{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('../input/melanoma-skin-cancer-dataset-of-10000-images/melanoma_cancer_dataset/test'):\n    for filename in filenames:\n        #print(os.path.join(dirname, filename))\n        x=1+3\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-11-21T05:03:24.505032Z","iopub.execute_input":"2022-11-21T05:03:24.505506Z","iopub.status.idle":"2022-11-21T05:03:24.942347Z","shell.execute_reply.started":"2022-11-21T05:03:24.505419Z","shell.execute_reply":"2022-11-21T05:03:24.941241Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\"\"\"\n!apt install p7zip\n!apt install p7zip-full\n!pip install pyunpack\n!pip install patool\n\n!7za e /kaggle/input/cifar-10/test.7z\n!p7zip -d /kaggle/input/cifar-10/test.7z\n\"\"\"","metadata":{"execution":{"iopub.status.busy":"2022-11-18T06:13:48.192813Z","iopub.execute_input":"2022-11-18T06:13:48.193149Z","iopub.status.idle":"2022-11-18T06:13:48.202308Z","shell.execute_reply.started":"2022-11-18T06:13:48.193119Z","shell.execute_reply":"2022-11-18T06:13:48.201013Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import tensorflow as tf\nimport tensorflow.keras as keras\nfrom tensorflow.keras.datasets import cifar10\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cv2","metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","execution":{"iopub.status.busy":"2022-11-21T05:03:35.149276Z","iopub.execute_input":"2022-11-21T05:03:35.149652Z","iopub.status.idle":"2022-11-21T05:03:46.21149Z","shell.execute_reply.started":"2022-11-21T05:03:35.149621Z","shell.execute_reply":"2022-11-21T05:03:46.210315Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def show_results(results):\n    plt.gcf().set_size_inches(20,5)\n    plt.subplot(121)\n    plt.plot(results.history[\"acc\"],label=\"Train\")\n    plt.plot(results.history[\"val_acc\"],label=\"Test\")\n    plt.title(\"Accuracy\")\n    plt.legend()\n    plt.subplot(122)\n    plt.plot(results.history[\"loss\"],label=\"Train\")\n    plt.plot(results.history[\"val_loss\"],label=\"Test\")\n    plt.title(\"Loss\")\n    plt.legend()\n    plt.show()\n    \ndef plot_model(history): \n    fig, axs = plt.subplots(1,2,figsize=(16,5)) \n    # summarize history for accuracy\n    axs[0].plot(history.history['accuracy'], 'c') \n    axs[0].plot(history.history['val_accuracy'],'m') \n    axs[0].set_title('Model Accuracy')\n    axs[0].set_ylabel('Accuracy') \n    axs[0].set_xlabel('Epoch')\n    axs[0].legend(['train', 'validate'], loc='upper left')\n    # summarize history for loss\n    axs[1].plot(history.history['loss'], 'c') \n    axs[1].plot(history.history['val_loss'], 'm') \n    axs[1].set_title('Model Loss')\n    axs[1].set_ylabel('Loss') \n    axs[1].set_xlabel('Epoch')\n    axs[1].legend(['train', 'validate'], loc='upper right')\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2022-11-21T05:03:49.031108Z","iopub.execute_input":"2022-11-21T05:03:49.032003Z","iopub.status.idle":"2022-11-21T05:03:49.055926Z","shell.execute_reply.started":"2022-11-21T05:03:49.031956Z","shell.execute_reply":"2022-11-21T05:03:49.05502Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import glob\nimport numpy as np\nimport tensorflow as tf\nbenign= glob.glob('../input/melanoma-skin-cancer-dataset-of-10000-images/melanoma_cancer_dataset/train/benign/*.*')\nmalignant = glob.glob('../input/melanoma-skin-cancer-dataset-of-10000-images/melanoma_cancer_dataset/train/malignant/*.*')\n\n","metadata":{"execution":{"iopub.status.busy":"2022-11-21T05:03:57.380944Z","iopub.execute_input":"2022-11-21T05:03:57.381336Z","iopub.status.idle":"2022-11-21T05:03:58.218507Z","shell.execute_reply.started":"2022-11-21T05:03:57.3813Z","shell.execute_reply":"2022-11-21T05:03:58.217425Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = []\nlabels = []\n\nfor i in benign:   \n    image=tf.keras.preprocessing.image.load_img(i, color_mode='rgb', \n    target_size= (280,280))\n    image=np.array(image)\n    data.append(image)\n    labels.append(0)\nfor i in malignant:   \n    image=tf.keras.preprocessing.image.load_img(i, color_mode='rgb', \n    target_size= (280,280))\n    image=np.array(image)\n    data.append(image)\n    labels.append(1)\n\n\ndata = np.array(data)\nlabels = np.array(labels)","metadata":{"execution":{"iopub.status.busy":"2022-11-21T05:04:01.340371Z","iopub.execute_input":"2022-11-21T05:04:01.340758Z","iopub.status.idle":"2022-11-21T05:05:48.240502Z","shell.execute_reply.started":"2022-11-21T05:04:01.340725Z","shell.execute_reply":"2022-11-21T05:05:48.239422Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nx_train, y_train, x_test, y_test = train_test_split(data, labels, test_size=0.2,\n                                                random_state=42)\n#print(y_train)","metadata":{"execution":{"iopub.status.busy":"2022-11-21T05:06:18.538119Z","iopub.execute_input":"2022-11-21T05:06:18.539399Z","iopub.status.idle":"2022-11-21T05:06:19.747678Z","shell.execute_reply.started":"2022-11-21T05:06:18.539356Z","shell.execute_reply":"2022-11-21T05:06:19.746516Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n\n#print(X_train, X_test, ytrain, ytest)\ny_labels = ['benign','malignant']\nnum_classes = len(set(y_train.flatten()))\nprint(\"number of classes:\", num_classes,\"\\n=>\",y_labels)\nprint(\"Train\\t\", x_train.shape, y_train.shape, \"\\nTest\\t\", x_test.shape, y_test.shape)\n\n","metadata":{"execution":{"iopub.status.busy":"2022-11-21T05:06:24.190933Z","iopub.execute_input":"2022-11-21T05:06:24.191499Z","iopub.status.idle":"2022-11-21T05:06:49.423239Z","shell.execute_reply.started":"2022-11-21T05:06:24.19145Z","shell.execute_reply":"2022-11-21T05:06:49.422136Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train_onehot = keras.utils.to_categorical(y_train)\ny_test_onehot = keras.utils.to_categorical(y_test)\n","metadata":{"execution":{"iopub.status.busy":"2022-11-21T05:07:03.125161Z","iopub.execute_input":"2022-11-21T05:07:03.125525Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x_train_normalized = x_train.astype('float32')/255\nx_test_normalized = x_test.astype('float32')/255","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_count = 8\nfig_size = 5\nfig, ax = plt.subplots(sample_count,sample_count)\nfig.set_size_inches((fig_size*sample_count,fig_size*sample_count))\nfor i in range(sample_count):\n    for j in range(sample_count):\n        index = i * sample_count + j\n        ax[i][j].imshow(cv2.resize(x_train[index],(256,256)))\n        ax[i][j].set_title(f\"Sample {index}: {y_labels[int(y_train[index])]}\") \nprint(\"CIFAR-10 Dataset Samples (resized for human-friendly visualisations)\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### We use input size (32,32,2), same as the researchers experimented (the sobel filtered images has been used)\n![canvas.png](attachment:canvas.png) 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"}}},{"cell_type":"code","source":"def rgb_to_sobel(img):\n    sample_image = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)\n    sobelx = cv2.Sobel(sample_image,cv2.CV_64F,1,0,ksize=5)\n    sobely = cv2.Sobel(sample_image,cv2.CV_64F,0,1,ksize=5)\n    return cv2.merge([sobelx, sobely])\ndef rgb_to_laplacian(img):\n    sample_image = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)\n    return cv2.Laplacian(sample_image,cv2.CV_64F)\n\nx_train_sobel = np.array([rgb_to_sobel(image) for image in x_train])\nx_train_sobel_normalized = np.array([cv2.normalize(image, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F) for image in x_train_sobel])\nx_test_sobel = np.array([rgb_to_sobel(image) for image in x_test])\nx_test_sobel_normalized = np.array([cv2.normalize(image, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F) for image in x_test_sobel])\nx_train_sobel_normalized.shape","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Experimenting with sobel filters","metadata":{}},{"cell_type":"code","source":"sample_count = 8\nfig,ax=plt.subplots(sample_count,10)\nfig.set_size_inches(5*10,5*sample_count)\n\nfor idx in range(sample_count):\n    sample_image = x_train[idx]\n    laplacian = rgb_to_laplacian(sample_image)\n    sobelx, sobely = cv2.split(rgb_to_sobel(sample_image))\n    ax[idx,0].imshow(sample_image)\n    ax[idx,1].imshow(cv2.cvtColor(sample_image,cv2.COLOR_RGB2GRAY), cmap=plt.cm.gray)\n    ax[idx,2].imshow(laplacian, cmap=plt.cm.gray)\n    ax[idx,3].imshow(sobelx, cmap=plt.cm.gray)\n    ax[idx,4].imshow(sobely, cmap=plt.cm.gray)\n    ax[idx,5].imshow((sobelx+sobely), cmap=plt.cm.gray)\n    ax[idx,6].imshow((sobelx*sobely), cmap=plt.cm.gray)\n    ax[idx,7].imshow(sobelx*laplacian, cmap=plt.cm.gray)\n    ax[idx,8].imshow(sobely*laplacian, cmap=plt.cm.gray)\n    ax[idx,9].imshow((sobelx+sobely)*laplacian, cmap=plt.cm.gray)\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## In the paper researchers use three different model.\n![canvas.png](attachment:canvas.png)\n### I will be using the model B (Basically simplified VGGNet)","metadata":{},"attachments":{"canvas.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAzAAAAHDCAIAAAB082jSAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAOapSURBVHhe7L17rFXFtuevvz86nZuLCJx0OlwPQcC0RAzIQ044YMQERDBGIh5AjU0HIgIGry9QkJgbHgKi58YosImkuUZ5iYEY2bwSSYRDBNGwI0Y7yg7xeE2nwwYfNzed/uf+Pnt9a9cpaj7WXK+9NjA+CZOqmjWrRo0aNWrMteZc+/r/+I//uM4wDMMwDMNoHv+f+98wDMMwDMNoEhaQGYZhGIZhNBkLyAzDMAzDMJqMBWSGYRiGYRhNxgIywzAMwzCMJmMBmWEYhmEYRpOxgMwwDMMwDKPJWEBmGIZhGIbRZOyHYatk27Zt//Iv/+IyhmEYhnFt8+c//3nEiBEuY1SOBWRV8s///M/PPPOMyxiGYRjGtc3Ro0fvvvtulzEqxwKyKtm2bZsFZIZhGIYh9u7dawFZLVhAZhiGYRiG0WTsoX7DMAzDMIwmYwGZYRiGYRhGk7GAzDAMwzAMo8lYQGYYhmEYhtFkLCAzDMMwDMNoMhaQGYZhGIZhNBkLyAzDMAzDMJqMBWSGYRiGYRhNxgIywzAMwzCMJmMBmWEYhmEYRpOxgMwwDMMwDKPJ2N+yNHo07e3tW7duXb16tcsbRgNYv369SyUYNmzYP/zDPwwfPtzlDeO669ra2nbv3v3FF1+4/HXX3XPPPePGjRs/fvzOnTtvuOGGqVOnuhOGURgLyIweDTvl0qVLz507N2jQIFdkGA2gtbV18eLFWBrpmTNnjhw5ksT58+cPHz5M4eDBg1etWjVr1qxSXePahVAMj3To0CFM4oUXXrjtttso/PHHH7dt20ah6qxbt27JkiVKG0YFEJAZRo+lT58+WOmCBQtc3jAaBvuovOKxY8dcUYnNmzerfNmyZa7IuCbZsWOHLAFTcUUBmI38VepZwyiLPUNm9Fx27tx56dIlJTo6OlRoGN3M/PnzuSUgsWbNmvb2dhUa1xqtra2zZ88mkfUB2Pjx4z/66COXMYzKsYDM6Lls27ZNH1oQlm3dulWFhtH93H///UocOXJECeOaghvCxx57jMTgwYNzvo4kJlPsbhhVYAGZ0UNpa2s7derU3Llz7733XrJbtmxRuWF0PzfccIMSv/zyixLGNQU3hPq0/oUXXlBJFvPnz3cpw6gQC8iMHkpLSwv3mv369ZszZw7Zc+fO7dy5U6cMo5s5cOCAEro9MK41PvjgAyX+8Ic/KJHF8OHDZ8yY4TKGUQkWkBk9kY6Ojk2bNs2dO5f0rFmzBg8eTGLbtm2lk4bRrRw/fhxrJMEdgv3+xbXJ6dOnlShiAPZKuFEdFpAZPZGtW7fee++93q898cQTHA8dOsTWqBLDaBzbt29fX2L58uVjxoyZMGHC5MmTd+zYsXHjRlfDuJYwt2N0DxaQGT2RLVu2LF682GWuu04flQE7pRKG0Q188cUXenLo9OnTX331lb3qe40zevRolzKMBmABmdHjaG1t5Rj+1HW/fv307tKmTZvsdweMRvPII48sKXHw4MHvv//+2LFjFy9eXLNmzS233NLW1uYqGdce/otLw2gEFpAZPY4333yT/W/K5Rw+fFhn9+zZo4RhdA/+96UuXbr00EMPqdC4dsAAXKr0eKtLGUa9sYDM6Fm0t7cfOnToxRdfvOdynnjiCT3av3btWvOJRjfDlqyvq86dO2dPFF2D+C8rT548qYRh1B0LyIyexYYNGxYsWKAvjCL0C0CXLl2yH+c0up9+/fop8euvvyphXDs8/PDDSnz88cdKGEbdsYDM6EF0dHTs3LnT/yp6hP91n5dfflkJw+g2/Oey//AP/6CEce0wd+5c/Z1KHFTZx1jtI3yjOiwgM3oQW7du7du3b/g4f4h/tN++NjK6GexND3QPHjzYforsGgTn895775G4dOnSzJkzc0IuIrZFixa5jGFUggVkRk8BH7d27dohQ4a4fBr+w7NVq1YpYRj1wv9ZpOhLSbbYBx54gESfPn0+/PBDFRrXGtwo7tixgwSh+ZQpU5L3hO3t7bNmzfr000/ffvttV2QYlXD9f/zHf7ikYTQPvNszzzyDp2PP27hx45133hn92jXh2jfffPPWW2/t2rVLJdynvvTSS/ZxhVE7mN/XX3+NOelXx8D/iaRDhw5xxCzZa59//nn7EfZrnLa2tqVLl8oqBg8ePHny5IEDB54/f/7zzz/HeLhRxE5U0zAqxQIyo0eAm/vtt99c5rrr+vfvnxqQuUwXvXr1soDMqJ38b8CT1mhc47S3t586deqHH35w+VIEb77IqBELyAzDMAzDMJqMPUNmGIZhGIbRZCwgMwzDMAzDaDIWkBmGYRiGYTQZC8gMwzAMwzCajAVkhmEYhmEYTcYCMsMwDMMwjCZjAZlhGIZhGEaTsYDMMAzDMAyjyVhAZhiGYRiG0WQsIDMMwzAMw2gyFpAZhmEYhmE0GQvIDMMwDMMwmowFZIZhGIZhGE3m+v/4j/9wSaMS/vmf//lf/uVfXMYwDMMwrm3+5//8nyNGjHAZo3IsIKuSbdu2/dM//ZPLGIZhGMa1DQHZ3Xff7TJG5VhAZhiGYRiG0WTsGTLDMAzDMIwmYwGZYRiGYRhGk7GAzDAMwzAMo8lYQGYYhmEYhtFkLCAzDMMwDMNoMhaQGYZhGIZhNBkLyAzDMAzDMJqMBWSGYRiGYRhNxgIywzA6mT59+r59+1zGMBL8j//xP7Zt2+YyhmHUGwvIDMPo5MyZM+fPn3cZw0iAhbS1tbmMYRj1xgIywzAMwzCMJmN/y/JqZufOnV999dXq1atdvmF0dHQcOXJk3759P//8s0oGDRp011133Xnnnb17916xYsXGjRtV3nPgXv/QoUOffPKJy1933ahRo/70pz8NHz7c5QvQ3t7OwPfu3XvjjTeOHDlyyZIl7kQCVPToo4/OmTNn1qxZrqiHcfPNNz/99NP/+I//6PKVwxi3bt06YMCAZo2xtbX1448/ZlKUZVImTpz4hz/8gTldvnz53LlzMUud6iFo4Xz66aehzA8++OCkSZP69eunkrL41Ud68ODB+cNcuHBhnz59qvMJd9xxx9133/3nP//Z5SvHLKRS6mIhoIFfvHixb9++jzzyyPjx492JBLVYiFErBGTG1cf+/ftxzczvvffe64oaxrp161jA9LVgwQL6PVZi2bJlKhSuas/gzJkzqAXxkJy0Ci9cuLBjxw7KR48ejfwqzIH6jJehcTx37pwrzYaWqUyPLt/zGDhwIHuty1SON4OmjDE0eOZRRrh582YViiLT2p1IYwiM8JiTCjFI1g5ic/SFOfhGioyOyuiByi5fISNGjCBed5nKMQuplLpYiAYOjNoVZVOjhRg1YgHZ1QbBAQvPO5qGLi3cAeELvXD0kY2HswpZwBX1AFAOPm7mzJlZvgz/RYV858VgqQNUdkW5MB3SQ1O2ooJUHZChDQbYxDHKzLKmwwvWc7ZbbI+FmWM/VMBEWVZZVgqc0uorqHCmSXqo2idUHZCZhVRKXSwENPAcdxdSu4UYNWIB2VVLN9zraD/Aa+SsdgSgjss0G8IshME9uXwGireyXKHO0g4JV5QL1VBCRXtnU6jxEzLQXHfzGJctW0ankLObai30kO2WxSJjKGs/7KbUdJnL8Y0QTLiiXKjP7GD5XFK1T6jxEzIwCylCXSwEfDTm8rnUxUKMGrGH+q9p2tvbi7w2RZ2Ojg6X6WL9+vWnT58msXHjxpynGXrO02MMVo9HvP32266oBEM7XsLlr7tu+PDh77333mOPPZYcNY1MnDjx0qVLePCCT5vNmzePyhU98HEVgy35p2FyCKcjB6qtWbOGBHtPzmMxS5Ys0QfGPYE33niDhZO0HzTDcELlaO2w0JQNmTJlCo2wcc6fP98V5UKn06dPHzlypMv3YMxC6mIhy5cv37RpU9LdZXEFWcjVjAvMjKsO3fPl3+vos/r8m2zdZkVf4XE7pU+JOLqibAreojWa1LtzfYNJOTeayKkbU0bHKeonNaNG8N0uXw7uztVjau89iu75hAwlo/CcjyL08QB1inwAqR6hbGWmsid8/oEMSMvoZGNCH05Qjl1xSisXy+EU9SlRNY8qQJGHF2H//v20T0IXKl0F3fMJmVkI0tZoIWoE8h+98NTLQowasYDsqqXg0lK8lRWTZZ1lAVMORYKtaNvAudAsggH+BRA19D5A+9SRo8GTUlPdUaiaFPpGkCH8ehFpVR46I2RQC6Ew+gYT565C2iHLhf6sTwvv5qQQrqIkGl0IZ30LJLgwfytqLt0TkDF92k1RjisKyD8bQeXSbBS6K6AyuEwJ5ERgUI+YUzSV2Bh7HvaJMFxLBXXHJdrdKaSCGoFwmWCQmBOF2jU9aoSjy3cNmUJdTsulTv729ZkEUBqoj7RU0NIjy1nJkwoVaIEj6YI+IYvuCcikkJ5gIQpTBI2g8KjTnmkhQLNU8KPmbFQhhPbrZSFGjVhAdtVSfGnJBYTOQmSVgxqHfN+aBG+CmwjdDe3TDh7Hb4dEQoqNgDSn6AW3xYWUeP9FfdVJysCowy7A9+LyJTdEg5TIE4EG5VvDhUV+XAoBWpPLE7izMCIUcqN+UKpfqbq6k257hkyaQbfRJpFVngXVSuqveP9QR+CnXnMddk0CS1P7TDeVyTI05poSsqoGKknKoMou0wVdUDm0FqpREt48kAWXKWk1XIO6iwDk8QYJtJyqeZaS7059VaouT7c9Q9Z0CwF0S0c+0mUh0zVN+bnosRbiHSPlXKsGBYPyNu+po4UYNWIB2VVLRUtLnj1c1cmSEJrlLNCLKyoAvgAHlPRBCr9CF+bvAhHDFXVtRfgXl+8SMtWp+UhIJLWhktCzRyUkopblkYGaah85fewYxWSUhz5UGqtIXd1Mdz7Un9xZK91rQfMFBY3coymLLARTpzCyHLIUIpjfxvxu57dqHyFFDUYGIFQzHDVdRPJTISwhHbbjgwDalwz061XBWVUTDCpcQapWqbo83flQf3MtRMYQTZ93SuFK74EWgnhqBKkkKo1QwYuqaqK+FmLUiAVkVy2VLq0wAgvTqdAsFSCKQvKRm4v2DPAuLHQrKgmdL2kVunxwYVgNkfB0LtOF9uCwa4ItPJTLlMBVUcdlSt1F2it1dVmMKKSNMHpjIJEMqlNkK2oW3fyWZbi/VrHXgt9ukxaVg7bVaFsSzCCnwvlNHVFnl5ebnC4Mq2kfdZku/I7OWZVgJ2TDhabdNGwKGcK+ksYmvDb8rk93DNP3BapDCy5fId38lmWzLAToiKtC1Qk5xlD5PdBC/Kij+I86Kvdutu4WYtSIvWVpODZu3Ii7efLJJ8eMGbNp0yZcQJEXuM6ePetSBdi7dy/H3r17K+sZNGgQfoFEpX/cmgsVaW3fvl0l8O677z7++OMu04XeHvriiy+U7ejowFvdeeedykJ7e7teW3P56647cODA9OnTXSaAwMWluli8eDFHGtSbXzT18ssvF3y56ZqlX79+Bw8eZKN64IEHxo4di/Y++uijnPfgcvDTWoRDhw5xTH3pVba0c+dOZYvzwgsvcNyyZYuysGfPHm3eIf6luW+++UaJr776iuNtt92mLHz88cccx40bpyyGeurUqaRahgwZ4lJdzJ07Vwm6VmLevHlE2Ffu673NshBW8aVLl0gkVXf//fdzRJK2Cv+mZ/dbCER/dYA6crP+L8Rf6RZy9WEBmfE3iMlwf8Ql7Ez50dg999yjxC+//KJEEbQXpiKn4P/yUnEUexFB6icqFFdNnTq1dPJvKAr0P2Px448/KuHRNubHRc1du3ZNmjRJ2Xx8dydOnOC4cOFCXJ65ubKgIpTM5scO9+qrr1a614Z7khJFCP9YVoSMRJtxRcyYMYMjo2htbVXJa6+95iOkEH308uuvvyrLJUoIBqJw0Kti69atyW07FZSp2wkNcP369Q8//HB14UvPoSkWolWcyg033KDEb7/9pkRBeoKFACbBUX746rCQqwwLyIy/QSTB+set4wRbWlpcaRrDhg1T4vDhw0rUiI+EKoVgSF8HHDlyRMcnnniidOYyFFoRq+lXfLgTxfHhmHSnyz2xbl4HDBjAERYtWrRq1aoq/rAdTdHshAkTrr8cOcGlS5cqq8/SrnHYXQj9mQhu3F966aVKdTJ06FAlmNaKdtws/P5dKcQN2hT16QVGNWTIkFTj0d9wVDWYOHEix7feeosjQ1ixYgUJ2TPQzgcffPDss88qWxFYmjc2DyWcwhSVTf39qh5FT7OQqsMXsxCjCBaQGQ6iMX1TefDgQXzHk08+mROTjR07VndyPsQpgj4wz/mI4sYbb3SpStDXAa+//jpH7jt1MxqB79PnB/4LnRdffJEj/m7KlCncE588eZIR0Qg+aMyYMQ8++GDy7x+rhfwPBXv16kW1JFIXnlRZqqn+NQu7C5rnBuCjjz7y30xVtOP6z4RA4XgRRo0axVHxcSqaqUrRJ8r6pJaFM2fOHJVHPPLIIxx37typ+ABbpTvufzA57gFoBLNEJ8tLzJs3D80wzNKlDt26lA0vZGYR2sjpUVl/+9EzaZaF+LvNnL6qWL/dZiEFPxeUDURcWRZyFeKeJTOuOip6PFN3b+Gjo8mSCP/oaNkHZv1Do9TUJcqGICfllT7UL2hf5UjL/bQrTYAXw9HAma63n0js37+fcmVphyyET7mG0D69EFa6fIAECKWN0ADDR3F7GlfcW5bgTSJ1UiI0rcyvLkl2JJPG8l2+8CPbQvcbVGZXc0VpaBX4XmR1YWuYJQvBW2kE5aX+Ux45L6t/DZBqLl8hV9xblkDlTmVVYiEcdUlSTrUWzm8PtBBAS7TAVS7fRVkDKFvBaCgWkF21FF9aWbFX2ZhM/gXCQCpCblS+g9Anqz4eJHJSqhm6Ie9bXT5AokLSB4V4f5rjy3JgLPJ0PoYTGle+ky2+FTWLK+53yISP8v0GlgpnvSUzU6n19VB/OLmpI+rsLG27xap1quwtipotWy0LXZ5cRBpXZJwhNW63V9zvkIkqLIQ09ZMrWrdkoeZ7poVoopPj1biSluOp0UKMGrGA7KpFa6/s0lK1rKgr/6z8IxWAlUzWneiClY9TCx2TPBqFYWUKIyfrPwYIC0mrMLnlqH7SgSahEfqCLK9EhZwNQ/JHn8PJi4WiJim4FTWR2gMyBQT5Y8zfU6vbcWWlwLwkbYOmaDO0YW9dYVyuwsjUkYTCaESlS9N3NdXPCYkEw5TMqQIDFZAkSwne2sNFRGUK85UvQ616u609ILtSLIReJGqyMFr7PdNCvPyhhVNI7/mzX6OFGDViAdlVCKsUX6CVD6QpYTW60wEsaSqETieJ/ELOJ0+sYd8XK5kskKCQa5PeRLLhASUYd4H4jmhr9A6UhJwOhbTpC8P6ggbp12VyQRV0igySUAIDaSShl/yP2RAbGXCUCAZcSDsUutMZSHgqu3zPo+qADH1KD6XJ6QwUom9YQtAb6so6C7SmHbfsphVCj9qBgMuZX+ShL01osjvsh1P0ol2NI9lwEuldq0MNUgfBKKTlsNDV7oKzdOcy5eByGlFTSOuhBQQjkaMB5KcOF0rVWlP07k5nQJt0V1zCiKoDsivRQuiFcipTUxrWVe50j7cQryUkpx2GT5YLKXc10qBNuisuoVFfrudfyXiMq4fUZ1GHDh0aPfsJbW1t33zzTfLp9YiWlpZJkyblv3JIp+Hr4gMGDOCSZI+io6PjyJEjP/zwA+lhw4ZFv1KBVNFb5ePHj48Ke/Xq5X+zRyBA6hhzaG1tDX9HLV/mkPb2duTX0/0Fr5L8/fv3r+LNze7h5ptvfvrpp9lxXb4wzKb/2aSQ1FfS0PkNN9yQ/7YaDW7dunXJkiUuXxiU/Nlnn/m3Lnr37p1vt94AkpPIFP/0008uUwLrouWoMBoIVzHLkWXmE9qSYDss0kK4iMqOVGhQybVTkDvuuOPuu+8manf5wly5FkJ9vf+RrNnzLQRCF1fkqhotxKgRC8gMw+ik6oDMuEaoOiAzDKMI9rMXhmEYhmEYTcYCMsMwDMMwjCZjAZlhGIZhGEaTsYDMMAzDMAyjyVhAZhiGYRiG0WQsIDMMwzAMw2gyFpAZhmEYhmE0GQvIDMPo5Pbbb/+7v/s7lzGMBP/tv/23in542TCMirCAzDCMTr766qt///d/dxnDSPC//tf/6ujocBnDMOqNBWSGYRiGYRhNxv50UncT/gW64n88sTjJv7Dm6Ya/UNbW1rZ06dLp06fPnz/fFRUg9Y9virJ/npJrDxw48Kc//alBQ9Mfv2O+Vq9e7YoqIfrbedXNuP6YHU316dNn7ty5OX96b+fOnRzL/nHSVBrxp5M0s/l/l7BeRFZU5M+GRoul7gskZzF2j06gjguk0j+dhK/bs2dPRa6gvkR/rHbcuHFSO0uJYw/8c41o7OTJk6HMxf9spcDkTp06leMBGHvWX+cMCfepgn8p1agDnX9h3Ogu9u/fz57qVF9i8ODBZ86ccafrwblz53bs2MEydh2UlrRQdvTo0VRwtevNsmXL1IvLF+PYsWNc6DWDTiQwCbKUL1iwgHG52l2sW7dOFYAWXGn9oE36VfsI40oLg5IRD5CTpgTDpIRmC046l9A1GqCRCxcuuNIMsK7qRBUDBw5kr3WZmpHkyIPkrqjB0CN9eSsqogdJKJia+q5EwGhDkUjQo++UlVhkWquGxjE29YVyXGkNjBgxgnjdZXJhUH7grqgbCXufOXMmaWDRqQT9k6DE1e4ZYCryNhxxHcwXkEB+JnHz5s2uXja+BQboii4HtXjb82D27nSA9ilUJDFIZNU06osFZN0H7h4rZ43JBZPwDkIV6ghdlJZb3LjvlM1AYtQXreTqli5XSWZcgCsqeRlcksoR3pUG6FR4SX3J93GpoFhkRg9ZbpRyWYLLZyCF0HWRmaJOjeZUr4CMKWOAfta6eeeja/UL+VbBWVevtAu60gbgRQpVwUpRtMSs1T0QDFHXdVkgBQMyhobp+ljQlXYXKFNdsxAwRVdagjWi2AK62SzzkdtMCiwo5FSOx6YcZ4IN53sAWkAzDByYIFVOqgIFJgtlw2VdllEjFpB1H2xR0XpjHZZWxHVFdtxKUcvJxen3oWjJNR3kkWDJzYNRUI4HSSoq65J6IamyfFwSJMTxld1lfXTu8gkUCBYPFLAu+q1I1Ih6BWR+jiRPN5sZluB3mnztcdYHDQ0VMmvFoSiJmmrY9UJd12WBFAzINBbv3FTYPWhZ0SnLwRUlUGzRzWaZA04AecqudCpkxWS+0N/CKRuieySXKcFV3v7DZumIkshgqEBh1b7FKIg91N9NtLe3P/7449HX8FOnTnWpbmT8+PFah1u2bFFJz2fOnDkcL126dOTIEZX0WBYtWnT69OlXX301/8kPzn700UcLFy7EMFxRAOWbNm1imlauXOmKcmlpaaHyww8/7PJNxT8h16yfSLjzzjtl4egwVb1AOWdfeOEFl28G6Eeb3xVh2MXRvN9www3Kdifz5s1DmcRkb7/9titKMH/+fN3g9QTa2tpmz55dZKVv3LgRxb7xxhsuH+AXWu/evZVIsnfv3kgnXPXmm28q/c033ygBqUvGd2E0FAvIuglCsazwi9WYau5TpkwZM2ZM1o4ili9ffv311+sZ1eIMGTKE47lz55Tt+dx0001K6CHTHktra+uuXbuY0PBB5o6ODgIspglmzZq1c+dOZvb48eNExmQ3bNjg6nVBI8QKJLZt21bEDzL777zzzrPPPuvyVxQNMvJVq1YpkVSv2LNnD7vybbfd5vJNwu+gPdywG8T69ev79u0bvY0RwXqhDrccLp8NNbkXIkGYm79wFi9e7FLNhgiSI+YaCowHwEVj81IOawQHQjkx2Zo1a/IXSyq4oOnTpyd1kvOJwFtvveVSJTRH9lx/o7GArJngQbiZ+/DDD13+ctatW0fMNHPmzKwVyCplfW7evLnS14VOnTrFUd8oReALaJYtUF4guQsijCpwxJ/qtb4QteAyATgFvCrNguISd6IAX3/9tRLDhg1Toizqjo7UIwJnqRFfg8Blqwk1y8BFNIpXXnmFY/i5C/VplgCL2bxw4QIelhviQ4cODR06lLPEbUk9aLdgdgq+iIdPJyArErr1QBpk5My7/5CMKVChh5K1a9fm78rII/uRbUQBAUbuLKAEWdp0mRKuXmHCzzbomhZK9tgJXSeHIDAeb7ok8iObHsjcuXOZpgceeCBLcgbIepk8eXKRVzW5gVHivvvuUyILApFx48a5TAk0XNZd4Awp14JFYFVG7b5mZBXeeYa2EY6U+kSQbAE05YpKQ542bRqFx44dO3r0KMrBXeh7WOIhVkoVH6biHPIV2KtXL5fq+jqCG8vQNREyIsPzzz/v8kaDcF9dGt2OHj4t8qQRe3P08Bnoyw42KpdPoPlNfuuvRyhg//79rqgL2kQk9UX0wLX0HkooefzjBWpKaa7icjkOX+ihL1r20uJWqBM908DerGuTjy9oc0UPrigg9RLpFnn0bARnFX1G6uIsMjBMDZmrqBMOECRVqEauooQL1biHRiiHcLI0Tf5ZMVomGw4kqu+fvKFfrpLYiER30RgF1cCnqZyc8YLU9y1LQBINxOUzqMXIk6AlacDbeVIAFIt5kNB0JOtIJF+oSUEYZYUe/QnLZQBJabN6AakI/Nhplq59I8pCNPvYHtd6pXHUmvI271H7qcZTKcXfsgQ/apfPAGkZRXKAIA1HXiIHdQcuX5iy7gL1yhSBSQTqo3/EpoS01zmnVC3yrlSgkLMuX0JtevsBmRC9+wZlIb41WgjrR6h32X8R1J3WQohsCehLpoVIaMmdNhqGBWRNgMXGytFiLmvoqduVVnL+RlVaUJ3t418E9bW8KYz8Bcj9hYs56RBJR6udLC27TNcl4PIlGAIlYY8qiQq9L0NOCcxZvQ0EDNk7qRBdEspANepHcqI9CqmJhK6o60F4lykhTxQWRj6OxkmHjXiQoVOUYOzyd6EDVR0G5fIl+UMD8EpABumBvuhRhVG/nM0RtVKaFZBB1UaeBJ1IAzIDWuAYWQ47kDTppywSkgoUctblu0rCmQIp3LfPtIYz68nqBRmictWMqrEKKKSXsHfGmBwXCqRmaG/Q2UEPDsiAUSA5wwmFlHJC55OPdyngioohO4lWTaq7kD1T7m1S14bVfEk0iWowmq/kApHNh6tAdfyFVM7RCWepHI0lByk5HKOgO0kiIosyGocFZN0NTocFA1q3QCJcgUmi7argRuUbV3faUYAE1yZ7pJCz4WqXS+Valy95/MitsJhDN5rqhekRXKYLNRWKIW8CnJLMygJDTkaQQhVCGaSfpJdR+17+VGekzS/cVkMfx0QwkGhX9qjBcKS0Q0kom5QcjoWsS5XQqCMlgyLFsJyENwlRqTuOaGJABtUZeRK07TXgLSqcZSp4NXpzjYSkAoXhRGss4VQKpptyJppTpKNZE74XBkUaGJcahLBrtZZcm6rsxyVLozVlPb4jEq7oSgjIQMaM2iWnBlg8GgPfHe24omIUdBe+xM+C0NSEk6hqoR8AZjwZrGu6fdd0lGxfi8JlSo2HfUWo66iFHBAgR10amqhiJRpVYAFZ02D5acOGpG+N8NuVNuYiy0MtR4uTUIBGKKe1pA/CqYU7itxi2IJ6p4Us/570wkhONumMksibQNg4m5MvR5JQPKFT4SUMLSoRNKXKGrjGkmwwwvs4BsLAw006wn+S4fJdHs1lSkj5vlO1qbTQJaHOhRfeTz3yR2ZQqTuOaG5ABlUYeRLm3WvAKy3cHTnrRfLmGgnJheFEM1+auKRR+RaQPHlW+DqIQe+CJc8A6chV6lop4PIBWomg+tJPqmK1x4f+pHRdTw/IQEpGjfKKjNGdKIbvDlxRMQq6C0hdX2QpDOeCgZSuu+y+i3kJ51rIqLydawhhU8n7Q9QSNhuRKmEW9MvYk1IJTAjxVIc2oewmZdSOPdTfNPr167d69Wp5n/DxyVSGDx9+9OjR06dP79q1i1VX5BHXVKZOnXrw4EG8w6VLl2bPnh09Szt+/Hik8s+3fvXVV+5EFy+99BLrEzEmTJiwcOHCrGeNQ/yf6VC2UgYNGrRkyRL8AmmaevTRR1WeRVtbG0NzmcvxrwjpjTY0ybHgs/CnTp2aOHEizsu/75lE7/mHvWvsnvb2dlSHm/Odome9ZlUWhJf7xgw4tra2cqzaDHom9TJyD0pjFyHBxGmJMQVMyowZM0rnM+FCvUPAAlm+fPmKFStUnoQloyXct2/fsi9hPPHEE6w+sXHjRgbobRI+++wzl0rgrU5/iOnw4cPKJtEL1MkH0ns+LArUghrXrFlDSFHWJUboLZlKKe4uisNAZHXvvvuuSrAi5iWcayE/4P+u2okTJ5TwfPzxxxz/+Mc/Ksu0MvV1+bEkmnrttddYbkmpYMqUKZ9//jnTgYlSR55n06ZN6yt/W8WoCAvImszcuXM5ZjmFEL3nRTz0wQcf1OJw8QL+RwGid5v1NhBBz2233YZDTL6spC1Td+Gsz1tuuaVSv1kd+AV1ym6afPEz5LfffnOpNORZquDOEkxTTkTIfqy7SUVLoLtnH/USwnIcM2aMspTj9YqHHXLfP//8M8fHHnuMIKP0Qtjf0A/LETsq2z1TU1/qZeQe/16YXsHbsGEDm2XqJhTB7KBD9shnn31WPwHlTiTQMmE6atyu/MacJIo2ctzFqFGjXOoK5MiRI6iR2ceG/aopCBMkFwHFr22Qu3jkkUc4cl+hW9bt27fr1cWI6dOnc8TUldWLn5988omyjEI/f3PrrbeqBAfy3nvvKV0LSEVTH374Yeqby5gxbva5556TzVOHyEyqWLp0aZGbcKNqLCBrMuwN2sXzYf2wODdv3kw8hM+amf0zAUXwN9w0pQSw0tiB8A7vv/9+zr0+6/PkyZPrSi8lsDHMnj27yMbvvUzV6NYf/vVf/1WJVPr376+E/6WMkOS2Wtx3oxaGjKtavny5K0qg19d1Xwvywg888AA+jlMojT2De1w6JfIgLMbTqaan4IaK5k+fPo0wIZpNTil7xf20VX2NXLC+FBajEAJlbLXIq/tUmzBhAtO3ZMmSnFBMPPPMM/pEhO0q/24hnwEDBiiRbCSSQZHHl19+qWxI1R9FNx10jjNh0r/77jsGmPNbGFlMnjxZiQMHDihRlkrdRUHwn4pg9uzZg19lyYc/bOGZNGkSRxay7JyrGDiGSmWcDKPQV9U0gipwzhhk7R+Pyc8vXrw4NRqDtWvXcpRsAj3gqbRP4fxVaDQCC8iaDzto/q2Y36jmz5+vz6jqtV2FHmfFihW4hnnz5uW4IW0VVGCj0rdvZBGvdDIdbTN4mVRp/YdJxcn/BXA2YG1XaEklIfoBNu3QqsbNa+eJy8H9JW8EGfVHH31EYs2aNVkx6MqVK3FbTJYUhWPFpbJbs02+/fbbzB2u+YknnqBTSojwkqrW1xOoS9kkitjcEweXEz1BwhyVrrgyaJyRv/zyy0o89thjd955Z9mPx+iRyIB5TN1EI9g4iQM2btwooyr4BXQqyKZE8rtLmRMi6U5JkUfqF5eK0vTRyxWEj8ZIsCjY/quIyXyo7T+aKktxd1Epzz33HMfXXnvtyJEjjEuFEfSuNatvvYE5JT1y5Mj77rtv9erVciDnz58/ceIENYsYZFkWLVqEbMnAzi80ff4auSayss+//vWvKjEagny30Sz0qUbOc5q6+WajcvkS/vFnLndFCUrTm/6Ap9qEsNlkiR4pDVvgQpcqcaHrHW+XT3uSVwMEpHVFXdBa+CytfBPkPGBLd66oC5Un26Gmf3ZeqBE/HK+ESPnoFgfqMokoxzcePvQdokezqZAzNfloh0iahMLfpHI8kaiV0sSH+qs28iToDQW6TBdSHUTaI6vyUEg/xS5fIlX5ZL2ZISTpqCmR2ksq0lhyBvVJib/cNxjJA4zdiySyalZBgx7q1+jCRQcMAZ0zlook19xB5KkiMC1fwU93vrsA1YxmJ8vIaU32wDHfgBWuhU/uV02qhCGMGm27TAAK8atP/ifp3zTSuhiSkYUFZN0E65PVklwMrMYc35G6UYmy2xUXQnJx0ppOcW3og+Q+qK9C2vfbGFkVcjZakNQBl8nwwv7+j5pogDqQHLi8CXDWFZVggFyoU0kFqjxUEaKqfuQl1b53NH4HBSSUVLSPPwqdUdLHyTFRLdReCDEBLUM0kJBkvOXhVNQjaHuINq2IpKgVUfeALHUWktRi5EmkhGhqmFYKk4EaE0Q5hNuhXyCaI5ryJRqLGudIg+E8+mqh/YB6h7Kq0HipGVkOcxrZm5QWzbWGE2mys+MM9VZKRQGZzBhcPgMpJ9WwGS9Tn7+OkkgzQCLUmAdVoDc/R+qF+tHsyJDCqUzVOVkKU8Mp+b2ofhIEUEyWJTBg/5FRpZLfI+0zUsYVwVUo2XdNiYRR1oMFgssYjcECsm7Cu36WBK4K66eEdYj1uxoJtLpyPKncNw1Gy5isFpVgaXUuu9JPPLOifGF0lboD6tAmqzqUWeuTQs5614AzRYDQU/hGIrG5XOUexh4KQCNeNhISGOTvVBhufsJLGDl0H8MhBr0AiUhUkALVgiAb9kI7EoDevbQkVJnCrK2CC+VkEYOuqSZoHBVxIQrJCTK4hGv9HGk44GVIBXVxVZY7Lkt9AzIG26mjkjw5Yldt5KmgXk1oUr3oHHN1mRK07K0rnHcu9FZBBV2o2ZQkXIgwlJDWJYJCXUW5753KZFVOU2RVngV6oxeQPLTDWJLDJ6s15UeqsUdj9LMQLZDqKB6QST/q2is2CafyZaMdxs64yuotRIudljmiH3SCHjj6pRcpEwVqjriQU6AWwk4p5FrqcCStQm8qYaGHs5yKZiQLqtEUIB5qQWCBJOiH9su2Q3eSkEZkEiGyliw46+qVUGX8iQbFkRKarWgWjCq4nn+aEqPRrF+/3j/bfuONN06cOHHGjBnJp4g81O/du3f+W3htbW1Lly4Nn0Y6fvx48vXpkHHjxg0dOjS135aWlr179yLbgw8+qOcVJPM999yjB5L0yNfZs2fPnz9/8eJF1v/cuXP1UE57e/uePXtIhISPMXHtu++++/PPP1P/kUceCd8boBeXSmPAgAEInHwENXkVQwub3blz5759+/Ra4vTp01O13dHR8cYbb3zxxRekozpJTSIJaon6VaHLXA46OXLkCCp1+dK8o9siTzLR+/bt2/VgRzgjOUjgHHnyufnmm59++ml2XJevllRLgNRn2qoz8lSS9sBsej2jnMg2ki89eNXRI2uBgYwaNUoWTsmrr77KqZdeeglT9H2F2o4EoPeffvopdTHmP96HTaJAmU3+1IdG4kXVKUgqJFoglXLHHXfcfffdRO0un0GqblO7xid8/PHHGzdudPk00MaiRYueeuqpiiTnKpbep59+KuUIFvikSZOyll6Ou0iaNMPp379/VBjam1i+fPnq1atdpgBSSCgz03rfffeVHXtyrsHLk5Q/ghuPyMFiWm+99Za0AVn+06gvFpAZhtFJvQIy42qlYEBmGEZ12FuWhmEYhmEYTcYCMsMwDMMwjCZjAZlhGIZhGEaTsYDMMAzDMAyjyVhAZhiGYRiG0WQsIDMMwzAMw2gyFpAZhmEYhmE0GQvIDMPoZMSIEX//93/vMoaRIOsHpQ3DqAsWkBmG0cmZM2f+7d/+zWUMI8E333zT0dHhMoZh1BsLyAzDMAzDMJqM/emk7kN/ajD/z9hVCm26VIJGf7+gvzHXt2/f/D9FF9He3v7TTz+5zOX06tUr+QcrQ9ra2j777LPevXtX97cay4Jsp06d2rdv386dO11RJaCQkydPnj171uXT/kJcPprNnL9bhwYOHTpEAiXk/FU+BnLkyJFffvmF9IABA6hZxBIq/dNJUleD5qIIra2tobb9X0tESxwr0nz3ULuFlNV5QQvRn3rUX5zMrxnSiD+d1J1WhHJ+++03lyngcABFffPNNy5ToqI/qVkWhp/lD+vbUQ6N9qtGBXT+hXGj8Zw7d65Pnz51V/ixY8eWLVumlmHw4MG4ePB/9n/BggXJv/xfF/bv369OkcEVFQBhduzYgYS6FiQwKDt69GgquNpdhJesW7fOldYPpArV6EoLw+XomQs5IioKARIzZ85kIjZv3uzqZUN9DTBrdBcuXPAa8CCzOx1ACwzEi4EMyrrT2QwcOJC91mVy8eNFJFfUjaAKjREBGB1pQB6VIBIJSlztnkHtFlJW58UthJUrFUkMElk1I0aMGEG87jI10/1WdObMGVSNhynpptM9ojR3LgMpRzBZqM6dqBMogS5kukACbfh5RFTOlhWyarBA3xcduVKjeVhA1k14u3f5uoIzVeO4V1dUWup4EJUX2Y8rhfYV/1XhL7hEgnG5KyqBnPJNeKJks9Jh4xwHvZeEqmyOtL0hGwpxRQEUcip1OIIKbBJ+prJGRwtom7MQxo5RfZqiMDQDUOOp4oUUCcgYBVoKox93ortgT9XNRlLhyIY2OAWRWppLjRZSUOcFLQQFJgtlNmW9RL0CsuZakTQgIj1EIKdXI7p1pQ1A+odQHsxGpo4MyOxKGwD6j7o2moUFZN1BeFvmiuoKa0mNRzsxaLGxpLPcfbOQwElfzBB0KukgGu04fNcuXwDFcGwtLp8BFbJ2XF8oC0kdnSI2lynBVXLWEDarQpfpAs9OYVm9FQzIlNA9QPdvpdogI22EaG9rnJFUSh0tJEfnxS2EjiiJHAUVKCw7m3UMyJRoihUBncqQ8sMs5s7rsKFCZjk9FCU5G+rAGVqya6Mp2EP9Daetre2dd96p74MXxZkzZw7HS5cuHTlyRCU9nPHjx8sJbtmyRSU9FmZ29uzZSLty5UpXlMHGjRv79ev3xhtvuHyAf7or5zGvvXv3vv322y5Tgspvvvmm0uEzLufOnXOpgBtuuMGlasYL2bt3byW6k3nz5mHJbE6RNkLmz5+vDaYnUF8LydF5cQtpb293qQDfRffQXCsSel6K9ZLzwOjLL7/8wgsvuEwzQFEKoK8gB27UggVkjaWjo4NdhIDM5TNYv3593759c57QBxwHdVpaWly+GDfddJMSeoD3imDIkCEcU2OLHgUzy3HVqlXhftba2or8119/vSZ0ypQpCxcupJwdd82aNanbYT6Y0PTp05Nb5tSpU10qQWQkJ06c4DhgwABlu436WjU1T58+TYItKj+AWLx4sUs1mx5rIW+99ZZLldAcFXmuv/tBP2PGjMlXy/Lly9En4a/LF+D5559XgqhLiQh0cvHixRkzZrh8k/Ax6xXkwI2qsYCssXDLi1Mu+y7P3LlzuY1+4IEHsnYvdiNutSdPnjx//nxXVIyvv/5aiWHDhinhwX/hyNgMuFkE9gl3IoB+OcXOSjXSkXhqIdUP6kKcKbDdsme4EwU4deoUR/8lbxHoDgnVHYksNeLWEbhsNQ/NMnYRjQJ1ER/06dOHYbqiUv1p06ZReOzYsaNHjzKhhw4d0pcO7HYzZ86s4jaXjTZ/0nv16uVSpWCF40svveQnBZnXrl2LMkM5u4f6WvW2bduUuO+++5TIgkBk3LhxLlMCJTB93iCxgeQGL2NGJNIIrMoYia9JC84USnhj4EJXtH592GzPtBB9ZL5r1y6NVBAyIoOPUXoU69at494MzWTFZMwRgezmzZsrel8VbWux0Hiq60Mn+aE/BoAOmVy5FI6Rf3A2UYIsZ12mhKtXmPDTRFRBCyVb7iTHwSJhEcdo9BTcV5dGA8Dn+icP/FMCyia5cOECu6Y8tSvqQo+h5Dw3g89S49G1tKmv/2jZFXWhNn19PcxBobICf0SnenYBt0Uj/jkD6qvlsBFx5swZalJBF+qZHipHz0Do2uSTGaoPyReaUp91oFnK6REJycp3Uw3hox65EDEkLackP93pLHCqs+PL54iWIflQrbw5R5cvdU0JlX2/EtgPBAHC+hGpo8tB3TEKly/hxwUMTXPhJzGf4m9ZAnLSRXL6IugXAaqz6ojSmDpx+cKgBHTi7QFJEIl2/NSjSc0mMC6gPkNTnEQ6mlCI9Kmn9KIx1t1COEvlsjr3pFoIaIEAfVGHLCIlLTxJfd+yhIIjQjbmAiGR1hV1ISWHq7gIXMKRZju1kCaAVKfuUuvIsP3KoqYkDA1DFg5+TtVsUlrveVCIK+pCFgJ+7DRLX74RZSEyPyThWq80jpp3hAmFBHWR7NrofiwgaxQYPa7QryK/5JRNhUtYP9HSKrJvya8Bq5RrARdPSKSFmlyB8gvg8l0lVHb5Lm8VSkI69Eq+kbCORk3XLl+CZqkWFepaxtspbgmEl2ugMBmNQarjoJD2owHSAjW9HwQap5qfDqCkU4Jgc0UGlSgLtBCJ7UkKQ2VKwi5Ux7dP5Zx5TB1dDjIMji7fBQJo+CJZIYtGBGTA8Kuz6hC/d4IrKga903UkJyqSTYbKkf4p91udrg2reQsJxwJUSKoiOaE1WkhxnQspORyjoDtJIsJlkk+zAjJIjck0Cj9fxeEqJaR/iCaUlr1aVCESUl0nJzcSRgNEck0xziTVn3i7ChsEzWBYrppRNd0P0AtackWlofl+PUnHCFJC1KbRFCrzbkZxcKxhVOGXnMtnwPoJdy8tyLL7lpY9cC2rSwtMsPaSwY22N3px+RKq7zIZr+ZFK1mXhL5MXil0moAPojASo3RppwwSmDBOJSRwalELQuMKRZJ+IqnAa1uy0VqyGoX0jsZc/vI50kQkNzOPBPYVqE8WCZUVUftIHukzJDm6fBAgbDwEYei6cyQlDYcTlEODAjKozqpD/NRkDTkLGWRyHiU/IiFbWBKNKDkpKolsKVrsou4WwqlkCznkWAhoIKJgTNPEgAyimEwzW0U0BlyohLercEK1fPyqUYVISGacwrB3jSU5fTIDfCANkvb2FhKKQRpo2U9Q2KZaS7pHVfZCaolFVgq+IxKuqHLPYzQOC8gaAssp2m/8SnD5bPzupTimyL4lXwDhMmPR+nKWXOQI8G7hqiatmi4ffCZBI6lOBFQh7DTaYHLQtd6DCHY1LqecdpKbaNJxyC2muhJ5LrkkpoN0csuM8HPEeOkrKUCI5PQeWdeGkiiiDW+IU7dtT0VukX5RUdIvg+yHgct+RP5YROMCMqjCqkP81IArKgY9ckloosIbvNdM6oiSk6JpBb8oaApjUzqk7hZSkc5zLAQwD8RTHdqE5OadpLkBGfiYTAvf67ZSuNaluhwFeF0hVSiSzkZCMvuRUaFAqoXzK7zpInnSDoWvgzB0JGiQAYYz6H2yywcoAgPVL+gYBX1lVTa6GXuov/60t7e/8847OW/m59OvX7+DBw/27dt3zZo1LJXw2duKGDRo0JIlS1jSpA8dOvToo4+qXAwfPlwvVempzz179qjcQwW5mKVLl44dOzb1udeI48ePX7p0Kecx2LJMnTqVseMyaGf27NllH0E9fPiwSyXQq5p6EHjv3r0ci//6A+NFY/4F1VQ0TP15ItCbjCEff/wxxz/+8Y/KIgnS5rz4Vhyaeu21144ePaoZDOEUwo8ZM2bjxo2rV69md9eOW0SZDaVGqx46dKhLVUJbWxuG5DKX41VX6ctrzKC2NL9kSDzxxBNKh/RMC4EpU6Z8/vnnTMf8+fOpo8Bx06ZNVTxp3s3glBD49OnTu3btIoCo9A2nVFatWqXEhg0blNiyZYtefciBydWfNkLVy0uvRl28eFGnIqimmxCMv+xfQ8KQmBfBEmaA4Qx+9tlnLpXAOyv9IaaCjtHoaVhAVn9YfmwDBEB6t0U888wzOqssa1jZVI4cOcKNDlvpqVOnatxHWdLaP4gwotch2RTZuUmw8lP/wiblciUIM23aNMTOepenjuDpvIuMXs5PkrXdwqhRo1yqcrS/PvDAAznjnT59OscPPvhAWb3W98knnyjLrLHDkbj11ltVgst+7733lK4FRKKpDz/8MPWdMk4xWf51OXZ3NjDFZN4Cm0UtVo1VyIyh+LXhHy5MokCkCmQe/rds2L9TfxyhZ1oIUReu4LnnnlO8SB32fqmCW69uWOA10lL6hRSsCMXWJaqYNWuWTAt/yPB1qxC+GJsFvaNnPOTcuXNxlSNHjnQnEui9YIy/xpDXB/dJojuWBjlGo9FYQFZ/fv75Z9YeXi9EP6EEyn7xxRfKJsEjzJ49e+bMmd999x2eIudXAwqiWyL413/9VyUAVwJsKvmuZ/Xq1ce6/swiYheJyajmUtXi7/ZQoxJZyJN++eWXyoYkf3My+RFFFoSnDBmnxnhdUYJJkyZxZFq1K+j3bBm7XoM/cOCAvkTYs2cP00c73HPX/uEHyqepxYsXp+61BNwIwOYa3lVT86OPPiKBqE3cbmu36smTJyuBbpUoS//+/ZXwP/4SooikCtiAOaJPFM4oIoV7eqCFwNq1azlKNoEeiMkUtZ88eVKFPRNcFlHs5s2buc3AOWBOdYnJFGGz3rdu3bpt2zZ/Q5gDU68oFsNOnf0Q7oX8tw3RXXFF+J8STDYSGXNFjtHoOVhAVn/wbu4L4QD/lICy1FE2wu9bJOQoq9u9UvHf2dE4fo0dLstrA55O+zcbCWLoq092l+SXmx6//9G+EiFFvvSMKLtlapNO/XxezkifUshjpv70P2NM1e3777/PFsV42QNc0eXQ5rrSEzD6EBEQgzQ3ytwQE8iy77Ljnj9/nkCQmkXuucuyaNGi5557Lrlta1vSB0JJpfkvSn788Uclupm6WLX/2G/Xrl0FI0vmSDsT+7dKQvRzd7rZqAiGoP21paVl+/btjz/+uMojeqCFgD47iYyE7J133knir3/9q0p6ID4amz9/Po6rjjEZEbbiUaJVrCKMVrN46KGH0GSR70wJvnFTGzdulKXpt4KrQ3MEye8uFaIxCi32go7R6HEoPjAaTRSQpaI7ZlyMy5fwT0NnPRAK8vuQrOM/ZKIFV9T1FGfYEb2omsuXBEYelymhXji6fNpD/dr/6It+XVEJmgovBF2LJC4foN0OcL6uqITEDtvxWk0OHEkQQ09eS7HAdqizgrO0qTrgW4uykR5C0CEVomarIzm6CNSSKsmZM2ekKM11ONGezmGUW+wNeqhfyq/CqpOoU0AVrigNFOIr6BJvCR7pKpQ/dURZkyLboFnMzBVlUEcLKavzshYCWqGUKOvRSPOno4kP9csnRA6BUTAFGFLkbfLBEmgqusQHzcm5VnkopPcMoRrVQnQ5Nb3t0SPpZB3wDSZPRWiakhrTKvOX+waTExo6RqE2y3ZtdAMWkHUTfoW4fILUfUuU3b1YS2o8qoAL0OfqEHpqbRI0KK/EUf4OaEFrlUS07PVSWPgWmL/E5YN30Ggc78kpQDzECF0AqFrSs3CVTiUvkeOIVCTJo3bolMLQfXs9cDlCUoEjhaFadBW4fKDY5AYmkFDKRIxIWg/qzbo8RBJmuUXapwJnI9gGQvcqFUW7MgJIQpfPoKKATNtPcvoiarHqVLyhZimcSUcqr3D1Qn10pRJBlsJwXtRyNCLpMzWcyp8vDwLUy0LydU77RSyEEgmjrId9Glwmg7oHZAWtSFMTRWMCvTE6Bi5XVgQt89CPgdYIJNtROV24fKlTFXrDoDXEoERjkbY5otKwI+/counWMgFmxxVloPFSM1o19EtfoYGl2rPGHmmSOhSmLlKjm7GArJtIbvYhLNr8JcFKwyOwFJOOmxKWohonUXLCnWiZqTDyPuoOaFAr2a9z0nI9Eth7HAkQ+nHfSCS2dy4eLgzFpinEc+dKe4MEJuEHQjp0LoCj1FmO4SnScj0c5UwRjLFEQYnkL7X9N+jUnS5V0PYAobS6igZT9wNBX1QAZKB3VCe4BOUgcCRMEiqra6YjGjhogFlw1tXrGiaS+B7RCSWQbDaieEDm54KOkhuYRxZSnVXngFa5RL0zdkaK9jgyfUhFSTRSaYD6XMgpUAuRTSatiwvVUVjooUdO5Qw/hMo0BVVbSL7Oi1sIqDLGr0FxpIRmy85CfQOy/BF5tCpRlMsnQGxaKGLhQGUZA8dovCgBXKYEUnmfALIfnVIjQIJRUI2zKmEJqyYzy1nVFxSqDuV+yF4koKmys4DZMF7AkMjSDmLTQjR8spTTJkf1leoYOaVZ4Bi1YHQ/1/OvZAlGY2lvb9fTV6nvM7a2tn788ccbN250+TQ6OjoWLVr01FNP+UeCIP+1nQEDBgwdOjT1QTF6fPfdd3/++efp06fPmDGjX79+lLz55puDBg1auXIlWQT+9ttv//rXv7a1tbGMWbcPPvigf8wl2S+N6FEt4JKWlhZauPHGG8Or4Pjx4/kP148bNw6Zo2dcdu7cGf08AUOLmt2+fbueJhk1atTcuXO9MCFIpZ/AiOr42QlhppL9pk6f0CRKBkEv9913XzhfSVK7Bt9RVgUPG0A0xYjt//JjcgqyuPnmm59++ml2XJfPINXkwtn3VG3VZeGqI0eOfPrpp6G2seRJkyalzjugk3379mHwpL3N61RSwxhh//79o8JojMiwdevWHHtIUp2FQL7Oq7AQ1stbb70lbUCkkCzuuOOOu+++m6jd5WuguBVRs3fv3vlPa+Fwli5d+v777+cPIbmcmWivfM2LFyDLU2nGmf033njjiy++oP4jjzxCI5SsWLGCRhYvXjx16lQ/wNBTRaNmvD/99FNOL1nQFzMuV5a/wPMdY1m/anQzFpAZhtFJwYDMuGapY0BmGEYSe8vSMAzDMAyjyVhAZhiGYRiG0WQsIDMMwzAMw2gyFpAZhmEYhmE0GQvIDMMwDMMwmowFZIZhGIZhGE3GAjLDMAzDMIwmYwGZYRidjBgx4u///u9dxjAS3HrrrX1Kf7rAMIxGYAGZYRidnDlz5t/+7d9cxjASfPvtt5cuXXIZwzDqjQVkhmEYhmEYTcYCsm7l+PHjLS0tqX/H7UqEsezcudNluoW2tjZ06DINoL29nUG5TOV0dHQgnseVFoB+dQktuKLCtLa2ulQGKI1pwur0J+26E4bT/Z0aEVhI98+C7Bnbc/lmkLUeKQeX6WFkyVwWf2EVc13Wh9AmdfAhFYlkVEzpT4wbjUV/kL9Pnz4cjx075krrwbp160aPHu3m8nIonzlzZvS3/evI/v371dGZM2dcUQEYvlSha0MovPfee5ctW4a6XO0uGAVj0VUM2ZXWD3qk38GDB0sSV1oJSIjCaYHRISGQUDZfP5xl1OpXcMmFCxfc6ctBSFcpgIlwpy+HRhADGWifOlltegYOHPjnP//ZZWpGvTNlHF1Rg6GjUJOo0Z3Ihjqudmm91H2xYO3R5Hpk6vX1BhGhQurS0YgRI/7xH//RZbLRvKtfwAKzTLRxMF48Br3Lq4BWNwnEY667zSyLU7UPYa2FlgwMM+uSinwIeKlIJD2zUV8sIGs4rCssHptunDX71ejXFX2xfuSDOFYUMxWENmkcf1fFuLikJG+nWlxRV6ymcryGK+0Cp0NlTqFPV1RvNFPg8sVgLAiGHlK3cwo5lRyOQIecZYJKW8a9pCVAqBYPGvAVPFzrTl8OlkBlKL4T1zEg87YHjZuvVML9Jt8yvRECynelDcCL5M2ASfGFdF02Vq4aP8ZuC8i0hEvmfK8WrOi2mAxl+lAsWpIoAakkTzebZT61+BA5RipI537dUZK0/4p8CN5JM7h582ZXZDQYC8gai9wTR5dvDD6SiNwuy0/rM2vJ1QgLvuq9RALjQVy+Cxa/TiV9E5Upb5wnRXvq2uULoIgKt5WjB3nMVBugPHR21PQhadIJMnB2GoQMSe0X1dFCvlRJ6hWQoRP1K9vr5p0PnZT010nWHiY46zenhgrpRYp60S0NMFOuqAGoC2Rw+RooG5ARdTGWMA6gXymZoytqJFprdJdj/Fpi3WyWOdToQzBjysNrvQvFXbiiLor7EEkFJFyR0XgsIGsgWvksJJdvGCwzrUBWlyvqwi/ObrtDLYikSgZkgBfgVFJvPS0gw5HJZyXvRCOoSXQSxVh4uqTHVE1kSGqmSEegaKxg5ZD6fmUJjZ6vVJhE+mX4UkLqZgOaO/8xVUOF9HaV7MXH341bnmo/6RmqoGxAhj0n92/vguoiQz4IoL5ywggtsW42yyxkh1CdD4HUSM7rweW7KNIRKBrj8p62a1z12EP9jaKlpWXTpk0k3nnnHZXksHPnzlmzZuU/jNnW1kadSh+iv+2225Q4e/asEj2fO++8k+Pp06eV7bEsWrTo0qVLL7744qBBg1xR6enXhQsXTpkyRRO6fv16LKFfv37btm176aWXwkeJd+/evWbNGpfpgpremYYw73379j1y5Ej+Q7UYyezZs0m8+uqroVQ9luUl8p+wbm1tRZ8VPR7OpHBkdrZu3aqSCDSJPu+77z6XbxIEwUpcQcszCyYRlQ4fPtzlu5gxY4ZLJaiv32Np7Nq1iwRhblIMD0ts1apVLtNsavQhLI3nnnuOUy7fxeOPP+5SAQV9CO0/9NBDSIUjmjp1qis1ugcXmBl1Rfc9qDf1Q+YkuiPhXifr9kUVuENK3gwBd3uazeQ9qD4vgeQ3gMANENcCN16pXVOoClxOOlmHFpKFQCFt6trUu1VJlfoJmT4i4ujyXeR84kJ3SKjuSKRqSfghJ6v5TzJcvlQSEtZnUKocFTJNQPsMXwPxNsD8ps5CBNdyVaQZNSVonzZT1S4VJVVXhKZ8QoZCqMNwsqZMFdgbXL4cTBP90hpakq7cictBRbRMZepAqpDMJuUismGyJYtwkKVHlynh6pUgm9ULJamnuKSz1xJR1yFFzF7tRyJVR8GH+pNkycDQmKCq/V6Ev5Mp+7kOraEul+mCvrzLymqBco2CFlQ5bIcWOOvxgwptIxwp9SVwODoNGWi8ah9CR2rZ5UsU9CH0qzpZk2I0DvuErCHs2bNHv6B4//33czui2x2OWfd53M8dPXqUBYBPSd4vco84ceJEbm5OnjyZvBnKh5sqjiy/SZMmqUTQJg1yX879KxvY3r17WerRhxBIizzjxo1bsmQJWSowLp3izozh0MK0adN++uknFQpusJYvX86Fv//977n2/Pnz+HFu9dzpcqAu9EDiiSeeUElZuINENhJ0N2DAgJdffvmWW25JqpqWhwwZwpCpNmzYMOSnWv7NYv/+/Z955pkHHnjg66+/7tWrV6j83bt3c2SYvpCBM00kmEo0Nn/+fLoje9ddd5XOX/fwww9/9dVXSufwyy+/cJw+fbqywLzQlPenmNamTZsYcvSyOtUOHTpEQlbELOgmm9vr8K66R4F4bDAMZ8WKFUkhmcTZs2cznKyFkwWTwn5DAl0lr0VvFy9ejFZECJKMGTPm1VdfZWmwQJgRbDhsB2PYvn37hBJvvfWWbOPXX39V1lUqAKtDCQWvwCTSNY1gpaDlwzwmlVPQ7JuOJMcFjR8/XiWe+vq9w4cPKzF27FglsqC10CMhIdl58+bJZVHy2GOPseK8P9RHVnJ3J06cwGmg6ieffHLp0qXYJ7OjapgBEyeroFrv3r1VnmUbjfMhdMeRlpWFgj4E1q5dy5FTyI/rYHSAsSUnyKg/LjAz6op3ryTYGLjn8GsDQ8+629O9ERXCWxMV5t8j+nsaboxUQmVu5iQG19KIygVnaZNTLt91r0ZNly/dfFMS3imycTIKpemILBXAdyroFFym1JeqcbfnikqoMKyJDAxEgqU+jk1lTlHH5UvoMaBwgH50YY+6ZQxF1YXUdPm020rGGE2HJymMZiHsQnX85VQIx5sFPSJV6nTTFCJ5rwphdxIAaIGZIovtSRUcIxtI0sRnyGRLSBuOWoXe5AqCQqRkGTCEVi2oIKn8jEdCSvJQt1JjZAla1L597C11frN60aIDP0bap6NoyFxFHXoJlVPQ7KGzg8QirY7qPiHDhyBAjhkwCiSvwu+F+OkGV1QYuo468lMWaphqlGhxSVRfzftJLwbWqxJB4xSGA4TkAtFch5OlOv5CKqTaWITMI3XSaSrHh/gRUYGO6I6mfOVoUEbdsYCsIcgzRotcLoZyNh5XlCDyTQW9kpZxKiyn5LV+1bl8iahEbjRagdFWoUvC9SxJQi8G2rc45fIlSpemwGBT/Qgk/ZcGEkkF2stBakQDqDGqhpBUoNzrJ1ILqsuZKXnncPPTxLlMCcYCLlNSTqSEJJKhbDX6LUn6t2gApGcI9c/oJGpYM5XmPtSvKfMxmbLJmS0LCvQ7Fq3RCIT3FZgEJTIMP+ORkCrkrMunhWggu6Kc6aBBNKxmI5K9MEGkdS0tewtUL9HyAVqm3FujGixr9kIlkeTVUV1AxqDCVZYKQ9bykeTKMur8q0K8ksvaeQQTwVVRFAsynrA1RTnRGteUhfajC6NqTI2fPo8WZt19iMwSwVw+g1Qf4gsjhfillGrhRr2wgKwhyHaTK4cSncoxa++b2EUKeiXfrHe7JHABchaQlIT1Fu5SoJou0+XgECB05ZHYuiSsUPIesUNE/rCO0LXea9AydXB59Eg5w09uS1m+Lzk6UDs0SFobFUed8tBFOCINGUjTMqg8FYYZtqlr1Z2gcUrCRkgnZYhgjF4n+WhQ4BUl/SQv9+PK773pb1lqRGhJu0IV0RgwWK8BTQGEOumc18sjG4iEZGlEG5LGQn2X70KiasFGl3h8LyHYDwMMZwRT1CmXD1AvIFeA/KTLmr0oXde0gExjL9I7kyU1Fvd7IV7JXOuKiqGFnJTQG4/3kyicbLS+kkbu59EvTKBasosG+RCEQQlFtKcVB15UjRGU9dCaTCsUxqg79gxZt+JfODp16pQSSfxzFdOmTavuuTEYP378rFmzDh48KN+9dOnS5cuX65SYP3++3qBpL73Fs3DhQpV7aAHneOnSpQkTJtCUHiDIf3Gvra0NsfXQQwjyJx8fiaBl6qxevZqxs/JPnz49ceLEsk8t+KdGkuhVzS+++ILjvn37ON50002dJwJQdeqIGO/OnTuff/55l09Dw/zhhx+U/frrrzn6p0ZAD4j4hz86OjpoM+e5JdDDXu+//77L54KQcujffPONSrJAsar56aefqqRnwojYITZt2vTkk08SrNT+OBTzq/3y0KFDelgQ9dI+xl86nwlLQ3U0awj2/fff61QE1eiCZQJlmw23bRpUy+5crk/w70prrguafdNBe3PmzGFOyy5/qNHv9e/fXwkmQoki4GF8/BSBPEpU+gIsLkVWx3JWCb0w3UklNMKH4ITXrl2LJotor7gPoTXZao7tGbVjAVm34rd/vwjzITTRI95Vs3LlSt3ZrFmzJopvyBKHbdiwgTBx48aNrjSAeI6YjMSuXbtIELepPIvffvvNpWoAP+h/swDZVJhFljOFMNL6+eefXaow9E5M4DJpjBo1iuOXX36p7O9//3uO/hltdMvGT0IbJLzxxhuMK8dL4kn37t2LzovvQ3rvoYgtyfVHBtCTYUtm+3GZGnj55ZeV2L59O8etW7eyWfq9Ngd6x+AXLVpEHM8umLzN8OhDBUwx+Xx0ReTMo482REGzbzorVqxYtWpVGHQWpAq/x8Dl6MA/jF+W6IWkCAUrVUAYytHfUWB1L7zwgtIhdfchGC333kRjRSxcFPch+omWHNszascCsoZQ9UoGvIneLTpWekeasKCWfZTV69fzt99+qwTgLBCSey9CsSwnzrWff/65PgAnQGGpJz9IS5Jzo18QvegE9K5EFnLB3oWFyH2E6Aa0CG+//TbKOX36dM54//SnP3EkVFXcMHbsWITBgXJbzMa8rPT8GeXSOVs7CtTLqqkw6Q899ND7779fPBrzeHX1qM24CrBJvVOpz8lS37usFP/pIA2yjrZs2eJDtByYjltuuQW7QqSyn+68+uqrum957LHHahHYfzSSXO/RzFZk9s2CtYMwxaOx2v3e5MmTlfjss8+UKEuvXr2U+PHHH5UIyYnC89EnTyx5xWR4idQfY6uvD6GRKVOmYN7FozGP9yEDBgxQwmgKFpA1BLkGf+uTJMvuvVc6efIkO4E+w68xJvPccMMNShw/fpyd7957781xl35rwQV89913+rgIZ+Fv+5LIu+E1Un9LgqG5VGHKRifSc+qn6NqudA964403cty7d2/niQRJwej3ww8/JJEzXryedKKfHuWS9957j42ZXs6ePctVzz77LP508eLFeEn22tTPIAWqJhqjxyqiMfAfn+irjZyAuCdHbGjM/8IFZlnHmMz/CqispWyARY+sQcw4Z8o8SPvzzz8fPHhQG/CiRYvcicr5wx/+oERyBv3yHzp0KMeCZt9E9Gl6TvQQURe/99RTTylR5Le4BatY0W3qt/n6nnrYsGHKVoQ+edq2bRuhFfOVurTr6EPg0Ucffe6558qadyreh/i79yx3rdsbo1G4JxqMuqLHM1nqLh8gteNuXD5Aj7Vi8eHzmCpklaZeIvyTmPTrirrwD2OCb1ZegKWurFAdlykNIXrqX1eFz8HokrBT9YW00SOljCJ8WBV0LUGhywf44USX6MmMUAD/UGpSOfrQQuX+mWjq66wHJfhrNWugrL/KP/EawRjVS6SoiqARxpXaBSON1BhB76ECqSz9J1uT6pLDD2niQ/2aRwzM5UuokAnKV0IEk4haXKYLv4tEGvAzHgqpeecSly+hsYSmDlgOCpf9+KaSxpDaSyqSM1qYQJthuTQD+WYvVDOSvDoKPtSPeMkhAPOYqgG5uCr8XhL5KMh6wUKEkiAq9aPpBupE5VxCSbjiIMvIEZtyYAg5yqcXTVktPgQYRWTbgq7zW6b31BElFajhp86sUS8sIGsUMutokcixRnGGSPVKoqxv0lKB5MqXu4GwU8kW+ppk8EFT0dqjhArhiHRJ2KmXhC7CpugrEt5Xc/kuNFhOcYwukdiRVHJnUaG8oR8yKvVtMlJpmCNXhWrRAMHlu/w7VyUnRSAtQ6NOqjf0ZF1OOfIjEl1HoEk/KCp4sT3YEoJ5JYtUp8mFFNKRy2fQrIAM1VEtisaETjGcLAUmkQZcpgsVoi6X7wI9Uw6hkKoM3vxQskqYBZUIBhheiC1RJ2ktWvUQVk7F14wsHw3QbFhYxOxFqb0y9lmQIgEZHSGbbDiEoVEejQu03qvze0loRJrJGTLNMnHIoyyN00uyvmzPVwPNb+Sycoyc2eFU6GZTQZ5afAjQEThFB2guVKe4D+HCVLFpKjJCo+5YQNYoMFxZsDd3OQvIcj2pXkmoAtemrgc5BWBX01IEfITWOUQ+mpWpchpUNV9CC9oaaYFs6I8oD4VHEl0SOSO5oRAkD9sBNa5TeI2SvJ3uw1+LSJGbAHrnFEeXLyE9U+7dmfRMU6EypcBS238j8q20oHJ/IQnpkAaT8gjqSGxao4WoUxRLC6n+mpqSPAt0omrKhmqkoyyRmCYqe20A4nFtlvye+gZkXmx6d0VpSOcyuVRUIZrNLLxKUbsrKkE5GkjOgrZYYI58+yhKhbqEaaV3NcuRSzQvWji6RPghR8PRjEBkb6loJdKRlwcNIIk69RQ0ey9S5AGqo2xApsnKIjl8VM3QQuVHqALjSvV7qdBUqHBE0rUcWT7oh+6itYBu6QV8ufqNrEgK5+jyJWgtWShollNRI6kgM4J5gUNtIEmODwFdmIWu8mbAoIr4EBkhLbt818xydHmjMVzPv9JMGfWno6NjxYoVO3fu1BfzZB9++OG5c+cmnydoaWl57bXX8t/01mMW7733XvgHX9evX//ll19mvUU4aNCg4cOHT5o0Kfnw0PLlyzdt2nSp9C7hSy+9RDWaWrp0KSt/27Zt48ePp7vdu3fjxfr27csy/uKLL2hk5cqVkjDq95577pkxY4bvRcPhWi6cNWvW888/708dP378wIEDOa/l09SwYcOiP2qLDj/99FP/QMmoUaNuv/12/wAcit26deuWLVtIDxkyhOxzzz2XfDyOy9mW9OeH8URhHU7RgpcKaYlOlixZQr/79u0Lh5n1WIzUReOM2hWVemHGQ82EoMNPPvnEZRLceOON9K40NdeuXctkkUalzNG8efNoNstaqI82qEkFtDFmzJhwCrK4+eabn376aXZcl68WlLlnz57QPJiv3r17p6pOU+BHmgpnZ8+ezaaSszog0ifjDUeNTYYai2ZWzJkzx8vz8ssvM5XM4D/90z9hja2trY899hinXn311fnz57N8sBbm6MEHH/RWpEKl1ftPP/301ltvhb1wyciRI/MfrmKNcBW2xPacM31lzT65SMeNG1fdM0bijjvuuPvuu4naXf5ymPf8l34WL14creuq/V5Z0OH27dsPHz4crkd8XThfIQi/YcMG5l0BFsennnrK6yryD9jzfffd179/fy7hlC/Et0fTxNwVf2+6Ch+CWb755psuk4Z/Z6tSH0LLr7zyCvVlWpFCjAZhAZlhGJ3UKyAzrlbyAzLDMGrE3rI0DMMwDMNoMhaQGYZhGIZhNBkLyAzDMAzDMJqMBWSGYRiGYRhNxgIywzAMwzCMJmMBmWEYhmEYRpOxgMwwDMMwDKPJWEBmGEYnt95669/93d+5jGEkGDBgQK9evVzGMIx6YwGZYRidfPvtt//+7//uMoaR4Icffvjtt99cxjCMemMBmWEYhmEYRpOxP53UcI4fP67E1feHwFpaWnr37p36t+EaRFtbG/fojdNke3v7kSNH5s+f7/IV0tHR8c0337hMbTOO2QwdOjTrL82hh+HDh7tMGlIUieIyVPGnk8qK0VCytE05x4J/QLCbqdpC/IX9+/dP/bOGObS2tub/IUjM/ttvvz179mz+H7us6E8nye/16tXLLKQiqraQJGoqq4Wyixer+Omnn0jkOCKjznT+hXGjMWzevLlPnz5O0Zf/pf16sW7dutGjR7sOLofymTNnNu7v8zMWdXTmzBlXVIBjx44tWLAgVIuHwnvvvXfZsmXnzp1ztbtgFIxFVzFkV1o/6JF+9XeFwZVWAhKicFpgdEgIJJQtq58LFy6o3xBac6cvBwWiJSq4fALmxQ8EUFpBjQ0cOJC91mXKwXjpBUlcvntBCdiDRocMoCGTYLCoruCQu5OqLQTzoE5pMh0MM+sSzNhVCshxO14qEsl1FzFixAjidZfJhqGFC5zG6+73ynJNWYjg2pK+LwPjcacDyvoQulMFDzKkNmXUFwvIGgXLibVETIb1a/eSZZf1elXg/bV3fPTiO+VYZD1XCm3SOP6uihFxSUney8IOFOUHwr7iSrvAHcjjoFhXVG9oWb27fDEYC4KhBxTuigIo5FRyOCG+35Bka3Tk9QOu9HK4ilPI07kFBS41XwBRMCDz3hxIuNLuAjPwG22kolCwxhlJFdRiITJ7KnROZ1dUAZQk1x2VKVcFD5e405fD+tWCwke5onIUCchkopJW7Ytui8muNQsRDE3jCmEu3OkuivgQDIPuMBtNorcoJHQ1jIZhAVlDwO4xZVyDy5fchJxpIxyB39FZlq6ohO80yynXCMMMx1gREhgtuXwXbA86lfRNVKa8cZ7UOzWXL4CcF64qRw+cokLSOQrO0gLbFb17UgNoyjmiliwhmQ4mWtWEHL3qp7YZUiQgo0Eg4fddlXcP0iT95ihcgvWc7bZGC2Ebpjy81i8Qwg5X1AWjprBkQX8jtV9JBWWtIqRsQIYZMxBZiEAAekFajq6okUiTdHftWIhgJTIizbjHnQtQYY4PAToKY3S6lsageOxuVIcFZA0BN5pcXRRi0zmLqmpYilowyUXo3Xf3f2uQj6RK3dHlwfELLt9FTwvImGJEhXAHSoWahEqp7ozCikwiR0iaSnaBu1f9snqr6CtLmVw3B2T65ANywgipunFGUhG1W0jqPu314PJdFOkIFAFweaU+oWxAhmDJqWFQkjY1RKgv16CFAIPllMsUIMeH0BQ6dJku1DX1u3m9X4PYW5YNYfXq1cmnIHv37s0x9TnKnTt3zpo1q7293eXTaGtrow41Xb4Yt912mxJnz55Voudz5513cjx9+rSyPZZFixZdunTpxRdfDB+yZhIXLlw4ZcoUTej69etbWlowhm3btr300kt6mjjktdde49ja2po/+0U4evRo8nUE7I1N3WW6l/pa9fHjx3ft2kWC+DXnYWRUvWrVKpdpNjVaCFbx3HPPJT3J448/7lIB6LBv375HjhzxbxGlQvsPPfQQUrHv5j/pXym0jADJqZkxY4ZLJTALqYsP4eyQIUNQEbpyRdWye/fuNWvWuEwXdO0jXaOxuMDMaDzcXqTe74LuWTmbdZ+kCtympF6ujysgeQ/qP51OfgMI3CJzLXDjldo1harA5aSTdWghWQgU0qauTb1blVSpt1y6G0ve8+V8QkZ3SKjuSKRqSfghJ6slbxwpCQnrMyhVjgpLt7udz9EzfA3EfwDG/EazQFaNiGSFJIihyi5fgBy9hdT9E7IarTrCbwllP9ehtaQa6csbZFYLlKNeErSgymE7JRP4G15mRueKGmAhqdCRWnb5EmpK0D5tpqqdflUna1JyKPhQfxL1iNgu38VVZiG00GkEXfhBUdkVBYVAfQlci4XQoBoR1OfCfHUhhiq7fAFok/r5692oHQvIuglWEdacs060DlN9k07leCWtFmCluaIutBlzeXSt2uRCuiNNNbIk3OkSyIw8apO06usUjgkfQUmyUzpatmwZF8p5UY06yY/BO8VNW+HeWfi+PBpLspwSJEFCruWIopR1p7vgrLwVCWSjDpB2p9P8FMphIFTDM0bK0RfQ4bgYuNr0NSWwl4SuuUppQVZ1Qmgzx06SQpaFIVA/kj9JI76ypFMUUp1VR1C5NO70F8dyoD4q9QYpa6FfrxBk88Ys8/B9gR8j9blKhTSlQqB9aYNjKFtdLCQVeo9apkGa8uIJOgrlFBRyCm0gDFbNVUC/yQlKUl1ARkcSxuUvR2ZwdVgI1WhHhZEx+CkLh1kXC+EUdUJ5gLH4FpJU4UMkKgbj8kZjsICs4bDGWEVYMyucleBK00j1TUW8ktoH3z6VcQFazKFnEVr2nHL5Ui+q6fJdN16hQ2fle99BR2SpANGg6BRcptSXqkWLWYVhTWRgIBIsdVuiMqeo4/Il5CnCAfrRhT3KB4Wi6kJqunyan2KMqVsFJIXRLIRdqI6/nArheEO4SpGryKoGSSHzkf5zGvQ06BkyGXAVVh0iaxSuqDB0HXXkdRiaDdUo4eh3TV/NrwJv85FJUCFcO0KzXy8LCZHphu14aAohEYYKIqzmR0QFOqI7mvKVudDVy6C6gEyxSKiHiKvJQrwYkTK1DMMBQn0tBFE5i9KoDySyVOfFdvkCMOqcBo16UbHtGhXh76s8+V4v8k0FvZKWcSo43OS1qQsyKpEbjaQNb+ZAl4TuQ5KEXgy4ikJOuXyJ0qUpMNiwwZCk/9JAIqkAsUuNOUeGBlBjVA0hqRB6mUgtqI7pUzqJvHMY82niXKYEYwGXKSknUkIE0qpZyLKT1LnLgR6pnKXSkMY91F+dVYf4UYf6LILkTN7ZK/wNW1OUE81g0uS4hBIqu3wJWkvOVyMsBGTMZTVPv/QOYfu+MFKIvxnwG38q1QVkiIrA+XN9NVmILoyqYR6Uu0wXjbAQNOZnM9mjqNSHqH5+v0ZdKDolRi2wSLBmrYHivomQqKBX8o37fZcELkDOApJrCS/g7+qEarpM1yJEAN8mRP5al4QVIvchkD+sI3St31domTq4PHqknOGjB53yZPm+VE+hdrRxKj5Lbpl0EY5IQwbStJzlzgTDDNvUteE+TeOUhI2QTsoQga7kppNRpgiFLAutoYdU/SRp6FuWVVh1iB8117qiYmiauNzlu9DsgF8FqSNKmpxsKRSDgaQOp0EWgjD0XkR7EhXoSCUaIyjrkZ1Qnm/zVQRkGnVS/0muGgvBpZSuu+y+lGrJLhpkIUA1LsxShdeVy5cD4cFljEZib1l2B/369VuyZImWwaVLl44cOaLyVIYPH3706FFW9bRp0/r27Xvy5Mnka1ZlGT9+/KxZsw4ePKiVuXTp0uXLl+uUmD9/vt6x0ls8CxcuVLmHFnCOSDthwgT/JlT+32xpa2tD7CFDhrh8F8hf9g+A0DJ1Vq9ezdjxI6dPn544cWL+61dw+PBhl0qgVzW/+OILjvv27eN40003dZ4IQNWpI9JbXc8//7zLp6Fh/vDDD8p+/fXXHPUirdi9ezfHu+66S9mOjg7anDRpkrJZoKvnnnuORF1eMl20aBFjwfZcvnnUaNX9+/dXAoNUogjYj98dI/xbeJW+fcwMYp+I4d/7YzlPnjw5OZxGWAhLbO3atWiyiPaYem354Z/iSYXWqEwiZ0FVASOaM2cOAUTZ5Q9XjYXgUghfSLS0tKiEXr7//vukEhrkQ0C+C1WUdaFlYRT0+/7777u80UgsIOs+WJD68s6vwLLg+n/55ReXqYqVK1fSCIk1a9ZEi5MscdiGDRtmzJixceNGVxpAPKdPa3bt2kWCuE3lWeiPJ9YIfvDFF18kgTdBNhVmkeVMIYy0fv75Z5cqDL1rsrIYNWoUxy+//FLZ3//+9xzPnz+vLLrdtGkTCcWF8MYbbzCugvsox5yhFQTfzcaWOrPNpQqrZjZlxlD83X79Jb4sFKxUio9dFOXD66+/nvrHT+tuIeyL3FkRtfhYoSxPPPEExyIOZ+DAgRxrt7qQFStWrFq1SuqqiCvaQoAwlKMP2bdu3frCCy8oHdI4H+KDwvwBlgVN7t27l42gouDYqBoLyLqVkSNHulQ2rIGJEyeylR4rvSNNWBAFUhXBQvLr+dtvv1UCcBa4G+692LDD2CWEaz///HN9RUKAwmaQ/CAtyalTp1yqWsaNG6cEvSuRhVywd2Eh2mBCdANahLfffhvlnD59Ome8f/rTnzgSqrJNkhg7dizC4EC5oWxtbV1Wev6McumcWBYFVvRJlfxp1TC/27Zt6znRWO1WPXnyZCU+++wzJcrSq1cvJX788UclQpIf5RZEHz8w9QxBo0iNkOprITQyZcqUl19+uXg05vELasCAAUp0A6wd1mDxaOxqshBGjQNhQhWTYQOpP8bWaB8CRT6bzIIZeeihh95//32LxroNC8i6FX0cneMWvVc6efIka0mf4dcYk3luuOEGJY4fPz579my2/Bx3KR8BuIDvvvtOHxfhLPxtXxJ5N7xG6k9TMjSXKkxZRyAXnPo9i6I03YPeeOONHLnV6zyRICkY/X744YckcsbLviidcPvLkUvee++90aNH08vZs2e56tlnn8WfLl68mH2Uea80NsqKkouA/l9//fWe8y1DXaz6qaeeUuKdd95RoizMkUL2Tz/9VCUh33//Pcdhw4YpWxz/8cOePXuYfX3FnKS+FvLoo4/SUXX7q/86z9+bZS3Gqj8TitCn6cWjh6vMQkCfTXJTRGiV+o02NNqH1DKb+H+iMdxgquRGo3DPkhndAjc9rP+sh1X1WCurKKzgn3XFQ7miBP5ZXW4uXVEX/nFd8M3KC7DUlRWq4zKlBz+jp/51Vfj4qi4JO1VfSBuNkVGED6uCrmVjc/kAP5zoEu2CoQD+seWkcpDBl/uXy5LPw6IEfy0DUTVl/VXhw7khjFG9RIqqEVqjzeRUikjIJEiLoiL9i0ifEQ16qL9qq04iC4TkO3EhdOSNhPmlPr0r66FOVJ46oqTJCRkel0OqqgWn6mIhjCL1Ue7kIo2g99QRJRWo4Uc+IaLgQ/2ImtpOOC8hV6WFIDblwBCy1jLUy0IiJHyqtqGsD+FyxsUUuHwAbXLWZYx6YwFZQ2A1JhchdoyLyVp4qV5JlPVN8hSQ7FTuBsLNWE4k9DXJ4IOmIq+qZRxuDLok7NRLEq5nKtBXJLyv5vJdaLCc4hhdIrEjqeTOokJ5Qz9kaV5tMlJpmCNXhWrRAMHlu/w7VyUnRSAtQ6NO6n7pSb2ca1FXqD3BiKLhhCSFDJE82BjVIhhLvpCNCMg0m9VZdRIa0XRD1lhoFqn8KqNxTX1UnyyF4WLEEiiJRiSTY7AuH6B5z5kpoRlJChCRZWBAF+BmMYAG0YbqYNXesD2MjrEjgMuX4EKEQSSX74KmqJw/F0UCMkklCUMQJnWur2ILYdY4lVR1RC0WQiFdJy9EVPSQegkwHXQHLn850iHmpIkLoS8G5eoZDcACsoagVcoyw4hVwrKnMMdHZHklke+b1B2w6WrlAItH6xzCsANYbCqnQVXzJbQA1KEFsqE/ojxc5EiiSyJnJDcUguRhO6DGdQqdlOTt3GD8tYjEkF3tLuidUxxdvgRiqNzrVg6FpkJlSoGltv9G5FtpQeX+QhLSIQ0m5RHUkdia36hTFEsLkYqE/DugWF3FWEhHkxXhr0paQuoYPZwKZUtSUUAmnTM0l09D8lAnq19VoKlUq06FplCRRiSF61qO2BgTkbQcTItewJerX6ZGWaERcXT5En72XT6AOeVU1FcqyFydhYBfEanoKhpUlkH5hUZHiJ0qnhY7Lbt8l+VzdPkMygZkaieLaLmBJuJqtRCa5VTUSCpVWwgj4irwc8211KSdLJVCjg/hKg0zCwblqhoN4Hr+OU0b9WPnzp0LFy68VHoBm4U9efLkkSNHzpgxI+vBoJaWltdeey3/TW89ZvHee++FfxJ4/fr1X375ZdZbhHQ3fPjwSZMmJftdvnz5pk2bkBDn9dJLL1GNppYuXcrK37Zt2/jx4+lu9+7drNi+ffsyhC+++IJGVq5cKQmjfu+5555wdBoO13LhrFmznn/+eX/q+PHjBw4c0K9RpEJTw4YNi/7sMfr89NNP/QMlo0aNuv322/0DcB0dHVu3bt2yZQvpIUOGkH3uueeSj8dxOZ5oV+nPD+N0wjqcogUvFdISnSxZsoR+9+3bFw4z67EYqYvGGbUrKvXy8MMPZ807l6DwQ4cOKYvmsZNHHnkk6zkhhPnqq6+8kDfeeCNGNW7cOF+f4eS8T5ojvLj55puffvppdlyXz4CpP3/+vJ8L6Sp1jFVbdVmwou3btx8+fDjUNpb84IMPJucdkHbDhg0oUNsnx6eeesrrLZp9rOu+++7r378/l/hhUjh37txwjJxijR88eNDly1GFhbS2tr755psuk4Z/I4dJWbt2rXc4DHDevHk0m6V5Wn7llVeor/USKSSLO+644+677yZqd/nLkUJcJo3FixdHs3x1WwiMGTOm+CuKVVgIc7dixQo8ubLaa7LGCGV9CIb0ySefKJ2E+rTgMkYDsIDMMIxOCgZkxjVLfkBmGEaN2FuWhmEYhmEYTcYCMsMwDMMwjCZjAZlhGIZhGEaTsYDMMAzDMAyjyVhAZhiGYRiG0WQsIDMMwzAMw2gyFpAZhmEYhmE0GQvIDMPo5L/+1//6n/7Tf3IZw0jwX/7Lf/nP//k/u4xhGPXGAjLDMDr53//7f/+///f/XMYwEvyf//N//u///b8uYxhGvbGAzDAMwzAMo8lYQGYYDcH/kTvDaC4dJVzGMIyeiv0ty2uO48ePr1q1quyfmq4vffv2/e677wr+kd1a0Ohc5nL0Z8vHjh3bODHa2to+++yzo0ePHj58+MUXX+xODddO3f+WZTdbWvR3kadPnz5//nyXyaClpWXv3r0uc911c+bMyfqrzNXRXGuk9xMnTqCTQ4cOHTt2rOzfDi+L/S1Lw2gsBGTGNcK5c+fuvfdezfu6detcaePZsWNHN/c4c+ZMDZMEWxHQ++jRoynp06dP4yQ5c+bM/v37Bw8eTEfdOd66MHDgQPZal6mNZlka+Klnoi9cuOBK0+AsdVSZKcuvXAvNskY60tID0q60BkaMGEG87jKGYdQb+8ryWqGtre3bb7/F+/tNqNt4/fXXOa5du1bZbuDxxx9XYuTIkeNLLFmy5PPPP9+8efOlS5eWLl26cOFCVagvw4cPnzp16pAhQ1z+mqSJlgZ+6pnoPXv2KJ0KZ6mj9BNPPNG4T6qaZY10VN8P/AzDaCgWkF0rKFbgeOedd7qiboHt+dy5cyTYe3bu3KnCRnPDDTe41OXMnz9fn9xs2rQJwVRo1JdmWZpg6vXhE7z22mtKpMLZBQsWuEwjMWs0DKMIFpAZKaxfv77sDnH8+PGWlhaXyYY6bHvaI19++WUVNpF77rlHiUOHDilhNBFi9NbWVpfJoL29HYMs/lh6v379FGlxJ5B1D0CnFy9evP/++12+SZg1GobhsYDMSOGDDz4YMWJETkzGPjdhwgQq5G+TnOXuf+7cuc899xxZNkjCOJ2qBZqlHajvm4wMR83mD4pOVc3eo6ydr776atq0aTmRPZNCNI9B/vLLL66oAM8//7wSWfcAb7755osvvpj12ZVHEw0u3424jst1bdZoGFcNFpAZKRw8eJBdMCsmIxqbPXv2ggULVq5cmf/kzdatW2fOnDlo0KBJkybpiaK33npLpwShz/Lly68v0bdv33Bjbm1tHTNmDOVUcEUl1q9fP3bs2BMnThw4cGDw4MHUoUQU2ZP8vj5s2DAlgH6HDBmye/duml21atXvfve7hQsXJsMytj2qbdiwgWrbt2+n9ylTpuSErUZZVq9ejSE9+eSTqTEZup04cSJ63rVrF1bkSgtAZf8hGbOmQg/NHjp0aMaMGS6fBlaHaX399dc//vgjJoFxhhPtjRNIkPWWjIVgiq5eOZLWqHZoBBuDOXPm0HVqg2aNhnG14R7uN64Z9NhK2Xe7Lly4oO8Zz5w544pK6L0tdrsib6WxSezfv1/pZcuWdRpcaY9UiUci0Z3Ld4GQnHKZEmrEt7B58+ZSk9dRTmVffuzYMZVHw/Qv1oV9MRYKw2GqWYQPx6iBc3T50juVlHBt9ApbQQ33NOr4lqUorgcFT6jd5UugXnTLTCUNJgfmQjbjbSAyIaA7IJFlJzIzP62YAVmECe2BtBaIb5/6kc2IrF6oGVmj2oSwEb2kydHlSxS3RgohKqwOe8vSMBqKBWTXHMW3Sb/l+GClomiMyuxPLlP6KYTOnaF0uSvqwu9YYVQE9B5uJGohbBPIUhjt5albIIUaDpuW74h4kRI2YGU90pLfa+lawYGyHtpXg6FCimu4R9HEgAywCir7eawuGgNm2c+aBIDQrpgpX5JqJ6DC0PbUVFgCNOJr0iymGFUQqb1QmLRGBYL+HkbQMnXCyyuyRkogVbBKsYDMMBqKfWVpZNKvX7/wu8vi31SKbdu2vfDCCy5T+hZJ9/q0E30bOH78eG144fdW7e3tly5d4pTLX3fdt99+y3HI5T8qMXnyZI5ZDxht2bJlSom+fftOmDCBnQz5v/vuu+HDh6vCK6+8wvG+++5T1rN48WKOhw4d0ndAW7duRZiHH364dPJvzJ07lyOn8n9hwSjLxo0bmRp9d4nOq/umMkKTCKFdMZUYmzeAVAiMsNWhQ4e6fAY0ohBqzpw5K1as4JLQXJPkWyOLYs2aNSSmTp1aqu7w7yj4H44xazSMqxILyIw8wpisomiMcCr5mI5+kCl1w2BL4xjGauw6YTznid5HGzhwoEulQbj2comPPvqI/e/ixYts/F5++jp9+rTSEX5TVHeHDx9WNoKmFEqGP/huVIePyTC22qMxYBL1AeqmTZv884WENT5Qy2L16tWYouyEC5cvX/7999/rVMSzzz5LF5gWFkLalWaQb40nT55UIoluGFg4eh7OrNEwrkosIDPKgJefN2+e0o888ojfP/LZsGEDx9/97nd68FlMmzZNZ5M/EDVr1qw+ffqw5Rw5ckQlbKJRPHfrrbcqkfyhhPAJ/RDCtfFdJHf3b775xqXS0PdEIitugxtvvNGljJrxf+zo4YcfrjEaE/4vFxHfcyTM6tu3b/QRVBZEP5glNw9z586NPpf1sBy2bdtGggDrxx9/VGEW+dZ49uxZl0rQq1cvlyph1mgYVyUWkBllYA978sknF5R+S0w/deFOZNPR0cFVO3bsOJZAX/Gwe1FBlT0vvvgiR21vhFzshVHwxx6mp2ReeeUVfZDGccuWLchWcIvN4tdff3WpgOTvmqZ+MTpy5EiXMmpD31RiZkzo0qVLU9+7rBQF+iSI77GW119/PeuPS4ZQc8qUKc8888zbb7+9ZMmS/NDwxIkT6sLft9SIbDsk9QtWs0bDuMqwgMzIg7DJf1OZ/1sYIXv27Bk8eDB7ofs0IECPuYACrxB9Hnbo0KH29vZ33333kUceUXkIuyNx3unTp2+55Ra2zEcfffSJJ57YuHGjO10hyKPEX/7yFyVCtC8OGDCAo74JSv2qSPtiXT7OuZYJnxsLnydzp2tAgf6lS5cWLVrEnQBmqfIcMC3sEIMv+3kwYnNLcPToUWIyzLL4D14kGTdunBLJ7y59iNa/f3+OZo2GcVViAZmRSRiNsTNBwZjstddey/q0gK1Cj/az4UWN+FPLli1jb/PRUgg70759+/T8DcIAIZo7VxXqMXVvQwZ22UmTJpGePn26SpKfXnzxxRccU8NHoyBhNKZgoo4xGfcA+gSLxhWc5YPZM9EIUzYawxgeeughbi2GDx/+6quvUrJ06dL8pZEDBi85k7cH+m6dOEzKMWs0jKsSC8iMdKJoTIVFYjIuJGDK+dVN/7eWk3vtU089xZGNM/VxfpgyZcqDDz5Yxw8A1CN7W/TzoXpMjf1bY2c42iz1KJKHHZHIkp0yGT5mvfhpRCSjMVFdTPb111/rPQwPM+g/FfMf0Obwww8/cMSGfbhD4tSpUySip8TeeOMN1oKm3v9hylq+uFS8qG9XVSIOHDjA8eWuvzpQhTWmfilvGEbPwv38hXHNIFe+LPHLWyH5vzdGIfsQFcKfdxL6NaaydkUFkWxBl6f2e6zr95zYcgSjWFeC7dNVKqEn1YAhuKJs9DOwjMh3qgHOvPynOOkd1UEoM+1TM+odwWiQo8tfIdT9d8iKWBrKpFpShx40TCPR78zloI88mSyXL0HjFCaNQXYO4WTpp+lUSDtYF23KLElwVgZAAslDQ1UvwCWuqERF1ij5w5pSUfKX9iiEstaoriORqoPbMPsdMsNoHBaQXSvgpnHKihUAV65oxp2+HCrg3FOjIqGQhV3K5UuwSWjfAjpit3MnAkIZQGK4cyXYeHL2Lb+3JVF3yKAtzUO27G7E5orkQPvA0FIvYfNDeGRGQipQLdISMlDiOi7FIqlK6JnUKyCryNKolhONCak0vw5oRnynXBVeEmWZl8hOuNZPVjSJTDGNK8tVZCmkCwwmnF8KVQfUXXXWSAUalxHSDonop2JFWWukMJwFssjjzlWFBWSG0VCu559WrGF42trabrrppvxnaDo6On788cf8H9isAn1Zk9X18uXLb7/9dmRT9sSJE0p8+eWXhw8fvnjxorLVwah/++03EqmPr3na29t/+uknEkOHDs1X0ZXFzTff/PTTT7Pjuny3oJ8HK/sd9PHjx/Mnpe5olsMp1nf0dTf4LPQdeq9evfJ77E5rvOOOO+6++26idpc3DKOuWEBmXDEsXLiQY9Y7lVOmTDl48KDLGJXTlIDMuIKwgMwwGoo91G9cGezcuXPTpk35v8tvGIZhGFcoFpAZVwZ6aXHLli3R65DQ1tY2a9asdV1P+RiGYRjGFYcFZMaVwYwZM0aXnv6eMGHCkCFDpnTRt2/fpUuXrlmzptse7jEMwzCMumMBmXFl0K9fv88//1wvx/k/LHjPPfccPXr04MGDdfxlMsMwDMPofuyhfsMwOrGH+o187KF+w2go9gmZYRiGYRhGk7GAzDAMwzAMo8lYQGYYhmEYhtFkLCAzDMMwDMNoMhaQGYZhXJHorzkZhnF1YAGZYRgV097evnDhwilTprh899LR0dHS0jJmzJjrAxBm/fr1x48fRzDOuqo9BjS2fPlyZPa/okdi1qxZO3fudDVy4Vpd5aGpXr16udMBKAc99O3b1+UTqAK9S2+0U1AGwzAaS+dfGDcM45pn4MCBf/7zn10mmwsXLixbtkze495773Wl3cjmzZv79OlD7wsWLDhz5owKz507t27dOpUDaZX3EKQxBEZOV1QC+SlE7P3797uiNLhK4wpJVb5XDriiy2H6Ro8erQohiOFqZDNixIh//Md/dBnDMOqNfUJmGEZR2tvbT548+ac//YlowBV1LwsXLnzyySdJHDt2bOPGjf7PMwwaNGjJkiXfffddarTRRDo6OsaMGbNp0yYJHP2CMfJT+NFHHz322GM5H1Nt2LBh5syZRJkhyb+yTwt/+MMfCK1cPo1FixYNHjzYx4VIJY0hYWtrq+oYhtEctCwNw7jGKfgJmSAgwHt08ydk6hRyPk8i1OjTpw81Xb7ZKNwh7nH5DM6cOYPYqdU0ogsXLrh8OWhEWnL5AJpCHpfpQu1Tn5jPFWVgn5AZRkOxT8gMw2gIx48fL/ssl54GK/Jwent7+9KlS0kQBU6dOlWFSQYNGvTiiy+6TLNZv3796dOnFyxYMH78eFdUYufOnZxi4AwfLcHw4cNfffXVOXPmuBoBe/bsmTx5sv64fo0cOXIk+Tv7aGzWrFkkfv75Z5UYhtEULCAzDKMh/Prrr08++aQ2+1QIR6ZMmfLSSy/99ttvriibDRs2KJEatYTMmDHDpS6HsE/RD/26osuhnLBPaVX22SrwEeTzzz+vEqCLMWPGzJ49+5NPPjl69OiiRYsmTJjw1ltvcWr+/Pkco+8Nqb927dpdu3YNHjy4b9++CxcuRCp3rnL+8Ic/RKGhGDhwoEsZhtE8LCAzDKMhTJ06dceOHQQTqTGZorFz58599NFHqVFCxOHDh5W48847lchCz5O5TImWlpYhQ4bs3r37xIkTq1at+t3vfkdk48MyEjt37kRIyvfs2SPBRowYQahEGLR+/XpVoxFCIr2ZuHz5cn85oRvXUsgxDOCOHDnCcfTo0eFzY7R8+vRp1HLw4EE6ZfgUTpw4UWdnzpx59uxZpQXyXLp0SWkSmzZtQqpQ+Irwj9ylMmrUKJcyDKMpuK8uDcO4tmnQM2QEH9SMnk/Su35ZT00loX7JXXXiigqj1xj9+5iwefNm2iHY0oNZHBGDLIXIiWDITIl/ZM1f60uiJ7qonHzMS+89hE+zSRX07vJddcL2U1VKBa5SZZF8FMyT8wxZFmqZ6NDlM7BnyAyjodgnZIZhNJBZs2ZFn5PpI6jin43BN998o4QePy9Oa2vrpk2biMnCD4fmz59PCIIAjz76KNl+/fohxuTJk0mfPn364MGDiErJkiVLiHsoPHToUOdl1103d+5cCbBnzx6ViAMHDrz44ou04/IZvPzyy4R9+mpSoAoaDGVL/ZiKClyFYARbEgk5/Ud3NdLe3s4AUVH0BqhhGN2MBWSGYTSWMCarIhoLyfm901ReeeUVjvfdd5+ynsWLF3MkEPHvE+g5qiFDhoRxVRRjkdUbA6+99ppKBENLPrh26tQpjsOGDVOWjhi1wj5BJERcFZb88ssvAwYMcJk00BhhmWKyLVu2qLBGNmzYQJi4cuVKlzcMo0lYQGYYRsPxMdnvfve7KqIx/5P0XKtEEQj+iHhc5nL8e5r+06+CKPBCDP9wfWtrKxFS8uMlPevmnwnTh3zh4/N6yMw/QAboZ9KkSS6TAUHhO++8QwIZqnuSLIRR7Ny588MPPyz78Z5hGI3GAjLDMLoDQg09p0WkUulnY+GXesXffPRfdKZS6befgsBr5syZJLZv366Sd9999/HHH1c6RCHa+fPnlRX+1yuIpfQx2x/+8AeVtLS0pAZ2SdCGPiTLH2BZkOGZZ545evRoqF7DMJqFBWSGYTQcfVN58eLFdevWHTp0yD9PVhz/SLu+CqyIX3/91aUCyr6tmcVTTz3FcefOnQwKTp8+nfq7aPfffz9Hqimr7jZt2tTW1sZVK1asGDJkiE7B8ePH9+7d+/bbb7t8OfSB1tChQ5WtAmRgRt555x2Lxgyjh2ABmWEYjUV7v76pXLJkSfSMf0H8z4/t27dPibL4z+H+8pe/KBGCVBzzn9lKhWYHDx586dKlI0eO7Nmz54knnnAnLocobfTo0VRTTDZo0CCCUbIjRox49NFHn3/++Y0bN9LOQw89hHLeeuut999/v6LvDfv06VPL94x0+txzz1k0Zhg9BwvIDMNoIGE0pggpfMZfdYpAZX1Px4VlfxzV/7yqvl70v2EWcvr0aWKass9spfLCCy9wfP3111977bWs36GFd955hy4WLlyoVwcIRvXTGAcPHiQ+g++//x7Z9JtkFUVXp06dquJTRg8iEY0lW0BvilMNw+h+LCAzDKNRJKMxUV1MpviGxAMPPODfjoygR6IN/yi9vl4k9opiOEVsRX6rIhWCMCSh2fynvoYPH/7ee+9dunTpoYceksDJ7nIuB8ROPjPXUvqDVOEfAKgI9HPXXXclNU9f7777bi2fuhmGURP6OTLDMK5xKvphWH3yRDji8mlcKPfrr8RkNBL9Zmw+Z86c0ZsBNLtu3TpCPXei1B0NcpajKyqhn4FFEv/DrRIs6lfPqHF0+RJUy5JwwYIFnMr5M+ceyZwUWHCWppYtW+byATROF8BZLzzKpDX/W7JJNF7wl4RI7Cwi1UXYD8MaRkOxgMwwjE4KBmREFeGmTjo1zgDKc6IxoZgsPw6IIM6gZYVlQIIoShEPwqRKQmRDBSCyAcIsWnDn/uM/uIRCtQakkZnCcJgURi0rzHKZAhAnKbzjiMACmTlmDZ8e6UICkCAupDJSpUZaQDvISZu6hMoMM9R/OMwkXOjqZWABmWE0lOv555ajYRjXMDfffPPTTz/NjuvyNdPR0fHjjz+WfWy8ra3tpptuquKbsvb29p9++sllgkf4s6Aj/RXzsjULggD5XzgmiWQeOnRo2YGHX7bWS/LquOOOO+6++26idpc3DKOuWEBmGEYndQ/IjKsMC8gMo6HYQ/2GYRiGYRhNxgIywzAMwzCMJmMBmWEYhmEYRpOxgMwwDMMwDKPJWEBmGIZhGIbRZCwgMwzDMAzDaDIWkBmGYRiGYTQZ+x0ywzA6+fnnnzneeOONyhpGBBYCAwcOdHnDMOqKBWSGYRiGYRhNxr6yNAzDMAzDaDIWkBmGYRiGYTQZC8gMwzAMwzCajD1DViV6vtVlDMMwDOPaxl74qBELyKrkn//5n5955hmXMQzDMIxrm6NHj959990uY1SOfWVpGIZhGIbRZOwTsio5c+bMv/zLv7iMYRiGYVzb/Pf//t9HjBjhMkblWEBmGIZhGIbRZOwrS8MwDMMwjCZjAZlhGIZhGEaTsYDMMAzDMAyjyVhAZhiGYRiG0WQsIDMMwzAMw2gyFpAZhmEYhmE0GQvIDMMwDMMwmowFZIZhGIZhGE3GAjLDMAzDMIwmYwGZYRiGYRhGkyn6p5M6Ojq++eYblynA+PHjXSqb9vb2I0eO9O7de9asWa4og7a2ts8++6xIzVrYuXPnnXfeOWjQIJdvPC0tLfPnz3eZxoDqdu/effvtt+erjvldtGhR3759N27c6IqubVpbWz/++OOrQxvr16/fsmXLuXPnSM+cORM7V3mjkQ6x8OHDh7uiZtBDbLt7nJjRaK4mz9D9aNP/4YcfVq9e7YquNI4fP37gwIE//elPDXFrBGRFOHbsmLugGO6yDM6cOcPGoJrr1q1zpWns2LHj3nvvLVKzFuhl8ODBdMEwXVHj0QbZuB5pubjq9u/fr5rdqYGeyebNm/v06SNtuKIrnwsXLowePZoRMTpX1Ej8goKmW1TTbbt7nJjRaJi7q88zdBvsdwsWLJD2WA6u9IoCA2i0W6vsK0sUilrdpV0frYXK5ax3PTkQWnKbXqQmd5MHDx4sUrM6iHbXr1+/bds2hUfdyYYNGziuWrVK2bozfvx4VOcNKJ9bb72Vmuh56NChruhaZf78+R999JHLXC3069dPFv6HP/xBJQ2FZXv48GGXaTZNt+1GOzGje1iyZMnV5xm6jUGDBm3cuNHHZFciGMD333/vMo2hgoAMp4ZC87/R4+z777/vMlcC+Gi03P0yd3R06JujQ4cOtbe3q7ARDBkyxKVyYeIwNbYNdm5X1EVLS4tLNYC2tjZiYpcxGgZ6vnTpEvf33fbtYXd+9Z9Pjm0bxfEuy2g6DfXJnkY454EDB7qUkUYFAZn/kjEfvF7Bmj0B+eju99RHjhy588479QG4PirrmbAmn3zySZepN7j4hx56yGWMRvLZZ59xxOSUNYxKWbRo0Q8//OAyRvNoqE/2mHNuCkUDsqFDh86dO9dlyrFmzRqm02WMNF5++eU5c+bo8d5Nmzb1THU1ek3i4rv/m+Jrk6NHj3K85557lDWMimhpadm1a5fLGM2j2+Ikc85Noehblqlcf/31HO+9996DBw+qJETvU7AT9O3bl/T06dOjNwqnTJly6NChdevWLVmyZOfOndu2baNw0KBBydeywpquqAsu3Ldv388//3zjjTc++OCDVb/BpLEcO3Ys9f1QiUcUVeTt0bIcP36caOz777/nXmfEiBGUpA7NQ+/cm1KhtbX1zTffZKRvv/22/1SP1rZv367vPUeNGkXcHH5blKXk559/PvpSCWF2797tXx4hO2/evNOnT5Pmco4DBgwI1Rv2y+TOmDEj+UFjlmy4FRa8XPyCBQv0ObYk9HfhUgjX7tmzRyUQaolTW7duReCbbrppxYoVZH2MC3TBhXv37iVNp4888kjxuUPsCRMmkGB10OyGDRs0hKQNg/R26dKlixcvkn388cenTp2qUx5Z6ciRI8+fP3/XXXchcCRMWWVS4a233lILTNDvf//7ZC85sAaREPPmzkqvS5Oo/YNhKadPnz40ziRGFuLXFH0xU5988glZgkLMINVUDhw48MUXX5CO1jJTyeVKw7Bhwxg7a+Hs2bOuqKuQBNOBwZMIhYlsW4QvgfqlkSUeLRCUaIKw5CrescpyYjn248fi8bKhrhMnTpDo3bt3aJNlDSnfmaSyfv36pUuXksDVK6anWXz7L7/8Ujrfide/1p1OUV9aQh4KNSNaC3hsVmXS1UPZIeTDhfn7jiroRT/SWt3o4amnnkq6CIbzxhtvyCwZO8OcNm0a6Ur3TUb96aef0hHpyB2FGvNG6+c3LMQesnwyjTCor776ikF5BTKo0Jw09UrneFeaSnXOnTUKoOHI8UYLGWRLihmQE5+mvTtV+VqhuBf0n2otBZewlph+asB3SoPJTRDQCd5GM57cTyE1VGDUXIVz1rde9FXlLzZ0PopfLWoB5bp8wJkzZ5AMo1FWLzoxu8oKLqSQOsmvOHfs2OEqlfA1Xb6EXiDYvHkzqgG9RBZ1UZxSt5mvTugsXbh8bTBePxYNbfDgwcqGMECGowmmmn9ZDHT5hQsXaIpT1CSLzjlF/XAUXnU0hfxk9Zg/1bhcdZYtW6ZC8NfSGrr1hUCJTgH9cpXKaZk6NO4bhHzZKCRRarvzvb9SM65fbwzKiqgQ89B0Axf6NKiCCtEYCY7SYWRUOXCVWiOhaz3RMGmTQqopi07IRh2hH+TXVQycFiJLLqtMGmSCpEnKqYNidaoIUj7QL+3QFy0wruIKSYVm/YSCrEVpoU7RP8NBYJAyk8Ijkm+KASIn1bhKQwZOlRq7jpoqAWkb6MIVldBK0bXUSdo2CWRQIZXRvyRUCVlV84SzrFGrplCdsqj9aOrL2o+fu6RUNIUkoZ3kGBLa0KRTiCRSkYhEiuByWqMdatKCGlebdKcWEFKVPRSqWX+tSmiB6fCqhsgIc4ZQBM2OH5GGSTvKAo2HSpAw3kKigfjWJACi+pqqUASupQtGIYPkKL0hlR8XCbVMTZVAshB5kj7ZNwjUREhkJqFhQjh8X9PlS0SFNEjLKomcc1mkUq5SVi1zVBZQJiWSk5okvHlEvchUpDTpkBH5CaIyJbowZwlzuawI6BrUqZRD2k+BQHLOlkZ8TKoOOxWlxi6TlkboncaV5RRXhRWKU4FhJZFkDMDlAxhqJLRKwrFJfZT7+aO+pofxaCaEavoBC2r6C4H61IGwsDi6NkuJkorF7PI1IDn96LDLUs+Z4YLsCS2pd6qhDalRVliq5dACCAulOvTp28d6ZI6p2gs1QFqFLt8FkoRrDDS5YWER2UptxzpP7TRZ6AWWNhgU7UtFnIpWkVdytLSy8N3RpmaK9jE/FXoz8DIoCyqhd5fv2lDDMZJGZpcppkwaDI1fM+gyBZBngdA1k6aRULBKQcJwIEA2bFCdMhyvdq/YcCKk2EgS1EIh1sJgVUKaEq98wRAoxCO7fAnGG47UT1PUhQoRz5d78TTpQpeHI5U50bWXrQi0wFXhPBaxH5AHiFYTUB5qo4ghqSnKdWHoTPJJCg9e/kj/Kg+VI0k4ei9EpyoM1VhkCPmofjjRKknaG/2iDd+1BhiZjeq4fAm/lFy+ALScNBUZc9i4pKKyy5dIFnoTdfkuNLN0xCXqi6MGBUkLV1akFqok1GRZUDKXhMagkrAwVL5KQHKGs6wlFg5cJZElUAJMcXKATJ9KQO3Tqd/yUA5ZCr1BAkLSVDhTXqsuX6Kz9cs1I6twmRIME1ymEiowrCSSLNSaR6MNl4GUEg4jOQ3gpzA5YeEINT2Rlata0nMVobPLXPsLx1ILOJ1waCCXkSU2o+ZsZCggDYT2BJgUhaGblk7CEkDnFEZiUAKhBrxxu3wJedvIBUtIkJAFZStdEes8tdPUQpX4NeZhXEllqnKkhyx8d9GkS2+g1S5bTV2uLtM16mhxes0XUSZH0syjzopo7vJJnW4JFq2+ikDJjD00S2Y8nM3OMSRGF/lBaQDzVtZDs6rpVSejirTNoCiMhoZgkVFRB6LCpFeBZE1NR9lqZUl2V8R+QCqCsDvpR3YIRQzJlySdSVlSdQXSf2ScVItmRJdHq4/hUAhawgWHkI9sJly26jpUne/X5Uuoo3AgutBrWKRem4OMNtIG+Ha8YEkBIFmYJYBqRn7PLyK/zFMvTy1UiRevCNhVciHLS3g1psqpgCa8ViWhd5KQkX40R7Tp8iUogVDyVN0mr0WALBMNzVIlYftcFZUw3kiqgjTqTyedPn2aVeG/8e0ooXTEyJEjXaoEl2j/yP8dI31b/Oijj04JOHXqFIX6ir3uJJ91qI5NmzY98sgjLlPiiSee4IjYx7PfMR4yZEj0IMW+ffs4Tpo0SVkxdepUJjX5I8i9e/d2qRLSuR5oqBQ9c/DKK684pZfYsmWLzur5pIpkq5HbbrvNpbpAw6wHJ1kXOqUnA4oTTfpLL72khCyNsxh5aG9Jlf7DP/wDx6VLl65fv94vAf8z30WUybzj0Q4dOjRr1izffkU/FK6lFFmdqGWx4L8uXbqEwN5ukTD5IMgNN9zgUiX0pqd/luXIkSMckz/Owqj16InXBuaEHuixtbVVJaD9JnwtBhVRJylG1Zw/f94fPfLmNVLEfmDQoEHqLvzNQvQWPqRSxJA8SWdSNTIqjDP0XfSbamyRF2KO2JhJ6I2TioaQBcosuO/kw0QwKKyrmseAAuQJk7/1wNhZPiS2b9+ukroQTStZRczd8KOAbW1teF3t3SGff/75xYsXIzVGcsqHc7myMH/+fOKbt99+2+Wvu+7HH390qcaAASPArl27nOWVeOaZZ3Q2fFw1yYABAzg+8MADO7t+F4bxFn/qLqRRARkCaVUwzuXLl69YsULlRVC4EE5PElYLBn3wcpj4UpTpYtgeCBPGbnHixAm2Z4/39RUtTj10WS/HWhw9mk3vTuklvv/+e2leG2GzZANtDOzlTrIuJB4JVasO7+h9PEGJfA0zu3DhQm0qIVSQTyQmGzt2bBhMQBFlgsIvqrGBYTAqLAiOEpMjEcUo+S6mCISn7FhsgRMmTGDs1e182oxT0Tbm/QDmpBDt3XffVQkQimlTV2AHW7dufeGFF5SuCxIjdUvr1auXS1VLWfsRL7/8Mkecno/Y2OnnzJmjNBQ0pLpDywoWve/C3jgW7HHy5Mkcf/75Z451GUIt+06I7rhq/5mYnEhINyGpIXgd+eMf/8hRHqChYJwco5i7FphxljxepaWlhYX/1VdfuRONQa9QcM/jLK8E0aTMLz+6mjFjBl4IJc+ePZswTkugOhoVkAFLAuEY57PPPsuOUnx7HjZsmEvlkh+x9Uxef/11Zg7XE8Ka1N0Su0uj12e9+Omnn1yqR9I4NWr7D2ErHTNmDAmMPHXdUq7PtLHYadOmsSii2KWsMvFHO0rP6rLmCexw5cXXvH6BLPmJDobHMfnRVHHY+QinvOnecsst/gaxONqMU0n6Ab0ux54tBdIdW+Zzzz1HmpXFkXIkwT+SrhdqjblTrC8IFLAE7f01UtZ+wMc9+s1ChslOH30CDU1ZlQoWve/avXu3PvIvQnL/rn0IVe87If6Oq0ZyIqFRo0a5VCOJPp++gsDIufl89NFHb7vtNtbIfffd5040kurmHRsjetMKJTAdMWIENwM6VSmNCsjQIPfN3MPhYipdErIhOfp8aglFux88BW5doXfEm2++qTrh6/1FCDeJ7uTrr792qWyaJRvoBrcRyJj1GTUsLPHOO+/ow5ssVq9efazrtSBWbBSTFVEm7Z8+fVoftmFFEydOLGj8qqZfKwjRHe306dOVrQ4ikpMnT64rve6nG8RKYzJ9PpQ6X8m9hO4UEOuTJEIBQDP6oI6A4MiRI2Sr24OzQELmji70lQRWzYyTZdm6GjVQ0H5g8eLFHBX3ZA2ziCHVHYJFTYqCRcLlSgPiG2+80aVqHkIt+06S2t2IdrEvv/xS2ZA6fphUFszVpRqMbvNqB/eIk/zggw/ef//9gh+O1oXUmSoCxoZD4LZZM75mzRrWtU5VREMCMlwGrhkjKOtlUtGHh/o0OwsNW5tKBL1Xuit0D9u3b8/aLaZOneo/aQi36hxUP/VbTvaMgo1UgXZQ/cxMEn2h1izZoH///hwJDlLDQQxD9/FVI5PTdxm0xnxhqDmflNCdxotbYcXqYVWiB4UURZQJCqqozB2/ggMG+Oqrr+psPhqvjyCF/+Y0+ommipBU2DObHyPSrlypG7rrrrs4MpxkfKm9OXoqRZ+HEcEwhCFDhshZy89wM/P666+nPr1UI/RCKAzcQOOd7r///s8//7z2/b6I/Xi8i8ByksMsaEgNQpPCcIBJkTBF0NMa3F1wrH0INe47IVovmGWN7kK7WOoXl9r7a7wjKosevcrfTOuCNIZ7TNVY9KhGWVasWIFLmTdvXu2rrCCSn5lK3Z7yzc/7LgyPG1TdNrO0kz6tLA0JyFKfhCi+DWuVPv/888qmIgtbunRpNGZ6YUtIfphfOzWuTC5nhnJ2C33OjwvwT8PkIw3QZmTrKOStt96qix0ruIEwuNEOysJL/j01ND9u3DgSBWXTBqP4O5/iT3Ti1tUsd8mRydH7vn37iu8WSWQDrDc1ojdLQpJG/tNPP4UTSgCkV370O5BFlAnhWYID/YXjnC/7QlK/GdEnsooOBZKnhrA5hFKhEMJNRYquqBh6VJ9E8uZKS/upp55SVqg+znpxCRUqrMTe6L0R99PMxR//+MfVq1cTeoL/pc0aKWI/IXqof+3atclhFjSkqpHBZ33+wT7EokMq+gqfbCsL+x+zqU/Uah9CjftOiH96rNKvLCLk8NFMcnFhw37sWUSvkkCqT87h008/5RgtoohU71rcOQuvseRz/Uxfpd+cspZdqotff/3VpRqD5GemFi1apBIPtxkulQFG68MDdjdum/VlyG+//aZCKBg/1CEgS1q8PoxlbNqPqcAaw/5Ia4LDS8Kfewa8MJPBVpHcOMOPE/2dPXdXNM5VtInixo4di0eoNBwpu2jHjBmDgVb9xTDo8/yc3cKvTD2TURYCVu1k06ZNQzDWJ6CBhx56yL8MmEWk8yz8FGgxoCX0zI6otfrkk0+qX9JM9JTSm4waYEHZ9ACTF0ZN+QelvV+m3Ouk7EyBnuk+V3rRkk5ZCcAtThHNRETdLSv9yujKlSuV1VctbCpabBz9Q8TI7K+N9l09GqVjEWUCowglGTp0KMeCz6Do2YtwxlEsHgS/6RcRjd9yyy0TJkzQw0wFYbwSWLDoGEvyGbuISKXyXySIM8JTpBk1QkZLhvr+8w8fGOmrTBxORY/zf//99y6VIPT+iIFHOnv2LIMV7kRVhE6soP14fNyTfEiroCFVjd5s8CIlleDfAEUSJZJE3wdhhyxS/5hX7UMouO/k3N35AeL69NznmjVrMAAVgv/UPyzMAbH1eUn4hiwwOsb+6quv+q1KwrMwdR8CCK9eQktI9ckqAUw6rKzNFAG89op711TnnIPXGArHjSA5l4BWa/70JYMtbR979+6VSAzklVdeIYF+OHo5Cy7hZFzr8QNEfoWSu3btQmZMSLolmnz99dfnJv5uZPTdenQzIOcsRw20hm3rT0eoJBO9RFApGJNu9AXxU/g7MZyVQoFQEVF27Nih0VKO69TvxPgfFqIpSrhKTy7TmtoRlGuhcgx70eURFLrTleDHgkm5osvRWSR3+QrxH0WEP1USwSnVAZaQRsrYFWtD8lqU5vUsyIa/mEIFldOIVx0Jr0/aVyFXqSbTpBKh3mmWhG8k2S8gsy4RZWUDr3Za9lYBMhWgECE568WLjAeoHFqFkBMMoXfffllokI64Sl2TRfl0RKHXGHippB9E9aMmTWXqaFq9XanlUFdJRUGkTEq88oGzNJIcdRaqT0dcIp1zdOdKhLZXfAX5ISurxeuz3ubDNcVZFUazpsoSkizHSEshkjaSUy2k6iTVtkNJXFFGTV8YwWAjNeaQ6sSK2E+EzD40Qo+/NiTUYb4zycdbCFJBqpEgc9aUqV/EQ7E0hSTMF9monbJDyIdm/eXSJ+3TI1nKEZv2qaYSUBaYEa+ZsJBLVEiCChwRRiU0nrVZRNCOruKoiWPek2OnGm2qcfqiAu17D4nM3my8MkmAylVTw6R9OuKYOiN++NSkAi14O6TED993TYWwPB+vH08ouR8jckoVwovkzZKxq4T69I4MoQVqUIikEi7XVeDH4gt9pxy9JN5UwkISNF66+m9Q4iuAFyPsVLqia2URjMbD+fXD8QPMopq/ZUnYm/pJ5owZM3z8TmhJDE486P8aFCV66uWll17yz0xQ4Ujp747p+5fp06dzn+QbAQLt6MWHAcGfqOPyrbl/dqosqWMJuxCMhWidO4kq7jWjLpKNQ+pX1Ogz+SH8ksvfwyKK939qLfq7b8mhcfann35KFiZ78VNJ+9y1o+d7Lv8DYZRzlR74oOYjaX8pMkc2DwP/5JNPaCH6s2JSuP9TaBgP90ZeqqRVjBs3LhKA4XM7qzuSrN7z4bbmL3/5i+Sn3/vvvz/5XRV13n33XazXd0HJm2++Sf2VK1eSRYBvv/32r3/9K0OQG0r+xdWyytSNsl78Rp4qhkML+qCO9lP/ghsKZxki3sMPPxzZWBaMlOPZs2e5Ab148SKuzS/A5ATRZmqhS12+llOVELJ8+fLoB+3QIZcnJU+uLFSXugqShb41WanSdKQPXQS+uKy6kgP3TqCs/ai+R/OY9T5BjiGh3rLOJB8JlvVnB2HIkCHIlnpqStff8WTg+7r+kGWqHZZdC/mU3Xcie9BERIWhM0HhXmCE6dWr1+7EH0UtQuiOsrYqxi6H6QdO75SzJ4aWQLWkT2YI+huRbFLqKGem8r2rR2ZPYepMZeFN2o9C5UkLRM/9+/dPFuqSSEhKJA+jxm6r3t1SOw3H7mc87FpEdgKSFmFI07X/KC6yW8ZOTNa3b199G5CH4jLDMAwcesFb4WsHfcriMpfDPTEac5luge6iT1Z6CNz6E5G7TALEZq8hIHN5o97oQ5putkaj7jTwd8gMw7iC4NaQe/dKb/2vbrjdnz17dtYz0fV6ur8g3GefOnUqvGXvOWzfvr2+v8drGNcgFpAZhtEZjX311Vd1/8NWVzp6HTXrMfD169f7Nz0bBEEYQSHHjo6OmTNnvvjii+5EDwDB9GUNxgMzKvz5McMwIiwgMwyj8zkVi8aS3FP6Qd3Zs2cvXLhQ8YcgFJsyZcqAAQMa/SHZ5MmTp02bNnjw4N/97ndkK3rqq6EQgSHYhAkTrr/+evTz3nvvVfRQo2EYKbivLg3DMIwE60p/h8C5yxJk/RtzjWZm1wtoxGQ96vE+/44b5LxviJZ2dL0QzRD2799vDynWlwsXLqBVdCslo+2yr/IZPZZq3rI0DMO4pjje9TtMvXr16ubH7NR16utyzaW9vf2nn37q379/zit4quMyJbpfgVc3HR0d33zzjct00QOtxSiCBWSGYRiGYRhNxp4hMwzDMAzDaDIWkBmGYRiGYTQZC8gMwzAMwzCajAVkhmEYhmEYTcYCMsMwDMMwjCZjAZlhGIZhGEaTKfqzF+2JP9XuGTZs2K233lr8r8FXBwKcOnXq008/vXjx4s7S38AXlI8ePbpv374nT57szp+Kbmtr++yzz44ePTpy5Mie8/PZdef48eMHDhzYtWvX999/74quRjo6Oo4cOaI/8n/w4EFXerVzFYwa+zxx4sQHH3zw5z//uZbfXmptbZ02bdrMmTND39J0cG5M0N69e13+uutGjRr1xz/+cezYsT/++OOhQ4d6vufJ8ts10nPmq6AFNkgPTYQdcPfu3WwN27ZtuyJ+9gx398Ybb5w7dw53x9oZPHjwE088Ea2gevmT6un8ddhinDlzZsGCBbqqT58+60qwJPQz1gxvx44drmq9OXbsGH3px4ijP2i/f//+kkTXdefPEzPSZcuWaeAI5kqvOhga2pZ6XdHVCEvUz2ZkXVcxV8GosU/vkWpc/qhC7bh8s7lw4YKGxgQhG6MDfJ0fL/T8WUPmLL9dIz1kvgpaYOP00CzYARlI2YH3HIheWEo+RPF/Z4I1pRKooz+pmsoMGicucSOrYiQqZzyuqAGol6hreS5OuXw3IotsStfdBnZZmtgeHZBhAy5VA3LxV4e7LM5VMOqSedbqQHHQ3FuG3rmJaPNgUIiUtG2c8OjRozl7pcxaqt+uiKQSetR8ddpfAQusXQ89Cial4MB7AkTDUXBCcMY6YjW5fBdZg6rLLlOWyp4hy/pecsmSJQotN23a1M2fx/br12/jxo1X8ZeGRj7YW/Ivh1RB7969Xepa4tocdZLhw4djSI3+S+FFaG9vnzhx4qVLl9i5ESn5GAZO+ODBg4rYrhFWrFjhUl30nPm6ZrmC/pY8pkLgNXDgQJcvMWvWrM8//7zgo1b6utNlGkndHup/5JFHlNi3b58ShtFo2traFi5c6DKGceWDPRONkeA+UyVJdBfqMlc77Kbc57uMYVTOp59+6lLVwi3BF1984TKNpP5vWf78888uZRiNhLuWefPmafcyjKuA48ePHzp0iMTMmTPz7925v7/xxhtd5urF7riM2mlvb+c4btw4ZSulO28J6h+QhX6ELZPlNKXEkBLr16+n0J0OaG1tVTW4/vrrcTf4JncuF3Td0tIyZsyYsD7LePny5XRHoWSgTaBxTrlKXVCByrTA2b59+9I1E8CFkKxcBOShKTqlKRqkcXeiNEwKOwdZqkBNd6I06zpFHVdUkg2NMRDJT4VIJDUorXItdRiINKyBU8hZdUcXuioHRk0L0gZ8/fXX7sTlIIYfCGOkftnGkcePkSySa1xcjoSqE5JvEggwduzY06dPk37mmWdUTaey4PJQZhQiRSVBHipkycZVzCmNyGwYRTR2ZGuE+VFONTVFIrSTHLiqc8BTpkjUVFWL/FEjjMxJQ6O1SDzkoQLXkuYUNSVqpGe/YNWF75Q2kyPiQipwSk2hnKQCC0LjdIrwGgVHd6IEzWpcLl+Ckk7FpRGKWkchDxw4oMTEiROVyGHNmjUuVUKWJvEQBpAq1DzpsguQQr9GaM0PhGulDc5imSoE6qsyaOzh2VSkLl1CmyrkKq9tX4i0+vaWdHQqdb5EWZEqskApBNAG1UiEKq0LOV0wTD+nlIcTyqB0ys+dyPcSapCmSKNA6tCjn2Waklo6pSl1p/Ky0AKSqFMSvkFP8T3RX5tqcjIe2S1wKtlXiFo4deoUab9NeJCEo2pmQQuzZ88mQSO6Cg3rFGBLyEw7CMORdGQeGh0yU861VEPDUZ3LcM+SFUZXJZ9MTL6eoEdTwyfpNm/eTIXkk3TUoSb1lfVPrVJfJSL5UOSyZctUE3y/JPw7OLRABbJcq5dcyKqauHDhAiW+Tf84LZUp5EKVp0IFakYP9UsP/gHAHTt2kA3bQTxKIBod0GCoLhpBDP/gqpryiqIdbqPVFDKQljwgVTCuUDYq5A8H6IL2vSaZCCkNVCIkCUdlkVOjpguVpIIwmiwpliMluhCi53MLmoSu9QLnQI+04Bukay5kdH6mVKIpALJcUmr+byMFGQy4fNeMe/kRxl9YR/MLp89XCwVLQuM0woV+jGS5KpymgqNWj6E8SEiJX8hUpll/IYNSU5LTX0h9yn2h+qVEgoUNAmLTS5b9eyiEfBugES70eqAFLlGWHpFBswOqIJAqMkvJSWXfVEEhC6L2oYhJh2iCGIjLpzlb9EyWQnqR8inhklKHfzNgDQGSMlDIWZepwW+DRlq2EBkoAZfPnS8oKxIlxS1QqvCa4RRZGlTWQyGUnbJUPZTtAgOTtEnvSoPowWVK5HgJWpZyJIOGrCxpKkgbpUs7ke9ymQy4FlSTRkDajqSiXwr9ktGQvZygEsg3uarXmiZXwxRcIhOKpgMohFASLkytSR3G63vXMCnRSDnrXSItkJYYkGMq9QnItPjBa1m6A2U9EjGcaV3rlS4YZKm9y/bpVL3QkWpGg5Spee2AbB3C+VObfrmCSpKSJ5F+qe/ygdguH4jn8iV0IfPn8l1EtkW10GpBF4baI02JvxB1aSxog/JQJww/Ul2ELgmHAzRIIbh81xgj4TXdlEcCR3g7Cadbdhy6m+ImoZIc+xZauqFuvTH41jTvkJQt1JtkC0ukoki39TU/CsNJBy+tbz8JwtNOWEF6CK8qOGotWyq7fNfrmWGJn6DQNtQj2nD5EmotsnbZT6gKBChr/0AJ5NsAF4LLlEDI8JKknUPUu7fe8EKaLSJkQdQ+lDXpEK0+cPkuspwtJKc7XICai3AegckKu1BTYTvgbSBcpLKxSP8FC9FDZ3OJwCt1voqLVNACtYrDuUCZUQlQkixMkjrkIl3owtT5DaWlWmR1uhD8eteypVNdyPCpo7OUky7VciR7jCi1ne7lvPxe/8oClaMSKGJyqK66taZq0eiknGg6gEJI6j+qyShQY2hUKqFmKKRmk3JZGvXDKUsSG3pZOoUtGQdSAhqUKv0cC9IURuoDP2F+2WgMfkY9chPhfKTqBTqbS6yH1DlI1lS1sCRpQFkku2B0DCeyj2Rr3ptQ3xWVtq7wQkSiQjQoaSC8UDIk9awuQicL+aageYwmQmKAy3f5stAQBfKrZjioiGRrkJzW4ibR2VZCS0mSkwIojbH4XlKtS4PicpfvUmyocA0qujBpG0AJhNKqWliS6r8QIGrKazLafjxqJzIMmmLIYWHBUXMJJeGk68KyA/RyunyJ1E4jjenCsCnQhRDamEqimhFyi/KJgvpRVu24fImwF9Iyy+TUFxGyILoQksafQ3FnmzrM5HR4AwjFoP3QgUgbSTkL+u2ChakCQ2p5jSKRpZBTLl8yG9oM51F1ohmnJFmYJLXTIl0wolIPly12Ta4fLAnaCYUHryV/oWRIOkN1gYpC7eXvF1BqOx649hF/LXIiWNSjLnSZEmVNTmOJ+tJwIFRgkuTMQup0QKm9yzpKralCl+lCHYWWppLk2syi1mfIBg4c+MILLyD9xYsX58+f70qvu06/Lp18qX7QoEFyjnoZ8/jx43pEIPkO7f33388RRed/SVwj+gH68Emp4cOHu1TlMDr08PnnnyvbUXpoQ+mQqVOnymo3bNigEkAhzz33nMt0PU2yatUqfW8tPvnkEyYYfvvtN1UTST3feuutHHft2sVVXoHhBEVQB1UjVdmXmWmT4w033KCsZ9KkSUocOXJEieqou0m0trbS4MMPP+zyXaxevZrZyR/vTTfdxDF8b4C5Y9lwrbLt7e3+uZ8qKGJ+J0+eRIAPPvhANiAwDFmCq5Rg9+7dHO+77z5lBYNlyF74LJKj5hJG7X9lAP1jiko3iIrsvyxjxozhOHHiRP9YzPjx48uu9PBxWFwqCmGBPPvss66o3kKGVPRLLgWdbXFmzZrF1sh4w7W8adOmGTNmKN10v52kESLhyfHn3gzwJHX/gyVFumBEiilffvlllQBTQ6EfbEVeIqkiSrBtVDR27FhkUGHOfpGDHuz75ZdflC24J5Y1ucattepA1Qjs5OiCcoRJPtpY/NeFqgzI6HJJF0xb8o8M6F2hVGQNehnzxIkTpbIU/JbfUF3LYR09elRZjwKmqtGzfm+88Yb2tiQYFkdMU8/3cSTQ8TEN6CVbwvCDaRTZS3TPwUSMGDFi+fLleQ8SXnfdZ599xjFpSRH+4cokfpH7pVgddTeJs2fPulRdQRVM8datW2+//XZXVDlFzE/yz5s3z8395eDIVC2iQS9pY7H0iLWMGjXKFTWGGu0/YuXKlagad7906VKMPMeMU2GL0n3Itm3bws2svkIC3lyJX3/9VYkiFHS2FaEI4PXXX1dW8+7H3nS/naRxIuE5ieNZ7ExKWQ9ZHWW7eP755zkSMHnTfeeddxRoiuq8RMiHH35IhEEX06ZNI7ZgC3Mn6kTZPTHf5Oq+1mpE75O57hOoThXU/y3Lstxzzz0ulUv3/CWpp556iiPe1t88yeIVMFUBSwuzmzlzJkto9erVWaPA1Nh02SHY0clGtzueWpwaXewvPctMes2aNbfcckvODWKNUZTw20mDqMUk6jJAgRpxWNu3b2ebZ4qz/EsRiptfdfJXtK/nQ1DCVoEYOEruwbrnF2XrtamzsvCS+radLWfChAksUp0qCyv6scceI7Fs2bJUC6xj5OHD3L/85S9K1EhBZ5tk7ty5HNl1tDHv27fP/9JkPt3jtyuiFpGIk8aOHTtu3LiNGzcWCWuqoEgX3GArXpFnYFLYO5K/i1uLlyOm4c5Qt4j6U4+sdJ2qkYJ7YhGT6+YoPx+mwKXqR6MCMs1rzvca+hGdYcOGKZtzz9qrVy+XagAYx5nSk4MTJ05cvnw5a2POnDk7duyobu1heWzVhw8fxvv7T6GzeOGFFziuXbuWI3fe4e2OJ+u2j45cKhdW7Hfffadvu7Eexph/35Nzqy2GDh2qRM79aI1bdYNMgklxqdogckKNWC/eMxlAV0px88taSvmWUK99HdfMfTMWW923GFVTo/2HMFmM4ljpxSiymzZtQuE6lc+iRYtYO9zYhF9WhtRRSP8Vsz6QK0hBZ1sRuC/Fr9wx4jTYJsN9tOl+O0kjRCKMWLp06YcfflhLSJdP8S609HDRuKA9e/ZodiKq8xIeYrLPP/8c/6Pb+NmzZ/uvL6uGrgvuifkmJ+q41upClrFVLU+jArLJkydzTN3gZTQPPvggR+4MSmUpitZQ2au64dNIpP3oo4/whtymfP/991XfCWFJmBGtFdmqZ8yYgd3j69mG6TS63dG98pYtW5JTS0nZP+PAolXshSRLlixh11dfrGRViPDuzH9Ukwqt5Xh/TXf4xWsVVGES+Z8DaWjMS3LxoMnwp+CKgPdEjbLeepFvfpJfjlglIcm/KiNkP4QdSfuhnYr8LC3o86QaZ7YiarT/CD9eXDxbjj5siH7HKxX/ZeV7770XLmrZUn2FBMTTx8znzp0rO0e+04LOtlIef/xxjpgQTkN3j57u99tJDUfUXSSuYuwN3YAq6oI6sg1cFianz5M81XmJEG9v+B9u4+Xn33zzTRVWTUV7Yo7J1X2tVUrUr/Tz1ltvKRvCtFb9IHWjAjJvLsmPPU+dOoUJatdhkuQcUXTp5N/Qk85Vf3VYEMx33rx5K1euxBUKd6Iq5P7CT6F8OmlGjP3FF18kwR6fvN354x//yBG/HK0l2uGGI1qNSX777bfQJljM+X9rBXemuyKEUUmEl19vHrDyw2GCHAGzWfajwXwqMgmtivynxPzQHnjggVBmabLSIEPb3g8//KAs/PjjjxyT81uEIubndxpqRr0QJ911110uczn6rIXY8dFHHw2vUo/JLzty+Oabb2iHxE8//aQSOH/+PMc6fhEcUaP9R7z77rsuVYK1gAtymWzoS19WsjwjjekR4+JC4gZTd8okyCZzpeucSzhFF5rZgs62Uhgy1zL1+AT/bLWol98OLROSt3n+I/mybznUfStRYMfkKiv0xH29ngSotAs91E+8goVEbrY6LxESrhGUWdFntDlUtCfmmFx9HUJF+Ft6ZYXugtBStOhYmM8880zVn+nE723mc6brnXw2QleUjV7GRr/Rm7QYU/hOKWflHMOXbFWIH3T5EvrqDVy+CxVGP8Qgp8YlLl9CNXcELw8rEmIlUxOQAdmAiXc1stH9CkeX73q/GiQMHflIi5LwNXvBMCVnanf+kSy0jWxItaz0S5uh/KqTfKuWypHmuYqaSRk8mi9gFLqQoyIeoAvaVE0NKpodslGPSXwXLl+CoVHCtS5fiUlo+CpEh6FmQjR2QHt+rmktbJ8BUiGcTfAXunzXS90c0SQi0YJXEVmv3vqaH6d0Lf0iJ7ZETdJc6Gqk4a1RV9EIRwQLbaDIqBFGWaoxRrJcJT3ID6hBjqrm7QRIqzAckUYddaqpDEekEsixf1AdKrh8GjQVzjUgf2hLXk4G6Iq65KTHsBAYqRe+iJB0rTqh5nOgmuwnOVhAGDqiu1Cl6oJBhaJSSAvhdHhJXL4Erelalw9Q/VQzoyPZAHVcUcYiVfvg8iW8jelyhsxVWkq0EA4trMZYNByOKg/HW1wkzayfRKGp9IP1EjKhZOmRU5oXSmjWC6lq+RYIST0U78IjIZNWAb59xsvlqV5CdbRsIygP7ZOuKQk1GYF4nZ1l/OyF1wa9q1rBPZEeORXK7Cmy1lKJRBISLLIBKPVwWWVvbJIWCRk7aKaAEVHIcDR94aAks+a3CJctkhzonmFrwQj6QFDKXY00uAT5uIqE1IdqknNAI15uqqmjcAwYB6P1vTN4qlGuNlXIWclDZU1qVBjVVMv0pcIkoQARDEFDoxpHb1gc/SQB80TXsgZIzj3QC/K4zOV4tXhonH51NhymykPFMkDKuVzDR8Lw2iy8igSXqAsSmBqNuHpdNRkghUCCvujInU4DScIZlLRc6/VDIYNSZT92OuJCJOfa5Ixwua4FKuQI4OdLaGg65fWjU9REDApDgVmfko2zKhEqd5murJ8XLpf+KfS6VaG6jloLiQbrL/cktZEkugpV+64rGrX3qsAlXOglV5bZ9HVIqBcKvQFTSDbsVBeq06g1SiRhjv0DvSChTkmrkjYJ7XCtDEyiUt/3woWhnBIeMVTCKXrxaDvhWGq4vJDAVTolmy8CzXp90iDd0QizqYGQlvAhdMopxkWCIVA5crYU+pktuwAFvVAuhSTxY6cv6tAv7YdmSWu0H3YaNiVNCiSnphSlRvwASasOoBPKaSQ5X6KsSFSgI7qjjjrNskDwkgNpWY6yKI0L6cJPLhXIRgoUWXoo0oVa8NAOp7jQ5S8n1JXwY+cSxujnGrFDvQGFnNXYOYUwSOLOJQjlROFqikKGpkIupykVStuCSad9LwbXltr7G5ylPJJNcMrPu6BlBuVOp5EUiUY0Hd4GaEF6ptOs2SSrcvA90rgfiCBLoc5yre9avfhTOVzPP12TT0dHR+qHxkOHDu2X+90wFx45ckRf8QwbNiznu5K2tjZ9H9S7d+9JkyaFH8m2t7eHX5eI8ePH6+GAEOT55Zdfosqphfp6CPEWLVo0ceJEffly/vx5faBK+enTp1Fi6mePiBq97tGrVy89B8DljJfWMB1fsmfPngEDBqQ2ReGDDz6Y8wmnVwstoBav7aROvAyA/Bx//PFHZg3lJ1WahZdf3ZH49ttvU2fN1yTtB5tDNFmSNirs379/KGSOSXho4cSJE1nqDYmG5jWZtG3EoMdkoQSQVKFIkmHcuHEYVXJe6mV+Xv7iswleh9Hqq3TUra2tZ8+eDVW3c+dOTGvGjBlUSK4IBhgVMuM33XRTkU5Dr+Llj2YNksvfSxuB6tSLviQKVZHUA6TOY0jUUY6QQBd4AAQoa6IRXHjy5MnwG/l8F0p9LCTL2VaxAIF5z+kR/NiTZpmqQ1m+iIwKYVJnUJam9ZU1Xy5VIkek5OU5Fuj16ZVJiR7DxewlcKn630iVP0cPZbvorBrQ0tLC6HKeP8nyEsmBQ6g3LuSIt5e95bt0ZCi73kHte5EK7on5JucnN3WtRSRFSvXGmrWc2dSkcKHcnc4K2TCJaMUlJz3coLMoGpBdlaDlKVOm/PnPf47Ws5CilyxZ4vINgDkj9P7uu+/yrcq4Kmm6+RmGcWUxZMiQbdu2pXoM4yqgCb9D1nN49NFHT6e9W+vhJsOlGgNBN7cIFo1dmzTd/AzDuILQRzgWjV3FXNOfkF1//fUc9+/fn/yAtK2traWlZeXKlXWPltaX/njLsGHD/vrXv7700ktsycmPuI1rgaaYn2EYVxCtra1/+ctfbr/99htuuOGxxx577733cr7OM650rumAbPny5fototGjR/u/dfjLL78cPnx48ODBb7/9dt23QzbaESNGuEzp6c5u/qVNo+fQ/eZnGMaVRd++ffWjMzBz5szkL5sYVxPXdEAG2Pfrr79+uusnRtgIJ0+e/MgjjzTuY+FZs2bt2rWrT58+L774oj0hdI3T/eZnGMYVREtLy5NPPkliwYIF9pH5Vc+1HpAZhmEYhmE0nWv6oX7DMAzDMIyegAVkhmEYhmEYTcYCMsMwDMMwjCZjAZlhGIZhGEaTsYDMMAzDMAyjyVhAZhiGYRiG0WQsIGsyHaW/Am4YhmEYxrVMnX+HbMyYMefOnfvoo4+6/5ct9ffY9+7dq+z06dN74I/g6+8mhZw/f74n/NxfW1vbZ5999s4772T9retG09raOm3atCI/RX38+PEDBw7s2rXr+++/d0V1BQG++uqr1atXu3yPgYFv3769vb2d9KhRo+bOnVvfP7qVNM4cZsyYQe+Yze7du5mLq/UPHqPtU6dO7du3b+TIkRX9jDOTdeLEiQ8++KBZC6q+NN0/XOlUbUgVUVEvOLoffvjBZS5n3Lhx/fv3b9Df9Av9TO/evVO3aS0flykxYMCAWbNmucyVRgV7FgFZvbhw4YLaXLdunSvqLs6cOTN48OA+ffrce++9JCTG6NGj3emewbFjxyRYyIIFC9zp5rFjxw7EkDwI6Uq7l2XLlkkAl88A02KKi9Ssgv3798t46MIV9Rj8BIUwce50PVCbWkSCtApd/t57WVMqwU7onRKfda1cRTAozFJKqMinUbnpC6qO9AT/cEVTtSFVRBW94PH8GmdpcxWwqFVICRVc1bqyefNmdQqkXenlsKfL2zROjO5BKtVgXVE2dd7V6JulS2Tm8t0FAw7n1c93fXesGtF+xtEzc+bMc+fOudPNRhqro8OtyAxYfmijyMJDQonq8vWAWcBUMJueGZCxrBAMCRk7kPVuFL25SjVDayxelymBHtSLy5dgjihBDNL+HkzZqxIpAZ27fGGuMs1cZcNpyiaFAqswpIqotBd/JxzOLMrxIXiDBA5jsiwnhkicrW4T7/75zUEDAZfPps4BWVNgwpJzxu7O+KMNpokwJeypLtMjkcXUy+GyHljqLlNXiht3FeB9aLlHBWRokvAr8i9eCXW0cFqLekkNyIDF5e1EFeplNj0QC8jE1TQcIoCsD2YaR88MyOTxIDmz/rumRnxqQHf+rjLp3zycrc7kes7WDwxBI3X5bK6Gh/pvuOGG5LfLI0eO5Dhw4EBlm8727dtXrVrlMtcAK1as+OKLL1zGqIEjR45s3Lgxespw/PjxuuXQI2V1AVde8FnGBx980KUM40qjo6Nj3rx5v/zyi8sbGcjDwE8//aREfbnzzjv1OdylS5emTJmiwrqwc+fOTZs2ucwVxdUQkE2dOtWlArTexo0bp2xzYdfEPmbPnj1mzJiFCxe2tra6E1cpV+566IGk3m+AbjnqyMGDB12qHMgzdOhQlzGMKwruFU+fPu0yRja9e/dW4scff1Si7nCrqQfFmBF2RhXWSFtbW72a6n4qC8i4t2CoBLPr16/nSDp8J46zZHHW4WsUaIeaqUTbzPHjxym5vsSQIUMqeucrAkkICHbs2FHRC0F+dIAAkoFCd7oEoRWFnCLNYPv27Yu0oRJS2bBhgxKYHYJNmzaNFtCMCgsiO0O25cuXczmxXdQCcR4VEIm01C5lUhiNAhgI2qYdqtFUFTEieuBCjrQDfr4Qj9CTxKlTpzpVOWWK1w+9qCbyUI5stCDZEFjjUs0QDINqoNa+/vprd+Jy6mg/1VHEfjSJyEma8SIq8xXNY0Tq/YanQW9ClSX5WVpociSSg8LkZJ8adapZpsKFLS0tGIDmlDS6VSPetDjlC1OnHnlQe2lyplCH1lKXLSLJDqnme0yFy6nQOdqSGRdcQdISY6cXJfJnHxAJMTrlLq19FWLtaiEsFEiCPBTSOGc5uhNdFJmI2v2DFjuoLxJRLzneAKq2FshSsjrSveKWLVtIg9c/Z1WfI5Iw9shCEFgikVYXnXOf4WDVWllDKqslOuIU5aRpUNqIZC5irlXwySefKJG8+6Jf+qJfaQDxvEgeKkikzuFdHhWEcB+o7y6Zl9QlmSTHNmhh4sSJly5dIo1OAP3oVD75ExG5II7qHQ1woeqEFNyzUnBfXRaDYDb8cnrmzJn+OSGiH//ESVjn2LFjlIffEPvvU6PH8H01joqaq/saWJdXeu2ZM2cwi/AqPXVIU/4bdAbrv1ZnFJwq+Aw4jVMftXgVieSjb1lIvPCpLLqmxMtGU/4TZtKaKerL1qPHudSa1z8a89cipwrzocFw1DRFjy6T9iQWzfouOEvaq2LXrl2o3SvWXdAFY0FULxXjTa1ZL/tJSl6QsvbDkbOaDtqXMShLWpdUhPRZcL6qw8+Ry6ehCjIABgJSPtPkapRATsrRkrKogjqUaMpy4EKMrdRJ54QyarL79++nIxXSJnL6Qqk0UgtWRKFfbnQqAWhNJUKWwykvle+FhEqExFBaU08d377QhaEk2AA1fQm9oKWCb5BpLji6fIlkIa3RhZcf2ajgs1BkIjSiWvyDFO6HxsDJ0ouyNOIbRLGkNRBQ+0WEzKKsktVXNKFUo31w+a5O/YWMyMtMmpq0gA1o6r0xCLVW1pDytVTWYxTsJR9/STSzkg1C6xJ0F6pUNRHMzxegkPDCaIMAuvMVZKUibAQoiQQraxtUKLVUQWxTdiLQkp9oxKZTSkhQQjkVVFPQGoVebM6m7lmpVCC0xhlqh56i2WIM1AmtgfrhYtBcUie8UC17hYJKILy2LKHigC7CNnOQVODyXWgFhpaEPGqcKSHLhfQY2VA+CCkbguRcZiFJQsViGVGJN2vJJvxqcfmSzGSjiaNQ14bzmwM1w64h1B6nqBB1ATIPepfGUKZ3+l6xygqZQdRRsma97AeyJM+nuP1o1tCABo4e6DGUvCCpk1h3aL9TiQUCMsboR4FJq9DbkqQN50Il1Il2siwkSdgLROYkpGE07/Jd6yJcFEA7zFckAL3QWtgFqJfQCEmHcwoyGwivVUm4oFTNZUqwPMOWc9C10YwnC0lHdRh4RROhkqgRCqkD4XByUJthZakxWULNyBsUETKHskpmaFSI1E7XFIajRoCoRIYE+Q4WuIoSxHb5EklD0qDytSR7pmbSYxTsJR9qUh98pyT8DsU6itoH+o0mghIq07XLl9sggF5C3UqNEI2IklAbnKJCvm1Qv9RSnteKUAthR8mJkDOnZuht5EP8LgbqPRq7bAlcPpsKvrL89ddfOb711lvKwqBBg6ZPn+4yJZJfZAwdOnTs2LEuc911b7zxBv6aUW3cuNEVXXfdqlWrmJvwWv9V48cff6xEEfbs2cMRY5J+Dx069Oijj5bOlIELkUoaD1mzZg3H06dP+49Sb7jhBiVWrlzJEZmXLFkyfPhwFRYBpTF22d+lS5e2bt2q8nw08cOGDVMW/Bf8Hi/GI488ogTcdNNNHPURrlixYgXZxYsXu3yJgk9zC32Wu2XLlvBD3RdeeMGlslEvTJBEnTp1qv9VQK/YkDlz5nCcO3eusiJZs172UzXF7Uezxmxq4OgB+6lI+UJm451p0/nzn//sR4GFy1z9B/VIi8mF371S+c477ySxa9culRTh4YcfDnVFliPthAvw9ttv5/jzzz8rC6+++irH+++/X1lBO3rJhjlqL70YwRzhMTDOaDqiLDa/du1ade3xz6oeOXJEiVT0YGtLS4uyMGnSpAEDBrhMPUA8RhF+f4Q36NWrl9JFJqJ2/wAYAE64f//+Lp/WgkqS3qBGa6lOyb///e85jho1SllI+pmCDragIUERLWV5jOK9FGTChAmlrx+vJ0Hj+BYiDxJRg8ePH6ff++67z+VL3HPPPRxxdFpKVWwQs2bNYiwk0OSU7Af86+VJIopMhJ7ZjbzNkNIzNuELIgX3rCwqCMhuvfVWjgwbffkFn/ozuyEMzI+Nq7RF4SL9sy/yIN9//33py9a/wRYLCq0KgqXC6tWrv/vuO+2OkW/KQr/vnwxxEFKR8r59+1TiSU5YRWB/uvUp+CoigyJ89obIoPwX/JXCGuOo2awOxo4FE4IQavtv0Mtagiep51QYI13QUb6q62g/VVOp/dRoPHg9YgLC+tA19CgiP/XBBx8wEW5iuqCcCVLNOqINEpNQFuSpkz6RfVoJRVGaI8VzOZw8eZItgRG5YZTQLQG4Shmo8SeffNI/9YIl4ApKJ+vDmDFjOE6cONE/ssPNibeTIhNRu3+Azz///OLFi97J4yWyfqM8uWpqtJbqlIxrxcH6v8/BEjtw4IDSlVLQkKC4lpIeo3gvBTl27NiFroc9sHC0nepepBYM3k1MCTajTuu/997ffvuNs0hbxQZR5AH/BnmS4hORT8E9K4cKAjLE1ec6OLsRI0YsX748jH+LMG/ePI7oLpybb775hiMzcTCN6v6CDep4++230Qvp0DVnkVNHmg1vuOuFgugi4oXgLnEun332WXgzVxwsRjdz3viq48MPP2RhYHzTpk1jSejGqL4wRo5ll1kj7KdSutl+8FY4r/pu5A0FD8vRTUkC1WkQ3NC7VALvNBU4Hj58mKPiuRzOnj3LEVfmpL+c/EnhrD4G2LRp0y233BJ+ilMvVq5cyVpgjS9dupS1Ew2/7ETUyz8INgjiQsz1119/Lb5f1mgtNSoZjSHw1q1bq451ChqSpzotVdpLEVgR77zzDo6dNDF96v6uTxDWrVvn5uNyfAxX3QZBC+o96wH/hnqS6iYipOCelUNlb1li6/u7/tjCmjVrMHcWsE6VhaGiTa4Nv6z01D3iwbaSXyFVgT6MbQRyeYoai0DYzkyzeWCpRLQFP2eK0B1M7bDwjh49qhsawhFGkbp+aiH8HLgsjYiY60Ld7Qd/MWfOnCsoGhPa5nsa3By6VIUSVmScIXg/fdFMd08++eSY4L3CuoDfY2eS62M7nDBhAgajUyJ/mPXyD4DDHzt27Lhx46q4eajRWqpTMnsZocP27dsJarmRqzrWqUj4qrVUo4qywLFrg6Z9tJGlt7J2Ut0GgfVylQKM2bNnp0YXDRp4LebqqdoteCoLyGDq1KnfffedN3fi6CLBL5rljo3Eiy++mHrvlfUZQy3eqvjDGbKbnC8Bb7zxRpeqNwWjaayZW40XXnih+DeD+eR8clAQltznn3++o/RGCVnWj/90uo4U/ASxEfZTnG6zH22uV1w0JrJMrqFz5N/Yj/5WcUh4b5NTLSRrrouMZcmSJYRKipm4R9V3LnWEXQ13caz0MhrZTZs2RW/+F5mIGv0DhorD//DDDyv64aGQGq2lUiWzQ7GXsU7Zj1GgK62BIoZUu5YKmmtF4F70OA16W7FihQojsvoNZ6e6DcJHhPDQQw8pEVF3T1L7RIQU3LNSqSAgw2QVe2GvmPuZ0qvRxGR6lD4ffVmJg+BClQg06z1m6mfLW7durfrrMIWr4U1wFpMnT+aYqkd53kb8NLnGFb0VkQp2pp3YP/VSNV7bFfwyShp+abF6CdDl+t98800ViqqXh/BvMKTeJ3kaZz/F6R770S2m91Ye9FOjqhuNzCN8H8iDB8h/EL5GcFY54bKmTMtK1b788svOE9nILLkw1SyzNjDhVw03pcymngBh26sx+gnxXbC1sB3qyzs9uQtlJ6Iu/oGmiAIHDx7M5uqKKqFGaymo5OjDDPZj9rK6rNOChlQXLZXtpTpWr16t9pEw8qt6VCZ6YF9Q8sYbbyhdZIPIwkeEhNQq8VRkGwW9Yo0TEVJwz8qhgoDst99+CwccRrL56MtKEu+8845KBIr49ddfvcd86aWXomGwnM6fP1/10wy4YO6QimjZvxOhPS/k1KlTTFUjPpMgkCWinTFjhstn88033+hz2vBPWKAZjpV+Roq2FaGi7VR71bu0ZXn33XddqtRm9IaL7FKTXjVjx47VrZU+W00i+RtnP8XpBvuh5U8//TS54lDCq6++ihKUZU15V9hzUMCKkUT6Yb6eeeaZRiyukOeee44jIVQUmstaCFlkIXpxEiHRYefpy/ELDbNUgpvMaAVx13TXXXe5TBpnz54NTZSBK2AqTtRjMsoMFyZgMJifyxSYiLr4B318Eu2mekq6yOU1WktZJSukiN6mUmj+ww8/KAv6efpUJeRT0JBq1FLBXqqGluV+n3zyybCLP/7xjxwRO7r3QFFTpkzxnjB/gwDUi290mQREhKmfpBSxDX9ToceLy1LjRIQU3LPyKP34RSGOlf48dvgbIbr5CH+WQ0oMf6SEs6V+Un4ZhWhJv/Ohn+4ABsPioVmgKTbasLssaJnKXOLyJcgWvFzod2iiAVKISBJSeFGLt6yvd5EwVJQ+XwxLcvC2QiP0SxYNy88yRiqoHa/qVIG5SiW+Gtf6Qu+z6IJLyo6OmqHwktD/HIvvVHUoV4M0TmFoHp5UxWpSAPFUzlHhF9CORuqvrc5+QqQHrnX5whS0HxmDZq04DEdSJaF9r3Y/s76kRryqvZ1EMFhVCMcIMk6/5KmGnKrJqke8/fv3o+riS4CRhg0Kv7JcvoR0BS5fQl9dcXT5EmTD+SIhscMp861xCpmlB/WrQoyQckZEmhHpKqE6ocykIxmkBJfJxUuiyUVvNKUJoms/QWgjmn3O+k6LTIS3Ihqvzj94UbXSqa8uVMK1alZzmvQGRYTMoaySNX2ohY5A6iKrQrpQoTd+surXaya0dtIq9Lqivlqj0xxDKqglSZv0GAV7UXkOfmap74q68EOLNK+JA6RCPKohMHXo3dUot0EAl1CSI6EfoB8dUCgVQY5tqIK64/KwhSQFJ4LuKIm8jVRBfZcvvGdlUVlARot+QaKIcA5Qhx8Y5d4avCjMnAexpGuvRK71ihZ0pPGURZrSJaiDpmgfCl7ukQwITIIxSshwmhmUJgDoVHr4/9s7u1+tjioO1z8AadUrYgihmJS0RtNiTRo0wQRE40UbqC2VGBMvWrDxQmuLBeOFWioVr5oCpiSmacohYuQG5EBijf1I2lIjaU2btJCmIb0qRewfoA/7N2dlmP3x7vfjnPdAf0/CzszaM2vWrFkze/bHe0jn2okRAnSSZajQ02e2BFRJKubcW3gbO6MMCY06wjAYYYe3Qxv9Ij2wX5TUnKckbeE09KdzFTHugEI6G36gaSRhjDTkdsp4gcckF9SVHhKUxAAVq/cIhUMFAI3mSkgjGUqDqrfFD6qQK+yBWZB3swNqqUobYSQKJcFFkowGCnFs7nl1qojYPOTwtrqDMAaaWjFAyKPvophcbdAoSjQuVNGgyMJQiG1SxdkIPMzI9as7hDcVgUQ9QupG0kFFJmpRGB4owhKQ6BRgBuMrORXJqqKElKQhzQj61ccJIhY6oCK9kEKaQKf6grWckqloxn7O5t2s97E+EPhTDg8iCPusD5yNUQDSskRZmvv73/8eQUJDMXxBHyPbGOhkhNG7OBUdFCihWMpUWYpFF0hEtCs8JIxWSBT2c7YIpIFeev3117Eq9GCDGg36tJKKNkH1PIypQnNFFTkTcFQoxPLotdAgqopAiG2EKIU1K7FNp8hGu2q0LZywEM0De00WYTpdkfcr2m2jz0DQEcUMR9IavogZCaMXeevAWUU7CdRSUsUa+RT/VG0getp2/vz5t9566/3331+6dOn69evjfdCZM2eKX14sW7aMs40PVIP8Gzr0nzp1Sg+NGe/+L3SpeOTIkXhIe8stt3z1q1+NlzhDkduAnvwP0EG9L6tXr+7TEBXjK8jCb/05fvz4m2++uXz5cqqr0ZmZGUzdvHkz2ur+x7eFcMmSJeHV6GnYg7bQPBC9/Xn77bf1VwDq4xWDIvMon79vhTCGkvVny3lgUBdTUaW+k6DdYmggH7uh4kc0BmrP8Q064mdgN9uoj2xOPqbAIMrno8W/aDQVNKNTpsmweshB3kfFMIn65GqjHjl6JVFYKD8Ug1g4JwKJdEeEhJEqQ/amm26qT9jQVp/R9ViS66jCkbP//ve/I56HGqliEUBVMSg0gT04RwtOm58HDkRE8mjrQ1QP/Uj0tTHBSd/bVoOcEaIF+jiZMhhD1/KZQujOzs7mo4kS3HjHHXe0BXYhLHoxMJC6vcSxz4oxsJU26j2CIpwgD+b8rNxFos3DHBsvELlC0bHMtnUnet0WG7o4auySqJ1hBwI/aJalfEXeC7qvxaH7mlVniA2ZMcYYY4yZD4b+sxfGGGOMMWayeENmjDHGGDNlvCEzxhhjjJky3pAZY4wxxkwZb8iMMcYYY6aMN2TGGGOMMVPGGzJjjDHGmCnjDZkxxhhjzJTxhswYY4wxZsp4Q2aMMcYYM2W8ITPGGGOMmTLekM0X+t9VTR/OnDmTUp8kHCEFhIF9MgKL2W8vvvjihQsXUmY+oZWrPXgc/Kbvfy5OrDz66KP/+c9/Uv66666//vo777zz3nvvTfkKpt+TTz4ZxW677bbf/OY3SgczMzNHjx49efLkxYsXJbnxxhs3bNjwne98h/TWrVs/+ugjyQuw4eDBg1Q8ffp0ElX/jfxdd9118803P/fccytWrHj44YfTiWnAovDKK6+89NJLMtL/cXsHjOarr76qSLj99ttPnDiRTswnxCdxUix8K1euvO+++9auXZvy84kjpA12FV/+8pdJnD17lhGR0AxkMfvtwIEDDzzwAMv7u+++m0STJpaRw4cP//a3v53u+j8CsSBg/6pVqxZmGTSLFy4J/bnnnntUa82aNUnUBGcpc+jQoZSf41//+pdOMUU5++GHH0r+wgsvhGaQsIDJprPbtm1Dj4SsQei54YYbdIoykk8LekRf2LnKniQ1TTB2+IpIwFHsqpN0QTh27JgGCCKWFgZHSBsxKAs8Ilc7i9lvinPW55SfB3QJkAemvv6PgBYEFkDsX+Bl0CxChrskED3a/XCM7VQdAqseW6wXqsuOKomuJOZVymdQBTnVGxcdLNE+b7ITkqmeUkPCBKv6sUgvtyP3az7QPnvhVyINUPd9xfyxyCOkPyPHUmNFVgA8kzKmN4vWb6zM+/fvn/iCU1fIAsJsuho3ZGJay6BZbAz3DdlnP/vZHTt2kLh48eKpU6ckLLhw4cLs7OwPfvCDlK84d+7cunXrqHXPPfc89dRTSXol9957r+KyYOfOnfv27SPx/PPPf+lLX5IwB6tOnDih3d6koNEPPvggZa4hjh8/fuTIkZT5xEPkpJQZHiZ1POoblvyJeMAKsDAvjq8xFq3fmF/333//ZF+kjhN1xixyhv6of/PmzUrs2rVLiQI2auyNim/Ltm/fri/GHnvsMUka+eEPf1jsq1588UVV2bZtW+NuTMROcSKwa+m28yqFtWzr1q0pY8x4sKnKPyrtD3c7+WegxvRn5KgzZvEz9IaM2x29QDx79iy7JQlz9u7dqwLBmTNnZmdnSaxZs6b7bol91bPPPpsyFU8++aQS+uS/AzZzd9xxR8r04MKFC9gPJJKoAmvnddfCrkjtpnwPsJBaSmMedSNbp00/SljL4ocUk0VWQeHMHNkst3fY30Fb1yZL7m21SO+UFY3CIOTqKSjbn6jY5sy86caSjcIcjVdbF6gYHlDJyAbcYnVsquSixoozMzMddzs03Wa2FELK1+hjtgjzUr4f0gltFhatKx2M1ihEuynfROW21uEeiJrgmPJXguaejm2ksXC0hfKqc73c0h11gRR2GDnyWETFDm/HeHWPSH8bVKyjO+baIb26HAa2Yqrb+KEY8uIdf7yIHOEdvypCyk+IQ4cOsTvkeOzYMXpx44036iMM7NfnCEAB0oB8//798eiO7Sa1KjXpIwkVju/b2r4QogBbIulEG2BAOtcEyimglzu4jiwVpRnqzuzQT11OIaEinVUZygOWSyEFyFIYzSoJjz76qIRAr1U4/2CF7qOQYtRCJ2fxD82l05VVnEUhBUhLM0cFCUKyVFTh8CdQK/cPWYphA0JahHRiJNREtAvYU3hbaUGnVEZ9FEV1LJd/yGKtygCdjYARbRFCozSEWqCDFEBPOlfVIis5WXTGMJHQMOWBWm8XkEg/UAZtMb603jPewgyakKoYKbThBM0RilFArhMUi3hTRXUwbzqPLuCUmsMAIIHO3Jj+Zgu0UQDzqIU2SCfakdlUpBbdQS2SdG7OADVKVgVAXQN8gnmXe9tv4gfhTHpBgopKgzTU/SZ3yQDKEw+VpstgvJwfhsFE4qGRYkYIlNM6rUiIfoys9F1uWmtCG1GRKjI43CjDMAn9KiPynopxxoIq8r98gnJ8kk5X0F9aR04ZClOG8kUZQIIGSlKMwrhICqmSSswhPSA3kqhrM9cS5SWhJ0QG8QHFFCL+CJ2UmSMKF0vtQOK6hYYkmgRSm1uO/nxaqtHCWgpIHgtWUBQOs1O+glpM/miUrMp0LAfMPVRpfWHG4lgKI2GWqm5uSR/9Ggiqp3xFWJt3gZVFwpSfg+WDIU6ZarhpNDdDFbFZC0duLXXpBVmqkFUtnc3HF4Ppaa4TKIbOlKk8k3d2BCqLrmgXbVycZBtyTCWrIVBhuoY8hBqXcC8Wyr2AW4AynJVCyN0bOlO+ArXoRH+epQyqJKEWaVUkQYt4iWKxmudCvI0wdxqoC5yNLGViBJFHvwbGm4RoS/kKfIi2XCgzKJzyLX2nQHSNAklamYQZEDaD+stR2WHNzn1C3YGBxIigJO8CTSAJOzkVQ49LFeekZSGt502QVcmInDbkzBh9TFUfaZ0WVZ1Eo99kDyVTfg6ZmjITjYcCjM/dkqRV96M6lgMK8bBcGmPahurmXRBqiOok0IZO9QVyt4w8FoqBXJXcwlinfI/FUJCms3lPkVCRkkW/KJPrJ0sZ6ubazDVGecXtCaFJcABRmERzgVUPbk02yAM6BzkzrYBpo2kA9RkYpNIZA2cXZdCZhzUNIUyZlg0ZaBLmJYG6yFOmIpyT8hWUYX6mTAWuo0xRt04Uyw2WS3NL+ujXslXYD5Qp5LSFBIo1l5Ih0ejkS4ZQK/mQSUJsqAuMbCih0bwwWczOeyooQJdTpoJ228KpDzSatxvI1OLCoKWwGILCcohxzw3TZbWtZMpX0PGiXSJZxeISEhXzcSEtYREAEoYxKCFbH1CENJ3yveOt3n1o88nAvgvJw2BglJEQaSlfgWFyaW5PT7OxZNhAokqhRN3MJdGpECKRJZg0zsTPVzPFA31P+TkuN3yl38KeYrjpexSbbDw0EmakfIXahdwtsdSnfAvyfBF1oDHCqm4jRxsLTeFioVNkRiz1Xwy1mMSMFvV+MdbFQIffBl7gzNXLiH+pf+3atZq6+/btizfl+t3l+vXrlQ0GfreEts2bN//zn/98pOJvf/sb2ZUrV/73v/9NJdp5+OGHb7nlFlWE5cuXF78nqHPp0iWOv/jFL5SFb3/7232+P/vZz37G8fHHH88/DnjmmWck72BmZkbTSV8DCJ1C3vbphlixYgXHVatW5T8JLH4eOI5+kP1/+MMflAX0ayk5cOCAJIAe1oj4acUvf/lLjt/61reUDX784x9znJ2dLdrdsWOHzGZkG3+fsX379vfee++pp54qegc4/PTp03Qz5atPBpctW5Yyk+bWW29NqQpliyEg6jg2fiaS/+SNzurnJjik45uSc+fOMZU+85nPpMGrUKBC/RfNuQMjffPNNyshdEGKSfS73/2O48cff5y0V2hnkzu2T7y1QcxA8QvrkcFd+tqM6SmJwBhdR5mJkkBPs0cIJLqjfqV8J/GHSYkBWh9nYr722mscP//5zysLt99+O8c+n4HSugzO5y8xRt0IzgWIh0biM+I8XD/96U+n1BjcfffduWEbNmzgyNVE2ZHH4uDBg/jtu9/9bspX/OQnP2EX9fTTTyvbczHkePjwYYam+1tq2LVrF1fYZGVF/Or/6NGjSphrj9H/66Rf//rXShCvSuzdu5fNUH2uMplTqh0C9MEHH1SaFVDxmi9GHbBea/UhggfuxkDThusfhhHoEuYX0TbYJrJgMTnjL0ewvjPB4penbfzjH//gyPX15QxWOqY0LFmyRMVGZkz9sp9VKbxBv1599VUS+dLM4v7Tn/5UaV3YlC6Iy6d+ydEHtLEb4wrU9idRWGo5btmyZePGjfq4lQhRkEwLXUL6fGIc1/K33npLiTryNqTBq2BANYLFTqsnmolvvvmmsidPnmSCJNVz4Fj0T+oXymwNT5w4EXOQ4Ikf5YzAK6+8klI1NIWZiRGxPRkhkOgOndKul0BlFvzpT3/SqYGMP/Epn1LZVqYP2hYzBNgsCQt1fuu4APEwXZYuXZpSFSOPBSs8x+IeksnF5juioudiqONtt91WyVohMlmNaSJZOYdMvfPOO1M5c+2RnpSNhG6nOJLWe5PiAbjQQ1roftYaz7fjobp0AguHJG3omscx5ece4+fkZ/XwX2Ae0Z9OVEgeZuQwHzgV9qCneM0E9Qf1Mq9R4UDUYm48SCGn8uxA/UWtHD1yiFcV6pcesWjUPqw+YYk3AtHHxkYVGIV5je2qd2hWlY4IkYWCWvm7iRGQnsKr0GiqjCwK10e5LgkkD1/VS6qJot06bU1IGPpF0ReVUbqDxs7W3dJYTDA0nCV+jlVf8xTF2roAkkcv1AQomxMrQ1F4oNkwWiCxRFARaFpt5WrbOiUDwsihkJ3xUkzUJXC54aZWijlFNu+saqVMO/0dW6fNLRLmBreVLGg0BhrtKQqrzAhjQS1ImSbC+Ebl+WLY5rfCVCmMrPnkMPoTMtCNFPep3Idx48hKUdxGiHXr1imhe5T+oE3RzGqo29n+EM0F+X0Jd70EPfsA0twAYXn364NAfykNe/To6OjRo9///vd1aiB93sCOwzj677vvPo5xP71r164HH3xQz8P0hPzIkSMbNmyoP/5sbFTvVvqzatUqPRvbvn1720BQYP/cTwgfeeSRjRs3xq3/NQO37yk1b8y30wihL3zhC8uXLydRvGocmbrNjetMT0YIpJ07d7JE3H///dQdoenRJuZDDz3EApW/YGWNxex4TTYQrc9//OMfOR4/frzx9cW1N4m6GXmR7HMB6rkY9pzmn7ShMTDWhozdiRJcv1k14n1Wgd70kYjrfX/i4fmwf1/+RI3ivzlfu3btu+++q6WZPSW7xj62saLpjczevXuZoidPnux/1XnppZdS6kp67gUHMo5+vMHqjx9OnTrFMLFDQqLPAdmw0tMnnniCC5IKQ7zhbWxUnuSqrGwf8Oq2bdswYNOmTW0DgQHvvPOOPqflQvW9731P8quF1atXp1QLhFNKXcmkIgTa3gNOpAmu+lu2bGEcNUfGJD7rrNscETLad4RDBRK7sccee+zZZ58deRc42sRcuXIlHWcjiEu/8pWvsHF8vqK/GfoUYXZ2lvn7zDPP6KarYF7jYREy8iIZHxXkKA6HXQxff/31lOqEyGxcCRnNthXSXO2MtSFjd6Ln6merjyXrn/MLiu3evZsEl9vf//73EvaEPZ+eYz3++ON97lH6kH/oytLMGqc9WfGJz/nz51PqSrhz5chsYUFX9wfyjW98g+O+ffvqXWAh6P+tVRtD6X/vvfdS6kr0fQn306D/hiHGl57iouJKoDfRjXsInEP5tnho41e/+hWXH2KpfoGMIcMkNtaHqjfO4/ttYdB3Tt/85jcxXpI62nzgN/Y0kgQsvnnEjgwGcNTXxwXo//jjj1NmGLilSakKfcL8xS9+UdluBk5nrnO6katf5zRV6dFQn1XBsIGEkfphwU033STJUIw58bk7uvvuu//3v/+99tpr3FJypzTUphDnaJIyfwmt2DeI+YiHhaGIup6MPBZyFEtifRvE5UzCnouhbKCt7h1VjFTj5VK/xjDXJGNtyEC7E2h8Hh6w79GlndUtnsD3AZ1//vOftWEi6CdyZ3Dp0qX8sscaJ9viu07NwPfff1/ZApY5lT98+HDx05uCsFYK613gUr1p06Z40DgyPfVrOWi7EMb9NOtdrAi6q6an9cef+hEGy03xYbV8u2PuN5X9ofzTTz/NWGPDzp07k7TiL3/5S94vgo2tm67WgkY7XndOl+eee46j9rgF0anYfGzdujXvBYO1cePGxmcbw3LXXXdxZLxwlCSCqy/uLa7WA9EOUndigbJvvPGGsvBy9U16fgWNhvr8X7F6QM5FNB99+Otf/8qx0aXdDAykgjDy7bffVgL0w722G5uccSY+IU0H42ebo6FJyvyt/xJ8svGwMDRGXU9GHgv9PEI3inlF/LZ06VKtcj0Xw/j5149+9CMlhF5i5tMEIzkWl0tap9EVK1bE0rpnzx6Wytwqc3WjT8nGQaHT+Dl/gT5dBKoU5Ql36WFxrKtCwrqps9zUFt/hItGSyj4piTrBDLSlTAVzNZfIEiQvVH/bs/jTNaAVoVASNH7jqfcjguZkQ2NnC7SOcEz5CuoixM6U76efjnAW4bEKqqQTc6jjdDnlK/TFfeO3z1JIQ3GWBNncMJC1jaMjs/Pe6aEF5G6nQG4trWBSSMiqSuGlNqI8hOUCDQgLz8hIepHyFeo7pHw27nlP8TOSwqWNEaKSgrZotD4cUTE3O7qDhiSqwEUIiYSUnxsI4BTKaYLBRZhrkwcKT6piPqzEVaXp8jBhFV1GiYqhHCHGUB5UjGx0RIZRhYqYJydELwpfSUPuUppWEylf0dNsCuSDS6OoKoY7J6zCUeoCGkASLJfPOaoYK4MqBgoeQcVhJz6tVA1e/kvF1IXCP21+C9DAWYqlfIb8A9iD/nHioZFGt0QMa9xFlOx2S2PUIVcfi3FU5OSWjzwWNJSqVRVB45JOV2hByL1HgmzhJRqVHuR0AaiIDRLSkMYRj4WQtpADEo7SA+E0dCaRucqZwIaMkCLsUmYQxJkCS3Gm4Nb0JkFoNi4cgkhVSRUWqFK8YkYqNwjCl4qoojl0MjHI5u1qxVdDbYs1VaieMnPQO5RrdQCaIBuLEelQC2joXgiomK8gpOmjHJhElbC/fi0QcbbuavRjfMrMgdp8FShgUaAKYAmoy+lcpVDLokBPXDY4FZEA1NUpqksCGCkhStDMkSxuJ035Sk0CAyjfYaegUSqqsJDlyHVKQqzCDBwLCMPIsD93tayScknUBIWxnwQuqhq/DAqpGwYUESJn6hQUdSkZwxeNcqQVCSkvbVgSbo++UJgRxyrJBdkIA3U2negRbyGnXUVaPnEQUh3lknCMvmCzyiAMf0YvcoMFWYTyFY2SKFza3+yBgVQnrAWMRE8MNNXpMhpi1GIIUuUK2a8CgJLuiS8oE2oLaFejhuYOvwm6iStS5komGw85CDEm7MdgsshxJum6MC+pkGgjWqcKLsKkPNRlD8LccqqEkbQ4wlgAFVOdOVenExmav0DrEB0sKGygpCTYmQc2hsXgQr1ROqVT3R4zVxGf4p8GdRzOnTs37MccZ86cyT9T6P+E/MKFC/nHXsuWLRu26UA2NGpQK23KOfu5z32OlWvYt3KgZ9rjmN3NQP0UWLJkSdvHKPWh1PPw7p7GaM73mw71bvXq1YU9Gq/pvmfBtq997WskmFMRpSOYJGd2jNGYhG11Nw6LTC36WASYmivaIsw++OCDoQzQ0E/KLW2B1Ii6kM8p2T/U4KrF/hMf3+7evfvnP/+5Ztb58+f1EcWlS5f0CjL/kU0HGM+xo5sTjIeFoTHqhmLYsRDhqO6mey6GeQR2XEAVaSQatXGWtuZpoTALz2Q2ZJ80Dhw4wKxr+yum5pMJK2xsyCQxZjRYXtatW3f69OnG6zSR9vLLL4/5eZkxZrEx7kf9n0C4T3riiScm8p21McbU2bRp08WLF9uempw/f36ovyljjLkq8IasF+fOndOfAtqzZw/HNWvWjPPA3BhjOvjoo4841n9AR/bAgQNvvPHGRP7SmzFmUeFXlr1gEXzggQeUvuGGG955552r4mMLs5DMzMxs2bKFxNmzZ4f6NsWYgogliC+72Y0RWrt37+759Zgx5urCG7K+bN++XZ9ePvTQQ77cmoI9e/boz1PB9ddff+utt27evNlxYkaG1ebgwYPxV92Jpa9//evr16/3raAx1yrekBljjDHGTBl/Q2aMMcYYM2W8ITPGGGOMmTLekBljjDHGTBlvyIwxxhhjpow3ZMYYY4wxU8YbMmOMMcaYKeMNmTHGGGPMlPGGzBhjjDFmynhDZowxxhgzZbwhM8YYY4yZMt6QGWOMMcZMGW/IjDHGGGOmjDdkxhhjjDFTxhsyY4wxxpgp4w2ZMcYYY8yU8YbMGGOMMWbKeENmjDHGGDNlvCEzxhhjjJky3pAZY4wxxkwZb8iMMcYYY6aMN2TGGGOMMVPGGzJjjDHGmCnjDZkxxhhjzJTxhswYY4wxZsp4Q2aMMcYYM2W8ITPGGGOMmTLekBljjDHGTBlvyIwxxhhjpow3ZMYYY4wxU8YbMmOMMcaYKeMNmTHGGGPMVLnuuv8D6n5I/1LazngAAAAASUVORK5CYII="}}},{"cell_type":"code","source":"model = keras.Sequential()\nmodel.add(keras.layers.InputLayer(input_shape=(32,32,3,)))\nmodel.add(keras.layers.Conv2D(64, (3,3), activation='relu'))\nmodel.add(keras.layers.MaxPool2D((2, 2), padding='same'))\nmodel.add(keras.layers.Conv2D(128, (3,3), activation='relu'))\nmodel.add(keras.layers.MaxPool2D((2, 2), padding='same'))\nmodel.add(keras.layers.Conv2D(256, (3,3), activation='relu'))\nmodel.add(keras.layers.MaxPool2D((2, 2), padding='same'))\nmodel.add(keras.layers.Conv2D(512, (3,3), activation='relu'))\nmodel.add(keras.layers.Flatten())\nmodel.add(keras.layers.Dense(4096, activation='relu'))\nmodel.add(keras.layers.Dense(4096, activation='relu'))\nmodel.add(keras.layers.Dense(10, activation='softmax'))\nmodel.compile(\n    loss='categorical_crossentropy',#'sparse_categorical_crossentropy' , 'mse'\n    optimizer=keras.optimizers.Adam(1e-4),#optimizers.RMSprop(lr=1e-4),\n    metrics=['acc'])\nmodel.summary()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"supervised_clasification_results = model.fit(\n    x_train_normalized,\n    y_train_onehot,\n    epochs=150,\n    batch_size=660,\n    shuffle=True,\n    validation_data=(x_test_normalized, y_test_onehot)\n)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"show_results(supervised_clasification_results)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Base CNN Network\nCNN = keras.Sequential(name=\"CNN_Base\")\nCNN.add(keras.layers.InputLayer(input_shape=(32,32,2,)))\nCNN.add(keras.layers.Conv2D(64, (3,3), activation='relu'))\nCNN.add(keras.layers.MaxPool2D((2, 2), padding='same'))\nCNN.add(keras.layers.Conv2D(128, (3,3), activation='relu'))\nCNN.add(keras.layers.MaxPool2D((2, 2), padding='same'))\nCNN.add(keras.layers.Conv2D(256, (3,3), activation='relu'))\nCNN.add(keras.layers.MaxPool2D((2, 2), padding='same'))\nCNN.add(keras.layers.Conv2D(512, (3,3), activation='relu'))\nCNN.add(keras.layers.Flatten())\nCNN.summary()\n\n# Over-clustering network\nHeadA_FC = keras.Sequential(name=\"Head_A\")\nHeadA_FC.add(keras.layers.InputLayer(input_shape=CNN.input_shape[1:]))\nHeadA_FC.add(CNN)\n#HeadA_FC.add(keras.layers.InputLayer(input_shape=CNN.output_shape[1:]))\nHeadA_FC.add(keras.layers.Dense(4096, activation='relu'))\nHeadA_FC.add(keras.layers.Dense(4096, activation='relu'))\nHeadA_FC.add(keras.layers.Dense(70, activation='softmax'))\nHeadA_FC.compile(\n    loss='categorical_crossentropy',#'sparse_categorical_crossentropy' , 'mse'\n    optimizer=keras.optimizers.Adam(1e-4),#optimizers.RMSprop(lr=1e-4),\n    metrics=['acc'])\nHeadA_FC.summary()\n\n# Semantic-output soft-clustering network\nHeadB_FC = keras.Sequential(name=\"Head_B\")\nHeadB_FC.add(keras.layers.InputLayer(input_shape=CNN.input_shape[1:]))\nHeadB_FC.add(CNN)\n#HeadB_FC.add(keras.layers.InputLayer(input_shape=CNN.output_shape[1:]))\nHeadB_FC.add(keras.layers.Dense(4096, activation='relu'))\nHeadB_FC.add(keras.layers.Dense(4096, activation='relu'))\nHeadB_FC.add(keras.layers.Dense(10, activation='softmax'))\nHeadB_FC.compile(\n    loss='categorical_crossentropy',#'sparse_categorical_crossentropy' , 'mse'\n    optimizer=keras.optimizers.Adam(1e-4),#optimizers.RMSprop(lr=1e-4),\n    metrics=['acc'])\nHeadB_FC.summary()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"results = HeadB_FC.fit(\n    x_train_sobel_normalized,\n    y_train_onehot,\n    epochs=150,\n    batch_size=660,\n    shuffle=True,\n    validation_data=(x_test_sobel_normalized, y_test_onehot)\n)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"show_results(results)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def build_model(lr = 0.001, dc = 1e-5, dr = 0.5, input_shape=(32, 32, 3)):\n    model = keras.Sequential(name = 'CNN_cifar10')\n    model.add(keras.layers.Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_uniform', kernel_regularizer=keras.regularizers.l2(1e-4), padding='same', input_shape=input_shape))\n    model.add(keras.layers.BatchNormalization())\n    model.add(keras.layers.Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_uniform', kernel_regularizer=keras.regularizers.l2(1e-4), padding='same'))\n    model.add(keras.layers.BatchNormalization())\n    model.add(keras.layers.MaxPooling2D((2, 2)))\n    model.add(keras.layers.Dropout(0.2))\n\n    model.add(keras.layers.Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_uniform', kernel_regularizer=keras.regularizers.l2(1e-4), padding='same'))\n    model.add(keras.layers.BatchNormalization())\n    model.add(keras.layers.Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_uniform', kernel_regularizer=keras.regularizers.l2(1e-4), padding='same'))\n    model.add(keras.layers.BatchNormalization())\n    model.add(keras.layers.MaxPooling2D((2, 2)))\n    model.add(keras.layers.Dropout(0.3))\n\n    model.add(keras.layers.Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_uniform', kernel_regularizer=keras.regularizers.l2(1e-4), padding='same'))\n    model.add(keras.layers.BatchNormalization())\n    model.add(keras.layers.Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_uniform', kernel_regularizer=keras.regularizers.l2(1e-4), padding='same'))\n    model.add(keras.layers.BatchNormalization())\n    model.add(keras.layers.MaxPooling2D((2, 2)))\n    model.add(keras.layers.Dropout(0.4))\n\n    model.add(keras.layers.Flatten())\n    model.add(keras.layers.Dense(256, activation='elu', kernel_initializer='he_uniform'))\n    model.add(keras.layers.BatchNormalization())\n    model.add(keras.layers.Dropout(dr))\n    model.add(keras.layers.Dense(10, activation='softmax'))\n    # compile model\n    opt = keras.optimizers.Adam(lr = lr, decay = dc)\n    model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\n    return model\n\n\nmodel = build_model()\nmodel.summary()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#data augmentation\ndatagen = keras.preprocessing.image.ImageDataGenerator(\n    rotation_range=15,\n    width_shift_range=0.1,\n    height_shift_range=0.1,\n    horizontal_flip=True,\n    vertical_flip=False\n    )\ndatagen.fit(x_train_normalized)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"rlr = keras.callbacks.ReduceLROnPlateau(monitor='val_accuracy', mode ='max', factor=0.5, min_lr=1e-7, verbose = 1, patience=10)\nes = keras.callbacks.EarlyStopping(monitor='val_accuracy', mode='max', verbose = 1, patience=50, restore_best_weights=True)\nmc = keras.callbacks.ModelCheckpoint('cnn_best_model.h5', monitor='val_accuracy', mode='max', verbose = 1, save_best_only=True)\ncallback_list = [rlr, es, mc]\n\n# run model\nhigh_acc_results = model.fit_generator(datagen.flow(x_train_normalized, y_train_onehot, batch_size = 64),\n                                 validation_data = (x_test_normalized, y_test_onehot),\n                                 steps_per_epoch = x_train.shape[0] // 64, \n                                 epochs = 40, verbose = 1,\n                                 callbacks = callback_list)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_model(high_acc_results)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"saved_model = load_model('cnn_best_model.h5')\ntest_loss, test_acc = saved_model.evaluate(X_test, y_test_cat, verbose=0)\nprint('Test Accuracy:', round(test_acc, 2))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred = saved_model.predict_classes(x_test_normalized, verbose=0)\ny_pred = [y_labels[i] for i in y_pred]\nsubmissions=pd.DataFrame({\"id\": list(range(1, len(y_pred)+1)), \"label\": y_pred})\nsubmissions.to_csv(\"submission.csv\", index=False)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# autoencoders\n# knn\n# kmeans\n# iic\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}