Warning, /RecoTracker/LSTCore/standalone/analysis/DNN/train_pT3_DNN.ipynb is written in an unsupported language. File is not indexed.
0001 {
0002 "cells": [
0003 {
0004 "cell_type": "code",
0005 "execution_count": 1,
0006 "metadata": {},
0007 "outputs": [],
0008 "source": [
0009 "import os\n",
0010 "import uproot\n",
0011 "import numpy as np\n",
0012 "\n",
0013 "def load_root_file(file_path, branches=None, print_branches=False):\n",
0014 " all_branches = {}\n",
0015 " with uproot.open(file_path) as file:\n",
0016 " tree = file[\"tree\"]\n",
0017 " # Load all ROOT branches into array if not specified\n",
0018 " if branches is None:\n",
0019 " branches = tree.keys()\n",
0020 " # Option to print the branch names\n",
0021 " if print_branches:\n",
0022 " print(\"Branches:\", tree.keys())\n",
0023 " # Each branch is added to the dictionary\n",
0024 " for branch in branches:\n",
0025 " try:\n",
0026 " all_branches[branch] = (tree[branch].array(library=\"np\"))\n",
0027 " except uproot.KeyInFileError as e:\n",
0028 " print(f\"KeyInFileError: {e}\")\n",
0029 " # Number of events in file\n",
0030 " all_branches['event'] = tree.num_entries\n",
0031 " return all_branches\n",
0032 "\n",
0033 "branches_list = [\n",
0034 " 'sim_pT3_matched',\n",
0035 " 'pT3_pt',\n",
0036 " 'pT3_isFake',\n",
0037 " 'pT3_isDuplicate',\n",
0038 " 'pT3_eta',\n",
0039 " 'pT3_phi',\n",
0040 " 'pT3_score',\n",
0041 " 'pT3_foundDuplicate',\n",
0042 " 'pT3_matched_simIdx',\n",
0043 " 'pT3_hitIdxs',\n",
0044 " 'pT3_pixelRadius',\n",
0045 " 'pT3_pixelRadiusError',\n",
0046 " 'pT3_tripletRadius',\n",
0047 " 'pT3_rPhiChiSquared',\n",
0048 " 'pT3_rPhiChiSquaredInwards',\n",
0049 " 'pT3_rzChiSquared',\n",
0050 " 'pT3_layer_binary',\n",
0051 " 'pT3_moduleType_binary'\n",
0052 "]\n",
0053 "\n",
0054 "file_path = \"pt3_500_fixed_new_2.root\"\n",
0055 "branches = load_root_file(file_path, branches_list)"
0056 ]
0057 },
0058 {
0059 "cell_type": "code",
0060 "execution_count": 2,
0061 "metadata": {},
0062 "outputs": [],
0063 "source": [
0064 "eta_max = 2.5\n",
0065 "phi_max = np.pi"
0066 ]
0067 },
0068 {
0069 "cell_type": "code",
0070 "execution_count": 3,
0071 "metadata": {},
0072 "outputs": [],
0073 "source": [
0074 "n_events = branches['event']\n",
0075 "\n",
0076 "# Concatenate the pt3 branches over all events\n",
0077 "pt3_rPhiChiSquared = np.concatenate([branches['pT3_rPhiChiSquared'][evt] for evt in range(n_events)])\n",
0078 "pt3_rPhiChiSquaredInwards = np.concatenate([branches['pT3_rPhiChiSquaredInwards'][evt] for evt in range(n_events)])\n",
0079 "pt3_rzChiSquared = np.concatenate([branches['pT3_rzChiSquared'][evt] for evt in range(n_events)])\n",
0080 "pt3_eta = np.abs(np.concatenate([branches['pT3_eta'][evt] for evt in range(n_events)]))\n",
0081 "pt3_trip_rad = np.abs(np.concatenate([branches['pT3_tripletRadius'][evt] for evt in range(n_events)]))\n",
0082 "pt3_pix_rad = np.abs(np.concatenate([branches['pT3_pixelRadius'][evt] for evt in range(n_events)]))\n",
0083 "pt3_pixRadError = np.abs(np.concatenate([branches['pT3_pixelRadiusError'][evt] for evt in range(n_events)]))\n",
0084 "\n",
0085 "# Build the features array using the helper functions\n",
0086 "features = np.array([\n",
0087 " np.log10(pt3_rPhiChiSquared),\n",
0088 " np.log10(pt3_trip_rad),\n",
0089 " np.log10(pt3_pix_rad),\n",
0090 " np.log10(pt3_pixRadError),\n",
0091 " np.log10(pt3_rzChiSquared),\n",
0092 " np.abs(pt3_eta)/eta_max\n",
0093 "])\n",
0094 "\n",
0095 "eta_list = np.array([pt3_eta])"
0096 ]
0097 },
0098 {
0099 "cell_type": "code",
0100 "execution_count": 4,
0101 "metadata": {},
0102 "outputs": [
0103 {
0104 "name": "stdout",
0105 "output_type": "stream",
0106 "text": [
0107 "Using device: cuda\n",
0108 "Initial dataset size: 229144\n",
0109 "Class distribution before downsampling - Real: 192431.0, Fake: 36713.0\n",
0110 "Class distribution after downsampling - Real: 192431.0, Fake: 36713.0\n",
0111 "Epoch [1/200], Train Loss: 0.2689, Test Loss: 0.2072\n",
0112 "Epoch [2/200], Train Loss: 0.1906, Test Loss: 0.1789\n",
0113 "Epoch [3/200], Train Loss: 0.1737, Test Loss: 0.1718\n",
0114 "Epoch [4/200], Train Loss: 0.1693, Test Loss: 0.1684\n",
0115 "Epoch [5/200], Train Loss: 0.1665, Test Loss: 0.1673\n",
0116 "Epoch [6/200], Train Loss: 0.1653, Test Loss: 0.1661\n",
0117 "Epoch [7/200], Train Loss: 0.1661, Test Loss: 0.1631\n",
0118 "Epoch [8/200], Train Loss: 0.1633, Test Loss: 0.1639\n",
0119 "Epoch [9/200], Train Loss: 0.1628, Test Loss: 0.1792\n",
0120 "Epoch [10/200], Train Loss: 0.1621, Test Loss: 0.1624\n",
0121 "Epoch [11/200], Train Loss: 0.1610, Test Loss: 0.1613\n",
0122 "Epoch [12/200], Train Loss: 0.1609, Test Loss: 0.1631\n",
0123 "Epoch [13/200], Train Loss: 0.1617, Test Loss: 0.1628\n",
0124 "Epoch [14/200], Train Loss: 0.1607, Test Loss: 0.1597\n",
0125 "Epoch [15/200], Train Loss: 0.1595, Test Loss: 0.1610\n",
0126 "Epoch [16/200], Train Loss: 0.1584, Test Loss: 0.1641\n",
0127 "Epoch [17/200], Train Loss: 0.1576, Test Loss: 0.1582\n",
0128 "Epoch [18/200], Train Loss: 0.1581, Test Loss: 0.1578\n",
0129 "Epoch [19/200], Train Loss: 0.1581, Test Loss: 0.1707\n",
0130 "Epoch [20/200], Train Loss: 0.1589, Test Loss: 0.1618\n",
0131 "Epoch [21/200], Train Loss: 0.1577, Test Loss: 0.1579\n",
0132 "Epoch [22/200], Train Loss: 0.1571, Test Loss: 0.1579\n",
0133 "Epoch [23/200], Train Loss: 0.1563, Test Loss: 0.1609\n",
0134 "Epoch [24/200], Train Loss: 0.1558, Test Loss: 0.1552\n",
0135 "Epoch [25/200], Train Loss: 0.1579, Test Loss: 0.1723\n",
0136 "Epoch [26/200], Train Loss: 0.1571, Test Loss: 0.1547\n",
0137 "Epoch [27/200], Train Loss: 0.1547, Test Loss: 0.1582\n",
0138 "Epoch [28/200], Train Loss: 0.1549, Test Loss: 0.1542\n",
0139 "Epoch [29/200], Train Loss: 0.1536, Test Loss: 0.1556\n",
0140 "Epoch [30/200], Train Loss: 0.1528, Test Loss: 0.1554\n",
0141 "Epoch [31/200], Train Loss: 0.1523, Test Loss: 0.1540\n",
0142 "Epoch [32/200], Train Loss: 0.1527, Test Loss: 0.1526\n",
0143 "Epoch [33/200], Train Loss: 0.1526, Test Loss: 0.1558\n",
0144 "Epoch [34/200], Train Loss: 0.1533, Test Loss: 0.1523\n",
0145 "Epoch [35/200], Train Loss: 0.1516, Test Loss: 0.1517\n",
0146 "Epoch [36/200], Train Loss: 0.1510, Test Loss: 0.1535\n",
0147 "Epoch [37/200], Train Loss: 0.1509, Test Loss: 0.1519\n",
0148 "Epoch [38/200], Train Loss: 0.1498, Test Loss: 0.1517\n",
0149 "Epoch [39/200], Train Loss: 0.1513, Test Loss: 0.1525\n",
0150 "Epoch [40/200], Train Loss: 0.1509, Test Loss: 0.1496\n",
0151 "Epoch [41/200], Train Loss: 0.1491, Test Loss: 0.1498\n",
0152 "Epoch [42/200], Train Loss: 0.1497, Test Loss: 0.1517\n",
0153 "Epoch [43/200], Train Loss: 0.1484, Test Loss: 0.1499\n",
0154 "Epoch [44/200], Train Loss: 0.1475, Test Loss: 0.1513\n",
0155 "Epoch [45/200], Train Loss: 0.1470, Test Loss: 0.1488\n",
0156 "Epoch [46/200], Train Loss: 0.1512, Test Loss: 0.2419\n",
0157 "Epoch [47/200], Train Loss: 0.1649, Test Loss: 0.1603\n",
0158 "Epoch [48/200], Train Loss: 0.1575, Test Loss: 0.1575\n",
0159 "Epoch [49/200], Train Loss: 0.1561, Test Loss: 0.1585\n",
0160 "Epoch [50/200], Train Loss: 0.1551, Test Loss: 0.1543\n",
0161 "Epoch [51/200], Train Loss: 0.1542, Test Loss: 0.1568\n",
0162 "Epoch [52/200], Train Loss: 0.1529, Test Loss: 0.1546\n",
0163 "Epoch [53/200], Train Loss: 0.1536, Test Loss: 0.1562\n",
0164 "Epoch [54/200], Train Loss: 0.1522, Test Loss: 0.1531\n",
0165 "Epoch [55/200], Train Loss: 0.1514, Test Loss: 0.1563\n",
0166 "Epoch [56/200], Train Loss: 0.1505, Test Loss: 0.1525\n",
0167 "Epoch [57/200], Train Loss: 0.1503, Test Loss: 0.1511\n",
0168 "Epoch [58/200], Train Loss: 0.1491, Test Loss: 0.1507\n",
0169 "Epoch [59/200], Train Loss: 0.1495, Test Loss: 0.1497\n",
0170 "Epoch [60/200], Train Loss: 0.1493, Test Loss: 0.1484\n",
0171 "Epoch [61/200], Train Loss: 0.1494, Test Loss: 0.1530\n",
0172 "Epoch [62/200], Train Loss: 0.1481, Test Loss: 0.1469\n",
0173 "Epoch [63/200], Train Loss: 0.1460, Test Loss: 0.1493\n",
0174 "Epoch [64/200], Train Loss: 0.1462, Test Loss: 0.1473\n",
0175 "Epoch [65/200], Train Loss: 0.1460, Test Loss: 0.1468\n",
0176 "Epoch [66/200], Train Loss: 0.1450, Test Loss: 0.1450\n",
0177 "Epoch [67/200], Train Loss: 0.1444, Test Loss: 0.1503\n",
0178 "Epoch [68/200], Train Loss: 0.1473, Test Loss: 0.1676\n",
0179 "Epoch [69/200], Train Loss: 0.1483, Test Loss: 0.1446\n",
0180 "Epoch [70/200], Train Loss: 0.1445, Test Loss: 0.1468\n",
0181 "Epoch [71/200], Train Loss: 0.1438, Test Loss: 0.1471\n",
0182 "Epoch [72/200], Train Loss: 0.1439, Test Loss: 0.1545\n",
0183 "Epoch [73/200], Train Loss: 0.1427, Test Loss: 0.1447\n",
0184 "Epoch [74/200], Train Loss: 0.1432, Test Loss: 0.1433\n",
0185 "Epoch [75/200], Train Loss: 0.1415, Test Loss: 0.1472\n",
0186 "Epoch [76/200], Train Loss: 0.1418, Test Loss: 0.1480\n",
0187 "Epoch [77/200], Train Loss: 0.1411, Test Loss: 0.1423\n",
0188 "Epoch [78/200], Train Loss: 0.1420, Test Loss: 0.1507\n",
0189 "Epoch [79/200], Train Loss: 0.1409, Test Loss: 0.1429\n",
0190 "Epoch [80/200], Train Loss: 0.1404, Test Loss: 0.1422\n",
0191 "Epoch [81/200], Train Loss: 0.1413, Test Loss: 0.1446\n",
0192 "Epoch [82/200], Train Loss: 0.1417, Test Loss: 0.2182\n",
0193 "Epoch [83/200], Train Loss: 0.1450, Test Loss: 0.1410\n",
0194 "Epoch [84/200], Train Loss: 0.1415, Test Loss: 0.1561\n",
0195 "Epoch [85/200], Train Loss: 0.1441, Test Loss: 0.1430\n",
0196 "Epoch [86/200], Train Loss: 0.1410, Test Loss: 0.1409\n",
0197 "Epoch [87/200], Train Loss: 0.1392, Test Loss: 0.1431\n",
0198 "Epoch [88/200], Train Loss: 0.1410, Test Loss: 0.1445\n",
0199 "Epoch [89/200], Train Loss: 0.1387, Test Loss: 0.1401\n",
0200 "Epoch [90/200], Train Loss: 0.1394, Test Loss: 0.1408\n",
0201 "Epoch [91/200], Train Loss: 0.1389, Test Loss: 0.1415\n",
0202 "Epoch [92/200], Train Loss: 0.1393, Test Loss: 0.1410\n",
0203 "Epoch [93/200], Train Loss: 0.1379, Test Loss: 0.1395\n",
0204 "Epoch [94/200], Train Loss: 0.1381, Test Loss: 0.1409\n",
0205 "Epoch [95/200], Train Loss: 0.1404, Test Loss: 0.1427\n",
0206 "Epoch [96/200], Train Loss: 0.1392, Test Loss: 0.1404\n",
0207 "Epoch [97/200], Train Loss: 0.1387, Test Loss: 0.1390\n",
0208 "Epoch [98/200], Train Loss: 0.1384, Test Loss: 0.1421\n",
0209 "Epoch [99/200], Train Loss: 0.1383, Test Loss: 0.1411\n",
0210 "Epoch [100/200], Train Loss: 0.1374, Test Loss: 0.1375\n",
0211 "Epoch [101/200], Train Loss: 0.1365, Test Loss: 0.1389\n",
0212 "Epoch [102/200], Train Loss: 0.1369, Test Loss: 0.1383\n",
0213 "Epoch [103/200], Train Loss: 0.1368, Test Loss: 0.1435\n",
0214 "Epoch [104/200], Train Loss: 0.1369, Test Loss: 0.1376\n",
0215 "Epoch [105/200], Train Loss: 0.1370, Test Loss: 0.1374\n",
0216 "Epoch [106/200], Train Loss: 0.1364, Test Loss: 0.1435\n",
0217 "Epoch [107/200], Train Loss: 0.1360, Test Loss: 0.1390\n",
0218 "Epoch [108/200], Train Loss: 0.1368, Test Loss: 0.1376\n",
0219 "Epoch [109/200], Train Loss: 0.1362, Test Loss: 0.1407\n",
0220 "Epoch [110/200], Train Loss: 0.1360, Test Loss: 0.1387\n",
0221 "Epoch [111/200], Train Loss: 0.1354, Test Loss: 0.1364\n",
0222 "Epoch [112/200], Train Loss: 0.1361, Test Loss: 0.1387\n",
0223 "Epoch [113/200], Train Loss: 0.1362, Test Loss: 0.1387\n",
0224 "Epoch [114/200], Train Loss: 0.1358, Test Loss: 0.1378\n",
0225 "Epoch [115/200], Train Loss: 0.1349, Test Loss: 0.1368\n",
0226 "Epoch [116/200], Train Loss: 0.1354, Test Loss: 0.1367\n",
0227 "Epoch [117/200], Train Loss: 0.1361, Test Loss: 0.1434\n",
0228 "Epoch [118/200], Train Loss: 0.1364, Test Loss: 0.1370\n",
0229 "Epoch [119/200], Train Loss: 0.1375, Test Loss: 0.1418\n",
0230 "Epoch [120/200], Train Loss: 0.1373, Test Loss: 0.1389\n",
0231 "Epoch [121/200], Train Loss: 0.1353, Test Loss: 0.1392\n",
0232 "Epoch [122/200], Train Loss: 0.1354, Test Loss: 0.1377\n",
0233 "Epoch [123/200], Train Loss: 0.1365, Test Loss: 0.1422\n",
0234 "Epoch [124/200], Train Loss: 0.1359, Test Loss: 0.1354\n",
0235 "Epoch [125/200], Train Loss: 0.1343, Test Loss: 0.1370\n",
0236 "Epoch [126/200], Train Loss: 0.1340, Test Loss: 0.1358\n",
0237 "Epoch [127/200], Train Loss: 0.1347, Test Loss: 0.1373\n",
0238 "Epoch [128/200], Train Loss: 0.1352, Test Loss: 0.1367\n",
0239 "Epoch [129/200], Train Loss: 0.1351, Test Loss: 0.1360\n",
0240 "Epoch [130/200], Train Loss: 0.1344, Test Loss: 0.1362\n",
0241 "Epoch [131/200], Train Loss: 0.1360, Test Loss: 0.1722\n",
0242 "Epoch [132/200], Train Loss: 0.1371, Test Loss: 0.1358\n",
0243 "Epoch [133/200], Train Loss: 0.1338, Test Loss: 0.1354\n",
0244 "Epoch [134/200], Train Loss: 0.1335, Test Loss: 0.1359\n",
0245 "Epoch [135/200], Train Loss: 0.1354, Test Loss: 0.1755\n",
0246 "Epoch [136/200], Train Loss: 0.1396, Test Loss: 0.1357\n",
0247 "Epoch [137/200], Train Loss: 0.1346, Test Loss: 0.1348\n",
0248 "Epoch [138/200], Train Loss: 0.1334, Test Loss: 0.1362\n",
0249 "Epoch [139/200], Train Loss: 0.1340, Test Loss: 0.1376\n",
0250 "Epoch [140/200], Train Loss: 0.1339, Test Loss: 0.1346\n",
0251 "Epoch [141/200], Train Loss: 0.1336, Test Loss: 0.1388\n",
0252 "Epoch [142/200], Train Loss: 0.1350, Test Loss: 0.1368\n",
0253 "Epoch [143/200], Train Loss: 0.1359, Test Loss: 0.1358\n",
0254 "Epoch [144/200], Train Loss: 0.1348, Test Loss: 0.1392\n",
0255 "Epoch [145/200], Train Loss: 0.1339, Test Loss: 0.1440\n",
0256 "Epoch [146/200], Train Loss: 0.1344, Test Loss: 0.1351\n",
0257 "Epoch [147/200], Train Loss: 0.1330, Test Loss: 0.1362\n",
0258 "Epoch [148/200], Train Loss: 0.1339, Test Loss: 0.1373\n",
0259 "Epoch [149/200], Train Loss: 0.1343, Test Loss: 0.1495\n",
0260 "Epoch [150/200], Train Loss: 0.1360, Test Loss: 0.1357\n",
0261 "Epoch [151/200], Train Loss: 0.1328, Test Loss: 0.1360\n",
0262 "Epoch [152/200], Train Loss: 0.1329, Test Loss: 0.1373\n",
0263 "Epoch [153/200], Train Loss: 0.1331, Test Loss: 0.1389\n",
0264 "Epoch [154/200], Train Loss: 0.1346, Test Loss: 0.1431\n",
0265 "Epoch [155/200], Train Loss: 0.1355, Test Loss: 0.1465\n",
0266 "Epoch [156/200], Train Loss: 0.1343, Test Loss: 0.1359\n",
0267 "Epoch [157/200], Train Loss: 0.1326, Test Loss: 0.1340\n",
0268 "Epoch [158/200], Train Loss: 0.1327, Test Loss: 0.1340\n",
0269 "Epoch [159/200], Train Loss: 0.1342, Test Loss: 0.1459\n",
0270 "Epoch [160/200], Train Loss: 0.1334, Test Loss: 0.1347\n",
0271 "Epoch [161/200], Train Loss: 0.1332, Test Loss: 0.1379\n",
0272 "Epoch [162/200], Train Loss: 0.1334, Test Loss: 0.1357\n",
0273 "Epoch [163/200], Train Loss: 0.1329, Test Loss: 0.1396\n",
0274 "Epoch [164/200], Train Loss: 0.1341, Test Loss: 0.1364\n",
0275 "Epoch [165/200], Train Loss: 0.1334, Test Loss: 0.1348\n",
0276 "Epoch [166/200], Train Loss: 0.1328, Test Loss: 0.1346\n",
0277 "Epoch [167/200], Train Loss: 0.1327, Test Loss: 0.1343\n",
0278 "Epoch [168/200], Train Loss: 0.1343, Test Loss: 0.1438\n",
0279 "Epoch [169/200], Train Loss: 0.1343, Test Loss: 0.1335\n",
0280 "Epoch [170/200], Train Loss: 0.1325, Test Loss: 0.1337\n",
0281 "Epoch [171/200], Train Loss: 0.1322, Test Loss: 0.1363\n",
0282 "Epoch [172/200], Train Loss: 0.1334, Test Loss: 0.1340\n",
0283 "Epoch [173/200], Train Loss: 0.1336, Test Loss: 0.1366\n",
0284 "Epoch [174/200], Train Loss: 0.1326, Test Loss: 0.1357\n",
0285 "Epoch [175/200], Train Loss: 0.1324, Test Loss: 0.1390\n",
0286 "Epoch [176/200], Train Loss: 0.1331, Test Loss: 0.1346\n",
0287 "Epoch [177/200], Train Loss: 0.1327, Test Loss: 0.1356\n",
0288 "Epoch [178/200], Train Loss: 0.1327, Test Loss: 0.1337\n",
0289 "Epoch [179/200], Train Loss: 0.1326, Test Loss: 0.1346\n",
0290 "Epoch [180/200], Train Loss: 0.1329, Test Loss: 0.1360\n",
0291 "Epoch [181/200], Train Loss: 0.1317, Test Loss: 0.1336\n",
0292 "Epoch [182/200], Train Loss: 0.1315, Test Loss: 0.1345\n",
0293 "Epoch [183/200], Train Loss: 0.1334, Test Loss: 0.1330\n",
0294 "Epoch [184/200], Train Loss: 0.1321, Test Loss: 0.1344\n",
0295 "Epoch [185/200], Train Loss: 0.1321, Test Loss: 0.1395\n",
0296 "Epoch [186/200], Train Loss: 0.1332, Test Loss: 0.1420\n",
0297 "Epoch [187/200], Train Loss: 0.1329, Test Loss: 0.1348\n",
0298 "Epoch [188/200], Train Loss: 0.1313, Test Loss: 0.1343\n",
0299 "Epoch [189/200], Train Loss: 0.1312, Test Loss: 0.1338\n",
0300 "Epoch [190/200], Train Loss: 0.1328, Test Loss: 0.1375\n",
0301 "Epoch [191/200], Train Loss: 0.1319, Test Loss: 0.1368\n",
0302 "Epoch [192/200], Train Loss: 0.1322, Test Loss: 0.1353\n",
0303 "Epoch [193/200], Train Loss: 0.1323, Test Loss: 0.1349\n",
0304 "Epoch [194/200], Train Loss: 0.1318, Test Loss: 0.1340\n",
0305 "Epoch [195/200], Train Loss: 0.1317, Test Loss: 0.1378\n",
0306 "Epoch [196/200], Train Loss: 0.1334, Test Loss: 0.1360\n",
0307 "Epoch [197/200], Train Loss: 0.1337, Test Loss: 0.1331\n",
0308 "Epoch [198/200], Train Loss: 0.1335, Test Loss: 0.1334\n",
0309 "Epoch [199/200], Train Loss: 0.1331, Test Loss: 0.1359\n",
0310 "Epoch [200/200], Train Loss: 0.1324, Test Loss: 0.1337\n"
0311 ]
0312 }
0313 ],
0314 "source": [
0315 "import torch\n",
0316 "import torch.nn as nn\n",
0317 "from torch.optim import Adam\n",
0318 "from torch.utils.data import DataLoader, TensorDataset, random_split\n",
0319 "import numpy as np\n",
0320 "\n",
0321 "# ------------------ Preprocessing ------------------\n",
0322 "input_features_numpy = np.stack(features, axis=-1)\n",
0323 "mask = ~np.isnan(input_features_numpy) & ~np.isinf(input_features_numpy)\n",
0324 "filtered_input_features_numpy = input_features_numpy[np.all(mask, axis=1)]\n",
0325 "t3_isFake_filtered = 1 - (np.concatenate(branches['pT3_isFake']))[np.all(mask, axis=1)]\n",
0326 "\n",
0327 "# Convert to PyTorch tensors.\n",
0328 "input_features_tensor = torch.tensor(filtered_input_features_numpy, dtype=torch.float32)\n",
0329 "labels_tensor = torch.tensor(t3_isFake_filtered, dtype=torch.float32).unsqueeze(1)\n",
0330 "\n",
0331 "# ------------------ Device Setup ------------------\n",
0332 "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
0333 "print(f\"Using device: {device}\")\n",
0334 "\n",
0335 "# ------------------ Neural Network ------------------\n",
0336 "class BinaryClassificationNeuralNetwork(nn.Module):\n",
0337 " def __init__(self, input_dim):\n",
0338 " super(BinaryClassificationNeuralNetwork, self).__init__()\n",
0339 " self.layer1 = nn.Linear(input_dim, 32)\n",
0340 " self.layer2 = nn.Linear(32, 32)\n",
0341 " self.output_layer = nn.Linear(32, 1) # Single output for binary classification\n",
0342 "\n",
0343 " def forward(self, x):\n",
0344 " x = self.layer1(x)\n",
0345 " x = nn.ReLU()(x)\n",
0346 " x = self.layer2(x)\n",
0347 " x = nn.ReLU()(x)\n",
0348 " x = self.output_layer(x)\n",
0349 " return torch.sigmoid(x) # Sigmoid activation for output between 0 and 1\n",
0350 "\n",
0351 "# ------------------ Loss Function ------------------\n",
0352 "class WeightedBinaryCrossEntropyLoss(nn.Module):\n",
0353 " def __init__(self):\n",
0354 " super(WeightedBinaryCrossEntropyLoss, self).__init__()\n",
0355 "\n",
0356 " def forward(self, outputs, targets, weights):\n",
0357 " eps = 1e-7\n",
0358 " loss = -(targets * torch.log(outputs + eps) + (1 - targets) * torch.log(1 - outputs + eps))\n",
0359 " weighted_loss = loss * weights\n",
0360 " return weighted_loss.mean()\n",
0361 "\n",
0362 "# ------------------ Class Weight Calculation ------------------\n",
0363 "def calculate_binary_class_weights(labels):\n",
0364 " total_samples = len(labels)\n",
0365 " count_positive = labels.sum().item()\n",
0366 " count_negative = total_samples - count_positive\n",
0367 " weight_positive = total_samples / (2 * count_positive) if count_positive > 0 else 1.0\n",
0368 " weight_negative = total_samples / (2 * count_negative) if count_negative > 0 else 1.0\n",
0369 " \n",
0370 " sample_weights = torch.zeros(total_samples)\n",
0371 " for i in range(total_samples):\n",
0372 " if labels[i] == 1:\n",
0373 " sample_weights[i] = weight_positive\n",
0374 " else:\n",
0375 " sample_weights[i] = weight_negative\n",
0376 " return sample_weights\n",
0377 "\n",
0378 "# ------------------ Data Preparation ------------------\n",
0379 "print(f\"Initial dataset size: {len(labels_tensor)}\")\n",
0380 "\n",
0381 "# Remove any rows with NaN in the input features (if any remain).\n",
0382 "nan_mask = torch.isnan(input_features_tensor).any(dim=1)\n",
0383 "filtered_inputs = input_features_tensor[~nan_mask]\n",
0384 "filtered_labels = labels_tensor[~nan_mask]\n",
0385 "\n",
0386 "# Print class distribution before downsampling.\n",
0387 "num_real = filtered_labels.sum().item() # label = 1 means real\n",
0388 "num_fake = len(filtered_labels) - num_real # label = 0 means fake\n",
0389 "print(f\"Class distribution before downsampling - Real: {num_real}, Fake: {num_fake}\")\n",
0390 "\n",
0391 "# Option to downsample the majority class.\n",
0392 "downsample_classes = False\n",
0393 "if downsample_classes:\n",
0394 " downsample_ratios = {1: 1.0, 0: 1.0}\n",
0395 " indices_list = []\n",
0396 "\n",
0397 " # Process real class (label 1).\n",
0398 " real_mask = (filtered_labels.squeeze() == 1)\n",
0399 " real_indices = torch.nonzero(real_mask).squeeze()\n",
0400 " num_real = real_indices.numel()\n",
0401 " num_real_to_sample = int(num_real * downsample_ratios[1])\n",
0402 " if num_real_to_sample < 1 and num_real > 0:\n",
0403 " num_real_to_sample = 1\n",
0404 " real_indices_shuffled = real_indices[torch.randperm(num_real)]\n",
0405 " sampled_real_indices = real_indices_shuffled[:num_real_to_sample]\n",
0406 " indices_list.append(sampled_real_indices)\n",
0407 "\n",
0408 " # Process fake class (label 0).\n",
0409 " fake_mask = (filtered_labels.squeeze() == 0)\n",
0410 " fake_indices = torch.nonzero(fake_mask).squeeze()\n",
0411 " num_fake = fake_indices.numel()\n",
0412 " num_fake_to_sample = int(num_fake * downsample_ratios[0])\n",
0413 " if num_fake_to_sample < 1 and num_fake > 0:\n",
0414 " num_fake_to_sample = 1\n",
0415 " fake_indices_shuffled = fake_indices[torch.randperm(num_fake)]\n",
0416 " sampled_fake_indices = fake_indices_shuffled[:num_fake_to_sample]\n",
0417 " indices_list.append(sampled_fake_indices)\n",
0418 "\n",
0419 " # Combine indices from both classes.\n",
0420 " selected_indices = torch.cat(indices_list)\n",
0421 " filtered_inputs = filtered_inputs[selected_indices]\n",
0422 " filtered_labels = filtered_labels[selected_indices]\n",
0423 "\n",
0424 "# Print class distribution after downsampling.\n",
0425 "num_real_after = filtered_labels.sum().item()\n",
0426 "num_fake_after = len(filtered_labels) - num_real_after\n",
0427 "print(f\"Class distribution after downsampling - Real: {num_real_after}, Fake: {num_fake_after}\")\n",
0428 "\n",
0429 "# Calculate sample weights after downsampling.\n",
0430 "sample_weights = calculate_binary_class_weights(filtered_labels)\n",
0431 "filtered_weights = sample_weights\n",
0432 "\n",
0433 "# Create the dataset.\n",
0434 "dataset = TensorDataset(filtered_inputs, filtered_labels, filtered_weights)\n",
0435 "\n",
0436 "# Split into train and test sets.\n",
0437 "train_size = int(0.8 * len(dataset))\n",
0438 "test_size = len(dataset) - train_size\n",
0439 "train_dataset, test_dataset = random_split(dataset, [train_size, test_size])\n",
0440 "\n",
0441 "# Create data loaders.\n",
0442 "train_loader = DataLoader(train_dataset, batch_size=1024, shuffle=True, num_workers=10, pin_memory=True)\n",
0443 "test_loader = DataLoader(test_dataset, batch_size=1024, shuffle=False, num_workers=10, pin_memory=True)\n",
0444 "\n",
0445 "# ------------------ Model, Loss, and Optimizer ------------------\n",
0446 "input_dim = filtered_inputs.shape[1]\n",
0447 "model = BinaryClassificationNeuralNetwork(input_dim).to(device)\n",
0448 "loss_function = WeightedBinaryCrossEntropyLoss()\n",
0449 "optimizer = Adam(model.parameters(), lr=0.0025)\n",
0450 "\n",
0451 "def evaluate_loss(loader):\n",
0452 " model.eval()\n",
0453 " total_loss = 0\n",
0454 " num_batches = 0\n",
0455 " with torch.no_grad():\n",
0456 " for inputs, targets, weights in loader:\n",
0457 " inputs, targets, weights = inputs.to(device), targets.to(device), weights.to(device)\n",
0458 " outputs = model(inputs)\n",
0459 " loss = loss_function(outputs, targets, weights)\n",
0460 " total_loss += loss.item()\n",
0461 " num_batches += 1\n",
0462 " return total_loss / num_batches\n",
0463 "\n",
0464 "# ------------------ Training Loop ------------------\n",
0465 "num_epochs = 200\n",
0466 "train_loss_log = []\n",
0467 "test_loss_log = []\n",
0468 "\n",
0469 "for epoch in range(num_epochs):\n",
0470 " model.train()\n",
0471 " epoch_loss = 0\n",
0472 " num_batches = 0\n",
0473 "\n",
0474 " for inputs, targets, weights in train_loader:\n",
0475 " inputs, targets, weights = inputs.to(device), targets.to(device), weights.to(device)\n",
0476 " outputs = model(inputs)\n",
0477 " loss = loss_function(outputs, targets, weights)\n",
0478 " epoch_loss += loss.item()\n",
0479 " num_batches += 1\n",
0480 " \n",
0481 " optimizer.zero_grad()\n",
0482 " loss.backward()\n",
0483 " optimizer.step()\n",
0484 " \n",
0485 " train_loss = epoch_loss / num_batches\n",
0486 " test_loss = evaluate_loss(test_loader)\n",
0487 " train_loss_log.append(train_loss)\n",
0488 " test_loss_log.append(test_loss)\n",
0489 " \n",
0490 " print(f'Epoch [{epoch+1}/{num_epochs}], Train Loss: {train_loss:.4f}, Test Loss: {test_loss:.4f}')"
0491 ]
0492 },
0493 {
0494 "cell_type": "code",
0495 "execution_count": 5,
0496 "metadata": {},
0497 "outputs": [
0498 {
0499 "name": "stdout",
0500 "output_type": "stream",
0501 "text": [
0502 "Baseline accuracy: 0.7410224080085754\n",
0503 "Feature importances:\n",
0504 "Feature 2 importance: 0.1705\n",
0505 "Feature 1 importance: 0.1614\n",
0506 "Feature 3 importance: 0.0985\n",
0507 "Feature 5 importance: 0.0871\n",
0508 "Feature 4 importance: 0.0293\n",
0509 "Feature 0 importance: 0.0219\n"
0510 ]
0511 }
0512 ],
0513 "source": [
0514 "from sklearn.metrics import accuracy_score\n",
0515 "\n",
0516 "# Convert tensors to numpy for simplicity in permutation\n",
0517 "input_features_np = input_features_tensor.numpy()\n",
0518 "labels_np = labels_tensor.numpy()\n",
0519 "\n",
0520 "def model_accuracy(features, labels, model):\n",
0521 " # Move the model to CPU for evaluation\n",
0522 " model.cpu()\n",
0523 " model.eval() # Set to evaluation mode\n",
0524 " with torch.no_grad():\n",
0525 " # Ensure features and labels are on CPU\n",
0526 " inputs = features.to('cpu')\n",
0527 " labels = labels.to('cpu')\n",
0528 " outputs = model(inputs)\n",
0529 " predicted = (outputs.squeeze() > 0.5).float() # Update threshold as necessary\n",
0530 " accuracy = (predicted == labels).float().mean().item()\n",
0531 " return accuracy\n",
0532 "\n",
0533 "# Use the original input_features_tensor and labels_tensor directly\n",
0534 "baseline_accuracy = model_accuracy(input_features_tensor, labels_tensor, model)\n",
0535 "print(f\"Baseline accuracy: {baseline_accuracy}\")\n",
0536 "\n",
0537 "# Initialize an array to store feature importances\n",
0538 "feature_importances = np.zeros(input_features_tensor.shape[1])\n",
0539 "\n",
0540 "# Permute each feature and calculate the drop in accuracy\n",
0541 "for i in range(input_features_tensor.shape[1]):\n",
0542 " permuted_features = input_features_tensor.clone()\n",
0543 " permuted_features[:, i] = permuted_features[torch.randperm(permuted_features.size(0)), i] # Permute feature\n",
0544 "\n",
0545 " permuted_accuracy = model_accuracy(permuted_features, labels_tensor, model)\n",
0546 " feature_importances[i] = baseline_accuracy - permuted_accuracy\n",
0547 "\n",
0548 "# Ranking features by importance\n",
0549 "important_features_indices = np.argsort(feature_importances)[::-1] # Indices of features in descending importance\n",
0550 "important_features_scores = np.sort(feature_importances)[::-1] # Importance scores in descending order\n",
0551 "\n",
0552 "print(\"Feature importances:\")\n",
0553 "for idx, score in zip(important_features_indices, important_features_scores):\n",
0554 " print(f\"Feature {idx} importance: {score:.4f}\")"
0555 ]
0556 },
0557 {
0558 "cell_type": "code",
0559 "execution_count": 6,
0560 "metadata": {},
0561 "outputs": [
0562 {
0563 "name": "stdout",
0564 "output_type": "stream",
0565 "text": [
0566 "ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_layer1[32] = {\n",
0567 "-1.0316417f, -0.5292138f, -1.3838978f, -0.7517025f, -0.8418103f, 0.8087351f, -0.1664009f, 0.2809185f, 1.3140178f, 0.7565588f, 0.0890440f, -0.4908848f, 0.4201532f, -0.4334770f, 0.5002150f, -0.5591785f, 1.2298888f, 0.0346711f, -0.4166603f, -0.0064792f, -0.2969901f, -0.3028315f, 0.0721094f, 0.2584246f, -0.2035742f, -0.2888707f, -0.1322349f, 0.5589037f, 0.4285649f, 0.1511498f, 0.1774099f, -0.9249431f };\n",
0568 "\n",
0569 "ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_layer1[6][32] = {\n",
0570 "{ -0.2303199f, -0.0356287f, 0.1646899f, -0.1455843f, -0.0290584f, 0.2789465f, 0.0559607f, -0.2009920f, -0.0174202f, -0.0237667f, 0.1854273f, -0.0269862f, -0.0826555f, 0.7338054f, 0.0937388f, -0.8827202f, -0.0182344f, 0.0352709f, -0.1331034f, -0.0432344f, 0.0208153f, -0.1124316f, 0.1652907f, -0.1187222f, -0.0506563f, 0.2842667f, -0.7510378f, 0.0503595f, -0.0329987f, -0.8018139f, -0.0368841f, -0.0285996f },\n",
0571 "{ 0.7176192f, -0.1105556f, 0.8051441f, -0.4914712f, -0.0188390f, -0.2385275f, -0.2936262f, 0.3048217f, -0.2883293f, -0.3593645f, -0.4168207f, -0.0530377f, 1.0154706f, -0.5427814f, -0.9457486f, -0.4481009f, -0.4298896f, 0.0229893f, 0.0285739f, 0.1118112f, -0.2768153f, -0.6592747f, 0.5621189f, 0.7928397f, -0.8139476f, -0.1173517f, -0.2300215f, -0.4201365f, 0.2074883f, 0.3603764f, -0.3421177f, 0.1743037f },\n",
0572 "{ 0.0283383f, 0.3209993f, -0.0528482f, 0.9225417f, 0.4743758f, -0.3381493f, -0.1023181f, 0.0761002f, -0.4110529f, 0.0935502f, 0.4090216f, 0.1871852f, -0.7008697f, 0.3572226f, 0.6193281f, -0.3993059f, 0.4011126f, -0.3016730f, 0.1985772f, 0.1066906f, -0.1462973f, 1.0643306f, -0.6703950f, -0.7412036f, 0.8931519f, -0.3644519f, -0.0444721f, 0.7265502f, -0.2158535f, -0.7507963f, -0.2511281f, -0.2659404f },\n",
0573 "{ 0.6280951f, -0.1979905f, 0.2835749f, -0.2547665f, 0.2009394f, 0.5826684f, 0.0070924f, 0.7995070f, -0.1270987f, 0.2988422f, 1.2406983f, 0.5875431f, 0.2251529f, -0.5355389f, -0.2763741f, 0.0566486f, 0.8032280f, 0.1221172f, 0.0441896f, 1.3281344f, 0.0374645f, 0.1209982f, -0.7381684f, -0.0807755f, -0.4638562f, -0.2137405f, -0.1491151f, 0.3022155f, -0.1751741f, -0.0065741f, 0.1841483f, -0.0963122f },\n",
0574 "{ 0.0176033f, -0.1465057f, 0.0613830f, 0.1969667f, 0.3062224f, -0.2499068f, -1.2580094f, -0.1081307f, -0.3870914f, -0.2593474f, 0.4072658f, 0.0381806f, -0.3507286f, 0.3829739f, -0.1012198f, -0.6614094f, 0.0203835f, -0.2897506f, -0.4447211f, -0.4430839f, -0.0040086f, -0.0372864f, -0.4039490f, 0.4331785f, 0.2907649f, -0.1092023f, 0.0977807f, -0.6611776f, 0.7277890f, 0.5314985f, -0.0860426f, 0.0131469f },\n",
0575 "{ 0.0536266f, 0.2549676f, -0.3011957f, -0.4934275f, 0.6024259f, 1.5041729f, 1.3324199f, -0.7268062f, 0.5070686f, 1.0215880f, 0.7595061f, 1.1927116f, -0.6326223f, 0.4896784f, 0.1556831f, 0.4490171f, -1.1741296f, -0.0409376f, 0.3861921f, 0.4442228f, 0.0257449f, -0.3155618f, 0.0957184f, -0.0695736f, 0.1083985f, 0.1015484f, 0.8968495f, -0.1153277f, -0.5456764f, -0.1840984f, 0.3110001f, 1.6959499f },\n",
0576 "};\n",
0577 "\n",
0578 "ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_layer2[32] = {\n",
0579 "-0.1015397f, -0.2844371f, 0.3264084f, 0.1813205f, -0.5612066f, 0.0906685f, 0.0845674f, 0.4616135f, -0.2177648f, -0.1652546f, 0.4002015f, -0.0791563f, 0.2383104f, 0.4796737f, 0.4520915f, -0.1967489f, 0.3534851f, 0.5968352f, 0.5477327f, 0.5137501f, 0.3921396f, -0.3068429f, 0.3759635f, 0.4266470f, -0.0625485f, 0.1195836f, 0.3834727f, -0.1557929f, 0.2742889f, -0.3761551f, 0.1094945f, 0.0651921f };\n",
0580 "\n",
0581 "ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_layer2[32][32] = {\n",
0582 "{ 0.0398679f, -0.3098788f, 0.6160386f, 0.1663042f, 0.0616995f, -0.5468591f, 0.0629986f, 0.9667135f, 0.1226420f, -0.1594982f, 0.8811188f, -0.0122212f, -0.1866431f, -0.0127864f, 0.8248008f, 0.0109573f, 0.6348554f, 0.7169302f, -0.4604602f, 0.7598190f, 0.4718021f, -0.0390644f, 0.8688850f, 0.1144402f, 0.2909767f, -0.0373881f, 0.7398790f, -0.1475801f, 0.1209616f, -0.2457073f, 1.5780616f, -0.9071835f },\n",
0583 "{ -0.1679361f, -0.1377337f, -0.0434273f, -0.0693874f, 0.1072921f, -0.0385521f, -0.1659795f, -0.0850864f, -0.0472408f, 0.0961263f, -0.0083223f, 0.1163423f, -0.4766918f, -0.2465681f, -0.0924990f, -0.1210379f, -0.1053123f, -0.5663610f, -0.0639742f, -0.2473114f, -0.1664381f, -0.2532910f, 0.2278630f, -0.1883525f, 0.1034932f, -0.1848536f, -0.1000912f, -0.0118117f, -0.6502397f, -0.2686550f, -0.0506822f, 0.0806257f },\n",
0584 "{ -0.9741716f, 2.8353660f, 0.3112126f, -2.3845534f, 1.0973041f, -1.0202811f, 0.0325992f, 0.3779927f, 0.0220244f, 0.0176618f, 0.5749296f, 0.0441563f, 0.1922859f, -0.1661071f, 0.3079973f, -0.0444220f, 0.2683854f, 0.6849532f, 0.1716617f, 0.5063406f, 0.7730200f, 0.0671466f, 0.5224924f, -2.8718150f, -1.4443614f, -0.1455812f, 0.0810298f, -0.3216130f, -0.3000851f, 0.7883633f, -0.6158253f, 1.2222605f },\n",
0585 "{ -0.5756851f, -0.0641467f, -0.8851644f, -1.1059726f, -1.4682759f, 0.3964167f, -0.0009879f, -0.6186015f, -0.0061868f, -0.1787315f, -0.4631928f, -0.1590177f, -0.3159569f, -0.5368351f, -0.4180341f, -0.1800581f, -0.4896149f, -1.3214008f, -2.5101123f, -0.8682919f, -0.5497130f, -0.2457729f, -0.3258826f, 0.5641282f, -0.7060823f, -0.1749261f, -0.2885247f, -0.1631423f, 0.0304128f, -0.0907670f, -0.2878378f, -0.6338934f },\n",
0586 "{ -1.0935251f, -0.9992083f, -0.0633413f, -0.1609752f, -0.3885693f, 0.3524578f, 0.0127510f, 0.1163242f, -0.1016347f, 0.0687914f, 0.0947863f, -0.0067241f, -0.1876621f, -0.0223206f, 0.1423765f, -0.1802772f, -0.0291052f, 0.0450566f, -0.4598204f, -0.1165596f, -0.3479767f, -0.0701668f, 0.2881460f, 0.1044018f, -0.0239950f, -0.1551994f, 0.2170918f, -0.2169842f, 0.4257921f, 0.3812013f, 0.4517531f, -0.1721758f },\n",
0587 "{ -0.2656136f, -1.6221433f, 0.0597067f, 0.4315053f, -0.0760466f, -1.1311982f, -0.1951133f, -0.0456391f, -0.3352800f, -0.1049919f, 0.3278280f, 0.0194480f, -0.2923800f, 1.0222012f, 0.0098873f, -0.0653058f, 0.2413959f, 0.7756509f, -0.5178695f, 0.2742799f, 0.2555766f, 0.0098521f, 0.1389581f, 1.2634234f, -0.1592175f, 0.0732114f, -0.1383710f, 0.0594422f, 0.2089978f, 0.4949785f, -1.7492013f, -0.6352664f },\n",
0588 "{ -0.4342200f, 2.3557084f, -0.3712214f, -0.7716209f, -0.4416530f, 0.1379044f, 0.0727059f, 0.5425858f, -0.0196064f, 0.1223867f, -0.0692875f, 0.1327683f, 0.4494519f, 0.0952457f, 0.1334870f, 0.0676249f, 0.3728006f, 0.7255252f, -1.6268474f, 0.8089049f, 0.1800606f, -0.1145329f, -0.1641574f, -3.0665483f, 0.5469566f, -0.0040791f, 0.0806037f, -0.0413218f, -4.4606824f, -1.1090307f, -4.0268488f, -1.1893214f },\n",
0589 "{ -0.2475177f, -0.6829165f, -0.0234359f, -0.0061992f, -0.0379326f, 0.4335875f, 0.0828241f, 0.0840699f, -0.0683035f, -0.1582910f, -0.0177477f, 0.1024636f, -0.0703338f, -0.1498718f, -0.0622758f, -0.0744936f, -0.0358834f, -0.1732212f, -1.1194228f, 0.0808573f, 0.0806586f, -0.1502637f, -0.0854174f, 0.6450295f, 0.1303551f, -0.1633945f, 0.0709481f, 0.0788564f, -0.1573547f, 0.2853298f, 0.3992899f, -0.2722275f },\n",
0590 "{ -0.0024052f, 0.0832400f, -0.1240560f, 0.1827071f, 0.5078100f, -1.9449767f, 0.1166574f, -0.4264094f, -0.3462796f, 0.0000746f, 0.0312783f, -0.1609135f, -0.2486863f, -0.0109304f, -0.3259882f, -0.0161246f, -0.1272729f, 0.3718676f, 0.7143661f, -0.0934052f, 0.2293939f, -0.1234700f, -0.5969369f, -0.0931792f, 0.1510706f, -0.1806178f, -0.3079583f, -0.0738148f, -0.3563280f, -0.2019677f, -2.4623275f, -1.5662863f },\n",
0591 "{ -1.0340914f, -0.0264978f, 0.1637420f, -0.2267127f, -0.0878844f, -0.9779887f, -0.0298158f, -0.0878086f, -0.2519222f, -0.1923419f, 0.1495816f, -0.1703948f, -0.4357837f, 0.2593633f, -0.1618164f, -0.0063047f, 0.1708476f, 0.1812407f, -0.1949099f, -0.0840897f, 0.0365015f, -0.1204635f, -0.1736511f, 0.1234083f, -0.2201642f, -0.2357494f, -0.1014172f, 0.1092978f, 0.1652685f, -0.3343832f, -0.7220409f, -1.6212125f },\n",
0592 "{ -0.2753425f, -1.0450485f, 0.0862796f, 0.5330997f, 0.2215044f, 1.0313069f, -0.1288254f, -0.4092823f, -0.2298029f, -0.1440723f, -0.4615285f, -0.0421680f, 0.2838994f, -0.4270649f, -0.4212369f, -0.0371355f, -0.4017382f, -0.3131837f, 0.0822975f, -0.1237065f, -0.3185453f, 0.1537958f, -0.2095980f, -1.3749454f, 0.8085594f, -0.1836801f, -0.2636165f, 0.0256138f, 0.2858065f, 0.2371098f, -1.3770546f, 0.1130755f },\n",
0593 "{ -0.9280756f, -0.7408227f, -0.2019696f, -0.2414151f, 0.1294307f, 0.5904933f, -0.1836856f, -0.1364715f, -0.1818316f, 0.0698972f, -0.0663465f, -0.0624342f, -0.5138503f, -0.8630795f, -0.2610115f, 0.1374615f, -0.0169794f, -0.5805362f, -0.1026141f, -0.1647637f, -0.2088510f, 0.0510566f, -0.1424105f, -3.4270186f, 0.2712817f, -0.1466969f, -0.1463187f, -0.1640311f, 0.4300542f, 0.0572317f, -0.2324502f, -1.0792063f },\n",
0594 "{ -0.0307543f, -0.0768560f, 0.8240007f, 0.7662915f, 0.1543863f, -0.1713554f, -0.1223227f, 0.4386547f, -0.2540433f, -0.1332257f, 0.7341567f, -0.0580215f, -0.2693185f, 0.5857823f, 0.4592545f, -0.0942196f, 0.5861048f, 0.5916333f, 0.8047288f, 0.6647347f, 0.6680315f, 0.0370881f, 0.5487651f, 0.4163764f, 0.1050351f, -0.0590813f, 0.7615875f, -0.0326647f, -0.3096285f, -0.0770774f, 0.4334740f, 0.0817739f },\n",
0595 "{ -0.3358265f, -1.4673307f, -0.0814484f, -0.1950698f, 0.2350309f, -0.8171171f, -0.0790631f, -0.0033560f, -0.2415340f, -0.1976242f, -0.0537354f, 0.0254026f, 0.6134555f, 0.0668075f, -0.0584858f, 0.0701805f, -0.0682784f, -0.1794098f, -0.0583040f, -0.1624253f, -0.1456280f, -0.2352183f, -0.0143530f, -0.1338016f, -2.4032581f, 0.1073783f, 0.1195123f, 0.0956468f, 0.1293423f, 1.1435740f, 0.3386673f, 0.1772921f },\n",
0596 "{ -1.6261140f, 1.0616504f, -1.4311931f, -2.4396446f, -0.9056627f, 0.4331071f, -0.0649640f, -1.3435462f, -0.0458936f, -0.0711545f, -1.6348053f, -0.1845289f, -0.0306016f, -1.4271052f, -1.3648096f, 0.0524831f, -1.7654743f, -1.1757706f, -0.6089430f, -1.3503740f, -1.7567536f, -0.0602079f, -2.0782607f, -0.3474989f, -1.6194628f, -0.1068030f, -1.7532518f, -0.1791241f, 0.8444587f, -1.6989282f, -0.3120632f, -0.3003300f },\n",
0597 "{ -0.1823801f, 3.2780595f, 0.0046086f, 0.3211953f, 2.8119521f, -2.4034190f, -0.1202310f, 0.0593807f, 0.0990012f, -0.0647286f, 0.1925517f, -0.0071298f, -0.4552369f, -0.0450624f, 0.2038983f, -0.1544811f, 0.2431807f, 2.5275569f, 0.4051182f, 0.1717180f, -4.4179425f, -0.0382397f, 0.1282281f, 1.3789687f, 0.2157716f, 0.0339806f, -0.0445110f, -0.1175756f, -0.2876654f, 0.6454542f, -4.1663256f, -4.9532986f },\n",
0598 "{ -1.5354282f, 1.1725403f, -1.3999028f, 1.4599746f, 0.7842735f, -0.2761331f, -0.1681113f, -1.2318107f, -0.1268664f, -0.1008263f, -1.1240377f, 0.0714637f, 1.0816231f, 0.0337652f, -1.0600177f, -0.0118573f, -0.9776441f, -1.1751970f, -4.1702838f, -1.4578556f, -0.5371085f, -0.1503768f, -0.7648986f, -0.1425661f, -3.3017726f, 0.1108525f, -1.1741784f, -0.0334351f, -0.5934044f, -0.0111621f, -0.9253456f, 0.9225865f },\n",
0599 "{ 0.1424126f, -0.1699037f, 0.0217177f, 0.1220412f, -0.0906500f, 0.0009671f, -0.1639844f, -0.0527305f, 0.0128741f, -0.1168858f, -0.0302848f, -0.1783690f, 0.0221473f, 0.1693649f, 0.2265290f, 0.0589060f, 0.0021857f, 0.0242652f, -0.0775702f, 0.0388727f, 0.0707951f, -0.1567841f, 0.1732654f, 0.0437058f, -0.1407849f, -0.0657614f, -0.1040369f, 0.1652682f, -0.1807958f, 0.1050604f, -0.1204055f, 0.0295164f },\n",
0600 "{ -0.0865003f, -0.5563657f, 0.0262170f, 0.1092112f, 0.1299964f, -0.2665305f, -0.0594379f, -0.3092700f, -0.1140931f, -0.1025264f, -0.0924060f, 0.0661781f, -0.6323586f, -0.1840952f, -0.3743987f, 0.0433477f, 0.1879622f, -0.2726538f, 0.0839276f, 0.0398268f, -0.2266287f, -0.0569720f, 0.1651291f, 0.0004582f, 0.0672146f, -0.0313096f, 0.0879014f, 0.0798023f, -0.6620474f, -0.3207071f, 0.0426169f, -0.2612755f },\n",
0601 "{ -0.0320727f, -1.2532046f, 0.2544250f, -0.2982330f, -0.4790863f, -0.5560963f, -0.1201061f, -0.0375240f, -0.1868488f, 0.0521472f, -0.0850537f, -0.1624364f, 0.1665921f, -0.1470270f, 0.2794432f, -0.1671568f, 0.0176426f, -0.4760583f, -0.1061204f, 0.3590186f, 0.1793246f, -0.0764172f, 0.3327765f, 1.3718973f, -0.6579911f, -0.2003471f, -0.0251827f, -0.0495654f, 0.0083008f, -0.2771839f, -0.8300866f, -3.0943394f },\n",
0602 "{ 0.0621519f, -0.1735040f, 0.1486671f, -0.1150359f, 0.1008294f, -0.0092464f, 0.0799274f, 0.1733976f, 0.0848012f, -0.1365137f, -0.0797485f, 0.1167114f, 0.1286604f, -0.0544559f, 0.0424396f, -0.1382235f, -0.0363623f, 0.0376997f, -0.0940855f, -0.0822849f, 0.1207218f, 0.0484856f, -0.0699596f, 0.1332577f, -0.1111775f, -0.1007963f, -0.0908793f, -0.1231520f, -0.0480829f, 0.0872541f, 0.0143959f, 0.1434527f },\n",
0603 "{ -0.7731760f, -0.1756437f, -0.5544511f, -1.1350045f, -0.8066334f, 0.4560536f, 0.0514501f, -0.5005841f, -0.1125634f, -0.1759978f, -0.5758266f, -0.1267792f, -0.1993223f, -0.3803072f, -0.4573608f, -0.0674755f, -0.4743742f, -0.6225397f, -1.0214270f, -0.5803681f, -1.1637334f, -0.0488763f, -0.4069555f, 0.3787926f, -1.1097326f, -0.1770509f, -0.3172881f, 0.0409949f, -0.0639247f, -0.3521400f, 0.4321971f, 0.0638231f },\n",
0604 "{ 1.1334474f, 0.2912367f, 0.9589775f, 1.3626057f, -0.5325903f, 0.0380656f, -0.1852983f, 0.4992394f, 0.0062829f, 0.1194302f, 0.6635179f, -0.1749409f, 0.9010410f, 0.4779315f, 0.6941727f, -0.0554726f, 0.6099629f, 0.6952612f, -0.6230863f, 0.5901819f, 0.8190935f, -0.3441652f, 0.6224667f, 0.4739881f, 0.6536815f, 0.0891643f, 0.6636505f, -0.1713116f, -0.1173943f, 1.0364656f, 0.1431172f, 0.4249419f },\n",
0605 "{ 0.8033506f, 0.0300558f, 0.6656652f, 1.2101313f, -0.8615839f, 0.5132746f, -0.0265759f, 0.5377895f, 0.0152243f, 0.0114079f, 0.6099907f, -0.1249313f, 0.2723404f, 0.3610747f, 0.8165271f, -0.1239832f, 0.4873835f, 0.8119840f, 1.0779978f, 0.7688931f, 1.2414374f, 0.0522347f, 0.5015374f, 0.3038165f, 0.7020112f, -0.5665528f, 0.5736545f, -0.2835588f, 0.2212313f, 0.1219948f, 0.3844957f, 0.3401546f },\n",
0606 "{ -0.7439671f, 0.1166124f, -0.5117782f, -0.8712475f, 0.5832991f, 0.8349549f, -0.0009121f, -0.2292912f, -0.1560434f, 0.1009018f, -0.3865246f, 0.0456842f, -0.0389204f, -0.4212565f, -0.4042930f, -0.0817643f, -0.4844481f, -0.5429400f, -0.0883068f, -0.2933508f, -1.4754379f, -0.0470565f, -0.3384019f, -0.2567133f, -1.3822908f, -0.4623447f, -0.3862867f, -0.1395572f, 0.4184268f, -0.4738576f, 0.5919805f, 0.4029315f },\n",
0607 "{ 0.0951967f, -0.0264262f, 0.0745322f, 0.1399388f, 0.1192624f, -0.0836915f, 0.1539033f, -0.1881727f, 0.1124343f, 0.1572933f, -0.0205816f, -0.0527924f, -0.0939525f, -0.0599392f, 0.0312916f, -0.1276211f, -0.0861412f, -0.2229238f, 0.0862552f, 0.1315805f, 0.1222533f, 0.1245137f, 0.0553789f, -0.0982146f, -0.1024362f, -0.0534966f, -0.1330877f, -0.0255273f, -0.0186730f, -0.0708270f, 0.1567561f, -0.0844505f },\n",
0608 "{ -0.0146923f, 0.2591865f, -0.2036884f, -0.0632321f, 1.3047949f, -0.5502592f, -0.1978513f, -0.0660495f, 0.0529672f, -0.0396357f, -0.5039385f, 0.0635856f, 0.2173319f, -0.2249199f, -0.2624365f, 0.0801869f, -0.5361095f, -0.7585190f, 0.2618334f, -0.3435789f, -0.0586956f, -0.1631703f, -0.4265099f, -0.0447214f, -1.0177643f, 0.0323080f, -0.3818924f, -0.1746079f, 0.5966771f, 0.6934303f, -0.2669153f, -0.4290505f },\n",
0609 "{ -0.7751231f, -0.3161191f, -0.2155289f, -0.6773937f, 0.1730236f, -0.4401509f, -0.1782488f, -0.2299091f, -0.2481384f, -0.1676966f, -0.2118478f, -0.1280043f, -0.3889951f, -0.2434384f, -0.3576782f, -0.1191592f, 0.0488413f, -0.3035202f, -0.1206791f, -0.3636490f, -0.4272992f, -0.0747663f, 0.1205972f, 0.3637502f, -0.4497322f, -0.0998606f, -0.2597706f, -0.0395944f, -0.1047684f, -0.1457169f, -0.3183976f, -0.4862959f },\n",
0610 "{ -0.3920153f, 0.0499827f, 0.0303043f, 0.1732283f, -5.3812666f, 0.5284207f, -0.0502357f, 0.4224207f, 0.0811522f, 0.0420046f, -0.0458250f, -0.1272357f, 0.0563048f, 0.0414863f, 0.3250322f, -0.1368608f, 0.2093009f, 0.2632169f, 0.2363917f, 0.2263645f, 0.2523394f, -0.0778029f, 0.0646684f, 0.4740721f, 0.3888938f, 0.0414786f, 0.1371806f, 0.0576984f, 0.2861594f, -0.3092689f, 0.3796774f, 0.4443598f },\n",
0611 "{ -0.8123505f, 0.2358683f, 0.1019373f, 0.2282849f, -1.2557510f, 0.1555537f, -0.0974192f, 0.5895486f, 0.0036116f, -0.1097254f, 0.1486044f, -0.0238196f, -0.6786419f, 0.3634615f, 0.6986865f, 0.0832033f, 0.2520329f, -2.2602396f, 0.1090904f, 0.7121069f, -1.6000881f, -0.0334629f, 0.5868925f, -0.1816964f, 0.4748906f, 0.0168051f, 0.6019380f, -0.1252660f, -0.6808432f, 0.4272106f, -0.0474080f, 1.0566397f },\n",
0612 "{ 0.0639796f, -0.0572261f, 0.0528333f, -0.0733780f, -0.0679085f, 0.1477537f, 0.1640870f, -0.0076429f, -0.1674436f, -0.0890600f, -0.0843337f, 0.0499240f, 0.1675422f, 0.0999710f, -0.0753364f, -0.1303664f, 0.0212046f, 0.1698232f, -0.0764421f, 0.0027978f, 0.0988120f, 0.0586733f, -0.0051097f, -0.0913044f, -0.0019131f, 0.1084543f, -0.0796013f, -0.0472390f, 0.1328194f, -0.1429810f, -0.1264632f, -0.1601117f },\n",
0613 "{ 0.6985479f, -0.0771215f, -0.0554999f, 0.6879453f, 1.2087054f, 0.5267281f, -0.0005624f, -0.4339048f, 0.0174754f, -0.0884810f, 0.0628883f, 0.0359151f, 1.9782046f, 0.0839531f, -0.0512894f, 0.1214923f, 0.1118447f, -0.2496938f, 1.8209995f, -0.1433189f, -0.6471683f, -0.3243633f, -0.2157310f, -9.1764698f, 0.9299871f, -0.1122522f, -0.0976612f, 0.0127003f, 0.8123280f, 0.9145623f, -1.1885530f, 3.2752204f },\n",
0614 "};\n",
0615 "\n",
0616 "ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_output_layer[1] = {\n",
0617 "-0.2746492f };\n",
0618 "\n",
0619 "ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_output_layer[32][1] = {\n",
0620 "{ -3.9178145f },\n",
0621 "{ 2.2943201f },\n",
0622 "{ 0.9315882f },\n",
0623 "{ -5.2151937f },\n",
0624 "{ 2.5372679f },\n",
0625 "{ -1.1143198f },\n",
0626 "{ 0.1008911f },\n",
0627 "{ 0.3630357f },\n",
0628 "{ -0.1294091f },\n",
0629 "{ 0.1117750f },\n",
0630 "{ 0.6620483f },\n",
0631 "{ 0.0155139f },\n",
0632 "{ -0.4766000f },\n",
0633 "{ 0.2999536f },\n",
0634 "{ 0.7013811f },\n",
0635 "{ -0.0866368f },\n",
0636 "{ 0.5150933f },\n",
0637 "{ 1.5360959f },\n",
0638 "{ 1.9393219f },\n",
0639 "{ 0.6595656f },\n",
0640 "{ -5.3660374f },\n",
0641 "{ 0.0038123f },\n",
0642 "{ 0.6477750f },\n",
0643 "{ 1.6103860f },\n",
0644 "{ -3.5332921f },\n",
0645 "{ 0.1317881f },\n",
0646 "{ 0.6166227f },\n",
0647 "{ 0.0163189f },\n",
0648 "{ -0.3913481f },\n",
0649 "{ -1.1696485f },\n",
0650 "{ -1.3807020f },\n",
0651 "{ -1.1326467f },\n",
0652 "};\n",
0653 "\n"
0654 ]
0655 }
0656 ],
0657 "source": [
0658 "def print_formatted_weights_biases(weights, biases, layer_name):\n",
0659 " # Print biases\n",
0660 " print(f\"ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_{layer_name}[{len(biases)}] = {{\")\n",
0661 " print(\", \".join(f\"{b:.7f}f\" for b in biases) + \" };\")\n",
0662 " print()\n",
0663 "\n",
0664 " # Print weights\n",
0665 " print(f\"ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_{layer_name}[{len(weights[0])}][{len(weights)}] = {{\")\n",
0666 " for row in weights.T:\n",
0667 " formatted_row = \", \".join(f\"{w:.7f}f\" for w in row)\n",
0668 " print(f\"{{ {formatted_row} }},\")\n",
0669 " print(\"};\")\n",
0670 " print()\n",
0671 "\n",
0672 "def print_model_weights_biases(model):\n",
0673 " # Make sure the model is in evaluation mode\n",
0674 " model.eval()\n",
0675 "\n",
0676 " # Iterate through all named modules in the model\n",
0677 " for name, module in model.named_modules():\n",
0678 " # Check if the module is a linear layer\n",
0679 " if isinstance(module, nn.Linear):\n",
0680 " # Get weights and biases\n",
0681 " weights = module.weight.data.cpu().numpy()\n",
0682 " biases = module.bias.data.cpu().numpy()\n",
0683 "\n",
0684 " # Print formatted weights and biases\n",
0685 " print_formatted_weights_biases(weights, biases, name.replace('.', '_'))\n",
0686 "\n",
0687 "print_model_weights_biases(model)\n"
0688 ]
0689 },
0690 {
0691 "cell_type": "code",
0692 "execution_count": 7,
0693 "metadata": {},
0694 "outputs": [
0695 {
0696 "name": "stderr",
0697 "output_type": "stream",
0698 "text": [
0699 "/tmp/ipykernel_1882258/1646812576.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
0700 " inputs = torch.tensor(features, dtype=torch.float32).to('cpu')\n"
0701 ]
0702 },
0703 {
0704 "data": {
0705 "image/png": 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",
0706 "text/plain": [
0707 "<Figure size 640x480 with 1 Axes>"
0708 ]
0709 },
0710 "metadata": {},
0711 "output_type": "display_data"
0712 }
0713 ],
0714 "source": [
0715 "from sklearn.metrics import roc_curve, auc\n",
0716 "import matplotlib.pyplot as plt\n",
0717 "\n",
0718 "def model_outputs(features, model):\n",
0719 " model.eval() # Set the model to evaluation mode\n",
0720 " with torch.no_grad():\n",
0721 " inputs = torch.tensor(features, dtype=torch.float32).to('cpu')\n",
0722 " outputs = model(inputs).squeeze().cpu().numpy()\n",
0723 " return outputs\n",
0724 "\n",
0725 "# Calculate model outputs\n",
0726 "probabilities = model_outputs(filtered_inputs, model)\n",
0727 "\n",
0728 "# Calculate ROC curve and AUC\n",
0729 "fpr, tpr, thresholds = roc_curve(filtered_labels, probabilities)\n",
0730 "roc_auc = auc(fpr, tpr)\n",
0731 "\n",
0732 "# Plot ROC curve\n",
0733 "plt.figure()\n",
0734 "lw = 2 # Line width\n",
0735 "plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = %0.3f)' % roc_auc)\n",
0736 "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
0737 "plt.xlim([0.0, 1.0])\n",
0738 "plt.ylim([0.0, 1.05])\n",
0739 "plt.xlabel('False Positive Rate')\n",
0740 "plt.ylabel('True Positive Rate')\n",
0741 "plt.title('Receiver Operating Characteristic')\n",
0742 "plt.legend(loc=\"lower right\")\n",
0743 "plt.show()"
0744 ]
0745 },
0746 {
0747 "cell_type": "code",
0748 "execution_count": 8,
0749 "metadata": {},
0750 "outputs": [],
0751 "source": [
0752 "# Ensure input_features_tensor is moved to the appropriate device\n",
0753 "input_features_tensor = input_features_tensor.to('cpu')\n",
0754 "\n",
0755 "# Make predictions\n",
0756 "with torch.no_grad():\n",
0757 " model.eval()\n",
0758 " outputs = model(input_features_tensor)\n",
0759 " predictions = outputs.squeeze().cpu().numpy()\n",
0760 "\n",
0761 "full_tracks = (np.concatenate(branches['pT3_isFake']) == 0)"
0762 ]
0763 },
0764 {
0765 "cell_type": "code",
0766 "execution_count": 11,
0767 "metadata": {},
0768 "outputs": [
0769 {
0770 "data": {
0771 "image/png": 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pP/30UwnIH/zgB6GyFStWSEC+/fbbQ9jj3gwmTrrF87p16/osu+GGG6TNZgt9P+200+TUqVP73YbNZpM33HBD6PuDDz4obTabBGRcXJx86aWXpJRSnnrqqfLaa6897H2Q8oA4kTLwe6anp0ufzycbGhqk3W6Xjz322JDMc7qP3apVq+Tq1atD5YOte8opp8j4+Phe94CD6b7HHHw877vvPgmETFNLS0ulxWKR3/72t3vVa2trk+np6aGH7O77x4IFC3pdM8XFxdJqtQ6bOJk4cWLIBG8gBjrfu6/zH/7wh4Ou332/9ng88sILL5RxcXHyrbfe6lXnW9/6loyPjz+sfZJSypqaGgnIX/7yl32WdV833edfN9dff73UNE2WlJRIKQ9t1qXrujzllFMO2ZfJkyfLM844o095ZWWlBPqYRB7MQGZd3eLpvvvu61XeLSL+8pe/DNrua6+9Jn/4wx/KV155Rb733nvyj3/8o8zOzpZOp1Nu2rSpT/29e/dKQD700EODtqtQHGsosy5FL+Qg08dJSUncfvvtPP/884NOi/fkv//7v0lMTOT2228/rH7Y7XZeeOEFduzYwW9/+1suu+wy6urq+PnPf8706dNDpiQff/wxra2t3HTTTQM69O7evZvKykquuOKKXmY2LpeLCy+8kE8++QSPx9NrnQsvvLDX93//+98IIfja176G3+8PvdLT05k7d+4hHTivueYaOjo6ejkQP/roo9jtdr7yla8AkJiYyMSJE7n//vt54IEH2LhxI6ZpDvk3649uR/hu06NuM4mSkpJ+zcxWrVpFWlpa6Luu61x66aUUFhaGTJwWL17Ma6+9xh133MG7775LR0fHYfXpV7/6FevXr+/z6rndwXjuuec4/vjjcblcWCwWrFYrjzzyCDt37jysfnSzdu1agD7mI4sXL2b69Ol9fqeEhAROOeWUI9rWoRjoHD64fDDn9Z7LbrzxRhobG9m5cyc1NTWcd955/P3vf2fLli3cf//9NDY28tWvfpWUlBQmTpzIww8/fFj9vfrqq6mpqeG1114LOf4fbErTk4cffpgFCxYQFRUVOnZvv/32kI6dx+Phvffe45JLLgmZ/wzGeeed1+v7nDlzAEImjWvWrMHv93PllVf2uqajoqJYsWJF6Jruvn985Stf6fXb5uXlsXz58kP2Y6icd955IRO8ngzlfH/ttdeAQOS1Q9HQ0MApp5zCZ599xocffsiqVat6LV+8eDHNzc1cfvnlvPTSS9TX1w+p/90mc6mpqf0uj4mJ6XNMvvKVr2CaJu+///6QtuH3+/u9b/XHUK+Rw+Gdd94B+t4rLr74YpxO5yH7duaZZ3LPPfdwzjnncNJJJ3HzzTfzwQcfIITgJz/5SZ/63b9lRUXFEfVXoRivKHGiCOF2u2loaCAzM3PAOt2hGf/7v/97SG3Gxsbyox/9iNdffz30EHg4TJ8+nVtuuYUnnniC0tJSHnjgARoaGvjxj38MBOzYAbKzswdsozvaSUZGRp9lmZmZmKZJU1NTr/KD69bU1CClJC0tDavV2uv1ySefHPIPfObMmSxatIhHH30UAMMweOKJJzj//PNJTEwECPmlnHHGGdx3330sWLCAlJQUvvOd7/QbxvlQtLW18dxzz7F48WJSUlJobm4ORXASQoSES0/S09MHLOv+HX//+99z++238+KLL7Jy5UoSExP58pe/zN69e4fUrwkTJrBw4cI+r/4ezA7mX//6F5dccglZWVk88cQTrFu3jvXr13PNNdeEfCkOl0OdHwdHy+mv3tHSHamnv8g8jY2NoXOku25/9dxuN16vt1ddAKfTGfKlaWho4Hvf+x6/+93vSEhI4L/+679obGyksLCQZ555httuu+2wrtO8vDxWrVrF3/72N/72t79x2WWX4XA4+q37wAMPcOONN7JkyRKef/55PvnkE9avX8+ZZ545JIHb1NSEYRiDXus96Rn9CAIDHkBoWzU1NQAsWrSozzX97LPPhq7p7t96sGtjOOjvvBrq+V5XV4eu60Pqz549e/j0008566yzmDVrVp/lV1xxBX/7298oKSnhwgsvJDU1lSVLlvDmm28O2m737xoVFdXv8v4GHw6+twwXA10j3VEeD75GhkpDQwMWi6WPOBZCkJ6efkT7kZ+fzwknnMAnn3zSZ1n3b3m4A0AKxXjHcugqikjhP//5D4ZhcPLJJw9YJzo6mrvuuosbbriB//znP0Nq98Ybb+R//ud/uP3227nxxhuPuH9CCL773e/y05/+lG3btgGE/iQOdlzuSfdDSlVVVZ9llZWVaJpGQkJCn231JDk5GSEEH3zwQeghpyf9lR3M1VdfzU033cTOnTvZv38/VVVVfRyH8/LyQqJhz549/OMf/+Cuu+7C6/Ue9qj2008/jcfj4bPPPuuzfxBwGm1qauq1rGes/YPLun9Hp9PJ3Xffzd133x0aNb/jjjs499xz2bVr12H18XB54oknKCgo4Nlnn+11jAbK3TIUep4fBz/4VlZWkpyc3KvsSEddB6P7IXHr1q19Qg5v3bq110Pk7NmzeeaZZ6iuru71MLp169ZebfXH9773PY477jguv/xyIDDi/uijjxIXF8eiRYs4/fTTefXVV1m5cuWQ+37NNdfwta99DdM0eeihhwas98QTT3DyySf3qTNU4Z2YmIiu64Ne64dD93H95z//SV5e3oD1us+Pwa6N4aC/82qo53tKSgqGYVBdXX1I8bxs2TIuvvhirr32WiDgxH5w4Iarr76aq6++Grfbzfvvv8+dd97JOeecw549ewb8rbp/z4HCvHeLwZ4cfG8ZLmbPns3TTz+N3+/vFWBiKNfIYCQlJeH3+6mrq+slUKSUVFdXs2jRoiNqV0rZb/CM7t/y4HuQQnGso2ZOFACUlpZy2223ERcXxze+8Y1B615zzTVMnz6dO+64Y0hmRzabjXvuuYf169fz3HPPDak//QkJCDwstra2hmZ3li9fTlxcHA8//PCAJmlTp04lKyuLp556qlcdt9vN888/H4rgNRjnnHMOUkoqKir6HfWfPXv2Iffp8ssvJyoqiscee4zHHnuMrKwsTj/99AHrT5kyhR/96EfMnj2bL7744pDtH8wjjzxCTEwMb7/9NmvXru31uv/+++nq6uLJJ5/stc7bb7/d6yHCMAyeffZZJk6c2O+IdVpaGldddRWXX345u3fv7mMed6TY7fZ+RwuFENhstl4PatXV1UOK1jUQ3SZaTzzxRK/y9evXs3Pnzj5mL+EgKyuLxYsX88QTT/TJQ7J7924uuOCCUNn555+PEILHH3+8VxuPPfYY0dHRnHnmmf1uY+3atTz33HM8+OCDoTIpJW63O/S9vb39sCMDrV69mtWrV3PNNdewdOnSAesJIfqI+C1btvSJmHfwDEc30dHRrFixgueee27IpkaDccYZZ2CxWNi3b1+/1/TChQuBwP0jIyODp59+utdvU1JSwscff3zU/RiMoZ7vZ511FsCg4rAnX//613nmmWd49NFHufLKK3udcz1xOp2cddZZ/PCHP8Tr9bJ9+/YB28zLyyM6Opp9+/b1u7ytra1P5LCnnnoKTdM46aSTgIGP/eGyevVq2tvbef7553uVP/7442RmZrJkyZJB1x+oH933goPvFc8//zxut/uI7hVFRUV89NFH/V473ZEgZ8yYcdjtKhTjGTVzEoFs27YtZF9dW1vLBx98wKOPPoqu67zwwguHtOfWdZ1f/OIXrF69Gjhgyz0Yl19+Ob/+9a9DttGH4oYbbqC5uZkLL7yQWbNmoes6u3bt4re//S2apoV8WFwuF7/5zW+47rrrOPXUU7n++utJS0ujsLCQzZs388c//hFN07jvvvv46le/yjnnnMM3vvENurq6uP/++2lubu43k/3BHH/88dxwww1cffXVbNiwgZNOOgmn00lVVRUffvghs2fPPuSsUHx8PKtXr+axxx6jubmZ2267rddo2ZYtW/jWt77FxRdfzOTJk7HZbLzzzjts2bKFO+64I1Tv2muv5fHHH2ffvn0DjmJu27aNzz77jBtvvLFf/4jjjz+e3/zmNzzyyCO9ssInJydzyimn8OMf/xin08mDDz7Irl27eoUTXrJkCeeccw5z5swhISGBnTt38ve//31IIg9g7969/ZowZGdnhwRQ9+zAs88+y4QJE4iKimL27Nmcc845/Otf/+Kmm27ioosuoqysjJ/97GdkZGQM2azsYKZOncoNN9zAH/7wBzRN46yzzqK4uJgf//jH5OTk8N3vfveI2u3mn//8J3DgQWPDhg2hsL4XXXRRqN6vfvUrTjvtNC6++GJuuukmamtrueOOO5g1a1avGbaZM2dy7bXXcuedd6LrOosWLeKNN97gL3/5C/fcc0+/JitdXV184xvf4K677qKgoCBUfsYZZ/DTn/6U2NhY9u7dy9tvvz1kk81uoqKiQvs4GOeccw4/+9nPuPPOO1mxYgW7d+/mpz/9KQUFBfj9/lC9mJgY8vLyeOmll1i1ahWJiYkkJyeTn5/PAw88wAknnMCSJUu44447mDRpEjU1Nbz88sv8+c9/Pqzkj/n5+fz0pz/lhz/8Ifv37+fMM88kISGBmpoaPvvss9AMoaZp/OxnP+O6665j9erVXH/99TQ3N3PXXXcNq1lXfwz1fD/xxBO54ooruOeee6ipqeGcc87BbrezceNGHA4H3/72t/u0fdFFF+FwOLjooovo6Ojg6aefxmazcf311xMdHc3xxx9PRkYG1dXV3HvvvaHZtYGw2WwsW7as32sbArMON954I6WlpUyZMoVXX32V//3f/+XGG28kNzcXGPzYA1gsFlasWHFI346zzjqL0047jRtvvJHW1lYmTZrE008/zeuvv84TTzyBruuDrt892PSrX/2Ks846C13XmTNnDqeddhpnnHEGt99+O62trRx//PFs2bKFO++8k/nz53PFFVcM2u6pp57KSSedxJw5c4iNjWXr1q3cd999CCH42c9+1qf+J598gq7rIfGmUEQMo+WJrxh5uqPYdL9sNptMTU2VK1askL/4xS/6jYDTX3SjbpYvXy6BQaN19eSNN94IbftQ0brWrFkjr7nmGjljxgwZFxcnLRaLzMjIkBdccEG/EY1effVVuWLFCul0OqXD4ZAzZsyQv/rVr3rVefHFF+WSJUtkVFSUdDqdctWqVfKjjz4a8v5KKeXf/vY3uWTJEul0OmV0dLScOHGivPLKK+WGDRsG3Z/+foM9e/b0WlZTUyOvuuoqOW3aNOl0OqXL5ZJz5syRv/3tb3slDfv6178uAVlUVDTgdm655RYJ9BsBpps77rhDAvLzzz+XUh44bg8++KCcOHGitFqtctq0afLJJ5/ss97ChQtlQkKCtNvtcsKECfK73/2urK+vH3TfDxWtq2ekoeLiYnn66afLmJiYUGjnbn75y1/K/Px8abfb5fTp0+X//u//ho7boRjo+BqGIX/1q1/JKVOmSKvVKpOTk+XXvva1XhHEpAxEHZo5c+Yht9OTwfb5YN544w25dOlSGRUVJRMTE+WVV17Zb8JIr9cr77zzTpmbmyttNpucMmWK/P3vfz9gH370ox/JuXPn9grZLKWUtbW18qKLLpJxcXEyJydnSJHiekbrGoj+oi51dXXJ2267TWZlZcmoqCi5YMEC+eKLL/abyPCtt96S8+fPl3a7XQLy61//emjZjh075MUXXyyTkpKkzWaTubm58qqrrgqFcR0oImD3+XdwWO4XX3xRrly5UsbGxkq73S7z8vLkRRdd1CeK1V//+lc5efLk0O/9t7/9bViSMHZH67r//vv7rT/U890wDPnb3/5Wzpo1S9psNhkXFyeXLVsmX3nllVCd/qIrrl27VrpcLnnmmWdKj8cjH3/8cbly5UqZlpYmbTabzMzMlJdccsmQwtk+8sgjUtd1WVlZ2au8+7p599135cKFC6XdbpcZGRnyBz/4QZ9zcrBjzxBDCUsZiLr2ne98R6anp0ubzSbnzJkjn3766SGt29XVJa+77jqZkpIihRC97rcdHR3y9ttvl3l5edJqtcqMjAx54403yqampkO2e8stt8gZM2bImJgYabFYZGZmpvza177WKzx/T0488UR57rnnDqnPCsWxhJBSZfdRKBQKhUJxdHR2dpKbm8v3vve9XhEaTz75ZOrr60O+gopDs2/fPiZPnsyaNWv6JLVUKI51lM+JQqFQKBSKoyYqKoq7776bBx54oJcvk+Lwueeee1i1apUSJoqIRPmcKBQKhUKhGBa6/QX3798/pEAhir74/X4mTpzI97///dHuikIxKiizLoVCoVAoFAqFQjEmGFWzrvfff59zzz2XzMxMhBC8+OKLh1znvffe47jjjiMqKooJEyYcdu4HhUKhUCgUCoVCMTYZVXHidruZO3cuf/zjH4dUv6ioiC996UuceOKJbNy4kR/84Ad85zvf6RPLXKFQKBQKhUKhUIw/xoxZlxCCF154gS9/+csD1rn99tt5+eWX2blzZ6jsm9/8Jps3b+6TyEuhUCgUCoVCoVCML8aVQ/y6dev6ZNQ+44wzeOSRR/D5fFit1j7rdHV10dXVFfpumiaNjY0kJSX1yrqrUCgUCoVCoRgbSClpa2sjMzOzV8LisUJnZyderzcsbdtsNqKiosLS9nhgXImT6upq0tLSepWlpaXh9/upr68nIyOjzzr33nsvd99990h1UaFQKBQKhUIxTJSVlZGdnT3a3ehFZ2cnBXkuqmuNsLSfnp5OUVFRxAqUcSVOgD6zHd1WaQPNgnz/+9/n1ltvDX1vaWkhNzeXnevziYsJXz/HIibmaHdhxDGlhrt1BslxhWgisva/S/pHuwsjjik1ipryiYndhYiw4x1ZextASo1djYvBtT/ijve69kmj3YWRRwpyfAl4HGUgxoRF+oix1LFvtLsw4rS3SS48vpiYmLH3sOb1eqmuNSj5PJ/YmOGd1WltM8k7rhiv16vEyXggPT2d6urqXmW1tbVYLBaSkpL6Xcdut2O32/uUx8VAfGxYujlmiay/7gCmBF1aiI8VaBFmxtcVWf/dQOB4x/h1YmMhwg53RF7fUoLTZ4EYEXFmunYxrv6+hwcpiPZaMZ2WiBMnLudo92A0CFzTY/nadsUIXDHD2z+Tsbu/I8W4urstW7aMV155pVfZG2+8wcKFC/v1N1EoFAqFQqFQKMKBIU2MYdbJhozEoabejKqHUXt7O5s2bWLTpk1AIFTwpk2bKC0tBQImWVdeeWWo/je/+U1KSkq49dZb2blzJ3/729945JFHuO2220aj+wqFQqFQKBQKhWIYGVVxsmHDBubPn8/8+fMBuPXWW5k/fz4/+clPAKiqqgoJFYCCggJeffVV3n33XebNm8fPfvYzfv/733PhhReOSv8VCoVCoVAoFJGJiQzLC2DRokXMmDGDP/3pT6O8lyPPqJp1nXzyyQyWZuWxxx7rU7ZixQq++OKLMPZKoVAoFAqFQqEYPdavX09sbIQ5RwcZVz4nCoVCoVAoFArFWMAMQxzUSIysejBjL6uNQqFQKBQKhUKhiEgidubkB5XLsLZFVoSvFl/0aHdhxBFSMMWMoc4dh4yw0JORerzj3FmUNUyPuOOdHOUe7S6MOEIK5hBFq+6LuNCyCdbIO95IQVVbAXUiNeKu753u9NHuwojja/MBRaPdjUExpMQYxD3hSNuMdNTMiUKhUCgUCoVCoRgTROzMiUKhUCgUCoVCcaT0jK41nG1GOkqcKBQKhUKhUCgUh4mJxFDiZNhR4kShUCiOFkMQs2Ui1oZYfEmttM3ZB7r6g1EoFAqF4nBR4kShUCiOgoT355D7+wux1yWEyrpSmij9zvM0nbRlFHumUCgUinCizLrCg3KIVygUiiMk4f05TPrxtdjq4nuV2+rimfTja0l4f87odEyhUCgUinGKmjlRKBTDhyFwbJ6MXh+HkdyCZ+7eY9O8SYLmtpP3u4sBEIheiwUCiST3DxfSdPzWY/M3UCgUighHhRIOD0qcKBSKYcH17nzSfnsp1trEUJkvtZGa7z5L+8kbR7FnByFB67Chu6PR3dFY2qOCn6PQ24Pvvcp7L7MEvwtDH3QzAoG9NoGsR86m8bQNdOTUgkVl/lUoFAqFYjAiVpxsachA67SPdjdGFO8hHqaORTQJFt3FvnYdKQ5d/1jC3WUbsW1lrpvO1F9e2qfcUptA1ve/ybo7nqVy2c6j35AEvdOG1WPH6o4KvHuisLoD7zZ3FLTHEe8RWILLbZ7uesG6HjuaOXIWrVlPnk7Wk6fjt/lozqulqaCKpoJqmiZU05xbg2kzhmU7jS7HsLQznhAS4m02Kv1mxCXlS49qHe0ujDhCCqQvmjrhirjj3dwVNdpdGHGM9q7R7sIhMYOv4W4z0olYcaJQKIYJQzDvf88CBjZvmvfXs6hctAuL1xoSCN3iIiAiogYUHL3qeuxo5qFF9lByKZuaic/Rhc/RGXzvwhv6HHx39iwLlHsdXficncQXp3HyL75yyO005dbgqo3H2mkneW8WyXuzevWhNbuOpgnVNHaLloJq/A7vEPZAoVAoFIpjDyVOFArFkBF+DVt7NLb2aKztUdjao0namYOjIW7gdRA46uO48KKfIOTwzFiYmoHPeUBE+J0HRIUjGmodbQdERXCZt5cQ6cQf5YOjmE3rjG/HndSCoyG2jygDkEg8Sa2s+fVfkEISU51IQlE6CfszAu9F6US1OokvTSO+NI2Cd+eG1m1Lb6CpoJrGCdXBWZYquuI8R95ZhUKhUAw7RhjynAx3e+MRJU4U4cUQJO/II6oxhs7ENupnlESGc7AhSNyWj70xlq7EVhpnFY+d/TZFwNypLSgy3FGhzwHREY0tKDys7d3lUVjbo7EehSlktzAxdSMgEpw9Zyh6iIbu8tDyvnUNe//CQpMwwUhgN61hnxqXuuSLa9Zwwv0XYwiTmkkmnjiJo0WQVqihScEX16xBBo97W2YjbZmNlB6/I9gAOBpiQ0KlW7Q46+OIqU4ipjqJ3HUzQ9vzJLYGBcuBGRZPSstRCSyFQqFQHDmGDLyGu81IR4kTRdjI/HgGc/7ypV6j6p6kFrbc8CqVy3eMYs/CS9qHM5n+8DlE18eHyjqSm9n5zX9Tc8L24dlIt+9FW0BAWNscPT4H3mlxBUSHOwprWzQ2d1BkuKOOegbD6+zA6+rA5+pEIkncl3XIddbd/gzVxxVi2I5uxmIsUb50F6/e+T6NUQvwxB3YKUeLJLHzC1rn7Bp4ZQGe5FY8ya1ULNoTKra1RpNYFJxd2R8QLjFVSTgaY3E0xpL1+ZRQ3S6XJyRUvDNKaJ1YiTurfuwIYYVCoVAoDhMlThRhIfPjGSy597I+5dENsSy59zI+/f4zx6RASftwJvPv+Wqf8qj6OObf81U2/ujJXgJF81r6iApLezS2NgeW7rKeAqQ9KvgejXaUAQ78UV14nZ0BkRHTEfgc04HPdeCz1xlc5urA6wrWdXT2fvg1BF+6/rtED2Le1JHcSsWSXcfcQ7OvcxLlaQv6lHviwBO3gOjOCqxRhYfVpje2g+q5+6meuz9UZumwEV+cRkJROonBWZa4shTs7Q7St04gfesEeHk5EDiubROqaJ1YSevkSlonVtKeV4O0Do/jvUKhUCgCKIf48KDEiWL4MQRz/vIlYGAH6Tn/exaVS3aG92FVgjA0hKGjYwUz8FmYIliuIUztwOce3zVDAzPw3n89EWgr9FlD+DWmPnbmoPs991eX4fl7fUBstEejH2VELdPqx+fqwBfjCby7OkOf3Q4vvuAMhzemA19QiHiDQmTYHlZ1yabrX2PZLy9FInvtuwzazm667rVjTphIKehsWxn8drAoE4Cks+1kLPZ9iKOMLOSP9lI/vYz66WWhMs2nE1eWEjIHSy1JJWZ/BpZOOwk78knYkR+qa1r8tOXX0DaxktZJFbROrKRtYhVGlO+o+qVQKBQKxXCjxIli2EnekTcEB+l4TvjxVXhjPX0FgKkhDIFm9hUG2sH1gsJAO7ieGfjezaSR2PFDIBDoPgsxJb1jSUnNDPhchARGYLbCF9OBz+XBF9OBPygqAu8e/ME6A/lewMiGEq5ctpN1dzzLvP89q9ex70huZdN1rw1PGOExht+bgzRjBqkhkGYshi8Li6182LdvWg2aJgRCEgMkuDxgCJwVycQWZhFbmEnsvkxiC7OwtkcTV5hFXGEWrFkEgBQm7uz6wAzLpIBoaZtYiS+2Y+idMASJ2wqwN8bQldhG46yiY06EKhQKxUCYCIxhtlM2jxW756NAiRPFUSEMDWdVIjFlKcSWpRBTlkLiztwhrZu6dUKYezcwpmYgdfPAS5NI3ejxubvc7PPZ7KdMaib2xhji9+YcctuFl75DzQnbg+KjA7+jK+DJPc6pXLaTysW7SNmRR1STi86EduqOkQAIUoI04jD86Ri+A6+h0OWZD8KPbqlGhPs/R5e4c+tw59ZRdcqmQJmE6JoEYvZlBgRLYUCwRDXG4ipLxVWWSua780JNdKQ2BcRK9yzLpEq6klr7iOC0D2cy7aFz+/hW7brxleHzrVIoFApFxBGx4qS5PRphRlZSI8N35D4KulcntjqRuMpk4iuSiatIIr4ymdjqBHT/kZ1GO07bQEtmQ4+Hfnngsx5IqmbqPQRBUDT0/dx7fTMoFkzdRGiSKfZodotWjG7RIWRYHLLTduRyxs++dsh6+6ZUUpPcFvjis0KLdfg7AxjekUs02JO2nBrIqQl+GdlrTAO8mpVOX9RR2e1KaUeaqUiZhmmmIc1UIPqI2jK6JuPpmgx40PQSNK0YoZUhhP8oetiX2sGub7sfZpQGXkGimp0kFqeRWJROYnE6icVpxNYkEl2bQHRtAmkfH4gU1hHrprGgmsb8ahrza4hqdTDv0TP6bCaqPo55P/sa7936PKWLdw/r/vWHBlQ7bezzmBGXZLU+KjKTbmZ5o6n2uSLuePuN0bmfjybmOEj3ZMrAa7jbjHQiVpwo+sfSYSOuMon4yqSACAm+u2rj0QaI8OSze2nJbKAls4HmrHpa0utZ+viZRLc4B3SQdie2sv6Kt5BhnjHQANOqY5j+sDuZ1U4rw53YiqMxZtC8F7XTyvpZWzFaSKkjZXJAjJhpmDIVZHw/NQ2EqEdoNQitFkEtft95gJP+1a4EuhCiHClzAQemMR3TmA74EVoFmlaMphcjhDt8OzgAnfFuKuftp3LeAcd7q8ceECw9REtcRTLRrU6yNk8ka/PEHnsnB/StWvT4aZQt3BP261uhUCgUxx5KnEQo9rbokPDo+e4axFeky9FBc1YDLZn1ARGSFRAj7sTWgAroiRCs/J8LBnSQ/mwEhMlIIzXJ+q+/yYrfDrzf66889vZ7PCElIOMxZVpIjEiZBPQz6yCa0URQiGg1AWEiektcCx/i951BQIj0fFAPHGOL9V00fT9SakgzE9PMxzTzQMYhzTwMMw/DvwIh6hBaMZpeghC14Tf/GgCfo4uaGaXU9Jhh0b0W4ktTSSxOI6konbSducRVJvcrwCEgUJwNcaTuzKFmZmm/dRQKheJYwAiDz8lwtzceUeLkWEZCdJOLuIpk4sqTiS1LJb4iibjKwEjoQHTEtdOcVU9zZgMtPd474txDNocqXbSHtf/1L5b8/TScjbGhcndiK59d8RalPfI6HEuULt7Ne9/9F4se773fnqRW1l/51oiYuigOIGV0YDYkaKIVMM/qL5GkB6HVomk1AXGg1SJE1yHb1/T9WFiD33cC4OqxpB2L9SM0PTArIYSJ0MvR9HKk/BApE5BmPqaRj5TpSJmCNFIwjUWAu4f5V/mwm38dLobNT8OkShomVbIXyP9oBif9YfUh15v2+iLcya20pzWHvY8KhUIxGihxEh6UODkWMMFVH09sRRLx5SkhMRJXkYzNM7DNf3tSS2AGpNscKyhEvK7OYelW6aI9lB23l7RdOUQ3u+iIb6dmWtkxP3NQung3ZQv3kLorh+gmFx0J7dRGwH6PNlJaMM0Uavx5eH3xmGYa0F80LX9wpiIwK6KJGhBtRzxboen7sWpFSDMDiQOBB6FVDRg+WAgQogm0JnTLRqSMwjTzAkLFzAGcmMYMTGNGoK9aedD8q2RUzL8OpiO+fUj18tZPI2/9NKqnl1C4cjOli3fhV6GLFQqFYszi8XiYPn06F198Mb/+9a9HrR9KnIwQwhSk7swNPaTXTi897IdV4deIqUkICY+QCKlMxuLt36naFCbt6U20ZNXTlNEQMstqyWwYkQcFqUmqZ0SeaYfUZC/TGMXwIqVAykSkkYo004MmWkmARmXvmgjRGJwJCZpoicY+5llHixASV3oH1igTX2cX7rqhX9tCdKLru9H13UHzr6yQWIFYpJmPYeZj+AnO6pQE/VTqRsX8q3b6oX2rulwdNOZXk7G9gPSdeaTvzMP76BmULN1J4cmbqZtSHpagFAqFQjGSmFJgDnN0huFu73D4+c9/zpIlS0Zt+90ocTIC5Hw6lUWPndHHvGn9VWsoW9LXzEf3WoitTCSuovcsSExVIvoAWcENi5/WjEZasutoyWoIvtfTmtGIGUy2dzTRuhSK0SLgJ+IKmmelIY00pJkC9JPHRbQTpzfgphJEDUKrQ4jwivDYrFQy503F5jgwS+n1dFK5aTetFbWH1VbA/KsMTS9DWj4MCLCQ+VcaUqYijdSg+Vd7L/MvGJlZCalJ1l/1BiseuHBA36pPbniV0sW7cdbHMuH92Ux6dy4xtQlMXjuPyWvn0ZLRwL6TN7PvxK10JA5tJkahUCgU4WPv3r3s2rWLc889l23bto1qX5Q4CTM5n05lxQMX9Sl3NMaw4oGL+OIr79AZ5yauIpn48mRiK5KJqU1ADKCcfXYvrVkBh/TWrHqaswPmWO1pTchjIJ+EYnwjpUCamUjpQAgPQqs87OzoUloDjupGdxjfNJD9+Uh5AzMhek3AV0SrQdfcTNBi2Olzhz06GwSESd6yOX3KrdF28pbNoWTdlsMWKN0EzL8aQWtEt3yBlNGYRh6m2W3+5cI0ZmIaMwEfhlmGbt2PbitCaJ6j27FDULp4N+/d+jyLHju9r2/V198M+Va5k1vZesFHbP3yR6TtymXiu3PI+3Q6cVVJLHj6FOY9czKVc/dTePJmyo/bGxpIUSgUivHAWPE5ef/997n//vv5/PPPqaqq4oUXXuDLX/5yrzoPPvgg999/P1VVVcycOZPf/e53nHjiiaHlt912G/fffz8ff/zx0e7CUaPESRgRpmDRY4FcAP2F3AQ47qlV/a7b5eygJauelqD46J4RcSe19I2MpVCMAUz/BPzeE0H28PMQbVhsH6BZ9ve7TsCMKSkgRsw0TCMNZAJ9bX5MhFZ/IHKWXoMQTYctfIabzHlTARAH2VcJIZBSkjlvyhGLk4MRogPdsgudXYHwx93Rv4x8IAbTNwHTNwGfB4Reg24rQrfuR+jhMf8K+VbtzOlhrjqAb5VGKArYZ1e/Qf4n05n47hzSdueSvWkS2Zsm0eXysP+E7exbsZnGgprh77BCoVAco7jdbubOncvVV1/NhRde2Gf5s88+yy233MKDDz7I8ccfz5///GfOOussduzYQW5uLi+99BJTpkxhypQpSpwc66TuzO01qjgQDflV1E0tD4iQoCDpPIzIWArFaGP6J+DvOqvvAunC33UWFl5D6PtBxvYwzeo2z+rnNiRa0bRqhF6L0KoDwmSUo1YdjDMloZcp18EIIbA5onGmJOCuaxrWbQth9DD/+iAQDlnPwfAWII10pJGGvyMNf8dSEG3otmJ06340axlCDN/shNTkYYcL9kd7KVy5mcKVm4mpTGTSe3OY+P4cHE0xTH99EdNfX0RjXg2FJ2+i6PjtdMV2DFt/FQqFYjgx0DCGecS4+w7d2traq9xut2O39xdtEs466yzOOquf/+AgDzzwANdeey3XXXcdAL/73e9Ys2YNDz30EPfeey+ffPIJzzzzDM899xzt7e34fD5iY2P5yU9+Miz7dLhErDgxuqyghSczdze22oFzhvRk65nrKVq2s3dhVxj61hl5Uy4SMJ06hl8fETOfsYT0jczxllLg958U/HawohaAxN91OgGfiP6yrHcGc3v0fAUfSCWBO7UBsr9cJAf3BdCkhtYRfv8qW8rQMsbbiKajrfXQFY+KZsyYBjTrJqTlIPMvGYPRNRujazbg6xH9q/jA7zxKNMe3seH8j/j83I/J3J7P5A9mk7tpEoklaSx+/AyOe+JUyuYVsveErVTMKupjuioBN3ZaDEfEXd8+V+T5EGoS4k0bLURH3PGORGRXZJuq5+Tk9Pp+5513ctdddx12O16vl88//5w77rijV/npp58emiW59957uffeewF47LHH2LZt26gJE4hgcTISDDXk5lDrKRRjDSltmOZEeuf4OBhB4FZjIZRlXdSEhAi0jFrSwSPFEm0jNi9lSHVT5uUhTZPWkvqgd3946Wv+ldXD/MuFNAswzIJg9K+agEjRihGiYdSOg9QkFbOLqJhdhL09igmfTmfSh7NJLkkn//Op5H8+FU9cO4XLt7H3hG20ZjSOTkcVCoWiBzIM0bpksL2ysjJiYw9Y3ww0a3Io6uvrMQyDtLS0XuVpaWlUV1cfeUfDiBInYaRmajnuhFYcTQOH3HQntlEztXwUeqdQHBopdSAGKWORMgaI7fW5/4SG/SPEp+j6pmEP4zuS6HYLybPzSJqRjWYJjFxLKfv4nHSXA0TFu8g9ZTbetg7qt5fRvKcK0z8yjt8B869SNL0UaXkfKZMOiv6VhuFPA5YAbT2if1UMq/nX4dDl6mTnqo3sXLWRhNIUJn84m4mfzMDR4mLOa0uZ89pSaiaVs/eEbZQu2gWOUemmQqFQhNUhPjY2tpc4OVoO/p8a6L/rqquuGrZtHilKnIQRqUk+/drbrPzDlwcMufnZV99WyfkUo0ZghMaFlLH0FSExQH9Rsg6mExjY96IbTaset8JEs+okzcwheVYuui1w2/TUttBW3kDq/II+N/luYVLxwU5sMdEkTs/CFhNN5tIppM0voHFXBQ07yvF3eEdsHwLRvxpAa0C3fI6Ujh7mX9lADKYxC9OYRcD8q6xH8sf+zb8C0dmGlnzySGjKreOzr7zDhkveJWfzRCZ/MIesrQWkFWaTVpiN76lTaFmyj8aTPqdqWqkKFqJQKBQHkZycjK7rfWZJamtr+8ymjBWUOAkzJQv3sPbbL7LkiVU4m3rmOWnjs6++TcnCPaPYO8WxTuAZ2TGI+HBx6Ce6LqANIVoRog0IvAvRCrQBBn7/1wgImf5GkCTQjhBVw7RXI4fQNZKmZ5M8JxdLVCCvSkdDG7Vf7KetrAGAriY36UsmY3MdEGg+dxfVn+6ltaQOgLotJcRPSid5Vi72OAcpc/NJmpVLy/4a6reV0tU08pnfhfCgW3aiszNo/pUdNP/KI2D+NQHDnIDhl0Hzr5Je5l+mMQG/7wR6m/S1Y7F+iKb3H53tSDEtJiXH7aXkuL1ENzuZuG4mkz+YTXx1EskfTOf0D6bTltJE4Ulb2XfiFtwp4fbxUSgUCjCkhiGH2SF+mMerbTYbxx13HG+++SarV68Olb/55pucf/75w7uxYUKJkxGgZOEeShfsJW13dijkZs3UcjVjcgwTGlGWToRwD/uI8oHtANgD4sM8YHIlzVikGRAkh77M/QTExwHBERAi3eKj65C+CLr+IYZxBgEh0rOyDC7/aNTD/h4OQhMkTM0kZW4+VkfAdK2r2U3NxiJai3qHBm4tqaO1tA5nWjwWhx2/pwt3TXP3rgMgDZOm3ZU07a4kJjeZ5Fm5ONPjSZicQcLkDNrKG6jfVoq7cnijeg2VgPlXCZpegrSAlMk9zL9SkTIdw59Ot/mX0BqRZm4/LTnx+87AwpphFyjddMS72XbWZ2w78zPS9mWy6JN5xH08mZi6BOY/fxLz/nUiVTOLKTxpM6WL9mDYxlaUN4VCoRgKixYtQtd1br75Zm6++eZB67a3t1NYWBj6XlRUxKZNm0hMTCQ3N5dbb72VK664goULF7Js2TL+8pe/UFpayje/+c1w78YRocTJCCE1SfX0stHuhmIEMP3BEeWD831YPxww38dgSGkJiI9u0SFje4iRGA7t92ESmLnoO+sRePcctSO0phUBazCMviPpuv5RcPk4QAjiJ6WTOi8fW0wgGpe3rYPajUU076sZ2KFdgru6eUibaCutp620nuiUWJJn5RCbl0pMdhIx2Ul0NLbRsK2Mlv01SHN0xFzA/KsetHp0y4ag+Vd+L/MvacYMtDYg8fuOx6oVhVeQCqibVEnJnEb2fO1Vcj6bxqT355CxI5/MbQVkbivA6+ikaOkOCk/eTP2EKhWeXaFQDCsmAnOY7UnN4MjW+vXrh+xzsmHDBlauXBn6fuuttwLw9a9/nccee4xLL72UhoYGfvrTn1JVVcWsWbN49dVXycvLG9a+DxdCyhEIHzOGaG1tJS4ujryH74LoQ9vJH1NEYChhDZjudLDT3z4ioScDiQjPDH7rO4Ngsb3eR6BIqYF0hUSHNINCpPvzkDx+PQgtONshWhFaKxjtQfHhHjFfDykFUmYQ6LMHIcIzYzQQGjBLutjZ4Tns4x1bkEra/ALs8QE/G5+ni7pNxTTtqQyrULC6okiamUPClAx0a2C8yOfuomFHGY27KzG9Qxv5N2PC78AupQXDPxvTWHbIuhbri2h6ZVj7owEzHVHsNA5c367aOCZ+MIdJ78/GVR8fqtucVUfhii3sO2FbII/UOMfp6hrtLow4moSJZjy7aVWhhCMA6elk99d+SUtLy7A6hg8H3c+S/9kyAWfM8Ib1drcZnD1n/5jc75FCzZwowoqUAkkGUjoQwoNgZB9WRxIpRdAGHwbM9+E9Gc1MBNljBkQ6ObTfR2dg5iMoQIRoRWjBmQ/R1m+CQjnMdrBDQQiJEOF9IB1uYnKSSF0wgeikwGyAv9NL3ZYSGndWII3wPwL52jup/nQvtRuLSJyWRdKMbKxOO+mLJpEyL5+m3ZU07CjH194Z9r4cCiH8CK3tQJawQZCjFEarPbWFzRd+wObVH5C+I59J788h77OpxFeksPCpVSx4ZiXl8wopXLGF8nmFSIt6zFUoFEdGOKN1RTKRK046dBhCUrdjCv/InvAmBfjFchBBMx8JyHYs8mM0RtDMp1ODzsM/1WUoP4cVsCKD7wd/BitSWJHEgRjI3AUCAiUa07+kn435gHYErQjaELINQdAMizYEwahOAz4U9t0/3Rd5NzgN0DQNS5t2yJFVR048KcsKiM4IJEs1uvw0flFG08ZyTJ8RvDuMpMAzaf6ojJZPyomZkkrighyikl0kz8olaUYObXvraNxYRmdNW79re0fodiboHNLPYnjnoMkmBM1h7Y9p0zEHSLJaObWMyqllWL/6JgWfTWfy+3NI3Z9J7hdTyP1iCh0xbvYt30HhCVtoyqkPaz+Hm47O8CYRHotogNe00GnYIm7mRLeOTmjv0UT6IuwZTREicsWJIqwEhMlp/Sxx4henYZFvDqtACbpdc7B4kFhpMl34MQ+UCRvdoqOPyOj5XYTnz1/ICjRZAbQFhUcr0KHGSkaIqPRYUpYX4MxJAMD0GTRtKqfh8zLMrtF3npaGpHVnDa07a3DmJpC4IAdnXiKxU1OJnZqKp7yZxi/KaC9qGJX+CapBtgNO+nVW6rYUFun4uBiNnejycwSjl43e5/Cy5+TN7Dl5M3EVSYHcKR/NxNHqYtaaRcxas4i6gioKT9jK/qU78Dojz2RKoVAcPuGJ1hW4hx6OQ/yxhhInimFHIgIzJtD34UUIkBK/WI5FViL6EQm9PvcQEoeavUD0f4Mo9nN0A+DSBHyAF/AHZzH8gA+BL7jMBzIKU5t6yOZ0+Tka4y+s7njHnuwiZXkBroIkAEy/SfPWSho2lGJ4Ri7fyOHgLm3CXdqEPdlJ4vwcYqem4siOx5EdT1ejh8aNZbTurBkR87NuBBKL/Dgw+CBl72s8+Keqy48wyUaKfExmYjIZXW5CYyuC0RWALVkNbLj0XT6/8H2ytxYw+cPZ5GyaREpRBilFGSx6+hRKF+xl70lbqJpR0m9URWGKQPTFFhcdcSr6okIRqQQc4od3aLG7vcNxiD/WUOJEMexI0g+YcvWHCCT+84urwtSBA4JB4MOhGXSYXaGy0LKD6vVa1uv70CxAJQJTZjH4iLI7MPKsGDFsCQ6Sl+YTOyUVAGlKWnZUUf9ZCf628TFC3lXvpurNXdStKyJhbhbxszOxJzrIWDWVlGUFNG2poLa4HKPLNyL90SjCIt8MDkL0vNbdIbNNXW7HlBkYYilSpGKIxRhyJrpcj8YeBKP7MC8tJmXz91E2fx/21uhQ7pTE8lQmfDadCZ9Nx53YSuHx29h7wlba0poByNswhSVPHpS3KqGVT1XeKoVCoRgWlDhRDCsSCyaHEZpOGgxFIPQUEn2W9VnvABowxepgZ+fhR286XIYyomyRH4/6Q1mkYI2NInlJPrHT0hCaQEpJ6+5a6j8txtc8eiZGR4O/vYu6j/bT8FkJcTMzSJyfjTU2ipSlBSQtzKVpXzX1O8roavWEvS8aRVhlMVKmH8gQT3Wv81ujCiFfwJQTMcRiELEY4mRMORtdfoJGedj7ORS6YjvYccYGdpy+gaSSNCZ/MJsJn8zA2RjL3FeWM/eV5VRPLaUxu47pby/os76jKYaVf/wya7/1ohIoCkUEYaJhhCmUcCSjxIniqJHYMcnFFAVIsofsq6Gb/0anIsy9G1mGMqKsCC/SYSV14WTiZmYg9MCfRlthHfWfFNPVMP5DyMIBP5mmzRXETEom8bgcotNiSZqaReKUTFrL6qnbXoqntiWs/RBIxCFMFAWgsw9NFmHKWRhiAVIk4RdnI2RZUKQ0hrWfQ0ZAQ34NDfk1rL90LbkbJzPpg9lkbS8gfXcu6btzkUjEQXOpAoFEsvipVZQu2KtMvBQKheIoUOJEcURIHJgUYIp8JJm9/T1kKxBFwA9kYPMmjfEVcnaoDGVEWTH86FFWUufmwvRs4i2B87G9pJH6dUUDRrga90hJ29462vbWYZ0aS8rMHGJzUojLDbw8dS3UbS+jpbRu4ASSI4TARGcLmtyNwQJMZiJFDn6y0diDLtcjGDvi0bAZFC3ZRdGSXTgbYpj74vFM/WBuH2HSjUDgaowlbXe2SrirUEQI4XSIj2SUOFEMGUncAUEi0notE7IBQTGaLELQgOyO1hWh5k1DGVFWDA+azULy7BySZmaj2wK3NE9FM3UfF9FRGd6Zg7GEu6YZd00z9lgHyTNySJiUjiMljryT4/C2dVC3o4ymwipM/+iGJBV0YZHrkGzDYDGmmITJVEwmoLEVXW4KmmqOHdxJbVTNLGHqB3MPWffUBy6iMb+Wpuy6Xi8VAUyhUBwOKlqXQtEPEpAkY4oCTApAJPRaLmQ1mixGoygYDrfHMmXepAgzwqKRNDOb5Nm5WKICpoQdda1Ef1FF+a6qiMuD0E1Xq4eKT3ZTvWk/yVOzSZqWhS0mmqwlU0ibV0Djngrqd5bj7xjdCGWCNizybUy5Neg0n4HJAkymo8sNaOxCjKGj2BHXPqR6Vp+NtL3ZpO3N7lXuTmylsadgyamjJaMBUyWBVCjGLSYaZph8TlS0rghE+ARYIiyzxBAmKiQCKdIxRQGGyO+dVFAaCFmJLovQZAmCwZ1udYrQKEaKHuZNcmTNmwQgZPA1YlsdG4hj9JlH6BoJ0zNJmZeHxWEDoLOxndoNRbiL65lhcyL1IZ3uxxTaQUk3pc9P3YZi6jeWEj85neTZOdjjHKTOzid5Ri4t+2qo31pGV9PomlJp1KHzCqaWh19fgtTiMcSJmOZsLManaGbJgNeuBkecZPVwqcmqxh3fhqPZ1a9pl0TiiW/jzW+8SEJ1EgmVKSRUJpNQmYyrKRZnY+CVs2ViaB1TM2hJa6Ips56mzLrgez3tia2HvGGNfkaekUcDpK4hfXrEXd9+M9L+wQBvxD6iRjzqyCuQ6JgiG1PkY4o8ENE9FvrQZBmaLEKTpQcylQ8RgURIZd6kGAaEIGFqOikL8rG6ogDwtnZQu6GIln01IEc2n/t4QRomTbsqadpVSUxuMsmzc3BmxJMwJYOEKRm0lTdSv7UUd0XTqPVRALpZgmaWYmjT8VuOQ2rx+LQzEGYVVv8naLJu1PoHIDXJpxe9w8q/ntfHKV4GH5U/vWgtTTmBWRHYFVpu89iJr0oOiZXul70jioSqZBKqkuHzaaH63qgumjMaQoKlMShavM7OEdtfhUJxaAwpMOTwCsfhbm88osRJhCKxYYrcoCDJ7R1hS3aiyZKgIClHMLo26ooIR0DcxDRSjsvHHucAwNfeSd0XxTTtrh51R+/xRFtpPW2l9USnxJA8O4fY/FRishOJyU6ko6Gdhq2ltOyvRZqj85sKJBZzB7p3L359HoY+G6ll4LWtRjP2YfF/hsboBTcomVfI2uteZsk/T8HZfGBW2R3fxmcXraVkXmG/63kdXdROrKB2Yo/ohBKczTEBoVJxYJYlriYRW6ed1KJMUosye7XjjmsLza40TaihKbuO5swGTKu6RysUo4ERhlDCRsTNC/ZFiZMIQhKNKfIxRAFSZILQeyxsD5prFSNk1THtrK4YP8TkJ5O6sICoxIDfkr/DS93GEpp2Vo5oVvRjjY66Nsre2YHVtZ+kWdkkTM0gOslF9skzSFs0kYbt5TTuqsT0jo7xkMCH1ViPxdiBz7IQU5uCqU/Eq+WjG9uxGBsRjI6Decm8Qkrn7COtMIvoVhcdse3UTKo4/PDBAtwJbbgT2iifecAHTxgacTUJvczCEiqTiWmMw9kSg7MlhuydBfB2oL6pmbSmN/Zyvm/MrqM9uUVNJSoUinGJEifHOJJYDJEfzEGS1itylpBNgdkRswhBfcT5ZCjGLs7sRNIWFhCdGnAGNLp81G8upXFbxahHmzqW8LV3Uv1JIbVfFJM4LZOkmdlYnXbSF08kZV4eTXuqaNhWjq99dMyJBG5s/vcwxTb8liWYWjaGZQ6GPhWL8QVWY/uo9Etqkuop4UkgKXWT5swGmjMbeoUNsXbYAiZg3YKlJomE8hTs7mjiK5OJr0ym4LPpofo+u5emrPqQ8323cOmKOfIEpMIUpO3OJrrFRUdcOzVTy1VOF0VEY0oNc5hDCZvKGkCJk2ONQIStpECELZGPFEm9lgtZg2YGQv5qRE6YVcX4wJEeR+qiCTgz4gEwfH4at5ZTv6Vs1EbxIwHT66d+SykN28qIm5hG8uwcohJdJM/KIWlGNq3FtdRvKaOj/iCTKgHO9Hgs0Tb8HV7c1c1hiUSgyQasvlcxRTZ+y1KklojfsgxDn0mTsRnJjuHf6BjDF+2ldkIltROC+aFcfpDgaHaRUJZCQnlK6D2+Kglrl43U/Zmk7u9tGuaJa+8d5jgnYBpm2Aa/vvI2TGHJk6twNh2IHuROaOXTr75NycI9w76/CoUiclHi5BhAIpCkhUy2ED1Cz0kTQVXAZMssHlNJzhSKbqKSY0hdVEBMTkBMm36Dxh0V1G8qxegcWzkvjmWkKWneW03z3mpcWQkkz87FlZ1I3IQ04iak4a5qpn5rKW2lDcTmJ5O+dDK2YHACAG97J9Wf7KW1uH7Y+yYAXZaj+Z7H0KbgtyxEiliKjRMRYmow03yEBd8Q4Elox5PQTsWcHqZhfo3YmgQSy4OiJfiKqYvH0eLC0eIia3tBqL4pTNrSmmjKru8lXNpSm5GaJG/DFFb+8ct9Nu9oimHlH7/M2m+9qASKIiIJp8+JynOiGHdINEyygjMkeSAcPRb60SgL5CCRJaNmm61QHAp7gpPUhQXEFqQAIE2Tpl1V1H1RjN8zunk4Ip32iibaK5qISnSSNCuHuIlpODPicWbE43N3hcI498TqtJOzahZlb28Li0CBbqf53ejefZj6bAzLPEyRil+ch5BFWOSniAifFZYWk5asBlqyGihaciBqmKXDRkJFclCsJIdES1S7g7jqJOKqk8jfMDVU32fz0pxZT3xlMkCfEMoCgUSy+KlVlC7Yq0y8FIphROU5UYwLJFZMkRPM0p4LosfDgewKRtgqRqMMEZFR8BXjBVtsNCnH5RM3KQ0hBNKUtBRWU/t5Mb42FS51LNHZ6Kbi/V3UbNhP0sxsEqdlYnXa+60rhEBKSfrSybSW1Ic12YzAj9XYyNToYnZ6Z2IwDSkK8JGHxk50uQGBOpd64o/2UjepkrpJlQcKJUS3OHuZhSWUpxBfkYzVayOlOHPgBgkIFFdjLGm7s6meXhbmPVAoxhYmwx/6V4V6iWBxovkE6GPfBVwShaHnYej5mFoWiB6HTLrRjWJ0oxjNrDwowlbffRP+sb+/w40GaJqG7taUw/9IIMCREY/FacPv9uKpau71gGpx2UlZmEf89AyEFjgirYW11H5WjLfJE2ziyI+UALASeRkYAc0b3jPc9Pqo+6iIjrIWcs+ZM2A9IQQ2VxSupAQ8lc1h7ZMG2P1dRHV+iF9sw2ddgqnnYTITk8lY/Jux+Lcck+HQDevwmZJ0RHfQMbmUysmloTJhCGLrEpj63lxmvrPwkG2c+sBF1BdU05BbQ0NOLQ25NbSmNw7/bEqUQBoRGE9ymJ2uxwW+CNxnBRDB4mSkkQhMLR0pHAjpQTMHzpRuChemlh8UJOkgDlygwmwOCRIha9UDt2LMEDMhmfQTJmGNOeCD4GvrpPrDQjxVLSQfl0fCrEw0PXA+txU3UPdpEZ317aPVZcURoNmG9rfRn9lXONFkM3bvGgwtA591KVJLwW9dhN8yHatvA7qxV4VIPwykLmlJb6R0XuGQxInVZyNjTy4Ze3JDZT6bl6bsupBYacitoTmzHtOixoYVxwYmGuYw+5wMd3vjESVORgBDy8drXQ6a60Ch2Y7N9zG6WRyIsCUSMPR8DL0AqSX3Wl+YdT0ESZMSJIoxR8yEZLLPnNmn3OKyk33mTKRholkCeXXc5U3UflpER3XrSHdTMQwM1RdImqPzAKqbVWhdL2DoE/FbFiO1GHy2k/Gbs7H6PkE3Kw7diCJEzeRy3AmtOJpi+p3VlEg8CW28eeMLJJWnklSaRlJZGonlKcGIYVmk7s8K1Td0g+bM+pBYacippSm7Dr9dBb5QjD8MqWEM86zWcLc3HlHiJMwYWj5e22l9FwgnXttpaEYxUktAavEHlkkTzawOmmsVo0k1sqwYwwhIP2FS4KM4yGE2+F1YdDpqWqn9pAh3edOId1ExfHiqmvG1d2Jx2vscbwApJUIIMldOo9ZRRNP2ihE3sxOAxdiHbhTjt8zEb5mP1JLw2s9GM8qw+j5Fk40j26lxitQkn17yDiv/fD4S2UugyOCB/fSSd2jKq6Upr5bC47cBgZwosbUJJJWmkVh6QLTYPVEklQU+81GgHVOYtKQ30thjhqUxpxavQwVzUSgiESVOwohEBGZMoFfyw57fTUswnKP0o5kVwRmSEuXIqRg3ODLie5lyDUTNx/vD7oOgGAEkVH9YSPYZM0NCJLQomDysq9mDPd5BxkmTiZ+WTtV7e+isaxuoxbAhMLD6t2Dx78ZnXYChz8DUc+jSstGNPVh96xF4Rrxf442SBXtZ+42XWPKPUw7Kc9LGZ5e8Q8mCvX3WkVrALKwlvZH9i3cGC8HVEEdSD7GSWJqGo9UZSDBZlczEz2aE2mhNbu4hVmpoyq2FQ99qFIoRw0RgDrM9y3C3Nx5R4iSMmFp6b1OuAbB4N2AxtiJQ09qK8YM1JgpHZhxx09KHVN/iHFkfBEX4aNtfT/ma7QEfox55TvztXVR/VEhbUT0JMzJJXTqB6NQYCi5aQNO2Smo/LRqVZJqCLmy+dZj+bfgtizEsEzEsUzH0iVj8W7D4N6v77yEoWbCX0nmFpO3NJrrFSUecm5rJh5khXkB7cgvtyS29BE10izMgVkrTSCpNJbEsjZiGOGLr44mtj6fgiwPhjb0J7aTnVgf8WPICwsWd0NZfDBiFYlyj8pwowoLsmXtkEAQt6o9RMeaxxkXjzIzDkRmPIyse2xBmS3rid6u8JccSbfvraSuqD0Rnc9jwe3pHZ2vaXknr/jrSlk8kfmo6ibOziJ2YQvVHhbTurR2VPmuyDZvvbUz/VnzWpZh6On7rAvyWaVh9n6Mbu5TT/CBITVI9dfjDBXfEuSmfvZ/y2ftDZfb2KBLLDsywJJWmEVubgK3JRW7TJHI3TwrV7XR5aMg9YBLWkFtDa0ozyq9YEW7C6XOi8pwowoKQQzMXGGo9hWIksSU4cGbG4wgKEqurd24LaZh01LXhqWghfkY6epR1QB8Ef3tX4MFVcWwhGdRUz+jwUfn2Lpp3VZNx0hTsCQ6yT5tB+7QMqj/Yg7e5Y+T62gNN1mLzvoyp5eOzLkZq8fhsJwad5j9FM0vUQPwo0+XqpGp6KVXTD4Q3tnVamV+bS9u+OBJKU0kuTSO+MpmodgdZO/LJ2pEfquuN6qIht5bGnBoacmupz6uhJb0BqR+++BSmIG1vNo5mJ574I5gxUigUh4USJ2FEM6vBbAfh7OtzAiAlSHegnkIxytiTnDgy40OzIweHgjUNk86aVtyVLXgqmvHUtCJ9gfwRHbWtgahcA/ggVH9YGJG5RxQBPBXN7H92PUnzc0g+Lg9XTgITLl1Ewxel1H9RijRGPrKXAHSzGK2rBEOfgc+6AKnF47WfgWZUBp3m60a8X4qB8Uf5aJ9Szc78faFEdbpPJ74imeTSNJJKAjMsCeUp2DrtZOzJIWNPzoH1rT6asuuo7zHL0pxVj2EdOA9O3heTWfLMKbh6+Nq0J7Ty6WX9+9ooIgsDDWOYp+iGu73xiBInYUQgsfk+DkTrkrK3QAk+tNl8HyszAsXIIyAqyYUjKz5gppUZhyXK2quK6TfoqG7FXdmMp6KFjprWAR8i2/bXU/769j55TvztXVR/WEjb/vqw7o5i7CNNSf3npbTsrSX9xMnE5CWRsiifuClpVL2/F3fZ6ETPEkgsxnZ0Yw9+yzz8ltmYeiZd+mp0fyEW/3o0OfLO/IqhYVgNGvJraMivCZUJv0Z8dWIvP5ak0jSsXTZSijJJKTqQ9d7UDZoyGg6YhOXV0phdiz/KR94XkznlofP7bNPZFMMpD53POze+pASKQhEGIlec+MWIZEy3UALmW/jsy5DigHO8kG6sXeuw+EsYKU8+PQKjMmoAztHuxSjR87TSBNHJrpC/iCM9Dt3e+/I3fQaeqhY8lc24K1vorG1FmnLgNg+iraietuIBfBBG0EZG7wSrGyItzZs5Tu7m0tNJ1XNbaZucTMopk7HFRZN37hzadtdSt7YQf/vQfZM0gGEyyRb4sPrXoxs78FsWYeiTMSyTMPQCLP5tWPwbEQT6djhJdcOF6Iq80VUBCE2gdR5631uSG2lJbmT/gmCkMBNi6xMCfizlqYFIYWWpRHmiA9/LU+Hj2QBIIWlNbsLZHBPc7kEh0hFIJEuePoXyaftHxMRL2iLtjgaMwDPa0WJKgSmHOVrXMLc3Hhknf2fjG4u/GN1fgqn3+DMzRv7PTBE5CE0QlRaLMyvowJ4eh2bVe9Uxuvx4qoMmWpXNdNS3w8Fi5HA5hA+CQtFN+9563MVNJB+fT/yCbGKmpuIsSKT+wyKaN1aGZpdHGk26sfneDTrNL8HUs/Fb5+K3TA0IFNONz7p0wKS6ijGKBq2pTbSmNlF83O5AmQzMggSSRgYESmJZKs6WGOLqEgdtTiBwNceSti+L6snlI7ADCkXkoMTJCCGQ6EbVaHdDcYwidI3o9NiQA3t0emwoI3s3/k4fnsrAzIinspnOhnblB6IYVaTPoO7dfbRsrybttClEZ8aRespkYmemU/vmHjqrR8+cSpMN2LyvYmo5+KxLkFoifuuy/kVTMKmuzfvmMS1QJAIp0pE4EHgQcpwPsglwJ7bhTmyjdG5hqDiq1cHMtQuY/daSQzYxe80S7O5oqieX0eVU+ckiDTMMPiem8jlR4kShGI8Iq44jLRZHVsCBPSotFk3vfUPze7whEy1PZTNdje5R6q1CMTjeOjdlT20kbk4GySdOICothpyvLqBlcxX1H+zH7Br53CjQ7TRfhtZVjl+fgt96Ioh+HhyEACnxWpcT1VUyvh/YB8AQBfi15dDDPBnZjsX8GF0WjV7HwkBnrIeKGcVDEidZu/PJ2p2PFJLGrFqqppRRNaWUmknl+O0qRcCxjik1zGEOJTzc7Y1HlDhRKMYBmk3HkRE00cqMJzrFhThIjPjau4JipBlPZQveZhWieqQQAgpyNGKd0OqGojJztKySxjUtW6poL6wn+aSJxM1KJ35eJq4pydS9u4+2HTWHbiBMCCSabO1fmIQqCRAuOqOuAnwI6QcCr/4+H2r5oJ/Dtqf9ExAmp/WzxBkoN9885gRKzcQK3PFtOJpdfXxOACSSLmcHRQt2k743h4TqZJLK00gqT2PWOwsxNYO6vGqqppZSPaWUuvyqQaOCKRSKAyhxolCEA0HAMdxpw+/unZxuKGh2C47MuKCZVjxRyS6E1vsP0tvWGfIXcVe24Gs5KGeE8qkbEWZN0TjvVCvxsQd+8OZWyctv+di2JwKdWI8Sw+Oj5vVdtG6rIvW0KdiTnGR8aTpxs9KpfWsv3sbREd1DTaqLsAJWevq0DrtO7SVUDnxGHvS9e7nsp26vZb5+lpsIAqZcfm15cN8OuqkEZ4z82nI0o/iYmjGSmuTTC99h5SPnIZG9BIoM7ufHl71J6byAOVh0q4P0Pblk7MklY08OMQ3xpBVlkVaUBa8vw2/1UTuhkqoppVRNKaUhp+aIcq4oxhYGAmOY/2y721MZ4hUKxbARMyG5T0hdX1vnoCF19WhrKKSvMzMee5KzT0JDb0sH7qAY8VQ242uLwPBrY4xZUzSuWG3tUx4XA1estvL3F5RAOVI6ylsoeXwDCQtzSFqWhyM3gbyvL6RxfRmNn5Qg/SP7uw41Wa7V+w6a2YgUFgJ/sZbh+Sx6/F2Hlh8kfMQwCiFp0i1SEFED1xMCcCFFOkIeW36VpfMKWXvtyyx5/pRQ5C4Ad3wbn124NiRMADpiPRQt3EXRwl0AuOpjydibS/qeXDJ35xLd5iRzdx6Zu/OAQJLI6knlVE0toWpKGc0Z9WpASdELlSFeoVAMCzETksk+c2afcovLTvaZMyl/fTtt++uxOGzBsL5BMZLYN95xV6M75C/iqWzG7x56iFVF+BECzjvVGvx8UKhRIZBScu6pVrbv7VImXkeKKWn6rJS2XbWkrpqEa2IySUvziJ2eSu3be+nYP3K5UYaaVFc39gVmEIb5mAeaswA6UlgwLQcEDMKCpIegoaeg6b0s9H2AZYhgIA2hAb0TsQ6GIaaA7EDQfEw9Y5fOK6Rszj7S9mUR3eKiI66dmokVhwwf3J7cyt7kbexdtg0kxFUnkrEnj4w9OaTvzcHeEUXutonkbpsIQIfLQ3VwVqVqSiltyS1KrIwDlM9JeFDiRKEYLgSknzAp8HGAh9WsU6fjc3dhj+9rItJZ397Lgd3oUM6UY5mCHK2XKdfBCCFIiA3U21+qZk+OBn9rJ5UvbMM5KZnUUyZhjYsm64I5tO+pg23l4Am/qddoJ9UNbK3b/6SLXupnoE0eQVckGqDTLVYMkYGhrzzkeqY2DVObBrIdTVaEXoLx7/smNXl04YIFtGQ00pLRyK4VGxGmILE8NWQClrovm+h2BwVfTKPgi2lAIAt91dQDYsWTohKBKiIHJU4iCDEavngCorMP+F50lDePaPhaAQgZfA1bowLNqqPZgi+rBd2mE50W28uUq+9qAmHVscc7kFLSVdd+IPt6VTNGZ++IREfd3wgcrReAZgYSMYZ70DF+iIPK8bZAf8KNiIC7eee2esp2N5F4Uh5xS3JwTUmBgkSSPiuifmvF0efpOQRjKamu0A9d54jaRXLA7wQEhRjaImCQGSO8CFmPFGkgXJhiKiZTA+vLZjSzIvCSlaFElkfWNxB+geYd/1MKTem1NKXXsuOkDWh+jeTSjIAZWGEOySUZuJpimfzJLCZ/MguA5vSGgFCZWkrV1DK8kRC22Bz7x9mAMPicKCLg70wxWrgmJ5NySl/fi7p3Cmnf27/vRTiQBPKA6DY9KCosaDYdvftzt9DosUzrsUwPCpBQufXongoaPi+l/vNSTO/ohEdVDA+t7cNbTzE0pM+g4e39tG2pIeVLU4jKiSP1+EnETkun6r09dFS3hnX7kZZUVyCx+j/GZxl4xsjqfw9dFiPRMUUappaFKbKQIhkp4jH0eAx9JkgTIesDsypmJZqsRqhHMUyLSe2ECmonVLD5jHVYuiykFmWRXhgQK0nlacRXJxFfncT09+cHwhZn11I5NSBWaiaV449SM+2KYwclThRhwTU5mYzz+ve9yDhvJlUvbz+kQAnNTAQFg+jxWevzeYBlVh3sFqZowz8CYxomptfA9PkxvYE/2Khk1yHWgvbSRiVMjgGKS0yaWyRxsX3N+ACklLS0Buophh9vnZuqxzdSsDIX47gcopJcFFywgKYdldSu248RxtwokZZUV5fF4H8Tn2U50PMe58bq/ziwHBAY6LIS3agE1iOxYYoMTC0bU8tEigSkSMUgFUOfD9KPJmuCsyoVCFl/zIq8w8Fv91M5rYTKaSUA2Dx2UsqzyNidS8buXBKqkkkqSyOpLI3Zby0KhC0uqKJqahmVU0uoK6jCVGGLRwTlcxIelDhRDD8CUk4Z3Pci/axptE9uGFRYhINuMWF4DUyfgen1B997fza6l/VXL7h+HxMSAZOuXIrFZR/wYdUfzEWiGP9ICVu3+zlxuRUpZb/HfM3bPuUMH24K69m/u4rkZRNImJ5BwoxMYgqSqVm3n5Zd1aPdu2MGXRaj+UowRToIB0hPcOZj4BNc4EWXJehGCRggcWBqWRgiC1PLDJqAZWFqWYEVpBdNVobMwI415/ojxevoonRe4YGwxS2OkFDJ2J1HTEMcafuySduXzbxXA2GLayZWUDUtMLOiwhaHD0NqGMMsJoa7vfGIEieKYSc6O/7Qvhc2C7Ez0g7ZljQlZtdBAsLb3+e+y6TXQHoN8lxW9ja14/cb4fXDkFDzQSFZZ83s87Aqg0+oNR8URqQvyLFIbAwsnB+4hXZ1QVSPU94wJLouKMjT2LRFzZyEG6PTR9Xa3TTvrCZjxRSikpxknTKN+GnpVL+3h66m8e+UPRYQSHRZdcT3MIEH3dyLzl6kAZK4gAmYloUpMkHYMUU+ppYfWEG6g+ZfFehmBeAerl0Z13TEedi/eBf7F3eHLY4jY3dOSKw4Wp1k7cona1c+AF3RndRMLqdqaimVU0tpzhxa2GJhCtIKs4lucdIR56ZmUvkho5QpFMPBqIuTBx98kPvvv5+qqipmzpzJ7373O0488cQB6z/55JPcd9997N27l7i4OM4880x+/etfk5SUNIK9VgyGNX6QmPg9aNleTWd5ywHTqK6+MxhHm8tAA4TmwPSNzBR32/56Kl7bTtqJvX1t/O1d1HwwcJ4TxfjjwvOtREcLyitMHv6bl9wcjVhX0MdESm642s7i4yxs3mayv0gJlJGgo7qF/c9tIGlONimL8nFmxjPhkoU0bC6nbkPxiOdGUQyMAAQtaGYLmDuQCKRICs2kBGZonJj6ZEwm4yfgXF8ia/FrJQizEoHK9QTQntzC3uQW9h7fHbY4icxdgZmV9D3BsMVbJpG7JWDR0BHjpmpKGVXTSqia2n/Y4ryNk1nyj775XT695B1K5u8dyd0b00hEMF3p8LYZ6YyqOHn22We55ZZbePDBBzn++OP585//zFlnncWOHTvIzc3tU//DDz/kyiuv5Le//S3nnnsuFRUVfPOb3+S6667jhRdeGIU9UPREi7KQcFwOCQuzh1S/dVs1HWXN4e3UKNC2v562onocmfFYHDb8Hm/AlEsNOB0zLF2kM2WSjs8n+ccLPgwDiop7P/iu+8zPssUWLjjXwv885MWn/FVHBlPSsKmMlsJa0k+YTOyEZJIX5BI7OZXqD/bSXtww2j1U9INABp3l68HcHHSuT+3hXJ+CFPHUEw/2KSAD9XWj2wysOpjhPsIR0JLRQEtGAztXBsMWl6WSsTuXzF25gZmQNicTPp/GhM+DYYsTW6mcWhKMBFZKalEmK/9yXp+mHc0uVv7lPNbe8LISKIqwMqri5IEHHuDaa6/luuuuA+B3v/sda9as4aGHHuLee+/tU/+TTz4hPz+f73znOwAUFBTwjW98g/vuu29E+63ojWa3kHBcNvHHZaPbg1mLDRM0MbDvRVtXIKzwsYoET0XzaPdCEQaSkwRnnR44z19/y09dff+qc83bfqZP1UlK1Dj1ZAuvvakenEYSf3sX5a9vw5WXRPqJk7HFRpH7pdm0FdVT/cFefO1q1H0sE3CurwoGHtiAxIoUmcRb86gzU5FaIlKk4NdSgLkgDTQz6FxvVqKZtcq5nkCOloa8Ghryath2+no0n05KcToZu/PI2J1DSlEmrsZYpqybzZR1swEwtYClgThoBF8gkEgWP7eS0rmFysQL5XMSLkZNnHi9Xj7//HPuuOOOXuWnn346H3/8cb/rLF++nB/+8Ie8+uqrnHXWWdTW1vLPf/6Ts88+e8DtdHV10dV14E+otTUQZlIj8pKvDvfpLmw68QuyiV+Ygx4VtL2vbafx4+JAQsLzBva9qF9byEjc17qPs7rUI4NwH29Ng0tWW7FZBYX7DT79zBhwW74uePnfPq78qo0Tluls225QUan+zIeToRxvT0kDRRVNJC/MJ3FuNjEFyTizE6hfX0zjlvKw50ZRDBc+NFlCrqijo8uDn2hMPQtDC7yk5sLUMzH1zEB16UU3q9CNgL+KkE3j+j9/2M5Sq0Hd5ArqJlew5RywdFlJ3ZdF+u5cMnblklSahmYOHJBGIHA1xZJemE3NlLLh6pVC0YtREyf19fUYhkFaWm+n6LS0NKqr+4+wsnz5cp588kkuvfRSOjs78fv9nHfeefzhD38YcDv33nsvd999d5/yabZodFv00e3EOGO4HtikVUPOTsOclwFBUUKDB219BY79TTiD9cw1hZgn5ILLHlpXtHvRPiolp8IDjr5Z0ocbDUi32xEmRJq1uRaBA/UCyBZ29NjwPHPOXirJyZZ4O2HTWxamuKyDr1AN+3eaTJguuHy1nf88ITDDlFhMjroH4cgjgFSbHc0YwvW9qRpZ3ALL89DSY0ldPpHU6RnwcTGiRiWjGQ9oQKbNhpDdx7sSqESyni5iaCOdVtJpIx1D2DH0PAw9DwALHcRQQyxVxFCNfbw514drxCUamFVP16x6ivmC9g+mkf/EyYdcbUprEslaU5g6FcCv6RSFdQtHjykFphzee3p3e4sWLULXdW6++WZuvvnmYd3GWGfU/84ONvsZKCQnwI4dO/jOd77DT37yE8444wyqqqr4f//v//HNb36TRx55pN91vv/973PrrbeGvre2tpKTk8Petg6EL7JGzPSOo1tfWHVij8skfmkuuiPwUOatd9P0QTHunXV9V/jCAxsriMqJR3fZMNq9dJY1j6jvhQb4o2BPuyfyxMmRJ2Iet2iAVUBho2fYQ/hmZArmLLUBgpdf8rKleGhnVOW/4Nu32ElIEWTM8vLe2vAEZzBH/W4+8miAN+Ywru92D/yjgdjp6SSfMAFLggPOnkHz9irqPtqP2TmOHIMi6+8LCBxvwyrZ7e7veHuAGmAzdsDUkjD0gHO9oafjF9E0kU8T+QAIszUwq2JUoBuVCAbOuC4RmFqPpJvmyCfdlIcYBxku0uJqg7/Q4BRGNVLTEd4oeLJz4GMSCaxfv57Y2NjR7saoMGp/Z8nJyei63meWpLa2ts9sSjf33nsvxx9/PP/v//0/AObMmYPT6eTEE0/knnvuISMjo886drsdu93ep9wk8sy6jnR/hUUj7rgsEpbloDtsAHgbPDR+UEz7ztrB/yQleEqbj3DLw4MkcLwjTZxEKpJADpLhnDmxWODCS6zoumDrFoNNm4Z+NrW3w39e8XHJZTZWrLSwbatJXd3wP9hE6vl9JNd3885qWovqSV0+gfhZmcTNzMA1IZnaj/fTsj1ykiuOR4Z6vIXZgMVsALYg0TC1VEw9K/DSUpFaLH4tFr91eqC+UY9uVqIZFWhGVci53tDz8dqWg9Yj+aTZjs37MbpRHIY97J+RkkLVEytwx7fhaHb18TkJ9EPiiW+jemJFJOrjPhhoDGzce+RtRjqjJk5sNhvHHXccb775JqtXrw6Vv/nmm5x//vn9ruPxeLBYendZ1wO2kVJlOht2hEUjdn4mCctysbiCoqSxg6YPi2nbXovKLqeIFE4/00JqqkZbq+Tllw5/dH3LZpO58wymTtP58gVW/voXr7p8Rhmz00/1O3to2VFN2sopRKW4yFg1lbjp6dSs3UNXwzgz+xkJBDgy49GdNgz3+IlCKDDRzWp0sxp8nyOxYuoZgZkVPROpJSH1ZPx6MljngDTRzBowPZiWCf006MRrPw1b15sjKlBGAqlJPlu9lpMfPReJ7CVQZPBgf7b6XeUMHyScZl2RzKgaAtx6661cccUVLFy4kGXLlvGXv/yF0tJSvvnNbwIBk6yKigr+7//+D4Bzzz2X66+/noceeihk1nXLLbewePFiMjMzR3NXjimErhE7PyMgSmICs06+pg4aPyyhbVuNEiWKiGLCRI3lxwdulf963seRWjK8/KKP73xXIy9fY/FSnU/XjUzuHcXgdFS3UvzM5yTMyyJlSQGOzDjyL19I46Zy6j8tRo5QjqSxjmtiMmkreudv8rV1UvNeIe37xlf+JoEP3ShFN0oBkERj6JnBmZVMpBaLqWfAQH7hQoCUeG3LieooOeaigpXOLeTdq19h8Qsre+U58cS38dnqdymdWziKvVNEAqMqTi699FIaGhr46U9/SlVVFbNmzeLVV18lLy/gwFZVVUVpaWmo/lVXXUVbWxt//OMf+d73vkd8fDynnHIKv/rVr0ZrF44tdEHs3AwSl+dhiQ2KkuZOGj8qpm1rjYpqo4g4oqLgwosCxt6ffepn754jN55qaYE1r/s573wrp59hYfdOg+bmYeqo4uiQkqaN5bTtrSPtpEnETEohaUEOsZNTAg/fEZ481TUxmayzZ/Ypt7jsZJ09k4r/bB93AqUngg4sxj4w9gFgihj8lhkYtrmDrCRAuDC1dHTz2DMFLJ1bSNnsfaTuy8LR6sQT66Z2YoWaMTkIEw1zmM2whru98ciou1DedNNN3HTTTf0ue+yxx/qUffvb3+bb3/52mHsVYWiC2LnpJCzPwxoXGBXztXTS9HEJrZurlShRRCznnGclLl7Q0GDy+qtHH/5s/acGc+bo5BdonPdlK//32DhywI4A/O1dVLy6HWdeImknT8YWF032ObNoL2qg5r29+FqDDrrj1LzpiBCQtiKQWfzgYDVCCKSUpK2YFBBwx8hvoMk2NLOeocyZmVryMSlOIGDiVTO5fLS7oYhARl2cKEYRTRA7O52E4/OwxgdEib+ti8aPSmjdXAXGMfJPo1AcATNnacybr2Oakn/+w4d3GKKfSQkv/svHzd+xMWWqztx5BpsPw7leMTK4SxopenI9SYvySFqQg6sgCUd2PPXrS/A2d5B24sS+5k3vj0PzJouGZtVDLxH6rIW+25Ndvfa1TxtCYI2JwpEZf0wlnhVyaPabfttSTD0dq28jmjm+jr/i6DGkwBhmH5Hhbm88osRJJCIEMbPTSDw+D2tCINeLv72Lpo9Lad1YFcjurlBEMK4YOO/LAXOu998zKCsdPqFeXy959x0/p51h5exzrRTu7cKtfK/HHNJvUr+uiNZdNaStnIwzO4HU5RP6Db5icdnJ+tJMKl4Ng3mTECHBIHoICc3SLSh6C4xeIsOiodl0hEVHsx1Yp3u9gcL2HwnRWXHH1AySZlaD2Q7CGTDhOhgpAQOEBdNSQJelAM0ox+LdhGZWRlw0UIViOIlYcSIFERdLWPcLXLNTiT8pD2tSIAGi0e6l+cNS2jZUIv3HnqWjBuidYGmPvFCrxsCDnccsErC1SexN8qjiNlz4FRtOp6Cq1OTjf/qIGmaf6PUv+Zk9Qyc9R+O8Myy88MjRm3d5UiPshhZEmCCM8N3OffUeyp/bTOz0NNJPn4bQ+m4pZN500mS6atoDoqDPbERfMTHYstByS/jvyqbPCLy8BtJvYPpMTG+gTLNqOPMSD9lGyrICEuZl4ylupL2oAXdxI2bn8GaCFYDeJbB4xMjcz/3r6Iw9NSBEegqU4M0lqnUtmtGC1zEHv30Spp6NNzobzVeLzbMZi7d42M5LX9wxovqOMVS0rvAQseIkohAQOyWV5EX52JKDosTtpfmjMtrWVyB9kfbYrlAMzPwTdCbP1vH7JC896sUMQ7Am04R//93LNXfYmbnIwrbPDPZuVdfhWMbX1tWvMOkmYN5kZ+I1S8OyfWnKgGDwG8igkOgWFdJn9vhsHCQ2zNDn3svN0PdBETDhuqVYXPZ+Z1qklEi/iTQklmgrsdPTiJ2ehjQlnVWtAaFS1EhXXXtYfpdwYvUWQ+tbdLmWIfUDeU6E6cbevi6wHIhuew/T/Tne6Nn4oqdhWlPpjDsNzd8cECldhYyQnFIojgmUODnGiZmSQvKSfOxJTgAMj4+Wj0ppXV+J9KoQmQpFTxKSBacFo3OtfdFPfVX4RiurSyWfvOln+RlWzvqKjdK7O+mK7ITIYxqL0zakeqYRmHWQQSEREgWhz2YfEdGfYDAPWi4P9gEcqYF0CbVrC8k8dyZSyl4CpdvEreq1nbTvayA6MxZnQRKugkTsKS6is+KIzooj5YQJ+Nq7cAeFirukadyEaLZ6i7E0lmBY05GaA2F60H19M8RrZjtR7nXYPBvxRc/CGz0D0xJPZ+wKhHEcto6tWDt2hZI7Ko4NpNQw5fDObsphbm88osTJMUrMpBSSl+ZjTw6Kkk4fLR+V0fpphRIlCkU/CAHnXW3FFiUo2WPw6Tvhf4h4/99+ps3XSUzVOGW1ldeeVtG7xip+99AiIpT/awsd5c3h7cwI015YT+Ur20ld2TvPib+ti9p3C2kvDPjZdFS00FHRQv2H+7HE2HHmJ+KakIQjNwGry0787EziZ2ciDRNPeQvuogbaixrwNXWM1q4NCYHE4htaRC5NdmL3bMDWsRlf1HS80bORuosu1zK8jvlYO7Zh69iBkF1h7rViJDAQGMNsVDrc7Y1HlDg5xnBNTCZ5aT5RKYEpaKPLT+PnZTRtKsdaq0SJQjEQy063kDNRp6tD8vJjvhEZmfb74D9P+LjiVjvHrbCwfYNB6V5l/jEW6ahoxtfWOah5k7+ti45jKGJVT9oL62nfV090VjwWpw2/2xvY1wGuE39bFy1bq2jZWoXQNaKz43AVJOGckIQtPhpnXgLOvARST56Et7kjKFQa6ShvRvrH/zUgpA9bxxasHdvxRU3G65iD1OPwOhfidczF2rETW8dWNPMIs7oqFMcwSpwcI7gmJAVESWogm6vR5adpYzmNG8sxu9Q0skIxGGnZghXnBm6Ha/7ho6Vx5JxPS/aYfPG+nwUnWTj7a1b+954u/GoCZewhofbdQjLPGdi8qfa9wmMmWlW/SI5oVkgaJp6SJjwlTfBuIdb46IBQKUgkOjseW3w0tvnZJMzPxvQZeEqbcBcFHOv9beN7hkFgYOvchbVzN357AV7HPExLEj7HHHzRM7F27sXWsQXNaBntriqOAFMOvwO7Si2nxMm4x5mfSPLSfKLTYwEwvEFR8oUSJQrFUNAtcP7VNnSLYNcmgy3rRn6G8e1/+Zg0RycpTePEsy2sfVFdu2OR9sJ6Kv+9ndST+zFveu+AeZNicHzNHTRtLKdpYznCquPMjcdZkISzIAlrjB3XxGRcE5NJA7rq3SHzr67K1tHu+hEjkFi79mPp2o9hzcbrmIdhy8AXPQ1f1FQs3iJsns3ofnUOKRRKnIxTnHmJJC87IEpMr0HT5nIaPy/H6FTDrgrFUDn5PAupWRrtrZJXnxiGTItHQFcnvPaUl0tvsrPsNAs7PzeoLlPDZ2ORwzVvUgyO9Bm072ugfV8DAPZkZ1CoJBKdGYc92Yk92UniolyMLj9mRSsxe2ppL27E8IzO9Xo0CMDiK8fSUo5hSaXLMQ/DnoffPgG/fQK6txybZxO6r0p5HowDzDA4xA93e+MRJU7GGY6cBJKX5ePIjAMC8embNlfQ+HkZRocSJQrF4ZA7WWPpqYHb4H/+7sUzitFO924x2b7Bz8yFFs65wsbfftmFOf5N749NjtC8SXFouurddNW7aVxfihZlwZmXiLMgEWd+IhaHDSYkkj4hkHelo7o1EP2rqIHO6rZR7vnho/trcbS+gaEn4HXMxW+fiGHLpsPWnStlExZviRIpiohDiZNxgiM7PiBKsuIBMP0GzZsrafi8FMOjRIlCcbjYouC8q6wITbDxQ/+YyDPyxrM+CqbppOdqLD3NwsdrlHmXInIxO/207a6lbXctCIhOiyF3WjqdmbFEpccQnR5LdHosycvy8Xu8uIsbce9vwF3SNK7MmnWjiei2dwO5Uhyz8UVNDeZKOR3N34TNsxnJ3j7hixWjj4nAHGb5ONztjUciVpxoBohxcO+Kzo4jaXk+jtwEAEy/ScvmSho/K8UIhrYc8gRgpN7XNJB65O2+Pv4sHo4aDZAi+DpE3dMvsRKfpNFUb/LGP32MhaS87e3w5j99nH+VjZPOsbBzk0Fj7dDOXFMPc+fGKJoftEg81yNwn432NkSHQen7exEOG678wIyKMy8wqxI3I524GelIU9JR1UJ7USPu4ga66t2j3fUhIWjD7vkYa8dGfFEz8UXNxLQk0Bl7MsiFWPyb0Y1dCCIk8ubojxeNKosWLULXdW6++WZuvvnm0e7OiBKx4mSsE5UZS9LxBTjzeoiSLZU0fVaKvz0C/5UUimFkyhyNecstSFPy0uM+vGMoINCWTw1mLTaYOEPnnK9Z+b/feiNPWSsUh8DweGnZUU3LjmrQBI7MuEBelfwk7MlOHFnxAUuDEybga+ukvThg/uUuax7zCSA12YG9YwO2zs347DPwRc9Cai781uPxWxZg8W9DN7YjUM8Co40hBcYwj2x1t7d+/XpiY2OHte3xghInY4yojFiSlufjLAjY1ErDpGVrFY2flo77kIoKxVjAEQPnfC2Q7XvdW37KCsfe8Nx/nvTxzR9r5E3WWXCCzhcfjO2HKYViVDElnvJmPOXN1H24H2tsVEioOHLiscZEkTA7k4TZmZh+k46KZtqLGmgvbsTXPIQEkAIcWfHoThuG24tnhAIgCOnD1rkZa+c2umInY+hzkVosfusi/Ja56MZOLP6tCFSulNFCOcSHByVORgoB0dk9oruUN/e6udnTY0heno9zQhIQFCXbqmn8tAR/qxIlCsVwcc5XbDhjBDXlJu++MjZtO1saJe+87OPMS2ycutrK3q0Gbc2j3SuFYnzga+2keUslzVsqEbqGIzseV0EizoIkbHHRASf7vETSAG+Th/biQE6VjopmpNFbdbgmJpN2UOhoX1snNe8W0r5vZML+Cgwsxk50YxemNgG/ZR5SS8KwzMXQZ6Ebe9CNzWhy/IZaVih6osTJCOCanEzKKX1vbnXvFOJr6STp+HxcE5MBkKakdXs1DZ+U4G/pHK0uKxTHJHOX6Uydp2P4JS895sUYm9oEgA3vGsw8ziBnos6XvmLj2QeVCYdCcbhIw8Rd0oi7pBHeLcSW4AjMqhQk4ciKw5bgIDHBQeL8bEyvgbusifaiBtzFjUSlxZB1zsw+bVpcdrLOmUnFv7ePmECBQK4U3dyH5t2HqeUERUoGhmU6hj4VzSzC4t+EJhtGrE+RjokY/iSMyiFeiZNw45qcTMZ5/d/cMs6bGcoyLE1J644aGj8pGdo0s0KhOCziEgVnXGwF4N1X/NRUjG1HDinh30/4uP4HGlNm68xcqLN9gzLvUiiOBm+TB2+Th6aN5Wg2HUdOQsCxviAJq8tOzMRkYroHC42AyWf3/3Q3QgiklKSdPIn2/fUj7hMmAN0sQ/eWYYo0/JZ5mHoepj4Rrz4RzSjD4t+EkCpXimJ8osRJOBGQcsqkwMd+bm4AUkradtbSsK4YX5MSJQpFOBACzv+6FXu0oLTQYN2bY3jKpAf11ZIPX/Nz8nlWzrjEyv6dBh3jI/CQQjHmMb0G7fvqQ7Mf9hRXSKhEp8ci9IFt/4UQWGOicGTF4xnFnDearMHmW4PpT8RvmYupTcTUc/DqOQizGot/M5qpcqWECxmGUMJSHa2hR6FVHD7R2QFHvIOFSU+EELRsrVLCRKEII0tWWcibotPVGYjOJcf2pEkvPnrDT22FiTNGcHpw5kehUAw/XXXtNKwvpfQfG6l+Z/eQ1tGdtjD3amhoshGbby0277Po/h0g/UgtHZ/tDLy2izC0yeqhVzFuUOIkjFiGeNMaaj2FQnH4pGYKVp4XmCR+458+muvHkTIBTANe+bsX05TMWWJh4kx121Yowo2veWg+n935xsYKmmzD6v8Qe9fT6P5NIL1ILRGfbSVd9svw6zORRGhSpDBgShGWV6QTsWZdmh9EmK9Ps3VoNy2z1Ys2AlYmRnT4tzHWkAAmCIOIGzMaiXNqrKEBplXgjxJICboO511jw2IV7N5usOFzE6LG35lQWgOfvGewfKWFL33Vxp9+1dUnN4sZoZMqmhcsHZGXr82M0H9vSydY3eE/3r49zfhbO9Fj7ANaP0gpiXY58Lmbw9wbMG2He9/qRPetR7IZv206Puss0GICuVL0BVh927B4d4zpXCnSN/7u1YrhQQ3BhZGOsmZ8rZ3IAWxIpJT4WjvpKGse2Y4pFBHCyWdYyMjScLdLXn7WN9rdOSreed1PY4NJfILg1LMj9MlUoRgpJNS/URj4eNB/uJQSKSVCCFLOnELGV+ZiiYvqr5VRR+DF6t1MtPsZrJ0fIsw20KLx2RfR4bocr20xUkTgyOUw0Z3nZLhfkY76BcKJhPq3Br65QXD5+LIyUSjGBTn5ghNWBaZHX/mHj/a2Ue7QUeLzwsvPBqbDFh2vk1ugRhUVinDi3l1PzfPbMQ5KgOxv7aLm+e3UrdmL6TVwFCSQc/1CYuZljFJPD43AwOrbSZT7WWwd7yCMRhA2/Pa5dDgvw2s/AVPE9FpHIjD0DPyWiRh6hvJZ6Qdl1hUe1PBbmHHvqaf6he0knzoJa+yBkRV/Wxf1bxXi3jNyMdIVikjBZoMLvmpF0wSb1hvs3HpsGP0U7TX54hM/C5ZaOO9SKw//2os/As33FIqRwr27HveeeqJy4rG4bPjbvXSWNYcGFT37Gkk9ZyrRufGknj0V17QUav+zu4+gGSsIJBb/PnT/Pgw9F799Hqaeht82Hb91Krp/P1bvZkwtFp99GVJzHVjXbMfatQ6Lv3j0dkAREShxMgK499Tj3ltPdE48utOG4fYGTLnUjIlCERZOP99CYrJGc6Pk1X+Nb3Oug1nzsp/JM3RS0jROOs3CO68pdaJQhBUJnaXN/S7yN3VQ+cQm4hZlk3hyAY6JieTcsIiGN/bStrVmZPt5GAjAYpSie0ox9XR8trmYllwM6yQM6yT6C2kohRNv1KnQ+ZYSKEHMMIQSVkkYlVnXyCGho7SZ9p21dJQ2K2GiUISJzImShcsC4y4vPO2ja2hBd8YNnR3wn38GBNcJq3TSMtUfmUIxqkho+ayc8kc+p7OiFT3KQup500m/eBa6a2xH4xSAblQT1bGGKPfzaL59AWEiRODVq3Lgu8++TJl4KcKKEicKheKYwRENS78UUP4fv+unuPDYMOc6mJ1bTXZsNtB1wfmXWdHUnVyhGHV8DR4qHt9Iwzv7kX4T55Rkcq5fhGtG6mh3bUhoZiNW386+oqQnQiA1F6aePnIdG8Mon5PwoP7SFArFMcOXz7YS7YK6apO3Xz22zZ3+8y8fHR5JVo7G0pNU3gKFYkwgJc3rSin/2+d0VbWhO6ykrZ5B2gUz0R1jP963FI5hradQHAlKnCgUimOCebM1Zs3QMQ3415M+/MeWq0kf2lthzUsBAXbKWRaSEtRom0IxVvDWuSl/7Asa3ytCGiau6Snk3LAI59Tk0e7aoAjpGdZ6xzpq5iQ8RKxDvOYdfObyWMQc26avYUNaAvt+bBr4DIw1gv474uLg/DMDo5JbPxRUVUSGU9fGzwxmH6cxcYrOJass/N8fxm5CtXAgBDAZpBZ5bnxaZB1qIDCaKvVAAsrxcT+XNHxSQtv+BtLPnoY91UX6RbNo21FD7Zt7MTuHPrurjdBgi/BV47W3IzVn/w9JUiJMN5bO6rB7nchjfIBJMTBq5kShUIxrhIALL7ASHS0oLTXZvm60ezSyvPIPP94uScEUnQXLlXmXQjHW8P5/9u47vql6feD455zM7klLd0spo6yyh6KCijJEcaGIIIKK4OR6vXq97j1+OK7rehVcqFwciILKUkBQmWW1rNJBS/dKZ+b5/ZEyy2igaZLm+3698qJNTk6ekCY5z3m+3+9TUkveJ1so35CDYlMISI0kYfpA/JLDXB1aMxIKuvqmD9GTV+xq+l1X/weS150OODVROXEOkZwIguDRhgxRkZyswmRS+OYbM4qXfbBXlitHlxO+/GoN/oEuDkgQhOZsChXrcjj02VaMZXWo/XVEX9+LyDHdkHXuNYhFbc5BX7sSyVZ3wvWSrQ597UrU5hzXBOaGRHLiHCI5EQTBY3XoIHHFKPsX+08/W6io8M6zeX+utZKfY0PvKzF2opeO3xQED2AsquHQx1uo+DMPxaYQ2Ksj8dMH4psU6urQTqA25+Bb/RV6w4/oalejN/yIb/VXIjER2oRITgRB8EiyDDdcr0Gjkdi3z8rGjVZXh+QyigI/fGHCalXo1ltFapr4aBcEd6VYbZSvOUj+gm2YKurRBOiIubE3EVd0Qda6z9BMezf5QjSmLNSWQjGU6xQUjjVibK2L+F8WyYkgCB5qxAg1MTEy9fUK334nZk6WFCr8vtw+vGv0DVr0YqVPQXBrjYcN5M3fTOWmQyiKQlBaNPG3D8QnIdjVoQmCS4nkRBAEjxMbK3FxU2+PJUvM1NS4OCA3sW65hdJCG/6BEldc6/49FQTB2ykWG2Wrs8j/Ih1zVQOaID2xN6XR4fIUJI37VFGEUxNzTpxDJCeCIHgUjcY+nEulkti+3crOXZ6xqGhbsFpgyRdmFJtC2mA1nbqJj3hB8ASN+dXkzttM1dYCAIL7xRA/bQD62CAXRyYIbU98cwmC4FGuvEJNeLhMdbXCkh/cYziXjEJ//yquCCmhv38VsgtHDefn2Ni41j7/5qqbNGjE/HhB8AiK2Urpiv3kf7Udc3Uj2hAfYielET4yGUklDtfckaicOId7rV/XhmxakLzsS1vlRU35jpABwsDp3aLckMXH1RG0vpQkmSFD7B9bi5aaqZWA456nDOgrLPgXmJst0e8swzuWc1/PbCJ8jnXFK2nQ8tauJNYVtV0fg5q4Y8O4VvxioUtvFSFhMiOu1vDT9y1v9uZJJAlQQLJ531tc9sKCoSyBbAR1vac0YTw35sxKCg5uIuyKZAL6RxMyMA7fzmEcXrmHxmKDq8NrM4oY1dZmampqGDlyJGazGavVyn333ccdd9zhsnhEKi4Igkfw0cMNV9kPwNdvsrA/2/WHJ8M7lvPsgL2E609s1x2uN/HsgL0M71jukrhMJliyyF5VGnyhith4bzt0FwTPphitlC3ZR9FnO7AYjOhCfEm8ri8dhnYSVRQ30l4qJ76+vqxZs4b09HT++usvXnzxRcrLXfP9BSI5EQTBQ1xzpYagAImSMhs/rXZ9JUBG4b6e2SjYz+iecJtkX2Ly3h7ZLhvilbXPxrZNVmRZ4pqJGlTiLKQgeJyGAxUUvLOJqj1FSLJEeP94km7sjz4iwNWhCbSf5ESlUuHra1/isbGxEavVitJWww9OQSQngiC4vT6pMmk9VFhtCguXmDG7Pjehd5iBCB9Ts8TkCFmCSF8TvcNcNwzj5yVmamsUIjrKXHSp147iFQSPZmu0ULhyD4eW7sRSZ0IX5kfi9f3oMDix+ZkRwSutXbuWq666iujoaCRJYvHixc22effdd0lKSkKv19O/f3/WrVt3wu1VVVX06dOH2NhYHn74YcLDw9so+uZEciIIglsLDLBXTQBW/24lv9A9WlSF6Uxn38iB7ZyhoR6WNvWAGX6pioiO4kBGEDxVbXY5B7/cSPW+YnsVZWAiSTf0Rxfm5+rQvJaiSE65ABgMhhMuRqPxtHHU1dXRp08f3n777VPevnDhQh544AEee+wxtm3bxvDhwxk9ejR5eXlHtwkODmb79u1kZ2fzxRdfUFxc3Lr/WQ4QyYkgCG7thrEafH0kDh22sXq9G5RMmpQbW7aiRku3c5bd221k7rKiVktcc6PGPolcEASPZG20cHh5Jvk/7cbSYELfwZ+kG/sTPiBBVFHambi4OIKCgo5eXnzxxdNuO3r0aJ577jmuvfbaU94+d+5cpk+fzowZM+jevTtvvPEGcXFxvPfee822jYyMpHfv3qxdu7bVnoujRHIiCILbGtJfRZdkFWazfTiXzfVz4I/aUR5ISYMW22kKOTYFiuu17CgPbNvATuHHb8w0NijEJsgMGS4mnwiCp6vJKuXgF5swZJUiqWQ6DEki8fp+aEN9XR2aV7EhOeUCcOjQIaqrq49eHn300XOK0WQysWXLFkaNGnXC9aNGjWLDhg0AFBcXYzDYhyAbDAbWrl1L165dz+N/5vyI5EQQBLcUHioxtmmexLLVFkrL3WM41xE2JN7alXTaJWwl4N+7k45+0bhSjQF++cFedbr0SjXBoa6PSRCE82NtMFPw024KlmdgbTTjExFA0sQBhPWL8761tduhwMDAEy46ne6c9lNWVobVaiUyMvKE6yMjIykqKgIgPz+fiy66iD59+nDhhRdyzz330Lt37/N+DudKzJAUBMHtyBJMHK9Bq5HYn23lj81WV4d0Snur/bEqoD7FgYDBpObPkpC2D+o0tvxlpXc/maTOKq6+Qc0n/3GPBpaCIJwfw74S6vOr6DiiCwFJ4UQMS8a/UziFK/dgqmpwdXjtmjNW13LWal3SSWN6FUU5el3//v1JT093yuOeC1E5EQTB7Yy4QEV8jExDo8KiH8wu7Ld+ZpM756OWYWtZAPdv6MEzW1L42x/dKWnQEKSzMDbedRMKT+X7/1kwmxWSu6joO1AM7xKE9sJSbyJ/6S4Or9yD1WjBt2MQSTcNILRPrKtDE1wsPDwclUp1tEpyRElJSbNqirvw2sqJuh4k9zwZ6zSq0y/00G7JEsgmUDV6YZXbjeZnOCKmo8SlF9o/mhb/ZKa62rH7W3UyFl/Z6R3iI/WNjIkvAeDD3ES2NwRB00nKT3NNPNQti8kpBSwpi8Jkc/55oIBDZ6+EmA/B+q8ULrlVz+hxagpXGamrdtfU7+wkCYgDt81enUjV6OoI2p4kgcoE6kZOO9ervZJbuOhfzY4iGg5W0nFUV/wSQ4kc3pmApHCKftmDudqz/mgU1y102GLHr67VmvsEGDhwICqVitmzZzN79uxz3p9Wq6V///6sWLGCCRMmHL1+xYoVXH311ecdrzOIyokgCG5DrYabJmhQqSR2ZFhJ3+W+GdaUxHw0ssLmiiC2VwWdcNuPhyMpatTRQW9ifHTRafbgGpuXminKsqL3l7h0+rmNYRYEwX1Zao3kf7uDohV7sZks+MYGkzhlIMF9ol0dmuCATZs2kZGR0aLEpLa2lvT09KNDs7Kzs0lPTz+6VPCcOXP48MMPmTdvHpmZmTz44IPk5eUxc+ZMZz6FcyaSE0EQ3MaVI9REhMsYahS+W+a+cyI66hsZF2UfsvXRwfhmt5sVmU9z7MMpbk08hFZ2nzKtYoOf32/EalHoOkRDyiCvLaALQrtWvbOQ7E83U5dXiaxREXlpF2Kv74M6UO/q0NoNd+kQv3nzZvr27Uvfvn0BezLSt29fnnjiCQAmTpzIG2+8wTPPPENaWhpr165l2bJlJCQktOr/R2sRyYkgCG4hOVFm+BD7gfLXP5ipd+N5nFMTD6GWFTaVB7OjOuiU2yw9HElhg45wnZkJMe5VPSnNtbFxiX3MxGXTdehEDzdBaJcshkbyv95O8er92MxW/OJDSJoygKBeUa4OrV1wZhNGR1xyySUoitLs8vHHHx/dZtasWeTk5GA0GtmyZQsXXXRRK/5PtC6RnAiC4HJ6Hdw43t4F/s8tFvZmue9wrih9I2Oi7HNNPspuXjU5wqLIfJITB8DkxHz0blQ9AfjjGxPlBVb8Q2QumSyGdwlCe1aVXkDOZ5upz69C1qrpeHlXYq/tjdpfvPcF9yOSE0EQXG78lRqCgyTKKmwsXeE+XeBP5UjVZGN5MDurz9xgcVlhBAUNOkK1Zq6NLWyjCFvGaoZf3revktH7Ui3xPcXqXYLQnpmrGji0KJ2S3w5gs1jxSwwlccpAAlM7ujo0j6U4YUjX8RPiU1NTeeedd1z8LNueSE4EQXCpnt1k+vdWYbMpLFxsxuS+U02I1jcyummuyYenmGtyMqsi83FTdWVSQj4+KveqnhTstbLtF/vwrivu1KPWujggQRCcS4HKrfnkfraFhkIDKr2aqCu7EXN1T1R+4gPAnTgyIb69EcmJIAguE+AP1461D+f6bYOVvAL3Xh90StIh1DL8WR7MbsOZqyZH/FIUwaF6PSFaC9fFHnZyhI5b+4URQ5mN4I4yF04UQzwEwRuYKuvJ+2obpesOYrPY8E8OJ2nqQAK6RhzbSAKf2GACukbgExvshevxn50CKEorX1z9pNyASE4EQXCZ68Zp8POVKCi0sXKNew/nivFpYHRHe9VkXguqJkdYFYn5R6snBfiq3Ot5mhpg+X/t/Q/6j9XQMVl8LQiCV1AUKjblkbtgC43FNaj0GqLHphI9rgeBqZF0mjGE+BvTiB6bSvyNaXSaMQT/zuGujlrwAl67hqQi4XVnAWzeOKRcAslmb7jpZS832hpXR3Bm/Qer6J6iwmxW+HaBGXXV+X8gSRJojRb0FeZWb8J4+4Bc1DJsLAwiK8cHPS0ff7auMpi8OD3xgY3cFJbPF3tiWjc4oDFUc8733b9XYdcGCz2Hqblilg/zn2rA5l4j0E5JkkBbq6CrUpzedNPdKF6YQ0oSqBsV1HXe93pLVud9g5lL6sj7fCuhg+MJG5pAQJcO+Kc0T0LU/jqir+pB4fe7qd1f5rR4jnLfdVGOsiEhtfLRhc3rjlaa88KPN0EQXC0kTOLKq+ypyKqfLZQWu/eRRrRfI5cn2L+MP94d6/D9bYrEZxn2hOTGroX4qd2regKw8gsj9QaFiDiZoWPPPdERBMED2RQq/sglb8FWFKsNSZKQpBMPko/83mFkZ+872ye0qXNKTrKysvjXv/7FzTffTEmJfUnNn3/+md27d7dqcIIgtD+SBNdO1KDTSWRn2fhjnfufop+cWoBKhj8Lg9lb6X9O+/jtUBi5Bj0BWivXprhX3xOAhlpYscC+etewqzSERYmjD0HwNrJWjaQ6/aGhJEloAvX2OSiC2/Q5aW8cTk7WrFlDr169+Ouvv/j222+pra0FYMeOHTz55JOtHqAgCO3LhZeoSEiSaWxU+PYrk9sPz4jxb+SypqrJp7vPfTiWDYlPmqou13cpwl/jftWTjL+sHEi3oNZIjLldhyS+IwXBq6hbuGJXS7dr75zZIV4sJeyARx55hOeee44VK1ag1R774xwxYgR//PFHqwYnCEL70jFaYsQo+3CuZd9bqK5ybTwtMbl7ASoJ/jx87lWTI9bmh3Kw2gd/rZXru7hf9QTg509NGBsUYlNU9LvUa6clCoJXstSZWnU74dyJpYQdsHPnTiZMmNDs+g4dOlBeXt4qQQmC0P6o1XDdTRrUaomMXVbSN7v/cK5Y/wYubaqafJJx/pPYFSQ+baqeXJtSSIAbVk9qKhR+/Z/9wOOS67UEhonyiSB4i4b8KsyGRpTTlLQVRcFsaKQhv6ptA3NTrb6McNPF2zmcnAQHB1NY2LzT8bZt24iJaf0VaARBaB9GXqEmMkqmtkZhyddu3GnxOJNT7VWTDYeD2XeeVZMjfi8I4UCVL34aGzd0da+u8Uds+81C3l4rWr3E6NvE8A1B8BoKlK4+YP/xpKPkI7+Xrj4gmnEITuVwcjJp0iT+8Y9/UFRUhCRJ2Gw21q9fz0MPPcSUKVOcEaMgCB4usZPEsIvsa1l/v8hMfZ2LA2qBWP8GRsbbq8GfnsMKXadjr57YT+RM6FxEoNYNEzUFfppvxGJW6NRLTc9hYniXIHiL2v1lFH6/G0uN8YTrLTXGtltG2EOICfHO4fA3zvPPP89tt91GTEwMiqKQmpqK1Wpl0qRJ/Otf/3JGjIIgeDCdDiZM1CDLElv+srA30wMWrwdubaqarC8IZn+VX6vue/3hEPZX+pISUs+NXQv5cGfLmzq2lYoihd8Xm7nkBi2XTdJycKeFejfvnSMIQuuo3V9G7YEyfGKDUftpsdSZ7EO5RMVEaAMOJSeKonD48GH++9//8uyzz7J161ZsNht9+/YlJSXFWTE6hxc2YVS8sAmjItmblSkq7/tMldxkOsPo69SEhMpUltv4+TuLU+OSJDAGqmkI157XuN0E3/qjVZP/Hk6iIbz1hzZ9cCiRV0MyuCalmM/L4qk0n99jNIa0ftuq3zba6DrMRlSMzMjb9Sz63L2qPJIEVp2E1UfC5mVvcH2FZyT5rUmSwBolYfGRvG5cvuSil7sxt+pYDG394B7wJ+6MSoeonDg4rEtRFFJSUigoKKBTp05cf/313HjjjZ6XmAiC0Ca69pTpN0SNzabw7RdmTMaz38cdTEvMQ5ZgTWkY+2pbZ67JydaXh5Jp8MdHZeOW+HynPMb5stlg8UIzVqtCr74quvYQfXsFQRDaglhKuKUbyzIpKSliVS5BEE5JkiAxWaZnX5luPWXG32jvNL7hNyt5Bz3jVGeibx2XRZQCMC/HmcOtJP6bnQDAtTGFhGrdc2nOwgKFDWvsK6tddZ0Gnd7FAQmCILgJZ/Y5EUsJO+CVV17h73//O7t27XJGPIIgeKjuvWQefFzHtHu03DBFy83TtfgHSFRW2Fi9zE3GmLXA7U1Vk99Kw9jvpKrJEX9WhLCrOgC9ysbk+ENOfazz8esvFspLbQQGSYwaKybHC4IggFhK2FkcTk4mT57Mxo0b6dOnDz4+PoSGhp5wEQTB+3TvJTNxmobA4BOvVxSF4BCJLqmeMRwoybeOkRH2lWjmZbfFJHWJD5uqJxOiCwnXuue4N4sFvv+ffb7JwGFqEjuJMdGCIAiCczh8CuyNN95wQhiCIHgqSYLREzRNP0sn3SahKAqjJ2jYs8vo9meEjsw1+bUkjAN1zq2aHLGxMpjtVYH0CTZwa0I+r+9PbpPHdVTOQYVNGywMHKbm6hs1vPOaCYvnFMQEQRBanb3S0doT4lt1dx7J4eRk6tSpzohDEAQPldBJJijk9B/OkiQRFGLfLifLfZdf6eR3XNUkJ6ENH1niw5x4/p22i6ujC/k8L5ZSo64NH7/lli+10LWHirAOMiOuULNiqchOBEEQhNZ1ToOHrVYrixcvJjMzE0mSSE1NZfz48ahUXrhWrSB4Of/A1t3OVY5UTVaXhJNV17p9Tc5mS2Uw26oC6RtsYEr8If5vf+c2ffyWMjbCD1+buWW6lmEXq9iVbqWwQJzmEwTBO4mlhJ3D4YHgBw4coHv37kyZMoVvv/2Wr7/+msmTJ9OjRw+ysrKcEaMgCG7Kzx/6DmrZSYlag5ODOQ+d/Oq49GjVxBUNEY+t3DU+uohIXaMLYmiZvRk2dm6zolJJXDNRQ6fOMr36yiQmy0jiO1UQBEE4Tw5XTu677z6Sk5P5888/j06ALy8vZ/Lkydx3330sXbq01YN0BtnqPk3q2orNCxfZUQDZAiqj1/XcxOLrvH3LMgwerOKyy9To9fZ5JdB8zgnYJ8VXV0NWkQ3FiTEByBJo6mzoqi0Ojdu9o0sOAL8VhFFwWIeOtv9wyKz2Z2tsIP06GLg9Kpe52x2be1If2fqNIk/nx6VmuqTKRMXI3Hb3scetrlJY+qOZjN1tM3xPlkDdqKCuU7xunLZV522faPb5bZp6GzqDzeteb1OgZywq0poU91wf5AQKrd/g+cj+Bg4ciEqlYvbs2V63nLDDh6tr1qw5ITEBCAsL46WXXuKCCy5o1eAEQXA/iYkSV12loWNH+5dlfr6NXbusXHGFGkVRTkhQjiQtS5ea3fZgIjmwjotjKrAp8MmeWJfG8vGeOPp12M3ohFIW7I+huN49m4okJMpoT5ELBQbBzbdo+HJB2yUogiAI7dGmTZsIDHTz8dBO4nByotPpqKmpaXZ9bW0t2lN9WwmC0C4EBMAVV2jo29c+jKu+XmH5cgubN1tRFCgvVxg3TkNQ0LH7VFfbE5OMDPc9UJ3Szd6d/beCMHJqnFzaOYud5YFsKgliYEQ1t3Yp4LV091u5S5Jg7DjNaW6zV9HGjNOQmeH+q7MJgiCcDzHnxDkcTk7GjRvHnXfeyUcffcSgQYMA+Ouvv5g5cybjx49v9QAFQXAtWYYhQ1Rceql9CJfNprB5s5Xlyy00NBzbLiPDRmamkcREmYAAqKmBnBz3Hn6RHFTHRdH2qsmne11bNTni48w4BkZUc2V8CV/si+Gwm1VPEhNlgoLPvDpbcLB9u+xs901KBUEQzpszx3V5MYeTk7feeoupU6cydOhQNBr72TOLxcL48eN58803Wz1AQRBc51RDuJYsMVNwmhWaFAWPOiCd2tXelf3XgjByXVw1OSKjMoC/ioMZHFnF5K75vLLNvVbuCmjhKIOWbicIgiAIx3M4OQkODub777/nwIEDZGZmoigKqampdO7sXl+ggiCcu4AAuPJKDWlp9iFcdXX2IVxbtljduhLiiM5BdQyPrrRXTVw81+RkH2fGMjiyilHxpSzYF0NBnY+rQzqqpoWrrl08Qk1Dg4X9+zwnWRUEQXCIE4Z1IYZ1Ob6U8BGdO3fmqquuYvz48eeVmLz77rskJSWh1+vp378/69atO+P2RqORxx57jISEBHQ6HcnJycybN++cH18QhGNkGS64QMWDD+pIS1Nhsyn89ZeF1183Hp1b0l5M7WavmqzODyev1j2qJkfsqQrgj6JgVNKxOTHuIifHRnWVcnSxg5Mpiv22yEiZqdO03HW3lpQu3rfSkCAIgnBuHP7GuP7663nppZeaXf/qq69yww03OLSvhQsX8sADD/DYY4+xbds2hg8fzujRo8nLyzvtfW688UZWrVrFRx99xN69e/nyyy/p1q2bo09DEISTJCXJ3HOPljFjNOh0Enl5Nt57z8SSJSfOLWkPUoJquTCqEqsbzTU52ceZcQBcGltGnL/7vACKAkt/NDf9rJx0m/33774x8/s6CyaTQly8PUmZOUtLl64iSREEof1QFOdcvN05LSX85JNPNrv+yiuv5LXXXnNoX3PnzmX69OnMmDEDgDfeeINffvmF9957jxdffLHZ9j///DNr1qzh4MGDR5cyTkxMdPQpCIJwnMBA+xCuPn2ODeH65RcLW7e2r0rJ8aY2VSNW54dzqNZ9hkwdb1+1P+sLQ7ggqpKpXQ/x3JYurg7pqIzdNr5cYGbsOA1Bwceur66GZUf7nNhYt9bChcPVDB6iIjZOZsptWvLzbfy6ysLePWK4lyAIgtCcw8nJ6ZYM1mg0GAwtbwFtMpnYsmULjzzyyAnXjxo1ig0bNpzyPkuWLGHAgAG88sorfPbZZ/j5+TF+/HieffZZfHxOfYBhNBoxGo918jkSoywhuhl7ARl780VvPF97tr9vWYahQ1WMGKlGp7OvwrVpo5VVq+yVEqkF+3A3R97XZ4o7JaiWC5qqJp/tjXXr5/jJ3jguiKpkRGw5C/bXn3GpY7mNn8eeDBt7M40kHLc6W27T6mxHYmmogxU/W9iwzsKwC9UMGqIiNlbm1qlaDhfY+G21hT2Z556kSJJn/p16KkmC+GQZ/yCJ2mqFvKy2XY3Pm19vb/wO84RzY2IpYedwODnp2bMnCxcu5Iknnjjh+q+++orU1NQW76esrAyr1UpkZOQJ10dGRlJUVHTK+xw8eJDff/8dvV7Pd999R1lZGbNmzaKiouK0805efPFFnn766WbXJ0T4oNa75xlTZ9HUuTqCtidJEBmoQ5E944OuNanPMBIoMl5h4GUKweH230sLYOMKmcoSFfEaLZy6jYXbkySI0oAqXHfabWZ12wfAxtJI9L7BdHav6SYn0bG1PJx+YWXc3fsw/93X47RbNsqnf85OlWt/b/kDPSTsR48na4DCFfDzeuhyIXQeDNExMpNu1VJ5GHb/CoczHX9oCQiL0GPVS9i87A0uW9r28eK6KPS/XMHvuFXY6gywZYXEoX1tdDAlQXiwDrOv3G6ruqdj9nN1BG3P0iixy9VBuJDoEO+Axx9/nOuuu46srCxGjhwJwKpVq/jyyy9ZtGiRwwFIJ50CObnD9PFsNhuSJLFgwQKCmjq9zZ07l+uvv5533nnnlNWTRx99lDlz5hz93WAwEBcXR1ZVA5LOuz7dtC0vbLUbkgQWPeyvrMfbBpFoaptfFxgEV4zT0LNpCFdtrcKKZRa2t5NVuCQJAmsk8g7WnPL5dAmro09oOVYb/HdDOAWnaCjrbj4o78D7V5UxMLyUD/8oIafq1CdV6hrb+Gj1HGXuB59FMPAKDf0v1RASLXHhLVCcZ2X9EjP7tlpbfCZBksAYJHOgvL5d/P06QjK33WN17yUz/NojZyyOfT/7BigMv1bhfx+bydzp/E9YqSn53V/mfa+3yeTqCNqeYmx0dQhnp0itv7pW0/5Eh3gHjB8/nsWLF/PCCy/w9ddf4+PjQ+/evVm5ciUXX3xxi/cTHh6OSqVqViUpKSlpVk05IioqipiYmKOJCUD37t1RFIX8/HxSUlKa3Uen06HTNT+jaOPUJ/jaM2/7MD9Cwf56e1tycvzrrVLB0OEqLr7s2BCujRusrF5uodF95lq3ijNNKpzc+zAAq7NDyTe4V3PD0zlY6cuanBAuTqxkcu/DPLvm1F3jPen9XV8Da742s/EnMwOv0DDgcg2R8SquvUdFySEbv39vYu+WliUpCvbn7nWVkzZ6vpIEV16jafpZOuk2CUVRuPIaDZk7jW3yN3jk9fakv/fW4G3fX+AZox2c8bfobX/bp+JwcgIwduxYxo4de14PrNVq6d+/PytWrGDChAlHr1+xYgVXX331Ke9zwQUXsGjRImpra/H39wdg3759yLJMbKx7rrgjCK7WKUVm7NVqOkTaRy3n5dj48TszRYe96xOwa1gdg2MNWG2wYEeUq8NxyOfboxieUMnwhCo6hdRzsNKtx6K1WEMdrP3WzMZfzAwcZU9SIuJkrr1HT8khG+uXmNizueWVFKH1JXSSCQo5/ak8SZIICrFvl5PljYfQgiC0tvOaY9XY2Mgnn3zCu+++y/79+x2+/5w5c/jwww+ZN28emZmZPPjgg+Tl5TFz5kzAPiRrypQpR7efNGkSYWFhTJs2jYyMDNauXcvf//53br/99tNOiBcEbxUYBDdO1nDbnVo6RMrU1ih8+5WJj941eV1iAnBrH3vVZNXBMA7XeEbV5Ijcah/W5IQAMKXpebQnjXWw7jsz7z1Uz++LTTTWK0TEyUyYrWfGcz50H6TyyknQrubjC736tewwoVMXGdkbZ20L3k1x0sXLtbhy8ve//x2TycSbb74J2FfbGjJkCBkZGfj6+vLwww+zYsUKhg4d2uIHnzhxIuXl5TzzzDMUFhbSs2dPli1bRkJCAgCFhYUn9Dzx9/dnxYoV3HvvvQwYMICwsDBuvPFGnnvuuRY/piC0dyoZLhyq4tLharRa+xCuv9Zb+XW5hUYPGMLrDN3CaxnkoVWTIz7fHs1FCZUMi68mJbSO/RXtb4ZsYz2sW2xm43IzAy/XMGCUhg4xMtfM0lNWYK+kZG5sH/Oj3JWvH3TrqaJHmkxSioxK1bKs8OJRavoPVbE73cqubVYO5SjidRIE4Zy0ODn56aefeOGFF47+vmDBAvLy8ti/fz/x8fHcfvvtPPfccyxdutShAGbNmsWsWbNOedvHH3/c7Lpu3bqxYsUKhx5DELxFSieZ8Veq6RBuP4WZe9DGj4vNFBd691HCrX0KAVhxMIzCWhetanWeDhn0/JodymXJFdzap5Anfu3s6pCcxlgPv39vZtNyMwMu1zDwCg3hMTJX363ngqttbPjBRMafVleH2W74+kH33ipS+8gkdT4xISkqsBEcKqHTN59zAvZFbEwmsFrAP0Bi8HA1g4erqa5U2NWUqBw+5N2fP0L7JZYSdo4WJyd5eXknLBW8fPlyrr/++qNVjvvvv58xY8a0foSCIJxVUCBcdYWGnt3tq3DV1Cos/8HM9q1iDHj38FoGxtirJl94aNXkiAU7ohiRVMGQuGq6htWxt7z9VU+OZ2yA9UvMbF5hpv9lGgZdoSE8Wmb8XXouGG9jww9mqg3iwPdc+PlD914qUtNkEpNPTEgK823s3m4lY7uN8lKF7r1kJk7TNFtNU2kqjXy3wMze3TY6dZHp1U9Ft572eSoXjFBzwQg1FWU2dm2zsXOblRIvP1EiCMLZtTg5kWX56AcRwJ9//snjjz9+9Pfg4GAqKytbNzpBEM5IpYLhQ1WMHK5Gq5Gw2hT+2GhlxRoLSpmro3MPt6Y1VU2ywijy0KrJEQU1elYdDGNU53Ju7XOYf61uvkJhe2RsgA0/HEtSBl+pISxK5qo7dRgqFFaukNm+zYZN5OJn5Odvr5D06COT2FlGPq5z5+F8GxnpVnZvt1FRdmICkbnTxsL5ZkZP0BAUcux6QxX89N2xZYQP7LFxYI8NtRo6d5Pp2VdF154yoeEyF10uc9HlakqKbOzaamVXuj3xEQSPJ/6MW12Lk5Nu3brxww8/MGfOHHbv3k1eXh4jRow4entubu5plwB2R4oKULk6iralbvC+d9DRk3zt8Kl3SZa5arSaDmH2IVzZuTYWLzNTXGJ/sioPbaR4PmQJGsN01DVaUBToGWRgQLQBi03iw5Ik6mI8ayL8qXxYksjITuUMijWQ2N3MboN9HXzJCw7MzfXw5xIzW5eb6XeZhkGjNQSGSlw7UcslF9vY8KOZXRssKF7wf2EKaNnsc78A6J6mIjVNRcLJCUmejYxtVjLSrVQel5Ccas97t1nZl24lPlkmIEii5rgO8Sdvb7PAvnT79hotdOmpomd/FZ27y0R0lBk5RmbkGPvj795qZfdWK9WVZ/+QliSQLQoqs/fNZ5G8caiPl73GwjEOTYi/+eabWbp0Kbt372bMmDEkJSUdvX3ZsmUMGjTIKUEKgnBMcBCMO24Il6FGYdkKM+lt0ATN00xPsi+osawwgsJGz09MAAoafPipKJKroouZ0SmPB9N7ujqkNmdqhD9/NLN1pZlLr/Uj5QKF0I4y42bouGC8hg0/2JMUm5dOS/ELgNQ0Fal9VSQky0jHJSQFuccSkqpyx47+FAVyDzj2OWM2cTQB0flAt14qevRXkdxVJjrefrn8Gg2HDlrZtdVKxjYrtV7YMFjwTGLOiXO0ODm57rrrWLZsGUuXLmXUqFHce++9J9zu6+t72ontgiCcP7UKLhqm4pLjhnBt+MvKyt8sGL2we/DZ9AoyMCisCotN4pOcOFeH06o+yY5jdMcSBoVV0Tuomh3VQWe/UztkNsLe32Hl4nr6jtAweLSGkAiZsdN1DLtKY6+krPeOJMU/8LgKyUkJSX7OsYSkusJ1p6ONDbB9o5XtG634+NkTqB79VCR2lonrpCKuk4orr1XIOWBj1xYrmdutNNS5LFxBODtnLP0rKkaONWG87LLLuOyyy05525NPPtkqAQmC0FyXzjLjR6sJD7UPoDiYY+P7ZWaKxZjt05reKReApYURFLWTqskRhY16lhZGcHVMMdM75XH/tl6uDsmlzEb46yczW1eZ6TtSzZDRWnuScruOC5qSlJ2/t78kJSCoKSHpqyI+6aSEJNs+hyRjW8uGTLW1hjrYst7KlvVW/AMhta+Knv3sCUpSF/tlzI0KB/fYh37t2WHFZHR11IIgtIVz6hAvCELbCAmSGHelmh7djg3hWrrczPZdYgjXmfQOqmZgaDXmdlg1OeKTnDjGRJUwILSatOBqtld4Z/XkeGYTbPzZwtbVFvpeombIGA3BHWTGTDuWpOxY59lJSkCIRNcBKroOUROffOLEyUPZxyokBjdMSE6n1gAb11jZuMZKUKhEj772OSpRcTIpPVSk9FBhMSscyLBRlqWQW4VIVAQ3ITVdWnuf3k0kJ4LghtQquPgCFZdcqEajkbBaFdb/ZWXVGjGEqyVub5prsvRwJMXtrGpyRHGjnh8OR3JtbBEzOuVyb0UvxJeancUEm5Zb2ParhbSmJCUoXGb0bfbhXn80JSlWi6sjbZmAUIluA1R0G6gmNuXEhCTvoL06kpluw1DlOQnJ6VRXKGxYZWHDKgthERI9+tkrKh2iZLr1UUEfGDRWz95d9nksBzJtHvM6CoIjBg4ciEqlYvbs2cyePdvV4bQpkZwIgpvpliJz1ZVqwpqGcGVlW/l+mYWSMs8/8GgLXX0qGNBUNfk0J9bV4TjVpzlxjIsupm+IgX4h1WytDHZ1SG7FYobNKyyk/2Yh7WI1Q8ZqCAqTuXKqjmHjNPyx1Mz2te6ZpASGSnQdqKL7QDUxnU+qkOyzsnuHlYztVmqqXBNfWygvUVj7s4W1P1uIiJLo2V9F2iANASESvfqr6dVfTWODwp4d9sn02XvFctJCG3PinJNNmzYRGBjYyjv3DCI5EQQ3ERpsH8KV2tV+IFJtsA/h2rFbfNs6YkL4AQB+PBxJsbF9Vk2OKDXqWFLQkevjCpmenMfWzUGI6klzFjNsXmlh2xoLaRfZk5TAMJkrpugYOk7Dn0vNpK+1YDW7Ns7AMIluA+0VkpjjhmwpNoVD+23s2WRh72YrtVVKi5cSbi9KChV+XWrh0GYNdbpGevS1T6YPCpFJG6wmbbCa+lqFjHR7onJkmWNBEDyPSE4EwcXUarjkAjUXX6hCo7YP4fr9Tyur1lowiSFcDunvW0WqbyUmm8Sn7XSuyck+y4nlqugi+oQYGBBaxeaKkLPfyUtZzbBllYX0tRb6DFcztClJGXXrcUnKGguWNkxSgsIlug1U022giuhOJyUk+5oSki32hEQAkCg8pHA4z8KKJRbikmR69rOvUuYfKDHgQjUDLlRTU62Qsc2eqOTniBM8gpOI1bqcwuHkpLi4mIceeohVq1ZRUlJyQtd4AKvVM2YaemMTRrOf951RlSV7czrZTf8su3WVGTdaTeiRIVwHrSz50UJp0xCu8zk3KrvhUBVnmxmWA8DP+8OpO2jDjwbXBtQGGoClYR24NrWE27vlsXZ/GN5SPZEkkKwKssWxpnyKBdKXm9m52kyvi9QMHa8lMFzm8sk6hozVsPFHM+mrzU5LUoI6SHQdpKbbYDVRxyUkNpvCoT1W9m60sG+zlbrjEpKTPwt01d53wC1JoApVUDcee70LM6wUZlhZ+QUkdJVJHaCmWz8VAUESgy9RM/gSNVVlNjI2W8nYZKH4kGce+Uk273hPn8D7/sSFJg4nJ7fddht5eXk8/vjjREVFIUle+IYRhPMUGiIxboyabkeGcFUrLPvZzE4xhOucDfCrZICffa7Jwl0dXR1Om1q4qyOju5XR26+GYQGVbKgJdXVIHsFqgfTVFnautdCzKUkJCpe59FYdg6/S8NeRJKUVKphBHSS6DVbTddApEpJMK3s2Wti3yUq9wTMPnl1NsUFOpo2cTBM/fwGdUmVSB6rpkqYiOFxm2JUyw67UUF50LFEpKxT/18J5UiT7pbX36eUcTk5+//131q1bR1pamhPCEYT2QZIgMUEmIABqaiAn1z7+WaOBi4erGX6BCo1GwmJRWP+HlV/XiCFc50dhZoS9r8nvxVGU1WtdHE/bqmzU8HVZNLdG5DOzYw4bakLwlupJa7BaYPtqCzvXWOg1XM2Q8VqCI2Qunaxj8DgNG5fakxRz0/K1kgSx3VT4B0vUVink77GesnITHCnRbZCaroPVdEw8MSHJyzhWIREJSeuyWeHAThsHdppQayC5l4rUASpSeqsI6ygzfJzM8HEaivNtZGyykLHZStUZekZJEsSlyPgHSdRW2+f/iPksAoCi0Op/C+Jv6xySk7i4uGZDuQRBOKZHd5mxYzQEBx07OKyqVkjfbqF3LxWhIfYBGvuzrPy49NgQLuHcDfSror9/NUabxE8FCYD3ZXqflMRxfdhhevrVMDywgnWGMFeH5HFsVtj+m4Wd6yz0vFDN0KvtScrIW5qSlGVmaipsXHKTjsCwYwOtDOU2Vn1mZN9mKyGREl0Hq+k2SE3k8QmJ1Z6Q7NloYd9mCw01rniG3sdihr1brezdakWrg5Q+KlIHqkjuoSIyViYyVsuICXA422qvqGy2UnNcj5iufVWMmqghMPS417vCxvKFZvZuc9PxwoLg4RxOTt544w0eeeQR/vOf/5CYmOiEkATBc/XoLjPpJk2z64MC4ZKL7NdXVSss+8nMrgwxhKt1KMyMzAHgu8poqkw6vDE5qbBoWVgWw22Rh5jZMYd1hlBE9eTc2KywY42FXb9b6HGBPUkJiZQZcbPulCfnAkIlrrlfT3WpjeCIExOS3Awre/9qSkhq2/JZCCczGWH3Riu7N1rR+9oTj9SBahK7ykQnqYhOUnHZDZC330rGJitmk8K4qc2rsAEhEtfN1PLN+yaRoHg7MSHeKRxOTiZOnEh9fT3Jycn4+vqi0Zx4IFZRUdFqwQmCJ5EkGDtG0/SzdNJtEoqiYDTCG/82iiFcrWiwXxX9/AwYbRLzS+NIcHVALvRpSRw3hhfQ3beWiwPLWWMId3VIHs1mhZ1rLexebyH1AjWjp+uQVc0TviPv9+AIFVbLsYRk/xaRkLirxnrYvt7K9vVWfAOgWz81qQNVxHeWiU9REZ+iOpqInu7z/PKJGvaln3pInyAI5+6cKieCIDSXmCCfMJTrZJIkoddDTLRMtljaspUo3NVUNfmmIpoyi86rk5Mqq4avymK4PfIQM6NyWGsIQxHVk/Nms0J1qXLKxORk373VSNZWcTbdk9TXwNY1FrausRAQLNF9gIq04So6RJ1+SU9JkggKlYhLkcnbJz7PvZaYEO8UDicnU6dOdUYcguDxAgJadzvh7Ab7V9LXz0CjTWZ+qXf0NTmbz0riuDH8MF196hgRVMbq6g6uDqld8A9u2QGDVicOLDxZTZXCxpUWaqsVJtxx9n4D/mc4ISUI3mLr1q1oNBp69eoFwPfff8/8+fNJTU3lqaeeQqt1bJGac2qjYLVa+eabb3juued4/vnn+e677zymv4kgOIulhX1FasRE2FaicHfTCl3fVERRZtG5OB73UG3V8GVpDAB3dcxFEgOYW0VLmyCKZontQ211C1/vFm4ntE+S4pyLp7nrrrvYt28fAAcPHuSmm27C19eXRYsW8fDDDzu8P4crJwcOHGDMmDEUFBTQtWtXFEVh3759xMXFsXTpUpKTkx0OwhVUjZ75B3BevO35NpFszn/Dd+kic81V9vkmiqKcsv+PoihUV0Nutq1N/vZUpvb9gg8JrKBPU9Xk04JYVBYFSQJMCpLR4nUNvNSNx17vrw7FcHN4ASk+dVzhW8rKivZZPZEkkFX2JqvOHvd/eLcVQ7mNgBAJST7F+9umUFOhcHi3FbkN3npyg/edEGzLtmqH9tswVDS93qf5PDdU2pcVbgvq9t9PthnFSU1Qhda3b9++oy1GFi1axEUXXcQXX3zB+vXruemmmxyeEuJw5eS+++4jOTmZQ4cOsXXrVrZt20ZeXh5JSUncd999ju5OEDyaWg1jx6iZOkWLv79EZaX9i+rkFX2O/L50mVlMnmwVCndGNVVNSqMoF1WTE9RYNXxRGAvAjNhcZG89M9GKFAVWf2IEyZ6InHCbTQEJVn9qFO/vdkJRYPlCc9PPzT/PJUmitED0O/F6ipMuHkZRFGw2+/HPypUrGTNmDGBvP1JWVubw/hxOTtasWcMrr7xCaOixDsRhYWG89NJLrFmzxuEABMFTRURI3D1Ty7Bh9gLkhg0W3njTxBdfmjEYTty2uhq++NJMhlg+uFUMDaykt3+NvWpSJOaanMqXRTEYLGo6+dZzWVipq8NpF/ZvsvL9640n9MEAqKlQ+P71RvZv8r5qRnu2d5uVb943NXu9G+rsCWnnXmqGXuHwABShPTkyIb61Lx5mwIABPPfcc3z22WesWbOGsWPHApCdnU1kZKTD+3P4XaXT6ag5xaD52tpahye8CIKnGjxIxejRajQaidpahW++NbOvacWWjAwbmZlGEhOP6xCfI86wtR6Fu6LtVZOvS6Mot4jPnVOps6r5ojCGmXG5zIjNZWV5B2xi5a7ztn+TlQOb64ntpsIvRKKu8vQd4gXPt3eblX3p1mYd4geMUDPqJi0jr9NSU62w60+RmAre6/XXX2fy5MksXryYxx57jM6dOwPw9ddfM2zYMIf353ByMm7cOO68804++ugjBg0aBMBff/3FzJkzGT9+vMMBCIIn8fWFaydo6N7dvorLvn1WvvnWTO1JvQwUBbKzRZXEGYYFVtLTT1RNWmJhUQw3dywg0aeBUeEl/Fzm+BksoTlFgUOZ4mDUWygKzZYL3rTaQkCIxNArNIybqqW22khOpvjM9zqiCSMAffr0YefOnc2uf/XVV1GrHa8uOjys66233iI5OZmhQ4ei1+vR6/VccMEFdO7cmTfffNPhAATBUyQny9x7j47u3VVYLApLl5r59LPmiYngTMeqJv8riaZCVE3OqM6q5vMjc09iclF54reeILip1d+a2b3Rgkolcf1MHZGxojIptJ6BAweSmprKO++84+pQzqpTp06Ul5c3u76xsZEuXbo4vD+H05ng4GC+//579u/fz549e1AUhdTU1KMlHEFob1QquOwyNRcNt79dSkps/O9/ZgqLxIFeW7sgsIIefjU0WGU+K451dTgeYVFRNJOiCoj3aeTK8GKWlnV0dUiC0D4o8MPHJvwCJRK7qbjpfj3zX2zEUCG+G7yGEysnmzZtIjAwsJV37hw5OTmnbCliNBrJz893eH/nPJMrJSWFlJSUc727IHiE8HCJG2/QEBNjLzL+9ZeFn362YBZLHLqAwp1NVZNFpdFUiqpJi9Tb1Hx+OJZ7E7KZHpvHz+URWJVzanElCMJJrBb4+j0jt/5dT2SszM336/jk5UYa610dmSA435IlS47+/MsvvxAUFHT0d6vVyqpVq0hKSnJ4vy1KTubMmcOzzz6Ln58fc+bMOeO2c+fOdTgIQXBH/furGDdWjVYrUV+v8O13ZjLFmGKXuTCogh5+tdRbZT4VVROHLCqO5pbofGL1jYwNL2ZJaZSrQxKEdsPYAAvfMnLbIzrCo2RuvEfHF68bsYiTWO2fl885ueaaawCQJImpU6eecJtGoyExMZH/+7//c3i/LUpOtm3bhrnpVPG2bdscfhB3pMicw4wbz6aoXB1B2zthdb4W3kevhwlXa+jV0/4flnXQyqKvzRhqwJMWO9JVtadESmFmSlNfk0PRNJSp0Z2iy6IkgVxTh1xS2awXRXunrQk47W02JBYcjOXertncHp3HyuwOWNpB9USSwBImYfKXvW61LMnq+a+foyQJrBoJi15yu9e7shE+f9/EtAd0xHVWcdUdOr6eb2q1ODV1bvaE24DN7H3P2dMc6W2SlJTEpk2bCA8Pb5X9tig5+fXXX0/5syC0N0mJEjdcryU4SMJqVVixysK638Uyoa52YXgF3QJrqbfIfJkX4+pwPNLi/I5MSswn2sfImOgSlhSIuSeC0JpKixQWfmhi8iwtqWkqrrxWw0/fiPJJu+aMviQe2OckOzu7Vffn8JyT22+/nTfffJOAgBPP0tXV1XHvvfcyb968VgtOENqKLMOlI9RcfJEKWZYoK7excJGZggKRlbiewu1JeQB8kx9NtVnj4ng8k9Gm4vPsWO7vls3UpEP8dDgCczuongiCO8k9YOO7z8xcP1XDoIvVVFcpbFhlcXVYguB0q1atYtWqVZSUlBytqBzhaG7g8DfTJ598QkNDQ7PrGxoa+PTTTx3dnSC4XEiIxJ0ztIy4RI0sS2zeYuHtd00iMXETF4ZX0DWwjnqLii9yRdXkfHxf0JHSRi2RPkbGxhS7OhxBaJcytln5ZbG9YnL51Rp6DfDCMdVeQlKcc/E0Tz/9NKNGjWLVqlWUlZVRWVl5wsVRLa6cGAwGFEVBURRqamrQ6/VHb7NarSxbtoyIiAiHAxAEV0rrIzN+nAa9XqKhQWHxEjM7d7WnuRqeTmF6J3vVZNGhKAwWUTU5Hyabis9yYpnT7SBTkg6x7HAkJpuonghCa/vrNyuBQWaGXarh6kkaag0K2fvEd0u74+UT4o94//33+fjjj7n11ltbZX8tTk6Cg4ORJAlJkk7ZUEWSJJ5++ulWCUoQnE2ng/FXaejbx35GKyfHxv++NlFV7eLAhBNc1KGcLgF11FlUfCXmmrSKHws6cktiPpF6E1fFFPHNoWhXhyQI7dKKJRYCgiV69VczcYaW+W8aKRYVeaEdMplMDBs2rNX21+Lk5Ndff0VRFEaOHMk333xDaGjo0du0Wi0JCQlER4svOcH9xcVJTLxBQ2iIjNWqsPo3C2vWWrGJk1puRULh9qRDACw6FC2qJq3EZJP5NDuOv3fP4takfH4oiMRkE8NOBKHVKfD9AjP+gRJJKSpumanjo7lGqitFgiK0LzNmzOCLL77g8ccfb5X9tTg5ufjiiwH7jPz4+HgkyfNWExC8myTBJRerGHmJGpVKoqLSxv8Wmck7JL4o3NFFHcpJOVo1ESc+WtPSgkhuTcyno4+Rq2OLWCSqUoLgFFYLLPzQxLT7dURGy9xyt5Z5bxhFk0ahXWlsbOSDDz5g5cqV9O7dG43mxJOJjvZAdHi1rtWrV+Pv788NN9xwwvWLFi2ivr6+WRMWQXAHQUFw/XVakhLt4+vTt1v5/gczRqOLAxNOSTpuha5Fh6KpEVWTVmVRZD7JjuMfqQeYnJjPkvyOGEX1RBCcwtgAC943Mv1BHR06ytx0h5bP3jFhFYt4eTyJ1p/A7omn/nfs2EFaWhoAu3btOuG2cylmOJycvPTSS7z//vvNro+IiODOO+8UyYngdhJSFG4cqcPHR8JoVPj+BzPp28UYLnd2cYdyOgfUUyvmmjjNssMR3Jp4iGhfIxPiCvkqN9bVIQlCu1VTBQveN3H7/ToSklVcO0Xbqk0aBcGVWrsHosPJSW5uLklJSc2uT0hIIC8vr1WCaguyyT7Mx5tIVldH0La0Whh7tZp+AxVAIu+QfRhXhZeM95Utnvk8T6ia5ERT16hCbuHyJZIElphQTGat+NI/C2tT9eTRHge4JbGAxYeiaPTE6omt6bPNy15vnzKTq0Noc5IESmc/FBmPfH+XFCt8+ZGJW++2N2m84joNP33bsiaNxiAvO2ABFJMHPGfRhNEpHE5OIiIi2LFjB4mJiSdcv337dsLCwlorLkE4L9GxEjfcrCG8g4yiwJq1FlautohJ7x7g4shykgPqqTGr+F+umGviTD8XRnBrUj6xvo1cG1fIF6J6IghOlZtl47vPzdxwm5bBF6mprlTY8KsY3yV4thEjRpxx+Nbq1asd2p/DC9zfdNNN3Hffffz6669YrVasViurV6/m/vvv56abbnJ0d4LQqiQJLrxYxZ2ztYR3kKmuUvjla4mVq0Ri4gkkFKYlN63QlRtNrcXh8yeCA6yKzMcH4wCYlJiPj0ocJAmCs+1Ot/Lzd/bK16irNfTq54EVS8FOcdLFw6SlpdGnT5+jl9TUVEwmE1u3bqVXr14O78/hb/7nnnuO3NxcLr30UtRq+91tNhtTpkzhhRdecDgAQWgtAYFw3UQNySn2D/pdO6z8+K2ZqGBfF0cmtNQlkeV0aqqaLBJVkzaxoiiCqUmHiPNr5Pq4Qj7LiXN1SILQ7v25xkpgsJlhIzRcM0lDbY1C9n5xBs3jiCaMALz++uunvP6pp56itrbW4f05XDnRarUsXLiQPXv2sGDBAr799luysrKYN28eWq3W4QAEoTV07yFzz4M6klNUmEwK3y0ys/BzMw0Nro5MaCkZhWmd7XNN/ieqJm3GqkjMOxgPwM2JBfiK6okgtIkVSyzs2mpBpZa4abqWyGgx10BoXyZPnsy8efMcvt85f/t36dLllJ3iBaEtaTRw5Tg1g4ba/5QL8m18/aWZslIPPPXg5UZ0LCPJv0FUTVxgVVEHpiYdItG/gRviD/NJdryrQxKEdk9R4LsFZvwC7E0aJ9+l48M3RJNGTyIpTlhKuB29/H/88Qd6vd7h+7UoOZkzZw7PPvssfn5+zJkz54zbOtpoRRDOVccoiRsmaYiItBcA1/1mYdUvFqxetipZeyCjcFvTXJOFOTHUiapJm7IhMf9gPE/33stNCQV8c0hUrgShLVitsHCeiWn32ps0Tr5Ly7y3jDSIJo2CB7n22mtP+F1RFAoLC9m8efM5dY1v0bfPtm3bMJvNR38+HdE1XmgLkgRDLlAxaowatVrCYFD4dqGZLDFe12ON6FhGon8DBrOaRblRrg7HK/1aHM7U2kN08q/nxvgC5h1McHVIguAVGhtgwQcmZjzQ1KRxupbP3jNhESMs3Z+YcwJAUFDQCb/LskzXrl155plnGDVqlMP7a1FycnxzldZutCIIjvDzh2tv1NClm33S+54MK98tMlNf5+LAhHN2fNXkq5xo6q2ed8ZeVmz0qsoh1FRDhTaAncGJ2CSHp/S5lA2J+VnxPNtnDzfGH2ZRXjQ1Fo2rwxIEr2CoUvj8P0Zuv6+pSeNkLYs+EU0aBc8wf/78Vt2f5x0FtBap6eJNPPz5pnSVufYGDf4BEmazws8/Wtj4Z9MYrtM9NwmsOrD4gLfVVRrCPWN5yitDi0n0b6DKomZBbex5xS1JIG3PQt56GMXWNt/qFyoFzCKdDhxbfaEUH94ljd+ltutuL40fdN77WFMUSlYnX5ID6rkpvoAPD7h39USSQFNnQ1dt8bqDOHVlo6tDaHOSDLIFZLNnNmE8m7I8hYX/MXHLbHuTxisrNfzytX3UiramHT7hs7CZPeA5i8rJCbZs2UJmZiaSJJGamkrfvn3PaT8tSk5OHkt2Jt9+++05BSIIp6NWw6jRaoZeaP9zLSq0sehLMyXFHvwOFgB71eSOKPsKXQuKY6mzedb5kguVAp7gj2bXh9HAE/zBM8rQNk1QzpeCxEcH4nmh7x5uSDjM/3KjMZhF9UQQ2krufhuLPzVz/XQtg0eoMVQq/LFKjO8S3FtJSQk33XQTv/32G8HBwSiKQnV1NSNGjOCrr76iQ4cODu2vReMOgoKCjl4CAwNZtWoVmzdvPnr7li1bWLVqVbMxZ4JwviIiJe66R3s0Mfnjdwv/edskEpN24orQEhL09qrJwhLPWqFLVhRmkQ40L9zJ2E9+3U06soed4l1XEso+gx++ahs3Jxa4OhxB8DoZW60s/8ZeMbn8Wg09B3hGFdwbHVmtq7Uvnubee+/FYDCwe/duKioqqKysZNeuXRgMBu677z6H99ei05THjyX7xz/+wY033sj777+PSmV/w1itVmbNmkVgYKDDAQjC6QwcomL0ODUajURtrcK3/zOzf6+3Dc5qv1QozGiqmnxWFEu9h1VNelJ6wlCuk8lABA30pJQdRLRdYOdNYt6BOF7qt4dr4wtZmBNDlaieCEKb+nO1hcBgiSGXqrn6Vg019aJJo1tSJPultffpYX7++WdWrlxJ9+7dj16XmprKO++8c04T4h2esTlv3jweeuiho4kJgEqlYs6cOefUaEUQTubrC5OmaBg/QYNGI7F/r5V3XjeKxKSdufK4qsmiUs+qmgCE0bIx/y3dzp2sLw1lT3VT9SRJVE8EwRWWf2dm9xZ7k8aJt2uJjPK8g1bBMxw6dIhLLrmE1NRUevfuzaJFixy6v81mQ6NpfhJLo9Fgszl+7OZwcmKxWMjMzGx2fWZm5jkFIAjH69RZZvaDOrr3UGGxKCz7wcxn883U1ro6MqE1qVCYHpULwGdFcR5XNQEop2WNpVq6nXuRmHfA3ojx2vhCQrQmF8cjCF5IgcWfmsnZZ0XvI3HLXTqCgkWC4lYUJ13amFqt5o033iAjI4OVK1fy4IMPUlfX8mVQR44cyf3338/hw4ePXldQUMCDDz7IpZde6ng8jt5h2rRp3H777Rw4cIAhQ4YA8Oeff/LSSy8xbdo0hwMQBACVCi4dpWb4JfY/ydISG//7wkxRoQcOvhTO6srQYuL1jVSaNfzPA6smALvoQDVaAjGdcrE4BahCxy4cmwjoLv4oCyGjyp/U4FomJRXwzt4kV4ckCF7HaoGFH5iY9jcdEVEyk2dq+ehNI42nH1EqCA6LiooiKsreYywiIoLQ0FAqKirw8/Nr0f3ffvttrr76ahITE4mLi0OSJPLy8ujVqxeff/65w/E4XDl57bXXeOSRR3j99de56KKLuOiii3j99dd5+OGHefXVVx0OQBDCwiXumKU9mphs/NPCe2+ZRGLSTp0w16Q4lgabZ072jMeAHgsSzU90KdgnyWuxEoGnNuGR+CjLXj2ZEFdEmKieCIJLGBvg8/+YMFQpdOgoc/MMLWrPKza3S+4yIX7t2rVcddVVREdHI0kSixcvbrbNu+++S1JSEnq9nv79+7Nu3bpT7mvz5s3YbDbi4uJa/PhxcXFs3bqVpUuX8sADD3DfffexbNkytmzZQmxsrMPPx+HkRJZlHn74YQoKCqiqqqKqqoqCggIefvjhE+ahCEJL9BugYtb9WmJiZerrFL74xMQP31kwm10dmeAso8OKidM3UuHBVZMgxcgzrEeHjWwCKcPnhNvL8OEwfvhh4XnWE6B45oH9xrJgdlYGoFPZuKVTvqvDEQSvdaRJY2ODQkKyigmTtUhihFe7ZjAYTrgYjcbTbltXV0efPn14++23T3n7woULeeCBB3jsscfYtm0bw4cPZ/To0eTl5Z2wXXl5OVOmTOGDDz5oUYyrV68mNTUVg8EAwOWXX869997Lfffdx8CBA+nRo8dpk6AzOafc22Kx8Ntvv5GVlcWkSZMAOHz4MIGBgfj7+5/LLtucyoLXvbG1hravREgSxHeW8Q+SqK1WyDtgQ1FA7wtjJ2lJ7W9PaLP3Wvl+vomaatC28uPrKxR8S5R22bTrTCQ3nAKmkmzM6NhUNTkci8mkcvwMyRlIEkhdE5HUYU4bt6u2WXgy8xOiauo5rAvhoZ53UafW09OQS6i5hgpNALsCEwg21/HWrg+IN1XzZMAOHu0+BYvsvNOd9RHO2fe75Um8F7KD8XFFfFSTQKlZ55THOReSBMYgFfWNGq97f6tXZLg6hDYnyRKq4R3R1Nm87vVuDFVRXAxfzjdz650aeqSpqDFo+Glx++2BosgecJDmxCaMJ1cunnzySZ566qlT3mX06NGMHj36tLucO3cu06dPZ8aMGQC88cYb/PLLL7z33nu8+OKLABiNRiZMmMCjjz7KsGHDWhTqG2+8wR133HHK1XqDgoK46667mDt3LsOHD2/R/o5w+NssNzeXK6+8kry8PIxGI5dffjkBAQG88sorNDY28v777zu6S6Gd6pYmM+pGDUEhxw4/qyttbF1rpd9wFUGhMlarwm9LLPyxwvs6PHujMeElxOobKTdr+MbD+poAoCjcm/0jvWpyqVPpeKLrLdRofAHYEXTinIwKbQD/6jaZ13d/SJ+aHB48uIRXkyd43FmRjTXBbKsNpK+/gWmRebySn+LqkATBa+UcsPHdF2ZumKJlyEVqqqsUNvxmdXVYghMcOnTohIN+ne7cTgyZTCa2bNnCI488csL1o0aNYsOGDQAoisJtt93GyJEjufXWW1u87+3bt/Pyyy+f9vZRo0bx2muvORyzwyct77//fgYMGEBlZSU+PseGMkyYMIFVq1Y5HIDQPnVLk7n+Ti2BJ60sEhgsccl4NUGhMuUlNua/amTDcpGYeAOVZOP26CNVkzgaPXCuybVFfzC6dCtWJF7ofAN5vmfuX5LjG8lzKTdiRebysnRuKVjTRpG2Jon3CxMBmBBWSKTG85ZGFoT2ZFe6jV++t499vmK8hp59W7P+LDjEGfNNmo6HAgMDT7ica3JSVlaG1WolMjLyhOsjIyMpKioCYP369SxcuJDFixeTlpZGWloaO3fuPOu+i4uLT7mE8BFqtZrS0lKHY3a4cvL777+zfv16tNoTB98kJCRQUCDWwxfsJ4ZH3ahp+lk66Tb77yajwocvGjGJ4xyvMTa8mBh9I+UmDd+URLk6HIcNrNzHHbm/APDfhCvYFNKlRffbEpzCv5PG8UD2Eqbmr6ZQH8Lq8D7ODLXVba4NYXNNEAMCqrk9Mo8X81v23AVBcI4Na6wEBksMvVjNhJs11NWYyT7ghmN52zsnDutqbScfjymKcvS6Cy+88JzagcTExLBz5046d+58ytt37NhxdBUwRzicbttsNqzW5iXE/Px8AgICHA5AaF+0Oug9REVQiNzsjXDidhJRceJsj7dQH1c1+bQwDqOHVU3iGkr554FFqFD4uUM/vu041KH7L4scwMKoCwGYk7WYXoYcJ0TpXEeqJ1eHFRGlFWcVBMHVflliYVe6FbVa4qZpGtGkUTil8PBwVCrV0SrJESUlJc2qKY4aM2YMTzzxBI2Nzb8TGhoaePLJJxk3bpzD+3X46PDyyy/njTfeOPq7JEnU1tby5JNPMmbMGIcDEDyL3hciYyW69pEZNELF5ddruOFOLTMe1fG3V/X84w0fxk9p2ZR2/yDxQeotxoYXE603UmbS8m2xZ1VNAsz1PLNnAX5WIzsDEngradw5zRuZF38Z60JT0SpWntz3JTENZU6I1nm21QXzV00wGllhemSuq8MRBK+nKPDdF2ZysmzofSQm36klKNjVUXkZJzZhHDhwIKmpqbzzzjvnFaJWq6V///6sWLHihOtXrFjR4onvp/Ovf/2LiooKunTpwiuvvML333/PkiVLePnll+natSsVFRU89thjDu/X4WFdc+fOZeTIkaSmptLY2MikSZPYv38/4eHhfPnllw4HILgXX38ICpUIDpMICpUJCjvys0RQmITe5+wHZcYGBV0LtqutFhNNvIFasjEt5kjVJBaj4jlVE5XNyuP7vyLGWEGRLphnutx0zituKZLMy52vIzzDQPfafJ7b+zn397gDg6ZlTa7cwX8KExkckM5VYcXML46nwORz9jsJguA0Fgt8Oc/E7fdoiYySmXynlo/+bRJNGtuBTZs2nXIVrFOpra3lwIEDR3/Pzs4mPT2d0NBQ4uPjmTNnDrfeeisDBgxg6NChfPDBB+Tl5TFz5szzijEyMpINGzZw99138+ijj6I0TSCWJIkrrriCd99995yqMw5/y8bExJCens5XX33Fli1bsNlsTJ8+nVtuueWECfKCG5LAPwCCwuQTEo6jP4dKaHVnTyrqahSqyhWqyxWqKmxUN/1cXaFQVaFgNsK9z+sIDJZOObRLURQMlfZlhYX2b1x4MdE6I6UmLd95UtVEUZids5Q0Qw71spYnut5C9XkmEiZZw5NdJvHW7g+IaazgqX1f8o/uUzHLp59Q6E621wXxhyGEoYGVTO+YxzN5XV0dkiB4vcYG+Py/JmbcpyOio8zNt2v57D8mLO13lWG3ca5NE8+2T0dt3ryZESNGHP19zpw5AEydOpWPP/6YiRMnUl5ezjPPPENhYSE9e/Zk2bJlJCQknHe8CQkJLFu2jMrKSg4cOICiKKSkpBASEnLO+3QoOTGbzXTt2pUff/yRadOmMW3atHN+YG8jSZCYKBMQCDUGyMlp/XXaJQkCAyE4RCI4WCKk6d/gkKZLkIRac/bkw1ClUF1uo7qiKQFpSjyqK+w/W1rQIHH5/8xcf6f2hAlXwNGsevkis1ihywscXzX55HCcR1VNri7+i3Elm7Eh8WLKDeT4nt/Y3COqtP481u1W3tz1X3rW5PFQ1mJe6nwdiuQZc7DeL0xkaGAlY0OLmFcUT76ongiCyxmq7AnK9Hu0JCbLTJik4evPxPest7jkkkuOHl+dzqxZs5g1a5bTYggJCWHgwIGtsi+HkhONRoPRaDzjRGeP4YwVFk4jtYfM2Ks0BB23rG51lcLSH8xk7G559UCWISj4uMTjyCUYQkIkAoMkVKozvzY2m4KhGqoqlaOXyuN+rq5SOMV6B8foJdCfPdYdWQqWz8yMGa85YQxsdRX89IOZjCwFApz/dyRLoG5Q0Bq8r2mX2d/1B7vjOxQR1VQ1+b6kY5s8plxajepQOYrt3F/wfo15zKz4CYB5gUPZXBOCqqaktULkMPBc8JU8V76EEeU7KTTq+CxwyHnt0zK4bRrgphPI2tpQLvKvYHpsLo8XdWuTxz0VWQJkUFR43ftbHdU27yd3IskSimRvzudtr7etBUdrRaUKX3xqZsp0DT3TVBhqFH76wXPLJ972GgvHODys69577+Xll1/mww8/RK12Xrfj9iK1h8zNk5sP2QgMgpsna/jy82MJilptTz6aVTyargsIBPksHVMtFgVDdVPCUXViAlJdAYZqhXNYLe6cZOyykbnbSELSsYpRbrb3JQneSiPZuC36EOBZVZMYSyWPVv6MCoWVPl35xq+vUx5nuy6Wt4IvYU7VaibVbqZQHcRK3+5OeazW9n55Ahf5VzA2sJg/64KxIlNm0bK1IQgb7eDklSB4qOwsG9/+z8yNk7QMG66mulphw1rRpFHwLA5nF3/99RerVq1i+fLl9OrVCz+/E8dgf/vtt60WnKeTJBh71en7fSiKwvUTNRQXKQQHSwQEnv1L3WxuSjhOSjyO/F5Tc/qzDZILpngoCuQcFHNLvIWMQlpANeFaE6l+BjrqjBSbtCz2kL4m/rZGnir/EX/FxG5NR94KHunUju4rfFOJshi4uXYz91f9SqkqgO26WKc9XmvZ3RjIrgZ/evrU8kL03qPXF5m1vFLSmVW1HVwYnSB4t53pNgIDzVw5TsPocRpqqhV2bhffw07hxD4nAwcORKVSMXv2bGbPnt3KD+LeHE5OgoODue6665wRS7uTmCSfMJTrZJIkodVCXPyxbYzGY8nH8cOtjvxeV9sWkQuC4y4JKeNvCQeI1JlOuH5DVSgmxfVDzM5GpVj5Z8XPxFqrKVYF8FzoGMyS86s9nwYMpqO1mhEN+/lXxTLmhF/PIU2o0x/3fFzqX0oPffMPowi1ideiM3jocKpIUATBhdavtRIYJDFsuJprJ2qorTWTnSUSlNbmzAnxjqzW1d44nJzMnz/fGXG0Sy3tSbl+nYX0bVaqqhQa6p0bkyA4wyUhZbycktHsBJKiwDUdivijKpTfKsNdEltL3Vn9O31N+TRIGp4OHUuVyrdtHliSeD34UiKstfQwFfJMxQ88GH5D2z2+g2QUHo44gALNBnDJEtgU+HtEFr/WhoshXoLgQj//aCEwSKJnbxWTpmj48D0TxUViXLXg/lp8OtNms/Hqq69ywQUXMGjQIP75z3+esiOkcExNTcu225Npo/CwSEwEzySj8LcE+8HqyVOiJMleoZ6TkIXcVitQnIOxdTsZX78TgFeDLydb07aJlFlS80zIGA6rguhoreHJiqVoFfecyNrPp5qOGlOz1/oIWYIojZF+PtVtG5ggOImMQt/gKi6PKKFvcJVbf5YdT1Hgm6/M5By0N2m89XYtgUGujqodckIDRm/X4uTk5Zdf5pFHHsHPz4+oqCjmzp3Lfffd58zYPF5Oto3qKuW0y7spin24Vk62KLUKnistoJpI3ZkPVjvqjKQFuOfBah9jPndXrwVgfsAQ/vDp5JI4DCofnggbR42ko5u5mL9XrkByw9UjwtWms2/kwHaC4M4uDi/jm6EbeafvTp7usZd3+u7km6EbuTi8zNWhtYjFAgs+MVFSZCMoWGLKdC36Fqy4KQiu1OLk5OOPP+bf//43y5cv5/vvv2fx4sV8+umnZ11X2ZspCiz9wdz0s3LSbfbfl/0g1iEXPFu4toUHqy3cri1FW6p4rOInVCis9unC//z7uzSeAnUIz4SOwYzMhY1ZTKv5w6XxnEqZRduq2wmCu7o4vIwXembS4aR5dB10Jl7omekxCUpjA3w6z4ShWiGyo8ykqVrEYqutpLWrJqJ6AjiQnOTm5jJu3Lijv19xxRUoisLhw4edElh7kbHbxpefmzGcdNK4upoTlhEWBE8UpjFxcUjLvqDLTO51sOpnM/JUxY8EKEb2aCJ5w8krc7XULl0MrwdfCsANtVsZXbfLxRGdaGtDEEVmLWdqI9Nok8lsbJu+K4LgDDIKD6RknXK4qtw0XPWBlIMeM8Srugo+/chEY4NCUrLMtRM17vBxJ5zBwIEDSU1N5Z133nF1KG2uxbmzyWTCx+dYJ2D7SlNajEajUwJrTzJ228jMMJKYJBMQYJ+LkiP6fQgeLExjZEpUPhMiC9HL9gRbUU59bG9ToMSkI73GfQY7y4qNRyp/Ic5SRansb69WSO5zKvFX365EWau5tWYjs6vXUKwKZKs+3tVhAWBD4pWSzrwWnYFNOfHA7chnml62MT8+nQcKenLYIsaQCJ6nT3A1kfrTV3tlCSL1RvoEV7OtKrjtAjsPxUXHmjT26qPCUK3w84/uObfNU4jVupzDoW/jxx9/HF/fYyvImEwmnn/+eYKCjh10zJ07t/WicyKzD0i6tn3MfcU2KG76xQUL8ai9cf0CCSw+EqZA2euSQckJfbc6aIxMiT7ENZGF6GT7f+jOmgD+qg5mesyhZgerNsW+otPcnGRsivucprvDsJ4BxjwaJTVPh46hUuV39ju1sS/8BxJlqeayhr08VvkTfwu/jpwzTNRX17VdbGvqOvCwMZW/xx4g8rjhesVmHV+XRnFTRAFd9XV8Eb+Vhw6mkl4X7JQ4JAm0Jhs+5Vave39b4yNcHUKbkySw6iXMfs7vEB8cYG7xdmaz8z/btDWt84QL0q388ClMuF3LBRepqStR+GuVeyYoVrOXvamFo1qcnFx00UXs3bv3hOuGDRvGwYMHj/5+cqNBQRDahwitPSm5OuJYUpJuCOTDggQ2VgcDEvvqAvhb4ol9TkpMOubmJLvVMsJX1u3mmrrtALwWfBlZWjc9yJMk3goeSYS1ht6mwzxd8SMPhN/gNonUr1UdWFMVTl//asI1JsrMWrbV2jvEL6uI5P+Sd9Pdt5b/dNnBS3kpfFfuGY04BQGgzNzCuVUt3M6d7NpkJSDYzGXXahh1vYbaaoXdm0UX+XPixCaM3qzFyclvv/3mxDAEQXBHkdpGpkYfYnxEEdqmpGSbIZAP8xPYZAjm+E4Xv1WGs7YyjLTAYwer6YYgt+p10ctYwOzqNYC9+eF6n84ujujMzJKKZ0PHMLfsa+IsVTxV8SMPh12LUda4OjTAPsRrS21ws+uLzXpm7E3jiYS9XBFayr8S9tHZp5bX85OxtHyqoyC4THpNEBVmDSFq82nnZlSZ1W41XNURf6ywEBAsMXikmvFT7AlK7n4xB9ZRzhzW5c3Et4QgCM101DbySNJ+vk3bxPUdC9HKClsMQdyd0Zu7MvqwyRBC8xZ89oPVrYZglpdHsNUQ7FaJSUdLNY9V/oQaG2v0KXzpP8DVIbVIraznydCrqJb1dDGX8nDVcmTF/Q8iGhUV/8zpztsFiQDcFHGYf3feSZCqZcNlBMGVwjQmNJLN3qvppIPFI7/7qSz0cdMl0ltixddmMrdaUWskbpypJSLafT6vBe8mkhNBEI6K1jXwz6R9fJu2iWsjC9HICpurg7hrd2/uzujDlpOqJZ7C12biqYqlBNka2aeJYG7IpW6xMldLFaqDeDp0LCZUDGvMZoZhvatDaiGJ+cUJzMnqQZ1VxaDAKj7ttpVO+jacICMIDlJLNl7qnEmA2spho47Sk4ZuFZt07KgJQCPDqykZJOg9s4OyosB3803k7rei95W4+R4dgSGe87noFsRSwk7h8uTk3XffJSkpCb1eT//+/Vm3bl2L7rd+/XrUajVpaWnODVAQvEC0roF/ddrL1302c01kEWpZYWN1MHfu7sOszD5sqwl2dYjnTFZs/KPyFxIsFZTJfjwdOgaTG63M1VKZ2iheC7kMgAl127mqboeLI2q5NdXhTNvbl3yjnlhdIx933cbwIM/oESF4n/vjDtI7wECNRcWsPb25Kn0wd2X25rED3bgrszdXbx/ErD292VEbQJDawhtddhHsoU1HrRb43/smSgttBIZI3HyPFp3P2e8nOJ83LyXs0uRk4cKFPPDAAzz22GNs27aN4cOHM3r0aPLy8s54v+rqaqZMmcKll17aRpEKQvsUq2vg8U57+TptE+MjilHLCn9WhTBjVx/uyeztseOpj3e7YQODjLkYsc/fqFB5bv+NdT4pzAsYCsBd1esY2Jjj2oAckNXox5Q9/dhUE4yfysrcTruZFpmHOE0ouJMrwkq4qaO9f9sTB7tRYPSxD1etCWZ5RQRba+zDVY2Kiof29aCgUU+svpH/67IbnTOWSGwDjfXwxb9NGKoUIqJlbpypReV5529cw4mVk02bNpGRkcHs2bPb7Om4C5cmJ3PnzmX69OnMmDGD7t2788YbbxAXF8d77713xvvdddddTJo0iaFDh7ZRpILQvsTr63kqeQ+L0jZxVUQxagk2VIUwfVca9+3pxY5az09KAC6vz+C6unQA5oZcxj5tpGsDagWL/Pvxs28qKhQerfyFTuZSV4fUYtVWDffs78XCkmhkCe6Jyeb5xEz0HnpQJ7QvyT51PJa4D4CPCuL4vSrsjNtXWrTcv68n1RY1vf1reCp5L5KHJtuGSoWv3jZibFBI7KLi6qkaJBkSUmR6DFCRkCJ70khYwcO1ODc+WzXjiPj4ljUKM5lMbNmyhUceeeSE60eNGsWGDRtOe7/58+eTlZXF559/znPPPXfWxzEajSc0ijQYDIA9K/O299nJXW69gSzZX2dv/FA91XNO0NczLSaPUWElqJpuX18ZyocF8WTUBZ72fp5CkpouskSq8TD3Vv0GwIKAgazz69JO3vMS74RcQqTVQF9jPk+X/8iDETdS6iFPzorMqwUpZDX68XDcAa4MLSVe38BDB3tSYnas+dTR19tDnntr8tbn7KzPcz+VhVdSMvBR2fizOpj/Hk5s0ePkGX15+EAqb3fZyWWhZRyOy+bt/E6tHl9bvN4lhxUWfWDi5tlaegxQ07mnCp3+2AMbKm38ssjM3vS2WZDDE/7GxWpdztHi5CQxMfGUfUwURTl6vSRJWCwta+ZTVlaG1WolMvLEM5mRkZEUFRWd8j779+/nkUceYd26dajVLQv9xRdf5Omnn252fTcfH1Ra7xpYqbig8aOryRJEabRItuYrrrR3Vt2x92u0upbr/A8w1KfwaJK6uTGCb2s6c9AcBKGQFOqiQFuRJEFYBz+C9Vb++dNHaLCxJb43a4ffRKzk8il2repj0ywif3mT6OpiXjD+woumHjRqPacb+4HyRF5rDGJ20nZSfWv5sutW3j7Yh6z64BbvQwIiQnVgVTz0fPW50wS5x3LSbS0wWAenWEHr/Cg8GL6NeH0DpRY982v60Smy5f1LavDlgwqYHb6DKVH5mHSBrK5t2YnaFkfYVmcX62HfNhvdB3JCYgIQECxz/R061n0rcWif8+Mxm2Q2O/1RBHfU4uRk27Ztp7xeURS++uor3nrrLfz9HR/LfXLCc3yyczyr1cqkSZN4+umn6dKlS4v3/+ijjzJnzpyjvxsMBuLi4sgqb0DWeNfXmaJydQRtT5JArVPIKq73uuTE7CvRSVfHjMhcLg8uPZqU/FYdxn+LE9jTENC0pWeuNHMqkgQdqow8uPw/BBjr2O8XxdMdx2M82D5Xh3qk0yTe2PVf4ioPM2Xpf/lX3ynYZM95o+egYXtOT54bvJfkoHr+3nkzc7d3YvmhljXGlCT751r2oTqve3/rKo1n36idkSQIi9VzoLR1P8+ndDzEQN8STDaJh/Z1J7PeAjjWNf0AwaiNCdwVk8ttIRnsKJP509B6Z3xsqrZJTiQJxqXoUJTmx2f2ZZUV+oxU+HWD0envOZu50bkP0BpEE0anaHFy0qdPn2bXrVy5kkceeYR9+/bx8MMP89BDD7X4gcPDw1GpVM2qJCUlJc2qKQA1NTVs3ryZbdu2cc899wBgs9lQFAW1Ws3y5csZOXJks/vpdDp0uuZDBRQFbF72B+BtX95gH76nYH/u3vT8k33rmJaQy+XBx1ZEWl0VzgfFCexr8NwJ4Wcj22xMX/c5SfUllGv8ebLLJBplbbv9sC/ShfBk10m8mjGfIWV7uWfPD7zZ7WrPGA/RpKhezz1re/Jo/wNcFF3BI/2y6BRYz392J2BTzv48jry3ven9Dd73fI9o7c/zAQGV3B2TDcBreZ3JqAs4yz1O78PD8cToGhkXXsyLyZnckdmH/a30edtWr3dCJ5mg4NO/7yRJIigE4pNkcrKcO7zLI/7GRXLiFOc0zmHLli1cfvnljBs3jiFDhnDgwAGeeuophyonWq2W/v37s2LFihOuX7FiBcOGDWu2fWBgIDt37iQ9Pf3oZebMmXTt2pX09HQGDx58Lk9FENqVFN9aXuqawRdpW44mJiurwpm4pz8P5fRo14kJwOxNy+hTsBuTpObpLjdTpmsfE/vPZK9/LC93vg4bEtcc+pPr8353dUgOa7SqeGpjFz7eEwvAjZ0LeXFIJn4ax85eC4IjIjRGnk/eg0qCH0oj+a6043nuUeL5nBQ2GYLwU1l5vcsuIjSeVeHyD2zd7QThXDiUnBw4cICJEycyePBgOnToQEZGBm+//TYRES0rwZ9szpw5fPjhh8ybN4/MzEwefPBB8vLymDlzJmAfkjVlyhR7oLJMz549T7hERESg1+vp2bMnfn5+5xSDILQHXfxqebnrbj5P28qIsDJsCiyv7MANewbwcE4P9je276QEYOy+TdyWvhqAucnXsCcgzsURtZ31oam832U0AHfvXcYFJbtdHJHjFCQ+2RPHkxu70GCRGRRZzbsX7STOv8HVoQntkFqy8WLnDEI1ZvbW+fFybmdaY5kciyLz8IEeHGzwJVJr4vUuu/CVPSfJrjW07nbt3ZEJ8a19Ae/uc9LiYV2zZs3io48+YsSIEWzevLlVmh9OnDiR8vJynnnmGQoLC+nZsyfLli0jISEBgMLCwhavEiYI3qibXw3T4/K4KLQcsA9VXFnWgXn58eyT2n9CckTv4hweX/s/AJb1vIxfA3p7XWl8UcJwYuoruDr/T/614yvuH3gX+4JiXR2Ww9YeDqOgTs9zg/cQH9DIuxfv5JlNKWwqCXF1aEI78mDcQXr712CwqPlHVirGVpyUWWtV88C+nsxP3UYX3zpeSM7kb/t7YvWA9QJzD9qorlQIDG4+5wTsc04MVfbtBOfatGkTgYHeWaKSFKVlo/pkWUav19OtW7czbrd169ZWCcxZDAYDQUFB9JnxApLGc1a2aQ3eOCFelqC73oesovY1Ib67fw0zYnO5MLQCAKsCK8o6MD8/gZwG+7JsZl/3/yJsDR1rK/nsu9cJa6jl18SefHXBNHIOet8EaXOgFtlm5YVtnzC4fB/l2gBmD55FsY9nHtQHa808M3gvvcJqsCrwn90JLDoQxfFntyUJEpL8yc7zvtfbayfEX9yBAyXn93k+OqyYZzrtBeCBfT1YX33mfibnKtXPwH+67kCvsvF1SdR5VWfaakI8QPdeMhOn2VeDOz5BOXK4uHC+mcydzk9ObOZG0j/5J9XV1W53kH7kWLLbvS+g0rXusaTV2Mief7vn824rLa6cPPnkk86MQxCEFujhb2BGXC7DQioBe1LyS2kE8/PjyWv0vrWifcxGXv/lI8IaatkbFs3jI28h1dS+lgx2hE1W8UyfSby18X2Sa4t4cevH3Dvobuo88ERMlUnDnN9TeaBPNmMTS5jVM5dOgfXMTe+E2ea9r7Fwfjr71PLPhP0AfFgQ77TEBCCjLpDHD3bj5c4ZXB9RSIFRz+dF7j/cNHOnjYXzzYyeoCHouHMbhir46bu2SUwE7yaSE0HwAD2bkpKhTUmJRYGfSyP5OD+eQ43e1a/nCEmx8eyvX9C1/DDlPv7MGXU7DRodmFwdmWvVq/U82u823vnrXZLqinlyxwIe7XsbVg9aYvgIiyLzWnonsgy+zO6Zw5XxpcT5N/DEX12pMLa8D4UgAPirLLzcOQO9ysYf1SH893CC0x/zt6pw3jjUiTnxB7k/LpsCo55fKzs4/XHPV+ZOG3t2GUnoJOMfaJ9jknvQ5nUVyrMRTRido8XJieD5tAbv+4uXJFD8wKbxkGUJT9LHv5o7onMZHFQF2JOSZWWRzC+MJ9/YlJScphebrZ33aJv958+MzNmJSVYxZ/Q0CkJD7b1cTDYkowW87OSeLfxYdaRYG8qjg27jrQ3vM7B8Pw/s/Z5Xe1/nUUsMH++b/BhyGvx4qu9eeoTW8t4lO/nX1m7sM5z7sq+eTpVX4uoQ2pwkS+jLQ/EvMDv8eS6h8MzgvcTrGymq1/HS78n4mttmovoPBREkWeqY0KmYZ5P28GCBisxKx/52a2Nc84Get8969GeJ1lgywAFWD/zSFlpFi5OTESNGnHJy1PEkSWLVqlXnHZQgeLu+/lXcEZPHwMAqACw2iR/LI/m4MI4Co3dWSo43et8W7tiyEoBnRtzI9qgkF0fkfvYHxfJMv0k8v+kTxh3aSL5fOF92vsTVYZ2zLeXBzNzQmxf6Z5IY0MC/h+zi5Z2dycb5Z78Fz3dzymEujKrEZJV4cmMXDOa2PNiXeHtnEpG+JoZ1rOT5IXuYvaYXhfWeN9xSOInoc+IULU5OzrQ6l8Fg4Msvv8Ro9L4JeoLQmvoFVHFHdC4DAqsBMNskfiiL5OPCeApN4osMoGdxLk+tXgjA/L4j+LHbQBdH5L7+iEzl7R7juX/398zcs4xC31B+i+7t6rDOWUG9D7P+6M2/0vYxLKKSJ9L28UOxmbl5HVE8YCUkwTX6daji9lT7yp9v7khiX1Xbr2RoUySe3ZTCm8N30yW4jpeGZnLP2p7UtGmSJLQ6kZw4RYuTk9dff73ZdRaLhXfeeYfnn3+emJgYnn322VYNThC8g8KAgCruiM6j33FJyfdlHfmkMI4ikZQcFVFbxevL5qGzWliTmMq/h4x1dUhu79ukC4ipK+P6nPX8M/0rSn2C2B3iudWGOouaxzZ3Z0bXXG5JLuCqyGxCB1Tx3LYU6i1ipLJwog4+Rh4fsB+VBMtyO7AsN9JlsTRaVfzzz268c9FO4gMaeWbwPh7e0F0s8CAIJznnd8SCBQvo2rUrL7/8Mk899RSZmZncdNNNrRmbILRzCoMCK/mg23be67aTfoHVmGwSi4qjmLBzIC/npojE5Dh6s4k3ls2jQ30N+0M78ujlk7HJ4ku9Jd7pcRXrI7ujs1l4ftPHRNWVuzqk82JD4oO9iTy3PQWzTeaCjpW8c8FOon0bXR2a4EY0so2nB+4jWGdhX5Ufb253/fDP8kYtj/7ZjTqzirRwAw+lZSFOlXsuyUkXb+fwN/vPP/9MWloas2bN4rbbbmP//v3MmjULtVqcsRKEI2QU+gVUMSq0hH4BVcgnfPkoDAms4MNu23mn6076Bhgw2iQWFkczYccgXslLoVgkJSeQFBvPrPqS1NJ8KvR+3D92OvVa8X/UUjZJ5tm+k9gbFEOIqY6XN83H31Tv6rDO28rDEbyYNYDSRi2dAht4f/gO+oZVuzoswU3M7plD99BaDCYVT27sgsnmHivWZRv8eGpjF6w2GBVfxtRu+a4OSRDcSouTk40bNzJixAgmTJjAiBEjyMrK4vHHH8fPz8+Z8QmCxxkRUsaS3n/xn247eD55D//ptoMlvf9iREgpw4IqmNc9nX933UWfAAONNpkvi2K4ZscgXsvrTIlZ5+rw3dJdm5YzKms7ZlnF30ZP43Cg83oTtFcNah2PDryNEn0QCbUlPLvlM9S2tlmtyJmyG4KYua43GZX+BGktvDZkN9ckFiLORnu3UXGlXN2pGJsCz29OocjNJp9vLg1m7vZOANzWLZ8r4rxv9bV2QXHSBRg4cCCpqam88847bfZ03EWLyx1DhgzBx8eHu+++m8TERL744otTbnffffe1WnCC4GlGhJTxcnJGs8OiCK2Jl5Mzj67k2miV+aY0is+KYikXCckZjdqfzsxNywF47uLr2RbdycURea5yfRD/GHQ7b294l37lWTy04xte6nOjxy4xfES5Ucv9G3ryUO8srogr5cFe2SQH1vPmziQsihj6522SA+uYk5YFwKd7Y9lYEnKWe7jGstxIov0auaXLYf7W9yDFDTrSy4JcHZbgJjZt2iQ6xJ9NfHw8kiTx3XffnXYbSZJEciJ4LRmFv8UdQAF7v43jHDn2UxT4ojiGTwvjqLCIJnJn073kEE+v/hKAT/tczPepg10ckec7GBjFk/0m89Km+YzO30KBXzifpVzq6rDOm8km80J6Z7IMvsxMzWV8QjEJ/g08vrkr1SaxIpK38NdYeHrwXnQqhT+Lgvl0T6yrQzqjjzLiifI1MjK2nGcH72X2mp7k1fq6OiyhhUQTRudocXKSk5PjxDDankUHkpedsFZ741xRCaxaCatewubkN3x/v2oidWduTy5J8Gt9OCVqndNboAbke/aQnbCGat5a+xE+FjN/RHTlo4TRZ31OkgS2UD2WYL1HNt08H+q6lr/e2/yS+XfKVTy473tm7P2FYlUQqyPTnBeck0gSWPxkTMGqo6/3ZxXxHNjtzzPd9tAnzMB/Lt7BwxmpHKhr++VjnUndNdrVIbQ5SQJV+n7UWw+jnOIDXZIU/nmXlRg/hcIyeOX/alHVb3FBpI557VeFyPskeiRbeSltO/e9pqaq5sQzXOqJQ1wUnetYLV72IS4cJerdgtBKOqjPnJg4up0301rNvLDxUzo0Gsjxj+DpAZOwSeLjqjX9GDOYhXHDAfh75jf0qsp2cUSt54/KUGakp5FX70OU3sgHfbZzSViZq8MSnOzmUTaG9lIwmeGZD9XU1HvGcEWzReKJ/6goKIGocHh2phWdRhyYewQnzjnxZg5929tsNubNm8e4cePo2bMnvXr1Yvz48Xz66aco3naaUhCO4ydbuCigZQc/pWI415kpCo9u+x+pVYeo1vjyjyHTqNP4uDqqdum/yVewtkMPtIqVZ3Z+Tkx9+zmAz23wZcb2PvxVGYyPysaLqZncHp+LJL7526X+3WxMHWcD4K2FKg4c8ozE5AhDncRj76ox1EK3RIVHbrMii/E9nkEkJq2uxcmJoiiMHz+eGTNmUFBQQK9evejRowe5ubncdtttTJgwwZlxCoJbUqFwXehhlnTdyOiQUoDTDieyKVBk0rGtTkx4PJMp+1ZzWcF2LJLMvwbdymE/sTKXsyiSzEvdbyAzIJZASwMv7PiEQLPnLzF8RI1Fw9929eTL/BgA7kjI47numehlq4sjE1pTRKjCo9OsyDIsWy/xyx+eWWUtKJV44gMVJjNcmKZwxwSbq0MSBJdo8Tv4448/Zu3ataxatYpt27bx5Zdf8tVXX7F9+3ZWrlzJ6tWr+fTTT50ZqyC4lWH+FSxM2cy/YvYTqjaT3ejDhyVxKNBsfsuR318tTMYmWiyd1kWHd3LHnl8AmNv7GtLDk10cUftnVGl5vPetFOmDiW0o55mdn6FpB0sMH2FF4q3sTjy3LwWTTWJkeDn/6bOdjjpvnITX/mjUCo9PtxLkD/tyJd7+n3v0MjlXu7NkXv3M/hyuv9TG+ItEIu3OjkyIb+2Lt2txcvLll1/yz3/+kxEjRjS7beTIkTzyyCMsWLCgVYMTBHeUrKvj7cQdvJO0k2R9PVUWNS8d7syN+wfwTnEn/p6XSon5xKFbJWYdf89LZbWhg4uidn8pVQX8a+tXACzqdAE/JHrfBFBXqdQG8M/eU6lV6+lVnctDe745fQnQQy0t7sg9O3pTbtLQxb+Oj9LS6RMoGjZ6ulnX2+iWqGCohWc+VGG2eP7Jn9+2yMxbYj88m3WDjcE9RQVF8C4tTk527NjBlVdeedrbR48ezfbt21slKEFwR6FqE49F72NhymYuCKjEbJP4tDSW8XsHsbA8BkvT22m1oQNj9w5hxsE+PJrXnRkH+zB272CRmJxBaGMNL/71MT5WMxs7dOGdHuNcHZLXyfWL5Kkek7BIMpcVb+e27JWuDqnV7awJ5PZtfdlT40+o1sy/e+1kfMdCV4clnKNRQ2yMG27DZoMXP1ZRXOH5ickRX/4i89N6CZUMj91upYt/ratDEk5FTIh3ihYnJxUVFURGRp729sjISCorK1slKEFwJ1rJxrQOeXzfZSPXhxWikmBldTjX7h/I60XJ1Nia91CwIbGlLpifqyPYUhcshnKdgdZq5vmNnxDZWE2ufweeHHgLVtmzh2Z4qm2hnXm96zUA3Jr7K6MKt7o2ICcoMemYuaM3K0vD0cgKj6YcYE7yAVSSODvtSZJjFe6baB/y9Nkymc2ZnjnP5PQk3vxKxZZMCR8dvNZjNxE6o6uDEoQ20eJ3s9VqRa0+fWMGlUqFxdJ+xikLAihcEVTCd102cl/HbPxVVnbXB3B7Vh/+nteDfJNYQeq8KQoPp39Dz8o8DBofHhl8G7ViZS6X+jlqAAsSLgFgzt7vSKvMcm1ATmC0qXh8Tzfez0kA4IboQt7ouYtAtdnFkQkt4e+j8OQdFnRa+GuXxIKf21tiYme1STzzoYrsAuigM/F/PXbhqxLHWe5EzDlxjha3gVMUhdtuuw2d7tSdC41GkdEL7Udv32r+FpVFb98awL7K1r+Lk/ipKgJFVEFazS37f+OK/K1YJJknBkwm318MfXMH85MuI7qhnBElO3lq1wLu6zeTPL8IV4fVyiQ+ORTPwTo/nuy6lwHB1XyUls7DGalk1/u5OjjhNCQU/jHVQlQ4FJbBSx+rUJT2+5lc3yjx2Htq3npcorN/Pc93z+Sh3T2wKu0zIRMEcCA5mTp16lm3mTJlynkF05ZUZrzuENOq8bZnbO8orG5UUNcpLZrfG61rYHZ8DpeH2ZcFrrfKfHo4jgWFsRhtqqY3jGec1jAFuPfQqIsO7eLOzJ8BmDvgGv5I7nbe+5QkkHZmIZ+mg3R7Vn7nsFbd36PJk3n/x/dIK87h2cxPmTrhfip9Alr1Mc6XJEFcg4K2xnbO8/f/qgnhnopePN8/k1jfRv7bezvPbe/ChpLQ1g22lWkLDa4Ooc1JMowZVs3gHgpGEzz9XzW1De3/e620UmJ2cS8+jk1nSGgVD/Q+yDMlKbT3oxjF6AEJmDPmiDTtb+DAgahUKmbPns3s2bNb+UHcW4uTk/nz5zszDkFwKT+VhWkxedzUsQCtrGBTYElJR/6Tn0C5+dTVQuHcda48zNPrFyCj8HWXYXzT9QJXhyScxKTWMOeK2/lk8ZvEGcp5/ed53HXV3RjV7a+J6MEaP2au78PT/fbQN8zA8/0z+e/eBL44GEN7PwD0JP3iaxnftwqwN1rMyvee1ybTGMDDRd15I2o31wcVcsisZ35lvKvDEpxo06ZNBAYGujoMl/CAtFQQnEeFwnWRh/k2bRNTovPRygobq4O5dWc/XsjuIhITJwhprOG13+bhazGxsWMKcwdc4+qQhNOo8vHnvtF3UK3zoXdJLk//+iWS0j4njlebNfxtYw++y+2ILMFd3XJ5PG0fWtGw0S1EBJh4eNRhZAmW/i6z/E/vO3xZUxfOK6WdAXgwPJsr/EtcHJEg5pw4h/e9uwUBAIWhwRUs6L2FfyQdIERjJrvBhwf39OCezF7sr/d3dYDtksZq4eU1HxNVV8mhgHAeGz5FrMzl5nKDI/jbqGmYZRWjDm7nno3LXB2S01gVmTd2J/N/uzphsUlcFl3Gv4fuJFyskuRSGpWNx8YUEKi3klOq5d1vvPcz48vqGD6vjAHgucg9pOlFrx6XEksJO4VITgSvk+xTx1vddvFmt1108q2nyqzmlezOTNrRn/VVYYhhHE6iKDzy19f0Kc2hRqPnb5fcjkHn6+qohBbYGt2Zpy+eCMC09NVMyPzDxRE515K8KP62sQdVJjXdgur44ILtdA+qcXVYXuvui4vpEtlIdYOK936NaPNGi7Ki0FspYYSSR2+lBNnFDUpfK0tmdW0YOlnhzehdxGkaXBqPILS2Fs85EQRPF6oxcWdsDldHFKGSwGyT+KoohvkF8dRaxVvB2W7J/I1xBzdhlSQeGz6F3KDT900S3M+yLgOINZQzc8svPLruGw4HhPJXbFdXh+U06RVB3LW+Dy8OyKRTQD1vDtnJ/+3qzC8FEcgo9A41EKYzUW7UsqMiUPQycpJRqVWM7lmFTYFXlkdTUde2VZMLlQJmkU4HjiUApfjwrpLG71JMm8ZyhA2JR4u681Hsdnrqa3gneie3HupL9Sl6bglO5sQJ8d5MHJEJ7Z5GsjI1Oo+p0YfwU9nHj68qD+ft/wRjagAAlZRJREFUvCQKjKKnRlu4ID+De7YuBeCN/lfzV3T7Pahtzz7oP4o4Qxlj92/hlRWfMGP8bAKNDYTXGyjzDWRbx07Y5PZTkC9q0DNrQy8e67Of4R0r+Gef/QyPLKNrUB0RPqaj25U0aHkroxPrisNcGG37k9yhgdmXFAHw2Z8d2HbInxi/tqsSXKgU8ATNq4RhNPAEf/CMMtRlCUqDouLewz35PG4ridoG3ozexZ0FfTCJJYaFdkAkJ0I7pnBFcClzYrIJVzcCsLs2gDdyO7G9JsjFsXmPpKoinl3/OTIK33Uewv+6XujqkIRzJUk8c/FEOtZW0r/wIF98MxfVcUNcivyCeG3YBFZ36u3CIFtXg1XN41u7MS0lj6kp+QzvWNls2eJwvYln++3h8a3dRILSSvx1Vv41pgCtWuGvbH8Wbgpr04HosqIwi3Sg+UBfGbABd5POBiUam+Saqlm5Vcs9h3vxSew2+vkYeCZyL48WdRO9uNqQMyawiwnxYs6J0E718avmk5RtvJCQSbi6kWKjlsf3d+X2XWkiMXEi2WajX9EBRmVvpV/RAUIaDPzfbx/hZzayJSKZ1wZOsDenEDyWWaVmcddBKHBCYgIQUVfNKys+ZuTBHa4JzkkUJD7eH0+1SY2iNP8TliX7SIx7Uw8iizEZ501C4eErCugYZKawWsOrv0S3+QF3T0rpQMNpH1UGImigJ6VtGVYzWSY//lbYA7MiMSaghNlhOS6NRxBag/dWTiS8bt6z4gULnMRoGrivYzajgo81UfzRkMzbWRE0WlVe9Zpb23gV5BE5O3joz8VE1h1bPcYkq9DarOQHhPHw5VMx6p37kePNeY9fkaVNHkdWbNz750+nvg37GeW///4dm3y6YZOce/5LksAWp8eqkc65CWNL9Q6pJkh7+v9jWYJIHxM9Iwxsqwx2bjBAbVf3bhJ5Pm5LzmNgYh1Gq8w/d6dSnGRfPVGSQNKqkK1BOHtF6/BKG+S2YLuEcOSQVOcGA9jO8P39hymEp0q78HzEXu4MzeOQVc93NVFOj8nZPOKYRcw5cQrvTU6EdsVftjA9IpdJYfYmilYFvq/syPslSXTwCcJoq3d1iO3aiJwdvLzqk2bXa21WFODL1Aup1vu1fWBCq+tdnk1E4+mXL5WByIZqepdnkx6e3HaBOVmYztyq2wmnNji8kmnJhwB4NSOZAzWuWda9Qt2yx23pds72fW1H4jQNzAzJ44nw/RRa9PzZEOLqsAThnIhhXYJHU6FwQ2gBS7pu5LYO9iaKf9YEc/OB/jxb0JVyS/vrZu1uZJuNh/5cbP/5NNvcumsNsq19Nu/zNmGNhlbdzlOUG1u2ElKSX50Y2nWOonwaeaL3PmQJFud15JfDES6LZZd/POVq/zO+kjUqPbv83adL+9uVifxYE4FGUng9cjedNXWuDqndkxTFKRdvJ5ITwUMpXBhQzv9SNvPPmAOEqM0cbPTl3pye3J3Tm/2N7nE2yxv0LT5IZF31aT9MJKBjXRV9iw+2ZViCk5TrA1t1O0+xvTKIkkYtttMcNxw5npjaKZ9Phm7lkogyJJGktJhWtvFsnz0EaixkVPnz1p4k18Zjs2CWVEg0H2Vz5PcAayNjyre0cWRnIvF4aVc2NwQRIFt5t+NOwlWigahTiSaMTiGSE8HjdNbV8m7iTv6duItO+noqLWpeKOjMxP39+b1GNFFsa+H1LTtD3tLtBPe2IyyJEn0Qp6uDKUCxPogdYa49uGxtNiTe2NsJCZolKEd+/+VwBwxmNUn+DTzXZw8fDU5nWHgF4mjj7B7snkXXoDqqTGoe394NswuXxJUUhb/nLaajuZpaWdds6FapJpANgV0AuDf/J64q3eSKME/JjMx9xT3INvkQrTHyTsdd+EhWV4clCA4Rc04EjxGmNnF3ZA7XhBSiksBkk/iiPIaPShKotYk/ZVep0LesSlXm277OpHsrmyTzZq/xPLfpM2yceIZLwX5qYHdIvNMnw7vC2pJwHtvRjQe6HiRCf6zPSWmjljf3dWJtSTh+agsT4wuYmHCYLoF1vNI3g91VAfw3K57NFcGIkyfNjYspYlxsCVYFntrelZLGNl7N4yS3Fv3GhdV7MEkqHk+exB7fGHrW5hFqqaVC7c8u/3hsSEwvXMmNJX9wT4F9gYgfOgx0adxHGGwaZhX1YkHMNnroanklIpP7i3uIRqFOIJYSdg5xRCe4PZ1kZXJ4PtM6HGuiuLyqA28VJVFgFk0UXalPcTYP/bH4jNvYgBK/YLZFdmqTmATnWxvdi38NvJX7dy45YXK8QeNLkLmeSwp3MbBkH5siurgwSudYWxLO7yVh9AmpJkxnptyoYXtl0NEDvzqLmnkHE/j6UDSTEgq4Lv4wPYJreKP/brZVBvLfAwnsqBLLmR/RJbCWB7rbh3x+tD+eLRXBLo3nosrd3FK8DoC3YseS4RcHwI6AxGbbfhR1GYBbJiiHLD7cW9SDeVHbGeFXzsNhWbxU3tnVYQlCi4jkRHBbEgpXBpdwb2Q2UVr7uNld9QG8VpjM9nrx5e5KAcZ67t20lGv3/glArUaHn9mIwoln0o8M/fm/IVe3q87hgj1B+T2qB73LswlrNFCuD2RHWBIP7ljMNTl/8viWL5l2yQOU+7S/96oN6azLBRvMGt4/kMjCvGhuTcznmrhC+oYYeHfgTv4qC+a/WQnsMQS0TcBuKlBj5rm0PehUCutKQvk8O9al8XSuL+Rved8D8E2HIawISzvzHSSpKUGRuLFkA/cU/ISEwpIOg5wea0tsNwbxaGl35kZmMDmogHyzns8Nrv0/bnfEUsJOIZITwS2l+Vbzt6gsevrWAFBo0vFWURK/VEeI7reupChccXAbc/78nrDGWgC+7zKItwaOo19RVrM+JyV+wfzfkKv5NbH9dAwXjrFJcrPlgv/d8yp6VOSSYijkqS1f8MCwO7HKntCwwDkqTVre2teJr3JjmNLpEOOiixkcXsXg8CrWlYTyYVYCWbXet8y2jMITvfcR5WMkv17PCztTXPrZHmKu5anshegVC5sCkvkw+rKW3VGS+CjqUhRgYskGZhf8DOA2Ccryug78X3kSfwvL5uGwLAosen6tD3d1WIJwRl6bnCgSbbocgIxCP99qwlUmyqxattYHtfn4T1vLVsJ0qRh1Aw+GH2RUQBkAdTYVH1XE8VlVLEZFBXrH9qdgb15lU+P0Jm3uRt3YuvuLrSnj7xu/YXDRPgByAiN4adD1pEfaD07XdezN+vE9SSs9SFiDgXKfQNI7dMImy60ey+l4cxNGs797JABmVPzz4ql8/NPrpJVnM/XgKv6TNtopjyVJoMhNFzd/fxebdby6tzMLcmO5LSmPK6NKGB5RwfCIClYWh/PRwXjy6n1bvL+AzAonRut8kweXMji8ikazxPPfRUG5gbPNXpNksFwQi6mDb6u+3hqrmcc3f00Hs4Fcvw48PeBWGjWOfdm8H3EVlgNabsn+jdkFP2MN0PBd/AWtFqO29tzvu6A2jkQauS6skFciMpmRlUZmg/tX7Wwe0DJIzDlxDq9NTtrSpf6lPBx5gI6aYxMoi8xaXinuzKraDi6MzH0EyBbuCM1lUtCxJorfGaJ4pzyRcqvoVeJKaquFyZm/MW3nCnQ2C0ZZzcc9L+Pz1BGY/5+9+w6PougDOP7dK+m9kgRISOi9CyhIRwQEhRekoxQVBCnSUXrvqBSlCkgvCqiISBWlho50EkpIA9KTa/v+ceQgJIELJLmQzOd5Trnd2dnZ2+zd/naaMu1XiNpazZ1SFbj7OEjI7b9uSQIXbycS/ZORMxvzNZ+yc807/a80rkX5plUfBp76hQ7x17ihvc9Zr+wfvUuSwNXJBq8k2aLBiSzLPIhJRm/G39y9ZBumXCrJmpDCfFwslCaFomjsHUUDryj+uO/F8htFuZecxacwr5kaAXF0fsP4AGrBXz7cirbg8coyQy5uo1xMKLEqW0ZX7k5CFgMTACSJpcWbAdD55n4G/LcDSYat/tkXoLw8iWl3S1BIncybTg+ZH3CO7teqEqbN339nr7saNWqgVCrp168f/fr1s3RxcpUITnJYI4dIZvldTLfcS6Vhlt9FvrxbtkAHKCoMtHUOo6/7LVyVOgCOJLgyOyqQqxoxV4mlVYq4wYijmykWGw7AsUIlmFGzHXcc0zYLkIBmNYtSu4IvaqXCYlUYEqDW6tBpCt7QmQZ7y45wlF5VLqS8j50uhQ8lica2TuizeQQvCVApJHR6ywaisiwTG5/M95vOEhOvefEGQGiiHeMulGZ1SAI9i4XwttcDmvtE0MQ7kp1h3qy6WYSIlLx2Tl9dIScNQ5veA2DHWVf2XbZsn6T2IYdoFnYKvaRgfKVO3LV/hSZPzwQo/S/vQEJmi/9b2VTal6dHYkRoWZYFnaakbQLzi53j42tVxEiXryoH+5wcP34cJ6eCOcql+KvMQQpkhnlfM/77mXs1hWQcG3+o93X2xXvk2yH+FMhUtY3BU6khUm/FqaTU5mwyde0eMMTzBoFWiQBcT7FjdlQghxPdEMNtWpZTSgKfB+/kvevHAONwwfOqtuaPgCoZBh7NahalYXV/XF3dUags237QRqNFk6SzaBksweCcd2pOUknI+MY9wNqgw0dpxX0H12z/HbdWKtDoMpt1JXfIsoxD3ENavB3Iul3/ZekYr8fbM+pcWUo7xtErMJTaHg9p43efd33C2X63EKtvFeGBJn/UHlspDYxpcQdHGwP/3bfhh0OWmwEeoFbkf3xyxTjK1nelWnDKvcSrZ/o4QJGR6HJzH59f3gmQJwKUBIOKAbcq8GPxUxS3SWSm/wX636qAzoJzyrzuRLOunCGCkxxU1S4mTVOuZykk8FGncKjEYWINapIMShKfeSUZlCTJigyXJxqUJMrplyXLCvLCzX0j+0iGe6Zvzrb6YWHqOjyglt0jAB7o1Cx8EMCWGB/0eaDcBZos0/zmSQac+gXXlAQAthevxcLKLYi1zrg9vI2VktoVfHF1dUeVSZrcpFTIKAvicCfKvNmpLMrRg4DYcNz0GgyaRCKzefQupUqBQrZscAJg5+BMyYBE7G3VxCdlvbH8f3GOfHmmHBWdY+gdFEJV11jaFwmjlW84W277sDakMLG6vHmOzSPTr8F9gjxTeJSoZPKvhdHqLXdT7B8fzpiz61Ags9OvJtuK1Mm+zCWJZcWbIgNd81iAEqG15oub5VkWdJo3HB8x2u8q4++UJC/cMwhCKhGc5CAPpXnV+w5KAw7KlGzbr0HGGKjITwctiieBzLMBjZxxsJNoUJJkykOBLgsjCDSyj2S2T/rmbN4qDV963kB6PInimkeFWfqwqKhazgOKxEYy7NhmaoQba/uuOxdies12L+wr4GRnhVqpsHiNiZA3aZQq7tu74hf/AI+kOBJV1i/Xpj+PUyiUKBUStraqlwpOUp2Ncab/qQpUc43hk6AQyjnH0SXgLu8Xvs+GUF/Wh/qRoH/9vi+bl3tE07Ix6A0w7Xc/ouIt933hpElgcvCP2OtTOONajPll3sv+pqiSxPLiTYEnAYqEzGb/utm7n5dwOdmR4aFlmRdwntZu97mjsWFZhL+li/V6EkMJ54jX7xvuNRJlZkfuMfdKckNjj51Cj51Cj61kMP3btOzp95Lxve0zaewUxqeHCgnslXrsyd529ykGKU3Ak/RMIJO6LtmgoJPLXSTSf9+nvk8yKGgXUo3bOss/aS/o1Hod3S7spfuFvVgZ9CQrVSyr0JR1pd9Gp3zxV4SU0YkWhKfEWtlhZ63BNSUe3/gH3HT2RpcPhxeWJAkpW55AS5x86EKfE87Udn9In6AQSjom8HHgbdoVCeOnED92X7EhWft6NMcp6ZXEZ/WN/dZW/ePJmTuWGzpZadAz9uxP+CVFE2bjythKndEpcuhW6HGAIksS3W78Rb/Lu5Bk2BRg+QDl7zh3pt8twajCV+lX6BZ3NTb8/sjb0sUSBEAEJznqVKIz97VWeKk06fqcgLGGI1xnza7YQtnS50RCxuY5gY2t8qmARnomsJFS1xnSrLdV6FE/bgBprZCxRmfquP4qbBUGvNUaEZxYWNXwaww7tpmA2EgA/vEpxcwabbnn6G7hkgn5Tbi9M7a6FGz0WvziHxDq5CkeEL6QxD/Rbvwb7crbntH0CgylmEMinxYP4UNfJRtOuPPrOVc0Fmwe9SJONjpGvXsHtVLmyHUHNp207HdLv8s7qfrgOolKK0ZX6U6MVQ4PvCJJrAhqAkC3G3/R98ouIG8EKJsf+FLYOolunncYV/gy4RprghNdLF2s147oI5L9RHCSgwxIzAgvziy/ixjktJ3iU0ecnBkelG2d4WUkkmQlSXol0RlUmsgv+aBShSFdIJMm+JHSrittHU9t+0cvzNfTzGZvQvZzTo5nQPAOWtw4AUCUjSPzqrXmT//KohZEyJJRQ78gNjaGb5esfG46GYm7ju4ExIRjp0vBIykm2/uf5FcyEvsjPTgY6U5j70h6BoZS2C6ZT+pF0LbqA9Yf92D3BRd0hrx17SokmeHv3MPbScedh1bM3uOLJfs2tLp9lPdv/4MBiSkVOnDTsVDu7PhxgCIj0f3G3scBisymgHq5s//nmB8WiK86mcYuUcwOuECPa1UI1YiHhoJlFdjgRKEBRS5Eu/tTPBmmK8uXvtcoZPXkZjxCa82se0Hsj/Ek7zduUJCIgkTMayNczf4RtYs/emG6BwlWqBJfsWgvIEmgNshYPzLk+UnaspvD9bj0C2WZpg/O0OveHpz1SRiAXe7VWeHTkIQEG5wvxmZ5P46F9Ci0BhQpBpSKV2tKaNBD5EkrkiKV2Hrq8aymIcutf6ytzI7E4xPimb9gJn/u/Z3oB1GULVOeUSPGU7FCZVMaWZb5duEcNmz6idjYR1SqWIWvx0ymRPFSpjRTp49n2/aN2NnZM3TIaFq829q07tffd/DLL1tYvHDlc8uyddtGRo4ZbHrv7u5BxfKVGTJ4ZJp9ZUZ6/NE3alCDbt17071HH7M+A3PdvXObxg1rsnX7HsqULW9aPmr0RGRZNu3/ebSoCLd1xTfR2P8kSWFNgsr8/idH//2bZUsXcvZsMCnJSfj5FaF+g0Z069YHr0I+ZuUxcvgXxMXG8O2ilWbv1ywGQJZRJRtQJeZcB/2/bnpw4JYbLR4dp8u7erzddHze4D5tK9xn7W9K9hyTMOSRIKV7Sz1VixpISoHxCw3E3rv5SvlJCgmrUi7Y3H5EVsdAqJAQwoDbPwPwo0ddTib7YRP66JXKk1Xr1dVRuSfTOfpv+l75FauHSWxxf8OsbZPdc25W90kXSlKoQgrlneL41v8cvc9U4pHW8iPE6S08Cp9ZZDn7Z4AtaDcrGSiwwUlu+ivGk/0xHlSxj8FDrSFKa0VwQu7PEJ9bghOcua+xwkudeXO2CK01wQniqWluKpIcRf/bu6iUEALADRtv5hdpwX/2hS1cMqPbe2w4OdWZpPAnX0u23jqqjYyhSJOcmWJ+zNdDuXr1MjOmzcfL05tfdm7lo14d+fWXv/D2Nt7s/rBsIStW/cC0yXMICAhk0ZIFfNSrE7/vOoCDvQN/7dvDzl3bWfbDT4SE3GTkmMHUqVMPVxdXYmNjmDd/OiuXbTCrPA4Ojvy+8wAyMuHh95k5ezKffNad33cdxMrK8jcLGXF0zNo4/LFqO2ytUnDVJOCT+IAQB2+0ZkSgG9b/yIRxI2nzfnsWfLMUP78ihIXdYccvm1m+fDEjRo1/2UN47ehlBb//o2DvcYnmdQx0eseAjwd82VXPh03hx11K9p+SkGXL/ca8Ud5Al+bGm8u5Pym5dc9yZSmkecSYu9tQYWCfU1k2uNe2TEEkiTWedZGBLtF/0ytyH4DZAUpOSTEoGXaxLD9UOkNh22RmlL1I/3MVSDHk/UenQv6Udxuq5jMGJE4muLD7kRcnE1zybWACxmOdda+48d/PPABIfT/rXvY1ZxOeT23Q0S1sH4suL6ZSQgjJkooffBvzealeeSowOTzIjaTwtD+GSRFKDg9y4/ae7B/dKTk5iT/2/MrQIaOpUb0W/v7F6N9vCIX9ivDT+tWAsdbkx9XL+LRPf5o2eZeSJUozfcpckpOT2LlrOwDXb1ylZs3aVChfiZYt2uDg4Mjt28YAcObsyXT6sDu+vn5mlUmSJDw9vfDy9KZC+Ur06Nabu/fucPPWdVOaU8En6NytLRWrBvF2oxpMmvIViYnGKshuXT7g3t07TJsyljIlfShT8kltQvCp43Tp1IbKFYrRoF41Jk8cY9oOjDUuSxbNZ/TIQVSrUpyGb1dj4+PPAaBxw5oAfNCmCWVK+tCtyweAsSbi8896mNJpNClMnjiGN2uVp1L5ADp/+B7nzp42rT929Ahulcry24lT1OrahQqVi9OxQ0tu3riW6edy//49Jk/8ii7dejJ56lxqvlEHv8JFqFGjNtOmz6Xf58Yap28XzOL99xqn2XbVyu9p1KCGaf32bRvZu3e36fM5dvSIWecmL9LqJH45qKT7WBVLtip4FAeFvWHUx3qWjNTxZiUDlhj6x8dDZkR3Y1Xa9v0K9p2w3K2GnT6FsXc242RI5opNIeYVam7xpqtrPeuyxt04c3yvyH20jT5q0fIAPNRaMeRCOWK1Kio4xfFVySsoMVDF+RFNPCOo4vwIhegllk7qPCfZ/SroRHAi5Ii/YjwZFlKWiGeqhiO01gwLKctfMZ4WKlnBUjnuBosvL6Zz+CHUsoGjTsXpU7ovm73qoJdy7qmYLIMuUTLrpY2TODnF5fE91DM3DbIEMpyc6ow2zrz8zK0R1+n16PV6rK3TzsJtY2PDqWDj5JN37oQSGRXBW2++bVpvZWVNjeq1CA429tcpXaos58+fISbmEecvnCU5ORn/ogGcOHmMC5fO07XLxy/1GcbGxpgCINXjYZovX7lEzz6dadK4Ob9s28PcWYs4eeo4EyePAWDBt8soVMiX/l8M5eDfZzj49xkArly+RK+eHWnS9F2279jLnHmLOXnyKJMmjEqzzxUrllCufCW2bt9Dx049GD9uBDeuXwVg42bjZHXLV27k4N9nWPDtsgzLPWvGRP7YvYup0+ezZftuivoXo3fPjjx69DBNulGLFzNj4EBOrFqFjSQxetTgDPMD+P23HWi1Gnr26pfheicn82phP+r5Ge80f4+6dRuYPp/KVaqbtW1elqKV2LxXSbexKlb8oiA+EYr5wbg+er4bpqdG2dwLUqzVMmN763Cwgws3JJZstdxthkI2MDRsBwGaKKJVDkzwa4tGkTeGPH82QGkX/a+FSwQhSXaMuFQGrUGikWcUu2v/y8KK55hQ+jILK55ja81jvO0eZeli5i1yDr0KONGsS8gxBa05W17ikhzPoJBtNH54DoBolQMLC7/DYecyufLUUJ8ksamGbzblJpEUrmJzLfPy63Q2HHM6cjnYO1ClcjUWLp5HYGBxPNw92fnrds6cDcbf3zi3S2SUcRQz92fae3u4e3Dv3l0A6r5Vn/dafUC7Di2wsbFh+pS52NraMX7iSKZOnsu69T+y+qcVuLq4MXH89Of2H4mLi6VK9ZLIyCQlJQHQsEFTggKNNZHLViymVYs29OjWC4AA/0BGj5xA1x7t+HridFxcXFEoFdjbO+Dp+WT27WVLF9Ky5fumfigBAYGMHjOJbl0+YOz4aVhbG2um6tVrSKfOPQDo1edzVq38nmPHjhAYVAI3N+MoSy4urmnyflpiYiLr1/3IlGnzqPd2IwAmTJpF4wY12bJ5HT179TWlHTBoJKVqVMMv8QFfdetCi4EDSUlJNpXlaSEhN3FwcMTL69WGOrW3t8fGxgatJiXTY3idJaVI/LRbyS8HFbRtZOCDBgZK+stM6afnwnWJFTsUnLmak8GCzIAP9QQVhoexMHGpEp3ect/33SIPUiv+GhpJyQS/D4hWO1qsLBlZ61kXWZLoGnWYnpH7kYBN7rUsWqbgGBe23vOhQ+F72KvSdiTztNIwtcwlRl4qw4HonOsDIwgiOBFyVGpzNiF3SLKBlreO0/fcLpy0xg7vOzxqsNKnAYnK/Dfx3auaMXU+o74aQr0G1VEqlZQtU56WLdpw8eL5NOmkZwI6WZbTVPL07zeE/v2GmN5/891sateqi0qlYtGSBezY/if7DvzJ8JED2brpt0zLY2/vwLZNv6HT6zl+4l+WLV/E+LFTTesvXDhHSOgtduzc9qQsyBgMBu7cDiWoeMkM871w4SyhIbfYuWNrmmN4drtSpcumOWYPDy+io6MzLe+zbofeQqvVUqVqDdMytVpNhYqVTTUwqUqVLkOc2o6HVhp8PIw3OrGREXgWLpouX1mW050DIXPxSRKrdirZvl9B+yYGWtczUC5IZtZAPcGXDazYoeDSzewPUlq+ZaBpLRm9ASYvVxIdY7lz1iDmAh0eGGsj5hZ6lyu22fWwJHv95GGcNb5r1GE+jtwPWDZAUSBT3zMKWU7/HEshGZtmDwy8waFod/GgEZAMxld251nQieBEEPKJgNhwhp3aQqVo44g412wLsaBwCy7bm9ffITspbWX+d/yeWWkjTlpx4NMXP4V7e3EUXtVePPy0ylaB1sxRqosWDWDNqi0kJiYSnxCHl6c3A4d8RuHCRQDw9DA2P4yKisTL88lT++gH0Xi4Z9w08fqNa+zYuY1tm3ezZdt6qld/Azc3d5o3a8WoMUOIj4/DwSHjJ7gKhcJUaxMUWJyoqAgGDenL2h+3AGCQDXzYvjNdO6dvKuZdNP1NfSrZYKDDh13p0q1nunU+Pk/+PlSqtD8JkiQhG8z/pZQft6lLF0hkcKeT2lQt0tYZzePJPj0So5EogvzMTU9AQCBxcbFERIQ/t/ZEUihMZUil0736vEyvq5h4iR+2KdmyV0HHZgZavGWgSimZKqX0HD1vYOVOJdduZ88NZil/A5+1M/6tLPs5p2tonq9k0j0G3v8VgA1utdjvXM5iZTHHTx5vISPRLerQ4wBFZpOFOu1Xco7B2zrzL1CFBIVsUqjkHENwjEvuFUwoUESfE0F4zVnptfQ5/xsr/5xLpeibJCqtWFChJf1L9rJIYALG+1CVnWzWq1CdFGy9dZn3ApRk7ArpKFQnxaz8XuYBu52dHV6e3sTEPOLw3wdo1KApAIULF8XTw4u/jxw0pdVoNBw/8S9VMuirIMsyX48bzvChX2Nvb49Bb0Cn0wKY/m/Iws1+j269+e/yRfb8aaxtKVumPFevXcHfv1i6V+poXmq1FQZ92n2ULVeBa1cvP3e7F1Gr1Y/Ln/mYwUX9i6FWW3Hq5DHTMq1Wy/nzZwkKKpHhNgYkIm1cALDVa/FITj+cdbN3WqJWW7Fs6XcZ5hEbGwOAm5s7UVERaQKU/y6lrQVTq9Xos3AO8oMHsRLfbVLSY7yKX/+W0OvhjfIyi0bo+LqXjgCfV2vk7uwg83UvPVZqOBQsselPy91auGvj+PruVqxkPf86FGeV59sv3igPWOfxJj96GCdm/DjyAO2j/7FIOTyszHuyY266fE/0OckRIjgRhNdYzfDLrNkzi+6X/0It6znsU5YuTb9kQ8m3MUivx+WtUEK1kcaby3QByuP3VUfEZH2+EzMcOryfg4f2cftOKH8fOUi3j9pTLCCQD97vYNy9JNGta0+W/PAte/78jStX/2Pk6EHY2NjSskWbdPlt3LQWdzd3GjU0BjdVq1Tn36NHOH3mJCt//IHiQSXN7rwNxqGF/9e2Iwu+m40sy/Tu2ZfTZ04yfuJoLl26wK2QG+z96w9Th3gAP7/CnDjxL+H3w3j4wNgkq1fvzzl9+gQTxo3k0sXz3Lp1g7/27mbShNFml8XN3QMbGxsOHdpHVFQkcXHpgwg7Ozs+7NSNmdMncujgX1y7dpmvx3xJcnISbdt1zDRv7VODM7inxOGgSzt0tI+PHyNGjWf1qqWMHjWYY8eOcPfubU6dPMbokV+y8Lu5ANSsWZsHD6JZ+sN3hIbeYu2aFRw6uC9NXn5+Rbh8+SI3b1zj4YNotFqt2Z/B6y7igcTcn1R8PFHFn8ckDAaoW0VmySgdI3ro8PPM+l2RQpIZ2UOPlxvcDodZa5RYaqJFK4OWr+9uwV0Xzy0rD2b4tEJ+jZoDrvN4k1WPA5SPLBSgRGnMe1hhbjpBeBmiWZcgvIbckmPpf3YHTW+fBiDC1pm5lVpz0Le8xYfJfBlFmiTz1twH6eY5sfPWU3VEzs1zEhcfx5x507h/PwwXZxeaNmnOoC+Gm2oJAHr37EtKSjLjJ44mJjaGShUrs/yHtTjYO6TJKyoqkiU/fMu6tdtNyypWrMJH3fvwyWfdcXP3YPrkuVkuY7cuPVm9djm/7d7Ju++0YvXKzcxbMINO3T4AWaZIEX/ebf6eKX3/L4Yx7qthNG1cG40mhUtXwihVuiw/rtnGvLlT6dK5zePtAmj+7nuZ7/gZKpWKUWMmsei7OXwzfybVqr/Bj2u2pks35MvRyAaZ4UP7k5CQQPnyFflh2TqcnV1euI8YtT0AhTKY/6RT5x4EBASyYvkiBvTrSXJyMn5+hWnUuClduxk7+gcVL8nX46by/eIFLF44lyZNW/DRx5+yceMaUz7/69CZY8eO0K7tOyQmJLBq9RZqvlHH7M8hP7gXKTF9lYp1u2W6t9BTr6pMoxoy9avq+OOoxNrflIQ/MO97pHtLA9XKyMaJFn9QkZhsoe8fWWZQ2K+UTL5PjNKW8YXbkaS0fvF2ecx6jzcBie5RB/ko8gCSLLPBI/f+Ps/EOBOeYoWn1XPmKUux5kyMmKcMcmboXzGUMEjysw1087nY2FicnZ2p9PEUFOqC1UG4IHaykiQo7WTL9fuJ+WLSVUk20PraUfqe3oWTJgm9JLGp5Ft8X+kdEp/5e3a+EJMrZfIs5ECvoW/h7eWLUvFqzzuyY4Z4KxsVmpRXm6n+dfQ6PSHOjIRM0cRIbAxakpRW3Lb1SNf/JG16sFJLeeJ86w06wiPusXTmYSLvx+f8/hyz78l1cdcEule4S20/43eGVi/x63VPfrroQ3TSk/0oJJkKnnG42Wp5kKTGXq1jQj3jHDyT/g5kf6h7tpUpI5IEQbY6Qi9EIz8ziVaHuBP0iPsXHQpGub/HOeu8MYfTy0o9HoBVjrX4tu3/cm3fDVyjmF78IjKkCVBSP/IR18qy72HOj9Zl0CZzau1oYmJicHLK2mSvOS31XrJm60mosvleUqdN5tjPY/LkcecWUXMiCK+JoIdhDD+2iYpRxgn+/nMrzNSa7bjsXsTCJcs+CiV41xRtmQsqGYl7Nm74J0Zgq9fgoYkl0ko8oc1p1x7a89XBkpRxj6dHxbtUKxRL65IRvBMYyS/XvNhw0YfynnH0rRqKl/2TZnCpN6tbL3vleGDyPLWSbphu5Bc613vtAxOADY7GPm094v6le9y/JJ5xYXmlJrmy730PPRh+rSxDil5L0zk+QmPNnNCgXAlMXhuyTLY/+cwPT1JfkQhOBCGPs9Zp6HnuDzpdOoBKNpCgsmZJpXfYUvJN9DnREUMQLEirUBFu44pv8gPcNPEkKq1JEMNg54pL0Q4M31eKSl6x9Khwlwpe8fyvdDjvFY/ASpn+hkkhGe+jLkQ6ZJBb7gjQRjHs0R4AfrGrwG/25S1Wluy2wbE6MvBR3L98Fvw7QK4GKAceulPZ8ck8ZafjxDxlzxLNunKGCE4EIQ+rffcSQ49vxTfhAQD7i1RgdvU2RNq5WLZggpCD4lS2PFTb46pNoFDyQ0LtPNFK4ucqt5yJcGLQXkeqF4rlo4q3KeWelGlaGfikyh0O3XHDIOfujauzPomxD3ZhK2s5bVWY753fytX954aNjtWRkfg47h8+C/4dCViWSwGKAYlTcS65si9BeJr4theEPMg9KZZBJ7bTOPQMAPftXJhd430OFc4/TwUF4XkirZ2x1WuwMWjxSX74wv4nQnaTOHHfGa0BZje6kmkqhQRe9hoqeMZxJiL32serZD2jH/5GIX0c95TOTHZ7B72UP2uSNzlWI7GEK5+f+pVPg39HkmWWVm5q6WIJkDND/4qaExGcCEJeojAYeP/aP3x2+lcctMnoJAUbS9Xlh4rNSFK/fiPPCMLLEv1P8gY3W/MmsXSzzcUhmWWZvjEHqKC5R6KkZrxbC+IV+bvp36qKjZAlif4nd/HJ6d2AzNLKzSxdLEHIESI4EYQ8osSDu4w4tply0aEAXHAvwvSa7bji9vp37hSEl/Fs/5MkpTXxov9JrnqQpH5xoiykyw6tEs7SPPEiBmCaazNC1W65tm9L+rFCQ2RgwMldfHL6DwARoFiY6HOSM0RwIggWZqNLodfZP/jwv4OmDu+LKr/L1hJ1MChej4kUBSGnPNv/JET0P8lV5yIdiUhQ42GnzXTei6hEK85FOuZKeUpHX6fPo0MALHeqw3GbgFzZb16xukJDZEniixM7+eT0H0gy/FBFBChC/iLufATBgt68c5F1O2bS5dJ+VLKBP4tWokOr4Wwu9ZYITAThsUhrZ5IVapSyAZ/kh0iiUXauMcgSC08VReLJ0MFP1hnnmll4qkiudIb3S4qi9+lNKJH507YUW+yr5Pg+86I15Rswv3pLAPqc+YPewbstXKICLHUo4ex+WcD777+Pq6sr7dq1s8j+n1ZgHz9JepAK2r1fAe1LqrWX0DhL6X5YLckz/hHDD2+j0Y1zANxzdGVq3bYcDiibbfuQbXLn8patlca/LUnKE7PTSwYDkt5Q8G5fVfmzMzC8oP+JQgKFIg+cb+Pfv95ena0TJGZGGZd78wH9c8mBiUn+fFbnHp4OT/qWRCWoWXzEl39uOaAkZ8tjp09mwpU12OuSuehQmLll26NX5F5TMkt7tqnP2nINQIYvTu6kz5k/kIAf8lkTL9G8KXcNGDCAjz/+mFWrVlm6KAU3OBGEnKQwGKgSdgPPxFgi7ZwI9gnEoFCgMBhof/5v+h39FQdtCjpJwerKb/N99aYkiw7vwmtoxMiBxMXF8N23K3J0Pxn1PxHzn+Sev2+58E+IM+ULJeBmp+VBoprz9+1zpcZEIRsYdWsLRVOieGDnwoRSHdEWoMAkM2vLN0CWJAae2EHvM38AMj9UapYnHhIVFPmpz0mDBg3Yv3+/ZXb+jIJWdyAIOa7h9bPsWj2RpT8vZOqeNSz9eSG7Vk+ka/A+ftwyn+GHt+GgTeGsd1E6/W8wC2q3EoHJY3oZgh8q2RuuIvihEn0Of0nHJ8Qzefo4GjStRcXqxfmwSxvOnj+dJs03C+fwTqv6VK5Zkhp1ytOjV0fOnA1Ok2bqjPHUfLM89Zu8wa7ffk6z7tffd/Dp5x+9sCxbt2+kVIUipledt6vw6ecfcfXa5SwdU8NGNVm16ocsbWOOO3dvU7qML5cunU+zfPSoCUydMi/b95eRPSdP0WTgYNwbN6ZEtTK0aFGPSZPHEh4eZnYeI0YOpJ8Z50NIzyBLnA1zYP91V86GOeTavCY97/1JjbjrJEsqFr79MQ+tcqd/y+vgp3L1mVf9PQB6n9lDn9O7xQzjuUnOoVcWHTx4kFatWuHr64skSWzfvj1dmoULF1KsWDFsbGyoVq0ahw4dyvqOcokITgQhGzW8fpaZu1filRCTZrl3QgyD/tlBucjbxFnZMLleO3p8MICrHr4WKmneczBSxYf/OjDojD0TL9kx6Iw9H/7rwMHInKvgHTN2KEf+OcSMKfPYsXUPb9apx0e9O6W52Q3wL8bXoyayY8sefvpxC35+hfn4k848eBANwF/797Dz159Z9v1avhw0kpFfDeHho4cAxMbGMO+bGXw9epJZ5XFwcOTwvpMc+usESxauJDEpkU/69UCjzb0mPFnl6OiEk1POD/G7fsNqPv64A07evqybMYuLGzeycOQo4uPiWL5iSY7vX7CMJtGnaRf5LwCzA1oT6l7EwiXKe34q97YpQOl1dg+fnP5dBCgFTEJCApUqVeLbb7/NcP2GDRsYOHAgo0ePJjg4mLp169K8eXNCQ0NzuaTmEcGJIGQThcHA0MPbjP9+Zp30+JWkUtP2w2FsLl8HucB1esrcwUgVX1+wJTIl7ZPYyBSJry/Y5kiAkpycxB9//sbQwaOoUb0W/kWL0b/vYAr7FeGnDatN6Vq1eJ86tetSpIg/JYqXYuTQr4mPj+PylUsAXL9xjZo1alGhXCVavtsGB3tHbt8JAWDmnCl06tANXx8/s8okSRKeHl54eXpToVwlenTtxd17d7h587opzanTJ+jcvS0Vqxfn7cY1mTT1axITEwHo2q0t9+7dYeq0sZQu40vpMk+C31PBx+nS5X0qVQ6kfoNqTJo8xrQdGGtcFi9ZwKjRg6harQQNGlZnw8Y1pvWNG78BwPsfNKV0GV+6dmsLpK+J0GhSmDR5DHXerEDFSsXo1Lk1586dNq0/euwIpcv48s8/h2jb7h0qVwnkw46tuHHzWqafy/3795g8+Su6dunJlMlzKf5WM4r4+tGkcgWWfP01/foOAuCbb2fR5v3GabZdteoHGjaqaVq/fftG9u7dbfp8jh47Yta5EXJf2YTbDLizC4A13vU45FrOwiXKu34q9zZzHwcoPc/+KQKUXJLarCu7XwCxsbFpXikpKZmWo3nz5kyaNIkPPvggw/Vz5syhZ8+e9OrVizJlyjBv3jyKFCnCokWLcuJjeWXi7kgQskmVsBsUSoh57kVlq9NSNCYq18pkKbIMSXrzXvE6WHA1te/As81EjO+/uWZDvM68/Mz9Pdbp9ej1eqyt0japs7G24VTw8Qy30Wg1bNi8FkdHJ0qVMg5eULpUGc5fOEtMzCPOXzhLckoy/kUCOHHqGBcunaNr54/N/djSiI2NYeev2wFQqYzt6y9fuUTPT7rQpHFzftmyh7mzFnIy+DgTp4wB4JsFSylUyIcB/Ydy6OBpDh08bdquV69ONGnyLj9v/5M5cxZz6uQxJk4alWafK1YsoXy5Smzb+gcdO3Zn/PgR3LhxFYBNG381plm+gUMHT/PNgqUZlnvmrEn88cevTJs6n61bdlO0aDF69e7Eo8e1SanmzZvO8GFj2bzpd1RKFaNHD870s/j9951otRp69uwLPOl/AuCcFIePvXkd0D/+6DOav9OKunUbmD6fKpWrm7WtkLs8NTF8fXMjVrKeQ85lWFPobUsXKc9b90yA8mmwCFBeZ0WKFMHZ2dn0mjp16kvlo9FoOHnyJE2bNk2zvGnTphw5kjcfzogO8YKQDTwTYmh96Zh5aRNjc7g0lpdsgOaHnLIpN4nIFImWh83L76+GcZgzbpWDvQNVKlVj4ZL5BAYWx8Pdk52//syZc8H4+xdLk3bfgT8ZPLQfSclJeHp6sfz7tbi5Gid+q/tmfd5r+QHtOrbExtqG6ZPnYGtnx/iJo5g6aQ7rNqxm9boVuLq4MXHsNEoUL5VpmeLiYqlSsxQyMklJSQA0rN+EoMDiACxbuYRW77ahR9degLHJ2egR4+n60f8YO346Li6uKBRK7O0d8PT0MuW7fNkiWrZ8n+7dexu3Cwhk9OiJdO3WlnFjp2FtbQwO367XkE6degDQu9fnrFr1A8eO/UNgYAlc3dwBcHFxTZP30xITE1m//kemTJlLvXoNAZg4YSaNGh9ky5Z1puACYODA4dSsWdu4r96f88mnXUlJSTaV5WkhITdxcHDEy8v7yWeVZv6TR4TYvbiDtL29PdY2tmi0mkyPQbA8a72GcTc34KpL4LqNNzOLtkaWpII64GSWrCv3NrIkMfj4z3x87k8kZBZVaS46yecUg5x+jO3syBO4ffs2Tk5PfvesrV+ub2pUVBR6vR5vb+80y729vbl//77pfbNmzTh16hQJCQkULlyYbdu2UaNGjZfa56sSwYkgvCSXpHga3ThLs6vBVLt3A3MHM420y66bduFVzZg6j1FffUm9RjVQKpWULVOelu+24eIznb7fqFGH7Zt/5+HDh2zc8hMDv+zLprW/4O7uAUD/voPp3/fJk/9vFs6hdq23UKnULPp+ATu27mHfgT8ZPmoQWx/XQGTE3t6BbRt/RafTc/zEvyxbuZjxXz95Wnbh4jlCQm+xY9c20zIZGYPBwJ07twkKKpFhvhcunCUk9BY7d259sp2cfrtSpcqY1kuShIeHF9HR5tf03b59C61WS9UqNU3L1Go1FStU5vrjGphUqTVPgClQiI6Owte3cLp8ZVlGyuDmKtLaGTtZi7VOg0/SQ3Hzmg9IssyXoT9TPOk+j1R2jAvsQIoy54dmzk/Wl60HwODjP/PRub0AIkB5DTk5OaUJTl7Vs9+hz36v7t6dd+bLEcGJIGSBQ0oS9W+e551rwbxx+woq2WBad9rbn8CH4ThokjNs2mUAIhxcCPYJzLXyWoqNAn6ra14N0dlHSoafs39huukVEqjoojdj3xLaF6YyKlokgDUrN5OYmEh8Qhxent4M/PIzCvul7XRrZ2eHf9Fi+BctRuVKVWnaoi6bt63nk16fp8vz+o1r7Ni1jW2bfmfLtg1Ur/YGbm7uNG/WilFff0l8fBwODhmPNqRQKPAvaqy1CQosTlR0JIO+7MvaVVsAMBgMfPi/znTtnH60qUKFi2Z6nAbZQIcOXejapWe6dT5P9YdJbT6WSpKM25pLftyE5Nl7IBmZZ597q1RPfn5SfyANmTRBCQgIJC4uloiI8DS1JyAR6eCOz6P72Bo0OBg06Vqx6HTm/jUIeUGn8IPUi7mEVlIwPqA9EVYuli7Sa2l92XrIwBARoOSslxxd64V5ZiMPDw+USmWaWhKAiIiIdLUpeUWBDU6UyTLKnB6nNI+RC2API0kCu0g9jrd1L9301lqvoU74JRreO8MbEf9hbdCZ1l1x8uUv38r85VuJcDtX6oadY+LJ1RhI26HLgLH3xLelWuFwx/B4Sc5Shkbk+D4AFHpnJJ0BtDpTlxEJsDVz++oOOjytbIjUKMh4plAZLysD1R1SUL4gNjHOA5n1+Q/s7Oyws7MjJuYRh48cZOigUc9NL8syGk36EbRkWebr8cMZ/uVX2NvZY9DrTTfHqf83GMw/9z269mLFjz+wZ+9vNGnUnLJlynP1+hVTAJNm32pjYza12gq9QZ/moyxbtgLXrl3BPyD9dmmkjtyQwXK12vi56g2GTNMU9S+GWm3FyVPH8PUz1oBotVrOnz9Lt2690m739L6eXf6MZu+0YPacKSxbvpCRI8c/OWZAp1BxRaOkjJWOok72REeGg8FgCnguXbpg3NXjLwArtRqDXm96n21k438UOgMKbc5f3zHlcn6EtNzWIPQM3U4fAGD6G+34J6hCmvWSBGgMSCm63PgKzVOsYrN+wNsKv4UyBQaeNQYoqhQDS8q+PgGKXlfATvIzatQw1uj369ePfv36vXQ+VlZWVKtWjT179vD++++blu/Zs4fWrVtnR1GzXYENTgThedR6HTUiL9Po3hnqhF/ETv/kRjTEwYu9vpX4y7cStx3Stls/5FOBr6p1ZcCFX/BKfjKccKSNM9+Ue49DPml/bAVQStC/WCJfX3bAeIf39A+n8Qby82KJKHPg9/TQ3/uRZZliAUGEht5ixpzJFAsI5IM27QFjH4rFPyygYf2meHp68ejRQ37a8CP3w+/zTtMW6fLbuPkn3N09aNTA2PGwapXqfLNoLqfPnOLg4X0UDyqZpWF3HRwc+d8HH7Lguzk0bvgOvT/uS4cu7zF+0mjat+uEra0d129c5cg/hxgzdgoAfn6FOXHiX1q0aI2VlRWuru707t2PDh1aMmHCSP73v87Y2tpx48ZV/v77IF99Ndmssri7e2BjY8Phw/soVMgHa2trHB3TNjmws7OjY8duzJw5CRcXV3x8/Fi6dCHJyUm0a9fR7ON+lo+PHyNHjmPixNHEx8fRuvX/8PMrQnj4PXb8sgUba1tmDxpI/WrViJ4xg+VLv6VJs1YcOryfQ4f24eDgYMrLz7cwhw/v58bNa7i4uOHo4GgKvATLKfngDmOPrANgXel67Ax6w8Ilyh82B72FDAw6+zNdr+xDkmFxudcnQMnrjA8+sj9PgOPHj5vdrCs+Pp5r156MeHjz5k1Onz6Nm5sbRYsWZfDgwXTt2pXq1atTu3Ztvv/+e0JDQ/n000+zt/DZRAQngvCY0qCnSvR1Gt47Q73753HUJpnW3bNz4y/fSuz1rcwNx0LP/WI/5FOBvwuVo2L0TdxTYom2duKsezEMYujgTNVz1zKhVDzf3LQjUvOkO7uXlYHPiyVSzz1nmubExcUxZ/407offx8XZhaaNmzNowDDTzapSqeDGzets+6UPDx8+xMXFhQrlKrF21eZ0HdujoiJZsvRb1q1+0h+kYoUqfNStD5/0646bmwfTJ8/Jchm7denJ6p9W8Nvunbz7TitWr9jEvAUz6NS9LcgyRYr48+47rUzpBwwYytixw2nSpA4aTQr//XePUqXKsnr1VubOnUbnzu8DMkWKBNC8+Xtml0OlUjF69EQWLpzLggUzqVbtDVav3pIu3ZAhozAYDAwb1p+EhATKl6/I0qU/4ezskuVjf1qnTj0ICAhk+fLF9O/fk+TkZPz8CtOoYRO6de1NlJUTAUElWTh8OJNXrOTbxQto2vRdPv7oEzZuWmvKp/3/OnPs+D+0/d+7JCYm8OPKTbxRs84rlU14NW5Jccw4sAIbvZZ/fErxbZWWli5SvrIl6C3AGKB0uboPEAFKtpHl7B8R7SXyO3HiBA0aNDC9HzzY2Aeye/furFy5kg4dOhAdHc2ECRMICwujfPny/Prrr/j7+2dbsbOTJMsFa5y52NhYnJ2dqdZhMkp1+lFh8rOC2qyrrL0NN28nZHi9S7KBCg9CaHjvNPXDzuKqSTCti7J2NAUk/7kUee2+yO1O5c7kSh5+zvSa3BwvTx+U0qs979DLcDZWxQONAjcrAxWddFmqMZEksLJTo0nWZ3sz4LwutVlXQWOtVqBJMbb3U8t6iiZGoETmgdqBKKvcG3xCb9ARHnGXH+b/Q2R4wos3eEWxQS/up/U6UOt1fPfnQipGhXDLyZNezb4g3irjRqGSBBU0EHothix0hcoXEopl3E8tKz64/jeDz24HYG2J+iwq926e/l3T65L5d8dXxMTEZGvH8OyQei/5ZqNxqFTZey+p0yXz995xefK4c4uoOREKHlmmVMwdGt07TYN7Z9M0v3qktuOAb0X2+lbinJuo7chtSgmqOOtenFAQMqCVlITbuOKb/AA3bTxJSisSlAXrIdRrRZYZfmwzFaNCiLWyZejbPTMNTIRXtzXoTZBg8JntdL66H5BZVK5Fng5Q8rqnJ03MzjwLOosHJwsXLmTmzJmEhYVRrlw55s2bR926dTNMu3XrVhYtWsTp06dJSUmhXLlyjBs3jmbNmuVyqYXXUbHY+zS8d5qG985QODHatDxeZcOhQuXZ61uJUx7F0SsK5hNoQcgP4pU2z8x/4olOEtd0XtTxv4O0vHEcvSQx5q2u3HbytHSR8r2tgW8CqQGKcfABEaAIeY1Fg5MNGzYwcOBAFi5cyJtvvsmSJUto3rw5Fy9epGjR9MNiHjx4kCZNmjBlyhRcXFxYsWIFrVq14ujRo1SpUsUCRyDkdYVjo2h16RxVLh2nWFy4aXmSUs0R77Ls9a3Mcc+SaJSiQ6wg5BdRVk7Y6DXYGrT4JD/kjq07spgFJU+pffcSnwfvAGB+1fc45pP55KRC9toa+CYyEkPObKPz1QNIMiwsLwKUl5KDQwln12hdryOLBidz5syhZ8+e9OplnO143rx57N69m0WLFjF16tR06efNm5fm/ZQpU/j555/ZsWOHCE4EE6+EhzS+dYZmt4IpE33HtFyjUHLUszR/+VbiiHdZklViYi9ByI9kJMJs3PBPjMDWoMFdE5er/U+E5/OPCWfi32tQyjK/BNVkY6mMW0sIOWdbYB1k4Msz2+h07QASMt+VbykClDwkK6N15TcWC040Gg0nT55kxIgRaZY3bdqUI0eOmJWHwWAgLi4ONze3TNOkpKSQkpJieh8ba5wYzjgfwksU/HWWj4/XLSmOhiFnaHLzNJUjbpqW6yQFl4qUYpdbBQ4XKke8+kl75nz8cQAgKXLnCFO75eSpa0oqeO12C9jhAo+H8UydL+XZyRct1P8kt66DPHOtZZFTSiKzDizHQZtMsFcgM2u2Nfu7KvWzlSTIcKbbfCwnzvfPQXWQJBhyehsdrx1EAr6rkHcClDxSjOeSZDnb50zK9jmYXkMWC06ioqLQ6/XpZqf09vZON4tlZmbPnk1CQgLt27fPNM3UqVMZP358uuUBfraorHOv451kMFD69jVc42N46ODMf0WKIyty99tVmX7OuNeaXXICNa6eptZ/JygXehnF4wvagMTlwsX5p3R1TpSqgqOnO+FRybgD7pYtcq6ytg3Ilf24uNmitFahtlWjtHDzOEkCpY0KlVXB62Mg5cLEf3mNJIFKrUDO5CZGgz0xkhbnpDgKpTwizKUQOmXO/G3o9QZUagWFAp2wccv56yDFxeJdRrNMYdAzdOsPFImLItLJjR8++IQAuyyMQiWBt8aAQmuX7aO35nUPK7z6aF0ZOVahGT/42dJ71098eO0gDh5WrG7aNk9EBvoUFfxi6VIIlmDxbzfpmQtAluV0yzKybt06xo0bx88//4yXl1em6UaOHGka7xmMNSdFihThRlQSCnXufLs1uHmWIf9uxzvhyahQ4fbOzK7Vhn3FKuZKGQBUybm2qxxjq02h7p0LNL4VTK2wy6gNT6YMv+BelD8DKrO3aCUi7V0AkGKhpCKZm3czHko4P7MLjc+V/SQW0qPTGdBoDCgVL5jCPReoVQpSNAXvRl2hsfxnbwmyQkKTYsi05ihc5YiVMgVbvQb32Chu23nkSP8Tg8GATidz924CkZGJ2Z7/sxK9X79mqYNObqd86H8kqqwYXKcH12OUEGP+ZyVJ4JCsI/TKowI3lHC4b86d72sB1Yl4W8PoA5tp+e+fPErSMvfN9yweoBi0r8FNi+HxK7vzLOAsFpx4eHigVCrT1ZJERESkq0151oYNG+jZsyebNm2icePGz01rbW2NtbV1uuU5MW9ORhrcOsu0vavSLfdMiGHa3lUMb9SdfQG5E6C8rjfn1jotte9donHIad66exEb/ZMJ+a66+LDHvwp/+lfmnuNT9SJPHWvquX5dj/9l5dqPt/giFSzseZf2k/4n4djqNXikxBJp7ZxzZcml75rX7fvsvev/8r8rhwGYUKsj11x8X6otoiwbv9sKWnBiyOHzvblcHQxIfHVgE13OHABZZm7tVlS5fxPPxFgi7ZwI9gnEkIstPl6Hv3HRrCtnWCw4sbKyolq1auzZs4f333/ftHzPnj20bt060+3WrVvHxx9/zLp162jRokVuFPWlKQwGvvx3u/Hfz67DeE835N+fOVC0fK5e8K8DpUFPzbArNAkJ5u3b57HXPek3FOrowR8BxoDklnMhC5ZSEITXgVah5L6NK35JD3DTGPufxKvEfBq5pXLEdYae2ArAkgrvcKBwBQuXSMjI1nK1AYwBytmDvH/pX+y1T9qD37d3ZuZb7/NXUO61+CjIxGhdFjJ48GC6du1K9erVqV27Nt9//z2hoaF8+umngLFJ1t27d/nxxx8BY2DSrVs35s+fT61atUy1Lra2tjg759yTsJdVJfxGmqZcz1IAhRIe0ePMXk4XCiTOypY4KxvirG1JVFsj54MJABUGA5Ujb+CeFEu0rROnPTN/8qIwGKgccYMmIcE0DD2Ls+ZJdf99Oxf+9K/MnoAqXHb1s3h1syAIRiNGDSI2LpaF3yyzdFGeK15lywMrB9w08RRKfkiInRVaMadRjisU/4Apf/+ISjawp2hlVpZtZOkiCc+xtVxtSkfe4X8X/0kTmAB4JcQwc/dKhjbrIQKUVDk4lLAYrctCOnToQHR0NBMmTCAsLIzy5cvz66+/4u/vD0BYWBihoaGm9EuWLEGn06WLIrt3787KlStzu/gv5JEYa1a6vqd+T7fMgES8lQ2x1rbEW9k+DlxsibeyIc7KllhrO9O/46zTro+1tiNJZWXxG/j6oWcZdHI73olP9bWxc2ZutTbsL/r4i02WKR8VQpOQ0zQKOY1HcpwpbbSNI3v9K7HHvzLnPfzzRbAmPJ8BmRvWWuKUBhz1CgJT1ChycFy1hPh45s+bzp97fuNBdDRlypZn1JiJVKhY2ZRm5LAv2L5tY5rtKlaqyobNu0zvp00Zy/atG7Gzs2PIsK9o0bKNad1vv/7CL9s3s+j7H59blm1bNjBqxEDTe3d3DypUrMLgoaMpUcL8OSAaNqlFt6696NGtl9nbmOPO3ds0alqb7Zt3U6ZMOdPy0SPHI+dSM4R/j/7NshWLOXs2mOSUZPx8i1C/QUO6deuDt7fPC7ePsnbiizHDiYuNYd2cBdy2z5n+J4KRnTaZmYdW4JqSwCXXwkyu2d7iv0vC8ykMBuqGXEQm/YiWqS0+hv69nf3FRIsPIedYvEN837596du3b4brng049u/fn/MFykZRduZFvNedvVEg46hJwkGThI1ehwIZJ00STpqkl9q3TlKkC17iVbbEq1ODGFvi1DZpAp84qyfrU5TqV/oRqR96lqmHVqV7oOCZGMPUQ6tYUKUVrinxNAk5jU/CQ9P6GCtb9hU1BiTBXkHiy68AOWebzHbXeGJUTxqTO+sUtHnoQIWknBkCdszoIVy98h/TZ36Dl3chdvy8hY+7t2fnbwfwLvTkZrduvQZMnjbP9F6tfjIa0769f7BrxzaWrlhHyK2bjB4xiDpv1sPV1Y3Y2BjmzZnGih/TBjeZcXBw5Nc/DiPLMhHhYcyaMYlPe3fhtz/+xsoqb3aAdnTMnSd76zeuYfzEUbRp/T8WzPseP78ihIXd5ZddW1m+fAkjR457YR4yEolKK2TA1pDz/U8KMkk28PW/6ygeE0aUjSPD635EiphbKs+rEnaDQi9q8RH/iCphNzjpVzz3CpZX5UQnM9HnxPLBSX4W7B1IuL0zngkxGQ7JbgAi7F3o+MGXaW7CrXRaU6DiqEnGUZOEY0qSaZnTU/92fCqNQ4oxmFEb9KhkAy4pibikJEJcBjt/Aa1CSdzTgYyVzTOBzdO1NamBjTEYSlDZMOjkdmQy7msjA188nhkYIEFlzcHC5dkTUJljhUqiU4o/y4LmnG0yqzzS1zTGKA2s8oilexTZHqAkJyexZ/cuvl20kho1jW2tPx/wJXv3/M66n1YxcPCTOZisrKzw9Mx4VMDr169S4406lK9QmfIVKjN18tfcuR2Kq6sbs2ZMpGPn7vj6FjarTJIkmfbj5eVN9x596Ptpd27dvE7JUmUACD51nNkzJ3P+3BlcXd1o3KQ5g74chYPKmq492nH33h2mTh/H1OnjALh8wTgR6angE8yeO5Vz50/j6upGk0bvMHjgSOzs7ABjjUv7/3UmJPQWv+/eibOTM599MoAO7bsA0Kip8TNq064ZADVr1GL1ys3pmnVpNCnMmDWJXb/9Qnx8POXLVWTk8LFUrFAZgKPHjtDto/asXLaemXOmcP36FcqULseUSXMILBaU4edy//49Jk35mq6dP2bUiHGm5YX9ivBmvbeIjDQ+4Pjmm1ns3fs727f/aUqzatUPrFr1A3/9dYxvvpnF1u2bjZ91jRoAbFy+lop1Gph1fgTz9Tm3m7fvXiBFoWJ43R5E2okg8HXgaWaLD3PTCcLLEHeBOcigUDCrVhum712FgbQ36qnPhmfXap2udkCjUhOtUhNtZs1LGrKMtV5rCmZSgxcHTRIu8Yk4apNNQY2D9kkaB00SjtokHDTJqGQDaoMet5R43FKyfzja1PqYk16BbC5VlyO+ZUhRWXZ+DCF7ychozKx4MyCzzfXx39mz2zyeXG+7azzFk63MauKlMrMBsF6nR6/XpxvNz9rGhlMnj6VZduzoP7z5RnkcnZypUbMWAwePxN3dA4DSZcqyacMaYmIeced2CMnJyRT1D+DkiaNcvHCOseOnm1WeZ8XGxrBzxzbjMamMX9VXLl+i18cdGfDFMCZNncPDB9FMHD+KSeNHMW3ibL6Z9wOt2zalfbvOtG/XyZTX5SuX6NmnM1/0H8rkiTN58OABEyePYeLkMUydPMeUbsXKJQzoP5RPe3/O7j92MW7iKKpXr0VQYHE2rd/J/z5sycpl6ykeVDJN7dHTZsyezO49vzJtylz8fAqzdPkievXpzB+/HcbFxdWUbu786YwY+hVuru6MnTCSUWOGsH7t9gzz/H33LrRaDb16fpbheicn8258P/74M27cuEp8fDxLxo3HVZOAs4sLYQa96H+SjZqEBNPj4l4Aptb8Hxfd/S1cIsFckWbed5ibLr+T5Oyf8LegTSCcERGc5LB9ARUZ3qg7Xz4zz0mEvQuza7XO/mGEJYkUlRUpKiui7NP+YJs1z4ksY6vTpAlcng1eUgMbp6dqbxy0T2p6FGbeHG4vUZt9RUWnuvxII8HoIpHZk5kEMSoDXxWJMiv5jAcv7nsAYO/gQOUq1Vn03VyCgkrg7uHJrp3bOHvmFP4BgaZ0dd9uSLPmrfD1K8zd26EsmDeDHl3bsWXbbqysrXmrbgNatW5L+w+aY21jw9QZ87G1tWP82BFMnT6P9T+tYs3q5bi6ujF+0szn9h+Ji4ulWqUgZFkmKcnYpLNho2YEBpUAYNnShbRs+T7dP+oDQEBAIKO/mkS3zh8wfsxkXFxcUSqU2Ns7pKnpWbZiMa1atDH1QwnwD2T0yAl07dGOcV9PwdraWCtVr15DOnfsDkDvXv1YuXopx47/Q1BgcdzcjEN1uzi7ZlqLlJiYyPr1q5k6eQ5v120IwMTxM/j7n4Ns3rqeXh8/CS4GfTGcmjWMtTF9evWlz2fdSUlJNpXlabdCbuLg4IiX5/OHmX8Re3t7rK1t0Wg0KP2CcE6MwlavwSfpgeh/kk3KRIcy+tgGAFaXrs/ugGoWLpGQFcE+gdy3d8breS0+HFwI9gnMYK0gZI8CG5zobSUM6tz5IdpTphJ7S1Wg6r0beCTGEmXnxCnf3B0vHMBgVuWEhAYbYrABXF+YOt3WsoE3Qy6x4PcXj9xz380ZjWPOngNJAq2DgmRXZYFrxmkVmzP9NJ6lc7IChQRKCTn1b1qy4CQEj59kmXO6p8/8htEjB/H2W1VQKpWULVuBlq3e5+KFc6YM3n33ydDmJUuUplz5SjRuUIP9+/6kaTPjcOaf9/+Sz/t/aUr37YJZ1K5TF5VSzeKF8/h551/s3/cnI4b2Z8u2PzItj729A1u2/4Fep+P48X9YvnSRseblcVkunD9LaMgtdu7Y+uRwZRmDwcDt+3cJCiphnC1dISGrnny/XLh4jpCQW+zYte2525UqXTbNdh4eXkQ/jEZWKZCVxuWyKm3eskICCWSVgtB7oWh1WqrUeMOURqWypmLFKly/eS1NPiXLljOl8ShkHBI8KuZBhk3gZMnY5O3p/b6qJ/OfRIj+J9nEIymG6YdXYq3X8bdvGRZXfNfSRco33M/l/MSeqRYXfZexl9Zl2OJDAhYXaY7rhZyfIFGvfw0mYczBPidiKGEhxxkUCk4Uzv+dx2RJwZGiZV785MVePHnJz9SyxOR7GT9df9YNKw3LPB69MF3PKBcCNS/uUGtlLaE1s/auaNEAVq/dRmJiIvHxcXh5eTPoi0/wK1w00228vLzx8S1MSMjNDNffuH6VHTu2snX7HrZuWUf1GrVwc/PgnebvMXrkIOLj43BwcMxwW4VCgb9/MQACg0oQFRnJ4IGfsOan7QDIBgMdPuxKl249023rW8g30zIbDAY6fNiFrl3Tb+fj42f6t+qZ5pWSBIYszHYnP/7cnx1LQ5ZlpGcWpjZVM6aXHpcz4/MWUCyQuLhYIiLC8fLKvPZEoVCku0/Q6bQZJ+bp+U+ixfwnr8hap2XaoZV4JsVyw8mbr2t3FgOavKYOe5RjfJmO9L2+Cy/Nk74lUdbOLAx8l8Me5Z6ztZBdxFDCgpCNDAoFs958nxl/rMy0r82sN9uIH658TELCSjavVqxkijXOOgUxSkP6PicAMrjoFZRMsTarz4mU2lElC+zs7LCzsyMm5hF/H97Pl0PHZJr24cMH3A+7l2HTJlmW+fqroQwfMRZ7e3v0ej1arfHmOPUm2WAw/2a/+0d9WLliCXv++JUmTd+lbLkKXLt62RTAPC11VmG12gq9QZ9mXdmyFbh29UqG25krtY+JXp95+YsWLYZabcXJk8dMNSBarZbz58/SrfvLD23crFkLZs+awrKlCxk5any69bGxMTg5OePm5k5UVESaYOjSpQvpjkOvf/L5xKtsnpn/RI1WIX4as0SWGXF8E+Ue3CbGyo6h9T4mUZ07NbdCzjjsUY4j7mWoEHMLN00cD6wcOeccgEEM6Z+GZMj+hgKWbHiQV4i/MiFH/BVYkWFNexDxTL+XCHsXhjXtwV+Boq+JYKRAonXM46dDz8YUj9+/F+OUI/OdHD60j0MH/+LO7VD+/vsAPbq2o1ixIN5v+yEACQkJzJg2nuDgE9y9c5tjR4/Q99PuxtGumqRvsrJpwxrc3T1o2Mg4olXVqjU5+u/fnD59klUrvieoeEmzO2+DcWjhdv/rxLffzEKWZXr1/pzTp08wYdxILl08z61bN/hr724mTRht2sbPrzAnjv9LeHgYDx9EA9C7dz/jduNHcunSk+0mThyd2a7TcXf3wMbGhsOH9hEVFUlcXPrReuzs7OjYsRszZ0zi0MF9XLt2ha++GkpychLt2nU0e1/P8vHxY+TIcfz441JGjxrMsWP/cPfuHU6dPMaoUUNZuHAeADVr1uHBg2iWLv2O0NBbrF27gkOH9qXJy8+vCFeuXOLGjWs8fBiNVqslytqJJKUVSlnGJ+khUrbPqpa/db20j3dCTqGTFIx6sxv3HNwtXSQhGxgkBWdcAtnnVYkzLoEiMMlIarOu7H4VcOLxkJBj/gqsyP6A8lQJe9LXJtgn9/vaCHlfhWQbuj1w4Wfn2DTznLjoFbwX40SF5Jx5ChsXF8fc2VO4fz8MZxcXmjZtwcDBI0y1BEqlgitXLvHz9k3ExcXi4enFG2+8yZx5i7F3cEiTV1RUJEuWLGDd+ifDZFesVIUeH3/Kp3264u7mztTpC7Jcxq7de7Fm9TJ+/20Hzd99jx/XbGPe3Kl06dwGZJkiRQJo/u57pvQDBgxl7NfDadK4DhpNCv9dvkep0mVZvXorc+dNo3On94H0272ISqVi9JiJLPxuLgsWzKRa9TdYvXpLunRDvhyFQTYwbFh/EhISKF++IkuX/oSzs0uWj/1pnTr3IKBYIMuXLab/5z1JTk7Gz68wjRo1oWtX4wABQUElGDt2KkuWLGDRonk0adKCjz76lI0b15jyad++M8eOHaFdu+YkJiawatVm3nijjuh/8pLeunuBT8/+BsCcqm045Z3/my8LgpCzJDm3pvbNI2JjY3F2dqbiJ1OQCli1s0Jn6RLkPkmCUs62XAtPLHAPI5xCUnJlP56ednz62Rt4e/uiULzakNAGZG5aaYhVGnDSKyimMW/44KdZ2SjRaAwF7tm3VND+wB+ztlKQkpI97SAcdMn4JRlrm+7aumWp/4nBoCU8/B6LFx0lMjLnOy8neuf+hIYKg4FKkTfwSI4jysaRWCtbluxdiL0uhS3FazOretsc3b8kQcVkHaGXH5GFrlD5gtbT4cWJ8hm9Ppn9RyYRExOT5/pepN5L1q8xGpUqe+8ldbpk9h+fnCePO7eImhNBEPIMBRJBGusXJxSEHCD6n2Tu7dvnGHRqO95JT4bE10kSKlnmhFcQc6u2sVzhBEHIV8S3riAIgiA8FmXthK1e83j+k4di/hOMgcnUv1elq41Uycbx2X4NqI5eTGIpFECSLGd7rXVBrQV/mmj8LwiCIAiPpc5/opcU2Bo0eKak7/hfkCgMBgad2o5MxjcMMtDn/G4UWRiFThCEF6tRowZly5blu+++s3RRcl2BrTmxeiijVBes6NQ2MvPx/vMrSQJFBVuU2oI3AIbV5Xu5sh91vDOSVg8punTzW+Q2SQLJWglywXvWLekK5s2hbK00TjqZjXlqFCrC7FwpnBCNqyaeRJU1cern9z+RZQlZAr2dCp19Lvy05sIfuLVOQ5NbwWmacj1LARRKfESlqBsE53Rn+IJ2UT8lxS33+xhZmv51+E7LwUkYxTwngiAIgiCYxKtsibZ2xD0ljkJJD0hWeufL/idKgx6fhIcUiYukaGyk8f9xURSJi6RQ4iOz8/FIisu5QgqCUKDkv29aQRAEQcgGUTZO2OlSsNVr8E18QKiD5+vZ/0SW8UyKfRx4RFIkNpIi8VEUjY3ELz4a1XOGvopXWeOge/HIf1G2jtlZYkF4Pcg8mV06O/Ms4ERwIgiCIAgZkJG4Z+eGf3wEtnoNnkkxRNi6WLpYmXLUJFIk1ljzUTQu0lQbUjg+CjudJtPtkpVqQh09ue3oQaiTJ7cdPU3v49W2bP1lMp5JMRn2OTEAEXYunPEMzLHjEoS8SnSIzxkiOBEEQRCETGgVKu4/7n/ipoknyYz+JznJSq+lcEIUhR5Fp2uG5ZqSkOl2OknBPQe3NIFHqKMnoU6eRNk6IT9n9u+51dow9fAqDKTtFG/A2A1kXtXWYnJdQRCyjQhOBEEQBOE5nu1/kqL0RmXQo5L16CQlSars7aysNOjxTnpEkQRjrUeRhEiKJERROCGSQkmPnrtthK0zoY4e3HZKDUKM/7/n4PbSw/0eKFKBkW91Z9DJtPOcRNi5MK9qaw4UqfBS+QrCa08mBzrEZ292ryMRnAiCkGfIyCTZPECnSkals8E22Q3pdWzjX4CMGDWI2LhYFn6zzNJFyVFRNk7Y6lKw02sIiLufpgZBKym5Z53FPheyjFtKnDHwiDcGHkUSoiiSEIVvQjRqWZ/ppnFqW0Kebn7lZKwFuePgQZI6ZyYxPVCkAof8yhlniE+KI8rWkTOegaLGRBCEbCeCE0EQ8oQ4+zAiPS6iUyWblql0NnhGlcUxwSdH9hkfH8+C+TP488/fiI6OpkzZcoweNZEKFSub0iQkJDB79mT2/rmbR48e4udXmK5de9KxU3dTmqlTx7F920bs7Oz4cugYWrRoY1r326+/8PMvm1m8+MfnlmXr1g2MGjnI9N7d3YMKFSszZMhoSpQoZfYxNWxSi25de9GjWy+ztzHHnbu3adS0Nts376ZMmXKm5aNHjkfOpTbS/x79m2UrFnP2bDDJKcn4+RahfsNGdOvWB69C5v2NjBz+BXGxMXy7aGWW9i0jEWtlh22SJl3fC5Wsxyv5Eff06Ydrd9AmUTghisLxqcFHJIUToigSH4mdPvN+ICkKFXfsPbhtbww6btt7ctvegzsOnsSo7dA45f6khwaFIueHCxaE10kODiVckIngRBAEi4uzDyPM+1S65TplsnF5eNUcCVC+GjOEq1cvM33GN3h5efPLL1v46KMO7Pp1P97exv1NmzqWo0ePMGPmN/j5FeHvvw8wYfxIvLy8adT4Hf766w927dzG0mXrCAm5waiRg6lTpx6urm7ExsYwb950VqzcYFZ5HBwc+e33Q8iyTHj4fWbNnMQnn3Tl998PY2WVN+c5cHTMnXH4129cw/iJo2jT+n8smPc9fn5FCAu7yy+/bmX58sWMGDU+R/cvIeOenPFwual1e06aRFre+hfP6/+ZmmO5ajLvB6JHIszOzRiEOHiagpHbDh5E2jg/tx+IIAhCflVggxN1gh6lKvNq8/xIZ1vwfugkCZTJBtTx+gL3MCK2tn+u7MfGzQ6DtQrZTo1BqQaMzbNkzLu+ZGQiPC4a3zzbgksCZIjwvIiN5GtWEy+VXm/WhITJyUn88cevLPxmOTWr1ARgwGeD2bvnd9atWcWgL4YBcDr4JG3ea0etarUA+LBtJzauX835s2doXL8pN65eoWaN2lQsU4GKZSowdcpY7ty6hZujC7OmT6RTh274efnCC8ok6WUkScLL1QMAbzdPenTtxWeff8Sta1cpVbIMAKeCTzB77lTOnT+Nq6sbTRq9w+CBI7FxdqRblw+4e+8OU6ePY+r0cQBcuhIGQPCp48yeNZnz587g6upG4ybNGTRkFHZ2dgA0alCD9u27EBp6i99/34GzkzOffjaQ9h92Na5vWhuANu2aAVCjZm1+XLM1XU2ERpPCzOkT+XXXduLj4ylfviIjRk0w1UYdO3qE7l3bsnzlRmbPmsT1a1coXaY8U6bOpVhgxk/l79+/x6QpX9OlW09GjppgWu4XEEDtBnWJjH6IrIRv589i757f2bbzT1OaH1d8z48rfuDPg8f5dv4stm/bCECZksbgc+XaLdSsVee55wbAVqt5blMrCVAg0+HmIWxuh6RZF2njZOqA/nRfkLv2buiUL/czbHc/81qX/EqSALUGYhPAULC+0BV6N0sXIdc953LLO1JHhcjuPAu4AhucCIKQc2T03PDekT2ZSaBXJnPTe6dZycuGt8KcXwudXo9er8faOm0bfRsbG04FHzO9r1q1Bn/t20O7Dzrg5VWIo8eOcPPWDUaNMD6pL12qLBs3rSUm5hG374SSnJyMf9EATpw8xoVL5xn39VTzj/UpsbEx7Ny1HQCVyhj0Xb5yiZ59OvNF/6FMnjiTBw8eMHHyGCZOHsPkGfNZ8O0y3n+vMf/r0Jn/te9iyuvK5Uv06tmRAV8MY9KUOTx8EM3ECaOYNGEUU6bNM6VbsWIJA74YRp9PB/DH7zsZP24E1WvUIjCoBBs3/0b7ds1ZvnIjxUuUQq1WZ1juWTMm8sfuXUydPh9fv8Is+2EhvXt25Pc9R3BxcTWlmzd3GsNGjMXNzZ1xXw9n9KjB/LT+lwzz/P23HWi1Gnr26pfheicnZ7M+0496fcaNa1eJj49j8gzjcTs7u5i1rcpg3p3Sf65+HHcsZRwJy9GTO44eJKptzNpWEAQhVY0aNVAqlfTr149+/TL+7suvRHAiCEKB5GDvQJXK1Vi4eB6BgcXxcPdk56/bOXM2GH//YqZ0Y0ZO4Kuxw6jXsAYqlQpJUjBpwgyqVzPWttR9qz7vtfqAdh1aYGNjw/Qpc7G1tWP8xJFMnTyXdet/ZPVPK3B1cWPi+OmUKJ55/5G4uFiqVC9pHBggKQmAhg2aEvS4RmHZisW0atHG1J8kwD+Q0SMn0LVHO76eOB0XF1cUSgX29g54enqZ8l22dCEtW75P9x59jNsFBDJ6zCS6dfmAseOnYW1tvHmuV68hnTr3AKBXn89ZtfJ7jh07QmBQCdzc3AFwcXFNk/fTEhMTWb/uR6ZMm0e9txsBMGHSLBo3qMmWzevo2auvKe3AQSOoWdNYY9G7T38+7dOFlJRkU1meFhJyEwcHR7y8vDP97Mxhb2+PjY0NGk1KpseQGZ2ZI11tKFmX/QrPlymeIAivmZyc5+T48eM4OeVOs9m8RgQngiBkOwklgeGtzEqbpI4izO2fF6bzeVAbW62HWfs2t158xtT5jPpqCPUaVEepVFK2THlatmjDxYvnTWlWr13O6bOnWPTtCnx9/Thx4ijjJ47Gy9ObOrXrAtC/3xD69xti2uab72ZTu1ZdVCoVi5YsYMf2P9l34E+GjxzI1k2/ZVoee3sHtm36DZ1ez/ET/7Js+SLGj31S83LhwjlCQm+xY+c20zIZGYPBwJ3boQQVL5lhvhcunCU05BY7d2x9sp2cfrtSpcs++RwlCQ8PL6Kjo836LAFuh95Cq9VSpWoN0zK1Wk2FipW5cf1qmrSlSpcx/dvTyxgoREdH4etbOF2+smxs8mZJSSprtAolKoM+w3o5GdBLCv5zKwKPkjNIIQhCviM6xOcIEZwIgpDtJCQkM79e7LTeKPU26BXJGbfGkkFlsMVO621Wn5Os3MIWLRrAmlVbSExMJD4hDi9PbwYO+YzChYsAxn4pc+dN59sFS6n/uCagdKmyXLp8gWUrFpuCk6ddv3GNHTu3sW3zbrZsW0/16m/g5uZO82atGDVmCPHxcTg4ZDzsrEKhMNXaBAUWJyoqgkFD+rL2xy0AGGQDH7bvTNfOH6fb1rto0UyPUzYY6PBhV7p065lunY+Pn+nfKlXacyZJErLB/AbQqaN2pQskZPlxh4EnUpuqAabzKmfSjyAgIJC4uFgiIsKfW3uiUCjS/bBrtTqzy/88sgThdi74xUcjk/bvLHWPsVa2yGJoXUEQhFcivkUFQbAoCQnPuIrGN8/emz5+7xFXIUfnO7Gzs8PL05uYmEcc/vsAjRo0BUCn06HVaZEUafetVCgzHD5XlmW+Hjec4UO/xt7eHoPegE6nfZyX8f+GLNzs9+jWm/8uX2TPn8balrJlynP12hX8/Yule6WO5qVWW2HQp91H2XIVuHb18nO3e5HUPiaG5/S9KOpfDLXailMnn/TZ0Wq1nD9/lqCgEmYf97OavdMStdqKZUu/y3B9bKxxYkA3N3eiIiPSnJv/Lp1Pk1atVmfpHDwtzsqWuw7u6Zp46RRKwu1cSFHmzRHVBEHIIak1J9n9KuBEcCIIgsU5pPhRKKYmSkPa/gYqgy2FYmrikOKXyZav5tDh/Rw8tI/bd0L5+8hBun3UnmIBgXzwfgdjuRwcqVmjFjNnTebosSPcvhPK1m0b2f7LZho3eiddfhs3rcXdzZ1GDY3BTdUq1fn36BFOnznJyh9/oHhQSbM7b6fu/39tO7Lgu9nIskzvnn05feYk4yeO5tKlC9wKucHev/5g4uQxpm38/Apz4sS/hN8P4+EDY5OsXr0/5/TpE0wYN5JLF89z69YN/tq7m0kTRptdFjd3D2xsbDh0aB9RUZHExcWmS2NnZ8eHnboxc/pEDh38i2vXLvP1mC9JTk6ibbuOZu/rWT4+fowYNZ7Vq5YyetRgjh07wt27tzl18hijR3zJom/nAlDjjdo8eBDNsu+/IzTkFj+tXsGhA/vS5OVbuAiX/7vIzRvXePggGq02/dwkzxNnZct1Zx9CHo+2FeLoyXVnHxJyaPJDQRCEgkY06xIEIU9wSPHDPsWXJHUUemUySr0NtlqPHK0xiYuPY868ady/H4aLswtNmzRn0BfD04xENWfmQubMm8aXw/sTE/MIX9/CDBownI4duqbJKyoqkiU/fMu6tdtNyypWrMJH3fvwyWfdcXP3YPrkuVkuY7cuPVm9djm/7d7Ju++0YvXKzcxbMINO3T4AWaZIEX/ebf6eKX3/L4Yx7qthNG1cG40mhUtXwihVuiw/rtnGvLlT6dK5zePtAmj+7nuZ7/gZKpWKUWMmsei7OXwzfybVqr/Bj2u2pks35MvRyAaZ4UP7k5CQQPnyFflh2TqzR8XKTKfOPQgICGTF8kUM6NeT5ORk/PwK07BJU7o+7ugfVLwkX02YyvcLF7Do27k0bdaCj3p9yqb1a0z5/K9DZ44fPcL/2rxDYkKC2UMJP02WIFEEI4IgiD4nOUKSc2tq3zwiNjYWZ2dnareYgFIlhnfM7yQJihW25+adhAJ3vRtUudOB2MvNjgFdquFdyBeFMuPhZXOTDaBJeR0GyM9eBnXuzxhuaRKgtlGQorX8xAAGvZaIsHvM33CK8AeJOb4/u/CCOc9JcbWG0AvRmfZPyq8Sq2bepyy/0uuS+fv3r4mJiclzo1al3ks2KjMElTJ7H1To9CnsvTQ7Tx53bimwNScaZyWKAvZjrtQUrC9zMP6YGVQSeitFgQtO9Na5E5zobSRkCdPLoiSQtAYkXcGbdFORWDBHiJKsHZEsH5uYyqDQyChTcv6Pz6AueK2yJQnGrfwELy8v4+AHBcjb786wdBFyn/41+BIXkzDmiIJ1dQuCIAiCIAiCkGcV2JoTQRAEQRAEQXhZOTkJY0EmghNBEARBEARByCrRIT5HiGZdgiAIgiAIgiDkCSI4EQRBEARBEISsMsg58wJq1KhB2bJl+e67jCefzc9Esy5BEARBEARByEOOHz8uhhIWBEEQBEEQBMFMos9JjhDNugRBEARBEARByBNEzYkgCHmHbMDu4VVUKbHorJ1IdC0BkniGkpeNGDeM2PhYFs5abOmiCIIg5LIcqDlB1JwU2ODE+WoCSqXe0sXIVbFB9pYuQu6TQJVsQB1f8GYMt47JnQO2kdUo9DIKnYziFT5kh4hgvK5sRJ3yyLRMa+1CRMn2xHtVMTsf32KeqFQqJOnF0/bGxcXx1VdfsW3bNiIiIqhSpQrz58+nRo0apjTh4eEMHz6cP/74g0ePHlGvXj2++eYbSpQoYUozePBgVq5ciYODAzNmzODDDz80rdu4cSOrV69mx44dzy3LypUr+eijj0zvvby8qFmzJtOmTaNcuXIvPJYrF+8C0LBJLbp17UWPbr1euE1W3Ll7m0ZNa7N9827KlHlSntFfTUKWZbCzydb9ZeTfo3+zbMVizp4NJjklGT/fItRv0JDuXfvg5e1jVh4jRg4kLi6G775dka1lkwwykl7G+qEGm6iUbM07I7Kq4AXtkgRfvTeNkFP3kA0F7Av9nRovTiMI+UTB+3YTBCHPcYgIxvfc96ieCkwAVCmP8D33PQ4RwTmy3169erFnzx5Wr17NuXPnaNq0KY0bN+buXeONvizLtGnThhs3bvDzzz8THByMv78/jRs3JiEhAYAdO3bw008/8ccffzB9+nQ++ugjoqOjAXj06BGjR482e7QVJycnwsLCuHfvHrt27SIhIYEWLVqg0Why5Pizg6OjE05Ozjm+n/Ub1/BRr454eHixYN737PplH+PHTiUuLo7lK5bk+P4FQRDSSe1zkt2vAk4EJ4IgZD9ZRtKnmPfSJeF1ZSMAz9Z1pL73urIRSZdkVn7mfrEnJSWxZcsWZsyYQb169ShevDjjxo2jWLFiLFq0CICrV6/y77//smjRImrUqEGpUqVYuHAh8fHxrFu3DoBLly5Rv359qlevTseOHXFycuLGjRsADBs2jL59+1K0aFGzyiRJEoUKFcLHx4fq1aszaNAgQkJCuHz5sinNkSNHqFevHra2thQpUoQBAwaYAqWuPdpx994dpk4fR6lyhSlVrrBpu1PBJ+jcrS0VqwbxdqMaTJryFYmJiab1DZvUYvH33zByzBCq1ChF/UY12bBxjWl9o6a1AWjTrhmlyhWma492AIwYNYi+/Xua0mk0KUya8hW161aiQpUgOnZ5n7PnTpvWHz12hFLlCvPPv4f5oP27VKpWnA87t+bGzeuZfi73799j0pSv6dr5Y6ZOms0bNetQ2K8INarXYvq0OfTrOwiAb76dRZv3G6fZdtWqH2jYqKZp/fbtG9m7dzely/hSuowvR48dMevcCIIgpJODQwkXZAW2WZcgCDlHMmgouX9g9uQFqFMeUfLAYLPSa/1Xg1r9wnQ6nQ69Xo+NTdrmSLa2thw+fBiAlBRj85yn0yiVSqysrDh8+DC9evWiUqVKfP/99zx8+JAbN26QlJRE8eLFOXz4MKdOnTIFOln16NEjfvrpJwDUj4/n3LlzNGvWjIkTJ7Js2TIiIyP5/PPP+fzzzxk5dBLfzPuB1m2b0r5dZ9q362TK6/KVS/Ts05kv+g9l8sSZPHjwgImTxzBx8himTp5jSrdi5RIG9B/Kp70/Z/cfuxg3cRTVq9ciKLA4m9bv5H8ftmTlsvUUDyppKtOzZsyezO49vzJtylz8fAqzdPkievXpzB+/HcbFxdWUbu786YwY+hVuru6MnTCSUWOGsH7t9gzz/H33LrRaDb16fpbheicnZ7NaaX/80WfcuH6V+IR4pkyeC4Czs4sZWwqCIAi5RdScCIJQIDk6OlK7dm0mTpzIvXv30Ov1rFmzhqNHjxIWFgZA6dKl8ff3Z+TIkTx8+BCNRsO0adO4f/++KU2zZs3o0qULNWrUoEePHqxatQp7e3s+++wzlixZwqJFiyhVqhRvvvkmFy5ceG6ZYmJicHBwwN7eHldXV9avX897771H6dKlAZg5cyadOnVi4MCBlChRgjp16rBgwQJ+/PFHUlKScXFxRalQYm/vgKenF56eXgAsW7GYVi3a0KNbLwL8A6lapTqjR05g+y+bSUlJNu2/Xr2GdO7YHX//YvTu1Q9XVzeOHf8HADc3dwBcnF3x9PRKE2ikSkxMZP361QwbMoa36zakePGSTBw/A2sbGzZvXZ8m7aAvhlOzRm2KFy9Jn159CT59Ik1ZnnYr5CYODo54eXq/8Lw+j729PdY2tlhZWZk+Hysrq1fKUxCEAkw25MyrgBM1J4IgZDtZYcWV+vPMSmv78CpFzry4T8btSv1Ici3xwnTFVNZm7Rdg9erVfPzxx/j5+aFUKqlatSqdOnXi1KlTgLHGYsuWLfTs2RM3NzeUSiWNGzemefPmafIZN24c48aNS/O+cePGqNVqJk2axLlz59i5cyfdunXj5MmTmZbH0dGRU6dOodPpOHDgADNnzmTx4iejYJ08eZJr166xdu1a0zJZljEYDNy5c5ugoIw/nwsXzhESeosdO7c92Y7025UqWca0XpIkPNw9iY6OMuOTNAq9fQutTkvVqk8676rVaipWqMz1G9fSpH16X56Pg47o6Gh8ff3S5SsjmzXAgSAIgvD6E8GJIAjZT5KQleYFCYnuZdFau6BKeZSuzwkYB1XUWbuS6F7WvGGFs3ATGxQUxIEDB0hISCA2NhYfHx86dOhAsWLFTGmqVavG6dOniYmJQaPR4OnpyRtvvEH16tUzzPO///5j7dq1BAcHs3z5curVq4enpyft27fn448/JjY2NtNZfxUKBcWLFweMtTb379+nQ4cOHDx4EACDwcAnn3zCgAED0m2bHK/M9DgNsoEP23ema+eP063z8XkSDKhUaZtqSZKEnJWneI/bVknPnElZTn9aVKonPz+p6Q2Z7KuYfyBxcbFERIY/t/ZEISnSdTnS6bRmFl4QBCGLxCSMOUI06xIEwbIkBREl2wPpR3dPfR9R8n85Ot+Jvb09Pj4+PHz4kN27d9O6det0aZydnfH09OTq1aucOHEiwzSyLNOnTx9mz56Ng4MDer0erdZ4c5z6f4PB/Jv9QYMGcebMGbZtM9Z4VK1alQsXLlC8ePF0r9TmSWq1GoMh7TDpZcuU5+q1K/j7F0v3MrdZU2ofE70h8yHYixYNQK224uSpY6ZlWq2W8xfOEBT44lqvzDRr2gK12oqlyzLuvxMbGwMYm55FRUUYhzZ+7NJ/aZvSqdVq9PqCNYy8IAjC60QEJ4IgWFy8VxXuVeiDztolzXKdtSv3KvTJ0jwnWbF7925+//13bt68yZ49e2jQoAGlSpVKM9/Ipk2b2L9/v2k44SZNmtCmTRuaNm2aLr8ffvgBLy8v3nvvPQDefPNN/vrrL/7991/mzp1L2bJlcXFxSbddZpycnOjVqxdjx45FlmWGDx/OP//8Q79+/Th9+jRXr17ll19+oX///qZt/PyKcPzEUcLDw3jw8AEAvXv25fSZk4yfOJpLly5wK+QGe//6g4mTx5hdFnc3D2xsbDh0eD9RUZHExcWmS2NnZ0fHDl2ZMXsSBw/t49q1K3w1dhjJSUm0++DDDHI1j4+PLyOHj+XHNcsY9dUQjh3/h7v37nDy1HFGjR7KwkXzAKhZsw4PHkSzdOl3hIbeYu3aFRw6tC9NXn5+Rbhy+RI3bl7j4cNoU9AoCIKQZWK0rhxRYJt1pbjboFTl/KRheYlcEJtsS6CzV6BxURa4mlJVYu50qtNbK5AlkBUSsuLl/8jiClUlzrsydg+vokyJRZ8LM8THxMQwcuRI7ty5g5ubG23btmXy5MlpRqIKCwtj8ODBhIeH4+PjQ7du3fjqq6/S5RUeHs6UKVM4cuTJ0LQ1a9ZkyJAhtGjRAi8vL1atWpXlMn7xxRcsWLCATZs20b59ew4cOMDo0aOpW7cusiwTFBREhw4dTD9oA/oO4esJI2j8zltoNClcPneb0iXKsHrFJuYtmEGnbh+ALFOkiD/vvtMq7Q+hnMEPowwYZFQKJWNGTOC7xfNY8O0sqletyeoVm540a3i83ZcDRyAbDAwb+QUJCQmUL1eRpUvW4OzobEyTmv3TP8KpF+dzfpg7d+hGsaLFWLZqCZ8P6GWahLFRk6Z06dEHWS0RWLokX4+fyveLFrBo8TyaNGvBRz0/ZeOGNchq49/m/zp25tjxI7T7X3MSExJYtWYLNWvVyfJ5eZasl5CVEsluViShe+X8XsTmQcEMquSygci2ngXu+1wdX/DOt0Jf8I5ZMJJkuWBd4rGxsTg7O/PmOxMKXHCS7JJ5m/T8SpKghJct18MSC9yPWW4FJ17udnzerRrehfxQKF88hG9OC/J3N3uG+Pzkyvk7li5CrpMAlZMVKVrLj25j0GsJv3+PBWtOEvEg8cUbvKKCGJxIEgQUsSfkZlyB+z4viPT6ZPYfmURMTEym/fQsJfVesrHvJ6gU5g/CYg6dIYU/7y3Jk8edWwpszYkgCIIgCIIgvDSZHOgQn73ZvY5EnxNBEARBEARBEPIEUXMiCIIgCIIgCFklhhLOEaLmRBAEQRAEQRDykBo1alC2bFm+++7FkxTnN6LmRBAEQRAEQRCyymAAsnlAjsdzYR0/frzAdogXNSeCIAiCIAiCIOQJouZEEARBEARBELJK9DnJEQU2OFHHalAqC1bFUYqzraWLkOskQB1rwCZaV+Cud6197sxrIysk4wQEecS9m5Fokgve+S5Zqaili5DrZFnm1u3IvDH05uMhRRV6GYUuLxQof1Ldf4j6RjRyAZtFO7Fqwbu+9TrLz18kWEaBDU4EQRAEQRAE4aWJmpMcUbCqDgRByNv0euyOHsFxxzbsjh4Bvd7SJRJywMqVK3FxcbF0MQRBEF6NQc6ZVwEnghNBEPIEh927CKpfg6Jd2uI3uC9Fu7QlqH4NHHbvyrF9xifEM3nGeBo0r0PFN0ryYbf3OXv+TJo0UdGRjPhqCG81qUGlWqXo2bcbt0JuPjffrT9volRl/3SvlJRkU5pfdm3j7Wa1qFmvItPnTE6z/Z27t2n2Xn3i4+Oeu587d2+b8pYkCWdnZ2rVqsWOHTuy9Dn06NGDNm3aZGkbcwUEBDBv3rw0yzp06MCVK1dyZH/PCgm5yajhA6n/VlUqlvWncYOaDBn4GefPnTY7j21bNlCzaqmcK6QgCIJgIoITQRAszmH3Lvz690Z1PyzNclX4ffz6986xAGXM+OEc+fcQMybNZcemP3izdj0++rQz4eH3AWOfhn6DenP7bigL5y5l2/pf8fPx46NPO5OYlPj8Y3Jw5PCfx9O8rK1tAHjw8AFjJgxn+ODRLF24mu07trD/4F7TtuOmjGbIF8NxcHA06zhWLllLWFgYR48epWbNmrRt25bz58+/5KeS82xtbfHy8srx/Zw/d5p2bZpx69YNxk2cwc7fDvDNd8sJDCrO9Knjc3z/giDkb7JsyJFXQSeCE0EQsp8sIyUmmveKi8N74hjjNs9kIz1ue+s98SukuDiz8jO3vW5ycjJ/7P2NoQNHUqPaG/gXDaD/Z4Mo7FuEnzatBuBW6E1Onw1m3KjJVCxficCAIMaOmkRiYgK7fvv5uflLSHh6eKV5pbpzJxRHByfebdaKiuUr8UaN2ly7cRWAHb9uR622ommj5mZ+2ODi7EqhQoUoXbo0kydPRqvVsm/fPtP6u3fv0qFDB1xdXXF3d6d169bcunULgHHjxrFq1Sp+/vlnJElCkiT279//wu3gSY3LrFmz8PHxwd3dnX79+qHVagGoX78+ISEhDBo0yJQ3ZNysa9GiRQQFBWFlZUWpUqVYvXp12s9Tkli6dCnvv/8+dnZ2lChRgl9++SXTz0SWZUYOH4h/QDHWrNtO/QaNKeofQJmy5enXfwjfLV4JwLGjRyhTwofY2BjTtpcunqdMCR/u3rnNsaNHGDViIHFxsZQp4UOZEj58u2CW2edGEARByBrRIV4QhGwnJSVRqlJQ9uQly6jDwyhVtaRZ6UODr4LC6oXpdHoder0ea2vrNMttbKw5FXwCAI1GA5AmjVKpRK1WczL4BP/7oGOm+ScmJdCgeR30ej1lSpXli35DKFu6PAD+/sVISk7i4n/n8fUpzLkLZ2jbpj2PYh6xYNEcfvxhvVnH+iytVssPP/wAgFqtNpYjMZEGDRpQt25dDh48iEqlYtKkSbzzzjucPXuWL7/8kkuXLhEbG8uKFSsAcHNze+F2VlbGz3jfvn34+Piwb98+rl27RocOHahcuTK9e/dm69atVKpUiT59+tC7d+9My71t2za++OIL5s2bR+PGjdm5cycfffQRhQsXpkGDBqZ048ePZ8aMGcycOZNvvvmGLl26cPDvE9jaOafL89LF81y7eplZcxaiUKR/DufklH6bjFSuUp2RoyfwzfyZ/PrHYQDs7OzN2lYQhHxOzoE+IqJDvKg5EQShYHKwd6BKxaos/P4bwiPC0ev1/LxrK2fOnSYiKgKAwIAg/HwKM3vBdGJiY9BoNXy/fCGRUZFEPk6TkcBiQUydMItF85YyZ9o3WFtb07FHW1NfFWcnZ6ZPnM3wMYP5X5f3aNOyLXXrvM30OZPo8mEP7ty9TZsOzWnZtgm/73lxk7YPe3yAg4MDNjY2DBkyhICAANq3bw/A+vXrUSgULF26lAoVKlCmTBlWrFhBaGgo+/fvx8HBAVtbW6ytrSlUqBCFChXCysrqhdulcnV15dtvv6V06dK0bNmSFi1asHevsYmam5sbSqUSR0dHU94ZmTVrFj169KBv376ULFmSwYMH88EHHzBrVtoaih49etCxY0eKFy/OlClTSEhI4Mzp4AzzDHn8WRcLLP7Cz+95rKyscHR0QpIkPD298PT0wt5eBCeCIAg5RdScCIKQ7WRbWy6fuW5WWtvj/1K0V+cXpgtdupakGrVemM7G2hpSzBvla8bkeYwaN5R6TWuiVCopW7o8LZu35uJ/xv4aarWaBbMXM3rcMGrWq4hSqaT2G29R7836z823csWqVK5Y1fS+auXqvP9hC9asX8mY4ca+Dk0avkOThu+Y0hw9/g9Xrl7m6xETafJePeZM/QYPD0/+16U1Naq9gbubR6b7mzv9Oxo1r8uVK1cYOHAgixcvxs3NDYCTJ09y7do1HB3T9l9JTk7m+vXMz5G525UrVw6l8smcOj4+Ppw7d+55H086ly5dok+fPmmWvfnmm8yfPz/NsooVK5r+bW9vj6OjI9HRURnmKT9++ijloTl4BEHIZ2SZbJ9oSdScFNzgRGuvxqBSW7oYucr5QsyLE+UzkgL0dbzR2SoK3PXueDQkV/Zj7+eMon0lpCQtCunpD9m8r5fkKrXRevugirhv6mPyNFmS0HkVIrlKbST5+RNLShKgVIDCvJNdNKAYa1ZuJjExkfiEOLw8vRn45WcU9itqzAcoX6ESP2/ZTVxcLFqtFjc3d/7XqRXly1Y0pXkRhVJBhQqVuBV6K8NtNJoUxk/9iplT5xNyNxS9Xk/NN+oAEOBfjDMXztCwfpP0GT/Oy8fXjxIlSlCiRAkcHBxo27YtFy9exMvLC4PBQLVq1Vi7dm26zT09PTMts7nbpTYfSyVJEgZD1jt0PhtEyLKcbllG+9LLBpDS3x74FwsE4PqNq5QuVz7z/SqM+zAgIz/enVavM5ZBevJKfZ8ZWQIkCYNagd4q5xslqB4mvzhRPiMpwFDcGYOXgYLWZ9juVKili5DrdGgtXQTBQkSzLkEQLEupJHLYWMAYiDwt9X3ksLGgzLkZ7+3s7PDy9CYm5hGHjxykUYOm6dI4Ojrh5ubOrZCbnL9wlkYN06fJjCzLXPrvAp6eGY9Q9d3i+dR7qz7lylbAYNCj1+lM63Q6HYYszPfy9ttvU758eSZPNg5PXLVqVa5evYqXlxfFixdP83J2Nva7sLKyQv/MPszZzhwZ5f2sMmXKcPjw4TTLjhw5QpkyZczeT7o8y5YnqHhJVi5bnGGwlNoB3tXNHYDIiHDTuv8uph3pTK1WozeIOXcEQXiGwZAzrwJOBCeCIFhcfKPmhM1ahM4rbZ8EnVchwmYtIj4LI1dlxaG/93Pw8D5u3wnl7yMH6dazA8UCAvmgTXtTmt927+To8X+4fTuEP//azcd9OtG4YTPeqvO2Kc2wUQOZPW+a6f23i+Zy6O/93L4dwqX/LjDq6y/57/JFOrbvkq4MV69d5rfdOxjQ70vA2F9FUijYtHU9+w/u5cbN61QoXylLxzVkyBCWLFnC3bt36dy5Mx4eHrRu3ZpDhw5x8+ZNDhw4wBdffMGdO3cA41wkZ8+e5fLly0RFRaHVas3azhwBAQEcPHiQu3fvEhWVcROsoUOHsnLlShYvXszVq1eZM2cOW7du5csvv8zScT9NkiQmT5/HrZs36NaxDQf27+V2aAiX/7vI4oXz+PyTHgAU9S9GIR9fvlswm1s3r3Ng35+sXL44TV5+hYuQmJDAP0cO8fBBNEkvGEZaEIQCInWG+Ox+FXAFtlmXIAh5S3yj5sTXb4rtqWOooiLQeXiRVLVmjtaYxMXFMWf+NO6H38fF2YWmjZszaMCwNM2HIqMimDZzAtHRUXh6etG6VVv6fvpFmnzCwu6ieKrWJzY2hq/HjyAyKhJHR0fKli7HmhWbqVihSprtZFnmq/EjGDlsLHZ2dgDY2NgybdIcJkweg0aj4etRE/H29snScbVs2ZKAgAAmT57MwoULOXjwIMOHD+eDDz4gLi4OPz8/GjVqhJOTEwC9e/dm//79VK9enfj4ePbt20f9+vVfuJ05JkyYwCeffEJQUBApKSmmviBPa9OmDfPnz2fmzJkMGDCAYsWKsWLFCurXr5+l435WxUpV2LT9d5YsnM/Y0V/y8MEDPL28qFK1BiPGTACMtSKz5i1iwtcjeL9lY8pXrMSAQSMY1P/J6GJVqtagQ6duDPniEx49fEjf/kP4/IuXD5wEQRCEzElyRr8U+VhsbCzOzs682WQ8SpWNpYuTq2zCEixdhFwnKcC7jjc37yQUuIcRDsdzp42yh58zvSY3x8vTB6Vk2ecdkgRWdmo0yfrs7qKY55UsX9jSRch1sixz804kGo3B4ufboNcScf8eC346RfiDnK9Zcbwam+P7yGskBRQt7kzotZgC1+dEEfHA0kXIdTq0/HlvCTExMVl6IJIbUu8lG9p9iEp68dD1WaGTNfyVuD5PHnduEc26BEEQBEEQBEHIE0SzLkEQBEEQBEHIKjGUcI4QNSeCIAiCIAiCIOQJouZEEARBEARBELLKIIMkak6yW4ENTmwiElFKBWvc+t3B4y1dhFxnMBj4tOM87ELjC1wHSl3Y/dzZjzoFg04HOh3ZXr2dRTLgW8YHlUolZgYvIJQpepR5YQAEgx5Jo8f+8gOc78fn+O7iyrjn+D7ymoJ8ScfW9rd0EXKdXpcMP1u6FIIlFNjgRBCE7BEXnYBOo8eAHiU5N+yvIORlMnr0egNJCRpLF0UQhNwiy0A2P/kUNSciOBEE4dWkJGg4vuMMdT+0xs3FDYWFA5Tk5GRRc1JAyLKMTq9Fb7D0UMIyCYmxXD9/n6R4EZwIgiC8ChGcCILwyvavOAJAjVaVUFkpLRoY6G00KBQKEZwUALIs8yAqFr3Wsm02ZRliHyXy55YL4qGnIBQgskFGzuY+JwVs+sEMieBEEIRXJsuwb/kR/l5/AicPe4sFBpJCYtr+0bi7u6NQiMEI8zuDwcCqucsIuxln0T5lBoOBmKhE9HpxUyEIBYpsIPubdVnmy2znzp0MGTIEg8HA8OHD6dWrl0XKASI4EQQhG2kSNUSFWq5Zi6SQUKvV2NjYiOCkADAYDMREJxIVZtngRBAE4XWm0+kYPHgw+/btw8nJiapVq/LBBx/g5uZmkfKIX29BEARBEARByCLZIOfIK7cdO3aMcuXK4efnh6OjI++++y67d+/O9XKkEsGJIAiCIAiCILymDh48SKtWrfD19UWSJLZv354uzcKFCylWrBg2NjZUq1aNQ4cOmdbdu3cPPz8/0/vChQtz9+7d3Ch6hkRwIgiCIAiCIAhZJRty5pVFCQkJVKpUiW+//TbD9Rs2bGDgwIGMHj2a4OBg6tatS/PmzQkNDTUeRgad8C05qEyB63OSegJ0+hTkAjaYT2xsrKWLkOsMBgMabbLxfBewNukGWWvpIuQ6SZaIi4sTfU4KiNTrW6tPyfY+qXmdTpts6SLkOkkCjawokN/nOq3a0kXIdXqd8W88L49epUOb7XMP6zD+dj97z2ZtbY21tXWG2zRv3pzmzZtnmuecOXPo2bOnqZP7vHnz2L17N4sWLWLq1Kn4+fmlqSm5c+cOb7zxxqseysuTC5jr16/LGP+UxEu8xEu8xEu8xEu8xCsPv65fv27pW8d0kpKS5EKFCuXYMTs4OKRbNnbsWLPKBsjbtm0zvU9JSZGVSqW8devWNOkGDBgg16tXT5ZlWdZqtXLx4sXlO3fuyLGxsXLx4sXlqKio7Pq4sqzA1ZykjjwQGhqKs7OzhUsj5LTY2FiKFCnC7du3cXJysnRxhBwmznfBIs53wSLOd8ESExND0aJFLTZi1PPY2Nhw8+ZNNJqcGZ1SluV0zaoyqzV5kaioKPR6Pd7e3mmWe3t7c//+fQBUKhWzZ8+mQYMGGAwGhg0bhru7+8sVPhsUuOAktamHs7Oz+HIrQJycnMT5LkDE+S5YxPkuWMT5LljyahNdGxsbbGxsLF0Msz0b7DwbAL333nu89957uV2sDOXNMy4IgiAIgiAIwivx8PBAqVSaaklSRUREpKtNyStEcCIIgiAIgiAI+ZCVlRXVqlVjz549aZbv2bOHOnXqWKhUz1fgmnVZW1szduzYl267J7xexPkuWMT5LljE+S5YxPkuWMT5Nl98fDzXrl0zvb958yanT5/Gzc2NokWLMnjwYLp27Ur16tWpXbs233//PaGhoXz66acWLHXmJFnOw2O0CYIgCIIgCIKQqf3799OgQYN0y7t3787KlSsB4ySMM2bMICwsjPLlyzN37lzq1auXyyU1jwhOBEEQBEEQBEHIE0SfE0EQBEEQBEEQ8gQRnAiCIAiCIAiCkCeI4EQQBEEQBEEQhDwhXwYnCxcupFixYtjY2FCtWjUOHTr03PQHDhygWrVq2NjYEBgYyOLFi3OppEJ2yMr53r9/P5IkpXv9999/uVhi4WUdPHiQVq1a4evriyRJbN++/YXbiOv79ZXV8y2u79fX1KlTqVGjBo6Ojnh5edGmTRsuX778wu3E9f16epnzLa7vgiPfBScbNmxg4MCBjB49muDgYOrWrUvz5s0JDQ3NMP3Nmzd59913qVu3LsHBwYwaNYoBAwawZcuWXC658DKyer5TXb58mbCwMNOrRIkSuVRi4VUkJCRQqVIlvv32W7PSi+v79ZbV851KXN+vnwMHDtCvXz/+/fdf9uzZg06no2nTpiQkJGS6jbi+X18vc75Tieu7AJDzmZo1a8qffvppmmWlS5eWR4wYkWH6YcOGyaVLl06z7JNPPpFr1aqVY2UUsk9Wz/e+fftkQH748GEulE7ISYC8bdu256YR13f+Yc75Ftd3/hERESED8oEDBzJNI67v/MOc8y2u74IjX9WcaDQaTp48SdOmTdMsb9q0KUeOHMlwm3/++Sdd+mbNmnHixAm0Wm2OlVV4dS9zvlNVqVIFHx8fGjVqxL59+3KymIIFieu7YBLX9+svJiYGADc3t0zTiOs7/zDnfKcS13f+l6+Ck6ioKPR6Pd7e3mmWe3t7c//+/Qy3uX//fobpdTodUVFROVZW4dW9zPn28fHh+++/Z8uWLWzdupVSpUrRqFEjDh48mBtFFnKZuL4LFnF95w+yLDN48GDeeustypcvn2k6cX3nD+aeb3F9FxwqSxcgJ0iSlOa9LMvplr0ofUbLhbwpK+e7VKlSlCpVyvS+du3a3L59m1mzZuXZmVKFVyOu74JDXN/5w+eff87Zs2c5fPjwC9OK6/v1Z+75Ftd3wZGvak48PDxQKpXpnppHRESke7qSqlChQhmmV6lUuLu751hZhVf3Muc7I7Vq1eLq1avZXTwhDxDXtyCu79dL//79+eWXX9i3bx+FCxd+blpxfb/+snK+MyKu7/wpXwUnVlZWVKtWjT179qRZvmfPHurUqZPhNrVr106X/o8//qB69eqo1eocK6vw6l7mfGckODgYHx+f7C6ekAeI61sQ1/frQZZlPv/8c7Zu3cpff/1FsWLFXriNuL5fXy9zvjMiru98ymJd8XPI+vXrZbVaLS9btky+ePGiPHDgQNne3l6+deuWLMuyPGLECLlr166m9Ddu3JDt7OzkQYMGyRcvXpSXLVsmq9VqefPmzZY6BCELsnq+586dK2/btk2+cuWKfP78eXnEiBEyIG/ZssVShyBkQVxcnBwcHCwHBwfLgDxnzhw5ODhYDgkJkWVZXN/5TVbPt7i+X1+fffaZ7OzsLO/fv18OCwszvRITE01pxPWdf7zM+RbXd8GR74ITWZbl7777Tvb395etrKzkqlWrphmarnv37vLbb7+dJv3+/fvlKlWqyFZWVnJAQIC8aNGiXC6x8Cqycr6nT58uBwUFyTY2NrKrq6v81ltvybt27bJAqYWXkTqU5LOv7t27y7Isru/8JqvnW1zfr6+MzjMgr1ixwpRGXN/5x8ucb3F9FxySLD/uPSYIgiAIgiAIgmBB+arPiSAIgiAIgiAIry8RnAiCIAiCIAiCkCeI4EQQBEEQBEEQhDxBBCeCIAiCIAiCIOQJIjgRBEEQBEEQBCFPEMGJIAiCIAiCIAh5gghOBEEQBEEQBEHIE0RwIgiCIAiCIAhCniCCE0EQhFewf/9+JEni0aNHObaP+vXrM3DgwBzLXxAEQRDyChGcCIIgvMCRI0dQKpW88847li6KWW7duoUkSZw+ffqV8+rRoweSJKV7Pf1ZSJLE9u3bX3lfgiAIgqCydAEEQRDyuuXLl9O/f3+WLl1KaGgoRYsWtXSRctU777zDihUr0iyztra2UGkEQRCE/EzUnAiCIDxHQkICGzdu5LPPPqNly5asXLkyw3R///03lSpVwsbGhjfeeINz586Z1oWEhNCqVStcXV2xt7enXLly/Prrr6b1Bw4coGbNmlhbW+Pj48OIESPQ6XSZlimjmgoXFxdT2YoVKwZAlSpVkCSJ+vXrm9KtWLGCMmXKYGNjQ+nSpVm4cOELPwNra2sKFSqU5uXq6gpAQEAAAO+//z6SJJneX79+ndatW+Pt7Y2DgwM1atTgzz//fOG+BEEQhIJNBCeCIAj/b+feQqJa/zCOf2enlZ3Ms6VDEEOjwTRURJlRFIYp2GmkIIQCCywPhSVzYWYHO4FDEZKITWnQRTcKYmGmXRRIjFFKkFgQYeUEXczNVFiT878QF3t26qb+m82wez6wYK13rXe9a9bdM7/3XVO4ffs2VqsVq9VKQUEBN27cIBgM/nBdRUUFtbW19Pb2kpiYyNatW/n27RsAxcXFjIyM8PDhQ54/f87FixeZM2cOAO/fvyc3N5dVq1bR399PfX09brebmpqaX35mj8cDQFdXF16vl5aWFgAaGxuprKzk7NmzDAwMcO7cOaqqqmhubv7lsXp7e4Gx0OP1eo1jv99Pbm4uXV1dPHv2jOzsbPLy8hgaGvrlsURE5L9P07pERKbgdrspKCgAxqY3+f1+uru7ycrKCrmuurqazZs3A9Dc3Exqaiqtra3s2rWLoaEhHA4HNpsNgMWLFxv9rl69itlspq6uDpPJRFpaGsPDwzidTk6cOMEff/z8f0gJCQkAxMXFkZycbLSfOXMGl8vFzp07gbEKy4sXL2hoaGDv3r2T3q+9vd0IU+OcTidVVVXGWPPnzw8Zy263Y7fbjeOamhpaW1tpa2ujpKTkp3+TiIj8HhROREQmMTg4iMfjMSoPERER7N69m+vXr/8QTjIyMoz92NhYrFYrAwMDAJSVlXHw4EE6OzvJysrC4XCwbNkyAAYGBsjIyMBkMhn9MzMz8fv9vHv37h9b3/Lx40fevn1LYWEhBw4cMNoDgQDR0dFT9t24cSP19fUhbbGxsVP2+fTpE6dOnaK9vZ3h4WECgQBfvnxR5URERKakcCIiMgm3200gECAlJcVoCwaDREZG4vP5jHUXkxkPHPv37yc7O5s7d+7Q2dnJ+fPncblclJaWEgwGQ4LJ+Bh/7j/Rff86tWx8CtlkRkdHgbGpXatXrw45N23atCn7zp49G4vFMuU1f1VRUcG9e/eora3FYrEQFRVFfn4+X79+/an7iIjI70VrTkREJhAIBLh58yYul4u+vj5j6+/vZ9GiRdy6dSvk+sePHxv7Pp+Ply9fkpaWZrSZzWaKiopoaWnh6NGjNDY2ArB06VJ6enpCwkZPTw9z584NCUV/lpCQgNfrNY5fvXrF58+fjePp06cD8P37d6MtKSmJlJQUXr9+jcViCdnGF9D/qsjIyJCxAB49esS+ffvYsWMHNpuN5ORk3rx583+NIyIi/32qnIiITKC9vR2fz0dhYeEP057y8/Nxu90haydOnz5NXFwcSUlJVFZWEh8fz/bt2wE4cuQIOTk5LFmyBJ/Px4MHD0hPTwfg0KFDXL58mdLSUkpKShgcHKS6upry8vJJ15ts2rSJuro61qxZw+joKE6nk8jISON8YmIiUVFRdHR0kJqaysyZM4mOjubkyZOUlZUxb948cnJyGBkZ4cmTJ/h8PsrLyyd9FyMjI3z48CGkLSIigvj4eGDsi13d3d1kZmYyY8YMYmJisFgstLS0kJeXh8lkoqqqyqjeiIiITEaVExGRCbjdbrKysiZcj+FwOOjr6+Pp06dG24ULFzh8+DArV67E6/XS1tYWUsEoLi4mPT2dLVu2YLVajU/4pqSkcPfuXTweD3a7naKiIgoLCzl+/Pikz+ZyuTCbzaxfv549e/Zw7NgxZs2aZZyPiIjgypUrNDQ0sHDhQrZt2waMTS+7du0aTU1N2Gw2NmzYQFNT099WTjo6OliwYEHItm7dupDnuX//PmazmeXLlwNw6dIlYmJiWLt2LXl5eWRnZ7NixYq/e+0iIvKbMwUn+iamiIiIiIjIv0yVExERERERCQsKJyIiIiIiEhYUTkREREREJCwonIiIiIiISFhQOBERERERkbCgcCIiIiIiImFB4URERERERMKCwomIiIiIiIQFhRMREREREQkLCiciIiIiIhIWFE5ERERERCQs/A/RzzHqnONsugAAAABJRU5ErkJggg==",
0772 "text/plain": [
0773 "<Figure size 1000x600 with 2 Axes>"
0774 ]
0775 },
0776 "metadata": {},
0777 "output_type": "display_data"
0778 },
0779 {
0780 "name": "stdout",
0781 "output_type": "stream",
0782 "text": [
0783 "\n",
0784 "pt: 0 to 5\n",
0785 "90% Retention Cut: {0.9204, 0.9421, 0.9441, 0.9532, 0.961, 0.9352, 0.8842, 0.8606, 0.8345, 0.7997} Mean: 0.9035\n",
0786 "93% Retention Cut: {0.8482, 0.8907, 0.8972, 0.9145, 0.943, 0.9027, 0.8286, 0.8234, 0.7881, 0.7249} Mean: 0.8561\n",
0787 "95% Retention Cut: {0.7417, 0.8096, 0.8166, 0.8495, 0.9192, 0.8586, 0.7761, 0.7821, 0.7431, 0.6526} Mean: 0.7949\n",
0788 "98% Retention Cut: {0.4325, 0.449, 0.5454, 0.6257, 0.8199, 0.6123, 0.5456, 0.5901, 0.5441, 0.393} Mean: 0.5558\n",
0789 "99% Retention Cut: {0.2897, 0.2835, 0.3589, 0.4254, 0.6923, 0.4273, 0.3172, 0.248, 0.4203, 0.2651} Mean: 0.3728\n",
0790 "99.5% Retention Cut: {0.189, 0.1805, 0.2267, 0.3104, 0.4719, 0.3159, 0.1372, 0.1571, 0.3198, 0.186} Mean: 0.2495\n"
0791 ]
0792 },
0793 {
0794 "data": {
0795 "image/png": 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",
0796 "text/plain": [
0797 "<Figure size 1000x600 with 2 Axes>"
0798 ]
0799 },
0800 "metadata": {},
0801 "output_type": "display_data"
0802 },
0803 {
0804 "name": "stdout",
0805 "output_type": "stream",
0806 "text": [
0807 "\n",
0808 "pt: 5 to inf\n",
0809 "90% Retention Cut: {0.5279} Mean: 0.5279\n",
0810 "93% Retention Cut: {0.3964} Mean: 0.3964\n",
0811 "95% Retention Cut: {0.2557} Mean: 0.2557\n",
0812 "98% Retention Cut: {0.0473} Mean: 0.0473\n",
0813 "99% Retention Cut: {0.0091} Mean: 0.0091\n",
0814 "99.5% Retention Cut: {0.0024} Mean: 0.0024\n"
0815 ]
0816 }
0817 ],
0818 "source": [
0819 "import numpy as np\n",
0820 "from matplotlib import pyplot as plt\n",
0821 "from matplotlib.colors import LogNorm\n",
0822 "\n",
0823 "def plot_for_pt_bin(pt_min, pt_max, percentiles, eta_bin_edges, eta_list, predictions, full_tracks, branches):\n",
0824 " \"\"\"\n",
0825 " Calculate and plot cut values for specified percentiles in a given pt bin\n",
0826 " \n",
0827 " Parameters:\n",
0828 " -----------\n",
0829 " pt_min : float\n",
0830 " Minimum pt value for the bin\n",
0831 " pt_max : float\n",
0832 " Maximum pt value for the bin\n",
0833 " percentiles : list\n",
0834 " List of percentiles to calculate (e.g., [92.5, 96.7, 99])\n",
0835 " eta_bin_edges : array\n",
0836 " Edges of the eta bins\n",
0837 " eta_list : list\n",
0838 " List of eta values\n",
0839 " predictions : array\n",
0840 " Array of DNN predictions\n",
0841 " full_tracks : array\n",
0842 " Boolean array for track selection\n",
0843 " branches : dict\n",
0844 " Dictionary containing branch data\n",
0845 " \"\"\"\n",
0846 " # Filter data based on pt bin\n",
0847 " abs_eta = eta_list[0][full_tracks & (np.concatenate(branches['pT3_pt']) > pt_min) & \n",
0848 " (np.concatenate(branches['pT3_pt']) <= pt_max)]\n",
0849 " predictions_filtered = predictions[full_tracks & (np.concatenate(branches['pT3_pt']) > pt_min) & \n",
0850 " (np.concatenate(branches['pT3_pt']) <= pt_max)]\n",
0851 " \n",
0852 " # Dictionary to store cut values for different percentiles\n",
0853 " cut_values = {p: [] for p in percentiles}\n",
0854 "\n",
0855 " # Loop through each eta bin\n",
0856 " for i in range(len(eta_bin_edges) - 1):\n",
0857 " # Get indices of tracks within the current eta bin\n",
0858 " bin_indices = (abs_eta >= eta_bin_edges[i]) & (abs_eta < eta_bin_edges[i + 1])\n",
0859 " \n",
0860 " # Get the corresponding DNN prediction scores\n",
0861 " bin_predictions = predictions_filtered[bin_indices]\n",
0862 " \n",
0863 " # Calculate the percentile cut values for the current bin\n",
0864 " for percentile in percentiles:\n",
0865 " cut_value = np.percentile(bin_predictions, 100 - percentile) # Convert retention to percentile\n",
0866 " cut_values[percentile].append(cut_value)\n",
0867 "\n",
0868 " # Plot 2D histogram\n",
0869 " plt.figure(figsize=(10, 6))\n",
0870 " plt.hist2d(abs_eta, predictions_filtered, bins=[eta_bin_edges, 50], norm=LogNorm())\n",
0871 " plt.colorbar(label='Counts')\n",
0872 " plt.xlabel(\"Absolute Eta\")\n",
0873 " plt.ylabel(\"DNN Prediction Score\")\n",
0874 " plt.title(f\"DNN Score vs. Abs Eta for 100% Matched Tracks (pt: {pt_min} to {pt_max})\")\n",
0875 "\n",
0876 " # Plot the cut values with different colors\n",
0877 " cut_x = eta_bin_edges[:-1] + (eta_bin_edges[1] - eta_bin_edges[0]) / 2 # Mid-points of the bins\n",
0878 " colors = plt.cm.rainbow(np.linspace(0, 1, len(percentiles))) # Generate distinct colors\n",
0879 " \n",
0880 " for percentile, color in zip(percentiles, colors):\n",
0881 " plt.plot(cut_x, cut_values[percentile], '-', color=color, marker='o', \n",
0882 " label=f'{percentile}% Retention Cut')\n",
0883 " \n",
0884 " plt.legend()\n",
0885 " plt.grid(True, alpha=0.3)\n",
0886 " plt.show()\n",
0887 " \n",
0888 " # Print the cut values\n",
0889 " print(f\"\\npt: {pt_min} to {pt_max}\")\n",
0890 " for percentile in percentiles:\n",
0891 " values = cut_values[percentile]\n",
0892 " print(f\"{percentile}% Retention Cut:\", \n",
0893 " '{' + ', '.join(str(x) for x in np.round(values, 4)) + '}',\n",
0894 " \"Mean:\", np.round(np.mean(values), 4))\n",
0895 "\n",
0896 "# Example usage:\n",
0897 "def analyze_pt_bins(pt_bins, percentiles, eta_bin_edges, eta_list, predictions, full_tracks, branches):\n",
0898 " \"\"\"\n",
0899 " Analyze and plot for multiple pt bins and percentiles\n",
0900 " \n",
0901 " Parameters:\n",
0902 " -----------\n",
0903 " pt_bins : list\n",
0904 " List of pt bin edges\n",
0905 " percentiles : list\n",
0906 " List of percentiles to calculate\n",
0907 " Other parameters same as plot_for_pt_bin function\n",
0908 " \"\"\"\n",
0909 " for i in range(len(pt_bins) - 1):\n",
0910 " plot_for_pt_bin(pt_bins[i], pt_bins[i + 1], percentiles, eta_bin_edges, \n",
0911 " eta_list, predictions, full_tracks, branches)\n",
0912 "\n",
0913 "percentiles = [90, 93, 95, 98, 99, 99.5]\n",
0914 "\n",
0915 "# For pt <= 5 using multiple eta bins\n",
0916 "pt_bins_low = [0, 5]\n",
0917 "analyze_pt_bins(pt_bins_low, percentiles, np.arange(0, 2.75, 0.25), eta_list, predictions, full_tracks, branches)\n",
0918 "\n",
0919 "# For pt > 5 using a single eta bin\n",
0920 "pt_bins_high = [5, np.inf]\n",
0921 "single_eta_bin = np.array([0, 2.75])\n",
0922 "analyze_pt_bins(pt_bins_high, percentiles, single_eta_bin, eta_list, predictions, full_tracks, branches)"
0923 ]
0924 },
0925 {
0926 "cell_type": "code",
0927 "execution_count": null,
0928 "metadata": {},
0929 "outputs": [],
0930 "source": []
0931 }
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