L330_ylk25_Assignment_code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.pipeline import Pipeline, FeatureUnion\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.preprocessing import LabelBinarizer, LabelEncoder, OneHotEncoder\n",
    "import tensorflow as tf\n",
    "from functools import partial\n",
    "from tensorflow import keras\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.naive_bayes import GaussianNB, MultinomialNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "import sklearn.decomposition\n",
    "import sklearn.cross_decomposition\n",
    "import sklearn.manifold\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.utils import plot_model\n",
    "import pydot\n",
    "\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data():\n",
    "    markets = []\n",
    "    path = [\"Processed_DJI.csv\",\"Processed_NASDAQ.csv\",\"Processed_NYSE.csv\",\"Processed_RUSSELL.csv\",\"Processed_S&P.csv\"]\n",
    "    for i in range(5):\n",
    "        csv_path = os.path.join(\"CNNpred/\", path[i])\n",
    "        markets.append(pd.read_csv(csv_path))\n",
    "    return markets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class add_missing_attributes():\n",
    "    def __init__(self):\n",
    "        self = self\n",
    "    def fit(self, markets):\n",
    "        return self\n",
    "    def transform(self,markets):\n",
    "        a = markets[0].columns \n",
    "        b = markets[1].columns\n",
    "        c = markets[2].columns\n",
    "        d = markets[3].columns\n",
    "        e = markets[4].columns\n",
    "        unique_attributes = list(set().union(a,b,c,d,e))\n",
    "        new_markets = []\n",
    "        for index,m in enumerate(markets):\n",
    "            dataset_missing_attributes = set(unique_attributes) - set(m.columns)\n",
    "            dataset_complete = m.copy()\n",
    "            # print(dataset_missing_attributes)\n",
    "            for i in dataset_missing_attributes:\n",
    "                if(index != 0):\n",
    "                    dataset_complete[i] = new_markets[0][i]\n",
    "                else:\n",
    "                    dataset_complete[i] = markets[2][i]\n",
    "\n",
    "            new_markets.append(dataset_complete)\n",
    "        return new_markets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class encode():\n",
    "    def __init__(self):\n",
    "        self = self\n",
    "    def fit(self):\n",
    "        return self      \n",
    "    def transform(self, markets):\n",
    "        new_markets = markets\n",
    "        encoder = LabelEncoder()\n",
    "        encoder_name = LabelEncoder()\n",
    "        prune_attr = ['EMA_20','EMA_50','EMA_200','ROC_5','ROC_10','ROC_15','mom2','mom3']\n",
    "        attr_fill_mean = ['EMA_10']\n",
    "        attributes_fillna_bfill = ['mom','mom1','ROC_20']\n",
    "        other_attr = ['Oil',\n",
    "         'Gold',\n",
    "         'GBP',\n",
    "         'JPY',\n",
    "         'CAD',\n",
    "         'CNY',\n",
    "         'AAPL',\n",
    "         'AMZN',\n",
    "         'GE',\n",
    "         'JNJ',\n",
    "         'JPM',\n",
    "         'MSFT',\n",
    "         'WFC',\n",
    "         'XOM',\n",
    "         'FCHI',\n",
    "         'FTSE',\n",
    "         'GDAXI',\n",
    "         'DJI',\n",
    "         'HSI',\n",
    "         'IXIC',\n",
    "         'SSEC',\n",
    "         'S&P',\n",
    "         'RUT',\n",
    "         'CTB3M',\n",
    "         'CTB6M',\n",
    "         'CTB1Y',\n",
    "         'CAC-F',\n",
    "         'DAX-F',\n",
    "         'FTSE-F',\n",
    "         'HSI-F',\n",
    "         'KOSPI-F',\n",
    "         'wheat-F',\n",
    "         'GSPC',\n",
    "         'NYSE']\n",
    "        \n",
    "        for i in range(5):\n",
    "            new_markets[i][\"Date\"] = encoder.fit_transform(markets[i][\"Date\"])\n",
    "            new_markets[i][\"Date\"] = encoder.fit_transform(markets[i][\"Date\"])\n",
    "            \n",
    "            new_markets[i][\"Volume\"] = markets[i]['Volume'].fillna(0)\n",
    "            for v in prune_attr:\n",
    "                new_markets[i].drop([v],axis= 1, inplace=True)\n",
    "            for v in attr_fill_mean:\n",
    "                new_markets[i][v] = markets[i][v].fillna(markets[i][v].mean())\n",
    "            for v in attributes_fillna_bfill:\n",
    "                new_markets[i][v] = markets[i][v].fillna(markets[i][v].mean())\n",
    "            for v in other_attr:\n",
    "                new_markets[i][v] = markets[i][v].interpolate()\n",
    "            \n",
    "            new_markets[i] = new_markets[i].dropna()\n",
    "        \n",
    "        r = pd.concat([new_markets[0],new_markets[1],new_markets[2],new_markets[3],new_markets[4]], axis=0)\n",
    "#         r[\"Name\"] = encoder_name.fit_transform(r[\"Name\"])\n",
    "        r = r.reset_index()\n",
    "        return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class encodeName():\n",
    "    def __init__(self):\n",
    "        self = self\n",
    "    def fit(self):\n",
    "        return self\n",
    "    def transform(self, data):\n",
    "        name = pd.DataFrame()\n",
    "        name['results'] = data['Name']\n",
    "\n",
    "        encoder = LabelBinarizer()\n",
    "        name_1hot = encoder.fit_transform(name['results'])\n",
    "        \n",
    "        m = []\n",
    "        for j in range(5):\n",
    "            m.append('Name-%d' % (j))\n",
    "        for i,elem in enumerate(m):\n",
    "            name[elem] = name_1hot[:,i]\n",
    "        name = name.drop(['results'], axis=1)\n",
    "        result = pd.concat([data,name], axis = 1)\n",
    "        return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class scaler():\n",
    "    def __init__(self):\n",
    "        self = self\n",
    "    def fit(self):\n",
    "        return self\n",
    "    def transform(self, data):\n",
    "        \n",
    "        scaled_features = data.copy()\n",
    "        attributes = list(data.columns)\n",
    "        attributes.remove(\"Date\")\n",
    "        attributes.remove(\"Name\")\n",
    "#         attributes.remove(\"Close\")\n",
    "        attributes.remove(\"index\")\n",
    "        features = scaled_features[attributes]\n",
    "        scaler = StandardScaler().fit(features.values)\n",
    "        features = scaler.transform(features.values)\n",
    "        new_data = pd.DataFrame(data = features, columns=attributes)\n",
    "        \n",
    "        new_data[\"Date\"] = data[\"Date\"]\n",
    "        new_data[\"Name\"] = data[\"Name\"]\n",
    "#         new_data[\"Close\"] = data[\"Close\"]\n",
    "\n",
    "        return new_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_prep_pipeline = Pipeline([\n",
    "        ('add_missing_attributes', add_missing_attributes()),\n",
    "        ('encode', encode()),\n",
    "#         ('fill_missing_val', fill_missing_val()),\n",
    "        ('scaler', scaler()),\n",
    "        ('encodeName', encodeName()),\n",
    "    ])\n",
    "\n",
    "markets = load_data()\n",
    "m_pd = data_prep_pipeline.transform(markets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "lag = 60\n",
    "m_labels_pd = m_pd.loc[\n",
    "    ((m_pd['Date'] > (lag)) &\n",
    "    (m_pd['Date'] < (m_pd['Date'].max())))\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_label, test_label_temp = train_test_split(m_labels_pd, test_size=0.3, random_state=10, stratify = m_labels_pd['Name'])\n",
    "val_label, test_label = train_test_split(test_label_temp, test_size=0.5, random_state=10, stratify= test_label_temp['Name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepareSupervisedData(index, days, data, targetm):\n",
    "    new_data = np.empty((index.shape[0], days , data.shape[1]))\n",
    "    label = np.empty((index.shape[0]))\n",
    "    \n",
    "    for i,(_, row) in enumerate(index.iterrows()):\n",
    "        date = row['Date']\n",
    "#         name = row['Name']\n",
    "        name0 = row['Name-0']\n",
    "        name1 = row['Name-1']\n",
    "        name2 = row['Name-2']\n",
    "        name3 = row['Name-3']\n",
    "        name4 = row['Name-4']\n",
    "        \n",
    "        \n",
    "        row_input = data.loc[\n",
    "            (data['Date'] <= date) & \n",
    "            (data['Date'] > date - days) &\n",
    "            (data['Name-0'] == name0) &\n",
    "            (data['Name-1'] == name1) &\n",
    "            (data['Name-2'] == name2) &\n",
    "            (data['Name-3'] == name3) &\n",
    "            (data['Name-4'] == name4)\n",
    "        ]\n",
    "        new_data[i] = row_input\n",
    "        \n",
    "        targetm_char = 'Name-'+str(targetm)\n",
    "        close = data.loc[((data['Date'] == date) & (data[targetm_char] == 1)), 'Close'].item()\n",
    "        next_close = data.loc[((data['Date'] == date + 1) & (data[targetm_char] == 1)), 'Close'].item()\n",
    "        label[i] = np.sign(next_close - close)\n",
    "        if (next_close - close) < 0:\n",
    "            label[i] = 0\n",
    "        elif (next_close - close) > 0:\n",
    "            label[i] = 1\n",
    "        elif (next_close == close):\n",
    "            label[i] = 0\n",
    "\n",
    "    return new_data[:,:,:,np.newaxis], label\n",
    "\n",
    "lag = 60\n",
    "m_pd.drop(['Name'],axis=1,inplace=True)\n",
    "x_train, y_train = prepareSupervisedData(train_label, lag, m_pd, 4)\n",
    "x_val, y_val = prepareSupervisedData(val_label, lag, m_pd, 4)\n",
    "x_test, y_test = prepareSupervisedData(test_label, lag, m_pd, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model: 2D CNN + ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout, Reshape\n",
    "\n",
    "\n",
    "# checkpoint_path = \"/content/gdrive/MyDrive/Trained_Model/training_1/cp.ckpt\"\n",
    "checkpoint_path = \"Trained_Model/training_1/cp.ckpt\"\n",
    "checkpoint_dir = os.path.dirname(checkpoint_path)\n",
    "\n",
    "# Create a callback that saves the model's weights\n",
    "cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n",
    "                                                 save_weights_only=True,\n",
    "                                                 verbose=1)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    Conv2D(filters = 40, kernel_size = (1,82), input_shape=(60, 82, 1)),\n",
    "    Reshape((60,40,1)),\n",
    "    Dropout(0.2),\n",
    "    Conv2D(filters = 8, kernel_size = (1,40), input_shape=(60, 82, 1)),\n",
    "    Reshape((60,8,1)),\n",
    "    Conv2D(filters = 1, kernel_size = (3,1)),\n",
    "    MaxPooling2D(pool_size=(2,1)),\n",
    "    Conv2D(filters = 1, kernel_size = (3,1)),\n",
    "    MaxPooling2D(pool_size=(2,1)),\n",
    "    Flatten(),\n",
    "    Dense(units=104, activation='relu'),\n",
    "    Dropout(0.2),\n",
    "    Dense(units=1, activation='sigmoid'),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 60, 1, 40)         3320      \n",
      "_________________________________________________________________\n",
      "reshape (Reshape)            (None, 60, 40, 1)         0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 60, 40, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 60, 1, 8)          328       \n",
      "_________________________________________________________________\n",
      "reshape_1 (Reshape)          (None, 60, 8, 1)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 58, 8, 1)          4         \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 29, 8, 1)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 27, 8, 1)          4         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 13, 8, 1)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 104)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 104)               10920     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 104)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 105       \n",
      "=================================================================\n",
      "Total params: 14,681\n",
      "Trainable params: 14,681\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "103/106 [============================>.] - ETA: 0s - loss: 0.8150 - accuracy: 0.5121\n",
      "Epoch 00001: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 11ms/step - loss: 0.8126 - accuracy: 0.5130 - val_loss: 0.6896 - val_accuracy: 0.5461\n",
      "Epoch 2/100\n",
      "105/106 [============================>.] - ETA: 0s - loss: 0.6990 - accuracy: 0.5171\n",
      "Epoch 00002: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 9ms/step - loss: 0.6990 - accuracy: 0.5173 - val_loss: 0.6953 - val_accuracy: 0.4920\n",
      "Epoch 3/100\n",
      "101/106 [===========================>..] - ETA: 0s - loss: 0.6952 - accuracy: 0.5350\n",
      "Epoch 00003: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.6951 - accuracy: 0.5346 - val_loss: 0.6926 - val_accuracy: 0.5170\n",
      "Epoch 4/100\n",
      "102/106 [===========================>..] - ETA: 0s - loss: 0.6936 - accuracy: 0.5283\n",
      "Epoch 00004: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 11ms/step - loss: 0.6932 - accuracy: 0.5295 - val_loss: 0.6858 - val_accuracy: 0.5656\n",
      "Epoch 5/100\n",
      "104/106 [============================>.] - ETA: 0s - loss: 0.6896 - accuracy: 0.5305\n",
      "Epoch 00005: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 11ms/step - loss: 0.6896 - accuracy: 0.5308 - val_loss: 0.6826 - val_accuracy: 0.5704\n",
      "Epoch 6/100\n",
      "101/106 [===========================>..] - ETA: 0s - loss: 0.6869 - accuracy: 0.5427\n",
      "Epoch 00006: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.6869 - accuracy: 0.5417 - val_loss: 0.6805 - val_accuracy: 0.5718\n",
      "Epoch 7/100\n",
      "104/106 [============================>.] - ETA: 0s - loss: 0.6815 - accuracy: 0.5508\n",
      "Epoch 00007: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 12ms/step - loss: 0.6816 - accuracy: 0.5500 - val_loss: 0.6779 - val_accuracy: 0.5802\n",
      "Epoch 8/100\n",
      "101/106 [===========================>..] - ETA: 0s - loss: 0.6788 - accuracy: 0.5551\n",
      "Epoch 00008: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.6783 - accuracy: 0.5569 - val_loss: 0.6758 - val_accuracy: 0.5684\n",
      "Epoch 9/100\n",
      "102/106 [===========================>..] - ETA: 0s - loss: 0.6719 - accuracy: 0.5722\n",
      "Epoch 00009: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 11ms/step - loss: 0.6724 - accuracy: 0.5704 - val_loss: 0.6763 - val_accuracy: 0.5850\n",
      "Epoch 10/100\n",
      "105/106 [============================>.] - ETA: 0s - loss: 0.6667 - accuracy: 0.5807\n",
      "Epoch 00010: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 9ms/step - loss: 0.6665 - accuracy: 0.5809 - val_loss: 0.6760 - val_accuracy: 0.5788\n",
      "Epoch 11/100\n",
      " 99/106 [===========================>..] - ETA: 0s - loss: 0.6652 - accuracy: 0.5791\n",
      "Epoch 00011: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 9ms/step - loss: 0.6646 - accuracy: 0.5806 - val_loss: 0.6664 - val_accuracy: 0.5926\n",
      "Epoch 12/100\n",
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      "Epoch 27/100\n",
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      "106/106 [==============================] - 1s 12ms/step - loss: 0.4795 - accuracy: 0.7381 - val_loss: 0.4663 - val_accuracy: 0.7571\n",
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      "106/106 [==============================] - 1s 10ms/step - loss: 0.4334 - accuracy: 0.7711 - val_loss: 0.4111 - val_accuracy: 0.7988\n",
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      "106/106 [==============================] - 1s 9ms/step - loss: 0.4134 - accuracy: 0.7864 - val_loss: 0.3995 - val_accuracy: 0.8022\n",
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      "106/106 [==============================] - 1s 10ms/step - loss: 0.3963 - accuracy: 0.7956 - val_loss: 0.3732 - val_accuracy: 0.8133\n",
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      "106/106 [==============================] - 1s 9ms/step - loss: 0.3995 - accuracy: 0.7922 - val_loss: 0.3669 - val_accuracy: 0.8203\n",
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      "106/106 [==============================] - 1s 10ms/step - loss: 0.3586 - accuracy: 0.8180 - val_loss: 0.3749 - val_accuracy: 0.8050\n",
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      "106/106 [==============================] - 1s 9ms/step - loss: 0.3592 - accuracy: 0.8228 - val_loss: 0.3456 - val_accuracy: 0.8279\n",
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      "106/106 [==============================] - 1s 10ms/step - loss: 0.3581 - accuracy: 0.8183 - val_loss: 0.2908 - val_accuracy: 0.8543\n",
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      "106/106 [==============================] - 1s 9ms/step - loss: 0.3170 - accuracy: 0.8513 - val_loss: 0.2924 - val_accuracy: 0.8654\n",
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      "Epoch 41/100\n",
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      "Epoch 00041: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.3050 - accuracy: 0.8554 - val_loss: 0.2560 - val_accuracy: 0.8855\n",
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      "106/106 [==============================] - 1s 11ms/step - loss: 0.2934 - accuracy: 0.8629 - val_loss: 0.2680 - val_accuracy: 0.8869\n",
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      "106/106 [==============================] - 1s 9ms/step - loss: 0.2748 - accuracy: 0.8732 - val_loss: 0.2126 - val_accuracy: 0.9056\n",
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      "106/106 [==============================] - 1s 9ms/step - loss: 0.2608 - accuracy: 0.8809 - val_loss: 0.2003 - val_accuracy: 0.9126\n",
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      "106/106 [==============================] - 1s 10ms/step - loss: 0.2511 - accuracy: 0.8875 - val_loss: 0.2500 - val_accuracy: 0.8890\n",
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      "106/106 [==============================] - 1s 10ms/step - loss: 0.2324 - accuracy: 0.8979 - val_loss: 0.1682 - val_accuracy: 0.9257\n",
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      "106/106 [==============================] - 1s 10ms/step - loss: 0.2192 - accuracy: 0.9068 - val_loss: 0.1716 - val_accuracy: 0.9278\n",
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      "106/106 [==============================] - 1s 10ms/step - loss: 0.2127 - accuracy: 0.9086 - val_loss: 0.1454 - val_accuracy: 0.9403\n",
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      "106/106 [==============================] - 1s 9ms/step - loss: 0.2177 - accuracy: 0.9056 - val_loss: 0.1569 - val_accuracy: 0.9368\n",
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      "Epoch 57/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 00057: saving model to Trained_Model/training_1/cp.ckpt\n",
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      "Epoch 61/100\n",
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      "106/106 [==============================] - 1s 10ms/step - loss: 0.1624 - accuracy: 0.9331 - val_loss: 0.0701 - val_accuracy: 0.9743\n",
      "Epoch 75/100\n",
      "100/106 [===========================>..] - ETA: 0s - loss: 0.1487 - accuracy: 0.9394\n",
      "Epoch 00075: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 11ms/step - loss: 0.1469 - accuracy: 0.9402 - val_loss: 0.0882 - val_accuracy: 0.9667\n",
      "Epoch 76/100\n",
      "102/106 [===========================>..] - ETA: 0s - loss: 0.1505 - accuracy: 0.9372\n",
      "Epoch 00076: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.1495 - accuracy: 0.9380 - val_loss: 0.0733 - val_accuracy: 0.9709\n",
      "Epoch 77/100\n",
      "105/106 [============================>.] - ETA: 0s - loss: 0.1571 - accuracy: 0.9379\n",
      "Epoch 00077: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.1575 - accuracy: 0.9379 - val_loss: 0.0838 - val_accuracy: 0.9715\n",
      "Epoch 78/100\n",
      "103/106 [============================>.] - ETA: 0s - loss: 0.1569 - accuracy: 0.9367\n",
      "Epoch 00078: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.1563 - accuracy: 0.9370 - val_loss: 0.0809 - val_accuracy: 0.9702\n",
      "Epoch 79/100\n",
      "103/106 [============================>.] - ETA: 0s - loss: 0.1420 - accuracy: 0.9421\n",
      "Epoch 00079: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.1416 - accuracy: 0.9425 - val_loss: 0.0616 - val_accuracy: 0.9764\n",
      "Epoch 80/100\n",
      "102/106 [===========================>..] - ETA: 0s - loss: 0.1312 - accuracy: 0.9490\n",
      "Epoch 00080: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 11ms/step - loss: 0.1321 - accuracy: 0.9486 - val_loss: 0.0664 - val_accuracy: 0.9813\n",
      "Epoch 81/100\n",
      "103/106 [============================>.] - ETA: 0s - loss: 0.1435 - accuracy: 0.9446\n",
      "Epoch 00081: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.1438 - accuracy: 0.9447 - val_loss: 0.0745 - val_accuracy: 0.9757\n",
      "Epoch 82/100\n",
      "101/106 [===========================>..] - ETA: 0s - loss: 0.1537 - accuracy: 0.9409\n",
      "Epoch 00082: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 11ms/step - loss: 0.1540 - accuracy: 0.9410 - val_loss: 0.0730 - val_accuracy: 0.9792\n",
      "Epoch 83/100\n",
      "101/106 [===========================>..] - ETA: 0s - loss: 0.1575 - accuracy: 0.9356\n",
      "Epoch 00083: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 9ms/step - loss: 0.1547 - accuracy: 0.9373 - val_loss: 0.0728 - val_accuracy: 0.9771\n",
      "Epoch 84/100\n",
      "102/106 [===========================>..] - ETA: 0s - loss: 0.1316 - accuracy: 0.9493\n",
      "Epoch 00084: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.1315 - accuracy: 0.9498 - val_loss: 0.0556 - val_accuracy: 0.9785\n",
      "Epoch 85/100\n",
      "105/106 [============================>.] - ETA: 0s - loss: 0.1272 - accuracy: 0.9448\n",
      "Epoch 00085: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.1271 - accuracy: 0.9448 - val_loss: 0.0545 - val_accuracy: 0.9854\n",
      "Epoch 86/100\n",
      "105/106 [============================>.] - ETA: 0s - loss: 0.1191 - accuracy: 0.9528\n",
      "Epoch 00086: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.1191 - accuracy: 0.9529 - val_loss: 0.0555 - val_accuracy: 0.9806\n",
      "Epoch 87/100\n",
      "102/106 [===========================>..] - ETA: 0s - loss: 0.1310 - accuracy: 0.9479\n",
      "Epoch 00087: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.1308 - accuracy: 0.9480 - val_loss: 0.0645 - val_accuracy: 0.9757\n",
      "Epoch 88/100\n",
      "103/106 [============================>.] - ETA: 0s - loss: 0.1235 - accuracy: 0.9499\n",
      "Epoch 00088: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 11ms/step - loss: 0.1243 - accuracy: 0.9495 - val_loss: 0.0599 - val_accuracy: 0.9799\n",
      "Epoch 89/100\n",
      "105/106 [============================>.] - ETA: 0s - loss: 0.1406 - accuracy: 0.9424\n",
      "Epoch 00089: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 11ms/step - loss: 0.1405 - accuracy: 0.9425 - val_loss: 0.0658 - val_accuracy: 0.9743\n",
      "Epoch 90/100\n",
      "100/106 [===========================>..] - ETA: 0s - loss: 0.1310 - accuracy: 0.9497\n",
      "Epoch 00090: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.1320 - accuracy: 0.9487 - val_loss: 0.0518 - val_accuracy: 0.9827\n",
      "Epoch 91/100\n",
      "102/106 [===========================>..] - ETA: 0s - loss: 0.1152 - accuracy: 0.9539\n",
      "Epoch 00091: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.1151 - accuracy: 0.9538 - val_loss: 0.0429 - val_accuracy: 0.9854\n",
      "Epoch 92/100\n",
      "102/106 [===========================>..] - ETA: 0s - loss: 0.1145 - accuracy: 0.9562\n",
      "Epoch 00092: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.1138 - accuracy: 0.9560 - val_loss: 0.0553 - val_accuracy: 0.9785\n",
      "Epoch 93/100\n",
      "103/106 [============================>.] - ETA: 0s - loss: 0.1091 - accuracy: 0.9551\n",
      "Epoch 00093: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.1090 - accuracy: 0.9550 - val_loss: 0.0495 - val_accuracy: 0.9833\n",
      "Epoch 94/100\n",
      "104/106 [============================>.] - ETA: 0s - loss: 0.1246 - accuracy: 0.9501\n",
      "Epoch 00094: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 11ms/step - loss: 0.1244 - accuracy: 0.9502 - val_loss: 0.0465 - val_accuracy: 0.9889\n",
      "Epoch 95/100\n",
      "101/106 [===========================>..] - ETA: 0s - loss: 0.1183 - accuracy: 0.9565\n",
      "Epoch 00095: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.1192 - accuracy: 0.9563 - val_loss: 0.0429 - val_accuracy: 0.9861\n",
      "Epoch 96/100\n",
      "105/106 [============================>.] - ETA: 0s - loss: 0.1054 - accuracy: 0.9558\n",
      "Epoch 00096: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 11ms/step - loss: 0.1053 - accuracy: 0.9558 - val_loss: 0.0474 - val_accuracy: 0.9861\n",
      "Epoch 97/100\n",
      "103/106 [============================>.] - ETA: 0s - loss: 0.1048 - accuracy: 0.9569\n",
      "Epoch 00097: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.1051 - accuracy: 0.9567 - val_loss: 0.0383 - val_accuracy: 0.9896\n",
      "Epoch 98/100\n",
      "100/106 [===========================>..] - ETA: 0s - loss: 0.1178 - accuracy: 0.9513\n",
      "Epoch 00098: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.1175 - accuracy: 0.9515 - val_loss: 0.0477 - val_accuracy: 0.9861\n",
      "Epoch 99/100\n",
      "104/106 [============================>.] - ETA: 0s - loss: 0.1303 - accuracy: 0.9521\n",
      "Epoch 00099: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 10ms/step - loss: 0.1307 - accuracy: 0.9515 - val_loss: 0.0837 - val_accuracy: 0.9743\n",
      "Epoch 100/100\n",
      "105/106 [============================>.] - ETA: 0s - loss: 0.1252 - accuracy: 0.9485\n",
      "Epoch 00100: saving model to Trained_Model/training_1/cp.ckpt\n",
      "106/106 [==============================] - 1s 11ms/step - loss: 0.1254 - accuracy: 0.9484 - val_loss: 0.0366 - val_accuracy: 0.9854\n"
     ]
    }
   ],
   "source": [
    "model.compile(\n",
    "    loss=\"binary_crossentropy\", \n",
    "    optimizer=\"adam\",\n",
    "    metrics=[\"accuracy\"])\n",
    "\n",
    "history = model.fit(x_train, \n",
    "                    y_train, \n",
    "                    epochs=100, \n",
    "                    batch_size=64, \n",
    "                    validation_data = (x_val, y_val),\n",
    "                    callbacks = [cp_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46/46 [==============================] - 0s 3ms/step - loss: 0.0383 - accuracy: 0.9840\n",
      "Accuracy:  0.984049916267395\n",
      "F1:  0.9854153455928979\n"
     ]
    }
   ],
   "source": [
    "score, acc = model.evaluate(x_test, y_test)\n",
    "print(\"Accuracy: \",acc)\n",
    "y_pred = np.round(model(x_test))\n",
    "print(\"F1: \", sklearn.metrics.f1_score(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simple ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "model2 = keras.models.Sequential([\n",
    "    Dense(units=1, activation='relu', input_shape=(4920,1)),\n",
    "    Reshape((1, 4920)),\n",
    "    Dense(units=104, activation='relu',),\n",
    "    Dropout(0.2),\n",
    "    Dense(units=1, activation='sigmoid'),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "53/53 [==============================] - 1s 18ms/step - loss: 6.9941 - accuracy: 0.5404 - val_loss: 6.7939 - val_accuracy: 0.5545\n",
      "Epoch 2/5\n",
      "53/53 [==============================] - 1s 15ms/step - loss: 6.9978 - accuracy: 0.5411 - val_loss: 6.7939 - val_accuracy: 0.5545\n",
      "Epoch 3/5\n",
      "53/53 [==============================] - 1s 15ms/step - loss: 6.9978 - accuracy: 0.5411 - val_loss: 6.7939 - val_accuracy: 0.5545\n",
      "Epoch 4/5\n",
      "53/53 [==============================] - 1s 15ms/step - loss: 6.9978 - accuracy: 0.5411 - val_loss: 6.7939 - val_accuracy: 0.5545\n",
      "Epoch 5/5\n",
      "53/53 [==============================] - 1s 15ms/step - loss: 6.9978 - accuracy: 0.5411 - val_loss: 6.7939 - val_accuracy: 0.5545\n"
     ]
    }
   ],
   "source": [
    "x_train_n = x_train.reshape((x_train.shape[0], -1))\n",
    "x_val_n = x_val.reshape((x_val.shape[0], -1))\n",
    "x_test_n = x_test.reshape((x_test.shape[0], -1))\n",
    "\n",
    "x_train_ann = x_train_n.reshape((x_train_n.shape[0],x_train_n.shape[1],1))\n",
    "x_val_ann = x_val_n.reshape((x_val_n.shape[0],x_val_n.shape[1],1))\n",
    "x_test_ann = x_test_n.reshape((x_test_n.shape[0],x_test_n.shape[1],1))\n",
    "\n",
    "model2.compile(\n",
    "                loss=\"binary_crossentropy\", \n",
    "                optimizer=\"adam\",\n",
    "                metrics=[\"accuracy\"])\n",
    "\n",
    "history = model2.fit(x_train_ann, \n",
    "                    y_train, \n",
    "                    epochs=5, \n",
    "                    batch_size=128, \n",
    "                    validation_data = (x_val_ann, y_val))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simple_ann(xtrain,xval,xtest,ytrain = y_train,yval = y_val,ytest = y_test, model=model2):\n",
    "    print(\"simple ANN\")\n",
    "#     score = model.evaluate(xval, yval)\n",
    "#     print(\"Accuracy: \",score)\n",
    "#     y_pred = np.round(model(x_val))\n",
    "#     print(\"F1: \", sklearn.metrics.f1_score(y_pred, yval))\n",
    "    score, acc = model.evaluate(xtest, ytest)\n",
    "    print(\"Accuracy: \",acc)\n",
    "    y_pred = np.round(model(xtest)).reshape(-1)\n",
    "    print(\"F1: \", sklearn.metrics.f1_score(y_pred, ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simple ANN\n",
      "46/46 [==============================] - 0s 2ms/step - loss: 6.9372 - accuracy: 0.5451\n",
      "Accuracy:  0.545076310634613\n",
      "F1:  0.7055655296229802\n"
     ]
    }
   ],
   "source": [
    "simple_ann(x_train_ann, x_val_ann, x_test_ann)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model: 2D CNN + Linear Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn = keras.models.Sequential(model.layers[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 60, 1, 40)         3320      \n",
      "_________________________________________________________________\n",
      "reshape (Reshape)            (None, 60, 40, 1)         0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 60, 40, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 60, 1, 8)          328       \n",
      "_________________________________________________________________\n",
      "reshape_1 (Reshape)          (None, 60, 8, 1)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 58, 8, 1)          4         \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 29, 8, 1)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 27, 8, 1)          4         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 13, 8, 1)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 104)               0         \n",
      "=================================================================\n",
      "Total params: 3,656\n",
      "Trainable params: 3,656\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Gaussian Naive Bayes\n",
      "Accuracy:  0.5707350901525658\n",
      "F1:  0.631328171530673\n"
     ]
    }
   ],
   "source": [
    "def linear_model_gbn(xtrain,xval,xtest,ytrain = y_train,yval = y_val,ytest = y_test):\n",
    "    print(\"\\n Gaussian Naive Bayes\")\n",
    "    reg = GaussianNB()\n",
    "    reg.fit(xtrain, ytrain)\n",
    "#     print(\"Accuracy: \", reg.score(xval, yval))\n",
    "#     y_pred = reg.predict(xval)\n",
    "#     print(\"F1 \", sklearn.metrics.f1_score(y_pred, yval))\n",
    "    print(\"Accuracy: \", reg.score(xtest, ytest))\n",
    "    y_pred = reg.predict(xtest)\n",
    "    print(\"F1: \", sklearn.metrics.f1_score(y_pred, ytest))\n",
    "    \n",
    "linear_model_gbn(cnn(x_train),cnn(x_val),cnn(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Logistic Regression\n",
      "Accuracy:  0.5742024965325936\n",
      "F1:  0.6405152224824355\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/elim/anaconda3/envs/ds/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    }
   ],
   "source": [
    "def linear_model_log(xtrain,xval,xtest,ytrain = y_train,yval = y_val,ytest = y_test):\n",
    "    print(\"\\n Logistic Regression\")\n",
    "    reg = LogisticRegression(max_iter=300, random_state=42)\n",
    "    reg.fit(xtrain, ytrain)\n",
    "#     print(\"Accuracy: \", reg.score(xval, yval))\n",
    "#     y_pred = reg.predict(xval)\n",
    "#     print(\"F1 \", sklearn.metrics.f1_score(y_pred, yval))\n",
    "    print(\"Accuracy: \", reg.score(xtest, ytest))\n",
    "    y_pred = reg.predict(xtest)\n",
    "    print(\"F1: \", sklearn.metrics.f1_score(y_pred, ytest))\n",
    "\n",
    "linear_model_log(cnn(x_train),cnn(x_val),cnn(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " SGD Classifier\n",
      "Accuracy:  0.48751733703190014\n",
      "F1:  0.3138347260909935\n"
     ]
    }
   ],
   "source": [
    "def linear_model_sdg(xtrain,xval,xtest,ytrain = y_train,yval = y_val,ytest = y_test):\n",
    "    print(\"\\n SGD Classifier\")\n",
    "    reg = SGDClassifier(max_iter=4, tol=None, random_state=42,\n",
    "                   loss=\"perceptron\", eta0=1, learning_rate=\"constant\", penalty=None)\n",
    "    reg.fit(xtrain, ytrain)\n",
    "#     print(\"Accuracy: \", reg.score(xval, yval))\n",
    "#     y_pred = reg.predict(xval)\n",
    "#     print(\"F1 \", sklearn.metrics.f1_score(y_pred, yval))\n",
    "    print(\"Accuracy: \", reg.score(xtest, ytest))\n",
    "    y_pred = reg.predict(xtest)\n",
    "    print(\"F1: \", sklearn.metrics.f1_score(y_pred, ytest))\n",
    "\n",
    "\n",
    "linear_model_sdg(cnn(x_train),cnn(x_val),cnn(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model: 2D CNN + Ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Random Forest\n",
      "Accuracy:  0.7635228848821082\n",
      "F1:  0.8181333333333334\n"
     ]
    }
   ],
   "source": [
    "def en_model_rfc(xtrain,xval,xtest,ytrain = y_train,yval = y_val,ytest = y_test):\n",
    "    print(\"\\n Random Forest\")\n",
    "    reg = RandomForestClassifier(n_estimators=200, max_leaf_nodes=24, \n",
    "                                 n_jobs=-1, random_state=42)\n",
    "    reg.fit(xtrain, ytrain)\n",
    "#     print(\"Accuracy: \", reg.score(xval, yval))\n",
    "#     y_pred = reg.predict(xval)\n",
    "#     print(\"F1 \", sklearn.metrics.f1_score(y_pred, yval))\n",
    "    print(\"Accuracy: \", reg.score(xtest, ytest))\n",
    "    y_pred = reg.predict(xtest)\n",
    "    print(\"F1: \", sklearn.metrics.f1_score(y_pred, ytest))\n",
    "    \n",
    "en_model_rfc(cnn(x_train),cnn(x_val),cnn(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Ada Boost\n",
      "Accuracy:  0.955617198335645\n",
      "F1:  0.9596977329974811\n"
     ]
    }
   ],
   "source": [
    "def en_model_ada(xtrain,xval,xtest,ytrain = y_train,yval = y_val,ytest = y_test):\n",
    "    print(\"\\n Ada Boost\")\n",
    "    reg = AdaBoostClassifier(\n",
    "            DecisionTreeClassifier(max_depth=3), n_estimators=100,\n",
    "            algorithm=\"SAMME.R\", learning_rate=0.5, random_state=42)\n",
    "    \n",
    "    reg.fit(xtrain, ytrain)\n",
    "#     print(\"Accuracy: \", reg.score(xval, yval))\n",
    "#     y_pred = reg.predict(xval)\n",
    "#     print(\"F1 \", sklearn.metrics.f1_score(y_pred, yval))\n",
    "    print(\"Accuracy: \", reg.score(xtest, ytest))\n",
    "    y_pred = reg.predict(xtest)\n",
    "    print(\"F1: \", sklearn.metrics.f1_score(y_pred, ytest))\n",
    "    \n",
    "en_model_ada(cnn(x_train),cnn(x_val),cnn(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model: PCA + Linear Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_n = x_train.reshape((x_train.shape[0], -1))\n",
    "x_val_n = x_val.reshape((x_val.shape[0], -1))\n",
    "x_test_n = x_test.reshape((x_test.shape[0], -1))\n",
    "features = 104\n",
    "\n",
    "x_train_pca = StandardScaler().fit_transform(x_train_n)\n",
    "x_val_pca = StandardScaler().fit_transform(x_val_n)\n",
    "x_test_pca = StandardScaler().fit_transform(x_test_n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = sklearn.decomposition.PCA()\n",
    "pca.fit(x_train_pca)\n",
    "\n",
    "pca_result1 = pca.transform(x_train_pca)\n",
    "pca_result2 = pca.transform(x_val_pca)\n",
    "pca_result3 = pca.transform(x_test_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Gaussian Naive Bayes\n",
      "Accuracy:  0.5804438280166435\n",
      "F1:  0.6068875893437297\n",
      "\n",
      "\n",
      " Logistic Regression\n",
      "Accuracy:  0.6074895977808599\n",
      "F1:  0.677309007981756\n",
      "\n",
      "\n",
      " SGD Classifier\n",
      "Accuracy:  0.5305131761442441\n",
      "F1:  0.5728706624605678\n"
     ]
    }
   ],
   "source": [
    "linear_model_gbn(pca_result1[:,0:features],pca_result2[:,0:features],pca_result3[:,0:features])\n",
    "print()\n",
    "linear_model_log(pca_result1[:,0:features],pca_result2[:,0:features],pca_result3[:,0:features])\n",
    "print()\n",
    "linear_model_sdg(pca_result1[:,0:features],pca_result2[:,0:features],pca_result3[:,0:features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Random Forest\n",
      "Accuracy:  0.7337031900138696\n",
      "F1:  0.8028747433264887\n"
     ]
    }
   ],
   "source": [
    "en_model_rfc(pca_result1[:,0:features],pca_result2[:,0:features],pca_result3[:,0:features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Ada Boost\n",
      "Accuracy:  0.9563106796116505\n",
      "F1:  0.9602021478205938\n"
     ]
    }
   ],
   "source": [
    "en_model_ada(pca_result1[:,0:features],pca_result2[:,0:features],pca_result3[:,0:features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "model3 = keras.models.Sequential([\n",
    "    Dense(units=1, activation='relu', input_shape=(104,1)),\n",
    "    Reshape((1, 104)),\n",
    "    Dense(units=104, activation='relu',),\n",
    "    Dropout(0.2),\n",
    "    Dense(units=1, activation='sigmoid'),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.7182 - accuracy: 0.5307 - val_loss: 0.6782 - val_accuracy: 0.5559\n",
      "Epoch 2/5\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.6748 - accuracy: 0.5827 - val_loss: 0.6589 - val_accuracy: 0.6037\n",
      "Epoch 3/5\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.6563 - accuracy: 0.6081 - val_loss: 0.6449 - val_accuracy: 0.6204\n",
      "Epoch 4/5\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.6356 - accuracy: 0.6349 - val_loss: 0.6273 - val_accuracy: 0.6495\n",
      "Epoch 5/5\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.6073 - accuracy: 0.6652 - val_loss: 0.6079 - val_accuracy: 0.6738\n"
     ]
    }
   ],
   "source": [
    "xtr= pca_result1[:,0:features].reshape((pca_result1[:,0:features].shape[0],pca_result1[:,0:features].shape[1],1))\n",
    "xv=pca_result2[:,0:features].reshape((pca_result2[:,0:features].shape[0],pca_result2[:,0:features].shape[1],1))\n",
    "xte=pca_result3[:,0:features].reshape((pca_result3[:,0:features].shape[0],pca_result3[:,0:features].shape[1],1))\n",
    "\n",
    "model3.compile(\n",
    "                loss=\"binary_crossentropy\", \n",
    "                optimizer=\"adam\",\n",
    "                metrics=[\"accuracy\"])\n",
    "\n",
    "history = model3.fit(xtr, \n",
    "                    y_train, \n",
    "                    epochs=5, \n",
    "                    batch_size=128, \n",
    "                    validation_data = (xv, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "simple ANN\n",
      "46/46 [==============================] - 0s 705us/step - loss: 0.6167 - accuracy: 0.6782\n",
      "Accuracy:  0.6782246828079224\n",
      "F1:  0.71\n"
     ]
    }
   ],
   "source": [
    "print()\n",
    "simple_ann(xtr,xv,xte, model=model3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model: tSNE + Linear Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Indexed 6727 samples in 0.030s...\n",
      "[t-SNE] Computed neighbors for 6727 samples in 2.994s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 6727\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 6727\n",
      "[t-SNE] Computed conditional probabilities for sample 3000 / 6727\n",
      "[t-SNE] Computed conditional probabilities for sample 4000 / 6727\n",
      "[t-SNE] Computed conditional probabilities for sample 5000 / 6727\n",
      "[t-SNE] Computed conditional probabilities for sample 6000 / 6727\n",
      "[t-SNE] Computed conditional probabilities for sample 6727 / 6727\n",
      "[t-SNE] Mean sigma: 9.412183\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 84.774216\n",
      "[t-SNE] KL divergence after 1000 iterations: 1.713599\n"
     ]
    }
   ],
   "source": [
    "pca_h = sklearn.decomposition.PCA()\n",
    "pca_h.fit(x_train_pca)\n",
    "\n",
    "tsne = sklearn.manifold.TSNE(n_components=2, verbose=1)\n",
    "p = pca_h.transform(x_train_pca)\n",
    "tsne_result1 = tsne.fit_transform(p[:,:50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Indexed 1441 samples in 0.004s...\n",
      "[t-SNE] Computed neighbors for 1441 samples in 0.167s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 1441\n",
      "[t-SNE] Computed conditional probabilities for sample 1441 / 1441\n",
      "[t-SNE] Mean sigma: 12.532647\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 70.809723\n",
      "[t-SNE] KL divergence after 1000 iterations: 1.006127\n",
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Indexed 1442 samples in 0.007s...\n",
      "[t-SNE] Computed neighbors for 1442 samples in 0.242s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 1442\n",
      "[t-SNE] Computed conditional probabilities for sample 1442 / 1442\n",
      "[t-SNE] Mean sigma: 12.833691\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 71.933464\n",
      "[t-SNE] KL divergence after 1000 iterations: 1.019098\n"
     ]
    }
   ],
   "source": [
    "a = pca_h.transform(x_val_pca)\n",
    "tsne_result2 = tsne.fit_transform(a[:,:50])\n",
    "\n",
    "b = pca_h.transform(x_test_pca)\n",
    "tsne_result3 = tsne.fit_transform(b[:,:50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Gaussian Naive Bayes\n",
      "Accuracy:  0.5450762829403606\n",
      "F1:  0.7055655296229802\n",
      "\n",
      "\n",
      " Logistic Regression\n",
      "Accuracy:  0.5450762829403606\n",
      "F1:  0.7055655296229802\n",
      "\n",
      "\n",
      " SGD Classifier\n",
      "Accuracy:  0.49167822468793343\n",
      "F1:  0.5351934051997463\n"
     ]
    }
   ],
   "source": [
    "linear_model_gbn(tsne_result1,tsne_result2,tsne_result3)\n",
    "print()\n",
    "linear_model_log(tsne_result1,tsne_result2,tsne_result3)\n",
    "print()\n",
    "linear_model_sdg(tsne_result1,tsne_result2,tsne_result3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Random Forest\n",
      "Accuracy:  0.5249653259361997\n",
      "F1:  0.6806526806526807\n"
     ]
    }
   ],
   "source": [
    "en_model_rfc(tsne_result1,tsne_result2,tsne_result3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Ada Boost\n",
      "Accuracy:  0.5256588072122053\n",
      "F1:  0.5933412604042807\n"
     ]
    }
   ],
   "source": [
    "en_model_ada(tsne_result1,tsne_result2,tsne_result3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "model4 = keras.models.Sequential([\n",
    "    Dense(units=1, activation='relu', input_shape=(2,1)),\n",
    "    Reshape((1, 2)),\n",
    "    Dropout(0.2),\n",
    "    Dense(units=1, activation='sigmoid'),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 4.0073 - accuracy: 0.4907 - val_loss: 3.5046 - val_accuracy: 0.4754\n",
      "Epoch 2/5\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 3.8567 - accuracy: 0.4833 - val_loss: 3.2986 - val_accuracy: 0.4774\n",
      "Epoch 3/5\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 3.5957 - accuracy: 0.4885 - val_loss: 3.1407 - val_accuracy: 0.4698\n",
      "Epoch 4/5\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 3.4385 - accuracy: 0.4871 - val_loss: 3.0147 - val_accuracy: 0.4684\n",
      "Epoch 5/5\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 3.3893 - accuracy: 0.4813 - val_loss: 2.8212 - val_accuracy: 0.4705\n"
     ]
    }
   ],
   "source": [
    "xtr= tsne_result1.reshape((tsne_result1.shape[0],tsne_result1.shape[1], 1))\n",
    "xv= tsne_result2.reshape((tsne_result2.shape[0],tsne_result2.shape[1], 1))\n",
    "xte= tsne_result3.reshape((tsne_result3.shape[0],tsne_result3.shape[1], 1))\n",
    "\n",
    "model4.compile(\n",
    "                loss=\"binary_crossentropy\", \n",
    "                optimizer=\"adam\",\n",
    "                metrics=[\"accuracy\"])\n",
    "\n",
    "history = model4.fit(xtr, \n",
    "                    y_train, \n",
    "                    epochs=5, \n",
    "                    batch_size=128, \n",
    "                    validation_data = (xv, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simple ANN\n",
      "46/46 [==============================] - 0s 625us/step - loss: 2.9197 - accuracy: 0.4695\n",
      "Accuracy:  0.4694868326187134\n",
      "F1:  0.3224092116917626\n"
     ]
    }
   ],
   "source": [
    "simple_ann(xtr,xv,xte, model=model4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model: No Dimension Reduction + Linear Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_n = x_train.reshape((x_train.shape[0], -1))\n",
    "x_val_n = x_val.reshape((x_val.shape[0], -1))\n",
    "x_test_n = x_test.reshape((x_test.shape[0], -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Gaussian Naive Bayes\n",
      "Accuracy:  0.6393897364771152\n",
      "F1:  0.6353436185133239\n",
      "\n",
      " Logistic Regression\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/elim/anaconda3/envs/ds/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.8793342579750347\n",
      "F1:  0.8900126422250316\n",
      "\n",
      " SGD Classifier\n",
      "Accuracy:  0.4549237170596394\n",
      "F1:  0.0\n"
     ]
    }
   ],
   "source": [
    "linear_model_gbn(x_train_n,x_val_n,x_test_n)\n",
    "linear_model_log(x_train_n,x_val_n,x_test_n)\n",
    "linear_model_sdg(x_train_n,x_val_n,x_test_n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model: No Dimension Reduction + Ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Random Forest\n",
      "Accuracy:  0.8918169209431346\n",
      "F1:  0.9097222222222222\n"
     ]
    }
   ],
   "source": [
    "en_model_rfc(x_train_n,x_val_n,x_test_n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Ada Boost\n",
      "Accuracy:  0.9965325936199723\n",
      "F1:  0.9968294229549778\n"
     ]
    }
   ],
   "source": [
    "en_model_ada(x_train_n,x_val_n,x_test_n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9b2be427f0>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca1,pca2 = pca_result1[:,0], pca_result1[:,1]\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(10,10))\n",
    "for lvl in range(2):\n",
    "    ln = [\"Price Decrease/ Remains the same\", \"Price Increase\"]\n",
    "    i = (y_train == lvl)\n",
    "    ax.scatter(pca1[i], pca2[i], label=ln[lvl])\n",
    "\n",
    "ax.set_title(\"PCA of the 1st and 2nd Component\")\n",
    "ax.set_xlabel(\"1st Component\")\n",
    "ax.set_ylabel(\"2nd Component\")\n",
    "ax.legend(loc='upper left', bbox_to_anchor=(1.1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9aa00f20b8>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Va9asyf7Pf/6TDTBz5szKTz/99PM1a9asXrJkSYelS5dmxz9mbW2t59RTT92zdu3az0499dQ9/+///b9DAaZNm3bku++++8XatWs/+7//+7+v4u93//33VxYWFu5Zs2bNZ5MmTfoe4LPPPsspLS39+vPPP19dVlbW+auvvvJt3rw544EHHjjygw8++OKzzz77/KSTTqr5wx/+cHj8+WbNmnXEp59++tkXX3zx2QsvvPANwIknnrjvv//975rPP//8s0mTJlWOHz8+IrC/+OKL7Ndff/3rjz/++PMHH3ywICcnJ/T5559/VlhYuPfJJ5/skuz1c4PkDAuCIAjCAcSsRV8W1AVCMcWuukDIM2vRlwVjhh21Pd3z1tXVefr3738cwNChQ3f/5je/2bpw4cLcgQMH7u3fv399/PELFiw4ZMyYMVs7dOgQAjj88MOD1dXVnvLy8txLL720t3lcfX29cvP4WhthGk7n+M9//tPh73//+3qAjIwMunTpEty6dav3yCOPrB8xYsRegP/7v/875IMPPjjkuOOOOw6gpqbGs2bNmnbnnnvunoceeujwefPmdQKj+rl69ep2AwcO3Ldx48asK6+8svsFF1xQfdFFF+1yWsM///nP9ieddNJep9ewsrIy8/jjj68ZOXKk47kAzjvvvJ0ej4eTTjqppkuXLv5TTjmlFqBv376169aty/rRj35U++KLL+a98MIL+YFAQP3www++lStXths6dGht9GP7fD79y1/+shpgyJAhexcuXHgIQGFh4Z7Ro0f3vOSSS3aMHj16h5v3oqioaFeXLl2CAMccc8y+devWZW3fvt27bt26dqecckp/AL/fr4YMGbIn/r79+vWrveiii3oVFxfvHD169E6A7du3e3/xi1/02rBhQzullPb7/ZHn/6Mf/Wh3586dQ507dw7l5uYGL7300p0AJ5xwQk1FRUVOYz5TJiKGBUEQBOEA4ofddZmpbHeL6XeN356TkxOyOl5rjVKxmiQYDNKhQ4eA1XmS8cknn+T07du3Np1zRK9Ra81tt922edy4cVujj3n77bc7vP/++x2WLVu2pkOHDqFTTjmlX21trefQQw8Nfvrpp5+98cYbhzz++OOHvfrqq3lPPvnkt3ZrePvttzv+7Gc/s7RImK/htm3bvGefffYx06ZNO+ymm27a6vR82rVrpwG8Xi+ZmZmReD2Px0MgEFBr1qzJ/NOf/nT48uXLPz/00EODl1xySc99+/YlXPnPyMjQHo/H/DuBQEABvPzyy9++99577cvKyjoOGjRowIoVK1YfccQRjsbq6HV4vV7t9/uV1pqioqJdb7311nqn+/7zn//88p133ulQWlraafr06V2//PLLT++8886C008/ffeCBQvWrV27NvOMM86I2DXin7P5epjPvzGfqch5072jIAiCIAhtj0M7ZCVUaZ22Nxc/+9nPdv31r3/NN1MntmzZ4s3Lywt169at/rnnnusMRkPbhx9+mHBJP5558+blzp49+9Cbbrppq9M5hg8fvttsSgsEAmzfvj1B55x77rm7/vrXv+abftb169f7KisrM3bu3Ont2LFjsEOHDqHy8vJ2K1eubA+wefPmjGAwyFVXXbXzvvvuq1y1alWO0xo++OCDQ84///yE5rlounTpEpw1a9a3f/7znw9v3769Tuc1MdmxY4c3Ozs7lJeXF9y4cWPGv/71r45u7wuG3/aMM87Y+9hjj1V17tw58PXXX8d8aerYsWNwz549SVMvfvKTn+xdtmxZ7qeffpoFsHv3bk9FRUVW9DHBYJB169ZlXnDBBbsff/zxTbt37/ZWV1d7d+3a5e3WrVs9wJNPPpmfyvrT/UxFI2JYEARBEA4gbh3RpzIrwxNTrc3K8IRuHdGnsiXX8fOf/3zXueeeu3PQoEHH9u/f/7g//OEPRwC88sorXz///PP5/fr1O65Pnz4DXn/99U5W93/rrbc69+/f/7iePXse/+CDDx758ssvf2V6gO3O8Ze//OXb999/v0Pfvn2PO/7444/75JNPEkTRxRdfvOvSSy/dfvLJJ/fv27fvcRdddFHvnTt3ei+55JLqQCCg+vbte9zdd9/d9cQTT9wLsGHDBl9RUVG//v37H3fNNdf0mjp16ia7NVRVVWVkZWWF8vLyLKvl0QwfPrz22GOPrX3mmWc6u31NrDj11FNrjz/++Jo+ffoMuOKKK3paWROc+O1vf9utb9++x/Xp02fAsGHDdg8bNizGXnHKKafUZmRk6H79+kUa6Kzo2rVr4Mknn9zwy1/+8ui+ffseN2TIkP6rVq2KaWQLBALq8ssv72W+PzfccMOW/Pz84J133vnd5MmTu5100kn900n7aMzrB6BMD86BSmFhoV62bFlrL0MQBEFoIyillmutC1t7HamwcuXKDSeeeOLW5EcazP7om7xZi74s+GF3XeahHbLqbx3Rp7IxfmHBHY8//njepk2bMh944IHvWnstQiwrV67MP/HEE3ta7RPPsCAIgiAcYIwZdtR2Eb8tz0033SSv+X6I2CQEQRAEQRCEgxYRw4IgCIIgCMJBi9gkBOEA5uOyJ+n+yQwO0z/wvTqUjSeN4+TiG1p7WYIgCILQZhAxLAgHIKXllSx98wl+r58gR9WDgiP4gcOWj+fDDR9y6q0vtPYSBUEQBKFNIGJYEA4kKkqoe+t3XFhfzYVAXN49HgXDtr1BaPIbeAp/BefPbJVlCoIgCEJbQTzDgnCgUFFC8I1fk+WvRqlEIWyiVPgf/rJn4cXillyhIAj7MV6vd0j//v2P69Onz4Bzzz33aHOYRjyDBw/u39jHmjVrVpexY8f2aOx5BMENrSqGlVLPKaW+V0p9GrUtTym1QCn1ZfjPzlH77lJKfaWUWquUOqd1Vi0IbZOadybi1YGU7qPXv8/HZU8204oEQTiQMEcJf/nll6t9Pp9+5JFHDo3eHwgY//8pLy9f0yoLtMDv97f2EoT9gNauDL8A/Cxu2wRgkda6D7AofBul1HHAL4EB4fs8rpRKOh5QEA4W2tWmnvGugMLl4wlN6Qxv3970ixIEoXX4+Nk8Hu57ApM7DeHhvifw8bN5TXn6oqKiPV999VXW22+/3WHo0KF9L7jggl79+vUbAJCTkzPYPO7ee+89vG/fvsf169fvuJtuuqkAjPG/p512Wp8BAwYcO2TIkH7l5eXt7B4H4JJLLul51VVXdR88eHD/bt26nfD8889HimRW5z/llFP63XLLLQUnn3xyv/vuu+/wf//73zknn3xyvwEDBhxbVFTU55tvvvEBPPLII/nHH3/8sf369TvunHPO6W1Wup977rnOffr0GdCvX7/jCgsL+4Eh9G+44YZuxx9//LF9+/Y9bsaMGSmNDBbaNq3qGdZaf6CU6hm3+ULgJ+G/vwj8C7gzvP1vWus6YL1S6ivgFODDFlmsILRxdoTa08WT0hROwLBNKB0ybBMgPmJB2N/5+Nk85t91FIE6o+C1Z0sm8+86CoCTf9XooRB+v5/58+cfcvbZZ+8CqKioaF9eXr66f//+9dHHlZSUHDJv3rzOy5cvX9OhQ4fQli1bvADXXnvtUU899dQ3J5xwQt17773X/te//nWPjz766Aunx9yyZYtv2bJla1asWNHuoosuOubqq6/eYXd+gJ07d3o//vjjtXV1dWrYsGH95s2b91XXrl0DTz/9dOff/e53Ba+99tqG0aNH77jjjju2Atx6661dZ82alX/PPfd8P23atCPffffdL3r16uXfunWrF+Cxxx7L79ixY/DTTz/9vLa2Vp188sn9L7jggl3xz1nYP2mLDXSHa603A2itNyulzDnYBcBHUcdtCm9LQCl1PXA9QI8eYjkSDg68HhuTcCosf0HEsCDs77z/UEFECJsE6jy8/1BBY8RwXV2dp3///scBDB06dPdvfvObrQsXLswdOHDgXitRuGDBgkPGjBmztUOHDiGAww8/PFhdXe0pLy/PvfTSS3ubx9XX1yf9n1dxcfFOr9fLkCFD9m3bts1nd37z+Msuu2w7QEVFRdaXX36ZfcYZZ/QFCIVCHHrooX6A5cuXZ0+cOLFg9+7d3r1793pPP/30aoDCwsI9o0eP7nnJJZfsGD169A6AhQsXHrJmzZqcsrKyzgC7d+/2fvbZZ+1EDB8YtEUxbIfVPxZtdaDW+ingKYDCwkLLYwThQKMjqVeFE9DB5McIgtC22fN9ZkrbXWJ6huO35+TkhKyO11qj4jp5g8EgHTp0CFidx4l27dpFfpdrrW3Pb2IKZK21OuaYY2pXrFiR4GO+/vrre/3973//6tRTT62dNWtWl/fff78DwMsvv/zte++9176srKzjoEGDBqxYsWK11lo98sgj315yySW7Ulm3sH/Q2p5hK7YopY4ECP/5fXj7JqB71HHdgKoWXpsgtFmqyW38ScSGLwj7P7mHWVcr7bY3Ez/72c92/fWvf803vbhbtmzx5uXlhbp161b/3HPPdQajUvvhhx9mN9X5448ZOHDgvu3bt2csXLiwPUBdXZ1atmxZO4CamhpPjx49/HV1depvf/tbxFO9evXqrDPOOGPvY489VtW5c+fA119/nXnWWWdV/+Uvfzm0rq5OgVFx3rVrV1vUUEIatMXKcBlwJTAt/OebUdtfVkrNBLoCfYD/tsoKBaGtUVFCtq6xvn7iEq1BFV7VZEsSBKGVOP3OyhjPMEBGVojT76xsyWX8/Oc/3/XJJ5/kDBo06Fifz6fPPPPM6j/96U+Vr7zyytfXXXfdUQ899NCRgUBAXXTRRdtPPfXU2qY6f/Qx7dq103/729/W3XrrrT12797tDQaD6te//vWWwsLCfRMmTKg65ZRTji0oKKg/9thja/bs2eMF+O1vf9ttw4YNWVprVVRUtGvYsGG1Q4cOrd2wYUPWCSeccKzWWuXl5fn/8Y9/rGuq10poXZR5uaFVHlypVzCa5fKBLcAkoBQoAXoA3wKXaq23h4+/B7gGCAC3aa3fSfYYhYWFetmyZc2xfEFoOzx6PFRvTPvuWsN/1UCGTv53Ey5KENomSqnlWuvC1l5HKqxcuXLDiSeeuNX1HT5+No/3Hypgz/eZ5B5Wz+l3VjZF85wg7K+sXLky/8QTT+xpta+10yQus9k1wub4+4H7m29FgrCfUFECi6ZC9SZqso+gXe3mRnmelIITsn9osuUJgtDKnPyr7SJ+BcEdbdEmIQiCExUl8Nat4DeuKubUbiakaZRFwjhP6jnFgiAIgrC/I+ZvQdjfWDQ1IoRNPMqwOjSKjt0aeQJBEJqRUCgUaoL8REE4+Aj/27FMPQERw4Kw/2HjDbZJGHKHLxtGTGzECQRBaGY+/eGHHzqKIBaE1AiFQuqHH37oCHxqd4zYJARhf6KiBMMP0YSNr8oLF8yCgaOa7pyCIDQpgUDg2u++++6Z77777nikkCUIqRACPg0EAtfaHSBiWBD2JxZNpUmFsC9bhLAg7AcMGTLke6C4tdchCAci8u1SEPYjdBrxaVrDtlAuIRR1vo6QnQco6NhdhLAgCIJw0COVYUHYTygtr+QCrfCq1CrDIRTFOX9lyYQzyGqmtQmCIAjC/opUhgVhP2HFvKfwpGiR0BpmB0dQtTPl4U6CIAiCcFAglWFBaOuEB2xM8m90nRihtVERnh0cwaTANRR0ym7eNQqCIAjCfoqIYUFoy0QN2EglOm0HuZxU9xQA2T4v487p10wLFARBEIT9G7FJCEJb5p07EwZsJKNGZzLZPxYAr1I8ePEJjBxc0ByrEwRBEIT9HqkMC0JbpaIEare7PlxrqNT5TA+MoixURLbPK0JYEARBEJIgYlgQ2iqLpqZ0eKXOp6h+FgCdc3xMumCACGFBEARBSIKIYUFoi1SU2I5dtiKkYXrAyAt+7BeDRAQLgiAIgkvEMywIbQ2zaS4FFFAWKqKgU7YIYUEQBEFIARHDgtDWWDQ15aY5Dfw88z+SGiEIgiAIKSJiWBDaGtWbUr6LR8HU9q9LVVgQBEEQUkTEsCC0Mep8h6R1v5za75p4JYIgCIJw4CNiWBDaEKXlleytD6V3547dmnYxgiAIgnAQIGkSgtAGKC2vZMb8tVTurOXrrN2pn8CXDSMmNv3CBEEQBOEARyrDgtDKlJZXctfcVQzZtYDFmbeSwtRlg47d4YJZMHBUcyxPEARBEA5opDIsCK1JRQnD3rybzzw/oD1GI5xbAt52ZFz4/0QEC4IgCEIjkMqwILQWFSVQehNH8ANKuRPCWkNIK6rIFyEsCIIgCE2AiGFBaC3euRNC/pTuUqnz6Rd4mf9e+IEIYUEQBEFoAkQMC0IrUFpeia7dntJ9anQmf/Zczoyfnyh5woIgCILQRIhnWBBamNLySha/8TgXKnDqlqvXGezW7eis9lKluzAjMIo/PvCHFlunIAiCIBwMiBgWhBZmxvy1vMrfUA5CWGvwEUApuM3/a8pCRXTK9rXcIgVBEAThIEFsEoLQwlTurKVAbXU8RinjJ0/t4Y++x5mS8ZyjeBYEQRAEIT1EDAtCC+NVimAK//SUgiu8C/nxvn8246oEQRAE4eBExLAgtDBBrfGQ2shlj4K7Ml9rphUJgiAIwsGLiGFBaGE6Zfuo0vkp3+9wnK0VgiAIgiCkjohhQWhhlILpgVFoneL9OnZrngUJgiAIwkGMiGFBaGF21vgpCxWxT3vd38mXDSMmNt+iBEEQBOEgRcSwILQwnXKMiLRale3uDsoLF8ySiXOCIAiC0AyIGBaEFmZvXQCATuxJfrA3Ey56QoSwIAiCIDQTIoYFoYWpDxpmYVdNdJm5IoQFQRAEoRkRMSwIrcT0wCjqdZIhkLU7WmYxgiAIgnCQImJYEFoYc5BcWaiI3/mvZ7vOtU+WkAQJQRAEQWhWRAwLQgszeliPyN/LQkWcVPcUv/HfRK3OjD1QEiQEQRAEodlJco1WEIQmo6IEFk3lvuqNTGnnwaNDVOp8pgdGsa1XMdlDB8OiqVC9yagIj5gofmFBEARBaGZEDAutR1gcpiL+SssrmTF/LVU7a+naKZtx5/Rj5OCCtM/XYlSUEHjzf8kI7gPASwgUdFNbecz3OK9s/JLSwj8y8reftvJCBUEQBOHgQulUx2DtZxQWFuply5a19jL2f5yEZhIRailgvUvgrVvBX9vwGL5s6zzdihJ450507XbQsF3nMiUwlrJQUeSQq3L/y736iYjYBKgli09P+gMnF9/QLC9JKtQ81J+c2s22+0Ma7lG38uDkP7TgqgTh4EQptVxrXdja6xAEoW0gYlhoIF7U9jkbvnwXqjditH1FfVZM4QqJojZ8bE32kfyh9lJe2TcMgGLPYsZnlNBVbUUrsJq/FtAePCoEyrAR6PC5VNxxofBSduhclILO7EHFHwRU6nw+HvlBQ/W4lQhN7oQH539rQa14/ajfM+qaO1poVYJwcCJiWBCEaEQMCwYVJQmiVkOCCI2mJvtIdtUGOIIf7I/RmUzwXwvANN8z5Kj6JlqwO0JacZKnhBWTzm7Rx41n08TedPNsTXpcvc5g5ZAH2kQ1WxAOVEQMC4IQjXiGBYNFU+Oqu85CGKBdzWaykxyYo+qZ7HuJQ6ghQ4Uau8qUqdJd+HHdP/lu8k0cpn/ge3UoG08a1+Ji85nMMYz3P570y0CmCtD9kxnQXOurKIHSmyEUtY5ep8OVZemdq616tAVBEATBJRKtJhhUb0rrblbWhHg6s6dVhHCNzmRRaBAP+57iCH7Ao+AIfuDE5XfzcdmTLbqWQeddz0R9PZtC+YQ09rnCwGE6eQU5LSpKYO71sUIYYP378GJx6ud669awhUYbf869Dv40tMmWKwiCIAgtgYhhwSDF4Q5ag8eFEAZ3grmp0Ro8aH7h/ReZKhCzL1MF6PNJyzaqjRxcQLuTfklR/Sxu89+E01eDXSq3eRaxaCrY+ZbXv5/6ueKuJACwdU3qwloQBEEQWhERw4JB3tGO1cp4WkPgpoJS0E75ySJgub+j3t3CK4J/rvmBYs9ipvmewevw+mX7mumfZZrV/5TPlaqwFgRBEIRWRMSwYLBhcZsXuOlg+5xa4bkW7lrATN8TSX3DWf5dzbOAphztLGOiBUEQhAMEaaATDHSwtVfQotT7OpLVkg9YUcKMzCfJcDRIhMnunPr5XyyOrchaNcWNmGh4hq2sEr1Oj71dUQJv3gxBm0a7ERMNj7AgCIIg7OdIZVgwhE8L4MaGEXI4pqlSADWQdcHDTXMyt7xzJ5k00xeOeCEM1k1xA0fBxU+BJzN2e7xwNhvtgg6NdgNHQX5/6/XEC2tBEARBaMNIZVgIN1a1AXzZrO96Ie2/WcRheis7dHuUgk7spUp3IZt9dFF7Gv0wCuDbj1o2Bqx2ewrH7kjt3HYeXavtA0clf95uG+1uWequIi0IgiAIbRgRw0KjGqu0jvXlhrQhNq28ujvIpZ2uj/PMhifbdewOIybSO0qo/Ts8xrlyp5FaUOxZzGO+x21TLEKEL3Uor2H7yM6zF6HLX4DzZ7p+no0i1cp7a/txU/k8uBC+o5/+kCXrGt6H4b3zmHPdqemsTBAEQRCaHBHDgiG+qjemfDet4aXgmYzwrKCr2kaV7sL0gCFmE6bN+bLJu+BR4+8uBzWMHFwQGaNcWl7JjPnZzN7zBVdkLEzsf/P48Ix8PPFckzvaLL4FPdKpVN592cZr0pqk+XmwIl4IAyxZt53RT38oglgQBEFoE7RJMayU6ge8GrXpaGAi0Am4DiLzf+/WWv+jZVd3ADJiIvr169JKk5gUuIZJgM+r8AcbLq1nag9Tc14np/a7RNGbhj2hQRifYVRa37mzoeqbnQfnPmR9XrNKbLW9hdDVm5KGV2gNVeRTdcJ4Tg4/j9JwZbxqZy1dO2Uz7px+kS8HMfQ63doSka53N5VGuyTEC+Fk2wVBEAShpWmTYlhrvRYYBKCU8gKVwBvA1cCjWusW7n46wBk4ir+WlHCFd0FKgjiEoiAs0oAY4VZ0zk3kDL6/2dbrWlAPuQqWPWu9vYXYQj5HRL6/2TO8bhYFn2WzpNgQwnfNXUWt3xDylTtruWvuKgBGVj5i2Dx00BD15nNJwbvraF0wX1unNIn9iNLySlbMe4pr62fT1bONfdlHkHPuVBkdLQiCIABtVAzHMQJYp7X+Rh2IQbhthImBq1kW6sOjvscdB0JE41WaJRPOiNy2rFq2NqYvOF48NpNf2Kqa+8/6S3kw3jYSRzAc7FIV9kfPmL82IoRNav1BgmW3g/6/ho06aIj9wl/FCNXRT3/IkgnzIrejxa4r60IqXzjaMPeWrmL3f182Xn+P8frn1G4m8Ob/NvzPz6VtRxAEQTgw2R/E8C+BV6Ju36KUGgssA+7QWie03iulrgeuB+jRo0eLLHJ/p6BTNmU7i8CPY5NaDB27N/u6moTzZ7ZIs5xdNbddu58yYR+MzyihQG0FYhsMtYb/hI4FoGunbKBBFMdzYehd64EhUQ2BycRuc1gXSssruXtuBTV+I0dZKRg9tAfDe+dZnnd477y0HyuVNc3+6FsWZ5YkfBHJCO5LzEmu3kjt3Fv4dMMOTi6+odnXJwiCILQN2nTOsFIqEygGXgtv+gvQG8NCsRl4xOp+WuuntNaFWuvCQw89tCWWut8z7px+ZPu8lIWK+GvwzOSZvm2h0auNYVfN/Vno39zpK6Gr2kqlzmeNLoh5fZWCQs+XXJSxJGI56ZTjs3wMr7IZ2hHli25qsTv66Q/pOWFe5Gf00x/G7C8tr+T2khURIQyGwJ/90bf0OjQ3Qfi2VJrElLdWA9A1/AXEDdnU0Xv5VKom9yY0qSPfTT6Gj8uebK4lCoIgCG2Atl4ZPhf4RGu9BcD8E0Ap9TTwdmst7EDDtDjMmL+WyTuvYX274/ld6Dnah3aBhhqVRYYvmyx/tVxOtsGqmlvsWczvdYNFopvaiiaxuJuj6rnD8yoXvPUTbnt1heX5fV6FVh6UthDEzdQQaFdlPmvmv/hhdz07a/2O939l6UbWPfg/zbK2aD4ue5Lun8zgMP0D36tD2XjSOE7bt4HxmSUpT97uzB4Ue0DBEfzA4cvH4185Gd+Ff5TPvCAIwgFIWxfDlxFlkVBKHam13hy+eRHwaaus6gAlOsoMzgOmRPa1b5UV7V907ZQdyUQ2GZ+ReIneTpx1VdvYUWMvLgNBzfoeo+j9zd8Sd6bQEJiKdcGumvzl93tdPVbQ6hJDRUnjfbpR56jzdWRg/W6yVDAiYDsvv4uHfYpMFUjtvCRmZCsFvmCNYat46zbw18gXQkEQhAOINiuGlVI5wFlAtHlvulJqEEbm04a4fYLQqow7p1+MZxhSu0Rfpbs47tfAiLXFTGu3g1FqAR4dsmwITCZ251x3qnWaxJGvwZTz4lIqRrhevxXeeGVZUQJv3Qr+8JeG6o3GbRMrkRwvnvucDStfjpwjy78z4RtGlmqmHGn/3si6a+fewoS/lbPskLPsY+8EQRCENo/SSc2h+zeFhYV62bJlrb0M4SChtLySO0pWRiqiizNvpZsnuSCu017G+W+gLFTk6nGyfV4evPgEWwGW8tS3t2+3jKB7MXAmkwLXuFqTFdP7rmFU9fMNQrZ+r/VUQOWBOPtHjc5klerPKVTEaF0rm0lrsSmUT1H9LBQwelgP7ht5QmsvSXCBUmq51rqwtdchCELbQMSwIDQV4QpmaOdGduhclDL8pxqSpnPs0xls1Z3oqrayk1y0hs5qD1U6n+mBUZYiuaBTNksmnEFpeSV3vl5BXaBBTKbcpDYlz3I4SRAPvffNdn+eKC7x/YeHMp8xkhvSJH7cd1tDa0Ocm+93J/ZEPMsn9+wssW1tFBHDgiBE06bTJARhv8G8/F+9EY+CLp495Kk9KGUI4WTfObMI0M2zFY+CPLWHLp49eBR082xlmu8Zij2LE+5TtbM2kuQQLYShIUrNNTbjqb2EErzEfQ5rj89F9t5v1d8aJYShbQthIPL+mu+3J+xZPnH53QTf+HV4rLVusINUlLT2kgVBEIQ4RAwLQlOwaGqDD9aCZKLOaX+Oqmd8RqKI6topmxnz1xKyEdopRanZplEo5lSdx4Z2l7Mh+wo2FC1i6NFdEhrjrLSxnV/6AL8YBUCmCuDVcc17/lp440YRxIIgCG0MEcOC0BRUb2zW03dV22JuZ/u8jDunn+1wDitKyysZPu09ek2Yx/Bp71FaXtmw0zaNQjdUjXUQvexZ+iybnCDArQR5lc63PGNbrPZq3UIiXQfRc68zPNqCIAhCm0DEsCA0looSmrulKzppQgGXDDFi8MyJdckoLa9k8RuP82rNdazLupxXa66j+vVb2TaxAD25I3rZs+DNNBrZwLZSrIDR3vdcPeb0wChqdKarY1sTreGl4Jn8xn8T9br5A3YUGK+3VIgFQRDaBCKGBaGxLJqK0UbVPGgNi0KDGm4D/1zzA2DEudnZd6O9vkvffIKp6qmIL7mbZytjvQvp4tmDIizlg/XgyYCLn4ZJ9hYLLzZT8OIoCxVxT/A6Qqpl/zcTX+ENaIXfmxO57fdks113IKQVm0L5/MZ/E5MC11AWKuJ3/uvZFMpH07zVYgXUvTUOHj0eJncy/hRxLAiC0Cq02ZxhQdhvqN7UrKdXCkZ4VjApaptpjzCj1ZzSJO4tXcXNoZfJ8cQN/7AS0cF6eOdOI/VAeW0TJtzQPtPL6RfdjHrzcVfHNxatYbvO5R/6VEZ4yzlCb+N7lc/GIeM4ubghktwHfFBeyeSy1QkT9MpCRWzrXsyc606ltLySn7w5lE7sdnxMM+otVftHpn8nVO80blRvhLk3GK997Q7I7mxsr90hSRSCIAjNjIhhQWgsHbs1mWfYLkos3jN8Ze5/4dE7oXoTIzt2Y+Qoa7FUWl7JnI++ZWqW++Ef1G43qpQ9i2D9+7Hrw6gMr8+6nB3kMtk/1jYb2ef1MHJwAd+9mc8R/OD+8VNCoYEt5POg/9LIAIyu4S8JR4R/4jGnLZaWVzJj/lqqdtbStVN2zPCMkYMLwDszdkhIHDvI5aS6p4w86RQGrBgrjyfUkMEcncVcvRFKbzL+LoJYEAShyRExLAiN5OPe/8vxy+8lO27scjwB7cGDJoQiQyVaDULayKvtovYk7Iv2DF/oWcy9+jmoDseWVW80RgWXz4Yry2LuN2P+WjRGM1tKYu2dOyGQKABV5D+Qxx4e9j0FfiwFsVl1fbD+Uh71PZ40azmepBnDvmy4YBZq4CiOAP6Y2umB+BHkFpji8507E4eF+LJZd8JECj7LZsauUWk9R9eE/A0Ve0EQBKFJEc+wIDSS2z7rw53+a9kUyiekFdtCuQmNWDU6k9v9N3J03Rxu99+Y0FgW0vDX4JlMCYxN2FejM5keaBBB4zJKrPN717+fkFJg2ilSbmar3e4YFWeSqQKWsW+AkY386PE8mvmXtNoLQyj7hraO3eGCWS0jDgeOgjvXG17qjt0BFXn8k4tvYMmEM/jjAw82e0qGrtlOzwnzGDz13dgkEEEQBKFRSGVYEBpJ1c5aKimirL6hOlrsWcz4jBK6qm1U6S4xU+TKQkXgx3a/4z6gqyfWMhHD8hfg/JkNx3bKpnJnbeQx/+h7vMlFW7yFA2BKxnNckbEQqsPfuNN4TA+a3/qvj7wW+3KOIOfcqa1XHR04yvGxVcfuzR6xV+xZTFlNEeP+vhLAuaotCIIguELEsCA0ElNwQrQI3spm8rnN/2tLC0FZKFY8x+8jQOQ84zNKIGBsz/Z52Zd9BDm1m60XE9fwNu6cftw1dxW1/iBloSLG65KUva3J2EyXmNvFnsVc4V3Y6MtOW9ShvBUqYnnOWTFe3jbLiImE5l7XbJfblDK+zIzXJUwPjGLG/HZt/zURBEHYDxAxLAiNxBScZwXfZ5rvGXLC3uECtvKQ7xlbT60dxZ7FMefppoyRzHm+TAaddz053qmGR9iKuHxgUyzdXrKCkDbsEtHnTgUzNSGermor69tdTkgrZgdHcFbGCkdBGO8FrtNeFIpMFTWxzZfNERc8wPqB56W8zlZj4CjWL19Erw1/i/EOJ/U+p4BSDZ+Hu3YBnNE0JxYEQTiIUfoAn41aWFioly1b1trLEA5wSssrGfbm6ZapCZU6n+F1s1yfa3HmrXTzWFRvO3aH335q/P3F4oSkhxiUB4ZcHbFM9JowL5KEXOxZzEzfE5ZNfG6wE8Wu6Nidj3v/L0cumx5jAwHajh2ikXxc9iTdP5nBYXorVboLi0KDGOFZQYHa2qQWle84lCMmf9V0JzyIUEot11oXtvY6BEFoG0hlWBCagJGDC+BNa/vBkWyj2LOYyb6X6IyRFLFHZ+FXPjqxhyqdH+ML7mpnY4jOM76yzGiWW/6CZRYwOgTLnjX+fv7MGCtHWaiIIcEvGOtdmJY4MxIlrDOIwakSqmDERE4eOIrS7uczY/5aKnfW4lWKoNb7jx0iCScX3wDhXONl5ZU8NX8tk3fWsq7d6HAQXNNwOE1rdxEEQThYkcqwIDQVjx5v2UC1kw7k6NpYG0AcNTqTCf5rKQsVuasMu3hcwBCtk7ZTWl4Z8Q4DfJJ1PXkWEW5NgdZQS2acFUNB4TXcG7iaV5ZuJKg1XqW4bGh37ht5AmAMB7HaZ7d9v8PpfUoHu8+DkBSpDAuCEI1UhoUDhz8Nha1rGm7n94dbljrfp6IE3rzZmLxm0uv0hLxeV4yYmDigwZeNL+QhM2gvhAFyVD0zfU/wGI9TTQfq8ZJJVOXVlw19zoYHukL93vBGQ2A6TsALV2/NauuM+Wsp3LWAzo0Vwg6V4SAeJvivjdgevlNd6Hrxg9z79bHM/ujbhuO0jrlttW/p19v48vu9lvfZ7wSx1eejsecTBEEQGo1UhoUDg3ghbOIkiCtKYO71YHXpOl1BXFECi6YaAjU8Rjc093o8KV4eDyof3nYdGsbx9jkbPnkBQhYC1Nce/HsTt0OkMhzDQ70SB0ikQK3O5B+eM7gwNJ8MFfu8tIaXgmcyKXBNzPYN086j913/IGjx/xtv2FNhtc8Or1Kse/B/0lh9K1NRYj3AI1Uy28PdVU2zpoMQqQwLghCNDN0QDgyshLDTdjBEq51IdWpOc6A0OJzhdbPotW8Ow+tmURocTlWoS/I7xuHVfmrIgsk7jUvhX75rLYQhPCnO5p/ykKtib1eUNEqIhTSUBH/MHbVjud3/a/boLLQ2XsWgVpZCGKDnhHm2YjeodUpC2LzPfknUAI+QSu9/v/U6A85/rGnXJQiCcBAjNgnh4MXJXpAG8b7cyp213DV3Ff/DL3lQP+HoGbaiXe13DTccrRAhYzraW7c1VIiVh3U9RnHJ8p+xc/E8ADrn+FiSNZGclFYRi0fBCM8KJhHOSq4zmv6yfR5q/emlU6RbGd6vGTgKDxB84ya82u/qLlobySTPZI5h8n6atCEIgtAWETEsHLx07NakDU0z5q+NCGGTWn+Q1/kRfk8okibhVsdVhbrQzc1alTdhOlppeSXjXluJP9QgtHbU+GkX3NyIXDQDq4lzE/QzjGn3Hh4dIoiHOcEzLCvE8UzJeI4xGe/hIURQJ96vz2HtYzzDJkGt6Tlh3v7dUDdwFF5wZZvQGn7jv4kF3tN58Lz98LkKgiC0YcQmIRwY5PdPbTuEG5BslGGv01NeQtVO+8aoslARJ9U9xW/8N1GnvbbHmdToTGYERlFaXtmwVo/N/eKtEBjC3B9KrLRW6fykj52MKh1r+5iS8RxjvQvxEkIpyFAhxnoXMiXjOcfzTM14nrEZ4fsRez+vUowZ1oMFt/+EMcN6RCrB8e+W2VB3b+mqRj+vVsG0TXTsbnuIBv4aPJPlh5zFgxefsN9HzwmCILQ1pIFOOHBo5TSJ4dPei2T5Quxo5iqdHzN8AayzeM1L4WbucEGnbJZMOKNhrW/flpgm0WNYwnP4IDiAsf57Es4fP90uVWrJ5M76a2Mm6n2VNcZygEdAezimbrbleTZMOw+m5FknUlg1/YVxasLbLxvqTCpKoPQmCFlYJgp/FRmeIjQN0kAnCEI0YpMQ2ibxE9bcCNRkwteKOHtBYzDHMtf6g5Yjlceq5EMudpBLUX3DtLqYarPVWm0SMU7zruYl7k8QxGWhIvA3THvbSXvaU0uWsolJ04qduj2d1d7ItLj40dJerL3CdtvbZ4Yr3DbRbLbbsfcV77cNdSbm+xptmcjOg3Mf2m8n8QmCIOwviBgW2h5Wo4bXv29sTyfurCl4+/aGiW5ApCp7/sxInNrI6k2cnXsE0/2/4Nr6koTqqxuvcHtqKfYsbphG1yk77P9dQXR/2vDeecy57lTbRAwFnOZZnbDd51X8O+unlNU0CNpiz2L+6Hvccn0KGFL/lOOag3jIsBC+QQsX1siMJTzQfi5M/i5hX8ODRtlB4j4LL/mOZ6z/7oS77PcNddCkX8wEQRAE94hnWGh72MWapRl31mgShDCANra9WGwMUqjeCGhyajczWT1pPUHOBVkqyKSMlwDI9nn5af9Due3VWCEMsGTddkY//aFzyoSCTtm+yM3OOT5m/PxEJl0wgGxfg+AsCxVRhbWXON4fbMWc4BnEF2a1NrZHc1Xuf3nE9yQ5tZuxjbSDBg+0xZei07yf8pLv/oS7XDbU3nMrCIIgCE5IZVgQkrH8Bft9VgLdX+s4oS0ZeWoPV+X+l0HnXc+M+Wttj1uybjscbp8yoYAVk86O3C4tr2TG/LVU7aylY7aPdj4PO2v8dO2UTdVx4ylYNSlmOlqNzmR6wLpSOSXjOUZ738OLkR6xRhfQh82R21ZpEpN9L0HAIV5OeQ0hbPpjLV5bhWEB8QbU/j+eWRAEQWgTiBgWDj5SbbRLR9TqoDFCOUpcau3OKqEUTG7/Ogyewm9fXeF88IiJzlP0wpSWV3LHaysJhhMmdtb68XoUj/5iECMrHzEEf9TzrMk+kun+X1BWd0rCac30CPO5ZBCiP5XMDp3F7/1X26/VKT7s4qcNy8ey54wBIw6jhhXs381ygiAIQptCxLDQ9uh1unXF1SHu7N7SVcz+6NvIbQWMHtYjsWJoNbZ56xpju50gTqfK27G7IeiiRjN/5C2k37YF7rKGw/aHrp2yYxIqEjA9pkkSMe55Y1VECEenXNSUtkOrfTGRZRooqx3IC/sahHB0JRgSRb1ScJl3ka0Y7pTtc3RGxAj66o2G9UQQBEEQWgARw0Lb48qylNIk4oUwGLLK3BYjiNMZ2zzkKgvPcNS6Nv03pgKML9sQwnENUb+b9h6VdZdGidFtaMCrLFRiR2Pcxrhz+nGbTXV4eO884y8uGq/21htiPj7lIpd9Cccq4Od6ARO4EkisBNvh1Q3G5mjBvZl8qgaPp25VJ7L8O23uHfca+GvBkwkhiwi4NDKgBUEQBMEOEcNC2ySF1IhXltpPkXtl6cbG+0lND2uSNAmzAhwRwnGYMWlloSLK6o00B8vcX1NMQ2TAgm2ahAXR3uCunbJ57LgvWZw5na5qKyE8lpnA8UTHoo32vufK3hHEQ7FnMZMyXiJPNVS/C9hK10/Gs1dn4Y17fI3DQLyQP/EqQZoZ0IIgCIJgh4hhodW4t3QVryzd2OhGKKeM2SbLnz1/pv3gA5eRWFaWh7JQEXm+TMMjXL0JsjsbO+ZebwjsERMZOXiU66lj95auYs5H30bqrEN2LeD45c+Q7THEtscm+9cKM+LNLi84Gq3hP6FjbQd6KCBX1VGnvezSuXTCyC3u6tlqL4Y7dosVvhUlRg7v5I7GbcnhFQRBEJoAiVYTWgXT2mCK1caM1XXKmE3Yl87Y5iZi3Dn9YiLNwIhPG3Te9fDbT+Hip6CuOtxopg3vbOmNhgh0QWl5ZYwQBmO4RnYa0+aUMu4L1nnBYAhgrY1Jcy8Fz+RotSXpZLssFaRGt+PoujkU1c+iKmQ3HlrFNtGZE9qim/BqtxteaZevjyAIgiBYIWJYaBXmLP02pe1OOGXMJuy7ZWmi8HUztrkJGDm4gAcvPoGCTtkooKBTNg9efEJD1fft2yAU16gXChrbTV4sNiqj5s+LxUA4LaJkZYwQLvYsjox+ToeuahtgnyP8UvBMetW9zDF1s1ke6uv6sQrUVr7OupzFmbey1Fto2EJiCFtQoiu+i6ZajyoO1ocHjwiCIAhCeohNQmgV7NwL6bgaTGuFqzQJaBHhC4ZAvXtuBTVRZt/2mV7uv+gEa9tD/V7rE5nbbSbzbfl/5zBu829iLCGmF7kxg9n26kyASF7wmIz38OjEHOFUH0sp4/3pprZyGP/k9cBPGRpaRlfPNvZlH0HOuVMTrQ9Ow0Wc9gmCIAhCEkQMC/sPDvnA9408odGNckPvX8CW3Q2X+Q/vkMnSe85KeS1gCOHbS1YQihP3e+uD3PHaSgDXPuAINhP4Dtv2Ef5QbBTZ+IzEcdCpEn3/SYFrEoZoNMVjZeo6hoaWUVQ/C4DsoJcHgycwMv7AjvbDRczkDUEQBEFIB7FJCK1Cjs/6o2e33TEfuAmIF8IAW3bXM/T+BWmtZcb8tQlC2CQY0jaT5exKq0lKrhaP07UR9ggTj2MwsEFjrRhgrLXYs5jFmbey2vMLhr15eqIPeMRE8PgS7+zNdBzQIQiCIAjJEDEstAoPXDwQT5zG8yhjuyXp5AOnQLwQdtzuYi1VToMy7PYXWldebbc7UEOWq+PMJjgrohvnpmQ8x1dZY1ifdTlfZY1hSsZzTWLFMJnpe4Junq14FBzBD4mNcQNHwcjHjQQJk+w8uPDPkiYhCIIgNAqxSQitgmkRiM7DHXdOv9StA0kY/fSHLFnXkEDglM/blCSbHNe1U3zTGA3RbeZoZOVlXY9LuWDpWdQsnsdLvgGc5lmdID4/VLH2kCkZz9GeOlfr3EEun4aOSjiv1kbjnHm++PHLY70LucS7uNFWDDC+BCVEvgXrjRi1aKHrMsJOEARBEFJBxLCQHi4HTTgxcnBBk4vfaOKFMMCSddsZ/fSHzS6Ix53Tz9IzDOD1KMad08/6jlF5xg2+Y0MojvXfw0u++znNsxrCTWjkHsmwvatZn3U5QTz8J3QsRRaC2YoanclbwWGM8KwAGirE8Q1yVkM3lLKeXtek1G6Ht2+3z3cWBEEQhCZAbBJC6lSUwFu3hhuawnm4b93avHmvaeQDxwthp+2Hd8i0PNZyu4u1jBxcwMxRgxI80O0zvTxy6YmuvgRY+Y7H+u8Jx5m9AoW/gj2b8egQSkGGCnGaZ3WC/cQKrWFZqA+Xej+gm2erkfAQvl+1zmF5qG/kWDdDN5qNZc9G4uMEQRAEoTlQuqkmdLVRCgsL9bJly1p7Gfs18eN9F6ibyKndnHhgx+7G8IjmIkmCQzw9J8yz3bdh2nkJ25oyTcKRihIjOzgmSi1qvHOYXhPmObawbci+wrBTpElQK7zK+hFqdCYT/NdSFiriq6wxliOcNVCvvWSp9NfgBg2oi58Wi4TQZCillmutC1t7HYIgtA3EJiE4UlpeyV1zV1HrNwRP5c5a2mV9Zx1wYJX32gR2igjNnA9sK3ytSHctFSXGVLn44RpoowoKEUHs5Dv2KtUoIQzOaRE5qp7xGSWU1RcxJ3hGjGfYRAF7yeaHUDsK1Na0Gum0Jun9FOAvvRWfiGFBEAShGRAxLDgyY/7aiBA2qdJd6GYVp6U8hsfzy3cN8ZvdGer3GM1Q0GCngBap8mVleKgLJFY0szIa7w4yJr6tIBilJ/sc1p4Ft//E+Y6LploI4SiWvxARw06+48uGdoeVXktB7EZgQvJjzAl0kwLXMNa70PKYTuzlpPqnKPYs5qHMZ8mOatyrJ4PdoXbkqT2NTpzICNbyt8mjaHfho83qMxcEQRAOPsQzLDhiFQE2PTCKGm3hpdVBo7ppeolrtzcIYRN/bdONz60ogYd6NYwmfqhXjG/5oUus49seusQmvs0lpeWV3PZqrBAG+PL7vZw181/Od042LS1K3Fr5jpWCMeZkvSFXJd49Tggnc0E57d+h20f+XqnzLY+p0l0AKAsVMaH+V4ZVBgUdu5N58V/oMrUS1cl+XLZblIKf6wWMe20lpeWVjT6fIAiCIJhIZVhw5Mrc/3Jt/Wy6qq1U6XymB0ZRFioiz5fJ5OD/S+9SvdvxuW/fHhMzxpCrGjy1FSVQehOE/A3H12438mkBBo6yjG/r2SWb215dwW2vrojcLdW4NeuBGQZffm8zUtnEaZIaGM8zCsfEjfgoNhKrvUo5V4q361w6sdfSOxx9n+mBUUzzPRMTpVajM5keaKjwvxkq4s0tRXiV4rJe3blvYDjybcTExPcKqNcZeAkQ+4zt8RLCHx5YItVhQRAEoakQMSzEEN0sd2Xuf7k7+BcyPcal725qK9N8z5CpPQw67yZ4849pPUadryNnTHvPOV/47dsbPLTQUHUGQwQumpogrgCjEr1oasSGES0m7y1dxeyPvk24S6pxa8kGajgyYqKNZzhMfLU3WaNeVBQbkztan1OFh9TFieIancmUwFge8z1uebdONAj7slAR+I3Ry13VNqp0l8gXo3iCWkde5/tGntBgiXnnTnStkeSxQ+cyy3ctVxR8x9Hf/C3ZjD3jvOELWY16/QVBEAQhDhHDQoT4Zrlr62dHhLBJjqpnhvoT6l9vGp7gWuv4MidU/W6G+BdQSRGVO2u5a+4qgFhBvPx56zsvf94Qf07VZZt9ryy1r8jaxbBZkWyghiOmMHSRJuE49tmqgU9Ze4hV1F9MW0RlVJV/vC6x9IDv0O1ZnHlrzFWBovpZKOBHvfPYsK0WtbPWtg3vlaUbDTFsPu+BoyJryQMmmwe+3R697DlAgwa/Uvi0th0CYjmwRBAEQRDSRMSwECG+Wa6rVZMcYXFVvZEACi+xwRLxl+RDmgTfbqYKRpIKAGr9wcRL39om29bc7mQ36NjNcnOwiWIEx53TL8ZmEU2fw9obubjr32/Y2Ot0uLKs4bbbSWqpjqAeclVsNd0C0zaxKDQoUtW1skDUaS8d1D66qD1Aw1UBT0Dxk5/fHPNe2UXYuX69z5+JivoSkAmse/4GjtpQgpdQzBAQn9PAEkEQBEFIAxHDQoT4y89VOt86NSJMhkVNUCkIaA8eNFW6CwU29zeTCuweOyk2PlS8mcY+C7xKpSaI460aAL1OZ2RY2FqmSXSZGSuEwbj9YnGsIG4k1mOmEz3EVigFY70LGeNdhAcdmVx3tNoSsUDkqH3khYWwSY6q54FD5pIz+IGY7Xavq7cRERK9r36S0vKJTHlrNTtqjPe4U7aPycUDxC8sCIIgNCkihoUI8Zf/rSqGbvASisjkvWSRS13CMWYKQfRjx+BrD36LZjRfOOEgyocasWpk58G5D9lWXS8b2t3SMwyGmIzBSghDRNiOvLLMWpRNvtDy/AkCuRFoEm0dDb5nFx5iDEHsDb9LGRiT614KnhkZwfx11uWW98up/S5hm93retnQxqVINPe4bkEQBEEAEcMCRAZjLN63iaqsLjzkN7ykZtPUpIyXUsqKVcqwTnRTWy2ju+p1RkwKQbbPm3jp+4LHYO4NEDMK2GNsN3FrNwhj+lfjhZtlmsTyF+xP1ETC9t7SVbyydCNBrY0EhqHdGzy2YDTLWVgi1oSsBWKC7zk7z7WnWykY7X0vIoZtrwpYWFDMNTs+F0EQBEFoo4gYPtipKDEGYfhrUUBB2BuKn4inNFvVWwphV9PDrO6X2Z7l2WehnNIkTJGbxvQ6awuBIXbvG3mCO5HWyOluyYhPtkhIYACjSc4iTeLcTdY2kATOfcg5uSIOb/iLh1cp/tPzJkZtnmHkQpv4sm0tKK5fV0EQBEFoY4gYPthZNDVW8BA7ind8RomtTSJdS2iWfxdL7jkj+YEpVn4hUQhD6tFpgG0yQ1J6nW5dOe51esxNu2SLmAQGsE6NsGlYS8A2ucIa5fGyYdp5iQ2AYAzTaMwobUEQBEFoo7RZMayU2gDsBoJAQGtdqJTKA14FegIbgFFa6x2ttcYDApsYMrPBza4BrjHUZB9BTtiakbTqW1GSki/YLiLNavtZM/8VMyQjZpyyUzJDr9MpLa9k3Gsr8Ee5OIwKdFnyNAnskxbcNPgN751n+XxifM/xA0t6nQ7bvzZeb1+OtR97yFXWQhgg72j3QthpWIogCIIgtDHa+jjmn2qtB2mtC8O3JwCLtNZ9gEXh20JjsIkhq9JdKPYsts2QtSOZlqvTXv6+ewA1r9/cMLa5eqNh1YgapQwYt9+8Odb3WrvdSJGIPzZF4oUwxI1TPn8mFP4q8Y69Tqd04F+47dVYIQwNFWiuLIPJ1Q0/FikSdkkLke1v3w5T8owmuCl5xu0wc647NaHhL8b3bDb/mZVtHTQEbp+zYfJOuKfKeG7mtDvlNW6fb5GEYeLWJ2312MuejVm/IAiCILQllG6i7NWmJlwZLtRab43athb4idZ6s1LqSOBfWmvH0NHCwkK9bNmy5l3sfszHZU9y/Ce/Jzsq8aFGZzLBfy3jM0ro5kmvMmznJw5qhQdtbbHo2B1++2nD7UePd8gSjjs2jF3mLWBYAFI8zorh095zHLoRc/+KEnjjxljLRX5/7u32jGUCw5hhPbgv43nrqrQpWJMxJc/a4qG8MClJQ51DAgWTq5v3sQWhhVBKLY8qsgiCcJDTZm0SGAlS7yqlNPCk1vop4HCt9WaAsCA+zOqOSqnrgesBevTo0VLr3e8oLa/kro+P4qzgryzH7D6mrMf0usHOT+xVDl++4i0baUyZc2UhaCSuM5ErSmDudYnbt67hPq6FYc9YJzBMfs76fMuecyeG7bzO4e2O9pAkODUnunnspAkagiAIgtDCtGUxPFxrXRUWvAuUUjZjtxIJC+enwKgMN9cC93fMiXNlFEWmwUWzQ+dGJpC1BHs8HciN3pDGlLk5152aXLAlobS8kjtfr6Au0OCDiD6H63HMi6ba79u6hvtusUtgsPvIxm1/uD/s2dxwO/dI+N0a++Y/5XW0hyy4/SeODYCumhMdHttVgoYgCIIgtDBt1jOsta4K//k98AZwCrAlbI8g/Of3rbfC/R+nCmexZzEd1L5GnT9VB05msIaPy55s2DBiojFRLh6PzzbiCwxBvGHaeZEfKyHc57D2lvc9vEMmt5esiBHCEOUHBsdxwDEVaKfKdmOJF8Jg3H64v9GwZsWQqxKEsElk+5VlCckXZgOgq+ZEh8d2StAQmgkH77kgCIJg0CbFsFKqvVKqg/l34GzgU6AMuDJ82JXAm62zwgODhKlvUYzPKCFTBRr9GFq7F8WZKkD3T2Y0bBg4Ci78s5EgYZKdByMfh/LZxi948+fF4pTWteD2nyQI4j6HtSfD6yVks15T9I0cXMBjvxiEL+5fT0IF2qZ6nZRMa6Eesz1eCEdvN5v/rBrk3OCiAdAWh8duTIKGkDrrnr8BHdfMqJc9y7rnbwAMy8pt99zNpom9CU3qyI77+za6MVUQBGF/pK3aJA4H3lCG8TQDeFlr/X9KqY+BEqXUr4BvgUtbcY37PePO6cddc1dR60+8rN0UkWrp5BAfrn+I3WCVNWwV/xUek5yKcLPyyfZymeFrOyq4ogTeus06uiya/P72+85/LHFYhsdrbHfL+TNbL87M5rG9SlkKX7tkDSFN3r6d0PLnOToUSvg3qICjNpRw1szLOHbr/Jhx6539W6h/4xYyQfKkBUE4qGiTYlhr/TVwosX2bcCIll/RgcnIwQUUbHybgk+mc4TeSpXOjzTPBfGQQSj5SdLENm1CeYwPpVMOcYrxX6l4iN34gUvLK5kxfy1V8RP0KkosRkhbkN+/YZiGxYS5yL40pu8lo89h7S2tEn0Oa580H9ixOdFFtvJlQ7tbJmhcNrR72s9HiCMcbecBQ/la4CXE8zt/RXbGvoSBOpm6zvjciRgWBOEgos1GqzUVEq3mQNQoZhMzVu2PvsfTnjDXGDSgCn8FK19OHAV8wSzjl3QK8V9WTV8QJ4ijhHdN9hFMqL4oMoo6/j6XFvawrKYP753HnD3X2Tf8WUWLxQthk2hBDNYidc0/rK0SZhOdA5ZpEn3LXMW5WX6xyHzQfupenCCWNIlmxi7azgL7cerKyKM+gJFoNUEQohExfDBjk+O7T2eQSQBPq129VlgmKpjZwimI4aR5whbVXL9W3OH/dYwgPiTLi9frYUeN3/Z869uNRjmNKYnP6XXzPMwhFvEU/ipREFsI4dLySu55YxV76w2BpIDRw3okCtBk+cBOVePGZhMLTYfTe5EKvvZofw1byOfB+ktZdshZDVdADgBEDAuCEE2btEkILUBFCbp6o+WV1CwCrVIVbsBGUJrpDA7xXynz1m3E2xp8SnN/xrMxcXO76oIYk8HtqdJd7L3WZkNZqix/wX57kiEWpeWV3PHaSoJRHYEarOPMnPKB4wW5OVUO3PuSZURzy2ATbWdfBbbBvxcFHMEPzPA9xe6al+hcuoeqN/OpPGk8Jxff0GRLFgRBaG3aZJqE0MxUlFD/xi12lsIWFcIpXZgw0xkc4r9SxqbRLVfVWW534iH/KKz+SWngRf9P6TlhHj0nzItEtLl66kmGWDgxY/7aGCEcTUKcmZ1YV15nQe6GA21E88P9Y5NMHnZohmxpLKLtGnvxL1MF6OLZg0dBV7Zy/PJ7YyMQBUEQ9nNEDB9ElJZXMnzae2z6+11Go0wTks4v3HqdwXada7M3TpH7smOzhV3Gf9lNnnMzka7Ys5jFmbfyddblLM68lWLPYsfjf+79gPgqcwjFS4EzmRS4JrJtybrtDL1/AWtCBQmvmwY2Z/ai913/oOeEeQS0zT9RU7y+WGwbMeeUI52Q6uCQD5xUkNtV5M3tjRXTbQmnfOe2gEW03dc9f8kO7P6dpU62qmfw8gkiiAVBOGAQMXyQ8HHZk5xc+mP+XXtRk8SmAQS1IqQVm0L57qqccezW7ZgSGEuNjhus4cuGwmsMjzDK+NNsnnMQf0DC/jmbixl3xIqYQ8xGuOHT3iOorcvgGpjme4Zunq14FHTzbGWa7xlLQVzsWcwXWaM5zftpwr7FweNihLDJlt31nFs/gzW6IJLFrDWsCRVw6q77I2J1TvAM6y8aQ65yjpjDOUc6Ic7MKZvYqWoMySv1jahum4x++sNIZT26ut7iOOU7txXOn2lYaCZXw6Tt9L76SdYNmUg9aVp1LMhQIQYsv5e7Jv+e0vLKJjuvIAhCayCe4YOBihKO/+T3ZKd46V9r0CgU2tI6oYCj6+YAsDjzVrqlKLI7q72UhYrI1B6m5rxOTu13zjFiyfKFrfaH6rl55wxuvvypyDmjvbSzM0Yw1rsw5vlpoIasBKtEjqpnfEZJjJe42LOYGb4nyVTWXwdO86x2fA3OrZ/huN8U0qO975GhQrF+W7tmqfBrMO6cfgmeYRMzziw23eFMLht6dWJz3ZCrrJv4oqvJThYVhxHNbnA1Blpw5OTiG6Bn53BqStNM/MtR9dwcepmz5g4HOGCa6wRBOPiQyvDBwKKpZJO6LaJadaB33Ry2qEMt91fpLpG/Tw+MSqzwJqFKd6GgUzZFF91Ezp1r4OKnjB1zrzeSLuKnYSXLF7bbjzZEQJh73lgVEYiTAtfwUvBMAtpjVGCVF1X4K3Lj8ldNuqptkb9n+zxM6/gGWcp9hTMdJgWu4Zi62ZFKn9vGs5GDC3jk0hNpn9kgOhUwJpwmMfrpD5n90beRKnRQa2Z/9C33lq6KPVFjJ9o5WTBc4GoMtBNt2ePbkgwcZaSxXPw0QdU0dZACtZUlnl9RXHoc300+RqwTgiDsl0hl+ADn47InKbRJjUhGp4tnsn7geVCx1zKPeHqgoXpbFioCvzHGuUBtTdqEF/C2o9vFD7Jk4BnGhvjM4+qNxm1omgEAZhIFRGLGTCYFrolUYDdMO8/Y+OW7lhW0Ou2hIHrQxuTLnB/X5nU4vEMmW3ZbC24r0p3SZjcpr7S80lZMzln6bWJ12GGiXdKhJub9WiNNwsnjmySP2ZLcI+3znfcXBo7CC9S99Tsy/dWQatJEFEpBHnsAI3kib/nd1K26nyz/riYdFiMIgtCcSGX4AKbkuUcYuPyutIQwytPwS2zgKMOzG+Xhne67KWEwRVmoiKL6WUzx3UbA2y72fB4fZOdF7p9x4f+L/SW5aGrskA0wbkdVdBuFmUThluhmvSiyPUGWHPkYgNGMGOpieZyJyj3SslnvkGwfY4b1cC1yLae02TWuebNjK6F/GppwyIz5a20fK5VmSCcLQwxxPtYWi1Vrao/v79YkCl8Xg07aHANHkXXPt6jJ1cz1/KzRiRMmmSpAlr8a0FC9kdq5t0i1WBCENo9Uhg9ASssrmVy2mreDj5Plsb6EnzR3dMjVsbcHjooRr4PKK8mOm8SW7fPy4MUnMHLweVAxILVxwlGVW9vtyfKF7fajYsSt1UiPYs9iHvA9B5P3RR1ljV7/PuO+WIk/qJnuGcUM35P2Vok9m7l03x9ZwhUxm80JcMOO7ux4ud9xSpuVT9qbDcG4LxVb1xiCOGqqnVPSRAQXI5YbbWFwgeMY6NZgfxO+SfAWz2T23CCXeRbhJdSk0YrZ1HHksukMXtGLSRcMEF+xIAhtEplAd4DxcdmTdF0+nSPZisJe8GoNlTqfrmobNWSRzT48ClQKl7BLyyuZMX8tVTtr6RptHUgHm2l4kalzJskEWvx+TyaM/DNnLTwsZgRxNMWexcz0PWE0qLlAA732vRxz/0kZL5Hn2WMpoQPaY3h+UyRi2YDE0c3xI5tNXE6DGz7tPSptBHGOz8Nnx/zF1YjlpBP+moikVgw7ZDqeK0rLK7l7bgVnBj9gmu8Zcmw88+mgNbwUPJMzvSvoqrah2oB9QibQCYIQjVSG93cqShoqsNmdObG2mkwXDV2VOp+i+lkAsR7YFLDzo6bFiIkJvuSEbGFIPljDYv9ZM/9lK4QB7vSVuBbCQEJZuSxURFl9EeuzLrcsKHtJ4dxWxAthsKz2psK4c/ox7u9GdTueBy4eCG8maVZsYdJOjTgQPL4tgPlvubR8INPnZXJt/exIBGNjK8VKEZvY0tT9AIIgCI1EPMP7MR+XPUnt3FvCFVUNtdvJTDIyGIxhF9MDo/B5FI/9YhBLJpzR+pcvLXzJkWzhRuIkhDdMO4+CqIQIN/w7NMBye9Dmn5PddtfEC+Fk210wcnABM35+Ip1zfJFtnbJ9PPaLQSl9Fhoz1KRFOFA8vi3EyMEFTL53Ct2mruPNkZ/ZDuuIzsZ2Q4Kg9tey6e93MXjqu5JTLAhCqyOV4f2Mj8uepPsnMzhM/8BgPK4qmtG/sHaQy2T/WD7I+ikzituYhy/Ol+yGtC+fR9Oxm/vs1V6nM/bzGyx3zQmewZUZCxO2v+07B6tkuz6HteewDllN64fN728tkvMT48SaorI/57pT034Pht6/ICZR4/AOmSy956xGrccSEb5pMXJwAR9vnEj28nvJjrJN1OpMJurr+Q1/o5sn/QE+XdU2dtT4Gff3lZHHEwRBaA1EDO8HmN7c6/f8mSu8C/EoQIHH5eX3Sp3PBRl/iTSwzGre5bYYTTaMYcREKL0RQrFVdQ38NXAmT+beHGMjUXfNs6yITQ5ew5XDjkqIEBt5/kz+HGfV6HNYexbc/hPL55GWoDe5Zal7f7ETyZoVo0hnrfFCGIypfEPvX9A8glhIi5OLb+BjCH8B38r3Kp+NQ8YxvPv5dC39U6PObeaU+4OaO0pEEAuC0HqIGG7DlJZXMuWt1eyo8VPsWcxY38KU/XtmHnD51LObZ5HNgUsx5zbJoM9h7S2tEn0Oa2/8xaxGv30b1O9FAyENs4NnGvnDO2u5a64xiGLk4AJGD+3B7I++TTjf6KE9bPN4hx7dhXU/7MUcBrdpRy2l5ZWMHFyQXEymUO0FXAnfpALcKqnCIk0iXewyllPJXhZahpOLb4Bi42rIEeGfk4FQmQd0en54rWFRaFDk9nnq35xc+r/oN9tGg50gCAcXkibRRrm3dBVzPvoWjZFW8KjvL3htRv7GY45RrtJdmB4YxVu6iPUPNl1nf7Ni1SwGloI4lSSD+Ca66MpsPFZJC+9kjqO/pzLSH7c5sxdFux8IjzEOx59lPJ84trjX6dzb8X5L8ewBZpoe3YoSeOs28MeJdnPSW1NUe8NYVdShkRXpFEknhaLFbBWCO5ySOlygtWHbeis4jEu9H8QkWGig3teJB/VVvLjnlMan1cQhaRKCIEQjleE2SGl5ZUQ8FXsWM833jGshbHJ03ZzmWFrz0wzNYoCt8AUMIfrmLRA0jL2LNfzbN4Cx/nuAsBBWlTFBEUfWr2ddwZQGQfr27YlCGGD9+5wdvJHZ3J2wK4Qx/GKkdwm8caNhrYjHPGcqwjdJRbclsoGbmpayVaTypemgp2N39157C8zpdTFJE+Y+IMu/k7v0/+PWrGw61e5hc2k+H28cb1SqBUEQmhBJk2iDTC5bHfn7+IySRmd+FnTKbuyS2iRNkmRQUQJv3BARwmD8kj7Ns5qXfPcDGELYyp4SLdCXv2D7EKd5PrXdV7UzPGXPSgg7nfvF4tgpcy8WN2yP9/quf79hfxvh8A6ZKW1vCVuFVQTfl9/v5ayZ/2qyxzigGDHRiD+MIp0LjU7WrywVJE/twaOgQG3l+E9+b/ybFQRBaEKkMtwG2Vnrj/y9q0q/WxuMqXDjzunX2CW1SRyTDNzaChZNtfQ9moLYNU5i1oGunbLtp+/ZndvKSmIKXrsc4NbIB64ogbk3QnTcX/h9WHrPWc1ve0jR82wXwecUzZcWD/ePzT7eX6PeTE9v1KTJubsGcFHo/4wm32Ygmzp4507xEwuC0KSIGG7jhPC4To0wqVFZKGhyn12LkGKzmKXHNZUhFU5CVIVnaLj5xa689oLY5v4ejOEX/CtJtJvyNvy9osTeMuJS8DZmvLFrAVtRAnOvS9we9T40q9/XqULeRE2AaREvhMG4/XD//VcQRwlTb3kllP5f8z5m7Xbj8yWCWBCEJkJsEm0QcxBCsWdxytPL/FrR/uI/sX7aeW1jmEaq3LI0Ufim2iyWiu+4Yzfb0yhg/bTzUHapDdHbh1xlf55epzNmWI+Yalm2z9PQPDdiYqzgjSf63Ium2h/nkjnXnZogfN00zzn5dhNwWmca/u9UbRVtqkIejdU0PKft+xkjBxewL6cFpvu9c2fzP4YgCAcNUhlug5w38Eh2/fdlpvmeSSlKTWu43/cbJu/vFZM0UxLSYsREwzNsFRFlZuq6ye4149Qs0iS4soz7gPtGnhDZfG/pKu4oWcltr67Aq3J5sM89jNr8iH2ahEkyS4XLfOB0UiNS8u0mW2eKtIStImkEXxhpsnMm59ypMPd6EuaWNyW1242mVYsYQ0EQhFQRMdwGCa4oYabvCVfT5aKp1PkMOu/6ZlrVAYr5xeHNW9BmE502Ri6P/fwGmDDPqJqawtcUxVvXGI1r0aLYJmM4nntLV8VErQW1ZvwX/akYNj9GMFviNC3P9MQ2Yz6wa1KZ6ueS5o5RW3D7T5IKXacmOxHEYQaOgm8/gmXP0ayCeNlz0GOY2CUEQWg0IobbAOaEuaqdtfyy3Uf8XqcuhLWG6YFRzNrPbBHNUmVLdUjFwFGUBocz7u8r8QcTf3lHptrV3ebei+zAK0utReIrSzc2iOG3b0+YZMf5M41K9lu3gj82B5n8/g2CtzU9sSYjJlp7hsH+fYCmE/IpTNCLJtlnr9FNdrlHWlsiclvAWtCSnD/TEKqLphpfikxPva994tUPG8xkCvurY9o4v4hhQRAaiYjhVqbkuUc485uZXMgeyAK0c9SQHTvIZfkh+9cAgmarsqUxknjG/LWWQthkybrt0K5pMpCDNvlTke3xmcU62HDbrDxHdfA3elqXy3SDwztkWloiLH275nps0iQsacqmt7ZSIY/nd2sOnDSJZMQ110WoKIFFUwnt3IjC/v93rv4/2MR2HEEQDk5EDLciH5c9ychvHiBTBRp1nhqdyQP6qv0uQq0xVbakFeUUfcdVcRPn0qaixGjuqY1La4gSYl6lLAWx1/ztb5dZvPwFQwzbiYx0SCHdIGXfbqrrTLXpLdkXntYWvnYciMI3FcKfi9Omvcfi2osady6HBlhBEAS3iBhuScIVEao3QXZnTqrdkdKIZZMalUWGLxufv5qqUBeeyRxD0XnX73/JEWmSdkW5ogRKb4ZQVHUzLFK7dspOGMGcMhUl8ObNELRoKIuqcF42tLvleObLhnY3/mIX0ZZmlrEjKaYbtJnxx6nE5zUhbpvshOSMO6cflDbmDMq4KiIIgtBIRAy3FBUlsV7P2u04hGklsF3nMqT+KbJ9Xh68+ISI8O0GTG7qtbZx0qooV5RYd7iHReq4c/5i6xmGcAZvXRIv8qKp1kI4+rFoSJV4ZelGglrjVYrLhnZv8AvbZRY7xa9FkzAaWkHhNW2i8952SEqqNNPY7mS4abIT3DFycAF1/+hIlr86vRMUXiN+YUEQmgQRwy3FoqmJTU8p0FntpWB/HKLhQItW2RZNxbazff37jLzSeE2nvLWaHTX+mN0Ngi2JFzkF/+J9I0+wT44YclViRJu5PRkJQhhAJ3qOm4FkQjd+P0Q1J153atpNb0lpYu+wCN+mIyvDC/7kx1nSBr7cCYJwYCBiuCWoKGl0zNT3Kp8lE85oogW1DVq0yuZCqI4cXJD8i4bTJXg3cWJ2KRHRmLeTHWeFnd/Y3Bd/jiZKN0gqdMO3rYhsb46mN7dNeSk2XApNRO2O9O7XsXvTrkMQhIMaEcPNjWmPaAS1OpONQ8ZxRBMtqS2RjvBNq6LcDLm3CYyYaO8ZBkNgJkuJMHGZWZyAk6/Yap/bdAMrURk1ECSp0HWLW+HrNj7PTVNeK/mPBdL6d1lLFtniFRYEoQmRcczNTZr2CK0hpBXfcSifDrmPk4tvaIbF7Z8suP0nCcI3aUV5xESMAcsWNPYyvMnAUXDhnyE7L3Ffr9Nh7/fW93Oq5qaKk6/Ybt/v1sDk6oYfN0IYDCH/9u3pr7UxNMXYbpNW8h8LwIiJBLztYjZZJQ8a/z80Bgt9etIfxCssCEKTIpXhZqS0vJLi6k1pfePYog7liMlfcQQckBXhxpJyRdn85RmfJgGw/Wujgm8eYxmP5rIJzSlObHJH6+1NmRJh5zc296WDXXUVrK0XNgzvnWdZKR7e2+LLAyS3LrRi1bbJGgEPdgaOIgOoeWci7Wq/oyrUhfdCg7jI829ylTERMoRidnAET+XefED1TAiC0HYQMdxMlJZX8v7rf+Z8r8LjMj7N5EC2RbQqplCNT/ao3hhrZSm9CULxXT1N0ISWbkpEKkMazLW1VJpE+Pm4EbpzrjvVvYhsKutCMzTlufFHNwkH0XCOnPAXyG7AIeWV/Cw8kbNruGn4ysEFXNm6qxQE4QBGaZtpWAcKhYWFetmyZS33gOEsYV29Ea3Bk+I0uSDwyUnTxRbRnDx6vLVP0WzKcfIwKi9MStEHa2KZ9ECM9zYBq6EY0LLCyK6iDTGvh6XQHboxvWl5To852SKKy6nxLllTnpXwBlvbRc8J82yXtmHaefbrToW28L4fwCillmutC1t7HYIgtA2kMtyURFUcncaMOuHNzhMh3NzYJUu4iUazqOyWllcmiWQLk05KRIpDMdxSWl7JPW+sYm997PMZM6xHYuSbXXUVYqwXCVXRihIovRFC4ceo3mjchgZB7CZdIxnJEiOSNeWlMb672Wmm910QBEFIRMRwE1BaXsmM+Wt5q/a35KlGTjFLN2pIcI9dB7s52jVZZTiK0vJK22EdlpfO002JSIaLMdDRa77jtZUEQ4lrNifjxQhiq8gzMCraAFPyrMXs27c1CGGTUNDYPnBUYqU8Ol0jFVId42xFMwlfGdAhCILQ9pE0iUZSWl7JXXNXMWTXAjqzp/EnNAWZ0HyMmAi+7Nhtvmxj+4iJ4PHZ3zeuCW3G/LW2U+vAXbTY0PsX0HPCvMjP0PsXJL1PDBUlhs85XghDQ4U0bs1WQtjklaUWXwauLItNnDCtCsuebaiWm2LWTJiot5kIaG63S9FY/kJiUoSJ3fYWxK7hL36709hwQRAEoe0gleFGMmP+Wmr9QSZnvZSWLSIGU5AJaeOqQcu8RO/kZXWZJlG1s3FXAobev4Atu2PTLbbsrmfo/QtYes9Z7oZiLJpq0fAXRVyFNNmag277CJzErJvqt12Khg42vXWhosSoSEcEukNTYRKPsdtGwLTGhps00TAUQRAEITkihhtJ1c5apmQ8l3ZVOKA9ZCidWnORYElKXf5OEWhO++Lo2imbykYI4nghnLDdzVCMFMZAQ/I1e91+q3MSs4CR62wlrMPnd0rXePt22PZlw+2w/cJShCZLjIj3LhuLtE4HcTmxrtlj1NwOQxEEQRAajYjhRnJl7n+5wr8wrapwjc5kuu8mJt87pekX1lZJcdxuKnmuTTYFLQXGndPP1jMMDhm6qZBMAKU4xWvcOf1sPcMAlw1tGHXr+Poni4orvMbaA9zrx/BQL3sx3aWPpZf43dVbWLLj8phDl6zbzujedzGnF/afq0VTE73LJvFV7KbwHzcVInwFQRBaBPEMp0lpeSXDp73HtfWzU45PA2Oa0hv6dAadd33TL66t4lR1s8Cp0tsUjH76wxivbjrnHTm4gBk/P5HOOYk+4xYbxJDM5xyXqTtycAGPXHoi7TMT842j0ySSvv52QzzM7efPNJrsTHGsvMZavv3Q2t+sPMbxZkU4jjNq/mG5fcm67Yyuv4ue+16O/Iyuv6vhAKfKeVMOPInDbjy449hwQRAEocWRynAqRDKEN3Gy7sIQ/yi6+ramdSqPgotyPiXnYJqmlGLVrTkrvU05OGHk4IK0p2Id3iHT0ipxeIfM2A0VJYnT8zyZMPLPDZYOl2kSbtec7PW/N3A1fQPfcrl3EV5ChJQHb+HVlBbcwd2/f4cafwgYgVIjGD00LLIfPR6C1tYQ2nUyBLRNooSXkOu1xryXTpXzZANPHEiWFLHg9p9ImoQgCMJ+gIhht8RlCBeorTzme7xRp8yp/a5p1iYA4POA30Iv+SyufzSp0G5Eo9fSe85KaKI7vEOm0TxnUlECc68nwX8bqg9vx73POUWbih33lq4Kx7BdzcTA1ZHtwzfn8eGSFUQ7MLSOimxzqtKaQt7GfhFM8UJW5L0cMdHCMxwmvrrtcmKdU1JEvCBucdpaZrIgCEIbR8SwW965s2F8b5h07BExSIxakzLj0kHc9uoKy+1pUVECb94cW8l0M70sbmxwfBXa5zHWZFZmY4SvFYumYt2IhrF90dT0hDBYNoe5wTJ+DecvE68s3ch9h7nwNw+5yrI6/F7O/0BdKqsMExnwcVti3NuyZw3fsJmPbJWpbPGFoVFJEc1JU42xFgRBOIgQMeyGihJrj2NjOJBi1NxWorxZELRQM94sy9NmehX1Fo1pmV7rbyGmuJwxfy1VO2vp2imbcef0S8/CYFeNjRePVmN8o7Zb2TH8ISKi3dXakqVFuE2TSGJTiV/rIVledtUlVlOH985Lq4Ie1Nr4zM+9zv6gKXmGMC38VcJkurPPn8lwi4Y+V2uJrpw7DfswBfH+SpLPoyAIgpCIiOFkVJQQeuOGpuk0VB7jmvGBFKOWSiXqwj/BGzeAjvIyKI+x3YLpPz+R3766IkaOqvB2O9z6d5MKbadqbArJAk5Cbcb8te7EcLK0iCa4wmAl2nfVBRMEsdkU2Puuf7jPIw7jVcr4zH/7kf2kOVOYFv4KJoW9yaWreGXJRoKL5+FVKmFktNXawbCbmOv0KsVlQ7sb92tsPrIgCIJwQCFi2ImKEgJv/i8Z2sKImiq+bLhg1oEhgKNJpRLlZthFFG4qvek2KCUV2ilm96ZD/PAL2+cyYqJ1lRoAFXOFobS8ksllq9lZGzuEY3jvPOY4rMVOtO+qC7Jh2nnGDdM+MBm+yoJ/Bwcw1n9PwuN8+PV2rFLbtNb0nDAPGMGF3iwm57xOZ/8W6wWFhWmDN9kgqHXMyOh7S1fx0deJI8zjGxNj7ueQj3xv6SpeWboxUUDH0eew9paWCEmKEARB2P8QMezEoqlkBPc16hQaUB27HziV4MaSwkALcK70um1isjsvOAhtt9m9+f2thb+LscFdOzWMhLaaRNfwXMKvV5I0idLySsa9thK/hRJdsm47FR0GMdC/InEhvU6Hz5MsNs5Hq4DTvKt5iQcY678br1K8k/cIfSuXobMADf8OGWLZHL0R/ZXyzWARb+4uYn27y7E0vYQF65woIRyNuX22xf4xw3rYeppfWbqR+9rZN+g5Ce9omjUp4u3bYdlzxHz5KfyVu6p1Iz6PgiAIBysihp1IYZCBHapjd/jtp02wGCEet01MdqLF0VLhVI2NThZIMjbYydM67px+gCFi7SbRRdbt4kvEjPlrLYWwSfHu8Ww49knr5rAJ8xzPbWUNUcCPvZ+y4Q/nhcXyssh2FPzYu5oNxzzJ8M232U68C2oPGSrxyksAD2+XVzq1DToKXjsLR1Br2wa9OYERtuezqg67Fb6pDI5J8DObWE3Ls6Kpx1gLgiAcBIgYtqOiBPtxsu6o0ZnkHChNcna08UpU2tVjU3gmS5MAR6Ex57pTk6ZJzJi/1tVziWCTdhBvu7DkyrJYK8AaxWWlqxxF++inP3S0WACOzXlV+26wvduc4BlcmbEwZpvWxvYVr/+ZxZmv0lVtpUrnMz0wirJQUeQ4J8HrVcpyv1epBkEZ16A3cbG1GE7VGx1NynnWdn5mc5+b6rAIX0EQhJQQMWyHY5xVcrSGP6gbefBAt0Y0ohJVWl7ZNMkPDriqHttFaaVo6bAj2RAPVyLWxCEerWsn+wqsiZ0Hd8ywHoC1d3jJuu3odljbGVzQtVO27bomBa4BYLT3PbyECOJhTvAMlof6Ms33NDnK+CLSTW1lmu8Z8ENZqIj2mV72+UO2gveyod0tLRSRUdPnz0wQlt4l1k2BXqtZ6y7zmlPOs3aaiNeM0/IEQRAOZtrkOGalVHel1D+VUp8rpVYrpX4T3j5ZKVWplFoR/vmfZltEIxuoqshn6IU3NtFi2ji3LIXJ1Q0/LoXwHa+tpHJnLRqo3FnLHa+tpLS8MvbAF4thcseGn6jRzU0y7jbFEdHNQbR3OJ6E5+JQgR13Tj98DuHXw3vnOVoLnET7v4MDrHfEDaOwYtw5/RwzuScFruGYutn0qnuZY+pmMylwDeMzSiJC2CRH1TM+owSAvfVBMjOsT2o2vY0Z1iMiZGNSKGw+UxGhbHG+GJrzM+M0Ea8R0/IEQRAEe9qkGAYCwB1a62OBYcDNSqnjwvse1VoPCv/8o9lW0Ii4Kg1UDRnf5FXOA4l73lhFMM7fGgxp7nljVcOGJKJjwe0/SRCLKTcxuR0RXVFijBKe3Mn4s6Ikdv/btxsZuZM7Gn++fXvM7tLySvrd+w49J8yL/Ix++kPAEIvZvkShc3iHzJSey8jBBcy49EQ6ZfsS9pk+VUcvrQNj/fdEhK/GuPLxQXAAvdfcyL2lq2xF8RftC7mjZKVluoQTXZX1mPOualvk7xP0M3yVNYb1WZfzVdYYpmY8HyN471tRxLqsy9jQ7nLW9X+iQQjbfKYcBXT88VakELlnS/xEPLf7BEEQhLRpkzYJrfVmYHP477uVUp8DLassR0yMjF9OFQWcXGzvkxSMyl7S7S5ERzKx2CQRWPFNTdUbjfG+YNgokgxxKC2v5PaSFQmCMN472hSWkWQ5y45e2mSE/cax9oNw4sKw+7mv1z0x780X7Qs5e9vtxNuNpvddw482PG7rBQao0vl0sxDEVboLAFMynmOsdyHmsjMIMTZjAWT0gBe/sv8SleQzdd/IEyyb5dLFzos9vHee9R1M60a6aRKCIAhCyrRJMRyNUqonMBhYCgwHblFKjQWWYVSPE0JGlVLXA9cD9OjRI70HjsnETTFVQi5nAil20TcTjY7Aqiix7u4PBY3xvgNH2Q+QWPYsnD+TGfPX2lZGzddn2Tfb+a56Hxr4rnofy77Zbi1qe51uLehc2BWApF7apzq/zBk1/4jx704KXBMRb3Y2iz0fvwL5XwMqkh997iu5xAvhYs9izv/mGXI81l5gk+mBUUzzPRNjlajRmUwPGP8uR3vfw1K/m01xVrio3JaWV3L33Apq/EbChVIweqhFddglVg2USf8dWPiZBUEQhOajTYthpVQu8Dpwm9Z6l1LqL8AfMH7D/gF4BLgm/n5a66eApwAKCwvT74IzG6gqSqidewvZWIwStuJAuZzZiIimZF30djkd6TZpUVESO8yjz9nw5btQvYkFHbvB5TY5z8nE5aKp9o9Zb92cF0+yBrlkgyViuLLMunlr9xbDomES915ZpWoAsYMl3r6ds2vfjrwJGYQY613IkR3acfZ1cyJri6fYs5gHMp6B6rBwrd4Ib93KeepqynRsxdfJCzwk9EVMI90PWUeR4/8WdJCA9vBa8McRweyNSS2OohFNZlYVfK1JfC9S/ELS0l8A3dJsOcmCIAj7GW3VM4xSyochhOdorecCaK23aK2DWusQ8DRwSossZuAoPj3pD2zXHYjXAkENQa3QGkLKc+BcznQas+yCZF30o4dZV+xjtttVO+O3V5QYlpbqjYA2/lz2bOztt25N9PmCIS7jzxedDNAEk+icGuTAOS8XDNES7TU+a9vtsQ2Lu7c4vld2QrjPYe1Z9+D/NIg8i1gvpeDsfe9EblvZKawELv7aSLNbNPZe4K2M9S4kQ4VQCjJUiCPq1kfEbYYKcan3A4o9iwFjQEaqmD5nS3t0r9MdK/gx71Gyz0xL8nD/2GbAh91FGjpFDgqCIBxstMnKsFJKAc8Cn2utZ0ZtPzLsJwa4CGixaRYnF99AaffzWTHvKa6tn01Xzza+owsP+Uex7JCzmiUWrFVJZcxyGpgCLHr07YN9PmfU+jthctSoZkgeYbVoanJvt7+WmncmctY/8qnaWUvHbB9Kwc4aP1073ca4C/9i/f45TqJTUX/a17nHndPP0jMMzkM5glq7y0lO8l45xcv1nDCvoTrsMKYYgLdv54us5/HoWAuFm2Y3EzsvsFKJVwXidXeOqmey7yXG6xK8hNA68Rg7dNREvJd893OaZzWYj2nmNDsMHkmoiLeG8I3n4f6wZ3Pstj2bje2/c/536nZgjSAIwsFAmxTDGN7gK4BVSqkV4W13A5cppQZhKI8NQIt2qRnNSVOAKQB0Bf7Ykgs4wIhpVqoogbdmNIhas5p7wSxDeJg2iPUfGGkOUTYIt3nQ7Wq+o7LOOP/OWn9ke+XOWu6aa6RYvLbs2xhxOu6Iy7jZ84jhEY6n8JqGP618w+H9psi+8/UK6gINl/dN72jvu+zzbVtCtAS1Ztd/X0Zn2thUlDfSJOgFUA0WCoWiOvNwOvu3JNzNbHaLbtqz8gLjy4b6Wlcemc5qD3lqT0rPL1oIA5E/ATZMO88YQnLXPxw/Ra4aDJPQ5B76eCGcbLsgCIJgSZsUw1rrxVj/amy+KDWhSUm5i96quuuvbfDsRid7mDaIFDHFmRW1/mCCWAWY8d0gOOIObq75c5RHWBlC17TD2Ew0i7bLOKU8ODW1WW1vaoo9i5nme8Zeiw65ytZCMdb3HlzwBDWv32zb7Lbuwf+h14R5aMJNcn7DWtFVbaNKd6Gu65kcveFvrtaauiRV9Kqzn5+XmI5hjV0GsVtSnkQnCIIgtBhtUgwLbYBGjllOuYvezptbvcmdDSIJ0eLMjnghbDLju0HcPK3K+QGcEgAqSuDNWyAY1YAZZfewsoyYTW2uxHCS98ouXs7E0vMbjdMXDx2EN24km2DEi1tpEZdmTqEr9iwOC+GthPBQoLaiv/mba7tD6mimZDwXmXQXj51f22RqxnOMyViIZwWwAshsD+c/FmnGdPsZT3kSXTPTJJGDgiAIBwgihgVrGjFm2SSlipeNN/c78jls50bHCWYmWoNWin3ZR5BzRF/YsDhSqS3jpwlZtk2G0+tUUQJv3AA6Tmibubfh7N45H30buUwfbZlwJVqSvFdW8XLR2Hl+XaODETGrNSwKDYq81uaVgHHn9GPX67cyxrsw8l56wokQzaaDw4zNWAiQIIid/NoAG4oWwbKFsRvr90Yypkcv7d661d7cI60tEblHJr1rYyIH20JkoiAIQlOidJLJU/s7hYWFetmyZa29jAOaJvnlaCZCRFWAa3Umd/qvZXxGCd08yQXbplA+RfWz+Hnmf5jme4aM4L7IvhqdyQT/tbaCONvnpdZvH8u14fK9sdFtI8JRbVapG9AgRh893jGn+t5Bi22rv+b0M0fRYhWzlqS5K94asDjzVlevr1u0Nlzc2zIO49AL74/EE4Zev87Vl5p0CWnrRjwIu8rD/6vbQS6vdL6Jm2+7x9Gvva7dGPuoto7d6bnlIdu1bJh2Xsztng7NeZFj377d0WpjSXwTXe6RSZvnGoOV3QP2P0GslFqutS5s7XUIgtA2kMqw0CjS8kJaCbjBYyAjOyKGd9KBif4rDPEaILHpKo5oG8Rt/C1GCENDlm1ZvSGGiz2LGe8roStb+V4dysaTxvHYloGWv+if6vwyzH27YUP1RnjzZuPvyVI3kkSzOV2mf2XpRu4beYJ9tc5pXLWDIC48Ko/Xl2+iNjxY4uHgKKZ7nyVTu8zRToIpSA8Nfg+lNxkbF01tciGstSFsO7GXKt2F6YFR/NH3uKUaVpH/QB57uHnndHjoGW4//FrDFx7HZUO7wwqHzOIUI/eSeuiTTDG0pRmFrxVtze4hCILQFIgYFhpFyr8c7QRc3LZoYWY2Xc30PUGGSvT1BrQnpuprd9m/QG1lceatLAoN4lLvBxFxfQQ/cMjye+nju4nvD/tpTBV23BErOHvn24knC9bDO3daP8doHKPZrIdYuNkHJB0tbFWxv7SwB+NeW4k/Kudtnj6N0YN7cvIn450fLw6NC4tDyG+8TrUJgyKbhBrdjsmBsZH3fqZ6ggy7gRzx1G7n1zXT+XUWMWOhh/fOM3zcK70OleFusM96lxVJPfQWDYqR7QdCbrkgCEIbRsSw0LK4GIkLRiX3j77HGa9LIiLlMR63PNaDjrE/OGXZdlNbuUItTKhS5qh6rq2fzat1p/LYLwY1JD886iB4a11Uw0ZMtPYMA/Q6He8aZSt6zTivdLyddhX7D9dtT5CK/pDmuvJerEj2XOIw05WTCuLa7dCxe+pjzZM9vood5zzE80XEh+wW83MQfR7Ww87Jl9KRoPVz83hhxESGL00tMcXRRpAs41kQBEFoNtrsBDpBUAq6eQyRMiXjOUI2H9fNxEamTQ+MokZn2p7X7nJ9V7WNWn+QGfPXNmxMdjncLl3D3D5wFFz0JHizYveHvb1OkV2XDe2e9qQwu8q8nVT8cd0/sZO1TgVqN84HrWHy3ktcpkGnTo6q5wHfc4z1LmzU/9DMoR4zfE/Sid3Wzy2zPYx8AgaOYs51pyYI37S9s8qb2vZWwk7o20YmCoIg7AdIZVhoFCnnCadBjqrnigx7oZOj9lHsWRypDkdn2Raora5ju3bo9nySdT2da/fAZCA7D7I721eAs/PcpW4MHBWJ4orHjFWLTpOAhuY5u8arL7/fC8eebl1p73U6fG7/PK0wRicnytWQhhqyyCV9P/Fe2vHCnlO4NSs35YEZbmnPviaJZ+vMHofzqJhYNbCp9qbTCDfkKusIuyFXuVl2i5FyZKIgCMJ+gKRJCI0mpV+OVp7hJsAqLaLYs5jHfI9bVoLjR/maCQiJx3qMjVYT6MC4/G8mS0STRBDdW7rKMlc4nqQpBC8Wo9e/H9Gx0ZPWUuHrrMttX6ff+G9ipu8vZKj0/l9Rp72M8xvDIpM1QqZLKqOZG3Wejt3htw5T4OMb4UwKf5VcEDt8Ztx+XgR3SJqEIAjRiBgWWp5mEsRmtJo52MGuKpyycMoOV7ltPcJxE+ns4tbCgsgu89esBkeTTAyXlldy26srXDwJgz6HtWfDthr8QePffbLXKvo1nex7ic40VHZTeQ3dvjfpUKMzyaIeb0uIYYCLn7aO2QOYkmft81VemGR8flIVtnZT8qw+LwkkiPO4z+pBiohhQRCiSWqTUEr9Rmv9x2TbhP2TVqk4mbFfNhm8rpqyLOiqtkVGCzdl9TFUs4Oj6+bgVYplubfR2b8l7ggNy56DHsPg24/s49aWv8C9gatth1+YUWrRJBu6EeNvdmBKxnOMyXgP764QoUwPb3A279cd7fhamQM0om+bb0wIhTcFF3BXtQ0wLCxl9UWsz7rc9X2d0BpeC/4YgLHehY0W2Eo5C2IN+Of+mkwCxobqjUY+NhiCOEkjXLywDWoduW33784ufs/q8xKDZZVau4tsEwRBOIhw029ypcW2q5p4HUIrYP5iNtMMzF/Mo5/+0P5Ob99uVL8mdzT+fPt214/V+65/0HPCPHrf9Q/uLV1l25yWrp7ZqzOZ6XuiyS/DV2mjQS+oNR3r44WwiTaqhXYRWQA66JgrbJUqseD2nySMyI1Ok6ja6TymesO089hQtIgrMxbiDbfPeXSIS/T/8WDms46vlVJwgfcjij2LecT3F7p4DD+tUuBVOqGxzukiU5XuQrbPaAYr9ixusmY6c42TAtfwUvBMAtqD1hDUUB/+u/mTyjlt90GDEDbx1xrvPSRthHMStnbYpY0kjd5z+iw67RMEQTjIsK0MK6UuAy4HeimlohP8OwDbmnthQvNj9wt4ybrtlJZXNsSLmaQ5GMCuGnZHVi6d1e6U1qy10ZCVRR2+OA9rrqpzVRm0qv5pjKSFeClTp72RYR5gH9sGhMW9U/SC11HAOEWpxU80M+naKZvKJILYTvjkuGiK68weJmW8lPBag/H6BbQHD5oq3YVFoUFc4P0ooQktoBV/9lzOgxeewIz5axlfU9KkAzg6s4diz2ImBa5JGLlssjjzVvv3rSkwv9glaYRLR9h6lXX8njfZh90plk0i2wRBECI4VYb/AzwCrAn/af7cAfys+ZcmNDdOv4CnvLU6caPTYAAH9nz8Coszb+XrrMtZnHkrxZ7FAPhIvYKrFOzUueymveW+VM5jVgwD2sNLgTP5bf1NbNe5ke3bQrmM898Q05Q3PTCKkN3L1rGbcxTWkKscBUw6UWrjzulne75IooeN8HH7cjklQHgJRaa/TQpcw1vBYYnHKM2DhXsZObiAcef0i1gmmgqlzDQMe6zi9lKtGDvSsZvx5/kzDW+4+TlQ3pjmObv3P9nnIpXtEZw+i20ssk0QBKE1sa0Ma62/Ab4BJDPnAMWu4gSwo8afuDGdwQAVJTyU8QRZyjimm9rKDN+TDAl+Qfs047oMMdV4FaMUbNe5nFT3VGRbWV2Rwz0Mz+uQ4Bdc4Y0b3OHLNhqpvv3IujKY3x/On8llAetmqD6HtU8epQYJzYcje50Ov/gL415bgT8qRDgm0UM5TFJLQjL/dvzgi9He9xK+lCiITFIbObiAHf84zMJ33Ti6qq2sazcaT/jzvEdncU/gV5Zxe13VtoiAH+L5otFeYw2oERMbNpw/0/ZKyWVDu1u+/07C1vQFp+ztt6tSm/uakXQGxQiCILQWSdMklFIXAw8Bh2H8XlOA1lof0vzLazySJmGPXZe6ScKleRed8vHU3d+DLH91wvagJu3u/02hfMAYyNFYtIZedS8nbDdTD7qqrTGjehP3b6M68zA6d+sPG/6dOGlOefmwczFjNo+KCJmjD83h6x9qLIWNY3rEsU/a5gqXDvwLd75eQV2g4fEjgtgu7ssFWsMO3OUDbwrlO6dETDY+B5Pvm8R4/+NpebtD2n5oSjx+rbjD/+uY9w1i31tF4yPZ6nwdybrH/t9RPC3atJpOmkSy3Owk2KWltCVBLGkSgiBE40YMfwVcoLVOMca/bSBi2Jk+d8+LqSiadMr2sWLS2bEbU8xQLS2v5MLS41KKNzM/jnYCxcwThqbJrDVzdOOFbvy5Qxr+GjzT0pM6NeM5rshYmFhBVV5Kut/D+C8Sp9TZxWI5iuF21gkMGuhd97KlfSNGEJsZtilgvj4P+54iUwUcjw1pRQhFhkr8QAXx0LduTuRKRPSXiZ20R2vDjmGb4qChUufztT6c0zyrXQtYw9McinyhgfQ+N7afV0Bl58G5D1kPVqkogbduA39YHCoPDLnaWYzGRw+GpxW2CHaxgCkI4qTZ2G0AEcOCIETjJk1iy/4qhA8KXiw2kh3MnxeLU7r7jEsH4Ysrtfk8isnFAxIPTuKHTDi3y9ivaMy0gnhMb+9rwR8b8VyhIib4r2VTKJ+QVml7P5WCSRkvxWwbn1GSIJY8Cq7wLoz4naO53PuetZVABxn+zeOWjxtpXoxL53gs96+Wx8cnSsRj52Nesm67keSx5Ex61v6V3nWvsMN3uOO5otlBLmWhIn7nv56Adv7fRZXuwpzgGYkpE8DswBkxlpyyUBFF9bM4um4OJ9U9xZD6pxyNL34ymB4YxdFqS0qV3AwVwhM11ntSxktpfYGye0wFRv70W7cawjeaihKYe0ODEAbjysGyZ+1TWKwyuNe/b/3vOs1kF0fsYgHttguCIBwAuBHDy5RSryqlLlNKXWz+NPvKhOSk8ovThpGDC5hx6YkUdMpGAQWdsplx6YmJSRIm5880LBGTq40/HSpcVTtr2UGu5T6zgc0tShnC5pfef/JJ1vV8nXU54zNKDIFUN4dKnW95PzePEW8B6GqTOuCxadQyI8usONImeCWotVGFW/ZsQ7VWBxkZeCdBECe9vJzkOcbH503aewlBF//0zQYzc9S1x+F5hrTRpLY81DdhOVrD8lDfpI9XZfMeAmSqQMTakC45qr7ZxkHHxKuZLJoKdq+ZXdOp3TCa+O3mVZqoz46jyBYEQRBsSTp0AzgEqAGir5lrYG6zrEhwj9tfnEkYObjAXvymSWl5JQCT/WNtL7Gn49XMUkGywlPQohu3pgdGJVz+TndEr1N8mlUSQhAPGTaiZzNdLLdPzXjetto2MvguI6f9LXFHr9Mt39v/qoGW57GjLFTEo/ovtp1xZtOcUtBF7eEx3+P8kccJ4rEUxDpsISkLFbEq8+oET69Hwf0Zz1JWn9icqGjQ8lbvYTRG45v1e+N2UIubz0Pao53jc7NtcrSNB2lktJlTsksrD9NINihGEAShrZG0PKS1vtrixzrMUxAwhPC411aiIXKJvbmmfueoesZnlERsE9tCuUl9x/HEV6+d4tPMARxgVEwXZ96Kh5B1cVZ5WXLUTZbnGZ2xyH5BdkLpyjJDEEfT63Q2X/i3lHN7PQ7l5PhTeVRDZT7+fQxpeCnKS52rrBNCordPyXiOr7LGsD7rctZnX8Ej2YZNxXwP7ewYIRSLQoMSItJCGtaECgiG7TJa29tG3JBuQ9135Ee+BAINcWuWD9LIaLN0kl3ckJ/ob3fcbkGyQTGCIAhtDTfjmPsCfwEO11ofr5QaCBRrre9r9tUJ+yUz5q/FH6VGykJFjNclzTb0IHrU73hKUClcCq/XGUz2j43ZZhefVqMzI01YF3oW86BFFdN81nt1Fg9n/JpBg3/JmLztvLJ0I+epf3Onz2gaU0mGcwDwcH/Ys7lhe+6R8LvEavLI8J/xaRJOOFWznYgftBGfspGMKRnPxUaZ6SAX83/sywxyT/3VkQg0qwpxhgpxqfcDXgv+mBGeFTERaVaJEU09ljua+Opxjc7kAf+lLJi7CjCutjBiouEZtnqd7aLNbKr/CV+C7OLyGiuyb1na6DQJQISvIAj7FW7SJN4HxgFPaq0Hh7d9qrU+vgXW12gO6DQJK88wtGz3uQW9JsxLkHrNKU6261xqdDtXUVlBraimPZ3Ym1TM/TzzP9zG3yxF1+LMWy2j3Sp1PsPrZkVuZ/u8PHjxCYz0LjGarPxJpsWB0ZS45h+xQtgkWhDbpA7YReZNyXiO0d738BJCK3cNA1aEtOLoujmR2y/57uc0T8OQFrvUhWA42cGqih1SHnrXzo58boo9i5npe8IymUJrQ8zPCZ5hO3Eu2TkaS1ArNusulp+Ngk7ZLJlwhnFgc6VJpJjsIsQiaRKCIETjRgx/rLU+WSlVHiWGV2itB7XEAhvLAS2GoXVjmGwYPu09yxHBzSFONODXXjJV8svDZiybUyUzEvnl2ca+7COYUH2R5fFfZ11uKeqsotoKOmWzJOtWqLYefx2DWYWb3NH+mMnVjl+Eeq+5MWGYSkJFNkK0a9cdm0L5FNUbgt8UwjGjrdP03IY0MZnOdq9x9OPU4SUL473frnOZEhibNCavseg4a0g8ClhvESFWWl7JjPlrqdpZS9dO2Yw7p1/jvPrRcXnKa1SbRQi7QsSwIAjRuGmg26qU6k34N6ZS6ueARclKaBWaWfim8wt83Dn9GPfayhirBBj2g8ewjhpLFwVJhbA5OGKyf2xSIRwtnHJqN/NY5uMMCXyRIHzsGrmUItLUVxYqMsR1TQnsS2IRSVXMODRPBvUNCZutJsMBBFH03jfHtWiMtooAlpm/6XpuPXHT7JwaGc3HaUfDe99F7WGG78nIaw8NlhezIu60RjciXmv4d2gAIzwruCLrcsuBLF07ZSfcr7S8krvmrqLWb6y3cmctd4UtFcu+2Z7eEA6HSXeCIAiCe9xUho8GngJ+BOwA1gNjtNYbmn11TcABXxluRuJ/gUPUZf8kgvjjsicp+GQ6R+jYCW6fZF3ffPFWCSgqdRce8rvztdpZH0IabvMbjXCTfS/ROZxmobGfhhb9z8pRYHXsDr/9NHF7ssqww/7eda8kVIbXZ11uKwDNCXzJrAnbdS5ZKkB79gHGbadBGY1hUyifRaFBaY1KNgd0pDpkI5kY1hr2kkUmwZh0lOhBMBFPeMduhmc4PIjD7mpJjs9DjcXUG7uhLELTIJVhQRCiSSqGIwcq1R7waK13N++SmhYRw+lj9ws8xhNpRUVJgj/WFAyTMl6ii6cFxHBYZDpNw4rH6bL8TjqQo2sTIuLSjuGKxqoqHN88Z2J6hh3E8L2DFid4hr/KGmMpcgPawzF1syO37V6DkIYAGYnPH/tIs5Dy4IkfTw3GPZTHMfkgpBU7ad+oL05awx6dRQePdcJF/LGQ/nu5XeeSrerJJkp0e3yQ1QFqd7AplFqzoVcp1j34P6kvJB2P8kGIiGFBEKJxkyaRBVwC9AQyVPi3hdZ6qsPdhAOAKgshbLm9osQYMFC9yYiTqt+b0ChmRqB1bqmq8K5KmNyRr7KSN1qZOF2W78RuS9XXJFXR8MCEd1dv4fod5sjlR1jW7hby2d5wXHTznEPqgFlRjL70/nGXCzl1+xuxD6thTjD2S43daxDCY50VbfOUtnQZxrvfd2CMZ0GsnxhQvhzw11CTfST1tXuM1zYOjY5U4NNFKcgluRCGhni9vDQfszN7El+LkN+YTkfDBLxoC4cT8ZV9V1SUwBs3xn7JMCfegQhiQRAEG9x4ht8EqoHl4PI3i3BA0LVTtmVlOMYTGV8FdmgScxqa0OSEK5IZKsRY70J6qc0crbbQVW219HmCkS/8mO/xlDN7m4ozav4BXB65XbjvTwzvncec605NPPjKMtvmydFPf8iSdQ0ietjRnTn1uhfg7byYhquXAj9N+JJgNfiiRmfGVjyT0et0fvbNLezw+wll6IYECzCKwuGqZU7tZjJ0htEEF+f79jbRe+B2yIbWsFofZemBbqrHiWRih4ePZPu87AsELTO4vUpRWl7J3XMrIjYKpWD0UAf7xKKp9tX2NjCMQxAEoa3iRgx301r/rNlXIrQadk1y487pZ+kZHndOv4Y7L5rqLi4M2KHbJ50y1hwoFdvo1U1t5THf4wwJxjbG2eULu0lccO0RdsBqrHO0qI3gkAMbL4TNc4x++kPmXBfbcPXu0x9C3LFloSL6dc5l5I7nOJKG2LDxGS5yoqNivXZMmEexZzEjPCvwEGrwF8fdJVMF2K5z8ek9rr6ExAvHxkyUM7ebk/ZO0+kJ4VQo8GzlQs9ilh1yFuPO6ceyb7ZbxuANO7ozt5esiBkeojWRYy0FcXNOvEuRs2b+K2YKnQzdEAShLeNGDP9HKXWC1npVs69GaHGcutzNJjnHNAmnX8Bx5IVH+9bQrmm8tvHYDSIg8bE8Cq7wLmR5qC+AEacWrhrPDp7JGZ4VdPWEc4tdRI8pZVRQl4X6pB01FnST/BsvhMG4/aehcMtSa/FMWFTH3XdOfn9G936MJeu2N2QQqxCq2suLwbiqcQDbsdoRoqqP8ekUXRzsManaIaJfy5A2vqqEUHiV9fu0g1xqQkYOdQgPXkLG0JE4D3U6n8dUP8cK+GP75+F/BsPAMyL/luLTJP655gfbKXqvLN1oLYY7drO/MpPCMI74irSJ26a+eCEM8OX3ezlr5r9EEAuC0CZxI4aLgKuUUusxbBIK0Frrgc26MqFFmDF/bUzlF6DWH2TG/LWMHFwQ+bHF7hew8kSsCpFNyvjw5IbTCJqcFKtfHgWTMl4iW9VHRFs3tZWfqw+Y4L/WqIZapEvYCaAcVc+PPJ/z79AAfuT5PCK6rCq+VueM9+9aEi+Ek20P807mONhaGbtx6xrm5N8GRcNh2cKoxQQZ6zVum4LYnAw32feSfVNb1Os/IbOEnBSsFW4FpdWXmoA2fOGjvYvIiBPEddprGan3ddbltBr+WsPbO/d66NiN+0ZM5L6Ro2IO6eXQ+GnrJx4xMdEzbGI38S6O0vLKhIq0iWNVOop4IZxsuyAIQmvjZgjVuUAf4GzgAuD88J/CAYDrJjk7RkwEX2yuao3ORFumCLQMqbQe5ak9CZYN09vZ1cEWYPcYGSpEkWc1XkJU6nxu999Ipc53XoTysiDnfMsmv+G985I9BVf0V5XWO7auMSq68UtSRjYxGFXeT7Ku54++x+nMHvvXN1x9LC2v5Ai2uV5bY68QmGOa5wRHsF3nRjzA20K5jPPfYNmwtpkk70mYZH1sSiVeN7Cr6MaeOAho44vkW7ca3vsorLKKTbx2L9jAUXDRE+BrH7VAT0pT6WbMX+u4/leWuhgcIwiCsJ+RVAxrrb8BOmEI4AuATuFtwgGA3S9du+2l5ZUMn/YevSbMY/i09ygNDocLZlGTfSQhrdgUymeC/1oqQ+7ERnOgtVEtNP/8on0hdrkHdrqiIDza2YoQylFxe8I+1G6erTzse4pFoUHU6MzYcyhvzJrOHnB4jPB9yXc/69tdzpzKnxkxai8W2z9gFLbi2Ulw2lTUvSrEhZ7FPOx7KpInbFb345++1vA3fSb3lq7irrmrqAp1cbXepiJH1TPCs4KT6p6iV93L9Kp7mSH1T9kmNzzkH5XwnsQL31Cy3OHwf81DzCzmxaEBSUV0DP5aw3sfxbhz+tl6qC8b2t3+XANHwT1V4Szqapi0I6XGuWRfgtNKuRAEQWjjJBXDSqnfAHOAw8I/s5VS/9vcCxNahnHn9CPbF+snjDTJVZTAo8fD5E7w6PF8XPYkd81dReXOWjQN/uLS4HDO0o9zdN0ciupnURYqYnogUWw4kervWKfjq3Q+x9TNplfdyxxTN5s/7zgFv9e+0maFKfys8CrtupqZqQJc4P2ICf5r2RTKJ6QVe3Q7lFkZhEi02pwjX2PDtPPYcOyT/Ni7Ola/rn/fEMT5/a0fKLx9znWnJgji4b3zHLWwnZ9UKS93Zb5mG6lmVmAD2sNLwTOZsO9KZn/0LbX+INMDo6jT7n2qbnF637sq99XoslBRzHuyKZTPS8EzY247rSGgPQmvqeEbb8fRakvq1e447/3IwQXMHDWIHF/D/6KVav5hHE4VaXCoSkfR57D2KW0XBEFobdxMoKsATtVa7w3fbg98uL94hmXoRnIs0yS8SxIGZ9SSxZ31v0qothV0yqYqLJCjKfYsdvaYRhHU9g1QqWBOizPX6HbMcHMSPeUN7AdgoLwwaXvy6XN2aRIOKROWjXfmMT2Ho5c9GyPuNKAKf0Xo42dtK5QhrTi6bo7tUpty2qA5Uvut4DBGe9+zfP02hfIpqp/VJI8H9hMJN4Xy6aq22gwnMermKcfz2U0itODe0lXpjW92gZNnGNJvomtraRIydEMQhGjcNNApIPo6ahDni65CG8UuQs2ySe7RxMi0bOpiclJNzPNZZRJnK7+rtSl00gisZIQ0/DV4ZoxYH59R0uxCONVEAduGOrcNgKbAjSZJyoSTWD5r5r8YE/gmkgccxGhIm/1FMX9VZRzBD5bLqNLOVojGDs2IZge5nFT3FADLQ30ts5DN8ctNhV3mslPUXJXuQo7al/rwjl2V8PbtcP5MSssrmfLWanbUGP92OmX7mFw8gJGDC7i3dFVMFFtQa9eNbW4w/z/QmDQJoE0JX0EQhGS4EcPPA0uVUm9giOALgWebdVVCk+MmQi0Gm8g0q0vRprCOzyS+01dCtss5LVU6n2z2WUZwhVB4bUy6WhuuTTMPN75q7dQE11Rs17nU6naGz9hCFG/XuTG3g3jIsBLEKcRfJeAmZcJKRGN0+U/imsQGvu/3svFH48hbfneCVaIer634vNCzmHEZJZb70qUze1ifdTlBPHgIsUPnso9MOrHX9r1vLNEJGqawr9WZDPF8QY7al/AlyBTKD/jS+N9jeFLcuh/2Mu6rC/EHGz7vO2v9jHttJWDfwGYXtxafO207xCWKpAkygiAIBxhuGuhmAlcD24FtwNVa68eaeV1CE+MUoWZFne8Qy+07ifX9mf7ikYMLePDiEyjolI3CsE649XBqYKm3kKmBsdSSFbtGstjQ8xfYXYyoIp/eUV7laIo9i109fmOo0ZlMCYylqH4Wv/HfREDHrjOgFVMCYyO3FfBxlwutT2bGX/U63Xq/3fZm5OTiG1g55AF20iH8xQPqfB1ZedKDvKNOSzj+oowlzMh6lm4e6y8G6WJ6uDNUCI+CLp495LKP2/y/tnzvbc+DUeGc3ncNS7Ju5eusy1mceavjZ6Ud9Q3DOTx7GOtdGGkohIbkign+aykLFdG+EYM6j/qmJEYIm/hDmhnz19o2sFltdxrAIgiCIDTgpjJsYmTbi0VivySVCLXS8kp+XB8iy+Kdbp+ZQUF2dsQa8dP+hzJj/lp+++qKxKEcjzoMAYhCAZccsppLfvsqVAym5p2JtKv9jqpQF57JHMOgQdfT+9D2sOw5onMMasmiash49H+szzs+o8TSu9mYgR9aY4wPDjuHauOaBI2qbzDmtol5ufvUwecljEZmyFUNXf8Oo5bTpTEezpOLb4DiGyK3s4DHnv4QfzBWaHXK9nF/u7lk1rbM1PZMFeCPvscZr0tsK8PFnsVMygj71sPvuVrdHoL1ELbwdFNbmeZ7BvwwxPNFg11EeajDl5CXHP/ZUQry2MP4jBJwmEniBq9DJGHVzlq8SlkKX6vGNscBLIIgCEKEpGJYKTURuBR4HePXyfNKqde01vc19+IEl1SUGNFM1ZuMIRgjJhoRS1HYeXq7dspO8BLX1AdYzm7Lh8ryV7PkHmM4xP9n79zjo6rO9f9dM5kJSbDcomICFKRWT1UqlRYs8XKk1LYqoq1YhaJtvbTWYy0VhMqPiwcFxSL1tJ56qW2paI2KMV56LGJvcJRTKAjaaq1ihcQb1woJyWRm/f7Ysyd7Ztbas2cyuQDv9/OJOGtue2Z2Ms9+9/M+75/r76Zq/RXcxHYao5Xc9q9JTH90P/PqX2FPc4xLe3+Z2eGfUhIPMGRjz1YnsWL4fzB179L2KnYrqIc3sm7M11lwwZi011k2bg6fHjGJ8AvPGAVCIRaJXEJZKYjqeOo2A9TelJCaUVJLqUqvvpeqeMpn/f/4GRPrV8ITBgH849HpjXOVxznNch5G37yS9z5sF2ZHHhZl7Y3j229vaZDLNRHsmCMqjAMRbN3/poojwPFVh1He8K7xPp2FUuli1iuIJ4RWszhyd9ZnQiz7tZarVm4u+Rm9VUvqsy0hQThgldfdjsWRuwt+LQBxZT9Z5x58msY3+8atCYIgCL4ESZP4GzBSa70/ebkM+IvW+t+6YPs6zEGfJrGpNiv1gUgZnHsndfGxKZHbpyzCvta2rFOw5ZEQsbgmltE+buukf5fDefG8PzAxvIbmFdekeYKbdDR1qtjl5ujPuSS0MvDpBFtihQLuuOgko5cxs6kIHCG0JPJTY+rAXt2LCvYX9TR+roSBX8XHMTX8XPZzjvomvLXGnvSQ9PlmCmGXNEFsaZAb6jPN7K1FZwOWynH0hvwf78gbjGcD3Azefmpv/kkLAWnQlYxtaU+TsO3DNjTFOe3VkTMPb3z0q3whwzMMEAkpFl/4yVQTXZA0iSCf+6GKpEkIguAliBj+DXCx1np38nJf4AGt9TmdvnVF4KAXw3ecYBQfTWVHcbK3worzhRotCbGvNTu1YEJodWrqWqOuZFXiJC4M/zGrk35m7HJWhk9nfe/rKG9+J+txMuOt8hUkpsdwqe5bxpqZnpHFHiuBBv4UP4GpsR8wMbyGW0ruNaZItOgwMUrorYp7Kj+hnSZAWxTXQLXTHqfmlyKRrA53RNgUdF+fKLah2+bYH++SfVkHZ+5+A5grtUVC49hSwjo5BlslukXcFiqG9+pSTmj5OWWRECGlUr+n3jQJnppmt9dkYKvgB2miO9gRMSwIgpcgnuEW4BWl1Eqc75vxwGql1J0AWutrO3H7hFxYUh96Nb+b1TAXS2gSsWxBlpnFO0ht50L1Rx6Jn8a40Eaq1I5Uxz7ASvUdyprNAjezaa4Qq4Kt8S7N35zhqVXAaeGXeetjd8PON2GPOU4tjCbagQYnGxp8EwZ+FLnLcsfOEYYdJkg6hQnXnrPqJvSebTTqAdwac/y8q6PXdpoQBmcfKCEBCnNaR4EkULyTGGBNCykWrje5OZYgElIszTwT8tQ0WOdJqkgOawGMgnj5FacUlCbRaRTZBy8IglAsgojhx5M/Lr/vnE0RCqKPuUnNNg7X5K01ZfG64229FdogAywys2cbdaUxj5U+SY+jadszHmN+yf1OU5NKwPxkNcz7peplyx/wO9ltrM4WgRCkDZjQwB4OY27sa6z/yHh0y09RpuaoXJXhoPgM3LB5gpf2/hXMm0zacOVR38w5vnfs8P7WJiynCl3B2OH3svx7p/DnDQ2sf/Y12N3cJTF3wTENlc5Ga1iT+ARHq/ecW3fAApEL737vpkekieH1vzDfcf0vrJ9Zj6kAZwphaJ+qKIJYEIRuJki02i/9frpiIwUfxs1xPMJeImXcF52Suji/5H7+UTqFLaWX8I/SKcwvuT/t5jaRUp2xnmuAhWnwwQ8TFxFXkfQbhqPQ/2hn0EAmkTL+d+jVKTk7v+R+poafo8Q95e2thtnoM8j/+k4gK2EA6MuH/OjwJ1nzpe2ETv66+Y4nX5ZzxDI43mATRx4W9R+4AakmOS9Le/+KiW2/IUsQrvuZU4H0wTTyORM3wmviyGrWzDyTpRedxDvYRxznS6suYUeiNxrVfmBlo6x/9uWyfjmfI6FCrNEnMCr0OoNCjh88lxDWGvZlxAN6r7Nh+t3Jani1HTT11LMLXnwPXgVBELqXIGkS5wD/CXw0eXsFaK21OYhW6Fo8p6W9aRInxcdStmIzM/W9aY1bJSSYGn4OcCZ5zSiptdZRNY4YdawS2+23007zkn3wQYYKiMfMX4KRCjh3KZNGTCKaTLiY3Px8/pW4cXOymwo7EV+P6J6tzracm6ywu35PAJQjPlUYSvtAiyc9wjtKGVh743h7msS8r5if2yOQs2LU5n/N/oLW/8I3nQLSK442T7K3ejxxZDV/3jqDfutnpx1QFeKv1Rquj11JfaIGBTz30XqG7/m1+cYqDDdsyV73GXntNuKFleLF8u9SHg8+wVADLTpi9aS7gngXvdEa+in/oSGz6za3N8fZziJ0ZFhLPpiquwHOJAiCIPR0gtgklgIXAJt1rm47oXsYMSkliv9cfzeDV/yACfoDPhOu5Eh2ZIlYpWBKeFVWg1wmIQVfCz+Xs/u/QZsb3gC+H3qYsM4MX7XsRm37U68jNQVrXp62hmGntx8g/OYGaO78TNWcYi7W7BysfO9lRzikvJ/J90HHHSHsFRaGqLW1N5onyPlh9Yz6VRN13Hd8c6F8esJVzNvUyOWtD6TORhSSLNGgK1PCcV7J/Rz9z+fsN9bx9vcxWgHnLIUND1hvntBwa8zZf+JaM6Dt/bwiJhp1pfVMi3dIx5PxMdkT/wykTZY7+TLzWRF3WIuNYnh1TUIYfD3LgiAIBwo5bRLAVuBlEcI9nz/X380J62czkA8IKahiOyHLxxZC+wrh1O1yCAHT6V0veflEdZzJ977A0JlPp37itl1UhdMmsmngj/HjGfq3qxg682kmrx3sVAQvuJeET3Zrl+FtdPTzfkJO20NQNNkDFlITyPyqie5116x10izcH3CEpfsTYHtm121m+KxnGDrzaYbPeobXj/wC4/VPaNSVBQnhVl2Str9NDj8fXKu27oPHr7KemtcafhX/XFqFtlEHt3a06hLK2J9ze5RytjsIaR7/c5Y4B0zu56PCuSuzfl7dfPCzM9j2Zy89aKqiIAhCJkFUwgzgGaXULKXUNPenszdMyJ/Bf1lMmfKfllUMtG7/KaOVeZFlzC+5n9XR9PG2E0KrSQTaxRzihLLE2wNtZ5rryJFeMHIKzNvD5Or/Ydj+B5kauzF1tSv66uJjmRG/miZt9tx2GV4fcy7vZ75JDhbP8auJ7ExmSApkv2qi6TofgW7zDx95WJQHXnw7JejiWrPmjZ18akgfqkLBRnVn0qLDaWI1nG9qhM+ENyCrWntb26SsfadFh9mpe5PQ0KZDqQxljWZAaG+g37mg2+2dLDe7bjPD13yOoc2/YnjLQ8z+5B9yV2S7wqsbxLN8aX228JU0CUEQeghBbBI3A3uBXkA3KwrBjyP0B8ZTupm+zKA1fpufM3OtP3vTfMnuJC6Fyitb94G2M7PWXHFyackq0uwVrfvgie8AsOYN86S0NW/s5K0dzTS0fpbWUCKZo7yDXbqCw9R+oqrdvtGRQQk5UWGnWXBen2RVz5JkUKj302Jp+KJPHnBKRGWMuLZWGn0E+vJ55givF9/cZbzLi2/uInREsFHdmfRWLayOXpvKw06gCAdIhQiC6SxEfaIGFYMZkVqOYofV37s6ei39Q3uz7m8joeDPFdcxoO19Gn389u5kuczBMnGtU5dNAze6lKD7rQhfQRB6KEGGbqw7kMPJD/qhGx7enfcxBvJB1nrmZK18hF9CF+brtNFGiO+3fovTSt/kfH5LSCdSwwOGrh5nvd9b/a4x+3/L+jN014+t97MFaE0IrWZeZBn9CFbJC4rWTnNUSCn6shci5cbxv0ZcIerT3OXaFYwT4zKa5AoZtmF93ADblInv8xuGcwQhc19OEOz0VhD2qzKiiWZfcWqiLBLmldBFgX9P3OEg3ixk0/TGKWOGpITu8FnmkeNhpXhj4ZfsT1bA52bE5hmGA7KJToZuCILgJcj3yHNKqc93+pYIHWZL/5qsqq/W2cXifMXfTt07cDU5FyGteSJRw/ebp3J820PUnfdXmLsz95eprREuR4NcVd8y43p9ooYm3avo1eAGXcmnWu5h5P67Yd5upynQhs37mSNqLVOwArz+/j7GL/l92prNvmBbD/q4RtyGP4+fOGx5c8NKOU2O594JfQbnVdfNfETTH7B3osNY1jbevM+qEIQihiugl24mpGBQyDmzcV5odc5tqe5bxsILTuR9dXjObddJW8U+3StrKEi5amVGSW3qslcIgzkf3Lue6bWffO8Lzg2K5dU12RzggBTCgiAImQQRw98B/kcptV8p9WHy51+dvWFCftRtaGDIjj9l5912UOxpFBU0F000egcLNMfiLH72tdTlfMVbkPtNP+tYyiLm07jFHgKR0KSau1Ii3M9POXenU53LPBi4Zm22IPYkOZgGaJjWTXnAY4f3Z3nLdcZGON/H9RPoFj/x6sN+YLyLe+qfEZPgey/zHmYhWejx1+Et/2RO29dZFv8ccdffDk6axPl3w8S7ktnEKjuDOEmpijM/+ivrc0wIrWbLkTewZv8FTPz9Wez76Diac/jSG3QlH2t5gHLMB0jeyYtPvZQ+6tzvwMI0djnVKFlMr+6l9ekNlfP2iBAWBOGgIKdnWGt9WFdsSFCUUl8AfgSEgfu01ou6eZN6BIuffY0/EVzcBbVKhNAFj9C1jSb2ctXen8D8r4COs1yF+W2/L3LlrktS16eiwG7tb60CL2/4AvFeIR5oOzPlMR47vD/LR2+FVVdwXngb74UruaX1Qp5M1KRElnU6XoE00Yv6RA1lkTDTzzrWWfTLhvWLvOpAhJmXrAlkvkkVPh5jv6g1y6n4o1q3MGXMEB5au5W41txU8nMml6wivDEBLyUnCZ6zhIWtF7IwY7JhR+w5bnPa3LZvpPaHsFK8MeynsOKK9hsOO90Z3W3Zr/roD43rE0KrWRL5b9iT3JP2bGX4vx7hjaEXUvbP5xios2PjvPu+bb/zHijubo5Rt6EhNYHu4tGD0zzDLrZ18CSJiFdXEATBl0B2O6XUBKXU7cmfczp7o3y2Iwz8BPgi8AngYqXUJ7pre3oSjbubrTFQmS1sCQ2v6uqiWR9sj7NT92ZbopKEVmxLVGZ5IueX3M/XSp5rF4s6zuebn+KtmlW8tejs1NjfoTOf5to9XyWGvVEnTIJLS55L3Xf56OSwiz1bUWgG8gF3Vvyc2s9uo1+5c5r8trZJtOjsxyz0fSmnJXXaPDVG15baUHFEcSKvTBgsCynyTarwkhm1FkCwL5h4Im8s/BJv1axiasnK9hQFd5LgU9NY95HxzIxdnravdARTI9zPSxaY32+/Jj6LGL8l8jNKVKYfKc7wxiepmvcG9RP/yk2R65zXg6KRSmbFLue58GmAOaHCdKDoPWuyYOKJTBkzJFUhDiuVZaUQBEEQCiPIBLpFwKeB5cml7yqlarTWMzt1y8x8BviH1vrN5Lb9GjgP+Gs3bEv3s6k2NXnuhV6VPNt2Eheq9EEazUTZwLGM0ZtTlaqQgo/yAfsopTfmSVkuQarHSmVX8pp0lPltU9PE74TQ6rQkgKNsU+3W/4LJ71yYduq3PlEDrfD/yh7l8PgHWE+ir/+Fc+p21U3ZzVmxZo7/2x3sjy1tf8wYzC1ZRn/lJAHsojfv6T4cR0NWAkeu9yLUdxBrvpeRiOGeRnYnzyWbBa0jpQNEXh1zRIXR0nDMERX+ld8c4tX3cZMYG+xybjH2LNp19/Mn9XNUJEGcEL+Kj2N94uMsifyUUL6xaTif0/8m/i1r/dTQK3k/FijeLJ2clSBRYZku5zZKThxZzcSGD2H9LtCaKrWLrw58hycandfj7nduuoktoaLRO475qWkseOkXLCj17ENiURAEQSgKQdIkNgEnae0EdCarsxu01iO6YPsyt+UrwBe01pcnL38NGK21vibjdlcCVwIMGTLk5H/+859dvamdz6barG78Zh2lNn5acnzyDt5hAItjk7i+pJZBoezTssWME3NHMler7cQJESKR1pU/IbSaW6P3UYZnFC/24V5D9z9ofa63Fp1tPTWvgZpej/On5vONp9k18N3Wq32TAlZHry38/SrrD1+8tX0Kno1cXf6pKXUePDaKglMfclzvl1JharADWNXrBoZjqLB6J9b5Pa8HrSGuQlkNZvmwLZE9EXFLr0vswzDCUcgxcjmmFTdHvstJZ1/JxCd8TkZZPjutYVn8c8apcxXRMPtas6001X3LWDPzTPO+AKnmNZNnGDwWowOAIOkoxUTSJARB8BIkZxigL+D+tQ32rdY5mL7PstS81voe4B5wotU6e6O6BUPls0y1clbJS5yy/06q+paxc18LzYkES9Rdnb45DUnhu8jj/RyktrMoch/E4IZIbZoQBp8pt0FySy1e3LgO0bC7mcao2ZepILVNNkHcoca65p2p/GNGTKJuQwOzVmyiOeaIu5CCS0YPYYHfY9jEj2ujuLS+cKFQeZzZEpFskPN7XFuD3bj9t/LWoJv8RzfbvNMZKEWHhDBAdWgHyyK3cGro5eSD2vc1reG7zZenYvZst40ozTx1L4ycD/Uh8/AOd9KhoQruTp0zieGbzz+RWSs20xxrf3/SfOd+EwvPWcLyK8w5zzYhnM9tuwK/FJPOFMSCIAguQcTwQmCDUup3ON8TpwGzOnWr7GwDBnsuDwIau2lbuo9NtVav40C2syWZIevmvNoadopVFW7RYW5rm8SMktqsEc/lqpUfRB/hSPKYOHbyZeCfamW0GmhgedyxKWQK88xtmlFSS31rthieEFpNQoWMp+h36t6U0Zp7jHW8FVbdRF18LNMe3pj2SAkND7z4NlMHjOLj+wz518NO9x9v29HJYX6NcOB/Xa7HxRkO8dDarcS3acKznuHi0YNZMPFEXug3gTE7Hu+8wSYeVCjCabyc83Zaw58Sx1OfqKG+xdkXwiHFP6IXm8Wzmxd98tfNBys6AfP7W0W/aepcWKmUv3zxs6/RuLuZ28uWORncTySg3ucgwrMeVMz6JU90lyAOmo4iCILQWeRsoNNaPwSMAVYkf07RWv+6szfMwp+BY5RSw5RSUeCrwKHVKr2pFuqutl/vHfubxNSwk4t8mshiRKhP1FgrqkewnYbEAON1RCuMebu+MWu/nJAtRlSYX7W1n4auT9QwM3a59XV4Y6xcJoRWsyhyn7Ey6XqgvY1evu/Rnm0sfvY1a43zizu/b4+8CjLe1kaOjGLA3gjnmzSRG3dKmnf88gMvvs34Jb/n4sYLWRb/XGp8cZsuxBGcjvHtD0UgkeNghXYh7B3hDRBP2D9UrZ0DzKGrx7Gs7XPGRj2/z850+weOqoX5/Zn4xCdY0/IVtgy6iS/r/3GG0eR4vEImFprsFH7rgiAIhwLWyrBS6izgMK31o1rrd0iKTqXUZKXU+1rrQH0zxURr3aaUugZ4Fida7X6tdSGdMQcuv7kBEjHjVc2UMvODc1m36Pn2U6xkN+wotLFC5+SxKhr1AN7UR1ITeiVQvFUF+5lfcj8JzBXVxsQAc6U2UgbnLDX6a62nfqMLzdXRoTXc/c53wNN0VJ+oYYauzRlj5TIzWks52UKqTYfSkjDcivLq6LX2aLY+g2h8zz5dLa61PfIqgKVg9M0ree/D9m098rAoa28cn7PyW7ehgRsf35zyqCpgsptKkCNpIleD3UNrzWcr3Pt4o84Abir5OVNL0v+MaJ2czuYZ420dC565EKmAc5emx6dlkpy6NsxnOl5CK8KZaRFAwvOMc9q+wZy2b/BGrynGiq+JN3t/inCrIq41YaV44KhaTtn5ePsNdDxYqoeLLamkmPjF/wmCIBwk+FWG5wOmc7KrgJs6Z3Nyo7V+Rmv9ca31cK31zd21HZ1N3YYGxi56nmEzn2bsouep29DgXGHJRNUabmj9Jk8kamjY3cysFZvTrq9P1FDTeidHtyynwRLBtoveHN2ynJrWOzle/TNwzqtS8LWS59IEjIsbGeVWat24qQ/CR3Dtvq8z9MGK9IlZOBXG4bOeYc0bO1MRUm8tOts5jWuzCWz5A9PPOpavRP+X1dFrebP0ElZHr+UPjKQt3CvtpgkNqxInZT3EQG22coTQRn+xLZqNcBTGzbFOvwP7EAXAX+QMOz1LCAO892Ero29OCktL5bduQwPff+SltGYtjWPbmF2Xvr+YWDntjLRkCUhvdLJNSbMxp+3rvPHRr9JGe7V4WfxzTIt9K1V9b9Oh4NaK8v65GxeT+L3/daGzsta0hgfi2ePCQybvsIWPt77CGxfv5a1FZ/PGwi9xyq48RaVtYmFnYRrBXKz4Pw+Z+1SudUEQhGLj5xku11p/kLmotX5XKSV/pTqRug0NrH78Lh7m11SVbqexqZKlj38VuJqJPvfzCjZvM04mt7VN4vbIPURVW9p6X/aypfQSGnRlKmosKKajqqyKaqLG6NOFdt/isMN7pw0RcE+1AzkzVSeG13BO5D5K4s6Er0FqOxeF/0TJkNGw5Y+4J9ZDCi4M/5H1iY+nvWeNekDgKnJFNOy8loxotj3qMPqetwRGTGJ6vCHLM+ziTmEzd9EnRY4lTeI9S1XTFci2BqnFz75mtQE8tHYrC0qNV6Xh19AUViovQawUnPPm+TTH0sXVhNQYZE04nzl0e7Y5/w473XzQNOz0lMD7RylgsEqEQ4rwhCXQUJ2Kw2vTIZbHzzQ2v8XJI/ki1gyPfwtWXOnYmfKxw6iwM6kwyeR7X2CNZz8I2gTnZneb1rPwOfAsJiunndHlaRKCIAherNFqSqm/A5/QWrdlrEeAv2qtj+mC7eswo0aN0uvWGRqVejDzFsxlRuyuNEtBk45yW+Rq5kWWGavDO3VvPtVyT+Dn2NjrKvpinrAFxYldS6A4ev/y3Df0YBNUYaV4Y+GX/CO6+gw2NxZabAeZEVzzS+7na+HnsvKSM4eFXBj9X+ZXPEavpnfTMmLLIuH0gRtgT5OYeKI1qiyXEBjqc4rfT+z87xs7faVlViKES+VxzB50X2qSXFipVGOcF9cznInNXlEeCdEUSxeSrm87Z5OigW2JSi4qv5fpZx3LKau/wRE7XkwZi19QJzJ8QAVH7ngx7T5e73BFNExV315p2zp2eH/e2tFMw26z5WV+yf1MDT+Xnkmd/E9RmwU9lWBblFr9YbcxIraxfcFiaQicJpErpu8ARqLVBEHw4lcZXgHcq5S6Rmu9DyBZEb4zeZ3QSVze+gDloexUhstbH4AJtzrRXfH0vN4n42OyHqe6bxlDB5RlfXGWRcL0wb/ym98XucLYzqSyhxaURcK+VWtbZTG17lf12/JH84NaKnDVajsTQqtTOcgXhv+YJoQTGh6Jn5YmhM8Lrebm8M+INreAao+P6x+JOhm0HiEMyQEMGWsundFF79cgVd23zCrqwkpZ/cazB93nW623iXqvaE6lTHjE9HKDcDYlkgRBJ60vDbubmf7IS8T1tST0tWm32bL9kiyjsVJwWvgV3vrPs61JC8ccUUEoOVgmE7daPDn8PGGcwSHL42cyLrSx8FHflcfBjtfTh7R4LBGmz3hZ5GZObH0l/fV5ovi8HCjZw4IgCF2Fn2d4NvAe8E+l1Hql1HrgLeCD5HVCJ1EVMntXq9QOhj1YwWOJM9OkpwK+Fn6OLUmf7ITQ6lRO6fIrTmHpRSdR3bcMBalxwcqQOpGLTJ2qtVORZtQ3nGa4DEI6QUhpBoW2szhyN5f1/j8WXuBvdfDzcg6d+TTDX/0Wf68wFHR2vgll/QK9DhelnMzhCaHVRhEWUjAutDFtbXpJLVGdPoGsXLUyr+KxbNG7qRbuOAHm9XX+3VSb1/bZOPIwczKIbd1l+lnHErYYwV3bhslvbGuMe2jtVt/q9hsLv5SqHi/YdjlvlF7MW70u4Y3Si1mw7XKjp9qWSJLLfaE8n1UsoY3CNRe2A4nX39/HkkknUR4x/7mc2/YNPtbyAMNaHuRjLQ8wt+0beSW4pDbV9QJfs5bZn/wDw1seYmjzrxi+5nM5Pd2nhl4xH8B2xNKQmXaSa10QBOEAxSqGtdZtyZHLg4HLkj9DtNYztdbmOAOhKOwvG2hcb9QD0MDo+LqsTvqQcgTBoNB2bo3+jGWf/mdKnE0cWc2amWeyZdHZrJl5prM+bk4+bkwA9urSVGPTtkQl341dzckt9zhVq3PvdGwKKExjC0pVnHmRZUwcWW2NTbun34P8vXQyW0ov4R+lU5hfcn/WbeJa8/kd06gdMiddgO/ZCvvzP3XrZg7bRFhmBJt1IIfrV3VxJwTu2Qpo598VVzinnp+alvd2ell74/gs4ZtKk/Bh4shqfnjhJ6mItjf9KWCKmyZhwa9aH6i6bYlseyZ8PWWR9AbERktzZxCq1fZU42S777g4TBxZzV//84upA8tc1CdqeCR+WqCIQgU0lR3leILPWWKNqAvS5FhULq23x/8JgiAcROQcuqG1bga6+K/wIcamWlh1E3rPNt6jkt+2fZILS3akjy7WUJaMMKvOcfq1jBaOWncbY/96DNPPOtZ8mn7EJJRPBFWmZ7hJR7mx7ZtZqQoh5fhiJ46c1N7Jb/MaJr3Opti0e/o9yOebn3IuJKeQTQ0/B2BsXBr7z7tAZZzyLzCft0ptt8fCZTTP2QaYbEsM4HTPkAnThMAUyca4Y46Y4BtV5odN+OZqkPKzbdiamPx83IEa5ixxYX32vcHCC05MDZuo6lvGT/Zfws36zqwkkyC2HZWcHuedfOjdX/+UOJ5Tw69kH6oFrHQ6/u/2SXHuGYUqtT1t9LjLuNDGwHajXs3vpv7frxK/YOKJ1s+4UyhU+D41LdWAaLJ6CIIg9CSsDXQHCz2+gc6tIHqEU5OO8kj8NM4Nr6Wf+jDtyztoY1tCK45uWU4kpFh84SfNAuiOE4wNZ273/LjQRqrUjjTPr4l+5RE2zPl8+4JFDGtgpHqE3c2x1P3mnnu8s22WyV1tOsTHWh7IWn+z9JLA0W+5SGiMj+V+Ds774AieVYmTuDD8x6zmRm+T3ZQxQ1iw8VQsYyEckukANgFat6GB+U++wq6m9JMwQVIDTA1SgG/TlJ/dYfTRA4yNcVPGDDGuu7yVnISYTyNW3YYGJtR9oiifbWaDZCSsWD3wR1lNdK5Ym/zOhUaROX3gRr6jHyKxZ1sqMxvIavRr0lFmxS7nieR+kM8+ui1RyaCb3gD8GyTd9zTzM85qnnPpjkqubZx4V8TBBUQa6ARB8BJkHLPQmRgqiOWqlXNCL9Kke9FfpSc+BK00KTR/Kb0SraHfE/vg94Ng3Jz0HNZxc7KEeDOl3BBzKsBzA76ETMFGWX9j4sUu3Zvd+9tvu6spxvRHXwJgYh4jbAHeoZJqCmxQ8qAtQrhNh3gkflqa8B2ktnOh+iOP69M5v/zlrDQJl4fWbmXBEYOsI7OdJ3Zeryk1om5DA9MffYlYPFtMBxmdm3ldkBG8fnYHdxtNaRJr39xRcHU7i021TPz9Teg8hbDtADHT4vKZof048opns8WajqPX/Yxz2cYaLk27z/SBG/nOvv+CWDMhHBvSosh9JFDG0ePTI7U80eI0ZGpbY2kGTTrK7fGLWJq87FeJd8n+/M+2DsgInB5RLExC2F3vIWJYEATBi98Euk/53VFr/Zfib84hxqZaq2Dqr/aicyQ++KEU9Gdvu313z1ZH+EJKENfFx7JRX8XliQeoCu1gf9lAZu0531oB9sOxSlQ7QmP/7qzrWylhXmxq1nosrln87GtMtMSfJZTZ1r5Uf5X53JMzeaBNhwih2aUr6KOajINBTIRJcE7oRaPgOb/8ZcpveNVawYtr7Rxo+E1C8xmlu/jZ14xC2MUrbExxZplipxgjeL3C1/Wwrn1zR7CM2MrjrJFtLn+uv5sT/vL/KKPF4Di306Sj7Cfq7OsZZFpc1ryxk9l1m1nw0i+ybquAr+iVzPSI4UhI8fWWXxkPVq1jvtnBhNBqbo/cY5xi58WdtrcucQzXhx+GeXdBn0EsPObrzPh79ljtVJNjkuyEjptZMC/d+x3kQEgQBOFQx68y/MPkv72AUcBLON8ZI4C1QP6KSWjHtUdYUCp3B33exJpp+s0cfhsf6zkF/xl+wWcAiLTaPaC5/KGLn32NiQ0/NFeFIhVM33epVWQ37m6GmsuM9w2P+jpT2oakvvRdX+gjrZ+lJZRIDbywjZf+F+XMi02lPlHD+uiVDMgYJmKrtKcOJgz0an6Xug0N/hW8EZNgwwP2bn6fKXONlvizTGy5vp0hdkw2itff38f4Jb9PE77u7dwDBUcY5x4R/en1t1Gm0lM6cqGT0XfrEx83WhZcO4MXZ7hIsLMQsYRO8/J6se03u6lgRklt1kAb22OUkEhPgtizlUlNi+Hj05n1+r9Zc50zP3vbcJq8DoRk9LIgCIcofmkS/661/nfgn8CntNajtNYnAyOBf3TVBh60+DVYJSlmZr9Lr6Z3mf7oS9nWBvwjqXI1SjXubnYaZky07WfdR+xJB1V9y5zTp6O+mTZy9o2PfpWT1n8h1VnfrzxCn16R1DbWJ2poppe/oFV7WRy5mwmh1fTLc6qe7XEbEwOYtWIzY442R7mlKniX1juvKf1Rc3on/cY4e7E1WkF+VV/IPRI3SGqEn2BORba5n/H2Vx2f+FPTWPzsaxxVgOVFKZgafo4ZJbU8Ej8tLekkc1DKhNBq/lJ6Jf+IXmx9vLjhz2FjInv6oB8VNOdscM0kaz+LNTNpz895Y+GXUqObM9M+/JrsCqKoo5dtf7k64y+aIAhCxwniGT5Oa51Kk9Bav6yUOqnzNukQITOKy0BRJ1gladQDfE/BF0pV3zLYb0lz0HGmn3Us0x95iViG2o6EFcuOfBjmf6W983zUN6mr/n7y9uke46znDSA8SlWcuSXLrEkQ+eBWHJsTcd7a0cyUMUP8J7Ods8QufC3V0ulnHWv1DEN7Q1zQ0cdBRvAWYyRuTsFs8Oqy7mdc1fZPGkOFfTZKtXu5vQLY8dc6749rW/Cr1moNy+NnZq0vbpvEwjwm4pUqZ3xz4BHNNty/D5tq4cnrIJZ8D1UITv46cT3OeLd8xmGnUczRy6O+YWmgy06FEQRB6AkEEcN/U0rdBzyA8+0yBfhbp27VIUBT2UDKm9+xXl/oOGS/+9lOHXcUd8AH9WbfLyqcSrOYV/9KWprEo4MfY/g/f91+26RAiq9vIJbI9hhnElTg9ld7+W5sasGjfsHxH8+MXQ7A6ui1VDVvJ7RlMAsuzmhMDIIle5cfj2Zi0j6QK00iaLSZKcpu7PD+LI8uhHntYmflsNNhWieeFrecObik5HmmtX6rQ5+Nmxdd3+qI4YtHD07ZBnLZFhIqxPL4OOa2fT3ruicSNegYLIn8NLDfPESCVl0SyCphpc8gRwg//q303ymdgHU/46aSt5lj2N7MoTVBDoSKjnvwJ9FqgiAcIAQRw18Hvg18N3n5j8B/d9oWHSLcFruIuXqp1etaaFXYvZ/WsI9SWnSEfmpfzng0G36+2ITWVPUta88ybrjMXBE6+TL45QQmbvkDE8Fxobt+xPlfNT7veYnf8n1yi+FViZP4mnouUITVjyJ3sY9exDWEC3h/Q8lKY5poMzQmAr4eWcCaveuu++UBu3gFXyaZYifLP+x3WtzgEz3miIqOp0b4JIasDJ8OMZK5vTtoIkqFyq+Zzjte+3evfsDY4f158c1dvmcPNIoT2h6kKZYudPuWRVIHbfWJGpZyV+Dt2KV7M79tKj+K3FXQ77cG9O6tJFZcZa0wTy5ZZRTDmU121gOhzm6e8zsjIgiC0MMIMnRjP3BH8kcoEruaWiFivq4Y9giloFVHOLn1nrR176CABCHCJGjQlSzlqzwRH5t2ar4sEubLJ1fz2PqG1KABd33hBSdmizVbRWjHP+zCyyaQfKpw/cqdN+7U/b/jwvAf04SwTWi4a73ZT5sOoXUi7/e5UQ8wjm0m1ux4wF0x7FP1TRPEHcS1Y/imSdiaovI8LR7ERpFTMFsSQ+I6RHMsTj011LfWUN23jJXqapTPmRMT7nhtYlC/u4ad+1r54aRPEvr9YGtqS4MekCWEAc755FH87tUPaEg2M+7SvbOaL/22oz5Rw836ZxxmaAp0m2P9hLJSGIfAuIRJ5LboJAkkfIedbv7sZfSyIAiHADmHbiilxgLzgI/iEc9a66M7dcuKRE8duvHuvI8xkA869Tm0hl30TqUpTAittp+KjpTx5xPnc91fj0lNA3Mrvn+uv5vBf1nMEfoD3leHs/VT0/n0hKuCb4jf0AWfSLVjW5YbPcYXfXowj61vYKX6DoNChXmA862+u4M1lkbuslShFczb7fxvkCETQW6Tq7qcC1P1F+zCx/P8Qf3DmbcrUdDm+cgeKVvEp/Um61NpDcvin0ubMhhoaIkP3mEbZZEQZyX+xG0ld2fZFloJc33rVcazJWGl+OGkT/K9hzeigb+UXkn/gGJYa2fLFfntY+59ApEc2lJUDqE0CRm6IQiClyA2iZ8B3wPWA4XNuxWyOLIIAyNy4caD3R65h7naHkEGQKyZT7/xX6yZ+XL6+qZaPr15LtAMCgbyAQM3z4Wh/doroR35Ej35MqO1InTy11lc/Unm1b/CaS2/c6rZySzk216+iObYZ6gqLfw9DCpStIYGz6jdGbrW7FHuMyi/DciVvVuM6nKBTVH5xqh5adMe4fzLCbDFLoTdSYeZ47YDDS3xwZvm0BxLUMdYElozL7KMfsnsbVXWn+l7vmq1DcW1ZuLIaq57eCMAffPI/Hbj//ImHzVsiuZ7ahqs/7njKwaIVMC5S4P72Q9S4SsIgpALa7Sahz1a699ord/XWu9wfzp9yw5yVL7iqQNEVRsDQj5C2MWUcGGKgHNtAdDxSCZDpJobPTZxZDUbz9/NnRU/Z1BoOyE05c3vMCN2FxNCq2nUlcGeowMkUFSp7cwoqWVCaDW3tU2iSUfTbtMW7uUM2ciHa9amDZ0A0iu/OTzFHcZ2+nvY6YFi1EyXs9ZzVJ8/1vJAlhAGz9CSSHrEXNCgBFNEWn2ihk+13MOwlgf5WMtDcMMW37g/txGtOhlzV8x9zRpfaPlznGaWUCFzNJ+b1KE9t47tgxVXOY14giAIgpUgYvh3SqnFSqlTlFKfcn86fcsOdgxf9t2OSaDbIuDc9SDVRx/hBThf7Of/1BnjnEyT4NZhzpe4ZVz1jJJaozAtNmGlCan2MbwAM2OXZ2Xa1sXHtt8pU+Ta1t3sXfenAD/x6JtXMnTm06mf0TevDHbHS+uzP5cuPi2emXzgXa+Lj2VW7IrU+9ygK9mtegd6XD+vLcDZ6k9w6zBW7z+fLaWX8JfSK5kQWp12m4TWDJ35NI17mgkpuK1tklWM55Nm1qZD/Cr+uaz9tklHWR4/07i+/lO3te8jc3eZG9NsGd8k2g9cBUEQBCNBbBKjk/96/VUayA7lFILjnrpcdVPBp4OLTv+j4Y4THKHbZ5Aj2PtYTlfnU9m+tN7fSrGpFuquBk+mMM074YnvQNwctVWldjinuJMJBNVqe6fkMntxRXhN652pCC+XF559rb2h8Br/iWtgGqVrbn7yY/TNK3nvw/T3570PWxl980rW3jg+d1NUN58Wt6VhxLVO2hPG8BBjAKfxc15kGTqABaFRV9K3LMK/9seyqrATQqtZHLkbmuPO43isRMRI2Sbcu7n+3+dKTmOnXmZsogu637m+8/pEDesTH08lZ3iTXjLXfxK6hIWuP/+pafa4MksjKhAo0zwo3ZJMIQiC0MkESZP4967YkEOSEZOcnztO6BmCeMsfSckANzLsk5fASw+mV2cjZXnbAia3zmLN/vamu7Gt/VnuXlh1U7oQdom3Whvs9kSPINyqqE/U8HTsVP5RegmFNly5BGmqq1Jmh1DWCGWfKq9tlO7aN3e0e3JzeYohSwhnrec6CLFcFzRGLeftDGJca/hT4ni+PusZLh49OC0RAdLTThqTXm0gcAZxiw5zW9skdrfGqIiG2deavu/MKKmlVGXvT1HVxp2HP8nT759qjBJsadMMuPgO53fC87sQxOab6TvPuDb1fwpHjLsHWmWRMAvPSx4gWQaWAI4gtvyeAPn72S1kCmEo0ujvjjaKCoIgdBBfm4RS6jil1Dil0s9PKqW+0LmbdfDz5/q7eXfex0jM7YPuYiFsP62bcUWsGV7/LZx7J/QZDCjn33PvbK9s57JA4P8lCvhXrnQ8y07SFu7FzS0XpkRLXGsadX5jc03s1L1Tp+XbtPlXw/Y8WSOUfzzaSYxwf348OnWVbWTu6+/vY3ZdcthjLk9xUC6tT7di2IQwpLzeK6edYRS+o48ewPBZzzB05tMMn/UMo48eYLxdStB7rBiadiE8NXZj6gAA4I2FX6K6b1kq7WRQaHuaNWVuybLAwziUR5rua40TDrWvhJWiOuTT7rBnm3WISVxrZ5/P+F1QZf7DK1p1Cd+NXe2cTUgKYdPr/FHkLjaXf4vLev8fCsernBZfaLNBuOumhjoAQvn72S3YRnznO/o7Db9GUUEQhC7CWhlWSl0LfAdn2tzPlFLf1Vo/kbz6FuB/umD7Dkr+XH83J6yfTZlqLbDtvAvZsw1GTGL2m//mVPDe04QfUlz85mbntH6u6iMBvkRtVgxwhMe4OUk7iWPfWLDvyzza+pm0m90am8Stkfuc97QAEhqeSoxJNXSZYuhsE/xSE/hcciRB+E2Ne2jt1na7RGdWx3J4vTNj1GzV7CljhviPbE7uB8NnPW1sHHtw7dssmHgijbubeTianeFcrlopI/hnGlVtaZPo4glNdd8y1sxMurrusO9r2xL2A6qUv9k9m5Ok9v4fcs4/F6Vtt/s6Gy3VYFNWtVLQO/Ev5oXvZt4lx8OIs9M3wFb1dddTGd/+aRI9zubQ2Y2igiAIAfCzSVwBnKy13quUGgo8qpQaqrX+ET1fwvVoBv9lccGirRjs1L0pozXjC1lhtBn0GWQVQkC7IO4I4+Zke4aBuIowe8/5PPRgBXAr/cojzD3jeH6ZjLvy0lH/cEjBheE/sj7xcedUtefxMn2dkZCid68SdjfF0ifwueT4gvcboxxkvLLLkYdFjVaJIw9zmrDqNjQwa8UmmpNDJUIKLhk9JG9vMtir2V7x7ie0bAkK7npV3zKqmvOLyrPZWjKtLFft/QnM/0rSaxvCtK+36hLfUeWZk90geYDw9+NYHbrcuJ+4lo+l6q40Yew3ES9rgIuLzQbhprBAzqlvnWZzEARBOMDxE8NhrfVeAK31W0qpM3AE8UcRMdwhjtAfGN9Bp1lHsZsK+hEgCq0ANPBf0cvZ1dTKrOgjHMl2J+btmM9bvcEPPZRbCHUI94v/Nzc4jXNAS6QvM5sn83jbmNTNdjXFmP7oS/Qtj7CrKdtjXJ+ogTZYHLmb0oxI7DYdYo8u981adhvk3Kqi17/pJZbQlEdL2DDn84W8Wt8xyraEBRNrbxyf1UR35GFR1t44nroNDUx7eGNarkJCt0+rW5DnNucS7x0VWtPPOpZ36iqpNuRvm96SJh1lP1H6G/J/vVaW+SX387WS5zwdccl3JBRFJ5szvYNpXNwDlrBSHH14OcvXvp1678ojIW65YETqAMG0n0wIreb2yD2pQR+D1PZUk16jrjRnVbuYbEOWPG67PSKbjtocxg7vb7xt5uhvQRCEAw0/MfyuUuokrfVGgGSF+BzgfqAICujQ5X11uHH6XINun5z1l9IrjV/0+U5Oy0QB82bPT15amH7lkDFpdgTGzYERk4g/+LTxsYJWMW1fosccUcHIm36bFLYV9C27m3nnHc/EkdWcueh5Gtqas+4Ti2u0hq9E/5fr+HVao1V9osbaILVHl3Ny6z28WXqJ75GcrUEuk6yGuTxYMPFE1r65w9h8lqpA3n4c7PWMI+59FFyfXXFee6M5K3fxs69ZA8YeWruVBcf5JE0YbC9h9S3j5+2K91xCqywSSlWovZRFHG/2xJHV/HnrDPr/ZbavLcLbjAbZjXWZVpbJ4efNn7eOM6zlQevzvLHwS0C2PQSgKZZgWu1Ga7Ub4Kbor4iSPvEuqtq4s8+v4Yu3ZjXipWFqeLONOvepBBeb5VecUnybRYBGUUEQhM7GTwxPhfS/5lrrNmCqUuruTt2qg5ytn5pO5fqZlKh2cdCmQ2lf4vNiU40d9EWpFt86DJp3pQleIMsP6WI7re+tYvqdkjd9iR5zRAVv7WgiFm9/3N3NMaY/8hLgLzZPb/kdi8rupyS+H3Cqbkujd3Fy29+tp6D7q728WXpJzryJoI14WQ1zXgJ8wa+cdoY9Xi1TCINz+fbjjILYhN/7F9fa7vUGY2Pdb/rv4/M7pmU9lsk+YGLhBSOyKtUA+9sSDJ35dPL1f5ZPX/ATtj06y2p10ajUASOQZWX5EV/lT71Oh+SZg7CyHBL4RZF5sNlD/IRwWCn68qH5yuadxjMhKfySWnLYILqCotspAsQQCoIgdDZWMay1trb4a63XdM7mHBp8OvQaOuNLOqwSjC55Pe0Ufadl6LpfwG58GlAXH8viZ1+jcXdzlg/WdlrfFUI5T8knBbGXsYueTxPCLrGEZvGzr1HVt4wGi6CbFX0kJYRdQsDXws+xS/e2ZsEGicDqqz5kffRK+qm91gYob8Nc5khiJ00h2Bf8goknmm0mmUI417oBv/cvdRBj8nrP62O8z8f3rUuLQcsnG9kV/d79ozwSoimWSCWbtPvQ/4146BJu1ncaP6/MgxWvRSESVlz06cHsX9+Quj6uQ2kHnSlUONBp/3w83C4Xjx4MG31u4M3yPv58J7FlzzaaygZyW+wifvlgBVXPPJ/tRe8gPdbmIMJXEIRuRukC/tgfSIwaNUqvW7euuzcjnfn9jZUpjfPlvTx+ZtqY2jdLLyHUiS7tprKjOHnvUppj7dtUFgmnRTv5DYkYu+h5X+HlnnL2Mmzm09YqrQLuuOgkpj/yErGMElwkrPh75BKU5d47dW96ZTUHFo47KOFpfSoJrdMOFDKFsEtavFghWASpc92e9v/3sVKYDlBcpozxaaIL8twGoT+5dKlRaNma/GyEleJPpf9BlcE7nNBwXexqQ1avE0X278cdnpZZDI5neGr4ueyDyeRIY9MZi6bWROqgsHFPszWKMPM5034vbh2WXfU1ESmDc+90Ju6t2Oz7OwjO52o7aA1Cj0uT6CaUUuu11qNy31IQhEOBIBPohGJjOUWrgBKVYGr4OYapdzhavUeV2k6CkHHErNawV5fSW7V0qHLcq/ndtC9hgOZYnMWeqWrWKiYBTskb8KtcVvUtSz3vD1ZsoilpvVAKLvr0YNQWezxWX/ZxXezb/CD6CAPZTkcHcbhNdU+21rBlUXrclUkI+60DxTslnMNK4b5/vmkSNpuEH5bYuOWV1zF5+NIsofXim7vyellxrRmotxvL+LZdvDopCmet2Jy1v7kHlZPDzzsV4gyvrVcIzq7bzPIX307tMQ27nVHMpj0opEgJUePvxRdvNSakZBFrht/cwJjmMK+EPqAx2n42ojkWZ179Kynx27c8wt79bakDxIbdzax+/C4+/9vHKG9+N9v2ZOBQFL6CIAi5EDHcHfhNi8IRfaeGXkkJ3BCJrElXbsVyRkkth6mWDm1OQismhFZnVdyCNokFOiWfwfSzjmX6oy9lWSUiIZWW2as9r1preGx9A+d9+j/49F9uwCRTGvUA59T5/hoU8EKv7xqbFfOhSu3w9wgHJUf+cIreR5ktEb2Pav//AFaKiSOr7VVD29CN0j7Qsif79q5Q9omNWz4vXVhmVmmDEFbK2mCqFGlpHy4Nu5tZ/OxrWQd0LnPbvsE9vb+TyhqefO8LrJnZ3hQ6dnh/Lhw1JE0IuyS0Y+lobmu3dLhpEr4V2bRx69v8s7SbdzIQQLWnTszVyxyrTrySVXtPYlx0I1Xx7TSWVKZFt92k7qO8OVl599ie/ASxIAiCkI6I4e7AFpPkIVNDKpwmuxA6Lct0qbqrw5tTohIsjdzFyfG/p9kzggrA6Wcdaz0lf/Howb6nZuc/+UoqJq1vWYR5E45PiQyTwGmOxbnur8ewZtQ3YN39eAVxZpKABm5pvTDwKF8bjXpA+lCNQgk6YOD6VwOnSfjiNwzFNnSjZU/2GOWMISq5MCUwZGJKtZ4QWs38isfoG/sgcIaw+1i2gzFI93jbIuD+b8tO6zmE5liCLTWr0pMcGi6DkUuMj5navzMbUgOOXY+qtpTvfZDazlTVbvMYpJypfG4/QdZ+bcspFgRBEKyIGO4OMmOSAhImkYqVcqu4OTNLDWRWmcE57fu18HOpoRNpU9U21Roj11z8Tslv+WCvb/6sX3XNVplu2N3M2JfPYemnjuX4v91Br6Z30w4QvLiNiDOjtRzFdsupdsvAEdoF9p2G7TzmiAqrZ7jD5Ct8M/EZt5xT2HZwiMrytf5CGGDymCEAqerxxPAabov+jGgseZbD4okwpX1o/BNPvL5bWwScIfUtxe1ly2CdZ+CmjqcOZie/c2HwfOVxc2h74j+ymj9zkXlQ4Fp3rMM7/MabC4IgCFmIGO5i2k8fjyOsPscDR9Vyys7H025jq4oplV4Zqk/UcFvbpPwrnyY1jCNgZ5TUsr58fHtjzqba9ExUy6lY2yn5oTPNGcVBgv797BcNu5uZ+uePsvCCVQApX6WJ+kQNT+6vYcsl+7LzXSNl8MlL2PXSU/RpfZ/dVKA19FP70gT2neC8F544rJVl/bml72Xcs7u9D+eYIyoYffQAhs96Ju/UhbzIZaXIMW65YALExvk5I8JK8Zv+P+TjG52m1gWlOJXnnW/Cnhx2n0gZt+0zVzzjWlMWCedsQMsXBZzPb81Xrv8Fa5rHGa8y7d918bGsjl3Odfy6wwkx1T4HwO9SySkzny6owU4QBOFQRMRwF2Iaa3xx44U8VAWn7KpPnYL9P47nRP2qVeB6J6XVJ2o4Of53JoefJ0wi0Bes322q1Pb0L9BVN2UPB+iiU7FuU5TNC+o2+a2ZeWZqe23JFlV9y2BEsgHOUOUeteZzVn9rv/KII4QzG6Kad/KD8I/5wSU/Sb0XvqOrizlgoKNWikwrhHcdJ7Vg+iMb0yqmY4f3Z3kHc2HfOO6nsCUj3SWnQFepz2r9M5Vg+HzdJrqOJC1kimmFU8EObexYVrHL4mdfo6H1szzKZ3MOf8mF7XdYa3g2/kk0zgHjrBWbATpdEJsjBs/o1OcUBEEoFiKGuxBbgP+UdybxxsJfpC6/s6GBRx6/ix/wC+tYZtc7OSG0mgvDfzRnqRaAAs54YjSElzgCz3bKtQtOxXq9w34V4hSbalmp5tCrNN02kWb5sAwW8Wv0mnvu8bDqLHMyQLw17cDA9hk/tHYrCxYGFJJBRW5HrBS2oRuX1lO3oYHrHt6YdZfU6f8cwtfNEDat512Z7jMYvvdy6uK/v2n2I//7cYf7Nwwm8cvavXDUELOYfsnS8KrCeb0U75kLWx52R1EKLgz/MWV3ykyFSfHUtKJNszNFDL7+/j7GL/m9CGJBEA4IRAx3ITbB5a7XbWhg7RM/5TuJBzlPbXfcDDm8k8Ymmg6gFM70LNcKYeuCN42MNVBQ0L/HozyxzyAmfmkOY5+ptArik+b/1plKF/0Z5bSkuvIXRe7j9NCbfLHXS5Q/8S783h49ZfOcKpKi/Akf8e85MMj1GeesoAadPufXHOde9qn8AlZv8OJnX7NuXtpnaRH2t1wwImtccUjBLReMgCesD+1YVjItLBnT2H73qjkZ5HevfpDT2w65RwobxbSt4fXkyxj7TsD9e1Mta3rNYqB2xodXqGC+4ULGr3vPHIHBe//UtPTX4/FAFyKIC4oYFARB6EGIGO5C/Jp86jY08IfHfsLN4XspDzni1vYd2KSjrEqcxOrotb7eQYBWXUKYOGGVZ96ua4UYN8fss02KFL9hHJBDfJjECxg9yktPnM+k/x1kbHPb3RxjerSWMtI9p+WqlS/rZ6FZtz/Wiisc3+8Xb00TSrYpe26jl280lufAIMjoal+CTJ8L0hznU/k1PobnukCRej4xcROTgt9YZfUTw+femVPMerdtQmh1qpFsV1NveKLVqdSDb8yYNWt3Uy08dR20uiJOwahvZDe8eiqpywkwyCLpu6+iOXWgFjRxrlBfsTd1IysVZv0vzHda/4tuH/csCILQHYgY7kL8xhovfvY1Hg49nLPKqzU8Ej+NC8N/9L2t1qSSJwBuj9xDVLVl3cb3y3bPNnNealKk+PpjMwRxFrbGvJIyo0d58F8Wo/lRmgDyjku2dtab5HPzTvSKK1jx+GNc3zw1JdYgPd1gbvmj9Nv4PmwZBMd8Hv6yLNsqEY6mVS9zja4uCkGb42ypEDnEtF/jYoocMXFWy4JfxdprYXEPlFZckcrlbio7ivklJ3BmaAPVyTMn7mTGAWovZDoZ8vG2b6qFum9BwvsgOr1ies6SdnvBup85/558GcuvyCEgDb77oo5XN+CeOVI4FpI0bF7nPD3QgiAIBwuh7t6AQ4kFE09kypghqSphWKnUaNzG3c0+gi6dyeHnfYVwXDvDiqvVdn4UuYtbIvfTorP9jbm+kN+lkmEzn2bsM5XUnfEszNvt+DcD+GNzYmvMs4ywPUJv57zQahZF7mNQaDshBYNCjhViQmg1u3Tv3M/pQQHnJ/6Hc0OrU41Goz7anzcWfom3LtnH0vL76Rd7D9COUH/pQfjUVCjznP4u6w/n/SRNbPl9xj2GHGLaL1PZ194ShEvrsyfdZVo83AMltxKfFGnlze/wtfBKBoWcJIZAI8qTFpa6DQ2MXfS8sz8vep66DQ3pt1t1U4YQ9uBWUl17gSsaXXvBU9MCbUNX4c3b1jiDatJer83rnKcH2sUWJViUiEFBEIQuQCrDXYxtrHFV3zIam3JnBisFJcbxFu2E0GlCtzf78x5K3Kyj3BK70LcrPac/1o88BUICxdLIXcbM1R9FChs84kbJ1bdmNBrZhPrrv4UbtqSvPzUNVlyJtwK9YNQ3WbAwo1q4qRYe/1Z69c3UPBdk+lwQcnmKfXA/Y2OaRD7jfG3bYNuOlG3GfjCVf0VV03Trcaze92UaWj8LWPZnv/3R/cwKtRf4WWyKjMY5c+TN285qovPxQBfCymlnSJqEIAgHNCKGewjTzzqWHz52EYvUTylVHTtdacsoDoIG9nAYc2Jf8/9CpYP+WJtAiFZAaxNecak1vmkZHTnl7K3Gp/yoQRM0MhuRXDKbkTbVOqf7MzGNYg4SmZarOa4jAzeS5ExmyBUTF3Qb0gSwffhJRyhvfoeb1D20hhKpfTprf/YTrG7FtAB7wZ/r7+aYPbvpU0AjXNbTBHgMBUwNP8e54ReTedl7adSVLP7XJMAZR+3ngS4UEb6CIBzIiBjuIUwcWU311o8S/kvxxUBQtIbp+hoeTVbQMslsrOqQP9bUmBeOQlsrmUK4s/2VE0KrHd+x22gUNEHDVil0r3PFxaqb7LczCcpckWm5muNyeYqDJE1kYno+sMfEBfE1Z/rGiySETftMZsICZOzP4+YYPMNJ3Iqpyi9i7c/1d3PC+tmUqdZUN6x77FjIPh30PkpBf/amnnOQ2s6i6M9g08h2S4/rgRYEQRBEDPckPv23RZDDAuGl2EJxp+7No62ftdbnMrvSXbuHNU3CL8vU1JjXui/LM9zZQti1Svwmfmq7VzZHgkYKv4Yj73VF9ow66QVXAVcBSfvCpXnYF3KJ6UxsVd5hpwcetmHEZEfpRLwJC5CxP7v7o1+aRJ72gsF/WewIYQ+dvT/bKKOlSwbl5EPOFA5BEIQuQsRwT8LSPGaj2F+s7uOZhHAkpIyNVTYPdKAs08wBGPP6FrTdHaVabaciWtJ+ytwnQSMNW6XQvc6liJ7RTAEBnmEY+QgJP7tE5kGM7TV2dLRzJzWW2X4vmihldfRaqtR23qGSxk/MIGUdAOtAlhRB7AWeuMAjtbbnI3YHXdzI50fR9mNBEIQiIGL4IKMj1eK+2EPye/cqyW+kayHNRl3YaOQlTojdzRmRabmEEdgrhe51LuPmmD3DkD2KOYdf2DTgIWu9EBuEi+kgphACbENT2UDKmy25yhaC7t+m21Wo/fRODruoZjvVm+dC6DV45fH2A9Gy/mkZ1NnVywtZPtey/2bYPrqrCmwl4KCcriDQfiwIgtBFiBgWUngrZ94MX4DdTYZRxH4UkmU6bg56xRVdXkwLkUDhxG/lJfhdUb/uftLq6aO+mS74XVGdK00i6PS5XASwQcyu28zyF99OOwswZcwQFrz0i+DP42J7Lp9tqNvQwOp9X+YmdU9eExTz8c1mrWUuxJqzD2aad0Ld1QBMXjs4v+plF9s+8qGZUsoybT6CIAgCIGK4Z1HWP2+rhJeOeojLPZUzd5wxMdKby4KSZ7MRQF18LOdpuvzUsgL+FL2W+56ewsSR8423qdvQYJ6oFrQRKUilOcj0uVwE8AJnDktxeeDFt/nPXvHgb/+w0wtOrlj87Gs0tH6WE0teZWr4uZ5VRU3EYNVNrHnvVuPVWdXLHJFwWoPGyf7OexJkB9EadtGb5z46jUk9yC8sCILQk5ChGz2JL97qJCrkidbFaabL3BncDvyySNh3EIMRW2apT5bp4mdfo0FX5vc8RUAlB3jMiN3Fd38wK2soQ92GBmat2EzD7ua03OWswQ1dgG3oxdjh/f2FqQe/oShxHfBPgiuyg07Dy8BNchgX2tizhLBLUH9t5oAQAw26kqNbliflcNeiFDTpXsx6/d+6ZX+14bsfC4IgdDEihnsSIyY5E836DEbTHsMUhM4SFFWhHSy84MT87APgVEtHfbO9EqzC2faBDBp3N3Nb2ySadP4HBEHROjmhz/DelqtWppfUZondxc++RnMsvcrt5tRm8ePRMK9P+8+PRxd1+5dfcUqWYEh14QcUpn5DUZbHzzRfMeqbMG9P+0+QvOJNtXDHCU5j5B0nOJeTuGcagk5dLAb5/D4F9tfmsEZ4p8E1dvKBnu31VakdxLXutgM4E777sSAIQhcjNomeRvJ0+hMbGtjz2LU5TyEXUhE2ZZ0mtHm87f6ygfkLYZc8s0z7lkeob6qBGCyJ/NR30EahKAUtOkIZZp+qG7/lHcqQma/skrX+49HZucGmwRo2Ak6f66hgsA1LAZjb9g0uHfPR4gxk8MbT7dnqXAYYMYnpZx3LrBWbadS5py4Wg7yEcCgC4+Ywdm1/Y0NXmojzqSC3RPrS3NLG0shdzNC1rEqcxIXqj3l5pPPB9negUQ8AzINzuhMRvoIg9BSkMtxDmTiymvElnXMKWQPfjV3NtkQlCa3YlqjkgfjnaFWlabdr0lHm7Ptyl1ST6jY0sHd/G+B4lKfFvtVpFeJy1Urcsuu7wgHaxa7NL521bhqg4beeyfWvZo9dzpw+99Q0mN/fqTrP7+9czhO/oSgV0bAjfOfudCrAc3f6C2FbQkU4ah5pnRxAMnFkNV8+uZrFnXwmwCVOiF30Nl+pPPtCWX+YeBeMmBSseulTQS6N7WFAaC+hpA1navg5Eih26t5o7RyAFptM0e+tTIPhAE4QBEGQynBP5ih2+F7fpKPsJ+pMm8qDRl1JfaImNY1LAZPHDOGWlx/l8tYHqFI7aNQDkmkSn+WFLqgmLX72NWIedVCfcCrEc0uW0V+1v75iHRyESGRV1bWGVYmTUpddsetWMb1WiYJ81C4mb69rIfFLjciV3Rwgzmz8kt/z+vvmCL1wSHHz+YbMaC+eHN1U/jJkN+1t+aP5/nu2OraJPoMo2fdlnkjUoGPO4JNqtb3T7D4lKkGFbqZVlxBVban1Zh3l5U8t4NMTrjLez7d6uanWM6DDRLoyVQp600KT1nw35iRWzCippUrtYJeuoL/a2+HXv49SdicOy/gdbp+6l3cjrCAIwiGAiOEejLLk7mrtNOW4FZ9FkfsCn3pt0eG0SlFYKX446ZNMHFnNsBff5hd8Jus+XVFNMj3HyaG/068IAsGEIltYK+U0dM0lXey6BwKLn32NUf9ayazoIxzJdtTvB0HYMIzDD5MQhuyBJCZyZTfniDPzE8LV3oQMG5tq00cW79nqXJ7402wP8R0n+DSVadizlVn6v9gZuip1YPZm6SWdGiRSquLEtTNpsS/7UmJx/V+PYc2E3PdPY1MtbU/8ByXx/amloEEobmNqTeudaeOhV0ev7bBlJEJblgB26dABnCAIwkFMjxPDSqnFwLlAK/AG8HWt9W6l1FDgb4DbtfSi1vpb3bOVXYRhLHBbuBcL1Lf45d7PEHK9nzGYF1lGP3ILx32UpX1RJrROCaCqvmU0GERpV1STMp97Qmg1Xws/Z/QxFwPb+1SttvNm6SW8qypp2DoDRjoVw4kjq5kYXgNP/tzqg6XyOLMlwjtYwy9lwW8gCQTLbvZpbLMJYYA1My2Nc16euq5dCLsk4s565gGBaaR1BqUqzi2RnzFD11KltpMgRCiPceSFEFbQS7dyXezbqd8DVcDBXtNv5lDuEcKQXyJg5mhocM5KTFXPZZ2t0Jj9/CZKVZwZJbVpIhsCHuwIgiAcovREz/BK4ASt9Qjg78Asz3VvaK1PSv4c3EIYHIFx7p3QZzCgoM9gSs77L+bNns+WRWfzw0mfROFYCj7Vck+aD9jWMJQ5Ze7S3v+X6vhfqa7mK9H/Tbs+r2rSLyekJyn8Mni5bfpZx1IWac8gnlFS22lC2A+lHOFRxXZOWD+bP9ff3X6lKTnA44PlmrXZE+UyB2v4kWvamy2j2Se7uajYLAHJ9cn3vsDQmU87Pw9W8JOK/2jfdy1U0MKg0HZCyrEy5NXoViBuZdYl82Bvdt1mhs96hqEzn2b4rGeYXbc5/QE21VKW5+S8TBSaf5ROYX7J/ak1U8ycUrCb3nn5izOFdnXfMtbMPFOEsCAIgoUeVxnWWv/Wc/FF4CvdtS09Ap9hDRNHVnPdwxtTl70+YNspV2+D2Fei/8tsfR/scSpc5c3vsChyH72jJfxy72fSh0vkosDhC97XAsms4d3NXRq5ZaNMtTL4L4vB9ZPakgO860GFr4lcotY2/tknu7kgMlMxAgj6zLHFAIvfPYn/HX4my793inNwZMAk/oqRmZ0LVzAq4N+POzy1njmQJK516vKCiSemcoU7unlKQQkJpoafY2r4ORp0pXWf78de/pQ4nprQK2kHiLYEGO/vOEjTnCAIQi56YmXYyzeA33guD1NKbVBK/UEpdartTkqpK5VS65RS6z744IPO38p8KUIigEu1xcJwX3QKRNKvawv34r7oFFTyfjdVPJbmeQQoie9nXsVjbFl0dn7VpAKHL3iZOLKaNTPPpLpvWdEzWXNVHG3XH6G3U7ehgbGLnmdbYoD5RkEzacGevgC5Ra1PdvPom1e2V2VnPs3om1dm3f2YIyqMD5u27hcPZ5WAyhhBBu3T2loifX1eWMajdcEZAVcwauCx9Q2pxBTbQJLUepFHLivVPvTF+u4qGBN6jV/FP5c687NT92afLs2ZHgHSNCcIgpCLbhHDSqnnlFIvG37O89zmRqANWJ5cegcYorUeCUwDHlRKfcT0+Frre7TWo7TWow4//HDTTboPNxHAPSXuJgIUKIgz7QXgWBtOOvvKLIvFhk/exMqSdjFW1vyu+UGDTt/qJKafdSxL+WqXRG652ATYu2pAavqceSCIgmM+H/yJLq03C+LMgSS24R2G2LPRN6/kvQ/TGyjf+7A1SxCvnHZGliA+5ogKVk47o33BLx5u1DfM19nWk9RtaODGlim06u45EZVLMHoHqNjyl1PrPpPmOorfQUBUtXFu+EVqWu/kuti36UUrh4Va0u7TEunLHH1lWk9AdzTN5bSZCIIg9DC65dtJa/05v+uVUpcC5wDjtHa+hbTWLUBL8v/XK6XeAD4OrOvkzS0uuRIB8sRrL2jc3ZxhbWi3WLgjhZuTVa2G3c00lg6g2nRqNqPSObtuMw+t3Upca8JKcfHowc4p407C2farue3pklTUWwLVoSEchVQbm3WU/1KXpCLV6hM1nBz/e0Zjn4aXHoQhY4KnSuSyjeQ5vCNTCPutpwnffHH3T9NAjtVPW++2+NnXaGj9LK2hRCpKTKGNn0lnWCT26lL2aHvcGLRbCWwDScLuRqlwbm93J9GPvUwIrbYOpCktP4yaL13NC8a/BV1D5oFZls1EEAShB9LjPMNKqS8ANwCna62bPOuHAzu11nGl1NHAMcCb3bSZhRMkESBPJo6szvmFZxopfGtsErdGf0aZc4zhEClrz44lgIfSJUDGbT44r+dKxq84hebWOBNCq7k9ck9aRqyXYokop3tf8b6qZOvJ0/n1/6YfGIwLbcz2abpNdF4xXIDvNkVHh3d0JpapgmOH26e1VW6p5+GokxjRqCu5LvZtZpTUGj3t++hFSCfymtLm99k36Sg3tn2T+kQNE0KrmVFSm5oI5xXFrpXg4tGD0/Z3l9Sgkm4SwgAouDV6HyW2xI092wL9LegsJt/7gvXA7KG1W0UMC4LQY+lxYhj4MVAKrFTON5wboXYacJNSqg2IA9/SWpuNij0ZW2WpkxMBTE009YkaVCv86PAn04coeESdn4cy7cstR8ZtYDxDHcZQyfj4hdTjDgdJf9+8HfbFSp6IE6Jk/i4GAgOBqr8+nxb5Zm3s81pLOjqWuTsJEg+Hc5C0/MW3U2MlKqJhjjmiIi2+bezw/iwfvZXmhvYDrkFqO4si9/FI/LSs0cQJDeXsZze9aU5E6af2odGECxxHHtewnyhLI3cxVy/jMLU/dTDlbgcxWBk+PWUlcPdp65mQPoM7ZJXo2EGbso4RB/LzrrsU43c2ic03Dnb7iSAIQk+gx4lhrfXHLOuPAY918eYUn65KBMjAliG87iPj4XsLrffL6aH0UuCXaIpkp77boDSQD1KCZW7JMiIq/TlDCuJaEVbF+6INqwRNtx5Hr+Z3aUwM4LTQJTwaPoVY3HmORl1pHozgFSJdXNk98rCosSJ35GE+nmubCLpmbc6qdubZAoB9rXHe3N7E0otOSq9M3nFF+pkHnGizc8MvMjN2eWrynDdLtz97aSLKdbFvszRyl+9rtwlLrSFOSWp64QCVPaWxXLXyg+gjnHneNWnbvGDiifYq5rg58MR3IB68cu1uj9/25iKuSghp81kRIOuMTiA6mACTD+Gu6IoUBEEokJ6eJnHw4ZMI0JnYGu1yNdfYvsQ65cvN0KnvZsL2N4gZgBDFrThp7UTMhdAMCm3n/+mfcrb+U0qo3R6/iFZVmn6nQoSIH5lZxTnW1944Pkv4HnlYlLU3jjc/jp8IAkf4ztvT/pNRzbadLYgndKoRLYWlGbNfcoR4Teud7KJ3VmXf/dwLTRVJoKyWGi8D2Z6frWDEJDjvJ+g8w9VaKOzMj9awLVHJDfFvEdfmP9da4zTL5jMJEYqSABOUlM1EEAShB9LjKsOHBBbfZWfi32hnJ6eHsphYTj8bm/w6AW3IbS1XrVxfUktdMr+5Lj6WkrjiporHKG9+12gt6TABqrOZWIWviQ6KIL9T3ll2HMtIcaWcwSq0tQvjTKrUDn4VH5c1lS2TTOtBk47Sy89OkLl9fnhsO+5nXRcfy4Q87Q6lxAuuCte03gnA4tIfm2+gKO7+VyA23/iRh0XFLywIQo9GxPDBwlPTzF3+HgpprsnpoSwiCRUipLObg+IqxL90Of0NomkfpYS0TvOeFurLtN0nc6LXo62f5YXycayZZxlhHNB360sP9hbbEhfAkGk7bg6suMJ8W7WdJZGfWt/3Rj2Ac0Iv5iWENfBY4nT+XW0w21m85KroPzUN1t2ffFRS47c36qsYpQfkfvwiEPecvGuwWHSay46ivNO3JDfLrzgla/jK2OH9WX7FKd24VYIgCLkRMXww4GYXu7jZxVCUCrSvh7KYGIQwQIgE/b98R5ZXs1WX8IPYNwFSkV2N2hIX1wG8E73cRIKq5u1wx2BzVThgZbczI+syI658bRN5YjtbEA6pdtuNp6KawO7HssXlaQ1l7LfaY1yyJtgB/642cFvbJBZF7ktPpQhFoPQwaN6VXtE3HUgOGZMuhF1izVyeeIDb2iaxOHI3pSpYukShVeGQJznivugUZuufpg3KaQv3ovyLNxX24EVOgAFE+AqCcEAiYrgnEqDKm0aRs4u7i4QOETKIo4QOEXr7RYjHUmt7dS9+EPtGKhrLHUMNsD56pbFhqpAmJq1hVeIkwBHCaQIrWSkEzILYh8CRdV5uPw72vtN+ufdRcH12BdpvCMfaG8cHE0E+Yt7dvsw0iZvPP9E585DRCBkiu4JrGyXsooEBIX8hbKNKbXf2i1j6QdKgCxZmf062A8lNvyZLCCepDm1nRkktUYIJ4Y7geqbdQTol4ePTbBslHbHoFCsBRhAE4QBH6YM88mbUqFF63boDaC5H5pezi1+T3bw+9sebt8f/ufIR3UHZVAu/uQGak6dLy/rDF2/1/dKu29DAeXWfMA9iIHsQsNawLP455ra1Tz9zq7ZuZTjt9Ll2mqoKSZ7YlqikpvVOVkevZVDIlCQxGL73cl6POXSmfUjFW4vOzl7MFMIuBkEc6LH9RJApGg6CZyXfcYLRJ5zQ6Z+jXxJER/oztYbvxq5OG6xR3beMNTMNtpb5/fPODjbtj51BQsP3Ylez7iPju3x4xsGOUmq91npUd2+HIAg9A6kM9zQKqfIWkl2cj7XC0ERkFbabarOjp5p3Qt3Vzv9b7rf42dcYZYstM6AUTA4/nxLD80vuz5gMl14JVgrCBSZPVKvtTAitDpYx3FmYhLDfei78qn8djYazvB+KYCK3o0ElSpGK5KtP1PinphQwRKMrhLDWUKvOoj5RQ1UXPJ8gCMKhjESr9TQKmVBnyyj2yy72E91e3FPee7YC2vl3xRVONfqOE5zrvay6yZzBmog511lo3N3MbW2TaNLpEWEtOmwVH+Gkn3JCaHWWEIZ2EdxRXHG1S/c23yAzkWBTrfPezOtrfo8OAibf+wJDZz6d+pl87wvtV1oSGroyataNZgNYeIFj35hdt5nhs55h6MynGT7rGWbXbfY5YFROg13mWg5aKXHOhBSBmfsvReOMTp+1YjN1GxqK8riCIAhCOiKGexq2L2e/Km8h2cVBRbch+zfFnq2w4kqnypxa86mS+lxX1beM+kQNM2OXsy1RSUIrdiR6o3wUlNtpP6OktmgT6GyUq1aUIkusZyUSbKp13pO0g4cru1QQ24Zt+A7hyIPMxABwpo+lBHExM5ctaGBHojet2n5yq0ptp29ZJCWEH3jx7VQKhuvRfqHfBPOdR33Dye7tMxhQzr8X3JO8bNge7aQ9bP3oVyBa4dynrH/BKdgNGfnKzbF4doZzBkaxLwiCIORExHBPo5AqLzjCd+5OxyM8d2du729Q0Z3TAqCdrntX7Pnltvpc5w4FqU/UUNN6J0e3LGe/6kUU8+AErWF53PGAWu0LRaYv+5gZu5x3OZyUQMocdvDEd8huvNLJ9XbGDjdXD23r9D4q8HquIRy+VV3IOfTDNnY3tT5iUtGqo1Y03NQ2letjV2Jre0gQSlWjbYNCprwzyX4gOWKS4wWft9v5d8QkR+hnVIybdJT5ketoPHkGwxufaD8Qat6JKmAsTELDbW3ZdiLTSHUXm9gXQSwIgpAbEcM9ja6aUBdUdOcaSgCAbrdAjJsDYUMFMhTxrRhOHFnNwgtOpLpvGQqn4emojHzf1LNlNM/txmJfKDKNegD1iRp+fNLj6QLJi21Mb8b68itOyRK+vpms17+aLXwtaRLgCOK3Fp2d+vEKYd+qLjhNcpmCOGjznMsXbzXYDIqHUjC9pJYnPU1ymYRJsLvJSSDxHSuez4HkiElZFePyL/+EebPn8+k3/stwFiWBilT4n9nJfG2Q1vznkpXh7GH52uyoO791QRAEoR1poOuJdMWEOvfxc6VJjJvjnObPVd9yK8iuOMwzTQKyh4LsnFthzJndRe+UEJ4QWk0F9opZIcS0ogSdNdXMrdb97tUPivI8eWeyWoRvPuSs6rp0ZOiH23AZa+7U5IUqtQMNvKcOZyDZn0mDrkwJSNugkLBS1G1oYNaKTTTHHA96SMElo4fYI+5GTDLvy7azKLEm5+DJkrJh2u6ySJjmWLtlKdfodFt1/CAPCxIEQSgKIoYPZYKI7hGT4O0XzQMIvHgryDaxUCS0To9RK1ZjliscMoVwQsMj8dNS1Tq/09Xdyfglv+f19/elLh9zRAUrp51R9Oexjd0dO7x/VsZwZ1q5d+kKwkqxsPVCFkXvo8wzgrlJR1nKV1MC0jYoZMzR/Zj28EbPaAvn807LfA4aQWgZPZ363Rg3J+29AfMo6fuiU1h49ol5j04XBEEQCkNsEkJuzlni2zyUc6xtgfQ1jF8G6Kf2sihyH4NCxRPC0J4+kfmYIQXjQhtTl/1OV1und3VgqlcQMoUwwOvv72P8kt8X/bl8LR5+DZdF5jC1n7PVn9BAUyKK1s7h2k7dm9siV1Nz/tUpAblg4olMGTOEcPLDDSvFlDFDeGtHc5oQ9vLQ2q3tEYRuY6kbQehtGnUx+InTfjcyLBZNZUfxoB6fahjdlqhkjr6Sk86+kokjq1kz80y2LDqbNTPPzCmEyyPmP+W2dUEQBKEdqQwf4HTmSN80vNXefHKHO8D7ltPfCULpY3a7gKqkfznX6eqiTfXKGnwRhgt+an2fM4Wwbd23qmt6XotX2Grx6IrM5SRR1ca8yDJ60Zq2P/SPxpl37vEwIl1AmsaKD/MZUBLXGtb/3Hzl+p9nV4fdzybjd2P2m//GQw89k/wd7c3Fox9iwcQTKQcqNjRwUREqwLdcMIJptRtJeE7ehJSzLgiCIPgjE+gOYDJH+rpMGePjdzyA+HP93ZywfjZlKv30dxmtvhXhjk4wM9GmQ4SUZn/ZQMq/eFOn2kCsE+AALrjX+Nz5TLTLbKJLVXU7OnkOAvtii4X1sw44FXDsoudpsNhewkrxRunF9jv7TXdM0pW/o3UbGrrGWpF5sAed0+TbicgEOkEQvEhl+ADGFhf10Nqt5i/aYlQsc1HE5/j0hKv4MzD4L4s5Qm/nHQZwa2wSM0pqfSfV5RLChYjlEuWcTC9vfsfxfUJBgnj0zSt578N2ce+NPEvhN+ltVceFuLWqG2DyXE5fssEX2y0YKtSmg4DpZx2b5Rl2uXj0YNgY7OlsZ2jy/h3tAJkNqJ2CSQiDfXqlIAjCAYAYyg5gfOOiMjF9iW35g7NeLDrhOT494SoGzvsHofm7qWm5k/pEjXFSXbFJaOenzfQrEmv2naZnI1MIA7z3YSujb14Z/EEsNoSwRdzb1gshkC/Z9cV2ds4wzlmCnZapgE1lA9Mu2yLlHln3NksuOokyj7c2pDyV20iF+ck9634Zv3n9jh4ImISwi22qpSAIQg9HKsMHMH5xUVnYvsT8vtzypZOfo6pvGQ27m51UhxgFp0nkun1cK74X+zb1iRreLL3EHImQKUoDeG0zhXCudSOW3OcfTjqJ6x7eaFwvFkF9yYyY5BwsNGd7kzuKu7s36Epua5tESCluUfemeYabdJQ5+75MzYaGVKXUL1Ju+RWn2Cuq5y6Fx7+VPplRhZ31JH7V37x+Rw90/EbGC4Ig9GBEDB/A2OKiLh5tSX3oQVh9qz5MP+tYvvfwRjTOUIL6VkesFltWeGeG2Zr40kSpyWu7/VVnvZC83srj7JYFS2qHK+Y65Bm1Pa9tIp0fndRIpxRsS1RS03qncxnoWxbh8tYHqFI7aNQDuK1tEvWJz/LCs6913DZgaYrzWlX8qr9Txgzp0t/RQn6vikYeg0UEQRB6EiKGD2Bcz2GXpEkUEb8paH5f3BNHVmdVPxt1pa9/2EtQr7BSTtV5pT6dPw75Nuf+c1FaE1+zjvLy8P/g0+5CAK9tXlyzNu80CcjtGc3pVzY9bz7Nc9483k6kyjOZsKpvGb/c/Rl+wWeyble0POjM3OxfToAVV6QuLoucwNTYD7LuFlYq+O9oERJaCv29yothp9vP9OQaGS8IgtBDETF8gGOKizJi+xIrZv5twOcIPAXNQHXSKuFyW9skFkXuCxS1ls+Z6Sq1g4Xnn8jiZ6Osjl3OjJLatMrj+r8ew5o8rdBHHhY1WiKOPMzgfw4iQN0MXC+WhkU/v3KWILZwzBEVRqvEMUdUmLclk0gZlJQZ7RP5NDU26gFMCK3mhkgtVft38F6vSm5pvTBthPGE0Gp+EH0E5k2GPoOYPvBiFr97UtZjZeYlO0NDroNY8nWqEJz89fbGMIMv/tTwyyzjZqbGbkxbd6u/OX9Hg53cSgAASSpJREFUN9VC3dWQcEZHs2ercxnyEsQd+b0KjCk6EA64NAlBEAQvIoYPFQrIv837lGuxMnZ9mH7WscxasTk1qrY+UUNUh7gtdBchbRufkD/vq0omjqzmew9vpAHHkuFFFVB1XHvj+GBpEkGwiU+3YTHjPS+GX3nltDPsaRLzL/C5p2qvdoJxvLdS7X5gP1HcpKOsSpyUdgA0kA+4NXIfxJz9YUJoNbdH7iFKm3OnPVv5TngpDLwuTRBn7c+bamHFVeDNltCJ9KQEw8GeAk4Nv0K4TRV2huY3N7QLYZdEzFnvzAi/Qil2Ao0gCEI3I2L4UCKPL7GCT7l28helyRtbc9bVhMKfLFqkV7OOsvXk6QykvWkvk7QpdHl4bQsSvib8OveL2RSZgXW8s581Yt7u9Msei0HaQ6C4LvbtrEp/QjuC022am1FSm3UmoEy18oPoIzy5v4abor9qF8Iu8Va+03If31m0JbVvr3ljJ0NnPp0+Pc82j279L3wrnwp4Y+GXrNf7Yms07IQGREEQBCEbEcOCkc485ZpzCloOzN5Yp4K2e8U0+ugP87JExFUJH+oyPqL38r6qZOvJ0/n0hKuA7Eo0GKbQddRrC8lK7/2kVUz9Tj33tM59FTZvk6mpqs9g42CO91UlTyZqqFAl/AcPMlDvSGVLey0QS9Vdxk0YyHa2LDob5n1o3sbmnf4HeX5Nfz3t/TbQ0d8rQRCEQxURw0KXs/yKUzqn633EJEY+WMG5odUpj+9uKqigmVLlETPhKER7Q/Mu6DOI8Lg59E2ejh6Y/AHgqWlMXP8LzgvHiYdDPNh2Jnf3/o45pcEifHMOqUg+j9Hy4DfIwCY+LQT1KweycZisMCdfZn4NpqYq02COSBkDz72FO+InMWtFmIdiY6yvxdo0aYmd8+J7kHfkIPv0PFfUd4b3vqy/uQqcZ1Zzp/1eCYIgHOTIOGbBSD7jfXsSpvG6E0KrmRmtpYodwTv1bQI1j0Yh05AKMAji+f3twlaFYa5BKPk1rAVsossUuqYmu6zb2SaQDTsdBnysPU1ChR0hfM4Ss3geOcWYnmAbj+z1FE8Irc5umoyUOcM+RkyCW4dZxeXQXT/OXk/y1iX7sj3DLt7Pvdi++E218MR3IO55PeEonPeTnukZPgiQccyCIHiRyrBgpFNPuRYhRsqGydawMnw6Z553TX6ZszZPbg7vqJfAQyr8Kry269xtCJgmAbn9yoGa7PwGq1xan/3e2KYSAnzv5ayHscWheY/ZvUNXqtQO9pcPpPyLnjHVX7w1PZ0BIBRx1h80bz7Qfn+/NAkoSPjWbWiwZ0AHyDIWBEEQOg8Rw4KRTjvlmtmxv2dr8jJF+fIvyvAJsItQ73pHfcIufpYHv0EG5yzp+XFWeU4ltDUsZuIOXQGoLitjzdtPZadURCog1pReeV6b7RkGz0FeZqZwvhgO9OriY9MO0Bp2NzNrxebUXZx9tYKqvncy/bwC9lVBEAShQ4gYFqwEFb55ieYnryP7NHTCWS9SJSzX8IksvMMi3NP7uRrCijl1zua3da87mMmoHK8YMIYz9n0vq2GxVyTErqaY6RG4au9PYN1z2VfE9jn2hiFjnJiyFVewHPhX2UeY3TIlrSnvrR3N1HnGNxfEptp0L/SerfDktWzUV9EcSx8K0hyLM//JV9gfSxhFsghiQRCErkPEsNAh8o5gi5mtA9b1jmISut5qaqb3Vsedy7a4NFecBpg65zukwkvK8uCTJpHHgA0Abj8O9r7Tfrn3UXC9/0S8QE12xWwgM1gojtzxIr8/4g4u2HdDWmUfSI3izuSSkuftz7H+5/CXZWmWiY/of3FH6b1E4yEebf0skCFEw2sKsyysuik72i/WzOWJB4wT8kzifnz8D5zxxOXwRDIRo6y/Y+8IeKA4u27zATeRUhAEobuRBjqhQ+TdaDevj/3B5u0pwhZ5CNIEZ2tec4WzTUgHfB2B0iQKfR1gFsSZQtglgCAuOE0CzE1lfg13fnnIhn1hdt1mlr/4dpogLouE+Wv4IvJI0kuxLVFJTeudaWuX9f4/5qm7s5IuUs15fszrS+YwEZIrDYlKqtR2GpNZyd6qtEtqWIjKyEgORWDiXTmff3bdZh548e2s9SljhoggzkAa6ARB8CKVYaFrUSFnqpdp3UtQP65f5TdIE5yfN7gInty8ha+JfAdsmISwZd0k1nMOBskU37YmOXcSni19we+AwvC4C4adzqiL/jvLD67q84uZc6lSO7LWLm99AELZ1V1W3ZQuRjfV0vSbOfRqfpfGxADui05hRtlAypvN7/2gkBMFN0htZ5FnWh44InhGSS3Vars5HzsRy35+Aw+tNcfCPbR2q4hhQRAEH0QMH4p08shkX07+uiWP9uvt/x/Uj2uzOIAjYoM0weUzLMJLHlPnjOTTfNdJAx9M0W+vv7+P8Ut+n5+Iz9UkV8i+ZRHYE/k2E2dmPF7DZfbKeaTCasFp1AOy1qpC2QIZcCwTLptqaXviPyiP7wccoTsjdhePt57OheGdRHVL6qYasqrW5aqVJZGfQtIlkRUTl+v5LcQtZ/ls64IgCIJDKPdNhIMKvypeAdii1qwRbOcscWwKrthU4ezs3gB+XMC/8us+tgnvuq1BLVfj2jVrs4Vv0DQJP7FvIpcwL5BA0W+bauHmKqeKO68PzO/nHIQUA5vP2M9CYVp396lM2Tnqm/DJrxofJq4iLCX9OgU0JrIFMpA+0GPVTZQkhbBLuWrldDYwveWbbEtUktCKbYlKbDq0RCVYFLmPuSXLcgth4F0qqdvQ4HubsGXsom09KJPvfYGhM59O+5ldtzn3HQVBEA4QpDJ8qJFn1FUuCopgK1YkWK7Kb5CpaF5Lha3JzkYhMWoQXOy7+KVNmARl76PsnuF8yIzBA8fi4jcZz4J5H+mAhSIT0z7lpjtkEq0gfM5SauJjeeHZ12jY3YzCqeLe1jbJPNBj3Jz2y5YqbZXawROJGp5orUlZH/xkaLlqpYzcQrhFh7kldiErcyRNXDx6sNEzfPHowTmfw4apQRZIPY/YLwRBOBgQMSx0mG4b95rL4hBU6PqJ83zsDMXKHc4k3wEb179aUJpEFqtuwjiNDdJ91zkSJvwTRwqz5wRqTDSlO0BqzPHnfzuOCfvfpTE6INXUljbQI7SDkDdNws0QNmZatNsujBPyCiSuYXrsKme7EnEWP/uaVQy7wrSYaRK28dXu84gYFgThYEDEsNDzsPlxAe44oV2cBK38WoRuzhiqfLKEi5k7bCLfanoA4Zsz+s3Pp+o9CPFrksMuqPyElp/ADux1tm3/nq1Znt9Fkfs4Of53xoU2UqW2s5ve7EpU0H/PNtSqm+DtF+GlB83iGkhoWJU4CXCEdFAhbPIUewkBSyN3MUPXclvbJJ7cnZ1C4WXBxBO7TKCKF1kQhIMFEcOHGsXMiQ1KvhXTa9aaxSWkBhkAuSu/PkkTmTFUca2zT/3mY2fI57a5mu86q8KcwcppZ/hXWPsMct5vE5k+5mI3YPoI7NctcX5Zwt66/cro+Z0afi6V5tCfve0qdc/W7AzoDEIKvhr+HesTH6dKbfd/bWlbkuN65dxmkNrO0shd/EjdBXcM7hHjmjvqRRYEQegpiBg+1MhRxSs6hVZM3evuOCFb0HijrmwV0xxJE90aQ2US+67g7awKs+Uz902NGDcn2zPs0lmT8Yq4b/55+H9wwvrZlHmqtG1aEVbaKEL9tV3uKmipijOjpJZGXcmggII4Hz0Z8opz94CwkwXx2OH9rRX8jniRBUEQehIihg9FChAXBQ+PyLdZLBPrqe4cUVM5Moa7PYbKJmzzfb+yxHMYLvhpukjKlQNsw32MJ69rjydTIScGz3sAksOfbBNUWYkjhW6nhev+egwnxy53/L9qB7t0Bf3V3oIGdASlSu3gV/FxTFXP5SV08ybWTNNv5lDeyWLY1CALMshDEISDCxHDQk6KlkdbCLZT3d6oKxM5kibCShmFb9qp33yyhDuaO1wIRitJHFZc4fyvK5TySBDJ9lH/GwtubLRvg2na3d53nPWkIPYKqvkl9zM5/DxhlUA1huGpy9qFdcDtDDrmunF3Mw3UUN/q+GxXR69Fqb3211IEdukKJoef71whnKRX07vUbWiwNtQVi25rkBUEQegiJGdYyEmgPNrOYtwcJ9rKS2bUlYkcGcO2U7xp6/lkCXckd7hQ/Krrq27K++FcH7V7kOD6qH0zZQNOu1t+xSm8VbOKS0ueo0QlnOqsa13JM7d45bQzsoSv6UxF3/JI2uV8vLyFoDVUqBZKlCWBo8g06gEsfva1LnkuQRCEgxmpDAudS0crpm51c9VNjjXCG3XlR46kicAxVPmI2UIGbmQK5mJVmANMLMuk033UQcZjByTIGYnMwn8+Xt5CUApKdazTHt+Lm17RuNucbiEIgiAER8Sw0Ln4NYsFZcQkq/i1epkDZAx3ZQwVEKw5rhjvF6TbSAImiGTaRlKWBhIwP49hJDZyDUkJuJ05h7wk84D/kthKY7QylSG8KnFS53t5u4iQggvDf2RLrxOAs7t7cwRBEA5oRAwLOQnq0bSSh5DLp1Evp5e5WMM0ikXQ5rig2+GXx+y1kSQTRPSWP6RCEf6UOJ6pf7uKYzy+b6+Pen7J/WlRY5lpHCnymXaXa0hKgKQT/wEep7RPnYs1E1JOJNmiyH0Qg3PDLxZFCGttT4HIlRtcTMpVKzMiDwPzu+gZBUEQDk7EMyzkJKhHs6P4iVsTBXuZ/Sq0PYjxS37P0JlPp36y3geTT5kwXHBvViW9bsR/M2z/gwxrcX6mxm4E0t9fr1/a2gSWaXW4/tVs4WubdmeLY/OuX1oP8/a0/2SkSOQc4GGYOleuWpkXWUY/Ot48l9D2kDWtu04Iu5Q3v9vFzygIgnDwIZVhIRCdnhpBFzbq5RlfVnCsXAcInOARsIrs12jlPo/XRx22jWE2VXaDjnnOYV0pSoSXxSvdj71FqQqHFLTpECHT+6O6XgznTFURBEEQciJiWBB8yCdWLqeYy6M5rtgHBkEbrVI+6vk5LA1BsOUPG6wrpvcOyJ4KmAtLFJ/V1qD9rzcRJmG0SnSLFfmYz3fHswqCIBxUiE3iEGXyvS+knYKffO8L3b1JeWPzLAf2MgcgqCj1E3OpaLLOil97ahrM65P+88sJaTep6ltmubMFg6VBa/hl7N/Nlo1M/PKHDdjsD5CecpE1qCNz3RTFF4pk38HDTt2beB6zVpTKTzx3Kq//tru3QBAE4YBHxPAhiF8TUneSr7gt2MtsiynrwICMoGKOa9ame2KLIYRNEXLu5LYk08861voQxvf3nCUw6pugwmgca8Cy+OeY2/YNwN/LDQTOHw6CN+Vi+RWnZAnitDSJEZPg3Duhz2BAQVl/X+WqFAwI7S3aH8JEFw0wTFFAhJ4gCIKQjtgkDkFyNiF1EyunnZG3P7cg726x4ssCUsiI58AJHrbsXkhLZXCnlH2/dmNaFdT3/U2mcQyb+bTx6i4ZukLGVEACTETzRvHdcQI0596vi1HpbdJRHomfxrjQRqrV9q6pHotnWBAEocOIGBZ6FF3RqAcEFr4djpUjW8y5uBPfXBQwOekxDnxgYMvuNTBxZHWnj+4tlLHD+1sPxmzTAoOg92zrEi+v1qSEcJXaTgLIw11dGKFI7kmMgiAIQk5EDAuCD0FFab5iLlMIgxPZ5W0YC3RgYMvuzZcc+b55k0/+ME61N1cDYiGpHu9RyUA+yG/bC2AXvflq+HeUqiJ8FkEpPSz3JEZBEAQhJz1ODCul5gFXQOob7Ada62eS180CvgnEgWu11s92y0Ye4NiEm605yUgBNoPuiCgrBkG2MYiY82IbfQzw4Nq3eeqld9jdnD7aN2vSGtjHTkPW5DYrmUIY2j3Hl9YXVh2//lV7moQFP/tDPqkeXha2XsjCyH2Uq1b7tpI9SEMn84RDAcrKTTpKRMcoDXWhEAZo3tW1zycIgnCQonQBfsbOJCmG92qtb89Y/wTwEPAZoAp4Dvi41v5lsVGjRul169Z10tYeuOQcaeuHaWgF+Apik5iBwgRxR6bUFUOA121oYPGzr9G4u5mqvmVMP+vYvO0HQy0+3FwYPydTE10+ld15fXyu2wPkfh8L2Z/yuY/f+/XWouQ44qemZWUYj335HE7+18rU0I1cPl5XBDfRiwr2+wpiraFBO+OefxS5q+sTJvoMhu+93MVPenCglFqvtR7V3dshCELPoMdVhn04D/i11roF2KKU+geOMD7wMsF6AIGFr4k8h1ZA8XJz86kQFlpN9KNuQwPTH32JWLILrWF3M9MffQkgL0HsHX2cD0Yrht/Y6SLh937lHJFcpPv4knlAkBwfveyj+zhn3/kQg8WRuynFv3qrkoMzerPfuYx9/HKDrqSm9U4AfqTuyn+bM8gr8zhSJn5hQRCEItFTxfA1SqmpwDrg+1rrXUA18KLnNtuSa1kopa4ErgQYMmRIJ2+qUExyVV3zEdWdMdFu/pOvpISwSyyumf/kK3mJ4YtHD87yDB+oFJJOUvREE0uqxvC3H2HhBXMY88Q1OYWwDZM4bdUl3Nbm+HUnhFY75eQOVobjhGjWEQ5TLf437DPYEcJF8gsfqPYlQRCEYtEtYlgp9Rww0HDVjcB/A/+J8/Xyn8APgW9g/qoxlta01vcA94BjkyjCJgtdQLGqrh1h9M0ree/Ddn/pkYdFWXvj+NTlXU0x092s6zZcH3FmmkSvSIjmmGUUcoHkek0MOz3bM+yu9xBy+pZtbikdd/adJ7YXdXs+1L2oT9QAMKOk1ream1lZbtUlaHRWs12JSlBKnIS2WDPK+sMNW4qw9e10xtkTQRCEA41uGbqhtf6c1voEw88TWuv3tNZxrXUCuBfHCgFOJdjblj8IaOzqbRcoaGhFkIEaflXXriBTNAK892Ero29e2SnPt2Diiby16OzUz5ZFZ7PwghFEfLq28mpyJOBrurQ+W/h2NE2iyOQcsGIbE+2uFzmPt59qF5BVaofvbZVyhpYktGJbopLrY1cyPXYVbTr7z29UtdkLzJ3QMNcZZ08EQRAONHqcTUIpdZTW2m1BPx9wO0TqgQeVUktwGuiOAf6vGzZRKGBoRZCIsiBV13ySDfJNQcgUjab1vmWRrJQHd70YuBXwefWvBEuTyEGQ1wRkCd/J977AGk/Tmt9zF5JOUsh9fCuVtlQNd6z0uDmw4gr7/fPkfVWJwhlzvV8NpLzZf7JeCM3RLcuZGF7DjJKHqVLb83dVyIANQRCETqHHiWHgNqXUSTgWiLeAqwC01q8opWqBvwJtwHdyJUkInUgB09qKcdo1nyl1hUy0y8W8Cccz/ZGXiHnm7kZCinkTjk9dzmwOi4Rg8YUnBbZ6ZA7HqNvQwLz6V1jzxs60VIVCxHEQ8m1uM8XK5dq2QPfJ54DLbSDMSJNIrY+YBL+5wTqNrk0rwugsu4PW0IYioto/77ZwL/YNGscL//wuRzR/wL/UYZSqCGFtt8rspoLzS9Zwa+Q+otrfE7yL3vTSrelxcNIwJwiC0Gn0uGi1YiPRagcOJ83/rbXqunHu5zv9+QPFd+Hf5GcSki5LLwouiL3PlSm+veQSnUFfU0fvU3QKiO/LyaZaePJaiDWnlrSGnbo3AANCe41326l706R7Ua22EydEWCXQGb7eNq0IK22t9rboME2qnH586LuJzZQys/Wb9CuPMiPyMOXN7zoV4SI2zHkpZuThgYREqwmC4KUnVoaFQ5QgVdfO5MjDokZbwZGHRdMu+4019ktDWPzsa3mL4cXPvmYVwrmeD4K/pkLo1BSCAuL7cuKKyVU3wZ5tNJUNZM6+L/No62d5s/QS6936so95bVNZ5BnekVlBLlH+RYVSFSeqP/RJnFDQZxBl4+bwo5TonZ/7NXWQzjh7IgiCcKAhYljoMbhCsaMDLQpl7Y3jcycvdIDG3c25b9TB+5i2P1MQF+M1FZRCkOdEuk5hxKSUKC4HajY08MKzr9HYVMkgZU6caNQDmFFSm3OKXU5sQribh2eI8BUE4VBHxLDQo/CruhbEplp4/Fvp0Vs+p9qLJXxNVPUtK+g+DQEFsS054sjDonnZG4I0t+WdQpAphMG5fPtxHRPEmaOk80zBSO1vmxZC3dWQSLfptFLC4rZJ3BH978K30X2sSB9KaU2zaYgXWBAEofsRMSwUlzxTJjqVTbXmBIHtrzrbWcB21W1o4IbHNtHS1p4F7PXt2oQkwPSzjk27HKQKPf2sY3N6hl0CJ0fkoJCGuJxkCuFc65XH2T3DLplCGJzLv5yQJohn123mobVbiWtNWCkuHj04lfOcwrUmeJvsyvoT/eKtjm3hjidhz1bry2vRYUpCIWsTXVu4F6XnJifMJ20anekFFgRBEIIjYlgoHqampw4ITwgmGK2susl+XQHe07oNDUyr3UimLvUmLZiEpClNwi//1/v6ih21FpTOetzABInvMw0KyVifXbc5bbBJXOvUZaMgtgnTcXOymu8S2nE+NOhK/nfo1UwaNbhd6Jb1c27UvAv6DKLEK3pF/AqCIPQoRAwLaXSomabITU9BBaOVPdsKel4bi599LUsIu3jFbxAhmU8Vt+jWEZcOeHjzzXAuiCKcUXhorbma+9Dardli2A9P853es433qGRh7ELWfWQ80886lknu5yNCVxAE4YBDxLCQoqeNZu3waf8+g3xPbedLIQ1wXUleyRFBPbxPTTNm9+adQtD7KLMlovdROV9XR4hboiNt674kK8cKZ5b8jzq0ZYIgCEJPQcSwkOKgG83qN3XMZ3S0jXya2bqDvNIwgnh4n5qWPtVNx53LO/4Bl9bnd4B0/avFT5MYdrrZKuEZLR1Wyih8w5nZaIIgCMIhi4hhoXgEaXrqStxT1nmkSfj5VKefdazRMwzpjWxBKqadlf9b1DSM9b8wr2/5gyOU3eluXiyVZMAofIM26hmb4C6tz5kmcfHowWmeYe+6IAiCIICIYaGYBGl6yoOiCEa/pqhMcjQAur5dvzSJoFaTzs40Lgp+087X3Q9DxqS/t7ZKMhiFc9Cxz75NcDli1FxfcM40CUEQBOGQRcSwkKIoTVFFjFHrcsEYoAEwVzNbPlYT2+vIFH9gqZh2JGM3iIdXhX0EsXaSE7xi2FZJXv8Loxi2RdBlrne0CW7BxBONtyt6fJwgCIJwQBLq7g0Qeg4rp52RJXy7ezTr2hvH89ais1M/Papy2gmYhDC0V0xT+GXsBuH6V7Ob1zI9vCdf5v8YmWkdNuHsV2EOQFGb4JL4VaV7BE9Ng/n9YV4f59+npnX3FgmCIBy0SGVYSKMrhG+nVHt70rCPDmCrgkJGxTRHxm6gqmeu5rVzljjNcrbn6jPI//5FInATXB6V8qBV6W7BZjd5a01++/RB8jshCILQ2UhlWOhS/LKDC8bP65sPtkY/z/r4Jb9n6MynUz/jl/w+7aY2S4l3fXbdZobPeoahM59m+KxnmF23OXVdR6qdLkWtel5aD6O+iTNewkMRxgh7mw791m3NbmnrHa2U9yRsdpPtrwavEBfrd0IQBOEQQMSw0KUUa2RwGsUa9nHN2mxB7Kmm+TXHueSymrg2CFf0us1griAuRuRXwVVP26n5c5bABfdAn8GAcv49987sxkQVNj+uZX35FadkCV9TBXvBxBOZMmZI6r0JK8WUMUPSfcABptEdMPjZSmxCOZMiD8ARBEE4mBGbhHBokSvr1uc0ctDmOD+rSa5mMFsUGGRUTP0ydv9mffp0TNVUl8wkiCCpHCdfln5637tuIWjDmq0JrlDGDu9vPDiwVasLphCrgl/jYgf914IgCEI2UhkWDh38pq51EbmawdwqaCZZFdNL69OGSwD5pUn4CWEvQSuR4IjmUd9srwSrsHPZlEeci05uIAtale4QhVoV/BoXbdV3QRAEoWCkMiwYyWvUbh50yrCJoMM+gkxd6yB1GxqY/+Qr7GqKpdaUgsmjndP6QZrBAldBLcI3UNUzqH3AW4kMUuU8Z4lV/Abep/LMKwYCTaPLpBDhm9fvRaFWhXOWOM1yptvlSvhw6WkDcARBEHowUhkWsgjijS2UtTeOzxK+vmkSPx7tVAfdH1NVLYfXt1jkao6r29DA9EdfShPCAFqT8gXbmsGOPrw8rTFv2Myn0xrrXPya71yKWvV0K5EdbMjKa5/yyyu20dFKeQA68/cii2vWdqzK3kW/E4IgCAcDUhkWsshncEQhBI5RyzERLo0u+JJfOe0M38rg4mdfIxa3p0E8tHYrbyz8Uur/3YloRx9envXeamifspasEvtOYnMryUn7w3KAXnRcELqVyABVTuPI5OR25bVPFeqX7ajw9RslTef/XmThU2UPhAhfQRCEQIgYFopLMbNNi90Rn2vq2qZaZ6ranm1Ohu64OVlNY35Wkcbdzb5P7/UFe20Qw2c9Y73P8rVvp40UNpGaxOYXL+YRinUbGjiCEzlFb8YaXmEQg34EEuo9mUKsGbkQq4IgCMIBgdgkhOLR07NN/aaubaqFuqthz1ZAO//WXe2s++Fp9Hq91xTml9xvvaktNs0vW9h7Vc5JbLnixTbV0nTrcUyo+wRDEu/wqq5Ga1I/7w0YA/P2OD9zd+YlAv2E+gHBOsvnZlsPglgVBEEQDgikMixkccwRFcZTvzbPbIoDIdvUNnXtNzdAIt3rSyLmrNsixTKqiSUkmBp+DoC5bd/IurnNL2xrqgt6u0DZxJtq4clrKY81g4JBajtNOsp3Y1dTn6gBoHpfGWts989R5cwl1PPap2zRYt4khaJPV7O9/+3rBf1eiPAVBEHo8UhlWMgi1+CILiPARDggWJNdLpotAyls62Bs6FIKJpc8n7V228dfZcErZ7Vv4/x+qbgwm0gGKI+0/4rabhfXmqEzn7bKOcCxf8TSbRzlqpUZJe2V78bdzfYGvRxVTpsgd9fz2qdsiQnuej5nIH45IX3f6MBEuh7ze2Eh13REQRAEwYzSRRj/2pMZNWqUXrduXXdvxqHBvD4+1+0p7DFzVQBNwsh0u1z4bPvsk1abG8OCvt5NtbDiSozVx2RCgGmEckjBC1V3cuSOF1Nrf68YxRd3ft9YiV0WuZlTw69kDk5Oxo790fj8Ca04umU5ABXRMPtasyuyWdPeDGR6hvO5rxG/Zrag77stS9nUVHhLFbQaGuGiFfCDxny3vssxJV1AzxLrPQml1Hqt9aju3g5BEHoGUhkWikfQSm4+XLO23cc6b0+2wA1qzcg1xEGZfxUSKPv45KDjh39zA9bT8Mnq8vIrTmHpRSdR3bcMBVT3LcsSwgAf37eON477qfGhpsZu5E/x49MXXeHXZ5DxPo16AACRsDIKYYDllol4XgKNTM6Hc5Y4vuUC/Msp8hnRfM5SCGV8bqGws34A0OVJF4IgCAcR4hkWisc1azvBy1kEgiQF6IT5vlqzOnotVWo7jbqS29omUZ+ocRIcxl4WbPywn9VCx1NV0Ik6zkQVhprLnO2ad775Plv+AFxlvGpq7Ebe+s+zs68YNweevDbNKtGko9zWNol+5RHmnns81z280byJ9q1PI2tYyC8nwDyP8Cxy7m9RcX3hOdJEik5P/H0RBEE4xBAxLBSXnvhF7jfEwRXDfQYnkyTSUcCg0HbAaTpbFLkPYjhNZ+59fbJpA2ET6sVkxCTY8ECqKqqBzeo4nkzUUBV1/gxMCK1mRkltlvA3kXMSW8CYt0x7SKDhIJ0VWTZiUueLXy/55GgLgiAInYbYJIQDmyDWDL8hDptq4Y4TkkI4uwkssy/MbTpLNYwFOZ1f1j97rf0ZzMt+09Yga8Kc7/pT0xx7iEecKuAzehPzSu6nYXczv3vkxyyK3Meg0HZCyjkAWBS5jwmh1VRE0+0DgSaxBbAomHzSa97YyeR7XzDf1yVoZJltFLPPiOYupYjpK7mmIwqCIAh2pDIs9GhyVg6DWDOsUV0heOI7EG9NLmgn11dZJSoAVWoHF48ebJ24Vnv/Dxn7z7s4iu28QyVvDzidU1qeyo5uG3a6XTTquP36Yaez/NJTglVVMy0i3pevYHL4eea2fYPpJbWUq9a068tVKzdEajlz4jVp68Xyp2YK4VzraQSpnF5an12h7kyrRjdaHnJNRxQEQRDsiBgWeix+lcMsQezHyZeZBaEq8Qjh5JIiJYht7IkeAWCcuFb+6gqua/5xSlhWs51+O57mhQHncEp8XbYfdX5/e+V6yx8gXAZxTySaR8ylvQeu6JvnXNwUOYkJH87gH6U/p8TntYRxvNJVarvx+iq1g4kjq+0P0NPpKo9yD7A8iPAVBEEoDLFJCF1O0DzUDlUOvZyzxIkwc1MeVNi5nGj1v5+JSBn9zl1gnaw2tWmZscJ64o5n4F+NgHb+fTuZEmHL1HWJNzsVxgvuhXBpUvBmZOYa/Lkntm5kWeTmlNi1PnzyT0CjrjRerywpFDnp6RaFYlOI5aEz0lcEQRCEvJHKsNCl+PlNO7Wydc6SbD+vT6Oa1ul+YTdRQX3yEgD+L3IF/dVeAHbRm3mxqQBUWyqsFbS0P4gpzcJtwjOx/VV4/KrsxAu3Ic1gpVAKTg29QpwQJRZBrDUsj58JwG1tk1gS+SklynPbUNipYGcQaBJbAIvC2OH9jQc2Xt9zQQ12BwodSF8RS4QgCELxkKEbQpcydObT1uveWpQeCZbPbQuh5eYhlMayh4HsSPTmqcQYpoRXEUKnieJWwpSgCWUIzDatSBAmqtry3ArVbptYcUUBr8KO1vCnxPGcGnolXdhrR5f/Kv455rZ9gwmh1dwSuZ8K9mc1DLpDQTIplhjzE7smm0zmbdLoSn9wJp0xcMaCDNjoODJ0QxAEL1IZFnosQSqHQMHVtYX668zS/0Wpaq/Itugw89ucKu/k8PMolX6wGMVcvS1RGshXCANoJ8niyWsLuG/S30x26oWLTQgrnNc3TL3D6NCrae9BGt74OQ/FEl1+Vd68bDIBo9w6jc6KezMgAzYEQRCKi4hhocey/IoAiQlBGpcso31/ufcz7AxdlczW3UGjHsBtbU7O7KLIfemWgc4m1mxPvQiVQMIutG3VX8gWycqTlFFCIut+WbjbYxuPbFnvcntDPtPmMrClguRFTx04IwiCIORExLDQpQTym3rIKaByNS75TJ+r6nsO9btroI3UsIm5Jcvoq/YRVl1vH0roBK8lqjlONaQUq6o8Dk77vtEz7NogpsZuZFnkZk4NveLcR9krxZnkvJ0K29/Dt9akv//J9d++8h5rdl2S9jDGFJBuxGbBcFNBgMIEsSAIgnDAIWkSQpeyctoZWcK3U72OluEVbX/+Ofta2ji/ZE3asIkBob2+QrhFh2nT2QqyTSvadMd+nRoTA/hi62KGtTzIsP0P8vHWh6gbu8KJYDv/bmIq6tgcdLoQBmcMc4OuDCyCA3PyZfYBIJYDkTObnjGuZ4rP2XWbGT7rGYbOfJrhs55hdt3mtOvzGiySBzYh7MWWFtITkAEbgiAIxUUqw0LHyfP0cJc2+VgSGsIk2N0c4/rSh7Oi0KwPpWF67CoA5pYsS0uTeDI+hinh5wrezCYdTVk0XGIJzeJnX2PiyGpmv/lvPND8C9/HsGUFF4zbPJfneOhccW7gCGFTTjO0V2QD2WRcfAaUZBIkmi/egxuLZcCGIAhCcRExLHSM7h42kKtxyeLDdfN1jyI/AVmfqHH+ba1JrU0IrWZR5D7CBVZlExoeiZ+WemwvDbubGTbzafyk2YTQamaU1PpOzXPJjIyz4k2RsHmZLcQDnHCyVV4fWrs1zZ4Q2FZR5Glz4aKX2IuLCF9BEITiITYJoWMUMmygmFyzNrtj31uZNgy18Obr2oZN5MO8SPagjXwIKTgn9KL1+lxCeHHkbgaFtltFrmur2Kl706j7+j5eilceb/9/22AQS1LC8+VfMq577Q22ymuHKrKX1jsxZu5PB1IkLh49uPDtEARBEA4oRAwLBz7XrE0XQd6K9DlLUqfKNe1e2/WJj7M6ei3VanswcQgkgDdLL2F19FomhFYDjhjtx94Ov4T+am/qMXMxIbSa1dFrebP0Eu6I3GWPRUuilFOtnRebSi/aglWQmz1WAtsEv2vWGtc/f8PyLF9vpr3BVnntioqsn+c4rBRTxgzJv3lOEARBOGCRoRtCx+iKYQMdOf29qdbJ8I01p5ZyZfOayLQXNOkoM2OXM6OklkGh4nh1d+refKrlHt/bzC+5n6+FnyNUgGZMaCekIsjr1hrWnXwbnx7aD568DmJJf6oKwclfh3OWdCiSLNMz7NJVQvSgnmwn5ESGbgiC4EXEsNAxTJ5hKF7GqmXccGBBfMcJzlCLTmBbopIqtb0gYWpCa/hu7GqjdxicivDSyF1Fe75c7OYw+rIPDA1xL/Q/n4sbL8xaTxOzOQ5iChHT3vu4FJwN3BFsucvCAYGIYUEQvIgYFjpOZw4b6GjleV5f/F23hZPQsEv3ZkCo4zYJl22JSmpa7zRetzp6bdGq0EHwa7Zr0yE+1vJA1npYKd5Y+KWOH8QYsFWTXbrM3pCZu+ylK0dACwUjYlgQBC+SJiF0nEKEbycIaGOlsc+gTqsMA5SqWFEfr0rt8Lmu64RwLmzxaamKbQcmwtnIlf2bmUTRadhyl6FrR0ALgiAIRUHEsND1dEIcmy23dsTHv86kpsVpnuFiEVJQoVuK+piNeoBxfUJoNRpFZ1W5M2nRYfZRRn9Lc6AtPq3QBrggHt5cSRNdlg2cK2auA4JfEARB6HokTULoevKJYzMMTTCt26qGs17/N/jkJRAoQ6F70ZqswRvgzTHuOkuTQvFkfAwxw7Q9gD8POM+4XkgkmWkinDu+2Usuod1l2cBueoYgCIJwUCBiWOjZXFqfLYgNvkzf3NrXf0tnVlQzn7rQAmUCxdLIXWnRbQAzSmo7lGNcCFHVxrjQRr4f+zbNibCTVexeGY5yyhlfYsqYIWn3SZOiAQ9iwD4RLnM9l9DusmxgW+6yIAiCcEAiNgmh5xPAfxlWyiiIw0rBnm2dsVWAucGskAKl1qQqv4PUdn4UuYs7+G8eiI/rNq9wtdrO3JJlRFQi/TXFW+GJ7zCieibQPnhDQ/tI5SJPhIP2Mc3dnibhpkb4NdEJgiAIBwySJiF0PZ0Qx2ZLGhg7vD8/bPgaA/mgoMftTNxfvQTKaoHQGvbqUg4LFdebXAwadCVjW7KTL1KJEgEZOvNp63VvLTq7oG3rMoos+IWuQdIkBEHwIpVhoeu5Zm3R0yQyq4ZhpRhzdD/+8vYebolfyKLIfV1uNciFW20N+RyQKgXltNKkox3e/kTyaYqVU3wU5uSLfBvZxg7vb7RK+E2K6zGI8BUEQTjgkcqwcNAydtHzNOx2UiQmhFYzt2QZ/dXegmwMXvzydzsDdxjHjJJaqtQOEihKlDnazI+4tlegCyGvynCOgx+ZCCd0JVIZFgTBi1SGhYOWxt3tcWr1iRrqW2uYEFrdYVEZVAi3acV+Sqlgf4fEc5xQavuhPV0i30pxqJhNhOEoa6qvhr9nX5XVyBYgSk+EryAIgtBd9DgxrJR6GDg2ebEvsFtrfZJSaijwN+C15HUvaq2/1fVbKHQ145f8ntff35e6fMwRFaycdkbO+1X1LUtVhl2KISpteE+y7KI382JTqU90TIBr7Qy4+EfpFJbHz2Ru2zeoT9RwcvzvTA0/l5fILliQhyIQjkIs+RmU9Ycv3sqkDQ9wYa+bUjETf0qcwG9P/ml2I1s+UXpFpND9RhAEQTi06HFiWGt9kfv/SqkfAt6Zu29orU/q8o0Suo1MQQPw+vv7GL/k9+nCxtDINP2s/2bWis00x8xDEuoTNRBzosuq1XYSKELogkVjg84epey1ZwC0UUJYJwI/h3u7EhJMDT/HMPUOU2M3cm74xU63amgNO3VvVg2ZxqRvfD/9yuT7rSCVqXZa+GVO23Mj0P0+2sD7jSAIgnDI0+PEsItSSgGTgDO7e1uE7iNT0BjXM4UwwJY/MJFvwwX/zeJnX8uqELt4K8Wro9cyKFRYjFmrLskamDEhtJrbo3cTpV2M96Lw8c1KwamhV5gQWk0/y2S4juBOnevLPhr1AG5rm0R9ooab3vw5zO/vTF5TYSdntxPGLWfRgaSGQPuNIAiCINCzh26cCryntX7dszZMKbVBKfUHpdSptjsqpa5USq1TSq374IOeF6klFBkfYTZxZDVrZp5Jdd+ynA9TSJ6vWz29PnalU2n2MKOkNk0IFwOlnMctJlrDtkQl02NX8amWezi6ZTk1rXdSn6hhfsn9fC28sn0EsY7b83VtVB6X3zpYD3D45YT8nlsQBEEQctAtlWGl1HPAQMNVN2qtn0j+/8XAQ57r3gGGaK13KKVOBuqUUsdrrf+V+SBa63uAe8BJkyju1gsHItPPOjbLMlEWCdMrEmJXk1OtbdSVDMohiDWwS7f7gaNhRWvcvIt11rCMKrWDnbo3A1THq8NNOsrM2OVZQt5lSnhVx+0YhUTpdUXlWRAEQRDoJjGstf6c3/VKqRLgAuBkz31agJbk/69XSr0BfByQ3LSDmGOOqDCe2j7miIq8HmfiyGoAFj/7Go27m6nqW8b0s47lew9vTN3mtrZJWQ11rbqED3Uv+ql060DqeosQhmDiuhAa9QBWJU7ia+q5gjODtXY8zt7XMyG0mlsiP6PC+TVLeajzwjZ9rQMZ0oVQrP1GEARBOPjpqZ7hzwGvaq1Tc3SVUocDO7XWcaXU0cAxwJvdtYFC/hTS3b9y2hm57zfsdHPF0CvMfjmBiVv+wESAXsBRp8PI+jQ/sbehrkrtMIrffLitbRKLI3dTqgJaJVQEtL+nOKGdMclfCxcuhAE0Kq3Zb0JoNUsi/02JJ4c4jPaXwpnvew+avhZovxEEQRAEeujQDaXUL3Ci037qWfsycBPQBsSBuVrrJ3M9lgzd6BmYuvuhiALFr9nK5D9N3qZuhH/iREdx0yQGhDItDWHw+ol7HwV73zE/SKQCHduH1sWbHrctkZ58kXfzYKQCbmxkdt1mlq99OxUrVx4JccsFI1KV+ILx+cx6iuAWDlxk6IYgCF56ZGVYa32ZYe0x4LGu3xqhGHR6d7+fQPJrsLs02z6xr6WN3c35pT5EQopYIvvAsj5RQyiuWBq5F+LePOOkEHa9s/N9Rg+37ec9DmegKk4zaJOOZiVf5OdvDsG5S5ldt5kHXnw7/bFjCabVbgSwCuJAFdtL6zuUJiEIgiAIQemRYlgQupKJI6uZuOnbsP8PsN9pklsTPYEprT9I3UYBRxwW5b0Pswd0jB3enwtHDWH6IxuJGeZpzC1/FGKWwR7bX3VEn7ZXprWOc4T+IJXn2xHadMjYMOfrb1Yh+Eg17NkGfQbBuDkwYhIPPfSM8eYJ7RxcmMRwXvm/InwFQRCELqAnR6sJQteQUYFUwNjQyzzcaxEKqO5bxh0XncTaG8czdnh6BXfs8P4sjy5k4hOf4PXoJWzpdQnLIrcAEFaKKWOG0C/2vv/zb/mDk99rIa5DNOrKQl9dikRymt2PInfxl9IrmRBanbrutrZJtOlsta01/DJ2JpN73wvzdsP3XoYRk5Lb5dM8aMl1lvxfQRAEoachlWGhS+js7n7fU++5GuwM1ylgNJvYsuhseGoa1H8FnoizXIWh5jI4Z4lzQ4OQPi38Mm997O72yuaWQbBnq/8LOPkyY36v1rA8fibrEx/n9sg9RFWb/+P44PUb92cvt0fugVhy8EiyeTAzTeKB+Djmtn0D3tjJ5HtfYPkVp6QeI6yUVRBXBch1FgRBEISegFSGhS5h5bQzsoRvsZrn/E69A44ozYz8Cuo/fWqaI1Izh048Nc25HCQPd9wcCEd9n6au+vs8mBhPQjsCWGunkrss/jlHjEKubIe8iaq2tAEe9YkaTmj5OcNaHmRYy4MMb1meem6ANW/sTLv/xaMHGx83pJxcZ0EQBEE4EJDKsNBldFasVaBT74X6T9f/wr7uVocNaOCEOf/DvtY4UMGE0OXcHvlvIkpnWX/fGzCGabUbSeiv8wO+bny8GSW1wSPa8qBK7Qh0u/kl9zM5/DzMS6RGMi+Y6Lz+fNIkJP9XEARB6GmIGBYEPxuFrfLr0/DmXE9SCDvUJ2qob6lhWeRmTg2/0i6Ih53OBe9cR0KbPbbgCNHqgGkPWpPXxLhGPSDnbeaX3M/U8HPtj+sZybxg4hIWTDwx8PNJ/q8gCILQ0xAxLAh+MV7z+/sLX4Ng1sCfEscbbz41diPVFWWsmXlmaq1x5tPWh59fcj9TS54LHCShlJMYEcKJtfDLJW7VJVkRayYmh583C2xLdXzyvS+kWSrGDu+f5jUuRPiKgBYEQRA6CxHDwgFP3qfeN9XCqpvSo8JsNgpLYxvgCOhL6/n74nEcs7d9sMufEsczNXajdXsbdjcz1EcAe5kcfj7vRLUQmqNbHmRCaHVqmt5uKojoGL2V0xy3i97Mi001TtcLK0Vl70gqRi6MIS8OjAcJmUIYHK9xZvNdPuQVxyYIgiAIeSJiWOiZ/Hi0k8Hr4g6nMJDXqfdNtfD4VaCTAm/PVucypCLD0jhniV0Mb/mDM3hix7QAL8hMu2DdTqOuTFVq3TWb5UFjjx12rQ/1iRrqW/MbJd23LMLGuZ9PX5wfNlfHDXFwmUI413oQJI5NEARB6ExEDAs9j0whDM7lH4/2FcSBeOKadiHsohPOukkM5yBzAls+TAitZlHkPsqVU4EdpLazJPJTwiRy+n4TWrGHCvqxN+22pulyXpZFbubU0Cupy94qdgh4bMhjMP+rjvhNNspZq+MnXxbshQqCIAhCD0ai1YSeR6YQzrWeD/GW/NZ/OaHgp6qI2gdpgFP9dYWwS4nKLYS1hgfi4/hUyz18N3Y12xKVJLTjFS5TrcwoqU0bqOGSat5TpH5ODb3CssjN9C2LsPLYeob/89fZMXIAo77ZXglWYeeyT5qGIAiCIBwoSGVYEGxkNtVlkiM7+JWbvpB2eeyi52nwTGarCpgQkYYKs6zt31P5v/WJGmgjbSDHoND2tIEaLqeGXsmyVigFp4VfcawR879qfs71v4C5OwOJ37HD+xstEZmT+/JB4tgEQRCEzkQqw4Jgw08Io+C8n1h9u6b16Wcdm5bukO+I5cnV/8PQ5l+lDcIAmBdZljWZLqramBdZlnujvNhSM9z1X06AeX3afwxV8+VXnGIeWV1g8xx07sAWQRAEQZDKsNDzqDzObImoPK7jj51rNHNQLrgHRkxi8pubjb7hyWOGZHmfJ1YeB5NW8IMVm2iKJbitbVKaZ9iPf6mPWJvQ+rE357oC/647cOwPtkY5U5V8yx9SiRpeOiJ8bYjwFQRBEDoLqQwLPY9r1mYLX580ibzoyGhmL2+/CPP7s2BjDW/0msJNJT8HnFiyKWOGsGDb5cYmwIlrLuCv//lF3lp0NnfespDyL/8E+pjHGqdQYWa3TMlv+3AsEFPGDAHas4+1aaKz+37YGuJOvizY2GlBEARBOABR2vjtePAwatQovW7dutw3FIRMbJ7h3kfB3ney171NZfP62B933p7cz+HlgnsZ+qDdH/tWv2ug2VA1LuvP8N0/Ie75HfemSShF9oHAU9Mcj7A3TeKcJcFfjyAcACil1mutR3X3dgiC0DMQm4Rw8JBHNnEgbJPpbOJ13c/yS1gIIoRHfdOJfHvQZ0jHF2+FuqshEUsttegw95VeniaEgbRhIG8tOjv7sc5Z0qUpEaNvXpka7gFw5GFR1t44vsueXxAEQRBEDAsHBwVkEwfCZJ/wq5Lmg58Q9lZlyZHSMOJsbnnmb0xtWkaV2kGjHsBtbZOof/ck68OHc+W3ZRLAa53vyORMIQzw3oetjL55pQhiQRAEocsQMSwcHBSaTWwazVzA8I0sOtoEONcRvqbxxi5uSkPdhgbu2T2Kewh+1jeuNbPrNrNg4onB7mCrkicPFgoZmZwphHOtC4IgCEJnIGJYOHTJdzRzPlyztmO2jXl90MBV8eNZw41pV2VGlS1+9jXfh5oyZgjLX3ybzO4ANwUjL0FsQUYmC4IgCAcqkiYhHLr4jWb2wxbDlrl+zVqn2c5l+6twe3tl+L0BY7IEqhdF+4Q4L5mV4kbPIA8TCyaeaH0eUyzc+CW/Z+jMp1M/45f83vfxBUEQBOFARsSwcHBgsx/42RLyHc3sEjSe7fbjslMn9r4Dtx/H5HtfYHTDtfwp7sSduT+ZuCOT/ajqW2a9Lt/Jb352h2Jz5GHmCX62dUEQBEHoDMQmIRwcdNSWkC9BcolN8WuA3vsOa7Y71V1vusOW0ksK2pTpZx3LrBWbaY6lD8w45oiKvAdgFGp3KGRk8tobx0uahCAIgtDtiBgWDh46S/gWmw5Ge2dWeyeOrAYc73Dj7maq+pYx/axjU+vgWC5MT5tnpoSVldPOyDtNAhDhKwiCIHQ7IoaFQ5dijWYuEn9KHM+poVfwpp65k+NcMpvnXCaOrE4Tv5lMHjPEPja6SMjIZEEQBOFARMSwcOiSIy6sw1gm1TXqfsabT43dyIO9FvJZNqfW1LDTOe3Set7q4Ka4iREPrd1KXGvCSnHx6MFZSRKF2B0EQRAE4UBGxjELQj7km0uc2UTX+yhmf+wRY5UWYOlFJ/lWeAHqNjRw4+Ob2dfqeIQVToU3cERaDgqxOwjCgYSMYxYEwYtUhgUhKJtqYcUV7Zf3bG2/bBPE12cP3liQ/PfBtW+TSB6LlkVCLLxgRCAh/P1HXiKeaD+I1eTIDDYIctN2uYjwFQRBEA4lpDIsCEGZ3x90PHtdhVMT4zqbsYuep8GSKxxWijcWfil90RTvBjkFsSAczEhlWBAEL5IzLAhBMQlhv/VOwG/ARtx0YGuJd7OuC4IgCMIhhtgkBOEAoqpvmW9luEvJ034hCIIgCD0REcPCoUtXDukoEtPPOjbLM+xy8ejBhT1oIYkaPtP1RBALgiAIBxJikxAOTTKFMDiXfzzafp9CRj4XmYkjq/nhhZ+kIhpOrSlgii1NovdR5gdy1zOFMDiXfznBf0PEfiEIgiAcJEhlWDg0yRTCudah60c+W8g1YCON61/1tzOYho74rQuCIAjCQYaIYUHIhx5uozAitgVBEARBsCJiWBB6EgdKU5plup7VliEIgiAIPRTxDAuHJj3A/5uFX1NaZzHs9PzWXa5/NVv49lThLgiCIAg+SGVYODTpIf7fNLqjKe3S+sLSJECEryAIgnBQIGJYOHTpLOHb00R2LoIIX0EQBEE4SBGbhCAUk0Ii2wRBEARB6DakMiwIxaSQyDaXDjaljb55Je992Jq6fORhUdbeOD7QfQVBEAThUEUqw4LQU+hAU1qmEAZ478NWRt+8sphbKAiCIAgHHVIZFoSeRIFNaZlCONe6IAiCIAgOUhkWhGLSEyPbBEEQBEGwImJYEIrJNWuzhW9PT5MQBEEQhEMYsUkIQrHpBuF75GFRoyXiyMOiXb4tgiAIgnAgIZVhQTgIWHvj+CzhK2kSgiAIgpAbqQwLwkGCCF9BEARByB+pDAuCIAiCIAiHLCKGBUEQBEEQhEMWEcOCIAiCIAjCIYuIYUEQBEEQBOGQRcSwIAiCIAiCcMjSLWJYKXWhUuoVpVRCKTUq47pZSql/KKVeU0qd5Vk/WSm1OXndnUop1fVbLgiCIAiCIBxMdFdl+GXgAuCP3kWl1CeArwLHA18A7lJKhZNX/zdwJXBM8ucLXba1giAIgiAIwkFJt4hhrfXftNavGa46D/i11rpFa70F+AfwGaXUUcBHtNYvaK01sAyY2HVbLAiCIAiCIByM9DTPcDWw1XN5W3KtOvn/metGlFJXKqXWKaXWffDBB52yoYIgCIIgCMKBT6dNoFNKPQcMNFx1o9b6CdvdDGvaZ92I1voe4B6AUaNGWW8nCIIgCIIgHNp0mhjWWn+ugLttAwZ7Lg8CGpPrgwzrgiAIgiAIglAwPc0mUQ98VSlVqpQahtMo939a63eAD5VSY5IpElMBW3VZEARBEARBEALRXdFq5yultgGnAE8rpZ4F0Fq/AtQCfwX+B/iO1jqevNu3gftwmureAH7T5RsuCIIgCIIgHFQoJ5zh4GXUqFF63bp13b0ZgiAIQg9BKbVeaz0q9y0FQTgU6Gk2CUEQBEEQBEHoMkQMC4IgCIIgCIcsIoYFQRAEQRCEQxYRw4IgCIIgCMIhi4hhQRAEQRAE4ZBFxLAgCIIgCIJwyHLQR6sppT4A/tnd2xGQSmB7d29EEZDX0fM4WF6LvI6ex4H4Wj6qtT68uzdCEISewUEvhg8klFLrDobsS3kdPY+D5bXI6+h5HEyvRRCEQxOxSQiCIAiCIAiHLCKGBUEQBEEQhEMWEcM9i3u6ewOKhLyOnsfB8lrkdfQ8DqbXIgjCIYh4hgVBEARBEIRDFqkMC4IgCIIgCIcsIoYFQRAEQRCEQxYRwz0ApdR/KKVeU0q9opS6zbM+Syn1j+R1Z3XnNuaDUup6pZRWSlV61g6Y16KUWqyUelUptUkp9bhSqq/nugPmdQAopb6Q3NZ/KKVmdvf2BEUpNVgp9Tul1N+SvxffTa73V0qtVEq9nvy3X3dvaxCUUmGl1Aal1FPJywfq6+irlHo0+fvxN6XUKQfqaxEEQXARMdzNKKX+HTgPGKG1Ph64Pbn+CeCrwPHAF4C7lFLhbtvQgCilBgPjgbc9awfaa1kJnKC1HgH8HZgFB97rSG7bT4AvAp8ALk6+hgOBNuD7Wut/A8YA30lu+0zg/7d3/7Fe1XUcx58vRW1BJsssEwvYoB9iAQ22Qg2zOUuH/lGLFcbMtWAFYVta+Ed/tdmsSGflGhIgJDmihDXTIqJcA+wyhBDXHLVJkthK0tpgeF/9cT6XTrf7M+See/q+Hv/ccz7n8/l832++3N3393M/95yttqcAW8t5G3wOOFA7b2sedwE/tf024F1UObU1l4gIIMXwaLAYuMP2MQDbR0r79cAG28ds/wF4GpjdUIzDsQK4Faj/ZWarcrH9qO0T5XQHMKEctyoPqtietn3Q9nFgA1UOo57tw7Z3l+MXqYqui6jiX1O6rQFuaCTAYZA0AbgWWFlrbmMe5wJXAPcB2D5u+wVamEtERF2K4eZNBS6XtFPSdkmzSvtFwDO1fodK26glaR7wJ9tP9LrUulxqPgk8XI7blkfb4u2TpInADGAn8Abbh6EqmIELGgxtqL5J9QGxu9bWxjwmA88D3ytbPlZKGks7c4mIOGlM0wF0Akk/B97Yx6Xbqd6D8VS/Cp4FPChpMqA++jd+H7xBclkOXN3XsD7aGs1loDxsP1T63E716/r1PcP66N/4ezKAtsX7XySNA34ILLP9d6mvlEYvSdcBR2x3SZrbcDinagwwE1hie6eku8iWiIj4P5BieATY/kB/1yQtBja5uuHzLkndwPlUq3gX17pOAJ49rYEOQX+5SLoUmAQ8UQqWCcBuSbMZhbkM9J4ASFoIXAdc5X/fjHvU5TGItsX7HySdRVUIr7e9qTQ/J+lC24clXQgc6X+GUWEOME/Sh4BXAedKWkf78oDq/9Mh2zvL+UaqYriNuUREnJRtEs37MfB+AElTgbOBvwCbgfmSzpE0CZgC7GoqyMHY3mf7AtsTbU+k+sE50/afaVkukq4BbgPm2f5n7VKr8gAeB6ZImiTpbKo//tvccExDouoT1X3AAdvfqF3aDCwsxwuBh0Y6tuGw/SXbE8r3xHzgF7YX0LI8AMr38jOS3lqargKepIW5RETUZWW4eauAVZJ+BxwHFpaVyP2SHqT6YXMC+IztlxuM839mu2253AOcA/ysrHLvsL2obXnYPiHps8AjwJnAKtv7Gw5rqOYANwL7JO0pbcuBO6i2Et1MdceSjzQT3ilrax5LgPXlw9VB4CaqRZU25hIRAeRxzBERERHRwbJNIiIiIiI6VorhiIiIiOhYKYYjIiIiomOlGI6IiIiIjpViOCIiIiI6VorhiNNA0ipJR8ot8wbrO1fSewe4/kFJv5V0QNJTkr72ykY78iQtk/TqpuOIiIhIMRxxeqwGrhli37lAn8WwpGlU9z1eYPvtwDSq+7u23TIgxXBERDQuxXDEaWD7V8Bfe7dLWirpSUl7JW2QNBFYBNwiaY+ky3sNuRX4iu2nyrwnbH+7zPUWSVvLXFslvbm0r5b0HUnbJB2U9L6yUn1A0upaLC9J+rqk3WX860v7dEk7yrw/kjS+tP9S0lcl7ZL0+55YJZ0p6U5Jj5cxny7tc8uYjWVFe70qS4E3AdskbXsF/9kjIiKGLcVwxMj6IjDD9juBRbb/CNwLrLA93fave/WfBnT1M9c9wNoy13rg7tq18VSP+b4F2AKsAC4BLpU0vfQZC+y2PRPYDny5tK8Fbivz7qu1A4yxPZtqZben/WbgqO1ZwCzgU+Vx1QAzSt93AJOBObbvBp4FrrR9ZT+5RUREjIgUwxEjay/V42wXUD3S+VS8B/h+Ob4fuKx2bUt5rPc+4Dnb+2x3A/uBiaVPN/CDcrwOuEzSa4HzbG8v7WuAK2rzbipfu2rzXA18ojw2eSfwOmBKubbL9qHy2ntqYyIiIkaFFMMRI+ta4FvAu4EuSWMG6b+/9B2K+rPVj5Wv3bXjnvP+XnMoz2bvmevl2jwClpSV7em2J9l+tFf/3mMiIiJGhRTDESNE0hnAxba3Ue0FPg8YB7wIvKafYXcCyyVN7ZlD0ufLtd8A88vxx4HHhhnSGcCHy/HHgMdsHwX+Vtu7fCPVFoqBPAIslnRWiXGqpLGDjBko54iIiBGTVZqI00DSA1R3iThf0iGq/bVrgXVlK4Ko9gm/IGkLsFHS9VQrrCf3DdveK2kZ8EC5FZmBn5TLS4FVkr4APA/cNMww/wFcIqkLOAp8tLQvBO4tr3dwCPOupNr+sFuSSiw3DDLmu8DDkg5n33BERDRJ1bbCiOg0kl6yPa7pOCIiIpqUbRIRERER0bGyMhwRERERHSsrwxERERHRsVIMR0RERETHSjEcERERER0rxXBEREREdKwUwxERERHRsf4F6B5wZutgq9kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca1,pca2 = tsne_result1[:,0], tsne_result1[:,1]\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(10,10))\n",
    "for lvl in range(2):\n",
    "    ln = [\"Price Decrease/ Remains the same\", \"Price Increase\"]\n",
    "    i = (y_train == lvl)\n",
    "    ax.scatter(pca1[i], pca2[i], label=ln[lvl])\n",
    "\n",
    "ax.set_aspect('equal')\n",
    "ax.set_title(\"t-SNE of the 1st and 2nd Component\")\n",
    "ax.set_xlabel(\"1st Component\")\n",
    "ax.set_ylabel(\"2nd Component\")\n",
    "ax.legend(loc='upper left', bbox_to_anchor=(1.1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>mom</th>\n",
       "      <th>mom1</th>\n",
       "      <th>mom2</th>\n",
       "      <th>mom3</th>\n",
       "      <th>ROC_5</th>\n",
       "      <th>ROC_10</th>\n",
       "      <th>ROC_15</th>\n",
       "      <th>ROC_20</th>\n",
       "      <th>...</th>\n",
       "      <th>NZD</th>\n",
       "      <th>silver-F</th>\n",
       "      <th>RUSSELL-F</th>\n",
       "      <th>S&amp;P-F</th>\n",
       "      <th>CHF</th>\n",
       "      <th>Dollar index-F</th>\n",
       "      <th>Dollar index</th>\n",
       "      <th>wheat-F</th>\n",
       "      <th>XAG</th>\n",
       "      <th>XAU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Close</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.026512</td>\n",
       "      <td>0.014905</td>\n",
       "      <td>0.014140</td>\n",
       "      <td>0.014884</td>\n",
       "      <td>0.013074</td>\n",
       "      <td>0.036268</td>\n",
       "      <td>0.052317</td>\n",
       "      <td>0.061095</td>\n",
       "      <td>0.064272</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017463</td>\n",
       "      <td>-0.007010</td>\n",
       "      <td>0.010069</td>\n",
       "      <td>0.014651</td>\n",
       "      <td>0.010654</td>\n",
       "      <td>-0.000803</td>\n",
       "      <td>-0.000693</td>\n",
       "      <td>-0.012812</td>\n",
       "      <td>-0.019746</td>\n",
       "      <td>-0.021366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volume</th>\n",
       "      <td>-0.026512</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.076513</td>\n",
       "      <td>-0.004393</td>\n",
       "      <td>0.014723</td>\n",
       "      <td>-0.003473</td>\n",
       "      <td>-0.048215</td>\n",
       "      <td>-0.019406</td>\n",
       "      <td>-0.041006</td>\n",
       "      <td>-0.025288</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006742</td>\n",
       "      <td>0.027267</td>\n",
       "      <td>-0.061320</td>\n",
       "      <td>-0.045736</td>\n",
       "      <td>-0.005965</td>\n",
       "      <td>0.001085</td>\n",
       "      <td>0.003683</td>\n",
       "      <td>0.019767</td>\n",
       "      <td>0.028697</td>\n",
       "      <td>0.036394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mom</th>\n",
       "      <td>0.014905</td>\n",
       "      <td>-0.076513</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.051408</td>\n",
       "      <td>0.023095</td>\n",
       "      <td>-0.064585</td>\n",
       "      <td>0.422857</td>\n",
       "      <td>0.279707</td>\n",
       "      <td>0.239659</td>\n",
       "      <td>0.195592</td>\n",
       "      <td>...</td>\n",
       "      <td>0.465489</td>\n",
       "      <td>0.173100</td>\n",
       "      <td>0.886430</td>\n",
       "      <td>0.975561</td>\n",
       "      <td>-0.028157</td>\n",
       "      <td>-0.231155</td>\n",
       "      <td>-0.232157</td>\n",
       "      <td>0.139613</td>\n",
       "      <td>0.203788</td>\n",
       "      <td>0.007269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mom1</th>\n",
       "      <td>0.014140</td>\n",
       "      <td>-0.004393</td>\n",
       "      <td>-0.051408</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.051511</td>\n",
       "      <td>0.023115</td>\n",
       "      <td>0.403718</td>\n",
       "      <td>0.273996</td>\n",
       "      <td>0.237265</td>\n",
       "      <td>0.192534</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.059843</td>\n",
       "      <td>0.073695</td>\n",
       "      <td>-0.044749</td>\n",
       "      <td>-0.044308</td>\n",
       "      <td>0.043212</td>\n",
       "      <td>0.032858</td>\n",
       "      <td>0.047704</td>\n",
       "      <td>-0.010123</td>\n",
       "      <td>0.045613</td>\n",
       "      <td>0.012436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mom2</th>\n",
       "      <td>0.014884</td>\n",
       "      <td>0.014723</td>\n",
       "      <td>0.023095</td>\n",
       "      <td>-0.051511</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.051504</td>\n",
       "      <td>0.445535</td>\n",
       "      <td>0.283877</td>\n",
       "      <td>0.242344</td>\n",
       "      <td>0.206585</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008653</td>\n",
       "      <td>0.018308</td>\n",
       "      <td>0.008665</td>\n",
       "      <td>0.019147</td>\n",
       "      <td>0.007930</td>\n",
       "      <td>0.001906</td>\n",
       "      <td>-0.015363</td>\n",
       "      <td>-0.004272</td>\n",
       "      <td>0.011694</td>\n",
       "      <td>0.005715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dollar index-F</th>\n",
       "      <td>-0.000803</td>\n",
       "      <td>0.001085</td>\n",
       "      <td>-0.231155</td>\n",
       "      <td>0.032858</td>\n",
       "      <td>0.001906</td>\n",
       "      <td>0.029169</td>\n",
       "      <td>-0.086384</td>\n",
       "      <td>-0.064194</td>\n",
       "      <td>-0.048155</td>\n",
       "      <td>-0.042955</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.543677</td>\n",
       "      <td>-0.334651</td>\n",
       "      <td>-0.198201</td>\n",
       "      <td>-0.226114</td>\n",
       "      <td>0.596993</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.953878</td>\n",
       "      <td>-0.159015</td>\n",
       "      <td>-0.372699</td>\n",
       "      <td>-0.347124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dollar index</th>\n",
       "      <td>-0.000693</td>\n",
       "      <td>0.003683</td>\n",
       "      <td>-0.232157</td>\n",
       "      <td>0.047704</td>\n",
       "      <td>-0.015363</td>\n",
       "      <td>0.034329</td>\n",
       "      <td>-0.082852</td>\n",
       "      <td>-0.065921</td>\n",
       "      <td>-0.048691</td>\n",
       "      <td>-0.044498</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.528321</td>\n",
       "      <td>-0.325971</td>\n",
       "      <td>-0.199722</td>\n",
       "      <td>-0.226215</td>\n",
       "      <td>0.572503</td>\n",
       "      <td>0.953878</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.156925</td>\n",
       "      <td>-0.359829</td>\n",
       "      <td>-0.334305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wheat-F</th>\n",
       "      <td>-0.012812</td>\n",
       "      <td>0.019767</td>\n",
       "      <td>0.139613</td>\n",
       "      <td>-0.010123</td>\n",
       "      <td>-0.004272</td>\n",
       "      <td>-0.024196</td>\n",
       "      <td>0.058995</td>\n",
       "      <td>0.030399</td>\n",
       "      <td>0.014995</td>\n",
       "      <td>0.023546</td>\n",
       "      <td>...</td>\n",
       "      <td>0.155732</td>\n",
       "      <td>0.148058</td>\n",
       "      <td>0.135562</td>\n",
       "      <td>0.133290</td>\n",
       "      <td>-0.100279</td>\n",
       "      <td>-0.159015</td>\n",
       "      <td>-0.156925</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.160597</td>\n",
       "      <td>0.109407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XAG</th>\n",
       "      <td>-0.019746</td>\n",
       "      <td>0.028697</td>\n",
       "      <td>0.203788</td>\n",
       "      <td>0.045613</td>\n",
       "      <td>0.011694</td>\n",
       "      <td>-0.021258</td>\n",
       "      <td>0.096985</td>\n",
       "      <td>0.042112</td>\n",
       "      <td>0.051754</td>\n",
       "      <td>0.049556</td>\n",
       "      <td>...</td>\n",
       "      <td>0.371554</td>\n",
       "      <td>0.867831</td>\n",
       "      <td>0.201130</td>\n",
       "      <td>0.194387</td>\n",
       "      <td>-0.264550</td>\n",
       "      <td>-0.372699</td>\n",
       "      <td>-0.359829</td>\n",
       "      <td>0.160597</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.792705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XAU</th>\n",
       "      <td>-0.021366</td>\n",
       "      <td>0.036394</td>\n",
       "      <td>0.007269</td>\n",
       "      <td>0.012436</td>\n",
       "      <td>0.005715</td>\n",
       "      <td>-0.006242</td>\n",
       "      <td>-0.001497</td>\n",
       "      <td>-0.032030</td>\n",
       "      <td>-0.018250</td>\n",
       "      <td>-0.014553</td>\n",
       "      <td>...</td>\n",
       "      <td>0.310317</td>\n",
       "      <td>0.684038</td>\n",
       "      <td>0.014880</td>\n",
       "      <td>0.005654</td>\n",
       "      <td>-0.321169</td>\n",
       "      <td>-0.347124</td>\n",
       "      <td>-0.334305</td>\n",
       "      <td>0.109407</td>\n",
       "      <td>0.792705</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Close    Volume       mom      mom1      mom2      mom3  \\\n",
       "Close           1.000000 -0.026512  0.014905  0.014140  0.014884  0.013074   \n",
       "Volume         -0.026512  1.000000 -0.076513 -0.004393  0.014723 -0.003473   \n",
       "mom             0.014905 -0.076513  1.000000 -0.051408  0.023095 -0.064585   \n",
       "mom1            0.014140 -0.004393 -0.051408  1.000000 -0.051511  0.023115   \n",
       "mom2            0.014884  0.014723  0.023095 -0.051511  1.000000 -0.051504   \n",
       "...                  ...       ...       ...       ...       ...       ...   \n",
       "Dollar index-F -0.000803  0.001085 -0.231155  0.032858  0.001906  0.029169   \n",
       "Dollar index   -0.000693  0.003683 -0.232157  0.047704 -0.015363  0.034329   \n",
       "wheat-F        -0.012812  0.019767  0.139613 -0.010123 -0.004272 -0.024196   \n",
       "XAG            -0.019746  0.028697  0.203788  0.045613  0.011694 -0.021258   \n",
       "XAU            -0.021366  0.036394  0.007269  0.012436  0.005715 -0.006242   \n",
       "\n",
       "                   ROC_5    ROC_10    ROC_15    ROC_20  ...       NZD  \\\n",
       "Close           0.036268  0.052317  0.061095  0.064272  ... -0.017463   \n",
       "Volume         -0.048215 -0.019406 -0.041006 -0.025288  ...  0.006742   \n",
       "mom             0.422857  0.279707  0.239659  0.195592  ...  0.465489   \n",
       "mom1            0.403718  0.273996  0.237265  0.192534  ... -0.059843   \n",
       "mom2            0.445535  0.283877  0.242344  0.206585  ...  0.008653   \n",
       "...                  ...       ...       ...       ...  ...       ...   \n",
       "Dollar index-F -0.086384 -0.064194 -0.048155 -0.042955  ... -0.543677   \n",
       "Dollar index   -0.082852 -0.065921 -0.048691 -0.044498  ... -0.528321   \n",
       "wheat-F         0.058995  0.030399  0.014995  0.023546  ...  0.155732   \n",
       "XAG             0.096985  0.042112  0.051754  0.049556  ...  0.371554   \n",
       "XAU            -0.001497 -0.032030 -0.018250 -0.014553  ...  0.310317   \n",
       "\n",
       "                silver-F  RUSSELL-F     S&P-F       CHF  Dollar index-F  \\\n",
       "Close          -0.007010   0.010069  0.014651  0.010654       -0.000803   \n",
       "Volume          0.027267  -0.061320 -0.045736 -0.005965        0.001085   \n",
       "mom             0.173100   0.886430  0.975561 -0.028157       -0.231155   \n",
       "mom1            0.073695  -0.044749 -0.044308  0.043212        0.032858   \n",
       "mom2            0.018308   0.008665  0.019147  0.007930        0.001906   \n",
       "...                  ...        ...       ...       ...             ...   \n",
       "Dollar index-F -0.334651  -0.198201 -0.226114  0.596993        1.000000   \n",
       "Dollar index   -0.325971  -0.199722 -0.226215  0.572503        0.953878   \n",
       "wheat-F         0.148058   0.135562  0.133290 -0.100279       -0.159015   \n",
       "XAG             0.867831   0.201130  0.194387 -0.264550       -0.372699   \n",
       "XAU             0.684038   0.014880  0.005654 -0.321169       -0.347124   \n",
       "\n",
       "                Dollar index   wheat-F       XAG       XAU  \n",
       "Close              -0.000693 -0.012812 -0.019746 -0.021366  \n",
       "Volume              0.003683  0.019767  0.028697  0.036394  \n",
       "mom                -0.232157  0.139613  0.203788  0.007269  \n",
       "mom1                0.047704 -0.010123  0.045613  0.012436  \n",
       "mom2               -0.015363 -0.004272  0.011694  0.005715  \n",
       "...                      ...       ...       ...       ...  \n",
       "Dollar index-F      0.953878 -0.159015 -0.372699 -0.347124  \n",
       "Dollar index        1.000000 -0.156925 -0.359829 -0.334305  \n",
       "wheat-F            -0.156925  1.000000  0.160597  0.109407  \n",
       "XAG                -0.359829  0.160597  1.000000  0.792705  \n",
       "XAU                -0.334305  0.109407  0.792705  1.000000  \n",
       "\n",
       "[82 rows x 82 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "markets[4].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = markets[4].corr()\n",
    "corr[\"Close\"].sort_values(ascending=False)[:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(markets[4].columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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