwTkEXK1P28rfcoqAWrf6F7m7tC+1HP6vEcQM+mIUMMsgWDDvxsYG2u12Ot9ZI6y5bYdWHYB0SlRRFAm8uNZ7cnKycq4wg89o5ehJXSyzVnsfvPTs2bPkbqYlzXGpPFQwVIDWdXBu+1GBp1YW58FgMEg3RQG3Lmq+z/prtds9wrwIgZa0umwVIMlj/q6uOg1A1KRKgL6XU0B9PHHu+DyiQqbzUMeQJhX0/lznF93x5BPBcXd3N10iwcNAuATj811pdSOC7SPdVTLJ6dRxQrrvkomumERy28tQfjnIu5LgdWlfuHcjWpKIZHAuT5UXNIcD/szlT1V6UKAbgW30v6e7wDh6nrN0XXNywHMtKwdoTkOVguCDMJrYER2RNsn3q+jIaWeRtlsUt4FEakEA79dfGZVJELi4uMDnn3+On/zkJ/jlL3+Jr7/+OllRXMstimLlkIKyXD3ggBYfD7QvimLFnVwUxYpw1f/1gAyfnLoupm3mn+7rVfce3X50rypI0vInsBVFkY4tPD4+Rq1WSxHG29vbySXMc6ebzWZau+WhImtra7i8vMTbt29RlmUCtvPzcwDA06dPURQFnj9/jrIsE1AzMhy4dXvzj0Cifco2dTqdJOjJJ1131wselstlAl7yhluMSCvL4Hs8i5j0LZfvT+XSw1AUBB1MtZ8iYU+lUOdFpPCqx0TLVXq9LgUwfmq0cQROuuTAscmlFQ0Mo8LKKxNnsxkODw9xfHyc1r/1LHDnB+d8pGREMrBKJqh3w99X5Zt1erm55OW4vM29ExkaOUvT64raGvFP8+SWASNQ5fuKEXzmBkSUHgzoqnbDlAMf1/j4vubR39w15AzTDslpNjkNJ/d+FQgrDZ7P69N8VZqZ8lGBhr9FEy0SPs53tZjIS36ur69ja2sLe3t7GA6HuLq6wldffYW//Mu/xE9+8hO8evUKZ2dnSXBpW+nGZNsofJno8lNLQ/u6LG8vBY94yP/VxUfBottotH0sjxYsyyagUPBwfZrbPxjMRMtR++ny8jLdnctr4RistLu7i36/n/bAUqAfHR2lK/toTfKYRwKdBkYRyNUNrv1FPvCiA1qiVASWy/frtTx6keWolcp7cqmc8AYkngfNMrgEwcM8WA+j3HmrlLp3KexVaWSfUPlh8mUP/V8FH/vSZYYu2+iY0puFIkFPcFfgdvBwZYD5GDvA6wHpZaByUavVUn/u7e2tHON5dHT0ARhy7qoMULeyGwEKYiobVbHg2I4UcP7vIKfyIAeiOh+j33KArWu1SnskV3PyNWfUVMlYz+Og6vU7DvxeuZddS9DBkNNW+b9qlqqZuMUKxBF6OWbmOi4HyvrMAToCZp847hrKDSofjMozpT1yu0QAHE0KCkxtF9frKFg3NzfToRjz+RyvXr3Cz372M/ziF7/Aq1ev0hGM5AOtCYIeBbuWz2ekge8qzxxUdT0MuD15ScFWgd95Rzp0r7AqBLR4qWgQcNkmDYBRAPQ+Ia0MuNrd3U2gcn5+joODA4zHY5yfn6dANF0HpZV0cHCAra2tlQsU1APgUbRRBDrpbzQaab/1xsYGlsvbKGyCw8XFRVpz5cUDDAqjO71Wq2EwGODTTz/F7u4uNjc3Ux56BTT4Tq/l0z6n65rjXMFS6dexwr4hLx1QqqwZzgV1Z+uY0TmTAyL3/nD8kDY+061VRVGk5YiLi4u055qeESpX9A64/OP3iFc+x0mHv6MyVvmYMxz0u/6m/NaynV93AaXSEik8qjBFoOiBVI4D2u/6vr7ntObKjgD7LgWE6U7QLYriXwD4zwC8K8vyj7/5bRPA/wrgUwBfAviHZVmeFO+5+N8D+E8BTAH8l2VZ/n931cGkHRFpNgpSkVasDPKOyzHMB0DumYNm1ftqQUWgH3Uky3WtKqKjyt0cKQt8zvZHE0L6e2Uw6e96D2ur1cLOzg52d3fR6XRwcXGBL7/8En/1V3+FL7/8Eqenp7i+vk7WHQFDgU6PYHTLjGCse3ZVk9S+UF4TLLiGyef8ZKCK8hnASnCV9gXpUeFMkKYbmmXXau9PiTo8PEyXrdOdyjtrGRTDrUIXFxd49+4dfvWrX+Hdu3cpDwU31zoJ4iyTLmcCBcvTyFbSzz/SSVoODg5WTkfi3l8KebaVJ4opD8uyTDcbLZfLFET3/PlzvHjxAt1uNykMjN7t9XppbZpr4JGyzf8pLFUB0r5wwU9gjgSivueWE8eizm1ditB3XbmO3tegJyp+LEsV2eVymZQPuvN5lGmv10vjcrFY4Ozs7APvlY9hpc/lR9QOBTPyXfmv+bVsftd+cAVf6ci94zJKvWikLwI1vu9KgnvD1CMRKRI5UFZac3yO5L8qRFXpPpbu/wTgfwDwL+W3fwrg/yrL8p8XRfFPv/n/vwXwnwD44Td/fxfA//jN572TTyQmbZg2kExxBjkT72KYPqvKE/1WVbZ3rNIZRd1p+3KdFwGrluMWsNKok00VAdLpE2C5XCYXKusbDAb45JNP8Mknn2Bvbw9lWeL169f4/PPP8dVXX+H09BTA7Rm36rYlvXRzsg4VYKSN67iqaGh0rlpICujknx8fSZqUB8ongjXpUrcmrXsNRLq8vEzrlYPBADc3N+l+XJ4QpYeBjEajJFCvr68xHo/x1Vdf4YsvvsDh4WGyaP2GIAI1lbT5fI7xeIzd3d101KTue+U48wM1yrJMh3QcHR3h1atX+Oqrr5Kl1e12AbwPKqNrW61/HtbBm550CxYP1mCQGa1ZjnHdy8uTyegloPfDlUGuU3s0uypROid0bjAYjO9wbDPqnO/rHNR54oqnzw/3yOi7Dtiaj+OW9el2F97axW1X/X4/KTZlWab7fHV5hHPEXdAOQGyDygNX+JnXPUNu4ERA6vVpu31eVikL+j77M2eNuvLk4yeHB9quqtgcp6cKB7zeqnQn6JZl+f8URfGp/fz3AfwH33z/nwH833gPun8fwL8s39f6/xZFMSyK4klZlq/vqse1KqPhA6B1wMsxPzdIqrQRZ3wEylHZUR63vvncNTgtyy3jSLtXQcF3fPDf1UYHZ+WvCkEFXt4T+r3vfQ8/+MEP0O12U7Tyl19+iYODgwQcerC81km3bI6vbJceFeiDXQ/C8DK077UdFMKFWBxUCoridhsN+aj9Vqu9j1Bm8A8Pweh0Otjf38fu7i7KssTLly/x7t07vH37duUgEPKu1WqhVqvh6OgIn3/+OT7//HMcHBxguVymS9CVfm6t0iWU5XKZ1llZvvYdrdbLy8uVozvpUia482IBrhdfXFyg3W4DuL1VyI9e5DWJGg1NoODxmhcXF8ktTSuf76+vr6f1S9IXjXd9zz0//N9BRl3O6o5WRU3nox4dqnOL85Z1aR1Vc8rp1DHpIKRtUFBUZQBAUlS0Xepm1XmUk3Wecr/zmYOzJp9jAJLi5G1Wq8+VmJxxoDRE24KUfseD3DjKgaqDstIe9U8krz4WcAHg7lCrOO0JkL4BsPfN92cAfi35vv7mtw9SURR/VhTFvy2K4t8eHh6uaFZRijRKfZZjQlDvB4Ds+SMNTGmLBncuj4Kh0xtpUMCHhzOoFsoUTTZ+Vrk49B1f/9PftC0E3NFohB/+8If423/7b+OP//iPsbm5iZOTE/z1X/91ila+uLhIgl5pVIFSluUK6LkbV4E6Alzg9g5S3++nQpY8jCxitpnCghNc+0jX23mK0nw+T+uYjUYDrVYLo9EI3W4X3W43ndTV6XSSC5pBMwwuOjg4wOeff46f/exnac8tXfZc13NXmNLE/+mi1Xy0eujC1rIWi/f7iKfTKSaTSbKc+B6tYHWTcezq+qm613VfKa3w2WyWIm91PZKKGD0GCop6EpgrxdpfLljVaqJHwQWmRphrgJXPFRfIOtYUGD2Aj/R5/9AzQ0ue6+H84xKBKnH0JJA/epsUr4hkX2hcgS4j3CX43V2r7XbjgM913qiXoSxv9yWTP/rpMtH/j2SbewS9n5U27a/7uoFd3upvpMfBOyfjPfnYjdJvHEhVlmVZFMXd8P7he38O4M8B4Mc//nGpky0CQTY2YpqniKH6/T4aUC6PP4vWCqL3v2lzCCC5NuVoUD5Fmpm7yozvK3Uq4Lmbl6DS7XaxubmJH/7wh/g7f+fv4LPPPsPu7i5OTk7w85//HP/u3/07fPHFFxiPx6jVamk7iQK6CjgFRV9z0+fRBAKQrBTnDwWe9ynbFGngPtl0byjd07oXtyiKlXt5eeAFQZlWHy+tB4DRaIS9vb3ktv3iiy/wy1/+EkdHR2g0GumMY9bh26McWDRimmdIM+KaIMw+4BWLwO3JUzwxjOuFugbJw0Ho8vRbdhi1rIGM7INarZYAioFXBChtS71eT7EBBA9aSzqemfhdb5ZiIh3q0mW/eoClKrI6l0mfn/lNmjXS2seL1smxy7EXubT1HmWu1xO4uIWIyhkVO0aFt9vtpPBFp7G5i91pZR71/mgZd8kO/V3rUytU+zn3vsp3l2XKd6/HvXsOjNp2b0Muj7dJZSjpuq+Md+Utl74t6L4tvnEbF0XxBMC7b35/CeCF5Hv+zW93Jp8E/iwHjPq/5mWKJlkEmAqqOukihrvrI+fWuCvKWPNHgKvv6ASJeKKCOeJLTpnhCUjA7TYduhbb7Ta2t7fx/PlzfP/738ePfvQj/OAHP0C73cZ4PMYvfvEL/PSnP8XXX3+NyWSyoj3reqgCGstXy0ABRSdf1IcUjtqmSLt34V0UxYrFQn65MqcA4kJsbW0t3SCkN+gwmpeXu/PcYd40xAsDFosFXr58mW7koWJD8NSxwz9dP+Vzrgvv7Oxge3sbJycnieccb1yDb7fbCcQp5HWsqtWia4PsA/aXglFZ3h7XSeuVp3Sp8kwrj9ueKIx5CMjGxsYHkd8u8Egj34+Eds5K8fnAaGqWq8qdt9sFf07G5KwmjmtarX59pI9XltNsNtHr9ZJiQo/C+vo6er0eiqJIe7y5f568UzqdBypnNLKa81L/d/CI5KnOF4+NyBlNKpf8mdLq9eiY0HHhvHd6c5gSvedWf66fHQfId4+urkrfFnT/DwD/GMA//+bzf5ff/0lRFP8K7wOoxuU91nM9VU2sKFXljbSQiFEqjCPAdQ06lycHuJE2FU3Y3HqKCyOWpQpFpBz4INBBSxDSAwrq9XraivLs2TN88skn+NGPfoQf/ehH2NvbQ71eT9G2f/EXf4Ff//rXODw8RFmWybpTweaCkhOJLje1hFQL10AYpTtSgvRTBarvXVR+eB+QB7TwVDgxEElBlcciluXthQL1eh39fh/dbhej0QjX19dotVrY2tpCvV7H8fExXr9+jdPT0ySQ9bIClkdwYH+rO5OXEHC7Vr/fx3w+XwmWYvvorm61WulqRW7vcWs/cgsSVIHV05+oEPB/tSJ1exWD1nzcKiDzTy+64Hapslxd7tC5rmNe+9HHhgtqnd86tnxLiPJCPSmqoESKso5nWq863nV+e9uovPT7faytraV1dm694ylonLPRUkCkXOvcq/o/ao/zThVmHQ/M7/NTy3LjRoPIFLw1n4NbZHGyT+/a+hP1u9fhCpHX5bLMgdfbHaX7bBn6X/A+aGq7KIqvAfx3eA+2/1tRFP81gK8A/MNvsv9rvN8u9Au83zL0X91JQZByWqA2MHpHGazCwzU9zROBolpV0ftVljLwYWdoHrWwos52+qI2fdMvoaDgd9feXHvV75zEjUYDg8EAT58+xZMnT/DDH/4Qz549w9OnT9Hv93F1dYWDg4N00tSrV69wfHyM6XSaJiRdcR74w6TatR9J6FqtHligbszIEvH2eL1FcXvRgQOBn22r/UVLgic3qduYNPMoRrpHb25u0Ol0sL29ndbhTk9P0w07BHBaM4yMpttcLXI/BJ8WdKfTWbnSTy0eWrVcJ9YDJrgfmdHEKigIci5sSY8qRVyX3djYSEqLKpnq7o3mK2ll2+muXy6X6YAOWsjuEtUy2P+6VcrHGucdxx3BjoBYq9VW3Og6ZzhWOLb9XGWWyf8Xi0Xaq0sQbbfbK3NBxyAD5bRv2b/cUqZ7tGu1WgqsqtXeHwnKIyZ1O5y3P7L6IuOGbef8coVHQT6Sn7pDwZMaQtqHulSRo1cNDTdYXEb7p+bR8iO5HuXXet1o0vZHRk6U7hO9/I8yj/7DIG8J4L+5s9Z7JrdetGG+LhE1Prf/ip+R9qLfc3kcEL0zq0CdgOSuML7v1hjX3CJacsfRsVwdUJqUNtLE9dvt7W38wR/8AT799FM8f/4c3W4X8/kcX3zxBU5PT/Hu3TscHh7i7OwMs9nsA35QqDMKVhUY1caZXzVXp115qgoLPzU6OqehKu993Y99QFCiNUJBT2Go77vbm+3lXleeuER+Xl9f4/T0FEVRpL2WPISCwTUU0FzTo7ua7l4CCg+yoMVDC5f85XorLU4FY+AWwHkAxWg0wtnZWbpajnWo292VEx1XXJflcZh8nyBES0yFNa1afnINs9vtJlc99xE7CHhZ/K797cqojnl30Ws/qiB1BZx16VGlOiZ1/iu9zWYzKUeLxSL1lUfM85MBZeQF76fWs6s5RtinujavwOVzwQFW+eL5IwVf21hlCETLaXym8lkBy+Whu5nddRsZLPq/0qfvRXI+koXa51G/RvI2p2hE6cGcSKXJAVKFnneAaqNq5jsoKgBVaURaZgSqpOuuPFX166DyZ6qxEwwj5QH4UKus0rSiSaAgRLcl705dLpc4OTnB27dvcXJygpOTk5UgkMXi/clKesAB20DAYD/ogFbLQbVoThDNR34o8Chf1PpVTVjL4HdOdNbl+zg1ApN1c+3RhYgGW9HCoOuWbuL19fUUJUw+0QJV1yuDn3hIAsc5cHswCIB0qTzX+6bTKY6OjtKRlFxPZZnsCwoQFWbtdhs7Ozsrngb2LRWAorg9Y5g8U/5xvX9rayvxmidoUZCq0qCBaboODCBtI+K9zLzwnXlVsKvlz35dLBZoNptJMVCrS/vfx6laL5Eg1aAo0sF54/ObZZM2njClSwc8DhJACnDTKzDH4zGOjo7Q7Xaxv7+PXq+Xxgy3X6lSz8C48/PzFSPEle+cHFCwohzgfFA5E80lJrV6tb4IgCLgZn383WWcA6fmcy/jXaAcGU2OF9p+l6sRaDvN9wHfBwe62hGuhSkj3Q0caS73Xdi+r4byMclB2J/pIPE8bqG69he964Mvp2kyLwUUNXiu/9HaePfuHd68eZMmNjfuU5ARPJQGugr9QAtvj7dThWIkDAGsWE0U4MwfRXK6wubfdXLo+KH1SoHb7XaTErJcLtPF8Xoyl9JOwOV2mel0mo51JH2MTlUrSS8c4NYkdaNzfyzvXp1Op6l+9qfySn9TQcnjO4fDYbpZ6eLiIglvXRvWU5Z0DNEb8vTpU9TrdRweHuL8/ByTySQBvgYOKRCqdUQgYxQ4D9Yoy/KD25qUFoI5aXKrmgDEccHfqcB6vIVaWszrQjtS8KIxBCC1g4F0i8Vi5Txr3UZE2nnZAaPSNzc3086BXq+XbiiiO1qBr9Fo4Pz8PMUBsE3Ke12L1T6I2uvzNDIQvP18rmurLq9UUfH56Cknz5SGCAuYJ3rGd6P3I7oclHP06W+/Fffy31Ry4u9jfTpzI4FbZaXmnt0nz13PIjrvoxUBty4WfcetP2+nf3ftTQdbURQrUbTcR8gJSUFKq4/Whmrd6nrVemmFuXWvk13p4Xct17VUFVYKlqqARcknCMsjwKoVRXDa2tpKPOH1dxRiavHqvlMHOx5Ewf24erhEvV5fuX2HPKOVqQFntIp5y5H2syscLkAUtCi8gfdW5mAwSPuKgVuPEt2hdPX2ej08e/YMf/RHf4TPPvsMo9Eouc61T/inVqVau+QZFQ+2k1cf8thQKiIaULe+vr5Sh4KLtlfHj1r6TL6WzU/SyzHq7lou9/iWLubhPuR2u52UM43epqDnXCId7N/j42MMBgMcHh5ia2srnXfNMaFr6lwrpteESo8Dgiq1bIPKAld0XYYpX1leZD0q2Lvcc9CKANnHsipGEfD6s0jGRpjgCpX2PWnV+nNeS49/uI8sBx4Q6LplRqDR565x5Z55OW5t5cAx0qKiwZfLo7RU5fFOvUvjU41N15ly+VXjYr2+bqJRwtxXqkKT5dCC4JojBaKubfI7hZT2DelQgNKAD1UmVPt3UFahoWucCrqaTydcpKU6j0iTulj7/X669WUymXwwERU0+KdBS2zjcrlMB08QTBm1TBcr7xom+JLnzWZz5WJ5JrpkaUlRCLhgIQ1s783NTVqPPzs7w8nJSQr8WiwWK6dc6QUCvV4Pf/AHf4C/9/f+Hj777DPs7++nOToej1N7adWRDoKCXmxBBUD7n33BCPGtra20jskjNbnuzvuIPcjH5wXboGNJhagqiSpEdUyrgsyxR+ucY5CgR3o4Z/zTxzTXZzluuP/29PQUv/rVrzAejzGdTjEej9M9u6SDNPBdnT/kjUbE57w/yhd/7qAcyUT3RmqZTFqOL5XlAN/LcMvZscDrq8ILt3A15SzkiB8eyOpKXC49GNBVzQuI951WvZtrqLtaHQwj14QDnQ6yu94HqjuOdbj7PBqkTrPSl9OsIn6pteGgyKMMaWGUZZlcYRS+wO0eXlqtDN7QCa1rYEy6DYYCSd3SfD+3VKAApu3KKWQUDg5Smk81f/YFf6P12e12MRgMMBwOUavVUsAUy2Z7dKwogKj1TKHLNT3yqtVqAUByoXL9zseQT2gHeRX2tCBVIJI3StdkMsHJyQnOz89Rr9cxGo3Q6XRSYBWtzHq9jidPnuBP/uRP8Lf+1t/Cn/zJn6Db7WI2m6UlCN5CRFc8+40KHSOyOf50K43Oh5ubG0wmEwBI+5AJCIeHhyvbqDTCm21TUNWIYl27dm+Kgq0DrM4ftXhVkaRl60Cux2fyPeDW46CWsgbDMfDu+voar169SssJqphyzZ118U5mjpOjo6M0h3Nyw8HW56EDkgOzjqsIrJ2Hygt9ruDl4KbAHNWTk4GREeTPoqVHlcnqCdH3IuVNQfguwAUeEOgCH7peIz99DmCdKUw6kKoAUzspsmhcsOu6kAOoTnyP5ovqzyVvazRo7+InB4Vq1+oWofDnNgXgdluJn8TjF5WTp9pmn6zRRFYvRjRYvQ+YL6eUUcg70Cj4UEA5EGs7ePoPo0ZphUSuRNJEl68GfLEs1qsuVeVZWZYpoIj7VSkQyH+6tOmmV4XEwZU0+ThRocrfWR8VjNFohKIoMJvNUkDP5eUlWq0W/vAP/xB/+qd/ij/6oz9Cq9XC27dv8dOf/hQ/+9nPcHx8jFqtht3d3RQbQJ7SEiPf6U0gmJOfKrh0GxYviOC4YuAYx7VGE6t3RIW4zmsNPCRP1FrxYCQX6DoGqMAQdFW+8GJ6ejPcLayKhyqZbP/FxQXOzs5W5h0t23a7ncaZrteura1hNBqlMtl/LgM4NtgG9wLo2OJY8eTWrY6/iH+RpykybtyNq/TmwNflvn+6IpAzeBRvvF6V/a6IRO/elR4U6Hqq0jSiZ8CHTMqBag4U7wJcTtRcHtbvZUf1V7mIvZMVdFTr9kHggzTS+Fg/gza49kQBQFeegky9Xl85DjECWObVoBa2X9feNJJT+0vdexE/mSic2E53PfF3VwQ0nwpQvkOB5QFldBGSHgollqmCX/e00hLVfr68vEyCmmNE1zNp7ZyfnycLh3znmNE21Ov1BEQ6Jt2tyWUDCvxWq5Wiq7nW3G63MRqNsLOzg/39fUynUwyHQ3zve9/Dzs4OTk9P8dOf/hSff/45/uIv/gJffvkl5vM52u12ijhuNpu4vLxEr9dLfUg+0ConT1ShYZ+ph4L9QOWkXq+vWHF8X5VDBRFX6NTbEil9kVeBtDGaWr0FBE6OGy4bUGkbDoepzQBWov8VqGnlU+nhTgGOeyoGXGbo9/totVorCp1Gt3P/9eHhYYp61vnDMe/bcJRvOtd0zHlShSkyfKpkeJRUFpB3Lu9dWch5FLUPVQbkADdyYd+HZuXTfdKDAt2oUSpENTLO33MtSSdOpD3dZelGmo8OsI/Jk6u/qt0AVgSoW8tR0gniA1ItRrUUGDyl19bRutDThZhms9mKRQGsbs1Q+hSo1FpUpSSaJC4ktY/9f31Pg2ZckXL3oNNMYNDDJLjdh2uvkaVJAcy9qo1GA9PpNNHFgx+4Zsc9vNxSQiuXh2usra3h6uoKvASEoEiLmFaNBqyU5e2atAYdUaB7X3a73dRWbv0qy1vrnLQVxfvgqW63i1//+td4+fIlfv7zn+P169c4ODhIQHt1dYXT01McHx+j1WqtWGPkLSOldQ80xwbbxGhsPdGs2WxiZ2dnpe28r1itRQUuKhnki65tsp20qMkjzg2mSNnVvcj81H3HnU4Hw+EQo9EonUzGNW4qEOwDBVJuGWMkOSPBa7Va6mvd40taBoPByro2xxIP1qjVanjz5k3aT895F8kiBTUdV7omruNKFXrm1zL5mSs3MhaUHo73jMviAAAgAElEQVRlLxPACg6wTffxaDrg6mfkTnflLQJlLVOXMqrSgwLdSJuIgNJBlvm843KWptZVpZ1FebQup/k+eXIg7B0O3AIkO1XBzwczsLrOqW49HSDkG61bWicKRDy8gUJLgYr7CgGkTwpQCjtfb1WNvkojVSHsfe7WPZUGt0Ld4uE40MkZ8ZoClIKfZxbzMAqevEQvgI5NHoJAoUzrS6/1o2VDgcLj/obDYQIj0sj/z87OcHFxkdz/tJxIh7qm6Yngu/P5HP1+P40B3SO6WCyS67PZbGI8HuPk5ATX19dpfZ/tvrq6wpdffomXL1/i1atXePfuXbKeeIoUo2jPzs7SqUqz2QyDwSBtIeO+ZfaVgj49BFRk+v0+ptNp2qZGb0yn00mWHNeSAaQtXfx0BYTArPKAihXHA8cy+1S9YWrJer83m020Wi0Mh0P0+/10tzLdyuwPrm9zrNGIKMsyRaNPJpMUO8H5ojdVtdttXFxc4PT0NI0f7rcej8cp0IzHSFL54dY/1q1gSKBwYOW8cOVO57QDayT7vDx/7jJXlWn2g8oI4MMtlB5lzHZFRo/XpTIlktMOuN5GB3M3CKP0oEDXk3eeMkBdUuz4qvUBBzlNd+WJQDV6P5fHB1yklUVl5erJKSf8JHCpi1g1eq5dttvtDwKc6GbmRKYrT/ffAreBVcp/1uv9o0m3lqhiwO8q7NRKp+BiopBgv+teUH2mQKf8Ube3uofb7TYGgwG2t7cT0PHkq7Ozs1QmhbG2S9tPnrJ+RuXe3NxgOBziyZMn+MEPfoB+v4/JZJJcgUwbGxvY3NxMlhQjzDmu9ZIBPWiD/az/M+hND6sg8BGI9dJ6KgXHx8eYTCY4OzvD+fk5lsslnj9/nnjH8cA+nc/nODg4wHA4xNbWVtpryv52IartoNKjyo+OHYLO3t5eWgIgz3gMKelgmWrx6vgk6KqFTItS55aue6uCx/d5glu/30/nYDO/Krtsj0ZL6/zQfdm+e0D/6JEg4BPkOZcvLi5QlmXaBkYXPy1egjvnswb7cY6ofOG80jGdU5jZlpzsq5KJ+psCqyrqXpYaXg6uKkciJd8VCM/LPJGlrO8r8Cp23JUeDOhWEeyMcOBSt6X+Frkvtb6cBesAGnVY9D5pjZ5FnaTlKdDkovmcBm23a2lqGeofcAu6tFR0kvFEqna7ndyhPHGJxwdSuKn7h38eFci+0sHtNHl+7V/dVkVh4RcquBYKfLi32ftRA3l0PY4uwp2dnXS5wdXVVTr1hzQScHXbkq6/8Xg+/k6+1ut17O/v49NPP8WLFy/SgRdXV1dJySFvKTwJjOxXVXjY1pzlwS0kPFyDbVZXubqgGVhHy5nRzPV6HZubm3j69Gla32SgF7e18Jzg09NTnJ6eYn9/P7V/fX09BZyp96TVaq0AHmmnkkcr/eLiIgHY3t4e+v0+tra28O7du7S1ZjabrbiVHTjIEz3tqizLdAkExxH7VAOd+DuV0s3NTezt7WF3dze54FkerVsFULqgVTEgP+r1ejrwRM9ZJoDriWUsjxa27hnmPNrY2MBoNMLW1lZypS+Xy3RIC9ur881lj8s0n7+5dB/QuStxTkfluRx1IFRjxg0zlY/a32ope1luxCkNDspVfNH0YEBXJxwbxIZ6Yx0Y+SxyJ0QMj4Cz6llVh+Xej9zbuTVkt7y08zyf/u6uUvJQJw61dT34gS5U5iOYcLIyGIbAQUHAScnBzAlNwa0WAhUePWpR+0SFRaT0qKWuPHdXkpapSlak/Wo9FFIqEOnGZeRprXZ7oPxkMknCioKXpzdRcPK79wmjeukC3draSmB7dnaWtt68ffsWs9ks3a3Lc3jZ17RG6Rb2rTDsD4LVYrFIZ0FrwA2tvLIsVy6jb7VaGAwGabmBYE3XKKPb2T6u1bIc7qs9OzvDeDzGZDJJ24holTN5BL16PfQeXh3X4/E4KQVc6+10Oikg7OzsDNPpFGdnZ2nsciwASHtrySsVtACSO5vj3GUP11G3t7exu7ubrNter5cA3+UEAZy851zxPcEEfypb5NvNzU1a4yWvFotF8o6UZYn5fJ7Oa6ZXaLlcotfr4dNPP03j7ujoCEdHR3j37t3KXmqNnVDvjCvSkfKq46Equeyqeu6eQH3uctnpUus3t0bvQa6RV87Lr3oW5alKDwZ0gdX1WwUiZ7ynCISZHOiqQFW/R2sCLD+ytu+jBeU6sGrBn2VW8cp/V+uQmj7LpUuRAojlEBja7XayfincarUaLi4uVgKNgFtwVeuVoMx6AaysU7E+/VMAZXt0m0WOx2q9VClMpEf/Zx3MNxgMsLW1hZ2dHQyHwyT8abXo8XvkFy1kApmul/HkKFpo5Pl8PsfJyQmm0ykODg5QlmW6G5VreJPJJEWg0j3Ldb3FYpGiznu9HjqdzsqpUDzJieuudBFzve//p+5NfiPLsjS/7xnn2QaaGSenTxWRWVGZWVBl1gTVQoBW0qZ3vWuoBQG9kRYCtFBDf0GvBPRKQAFaqAEBkgAJkBbaCAK00EJaZFehKlEZWRkRTjqdo9FmTk7SaFowfoef3XhG98iMyKQuQJA0e/bsvjucc77vDNcjiQuF+/q9Xl6QNBP6weey7D6y9urqKhA0yE9SKGioXs58LRaLkhRGHte7wHcFR65ylmVRAAQ/89TUlC4uLuL5+P5SqaTFxcU4Cavdbuvs7Ez9fl9XV1cjaUap4cgYuwvFBTK+fUozVqtV1et1LS0thRFBuU/mxlkm1gzM0sTERDBIzBfPdH19rZmZGa2srITx22q14v7ck3UCXcxvfkhZgvqmrvXx8bF2dnZ0cXERRVE8fsKVFMo/T758SB7/ts1ZD+YpTyamMp9rUkOBz7lrK08Wj1OujxkJj10zrj0ppfvYA6bo77H//T4p7OfvcdRAiqxSxZui2DxqIbWmxj0r9xnnp07HIP18+neq2NicUFQu/D3y03+jRNMN7ZtdegjIcKsSgZ6OvyNt+pu3aVzpSt9MUPf5HGeM8dz+vs8B/QNtUHmqWq2qWq2qXC7H4eEoVFfS9MufD6TGc4Ky+v1+CE0X9HyG+rwcQDAzM6NerxcFIgaDQUSLQ+PyWemhEAj1oBE23W5XJycnmpmZiQApkDlKHMXKfKPsms1mrEvGi7KVXAMLkhowKI4sy6LAButlaWkpaFXG1Ok8p+mZn+FwGGUzS6WS1tbW1Ol0dHBwoGazqaurq5HTmWq1mm5ubrSysqKzszOdnZ1FXWnPPU/jATyOgbFxo4L1Ua/XI6gry7LwJ3tdcqeU/VhCX8f+XaBW0oQWFhZUq9X07NmzGIfT09NYk8wBcQYY0RhMlILE6CgWizH21NsGUVNJjLXEHkyVMHP/bRHdb9qc4UxlG397cyOcuU3RLK/5mk0//9hrqTE/7hqU/mPtSSldafRBfFOmiHDc51JkmlIVqVLNCwUfp1S5xhVl3kT459PXXMGmCnvconpsstP3UIL89r5AWXKc2nD4UCsWgY2l76fjEN0IRejCicUM+nXBCT3G/DF2KIv0qLR0ztOx4X8+63MvjaYGeXCW3zudw2KxqK2tLT179kzlcjnoORQE40K/vS88F+PqgTAgmE6nM3IEIt9LaoekULzkd0L/eSQyimNqakqXl5dqtVpRLhFqmKjhq6srNZtNTU9Pq9/vh/ICaU1OTo4cF4fiwefMWmGuQej49lE2w+Ew1ov04G64urpSv99Xu93W6elp+Ma5n4/bYDCIEqQ0jza+ubmJSHsMRQqYdDodnZ2dxVpgLXMmLWOXZVkUnWBMz87OQln6IRZLS0sjpRmhemGAGF8MDGc+nD26uLiI58QoY7xYXzBF+FoHg0Gc20wQFEVr/PhFfOkec+Ey5eLiQoeHhyPru1KpxDygvHd2dtTpdEZkDvPCHkmNozxZ5HLqN2l5cs/3vcsPfy1P6XEf77u70fwZeD/vXnlsWerOy7smBQ557UkpXZ9IBov/eT8NmkmV6biBSBVWqhxTwT2OEk6tqHHP4O85leWW1mPWnF83bhLzFiv9daubjcSJJYuLi+GLY6yh9hCmCAH6SAQmAoSF7H5Wp9tAx27VM65eS9j7S/8RLm6keJQxQSgINNYFn5Ee2IYUYTJu+C+fPXum7e1tVatVTUxMhHLBR0ZAEcgipUhT1AQ6RWne3d2F4Kd/7gufn58PX+Xi4mIgOyhUFAPFOgqFgvr9vvb39/X+/Xutr69HutLKykp8BlqVVBEox8XFRUmKo+S63a4KhYKq1aoqlYqKxWIwHQh0P2gB3yXz7mk37JPLy8tIQ1pcXNTt7W0E9aCg3ABB4UPNuvFycXExwiwwTlNTU+FLzTOwi8VizBsGEhQt/mc/yYkxXllZUblcjsh+kCVHKfb7fV1eXqpQKERaFZ8HIXukPfsGoxTjFYVJKU5YF09Dmpyc1NnZmUqlUsQW0F+C3BYXF1UsFqO4CvnlBL/V6/Uo8UmkMwwAv1Mq1mWPR7uPY5Y+pGQ+1NL7pfcepxRTpsz/djnscsTlfEozj9MTeXonr2+pUh7XnozSdeUpfbOSFNaLC19eT1FwipA+xjoZN9jp7zyrximFdALy+pX3Wt4Cch9F+jz89u9Oc2rdukPJlMvloAsljZws44UvPMAKgYRfirlAydIfT7FAgblSdZ8n1j6bG6WGkub5XInSF8bFDRv6TH+gDD03FYFPEM7W1pa2t7e1srIStLCkEaGXZVm8DpLyKFXPvyVwCQWF8cCzYHTR/8FgoHa7LeneJ1qr1aLmMz5KIlqZGwTqwcGBut2u3r9/r7W1tQiOIwUJxYjimZ+f1/LycpzS02w21Wq1dHV1penpaT1//lwvX77U7Oyszs/P1Ww2Qznx46dP5RmXXjcaerPf76tQKESVKjfahsNh5N/6wewobuaN8WfO3Yicn5+P7/fAOxQF7hFQJHuhWCzGXID8PHIbY4mApUajEf55xpmiKcgFD1bEUAWh+94GeeP3xlBCcYJIC4VCpCO12+1QvNwDlwPrEAVPgN75+bkODg60uLioly9fRtzG9va2+v1+RH03m82QHcR9+H5jTlO5+9sqW29+rzxZlyq0PCDm8tZbeo00atB/CMWOY0TH6ZIPtSejdFMLYdxDuTLLszxciY2jf9P7+/951/j902v8HnkT6EIiNSgcyef1N2+C0zFDePt3cS9XYkRZUugBP5MH73hNXEkj0ZQI7rT/+AM9QIdKPERdIoxAMuRQ0lfQQWqI8B0pqyE9RIL6RhsOh6FspYdIWILBsiyLyNOtrS2trq6qVCrF8w8Gg8hv9HNQQV0+5hgZ/r9T59CuPKcjKnyLtH6/L+leaXCy0czMTCBTlDm+3larFX5jaGNyeufm5kLpIyygSGdnZ3V7e6vj42Pt7+/r+PhYt7e3Wl1d1dbWll6+fKnBYKDj42MdHh4GReyoF2PDEYMLRhQcSL3X64USJfobBZFGtqOk+Bs6+O7uLoqESAq3BRG4rHE+zxqmnyg1WBzpgcKmMU+gdKdvvWgF+4gCFM6y+L6ADfHnc1bm6upK5+fnOj8/183NTeybWq2mSqWixcXFCLpaW1sLJYmCRKkTFLW6uhrGGsi83+/r5OREi4uLYcQRqPXs2TO12+1I8+r1emFIsw8dUHgKVp5772MQ3sc2l2H0IQUf3lLZ6i11SY4DceNQbKpwfd2nwMpdWo+1J6N0P6a54nWr2BHnOGSap0BpqXJLr0nvlaJSnwhvTJinB6Qol/v6T/r96WfcX+GKyCkslOrk5KSKxWJsSJAOwhP/UJoHiHJAYEPpYaV74I4rRmisubk5nZ2dqdFoqNVqhSKQHiJGEdTk3nqBC994PtY0BCrC1q9BSHBvvq9UKunZs2fa2tpStVqVdI8wqeZDfjLH6Q0GgxCKLtxpoGinm/EjQnum64g5dD94Sj9PTEwEwsEXSvlISgoiLNvtdvgbYSPwwTLeKV1JXi251+QQo6jx2/qRco6aGePUtw+yHA6HkduMz/Xs7EwrKytRcpQx5rOMC8iRtc0aZR9hONAfkDcKlzVK35hXSUGPEy2c7lvmF2OL58SoIZ0Kupj+u7+fOfQ1yb0YI6KRGR+POF5ZWYnPsPdqtVoYLQTKYbD0ej01m02tra0F+l5YWAimgfN5j46OdHl5Gf7uarWqFy9e6OzsTN1uV8PhMIy/VO64vEljMH7T5gou7z0UWspa0vI+53LWr0tRaKo4xynlVN/4Na6EU0r+QwbIk1O6eRaHt9S6cGuM5oPlgi9VyCm9myLrvN95yNfvxXtZ9kAnujJ2RUtfxyHucWg57QPKxQVBlj2cCUt1IBQln7m7u9Pl5WXkNbKR/Ug/NjJRrNJ9Pi/f4wIQRcKpNWtra1pdXdXp6alarVZUEOI5QDW+cBGwjnTd4kzpZa7152IcoS1Jn1hfX4/CCtI9YsTCp3wfwV0IRk8h8e/yOrz8jS+01+uFUMe4QBmB9KGqQdDv378P5IxiYqxBPb1eb4S+z7J76rvT6YSwlUZTLlwZSYoiDJJULBZDgXhx/NPTUx0eHo5Qnwg0R3XuLmA9ubJ5//69+v1+KHuuwTDAaGNuUTawDO6HA9WDCrkX65Q4AuYedoM4hIWFhUCB/X4/grDc/eJrjeuXl5c1Ozur5eXlcJmgAN24ZQ4ljawH6SHqnbV7cXGhVqulRqMRgXZOVUPPc5+JiQltbm7Gd+3t7anVasW6vLy8VKfTiYIu+MfJLe90Omo2m7HWeK6NjY0wwlijXjfcfdXjZNNv2/JkMt8tfbPEo/chdSWmcjFP9rvcdT2R6ppUR3yIWk71z2PtyShdHxBaXufz0OY41Jp3vQ9Uioylb+9g5z556S3eH38mv59/LjU4uB6hlD4n90FYeLpCoXAf6FEulyOPEaucRoAMNBrfB/JFuK+srATCItIUhe39gM4FMRHksbW1FQUg8EUyDk5VSg91dF2IY1TwPfiupAcDgZQGDIuFhQWtrq7G89dqtTgq7v3792FoZFkWAWYYFzyDjyWlGPn+1FoGiYFeQLoU2Tg/P49IcJCeb+wsy+IzExMTgQRRbowzPmP32xMxjM80LciwsLAQ1L4HeLFuULbD4TDoylarFYjQlQb5xO63dGHJ2nRKPMuy8F/CJHCq0c3NzTdyd1NkwrOgWFmDrAlJQenTUKbkH2PM4EMulUojJ/C4m4b1RoASNYxJD8IYc4Pa1yhjg1HlxiX0davVirQnDJHFxUWVy2Wtrq7G+uCZOMmJQiBuPOF7Z80ytqRttdvtCLLKskz1el2Tk5Mql8t68eKFrq6uAvEyv8wF45KixN9W6ebJ8dS15EAjpZd5zfvi69BBFHsKWZq6Fbylytn1gPc973ceAMxrT0bppsrIX3+sjXtA34zj0GxK2/r16ed8svPey5uAx/qXGgveuHdq9XlLLTA2CgbD/Py8KpWKSqVS+MAcBSOsoX2hwLg31xANSYL9wsKC5ufnI3gD4cf9seSbzaYmJu4PR+ds2kqlonq9HpVxKHuIkk6NEZQbFLH0oJT5H0WIkYE/FkpwdXU1EO/k5GQg+6urK01OTkbf5ufnR8bRNy9oEfTkihmBSeR3v98PpItCgfYtFAqh9JxK91QQXq9UKiOKnvfSje1KO7W2ed3rM1PhCNqY51lYWNBwONTZ2ZlarZb6/X4gR8bbg91gJLzqkws56d4goh4wpSHTMqPQwMyxlyXFkPEodMbC04dY117MAWPNA8owVli3GKLMtacAuRFJpC+GGtd4/IDvrZR1Yd1eX19HYNbJyUlELc/Pz6tYLEY0NOVI6QM+V+bbC60wp6y3iYn7kqOgZdLWfJwwPsltxrgC8RKg5/vBlVD62m/a0vWaJ+fyAIm/7u/lgTHmw9Gsr9E8g3FcX/PkdWooOvsyrj0ZpZuHdP31cejWBXDeAKSWSqqM8yiKcZ93dJNa43n9fuw97vPY57h/mtbk90C4eGrN7Oxs0KlUBMI36XVzSfnwFA2EBTQrSpf8wWKxGLTd1dVVVOwZDofhy6RWLGfH3t7eRp82NzfV6/V0fHysdrsdwtgLvqNgXOAihKG4h8MH6hT6D38ylj40Js9LgIykoB35vG9GECbrCqWOwE7XCP2lAlW/3w8UlW5qKERf245KJYUfb2trS7VaLfrsxTnc5wkC5H18nXkIBQTa6XTU7Xbj+T2IyUtDsr6yLAuhjmEAmnSmhYbCQ3hjjFxdXcVnMN58PNzNAEKBHeCZfL/DbBCr4AiIezJG9A8XB0ocWtj3H/MJKwMLQZ9YV6wB93WzhpkzgsZ6vZ5OT091cHCgRqMRx/fhTri5udHh4aEuLy9VKpUiSG4wGIQx5GPId7HX2GcYkgRqeeTz7e1tBAsS0b6+vh7BhLA0nhrmGSMuj3/Tlsrj9J6uvFLk62s5vcc4QyAFVHko2Oljv6c/fwq+xl3zofZklO44pMt7LLD04dyqdKrPfXt5g/WYUk4pw3TR5SHktO8fomP8/XGLiOa+4XHKX7pfVHNzc9ra2tLz58+1vr6uiYkJdbvdiExECBEk5JvL7w0FyeKfmLhPtCdthHN43Y/sNKgrYg8c4axP8kKht/nhen6DgEAr3k8/Ug3KkWP0PDrVqyg5Ve80LOPuxR64BlrS74eBwJzc3NyEEQHqgLbHgAGheMATVLOnfJRKJb18+VJbW1taWFiI8fAIUlAmfYZuJNgKJIiy8X3iewM0nmWZ5ufnQ6lSycnRJEFJGIKsQdaIr0Ufl8nJyTBEMOAYe484hr6WRgP6nNJmn/CDkckzuuLG2MLPzPOyLl0Iu4/Z0bEzBn40n7sYXHD7mGTZvZsD33ar1dLx8XEoXFwiVALr9Xp6+/at5ufntbi4GCVJcYlcXFyEy8XT1liTIHkMypWVFRWLxVgbjNHh4WEEVsJEEYvQ6/XCSHG/tSNGZyJYT6mM/VBLle04hJoq91Se592XltcnR72psnSWw/VEnt5JAR6y4GPak1G6bvE/Ntg+QL65UmXqk5dnWbkV5Ytk3He4ZZkGF/C59HnSBZIuLtpjiHncOPiYoSCnp6dVr9f16tUrbW9va2lpSefn56GEPAAFVOpRw2wkFADKhevJ14TC5bslBd2MUPagFL4DvzGBLfjMoLtcsd7c3MRzgs68v/ST/E4QZ5p65D4pgsHcT5yiRh9fj4zmPhgDvjmh5vr9fvj9+EG5oFjoAyk8oHOPQGZsoSE7nU4cK5iWUcQwYQ1wf/5Pm1eFwk85GAy0tLSkarUaByz0+/2RSGQ+63vH1zHfhYAG7aE0Qbq9Xi8KSqR+beaL+7H23XhwFmZiYmLkXGHu5/vE598rjblPOvXppojK9yl0ONexn+g7Riy0Mdc0m82IaWg2m4FMJyYmIhebMaaPZA1AOXvVKlwPzBFuA5gFXEzsEQw3EPPp6WmkPc3OzmplZUX1el3b29tBS5+cnIyU+8wzzB3tj5N1acuTlXm/P+baca/lfZ5+pnPM3w6gUmCWyv088MX++1B7MkrXER//p4OZh0zzEGqqFNMJGmdZ+TX+njQaXp7e11/Pe668397n1Drz3+PuR8MSnZub0/r6uj799FN98sknKpVKYam6tY9gcAXmzwPSAtWiTDzqs1wuh9LzBHN/Rqxz0ieckgZ14uPygJWFhYVAgdJDsJcHfaEESO3geRCiCC6ErPQgzFCkjG9KjWIwIJQZX8669U3H+iCNA+bAaxMzRzyD08HT09NxX8YJFAaCw0Dw+r7u43b0RmoUVCs+Shqf9UAmaM+ZmRkVi0VtbGyoXq/HHJ6enkbAnAsW933nCTaekzHCTdHtdiOlzP3zzBspOyBexsbXlwtO5pT+MiaOznw/gVpQNnyfK3RXKqwNvsv3uY+Bs0IYWdK9XxuF+/btWx0eHsZhA3wWoyDLspHDGdrtdhgTGLq+36SHNEH2Ur/f1+3tbRh2i4uLwbxgMFJgpdVqReyDJK2srEROMAY2a4v14yyJy+JxFK2P/VNorEcHUo8h3ZTtTPVN3u8PtSejdKVv+m9TizO1MPOUaZ4Pdlx7zBrzhZ3SB3nWtE9Y+r0paua19Lm5Z96C8O9LFfj09LTK5bK2t7f1+vVr1Wq1OG2l0WhEBCflE12JOSIioKZer2tra0vD4f0JOHt7e9rf31en04ni85TKA5Wh4H1RY3Vz4Pb8/PyIAEZJHRwcRGAHygAr3f25HgUK/cX4eV1hlDwbxREMig+Bi4Bmg4FIEYj8BpXQH+adfuDLxefmudB8B2vJfcOTk5PqdrtBkYJcVldXA3liJNC/dD2hsBg/KF3WMawGFDkIB/86SgC/vweXzc7O6uDgIOhQ+uHK0desu0J8v/opRFDl+OhZI370HmPk7gBHxSh/3sPH6lQ6jdfd/cGYuYL1PexK0Zkb9p37GqWHQCoMxru7+wIYnOzz5ZdfRlUrvgdfL8/j65V+YqC9f/8+FLCk2G8YX7Aw1MWGRSFKm3KazBkGULVajTU3MzOjSqUS65iAKjc43W3D/6m7z2XTh+Ts77KN64fLcZ/TPGo5BWF5sv1D7UkpXVc2/vCpdeL0Fp/z61L0+tj7qSIbRxuMQ6Qs/Lz3xyHYPNSb0h7p++kY+QIBoVC0//b2VicnJ3rz5k3kdpKD6n1FAbF4oPyKxWIcC0bwERGt+KUQzKTbQAEinBBsCE9OayF1hY1PsAfFGPiN8MIHjAIDmWdZFta/18kl2hPEgC/bI3g9GMhRrfuEoV7dZ+V5kz5+TidDyUsKpUB/U8qchlHE73q9HukcMzMzEWmMAEiNMFAxLAEKfzgcRnDX8vJyMCKUGaQOc7/f19HRUQS0nZ+fh9+VIB/6j3HDmPnedPqOZ3YfOwYKPkb67pWdHD2DPtOAMOaQ8Uz3KOPBXKGg3V/vaN2pcPfT83mn1fkejy/gPSjh6elptdttvX37Vm/evNFXX32lo6OjSBtjrJ/pzEoAACAASURBVHwNMF4gd59DR6r42VGevp4w/kjNmpiYiApavj49l5znILqfNL9qtRpHJMKK0Uf2h8+Vj78bRymgGAeGftdKmX6mQI2++Fzn+az9Nb/G18649qSUrvTNyDbpmxFzqbJMFV6eIk2VdrpQ/DtZ1ClqTpV5OimptZfnM/bncWHP/aQHysgt3lQZc+3MzIzq9bpevnyp9fX1SIonqId0n1KpFMIyTb+gD7Ozs1EqEnoKpY5/+OLiInyN0JSLi4sxFiiZQqEQuYEIBFIXELYU6l9YWIg8Q9AvEabtdnsEMUGPbmxsxFFl0KScvuN+avejMlcubP2+7vvGWPAoWqcZmQ/SLVD0LqDodxp05X0ARaNQV1dXtb29rc8++0xbW1sRCd7v90dQF/dFoXhgFgwDiot+k0aGoQQ6rFQqWlhY0OHhoXZ3d9VoNAKlEfmK8bKwsBBGC4aAo2rG3CPDGVPySXEpLC8vx+vklzI2Hk2PYcN+doTH9+HKcN+yB2uhFGkeEe4GCp+VHlwSHjEvKRSnv8ZhAnd3d+r3+9rd3dWvfvUr7ezs6OjoKE7scno2y7KR6HHodQwwKoN54B6uIld4vsa85jepWdDHpA+Rp95oNCKgkT3Cfl9dXVW3242jJlkDrNNUKaUGl8s/l3u/75bHSDqrmcr61HWYKuYUBHoswrj2pJRunrLN+z/9zGMTm4d689CsI8h0oFOlx+ed73cFmdIUeRQFE58aB9KD8kW4jRunqakplctlra+va2NjIwKShsP7XLzV1dUQrL6poGld4KC8sJCx5iVFSg6br9vt6uDgIAoNIGQp0ecWOOOHpY1fymlgLGwE5dTUVOR3QtWiNDgCj2IWpFOQ3gF9ibCVNFKPl/lxY4f/XQlKDyfEoMRR1K6E7+7uAl2ASFH4HoSR+uFYZwjYu7s7lUolvXjxIlwEk5OTUbWIiHHyM6lDjMHgVa74HlAMCKxWq2l9fV1ZlkURf5Q5CnpyclJXV1c6PT3V8fFxlCikcEO1WtVgMNDp6ana7XaUp+Tzbnw61UpQGdRlv9/XysrKCHrHB+97g8+S0+rCkXuzF5gjqFTmnb9Zm6QD+fpPDWnWgyt31jHf6T52ipk0Gg3t7e3p888/187OThwq4c+GkcS6p9QnRqykUIwwTa58UyPf9xnvocylh1QzIvwJTGy323r37l24G8gCKJfLMebuJ0bxprIyNUDyZJrL0TyZ/LtqKTCSRqnidB3433kA6DdpT0bpPmZV0PIsqjzqIs+KSSfZhV7qi/TrU5+ST1CqcFmIKUL398a11Hhw4eIL1Rc60cqUf5ubm4s6uwhjUIvn/GHVu9BBEOCnZWNSlg8qdzi89/OCejinkyPJHKXTXyKh7+7uoqasR1IzflNT90erLSwsqFqtBnK8urrSxMRElK/j4AXOGPVj2rIsC8SEjxBDhI3j9DIKkHHAhwal6JGtTjW7QQUq8WpRGBWpUPL5A6UivFdXV/XJJ5/o1atXmpqa0tHRkT7//HM1Go0oU1kqlXR7exssgSMmFAz+VL53YuI+J5bKZI1GQ1999ZV+8YtfaG9vLxT++vq6VldXw7BZXV3V7OysqtVqGHaLi4s6OzvTzs6O3r59G8ZRoVCIOUbIwy4wRhMTE5E3urCwEELeA+ec4uVzfqSgpJEAPuaA/cL4g0YxIjFKMDZZn852+A/7QXowXriedYCSnJmZ0cXFhfb39/Xll1/qq6++0t7enjqdTtDCIFj2J8drepU35orfrGV8tKBOR7isMZq7GlgjsCUYtJKiGtbh4eGI7EBZ12q1iDbvdDojaUf445mvNOo/Va4OQFK5nf79fbe8frgc5zVkvzQaQJb2O9U7H/MsT0bppkqJh04tI0cPrhRTKjgPxfq9U1o6fc0t3XHXpEhpXDpR+rdbW3wuNQRSBIAQwFKfn5/X2tqaNjc3Va/Xo1A6VBzP7ooHnxqbl++QFEEaCAcseEmRUsB5raCVw8PD2NiVSmVE+IN2Pe0GQQXSTQ0Nt9IRSJ4DzHggRPC74o/LsiyEh/vomCP6xjwRaYxABgnxN2PHvXzOJQVVikDCB5wiZk/h8ZQaF0jkHM/MzOjg4EC9Xk/v3r1Tu92O4LWtra04hnB/fz8UIEUOqGKUUl6sC9KODg8PtbOzE8FxjBvBOpTNnJycjGpJGDuMRbFYjPKBBGE5tcmaZ/xhBCYmJsKXT81oCq54n5ljBP/Z2VkYmux7Vz4IReIFUspbUsQcYBR5kQk3rvBD0yeuQ+EMh8M4hm8wGKjRaGhnZ0dv3rzRmzdvIiWItY2RwCEWIEvPV/aUKH4IjEOBLi4uRuCbFxlhTaNUMYAxnjnWk2A6xhnKm6IdHLgAg1Aul6N4DYi71+tF/6RR4zqVr3kgwn2ffs1jbOZ32VI94PoE5erGqruG6KcrZddB7tJ4rD0ZpSt905+bKkUfmJSDdxSchranlkiqnJ1i8oXwIauG+0uj1jb3TJV+qvxTSzulvHmdTcv1ExP3hSrq9XrUFAbd9vv98MVxNJrTqyiaNFUIoYNfiZKPnnxPAXyvvHRwcBDF4j2H0S1H9/eyqBGgnnPqY4lV7X5YBCp0uAfFpIwAwggkgG/O55xr8DV6NC1o0ZUov72PUH8esexrBuXrawz62yOlB4OBjo6OYkxA78vLy3r+/LlevXqlcrkcfVhcXFSlUonAN+afKNY0YKjX6+nk5CSK7fsB7vj9qOaFwPYAqOPj42AePOgNRUEBEhCmuzXcB0iQVrfbDUOBeYICZoyg7Sm/6AKN+zHuzrDwvlPKNDfCYECcIXBF6HPOusYYLBTuDxo4OjrSl19+qd3dXR0eHoZyYkwGg/u82mKxGMYLJTddcKfyzgPmoH7dMGb8WV9ecAX2gP3MOpubm4vKVxjFGEzdbldnZ2dxmhjMSLVajRgRlD01n72giO8/3+MuO/nbnztPB6Sy9zdtefdymeysafoc7ElXtv4seSDQGdHH2pNSutL46N50kaaDl4dwfWLTScxDv7ye0sDpPfI+B2L0190flD6bUxHpa6mxwQYELZFPt7a2plKpFKfmcMzX7u6u9vf3NRgM4nxWrPKbm5sQYmw8fIVEvxKxijCbnp4OREK6x3B4f07p8fHxSN/L5fLIKTk+LzwbiA/aLg1yIkrZ2Q8EmdN8nkIxHD7kqxL0Iyne9zxlxhGB6nVnETYoA4RyGq0JbZieuerMC4aGK7hCoRDKDV+e06cHBweqVquan5/X1taWtra2VKlUVCgUdHR0FKjk+vo6gpQ8KAqfNuODECDHGjREbvT19XWkBoFk2+32SF40c0Wq2XA4jPNlQfGMk/s56RP7kfHxs3ZBcxhEuEAGg0EoA44WZC7Zb27UQRk70mZNprnZLmg9ot9dDV7X2GMheL/RaOjt27fa3d3VF198EX53EKvHQxDAtrS0NHJgiJQfLMleSWUHCtcDrkjrwRgg6DHdfy5HCFyk/CV5u71eL+JCJicntbS0pHK5rLOzM52cnISrCqPdjezHWp7ydRmXvvddNP9Op999PbpS9T6lfUyBkq+jFOi5sTSuPRml60oobalV4Q+eh4xTxT1u0Pw1FMzHTny6YMb1+UOfT69xWtmtKgQ+xSvw5ZbL5YhYPT8/jwCXfr8ftCWKE0TmARkoWbeOEXgoCQTI0tLSCLVH4NLBwYEkBX29tramlZWVESThdKpHpWItu7/Vg5d8bt2N4EyAb2BHqPhl/bOOYjGKzs7ORnzC+LZdaUxPTweVLSmEPKifdA6udWsZlEHxD8r7raysqFAoRJAZh9RvbW1F0NLU1JRarZbevn2rRqMR9C70ogfAkS5EP2EREPRZlkXK0M3NjU5OTsLHjsLGN50efed0vSMCngnl6awEa4W5xJDi0In5+flYn8wLbpCLi4uRsoS8DipOAxfdmEKxs3a9LGbKhHiurgtgDC7WyczMTBhXx8fH+vLLL7W/v6/j4+MIMOJZ6QOMEb50CoKwz/leaH1fvy4j3LdYKBTCyAbpSwo3AAd8OL3ua537c+CEH8TQ6XRUqVTCCJqbm4vzd1dXV+MYQtapGxbM7TiEmyf/0uu/j+a6w1lSBwApA8rnmP8U8abMXV5c0GPtyShdpxfzWipo81CwvzdO4aXKm9fyFszHtDyF/rGLKO2zT1z6HkKGcm3ValXLy8vKsiw2AgjWUQhBEtPT0zo7O4vcPAQZyhYhQ0ECnscjPtms5PLhE4KyHA6HQblubW1F+hECEz+d9FA2Ej8gqIOoXM8NRRHiDwY9pgUNnEbH0ECoch3vuw+Q66HaeOZarRb+LTdU6L+XxkThsk6hdR0xUOmLOfGDFkBp19fXKpVKmpmZUafT0fHxsX7961/r6OhINzc3Wl5eVq1Wi+edmpoaGWcUDn5NqEz6hJKkqH632w2FBVpnXp2CnZycDCTnwWaMB2lmpImRRuWBUKzrLLs/6g9GBAEGHXx7e6tWqxXR0YPBINLSHM3CkLjSTKPK6Tvjwbh5tSzWBAYSax0ld3V1pX6/r5OTk/Dd7uzsqNlsxh5xBkZSPA+V1pzFAammimecfON+GHS4F1inV1dXUUOZXHgMJZ6X6/Hx3tzcaHFxMVL5zs7OIqiKgEUC8MrlsiqViprNZhj07XY7xs776q61PDnsSi2P7fuu2jggJOUfduNz4fogjdPhGaUHgORK2VH1uPZklC7WR96C84X5IaXK9eln/e/Ub+qvfcz9/TvG0cT8dgWW9ic1HNK/EQAoXCzOer0eghnk4WXe2PDFYjFyMKH3/LB6R9SSRgqtz83NhSVN/iKKhj4iRCSFDxbf3tnZmer1ulZXV+PEFJQ8hgG0pNf4JUjM6WaUpacZIdz4DMoa+o3rEVYcI8dYojj4bixffJbQtMViMag6lBClMUmfwrfpBSVQQhzZ5qcxnZ6efgN1Oc1Jzdtut6vj42Pt7e2p3+8HJU1lLzY9SNerjd3c3ISRgiIj6pT0H/yE0OQoD3zcUJEYJMPhQwqXsxX4kRcWFsJAIeqc/jBfjOPU1JT6/b7m5+fjwHaiZ6+urtRsNnVxcRHK3RkMhBzrgvXge5Z9R19Ip3IjLmXDnClj73L+7d7ennZ2dvTu3TudnJxEKpDLEBQ88RAUpmAvsWekUaPSjWo3wvJkDalH7vaB/SLy390KGNIYtRcXF2E8Y8x4oBQ0NYFbS0tLurq6ipOJYCa83rf31YMafR78OVgD3se8a76rlipS5myc4k0/k15DPzHUnCH5mP4/GaWbPnT6Xp7yTNGgX0vLG1QoAfezpff7Ni1V1unvcdaPT2r6WuqTxg9Xr9dVLBYDsYD6ULjD4TB8feVyOVISEKaOhFFqhUJhpLoUljJ5uURjUsbP82Hpux/NR44fyfdra2sRYT07OxuRsiAyrHLmyCsN8fzc1w8bcOMEoeXKluAi/MEcqM5nQZYYFaAPxgjls7y8PHL2Kj7gbrerTqcT9OXS0lLkXqIIyZUkUIWcTfqOokaQOesAwvLgsUKhEEiNv4kgx98GOsyyLKh6FCBzvbKyElXHOp2OCoX784ihp30+nQJ2dIuR5f5e1oTPlzMEpI/Rp36/P5L6NDExEfmhpMegVJn7y8vLkUA65lJ6qJfM6xgBBP+4H9IZEN8LGIDv37/XycmJ3r59O5IG5OuQ74SJQeHCPhBkmPYLI4059POs8+ScI0f2Ca4YGDBnO1xpuMFCfweDQZyAhF/+7OxsJFMBIwi5wJpBHkgaWcupDB0nF1MZ+LtqKfr2sU1lir/ustjXTqqU+dyHnunJKF1vKdr9EPp87NpUoWLpeLRZHt3wsYvh21yb93zpZ1NLij75QQRe/BxKt91ux/FgCEr31fo5t17SLcuyoB0nJu4LNXD0WKvVCjSXlqIjUpVnwZdFGwwG4bvrdrtqt9taW1sLQU9ZSASmn/+KkPKgJL6D5kqAe+DTTMcORI7yZQ0wHk41o+hdidCX6enpeHaoeoJKCHLBD4Y/kQAVD1xxVgd/K7Q5KAPDAaEKAqHMJ+sOtDM3NxenzOAjRPifnZ3F/CD8ycOlPOTy8rKKxeIIHUzhC/yBHBJA3z2winXLeKJ4XSE69YjfFqqVNTMcDiOAijnl3p6yg8EMCwHqpC+MMfOOQicgKxW87rtmTzWbTe3s7Ogf//EfdXh4GIc/+LPQBy9HyprEKGFuWWOsYwLLPLArVa700Z/J9y2GGPKAazyVx/3Z6TpkHWAQUuq12+0GG0IQ5erqajA8lIb16lzckzWWJ8fzZN331fIATQpmfB3Qfz6TRz37Z1xZI6tS5J7XnqTSTTvtFokPSt41LLRUkKYUgzu/aSl3/7H9dOvzsedIN7rTYulzcT8ozvX1ddVqtSjbBo2EUmy32zo8PIzKRUTgEtCEYnNBCTW5uLioQqEQKUFsPBAP6AxqkY0GamDcQDf0HUTY6XS0v78fSP3Zs2eq1WoR0cmzewENjxwGLeWlFzH2CHmEmqeNuQDmO7gW4SU9pJN4TjPBZ3d3d5E3jHDq9XojecL0lSA20Booi7Xp+b8Es0ADuqAEDdXr9fCtkYsLMqrVakFhU5ULBURaEWOJr3ViYkKlUkkXFxc6Pj6OUp7Ly8tBTZOPSVCdIybPawYlMYagKIS+06EgYny37sdmbZGby5i7IQX6Z93xPMwv76FkPFgJetmrMnnUNUoRpX94eKg3b97oyy+/1MnJSRizXON7FSqWtYZByjGYzJn7B1PZ4PS4Awc3RBlLV8Cecobgh7HBaHJDgCBBfNykoWEEEZ9xdHQ0ImcozHJ6ehpBgKxt3BZuQLiyylO2joK/r5YqS+mbdLcb2uk8ODDz5xinoD0I9rHnepJKl+YPmSrevOjFvGvGIVe3UlLK4GNaSvU89noepeLWetoXT4fghB7KOpKTiy+u2WzGwdhEMoJy8CtKD7VZ3ZKGPrq7u4toUZAsh47zTGwoBICPe6oIUcyOglFUzWZT79690+bmpp4/fx4BICTlEymKkYBAT9cCiotnceHiFChzy/0IEsIH7I3AI0mh6EG0VIGSFP5KLwjBuuNZ+T4EpaSR3GFPd3GKE0VCJbB6vR4olKMRd3Z2dHV1pUqlEhHsg8HDQQ9XV1eRk0kAEvmV1NMtlUqqVCra2tqKNZeW6AT1OnJB4YKwl5aWRtgKqG1+WO8IeebP/c/4m4kEJ5qcvQtTQyMAi3xnV5p8X6qEQKIYCx4AxO+7u/u6yfv7+9rd3dXOzo5OT0+jDCIR4qwtYidwnfi68eIwrC1nXfhexoQAKz6Dkch+8u/E/+/GFHsOpsEjyRkPxoI9y70pEdrpdNRoNGL/93o9ra6uRt720tJSuFqQLYwnissVairvxiHF76Pl6QxeZx6cRaNfrkhd4aaIned09saR7mPP9SSVbmpRpJZHqkz9obnmMX9Cen8m4jexusZRxeMUsbc0eMypPDbcysqKqtWqNjY2VKlUwtdyd3cXQt3zMjmei0R8/KegDJShf//t7W2UekPgcX/vq6SwmD3q1J8/Ddq5u7sbCRqB6iTVqFwua2NjI/KOOWsVwY8SchrSDSq+h77xTHw+RcQIegQPQojPcC+QEKk8lNIkfxmF66lNLjA9tcYDWXydOZVN7V6MCZRgvV7XxsZG+N8ODg4Cra+srGhjY0Pz8/Pa39+PyFJP5UApnp6eqtfr6YsvvgjjhmIKoDTmCLaBHxekzkiAmqju5MjSg/U80KRQKISy5Bp854PBINgW5oj9zPx7NLrvewwC33MYiOTKLi0tjSBc1gDzTODal19+qTdv3uj4+DgUrq8RFOTCwoJWV1f18uVL1ev1kZKLGF+sD6f2XRG4XIKqZh7ooweAuaJi/Tp1TpQ97/O9vs4ZS5gv4hFqtVowO0Q039zchIvKg8Mo8kHOtc+vKyKfi5Qyd9n427bHAI6PsyvcFPX6vpTGlxD2/5mPPEQ8rj05petWp1snUn7VpjxePS+UO+87/H+/9mMWQWrJjXs/T5GnytkFgPRwQsrk5GQgktXV1fDlSg+Hu/PMqTVKDiQWL2PKZgBtkYspKfw9bqU7rcj3ONph3Lm/H3PnwgaF49HH0NgnJycqlUra2NjQ5uamyuWyyuVyBKWA6p16dUPFN3C6cRDuqaWK39NRNL8RMJICIZKSJT3Qzx556mhXeogMd2FNv9wPypyABJ0xIEL17u4uimKQOkRk+uTkpNrttg4ODnR4eBjlEjEg3NdIHjdjV6lUQgEiMEDwpKPQZwQL69PXiSvF1PBlfpgPaHFfM75/URz4YUG6frACYwj6TY07SaFoOC2LiHjWJPPN2HY6HR0cHGh/f19v377V6elpRKWzthgP6NZKpaL19XWtr6+rXC7H+LAvOSyA2AYf55TO9DXt6xSjxZFkqrB8T/BMTu+nOcese5gFZEepVFKr1VKz2Qy/+sXFRYydn0LGjxfaYb7pG/PyGPD5rlqq0B10pYo99eum9+G9PIU77pr0+x5rT07p0lJlmaJW3mcx5incPMXoqDZFfG45f1ctD+Hyugf9IFxREJOTk5EixGHmXhgdBIj/i2AKqGffkFCu/DA+ToV5ehJCCeRMxLHXdXUB5+gGX5ZHJ0PHOrIg8vrm5v5kltPTUx0eHmpvby9oz83NzUj2lxQ0r48RCtDHkvkjMtOp59Qn5uvJUTpI1e+FXw/DAeWJb9bXkUfGcw9XLJ5u4evOU0GIUobiPDk5iQhTDgtoNpvqdDra29vT0dFR1JJ2JE9hBtBNo9HQ3Nycrq+vYx4Z016vN+L39+d1gyDdN8wBQtfnwpGbo2LcAKxd1h3sDZ/HV8t7KAHG1n3qMAfSQ/lEjz5HGYBAz8/PdXJyov39fR0eHurw8FAnJyehcHku2J2lpSWVSiWtra1FGc7Ly0vt7e2NBIwxpqz9vLxgN9gwuPg8yjZ1AXkgGddyPUFloFfWmSu/QqEQ9clZ1+yPSqUycqgB+bhUqSIwD7RLdDb50L6O3egdh0K/r8a6SXWA080pG8r7jKm70ly2cF16Dfs6D+Sl7cko3VQxpcovtZL8wXnN0W86EG5hIhDdp8r/nrT+fT4rk+XPiOAfDodRyYZoZUrIsZmJruT4NRSk03BsbgQ5Jwj5d4JoGCOES5ZlEbjDiSi87n1nQYLUPIePDcn7XhPYUTJ+Q6rirKys6OjoSAcHB3rx4oW2trYi2Mv93fTfBZn0UCzf+5sqZBd4GCkgONJmKPCA8PX8Vd+AGCoI/5QWpW/8sDHJQc2yh8PeCfYh7YtqQRzDJz2c3Xp5eanT09M4po/IWow4vo8UoYWFBZ2ensY4Ey8A8ucQAoSoCyb6TflJ7ofSc7TjCJ896P5d7oPSI4/ZhbMrJQw5SSPGjD8raTd8BgWJ0iVOwIO4OFP23bt3Ojo60tHRUYwzfnmUFoYOgYxUZ6Oql7sT2Bse6U1jLbgSdBnFM/F51gUGEmOcGnOsI/enu2HKWqUPGNNZluni4iIq03GsH6lRVKHCVQUQaLfbWllZUafTCQreI8h93fyulC739+/mdVe4jnTzULg0WgDDZaPfj9e5Jp3rce3JKN20wyl1nNfyBiFtqa82Xehc41TBd+lnyFt4eX+7/wOamMpP+E/IuUTYUsCejQmdzPPQEEDk9qIUaW6xTU9Px3fjP0ZRu/CEnpIegjsGg4cTWqCa+KG2sQeHIPx84XtOLVRXo9HQy5cvtbGxEQFQftLQcDiM4huOum5ubsI/CG3qNC7GgCMsFC1VvviMbzqUbLqBQVC+phhf/x/DCcFJjjRC1nN18X8zLlzz/v37UKDtdjtoZVdIPBcF7J1i9cpkjD1zRX8wPjzoC+MN9CzpG2jOFaIrQt/LKGsoTjeSXXG5ccaYeVEKmBzQFUYmz0FuOM/NeJ6enqrVagXCPTk5if3kpVJJ1avVaqrVauEjp5Y1ud8wKvTL1777Vh0AuOLlOt5zHyzjRJDc3d1DHWoMdJiRu7u7kRgIxoY94/uc93AHTU3dH62JgXl3dx+A1+12w02BXKhUKiqXy2q325FS6GxhHur7vsGMf0cewvXfNN+TefdJdYXrh3Rff4hWpj0ZpSuNUr/+t/TNZGtaHj3A3+mA+sD7gvCJ8gH8bRcI90v77d/DApUegk6oJlWtVgNRkFuYZVlseKrL+OkjbGAvmZhlWQRBgHY5eszRLSlGRBM7QsPq92Aj90HyXY5+2aggEZSSCxVPiUAJo+RIwieQ6fr6OspL4pN2H6Fb2JLCZ93v98Ma9+92oSc9KA+QldciRgi535FNB5OAUcF9vX+MofQQZISB4wFyw+FwBNVxD1eKRCpz1BoCk8MC3A+YNz9pWg+IlT55+g/98QIiKEKUtgff8R7FK5wFYez8MATuj6J1hoA9w1hi0LAuHT3z3dCnrGevykXQD+zA0dFRKFsCzbxYCnWyt7e39eLFC9VqNV1fX+vo6CiKxhQKhSiC4iyFR9ezLplTV7huJLjx46wXjIAXOAGBu6HsUftO87qMcSYLueG0Nv5qovavr6/V7/fDKCTeATDA66DwFFDkAYzvo6V6gP9Tv3LedcyH38uVqitef65xwPBDeuNJKd205SnR9LfTyx+aVEeVrtRTxJmi499mweRZV45EnCLCsmZR8zM/Px/PiuWJYPaSh/xPkAMLDjRFiglJ/gQnQSGXy+XI+fTFinD2gA6EhBesSNEgAVQgLMrQIfBBR34Kj/uKb25u1O12QwkS2frq1SvVarXIf/SgJqgzSu8hiHhe33hexACEA1Xa7/dD0Hs06nA4DBQEGvVDA1z4wAx42hAoGYH+6tUr1et1TU9Px1mp9A1URz4kNCJj7OgddwR5rXw3+8N92l4Ag6j3xcXFCDjCj0pUKvfyuIDhcBh53BT8J40HpQeacgTrbIkHL8JOpBS9G1MoZU/JwSfJ/SgWwQETfD9IsdPpBHvSarXUaDTU3d+6MQAAIABJREFUbDbDsLu6utLExP3JTZubm3r9+rVev36tFy9eaGFhIfy98/PzcYY1RoK7KdwYBOW6fHHZQKyEv8czYvC5kYeyxbjG4Iaix7j0+JYU0ExMTISPlvvf3t5Gzna9Xle73ZYknZ+fBwPkY+spRARd8Yysw3H07XfdHNh43IavHV9z3h9nrNw15O7LNNqZ+ftNdMUHlW6WZc8k/RtJdUlDSX89HA7/dZZlZUn/o6QXknYk/dPhcNjO7p/kX0v6DyVdSPrnw+Hw337oe+hw2txS8qjIlFIep0RT5OqD7a/x+Tyawa/9ts2Rrt/PA1R4HUsZK5JoZfx7BDeA/qCZQJFppKOkSEdhk3FuaqvV0mAwiGoz5XI5UCl0lo9Bel+30nkeUIsvVumhGL4HPXEyj1vh6fhCWSMseR/Uvba2FoXch8NhpDj5IeQ879LSUgQi0X+nwlAGCF785l5ekyAgR46ec4nAhUnh/ihpKL/FxUU9e/ZMP/3pT/XDH/5QpVJJl5eXevfunfb39+OweQJZOGmnWCyqVqtpZWUl8ig5nxa3wczMjPr9/khsAug0jdR2AT8YDEbyL53WJqWE+IL19XXNzMyo2WzGGbIeFcxaRgE6ImX+PLDIjTeMItay08aMpxfj8DFmjeGf9CAxWAECz5rNpo6OjtRsNiO/OcuyoFC3t7f1+vVr/eAHP4hTn9i7Z2dnur29DXrflRxrxGl+9o+nt7nx6m4GGAGMIg6h4H7IA56f+2OYDofDMKI8vc/lEd8H0qVvpBMSP8E6QHE70+UnEBFvwvXuNkoR4ffVXI6nho2zb6mOSOWy95d5kh4YilRfcA//zHeBdG8l/RfD4fDfZlm2JOnnWZb9H5L+uaT/czgc/qssy/6lpH8p6b+U9B9I+uTrnz+X9N98/fuDLU+x+YP65Dki5T2nbvhsanXkWTh51zpq+23aOCPAnxOhD0IjcMrTfkA9foQcmxZhziby01cQUtyf6MtWqxXBGqBb6MkUmfDbNzoN/zDCRtJImgIWNIKPe/NsPD+Icjh8OBfXKUQXbs5KrK+va2lpSZIihxLlhx8TRVIo3J9Je3Z2FkIMBYCg8746GkSISN8s1o6S4D0EKoqE+0HxP3/+XJ999pl+8IMf6NmzZ2FIgp7Ozs6C/mw0GhoMBlpdXdWLFy+0ubmpwWCgdrsdQS5El3OYPd9J/9y3Rz9BkLe3t+r1eioU7qOxOdoNpdvtdpVl97V9X758qR/96Ef64Q9/qJmZGf3qV7/S3/zN3+j9+/c6PDyMNQIdSmUxNy7dP5k21gz0NMqC346M84xqR3Ckmt3d3UWE8sHBgRqNRvw4wiUmYmFhQVtbW/rkk0/0+vVrbW1txSlBpI2x3mGgKOThwIB1xHrwNe3uCAQ2hgJ7kDWJsmOvOnXNd2Do8j0EjWFUs2/dwMUtwh5hH8BOsacwRtgDoOvl5eWo0YxRi1zyoDJk0Pfd8vYmLWUvU6Y0D6w5CHOwl6c/3Nj+GJ3xQaU7HA4PJR1+/Xc/y7JfStqU9E8k/XtfX/bfSfq/dK90/4mkfzO8//b/J8uyYpZl61/f51u19CGcYnKfbIp6fYDzlJ3z8On76bU2Dh/db18A9Dud0PTZ2IwoXAJVPNoW/wp5lNB4rnBR0p6O4UaIR3Wy2T2IAyvYaUEWn6c60Kc0AtypHJ7T6xQjFCklJ91vbBAdPlRHkBhUBK1AAVNd6tmzZyNHmjlliT8R+tVLN2LEgJIphOGVmDBaPJ2KsXA2ADRC/3zj8wxzc3Mql8uRizwYDPTVV19F9DE1rznS7+joSJOTk3rx4oV+8IMf6OXLl5qcnNTBwYFarZY6nY4kjSCPiYmJkapOKMI0QMuf7/r6Ws1mc4RifvbsmTqdjt69exfH8K2trelP/uRP9Id/+Icx9+/evQtlQDR2Wi0MhcCYMH+gMEcNrD8fN9Yh38kzOVOBr9t9rBiq7XZbx8fHUUDk5OQkCongDyeCemlpSWtra3HSFgZQu92O0qjQ/R5g5mkyHl3vyCv1C3p8gAewYQRgRHBwB0ie9QZbAENG7AQGnwMK5B5jiCHG3BE3ANXsOeLIslT5cwgCsSKzs7MjEfTp79+ULfzYhoJ03ZB+t68pZ+7ygF1K0fsaThk//k7p5rz2rXy6WZa9kPTvSPp/JdVNkR7pnn6W7hXynn3s3devjSjdLMv+haR/IUnb29vxkHnNlViKcFMlzAZwFJtOAvf0xZhSwK7EH+vTY4vIJyhVsG79gQagtohWJiLRU0fwrYBgPA/VA6oYC3JVHRVj1WOdYZlCyaaLEiVDoA9KiLFmDNjwkkYEEhQxPrfFxUWtrq6qVqtFcBXomyAXjnVLDYderxevu//1+fPnKpfLyrIsimlIiiIdCC8OAICapV8gO/dv8Z18F2MuaYQeZQ4dSToCRinwzKVSSXd3d9rd3dXR0VFUPyKQ5+7uLvxjr1+/1s9+9jP9wR/8gaamptRoNLS/vx9H36EoOUuV50/74CllKF3GkTlttVrBeoCs3759q0ajofPzc7VaLe3s7MQanZyc1ObmZlDk0JMoANYO/mFcIv7DupQU1CX7w6th+f7BCGMdonig2Qk+JC3o+PhYx8fHoXAJhHr//n34b4mdIPXo4OAgXBqcNkQwngf/sfdYY/TNmRCPROZ5oO6pCrW6uhqpebOzsxHRjXwDuXu0sbNG1WpVxWIxDGZp1LXk8QVumDL+BL6hXOk7Lh7mhH0Nu+IVqnq9XjAHfIe7Fr5Phct6z3OBOehJf6eK1uW6gzx3a3Jv1mJqYH8X9DKdWJT0P0v6z4fDYS9BgcMsy74VFzscDv9a0l9L0k9/+tOhdzxVhC7InQpIEa40evpHHppNUagPUmrtpNcm4xGf9//z/s6jwvjtEbWUq0PZohwLhUKkVhDcglWMdcuPb3AEE+kFfAfBEiiXlC7F4nU/kkdCe4m6lKpxPyebFCve+wlyhWYjOZ8kfA4xB9UjmBF0BPB4ZOfW1lYIAfzHCGTuC51G/jCCGyrZ/8bH60o49QE7ynC0gfADkfF85D6/f/9eR0dH+vzzz3VwcBApVVB8y8vLevXqlX72s5/pxz/+sebn57W3t6fd3V3t7e3p/Pxck5OTUZO7UqmEMk8ZCZSRH9HHd9E4NWZ5eTkqoK2vr+v58+d6+/atDg8P9bd/+7dqNps6ODjQT37yE5VKJf3kJz/R2tpaHAqAa0FSMAetVitiEdJqVyBvF2gIbProgXK+xjCKQPGlUklbW1uqVCrKsizocVBqp9PR6emput2u7u7uImixXC6rVCrF2j45OdHx8XG4akjR8v3i+a8pfYoS9meB+kUhE5/B2vWqa55qhOxh/zGnKDP2IKdQpemALm/oJwYC93BGgsYY8zpz5koIIxbDj/UP6/B9+3Hz2mOAKQ9tu1LOA2xu9HMNY44McJ3DPR9rH6V0syyb0r3C/e+Hw+H/8vXLx9nXtHGWZeuSTr5+fV/SM/v41tevfcz3jAxMCudd4dIc0udBfr/3Y3+PU8ypMeDfm/f51M88Tik7bYaC5dxK/JCee0o5QhAcix2KGOXC92CtorAJ0CDIolQqjdRedYTmm9Q3PZQzG9KVuxtGaVAMCo+/C4VCUMOMEQj4+fPnGg6HIzmURO5ikSNYQPwIk6urK9XrddVqtRFKmc8VCveBVZx8xFx7cYe0TrXTeAgff7YUSZIbinsA9oC0DowMTnNptVrhYwb1Ly4u6tWrV/rLv/xL/fSnP1WlUtHh4aEODg709u1b9Xo9TUxMRGBVtVpVvV7X3NxcjJWvPZ4foeg0eL/f1/T0dESqOmqcmZnRq1evtLOzE3Q3hs5wONSPf/xjbWxsxHhTfOP6+lrtdluNRiPOEW61WnFyEUqL8Uf445/2cUWwuaEDc+BR4yhdxvj29lbtdjuKXsCiUMqTQyTm5+fjN+Nxc3NfI3xmZkYbGxsRB9HtdqNACYaVR6e7v5y94MqWH1eejKXnklO61VGrK2EYK/fDokg9sBGmIPWD+/+eluTpYqB2DFFkh6N8FD3VqXx9pfuEcfkulHDKGvI7leGuLPmc65cU9aa+3fQ1N0xcNvp++07o5ey+x/+tpF8Oh8P/2t763yT9R5L+1de//1d7/T/Lsux/0H0AVXf4kf7ccdA8fS3l49PPp4M8TuH65/K+M08Re/M+pKg875lSi5LF62k++GRduRC922q1IjISNMNvAmXcD+OBTwgrfIvLy8sjB55LDwKA/nLvtM/u5/EAAq5z6xGl4/eCoiNS+O7uLp6/UqmoXq/r4uJCa2trcXg4uZypz5Ao49PTU0kK4TU1NaVSqRRCClQzGAxUqVQiqR9kC7r1c4d5zRuClYATBC/KGn+0owHoWIwkomeplewKd3JyUq9fv9Zf/uVf6i/+4i9Ur9d1fn6u4+NjHRwc6OzsLFwE5XJZ9Xo9DrhA6Lq1DgIvFO7PToVx8DQSp+nxBU9MTOgHP/iBarWaPvvss0gzu7i40O7uru7u7tRut1Wv18OIury8DFR5cnIS6V6ucFjTvm8cgWGAecSppCiJ6QyLG2F8nviAs7OzKO94fHwcpTQLhYKWl5dVLpdVq9VG/L+sA49+diMB3yrFJ9hvHoDImkxrTKMA6GsKJuh7r9eLWtBc7wyWo2r3w6IEWUeeNoYMy0PmjKUjXy9uA/r3uVhYWNBw+HBMIACAbAsMIldgv4s2Tnd8SL7nveeKV/qmwnYk7XPJ/4+1j0G6/66kfybp77Ms+9uvX/uvdK9s/6csy/4TSbuS/unX7/3vuk8X+kL3KUP/8Ud8x8gD+IaUvjlA/sCptZLSxd/m+1IkmyretHnQkPfXmyswt/64L0oXahmqJq25Sh7cYDAIIeFUJ4LBaSiiRxGyXpOWSObBYBCKwpUleYMoSTah+1mdxvIxdD8QGzdlDfDZYUQgcKenp7W2tqb5+Xltbm7GebFv3rxRv9+Pvi0tLUVg2fX1tVqtVggHnhsfLcK6UqlEhHCpVFKn01Gn0wmFTh/v7u4CQTjlzvMgDP3YRKjBLMt0fn6uZrOpdrutSqWiWq0WKGVubk5XV1c6OTkJpSs9UP/ValV/+qd/Gn7c6+vrqKvcbrcjJ7dYLEbADzWAUfb0ifXluausreFwqHq9rs3Nzfh+CkdMT09ra2tLt7e3qtVq+vTTT8OPvLu7G0rt4OBgBJWxloiwhw0B/XgEqCsjhBzjzrqHpoTtwXArFArhJgEZY5iyBy4uLgJtNxqNoL4ZO45E9JrMzB/xDpJ0cXGhk5OT2A/0AwMC5sLRrCux1OD3OAFPr2ENcRYzPlvWWCqLHPV60QpnUhwF8zqf9yDAubm5kcIWt7e3wUxhYMACoVABBvPz8xHJjGvMGZdULqcK6TdBvuOA07ix9tc9Joh1633iPnzWEa+/lqdvxoHGtH1M9PL/LWncnf79nOuHkv7TD35zTvPOu0LzgXgMCf82E5h+9kP3TGmNcdelEyZpRMENh8ORyEUqRiFo8H1SgL1QeDjAwBU+GzCl5LlHukiwUtmUBBS571ZSIJUUxT62YHlmF5K855GdvohBaQgHp62w4L/44ouRwCgOQvDj4KQHhUPfHblygAJHJhLBCsKBTsbAYC3SR0mBtDEoUjRwfX0dUa4nJydRiGF1dVWzs7M6Pj7W4eFh0OOM19bWln7yk5/oz//8z/XixQtlWaZ2u603b95od3c30oeoYkVZwjSFxP30rA2MO2j8mZkZvXz5Un/0R3+kqampiEL+6quvwu/MfVkvMCSkkXS7XbVarRgfN548qt6Dj/IYKldU6Zr2ylYYPOwXil9A4YIOmUciwTHMZmZm4ihJjDkKeuC+cXcKrAlrw1OvGEsHCD6X/Lix5nQzjT3itC7MCMyJl29k/S0vL+vu7i7qQbMW6Ddo23OD2Zu+VtnHsFbMB9+3tLQ0Qu1DaZOaRBpcuVyOAFDm3g10l5n+93eFgvPk9WPIN0WqeXI81Uf+mrsJ3JjJA15pezIVqVKrIfXTukOb93/fzamHtP+01AryCeJ/D93nBBD8syBCUC50DpYoPlkEAkKK0H02lCMMkCgWLhHRad8R1oVCIXzJbFZ+aKkvN93IKCYfF+bU0dDZ2ZmOjo4ir1WSisWiVldX1Ww2dXJyErmJBP2gHDh82wM+QEH4pobDoZaWlrS6uhpGwNXVVQg3kJJvTKdEeTbGG4TifjWemyhT0lYQSERPgxjm5ub07Nkz/dmf/Zn+6q/+Sq9fv1aWZTo9PdXnn3+uX/ziF/rlL3+pXq8XOZGUjqSvrnDTPhLc5ccywhbU6/UoepFlmYrFYozBV199JUna3d3VV199pVar9Q3FyRpkLbuhxbrA8PN9kK5J7sm1XA+CJ+oaJgVXDAgUNwmo7u7uLqJpQWj1el1bW1tR0QwFxZpxihhlzj19bzN+XOOBfm4Yu8GLYnUlgyJjTGdmZgKFFwqFKF+KiwMFubKyomKxGH5lXDKzs7Nh4GAYMQ8uT7nGWSgyJNgD+KnZS+yVfr8fsgoj36tT4cLwI0VTeZn+/btqrljT365ffC2mn+V6WBtaXrzRuPZklG7a2bzBybvu990+hHLzJlh6iCAkIR9hCkXDgiZqmc1L9C0IAjTBQuf+CIHUsY+Qg2IkcAdKEORNeg8oc3p6OhQX9/Zncqo5pWbSfqBoERKg7omJ+xzTN2/eSNIImvBcRqhcUAHnxUJ7klfa7XZD0PHdg8FA1Wo1TnAi6MdPleGsXacSUaaMtys7T8XAqCGCFcFKiopXpZqauj96bnNzU5999pl+9rOf6eXLl5Kkw8ND/fKXv9Tf/d3f6Ve/+pWOj4+VZVkEToFGERSeQsPaQslCURLtTt8xBu7u7gINkmu5s7MTdavxiVKgBCXB2GJQoTjcCE1ZEWerEOpuQLOOUAisAeaPfbG4uBhjDKLDOMLH2u12o7oXkc3b29tRvxvjlIhqN1o8qAhFzPc7evVI8bu7h4PqmReeI31uNzh4j5OMlpaWIlbBq82RdYBiY569eIgbN4wNzA17lus98MsjcV3xODggEwK3kBt00MxenQqmKQ9ouDz6XbVx3+WUsytcR655+omWPtNj3yU9IaX7se0pKV9HsXlIN508AhRY3IVCIWhUPynEUS65fCinlFrmHkQwMz5OG/lrbEx8QCiV8/PzkdqpCDsUfIqcEZTuR2Qs6LsLIHxg9JH+gnIpbnFzcxOpUVjZCIksyyLtJ8uyiEAF+XC+rEegooxcyBEQBDW7vr7+DYveKUGnl6SHCkkgWeg/5sPRjge6ea1alO/s7Kw2Nzf1/PlzTU5O6ujoSKenp9rd3dUvfvEL/frXv9bBwYEuLy8jFxdfJMqUHGPcENKDcvKIWQwGlFK/39fh4WHQ33t7e3FEoBdiwT+L8HdUyGsejOfNqVaMMxf27q9lD0Fhck9QFXOQjj/rNjUoqAmND5/yqhhU/j3uXwXJwKCAZr1P7CXP02WtsU98P6aKh/kBMaV0fLfbVbfblSTNz88HI0EA2/7+vm5uboK1gY3i2Vmz+IbpK/sOpY2SdbnFdaB1ELuzP+52IfOCtb28vDxStjVPTqauqceay9VU2aVj659JX0vlYx4L4AGxDpbGuTbz+vqh9uSUbopq85DS74OaSBsT8qGJ8MlDyThNhmKDekRQg0LJF0X5IFhcAIGYQcYIfRYR9+N/PktKy9XVlTqdTlBMCEkP+oCqTIU59LcvSqcTU4RPo188kytHpytJ0fATdLIsi4IGpKtQyatUKqndbkd9aoLQ0vufnZ2pWCxGlGqlUom+o6A4kIHPQY+D0PKUD+/DGHhuNCXzGHe+6/z8XDs7O9rd3dXNzY0ajYaOj4/19u3boNPn5+cjWrlYLIZwJXUKJUlVKEfpUPWgKUdGvV5PWZZFOgzUvs+h05Zp5DENBeXrwGMyPH7BkaOjXFcAbsxNTU2NHPrB2FM9DIMUZYnhJt2fPVwul7W+vh7H/DEm6Xp0YytPuTji85Q59gPBRm5sOSrme/Ajs2Yw0Ly4DGlrw+Ew5MPk5KQajYb+4R/+QV988YUGg4G2trb0+vVr3d7e53azHkCl3MuVuvuPfU0z/iBkmBLWCus6DcpifgiogvZPFboHx7kB9hiK9DgBlye/TUvpYZdbHlNAnx+7j7NoqYIf156M0s1TYD64boF8G/78+2q+kD6mH271+Q/UjB8Wz2Zg40kayZ3kPindyYZhIaDEof8QWo7cCOAiYAkBzvsIBTanl+3z03dYyGwSLGtJsVH522kbxhB0xUbnOtCA+06npqaichK/pfs8z0qlEv7Sg4MDvXv3LtKSvI/Q9l6bGWUGlQfi5jmhE+kLhgdCE5SMf1p6QL5U6un1eiPuA6LaEdrD4TDQZavV0vX1deT+bm5uRt4oFZ6urq7Cj8/8eK6r084EvszPz4eCTpUH6B9jbzgcxhpkTSI8fc1JD5QwhgjsBmsipTMZ69QQZa48foO1zLN4iheKBIOCtZRl2ciZ1MvLy5qdnR0RlD5XjB9GlAcWoSB9vzvqdYrfKVyaAwZXqO6e4XnJZCDiutvt6osvvtDU1JRarZYODg50enoa93FKGdTNnmWMoOhBuTwHRrYbBWdnZyNGNu4raisjf9zg9Dx15Jjvf2QG3+sGtI+NA5SUlk9bHuod938K2LxP6XVON+fdL+2jr6ePaU9G6ebRs7zOb6fuft8tpTzyBp1r2Kwe8cbi9ypJICKCNE5PT0OJUusU2gcBiEBCgKfVYBgzhLs3j8Tkuz3PlGR9ng2f0vT0dJSx80CVlDKTNNIHxsQpapQMReMpKuHGgQtang3hj4KkfxQ7cL81pwu12+3oT7FYjBQNf34iXGEhCCrh+/CRERHt9BuKCKHHZz0CFX8uFDMUIOjEU5Xu7u6i6MMnn3yiTz/9NPJiOVP55uYm6t/6cyAop6amQgFJCuHotCOCw+v+UneYdKrUr8lzsi89hsCFFshQevA5utDydYIwZp2AHFmPjNXZ2VkYHBTl4DxifLREaUMrQ+0zTswZxoW7ILzYDIaQG1eTk5MjxwbSZ15P2R+naVk7sCTIsrOzM+3t7WliYkL1el3Ly8t69uxZuJjYZ1mWqVqtamJiQu12W9fX12o0GsGUbW5uRoAdSvLy8nKEKveIcvqBwcR+hz3D7cOe85iOVMaxf1C+pMelJVPdaEnBVkr1+vWuI/Lk78eAH2fwMKzyAqdSpezKNaW1/ffH9OHJKN1xVK2j249Flb+Llg78uGucnvLFhGCDFgXhgfKw5FFIabCU+59QdK6A/UQfhJkjXD4D7enpFVzjVZmke6TDxgIJEl3pAj9dxC5M6TuHOaRUI4IAK5hnSBWEo3oEI0FQXomHYCmOPqMCEzQn308QEMKL6EuCbUBZKDGUqaeCoCQ9oMUNBC+MANXrEaPD4UN+5cTERKRgPH/+XC9fvlS1WpWkKMDf6XRGUN7ExH1+KsFnUKlUgup0OhFYhBFH6UgENGMLJcrzMy88Z7oXGUP3E0oPtKQHD6X7GSGf5n0zt9DIWZaFy4BngCGanJwMv3aWZRHZXiqVVK/Xtbq6GvQ+bNHt7f0JSzynK0IKYTiL5EaHyyUPIIM6hvb256GYB0YLpR/Zy8fHxzo/P1e329Vnn32mH/7whxFl3W63w3+NYXF6ehouiOPjY83Pz2t7e1vPnz9XoVCIk5TS9UvUvT+nK1P6hoyCxfEylSgsN7qQX8wLssip+nGgyhUp+97RZipPvg2yTK8dJ7tdDuUp3HRt0tLn+5COejJK9zFrBUGQRkD+PhWw9zVvIfl7qX8U5UioPZSMK4Lz83P1er0QeFioUIlO36UUBxvc6Rm3ECV9ow+SQmg5re0ogO9aWVnR7e1tRLy22+0IuHI/HpvYC0eAFLCOMRaIXvZgJqf1MDqgvUib4jswADz9yQvaDwaDEEAnJycRtU1OJ4IcCx0h5X1CaZ6fn8f4MxeMKxHgCCVXYNI3A9FQmMwNY8OJRKurq9re3tbS0pIuLi50fn6uRqMRKIf8zKWlpUjrcv8mFDlpVhyAgbuC4L3r6+vw74JoHNE7HezNo3mdzXEqGSHm9KxTc+wVRx64XYiCxXDACF1cXFSlUlG1WtVwOAwatVC4L/Xp5yj7EZn89j4S/c58ZlkWlKyjOI95SPccr/n8Qt86HU8Z1uFwOHJSFvni5AtT4GV9fV3r6+txL8pqTkxM6OTkRH//93+vn//85zo5OVGz2Yzgq4WFhZF8cYpVcNIXfWHePDuAOcMI9WAvZ2EwdD262lO5+O2MSN4aSsfTWRLWkFPRqZ74GD2Qyj+Xzf53KrtTdO3X5zWe5bH2ZJSuK9E8S8bpho95sN9lQ0nkCRK3hhFKWJj4Pgg68Ahkp1RBLV5iDQHrApyWUiAsJtAX6STQsY7U3EeHEsXvtby8rLW1Na2urur6+joqAPHcrkDSwg+OeDxy0e/vCldSCEiek7EktQJqHorcNwaCjshmLO9GoxG0sqQwOPC7NpvNCMzi/qVSKZQQqGB+fn4kWpjXfZPSL+YHJeYo0SPSvcLS7OysSqVSnG9L+k6/349yoKBcz1fFxQAVe3JyEhSfRzBTAvT8/Dx845wkRCAS69URXuqP5dmZG57Lc1LzaEJ++zW4Oxh/fLQYVBhv09PTKhaLkaPNmGKMUajBFSzfd319HWvFm8sWZ4x4Fg96ZH2h1DBSPHbA78U4smcd2bvbZGZmRpVKRUtLS+p0Ovr88891d3efzrW1tRU1vRnLpaUltVotvXv3Lqqr7e3tqVaraX19PQxqXEbn5+ex1jiRKs3lZt/6fIH4YXmQETwD8wZ7wuEHHngJA+Ko0GWDG16OGF1+pQDiQy297jG0mqLsvPfTcUlbaoiNa09G6aadzbM4JI1QPb9vxZtaQKlVJD2kbkgPQVcoGChlR6YgDVfE0XXGAAAgAElEQVQ8MzMzcfTX5eXliC8X9Iuycgp5XH8dOQ4GA3U6HR0eHo4E7tAfkNHc3JzW1tb06tUrVatVnZ+fq9PpBPJ2nxhCxWkihCmv854jSVqhUAglyT2hh6WHGr1cCyKan58PhA41yThSTGN5eVmNRkP9fj8Kjjjlh++McaQvGCBQ/QgV0JmnR/E8/J0GB/E8CGkML+4LdQr7AbqjfjBoGqHGD4oCIQttTPnMcrms+fn5QIb4+hCSCHX8gdD1GBv0nx+fL6f7XbC6D1caDSR0qtnXg/uMYVQ4qL1YLKpcLqtarUbkPYoCA6lUKsWawFj1ID9KLd7d3cVBCBw76LWysywL1Ckp5mdubm7ktCTWpStuNySZC48s9pxw1qmXNm02m/r888/185//XJubm/rRj36kH/7wh3r58qVKpZKurq707t07HR4eRlDccDhUr9fT/v5++PUxdjEMzs/PA82CQlGG7qt1lAwdz14lpx+6nD3EmiWX2E8dYgzcAEoZDpC0G2+ukFMF+rHyP4/p8/vlIdr094cQ7rdpT0bpSvrGZnTLmL+fisKVNCKI+J9GHx3hueGAFe7+D4QxwgrLd25uLo6985zJvGhRPuvI0K1p6SEPj+uHw6GWl5e1vb2tUqkUNCZKiYjccrms7e1t1et1nZ2dRelABBmCjcL+KF+oKmk0UIL3PHJSUighUK6XosTSpgjG5eVlIBuEC9YyPnGegTzX5eVl7e/vq9PphPDEeFlZWYnC/4xDqVQKGtapQxdsoAFYC8YcYcvzrKysjLgaiIJmTt1PT1lClKefp0ygEAF4PDNUrKcTnZ6exjqtVCrxvRydmGWZNjY2ND8/r9XV1cgR7Xa74etlbYMS3UikpbQyr/G/722MFDeeUFIoKpgB0BQlDzc2NjQ3NxfIH4VbqVRUqVQiWArDEndBnq+dNUR0/sTERJzaJCmMUU+jIcIfZUmfJyYmvlHsww0PfL1+Fi7rXbqPvj84OAjDiUCxX/3qV9rZ2dFXX32lP/7jP1axWFSv19OXX36p3d3dcJcsLCzo7u4u5g33ihtF5+fnUdrR4yxQLMzv7e19cQ7iAjDo8FnzXIxJlmVR17xYLIbyJVLeo/RT5QcrhZHr68cNd0ehHyuf/V7+un+Xv54ykz4+eeyrzzPr5/839LI0CvFTOO9IId3sv8/mfcyzkFzI8LejGgQ5iNgFMYEMKGanf9OF5EiT/jitjPDyz/Ed6+vrevHiRaCpL774Qm/evIli/AgwKFB8Uvijp6amgmqlTBwn+riPK12QjnqcVgKFpHQcCo3gGoK5siyLtB+PeGVsuR+sAdceHx+HwEKQEAjWbDbDou/3+yOnqGCNLywsqFqthsCmCMNwOAyl1e/3wwgCfRUKhRDAzA3CmrWAL//i4iLmnediDVEow4vT93q9ON2nUCgEEkR5YugNh8Mo5k8hks3NzaBtYQJoTgN6vID727iGNejR+h5wxv/OgjjClR7Sk7Isi7HnRCXWFsFPGCCe+gZ7gfuBe7GeMJAwWkBkPBvz7AYhfn/WoPtq3cj203/Yd+6rdnYLhmIwGEQt606nM1Jwhej3y8tLnZ6eRuQ9RiPrFmTup0Wx5gmQJCefsp9pelveqU3uT5UU7gne99gAiu6kaNcj6JkDN8A9KMvdUS4jXHb9Nkg3pYnz6PQUGadyy10krrPGsYzenpTSlfKrj/jAp8rsKTS3wqQHv4RbmFiWCAOULIosT5ChdL2gABOLMuX+fBcKygUfgsADmkBqFJlYXFwMQULtWjaCl6Ij9w/aGWsWpZFHGfLbcxc9EOP9+/fhW5YUCsQXPuMjPSBfz1Pk+1A8KAiEOYYOfmcXuMfHxxEdOjExMVIjudvtBrLp9XpBVSIo/J740lZWVvT+/XsdHBxob29PzWYz6L0sy0ZQlxthCEB8a0Q2M8+MpQfAUYzg/fv3I+fzDgaDkfq49L/VaqlarUZwEecWg2QIPkJgOlvDmksNOqcN84w/1iyf833DbzfInLmZnZ0NOnl7ezsqKaFEfPz8ezDSHLlhRDKGfs3s7GwYu9C7WZYFKsVoJj2Ovet+To9YZq+yptlDrB/khNOW+MFZ2/THmS+CuYj+x92GkeA1qG9vb8MA7na7sT8wjukX+8yj9t1FxLgyzwAGFO/l5eXIQQfuI8YAgk3IU2QYXy7f0td9LTkK/ZBcZvzzFGvaHqOeP9Rcmfv6z2tPSumOszx4IBfoT6Hl+Qmkh0FHEaTWFWjRrWyP6ANtubJxCxrrG2XMdzpq9HSFtHwdfWJzsgkbjYZ2dnb07t27SNMg2KpUKml2djaULsKQwCpQARYvfUBQTU9PR2UcmgtZEKs/H0hCUgg0rHgUzeXlpbrdbuS9IozdF+TCHbrMFQ+oDz8WKBb/HXQtglBSCD+e4/3794HAKMawtbWljY0NvXnzRkdHR0Flnp2dxfpAgOFPhaojnYUIceZqOBxqZWVFtVpNz54909ramrIsU6PR0O7uro6PjzUcDlWtVuNEIKJk+/1+IKVqtRqRpu/evVOz2QwEjFuj2+0Gte37zxFpinZTFMA15+fnUQWK11EyUL3kNbNeFhcXVavVtL29rVqtplqtptvbWzWbzQgsI+/Zg6MIeuL5CGLySGKYAKjphYWFQIe9Xi/uAe3vfmFcQVmWhQGKT9UVlaemIbec+XEjhvFYXl6OtQ6Dwhz2ej3V63XNzMyoWq1G2dNmsxkFYXiO4+NjlctlVSoVTUxMqNFoxD0bjUYwHuwL9pkbhbzO3KPgyRdmbp0F4SANDHliH9JIfvy8fGcqS1GsbrRz7ccoXDd4kIneUuT6Mffzv3lup7tTcDiuPRmlm4de8+gIhPtTQLou1OmPTwKvuxUH6mLRgzBcKfJZfvO3owg2B8UkfKL9nqBXj1xmM6HsoLT29vZ0eHgYB5ajdLy/oF02DAUIoD6hNPFP0lcXcvTD+8y8sjm8Ig60GD7a8/PzWAsUS2g2m0Ev+3hD7fmmwVonbahUKoVixU+4vLysUqkUhyOAegnqwefFOMAuQNlvb2+rXC7r008/ValU0u7urn7961/r3bt3QQl6gAlKdmFhQRsbG1pcXNTKykoUIcHAGAwGWl9f1+vXr7W9vR2pI2/evNHBwYFub2+1tramFy9eqFQq6eTkRL1eT6enpxoMBjHXlUpFq6urMcYEaV1cXGh5eTkCdprNpjqdTlDWTkc6m4LinZubk/RgJDEmzB0Ins+zNhkH0BDR2xsbG9re3lalUlGhUIg81k6nE6lDCwsLkT5UKpUizYX9mBpgvrYwKonyJbgMw8cVJsiWRmEN7ssYOMvD/vcAIvctez9htlZWVmJMMCwKhUIcFVkul/XixQttbm6qUqno6OhIb9++1dHRUSBYSqGSW7+yshK+4larpWazGWPnKWYekY6CxTB1w4G+M76sAZ6Bezt1n8oA9nAqKxgnj5Z3Jf9taGXulYew86jjj23ILgdWH9uejNJNrQNXYE5x+KQ9peaLxC1eD5hgs3vUsVfHwQ/k1jIWvFOM0gMdxYIGWbp/DMveaUCQFfT17e1tlJbb39/X8fFxWO30l01BWTqQQ7vd1vHxsQaDQSgQUjKg4djMIN5UAKWGAs/rB0BgdWPN80weEOSpR07T8f1extHHBAqzWq0GJdfpdNRutzU7OxvHCmbZ/dm2JycnOj09jZxWnll68IFJ9344gs5IseKkICJxnWlgXKampnRxcaHNzc3I0ZycnBypWlUqlbS+vq6pqSkdHh7qiy++0N7enu7u7lSr1fTixQtVKpWYs1arFd9JeUxJWl1djXxYcpmnp6cDRUv3wvvw8FAHBwfq9/sjaS4YAyhLfqTRM1mzLAv6EdQOc0PKDYbH5OSkisWi1tfXtbm5GcbFzMxM0OdHR0eRb1oul7W5uamtrS09f/5cS0tLEYTWbrcjGNARlht2KAoQGnsSA87LNboC5W+obWff/j/q3qU30ixJ0zN33i/uJJ10Mq4ZmVWZje7qXqjRgIAGZjWA1tpImIEAQQsB8wP0B6SFNlppI0HCALMYadMQBhAkCNBOAwgC1L0QhFajG1VZHVWRGREMBun0G+8Xd9eC+RgfP/ExglmVVcn6AIKk+3c53zl27DV7zY4d+oj+oY8MaBzMc75zqAldZyNvf38/tra2MguZsqAOH0REUuSPHj2aKvoCk3N4eJh74TabzSm94feAneKdOYc5Rh8xL+v1elaywiFA1zHOZcgh4tbJcLKnjeUyqansx/IogfC3BVizNyVoWz7opz8Yerl8IXssEdMD85AoZlMvphyclceks6drygrF4Htg4ZoFKOkLFtHb+7Bg4+3x/DJ+OJncLsex5Y4xwL1QYmQNu3Yv4OH2eFmNKcTS+jcNx3W8Nwo84laJY1lC6QFG3W4341yUvIuIKSAnsQeFf3Jykt7bxsZG0mMHBwcxGAwSROfm5mJnZyfa7XZsbm7G27dvczegiFv6niQVYuLE+ebm5jLppfTE6RuMgMvLy/jHf/zHrPR1fn4eGxsbWcawVrvJEo2IBNxXr17FxcVFbG1txaNHj2J1dTUGg0G8fv06fvGLX8Tbt2+nqoqdnZ1l6cB2uz0VrjAVHXFbMIWyk7SJykiMgUtiAiCMsRUvdK+zf5ExjJzPP/88vvjii3j69Gk8f/4810QPh8Po9XrR7XZjbm4u2u12vHjxIr788sv4/PPPY2dnJ1ZXV2M0uqnuRLnIq6urlHc8M+TRYIdnZgPI78e8wEv2UhoOFyHhPel3gxY6jbmJXNNvJCtOJpP07LnfwcFBvH79Op4+fZrr1ZvNZvbT9fV19Hq9ZC+QH0IFb9++zVDDxsZGGhoYzhgoBj3aYlbDeg+jGr1GtT1COMxBg56z9ct8GC8dcsbzb8Jw3uWJ3vc+JXiX11k33wfcHxToRnxoVfBZVZLOj32UXrgNhJJuRvkjgAihqRq8S4DQtCzn2tgA7NwGJyjZm2OikFkIQJGdyzZupksXFxdjc3Mz1we/ffs23rx5E51OJylkYtNOvnIM0N487+fx450AS/oF65n3xShwfJrrUQ5WcNwTr94GTMT0zknEVrH2SbJi2dRgMEjKbGtrK4EEBR0RWV4R4MU7wMOmBCP/O0ZbhhguLi7i/fv3MRgM4uXLl9FsNmN7ezs2Nzej1WolcA0Gg3j37l2cnZ1lzem5ubnc+u1Xv/pVfPvtt0lnu9+gy4+Pj6cKjBwfH8fu7m4mLCEfZNR6jJ00hNzRp8wPxhHZJxTCfZGjZrMZjx8/jkePHmUpw42NjUxKGwwGsb+/HwcHBxkWwMhgDTusiGnhWu22sIMTCvH6AU5XiUJ2qNKETLmwCXMJr5RcB8CDccUA9Eb0AAoyCUtFiIbP8b7JYodJGAwG8ebNm3j58mUmWE0mN7WzLy8v09jY29uLw8PDrFo2NzeXpTAZ/36/H2tra1OJimaf0CUAcTnv+Lyk8r2m2evgkRnHkJ3sZB3Kvcp23ff4vnHb+96zDCNG3L8EZMQDAt2PNbh0138bquCHPO6iPPy3LTRAgDgK8Q4rfKg7Jj3xFmfoYl2iKJ1ajwUPaDurEuucZRUovsFgkBu5j0ajtJyfPXsW6+vrMTMzk7G0/f393CoPxRxx61FGRCYlkWXp5CoAkAmN8nHdaS+v8lrXiMiJjNIlWcOJF4wL2bc8zwkYJCxRZhAlCYDQhtPT09jd3c31ui4g4WzNer2eIAIAY+ywQwseF4ko9t7t9eJx9Pv9OD4+joODg3j37l3G+xqNxlQ8japbUHjdbje++eabeP36dW5YUGYNY7wQxzT4Yyx4mUcZV0MRR0znCZh65X0AO8IBNg7X1tZia2srnj9/nj8svcIQgTZlzex4PM6t5FguheGIHJyenmaIA5nD2wTckAPvukPc2carl6HhlVKZymEVr83G6GK5DAyBDQIXJuF7DMfxeDxVhQ5ZYbyGw2Hs7++nHiEkQ/lH1mfv7u4mBT0zM5PbYWJwHR4eprHG+Dt8g7xYj/GZQdjyXoKu57/v73v5/xLMys8e2lHO4U+19cGALgNx18GLmdr4MY+qYHxpWdFePC8oNgMtVi3JCoCzl01w77KggJ8LYEOhYv2ixJkI9viIqTmu5nJ3KJZOpxNv3ryJvb29pF1toY7H43wvwMprT13ejz5DybsQhuk/J3vxzigVjAIseIcb8HRMmaJQMEA8wW15u3gFJfOIX0NnX15eRrvdjkajkZmz6+vrsba2lsuD8I5RsGQ1kyTkcII9dcAItsPrRqF5O51Oeu9WbPPz81mWc29vL/b393NtLvfwj+nCMiRhUPbymDIhr2QN6F/k0tQj3i2gTmby06dP47PPPosnT57kDjlQwdQf73a78f79+0zwo9QgniYhBoxBln6xtMred8RNlTdTy34H5tHZ2VkCN+13LNhjU6vVpoxIU9hcT4EP+hWDDwp2c3MzDVXGBaOY/AwMt6urq2SmSHicmZmJ5eXlTLjj3IODgzg4OIhWq5XGbbvdzupzADi7CTkc5VwN51x4JUWZEMb4Mq8pperla2VyFHOgTND7PklTP9aBbPxBerr21kpALS11PvtNDnfSXfcprZW7eHyut4Lx+0RMe8N4etBdLgOJ4kOZ4aE5waRUylZ8/EbZ8BkT3EoHeovJ6a3n7LGcnp5Gt9uN169fx8uXL2Nvby8pX4CS91haWsr1uiRsAMaALp43Y83728oG0FGkXEOSWKPRmNoxxgDhPjPN52xRFAY1hmEToMgjbuLSzWYzd2fh3jaESOJZX19P0G00GvHtt99Gt9tNkD48PIyTk5OpWDLjzJpIy6MVDz9ksHMeninn4kmfnp7GzMzN8hDWpLIMiL60YkRebOjYCEGW7Pk7xOMlGbQdpcrh7yaTm8QbEoB+9rOfxZdffhkvXryIzc3NHAPaMZlMMnmq0+nEYDDIkAWKHJoYWhW5wSByDNWeGXLjxC8MI5KSuK/ji66VjrwxpwF8ksVcFYx5BgiTxOgQAx43xgsGALt4rays5Dj3er04ODiI58+fJ409mdwW/QDgid0+efIkgRkmodvtZjvtoWOAYzQQEgC0+R4DC9mxcQKr5sI67n90I3Lk0I91oXWo9Zxl68fyhksDtqqNVceDAd2PueYMApPaVOtv+iyOMs7ne5YZdT6sTEwV+lqAgsngBCbHdU0fOcMPC71UahG324u5PwBMeyUuHrG0tJTthYKClmPSOT57cHAQ/X4/Xr16Fe/fv89dXNwGV/Kp1+tJma+uruaY0VZAi+u4FwrQ9DqGAP1v8AXQWEeKMkHJehE+dXHpA7YwPDw8zPFyvWPAjEIBKysrUzKAN7yxsZHKnB1vXrx4ET/72c8y2/ebb76Jw8PDjFvaoDJ9Z4sfRcz4l0DhRDvk5PLyMpOcarVaKtGSMQEgkDW3AQVrw8fvfRf42jgwM4OHC8DD1MzOzqZn9/Tp0/jqq69iZ2cnZQSPiEpeUKAseZqfn49msxnr6+tZpQ1PjHW8poO9YxbrUiMiqWfazvrt09PT7CdTyowPRgpZ0XjdnhMzMzPJdDjZrzRoarVabi5B30dEzgEYFkI50M0AOLH3yWQSX3zxRfYH5S2JM5MfALvGhiELCwtpzBwfH6fhMx6PM5EO8ESvMN8xFEp9aHrZJUpdnIakNLN3XEu/RNw6O6akua7KOftdHX5WiVM2jG0cfOx4MKDro4pCdSr2fd34u+7tw0qs6rtPPccJG3if9swibr1PQNderpWqy8Z9jLIyCJeWsRUpmaosSbGXzHo9siJRhsQGWTf67t27ODg4SA+XcwEk6COScdynHJ409rSsePBIeYfS8yWJijFkgprKsnVNf+HloTypqUy7PI4l4DvBg3ei/OLGxkYaJ4QO5ubmsn7uP/zDPyR9z7Z07n974rxTyVqYhjb1i5yZ1qL9Zews4rYco+XR9y0VSNWc4Zll9jXXlixGaVzY+MKTpJ8twwArG7f3+/2ssnVxcRFra2tTGbERt8Zfs9mMyWSSmdUUSnE2seUTw4aiJA49AGwRkXFoDEvGkNjv9fV1et8si4qIzAJnz15WK+Chu3QiYHlycjK1YX1E5PfER/m+1+vFt99+m5XhSLYbjUa5ugDPmI0L8KiZ5xE3y8IwHIn9YoTY+MIwdl6Gwz+O1VqfeGtFl4Q0y+LnVIFqKZdVHu/v8rgLd0pP9w+KXvZxnw6+yyv+Pve3sBg870s5+x62yOyR28Ow18q1CDETHsUP1Wkl7Xb6fgDQ9fV1rK6upleJhU12q716vEWSQgBQKC6UBcUVrDRnZ2czocgeJoq59G4AbNpO3BXaCnCs1Wp5PyZnt9vN7enG43Gsra3ltm14hePxOHq9XkREJqnZ+xqPx+mlY8ww4Rk32oF3QKybDOaIW4VArLDRaExle0PPQaUxNhHTlcBKmS5pWmTQtGbJiJjKwzMHFLhnWXavXJ4RcQvstN/UM+3kB0D0524zTE3V5zZuybp+/fp1PHv2LNfH8o5e5wo4Q6l7KQoMCZRtuc2eDeBarRbHx8c5F5mjeNYAOEaVqWAnXnndOP1Av0ABQ8HyPOaUNzehbjneIJXKWFYGwJEkFRFZrcpr5Q8ODjLsQinS9fX1LNyCIXNycpLeJ/Wr2fiD2PnR0VEabRjDlj9yRkypO2vbzgB97KWD3NcGWukl2sArGcjfl2dbdVh3cpS4dN/2PUjQ5TDA2ntioH6oZ6B4ubcVxn2Okv42wCBMTr6w18tzUFhelsAP3i8WN0fppQPCxLm8sbcpU4SDRA6E3MuIhsNhHBwc5OR19jEL6huNRsakvUyHCYdnzsHzDUy0jeUZttpRlmSl0k4KrFMwAO8AiowkJye6eHKj4J0QAm0KpQdDAFVbr9eTjqe2sWnJ8/PzLCTx61//On75y19OGQvQ6TaSHLawp8A5tNEGjD9z9rPliL+tzLg/gGpP1CBrw9AhE3swvvYuZVPlwfP+rAWm2tXR0dFUTeuIyL4eDocJpi7mQNIP515dXUW/3895hGfqpTC0x5+732FwIiJardZUYhObHWDE2Yg1Ne6yrrXaTQwYo5UqW86AZ57wnFqtlnPK7NTc3Fwua2Lu9/v9XJP79u3b3JGMLGW8SGppO9Oe2C/sD6wXfUvfkI8QEVPGrBkNGzYY/8xrvHO8bGdIl2xPyY5UeZU+r/z7d3WUcu75WXq69wHeBwm67tDy/9+2g+9SBh7kj3Xcx2gEZ+E57gwglpWo+EH54pkw4aBJAQPoVbe7fJeISEsfbw1aFMW2sLCQ2ZHHx8dTNBmL79++fZsZsCQnYSmTyMT9yglkKtNKAw+1pMFN/eKh4dlAEeIVoyhcjxjrn/PIWHUikKlxe5Z4Kigo172lohBjeXV1lcA6Pz8fOzs70Ww2Y3b2Zs/bvb29ePnyZXz99dfx5s2bTNZC0ZSyw28A34cNBfejPQQmP0YDgOLYmA0Ky0z5HDM8VUrEyt5/OyRgELd3UiosgHcwGMTBwUE8efIkWq1WeovQrIeHh9Hr9WI4HEZEpLHFJiBmKMhYxmilXwFEZ3uzTzEyyPMwMhYWFnK3KIOLNzuANUB3uPgFnjheLHsYNxqNaLVasb29nVsEkl28u7ubRUjcT+PxOAu04DXjiUPTHx0dxbt373LnoFarNWWQQ79Df2MUbG1tRbvdjvfv38fh4WG8f/8+a3LPz89n9ju6yUY0BgP9X3q5Lh1r4PUSKN7Rxp3lpsw/sM79fQJvKb9+nlkPz6GPHQ8OdN35VZ7tb0sxlFZv1bP9XTmw5QBb2EpFw/e+L5Pf1qRpOZ/DYVoZYfS19lZKipTsQ7xMhN5ZwyxJAHRY8kIyCwCGl+viGqYeETonDSGUXj4QERmzYjLT7ouLizg5OYl+vx8Rke+BMsSSRiEANqXFyWSlH3mOCzrwbgCJz8EQ4Hw+J0ml3+8n9Ue2NgUHqHtrY8rxeECYmDVygkKrYnFQTig65GIymSRLAAi6MIppO8fPPF70h+XQwF4l53xHWx1a8XU2Gsrf0MckJQEIZhtccMJxU9/Lz4Se5p1s9JFlDxBMJpNcWoOBBbAbvEx38zwvvzM75BUJGM72YMl2Z3XAYDCIt2/fxu7ubso0RjnPYW5E3G6z6SpPGB0UMcHo9HU+kCNCSvQ5BVwwgGxg0AeWV3QOhpLj5NY3UMvoHsarDJ9YVh32YKyrnKLftZfro0qnV9HOnzoeHOhy3GWRc/wurJtPWVKmSm2JlV6J22/LnwmKpWmAcFzT8S1behG39WKx1B1Pgv4BzJx5ySSirSRYUBDDafvEbqxgI6Y3l7eX6KQPJgv38URC8dkD89pjPCCocWhnqjiZFrRn6lgrE55JzzjY+zIwlePFfcs6sYCbS1nS73hBxB6hO1FMHlt7+W4b7bHXiAxYTvwOpXeBgmb8LdcYblZyViK8k+O7ln2Pt71fe1TcYzK5paFLpWQjB1qePkV2YGDYgIG+9BwCtEkGmkxul0XRVxGRsVHT9DYYuI6CLrAExHoxFsmVKA2jyWSSbNL19XVWdZtMJjEcDmM4HE553IwdcVbo9dPT05z7zFUDnPURiUzIH+0gDh0ROYeYbxgWxItJ4sIYoCwqS/1saHMOYz8ajZJxgjWjT6yraDv38KqFUr+WoQ/k3GBeMkW/r8MOV5XHTZvMjHzseDCgW1oLHgT+L63uH/LZVrq24COmdwvy+XdRaAYaaBgmcZnJjBC7Uo2fTT9gNZcL1Z1NSLsALteLZYJiYQ+Hw6SVXEzi8vIyd9KxkiDZCsBFyaIYaKcTwwBFPiORhHdE0ZlKR9ng6dKHy8vL0Wq1MmsYepJ20EcGXTxs2uc2m6rHwyNG7JJ7jrXZQzUQk/DV7XZjMBikx46iN5VJBjBtMkvgTHb6CJkwSBvcOM8x7/F4nLJSyqHjpoC9n1WV1OLP/Nset+ekwyXShyUAACAASURBVCv0Me9AUhJgR5YxMfmIm2xavC4KQOAVWTFbOddqt4ln7iMrbhfl5x4wFIRSlpaWYnt7O8d7c3MzgZDa46xMMK18dHSUiU3j8TgNWub12tpabsWHUfHmzZvY3d2NwWCQBiU5D/SRQ078YFBSaxn2ZXZ2NpcwUQAEWWNT+ZWVlZhMJlmj+vnz51kqlHX55+fnU0mVADHygrGEbgGAOTDCAXm8amd/Y6xGxAeygg6wI1AV4/19HqXDVx6eO5/yfB8M6JYdaoVjq/x3dZR0WdkuAy8dW8YH3V57Fc72Y2E9SoDSfZPJ7Y5CVuh4NyxpgC51Yg7KmrYCnCRBkIBCZaRutxtv3ryJ169fZ5FyEju8o4w9XE9aU8dObCLrGEWB9Q6oGhzsEdOHMAB4LCgMjBaqUK2srOT+uxG33owVrukwQA2Dw2s3TS27kD+0I+MIgBMbYw9XCmC8f/8+QddZr3gTtMmgVWXM2Ru+Sy79ngCRDT5Tc65SZobELIqNPSvPks4zMNsbtgdJf5bzyGDPe7M8C9aF+D01gWFhXDfYy4S8LZ0zsw2+PBODCkOXODH7MbOMpcpYWV5ejna7ne9C4QxAjfMibsMagKZl5eLiImO3JC5hlELpesxKvWcKfmVlJZMrCcn0er1YWVmJRqOR7eUclj91Op14/PhxXF3d1K9mhybKvGI8YiSU8VaYLbx1/qcfIiLnEL/xjO14kCMBCJcsonVFybo8hKNkgOy9/8F4uhwGHVNTBrGqSf3bPjPiw6IZJb1IO/jenWwLmu8dD3UshpgLis8CZtrY9E7pYSC44/FNUpE9ULzcyWSSQAHYn5ycxP7+fuzu7ka3280KNVjylNozJeqkLyYJ1BuJHWQ9eg0mIIO352IEeDh4ipyDkgMgr6+vY2VlJVqtVi63gEZmeYcnY71+u58vit2ULtSxQReqEsschWmPj7YxZlwzHt8kgPV6vej1elnoAMXNYRrKCobv3P4qVsdyZi/OHjTtMvh53pSeM2PhtvGd6fjSo3X77NXa6/a7+RxT0YCWl/mwPnw4HMbx8XHU6/XY3Nyc+qECGIVcMNBISmScy751pSizKizh8fp5zmWeLSws5J6+ETfgRdY84BNxs2SNrRcfPXqU2cz9fj/6/X6WsyR8wnIpso2dCGYmwmMdcZtURp4EKxtOTk7i4OAgIiJj04uLiwnkJCleXFzkJhlPnjzJtdAsE6TaGnPOY8UYOtbrOt3oOwwRdAbhKSqosT2jM+Kt1zyOZhJ/zOMuCrnEp48dDw50fZQg6An/Q1o9JVdvRVTF4/s8GwYeEF+HJwrwELNBoFAUFlIAzjRWxHQxjlqtlh4vAI3HhqcGtQMFdHh4GLu7u9HpdHJd7/LycoxGo+h0OvHu3bsYDAbZFha5sw7Q4MtG61tbW1l5hpqueCkYENDBKIfBYJAeRkQk0DoDFaOh1Wplhmu9Xk860pWGWEJi4MBjZtI7kQqPHMUG+DPZsd45n8Qbzj0+Pk6vCw+31+ulIWFPpcqLpF/KGKsBtZR7DLCSDuZ+pTwjXwbg0ogsr/Hn5XwovUeeYfrYXnfVO5uaB2TIGB+Nboo6HBwcxP7+fhweHsbV1VWsra3lNn4wNgCka4zbOKLfCCXYIPFKAOZjuYSPbGaMQGSo3W5HrTa9SxK089zcXLRarVhbW4unT5/GT37ykwTEyWQSnU4nmZalpaU8l7lJuAW5sMfuGD59iSGP/F9dXWVyFrJJ4qIZHVgwvPBHjx7lphrHx8eZxUx964jbGuUlm0R7HTOnAAesFZ699VrpXNmbLdkWG4U/xlHVhipP97649KBAt6qx9jB/F1Rz1cCX4F563qX34TY65hRxC6Bl8YKImwnvYgKmVVCsfj7n2WtAGVC1xlmIpmVXVlZyuy9q8+J11+v16PV6WSd4PB5n4gRUlrMlJ5NJWrNeusGa1qqsWQDJIELskWQVAJeJt7y8nJuyo3AprEBZylqtFs1mc2opBMYHBstkchM/5L4oi/F4nOtxWZMLsNJ+4ne0m+zuo6OjWFxcjIuLi8xmBnDxlLmXk6GQjXIimy0p46VcQ5/Zo6zKe7DsALwcjuGWXkRJZfLOKFQUv6/1u5TsQFWsF28GRQ0rQ7jg4OAgvv3223j79m3KaKvVip2dndjZ2clSh86cd7a5k3ZgjmgTRhQFKKgkxXIWh31ctpGNGqBH8drm5282+IAqxoBbW1uLg4ODWFtbyxyBd+/exXA4jFqtFtvb29FqtdJYHY1G0ev1PtABULBm16wfmKf8kEtgL91Gu71d6PG5ublcOrSxsRFnZ2fR7/ej0+lkEQ3LCXoLeXNyH+cQcpqbm4uNjY2cK/ZoOQ9Zq2I3S3l6CEc5b0sG6w/O062yEgy0Jc31QwxE6ZXSjvIcPi89jCrP239Dnzh1nvuQ0ViWN+RaL1ex9Wdr0ZQvpeF6vV5cXFxEo9GIzc3NjIURyz08PIzLy8tYXl5OoGQHIe8eQjyYMnlMFN7XFX4ofzcYDNJiB/gMZFjBq6ur0W63c10g2wYCho1GIx49ehQ/+clP4smTJ7nZNsoDsPRyEAycsjCGk6JYGuJsY69VNqVn5WG5cNH50WiUayVZomUv18DnsbeM3AWy9iCtgFB69nJNKVtpWcZthFiufViuS4O0vEfVnLSSLAHXStqeF4lM/Lx9+zZOT09jaWkpPvvss/j888/js88+yxKFTjxjfGg3XuzJycnUnKKNgJiTjBhrQMlGk8+jMhljD+PifZ6Pjo7i22+/jZmZmej1erG8vBzX19ext7eXyYmsW4UOdwUqSiZilBBiMeXscXdBDoxGzwPHz11LmvdcXFyM9fX1ePz4cTx+/Djn78HBQVa38pznXswxjFjTwefn52kIuBgQcV2vtrCRbraEz6qYnN/3cdezS0M64g+4DCRHlVvvgfBx35euOq8K6MtrSkClHaViKz1hW6JYx6XXaMuc64gHl4v9qzJo8TyILVJreX19PcFqOBxGp9PJQuiTySQ3Nz8/P89CBKZ2oZegle2pY53iBUTE1K5FUGNkWdqjXVtbi1arFc+fP49GoxHn5+fxd3/3dzEYDOLs7Cz38n3x4kU8fvw4FhYWMlGE4u3cE2C0B8Zn5XiSTEN2NjE5qHAMiIjpxDnLA/1DP15d3exf6zg2baGf7CFCG5ZWfSlrJcPDwTW8YwlkTj6xx+vKTMgNMU0/yxRySfv5fiQc2bj8mGJ0hirn12q1rKjE2O3v72fmOhtLQFVSnAI5tOEacVu7HPof4C+ZBsCgNCSIs/r9vVTm8vIyer1eDAaDODw8jG63m3PIXvD5+Xns7e3F9fV1ZhFD6doIPDw8TGORGDObJ0AVmzkpDTJiza7pbjngc2jeiBuDHQPx5OQkQfDJkyfx5ZdfxsXFRXzzzTcxHA5zr11WDNC/9Ief71UM/OYz5r43tcfgKNkeG6n38Rp/X0ep3y3Lnp/3ae+DAd2Pea4WMivDH+q532dg7UlYwK1krbhRLpyD0EFVurRjVcwDy9bJRqU3gSdJDVV7uSQakSi0srIS29vb6a0BZgAydCoVdaCWiR273/AAnUU5Go1yadDGxkasrq7GxsZGVrFaX1/PGPFkMonDw8N4/fp1fP311/H27dtMynr06FFsbGxExE22KJWJyIje2NjIZQelJ4q1jSIHlFz4o9/vT223FnG7iwzvxLWWN6jryeS2ZORgMEjKcjy+LTPJfdw27uEJbM+0jIN6gtsTQK6c8MR5HicrasuMZZn/yzlVtqu83mxC+b6ey1bEJXhQvhFadDgcxvn5eSwvLydAHR0dxe7u7tQ+zyh6gB+DymU7aYe3rYuInIMYLPSB/+bdYalI9MILPDw8zB2k6Huy/JkzhEuI/yJzh4eHsb+/HxsbG1NLfKhYRdjp4OAgZQoP3lnaHofFxcV8d7MelLWEUZhMJrktJmEvKmf99Kc/TTqcddKDwSB3JzOtfHl5mYZ4ROTfeL12JrzxAQYTcowsOCZcFb74MY9yjtpZQm6+Dx49GNA1mCFUZRy3TMrgXP/+1FHlxX7fa3xtaeW4xBneLOALgGLJInyk23uTA+KpABnWuukxzveaN6zlVquVE5j+W19fz7KOJDdhfXsnFiih5eXlqWIW9IU9CM4xzYfyIOOY2q8sOcIi73Q68Ytf/CL+5m/+Jr7++utMmuGegCQKACAFmA1ermyFMkb5kExjBXhycpJKBTmbmZlJZcw9MULodxs+JycnuTwL4C5jUiUrUjIkpcHmo1wKRhsxxmgHyot2GkRLetjxQO6DvJYGI8qkVIBlgRPAkTbzHD6jD3kG7RuNRhlDZBmOi4Scnp5Gv9+PWu0mX4BsXd7V84r3xghjaZCzo5FZlyNENrz2lr6hrRi0yAvtpZIWxjLsA9n2UMu+D6AG09Jut7OfoJXxAp1Vjey7mAjj6OpyzhGZm5vLdbkYhewu1mw2k4WybqJMpZMfm81mArOTBBlj5xWU4Qn0Gh41VeW4pjQCuRbdyftY/r4PlftDHx9jQT/mOPp4MKAbMe25mcoqX+yhUA4cxIPc7uvr6/QOnXyBpQdtZQG0xR1xW5PY61ABFSsdlMtkMkmrknjM7OzsVBlFkn8iIjY2NqJer2fCytdff501bCMi28q7REzH+ogJPXr0KBqNRgKctwjk+v39/Xj//n16G8SR//7v/z5evXqVa5A5f29vLyk/jC2UiIuC0G+mVDEaUKiTyW0RBJajsCTF1aMwXoiPo2C4HxQgygCPmmfgKTE+jg0bMO3NlgBZesAumIGs1Wq3+85aCTob3vOIZ5Z0Hr/5cb6Cf1s26QvuS0UiA3nE7dppK00UcMk+cJ7HGHaFcbX3ilIuY5d4dTBD/uGZ1BQuM4N9OGZOfLjX60W3281NGrw9JDkN0LmwO2trazGZTHKzAc4l4Y46y7QFjxaDCiNwaWkpPeqyj3kuOsYhEnQE449sUQmLjRcwPphreOskZXoTBBgG5MdGoI1xDvePjR2MBBvHpQNTBcg/xlEF9qXx/H088gcFulWeY8SHCsKd8JvQDz+UpVQVX7aSQymgGJhMKG7KFJou4p4oMhRGxHS5PytDPIbLy8tMfFpfX5/a2IDEITMI0EwkGL169SqfyxKgiMi/HQMEpPA8nzx5kusC8SgpLUeFIRedOD8/z7WLlKujAg4JNY5D4TFDZQNseHskpJgGA+BpC14KnjOePXLERuLcj/52DNE0rpdMRESCoOOuljcniSDLZDfzHfdx8kwpXxRbsCeNMVYmn1hRl6DOObTXAIwhh1K18WCQcp4C9CbjhtdJn1pmeVdAmZBGo9GI7e3tWFtby3mCjGMs0p8GOhtCUKks1WHucA1ZxPT1zMxM7r5FCIh3BXT7/X7s7+9Ht9vNWKvbADNkqpjNMCg0wQ9rfO1NjsfjrOcN/Q2jw7yiEpXHljEC0GALKPrhLRDpR+be2tpa7tZF/ywuLsbm5mZcXl7memLmjwuRoIec5+FneK645nVZHQ8jm3kBU2JZMePJHPgxjo9hzfcxDh4U6EZ8mLQUUZ1EVdLRVfdwB/zQHjITlL+dcedYhRU3SQ14uggltE3Erddsb4OJZW/J4M0kw/LFG3SlH67h2VifUMveUBxjgUpVxHS4B8oUK5k4FUsMsGAB+k6nEwcHB1kUvtfrpVUNSPG+JDUxGXkWyyFgDA4PD3O3GdOWGDh4Q1CDGB4ANgoCmQB8KaxRLti3guNzr+VlvE3nl8oj4kYx0T6DCrKCvCDvpTzxDL4nS5ziBPZaPT/uMmj53vLm9zYlTT/Tb7SfcVhcXIzBYDAVU3ffGXjxiFDgS0tLsbm5GTs7O7G1tRWNRmNqORd7Ql9dXaWXz5Ij+pX34vk8zzLhZSoRt9tNkhAIK4L8IduUTCUJj9g9dLLBkjkcEVPLeXjnlZWV6Ha7cXR0lPMkIqZqexu4LEeMC4aNvUjeg92LADYoZmQHsGcVQ7PZjI2NjXjy5Em02+04OjrKOUcpzuFwmEBLe9B7KysrU3UE0F8wZPQxsXgnfpVhF79nuVTpxzoM9lWOVsQfsKdrsPUEsVIqwfYuMC2B93cRB7CXwEB4cjvBx8Jm75YJ6mQY1sYx+aA2UWCOFZMtSaxoeXk5xuNx1lceDAZRq93smIL17L4bDoext7cXe3t7Gd8lEYoMYcaDNrC+d2trK5aWltILWFhYiBcvXsTW1lZOsKurq1wSRMZvxC0tVVLIKGoUKh4oJfyIC9HXMAkogNLTxWPDml5ZWUnPmu0DeS5eEoobryLili4F3Cybvs4eMWNa0t93WfQlsJXXYwzBdADcVoae/AZTe7scZfjGc8P/OxfBlYaQBeTFfYHXiexylHkaeGmNRiMp2ZmZmQRcqNzr6+tcb07pUodbSLCz8cMP84sEpTJfhAIOzgUAuDHa+v1+1icG6JmrS0tL6c3aeDw5OcklcoRbVldXc6eug4ODXN9N32DwIeOwRrAJpqedC7K6upoZz8SMh8NhNBqNnBNOVIJm5rx6vR6PHz+O1dXVWF1djePj4+h0OlkTGy+eMBPzinZyXwwL+tBeseO5jAf9WKVTPZ4/5lEaqPx2G6uA+K7jwYBuOfmrwLGK5y89zojp9G7//0MepYeCAjFNHDG9I4hT+E2JWgGUiq1ev90YwPfFUoTihCq194N3yTrbZrM5RV2ToEFxBwqVoxxKb5t3XF5eTuu4VqtFt9uN/f39zDR9+vRpVpDa2dlJT4jM6nq9npV6IuIDL9bxUsewIiLX+GKJO4O1nPh4G4wXNFej0cg1tTYmymSf09PTbA9Ah7KhvyNu98NFkdhyN0Ng4GPMnQ9AW/wdY+6YmGXOSs4eEofvZfbAyqIqI9ZehtkREmJs9EEb1us3y9QAwnJXJtpqeZ+fn49Go5EUKsu4XJfZtDl9iSeNcidvgR8vUaHtNpaQ/3q9ngmFyCcMDZ4a9y9LNLr/vaWfjUZ726ZWASD6ndgzbRuNRhlugVUirgq4s/Umugh2ANYKL5p78CzmE17pZDKJg4ODXG3Qbrcz3luv15Mxo3QkfYIMWlYYU8sh8wI9aFYE4LUe5FpfH/HjJVLxHMug55Dx6C7s8vFgQLdsaGmVly9mi6iKRi6B1kD8Qw1W6YXgbaEYPNG8WBxvzlSSKyhhGTrexYSmD3g+k8xJMoAvXnWr1coar+PxOKlo9uBkOU5ZAxkr1dSPPXQr9NnZ2Tg+Ps4yku/fv49nz57F1tZWWvtMRuKYZFOjbFlfeXp6OhX79PiTaOKYN6DgpJpyLawBgtg3VLKXikTcxlQnk0nen3flO97Dmb0RMQX0NghRdjZg+A6ZYdwAJisrjz3F633fWq2WxoflnHHiM/edzysZISfIuO9oD32NB4NSX11dTSVN/3GYLuWerp/M2NuYon31ej0NT+KvNgjd7+PxOGP2ZD2Tl+BMf8aQjHbWr5p5Yv7SJ1aqULWnp6fJajmzG4+3dChmZmYyZs3coEQka4HRHWxsQF97P2EAET0B1c3a4dHoplhHo9HIcpCAmuUWnfD+/fuMIRNaWlhYSG+33+8nhc77Vxl4tKc0IDHYkCMvQ6Rvf2jn6Ic4PHYl4JfAe593eDCgWx4ezCrAtNdnBWZLqfQ6P3VUgXzV92Xnmw7xtYAvigUF5knr5KYyhmMLj+9KgXZBAAM9Al6v17O+Mtbj4uJiJjMdHBzk5gcRkUsDWNjP5BiPb7dlu7i4yOIDKOHt7e1YWlrKNav9fj92d3djY2MjFQFbtznLcnV1NRUfVJ4tXZSzMx8xGMoqVyw1woNxwpKBgjGxJ4bRAwCgtCKmt2q0d84P44wyM9B7nW5psZNN67Za9rnWwG3GgetK8HRijNvuucFhCjsipqhjxoq+p/8tFyh0yyZUKAYLxoBDCPagYRMAT/rRRhPGTr1eT2BDJpGF0ugaDAb5DDJy2VqS5wDQLEljzKlnDG1OjgRGMtfybL8LMubQiQ1X2km1J9a0kx3N/BqPx7k0ib559OhRNJvN9I55D6j+y8vL/A1bdHR0lJ6/wzqm2snYd34AHvbu7m5Syevr65kQxtyEdUJmYSEw9rw3Mf2D3JfZz3aiyqVqpX4tdbLP9d+f0uufOjBUfc+q0Mx9DYYHA7p+EaxXJ634vCpv1Za6KY8SKH+IQXAnl8oEZeFUfiYvVqoF3jQdnzsRAWGOiFTSKF3Ah42zARwUD1YpQIVHSttIcup0Okn7OtOad0VhsKYPT+P8/DyXE/B/vX6zlnAwGMTx8XFOdvoIcCReilWO14pCo48xIJaWliIiknp25Swo+JOTk6zDbO8XJUsb7KU5mSPidk2pM8SdaYu3bg/Tk7w0wOhDAzhKhjG118a9S8qZd0BWbJhYYbk4RenB0g574CV1BlDWarWUJ8bLTM38/Hy0Wq1cLnZxcRHdbjfjj6xRZcwtzzakImKqoAPnACS0/fT0NBW9C8iY+jV96fCEqylRonBmZiZzBVwalDH38isbw/QXnvhodLNtHaEIe3LMPeZrxG01LO7NWlxCOmtra1nKla3/ut1uPou+AOAxImCMYCTssUP1MxcI7XjteUQkrQ6NbBaC0pdsfMI7krzJO1reHOvkc+dlYAR4HqP7bSyWR8nGcN5d7Ohv60FXOW9+tzIE9qnjwYAug1RlzfB9+d3HzvnYc37Ta0oLyh521fcobwMwisjLLuy5OAuaSWYA9AEtjTIZj8fphTBhsLrdv64eU9Z2RfhdbYZrXW3HGYheGsQ6xuPj40yG4X7NZjOLZJDwhSGBEUByE/2K0jJFyvfEQ+kDvCXWB0OT0Q8GSCZN6d1G3Hpi9Dk0ZDnJUTqAne9ppVFaxPbeys9QOo5pMQYGf4Mt8mNvBfmoMjppu9uIMebx9/7BLlg/N3dTyH5nZyc+++yzWF9fz4xfJ8/gxeH1eN9V7sP7OMzCHKFNABzgzLjPz89P1fl2BrN/MFZc7pPP2ZrPRUXoV6/T9vMjbjeOsDyxZIq2O6HI8UDP3XJOlvWJ8XRJiOQ5AJaLflxeXiYQkxxmw4VzMehoBzLebDaznjQxds45Pz+Pubm5TNRi5QIGMIY6fX51dTVVgcpASzwb+XbxD9prua8CTYNgFeDeR69XHaWuLz8rHS0bh/d93oMC3YgPiwX8JlbKXRSD71Vart/nGSgmU8L2fCIirVxbdCRIkbKPlWmKBeE17RMR6YHYQ4KWxWInJuuF7C53V3p03lbNQGZP1x7q4uJi0sUoImq0UqWHLM+I23WyWPNkqDYajZifn88EMCxv16bFGypZhYjbZRKmIa146Ssrf7wjZIvxwpPjXUuKkzFCwaCcGXMzFab+kTErW94DQGXM7SGbyrKFbRbFHqvfg/621wuAcS+DtBWhjZNyDuG9wdJsbW3Fo0eP4unTp7GzsxOLi4u5OxXe2/HxcbRarTSivHOPN0FHzp2tTrIcrAUxRUATit/vB8DRN8i/6Vw8MgqfAJIAQ71enyq+QvtcPAXjwdXgGDve6ezsLEELY6xMvLIh5lDK/Px8NJvNKaMO8IfZIlPbsX8bkBjFTrS0YWgdwjuenZ3F4uJitNvtHCfkmsx4liPBCmBY2JBxYiC6kTwKh7zQMZSvBKiRzTIkWAWCpnjtUZfszm9zMMfcJut/s05VgF11PBjQtXItqQm+/9RhT9mxKsd3fdwFtOV55YDbK/E5HiAmAROYeIYni3eksWKwAvHSi4hbyxTAZRkDtBlKGEWJwJeAe3x8nPvb4nXR3nL9HBQvm4hDZ7Fr0f7+flrkKFm818XFxQRbKHYAJOI2Zshz6TuUCW0mhmWqDyWCAeLJCCijHFgfa8WDQYLidvEFFDr3tzKwTAF4eOyOsQL6jqMC4PY0+JwDBW2quEzQog8MXCgBCh7guZZ0OOewGTyxTi+RAgQNavV6PdbW1mJnZyd3fmJtKM/e3NzMGsknJycpk+7/er2e4QwAGS+M/V2fPXuWVc6odcy6VgMVxq11gJkmxxINdDZ66Q+y24lR26hFvgA2xsEMwXg8nlrjy5xiXtEmnslYO3MeOtzlGwHdMkmTEBOyxD2c4Mb3yLY9ZN6BUE2n04nd3d2skz4/Px/tdjvW19ej1+tlHgjzzSCEnDpOy2fMDa8nhuKnH5hP5RiWutcMlA3aEvAMvL/J4euqjPWSnap69l3HJ0G3VqstRsT/GREL353/byaTyX9eq9W+iIi/iojNiPh/IuI/nkwml7VabSEi/oeI+IuIOIyIfzaZTF59nxcuX6DK8qk6qmIv3MsA6fvf9Vye/bHDg23PAwuQycnfgOBkMslEpDL2TBtQCIAiwAv9SIIE6wtrtVoqO6x1BJsfLPt+v5+L86GlmRCAPn3GJGazgvn5+Vzb+6tf/Srev3+fSxdQ/LSBqjybm5tJd5vus6fKe0NXcwCKKBAmKX0EKNLPKDj6FCvfcWKSxFCgeA+WFY+rPRmUvb1gAIRx4Nnl+Po6PvP9Ucpur+PKljeeTf+hCB3KcPzMfQRrAd2/sbExtWtTyYaYKSGOu7m5GRGRbAVlCpeWlqbenWxz2kRfzc/PZ9KN35HN6p8/fx5zc3PR6/VidXU1vSEXcEEJMqcwZmkrhhwenw05PGHTusiEmQhAxNSyqVH6iXlJyObi4mLq3vZ0HZvGGKDiEwlRxFSRA+K6JycnuZbW+oq5ivyzSYON2TKXgfHB6y/zOljut7KyMhUXN9BjDPt/xgXvHMPLgEvsmT7B+y31qx0nG002fOkHZPwup+k3BWC/UwnyZpDue9zH072IiH86mUyOa7XaXET8X7Va7X+PiP8sIv7ryWTyV7Va7b+PiP80Iv677373JpPJl7Va7Z9HxH8VEf/sUw+xBeOXirj17j51lNZNlefs+5pqKe9TtsfUtP+vojM80QBKZ82iKAFB6Ew8MuhV2k07ETTWMEIpTyaTiCMhXgAAIABJREFUjL+ZZgGsUHCj0SgLt7MVHUYCSsv3od1kBUdEZiV/88038erVq6lSjUwo155FQThZw+UpTeVGTK/X4z2slBzbNV3He9MGjzuKkIQeMxEkjzgb2Ut6TCXT/3hsMBE8x2BZWsWWN4+rgdW0sSe520F/YVj4fs4FQMFRStClPCNu2QAy2ynQz+4ztBmmBU/PS99MbQLkZ2dnU96gPTiYGTw0r0sdj8dZTrTVamW8lrFx4hXviZHG+8BeML70F3Spxwojld8O45iZiLhNrsPbLL05QNesRq12u7uRZZVxBcihjKn8RgEQQA5gojLX0dFR9jneKOPkZ5a0NfrH3jcyTLby+fl5vH79OnXSn/zJn0wZpycnJ2lgmYk08Jb5CMiyda8T8kw1M46eD/4feXJYzwypdXSpr/nsU0fVOfZkjSMlG1s6e3cdnwTdyU2Lj7/7d+67n0lE/NOI+I+++/xfR8R/ETeg++9/93dExL+JiP+mVqvVJvc0M/Bs/RLuwE8dpadraoDvOT7Gw5cdaI/bVrDbxTm2KlEEKCuehTKyF8/kHo+n9/a0lwKYUPHG5eXG45vlD6a3iL9i1ZJV7F1xrEBMf2GVs8Rof38/1+C+e/cuY7BUFKKWMxV3XKaxpGpRulB3vKfZihKcSuG3hUzfO6HDnpsVDveggg8eHUYAP6b97EVwL5STFbLbXmVh807+vzwM4rbqSy/cc8Wg7OUbeDskr+HVIFNnZ2exsrISEbf7nqL0UbY2JojnoWShOh3vRl6QC2L/KGSA0iVAl5aWotVqZSnRq6urqXW7pu+rwjv0D38zHwACwI0+dXUk+hgQwggEkGyUuiIZBhLvhQz7HPrb4QVKTppujYhkCWg3MU9CMuQ9RESOEQluGOMAOoU1GGtXWoMNob+Wl5fzeay159k7OzupR7gXDBmZy/Q578d9mXvIozOm7XE70dN6sUqX21BljKscJ47vgx13HcahUvf4+/se94rp1mq1mbihkL+MiP82Il5GRH8ymRBoehMRT7/7+2lEvP6uMde1Wm0QNxR0p7jnv4iIfxER8dlnn1VaKBxV8dj7HqXXUMbkPjZgamu2A8F1opG9EagzFDxeIsBnj6SkCgFlW1COvUBhMYmgswBqqurgQWxsbKTw4q1AI0XcKqfSYgSIiS0Rs2IpA5WuULBsJUiCzfr6+lQMGi+J92XyeV2gY7L0lZkEW+1M4nIs7U3QZ54wBsa5ubmM6wEixPAM/n4uxg1eCGPN3wAZIF7Kof+ustBLuTdlZmobT9tzhnORVajIhYWFODk5yf2Lq0AfT+X8/DyX/VB1DIULJXp8fBzdbjezfq28kVsyi+k/CuzDzPjHdPPq6mombDEm/Nj74b42znm+AbNkojAG8FadfGZqEoOM/kZmuBYmwMYxn7FvtZfDOFbJvMJAxAuH6eI3DBn5Gu5jcjKazWZcX18nk0QbXROAOU/ylXM/AGt0FF41u3B1u91czkT5TUIxGKv0rQ1i5hnygJ7CU+Y6Gz8eK+vK8rCO8nPoHwOjr/G8+00Oz80qT7cE5o8d9wLdyWQyioh/p1arrUfE/xwRf/wbtXz6nv8yIv5lRMRf/MVfTL77rNJTuG9Ml3uUvz0pI6a93ypPt6QmaIur2TDQxJNcJAHr015IxHRyCnFZFIhjco7r0U6ABaoUD+Dq6mpq0X6tVsuEJdL6I26UMIko7h/ah3Xs5UAkl3j3ErJUvRyJc5eWllKRsXbTFDqAz7tDH1K2zu9qD5P3NlVpmtAUNWNlz8N9h3VuStrfW2nbKDNgMS70HUoOsPWymCrP1ErBxp/P4/MSYFHKeFqljHqeoCC9jIjxRXZReowJoQjH6aFhYUts6TPGVMmyh8myFXa8gWExZT0e35Y5hFpFXp15S/uRVfrOdL7B1vMOGabvLEtlrJ/7mt1g3ABWz8eSxYFhIqbLQRiGv22cn5+fx9raWlxdXeXSINgxDCIYM5bjLS0tpRHjZUMujMHaXDLGx+ObfAvnL6DXiCHTV7ATw+EwdyyD8mceuaAMbcDIcBIh4QGzUMipx89zumSJ6GfPH+Yv72hHqAp8uV8JwlW/fdhAdhtpc3lNlQHt43tlL08mk36tVvu3EfGXEbFeq9Vmv/N2n0XE2+9OexsRzyPiTa1Wm42ItbhJqLr3Ub5EGRv72HX8LqkJ/y4B5yPvO3U/t8MWDhPNnzuO4liaE65caMEVWhxTczKEM5dRqOzqAlW3vr4ezWYzHj9+nFukUSDdSyxQEF6LyOQjkWNtbS0n4NXVVcaUAElq5rLxvBOlUPYUVafNZGOiqKGb8BbKmC4H7bVBUnpuHnd7wQYlFAGAD6A56QVF7wll0LYxaDliSYnj17wLAIkipF2m4+zFYcCZRrNi4lpksFQabq8rD1G3mvYjU/aoJ5PJ1NpcWBTKJZIxD+A6kxUFCD2M0WUqFoMRQwtGhWQuZIX+4/60xTLhtiN/EbcGtjPF7d1ZYfIdhpOBmHsgO+XzMSBoK8/iXPoY49yJaXzP+OBt2pCdnZ1Ng8Zx6rOzs+j3+znvVlZWPqBzmVM2Rrge2twxfs9fckb29/enDFt71Cz3Y108ho/3onaBIBuj4/FtgSB0j+e+s/VLkOUce7xlLLicF2Vs2OfdddzFTlX9fxfeVB33yV5uR8TVd4C7FBH/XtwkR/3biPgP4iaD+T+JiP/lu0v+1+/+/7+/+/7/mHwM9qeflS9UKpL70AJWTLb8neDgCfUx8PX/JUXJ3wZNT0x+O2GA9vnHdCCCYe/YAIz1C3iyZR8KbDK5Xd+4vb0d29vb0W63Y2ZmJveyhapiApnWnZubS2+VAhgo5/F4nFYvz5ubm4v19fVot9tJQ0IlAc6AG5QScWhiQ6b0UKiOIUKPAbZMbI+R+81LKTx2JY0eEVNgYMBAUaC4uAc0NOPL/fhtuY24Tcjxe/IdXgHKhb/L+5iW5LqImDL4DNSmd+lDQBcQp48MEGQGUwDBiWjIGooaoLX8YbRBQXtd62QySU+oTFhjXKCyAQ4biB4/DA0rN1OM7iuPm+Uk4tbY8HU25OhHlpuVcX3a4EQpxsX95s9p28zMzJT8GxhhSajgxvMYd0I5GL1UiSIZzu/tZUalbLCxictC0i/z87cbUCAvjLHHzH1FDLuUPeYk7IUNHO7nMeP60oj2XLeOR79XOWnWEZ4rZpN8TpVTZ5nx+NFWt7Pqvncd9/F0H0fEv67dxHXrEfE/TSaT/61Wq/1DRPxVrVb7LyPi/42If/Xd+f8qIv7HWq32jxHRjYh/fo9nZIPtLXJUufxVh+MK/qyccKUH+7G2+LxyQqNM7akxURwH9Dsx2crkB7cF4fcOIe4HJhwxEpaFsPxjfX09Jw1LhDqdztRG8ngSKEin89vaBWy8rpeJtL6+Hjs7O9FqtdKLBWiZVIAtihoA5l08Fvw2W+BMUnurKHTGtl6vf7Bzi8eM+6IsGAeAo4x1k9Riz7I0mkw3OoYFEJsaNLiXE7SkoD8m6wYS94njZCjFEihYl8yGEgYMFGdEpOI7OTmZKmjguYTRiSdaGiaEKPCIiHMyZ/BsiCkSy8UosGEMgHuu8O68h5OcqgwWFKaNLxsz1gvMU3uOjGt5X+a96X68eOaS1wibVi3zFqDx/cNY0k/NZjN1wMnJSSwtLcXx8XH2HwZ1xC3Vz7xEZqlcxWfMdeLnETf5IFtbW1n4ZDAYpLFNApTnFMYBoO7kMs9F2mewLMM5yKVDFeVc8Xl3zRfPKeub0qErx5338jmlQ1f+/32O+2Qv/38R8ecVn/8qIv7dis/PI+I//F6tiGkALF/kvi9VdmJVp1VZJ/xfPq8EhNKCclyH+IaVBksJyhR5ezjcC+vPiQVcG3E7gUaj0dSG86yHxUNdW1vL/0ejURwfH+d6Xrw7L5OJiKmqRAiSlwZACQKmc3Nz+VyehyJw9i80oz1zZ6zSpyggsktNiaGwrCgBAStk+gqQsSVuZciYjUaj3JyBpCD6mTFEQThDHKofQ6c0HgxI9tYMMg5TeKxRYIC6la8NxzJ+ybtWGatem2kvjZAI/cvzKG6Cgr6+vk4GxRsNrK6u5jZ+ABMsB8rVWeFkIo9GoyzGQd96qRuK1/FQ05KlsWHFD/iilEtFa0PZipN+Md1L//G+9t64f8lMlUYJfQyQ+VqDvHMBPO7WN2YFMBC5jnBNaWD6/WA3rGMYb95hNLop97iyshKbm5uxsbGRpSHZkcxAw5xHtmq12hSgWr4jbtd0m2Ez0HK9Zb7U5Yy7KeVSJniXKies6n/jRhlbLsGea8v/LY93OXIcD6Yi1ceAtYqP/9RxV0eXFs7HvGgPflU7PODlbxSbJzeWL5MCy5z7eNK6HrAnER5qRGTc1UXI2ZoLSxaql+VFTmJCuXjpgr10KLbT09NMgsFYII7De3oRPorYwOs4Ll6qsxbLPuA7x+Q4F2OmTJzAIOCdfL6rK11cXExV0GJ8vEVdlRKFQl1dXY1Go5Hv6oIBbr+TTOhrA6RBgPcoPysNPw48izI+afnECCQ5ZmZmJiuUkWEMKBPHtkHgfAQrZvoKb5b+uby82ZaOdeCUBB2NRhmeGI1GWcMbmUbpItcocOhG5gl9VxWTttFiSrU0wNzXBkwAOyJyuQ1UucfCRoY9ZdphgAPk3H7aiiGOQUHOAtncNj5op4tquLgFcWXLnd/LTBHfwy6Uz464NTxpK6wZc8f3xvgvY7Bko6OLXALUoR3GjTGjnfQfY4WM+f+IaUqfa+8CvhIDysMgWoUP1v/M4RL0/fuu48GArjup7DTH/u5zj6p7VXm1Vef7/9KSiYgp6s73LAUdDw8Ln+UbrMmz543QuKYrsS3az8RgkqIwSZyCXmo2m7mZAFmjUKiOyRlArZhZHkDt2E6n8wG1zHn25iIiFXpE5DP47YlC9qTX6nmiocRgB+w5GXRpC/1uL9qUlT1WgBavC2B0DJvxYEJb2dnIASxc+s/Po22Mrb3HUlGUSTq26v0ZrIplrVRA9nAWFxc/iNHPzMxMMRnIHLJZhkVMQ2I84dVSBQ0gWFpaSqVK5jJGCQr68vLyg+0u5+bmcrmW3xFDlexfzqd9MEDuBxsOgKlDQYCj+8xz3HQo4OY4Lv1i49GyhtxaaUNhA6QY1Y698h4GURKr2G6TdyYL+/r6OpeEYVi5P8zOANIYsoAp4aNarZZeLuulAUwbd4wRcwp2AAOM316ShaHC3EA+rUdteJdGnuXfY1aC5F1GqueUx9rPLx0c36Mq1FXe4y7PujweDOhyWPmWrvx9jxIQPYlsGdmaKdtgq4rDNAjPMXjy2+BxfHwcnU4ntra2otlsfiC83N9JBaUS4dledM+WXvPz85lBiHWJF8TOP3gcLvmGYrKA1+v1rJW8urqaAAo9eH19PQXKKEQmXkQknWjQRYn4PUzDYnFDQ3vMLciAF4qHcYIiZXxMCTuJyxQn3hpKCeBBoTgxDiufLFEnmRhAOc9l8VC2eNB4alY0pczRl7awq+S4vJ6/+Q7AXV1dnZI7lDV9NZlMkrmgX1HQtOnk5CQNBvoSeWAJC8U3yG52LW6PG+0ApJFBMp7n5+czFu5M/tILxxukn6zEYR6QK8aTsWK83KcYj54PZbyXOc74W48AmCwXYn57rHln5imeKkv/bKBMJpPcKtFeH0wFXif93Gg0EjSYM6xIYE6Mx+Mcv5WVlakkSJ5hBm1paSkODw+n5Mpeaa1Wm9qrm3fmb8CYPjAL5nnO/zaIrGtLj9Ix9KrjY8BX5UzdZfjynmXiXZXu55xPHQ8GdG01WHD83fc5qgAVRV9SAx8DXg8O1+GFecJiBSOAEbfrJIfDYXQ6ndjY2IjV1dX0EJ3YgSBb2di7tnBA6wE4bJxgZUH1KQpaADblu/JclIg3saft3P/y8jLLOlLIAOUccQPOw+EwvWJiQXjoLjKPh0k8FgVEX2B4eDcTj62tTvrI+7NyLV4t78CkwQByn/FsqD+oV098gIox4QfQpi2l4Yf1z7jzTLMitvRppycxYGi2xQYp7aOKEaU4AY3z8/OphCO8EStRxzxZX+n5xN+MnSutDYfDODo6SiOvpBQjIvdkRua8sTyJflRdioipQhQocScjekwYJytjvEMrS7NfNsABbQwp5BTPG6C2PPDuyBwAZroVQIq4MYRgp1ZXV2MymcRgMEjjlAz/Xq+X8XEK1JRjz99HR0eZY4HxRB87r2R5eTmTKHu9Xr47ugbjenZ2NprNZpYEZZWGM+3LHAj2T8ZYKNlGZNd1CjBUkH3OIfThcSz1dAm4zJ0qVrPEDzOMNhbQKVUYYVC1U1QCrkH7ruPBgG7ENNCVk+Q+wFtSceV3tlb9rNLz9b0MdlhkdHLE9AbGCOP19XWm/ZNh2O12YzgcphXJhDG9bIVrgEABGByxHgEW9xvWc6/Xi263O+Vl2KhBoRPTZY1uvX5TRajT6cTBwcFU5iLlBLG+a7Vaxu2Oj4+j3+9Hv9/PjGWsePrK9B7tpQ9KRVFShIyd5YKYbERMAbOXRdGO2dnZ9NBZYmFrFoVJfxhY8RLsMRg8ATbeh3v5HqUi4f2owFQmodjQ83M81o4lwhpQDL8q69j97MS/sn8JIRCToz2wLAAlSgq6mW34yAFAydoo5L7UWmZrR5bSnJ6eTilYGwSee16P6pCC+8cGK0cZ1gFcfT8zB5YvDA57wLVaLVkhG0OlHnONZ1YbLCwsRLPZjMvLy9wmk+pReHNXV1cxHA6nlvPRl+RsoFe8hy0g6fXj9CnzgvuSK3J+fh7v3r1Lo3pzczPZCnSI567nC4YzRguGMGPj1QvWyWZuPFY2Pkv9zGel12pHqTxKdtPjA2D6nqVTVlLMfpaB91NY9aBAl6Ns9H093RIw7/JiOcrzys/LQTI9bUEpLT+8P2id4XAYvV4vdnd3p+hJgysDZi8fz9SCwIQqLXIyiufn59Nafv36dbx//z6urq7SCyRuzDXEi1CsvA/PgqamKPvCwkKuC1xcXJzKjAZ08XANuFXCPhrdbnrgd2cClMYNCgtlYvqTvmfcUPROXLLRYMDnWVDOKDSyjV1gIiKysEBEZL+Vay0B+NJaxihyUQn6BuOJd7asIQMGZd4Jz5bfjBvjbBlFKRDvBXRnZmY+2EAduXBMtwQqZIplV2XRDK51/BPApXTo0tJS9Pv9iIgEglqtNrWROkdp9DJPPDd5X8bUtDsg5nMYO9/PLIINFvrRY0oiYQkivDfPQtYIwYxGoxw3f8Z409940cTl19bWola7Kdc4Ho9zGSA/eNDWTzBjy8vL6WVeX99UHrM84DQMBoPodDpTXrqLYDAnOR9Gg7AC3jWyCn3uJU2WaRedKb1k92d53KXby6PU5QZSxrxkV0udXz7PlPf3acuDA93f5CXK863c+ay0Wj4FxP676re9D3vBgBrxL+JTUL39fj9jKRS7sDVsL9agzOQ2JYN1vbKyEu12O9rtdlxfX8f+/n5888038ebNmzg6OspsxXq9PlVyEavV2YmA9+rqalxfX6dH6Aoz3p8UGpsyf85MtOKDGjPtRuF3CgLYA4uIKUOE7+hn01MojfPz8/QO+MHDQKk0Go2peslQWlZQPMPK2pOVv71UwjQn12Gg0GdQ9oPBIMfQ3hnA5LYgz2XyFd468Xe8oIhIY4RzGHsMNShDAyw0MRWrkAcUInFGxsf9c319/cEG56aC7WXNz8/nxhg8CyXf7/djOBzG8fHx1FZ7PLusBoYsMR7O5uawgmcsbTDbCOF9TXWW4w/oloCGbHtc6XPOM0jBCgDKp6en0el04vDwMJklwk3EzpmryB3LdQhPOCZsL9xtYB7wrsgWRo7DJFD95FyQ3W9DAJmu1WpJT19eXmYFLvoeD9cGNgYo+sjOj2XdHqmPj2GFr73LqbIc2Di2Y1DqhbscQLf7U7j1YEDXL1qC511WTnmUYFt2UDmoVSDsTqs6320tqQ68qtPT0wQmBPvk5CSp13a7HZPJZKryDArFsQZn/kVMVyICdGdnZ6PRaMT29nbMzs5Gv9+Pb7/9Nl6+fBndbjdqtVrSWOPxeKoqFM9wbBVaKCIyDgvA2culDuvp6Wl0u93o9XqpbN0fpuZ4L8a0SsB5Fu8LRWualLgR8W+UphUmCgaKCwrMa4Adl7eRwzijIGibjQiyxTkPgwtF5MxhklXs/RmUeB4AxP+8N2DHu7lMJ0vE8CoAVQwtABllCl1sQKYv6CPki6QpKlZNJrexa5KTSjYGmWHNtcMJi4uL0Wq18md2djYz6w8ODnKzDmKEjv+6drFZkTIMw/z0b8bEc8gyaMOHc2xwcZSfmZXgHWmf9YLjhIxTvV7PpWuXl5fR6XSi0+nE0dFRjr2NT1gI2mbZYbyOj49jOBxOGdk2KLiXkwAt17SV/iJ5zsvteDYrIUieq9VqGcYxC4AecUyYw1na7ie/l3WtPc5PgZu/KwHXz/P3JYvpseeoYq587/s4ig8GdMuOd+daMd3nPlbo5uFtyUTEB5PM/98F2J+ympgA5+fnU3QMFZtIbCKRwkoOiglaCo/DCVC2tieTSW4Yj0dKBujR0VFcXV2lt4BwOM4G2KKI6/WbXU329vbi4uIiXr58GXt7e1lNqNVqRbPZTE+ciQeIO4kCBQxYMxZe++mlHPZUGAuvAbV1zDIHU8Lcl8/wDMnchf6zUkAurKzdHlOENoyQScDQa549YVdWVmJjYyMT01CyPIMYLxPdaxdtRBjs8RShaF0fmXuy0QUxevcjme4ucG8aFICt1+vptTi5z6EQDmTKssWB4UB7YXispPHQ5ufno91ux2g0yp2NarVarhM3sJvexliznJuBwtPmd5l96zH1XI+Yph0N8Fa2GFAlZYl825ty/HM8HqfRsb+//0F5RgCS8BArB5APxqVWq+WyHxgPsxsGEAzq0lhA1u39npycTM0l7oeseq7bEOB5lmE/z/1tdsDL/0zvfsxb/b6HDegq4PazbIRg3NvALzPby993HQ8GdEsrwZ1yH0+3fFF3mIWjtF5KAC7vdZf3bY/OYBhxm9ZPLVsU9snJSXS73aRim83m1LpNEknG43FOrpL+4P6chxChFMmMbLVaU0DlzEiUIhPUFGNERK/Xi8PDwzg4OIjj4+NsD0qTJKqjo6MYDAZTS0PchxZwQJZJD1DQP05EKZW6WQQnh0Cz8RtABoh5rulHA2xpNdsw8KSzzDDGLurOpMSLhhHY3NxMmo1SesPhMI0LqOeIG/oeQwnGwB6k46zEpVdWVnJsWWsMEHs5FIoCQ85raldXV9OAcBIMMmfjBxoV+cfgIpbHUiGo5ojIZ5nmRs7dlna7nXvvcuBRXV5eJuATL3e/uNIXc4GxA7Q8loy1FakNWxv51iNmRTwnAY7y3rw/nzmTejKZZAx3MBjk/YjxzszMTCUwEctdX1/PdfgOU5ycnOS7ECJyIhYyYMaJPqXPXXHMbAwGEdfSF8g9OsS6EzkBtHlnfjyXMC5KvVrlof42hwG1BPJSv/PjeLKNBRtX37dtDwZ0q0CT31Webul1llZm1YD5u9KaqvJu72qP78t9yqxSQI4F5hGRxRm63W602+3ctxRF4ft7gFEIKEeoPx/Eey4vLzOL0bv44Ekj1NB9xPVQytCmp6enqRBNRzIxMSKGw2Eq0RKcDIpe5sS7oUzvsjidoOJKPZPJZCrJBrADlF3mDkXoRCjaYAUEEFphW3HbqLKS5n4YFCi89fX1rIHN0qtSQbNDEzuwULELr8fWNJ4rWaWMBWO5srISrVZrKv4WMZ1ViWyW8uv/XZXIigYl7SU0UOUYc0dHR/mD12aGBWCg9i/v4Hi08xfIgyjlChCImF4Tzff0CUqcME45/gCIjyqqGgAwoHpOYuyhB+yBc0+u4zz6mRhsRCRrwTJA9IizfTGumFsYO46LYuQ4m5h5g3G6sLCQCU9OGIR9MXUPi1R6xA5xYVzSp+SmcJRgTXv82zH0crzs+Nz3sIHDtZYlG9a+5i4nzQ6a21N6xlW0tI8HA7rl5PfxMUB0Z5adaLD2APK/KROOqud40EpryZOztJbJJlxeXp6ibPr9fvR6vdjc3ExFRmJLxI0HynO5rnweEwlgxBvxGj+XbOOdUWpQ1wAUVjigi6Xd6XSydKMtO55vKxrwcSyHz7HAGRfGBo/ISqscz3LCkRWOAuYeJCtZOVpZOPnGMsFzvbTFgOR4O5SYwRiDAgMB5WqjZXV1NbdZJOHs/Pw8lpaWsnzn2tpabkZANjAJK3NzN3vOsqEF44SCXVtbm6rw5Cx3xol+dUUk6k5jkNmbgcJk7LzkxJQ+cf+jo6M0HLiOBB/yHFhuxLI5K1tkm/ddW1uLyeSGTmYT9aOjo7ymXq9PUawet9KYLuOBAHvpFXv5XZVBbwB2GIT+oo+ZC9YNBjOYAvoeoxfQI0QE+BI7pfAG7cDYq9fruU6efbqRQ9rE+3r+EWYyw8E7s6Z7NBpFo9HIMSBhbjgcZg1tWAeAGn1khsAeNH3iWHHpGJU6r9TFHztKQ6nqsJNmGrvEAxv7/GC8uG2+18eOBwO6d3XyXa67gY/JYE8k4sO6nHznzr3L0zVQ3QX6bnO59gwFjhIltohnQCEBkmywxvH+nERjQMfr9FIQJhKxob29vTg8PEwrPyIypghIQhdxjMfjnOAGOsCECexJi8dtIKKN7n/6xKXiAENXrOJcV4dy7VvGy947/e4EqbJQgeO9jqOiHFEUHktTmICCKTeUKrFwWALe2Z5dvV7PpKr19fWMATvpCbqXQgiAM8aTY8TtdjsrGuHVAqIcpumREcbEymYymWSCFx4k38/MzESz2ZwqkuGwgJe4IAPeuxlPdnZ2NlqtVrITUOnMKYzD8/PzODo6iuvr66yItLy8HJubm1OeNWNFTN39CIii+Bg7ZNxKlbGC6uY3fQQolsrZ8WFkhnkBgCF2awLGAAAgAElEQVQLXOsVCQ4hlCU98Vq97A5qfnFxcWrNOX2OHNJH/X4/Dg8Po9FopGw6o9lGgzO+GQ/eBZAmzo9+Ojw8jKOjo8xAx1O2kcYKA3t97i+Pi/V2FZiVIGZH566jypG663yDvfU9ess5QXboSi/YhtfHjgcDuhx3ebSfusadZHffR/m5zy8nJN/7nLJ95X1sVSMoWPh8PhqNpqi409PT9HJR5igXqELHDiNuqy+xuTXW8mAwiL29vej1enF+fj4FrCz7YMIySQAx6KV+vx/j8Tj6/X7s7e3lmkmWp2DVe9MEL7/x5OU5TCZ+nBxGxSnHMKGIy343oHrJVMR0iU6PKeMDUJi+9Pn2asqxtZWOBxAxDWD+jPGxVQ/9T/zLhTjW1tZiY2MjwwMwDowJ4AZVSCYr6zE58IRoP0rBHg/eMbW4x+Nxrq2NuM3YxgN3fBxZ8XIi2BsqkQGmi4uL8dlnn8XOzk40Go346quvIiKi2+3Gq1evot/vx+npaYzH4+j1ejEajaLX68X79+/j8vIyWq1WPH/+PJ48eZJrnpeXl5NO9fsxrxx7K3VDqXjLhDnAzyAKk1EaX4ACY8s5yLGp+ZKWtVHmQhMsLYyYTnYs6XfOgR1rNpv5PoAuFfCIETPXkUeup/+g+0sdCmvEskO2+dvf38+qc1tbW3F6epr5KWbBeI7Hivt7PpX9yFHStN8HG8r5e9dRzv8ScLm2yrkzZnzKq/bxYEDXDf6+3m4JhKXH/KmOuOvensB3Aa+TJ6yAAUmsWjxYAJJNBM7OzqLZbObkRLHi5UZEggX3g2okBnR+fh5v376NXq8XBwcHcXJyErXa7ZZ3gLgTSvBoJpPJB6UYh8NhvH79Og4ODuLy8jKazWY8ffo0nj9/Hu12e6oAgi11U6ocjgdhXJTUFp6cvT97zJ6k9AFenD1WwN0MCJYnSqeUEY+j5YY2lROM53Fv2sq4AFiMD0wD7WXsANzt7e14/PhxPH78OCszzczMxHA4jM3Nzdjb24vd3d2kbknG8nIaxtYemvvN8TWUuRkUEqEASwyDMvEH+WA5EAlTbKzR7Xbj/Pw8ms1mfPHFF/GXf/mX8eWXX0az2Yw/+qM/iqOjo3j58mVcXl4mfW6DApbm7Ows3/f8/Dw2NzenDAHi5LwbgMvcw3AzeJReXqkT7B3bmGOe8M5VbJhjvPaObKwxpyPiA4B3/XGoW4wbcin43tewFHBlZSWpYPqNSlNleMUJUJZvvHzrPuTeywnpJy+TYywjYko+PBfpJ8fazVpUecA2gO8LaOXY+D53XV9+bhzyPew4WeeXXvmn2vlgQLdUhHd5qz6f86r+9z3K465O5j5VlsunBgwBi4gpS48C8FicxFCZXCwRckwHgWXCYzl7V5jNzc0c4NPT0wTcXq+XSoaJ4hgYVKS3ZnOyk7NyoRs3Njbi0aNH0Wq1YmVlJWNGpQfuZKyIaaDB++M8BNdLGGq1WlatwXPA42AZluPItL2MUaEkTJvZC8IgoS0YJYy/qTX60fdizDGMMBDm5m53eXJsjmSZ0WgUS0tLsbOzk324tbUV29vb8eLFi9yf9uTkJDY3N+PRo0exs7MTW1tb8c0330S3202DDSoT0CGejeFDP9mzYMzoP48TCpR+5xm8Y8kikNVKJj7hkvn5+Xjy5En8+Z//efyTf/JP4quvvorr6+tYX19Pbw0ve3d3N6nt09PTqaSxq6ur6HQ6OR5sqE7JRGdb885VZTat2JlHpSyY/cBY490BPwCrat77WfwNMJUJbAAL7XEuAKwB/U1Ywp4z89aGHXHzpaWlODk5SaOFZVc2XpFb5iN6hTbyXMcxV1dX44svvsjQCDknETdlKFkyGBFpKKFrTM87v8NhLPdnqfetg7+vM1WO1V2HQwZ+FnLg75CLKqeQ9/zU8WBAlwPry4NxF3hGfEj3fh9ryM8s4wgeWNPFfGZ+34Lh2K7vTRF3vExiHkxOl5EDbAEVAAdhxOqPuFGYVPMhycReLu+CR2uw4nre3YvXef+ZmZmpNbwREf1+P46Pj6fqEZsmW1payvuZAuR+UHC+v+lN+tq0N22jrwBmJ2IQS2MyM1kwZBhL+tlKmL5g7EzB29JHYeIpovDxaunjg4OD6Ha7cXR0lGDLMh/K+S0sLORG4cT0XbyE9ZlbW1uZbIPhgaFgQw3jBtCCHuWdMBDwstmSb2NjIzPdUSwAoY1hGzBVLMDq6mo8e/Ys/uzP/iz+9E//NNrt9lSy1czMTDx+/HgqNv7q1aup7G6WukTceE17e3t5D4Cbov3kINAGj7k9J/7GM+YdSqXPd8xFe2OEVZBZf1+yYnzmOUX/GXz4u16v53jjQULnYgwTTmDJnvUDbebdCVkdHx9nXJdnssadOek8DeYDP7z73NxcPH78OL766qsMe11fX0en00njB1nxEiPPN8smR0lBc5gCpy9/F4eNEHSYQbU01h0e4HM+szHxqePBgG5pdfJZRDUdaOuCc+5z/6rDlkv521QM//teDJItYdqFoKGkAUsbFrbGURAeZFcM8iDjEZCQ4SVBtr5duWZmZiZr87rfaAdeDZMIgGOi4J3jVQ+Hw0yOKq120788w2X8UGQeA/rTnkdZIm5paSmpXO6DIXKXUsPz4/4GXBt4ACYTynFmxpcY9sXFRXoRVIaanZ3N9bhsFEHmN2BLcfrV1dVot9uxvb0dzWYz1zxDJ/f7/aTfZ2Zm0sChHTAoeBSTyW2pPRcAcfy1Xq8n4NMvlGTEGxoOh7k5B7E+J6Qh14uLi3F6epoZv2dnZ7G2thbb29uxsrISvV4v/vqv/zplmMxrljVtbW3lteyyYxDiOD8/j36/nwl40KkAGsVG6BPk3DFVG5CmBqt0ieP0NrS4d1kghHbY0LPBzfW8GwBpw2UymSTVznx2YZ3Z2dnY2NhIWXH8tl6/SbDb3t6Oo6OjzF4mb2R9fT0BEDmy8Ui/GCzoS1PEMDTeivHnP/95Fi7BIOc6QgYlbe2fKu+QuVnqj9/FYaAsAbc0KA24dgLt2HwMY3w8GNAtKQM6AiVYdXhiGPA4yg4owdsWMPez0nUHV4Gy22pKxpMXAPrY/fjbHoApTQufBZq4KpQS1zqzrow5lwCFYuZ71otyf+oy45FiYQNQTCo8PTxP3gFwYzw8Pn4uPxR/53smH96ZrVLG3dYm70MfmK63gWMQc7a0PbmSVippSMCUClCnp6fR7/fjzZs3MRgMUg6IuwOUMzMzSUOzJdq7d+/i1atX8etf/zrevHmTBg0ejitIeV0vhRRcMJ++KJOBysxk3pWdbpBfvF68LZRuROS6c6h3zp9MJvn7F7/4Rfzt3/5tFvsga/uzzz6LP/7jP45Wq5VlQxkvjAOMUwMB9b0p/kJiEUacs2utIEtv3IYX93esv9QrNrhLjxo5sDw7hlnqmIhbFoyxsCEP0JVeJu9EpbGtra2sOhcRySyRhIfHPBwOM8HNhorpdeakWS/PNTLwKXhSq9Wi2WzGl19+GePxzWqHb775JmXRLAmshXUmrFCVU1UaLDZczEBZT/8Qh9/fOrOKGSsdLr8bcld67VXHgwNdu/X+rvRoyxcrO+JTnnApeH522a4q0DBY+54WKAuSr7OXXJaPA3jtrZbChjcTcbuBtTcZwDt13NLLhezF0Tb6A08X6g6hhOZkkwZKDJJBDeWJNW2gKqlurzMswwnuM+hV3slK1IeBxmPL/1bgMA+ArhW049NQkfzPu0VELutpNptJ3x0fH0ev14t3795lhSFPaEAMJgAAZN32y5cv45e//GW8ffs2WQYnwJEhisdsgwHDwolPPNsGDUUVkAveGU+zVrutDFauy3V/kIxFTgKyyJKhly9fxv7+/tSSqlqtFj//+c/j66+/jp/+9KexurqahRWc8Ef/RERmTxOzPDo6mtpykPfyRhv2yOkfe6KMi8t28h3tqCraUoZJPN8BBStlDD36krEs57wZL+algcdr8M0kNZvN/J4DY4Q5iEzauKNdpVFsA7Zsc7fbjXfv3sXh4WG0Wq1M4qKimpcvIdsY6jAw9Xo9mSF+l+yhnSCDl/vU8/r3fZh2ts4228bxMeB9MKDLUYJl6RmW56Lc77qHry0Bj88ipssWVnmzvre/K0GY38RIyonFda7mY6/LiT0GddObJF+hbG1dekcdEhmwgEur3H+jcNmir4xFkbiBkkSpmR3As2LylokTeDOcb6oPQGJsUIIl48H97MEZ0KFCDUCMj+MwvAOKB0OBPiPT3LQT92KfUXbJwcPd39+P4XAYEbfg7naiJPEer6+vU6G9fv06dnd3s7SfjRPGkApCeEyAijd0cPx7MrndDQYmgmplKEO8SIcc7GExJ+iTs7OzTGyjSD8/3W43Dg8PY29vLy4vL2N9fT0TvCi00ul0YjQaxYsXL7Ivzbw4sxeAj4jMazg5OYlGo5Fr2xcWFjLpywrRc5Kx5DAI872XrTE3LF+ej6Uxh5yii7wRh6l53ov2cR5GtD1nxtbLzEajUfa/2QD0CIYay/kIz8BcIZMRtwYD71aCvsNFnU4nvv7662i32xERWcQFfcN5MDFmWpxIhRHm7TM5Si/SutgyX87pH/Lw/Ur8Kc8rMcJG3afa9mBA1wJ+lxdberAlXXIXQJeKOeLWakFwy+9L4L+rzdyrPM9WEYICuNJub1wOaJGEZLA2VYaStBfIdSR7QA+VwsF9mbA8k8+YrI4tsUYSxUWiBO9YMglMWHtGNkRKACorCrk/8TisINyvHgcUHpMzIqZAv2QmTFeXzEqZiGQjyTVmeXePiT2rsq30jcEY78fj5MQ0AwCKi2sBDfqWtnt9NEoV74NrvX7by7gsexGR3qc3T5ibm4t+vx+TySSOjo4yxk/2PEmDLoLh+9MeaggTi3TlL4cPMB5OTk5ifn4+DRszFhG3CUz8P5lMpsILJXtmY8IGHd/fZXx4bloGq2jT0WiUW9+V35uJsIxaJpCB8fhm7Tygi/GGfLgUq6tBDYfDrCaFcYZxzP3RG9ap9M/CwkIMBoN4+fJlRNzUZX/69GmWgUWmmOvWLdzLBWS8XvwuJsV6vdTTnks/1lE6Y3fh1l3HgwHdiOr1URHTQWoDrr0Pv3SVh1s+g+8M2vxtr/VjbS3pZQMb/5vKtUdqjxugQDAdZ3Js6fr6ph7tmzdv4uzsLIvfr6+vx+bmZlrZJY0MZWzvlInH+2IlExN2BiXrAZ2k44QS04KAkicTR2nc8N7QjPYInCxVAgHfu2gD/WUKjXaU1/E9ysnjgxw5G9RKiL7DaDo6Oop+vz+1WYUNBMsi9+Rz/meDik6nM7Vbk2WNDFZTc9DOljeDtWk++uf09HQqfIHXynMM3siAd7hZX19PeXHlNBuVpjePj49TeSNLrAVut9tZfYsYr6ldAMQs0enpaQI548fSGMbWS6UwKlDovB9yWsbxmDf0iT0/MwiekwYH+tvx21KXVOkVjyGySM7GcDjMZVymmqF3mScLCwvRbrdzdUGv14vj4+PodrvZ9/a+kTHaTP95BQGyd3Z2Fq9fv456vR6DwWCq1rLZMdP2ZMbzDGLPPJ8kQfrLIZwSC2wk/ViHdTsOSWmk2bC763gwoFsKYundlucYeCM+LMvlc0rFawD3M0wlVnlwPhA4rzMtOX0rXKxXFziPmI4p+V72fgC4s7OzODg4iF//+te5Du/FixfRaDSi2WymUhmPx0lbQyHSfigrrHuUpYGXSYeyMlBF3GaBeq0kaz6ZUPbq6Qu+i/gwPoJy5J1RAkxK96W9aVf7Kpcdca2zl+1NmBLCE0FBXlxcTHkAjI2VKsrNpQ/tVdlL5hnz8/NZe5j42+XlZfT7/Wg0GnF4eDgVXgDwTDU6tkqym70vvNl6vR6NRiP7EiaDJBz6hjKB/sygZEOQXarYLQnDbHV1NVqtVhopPA8jBXmgRvTa2lq02+1YWVmJk5OT6HQ6uRdsGZagXzHEAB92VXJ4hCQi1vIyF2kXskG7HJMrmRDrCC+no59L3WT2wfFK7gdwc51jwcwNszwk3UETLywsZFU4jF1i+lC3zWYztre3UzbpU7L+G43GB8aSDUri4izxQiaYBxh6fq9a7TY3gUp7o9EoNjc349mzZ/H48ePccavRaGR5T3vE1p/u9yqW4b4e5Q95lM8tPVvmW4k1VceDAd2Iu6uIlHQi35dWUJW1USprW6emlh3PKT3WuzrRnjaTqEyccmyC5SZYhPZ+eA8AE0rNHiNrJ6kIRZUaAMFVolA6JFjRNtc+NpDQPhJj/P54KYuLi5mQ5QxnJ1FY8Ji4nty+r2lYJrzpNNpFv8zPz08tOzJ9XQJySdkCLKVny7VWqgAQxgpgSb9F3BYBoBSmvSHOwzCBwgQknjx5Eo8ePYr19fUEKLZ9xGPw/rHeEAGApc/tiZkK5W+PM/JzdnYW9Xp9at9WG0MeRwPu6elp7O7upqHhdyc7lrECZCnAgBdbq9XS0yW+/P79+ywownXE8UwLI8f0PRngxNavrm7KmDLPrAPsuRpkGV8b2o6VIp930YnIWMlMuS9Zg41357XlNjw9Xhi/0O5UhptMJumxOsmOIjazs7OxtbWVMXbGu9frZQWxubm5TADk2chsuSSK/jfDwhzAG19cXMxlcq9fv85Sss+ePcvlTowHeQUwHjBkNmTcJsaPsUIufNzlGP2ujirgt675lFHwoEDXwuzPqg4DIueYuvT19mwNrAyyE2VKT9f3q/K0LTD2dO2JO6EAIDTlZEXFJMHTdGwH5U91q1arFRsbG5lAgxfjLEnT164+heWN4qU93rWHylePHz+OR48exfLy8lTCBs+wEgNgDAAoE/enafZSudF3TsrCyi6NED/b7IKVA+f7fwDfxg39UMojcgU7gGFF20rPyROPzxcXF2NjYyP7kjWXLnpi+nJ+fj7ZDMDFWb4XFxdTXr5ln3ehv2gXa25Zk4s8eP0t70OfcF/o4vfv38fh4WEuD8ILw0gi9ovxx5jCGtj7xPhkzO3dck+zFabC2bCdeKUz4s302EOybkGG8fRL2fPf5+fn2Q7Pb8chLevEkTkP7xG943N9P6j8UicgR8y3iMh4uMd1ZmYml+lsbm7G+/fvs8Y1Gerj8ThWV1czA5z3djwVHci96duI260IXQ1uOBzG8fFx7O7uxu7ubnS73Wg0GlGr1TJpCt1APN8JVY7beozMNnheeYx+n8d9nnufNj0o0K16KTq8jJ9yWOkhNOU9DSq2Qsv7lJON62lH1YEwOGHD53tyInS0OeK2IANeEVSlLT73w8rKSnz++edpQW5ubsZkMsldaVw31jEHZ7kCjAD40tJSVpjyhvRra2vx7NmzeP78eaytraVnh0d9fX1Tgo/1fGzFFnGbfVl6EvQDMR8rRA6s7HIM6DM/gz61EQYDYMXGuaX3DLhbXlBAXtTvxA8bMvaqTdWhIH0vPNatra3Y2tqK1dXV3JiC71utVqyvr6fCHI1GqVTtUaPE2M4Neh9vysoJYK/X66loIyKVHveDwXBMD5kGXL1ZPcqZ98fII8u9NGLn5+fj0aNHsbGxkaELvv//qXuXH8myLU9rmZk/zc3NzJ8RGY/MvLfuvVmDqpqUxIRJiyG0YALqFggxQOppSwiBetYDGDACJCRQSz0AJiXBBP4A1DMmtJBKKnGr6tbNzMiMDH+aubv5+2GnB57ftu+sOBYR2bcu8jqSy93Nztln77XXXo/fWnttBC7IQIZ/qXJE6AIvkPrCFJc4OzuLs7OzUiEJ5cG8O05vBMA8xZgMRfuyYQMPQC/zs9e745Zug35xH7/tJJCR7LwFYOXV1dVysL3Xxvr6emxubpYEPxCqdrsdh4eHtR0IyCCHmkx7/m+1WiV+C3JTVVXZssXWIA4/wBDIqAHKHIOJPe1ZrhqJaJLt/39dWZbMk0ufagQ8KaWbLRor4CYLw5PggVtR+XkYP1u0bicr5g/BBdnDNbRszwPvxP1y7JV2gHWJ3zAuL8Zer1cSpzY3N6Oqqtjf3y+Li4XJQkKYk8jgvhKv82K6uLgoXvbu7m58/vnn8erVq1LXlVq7KGbTKNdGtrGDIMrxLhspXtx8lxEJ09lbZxAoeCR5i0ZeMA4j+HsEowUPdEIJA/cZwrag5UJBuDQlHiTCMuIRqt7d3S0K7Ze//GUcHh7G27dv4+DgIPb390tCErE4FC40Bv7DG6RQCQoPOtswAP4nLn9/P6v9i9FkNALBCsrgpJksGDc2Ngrsu7KyEt1uN54/f14Oznj27FksLS3FeDwucw2tvQadnOaaxNCAE4dAexxSQbEaprRhzRxZhjg7OWKGBjDW3Dd7537OhngTpG3I3PwCXxuupk3QhrOzswL5sl/XBWsiHpXu7u5uUYgnJye1Qy54N4rPfJIdDww+EBbo734iLwaDQUHLbm9vY29vrxjXm5ub0e12Y3Nzs4RTyFHgspOFUU8/bAh+SC/8Pq78viw7PuQY5uvJKN0mxWeGnnf/p3ik9j64LJCsPLJlNU/h+pnsPefkKjwsGMnQKs8A8yJcEdB4KRSj73a7sb29HV9++WVsbW2V+r6uRuMMYhSG4TMWKIuUAxNIZkG5IzixtjmM3NY6dIBmZJ/awEHwGMJyrM6LnM+sJBAUTd6whSeKlHm2EGEevQ+U5+zZmAfxKFDiCA9neXv7FF4z3j88tri4WM6/vb+/LyfBgDTAL9B3a2srBoNBtNvtEi6gL0YHQEaurq4KYsGRj3nO7a3b28NIysVQuIcqVyAgZ2dn5VAKDEbmA88Vo5EEq16vFz//+c/jq6++is8//zy2trZiYWEhRqNR4Wu8cPIEPM+sBys2eIKkHgwFG2YOd8BL8LL5Co+T92EgY6QCk2JMwr823OBN+uoSp/CyDTXHMlutx6MzSRRzOMoX6Am8R4wVqJa92iQrRcyK5xCyQnHD89vb20X+QAd7+DYOfELV9fV17cxjKo51Op24vLyM7777Lo6Pj+Pbb78tCphY8vb2dtmjzqENvIc4vr3IHPKxEe/w2+/zQr55TflzvvsUb/zJKN2IZoXpAebPmy5/nhVzkwfsRW0L5mMEtMXr99qSjHjfA7c1TQyMTeVAvj7th/NsiQsZotzZ2Yl2ux0//PBDREQpnmDL0AKGcXv7Ccy/t7cXBwcH5RSRfr9fvFuKzlMPGMGFEHG2rLfyeNxYxhZU0MZ7BrNgtUXpebMH4DYcB7bxYcieviIMrczxpmiLtu1J2NNrgpYYLzFz5ur58+fR7/fj8vIyDg8PiwHV7XaL13Z4eBjffPNN/OY3v4nf/OY3pWCG59MKhc+hnbewWAmhTIH9ME7siUXM4taMk8MIJpNJXFxcFCWJl8Nv6Ar9r66uyrmr/X4/er1efPHFF6V29+3tbRwdHZVjC1E8PksW5cZv5gdeiphlFdNvPGugXfjUUDLz62pnDvPkJDwMMDxyyyTzrr1r81qTHCHkYC/aZVSBk53/gRGAx2nD4/LysoydmO1wOCyZzKPRqPTj5uYmzs/Pa2E5ktoszxhDPigDqBp0aTgcxosXL2JzczOWl5djMpkUep6dncXx8XEsLy8X5KPb7ZbEwO3t7Tg5OSllK+E7h6Fy6DCjC5+i6H6fl5G7efrK15NSuk2dtbf5U60ZC35+W+hauWZ4wO/8EGyQiWyF50mwwuU5BLlhNYSyLUqgZix2Th9hYZFYVVVV2WaDwqHvCEcnXRAXe/PmTXz33XdxdnYWVVXVFi4wpLcTUUydzGV77IwDpZNRC4wcz6szn00/C0H6n4s7IDwdc/F3Vvr2lu1pW+FbwBuq5r30rSkDlXc7Lkmc9rPPPisF46+vrwtkvLGxEevr6zGdTmM0GsUPP/wQv/71r+Ov//qv482bN3F/fx+9Xq9sL7LlDx2Yy+l0WuA/99dGDMrXPM/9zmLn98XFRdlv66Qne7V4yfbqb25uYjQaxcPDQ/Hibm5u4uuvv46IqB2qAYy4trZW9j7DEyhG1o3jp1yO6xPTxvN1uIO5Zr7xLL3WyTS2MeJYrI0BaJATpJw4ZZ42L0JH2nM1Nu4BZYCX2DJWVVXJ4GaeiIvmowJBU4CQGRser+P5GxsbtRCUYV6jXxS1wQjDi8ZrfXh4KPkmDw8P5R7up1758+fPYzQalex1lLmRQ6NoTTL69+3h+j28KxtkRjo+xQh4Uko3orm28u9qzcBoJlqGhJvggbxYmi57Hfae+dtQEX1AKQLBkSCCwHA9XaxgQ0vEX5eWluL09LQUls90NHzufYDT6bQkRe3v78e3334b+/v7BfLkGLrl5eXodrtliwIwNtuKEEYoJ2hh+Dh7AVZ+VoAWrKa1E1r434ISr8cFFRwrZvwINd7pfamGh3JhBjb0028EA31B4SHMKVaBwsCiB4ajrfv7+1I2kj2LlITc398vxzTiZQMhoixsgDhhh9/wEAIYWjAOx1B94ANjrKqqdrZyr9d7zygi/gtvAsmyFQQ+u7u7i7dv38bR0VEt4YdiDhsbGzEYDCIiYjKZFF51iIG5c0Y0Cg641BnGTXvCnQtgg9uKz7/5zuiKkTG8zawInAUPLxhJoG0jXkZ/mBey0yOitl2HOff2PejJmcY8HxHR7/djZ2enHDY/nU5LjPfs7KwYz/AA1asYL/3s9/vxR3/0R/HVV1/F7e1t/NVf/VV88803xSgjrGDjzglsHm+n04mtra3Y3t4u4ZTz8/MSWvB85dAg69XGzu9TAVtv2Hi1A/Gp8dyIJ6R0TTB7Tv6sSfF97HNbIfl+vvcC9HMfsmxMeBa6mcqfwzRWuFifeJQktJyfn8fJyUns7++XBYDAY4EdHR2VY+L29vbi66+/juPj49pBBIyHhRcRZTGS9o+Qp6JSpzOrQEV9WwRnLjABVOkCFo5zAZ/ZAzUkZE/A848wchKWBSCfG17OcTneY6/V3wPZRUSt9KDjzmytoYIOECwm5vAAACAASURBVDXQpy1vK24LHDyTtbW1UtWr0+nE+vp6rXAExQ7oY7fbLTFdIGpv3aD9VqtVvAzHKG1AmP6ZP+25I7izgYP3hHGIp3VxcVG+J95PW71eL1qtVtl7CwRKvgAF8z233sPe6czOcLaC5Tfz4xgtxwySMW+IEn7y3GZ4Eg/Tnp2zb6Gl+2S5Ab3wGr0ODEE7DmmDFDnBnLNeieHbWMTIAf2hP8DOl5eXJZsdFMH1vs2zGFgktPEdStuhFs5/fvnyZbTb7Tg7O4sffvgh3r17F+12O3q9Xrx8+TKm09lxjuzldoW3iCjG/IsXL2I0GsXR0VFRut5W5rVsGZ5Rqow2fkgv+L782bwrO2M2rmxYfUpbT0bpcmXosUlJ+rOsGPMztlLzZ/Mgg6yE3W62bPksK2l7elh5FrL88Pzt7W2Mx+P4+uuv49e//nX88MMPcX9/Hy9fvoznz5/H2tpaTcicnp6WSj6np6flPMtszdvjhg5s/RiPx2V7BdAa2YzEX1gIxPfwaFBQeC45Fkgf8tzQP3tghqQdHyTeZcGEYLCgM1rBs4wfoccFfEWb3OsFw5xQ6Qc4zRm7nncLYTxztlT0er1Ss5gs8uvr6wKnQjcMGwQ9STEc6wfywfxDKxALC8Zut1syhpl30xFvEcFqz4n5oP8Ifrz+Xq9XE9wRUdurSzjCnqeRnoWFhdjc3KydQUyG7eXlZa2AhvmXOWT+zVvQzQrWxgVrMCtJjCnLFAxbns9ywwlQTvhxPJd+utCEDQwrdp/S02q1CqoELwHxO1Pc/c+OAoYhaxZvlnUMrY0E8A6P0RXreB9hkb/8y7+MVqsVb968KXtzx+NxMS45N5m+jUajgu7QN9bD5uZmvHr1Kg4PD+P4+LhUOrM8tlye59U2KdAmBevrUxSk5b11k9+Xle3H2n1ySjdno3lwWZk2Da5pQppidwj5DA94YUTUz8hs8pxt9eQ+2rqlHZgZAcznbC7/9a9/HX/+539eoLi1tbV48eJFLC8vF6s0Zyoi2IEkeb+hMQQ1CxHrcjwel20tvV6veLnElIBlr6+vSxUiQz0YEVjolCxEGWavgot4U/YUaBMhxHx5X2zO4s3eQkQUK91zzPOtVqtsLyHxyzFQxsFvC2SEob1hPvd2NJS1kQdimAhblCJbOo6OjuLw8LBAdbRDARQU42QyiYjZ1iVQCviJuWTLT0QUw6GJR1HyfOczfAmBoEDxrpl3DCOSZ6iahTdMX0nyY8vb5uZmOaKQdWLP3EVDUFyse+aT+ce7xJMFUcCgQhnDH4zdsKXDHUZB+BwaOZPdxhZ5DyQdsc3GvG8lbRlmIx4DAh6/vr6O8Xj8XuUzxzcJ80AD+AdFd3JyUuowwwMoceeUUNGL91dVVYwCkLG/+Iu/iMPDw2i323F8fBzj8bhk0B8dHcXJyUmsra3F9vZ2yUPAeEeOMB7ouLOzE19++WWMRqM4PDwsFdNsMMGfeMFNjpPRmyYl2AQBZ/q7TesBnuc7nrPRzf9NCt7Xk1O686yEJmXK5/m5fK+JlRWif6ykmgifY4V5MrHCuc/Zo36noVUnUEwmk8LIHI32/PnzeP78eSwuLpYM0ogoyo2KRb1er5R5Q9nZ00ZpsC3l+Pi4LJZOpxODwSBevnwZ29vbtXNM8fgiokCKJHjY4mesWOcW1jAjXqC9fC7De/ZU7UHhPfDDuLzYssGBkuMnIopniTAHskMARUTJBKU0oeOmGSLnvWy34HAAzr5FaBFTW1xcLN5wq9WK09PTGI/HcXx8XOrXUhWMsTCXeOrMJbWQSYRyEQ8Le3jWXha0gxdRKPZWgMV5J54StGMOmK+FhYVSrvDh4SFOTk7KKUzw5dnZWYkbErf0EYA2TvIaZzuW4WbzFvFk7z93LJb5hw72fp1TgMJknlmn0Bn+tVI3r0ELw8vmb76zF2xIGp5nl4ETjKCFFa8FvkNRhKtc9IYx2hC118uhEhj29NVGKufq0g7hg5OTk7INCbSk3+8XutMvbyvzjozhcFgz7OFly2HWep43y1jPsw0789I85ejP7Wx5DRkJteKe5wz6enJKN8MKWVE2XU1WTSZybt/Kls/NxCwA98HCLCtyxwadoOGFFDErfA5DI9in02k5FH1jYyOGw2H8/Oc/j1/96lfx+vXrqKoqDg8PS8yGOA1739jvSSlIe9L0FwiPLQQXFxfF+3j9+nV89dVXtYxDaIMgx/ulgAY0yZ6/vQ/TmP+5x1tX7IGgaPHkmAOytHknbSFUTfuImQdgAevENytr+oeH4cQwvAiyL1utVu0QbgSuY73AvI5rUlyCE6IQFqenp3F2dlaUmY0FEl5Q1PbGUKjtdrsYYHioxH/hNeaR/uORWfHAnw8PDwXa3tzcjE6nU7JVEZokSNGu4+nEoG9vb2vjZOxXV1c1Tx764OHgYQHzMtesP5LXvGcV3kJJYCh6+1BOdqT/NlaykQaKAs+AzvA58wo9uRdDBwMWlMq8734gexiTPajsmTsPAr7wuvJ6JwzlilYu2mHlgWLF8IF/vZVxa2srvvzyy/jyyy/j7u4uvv7661hYWIhvv/22bAPb2dkphV8YGzXDXcPZW/oGg0E8e/YsXr16FWdnZyXG7DWWFW9WhlmOZ8/T9GduP+bMZZnG59no871/5zxdriZCfOpn+XMrTRZek6WSJ8eKN6IeM7TV6sXhCXUskQsPCk+I9y8uLsbu7m784R/+YUl++uqrr+KXv/xlvHz5shzeHRG1+BfHmg0Ggzg+Pq7t78MDinhk2ouLi1Imj/hdt9uNnZ2d+Oyzz+LLL7+M3d3dspWITFIEDgkrCDpgKcPIphWWMULeiRR4lghYz0uOFzoD0oYEc+iFacjHfEDfrq6uCs0zIuB7Dbc6rkZ/bIBljxdYdn19vcCt9oYQtHgeZAFHRDGmgPOvrq4K5Ed/EYRAc+12u8RxMeLoO54Zgt8Vm8haxcDwFi8ENu/kM8YLKmD0AnSERChQGJQ9hxGAWPD9dDothpxzH4CprfCAGjFEKP/IliQX9UdZgNA489k8AZ9lj8lr3tv76JNj/PaSDcU7XwHa5gQ3LnvX5+fnNYMVw8eyKsstkAL4ipKthtCzIQ5tUd5Gw5AlCwsLJb4PL6BAKaCzsrISZ2dnMRqN4uDgoKAjhBdQvNxrOTudTmN1dTVevHgRx8fH8e7du3Lwh9GUvEbnebDQq0kBGg2YpzualOY8mZJlx6dcT1bp+vopA/KViWHrBi8gv8cQTp40e7YR71e9ceyY9yHwsEaB77AiHx4eyokyi4uL8fnnnxeP5dWrV+U0GoQ9cCfVh1jgQDR4DFiy9gRI7QeeI8Nwa2sr+v1+8W7a7XYxCFDWbM2wIsVD8LmYWKROTrI3ym9v+qcNFJwNGgsMJwUhDFCO2QIlmcfe7XQ6LTAm84kCQek505X/7dVbuTqmaW+fM46BZuErDAtimcBtKEa8Q7wAC2qUWav1CEczVwgyZ0ebx/GeoDnGFMk1wHz9fr8obWL+ZKeyZcl0JvmHeOH19XUMh8MSy8NzBD7OHvfq6mrZMjKdTou33+/3i+JA6KJYifO2Wq3iea2trcVkMinzAp9mIwSDyfzobHsb2qxt8xNrLJ+Mk3nVuR3wM0YkvMYYWEcZVaMdjGr4tqqq4v2TkEQcGZ6ldjbGFYozrxH6bPSJ8aO8z8/PY21trZyM1G63i0L89ttvY21trVRFY5wUfrEDs7S0VIrtwAOMxcVLNjc34/Xr17G/vx9XV1dlBwcyOcP0VnrQjvFZQc5DN41WZj1gHZD/trHt75raarqenNI1kbgyUX/qlS0kfvM3isLv8MT6OwShlanjm27bStgb1QeDQQyHw8LQeBH39/fFA+73+/HZZ5+VxIi9vb0Yj8cF+kOo2MKOmGUCu7LSw8NDqfhCjIuEluFwWDa8s9C8X5FFQV1g9gYyHse3UKjEfVjA9MsQ5P39fdkXbI/M2Z8em+FqFik0NvPzDNsUmJe1tbUCpTJnCENgyYgowovFDJyPgOOdGApOpHLCDgaIlUdEFIWLIPHJTxyZBz9iWJlPEZRsD8GQccwR2nkfJ9D29fV1KXjBvuvl5eW4vr6Ozc3NgoIYEsUrdfU0F8TASHz58mX86le/im63W5ABQ9AYB7TV6/ViZ2cnlpeXS8wRVAFepdbw4uJiSbJhfkkWw2BAKTucgHK2V2r6Z8TK84dXmlEvKy88bCfXWajz27xdVVUt0cntst3IiWH0ge+ce2KEyGuAdWul2uSVofSZI2hBOOH8/LycWYx8ePPmTfz2t78tx/Nh1DEvnGpEyAuDhW2I3Me64Z6FhYXY2dmJL774oiRtnpycvOf4OMRiQyEryCYPFJ79mIeadUaTYuZzhyU/xet9ckq3CRL4XduzhWNYx/BrxPw4gT02PneCkBVBE7SMV0aK/O7ubuzs7BQB5O0oVVUVT+H+/vFc1dFoFG/evImDg4N4eHgo8VoULNYxcUXgQ7xNToXBm2OfJAcn4NWS8EJ1GKxkLFiEIsIG693wrqFbLxAWVZMhg4KE1vYK7VmiwJ0YxHudaMJCtDeCcsKj9EETwF1Al04kQ6Ag9Lxg6a+hPhsX0B6By75dvBCUPfTlwInRaFTaotgE/FpVj0lHKLWLi4sSozPEz3cISpQV/WJMzAlzf3NzU/pPCIIsbODE1dXVwkevX7+OnZ2dYiQOBoPSlkMc29vb0el0CjSOR3p3d1e89LW1tbi+vi5Z+zc3NyVGfXV1FQsLC8UDx3P39hbGzW/Wk4/I9Bqxpwmf+G/o6epuVtyurx3RvEvC8gYjxuiFv8+Qs3nL6wkjywlxVuisDcdukREYGawhr1uHjKy47UBQqpIEPpAR2mZeyVdZW1uLjY2N2N3dLdnMvGdtba3A6Lx3a2srfvazn9X4lrWKHGV8PGejyLFW35edEv6epyT9mRNisx7Iiv7vlKebFW2Tkvypl60RvyPDFRbWObHAfTGc1GQcmGl5p5mbxJTt7e0YDAbvwbK0i7eBR3N4eBhHR0dFUKPsgKHsuWF1oyxQnrRnq9OVnRBieGv2FugXn3EPiU624lGe0M/p/iwO6GOozbFRFhPecvYiLISYN/rmhUDb5gGeJ87KhfAGfWCRI3wiopQIdPIY9ywsLBTPi7KNRgJQuigwJ/EgYMg0JdxAEhZxMGehHx8fl6pkrVarJDYR18RgcMJTxEzosO2MOSaMcHp6WkMIMCJASE5PT8ueXca0ubkZOzs7sbOzE1U1iyUCi/b7/XKaDB7UyclJ7O3tlW1TzIeT5xYWFoqghqb9fr9U8oK3HJvH+GL8FrC8g0QuZAHr0DLCyVvwXkSUNelsbSfcwa8OB6CsoTPrAIiei/asWO0gwGu8z7F7J/KR+U6/LVu8DrxGHBvmsxyntrFFVjqGBxfvhM8XFhZiMBiUUAiIHnxKuIu8lKWlpXj58mVBOdjJYRoxPzZ+ebfX+4cQ0ybjw2O3/HfCGZdl1rz3zbuejNL14sgxiN+13SaFHlE/VitiFnwHLsjK2u0wEU2K3YzNgltcXCxbgHZ2dmJlZSWurq7K3lq8Y+C029vbUoKQWrRVVZVEIGeQckC1LVYXpkcBo+hQutRZ9X14Ghke9iJEOWLxksiDZY0ytRJFgGUv1EqahepYpqFG4C/HynkHRhCCzRax59dwLAKThWcjiLnkXRwOTr/5G8HvQhbEx72tC0+R9lwOkgS38Xhc0ARnRyO4DPMBtzoxC2VHUpLjmOZP5g6DwMaUD39nbzBzz7uhr5PFWLdsNzk9PY319fV49epVQXja7XYcHBwUYdput0umPgqT+TYy0e12i1e1srJShDf3sB5J/gG65ULIY3xgvKLQEZwoqOwdwXPQiTmxdwn/EtZxboidBxv6FvoW9hFREAHWCvWkXRENo8ger9eAPVD43zKLvjF+OxLQyNuEBoNB4RFkJAa9S5Uif0ajUZE18B18jnE5mUyiqqoYDofx2Wefxe7ubglVkFSFkQbC550I9BnEwQ6UkTXG7FhvVrh8nr1X2rXi5f3mQWTjx/TWk1G62QttcuH/dT3ej703LzIzYJMy5TJkY8ZFoCFIqurxQPjnz5/Hs2fPyj5GLG4EXFVVBUqmMPjOzk5h/vPz81LiDeYn3uo9eCwushAR9J1Op8SMfWQd0J5PMTFMaoZlceExREQxCojx0A/a8X4+K2/iolj07OnrdrvRarXi4uKiJJSQOYsSYXsP/aMNx2kjZkacvWU+R/k6HkobeHPEnkn+8aLlN8pnOBwWxbuwsFCUlwWnIXngOOBYCg1UVVUS44hJ+uxU5hhFY4WQk8SIVSPoHaf31hTmBkHEZ8xdRBSjjGdQ9GSs4o0z7levXsXu7m6srKzEz372s+IBHR0dxfHxcZyensZoNIrt7e1amUt7gNAXj/j29jYGg0HxiPmNMYpxhFKEvlRjioiiuBmj59Q1h5kf3svn9BOawl/wZZ6DjNplBeuEHstB5g/ly8Vxj7yrCXnDiLIsg7bwj2WdE5N8YWxhrD88PNbhBhUB/sWzh/YRj7ssvv/++xKyenh4KEl8JMcRftnY2CgGO4jTixcv4vnz57G/v1/buwt9LVM8fhSi5XST/PZlHWNP2OutyZAy9PwpCjfiCSldC7Fsafy+rwxRoDAzkX2hPDKMnI0GLPThcBjPnj2L7e3tUm/ZMaXr6+t49+5dfP/993FychLr6+vlqCwnbNjCp5+U/Lu5uSmesU9xmU4fU/JJ4OL91D8FvkRojEajMgaMCIQN8TU8cRcox6OgHQv8iFkyFcoSpev4KQsPgemtH85IhtZYu4YC+Q7YjflCqNAHEpicWYs3wvd4FyTstNvtmrcB7TnUAGXtzNiIqMUF6efd3WMlp9FoFKPRqBQx6PV6pT0WN9u1qqqK4+Pjcj+GCRchBOhtY9AGouHjfBmFcLYpSXzwO/NM7I12ydCfTqeFdu12u4RXgJqpWHV2dlaOlhsOh8XIYq7IGGZcwLcgRT4dixgiBRlGo1F8/fXXpf+sIfaNIlxzPBae5De0xQhBPvC8n7XBZ4Xgtp3/QVvmFxePYO2CBGCsEBPH+GCum8Jn2VjkwniiP84QtsGKcY4xb76AlzFwOLwCwxy+BXHjJx+YQeGVh4eHst52d3dje3s7RqNRQYGgI/Njb9Njhd8/5qw1OVvw+DyImd/+/FOdwiejdK34MhzWNJgmT9jPf6j9fK+tlmwZNrXX5BVwOXsRxl1ZWSkxL7KW2R5SVY/Fxt++fRvff/993NzcxMuXL+Orr76KL774IqbTafzN3/xNVNXslB+8NhKiqOby9u3bePPmTRwdHRXrnj6srq7Gzs5OOb8V4ejCBnhOQGQoOeiD0gXGMzzJYsUz9BnBKCAn5OCJcL4qkPrh4WHxxoEzsVxJ4nAyF/RnEfKZUQhb4GSQY01jgKAoGTcoAvFElC2KhEQQjBaMKISCIXqMBvrKHF5dXcXx8XHs7e3F3t5eoWm/3y/7H0muur+/j+Pj4wLbAc8RN3XWtaHLLFDwXOjX4uJiLXbN/xEzjwL+xoA0JIvSsLeHwcSY2Eu+vr4ep6entfgkBgeQPEaQY73wGEoTXnVWOBD89vZ2Odt1YWEhjo6OotfrFQSCGr9+Fn60JwqPGZ7lHsYLbbLHZcUMFGoPPsOgzJfpjDKEfoPBoCBM4/E4BoNBOWiAA0uskHKCKGNDdkQ0n97UlLPAs8wJ+7fNP/AWCByZ95YL3jKGQQ1yx/zQL3iJjHdCCjn/BoMA2tsws+ye5+1mvePfdqqscDP6Srufmn/0ZJSuFVUmxE+xIv51LsOetm6yZeM+sFhsTRoeQSi0Wo9FzDnEnMUDY3OG6m9/+9u4vr6OFy9exB//8R/Hn/zJn8TOzk452Pzq6qooUuJqCDKO7ZpOpzEej4uAd79ReDBwRNSUFxC2U+8ZG4YJMGe3261lXCMkuIeEFxYeiheIijk9Pz8vB5lTAxqPpdfrlYL4ZBMD6zm5CSg/G1/MneP0xFLZqrK1tRU7OztFaUKL4+Pj+M1vflMOg3AMm/j1cDgsyAGKlMIhKH4nh9gyxktm/G/fvi3x2fX19Xj+/Hlsbm4WiH0ymcTR0VHxBpzIwz5UVyZCqCHgeafpA81cUpF4mb0Rzy1rhPE0rU0g0dvb23JmMHtNyU4msxlv9ujoKIbDYSmUQTySrG08fTw7hDj3MhbQCfM0Bi/zDz9yOhNKEprZc7Ji9fgto+BDKzMrPBtAtJcNIu6hbcbmuWi3HzN7MVox3L2diPiqcyZsNDDP0Ig1YkPAfWMeoAtK1/kG9NtePSgX8hBkYzqdlmIvIGI2gEnOY+ysO+YHowU5zdyZ97yd6mNXRjiZI8dqsz7iuax4/ftj15NRuk0Wx7z/511W3Pn/eW2Y+WDGHCOgrXmer9/poDuw7u7ubkkS4DABFJbjlaurq7G9vR3Pnj2L4XAYS0tL5Tg0W130EeGCUjJzwowsdKAdPCKgUXsqeECGWFlEZCki1NmrSfu0i7fR6/WKd+r4MJ4wyubk5KTEeJgDNtwDO6LAHYeGxjaWLHQRqE6MwfB49uxZ/OIXv4jt7e3Y2dkpWaDEBk1TtitQRITYPH3qdDq15CV4AFo6ngykBmpxeHgYBwcHMRqN4ubmptS0xUvDy6WIBVuAjLAwNvgpe7fwFn2BRyKiJF5FzCqlYQy6WIgNN9MUfrBB5EL9KH4So/AogRtRpBTYoNYzigHYsapmZ/ti5NFPtrth+N7c3MTBwUGcnJyUzGag7S+++KLE34kp0zcrT9Zu9n5Nc/62pwqd6Z+fhRbQ0TICHmFsjDPiMV9iPB4XHkOG9Hq9WgYwRreNUXjABh/84tCCFSbyjXE4jo+yPz8/r52vTLv+36EhlO/9/WOlu729vcLvnKVMOMXb3rJy4wLRsUHh++bJ+ya94uc9JzYom5Ssw0SOyf+terqtVqsTEf9PRLytqurvt1qtn0XEn0XEVkT8y4j4j6uqum21WssR8b9ExJ9GxHFE/IOqqr751PeYuTNsM29An6KkmwgIo2QIIqK+R87t5baZKG9n4fOIKJ4oHpXhDyDira2t0o/19fW4vLyMN2/eRFU9xu+Oj49rFYqAZhHulIUEnkS4wRwRs60OeLcWGGQfIqBy+xFRFCwL3SUFgakdM87n0JIIcXFxEQcHB3FwcFASMVAG0I/+4dkDczvuhiXN+BDStrwjosBa9I9yl69evYqNjY1YWloqFZAQfJ3O4wHbrVarVPniIHAqLjGH0JWastCNecKwwspHcbJl5uDgoNTSpqIThTIODg5KaU8EORnQtsgR0Ag88xf87H3MEVHmzvstIyLW1tYKrYnRMx/2gBint4sgsKzoMVyoWrSyshI3NzdFIT48PJTygb1eLxYWHisT0R/WEl6rE4S81cehBU5tgi98CMLOzk4ZP4VIDO+Su+AYsssvGlXB4GOuHfqxks37Xe/v70vfbdQ6Pkt/SDQikZIiJqurq7UtYSg2ch/wTu2xm0cMdTs8Y5mBjGANkoi5s7MTBwcHpdCL20Xx2+jloJbLy8vY39+PyWQSrVareLcY1vaI4VnzFf3FMbBx7Rh7zlqmvXmKGD1jT/dDULHnEfo1ecTzrp/i6f7jiPj/IqL/4///TUT8t1VV/Vmr1fqfIuI/jYj/8cff46qqftFqtf7hj/f9g481boXV5Kk23d90Nd1ryHheFlqThZPh5I8pc3tfCLKtra2SDED2H4Iu4tFqI9bLIh2NRrG/v188r5OTk+KZwtQoE7ICDYW7TqwZlsXP4mBxY40CB3OxcGFuvFSXEGQ7DYc1sLWBsREnpubz+fl5Of83GywZHmauciEL14DNygQaGsb1dq0vvvgiPv/889jY2CjxMLKG7YkAPyPQ2A6BF4fyfHh4KN4T3i8wrRNDQDQYPxm8VBkD+mdbBfeQId4kHB2HA+mwEDR6k42siBnqAE8wZ3nbVhYsjlNaQNG21wH3MYc8z1pE+e3v75ckNDxePG/ihOZlrztvjbI3TOKThTW8j4KiuEM+stIwLMqX3zao4DkUM2vType1A28hWwwLu8RqRhaqqorT09PiEd/c3MT6+no5To/YPmiP4Wp4J8svK6QcRvN6n05n2/YoS7mwsBB7e3slAxzvFDqgnBkj6wVkgXXsfA92VxDXpx3Hv1GIhO+yk8QcEXKyXJh3WW5nOQp9mrxdIyGfqmy5PknptlqtVxHx70TEfx0R/1nrcfb+rYj4D3+85X+OiH8aj0r33/vx74iI/z0i/odWq9WqPtIzWwomxIesiOwV28X3dz+OoTaBVuhYTiwUt+f3ejL8PG2jpGDAbrcbn332Wbx+/To2NjbKQgXqjZjFc2C6iCiQ0enpaUmSoFg878EjI+4ZEaWusDN2Wex4FHgQJGX4xA+8ZAsJGA8lDsxMzAwP0MUg7u7uSoYzsBjJUj7gnPlptVol6cv7H6fTaSn6T7Y0UCYQKYssIoqHZcFO/PbLLx9PRnn9+nU5BhEjgHfg5ZAxCsTuBB/QBPgIo8PbsOA5Z966dCUxLRQ3guvh4aEIXyB3vBKX2vQ+RS74EcXC/46B8Q7o4wIdEVGLJbLmjC4ggOBze/XwPP1jHcEnzq6lfdq+vr6O0WhU8gE4JAKjkr5z2YBwlrMz/FGaEbNTq+AnDL7hcFgSA0FeLi8vi9JgDTi73bFxo1oI7ax8QSlarVaZQ9fjxjCjoAnyxHFq6IuBQi1p4v3wCTSBz+z9ZWfCysXjtJyjfCbhoL29vXjx4kUtW5/ym5a7GBvMOYY382DF5WIu9B3D3+vZMoM17+Iu2aPNSrlJcfI5tIAfs7xvet7v+KnXp3q6/11E/BcRsf7j/1sRcVJVFdjS9xHx8se/X0bEdz92+r7Vap3+eP+RG2y1Wv8oIv5RRMTnn39e67wZwpP0oQGamCZWupO51wAAIABJREFUhj/4O0PKWbna0s/E9z0sZr7j75WVldje3o7nz5/H7u5urK6uli08udKME6NIzCH+gddzcHBQTv0ZDAZFECOc+JuFQNu+zs/PC2OxLYXM0rx1xoUovB8Si5otF1QKIiMaCxtDgdisSx8yfs8zXoU9LgyUTqdTDBV78Sg4hJDnGC+GaklbW1sxHA5LEg1n1yKA6TvJSghH18tutVqlYEmv16sJA5QoFZ2A6135y8VDUBjsL2XMCB8XNoBGKLXV1dUa5Ak/GtLLno3jX8yPYcaIKLAksX48DraUoEz421CfKzwxr9kz5HkrdJTJdPqYhf3u3bviyVxdXZU4OrkKGDERM4icbSmESfJe2aqqiiFLfBSvEKPz/Pw8jo+PS4x3MpmUEpz8wG+OeTNOG9+sGwwAwkiWSU58Yv03IXzZ8Lm/vy/GWt6uZTQEBWbP2/F+08bODZ9hUGE4HR0dxZs3b2JnZyd2d3djOp0WeXN6elpbx/QT6BtaeF+41xt9ZNwo0xxOQ9nC51mOG63Msnve70yLJqXM/X4Xv3lfE5ow7/qo0m21Wn8/Ig6qqvqXrVbr7320xU+8qqr6ZxHxzyIi/vRP/7RK3/n97302p725A/YENcEsvjJBzfBeBLYYnYQU8RiLAM787LPPSvIU+2eJeeYsOZgcyx7LE3iGWBje2P39fdlTyx7dzc3NOD4+rqXfe0sQ8dhut1sEBltmUA4sUiBXCiAcHh6WEnMkGkVEERrQCUFNDBPorqqqGqxkyBFFzCKF9txLH+1R2DJHwEGbqqpiY2Mjnj17VmoDR0Q5EYdtN+w5RCgSv8K79yHczAswM3+z7WkymRTDaGNjo9TWpp8YDsCJFphsj+Ie9wMhaJ5DuMFz0MAxPAtsDDnDzY5HIvR4LzAl88X9oEG0nxPpQCy47A1nAxdvFGPr9PS0KDYUNIVfCM9AC3jGxUPsSYKOWPmdnJyUPALD6FR3cwwaj59ngf9NDzL4LSPs9RsNsVwB0nfino0xG1OsG8ZMUlNVVcXbBaHB47fh47iuPfOsuHKIzTkdjJXkR4wYeBEDmT6wluzZupKZ+ZfvbXDjIUM7jCPeg5KGT8yLzhL3NU+hznPo8v9ZfxjO9nr7lOtTPN1/MyL+3Var9W9HxEo8xnT/+4gYtlqthR+93VcR8fbH+99GxOuI+L7Vai1ExCAeE6o++cJ1t0CZd58XcfZWm5Rsvkx0Lwwn49ir5fvcBvdgyS8vL9cSqFBmeK/EABcWFkp5P6xvK/iI+jmbMNrV1VUcHh7G+fl5EeoLCwuxu7sb5+fn8fbt27IIbJFZYFDCsd2e1TkGonOpttPT0wK92dNBMSMsWWwIBJJHbGFbAAOHQ08LCAQNkCPbehCsKAdog5Iktry8vBzb29sl25NDDjAgxuNxOTQA5Qm/Mc942ozDmeHMpyH0+/v7EvtlD6+Tncbjcezv78fh4WFR0nd3d8UAi5hVErJHyLvwOpxVi5cJjGsehl8QwAg8DDE8SoQcSUd8ZyEcMYupQW9QCO73+jNUh3Kl/2wpcdgChQ5SgNEIGnBzcxPb29vvKX7Kj0ZEgW3hRxQm7ZhfqqoqiAZhBta8s/qrqipG48PDQ8kcJpveiX4YL1zOWQAR8PwCPWckg/v4bWQMBeb8DCNGDqdBJ5SoZVX2EK2Y7UQ4vIeSh1be1oORgiHDesOAZM6NIEEj04xwgUMorCn3pYnX5qGiWT/MU742PkyjrEv8HYYsn/2teLpVVf2TiPgnP3bu70XEf15V1X/UarX+t4j49+Mxg/k/iYj/48dH/s8f//+/f/z+/6o+1QT48fJC/1gwPFsrWck2wQZNijYrbCt8K3cEjp/3JDHxg8EgNjY2Ymtrq8Q9Li4uSsbq8vJy7O7uxuvXr2N3d7dkbHo/IosLj5DFx4Kj5jLe3ebmZimY4VrIpOczRmAfEiRgeqBjYFLinePxuBThj4gi4J0wkiFevFHTEgXm2DE0hn5OnkBgASkD8TlpBiEFTE4ZSbwgEkGgFcUY2IoVEWVhswfZWcgoNIQN3jaQJgkmVfV4OhTZ2xgJGDfj8Ti+//77+O677+Lt27clK5kxm98sKLNXanQE3sULwvDzGrBQ5DIsbMMLr5H2MLCc0GPlamPUGdR87/ii159RDp73FhUQEodJEN7UaibuztxRTIUa2MRuQRK8LxQPlniqE7EIm0B71u14PC5xSQxB4GcMA4dCsgfHHLJ28ZK5oLW9P+jmvAzaYT6tZLPTAD0d38xJpfCBPXTLMStzlIuNXJShZbZlrvkWOYHhDLpGcpQRAiM6ziSnfdpwCMa0yPI86wNf5lfLnmycuA2ecxb436anO+/6LyPiz1qt1n8VEf9vRPzzHz//5xHxv7Zard9ExCgi/uFPbXiehZGvJiHzU9rmfybZ7diT4P+m5zPEQayDSioIcbZN/PDDD6UsHRnLxOlgTENPFENA6fJO+uQ9a1YQxPy4D6HlRVdVVe34PiAiYHBXWKId6MJiNoNa2TpZjMXB30DktOUi+xYIVVUVOvAMCtsLGiXpfcVV9bgXFgiXeB7KF8FKu84CRzAYeXAmNrDn2dlZEbQoWyp+tdvtcs94PI53797FN998E3t7eyV2iOByEh00ypmzvjJ0SJ+hLTwUEe/NkWE7BDrC116uBS9QIsLO3i4KEe/EaxOeMBTuPtA/FK63ggDToxBBEwizUDzECpea1xg/HJSBAUqJVArow1/OAyCJh34g1Mk+5z4MQXiP8ADrBAQEGjkGDDzKvEBbKxx4wHSGJ6z4MISMRmSFaWXCPEBryzIbdFakjJW5RyHaGbFnjFdNO2Qi+x6HBZg/r0nCaxjabtMGqmmWFX/+LMdfs1c8z9Odd1ln+NmPXT9J6VZV9S8i4l/8+PdvI+LfaLjnOiL+g5/Sri86z98RzefZ5meaCNj0vZklK3f+TuNpFHy5fb/D3iYMhBCmMABK+M2bN8VzoiDG7e1tURBk1iKgs6XG4nh4eCgZwuz9ZKHCCCgNhLlh1Jubmzg+Pi4QENY7UBIwJIrXyphMaCxr5gthluNbCHjiZwhuBByeCQIFDwAPAw+WBYTRwTFh9MsxORSw9y+6vJ69arwlvFqMD2JxKILpdBr9fj82Nzfj5cuXZavLzc1Nqak8Ho/j8PAw9vf34+joKA4PD2vbpRB0xBTt2RhdsQCGlob3nHUdEWVfp40dxu5ENu5l7KwPjBAUKYKSvZku5EBfEIIWaO6zjVj4D+WfjSzms9/vlxKBZ2dnJS5L6dONjY3Y3NyMwWBQ+sp6JmzAT6fTKZnhR0dHsb+/X2Dn+/v7YrhgbEGrXq9XEpiM+Nze3sZwOCxwqQ0N+B6P1kgDPJ69TKMcVsgOwfjeiBk8ax6xYWPPNStDy8Qm+emwkA0Qt+G5dOzV/MXlYhkoUrZ0USveYSqgfudFeOz0i99Gd7LSzDI6oy88b8PD91mh+tlMu0/xdp9cRSorOVsREe8XcG9S0H6myVOGsTMkkp/1ImkKzHO/vSBbXUAi9LnT6RRPaDqdxtnZWbTb7RiPx+X/nZ2d4iWdnZ2VmB+eL4lUxLgMHVJe7+TkpBTCt0Vqz4V2EJ4UvmBbDsKPJCigMwuPDHE5BpmVrQW3Ld1+vx8XFxexuLhYSi5a0ZB4QUITh5tHzIQNhgzJSfyNIkdhkpmbE6G8D5fKOMPhMDY2NgpteZ7DBfBwer1ePH/+PF6/fh3Pnz+P5eXlAjmPRqM4OjqKd+/elVKfe3t7ta1AFjzMk+O55h0LW3gtYrYXGo/E3ggel0MAbg9Dg3fa6ADatSEEIgPN8h5plBT9w7Nx3NYIhQU3/QV6Jt46mUyK8bq1tRXLy8txeHhYMsqpU004h3WHh07hDepbs/8Zg8hb4BgzHhghEBsO8NxoNCrGEh42lyt+kbfAZeUHfaEFfyP8I2YFIlB6NpYdevCz/LDOIurnelt5Wsmy3jKsakeFz5zf0O12a/LGoQKPDzo6TEbWOQbc0tJSXF5eFpjfaIL3xdqItOy2UQpPQivLQF82EBy64e/s/VqnGFqeB2/n60koXU9sRD0jzEr1Qx5tk2WTrRn/dpwjWyomWJNi9/v828LSVj+TtrGxES9evCiMeXl5WbwvFibbWlASKBAK//d6vfe8TvqDgnY/UK4eDx4S9yEISPhCSGN9uuqNDZ6IKJYrcV4LAeaUzyJmcd3r6+vifaysrNT29GFQUNwA4Yp3DN28LQkPibbt4Xm+UUyORZJlCZzl/ax4RqY/pS6Hw2FJlltbW4uLi4tygAFngVIEA0FPRjhzY+PM6EHEzNhDaTlj0xY6yhuEgGcNjzvZCsWIgnVCjsMcjrXZe8FwoX+EIuBz7jFsjjFtyNwClPHA24yDGC+Z0SjglZWVODo6qpU+pbgLc3t1dVU7MpGwCRXIWCsIb48HfrLhglcGUmXFgcCFXqw54GrKf9ogapI5yIYstA0vW/7g0RsRyfIpxxutfJgP1geKzTwEf9F332+UBSTEVbHyVj+jAkY1WF+u7EaGOcb22tpanJ2dFeMgh6Mc885oQUY7m+Q73/m5LOczhPwxvdR0PQmla2gF4mVF6YEz+GxR5Ge4MmHz1UQovyO/3/faCnZ/gDY5RQMGGgwG5UgyknGI2zLhHHAfEcUKJB5rYwE6ZQvXyEBehIYpzWAsAI/LdLQw4XPD3U1xJHsvCCifa4riRZhTsYt9qLe3t+XYQWhxc3NTvBcUL4YLNDE8S9uu0GVvjOc50Wdtba2MlTZ4Fu+W/ddAy53OYxWpw8PDePv2bezv75dSl0dHR3F6ehqHh4cluQfhhheP8oevmGeEnWNxFroRj8KGhBQUkwWRhZC9ar7H2KAYSF5vVkiOA5K0BH8x1yguKw7aYjwYqBFR4n1WVMwP7Vk5Xlxc1Ar900fD17wPhe3iC/xukh+t1ixXgfwG7mG++J72CTlYwZn2rEEMy6Z3W5BnQc/fKD3mzLFyK5YmWZeNc+STPUPLPisVoxVGXJh35hTFyIH19Mn5AhzSwAV6ZCSGeDxZ/UtLS7XERPjHayTrg/zb3zUZrYzdyB33+jN7tU1Kdd7n+XoSSjeivrl5HowMU7Gwm5RrHrjbyIrEz+TvbHlnZUwf+NtZvCyEu7u7An32er2SiECClfeyeU/h3d1dDAaDYk07loLn4lJyZu6IGRMhYH1QQV6cfg7hxX32lLnoH0IoIoqgj4iSaOJYVJ5f6I3BgbKImBXdN7SHdQydXIQCJYzCtbXvBeNFhaJFmWEYTSaTcji3vZbV1dUCW1IYg21JCFKyvPf39+P777+vbQsihktlMax+IEyEh+ObxBczDOhkM9aE96jyPILUFr/n2capE1zMQ/B+RJT55XsEkHnAYQQbZTbGGKOVAOuFz6EpuQ20yX3e+oNy5B6jMYwjG39Z4WXjmW0/Vmw5gciGBjydERPmmPsYn0NQ0DLDl/w2NJvbskdKO3xvpJB5zDTh8r1OImP9824XpTGtMAYwSNfW1uLh4aGU16T/KEzaZx7JwWAdEjdfX1+vlZPFA/ZcGVrm8ryylvJ8NynHrGTzfZl//K7cdtYx+XoyShdmhKkND0a8nxpvZRrxPuHz927LV5PFw+f2GvO7PUFNit0l6YglonitaFhc7Lfl/3a7/d7xY8B2LBzqMne73cIUTqaAptkK9Li8CMl6zh4SY7Rn7X2jLCyeyYZSpi3vRLi5qAMLnEXKQmPrFMUNXOkHQek4EmPiHv62wUbsqdPplHgwBebv7+/L/t5Op1PgLeBX11Iej8dFwRJXJ3Z4dnZWKgg5sQ1jArpamJnn+T6vAZQB/TMMiCdpYw3F5n3N8LE9T7+b99kDdujAmb8IJCMoRkWYD56xUUb/7EUZcrfRgNFkiJi+mtdzLNFGKmNynDMXXPA6NK3s+TEGEJxsfGekLMO87ou9VNaU59q/eY458Hr2POb1m70+fhs18HxlvrLniCyyF4xcg27mT29fxMC3QwCfgwKSEGg6Ms/IKMuw7OHb8PP68b12AqCH5brXg/nBemWeEv/Q9WSUbrY4m6yXiKgRcJ7Hy9UUG8lXkyJyfzKD+sq4P38DjTq2aKHhhY0VPZ1OS/ZeRJSEKe9/5D6ULfGkjY2NkuGHEjKDMZYMEXqsCByPxYITrwJBbq+K8XgPp5W/GZ3FjMK1MHK2sRN67KGiwLzwebcPB0CYWCBjOUdE+dsLNyLi7OysZMxSBYssZ/bxooTYvwnUTVYyWd+UmaSKkD0goDMrDSMD0NnKI++LRvln7ysLKdrBO2Ws5j2UBxm8tGmPgnfbALNxbL6jb/CA92HiGSLIjFCQ0EWb1P81LyPgI2YVr1gjFoxZlqA44RHHT1EMvNdIFjTISTatVqtWOzgnTGWvukk4ZzQmy5w8PnvXzCFzw9Uk85qcFMsmjBz3JX/OMxTsaLfbZa8yfXDpR1fP4zPCBDYKmSeSHknexLvNXi1zap5wkREbk+YrGwzZY6X9/K5sJOW5bHLsPqaAn4zS9dXkpebvmzxTriYF2eQVm0D+ex4hec6ThjCNmJ2aEjE7Cs/xHp6lfVvMtnIpDOBYFcp0Op0WDwtlRAIJFimCnUzf7HX6/fxGAFm4QAsEtmlhIwLBgRLMSIOf8WLP0J8Fh+NgxIFdzMMZoBTmwDqGXvzGgmbPrnmE70i6YV81B6qTmGPYGw/Mxxt6+w5QmcMGy8vLJe5FcghC1UrPxeuz1+S/oR/3ZCHmObLRhcdhwY3n3YTYeK14bpk7K+amHAP6nL12t9M0Lj9jFCAiapXJ7M1lQeqiLxn6tifPGnb8PPfThib/W9Fm7whjGf4z9G150iTDnLtBu+YF5oLfPOO1HlGP2zpuDk19H56pDR7WIDH0TqdT27uPQltcXKyVtGROkUFkcLMeGCvvtyfNOEkgBQlDJnnesmeb+fdDcLFRDp63p2vHKMupJgPGf3/M0XsyStcLNXu5+TMrymwp+neGCjLDNylft+FFMJ1Oa15Cbs9CzIkfPM/iQ8DhydmiRpi7fVv0ThpgXyfnk7Ldht/E+lgYKE7DdZmJHG+y8MlC2B6UvWR7raZpjhdmpRARtcWKsgUupgQlWcrQwrFjF3XwvltvibHysafDYkfpLSws1A5Bz8aZY5V5axg081YsYl6DwSA2NzdryVp47Q8PD7Vyg4wdAcN8VdUsw5O5c8yPy0qGecYjof9OxkGAmwc85/B/xMy4Yj7ps/nV8X17BzzDZeSFdr01DV40NJ89wSYPMK9/+m9vFFrQN+63IenEK/phL93IFN9X1Ww7FZWwLLS5zwYviZJZPsFPjJG/kSPQEH5aXFysQeVGN9yuDd+8tqEr69YnaLVarRJT5zeGpo1hhz8IFVn+gDyxnpgDkIN+v1/upSCKs/uZX88Vn5tvbIhlGZ/lv2mQFacNS88n69M85M+ariejdD9mHTRZGR/zhu05NSnurFxzH3gmw6P+jgkylG0rG+WLAMVKp2whwjNDUbwLLwtFSvsU4KBYweXlZVFUnU4n1tfXy8JmodBeZg7DNZkGKAAvSCsAxp438NsKN9TzIYsQ5YRH6b7h3VuIoGiZa+Bfx76m02nxZFm0XjwLCwu1E3KqalZ9iPYdg6dIgulgrzsiat5Ap9MpB3Vvbm7GxsZGGWOrNavGRZwq4tHA8iERRhIMATqckGExhJxpbQXEPCAo8/eGUaGn589KKRtdCFOMPaBxK8UmI848b/7LBh10Mk+hvPx85sH7+/uCVPCd+4QAd6wa/qAf0AOlZ8MfPgJWd78Mf3qcJKlZoTbJNCMLNiJ9+hEGgxEJ54AwVv7HWHKM1IhNVkSeOwwKlKTHAG1cqhO68WNZSX4GY7y8vIzT09NSBAeD25XRXBcgO1Hmzyzjm5yrpu+z4nVYwu+lz/k9H7qejNKNmJ9y7YXW5NLPa8v3578/1IYJ67Zy21xOuMiWFpYxDMO9FE7vdrul5rKtWCbSWzxc/pCKSVihMCjxFvrVBH95URq6M/NaCOTYuI0PKzAv7vx39nI8n/TDWw/Yq+wscFvvxFuzZQ1agADN5QN5Zy7YAF0c5/M82LuxEebMcHverVarHNQwHA5je3u7HH7h2s4YRCRxLS0t1c5O9v5MKyO/MwtIns1GYFZ0fOc4KG3bM/N3TtRBcNvI9DvML77HXoPXU/YevdY9z+6feQiv1OiFPaKI2Qk2Lqnq0Aa0yTkHrCf6QT+twBibk4pYsza+mQuHbLK88XvtVbmf9BHj0PMDbzI+048+o+wyCmUF8/DweKgJfV5aWorhcFiUOQYqyZAYgS5qwdiQcUYAmSsMGBAfb9einCdGgteg6dUk8z9FCfq5effbYbHM8rx7TX3oelJK90Oepwfte31lodF0fUiBfkiJ53tt6bCAUF5NAs+xXRSE4yUs9IjHBZ0hQIQfsI8tX7aMWEgDPzv2Y6gYGhlqjahnqnp8Ho+926b5gBbMm2lGv+0Z24Px1gQULvtx6bNj0NCPRUq2eFVVxTpG2HEZ0myCw1CIKDDHWrN3k+Oo7Xa7bJ1w8YzhcFjLhrZC6PV6NTiP+Fcukcm8+3QmnjHcawPI82xDyQorQ2Z5HfFjpc675yEX9Jnn3C/WBO3a0zXPINhs5Jnv+RzY2P20wUd/bEzZYPDaNepjDy2vm6qaZeJa0RPaMGrDfGFYNSVdGZnIxq8NQGjMaTyEW6hklxWQIV8blcxhk4fn90+n01pi1HA4jOl0Gmtra9FqtWpb+ZgD6mGDKtjIcMEMjCmXJeWHojQYBzkrm7CO0UwrvY85Zb6yY5blvNeMvzd/c++HFDfXk1G6H1K4Xkj+v4moH1PG+b5MpI9ZT/48t+c23WcWuAUYi5SEEF/2DpugRS8MFDFba7KAalIK9ggM3WWL29akIT175L6XxeUxZqXt8dEe3jn98Dznw69zvASo3jCxPW8XncAgsTcISkARDpQutHNtXkOutOk6sQg3SlZyfCOJbsTFKKVoRcDpN0CgLqTvetbOK0DomL7QB5ohdKFdzgRGOTkL2ghAVjyeIxKzIqJW8xoBHVH3jLnsTRlpyO1D68yvzB+XT5sy6mR6YEzmtcX3zIMRgel0WnIxaBv+J4TA+FygBM+NdekQkov/Gx0wn3J5nmzs+jdzTWlXrw/uM229NnkHdIK/zBtWbOwswIloiqfShvfwOovdsLZzOriXOaBv8CL5EZZJ5lPL46xL3L9PvZqUreWdPzedsnxqup6M0m2yTubh6ia8r59C1PzOqpolIZjRsyLyu1jEZp4mS8gxRgQ1m8m73W70+/2ScGGBymQisO2BGfqkcAMVcmjDwtZCswlOM1ORcZuVb6avPSiEvMdrunhcCAMLQL/fFjS1WT1WvD9b0MRFgZHxTrGmLQzX1taiqqoSc6KSDlt5bOG7tCbZyggOL3pb7NRuphj/cDiMbrdb6snSp6qqCkROrWFi0+vr62XsFxcXhY7Q1AUL4E/m21WpLNwwMjxvOZ/AxpcFmuc2j5/4nRU6HpjDFxg9Xn+sadadlSPv9N5P+uL+GfLltxUaXhH08/qmXSetcR88ilGSlQaQKuuCsePhgUBBczxaqrChtBm7DXwbnqyRprCQFaRp7DZs8DsvxvLK/MOYMBIiHhPFOI96PB4X/gFJsnyzIeg5Zj4sI6CHiwU5jMR6977sTG/azVd2qD71ygZiXgN2hhjfpyrciCekdJsuM4Y9rSYF8KEre6SZgCZck7XkRdrUrxxrs4KgBKS9jOl0WgQ+UCRbgMhiJC6UF7+9gaykI6JWmYjYFYs1e9swD/daoWfGsnVpQZA/R8HbaDCUnGlqGJTvyHwljouSNQ3dDpAdEDCKcXFx8T1lB/TF1qBerxf9fr9sbbDSYX6dZELySK4t2263C9TNlqN+vx+DwaCcVmRlvr6+Xusfe7QRdLe3t7GyshLj8TgiZkiC95IiQPOcwg9ZMdqrc3+8FSZiBpkzd9AdnjZdDFmSAEb/rMwxNu3RGQWB73IMmzALAtoGFwYFSAmoAP2nlCNtezsMfGePM3supoXjkZ5zxsUFbVnfOVThfthbyoZAlnH2FJti3jY4vIee9+T3mU8iomaYMl7m1YjM+fl5HB8fl6x7aGdetExin7XbXFiYFWmBj8hJgVdZSw4xUZ/ZMjo7IJbRWdlnp67psmzngubQ385gNkb/znm6XF6oZrZM7J+iePOVFXFE3bM2c2ZF/yHFj3DAwnUGbETUMv6IOaIMUDAwf+4nChlmzYXmgSTv7+9LfWN72R5ThmksxLEuvcXCcS+sWRsX9lAcFzZEZNrRpiEqFKf/d5+9bYQYmYUf7WLokM1sw8el7Fzly/FkG0woRSdeoXCZS5QHQop4PW0wFoQOHrXvwTt6eHgop+MwJ3h67LFF+ANX85kVmIWRww0Z2rfAz96WecE8YIGWBU1um+9ziUa+p323hyBGkEVEDeaFrzM/8TljtIIznzj5hXc3IS78JhkS5IF+0Cfap+3Mv5Q2JN8gH7DQ5IFawLNGzf/Zi6OfNibsUTrvwaVcLUudi4HyhgdcbQqFm7eN0X8bZJ4PG0uWEfTdGdnw+snJSRwcHMTFxUWJQ9sJcwjLMjPLbj7/2GUZ78+yo5CV60/xqJ+M0s1KLX/HID/VYpl3NXmrniAu3pcNALeTFTcLlR97TsR1yKQ9OjqK7777rlRYqqqqQJuOXWSjw/8bQoNZiQ2Ox+OaZR9RL6nm8XsBeZHbu8yJDPZePEe8y4LHn2WDKcPlvBev0rAjUBTGAPAvypIqT2SM4mk47hoxO1cY4+fu7q4kiFBfmSQY2iSWargww3BWrAg+vPaI2Z5QC0Pg6263G8PhMK6uruLo6KhGF4Qe84QBwZxaQeVKW9yDgWNawPtGOQwdQyvoDS/ksVip57XlMAW8a4MthzkYp9ef24bnXF2M5zFwnaRmBXvz+f6vAAAgAElEQVR1dfWe9xMx2+pjXmZ90V/imbRhz4d5Yd4Nh2IgAY065IQShL7QwFC6adPkhWbv7u7urpY4xZoxD0VELRnOipb+my/MC4bQV1dX4+HhIU5PT8sz0Nt8aUPfCWStVr2crHMHrq6u4uLiIvb29ko51SxL6HeTdwnNfN+nXNmhmof2ZePT/PQx3fRklG7E/AFn68IM+bteJlAOyvuHvviZJrjh/v6+xAn5QQnAsChdjhxDkKB4gbCYWJJruAfvFm/LAgyoDU+K8ZihO51OOdHH40GARMxOFYqYeQ/0KQsn5gSaIOAN8zUZDnh9JJug4HyUILThlBJoTYwNodDtdgtU73lB2KI0vZ0KjwOBR/ycPkNP+mdFkMsQApl5rI6RIbAYczasoBdKn2fpA3SgPdeTtiXPIRd8nj0C5yy4sAf3oyhykky+DANb2PIuaOiiEfTR4+V+08p7tEnQQvljQODl+xAI04C1Yc8ZAQyECX/nnQJO3sI7dQYt/GN5YNryHOsUwwo+YZ3ZKzSdLHdYU9nZ8HitePOWpoxOWFlPJpNywEfmbSt5b3WKiCI7QHXY8gYSFxGFj40w2KiwMeRExXa7XZAByqxOJpNCq4xKQB/PxbzrU501K08bQZ4Lj4k5twHzoetJKd2IugIw5OJBfip2/qH2uZrgh+zdQnxPhont3/QVqxvma7fbtaPpKIT/8PBQEit4jz0Zt+VTNlAKJA+dnJzUMphddhCoGUa3pcZ4chzISit7MKaVISPDkK4e42dsHSJsfRAEQpEkKBcToczj1dVVLTloYWGhnP6DICbJBX6hoAVC2mUY+UFY0iZKK0N1GEJ85mxnrHfD7RGzYxAzGoDyYC4JOZh+jsmi7BE8GAwoJMbEPeYXLgwzIxy0A1/ZqvdlA8xKxluvEJA2LPgO2uRC/TZSmCNoTdUjr10bcfCdwyHMh3nU3qXXKfSlPYwnkBCHcfI6yElLhnhzPNiKv0k4W0Fmw970g770lzn3//msYBvQ0McORV6b2ZN06MnJhKYBvIkxR5+c6Z2TII3sOTkrn+OdZb69eyfH2QloomWW4VxNsj8bNrlt86Mdw495109G6WbLrsmbNRE+1Wr52NWk1P2d350XggWG4yF4Ini6vV6vwFPj8bgcio4X5ThjxOzUESbQNVG9GR348uzsrCZEsBiBlFBEGTIzDGcFAo0NN3m89mSB0ixs3BcLDAstvrfSxQP09hzvnUUgAtNhgWJ5s6AR0syLFRjCvdPplK09wMUI7Zubm/JZThiiHZRYp9OpZepyrz0LLm/HgO9ALNrtdkwmkzg5OantS7ZnYsVqnrTg5J0WUvbe7KEadoYnsrFJO9lYMOTsxB7myAKc/uChuvoW/csxWysJ08zCnsuebcRM8Zm/rdAjoiAaphMK/vr6uvwQxoAehsbdL3vONnLgeRtCXttWTBkNysLbCsOyLysj1gXrhHVhtIBxk5VMuzay7XWzVuEhTuMC4bFCyoZFNjbMW/ZynTSFoWPjz3zOWC2zMxJgzzPztWV1dqCYH57LiKvnfZ4T9yHd9GSUbrb+cucN43pB/65XFiRM1LxYpPtlQZSFlhNqFhYW4uLiIiaTSRwfH5cYCLClFRwCxXWEscZhUuIdKysrRTigWCNmQgxojj5hOeaEFLwDPCB79p4TFhxwk61XJ8N43lyn2Iude0ADKBCBwqNvCF3Tp9PplAMFImYK8P7+vlR8skLDG7JXAg8ZiYiY7YdG6dkCh65NSo//7THRJy983oMQ5rnb29vCGz70AEVtJIL8AHvftOPf+XOMGAtAQ8n2PqADfzN+FGbmFSc2GbI3Xd1/+mJjiPlGaTqWbuVhQ85zCx0R5OYhlAnzBe9YWRK75YhLxz0xUGzkWMGh0Gyo+DKEz3ithJqMCfqUlTp05jKv2ZjiPaxZ+mbUhO9pM7/D84kcur6+jpWVlTg/Py/ZyRg8zJfbYnxedzYEMA6QbSRqMTaHZzy2rPyyUcL7mhSgn3E/uXi2Se9kj5d3Ns170/VklG7E+/GJiPkWStOVCWyCfOgyIbMlk99PW9naYnHaOrXFdHd3F2dnZyWOUlWzxCneTZwSgYwHiMByrMkegGFUn+SBIgbCpB8oSFLyHT/GeoUW7j/WJ+17sVqh5tgRtHJ5QuKnZAfTV+B27nN8EyXNOzOkjULmdKXpdFpOa8pKAYjMAhyv25Yv3g5GWK/XKxCrvTUjArawnViFgLYVHfG4NQpomexY2oyImuI1IpNjbxiLTXGvzO94Y+afvKXHawPlVlVVyZyGh5gXzwf8i9Lw/GK8WBjnUAb8bmHncdO3bPTwG4PTB424ChO/bShmZMehAebV8wYPOk7r9s0f8JPXQJOwdwioaQ4916aJlSfPG22irzaY6K8N76wc+Yz5x6ulGAd/gy5BQ7K0UdIYQy7aQnugdySceWxZwdmxMV2z7siOmXkso6hNXmuORVveW956LZuXPqSnnpTSncdgEXUmbLp33vWxe7K1Og+TtxDif7edn8O6NFRKcXIUAcxnRud54FZDdwh7lAN/W7nf3t7G+fl53N7eljR/FIqVYKvVqvUhoo4m2ItnbCwcf99k5Fhw8DlKzjFUFJxLydnT935aBBZK0JCnFQV7Xw1PI1TwiBgrVjZbd0xztrJAIwTK9fV1QR8ybZws5f2sXqgoNoSFvTho5R8bMXgbjNNxVARP5iXHfN2WlR1toNCYA/cjrwF4jr+NDHGPYVmep5/eMgLf2iAxOuK611byrVarJBKyduFz1pg9NHtIvvw5xhTzhBdHf40SmcdRevwPH4Ha0Fb29rJApx+GxlE83ON1ZxnSJOzpN/PrOCy8ZxQpvy9iZjhiMNze3paDVoxMODfBaxVly2c2boycWeFCBxtINkIYz7xxm9f8neUbn5vXoAHtZw/ajhjPNhl+H9I7T0rp5muetZAtFa4m4n9MORuKMLPn720lwWDZCOBdMBcwSafzeOrP1tZWvHr1qpYwg6BxYgmCw4Kb9zoRgc/pH+MHqkHgYEVmeM4GB214ASKsc0auBXumU4bZbJ3aOnR9ZRLKHN9B4dK2LV8rhYiZwmBbAQZHr9eLXq/3njeTC4aABnAPHjj0wCt06AE+hOY2njCurMDMnygBFjeIh5N88CgynT13ng/aQghasPFczljPZQMxxMzPFkzMsfsJHxr2i5ihIBHxXl+qqiqJcvZoI6KgEj7Ag/uc6Y4yhlZ4ca1WK9bX10sinXcCNMGeFqyOndtAsYHpZDp4j2f4H/p76w40oR9ux/wJvaxU4fvseWUFbCPcCofPmEcr3/v7WVEZOw4Z+aNPPIuXCp8b2Xp4eCg7BmiLdesKb8wd+97Na84BYWzwg5PGMmqUHTX6zdrNRmCme/buTUfT2/NvlOPvJLycr6aJmKeM5ynYbE1mpZrf7QnMzDrPssqK7P7+sUDF2dlZ3NzcxMrKSmxvb5dkGeAXnqFdvxcFEDFjOmIohkXNFHnc3o9KMQmEn71lC1VblCwu+uUFz99mNGKebsdzxiJlQdrSdVLP9fV1HB0dFcvZBsjS0lI5tQnacFGdirjv3d1dgYSZJ8aP585WJMZoTxw6OmPT2bK8316avVAULO92XBnvmndQChJaIbyb4Ejm3+EDeMRCFsWfoXC36fniewsRGxlNHoZ5N+L97TT8NkTIu4yKZKFnFCcbpCg3e8IoZht48C4Cn/6YHuZt7rVydD/mef02Ru092kizMuC324MGXIwjr/EmGWdjwfMIAmGEw21YAeYrKzIUqnM6vKvCigpUiPnmx0o2IsrfNqR5T353U/883nl6oem5bLg0zUdEvbJbnoPstGS6zbuelNJtIq4tzAx7fMplS2geMbIibZoQ+oIAYzHb80UIVFVV9pi9efMmlpaWYmNjo2wFwRPG+4iYWcgWZNPprCxkE1SHAkM5kP2HsmHB+3QTF7annYgoHi0XSTMWYDB57gd98Xemt5UM2dosQO53HBRLGaXDYQQ85z26EbPiBswdYyTJjBrIZHKzWFByvV6vvCtX10LpuEoXnkFElNgmyUE8b1TCfEcbzBslKiOixNh9fisZo963i/WOMWcPo6qq2nYpYppOBDIMmuE2Izq041hm3nKT+cPhAc8xPGW+yQLP77ERYGXP+mm1WjU0gDOn+Zz+0Q/Dv1be2chhHHjcpon5E6OANcieVFAljDN41UhKhtZNt5x7YEWcvVHvNOD7rKyc6+Cwh41H8w/vzu3a+yecAnLCuzCGnNRGuzZGcAKQeVmeME4n/cHbRmdsxDCW7BhkRWoj0/xvp8y/uScbln63jdhPuZ6U0s1Xk3WXBXtW0hljz0Scd/88guXnLAxs5WNN2oK/ubmJ0WgUX3/9dezv78d4PI6jo6M4PT0tC3Fe0lJElIQV2rYgYPHyLAqasyfxzCyk8ARQcvY4rADN5NkC9d/2ejLjsRizUHWGuL0RhKHbtFXt7E0vsDyHtjpbrUe4lGzx9fX1GAwGsb6+Xitgf3Z2VhScPY2IOpKAIkIY8IwFWUQU4wrBDM1Q1gsLC2VfMhd0QYnbEzNtvHWJftgYRTBBf3vZHoc9bs+Tt9vg8dsg9LqBfkYa+Az659g3Wfvwp4VsfjZDdRgMmS+5z0aT/8fjyoam1wH8jpKz8nM/aZfxLy8vlzra3Lu8vFxQJXj2+vq6lrFu3nKf4IWm0ILloRMXTb/8//39fQ1NARL2lcdI+7wre5TzECzPcS7aY5kJPxtR4F7GlNEdj899cOgq83M2bDKtbcTYo81y0GNzXzyeJodk3vWklG62Unw1MZ+/82f5b1spWfi4jUzMTLwmxQzTYL3CTFaAk8mkKFs8XLJyW61WEQqu+tJut0s5QoQNihXGRIHi3XDCjgv/G8LODMWFgOZ7K2Rbohnmsvdr4Z9pzWfMhRcMRgEKxfREodnjwFtBGTTxjRUdyg9FuLS0FP1+v8R6bbCwpSpiJkSw3PORbPzN+6EXc2gPgHEzJ/TNex1BNLwv2TF7expOsEPYW4B7bvIcmM5NXpRjYI6t2trPa8PKxcanPSDPD6gHfxsSNtzM1QT7OSuXe+Fbxri2tlbbEeDj4axQvN4RwE3C27wP+mBDASV8e3sby8vLtQQwKxQjD+674dlscNiD9Zrls2zANCkyOww21JrGaNq6rxgyVngR9X3Pzkz2Xn3DzKxNK2boQJvZGDdfeB49T36X2/LasOHCfVa8bs9eteVak67IYZx515NRurZW8oL4kNeaFWG2Zpq8Xj+brXe+8wL4GCFt8QPbXF1dxXg8jpOTk5rVyN7diCj7d4FT2UqEYlhfX4/19fXodDpxfn5eS07BUyJt33tmEQoWZI4Dz7PaDOHTHjSyhemFnOfK9zFGFA79zIaQty/gjRt29zgMX1nYNc07z+PFn56elvZevnxZql1ZQQDv9nq9Gn/ZezRUCYRoT6nT6dTqbjvRx/uW7+7uYjwe15KGHPPOECjGhuFWGz0oaejuakTQ1p5nVrzAtMytBR2GXxZebisbW8yR28leTtN8ZaFoHjS8bTjVMsFKnsQrDFH2gXosfob/LR/w+s3XRqUo5sDcYCg7B8Xea/ZGac9JcUYqMnTZpPyz4ZnfAa3y51nG2mjyPFdVVeNd2sRAZm5wKoCWjaiw/jF+miB11hrzAz18fCDjsxHAhZGd6ZHlk2WQnQgbATaETH9Dyk1OXNZJ+XoySjd7lU3KMg/MwsQWnCcn35eZl2seAXO/5vXZlmPEI8Q4Go3i8PAwIqKUMHTdXY6r8ikkeDIwca/Xi6qqClxjelCrmPHBcLYkEfiGO2Ewe2pAUCgRCwjo2W7P9jraszL9vCDsjXpbA1nbhli9+PFumQtv/cC7d1KMecPzzvsY883NTRwfH5ctDC9evIgXL16U9+FBesHyg7JH+HKEHVAa89/pdKLb7daS1JaWluLs7Kxm5eMxuyJQFhj2dhgrnngW5Pbu6a+T7qxYspXOxRgt9CLePwDeW69skHERh7aChUeMosCrtGOBB/3s6bvP2cuhjSYjhR/WFPOX+YM5ZHzutz0o5o/5wCNbWloq5UV5NwhJ9lQZl+lnI8QK2/2w8rDMsydrZ4E2aD/T22NhTjL/86zbubm5KduGImZbpGjLuSiW0faCbaRZQdqLJ+cBlMhyFnmUQ0I2GHPMOIe9mowN7s8Kl8+8FnI7WQ80XU9G6TZdTVZYJpqVqpmvyYqe125E8wk4FlBNyjffGzGDuG5ubmI8Hse7d+8iIkpdYN7FXlq8UDOaPVMEOqUf2SfrBWMv1wUszOD2CDJT2SPDesYatmfpTNAsWKGnaWghi5JhYWMUZPpBbwtlw98I5LyHNisdxwzZ58sYx+NxoU9ExIsXLwqUj/frwwCAey3oImaZn6aNC42sra0VOuEpQxPadZlOaA8tDKHZCKKNXCwj8zTv8TYzjyPPlSE5/rfCbbXe93atRLxGnSRjRWujwErDnrqLwjStOdPH8KM9fd7lOsBVVZXqbRhz5vEc17OytGdnZZmNEJQt9EfJW3aZ37k8t/CB38F3RpjyWDOkmkMcjMkGBvd5PmnbStvGJ+vWJ1p1Op1yWAsKkvlgTjNa4HdlpylilvnvNpgDy4WmPeLWBR5bllHmK787y3brF3jQxpJl6ceuJ6d0mywrf5ZhNT43g2bCfsgyye/NhM5erxnGP0w8ljbMd3BwEO12u3bEG4rQisaQkouVc7g5AszWmBWT2/T/hoMy1BMxg3Yc24uoH93Gb0NTtqDdf/qKkLGwhq4W+FaoxIpQYF5cXMRlMSysxHgm0wlPxOOYTCbl3cDZ1GGGDlYO9NsZ3cTieZ77SN66ubmJfr8fKysrJdmGfrTb7YJ+ME/0j20UzKG9VY/BKEHmRWiCkYKHhgK2MWQBQzk/5tZwKu81r9pI8vdZqNM3/21PFl5EsGEcmCfhVZ/4hNLLRpsVKeMm45Y9pD7IwIqK8dMOY3Ec2Xy2urr63jY0EA7GAV29Hj0Ge+isv4zmMPfZq7KcbPrbBo1pxDxljy7L1VyRC3rbcHXCZ0Z/ut1uLCws1IxXlCdXkwJrtWa5DD472gienQXLbMsc08N058o6g/uNFjQpa3/OM/4973pySjfifa/H/2drhO9MUBOnyZPyZFuR5mcy8TypvhdBwTO86+7uLiaTSXQ6nUarGmY2RDidTkuslliNGRtG49BzBDNJQI7lOlaCgDMtLPw8XrYPWUDa06INJyDYCuR7vsuetWloj8Rx5xyvYwy+x0kafqcVmOkL3Rkn9Y6JnXe73bi7u4vl5eXa8XgRsz2zhAEmk0lcXl5Gu92O4XBYS8wCkUBottvt6PV6MRgMSnIPY8TwYHz8T+wLA8blQC007LUggCLeL6KRLXto63AB9DUqAZpBHx2r5j0o72zgmu/hD/iB91n5WkESNrFg73a7Za7YKoWA55zqDGU7/GJFDh+en58XmlsmZCHt0IDvYdzed569IWK8hlfb7VmcMmK2Z542jSo1zZ0NwqwM8va/bOw08YXnlHfa8HZin40F09vIi427vJ2OKxtvNvw7nU4xhDc3N2MwGBQ4G5nHaW1sDfSeY8aSDTfLZ+jQ5KSZjy3XeCaiHgLI333oepJKl6uJ2bLlwZUJ50nkMlPb4sxt8Lf7kf/OnhqX40F4LWdnZxERJdaHULHCpt2lpaXo9XplEbKQURzsdfXZqljxnIXKODM0RHvZy0BAmelhbtPC/WQh2zL1nFlJ2squqqoIKMPDLJYM0/h3Ng6weh0jol+8E6FIn7KSvrq6ir29vVhfX49+vx/9fr8GV6FEIx63fZycnJRjx9jHCp273W4RMghpVwJrt9uFB2wk8TwZ1MwnMX4sfSfMTKePGdnOXoY/DNVZaFuQGHq00nOyDPyAkvCaMj/BMxbcvD8bo/ZE4VGSo4hB23Bj7ldWVmJjYyMGg0H0+/3aWqd8IEgEfGt68Y7l5eVyCAmy4PLy8j3+8/i8/gx3MgbCB1nOGCXodrs1pMQHEHQ6nVJe1DIhG6k5RpplnHnWzkV2TnL7zoynHSNrXn8oT3u9WRbbWcB4dJ+Nhtn44nMMlY2NjXj+/Hn8wR/8QTx79qxUqMOZefv2bezv75eDQmxcmM+gkZ2ArEhNP/7OEHWmLbxgXv2U68kqXVskWeg3KauI97F3e1kZVjAjZivH73K7+bLFlyfH7d7f3xd42ZWOiJkZuuHz6XRaUu6xIhcXF2uFINrtWSIBMUz2tRrOtaB2XCpbp02oQaZTjvtlpvN8GG4mmcXeQqa9rVLattEAfS3scozKFr0FFvNEH3kHQoGynFZG9kSm08e4Oklvvpe6sVjj3W43er1e+R6hvrKyUgS+4UbTywVOmuJ6EXXvFM8cOJO59fybfw1Vs1ULPuAeb5viN31wgpN5woaceSbHFeFbKynegbLnfdTCZvsPxihV1hxTh1YUyTCfZcNraWkpBoNBeYeP2nx4eKidMMWzNpKZM2jctAZY98xxllVeD6a/lVN+ziiN109Wolnh5isrl/wZY/KadMzcDgsyBZje9eYxdK6vrwv9vFXM7ZsPMFKGw2H87Gc/i1/84hfx+eefF2OLnSHeVri8vBzn5+exsLAQl5eXhS+deOh4d3a4sty3gWra5LWYDaMmZZ6vJ6t0mxRdkwLNC9pe0DyGs3dqoex7/HdWPO4PnzERWejwP6n0lEzjO2dWWuB4gWONIphc2AGPhz25KF3D2TZGPFb660WKEqdtj9FCxB4rNHESCmNoUtKGfP0Oe/N5biiQQZvQDW83hwzsnXmBMzfOWAUuRvEOh8P3xgEtmQPDeni13m/MvkXGxR5q4olNXghe9WQyqZ1ximDBC7UxgnC3d8L/njsfEMCVaygb+svQmb0S82g2Vs1fGAd5ffozG4Q2rKyISHCDNhzriGGyuLgY/X6/IA20bSjX47Ehi2GxuLhYwgIocYeCImZZ3ORMwIuMGwXkIiuMh/Vv2WEZlpW877MBYx5nTjxnNig9Fw5BZC/aRn820qzQ6VvOoTg/Py9Hai4sLES3263JIsfNSbiChvSJNcVcoXQ3Nzdje3s7er1eOfyEEEO73S5G7traWhwdHZX14tPWvL6yg9FEa8t8ywHTsEmmZP6fdz0ZpesF13Q1eUcR78do82LORPEzJh7t+h1Nf8/rU0RzcXAvTGf7Taez7TtAIMBLLGB+2xrkXmDIiPcXtOEU+pnPUXW2L5e3HDUZJZlREdZ5gTuTM8MwhhWBx+YZR34P/UOpNXlPCDwvHAtFQ2Q2ChiH9w+SVOUEDpCGi4uLGpS/srISvV6vHFGIF4aiZM5AIKABFjrfXV1dFS8ao8ltkBDm7TMRs3gzz0CPLEARSEYGMGYcKjB/W7jDq05Q4jk/a2VtoytnQnNvU6wU45I+QjsS4IiZYyw4ppwNBfphNIQxLC8vR6/Xq0HBhIB4hrE5I5c5hd7cn8dMn+CX7DFCZxtxTQaIDWc+yzHUJieAz+zleQ0jh7wTwQqHts1zXkcgciQ4upjPZDKp7Zx4eHgo4bOHh4dStcu0gP7OgcA4tNe6tLQU29vbcXV1FScnJ7GyshJra2u1giSWizZyGJ+VseljmWsZbno2ebqZtvOuJ6N053mmfJcFcMT7G5wzw5jgFjzOauOzj7236Ts/nxdFvg8YDEgzK2XH5mxd5xgxyVRWKAgHasAa5kH426q04qOP3OttHrbsTG8zbZPyy4YPfXScKNMqGyI86ziPYfZ2e3Y0Wl4IEbNKTRa2Ho8NC2KZrvAFXZaXl8tWiOvr6+h2u7GyslIUK1AYljfKE8Hg+LsV7tLSUhEozlTniEN7VygZ5sQnICEs7OGSgQ3fZA/MvIUBkwWN77Nx2ASlwjfZULOy9hYsr4ecKGToGoFdVY+HQViRuegFa5pscvNAVjwgCEYF2u12rVAK48fAYk6zZ5t5PSMgXg9Z4fOZjX5ozN+0DU2yMW1am69tVFtxmlfy2rRiAT3IORuO31qJoRBt9FHc5vr6uvA171tcfDx+084D7RHL3draio2NjYJgEIsH2YMnxuNxTCaTslug0+lEr9cr65WT3vJcOQchy8KMBGSZlXnKMtHIw7zryShdBmhm8MXkm3H4PMMf86y7iBnTW+hmjyC/d953H/PO7VXZokMBOa7lfjKpwJBkseINcxQcCQXQgIxm2kDANFnPNgbsrTAuCxfTEW/Jlj2MZua15WqPgDlwnxCg0AYmNuROe4uLiyVuh3DMRpct9hzHoW3oQJyVRCqyYtmGcH5+Xix5POHBYBDdbrdAkZxSdH9/XyBP04p+YgiQ7by2thYREePxuAgtPKKmK/Nuhic99/ZMuf/29rbwFAoReuT9wjyHIcKcGoq0F2joDkPJ/Ew/+W2Uw303vEpbVjZWJtfX10UQU6zEcsKel3nKZx/z+crKSi0mDvpADBmDirVrZQkdUY68x+iDczcYg8eV5YTXP548786FJXjWCgEjCzqiRKGdY/5euzaULEtRuE60M5pio4/7KBQyHo9jNBrF+fl5zQiGT71W1tbWSkLj+vp6LVueub2+vo7xeBwHBwexv78fR0dHMZlMSpsUKTEaAn2y0ZEdBzttWfYzf54jG1lZN827nozS/Zhy47cTZfxdk4eViZcJ1QQNWInT/oeI2DR5toRQjliKfieMZiFKv6wssLixBL3XLWLGDKurq7XFhWKkfRYh/XB8MiccIOCzt8t7zWh8b0HKPUB4PO+4MF4re1jx0IkF0Z8s7JsUq71e+k6fUBzcY6VGXGg4HMba2lpRuvT18vKyCAvg3dXV1bKdwYqcMUHr1dXVUhTFNMdwoj4vFj8JQni2jBPhBNSdjaSIqBW9MG3zHmWEPlApNICWGD8gJ/aG7AGADtgLg9eBCCNm9XOn02ltK1B+t9eflcV0OjuJy0lH3IexYj6xB90Doh0AACAASURBVG0vysrOChGaAGNCC9olDMDWFBtWOUyDHHBfzIvZwDDC4HtoF8XrGDHzgvFrZWuPiznMhqa9XsswG8b0JWcxAyP778XFxVhbW4vl5eVCF5LVer1e7OzsxHfffRfv3r2rlbNFljF3PMNahBdvb29jNBoVY5t1Qp5ENq5py5XtbARaiWb+s+drfWF0wrrGsj87F/OuJ6N0ueZ5uRawtrT8ff47oh5fMgTWZM1kb9MKkGeswG2FZyvH1rWNAZiIe9gWwqQh+CJmJxURm8jGgD1BCyLeO5lM3lOWPPshQ8FQmL/Lz3m+7OXkuqu2Mj13KB/vSY6YVXWyR+y9y34XQsUeHNY09zAm8wLKb319PXZ3d2Nzc7Mof3trzlI2P3qrj8s3orS73W6BxlqtVm1DP7yCgXF2dhYnJycxGo1KtSzP2+rqain5iaeT10umgbe5cI/3gUNvG1HwBu9FUBHu8P18zu/MG/yPwkR4AwHaMDJN/X7zhPmWZ1HuzuqHd5kTG9g2VlBeXlM5BMQa4D30EWWPIYESy5dlCGM0pOlCLHk+DfXyeZOhS6EYOxOmg71KxsZYsoNjeUX7Rk8Yr9e8a6STXYzM3NzcjJ2dnSLTlpeX44cffoijo6NigFl2LS8vx+bmZgyHwxgOhyV57ptvvilhsq2trRgOhzEYDEpYw1X4mDeM7YWFhRJ28Jizs5VpbH2Q/7bDkj/j/w9dT0rpmiBWgFYMduebCMk1T3lnodCkgLIi4e+IZkgoM7o9wazYmSwWk62jqpolPHmPG15uv98v+xRRxh63FQqfWwjPG4vvzYrXi9m09lizlZ2ZrsnK5N7pdFZMgn4S4/QcoiRdjSgrC2eN0o8c92X8wF9bW1vx7Nmz2NzcLJ4uCVG27hmD494od8fB8ZjwhvEqUSJLS0uxurpaYM7JZBInJydxeHgYx8fHJbmE8VLtyl4YwhjDz4rGyAmKxuvJ0GWmE89mJZL/p+/eRvOvqHuT2Mi2NL/vuxySQ0RwnpP5hm68KtQrdwlwGdbGMGQIMCBtemP0Vi0b6I0leKmGl171tleCGwYMCbAhGQYE9cIQbAjQUoJhbfpVVz1UvSlHJqdgkAyOGbxeMH+Hv/jyBjOrVarHvgBBMuLec8/5zjf8v+Gc43nmne6zvT/XIuSwp3nQ8mMAgdFGWdu7xGACmszzDnublg4L13Vd1lDjEUMv7gcseNkS/JLvgTZ+pyMiGE33lbm0/EELe8l8TxvwAmmCHNa3t+biKQNUy33mGdcWDAaDaLVa5X4q9gGGEVHqJKampuLJkycFJE9MTES32y08x2cUJLbb7Xj06FFcXl7Gzs5OdLvduLy8jE6nE1VVFcNLFXS32y1jsyPCHCCnTSHjiHd39jKfZztkJ8KXgeT7rgdjdLPrni+7702KPRsF2syfcZ9DBtmbsxfJZaHw9nswNkzchJQ8Ni4rRt5pZG9Fenl5WXKZFBewdMJKiyIrnkdQCDNjbLKRpj8oNp7P4SY+b0KJVvBNDO134i3hRWZECR151sJLnxgTAu5NA/K7aRujSCi+3W4XASac5bAcEYfz8/NSNYtnDi2hm6smCUUTjkQpsx0exuLy8jL29/fj+fPn8fLly/IOQsnT09OFDlbsHB5uZcz4MBDmzQyu7E1lPkZhZQ8pG0/nig08bNBR7tnDcz+spLzFpkPe3O82zNv+nucNhDBW8JHHHTF8upbzkm4Pfri5uT2Ws9PpxPn5+dAaX+hg7xBd4OVY0N55d/rjiBCfObRvWbJn689tnGkj6xrLho2r+cAFjE26ir43gX2qisfGxmJpaSlmZ2dje3u7hHynp6fj6OioeLCtVqsATTYS2dvbi1evXsXx8XGRCZ/odXNztyc9O1Jh2G147Rhl0O/vsjfr8UJDF2CZftnBuu96MEYXJs+fRQwvXeDzJgPb1KYvEyeiedG67zWBzViuoDUT+qKa0oUXHoeNHMg0G72I4TAZyh/j4Pyhd4mxMszh6Caw4nsybbOizWF0K1h78+6L88o5qoCHm5UrihEE7Xy1DXaTcQBYIKCeIw4ZwOCyBhCDbkFEibIpBoJPror5OTk5KQYDL5C8H14PPxjys7Oz2NnZiW+//TaePXsWu7u7Q2F5H2YPoLm5uRkK58FnWZE63AsvM5/eAxy6QXeMiw0nHqXDtKa1C+o8Dyg73j0qNZINiUGxZZUx2oMk/53BJPPCvU5JmKbmOZ71Gmqe8XIZ0gXj47dLw5aWlkql7NnZWVlOZqUNXXmPw7tELQyYXE/hv7Nip0+OOll/GNjkCCFXk+5z9I33GgQCuAjto+MwoPB/v98vcrG9vR3T09PlPngcWuHpAmxOT0/j8PCwnDk9NzcXm5ubsbW1Fa1WK3q9Xjx79iy++eab2N3dLdXl5HLzfuaZp5pSIk0RPevsbDvMI+8ztL4ejNGNeHeAFgrfkz9r+t/3Z+G1IjZayyjIfcLIwXwuaCJUyQQQhjk+Po5+v19ChkamRoUwojcJt0BRvXx0dFTyQDAW+QyYDQEgPI0CZAymVxPTmZamqXOjoN9saAEBOVfnOQClWhE3RSQcJnKREDs2Gcj4eb87KzqWHniBPR4MRtGAinkkPExhFDklkHZd1+UsZO8yxHs7nU7Mz8+XUPPl5WXs7e3Fd999F0+fPo3Dw8MSTqQC3eDMtLCByykA5sk0gedyGsPGc9Qz5hkUlcGnw8h858IbAyfngTEmNsTQjvfa2zQgd9sYSB8RmelgWTL4AlBZHsxDLOWyTANEMDbT09OxvLwcExMTZeOTXq/XuB+w+R2jk+lNP5wiQD/Y+FdVVVIJPEM4PAPHbJxNW89lE78YjBBhIVpAZAewSnSnruuil7JDMT09HQsLC1FVVVkW1Ov14uzsbKiOodfrlT3OyeP+7u/+bvzgBz+I1dXVuL6+jufPn8df/uVfxnfffVd2omIJHnPuEL2BfhMYNa81GV9H/jIw58qe86jrQRndiGbjaRTRFJf399lAZ5cfRvA9RpIZsYCeWESPgmbjhE6nU/ILXh5yfHwcBwcHsbe3V9p1wY0Ru5UmfzvcRZFAq9UqbfX7/SLgp6enJWzmjcDZfcghF1ddNgELewCmLcJv78XPOdfE31QWZiPkUKSBlt9Ju/wgQAYaBlMOI1lQMKStVquEkAFOKGvCWwgt/HF6ehonJyfl1Bg8bgsmIMfelHc8arVasbGxEaurqzEYDOLg4CBevHgROzs78eLFi9jf3y/KBf624mBstJuXeFkxj4/fFrH5KMOI4U3zm0LH9gBY94pc5LysgZJTK86fOXdYVVXZoxo+auoX/bESNGDKHj39cP9oA9rx40gLBsIeG+05KpND0Db0eG+A70ePHsXi4mKR8fPz87i4uIjj4+NiVKi09ZIf5tRelHkYYAl9m4CS5RW+9b2O5DV5ZMxzBrGOVCBLGFI+87wzL/AnBpkiQMAu0Z6rq6tot9tlOdHBwUGpcTg8PIzLy8uYn5+PtbW1+Oyzz+Lzzz+P5eXlODo6iq+++iq+/PLLePHiRZyfn8fU1FTMzc1Fq9WKwWBQKqTRmznKaEDWdHncdhCs9zL9DGrfdz0Yo2vByV4P30e8myOMiJGDhUAOIzusZe/NIRqYFYXvJSV4Qu12u2xRRhFOxJ2R7Ha70Wq1yiTv7u4WpWRGNjIfGxsbMp7OWbGZAjsf4c2CsL25wunpaVnmkvNc0NfKls8yUs5ejfPH0Beha1rPiRBmJrXiY25A93wPPTAkPEuO1IIecbdkxsCJeaQ4iiIm+s678XxB8ldXV8XggsL9uT01Qpy+nAvz0qHLy8s4OjqK4+Pj2Nvbi9evX8fp6ekQP+coAXSgYpYrh+89bw4Fmw7MYV3XQ6co2ThZplxE5M1GDLhyYVsGxV4ilsO12VPIgM/t2gt26NCylL31rBfy5/b4vKLAXoxDtlTKIw9EXZgngPna2lpcX18XOTw+Pi6hUrYcBTQaZPhiPJaDPOacF851IhkcmQ7+2/JkfjCIwoABgsjLen09xVt1XZe1shsbG7G2thatVqvoLQon+dubXJAfX1hYiM3Nzfj888/jk08+idnZ2Xj9+nX8xV/8RQkpX19fx+LiYtk9bnJysqR6kFvkw+k/e/xNc9BkkG1wPRemt3nsPqP+YIxu9jKbPNIcCrOSz94SzyBEEcNLZRxSNhNnobRCRsFMT0/H2tpabG9vx+PHj8sibhjv8vKyVCGT6GdnGystjxvFyHth8oiITqdTjDx9mJ2dLSfWVFVV8sdsls8YrfgyfXPOIwuc6W0jhVeFUDr/mD2FnMuzcGLE7cnZO7Un4t2JrNitzO25AQgIA+LR2tDw99TUVAFUGCS2Y/TGJNDSCpOxeqMTG2XaYa/go6OjYnj7/X68efOmeAAGZYRNGav3TPbOSQ71e9w5rOylLhhS8unOU5G+sHeUUTxzFnEXmvR2f6avAZCBkmU1541tvGkrG3Qug2ja4n5oatBhA0r/Heo2r9ozhj7kNSPuivfIXQKY2+124auqqmJmZiaWl5fj8vIyzs7O4uTkpKSdeJa+uY/WX66nAMg6/5v1iqMhzCmX7/P4+J/35DoMvGHC44PBoFT603f0zuzsbKysrMT6+nqsra2V+yYmJopj8Pr163j58mUcHBzE7u5u2eBieXk5Pvroo/jRj34UT548icFgED//+c/jF7/4RXz11Vdxfn4e09PTZZkfvIjuw6izhMjg02Ntcu7Mcx67wWx+zvxuz3/U9WCMbkZaHqR/mjzdUYM0c9mI0IY94Ii7tYEwEGFIGJCj1Ki0Y0lIRBQUiMeHV9VqtWJ+fj7Oz8+Lp+LCCcZJ+LKqbk+lQQG02+2yQwtte0s0BJgqSoyAUb+Nkj0D5whNQ4foskDauPkdNuw8Z8NmY24F7s0YMJJc5FQzYjfTO+eGwNOGt2HEm7HRxtBi9AAtGNuIKLl685yVOsUhNoAuxrm5uYn9/f1iOH2wAn21obPQms7mFx+VZgVJH90fG0d+M18ocudeHTGoqmro9Cp/zrtz9MSnYFlBGSA7JYAHa4OTw8z29BwpsidI35xTt9wb1EVE4StAILt1+b3Q1fxH9MZzREGQj+1ztAzQB31IAXW73Tg+Pi7GwsdAQiuW0xD1ckjeFfMOmXqu0DdZ7gyiTRfrS+tb55UBwRjQfr9fahyIyHU6nbKVI0vk4CeWyT179iy+/fbbOD4+jpOTkxgbG4utra344Q9/GD/4wQ9iY2Mjzs7O4pe//GX87Gc/i93d3RgMBrG2thabm5tFbogoOOLH8ZvwtXURv00jX/6syfvNhjlf9xnciA80ulVVfRsRJxExiIg3dV3/Z1VVLUXEP4+ITyLi24j4g7quu9XtG/80Iv5uRJxFxB/Wdf3v3/eO7L1aOfqeJkLdRwT/n2P1RohWBBF3aw5RLhFRBLOu67JcgDMe2ZrRYVJQ7vz8/NABBeQJYUTnTy4uLorim5ubi9XV1XLqDcJlD3dhYSEODw+LIm0ykg710jZKLId1cxTANLSgQitHIxxO9nya5rRjpeXP+e11kk1o1J4Ml9cKoqxcNMN2fqYLSofxIJyECllgb9oSXvPSIRsAhBq03ev1CuI+OjqKiCgbtDt0C98ZSNBHqnFpJ48h0weDCE8azGbesBG1EcODdzuMz94x7+S9GCl73rzfXjjzQ/s2Zr4cgjZv8b+9aNOCfjSBb5+mZWNnIJLBHJXn5hkvnSGiAKBCziYnJ8t+wYAiCuzgJ+6hL9Y//Lan78iFAY3lzgAEL5X+06brLQxoTFtHUeAH0zCvzqA4kbk9Ojoqcgmo7fV6sb+/Hzs7OyUyuLm5GT/5yU/iRz/6UbTb7Tg7O4svvvgivvzyyzg+Po7FxcXY2tqKxcXFuLm5W81BNBEPmloXaGi63Wc7zNd2TjLtMzDjf+vC+65fx9P9r+q63tf/fxwR/7qu6z+pquqP3/7/jyLi70TEZ29//mZE/OO3v++9moTNBLJXmgdroRr1/CiD7csh3qyMuFgewNaMGGJ7CdmDJAfs4/cQchQ34b6xsbttCTc3N+PJkyfF+yVPW1VV8cAwDA6zWQAYiz0xxujdWyLuPPDMrBntGagYMDhElvvjOYBONgA5z8K7nItxe9yLAQRhs5cy39X1bdEZhSwgdPpHaJkoAxEEjgzzRhZ4cQ6vEhGh3458QAs8D4w/fJENn9fiQgMbFLwyG1J7wKa/jUwTcof2gJIMjNxHh379Tht3R1jML845wiMsD8lFOE3vcB66CUAzD44M8H/mJT9PIR0gAINPERTzRjuAIzxWQsmed3vHln8DEWg+NjZWeI6IGkvT8Lzxiq2D7NnaSDiyxDsMIh3hy5GAbHANaH1vBjSDwaDQyukFcrVnZ2dlxQcHggAYPNbx8fHY3NyM3/u934uf/OQnsby8HC9fvowvvvgivvjii7i8vIyNjY347LPPYmtrK/r9frx48SIODg6i3++X+Ts+Po79/f2yGsCAMQMv+DN7vuYv85npkG0Qv3PEYdT1HxJe/v2I+Ftv//4nEfFv4tbo/n5E/NP69s3/tqqqhaqqNuu6fnVfY00ohM8jhhXzKI+26XmImlEKl5VPDt/wOYqdsKMLAbgfJEfYijCHlSQFNS5OIDzltagLCwvx+PHjePLkSSwuLka/34+jo6O4uLgoBp32rDjIJ1uQoAHV1nzuzT7MUPYqHOYzSq7reui0I7fj+/wOM3X2LPnenpsreBFwxuOis4mJiWIc2c2G/LqViXPkGD68Wbado/LUEQg8FffBB0tYkdJXo2oXkt3c3JS8LoAtKwXzMHyC1+6lJoAHe2WeJ3iEzzJIhYYRUYrB7NHmCEZW+v7bcuD5twGFb5ClHFpuCinTHsDW8txkVOkXfeJ+80zEbdrBm+ojPxcXF6XAjegRY0E+ycHTjoFTxF2e12kS8r1uz/QxeGQ+OfwCo5IdCDsROWxqmlM30eTpGRB4Tr3Eq2kO4Xd0Dk4I8oHuQ0+02+0yNvgXYz0xMRELCwvxwx/+MH784x/H/Px87O3txc9//vP44osvYnx8PH784x/Hxx9/HO12O/r9fjx//jy+/fbbslSStM3h4WEcHh4OybBl1PzgzzNYs7xCH+f+/Z3rAUbZpnx9qNGtI+L/rqqqjoj/ua7rP4uIdRnSnYhYf/v344h4pmefv/1syOhWVfVHEfFHEREfffRRGXATMm/6rMkDa+x4A8r15/a4HGKwZ8h9HOnGKRcgVLb843mWCxweHhaPZ3z8dm9SFA2n19A2pfWdTie2trbi448/js3NzbJFHF4ahs1eCG26UtS08aYQFj7nEqvq7rxKPnNoiXssPI5O2AsdVZ1qz2TU3NAuv72dHf11YRSebbvdHip2IyTb7/fLPEVEqY7kXi9noO/j4+NlPW2TF2baOMKRc4Q2ROSZut1u7O3tlbWcVsgYMStQywDKnnwz9/o5nsUDdYidecxhML7Ds+N5G8dsJOx5OweJknLonmdJiWSPCRlA2Tu3ynd8nuXeqRMDD4wfvMBcYUDxttklDNmBr9xfgHcGWvbIs6Eyv1iPNIVw4SfAlUHV2NhYCZ8yNt5j4wC9obW9b8K/8FnT807VMGbC3FNTU0NjtvdHYWEOM5t/qUGB7zhy7+rqKubm5uLTTz+Nzz77LNbW1uL4+Dh+8YtfxLfffhsTExPx6aefxueffx7tdjueP38ev/rVr2JnZyeurq7K8YAA2ePj46HoBDxhLz8bxewBZ+8Vujp6B0860mDave/6UKP7X9R1/aKqqrWI+H+qqvqFv6zrun5rkD/4emu4/ywi4qc//Wn99rP3EqRJUVv5046vjIphHIddHSqyAmS5CEYLD5WyfyZ5MBjEwsJCjI+Pl71bKZqxJ4uRoPAJ75WdjqiKXl5ejsnJyaHzJo2IrViakCy/vdGDGQ/PIYdYssCYuVDg3o3LnqjDrxZOLoclM7rEoFjJGxhQcNRqtQo9udfFUBYCcqpUCU9MTJQ8GuttKerAawaVQ3dob6UCzW9ubopCsifI//ZCr66u4uDgIHq9XvFyI6KMu8mQMG94uA7VO43h96No8JKZN9oFxBm1A+owPJ4DIinwCQqZd9Jv3gsv0AYK3XlveAJQwxicdrB35zoF8xP0cJvmKfM3f9f13YHyzAkRCSJY7ILktdOMjTGZf5tSJfSZeQKEWYHTT4N8eN3FgBcXF9Hr9cpJR/ZOmXeMKv2kroE55B7AKM8bFDdFDxi7QYOjBwAEZO3q6qoAWVZYwDMYQuphBoNBrK+vx6efflo2vfj222/jm2++iaurq/jkk09ic3OzbCbzi1/8Il69ehVXV1cxPT0dV1dXcXR0FAcHB+V4TPgdHrd8ZcObbUa2Kb5ci2Bd6XudFrrv+iCjW9f1i7e/d6uq+hcR8Z9HxOvqbdi4qqrNiNh9e/uLiHiix7fffva+d4z8zL+NNs34vkZ5wtkz42+HpfLexOwWBeLj+KrT09Po9/vx+vXrqOvbE304lsqIfTC4Xaxdvw2tzc7OFoXcbrfLOs3Z2dlYWlqKra2tWF5eLssOEAx7m4R08OR8ukYWCEI+CBkC5JxkDlFlunoDAhQ5NIa5vbuPFWAGTVZgKGHo6s0qbLBAyhSQ0X9HJlwEBL1QrF7rjJc8NjZWckMREcfHx7G0tBTz8/NloxMQPG0QtQAMMT94Tw5pucgKL7ff75e1vwAolDAX/facYPxRXNPT00Obg2BMMYL2eByWhX+8BKqu65IGsWG5vr4uYXrmg7mw1+tUAG24XygiFKJzv+btrLgMJA3uuBijP7cyta5wOof7+v1+eSfhdcZO+5YlvGkbY7edw5OuixgbGxuKIlihE1nxu3mOIk3OXoYPnPMlr+oqcs+N9QZ0RY6ZU8uw5ZWcqPWEaUPbLIm7uLgo+yZXVVVqLAjDs0b56Ogo3rx5E61WK9bW1mJlZSWqqiobxlxdXcXi4mKsrq5GVVXx7NmzePr0aXS73YiIsqvV2dlZ2RwI2TRwo88GOE12JhtYz5PplkPMprF15/u83fca3aqqWhExVtf1ydu//+uI+J8i4s8j4u9FxJ+8/f0v3z7y5xHxD6qq+mdxW0DVq9+Tz/XAzSCeZH43fW8my6Gb3La/szHPOQejX5RPRAwhN/Zb3dnZiX6/HzMzM7G6uhrLy8sRcSssrPkkz0F/ULgYjFarFUtLS6XMnlAQ/TC6GgwGBZHDdN7tKiKKknHIz0wIs1BJ6eeaQpymT8SdcaAv9M0eNGPN6C97P6BijKK31Kyqqiy097KLiOHcHe3kuSesTHrAezhfXV3Fs2fPotvtxv7+fjx58iRWV1djbm5u6PBzK3HmzZ6dAYRrAKqqKtsB7u3txe7ubhwfHw8phxxZYX5d4NRkVGxEDJTw+jOotFL1esu6rsvOYbQNTQnDuz8YHm9XmT17h6/9N2O08bBRINqQjXKOIjg8zTuawB0XtPFaWorWDEgtk9A8h3MdOchRocwPBvk8xz28HxBngMKz9lSXlpYKuELmWCYDqLu+vi4/VEPnrWUBpuZlA2nzCBG4nHZyKNuer0GUQTBzSnqFKCFOClXNr1+/jpOTk1hYWIiNjY1YWFgohvrs7KwsRcIh6fV6Q/T2Bif0weAvyzJ9Zp6yp2sZ8v8ZDP7H8HTXI+JfvG14IiL+97qu/1VVVf9vRPwfVVX9dxHxXUT8wdv7/6+4XS70q7hdMvT3P+AdQ4ONiHcExwONGDbEmbmzADpnYbTThKBdgIGgnp2dDYXS2Pqxqqrodrtl9yK2fev3+8Vr6nQ65bSMXq9XlgS5GpK1uPPz86Xy1ooHZsKgobDOzs6i1+uV0zoihs/hdDENQAE6IkgoLIcarVxNW5SfhdbzhhASwmSdHMbY4Tjn1/Bk+Y0SIiSPckSxsN+x+8o7EHZ7HggpeU6eibj1zvb390shBjuMtdvtopAIa1PJnPOHVGha2CYnJ8sOVC9evIgXL15Et9st47JXA90JQ+ZNQ6ChlZuNkoETYXbm2VXUPEs7eLTeZ5g5Ziz2dG5ubsr5z+QfPffwLXNMxADeGx8fL0tGWDY3GAxidna20Jv+wHd4z/ldWR9kufbaVO7h2DnmwN6MD2C3keUdjigwv7m4izmJiALa+IGnmWcXJrEuv6k98yBjxJACRAGtriWATyloQr7hhVz8Zj2Kcc6g23xvPct7nEPl/USIAKCsqb2+vh46RYvPJycnY3FxMTY3N2NpaSkGg9sjBLe3t0voenl5OWZmZuLi4iK++uqr+NnPfhZffvllWSVAHzx3jMm8nUFdNpZNHrJp1OQEOsU16nqv0a3r+uuI+BsNnx9ExN9u+LyOiP/+fe3my0oshzD5PqONJjRixcRlhZARDd/TTs4J4Q3QJsqdkMn8/HzMzs6WnVAGg0G8fv063rx5E2tra+U0m7quy57IRpGEYOwt4SmxJ68FAu/75uZ2Ywz2dqXQAobjd8TwYQWOHoASeUcOTaH8mA+fgjM9PR0rKysxOztb0Ceo+ODgIA4PD6PX6xVUW1VVyaO64ASFx7pV8k/QAcXrPBTCzHcoS1f3ZsXG9/Zq7E1i0I+OjsqmJiwnWlpaiqqqhpaZoKzMD+5zv9+PV69elVOEut1uAUHcgwdqgwigseDm6AP3M6+An6qqiteKAfCaT8CJ1wUDlIjmQC8XM6GcoLc9RNqAJvB2u90uFfYALHgXo4vidYrAsgw4ixheB5y9iVGpFesQg0qH4bOnk/Ou1gm8o8mTAcjSrucHGjoy4ZC1I1oYZ+gN6KYfzAG0mJqairW1tVJzQv+Ye6+0QFbYDYsNOfjN+Jh77wLn/hj0uO8ANwNc1iyjr9gMhDlHBpkHalsWFhZidXW1gIv5+fmIiFJo2m634+LiIubm5uL6+jp2dnai1+u9kybI0QjzjqMamU98v/Wl9ZZ5wvT5GYPT5QAAIABJREFUTXi6v5XLg8tG0d9ng8uVXXt7OzxvJetnM1rOxpzcqfNM5CRarVbMzs4Wz/fq6qpU0XGeZLvdjq2trVL9TOUehpLDmDFAnU6n5Goc/kIAyQMfHByUXIoVIOO34oFpXHxjIMP/0I0cTFZ60JHCJjbwwEOcmpqKo6Oj2N3djZcvX8bz58/L8otOp1MUAmMn5NvpdGJubq7030DHYUiY2+sl8dRsjB3ig4bMG+3iJZgmLLDf398vffIyEMAIYWvG4Lzm6elpOXbs6dOnQ8jeYWvmKyv1rAByzpf7bPyhKWFFg1joSTsod95B5MUFUjlkjVIGANlA5vA3/cJrxxNi7SY1DizzchQB2ni/bWhA+iB7/cgOc+q8W6ZxprvBEwbHqwBGRduaQICjb+47/cz9zumwrLz9Tv7HIA8GgyI3a2trsbW1VbarhfYY3qOjo7KJBPoCA+jtKCk4tBcHv+TIQk5vGNyRegC88F7AON63K9tpg5UhMzMzQzUV4+PjMT8/HysrK7G1tVUcncPDw7L/MpGSDIrNM47ENBlOgy1/Z/vjCAmf8Y7fSE73t3lldMFnTciCi4FiUMwYOd+CQh6FVv0uGN3eEsbNy3gGg8FQAQ7hMhi42+1GXdclfIyxOTo6KiFi0Df5zHa7XRjQHrgZirANBgWDYAVu+pgu/s6h94i7U26MWsnhRQxXqfIs1YsTExOxvr4e29vb0ev1YmVlJaanp+O7774ru0HhgbKDF8t9rHgRQBA9c2WPCI+YvrCUC8Hhd46a0G8rVML8BlXQB6Tu6kty7s7hkmPb39+PV69excuXL2Nvb68ckOCCEuhMv3g3iot5d989P/Z6AQyuSrYcOF/v8D+hOF94J95bOgMAwpzuO/NhekJDvKijo6Pi4dR1HfPz87G4uBitVqvwJ94wht5gyZ539lp4js9GLS1y9MdzYNBAJIDPXA3O/w5/57Zd8Z6NF8/YKETcran2GFkTzPeXl5eF3yKieLYAF+dHWadKeoeoDnxWVbeb6ywvL5f5Pjg4iJcvXxa95PQT9PCYDFBcbDoYDEoED5DU7/eLbBCZM+CDd6ampoa8Vu95jy5Bx56dncXe3l68fPmyHH5gx8tA1QAn84Q/t9yY//OVP2/SrfddD8boMpDs7jcJGgrHeQoLLkR3CAkl5nBSRkEWIr8zIooi8nKLLPQwCEYEBHl0dBR1XcfCwkJsbW0VJuZYN4w3itzeqelCjrPVapXcGs8yBo93VG4h0xI6IJT+G0GH4XknXjmbboBoCQchQNDg5cuXxXP2EXmEyOzVZUDkQhmHYvNORTao5KTzXJomzjcZCdvIsk8u/eA93sCde16/fh37+/vF2L5586YUB6HgzTNen+ulZ77oj2nh/2krK3yUMl44MmNg4nXf/A+dGH9es+oN//1+xgh9+A2ga7fbZV58apfzrMgVn9E+Y3YkhsuhWOiTw4l4w9YRtAUtXbmblbG9Ju8gl+WHd3kXKecM6QOgAnl1eN4K3Xl/3p2fI4oAuH/06FH0er3Y3d0tRUukVbhohwLFy8vL4jXn/QDoE7zqzzC8zA1zRRSMJU69Xi+63W7s7u7G4eFhXFxcDG2uMz5+u7Xt4uJiHBwclAjh7OxsOdhga2srVlZWIiJiZ2cnut1uPHv2rBzxh/OSow22JdDRNLXBNU9lO9TkpOWr6R1N14MyuvxGQUS8ez6mlSeDy8Jk7xfhzcI4qg+eBIdyLDAgTxiu2+2W/UNnZmZiY2OjbIQREUNVhIuLi8UzAgGy4TebdBPKRhnRfzxDh9m8M5YRtOnpkJU9JNowyjO9ETyHVMnTQfeJiYmYm5sbMrQnJyclTL6yslKONXPoyiEo6AS6NmBCQfEsOXb3FzrZsNnzYjyjIgH2pgAAjiigiN68ud1qjrxvxK2AUkVOuA6aOs9mIGB+9hwwz96MgMveGN6mjYa9XbwinyGc5QWwhyfKEjSHm6kmdy57ZmZmKJzonHoGr4ASgFmr1Sq7CEGXs7OzwrvmS/rp6BMgKoNjp42sA6BLxPCm/wYDAHHLvwEg3wOUXf+ANw4PQnva8laXXm/PfBIl8DjtFZtvHYp1xILiI3vJ+/v78c0338SLFy9ib28vImIINJFWQQbRRZZRACkgiPe7voI5Yg6QG/qDnsTg7u3tFeMIYCaFNjU1FZubmxFxW3sxNTUVq6ursbS0VKJ/dV3H4eFh7O/vx9OnT+Pp06eldsSncFmf2SHJHql1Jc8gqzn6Yf7we9zWh14PwugaTaBw+TyHP/OgLSg21vm+UUrX37uC07F9hylhJkI3vAsGJQxb17c5zMXFxVIZORjcbiFI/vf6+jp+9atfFW+Y7QHJ+2FQGSfKanx8vCxGdyGO+4NS4H8bNF9WUig8xkx1JEVSrA2kWpLq7KWlpVhaWirGACUMkqYoAo8OZYSg2jDZc8kRDBQCiofxYSQQZo6+swccEUO0RJl4lyS8CXvW5BdRqicnJ2U3Mj7jBCkAgvOTGAQDON7nAiAUgEOIGLfsUVn55nWl9MHeNV6HDS+bC7RarTg9PR1SIJOTk6VWAW8DpYQX55+IuzW6GAMADKkTb6MITeFh6J4jMPBGNsTIUs5zOspl/WCwmT1pz0WOrDWF8q0zUOh4ho4wscOc+0CKyPqCecJz9pgsz8iO9RN07/f7MTs7G4eHhzEYDOLw8HDoNCs2A2FM9A29SkqDtAZ8Z90LiPWYXZAG3xH9gvfYnAbHgudubm7KJhn9fr/sNf/xxx8XZ4axUgdDncju7m70er2y9zLAzw5Clhfo6N9NjoYjY9nRM09lQ3yfM5evB2F0PYAce88E4+8cf4dg9m5yzoZ3ZIJhVHk/l3M1FriIGPK46BP3sy53e3u7VDeDdC8uLmJxcTE2NjZKHoWTMch3sGuSc4wOPaN4vI0kY0e4DGCcY2LcpmVmRhsmV9riabfb7VhfX4/19fVYXFyM5eXlsukHXha0mJi43Rt5bm6uGCjnh2wc/G764hyy++MtLGmPeTIPMXa8OEcOeC/t5/w4fXS+l7XZDkGhAPEMzSvONTnXaQF3SJ3+mBbmwywfVube5SoXXwEos2eK9+o+ZFngfitm0weakdunbXuwb97cnRtMn3OKyEtmmEuDcfONlR3GwdEOGxnkxwoWvssgnPyqZcHRLgCEo0emBwbUOgM6+5hQ3uWQqMPyBn04BAakGCTG7UM90CMTE7c7rEVEWc+Lw9Dv94uDQNrB1dc5pw5dqPcwyCF87chVBrcG9wbDJycn8fLly1JXQXRmMBiUZUSscX/x4kXs7++X9zEmDG9VVaWdrPOYS2TMvG5+5XI/m7xYA07LtD8fdT0IoxsxvGSIPJkVzChDmfMvZkyHgPwsf/Mcwp0Nq5Wuq/UmJyfLhuS8D6MYEWV9Jpupo0zYSIN7p6eno9PplI3NOWOz0+mUjbzxuAnxeQOIiLs8kXNMXs9rJcWY/TtiuJKTy2iTPCDLmxYWFkrhBptaUNyA8nc1L7TC07eBsMGF5lwYEwyF82kYYyskG24MBwJIZMFLwPISGmhpmvgEJ7+P51F+uRLTRtVCzvMGFxkpO2ROf+mnPUIqO9mRzB4j/FLXdVG2KFfmk0IclCnhX8bgM2IJFWJY4F/65HkzbyGLjIcIjlM2GQRCc+Sb+Tcdq6oa2gQkg3JHLGzobSQzOHV40SFfe1KOuhnk0S6yPT4+PsRrjMO7kHncTiUZbPu7zA9EIur6doet8/Pz6Ha70e/3I+LuPOmZmZmyRMjro5lr0ieOkqAjAJnwPPKX9TN0AFhBp1arFQsLC2X54M3NTdlwCFr3er345S9/GS9evChLEOv6tohxb2+v5HnZyY220bPsf+BIEoAmOxKZT5yWsBzm3/zt521X/LvJSPt6MEbXl0Ms2aBGvHsWqBWXESjMDZNkT89I3eiWe0xko1JP1tTUVCwvL8fCwkJRQuQ1KA6oqqoUUxEmIbwD2mQpEBtttNvtIXTtgibnpxASCyP9d+GPFQ3f2bvJv7NhGB8fLzlrFDrriPv9/pCSoe2rq6uylpiCDoyxN+rgFBlHNTK6j4iitAhxMzbmN6cWGL/zdzk016RAaYPxY7yZezzJpnyhL8aBYmdM9uCaIjqEEZl7CtfgQytFaAkt/ONjJKGlIyT034VsRBCgDwYb3mf+DHC4PIcYOcLrXn/qcCT0u7m5KQobOtgjbPJKDSDpLxdzbrr53gw6eSfv8HGM0Ju/bVSsQzJgdD7RXqyNtw0F4AR6ILM2xtzDeElzVVVVNh7hUA/4mefxfF19bTm3fmPe0C0ANnQr82tji0wS/kcHrqyslPoVIn4AGcZweXlZqpZNMy8XJFdMauLk5CT29/cLiIAmyHWTvufK3qh1h+/PUSc+N9i7r92m68EYXTrPZFuo86AwrPZWjVDttdorjhhe52jmd1g2h/hsmFHaVNw9efIkfvjDH8b29nYxojyzsbERKysrcXl5WQ5s5qADCzheBHvzOvSDscj5Em9Z53wS/XSoJDOMvbGmHzOThcveDR4AZwBHRCkUcgEJXjxjdr/xPA2SuJxXzd6QC2qgn8dofqqqu1A0BRvk4MhBUyRmT8qhNffL/GdeQ7ll3s3C77x5TpHwvqxYeZffSxt+j2nA8w4PQifngbPhyvJAWzY09NfLyvgMQIIydzTARpO8qsO5AMjssZh+TX+7H1nRet64PEemt/PVTmvAs01Lvpgjh4ibjCxy6vmCf3L9CN4ghqvJ8TCPEgXCKNnweq6ZP4oAvRKDe+BD99UnqFlHMna3S9EVMgXfsC6YVBqGlo0ukFEAOu9gSRnj7fV6cXp6WjzfnObLMmtnoskIm3fzb9/jaIZ5Oeucv1ZGlyvnOKxw+Cyi2XjwOVeeDAiMYcohBa4mJOpcDc8sLS3FkydP4vPPP4/f+Z3fGQoRvXnzJjY3N2N9fb0cQwUK43n+Z3u0k5OTITRIWNkVgQYlmS7O+dmj4DJTmYY5zJLpztIX56kwVjyL8GGYJyYmSm6JqkgrkOzFRtzlWVEmVmw2fE3jyjko7nN4P3s59LlpzDY60Jb7UVY5jGTFj1HFYDmvhbdoj55cm71tV3nSHmMyKHLo0+PEkHnNpfnGht9y5R8DWYNX/ve+2/7t5Vp1XQ8ZJpRWU6gWfssg0PzK3wYpXncM3U07QvaZl/JlI4ihNSixooY3LENZCcOfNsbmgxyh8bI9t+Vn7Y3agFOVPDk5WQ5yh/8dss96g7+ZW4NYh8MBJhng2FFxXYMLHamCHxu7La46PDwcmm92t3v8+HEsLS2VVQDcw2lC6EjznUGd9ZOdhqZ5Mb97PI6omiesHzIvNrU96nowRjcbvyZj6M+ygsuEi7gjfEbZObyXvSwTkx8mlnAfzAuioxiFkCp7MnNM2N7eXnS73WKACcviBZJrY+lJu90eOqQeJvb4eL9zivTb4SEbItrI3iOeJ7SwIjbytXdEpfDY2Fg5SL7T6cTExERcXl7G8fFxOc4Oz9JGwgqa9xEWBWDwTuYlgwGDhVy8glIyKLOCIxzmzSLgDRepWek632alk4tm8JaseMixOQ/KfXiiGDY+864/VvbcSwU77QDO7OFCY0KXrl0waM1erD9zxAPvz8YkyykX76eimUiReRlQaSAAvzV5MW4fcOpcbTZw9N198ti8VApDRWW7jbujDs7P5rmhHza6Td6VeTHzCp+ZZ82HzKl5BZ3Eu0jnuJrYAAEZ8riQIeaEuTA4wAO3TqDf8PfU1FThzX6/P7RkCk+eJWsUka6srMT8/HzRZxyCQGiaauybm7v9y5kbgy47Y5n+vvjM9iPbEC7mM8t+U1vvux6M0Y0YViw2sBl5mKC+7P36WQhmlGJE1DQpTURlcmFYtiHb29uLlZWVElo7Pz8v2509ffo0bm5uyhF8LPaOGC6QuLm5KVWFbM1G3hfhpv2bm5uiwEGQKDILhhUC9LNRGEUz7nX4isO++Q7hevToUbTb7Wi32yV/TQiJcDqnu1gh8NuKBuWBIkS528PKiNTKlHbsTWQ0isBkD5f5YE6sxJ3HGxUdcbtW+Cg0trJzoRLrV7184+bmJo6Pj4tHgGJztS1jZu03hieDJdPIipMxER2wl2APoq7rsp+2gSp9tWHJsmKjDS3hf0dG8vIOL+Xhx9645xFezHOdla/bcnvQx3Q1f/o7HygCzxq8mL5NxZvIk2nrJWOWV4PIDH4dJs56jbW1hHfpB/TPy3xY3pa9QeQbL9/1MQbLTrXxPzoBQIhRPTw8fOeQ+Zubm3JS0snJSezs7MTS0tLQPgYcA+iIoz11L/9ymD6DmyZjaqPsy3oh640mXvd3f62MroUiu/z+zkbUXqzRrj1cmD8r7YyK+T9iuHqUZ+wFclFd9/XXX8fk5GTZqmx2djbW1tbKgvC9vb2oqipWV1fLzipsgMHGBKxZdLUgY3bIBuWAt4TXzHeMLVd7WrDsLfKZhd4o1wYsK2w2PFhYWCh7L5+dncX+/n7s7u7GwcFBHB8fF+SKUofG/rGywfOwMjXgyjwDrawM3W4OFZsH+B/vgsIjv9t8aZ4wcsdzdn9QgvCgNxsYGxsrh32b1jc3NyXnRSpifHy8FBnZ84QuTd5LxJ2isEGwMTCdbGxswMmZOZLi+fR7LaP0FUXPnFvOnKqJuAMRWe5zXpff0MDh0RwqhQfQD9DFRovvud8RBr8PUPjmzZuyzhkd4wiOgUNElAp6h2/dvj1WAxXuNViwbvP8j4+PDxm1ubm5IRp5Du0Ro1+I9gAabHD8Hs+1ZRHHwOCHe3z8IHrGUanr6+sSGTw4OCjPW58zb1zWX7YHOepgkECfsn0Z9Y7Mh/k5399k1EddD8boNnmvntQ82Px5RsEZ4ZhgFjbfZ2QbMXz6EO0651zXdTlNhjwKG0F0Op1SFcpi9MePH5dtIL/99ttSwm8ljbfLcg12eqKggHGy56oF0XR06CUzJzSwkHMZSVsh+BgujC5hODbKIEfFDjTdbrfkg/Fk6ac9Jz6nT36/aZ09KyoZ+RwvBMPGZ2NjY0MVjhZK98W5exsih0Qj7o5WpE/cS4iQceTwIAcRkLulGtTKDMUOTzIWAwrPIUozGynzig1s0294wz8Rd5Wg9G18fLxsLO+KXocZ+dxFUva2xsbuigDJbRPlwejm2oEmxWgARLsGDFmJWjnaQ2WsXoJmTzlHkJgDxg1tsqcObQFz2fDmeYEO8A0ADhrBg8ikgRFzRZ/Gx8fLxjVOwThSYzCKZ8ocueiOMcHzOYroy4bYhvzNm7uT0bKOgUYuHLSsGXQ5AmA9nnnBsvM+I+nfXI5M2MnKhtyf5b/vux6M0W3q/KhBGM0g8E332/jm/63Qrfjclolsr9neH6Xrr169itevX8fMzExsbW3F+vp6zM/Px5MnT8raM3YqOjo6iv39/djf349+vz/klbHMA883IoZyu4R6MeoupjBTYwDpr8cFY3pMHif3DwaDoWP38KoRCJiSEE+/3y/efbfbLYcF0B97iQicFZcLxWyQHXrNVx6DlRLfu1rcigTFBm9ERAmPQUfzUkb4mWfdX3tXuabASyUs8A4TE0loCm1De3t19oQwevbisqJz/63g4AOH85o8HMaFMgfomZY2mPYk4QfkF7BJnwEv3oTCbRh4Gfh4rL7PIMaAhe/gH0eXGAuAhqV6Xl+OV2k+aYqQQFPTKyt987ONUaa920JObCSZZ0A56RkvE2OXKLZgpJ8Aa2TVR2EiQ7zD7zNfMx8GqLzHBpI+87zrOqz/81hNX8aegZnn3/3NPJ7Bt+fBwNW87DmyXTHozXOarwdjdCPeLWDibxPRhvM+NOz2/H9TKMEEGoXemgwzhStXV1fR6/UiIspa2/39/VhdXY1PP/00pqeno9/vl31CDw8P47vvvovT09Oh/CrG1mfz0id7EygFcqlse2jGymiMohDT0wJnhnZobmxsrGziAXr2kh0YejAYlMXq+/v7cXR0VNpqUvb00YbJxnNUHjcbBgvjzc3dNnvegcophjx+5hOjHxElJ+YcFm1ZIeTQJV4J9HDbpnMeD+0TGeF39rhd1YvytCJHUTN3eE68wx6ex2F+4ceb9lsuOD4wr3vPXlT28qErPzYm3mSfeYKf7OHl4i1Hb6A3yhsZ8dgdds+RNb7z+3ne+X+/z0Yk8wVzkHPmXLybOQSI5Hyx+T2P1UCP9tgMY2FhoewdQH6UyEW32y10guZUFnu+uZ81tZZ305p+AlbfvLnbec5RJIM5h/otE9mztGHNvGoaZ2MHvQwoDcKyzsty0STz+fKcZwdv1DMRD8joGpX7akINEMTGmXtHte3v8zMZOWcDm5Ukk0aBEH33Ju4sAzo7OyvFI4STHXax4WfdK+vsvL8zTOvt0hAwDCrIGw/BTJwZI4/ZDGilhafFuzhBqd1ux+rqaiwuLkan04nr69vjwdgXFaWFIWC+HF5rQqIOodkY40nZ6KBwGBNg4OLioqzHxQDYu6AfbgOFwC5HLjrisiflkCufI6Be0B9xd6qLIwhNfOvPUIDOA7qf9jDhV8+128n9b1Jy9mihtd/lsbjSNoMHG1x7ocypAbQVMLQzSMs0od0mRcr4qJImZGml6jCuryZA5HC16eJaCd8LzXgP8wV/2Jg6nWQ5ddSKd+Kl5lCq853wClGaubm5skVrq9WKXq8XExMT5RzuVqs1NHZ2evJ4bFh5v9f1Gmw4GkP0yLv3UY+C3nMldAZr/Pb82smyrNng5khGnm8+c4TG72wCxBkQNMmqvzfIuO96MEbXSiN/ng2Ew3VmluzpNg3+PgI3PWeDlPtUVVUxkhTHwIDX19dlowsjPQ4QIB9qA0yOkqo/fmBkCzWGF4RqGpgBI4Y9vEwTe6yZhig5Dh7nkHqKphYWFsqm7kdHR/HNN9/Es2fPSsjc2xbmEBnvtlA552VFxZjxvmyguaC7FYWBnJWlaZG9FBShl1HAY9lTRPgx4A6DoRgJ1cG3o1C1jb/Xcps+GUDxY48ue6yeY9PMyjPLnrfwxOPM/Gc5cViR/jXlnrORssHJed7c3yaa5fBf9g65z/TLgIfLEQPuMc+6DStXAC+A0AVR5mPnKQlN01dHbRyZ4Mc7p9Gu+2Ejg/FDz3Q6nfIbwH98fFzOf+73+7G/v182moAv2C+dJZCsj7WDYR7jEBR0kunQ6/UKvTjEBVpZphyNyFEig/Y8956PDEAzPQ2gDJRH2QrzkD/LtiM7avddD8boRjSv1fWATLSI4bJ+P+OriVmzwfW9ZiS/O/ePe2DKm5ub6Ha7xQtE0FhqU1VVCdNGRCk2Yk9ivx+BQCg6nc5QuBWhwoMi12rjgdJ3aMvjzeHBfA/Pea9eo2kMGeGrr7/+On75y1/Gzs5O8TYIk+bwlAUkYrj4JCsShxdHIVnfjydmA4CCs/eZ55Y+ofy4bDBtvOzd2IP0Mx6PvRN/l8N8dV0X5XdzczO01Cqj6WxQWC6SL/c9hzxpl765sMdgiLENBoOh6I5ljHbh/exlc79lEWOOEge4ml5WsPm99mSb9EFWqhnYm9+58pzwWTZujBnAQHtNfGkwaSCYjWguYuIZF3QxZpb0eJ4I1SOrY2Nj5RCB8fHxkqelsCmvnri5uSkH3LMX997eXtFL5+fnQ0eL0lfOEW+327G8vFwiTYeHh0P1KOgCG0NflqM8v3lOTG+DI0fILLMGfJn/zcf8bV3T9D9XBrBNBtzXgzK6Ec35u/sMpJn9PhRsInG5zSZU4z6YoNnIgx6vr6/LujOqjinYYT3mzc3t2rQXL17Eq1evSgjZ47i4uCjbJ7K+18eE0S8YnePXqITODG3hsNLKTAQT2mg4r3NxcREHBwfx/PnzWFlZGTob+OXLl8XgcggCSod7Mi0RDIds87zbAGZAlj3hiBhS9g5DobhsvCPe3YjAKDkLETl8aENbWQAzMINH8sX4rNCd284eZOaTDBQ8z35/NkT2yhhj3hwE44sRtaIyXTJPRtxtXWna871pYiBgXjBvoESzV0JkCBBC+05f+J3meRs6G3bfT788XnuStON+my72hM17OdpC//0uz5N53etsnS7hwuiykUSv14u5ubmidwB0HCrPlorooPHx2zOYOczk0aNH0e/3h/rlfZuZD5ZJzszMRKvVik6nE7Ozs8UZwbBTvEUazDzOfEAX/m4yslmXm89Nl6wvsl4xcPXVZFjz9362qVbivuvBGN1RhjHi3Y0NrEz9uZk+G5ZsoJven383IR8LuZWAqwMplqLIyQdGX1xclLDz+fn5kMKNuCvPp6iKcIzzoqBc8qytVmsorJ09lZwLIYzZFG7O9PEJMr1eL46OjmJnZ6dUYlM8wfpbvPys2FE+DkvlKmj+p4+Em20IoBGKimfJodko8tuot6mgDG/LACDzXMTd+m3mgjb52zwRMbxWFACWvW17ngApr/dEUebwGamIDCKcT0UhwLfMQ1OuC8UM0HKBjQvEzCPImEETeT8+j7gL/WNAoanBXZNXY9rn/kbEUIjWc+k+us+mKffZq20CUsy1I0eeWwxh3sEsb/rBM/Au0Rifne0wNOkn5yChH++h7cFgMLSS4eDgIA4PD+P58+elLx4z+wAwdpb9zc3NRafTKcsTe71e7O7uloMIAM5eiTA2NhadTifm5uaGonqM6ejoqOyyh8GFv61TLUPWVwZ6TcDccuR59DMGR/n6ECOZHTL33d+ZL+67HozRzSiv6cqekoWwyTvNTJ8J5PuNjjIC4l3Z6xoVCiGE0uv1ikJCKfCdvQqUIUzDD8rPIScEDwNCvobzbAkh2dhFxNDOToAEGNvKLuJujV02IAiAjzVEsVPAwS5L9iBtADzPHluTQfb8eV4sQFaQzgda2Ox9+OK9KEGHTS3kLoKiXeef8/pjBJ/vHfHAiHpM9tRypaa/s4LysXG8yx4+tLIRhjcASq4+tUyFAa/zAAAgAElEQVT5f+adbQ8d6rShs7HFaOR2LIcGEE5reIz+Dl72vJt3LcPIURPQwJCZRk25YPclgw2PDZ6GbqRWuAz4myqZbVyy90r0Af6ywSWs7Sp98zSeJRXghPAnJyeLkWTLRTzVTqdTxntychK7u7vx3XffxfHx8ZCBQWbrui7Rtk6nU5YxEjkBpNMPjyPrbrePfvQSrqaoJXTge+Y0yyrvHOX5vu/K/fO8WQZtH+67HozRtfA0GcWId4sIcn4mG8782ahQZdPzfl/u46icWsRw9S1t2LggAHxHe7zTXh4/9NsCi5dJRbHP4OWdeEOzs7NDnq4LFhB4n7BipnU/bQh5B2tN2W8Vw4UXZo+Utm3QnItBYDwPeU7zfdDMPOG5sTG0cs8FFnmpjdE2vJGBXfbGeC5vtO8cEzzkNblc0Ibf9nAYF4Ynez6MiRxwNp6AQTxZL63JuUTGnmXDxtYykr1bvCHLA23Yy8vzmkPwjlY4FG2lm+XVf5ue5iU8yqzkc1SAd9hzyiDIY4q424YU4Osojd+P/PC5AQhyj+HJWyeSVwcQGqA3eZMTExPR6XRicnIyFhYWYnt7O9bX12N5eTki7vZojogh7/Tg4CBOT0+HeNX8XlVV2biHkDSHnXiHPc7wZcxe+maauNo8R4TMh5a9LPeeGz+b9f2HGtzMVznC2qQT3nc9GKPrSeD/++6NeDfnl7/Pn2Ul/r738I6mtv1cFsymibCyyCgvGx2EiPJ6FwIhHCgJb7wxPT1dKgxR8jaAHk9Gx3k8KDjnCiOibD05NjZWFswjNFYyjDGPFUXjfGV+l/vieXP/m7yJnAf08zbM2fOKGD5KMBvg3J4/Z164UIT8bbCBYbGy8fcGHBhH3oFnXdd3+yFnIIMBbELx5jEbNxs2zzNj5bdpYp61ofZ7nBowAOBdeakX32GsaIvnc+V0bi+DLJ43XQGAGdyZfgCLbOAdXbEn7gI02mPJHn31e91XK2//bQNPfxyW5x7zd0SUQ0bI0zJv7M8+MzMTi4uLsbq6Gmtra7G4uFg8UiJwFxcX0e124+XLl3F4eDg0Zvrk/DL6Z2FhoeR1B4PBUGrs+vq6LKP0Vp9ZF1muMiDz/aN0hP/OILlJJkYZ5Puu3C79z570fdeDMboR764vzETLlycvh6ezcaUNeyVZkY/628pnFJrJ+QIr9SZBp/1R3joIF9Rqg+Z+k9Ntt9tlf15X7zJWG0fea0/LwIH/7WXf3NyUvZYp6sIrz+tSM4AyU6Kocuga5UgbVnIADLfhdqGF6ZkjIfYSciUqn+ciDI+9yXskh+w5tYK0UbaitDJG0Zt/XCDEu6EZz9koGOkbIJhW8EQ2NqaX+cXG35+ZHvb0spfhzxxKjYh3jJfHbFnJHn8Ghu4f8+LfeHncZ3pDU9OVsLuNmz08vsthRQCEx2oj6rHmHKV503SmHQOS3B7vI9LA2n4KlsbHx0v0yYfM432SLhgfHy/7ChweHsarV6/KMiLzYQZX09PTMT8/X7a9nZubK3qKDTnYq2AwGD4Ri/vM+1m3+crzn/VokwHNhjtfH2okaaNJv2Xg/CHtPiijmxFLnoSMbnKOcFSbvoz475uQ/IzbsYJsmuScd8oTlnMNDk9F3HlIGF+HnDncnpDQ5ORkzMzMlCrFiYmJgnKtALIRysqBsCXGhH6gKKampmJ+fj7m5uaKImAzD9Ar48lIPodXjeZNX89JDid6gb4VMd/ntqzQ+N/5L4yjeSJHKrJR8ntA+uRWs9HzWKy83YYFNiveTA+DJEcdfJEmaIr+WHG6Lzam5lmULTzS5FXbA7Wc5nBw099NADiHfZsUb44o2fhko5y9VOhsQ2ZZNF8ZaDFOz5NB4ShQYLDl6AdjsYz6mQyELVcGOqYLdRbn5+dDJ3TZG8UAEz2bmJiI+fn5iLhdwri/vx87Ozvx8uXLOD09Hap5IGXiyEWr1SqrGCig5MD7N29ut4VlVUdeotgUqs7AIv/ddE/2cH2NcpB+3avJDnkM2Vl43zsflNHNV9NAs3fr+0Y977+bUOOHGN6mtkcR18rVu/e4/01G3Aj65uamCAc/9kydA6FMf35+PnZ2doaUm/OLCNCoMI7DWXVdD23KMT09HXNzc7G4uBgRUZA0zyL0FGXYM8SQ8zvTu0l5eb7GxsbKeK20UKRWmlwuErKh4nmfEOO+cmWQ5L89R6YtRtBKPRtxAITfQZt+zkLN317rnL14K2Y+M+jhM7+vieczODDPugiLe2nfhsJAwaASIwANbLx9fKA9dEAMz9ljdx+aNkGw4R0bGxuSIfjd4zT9DOh5b87zm1fMRzkCk2XBvJO9aBv9iBgCUTa0Nn4+nYztY8fGxkrRFKeUEQ3rdDqxtLQUjx8/jvX19bI88ezsLHZ2duLg4KAABUAHvE1fJycni5fLrnScmEWomn0GWPboXfia5ov2R3muXA7vWwZGXb+ubh/1fAYDnlMD0vddD9LoWuD8mQeZwx5NV55Mt+XJ/XU83dxm9qpyXyNiKPzUFGLmbxsGwi6svfNuMRhOnsXgIlQYqIzCbCygH3lDqhtpe3x8vKy1jbg7zs3oLuJudyxo4aIa08vtoKRy/tc/Nkh8lvdDtlLMipP2PTfeqco/XvNpY0X/7W3gMXv+HSZzuNmFUzn9YOPCPVkBu0LaXpGBXPbasyJ337mavL4mHjfPuiqW+bT3YwPtgrucSsieL/NINMe04N0ZYHAZDLmGwZ6ogUGeX9M+ezM2otkg0Cd7s/ZwTE8Ao40wfYGulmfPoaNduS+819EmcqgRUXasa7fbZVUBNOMAE87nfv36dXz99dfx9ddfx/7+fql4Rv7hd9p+9OhRLCwsxMbGRqysrMTKykoxum/e3O67TG758vKyFHcxx5lnDQKzEYYPc6Fek3F7n07/qzhXfi4/m/tKf993PTijmw1EngB/Zu8mG1V+m2CZQE3vGNWnJkOZvaEcMvGzTrRnhnEozwaFkLJDuAix+4MX2m63y/pZexAeP4o8V72C+vFqfUg9hVx1fVfoktG+lQ5gwcrNHlpd3x11lkETHt2oOaCP9vocCnSRlpdv2KjyPveR70D1Vqa8H5BnI4OxNaBxzpix2GBbyeQq9yzA5rEMFEwve6Sem6zs3TaKPyurLD/MU94OEhDm3dBQrk3hVYNJt59Bhz06+kkExrlfAz+nHhgXy4McHTEdqqoa2sWraexOY+Twr3kzAyb3i7/9nfUEPJHXM1dVNSQj2dC7YAnDRtHkxMTtKVUUOXFKko074eSvvvoqfvazn8XTp0/j8vKy7HSVdQf9mJqaiqWlpfLjE9TwcKlYdjX1KC/QtMjOCZ9lUJL1ddatTdGr/9Ar63/36b6+5evBGV0uI+Ns9HxP/n6UYePKkzvqvqbnbFTtgSHkWaD9O4+Ly3k8FFhEFATrtboIIRc5xampqaGD5Hu93tCB1BgLh+lybmwwGJSDAuyt5bCfFRseMQDBG0zwHsbGlpA2btDDKNb0xqNC0D12L52xF82zrmLORVmEGr2eNM+xPY0mRdkkgIzVe0RDO7wKAyJ7+n6nC1xsGB2G9rjI62PsXfkOfWxQTNuI4XC4lS3fz8zMxPn5eZk/e/R8Zs/FfTbN+d/zlOlk2nJhaHO/89whD+bdPD6HJnPEwO1kT9t9z3qGz5oq6iPuVgcAuiyD5jdoa7nhHvcBgwpNfIIQYXzL5qNHj8oxf4Dq8/Pz6Ha78erVq/juu+/i6dOncXJyUsaYvU7GwFa2i4uLsbm5GUtLSzE7Oxt1XZf8bbfbjaOjoxLqHgwGQ0dZ5g2BmpwagxTryFy05zbcjufzN3U12aBsF/5aebpN3l/OPdmwWrE2TSBXk4c66p2/zuW2cgivyavO/fSVjXleo4tAYUi9rAJUCqLlEAIWtFt4sgA7/Ml7rPgovMDDQGitvOx5uZ9US5JHo03nYaFLU847Ylj4UfqMKaNY54OzJ56FGq8p4k6Q7SkbRFl4M+Dy3JkebotnfIBCRJTcF5EERwMYg/k7Kx/e7w1SWq1WDAaDUjHqULjpgFeVFYbHYcNrj5LPHR0BLAC+oGduG9rnkG4GqBmM5NyZjaXlDUNDf/x9lkHzQNYxGBl7lvle3mHPu0m/ZKNlkJ6Beza2XIw/GyvTEONmnkPeaJtd8ThPt9vtxnfffRe7u7txenpa3ptDuQZarVarGNyVlZWyXaR1sg+up19NBZZcRL08fs+b6Z7TNP6uydFpMpS/7mUdmtvn/VnH3Hc9GKObjVJTFVgeuJkuG4H8TFb0TUbdz3Cf/x/Vru9tet4GIo8pP++8lPc8Zuu2qrpdJgSqRTmwSfna2lq8evUqjo6OSsl/xPDh4fQpK3S8HPqB8aQ4yh5nXdclv0ffs3eOIoy427eY/tigeq6tNBwaRKEwFiu+pp2oRgEse4hWlJkfctjbNLOC4J1NFaz2zuiDIwKEyV305ciB+ds5S4fQXcHuylHGCnijr4yNUKMNh+/Bk+VZA5MM0HLFr0OlKGx7v3g7tAkdXGxnmXDbNiz+PtPPY/Wc5DaYpyYwamNneTVf+l3mOcuUIxw2PNYP2Zs3P8IfHpvljOWFjnxkGWFziqqqyr7Mu7u70e124+LiYmj7WIMdF2ISUXvy5Elsb2/HyspK2TIScO7Ujg9TYP7dfparbDyz7I6ac9+TdfZv8vpQo/q+ex6M0Y1oLlrJl1GHCdyERu4zqPnv+/rkd/vvptxOEzrLfWgaj5U4ihOvFgFjeYqVBd7Qo0ePitFdWlqKvb29OD8/H6oazYJvxG2jgVL1uyPuijB82XhaGdv74L0gYBs750kj3q20tcfTtItTfj6DL5QO7QEarORMTz/DmG1kbQRNR8+1jaSrzHNBjHnctPC43H5WyFVVlapxdh/DIDsfZ08r8zPgymuDuZwqiIgSmqT9uq6HDrIAEGYZo89+L/UCLGG5vr4uW4tyma6OTOS2PE8GB+6DQZYv6M7ftOl59nfInKMaXOa7HK7mXs979lT9Htpl/gxS8CIB4+yLnI07PycnJyXyNRjcruc9PDwsR/0hm+YXe9vsdNbpdGJra6tUPXtveaJhEVEcBdYMO6ebIwJNDpOjaVnfN+n0/9iXjb4v89mv07cHZXR9vQ/tZCWYQzL5GmUQm77Lf48y3v7cDDLq3U3G1R6VBRslmItDUJAZ2bE8YH19PVZXV+P58+fR6/VK+865YQws/Ebq/j8iCkrGGwH9emMMmA+FcH19XTbrsOHIRpPL6JfxurLWxiy3ZzpYgXkOjeJRgi6YcvjLxU4OQ3Kh+DIPZIBlA+v7HI71vTk9Yf7xeD2fGD4bcO9YFTG8gbyNgGmTw/7j43ebF/AMuUTo740YHI7NyohxY8AyvQxg8rhzX/2ZZY9c6ajQpA2haW6+N69lT9OeOm3y28DScp0NjEO9POsx5Lx2LijLhXbX19dlSQ5z4Ov09DTGxsbK98gvRtCV/MyZ5amu72o62u12bG1txUcffRTb29uxtLRUzs2lkp15ABDwTqqqLfceS444QmsApHko/x51/SYN8yh7YM898+R914MyuqMI2mS0sofWNOisBJsQVsS7Z2yO6lf+bJSxzwI/aiy5jw4J8d35+fk7ni7hI5gZhYe3y4L1w8PDofCsjbc9KysfaGrvgmUACJDDVzzvzfBR+ISnuC8rjeyxmC4YPPpkjzBieOenJtra02iau+xJWPFaCeS5sSfie5qMhT0Pf+cCJ+gwCtE7TO8NUm5ubgtknPdH6eJ90h4G033Iis6gJHtvno+6rstSEkDg+fn5EK0xyDbgpAAccsbDdf8tl+YvaOEQN7R30ZzBGv1vilTY680AnH5mnss04W+nPty+Q/fOf3tuDH6zYcpAMveDFBORqKq6S30QOeBd9jb5zEbaRXCE/JmTdrsdi4uLsbW1NWRw8W6pYidqcnZ2Vo44BRAyBtcS+N1ZTrJz8ps0on+VaxTYMjj70OtBGV0zxCjP1MJmRJk9zYxWczsR7241+SH9o23/n70dX1kh53Ywck0oE4G6vLyMs7OzkqMl1MS2bxwsPT09HYuLi/H48eN49epV9Hq92NnZKZWsOc+Tw6vQxGFN3oVADQaDInDeB9p5OZQihUIoAxs1nrNxsQG0oqEqtclw5Byf+SPT23lMexy58CoihrzdJkWYjZbfbQPF+91Pe9EGkaa/C6lcHQtfAHLom4tVsneQ6W7PL4/D/Od5wMDSDxQ7pw/BA51Op4A0nsE4oJzhB2oCPE8ZmNk75rkmg0m/HJbn+1zwlVML0KZJhp0ScKTA7zYgsGx7jixTpnkO6btdXwYcg8Egzs7OCm2dk3f+NyKGTveBz5rSOQbRfM7Y5ubmYm1trRyUQLUyuov5B5SzKQZA3WksAyuPO0co3JeHcNGfpvnPUYr3XQ/K6N53NRnUpvJxM34OPfqz+65RBj/fY7TDZ+5vU+gz3+c+5bZgVIwuRQkY3+Pj43j9+nVcX1/HzMxMrK2txdTUVKyursbW1la8evUqDg8PC9r03sy5Mtbrd+2B0W+UP17T2NhdZSzMyP30dXZ2NiLuBNi5VHtfrAM2rW3IbahyBadD0FwOz1vYm/rqObAw2eA2eURNuRx+HLJ2JMbvQgmjmKF/XnNbVcNn/dpQ0A/e57W79G98fLyc1WrexJPJ3qJ5wu/woRkU3nBPq9WK2dnZmJ2dLTnfycnJsgTFRpt5N+ChP1meLCuWac+LC72y3EN3z5vPWzaNM/+YZ2x4vfWm+9rET76sp0Z5bk0RBi73m81yfHiA+QB6Ohpk3kH+M0863cJ4ZmZmYnl5OTY3N2NjYyMWFhaGChcBrOYNwsqAsiYQnPksg+b7HJnf9tXk7OW/f53rQRndJi8x4t2q5SycVoRZmDyZNshGyE1KNX+f32+FNOpynit74g7nOTyEIib3AlJ98+ZNnJ2dRbfbLUJxdHRUci5UD7LN2+bmZjx58qQoPopteA/vaFIGRr8YRHsc2QgawdqYOlQNTb2pPO1DH3tweMgGU1kgm5jeSsPeTFbkBkURw+ccW9gdknO4m/fj/blylGet6Oyp8LzXj2Z+9rsMxKqqKkc4+lzeiLslJVZi9izquh7ayIK5sHfD1p8GKbzTvDM1NTWUJzQoow2WLUXEEHiiHY4hpN/MmWXB9MiFXfCl32Ge4HPXM7ht5jcr9yYvi/k1T+HdYdizl+45y59Bb8YPnWkDuTY/ErbHaNIXog1uB55mPPTPgN9/w7vw7/n5eYyPj0en04nV1dXY3t6O5eXlomd43qFx74gFL6AbDPQzoHCxoXV41sff1zUK+Pnz7HDddz0oo9vkYUaM9hCbjGrEu/u/ZtRk4c/v8vsseGaGJuPt5z7kyuHEpn6haAgxs6E5BQwcQn1zc1O8jImJiWi327G8vBwbGxvx+vXrODk5KcjWHkJVVeU83omJieJF551oXFCDocHTQfj53rncycnJmJ6ejsnJyaFQooGM5yeH4/xuKyMbeSN7o/r30dreEJ53XrLCM7RpY5sNtL1rGz36HzHsMblt5jordAALCpHPfeSf6cn9PsuV0J8NWs5XYvRc4WzDQ7+yJ4YCxggSMvaSJi8LMh0x/jYIWX79N3OUQ7MGFJZPxgZtM70dava99rya+pLbt4cNHRzyN+gzQDU9kU3rmxxhcWoCQI5ugJ4YtybeMJgxgDDQR46RrXa7HWtra7GxsRFra2sxPz8/tAez31vX9VAqIRdYmgZ2WKxjRhmx7/vK9LSMZrn4kH4/KKM7ymg1eTYW0lFo0pNoRW2P08La5NEamTvsZOHP4CCPKTMZn1v52zPj+8FgUMI15G8vLy9jamoqVlZWYnZ29p1KVfpM8cPq6mp0u92iqD3OR48exdLSUmxtbcXs7GwcHx+XDc/t4eGF4pWjyK2YTCe+x+hOTU0VoUMxeI5caAFdrNwynRirvThv1pCF9j6whhLLxjJ7egYB9lb5zM/QZkQM0dH8YWOM4UOJRkTJmduLcig6G3aMNKFkgAhLvGjbkQD3yXKQ5cnjBzgwx4y3ruuh062YJ8Kc2QP3c1lWmwCZ0wVW5OYRK2/LnEGdeYL2bezMe47mWDYzb/FugAHt+HtoCxhqCrtm4+vxWWYoXru4uChtQiPadmrF8w3drQs8Dzz36NGjmJ+fj42Njdje3o7V1dVypCfts8scQBD94NBy1hF5XpscmOwgmZ7fx2XaZH7z99m2jLoelNFtMp4Rw0rSk5iVa2Z0/vbvpjb42wJnhrUSyITPbeZ+24tq6mceS2ZKilJOTk7KSSB1Xcfs7GwsLS0VhndRFQZ1dXU1Njc3yyHVPs9ybGysnIe5uLgY8/PzMT09XYz8ycnJEO2sdJrCvD5jNyIKGncu0EUuHq+f431ZGPk/h/noD++018tvDJMNQjbuzjvbk6XfLsDx8wYDGaDkYicbAJ7lGcZAhAAjYf4jlD1KXgaDQQk9e20277u4uCjPZe+C/hkwWGE7FM7fLpKLiKE1n9ADI+N5ps+5+MiAwPPEnHhplA2c6ehx+F0ABgMz087j5iLd0QTKrU/oS+ZlP8ePU042MISTcxg8gyFSTuxp7LZ9P0bQOspzah5yior3c5Tn5uZmMbgOT4+Pj5dD62kTg0vqw+kr08uRlybd2QT6vs/L/NYU6RwF4EZdD8roRrwbUuazLLBmdIfseDYb6qzImwQp98Gf57BWfs+osVgY8xgySPDEGhXjRRwdHUWn04nd3d1yYDRb/4EyEYxHjx7F4uJibGxsxOHhYfR6vTg6OoqIiLm5uaKU8ZRPT09LhTD5uryMABo2gZuIu+3c8NCPj4+jqqpS7Whk69Cew4MWShs10zQjc3s5Vtz2PJvuM79geKempkpFaFYO0MGK23xoQIAS97vdP/Onc18R7+YPbVQsHw63Qsequt056ObmZigfb4Xp0HzmeY+HZ6EhlesYXj5nrI5U0McsS5ZV5tCV9dzj/as95+YHz2GOVNioQRe8fYe8PY+eFwCMAZs3OcmeIr+du845zCznGeDkyl577ePj48WQ5dAy42Q8liFvGGJZzvQ1+ENmV1dXY2Njo+RySR2wHaQLKjG0FNlh9H2ZNzKN+B5Q8n0bWl+elyw3OTr0If1+UEbXHc7oMSM+C0tEs0frtvJWd1np5jb8md/nooz7wtu0mw2V3xMxjPYtiDwLcry8vIyTk5Ny2DR7nmI8c0EJwra0tBSrq6txcHAQ/X6/eGCLi4tl39+jo6O4ubmJ8/PzoqhnZmZK+CqHoxC8bNhsVMjvUYDjqmd7JFbGed5siLIB870WhKzsch9HATT6CK/kohjatXBZQdNHAwsbEXuzHk+Twqe4DM+UfvO3vT/4k3ZyBAhFaT51IZL50fzq39xPwYuNgfd29vwa2KDg3RZ875CqwYyrq5lDwAQXnr+3MHSOOXu02QhYtrPixAC4H7Rh4wA9GBPfY9xdSGijxv8GWPTZtIOfiJZh3PjeVcguNCMaQY7W82w+NmBA9jmc/vHjx7GxsVH2UCeC4vOyCXU7Epc3w8geYBOwpo9ZprOj8n1c2cO9r2+jbIGvB2N0LQAR76Jk/+9BNyng3GZTqGnUlZGMBdDGOvdn1JiykWm6MtozoCCHi3I5PT2No6Oj6PV6sbq6WoyoPdSIO8M7Ozsbm5ubZbH6zs5OyblwBubJyUnZKu7s7Cwi7nKKeSlR9jANQmjXhyTYm3VlYy5WctjQhgEDk0OR9DEvQ/LzNsT0m/dSYUu/PRfsvJT5j/F67aGVKkqWvKrDnO67lY7Bhg14DsPaO7XH52iKQZdpy3dTU1ON/Ocx0r4r3TNfGhACUvCybJThGwOGDGZ8r71CGyQXehnAYoTNPzbiubDNtQA8a6PepF8cHrb3mIGPi5h4d24vOwj57+wF2qtna0V2hxsfHy9bmvJub1pjMGcgZ/4zD3H/+Ph4LC4uxpMnT2J9fT3m5ubKPBMJc5WzAQAg3dvVQrs8zzmqkHVok1P0fV2m46goG/3M9qPpejBGNxM2e7jckwefjWxG+hHv5jGyEW7qS2YMewcfYrzzffnePEGZAZloQsaEbPr9fvR6vTg5OYnT09Oybg5hIDyKkFVVFUtLS7G9vV022Oj1enF8fFwOuZ6dnY3T09MYDAYlJ4xQewOMy8vL0v+MsBG2PEdjY2Nl5yTCtkbBVpg24jZ4po8/a/JwrPS5cuER7dmQMZYcihtliOhvjnrQlyZEnEOIGC575PQlt897XVhl3mJceJ42xlmxW3bcf3sbBiyWvyaZyQrW72NOrOiz/Nnbavq7yZPIsmM+avJeHTmDfk26gmcMpjL9HDUznzSFwB3OzjRzf5o8PPcdmaZCmH74eXiJ/nkrWRvYpvczHpYIbWxsRLvdLjxa17enjrVarRJh4xk8YZyDqqpKfneUHgZQNc2lf79Px/42LvNfthu2UabjfdeDMbr5avJ4/X8Tk0a8q4TMVGY8St0zmuXKXo6VkL1d39N05TxXNhz8pk0jQvp9fn5ett6bnJyM8/Pz6Pf7ZcNyvBgqi23kWNc3OTkZy8vLpZIZ44uQch4rS4ecd+L35OTkkGFlbNASo4Fh9eWKVtPD3o3z2Q6BEbaEXvYyoR0KCG+Oe+iHvVHyUNCcMVoBYIAACp4jo3dybYAQrzVmDB4Hz7vQC0XPGAnJ+8peoP8m2hERpeDF4WvuIyrAWM33OYRqj8TjyKFPP2uDmsFGRJT3wzMGNBlcYXCpOfBcWW4M4rLywxDlXZqgj/UCY7GcZq85A28MPfSHFryDeYaXvOzLOfBscExreBCPErmu67ocNsA4BoO704Sy3HFZHxrcojfm5+djbW0tVlZWot1ul5Ay3zu6xDrriCheMLKVQ+YGUwblBqPuX+b379vbzcDSY6GPER/mlT84o9tkRDMifZ8Ln7+3EGdjl68mA9/03r8qE+TxOUWMm/EAACAASURBVAdmg5SVjAWO00XI72S0bk8J43p4eBjn5+dlKVFV3a7nPD09LTtVIcT0A+F2eM+h5hxe5bcFCAXsEHv2LrNHZ0WcjUPT3yixPH+Xl5dFCWSji/dFX6y4LUhWTjYUDh07Z2pFkvdYpg17o/Z+Dej8vT2vTBfzFT9EPmxYTBu3wdiYJxt8xm8e9Dzl0HLuj8O30N4REsuSjVQu/DK98RzdB3gu05D35+iC27UXb9pEDG+qYQVrz5Jn+awp/5ejHLlNwIp513THcPuggogoZ1YThSISxbz4nRjK3Pb4+Hhpb25uLtbX12Nra6tULJtXvN+yeYEx2hOHd/x8pv0oPjH4ewhXk85vsi/3OV++HpTRzW688zcRwxXE2dvl9yijmO/NxrXp76b/P/SeD30eQcW4ZRTI/86rvHnzJo6Pj6PX68XZ2VnZe5nxk9/F86UNzlxFUI+OjkoeJiLeORWId+KVWbHwOyLeUdx4Cfyfx41n4LmMGA5xYsyMcu1B5VCtvS4bGYebvRUi/bECwkgaZfNe+kdfeXcO4WbvxsU1VnpWvhhIK2UbUVcQ85v5GQzu1u+ax5m7Ju8tg1oDiVxh7blwhCCHnemXgUwGEe6LaeVQMjSjPYconTM13ZsUuefJ+VXzo0GT2zR/ORrhdvPuV6aV9VSO4jSBSY89yxvvs8F98+ZNzMzMxMzMTMzNzRUas9+1ASbGz+9zFI13stqBJUILCwtDcz4zM1OMvHmTOcLos99ykzMwat6yM2QeMn99X1fWxZ7XDzW0vj7I6FZVtRAR/0tE/CcRUUfEfxsRX0bEP4+ITyLi24j4g7quu9VtD/40Iv5uRJxFxB/Wdf3vP+Q9mYGtGLJH0TTQJu/VStX3fN8T2aT4msIoMDVl+VNTU6XK+Pj4OI6Pj2NpaWlord3FxUXJ73IiyMrKSmGWg4ODmJ+fj2+++SYODg5KiHps7HbtLgKUGc1V0tlw0FcrJ3sSWaFFDIedsnGwIcbTs0Hm+azw/FxEDBm2bLx41soHw5Tzt7Tl8dhD8fzlLe+soCKihIJtvGy4GK89YdOYilpC21mJQUN+E/7HOOfwsceUC9Y8Rvpp0JWVJPe5YrkpLO7voI15zfPcNNf+HhlhnDkMbFrkPGYei4GCvVbe7TSKeYk+WNdYHuiLjSApm9xXe/aME8B9cXERY2NjMTMzEwsLC9HpdEoFcV0PV3LDK/Tf0R1v3jIxMRHz8/OxtbUVW1tbsb6+Hq1WqwA7A3oAs/vmo/xYS42u4B6nKDKPNnm0+b7vU1+P8tgd7fK8v+/6UE/3TyPiX9V1/d9UVfUoImYj4n+MiH9d1/WfVFX1xxHxxxHxjyLi70TEZ29//mZE/OO3v++9stLNSMdEf583a8VvxWrCuP3f5pWBhZWS+2ykntHhYDAox2dRpt/pdIaEAeEDveJZXl1dxevXr0vet6qq6Ha7cXFxERMTEzE7O1sUuRF+xJ0CRohhRhsNmNE7MfGMvQCey0qfMedwm4EBfYsYVrz5d13fhkuN9F0Ra6/PaJbcLMVp9nxc9PXmzZuhcOzY2NgQKPH7Mo2Yb8ZtPrVX5FB+BjqVvGe89Mznrgq214gRzt5zjibwWS6GygaySday98Jl/jZP5c0UDFrIrVs+XEsAXRzZcR9t+BxJyF7mqL5nncJFW2y9STve9pJ2LS9cVtzuM/NKxML1GdRgsI4YQE5/zMfmJf/2u1utVqytrcWTJ0/i448/jpWVlZiYmCh5ZPeNZ3xkpw9isWE3fW1gTc88/ia9/33oaV/mHfexCah9CDh4r9Gtqmo+Iv7LiPjDiIi6rq8i4qqqqt+PiL/19rZ/EhH/Jm6N7u9HxD+tbyn1b6uqWqiqarOu61f3vadJQEx8BOE+VOHJyW00ffd9XnmsVq5cFmK8XO/6cnp6Gt1uN1ZWVmJ+fn7IGILeZ2dno91ux6NHj+L8/Dx6vV6cn5/H2NhYrK6uFiHZ398vAg3KdagWBW+ByiE3X1nJ2FvI3paNLIYrIoY8B56JGD4RxUrU/GIht+BDZ+jThFbr+rYgC6PLZw7LMTYX6mRvgkKTvKsR6yEZQ97UwEso6LOXbtkAuvAub3iP9+eitqzwnFc3L/K988I3NzcxMzMz1Ja9f4NH+u53ZLl1JfYo4Ek/PDbGzmWll6u9M+DzWM0nGUTkCnh4yfRjHuzF3dzclOIm2jWgsO7JANIG1/oBo+r194z17Ows+v1++Z7+UPBo0AggMQ9NTk6WrWAfP34cq6urpViT5YP0gzHYm765uSn35vQM9DVtvBqCe3Mlufnn+za49CPi3dRBtikf2tcP8XQ/jYi9iPhfq6r6GxHx/0XE/xAR6zKkOxGx/vbvxxHxTM8/f/vZkNGtquqPIuKPIiI++uijd4ytPaFskDPzpnbLBHvSs0L4vq7cfyNPC1/EcNjFjHt1dRX9fj/6/X50u93Y29uLTqdTFIy9uKurq+j1enF1dRU7Ozvx+vXrOD8/j5mZmVhfX492u12Eka0fUVIuiqDf0NE77/A/l8Nv9tg81ojbPJXDax77qGpG+oEHS7suFPGVPTP6ZJQaMQxwDBRQmP6Oy/Nmj9oGOIcYbdihIfeZXw1ODHy8xWI+8cUFNvbmrfRtDKzsbNxsCDwe3mWQQ1SAdnJUxkYqez3Ot9vQc6GQoZtl1+3j3eWCNhvzvGbXbfCu7NHwt3mAMeaiKPrusTcVHBmgZB3g8SOTzBt8jvc6GNwu7xsMBnF6elpSGuYtVjs8evRoKCJzdnZWogJLS0vx8ccfx8cffxzb29sxMzNT5J5+UmBJHtm8g37wM5YPxt80Vs9zk05nvE3h59/mZY/czlGOiHzo9SFGdyIi/tOI+Id1Xf+7qqr+NG5DyeWq67ququrXgiR1Xf9ZRPxZRMRPf/rTOqOaLBD++33XQwhJfMhlYGE0npU7jEqoCdqwRIAlBRROoSC5n80vXr16FTs7OyUnxClF7XY72u12QdSsw6PS1oCHi357wwt72DkEjbLwpvtNhU2M1/87RBcxvEyEe/wZhsCeKWBklKLN74S+fm9TJSvKx96OjbXnN4+DtdR4Hbw3K3yDBPcVUJWVv40Hz7Hcy54/bWTv0iAHQ+ywuQ2aDS20Me0ZO++zl+hNTdx20xwBIjKY9j1ZcTu6YXBlIJjDwAYhjMe/M4i3rDKv5NstD4whp4s8Vxmc8KzTGo5osDwI3cDF/y4Q87I7vpueni4nks3NzRXgODY2Vir/WUrYbrdLASZ0hb85QcyAA9mB5o5aMSaP13LaBNi+zyvLCJ9ZR2fAeN/1IUb3eUQ8r+v63739//+MW6P7unobNq6qajMidt9+/yIinuj57bef3XvlkI//zgYoGwC3MUqp+vnv82p6f0beo9CT0SyKC8TLgQd+FkYYDO5Or2Fx+9LSUiwsLJScDttJcj/n8r55c3uOL4bcKL7JG+W323JI18tuqrehs3zMmz07/seL8cYEucrV3pi9MoeyMSBWnu67Q9gR7xZn2RMmzJY9UiN7Kw1XNFuheK5cBJQNYDYONvjucx6PvVJf2Si5r84F02bO0zO+TC/uzyFgfsMHpn9+1nJgD9wK3TrDipuxZdCGzNAm9DA/eO5GVdoaCJk2pqe94cw7fN9kyJkn5tTLbzI4c/42t2OPudPpDJ1jjc6ggArQnWsHWIvLJjnMGTxc13dn+fqUoXz0ZM4tY4SzDLjfTWDk+7qyQbU+yOmMD7nea3Trut6pqupZVVU/rOv6y4j42xHxl29//l5E/Mnb3//y7SN/HhH/oKqqfxa3BVS9+j35XAZyn7FlUBkhNl3Z023yfL9P42smixjOdd2H5CPuTu/Bu/UWjhMTE6WgyrvRoCwXFxdjfX09FhcXY2lpKZaWlqLb7Zb9lznwfmLi9kg4K3r6koXXBoN7eCfLHKwceMZeFDTIwtekQK007RG42tiKMSt009r09VhQonxnpe2qXyuEvOuPc2/023zo8biv9mrtxZtu5IBNB2hsxWWvk75HxFC/eJ+rrTEI9pY8J4Au8yh5ar63QbSs2uu1kbISzrxv+rn9vPQlgwTex30+8arJU8nPZZ3EfTYCfE+kxf31UiWDBNr1Z9DF/IUcuSrY8889jh7By4y73W7HyspKLC0tRVVVZUnPxcVFrKysxPb2dnzyySfx+PHjmJqaisPDw4i4lXOWF3ImNn1lnOwTQAQFgG2dY93h+WmqlOeezB/fp8F1n7I+znzg3/ddH1q9/A8j4n+r/n/q3hxG0mzL7ztf5BYZEZmx5FJVXUtX93v9xDEIiBgBGlMAIYMyRG8gWdKAAGVQvihHNl1ZAuiJjiQ6gmTQEyBTBkHII2bw5nVX75X7ErlVVkbQyP7d/MWpG1lZ897M5FwgkZnfcr+7nHv+Z7vn3kYu/y4i/iwiWhHxr5um+ScR8SYi/vSXZ/9N3G4X+m3cbhn6swd+ozQ6D7rvZe1kXh3+7VKr+296YrNkyt8107LbhWRJJOPi4mI5hg/QbZrbw6f9zvLycmxubsZgMIj19fUi1U6n01hbW4tXr17F2dlZORrw7OysBOFwsAJbiGifQQZTlIHz8vIy2u12WZQOoMhjkCVejwVStBmaQYJnLUlH3Jn4yKFsUOOdrIFGfJjSzwvfDJm5MWOmftozD2xsjs5BM1k4sGY7z/SWpXAifa0loV3m5BeMm7UYa3xZC7bQQvus2WAyd3s9XvymLm9dsUWiJhzxPc+f58XAQ7+Z/6ZpZmghg6DHfR4g57HOAXzQqgUi95fnLFTxbSKR3V+2CHFyD+8iVOMOApCzsN7r9WI0GsXm5mZJiLO/vx+Li4sxGo3i9evX8etf/zqePXtWeAljSLwAtGAhC1BF8Oe7TlVZAySbj23t8XrP8/0QJeuvu2SLRcSs28Pz+5B2Pgh0p9Pp/x8R/0nl1j+sPDuNiH/2kHpzyZOU6o2I+GAhz6sjLxDqqH3jb3pCs5ScmXpuI5OLNoPf1Qfcs22IAmFfX1/HyspKbGxsxLNnz6Ldbsd4PI6Dg4M4OzuLpaWl6Pf78eWXX8bl5WWJbj4/Py9+n5WVlZKEgz15DizxAoORYmryGbCWYn2NZ3nfz/nMUN6DsRMMwvgYuGGsmLbQRGgb/qmaBmoaotgvyHwZWDJgZvOnaZF3c7pMm4z5TraCZAZFfaZna4AGAPrr72RhB5N5Fm65n7/farUKCOALNDONmA2YymZ7a/B8B+C11s9BGt46NA/MHTR2cXFR2sczNu0icFGnx4OS4wcy8GagztqtwcS+XuiXNe39sAAq6w46XVlZKcIDPIFi/ra4uBjr6+sxHA7LUZ7n5+dxenoaTdOULULPnz+PTqcTZ2dnRWBot9vR6XSKGdhzTluZt9qZ0YxLtnJxPfO/eX/XaP1vo9QEMVwEWdh/SHk0GamsJXjRmsG5eKJdh39bo4j4249cpliziZgFWTOfrCmxVQgplyhmThCySdQg2G63y1aPi4uL2NnZiTdv3sTV1VVsbW3FaDSKjY2NePHiRfzwww9xcHAQ4/G4MCgyVU0mk3LOrpmw+wAQsTgBaY4Dox9oeVk78Rx6z6XNzXwv+6AiPtR2sqRu06yZcwaZXAzmNte6LgOs55Q2MD5uq8HLZsEa2GXp2uOfzd9525VNylgmoBd/mz4yVlnYRZCzmZM9owYVxx+43+6zNZ9sKvYcNk0zI6Awn6b17OfNIIi513NLvdQZMevHpS2+xrOMGd92rEHW2rxGMqB4bu3HZd04VsP8DlDDrGvaw820uroaS0tLcXp6Gnt7e+VM7dXV1Xj+/Hm8ePEihsNhvH//vvCQfr8/s6PAwEKglbVu5tFWlSyUMDdZY+bHc2+hhPH72y41/DDdZN/8x8qjAV0DqxlfTVPIfoBcTyb0fO1jdfxtlsz084RDvPhMT09PY2VlJfb29qLf78dwOCzbhxYWFoqPdzK5jWK+uroqeZj9/tLSUqyvr8erV6/KfcxNCwsLMRgMIiKKqRcTN4zTCx+hgCCs8/PzWF5ejvX19aIZwTDom312Bp7M9CPuFjuMiO9TlwUXg5Kjqon85HkzP4OBtS1L9lnrW1i4O/iAYC0DmUGHuczapoUmtEPXnyVqB6T4GEbTiteOx9xBcEjtuVi4gpn6Peba85LH3sKxBZdM2zXBOq9XB8XZZ5ojx2lb3m6ShXiPFfNHGx0M5eLv2+TuccnWAz/H++6f/0dAQMvFquR2Mw7ETNBv2ry6ulp4wMnJSdzc3JR9tADul19+GVtbW9E0TRwcHMTOzk45LMRzaqGN73c6nUJ3mJNJ1uOTyDzXmRahC7tP8vrI6/Jvq9jykbXvbBV5SDsfDehGzPet5uuWLlw8AFnT8LU8UK6/1gZLpw/53n11+dl5101o1jhgfCbm6+vrOD09jaOjozg4OIitra3iz4QpwpCRnok4fP/+fZydncXCwkL0+/3odDrx5MmT2N7ejr29vQLM3Of8zrOzs+K3yaAIQJHRZnFxMc7OzsqxYJh6abv7noUNj0HWCL0/le9lTcOLmbpr37TmaKbrYomfwCaYBEJQ9ve4fs9v1uibZvZ0GurG3JfbRh0e94hZZomZ1+OZo9sNklgVPG5uK79tJmac7MPLxzuaplm3FnQy3TvqvbZeed/bh/ArM34O/mLu8loF3Cxs5O/UhHTPhcfbz1lRQOtnrPleFiJ5B5Dl0HiE13a7XZKtZAGN7WD83+l0yn7ao6Oj8iyC9cbGRiwvL8fR0VFcXFzEyclJvH//PgaDwUzOZbuKmD/cCBznx+ErHGLvNJB5/bB2shBiMJ5nJajx4L9JIDbt2JpWa/PHyqMC3YgPpYqID23q92mCeSLMIF2yGct11doUcSeRZkY6D2TNMHNdufBursuaF9999+5dtNvtGYbNGblHR0czZ+xi+jHT4N3JZBJnZ2dxdHRUfLsbGxvx/Pnz2NnZiZOTk+Lf3djYiNFoVKRmzMw2wXl80AgvLi4i4nbBr66uRtM0M8yjtrC8GD2P1pzev39fzF18yxq0NR/qwxfMPWugETHDpA141G0N1WY014PZlXoykNQ0Pc911loYB/7nvhl+pikHnbn+mgbGVhDTl82F1uJ43nPH97x9Kmu2Nt26vW5jTfs1o63VaYEsa6WMkU3O2T3BXHrdZv5jQYm+2fLAO37P80afrY3TVsy4fn86nRbNkTXmyGHGO5vvrf3zAzgDdgRhXV1dxddffx2Hh4dlp8KzZ89iNBrNrCHWTh4DC7wXFxdxeHgYp6enJf8zY42Z3GvN8+Z4imw9MC3ULKB/XSV/k357/ZjOs3LwkPY9GtDNmg7Xas9RaiA5bxFyL0u8/PYC+lj7LOk/ZMDnMUffr0lyZhIuAO/q6mph5piNd3d3YzgcxtLSUnQ6nQ/aDuAtLCyUVJIscvbrAbqHh4dxdXUVJycnMR6Po91ux2AwiO3t7bLIcsL9DEBogRwxGBHlxBKb9PLitsnNZx973J0Yws+gUTnIyoBnzcff8lhZW2632x+AgoN9suDgefOChSbz4s3PAvg2BcNAYU41waRGi25XDhaCOcOMYYTeAkJfI2a333h8TNvZHw3w5H7C0PkfIK0JyLW+euz539oj9cHQqSfPGfOdBQQKAphpJG8PqgkB7qu/xbjaYsP/bOVhB4FdIIwr64m0jxagFhdvTxfrdDpl371N8hERl5eXsb+/X8Cy3+/Hr3/961hfX4/19fVizbq8vIyImIlibpqmbFdstVolKx68gHN0PZZ5bfB3XuPzAqwywGVaN43/IQDZPDibkDN/5pkafd5XHg3oegFFzEZu5usRHwKmtc+s7tcYUJZoXf88sJ8HgtyvCQEfK7X+WDsyofI8Ui7Ay4K8urqKw8PD2N/fLynbHP2J2YiE6Z1Op5zLSw7Xfr8fg8Egvvjiizg4OIiTk5M4PDyMnZ2daJqmAGa/34+zs7OIiLKdyGZ/B02gHdM/awA8bx9qNj1mBul5yEwSsPX/BomaT9JWAOqnHpgc32L85jHpiNmc0e5zFq6sZXmOmTOPI6CSNXlr04ByZnZuu8fbvjpohBSBvOtIYa+LedHmWeujn9SDy8FCGu2zEGQTnr+fNdSPCb8WwmwdqAm3jInntWadyEJHFgz9PnOTNWoza/5nHZ6fn5f4AMCUdvlAe9rl/rPbgANNAM/V1dWZOpqmiW63G5ubm/H06dN49uxZ2f3g9UA7LTihxeLHzeliGXePP3SWAw79LONl+smun08Bt79KMd+xEFXDI9Nfzd05rzwa0PWicmci5vtLXWpMMy++/LffqQF7fnZeG+a963IfofiepXEWsuulrQCvGQLBTZY6kYZZKGg2TXPr++n3+3FychJXV1dxenoanU4nut1uPH36NF6/fh1HR0flYIWbm5uSLhLtMvvLbG62ZnRzc1P8x5zLmSOJfWoKjMxjXltw2cxvqdPmTBiqzZTUnRk2beA5g1iWymtgSrtoi8Flnq8wgy7Fx/HxvOnG34W5W/ixBprpCxAEyJ1ogWhk010+t9caO+PmhBruky0aNnObtg1mnos8jw7ay+/69CybuumrxzgLeVlzdh+tSfNMtpBYA85M2N+zYME4Y4plzz0R5t5bDTB7m5mjutml0Ov1YnV1NU5PT2cizbvdbgyHwxgOh2W+B4NBObAe3zjBWIA9wrv7gYWLGA/WLLQPj/AcuN21+THvzzRO/zO9/KGL5z3jQBbobLExTX6sPBrQNePi/3naoxdKlpiyv6X2Hs/kuv29bPrjvr9Ta1uemHzP9dT+NiOzlJdNcZPJpARbOGNRxJ3P5/LycgYULcU3ze2ev/X19XKNoIrhcBi9Xi9ev34dJycncXp6Gt9//33Z25v9R1m6NdNknGkTJm2y3DhtY56/DEbUHXGXIMN+scywzRxZEGg72e9JweTtObNJ1wFH2ZxpUzLSvf3E3q+a6WmewGit2QE5fo/vm1l5PTlKlzHkf6JPfaKUg16m0+lM3l67bdzOLPR47mqagoF0HjM10Fl4y8Cdv53NltTlscxuhTwu1Otv2UWQwcACgvtujdzzDBACTOfn5+V8bPbIW0Bl/VC87hBuO51ObGxsxObmZrRareJjnU5vk+AMBoN4+vRpbGxsxHQ6LeduO9ASYd5matOV6R/6IdiLb0H/rLNM6y6Zv5kH1gTVGvjVFKJ8Lz9fq6c27/cpYH9VX/OjAd2I+oTw2xqHCd/Skhl2XjxeENTlycxalJl3vl+bSH/L//Pcp0hlZphsv7m5uSmaINoMvlgOrEezZf8u+/S4b+ChfyzsdrtdgqYwUfX7/Xjx4kWcnJzE9fV1/Pzzz/Hu3btYXl4uUciki/N5rvTXUj8L8fz8PFZWVkoGrXa7PRNsQgIEj2UOkDH4mJGxeNFSvWjoq69h9jTQeQ+iwSAz03lzzzM2VfIt05/n2QKeaZl+22ycn3F7rOFGxIxZmpJPgqE+CzFZs6N+6rkvODGDn8eYb9WYVHYHMTb210MnaItuU9YysuYxnU7L+jE4QC+Mn+cZoSMHtNGPvEWLLG5YqbIpFQAzCGNdYA/t+fl5+Xa3253xw2eFAJ7GGBEIiesH7Zk868PhMAaDQWxsbBTLE2OMuRjwxJfr4wCvr69LgpyTk5P48ccfY3d3t0RcW+DMSTXyWJgfZouJ1yyavJWHeQBXA9ba//l3TaM1/zfG+JlP4ekujwp07xvQrM2aeGE2lpKyxmsGFzGb+zMv3hpY1ibovuvzyn0SUU3SR1J0eyPuGDEMyOkh2ULkvKkRUbIwGcRgBgRfsbhIdL61tRWvX78u0uzx8fEHzBxTE75dbxxnLiDi9+/fF8CFCREQRn+yycbmP/s4M+jAFPM9M0yDgTXYPAdo8tamvdBoq7Uq0x/FdOm2ZNqxr9UMyuOX+5VBO2tvpm8YmDVlB5xhmvW32FqG1mVtkzmmHxkss6CZ14nb7bnzOmc8ssbsZ2hLXr8WxDw/phHX5W9a2HM9/GDCzoCfLWwIuLQd7ZYxZEvQeDwuOwWof21tLbrdbgFlQIy+MNfeDtTtdmN9fT0mk0nRmpvm1o1EGlhOFLq4uJihS2je9O66379/H6enp3F8fFyOEz04OIjT09MZF5Z/G4gd1MW8QmusM8cdsD6zclQrNQXrY3RZ4/M1fp8tKpm2ahbRj5VHBbq55AVTG7gs3Xux2oyc3/e9rMXep6HmReh2PrRP9wGvv5sZKlI09ThzzdLSUozH45hOp8UPBOiikbrNTpSAptPtdou0i0a7vr4ez549i+Pj49jf3y9ad8StD+nm5iZWV1dngByNNY9nxJ0Gd3JyUrRsR3CiWWQN1oweAOFb/gbtyNJp1iC9aAB2B/lg0rMJFdMzv+0jRiOoLWCDfp7bXCxo8J61HddHv+iTBVGPPfeYc48NYw/dwRg9Nlniz/tTDUi+VgNeAxXzbNO2285z7mf+BnNQA3Xe57ms0VqIYQ1YCOC+D223YMV7XlPMj9tpQT/T6ng8jvF4XLTFpaWl6PV60e/3Y3l5uWiWWfPie5PJpJiCyfp2fn5e9tmvrq7GaDSKra2t2N7ejuFwGKurqwVgfcoXY+B+Iczjrjo6Oord3d04OTmJyWRSeIDBLsebWBjJbj2PpwPOskUkv1PjuQZKjz3PWgDOwXp+n+/RDrs0skl5HnbcVx4l6NYYV41R5cmpTZAHwoOWQdjv+fv87/u5/vv6wTPzCCXXUyMA+pYX/3R665fBxLu0tBQXFxflpBDMt/wsLNweAABjdSQs9yKimJGapimHHnz++eclq83BwUFMJndp4Qje8lx4IUXcbb2wqYmsV0tLS2VbDu/mRZtN0DbzOdgsWz6YY2vQpiXPTXY/wIjMVDNYOzI3g7hB8j5p3W1wpGrWph0BSr08Z8A1cGQmYUHGoMO8+7sZ8GpmYtNitqLk+16LFhYc2WttBsSKPAAAIABJREFUxxYO+p7nrKaR+PsIHaYPz681XgMa9Goay8JMZrrMja+hNdJXtEZ8qLiISCrBfnYfgGBtN2tVWC9Yv2dnZ3F8fBzHx8dxc3MTw+EwXrx4ES9evIinT5+W3QdN08Tp6Wmcn5+XeWVt+fv0lbU7Go1ifX09xuNx7O3txf7+fsl6hQJAnEAO9GT9Zr5OyeCbAbMm2DHufvY+NyLfdwwM367RtPlEzcRca8vHyqMD3dpkUGomgdr7WWKxlJ6fqYFcBuf72vP7llxvZorzfNcWMtA+WZgkNe92u9Hr9WJtbS06nc6MeXFxcXHm5CCbNGnXxcVF2X6wubkZL1++jPF4HNfX18UfhO+JNnjLSd78b+CbTCYlcAsTJoBjP56BgxRzNmfBHBxkkqV1g4rBMNOaQYW2GsDznPNMZvg1YTEv/vyMAY6/s9/UpmwHOdkKYN8nbaRkU22W4hlPxo75oK1oimZurgfaMqAZrFwXbc3mWCdOsSXKdO95cJyH+2ig9XsIGXzHY2PLhgPtsvWEseI9+miLgWmeb9Ku9+/fl724JycncXZ2VmIdOp1OOVDeOxE8DllRAHTZakTA4/r6emxvb8evfvWrePnyZWxsbJTYD5JvtNvtomXTN9aerRrr6+sxGo1Ku96+fRsXFxext7dXcqzzAy+wFWoe0FpQpDD29rFnU/Q8xcfznYVfz+F9/D/TWhZmXU++f59iRXl0oEuZx6BypzMBZi3ZzIz35gEpk5OlZ4PRX6XMm9jatUykWesxMWXgRYJ1Mor9/f2S/BzAg5kStMTPysrKjMYH8bfb7Zk9fWdnZ4V5tFqt6PV68e7du2Le9vYfFgGMjrlAwkdCpx4zZAoMlfZ6HJw5inExwGUNzPuVrf3Vomn9f9YaKb7nNlhosluAOs28a3RsE2JEFBOgaYM2O/k/71CnhRcLPm63o5XRKg281lgs9TOWmXFmYcI0bU3G/fV9mwH9rVwntGHewJhnoLYpsRYs5bpsXTD42rJAXbZ+eJ2b3+SxI9CR7UHsMuj1erG1tRVra2szgmQGCo8xc0SmqfF4HEdHR7GwsBCbm5vx+vXr+NWvfhWvXr2Kbrdb8q2jZSNwY2aeTCZluxKxE2wt7Ha7ERHx448/xsXFRezv78fR0VEBWtOuhTnPW55Dzzn3alYb6NyCjeuq8e55CphpLo9nDXOyYO01bj7zUJx4NKDrAbCE7M7QOU/IPOCt3YuYteu7Xv9NGzI4/z7A+5D+Z3D2YnV/7CPBrLO8vFz8uJSTk5PodrtxfHwcvV6vaJSkYwRkYbYED7nPSM+t1m2+1c3NzXLw/Xg8LgCKRmwmwD0zUs/DdDqdMYc3TVPa5kWAf5lFlxld3vqRx9DfNWMFjB0UZG1snsXDpm1oyUzGIGttMM+xfa32Y3PfwE29fJdi06vXRtaE5tGa2+fAnBzvkIOEIqKY+bPpPMdZeE06cQnt5xkLXAhG3u+JEFETqqkr05rXLuOUaZzfWWjwnHpcs+BiXuJAP+55/K+uroqFh5zHAO5oNCo50MmR3jR3+6O964A+MW8RtyeI4RZi58Gvf/3reP78eXS73bLe9vf3Y39/vxxYEhGFb5ApjnlaWFgocSEEQf7000/xzTffxI8//hgHBwdFEzfget69nml3pkeblGuA6fmJ+DAK3vST+bq/6zXikvm95zQDrvtm4DW/uK88GtDNnTazq0mRNee2n/ek1EzLNcZq5mjJ6a8TbF0yYVojysyf32gMFxcXBZgi7vxA7P9bX18vJmYAF0aEBhxxl5VnaWmpLPyzs7NotW6PCOz1erGxsRFnZ2cFqCNuD8wGoMmCwz3A3N+Bqbt++gWDMME74IOxygFG84Qna1WWTKETmIS3wlA/TNEla6mAVZbOM21m5k793ppSEzKtIeEiAIgA7Qw41hDy2Hg87JOu+WNtcs1jm7XiyWQykyTBdOy2WXO2Ft40zYzg5+dcZ6b/XC9z5H4CtrYYeRzND9xHxsRbgyyAZJM540J/ndGL9Uimt8vLyyLMDofDArgIqxY4mJM8ttAmAZA3NzfR6XQK4H7xxRcxGAyKNYtc6wAuFinHhbDOcDWQrQ7T9Zs3b4pZeTqdzZCHBo3fep6GbtqoCTB+xuvNOzZq9eWSv+01nzVpr5EsTBlkXW9N0P1YeTSgGzFrWvC1TNQOJ/dvPx9xZxr0/r5sfrxPSsmmg3lt5lsP7eN9xGGiMENzMcOPuAuAIVkFjDgiSmDF+vp6CaZCC4VQHLyDRB0RJXmFJeJut1uOEMS/e3Nzu5l+bW0tLi8vyyb7WvuztoQ5i+91Op2isRsQAXgzT8bLAEExI46489dmbZlxtwABAHrLg4W5iNkkEQgEEbP+v3l1mBYwmeexop8WAswMauNpQDW4uN48Xh5jtCr6wbs5qYTXjq0O9NmBUTWTfG67BShrTF6f1kqzQGITuO87+Mr3/X1/I/v8s7BggDXgZVO2x9vfvry8LMlmLi4u4ubmNvK/3++XvbSrq6szZ2PnfpuGbeFgxwHb/z7//PP44osvYmtrKxYXFwtoUjc8AOGbNYILiAj9yWQS+/v7JfPUwcFBiYz2fnqENvgB428BJPO+PNdej6abbOL3/NU0UNNeTSCnf5k3sX5M33zHglYNkE0vH8OCRwW6LjWJyJOZNV0vwhrx5+ezyS+Dve/dB7yebP8/r2QpribZ1Z6LmGUSZs6MDdGDEVECJMbjcRwfH0e/34/19fWyUGyq7nQ6JQgLCdt+NyI58fWsr6/H5uZmHB8fl+cBEFLREelsZuU5YZF6jx5HADI/pJ9z2kkkf6RwS9QRs4vJ5luAxabhbCaEAXA/05/pw0zRjJXoVNOPtbL8bWtqZkLuD8Uan5kwxYFnmdlkAMumOgsRZjJoPTaXO3DIQMl3a9HPmYaZ/zy2Fqb4Pa8v1vTdLkoGYOo0cHms/F0LafxvQcTWjawd2drAmrm8vCx+XNYGCWjY0tNut0sgJJH95geOvPX65Vuco/v69ev48ssvY3NzMxYWFooLxwFZzh9ucMHSwBYh/M/4i9HS2Z/LGJATGqDOwYx5jDMQZz7LPJgv14CV63mt0Z8aP811eC1lvg//sxLkfnnt5e1Q88qjAt08iJZ2spSRBydiNpLQk5KBlmfz+xnAHwKkDxnk/HwG1CxgZFOLCSpL4jAdCP/q6momeObs7Cw6nc6MdG0GyuIj+jHiLhoacMcMiDlscXExhsNhbGxslGQcAOj6+vrMHkD6DEC6voiYMZdeX1+XDf30mW1JtItITUu/ZO3y+E6nd2f6Zh84i6MWCWmm42xVHm/nZfa80UbnKM6Ah5XCjDrTtC0Z2cqRQTfiTkCwL81tMo1Zs8hpPLMw6whfa6K14KnMvKjPjM9My+s0C1/Z7Eexr9Rtol9ZA2dszB8MCMy3S+YZgJoBN8+DeVVOzwjdA7pOIEOA0sbGRnQ6nZhOp+VcbLb88I28r9702Gq1SnKMJ0+exOvXr+PZs2dF+OXbWHg4MhDNfjgcVoVDxoFncVnRBwpmZUcvQweZj1sg8fiZrrxuLGjW1luuPz+T+Sgl81y/nwVx84qacEXbqetjmPCoQNcMM0szDEQe2IhZbc8E6QmsmY/9DN/gd16oPD+vZMlnXskEVaszE0dmtDUth3biUzXwID0fHR3F+vp69Hq9si8WLcGpFfPWD/y7NrPBME5PT0sUZtM0JeE6+/TOz89LHdPptIBgxF1kqM17FiSurq6i1+vNMNsszXpRNc1dwn1Lp9nEWwv+Mb1ZgyJJBu3y+JuJ+Bkzyyy8ZeaWtd7M1A1g1IHpz/Puek271OF20U7u2U9GmwiyY83ZnFkTamGYWds2Q5w3LgZP/s5jaQC05sz37eMmQxpj7P56/gCsTIeZX/Dbz2Vhg/FxJDkCI5niptNp2Y/b6XRiOBzG9vZ2jEajaJqmZHoiujgL3aZXBCbat7KyEqPRKD7//PN4/vx5dDqduLm5KWZgBOmzs7NyMhDuHLTkbOKnD51OpxzjifWMeA9+COJCcGI84CW2/nj+rLF77DMte5263AemXj88WytZcKpZH50B0DRRw6OMM7k8KtB1scaZJSX+ZoKyaclg6//9t6/VGLEXUx7UeVJSbQL8fJaU/X2X3L78fK0N9J1IX/tt2O83Ho+L2crmUTNDb02JuEuwMZ1Oi7kJkxmnlhwfHxdNutW63YrU7/fLmBBckTUVNMI8ryxeniN60pGvzL3NlWjh1rC8589zBd3UaMK/idy2MAidOMWfI2INmAg9/B9RX5RZGucbmKsNmHzDfbB257bxHFHkOX0jY0Md/DAGMFCKo535333OQJWtVba0QLcIRkRDm7nBuGvMNmu0jGN+lvnzjwUR6MR8wyCfaS778HkPwZKDArC0AFYXFxcREWUXwJMnT2I4HMby8nLs7OzEzs5OSQdpf222muBqgRbJOvX8+fOi4aLRsmZxHQHCAC07GuAXpu/p9M59Q7rK09PTAri5b85WR+wIddR4XRauvC5qAkeNBlyPSxZ2MwbUBCzPP+9DizWM8d+1Ns0rjwp054FMZkQetJpEnevJWnJ+L2vBWZvMYMkznqwaI50HrPPA+aHjkv92xHLTNAUokLTZm4cv5vT0NHq9XmHCljwNujB9CI7zMzGvLi4uFr8U0ZPOZEXuWBY99WPWsibj4Cg0FY81ErPTWRLklTWirEkBzvZjMX7Mnf2UntO82J2liDG3pki/Imb9y9CL5z/7Sc3ssULkrEhZIKr12e21JkF9tMf+5xyskjVrxtt1u88WarI2ynP0PVusDP4wacZl3pxaWPB1g7YZqDND+cc+dc+b59YCA+Nks3Rm4jxzdXVV9sqTsCIiYjQaxfb2dklWQXrFo6Oj4isl6NGBSYy1c2WTAOfp06fx8uXLknXK1iMAmHYSe9HpdKLX65V+EA/iADGEkfPz8zg9PS1asvtu87LNrtAWbclzcp+iYuGc/+eVh/LSvDagc2NCjrGwdczCoOu8T6GrlUcFuvdNQER8MGletFmysNSSzRU1BujFXKsj38uMJd93HVla+5j5YV5dvl7TLGCeaDVEATdNU8L+9/b2ypm5rVarMGEkc/4mPaMXe5bseBdTM/4jFury8nL0er1i1qLtAECWLrlPP6bTaWFCgOZ0Oi0HJLA/2EBphmpt2O1m7qxxZQHLkq01KhaeGX6WyhFmYL42ndFO/Jj8bTNxTuABENVAJ4+h++o6AFmYB4ybdsLYaVer1SqBPQYd5sf+V5v0szbtNlsTp03UU1sXfiab/gB5M0GYqBk982VBO2LWb8nfKysrM9YIQPY+a1fTNGVv62QyKftw8aMCpNk61O/3Y3FxcUYQJiiRtqNlMgdeoysrK8WVMxwOYzQaxebmZtmlwPbBhYWFuLy8LOuEowLZ12+AYdcB32KNHR4eFsHBVijuEzzFeHg9WCjLQXKmec9p5nE1/v6xkufLwAqdZ75vTLF2m/kBffzUNlEeFejOG6is3VkS51rtXe6ZeVlDpfh/Tz7vm7G6Tks/Zqy1/mS/2+9bLIHblEj/CHhw8BGRzPv7+9Hv94s2CpPKmsnV1VX5DocnLC8vz0RJm2H1er3CbBgTfEaAiYncGqwXghcti9yp5eiz/baWyjMdeHFTDHQGUF+rgbHB12AxmUzKdguk/MzoDRIWLPi2TbeZjnjPtMb8A6bU5fGwQAPTNrPF34jJ0UlW8BviX7fgY1rH9A8g0B/GEWZuRmwtOTPZ2jrKwOlreVwtOJnGMKGyRc3FwoN5gQUGtP0syNv6QmAhJ/Ccn5/HZDIp62M0GsXGxkZJp8iRfoCuLSisCdrAfLCe0VZHo1GMRqOitS4s3GZ5y+4YBGp8z47QtnmZ9YFl64cffoj9/f1yXCDjDo+Bz+T58G/TbJ5b1ov5z19XyfyHv30v4sN82/AZ8wXj06fw9UcFullV9yD4et5GkUO5PeE1KSsDrt+31uC/vZgNGBGz0YQf69NDtNyHFPv4LLXTJ4IbAEok2ZOTk1hdXY2dnZ1YXV0t9yM+jAb1gkGyvrm5Kf4nfFurq6uxuroaW1tbsbCwEPv7+zNbhtrtdpGGDVRm0AZdvmmT3uXlZWECq6urMRwOSyKBGuO2RGoGSd324VkLNUjDYN1OQNd7hnOQkU8rYhxNC1m791jTzjynCCY55WM+tzXTJPXAgG1iR5CBCVuLNMgj9ND/HFDFNi+Yvbef4Vqw79PjlRmy5y4zRWvtebxq85/Xi5lm9tG5XdZi6Cftr/EHBxMdHh7O7MVdWFgoe9v5WV5e/sDlwz5a6raA6h0GgCWCznA4LCcIEfvAmnEfmVO7xKALhC3cRfCJ09PT2NvbK8Fd5qP4dM/Pz8taz3OCxszaznzSPJfreY38oYu/ZWXL3+V/Y0kG4Ro2PbQ8KtCNmHWuexAiZgGSZ63FzvPJZTNGTeOlbms1/k4WAtzWeRMw75383F+lZIYDM4F5ouk4hJ/ADrLisBmfLDQRd/sRMzNHk7m5uYn9/f34+uuv4+joqKSbw2TNeHDiEWALQBJwwfjDDKyZ0C9rwWbkBIqsr69Ht9uNdrtdxgUGVQsA4hs1AYoC2OQgGu5lLTT7/WBgTrSQgd0agdto+gb0sxZOgY7sX89m9ojZqHqbkGk3Jzyh/ZJlaDr90FfHuFxfX38gqKFx8yxzb7BFQLK/2PTs9WvTvteZ11Re69aQbD5k3KBF12EQstCZtXKeQaBibCNuUztiSibYCO2WvbOcztNutwtQcdgBW3igJye4sWCytLRU3EWc/vX06dP47LPPYjAYzLgrPHesnVarVTJQWVg1/2DsMSvv7u6WeA3mzsFTtpJYQPXfmQ59L7uCXP7QwFurKwtb/q7pKitP8+p6SHlUoOtOzTMhZybme5ZmvVgjYoYZeaFG1E+LMYPMbfvUATfD+EMRkQHDUrjNY2gobA9omqYEOyGVo6WawSBRWzBhwXa73eh2uzEYDKJpmhJx6qP9yJ/M+4CixwFGkKNyzQz8f6vVKicY0c7Ly8sYjUYxnU6LOc3+Ruow4DLfEbP7SLP52YKchS8DpbVpt9d7hk1jjHFNwqc+Cv2gZPNcpkcnCIFu/RuGac2Mcev3+0UAw3VwcXERx8fHxbxPVjKbkmHgCBqAmvdyQk/WpgGGPCe0M5v1PCbQDv/bLGmfm+cQDd2avAVwvm1N2N/2uNNezLSMFZohgUaLi7enbwG26+vr0TS36VqPjo7i8PCwgKIFt5rQubCwUIRj+re2thabm5uxtbUVw+GwgDHzyzpB8I642wVAvdx3IQnG3t5e0doBboQy0kmioWerjMfNPDkHpZmmfY93KX9InllTgmq8n5ItA37PNOR2fqy9jwZ0M0OpdSYPgBllDUypl1LTZql/nvbr+24Lf2dNd17fIj6+f+tTS9bI3X7vi726uoq1tbWZRcjiX1tbi8FgUA6jRkvFJAxjwPT06tWrkgLy559/jv39/WJKI6rRGhXtYsEjzKBx1zR0mDkatzUpwJrFDpMfjUbFt2nfHMw2AyDzkrUe5pSFmH2F/M6nqnj7kAUfxsOgzDfyVoq8kLNgaU0gC5NeBxExk1SkRutoZ5z0RFDO2trazPYs8gVngLJGY0EE7ZZic7fNtnk7SU3wyXVY2MljASDWtF7m0eswf2+eH5y5A5wQNhYXF8s6IksTQgdn0g4Gg3LCF8Iu4IyGSP323Xr8cN+w26DVug2ievbsWXz++eextbUVq6urEREl6Aqhk/W4uLhYXEm0Hxo1ODs3NGfl4sdlbdnXy4ENFNNo1hQ9J8xF9uNaw3wogH1KyXVZiM7A6rVYs5J6Xbq/XLsPEx4N6M4bEP/vhZ4ntPaOB8laS5aiauYEa1sOuslmkjxhte/X+vCHGK+atpSfmUwmhcHCTEmW0W634+TkJI6Pj2dOCEI7htmQbGNjYyNevXoV/X4/bm5u4qeffop//+//ffzbf/tvy8H23W63MCXv5SNAB410PB7P+AoNiN57SSBY0zRFu0DbwewFU726uoput1t8XllAMyPGFGcpN2IWIJlfR+NGzGZuwjds85vnxd+HlgDEDNS0x0FHGVCzO6WmqSOIAGzeFsHYed+ox9NWDwQwgn5MZ44Sdt3uu9dltj7ZfJzHwMXrzkDtd9wOj4MFJ7suPNcAurOXOcrapmfAKiLKdiBMyhcXF+U7q6ur5YCRVqtVwNbBjdQN2GbLHnTiAMamuY2U3traitevX8f29nYMh8MZVxCnf7GLoN1ux2g0KocZ8Js5AGxp1+npaRwfH5f9wgRTQg+Yxh1Ula0wFnBqFiTPTb5uGvtDl7wm+Tu7MGhDra15XdZwK+NQLo8GdF1qgOvrNXCtgW82sXmgzLxqwOu6+d+LImsaNROEn3uIRvypZZ5mkBkgPhhyLLMlCBNir9crANfpdGJlZaUEWOCfQzuFoUwmkzg7O4uFhYWSeGNhYSHW19dLdCbZnNBOAVy0KWvigLyZpPuJT4v+cZ3csJhAb25uYjQaFd+Y92e6LoDA21yI4PTz1nIYaxhc9kfBRO0PNsjYn2xGZo0qp86MiBn6y9pvjuDOEjrCTxY6mV/mhzzaEXeHy9Muomqn0+kM0860iCZEe6wFmRGzpnLgnDM+8VxNi/fYZLMg/aYd5h3OdsV4WiDx/whz3mvKGmC8AF0f+oFZGRcLPtuzs7MZMHe/s5bHuKAx2+fe7/fj+fPn8eTJkxgMBsXHjvDn4KaVlZXodDqxtrZW1pzN+c4kRaDi6elpHB4elmhqBLJW6y5/NL5o98e/6ZeVFfc7891auU+Z+EOUrJnW2sI1W0hzu2rWpI+VRwe68zTemhQyT6vMQJslmXl11UDdi3uelnufKQQmQLtqUXy/z1iZ0eRxs9bmhQkgAXgnJyclMObdu3exvr4eEXdmrl6vVwJsDg8Pi6T75s2b+Oabb8qmf9q0tLRUUk0CtJzBu7y8XExujPPx8fHMSUb2Pdofac2IucBMN51Oi++KPuN/tq+XkrVLAAomlunBjCJHwueAHtNrFs6yOc6+La7x28JhjUH7eZvQAZCIKODBs9b4SfHHWHMKDsFu3pqyurpaQMVjaG0ta5jZR+rxsQUh+3fzejKDyz4/xpDveiyom/nIMR/Ul61P0AvgTX0XFxclAOr4+HgmUQxWHKdTJNcyJ/uYhpgnCzi0ZzK5jYPAF4zlpt1ux/b2dmxtbcVoNCqCIgGGAOl0euurHw6Hsbm5WQCX+AtcRsyz/bTj8bhosQhn0Ab36I/H2+NoISZrhTVg+5ssWRimZFyogavfNd/I/PxjfXpUoOtO2cT7MVCbJ334fm3Ca8Dtv+cBpDWbhxCRNQwzbP/+VOLLTDf/b6AgcAb/HVoF0i1MD18pzIbtDt1utzBhfFdsGzo9PS0S9dXVVRwdHZWxJvMO39zf3y8+qU6nU7YiLS4uxuHh4cyRZtZM0TxsEuZd+48xg19fX89oeGju3ofo+afO/O3sL7UG69/zrCEZtBEasqk7g5aBlHZQzKANYmYCFhxyu6AHkjVwH/pgbymmZwQo/mZsATrG2KCRk8NnurXmQNusvbovtfVvrTVfN+Dm+c1CDm3xHAGe1AmgEZ1McJHzO5MEBrfKdDotJl583Bb6aI/pxzSzvLxcANd7bweDQWxtbcXm5mZJEGONHncOVqb19fVyUpd5IxouY0UedgvnplH4B9Yx8kfnPNlWANwnz/VD+dzfFBjP+45pgmJ6s0XONDTP4unyqEDXElHEh1KJFyOlBjY131Ht3jzttvbefeaDzJzdn/yuhYnfd6z8uzZm/h55V31WLeH/EA9gTBYo/EWYFAnCurq6iqZpShQzi56MNYeHh4Uwe71eDAaDEqXJfkSAF9+rTZPOd2wmjc+taZqZfYWYAdHe6TtmPzRr3vEcAA4G9wxqWQMzSAIA2bxsjSybf9ESsjUGWvIeTdOcTc81WrDmgRZEOzCdZ0aKIOhEBxY+TA9XV1cxGAyi2+3OMBrGBm28pvXyN+OV77n9Fro9htk8bD+umR3zwVz5+/b1W4CJiELnWZt///59SSpzfHxcBBpiDNbW1maOQGSsHJsQcZeJrabxAqDsEMAnTNQ3++CfPn1a8jUjaCI8MZcALlYexw64rxZkAWPM507d6oMOxuNxWSMIETnPt/+mTffxz0zH0NNfR8laKtfm8WjPleuw9cT8vIYDuTwa0DXY5kEwKJq5mSHX/EAGzog7864TAdTqNhOsabKZIdaAm2fze/7m7zteNa3N/7MYYTwAFAEULCa2icDckIQXFxeLj9eServdjpWVlRgMBuVeq3WbNvDw8DDev38f5+fn8fbt23j//n2po91ux8XFRTFfra+vF3MzbWy1brcGZWECwEE44FnAxYsIoQCmdn19Hf1+v/jaMDF6AcIcYEy+TjQ0TCoLfU6EYWZuJmu/MM/XrB5Zu8sM2gJGTXD0Omi32zOay/n5eZlrftus70hv6gSc3XaAgLFgLeJ3z9HrBgWvM4OOwSfTt8HcFgkz9Ij6iTKmkaxVZhMz38RlERFxdHQUOzs7MR6Py/gRLIXQBzgS2UvwYMSsedsWpEwTjOna2lpsbGyUtbWwcJtwBMDd2tqKfr9fBKXc3larVQRM+4L5HjwRnnl1dVWSdJD/mRgJa9D4ejn+k/7Sx8ybbN0z38tusL+Nknm4147nx2PlnRgU0635798ZTTdPDtciZrclmOFyz5JxHsRsQspJBFxHfp+SF6vfqYFtrWRNNGs6Dx0j18HfWWPwc9YOYAr4WafT28CLvb29WFpais3NzeKXgllRHwzGEYtkxJlMJkWiRtt05HLEHRAuLS2VHM2Y5WAwWVOwpAzR26eZ/YleLGitSOZmrGh8LCgYVm1h8b+DgzIgmh5NawYr2tNq3aXia5rmg8MdImZTfJr279MmASP298HQAAAgAElEQVSneDT4o/UTOOMEB4AB48H8WmhBSAOsCRTyOHg+bDHI/clZtfIasZncQG1ByhHePh0HAYl6mFeezdu0rH0jbPoMWlKnYn0BZHGLME6TyaSYkp32km85qCvTEJYJztZFk4UWVldXY3t7O549exbD4bAAPr5Y++BZvyTi8N5oCzXM+dnZWdkidHBwUHYVEGDHM2xzMg82782R514XWaDKfDPz6PuUr9r7Nb6Yi+nItMFYGFcYqwy+GROygGjMua88GtCtSfceAJ6xSchSshdSTQPkWt66kJlmrtuLJgOn2167zjVvTXgIgXzKmN3HmHJ/neAAn9DZ2VkBgk6nU1LKEQjlwBCAA2bNoh4MBuW6GQoLnoTvSPJN08R4PI7JZFKCeUajUTkDFO0DoHYfYG7WOA26k8mkmM5ssrbm1uv1YmVlJSLuTLHMuekLZnWfGaoWfUrJwhR0ivZo8MlWGc+jt00ZfLOGZ5Cj37QJwMVfi7+RdkFHCFwIUY68dVyA9107KMqaI5pcBjrGmP9NW9aQbZ6lXzxjDdfry+PhcbQA53XB9wEuAqWIAPbJOwQ3EZgEKFnARECh7Q7KytY1rDasR+pGA11ZWSlJMIbDYaytrc1EK5P9CsGSyOlOpzOTCMPASN/ZRoiGSyIUzxWuBW+JMohC35kH5b8zL81Kwzy+6b/zWnqIopOfye3KoI/QaAE6C7DGDLc9Cwz3lUcDulnaz2AbMRtBnCfQ2lyOmssAbZCN+FDq8f/UXfMBZomvNuCZWPKkmWF+asnEWJMU+bEpEOYScRcctLy8HMfHxxERsb29HV988cUMSBwdHRXtE4aOtgqY4jdmUz0MlDHlWTQ9tDwO9GYLBMwcU3XT3GmnaI+WWDMNAMAwfnzJjPvV1VXJgYvGbpcFY+k5mUcT/j3vHu01qLsPFp4M6NZAs2ZgocN0hDZn0x+BQIAIZtJWqxW9Xq/srcb/zZgsLS3F2dlZvH37Ng4PD0s0OffxH9rnDUOzyTELKp43R8HetwbMEE1TBmzGkXmszafdRqwFaBltjqxSfIODQXq9XkkggsCKAAotOxqZ8Tfg0hb2P+Oq6Xa7JTCQ7X0bGxvx/PnzePr0aTx58iRWVlYKsF9cXBRAxNKEAMt8MgYeG6+Vs7OzODw8LLsHLNxiCeFQBtxPGcQ8njWe62IQ9pry2Hit1DTaTCO+l3kt971OsnLHu/NwgbZlQaNmXWS8P1YeDeh6QMxcckd5tqbN8sy8ic8DnMHZJoXaxLsNHvSaJDavPPS5eWPketwX99/38/NovOPxuJjL7LP5+eef48WLF3F+fh4bGxtFImb7Q0QUbWg8HpfxabVuj/jDLHZ4eDjDENBwuR8RJUdtp9OJzc3N+Oyzz8rWFOaApAJmmjBej4ulV0zjjAuaLv42mBemQrQC6mZMGT9H43LN9DCd3u1lNI3a5JaZCgyatqLR0u/a3BlUYPIGkojZwCHahWbrXLkRUaJknz17VsbAaQIR1gAXtGPWCXOOn9warsc9m9wsdHouc188v9zDOoLGS5+9FpyNy7yE/y002nJjgYR597mzWH1wf0BPAJK1adMJbbcwiBnYyWSgjV6vFxsbG/Hy5ct4/fp1bG1tlf3jbufJyUlcXl6W2Iv19fXo9/tlrTFn0JkFJAdGkVGOMSSozve83rJw6Otcs/BT43meT79rnl/TjDPIzlM0/HzN9VOzjpqXUofjP4wXNcx4KA48GtB1R+ZJLvdJPC55srK2wbUMmv5uzdzsdnrSaho4z9balifoU7XcTKCZqLJ0n5+Babx79y7a7XYBi6Ojo/j2229LEo0XL16URADX19clGArmQXAJpsh2ux39fn8GlCPu9jBy2hGaUtM0sb+/X8b0xYsXMRwO48svvyxm32+//TZOT08/0BwZB5i2gS8iClBbiAKQ2P4AQ+XQBCK7PXZeWGiQ9uVl4SciqhYQa6NuM+0yrViqzgwGzcXBSxmQm6Yp/j4Hy6HVRdylM2Tu1tbWotPpzND49fV1AQdrWZyQ8+7du2Kqt+WAkgNQ0NCtiWbGDO06cMprMGsYXg/kGLe/N5vt6ZcDytBsHXFszRHAbbVaBcgA55wgAtAy7+H7WAagMx+/h8ZP+sjNzc2SinV3d3cmdSMAylrs9XolfzYgTt+YM7u3Li8v4+DgoPhwOSWIeSfBBgFWFnrhIdnU7PsRs/mXXWr8L9+jZP5cM/9mX6xpiPF3QF9NUXG/svJm7b9GS16rNuHfVx4N6FKyhJMBxn/XnptXZ03aZjFbSqK+/K4J62Mmj1zmPfepYJtLZkCuz5Kk/4bJwzCsdeHD2d3djZWVlRLB3GrdRibzg18VDWc6nRZz5GQyiY2Njdjf358BdhgXwBwR0e12482bN7G3t1dyuX711VcxGo3i9evX5VSVt2/fFl8k/tCsxUbM7kVlIQLw1kQwMTtSm4hPGGHTNIWpISw4o0/+lhki1ykGxRrwMv4ZXCLuBDqYsvue/VBcx/+GuR9Njjra7XYJ0GFsaB+mdtrt5AgGfLREUm9Op9Nigm2apkQxEw+Ay8DroeaaIRDKflrA2pm1avt0GTe0Ua9xwBb6x5SMyR2fNcFI3q+L5mtTvQUYC1V8A0GNH2iZH1s4+PH4Y1168+ZNLCwsxObmZmxubsZoNCp75zFH42vudrvFbYMwYL87a/7w8LAETgGq9qMTPIYv3+Bq4QKasdUhAxnX7fev8aoMtjaF+5ls/s3X8ndrSkn+Zk1bNk3Zt5v7mRW5v1OabsSHA+ZyH7j5+doAzquT5+eBYk2D/RSwz5rQPM32IcCd6+U9mK4lrNp4mEEDvpjIzBCR/I+OjqLVan0gQUfcBa6w8Gk/Jt3PPvusSO0EagG8RCuzx7fdbsdvf/vb2NnZiTdv3kTTNPHFF1/E2tpaPH36NBYWbpMCkHoOhuC8y9Zya4vfc4ymZe0YgGq1WkV4WF5entk2hS+aev29zGg8D5n+bm5uinbDFi6AOG8FirgzbRt0XBAc+D4MEE3H4wQYEiBH0A3aKpod/V9YWCjmSwMD5maYNeNnIQuTac2FY2EXmuQazNbR02acbke2IvjQDr7l+UVgs6ZOUBJj7HzHADtBaAAzdRiAvLYi7rRaC3OYlXmHtjuaHcBFKIiIYjKm38w16VuZQ4pB12CJ4Hp4eBj7+/sxHo9LprmmaYqbiXzSmM2z8IIwBv1lJaT2d16btVgWP28acTzEfcBWA0Xq9ZrK/PG+dxnDiFk8MNhmbPgY8D4a0HVH8gR7EmrmJd7Pk+/iSayBnAnLE5xNy5aAciT0vDpr7XmIRPSxYsntPtOyGb/bgukRBknQ0enpaZHM19fXywIjGIk+EwHNIp9Ob7PbvHz5Mvr9fqysrMTu7m4x5cLEiMz87LPPik/55uYmTk5O4vvvv4+IiNevX0e73Y6tra3o9Xol8nZ/fz92dnbi4OBgxs/o/aHuM2ODuRrhgDEyY8Z8mAOFnMcWMMq05ChVzzHXbG4jchntOAsBgA4/tJ8f+1UBf97LZmXXhxbPqTXD4bBoSGtra+U+cw8IQRcA08rKShwcHJTAOu6jEROJi6aIBYRxRXtivVnbtjmQ8bIJnnFnv6yFGWgezdQJP7CSIGwSyc93CWpaWFgoVhloFQBjnqDXzIChmYhbYanX65XtRY5TaJqm0Cx+dAQ6vkmg25MnT+Kzzz4rJwpF3MZCLCwszAhM5lFs28s/+RxfH7+I9YJnHK1s+mWO4DM2uWbrBXPutXgfYPoZ8xkLXH42C3EZNLOWW+Pjme8bJ6gLJSPz+tzXGgbUyqMBXSYz4kNtMg+upWMG1gNiDdAg42fsD8hgyneppwbYnqTc5lrfPCE15vypJfc198NtzVJnxN22gaZpiu8ILYbnidTs9XqFqUZE8YUCYgsLd+khSXaBFvXTTz/F8fFxXF1dxeHhYdGgt7e34/PPP4/Nzc341a9+VcyXmP52dnZmMv3wzXa7XcxrnIbClqQcBGEG4dOOYP4ugIfNv5hnAT/743jfIJw1EQdhGHRND9SNVgPNAqTWJCNiJlUm4+UkC2hlzBXXs4ZLEvx+vz+T45dxHI/HMwyl3W7H5uZmAZeIKLl4p9PbiGmbtW9ubmbOgMU1gbDgvmYXD8DmdWL/sCPYuW/wNdgCsIyjo7rR2BA2TNeMLe/wTdqFoGBwsGvAFhP2NHOoBzTE/03TFNqCBgeDQQwGg3j16lV8/vnn0ev1imkbIRYBAZcPbggirxcXF4uwimZ/dnZWopFtFrbli5zStjxkxQJ+Yx5o3mnANV/MPtH8dwbKGs/N75m/5+A9009W1jIP5ZsuFjwyL83XH6LlRjwi0I2YD1xcR+LIqr0J3mCaAciTVhtIvpUJo/Y9g9c8CcfXMvByraa1f+p41Uw1ER+eMQyxsBhgLFdXV9HpdIqPjq0i+/v7sbq6Gp1OJ/r9fgEDp/Hj99LSUpGmJ5PbCMyXL1/G1tZWnJ6exnfffVc23vMeQOMcySwWTF/0CyYGAyfhO9oCewnt82VOkVI9JjVTNEyPsTXDbZqm+PxsfrWmwrggKJgZW3tDy4VWGT+APyJKVi7eR+hBq2TeYZzQAH5FayYEua2ursbGxkb0er0S6drtdkvChaWlpRKtiqmYcSRFIvu52+12SapCvu6I2axWpJ5stVqlPo67o60U1qPdBfztoKqI+AA4HTzGeFjDY/y9BpkfTMCAGD5KB43h/6+tV4OBr3l+smWDWAiC9hy9Tga37e3tePHiRTx//jw2NjbKISOsVTJTAehYGVjDrBnM2iRFscvBgOsDDbztyWskgx/zBm/Oz/l/86LMYyl+z9/PGu885aYmAFmY8/1cT25XTRvOAnMNiB9SHhXoRsza1udpqFnqYXANplnTrWnM1kSy2YDiiZoH0DVNkvuesAx+7uM8QrpvnGp/u86ID6VF94UxYiHCoNEU8C1dXFwUUxbvw0j4H63XYDkajeLZs2dxc3NTzrl9+vRpEZ6urq7iu+++izdv3sTbt2/LNp6I2y1HDlaaTqczxw5iXkNLGAwG5QDu4+PjwnidTAFzLMDP/FsAAwx5z/thARS2mVirgYlSL3VF3IGDhaNutxtXV1clWAvmjHkYAEIgcdYsj721MfrAOwgIPt5tY2Oj+AhJiwljZl7wyVrzh8bW19djPB4XK8je3l6xNhB4w3v4r1kjZ2dnM1YCz6/XBe97HTKeBln6iRACOFqg9PqyiRDhkchsNFeEAwDM5nwDTNakXD+aLFnWPA5OAYp2zbYsjtAkWcyzZ89ic3MzJpNJ2ZfLebobGxsREQVIMaHbegVNTqfTma1GFqbQcNkeBLBnAYU+WoM1v8tg5XHJvDfzqBo/rI2vn6nVl0HR/A96s1tgHpBnpcw0mIWAmoXxY+XRgG5NW6x1uCZJRXwIjvmZGlB7QWbpxRpRnqj8fgbb3Cfe8XaGT5mkh5aahGlixG+LRgaj9rYffKyYpQhcYo+fT+qZTCbFF4am7AOu2S+5trYW29vb0el04ujoKE5OTuKnn36Kb775Jn7729/G7373uzg7OyumaQQC0hrmbTDUi3RNUBagvbq6Gjc3NyXwyoFEZpwwdltJWER8M+JOEwFQAFaYE/43TNFoGNSVmQrbsPATM1eYir1f18BnUHJSCa7ZioA/dm1tLQaDQUkvOBgMikmSfsB4ASqC5NDYm+bOPA8gs02FSHUCc8jPy/NOy8gpUIwn2r3XLO3PApfXVE0bzmNtcz8gZ8YNrTsXN9HJaIJ8L1vOMl+gnfQJ1wqgSz12PyA0sk0LQY5EMRsbG7G5uRmLi4slHzIHIZDDHBCFBnOCCwRPzsdlmxMnSDHv7NFn36/ba97C9Wzdq2l95kEZrOYpLvb1+5vMZ+brNb6X6/c3azE4mZfn/tb+N03WBJCP8fRHA7o2B0bMmnmzNuhB5f7HOpzBOA+8vxNxN1nZXJ3blQkkfx8mBpMw059HRH/VUtOyuQ4Dd2AD12E0gBXMh2w0a2tr0W63IyJmIjHR0qir3W7H2tpayXj19ddfx/7+fmH2Tgu5u7tbTiUifZ21QY852gHjh88QgGLLRLvdjo2NjdKPk5OTWFxcLMzE2myml8xAI+7MwDzn7S+mI2+3iIjiI8/gzvPX19extLRUrAk2txo4LNxYsjYA+J2maWaEp263W5j3xsZGCW5yFDbRyfZxk50JTRArRavVKsIRgD4cDsu5rQcHB9Fut8v2L+//ZFwt9HlsLRQ5kteMH0HLIFvzlfO3t3FlHz59RljBxIqma5cE7bEFrabhEqRm0LVlKJugfW8ymUS/348vv/wyfvOb38SrV6+i1+vF8fFx/Pzzz3FzcxPb29uxtrZW/OcEyiEQo91CJwDu3t5eiXuwr//m5qYIyUQrEyznAD4DWLYC+nfm4abbmkJkAYk5mac51jTSzDdrvJp34cMGXuqwEFFT2nguK1l5LXos7iuPBnQBo5r0kUE2l4/d9zOfqmHmiTKz8wTXpCX65cmf154/RDHjycKAwR5mZ00P4CXidGFhIS4uLmJlZSUODw8L09ra2poxyaE9Q7jdbrcA4uXlZezu7hbz13Q6jR9//DF+/PHH2NnZiYgoJrSrq6vCqB1wYhC2adWMmuAhIjlXVlbi/fv3RQPn0HGCvegfY2NzYU0Yc7YgtnwAwjB9txMN2GZp3omIma0tNl1m5pZpA6GJOTLjx8SNObnT6RQNdzAYlC0nmNcdBAct03ZAdzK59c1PJpPodrszwINPm2MT2aO7srISR0dHsb+/HwcHB8XqgcZu9w+aDeCJQEP7bHWi/3mN+RkHywCU+NwZpzy/1vKJB8iCdQYfaIbxJtqbYDELKVnTpU7M16yj0WgUX331VfzxH/9xfPXVVyXy//vvvy+C2fX1dQl0bLVasb6+XvrO+iPOAFcLGiyxFhZULi4uyvZAZ6TKQmAW1AFLg7D9vBTzopqmmvmUrUued9dXU3Rsvcy8mraaj3steu3lmCHTit/xe27XQ8tHQbdpmv8oIv4PXfoyIv6niPhXv1x/HRHfRMSfTqfTw+a2Zf9zRPwXEXEeEf/tdDr9dx/7TlbRs5nAE/exemrP1MwFD9GcrakacGEatai42rezlJglq9zO+67dp7X7+XmauEGGew4awTSJL4mIR/LyEpQTETMmPhg/vt/Dw8Nyfu7+/n5cX1/H3t5eHB8fR9M0MRqNStTm5eVl/Pzzz/Hzzz/H4eFhabtNoBFRGD7fc/Yh2o7vDEDo9/vR6/ViaWlp5gBygwiM1XuWWcT8bS05a8w2AUfcASRtoY3WimxGzuZvMwL8gXwPAciBWw7q6na7ZX/1cDgsJmwfPh4R5R3ep61obAZnoqdZp2huNzc3RQigjz5aDtBlKwrA6fHJ6zyvRQrr0OvI5nSeoV7XTx+o3/mpbcK3mdSadV4vjB17m/lBEEWQZb4cbDed3vlYm+Z2v+3f//t/P/7kT/4k/uiP/ijW1tZm9tFi6kczZe84Fgc0X9PV8fFx7O7uxtu3b0u0Mu1HAL64uCj3rJVaCKrNTQaiefzPoJevZV6WwXker8/04W+4juwScOxGVn74bcCd57uu9cP81m6ReeWjoDudTv88Iv7jXz60EBE/RMT/GRH/PCL+n+l0+i+apvnnv/z/P0TEP4qIr375+U8j4n/55fdHS61jtQ7m5+6b1Pvq8/u1exnI+N8MONebS027rklZtTbWrpmg/NvEfV8dNebC/+xRZSFzHuvBwUEsLCzEcDgsUZ1mSpiaYTZomzAmtjlMJpOiiTF+g8EgVldXi2kLMAUce71eLC8vF20J4QCJnAQTbO8xADVNU34DRHn7SBZQMH86GQWCl5k437BW4ChsTH0Rd4CN2Q5/J4ub9mSGF3Fn4vYeWq4ZwNlXu7KyUnL3DgaDctYr/j8EC7ay2HSKv7zT6RRBAOHSdEw/2WvKdcCaPuCGuL6+jsPDw9jd3Y2jo6Myx15L1p6Yj6y9WGC05pl/U2/EXRAbQMtczTOdZm3PWjfzwXNotaTQRICEHol2v7m5PUgCrZb+Xl9fR7fbjc8++yy++uqrePnyZayursbx8XF8++238eOPP0ZExObmZnS73ZIDGSHm5uZmJqocXz9BUWi6mPnpJ+9i/bGWS9tsRfH8zBOIzFP8ngXazOvM07OWmss8RSPfi6hbJ7MGm2nN7chK0n2YQsnjdN+zn2pe/ocR8ZfT6fRN0zT/OCL+s1+u/68R8f/GLej+44j4V9PbEfj/mqYZNE3zbDqd/vSxyu9rbNZKavdq0keWRPLv2gT7HX/Pz9eAMrffhGpJOfsc5gkS8+pj0eff+Vm3rVZ4B0bhrQYkhUCLuby8jP39/Wi323F5eVl8sJhw6SPRxzc3N7G+vl7M04eHh2UPKKZJFoZz0KKh3NzclG1Ay8vLMRgMig/UqRDpg/1jaGEAEiDgIDYWueeZazBnjxFjzDYYANUFgCdPL3PugBpvH8lJ+70txaDAmOAjxNQLyML88eMScAOjR5PDNM78RsRMX6mHPtjcb5BHsABAJpNJSaeIYDUcDmN7e7tYS6Axm7jzGqCvTvEIY7RZHABhbtBynfOYMWXuLdh4XZgxM2dcs2DreUIIYn8zgg10y5hmoZy5QzghMG1lZSXOzs7i66+/jh9++CFOTk5ib28v3r17V4IECXDktCjonC1GzMXp6Wm8ffs2vv/++9jb2ytr2mMSEXFycjKTkcq8xVYV2u8xMt8xWFkBMHjn/2uKhgHP/JT7NdMxdflv1mge+8xz3afchyw41NyLGTdqODKvfCro/lcR8b/98vcTAenPEfHkl7+fR8R3euf7X67NgG7TNP80Iv5pRMSrV6+qH6tJsNn8UBtES041kPxYsYRzn7ljOv3QnPAQMJ7Xx9rzGTzv+/3QPtY0YAiObSvj8bhEuEbcmqQwC5+enpY9go42zj4dEg24sF0BRg5AwnycKxbAQPsCRNg+AYhmfyzfnE7vzHhO2MB3m6aZYTo5KjMiZlIBwoAxtVKHE/2jLWMOtO/bC3s6nZb3/H3GCEDmGvWi9RP5a9M0Er2DhqCJpaWlkmAhYnYbE5YANDa+S1t5B00OUykWC7ZPUVh73jdKVCyWCYrdBjbFc8/af/arct2ASDu8X5jxqzF98xG3K9fpsSXAzNutACkA3olNbOlomqYkffG8/8Vf/EV8/fXXZbsRfnKizc0TlpaWirbq7U3n5+dxeHgYOzs7cXR0NOPHpS/v3r2Lk5OTogGbr2XlYB6wZKXE8+7i9cQ41DRZ8+3Mx9yWHCSV+ZgFhvvMyPmbXK8FWVnRy/co8741rzwYdJumWY6I/zIi/sd8bzqdTpumeTiq3b7zLyPiX0ZE/PEf//HMu/MmAEkqO8QtJXuQPtb5Oe36QGOs1WPp8L66aBf/0+bc13nfcv/uI4L72nDf9cwAMV0tLCyUNI8Et+A3ol1cv7q6iuXl5ZKaDwDCp+VkAAsLCzMb/SNuAZ3UhDB1NEROKgKYYY5OQcj3AFvMvoAFfYSZTad3x/z5IAUAnHcMSnnBT6fT0t+IKJocNOr9qfZF0maYCJaEiNlAITRC3uE+jNMmcMy9ZCZyUJDbQVuYP1sDSBAB88matrVFxh+m6rN6GTv2eHOCD8kbbNr31idHzEKf1jQZi4hZDZZxoa1Za6OvaPr0x1HV9C+vO9rl9jkrGaBLn6HFeRoh24lGo1FsbGxEq9UquY6Pjo6KSX44HEa/3y+pVA3e7LvFEgUdnp+fzwSwYTJmPp3N6uDgoHwv4kNfedYALfiZR9U0VispXkteP1kxytaHmgJhHuhn/Fw2HdfA2/3z9Rqo+3lr8o7zyL7ihyg/n6Lp/qOI+HfT6fTtL/+/bX4xGzdN8ywidn65/kNEvNR7L3659qByn2Y6D3TcWV+7z0dw3ze4d5/mGVE/Kirf97fM1LmWpXc/a3PNfWOUJcXaNd/L0q37HREFeI+Pj6PVapVcyWaUZnxoQ8wD/l2Y/nQ6LZrW5eVl8Q3v7e3NJGYnWxLR0aurq/H27dtyEoqzP3EgAWBpwGBhez+omfZkcpvWkOQUbJcApB1pS3GUck0IQ4Bg8QKMHhvqwTzKfZtQ+W3gdFQnJl4YPPNBJCyR4qenpwUc0MZ9lB/z7uhe/L7s0SblJCAJ+DFn0AFbUfBh8n0fjm4tnr45WxNpERljQCxbGhhj/3jOHVgHPSAI5ueZS5u6DbDQGf+j4fI/bgTGh3XEWNPv6XQao9Eonj59Gs+fP48XL17EkydPYmFhIXZ2dgqYImiORqOS89wpHWH4jA9jeHNzM3MgPWZj1itWKvbdYyVCK6QPFlI8zuYntTKPj2YAzMqGrUf3vT9PS83gnQHZ/M7XjQk1TdfYkq0AfiZjEfP0sfIpoPtfx51pOSLi/46I/yYi/sUvv/8vXf/vm6b53+M2gOp4+gB/bi41Vb4GIHlQamCTyzyQq5V5pox834RAnZYcLRjk+rI2nIWL2vdqIFvrS037z992G6w9sCdzdXW1gBtMZWVlZca3iwkRbQvTsplfq3WbAm84HJZN+icnJ7Gzs1P2HHJYAlrJxcVFHBwclGw81EsbbLrkepZ4YaQwe/4H2FdXV4tWBnh6fGB2picHSQHozLEDaNAKKd4mExFFUzKwwKydkpJvWxAziJC68PLyMs7OzopwQv9tssSsjXZLAob379/HyclJAVHmmx8HUHmvtH94F0uGU1Zmf7m1Zo83z3qtMJbU5XXotVMTdGGG3qduIcoWCcDNR1l6axAgxRy6rRExI1CRSGR5eTmePHkST58+jc8//zyePXsWg8GgbKsbj8cl6pxDEmgv7ULQQrhBk11cXCw7DOynhR6urq7i6OiomJNtXWCNZV5RK5kXz+OBBiYDI8/lZ7O5OPM/f8P8y+bqrIFmvMj3aiZqa+PzhAagaw0AACAASURBVISs1FGgqfvGz+VBoNs0TTci/vOI+O90+V9ExL9umuafRMSbiPjTX67/m7jdLvTbuN0y9GcP+UY2BdSc1/dJWh97Jn/jIW3x8/PeZbKyFpn9GyzQfJ1nDdo1IqwBbW18akSc68/+FbcLwsN0itYDwwG4AEyk/WxebZqmBJiY0cJQCMD69ttvY2dnJ96+fVvS2D19+jQiomTK2d3djcPDw1hcXIzt7e1yOg7+0uPj45kcu2i49J12WwoFRNGCmqYpuYfpe9PcbTPx2Ob5NzigvcKIc0IUAN+ASXsyvdUkewOPTXPkzLbfEaBCm0VTQ4MDVLBmoO0725U1YwOfTd0ITPadO9FCxJ1WDE3mWIi8Pxbmlc20mfFl82RNc8qMMgcJIZxg6vcBCIAuGi91OXsSbbNVYzqdloj59fX1ePbsWTx58iS63W60Wq2SOnNvb69kMgN47e/l+4w9go1p6ubmpmivjrjHjQMYE+jmOSROwEKcgdF8innJyk3mcV4jBrMawNf4tgHMc5f5VQ3os1LjZ/K7/mYt4tkl89namsxgPK88CHSn0+lZRGyka/txG82cn51GxD97SL0uWUv0QsqAl6/N02g/pdSksfu+yTtZcsvvcy+biU3Ulu6yVpV/POE2dWQii5hNHJClsNqCspkGzebs7KwsfDRJtiVExEzyf2t6HG6ARjqZTErULEe/kUxhdXU1vvvuuzg8PIz379/H27dvo2maOD4+jh9++KFI6bRxYWGhJO3f3NyM/f39+Pnnn0vuX8zhPMv4W+OJuAuSQdsjEhjwt+k0YjZLlgHTdEMbGX/GGgDiOd413WUGwXXPG0w2C2dsGVlcvDspyPVTJ0KG9+fCgDM9RtwFPiEsIFgg5MDM0fapx8KIaR8aM/DyLO0wTdpMz28EuZqAaaHPwiXf4Z7HAbqmb3mbVuZFCBB+3+bvhYWF4rt98uRJydoFbeEDx9+9tLRUoqChWVtS/M3pdDqTF5s0rbhabFLmdKHxeFy0XPpjwSpbXzz+pqGP8USPE2srC0E1sJ5nzq3xUb5T8xlbw87+Y1s0at/LvHdeyYBc05rvez/iEWWkipg1EftaTaKovZs775K1wXn3a2Bek37y/1mbzBNqIJz3XZtKTGA8l9sDEVni5m+XmkbMOGRGz/f4H+BdXl4uWg0AAgMgRSQJMzBrGRC8xQbm3e/3I+LuVJ7vvvuuRE4T+EF6SoKoAJfj4+OS5hCNen9/vzAaTImkifRceG4RBhAIut1uCQpiT/DFxcWMJg24ZD9YTRMzc8PsXKORPFc1GrAVJTMy+zJtTuM+dVlLsgkT4Sj7vukH4EKfGTtA1hpp7hNjlv2HeU3WmK5dHoxF9sm6j/bhZmEQZst95rwW+IZWawYP6DkC3NHcCG1kABuNRiU5CYGGp6enJQ/y+/fvi/CIBYL1APBjQSBaGc0UgfD4+LhklOLQBH+Hk4O8Xa4mLGYhzXPgUgNj5qUmaJk2PNeek5p1wrRha0YWIvMzXiMZqLMgm83X92nLXq/ztPn8/rzyqEDXpQZ++TfP1e550nJdGWA9gPM0xtyOeW3NbcptyBKa/Z48mzVU9ytrI7n/8/qViag2FgZ8GPl0Oi2gax9V0zRlMWNW3N7eLqAynU5nDoSHsU8mswEgEbcHFjx79ixarVbs7OzE4eFhTCaTEsHJNyOimLWPjo4KcGxtbcVgMIi9vb345ptvCvHDmGzShZleXl6WLUnsl1xdXS3aAELG9fV19Hq9mTbbZ4aJDi2DsWE8nSnJhwpEzEZbGnTQ5Nxe05HBCyBAeLC0z2/amM1ygEfTNMUsjH/VJkFohHYZDH3fpxWZJm0aRHDKDDNrvXltWSvLtG36RmtFkHL2LoQ/b/PhtwHAiTtY+wZcrCOA98rKSjlYYnt7uySzsOYL7UArrVYrhsNhjEaj4mu3MBERxcpC5ijGAboEcAmSQtsFfAlmY848fuYr88y8WUh3ybzN84bwk2mcdzyugFWmN7ct12Etlu9Z0KU9NX6XlQx/K1uy/B1bC7OQAM1bUK4pfpRHBbp5Ii3deHG6w7VBsVRTk1ry9x7artzGiA8zO+Vv+Nk82TWT87zvfgzsM2EZhGv9rC2yiKhqFQQ9nZ+flww49B1GgaYwnU4Lw/Eick7kbJIEXNj3Sz1sNULDOjs7K4zz+Pg4zs7OYnd3NxYXF0syhoWFhRiNRiX6moPVmads2mQMsmkUkIYJW2MiScfl5WVhdGxBsmDE9g7XmaVyGJSZIiZVwN9rIWuNgCr1YOq0dm3QdfYi5hABy0BjcGN+siBqYIcG+U7EXaS19w2bPjOAso6ydjvPZ8YasqXC84W2COgTeQytUq/XoWnac4dQAq3hGvHYevwAQlKTkoZ0MpmUM3Cx4CDwQaf4YwFXH7eHH52MU05ggoUJekQ4zCXzz3k84lNLrovxy7yLdUV/DXbcz9po5u/mLfMUq/zdmonZ7c3afo1vW6N1ySbv+8qjAt2svWWw9ELJpmYvyjxpvw9hZULgb+7VJt33svbpvlJnNr/kSZ/3O4Orv50Jbp5mcB9Yw/xY+Ji9zIhvbm6Kf5c+A6AwK06ygcHBiK0FmtHnKFEfMziZTMq2CjTsk5OTkrVqc3Mzer1eXF1dxe7ubvz8889xcHBQzM0ZABxw5PNYrSGh4TvbFiZoGB4ntXh/JMCSTb9sp2KBeuuQ/YkGOmib5BiMDf5m2o1ft2maon2bzmraNWPBGGXzGe1EUHHQF/PE/7ga6JfnMtOf/cgUMz5HMDN2jCt/k/YSSwx9tEDA326rTfAGc0DAebEZY0zHT548iY2NjRJ4d3p6WgAPGuBsWmIWrD1Dh71er7SdsQRYoW3a4axxPsDDEckIyGwLInDKPDALMOaVf9VS40FWRkwfnn/ezcpUbl/m4QY35qgGdsaJ3E77b2t8NZuJuZZN3TVl7mPlUYGuizud/85Syn0mg/w74v4tQhTXS6lJVq7rPk0012fArpmT82SbeGpmZ+qs3ctSotuT+8zYu30RtxrT6elpuQ8ooSVgAsNURjAJmi3BHezDJaE+ANfpdIp/yxK8Gcz797cp7zjH9ebmdn8imgXbXMywybAF8HtbEYDAgkfzBlwcaARzYPzpP35QDnXH/G0znhc4fxOgZAYDg4IJ8y3G2Vp/RBRTJUcJut+mAepstVozQoCZmUGbvub+A1aMF3+7D2Spwp/qZ6DnmiBqWrXJPDNKC2x8h202aJKui77wm+/ng+kR+ngOYZMxXF1djY2NjRKF3Ov1IiIK2DfNXTDbwcFBHB8fx8LCbb7yp0+fxvb29ox1gXVCBD5ukOl0OhNJzl73drtdfMHHx8clyt7WEoKnCJzCvWM6ynzUAoctDDXhP/O1rL36Pbup/EyNf2btMfNs+0qhAwP0fcpDBnzzB3+P/melrtaOXFcG9Y9hy6MC3TyJNSmiBlBmHjwPAdUGxt+bJ2XV2lRr6zxAv08KmkcYBsRcB3308xC2+1+7lp/3WNYsARm4IUan8iM1I5G/AO/h4WHxqa2trRUAQTtstVozJuSIuxSFGxsbMR6Po9PpxPfff19MZGgT9AFwhGFNp7f+Y6Kf0VzxvxHgBTMlg5YZRNaQGA+YMQzZmYx4jwCstbW1OD8/L7l00eQ8nwBbjUlYK2AOMDGj+bo97969K31DaMgBVAYO+m/TP+8yPvSd7zbNnTXAmth0Oi2aLCWbeqENMzEn5mA88loyOJqeaRfCi7/pzF5eLwg9fI9x81rkWZKoIKhBOxyTSCYpn/1M+tLhcBiLi4uxt7dXhL/RaBQvX76M7e3tUjf1ArgIQtAwmup4PJ4RCBB6d3d3ixbtaGUC/lgrl5eXM2CSg6c89m7DfcpGrWT69bxmhaHG0xCyzG9q/NHzmhWQrLzUwG/eGPC3XVAZrGvXqC/7gf/OmpdzuQ/Q5j1fm9x5E1abuHlSWWagNWmv1ub8d60OS1NZusv33N5aqLuft+nIgOp9qzA7FqH/hvFhQjs5OSn1R9ylX3z37l2Mx+PCANkahOaLP4pn0Bj5Bok4er1ebG1tFeaLL81pBU9PT2NzczP6/X7RNJztiKT7T548KQLDeDwuxw1G3GmKPhTB0i9AzIK0/w4NDsZoM3Sv1yuaPcwMDSb7AE0Dme68xQqTNG1+//59yWJkpmFBCqbcarVmjm0EWOwXYzycuSprF3wH+jKwAaZo1T5z2c9TT45kRqjxFh3es2aPb9wasQU6M8ccpFgzoWYLQtPcmtmZy9FoFKPRqJwiNJ1OC/2ggeKjZbzX19fj1atX8eLFi+j3+8Uiw15qgBpzMVq3k5t47InWZ786mcKgKWjbZ0bbYlPz4frv7Ous8TCXDK6ZT+Z5yHRtnmWz/n3AawA3f6xZQXO//Lz753ftY871ZAHALpvsiqmNVy6PCnRz42tMqfbOPLCLuPPRGDjzu7mOLNXkuucN9DwirUmClJqJI+JOY/U7NuvVCDEXEz/ENs+8nIUBL4YM/OyZtQaGLw8fK997+fJl8YWaSaDdOoewEzOsr6+XyNDBYBC7u7uxs7MTOzs7xexG2/r9fiwsLBTGyLc4xB3N5OzsLHZ2dmJvby92dnZKlGeej6yN0Rf8rTwH00S7JvEHbUcAAPDY9pSZU17AmIItFJlRkVUKDcUJPLjGdqtWqxW9Xq+kvSRjEeZLQJJv4RcFBAB5gJJvZKuMg5cAW9OfrS32v1qYQ7N3UBRz7aQPFmQAX8aTceSeNV3mk/Y4MhyGG3EbTd9ut2Ntba0cAsHaOzk5ien0NmnF5uZmsfZgUp5Op7G1tRW/+c1v4ssvv4zz8/P49ttvY3d3d8ZqgSAEgCIwTqe3gYjQ1uXlZZycnMTbt2/LnLJGGAsEYZJg4Jv/FI21psh8DDzyuzU+moHL4OffGcDyt/M77l8G2cxLEdTyfuSaQkM7zJeNE2i3VnLu4/218qhANzc+TyAla5/zBihLW/f5QueBbO07ER/6PwxsJkAzVt9z/TVizeORx+Ah45cJIUuy8zQsmKMDZqzxEljV7XYLaGL2A1h5D2bM1h++m02hk8ntEXAwdzL3ABqdTqckmCcydDKZlJSHHGkHs+XkFVJT9vv9GI1GsbW1FYeHh/HNN9/EwcFB7O3tlUQeETGj8djn5QXuBAP44zgkHA3efky/Y4bPdbRBfH5ug4NpECqsQRLIZoYyHo9nhDZHha+trZWgL95hbJlLwA2fOnTvPMSu31HCaH30PdMQdIRvnbEi4QP1GaRzNDHti5jVwP1d1qM1GQcsQXO0kb9xmwwGg0LfEXc5ozkdC1Bmzyzm3uFwGC9evIjXr1+XHONOc0kb6Q+mY8ccMAfj8Tjevn1b9p/bh4v7BB8vOZctgGVeMA9wa/yDttZ4zrxrmbf5Wc+Rn80aMvMyT8ucp4l6zjNPY63WtOtcd8aCGgZk327m7x8rjwp0I2JmoDIw1rYM8c59gFrbQ5UnvAaK/O92cS2DsN+tEW++nhlH7n/uX/6dBYz87Lz38ztZyDGRejwt2CBhHx8fz5ihOcGHRO1ogO/evYuXL1/Gs2fPyl5YfLOkksSkyfdYIGYy+E53dnaKXwvmAwN0BPR4PI6dnZ0S7Uyi+cFgEMPhMHZ3d+Mv//Iv46effioRqDZbAgCeR88ZANJut4uv2OeXGqDoExoVNOmtLTBwNFT7YMlYhJZLEgTnSEbrJpkHdAVzxoeINgro+8ScLO0jWNB+TKw50MpacN7LC92w/Qn6wQ2Btu3tTQhNFO55fpibpmmKhcN+Z07hYXzRSjHtsgcWH+/Cwm0+ap/wQ0wB40nSi36/H51OJw4ODuKnn36K8XgcT58+jd/85jfxR3/0RzEcDuPs7Cz29/cL/WF2J+BrOp2WvhKRjtB6cnIS+/v7MzmTba0i69vh4WEcHBzEwcHBzHnC5p9ZgbmPP9T4UA2871OCMv+dx68ygHrt53vWiF0v7ZknIFvospadedq8d/0t6LlmObVC8zHgfVSg68m1T4Jrlpbm+V7zb7/HN3zNgz2PeHw9mzB4NpsteM/mCJ6x2ct+tRrY1yRPX8tSWW1B1KTG/DxjzbgsLCzMmKnIpkS7CQpxXyNiJoqULT6MT6vVKhmkMKlZI8lnjJpZWLtbWloqhyDADDGzeZvK5eVlXF5elj29Cwu36SM3Nzfjs88+i8FgEOvr67GxsRHff/997O/vzwgSDsCxNO1j85wQwloNcw7DZ5Gz0BFqHMACjXJCUKfTKecIk9UoIopWQ/+dghGTNmPSNM3MGca0n/ZwhnHE3R5t51tG40IAQQvHj+6AtLwmnI3LdGhhzPMP3WE5YMsL9GCmS5ttNWB8mQdoA1Bmby3jbsEB4Wk0GsVgMCgR4U1z66t1+lL8tN9880389re/jR9++CHW19dLVHO73Y7Ly8vY398v/tnRaFT6SmQx88e4IRyhvfK+aQqt+Pj4OA4ODmZODWKd1cC2JszfV7IW6XnNvCrfy/SceaefzUFUNdCbx6MN3hTzZ69ZX3cbXPxM5vPmk6ZF99X84r7yaEDXg53BI0tGWQqzJDQPFLOmmyWuDHI10DNRM8jZv5vrzsRiLc6acs1PXOtLvmYpLRNczRzj97IJ1AIAfpC8hcD+zRxYhOaE5tJqtcopJzDhi4uL2NzcLPPBe7TDiTfy/MP4e71etFq3EaBEfN7c3BQmbfMh4ODE+03TRK/XK0Eym5ubMRwO45tvvom3b9+WE3dqW0cAJGsq0ARMn+sWDk2ntGVxcbGk9zON2adM3WxFOjs7K9ujCMwxnaGZTiaToj0yZ2h39MUnNmVtljodaJZ9cD5gAZqzNuo1gGaNcIDABuABLFg3XI/pnjaYLs3c/TeJJ4gsB3ABaugUQY1c3r1er4w5e23Rglut233j33zzTfz5n/95vH37NlqtVrGgcADH0dFRCThEEMUtQ4QxVhHGgADB3d3d4kJhHKC3d+/elVOD9vb24vz8fEaIygqJed/HwIBxq/39MSWHuagpGW6T663x7MyT/U7NrJz5f/4WNJX5KO3NCsp99bgdrnceFswrjwZ0M7hlycydrQ0IA1ib6CxBMbCOAqZkQqgxzSyV1SY0tztLUXn7Ts1Ubcl+3jWPne9RnwHBY1frL+2tmXbcl9xfRzQ3zW1O5el0WjSzd+/excHBQQGwy8vLGAwGhZnxHSJyvQ0la4okjffxguPxuASR4NcCvHq9Xnz22Wcl6Tz7SM/Pzwv4IijgL22324XhkXEKU6fNxfTHgTztdnsG6AAR2s/4Nk0zEzxDwccL40KoIBHHeDwufTOIM185It0aq+kOwYTtXwgT9H86vd0CBhiTICIiChDTZzMqBDVbC/JvxoWDKSygMEYAOdYVtHVvhWGcLbA5kxfaKdqn5471wFjj98f8TFYwALjT6URExM7OTrx58yZ+97vfxd7eXmxsbMTf+3t/L/7BP/gH8dVXX8VgMCj7aEk1yhrAL0tCFQsUmIt3d3dLLmXa0DTNzDnJHHoPbdbWcV7j0MPHQCHzG9NuVij8fJ6//J3MU83vMs82j6mBvPuSFamIuFepqfHODLwWVjyOfDfzcz/3dwp0c2maJqKJiGndJ+kBrWmwNeLwpFgC9D3qzc9kQSCD039o79xiLUvO+v77qtZae++zT59zuqfbPZeemz2DjQ3GEIeLIDeiRIAinniIEyk8IPFCIpAiRaBIkfKYlxAiRShRElCkhCQmN8RDCDE85QFibC4Gx2Bkw3jsmb6dy76vtaoqD3VZtdbZp7sH4+nuw/5GZ3bvtWvVqqpVVf/vXrmqOG9T3u687UPOcVj3ts/htYsm4DYGY1tdcWMcgmt+33DB5s8cbqDHx8fJXhez7UQQsdZycnLSG6OYtrEoih4A5Ys8boo50BweHiZnoOVyFSTqGmPup81os9n00khGm6i1tucFenBwiNaK69evp/c/mUw4Pj7uqdBzT15rHVp3KuXc6zqCXnSsSnM5UOxXfF8RpGJf49jHv1i3VyO3PWcbEYivM76nOA8j8OUq7fx7/qd1QVWNKMsieZS7sO5msxlKdclQcuYzSqSx7ZGRjdfzfNfxNJ0oDedzNM6jeKB9HK+oTYke4EMQzu8tyzI5g8VwpaiSj97H4/GYUTVC6a7vUSMTwTY+K45lBOmo0v3iF7/I3bt32d/f58UXX+TDH/4w3/AN38Drr7/O/v5+cqiK/W1bf0ZxzrjFcKTFYpFC6I6Pj5nP52n+xv7HcYkxuNEEEpmPfE/L11ZcvzEf94OEgkjbBJ9t/972mdcxtJ3m+8U2YN62rw8Fm6FgNJQs8/K5ZJvvJ/mendeZ1zGUZOO92wSVXKofjv+D6MkBXZHujwxkwk+hENY5lIoAE4BZ8mr6hvb83yL+flwmySnlNy4RHA7nQFQEHcFYAwhK5SpuDTiUnI9Hy9uQ+hX6Y5z1GyUuPM8/60GMQt7+YbkHlYc+VzacpHmdsV95PUOJOK9r20KLG02uBrPWcnBw0FPtLpdL7ty5k6SZKF3kKr+4ceenAzkXPVFJk9w79QhKaayF9XqVQmIiUMcUkjHMCRxF4Z2HjLE0jQnqPx0kdEtRaMbjKkgSnTOXl3zXNE2LtQYREujG05fi2CmlMNZQ141vux14qQtYHOJA6YKqLNOcMm2LE1+oblqMacAZiqKiLCvK0oK0WAtCiXMKcIi2KFUiYlFqg7UOJQWgcTSgGqzNjrErK3RRURTxVJsCpTRFUWKMBYTxeIIxEcRt9h46CTvOjxwgc8Ytfk92ehzGWEQJYs7ngFZK9UwXEaQ9CAqjUcVoVCGiAAnvs6KqynROc5Tgo8Qbs5757UIQJWldj0Y+3E2LQsrS7xGBoqNTDDOr65pnn32WV199lQ996EO89NJLKTFGbl/1DF8ZQHTNculjeyND4JzrpWy8f/9+T1UcxyRqOE5OTlK5yKDGuRbHOgeN+Jc7sr0Tytf7RbRtj82fkzP7Q6HgYQJHPn/iWOTau+HeNBRohoKOtZExMReqs4eA39173st5+MzhXvwgenJA17PWoeGh8Y60SFz4XXnUQpB0G3TfI130kpVIWKygRGFNrnaQhJFxIFUoGzdNf4/pJPFA+YvJn59zfCp7kVlLUz1bQRvOXRuWu+hlx+vbJsU26fmi36Hz1o19zcc13puryXKVo3MuhZJEO9Zbb71F27a88sorKQVkdP5pmiaFk/iNXTDG0bads1KUErQukoOMtSapmfM+RIk03xSKouT0dMb+/n44aPxKcLa5grWWzaYGFHV9hohiPB4Fid0FG3LLer1KjkzROaxpmnRizHrjnYHWqzWtC1mtwvto6xbjLEoEJRoTmDpDkEZFaE3nhKaVQqREUAH8FCIaEY3SFqShaVvqTYuSAmtVYE7DGhJFWU7QEk/YUZSlpqz8n9ZCUWjqeo0x0Y5oArj5sTemDcBozs2nHFjjmOcq8KgBidKXAKY1mKalUd15xRGkI7DEmF7nXDhso2A8HqF1QVlWwevbv5vpdK+nCYhOYnm+cGst1jmaTRdaE0G2jlLzqALnaI3h3v173L1zl9PTU6qq4uu//ut5//vfz/ve9z4ODw9p25Z79+4l7+For/b9aIOt2iV7sFKSjuS7fft2cpY6PT3taXhivHs87D4mxYiaj5y52yYRxvWT274ftEc8yr4w3N8uuncIoPlecX7/2y4s5GAG5/MZ5PcOpeL8mbEeEbC2izzYZqMeAm/epqHGcBs9KvA+OaA7oOGgDwHhgYDjHEEOTpTLz5FT2WY7SBLxgMPLn5NU2wHEnQOtlH+AytpJtzAiPaqxfRsNJ9jDym2756J78wkrIp7RiSMWfnPheiyfMxYinat/DIGI/Y11xo0nqj7v3r0LeGni5s2bXLlyJSXKiM5RURUXxyxKUt522zCZ7DGZ+FCaq1evcnR0ldPTGUoVlGXBtWvP8Pzzt7h69SgxBYvFkqZZo1VD25iwOdZcubIfVJQjxuM9VqsNVTUKYOElwChhbzYrtO5rBCKgRJXTZDxmUSwpVUG92bCpNxjb0jYbbBjburVo0WlDiLZl67KNTYTWNogolIBtHM4pCj1CFw5RG4yb01iDaSuUm4ATEIeIBQeFLihUBeJQCqqqpKxKyrJAFwpdKBweJKzxkrl/ceBc66VncVhnKQsB0WmeGGtoW0tT1yiBQqu0DNq29fPHevSXsF5AhTVpcQ6sdThnEPHroywrxmOdwqS8pBpP5RlRFJ3DnneGKlGqy5Edxy4Py/J20YZN7fuoCx3OuW1ABK38NVrLZr3m5OQ+t2/fZr1ccXh4yAc+8AG+8Ru/keeffxatChZzHxJ0995dFotF0Ai0WOuStkYpFRz/YFNvODn2x1Levn2H4+OTIBk3ceHixFFvPODevefBfj5bsAhx6TlI5Ot2uKaj+n/b/vmoNASybYC2DYgv+m0o0V4k7W4TMC7Cge7+vqYulh860OKCAEXEB38t7uOC4IKW0zn8nu4cWjowVnRlU2tCWaXC1QcoFp4c0N3SyKHO3hqL0uc5nvTCnOuGIU0U69XA1kvKLox2Lv1FSTqCTM4h+dE9PzmstWHwo3rWpZfadcnhbDaBYj9lYEt50LC4bBKJV6/Gz3wdDcvlYxPb5x8W2RH/qQKnn7dNpemYRHDfb+cL+c9+AH4ck9w+nJ8+FMctSi1KKRaLRZCc/GYfQy4ioxJDc6KqMF88XiVdJM/Qqqo4ODjk+edfQKSzt8T8t4eHh+EM0jP29vapN01SW66WKzbrDavlkun+FK0KnBWm033KosI604sZjR67xjQoBUWhe/2ODIbem3Jlb5okoLpes9qsOZudYZ2lMS3LxZK2qTFGUZbd4eQE1amXzAxOLNAg0tI0BqFCSYNSLU6fIsUMnMZJASi8NmdD225QqsSikUIjylIUJWU1ShtLlGJ9OwymbTFtY9iHRQAAIABJREFUG+a0V117IGmpqhFaK5QoymQO8NqfaPZRStC6oG0NWL/+ylGJkpgGskuf6cI6ie81jm1VVUynUw4PD5PnsYhnFnImLNfARA/obSDUNF4T0BpD23pQ9kCte2Fmq9WK0/v3OQnxr4VWvPLeV3n9tdd47X2vcfXqEXVdc/f2V5I3eZ5VKiawiHOrLEuaesNytUjn296/fz/dJ6L8HqW8BmM9X6dDPo6Pj0NYUefEJjivGk/7ku0ARADnzQJt21334xzHYygpKlwG5qkaXKi7Y7zBgxM4JOxhvn6wOejhEOW1iEoJ1pk+QOGFFWsNKtpEleqAM+zlaUsS6doRawjzrJNYvdnPWYtIdtxkT3jq6o115L85z/2F+7L9Oe6rcShy82dWhYhgjT03xkN6YkBX8HgQMUEcSAC9CAhKCdgoXWYcVHpJ50ON8ledx1EJkmxqCVvy+xOACs52Eq2En3qcz8AxKj47qcGlU09LxLv4XkLbh959HUfnUg+cPe85l6TNLVxij0GI5cnb6Msr0vD1mJyLtACpXdJXN6d3GcC4bbsThqJqL9pwnXNJqj09PU3PunHjBoeHh0liiWrkGNoRnV6qagR00nN8t88//3zyno7e0DGzkHOOK/sH7E0MTVPTBoCr6w31pmY+XzKfL3sxoGVZoYt+qItSEjyz/Xsajepwhum6l1Wr1Jr9yQStCwqtceJT+x2fHbNpapqmZj5bsFyuqeuG9XpD07TUTefVKipoSrQgNDgMNqilndOIEnSh0VVB0xivLmYc3oOlNQZnQIGX7pRCSYFpHU3T0polSglNvcG03k5tbeslAufviSpZrRTsa6qqROm+RFNWFUVwHEq2N+m8qwXvsOXHtjtmr6zikXeCLgqme1Nvm51M2AsajCqEG3nN1PDcUw8ONlsbuTd5tA0bY1it11gRynHl1dMBwKPD1nq95O7du7x9+zbr9Zr9/Smvv++9fPCDH+TWCy8wGU84O/OgOTudUzeNZ1Csr9uY8G60phDPzMzmczabZYgrP2O1XrFcrqjrcCKWbZIUHm23J6enLMIRgesA4oXWSeMU17BEEDknbZLQQCJIZntCnywdiHagJ2ECxPujoDBUY6d9Jq59PACLA79926gbi9uG16FF0DQGrQLzkKRN1/nVZM/3z7Ld3IpmvoQREfBsmL/DkKMgYARmry+0OJRWnYbPdYAfmSLrbKfpG+pSw3uIJsQHAe8TA7oOL4VagvguZArOOOiSHI9Ig9nhYxyQbaqUDky95s3RDT4uAxuva+hPzlgu+7dIBFRJL2eo9onPEkgSdupOAk1w4hkAVPfZe15kQKJEoFXgB+JvAz4wl4JFfPnsmbkK3BIk/GzIInecj138TOCbVCh+wrvhpHMuSar1ZoMNKmUgHYU3Go0QQkL3k5PkaKOU4ujwkEIXyYM3JjKIXqZFUQapy6s5Y0af6XSazvKN+YXzA8G9XdWrRL2ncsVoVFBXVbDRNiyXG5q2YVRVFGWBNjqB9mhUJu9Irce9HMir1ZrFfIGxNm2IIiHX8GSMc5bxZMLB1UMv6TYNy9WK5WLNYrHkbDYPwD/3kmfbIpHLUwVQI9oyrUZoNaJQVyirAl1W6HKPQjSuPUC5AxwWpTdYu6LZOEyraGuHwbJerzDOBmc3k9Tupm2CFOuZVWtdAjOtNWrkwVNJgVYanTlSjbITheJnZD6U8lKxZz66UCBvly3QWiXb7Xg8pqwqyqI7h9c6r9YW8TbROK+60CQXPI47x6EItLma1TpHURVMRlVixOq6ZnZ24h2l3r5N0zZYB89ce4bXX3sfX/d1X8cLLzwPCHfu3Ob09CR4vlsI+1VrDK1pk3YlSuKnp6fM5jNOTnzGqPVmFdqV+TuIsAnmkpOTk5TwInrNR2bDa+L8erY56MU1SSbh50xytqZhOxTkMnDaE9N+mFG2DySGfsCQO+elXL81xM3Zpbrz1jiyvSs9l64vcV/J9yHJ7x0kzIhjFJ+bNH6S3lW+t7qs3ti25EQXQT5spk5IQob/uY8xXhB2nTy1BYMiPTGgm7/QnBtJ+gtInF7/PvovLafIjGRjP/zZf27hWnpNOx8XbCNgDtTEiQsMTEIEox5nmFiJ7rlDiXnYx04jLGkcEvcVVETdQszHxGXX/H9Roo99i8/PxyjZZiKCZ+qepA6M3HQ3Q319NnDDjrDZWZaLJc46bGtoDg4Q57UF3qlGWLdrr45CqNcbNjdvcnh42IWiuBYXbY0W1ERjtUkvsW0aFEKhNE4UaC+lCZ4jXq/WtNlGbUwb7Jnaqz2VUBaF53CtwVqwtsG0DmtabNugtAqqWItzLUXhwaQsKxiNmO5NOTo4Sk5DTd3igLIoKMrgVOQ8m6OLAlFwaC1t62jqhtl8wXy+CEcUNtSbNdYF72kE62pU0TIZV2g1Qcs+o/GEatSiyzWlHoHZx9kRSllUscKYNW3tWM42rNcNs9WS2XxOW29o23jaUJSKFKDAKW+/F9BaUtjNdLrH3t6Y0ahkNPFewUr7MaiCh3BVVpkdNUjiapCvOUgRWil0oXuHJcRYWy/B+ndrjKFp/QKITlx5/LNJDF2XVjGG1OTMcFVVTPcnTKf7FFqzWC748pe/wltvvcXx8X2apuU977nB88+/wMsvv8KLL/pTgk6O7zObz1OomYigpfO0N8afj9wa45nIkILy9u3bvP3225yentC0Dc5az5CJ1+Bt6pp1SMBxcurtu7OzWQpZ8mOnsPa8hNpXBfevbaMkGFxY4sH3P2rZtOWmvYEgOb9zGprzhte2PTsUyq7lasXUosF97oHfe/4tD27wI/XzyQHdQENbLWQAMOB6thnpz01OZNs4D9QxfQ4uX6xRLTp8yUPQCg87xwHloHbxRPX/xQ05qnrDj7Hq9CVOI+uc594COHaMZRwLOocAcum3A/yEpx1uIsp7+inlbeFRpRPte37XlJ4aL3d6yx2L4oKJiQzyGFJjTEp1GKXVpmmYh7zKN27c4Mb161w5OEAQ2iD92eDJXKz8xuezKo1CSE1nvy6KKj2nO8nFq5ObJtTXtBhrMa0PDXPWUY0KyqqLp/WqZUO7aUC8p64zLVQlrrA44+1JZVl56ThIxa3x4BpBxIcPOZrWpz9sjQ8D0hr2plc4OHqGuq65VW9om4am2dA03lO6tS1t0+BoveArFVV5QFlUjCaWsjKM9SHOjhBV4VgheoUoQ7Numc9WLBdLThZz9mcT5os5i4WX7NvW4qyfq1pplGhvHwsSafQun04njMYVo1HJ3v6YyWR87vjB3KkuqvyjxOqlZwvWZXOnk4g9Wep63fMPcEgIYcrNON4m6+dphXNdHHIuYcfEHx7MNWWlMG3Nl974Cm+88Qb37t1LceXvffWVdCSf96ZXnJ2c+Hjw1Zr1Zh3sq5LmeVmWqML3tV2tmAWJNeZEPj09DUkuQuyntRjThCP8lsxmp8zn3ZGVbdtiCV60AhabnO7ielVKJ7T1dk2XGGTJN44giKgQHpWblbIdKtszcynlYfARyvQk1CDl+skUSg3B6uGwlPH4QevopVqJwkQCQckEjKw/WT/inuX3RrelPQ+n7ffkqsv4Tz/m9iFPebJANwKgeCN7jKXzLzCABEOO5ryE9uBnxKqkd3sCoux2G5wScim6h4XbHpVfcx2w4bKXl72vKJFuBfFBfS5rRCwXQ5okfXYzVgYTcqgRkKBFiPa5eL9EFVHPLtQxKCZw9LFpCi/1JkzPuHAV1GPGOWzbslmtmInQ1jVt03B0dIQSYRRUcs1mg2ka2qZhvVzSbDbcqGuq4MW87Ug7D3KSHVHXhQZEj+oYQmLbEphiTBs2u5n3Km4NShcI0XaoiZPFufCJl7SLQqMKr4bF+uvWQYvfkLuwNEF0gcOXLUv/jvb29rHYxGS01iGqQIlwZTxmPBohzmFMjWlrjKmp2zXWSAIXoaTQIUSmatCFoTCHKBmDOIwsQZYgBjlQHB74jf5wecbpbMbZ2Yz1uqapvWOWNV4VrpVCq3imrfSyVI2DKn48rhhPRuyFEJ0O1IrkPxDVzrlPgQDG+LAocX4texD1szNXDUcAckFFH/1NfUxt9BkwaK2w1vROZhqPxxwcHCT7fydlW9brOXdu3+aLX/gCd+7cYTSqeOXll3np5Zd4IWQuGwWP9dl8wbreIKKSGt6bO5rkWR9j4M/OztLBAzHmNkrb/iCLLpxotVoxny+YzeYslzNWq2Xy9heRlIgkt6EmBjms5Ch9uQgyA6z0S7cr3Sk/+4o8pfz9fs1eBMznKbPwDQSl6N8xCP9M5bbnGMgpj5PuS5mZJi/rXyeGxDZlQli25z4ICM8JJg/7PTx/qGV4mDYBnhTQFTqbp8pluggMnTSWD3O82fU+e9VeyGelseuBWpYRpVcqK/NIHeoaL2E+nDPeRzR86OR+SHjUlvIPLuDbIAEYoiooMmoEiVupwGxEVbF0jmceg4KjVbSXhUQjIuIBO6jSbFidLvwZ51ISAJc9++DgIIFmtMfGNIsnJyccHfmDCW7cuMmoLNG6oGlamrpmXa8wTUs5GlNqbyPUKks/CBRKgxKKUYlzhsViRlFob7NtBK3LIDlorDM0tY+ptc5h2hYRxajQQY1aAUK98SpaJVGq9/1ZrRa0raExDjUaIUpjnI8NHlUVBaBLjRuVoBWqBaUrtPb9sgijqqDCgm0wtqYxCwoUjRmjdYG2ZygsrnIYDaWeUDYVpT2gVTPOWkWrK/YE9tw+TbVkdUWoNpbxXsn+/hhjDc4Kto1hNgotY0RpCl14hix4HBdlgVZCUergQezPUHY4r5YPi1ic/1QorHXUbRMYzjowKA5nDNa0JIeVIMm0prMv14EpU+IdrKyoJMVpKcNUdbTGe9ga0wY7f4zlHQd7sQdbay2rlc8WZazh5s2b3Lp1i/fcfE86/EKJomnq5LxnrE0e0U3jY2NHo4rpdA8JIVN5lqhok42nG/nwIYNzlrrehKxSc87OZsxm3nvZmCasnxiPrbNNLqxL8etoWxxpR9IDjKFPht/bNHloTd8pqi/tDp0n830oPvsizePw+ReVydsx3Fmjpi6va9sz+u3rmIFtbY4aku3l/b+d21aveCm5k/aIG2ncP228Gsuf62VHTwbo0g1s5FbzwXoYkJyzSwbadlcEviGHMuS8hs/v22QfipXdSxsU7L23R6B3Ymd5tArJwM6eH6Qw6exg3BNnGSc0oZ6srAqztqem725OKmjjHGazoTWGOjg3GWOSc5UugrevcynZxtnZMfPFgqY1tI3h6OgqEmJaVSMsVyuOT898OsDRCK10F8NZlohW2NbQWkPb1mw2NXVI/t+dm+oZkU29Ybla4qzFtm0IqXIUWlFWFZPJHloVNNWIul5Tb9bdcXa6OxnLGsvqbA5KUY73eObae9ifTilLb881ztuHbWu8BGgcpjFsTEuzslQaSi04MWgFhWhET1DKMmLEqLTU1T3m7ZyieBEta6S9R8E+hRoznm4ozRnK3gF7H62ucGVcAY7xpKCsCpragS2pysJv/LJH3FRUAF//9uK6hLLs1Mi5CSYCLdbQBFU6RHODBeft4xBCT2zME+y8dsMY720d6iuyNIzgtTmmaWmdCU5enUOSUlDibfqFLil16T28rUXroDbf22N/OuG5Z59jNBpx/cZ1Dg+8U9vszCegWC6W6YhED9CdHbYsq2S7tsZxcnzCyf1jbt+5zf37xyyXC5bLVUqCYkzLarVktVqyXPlY2/VqxdnpnE1dY1qLKHxmrsCgu2Rf7xDByyIKhw3RExmguBBjmtZ0kFzjZ9DG+GUYvXv7THwMyInvNw+FjPpFF7V1Ls8IuHV7SQxR/NaFHvq64h6TNMQi2c19KbfTNkUnqa2PzTR9+bUYvRLF8vMaRRGyscqfmwsinuK4xGbmOBJdrPy+t72NkZ4M0A2NTJt0DnBb1BDnQDCbIPFzCFXx2lbpd8BJXZSUI9aT82U9wTV/bpRst/X3CaGLmZnIZfb1ApEThLDQLRCcQlK+kPS/Toof2s87O2mbPDXrpuEwJIg/ODgIkko4oq/e0NiGddMwX6y4d/+EF164lQ63L4oCVXib2mazZrNZM65GrOu1PyfWhoMVnPUpFp3FKUGXFZOiS54APszDAhMIGbIaH8ZT19RNy/r4BH02YzLe86CgBFEa8Ju0axqUKijKkqKccPPgiL0rVxhN9zm6es1vXCEErTENq3rNyjS4xmDqFiM189kG19aIM0wqz4CUSmjdkqIqUNpSjk5g9DlaOUPJdVpZYt1NVDPBmBWqmiLOYOULuOpLmNaxaQ/QHLG3P2GzdkDL3nRCIV5l27aWQlU412JaS1EIo5FXHxPeWRE8553rYtOjWrjebDCZw4sHw2DXN9A0Nab2DmkqLETvWQxNSBARN7qq8FmrksTjfIYsa6KTm8vqD0f8ae01NM6FzGceaEZjnyLy6tVr3LjxDEdHR95MIcJyseTNL3+Zt95+Kx0q0DYN1kYGAHRRUJU+deNqsWRmDPPZnHv37qdTg5q6TiFZrXPUKV/yWbLZbup1mkueGejantZJ2s0zXZuoEH7FOdDolQugkjs2aqWTOai/ll0HfqFsF+7S3xvOCTO2L4h0kqoEJqHvX5McLzPQi03p192Vs7bzR/GAalObk0YyrCVH3p7BDt+Tjj1T/VDhLDI1qhtj7xBL/zOTWXLV+cNQ98kAXQbAmUmXQ2epYfmhBJp+G0yeKJ1BHwiHdeaqkyHgWjd0fT//wuzwvkE/Il0kYW+jbfdedN8257IL68zUIPl9drj4B3VFNU1iYIPTR+xvzmwk72dIUn8My1IhtGC1WWOP77OuN1y5cgWL4wDHBEcpPimGbVua+YLVuma2mLNcr5lOpxwdHnJ4dMRkMmGyN+48h60HDrd2NNZLoV7V7FVFccP2eYeDNCUKkRaHQoeQFV34RTqfzTk+PuFsdupzO6uC6XSPg/09ysKHzzgIcbYNZaWY7k+59crLPPPcc2ysY7WpWS1Wfk44sMbQWAFToigQ55NMtLWwXtaUCpQRGDt0YVDO0TRLjBWeuX6bV17TLOtXQb9Cs1Yo59icrHnjjd+nmBziuMozN0peuDVhtrxCbV5FuSmKCUUx9qkI3zpmtTCURYmIom0Mpt3gwqEOWgtlGc/b7ZzqfNydo0mJNMKRj8GGbq2FIJ0ZY9isN9TrFW0b1KlZmshk99Xdmc7JbyD8VjdtcJATQCGqCBm1vKq7CIxT2tidV2M7HG3dUGnN4f4Vrl+9xt50GhJc3OGtt97izTff5OzsLKUhRXwGr1KXGGeDytfn8T49OeXszNvE54s51tgubWXbUm9qlgsv3c5mPq/yarVkuVx4db7rMuHF/hH6myIQolQX1mUnOYZrwZTD4HoPiKPm0JhefVtBYaCRyvdJt6WcdV1egFRnkppDNbHOUEdiuPPqeo2IErB/xxHvUiwyhJwK0uP+cyEnr7Xf9mwfs12McNpXg8gaVc6ZOjB7joQ+Be0C/dCtpFZ2MuzYOXqiQLdnjN4CVEMacn0xkjV/sT0bbTYR8jrODdyQm4xlh9cGXGnvnhyYt0jLZOW2weKDADNv40X3XDSGiScNCyX9GscgK9cbhwFnjZCyWfnyUUuRq2cG45e1Oc8Zq4IqOdpxY2xuPAc1puuL6mZ/aMJt7t8vuH98zNWrV7l27RrTvT3GkwmjeLweXuLYhMPSRamU+aYsNSLem7Rpvd1N6yLFWYK32xaFl54KXVIUFaPRhJOTEzbrNc461psaR0Wh/UHpuqzY1A1Xr17j5q0XOXrPdSgLZmdnLDc1m2aDbTygmNbQ1AaMd1pqGkfTgDWCtZrleoNrHYoxxWgEssKyBjfnbPlZamP51Ce/xM/+zMdpNpa/8Jde5mN/64M8W36FL/xxgbMvcHi94d79L/Mf/t0bfOr37mObDbatfKxtIXzbn/9O3v/ah3AVtMYGW7gGMeGdbIIk2/fkt0FTaZOU4yUSrTWm3fhTn1oTDodoMcF73TkXtA0+M1ShdVoDw+UuYV5FsNVaoVSBiKIovdmgqspwQMWYMswjYyzWOEyw5UYg9mfRnnH/vre93r19h9lsFg7DUEG6Krw6WhdJmlnXazZ17bNE3T8NQLpKZxPHoxfj3JwF73t/CtYymU98Ws7+WszzsqPPn/kd11xP+5allc3BJq3/IPFFAPFJKlwow2DPkfTXqX4DqIqEsEAvYdrwjJSwIicXVcD+uT2p0/az+yWBgOEeN5SCQ5sCf5+EgS17ytad0oG4kGBJsuMD3WBvStjb7XES78/KZbwMEDL3ZeMZy8bzAS6iJwZ0i6JImYQid/uo9Mh2z+GokUmE5ys9D/rOe/XajCPNJWoZ3A/9sd8yVS9WP29p6yPRI9znhmV6HF8/XOqdPfq82j/aoLaZA4AQSO9647kMyRssDtHKH82WnatbVKXPUdvWmHBs2mrl8+Neu3aNw8PDLATF9886h20aaucQ5UCNvIo8eJSuN2uUKKbTfa5cOaQqfaJ/3yTN3t4+ZTlib7rP4dEhi/mSpvb5lJu2wVhFVSmfVakcceull3ju1os0CLPFjM3Sxylr8ZKKj1luvd1YQvuUxYjBFY6GhtZu0K6kcROqtkB0S21rWnfCM+6Ismg5PnmDZj3iw9/8IuNqzP27a0aj62zWM8YTR1kV3H2zwmyu8YEPHLA/epblzGdw2puOufX8eynLKaNRxVgJba3A6eAA5ELMa1TzhY00qCyGZ+XioN74zFzxZKf1ykv2Pq3jHlorGhGc8++zCAkyvBQSxiWAswsMXFkFyVBpb5tVCl3GFI46nJDUZSVTyuIKaFuFtm1wzoPjkxPOZjNM23rgXCzTwQwmnJ6EiukhQ8KTpT+l6vTsjNlszny+6J2AZEJMbp6IZbFYsFguiCdl9bRp53aAuN4gMjZ+KXYA4AUGHrBZbFuMkWEmU78OMUt69eY2VA9Otg86AXzjt+7/PaVYV+fQoSV7bLo5olv+s4T5lhrmto5Dvm31vp970Ha6OLCnF3j1wC7EevJxOjg8eOCj5Z1urF8L+uhHP+r+88c/zh/98R8lzgb6YLpNHkyD5kgbd8/FPLyg3kROE4JOKnMZlxjfbALNTtnSc1DoZuoj07Yg60cOvP5a0rbHDzmE/Hu+sB5AMqh36BHYFczGOoK0Ep+kvyhQ0Rs5JBNPccE4cF2cp4/XrZJ9dmifStw+LnjnEiQvg7E+P6z31C29RBy6GB1dPNMe0iIaG9SqPilDZBpUyC3sz3IdY51jYwyNNTj8ZmJbL+naOCa+BsCHy9i2xZoWZb2NsNBBFUmDYYV1isP9mnL0FU7mS46PNVevPouSM6ajI6S9zrw9pcVyZWLQvMm944Zi7zoj/RJNU6AQlHbgFM4KOkRImSYeRODHzAaRNgo8XuIKr01lWpXwHmPmsfgXGVQd4qn9++/n+lXx4AhzPvduVJ8I4X0M1LLxud5cQNrok3bQmuRV79+T/9Ea45+XNmxH1AzqBIDhfZjulKQmHuAQ74nvLPTVx3y3KXVmmuLkeBWCfcRrB6Jq86K+x3viOkl1ucyUI9CFFmTlPHol4OoQalAuDlp3Y48xPycw9OrKfsnRd4CKCciixNur03bPBT8vs3t8NdJpLHMtHaHxjl6bh207d8+FAsrDNvXYxkHd4XtRFPzI3/u7fPrTn95a0RMBuiIyAz73uNvxNabrwN3H3YivMe36eDlo18fLQbs+Pj562Tl3Y9sPT4p6+XPOuY8+7kZ8LUlEPrnr49NPuz5eDtr18XLQ09hH9fAiO9rRjna0ox3t6E+DdqC7ox3taEc72tG7RE8K6P6rx92Ad4F2fbwctOvj5aBdHy8HPXV9fCIcqXa0ox3taEc7+rNAT4qku6Md7WhHO9rRpacd6O5oRzva0Y529C7RYwddEfkeEfmciHxeRH78cbfnT0oi8m9F5LaIfCa7dk1EfllE/iB8Xg3XRUT+eejzb4vItzy+lj86iciLIvKrIvJ7IvK7IvKj4fql6KeIjEXk10Xkt0L//nG4/qqI/Frox38SkSpcH4Xvnw+/v/I42/9OSES0iHxaRH4xfL9UfRSRL4rI74jIb4rIJ8O1SzFPI4nIkYj8vIj8PxH5rIh8x2Xqo4i8P7y/+HcmIj/2tPfxsYKuiGjgXwDfC3wQ+JiIfPBxtumroJ8Fvmdw7ceBTzjnXgc+Eb6D7+/r4e+HgZ9+l9r41VIL/H3n3AeBbwd+JLyvy9LPDfDdzrlvAj4CfI+IfDvwT4CfdM69BhwDPxTK/xBwHK7/ZCj3tNCPAp/Nvl/GPv4V59xHsjjOyzJPI/0U8D+dcx8Avgn/Pi9NH51znwvv7yPAnwOWwH/jae9jPFnncfwB3wH8Uvb9J4CfeJxt+ir78wrwmez754Dnwr+fwycBAfiXwMe2lXua/oD/Afy1y9hPYA/4FPBt+Iw3Rbie5izwS8B3hH8XoZw87rY/Qt9u4Ter7wZ+EZ/37rL18YvA9cG1SzNPgUPgC8N3cZn6OOjXXwf+z2Xo4+NWL78AvJF9/1K4dlnopnPuK+HfbwE3w7+f+n4HNeM3A7/GJepnULv+JnAb+GXgD4ET51wbiuR9SP0Lv58Cz7y7Lf4T0T8D/gE+/TP4Nl+2Pjrgf4nIb4jID4drl2aeAq8Cd4CfCWaCfy0iUy5XH3P6m8DPhX8/1X183KD7Z4acZ70uRXyWiOwD/wX4MefcWf7b095P55xxXp11C/hW4AOPuUl/qiQifwO47Zz7jcfdlq8xfZdz7lvwKscfEZG/mP/4tM9TvNbhW4Cfds59M7CgU7MCl6KPAAT/gu8HPj787Wns4+MG3TeBF7Pvt8K1y0Jvi8hzAOHzdrj+1PZbREo84P5759x/DZcvXT+dcyfAr+JVrUciEvOU531I/Qu/HwL33uWmvlP6TuD7ReTrGewxAAAB0ElEQVSLwH/Eq5h/isvVR5xzb4bP23g74Ldyuebpl4AvOed+LXz/eTwIX6Y+Rvpe4FPOubfD96e6j48bdP8v8HrwnKzwKoRfeMxt+tOkXwB+MPz7B/E20Hj97wRvu28HTjN1yRNLIiLAvwE+65z7p9lPl6KfInJDRI7Cvyd4e/Vn8eD7A6HYsH+x3z8A/ErgvJ9Ycs79hHPulnPuFfx6+xXn3N/mEvVRRKYiciX+G28P/AyXZJ4COOfeAt4QkfeHS38V+D0uUR8z+hidahme9j4+bqMy8H3A7+NtZ//wcbfnq+jHzwFfARo8F/pDeNvXJ4A/AP43cC2UFbzX9h8CvwN89HG3/xH7+F14Vc5vA78Z/r7vsvQT+DDw6dC/zwD/KFx/L/DrwOfxKq5RuD4O3z8ffn/v4+7DO+zvXwZ+8bL1MfTlt8Lf78Z95bLM06yfHwE+GebrfweuXsI+TvGalcPs2lPdx10ayB3taEc72tGO3iV63OrlHe1oRzva0Y7+zNAOdHe0ox3taEc7epdoB7o72tGOdrSjHb1LtAPdHe1oRzva0Y7eJdqB7o52tKMd7WhH7xLtQHdHO9rRjna0o3eJdqC7ox3taEc72tG7RP8fgDgsImElFjMAAAAASUVORK5CYII=\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "img_teste = load_img('/content/drive/MyDrive/schwannoma.jpg', target_size = (384, 384))\n", + "img_plot = PIL.Image.open('/content/drive/MyDrive/schwannoma.jpg')\n", + "\n", + "plt.figure(figsize=(8,8))\n", + "plt.imshow(img_plot)\n", + "plt.show()\n", + "\n", + "img_teste = image.img_to_array(img_teste)\n", + "img_teste = img_teste / 255\n", + "img_teste = np.expand_dims(img_teste, axis = 0)\n", + "\n", + "resultado_teste = model.predict(img_teste)\n", + "resultado_final = resultado_teste\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "imXLlCtLGAtl", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "90c4c393-0af8-48cc-bd26-ca3c9640808c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[1.8170601e-07 6.9799069e-05 4.5905031e-06 2.9673144e-09 7.8958334e-07\n", + " 4.8580534e-10 6.5576124e-08 2.0670046e-03 2.5904728e-06 2.6275066e-07\n", + " 1.0107833e-05 3.2708392e-09 4.9436137e-12 3.0666649e-07 1.0507600e-10\n", + " 8.6792012e-10 3.2188844e-08 2.8786542e-09 1.8884080e-05 2.0792250e-01\n", + " 4.8662610e-06 9.8221199e-07 6.3104468e-05 3.0804163e-08 3.1495922e-06\n", + " 2.8624127e-05 1.9884273e-07 2.4319982e-07 1.8917688e-04 1.8116678e-08\n", + " 2.0054727e-10 4.5870016e-10 1.6898479e-09 5.9449086e-03 4.2031112e-01\n", + " 2.3345638e-04 3.7321527e-04 3.6247358e-01 2.6458994e-04 5.2944989e-07\n", + " 9.8568635e-06 1.0099815e-07 1.4384935e-07 9.1477943e-07]]\n" + ] + } + ], + "source": [ + "print(resultado_final)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2MnkznmfYn_G", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c62df7ed-5262-457e-bc4b-57f5a1e25ac8" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Com base na diferença de densidade dos tecidos mapeados,\n", + "a amostra possui características compatíveis com:\n" + ] + } + ], + "source": [ + "print(f'Com base na diferença de densidade dos tecidos mapeados,')\n", + "print(f'a amostra possui características compatíveis com:')\n", + "if resultado_final[0,0] > 0.75: print(f'Imagem: Axial T1 \\nAstrocitoma \\nProbabilidade: {round(resultado_final[0,0]*100, 2) - 2}%')\n", + "if resultado_final[0,1] > 0.75: print(f'Imagem: Axial T1 com contraste \\nAstrocitoma \\nProbabilidade: {round(resultado_final[0,1]*100, 2) - 2}%')\n", + "if resultado_final[0,2] > 0.75: print(f'Imagem: Axial T2 \\nAstrocitoma \\nProbabilidade: {round(resultado_final[0,2]*100, 2) - 2}%')\n", + "if resultado_final[0,3] > 0.75: print(f'Imagem: Axial T1 \\nCarcinoma \\nProbabilidade: {round(resultado_final[0,3]*100, 2) - 2}%')\n", + "if resultado_final[0,4] > 0.75: print(f'Imagem: Axial T1 com contraste \\nCarcinoma \\nProbabilidade: {round(resultado_final[0,4]*100, 2) - 2}%')\n", + "if resultado_final[0,5] > 0.75: print(f'Imagem: Axial T2 \\nCarcinoma \\nProbabilidade: {round(resultado_final[0,5]*100, 2) - 2}%')\n", + "if resultado_final[0,6] > 0.75: print(f'Imagem: Axial T1 \\nEpendimoma \\nProbabilidade: {round(resultado_final[0,6]*100, 2) - 2}%')\n", + "if resultado_final[0,7] > 0.75: print(f'Imagem: Axial T1 com contraste \\nEpendimoma \\nProbabilidade: {round(resultado_final[0,7]*100, 2) - 2}%')\n", + "if resultado_final[0,8] > 0.75: print(f'Imagem: Axial T2 \\nEpendimoma \\nProbabilidade: {round(resultado_final[0,8]*100, 2) - 2}%')\n", + "if resultado_final[0,9] > 0.75: print(f'Imagem: Axial T1 \\nGanglioglioma \\nProbabilidade: {round(resultado_final[0,9]*100, 2) - 2}%')\n", + "if resultado_final[0,10] > 0.75: print(f'Imagem: Axial T1 com contraste \\nGanglioglioma \\nProbabilidade: {round(resultado_final[0,10]*100, 2) - 2}%')\n", + "if resultado_final[0,11] > 0.75: print(f'Imagem: Axial T2 \\nGanglioglioma \\nProbabilidade: {round(resultado_final[0,11]*100, 2) - 2}%')\n", + "if resultado_final[0,12] > 0.75: print(f'Imagem: Axial T1 \\nGerminoma \\nProbabilidade: {round(resultado_final[0,12]*100, 2) - 2}%')\n", + "if resultado_final[0,13] > 0.75: print(f'Imagem: Axial T1 com contraste \\nGerminoma \\nProbabilidade: {round(resultado_final[0,13]*100, 2) - 2}%')\n", + "if resultado_final[0,14] > 0.75: print(f'Imagem: Axial T2 \\nGerminoma \\nProbabilidade: {round(resultado_final[0,14]*100, 2) - 2}%')\n", + "if resultado_final[0,15] > 0.75: print(f'Imagem: Axial T1 \\nGlioblastoma \\nProbabilidade: {round(resultado_final[0,15]*100, 2) - 2}%')\n", + "if resultado_final[0,16] > 0.75: print(f'Imagem: Axial T1 com contraste \\nGlioblastoma \\nProbabilidade: {round(resultado_final[0,16]*100, 2) - 2}%')\n", + "if resultado_final[0,17] > 0.75: print(f'Imagem: Axial T2 \\nGlioblastoma \\nProbabilidade: {round(resultado_final[0,17]*100, 2) - 2}%')\n", + "if resultado_final[0,18] > 0.75: print(f'Imagem: Axial T1 \\nGranuloma \\nProbabilidade: {round(resultado_final[0,18]*100, 2) - 2}%')\n", + "if resultado_final[0,19] > 0.75: print(f'Imagem: Axial T1 com contraste \\nGranuloma \\nProbabilidade: {round(resultado_final[0,19]*100, 2) - 2}%')\n", + "if resultado_final[0,20] > 0.75: print(f'Imagem: Axial T2 \\nGranuloma \\nProbabilidade: {round(resultado_final[0,20]*100, 2) - 2}%')\n", + "if resultado_final[0,21] > 0.75: print(f'Imagem: Axial T1 \\nMeduloblastoma \\nProbabilidade: {round(resultado_final[0,21]*100, 2) - 2}%')\n", + "if resultado_final[0,22] > 0.75: print(f'Imagem: Axial T1 com contraste \\nMeduloblastoma \\nProbabilidade: {round(resultado_final[0,22]*100, 2) - 2}%')\n", + "if resultado_final[0,23] > 0.75: print(f'Imagem: Axial T2 \\nMeduloblastoma \\nProbabilidade: {round(resultado_final[0,23]*100, 2) - 2}%')\n", + "if resultado_final[0,24] > 0.75: print(f'Imagem: Axial T1 \\nMeningioma \\nProbabilidade: {round(resultado_final[0,24]*100, 2) - 2}%')\n", + "if resultado_final[0,25] > 0.75: print(f'Imagem: Axial T1 com contraste \\nMeningioma \\nProbabilidade: {round(resultado_final[0,25]*100, 2) - 2}%')\n", + "if resultado_final[0,26] > 0.75: print(f'Imagem: Axial T2 \\nMeningioma \\nProbabilidade: {round(resultado_final[0,26]*100, 2) - 2}%')\n", + "if resultado_final[0,27] > 0.75: print(f'Imagem: Axial T1 \\nNeurocitoma \\nProbabilidade: {round(resultado_final[0,27]*100, 2) - 2}%')\n", + "if resultado_final[0,28] > 0.75: print(f'Imagem: Axial T1 com contraste \\nNeurocitoma \\nProbabilidade: {round(resultado_final[0,328]*100, 2) - 2}%')\n", + "if resultado_final[0,29] > 0.75: print(f'Imagem: Axial T2 \\nNeurocitoma \\nProbabilidade: {round(resultado_final[0,29]*100, 2) - 2}%')\n", + "if resultado_final[0,30] > 0.75: print(f'Imagem: Axial T1 \\nOligodendroglioma \\nProbabilidade: {round(resultado_final[0,30]*100, 2) - 2}%')\n", + "if resultado_final[0,31] > 0.75: print(f'Imagem: Axial T1 com contraste \\nOligodendroglioma \\nProbabilidade: {round(resultado_final[0,31]*100, 2) - 2}%')\n", + "if resultado_final[0,32] > 0.75: print(f'Imagem: Axial T2 \\nOligodendroglioma \\nProbabilidade: {round(resultado_final[0,32]*100, 2) - 2}%')\n", + "if resultado_final[0,33] > 0.75: print(f'Imagem: Axial T1 \\nPapiloma \\nProbabilidade: {round(resultado_final[0,33]*100, 2) - 2}%')\n", + "if resultado_final[0,34] > 0.75: print(f'Imagem: Axial T1 com contraste \\nPapiloma \\nProbabilidade: {round(resultado_final[0,34]*100, 2) - 2}%')\n", + "if resultado_final[0,35] > 0.75: print(f'Imagem: Axial T2 \\nPapiloma \\nProbabilidade: {round(resultado_final[0,35]*100, 2) - 2}%')\n", + "if resultado_final[0,36] > 0.75: print(f'Imagem: Axial T1 \\nSchwannoma \\nProbabilidade: {round(resultado_final[0,36]*100, 2) - 2}%')\n", + "if resultado_final[0,37] > 0.75: print(f'Imagem: Axial T1 com contraste \\nSchwannoma \\nProbabilidade: {round(resultado_final[0,37]*100, 2) - 2}%')\n", + "if resultado_final[0,38] > 0.75: print(f'Imagem: Axial T2 \\nSchwannoma \\nProbabilidade: {round(resultado_final[0,38]*100, 2) - 2}%')\n", + "if resultado_final[0,39] > 0.75: print(f'Imagem: Axial T1 \\nTuberculoma \\nProbabilidade: {round(resultado_final[0,39]*100, 2) - 2}%')\n", + "if resultado_final[0,40] > 0.75: print(f'Imagem: Axial T1 com contraste \\nTuberculoma \\nProbabilidade: {round(resultado_final[0,40]*100, 2) - 2}%')\n", + "if resultado_final[0,41] > 0.75: print(f'Imagem: Axial T2 \\nTuberculoma \\nProbabilidade: {round(resultado_final[0,41]*100, 2) - 2}%')\n", + "if resultado_final[0,42] > 0.75: print(f'Imagem: Axial T1 \\nNormal \\nProbabilidade: {round(resultado_final[0,42]*100, 2) - 2}%')\n", + "if resultado_final[0,43] > 0.75: print(f'Imagem: Axial T2 \\nNormal \\nProbabilidade: {round(resultado_final[0,43]*100, 2) - 2}%')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "J7Pi9wtggW_P", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 486 + }, + "outputId": "a1871a7b-c82d-4322-9859-f2b5f9598c19" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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4RonOgTypa+7vqnfKep5in30N2YbU1FX3WDl167Y6+/rgwFalbGP15dznsyA6kGVf7irWUBkpM+mrfF/HBNPxiLm6VUExyHJM0/spej6AFeW22207OTmxTqdjJycndnJyYq1Wy8wsw2p5CRCDGIOKmYX6zedzW61W9unTJ/v8+XOINz48PLhgix8vFqtgy33GbWIXNDKb2Tjge2BEsPsadUciF+59fHy06XRqx8fH1uv1MsB8dHRk3W7Xnp6eAuBy3BoeAt28AiCMe8CU2Y2t8wT/A2TxubrZ2T0em39adtn3bhcgKeMu/Z49cHnvZ16by3rgylz7VlJ2DA8ebFn2NTm9QdunO/WtX7AqE9RjFnXXRYGY3cQMBAq0efVlkGYGi7KOj4+t0+lYt9u1TqeTyQY2swzQsmt3s9lkWBpYK5KEADTI4kVMVAGaXZ9mr9fmeqCrP9xmdaVzv7KXAM/h/gBY8/9wkQNA8T8yq9GHnMFsZgFI2b3PrnjUHW51MGP8xg9czcpSYx4PTqBKkiRjvChA7/N9e0uv0CGKp5P3qTvy/t/383eVgwbbKgNZ54DHXDp1lV+HqDHgsUe9Nk/q8AwU3Q9AiAEHu0ljoK8sj++Fe7XVamXilRzjBHhobBdKHUofyhvsFLFKJAkxW318fLTFYhFAyuwlaYjjjN68KgJVM3vlMlWWh/u0Td7zYLhodjU8AChnPp/bdDoN7B9jw6xa3cFJkoRYL/cxwBBgDlcz1vXO5/PQvwy6+K1zG/XncUuSJJMIVjQXdV5693hjUfRZVXlLsNqX7LO+X4P9xzwUVVi3JwcLtrsyLM9tWfSsqrINqG3LmMsOet0xi12Ex9B7aRQoWZGrcgUI6D3sFm2329br9QJYc2INMzN2BQNEwNDAuDgZiNkXL5thRqVxVgAF6mv2MhbeEhkGGGapHIPVJTDcP9zf+jc/i39zEpi3Qxa8AlimxKCrRg2PobcTF7us0QfoZwD7dDoNRgvHtLXvAOIwEI6OjkKmN8eSvfmonhX9X/tvGyAs+45/yy7koroX6ajY/VX0dt2ybyPnIMH2a1t2Zdhz7CWtQ8qUXSY+lKco9lV3dfl6QMAMjEFWXY+afczMF+DKYNtqtazT6WQAm12TqANnzYLFgb0iSYiVPmfXMttCWXBlKmiaWQBgfK+Z0eo61c94vWyM+eJz7T98p9fhXgV9LKtBXR8eHszMbDabZYwVgC2PAZLM2u12hjXzbzBS1JPjv7PZzMbjsU2n05CsBQYM1hp7LzA3UD7Giw0fnYceIOYp9tizq4BO0bXbGuLb3rONlAXJfT4rJjGj6VCMmoME2yJ36NcGY4gqsrJStv4ee6lSp12fv62gDjGg1VgquxtxDScCMbjCrQkFz9fhWmaBzEahsAGsi8UiMFZmsNjMgRkq2sWs2+zFXewBWux/77u8cAW7hvEZ/mdDAiDGa2vLzB1+NhsOKBvratnlz1ndDL5s+CDjmzO/OQv6+PjYut2upWlqp6enNhwObTweB9CF0QOjSA0OrjfqlKZpJgEL9fYMT++7OhRzlffrkMAgT4pc7Yeik+uUusflIMEWwgMZiz/x/yqxCVDnBC9bjrqw3nJyvtWz8pgzuxPZJYlMWma7cF12u92gxDn2qpm1XI6ZZbJ94QpG7BUJQGBTi8UiMCjsnKQuXbP4mkw1LKp4GfhediczqweIgq3zD9qK69n1zN9xPfPGTQ0DjgmjLNQ3Zhix0cOZ3/1+37rdbjCUHh8fMztpwS2Nv7Gsqt1u22q1Cp4GPiwBz2dDgNuEz3jJUWwMquiDvGt1HuSBEeuCfXrK9iWxfstrxy7u522lDCGKEbs65aDBdheJuW90gh+aeHU9NMlT2vyjbkuO6zGjNbPgXmy329btdm04HNr5+bl1u92wthJMhdfFcuYvb7zAOx6BqQJMwfw4FusttfHahf9Z2fNnHFfNK4u/Y1Bjtye7YTnDGuwQ7db1usiINrNX8Uvvvchj4kmSBMOGjREIPAYPDw8ZgIMHA96IbrcbABfAis/6/b71ej3r9Xph/Lvdrk2nUxuPxzabzTJgCsDV/uR+4/7ibSbVjZ/XL56UdQVXve+tpArg7FIWf1+l3H0y5ZiH463kmwDbbSZqmUGr6s6tIkXxgzpfvjLtqGNSKXPVPvZclgwazGihOAEKcDX2+307PT210WhkFxcXYa3nYrGw8XicWWYCIHl6egoux/l8HhJumA1pljH+5t2N1PMQa1ceSKmLuag/mS3yPsn4H+52sxcXfKvVCi5kZrgcX9YNJ5Q5sUHEbVAwwvrkZrMZDBTdgAO/9WSeJElCAtR4PA4JbGCu3W7Xer2ejUYjG41GNhgMAhPG3OClRJy5DOBHO7idnEDFngDuHx2r2LjExluv5z5UUe9H7NqqLPEtpUzdYyGLbYlNWQNIrynS+dvqXn2HqpbzTYDtPmSfDLKu8spMnjxLrU72nuf+9sAIE5NBlndnwvX4HuwGsTsoZSjbh4eHTCYxb6qwWq1sOp0GxY54LNisbhGo7mENV2j2sNnrdbHcVhUPvPOEQZn3ZQaDB1B4AIC/YXQog4spcI0ze9cA8AGK6/XaFotFcFOj7oiR6mYdnIhmZjafz20ymYQYL+K4t7e3AXBHo1FIcjs5OQkhhX6/Hwyp2WwWjClvXJjZI6GLvSNmFurk9YsaXTxGReMZe+e8d/lreNfe+pmH6kH8GnLwYFtmsMq8ACxVB7+oDlXAtc7Jl/dS1ylVvAPsXgUz4+QZdp1ybHY4HNpwOLTT09Pgsry7uwv3c9IL3MPMZieTSWbHJnUZKsCiDp5i5WvM/FOBikC3rAcD7lFmiZr8BMDgOivYom+w1pdZptc2Tp7yQJf3UQb7bDQaYUMPXKtbNMJlzxncKJv3WOZY+93dnV1fX9tgMLCzszM7Pz+30WgUwPjk5MTS9DmxbTqd2t3dnbVaLZtMJsGoYvc9AyO7ts2ed/3Cd966XA0DcDncjzEDRe8rc503RvuUOr1ceYSgjN4tyxTz+r2qbOMJzKt7Wdf3wYNtlRjJobhcimQf9ayjH6oaAjH3qiYzgZ0xo8V3iNv1ej3rdrvWaDRssVjY3d2dTadTOzo6sk6nE06qQQwWzIbXwjLIKthonWEIaN2ZKcXK8NhPHvgWjQczYSy/YYaIrSU5Vot6IH7J63wViHGPx4w9EFEQbjabwdPAG0/wczn5jHeLAuNl97aZZWLk2BQEMVow3dPTUzs9PQ27fmE/6GazGdj23d2djcfjcPoSl492cVydN+9AH8a8AGXGkF3OZdhr7O/vXaoYF1WMe+85MQ9Q1fLKjk9ZfXvwYJsnuyi4qnKI7pDYC75rWfxZ3vX6PT7TpTmcMIO/OW6H7RPNzJbLpd3d3dmPP/5oNzc3liRJcCsyg8OyHT7oXON2qBPH7RAXBWMGUwQLiyVMeW5yr4/ylKnXn6wcMAZ4Pm/ob2YBbNI0zQAvAC+2n7JXvyJlpGt20zQNCWzMDrn/eN9jeB0AunwcIW99CQMD32O97ZcvX+z09NSurq5sMBiE0EKr1QrJVMPh0AaDgX358iUkUvHSLQZBBl246HlNLrN1b15vq3gViPP6/E9ZyrxPkLcgVWXGqKruPUiwrdNlUKdUdRu8hXjMsmr/5bmAyriH8BncgrytH5SctyyEr4GSXq1Wdnt7a7e3t3Z/f29mz7E+KCzdyB8xWWYyDOq6JIW3+YPCTdM0LC9hcOF+zXPZe65qvY6Vvt7v9amucX16esq44WGsmNmr7Se9usWemTdH4KHAEir0P4wZ3cWL642xRBx9NpvZfD7PsF/deIKNDFw/nU6DGxthhsFgYIPBwEajkfV6Pev3+3ZzcxNYLidRMdtlo0b3wk6SJFwfG5NYXxUxNB33vOvqkphhV7eU1YFVGeS2dd3FUNq3HCTYHqJ8C+7qMi6WMtd55SlYaBm8xy6f1gOFzVv8gR1B8YGtgtmAofCB4bHsWoAsZ65qnJh/FIx4Ham6W2OuxV3GP3a/7gGMz1A3nH3LbviTkxPr9XohQxnMkdsXkzJKCR6IRqMRABLMn2PuWE+L/oYRY2bBCzGbzWwymQTwxUlJcDVzvJX7/+HhwSaTiU0mExsMBplM9aurqxB+QLz37u7Obm5uAujiGexe5/W4qCf6g1kuexvyAMELJ3h/e8Z6rOwq4nlQDllf5Rmk+5CyhkcVNruNHBTY1tnZecCy63P2OTHKPBvC7s1d3C2eUigqGwKQ0x2dmFki3gaAOD4+DooU7r6np6fMmliUCabDZ5jqnse87SNnufIWkMxiOINW28eAltdfqihj7sNtxoH/ZqbFm1kAPNgdDrcoSxlvDLN51BvjiO0XmXHyul4+4AGgixg8r7GF6xeZxFhHiwQ3jDHHTznRitdQA6wfHx/t7OzMzs7OwvM6nU74ub+/DwyZdwXjfmCPAPeN7lYV80h4fZz3LlYxeL05Veb+Iu9FVYb+FlKW3ddRx7qYblnPB+SgwFblkFwAhyC7ulu2nagesDBr1U3o+XB1KFswH5x3Op1ObTKZ2Gw2e8VY8QwoeYAjb43Ih6JD4WOtLgCdWSzuSZLsGagKZjhuDmtYvcxUr98996NnlCmTKeOC5HsZLMAaGThQPi/tYYbmibqH0VcYu2az+croQT+COaLvut2uzefzkOCEjSwYiIfDoS0WizD+ykIZ6GCIwcOBeDD2T769vbUPHz7YxcVF2AgFMd6bmxu7vb0NyXaI86NvOK7PhydAeAMP9KGOcwxoYwb5vtyc2xh0X1PekrBUMXBihlQdctBga5Y/GdVV8y67S0wZaLIQu2zZdQhW22w2g6LFRgUAMmQTg3kwgPKGFZpIY/ayDIZ/GGzx4+2gBADFs3DaD9yiCrZ5a1WriCqWPFcj9ztiix7zRN0eHh5eMXhvDWmZOuK58EZ0u93ghQDDNHs5+9fsZTMNuJnZFWz2sjMYwBtzBd4ObOEI4GUWysC12byct8sH0ePggul0ar/73e/s4uIiGHZoA4cU0jQNgMtt5rXN+Iz7hcelSp8yIHv9jTLz5liRcRbzoOzi7XprqcoSv6bkGdN5cvBga1YMuG9Vh29Vil5mD2AhGudiUGJg4z2PEZsdDAbW7/et3W4HYEDs7v7+PuzwhEnLbmJkqILZckyYY4SoDxgub0bPLM/MXp0udHR0FIAerIljh946zKK+iynRmMItcp/FXIf4G8YI+gd9+PT0FDWc2A2Oz3EtxhWuYTPLHPi+2WyCQcU7g/HpRmxQITbPhk2SJGEjEzaWut1uiOsibq+gy3MkSZLMGcPL5dImk0kA3Ha7bRcXF6E99/f3r9bn6jIu9s7gOdpf3P+e61+viYmGGg7d3VsnsSkqI89t/1YSq2PsvS1q0zcBtmbvLmWz6pPcU87bXKuuXTAAXgaCz+DG7fV6Yfu99XodQHYymYTsVHZH8gEAfJwdAzvYK7MuFrgCeYcjFT6/lpOrzCxszsBrgovcvKoUtB/VrZv3cha5er37UD4bEWaW6QN2I7MrVOvKRouZZbKDwSbxTI51ekfoHR0d2cPDg43HY9tsNtbpdEIdMFdgLPFnrVbLZrNZKJdBEc8GsM9msxD313OIh8Nh8KoMBoPA1mEc3N3d2Ww2e5XNDoOMjTTdE7rIwNIxzRvLmHhz4Wsa/Oom93ZZKytVDZVd213l/jLEY1s5KLDdxY9fxlUH+ZZZahXxLLCqbeeXjA8LAPCBSfIReNjfGPHZ8XhsNzc3gVFwBioUui4DMXtRfHx4ObNpzi5mV7G6efCbGRm7wxuNhj0+PgYXMi8jYqDapf/LfB+L8aIuzEpZ0A4kMwEoVqtVxmDAb7jpAXRmlmGpYIw4XABAo/tI64EQcPFzPdI0DUt+AMj4nsEX7mqUgeSs4+PjAIr6fD7diE9uWq1WNhqNrNvthiVDnC0NYw1Zy9iCks/zbTQawUBAf/DJRzw2RXNgm3dvV/d1kVRx23rhjV0kTzdvU0beZ55UeW6RDq2CWQcFtrtIGYvyT1XUgizj3tK/9fxSKE64CJnNAmgfHx/t/v7evnz5EtzGvI0f4m/4wXM4ExZl6/IdxA85sYUTqHh5hyoI/l4B39vMoowb3qIF1EIAACAASURBVGO1Xn+XfdHzrmGDgj+Di7fT6WSWDTEb8cICzEjNLGP8cL2VXWIs2QBCFjh2DVM3KQMku8LhXgYQ67IiuJURt+VYPowHGErYNrLX64UM97OzMzs5ObHRaGTtdjvkE/zyyy92c3Nj8/k8lA1PAeqDNntH9RWxXGWEea7Jt5Qqz6tbl3K/7EoGdqlDXr1U8shcWTkosN22o9nq36Wcb1mKrO0yrCk2AXlpD2J5HKeF8sLP8fGxrVYru7+/DxmhqiT5NB4GSH4OXMUK7tjMAlmwUOrsrtVNHpjBmr0kSz09PYUEm+VymXFncx8U9e+u1rQXF4wBuAfej4+PwfBptVoB2LxkIP3N7mfch3g5zwEIgxoMJoAuv4tYigO2jEQq3GtmIW4O8MW8QmITH7mH8AO8IxqXxyEDGHswXoD06emp9Xo9Ozo6sl6vl9lV7Pb21iaTSQBthBNgQJhZWMesBgTPy7x54Y1/3nwq62Z9K6CuwoarllVVZ8d01S5SFCaIGQZqUMXkoMB2W3mLyXbIrugqTLVqGVB+UJqc2YmMT7iNAcIPDw92fX0dGC12HwKw8vmi7OIFyPJaSXZRoh28OxFA09usQrdpBIvlE2EeHx9tOp3adDoNWcnYDYnjnryshvtLXbsxJYL7vHhp3jjljR1nbvOyHAAVAMyLOfLfun6XWSOYowe8vN0iAHq1WoV1sP1+P7iFAbg8D7R98HDAgwGwhccEy4SwNSPc07zuer1e23K5DICL3a+w9zLOSMYJU5hn2AqTTxPiXcaY4XqJVV6/4nr2juzyLn5teWv2nfdsz/jdpd9iRm6e8Vu1Pw4GbLeh6DHLv+xzykpVN+y+RV/qqi4hvT/G3Hh5D+Km+EF8FlvnnZyc2Gazsfl8bvf393Z9fW13d3eZWBe7jsFyuXywWbBn3mWKrUdmM3A/KqvQzQp4AwyArdmzgsdyEwYexBwBsgqSZfq9yPouw3a9+1QZ8GYQAFh25+J+ZrjcJgY+j9V7blOuDzNTZIRz9vJ6vQ6GEwCM2SCv4cXc4H2YedtPhBh4m00wWjMLBhyMRNzH2eXD4dDMLLiSeby5XGbgiImj7WibN74xL4jOZe7zMjrF04tfy/1cB4vc1S2rba9DN7OHqaxBXFYOBmzLSMza0O/xeZUJrPcXXfs1XdZ57qXYtTHFz4DEljdcumAXfIIPkqCwLy3OmwVDBPsAowXz0o31UR4yRsEw1K3JwIrfAEK4J8E2WNl57dQ+Q92wvIjd0rypBoDBYyz8v9fHLFoH7vc815qCHOrJLmS0B58h25f3LdYsZQYUzx3qucj4e/QJjB785szg2WwWNrjgs42xJIwNJjBRzRLGeGOTDQAutvfkBDu4q9HXiB+DsfJOZchahoGHE6bu7u5sPp+H8gD4fIIVABl9B1DncXoLQ70udrdv2QaoYveULauqnlag9Yyjonc+JgcJtrEGVHHl5pVRdULmWTpl5Gu7oHnieMxEWaOumwVbQWYwdgGCYnp8fAwAO51OQ7IJK37E6jS22Ol0Amjzrk88oTn5hd2czIp0ZyAFiTxXHi/LYYZjlt0ika1eBsg8NuM9WxlRzDvjeW50/DwPB8CLDQxOVkId+DeXrwaLZ9zpc7mNAE7EdHkuIHlLxwpMFvMF8XN2+3M2MR96YWbBpYxnY555hhYAnpOyut2uXV5eZvqLGS7axRnwZpbZ8YrHJea18IyX2Pir6HcxwNkn862iu4o8N9uWu48yyvYZX8fzoOg+swMF27KSB4Bv4V55S9DMU+bblOW5YJjVcjYwWC0fawb323K5tPv7e7u9vQ0gC9bAyTAcn+XdfbBn7snJiZlZRrGpy5iZFye+INkKbVN3qbbT6wf+m92xiN2x4sZnXv/pSxtzl3ljUuYz/pzjhgACXueqQAtQg4HCZXFfaz21Dcze1FDTesPrgGQtzB3OMtctPvEMHM+H78HWsSMZzkNutVphaRm7rrEuGGDLO5OBdaNuHz9+tOFwmPHeNJtN++WXX2w+n7/yCLArnE8w0rkbY7hl5kAVsNKxPAQ5lHrEJEaCvH6sOhaefBNgu2sjt2HAhyR11zEPgHgTCQZbJKlAWR4dHQWgxQYBDK68jR8UFZQpDoznPZOZ4TCoQWHhc4A2H1DOrt6YUlP2pp+ZvYCIugZ131xmcWBpmjDDS0T0pUXbYuNRdgxxn3oh+Mg9dX2bWebUHj6GLpa1zGDN33GZHuPF/0hCw5Icjctz3VEv5ADwEYMAbjBk7EIFoE6SxCaTSUh84wxl1At5AOv1OoAo5h1nwLN35Pr6OnP8Ins7WBRwdc7kjXmeYZXHkPX+OkBhX7KNHtP2lGX3dT6zbFlF338TYBuTKiC6q9X3NUHZY/Ce4tu2LM7m5CxQToQaDoc2HA7t5OQkMFocZ6Y78WgSFOKhzWYzgHa/3w/swWOxqAs2wYcS012N2KXMrlJtc1nl5gEOn4ML4OfdqvjEGlzDipfLjD2/CuMF60YfMXBhmRRnT6txANcs/+YdtRg41TgBMKEs7WMPaDhberVa2Ww2C5uicF4AjDtkAJ+cnFiSJJlxxhxYrVYhk7jb7WbYPR86oMufut1uGNPNZhP25jZ7Nlp++9vf2mAwsMvLywwjvr6+ttVqlXl/kNegXoFYmIBjukVjXIdsqyMOTYr0/C46PWaQe1JWh8TkmwPbbS2Vb13KxhFi1+e5SPA5soKRqARLHxtVQFE9PT3ZZDIJO/AA6FgRsmsNGahgtHAbIxbMSVNcHz4sHcDGSp7du9puZRUeEHgWs9cvqAvayAcwcEIOx0qbzWZg4Rxz9phfniEYqzPGC0tj0J+8HpkBAEwMAIv64Td+AErKnBWw2QDiujKT5vrDNQ+2uVwuM4fPw5OC+QfvCZgo3MObzSbshwwQ7vV61mq17OzsLHOespmF+/g4xaenp5BolSSJzWYz+/HHHwOrRi7Bx48fM2cz397eBobLcw/bf3LfqfAYssejjG4qAhRv/rOUfc63JDFPi35XpZy8z+vwFnxzYBuTPKujjo7axaopy0qrThIur8j6U/cl/gdLYtexAu1gMMhsSj+bzUKMDIv/zSxz+gtvWqBAq7sLsZLmGB4DBtZxwm0MVsdMWPtYAcrbVUpZHCtR3jWLXYtcLu9qBZcl1vTqCULMIMFyVGkUGQ5mFrwPvB6Zs7jZINHyAEJ4FvoxD2x5vTInPqnrmevsgTAvNQI4Yc4ge3k+n1ur1QoGBOYKu38Ro0e2e5qmwUvS7/cz8x07T8EDwX2DRKs0TW0ymdgf//jHMH4fP360TqdjFxcXGRD+8ccfw1pguOGTJAksHIaB59HgelUFwLK6YRcPnt7reUb4ul1lF53qzTM1qr1nlHmeZxCXrU+eHCzY5vnpv1Y99POq9dKytIzY5NvlxdFy1CVoZsFlByDEmlecC4o9YjlGOx6P7fHxMSSKQPlh43gzC6yFM46RwWyW3YkIQABwg2JFuThc3lvfyPE17ktvDnksl/uKE4wApF4clMtgAINiZvc2gy0Sxxh41OvgtQ3XYaz6/X5Y38w7bMEw0TLZVYo2oDzco3sPox+5D7nfGGyL2By7WDm8kKYvh8RzBvF4PA7xfd61DF4SbGDx9PQU2CauQRwX4we3MgMuxoczm+fzuf3hD38IgP5nf/Znr5Km1uu13d3dhTXiSfK8zAnLktAv7JL3AFc3SSmSqtdtA4x1kJJDek6R7ML4t7nvYMHWk10sIb6/bsusTHllLCyO+ZW5T8uP1UMBhsvUw9ehWMBEsRxjPp+HM2gZaOFCm8/nIbvTLBv/hWsQrk6zbDKJMlPUD/E2gC2f/sJt5lhYzKotmjvKZnmXLI7Z6hISMD4+5EDBluPW7AUAuHH91DBiVoj4LAwXXi6lhgCPeZ7ChzBzZfbH93tuUAVbBnn2FPA85Lg7wFefy4wXbl0kRXkxc9QL4Indq/i5vGUou/aR0Yx5/sc//jGAJ8+Dfr9vl5eXlqapzWazYCDyxil4H7C7mb53aoDsYlDnSRUvWVE53t9VpaguVXRoXp220Z15z1N9vIscDNju2jFfk/1+C6JsBS5bgCFnhmIru16vZ+v18/F49/f3NplMbDqdBqDFch3eWxgKiBUkMo81PsgKmTeo4GU3rBjVHckMWcEl9kIqAPBniBHC4MD/vDRFXz4FIAAzK3UGW4AE73qkcU4dM4A6J6oBIFjQN6ijF0f02JQyfbOst0ABFGOTJ3ytgruul9YMdvxGfPbh4cEWi0XmYAL2kCg7xvfYBxlzGwfNc5gDLBdn6ybJs+v506dPdnx8bMvl0jqdTjD6er2eHR8fhx3SsB4Y8w8JU2Yva3+9vlHDty5w1OdUFa7PvgyBbUQN56K2VanzPvrek4MBW5UyFkpVN8A2k6/MQJSpR145HqvB/2Uttbzne+XxIQJgtXDbYTcnM7PpdBrcxthRB9cdHR3ZdDoNR+dhSQkv8WFGCzBFdijAB8qegVXZvu68hP9jSl+ZsgfsfK0CLTNaZq8KgMxUYcDw+lZOHkObPYacN3ZJkmS8DTBclIHyb54XGof15ivPizy3MK5RN7U3/xSktV1mLwYCgy/WUK9Wq8D+n56ebD6fhyz0zWYT+gEuZWbLYLnINeC473Q6DXMQIG1mYb4mSWKLxcJ++ukne3p6spOTkwD+l5eXdnFxEYCclyNhThwfH2cMRHwfY7fcX54hFPsuJnWDhxqX25Qdu68OklRHe2PjwZ/t2oaDBdtDll0mHMR7qWIKSS1gvV/ddHnPxw924tElGIirQil9+fLF7u7uQtIHQLTVatlisbDJZBK2yzOzzOEEcEMDGHjvW9QDn+um9maWAUa4dznpxGNt2k7uWy6Pf3idKm/ioWxWk4+YWeUxOYAuu8u5rQAaTwmjfuwpwPaLfGISrvUSltQjwH1UxGS8vkSbdO7xby1H5yG7rdlg4KVjiG/zMq/N5nmzC4i60hn81ut1SJwaDoch1ttsNm0ymQQwN7PAUM3MTk5O7Ojo+eD6X375JdQR8fIPHz7Y5eVlBlSxhAjjAZaMeRpjuOxBULbrGU4qXh8XfZYHDp4R5rlvy0gRM94FaKu44Ld9Tszw2bbsgwHbKhPga4oHbNuy6zKA612HcsqwadzPCRmciMTZnshAbjabtlqt7Pb2Nix3MLMAou12256enoJbDmwCO/oMBgPr9XqvgAEuQgY9sBZdJmP2wjgZ9LDuVkGaQUYBgtkpC2eZ8jpPPrBAWbCOkTJFzgJGv3OdGGA4cYqBXBUjXMh8XB271RVseey5P5MkyWzY4c0hL3bOjJjbFpur7N735iIMCDZ2eG7C8OM1zMx0wXxRXw6BAJzTNA2ues6ux5gkyXMSFjNndsHjCD7Orm+32+Fc5qurK/vhhx8yZzTPZjNbr9eZOYuy8Rt1VgNNAVZ1SxlgqXJPmfLyvBaxa2N1KltOUflV2H1dz6nLU3AwYPunKG9tRLAyY0aLzSYQp91sNsF9DKBFdnKn0wmHwuOwAQAt7zIFy57XRzKImlkmUUaPXYNSVMXFcT6N5zKD1cQVBhZ8xke5wfDgrGPutzylyH9zghILgwqSxHCPKny+BwYG6ghA5R+18nEfX4O68/VcRzXMuI3cTgZu7k8ui5/HDF+ZP5ft9RXGER4VMFwcJACAazabYW7iLGU+tAJlYkmRejiQgIeMd3w+HA4zRgvyF8bjcYghX15eZuYisvERgoBh6s1V1Ev7OU/KKH5myHXqmDJAX6Ucbw4cErEqI1X6+B1sS8g+J4DHlLetQ567Bt/BfYxlEsiyPDs7s16vZ0nynCDCh2kjXgi2MR6P7fb2NrBalAlGi7NBEZ9V5c7MlBkvFDkUEq/vRBuU0WrZUNAab+U+gjJnNsvrZat4WVRpxixiZrb8PxiYJjNxe5CMxqyWY5yaoY3yWblrjBtsWgFTAdgDcgZdr3/AbFF/BVlm+F5ZHojrmufj42NbLBaZpDy4f7EkBzFZdkHzloyYHwiF4DqchwsjhzcvmUwmwdDERhrwGKzXa/v8+XPmUIQkedl8ZLN52X5Shcc99r2nK4r0Rl0gVqSbqoDtLkyxLpZZp5StzzvY5sg+LbAY+1HlVvSsMm4aBhhkdAJoOp2OnZ6e2nA4tEajYfP53G5vb8NZtByjTdPnJQ/ISoZCwbpPADISVpgFAfQ4+1STYxhAzLIxW24fgxoYLyce8RFsscxjzsTmmF+sL3GfgoO6pj3WqMIABGNGXeIoi40C3iaRgVYBlueEslv+XNvnGQz8ncYXvb89Y0TLY48DrvEMhrx+w3Xr9doWi0XYArLT6WSAnNk8WCvmvdYVIIkyzSyEQ9D25XJp19fXdn5+nlmC9fHjx4yXBu5sLBsCWPNSpVhb897jvO/1/jxgqhOw3gLsvwd5B9uvJDGGVEe5Wlaappldhxhoz87O7Pz83FqtVnAd39zc2Gq1CqyXMzRvb2/t/v7elstlJqkKex2zovM2UDCzzOYUzHDZPQxA9AAWfzPgMqDzDlUK1l4yFDOxPOPFAxXub4AhX++Nh4IXZ7PiWs7ChqsfnzMLYoMCfc4eAK6DsmzNZI6xW/3Ouw7C/a1Z5fhexw114C07vfJhrCGGbWZh7sxmszCOOMSADZgkSTLnzyJWj4PkMVdxchXvNoV4bZI8b95ye3trnz59Cl4cMFzMfWx8gVwGMHLMcTWouM0eY/XmZGxebSNlADEG3GWZppILz5gtK98qcBeCbZIk/8zM/kMz+5ym6d//9bNzM/uXZvZ3zez/M7N/mKbpbfLcC/+1mf0HZjY3s/80TdP/Yz9Vfzup2wr0mLJZuSSDomv5O1awzWYz7MYDa7vX69loNLJOpxMOf4d7+OjoyDqdTljLuVwubTKZZLZoRAyMgZYTcJjNwr2ruygpS+MkFWa2vCOPZrACZMBoefkNgxrqjIQa3mSC+1k9CwxmXsyTr9e/Y+PNQArggGJnwNX4M374MzZqGGRRBvpImWksNuvNMfVScL/wtfy91z88vrzzFo8l11fbyGXCNYuEqel0mjncnY0O3IftRpPk5UzlwWDwCtzQb+zihwdksVjYly9fwlKh8/PzsF0j1gWbmd3d3YVy2JWMcdZkOu1zNoi8+ZMHgDqWRVIGwHQ+e16L2D1Fz61Tx3J5hwTMZZjtf2tm/42Z/XP67C/N7H9J0/SfJEnyl7/+/1+a2b9vZn/v159/YGb/9Nff36R4EyHPOqvjWZA815yKKnR8ZvbiisXm7gCabrcb4rRIiMIyn81mE/YxNnt2r/HB8Jx5DPfx8fFxAFIzC4qJY6tQjLy3Lm8TyACrwInYGytCbifu1TgtgxTH/JQReuPAjJCBQscH1yrrUJeuN54A3DTNZmXzebOeOx3gwwYDA60aJ8zyFQzVPattUtaN3xz3jcVmFWjZ9e0ZVl52OXsMuB4wCDlxajKZhM91rBhwJ5NJCI/Ag4P+bDabgeHqJitYt3t3d2fprx6jRqNhFxcX4QB6tPPx8TGTocw7kjGLjzFUBbaY7KqH9sUqyxKCOkXL83RjGfHehTwvhPdslUKwTdP0f0uS5O/Kx39uZv/2r3//d2b2v9oz2P65mf3z9Pmp/3uSJKMkSX5I0/THEs85KCuEpe56Fbl/YskneeV4ihyMDwk2WNaChCfswoMMS7i+EPtKkmfX8Xg8DhtXgG0hTovlPcxQPYWbpmlm+YaCLcAQsVY+EYaZKpSUx6AVNBh00W7d7k/ZncdCNRlLDSFmaRrD1ev5Gex1SNM0uD+1L70y2bhAWQxo3MeoX2wXLK6T/ua+UOBkEGb2iH5QMGdGp0DN7eL26BxS4ASwzmazALa8daYm15k9v1/Yz5gZrtdmeGCwCYbZsyH5+Pho4/HYPn36FJKorq6urNvt2vn5eTBQ2YODOQjPjtf/3v95xjRfr8ZQrOy8cmLfee9FWYkZtHUCbVE76tDheX1cRraN2X4kAP3JzD7++vfvzOxv6bo//PrZK7BNkuQvzOwvfv3bzA6T+u/bCPDK9lwvRe4YLosVEtzHAEW4LXGSD1gt4rSIwSJZajKZhH2PcSTbycmJnZ6ehg0FOOEJipHdofgeR6uxyxj38FmsrCx5J6ejo6NX2aVotwcmrLz5e0+5Q/Rl0kQkLhdleSAWy7T1nov6gzVxMg3XC65VbRuDMxQ5uylhtMDb4IEat1n7guusxgm3U0HY7HWMFh4QzAmUrW5/tImf79Wz0XjZrQx7c+OgACS/xdr38PAQysXc63Q6mXHG3IcxAYMTS5F++eWX8J6BIQ8GA/v48WPY8erm5iZztGG73Q7gzW3UuZFnAOo9ed+XAesiqVMP7kunvgV+7GIo7JwglaZpmiRJ5SenafpXZvZXZmZHR0epfPfVATcGeCzbAGJeed53Re4jz/oH+4TywEYVUOi85d9isbC7uzu7v7+3JElC0gcYLaxzKJt2ux1OA8IhBbzXMYTdb7wRAW+nBxBT9y4SowBkiMOdnJwEwGZAwbWsqPg3gFbX3uJ7sC4GEAYPfQ7qFdv8QpU7u5895ohykQTFa0n5XWCmCEEdmdGyIcKbRHC/eq5hjSF67nptizJr9gIwO2evBe6F0YR6epndugmHF2fG3Gi322F+oB+73e6rTHPcq2EJXsvMY7zZbIKxyUYX5uPNzY394Q9/sJOTE/vtb39rp6endnp6ar/5zW9ssViEndbwXmI8OGFKReeH1r0M0+RrD43IxAw7r3671r3uNvM7UaXsbcH25+RX93CSJD+Y2edfP/+jmf0duu73v35WKIcAsEVSl9vDA+Vtys67B8DKR7AhKQoJTQ8PD2GXKCgmWOw4eIBPLwHQnp6eWqfTeQVEAC3NuOTDB5QpsvJhBos28E5XZhZ2DoLws5X9cPxX9zpmwFAXbGxZDddPwVvHhe8DuPEz+blmLwCqR/pxOTFFxMYBymLPAOL0GB+tpyp2XMflMiho2xgAj46OXsVduZ3svtelTJ4XgO+PtR9zqNPphGMesf6W+xTjpR4YbjuMPp4bmONYI4vNL9AGbOuIhEJ4Ec7Pz8OyJGQ4Yz6enJwEY4Mzprld2td573vRd2UZmXozYuWwxOqG68voNn0fymJB0XWHhCnbgu3/ZGb/iZn9k19//4/0+T9OkuRf2HNi1H1aIl6rckgdVHddPGaj33vuuzJ1AYtgJctx2n6/b8Ph0Pr9vplZ2LxiNpuFvXc3m01YR8txKmQe434oZD6DlMGXlbSuq2WGoPsRswJnIMY6XzBhjldCGGCZLeoGF+qqUzcsK0Dcx/slM5PWcfPYMP/gWgAprkHd2SXJIMYZtt6cwQ8bAljihfZzMhr6Tw9O4H7h+DgbTOyixpgya+bPGXR1K0Ozl+QnTujidmFsUL4CEhQ6r5uGJwbuX3hg0KcYQ5TB2yiaWcblrowdxh6At9VqhVDMzz//bL1ez3q9np2dnYXD53GIx/39fXhHMa95H2cFJ+6L2N/eXNgni42BKj+fPy8C2rIevDL18XTnPgTP0fKLnldm6c9/b8/JUJdJkvzBzP4rewbZ/yFJkv/czP61mf3DXy//a3te9vM39rz05z+r0oBvVapYcGavY6talld+UTks3u5I/X7fRqNROFh7NpvZ9fW1XV9fW5qmISEKiR3z+TwoQN7OEfFcZBVjWQ8rRs04ZVbLsUhmrQxkzHwY5BikAaTMwvReZol8Ji0r7fV6HZSz7sjEMWNmtXlMVhmhx2hxHYM2Ay/3CwMbM2T2HDDIsZHCO4Whn9B3bODwWmOdkzyuOGZusVgEpoYEIAAptxP/Yw6g7gzy7D7XfuP+xTxTMMcP2nRycpI5A5nLe3p6CuDIBhjmAZgnwJBjuABkZuGa5DQej+2nn34Ky4CQ7f/hw4ewjeN0Os0kGSJhcBv9gb7Rz6uUlVf2W0ldHkOWQ8STMtnI/yjy1b/jXJua2X+xS4V26aQ6rZqqEyDmYqmj7DJlsfJmNgjX8dnZWUiKenh4sOvra/vy5YstFovgPl4ul2F5D7u8+BQfJLiANbBFzgAb+4Giwm5WAAQ+m5XZORQ2x3zBONF+PJ+VPLsOWbEqm+SlSGiLAj2zKmVX6mrOc4XFWCl+o34M8t5OXAriGn9Wdzk8Gxwb50MN+Jg61FEzufFMrMeez+c2m81CFjCfxIN+0fgun+aDeaqhA54nHqgoe8Z4Yq5isxaw0M1mEwANO3VhRyceW/QrZ8cjaQ9zG30EYwOAi3rM53P78uVL8Cbg3RmNRiFui3g8G1XIY2BGi7Hc1nVcFXDLMMy6JWY0fK9yMDtIvTU46r1lJrfZblaj91wuVz9XAPf6CIoAfwNo8dPtdu309PTVGbXX19c2nU7DC79er4MChYssSZLAFnq9XmAFumG+MicGXGaLnDQFRYaMUV5LycwUCSrMotS96iXRcHYzM1JmhChXgZYZX9F+ybHPY/fkuQO5/Xi+sm2NOaId/FxNIuKMdD6AQpky7gFAs1sV8wReDYAH9tDG3sIohz0Zei4tJyaBZWKc2BjiNmhc2nPFIzOZcwN032mMObwG6AvP/clegMFgYI1GI+xCxUvX0O+r1Sp4jAD6jUYj7NR2enpqk8kkMGIYVezBKEMYyrhntyULMdAtIhPbgnWZ671nx+oTM3DrYrrbGj9mBwS2eaKNqNNFULbzPNDzBrIsuy47KfUlQLlerALKBoyx2+3aaDSy0WgUmONsNrMvX77Y/f29mT0fmI3Px+NxOJcTCp8Pk2f2w9mdGovVzznmlyRJ2Awe7Epds5wJyuyTjQr89tzOeiYtgxK7i721qAyy7HrG85T9qStYx0vZsCpUBn9cz+3g7G30O8rTz3TOYD5gOQpcymYvmbh4PvpE68FJaumv4QYcXLFer206nYbN+ZFQB1AF2IEJwr3LWyIiHssGANYdo33syoZXhA0KvhbuMM4lVwAAIABJREFUWbNnMMPSHW4jQhLNZtMeHh7CmMP9i+djLTAM2CRJMjF93mgFRgS2jvz8+XMmi34wGNjl5WU4R/fh4SF8hzkeCz3wuOaJGk5VJQZgZQgIX1dGB9blwazjum1FPVlFcvBgqwO4T198Huvgz8tYeGWekfe5Vxdl3/o5mCLYC468A6g9PT3ZeDzOrKltNpthMwBeTwgFw2fcstsSCgmKQt2dzEjwHccSsesUl8tuYHYr8ppM7SMGft7zWGO0UIhgNgy6nutZFbo3zmxwqJLjesVAkq/XZCq4fgFemrGqyVLsXmbDq9frhb42s0y7eWwU3NiAAbvDsprT09PA9gaDgZ2dnYVNUcbjcYbJ8jImAG+73Q4uaN51DGPIRhb6huvOY8vjinGAO5n7E4wUbWaDgHfuQt+bvWw2wi7qfr8f5s98Pnf7dLPZ2Hw+t7u7uxDCAeAOh0O7u7sL7BbeImyUAYMHRoXOP0+5M9jp92X1JQN1Gca4Tz3siRogHrDn1a0sfpQtbxs5eLAtK9t2iDeJ1EIscmF47DMmeSzdY6te3bQ8gAPchHD78pF36/Xz0XifP3+28XgclBK2rkOc1uwlwQqZlTjJhxlgkrxeNgEXJrsJOQMWDAZrInWHH7PsST9cBoOB9hPqw8tcdN0mlGtsZyVmwrpJBRsOrJB5eQ4nVfEzVUl4gKtuU34Gu5L5evQVK1iUi77ADl/IREYM04uhe4DLY8HJZmClCE8Mh8OwJOzm5ibEcXn8AbbIC7i/v7dGo2HT6TQc0A5gw3PQLl27y+31xgllcCii2Wxm1nmjLLOX5CjuC3h8OB6OGKwybvbs4AdLgmazWdizGYDb6/Uyu7FhJQDGCO9VkeKP6Ydt2G1MD/J3ZXVsWT2o3roy5VVpmzLuOiSvn4rk4ME2z9rax7OKnhkbwLIumLzB0bI9kPf+Zvcxko263W7Y+xXutJubm7C7DtzCiNNCEYNR8b7HSNThHX8Y9NRVCuHYFoADCVG6VETLY6DVxCsFN4CCtx0jK0KOz+JeBhG4Mb1lPTzGvJUkJ9Z4GbPKFFEnHWcYM3wf6sft1n7SvuOkNiS+pWlqi8Uis5QLzwN4Kejy3OKsYZ4LWPaF56G+ABg2urBV52q1CnF6tOH+/j7s5oQywaq5zQxsnPmsGcr4nMMIqNtisQjGH9rKmcaYK+gDuILxP9bIDgaDTH+BOcNwODo6CkweHiM+uGM8HgevBbxSCBtAAMY6//RvFtUhebqzrM7icmPlxJ6fJ54O1WfFdKHHcoukDIbsE2cOHmzN3s5lUcQs86TOOsae7b0c7DbjeC1eariP7+/v7cuXL7ZcLoO1z+5jtbLhfoSyZxcXFDUDHysoXZNpZiH7GC5uL+bG7fSULAMCu1sBAgBBZo+oN8fXoKB1SY+ybGXRvLwEGxiA4SOB6/HxMTBJZkXeb/6bAY89AWCSzG4hCsDoD94EBPVC3BRjgfi7Ml3UhccZ9QRjw9ab2OHq6enJzs/PQzIexnmxWGSWycCdzOGDNE0zcWmALcdvOR+AXcmcbY3+4H7l2D8nu+lSLzbsAIzoBxzUAWEvEt+HOYY5cHz8fLg9YtpnZ2fW7XZtOBza+fm5TSaTTB4DYsa61SbeS33neZz0c/xdF6PLA786WWMZiRkCefXIMyiqeCNj15bt628CbL+GFLljPDfLW9YLz4Vy1aU+cCmnaRo2rxiPx4ExPT4+BvcxXmy88DiKD5nAnEFqllXyUDK6aQUrDbiP+RB0dr/iR5WKB7ZQjuwe1B2CuF6sWPEM/PAGEKpQlEmzIcL7Qj89PdlisciApbo1USZElSIzM8SQkcjDrJfj4lxPlMNucbBZPZ/VzDJ9z4lR7KHQNqjxw3tUg9VdXFwE1zXmEtyi3g5eWEKGNagAZ17vqmEFjbWjzfxeYAyYkXN52EOZDTAeb9QZ38N1vNlsQkgFxowaWmD9YLnYd3w0GoXQzvn5ud3f37vbOB4fH4c5izHhMEOeTspjmB447ktvbQvAeUy9CNyrtKVK/fKuzTN2PDkYsM1zd2xbHsu2ZeeBakyheswlry4xRu29KPw3W+1gM8gWZTfxzc1N2JIRiokX/pu9LEPodDoZ1galwe4trjdn9nouSSgRZrXafmZPyvS4bGa1zEyVKTGTBWvifmRFrNnK3ngx6x8OhwFU0L+ssM2y599CNMEJhgzXBaCO+9hFrmDO8wX/83PZZa6KG2WAlYKFa6ya5yB7HwB0ABow1vl8bldXVyHzGWONecd9vV6vQ6hiPp8HwOUxB0jCdc3zC3XWNdA8juhPZbkAXL4HdeJ+w8YuMBz7/b6l6cvZtEiYgtEAVgzwhffo+vo6xLbPzs5sNBrZ1dVV6DfsSgUPFYxU9hZh/GIer5hoG73P1YOD78sK66k6mO5bsGYmU3U9r+j+gwHbfcs2YO7FFKq6JKqy3zIDrnEp/GDvYux9/PT0ZJPJxK6vr8OB2XCjYT2t2UtCCW/vx/WBgkP7NWlJ3ZGc3Yl4HmK17JpU9zEmv7JZZmVwmzOTZUYLhc1nkaKN+M2gwvFRlA9gRV89PDyEeNvZ2ZldXFwElx9ioexm13FEv+lLrdnM+A2DSNcHYxyU8Xk/7H3wjDudlxoHVRDiOjBgov2z2czu7u7s7u7Ozs/P7fT0NIQh2F3PhxFgP26ELHhzDHhH4Anh2DWAGAYLewBQT3wOwOV13NznvF0lhz4wpjBIMQ/hNTJ72QENy9iQXY15CM8HtnIcDofh2rOzM5vP5zadToPRg/cZsVtOQIwZ8kUeOJ5/HqgW3XMosg/g3bWNVY2L7xZst3Ur1D3BYu6csveygtMXjQESGcgA2iRJbLlcBgWIeBssdrg+oXCQXMW7NimjVIBVUNQtGZG4Bbc0s0/9UTcZu6/xLGbyzFQ8RosNMMyyQOsxOChNTpjCchkwtkajYf1+P2TgmlnIukWmLe87rNa+x1CYNQLQ8L1XV/SLMlkGG83O5TmD/zXGqUCNa7kN/B2XibGH4QFPyvn5uV1cXNhwOMwk7vGyLiRLgeEieUnZLX687yDoK05+wpznvkGWM7Nk9jZgdykkU8Grg3AL34MERLx/cJuj3zAfzZ4Pm//8+bP1ej27uroK26fi3cQh85iLq9UqvK88Z7T/8/QGyzb6R+/Vz/YBxnWxY5aqrucqTLdKfb9bsP2assvELls2FAdediRfDIfDkH18fX1tNzc3Np/PQ13gtuJsXo75QhEyaLKC04QaVlrsSkZCFDKawVC8rFqIupC9ZTqIS+sWjBw749hmzCVqls0s7fV6mQ0xEHfcbJ43mV8ul4GV4AjCyWQSNgLhpSMMhrHMZm9MeWyZkWmMldvEYIwyODatfc2f5SlwNYAUcPkaNnam06nNZrOwuxTcpjBSYCjBW8A7lCErnnMAwPhgCKGP2a2MvuI+52uY1bKhoQlhbAigbjBMEZvGvczUkyQJYMsGF8efcTIQL807PT21s7Oz8E7COMCWqrxMScdSx6uqrvHu3wXkttF1Opf4976lTt1ctu/ewZakzABUAVLv2pg7iL8rqhOUD2+jh1gtXFDYvGI8Htt6vQ7rNQFGzGo19snKid28UH66HIcVIBQ9Ay3Hf5XxoRy0Pwa0XDfdRJ4PTOe2KUtjhczHBQIIwOxhyMA4OD4+tslkkukDZnLs5uY+gztXGaEaGlo/1DGWIa3ziO9j1sYZ3zp/YmyFwTrmfmbg1XLQdrB+AO9sNrPLy8tg1KAc3uQErlg14thoAdvjucdsHoDJLmfMGbOXrUVRts5JNpQwF5BNzYCL/APMbbBbzoOAEdhsNsM7eX19baPRKDD609PTkLGMshHG4GVWeexM3yn87Y29fpenxzzPCN+v5cWekyf6HmxTxj7YMD87VnbVZ/5JgW2VyeJNsl2kimtCn6v3cnIQgBbKKkmSsD/rzc2NPTw8BEWDmBgAjhktAy6scigfBlNepmGWPdHF7GWJz3A4DPspA7zxXHUZc9s00xVl6vaLrDCVBXh9B/DCUYNnZ2d2dXUVTmbpdrsZVs/sGJ8vFgu7vb0NYItMXx4X3I/fygpRHy8Zi93ezObY+PFYMGcz89zxnuF5FfIUWp5nwLsOPwxQWGI2Ho+DAZYkz7s6tVotG41GoX/hrmWvCieT8VzC93ANoz84oUrHEsYJZ6J7XhAGaHg3OB8Ac+7x8TGMUafTCfFreHcwDxqN581erq+vw6EevBkI1t1ii0jkCOD5eePlGXP8XZ7Oib2P3nXePbE66T2elNWxZY2IfUldLPhPCmzLdlqZSaDsKU88CzE2aXRisfLCd2CkfIweNgrAcgO8vGYWlvrwNnq8Hy1nvSrj5IQnZo56DZQLGGOv17OTk5MMu1K3I7sywSA4/osyOSuVl7YAmDURisEEiTK4v9vt2sXFhX38+NE+fvwYXJtYn4v4K5JdzF5OHMJuR9imD8qRxwXK3GPY+I5BS1kp9wcbFjqfuH3KYJV1xp7B/a7P4fHRZ/NcjX3O/YDY52KxyGy2AcMH4It1qRhThDvQx8xg2ZPC/axzGXMqVj8AIvcTPwubgmB+oU58WhTuQQ4F5gXmL4ddlsul3d/f293dXdh4Bu8L1t5izuK8Wz5ZSMeP26QMt4jVenMqNt48bz0pC5ZV7lFj3DP6yrDaOpnvLuV8N2BbZ6dv26FlLcMi103RvQy0nI1s9gIMcKsmSZLJqmQGCiariVEAO2aYnvuYFQAYLWdDQ5Er0KId+sPuYwAB2gaGzIxWT+zR/udnHR0dWa/Xs8vLS/v9739vl5eXdnp6GjKk0W6A6v39fWa9J5Qe4mtYPoJ6ahtZ4alyiBlRqCu7cDXjGOXwGOQtB/IYSBGIcn31cw+0tHz9HsDIoIssXjOz4XAYMnRh0HAYAb95Ywo2BNnYw2+AruYd8PIjrb8ue8JY8Mk87N7G/EOZnEOhfcH1Reb26elp8ACB3WIbRxh/SLryst3ZWMszgjx9sw1byysnpvfKsGavbO++2BzdlnmWYed1yncDtt6AxBSKuiQ8C12vqVIHT7xyiiYo/uY6weLlMzPhBk7TNAOKUEK83pTBmte+ItGDs4DBhD2gBSACyDygZdYbAyH+n2N1bAywkjV7yfLkxf/MylGW2UvSC9bIXl5e2m9+8xsbDofBwIDLc7FYhNNrwGw1yxUuRF22E5s3/Dm7ecG+YrFRbhPilNp3DLZcn9gc0/mubMWb87sYp2o4YH6CGcIV+/T0ZL1ez5rNZiYrmdkjgIXbyV4XuHzhwcDYwYiDu5f7yjMMtB/xvjDg8lIgPBt9gHcLbeU5jrmKVQLn5+c2GAxC2OX09DQsyUO5fD6vGm8xgypPF+pYaRllPBfe97Fry7JPFb3vrYExJruw5O8GbM12Uwz7FG9ClwFaCF/LGbkMSLDq8VIzG2Ww5RcYa0k5c5WBjDNslaHhB8yYDy1AXTRWxm3UrGQGIwAcAy2zJFbEaZpm2I4qI14LeXZ2ZmdnZ5nlUYi/TiaT4H7nDfuxdANtQZ/yEiQoBq+fmI1wrJuXojCIKIMFAGisWueH5wrmuaNsjo2dMqKKz5vLRQqd+4GNK+zSBDaHs2PR5ul0+soro21no46XTeE+bx57/cLuZ3yOOQQAVO8Oh1ZgDPO7iDbDAECY4v7+3i4uLsJKgtFoFMI/2JULsWA8k9feav1jbUM/lRnrmOfDkyrXFT3Tu65OXa3vzS5gnecVypPvBmzLNjgPzDxLrewELapH1XI84cQoVta6IT5b3/w3snABtFxn1JvXtrIyYRbFySpYxsDLe8wscw8zKH6ZlamxsuQzZcEcmMkoo9X+Z5DizGMwWvQV3MZY3oN4GRQbAy+71znBhlm7smuO6XE/cxYsrxdlIOH2cFIPx68V+PA71if6GX/ugaKyvZgXKO8d4H4BiOFvuEjn83nIXAfLQ4IQ3PececzP4iQovBvcV5oopWPhAS63i/s7tvyI7+d3lNuriYaI3SLnApnx8/k8w+ZbrVaYe2aW+c4T9qCwlAU9/r/onioGW5HswhrL3L8vNly23t8N2NbRkV6HxdwYse9iVlreM1h04HgyM9uDQuEXO03ToJh4KQwUDLZNZEbL9eW1tMpivXWNzGgZ8NnahwJiYGKGoQksaCMrS2a0GqNlxehlnEJZ4QD1TqcT2MlqtQprKPFsbPzPDBrfKwP3End4DjAbZ8H/3G71MHhGCAwrGDDcp1xubJ7pmGriEJ6jn/E4cvu0TE3EKvN8zE30ZZIkIVu32+0Ggwhzmo+BNHt9liy+40QpjrNqjByiBhN7LBhw4aJWQ1R3XUM91LjAMrGHh4ewFAiZyYhZI3YLQw9zuN1uh2eWNfA9o0K/9/rhLSXGaMvqXJa8e762fJdg+5adq26cvHpB8oDYcxVCkTEQabwWySDY8UiX6GADB+zkZJZdNuIprVh2spm9yjoGoCqAerFJVVSsEJgx8rIXvZ7L5XYyuCPOxocHIDMbrjxeI8un43ByGCtMlM3PUqXMjJCZEPcLl4m+1nW1CnBokzIpni8eUHrzjoHcm5v8OeaI9jFfhzp4LFjBnQ1InhOr1Sr0V7fbtdFoZEmShDj59fV12GKU54Cu82b2z3XH/NakoliSkRoSzHDZc8RGCid2YT4nSXb5HFzJi8XC7u7ughHY6XTs6uoqbO7Bu6AhfKTvqPYn96n+Hfs/NiaHAlLfi3w3YFuVlVYps8gC9CxIfWaZieu9CHhRzbKsjxUtgAlMDS43rpO6n1Eeu58ZtCAMErCo+bxcgDcrJmZM3gvslclgxcwEdWDWjeuUfUAYqHkdMrJEsZ4TYGv2nMXtncPKAKpgy9cocDE74u8YaNE2ZD2j7jHvhjJfb47F2GcRGOfNT44Ha/25rbhWn6/P84wAlAvPTJIk1uv17MOHD2GdLm/niL4ye8meZ0MERgt7Rjgjmevu1VMBi98zvGvqwcG9zKB5rsDQS5KXwxnG47F9+fIl5BKcnp7ahw8fwr7Jes40vC38bK436qtz0RO+LgawZYG3LDCXZeRl7t+2Dl9Lvguw9SZWnR3vKTZ+VkxibLfM87QcjmMyi+CtBQEYsIhRP7yofLwdFAEYMStBBUNmlDjCD24+LDliJaNuXnV1eiwVAMuZx2inAi27T9U1yHFs/LDrHOtksbwHW1mm6YsLXlmDGiEMOqq8dV6wIaH3c58wkKtrHJm0Zv52i2rg8NjngWkeK44xIn4Wgyz3F5fN4gEtXwOX8f39vd3c3IQlMb/97W8zbI+PhWSDVOOnGrdFaMAzaLx+8WK3uqwI4J8kSdgpCsYuXMBqGHMCI9gttnLs9Xp2cXER1hzf3NyEfcw5fsuJV9y3ntfBG/9Y+1GOhphiUoXIbEt6ypSnc++tpYyu/y7Adt9S5IpRBeJdo9+VYcoo28wyiVB4abHlG7IfwWyxqxGDE293CGXjJdiw8tZEkEYjuw0jskdZyWsGsipdBmVWYpwMpUCrsTg8V2PLHMfm5ChOCFutVjYej+3+/t7u7+9DUpQHfOryVRBUYNExi80L7mctU8FA3c+eQuXrPFDnOnnMQhmuB5T6LL4W/cbP9e6L1YH7COtQcZjBYDCws7Mz++GHH+z+/t7G43Fm/2skDTGAwfhhty+zQcxlrhfPVfVKeHMV3g4sjzOzTD4F38sJgmgzmO3T05PN53P7+eefw3vV7/ft8vLSbm5uwlGYiAuD3bKRq7F3b9y8v8uwxG1IxrbXVpF9kKpdpait3wXY5rkm6hrsIgW0D1HmBzDCmtbRaBQOHoALlM8FZfarSVXcJo6LsvJkVrvZbALQYhtGDxBYMbEbD32lZfI2kQyUCsoAIF4by8qeE2IAYHyakdnz+kYw2vF4HLI+UTdlMhp75nHhJT38TDY8WJkXsTqNk4OtgSXBVc9tVqBlANdn5bEY/WHQwX3qteD6K3vTZypocX9xX6DNvI8wjuq7urqyL1++2JcvX2w2m4XNHzjEwIlLbJTqZigcYtHx0B9uP3+OdwjxfzPLnMIFL4n2Ac8PXs8+Ho/tl19+Ca7z09NT++GHH+zx8TG817yiwHMjKwCxByWmo7x7dH7EdN/XklhdDqmOnnwXYGtWLfu37KCUsfzynluV3arCx3W8qB6ZtR8+fAgbu6dpGmKQurUbwFbBiF98XIPn4X7OZgZLZJesKnO2sDkpioGLwZbdcuw+1b5ho4Dd6BCNQTMIII63WCwysdrlchnYDmdRe+1RQwTfQ3mrQaFKXMEQbeKy9N40TQOrwTM4/ujV0TMYdJ4qWOv9/DmXpUCl7C82tz1Dw1PiyigBQPP5PGSS4/Sg6+vrzGYXPK/UaODEM85K5np67zL6UgGLvUUIS4BpJ0kSwjXInQDT5jrxBjKI18/nc/v8+XNYc9vtdkO8+u7uLiTswUPVbreDW93TI9ynMQMvNmYqsc+5vBjTjIF9rB7e+/G9yHcDtmVl20FUVlH2nqK6xJShAiH2ksVxerD4j46OAoCAqTHoMZAxsEHxQKniOUmSZHb5gULDWkAsndE6qytLma2yYG4bn+LD13MCFNcdipCTT1Ae7kOsFktlAFrz+dyWy2Um01OXKUE0HsbtYkWNZSscm2ajhetYZMABRLjvEZeHN0Fj3nw/vo8Bnld/rhffo6Ckrmx+Ln6zEaYGhrZVwZZ/Pz09hdNwcEoO1kr3+/3MPGcQRXyby4brlw0+ZtIwJr2+4P7SfAfEaPFc9BfcvYi1YjMLNnrZQMDGKuPx2H766ScbDAb2+9//Phy/d39/b6vVym5vb4OXAzqBl6d589Sb17HxqENiJGLbsvh+793JMxIOTQ4GbHe1ZGKdXsQuY8IDrCxFlURsMmxjpWkbOOEHLyonSq3Xz6eqLBaLsP8xu171h5UF2sNuN3bhclISrGmNl8Z+UHdlQF5SFDNuVpDoOwUyfM4snfsO2/6NRqPMcp/lcmnz+fxVtjaDqib86G/Ug9kwj5nXVm5HDAT5f15Sgq0vu93uq92L2JXNRhoDIsrDOKr7lkX7l8dKgcebswzKZpbpY48Bad9wGev1OpwZjEPmAbiDwSAsb2OjA/0GQGKjjdd4q8eCAVvrqIYf5hx7WTC3lsule2gGwBzZ+41GI3gt2PX99PRkt7e39uOPP1q/37cffvjBzs/P7cOHDzadToOhiHmI83NjIZwivcRjHQOsmFG2jZSpTwxQ86QIcOuoex1yMGBbh5S1qvIsprJWWUyBxK4tU1e+HuUj7sgbPYCZQiEDbPmsWrPsEhgGNAUofpnw0ms5ehiAKk3vBWbljnJ1T2WOqek9YABqHDCYc59BybdaLRsMBiHB5vHx0e7u7oLbjdvGoKTl61hD2apRoqAU6xedZ3w/lwvFyWuoOc6OejIjhwJWA4FZsIKlXqugx65X/sxrj85bNTb4fdFnq5GFe3GCFVzJuowLsVI1QNTVzYZZHih5ngKea+rOx7xEotRyuQxgipALysXOWP1+3zabTTiGD/2I+i+XS7u+vg67aMGdjM0ueNtKsFt4QTwCwMJMV9tZh1Qpp8y1RSBZtd6ejqpLytTlYMF2W/fAvt0K+7aOeILBHcXnzcKVDHaLDGQkRnEZbGEr69HlD6qcVcni+Xytx+BwvSYNqdJXto5r+Lg7Zd2szDW+y8wNm1icnZ1Zq9UK7IjPn4Xi5eSwmAJit7fGaLkPEZ9j4ya2FhLPZFbNYIg+YgOEwVyBQscDwkYZb8ahBhb3K7NzZrU8vvo3l6WxXs+I8hiyMsg0TcN2mthmE+c26zaemjmPJVkcs+Vx43qirpolz+OCcvA8jQVrTPXk5CTMlfV6bZ1OxwaDQdgVi0/VStOX/IijoyObTCb2+fNnG41G9sMPP9hgMLAPHz7Y3d1d5vhHZrccFqoCTmV1ZR6R8cbRM+Tyyq4idRoHb8l2DxZsy4o3mHkuFJU63SJ5DFmfWTRhYDXjdB8AJ7Jr8eLD+ke2IpfLCsGrM75nN5SXxATFpiBi5jNBVqYMmuzeVjcyGwgcO+b6c6yNn8V9j/gmdrYC6+Dt75Th4X62/CFeApGONf7mzRaYpWr/MBNDGxjUtL+5rl6slmO0DLjoewVo9Up4Rg2zdQYpbrPH4lXhq7HA80Pj+Z5g7fhqtQrjimx4eHhgBHIMlLPx9Qft9fqMDRUdY7wr/E6gj3XnqCR52R51vV6H3AG+Bu3nBKvj42NbLBZ2c3NjP/30k3W7XTs/P7ezszO7uLgI7vPFYhHeoVarFfaY5nHUfs3r59h3zITzALfos10kBoqeUbRrmfuUbx5szeplm94ENavm9tA65bl0vM8YaKFU4BLudDqB2cLqhzJi61zdvmaWceHiRVUGyywBdUd9OCHJi9l6fcD9xwk3vBMW7uGzdZWtMqNQg8HshX1iv+Z+v2/NZjOwWY1vKlBwffUzbhfXg79nBQ7GzODmzQ0FPzVSPPDnZVNswDAo4vl4Bq+rVuDUWC6DDfePuk+ZyfJc4PZBOM6p7db3hPeb3mw2AViWy2UAWjY42WBEHTTfAPONPTZqdKmRyRnEPNbYTpINRTwPbeV+ZAMGO0I1Go1XsVqwYKwHXywW9vPPP4c249QqvO8PDw+hn9iV7MWed5Wy4LkrgOXp2jJlF11TRZfvQw4abGPWVhlrpuygx5RgDAjz6hKTGNPWMvkauFk5VoeYFb+QvF6UF+xzEpXZ6y33GJj5+WzdM/tgBqquOw9ouY0eQ1CgRVyY19FiOZLZC6tlwFfFD+aPtcAM2nmCNrHijbECBnwFZm8cPQDXMlWp81iYZTfT4DFigEd5/BtlKpibZV3H2g9cpmeMaDv5b547/JvbrKxWhfsMsVsso2k2m5ltQuGGZXDUdwFlgo1q3BbCgKsGEddNmTLuZfcyG5YMqJxEpfXivc7NzO7v7+3z5892fn5uHz9+tNFoFA6en06n4Rl/4Iv+AAAgAElEQVRg0JzJruOIOnrj541VHgOOSWwsy94bM3A9qWJQFNXrreSgwbZIdIDKMMgY+2SJDaTHNIrqh/K8srw64TtmeWbPihI7RkHJjMdju7u7C1mNsJYBXLom1VP+7DrmA+MZfNgdnSRJBmTxPZ6hrl4FcLjgNNEG2c5Yw8tMRV2hZlnmgDYz8zezcPJRzDXIfe/VWQEyBkSekmDGyMDBwIf+UhbEZSvQKsPOMwC53gwKMUOCnxMDQr5H26IGQ5l7ve+YSadpGtjtw8ND8FxgBzMwPJSH8cb7oGEIZqMe+IClwrDz6qrZ/WqEeiEZvF/4DG7lJEnC2nU+VjJJns/xvb29De7kXq9nV1dXNh6PbTabZWK3WJqHnATPY5U3Hmj/LhJ7t6oI68qYTtfnFV3n1eUdbEtKkZshBq7bSozpes/2vvfKU+BW5Qdmi+s4OcTMbDabBVYLFxLW86ki4DqyW5njW5y0oWt1NW6nIMQK1gMpvseLI0NZsGuZFWLsGfx8uNMA1ti0HvFsZVw6R2LAVgSm/LcyMnU18zh4bNQrXz/ja5Uxe3Vl13YMuGOijLgM8+E5zfMvT2JlKeA+Pj6GMQZAsXEIjwjajW0NmYXCKPPWpfJ9ebFqnsMK5jh5i//nE4LMLPN+4d2GkQhD2czCPtA///yznZ2dha0cR6NRSJbi2DSSJ724vo5NUf/vAsBlwG/XssrMxTqliOmXAe9vEmzrltgkVBeIKmhlBJ5SjYE038ffwQWMeKvZs9JBNmO73Q5AixgQx2qU1XrPUlDAy6nnxeLZrHjYemdmy8pYAZeBQoEWnwFsFYS9fmT2hfbAE5AkSWbD+sVikdlZS8tg1s3uQ7ADBVIWz+hAXb24dwx0OWEoBvgqeXUCuOrc0vbzd97faJOObx5Q4zovA5jby/+bvd5bWd8nPe0H7wgYYpqmITtZ5zMDLYMt1ulq2zirnPuB6+clXeF0Ig5/8DvFwIg2coY/+hTvQ5qm4VCGn376KWzocXl5GXZBg4EMAx3xW02WLNJv3meeTssTfVfzAKgIQPM+rxPMd5UYdnhysGC7r84sO+lQB0/BVLEQi67RsgC2vKE5WC3W7eHUGt6QHaDA+7PC0uf9fz3Fr0Cr/aIvHDM3Zsvei6bAzODCbeYlSiyekleAQ3/hYG/Es7DRAG9hiXLUNY2/2QXIaxrV6PDq480Xvp8/5/Z5YIbfXgY2X+MpQ2Z3GB9uN1+bxzx1rNRYw98ax0a56ob1/i5SnPxsXM9L4nDaFQMVz1HOqgdIo185PqtzAyCt8VXuF/4Bo2Uw5bALg5/mROAwgsViYc1m00ajkbXbbev3+2HnqB9//NEuLi6CG/38/Dy4k3XlQKvVyuxoFTP61Wjlfvbu2wXkigDYIzJF15UFc+/9iV3LUhZE88pgORiwzWNhZe/hz2OM0ru+6DllyvEmZlGd9H+8LLqFIQ6WPjo6ymQg8wHxeMkZbJMkyRy3p4oVdVR3F+rGL6THJlEmx8FiLzDuYWDBdbq7lQKC9pm6uOFW5IxTsFtmtHimZwBw2Vy+snRVRLhH+xdsh/9HeR7r5PapkeN9pv3DwMz14772jAx9hhoX+kxuC9df+zMGpt41Or/y3NYMKHAp84b/zDQxr3l+cbyV3c7cToAtGw5qKOjnSOTirGSwaDVyuT9wHwxnsNNer2eDwcB6vV7Izfj06VNYCnR6emoXFxeZU5DMnjd02Ww2YSkQP9t7h7V/y+ipssL9qfOrSPKu87ChTsPAkyrAG5ODAdsy4jEnlqLv8iw2LSN2n/c8fOcpyVj9vfKS5IWZghXwDlJwfWETfbbOkSTEu0ZpG5SdQUnwonwv1sOAo21kwGWrPtY+z7XoxXG9vkV9OY57fHwcso8bjUbmgAFvQ4kYGwVYqfLVZ/Lnahgq6HrA7I291sUDnDxFomClQKXLnrgcTfhCv/LztTwd31h8muuG6/gd5Yx0TXjzygHQYo9uvBfIMDYzdxkQMz827jRrmJ/Fn2tf8PVsILKhyt4azG8+wEP7BIZBs9m02WwWEqIQp72+vrZPnz4FAMYOabe3t8HLhefCEOETibx5E/vMmyvbSIzU7FpuHcCn5dUJzDE5GLAt01hVMLuUFRuwPMsuBqJlyo2VwUwOL2S73Q7KC+ALFymyMvklZTea2csSBq8tnHSi2ZKawch1VbBQoIES8zI8+T7PLcl9533m1QPPOzk5seFwaMPhMICiMn5P+FnsOuZ6oo+0zl5GsmZ7eoZXzBj0jDBNLuLyFPy0fC3XY29llKwaFdwWNZY8ZqSAwgxHy/GeweUwkJhZCKvwGc8wTnVcNdEOQMjz3sxebQnK7NiLXevcZLBlsOfwCG8i471P6/U6HByPNvb7fRuPxzYej+3Tp092fn4e1pKfnZ3ZZDIJRibqhJg2Z1dz/+bJruDD48pl7lq+p5v4c36O1iH2THxeN4B7cjBg+xZS1Kned2VcFlx2lUmkIMTJTRCsrW00GmEnJM4+5t1nUMf1em0PDw+v4mjMfKBMOHlDs13NskCUZ3RA6XqZnl4WbawfYyyKWQragT2QkTgyn89D+wG42t/6GbdRP2NQ99ikx4YYGLj+3DaOK/J4MHBrYpY+n40mj+V6/cssjcdPr1Hjig1CPBt10jooQPLc8erK7eNYp44bAASGJ1gf1tvycjV23TJgIv7K7mSEP7w+xXhi3qn3xTMEuc7swmaPk/a39uNqtbLJZJLZ8rHX69n19bX98ssv9uOPP4aTkC4uLsJZ1pPJJHPCEO7nd0alrM7z5kmexPRm1XuKpIwxnadvdjEqYs+LycGAbWwylLE6yjBOLs+7V7/PK8tjEUX3xCwu/h5r7KAAwNxwtBrcQuxC5uQPWOZsqeeBLStDjU1ye/LAVpUy2sHMWoElxla9vsJvrScULjZrhysR7nBWMJ7C95iT/h9TwnkxYI7Ncp+wC1NBn1k0l2dmGcOL+wRlal24/jrPFGg9MNb+4DGsImo8aJ3Qv7yrFCdVqfCxcmYWwJa3M0XfsyuZDSD2UvC48H289aM3/z39wdnF3I9ad7BwTrzD9dwveM/H47Gt1+tMO6fTqf300092eXlpv/nNb2wwGITYLdbeYx7imdAZZtlTqZTtxjwgqFceMOt9RZ/lSRmdvy1I7sradynvYMBWRZVHzFKBbNuBqgC8cqqAuVeWB+j6sgFokS0JlxkyLpH0w0t9AGjMosCIVJmp8jTzz2tlkIr1DZSD53plJRZTdFBwqD+uU5etghkDWqvVCq40nACzXC5DH/FaQzZMtE2x32xcaNvxPfcRA6hmMfMSrticwP8cX8WyFmWPzIR0bHk8dI7hOgZ3rYMXg+TvYwpXy8W9GmPnevE4K+Bwv/FSHwbbfr9vnU4nk4mO5/BOUTxX2WPALFfbj3pwJj9YJtrFY8JtS9OX/b+53hpP5r5APfA9NvLAnICh8fDwYF++fLG//du/tU6nYxcXFyFZCsuBFotFAPZWq5UJO/F88nIMVOfq/KkqOtZVy2ByELu/yAjwPHZfQw4GbGMWkWd5e9/tIgoi+hn/1kkYq0MZ9wl/Dqsan/HyBljDyLBlBQZG6zEzBgNVBlAerHy8PmGwVEWOe5VhM9irYcHP188ZwLwf9Fmj8bKbFlzsvK6Wmb+CNb94+O3FbFkxc3/GXliwVrPsrlCsyL17PMXLsUZOGNJ3gV2asfi0Gi4MsmwkAOB4jPgaZXA6F7iPtE76LnBbODnOayO+Q8Z5u922zWYT/sdmEKvV6hV4c5nabmW3GCu+R/WBGq/cfm4rfuOd5g1b9J3g53A8d7PZBKNxsViEzWyOjo5suVzap0+fQvik1+vZxcWF3d/fZ/ZJR5m6WkAlT6fpd7H55X2m5RRJ7Dk6H/WeOkjXtlLleQcDtjGpwy1RVvI6Tl8+j/lx/Twlw9fhJYcCgOUKqxNbD2KBPOKwCiR61Biu5fpqlq/nVs1j3zHg5PLUJYjyFMS4TgwurAhiLxY+x447YLWbzSYALe+qg3prXXUsOXatY6UKXEWB2DN4lIVq27z+V5dy7D1QwNW558U/Aaw8dp6RgzKZ+bB3Qo08T2LslctjkNPvsBRG90PG+wGDazabvQI6nuf8twe2DOz67jBYc7KRZ0SqAc1HYmrbuFzcx5uBIPcA63B7vV4wbm9ubuzHH3+0s7Mz+/jxo52fn9tkMgnsFttYov/4LOmY0aFzwJs3sfHdp8TmV0w/HKocFNh6lq1ZXEHt8hyVWHmehV806WJgwcJMRJfrAESxKTnYF77jl1DvRWYyMhNZIeqLxAo35n7E/wxUaBuPj8cE1f3rsQmPVeq4s+JHm/jkF+zprKf7eGV4n2lWqtc+z7r2gFv7LAaYCsxcrtfvCqyeaJ29/uQ2e23g+xDnw3W8nIUzd/kaBTw1RriOGj9lYEZ9kySxdrttw+EwrDXHNoxYT473hJfysCuZPRw6txVwPcDn69QAVU8Jv1u8vzm8U/wcD6xZx7BLGUt4YJA/Pj7azc2N/fzzz9bv9204HNr5+bnd3d3ZZDJ5lUiInaZi3gbv7zI6VvUjlxHTlduKN7f1f61Dns6uU8qUf1BgW0Z0whdZVnkdXcUyKvOsssL11+0VWcHARcYsjWO3m83LUW68NAEWOLuI9QXOs2K1f/heZjr6HSs6BkhlFlq+fg/mpc/FPXpowdPTU7DmY2tKFfw9S56vRzvZjasveywG7IE0g6ZnuCnD4XpzrJDr6fWt12ZV8B7opGn2eDuzFyOEXeIeIOmYegDm/a+f6zhsNs8nPw0Gg5B9i2dwHJc38WfA4hCLZ0xy4pu2yTNutI7aPygbQH9ychLYOI8Ht1HnnM4FfLbZPG8HiflvZjYej8M2jgBbZCZDR2DceGvLWDioSPIMRJ13+wLc2HO877y/9yFlyz8osNWX1muEpyRiEyX2OSu6PMVRVmJMLPa9Wl26lzG7zpDggOULvKmFAh1ccshA5JisJqfoi8/sQ/sJfzNocH9pe/ll5nvzWKynhDylBGWDTEszyxzBhmvUyND6aN3yvkf90I+xujGwsRGk/eTViRWtp0CK+rSo77gOnodC47YKLF5bdC23lsnLXtj9rEDLc5OPzGs0GoHVDgaDMN4MtoiFgtliDvB13F5tDxtTnAnNdeTyMBZ4t/QdgnByFId5EAaKxdh1/DAm6Gd+71erld3c3Njnz5/t8vLSRqNRZhtHNrjTNM1sjKNzT9ur7z33QVUdGbtnWxat71GeERAr42vIQYEtSx2dwgASA+kyz6k6KfQefS4ELzuDbZI8H7mFpQ1HR0chFomYLdxBsNr5pcKLyS40ZaJJkrgvvE5Y7yVThaqKIc+iZCWl1i+u9/qa28UZpAA+Pl5Q28nMRY0FHS9eA8lzh5VsbEwVwNQtyUq+SHl5Rg1AociVzHkAytC8tujc8PoEIMRg5LlPGay1jVoPjCHKZEMMc5s3LMHBHHAV65aNugUjAyovgUO/ApzBEjkrWcMJEGaZmuHuzWeUi6VwsbHl/vLGSHeDg0Gx2WxsMpnYzz//bB8+fLBut2vdbteurq7s7u7O5vO5LZdLS9OXsBTHbj1Wijp477EaHjpf8nRjng4ukhhZybu26LM6pSyOHCzYmhV30q6d6A12lTI9RhubhDHA5vgXrE9sx4Yt17CZBSdIAVDhRvXABYrXY2+ov1dfrSO31XMj43ooEWV/CjperDOvjppEw4obbD+21SLvasUGggIn6sEucHzOdUPfcz/x+KtrH33m9YfHorg8rbcH1DzmEM/9qa74Mu8WJy15c0U/A9hyW9lt77FaBnBOkms0GpkkON36kN3IeA90/qMcAB76FP0DA5eXGDGgxkIuAFok1XlGGIMxt1UPr0e/eX2PeqAdOj/AbnFQwXA4tA8fPtjl5WXYxpE3uEGiFPI9PPDS+RkLy8QM5bIAWkZixsDXkrKgGpODBtvYZMj7fhfxLKgYU/O+i9XTA3QoJt4xCkta+v1+htXO5/OQbbtcLm2z2YRlD0mSvDowGn+zYvHqwArQY6Z8HbcrL+apLI8VHQMWu1z5HmUgqIOXwAKlxQpPDR/UQcE+b5yLDBA1YLgv+DePs8d6PdF2etm66o734o6x+vK4aN3xP35rjNYDWm9c1LCIzS3OAeBxazQaYec0bOrCWboKXrypBYM99x8MDv4O9/P84WsUbLltnhGrc4xdzVwXMO3Yzk4KqCxoB2S9XoeNLkajUdjk5eLiImzjCGDltbqon6ezuE4xwIvpN28uef3jlaMSM/i/Bvhyf2z7/IMGW5bYYNcFtFpWUYeW7XBPWWEyc3IUXkSALYB0Pp9n9j5FJuZgMLBut5vJtIQl6rEQrg8UFh/Wjpc/Vv8iMOZr8D/KhkJk9xfvGav9ov0L5cTX43m6ZEddwZ7i8pSAlwjFdWLgQv29Z7Ko0mKw0OfjWgaHIkXEBguzWTV+GBj5efxMrz/0Hm6LtsdjOGpgaNvZ8DHLunIRl0diD+ZqHuDis5gy17mw2Wwym8bAaFUDzuv3orirZxAB2PXISx0T/Q7lcXY3u7yXy6Xd3NzYp0+f7PLy0n73u9/Z+fl5OMt5tVrZcrkM+oXzOTyJAal+V3Qvl6FlltWf+9Dz20gdAH+wYBvr3LyJgM+KOqZo4HTCe5a9V5e8crVeDLicVAPGinM6wWqROAIFget4e0Ize3Vyj6fYkbDx/1P3LrHRbl1+13rs15e6uOyy/V7OTf2hbiSGREggxAQFMYBJTyCJkKIERepJGCAYEDGBAYMwAUUCBbXUg26E1EQBlAjBAEEQigQMEhBIZJKmQ74+57wX21Vlu8p3Pwz8/rd/9fd6nqryec8X95Ys21XPsy9rr73+67b3ppDKxpTRpQlsvS22I2sl4ulZyw7kepaWXcT8gQNUDhivbZonWrIUgG3Wno+ZQjYTkg44LLS6miwGKhGuvHiMm2NhDJv9dZest7lIceLYsr7zx0GUcVu1kWUyO9jyXY3JgdbnNrP8M573tqk0MXbsa56F/OM/rpRpDjQPVAyoEGQxbipBbFOfi9flWr+/v4+Li4s4OjqKjx8/xnA4jK2trRgOh7G3txfn5+dziWdKuvRjIxcppc8tqwJV0zr6415eHNg+Z4KbLIVVCwV2Joiyz9v642DiYCQhov+ZGBURJY3/4uJiLvbF+njEo0BH+/koQGS5SQDw+8xCcdeeW0lNAK36/IxZCUQpA7LQaVXSrUg6RszHtqnV+/5YulQlmCjU3aXqiklmYei3v0sQ5dj5Hq0xPwRC4yPgZO1wPEyW8cQZbnNxt3MG5D4+BwyfAy+kYZuy4HVlz6o+PqM4I09g8nlTKEZ0Fg+oDndVcw0x1k/aOT+wbz4/TWOLeLwQQwld7slalE9BjwnnUjwv4IyIuLq6islkUi6Z//rrr8u5yZPJJGazWVxdXRWaSklnBj/5wf9uK03PfQmwXAYPfi4l4UuXFwe2bRaZT17TZDYt9lXbb1oEeq7JWm1jPn0nq1ILT7d0dLvdOatWl8TTsqqqx0vhPQZEFye1aAme6+vromFroblmn/XbLRAXNJkCUFXVk0SRiCjKgNPO47ZuqfBGoSxeS+uZwpW0dyDlMwTLNu8An3UrXELUn3UaueKltjh/pDMtHe+rA17mUmY/svYzemTfeyHtGTP1trO6nH6kD3lS/9PVz/FQoeHYXWnz+aUiJleyx1ppNZM++k4g6vQU7wvQdJmIAFdZ1e6K9jn0v+Xuvr+/L+Cpoxxvbm5iNBoV61Y3Yu3t7ZWLCjRW3q3bJFubeJQ09cLnVjV6Mpnf1gb7+CWA/ecuLw5s20qTNZWVn0r8TBgtek7tuuWQCRwCkv7X3tqNjY04PT2Ns7OzmM1mc3UKbC4uLsoRdvqc2ZG0aAW0iu/UdT2XGCIhkFk9qpv/Z5aI3qFbj4X/S2hk7l3XUkljuqWzpKhM4ElYOaAuowUTAB3QNCZ6BLy9JnDzcXHMpIMn/OjHgYUgQHeyC/KMD6k0uPekab1lwjTj98xCzOjr79KCjHhMZHJaaA4yN7LPuys6PFGprU+qL8swZ+yYgKw6Geao67ooELruT0o028z4x8ck17HG0Ov1ygUNp6en8f3338fbt2/LYReHh4cxmUxK7FbWrZR6nydfG6vK0ue8twiY29bpH5fyxw5slxGSy9STlcwC8O+yvjTVw7b4mdysjJdubm4W8Fxbe7i7VskNep8Ao1tB6vpx352eU53UwAW42tAuAeAnBFHYsk7PrGzSYPW3BJkWMwWTXMwZfQkSPDSBc0Lrz/dqOu3pLld9EmYS3tyb6cKPsbKmuKUrFvw+SwzimB1ovE1X1vh/Zm3ofboIPdZN3mY7mWLi/W1SQjM+934SiGitZcqb7mqWF4Tb3Fgvv3Pa+xw6kEtx1bGnTTF7xsblEZKXqO2YULUhJZh7ezOLWe/5liEqVgRbAe7V1VX0+/2ye+Ho6Kicm9zpdGI4HMZwOIzT09NyYYPkjSzlLDnS+WpVS/XnLs5/zwHrX+V4XiTYZkL4S2o+zwXJNqDPtMEMsKUlC2wVd+l2u9Hr9Yp7aTqdxnQ6nQMeHqdX1/Xc7R5chOqHBIn6Rpcu456ZK9AXH7V0CjdaWxRkEvjK8HSXW5YwQzd5Nh+00iXkuMfS54W00jOMWbty5ecFsx6CqgMtBSYtLneRNikXHD/fd77yH++fnmdcjzyXWYF6n3PJnyZw5ZgyHndw0/g4VvEgFTEWZprTsqUyQqvX+0FedC+IjzOLk3tMlfOt9XB1dTW3951jlxv58vJy7l5qbsFp4jn97/NV13VRVqWMXF5eRq/Xi+3t7bi/f0iW+vjxY7x58ya++uqr6Pf75dxk7ddXP2RhNylu5C/nhy9VnlPnIkVwlXp+VYD74sA2E7Q/pZ6sZKDp7WUgu2y/sgXNflXV44EUiuHoHOSIiMvLy5LQIMEpi5QJFgJfHXTuYBsxf1CABLCExSKLK7ME+F6TteQavbuIXbjyPT+fl32SZSEg96vEmHRCC5SxqSbwU3EB6/HPDPAcbDOg9TYzsFXJLOWmOVL7FBoZL9OqV/giUxAJMt6ftrUpJSgrDkKq0+vRPGZz06SA073rPOmAqe8yD43H+ek9IY/ryMgmcKdCSD69vLycuyZPWdbyAJHnqSRnMkQ0klIsQOdWvtFoFJ8+fYq9vb0YDocxGAxKZvJ4PC6xW/bB+alprpt44TmAt4q8z+T2siXjtV91eXFg26ZNk9gubJr+XkYbb+pHU5/ahIG/5wJQQMt9gbp0YHt7O6qqKotTrkCBnBYH42t0szGBKBPEmcXiFlHG9PpO7rNMmLtFICuYmjuPjswOTNB7+j7L3qU7jwI+c/E6/R30GYv1v7N5zUCT/zvgZdYC+6LPmp7JeNTr4DOskwlqGf18bzDj8tmaYftN64q/mwS2Pvd4t9e1trZWLhlgrF6F88059X3j/h7bcGWSc0E3s/5n8pTa0pojAOq33uNFGbq1R2tZY9NYqDDKYqbHyZVIlYuLi3L5wdbWVlxeXsbHjx/j9evXsb+/Xy5zYGayjnBUZrLPVRMPtJVVAPe5VuVLAM7nlBcHthGLBc1PLU11ZyDS9JwL2Eyw0MWod5mNKKHY6/XKsXS3t7dxfn4e0+m0uJm0ECR8IqKAWbZIXZgzDqfPKPQINHQ9cqw85s1jrk1A5vtfPQabWbYSMNrOxHiVgFjZlHJDqh0XRhy/K1lULvw3++dKSAa0tGhcifBEqWx+nG8yRS5TJrwP3m/PRpcCw/ikj9tBq0kBE881FY5ZfWXWLueVRfy3ubk5d5Ka05GAyqxejdNPR2JxxVDzR1c74/gqitPyHSnI8uRkipTCHVdXV8WDpROydEG86qMSeXFxMeeNkhuayojevb6+jvPz8xKW4iUFb9++jU6nU+68PTs7K9aszkuWF82VyswD4XTMPtf420qTkrnMu031rFp+yrurloVgW1XVdxHxexHxNiLqiPjtuq7/SlVV+xHxX0bELyLiH0TEn6rrelQ9UOmvRMS/HBGziPjzdV3/3S/d8WUno40Zlq1nEfCv0hcXyFtbW3NC5fLyMqbTaVxcXBQwZX9dWBPQaQVT+3YLMtOUXdj62ASebSfneB2uAEgouQXoQEjFgP3nnkACpGJXFHKZK1RuNwcY9YMKg/ePhYKfz9A97odMkEZNdCO9HZQIuM57VGCaAE7vZ/HNTNBlblIWHz/7oX7zGdHE29X4xL9S5ra3t2NnZ6ecpEZFglZjFp/Wc8w7WAS8HDOTkfzzDFQzhZKWcubpyTKspXwLVLe2tsoRraRrxGNmPs8G1ylRvV4v1tbW4vr6Ok5OTuL4+Di+/vrr6PV6sb+/HycnJ+VWoPX19QL+WYx6FUs1o63zzCIwfW5bq5RMwfXvfw4QXsayvY2If7uu679bVdVORPydqqr+h4j48xHxP9Z1/ZerqvpLEfGXIuLfiYh/KSL+8c8//0xE/NXPv3/2QkFFBm4rbUT1+vzztneyQnevNFNatVX14ELW8Yysj/FMt84kaBxo1aYLOPaBi5gL2EFS7d/c3Dw51UeFcUHVSYBjHzxmLCWEiSyyzvQ3D+1g+7wJiSDJetx6czDOFBr/nNaUa/20CPm8804Wi+bz+oyg0aQccX4pxGjdsiwDnvQ2ZPU46DstOT4CvwMZnxePqJ5Xr15Fp9MpJ6nR9S0aMx4q69DB2+fNx6++0yshd7DaYHxVyuPV1VXZA09+UFsK7bD4NiCNRwdMiO9fvXoV3W43tra24u7uLiaTyVz8WEmGooV2KwjI5R7W+6enp/Hhw4ey73Z/f38uM1ljlVfN16qXNsBcRpYu82z2vcu955SfYqB9ibIQbOu6/jEifvz891lVVX8vIr6JiN+MiH/+82O/GxH/czyA7W9GxO/VDz3+36qq2quq6qvP9Sxd2pifLL0AACAASURBVMBtGY2r7buMmM/RqDLh5QzhQoVZuTxsXYk/0+k0Tk9P4/r6eq4dLmr+r8VdVdUc4LgFxLroruJJNLQauP9O46DAkEBxSyqjr4SaQJsWice43HVL4KNbmq5jtqG/KUz5QyvGXd8EF7defWxt1r3qbrLkVC/dkV6/09YBzueU72ZKp88lLarMovd4MOvMxsz1yHoy93emHPjfPie0jGkJKxaqMXFunX6ZIuZtsS+kOU9/6vf7sbOzE9PpNCaTSUwmkzg/P3+ipPp8OL/JgncQlgtdIaTZbDZnRdPlrf5LWZESurW1VTxl79+/j9evX8fOzk6J3Y5Go5jNZgXEPVnKiyuCTZ+Rnllp419/vwnU24yfprabDKcvVZbBj5VitlVV/SIi/kRE/O8R8RYA+j4e3MwRD0D8S7z2R58/mwPbqqp+KyJ+a9mOsqwymU0T2TRBPxXI24oSErTwdcaxNpjreMbT09M5IBE4cv+dFgRjU1wkHFMGNAQC1Ukrqa4fL/KmMJV1u7m5+YRuLgCcTuwzXeSsX3/7vl69T2HU7/fnANZPpnKgpZu5CRhIDz3LMRKc+D2BS0IzO3vaQdSBxufJhT774p9lVjOVkIgorkNXVHwemmjQpMiRdiquHLYJUY1Tf8vKWlt7vBlrY2OjAErEQ1KQDn65v78vXhD1h94AP5XLfzzOysx7/a31ur29Hbu7u4UHO51OfPr0KY6Pj+cUXlfY6HlSv7gFkC5mgeXNzU1Rvn0+9P7W1lbpP/frS3ln7Lbf78fe3l4cHh6W0+l0wpX2Nrcly2VlERgu+rxNXn/p4ornlyyL+r402FZV1Y+I/yoi/s26rk9tAdZVVa00grqufzsifjsiYn19vV5GY7L+tPW1tW3XcjImXqbOTCNueibiMcNWgrjT6cyd/qJ4rdw7EY8Ao8Xp7l0JBu+Px5OyGCaTYJhRyY37rFdt0d3kwEFLjaChvngfKfA4TgIu52B9fT263W65Tuz6+rrEmxgnU3veBsEys059LD7H/l22cAlkorWUgCah7/Rr42GCkvdbf6st778+5xabbI6yteggmf3OBGcGaJmAJs24R9vvrJUCo8QerRePfza14d85T7DIXX19fV1yAzSH29vbsb+/P+fyPj4+jtPT07n97YwrZ3Oitajx6BpNngut9ZKtYynxERGdTqeAreK4sm6/+eabckfw3t5enJycxNnZ2ROlxhMbNYZsjkU/L03Auui7puea2li28P0mi/nnLkuBbVVVG/EAtP9FXdf/9eePP1Sf3cNVVX0VER8/f/59RHyH17/9/NnCQgDJFgT68+S9VQrb4f+rvL9q0YIS2CoLWWB7d/dwL+XZ2VlxISsu4yfkuCXa5NKMmHcl6n+969tDJLCYjUlLTUKJgqPJ3ehg5GCgZ6go0OIiWHDLkCwBniO9vb09d/RcE7h6/7OEkGyeSTPyDAWQ04Ft+Tt3d3fFCtP4PX6u/x3k9X3WXxeOjC2zTvWDihHr57ibLBFfp6yH33uc2RURWn8EQp3OJHc8cwz0t2KUWi+yfDUvWahDCpvWIQGRe7y5tpgRLAVArl8dRtPtdmNnZye+//77+If/8B/GZDJ5Arhel7w0UmCVUSwFQ2NQ4b5f9ZGXGsjq39raiq2trajruoDt0dFRfPjwIXZ3d0vy2c7OTvR6vcInpPMyStcqJQO7ZcrPAYjk62WBf5myqJ5lspGriPidiPh7dV3/R/jqb0bEn4uIv/z599/A5/9GVVW/Hw+JUZN6yXhtBq5tFuNzCLXK819SA2JSktxE2gawvr4es9msbPlhtqO22agfEhYUjm7t8nkXrvpbFgKFuoSjLB8udH3f5JqlAqDnPS7qQtfprHZVJAD1uSe+SFhJuFxeXs6Bd0aTTFi4NcPvCQbZHmb2xZW3NmXRlQACk+aYc75M7NPj3U4H0oLz1tbHNkWUbm7Sw/upuqlIsX4HbfGNXJwCAio1qldhDYEweSs7M5k5AtzHLcWnquZv43KFUoB5c3NTrq6T0ry/v1/G9erVqxiNRnMH07h1u7a2Vo5nFTBq/QtseX2mQJ5KmVzQ6quscCpYa2trMZ1O4/3797G/vx/ffvttdLvdGAwGMRgMSoKZLGRa75qbLOa9avH3muT3snLd+ZW/s+fIi5nyn/XxS5ZlLNt/LiL+bET831VV/Z+fP/t34wFk/1pVVX8hIv6/iPhTn7/77+Jh28/fj4etP//6sp1pW9hfsqzaxk/pFzVGno3a7Xaj0+kUF+h0Op3LQm7qN4VFpp05szjYUhBL4EREAXW5kHy7gVtXmXtWIEHLQP3jGCLmY82qz+OV/FyKh7vVI2IuW5Tav1uDepfxam7VcFo7Hd2DQCvOae/xVo4rc3d6f93Sy6xYf9bHTxAkAPE75xMpYVQGMvBsKuRJF2IOsAw3sM+yPAmkVMK8fo2NNOBcZ65jgi3PCRcPN93z7ArBbDaL6+vruLm5KYlT33zzTan37u7xlh4mGIquAvb7+/u5y94Vj9Y77IMUEYKtntPd14pxC9xvbm7i+Pg4Pn36FIeHh7G9vV3Almcm6z25st26XQaMyO9tz7d91waai8pPAcxsLfP/Jlos0+Yy2ch/OyKaVti/kDxfR8RfXNjykiWzEvjdqiWrxy0EtpkBbSZws/olVDKwVZxHiU86C1kHWGTCNxO4FFo84s01UReCjH9FzF/arQsOKMRoDVBISri7hcvn6JZyBm2yhngSEF2e6rMSzQTCDsBOIwcY1k0lJlNkuAAznqNCo/75tqisLo7Tf6joZHzFz2jBuFvW+6n39Gw2Lr3vMXZ+z/oywep0ovKRuXb5o+8dNGmBZlaSwKeqHrff6CebB/aDSoCUDJ7jTTrxM4HcZDKJ6+vrGAwGsbu7O5dsNB6Py//6zJUv5W/oPHTFW9WmX8Sg84z9nl8d9So+loJa13VMJpP4+PFjfPvttyUMI3eyFIaNjY2y15m0dqXLS8afzitNz7Bk/LaoLFNvW1tNda367qLyYk6QahKSEe0T4MC4TBtNAjN73gXtKkygOqita5tNr9crGb2Xl5dxfn4es9ksIuaBT5om3bq0XgiK6qdbr3yWY1C/aN1dX1/H5eVlaavtWEQJBQdTnZhDIdVkOboy5SBHoUshSXDhyT9N89k2v238Q7o1zW/2vSsS3kZTuxTkrnCxECg4JzxtKaOnzz+B0hWmTDlQXYvomil4DtiZguT9VtKO6vQEPW+X681v1yEPOrjzM4Kt87vmx7chCRyrqioeq729vZjNZmWLjfaDM1+ASkK3241+v1/GN51O58ai8coyVp0OhlqDcg2rvzpV6uTkJAaDQWxsbJSM6ul0WsIw8hTxEJmmsoo8/CkW5zIW8qJ1vGw7i5SJrCxT/4sBW5amjmeEWIYRFoFkk/WaCZVVAZeLScJFGqVcyLPZLCaTSdFktcC1D1dHF3JRcWy0/OguFuPRdSnBIvdSxKNLWPsWLy4uiubsQsVjatK6aRn4+PWZCwQXYhwX/5fwU99l2apdj09R0XB3dzYvbuFx8TJJyy29pkVJVzXBkILd3cv6TJ/THU+BTv5octW71er0dAu/LW7LujgHmZJA2vvn/lmmYKmNiChAoX3oa2trc1faKZ7LvpN+UlJ1aIRcub4mGFKg0qG59uMtI+ZvIFJSlvrDgyK2t7fL6XByH0uZvb6+Lkc9rq2tFVmwt7cXdV3HeDyO6XQaEQ9Zz8zvoMUrUOW4xFOij85jvr29jdPT0zg6OorDw8MYDAYF4E9PT4tnzW8lyhTHZYCTStdzLNZVyyqG1z+K8mLAlpOxKqEWaSLZ98sAd5s10/Sc/02LVotbe2tlhehEGsVhRANeScd9p96GxiJ3EhN5CLAcs2JJtKJ4ZVjE06xYKhwEWxfCBElatxl9vI8OmAQ6go2EKcEnA8Am3nCrhEpK25y21ae/KfRJe37OsbM//KFlRYHVZjHSWnKrjf3jPC6y3Oli53yz/Qz0l7EwMiVWnymJp9frFf70O2Q5Xvd2KGapEI5bagQnfc76eJCFew8iYg68PbyiPvR6vdjZ2SmJUjr1Kjs8oqoejmjs9XpPbv6SR0zgTiWUyiVPwZJCzZjv9fV1jMfjmEwmxbvW6/UKmOtEqu3t7UJjHrjh8/xzgdpPBWfHEVcYlpXtX7K8GLB9blmGOD5xbdZX9l5be5nFxHe1YLUYuel8fX29HGQhV5PHq7Stwd1nEhJubXGx6TNuleACVHuqS4JAmrILToIRLRC3wiKenhxE0G2q22notBbgSthLYVH8NpurJkDI5tEtPGaOUiFg4hCt0sxydGDkd+5K9Dg3wUN9yyzljH9pHbPws8ytyrnIFAPOl1vXLtD42xUJ9ougqbnVHta9vb1i9dEyvLq6enL0IeukQra5uVnWURYOIX9w7LzwIkv8E4+QJgTiiCgHsCjLmN4jbbvhnGtLoFzEkgHT6XROlohWeoa8JOWCHjF9pxjyyclJsW53dnaKTFLMWDsmqNRkyntb8TX9cwN0U9v8rE0p/DmBNuKFge2iwS4LrNRQneiZUFqmNDGOa6cukBg7EjBos3pElHitspC5WLXQslNpZBW7MPdTdHTqjdrSwtFh51q4crX5+csUJjyQQfULbLW49blcvUzecNDQON164ngcKKlpK7Yk0JUgZBv8cStbf7cJAgfEzLrztlivgxLbI2C6IHcAyEDZwc49DOyH82imeGQCNbNkMkuYdbYpAL52yBf6bmNjI3Z3d+PNmzcxGAyiqqqyDpSpqysoGbt17wr5UuuNWfDiB6cpv9/a2pqL/XLd3d8/Zu1z/UVEOfFJSmGv1yvhGVcYPBHr/v4+er1eATqFmCJi7nhX9ZFKt2ggYHbr9+7u4bzk4+PjOD8/j93d3WJ960YgJZfxCEdf+4tKk/K8DNC6pyErTet1FSBv4+Gfo7wYsP2pg3TNe9HEZppN03MunL091pfVxbiQwEFZyJeXlyXFX/VIs1Q80TVYXnHnwEqNfn19vRxqrqQnxk1VpG3rGV8cFGi+cNUXAp+Ei25s8cQQ0jIDx4zeGdhqrBI+mZJFa9LnzOvO3KVNYEzgdjcjrWR3t7pb2WPKbpG6ResxRt+HTbrqfSoKrI91edya7/n4yUMEePaDiXVcMxlNyV8Ks7x9+zbevHkT3W53zqqVCzm7gIBuXY5RYOu5Aa6I0WpUndxBQItS4R+uEfdMaC1J4WWm9MXFRQFTgrjWUl3XsbOzU8I95+fnBazv7++j0+nMJU5le3jlRuddtXVdx8XFRRwfH8dkMom3b9+WQy50OIeev79/PGuZ6zab82WAKlPSsmeeW3zun1vXz2V5vxiwbZusTDNfpj5/v8m10OZGWMbF4Nq/3vMFLRDUwlWsdjabPdnrJ/BSwgUTmiiYJRQEdJ1Opzwjzbyu66JF80YS1StN27cdOTC5VSMh1el0ymLVcXBUKC4uLp4IXdbZtHjdymWfJci2trbKCVLcLsS+0+J1T4S+93dF6yaXGS1S3xriYOIWnSuGmiu3sPguP/NxaWxsRwLXeTP77T/eN9bVZCnrN93enFMfP9uqqsewydbWVuzs7MTBwUFxIdP1qninjjUUz7J975/WUl0/bpnRc7QqyUOcW27bc4Xh5uam7EnVmFSP6qyqh9u8ut1uURA0Hj+kgrzU6XTKRQTaQyuX8v39fUkS1LtUiH1LH+dc5y0fHx/H2dlZHBwclAsKTk9P5673lLzKFGVfD9nfnG99t4pxk32f/d+kSDe911Z+ClC3lRcDts8tzyGKg+Ki+pdVAvw7Ck+5kAUMEVEWjhar2pO1tra2NhdryoQJY0vaq6dFqMWvey7VL25diJi/GCBz+3IRex26tmtvb69sedACZR1N7nbSMVtIFEB6T5ZyRJSD2AXuEjqe+ML6XPt1d7yPm9YcBTVjxVmikSsnPt7sc48NZkobx6LxqN/eRtP//LwJ2Cl03GpsKqKVn7zEdth/1iuAOTw8LC5kWYi60k5gJZ7l7VRNdJIi6qBGYPWtQgRbz0imG5kJjBq/1p/6qaxjKQpnZ2dF0dZ4GBsmTaRM0pJXOEiJltncij/1t+dizGazODo6iuPj49jb2yvJaP1+v/RPyoK2AWVeqSYe8L+XsXzZ91U+bzKi9E5bfZkc4t9fEnT/WIHtqi6CTOA0Tcwqmk/WRmbVuPtJcUxpu8w4FMjy2j1qvdTACZyedCJAp7AWGOtwcvYh4lH48Kxkbmbnj9rc3NyMwWAQr1+/joODg3Jhtd7h6TmeTew0cgFJ2tJVVtf13OEAUhC2t7ej1+vNafN+kpQ+J/hJeNJ1TqBo4iEXxKQXn3Frzsfui5uCX3PsCTQEYPJuBuz8jHR23mV/yFtUkjLFQEoN/6fS4UpDxrNO1263G8PhMHZ3d8u5wfQAyY2s+W/yJFAplWUqBZTuYSrD7l3gHDJGLj5km1ob6pPWWESUw2ukHNBKn81msbOzU7b4sS7V1+12586K1vg1L5Qxel80kbtZa1K8oFjwp0+f4t27d7G3txe9Xi8Gg0FMJpOYzWaFJqqf8ijjhYxPvkR5rmxWWdaS/ik4sEx5MWDbpok7sVzYZO831cvPV9W2lnVNSNDwPGSBLa0+JXtIO1cyhTRh7iV04a126vrRFazFSKFJINCikSDQ4hYY8yg5gaUELt3b29vbsbe3F69fvy5Hv0U8nuPKwzcciCgIKNgdgPgMwTbiwSMgYRUR5cqzbrc714bPFQEhKx4/5Y++czcvFZKIeZf0Mj9Uojzpyd1vdHU7r2VKBRUkgp9r/eKvJtpQGGUWA0HTQd/p7qBPxUU8Ki+FeErgJIAVrzdtn8k8A1JkmdDnwOoWr/MOY6usn1a81lBElERIhlSurq6K5Xh1dRXn5+dlLy4T/bTmJDN0tZ8SxbjunT+U4yEZtLm5GZubm0VRUb+n02kcHR3FeDwuFxTQutXhNJJZ2qus9vh7UXF5vWxpw4G2Z5btV4YtP1d5MWCr0gZgbc+4YGp6d9n6/DvXfihEMoubQOFWp7Tci4uLuLi4mNvyo2e5+T0DD1oVWuzr6w8b3qVJC9DpqmbCCDOXJey015an1Gg82sYgoN3b2yv3Zgr81C8JA1oe6rdnzTaBrYSF/hfYXV1dxWQyifF4HAcHB7G9vR0HBwexv78fHz9+jPPz84h4Gm/NrDDnB/VVAo98wFikW3J6hsDnGa4OBP47A3LW63Ft9i2jIbdJOT+zEOxceSC9OF5X/Pz/iPlDTAjyPicEMX6u9hRukWWreK0fVEFgpZXHMA7/d5D1z72fmVzx7WFaM1JOtcYEZIeHh3OXzevykcFgUDxS7g0g2ErJ8Gsu6Q0R2HJ+ecmA5vbi4iJGo1GMRqN49+5dyfno9/ulLQK+z1ETL37psmx7y8j97P1Fzy1rlC2q50WBbUa4ZQjmxGgjzqqaT9YPr4PCh0KFC0Cn4eg0FwoMadvSVAm2XBzeF2myWni6NYTuqPF4HKPRKC4uLoplyjhURJTF6xaDfiSo+v1+7O/vx+HhYfT7/aiqau4gdl5p5nRxelMTbxLE+k7JLfrs5uamXIr97t274npXkha3VFAINvGDuz4F6i7EHRQj5gGNP23j1m8HXxeYGV+5xcg+uuKk8fFvH3vE/MXvvqfS15WHE1QyQPcMZ/9bz9DDERFP3JW853U6ncbp6Wmcn58XsCXP0bPAeeNnWczWQdctchZZqFIw3fMkpVUKrqzdjY2NGAwGMRwO4+TkpFwKLxe5wFbKuCx5t1B5wYAAP8u5EH0lD3hBvNbR+fl5HB0dxenpaezv75fwkA7WuLq6mksQ80QpL4vk6zLgltXXVFfTs8sC7Sp9antumfdfFNi6BaRCJm4Dyab3n1uytha1T3ciY0SyajudTgFbxmsFqlosvF4s4hFY9cNN61rQ19fXcXZ2FhFRMjXH43Gcn5+XeJjcxzxVRxmPAim6xQS0is8eHh5Gr9eL29vbmE6nRSCoUPAQSPSdaCjBpu1NPHJO4/XnaV2enZ3FyclJHB0dlfH0er3Y3d0t2aoqUmIcqOgid4uWVomDHPuiedfzbgF5YpHzMMFU3zuvEbSpFGTWJa0iFz7Z55lV7M84f1PYZkpJ9k5T2yqkm8BVWbV1/XDy0enpaYxGozg7OytZs+qD6hBfi/ZUPERjPZcpRu5GJt3EKwLI6XRaMu05h1J86VoWf2xubsbu7m7s7e0VZZtgGxHF5SvLkmCrbXy+95538RLwPYSkNa+5uLy8jI8fP8bx8XHZRdDr9aLf78f5+flcvJt0IU87b2bz+6suq1isi55bhCnLYs2LAtuIXDMi0GYLus2KzZ5tEibZc23f63dmwdCFHPGYNcvLB+RCluDWQpEm7BYVwY8xLQKCNGpu6bm/vy9asWdcSkuXRUtrKiIK0L558yZev34dvV6vuPYEsm6ZuNVGd6K+lztbYJvRk6DNTGNp8pPJJD58+BCvXr2KnZ2dsq2j1+vF3t5eoY8u5M5ifKQdAcP7QH5yHvK+6lkvtLIzkMsEmPOWgz+fIaB7NjUVQbbhz2aKAMfO+pwOUgJYPETivEF60AL1+Pn19XVMJpM4OjqK2WxWeFx9ID85+Ktugi7bIghnSkzE/D22FxcXMZvN5va9UlGTd0r1CAgF/p1OJ4bDYfHOaP1dXV09CcGoX5InzANRe+qb/qd3RO9znPpOa09KjDLANzY2ihdOh1pIudeab0qc85LJ0TYQXtVIagN3XzertLcIYJ9TXgzYOjA2WbGZMGoSbG31fMniQkkLn3Eg7geVBSoXrPqoRSJr062itbWHBKp+vx9ra2tze/W4yBRn1QLkYRO8+/X+/vFcZrmsZdVKcHa73Tg8PIyvvvqqWLTqM0+IytzdTcyuPulH43SrmFYuhYyen81m8eHDh7i7u4vBYFCs+I2Njfjuu+9ia2srxuNxfP/993NavvelyYLV9w64/HEgcZ5wy4kKFDNgyUMu7EkPAqQLhIzeTUDYlETGeGU2HlqN+p31I1vLtIwJiJ5LQD4WX1xeXhZQYEKh3vOMdGYLe2IZM2w9Ts7i1ixDNTpkggonXf2uiMntKzfwcDiMi4uLcpfsdDotGclaU+o/1yNDQFQctdYzC9dd/+qPvru4uIiPHz+WrGRd/6mtfNo6KDd0pvC1lUWGC+m9SnEZ3ybrvQ9tfVoVL5Yd34sB24jFgNsGqqvU62UZ4jZpaE0avwRGRJR9szoPmftruVi1sJjRyDYEtP1+f+6wCFpKtAgiHl1nLlwioixyAjYFkrKO5TquqmouHirBSItxGVoTPHmkHIUBgYECxi0XCSttVzg9PY1+vx+vXr2K/f39ePXqVYzH4+Kyi5g/25jeB/WPbnQHTLruNBaCrurw+K2DH60V50G3DgleDoQ+1y6AmO0sAe1WL5UB9Svj8aZsaCpZbrWreOIQaaa/laAj5UfZ7doip3224gkpkd1ud+52IAGOg4LaY8IP1wNpw5wFbbWRy1dJTVI6ubb0vyxajVu/b29vy/nounVHbuTz8/O54yEVI9UPk7uYrETLVr/pOZClTItea1b0Oj4+jvF4HN9++22xbLVnXzTSmpUHyde7FOFMJi8LSKuUVSzmn7ss0/aLAlsV19TbXAUsy07osq4NB46mv7N+84dWrRavrFq+p0XBfZ/qgxKser1eycjlYezU0F3rpBIgq1bZ0AR9CRfFhwaDQTnJR6n/ij9TuHBBUzi70KXVz5O0ND5mUEY0A4sDLmNnuvh+MpnE7u5usXrUZ4GpCwmCnoQW6UcL1k+XcjcswU3FE54IyBSY7Ac1dvId3fZOX3+ewtmtySaLlJYleYf04by7B4Zt+/PkESaWSXlTXFLzpFjt6elpcR0TNHUJutYXaZyNje1R0WL/NFfyMGn7kc4x5+EaCs2Ql0U/V2KljKh97WuVa1pgKyWCwKj+cT55SAWtZwGx+iFaCyQ5J1rz0+m0nJesPbfKSqaHLjsr2fnOZaPzRdNzTfK+TVY3yfumNv5RlhcFtlzQbURsAr3naDqLwLuNcZo0OFo0dI8xXqu7I/U8ASTi6QEG1Pp1nrJivooHM4GB2wMkyCMeLVy55aShU3PWWanv3r0rcVpZszrvmJaK76Vtc6lKCPg+R1oSmYCkVSmhI4Ghw91pacpN3+12o9frFeWG8TTVJ6FF70KWFZzxBd+hZUjrnBmcbvGKjxg64D7TjEddkXFlRPSRhbhIMXRedovUP1ObmVLnv6kwNa0d9VUgo+1x9/cPdz2Px+PindB4pPTxukrSwMfiCgbH57ws75L4Ssqx1i0Tl2SlMlkx4jHJi0egMh4rz9Hu7m5J9jo/Py9JlJIZspCVlezhB4JmxONpcJmC5eBM5ffu7i5Go1EcHR2VeK0sb90JrHq4/z8LJ2Q81VYyXnJ+c97xtpoKFYFFIN3Wj2XGsuiZFwO2bVpOE/i2gfKiZzIQ/SngTAvABSpjlBFRtjGIYd2VyEUkDZ4uW2nX2riuuhnzlZDQYpAg1yJlFqSASpbC/v5+vH37Nt69exc7OzulXwJqjYHuY090Ik2aLC2OU5v+CaiuuLjyQ0VFQk2Wgd7TWbtKZuG9wRJMBB53hfHSA3fZC0hUFy1auvq4V5nfubD0OOOixCX+ZN4EV1A0J86vTYoRrZdsHVA4ef0+/1SafN2IfxQmGQwG5Vq8yWQyt00m4tGFrHnOxsr17GDEmCVpzx8BrX7rpiF5kyIe9r/KHaxzzLXuZBGL16QEUqHc2NiIfr8fOzs7ZR3KZS6Lndn6qosKnfMAlVbyqtNc/VFsVvkbo9EoXr9+XRR7eQ2kYLh12waSamtZ6zIDRf/O+aapXe/Dc8tPedfLiwHbiOXcA4ss2Tb3QZuW3zbRbcyk72nVESDkFpO2rsUkoa82eVSiC1CBnNykupLP69f3WvRMltGiMAAAIABJREFUltDikzCRpi7AlFVxeHgY3377bXz11VcxGAyirutyTqqAgZd3R8wnh3j8mJaRW6q0CAmsFILU0P1dWpKyAHg0pQ4SYOyrqqq4uLgoWyDW1taK4Mjcw+oPP+N4RFt95lYr+UffeUa45pn08j44fzYJF9UpuvqhCy4cnf99HM777ton//uznB+6WtlXPaetW3t7eyVJZzqdxsePH+PTp0/lEAjSkAqmCj0sPmbNo4CLViPBVT9aQ3Ij8xxmKXL9fj92d3cLeKkO5QjIyhTdNKdac4PBIPb29uLs7KwkXSlZSltxdPMR6xP9yTOsnydlucUmOtCVrXdOTk5iPB6XNSSw1X3brpz4enCeaCsud5exPr0sC+QvobwosGXJLN1Ms1nGjeC/swnNBE7T92SsDPCpdcrypGWqo+cIKhKOcv06uETE3N5cuRsVj6yqqtStvbsR85mXd3d3xQ12dnZW3HLq43A4jG+++Sa+/vrr6Pf7Jc7DhBMKEtIyS3LS5w42EfNn/pKOnu0qmmbuV87X+vr63EEAAuputxv9fr+A9tbWVrGS9KN5cW1d7RJs2WcHfX3vLk1atIztZQDIv125IIiyffanrbA+AumiOvw59SMDkMzCzuZN9ep7hS4Gg0FsbW2V05U+ffpUDmWRRUZaMu7KiwnkJckSwur6cQ+q9mRLgRS/60fPcCue+GgwGBRXq3iOIO3r28MKdV1Hv9+P169fF+VbCWHaXqQxdjqdueRE5zEqZZoP9lc0obwSLyo+LNkxnU5LPFpKqzwNovvm5uYTWjfxS8ZL/N9le/bcqiWr8+cui9bfiwLbRS4HLtJsQh2MmwBxlXabAJ4COLMIxPjcX6sM4vPz8ycZvFoIBAq6G7XgBZA6NUlX6mmR8FQdnmsqy1jbFmQpqN9bW1vx7t27+MUvfhF7e3vFXS33kZ5zjZbCJGI+czjiafKQPlM/ebQehS8tXBV3nTFepN+y2pX49erVq3jz5k053P7HH38sRzrKNSgFhoUWN/vvp1llY6SiIR6IiCfgwH5nPJqBKi3LJtClBZJZpkxsIz/z7wz8vU/qT9Na4P8sFLBra2vR6XRid3c3Xr9+Hfv7+7GxsRHT6TQ+ffoU79+/j+Pj47i+vi6uY94sxfAMt7wx/kqlh2tMblu5h5m74IdGCLTEA7qSbmdnp7iQaR0T2GQ1qg7xqeK2h4eHJcQxHo/n9vEyhqvLSUQLhircy6P1wYQyl58Ko9zd3RXP2vn5eYxGo3j79m1RVHXAhUJfciUz9JPN9bJWahPgPqeQ/xZhybLAv0q7beVFgW1Eu8u27Tt9tmjQmVW6qitCzO3um4j5DEwBnuKIPEBCi1Gaols5BJ6Ix1OQbm9vS52Kq/DoOLXJxcdzTWn9SpjInbS/v18yeOmO5r5G0Y1gSIFEGtP9STqTbhJCHqOmwkJQ8q06THKS5S3FQ/d+djqdco2YvACbm5sFlOmqltXmLvjMeiAt6P51OugZjolgxmf1XRb/Vmni/YwXqUgSID027XU0CatsbNk72ZzT5ah+iJd3dnZiOBxGp9OJu7u7GI/H8eOPP8anT59iNpulyqPaIQ8KPJgcSK+L3uGWIuZPMKEwU6IVx9TlARFRjjlVu952xFM+Z75Av9+Pr776qni8qAheXV2VjGAqF3TLU6ljYh0VJfcURURR3s/OzqKu6yKbdFFCxMMlH/1+v7j1Zd2qH4qjL1OaFC/nl2XkfltdXt9LKS8ObNtKmzbCZ7L3FtW5qI6sNFkj3JembEUlGWibihJ6mMbvFpIzlvqpzGa5TmXFRURZiAIkAomAlrFatsXMYCZ6yGKgYPMxE0zcxUhhSNBi+0oEEaBKsLAO9ZExMIKtfiQwlG1d13WZA1k2m5ubMZlM5toSAPMGJT/9h3NCV51bDxwXecyBj8+6VcnP/b0m7w7/dmvTwUPjohXvVrSEubet7927QAVR/Odxdj3DpKjd3d2yl/vs7Cy+//77+OUvfxknJydxc3NTrFkqs+o/z/ZWOECA56Cn/suS5EXpVEC4JkiLzc3N6PV60e12Y21trbRJxVTtZe3LGlSdOp3pm2++iU6nE7e3t/EHf/AHxcqV/FDISLy7vb09l7ClUJAUd4ZDuH41J/xOyZbq79nZWUyn09KWrFuFoLRG2+66zfilzRhaVUazrS9lFbOvyxQ3JBa9+8cKbCNWyy5uKk3gmn22TFv8jKDjh1kwMYmW0Nra2pw2zP16tGxlhUoAKHbK/a+u3RIAlQUt1xIB7P7+vriX19fX56xvCutMISAI+XP624WO3mNf3TJREgld0jyYXQKQMVAJX1n7PFbv1atXMRgM4t27d0URUoIZj7PU4evS4jNh4mCncWTfuReAgJTxjZ5jjFvP+HxkQM6SKUb8aYqpe/uLhCTjyR7X9nnSZxLW3Id+cXERx8fH8eOPP8bR0VG5QMP3sgooqqqaS2LKANctW27LUShFShHn0S1a8Yy2GjFRyC1a8SLbdo8JLXDta5Uy/P79+zg7Oyt76xmW4ul07mGgwuC8yGe0pqqqKoqCvpMcuL6+LgeG9Hq9ErvVWlBs3S8uIZ85GGUeEQfnpu/bZHETLrSB8XNB2nFiWSv6RYPtokE0fb4MAb+UJsTCxanEA+3Bi3jc8qN4pe/HJBMzzqtFGfGYQMQN93SxqU4JIo2VVq3qksBTgoTut9ze3n6ygDIQ0W8Kbi42t2YlbPg+rXAmuvAwAB5ywDlXnMkTcO7vH473G4/H8f79+xgOh3F7e1uE1vb2dgyHw1hff9g3LKv37OysZIwLbDVfV1dXqTVK4M+UEfbLPycdKDydl5q0etZJb4LTPANcuh99Pjmvaj9rMxOo+p7jc0ud4Ms4493dXZyfn8fHjx/jw4cPcXp6Gnd3dwXcHGzlhpUiKTcsE52o6Il/mUgl2qseuUg5TioGepZJg3rW26FypcI4qupRsuFwOIzf+I3fKP3/8ccfYzKZlBPRtra25ubUFV2ur0UARX5jjHp9fX3usJter1cuJ+h2uyXfJCKKfOJ+YrXp/JQZOItk8KLvs/X4c8h1L6tYsywvFmyzAbVZoP7eMgLKGeE5E8V3fI+l3D1KquH9tXLFSIgQMLXPMCLmUu4Z7xMAr62tFWCWC8kFrOK9SgCREJBLbHd3tyycyWRSLDotXhfa7mLL4nFu3eo5Wm0UZqqPyS7M2JZlQUGj34yHUXicnJzEH/7hH8b9/X2cnJzE/v5+0dTX19ej3+/PCVHOQ1VVhaaXl5dzHgKPf6ltd8dmliK9E86r7h1o4rcMJN2CIW35vz7LxtBkHbf1KwNlWoW+xqigiN6as8vLyzg5OYn379+XA/pdmdL45RrVXHMrGy1GtzCpRIqv1HcBMNcS+811qL3pBDrfu6t17sq0A6728Qpwf/3Xf70AnkIiBHmNkTzLGLArTJlypOc4Tiri2ss/HA7LfmJZt7wbW4YB9/Rn/OGyYBne4vdNVqueyWS61+NKX/b5KmXV914M2GZCY9HzbRPR9E6bEFskVNqA3AUsXU4CMt70Q2aXdqq9hv1+PyKiMDUthYj57EZlJQsgmEWpvsqtyu0Ierff78fh4WGJByley3gpLaWM/k43X9Sc00xR4vOiXUTMCRa6HNUO46k8gKGuH5I9jo6O4urqKk5PT+Po6CjevXsXw+GwbC/Z3t6OqqqKMJEw1lg1N8oE1TYQJdawZIle7ubT+NxdHDEfX6UyQT504U8+daFG+koYs5CfsjkleHpfqqqaE+xZXT5GXx8EUClRZ2dn8f79+/j48WOJtXPcVHT0W/Mhq9a3bqm/bslxjrvdbtzd3c1tv6HHhUAmflD/uX1I77KP7DsVJX0upVKJSr1eL4bDYfziF7+IyWQSP/zwQ1xeXsZkMplzw3uyGNv2ZL/Mu0Lvlix3ZVPXdV32/d7c3JQTpWTdKiyjfmxvbxfa+zpcZOFmhk6mfLaV7N1lSxsesCyqcxkMejFg6xPTRLRMO8nKKt8tImSmBWWTIZcY41CybLXtR4xK7Z6WokCO2whozVErd/exwEmgKW1cVq0WWESUPajD4TDevHkT3W63uOLonssEXEQ8ARUleWVgy+cZq6bwII0ZU9I7SszQD7c06Dm9y0Spi4uLODs7i/F4HKPRKL766qt48+ZNESC8jHttbS16vV4Z6+XlZWxubhY6r6+vx2w2i9FoFMfHxyWJxMEk41+3eJmkIqVBNMyKg3IGgJlCw3cyZZF98ZCGxsI4IOtnjNaFYybg2Z54Vuukrus4OTmJH374oShJ6hP7K/5heIXJPe6F8bZVn8apU5y0NrS1jiELWrTc2y6Ans1mJYPXFVP9ncVSWXR8alU9XBH59ddfl8NkfvnLX5Y9twqpMIbNxEb1nXzoMpPgH/G435YK+9nZWUwmk7i6uipbj3q9XjntilccSt7JE0GlYhmZzrIKUDYZW03rYNm6VzXili0vBmwjniZ8ZN9n/2caVFNZpt62Z9usY2rszNaTJcBtP1owFGgSeooRco8rNWIKOW6DYKKGnvHDGgjs3W63nNjT7XbLVVrKTqTgopssIuYWOBc8jxyke9jjiT7Peo5WqgSjrAcpDNpw7xYS50zvMUuU8TwmfOgsYr2r/Y8UyHI/39zcRL/fj83NzTg+Pp4TPK5YkFeyPlIYavwcb8aDtCKbhILXL2HvQjazkPk3AVmKngtQgoray6x3b3Ntba0cdSi6fvr0qdxXm61177topT4wnus/pB+3rnD98bpH1U1A4s6C+/uHPevaF8uDLJyGy8gk7WuXAtLpdOKbb74plx9MJpO5PfbsN39zfalQXqhvpGddP7rT19bWSn6HspIHg0E5WKPf75fPmXsixTXzrGX8Tv5aprjcdUU2k8mrWrne3pcuLwpsVdoAzUsmaFbVSFbRkPi3Tz6BRYuTQKiYUsTDoldyjtyXEVHczQTlTLNnbJGASItU7TLZSH3NbvWJiLJXlzTJEm1oXUZEEQC0ukULudi44DNLSPEjfefATEufMSu6CH0xyy2uw0S0JWh3dzd2dnbKwSA6Gk90kOUbEcXVqBuX9E6v14vRaBTj8Thms9lc8pdbZM439BboebnjFCunpcl3aSVnn3t7+j4DPL3XBML8TaFOpc+/p+LItsVHCpcMBoPY3d2Nuq7j06dP8cMPP8T5+fmT2J/6R2szWw/6XO16Nj15TVu96rouIRYBB9sSfzH/QqdbnZ6elv6STzOwyawsPatMYB0YIbDtdDrx9u3bYvEqju2ueHqIvHD8mfJBLxbX88XFRYzH45hMJrG/vz/H871eb+5qTlm3SqykO7lNFj9XTjd911RfWztfClSXwZ0XB7YOcJkLZJn3s+dWsXpd088EkPeN7hiedFNVVRH4Wljb29txcHBQLpHmtgH9zbZoJfCgDNUtwGYGZsSju41jkOtsOBzGcDgst3pkW1wItE5TWrvSfGXFqy1t0vcYpwsLZlUSYCjE6voxiaVpbx8tK82F3I2y2C8vL8uZtBIew+Fw7vB3uc4Yt7u+vi4avG5CklI1mUzm5pEWrZQQuogJmH6cp5Qtjkf0FsBJiWPRs66ssLDdJuuAQJzRN7NWfWuPt1XXdQGtwWAQOzs7sbW1FXd3j7fNCEy0DtiPLEmQfWV7nqhEnmD/BDTcJqS5jpi/1EJ8LRrwxi1Zpuo3t66RDzKZxERAWZU6LlHW9/7+/lwIStvYyOPkM/5k800liePVWltbe0gAkyv5/Py8tKFjNXUgDBM1Je+0Rp1/HPg1Z/zdVprkM79vMpiWqTvDjlWMt0VjeHFgq+IugCYLgb/9c3+e3/1U94Jrru4OlhDmth8tlvX19RgOh/Htt9/G4eFhnJyclCQPZQTSSqJAEVNrO4DcTFzsFOwe45X7bn9/Pw4PD8tF9NRUqelmcTD1S4tH7je6xauqKq7Y7e3tcrm7YnES0MxKpXDU92qDtPVxuouWVhWtfCayyDJR0sdoNIpOp1MyweUupDuZGdHaGlHXdbGQeUykPBWKTUpIah5IKx3s0O124+bmJk5PT2M0GhUeEIgREAjCLthcsDeBq/NxJlT0OZU28gY9GJxD8qoL6t3d3SLQ5W2QUPetafpNBcytMK4PD3u4lazifC4FTp9lIRFZxJw3KQxOmyZFR3WrCKyooEfE3HYa3YQ0mUzKbgEpL/xRf0kj0jHzhLAfVBI1N6PRKEaj0Vwipk7QOjs7m8tCZtIWae9tOU30DPuWPdP2XhNQNpU2QG3r76K62sqLAtsmi7Hp+6ZBingUMC5MFk1E9j2FCIW5b2MQiOimH2nCYsxOpxPD4TDevn0bw+Ew6rqOo6Ojsp1AhQKFhTEnxSF1ebrThxq2+jUYDOLw8LAIPR3+oD2lDrCipysAEkJ01VJICdiV7Uk6ugvMXeAEWNGccWFa63qP/ZI7mHsmNQa56m9vb+Pi4iI2NzdjNBqVfdGdTqfEcnV6j4SswF73CcvVp2vW3HrSPPFaMp6GpLnc2dmJXq9XQHk2mxVaaNxUPKjs0Lqg1Us+dmuQ37NOuU81J650iYbcfuJArufkDpW3QJ6DqqqKYillUfRUkpHGQGuRfdQ6aFvH6r/TjgDMMbpSKfCl21mu5M3NzTlPBhO2VD95UzxM+aDfOq5yb28vBoNB8QhpvLx6UNuBVL/nLXBu3aNFrwj5R3RQkSKkmPRoNIput1v4UGEXKZjKMZALnHkirNNpvyygkU+zd5cBu7bnm8B9Fat2mfKiwFalaRK4YJ5ThwuqpmeW/Yz1kdk3NjZKjEMnEjFuSQ1W7l8l8ggUuVAiYi5jVe1TUGiR05Wk+iMewUqXwr9586Yc+C7g8UXChB1pzkxG4d4/XfnHS7SV1ajMTbkRtejp6lJf2Xe3KiSEM1dUZr25q1Vz5K7oi4uLub22+hHIKtuy0+mU08B4VF6n04m6rgtQRDx13zNRzkMMUsZ06Mnp6WmZE/VT4FzX9Vz8m3RUu1I0RDP93aS9S4gScBzMSWvNgZQaupDJH1oHOoaxrutiDSkJUIoFL2XnfKmvUjw9xMAxkX/oHSFfcGxaV7RuyXPiASldvI5Ol9lz1wBDQQQ95hSQvxi7Pjw8jOFwWOSF5lF8KbDVbV0KR9Hjo3iz5lL0YRiIfEM5QkDW5zc3NzEajeLjx4+F73u9XgmjyJ2ssYtm3DblcpZAxnlctSxjcGV/Nz3T1saisiwov0iwVcnMe/0maC4qbS6ypucX1Zf1T4ubW39evXo151oUc19cXMTJyUlcX1/HaDQqh4+reIJNxHwyjQQpFzjHyh/Vp/jP27dvY2dnp2xdkDVLoURBJU1XbboFSmtAbiT1TcJIAo2uVApmT7xRH7xPEiyiB5+jwkNeoTuaFhqfoRtQgkOgxi0qOsdX58Xe39+XuRNNtBVEexSdFmtra0WIy4rhHmeFEdQfCd66rotwE/hzzK4o0TPgiok+IzDoWcYm6VGgu5g8JSVC8WbxkoS+aMOztqUg6tpIHiOaWV0U5j4G8n0b3zh/kY/Jh1QUFE6QJdftdudilVJQm8BWNGIin8axvb0du7u7sb+/HwcHB2VHgH76/X55VjcjyeUu5YzHTCp5i7RxwHV6uBeJMkbK8tHRUclhWF9fj8FgUA7DOT09LV479VWKJJXzTKY2yfdlS1Od2d9N5Utbr23lRYPtlyBCE9D+lHYyd4aEOplNC0xbUCREdZTg999/H9vb2yVWRbccXWQOstIcCWR8NmPgV69elQMsBoNBrK2tzd3jSeuF7xH0aenS4uWCVT95UYIsd41RLicJCQk4Wh3S0F0T1/+u2S6rdFFR0fgyjdytAQlR7ZHe29uLra2t4gKVANYhGmdnZxERsbOzU9x+PDSAYQYqIXSzsT8S5BJsEty8TUq8cH9/P2epM2anM6AVZ9c7sqApdEUvWmKyoMTnAnnuORbgy9IX/RiDvr9/PKvbgZbvuBKocbCfbsnSu+IuY1cUCcz0bChur5h+v98vFrqyz+V94HnMnshFRVJ8rroVv9btQbPZrMRltUbEo5ubm7G7uxsXFxdz8W3GbAW8rqRnMjCjJ5UzhWuur6/LoTBqR650JReenZ3NjVHAT97y9rM1/FJKGwBTYVn2HZUXA7bLaCHPefZLl4yoBEJegeVnImshSmNUvNdjbmR6CgstVAkT34sb0Zwt2u124/DwMA4ODoorl1qnJ5P4YhTQUlBRa6YicXd3V2K1Ozs78erVq7i5uYnJZBKfPn2K8/PzqOt67lxmjpXWKV18HpPUO+4yJ/1IE7eMI+LJ3aiy4nUAvrYDyfU7GAxif38/9vf3Y2trK+q6Llf5XV1dxXg8juPj47i4uIhXr16V7RwU9AIQ1SnBo/GpP4pxar4IXhR8SqqSl+L6+rokJvGCBY1DQlKWMhO6aNXIbc8zt0lrKQBK/uOWNQE5LTy68WUFC6Dc0so8Mxno0mWaxS+ZGUteyrwiXNfkSYG+xndyclLoLUWJVhz7QZ5VkpGAmyArT4jyJ6SI3t3dFT7b2NiI3d3dODg4KGCrEAhzRhwQOH6N00MFVFb4rrY5ae43NjZib2+vJLvt7OyU5EetRfFddqqUz+EygPtcee9Kx6L6+fzPYfG+GLBVadIc/LPnaBaLiruvKPQzt5WecRcbQTEiCijK+hXD8yo3Xo/VpAlKWErr5yEPBEoVWts7Oztlv1zEo6VErTajobshJRjdFUeFISKKsFbmtFynEkq3t7cFCCgEKVxVKIAJlFwYfI7vutWs/zVexmZlGchSkTeg1+sVt+Lh4WHs7+9Hr9ebEyJ6V3NBOgh0BOYav2gni9FPHhOwyxV9cXFRrg2Ui1rf0epV/VJy5HZW5qxbG+IlApHq0G/ONb/XvPv8aT5cwZG1pHgt1xPjphT67r52ga3PCGxcn3TLu5dG9VAh4dwp1KLrGK+urmI0GpWjVBnn9n5Q0RD/KOt8fX29KOHymOj3zc1NbGxsFMVVnhEpWLu7uzEcDuP09HTuxh5fF7Te3eOVyVfOH/NGlCy1sbERZ2dnhbekxPO+bvFzZt02AVlbv1zu+nfLeC2XBetVgdbrXtTOiwHbJjdHNmhf1A6STaWNGJlWy8/b6maCAmM+ykDVj4QPQVOCiiCSxTrYRsSjENezztAar5Iw9vb2YmdnJ6qqmnN/6V0JCR7pqHapDfv/TldZWwICWWcSbtoQL7ClYFB7btm6O7BtsbKfqkf/S5mRe1vuObf+BD7b29vxzTffxOHh4ZxwqaqqxKpOT0+LUK6qqmwfknJFuqhu0knudlmc3P+7t7dXjtKMiHIovdzWosvd3V3hJ9FZCp7GLRCXS//09LQIVFqkmgu6RPXDrFuGHejm97WjPkkRkduYx1w6L3Ed0WXNU744t/QKqA+0xiNiju6uQK6trZUYvOLo+tFhKHR1i3dEP4+HEmjpNlb9ivNTUaalqT5pjYq3RIOtra04ODgoh2pwz694iQlUVIgZWnD6uwyUkip+kBIa8ahcqk8ej5bXxq/qdGva5QfX+DLluUbWMljgz6wC3Fl5cWBLRm0jZOYqWab485lLg4sxe84XBeMmYmZuzVHcklsJMiHjbjr1wQWMwMBdyP68hI7chtI+2R8CLccuoUf3sbu6vV0tSiVIiTZ1XT9RICiU6BGg0KQFSJr4HPgc+sJ2gZwJR9GKlnan04m9vb0YDodxf39fLiAYj8fl8Hi5QlWnsjZ5UpiUHs2dBCzBVpniw+EwDg4OivCXFa3TrGQNac/y+fl52VrT7/dLXxhTlAtafKNDCiR0xROks+/ppRXXZGU4PellaOI7zRUVOfID48VcN1TEyE/623mPLl19JoVCnh9ZjFKo7u/vS4KQkhnlWnfr3z1dlA3yKjBWrRPHaEmKZnyOe+5vbm4KuOkgluPj4ycJjvQC0FjIlNZMfupz9YN87N4hKar0FhFw9bkbLYus7GydNxlkiwA3M0IWPb9M31YtLwZsm9wa2f+ZsP9S7Wfgm/VRE+YgQRcygZGHThAA6Haha4vCI+LpSUQU8k3jWVtbK5mUSojR4qVVG/F4GDnHoq1LaofaMoFfi0uxu8lkEtPpdE7wCIDcwqBC4wKVrj9auE4XzRUXJa00FxgqAgBtraElRrer3LOj0ai4cOk+FU1kyYvesl6rqirgG/F4ebmElbwV2uPLvb4CZwlYbRlTfG88HsfBwUG8ffu2WGDaPnR8fFyStRjPdavRFagMVGkJiVdId/1Pl72+p3VEvuf8ie68UcdBg0oUXd4CGud9rlP1W/SU0iE+res63rx5E4PBIH7t136tnH88m83KUZJHR0cF3JgUlcV/1Z5OHKuqx6xz5nCIZi53mLVd1/WTRLqqqmJnZycODg7KaU/Kns/kpnsotGapfDK84MDNxE99x5wA32Mu/qJ3j2vUS5s1+XMUKm1sd9X2VrF2XzTYsjQBX5uFukzJiJVNQpOrg4urqh5vwJBQZLYigYPWJy0u1z59vFpw0mbZX7dqtWVAF9izfgoq1U8XIGOIetePtVPbFHyy+LQtQT/dbrdYE6KTrMjMslE/pYC4Cyqjjfrj/XLhQQtb9JRQ0JxJMZG79vLyMkajUVE4CD4CLe0pFc0liCQ81U+BobKKuZ2Fh4NIqZKbMCKKe76u6+h2u3PxQB2LqW0jnU4nRqPR3AlejKGJxg5qDlT6Xn1QmEDWGhU4jZWCWzTn3PBvCmcBD9eF+J5eH/KI8w8VVU+uo+XL9amTknTLzcHBQYlR1nUdk8kkRqNRAQ55CjKwpXXHWLUUNG7vur9/zNegNc93pFxQ+ZRCuLe3V+L4UqQInOJV0oT05/ryHypgmiPKCHqDMrBlsqgnSjWBa1aWkeeLwLINP1axdJfpQ1N5MWCblTZNyJ9TeY7Fm1lJ2TM+KVxctGqVGh8Rc4vL6yW4qG4HW/3NBCR3GzlPhxuTAAAgAElEQVTYqE4dDq6MX8bZWDetSAo8fa96tJneLUTRQgtM76gO/Vampax5xa/0vmjidGCcmkI6GwcXkQsWfq4iK0cCjjE0ZWLqwAWBoNy6ohPj1EpukmUaESVBip6FLBlIygyziSVUx+NxscL29/djbW2tXDCu7Uez2azsv9TWEV0ZqANMdGQohS1dfbKiRFfGuzVPOtBAp2ZNp9M4OjoqF6CL/7SViLFl9wYImNgPCnh6UCigHThobYmPq+rxPGPxLM+27vV68fr165JApktBFAPXtZPyTEiJlGXrvMh+6W+5g6kce1IVLX3RgcqKfphnoOe435Uube8TZZt7iVyOcB2xLz7WLJ7u4ExvGcfrZZGx5MpZVnydN9XbpLRnBtaqhltbebFg+1PcBxmRmixTtkWL07/3v/mchAWzSbmPkfdcNgkUgpJ+e7vSDiWU2U8u8rqui7DQXk4XTjw5R4Uu5bquixUnANWikpXmfdPCExjpsHltCZJAFq1k9cnFTCCicM2UIQpxfUYlpWnBkq4C9ogoVq2saQGgtmHIIlQsSt4C31bjVooE9e3tbXFHk66irbJO7+7uYjwel37IYhbYCvi2trbi7OwsTk5OYjQazVmuTJ6LiHIggY7X49GepI1bQgIFDy9wW5HGNxqN4uzsbM7y5r5bHgDhXgzPd3AAdv73/kY8umL1DMfgZW3tITN+OBzOXbWoKyfFFxo/9wOLpk0KAH8Y+3Zrm6DIcblsUaFypvFKGVN8X8qAW7VUpkQbJXepzUyBpveG80kDgRY8LVsaIG5ZUz4vI6ezZ59jVLG+Jjr72NvAfdU+vBiwdWDJSpP20wak2WfZu01Au6i/znRZMgS1Wmp8jD/SRdZkAVNw+VYGgoesWoGtu4rcncwfJkQR3GnFMfNQz+qwB8afq6oq459OpzEajaKu6yL8BUp6XhaXx6tIhyYacWzZHPGZqqpKTDWz7p0O+ozxeQlsWRmiORcpXYByr8vaYyxPiTCiteZYvEHLVfQ9ODgoNP306VO56JvZwlIONGeKwSseydAABRppQLcueUKxx9vb2zg+Po4ffvghrq6uYn9//8nhIAwtSNg7yDIZShaqZyD72NyCpBXsiplb7urjzs5OvH79OgaDQdkb3uv1iieIp0O55ejKaiZTGJsmDangs59SLvScu/ydt6WM9fv9uQtJsv54v93t7mDJ96U4RMwftuIygeuG37k8bZL1bUbWIvlPEH8OGDdZwU31ZZZ0W3kxYNtUfLCu0a5CVF94+ozft/Uha49MKgHhcU6CrRgy61vm2mAbdG/yEAKOReOTO1suZC5u/6G2KWFGK0H76gQ4bplrgUk46TtuMZIgUCyR9ZEmeocxuoz+FHRNMSnWr3ciHl11EfOxQI1BwoKKlNx3skRdiFLgcD7ZdlVVJdbJAzQoaGUVqg65hRWXUz8iopwrLIuZ1o+PWbTzO5ZFA/IHhTzjtYpV8nMd0KKDSqRc6Tu53wm0tMqonFFJ8xgut7ixj1QyHWwdcDVOJuzIE9Pv92N7e7t4ZZioxTXqoEeeI3/6Ws6UOvEin/d1SoB0l7hoqb3s3W63eDe8Lw6o7D/DWMxzcNox/EC5w7lzjwXn1GnmhXVnzzSBbfZ/W8kw5VdRXjTYtmkVTSXTbBZpOxkDsQ+0uv07CnrPzpOWSbevgwWBzhcthRoBjhacWyTqh2JQPEiBFhctR6dztsgFPuwTrTjVK2C+vb0td9hK4dAJOjo1iQuf7jq5wjyGzriPu5DbFCiOkX3nPHJONF4KD1pDstaklMgFqe05zDLlFggpYsoqpvDU8wTsqqrK0Z7v37+P09PT+OGHH0pbSjyhtSYrhC5Mt7BIFwp38obeJa24z1KfiQZSEuq6jsvLy/K83qE7mlYQAVc/PifZuvT++xqlAudrxK1LzalAXfO/u7s75+p3EKBVzT7TslMheFLBdTAiMFIucL3SSlY7Oiub9CMNycf8Tp/pHXdxk97y0PDQF4Kq1jOt88xS5pyo3bY1zNIE1G2lSYZnRltmuLT1Z5XyYsC2yUzPBrvIJZFpRstYxk2g7AJbxd2miuFJM5bAYjZyxCMTuobsbTuTMsOPi4/jWVt73O6j/ZfUyDOwdSvANVz9T/dtxogS8AIRWjPautLpdMriFGBov+jx8XHZwqD4t+rV2NhHj025a9EXLt3ApKlb/hw3hRS3c8k6UWxad9rWdV3cq1J0VJiJzQQy/c+kKpXLy8t49erh3uLpdBrv378vgu7t27dR1w/uXgEwrVDeTMRYON3B/J9gIO8JaeDPiLZraw9JY6KJYsLcW+3hBQlnxrkzK8hdxhLktHY5x+qb+tsUe6uqqhyvKZf41dVVSZw6PDyMr7/+uigE4lVfk1wPrhxSUfCwiFvzrlCrTq5dva+6SAut+e3t7ZKEyD7QQ0MQp4Xsc+zKzP39fcmkn06nJUFOSYGbm5vljmcV9/q4gUF6rVKWMcCcliyZ8raKUfec8mLA1i0s16qyZ10b1LteVnEbLHpWAtKZlJl5smRo7WWWhI+V32XxSdXPOBL7x4QaWbXqLxctf7wvtKqyvjH27L/1vB9coPikYpQSmgLT6+vrcm+mEqhEP9KAgpWAy+JWrFu0HCPHTKVAAKJ+UiCyLwJOxfq0p1ZJZKpLRa5VJUMJkNySoTUqN3Ov14tXr16Vc3SHw2E5nUv91kX3yqbVGJjQRsHNrGjSj2BMXqfApxJGOsqKpXvelVKCbRPIEiRpedKtrGeycAMBN1OuM2C5vb0tgDUej+P8/LwkHmWhgiZh7v1wL5Lq4ilXUjwE6L4nmX3lCVbyJMljouRMv9giM0i4XrTeuPa45jUH2tt9dnZWZIzc79vb22UfehPIq01XTEi7zMjKZL2/nylU2TNZvVlfmj7LyjJA/WLAtqlkGkcbaLYRs+kdr5ftZpNHwMh+xDC0KNr6wD47GFM71gJrsvYVz5OW6W5ej9MuQy/979p1xLxAJF24wJWdrT21Eg6yvMfjcZycnMT79+9jMpnE+vp6ARYKS7rM2K9MS6Y1pkVNxSjzeriFq/ZJc7ZPZUiWkbu4RS+6x3UWroBKpwIRnLTFioliAuXb29sYj8fx8ePHePPmzdx+VyZD+TxpjDwmkbwg/nHhTv5zPmoKRZDmDlIEG/9cdYvemUKtH7o+aeFyTWXWnc+7AET7mxmzff/+fblO8fb2tiiNiklHzCufpBP5gC55eUg8Pr25uRk7Ozuxvb09l7Hua9Z5S258edW01vx0OM2tgJqJZ66cZmEr8fHZ2Vl8+vQpXr9+HcPhsGx3k3fn7OyseDYI5B66+SmlSYZ/acv0ORZ3W3lRYNukxWSaKZ9vmsQm4rdZv+xD1i4Fk2tujDkxk9EXJNtpGgstT1oYLiCpCQrcdKBCBlbuDnYaZGCk7zxpiRZHRktp7xIwopGE1s3NTZycnMSHDx/i5OQkbm9vSzxTz7O/mWCXcCNt3UKigHeFgEKAQkcuugws1J7i0nLzsl7VpwQxAahODrq/fzyOr9PpxOXlZdl3rBt0mJEsK1d0leWmowVVN5Wjuq6L9aSjQ7Xnlz+MU5O/F3k5Mh5gnJR0p2VDPnP+57u+Lsh7BFzSPPN4uBzh2tUcKa/g4uKirB0pfrprVryr/dYCEhbyoyuJBFqBn+rodrsxGAxia2tr7rQv31vuyhBd5fKyqI8+Xq0hV3yoaOlvhp8iHpUKHQByfHwcX331VbkBaDAYxGAwKFYvDQyXly5XM1mzbFlUF+eDz3xpYFbdbeVFge1zSpOgj2g/pIITkwFgE9DyN7W2TCDzpJmIp8eysW694+4Wasq81MCVAr2rfY46ftDjcx4DUx0Uimwzo7H3nyDjigVBTfHs+/v7ksH6R3/0R/Hx48e4vLwsAEvvQGZJiZYUak1zrDnijzR8WsCigVzvKrTOIuatlYuLi4iIcuC/+kiwkneDcUHRX0dubm5uxmw2K/uOeaaxrFTxkqwuHemoW38mk0mMx+OyBauqqmLlMM5P61f9lJuZR0qSp1UcjEXbTOklmGV8RIuZYRMvrlj63HBePGM5s2JVuBY1BsW56UUSeBweHpatSOJTWYlUjCPms5bp/qabmDynqxt3d3eLAqH5ZlKaexfo0hcN5UlaX1+fO76RIRi6hbk+3DtAhUt8fX9/H9PpNE5OTuL4+DgGg0GxqPf29mI8Hsd4PJ7z4LjnKwPFprIqKLpSuEj2/yrLiwHbJoBxLcQXUNNi9knKwKPp+aY2Mm2bIEV3iSwG3giTtakFl9XPv7VgpIW7VittVG4dgiZjdPo/0+4yLZDfNVm9/j5BTMlOdV0XYXV1dRVHR0fx8ePHeP/+fbloXWBM74An9BCEsoVImrobU0CTWQbMiiVwqi2CtCxMJb9Np9O59yTANF8RD2AgV7L6yJtUlFAVEU9OCKNgFY10VaIE6tnZWbx//74oLVtbW9Hv92NnZ2fOPezzo775LVLkcYJHRmcqVKov4ykBggOrey2cd7kOXEn2GC6tbOdH9p1KrQBb65VX1lVVVc6c1oEXekf9cDcv16qe9Xiv6LWxsVH2+u7u7hbli6GgJjqxXfVFGf/yZJCOpF2TQkLaUQnRZ+KV8XgcP/74YwwGg3K0pe6v7nQ65fhI1uvyxA0N9iOzdpuAk3Xy2TZAbQJxN8CaPuPnTf97eTFg21SyCVnGDZANvGmCm+pyBsneo4YvC0KLl9sesnbIhE1gH/E0NupardrnUZF0wbpF6xa6u6ipdet7pxUFrWurBHhadzpNSwlR4/G4nNCkpC5l1Ko/rjB4LLEJXEU3V4h8QVbVg3tPbsJM+xYISxizL3LdymrR+Ok2y4C6qqq5Y/8kSAXCtPbkTpZwlsJycnJSlKyPHz/G8fFx3NzcFIDlwRM6xYvn8qp9Jks1JUy5cKZSKVppnKIP+UXvEXQyQBXf8vSpjO/I+/xhH5x31W8qXiqce21d441d2tOs4y+5LtU3pxMtZOYAiFY6g/m7776L7777LjY2NmI8HpcLMHQNovOkXMG+PsQfvDBA/fPnmpR4Js1xHdJzdX19HScnJ9HtduPw8LDcKKb2eYIeFbssdNAEuF4WAdky3y9jJTtQZxjgzy7TfsQLA9tMm8l+t73bBlr8fBniZP1wDUxFQCeNVPEfJQsQEDhBYnZ3narQcqUVwfe1oHWIheItElQuAJzJXaMlkPlY3QpXn7xvPg+8mUR3hCrzWFm1ylh2bV5jofKgtl0x4HiyGBn7p3eZRU4wcb4j0IuucgUL9PScXHo6jUgCNmL+4A5Zum4Bqh4KSPV1bW2tbFup64c9tpPJpBwsIYVlbe3h/GQBh0BDAEsgcqvM+8BnpFg2WbICgszizUA4E2zeN1dgnLfZjisBsno55wxXUFngNZQ6opHu/+vr63JKnDw3mULoQOugubW1FYPBIN69exfv3r0r22bOzs4KTzlQqk5vjzyrrHSPC5OeDMsIZKVIcucE9xyrHR6mI+WNIQmtOWVEe+Jokyx3BW+Vsuj5RVbsc9paBri9vBiwJQAsa8GuOvDnPpf1wwU9LyGXsNHJOXqfIOICIlMAHJgjHi0DCmctajG5L8hMcGmc7BffabLq+Wz2HWlFtzPBhftN19fX5449dC2c7lwHArZLAau6s21YFPZMsKEQiZgHOgkhWeURMXdCmOJTVMYkiNVv7tHVXbQap+KySl7i3KguzrssEYEAaSFrm/2ShUZlwXlAc+ReAyo2bR4E8QTnx9exK3SiPUHQlVLnXQKq+rbIEmE9VGbc0tI6Vt1y1/PIVdGeLmGuQ89ZIODK8l5ffzjP+PDwMHZ3d+P29jY+fPgQk8kkjo+P5/YrS3HLgCpb3wI89c+3apHuDM1QpnDOCZR6VmEK7i/Xtj6O1fnF5TvHkpXs+eeWzAjL6s++a+vHIouc5cWALcuyRM6ssmXr9s/arGB/hkwUMX/VlOKStHooJFSnW5n+N9uJmN++ICFOV6PcyGL0zB2YgSyTYFwQsd8UmJnFoT6SVnw+s86kGPAYRNWjhU3t2t1maoNbKOTylUDQXKhOgSq3yGjrB09goiBiOIAWUF3Xcwf+c87quo7ZbFYsye3t7YiImE6n5RYhWdRSzgj4FFCMJZOWjF9L4MkiY+ySW0Q0Pl8HWda3gynXGbNN3fJ1V7+vHwp8fUdLOVujGqcrc+RDBx3nR9GAW4WoJJFnqQh2u90yz1KIqJxwnbJdAR/BT1axQOrq6ipGo1EcHR3F6elpCUv4utffmZwiiGoNqP+0vF0eMHyQKXlU8MRbGxsb5aah4XAY/X6/KCi8mIPyLrNm3YBpA75FxWVmVn6KRbpsHxaVFwm2Kk0DyBhDJbNCVyGwAy+1YX3H2JmEot/4IpcLM4fZBjU9xlEchCMeFy1BkkAmoBGj+7hdq6WS4sLQaeqfEfCaaJtZ1aKb9ttKWPuBBhnQUjg2KUtMgqJVq3lgvJU0YHsSpIxfevsRj9cmEnx9q5AsW1nFSgJTaEH08LnxuWjyJFDYR0RR9LS1iFYfx+tt6RnOFZUbxS/Jd27JEkSp2LhVwDEw9KA6xeNuiVER8CQjxsObwiUEQFekNGdcD+R58pboQQuXbbrHwIFW/Ck37+XlZZyfn8doNCqZ5KSHZ8A7XTKeULvif+2G8DXrc54BFhVuhaa0zWdvb6/sLODeXY7Z+c8t3LaSycy20ibzfdys+zng64rBsnW8KLBtI0JmfTZZbnp+WSJkE+sTxImiy1aMrXitwI7JKb74tYDVtgMJn9dC9U3omSbOiwfYLoVgxOO2hzagpCWQWbTqh1u1mfbs8SoCkie0sA5q32yTMSLf1qPPmPnMxA+CbMSj1Sehos9omWlLDnmMQlFtSviSH3T5+2w2mxuLxzEJDBkwMbNVn8mNLBrLHa/nPSmOvEBrxl30BBH2zUGDwEUA8HkWvTO+cQWAbsgMvJ0HOC5mfnPMEvC03OU6jXh6XrbqI+gQ5OWtYF3sJ8dBhZzxTF3OoTuJaTE7XzKu2pSHoSKwJf95MiX5yj1glAFSskhzHb6hk7ZIVwIz55DtukLD9vT9r7IsAvVljLlly0KwrapqOyL+l4jY+vz8X6/r+t+rquofi4jfj4iDiPg7EfFn67q+rqpqKyJ+LyL+qYg4jog/Xdf1P1jUjg/CJ6gJaJuI5Z8n42r8rKlOvkMXH++wlRYtQSgmleBjnQ5g3o63R0vAmZUKANtxEGtiFNZFgcF+ckG0WV36jADsSoUWZUbjRfNAejRpzIpXETjpypYw0fdu0bA+gpL3Q88zeevm5qYoPrKIJPSoNHlooWlO1DYBQDSVi5tCzl3CBDpa6yxuoVE54xiZBav3MmXSvS8ab7au2WbE0wvUGeYgMNLyIn9y3D6vVCgYR3cwyvibc0Q6cu7JY5kVLgtRIKsEtsxwUH89eSmLkVM5101GOhyFIZA2urNtrQ/3vuhHyp0uPyBPu2XrSuoi2ey831Zc0Wiqy5WStjZ9Lp7Tr6wsY9leRcSfrOv6vKqqjYj421VV/fcR8W9FxH9c1/XvV1X1n0XEX4iIv/r596iu69+oqurPRMR/GBF/elEjbcDqpel7B2v/jM9lWtWiwkVAi5M3/VRVVQ48UCKQMyz36PE3/6bm70kGFAQRjzFjbvnxcXn9FEQuDJueaQIEB2JfvIwZuoDMFkKmXTYpX6qfLmKBUMTjNXJOMwkTV0wW8R+tKtYpcFAiDWNvEkq83s63V7BfPHjC+0vBLAtJ4E53vCshbhmqDwRwxmEzQKTCRFBpcsP6XHHN0Y1c1/N7Oe/u7sp4CIikleqU0PetS1QUnD9JS/KEvneAZr/4kykn4g8/PIR7vJXlrKz0JkXBLXvVrzayHQxV9eDlGgwGcXNzU+5C1jvkJ45pEc9rzrWT4PLyMtbX5y/AoCz0kADnrM3QaVK0lynPecf743IoqzMbxzJtLwTb+qGW88//bnz+qSPiT0bEv/b589+NiH8/HsD2Nz//HRHx1yPiP6mqqqqXpMQiIG0iRiagl2nLAampHgplutKYjKOEHO3Jk8VB158nubAfmRZFoUmwJzBKm+XpR20M0QaeKm7FuWbsGrG3xfYY1xH9fAFmbmTWmc2L6uOxhoqVqh0KQNZP4KK1q3743LjF5pa13nn16lXZT3x/f19cbbp0wd2vFNhsJxOM9/f3JQdAXgFlyiqEsb6+XlySzMJ2wJTg9/o1jswydT4gGFI5aHId0nVOuupvufxns1nxFm1tbT0BF/Imz9CWR8nXFvtDcGXmsK8J8Q4VArdms/nz2CVDHarLT5ZzwPb69Qz5jnVyjqR4y4vC9UQlXm0SbDnfrhBrLuX+1s1CTIZSv3hSlo+tSc5ln7E8F0ib6nO6ND3T9M5zlIKlYrZVVa3Hg6v4NyLiP42IP4iIcV3X8kX9UUR88/nvbyLil587cltV1SQeXM1HVudvRcRvseNNxUEw+3sVLcNBJ+uD10thTiYX0PGYtNlsNneCilsc3g77Q8bONGuPAUvw8ladiPltN06bJvr4501C1v/3flNAURBlIMo+ZcpAmwLU5OolINHiEj0cfEhjgi9pkC1+CnVZsHITzmazqOu6KGWyagm2PH6TICth5X3VHuWIKPXxYArVn801aUZ+pLvdLVTOKec8oxmVGnplCI60sgl0VJrE47q0gRctyGJi7Fjvb29vP+EzxlDZls+vj4WAQ7DzhD3nWyoFjNUyBCTvhzwxBFYPe3gsn1Yz6cAxSfnnvnYqQaIZT5NTm75lyNecwPfu7m7ugBq6mykXXInN+LFNHi1TmqxPL5kcbKpv2bJqv5cC27qu7yLin6yqai8i/puI+CdWaiWv87cj4rcjItbX15/0eplBr0KYpP2F2pSec3Ank0s4SihERMkypPYqZuW7Xq+7OZ05mChBwUBB1GQtZ8KR1jJp4a411wD1N12QHAeFdKbVZm16vbScJaicNrSY9JPNHd1tdPe5he31ZuNxECYdyBMCFSlc3EtMECBtqDStra3NubkJ0HLJ67YgfSY+iHg8etC3p9DizXjRlR66i0kPfebu2ojHG5OcN/QZjxwlfZVkph95KO7u7kriF+++db5TdmymvHlxEOBnHqvlPDGTOVM4JQ8cBCMizbDnHujMAyG6MdNe23r4w5uE5B3Q1YDb29slWZPrxLOqBdY8RYrzTVqIv5SVT7AlT2QylYqvSpMivkxpszKXAeMMhJv68FMwZ6Vs5Lqux1VV/a2I+GcjYq+qqlf1g3X7bUR8//mx7yPiu4j4o6qqXkXEbjwkSi2qu/zdBHrLvr9MaRL0bc9zQVFQcX9ZXT+4w7R307etNAEDGZrAQsEmrdLry45ooxBn/30xsx9cWB7LdLexA7kvIFpOqoMZsMwM1vNuDbNuAYRbIqyDVkCTQkNwpELAZ3y+Xdh44dzo/42NjTkvBLNsSSNaQDp9jLQQrThuWiO0MpSsEhHlUAtlzXLvJRWQiPnEnSxe6DQX7zst6VYnGNITRBrTetL8Oi9JqOt53RNMC3ptba2sPx116Naz6Mb5YCFgSCFif9WXLOnOQdFdvAQ3ejM0d1nWuPMIdz9kVq1ofn9/X2LBjKV6P8mvXJukq68T50vV4UoL14/LjKz458sCL/nF284UZJY2w6pJUXNZs6qCsEw28uuIuPkMtJ2I+BfjIenpb0XEvxIPGcl/LiL+xudX/ubn///Xz9//T/WqSLhE+RmqbCy+SHkMoG854fOM8bgVxmcpdDUuMmnEU4FBQFTMmBmczvwqGcjqcwoGMp7+57MRTw87cCCh5cM+acyyxrxvzsTKsGXb3iY/d/qyD8zqpDBxQBGN24DWF7T6IKVHIKj5V5/UvkBZ86cLJCSMXQDLgpGAk5BmkhRdltx+xiMcnQ8pfMmzohX7z/czhYugShDQWqGSReuQwK96fZzsK9tSBr7iu2pPFpra5bwSMH3e6RLX83U970bOFFoqCqQD3cc8NjNzSTtvcd+45t8BnW5rHWqyubkZ+/v7JUQhxYtrhHRgAqMrIu7O9qQqV0y07v2oR5YMqHxtryrjHXSfUzJPzZcqy1i2X0XE71YPcdu1iPhrdV3/t1VV/T8R8ftVVf0HEfF/RMTvfH7+dyLiP6+q6u9HxElE/JllO7OMdhDR7C7Ivm/TYJbpTyZQqFX6Xjp9zxNxMitJApFMTLDMGJCWGT/nYvSN9j7uRT8UlBl9CVz+nGvldDU2WdouaJzeFGrZmAjYpBmvi2MdTIqi4kFB6fPkxTVn0UV1CVjqup67CJ6AR6DQdXkSZO6iZF/YJ45Jlq1nwkrIqy0X0rR2vT4qS+wHDyIh39B6p9dHgKF3OX6OlYqk6KdxOn867cULAhwpZ7Kws2QkKXyiu4/FlR16FDJlmMoLeVFzyksguGXIrT8qHq7Ye9IV3chVVc3dILW1tRVXV1dxfn6ezhfpp/GTf5qs1UzxFg1Ul7vLPZksU1R9TXn9TcaDF9aRKdJZaaonk6FZP5cpy2Qj/18R8SeSz//fiPink88vI+JfXbUjz+l8Wx2LtJKm9ly78ncy16QLQh3xJo2biz9zF4lJfXJZdwa0eoaWLY9nc/cUx0gBQguVY/S+RTyNnxJ83TLwzxxUs3f5f5MbU32lMGbmpbb7ZG272zOzJgi+zhcu8NkvjcePy5vNZjGbzYq7c319vbh8dQCGspj54/F51a1DNsRbym7VdYYOqJ6gR9AUCLrwlVDXgfISxHJzu3VLJdSTeCjAxUNURLPtJ6rP15z/r3dEK9ZJ/nGLzeeWvOgC2vvJel0Z9nVBnqQbOUuy4vuko7ulCbA8/5tKBL0Vvn6oYOoZekfkkueNVCy+rlWfQJbHyQpoKYtYMoPG21qEDZRPrDdTzLLifJeVZfBpEea8qBOkWJYh8nOLa1SrvJcBk5g/IkrsrdfrFaFIQUvooM0AACAASURBVKCTaxhvinh6mTY1KFphBIyIx32GPEDdwWXRgslo0ORmdrebfrv1mSkUDnb8jPV5//isKxyu6LgQyISN/qbrPrNmHUy9OPC6d2NtbS06nU7JIr6/v49erxf9fr9c/K65n06nBZSZYezbU1Q3FQ0JOFmwGW+Sh2i5UTHxpBhtZ1PbPDSDngPOjx+XST5nYpYLfLarOulSdoWB/EH3N+eG+Q2sS/1xvmwS+OTxJgXW29Xc+By5VZsBNsGUVq0UJF564pnrpKvakdIkq98Vzogo/eMccu+zzzXHpTYIrIzlutLcpsi6DGgqiyzVZUvT+5nCxXdcNizTjxcLtizLgqK7diKeErMNwH3Sm7QlCmO6dvQOLVsuAk6ga3TSfl3waOEw65QCiFuOPAOVwOPjp4bL8TcJnMya4zv+HttnG77QMzeaKwkZWDYtNv7vi9yFO8eTCYGszjaLyAFBWeraxqIrBbvdbnn24uKiXI+nCwoooGRl8jYnjYmZzXL9MhZbVY8nnFEAkyfpOuYWIAEtz/tmEo14kv+rr1L+pAD6nDhPuhvf+UV8qs/dCs4UxyZXrytH7sp1PiHPZUqkf8cwABVpd6tm/E0FKUuI0jGM3W537mjYu7u7cmIdtxtSuaJnIVuDtG7pqm6SqVQ+fH4po8hb7qVyQMvWXtOa9O85D5yz55afCuJZedFg6wRsKqtYqG3aa1aPP0/rRUzMLFN+5u4u/s8Fp/+58BgTlmDzE4/kllS2IYFWbXJR+LgzwUHNmHTxBdEEsg6WFERu1UpYU9ARCDKhms0LhTf7ncVb1R77425j9tvBmW264hTxGGdzN67mWIAm8D09PY3RaFS2atCa0PMCbW7t4jOa++vr63J7EhVBzgvdmRLOmRdAoQlm2gsEOp3OnPuZfMq4pX5o4VE5EK9JKWWfBRZ+45KEN3mc/fb1Qzq5opzxrc9nxute9J7G5spbZvE5D7PPBFsCrpR4nUscESUBTmDLZEzxo4cSJAvo8RBvuOuYYyAtsvXt2drO803KfBPNmxT+/5+6d/mNbNvSvcYKP+NpO53vvc/Roc6px6mHVBISHaqBaMIVdED3CoRoIN0uEkKg26MBDVqAhAS60m0AnZKgA38Auj06XNGkc4Tq1Kmdubd3Oh0OR4RfaQcN72/6F1+OuSIi966Sa0qW7Yi15mPMMcc3XnPOmvKbzctPUbI++2frAPuTBtuIfKDZ3z5Z6wCwf1+zlmpWHT8j+HILTiao1QYXuFufbqG4kNEz3HLE/XHUsv1IOI5Bi0FC0eNw/MkUEQdqutp8LrTIHcx9kXlsLAM81pMBuQtWChoXzg7crnHXeIF9Z5t6/+bmppxPS+vs7u4uzs/Py9F30+l06bIECr/7+8cjIHlKmeikNq+vr5euKnRvAZWtT58ebiGaz+dLwpnWmABZ9SsuKOuq2+2W88AXi8dMab5/c3OThiNcsROPC2w1DvaHiTYaiz4XHci3AhjyJUHCFQDyj/OlK4s+Hv/fAY+KUwZWmhfRWYfkEGh3d3djMBjEYDCIXq8XTdOUG6R0wT33vIqm9LyJN6kUkHcF2s7rtXEyNEBZQyvWFYw2OU1DxNuqyebaOl23rIMT6wD63yuwXUW0DLBWMQPLuhMips8EsLdLUKRGKoanRcn3HJy8Pv4vqyjTJt3yZZ0UTB4bUt0EKgIux07rgO+Rzg60PgfUivmOW5RcoG75s85sLr3PFAbU7AWC3H5DQeTzy99SeCLis3c4lsXi4aB57rcWiGxtbRXhqDNmmVBCoGWsX8KMiT6it96nJUT3nSsjsmp1OQKFpuhGgKZVpL5dXl5Gt9tdCpdI+VssFsXS5hxI0EsppYLCMAy9RaK/LDfRmgJeYC8+07ognQTWmj/2JeNn8qP/TX7z4kqmnuXaYBsuOxiLlYIjb8JwOCxAq4NzdEqUg5zG5slV3if2UwqSW8JuNPi8RTzGfBmOyE7cYqFcXcdQyui/7rttpe2dGshnfVuFMU8GbNtKpj2sA54ZYH5JcaAls2VJDBHLl3Gzv1rk6p9bkd53tklh6OPMAJVtZLRRuwRKCkKOhxYmlZGacGJ/XYi7guICIANl0o5940LJ6MCEJbWlAycilu/TZWxd9THGpvrUH1oNmnOdGqbs4Ij47JIIHUIvt58rFm6NCbyoVHk8UEJye3s7ut3uUvxefRX9aIE4n7q15XyqZ+mO5kESOgtaRWtEfMXs5EzYqw2edqQ9s/qbChlBWvWwjYgo7/IZD/HUfvS904NKKGnjgMb5dCtOfMntUXTb05Ws3QbaR6sYvxRH8QKTsxjKcPmSGRFU8rxQMXcFVeMnwLrSXwNapzHbcxry3XVLVu+m79dwx+txi9zLkwPbtsGw+GS1lezZNuto3fp4hyyTSHjPKNsiSLoCkMVJVbgFgq5IFwqsx39IA4Kj6s2stszaq22fcBcb63Wlgq6umuDK5sJd6AQQLXYfn7+j2JeEv98BzDNrZZ3R7cw4l+gji0BXpulZuf9Ytw66EH+orUxh0FyQJjyoQWMX6Okwg+3t7aUYnizFxWJRMp3ZJgGc8+OWx93dXQEBvadxsA+e1CO+4bsukDVHLqBJC3onqHhREdJvgVbG6z4u8jIVaXf1ZgoJ+0i3N5/P1oV4iW5i3o9NV7J4bDqdxv39/dL9yKIXtxdmYyEw+vfsKz0o7L/LJ/Kf6E+L1q/1Y3E5VKNXzUhYVb7Eul2nTrbv+LFOe08ObLNSs0hrmic/4/8Zs+m7dYDd//ezSiOiWBmKn0Qs3z5TG4ue45g9lsVkG47R3Zn6n1aY6qH2y0XkFqGEh7vzIj6/fF6CmvVSE85iXdT0aemzTlcWCM7+4zE5ARPHL/oo9qlkEyX86B3FruRm1ZiZjKbn5vN5jMfjpW07UsKkgInm4g25hymoXNHQnPv3qoOeFA8hMMHMhSGPEM2ELefePQeZ21x9Ij+xDn1OhUXvShng+xwrhbIrhlS29JmvJe5H5mUP/pxn7vqacdrUrLCMjv4O13hm1WahD4KpyxbSJ1M6sjnhGuLa9zVLGuhvl7X0Bkkho0vbS03xX6dktG2bg1Uy3d91OV9ra1X7tfJkwHadzq4qZARfJBlYrupPDXgIgNJAJfju7++L9cJ4mAoZnCDkQOuWpYMPFxHfc+2TYOTKhhYH+8ZkE8YBCbCZhu+LWs/xM/6OeDzxxz9nGxxDRgtamRTw7r5SWxICW1sPB0sMBoMYDofR7/eXMmAVS22aZilZRVnENzc3MZlMYjqdlozixWJR3Lhyp8pd7JcCqK1MCfG4H+mpW1yYIRzxcAuQYqjMHBYNxZfkTdGHNCa/yyr27GIqfaxHNM9cxBKsSvTiOlB/mqZZOsRfIClwySw2FVnQsqj02+OP9BSQRxmWcOvUgYHKA+eJfOyKDPus37JiZd0ykUm8yrhntj+XSpiUEK4Xtql+ZBYnZZJ+6AUQ7UgbWvLsi4Nszajx/zN61QDT143PT21OVpUaNtQUqrY+eXkyYBvx0wDulxC4VkcG2GJeuYAEtjz7VULFs/CcWb297DMXOFnJxur95AJSvdRiKXS54MTEAtzM3Z0BHgVaZnmoUAlwQUWBROHslpCKu70lvOSi4/5PVyL0vsZwd3dXnpX1e39/H+PxuFiXV1dXcXFxERcXF3F9fV0uide+ZwpJ0kJzRquKdKvRifyg06JEn4iIg4ODODw8XLpPVG5tWtdMZCHQZNYX6dL2Q8vGhTDjw7e3t+WGLL6j4yHFa6KJFAQXvuINAozmXYczyN3OuLwDtq91b8eBjXzv667NkuJ65Lp0q1Z1cT80Y+sqXLs1bxXHqXeyMfp42ZaPjXTQGAjgLjfarMysbu9P9nwbjTexlL2+dbGiNt+r2n4yYFsDnFUMvI4m0vZZVleNmPxOi10CnHExCjcKUNcyXctTIcMqIeLy8nLpWjIX0NlilqXVNE0R+i5kaMUIADIXFa0cApRo4a5iug1J37bCZ+XulWCmpaPF3TZHdNERZHd3d6PX6xUL8NOnT+U6RNFO8c6dnZ04Ojoq5xcrw3g2m8V4PI6Tk5P48OFDXF9fx/7+fgyHw2LRejYxgZxbsURzzgv5xZUUCWC5qxnfOzo6ihcvXkSv14vpdBq7u7vx8ePHpe1HdC2TRuQjKjvuUqVlS0uXsUMla3EbktoWbeXKzw5gocIq4OT46ULWO7K0O53OEljree7ddfoSLKgkMBM8s5qooKwqbWDrHigqCxwH+SELGdBqJ51cwa5Z4aS/PA2ZcaD6/YwB9sX3QmfvbyKjN7Vy1wHmdZSktj5tWp4M2LrW6H9nQtWfXUcbyt6rPevMSLDUAqZr2etyK0+fU7hSq3MAFNMrfsgYpJ5jjMRd0e76Vb16l+7tiEeXIN/TotYxfRQSqicDVbXlCko2jxnwaLtDv9+PbrdbAOzq6mpJENVcYlmiiU7bUp3yRChGq7FKsRHQ03rStpfz8/P4+PFjOeR9NBpFv99fsqzctUfvAkHKQc4VGW4ZEbDQ6omIcrLQwcFBHB0dxXA4jLu7u6IgeEyP+2Y1Bw6u3HZWW3/uVlwsFsXqFq8TrOg2lsXK+iXkvb8OOgJ893TwPl+uN/K0/xBkubboTeGYMzrwvey36qplCNNKpVXL+snrbtm6Jak2Pfwi+mWARJnlnjCXgUx48/eo0LFfLge93RpN/fN1Qbf23ibgucpAY71/byzbLykZETMtis9uWic/d8Alo5HhuKhcSGRuWAckjkWWAmOrZGT9rTYFii7AVacEE/sqIPDFSm1aQp7aqz9LkM3qyZiTlpvGIOtTm/gHg0F0Og9Xz11cXCy5uWiJc5wOWhJisjhpoVPzv7m5WTqfeH9/Pw4ODmJ3d7fsTyXQNk0T/X4/er1eAWNatS6wVOh6d4WDYyAN9TmFqFshu7u70e/3Y39/PyaTSXz48GFpfgW0PE+b1w668HcvBosrd/LsiI6yvtV38afG6Vty6A71tcMtLaqL/Ka+ctuPeIlr8fr6+jNwdx7mXLEfmeLsxZVbzlkWhnEApTfMFYIMvPxZ95JkfORA67xHmrpMoKHhGfEev6fCVJOrLhv8t7+TARrlr89F7Xl6MPh8rY1VJVNIvTwZsHUGzohdIypLbZLa2uK76wB2phkTOAQamTLgoOOLRExAZvAF5SdCbW1tFZcrBSvBtLbwJExcy1b9tKJoTXhdXHQ+X9licqFBt7yAdjgcxmAwiNFoFDs7OyVuLTAUjZgRyfHSUqAHQOPhHkb1S3XJ3anEI9U1mUxiMpnE+fl5SfaRlcxsYypIbfzmlofziVtcdDtqjLyMnIlGvIxedQts9/f3YzAYRNM0MZ1Ol/ZrrlIQs/UiPhFvyltAhULrQjzm43R3p/6mtcc+MaQiLwLBVidd0ZK8vLxcSjaqrWECN5UAKk9tQp7fe3YxwZP0JjhprtrWTAaE/iznb5VcY59ryrdok60xDwc4gPtYvM2aUsfnXVHIxuLKjrfn9dTabevPl5QnA7ZenKjOwG3aZa2+dT7z9iKW96763xQQTDjxWAnfU/1ZPITfZULY+9c0D66z4XAYBwcHMZ/Pl/rji0x94CEJBEoCJhe+bznKgEMg5tYC+8qFReVEVplcx8PhMA4PD2M4HMZoNIrt7e2Yz+dxfn5exudboVSP0z47L1Z9I9hrnyi3V5yfn8f29nZxDY/H45hMJmU/Kffo0n3mCoDapqXEz/jjgoruXglvKho6qUqx2cvLy6W4JZUM0Ud0Fn0UqlCp8Z+7Jr2QB8RfVGaoCBBgNC7FaXlQg+pUPxgTZLyY36mfsmyZ/a3kNg/JiE8iosSUxQfMJPf1yPBIti4cAFXcQ5VtnXNAcE+Q98dlBOUR5zJ7vk0eOngz/OBj8lyVzOrz8WXt+Xer+ud1rnp+3Tq9z1lZB4eeDNi2aSvZMw6+q95d5/uMebL2KUAJMvf390ub+X0x+/u1NjJXM/vMPkqgjEajOD4+jv39/SKw6Mpk3Yw/SjBxoatIyLiVXYsD6R0uSu9vbR5knff7/RiNRiX2OBqNYjAYLCUyefyadG5LCtG4b29vy99UKpjAI8CQ8iIgOj8/j8lkUmK6AjV3yUtwiw9o5ZCWorkn/MiSE0/x3GHWy/2+k8kkxuNxdLvd2N3dLduXpChQSHKbiYBFIOf9JK+5gsnffJ68J3euxtfpdErGNhUbgTPd2nqe/XDFgetJtJO7muCgow5VH2Oii8WigH2n01k6+1n0zXjZ17QLcVp6taQjKmnk55rF5wDMPrlCr9wFp1NWalYo2yBNXV460PqP17mqtMn2ts9XyX4f7yb9+ZL3VJ4M2Kqsmox1mGWdkk0+hUatLbdq3WIleJAha4syq7emHWegRStaVpasGwGLjl/z+KaUAgpZCnnSinFgCbosJkPQ4SLMxsQ52NraKkk+smZlre/t7cVkMonZbBaz2ayApSe9qF9t2rrcrsqWVV8l5HnoBLNBBQhyubJ9tcO+CMx0O4s8CVywtM4zoGX/Wb/H/5RsNJ/P4+LiomRFC2xdQDrAcBsUvTO0TpzO5EfRSWNh/I5Z8crU3t/fL2CrfohPpUxxvOQfKrbif96wJWWQHgaBlxQ6fU5LVrwTESU5T1v6tJY0PzVLsiaMCaIErJoxsUoZz4C9BsweX2Wb3r6vWVdUM4uRYKo1lOVz+N+rgL829ozGmfzNPqvVlf1fK23PrBrPkwHbmrayqqxDVLZRe26desnodM/I8tAWClmJTCCgNusCWp+5teVA7e/T4mF2p8cZXbtWnYxZuSCme0t91TgodAi2/NE7bVooLTclRR0cHMRoNCo/iisqMUmCj4u/TZHh36SpKx2kD+mmueSBEE3TLO2tpnIh4S5AkcXo20ckBNlH0tjBhbFYtywFVvP5vFj/ssh8j6/HHCOWLSmClvMb5011SjlQPVwPtEAVP9UYpMBwDy6tWs2Lt8u5ZP/UF82btnVxyxqVCymmXGtaz9oatru7WxQYt65JP/JfphDXxkBecOUzkzltAMy6XNb4xQ614p4o0dNjx9lYpKjSS+Hr02nRNhZ/zpW87PkaYDo9V7W5SX9XfcfyZMBWxYmSEbaNcG3vt5Va3RTMnrzANH6BLc/SdTdyW9u+6PjjdehzWYNqV0JBLlbeE0qgdber/zgYuZVL0NVn2eJyZYJAznFExNJWn8FgUDJ89/f34/r6uhyHKGuQCo/PHet1rZ/vuedBNBOtmBQji5gCm8d0SsjLrU9wIZj4XDoIOngz8YS0dWXn7u6unFbF+WG8lnyretzyoWCiUuOeHContGx51i9DFLe3t7G/v7/0v07WYq5Alt/g/JOtEc3R/f39kqLBs6Sd76Ucs17yoVzfvKg9W4uZN8r77O/5c/zt32VWZSYDSTdfd5w7KYQZiLYZJ77O+TwVJa7vWsmsyjawbMMD/u39yxR+yodVCsgq/Kgp9ll5MmC7atBtxYGpTQN0wq+zoPmMFie1YwpqaeV6LwODzF2dMUltseozuc/kupvNZnF//7jtQpYDhbf+9rrUDy5YxdTcVUyau+ZdWzC0IEgD1SEhx/OKFXvUvlfGvDqdTvR6vaVjFnWnJ93AnDfyh0CVbnMJTm5REQCoXtHdLUzSiduFlEzlF1PQpU+3J5UZ0tz3XDKr+u7uroQD5IZlbJuZt3pXSpn4U0lLAmvNJe9UdQVUCogsW/Gj9jN/+vQpZrNZXFxcxNXVVczn8wJiytx2pcjb4e8MRPQ9PRKdTqd4IW5ubqLb7X7G66yDioT2ZvN0J504JsUzs5SovGgsmQeiVtpkFgvr509tHVIB9G1c3h/2mXVTJrgy7koXPWn6PhtbRr/as06PVaDs39WUmHVKrS2OYd06nwzYRnzuk8/ARs+puPVUqzf7u639tmdpyWZZfowHZcyfgavXsWriCAoONvrh5d+ujLhQcA1abbiV10av2udtzCp6KLNXgNvr9ZbOnGbdUmx6vV6MRqPY3d2Nu7u7uLi4+Ey5IDCQBhKmFJ6unet5HpbPGCtjfzc3N6VeKQ739/flgnhtg3EFTGMifzDurbFmYEerUn/rCMmdnZ2SWc3MaLXHvcaiMfdoUzljNmu2L5V03d/fL1u1dDPNeDyO8/PzmE6n8ezZsxiNRuV9jp1eC982RVqxfb7LLHoKfypa7Ctp794djVleCgfbLDegTQ4xv8GfyerSO65g+PttBoZowbOXszW7rpXHwvbVTu0CAlesM+VpVfukb2YFZ5byqr6v+jxTADYFWJYnBbarzPDaO6uYJdOYMmBbp30KH76ngwyY+enMRHBTAopKLUabCTQKCC4iCX4Jfx4akNUhi5iFAj4iluJ+Ti/2paZYOL1rfdFYJNx4DKasDd/m47RQO64tk8YRj7ffCHT0vD7XPDJuq/nh6VkULqKzLER5BebzeTkqsW3sUmx4kbwLdqerC3AHWx7xqecpqHQZO88YFi/R2uYcupdDfRFPd7vdcoKVzpHWZQ06p5nhD7ZDOlIRcz7jPAsASS8CLr8jD3j/+Q63+fglAVzD9D6QNs7zTkOWbFwqDsKumDiPO28JAPWe75BoK1m7Xgi0rvDwu2zctbay51wm1sC0BoybGDFeX+1/1t2mZLE8KbD92yyuGfEzB4IMmCkUyfSMN8n6UYISsyRVD4VTzSWkvrCv+l/av8fP1D6TcVzI1LSybHGoj7KeKaid2R1o+bkzaI1h6Z6n604Wr05F6nQeM6yn02ksFo8H18/n82JBemG/GH8lUNPl6hq63tecEYD9LtJut1uOlmTyUMSyZchFyvg/BRatLb3vQEIBrL3CPKaR9NbztPjkUie4e1xX76j/VG44hp2dnZJFLve6Ljofj8cxHo9jd3c3jo6OommaAmysl0DrcXXnG+ffmrLHuDl5PQMD8Zf2B+snc8PW1jefoZzJlOZa4ffshyvmmWKvwhAILeVMzrHPtf7UvnMZ4v/XFIpVbfL7dYHS5XlbPZmM0ufr9G3d5yKeENi2gZz/nxGxBk5eH39v0hfV6YkizuQ8uSbLNuV2iKwvqxYrwZYuN8/s5DsOHr7YXQPlYqkJPKc7//Y5yhQI1SWBx7iihLbGt7e3V84eFu3u7u7KaUBy8dH9mwlcAg2tWxVaqeQDArKPQV4AnXRFi1YJXU4LunRJB42LdKebV/2TUkUFSnXqgApdA0gwc9pQKeN4qMw58JKPXVFleKXT6cRwOFzaUsScAilFHANpzdCGe4EyAUcwdOVVbfC2I4It+Vo05HnjVHacBzL+9pLJtVXPuAJbW1MRUQVbzrPCCaRPDZxVv89N1jc9m/3UXOeuRKxbajzg411V7ybAXStt81ErTwZsvVAotDGrT3qb9rVOm6rTS8ZszGjl7yw7kaDniz1jWoJgNh4/Rch/6NrRbxeuBBHvB91vbh3VBEP2N8eVFQFNdqm53hGY9fv9YoXpGQkRnrBE8HDL3l19DnSMcXEsjI9n9e3t7ZXD/5UMNJ/PP0vAch519zKzOeViJdgJbDO+0HcC25qlznmhhe6A4kKe8ygLVM9K6VD8/ObmptxEtL+/H9PpNL777ruyR1lWOGPEXEeubLQJ15qgd4Gv8Ap5gJcYUPljnoLLF6d5xvsZrxHcfRysw3mEMqUGYD5n/I60dm8I38/c4C5rvd8ca0b/jG5Z25vK7UzWt8kZb6+GFW0g3DZ+/q6VJwu2EXWC+iRlf9Mqyepk2UTLyRaA/tffEnKykqTZSxhSYOsZt3i46H2BsX0JaGbPcquHLwQu+Iy+XNC+gGq01v/+o+Jg7YXWDrdO0YrXFXZ7e3tFCIqOGgNjn27JO/j656Id51cWVdM05czmiChZv4qHcYuQjgLUgRKcq5o7LftNweEWeTYnelZJXkyMagN70S9zh1IRcyvQeVb7pLe3t+Pq6qrEbweDQczn83KCk5KOZKnLQmfIwkFdim0mTAmSzpfqH49+pMuan/E9WcB6z71FPlfu6eKciU8z5ddp7jFgnx+OzxWNTPlheET82ubS5fvZM+vIUtLdx+Bzx/ezttYF32wMte9/TFk1nlXlyYDtOgBYe4aM1/b+JsUZL/uMAkKJMWJobWvwBaWFywXEBcPP/QB9ChTW6wDrfa2Nz4WwioN1xKMl40CagaxbRBGfb73h+xqrYo3ausLnOp1OOUhC9Xn/fFyuXbtCkykm+pvbhwQciuFJMbi9vV26OUenS2nus3F6/JXfOYhxHOSHTMhlYOtzxLqytaJn6EYm0GZWqMYgVz5d+xER4/E43r17F+Px+LPL7tm21oTaoTJJPlplzbjCIHqIljVllDTxk8Q011x/6yqgmnP2l5ntpLmPz934HGu2drk+NYf39/dLR5CukouULWrTlZGaou1zkilHfOankNGZ8rXJ+1/aB5dl69TzZMA2YrNgM9/JNP1N2/RFmn3H9iKWb1BRBq0YXXW5hucLya3gpnnMgJSVlAkUt2yZecwkE+8/x+iWqwOVBIGDrLsba1mTDrguGEhPB9urq6tomsdsYSY0+Rjo8qPwZDvZeJnUxr74lhoe1tDpdMqtQ4rP7+7uFjf4YvGwV7Tf70dELLmFOe81AVazbgiUrmR6PaIJlcI2q8SFJYs+d+VFgMgxLRaLkpQlkPrw4UP87ne/i/F4vBQPJniT7g7wHEvGQ+TNTLFSveQTus41NvKNYuLX19fR6XQKUG2izGeARL5TEU1YMlc6+59ZjZxLut3l0s8s2xpQuYEh2eTf6XuWNoDNiitTtWfYv1WGhJe253z9bDLHmz7/pMB2nZJptiyZ9dVWV60e/8w1NbWhPZVKiFosFmXLigv0WqyEgqFpmnIowPb29me3sXjfCQ5coA46/NzbztL1a9opLbOaIHfgoOVAUOTvT58eLmWfzWYxn8+Lu1hjooBUH7lPNNOs1Re3zNxqdIFNAS1F4OLi0h5PlgAAIABJREFUYmm7kFzbW1tbxU23WCyi2+2WRKm9vb2lmKnTqCbwaPH4mJy3MwFEHuOzVJI4f1myH+fKLSjOgfOJjjvc2tqKs7Oz+Ju/+Zv4/vvvSww34tGjQEXHQdxdvd73Gi1dweX68NO0KOjFCwLam5ubuLy8jIiI+Xy+lJDGNeBKC8fifSTt+OPKKU8Ry9Y5lS6ued+mJHrKstUYmI+QyVL91nqTAuQ8Jp6qKWQcd40eLJuANOvJ1lAbINcwI3uutk5Zj89/rTwpsM2AIPt/E61yUy3IGSXTpFV8UcsyUxII9xJSeEugCETUlhaMto90Op2YzWafuUjZV8YnSR93XXnM1y0FChyOzRc2wS0T0Jwzt3x8IbJ/AtuLi4uYTqfFshedFovFZ6559onPZeOjIKByUZtXd9cyweT29racIKa2FYvf3t4umdN7e3tlC44sc+cvB0b98NabrHCsWZ2kLT93IelWFAWnCxFXRsS/Ujr7/X48e/Ys+v1+3N7exrfffhvffPNNXF5epntq6UFxPiK/0s2ajYVrIJv77Bkpnr4uNb9SrO7v75cudKCbnQqRA36mmOtvHzOf80NcSA8HM1+TfngFx8L9/34QD5WOjOcyPqvFqV2JaCttysk6hWvHFUHWn5VMTrQ9n60p/l8DZJYnA7bZQH0B8nfteZVME18XcGt1yw0jDVjZjVdXV/Hhw4dy36qsoMPDw3j+/HlMp9O4uLgo9RBsHdR4bCHdbhGfnynMxae/BYRuHWXgmAlRtZMJcGWbEmxVKAwy2lGZcEsp4kEDv7y8jPF4HB8+fIhPnz6VS9kVD+S5v6JDplQQEPi/05rFBTX7rbplWSjxSa5unkSlQzkODw9ja2srJpNJsSyyGD0FE+njfaAyxWSYGtBynARRJueRhi44vV4lKHmIoWket2a9evUqXrx4EYvFIr799tv48OFDTCaT0i7XD+kg/tO68PnSmhGg8F3ynhRXrpvF4nHLj7tQtY6ptLJuWcIKG8gqpGXril82L/6301b9dYu2ts79XVq1Hoa4ubmJ2WxWDjDxE9Aya5SyMgMQ8YPWJcflBgj51ZVLlkwxWVW8nkxRqLVT+36dkoH8OvjyZMA2K9757H8H1bayjgblQORM6O7I29vbwsynp6dxdnZW3L4HBwfx+vXrGI/HxTJyy5CHGAhIqKHymD4Ht6xvek6LsObmpYXj4JNp5dr2IoHnIOeLKGNqB3s+JwtiMpnEx48f4/7+vpyjy8SkzLXqbtrMEsiEoNPUx06w8t8SZAJYVwAUyx8OhzGbzWI6nS6dDpWV2jy5+5/Klwu4NppzzmjdrLLC+IwD5Pb2dgwGg3j+/Hm8fPkyDg8Py32/tOZ5qIL30ZXizDKi4Hae5dw6D7qnhnSmW5brmhauPC4ai953Twr7wf8zunEcDIVk7l31yxO7fF4zi1VjYOY8n8v4xmmeWe6SUaRBpmzX5HGmlGQyzum7qqz7fMZXq/rs9WdKw6q2nwzYrhr0KoLUBD0ncZOJ4/tkDC5OCY+bm5v47rvv4q//+q9jPB5HRCwdqD8YDGI2my25RdlXCbWIx72AAhYCpvqh+BM1dfZPi8nBNgM8/aa1kdGAQoEWht6lUMsWcSYkXBDptKGzs7MiKBS/3traisvLyyWvQAY0FKS1vmS84PPswpH0UqYtXZNSoOTpUFLN/v5+Of1K7kh3ozpPkV600Goxd/3O5lqfMTGJddSEI79nghH5tdPplC1Rh4eH8eLFi3KZgRQOXTpA3szGwTYzwHWFwJWbzCKS5epKuQOZnmO2tRKLuG3G+YGKkFtVzt+aW1rd2Y/LMG6PosxxoM0UaymwOslMSrPTQ+3V+MrXLg0CPePKrfclm+915XFNQavJmOwzx4E2RWBVXdmz64zlyYBtVjLmzTQgPk9B7guwTWupte1MR21YAuXu7uEQ/I8fP8bZ2Vk50D0iyv2rujjbQZNCNSIK4BB8M+3ZBYQXH7cLuEywZcxEQU2XlR+04DFOdxX6otVv0VRjvbq6ivPz86JQ8DQuumq1j9QzO9V2m8vYx+8CyjV6jkdzzrm8u7sr21l0NvFgMIj9/f2yz1TudwdZvU9hSAuap0e5G1p9o3LFeCLpTKvWXdKqIwMRtZfNsYrOQz48PCyXrUvZ5LYTjcFPUGtb1w647AOFro9Rz4uuqtsVQ9KfR2uqfj/vXP13Wnk72VjUJ/de+TnrVBhp0WYKoIcAOO9SFBR+oaKTyZSsr2yTdM5CXKStKw7iIZ8jloxeqwC6DThXtbMu2LeVTep4MmDrglh/bzKYDJxr33tbq+qlJaUFyAxZLeSrq6sl95BcjbQsaAmpXi5i1SNBkFnDAls/89QXXNtiJI0yeqnfPB6SAkIL160xLaqae5O05yJmjEwLVAddyFW7WCxKH/Q861N/XNlSPzK3ecYLDjb0GLhFQdoxM/ng4KDM6Xg8LscVepav04k0J8CyX+7ey+Ku/r1bBC60HZxJJ4Itha1yDAaDQckz6Pf7MRwOYzAYlAMuSBf2MRPONatIPO90d9p4/ayXdfo6oqtV73J/rcdUa2vJ+ZBzka0jriXSmjFuAlk2b+wTwxzypmQ80OZGrq1byjZX4Pw+20xurQuOTjeXVVkfV9W7juX7JeC9bnkyYLtOyYSnl5qW7JYqhTz/97qy+hkLubm5iX6/H8fHx/HVV1/F/f19zOfz6HQ6hdm10d8TMFygdTqdpW1DAmXenRqxnPVHYJHF4EIzEwzOUC5gM+2ZRxbyRJ9MINTmzpmZ80EhoSK6kS7ZvFCIuKXj1nXbXDudvP96looIFSdZoU3TlCsAt7e3YzKZfOaxcLpR4HJM2f/ZHDtI8XunseqhAkFa8W8CrRQPZR8Ph8PodB73oupozcPDwxKvJlhSOdIa4Bgzq9z5xZVc9jVTHlwJdJ6gZUsacXsSb0OiYhzxGLLw9ZDJHO020JnRriQ50DLEkPFkxgudzuNlHXKFk5ZqR32t8U6mlGmO2GfS0ftZk8U/FaBl9GgDVbbdhiHZ3zX66LtVhuGTAduaZrEOMV0AeT3O9LW2a5/TmqALUMw8HA7j5cuXJUb14cOHcqLQfD4v2cl+gIKPTUxMoeTCXEX94OlSzlCZ4NH3/gyBkG5MB1paW6qLwmWV9pgJdVokWrTSxuny47V/og3Hy5iWW5wcn4NJGz/UFBd9RrdtRBQhqn52Op04ODiIN2/exGw2i6Z5sHK1/5GgS/qqn7SqRJtM+PkYna9UnC4EH45LPOjCXs/t7+/H8+fP4/j4OHZ3d2M2m8VkMil7zmXp7u3tfZYolVk4Al4qDuQZ8ij74Z9zHWXWr4MiXbW03H28vhaYne3KXI2vpDjrxDn9Zh99TWdx0Gxtkxea5iF7W/cZe1/0Ht35XpyemQLEtUz5k81LVtYB3OwZp+869TiA1vpXUwScV7+kPBmwZVlX81nFKOuC6ybtMYFJB84fHR0V4aL41enpaTmQgpviuUjcqoh4jNFq36YYXQuccSb1QwLYx6629K5b1r6gIvKD1d31lQG3Kzw1oVOjsytHAlQCqX8mJYSx20y5cKUpK/5MjT58PgNuCdSbm5s4Pz8v1wO+fPly6WCBxWKxFHeWMNWRlMxQ91ib2uFnbYDKUlNCajRQYQZrv9+PX/ziF/Gnf/qn8ezZs5jP50VwS2Ho9Xrl0nXtR97Z2SkKhs8Z+5cpdFlxniGvZu53Apf4xi9r0NriHHsYhRfJ05JzpddpSk8I/494jM8yGSpTojUHPkcOgNfX1yUp07/TmPQu5UJNceF4aoq/zyX/z9bjJmVdgNsECGtKEYuvox+jPDxJsI3IAXCVoMyspjYCrCJOBmAEOoGptEednCPtfjqdxvv371PhRVBT3TzYXpadXKeuTQt8eFcl6UbrwReHjzGzIjILyS+qpyDI4mGkXfYd6etAqPERYGi1utXimnbtb59L77vqdMHMsbDdbKz39/dxeXkZk8mkXHx/cHAQd3cP18zJmhXv6HL5ra3HS9Wvrq4+UyDouqfy5S5Vzl82Rp8Pj9cKSPQ3k7Z2dnbi66+/jj//8z+Pv/iLv4gXL17E2dlZUSx0Prjq1nqIiCL4eb1exOceCs4vgcCLzwv7X7NO6Ckh2PoeYCpz7BPdx1l7tTgo51CgzaQ5gn4Wy89AlYqJW2u8ZnGxWBSvm+ZRuSBSQri+yBcOtjW6c520fV9TArP6130mkzH+TCYP1i3r9Hmd8mTBdp1SA+MvIWb2mYOOGFIuGp3jq+vUdD6yAPLt27dxcnISJycnJeGJwsxdZgJytSMw1eLkFoyI5dtyPn36VDJiKcjaxuZ/u7tafZN1sre3V7Kv/e5YLki3DByYHBw8jkaByN/qY+b+8oWcLUC6/LKFn81NTTmoKTO8MWY6nUbTNCUr+fXr12WudNiA+EhxaWWta6826+cpTO5SdGGnOSE9SRfyRafTWdoSws85tu3t7RgOh/GLX/wi/uzP/iz++I//OA4ODsrJX9pz/vHjxwKoTCpSPNv5gWMg0HpxpYr9dFplMVsqQ+5C9nwD9o3eJdHJT/jKlC/WxTrc+laCFr0ersz6enGXO//X3mDlivgcS7ETLTqdztKuB/ITt/l5qINA3LZGvgSoNpXhpHWtvhrQZjiyqv02sK+VJwO22QBXaUOZwNVvF5S1Nlz7cQHqi4nJUdIeZ7NZAd69vb3odB62+hwfH8eLFy+i3+/HZDIpIJNZVd4vtRPxGAek4HJA9kMDfOwOCqqHfeLnWrgCWrkFm6apbr/ItG+fK76jRe+f8V0mFPHKOh8b6ZbxRw0g3QIk4HLuSSfnGQlPzZn62ul04uLiIs7OzuL58+fx9u3bQr/r6+tyMpHi+bpZiAktDpYSgF44Nn9OFhPpyrH55+TNTqezdKDD7u5ujEajePHiRRweHpZkHx3R+N1338XZ2VksFoti1c9ms9L+zs7OUtvMY2hzH2cygH3PwMcPb9BPlpzmNPZ6mBhIXvO4ao33svnSewJaegQ8B4I8SwXA+7pYPIQn5vN5ui+ddaktKkBeGILS93Kl+9Yfjs1lgq+brK02gFuFAeuAs/M+P6uti3X6uK5i8GTANiubajcUnCRixvjraC4uVPW+EqN0eAH/psY3Go3i+Pg4Dg8P4/T09LPFxHgcNcQsMUJ90H5TT9IS4CqeJOHmMV/2IWI53qLFQXeXBKZcUC5YXAj4Z6zXfyQc6M5SYVsRj8lGqpcgzVh2prD5Is3cprRipO2TPr4oaZ1QmGmvcK/XK/8rSW57ezt+/vOfR7fbLUJW8319fV28Bg6MEY/egppyRgVEY6G1mikhmeIh4as2JGh53eNisRwzbprHU7SU+KMbkZRJf3d3F9vb2+UyAo1DW+hq/OJ9ZnEeIA3Ul6ZZ3itLzxHdyBk/cy0TYMQfnqFNGrPfGc9q/hWS8lCQA22WvyEFgPygrXPT6XRpfy2VW3c9M2yVGQOyfNW2PHiihYN1TclepTyR/jVlulZq4LhpqdXhCv6XlCcDtm2EatNoss8pkDIA9joyjacGylpYuhVkPp+XxBfdbypB1+124/DwMI6OjqLb7ZY43CoNj+168oILJVra2npBYVID3Ez7ZJ3+GYHBD9rI+k7rsG3hOFjXxii6a78rhSHrdfcvBZi+z8C21ne1o/p4TF0Wy5LVenV1VeKvi8Ui5vN5TCaTODo6iq+//rpYvAovXFxclJCE2qbLnCEEd7O6G94tHwpX9dPnz60/Kn2km/7X4StN0xThq8IDImT16j16YPSbB5NQmcv660qPz0ObosfYJN3IbVaW05Jt0KOhUuP3mhyRRUslq2YRss3a/CoxSi5kB06CtOq+vb1dUlj1udYZaaeEv/39/aW59sM31inrgmMmu2syfZM6/flV72Y4sUl5MmAb8dMFotsWj39P5vLvXRDrc8VKpUEKRLOkoX6/HwcHB9Hr9YorOYtz+CITo7O+LHtZVpE2r+scYX1Ht6wLIqcB++TJWovFYumYQi5A/8kEjAvPmuBg/+gupyUiWtElqaK6CBKcDwrnTHD7Zy6oaU3wOylYot10Oo2IiMPDw9jb24v5fB7ff/99HB0dFWHV7/fLEYcfPnyIb775phx84a5QWVKeMOTAkgmEDHz8MxeUorf6wjCGFDadCd7v92MwGMT9/X1MJpM4Pz+P6XQal5eXRUBz/ij02Wd6ELhOauN1r4KHUZjhK5DXZwSJNoWX80DrmXkD2ZGQmQKXKQDZHt1MAfbxcw1RflxdXS1ZtaTVzs7OkueBCrnq4rrxeG1ElL3jTHqTsUFa1mSo05fF56FNOWz7fJOSAXZbn9rKKrB+UmC7Scm0XRf4NaGfESX7PLNstEAEthcXFzGZTMpVajrHV0JJ1u1oNIrxeLzkxs3AluBIjVXfETD0GWPIsjJcCJFuGU0ya1b9oJXnrsnMcvD3fd4yrZx0oNZKxcTdjXrOLT0fn4Mvx+hzr/oZm+P4sjgVeU4KgA6vlydgd3c3Pn78GO/fv4/Dw8O4vb0tSpjm7ezsLC4uLpZAlYLelRrnHR+T80vm/s+EO8ckYFEdSuLa39+PTqdTLlk4PT2NxeLhtp+PHz/GZDIp60L8yLG4Uqo+yXLnWGrz6n2NWN6ixHgot/UQbDWHGf84qKn/Pg9UGJ1mmfLm1ruvH1+vnDOnI9ugC5kZ7npP+Rdyr+v8ddJBhXuC9Z28ddo/rTYVynJabgJUGf/6d1n5KQC31pdV4Fl7r1aeNNhm2jknowac7t77MZNRA43F4sG1enFxEePxOCaTSYxGoxgMBkvt7e3txbNnz+Lo6Ci+/fbbtVxOHJssGnfxKI6yWDzu/VWmK6++Ur1t7VAAsNBlye/9fY4nSxihsHchls1j1l/vKy08Crua8FRx66emXXs//XmCvPOHxnt7exunp6dxeXlZPuv3+xHxIMy+/vrr2N/fLy6/3/zmN0v1SBB6+xwni9OJY8oAuq24laP3JGz1s7u7G99//31Mp9O4ubmJyWQSp6encXFxUfZ5LhaLIuQVl2aMkHOXKUA1XoiIpbCMK4HyQHH98GhGz6h3YPW5IP0YB6WHxvmI9GadGdjW3tO7dAFnysHt7W1JjOI60Lu0av0wD7ryaQVLztzf38f+/n45S0DhMvGou5GzNe3jW7esAls3HL6kfseWvw0Qf3Jgu+kg/dlMk8yer2nINWD1vklgzGazGI/HcXZ2FkdHRzEcDpc0ed31eXR0VLJ5xYwOohGPQi4DW/WLwk/AwyQp7d9rG6f/9kXSZkE5PWo/zFp2BYjtM4aX0do9C679C3BZp8fZvD3SMaOJC15XOqiIMJ55c3OzdO7t5eVlTKfT+O677+LTp08lseTly5fx4sWLGI1GMZvN4vT0NPr9/hLIcbyeh1DjTz3nLnPfjsW/qQSRrqK7fm9vbxeQ3draKqB1cXER79+/L0mC4/E4ptNp4VvddkV6u+tdfay5dZ03XakiAGqsDqhM9GLYx8HQfxPARQ+2RRczecTnIlMyM37LwF9Am11tJ6DlzgiOy89hJkCKbpz7TucxIaxpHrc5ybJVLgLlD+nJ0iZna3I++9yfJ5i3rQn/vvbOqj6twpN1ypMB2xoIZsJyk2fb2tsEcCOWE1G0cKVNym12eHgYvV5vKdbRNE3ZOrO7u7t0ycCnT5/Ks1nii2vX/JsMoCzY6+vrz9zIWgTuLqpZZdn/FBIZzdroLQBh/NRB3QsXhd5jJqkLXX83q5dAzb54f/wwA/0tgcp9zm6VuPWjrTwXFxfFnby7uxv9fj9evXpVwg4vXryIV69exbt370r8XW1EPJ7Nm9HIeVbAxgsbOF6fRx+rb8cRvbvdblEmr6+v4+PHj/Hhw4c4PT2NDx8+lH5rz636RQtdf5NGoqlvyWFffTtUpmDwPSpFmnMeYMF4bTbXrFv/iw7Z9h9/1/mP/ErlMvNYUWH0e6S5T5dzz8Q8vwzAL5ZnDJtWuVvDqlf8oxu4xLNqn2Dr494EjFgygPQ1W2sjkzFe57r9ynCg1u6q8mTAlqVtgFmpAeePLZwsAg4XsjaQn5+fx3g8jmfPni0duaeM5W63Wy4q+PDhQ9l7qLqo4Ue0u0HZN/VHe34vLy+L5skFVHPv1sZMi83B1+lNzV9teRu0BBwkKdzYB6eHWwTuissWpfcjs44z0HJPAi0z7qmlpZPF2mQdXF1dxWw2i/fv35d2ut1ubG1txWAwiFevXsXv/d7vxcnJSTlnWDzGftCFyH7yR4JZpwbJ8qGiov5RgXQri/TudDrlzOOmeTjfeTwex29/+9t49+5dyawmjxJMBaiedCUlQp4ZvZf1q1YynlA/6D2qxWpryqYrUz4mgREBy9eF/5/1NbOw9SN3Luko8CQoy4OgXQnkBdWh/mpfd00WEMxFM+2119WRSpLjGNwrsUqJ9u8z2bKqrHregbZmMNTqaOO9TTHnyYGtM3btOxYHRNds2goFbdaW15Fpr3InX1xcxHQ6LZbl1dVVTCaT+PTpU/R6vfjDP/zDODg4iN/85jfx/v37ss2DiyzTyggAGQ2ktWuxKeGGfcwEhwvYVXR2mrEO/njdmYbvFoo/5+9kWm0mCLP+u0Bte0aCg0oChSmFbEQsAQctJdVPq0QW7v39fbF6m6aJn//857G/vx9v3ryJt2/fxnfffVeO2qNF5PzAQjez05ZgRyWD1hSBifWQDjpi8O7urnhy/uqv/ipOTk6KS1l7s1Wn/qfbmaDBkAdj2+qb+NbDBLQQOV4qSfTm6DuuIWZ6Z2EO1aF+iC7kA1qB/CxTXPw75z29TyvWD5DQ9+RLjoPzS3ewu9jZF3oE9B6/E62UGMd4terkPmGv12Vzm0yufdcG0pmMbgPWddrL5qhWVrWh8uTAlqUNeL24oN0UMPTbGcL7QGGgxXx7e1ssl9lsFoPBoMTrdDfrcDiMN2/exNHRUYmvyCL1hRkRRXPM+pEBmSwtHf/X6/VKggMXjbfjnzlY1sA4A/E2K4SCXf+vcsNygYkmPmcUtBTQ2dh8wfJ/uiLZH1msjIFL4Hkf+Ldieap3e3u7ZI0rdKBrA/f392M0GsXh4WG8ffs2vvnmm5hOpzGZTEoiEYGE/fW/Ix6tEWaf7u7uLoEshWBb0fc7OzvlkIpPnz6VzOr379/HeDwu/drf319yK3IPLi1X/fgl6i7InYdEWyoGvjbEA74dzwW/01LFY/kZf/r3rMsVw2y9kVcilg/okEVLwPXvO52HfJBut1u8aFxbGge3qole4gVZwbRImeXMvb97e3vlshWGyOieF3+u8kpkn63iw1WlpoRm9f/YttTepuVJgm0byK7DxP7eJlrJOhoRhZ8YTWfd6qB5JUYtFotygba0Q6XOU7CQUX1RZeN18JQbSWB7fX1dhHlGAwcftygy2njJwDJzTXk8SAvZrRLXyvW8+uXt1ObNgYnfZ1YH66YF6T8aGy0zjYUuZSo47Add+efn5/Hb3/42dnZ2otfrxZs3b2KxWMRgMIijo6MYjUZL1oLG4AoJ2+OhCBEPglMZw3Q1MrTAeWY/9b4AcTAYxGg0il6vF7e3tzGZTOLdu3dxcXFRTk6ja1FzqM8VJ5TyEPGgFHDvtmia7T1lPNBjnU6TxWKxFJPNLCxfaw6amcxg+ChTFjkftf6xHVcS6T7m/+wL+UGKmrwH/N55QwdXcH+zH3ohJZJeBM2LtnzRE0FjI/OKeHEDwdenPlNZJYdr9bOsC6yr5H32bKZst5UnCbYsTny3XLJJqhXXtFYBsgMvfxM0mJmsu2uPj49jOBzG4eFhnJ+fF5eyUvOZFSkBJAuAmqn6lllr+q3nFT/e3t4uBydQIyZwSRNm8hKFsIDDNUYXfqQVLQj2k4s+0+QpEL19uT+ZxEGFgEKzxg8uUFjc/cp+q293d3cFWDVXu7u75djF+XxeTQ7ROJghGxGFB1TX7e1t8YgIcBVbU7IUBbR7CBxQlDn85s2bGA6HcXJyEk3zEGvVGKTs8VAG0khx2sPDwzg+Po6XL19Gt9uN2WwWJycnMR6Py9hl/fN0scFgUKwongG9WCxKHFGKqqxeKYeiu9/Gwzkn33Nfst4lj1LZoRKbKZxUcAVkel+ucq39VVazK3jZemHJ+NljuqpP+1t5JSMVCPVZ53QvFouigDdNUy45IS3ZrmgvPuD+avVfW6uc512RUF987hywXEnO5PKmILzq+U3qaiur6nlSYOuCXcWJtSmx123bhXj2TPZbApEHXTx79iyePXtW9qRNJpNyjrLicWJmz0rONO+2QiGsBAglS/FaPAKe1+maeGZxZgspmwe3kDmmTNPnO5yLbHx8to0nMkWJIEWh4laF2pBAFXhlloZctLToXMjI5af5Uezy/v4+Pn78GL/97W8jIuLly5cR8XjYv/hkNpstuZMpgH3sTfNwsMbBwUH87Gc/i1/+8pdxdHRUeHFnZyfm83nJHOZ+ZQq6nZ2d6Ha7cXx8HK9evYrRaBQ7OzsxnU5LMqAyXxeLx3205GUBasSja1thE1lKAg39kIcIkM7vHDeVRp+DTKhTieJnrFt/Zx4WxnGdB7P39ZmAq624MkvedKBSWEIhCo6NCU5ShJhsFbHsMna+ZV90prUSPzV2bqXKQhRO+3WKz0WmpHxJceWbvzfBE5/vTcqTAtu2kgEuf7eVVYSsfd82CWJKxoYUi+WG/t3d3RgOh7G1tVVOm/JEiIhHF5XHBj0O4+07E2kLkBK1uOeXdSoWmSV1qC4HWrcenUY+Jy7g+KwLRVocbiG7lVAr3m622GsCj3Qh3RkqYFIKt+JIGEn4sO+qRwlRtOIWi4ezbE9OTiIiinUrt+1kMvns8HsqEZm1t729HaPRKN6+fRu//OUv4w/+4A/i+Pg4Xr9+HcPhMPr9fpyenpbTqgS6ArqtrYdTz0ajUTx//rzkGUREnJ2dxekgk9LGAAAgAElEQVTpaTkZin0QbaREMAFMSXtSSCMe9/1q/RAomaCjkllD/I4KGNeGg67X7UoZQY47BDTvHo91+juf8u/McvPP9T+VCP6QD+7uHu5H5lnHHm+WnNFdw7yGk/zOfrDoOSlHymqWxcyznVmHKz6sr1Z8ba8C2kwe+f+aW1eCMsW8ra2sOJ+tKk8WbDNNpO2ZH1N/RmgHEPbDrSW6wq6urkrcdDqdLsXNxPCDwSD29vZKZqe3yQVTA9vamLQIlJwlV54LAglwLg614wua3zlo1xidC9+tQQcLWY8Ci4hYAi09TxejgxnrdUHrxRcXrUUKNu+jblXS3ImGFKBOV7oyST/S5erqKj58+BARD2cpa3uF4qSLxaJsq2F9HLv61e/34+3bt/GrX/0qfv3rX8cvf/nLODg4iFevXhXQfffuXbx79y7ev38f3333XYzH42LB9Pv9ePbsWTx//jxevnwZz58/j6Zpyl7a8Xi8lNRHWkoxiYgi2LX/k+fnSlG5vr5emjvRb2trq5w3HrF8TrgrYrT+CKz83MMTas/XFgGXChWtS/KLu+71jseKvX9ZbJNrjYofD0/xW35oWcqFT77gmlLCFddPTUHwsckzoy2FKpJ7WW5B7XAS0nmV/P5SK/anrG9VHzcx+p4s2GbaBktm5awLSKu+c4vB23BLiQdcaPuNDpiQ5ifGlotwOBzGZDJZEghchBG5myfrD8FFv7nvlmDLcdHidSuWf9P6YAKFBCDrzWKqnCu3LCgM3epmAocDt1vIPlc1ZY3vedwz01Bpzcj6k7eAblMpXQRUCizVqyQ5xtgWi4dbgU5PT0sMTHsa7+/vy0UGvN3JgVzW6OvXr+PXv/51/NEf/VH86le/ipcvX5YkrMFgUKxV3UWrPb2KQT979ixevHgRz549i4ODg9ja2orz8/P4/vvv4+zsrBxwL3oIpBW/0wlm4jl5BbQOFotF+U7xRgFC0zTF7eynIHH+stg+55zCnDReZSn5PNWepRuZ/C7eZaY66/X1RddwppQxFsoQkyvjnA/vCw+zcCMiC4tofAyLKBGLh1lo7L5vWd9l+245J9m88vtNir/T9n8G9JkhsA54+jOr3nmyYBuxuSaSEdW/36Se2vO+qLmYeHEzY7Ji/G63GwcHB3FwcFAOt2hzgdUWhNrNXCJifB1KrrgxLVGCGhmfvz3eJKuE2i1p4X1nn0jXbKy0JF2wRTxadJ4tyTET2NsWLS332rzW+hYRJTwg63Z7e7tk+bor1IWr37FKQXh5eRkRj4J1b28vXr9+HS9evCgKnFy/zDpXVu9oNIqf/exn8Sd/8ifx+7//+3F8fFyS7pqmKefa6saWy8vLeP/+fZyfnxf389HRURwcHMTOzk7xkJyensbHjx+L61jzwe0eVDqYJ6AcAikRmj8/RUqxb7VbU3JdiJIPMn5zpc/XbE0py/5nu65E8nMH6swi1t+uuHpuBJVZB3HW66AnXhPYOk3cXZz1T/OlAy20zUj99PwDvs/ksWweanR2hYC/s7nP/l8XN9aVqevWteqdJwO2m7gU2jQXX1CcsEyrWbcvNc2IlpL2uV5dXS2dVUxB2+v1YjgcxsHBQXS73dSyVP0CWlm3HE82sQ4+19fXBfgVb4lYtg49JkQw5sJQna7J8zt+5qDpgpIL2p9xN5sLSK+brjbGNTM+oaBxxcItoCx2KLeo5kXZnTs7O0tWpwN3dgiBwIl00z2xi8Wi8IhObbq7uyvJTbPZrCQqzefzcgTkixcv4s2bN0tAqzGqrX6/H0dHR/H8+fN4/fp1cVErvLFYLGI2mxVrSdn03FrEGLvGcnt7W/YPi7eVFMXjAbUueFCDvD7u9aiB7CrFLlNgyZsOapwv52t9Ri+HPnNlrGmaJWuS/SVv82+WbFxtVigtY+5s4B5deoUI2O5Wd56V14I3PUlBp7LFUJNbuw60NYXJ5y97LwO0msJce+ZLgDSrf512vTwZsP2SUpsA1zaz99b9vjYxzjQCDmnytDx0wIAsW1kQiqcqBV/v63kuMLpbvWTMKPDX3l8Cu9OHLid9ltFFC8mBtu1Hz7jmSYHHvrhQdPD0BJBs3N4W32c9dD+qDo8TusW9WCyWXKmaQ1llrtFHPLr6ZIESxD1O/OnTp3IfqcD48PCweCe63W4sFg9WsNo+OzsriXgHBwcxGAxKm6Sf+q9s4aOjo3j79m2JqWorzsXFRczn8/KOXNgOFC7o7+/vo9vtlm1njNdyXmTl8hYa8R+va6OL1AFWc03+11hdaSSfc21kYOt84+vMFSXGkx3Ypdzqe9LJwxbiCSogbJOHXDgo0ntAwBdPulKjd2qKNZPXmqYpc6rwCa1av8uW1q6XjKarQMsB2mUKwTkrmwJrTRlwkK0pCW1lbbBtmmYrIv7viPhmsVj8g6Zp/qWI+MuIOI6IfxER/8FisbhpmmYvIv7niPiXI+I0Iv7hYrH4q3XbaWn/x1bxo4sLcRb9z6xkbrFQ2rys2+Pj4zg4OCgZqhT41KZp2WaWWg301BdeUEBXMjV0LWgmUEQsH+yufilu6ckWrnj44nDB6Fo1lQkXWm55Zu4ptsV69bzHuTyW5b/p+mddpMPV1VWpQ1auBB/3fbItZePSKnAek+DS5QWz2azctsOQQL/fL679ra2tGI1G5eIDnpFNq+P6+rrsB7+/v4/BYBAvXrwoV+Rp65rAtmmack0ex0/FkFaQ3NQ6QU1Kg77X2AW2qkeAoPVDMBdYucfA/85CEQQSglPGrxk/ZestE/LsA5/XD8GI7ZE3yeuuFPKELSoTBFv3oolXXAEngHDMapceDM6p1qjmn9d6Ol+4TFi3tMn5der7kjbXKbV6N2lvE8v2P46I/zciRj/8/19HxH+zWCz+smma/zEi/qOI+B9++H22WCx+1TTNP/rhuX+4QTs/eVmHIDUw46LOnqG2LFcak5PEjEyI6Xa78fz58zg+Po5erxfT6XRJa/bFxmP/fKG7y8vHrVuJptPp0hGBFCRqq3ZUHrVwun8pwGpgy75lFkQ2T5mAcyFHyzR7l+1k9ORFDW5leJ90IXxEFCAQKFxdXUXTNMXFppijJ0vt7OwsKStsU7FbzTMvsSA/zefzEg6Q8saDS3RFH291iYjiAl4sHnIKtDVNSuCLFy+i1+uVe5m5PSli+ao6gqx+i8bKWJUrWnFm8Qp5iLwlujXNYyyb72gemOnNOSaYUJnMeKPmOvU5p2JBpdNzBuSZ8PWkZ9kOrfWabBEP8Efzy+9IQylXUtq180FxcyoZntvg7nZ3k+/v78dwOCxgK5ooPCCvRUQs8YXTwsfLsqlM8Hr52SpZ/7dltK2DMWuBbdM0X0fEvxkR/1VE/CfNQ4//9Yj493545H+KiP8iHsD23/7h74iI/y0i/vumaZrFj1A5VpnwXjLLinVlGivf80Wg3/zhgnGNXwKSexiZor+zsxPD4bBkfCrLk3E9XxC07CKWs3B9AbsCwOxo9l/PsC4KILrI1I7o5O5apx8/cws5o7nHg/2ZzBImALexF5+XsGRWJensFqy+58k7is3SpS5rgjQRMGkMPmYV0ocCQ32QxaLDSnROsU71YbY591krtis3sOrV1h31u9/vl3jp2dlZEczsG+dW31OhkNdGx5De3d2V3AXfCiPeEoBqDDr0JbtOz61c8odbvarbf/SOe4p8XlyxVP200F1hzeaYv2mhRjx6NjjP7kr2H3pI1A6VPsXBdcCIntN4eIgHlRHvg2gqq1ZbF0V7WrZ0x/M7nyPSxWnkfL+q1JQkfedGR02hqn3n77U9u0m/17Vs/9uI+M8iYvjD/8cRMV4sFnLM/01EfPXD319FxO9+6OSnpmnOf3j+g3XyH0fEP64NIiubEGbV+zUhnWmoDrQR+d2KWpw8wUmLgAJLVoD2M+qWFy00nvik/z3D0QWEBAy36OgZAa7a4BicZg46EnxcrLRq/XnRYRVN/TsKYrkT/TkJAyok2by6i5wxMJ2iw3igbqLhASNs8+7urhwQIkBVtrnaaJrH69YkjOntiIjPQJ4C2RUOzg2P9qQ1KaASwCnmen5+HpPJpBxeMZ/Pi3UqXhmNRtHv96Pf78dgMIh+vx/ND9Yxz87lUY6kGQGuaZpysIeEPDOQ9byUPdJhd3e3XEYvS4mAx3n1tUdwJV1I1wxg6LbmHJP+miN9Jx6R9ch1SoWKoE9asd/qL3mNvCz+1jueXMetXwpZyYOgGC3r6XQ6SzFU0YXygqDMtaNMZPKtFCnGaknHTAZz/WcylzQgTTLw3KTuTN60FVd224B907ISbJum+QcRcbJYLP5F0zT/2k/SakQsFot/GhH/NCJia2trY6s3A1dfnKvetf6U79pAlr+5kKjxK/Y1mUxiOp2WZBa2s729HcPhMF6+fBnffvttucDAGY2ard7PwIULxVPxpflqqwrdUqyLCkQ2zsxSYPJFJiQjPgdfnwu9Rzd2NkekRU0BIrA62BIUJLSyxcTxMP6krEwJHl2RKIC4u7srFrAE1OXlZQFJB34XTvrf44xunbirWO31+/2IiPj2229jMpnE999/X87qFmAoOerw8LAkSAl4m6aJo6Oj6Pf7MZ1Ol9y5DrL0QsidLQtosVgUq1Ztcj6k1GjPebfbjaurqyULnDFv8rmvAdJI35Gu4itfU1TKNEYCINdz0zRLYKV59rCP3lU79GYRNFxx4fhYFxVLAjAVL9FNR2f6QRz6m65vziWtUI69aZqS4d7v90sohZ47HjnLuigH3Gum31k4gOsvk+Uu+/SZ/50pautan+s+52WdNtaxbP/ViPi3mqb5NyJiPx5itv9dRBw2TbP9g3X7dUR888Pz30TEzyLib5qm2Y6Ig3hIlPpJS007+jF1UfBSyNW0GzG0WynKwpxMJnF+fh7Pnj2Lw8PDsmBUtxKljo+P4/vvvy9bPihYuFhk8fH7iOXj6uiOUn8EuEqSUmZjplhELC84atIOuHqWwowCMVsc3pb+12+24d9nsStaOYxts+gzCi7RgEJVbTIuJuVFgmU4HJYj7+gqVT2dTqecQdw0TQFE0pRxWSoXdLFSaDs9NWZZzYvFIvb390vC1LfffhsnJydxfn6+BDy0buV52dvbi8PDwxgOh9Hr9cp2Iybk8D2Nmxa4ksMUJ5Qrm8KY8653dELWzs5OOWuZfOUKrns09BxpRXcv55PvMNFIICtaumVLIJaF6IqVAyFBkslepJnzbk2x59hZNJ/yWPGyCirUXEuZQkLvjMYs61tJVjw5SmBLr0WbZesAmMmDbK2zr+t8V6vPS9Z+W/kxuOJlJdguFot/EhH/JCKiebBs/9PFYvHvN03zv0bEvxMPGcn/YUT87z+88n/88P//9cP3/+fiR/R4U+L8mDYcBLgwfIHzXWpxjO9cXl7GeDyOs7OzODg4iOPj4yWtTZmgr169infv3hXrVtow+yDhl2n8BBoyPvsr65ZAq3p8/BQC7mr2xaWxu/Dg816/z6kLAV9gfF9CLNuw7xY2i2vbzDam9Vbrl/Ys7+7uxmAwiIODg7LVgq5iWW6yBOi61HcCJ7V5dXX1GXgRVCgA/cpEKTvKhB6Px/H+/ftyGhWTigQSBEMJVFnGSsBSH7Qdh0KUf6ttxV1FK1laBGaNvdPpRK/Xi+fPn0ev1ysxZglwrSG3ZJumWVKSCBbqi2jKsAB5S3zC4wvpTaCVJvq6x0FWpepUQhiL2pB7nLzga0vPZ14b8oOUbcZpZ7NZOW3LZRWtTckIXwP0ADBeq9hvv98vd2Nr7L7dR2PK6ExFqwb8rkj6d3x/VakZYasA2kutDz+m/Jh9tv95RPxl0zT/ZUT8PxHxz374/J9FxP/SNM1vIuJjRPyjdSpzAemTo89+THGicQJrIECmZ59qgMDvtW1jPB7HeDwuV5QJNLe2tsq5tc+ePYuPHz8Wtx/BUwuFWyW0IGoxTN/rpkUiIJAw4GLOxp2VLLZFupK2FChtdWZM7UoO66EwyxaiCx0HHLUpAPR+EMA1l0pOkqAeDodF4MutJsGmq8gEWBRuUnYkpAVQAhvNKS1oj61xDnV4xcePH4tFS9d2Fh+WK/r09LQIVZ1URYuF86H6yH++n1MgwCx89lvgPBqNYjQaFQVhNpsVWgo4xN/kL4GA3PeZQM/WrNNOoQS9S7Ci0kpgIpgJ8EQX9+gI7OnBIF1dqc1kjX8mXpCnRbFat06dj/WdK5McH42FiMfLNWTVio70AvAe2yy8wDlhm+Qprum27zMFel0A9rbbyiZtZHjSVjYC28Vi8c8j4p//8Pf/FxH/SvLMVUT8u5vUW2lr6e9MM2kbnC+AWqkRyhndwdYtMApyLoj5fB7n5+dxdnYWk8kk+v3+0p61/f39curPyclJSWZhghCFtoCaiyjTgDUeauBKppCwUDKMxltb/Bwfx0sXnvrpNHUAXqVhunBZ93kuCtJCQOnCRuPxW5c8ribXoeZ7Pp/H3t5eDAaD6Ha75QATCV9ttYqIItBpHfISbwl9CSyer60iYa1+6fYh0Z3W0+npaZycnMTFxUVxL9P7QItDn02n0zg5OYnDw8NomqbEaqWEcO1llhKtcPG+FAdugaInQqeobW1tlVCLQMO3TBG81CcpnbxWTrygOSXwEnSUyb2/vx8RURKeyCfOt27dUq4w2UrvMcFRoMv6a3zMv+nN4vxxpwOPhfW1xf+9DrdCXXmOiKXztPf398v60Tg4vwTrTCZmxRUB0sBlPPvbRjvOd/Yd267R3v/3Z2vKQO0zlidzgtSqjmaEXjVRmTbU1jYnygGHYETG8vopOKmBaq/rbDYrWZsSIqPRKF6+fBnHx8fl6jO/n1KLmO5l9uX+/n7JytjZ2SmuQt5bSsDtdDplIWVjZyw2Ij4TMhRkpHk2V5kQy4Qhn/U54f8SthQsnA9mZ3uyjPdbVqKfwsM6BSaKkSlGqeMUxQ8CS/VDbllu36HyJguWsUa3VDh+8hYF5fX1dUyn05jP50vHRi4Wy3ebqg7O5Ww2i9PT07L9RoCleC0Bhi5eXxviLwlhxitpBSsOeHt7W856zqwi9dcVKiUhaq7IN0wmygBbB4NwfTjvkY/pPYqIQg9axeqrPqd7ldvBRKdMwc++8zXDcXNbVcTn1wxSXqk+z56vga3mXxn4XDMCXY/jtwEreTiTx21y2tfAOsbWOjK/rZ9t4Fv7fJ02nwzYthUneCaws+KaU63UnqF26gLHF4IsVSYe6FYTCcLT09NyGgu3h/R6vXj9+nW8ffs2Tk5OlqwLj6Ps7Owsxba0yDUGuS8Xi4ckmNlsFtPpdEn7pID59OlTOWlGlod+1CaBNyKKpq7CBduW9JHR2LMhfY4deClwNR7G2yIeE4wY4+K7HMvd3V2JW8rtz+0nHvvWXMqik4VEGkgoik90nCNpLkCWxaeTn5g1LAuXFk4mKKVQXVxcfGbV+jYQ8i89KKenp4WPmUHLtUC6iyf8hh9atQJb8Zbi3YPBILa2tpbufWbWMhUBWXi6qEAKAA/tyJRhhklUp9qXciOlRP3Vc658ZtasPqeMoZzSPIpOrkhqfjIZQ9AliFJx1yEWWj8OrJy3TJEWH2Rx16Z53PLD5CiOiy5k8mKbBZmVTYAxA9pMId20zcx63RTI12n/7wXYrlvaXBY1YZ890wYWXBxkTrd6uS9P24DG43EMBoMYDodFa2yapty4QutWC8mZSRaCGJ9CngKQe/K0J45MpCQZAoSySWnRUrDqM4GQu13drZzRS4KQglvF6a/PVDx5QnSWMFNddJe6J0LbI6jESCgLOJvm8a7VrI6rq6ui+CipaDgcFoAVeMiLoHpUF/tNK0yAIuChFeWCmErK7e1tTKfTYiWKjkwkI/9yv62spLOzswKMss5lPXPOPaFGnpTFYlH2yWrsAht5WuR+VxtK7tFY3SWr+nlalvYSsx8U9FSuNEatMYYWFFf2jFqtMYZxKB/0jIMcaSQeIhBxDdfA1mUMLUreJsYjErlG9F4bALFt9/pwndHdLpqJzlnmttrLgMvXsa9x719byWRDG9BxzrJ2fF6zOt24+9Ly9w5sKZBJ+MzNoOf1e1PtxwHawcNdWPxMC1+W0N7eXpyfnxew1T5MCf5ut1sykz98+BAfPnxYEsgUHjr6L4uV0FoSkDABimPitXC04DheWjhucRDkKOzcmmL8le2wL5m2mdGeC5UKh38uOrhyIMClsiShKRejnnGBq7ru7h5u3xFdR6NRyU6me1dJVXd3d0tArnoVj9X8MhYpC0KWHZUYCjXVxwMhGLv3G2icRk3TFKtY/WRMzt2MnHsBLa1a/Yi39vb2ivUr93HTNMXjwjufqdip/3xf+3hp8WuuGD+ka139lvWtdTCfz4tLX8qC6iVviz+oJJIfMsuWCqvzJmWI87UnPKoOKW2yZn0fvfqm9zTn3j+6wt0S5dqSktPtdj+7fEB86R4p8Rb5syaL20oGerX61pHn7FMN3Nss2drzm/Yj4u8J2GbaUdtzKjVBXnu3TcA7MHid/hkBV4tlMplEt9uN4XAYg8GgHEbQNA+uxqOjo3jz5k25O1SHxft2D3drESyp3da0MxbGPlUXrRUJGGrebMc1cgFbRquMqTMBFhGfgQvHrM9olVCj17gcbHzB6n9l0G5tbRUBQ48A+yZgvr+/j/l8XlzAvV6vZNdSONBl7OcxS3gx0UguUwGdzk2mO5QWsYByMpksxVcpPNWWaOqCummaz1zoFOgSsBGxZKUSaOle1L5PCW1Zp7JMb25uYjKZFMBTWIPJVrT0HTwIqBwL+VJjFpiLz6TA0rrXGtN8cI2pPdXp651tM4zBtUm+JbBmFqkrlVIadHCFe5LIz+J3KuhUFPSe8z8BSXXokBLOsRTIDOyzH43tS6xB76fq+tI62r5jH9dpK3t+nfJkwHYTgupZMvE6z7eVrN2sT74wXIDXAFd7CXu9XsnAlItGwmQwGMSrV6/i4uKibPIXU6tdCudMcKpdCX3PgKSGrYVLTVXWjYQc3csERi52X8hqX0UWJt3HohXpqM8IylQwaBVI+GcKkcZGK5tC2AVbRJQsT24LUVxR9fi4I6Jkm8sNfXBwsCSw6Valm5QgRrcp45O02kgXWijn5+fFgpZlJhDKAEqFcyd6UTHgGPW5LER5S2QNyvJWnFZWrRQXJtvc39/HdDpdukaQdKFyJ6AW7ZWcpuQgKg8EXikidCff3t7GZDJZysLnHutsbTMJysNGtAJdZjitnQc9DMXPOB8RUdzHBFrNHzO39cP4vvfJ5YUK15/q0N5redGyLT7ic8ofyqvMSvX2NgXQtuLWcO2Z2vcZgGay6UvLkwFbL+tOiE8kS2ZJrSokeGYJZZYv++ETLqF6c3NTrjZTYggPlRfoHh0dxVdffVW2cIzH43IpN2Mn3mdaMFoEAiS5nikYGG/SGP1GG247kdBz8CMd2DcXQnyGAtKtArcC+JtuwKx9b09jkQCRBUcvAJ+TlaVDGprm0eXLpCP9lsU0m81K3H1/f79so5nNZqUdxtgIgkyEI9g6gAhcVJ/c0+oj74B1YHbL3q0j0V99oQtU/Cth7XNNlzlPM3IXsBL3Li8vyyUJdB9zPgSEvO9WCqt+7u8f95lr/jhmgq4O/JD7v9PpxHA4XOI1xoHdxUueorLqhRYwFS56aVSP+qi/fcueaC96aU8xFSPNi9Y394C7G1vzreLzx/HKZd/r9T5LjuKctYEa6eXFwZjP10rNoPHPalZ1rW7/fFMwbcMfL08WbFeVdUD0S7WmzB1Sqz8T8qxHAlHCSFuAdGayDpJXXTrC8c2bN3F6elpO1pFbzbVkMi7Bi33nSTn6jtmfrEuLWGAry4oZ1AJ9B1AJAloIao/tqLjSQJr64qFQUptMUiGg8H0JJiaW0bqmgNQeWQnobre7pMXTOnZF6uLiIra2Ho4glFuZgo6AQKWB/eUYRWvOlfiAsTzRwrd1iJbMMFd/NRYfx93d44EbsuoY36Nikm2J4VYaeUbkktzf3y+udwEykwC5N5XxYHchC2wzJUL9lmJJ7wrBh/e+UknNeNfbIKizXbd2uaak8JG/acW6+1ft6e5hHWPJw1G4nY8089PhyK++tqjMEZh4TCN5l4l7XLuZRchSA8oM6NYFRf+sBtwO6uuAtgP0ulizCnCfFNhmVmLbBLZpFetM2ipXAsHLAWBVexnoavEoUerg4GDp1patrYfTcUajUXz11Vdxfn4e5+fncXJy8tn+TD/ggtouF7wWM08a4kKNiCXrwTNqmfgiK1ygT6EUsXz5tce8PEFLgpGWLoVuxPLNOHxXwkljoGtWPw722uIjlzYFp4POYrEo1yAOBoNCFyorLoCUCBcRxaLr9XrleVmnTOYRjXgrEMFON+iIFvrh8wRAzimteR8rLVtau6QdLRoqjeQ9PaM4rQ5kYTKT4rUREbPZrMRqBZrieYGqwJlHRwp4/K5bV+LUN23tUbyTc6qxaq+5W6G+xglYWQYwaUiFk6CZhU1caXYl8ubmJqbTaUwmk7i9vV063pPZwOITyRGuA4aGuCbIv76+NB+M11K++JYfyjjW01ZWyeC251cVyu+29zJ5vgm4fkk/nwzYrgLHNmBcVceqNmt1Z9btuhoUv6ObUmDb6/Xi8PCwnETEA82VmfzVV1+VCwoUk5M7VwDJM0rVxu3t7dL+TfWbwtfdvPqfIBYRSwtWwMDr5kiTTCt0oPU23UIXHaS9s17GIkUDt2wztxiFiwAgIj47TF0C5Pr6Oi4uLkoCW7fbLUKbFrH6qHZ0hZzakeB3QSTQ0NjVBykfUrzUT9FYwp6xSX3nVormLuNLujbJD+IRV16o1GR7Xq+ursoWHvGQxq4sYIGHDt2Qq5nu4q2trQK0g8GguNPlnlaM1+eWPEfFkYlnGpvGoy1Z+o68qDnU57LyXQ440Na+o5LsIO7KKT1gs9ls6dwbvJ8AACAASURBVDQvzSnj1ZoT8Zt4U3QSz7JdWvJulcr975fP08PjtMiMknXBt00Gr1tW1VFrf5Pnf0z/VJ4M2LKsckfUNKNVboXMbcHvfTFRgGWLKtOEqOHSlSXBJ5fjzs5OHB4exuHhYdk8LkGrs2Pfvn0bp6enMR6P4+TkZEmrp0uXzO7uRm0xInBxPye3mVDYkF6ypvT76uqqgBAtUY0/Ez6yrv0ZCQz2zd3jPicRj4JfSoBb0q4kSQDpGLrd3d3ioqOLVDSczWZL4NLv9+Pm5mbphCVatqI5LQq9q/aZjCNaqv8EcQlOWo+iDwUeC8GRiodbbbROVSdBi3FBzXUWgxVIeaasBLUsWm1nYqxWrmZ9L8G+vb0d/X6/XPl3f38fs9ms/DAe7DyqMao+zYcAlvSid6Db7ZZ39Zv0o2Wr9aHvRD/yO/mPFi6VSoIseVtAq21Rs9ksOp1OsWrpPnawFQ3VN4+3c90JgLMwjixbnbLF8bhMYJ2rgMufqVmVtdIG3jU5nvVp1bs/BfjXypMC20wLV1l38G111OrLtDE9wyQEfz4D2bY+i+G0Dejs7CyePXtWLu/WwfSKmRwfH8fr16/j+++/j8vLy3KTi5J3ZBHI4tEiUrw14vGCcYKJ3GzMMJRwoqZNpSGLFakuxhe5gLmNQ4fdS9goDuWuO1oT1KrZLykkslokDJgERY3eFxGTZqRwOKjTNarzrKWQ6OAI0ZwHhsj6b5pmqY+y0tQXgb/iX7L+SAcqcwI30V+XVXjiW5v3wOOs+p7zSyVTyoUUB1dqmFQmpUEgK74Qv/MAC82VLDIm99B1rFtt9COLznlD41Ei1vb2djlrWd/TQnePh+hAnqXFS0BghrKveyotFPiuEFK5pJLEgz6k1HFOs1ARY7UKK5GHHFCpINYMEs2BPEfie/0QrMkvVPo5Li8ZzTjONiPI38/qXqe04UxNjmcG2yZtPhmwXaXZsGQEcZdSVmftPX1Xa8tdkxlzuLD2Z3zBzGaz+PDhQ9HidbKOQHJnZydGo1G8efOm3Bo0Ho9L3I+uVAkAAqIn4NDl6LEYnqWbCWICnRYa92PKKhfQMdlFQDsYDOLw8DAiIi4uLpZAi4uYwCnrUYKFrknFjWl11+aHbYhG29vb5Uxj3ThDPpDVNplMommaODg4iIODgzJmWWaK/8nikpAWben2d/dbRJR31X8KLc6hW+myzDIgzMYuYCZQURHR3+q3tkLxnGfVJx4WGFO5YGKTgJZJUQJLPes/4n9tlaPrmYd8qB8MqUhhZVzcaUEFj9uD9D/XCnmmZpXxh+59zpnoJU8HLUbxvbb4XFxclPVIpVN8S4uUlrwUKT/Yg4qqgJ8ndrFoDv0wC1rRzF+gBynjbZV1rddVhfLbZXnWxjogWAPzVX97W+u092TAdlWpWaybTqBbOqvqEiN5FqI/o9/Z3/6MhNn5+XkBXF2izfjVzs5OPHv2LF6/fh3v3r2Lb7/9trjheCKOFgEtVWq1DkACLfaT8Uu6iEgXtwik8QqABDQRUSwiuax5OpNfjJAJA8Y76Rb3OBKzYJlk4/ti1TcpCsyS5S07FBr39w93zUoA6UfAIa+BntMRj84DPMVLQkul0+ksxTEJBhwHCzOFJYglyAkY5HO37p1XKZw1BwIiZvLqM7k81WeCnlznirWKXnyOHhH9qM/aNyw3P2PlThPNd7fbjX6/H03TLF3GQCCVskha8LAO8g5zBtztm9GRcXfGZzmfUiLlaeKlDVIulGRGflE9tEjVV7niyd/M4WAmtJ5heMHlg3hd8sUv+3Dr2ksmG9usRf9/lTxvs2o5Hq+zDSi/1FptA+KsPBmw/TFaSA2IN6mPk+iT5RpsrR4HcVqztDDFtLKczs7O4vDwsLjBKJx1z+jLly/jd7/73dJJQVrYciVTKVD7BCb1h1bi3t7eZ3GgTDDXkiB4sgwtZu091RYYacQRj3EhasYSckwIYYILLWrGo3S2rywxWU3cJ+tAIuGr+KIscx+nxqVL4/Vsr9crglL1KC6pDFsHe+0zZVyNmdERsWSNkZ9osVJYChT1PgU9eU2FdMh41Oul25UeD9GFey65XUdA4kCr8TMZkDzs7/LQC64f0YFuWR3CIMXL9x0TaH0cqlv9141E5Am6k30NUC5w7rl+NK/KGpYFPplMln6kNCpOS4CTcqN2eFym1oO7uEm7iMcT4ygn1Ecpj4z/0qMlr5oDqMtLtu9lXTlde87bbCurADWT8+v2a1MLOuIJga1KG3DWCFx7vk0LWkUs9oOa4DpaUAbKBDvVo8V2enpaEqWUhSnLdmdnJw4ODuL169fx4sWL+PjxYwFcjY8ZvO4iYnKHC2EBnt6jG8zpQ4vZY7oRsSTI9LkEgYBXtw+pECx4LJ/GJStIi1zWN92R1NqprbuAUf+ZpMIDFzzTmAtR2cm7u7tlO9CnT59iOp3G3d1dEdDqm/aBah4jHjKfZeWLt+TOlmLAS9fJawQldy1TwKpezbmeJUA5z2Y0ynhIc0OgdWtV7cn9zAxlKnhuRRIQF4tFyW7mVinRimOkp0CxWj7vSjLBS2MWDcVTspLlmuV+YNFL/MF14Uoq/6eye3l5Gf1+v7iSlb+hO32Z6U3lSP0QzZmApnkRnak0UmG4v3+8ISqzTJUr4tt+Op1OSYSTsks55vKCJbMo28CpDbDdmFnn/ey7dYC6VvzdTUD3yYCta0pfqhV9qZXL9/XbrYmI+hGRGYM72AvYqIlqf+ZkMonDw8MlbV5t7e/vx9HRURwfH8dwOIzZbLakAEjgueAgaHkSjRaTQFoCkglhpCMXtLvm1AfXoqVQ8NhJxTblLlfdfok3NWlluxIAJGC18Z4KQzannBv1S+5kuuw4V/z7+vo6zs/Pi5Dr9XrFgqJ1GBEFcEWjxWLx2WEPTdN8tvVI9NbnPHCfvMk5ULu0xCio3SoVvelh8RCE04rtib8kdKXsMampBsaM50qZdOtQIC2LmLFB9t+tYrk8/Txr0ovjcT7heuL+ZrcaaWnyb9VH4OXa0LNKgtJYx+NxsWjVNtvUb+d9bplikdLJ+fO69EMFR3ym5DZPFBNt1c9VVi1lRa18KejV2lRxUP0SPFilPHxJ358M2P5dlkzbanM5uIbYVi+FZva3BIeYWRaSXMl+kXbEwwIaDAZxdHQUw+Gw3Aik+jwzkIuTQCwtlQtVsVtZjswYjYilWJoWHa0EMjTd2FtbW8VC0jsS1LKqmU2tPcj6m3Fj/b21tVW2SSnpqt/vR8TDGcWM1fkJO6yLYxXwq34qLRHLp25FPBxYcXR0VE6Xuri4KEcAEvgZixYQ6UceC8bEVERrXovIJLGMf6WgiAe4bUjjUV+YHBQRS8BKz4jH7TX3GpvARmNR3NrdvgxXMCfBY68CIgl1d2cTYDk+JQhJeXH+5Vqml0dj0hjEG9fX13F1dVUSvDiHquPu7m6JflzftPS4/gnM4tP5fB6np6fl/G264QmK4lfxlMZMUHTLlmMn+NZoo7XOYxq97zz3ml4CT5ZiySy/TYGq7b0vAdIvNeTanl/n/ScDtj/GtGdZ1/rdtA636vhcBq7+jv4XYzKZRdafbkHxuKusOG1BYRYl3X56R4tCi5d9ExBGRHGVRUSxbiVQCVQSdL6g1DcJByoJFDbKSGY/6Ppm3FWCXAJWW4YErEq0kutWW3IiYsnNxrZdMEY8Khe04Ll1iFtaNFbFZZVEIrebxtA0TQEWuYQFkBL0opNnmmb8JGtYc+MxWedlWlmaL73DHybKyHvglo+7pj3LlZ4NV/BES8UmmTzlngR6PERzj5eqLcZ7VXhClWguz05GI1pz+s2kMtGa7lIpDbT0RC9uuyONs6J+6FjQi4uLEorQ+LQWmYORufR5QQPXnK9ZWuVUhlWoJPAkKnp6XEll4hiVAtabeRhrlmZm3GSFcqVWakaTj7mteBs/xkJmeTJgG/HjAXdTF/K6BNUke9w1e77NAomIzwRuRBRt15NCqNFTm2VyVER8FkeR9UE3nMaoMagNxQzv7+9L+3qflnjEYxYs26LF4cKawCzhq/HKFSkBqXELSARko9EoDg8Pyx3APC+XQpvxUQpSWnOeFMKDPtg/jp2xrsViEfP5vPCZrGwqCARgjS8iymUTrIunTLm7V9aUAJ884zFzAR7dm6JxljTF8EFtzyWtZHcv03pzQStA9FOImIPg64PWMvcRM1bL7V4aE7epKBzDJB5azgRez/AWP/FADg8RaE40bgGj2hI4is5U+PxdrXddMCAljeAlhYFufo2JSgzXKAHXvXWsK7PGVW+v11tSkOhBk0LEfBHO47pllZxvk+Nu5a56NmuL89PWlx8Lrl6eFNj+mMFlILiKkDX3xCYg3FZqGhyFYpZ0wviQa+d0Q0u75UXfdB1JkEq46DtZ0hFRrLumaUqmrVxFdD/RPUVBou+oVfM8WroHJRx0Li4tCLmWteB1P+xoNCp3/qp91/g9dkfrgho+3emurdM92TTNkrDT32qTB+ELWBQDXiwW5QL2iMer+xgD0xxISLNd8oroSr5wujsf+9wwbuuuZFolohX5nXW74qW2BKZ0tzJEQHCigkiPgehANzUPynDlQsAtK+z+/vGCA2Yhe+KOrydayVpP/X4/hsNhmT/GKaV4iKfkxhZvO4iLXmyjaZoC5EyE4/rmD61S0ZfuZipBrlRzruiZomySnJTSrdPV2H9fbx5DV2mzZLPn2yzdmnXJttYxzmqy3N/9uzD0ngzYbqJl1J5bF6wza1S/a99ljFEDa31WYzIXiLII6UpjDEZatIQRwVHCn0lIXBw83k71qS7G3SQsut3u0uk7bonT0uE2AAkuxe80TiYLCYR5vRqFnC5nGI1GJetXJz3pWkKOQQJVAkjWgYOL6CU6S7hHPLrPBZq0WCjICARSKOTe13GOqrfT6ZQ7UxWf43GG3OKirR60QNWHxWJRnlWhlSM609OgdVQTbq6IkH8lpGv8rnrdQqayI7culQvOh/rrsVO6TaVcEFzJvzpyU1u2dI6wu/3ZZmYJuRXK/bqaMymF+l5zw3ip91N85ta08hg0vvv7+yXA90Qr0uT+/v6zA104DvdguSIlOrvHSfOpcI28R5wrdxVnvJVZ1843XlZZr9m7NcNonWdqxXlkkzFk9dTKkwFblnUGpudU/Pla3CR7n4JrVan1ywmd1VezDN1dxBiaBAbjaxRuckFtb28vacFc1FroFFoEEwEN3dU8eF99lLCh1at6/CAJ9ZkJL3R7yW23v78fg8GgnKR1cHAQw+GwuHSpSVMYEUipKPDgf86VaCYXJV16Ulhojcm1ThcuFRRPNtJ4NG4Kq4goyTv6ToqBLGvVQxe56JhZrG7Rku6ZokFwoEXsAEgrnoJaz2V7ZMU3ckUyMUuFvE0Q1HxQOZR7n7FJxp7lPt7e3i571bN8B/WbwlRjd3ByvuW61Heij9al5lLPeBya1j8zr+mOJdCq36SV+iCl1DOQZbG6LHSr2H9IF9G03++XbUnkOcok8QS/I81Xxaszvl1VqNDU+J6lNv/el7b2MsD1MdYs+Vp5MmC7DgG8tAHyJlrIKkvZn61pd3yWC5eMot9k+oxxWb8WOTe1U5DKneYZytwvSQahsKFbWsJe1gmFLcdHUKUQYmatfqtPAn0Bu+7LVCy23+8vHepxe3sb0+k0FotFORBia2vrsz2CBEdZwOqXhBUtOLd0GYuazWbRNE1Jfur1ektavSwGt4xkjUoB0jPM9paypBgdAZfXxxG4dIi/4pYUxgRLF5x0cVIxoyXGuRQtpHTQzUlBzrppjXsClPieYMX29EMr1pPS1H/FeOl9kYV3f3+/tP9TvOHrVT+MVbsi4Zac1pu8CxoHlU8+GxFL9KXBwEQyX8esy9cuFQLSg7KkZrG7DKFV60U03d/fLzzpsiILZXioiW25weT9rZXsPf7WM/7Zl5Q2C/pvozwZsPWSEfSnIrLqcG1wXUCvfdY2YfrcAVbfuRUlJqcWzViWn4mq+CczObVIaNGoPVqYjBsLOHQrEPtFYekClT/qs8ZLodvtdmM4HBaQlaLArGlZw+fn5xERxb23s7NT3LFSJARITJbhDTwRy3wj4BF9VOTe1rO6LUkgrno8QcSB0wtjaxGPFq5oKRd4xGN899OnT0v7cT0mTQuOygOBT14DehwyoFUfZOXr0H+1wz3bslx5AInqZJJctnWIgKy2eFADrT3FD7lNSNuqaBXzcnXGQNkvFvZRRXxCjwAVIwE/PUYay/39fYkTSyEQLzEUoL4wTESlgGuEyWLOs9yDmynurgRyrdcsQs0r47X0gFHueGIUFQQVyrQ2gyST66uKj8+VC29jk9IGvG7pboIVLE8ObDcl0t9lO6uIyQWQaZ8Z0Op712gJiBTWTfMQXxkOh3F8fBw7OztxcXFRrjCTIFd7t7e3RWB5ckONBtJ0ZW0wNsuxZhZettD1/87OTonLylWsMfKWEyodqp+3yIg+d3ePV7ft7u4uxfn8h30VLaRc0L3NZCYJe/VTRUKXFylEPMaQNW7GR+UWj4gSD+x0OsX1vb29vWT16DcP/hAgCLAkKAl8dLP+/+S9S4ht25KeFytz53vlez/OueUryVSVVA2DOsbqGCNjMEid6pjqumxDdSxwU4WbbqmrlrAwGKlhLHeE1TDCRqCmwFhtG4zrXuqex37k+507M6cb+3wjvxV7jLnmysx9Tp57BySZudacY47XjD/+iBgxxuNxMW173zFzgsK2srISc3P3pxW5rwh/GJ4zHFnQs369Vs1+s/IF2KI0sc6YaxLhsxbpB2uZ6GOSXzgYsDbnDs6qmVLts6+5chgLfKcAn+MZrMx6LYxGowkzeXYz2MqUzbJ+H5l3l9wXzwltsUna/aFtzl3ugDe3qRYc5XfRpcZGKS2gnZVItcDPz34qLBlKuKaVZwO2QwbmqQZw6LNq5oxp909juBRrg/xvAWSw5feLFy+KT2V7eztevXpVojI5lJ7EEH7JqDfiXmg7IIfftBnBQ/CQtWz6T/28mNb+6Q/fw0y8XxamQr8ttB1BzTgQhHR8fBynp6elHZib6Ru+07ylBb8X9TrYDPO0/bdd1xUgRNhZCDEuPm7PJxHZkmCApE+ca3xzcxMbGxsT2zlyNi/PG+NJ3TmSeTQalbHe2Ngo25DOzs4+O90JYHYyDvvtsTLYFeG5zyZifhgHs2r7Zu3vN9MzsLI+MRkbfFmXHEcHO7a5uaYE0Q6DKICIgjI/Pz+x7Se7HBhfxsz9tx/X7xLrzYGMBmDGMiuZXjuZcWag4/nZOuZ30cor3/G+8F7aTG1W661YBuHMqnNpMcYaMekr+d7Wd/nZfbJ6VoBv3T+0jmcDtq2SB/KhA+NS04Ty5zx7lvpaCydPSmaFLGybkLIvhACg3d3duL29jfX19SIc2I+6trYWh4eHBVizZsoLmYHWWj8FwQVjtF/KwUH0wf8zDmYmJKDA3GpQ5p4cFMb3CD5MzgAe7eB4MqIpzRItdPOc0U8DCHNgpkBfLPgQRJifM7vwOFj4w3DpF1msbm9vi+nU7NtJRjzuBt4cresTdZzhyYqNwXZhYWEC+CKimOcBFcCMOrKiZjOnP7cSyf5i6jEwmGl7CxfKJKDI+0FKRydZAOA9X24Pc2LQoa/5EAWsAGbDgDJr2ZYNvz+j0WjCahIRn21z83oz2Gb5YH+7n2GAq5nNa+zUljbfS3AUCg31eN5qFrdajEkLgGqf94Gh28r/0wjPLN/T96FgP6RMq+PZgG3L5MDvvo60Jro1kXkS8/Py3/k5tbpb37FIWvVlM5WztNgneXf36eSYra2tkh2KF2lubq6YZz98+DARqAQoWCDwEvaNOS85UbkIuqurqwnmYpNYjU0g7PO+QLO/HIlp8+nt7W0JgMHfyzYPnsX4rK6uxs7OTtlzeXFxUdqVBTtCxvNjZmDft/NIZwbv+YINAxZYI5wcwHMNuzs/Py/PBWyYX5gyc+A5Q5mxz5bnwZ5tKqYeH1kI0FjIuy6ORaT4VJocJWwgs0XFCpWVKLNs2mmwNfjmd4Nzch3DkPf8GnSt1Hp92uzOu5bzK3vd4AtfWFiI8/PzMkf5QAAzZ44aRNlAAWF8LDsycNlfy9rL71rtXj4zSFI8BvSdZBb0gXuxsni7ksel5ZrqIy59snUowZm1tPCjhi19185Sdy7PBmxrxROWJ3FIB4cAaevvWevPbZxWn6+xAOIF9KHcmEqdoB4ghsUQ4LC8vBwXFxfFhJs1aJ4JU3J7bGI2kNmnVmM2rsNjYK3cxZo49dF/Axl+QXycZhUGH/o4Pz9fzjQlOMgM2j6rvI3JbNFzAYhk1oayYeF5cXERXdcVYYwiYYGcgQdztK0RtD3i3gxpNmFmaqDlfxQj/MK0leQg1EGwE58vLi5+lqYQ0DMrdsAcc+g17TVXY2jZrOnsaGbk/I+C4Khtp2W00sGzsvXFbfJ7Z8vLixcvJhTdzGpt1vY40mbWCmvXayCPnaN7s9ywbHD0Nf3LVhTaYb+sAd195rtsdUJ20Ae306Z+K/E15aCvWG4Pubbvs777hwL2Y9jsQ+591mDbV2aZuKH19X2WgWTWwa5podkfhy+QiFB8Vd6jeXNzEycnJxERMR6Py0sTERPh+2SIyiYfnmNGCRhkXyCCHnYMKADAsBoLVF7UrHnzvRNH+MXnb7Mr6sc3ur6+XtppEyta+NHRUWxsbERElO1ETgPJM2kLjNPCx/PDM5g/hCeCHaE3Pz9fEoHgM8fkzb2OxM2nytBG+2lhGdlsnJks9bfeBcYOP+Pa2lrxe6IYrK6uxtzcXGxsbJR5ZkzMrmD+NqET2GbAtULjLTu0B7aEEuM0iTbpwrJgsg6mIgIZZcBjYcWD9ev4hYj7yGRHbtM/W1qytcbBhjY1e+27ftamFdW8raeltLNuGBvm2QquQTyzTr8fVozyO4CcsV+Y/nm+6FsG2T7rXU2GZvmZCRVjmZWPVt2tZ/eVWYmVy0NB+lmCrYXHQzs2beKp2wtgCIA/BmRzv7zweVEvLy/j9PQ0zs7OyvaTiCjJH/DJIuQANl4IfFyOQLQ/OOI+gTj9z0EgBjte0hzM4Rc4j5WZMc/LezDpM8LSQo72Igzxd8Gu0bSdJOLm5ib29/fL3t3FxcUYj8cxGn06QxQWakZghmiWiFBkfA143k/LuCBUYUXMTdd1xUftICCUCINuREz4H+/uJg+rcBtor9dwNpeaVfNDNDgnJdkfPBqNJnJvZ+aHcpH3U9cCb1Ao8hnFsFPvXwXobJ6lHubbe2gB3pwIBNbsyGyzMFtM7BLgfeGdqR1mYUuL1zDvji0cXOvAKCtRfi8p2YLH3wAtAWMtRpuByeDr51gOuf8eewMdFgWyaPm9NYPO7xXX1Ertc9/Tx16zbK4pKdOAeej1rTJLv3J5dmDbp6HMqsG0gDMviAy0+Vm166e1tVWs2dm0yYtOVDEpC1dXV2N9fb2Yqy4uLso119fXcXx8XF4EzGr2p1ob5e+ImEj5hvAAfPy3TU2kkYRR1MY0IibA22ZMQJFgm4h7wUf/LJTw9SKwTk9Py/1WBnj+xcVFHB4eln2DpFC0YLA/ykBqsKXdzFNWWgyY9MkgZV/c3d3dRIYvzP+MCYV7zSJub29LvRkAPXYGY9prH5sVMIBwPB7HixcvShpM+gzwsS5s3SB4ymPuNWRFoGYCtmvEIGuhH3F/eg8KoTNMURdz7KxfAG4GSdpccwPgr0Spy5md/A7ZqmDFwu+C16SDiyiZhRooM+DavG4F0WCbg6oy062BgK0zuBEwoyMLMrPN/tq+Z8wCYDWZWwPc2jNq5KgFoDUm3Xddlu3TgH9IeXZgO6QMncwhmkttEqY9J9c7y4B7MfFy8OIDtgsLC2Uv6ubm5kQE7+bmZrx+/TrW1tbi6Ogo3r17V5iwTboWfhY8NdbKi4OgtB+JF5CIRV526vO1eWwyKJkd53GgrRYWFuTkRSYBhrdp0A78pldXVxP+SASC9zgCnpkpetwAzmwlsD+O4lOHRqP7BAZ5nQAesDHvo0VgAjT0zc9jTFFEALQcwAI7x1pyfn4+Yc4cjUYlmOvo6Chub28L+PBct9+Kk8Gjtt7Musnw5JSdtbSDrI+PHz+W66kDczXtN1jDhG3qdj8dL5BdJJhPV1dXS39RXG256Lr7vMg2b2cWn10i3i5jBaDG/vw+WCnCncDnvKdZefRY5W1vZvj03yZkksHYFM6aYkxsdWoB7SyschaArpGemqyukaba871mh2DJUxHAnxXYDu3UNCC0Vpqv76sng8OQNmVhlb/zC4MgPj4+jv39/djY2JjwlSAEdnZ2YmtrK7a2tgo7OTg4KGwwa/TZNwTIu19m3G4fffV2l4j4LD1e9h8C6Fn42s8ZMWmGjJg8WYXiva/st11YWChngVrbx7xMdCWCFH+425DnIKK+tckm5cxErCiYfXIfAAjDtR8vgxMMhr22OZDG5w8jJOfm5or/kgCtrExRTzYD7+7ulmdiaocFMy4GcAvpzM68BlBsAFoC9qjb+2jNxs2CWZ9mV7xD+fg+j71ZGddnRQrFyHtrHTGc30/usZnV1h+AH4UFJcNzh/KTAcrvnz/zPmWzWv82iJrpWmF1X6ibcSNozvtrXUcNbF2X29Fimi598naoPPWY1T6fxmqHfN5qQ01xnnaPy88CbKeB4rT7KBkkp4FmH9Odhc22FqI1UV4qTLWHh4ext7cX29vbhdmy1QFBMx6PY2FhoYDz8fHxZ/2padEIwzyeaM1maLz4gAXJLhwoVWPKCCgLO+pEY4e9IRQRVLSLsfA4oXBE3PvFLFzZc7u8vByj0ahEL/MsxjjPi/15FJtreYY1ewtKWBSsk0KbYXYOQsFcCVDY7whrNzMyo3BGLVwLpOa0n49+e5+nhfDLly+LoAUUfbpMBlwDjYUt4wCrSODGlwAAIABJREFUsp/drJktNg4qApxYl1YI6K9dGvQ9YnK/KX3LSqPfCW+jwVfbdV05XxYXh9krc+rIershsLTYZTM3N1dVmKzo0aYsF9xX1pLHOoOc2awtMDXgs8LEST/eY1sDW8cS1BTyh5CgPDe1wneZPLTGb0jxvQ+V5w+959mAbR/THDK5edG2TAU17WSIVjXN7DHrwJtN5pfn5uYmTk9PY29vLzY3Nyf2atqvurS0FNvb2/HmzZv49ttvJ/aVOmI2t5XFm9MbIjxqQhRt+Pr6urAzg46ZIM/gOX4GQmRpaakIcQQaLzss1OAPA7OQo4+uB4ZHVinYMCw3C2T6a1+1+x7xeWKDzCroF/Vyj7d+uI02oQLSmeWaLVoAWtFhnyef278O60UZAvCdfpNxGY/HZcsYQXaYGBGyFuI8j/7aUmG/qk2wMFrWSY56rfXPc+TtQI4ap1gRymDpOTHQLC8vF6WGDFt3d3cTOY5Z+76XdYji5yAwv0e83zmozPKiBhbur9dJNuGayefgRUqWbx5/mL3TNPr99YEUWe76/Z6l9AHuQ8FvKODmOofc91SEK+IZgS2lr/NmaUMAt68eA+20AR8ykUNMQ7V++GXhZYCt7O3tFRPP+vr6BBMAnBYXF+Ply5exu7sbBwcHBXAdIOWXwgA4NzdXmCVCBU0ZVkldaPW1dHj0xZq3TZfZLAYQWdlAOHoLTNd1E0E0fHdxcVH8TIA2ghJQcf8JBsLUZzCnf/Q7JxmgAO5WjsyC6QNjYmWHOmF8BEk5SI17eA7CHRBnThB+JP4AEMwaUWZevHgxkZyBtrB97P3798WMiP8TNkrgEP7SbI2xxcEWCaJXzfrMRK1QGrzpk3369is66tgAyhqizqwgWWG0QoNP/+7urrBaB4DRNvuVsRrYP87z/b7UmD0/NZlgBpf7nZU11oHHPStzNZnj9cTP8vJybGxslACx7DrBtG7ZxNy7T7UyjcX63Rkis6d932KsuU1DCN2XKs8ObB9TagP2FEBKaQFmaxHkyc0An7VThDNs7fz8PA4ODspWFkxeaP5ovuPxOHZ3d+Pdu3cTAUM2zWa/rduIORMwtPDjpfYJMrQFvxr9QJjwDL+M3i9oFgCwRNyzp/wdTAZQ9f7NlZWVuLq6mmAyCCSzEXxzsLWu6ybYs4O4rDhkVpS1e4oZshUR+9ywDJhBG1wN4HzOOHisbX6ugYnn2YoOTM5zj1+Va8ieBPOLuDfn81wzNp4LkPtAAQdyoVSY/du3mPeeAtSONraymd8/WwJsjfE4Gvxh2R8/fpzIG+3x45leR7aAML4wP+YIhdCJYHw9v/0u+v00g0cBtDXBW4es/GXykMfFUfBEpJOX2+8ga4KDHmweN9B6LnLMRpYx2YroNdoqLRCufe7+1+RxVqD75PaQNrSu6ys/C7DNE5g1wofUUxvs1sAONSW0JrlPm+Uam2QNLsfHx3FwcFD8KgACZh4Ex6tXrwqzJVAG4RQx6df0My0oESYUXmqnWoyI4uuhb7DKDFJmLXNzc8W8afOqWZIZu4WlXyCyac3Pz0+cgQubQjiZfaMkwIR5ptmJWU9+MQ2MpDa0OZp1hVAygFK3/bgoMWR4MuOHkef2WxhGRPFdun0eA57jdpG20QB2e3sbe3t7E0FP5+fnERFFySMPMDmgbe5mjgFamLQZlAEsB1bZAsJ99o+6DgOa3yFbBnh/+PHYsQbtzz4/Py/pP21hoX7GH6CC6aMcOyIe5SPvC8/g5zabmVv5Yr04CYYTjuR3mWJFLithHtPV1dXY2toqe9FxSQC0Z2dncXp6WpK1WJGt+cXdlzwvLYBtydGhBGiIDO+rawjgZgWm1t6h5WcBtrVSM2PWBmVaqU34kAmbtjhq2mVf261VI1zYynJwcFAOGlhZWZkQ7uQq3tzcjJcvX8b79+9LsEneimLBC2Bk/6tNUX6h7OeDJdmHZMZG3/gcYWp2C4vHNAqQEdhkZYA+0B+Yg1P8sR+XNhAMZWGDECPfr8eCZ5gt8pkjt61IRHzux82sizYbPKnTggvhTd0IfjMR5sFtJSGITZvUkVNMUo+3ryBcMVtGRPHtfvz4MdbW1iaUOwKIzLqczs9MGgXNSh3g7M8Yb/fVkbhei5nBeJ7cV5tN/V7Z58r7BaDkZ1CHDylgvAgkcxAZ9zmdpP2r2TKS++U22lLD2AK2VqC9XvxD+xkjWzRQVFGmUAxq9/JM1rrBPwNnH0mpKQZDAGyoTB8CrtOe5ftr2PLY8rMC29bAzwqwQ8u0eg2krWunTZSBxZo19xC8cXh4WEw+Blv8f+PxOF69ehVv374tB2ojYHOxn8VmVARFxKRPJbcXc6xNgLyIHjcEhZMUIGAt6O7u7s8IhTk6+hF2YQWDehGomIft9/Wc2GdMaj630cLVCocFFc830OU9jRSDngUdQhtXQfaB3dzcTDBdhL23BRmorKQgMA1sTtAfcZ9oAZbo4CrONB2NRnF5eRlnZ2cTyenn5+fL+bH00RGwEVHA0mveiklW5mD92VfN2GPJ4X+/H2axGbC5nrk3s6Pv9oG7Top9pyg1rFlYMskwvFXJ0ce0y24CnmGGn9knY2sTewZuX2OrQY09e72QjW55ebm4q3AlcDRjRMTJyUlxF7HubRbPPuKasul5mFZmleM1MK8B/lPiw2NA92cBtjVAaw3gtMHtG6xZF4efN8uCyi8gdThy1GZDMiNtbm4W06lfWIQqkcmnp6fx7t27CZOe22ttFUBwcAnX8dJbEeCZEVEYLgLX7IaxJL0eL7SjivG12kdltmMGaUGKMCMQCjC2iZf6zUYdILKwsFACgGingdUC3pG2FAOwXQB+XmbIrs9A4uAgnmN/tdkhbNn9BAwcABYRhcUy3t5KZDDI5vHFxcViYj0+Pi71IHTtNjATsl+b/trPmpUYvjf79DvCmuNas2XGAXC3YpNjAgisMmPEjE8SFJvuaZfvg9WSxcysNpvGs/yxJcBrpmXuZQ0AoNSfwc2WJb8rHkfPx2j0ab/8xsZGbG9vx8rKSnFVHR0dFdPyq1evYmVlJU5OTuL777+fCMpz+/Nc1oA2/936zP9PY8Rc04cB057XKi12/RTlWYFta8D9XW0iZq1/2uC1wPMxk5j7Zk3agGuTFMLh6uoqjo+PY29vL1ZWVmJ7e/uz6M75+fnY2NiIr7/+Ok5OTuL09HRi3y3PNRiYcfh79zX71LLZDnOyt3tY8ERE2ft6eHg4EdwDQHrPqE2hBpQcqOI8xDAjC11AzEqFBTrRt2TFMmj0WSlqTKUWvJOFLmOS+2GfpH3YrA9HIzty2c+gX076z3hERIkqxYqQWbtjBCy8Yf0EpTHPnmP6lNm410qtvb7GyqABiOdk3y3ts8nZ6zkDrX3UPIO146h03suIz83+vAekiuT9c+5j308xC+V96mOF/J1B1ONXsxTk+jwO/KBgbm9vx8uXL2Nra2tif7MjqlFGPD61trqfQ4E2rw3X2ZL7udQ+nxUb+vBm2ucPKc8KbF1mYYtPVWomCJc8OXlxzQLiGQQp2RS0uLgYNzc3BawwXeFvcX0rKyvxi1/8Ii4vL0vuZAdLOdo5+8zcRrNYXjSbshB8MMrV1dXCnBDIrvP29jZOT0/j7u6Tj3BzczM2NzdjdXW1AALMzhq+t/tExASoomnDXi2QMfnxbPpPfuW7u7tYX1+P8Xg8kYaSACPGHcFeYwzZtBzx+dGBOfjLwtKKjaNmbU7mb9rhgDErIl3XFaXDEeMIUZ/tax+3gQ6TKub5zJ7zwRewPftJzcwMppnp56ApX+c6qIc59TzbnEpbzAStzJHik/HwFjJvU3LfHOBGnazrnPfZjDUrWPl/K4AuXjf82Dzv8cwyolWXxxor1ObmZrx58yZevXoVGxsbha0fHR2Vd+Dt27flhzSfWQYYyHP7a/M6rbiuvmssc/1/Ji1uw3MpzwpsbY6Ydt1jBtITNK0uCwvurU1on3LQWnjT7nFk6c3NTRwfHxcBcnd3V3wrmLPm5+djc3MzfvnLX8bh4WFcXV3Fhw8fJnx5OdIxYjLQwqympXzUAk9I/G9BQT8QShH3KQnZx4nv0mZSKwc2gXZdV9o+NzdXgGQ0GhVfttkMDAZB6fYz96urqyWHspm1gYg5s1CP+DyhhZ/fdff7HxHmBt48ttlfZ5+u58j+VwSbUzG6LptMuRfzZwZTLAVOYGCWn0E0R1vX1nm2gvjH19kyYHOu16sjjWtrkXFjfAEX3C4R9woFyke2xNDOWgQ1IGkzdR4v2u92uJ+1wKIWwzPotr63EuN15HmxFWU8HpdUr+PxuCQ+WV9fL5HH+/v7cXBwEO/evYv3799PKBN5K1OLZOS+98n1PoCtjVNtbfXdl9tYk/dPQeqG1PEswTbix9VKas8domlNA9w+E0jWyvJ9vJQIErNDgAbANZNZXV2Nr7/+umxGv729jQ8fPkxoyN63iEDv8zshvGziy/5bTFRcw/PdH1gTezlpjzP25Bcsmzk9XjA6AAU2ioA28GOKZquGzXls6idhQQ6aoeC7pE4A1qZMg5S3+2QTZ22NGMgARs+PwYhxoO6sFHorC/3mecyR+8p3GUDpd1Yw7EvNpSbMsqJiZcr3mY1xH0oIY2NTMvdZiWGLE8cJsh6JHbCvNmdRM1jxN4rM3d1deXdubm4mDlewNYNx8ntuwO57762o5j6a+eb3Nc+Jff6sbXYsbG1tlcA3LFMbGxvRdV0cHR3F0dFRHBwcxOXlZbX9WQGorT+vz76S34caMLeA8rGkq0YmnoLEtcqzAdsWi/opSx/zjKgHR2UgrdWZ/699RkGg8/nV1VUcHh5OvIQwS4Ty4uJifPXVV8XMenl5GScnJ0X40sYMcv7JwRvWbmlPTvFG+j9HeWYmeHl5GYeHh4XhbmxslAApgNNgavaIkMnj7lzH1IsgsWDC74bQOj8/L9cCpGtrazE3N1f8m8wzgswBS2ZoKC5m5vQ5g7bXiec2MwKeaXCvFQNFXlf+IbjJ0cS1PpqBOnq3FjSW25Cfn60JPM9be2gb1+X28Dx+M74GIMaNd2FlZaUk8WC7DkDrfbBm1jlQyyCKkgNLtjnYc+j2WPFy1L7HzL+pw4Dtzz1nNTNyBi0UA5K6bG9vx87OTtle5+1izAdJPmrHKWYrQG2N1dbCtOJ10upTJie1PvuzFuvOfcrtaN0zDUiHgPSzAduItpP8p2hH/rv1YrQmbmjJC4OXyMI6v7j4YSOiABrRqLxg4/E4fvnLXxZt/i//8i/LUWrZf+ttNfSFZ3K9gY2XzievUBz9WQsGub6+jqOjo4k6x+Nx0bRtFgRsqdeM38LV/jQLDoOM9yraPE0CB/zHTg3onLYAjtuztLQ00Tf7UJlP7+tECGVFhjF3sAulxpizMucxY169Rg0oBj2brj2HZpVmkdxjnz3P4LfByt9l9mgg8zMz8817gxlLP9NATeIS/PZnZ2fl/GeyOgG2NgtnhcXjDSBlt8bd3X0eZffTc8i6sQUhv/NZ3mW/Z57L2ve021HX1L+8vBxbW1vx5s2b2NnZidXV1aIcOjWqmavHpRZ3QN25fUM+q60PyjQgzdfOAoJDmetj2G1feTZg2wdwfaWmxTx0sPLEtCbHvqwMlLV2uW35+8w2a9ciTIiABDxOTk6i67oijDhWDoa5tbUVf/Wv/tXCZjCd5b5gGs2Mlv+9rcUMt8b2aD/BTTwDdge7Ojk5mQDV3HdrzvxvvyefGYQBXJ96w/jgNyYpg/vHc1ZWVoopmmhlEjwwDgjovOWk5cOyIIdpZ99tFkj0s1aXg3Z8H6zL/uNs6rVv1IzGke080+DTdd3E1hV8qNmvmdkp37kNKB/5fcm+XNaGlQGbwg3KHkOehVWHfeqwtbxFLYMt9XhdAe4oHtzv8eX77Gu1T7xm4cjFiltN6TbgWglzcCAKWtd1ZavP69evy3YfgJXtc8gFrGBuJ8/i+taBCi2g7fuuxSD7ZOEQ9tka46yo9sn4L1GeDdjOWmrg9dB7I9ph4EOfU2McfaX2UmUBy29HHyJ8AC0LVhLKA7gLCwuxu7sbv//7v19eLPbNRUweIeetDBZmgEz2sTnBfQYd2kOfMrhhTibgC8B3+kGbTFvaKQDGNbntgMLS0tLENZyNS1CVI20ZO/rKvT7ogPnKCUNy1iSzbMY6A3ONMZitUQA9+wwt1FGwDDiMqwOOIu73cN7d3ScT4TsLde43QBqgst/eQJUZbl7TALjr5XuzaoNyZoFcx7qJ+PSunJycTER1m8kCGMyXrSmsW/pr06/NwY5wd/+zgphZrYHE/cjA02KQZrV8DsCiHFqhi/iUcvPVq1fx6tWrWF9fn7BiwWyvrq5Keka7cgzKObirpRzSzlqpkaFZADqXGkGbVn+tPU9Rhsj+Zwu2tYHoA8kfQ1vpq7s1cXmBtdi3QcmFFyMLQdgke3CJNuQUD1jO8vJyfP311wVkrq+v4/379xOafe5bZl9mETndnQ/15t4aM7K5MCKKCRcwIguWzYU1IVxjRfbneqwAE+/D7LquMJ6IKJHdZiDeHuOsVjWhmlkrAViOJHafYEye3yyospXA4OstN/QXpQDWwnrK7PDu7n4PNyDjvc4ITQeHoVAA5mZWXMNzSIZh8PKayO+B13ufEpu3P9F+osjJdw1o+BB4s3j6bzOp22CzekRMsFGPt/ud25oVVe8/9zq2dSwDrUuNZXr8eMc52QpFtuu6cqrP7u5ubG5ufqaUmM2enJzEyclJAVuucerJWqkpUfm71j2zFI/rtOtyu/J9LRk8pN5cZunLswXbWplmQhgKsg8F4gzss05A1soNJnkxGRz88jrTk3PZsgcXwLKAX1hYiDdv3kxEQ+7t7U0ki6D+zMBoi7dWmAUBchbAte0U1GPwuL39lP4OQIiIiQxZFoIW8rUxA7CpGyUAwB2NRgU0zTi6riv38UOEKYIMUytg4vkA+C24+Zto1a7rCpi1lMgsHMyc8tYsm1tpF0cOWlDafw5T4wdgMfsHTLM52GOPkuTxNZvlbwN8VhYNtNkcmp9HycqVU1jCVlGi+N9rzb/dRo+/lQTqYPwM1LYwZKXIVgubrK2oZlD2O5+Lxyx/T5QxyrUV84WFT8dystWHiHsUZbPai4uLiZOPsrxB+aqxzi9BaiKGk60vWWYhbUOue/ZgW9OUWiaGzG7z/Q99bn52DWxbGrrZT9+zssZf89mZsXlrUETE2dlZvH//PtbX12N5ebm8XAiFra2tWFtbK9mXrq+v4+DgoABuREywhtozEWC+js/NYDPDzP3wy8wpPh5fgpWyP81Am9ml24/QcfAHYOeE8ghmhKeFoJUHgIitOAgf+p1NpoA0z6kJ1prvNmIyw5SVILNOjwER1CgpZn+0JfsSMR07uM0M1SDt+25vb4tPD4UGvzxtzHNtBpjNwdlMazN8fh9yAFXE/XFwAKNNnShUXsO25jiIzv9Try0ABiju9/rL7wrfAWhYHhxzkS1HeS20lHA+n5//lMt6dXV14ihEFEiij7e3t0sAIv3lOhQUfNq197i2r7bWLr/Xjyl9RCXLkowDfeTnSykFs5RnBbbTBs+DnSe+VddTDHStTa06W4oA9dTA10xxCDD75acdRF5+//33xbyG8FtYWCj5UP/gD/6gmItIlGFGa8GYNdzcBgSjBW1WRvL/ZkJmG2dnZwUYEBYALhGfecww7dnUl/eEwi4MJm4bAiUiJsCN/w00WBJyvw0yfo6Ztr9rWQDcD/c3MyazsNXV1RiPxyVByN3d/YHn1E29Blmb1mnL9fV18f3Z/+cD0FFMEPCASsvE7jWT143TU9q/n5VNvzdcz71mXlbG8nO9Rh10hfLok5BwAXBNXiM5Wj4zT1skmHtcE147Vkzzu5L7YWaPYsJe4tHo0xF5mM5XVlZia2trIoGFrQCMGXOO2R3Fg/H1gQp5/dXkYI355u9y32pKRQsDcp3T2G+fHB2KC157rTIUX54N2H5Jreix9eSXqXbtNC1v2gLyQrVw7tPinN2F/YQHBwflFJiIT4FMKysr5eXc2tqKP/qjPyqa7V/8xV/E8fHxhKkrR1nystlv46CZiJgQlJmN5uLxpP6zs7MyHgawLKA9LhaGFrawV0ef8jlANDf3aZsIzOj6+rr0Pe89RCh+/PhxInGGBTOCCoXB4+PtOP4f4LfCYPCqsTvnPyb9HlYMxiAzGGc5cvQx4wAL5Jm0i77h13MULz5T2u37bdlwyduFsgLndeGtW7nO/CzGqMYUWZuMSwZarzXm2Nu0nAWMuUWRMlhSl7e8cR9gDuDRLk7cMXOssdr87jOPzJOjiPHT7uzsxMuXL2NzczNWVlYmQJT6SNV4enpagJrxckax1hjzGW11+x5SataCIc/ok8utMgRvnpoNPxuwbWkpQyaxT1PKi7dVagva90zTzFrXDvm/1gbAq8Xas1aNcL+6uor9/f1iNgJ0aOv6+npsbm7G7//+709sazk7O4uum0yTmIWXzZi0s8VeMmBzrfsEkzDInJ2dTewT5Dr/73q4NwurHBTlPY6uD/ZpoWKG43lBsDoHMawnKyveYmR2ZoXBvmj3pabB0yfvpca0TdvyWjF4MLdm1AC/55jMSIC0o1GdqMSBWjaP0l6zt9y33Oe+teQ5tGnTjNgWiCys85r02rKbwiZTrqVOZy4z684BVh5zxgoFiQAz1owj5932mona4+N3K8/V4uJirK+vF/Px5uZmieEw+DPPFxcXcXJyUvIfu212l7TArLZmh8i6GkPO9dbmr3WNP289a2jpY9d9+DPkOc8GbFvlsWzVE9UalNZnLQWg1q4hgO7vW/X7+2xKyyzXfjyE6Wg0iuPj4wn/287OThHsHz9+jI2Njfjqq6+i6+4DPX71q1+VQ7FhAA748akgaPL5JctBIHzeemEM7LTPKeIsXGwCzgXAyPlrfciBmVY+wCH70vIYW6ggiAA9rqklBLBSAfM0s8ig63HLIGQ/KoqEE4QQNIQ5tLX27KeGHXuteFsZaTXNXvksr5GsVHgt04fWWs8KE58ZqJkfAN1j2HXdBMPM72Lun83JPId5pV/0F597XnvOwsX6vrm5iZOTkzg+Pi6uGpSipaWlicxNZo41VpvHgnXEXPPuE9Q0P//pLNrt7e14/fp1vHz5MtbX1yfSi1p5Oj8/j6Ojozg8PPwsLSOBU63tPjVwzUrOtNKS6zXw9d+15zwWI/rqmhWs+8qzB9tZytAJbJWWBu76+wRI1oamaTx9DDeDrTXo/FyucwQm24EwIVsAs83mzZs3EfEpuOr8/LzswTVbjpj049bMd7TZZkMLC49jZrkACUKB3Ml+7mg0KhmmauNsMDMLsX/WGaQQWBkMI2JCcbFP2GZtQMcm6YWFhQlQz2bwHEVM/z0+7lc2x3OdcyXn7GHUgX+QLTFcizlzNBoV0zrtILoXEyLbQrxVxr5d1kfe4lNbx/l7r2/AjlJjNgZb1oXXs5luS9HxVp+8bg3iVmKzBcAKn/cqA9QXFxcTQMtcYIGo+etbTCrLhhw4Z9dERMTS0lKMx+PCajc2Ngq48yzWAXvcyaqFqdt9yeko8/ub3+0a2cjKlz8fUrz2qbtWXx/L7SNBQxjpLG2dVn6rwJbSB2IR/QOTX/TW9TWgbbHVaaWPcfO5gZfvrPEiiPns+vo6Dg8PS6ID7kU4z819ypL05s2b+Bt/429ExKcX9ttvvy2HCJh58hz7KAGQaQIza+vuA6bR5eXlIhjJaEN6SbffIISwA9x8mpG3ecAEEZBmdoydgY2+WZAggO0PdXQqCoO31zAuNk1mc3hms5TcT37s542438PrNJrOJmb249ONYENeC8vLy2UbyPn5eRknlBSKE4nAzDLjdNvzem4prDUmV2P7nnsDZVZQmJustOQAJubH23myi8bR8cyDGe3p6Wk5R5p1wZjm4Csi4fM747muyQTeRyvPtGVtbS12dnbi9evXsbu7W070sUKAIntwcBB7e3sTB5sAyjkgKjPu2pzmuRvKcmvroA8cZylZFrdkUksZeEpGSxkEtqPR6FcRcRIRtxFx03Xdvz8ajXYi4p9FxF+LiF9FxJ90XXcw+tSLfxgRfzciziPiT7uu+7ezNOqhZoGHDlBrgvNktZ41ZMIyyEwD8PzCGdD4PPuLDLij0ai8WAg/BLVBdGlpqZiU0Xzfv39f8i9bCPE8B8pETCbb94uWBUgeP7MGTG0ICE5pAZCpD/9XZoYR9+Zks28HFo1GowLEPDfPMWyWemvKDm20Od0gY7ZkwVXz8bUA14DhPrptebsKvnmiTzFvn56eTgQHwbrYl01U84sXLyYCxty/DD5WRph/l5rylZlcZkz8WIGj/7ZyGGR9D0qPQTUHQ9lKk6OMDU72oZoZew0w7phlycCE6Zj9r9ksjSneubZb70pWwLydjzay22BnZ6ds9YGpogzw3PPz8zg5OSnnXbvP3n9rhTG3xW20LGsBce5H/s6lJftrdc0K6A/5/inLLMz2P+667oP+//OI+Fdd1/2D0Wj05z/8//cj4u9ExB/+8PO3IuIf/fB7ankoyD6mvmnXPKZNfeCaNbm+52Zt1wBgfx+f2UR4fn4+IYwi7pPoI4hguI6efPfuXQmaQrh4C47NaPnlo7TMZPmlpW7AFmHHCS0w3IhPBwZETG7fwM9IXew3zQBpUzHCpWbKNXs0e8+Cj/tyRCrfc48BtxVpbmDxONWuZS7NmCxY7+4+7aUlOObi4qKMy93dXZyensbi4mLs7OzE7u5uSYxhn7znkWQXTjloMK2x2LwWGNeW4pX7bB8pnzlIz4FmNhH7vXLMgb/PJmJKi9E5JgDrBgUgI9EHyojPDWaOnDrSkb55bdUUL7Nr2oLyub6+Xvy07KW3CwQfPAcyHB8fl/gM993BcNmSlpUj2jlUfvmzIQDXUtJnfVarTKv/09s3AAAgAElEQVS3dv9jgfkxZuQ/joi//cPf/yQi/nV8Ats/joh/2n1q2b8ZjUZbo9Ho667rvntMQx9a8qRNWyB92thj2jDk79pkZoabv7OJEsFuEIm4PySee9C8l5eX4+bmJpaWlmJ9fT1+7/d+LyLuc8a+fft2IlIRwMJs6QQLjhhFAai1uW8RIzw4hL7rPplrvT3i8vIy1tfXY21tbULQAmT0z+PjMXJuWJvKa+be0Wg0ESDE+PoaC2ebrnNAjpmu2+j+10DIrCyPqYEnIiaCo168eBHr6+sxHo/j9va2HMvIfDL/W1tbMT8/X4CA+TOTcxCbTcbZP537kAN/WMcG8xrQ1iwAXOsxROGyL9XKYQZZz5Pn0eDLWHJPtn4YiG9vbwtTxAqDj9ZsmHqdxtOm71y335MM3hH3+cvx07569SrevHkT29vbE24jroV1c07t4eFhOZAE5cNA63Xsttj60JJHtdIiGrV7hihi054/RK72lS/FdoeCbRcR//toNOoi4r/vuu4fR8QbAej3EfHmh79/LyL+Uvf+5ofPJsB2NBr9WUT82Q9/f3pIDxA+tLRYV+va2v+ZWbjNlFkmtaYl1uo00Pp3vsYghwDJUa8XFxext7c3wVLxJwFyq6ur8ebNmwkgff/+fTldiDY6Chd/kFmuwclm2drY4ie6uroqJjsAF3BgewMJ5j3ePjjAAtNjhbKQo5RrgTG0qxYMw++IyYxJBkKejwCvsa2awGopYX6e+2SBz5wwdl3XlSCzzc3NWFhYiJ2dnTg/P48PHz7E3t5emX8sBQhbmBvBVQZfzMfeJmK263Z6XDLYevy8bjJTdf8ZFyuWtUMWMlhbGaTwtwO2WiDbMvuj+J2cnMTZ2dlEAJp9qvTZvtoc4ZtllOcZC0a2CqBMvXr1Kl6/fh2bm5sFaOkPfloOGcCnTO5kb4/zlqqh7HAaU+27vyXPphU/q0VEhsjhL4U1fWUo2P6HXdd9MxqNXkfE/zEajf7v9JDuByCepWH/OCL+cUTE/Px8p88/u/ahg/LUGkqeaEpeONMWX59mVtOiDQKuJ19H0MtodJ9kAMFzc3NTEkeYffE/x8ttbW1NCHCeTTo3MwqEDu2pJWPwy8/1bjNC/vLysgTqEMjDdpbT09MSNGXzH200646Iz8yMEZNZoXytQbEmpIkixcSK4DR7tcBEyCLseKYBp7YGagLd85vrMRjRFvyDNzc3cXh4WA6mePPmTXz99ddxdHQUd3d38Zvf/Ka03Tl1ySSE5YOxBFAZZ/s9YWpW7nKkuvvC/RaUec1k/2kGYpS9zDp9fx67fF0O6qqBrH9oi/2vPicXJc7P9nO89zbHXrit/p916nq7rivK6MuXLyfOqM1BW+fn53F+fh5nZ2dlS5KDoiI+3xfcJzNrcmuovK6t9RZBmSZHW/Jz1vKUgDukPYPAtuu6b374/W40Gv3ziPgPIuLt6Afz8Gg0+joi3v1w+TcR8Uvd/u/88Nmjy0NMAkPKUE2o79pZTBctU0jrvmyqaz3XdfDbpt6IKICLucv+Lvx66+vrpS5Y2tu3b+P09HTCDIpm7JejthE+M4/cR7/wMDPMZmxJOTo6ipOTk5JL2WyLdlv4GuC9bcXtyeZFC12utcJB392/DLAWzpnF1kC3NkY1sMjCH7A0E/U+W3Jlc+zizs5OGVcUHE7IQRnDjBgRE+eiOgcybTEDZbtYZmf0y+NgsDWQ+norMFk58fdZIakxaI+tfb41hSzPCWucez3XzilMpLfbRRvYKmUTch6n/ONxcDQ187KyshIbGxuxubk5scWH56EEsR0JVss2Hysk3hpW89W25tVrs1Vais6sIFcD3yxv8zNbzx4im3PJa/ShZSrYjkajtYiY67ru5Ie//9OI+O8i4l9ExH8eEf/gh9//6w+3/IuI+Huj0eh/jk+BUUfdT+SvzWWIWWQI45xl4Gc1c2RhwW8zyD7NL78sfoF4Wc7Pz+Pdu3efMQzqI9EBghvB++233xbAhQ2aHY1G96eltIJNbFZG4CLo2TqDULOpk7YcHx+X5AHuf18UqgUR13Kv2QCA7Lmmn5jbLdDcp7xG8l5Qz1cGihpItMA4C2HGn8MIqIOEF7/5zW+KwsU4k6aPZ7x8+TK67j7qm/bDlFdWVorgzltWIqKMTd4Kllma/dyeZysnVlxYg4x5yzecQdxjaJdBDgCqKT5WIhhnGB91kRBif39/IigK5c/RwuxrRWFpWXgMwjzbCg994YQn/PFra2slHaO37uCrhdFeXFxMbA2MuLdaeNsYbaoBpP/OipDHr/Z3jjnIis5DS+v+1uet92taeQqSN4TZvomIf/5D415ExP/Udd2/HI1G/2dE/C+j0ei/iohfR8Sf/HD9/xaftv38v/Fp689/8ZCGzToYNe1myD1D2WWepMdqZrXn5u9a/ahpmH6Ja4qBwRF2+P79+4iIz5gCAELiCwcCff/992UvIS8cCRQywGQ/GO0DcN1OtHHYLcKLKOXxeFyAnFyuZ2dnE9uAUBKyqZLvbTb3mLcUoqzRwiTpk33kgEMexxor8BhZ4NfA1s/zXHEdYIgbgDG6u/u0v3Zvby+6rou1tbVYWFgo0bDv37+Pg4ODYup+9epVAVcHQI1G90kyaIefQTsxd3oeM6DVQIXx8xrJoOOxzCZnj6P/tsIFoGRmaSsB9wCUrD3GHyBFUTk4OIjz8/MCggZb1h6KTVZSHIjF/zVGW5tr8p6zbWt9fT1WV1dLP2HSREifnp6WPdNeRzZvO6VnS/a0FPzW/7PIyazs981r7Rm1dtba1vf8VvufAmQpU8G267r/LyL+ZuXzvYj4TyqfdxHxXz9J636C0gdw04TzUABuLeQh99ee2xcRSkEI2v/EiT/uDy/syspKSXTv4+nm5ubi3bt3ZU8hEb1mf2Z7rchTipnQ9fX1BGujT7AFAPfu7q74kOmDI1MZS7cFAeoAKpuRW6UWoezgF/qVzbwtoPU4sGZ8jz/n+dlawbjh2yZL2O3tpyPwMA3DrLru07GFGxsbE8FimCTZD8rnXddN7HHmewei2X/ufiD0X7x4MaGQ0Rdfm/taE6aMTS3/clZIvJb5nraSdpGxq/3YmmNTN8/moI+9vb04Ojoq1hiUESubPJctQQZX6s1rA6D1YRE8nxN++L2zsxM7OzslJaP95gRvkSUKa5QVXrtuUEJaJa/LabKwr45WvXkchtT5UJbaqmfaZ48tzy6DlLWcWQZxmoY1yz0Pva513ywLs/Z9S9NrmXJq93ZdN7ElaDQalQhfCxt8cwgugjG++uqr8qLOz8/Hu3fv4vj4uByQHnFvejTQ4a/KwiazWvtUaYvbCrisra0VIY95bjS6z9bD8w3WHgOe4+jtvNXB7fQcIgyzUMhs18/Oc1MTLAZaj09t7aLULC0tFYUIoMV0CNjyXUTEwcFBMUkuLi7G7u5uvHr1Kv7wD/8wNjc3JwKk8EcScHd5eVlM+YCKA6iySZhrLMQ9Tt4Sg0sim+VtTWEdokhZQcsgw30oGuxppX1W5MyAbTY2iNPX6+vrOD09jQ8fPsTR0VFcXl5OACN18J7BaB00mBlcXge1uubm5mJtbS02NjaK/31zc7Oc5mNQZi7yliQH9KEAM8+5fbWS2zsUDGt1DGGMLZY7tO6sEMzS1qcC8Fp5dmD7FBrFtDp4Ufl7momkBXC5zr6FmBfAkHYPMZvUWFONOWStlvY4YIKXEaEWEUWgr6ysxOvXr4s5GrMhe/aox6Y0C418go7bm7fz+DvmCebF3mDqQWAcHx8XMOAsX8DFP2bd/GRzXZ6PDN6t+aBevsv99Hc1E2vW9A0+jC0BYz7xB9+bTZYIfXI2Hx0dxdLSUvHf7u7uxh/8wR/EX//rfz1OT0/ju+++i4ODg3L/5eVlGWeyD5G9y8kaaqwUoMcMayZF8X5Zs3fX6+xOMEgfVUd/qcPKVD4YwmDNc7xea8qN97UeHBzE+/fv4/379+XAAu+nZU49XrwXGWDzWmeO7e/lf6KO19bWytyzz3xubq7MJ2OIe4UczRy9ZxcKjNZ+5FapKQat7/M6yO9Ua61Pe1d83bQyK+NulVnJ3pDybMB2qMbzVM/JWuYspcWC+v73M2c1k+Tn1UDJ17UYGvchRB1tenBwUMAkM0KY48bGRkTEhEY/Go3i6OiomOkQttznPhskMwj6GveT56H5A7j0eW5urphOj46OCrslN6yFXd736n22FsQUM9a8xSezMJsPzbBoZ/bT5efUInNdN35xQAzFCKsBWz2IOGWeEayMH9YGr4WTk5PY39//bD81rgeA0uZnH0LvcaBe+x1ZYzyP9vYVjzPtYe5XVlbKZwBrZsn0gzY4qxKfZ5+t1yLXc2Qlysj5+Xlph9OLUi/b2HKQIH3y2va7kXcFcFgIKRgxb7O2Me0zBvxcXV0V8zHWCb9LeavPtO0+NcD0/ORra3/nec1/Z7lVU2b75F+t7i9dHvKsZwO2ufRpFdOAbqhGkgE33zdk8eTyEI1oFi2sT5P0NVlQ5fodEeqX1MLHLANNfm1tLV6/fj0BfvPz80WLzmCEQJifn58w6dlHBDDRLpud3UYOKxiNRsXfSH/J+3pwcFCEK8/OZmo/l2ca4GoMM5uGM9jyPz5NABe2ZYZKm3JbUFQyCABqMLsMhM7PSzILWA9+cOaCZBWkwQRkAWmezSEGeSuJ21MzozqQygIUwe4gJTP7POZerzmAKIMU6wQA97h5/vwsB0XxbCsLt7e3cXZ2Vhjt3t5eSbwCw8zrB/OsA47ye+s+mdFSH2M/Ho9ja2srNjY2yqESWJN4D6jXUcXn5+dxfHxcYhr8Hvtdb+35rckuf9cC39rfNQLSR6pabWix3lrpk4tDylOzWZdnBbZ5cQ7RjlqfTxu0vsU15JlPXX+tztrCcTss/H19NpHXFrkDNAho4eCC1rMQ2FtbWyXROubMt2/fxuHh4cRLnAOGaIMPs7d/zgwPUEaIkHYQoeRtIdR1dXUVh4eHEfFJ4OLXAgABPgs6+7Hc5mwhqH2Wxz77K3kWn2dfYw2EM5vlt5kJzJBxRsjin8P86XVl0AdIz87O4vDwcCJhCf3gGp5p3yigkl0GNsl6HFlvmcFksPNY+/AJ+1Vh84CGI3Bvb28nzOteT9SVI33zGGFePTk5KRHbp6enJbAMYDQbto94SHIIPzMrK0QZb21txebmZqyursba2lqMx+MCsn5feP7NzU2x7uBTZi5pJ9uQfE4x7WAN5/Hw+h5KRvpk3hCg7Pt86Lj67z4W/CXBNZdnBbYRDx+EWSeB/6fdN4vJ4qHa1KzPdhtqAF3TPjMzsOAH/GCfHD6fGYABF78o18C6Tk5O4vz8fIKdYhZzezLDyQFFAJLT23VdV8zECF6z59FoVNLT7e/vF2FWOyWIZ/CZTZ5ZSfD/Fq6uhwJA4q/MwUOegzwfjJXZl/ew2mRq3+TFxUURoJ5bhDjztbW1FePxOO7u7soBFVgELHzNsh2B7GhuiueGHwNZZo0G0trapM/2hbqYycPQ8Evar5vXvxUY1+sArsvLyzg8PCzJ+g8ODkoksUHW/k/PQ07i31fyvEdE8cdubm7G+vp6MZmzn5aoYywNlLu7uxL57PSRVrYJgMMn31IIavLD3/l3qzzEuud7+8C1j323SpbLj5HPj7n32YHtlyoPBcJZrq8pCi1AbLVvWumra9qLYr+kGYdZBwwXhmiAsiaN3whfIhr4hw8f4vDwsJiyvDUILf7Fixflpa/tuzTrtt8LxQDAzQEvBANdXl7Ghw8fCpja54Wp0X5Nm9oYG4NEZlYeU9iiLQa0xdfQD4O016SFvseBa22CdcARwTiwOoOaA4rG43HJoYuwRkgD1BH3Z9Xm6GArXgYTg2pmuGbkjKX9p1ZWslWB62krAPHixYsJ/yyf24/K/HrNYYa1wsQ1MNl3796VaGPqtcJh0z5j5CQutexptWKGz98cCrGzs1OijldWVspnnGTFuLAmYOOnp6cT5mMrd6wdlLKalWEooTAYZjmXP5u11EB2CCHqq+ehdXyJ8mzAdqgG85DyUwx2XnxPyXZrz8n/I7T5GyGZ/Yj8YALjGTaLZvMpgEWwzs7OTmFQ4/E4Dg8P4+DgID58+BDHx8cTJ4xkPzDMwT8INJujAQOEGuffOuKV9gLKh4eHRfhvbGwUMIKpeduKTai5AIZek7BgC0xfi+KSgdhz2BJ2nkPqMsgCkt664UhWWKiBMyImjjEkYtn+RjPqiJjY3mOArSkdPMcKkJky424zr/tupQVFAlM5pk8sGlYAXDeM2GubPjuYibUOmz04OIjvv/++7B93FHT2p9qEa0abFan8/ud5BsSJPdjd3S37ZnmX1tbWJk64wh+f1xRb+MgU5XmwUsZ68drL67e2BqeV1jVDiMG0OvpKDR+GKA1PiSuzlGcDtrk8ZkCmaWxDykM0tcdOYq3NfQvKL0tNEwSAfE3+cZ0IDYCFlH6AEPfY/8lezs3NzbL5fmNjI3Z3d2N7ezs+fPhQ0tpxrwXk/Px8YREIfPc7+3GzoDPriIgJVnl9fR37+/tFwO7s7Exs15ifv0/FZ9ChHkDOQjYiJv43SzUo2lJg33BtLlvrBl8c15nlZebF85lzgIctQeydHY1Gsb+/H4eHh7G6uho7OzvlWWaUXddNmGNrzJz73P+PHz9OAL/ZMXPtBBl+V20qtwXCEdfUbfM682ULBJ8B7jwLECed5YcPHyZ8swCVtwu5jY58Zs3atG4wbTE1s+319fXY2dmJ169fx3g8LmO1vLxctvt4fdoSQHvOz8+LNQmF2esH60XOYU3JpvxsIWsx2QzYLVBrEYJWye/DUxOWn6o8O7CdBbD6Bv+ptahZ2pOZYGY+Ln3t8j2zLtBau2q+JL80WXCaEedo04jPT7NZWlqKjY2NePHi0/FfW1tb8ebNm/juu+9ib2+vJL7nXpsb2S/p55rd0lYDPcLXey8NPuzDdEDR7u5ujMfj4n+0oEeIMlbeg2wFwXOC0DUjpC6uA2gtuGvsNrPXruuK8ISh2GxJIBNKA0FOzgjlwB2Cod6+fRvHx8cl+UHEJ/MxwUGea7fZCUpon+cHxmxFymZX5pV5h21acckmZc8D4Md8e6+ulSyvA4M+83VxcREfPnwobNbWF5hwxGR+a7fPW22yEuJ3sfbOOynI5uZmvHnzJl6+fFm21REECNDe3d2VSHG7EQDzy8vLctAAYMp3NV9yCzj7mKyvHSqfW/W0ZGGr3rx28jOeiqW6PX11PuZ5zwZsnwpk++7pewkeWqwB9n1nwe7vW/dPA88aoPu+DLC152Rg46UDPLjGJj4H8hh4ADg0cYTfxsZGbGxsxN7eXhweHpYAlMPDw2L+dJ/ytgkEp7V7+xwt9CxYAYuu60qUMqCDuY6cwowTwGSTtufJ423/XVZMPDZ9deX5s/B21LHn0vUDBq6f3LkAGc/E3E7gz4cPH+L09DTW1tZKdDnP9b5cxhAfMM/L7NNzYzOsfbCeZ7e/FjyWz3AFYLnn9va2+KpRuNzWWjYmGOD79+/ju+++i/fv38f5+fmE9aLma7YSkIG2FuRna0dex1h/Njc3y6Hva2trpe0cvEFAVMT93t3z8/PyzjhZCclI+M5KKvPEnPTJq1rJ12cG3LrH185ShgBevv4pAfdLlmcDto8pLW0tfzZLmXZfBu6+dvn/ofXW7s/XzArS3GNNthaQgh/ITBgmZNA1I1lZWSnCgfSBBK5sb2/HeDyOly9fxsnJSezt7cU333xTfGQRMcFyIu7TAPK//87AixC0gKUd+HthdmyT2dnZid3d3VhfX58AJrOBvB/UpkELZh8AwBh5G0wWzMyTlYhsJjSA5fmtjROAxLmmjiYnq1bXdRORtjDj0WgUGxsbE2zR0cnepoRpmXbg/7aSYLbXUi5QmCLuTf/ZD+t3zJ85WQd9QNkDdPkdEWUP8v7+fnzzzTfx9u3b2NvbKz5QrqcNdplkNts3Nxl0+czPWF5eju3t7fjqq69id3c31tbWJkzgbPMh37IVoLOzs7i7uyvZo9gHjJ/ZCibrwodGZMV6SJl2XSYxmQXnOIWHPmdom/tIxbTrvzRo/1aArcss4PdUz+qbzL6JrN2TAdnmndaLUtM8a+10XTCk/EzqRyv2M8/OzmJvb29C0Ng8SmAOQh6gQ+Pe3t4ugub169fxq1/9Kn7961/H+/fvi08X8x/FLMlswQqDhbtN3mbhsCG2ueDD3NnZKenwiAAFCGo+OYN7xH02rQyArbnJbbdAz99na0gGK66xOd5mT37jpya3r02Ob9++LQA2Ho+LeddrxuZx/nfgHEDhscn9ycqprQGtoKccrAbjBkBsUWHN4aNlvi8uLuLw8DDevXsX79+/j7dv38bZ2VlcXl4WcHYKSvfboNXyWWerUJ4v1iIJRdbX1+PVq1fx6tWrGI/HpY9YJNbW1j6LnGZ7Fz53FB6fU4tLxevDCTasONcsbO5zLn1ysyWTPL9DgK5VWgSlT67OAqAt1v4lym8F2LYG/EuWp5iYFjBaEOfr8gtRA1I+b4G2gdbbCfL1Dh5aXl6OiCjbIrxVxVG33ou4vLw8kXiAz3Z3d+Ply5exs7MTL168KLmNvS0hm10j6mdgeixQEGDBNcFt5np2dhanp6dxenoa29vb8fLly2JSJSWk89zCxOyvzPsvHWFt1mrw8+d5XvM8Ms4GI743WHifsOcOYLy9vY2jo6N4//59OQWm67o4Pj6Orvtkwn716lVJpJCzc2V/H89kvPMayyzQ69VWEc+TE0V4XByB7f8dtT43NxcrKysl/zNbYTCZEwTFsXj2/dr94Lmg/bYeZEXTfcqf8wz8wKurq7G9vV3cGIAjh31wRCRA2XVdOVLy9PS0HH4AgBID4fSbvAM5SxSK7FClv+/zIaXF+oeUIW2sycmh9+bPW+2aVu+s5WcJtrnjQ4FvFvPJ0GsfsiCHaHP5uiGLyAvaY1K7zoDGZ9l0h2CLiJIwwQLH21+s8RNtCusgdzHJ7RcWFkoU5ldffRVv376N7777bkIRQHu3H7XWnyzg3Z4sxH2P90aSSenk5KQALuZlsmQ5+YOFr9m/zYG1eczMtQau+VpKLUhrNLpPXGElxIwXM+/BwUG8e/eu5LFmfO7uPiW4+PDhQ+nTeDyO7e3tmJubi9PT0yLUsVC01or/5rrMBrPSkNc39WBdYF6zCZ41CdDyHLbAEAVPjACJVhyY56honumfzGrznOX3zmwOC4F/1tbWCnNlzjAtLy4uRsRktDPrk+AtLA63t/fH92X3CkFsKIceN49vbY3NUloKbx6XWevP68B/5/nPsmAW+Z6fl//+EuVnCba5DB2kWSbiIUD7WM2tJbymtaFmouO72vd8Z7DN/kgEIi88ZXFxsTCH7777rvh20aLJdmMgwBd4enoah4eHJcsTQg/gdYQwY0CGICc2MFBkRsF37gvXuV+MDaY2csq+ffs2tra2CuhubGyUPgG6sAaDAYIcPxl9QOnIJ94wHzWBkefWAJUjfAGPHJyD6XI0GpUIZEyojrimcHrT3d1dYflLS0uxv78f+/v75ZAH0gbmNVMzG9uiUFMiPA+Z/VqpA7ztFsAy4WQXl5eXhb3v7e0VNuicyQ608p5v1nkGugy0ueR3DesNSV/wHROMRyIY1ob3Hdvn7+fjc4e1E+iFr5Y2sCfZiUqG+kz7St8arf0/CyPMlppchsrBWa+tXVcjKg+tu1aeDdg+RWe+RJmmTT223r5Se2YWVrPWVWtDa8EbDDLgRsSESQ4N2sEYZg5Eu3ZdV/L4zs/Pl9SQu7u7sbGxMSE42LrDAdwkN8jbgWos3Z/X/LgR92BhXyOCbG9vrwR1vXr1Kra3t2Ntba0Ivevr67i4uCgMIifdMNAaVGoRq56L2npzW2tBRLkOlJiu+3QI/Lt37+K7774rB57bjRAxyejYErO6uloSlLx79y4uLi7i9evXBRzyVhLGESCnzXkNtwC2JqDdTzN4KzispbOzs+i6rkS6E7mblR4zWVtkMth6m02r1PpGYpeNjY1YXl4ufcDE7cPpeT88B4AtyqrbTLrFk5OTArQe/7wHOwdFuWSWXpMPfUDYktN9BKSPAft3thL0PbNmVam9V63ntto3q4wfcv2zAdufU5kFcB+qkfU94yEKSW0huy4Wnk3EfG9/J/s+MfMStIFJzyyU7TU+Fu7FixclrRwBHkRYciB6RBTQ47kR9xHABjhAPjMP9ysDs7Mc5THhWUdHR3FychIHBwfx7bffxubm5sQpLM7RbN8l7bEf0+NKP2Dc/J9fdNrUUrgyUCGcmStSEOKzPDo6Kn5zb4mJuM+te3Pz6bi9X//61+XAeOZqZWUl3rx5E3/lr/yVmJ+fL4BmFp/9mgBHHmuPuT/L8+EfzLoA7OnpaRwdHZU9qPTDvmr7Y7Pv3uzZfnzanU3ftfb7u7m5uXIM5dbWVqyurpZ2Y7JGUYFdY062yT+vTaxGFxcXsbe3V6KPUe5sTXFsQZ+1JINb/ttlyOd9ssoK1tCSFYSWRWFWudl6Vq7jS5XfCrB9ygFrMYv8/WNLTSOj/tqz88ud72m1scVo80uQtUJAlvthCXkfJmwQAUcd1tBHo1GJsFxeXi5AwdYgsw+2rsAKeB5ClGsRTJixR6PRhKZf6xP94JpsmjQ4UC/J3Q8PD+PDhw+fncgCU4FJEvSCwM5ZezyPefxr/3tN1EzmtN0KEW0+ODiI4+PjCd8eIFQLtoL17u/vl/SIfEcU7S9+8YuybxNTOm2wlaNPCfK1tXWbQdBZkM7PzyfMxwYW2uptPN5nm9e/TcbO052Btk8p4Pfi4mJsbGzE9vZ2bG5ulrXJ2seEvLGxUSwktno41abHluDBg4OD2N/fn9iCB5ijLNWij2vMbRrbG/LZEMba+r4lY2sEoHb/NED9EsD5FHX+Vu6d92wAACAASURBVIDtcy5DzTT8rplPatcPAf0swHOb/Lya/7MFCgSvWKvnAPq8id6+J0xmJLtAyzfruLu7K+eFEhX84sWLkv3o6uqqCGwYGgLGwqcFWrSJkvekUlAkuq4rfrKrq6vidyZvLblrOf7MW55qASoueauJ55jPsqnYfTK7Nes7ODgopy8RQESkK2wvK23MoQU8czMejyfaTYYj1oBTBFrBYV3VBL/XlKOWHU1L8nyb62HgVrpy8FgtIM5zkE3H9s1yPQpcxORhHIy7t6Ktrq7G5uZm7OzsxObmZjGzA/zr6+slucvW1tZEwooclc48np6eFhcKqRhhtFkJJq4hH+xR6/9QBtsHsEOBblqdLatBrc4hhGoWBaB137TrHkO2nhXYPlR7eEpN5sduQ2Y6lD52mq8b8hLUnplLFkh+jgUOAjHiPv3c3d1dyejj5OwG35WVlYmgKUdpcgYnzwW82H/oQJ3T09PPWD4A7CjSGmt3P81kDGoW3jYpmmWdnZ2VoKG1tbXCdDGZE1HN2NRYUgYmz0++LvcBAQ2rgc1imkcoZ1+lk0cwr2bOjCHzCOs6OjqK7777rqTkHI1GJbkCAOh+0rbcNzNKgB2WShStc/marXk8vL85R2Rb6bIv1MqlgdbKWW0OaqwYSwGn9bx+/bqcLgVbXlhYiPF4XA4ZgNXab+z3A2Xp5OSk/LCFiVgFWwYMtNl83Gr7kDKNyQ4hDkPLUDLyWLk8rU+zfPfQ8qzA9kuXx2gmfeDXd+20a1rsc+izhmp9fm4G9to12XRZe9kQVA7Wubm5iePj4wJ4CFD+t6/T+wvX19fj9PS0bNG4ubkpDIroTrLrkKIOn6FT+FkgtXxv9Amh6T6Y3Rp8LLQdSAPwHh4exng8jtXV1bJP1WZqj50jonOQEdfUTGx5TAEiB5HRfxQZ+5X97PzbW2Hwx9/e3hYf/IcPH6Lrujg8PCzBbPgcsxnb5ln/b1Ch7d4PymceY/oOY/UhErZIAD6023XkMc5tqyllefxdAPiVlZV49epViVxfXl4uoO4Tsd68eRPr6+sTVgWeQ78xkbNVCbBly1zE/fGH9AFfLkpuDuaqtf0hsiLXN01RnyYrZ5Gl057bau+XAMvHsNqI3zGwjZg9im6oyXaWkl/oVhmieT2E2dae3XqB/GP/pgUGW0Ys3ImWJIIYgCB4BKDherZELC4uTggQIjJhjhsbG3F8fBzj8Tj29vaK1r+wsFAEF6AEwwaAzGxs5qSvZos102TXdROJ+O07Yy8x+ytJJO9gJLOyPH99cwdbpy+OOs1pJR2Q5ixEnjeea/8tgTwk4R+NRmVLFPts2af7zTfflD2jHHLuBBO0DfZKm1EGaHMt8YWVPAOqA50y67eFwsDO/NgsbLNx6x1osdqI+7SLa2trsb29Hb/4xS9ia2urmI5Zs2yT2t3dLaZlQJI2YyVBYTs9PY39/f04ODgoWa5otxVBxtCHwdcsOZTHMMSHyJdaHUOv+RIg+VzK7xTYzmJmneW7mpn3MQBd09Ae44MYYlKqvaBWCvrAGR+uwenu7q4kQrAQNOsBRJzPdjwel//JGBQRxaSMOQ5f6du3b+Pw8LBsJUIw4U/M7DQDbgZhmyst8KnHZkIDBABIW4+PjyfA1m0xe7ZlI4+zTcbZt2jhyth5e4mPoKNPec9rjn7Nx9fd3NyUQB4Cftgjvbe3N2F5IABuNLo/HYg2+2xa7zHOaxXG2BdBTPutLJmp5vHJJu08dtPMo24bAXyrq6uxtbUVu7u7sbu7GysrKxOH2BMMxdgAsig4RA775/j4OI6Pj0tsgk/wYe3QDxgtvuza2q6to2lyqfWuZ+tblidZbkwjKrX1/lDzs9vTque5APjvFNgOKdPAtDaxrcXt0mc2fsiCmxV8+0yVGXgo3qfYYv6uH5+RzWVXV1dxfHz8WcTkzs5OudZ1O5iHY99on/1knMX66tWr+P777+Obb76J4+Pj0m4EGyzVPsjMat1fm50tuHPQDQw1jx334UfLJlublc3SqDMHRGUWx7j4egeT5bzS+M7Nznk2TN2fAXiAC4c0rKysTBxAjokWRu+2e015bq205HHpGwezV+Yt/51NyH62GaXXv+ciK6K0B+UIa8Xm5mZJwoK1hTXmE3tWVlaK1YAxoy6OxcP8j/nYeY4j7s3GtNf+edwGDj6rKcR5bea+1vrvZ/p3rbRArzaWtbpbz57WtqHXPhegjfiZgO1jzRhP3Y4MXq1F5ntqdU3r10PMPrX25ja1So355uCePsYLM+RzGCUHdQPG3ka0trY20U8EMj6xhYWFie09tIfvOah+Y2Mjvv3225Jo335i19tiOLTB/lxfW9tm4zGhDo+bBaEFu9dRBl+DEeBaWwMIbgARVgsjtSmVYv80bWROfFoS4+Uk/5j4OeHIgT32keZ17364/+5bjq71NR5/vsv+1hoI53nwZzUlM3/O82GzGxsbZa81SSvYpobpeH5+PsbjcbHO8J2TnoxGo7i4uCjxCScnJ3F2dhYnJyfFdeL1ajcA1hOUHs9trTwUXPPffZ9RcmxC6xkPuaZW+uTqUz3jqcvPAmy/dJnFzOLffaW1GPoAdBrTnOWZ+d5cT+u7DJ5mtxaYbqOFAmyJ4hy5p6enBXyvr6/j5cuXJTMTPl9HyyLkAeYXL14U4Y6wWVpait3d3VhdXY3d3d349ttv4927d7G/v1+2SmD6tb/QgJ/ZnvttoU2/YdgGywyarbHmf+p2XWbbztfLvBgU8rmrVh6yT5S6cvYornHkND+YhlGguu7+WEUfoYhyRcAO4J0ZpNeVzelZ4bQyl4HS7gjPm33ttlYYeFvvXWa3tJfEE7BYEpqQfIUzaM2AUQCzhcG5ijEhkywENgtLzVuZGFv8uvhyve/c4+ZSUwJbJcuAoYp+n8x5alb5JUnXl2w35WcBtg8Z3GmD91QTV6tnKGgP+XyoWaSmtfc9a0hxHbDCWiKCGhtxzlbMl3d3dyU3r38soGFZNmca1BYWFsrWEIJDYBMrKytlT+P3338fHz58KEkdzCzM+LI/l7+9vcJ9tYC3UKoJmjxXNUuAA2BqY2/Lgv3HXAPIZcXAwMF99MXp/BhT5tgMlnzDtUhXrzfAn/tzBHDuewaTGivNbJa+9jFaf956v1vvq/uRrSawWSK8OTwABcXKofeOe681p/fgayVv88XFRVn/zJfXvaOOz87Oylq2ElzrZ23N1sahdv3Qa2sKfZ+Cn+trWRumPatPvraUjqHs/kuWnwXYPlWZxfTwFM+adTKHtGOaxjlN0NTuH1KfmWDWpjMzBMDwPQHSFqwIIHxQV1dXxR/m5OxZoOA/m5ubK8KKPbpra2vlbFpMft9//33s7e0VVmBAyAFHOaqVPvPsmiDJAJfHjmKBbDNuBuCadcPjbeGOUkM/GJfsI/YWGdg9ka7eNhMRE8CMgD87OyuAYBN1DswBrGpg6T60+piDnTzGDrCzZSArSr4nP9NKkRVBxoYoYgKgtre3S/Q8TBcwdcR3jT1jwTk/Py/belD8yOfsc3kddW2XB2k3MTvnTGo15XoauGSAq103C7t9CsV+6DOGPOchsvfHKD8rsJ22SPruy5oRZdrEPMXE9dXRYuBDX4zas2p/t9rTV3+uK7Os3I8aAKHZO9IUwZiPAyOAigATJ8Ew6BtwFxcXC1Mj+fyLFy9id3e37N/d2tqK/f39eP/+/QSToK32q9kHWWO8tf5GTB4Yn4V6jc3mYp8w12U26OtgPwYhTO38ACC0xVtxrGhQD8/FN8jWHwt5+pwjsmtAZgDM4OB1iHLgyOWarzmPl+cmz0kNjKzsoITw9+LiYqyurpb1srm5WdgsIItp3RHlWbHw2ieHM4wUwGQrnJVU2kK9t7e3BaA5Kctj4P7VxqFV8rq1QtJ3Ty75+lnk1TR52pKJQ8osDHYam37o81rlZwW2P0UZOph9Jo4+oB1qoplWhl7vl6sm9IeaXGoM2oIGAZ7Zb8R9lOXNzU3xq/rMWxguiS0QcGYNBPBgysPnRZ7miE/m0a+++iq2t7fj8PAwtra2Ym9vL46OjoqwA3g4BtA5dzPoAgAZfGtsIgtGPqsxPYOd2W4NpL1Pk9OXELZ5+wvbTviupkhQN0CM/5Cx8T5ZlKbMKGtr3wCfBXDNIkLEeC2wyyDGum2ZqnM0N587GMuKCOtoZWUltra2im8WczFR3gDtwsJCYZwO3DOQY+aFweKfZWytmBlkaTPz4MQWfUpE7XdW9vI76PnIfz+U1OS6fgqGmWXqQ/oylNHPWp4N2A7p3CwDMPTaFjA+FQgOfX5LU2zdM61/Q+qbRTHwPX6BfU8GYAtT1393d5/MAsZkYQ/oXl1dla0UnEFKfTaBjkajkoyBJAxnZ2exvLwcOzs7sbu7G+vr6+XIPJIzwDRoE4IaEM9BVfbvevxqZrT8eWYdrWs9zhF18AVsrdTAEDMwGVizmZX5AujwgZtFZ4HtIxRz3/J6ys+qKSNuQ8Q9i831W5HLAJ/XZV77Vsy8LQ1zMNHFJOjgmqWlpYkUnFhYrq6uyolDXddN7DNmLV9fX0+cbIWv1cF5KAGOD3AgFLnAa1vPautnqMI/zfo1K9BkJav2zFY7nuL5tWcMqWOajH2Ktrg8G7D9McusYNnHTF2eYpHU6swL+LELqXV97SWdZg6qCdfadWZusBYCSbquK1lwMF/CdDc3N0teZfIlG/QQVAZeJ3GAGbONhSCXjY2NkoEqJ+03S8n9NzhlAMsmOQNFTSDyuwVQnheudR8xG/s6jw3jY2CtgW1EFGWHe2CZ7odzTveBX+5j3zhkhcysuXYvxduvPDd5LLKv1XuIbTYmnSJryQksyE5G6kq27FxfXxdQ9lomDoHDA4ggzuPk9UXE8enpaVECsdZ4u1lt7FpA6uuHvtdDycSPzViHltr7Okv5EiBL+Z0B22x2mXYdZeiiyi/9rM/JxcKx1abHtnXWF8vXZ2bi68yGWkICv6m3+tze3pZgHAudi4uLGI/Hsb6+3kys7/YgWDc3N2NzczOWl5cLkOLP5HB0DhTY398voGvWaAbS8uXmgJ7aHNVApY+p1P72mOYgLtePxcAnxFghcdsAUQr1OQgqAyvtypHBbqfry2sj9yuizmjzusnKTE2w0k8yYTmC2EFQKysrE1HsKGMoaMQMYOEgkhjTMfPnLFwA8snJSfG1YqlhPXmO+N+5kXkO70cNaIeAbM3ylK/tA1w/70sAT+1ZP3apPfupLZcuvzNg+9BF0zf4j9WicrFQycCW25GBuE/g1Z7z2EXVehFrgEuhH/zNdhyDrlmuI2dhuESLYoqujRVg7IMB7u4+bTvquq4A79LSUvHTbW1txeHhYRwcHJStRTl5g4OKMlN3dK7Zu0E5g1wes76x9phmSwHFYOzkIvgmMYUb2ABb2myTv6Oz/SzmPrPV1hrJfaytPz+HdeB+1f72Z1akfKACzBZWSypFwNRWD4AWszDR2wAoLgcYMSlBu64rrg+AFkXRLgj6ZjM9gX326bJ2WmbWPJ4el6GyyvMwre5Z6pxWvjRwP0SmPcRy+JDy7MB2KGA8pI6hbHOoycX1zjrRfdfmCe8D9WnPfgpNbSiDNgC7DzVzLNc6kT5+XAAB065PuRmPx3F1dVWSC+BfsxDzObcXFxflGoQvAVcvXryIjx8/liQFZAnCTOgTaRytbGDN2ZoALQOvAS1bCWpjabBpgRilFoiVlTDMzXlfK/fn3Mn5OTWWaZDNoJnZaA10W+shKyI1NusfgouYV+baeZ4JcnKmLUzLPk0I1psPVvA8Ly4uFjYMOMJMT05OSixAPss4z0ctRsGpSVtyqzZu2Vyf5ZhBuwWkfUpz7ftp3/0UpU/ODVEqpl3/2PLswPYpS35RhzKIH6NMW6h+iaYBZguc8/19z3yMlloDDTPNiM/PTUUoAECwBNgqe2E/fvxYzme9urqaEEwOXkGoskeU7UQ8f3l5uZjyvJWItgLWS0tLcXFxESsrK8WE7ZNsLHjz6TY56YLHwiBtJlmLqPV9LVCrmQkNVmb8CH0rJK4/R0hPa09+pksrgMnXDmVhNSXTe4gxDQOmVtYAWlgsvtecKcsJPJzQw2lDLy8vyzP9LIPl8fFxHB0dFf+s2+v+sw58CIEVtdaY9Y1XXh+te7IVaFoZCqazys2hyvtTlqHPmFUOzmIG/60G24gfR+tqgVffyzHt2lkYeV87Zr3+IS9jrs/MhOuyIDH4AgikeRyN7n1hfGeWi4DD70rwFFGbPBPg7bouVlZWJpJt0HY+c+Ygjo8jqYMFo/eqmu16b2pmNABg62i5aYyxFlzUB8Q1Juo6LHQzUOf/83c1parGYvNayMzUn3kuvD6yRcT7UQFTApR8zi17ZplLTMmsK/63LzcH2vGbOXaWJywtp6encXR0VNgsx0rSVo8P6457cVPk7Gl9DCzPcd//eazz50NkU03ZeQpgbPXzt708O7B9ykn40hrTLFrNkLr6tNj84gzRDrPA72vrNFbTKlko157f6pcBNzM8hKITYdzc3JQAqsvLy1heXi7BU2tra3F1dTUhgPFTXlxcTOwRdapGZ+yhfbDktbW1WF1dndgD7DSRMCGAFLD1Fg8Dldl69uV6rFrbdPL1+Vr/XWM7LfN0DZxbAJ5LHwjnNetgrZoymcHW59nmoCJA1UfyEfgEyGIy5nqbm7GImHlaMSISmJN5mLPr6+s4ODgorgaylzHnADXrzLEHWFtwTUwD1/xZa85q99TGuPVut4C6T649pez7XSnPBmx/V7UdSq3/mTVQhoxT62UZYraufde6d4iJyWZNC2d/7uthqBwiACvhPsCObDwwUAStD6dH4LF/1ADvoCy3BRCG/Zi5OhMQ2zM4k5fEGJgKAd+cYo9E/pi18zi05j0iJhix+1f7uyVAMxB6rfB3jeXmOeR6jyltzPfUgLZ2jRWfGtPlx3tmcSdwxrGBNDNhgqUM4rTZY+gTdnAl3N7exvHxcUmh6C097oP99LgaHHBXU6QYy2wV8jVDlJ9ZS82SQXms5cv112Tb75q8fzZgG/HznYA+YBvyeWvBZ3Mb19RYQ1/x9TzLWxG+dKmNgX25EZNC22CCgHLWH65BmF1eXhaQxXxIggILZYQv/8NwMTfDSmiPg2fYPoTpmkPUvScyRyvztzM3WZg7SX1LANcAiXGDrXpMcnCW10hmvrnU2CafGzBtcnXaTlsHuG8aG8ugn/fPmtXiT4WZArK4EsbjcVHMcoyAzc4GfsbFAOv4APa/Ari1FJa5LpuebTKuAWbNApHlwBCgnSYL+r5/CuCugWpucwtwf1dY8rMC26ElC4KW9vSlgbtvcUxbOEMZ5pd4STJzeWx9ubReOH9n0Kctd3d3E8zG5lFMgETWwgLu7u6KmY+9ikSKIpAdfYowdhs85pgC3W7AmHawpWg8Hsfm5uaEmdkRzDni14wTYZzPJ60xVDNSxoN2Z+Hs5B6OPmbsPW4e47zm8hgYoBzp66PkfF82bfOsnOe4BqoZIH3SDkoVJn5HGtf2EZvBOguT24fi5sji7FdFmeJvlFVbAlyPgTYHwrkNeR5z8XzXvnN5qKzok0X5+z5i8BzAss8K91OXnyXYTivPcaBz6VucQ16i1r3TXpw+s1Grjtb/fRp2q3/T+tx13Wcn2xhkPn78OMFUuQYGDAs5Pz8vYGuhvLKyUrZw2CcMg/XYmEHDTr1NZHFxMdbW1sp1gP35+XlhSACtE/l7qxDC3f5eA3HO0+wMTtlcm5WUaebHDMJ9824Trv2kWQGg3ppSkOsCuPP2G9grPyhJAC3gyxy673k8MsCivDBGzA2RxcyhD8jg+2wx8Hvg6HSUJ/tmh74HHvehVoi+Mk2hfiyz9L0PITvPAaB/rPJswPYxAPlzANfHlNYL0/Kp1O5/yjHKloVZSo3V5Dqz+dPAAtjAcO1/M+gaxK6vr2N5ebkAGv/DzBD4PovUwtuHoi8tLVVNqXNzc7G2thZbW1vlmT7o3iyJNlxfX5d7FxYWJvy7BOdYcGeztAGcdnqs8rrJPtAaGNcErz8zS+czz4ujjxlHA6nzFOezc53xyYn/nRHK23s87zUwZ2zNsGv+bgCSxBKYiPOear93rtdRxvxYwfI69rh7/GpK0GOBqAXSQ8F/iFLd+v6nKrOw9B+7PBuwdXkOA0N5LuaRoWUasNrs5c98/2Oe7We0vp/GyhFiNd+aWRPCEuFtMybgBOMkMxA/bBfynkqzKe/Zpa65ubkCujzXUc8Rn8y4sGn32yZjC2aiWvH7GqTpqxNx1PpfO82nZrY0W8/XZVNlZrVWMDIbAkS4Nif+9z5o+9ANtoCvwdXP9hYdt4dn5zXH/1ZMMnjip2U/dZ4Dg2B+pxxP4PlEmeoz/WaA7WOvtrLkdjxUPub7WgrXb0t5LvL72YHtQwamthCfErB/LMA1CD7UtDPNPNUC2vzd0Of2se5Z6qgJM+rJdZm9GTC8VcRM1/4zTJSYJH24AX5BwNjMyZHMjijNJkoHYJndLS0tTWwRAgBIiMAB4RytRj5dkjZ4n7DHIANILQjKjM7AkwU/P9ksbRaf974yRwZZp0zMqROdUAJTtDM+ZasCz7W52W3La4bxyf21YsIc5kMF8MVa0TGo0yabjO3j9TMZlzxOnju/L62SATdfX3s/htTXV57KEvZciMpzUR6eHdg+ZXmqQR6yOGd93tA6KX2+kb62tF5SX98yR8/yoszyArvumvmsJuD43MIlmwUz0OGbow5YKsBLFDOmyYWFhYksVTXzZcTkeb0RUYSrhahzE0fEBFCxN5cj3IhuJjHChw8fYjQalbYRbdt1XWFPCH0HHJm18r+3LeX8zhGTpmWPb/arZmbrdcI1NR+49zGbkQLOKDIeH/94/DPTzCZis/3MZh2Yhmnf/v2879XmdubcJmPWj6PKc/tqimuN2bZK7X15SKkpwEPkyKzf58+eA9C2ylOSsaHlZwm2LY2x9tlDGeKXKkP9IEM+r13z0BdoyPjUXq6+NvYJiRpbzXVZYJnJZBMd4OFkAvlQbj7nt7d52LzprFA2dcKODRwIYIORBSnbYrytyO3hHoAcZhgRsbi4WLYabW5ulsAu/Irew2sgyNuPrGzBkiPuo5Z9RrCDzvgsA282H1OXTcNmuo4uN5ADpMyJv2McaC/s2FtwaD8sM+emtvnY42R/OT59t4F+0z/G0RHGNjXXAqAy2/T3OXit9u5MkwMta1IuQ6+bVp7KyvYYYPuSdT1FGaIQ/SzBdmj5sYB2Vkb70IUyBKD76m4pIBZ2syoBX2JsEXJZ+MFUeaZNu/7Jx+ll8yfXIIgJlkKwI4zN1MzWbBb9+PHjBLDbzGl23XVdAW72xTKGNtcuLy/H1tZWSZCxtLRUkjV0XRfr6+sT7CqDivfwZn9iREz0yXObmSXf8eM0mBH3CkQORnJAVIsNU8xuc7pFH+LO6TsA3OXlZbnXJl0HadUYfU40AgOuKRHZJ2t/ezbb0xePZwZU/57GamtM1OWxbCxbJh76DluWPRcy85zLswPbpxTsX9L38BhzSa2uPk23r12Pua7GUmYtLbabWess7JyS/Vq1un2fzaiAXE6IYZYGMBHZTLrHfA6qI54z2BqI7S/msxpbIn8u7SS4y6DsM3gBU7YaRcQEqzKIGGhyLum7u7vis15cXPxszvJ4ZkWANjvdJWBJ2z0ueU4N0NlcnI/C815qzP4OYPLeXUd5O/App87M+5dzXIDZsRlwji73eNni0np/a+u2Jj/6LEd9ZVaG3Lq3xoSHAvOPaY79uZZnA7ZDfAQPrfcxZZqJ5sdaZEM04Yj+F2Ka2bevT/m72rWzjHULOGvtA0QzQ7KwrN1D3QAu5mXA0/UAVoCx93o6xV9muDm4BxbobEWZhRlwbMalsP0obxEajSZPRQKkcxYqKx0EAZ2dnZW0kj7DlbHKY5mFsFm6QdMMEL83/XYQG/PH8wHjPKaAN/32MXb85H3H+VCIbE6vsUp+6J8ZsvMY1/zcHg+voTyGtXWelVCXWVnsQ8zEfWy51e6aEjCE0XrtPKfypSxy08qzAduIp2OiX7LUAOspJ68P6IaU1j7WXN9DlYUhQD603mn98rjWhBQCvSYoPDcAAmALk817MSOisE7Mld7CYr+kGa+Zbf7xd/YhO/Uk7TRQw7Rs/qYfFLPpHIXN/943jK/TQWQ1UM2C1uDCd/aZkiXJbQe8ADvM+oyjmT5s0Uk8fOBDBlUDqv2xOfFH7V3IEduuO+9pNlj3yaYaoPeBUc283Peu5u+n3Vt7/mPkqtfBrID+nOT5T92uZwW2T1m+9KBOq3cIAGcTTV/9QzTIx5bWCzUNSPvMkLUyZG6ygM9AEBGfAW0tiCcrRDYzeisKgJQjXNmeY0bm7S21DEiuqxadSx30wQFUNQB0fwEGgC0DSg00AV0zcisYLVNhfj5s1j8oJ2Tlol7YeL4vIopJ2xHEzJf3qho8zW6zKZj7qcM//pz59H3TjkjM69H/t57XurcPrFpWpyFKbG5bH6i0FO3a+9Vq37TSWj+1z/ra0nffrOWpiVGeryF1PiuwfSgwthZqrb4h5o++MhQEn5LpTqtr6LMeutBa9/X1vfXyz1Ky8HLQlOeR5xl8fV2ec2/dcWBTNgdz7d3dXdnm4QCgmt82+4cz0zVLzmBPIBBMDaYHOAIUHz9+nABN+6EBla673/bD/lGeY4D2+GULgceg9XcWljDyvOWKv1FenGXJbJ1tTQZiWGfeH+zn19aZr/O2nVpQWWayfcK+D2hb8qUGMLU2Dyk182xNMZrlnZ32nesdWjxONQX4qWTktNKnvDxlmVb3swLbpygWHBE/nX2+r/QpB7UFmr/L9+V6+xSCaeataeUhINrSzi3ohxZr4H0vUV4HNWGIwI2ICdaZ94bm62FqGWQNxNmXa0DPWZOyGdq+0K7r7d3HmAAAFoxJREFUSjDTaDSaCOj6+PFj6WMOYPL2JvylTqyRo7NzsoraHPEcGLiDsUajUcmyVTPxMg7X19cleYfbCSjSvzzm9MvzmAOcXGpJPLJf1+y4xk5ra7PGTmvvQYux1Urfe9R6N/qsRn2WpYcSmoeWaXLmIfJk1j48F/n/Wwe2P2ZpgdtTam4ts0+t1EyuNY3yIW1oCeKnMu8MuWaa0pAFTU3o8LfBCUHvvbUGxxqbRkgDvnnrS2bKmd06bWFORUi7YN7Ly8uFUQISNjkDlrTLQMu1JNK4uroq1zooK++v9dgYaG2qB7Ra80FmpdHo3ndMisrMMP1j4MtAmOeCtpjBes+xc1Fnn24LYPvW1zTwaF1DnUNAdYj8GGph+6lLC/BnUfaHyr9WaSn7Dy0PHevfOrCdpvHNMlC1SX4KpjztRRnK/Kaxt/zZkEXm62uCp6+OVr9mGbNp107rQ7YMeIwsmPmcrTWwJCe+4LvaHlS31YDH5442tpk4B1j5IATfFzG5j9jbmeijzcH8n8EWsH7x4sVnW1dyXzxeeYysUPB93j7DZ9xDEorRaBTLy8vRdV3ZhuR9rjbp1kzcrTXA71rGqBqYG7jzeqmtoxZbzZ/XTM997K32rKHXTmv3tPf8IbKrT155rfTd81OVFqg/FXDPUn4rwHbo4nroINeY3TQQnFYey37ziz/tJWux71YbWoLG/9eUgawo+PMhCk9fW/qYaktg1drgCNn8ecQ9iBG5bF+s68qmV8+JsxLZr1oLlHLdmQFHRAFPitl4nneDLXtzmQtAKINkNikbPLIC4THI8+9+Y06+vLwsz+66riT9N/hlc25rXvOeVliso5gz0GaGzHjlua7N4RBgbs1/Hhdf3/cO1OTKLLJm2nv9kDLr+zqkvmlKx2NKa55qWDBEbuTvWmXaNc8KbB+jcUwrD6n3S2pnQxfw0JdnlrEbem0N3Pral79vAXxrkdcUiNYzWm2tfV5j/xbAvtYAZjCw6ZYf+y9Ho9FEsgeekUGR/bP2+dYCpWCiXM+WmqzkeAtNRExE2/IdwUX0KacnzBYAt93jQxv9vQtgyxhgtrb/2UkpuN/mX9eV598KkYObalmh3L4sZHPfaoDqZ+Vrcp9r/z+Ulbau7Xtf8vWzWJJmLdPe7VnrqZXH1j203sfU8ZDxfVZg+9tavuTid+l7iVvmnj4tcBatOd8zRDuv/V/TPvtKC5hr/ez7zM8G8MyALXxtVs3gbFDN/TTwet8rKR/zNiQHXuWMVLQjYtJ8DGOcm5srx/JlP2itzwZvz6W31fj5LYDiekDZ7YHhtnymGegz2Nns7EAnA20LpGvrpm9NTGPZQ8pD2NtjLV6PLdMU6S9JQH7sUpv7L9m/QWA7Go22IuJ/iIh/LyK6iPgvI+L/iYh/FhF/LSJ+FRF/0nXdwehTa/9hRPzdiDiPiD/tuu7fDm3QEM3w51RabO6nLtO06yyI+0oG4T6T1zQT2rS2DGmHmV++L0ew1gDe4GBWayYGuGZwMjvM+3fdzizo8RHXfLY58tl1man6b9pby+M7zSSZ/bKUzNhrxdHa3svbYpLZ3+ktP+5PzuSUUzC6na1+1Rhuvq7W3llKBsvaGuxb5/7sofLiS7PbIc+PeLwM/ykUj/wOP2UZymz/YUT8y67r/rPRaLQYEasR8d9GxL/quu4fjEajP4+IP4+Ivx8Rfyci/vCHn78VEf/oh98/auljZT9FOx7y/Kfsw0OFx9C6h1wzFJRdHiI0MltrCdDWSwXIZOFrZke93kOawdt7W21urrFhrq+1J0c5Z1NuzdxJFG72c7rvbre/NzPPpvY8jlZaWmPjevJ8OGDLUcQOajK48mNmaotDbS5bLLwFgrV2DlmrT/V+PVU9fUD/peRhHuvHyqwvWVqypaUoTVNSp5WpYDsajTYj4j+KiD/9oeLriLgejUZ/HBF/+4fL/klE/Ov4BLZ/HBH/tPvUgn8zGo22RqPR113XfTeoRV+ofGkTwZDnP/Z692FWrQ/hklkRpWUymla/v3fE7tB21czNrfZMa0cNNPx9TfC0khhkE7JNlAZK9yObeB0slE3MuR4HIrm+DFrcmxNZ1PoLy8ztc79cagy9xgp9Pe3O0dpWHLLZGBZq8ARoMYU7iMsKTW0uM9gbhLNSkPvS6lefUpbHZOgarj33sQJ8ljKkzmnv3Y8J1i7P0dI561gMYbb/bkS8j4j/cTQa/c2I+L8i4r+JiDcC0O8j4s0Pf/9eRPyl7v/ND59NgO1oNPqziPizWRr7GKb3lJP1Y5s1Ij43MSEQpgFbTRj73nxt3/19z6j9XXtGTfj11TfkuzwOtbqHCtpcZ03I5n7k53h8s583Ay335DSINicbwPg/K09ZEcvAlJ/Zuo6/DZY1JS+DcYuVG0xruYsBXzPXrGDUxr+2Lg2uNUZbA+natdNKa03k/1vyqvWs2py1rhsCinmOZlGCH1ryM74kOD9EeZimQPbV//+3d3Uhm1VV+FnNpE4Wjn/I0EgqieJFjoOUkkQphUl0JZEIiQx4Y2IQhBIE3dVNZhCS9HcjWdmPIpGZej2m+Tc6TY5lOKJOhVrMRWSsLt59xj3rW2vvtc97znuO37se+Hjfs/c+e6+9zt7rWWud857PsqPyuwUP2W4FsBvATcy8l4huxyJlnAvBRNR0FZn5TgB3AsCWLVuOnjvURZmjJ+RByaEoedee/kqRXwke8pN955+a0ZZy1ByGvvJJArbG0iJjSUAyIrTm0pXLLIKMbGUfMl2stctlz6NtK9LLSUb+xEfKnh9bBFTKhhDRMfdOcyKV91rzMazfv0o9e8i1RKitc7WurzV/rUxzVDQ5auUavOSpOdx9MbZd7Rt4TYWaA5XDQ7aHABxi5r3p+B4syPY1SulhItoB4HCqfxnAmdn5O1OZC9aisAykxCpItkXBQ4xjwZq/ZlCsjdmiL82IeeSqeeJ99ViKKmqk0LWvEb404jKCk7rV5ioNbk66Uq6uXD5claeZtb7ycSzHwvpdsOzDIh+pC80x1Ig0J1hZn8uhjSXL5LGWYpYoOYLaWLW1atV7o81S2+6alZws7XhKlHSnOSdDye5xNiwHfxlo634QsmXmV4noJSI6j5kPALgCwHPp7zoA30if96ZT7gPwRSK6G4sHo97kEe/Xat7vVFHtsmkaafTy8g6WIZT95H3IDTwEPItYXpuh01jW+GMaotJGk4Rr/U4zJ0IrOpJP9eYRcT6mTDnnfWjfrXloMsj2MhLVIAlQI1gtipXjauRpRaWa7JrjJfvyRJeW8yrH0757AwQ51tD2aypilhH1EE61dEKGtil9UApmcnifRr4JwF20eBL5LwCuB/AuAD8joj0A/gbgc6ntb7D42c9BLH76c327+BuN+RBKlca/BZZRtDbesrJqqcFcJx5Dock1JUpGy/L4Lf1q0also/XTd01JQ23JWTOmwNsp5a4f7VWQ+cNBMlq1yLa0NrpzSsY879Oqz/WtEYv2cgrr/LzeS66aPCVY/dZ00QLLrpTWVyk7IuVdVq6+55UyQrJcs1XWnlnGDpdQC1Ra+ildt756pam9AmBxz3bbtm3FNi3eeU0hQ11kK4Icsu8SLM95KGeiNE7tHGncrcxDrjutvEa2wMZX7nnJVqsvzUOWS+KrkX7+MJPWjyS5XFeWfrSnkq15W3Molcnf5sq5auUa2VoZgJrT6IlsSyRnyS3PrUWkHmj6rM1vlZFZCwkts1+s+hrZ1vTg4YAWeb02r4V7jhw58jgzX6zVzfINUjUv3Irypo7eVhl5e+eqpbhqEYFnU3rK5c9LtH4t4y2Nn7UWas6FtX5kH9ax5ixo8JR3L+nP55jPraSfjoxzfRHRMZGklNWSRyNxD1mX+pHl+fy6cus9xPJ7yYHxyFci+5a1XWprrQupEw+Z1taW1tbbvi8su+Edt3VP9ulPu559gw1rPLmel8EsybaDFQF5vZllxpwq4vfORTNUJaMpz/H0WzqnRH6dLF4deghTIyhv39Z4fa5xizfuJYruuxbxynppBPJrbkVqpTHlGBrRyYi8ZPC1OVlGUZNNc6JL0KJYea42nkWkfQ2rRcae9rU907pOrbn1tZNT2UIPlrF3rcS8LJHPmmyB/mmWqaPcPuiz6a3oQGvTx5FoWcxDOyrSqywZU00ub5pIm4fWx5iOmNVnF70y8zFPJVvEKB8GkjooeezdWFKe/B5zdywJ2ON05J+ag6g5UV6S1ubaAs8+ylHLdgxB2iVH0YuhApMhAxyrr759rtLWSwe3BbMl25Jn/k4k0r7Qoo8OHj3UjIhmjDUj3BoV14yNh8BL0Y92Tp914TEirXNYBtrcJEnlZfK//uTntVw3jbgtMmbmY+7lymPvPD167ZNS9tiI2tieLIFHpto5Hjm0utaouTRWDWNHtn1tusf5sgKNKXhktmSboxZRlNJIrQodc2FZm6BPFKsdjwFrQbd6dqX2tXR0XjbkZinJ32IESwTtMZZan91cO7117WXkKaNOrf+SHLm8NeOl/TODfJ75fzSSutDeCNVBi6a1uVjXS3vwLB87r6vpJdd3KXqtRbZelPa+J1tgnWut32WzW3MLdGp2aEh5l+3rHUG2HVrJaFks4xX2PXeMha0tSI/R6cq9epXjSELRxrQiF080YUUfHoPSGv1o3rImUyltq7W3jmW5JwLv2kn91GSX/Wtt8t/Kav2UnEfrd7m1MfM6zRnIy7U+POvAWpN5v8vAc53l3Fr2nIY+6e3aeHMj2RylPTmHtHmH2ZHtUOmEOS8OoEyqVgTcF5aB7IPWNGvfFKxGvKXoogUecq9dg5IerLou+ivBk7rMZfJmBbryUnsZQWupt/yzJlfpvFoa1Dqnq/PIUqovZWy853odKE9WRxJtae+M4QRYZUONN2Z/ss9alqePDFo/rXZ6dmTbYVWpgRKWiaQt738qJ6BkCLrP0iL1RJq1upLxsKJcTa5lUHM6ao6JRvhWhL3M+CV49F2KQEtGwtK1FUHKuXtIyJJP66vWj6fc4/SV9kEHy/nwXFNvnSbPKjCEIz4lWnTcolPNcZVlXt3NlmxbMVTkNiRqMrWmubRNvgqUUnUtkO8V9qZ787Y1pyBva/VdGteK/IbQ9VBrtESgWvRXIq5SpKvJb9WXSNI7j9LYtWtgyTakTcgjzr7roUU/Q6VAxxxr3bCM4z8bsl3mYnu8j1VDRjwd+qS9rLTaEGiJyoYerzv2onZvJjeEJaKxrk1e3/VrRUfSuHuIyENwnmvgIU9vZNjiSNSuVSlD0XfcsddkbX9qa6GvbH3m22o/AsNiyPU3G7JtgbVRvSlArzcvDbfVdmyD4EnFjjmmR5ZadF5LC8vymt4tB4G5fF+0FrWVUEsbWhFnKRJv1XNN3pwUSu84zvsoEbc1B3m+RbA1x0frT6srzbmEIRxwK6JtzVR4He3a+qjZv3cqhrBzpT68DnLLfmvBbMi2VdGl9GGfzTRku1yeKT3Rvqlfb3qvZXPXDGktHewdWzNY3nS+1taKmLXxtHPlOB6i9crbkoatQRJnSabadbH6zr97+7OIXo6Z63aMPZf3711bXnLXdGKduxki21XPoa+TNnR2dDZkm6NmRFom31dpcuO2pjtLm3DVJDykt6vpRSMVL7lZhrTvNe4jh5U27uqstVAiFk+kXyPoWqSvtWuFJzVsyWClWL2OkBVpyLHGSNcu01+Lo+fpW17zZR2mMZ2OOcK7T2TbWrDgzdx5MUuy9cKz6L2K71NfkmOohe4lLdk2N+hjjCe98NJYpbpSarEmuxVlaqlP2cbKgtRI2+t81cixFEkOuXY8UWgtGq2dr/VR2oPW9ZF6kf1a0at1TmsE7m3nsSstY/fZp95+LbRe39r5sm0tk9UXnj4s58ibCZLXY6j9OFuy1QychhZFeNNWfaAtttxL1S5eXyNnjSnrvH31jbS1zZ3fJ5QG14qAiTa+YaivHvKNVTN4WvpOYshNVyJZTZctkZIWiefltahY6lD7h/SlsWvG1oo+PNmB0tqx5qOhj+M5VH9WOly7biX9ePb8KiJaT6AydIZhmXm1njuGLmfx/2yJ6N8ADkwtx8xwGoB/TC3EzBA60RF62YjQyUaETjZiaJ18gJlP1yrmEtkeYOMf7q4riOix0MmxCJ3oCL1sROhkI0InG7FKnei/DQgEAoFAIDAYgmwDgUAgEBgZcyHbO6cWYIYInWxE6ERH6GUjQicbETrZiJXpZBYPSAUCgUAgsJkxl8g2EAgEAoFNiyDbQCAQCARGxuRkS0RXEtEBIjpIRLdMLc+qQEQ/JKLDRLQvKzuFiB4koufT58mpnIjoO0lHTxPR7ukkHw9EdCYRPUJEzxHRs0R0cypfW70Q0QlE9CgRPZV08vVUfjYR7U1z/ykRHZfKj0/HB1P9WVPKPyaIaAsRPUFE96fjtdYJEb1IRM8Q0ZNE9FgqW9u9AwBEtJ2I7iGiPxHRfiK6dCqdTEq2RLQFwHcBfBrABQCuIaILppRphfgxgCtF2S0AHmLmcwE8lI6BhX7OTX83ALhjRTKuGm8B+DIzXwDgEgA3pvWwznr5D4DLmflCALsAXElElwD4JoDbmPmDAF4HsCe13wPg9VR+W2q3WXEzgP3ZcegE+AQz78p+O7rOewcAbgfwW2Y+H8CFWKyXaXTSvZJsij8AlwJ4IDu+FcCtU8q04vmfBWBfdnwAwI70fQcWL/sAgO8BuEZrt5n/ANwL4JOhl6Pzew+APwL4CBZvvdmayo/uIwAPALg0fd+a2tHUso+gi51YGMrLAdwPgEIneBHAaaJsbfcOgJMA/FVe66l0MnUa+f0AXsqOD6WydcUZzPxK+v4qgDPS97XTU0r1XQRgL9ZcLyld+iSAwwAeBPACgDeY+a3UJJ/3UZ2k+jcBnLpaiVeCbwP4CoDupdqnInTCAH5HRI8T0Q2pbJ33ztkA/g7gR+l2w/eJ6ERMpJOpyTZggBeu1Vr+LouI3gvgFwC+xMz/yuvWUS/M/D9m3oVFNPdhAOdPLNKkIKLPADjMzI9PLcvMcBkz78YiHXojEX0sr1zDvbMVwG4AdzDzRQCO4O2UMYDV6mRqsn0ZwJnZ8c5Utq54jYh2AED6PJzK10ZPRPRuLIj2Lmb+ZSpee70AADO/AeARLFKk24moe7d5Pu+jOkn1JwH454pFHRsfBfBZInoRwN1YpJJvx3rrBMz8cvo8DOBXWDhm67x3DgE4xMx70/E9WJDvJDqZmmz/AODc9BThcQA+D+C+iWWaEvcBuC59vw6Le5Zd+RfS03KXAHgzS4NsGhARAfgBgP3M/K2sam31QkSnE9H29H0bFvew92NBulenZlInna6uBvBw8t43DZj5VmbeycxnYWEzHmbma7HGOiGiE4nofd13AJ8CsA9rvHeY+VUALxHReanoCgDPYSqdzOAm9lUA/ozFfaivTi3PCuf9EwCvAPgvFh7YHizuIz0E4HkAvwdwSmpLWDy1/QKAZwBcPLX8I+nkMixSOk8DeDL9XbXOegHwIQBPJJ3sA/C1VH4OgEcBHATwcwDHp/IT0vHBVH/O1HMYWT8fB3D/uuskzf2p9PdsZ0vXee+kee4C8FjaP78GcPJUOonXNQYCgUAgMDKmTiMHAoFAILDpEWQbCAQCgcDICLINBAKBQGBkBNkGAoFAIDAygmwDgUAgEBgZQbaBQCAQCIyMINtAIBAIBEbG/wHRavKTsoGhUgAAAABJRU5ErkJggg==\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "img_teste = load_img('/content/drive/MyDrive/meningioma.jpg', target_size = (384, 384))\n", + "img_plot = PIL.Image.open('/content/drive/MyDrive/meningioma.jpg')\n", + "\n", + "plt.figure(figsize=(8,8))\n", + "plt.imshow(img_plot)\n", + "plt.show()\n", + "\n", + "img_teste = image.img_to_array(img_teste)\n", + "img_teste = img_teste / 255\n", + "img_teste = np.expand_dims(img_teste, axis = 0)\n", + "\n", + "resultado_teste = model.predict(img_teste)\n", + "resultado_final = resultado_teste\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-gt0nHUrgW_T", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "70a59dd2-401d-45b4-d53e-93db6a792292" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[1.49426427e-23 1.02706000e-20 1.51871987e-11 1.40941436e-22\n", + " 1.18107332e-23 1.33995691e-14 1.14293212e-25 9.64622838e-23\n", + " 1.46982271e-18 2.28959857e-23 1.04927053e-16 3.63950883e-12\n", + " 1.04573393e-24 1.58711777e-19 1.45133912e-14 1.55698631e-24\n", + " 9.40127218e-22 1.51582663e-10 1.29658990e-25 4.68804890e-24\n", + " 2.48217893e-17 8.94520215e-28 1.03743019e-23 1.48085045e-22\n", + " 9.86864273e-18 4.70925890e-14 1.00000000e+00 1.82865506e-22\n", + " 9.57524359e-17 4.84704941e-13 9.79464046e-19 1.37653700e-18\n", + " 8.55395128e-14 8.95221443e-25 8.68064176e-21 3.25302050e-16\n", + " 2.41582720e-23 6.67367238e-23 5.99919894e-15 5.18775473e-23\n", + " 7.65302333e-19 1.53014740e-14 3.34975423e-19 2.43458500e-11]]\n" + ] + } + ], + "source": [ + "print(resultado_final)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Agj6Up49gW_U", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c8e87912-272b-48d0-d785-b959d33f2ddc" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Com base na diferença de densidade dos tecidos mapeados,\n", + "a amostra possui características compatíveis com:\n", + "Imagem: Axial T2 \n", + "Meningioma \n", + "Probabilidade: 98.0%\n" + ] + } + ], + "source": [ + "print(f'Com base na diferença de densidade dos tecidos mapeados,')\n", + "print(f'a amostra possui características compatíveis com:')\n", + "if resultado_final[0,0] > 0.75: print(f'Imagem: Axial T1 \\nAstrocitoma \\nProbabilidade: {round(resultado_final[0,0]*100, 2) - 2}%')\n", + "if resultado_final[0,1] > 0.75: print(f'Imagem: Axial T1 com contraste \\nAstrocitoma \\nProbabilidade: {round(resultado_final[0,1]*100, 2) - 2}%')\n", + "if resultado_final[0,2] > 0.75: print(f'Imagem: Axial T2 \\nAstrocitoma \\nProbabilidade: {round(resultado_final[0,2]*100, 2) - 2}%')\n", + "if resultado_final[0,3] > 0.75: print(f'Imagem: Axial T1 \\nCarcinoma \\nProbabilidade: {round(resultado_final[0,3]*100, 2) - 2}%')\n", + "if resultado_final[0,4] > 0.75: print(f'Imagem: Axial T1 com contraste \\nCarcinoma \\nProbabilidade: {round(resultado_final[0,4]*100, 2) - 2}%')\n", + "if resultado_final[0,5] > 0.75: print(f'Imagem: Axial T2 \\nCarcinoma \\nProbabilidade: {round(resultado_final[0,5]*100, 2) - 2}%')\n", + "if resultado_final[0,6] > 0.75: print(f'Imagem: Axial T1 \\nEpendimoma \\nProbabilidade: {round(resultado_final[0,6]*100, 2) - 2}%')\n", + "if resultado_final[0,7] > 0.75: print(f'Imagem: Axial T1 com contraste \\nEpendimoma \\nProbabilidade: {round(resultado_final[0,7]*100, 2) - 2}%')\n", + "if resultado_final[0,8] > 0.75: print(f'Imagem: Axial T2 \\nEpendimoma \\nProbabilidade: {round(resultado_final[0,8]*100, 2) - 2}%')\n", + "if resultado_final[0,9] > 0.75: print(f'Imagem: Axial T1 \\nGanglioglioma \\nProbabilidade: {round(resultado_final[0,9]*100, 2) - 2}%')\n", + "if resultado_final[0,10] > 0.75: print(f'Imagem: Axial T1 com contraste \\nGanglioglioma \\nProbabilidade: {round(resultado_final[0,10]*100, 2) - 2}%')\n", + "if resultado_final[0,11] > 0.75: print(f'Imagem: Axial T2 \\nGanglioglioma \\nProbabilidade: {round(resultado_final[0,11]*100, 2) - 2}%')\n", + "if resultado_final[0,12] > 0.75: print(f'Imagem: Axial T1 \\nGerminoma \\nProbabilidade: {round(resultado_final[0,12]*100, 2) - 2}%')\n", + "if resultado_final[0,13] > 0.75: print(f'Imagem: Axial T1 com contraste \\nGerminoma \\nProbabilidade: {round(resultado_final[0,13]*100, 2) - 2}%')\n", + "if resultado_final[0,14] > 0.75: print(f'Imagem: Axial T2 \\nGerminoma \\nProbabilidade: {round(resultado_final[0,14]*100, 2) - 2}%')\n", + "if resultado_final[0,15] > 0.75: print(f'Imagem: Axial T1 \\nGlioblastoma \\nProbabilidade: {round(resultado_final[0,15]*100, 2) - 2}%')\n", + "if resultado_final[0,16] > 0.75: print(f'Imagem: Axial T1 com contraste \\nGlioblastoma \\nProbabilidade: {round(resultado_final[0,16]*100, 2) - 2}%')\n", + "if resultado_final[0,17] > 0.75: print(f'Imagem: Axial T2 \\nGlioblastoma \\nProbabilidade: {round(resultado_final[0,17]*100, 2) - 2}%')\n", + "if resultado_final[0,18] > 0.75: print(f'Imagem: Axial T1 \\nGranuloma \\nProbabilidade: {round(resultado_final[0,18]*100, 2) - 2}%')\n", + "if resultado_final[0,19] > 0.75: print(f'Imagem: Axial T1 com contraste \\nGranuloma \\nProbabilidade: {round(resultado_final[0,19]*100, 2) - 2}%')\n", + "if resultado_final[0,20] > 0.75: print(f'Imagem: Axial T2 \\nGranuloma \\nProbabilidade: {round(resultado_final[0,20]*100, 2) - 2}%')\n", + "if resultado_final[0,21] > 0.75: print(f'Imagem: Axial T1 \\nMeduloblastoma \\nProbabilidade: {round(resultado_final[0,21]*100, 2) - 2}%')\n", + "if resultado_final[0,22] > 0.75: print(f'Imagem: Axial T1 com contraste \\nMeduloblastoma \\nProbabilidade: {round(resultado_final[0,22]*100, 2) - 2}%')\n", + "if resultado_final[0,23] > 0.75: print(f'Imagem: Axial T2 \\nMeduloblastoma \\nProbabilidade: {round(resultado_final[0,23]*100, 2) - 2}%')\n", + "if resultado_final[0,24] > 0.75: print(f'Imagem: Axial T1 \\nMeningioma \\nProbabilidade: {round(resultado_final[0,24]*100, 2) - 2}%')\n", + "if resultado_final[0,25] > 0.75: print(f'Imagem: Axial T1 com contraste \\nMeningioma \\nProbabilidade: {round(resultado_final[0,25]*100, 2) - 2}%')\n", + "if resultado_final[0,26] > 0.75: print(f'Imagem: Axial T2 \\nMeningioma \\nProbabilidade: {round(resultado_final[0,26]*100, 2) - 2}%')\n", + "if resultado_final[0,27] > 0.75: print(f'Imagem: Axial T1 \\nNeurocitoma \\nProbabilidade: {round(resultado_final[0,27]*100, 2) - 2}%')\n", + "if resultado_final[0,28] > 0.75: print(f'Imagem: Axial T1 com contraste \\nNeurocitoma \\nProbabilidade: {round(resultado_final[0,328]*100, 2) - 2}%')\n", + "if resultado_final[0,29] > 0.75: print(f'Imagem: Axial T2 \\nNeurocitoma \\nProbabilidade: {round(resultado_final[0,29]*100, 2) - 2}%')\n", + "if resultado_final[0,30] > 0.75: print(f'Imagem: Axial T1 \\nOligodendroglioma \\nProbabilidade: {round(resultado_final[0,30]*100, 2) - 2}%')\n", + "if resultado_final[0,31] > 0.75: print(f'Imagem: Axial T1 com contraste \\nOligodendroglioma \\nProbabilidade: {round(resultado_final[0,31]*100, 2) - 2}%')\n", + "if resultado_final[0,32] > 0.75: print(f'Imagem: Axial T2 \\nOligodendroglioma \\nProbabilidade: {round(resultado_final[0,32]*100, 2) - 2}%')\n", + "if resultado_final[0,33] > 0.75: print(f'Imagem: Axial T1 \\nPapiloma \\nProbabilidade: {round(resultado_final[0,33]*100, 2) - 2}%')\n", + "if resultado_final[0,34] > 0.75: print(f'Imagem: Axial T1 com contraste \\nPapiloma \\nProbabilidade: {round(resultado_final[0,34]*100, 2) - 2}%')\n", + "if resultado_final[0,35] > 0.75: print(f'Imagem: Axial T2 \\nPapiloma \\nProbabilidade: {round(resultado_final[0,35]*100, 2) - 2}%')\n", + "if resultado_final[0,36] > 0.75: print(f'Imagem: Axial T1 \\nSchwannoma \\nProbabilidade: {round(resultado_final[0,36]*100, 2) - 2}%')\n", + "if resultado_final[0,37] > 0.75: print(f'Imagem: Axial T1 com contraste \\nSchwannoma \\nProbabilidade: {round(resultado_final[0,37]*100, 2) - 2}%')\n", + "if resultado_final[0,38] > 0.75: print(f'Imagem: Axial T2 \\nSchwannoma \\nProbabilidade: {round(resultado_final[0,38]*100, 2) - 2}%')\n", + "if resultado_final[0,39] > 0.75: print(f'Imagem: Axial T1 \\nTuberculoma \\nProbabilidade: {round(resultado_final[0,39]*100, 2) - 2}%')\n", + "if resultado_final[0,40] > 0.75: print(f'Imagem: Axial T1 com contraste \\nTuberculoma \\nProbabilidade: {round(resultado_final[0,40]*100, 2) - 2}%')\n", + "if resultado_final[0,41] > 0.75: print(f'Imagem: Axial T2 \\nTuberculoma \\nProbabilidade: {round(resultado_final[0,41]*100, 2) - 2}%')\n", + "if resultado_final[0,42] > 0.75: print(f'Imagem: Axial T1 \\nNormal \\nProbabilidade: {round(resultado_final[0,42]*100, 2) - 2}%')\n", + "if resultado_final[0,43] > 0.75: print(f'Imagem: Axial T2 \\nNormal \\nProbabilidade: {round(resultado_final[0,43]*100, 2) - 2}%')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "t-X_WbOol-UV", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 486 + }, + "outputId": "7466541b-f517-4bd0-fc60-7b19d2f2fc65" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "img_teste = load_img('/content/drive/MyDrive/normal T1_original_c54617b3d769a9fcc9ddee9260055e_big_gallery.jpeg_2d1d7ff8-e950-4509-a661-20d0990d9a23.jpeg', target_size = (384, 384))\n", + "img_plot = PIL.Image.open('/content/drive/MyDrive/normal T1_original_c54617b3d769a9fcc9ddee9260055e_big_gallery.jpeg_2d1d7ff8-e950-4509-a661-20d0990d9a23.jpeg')\n", + "\n", + "plt.figure(figsize=(8,8))\n", + "plt.imshow(img_plot)\n", + "plt.show()\n", + "\n", + "img_teste = image.img_to_array(img_teste)\n", + "img_teste = img_teste / 255\n", + "img_teste = np.expand_dims(img_teste, axis = 0)\n", + "\n", + "resultado_teste = model.predict(img_teste)\n", + "resultado_final = resultado_teste\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iC6IXzCYl-UX", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "98a60f57-620f-4448-eb78-f41ba1333dc9" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[1.9888174e-11 6.8007550e-16 2.1659801e-15 1.9522935e-18 1.5041876e-22\n", + " 1.3925407e-17 2.3618248e-18 7.8963183e-19 3.8564663e-21 2.3776142e-13\n", + " 4.6361237e-22 1.7420837e-12 5.3229266e-16 1.9422328e-21 3.9355767e-20\n", + " 3.2401528e-16 5.4918149e-22 9.0568508e-23 1.1180110e-19 1.8579936e-19\n", + " 3.1258226e-16 4.8246901e-21 1.1605345e-21 2.5447009e-23 6.1316466e-11\n", + " 1.9199257e-15 1.1372841e-15 1.0273006e-17 3.0876693e-19 5.8658689e-21\n", + " 3.2629590e-11 4.6494515e-18 7.8936295e-21 1.1515621e-18 2.5441210e-21\n", + " 3.5052897e-21 3.6141628e-12 9.9719300e-15 1.3544618e-18 2.4046602e-12\n", + " 1.4256081e-16 2.0349766e-21 1.0000000e+00 7.9887555e-12]]\n" + ] + } + ], + "source": [ + "print(resultado_final)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8k05dYQql-UZ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7c71b41f-f92b-4e2a-fb0e-c67fecfa82be" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Com base na diferença de densidade dos tecidos mapeados,\n", + "a amostra possui características compatíveis com:\n", + "Imagem: Axial T1 \n", + "Normal \n", + "Probabilidade: 98.0%\n" + ] + } + ], + "source": [ + "print(f'Com base na diferença de densidade dos tecidos mapeados,')\n", + "print(f'a amostra possui características compatíveis com:')\n", + "if resultado_final[0,0] > 0.75: print(f'Imagem: Axial T1 \\nAstrocitoma \\nProbabilidade: {round(resultado_final[0,0]*100, 2) - 2}%')\n", + "if resultado_final[0,1] > 0.75: print(f'Imagem: Axial T1 com contraste \\nAstrocitoma \\nProbabilidade: {round(resultado_final[0,1]*100, 2) - 2}%')\n", + "if resultado_final[0,2] > 0.75: print(f'Imagem: Axial T2 \\nAstrocitoma \\nProbabilidade: {round(resultado_final[0,2]*100, 2) - 2}%')\n", + "if resultado_final[0,3] > 0.75: print(f'Imagem: Axial T1 \\nCarcinoma \\nProbabilidade: {round(resultado_final[0,3]*100, 2) - 2}%')\n", + "if resultado_final[0,4] > 0.75: print(f'Imagem: Axial T1 com contraste \\nCarcinoma \\nProbabilidade: {round(resultado_final[0,4]*100, 2) - 2}%')\n", + "if resultado_final[0,5] > 0.75: print(f'Imagem: Axial T2 \\nCarcinoma \\nProbabilidade: {round(resultado_final[0,5]*100, 2) - 2}%')\n", + "if resultado_final[0,6] > 0.75: print(f'Imagem: Axial T1 \\nEpendimoma \\nProbabilidade: {round(resultado_final[0,6]*100, 2) - 2}%')\n", + "if resultado_final[0,7] > 0.75: print(f'Imagem: Axial T1 com contraste \\nEpendimoma \\nProbabilidade: {round(resultado_final[0,7]*100, 2) - 2}%')\n", + "if resultado_final[0,8] > 0.75: print(f'Imagem: Axial T2 \\nEpendimoma \\nProbabilidade: {round(resultado_final[0,8]*100, 2) - 2}%')\n", + "if resultado_final[0,9] > 0.75: print(f'Imagem: Axial T1 \\nGanglioglioma \\nProbabilidade: {round(resultado_final[0,9]*100, 2) - 2}%')\n", + "if resultado_final[0,10] > 0.75: print(f'Imagem: Axial T1 com contraste \\nGanglioglioma \\nProbabilidade: {round(resultado_final[0,10]*100, 2) - 2}%')\n", + "if resultado_final[0,11] > 0.75: print(f'Imagem: Axial T2 \\nGanglioglioma \\nProbabilidade: {round(resultado_final[0,11]*100, 2) - 2}%')\n", + "if resultado_final[0,12] > 0.75: print(f'Imagem: Axial T1 \\nGerminoma \\nProbabilidade: {round(resultado_final[0,12]*100, 2) - 2}%')\n", + "if resultado_final[0,13] > 0.75: print(f'Imagem: Axial T1 com contraste \\nGerminoma \\nProbabilidade: {round(resultado_final[0,13]*100, 2) - 2}%')\n", + "if resultado_final[0,14] > 0.75: print(f'Imagem: Axial T2 \\nGerminoma \\nProbabilidade: {round(resultado_final[0,14]*100, 2) - 2}%')\n", + "if resultado_final[0,15] > 0.75: print(f'Imagem: Axial T1 \\nGlioblastoma \\nProbabilidade: {round(resultado_final[0,15]*100, 2) - 2}%')\n", + "if resultado_final[0,16] > 0.75: print(f'Imagem: Axial T1 com contraste \\nGlioblastoma \\nProbabilidade: {round(resultado_final[0,16]*100, 2) - 2}%')\n", + "if resultado_final[0,17] > 0.75: print(f'Imagem: Axial T2 \\nGlioblastoma \\nProbabilidade: {round(resultado_final[0,17]*100, 2) - 2}%')\n", + "if resultado_final[0,18] > 0.75: print(f'Imagem: Axial T1 \\nGranuloma \\nProbabilidade: {round(resultado_final[0,18]*100, 2) - 2}%')\n", + "if resultado_final[0,19] > 0.75: print(f'Imagem: Axial T1 com contraste \\nGranuloma \\nProbabilidade: {round(resultado_final[0,19]*100, 2) - 2}%')\n", + "if resultado_final[0,20] > 0.75: print(f'Imagem: Axial T2 \\nGranuloma \\nProbabilidade: {round(resultado_final[0,20]*100, 2) - 2}%')\n", + "if resultado_final[0,21] > 0.75: print(f'Imagem: Axial T1 \\nMeduloblastoma \\nProbabilidade: {round(resultado_final[0,21]*100, 2) - 2}%')\n", + "if resultado_final[0,22] > 0.75: print(f'Imagem: Axial T1 com contraste \\nMeduloblastoma \\nProbabilidade: {round(resultado_final[0,22]*100, 2) - 2}%')\n", + "if resultado_final[0,23] > 0.75: print(f'Imagem: Axial T2 \\nMeduloblastoma \\nProbabilidade: {round(resultado_final[0,23]*100, 2) - 2}%')\n", + "if resultado_final[0,24] > 0.75: print(f'Imagem: Axial T1 \\nMeningioma \\nProbabilidade: {round(resultado_final[0,24]*100, 2) - 2}%')\n", + "if resultado_final[0,25] > 0.75: print(f'Imagem: Axial T1 com contraste \\nMeningioma \\nProbabilidade: {round(resultado_final[0,25]*100, 2) - 2}%')\n", + "if resultado_final[0,26] > 0.75: print(f'Imagem: Axial T2 \\nMeningioma \\nProbabilidade: {round(resultado_final[0,26]*100, 2) - 2}%')\n", + "if resultado_final[0,27] > 0.75: print(f'Imagem: Axial T1 \\nNeurocitoma \\nProbabilidade: {round(resultado_final[0,27]*100, 2) - 2}%')\n", + "if resultado_final[0,28] > 0.75: print(f'Imagem: Axial T1 com contraste \\nNeurocitoma \\nProbabilidade: {round(resultado_final[0,328]*100, 2) - 2}%')\n", + "if resultado_final[0,29] > 0.75: print(f'Imagem: Axial T2 \\nNeurocitoma \\nProbabilidade: {round(resultado_final[0,29]*100, 2) - 2}%')\n", + "if resultado_final[0,30] > 0.75: print(f'Imagem: Axial T1 \\nOligodendroglioma \\nProbabilidade: {round(resultado_final[0,30]*100, 2) - 2}%')\n", + "if resultado_final[0,31] > 0.75: print(f'Imagem: Axial T1 com contraste \\nOligodendroglioma \\nProbabilidade: {round(resultado_final[0,31]*100, 2) - 2}%')\n", + "if resultado_final[0,32] > 0.75: print(f'Imagem: Axial T2 \\nOligodendroglioma \\nProbabilidade: {round(resultado_final[0,32]*100, 2) - 2}%')\n", + "if resultado_final[0,33] > 0.75: print(f'Imagem: Axial T1 \\nPapiloma \\nProbabilidade: {round(resultado_final[0,33]*100, 2) - 2}%')\n", + "if resultado_final[0,34] > 0.75: print(f'Imagem: Axial T1 com contraste \\nPapiloma \\nProbabilidade: {round(resultado_final[0,34]*100, 2) - 2}%')\n", + "if resultado_final[0,35] > 0.75: print(f'Imagem: Axial T2 \\nPapiloma \\nProbabilidade: {round(resultado_final[0,35]*100, 2) - 2}%')\n", + "if resultado_final[0,36] > 0.75: print(f'Imagem: Axial T1 \\nSchwannoma \\nProbabilidade: {round(resultado_final[0,36]*100, 2) - 2}%')\n", + "if resultado_final[0,37] > 0.75: print(f'Imagem: Axial T1 com contraste \\nSchwannoma \\nProbabilidade: {round(resultado_final[0,37]*100, 2) - 2}%')\n", + "if resultado_final[0,38] > 0.75: print(f'Imagem: Axial T2 \\nSchwannoma \\nProbabilidade: {round(resultado_final[0,38]*100, 2) - 2}%')\n", + "if resultado_final[0,39] > 0.75: print(f'Imagem: Axial T1 \\nTuberculoma \\nProbabilidade: {round(resultado_final[0,39]*100, 2) - 2}%')\n", + "if resultado_final[0,40] > 0.75: print(f'Imagem: Axial T1 com contraste \\nTuberculoma \\nProbabilidade: {round(resultado_final[0,40]*100, 2) - 2}%')\n", + "if resultado_final[0,41] > 0.75: print(f'Imagem: Axial T2 \\nTuberculoma \\nProbabilidade: {round(resultado_final[0,41]*100, 2) - 2}%')\n", + "if resultado_final[0,42] > 0.75: print(f'Imagem: Axial T1 \\nNormal \\nProbabilidade: {round(resultado_final[0,42]*100, 2) - 2}%')\n", + "if resultado_final[0,43] > 0.75: print(f'Imagem: Axial T2 \\nNormal \\nProbabilidade: {round(resultado_final[0,43]*100, 2) - 2}%')\n" + ] + } + ] +} \ No newline at end of file