Matrix Games.ipynb

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    "# if you don't have some package, install it right here with \n",
    "# import Pkg; Pkg.add(\"Name of the Package\")\n",
    "using LinearAlgebra: norm, opnorm\n",
    "using Plots \n",
    "using JLD2 # for saving data. One can comment it\n",
    "\n",
    "include(\"problem_instances.jl\")\n",
    "using .Problems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Euclidean run\n",
    "\n",
    "\n",
    "| id  | function  | n  |   |   |\n",
    "|---|---|---|---|---|\n",
    "|probl_1| policeman_and_burglar_matrix  |  500 |   |   |\n",
    "|probl_2| nemirovski1  | 500  |   |   |\n",
    "|probl_3| nemirovski2   | 500  |   |   |\n",
    "\n",
    "The first problem is randomly generated. For the paper we used seed `sd=\"1\"`. Set `sd=\"false\"` otherwise. For the first run of code set `m, n, N` small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Operator norm of A is 45.9,\n",
      "Frobenius norm is 46.5,\n",
      "max norm is 2.3"
     ]
    }
   ],
   "source": [
    "# Choose dimensions and problem instance\n",
    "m, n = 50, 50\n",
    "\n",
    "probl = 1 # 1, 2 or 3\n",
    "sd = \"1\"\n",
    "\n",
    "\n",
    "if probl == 1\n",
    "    A = Problems.policeman_and_burglar_matrix(n; seed=sd)\n",
    "    filename = \"save/probl_1e.jld2\" \n",
    "elseif probl == 2\n",
    "    A = Problems.nemirovski1(n)\n",
    "    filename = \"save/probl_2e.jld2\" \n",
    "elseif probl == 3\n",
    "    A = Problems.nemirovski2(n, 2)\n",
    "    filename = \"save/probl_3e.jld2\"\n",
    "elseif probl == 4\n",
    "    A = Problems.randunif(m, n; seed=sd)\n",
    "    filename = \"save/probl_4e.jld2\"\n",
    "    \n",
    "end\n",
    "       \n",
    "\n",
    "# Compute different norms of A\n",
    "max_norm = maximum(abs.(A))\n",
    "l2_norm = norm(A, 2)\n",
    "sp_norm = opnorm(A)\n",
    "print(\"Operator norm of A is $(round(sp_norm, digits=1)),\n",
    "Frobenius norm is $(round(l2_norm, digits=1)),\n",
    "max norm is $(round(max_norm, digits=1))\")\n",
    "\n",
    "x0 = ones(n)/n \n",
    "y0 = ones(m)/m\n",
    "\n",
    "z0 = [x0; y0]\n",
    "tol = 1e-8\n",
    "\n",
    "# deterministic algorithms\n",
    "step_dtr = 0.99 / sp_norm\n",
    "\n",
    "# proposed variance reduction\n",
    "K = ceil(Int64, 2*m*n/(m + n))\n",
    "p = 1.0 / K\n",
    "α = 1 - p\n",
    "step_vr = 0.99 * sqrt(p) / l2_norm\n",
    "\n",
    "# proposed variance reduction-looped\n",
    "K = ceil(Int64, 2*m*n/(m + n))\n",
    "p = 1.0 / K\n",
    "α = 1 - p\n",
    "step_vr = 0.99 * sqrt(p) / l2_norm\n",
    "\n",
    "# Carmon et al. Euclidean setting\n",
    "step_mult = 4.0 \n",
    "alpha_ = l2_norm * sqrt((m + n)/(2 * m * n))\n",
    "η = alpha_ / (step_mult * l2_norm^2);\n"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "#### Set number of epochs `N` and run the algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.374812 seconds (670.99 k allocations: 34.562 MiB, 5.78% gc time)\n",
      "  0.766644 seconds (1.32 M allocations: 252.624 MiB, 4.56% gc time)\n",
      "  0.600771 seconds (872.54 k allocations: 233.146 MiB, 3.98% gc time)\n",
      "  0.539822 seconds (1.37 M allocations: 230.313 MiB, 5.05% gc time)\n"
     ]
    }
   ],
   "source": [
    "include(\"algorithms.jl\")\n",
    "projection(x) = proj_simplex1(x)\n",
    "\n",
    "N = 1000\n",
    "@time res1 = extraGrad(A, projection, z0, step_dtr, N, tol=tol)\n",
    "@time res2 = stochMPCarmon(A, projection, z0, alpha_, η, N, distr=true, tol=tol)\n",
    "@time res3 = stochExtraGradLooped(A, projection, z0, step_vr, α, K, N,\n",
    "            bregman=false, distr=true, tol=tol)\n",
    "@time res4 = stochExtraGradLoopless(A, projection, z0, step_vr, α, p, N,\n",
    "            bregman=false, distr=true, tol=tol);\n",
    "\n",
    "@save filename res1 res2 res3 res4\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Timing is not important here, since we compare algorithms' performance with respect to arithmetic operations. And of course our VR-implmentation is not so good."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make a quick plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "  1051,472.322 1052.89,469.003 1054.78,469.473 1056.67,466.665 1058.57,467.135 1060.46,464.828 1062.35,465.3 1064.24,463.483 1066.13,463.957 1068.02,462.626 \n",
       "  1069.92,463.101 1071.81,462.251 1073.7,462.727 1075.59,462.356 1077.48,462.834 1079.37,462.941 1081.27,463.419 1083.16,464.007 1085.05,464.487 1086.94,465.558 \n",
       "  1088.83,466.039 1090.72,467.599 1092.61,468.081 1094.51,470.139 1096.4,470.622 1098.29,473.187 1100.18,473.672 1102.07,476.756 1103.96,477.242 1105.86,480.539 \n",
       "  1107.75,481.027 1109.64,484.693 1111.53,485.181 1113.42,489.404 1115.31,489.893 1117.21,494.693 1119.1,495.182 1120.99,500.585 1122.88,501.075 1124.77,507.109 \n",
       "  1126.66,507.6 1128.56,514.302 1130.45,514.793 1132.34,522.202 1134.23,522.694 1136.12,530.858 1138.01,531.351 1139.9,540.326 1141.8,540.82 1143.69,550.673 \n",
       "  1145.58,551.167 1147.47,561.976 1149.36,562.471 1151.25,574.332 1153.15,574.828 1155.04,587.855 1156.93,588.352 1158.82,602.685 1160.71,603.182 1162.6,618.996 \n",
       "  1164.5,619.495 1166.39,637.008 1168.28,637.508 1170.17,646.47 1172.06,646.873 1173.95,643.898 1175.85,644.299 1177.74,641.756 1179.63,642.155 1181.52,640.032 \n",
       "  1183.41,640.429 1185.3,638.717 1187.19,639.111 1189.09,637.541 1190.98,638.059 1192.87,629.766 1194.76,630.282 1196.65,622.773 1198.54,623.287 1200.44,616.511 \n",
       "  1202.33,617.023 1204.22,610.936 1206.11,611.447 1208,606.012 1209.89,606.521 1211.79,601.706 1213.68,602.213 1215.57,597.992 1217.46,598.498 1219.35,594.847 \n",
       "  1221.24,595.351 1223.13,592.251 1225.03,592.753 1226.92,590.188 1228.81,590.688 1230.7,588.644 1232.59,589.142 1234.48,587.608 1236.38,588.105 1238.27,587.072 \n",
       "  1240.16,587.567 1242.05,587.029 1243.94,587.522 1245.83,587.475 1247.73,587.966 1249.62,588.408 1251.51,588.897 1253.4,589.827 1255.29,590.314 1257.18,591.734 \n",
       "  1259.08,592.219 1260.97,594.133 1262.86,594.615 1264.75,597.03 1266.64,597.509 1268.53,600.432 1270.42,600.909 1272.32,604.35 1274.21,604.824 1276.1,608.341 \n",
       "  1277.99,608.813 1279.88,612.553 1281.77,613.021 1283.67,617.294 1285.56,617.758 1287.45,622.58 1289.34,623.04 1291.23,628.431 1293.12,628.887 1295.02,634.868 \n",
       "  1296.91,635.319 1298.8,641.918 1300.69,642.364 1302.58,649.611 1304.47,650.052 1306.37,657.983 1308.26,658.418 1310.15,667.076 1312.04,667.505 1313.93,676.939 \n",
       "  1315.82,677.361 1317.71,685.891 1319.61,686.474 1321.5,691.38 1323.39,691.963 1325.28,686.168 1327.17,686.642 1329.06,664.36 1330.96,664.836 1332.85,644.953 \n",
       "  1334.74,645.432 1336.63,627.568 1338.52,628.05 1340.41,611.918 1342.31,612.4 1344.2,597.775 1346.09,598.259 1347.98,584.962 1349.87,585.448 1351.76,573.335 \n",
       "  1353.66,573.822 1355.55,562.777 1357.44,563.266 1359.33,553.191 1361.22,553.681 1363.11,544.497 1365,544.987 1366.9,536.627 1368.79,537.118 1370.68,529.524 \n",
       "  1372.57,530.016 1374.46,523.14 1376.35,523.632 1378.25,517.434 1380.14,517.927 1382.03,512.372 1383.92,512.865 1385.81,507.922 1387.7,508.416 1389.6,504.061 \n",
       "  1391.49,504.555 1393.38,500.766 1395.27,501.261 1397.16,498.021 1399.05,498.516 1400.95,495.809 1402.84,496.305 1404.73,494.12 1406.62,494.616 1408.51,492.943 \n",
       "  1410.4,493.439 1412.29,492.271 1414.19,492.768 1416.08,492.101 1417.97,492.598 1419.86,492.428 1421.75,492.926 1423.64,493.253 1425.54,493.751 1427.43,494.578 \n",
       "  1429.32,495.076 1431.21,496.405 1433.1,496.904 1434.99,498.742 1436.89,499.241 1438.78,501.597 1440.67,502.095 1442.56,503.517 1444.45,503.977 1446.34,503.919 \n",
       "  1448.23,504.381 1450.13,504.786 1452.02,505.251 1453.91,501.416 1455.8,501.878 1457.69,493.858 1459.58,494.323 1461.48,487.024 1463.37,487.492 1465.26,480.873 \n",
       "  1467.15,481.344 1469.04,475.368 1470.93,475.841 1472.83,470.48 1474.72,470.955 1476.61,466.181 1478.5,466.658 1480.39,462.451 1482.28,462.93 1484.18,459.27 \n",
       "  1486.07,459.751 1487.96,456.624 1489.85,457.107 1491.74,454.499 1493.63,454.984 1495.52,452.887 1497.42,453.374 1499.31,451.78 1501.2,452.268 1503.09,451.171 \n",
       "  1504.98,451.661 1506.87,451.059 1508.77,451.551 1510.66,451.442 1512.55,451.936 1514.44,452.322 1516.33,452.817 1518.22,453.702 1520.12,454.199 1522.01,455.587 \n",
       "  1523.9,456.085 1525.79,457.985 1527.68,458.485 1529.57,460.907 1531.47,461.408 1533.36,464.364 1535.25,464.868 1537.14,468.374 1539.03,468.879 1540.92,472.954 \n",
       "  1542.81,473.461 1544.71,478.127 1546.6,478.636 1548.49,483.92 1550.38,484.431 1552.27,490.363 1554.16,490.876 1556.06,497.493 1557.95,498.008 1559.84,505.353 \n",
       "  1561.73,505.87 1563.62,506.504 1565.51,506.976 1567.41,505.333 1569.3,505.806 1571.19,504.645 1573.08,505.12 1574.97,504.438 1576.86,504.913 1578.76,504.709 \n",
       "  1580.65,505.185 1582.54,505.457 1584.43,505.935 1586.32,506.687 1588.21,507.165 1590.1,508.4 1592,508.879 1593.89,510.603 1595.78,511.083 1597.67,513.305 \n",
       "  1599.56,513.786 1601.45,516.516 1603.35,516.998 1605.24,520.249 1607.13,520.732 1609.02,524.52 1610.91,525.003 1612.8,529.347 1614.7,529.831 1616.59,534.753 \n",
       "  1618.48,535.238 1620.37,540.763 1622.26,541.25 1624.15,545.859 1626.04,546.391 1627.94,549.868 1629.83,550.403 1631.72,554.48 1633.61,555.019 1635.5,559.72 \n",
       "  1637.39,560.262 1639.29,565.618 1641.18,566.163 1643.07,572.209 1644.96,572.758 1646.85,579.534 1648.74,580.087 1650.64,587.641 1652.53,588.198 1654.42,596.589 \n",
       "  1656.31,597.151 1658.2,606.445 1660.09,607.012 1661.99,617.292 1663.88,617.864 1665.77,629.227 1667.66,629.804 1669.55,642.37 1671.44,642.953 1673.33,656.867 \n",
       "  1675.23,657.458 1677.12,672.9 1679.01,673.499 1680.9,690.701 1682.79,691.309 1684.68,710.565 1686.58,711.184 1688.47,732.882 1690.36,733.515 1692.25,743.083 \n",
       "  1694.14,743.369 1696.03,737.98 1697.93,738.268 1699.82,733.284 1701.71,733.572 1703.6,728.976 1705.49,729.265 1707.38,725.043 1709.28,725.334 1711.17,721.473 \n",
       "  1713.06,721.764 1714.95,718.254 1716.84,718.546 1718.73,715.377 1720.62,715.67 1722.52,712.834 1724.41,713.128 1726.3,710.617 1728.19,710.912 1730.08,708.721 \n",
       "  1731.97,709.017 1733.87,707.141 1735.76,707.438 1737.65,705.873 1739.54,706.171 1741.43,704.914 1743.32,705.214 1745.22,704.262 1747.11,704.563 1749,699.996 \n",
       "  1750.89,700.421 1752.78,695.907 1754.67,696.337 1756.57,692.337 1758.46,692.773 1760.35,689.274 1762.24,689.715 1764.13,686.707 1766.02,687.153 1767.91,684.628 \n",
       "  1769.81,685.079 1771.7,683.032 1773.59,683.487 1775.48,681.913 1777.37,682.373 1779.26,681.271 1781.16,681.735 1783.05,681.104 1784.94,681.572 1786.83,681.415 \n",
       "  1788.72,681.888 1790.61,682.207 1792.51,682.684 1794.4,683.485 1796.29,683.967 1798.18,685.258 1800.07,685.744 1801.96,687.535 1803.86,688.025 1805.75,690.329 \n",
       "  1807.64,690.823 1809.53,693.654 1811.42,694.152 1813.31,697.527 1815.2,698.03 1817.1,701.971 1818.99,702.479 1820.88,707.009 1822.77,707.521 1824.66,712.335 \n",
       "  1826.55,712.832 1828.45,713.186 1830.34,713.498 1832.23,712.79 1834.12,713.104 1836.01,712.713 1837.9,713.029 1839.8,712.957 1841.69,713.275 1843.58,707.686 \n",
       "  1845.47,708.018 1847.36,685.302 1849.25,685.649 1851.14,665.297 1853.04,665.657 1854.93,647.295 1856.82,647.666 1858.71,631.009 1860.6,631.389 1862.49,616.216 \n",
       "  1864.39,616.604 1866.28,602.738 1868.17,603.133 1870.06,590.432 1871.95,590.833 1873.84,579.18 1875.74,579.588 1877.63,568.887 1879.52,569.3 1881.41,559.472 \n",
       "  1883.3,559.89 1885.19,550.868 1887.09,551.29 1888.98,543.017 1890.87,543.443 1892.76,535.871 1894.65,536.3 1896.54,529.388 1898.43,529.821 1900.33,523.534 \n",
       "  1902.22,523.97 1904.11,518.277 1906,518.717 1907.89,513.593 1909.78,514.035 1911.68,509.458 1913.57,509.903 1915.46,505.855 1917.35,506.303 1919.24,502.767 \n",
       "  1921.13,503.218 1923.03,500.182 1924.92,500.635 1926.81,498.088 1928.7,498.544 1930.59,496.478 1932.48,496.936 1934.38,495.346 1936.27,495.806 1938.16,494.686 \n",
       "  1940.05,495.149 1941.94,494.497 1943.83,494.962 1945.72,494.779 1947.62,495.246 1949.51,495.532 1951.4,496.002 1953.29,496.76 1955.18,497.232 1957.07,498.469 \n",
       "  1958.97,498.944 1960.86,500.666 1962.75,501.143 1964.64,503.361 1966.53,503.84 1968.42,506.565 1970.32,507.047 1972.21,510.294 1974.1,510.777 1975.99,514.564 \n",
       "  1977.88,515.05 1979.77,510.124 1981.67,510.61 1983.56,504.922 1985.45,505.41 1987.34,500.335 1989.23,500.824 1991.12,496.337 1993.01,496.827 1994.91,492.906 \n",
       "  1996.8,493.397 1998.69,490.025 2000.58,490.516 2002.47,487.677 2004.36,488.169 2006.26,485.851 2008.15,486.343 2010.04,484.537 2011.93,485.03 2013.82,483.727 \n",
       "  2015.71,484.221 2017.61,483.417 2019.5,483.911 2021.39,483.604 2023.28,484.099 2025.17,484.286 2027.06,484.781 2028.96,485.466 2030.85,485.962 2032.74,487.146 \n",
       "  2034.63,487.642 2036.52,489.332 2038.41,489.829 2040.3,492.033 2042.2,492.531 2044.09,495.259 2045.98,495.757 2047.87,499.023 2049.76,499.522 2051.65,503.341 \n",
       "  2053.55,503.84 2055.44,508.231 2057.33,508.731 2059.22,513.718 2061.11,514.218 2063,519.827 2064.9,520.328 2066.79,526.59 2068.68,527.092 2070.57,534.045 \n",
       "  2072.46,534.547 2074.35,542.235 2076.24,542.738 2078.14,542.786 2080.03,543.291 2081.92,540.426 2083.81,540.932 2085.7,538.604 2087.59,539.112 2089.49,537.311 \n",
       "  2091.38,537.821 2093.27,536.541 2095.16,537.051 2097.05,536.288 2098.94,536.8 2100.84,536.551 2102.73,537.065 2104.62,537.331 2106.51,537.846 2108.4,538.629 \n",
       "  2110.29,539.145 2112.19,540.451 2114.08,540.969 2115.97,542.804 2117.86,543.323 2119.75,545.698 2121.64,546.219 2123.53,549.125 2125.43,549.651 2127.32,552.893 \n",
       "  2129.21,553.42 2131.1,557.244 2132.99,557.773 2134.88,562.2 2136.78,562.73 2138.67,567.787 2140.56,568.318 2142.45,574.033 2144.34,574.566 2146.23,580.975 \n",
       "  2148.13,581.51 2150.02,588.654 2151.91,589.191 2153.8,597.119 2155.69,597.658 2157.58,606.428 2159.48,606.969 2161.37,616.65 2163.26,617.194 2165.15,627.868 \n",
       "  2167.04,628.414 2168.93,640.182 2170.82,640.731 2172.72,653.711 2174.61,654.263 2176.5,668.605 2178.39,669.16 2180.28,680.592 2182.17,680.983 2184.07,692.421 \n",
       "  2185.96,692.807 2187.85,705.239 2189.74,705.618 2191.63,719.15 2193.52,719.521 2195.42,734.282 2197.31,734.644 2199.2,750.79 2201.09,751.143 2202.98,756.843 \n",
       "  2204.87,757.214 2206.77,748.004 2208.66,748.383 2210.55,739.899 2212.44,740.286 2214.33,732.482 2216.22,732.876 2218.11,725.715 2220.01,726.115 2221.9,719.565 \n",
       "  2223.79,719.971 2225.68,714.003 2227.57,714.415 2229.46,709.005 2231.36,709.423 2233.25,704.551 2235.14,704.974 2237.03,700.622 2238.92,701.051 2240.81,697.204 \n",
       "  2242.71,697.638 2244.6,694.285 2246.49,694.723 2248.38,691.855 2250.27,692.298 2252.16,689.906 2254.06,690.353 2255.95,688.277 2257.84,688.734 2259.73,686.971 \n",
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      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr()\n",
    "\n",
    "# some hack to put markers less frequently. From https://github.com/JuliaPlots/Plots.jl/issues/2523#issuecomment-607090470\n",
    "# now their frequency is controled by parameter `step`\n",
    "\n",
    "@recipe function f(::Type{Val{:samplemarkers}}, x, y, z; step = 500)\n",
    "    n = length(y)\n",
    "    sx, sy = x[1:step:n], y[1:step:n]\n",
    "    # add an empty series with the correct type for legend markers\n",
    "    @series begin\n",
    "        seriestype := :path\n",
    "        markershape --> :auto\n",
    "        x := [Inf]\n",
    "        y := [Inf]\n",
    "    end\n",
    "    # add a series for the line\n",
    "    @series begin\n",
    "        primary := false # no legend entry\n",
    "        markershape := :none # ensure no markers\n",
    "        seriestype := :path\n",
    "        seriescolor := get(plotattributes, :seriescolor, :auto)\n",
    "        x := x\n",
    "        y := y\n",
    "    end\n",
    "    # return  a series for the sampled markers\n",
    "    primary := false\n",
    "    seriestype := :scatter\n",
    "    markershape --> :auto\n",
    "    x := sx\n",
    "    y := sy\n",
    "end\n",
    "### end of hack\n",
    "\n",
    "labels = [\"EG\",\"EG-Car+19\", \"EG-Alg2\", \"EG-Alg1\"]\n",
    "plot(res1[1], yaxis=:log, label=labels[1], legend=:topright, linewidth=2,marker=:auto, seriestype=:samplemarkers, step=50, xlabel = \"epoch\", ylabel = \"Duality gap\")\n",
    "plot!(res2[4], res2[1], label=labels[2], linewidth=2,marker=:auto, seriestype=:samplemarkers, step=10)\n",
    "plot!(res3[4], res3[1], label=labels[3], linewidth=2, marker=:auto, seriestype=:samplemarkers, step=50)\n",
    "plot!(res4[4], res4[1], label=labels[4], linewidth=2, marker=:utriangle, seriestype=:samplemarkers, step=45)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For publication we used backend `PGFPlotsX`. It is slightly more complicated, so we put that code [below](#plots_publish) the Bregman case.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bregman run\n",
    "All problems are the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Operator norm of A is 25.3,\n",
      "Frobenius norm is 29.0,\n",
      "max norm is 1.0"
     ]
    }
   ],
   "source": [
    "# Choose dimensions and problem instance\n",
    "m, n = 50, 50\n",
    "\n",
    "\n",
    "probl = 4 # 1, 2 or 3\n",
    "sd = \"1\"\n",
    "\n",
    "if probl == 1\n",
    "    A = Problems.policeman_and_burglar_matrix(n; seed=sd)\n",
    "    filename = \"save/probl_1b.jld2\" \n",
    "elseif probl == 2\n",
    "    A = Problems.nemirovski1(n)\n",
    "    filename = \"save/probl_2b.jld2\" \n",
    "elseif probl == 3\n",
    "    A = Problems.nemirovski2(n, 2)\n",
    "    filename = \"save/probl_3b.jld2\" \n",
    "elseif probl == 4\n",
    "    A = Problems.randunif(m, n; seed=sd)\n",
    "    filename = \"save/probl_4b.jld2\"\n",
    "    \n",
    "end\n",
    "\n",
    "\n",
    "\n",
    "# Compute different norms of A\n",
    "max_norm = maximum(abs.(A))\n",
    "l2_norm = norm(A, 2)\n",
    "sp_norm = opnorm(A)\n",
    "print(\"Operator norm of A is $(round(sp_norm, digits=1)),\n",
    "Frobenius norm is $(round(l2_norm, digits=1)),\n",
    "max norm is $(round(max_norm, digits=1))\")\n",
    "\n",
    "x0 = ones(n)/n \n",
    "y0 = ones(m)/m\n",
    "z0 = [x0; y0]\n",
    "tol = 1e-8\n",
    "\n",
    "# deterministic algorithm\n",
    "step_dtr_br = 0.99 / max_norm\n",
    "\n",
    "# proposed variance reduction\n",
    "K = ceil(Int64, 2*m*n/(m + n))\n",
    "p = 1.0 / K\n",
    "α = 1 - p\n",
    "step_vr_br = 0.99 * sqrt(p) / max_norm\n",
    "\n",
    "# proposed variance reduction-looped\n",
    "K = ceil(Int64, 2*m*n/(m + n))\n",
    "p = 1.0 / K\n",
    "α = 1 - p\n",
    "step_vr_br = 0.99 * sqrt(p) / max_norm\n",
    "\n",
    "# Carmon et al. Bregman setting\n",
    "step_mult = 10.0 \n",
    "alpha_br = max_norm * sqrt((m + n)/(2 * m * n))\n",
    "η_br = alpha_br / (step_mult * max_norm^2);\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set number of epochs `N` and run the algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.223221 seconds (217.29 k allocations: 11.132 MiB)\n",
      "  1.081470 seconds (1.78 M allocations: 490.585 MiB, 7.70% gc time)\n",
      "StochMirProx-VR algorithm achieved 1.0e-8 accuracy\n",
      "  0.437424 seconds (622.27 k allocations: 169.861 MiB, 7.91% gc time)\n"
     ]
    }
   ],
   "source": [
    "include(\"algorithms.jl\")\n",
    "\n",
    "N = 1000\n",
    "\n",
    "@time res1 = extraGrad_bregman(A, z0, step_dtr_br, N, tol=tol) \n",
    "@time res2 = stochMPCarmon(A, projection, z0, alpha_br, η_br, N, distr=true, bregman=true, tol=tol);\n",
    "@time res3 = MirProxVR(A, z0, step_vr_br, α, K, N, tol=tol);\n",
    "\n",
    "\n",
    "@save filename res1 res2 res3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "  368.921,86.1857 370.793,97.9276 372.666,98.3757 374.539,110.912 376.411,111.392 378.284,122.137 380.157,122.669 382.029,133.918 383.902,134.509 385.775,147.243 \n",
       "  387.648,147.899 389.52,162.271 391.393,162.995 393.266,179.181 395.138,179.975 397.011,198.173 398.884,199.04 400.756,219.476 402.629,220.415 404.502,243.349 \n",
       "  406.375,244.359 408.247,260.236 410.12,261.564 411.993,276.287 413.865,277.729 415.738,294.119 417.611,295.678 419.483,313.938 421.356,315.615 423.229,335.98 \n",
       "  425.102,337.773 426.974,360.518 428.847,362.424 430.72,377.171 432.592,378.658 434.465,382.53 436.338,384.114 438.21,385.476 440.083,387.46 441.956,380.841 \n",
       "  443.829,382.911 445.701,378.578 447.574,380.72 449.447,377.695 451.319,379.5 453.192,371.773 455.065,373.725 456.937,368.235 458.81,370.326 460.683,367.159 \n",
       "  462.556,369.381 464.428,368.621 466.301,370.966 468.174,372.691 470.046,375.151 471.919,379.446 473.792,382.013 475.664,380.926 477.537,383.082 479.41,379.605 \n",
       "  481.283,381.906 483.155,380.8 485.028,383.231 486.901,384.585 488.773,387.129 490.646,391.03 492.519,393.67 494.391,400.206 496.264,402.926 498.137,412.189 \n",
       "  500.01,414.972 501.882,427.061 503.755,429.892 505.628,444.921 507.5,447.785 509.373,465.887 511.246,468.771 513.118,490.114 514.991,493.005 516.864,496.618 \n",
       "  518.737,497.89 520.609,490.421 522.482,491.79 524.355,485.401 526.227,486.882 528.1,471.78 529.973,474.655 531.845,459.528 533.718,462.552 535.591,451.086 \n",
       "  537.464,454.251 539.336,446.45 541.209,449.754 543.082,445.679 544.954,449.121 546.827,440.565 548.7,442.734 550.572,429.185 552.445,431.494 554.318,420.332 \n",
       "  556.191,422.791 558.063,414.055 559.936,416.676 561.809,410.449 563.681,413.244 565.554,409.651 567.427,412.634 569.299,411.85 571.172,415.033 573.045,417.284 \n",
       "  574.918,420.678 576.79,426.252 578.663,429.866 580.536,435.519 582.408,439.089 584.281,436.135 586.154,439.878 588.026,433.057 589.899,436.356 591.772,431.47 \n",
       "  593.645,434.938 595.517,433.43 597.39,437.064 599.263,439.156 601.135,442.952 603.008,448.925 604.881,452.872 606.754,452.981 608.626,456.434 610.499,446.902 \n",
       "  612.372,450.43 614.244,444.523 616.117,448.101 617.99,445.782 619.862,449.386 621.735,450.641 623.608,454.242 625.481,455.088 627.353,459.086 629.226,452.247 \n",
       "  631.099,456.257 632.971,453.437 634.844,457.425 636.717,458.528 638.589,462.457 640.462,454.919 642.335,457.987 644.208,442.97 646.08,445.995 647.953,434.565 \n",
       "  649.826,437.522 651.698,425.845 653.571,427.033 655.444,411.228 657.316,412.494 659.189,398.776 661.062,400.11 662.935,388.386 664.807,389.779 666.68,379.973 \n",
       "  668.553,381.418 670.425,373.47 672.298,374.958 674.171,368.822 676.043,370.348 677.916,365.99 679.789,367.55 681.662,364.951 683.534,366.541 685.407,365.697 \n",
       "  687.28,367.313 689.152,368.233 691.025,369.875 692.898,372.584 694.77,374.249 696.643,378.789 698.516,380.475 700.389,386.906 702.261,388.609 704.134,397.008 \n",
       "  706.007,398.726 707.879,409.192 709.752,410.919 711.625,423.572 713.497,425.3 715.37,440.285 717.243,442.004 719.116,459.496 720.988,461.192 722.861,481.396 \n",
       "  724.734,483.052 726.606,506.213 728.479,507.806 730.352,525.71 732.224,527.871 734.097,521.004 735.97,523.383 737.843,519.206 739.715,521.81 741.588,520.56 \n",
       "  743.461,523.394 745.333,525.354 747.206,528.423 749.079,517.53 750.951,520.187 752.824,498.409 754.697,501.28 756.57,483.478 758.442,486.554 760.315,472.627 \n",
       "  762.188,475.9 764.06,465.812 765.933,469.273 767.806,463.043 769.678,466.681 771.551,464.382 773.424,468.187 775.297,469.942 777.169,473.9 779.042,479.89 \n",
       "  780.915,483.984 782.787,494.449 784.66,498.661 786.533,513.917 788.405,518.224 790.278,538.68 792.151,543.056 794.024,569.247 795.896,573.476 797.769,591.861 \n",
       "  799.642,594.932 801.514,615.061 803.387,617.844 805.26,611.392 807.132,612.248 809.005,591.39 810.878,592.373 812.751,573.448 814.623,574.582 816.496,557.36 \n",
       "  818.369,558.779 820.241,543.721 822.114,545.405 823.987,532.535 825.859,534.466 827.732,523.816 829.605,525.639 831.478,516.473 833.35,518.146 835.223,510.72 \n",
       "  837.096,512.501 838.968,506.823 840.841,508.71 842.714,504.81 844.586,506.801 846.459,504.735 848.332,506.825 850.205,506.67 852.077,508.852 853.95,510.704 \n",
       "  855.823,512.971 857.695,516.944 859.568,519.284 861.441,525.508 863.313,527.908 865.186,531.879 867.059,532.747 868.932,520.568 870.804,521.643 872.677,508.249 \n",
       "  874.55,509.548 876.422,498.228 878.295,499.734 880.168,490.49 882.04,492.06 883.913,479.095 885.786,480.824 887.659,470.392 889.531,472.254 891.404,464.355 \n",
       "  893.277,466.325 895.149,458.649 897.022,460.601 898.895,453.289 900.767,455.239 902.64,449.967 904.513,451.901 906.386,448.586 908.258,450.494 910.131,449.057 \n",
       "  912.004,450.929 913.876,451.3 915.749,453.132 917.622,454.37 919.494,456.498 921.367,458.18 923.24,460.297 925.113,463.927 926.985,466.038 928.858,471.653 \n",
       "  930.731,473.763 932.603,481.433 934.476,483.549 936.349,493.385 938.221,495.512 940.094,507.67 941.967,509.816 943.84,521.027 945.712,523.824 947.585,520.347 \n",
       "  949.458,522.051 951.33,510.602 953.203,512.401 955.076,503.058 956.949,504.95 958.821,497.729 960.694,499.707 962.567,494.639 964.439,496.7 966.312,493.83 \n",
       "  968.185,495.966 970.057,495.349 971.93,497.555 973.803,499.257 975.676,501.522 977.548,500.818 979.421,501.544 981.294,491.669 983.166,492.479 985.039,483.671 \n",
       "  986.912,484.573 988.784,476.865 990.657,477.862 992.53,471.287 994.403,472.383 996.275,466.974 998.148,468.169 1000.02,463.959 1001.89,465.25 1003.77,462.269 \n",
       "  1005.64,463.653 1007.51,461.927 1009.38,463.4 1011.26,462.955 1013.13,464.512 1015,465.372 1016.87,467.007 1018.75,469.2 1020.62,470.908 1022.49,474.466 \n",
       "  1024.37,476.241 1026.24,481.201 1028.11,483.039 1029.98,489.452 1031.86,491.347 1033.73,499.276 1035.6,501.225 1037.47,510.751 1039.35,512.749 1041.22,522.459 \n",
       "  1043.09,525.353 1044.97,531.61 1046.84,534.506 1048.71,543.179 1050.58,546.064 1052.46,557.259 1054.33,560.118 1056.2,573.967 1058.07,576.784 1059.95,593.454 \n",
       "  1061.82,596.211 1063.69,615.908 1065.57,618.581 1067.44,617.154 1069.31,616.78 1071.18,599.905 1073.06,599.699 1074.93,583.582 1076.8,583.543 1078.67,568.23 \n",
       "  1080.55,568.357 1082.42,553.903 1084.29,554.19 1086.16,540.654 1088.04,541.097 1089.91,528.54 1091.78,529.13 1093.66,517.616 1095.53,518.343 1097.4,507.933 \n",
       "  1099.27,508.787 1101.15,499.54 1103.02,500.509 1104.89,492.483 1106.76,493.552 1108.64,486.799 1110.51,487.954 1112.38,482.522 1114.26,483.748 1116.13,479.678 \n",
       "  1118,480.959 1119.87,478.289 1121.75,479.608 1123.62,478.371 1125.49,479.711 1127.36,467.111 1129.24,468.704 1131.11,455.842 1132.98,457.479 1134.86,446.734 \n",
       "  1136.73,448.399 1138.6,439.692 1140.47,441.373 1142.35,434.633 1144.22,436.32 1146.09,431.488 1147.96,433.17 1149.84,430.194 1151.71,431.864 1153.58,430.7 \n",
       "  1155.45,432.352 1157.33,432.966 1159.2,434.593 1161.07,436.958 1162.95,438.557 1164.82,442.655 1166.69,444.221 1168.56,450.043 1170.44,451.573 1172.31,459.118 \n",
       "  1174.18,460.61 1176.05,469.888 1177.93,471.34 1179.8,482.372 1181.67,483.78 1183.55,496.597 1185.42,497.961 1187.29,512.607 1189.16,513.923 1191.04,525.969 \n",
       "  1192.91,527.378 1194.78,535.08 1196.65,536.46 1198.53,542.372 1200.4,543.08 1202.27,541.301 1204.14,542.012 1206.02,541.058 1207.89,541.758 1209.76,541.603 \n",
       "  1211.64,542.278 1213.51,542.877 1215.38,543.512 1217.25,544.804 1219.13,545.385 1221,547.294 1222.87,547.807 1224.74,550.241 1226.62,550.674 1228.49,553.526 \n",
       "  1230.36,553.87 1232.24,557.023 1234.11,557.269 1235.98,560.598 1237.85,560.743 1239.73,542.299 1241.6,542.442 1243.47,523.67 1245.34,523.977 1247.22,506.46 \n",
       "  1249.09,506.922 1250.96,490.704 1252.84,491.314 1254.71,476.439 1256.58,477.188 1258.45,463.697 1260.33,464.577 1262.2,452.514 1264.07,453.517 1265.94,442.921 \n",
       "  1267.82,444.041 1269.69,434.955 1271.56,436.182 1273.43,428.648 1275.31,429.976 1277.18,424.037 1279.05,425.46 1280.93,421.161 1282.8,422.671 1284.67,420.062 \n",
       "  1286.54,421.652 1288.42,420.786 1290.29,422.45 1292.16,423.388 1294.03,425.119 1295.91,427.929 1297.78,429.722 1299.65,434.481 1301.53,436.33 1303.4,443.131 \n",
       "  1305.27,445.031 1307.14,453.982 1309.02,455.929 1310.89,467.161 1312.76,469.149 1314.63,482.821 1316.51,484.844 1318.38,501.148 1320.25,503.202 1322.13,522.376 \n",
       "  1324,524.454 1325.87,544.762 1327.74,547.324 1329.62,553.392 1331.49,555.986 1333.36,547.597 1335.23,549.669 1337.11,537.237 1338.98,539.462 1340.85,529.575 \n",
       "  1342.72,531.941 1344.6,524.086 1346.47,526.165 1348.34,514.35 1350.22,516.469 1352.09,507.059 1353.96,509.202 1355.83,502.093 1357.71,504.242 1359.58,496.484 \n",
       "  1361.45,497.453 1363.32,487.729 1365.2,488.763 1367.07,480.408 1368.94,481.495 1370.82,474.471 1372.69,475.6 1374.56,469.868 1376.43,471.032 1378.31,466.55 \n",
       "  1380.18,467.742 1382.05,464.473 1383.92,465.689 1385.8,463.601 1387.67,464.841 1389.54,463.912 1391.42,465.175 1393.29,465.394 1395.16,466.682 1397.03,468.052 \n",
       "  1398.91,469.368 1400.78,471.908 1402.65,473.256 1404.52,477.001 1406.4,478.384 1408.27,483.388 1410.14,484.81 1412.01,491.145 1413.89,492.609 1415.76,500.365 \n",
       "  1417.63,501.874 1419.51,503.225 1421.38,504.98 1423.25,507.729 1425.12,509.543 1427,514.132 1428.87,515.999 1430.74,522.524 1432.61,524.439 1434.49,533.011 \n",
       "  1436.36,534.969 1438.23,545.717 1440.11,547.712 1441.98,559.911 1443.85,561.828 1445.72,568.13 1447.6,570.08 1449.47,578.551 1451.34,580.515 1453.21,589.553 \n",
       "  1455.09,591.021 1456.96,584.67 1458.83,586.132 1460.7,581.569 1462.58,583.009 1464.45,577.605 1466.32,579.087 1468.2,567.83 1470.07,569.399 1471.94,556.419 \n",
       "  1473.81,558.255 1475.69,538.27 1477.56,540.17 1479.43,523.146 1481.3,525.095 1483.18,510.842 1485.05,512.829 1486.92,501.19 1488.8,503.203 1490.67,494.049 \n",
       "  1492.54,496.08 1494.41,489.303 1496.29,491.344 1498.16,486.855 1500.03,488.901 1501.9,486.631 1503.78,488.675 1505.65,488.572 1507.52,490.61 1509.4,492.637 \n",
       "  1511.27,494.663 1513.14,498.802 1515.01,500.812 1516.89,507.056 1518.76,509.045 1520.63,517.408 1522.5,519.37 1524.38,529.527 1526.25,531.433 1528.12,522.107 \n",
       "  1529.99,524.009 1531.87,517.103 1533.74,518.991 1535.61,514.407 1537.49,516.269 1539.36,513.921 1541.23,515.744 1543.1,515.547 1544.98,517.317 1546.85,519.186 \n",
       "  1548.72,520.886 1550.59,519.426 1552.47,519.305 1554.34,500.746 1556.21,500.696 1558.09,482.998 1559.96,483.013 1561.83,466.153 1563.7,466.229 1565.58,450.186 \n",
       "  1567.45,450.32 1569.32,435.073 1571.19,435.26 1573.07,420.79 1574.94,421.03 1576.81,407.322 1578.69,407.612 1580.56,394.655 1582.43,394.995 1584.3,382.782 \n",
       "  1586.18,383.172 1588.05,371.704 1589.92,372.144 1591.79,361.426 1593.67,361.918 1595.54,351.963 1597.41,352.507 1599.28,343.334 1601.16,343.931 1603.03,335.565 \n",
       "  1604.9,336.216 1606.78,328.686 1608.65,329.391 1610.52,322.731 1612.39,323.488 1614.27,317.735 1616.14,318.543 1618.01,313.735 1619.88,314.592 1621.76,310.766 \n",
       "  1623.63,311.667 1625.5,308.86 1627.38,309.801 1629.25,308.049 1631.12,309.024 1632.99,308.356 1634.87,309.36 1636.74,309.805 1638.61,310.831 1640.48,312.411 \n",
       "  1642.36,313.453 1644.23,316.188 1646.1,317.237 1647.97,321.142 1649.85,322.193 1651.72,327.277 1653.59,328.323 1655.47,334.595 1657.34,335.629 1659.21,343.091 \n",
       "  1661.08,344.108 1662.96,352.763 1664.83,353.755 1666.7,363.603 1668.57,364.567 1670.45,375.608 1672.32,376.536 1674.19,388.771 1676.07,389.661 1677.94,403.091 \n",
       "  1679.81,403.938 1681.68,418.569 1683.56,419.369 1685.43,435.212 1687.3,435.96 1689.17,443.211 1691.05,444.315 1692.92,442.915 1694.79,444.123 1696.67,444.151 \n",
       "  1698.54,445.461 1700.41,447.03 1702.28,448.441 1704.16,451.672 1706.03,453.18 1707.9,458.203 1709.77,459.803 1711.65,466.755 1713.52,468.441 1715.39,477.474 \n",
       "  1717.26,479.237 1719.14,490.515 1721.01,492.348 1722.88,506.056 1724.76,507.946 1726.63,524.295 1728.5,526.23 1730.37,545.461 1732.25,547.426 1734.12,569.824 \n",
       "  1735.99,571.802 1737.86,582.933 1739.74,585.147 1741.61,582.943 1743.48,585.22 1745.36,585.359 1747.23,587.692 1749.1,590.212 1750.97,592.594 1752.85,597.555 \n",
       "  1754.72,599.981 1756.59,607.467 1758.46,609.93 1760.34,617.138 1762.21,618.385 1764.08,610.35 1765.96,611.786 1767.83,605.668 1769.7,607.29 1771.57,603.243 \n",
       "  1773.45,605.045 1775.32,603.231 1777.19,605.206 1779.06,605.797 1780.94,607.939 1782.81,611.125 1784.68,613.426 1786.55,619.417 1788.43,621.778 1790.3,626.224 \n",
       "  1792.17,628.158 1794.05,634.801 1795.92,636.689 1797.79,645.548 1799.66,647.368 1801.54,639.581 1803.41,642.072 1805.28,635.829 1807.15,638.404 1809.03,634.509 \n",
       "  1810.9,637.156 1812.77,635.664 1814.65,638.369 1816.52,639.346 1818.39,642.095 1820.26,645.625 1822.14,648.402 1824.01,654.586 1825.88,657.372 1827.75,666.328 \n",
       "  1829.63,669.103 1831.5,664.112 1833.37,666.328 1835.25,650.895 1837.12,653.465 1838.99,636.016 1840.86,638.797 1842.74,624.963 1844.61,627.93 1846.48,617.66 \n",
       "  1848.35,620.792 1850.23,614.066 1852.1,617.342 1853.97,614.173 1855.84,617.574 1857.72,618.008 1859.59,621.514 1861.46,620.387 1863.34,622.66 1865.21,617.587 \n",
       "  1867.08,620.01 1868.95,617.556 1870.83,620.117 1872.7,620.354 1874.57,623.039 1876.44,626.058 1878.32,628.855 1880.19,634.769 1882.06,637.663 1883.94,646.61 \n",
       "  1885.81,649.584 1887.68,661.733 1889.55,664.771 1891.43,676.155 1893.3,678.634 1895.17,677.812 1897.04,680.356 1898.92,676.449 1900.79,678.927 1902.66,674.67 \n",
       "  1904.53,677.223 1906.41,675.584 1908.28,678.191 1910.15,679.192 1912.03,681.83 1913.9,677.567 1915.77,679.577 1917.64,665.505 1919.52,667.637 1921.39,656.194 \n",
       "  1923.26,658.431 1925.13,649.55 1927.01,651.883 1928.88,645.513 1930.75,647.932 1932.63,644.043 1934.5,646.545 1936.37,645.126 1938.24,647.708 1940.12,648.776 \n",
       "  1941.99,651.436 1943.86,655.035 1945.73,657.774 1947.61,663.981 1949.48,666.8 1951.35,675.727 1953.23,678.629 1955.1,690.436 1956.97,693.423 1958.84,699.121 \n",
       "  1960.72,700.821 1962.59,683.221 1964.46,685.13 1966.33,670.065 1968.21,672.16 1970.08,659.647 1971.95,661.906 1973.82,651.972 1975.7,654.373 1977.57,633.949 \n",
       "  1979.44,634.829 1981.32,609.985 1983.19,611.071 1985.06,588.797 1986.93,590.064 1988.81,570.283 1990.68,571.707 1992.55,554.35 1994.42,555.91 1996.3,540.918 \n",
       "  1998.17,542.594 2000.04,529.915 2001.92,531.687 2003.79,521.275 2005.66,523.125 2007.53,514.939 2009.41,516.85 2011.28,510.854 2013.15,512.809 2015.02,508.97 \n",
       "  2016.9,510.953 2018.77,509.243 2020.64,511.236 2022.52,511.629 2024.39,513.615 2026.26,516.087 2028.13,518.05 2030.01,522.577 2031.88,524.501 2033.75,531.06 \n",
       "  2035.62,532.926 2037.5,541.494 2039.37,543.285 2041.24,553.836 2043.11,555.535 2044.99,568.04 2046.86,569.628 2048.73,578.922 2050.61,580.896 2052.48,586.611 \n",
       "  2054.35,588.605 2056.22,596.293 2058.1,598.302 2059.97,608.035 2061.84,610.059 2063.71,621.931 2065.59,623.964 2067.46,638.096 2069.33,640.135 2071.21,656.675 \n",
       "  2073.08,658.713 2074.95,677.844 2076.82,679.873 2078.7,701.817 2080.57,703.827 2082.44,728.858 2084.31,730.835 2086.19,742.472 2088.06,744.192 2089.93,739.652 \n",
       "  2091.81,741.927 2093.68,739.114 2095.55,741.55 2097.42,738.731 2099.3,740.97 2101.17,736.43 2103.04,738.697 2104.91,722.204 2106.79,722.119 2108.66,703.907 \n",
       "  2110.53,703.975 2112.4,686.617 2114.28,686.837 2116.15,670.442 2118.02,670.81 2119.9,655.474 2121.77,655.986 2123.64,641.795 2125.51,642.447 2127.39,629.478 \n",
       "  2129.26,630.263 2131.13,618.59 2133,619.501 2134.88,609.191 2136.75,610.223 2138.62,601.341 2140.5,602.486 2142.37,595.095 2144.24,596.344 2146.11,590.505 \n",
       "  2147.99,591.851 2149.86,587.625 2151.73,589.057 2153.6,586.503 2155.48,588.011 2157.35,587.187 2159.22,588.76 2161.09,589.723 2162.97,591.347 2164.84,594.153 \n",
       "  2166.71,595.814 2168.59,600.517 2170.46,602.2 2172.33,608.85 2174.2,610.539 2176.08,607.973 2177.95,608.446 2179.82,600.548 2181.69,601.136 2183.57,594.181 \n",
       "  2185.44,594.877 2187.31,588.931 2189.19,589.73 2191.06,584.857 2192.93,585.751 2194.8,582.012 2196.68,582.996 2198.55,580.452 2200.42,581.519 2202.29,577.946 \n",
       "  2204.17,579.009 2206.04,571.821 2207.91,572.976 2209.79,567.116 2211.66,568.36 2213.53,563.89 2215.4,565.217 2217.28,562.202 2219.15,563.607 2221.02,562.113 \n",
       "  2222.89,563.588 2224.77,563.682 2226.64,565.221 2228.51,566.973 2230.38,568.569 2232.26,572.051 2234.13,573.695 2236,578.986 2237.88,580.67 2239.75,587.852 \n",
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       "  1193.3,758.361 1232.65,765.302 1271.99,730.499 1311.34,749.621 1350.68,731.707 1390.03,739.914 1429.37,729.648 1468.72,735.987 1508.07,765.187 1547.41,761.109 \n",
       "  1586.76,809.078 1626.1,819.032 1665.45,804.911 1704.79,810.724 1744.14,799.499 1783.48,813.243 1822.83,800.571 1862.17,813.147 1901.52,786.508 1940.87,793.126 \n",
       "  1980.21,787.562 2019.56,792.468 2058.9,788.314 2098.25,796.364 2137.59,787.109 2176.94,786.938 2216.28,802.587 2255.63,805.409 2294.97,843.007 \n",
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       "  408.247,470.17 411.993,540.251 415.738,534.826 419.483,498.439 423.229,473.997 426.974,506.64 430.72,533.887 434.465,546.288 438.21,536.83 441.956,540.988 \n",
       "  445.701,526.722 449.447,490.774 453.192,450.482 456.937,432.711 460.683,444.993 464.428,467.776 468.174,515.932 471.919,599.963 475.664,661.234 479.41,617.201 \n",
       "  483.155,619.897 486.901,642.531 490.646,655.647 494.391,624.767 498.137,619.462 501.882,609.976 505.628,597.962 509.373,587.889 513.118,593.642 516.864,576.377 \n",
       "  520.609,586.691 524.355,610.548 528.1,638.008 531.845,623.96 535.591,582.331 539.336,555.724 543.082,561.53 546.827,560.245 550.572,573.12 554.318,593.93 \n",
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       "  595.517,565.523 599.263,560.627 603.008,549.669 606.754,545.785 610.499,541.663 614.244,541.55 617.99,532.393 621.735,521.706 625.481,522.864 629.226,540.507 \n",
       "  632.971,578.855 636.717,618.65 640.462,615.053 644.208,571.126 647.953,539.262 651.698,529.94 655.444,537.971 659.189,568.757 662.935,588.033 666.68,625.658 \n",
       "  670.425,660.424 674.171,653.903 677.916,643.586 681.662,636.181 685.407,634.014 689.152,633.769 692.898,626.855 696.643,555.024 700.389,494.666 704.134,449.575 \n",
       "  707.879,416.951 711.625,390.477 715.37,373.505 719.116,365.217 722.861,374.791 726.606,392.815 730.352,418.28 734.097,452.601 737.843,505.243 741.588,573.567 \n",
       "  745.333,622.781 749.079,686.399 752.824,778.487 756.57,754.242 760.315,735.054 764.06,743.594 767.806,738.386 771.551,742.7 775.297,768.736 779.042,800.312 \n",
       "  782.787,812.582 786.533,853.789 790.278,896.645 794.024,868.53 797.769,867.724 801.514,858.783 805.26,878.976 809.005,911.68 812.751,837.853 816.496,753.766 \n",
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       "  857.695,747.833 861.441,737.575 865.186,723.96 868.932,720.102 872.677,721.321 876.422,730.682 880.168,754.007 883.913,776.77 887.659,815.282 891.404,818.307 \n",
       "  895.149,822.87 898.895,832.179 902.64,827.589 906.386,817.83 910.131,811.393 913.876,814.243 917.622,830.165 921.367,779.499 925.113,750.237 928.858,729.249 \n",
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       "  970.057,811.622 973.803,794.448 977.548,786.036 981.294,781.315 985.039,778.014 988.784,771.497 992.53,754.755 996.275,748.684 1000.02,755.566 1003.77,776.659 \n",
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       "  1044.97,705.232 1048.71,718.908 1052.46,743.66 1056.2,779.198 1059.95,813.36 1063.69,848.863 1067.44,884.455 1071.18,922.862 1074.93,948.43 1078.67,968.145 \n",
       "  1082.42,931.268 1086.16,890.603 1089.91,852.746 1093.66,830.032 1097.4,816.409 1101.15,804.655 1104.89,810.872 1108.64,820.628 1112.38,827.038 1116.13,834.676 \n",
       "  1119.87,842.477 1123.62,840.768 1127.36,835.67 1131.11,817.471 1134.86,804.394 1138.6,800.496 1142.35,798.797 1146.09,804.591 1149.84,819.013 1153.58,831.354 \n",
       "  1157.33,853.075 1161.07,871.667 1164.82,880.901 1168.56,883.728 1172.31,898.675 1176.05,881.329 1179.8,856.574 1183.55,836.544 1187.29,824.603 1191.04,823.98 \n",
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       "  1232.24,934.649 1235.98,937.179 1239.73,939.956 1243.47,934.881 1247.22,932.722 1250.96,931.995 1254.71,924.29 1258.45,919.251 1262.2,909.972 1265.94,909.393 \n",
       "  1269.69,909.944 1273.43,911.669 1277.18,910.776 1280.93,917.486 1284.67,923.151 1288.42,934.621 1292.16,944.814 1295.91,931.241 1299.65,926.32 1303.4,925.017 \n",
       "  1307.14,937.818 1310.89,955.929 1314.63,968.427 1318.38,983.346 1322.13,1000.67 1325.87,998.551 1329.62,1000.77 1333.36,1006.52 1337.11,1010.93 1340.85,1018.89 \n",
       "  1344.6,1024.34 1348.34,1035.12 1352.09,1043.07 1355.83,1019.65 1359.58,993.586 1363.32,970.609 1367.07,951.025 1370.82,938.99 1374.56,933.876 1378.31,933.421 \n",
       "  1382.05,936.074 1385.8,937.201 1389.54,943.685 1393.29,956.905 1397.03,966.562 1400.78,981.065 1404.52,996.981 1408.27,1003.08 1412.01,1005.95 1415.76,1014.05 \n",
       "  1419.51,1017.98 1423.25,1012 1427,992.495 1430.74,997.214 1434.49,994.832 1438.23,986.903 1441.98,975.863 1445.72,964.272 1449.47,948.463 1453.21,941.506 \n",
       "  1456.96,934.95 1460.7,925.98 1464.45,919.489 1468.2,916.841 1471.94,916.439 1475.69,911.14 1479.43,899.335 1483.18,869.808 1486.92,841.754 1490.67,826.139 \n",
       "  1494.41,811.25 1498.16,802.65 1501.9,794.726 1505.65,797.2 1509.4,800.108 1513.14,808.213 1516.89,816.407 1520.63,828.358 1524.38,841.415 1528.12,848.845 \n",
       "  1531.87,862.463 1535.61,879.954 1539.36,893.891 1543.1,913.447 1546.85,930.317 1550.59,944.475 1554.34,963.141 1558.09,986.833 1561.83,1007.7 1565.58,1034.51 \n",
       "  1569.32,1070.8 1573.07,1109.69 1576.81,1148.43 1580.56,1175.69 1584.3,1195.76 1588.05,1221.16 1591.79,1259.18 1595.54,1285.55 1599.28,1300.02 1603.03,1329.75 \n",
       "  1606.78,1336.06 1610.52,1320.13 1614.27,1285.13 1618.01,1250.84 1621.76,1225.6 1625.5,1193.62 1629.25,1171.13 1632.99,1150.5 1636.74,1129.78 1640.48,1115.79 \n",
       "  1644.23,1105.15 1647.97,1100.97 1651.72,1096.17 1655.47,1096.27 1659.21,1099.01 1662.96,1097.13 1666.7,1102.23 1670.45,1112.06 1674.19,1123.69 1677.94,1140.09 \n",
       "  1681.68,1145.44 1685.43,1158.35 1689.17,1174.13 1692.92,1185.04 1696.67,1197.95 1700.41,1198.14 1704.16,1177.57 1707.9,1168.98 1711.65,1162.03 1715.39,1162.2 \n",
       "  1719.14,1161.5 1722.88,1177.3 1726.63,1173.23 1730.37,1156 1734.12,1136.56 1737.86,1130.11 1741.61,1121.76 1745.36,1108.87 1749.1,1091.58 1752.85,1070.22 \n",
       "  1756.59,1051.65 1760.34,1033.81 1764.08,1019.65 1767.83,1007.57 1771.57,994.711 1775.32,983.441 1779.06,978.275 1782.81,973.082 1786.55,970.749 1790.3,975.846 \n",
       "  1794.05,982.417 1797.79,985.485 1801.54,991.828 1805.28,999.982 1809.03,1009.27 1812.77,1023.73 1816.52,1039.69 1820.26,1049.85 1824.01,1054.07 1827.75,1070.52 \n",
       "  1831.5,1096.55 1835.25,1123.79 1838.99,1159.24 1842.74,1170.85 1846.48,1184.7 1850.23,1196.06 1853.97,1214.15 1857.72,1233.31 1861.46,1235.18 1865.21,1227.74 \n",
       "  1868.95,1215.95 1872.7,1203.75 1876.44,1190.22 1880.19,1180.12 1883.94,1176.75 1887.68,1172.71 1891.43,1175.65 1895.17,1180.29 1898.92,1187.82 1902.66,1202.6 \n",
       "  1906.41,1217.01 1910.15,1224.6 1913.9,1237.55 1917.64,1265.51 1921.39,1292.33 1925.13,1326.23 1928.88,1342.16 1932.63,1363.97 1936.37,1376.61 1940.12,1384.24 \n",
       "  1943.86,1378.64 1947.61,1377.25 1951.35,1355.75 1955.1,1330.92 1958.84,1324.03 1962.59,1309.01 1966.33,1298.59 1970.08,1300.04 1973.82,1291.71 1977.57,1290.61 \n",
       "  1981.32,1288.64 1985.06,1315.35 1988.81,1337.18 1992.55,1356.57 1996.3,1363.65 2000.04,1369.81 2003.79,1371.09 2007.53,1379.31 2011.28,1369.44 2015.02,1358 \n",
       "  2018.77,1353.83 2022.52,1347.04 2026.26,1335.81 2030.01,1330.17 2033.75,1322.45 2037.5,1314.9 2041.24,1309.29 2044.99,1295.82 2048.73,1276.57 2052.48,1257.52 \n",
       "  2056.22,1241.97 2059.97,1214.01 2063.71,1194.74 2067.46,1174.15 2071.21,1161.73 2074.95,1153.36 2078.7,1148.6 2082.44,1143.28 2086.19,1141.51 2089.93,1142.45 \n",
       "  2093.68,1147.98 2097.42,1160.21 2101.17,1157.77 2104.91,1159.86 2108.66,1161.07 2112.4,1167.44 2116.15,1181.22 2119.9,1193.12 2123.64,1207.14 2127.39,1224.72 \n",
       "  2131.13,1246.05 2134.88,1248.42 2138.62,1249.94 2142.37,1251 2146.11,1257.28 2149.86,1274.26 2153.6,1286.72 2157.35,1305.32 2161.09,1281.07 2164.84,1255.79 \n",
       "  2168.59,1243.27 2172.33,1233.31 2176.08,1227.8 2179.82,1213.77 2183.57,1199.18 2187.31,1197.49 2191.06,1197.41 2194.8,1195.7 2198.55,1200.43 2202.29,1202.23 \n",
       "  2206.04,1193.6 2209.79,1191.53 2213.53,1194.4 2217.28,1192.15 2221.02,1184.07 2224.77,1161.26 2228.51,1137.42 2232.26,1119.02 2236,1100.55 2239.75,1087.46 \n",
       "  2243.49,1080.05 \n",
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      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr()\n",
    "labels = [\"MP\",\"MP-Car+19\", \"MP-Alg2\"]\n",
    "plot(res1[1], yaxis=:log, label=labels[1], legend=:bottomleft, linewidth=2,marker=:auto, seriestype=:samplemarkers, step=50, xlabel = \"epoch\", ylabel = \"Duality gap\")\n",
    "plot!(res2[4], res2[1], label=labels[2], linewidth=2,marker=:auto, seriestype=:samplemarkers, step=10)\n",
    "plot!(res3[4], res3[1], label=labels[3], linewidth=2, marker=:auto, seriestype=:samplemarkers, step=50)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='plots_publish'></a>\n",
    "\n",
    "### Plots for publishing\n",
    "\n",
    "First choose the problem and load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Array{Symbol,1}:\n",
       " :res1\n",
       " :res2\n",
       " :res3\n",
       " :res4"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using PGFPlotsX\n",
    "using LaTeXStrings\n",
    "#import Pkg; Pkg.add(\"LaTeXStrings\")\n",
    "\n",
    "# load data \n",
    "probl = 1 # 1, 2 or 3\n",
    "sd = \"1\"\n",
    "\n",
    "if probl == 1\n",
    "    A = Problems.policeman_and_burglar_matrix(n; seed=sd)\n",
    "    filename = \"save/probl_1e.jld2\" \n",
    "elseif probl == 2\n",
    "    A = Problems.nemirovski1(n)\n",
    "    filename = \"save/probl_2e.jld2\" \n",
    "elseif probl == 3\n",
    "    A = Problems.nemirovski2(n, 2)\n",
    "    filename = \"save/probl_3e.jld2\" \n",
    "elseif probl == 4\n",
    "    A = Problems.randunif(m, n; seed=sd)\n",
    "    filename = \"save/probl_4e.jld2\"\n",
    "    \n",
    "end\n",
    "\n",
    "@load filename res1 res2 res3 res4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "</g>\n",
       "</g>\n",
       "</svg>\n",
       "\n"
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     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1, y2, y3, y4 = res1[1], res2[1], res3[1], res4[1]\n",
    "x1, x2, x3, x4 = Array(1:length(y1)), res2[4], res3[4], res4[4]\n",
    "\n",
    "\n",
    "fig = @pgf Axis(\n",
    "    {\n",
    "        xmajorgrids, # show grids along x axis\n",
    "        ymajorgrids, \n",
    "        xlabel = L\"\\Large{\\mbox{epoch}}\",  \n",
    "        ylabel = L\"\\Large{\\mbox{Duality gap}}\",\n",
    "        #xmode  = \"log\",\n",
    "        ymode  = \"log\",\n",
    "        \"mark size=4pt\",\n",
    "        width = raw\"1\\textwidth\",\n",
    "        height = raw\"0.8\\textwidth\",\n",
    "        \n",
    "        \"axis background/.style\" = { fill = \"gray!10\" }, \n",
    "   \n",
    "    },\n",
    "    PlotInc(\n",
    "        {\n",
    "            #\"no marks\",\n",
    "            \"mark repeat=1000\",\n",
    "            style = \"{very thick}\",\n",
    "        },\n",
    "        Coordinates(x1, y1)\n",
    "    ), \n",
    "   LegendEntry(L\"\\Large{\\mbox{EG}}\"),\n",
    "   \n",
    "    PlotInc(\n",
    "        {\n",
    "            #\"no marks\",\n",
    "            \"mark repeat=200\",\n",
    "            style = \"{very thick}\",\n",
    "        },\n",
    "        Coordinates(x2, y2)\n",
    "    ),\n",
    "  LegendEntry(L\"\\Large{\\mbox{EG-Car+19}}\"),\n",
    "    \n",
    "       \n",
    "    PlotInc(\n",
    "        {\n",
    "            #\"no marks\",\n",
    "            \"mark repeat=400\",\n",
    "            mark=\"triangle*\",\n",
    "            style = \"{very thick}\",\n",
    "            color = \"olive\" \n",
    "            ### other possible colors are red, green, blue, cyan, magenta, yellow, black, gray, darkgray, lightgray, brown, lime, olive, orange, pink, purple, teal, violet and white\n",
    "        },\n",
    "        Coordinates(x3, y3)\n",
    "    ),\n",
    "  LegendEntry(L\"\\Large{\\mbox{EG-Alg2}}\"),\n",
    "    \n",
    "        PlotInc(\n",
    "        {\n",
    "            #\"no marks\",\n",
    "            \"mark repeat=300\",\n",
    "            #mark=\"x\",\n",
    "            mark=\"diamond*\",\n",
    "            style = \"{very thick}\",\n",
    "        },\n",
    "        Coordinates(x4, y4)\n",
    "    ),\n",
    "  LegendEntry(L\"\\Large{\\mbox{EG-Alg1}}\")\n",
    "\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save all plots in different formats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_name = chop(filename, tail=4)\n",
    "pgfsave(string(base_name, \"tex\"), fig)\n",
    "pgfsave(string(base_name, \"tikz\"), fig)\n",
    "pgfsave(string(base_name, \"pdf\"), fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bregman\n",
    "Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Array{Symbol,1}:\n",
       " :res1\n",
       " :res2\n",
       " :res3"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# load data \n",
    "probl = 1 # 1, 2 or 3\n",
    "sd = \"1\"\n",
    "\n",
    "if probl == 1\n",
    "    A = Problems.policeman_and_burglar_matrix(n; seed=sd)\n",
    "    filename = \"save/probl_1b.jld2\" \n",
    "elseif probl == 2\n",
    "    A = Problems.nemirovski1(n)\n",
    "    filename = \"save/probl_2b.jld2\" \n",
    "elseif probl == 3\n",
    "    A = Problems.nemirovski2(n, 2)\n",
    "    filename = \"save/probl_3b.jld2\" \n",
    "elseif probl == 4\n",
    "    A = Problems.randunif(m, n; seed=sd)\n",
    "    filename = \"save/probl_4b.jld2\"\n",
    "    \n",
    "end\n",
    "\n",
    "@load filename res1 res2 res3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L8hFbO3R1dX/44Ydjx46pLUZ848aN27dvr1u3jno7cODAMWPGKOoHikQiysih6E8QRFlZWav9sGNGhfXo+lAgMBAIDAQCPYGAq/gTC3RYAmAJgCkAxvt/mWPUTsW8g+jVi7C2BgB6cDD6MSAQCAQCWTtqhclkWllZ1bb0woULDg4OjLf1jgBgxIgRS5cuFQgEbDabsouUlpaamZlRS8vKynR1dVvthzW3HPHp5NfjxrErK6moljyAPKpidU/ITAZnAMgAKGn4lmXjx+MnT9LCwqC6Gjgc9JNAIBAIBLJ2NAyCIIKDg/X09JQbuVyuUCgMDQ0FAB6Px2KxlLPLlZaWDho0qDV/qN69CV9fIZdLRbWYAvQD6AJgqqghqQvQQbM/5VrXMipyWCikP3iAfg8IBAKBaFHaZt4OPp9fXFysUjCQsnCkpqZOnjyZyWROnjw5JSWFqlKTkZFhaGg4ZMiQOrapUoFWQefOna2trf+bz6WjA4cPc/bu7fn8uR4A7a3gqKSWGjdcnEVFReG6ug5aWrhIVOTjk9YOwuaioqLQzx4NFxouBKLlzv9Xr15lZmaq7RMTE9M2ZQeVK01bW7um7FA4cBw+fNjNzc3AwMDY2NjT09PHx0erzkydH7YCrYLBg2HmTAgKEly9yvjnH9rbmrSNAcdxKqkiOWoUBAebPX2q19ZTUqK0m2i40HAhEC12exqs8qImbTZLqVAoBAAm870knjQaDQAUlX4sLCwCAwOfPn0qEAhOnz5dr2OHirVj+PDhKvV8/0smTpRNnCgDgIiImIh7Z07KsOKSLmbWwc7OkgbpDup/6fjxtOBgPDMTT01NZ/Tq0oVAvx8EAoGQXyGlUirDE6KhPHr0KCLivVIdWVlZbXMoKQ0hk8mUG6nEHsrygkajaf500kqsHSowGEQPnlbea1KqVXo71mrmcqFiUXo63q0bUVGB6erWU6xBNnkyrF0LALSgoIuVfceNkw0dKkM/GAQC0RQCAwNfvnypeMvj8cZqVoTh8uXLfD5f+RmPcrzz9/fPycmpbS02m83j8Xg8nr6+ftMPvqys7NKlS9euXYuPjxeJRPr6+oaGhnPnzl26dCmHwwGAmzdvpqamts77QuthxIgRKs/nbdbaQZ12KiWMKdmh4mfaBrCwMMx7DWxWSXLye1aKkycZ//uf2MuLsXatuO4tENbWRPfu+IsXguvB4dpr8/IwJDsQCEQTkUgkmZmZCQkJoaGhAoFAW1s7NzdXxeWuJk+ePHF3dy8qKmKz2Y6OjjY2NlKplFokEokyMjJiY2NDQkIAoGvXrnPmzDEwMAAAgiCysrL8/PyCg4Pt7e337ds3cODARh/58ePHN23apK+v/7///e/06dNUwGNWVtaNGzeGDx9+8uTJ3r17u7u7f/tR5To6evRoWVnZ+vXra+vA5/NPnToVHx9fWlpqZGQ0ceLEefPmYRjW7EfSNmWHnp5er1698vPza8qOoUOHNm6brWqS5T2Nz+kIABhG5uYWVldzqRhYiQQiI/H9+5kREbR6ZQcAyCZMwF+80I4OT2JVP3umY2xMbtwoBgQC0SY5fRouXGjMip9/Du7uGvadMWPGjBkzAGD06NE4joeGhl6+fNnNza1eU4ejo6O3t7ejo+PFixeVF82ePXv27NnUg2VZWdmCBQs2b96ssvrr169nzZpla2u7Y8eOOm6xtSEWi7/55hsfH59vv/32wIEDLBZLscjKyur777+fMGHC119/3bFjx9evX7f+71kgEOTm5t6/f//PP/8MDw9ftGhRbT1v3bq1aNGiH3/88fTp00wmMz093cXF5fz5835+fsqD0FDa0SQLADg5OakkO8/NzTUyMrK1tW3cBlutMU1Hx4J6wWFlHz8+8McfxSIR7N3LjI+nPX1KMzGpa4aluhrjcEgAkI4fzzh6lEGKRwhC/AWf797NXLRIYmpKAgKBaHukpUHjMgT27NmIlTgczty5c0NDQ728vOqWHQKBgCRJKhqAzWbX+qzFZpeVlakEDVBYWlr6+/tbWVn9/PPPPXv2nDlzZoMO1dXV9cqVK4sXLz527JjaDjY2NqdOnWqKKaWJFBcXGxoaatLz/v37ixYtsra2njJliq2tbXh4eB09HR0d16xZo0iw2a1bt6tXr/br12/58uUnTpxo9NGqnWRpC3k7qJSj1L8KFixYkJiYqDyz6Ofnt2rVKhxva6lKODrmb208r+/fpwFAbi6+dy9TLAaCgIIC7K2FUg0zZ7K2bmVu3KglGzlSxtYBgMlwh1oUHk5roQO+dg05ZyEQrYJKjf+a+Aji7OzM5XLDw8NTUlLq6Hb16tVZs2Y18UOZmpqOGzeOJEl3d3dpHZe/Guzfv//KlSudO3f+7bff6ujWu3fvTZs2fZDvKyMjY8CAARp2Hj16dFpaWnBw8I8//lhHak2hULh48WIA+O6775TbO3fu7OzsfOrUqbi4uOb9FB/xDUAikSxcuJDP58fHx3O53ClTpvTo0WPMmDErVqwAgJ49e54+fdrNze3o0aNmZmY7duzo1q3b2rVrG727VjvJoqNjSb3Q575+8gQnCMjOxoi3bh4yGeTlYZaWqhcNkoQvvmCHh9PCw2k0Gsyeze7Ua7Tl09tTIBADkgTs4UOas7O0eQ/1yhV6VRW2cyfT0VGqlD8WgUB8AEiAFI0792/a3YLD4bi4uJw4ccLb29vDw6O2bhEREV999dX58+eb+NF69+79119/5efnJyQk9O/fX5NV+Hz+1q1bAeCXX36pd1phxYoVVLWv/xiBQKDis9h0zp8/n5aWNnDgwJrSxNbW9vTp097e3gcOHGjcxtvaJAuDwfDx8amjw5dffjlhwoTw8PDQ0NC5c+c20SzWaidZtFgGdAZHKqnW03tdXo6tWqWl4hD65g1uafleC0HAzp3MoCCaQpqsXKn1s8lES7htCa/7QXwcfHLvXvNbO/7+m+7rSydJCAykz5ghBQQC0W5wc3M7ceLEmTNntm/frjYeNSMjo3v37s2yLypdAgCIxZr6qO3fv7+yspLBYDg5OdXbWVdXd/jw4W3je/nnn38AQFGPXRnKsuLv799o2dFmJ1nqQF9ff+rUqcuWLfuAU3H/Adra5gBgZPgaAP78kxEcTH9fdqi6Iqek4Lt3y5OaULL+8WPahkczqJZF5rcBID0dT0hoztPjxQs8PBwnSQCAgwcZNY8KgUC0YYYNG8bj8fLy8v766y+1Hc6dOzdv3rxm2VdWVhYA6Ojo9O7dW8NVrly5AgAODg4qlcxrY9KkSTUbq6qqoqKigoOD6wj0JQiiqKjo1atX1FuSJDMyMiQSyYf6XqhD5agryEU5kbx69aq0tLQZ94hm2TWl1U6yAIC2jkVZ6YueNlk0Gshkqs4TOTnv3eAlEggMlHfo1o04ckTk7MyuroaEcqsE6NMHEuYZBi5/sx4Azp1jbN4spnxOm866dVqZmXIdExVFO3yYuWuXCJ1XCES7MnisWbPGy8tr2rRpam/GJiYmTd+LUCgMDAwEgE2bNql1O61JWlpaRkYGAGguU+bPn09FRyrUw+7du1NTU21tbY2MjL7//vucnJzff/9dueZGRkbG8OHDi4qKZDJZr169kpKSjh8/HhYWVlJSEhcXd+/evR49evz3XwqVnpsk1VznFbVUExMTG3e/a1+RLM1Oa04Lo6NrCQAdjF47Okr9/Ogq509u7ntGi+RkfOtWJgCw2bBli9jBQTZ2rPTWLToABDOm9JEkGCZFWHKKXlcbnTjBCAykRUVVv5/utTGUl2NRUe8dRsu5rCIQiNbJV199tX79+lu3bhUUFHTs2FF5UXBw8Pjx45u+C6lU6u7uXlFRsWjRIs2d+RS2B5WjqgMLCwsLCwvF2x07dvTp0+enn36i3rq4uKxcudLOzu7q1auKWZuuXbump6dHRERQlhI/Pz+hUHj+/PlJkybl5ORcu3ZNsfp/Sffu3e/evUul9lYhNzeXeqFcNrVBtKN0Ye0NapKlqjLX3V3o5yfPxtOhA4njUFCAqVg70tLkt/9Ro6ROTlIAmDFDLjvyB02Af/eCTOZqGuyR4SKRwMuXuK8vY8GCphoAXVxYxcUYADAYYG1NpKXhcXF4ZSWmo4NidBGIFuP2bcjIUNPe6JJ1cXFw+LCadktL0MAlwsTEZOrUqQEBAT4+PqtWrVJeFBgYWFu5zdrIzMz8999/lY0liYmJZ86ckclkV65caVBEjCLJU4cOHRoxKiRJHjp0yNLScsiQIZaWlgohcvz48e+//3769OkKXxYdHZ3x48d36NCBJMkHDx54enoCwKpVqwwMDFxcXD7IOeLk5HT06NG0tLSaixISEt4+N5Y34x6R7GgLUDG0BCHp17fA2Fibz8cAYMkSSXg4raCApuJFkZqKAwCOw+TJcj/TKVNkWlpgYUEsOj2YHMbFysun4nc8QP4bOHy4GWRHYqJc64weLfv2W8msWSyZDGJi8FGj3vN1ra6Gs2cZjx7RfvtNpKVF1h63j0AgNODECbhxozk3GBYGYWFq2seM0UR2AICbm1tAQICXl5ey7CguLtbT02to3ZPY2FgjpaLZNBqte/fuvr6+ffr0UbiUanoJfevZoPahv15EIpFAIHj27Fl6erpCdmhra/N4vKdPn+bm5nbq1Em5P5PJTEtLU4TCTpgwYcKECR/qHPnss8+mTZt269atnJwcZfsNvPU2pWxISHZ8AFqzbweLLf/ticVFy5db3vL3SUyZMW8e++VLHN737UhJwU+cYABAp07EN9/IxQSXS7q6SlatkphY0mWffkoPCBiUd4fDImyHkA8e0FJS8LAwmoo+aBAFBVhREQYAHA74+goqK+XH8/Tpe5s9e5Zx6RI9LIwGAKGhtIMHRTNnomgXBKJNMWXKFBMTk4SEhMePHytyRvv4+MydO7ehmxo1atSWLVua5ahMTU3fXqwKGnMFZrFu3bqVnZ09cuRI5XZzc/OnT58WFhaqyA7KPFNHLAxJkgShpiSnVColSVKl3Jiy8Grcxz98+PCTJ09WrVp16dIlRaOXl9ekSZOuXr0KAMryrkEg344m0Zp9O1gs+TkhFBTNcXnGxr6tFuzr0OGmmVknAMjLw0kSqMz6GRk4ZQuxsXlvduPAARHVQTZhAj0ggFPJ3/P1v9O2ftK7t3Z1NXh4MEeNqjVY/P59moUFWVUF/furL107bx7rrayWsljAYpGdOpHZ2Vhs7HveHqdP0588kf9sSkowf386kh0IRJO4cAHURpBu3w6eno3Z4OLFsHu3mnaN8/DQ6fT58+fv3bvX29tbITtSUlKWL1/ecsMQHx+vVk+MHDmSKlTet29fBoMhkUjy8vI03GZ5eXlWVlbfvn3fmnvGUC+io6OTkpJevXpVWVmZlJQEANXV1Wq3oGJaUBAZGWlvb69WdijGUG27kZHRmzdvmA33xbO2to6NjZ0/f76Li8uSJUv09fWDgoKomjhUB81dXlRAvh1tFhZbPh8pEPDjn27BcYmOdtId/xkWFpEATJEICgsxY2NS2fLh4PCeXlaU+5FOmKCFYUCSX3e8TRr1W7BAcuwY4/FjWm2VbE+dYnh7MxITcRaL/OcfAY+n5qfy/Dn+1mIkD13p3ZvIzqYlJ7+THd98w4qKek+qBwXRqqtBXVQXAoHQDG1tUBvK0egqG1paYGDQxINauHDh3r17fX19PT092Wx2TExMo2tWaMi2bduop3YV8vLyqNgZLpc7atSokJCQBw8eaLjNqKiojIwMheyQSqWXL1/ev3+/kZHRV199tWjRIlNT04SEhAy1vjUAdDqdU8vVzc7OrqCgQO28RnJy8syZMxMTE9WuyOFwmI31/zcxMQkKCsrMzIyIiHjz5s2cOXOsra2jo6MBQEtL65NPPmnGrwPJjrYhO+TWjoTYPwryHlOvi4sSDE2iAMYAQEHBe7KDyYSVK9Vn0SFNTYl+/fC4ONa9u4JNP40YITt2jCESAY/HsbeXDRhArFghYTLJtDScxyMIAnbtYhYUYABQWYl9+inbz084fPja51DOAAAgAElEQVR7gobPx6hZFQMDUlHkpVcvIiiIlpaGy2RAowFBQEgIjQrAmTlT+uIFHheHV1dj168zXF0l6PtFINoSPB7Pzs4uMjLy+vXr8+bNu3TpUkvnGl+5cqVaD1PlguTffPNNSEhIRkZGcnJyr1696t1mQkKCg4MD9VokEjk6OoaEhJw6derrr79u+gHXNqlRWFiI43izhBmrpXPnzp07d1a8pfTNsGHDmE2PZkSyoxG0at8OliEABkDmvXkEAFosQ5GwBICk4/co2UFFkQBATg4OAGZmRB2laaQTJjDj4mjR0RifP3So3LZWVoYFBtIDA8Hfn25gQObnYxER1a6ubEpzUFRVYStWaEVGVitv/NUr+ZvNm98JnR49CAAQCuHNG6xTJ3LFCi3K+WPKFOmZM0KpFPr21c7Jwa5fpyPZgUC0Pdzc3CIjI728vJydnaVSqY6OTovuzt7evt4+Li4uHh4ecXFxR44cOXLkSL39g4ODFYXvPTw87t69+91336loDuWJkuTkZHNzcw1zkbUSHj9+DABNEVLIt6NJtGbfDhxnMLW4YlEZ9bbPJ0tevggoKU4SC0Pllo9iDABiYvAbN2gAYGFRV9iqbMIE2LcPCIIWEmLu4rJypTg+nlZUhFVUQHo6rohJWb6cFRwsnxaZOFE2cKDMw4OZlIQHBtKnTXtnHlywQG7OHTnynRVEUReXz8csLckbN+hvtZ0IAOh0GDtWeu4cIyrqnVdKvQgEgCJfEIhWSM1UVLNnz165cuW9e/c8PT2dNAuBaWkwDDt79uyIESOOHz/+7bffKmZParub8ng8RTatgIAAAKhpUElPT1e8vnbt2ueff96nT59W+AX99ttvMTExnp6eyrVtpVLplStXLCwsZs+e3egtt8fk6O0HhVcpAPTsvcC802gAqCx/TKcLFbIjMxOvqsLqlx1Dh5IGBgBADw4GgF9/Ffv7Cx48qI6Jqf7883d6wteXThCAA9G/P3H8uPDHHyWGhiQA7Nr1zhwnk0FuLgYAGAadO78T/h06yA+gsBA7coRBHd6yZRJFybrBgwkAKCnB0tM1OkVLSrDvvmMJhUCiPCAIhMYwNf5rCllZWSoOklwud9asWSRJ+vj4KKYqVGQKWfuPud4OjaN///5nz57FMGzatGnZ2dm1dSssLNy5c+fGjRsVLcXFxVAjvzifz6fyjlPF20QikSJlKkmS5H97qaLGX+1OZTLZmjVrzpw5ExwcrNx+6tSp/Pz8w4cPc5rbww7JjrYiO966d2jrmOvoWpqZ2wOATCY0N40HgIAAOkEANZEBABs21FkeiUaTjRsHALTgYFCK1MJxOHRINH261MaGMDAgcSC+gCtJwDv4Y5qREcnhkO7uEgCIi8MVjqv5+RjlFzVmjEzZiY1yNKGsHceOya9pixa9m08ZNky+34iIekLC+HwsOxsbM4Z95Qp92jR2aChKfopAaPZ8D9BP47/GGcaFQuGvv/6alJS0ZcsWldKpbm5uADB//nyVVcrKypKTkwEgLS1NbXJMRXtSUlJFRUXzjomzs3NQUFBlZaWdnd2tW7dqdggKCnJxcfH09NTV1VU0Upm+Tp8+rXwvX79+/aFDhwAgNjaW0l6U20RJSUlhYaFMJouJiakjXKUZEYvFMTExABAfH19VVVXjek/r1q3b/PnzJ06cqGh8/PjxunXrtm7d2hK2KCQ72pq1w9CoDwAYdehHve1sFQ8AISG0y5fplOzAMLC2rudcl06YAABYSQnt/WyGhobkhQvCBw8EvnOuxWIDLsOXNpA6/M11aulnn8m1wsOH8ns/5UoCAN9//57QUVg7rlxhZGdjADBhgoxy+KDg8QgDAxIAwsPrOUUjImjjxnEyMnAA+PdfmpcXA50MCERrwM3NTV9f38PDg8vlenp6GhgYKCfnGDVq1JAhQxYsWKBo8fHxMTc3NzExiYyM5HK5sbGxZmZmZmZmJ06coDq4uroaGBh88sknTCaTy+UGBQUZGxtbWlreaNaUaJ9++mlaWtqXX37p4uIyaNCgrVu3Xrp06fbt23v27Jk6derZs2fPnz9vY2OjvMquXbt++eWXq1evfvHFFz4+Pr///rubm9uPP/64cOHCuXPnbtiwwc7OztHR8eXLl2ZmZhYWFgwGg8vljhw50sDAYOrUqS00/oWFhTY2Nl27djU1Nb158yaXy01MTLSwsOjSpYuNjQ3lt0Fx8uTJV69e+fn5PXr0KCgoaO3atdOnT/f09GyutCiqepdEVmlNhgnDml1WNwtRUVGDBw8GgPt/f5uadB4APhm0Ypj9dpIkTv9hJpVWx8St9Ll4AACsrMjPPpN6eTH09cns7Kp6Pm9hoXa3bkAQ4rVrxe87mdPCwphbt9KePHmn6wcPFty7BwBSKXTpol1aig0dKgsJEQDA1q3M/fuZABAZWd2nz3tax8RERzma/e+/BQoLB4WTE/vvv2kDBxJhYdW1HeebN9iRI8zDh99JDQYDkpKqFL4jtQ0XokFnF6KVD5eurm7DruRPn0JcXGP2xOPB22QbbR6BQBAUFBQfH5+bm8tms7t27ero6Fgz8de7pzWpNCEhgc/nW1pa9uzZE3vrlcbn8zkcjoZF6eomISFhzJgxfD6/2T9sSUmJr6/vixcvCILo16/fF1980VDvVw3vkihvRwNozZEsoJS6w8CQBwAYhhsY9eLnx1hZyi8uWVmYry8DAIyM6r88kR06yAYOpEVH0+/eVcgOPDpaa9s22tt0uaShIdG7N+3hQ1p0NJ6VRVhZ0emwcKHkwAHmkye0qipMW5ukMqICgMJpQ0HHjoQiyIXJhP79VfPu2dgQf/9NS0vD6vAqvXaNfuzYe+YNiQS2bmUeO4Zq2yIQtTNwIAwciIahbths9ueff/75559r2J9Op/fv379mu7GxcXMdEoPBYDBaxKBrYGDg7u7e7JtFkSxNojVHsoCSb4eBUe+3+qMPPz/G0OC5kngHDWUHAMgmTKBFR+OxsVhuLlZRwdy+ne7vT3lskhyOZOlSyerVWH4+x9YWSJLu5ydesQLeuoKSJKSmYj17QkWFPGOHnp7qTrt2Jd8WfYRDh0Q10xdRcy6VlVhuLmZurv6Y09JwRU4dDIOePYnkZPz6dcb+/SKpFHv0iDZpEspzikAg2gg2NjYPHz78iA4YRbK0ZSjfDgyjGRj0pFoMjXoDAI4V6OgU0GigyKWhoeyQUu5FJMl2ceEMHUr38wOSBBZL8v331QkJ4m3bSD09wsaG6NULAOjXrytMFNSL1FQ8P19uo9izR43toWvXd3Muo0erEQddusg7ZGaqnqUkKY9YocraAYCZGTl8uOzXX8Xwtp6cqyvL1ZUVFEQrL8fQ6YFAINoGXbt2/dg/ApIdbUV2sI0AQFfPms7gvLV2yBPtdbVOvnJFMGSIjDIJKAfB1gExcCBpbAwAeEwMyGRAp0sWLKh6+lS0axepVBtaOnMm1Qd/9YoSE1Q6u8uXGXl58vu9Wk8LheywsiI7dVLTQZHSVDkjGQBERNCmTWPb2XGuXXtXw+XPP4WHDolGjZJSBsjffmPeu0cTiWDWLPaePUx0eiAQCASSHYjmt3ZQYSwUegY9qBf79z0fP152+LBo927RsWOiuXM1m3fAcdn48QAAGCZ1dq5+8kR05Aj5tqaziuwAALqfHwAwmUDlFf37b1pWFqYiIJTp3p0EAH198ulT9f6tHTuqkR0+Poyvv2aFhdESE/Gvv2ZRVa6srEgHB1mvXgSHA/36EQBARcdQHDvGiI9H5zkCgUAg2YFoftnRW9Gio9uJTmcBAA4vAKBbt1KX2bHz5jUg17h0/HjZp59Wh4UJT58mundXbxTp2ZPo0weU5lmGDpUBAEHA99+z6rV2TJkiqy3Zv6EhSZVxVsiO8nLs7Fn6mzeqkyb79okUPqcq4TAAIBaDkxP79Ws01YJAIBBIdiCaS3awjUDJnxQAMAzX5VoDQFnpCwDIzgzOexPeoG1KnZ0FN24QAwbU042aZ3n2DE9PBwDFjIlQCJT9g8rAoYK1NUGjwfTptZpeaDR5eg+F7NiwgUllD2OxYP16MeWFSqNBz57v3ETs7d/JDiYTVqyQAEB+PrZkCUssRqcJAoFAINmBaA6YWvoYRlO2dgAAV787AJSVpQPA68y7/Pyohm1Us2ooKvMsKo4a27eL1G5GSwtsbIixY+ua8aHmWSjXVIKAkBB54JW3t3DDBvHu3aKxY2V37giU066PGCFT7M7ZWbp9u+jrryUA8OABzdsbZRJDIBCIDwwKoNWUVp63A8NwjrYpV6+bcqOefncAKC9NJwhpdubfTGaLFD8kuncnPvkEj4ujX78u/t//zM0JHAdFzl87O1ltKy5dKqk72b+1NREfj1NlWdzdWdREybJlEqrUnJubZNIkqUpsrbEx2bMn0aULYW1Nbt0qAoA9e0T379NevsRPnmT07atGZ2dnY2p9WhEIBALRFFDejibRyvN2AICp+XAcf+8L1TfoAQAymejlCz9BdYGgmi8SFmuxDJt911InJ2ZcHB4fj6ekaPXsOWeO9Px5+ZF07VrrHX3u3HocTXr1Im7ehPR0XCyG+/dpAIBhsGzZu7XU5vP48kvpN9/I69IBAJsN33wj3biRmZKC37ljplwBWyaDiAiamxtr7FjZokWSwYNlzSzICHnccnk5xuGQdPRrQyAQ7QmUt6ONY2YxUqXF0Eheuznm8W4AACAL8p60xK6lbys+0/39AeDQIeGyZRIWC4yM1CQKU1BvnXoejwAAiQQWL5abOnbtEinyedTG//4nVmgOiu++E1MC5elTfeX258/x6dPZubnY+fP0b7/VosrzNhd79zLd3VmnTjGysvB9+xiHDqE4XgSifZGWlvb69Ws0Diqg56+2g7nlKJUWI+N+dDpbKhWUlqRQLfl5TzpZT2z2XRPW1sSAAfizZ/Tr18Xr1mlpwZ49op9+ElMmikbTpYtcPfj5yU/U0aPrN0jUdCWh02HUKNnFi/THjw0zMkRUEM2rV/gffzAUSU5TUnB7e/bjx9UqkTUPHtBGjmywFWT9eq0//2QIBHD+PJ3FAqEQtLTg/n3a9esCZPNAtGZkMhEhkzCYOh/2MAIDA1++fKn0BMIbO3asJitevnxZuWTJ8OHDBw0aBAD+/v5UGfpaHoHYPB6Px+Pp6+s346dYvHixiYnJpUuXautAkuS+ffsKCwsLCwv5fP4ff/xhbm6OZAfio4Hy5FAGxxmGHfoV5L2rNKj8upkNHjNnMp89wxMT8eRkKnWpoSHp5NSk3OQmJsTbHycAwA8/SFTqyWnO/PmSS5foQiHt2jX6mjViAIiNxX183nMyTU/HvbwY334rAQCZDAgC6HRwd9eaM0c6cqRMc/GRlYX//jtDUZmLiugRieDePdqJEwx3dwk6VxGtluh/90vElfZjdn7Yw5BIJJmZmQkJCaGhoQKBQFtbOzc3V0enHjH05MkTd3f3oqIiNpvt6OhoY2MjfftgIRKJMjIyYmNjQ0JCAKBr165z5swxMDAAAIIgsrKy/Pz8goOD7e3t9+3bN7A5Cta8fv06LCyMxWJVVlbWceQSiSQ/P//MmTMAcODAgSbuNCws7I8//nj+/DmHw+nbt+/o0aPnzZuH461rWgPJjjZOR9PBylKDnx9NkgSGNf9ZKJ05k7llC5Ak/fp18YYNzbJNExMSw0Bx/544UYo1dhpk5EiZlRWRmYknJOAK84Zi6e7dolOnGGlp+B9/MJYskVRVYV9+ySIIGDpU9uoVvmsXMzRUdveuQJMdicUwbBhbccydOxPKyd03b9YaNUrWty+BzkxEK6S6Kv9J5G6ZVNjnk4X6hj0+4JHMmDFjxowZADB69Ggcx0NDQy9fvuzm5lavqcPR0dHb29vR0fHixYvKi2bPnj179mwA0NfXLysrW7BgwebNm2sKhVmzZtna2u7YsWP9+vVN/AgXL14kSVIgEPj7+7u6uqrtg2HYhg0bACA+Pj4mJqYpuyMI4pdffsnMzFy3bp2NjU1KSsrZs2fnz59/6NAhb2/vfv36tZ7TrL37duTl5e3cubMNf0DjjrbUC65eVwAQi8tLipNbYkdE587EoEEAQL92rbm2yWTKU3cAQM+eBFVnrtFQN/tnz3CShGfP8KtX5Zrb3l62bJmEMnKkp+OnTjEGDOA8fEh79Ih28KB8xiUignb8uEbxt4mJeGWlXBx9+qnsyZPq27cFZmbk6tViJhNEItixAzl5IFopD+6tE4vKZTLxg3vrWskhcTgcSm14eXnV3VMgEJAkSdWXZ9fuOEYtUluG3tLS0t/fn06n//zzz9ff5j9sNNeuXaMK0vr6+tbbmbK7NIUjR47gOH769On+/fuz2ewBAwZ4enoePnw4Ojp6+vTpZWVlSHZ8eCIjIzdu3Lhu3bqTJ0+2bWsHAPTtv2zCtItvDR5PWmhfUicnAMBTU/HExObapiKx+qpVYm3tJoW5DhpEUMLC35/+/DktKUl+8v/yixjDwNVVoq9PAsDatVoqVWAo1q7VUlSeqw2RCNzd5blZeTxi3z4Rmw2jRslu3RJs3SqmIneCguilpShlKqLVkZ8XnfT8HPX6Rapf5su7reTAnJ2duVxueHh4SkpKHd2uXr06661vexMuOKbjxo0jSdLd3V0xQdMIUlJSOnbsuGTJEgC4e/duUVFR3f0xrEnXhMLCwgsXLtS037i7u/fu3TszM3PNmjVIdnx47Ozstm/fPvNtqqu2Clevy+jPjg0ftUffoCdTSw8A8nNbSnZInJ0pf056kx8UFAwcKPeo+OSTpk5MuLtLGAy5wSMtTf4jnzxZOmCADAA4HBg6lAAA2VsXDhwHXV0SADp1Imk0IAj44gtWSUldV4f0dJyq/4JhMG+eVFGPl3oxe7YUACQS8PGpf3JTJoN792hJSfjLlyjcDPEfQN67+wNJEhiJA0kDgLCQ/5GkrDUcGYfDcXFxAQBvb+86ukVERNjZ2TV9d7179waA/Pz8hISERm/E19d37ty5X3zxBZ1Ol0qlV69ebdEhevjwYXV19blz51Rv8Dju6OgIAMHBwUh2IP4zMBueKwBgGN7RxBYAGpyrVPPrlqWlbPBgaNZ5lt9/F/n6Cvv2JZQzoDcOHR3SxEQIAImJuMJuMWWKTEtLYQ557yLr4SF69qza2Ji8ckVA1bfLyMDXr9eqYxeKzY4cKVu8WDUZ+4gRsl69CAA4c6b++ZrNm7U2b9YaO5Y9eTK7bq2DQDSdpOc+uTkRAEAvM2GWmgJAIT8+7unxVnJ41DzLmTNnarNAZGRkdK+lblRDodHk8XfiJtRTuHHjxvTp042NjceNGweazbM0hcTExPj4+AULFiQlJaks6tGjBwC8evWqsLAQyY764fP5c+bMKS8vV729keSJEyc2b9586NAhDw+P1jOarRxjkyEAUFKcJBFXtNAu5PMsL17g8fHNtc1p06SPHlUzmiOzubm5EAD++ot+6xadsnAoq5nJk99d0WbPli5dKunYkQwIEPTpQxw+LKJSo/r60oOC1EcFCwRw8KD8KAcMIGomYMVx+PxzKQCkpeHV1XUdZ3IyfucO7dkzvLISy8nBPvuMnZaGnhAQLYVUUh1+fyMAYDIGo9yUXm6CS1gA8Chsk1BQ1BqOcNiwYTweLy8v76+//lLb4dy5c/PmzWuWfWVlZQGAjo4OZfZoBNHR0Twej8PhAABlpwkLC2tiAg+JRJKeni4Siai3BEEEBQXt3r373r17AGBnZ4fjuImJiaGhYY3rktwXXktLC8mOWikqKlqzZs3SpUunTZt28eJFxagpcHV1LSgo2LZt24oVKyZOnOjg4JCXl4euHfXLjo4DAIAkibLS9BaUHRrPs2C5uVorV0Ldd2CqZzM97ZuZCZXfLlkiHjLknYVj0CCCeothsGqVmAo669ePoFp27xZzuSRBwOHD6n1C09Px6GgaAFhaksuXq39OoqaKZDJITKwro8nixSxlnZGaii9froVOYEQL8fjRzoryLABgllhiBI6RGKPEAgCEguLIh9talcFDrWMpQRBFRUUmJiZN34tQKAwMDASATZs2qXU71YQLFy7MmTOHeu3k5KSlpUWSZB3ZO+omIyNj6tSpjo6O586dmzNnzrRp0/7+++9FixaVlJR06dJl0qRJRUVFY8eOLSgoyM7OrjkIqampAGBlZaWrq4tkR63o6ur+8MMPx44dmzt3rlrj1e3bt9etkztaDxw4cMyYMcuXL1d0+PfffyfXwpQpU549e9ZuLy5abCPqhVjcUl7NpKWlbOhQeFsWrq4zLzGRM3Ys488/2XPnwlsJ39IMGVKs/LZHD9WE5UuWSHAcLlwQUglSlbGyIubPlwLAo0e02Fg1PxzFDEuvXoSJiXrv1wED5JXqLl+u1b1DJoPkZFxhIHFwkAHAw4e0wEAU7o5ofirKs6OfHAAAXKRNr5I/K9Or9WkCLgDExhwt5D9vDcf51Vdf0en0W7duFRQUqCwKDg4eP358Mzw1SaXu7u4VFRWLFi1au3Zt4zZCEERwcPDEifKsjHp6elOmTIHGzrOkpqb279+/a9eud+7c2bp16/Xr183NzcePH//999+7uLjY29svXbqUy+UCgJGREaOGTVgmk127dg0A3N3dW88p1xovZEwm08rKqg4h6eDgoDy+I0aMWLp0qUAgoCKjhg0bdufOHXQ1UTewenLZIWrBYCrpzJm0f//F09Px2Fiif3+1fWihoax587DycgCghYSwvvpKeP48MFq8Qqy9fWGnTmR2NgYA3bsTQ4eqesw5OUkDAqTUfEpNZs2SHjvGkEhg/37m2bPvGU7KyrDdu5mUXYRy4FBLp07k2LGykBBaYCBtzx71fVav1qIyjGEYbNwoXrpU0rcvp6QE27iROWWKFJ3GCM0JDV6RnXmv7j7l5VlSSTWQoFXUCZTUMrOkk4CVSBDSS2eHc/W61L0RU/Oh46ecatHPYmJiMnXq1ICAAB8fn1WrVikvCgwMVCnVWS+ZmZn//vuvsrEkMTHxzJkzMpnsypUrTYmICQsLGz58uPIdas6cOX5+ftHR0WlpaZSnheYsWrSosrJS8ZhNmWFOnjy5Zs2akJAQCwuL3377rY7Vr169mpOT071795UrVyLZ0UgoITlp0iTlRi6XKxQKQ0NDJ0+e3IgNEkR7yd2kpfVWdojLW1B2fP651vr1QBD069fF6mQH/cIF1g8/gFgMOE706IGnpNDv3GG5uQlPnwYaraUHoV8/WXY2HQAcHGQ19YGWFhw+XKvpxdZW5ugo9fOjP36sepwZGVhiIk4ZRbZtq8t4M2iQLCSE9uYNLpOp/7hXrsh/lWPHyr77TszhwOrVko0bmWlpeFoa3qMHSjWG0JTysleFfI28rBhVRrj4vTkFXMyiVxpLdQvE4sp6N8J6a0ltUdzc3AICAry8vJRlR3FxsZ6eHr2BRQdiY2ONjN4dM41G6969u6+vb58+fWhNuwpduHBBxU4/bdo0HR2dyspKX1/fmjGudVBVVfXo0SMjIyNLS0ulR5dOBgYG//zzT3l5OWXnqF1Qlq9evdrY2Pj27dutx7Hj45MdfD6/uLhYJdEsNfSpqakNkh1JSUlnzpx59OhRWVnZsmXLevTooaKgVahNTXfu3Nna2voDjklUlKaRKTKZ3IviRVp8eVVUyx3SwL599eLiCF/fqGnTVPwyLK9d6/7770CSBIOR/NNPRfb2/dau1Y+Lo/v7V33xRdJPP0EL5/Ht0CEHoDMAMBjZUVFZavu8elXr6ubmlgDdc3KwnTtfT5jwzqMoNLQjQG8AMDEpjY2Nq/NbMAewkUjg7t14Y2NRjSsFo7zcHgBoNHLMmKTExEIAsLTkAAwFAG/vNzNnvm6dZxeiFQ6XuaU9jtdhRCSzM+8JhSVA4vRSi5qLmSXmMk4xSZMymdpW1hPqyG5sZNz3P/g4U6ZMMTExSUhIePz48dChQ6lGHx8ftdPxdTNq1KgtW7Y0+xFKJJKIiIg//vhDuZHNZs+YMeP8+fNqU2vUQVFRkUwmqzl1QrVUVVXVLTuWLFlSXl5+7949Gxub//j8f/XqVWZmpto+MTExH5nsqKiogBoJ5qihb2gWNh6P5+HhoXn/1ln4PioqavDgwRp3J59H0QlCamKiZ9uAtRoM46uvYM0aVm7uUBqNSl0KACCVaq1ezfDyAgDSwEB08aLViBFWANhffxHTp+PR0SbBwYYmJqLff2855REVFfXzzx1MTMSXLtHt7c0HD+7Y0C3o6eFnz5IVFVh0dI8NG949goSFyf1MhwzRrfsbKSmheXoCABgaDhg8WFbjCOVPWt27kytXWgNYA4CtLWzZQmRm4gEB3XbsMBUIMA6HbH1nF9IcrW64BtvV5aAQ9/R4Wsp1AGCWmuFSNeoEI2jMUnORUZZYXGVuOcJ22P8+8FMynT5//vy9e/d6e3srZEdKSoqyb1+zEx8fX9ObBABGjhzJZKp6l//111+ZmZn9a1h5qXDLlJSUp0+fal7wxdLS0tTUlM/nK1d1qa6uLikpsbKyMjMzq2Pd7du3BwcHh4SE2Nra/nfn29vzv44fwoEDBz4y2SEUCgFA5cumbGI1A16aFxVrx/Dhw0eMGPGxXRgxBlNXJCxpOZdSucBwctL66SeQyeh+fuJBgwAAq6piLVhACwoCAMLaWnj9OvF2jpPU1RUEBLCnTcOfPWP4+IC2tmjfvhZ8/jMnN28Wb9rUyJj8Hj2IadNkvr7058/faaOsLMzbmw4AHTuSGzfWs91OneSK4fZt2rBhqrJjwQJ5klPlRRgG334rWb9eKysLu3GD/uQJvn27GBCIJiASlj4K20S9JrSqRMYZtT2rAAmAQWT4r7y+X3G0TT7sYS9cuHDv3r2+vr6enp5sNjsmJqalb6vbtm1Tm+wrLy+vZtiIr69vcHDwgAEDVC+JUmmXLl34fL6vr3y17wsAACAASURBVK/msgPH8W3bti1ZsuTKlSsLFy6kGv39/SUSye7du+tY8ezZs8eOHQsLC+vTp8+H/b4ePXoUERGh3JKVlfWRyQ4qBEgme+9iTSX2aOnooNZp7WgoTKaeSFiicCkVCYszXwZ27eFMp7ObcS+kiYls+HDaw4eMa9fE27Zh+fnsWbPw2FgAkA0eLLx8mTQ2fq+/np7Az489eTKenMw4fhzodFFDDFGN0V8YNHquk6qC++YN9u23Wj//LNbVhZcv8VevcADo0oXQ0SHrFS6DBhExMfi1a4xt295TD2IxUO6uALBnz3uLbG3lLh2LFrEwDPr2JVxckHspovFEhm8TVMsLxEs5pfX2F4vKw+//3NJ+o/XC4/Hs7OwiIyOvX78+b968S5cubdq0qUX3uHLlSrUepnp6eiotVVVVycnJw4YNq9lZS0vriy++OHr06MWLF3fv3q15KnRnZ+eTJ09u2bKFw+EMGDDg/v37mzZtOnLkCJUORC1BQUHbt29/8OBB165dFY0EQXyQOrQjRoxQeT7/+Kwd+vr6NQ0blOyoeRIga4e6s1+vAkAkKquqzAkNXpqb85AkZcYmgw0MezWzwWPmTNrDh1h2NuPsWeauXVhODgBIp08X/vknqKvSRHboILhxgz1pEp6Rwfj9d5LLba4yts1Onz5y1Xv+POPSJcaAAbK5c+UKoEuX+uc+aDQYP14aE8N88waTSkHZEy4/H6NK1xobkyoFaPr0IahivFSQy+LFLH194aRJDVAeWVl4airm50e3sCDXrBG3fNgQovUiqObn5kSYmDbMTsAviKsoz9bldvqwB+/m5hYZGenl5eXs7CyVSuuoKd8s2Nvba9iTykxa29K5c+cePXo0Ozs7PDzcwcFBw22eOHFiz549gwYNunbt2rVr13r27BkfH9+xY62zw9HR0T/99FNISEinTu99Td9999327duVvWiRtUNT9PT0evXqlZ+fX1N2KKb6kLWjLmuHlh4AiEVlRYXP37y+TzVWlGc2v+xwctJauxakUq3vv6daJMuWiTw86vDbIM3MBLdvsydNwjMzmbt2AYslrtPJ90Px2WeyZcskx44xAEAqhago2rNnNMqCsm6dRnMflpYktW5uLqaYcwGAggL54HTurBquwuWSdnayiIh3Pva//socN06qoXooKsJOn6bv28ekZM21a3QPD9H48TJAtEvYHGOX+REfxaGSpKqUnz179sqVK+/du+fp6enk5NR6DvXChQt7aguLBxgxYoSVlVVWVpavr6/msiMqKmr69OlcLlcxyVIHGRkZq1evvn37trm5ucqily9f/veaozZrx8eXcdnJyenly5fKLbm5uUZGRv+l48xHLDuYegAgEZdXVb4LiKisyGr+i0WHDjLFUwKOizw8RHv21OsrSlpaCm/eJM3NAYC5ZQvjfYfw1sPChRJlkw1VJqJDB7J7d42iWy0t5d1ev35vQPLysLeSQo18OXxYpDwxFBeHHzzIrHtHEgnk5GAkCRcu0PfuZSou4Kmp+Lx57OBgGvpFIFo5WVlZKjkOuFzurFmzSJL08fGpef+mZEpNsaJ5h8aRmZkZGxvL4/Fq64BhGDUzcvny5ZqVZWo7Kj09vZ9//pmKpajHFsXnu7m57d69WyKRZL7l5cuXycnJFy9erNYgGfR/Rqu2dlD550Xvp7BcsGDBwYMHX7582aWLPIONn5/fqlWrWnriqm1MsjC1uAAgEpVWVryTHVRe5GZHOnMm7f59YLOFp05JHR01XIvo0kVw8yZ70iSMz9dauxZYLMnXX7e2YeTxiH/+qZ4/n7VjhzgggHb+PAMALCw0vZApLBy//cYYPvydyeHPP+W2C0XpWmV69iR8fISenowhQ4jQUFpcHH74MGP5cnFtTioJCXhaGu7lxfj0U9n+/QxlW0tBASYQwLx57MjI6q5dUSIQRGtEKBTu3bs3KSlpy5YtGzZsYCspfTc3t7Nnz86fP19llbKysuTkZABIS0srKSkxMDBQ6UC1A0BSUlJFRUVzeQQmJCTMnz8/JyfnypUrn3/+OaMWIySVPKOwsPDIkSPK+bskEsmrV6+ow1MpaLdo0aLRo0cbGRnp6Oiw2WwWi8VisYyMjMaOHTt58mSFH0lVVdXUqVOfPHlSWw3exYsXf5AvUe0kC9bsoq/pSCSShQsX8vn8+Pj4qqqqTp069ejRY8yYMStWrKA6XL58+dixY0ePHjUzM9uxY0d+fr6Xl1dD08U0CAzDNNGb/z0NjdmLfPBT/LPftbT0O1lPfJEirxHQtcfMcZPONP+gFRZy7OyEFy7IGj7/hT9/zp46FSsuBgyTuLmJd+4ka5ZWa/nhqu+yCCwWFBZiDg6cnBxs6lTpxYtCTVYUi2HCBE50NG5qSqalVSm21rGjDkkCnQ5FRZW1qeinT3EjI/LJE9rXX7MAICBAMHas+rmSX35hHjnCFL5/RBMnyry8hOHhNBcXFkHA5MlSX19hbemRUADtBzy7GoSurm4rvJI3BTc3twsXLtBoNKpwvEwmmzlz5oULFxS2gWHDhgUEBCiCSH18fNauXVtcXEyn02k0mkwmk0qlBgYGv/zyy5IlSwDA1dX19u3bQqGQTqfjOE516NChw9GjRx01fiiqSUlJiY2NTWVlJYPBIAhCIpH06dMnJiZGpduWLVsOHz4sEAiovUulUhqNZmRk9OLFi86dO5eWlmIYhmGYRCLR1tb+7bffFJlIYmNjv/nmG7FYrKenx2QypVJpVVVVenp6aWkpACxduvTYsWMYhm3btq3uNCT79u1raT8BDe+SBw4cwD7Sk7W0tDQ8PDwrK8vOzk7zeCQkO6L/3RnzeBeG0TqaDsnPjaQajU1sP/8ytEXGrbiYrFERUVPlERPDdnTEysoAgOjaVXTihEydi3hruDEkJuITJrBnz5bu369pcZmdO5m7djGVFcarV3i/fhwAMDMjU1Or6l69ogKzttYWi+GrryRHj6rZ6evXmLs769699wRFly7EkyfVlHXEzY1FpUP18xN89pkMyQ4kOxCtjQsXLqxevfrGjRtDhgypcc1J/Omnn27evHnu3DlXV9fWcLSay46PtbiUvr7+1KlT/8s9tpVJFj0AIElZaUnKu3tYeWYL7a7RmgMAiEGDqiMiWMuW0e7fxzMy2BMnilesEP/8MzCZGv0G+HzGn3/iL15Ip0+Xjh8PzWEsqY3evYlLl4TPnjVgmq9jR7lXaVYWbmVF4Djk5MgdOz79tH5PT11dcsoUqb8//dIlxs6dYn191VvO9u1aYWHvaQ5TU/LaNaFiRsbTU3TjBl0kgitXGLXJDgQC8aEQi8VLly7dvHlzTc0BAL179/b39+fxeIGBga1EdqilLUSyfEDaRiSLoiyLSFgCAHQGRyqpFgoKpdJqOp3T2o6W7NRJcPMm49gxra1bQSBgenrS794VnjxJ9K0rEzMeF8c4epRx9SpV2JZ+6RJwONIJE6QzZtA6dmyhQ7W3l9XM/VWv7ACAjRuZAwYQCxdKFLLjf//TKBxm6VKJvz9dLIZr1+jffCNRWfriBUZlt2EywcSE1NYmjx4VKddz0dcnx4yRBgXRL1ygL15Mq5kvtUW+UBI0TliAQLRrqOSkKnGw713ocNzS0vL169et+VO0kUgWRNOtHe9ufiZDWtrg0VQwTOLuXv3wIWFrCwD48+ecMWOYBw6ArMZtUiaj37jBnjyZY2/POH+e0hzAYgEAVFfT/f1ZCxfaOzmxZs+m+/piZc2fp7VBzkUmJnLZceMG/ddfmYMHc7y85G5o5uYaWcuHD5dRG9mwQau6GqshO+Q/7S+/lBw+LPw/e2cdV9X9xvHn1C3gkhKSyixMxJg1A2e3zo6ZGFMn6pwOnTVb0fmbOXWzZk1RRGZjKwo2YiDdXbdO/f44lwsiKaHA9/2Hr3O/95zjuQ8nPuf5PjFmDNO6dX6LrVun1Tc3blRGSou7u3jECMn582RsLJIeCEQxWFtbt27d+vDhw4VNn4WFhd2+fbtfv35V7qch2VGzMKvVXCTK7R5kY9dNq6zjA77kw+bq11dcuaJZsgQoCtRq0bJl0l698Jw8aiwjg/r9d1nz5pIxY4jbtwEAKIoZNkx5/XpWVJTq1Cl67Fje2BgAcI2GvHBBMm2aXt260qFDqYMHsbS0z/KLdN4OngeOg6Qk7M4dAgAMDfMXCisMgoBVq9QAoFCAv/8HF/L//kclJ2MAIJPxmzdrXF1ZN7cCPChffcUJGb8HD5Lp6RUoBRQKrHdv6d69lK8vOWqUpE8faWwshkIREIiiOXPmTFRU1MiRIyMi8icbXr58uVOnTvPnz1+4cGGV+11okqWkVI/YDn0DO9feBy96D+M4BgBsHXo9C/xdpUqOjvSr32jsl32qkprFi5mePSXTpuGvXxP370vbtdP88gseFkYeOYJla2MweVNT+vvv6WnT+JyCOUzPnkzPnmqaJm7eTP3zT6v797GkJNBoiEuXiEuXRMuWqdeuZUaNquRfY2XF9e/PXLxI5msN06RJKdJZe/dmcRw4Dnbtorp0yXVm/Puv9roePJgRmsZJC6l9/913zNq1ovfvcW9vcuxYuiJ+qVoNJ0+St28TeT0xXbrIOndmBwxg+vVDJd4RiEIdHv7+/qdPn3ZzczMwMDA2NjY2No6Li4uJialXr56Xl9eXX62qyiTQfoFUm0wWrW8j4bFGnQYAlrU7+F2a8v7dGanMfOzkdwBVwfutVIpXrKB27oQPiwhxTk70zJn08OGFPmMFczk7E3fukGfPkt7eWGysMM52767eto2zs6vkn5KcjPXpIx04kLl1i7h9m6hVi3/3LrtUBWi+/VZ6/z4BAEFB2bpyIA0a6MXEYADw5InC0bEoHcNxUK+eXkIC1qQJd+mSUl+fDwvDCQLs7Li8Z9eTJ3jTphzxSVMxgYF45865YUPNm3NPn2p/oZ4eP2ECs369unrcJVAmC6KC73zK9PR0c3Pzz9Jdpbyekii2o4ZSy9zZ2rartW1XghBZ23UFAKUiITnpRdU4eqlUvW6d0tubF1QCjjO9eyu9vRX379MTJhShObQQBPvNN+rNm7ODg1VHjwpOEeLKFWnr1tQffxQQMlKRmJry//6r/PFHze7dan19vnt3trT3E1090xcvtKKAYSA+HgMAiiqgyHo+cBxGjGAA4MUL3NOTmjJF0ratrHlz2fTp4n37KH9/E6USvLzIQYOk/ftLw8M/5Xbx9GmuWpk3T3P7tmLJEo0QWJqdje3YQf3xB2oPg0CU5M4ntbS0/DI1R6lAsqOmY2PnKixEhP1XhQ6b/eYbxf376tWrFY8fq06cYLt0KXWOBI4z/fsrHj6kJ08GDMMUCvHPP0u7d8dfvqxU+9vwMhnY2XFr1mi+/bbUMw5Nm3LCXWjfPkp43Y2N1eawLFyoKUmU67Jl2prr27aJTpwglUpgGDhyhPrxR/GiRc2+/14yYYIkORm7dYuYNEkcFFS6O8bDh8Rvv2kTnl1dWQ8PDQAsXqz59VcNQYCJCQ8AS5eKAwJQmXYEoqaAZEdNR9/AzsSsCQCEv/epWkfOGxjQc+dyeZo7f8pO5HL11q3KS5e4Bg0AgHj0SNaxo2jZMlCpKvnnfP89/QmBDnp6vDCNcvEiERmJsSz884/WeVDCnFiJBJydWQDQFJS3e+ECqZvL8vcnRoyQfJw1UwRv3mCC6wXHYf58ja7kyvz5mv/+U549q5RIgKZBJ00QCES1B4WUlpTqEVJaIPZ1+qQkvUiMD8zOjtXTs/rgqcxzGFb9tSn79deKe/dE//ufaPVq0GhEnp7k2bPq7dvZb76ptGPAsOIniApk61Z1375SAHjwgPDxIV+8wHV+lBLuYdYsOiCAoGkAgLp1uf371ZGR2NatosBAjOc/EBlhYfj69dSKFZoS7vntW+3BzJhB5+0+AwBff80CwMSJ9M6d1PXrRHAw3rAh6g5TlvMHpSUjvjhQSGmZLunqFFKaj7jYe96negBAz/6n7Bx65v0q6Nkep2bTqs3fsVhz4S9eSH74AQ8IAADAcXrCBM7ZGSiK19MTPAO8UAhEX58nSQDgra35CitBVkKysjBraz2OA4oCmgYMA+Gajo7OlstLenU/f44fP04eOUIFBiqMjXkASE/HLl4Mmj27tUIBGAbr16tPnybv3ydEIrh3T1Fgs7p8BATgU6ZIhAoi+/ervvuuAF/Oy5d4x44yhoHvvmP271dV77MLgcyFzFWFi6MjyhEDA20GhyI7Nu94fOz9Vy/2VyfZUSxckyaKq1epnTtFq1ZhCgV14AAcOFDE+ry+vur8efazprHp6/NffcW9eYML7gpBc2zerC655gCApk25pk01CxbQujrrhoZ83brZy5er//iDcnHhZsygO3ViO3SQaTRw7Bi5bFnxDo+XLwlBc3TvzvbsWfCMT+PGXO/ejLc3+eABmvBFIGoE6FJHgFRmgWHEx7Lj5bM9qSnBDJ1ds8xBEPQPPyj9/VlX12LXxbKyJIMHV3IU6sf884/Kzu4DkWFv/ylezI97u7i50Tt3qvftUwFAkyZcy5YsAJw/T3IlmA+JitK6/Zs0YYvQQMJsS0QEHhyMbkcIBJIdiJpwEuCkVGoGAIrsON2gQhEfFnKW59mkxKc10Cacvb3Syys7MjL73TvF8+eK588V9+8rbt5U3Lyp9PFRnjunPHtWs3QpYBiWmiodOBAPCfmMR1u/PvfoUbatrXYKCEBbeKM8zg3o1InVZcQMGMACwKtX+JkzxTtKIyOxnMMrSgN1784KFUE8PVFgKQJR/UGTLAgAAJm+lUIRn53H2xH84gDLagAgIe6RZe32NdMsvJERABT2zGS7deMNDcULFmDx8dK+fZWXLlV+wTEdUimsX6+WSvkhQ6Q8D/mcH+XF9OmatWtFSiXMmSNu3ZorQtzExGBCKZE2bdhhw4qqf+rkxHXpwl69SiBvBwKBZAcil2qcyQIAMpklACgV8dp3fY4JfqmNaUhKCER//cKg3dywtDTR6tVYdLSkf3/lxYu8peXnOpj+/RkA6NGDffQIL2Fjl08QN05ObEAAkZGBeXpSa9aoC8u+uX6dePIEBwA7O77YDJ169birV4l374rPxWCY0rXcq8y9IRCIfKDG92WiejS+L1R26FkCgCJL6+1IjA/IzooRlhO+7C5xnx3NokWQnS3y9MTfv5cOGqS8cIE3MfmMxzNnjmbpUnHF7b9ZM06o7nXwIHXhAnnggKp9+wLCRSMidHm8xU/3CB3pMjKw6GjM2rpQwZSWhi1bJmrQgLOx4Vu1YhUKTCQqvhJrXkJD8ZgYLCMDs7XlGjXiNm4UvX2LUxRs2aKuIKGGQNRkCmx8j2QHQpAdVgCgUCTwPPv44UaCyJ1lz8wIUymTJFIzZKVClceKFVhmJvXnn/jLl5IhQ1Tnz/P6+p/rYL75hh00qAL7q02YwBw8SLEsaDQQE4MNGyaZNYv+5RdtYsvz5/i+fVSHDqwgTUQiqFOn+Md58+Za6bB9u2jdukJbtAwaJH31ClMoMLmcb9mS8/fHGzXiTp9WCdVOi+XpU3z1atGVKyTDAEVBt27MxYvaG+DJk+Tu3QWn+CIQiPIFTaYiAAD09K0AgOdZhSIh+OWBQP+1AKDrDJeU8ASZqCgwTL1lCz1uHAAQAQGSwYMxheIzHs7MmZqK27mLCztrFm1goH3SZ2Zi69eLdu+mWBbu3iU6dZLt20dNmiS5eFEb2DFpUvGNbb/+mm3ZkgOAly8LviMlJGCJiVhgIC7USM3IwPz8CIUCCwggGjWS/fCDuCS9dNzdxf/9RzIMAABNg05zCB8nT5Zs24ZiWhEIJDsQlejtAIC4mLvZWTEMowKAWhbOOY+WCGSi4pXH9u3M4MEAQNy/Lxk1CtSfrbGqWFyx+//lF/XRo9q5FZIEnocFC8RLl4r79ZPme/yXPI9XmCuJiysgvGPfPmrmTMnIkZICSxsqFNjff1OLFomLzuldsCC38wuGQe3aufvq2ZMxMuJ5HpYtE/n7o+4wCASSHYiKRyrVltp8/+Zf3aCtfQ8cJwFAF+eBKAqCUP35J9ujBwAQ165JJk4Epno67WUy6NKF/fFHev9+1c2bCjMzHgC2b6foHL+GhQUvhEqUPPDCyooHgNjY/LIjIwNbskR88SKhEwQNG3KbN2slna7vzO7d1Nq1hfoqOA7++YcUJJGVFX/mjPLJk+yePZkePdjFizWnTqkuXVKKxcBxMHy4RKlEpzICgWQHoqJlh0wrO6IirugGzS1aCaGmSHaUFJFIeegQ26EDAJDe3pJZs4Crtn1GevdmvvuOadqUW7tWnbcfiIsL9+SJwttbZWDAl7x8iCA70tM/mJ5iWWjVSpZ3xMCA37lTPW0aPWsW7e2tvHRJuWePqkEDDgC2bhWFhxd8Q/P2JjMyMADQ1+dXr1a7urJSKfzzj+rff5U//6wBgEaNuBUr1ACQnIydOkWhExmBQLIDUQmyAwMAhlECgFC01MzcOSfUNBaZqOSuANWJE1yLFgBAHj0q69RJvHAheeIEHhZWXX/xyJHMuHG0zhFy9KhSX59v3Zr18lI5OZVUdlhYaNd8+TJ3muPNGzyv/8PUlJ86lRY8HOvWqbt0YSkKRo1idu9WYRioVDB9+gfTSw8eEGvXihYsEM+bpx3v2pUdPlzrgqIoAAA85xY4dSothKa6u4uzs1FbNQSiokCZLCWletftIAiRWGKkVqUKH1u0mh8bfVsqMxdkB/J2lApeLld6eUl79cKDg/Fnz/Bnz6hduwCANzdnW7Xi2rZlW7fmXFx4maza/OT58+mICNzPj9i9W6ULm2jThi35Hho35ggCWBa2baMOH2YBgGFgx45cx8OqVZpx4+j4+AIEgYsLN2kSvW8fdecOERuLCY6Ts2fJFStEuha4gqtjx45CA25EIli9WjNzplilgrt38W+/ZSvIVkoleHmRt24Rw4Yx7duzusKyCET1A9XtKOONdX71/oFSqblOdtjYuTZtMQsA9PRrw0e9WhDFKw9TU+W5c9SOHcTDh8Tjx6BQAACWkEBeuAAXLgAAkCTn5MS2aaNZtOgzVhgrL+rW5by9lS9e4J9cHbVZM65TJ9bPj9AJi/Bw/K+/qJyrT/PjjxrB4VHg5hMm0Pv2UTwPCxeKDx9Wxcdj8+aJExOxPKc3tGrFfdx0Ji+DBzNz5ogZBtavF7m6KvEK8AW/fo2vXy86eZIEgEOHqPbt2a1b1Y0aceiSQVRLCqzbgSZZEDn3ZVluA3e5YV2xxEQnO9Sq1Pu3F0eGX+Y4GhmqpMrDykqzapXyv/+yoqMVt26pN29mRo3iHB21XzMM/uwZ9eef1N9/V5uf3KQJV6q2t/kwN+cBICFBqxV0jorGjblJk4oJzm3RghP8ExcvkhoNuLpKdZqja1d2zBhayL4peif6+ny/fgwAPHhAhIaW/70xJQU7d44UNEfOuyDRrZvU1VVakh43CET1AMkOhBZZjuwgSZlMzyJnUJtY+/zx//47NyQ64hoyVKkhSa5FC3raNNWePYonT7LDwlQnT2oWLuStrQGAPHwYeFQfM1d26OTC27dYzgsTW2xoKobB2LE0AKhU8Ntv2thSHIfJk+njx5W7dql/+CG31kgRuLtrS548e1bAvVHIhcnM/MTIj7NnyVWrtOk2BAHOzhyOQ1YW5u9PTJokWbxYjE4EBJIdiJro7ZAb1dUVChO8HTpiom4gQ5XVC2JqyvTqpVm2TOPuDgB4WBhx6xYyCwDUqsULD3WlEo4coTZuFAGAqSk/e3aJfGytW2ujMbZs0T7au3dnN27Udo0hSlaPw8mJE0JNjxwh8yUhaTTg40NOnizp3l16+TJR2hQlhQJ79AgXhAWGwe+/q2/eVJw4oRSmpRgG/vc/CvXgRSDZgaiRskNeRzeokx1CAY+YqJvIUOUFM3IkyGQAQB06hKyh83YAwPHj1MyZ4tRUDADq1ePq1CnRE97Wlp8wgc67t40b1VQpk2HFYmjXTjtZEx39gVdj506Rh4foxAkyKAgfMkS6YkXBEiE+Hvv7b2rWLPHhw1Te1N/9+8mjR7VHM3s2PX48DQA9e7K+vspjx1SOjhwArFghun4d1StDINlRfblw4YKHh4e7u7uXlxdf4/2bH3o7PpAdevq1mzSfAQDJSc/VqhR02ZSP20MuZ/r1AwDy7FksLQ0ZRPB2AMDSpSKdL0HoEldC+vTJTT9xdWXr1v2UUM2fftLOs7x/n3t7pGm4exfPG/CxZYto0aL85WD/+48cMUL6ww/igwepGTPELVvq7dunlRqvX+NC9Tg9PV7XwgYA7Oy4vn2ZgwdVeno8x4GnpwhNtSCQ7KiebNmyRSKRrFq1asmSJZ6enmPGjEGyQys7DHO9HSQpE4nkrb5ealenNwDwPBcXew9dNuWF0MYFlEry1ClkjXbtWGdnDgDS0rAcGcGsXVuK/jItWmhlh5ERv2uX6tMOQydW8sqOgwcpXQ8XsRgsLHgA2LGDuns31znx+LHRjh1UQEDuVtHR2I8/ij08xFlZ2KtX2vE//1TLZPmVRbNm3JgxDABcv04cOYLqlSGQ7Kh2MAyza9cuuVyOYZiZmdnGjRv/+eefZ8+e1WjZIa2VIzvq5h23dehZr+FoM3Ntf5b01Hfosikv2M6duTp1AM2zCCeenG/a9INqGf37s0WnvOajdm1+6VKNkxPn4sJ9cvqrtTUv1NLw8SE5DtLSsKtXidWrRUI8qZ4ev3+/ys9PIUiHBQvEut479+6Z6aZIDAz4ESMYoYTJtm1U//6SBw8IACDJ3BiUfPz8s0YoM3/pEppnQSDZ8ZlITEwcNWpURkZGvnGe5/fs2bNs2bJt27atW7cuKSmptHsmSbJDhw5ETpiZmZkZAMTFxSFvx8eyo/03GzAMpyh9IaU2MyM8R3+8zUIt4soIhjGjRwMAHhiI12zVKyB4PHUFaAAAIABJREFUEQS6dmX79i11U5ufftI8eKA4dEj1yceA4+DiIoR3EDEx2Pbt1JgxkqQkrQNm9GhmwADGxoafN48GgOfPcT8/AgAOHaJu3TIT1jEx4b29VX/+qbpyRWFjwwPAo0faW83Kleq8vzEvtWrxrq4sAPj6kg8fIuWBQLKjEklOTl64cKGbm1u/fv2OHTum/Kg109ixYxMSElauXDl37tyePXt27NjxExTDgQMHnJ21b/C+vr4WFhbVqerop8oODMcpfQObvOMSqVnOy6g95OlG+zb4n4AHa9AlVEboceOELAvqyBFkjbyPZFdXxtj4E8McSpIrWwS6NNrFi8VHjlC6WulyOb9pk9a5MWcOLXT6PXeOBIA7d/C4OG3B0T17VIJwsbXlt25V502iadKkqHCTceNokgSVCry8UBkPBJIdlYiBgcHs2bN37tw5evToj789d+6cj4/PokWLhI/Ozs5dunSZM2fOJ/93ISEhW7ZsOX/+vL6+fk0+FQhCLBLLDeT2QkOWj9E3sAOALK23g3/35uTb4GOpKcHoKioLvLU127UrAJDHjoFaXcOtYWmplQvNmnHjx3+2/r26+u7e3rn5LNbW/Pr1Gt3cjUzGd+7MAsDhw9S7d/jr19ovunRhe/bMnUbp2ZPp2lX7sU0btuiCpJ07sy1bsgDg71+xd2ahcNnNm4TusBGISuNL1NQikcjOzq6wb48ePdqxY0cqT2Jc+/bt3dzclEqlVCoFgAcPHixfvrzAbTEMW7NmTYsWLXQjkZGR06ZNO3v2bJMmTdDZIJNZGBg6FCoHDR1yvB18fKx/ZkYYAATcX929z2FkujI6PIgrV7CUFNLHhxkypCabon59ztCQT0/H6tfnPtnVUY6yg83RD927s+PG0UOGfKCElixRX7ok4zjYsoV6/Fgr1v/4I//8zooV6mfPpAkJ2G+/aXS6qjBat+b8/Yn794nz50mhZGq5ExWFDRggjY7GFQowMOD792c3blSnpmK1a3M8DyJUOgRRA2VHEXAcd/ny5V69euUdlMvlKpXKz8+vd+/eANC2bVtfX9+S7C0iImLJkiVHjx61sLAIDg5Wq9XNmzevyWeDVGaet2hHftlhYAcADK1QKhLfvT4uDIaGnEuMD6hl4YKupU+G6dePNzXFkpOpQ4dquOxo1Ih7+VKxfTtVliLrZcfYmJdIQJWjH+zs+CNHVB+nnzRvzgmrHT1KCQLFyYn7uCtNs2bctWvKESMkukSbIpg4kf7rLzI7G/P1JSpCdjAMtG8vE2qiAEBmJnb0KPnkCS4WQ3w8Vrcut2aNWsgnQiCQ7AAASExMTElJyTcbIpfLAeDNmzeC7Cgh4eHhs2fPnjt37rt37168eHH06FHdxE2B5OtAq8Pe3t7BweEz2uTRo0fltSulikzLIAvbYUaqdsL7wf3zoa9P5AzzF32mNmy2q6pkRZWjucqRrzp3tjl9Gr969YWPj8rCooabS3it+Lx/KFPTttHRUmF52rSXQUEFx63b2bm8eWOgc4pMmfL80aPUQm4g+IsXJXqcN2nS7MEDk0uX2Nu3H0sk5dwI99dfG6emau+fBMHXrq2MjJQFBWkv3pgYonNn2YwZId99F1ljL0Z07yo7YWFh4eHhBX4VGBhYxWRHZmYmAOjp6X0sO9LT00u+H5Zlu3btGhoa6u3tLYyIRKI9e/YUscmX2YH20aNHrVq1Kq+9aRQNbWw729UpeIepKXohwQAAasV/DJMOAOaWbRLi/JXZb40Mwr5qMLxKXLflaK5yBF+4EE6fxni+5fPnmr59S7pVUBDn4CCUOq1R5qoEDh+GoUN5HAeRCObOdQAo+NXC3Z2aPj33Y//+XxU7jVIsffqIHjyAuDhJQEDbuXNLWraE52HvXiozE+vfn7Gx4T/2zQBAeDh+86b2bKlViz9xQtWyJbdsmeb333NrlPE87NjhaGZmu3ChpmZejOjeVXaKOFRPT88qJjtUKpUgEfIOCnmwHye8FAFBEO/fvy/Vf53P29GuXbvql/kilZkbGBY+ySK3B8AA+IhQXwCQ6Vn2GXT24B5bjmPS096i+0JZ4Jo04Zyd8cePyUOHND/9BCUoOoHFxUm/+061dSv77bfIgBVw32QvXlSSZDGxDmPG0DdvEs+f47Gx2Hffvbe0LAdP1axZmh07qPh4TOeEKP784WDUKMmlSyTDwMqVojZt2IMHVVZW/IfvWrBwofbHGBjwO3aoW7ViAWD1ak3//uzo0RJXV3bkSHrmTEl0NLZunahHD6Z5czTbgigTd+/evXfvgwqTERERVUx2GBgYCL6KvINCYQ/hq4rjy/R2lC96MksDuUNh35KkTCYzVyjihY8NG39PUfpSmUV2VnRWZjS6wMoIPX68+PFjPCKCuHmT7dKlGM2RnS0dNgyLiCCvXkWyo4Jo2LBED93du1VZWdjJk2TTppEA5SA7xGJo2pSLjyd0hU0L5M4dIjMT69yZkUrh8WP8wgVSJ0Hu3ydatZKdPasShIVAWBju66tdZ+pUulev3MCRtm3ZGzcUFhY8RcHx48pvvpFpNDBpkiQgQIFOA0RZaN++fb73c09PzyqWPWVkZPSxY0OQHYaGhuhvXEZMzVuQpKSIFfTl9rpla9uukNO0RaGIRdYrI8x334FUCiWpWMowkgkT8KdPAYC4ehWZ7rOjr89PnEiX4w6FPNtnz/DLl4kCfRs7dlADBkjHjpX07i3ds4eaPj33shU8ZRkZ2KhRkpCQ3Dt8cLB22cmJmzAhf7CqjQ0vZAc2b87Nn68BgDdv8Bs3UNUyRPlTxbwdhoaGDRs2jI+P/1h2tGnTpkL/65owyWJi6lT0CnLDOglx/sKysUlDANDTqw0A2cjbUWZ4Q0NmwADy+HHy3DksLY03Mir0bXjRIuLiRe0zJjgYi4ribWyQAasTQ4cyu3dTGg3cuEF8++0Hzl2eh+fPcV0XuoAAIiAgVxzUrs1fuqS8dIlwdxfHxWHu7uKzZ5UAkJqK/fyzdoalU6dimuRNmkRv3iziOPjxR/Hjx5Xk8OB5SEvDjIx4DEN//+pDdZhkAYDBgwdfunQp70hsbKypqamLS8XmcNaESZbCCoXpMMjxdsj0LIVa6XoG1gCQnR2DLrCyQ48fTx4/DioVeeIEPW1ageuIPD2pPXsAgLO3x8PDAYD086PHjkXWq064uLB16nCvX+MREfkd0uHh+IgR0sI29PJS2ttzU6dyL17g+/dT168TBw9S48fTYWFYWBgOAObm/OjRxThmbGx4d3fNpk2id+/w+HissGruZSQtjfLzI86cIaOi8MmT6fR0WLNGJJPB2rXqzp1ZAvlZqgVVb5JFrVbr/tUxYcKEoKCg0NBQ3ciZM2fc3d1xHJXbq3DkOZEfxiaNhAXB26FRpzN0NrJPGWE7dSq6Mxx55oxo+XIA4OzslFeu8LVqAZpnqabY2/MA8LHsePEC1xVONTXl9+5VmZpqZYGHh0ZXBXX+fI2JCc/zsGsXlXc/TZpwLVsWH7PSq5fWxbJ1a4WUD1Mo4MwZ6yFDpPv3U5cuESNGSKZNk4SF4UFB+MCB0okTJTW+YG915kv0dtA0PXHixMTExOfPn8vl8j59+tSrV69Lly5z584FgAYNGvz111+TJk3asWOHlZXVb7/95ujo+NNPP1X0UdWESZZi0dUwNTZtlOP2sBIWsrNiDI3roSuqTGAYM2aMaPVq/MkT/NkzrlmzvF8S9+5Jpk0DjuPlctXJk7ylJdulC3nyJHHtGnAcINldvbCz4wCIkBAsPR0zNNQKi9hY7OzZ3Ju2t7eyaVPO1pYfMEA6cCDz7bdMns35H36gV64UPX+Oe3uT4eFapVLCOFlnZ9bRkQsJwY8cIdeuLX8JsGaN+MQJW7oQt8uZM2RamvTff5V5ilEjqiRVZpKFoqjDh4uqtz18+PAePXrcuXPHz89v9OjRuo5uFUpNmGQpXnZ87O3Qry0sBAf93cBpvJFxfWSlMmnusWNFa9cCy1KHDqk3btSN46GhktGjQaUCkUh15Ajn5AQArKsrefIklpKCP3nCtWyJrFed+OorDgDS0rDdu6mfftKW0Hj+HD92THvTXrlS07QpBwAdOrCnTim7dmX5DydDBg5kNmwQqVSwZo0oORkDACMjft68ElXjEIlg+nR64UJxaioWG4vly8UtI0LbXpUKBwAMg+7dWUNDPiAAz8jA3N01vr7k7dvE9evE7t3UDz/Q6Eyo0lSHTBYdRkZGffv2nTFjRuVoDoROZBCE6EPZYS0sPAvcdvKwy7G/GycnPUeG+mR4a2u2WzcAEII8tE6Q5GTJ4MFYUhJgmOp//9Ol1zLduwOGAQB57RoyXTXDzY0WutLEx+fGWArxGQBgYcH36ZPr2xC6zeULxqxfnxP0yosXeGwsBgAODnzJq5k5OXE5L6zlHGexcaPo6VPtD/nxR83Jk8oDB1S3byuPHFHNmUOfOaN0dOQAwMNDXHQKMaKKgtorlxQ0yQIAGEboGdhmpIUIaSwAINOzwjCC57UzwZkZEfduLuo35AI6YT7d4TFuHHH5MpaaSvr4MEOHglIpGT4cDwkBAM2vvzKjRuVqFAsLrlEjPCiIuHoVFixApqtWt2YSLC351FQsIaEA2TFpEt2gQfHTJSNGML//TqWlafdgZ1eK8l+NG3MEASwLK1aIhg7NlTgJCdiDBwTDgEQCvXuXumsMx8GzZ9pf4eLCrVyp9b7I5XyHDiwASCSwY4e6Vy8py8KxY+SKFRp0MlRdqkkmy+cCTbJo7w6GdVhGJRIb5twcJQZyu4z03Ajf2OhbIW//daw3FNnq02D69uXNzLCkJOrgQWbwYMmUKYS/PwDQ48drPjoJWVdXPCiIePAAy8zkK7hiHqKSMTfnX73K9XakpGAnTpA6TVCSPdjZcV5equ7dpQwD9vYlCibVYWrKz5xJb99ORUXhLAtCagnLwrx54hs3iPR0jCRhwgR61SqNgUEppmDmzhWHhuIAQBD8qVMFl5Zu355t2ZINCCC2bRONHMno4mQRVY5qNcmC+FwYGNjr4kkFjIwb6JbFYiMAuHtjgVKRgGz1iYhEzIgRAED4+UmmTiXPnQMA9ttv1du2FaBRunUDAKBp4vZtZLnqJzsE74Lw0cuLFCRIz55M9+4lbRHn4sL6+iptbfk5c2ihDljJEZ73NA263Bk3N8mVK0R6OgYADAP79lHDh0tK05cCnj4lOA4A4Ouvk83MCtUry5drBJVz6xZKpa1uIG9HSUGTLDpvB0V90HvMyKRBRNh/gub4pvuOyz6jVcqk10EHW7RCbv9PhB4/nvrjD+A48sQJAOCcnFR//QVkAVcr27EjSKWgVBLXrjGl6cCMqCqyIy4Oy8rCUlKw+fO1JcLq1eP19ErhYPj6a/bOHQVf+qhQ3aRMeDhuZ8dqNPDff4RCoZUgEgmoVHD7NjF9uuTvv1Ul3Oe7d9rN58x5C9C0sNW6dGEtLPj4eMzbm5w8mUZlPKooaJKlTKBJFq23Q24vkZjkHTHO8XYYGtdzqNtfIjVTKZOys1Dd0k+Hc3JiXVyIgAAA4K2tlWfO8HJ5watKJGy7dsS1a8SVK8hu1Qxraw4AsrOx77+X3LxJMDlxFA4OpZ50EKJTS4uDg3arPXuoTp3YQYOkgp8DAAYMYHbuVI8bJ7l2jTh9mpw3D2/RIveo1GrgOMjOxkxM+LyZ3Rs3ijIzMQAwNeXNzYvJy/3mG/bkSdLPj7h1i+jSha0gI4eF4efOEQ4OfNu2bAUVRqvJoEkWRLnIDgfjD2uoG5loZYcw26Kvbw0AWUh2lA1m/HgA4A0MlKdO8bVrF7Vmt24AgL97h0dEILtVJ9zcaFtbHgAuXiR0ExnTp9Mln2EpIzY2XJs2LAD4+REsCw8fEjpBsG2bWi7nd+xQCaU13Nxym8K8eIGvWyfq0EH29dcyZ2fZ339Tutpf589rX3R//734WiC//aZdRxeCWs7inoNz58jWrWW//CIeM0bStq1s4UIxjTJ2Kx4kOxClQ25YR6cz8qoNADAyqQ85BcQU2ag5XNlkx7BhvFyuOnSIa9Kk6DVZV1dhAZUrrWaIRODs/IHC0Nfnhw5lhPzSSoAgtOVK09Ox/fspIaHbwICfN08jhGVYW/OzZmkAICgIf/MGB4CkJGzDBtGmTaK3b/H4eOz9e/yHH8SurjLh2/fvtc6SvK6RwrCy4mvX5gHg3r3yn2LheXj2DB8zRpKTpQ7JydiuXdSgQdLUVNQVBskOxBd1KxQbUpR+vhGZzAIAhFphQiUP1ByurLdFuVx55YpOUhT10ta4MW9piWRHtaRJk9zHs5MTd++e0sWFrcwDEOJLeB48PLRV0lu14vK6W8aP1879HD5MAsDcuWKdS0PH06d4t27SJ09wIZVXIgEbmxIpp6ZNWcFHUu7K4+1bfPjw3NY2tWrx9epxAHDzJvHNN9Jt20Q8mm+pMFBsR0lBIaVFYGTSQKGINzISZEdtAFAqk1hWTRBiZJxPhmvUqETrYRjbrRt59Cjh5wcMU2DkKaKKMn48ffw4GRKCGxnxly8r5fLKfhjqwh2ESFIch7lzP0iHqVePa9yYe/kS375dNHAg8/AhIcxTiMUweTI9Z45m3z5q0yZRejrm6qoNRV+/Xl3CUv7Ll2suXiQB4MkTvF278tRbAQHaEmoA0LEje/KkiiT5mTMlJ0+SYWG4h4do3z6yQwd23TqNrjI94hNAIaVlAoWUFiU7jBvExdwTOrbklEvnlYp4fQM7ZJxKgOnalTx6FEtPJwID2TZtkEGqDdbWvK+vctAg6cmTn0FzAECtWh+4JeztOVfX/I//RYs048dLGAaGDpUKVdhJEubO1SxdqgGAZcs0HAebN4s0OXKlbduSCogmTThzcz4hAXvyhAAot7CLR4+I5cvFul909KhKX58HgH37VHXrivbsoVJTsdBQPDQU/+8/8tQppYsLKhzyiaCQUkQFejvkhnVwnAIAmZ42/hFFlVYabPfuQis4NM9S/bCy4q9fV9jZfZ53bmGSRYeuYnpedFXIBM0BAF9/zQqaQ+e08PJS2tjwFAUYlpsgUxKaNeMA4Ngx8vHjUj+tFArs9esCtnr9Go+JwXKegmpdmg+GgYeH5uVLxciR2pmjpCRs6FDpJ/zXiCIokzUTExNfvXoVHx+P7FjjvR31DXOawOmaw2VnxSDLVA68mRnXtCkAEKg5S3VEJvts/7WlJa9TFdbWvKdnARkodnacEPspIBLBx01rXV3ZZ8+y799XdOvGlqroyA8/aACA4yAoqBThHSoVXL1KtG4ta99e1qyZbNs2UVYWpvvq1Svt8nffMR075ne9GBjwe/eq3r3L3rRJLWgpd3cxy6LT8LPKDoZhNm/e7OjoaG5u7uTkZGlp6eDgsG7dOoZhkEFrqrejoZFRvXyyIzM9NCX5JTJO5SCk0RIPH2JpacgaiPKCouD0aWX79my3bmyzZmyBrWgxDLZuzdUZTZpwBSaqUBTUr8+dOaMs1QF07swK0UohISVNMKFpuH2bGDRIGhGBaTQQGop7eIjat5cKHW2uXyd37tSGx/bty0ilBe/EwoJ3c6OFhr2PHhErV4rQyfDZZMerV69cXFwWLFjw/v173WB4ePjixYvbtm0bHByMbFoD0dOzMrdqnfOuIxdLTADg1YsDzx//joxTOQh9a4FliVu3kDUQ5YipKX/xotLLSykULC+QXr20nVMwDFauLKomB1bK7FSS1BZLff8e53kIDcVDQop5bN25Q0yeLMk3GBqK9+wpDQnBBS0CAB06sJ06FePEcHenhQyXkycprsICPLKyMD8/4t9/ydOnyZqQQVO6kNLs7OwhQ4a8fv26d+/eI0aMcHR0tLa2jomJefv27ZkzZ7y9vQcNGvT48WNpYQKyKoMyWYrGxi431dPYpFFczJ2szIj3b0+37fCbRGqG7FPhsqN9e15PD8vOJq5eZfr3RwZBlC8YVnBgh+7bkydV334rlUj4zp3LeUKibl3+/Xs4d46cMUN89iypUmG9ejHz5tFCKbN8qNUQGEikpGjVTaNG3Nat6kuXiM2bRTEx2MKF4vr1tb/im2/YfJErH2NkxM+cSc+bJ46MxFatEv36a/m3w1UosB9+EF+9SgjZxe7u/O7dqk6dOJLks7OxTysv++VQDpksCxcuVCqVT58+bdo0t5Z+nTp1OnTo8P333z979mzo0KFLlizx9PSsflcdymQp7qUkd/7ZxLRRXMwdAGAYVfDLv1q0WsBxDI4TAKgOT4UhEnEdOxIXLxKXLyNjICofe3vu7Fnljh1Uue/ZzY2+coWgaThyRLvz8+fJK1fI1avVbm7501t8fclNm7SrmZnxfn5KmYxv356Nj8cOH6YuXyb8/LQxIg0alMh9MXQos3079f49fvEiWRGyY8wYyc2bhC7NJzkZGzZM2rgxl5mJYRg/YAC7eHHpevx+UZQ1k0WtVp87d+7GjRt5NUdemjVrdvnyZS8vL6VSiS7CmoyxSW7BiVcv9nEc8zRgc1ZmJLJMhcJ07QoAeEQEnmcCFIGoNBo14jZvVpf7bnv1Yj52S6hUsGCB+MwZMjER27BBdOAAlZ2NAcCbN7jQ9gUATp1SymTaDVeu1AiTNUJZkW7d2B49SuSVMTbmhw1jACAoCA8NLeeUln//Je/cydUcLi6skCb98iUeEYGFh+Pbt1Pt2kn9/atVK7xSGDEiIqJTp0729vZFrOPg4DBo0KDnz5+jK7BGyw7TXNmRlRkVcH91oP/6lOQgZJkKBVVJR3x2RBUTedmypVYiyGSwfr3a11cpVGefMkXStKls1SrRnDliFxfZ1q2it2+1D7WBAxldDg4A1KrFjx2bm/RQty5XcheCUKmMZeHXX8v55/3yi1h4SZdI4NdfNdevK2/fzl+gJTwcHzRIEhVVfUIXSiE7DA0NLSwsil2tVatWCoUCXX7I26HjScBmjqNTU14hy1QoXMOGvK0tkh2I6scvv2iErnhbtqhnzqQ7dmT/+ENNEKDRgODkAIDoaGzpUtGZM9rIATc3Ol/46qxZtK6Trb19KaYtXF3Zbt1YAHj1qjy9HTduENHRGABgGEycSC9YoMEwqFOHO35cZWTE29rye/eq1q9XYxhkZmLz57dQq6vJX7MURjQ3Nw8JCSl2taCgoIYNG+o+ajSaa6iWQA1DIjUTwkjlhnV1g6nJQQAQGX45+OWBN6+O0nQWMlS5I8yzEDduAOqkiahGtGjB3bypmDaNHjNGe2L36cPkrZdqY8NbWvIAIDybW7bknJ3zh27I5byXlzI0NHvGDLpu3VLkpWAYCK1wQkJwTflFd0yZItH9urzpPx07sleuKI8eVY4cycycSS9apAGAxETxnTvVZKqldNqte/fuvr6+RawQGRmZmppqaWmZd+SXX35Bl00NdHhgGNG99yGCEOXIjlcAcMfP/da1OTeuuP1zoFHoOy9kqPJFSKPFsrKIhw+RNRDVCTMzPl/giIeHWmiY0r07+/Ch4sYNRcOGWjExdiwt1DvPB0GAmRm/YYN6wIDSVZkS0oNpGjw9y2eeJTMTi4vTujomT6YlHyb8NmiQW/vkp5808+drDh70FzwuNU52zJ49+/Tp0ydOnMjMzMz+kLS0tLNnz86ePXv58uW6wYyMjFu3blEUha6ZGic7TBtZWLU1rdXMsf4wYSQt9XVWZlRmRpjwUa1Oiwj7DxmqnGWHqysQBKB5FkQNwNmZO3tW2bw59++/Sn19vnZt/v59xZYtaokEdFmy5UXnzqy1NQ8AN29+4HLgebh/n4iMxFJTC0jTi43FsrMxlaqAHeoSaurW5SZMKMo3SVGwfLnGxqb6hC6UIoH27du3Tk5OPM//+eefRax29uzZfCPdhEJGVRxUt6O03g5hhqVx85kZ6eFxMXcYRvU2+Ij2QhIZ0JpMlNtS7vBGRlyLFnhAAHH1KixdigyCqN64uHCXLil0/WwJAqZOpdu0YW1syjnj1Nyc79SJPXaMDA//QF6MGiUJDCSSkzFHR27aNHrKlFwBsXGjyNeXiIzEbWy4gQPZ2bM1RB7FsmCBWCdoqvEfqKx1O0xNTRmGIUnSysqKIEo0ycSybGJiIl8t6q6huh2lwsTUSU/fBgDMajX/uuMarxOdASD45d8AQFIyO/seIW//zcwIR4YqdxhXV1FAAPH4MZaSwpuYFLc2AyRqQ42ownzcsKZ58wqpJyrk38bE4CwruBRh4kTJhQvauqKvXuHz5omDgvBNm9Q4Dps3i7ZsoYRGMHFxxKNHxPnzxP79amEnaWmY0IuOJGHjRnU1/usUWLejFHccY2NjgiCuXbvWqVOnkm919erV5cuXo2ujpmFu2QbHtWeXsWlDDMN5nhPcG+YWreRGjgCQnRXD8yyGEchc5QjbrRts2AAcR/j5MUOGFLYa/uSJePFipn9/euZMZDQEoliE5Beahpcv8WbNuLdv8VOn8j9A9+6lxGJYsUK9fr0oX/mqBw+Ifv0kly8rIyPx4GCtf8bBgRPVvGYvpYjtwDDM1NTUxcWlVP/BV199RaOg+pqHTnMAAEnKDOT2eRWJgdwOADiORl1qy192tGnDGxhA4eEdWHS0xM1N1rkzcfu26PffQa1GRkMgisXRUetEWbtWpFRC5865hTQmTqQDAxVNmnAAsHs35emZqzkaNODOnlVOnUoDQGgo7uSkN2CAZMYM7QxL1641sbNt6UJK7927JytlD2ZbW9vjx4+jU7aGY2LaRLdsVqu5nr6tsIzCO8ofimK/+QYAyI8S1zGFQvTbb3otW5JHjwLHCRKE+ucfZDMEolg6dGA7dmQBICEBe/UqtxbqmDHM5s3qevW4P/9UEQTQNKxerfVgODlxp06punVjN29WjxzJAIBGA7oNDQz4iijqWt1kR926dQscz8jIePnyZVhY2MdhHDiOF13YFFEjZIdZY92yqVlTeY7zIzOnymjyAAAgAElEQVQjIir8CrJPOTs8XF0BAIuKwl+/1g5xHHXokKxFC9G6daBQAIYxQ4Zw9vYAQG3ZAgyDjIZAFIsQqRoVhW/cqBUWjRtz8+drhGTNxo25vIEaMhnv7q5xcNA25l2/Xq3L6cUwMDDgGzbksBrZpap8osliYmK8vLzUanVERMTAgQMHDx5cJX78uXPnQkND09LS6tatO3LkSJToW5HeDq3sICk9A8M6HEcL0R5JCYHxcQ9s7LsjE5Wn7MjJHSOuXuUaNCBu3hQvWYI/fapVIC1bqteuZdu3p/bvF8+di4eGkqdPM8OHI7shEEUjtIaJicFiYrSPziFDmHr1ciNYp06lX7/Gd++mAKBfP3bEiFxBb2LCX7yoHDVK+tVX3I8/ahwd+QsXamhYW/nIjoYNG+pqgrVr165nz56lnYupfDZu3CiVSufOncvz/MiRI318fI4dO4auqwrzdjTJcXU0wTCcIMQyPcvsrJg3wUc16ozsrGg9fWtkpfKCc3TkHBzwsDDy1Cnixg3ywgVhnLezUy9fzgwbBhgGAPSYMaL167GYGNGmTcywYYDjyHQIRLGyQ0ezZty4cfkjFzdtUjs4cPXq8enp+Tdv1oy7eVNhaMgL2WPTp3M104yfIjsYhomNjU1KSsrXe0WpVD558iQwMDA7O/vLlx3Pnj0TDhLDsHbt2q1atQpdVBWH3LAuSckYWmFq1kwYMTZ1ys6K0ajTASA81Nep6RRkpfJ1eOD79+tqlfL6+vSCBZqZM0Gap6GUWKyZPVu8eDH+6hXp48P074/shkAUgZnZB0LB2Zm1siqgPMQPPxSaRWFqyiMzllp2nDlzZtasWbGxsYWtMHbs2Fq1an35v/zgwYO65du3b1eViaEqCobhxiaNEuMDTGs1zXF7NNNFdUSEXkCyo5xlh6srtX8/AABB0OPHazw8eHPzAl4hJk0SbdqEJSeLNm1CsgOBKLm3Qybje/ZkkU0qXHb4+/sPGzZMJBI5OzvXrl371atXTk5OAMBxXEhISExMzLZt277//vvyOrjExMQ5c+bs3r1bLpfnHed5fu/evVFRUaampkqlcsqUKWZmZqV/EGIqlerSpUtXr141MTHx9PREZ0OFYmLaODE+QOftMKvVXPdVTNRNms6iKH1kpXKTHV27AkWxHTqo16zhmjYtbDVeJqNnzhStWoUHBhJXrwqxqAgEokC6dmX79mV8fEgAqF+f798fxWJ/CqWbzZ01a9bs2bOTk5MDAwPPnz8/fPhwb29vb29vHx+f4ODgd+/e3blzJygoqIzHlJycvHDhQjc3t379+h07dkyZr+oKwNixYxMSElauXDl37tyePXt27NgxLi7uUzQXSTZu3HjAgAFhYWE+Pj7obKho2YFhhLFJo49lB8uqoyJQD5HyhDcwUFy+rPT2LkJzCNBubrxcDgCijRuR3RCIIh8ZsG+fulUrFgDq1OGQQSrc2xEZGWlra7t161bdiEwmS0hIMM9x3pqbm+/evXvSpEl///13WY7JwMBg9uzZNjY227dv9/f3z/ftuXPnfHx8/vrrL+Gjs7Nzly5d5syZc+LECWHkwYMHhdVFxTBszZo1LVq00MkOR0dHR0dHjuN69+7dsGHDZs2aoXOiomSHWRNDI0eS0gb9GBjWoSh9ms4SPkZHXKvjOBBZqRzhSlbZjzc0pKdOFW3eTNy5Q9y7x7Zrh0yHQBSGnh7v46MaP15Sty6SHRUvO169ejVixIi8I/b29pcvXx4zZoxuhCCIVq1avX79ukGDBp98TCKRyM7OrrBvjx492rFjx7zJru3bt3dzc1MqlVKpFADatm3r6+tb9H+RmZk5evToDRs2NGrUCAAaNGjAsuydO3eQ7KhI2dHYtFazPBIQNzVrGher7RKUlRWNTPS5oOfMoXbtwrKzRZs2Kf/9FxkEgSgCmYz/5x9laCjK/PpESmE4IyOjlJSUvCONGjXKG5ipWy0qKqqiXuA47vLly4aGhnkH5XK5SqXy8/Mr+X4UCsWTJ090xc2Cg4MJgujQoQM6ISoOicTUxu7bvCOm5s0BwNC4HgCoFAnIRJ8L3sSEmTABAIhLl/DAQGQQBKJoKArq10fejor3djg5Oe3atWvGjBlRUVEhISGdOnVq3bp1YmLioUOHxo0bp1vNy8vr119/raDDTUxMTElJ0dfXzyc7AODNmze9e/cu4X4sLCxWrlzp6+ubmJioVquXLVu2d+/eol0d+Rrf67C3t3dwcPiMf8JHjx5VlbONY23zHm1mllwssSGp+gBv09Ki8n6VlfFEX96ihpurMhF36dJ2716cprOXLn25YgUyFzq7kLmQuT6ZsLCw8PCCG4wHBgaWQnbo6+vXq1fv7Nmzy5cvf/LkycGDB8eNG+fu7v7999/7+/sPHTpUJpMdOHDg9u3bjRs3rqAfk5mZCQB6enofy470j4uzFMnEiRMzMzOfP38uFouvXLmST8p8zJfZ+P7Ro0etWrWqoldRcqLonaGKICTJCT4sk9aqlQsABgAadfrNa9tatZqCzFWZsGPG4H/9Vev27Tb6+lzDhsWai7x4katXjyukYULNfCqgswuZC5kLAIo41NI1vgeAn3/++bfffouKitLT07O1tQWAkSNH/v333//73//+97//CeusXLmSICqq5qtKpQIA0YetgoX/7uOEl2IxMDBo3759CVfO5+1o165dybdFFIiRSUP7uv2SE58BAMfRalWqWGICAIkJgdER1ziOpjWZwgiiEqDnz6cOHwaGEW3Zotqzp+jrULxsGbVrF1+7ttLXl6tTB1kPgUB8zN27d+/du5d3JCIionSyA8MwDw+PX375Ra1WSyQSACBJ0tvbe926defOnUtJSRk1apSHh0fF/QYDAwMAYNkPirRkZGTovqo4vkxvR5WGIESWVu0U2drkZ4UiQSs74gM0moyEuIfPn/zxbZ/DggsEUdFwDg7M0KHk8ePkyZP4kiVcIVOHeHCwZOJE/MULAMCio6V9+yp9fTnU7hGBQHxE+/bt872fl9rbkZycLJfLKYoSNIeARCJZvnx5YTmr5fx+bGT0sWNDkB354kzLHeTtqCBkMgthQamINzZpCACJCY8B4GmAZ0TYf4nxj2tZtERWqhw08+eTJ04Aw1Cenupt2z5egdq/X7x4MSgUAMDVqYOHhmKRkdI+fZS+vlzh2WcIBAJ5Oz7F23H16tU+ffpMnz59W0H3Ix08zx85cuTChQtRUVH9+/dfsGABVn7NfQ0NDRs2bBgfH/+x7GjTpg3ydlRFpDLzHNmhTWZJSggEgIiw/wAgPNQHyY7Kc3g0asT07UueP08dOaJZtCjvV1hGhnjuXPLUKQAAgtDMmaNZulS0YYNo3TosIkLaqxfyeSAQiJJ4O0qRQPvw4cNDhw799NNPRa82ffr0cePGBQYGUhTl4eGxdOnS8v0ZgwcPDg0NzTsSGxtramrqUrLiSIgv1tuhyI4HAIUiPiszNwE7/D2qHlu5Do+ffwYMA7ValBOtBQDEw4fS9u0FzcHb2Ch9fDQrVwJFaX75RVAnWGSktHdvvJDYdQQCgdBRCm9HixYt0tLSrK2tBQeDQqGwtLTMt879+/f37NkzcODA06dP4zgeGhraqVOnIUOGtGz5KS+sarVa96+OCRMmbN26NTQ0tE5OINuZM2fc3d3xCm7bjSZZKghKZECSUoZRvgk+YmXdUZH9QZfBlOSXGemhckMUtFhZDo/mzdlu3YirV8l9+6hu3YBhRBs2iDZsAJYFAKZ/f/Uff/DGxrkyxcMDeF60YQOabUEgEPko6yRLjx49pkyZUr9+/QULFly/fh0A7O3tN2zYMHz4cN06QnnQtWvXCiKgTp06+/fvP3DgQKlkB03TEydOTExMfP78uVwu79OnT7169bp06TJ37lwAaNCgwV9//TVp0qQdO3ZYWVn99ttvjo6Oxfpgyg6aZKk4pDLzzIzwlKQX5065mpjl7yES9v58M+fZyEqV5/BYuFB69SqmUNT980/p+vXE/fsAADKZev16uqBGj5qlS4GmRZ6eWESEpF8/5YULvI0NMiMCgShrSCmO46NGjRLmMoyNjSmKiomJGTFiRHJy8owZM4R1rl69amxsLFQcF/j2228LK7RV6OsvRR0+fLiIFYYPH96jR487d+74+fmNHj3a2dkZ/XWrNPoGthyrUamSWVaTGB+Q79uYqBtIdlQmbIcObLt2xL17Vjn9EbmmTVUHDnCFdzzQrFwJHCfatg0PDZX27au8cIG3tkaWRCAQH1O6TJatW7dOnTp16dKlQtEOhULh5eXl7u4+dOhQoSFccnKyo6Nj3k0wDLOwsOA4rnwnQYyMjPr27VuZlkKTLBVHvyG+ABARdvGi93cAPACIJcYadbpprWZJCU+yMiKQiSrb4bFggXToUOECpmfMUK9cCWJxMZusXo3xPPX77/j791rlUbs2siQCUZMp6ySLSqWSSqV78tQRkslko0ePzszMvHPnzuDBgwFArVZbWFjk29DKyioxMfHj8aoFmmSpaOwcejZwGvc66FDDxt83bzn37q2fjIzqJSU8ycqMRMapbIdHjx5cy5bs+/fs3r1Mr14l3Er92288SYq2bMFDQoQ4D97KChkTgaixlHWSJSoqqmnTph+Pd+7c+cyZMzpp8nGJ0tTU1IouqoGoHnzdaW39RqMta3cAgLYdVkeGXwEAms7SqNNFYnQKVSrqTZsep6Q069mzVFtpVqzAVCpqxw48JETaty89eTJvZsabm/Pm5ryZGW9mBhVWwhiBQFQJSiE77O3tHzx48PH448ePrXLeaRQKBcMw+VZIS0vLW14MgSgMkUguaA4AMDZplJoSLCxnZUaaINlRubCtW2s+qfWUet064Dhq1y787Vvxzz9/8B2GCeKDr1WLt7Tkraw4R0eubl3O0ZG3tgYMlaNFIJDsyANFUQAwZ86cn376ycbGBgCysrJOnjz566+/vnz5EgAyMzOzs7PT0tLybhUeHp6VlVUNLIViOyofAwNbYSE7K9rErAkySNUAw9QbNgAAdeAAfJj9DjyPJSZiiYnw6lX+raRSztFRUCH8V19xjo7cV1/xVXxmFoGo4ZRDT5bt27e3aNFi+/btpqamFEUlJiayLNu6deuAgAAzMzMPDw8MwyiK0vXK4zhu4cKF3xeUdFflQLEdlY+evjYPMxOFd1Q55bFxo3rjRiw9HUtIwJKSsMRELC4OS0rCkpKw+HgsMRFLSsIjI0Gl0m6iVOIvXgitXnRwLi6a6dOZIUPgw+6PCASiSlAOPVkcHR0fPXo0derUu3fvsixraWm5cuXKoUOH7t69OyAgwNTU9MaNGzKZrFevXu7u7ra2tocOHYqOjh40aBCyPuITkMrMCULEsprsPHVLEVUI3tCQNzSEevUK/prjsKgo/P17PCQEDwnB3r3D373Dw8NBoxG+xwMCJFOn8h4e9KRJ9OTJyPmBQFQDyNJu0KBBg5s3b2ZnZycnJ9vY2AhpsYsXL867zubNm93c3LKyslq3bn3hwgURelNBfOI7My7Tq52ZEZaVhWRHdQTHeTs71s6O7dIld5Bl8chI7N07wt+fOnAAi4vD4uNFa9eKNm9mBg+mZ8xgURsEBKJKX/eftpmenp6dnV1hpThGjx6dnJwcHR3t7+9fG+XuI8qAoVFdAEhJfolMUVMgCM7Bge3eXbNkSXZQkOrgQbZrVwAAjYY8flzapYvsm2+oAwdyZ2cQCESVgqyg/YpEomomOFBI6WfB3LJtVMS1lKSXalWKWGKCDFKzoChm8GBm8GA8IEC0axd5+jRoNPjjx+LHj0Vr1tBTpmhmzwaZDNkJgfgyKYeQ0poMCin9LFjWFrQdHxd7375OH2SQmgnn4qLauxdbvZrav5/avx+Li8Pi4kSrV5PHjql37WLbtkUmQiC+QMra+B6BqHwsLNsQhAgA4mLuImvUcHgLC83ixdkvX6r+/JNzdgYA/N07ac+eYg8PNOeCQFQVkOxAfNGQlMy0VnMA+LhFHKKGIhIxI0YobtxQ//47r6cHLEtt2yZr04a4i4QpAoFkBwJRZoyM6wNAZkY4MgUiFwyjJ05U3r/PduwIAHhoqLRPH9GyZfkLlCEQiC/tZRKZoISgkNLPhYHcAQCys6IjQn0f3V/VvNX8ul8NxjAcADSaDJFIjkxUY+EcHJQXLlB//SX6+WdMoRB5epL//afatYtr2RIZB4H47KCQ0jKBQko/r+zgee7axUk0nXXtv+8jG17s8u2etNQ3927+1GvAaUGCIGooGEZPnMh26SKePp24exd/9Urm6qqZPVvj4YFqmyIQnxcUUoqoksgN6wgLNK1t7vM2+FhczN3Qd15REVefPNqETITg6tRR+vholi8HsRgYRuTpKevalTx3Dn/2DEtNRfZBIL4ckLcD8aVjYOigW7a0apeU+JRhFI8fblAqEwEg4MEay9rtrKw7IUPV+JsZqZk/n+ndW+Lmhj95gj97JhkzRviG19fnbW05Ozve1pa3teVsbHg7O87WFgwMeDmapEMgkOxAIPIgk5mTpIxhFADQzGXuu9cn3r89nRD3UKPJAACeZ+/dXDRkFMpiQAAAcE5OimvXRJs3izZsAJoWBrGsLOzVK/zjtreCKJHLwcCA19fn9fVBLucNDXl9fTAw4Bo0oCdNAgxDVkUgkOxA1CgwA0OH1OQgkUhuY9c9OuI6AAiaQyA56XlKcpCJqROyFAIAgKI0P/9Mu7lhoaF4VBQWGYlHRGAREcIylpyc//TKyICMjALFBXHtmnr3bl5fHxkVgfg8suPrr7/29fU1NjZGhkNUJnK5Q2pykINjf4IQS2W1dON6elYKRTzPcyGvT5i0X44MhdDBGxvzxsYFpLQoFHhEBBYZicfFQUYGlpUFmZlYRgamW87KgowMLCEBUyjIc+fwt29Vx45xdesikyIQn0F2pKSk+Pj4jB07FhkOUamyw7AOANT9aggASKRmuvFaFq1UqpS4mDth78+3RrIDURJkMq5hQ2jYkC1yLSwrSzx1Knn+PP7qlbRjR/XevUzfvsh4CETZKV0mC0mSM2fO3LlzJ8MwyHaISsNA7iAWG9W27QIAUmmut0Nfbmtj7woAaalvVMokZChEecHr66uOHtWsXAk4jmVmSkaNEi1bBjyPLINAlJHSeTtomm7btm1KSsp3333XvHnzKVOm2NjY1BBLoXJhnxG5YZ06Xw0WmrPk9Xbo6dU2M3cWHhMJcQ/t6vRGtkKUGximmTePa9RIPHkylpEh8vTEw8LUu3bxqOctAlEyyqFcWOPGjQ8ePCiXywHg8ePHa9euTU9PHzt2bI8ePXC8mpcAQeXCPiMGhg6O9YcKy3m9HXr6tc0tW+E4xXF0XOx9JDsQ5Q7Tqxfn5ycZORJ/84Y8cwZ/+1b1zz+cgwOyDAJRLOVQLszLy0uek+bu7Oz8xx9/7Ny5Mzw8fNiwYevWrYuPj0dWRlSI7JDXsbLuKCxLZB/IDpKU/Z+9O4+Lqt7/B/4+sw+zAcMqqwub5oIiBlHizzS1MrfUzDK9dc1MLK9p3Vtq3qtZt9K+lmZmWimWfQvTcK1vaKFJpBgqiAurLA77MvvM+f1xcMJhEVR0Bl7PP3ycOXOYGd6cGV/z+XzO56P27E9EV0vTUCjoDNaQEF1KinncOCLinTkjfeAB/k8/oSwAN+dWmygUCsXcuXO/++67mJiYmJiY6dOnp6SkoKxwe/H5Iobhc9tisYrHE16LHX5E5OYeTkQN9VdQKOgkrEKh/+or42uvEcMwVVXSyZNFa9diqAfATbgN83ZcuHBh06ZNW7durayszM3NPX36dFhYWHx8/NNPP+3u7o4Sw+3GSKUeDQ0lRIyLzJeIRCIVERkMNSgNdOZ5xxj/+U9rWJj4hRcYrVa0bJlg2zbrvfdahg61REdb+2LaGIB26Vhrx7vvvsteC/hmszkpKWn06NFhYWHvvfdeXV3dtGnTfv7556ysrKSkpP79+yckJCxdurS6utrxq5CQkFBfX4+zwVlwo0qlUg8+X0xEIrGKiEzGWiJ8+4TOZZ48Wffjj9agICLiXb4sSEwUv/yyy333yfz8Br30kmj5ckFyMnP1KgoF0JqOtXZ89NFHkydPFolEn3766ebNm69cuUJEwcHBf//73//2t795eXld+1bAjBw5cuTIkefPn58zZ877778f7MAjsDZv3rx+/frly5fLMRehk/DyiebxBErX3txNoUhJRFar2WzSCoQy1Ac6lbV/f93Ro8LPPuOlpfF//50pLyciRqt1PX2aTp9uPCY42BoZyfr5Wf39WT8/1tfXGhDAenmRABNDA2JHR4hEov79+2u1WpZl+Xz++PHjn3/++Yceeqi1y1jCwsK++OKLl1566dNPP3XM3//cuXNo53A6cSPWXdf4IXblNgyGasQOuANYd3fj4sXcNu/yZS5/6FNSFJcvk9lMRLy8PF5env2P8fmst7fVz4/t0YP18zOPHGkZPRrFBMSONo8WCBoaGnx9fZ999tnnnnsuICDghj8il8uVSqVOp5NKpY72y+v1+sTExMWLFy9atAingvPiOlmIyGCo4QaZAty5xo9evay9epmnT/8jPT2qb19+RgY/LY134gQvJ4dXVERa7V+HWixMcTG/uJi7Jdy0Sb95s/nxx1FDQOxoldlsXrRo0dtvvy1oR1NhXV3dnDlzBg8erNFoJBLJTbw4jUaTkJCwadMm5fWLU7Msu3nz5qKiIrVardPpnn32WQ8Pj5t4/LVr17700ksMVpjsKrHDaKwhYonwB4W7xMXFEhtraTJRAVNdzRQXM4WFvJKSxo3SUqaoiHf5MhmNkuee0xMheQBiR6tMJtOSJUsE7euezMrK+t///d/du3evXr26Q/+1V1RUrFmzpra2NiMjIy0tbd26dXaxY+bMmREREStXriSiU6dOxcXFpaSk+Pj4dOh32b9/f3R0tIeHR00NroBwbtzYDiIyGmouZH8VEv4EagIOgnV1ZV1dqW9fuyVg+L/9Jpk4kamvlzz3nN5sNj+BkxYQO1qyePFi27jRGxo6dOiBAwdEItGIESM69CwKhWLBggX+/v7r169PS7OfA2rPnj3Jycnbtm3jbkZGRsbHxyckJOzatYvbc+LEiRUrVrT4yAzDrF69etCgQaWlpefOncPEo12D2Da2Q1+d/tu/A4JGNZ1AHcABWe69V797t2TCBKa+XjJvnp4IyQMQO1oQHR3dvN3ixx9/3LJlC8uyrq6ukyZNGjVqFHcMwzAPPfTQTbwmkUgUGBjY2r2JiYlxcXFCodC2JzY2du7cubbhI8OGDdu/f3/bT3H27Nny8vI33niDiCorK4lo9erVQ4YMmTFjBs4JJ4wdjZ0sJVd+ra8rLMg7GBrxJBEZ9FViiRvqAw6aPIYN0+/eLZk4kamrk8ybp2dZMz5/ALHDzuOPP56bm2u388EHH3zwwQeJqKamJjExcd++fevWreukl2u1Wg8fPjxmzJimO5VKpV6vT0lJGTu2vUtycNf3cttZWVkbNmx49dVXPT09cUI4I6FIScQQsXmXvieivEt7QyOeLLnyS7nmz779ny0tPuYXMAJVAgdNHklJjcnjhRf0REgegNjRASqVat68eStXrszNze3Zs2dnvFyNRlNZWWk3wQY38iMnJ6f9scNm69ate/bsUSgUCxcunDRp0pQpU1o70m4FWpugoKC7OytJeno6zmMeX2K16AyGaiIqyP8x7cQvF84ulMpCK6pccrPfCB+4WSjyRLlwdjliufh81Vtv9V+yRKDViufNy8vLK+02V9Xi7Oqq5crLy8vPz2/xrpMnT3bK3DWFhYWdFDvq6uqISCaTNY8dNzcydPbs2bNnz27PkY45ECQ9PT0qKgpvyJxMd9uaLKzVUFPxsbbhgkLp7qqoMJtrNMXvPzJpH48nRLlwdjliuaKiTBER/AkTmLq68HfeCQ4KMj/5JMoFzluuNl7q2rVr24odLMtarVa7PRaLpcWD9Xp9Xl5ecnLyunXrXnjhhU76ZfR6PRGJRKLrvy3wiUin03VqHe1aO2JiYuzW84W7SCRWNV0KLu/SHiKqqswWCuVEVFby2y//l3Df8PdQKHBMlujoxhGmtt6WbpA8oMs7duzY8ePHm+4pKChoK3YcPXp05cqVeXl5ly9ftu284dWzS5cuvblZNNpDoVAQkV30qa2ttd3VeXDZi0PHDpGy+U6joaa46Ghjc0jW9qulab5B/yTCFyxw0OShS06Wjh/PVFcjeUDXEBsba/f9/AatHcOHD//pp5+I6OzZs+vXr9+0aRMRtRYpZDLZPffcM2nSpDlz5nTe7+Dq6tq8YYOLHSqVCn/j7tzawW307P1YbW1uheZP7qbVaiIimbxHQ31xdVVOQ8MrcfdPRrnAMVkjI/Xffmtr87Bu2mQNDbWGhVlDQqyhodbevUksRpXA2bVrbEe/fv0+/vjjiIiIxYsXazSau/hyVSpVeHh4WVlZ89gRHR3dqU+NThZHxk3doVAGxY/+hM8XZ5/d9uvPL9nuHT/lx/TfVl7I/spkrGBZK8PwUDFw2DYP/fffSyZMYGpreadO8U6d+us+Pt8aFGQNDWXDwqwhIZaBA62DBqFi4Mg63MliZ+HChV988cVd/zUmTpx46NChpntKSkrUavWQIUM69XnRyeLwrR1M3Ij/EQhciCjinr/98dsqnU5DRCrXPnJFgKfXkAvZXxFZjYYaTOYBDp08hg7V/fCDcOdOXk4O78IFprCQWJaIyGLhXb7Mu3yZDhwgIhIItD/9ZB08GBUDh9XhTpbm3nzzzTv5ig0Gg+1fm1mzZq1bt67pNbpJSUmLFi1qbRVctHZ0i9ghUoZGzPQP/H+2PW7qCF2Rhoj6R75IRBKpmtuv15UjdoCDs0ZGGiIjuW1Gq2VycngXLvBycrggwrtwgfR6Mpsl8+drjxyh6y7UeqoAACAASURBVIfYA3Sd1g4ieuSRR254TGZmZv/+/W/lhZpMptmzZ2s0mszMTKVSOW7cuJCQkPj4+IULFxJRWFjYtm3b5syZs2HDBl9f31WrVvXu3XvJkiWdXT60djgyV7ewAYMXXrfHPby46KhM7hcaMZOIxJJrsUNfoaIQVAycBeviwg4adF1/itUqevtt0erVvDNnRO+9Z3ztNVQJumxrR3sMHz6cm278pgmFwu3bt7dxwNSpU0ePHp2ampqSkjJjxozIa18LoNsKCZ9mt/Csm3sEEQ0c/BKfL76utUNfgXKBc+PxjK+8Iti/n3fqlOidd8zjxlkHDkRVwFm0GjssFsu3334rlUofffRRbo/RaCwsLGwrlbPspUuXuKk1Ov3bravrww8/jL8fXGO/VJCbe7iLi3dYv1ncTYmttUOH2AFd4JNboP/oI5fhw8lkksyfr/35Z2qyTBWAU8aOXbt2ceuinTp1atCgQURUWFjYp0+fGz6i3SL1XQbGdjgXN3XEgMEvCQTSxthxbU1atHZA12Dt39+4cKHo3Xd5p0+LPvrI+NJLqAk4mo6N7eDm5+DxeLaZyLmV0tzd3fv37y9sKVmzLJuXl3f16tUuWT6M7XAuEom674Dnmnw5lAiEMrOpobXWjrraPIUyGHUDJ2J89VVBcjIvK0v0n/+Yx461hoWhJuBQOja2Y9SoUadOnZLJZCEhjePvlEqlSCQ6evRov379Wn0bGI3u7u6oNTgCblRH0yBSb2rQ68pbPPj82S/uGfSCrVEEwAmIxYYNG6QPPkgGg/iFF3SHDxMPc9KAo2trSOmgZnPRhISEtN3PIhKJuIlEux50sjg7qYtHfV1Ba60dJcXHPH2GBPXEgCFwJpaoKNO8ecIPP+SnpQk3bzbNnYuagOO4DRfQnjlz5obHtLbcrbNDJ4uz40aVtji2w2IxlF/9o7T4GGIHOB3DG2/w9+3jXb4sWrbMMnq0tXNW/wa4CS12stz+Frmvv/4atQZHjB1SNbVyJUv51VNms77kSiqqBM7HxcXw4YfEMIxWK16woHFKUwBHdftjR0JCAsoKjhg7Wm/tKC05TkQVmtNmsxaFAqdjuf9+0+zZRMQ/ckT45ZcoCDiyljtZiouL7VZ5bQ+WZS9cuKDV4oMbHBG3Sq3JWEfEWq0Wq8UgEDZepcW1c1it5rKSE34BI1ArcDrG//xHcPgwU1goeu0188iRrJ8fagLOFDvmzp37ww8/3NwjYt4OcExCoYKIWNZqNmmrKrOJ4Xl6RXJ7rpamcceUFh9H7ABnxCoU+nXrpJMnM7W14kWL9K13djMlJfzff+enpRkXL2a76BUA4CA6MKSUm6Jj8ODBbm5uDMO097zHvB3gwETixkBsMNSUFB+Ty/08vSJLrvxqtZoN+qprseMYCgVOyjJ6tHnGDEFiomDfPsE335gff7zxDqORf/o0Ly2NSxvMtcmmrT17mv72N9QNOk8H5u3w8PCYPXv2Z5991tHnwLwd4LixQ6S8dpbWlhUft3oPJqKcrMT6ur+uvbpa9rvFYrCb8APAWRjWrOH/9BNTViZ+5RViWX5GBi8tjZ+RQdev480RJCYidsCd12rsuLn00IXn7QBnJ7wWO0zG2tKS4yKxymzW5V7abTLW2Y4xm7TFhUcCgkejXOCMWDc3w3vvSWbOZCoqJM0iBevnZ4mOtkRH87KyhF98wU9L4128aG3HkhcAnR47XnnllZt+xK46bwc4O/G1Tpay0t/1uvLamtz8y8m2zOHhNai6Mtts1ufnJiN2gPMyP/aYecIEwe7d3ElvGTTIOnQolzZs40x5ly8Lv/ySWFaQmGhctgxFg7sfO9o/noNTW1tbWFgok8mCgoL4fD7KCg7I1tpRmHeQiOpqcy+c/8p2r6fXEKmLd2HewYLc/TRiXfMlbQGcheG99yz33muNjrYMHEgiUfMDrL16WYYN4//2m/Drr42vv44p1eHux46OKi4u3r17t8FgKCgoeOyxxyZOnNj1KoUrWZw/dii4jdLiVCJqqC/RaTW2e93UEWrP/oV5BxsaSmqqL6lc0fIMzor18jLNn9/2MeYnnuD/9htTUMBPTbXcfz+KBp3hNkyO3prw8PB//etftv+PH3roIRcXly5WPlzJ4uzEYhW3YbEYuQ9nq9X0V+xwjxBL3Ljtq6W/I3ZA12aeMkX86quk0wkSExE7oJN0bAXats5Xs7mkpKS8vNxuZjCdTpeRkXHy5MmGhoauFzvA2QkEUobhs6ylxXvd1BFisZtA4GI2azVlf4SEP4GKQRfGKpXmsWMF330nSEoyvvsuK5OhJnCHPoo7+gNJSUnz588vKSlp7YCZM2dy034AOBiGz3cxm+ua7uLzRRaLUSL1kEo9icjDK7K0OPVqWTqKBV2eecYMwXffMQ0N/L17zdOnoyDgiLEjLS1typQpIpEoMjKyR48eWVlZffv2JSKr1Xrp0qXi4uIPPvjgmWeeQVnBMfH4Mro+dvQOnZKTtdPNPYK76eUTVVqcWlmeabEY+XwRKgZdOXaMHMl6ezNlZcKdOxE74M59Dnfo6Pnz5y9YsKCiouLkyZM//PDD1KlT9+7du3fv3uTk5Ozs7IsXL6ampp47dw5lBUeNHfZ9f34BI93Vfd3UjbHD1S2UiCwWo16nQbmgq3/rFHAzmfJTUpiiItQDHC52FBYWBgQErFu3zjZuw8XFpelU6F5eXps2bXrnnXdQVnDUj1m53R61Z3+/wBG21g6JVM1t6HUVKBd0eaYnniAislqF33yDaoDDxY6srKxp06Y13RMUFHT48OGme/h8flRU1Pnz51FZcMTT/VprB3cxrUAgUbmG+PmPcL/W2iGRNMYOna4c5YIuzzpggLV/fyISJCaiGnCHvv61/1BXV9dLly413RMREfH6668/+eSTdocVFRWFhYV1sUph3o4ugM9vHK4fEPjg5YtJbu4RPJ7A1z/u2iW1JJF6cBsGPVo7oFswTZ8uzszkZWfzTp60Dh6MgsBtdKvzdvTt2/fjjz+eN29eUVHRpUuX7r///qFDh2o0mi+//PKpp56yHbZ79+7ly5d3vfJh3o6uFDsCe467fDHJTX0PEQkELgJBYysIWjuguzFPny5evpzMZuHOnQbEDritWpy3owOdLHK5PCQk5Pvvv3/00Ufj4+N37NhBRIsWLZo9e/aCBQtSUlLS0tLmzZv366+/9uvXD+UGh40dAoFLj4DhRKT2uMfuALHElccTEMZ2QLfBenlZRo4kIsGuXWQ0oiDQ2Tp2Jcurr76amZlZVFQkk8kCAgKIaPr06SNGjPjwww9HjBgxbNiwjz/++MUXX8SyLOCgp7vAhYjc1BEyma9AKPPwGtjsEEYscSd0skB3wg0sZSorBdeP1QO4+7GDYZjXX3/96tWr5eXl8fHxRCQQCPbu3bt8+fLIyMigoKBXX3319ddfR1nBMXGtHW7u4USkVPV0U7fQLMf1s6CTBboP88MPsyoVYWApOGDssIUPiUTS5GNasmLFipMnT+bl5b311lsdXb0W4I7Hjggi8guIF4tdW4gdUg9CJwt0KxKJeeJEIhIcOMBU4MwHx4sdbVu+fLnVanX83/zy5ctvvfXWkSNHfvvtt507d+7ZswdnQzeIHS50rbUjuPf4lj+BpWoi0qO1A7oT84wZRERGo+C771ANcLLY8eGHH1ZXVzv+b56bm7tq1ar4+PgxY8YcOXJk5MiROBu6/unOlxGRq3s4EXn7DGvxGCnX2oGxHdCdWGJirH36EPpZoPN14ALaurq6o0ePtpWXzea0tLTKysqKigp3d3fH/+WTk5OHDh2KxXK70ekukAsELnJFABExTMuZWyxRE5FBX0nEEjFEpNNelbp4mU3aqqrzRKRUBnHDTgG6EvPUqaLVq/np6bycHGtoKAoCdz92VFRUPPLII+05srKy8ra8OI1Gk5CQsGnTJqVS2XQ/y7KbN28uKipSq9U6ne7ZZ5/18PC4uaeQSqUGg0EsFuNU6A54fBc3dXhrgYPj4uJFRFaruaGhVCbzJaK0Y8uGP/hx8ZVfDu6dQkQisSpq2Ov9Bj6PekKXih1PPil66y1iWcHOncauOPcSOF/s4P5rDw4Ojo6OFomuW5yzqqrq4sWLhYWF06ZNk8lkKpXqVl5TRUXFmjVramtrMzIy0tLS1q1bZxc7Zs6cGRERsXLlSiI6depUXFxcSkqKj49PR5/o888///PPPxmGOXHixLJly0JCQnBCdG18vowb2NEG92uTeVRoTstkvmZTQ05WYv9BL9ZWN07RazTUHDv6So+A+Bs+FIATsQYGWmJi+MeOCRMTja+/TpgHAe567JDL5WKx+NChQ63995yenr5ly5YlS5ZwU3rcNIVCsWDBAn9///Xr16elpdndu2fPnuTk5G3btnE3IyMj4+PjExISdu3a1aFn6du377Jly4KDg4koNDT00Ucf/fPPP+3iFHS12CGQ2VZ9azVeew5kGD7LWjRlJwODx1RXXSBiMzM+FArlRMTniy0WAxHlXtztFv0qSgpdqsFjxgz+sWNMcTH/l18s8fEoCNzl2EFEERERgYGBrd0bFRUVGhr6z3/+88MPP7yV1yQSidp4lsTExLi4OKFQaNsTGxs7d+5cnU4nlUqJ6MSJEytWrGjxZxmGWb169aBBg4hIpVLZRnVERkaeP3/+999/v++++3BOdGEMI1R7DrzBW0Ioc3ULrarMKrnya21Nbk31BSK6lPO/XNuGu7ovS2z51YzcS98PRuyALhY7Jk0SL1lCWq1w507EDnCI2HHq1Km2D1AqlT169Dhz5sw999zTGS/XarUePnx4zJgxdk+q1+tTUlLGjh1LRMOGDdu/f3/bj2M0GtVq9YEDB4YPH05EPB6PiCwWC06ILs/LO+qGx3h6D66qzCq58kty0iM9ez9KRBaLoVxzmoiUql7unv3Lr2ZUlp+pryvkRqcCdA2sQmF++GHBN9/wv/+eef99Via7LrWXlPAzMngnT/JPnWIuXzZPm2Z69llWrUbdoENu/wW0wcHBZWVlnfRyNRpNZWWlXC63ix1ElJOT06EGlfHjxw8b1ngJ5dGjR4OCgoYOHYoTosvjlrxvm4dXJLdRX1dw5vTG6042VS8f33u57Zrqi6gndDGNE6U3NPC//57RaPgHD4reflsybZosNFQWGiqZOlW0Zg3/4EHehQui//xHFhEhfukl3kW8EaDTWjva448//nj++c4a5F9XV0dEsuszOBc7ampqOvRQCQkJq1ateuCBB8rLyz/99NPvv/+e66Npjd3C9zZBQUHcAJG7JT09Hefx7S1XQ53URRZCDF9bn82y1819V1HFM7GNZ9qZzKMlZQqUC7pSuRiV6l61WlxRIZo/n2c2t9AiwuNpg4JYPl9+8SLpdMItWwSffVYRE1M0dWr1wIE4u3B2EVFeXl5+fn6Ld508ebIDscNgMFxsPdWyLKvVan/++eedO3euWbOmk34ZvV7PtVU03cmtPKfT6Tr0UPfdd19kZGRGRoaPj8+hQ4duuHydYy58n56eHhUVhTfk7S5XFI14ukLz53df2Y/1GRg50tsnOitDZLEY1W5M1y4+zq7uWS7ezJn0wQd/ZQ4ez9qnj3XwYEtkpDUy0jpwIOviQkS61FTh+vWC/fsZq9Xj2DGPY8eskZHGBQvMEyeSQICzqzufXW281LVr13Ygdly5cqU9Iza2bt3adLzn7aVQKKjZIIza2lrbXR3i4uISGxvbzoPtWjtiYmLa/7PgjNw97hFL3A36SiLy6XFfaXEqESlVvRiGL5P71dbk1tXlo0rQ9ZhmzODv3ftXzhg0iG3p09Vy332W++7jXbwo/PBD4c6dpNXyTp2SzJnDLl9ufP558zPPsNdPfADd0LFjx44fP950T0FBQYfn7RCLxf369ZNIJNwwTFt7g1wuV6vV06ZNGzduXOf9Dq6urs0bNrjYcYuThdyQY7Z2QOdhGJ5vj/vyLu8losHRS1OP/KOhvtjFxZuIFMqg2prcutoCVAm6HmvfvtrTp9t7cJ8+hnXrjG+8IdyyRfjJJ0xZGVNYKP7Xv0TvvWdYt45bYQ66rdjYWLvv5x1r7VAqlUKhMDU1dciQIXfrd1CpVOHh4XZDVrnYER0d3alPjdaObqiH/wNc7HB1Cxs05B9nTm/g9ssVQURUX4vWDgAiIlatNi5ZYly4ULhrl3D9el5WFlNZKXn6afO0aYb33mM7+TshdNnWDq7Bo2/fvnf315g4ceKhQ4ea7ikpKVGr1Z0dhtDa0Q35+t9PRAKBi0zu2ydsalVlNrdfoQwkooaGUrNZLxBIUCgAIiKx2PTUU6aZMwV79ohffpnRaARff81PTdVv2GAZMQLlQWsH19rRsQtoL1682PblHreXwWCw/Wsza9asc+fO5ebm2vYkJSUtWrSoaacPwG3hru4rkXq4uocSMTyecMi1+cEUyiDuO562/gqqBHAdhjE/9pg2Lc38yCNExBQVSR97TJyQwGi1qA1QRy+gvTOLtZpMptmzZ2s0mszMTKVSOW7cuJCQkPj4+IULFxJRWFjYtm3b5syZs2HDBl9f31WrVvXu3XvJkiWd/arQydI9P0F9/eJ4vMYh0gJh45XbEon6WjKuRo0AmmM9PPQ7dwqSksQJCUx1tXDrVn5qqn7zZuvgwShO93EbOlnuDKFQuH379jYOmDp16ujRo1NTU1NSUmbMmBEZGXkHXhU6WbqnHn736/UV9qfotTnHjMY6lAigNeaJEy1Dhkief57/yy+8nByXkSONCxYY33iDOu1qR3AotzqkNDs7+/Dhw9nZ2WVlZTwez8fHZ+DAgaNHj77Fhd9ujqur68MPP4w/KnQ2X/8HKsszW4sdJlMLsUPbUOoi80HpAIiIDQzUJScLN24UL1tGBoNo7Vr+0aOGzZtRmW6rXbHjm2++eeutt1pbkOWRRx5Zvnx5l5/4BZ0s3ZObe5hQKLOPHcLG6fmNhhZix5Ef544at1MgdEH1AIiIGMb0wguW4cMlzz3Hy8zk//GHS1ycz4svEqYL6+puppNFo9HMnj07OTm5jWN++OGH/fv3v/HGG2+88UYXHteJTpZu+5HZfL03UeutHRaLsbjoaEb6u1Exy1A7ABtrv37alBTRqlWiDz4grTb8nXeMDGP817+IYVCcrqrDV7JcvXr1/vvvT05O9vT0/Mc//pGUlHT+/PmysjK9Xt/Q0FBQUHDq1KmdO3c+/fTTIpFoxYoV8+bNQ5WhO2jSyVJvd1dtzSWr1fznqQ9qqi6gUAB2gd345pu6/ftZLy8iEr39tnjePDKZUJhupdXYYbVax48ff+nSpTVr1hQUFLz77rsTJkwIDQ318vISi8UuLi4BAQGDBg2aPn36559/np2dPWXKlE8++WT9+vWoKXT9tw1PwPWhGA21dndVV54nIovFeOzoKygUQHOWmBjd//2fNiCAiIQ7dkgnT2bqMDS7G2m1k2XDhg1//PFHcnLy6NGjb/gogYGB33zzzfz5819//fWpU6d6e3t3vUphbAc0JRTKzSZt89aO6qrz3EZRwU95l/cG93oUtQKw/1obFHT6o4/ufest/vHj/J9/lo4apfv2W9bPD5XpYjo2tmPdunX//ve/25M5bNauXfvLL798+eWXixcv7nrlw9gOuC52iBQ67VVTswtoqyrP27ZP/PqvgKDRfL4Y5QKwY1IodHv3Sp57TpCUxDt7Vjp6tD4pyRoaisp0JR0Y25GRkVFeXp6QkNChJxCJRNwQENQaujxuVGnzeTu41g6uC6a2Jre46ChqBdAysVi/davp738nIl5BgXTkSP6xY6hKl9dy7Lhw4cK4ceNuYk7SCRMmXLp0CWWFLo+7htautaMg7wA3krRXn8aFN6sqslArgFbx+Yb33jO++SYxDFNdLZ0wQbBnzw1/iKmtJbMZxXNSLXeyaDSa3r1738TDqVQqi8XSJSuFsR1wXewQKYjIZKpnWUtlxTm1R/8L2TuP/DiPZS1E5NMjpqjgJ21DaVUlYgfADRgXLbL26iV57jnS6SQzZxr//W/jwoV/hYzqal5WFi87m8nN5TZ4+flsQIBx8WLTU0+RQIACOqwOjO1gWdbV1fXmnkbVRdc4xtgOaIrrZNFprx7YO6W2Jjf2gf8e/Wk+lzmIyNUtzM09XNtQalu0FgDaYJ4wQadSSZ58kqmrE73+Ou/sWZbP52Vn83JymNraFho8CgrECQnCDz4wLl1qnjqV+HzU0AHdhhVo24PB3C/QHVo7hAoiqq8rLMr/sbb60qEfplqtf00/4OYe4eYeQUTVldlELMoFcEOWESN0Bw+yvr5EJNi5U7h9Oz89vWnmYJVKS1SU6cknjYsXs/7+RMS7dEny97+7REUJvv6aumhDe9eD5imAm4odInnTm1brXz3NMpmvSKxydQ8nIpOpvr6uqPk8pwDQnLV/f+2RI9LJk3mZmayrqzU83BoRwfbsyW1Yg4JsU5oaX3tNuGOH6O23mStXeBcvSp591vqf/5gWLTI9/TRaPpw1dlRVVd3cI1ZWVqKs0OWJRMrW7nJTRxCRm3s4d7O66gJiB0A7sb6+uh9/JJOJbbu/XiQyzZ5tmjFDuHWr6P33mZISXl6eOCFBuGGD8bXXzBMmUNddqaPLxo6dO3eKRCKZTNaB04VlL1++jNgB3YFtNTgPr0HVlefFEjdPryF5l/cSEdfO4SLz5Q7QactQLoAO/FfS/osoxWLT88+bnnlG+NlnorVrmdJSXna2ZNYsa9++pvnzzZMnsx35Lwzucuy4fPny8uXLUSAbXMkC13/XalyWJTB4rKfX4ICgUbU1eVzs4EZ1SKUe12KHBuUC6EQSiemFF0yzZwu3bBGtXctcvco7d048f75o6VLz44+bnnnGOngwinRXdHgFWpVKJZVKOxBRWbaqqspoNHbJ8uFKFmjKthqcl89QL5+hYrGrbWYwrntFKFIIBBKzWa/XlaNcAJ1OKjW9+KJpzhzhp5+KNm1iCgqY+nrh1q3CrVutAweannnGPHUqq1SiTndSi1eytBo7Ro0adejQoY4+x4ULF0Ixuy10h9jR2MnCcJmDiNSeA4gYItb12qgOidSjvq5Ih9gBcMe4uJgSEkwvvsg/ckS4davghx/IZOKdPi1++WXxa6+Zx441zZ5tiY8nXHF597Q66GbgwIE38XB9+vSRoS8NukPsECmIyM09nMscRCQWu8oV/jKZr22PVOpJRHodOlkA7vD/bDzLiBH6L75oyMoyvvmmtWdPIiK9XpCUJB0/3uXee4WffkomE+rkQLHDz88vKirqJh6OYZjHHnsMZYVuEju8fYc13an26M9dxsKRuHgSkQ6xA+AuYb29jYsWaU+f1u3da548mcRiIuKdOyd++WWXe+/lHz6MEt15LXeyTJgw4aYfcceOHSgrdP3YIZS3EDs8BzRdpYUbVYqxHQB3GcNY4uMt8fFMebkgMVG4ZQvv8mVeTo500iTzQw8Z16yx9umDIt0xuLIZ4GaIxEoi8vIZel3s8OhvG9hBRBKpJ7V5JYvBUI05TAHuGNbDw5SQoP3jD8P//A+rVhOR4OBBl+ho8ZIlLU7BDogdAI5CKJSLxa4q15DrYofnAO7qWQ7X2mE2a80mrX3g0Ff+cWL1V5/fU111AcUEuKMEAtPs2dpTp0zz5pFAQCaTcONGl/79hRs2YIZ1xA4AR33n8AQ9/IczzHXvIIUySO1xj+0m19pBRHr9df0sBkP1zm39Tqa9ZTTUXC1NQzEB7jzWzc3wzjvaEycso0YREVNZKV661OWBB/ipqShO56Y+lKCdMF0Y2PEPerDZPkYg/OtKrqYzhskVgbb9dbX5JlM9t11WciI0YiaKCXBXWENDdd99J9izR/Svf/Hy8nh//ikdM8Y8aZJ5zBhydWWVSlalYlUqcnVlFQqUq6M6PF0YNIXpwsCOX8D/a/sAybXYYTeqtL6uwLZ9tfR3VBLg7jKPH28ePVr00UfC//6XaWgQfPed4Lvv7A/i8WwRxOrvb37mGfPo0Zj/o213aOF7gG5CoQxs+wAXmQ+3odWWNt1fV/tX7KiqzDrx6z8rK86hngB3k0Ri/Mc/tKdOmWfMaDlMWK1MVRUvL4+XkSH44QfJlCkuQ4cKt24lvR7F6xC0dgB0FqmLF8PwWNaqbbgudjRt7WBZ65+n1nt4Rbqr+6JiAHcX6+ur37SJefddprKSqquZ2lqmpoapqaHq6saNmhqmpob/yy9MTQ3v/HlxQoJo5UrTc8+ZnnuO9fREARE7AO4mHk8okXrotFftYgfX2iFXBDTUX2FZKxHVNQkiAHCXw4dCwSoUFBTU2gFMQ4Ng1y7h+vW8CxeY8nLRW2+J3nvPPHmy8aWXrH3x/eFGH4zd/Pf/+eef33333Q8++ODkyZM4G+C24/pZGupLmu5sqCskInePewZHv8rNpF5Xk49aAThNLpHJTLNna9PT9bt2We69l4jIaBTs3OkybJh01CjB/v3EYj4exI7m5w3Lvvjii5mZmYsWLZo0adLUqVMtuGIbOid2lFz5JevMFpa1/PbLqxl/vFdTc5mIFIrAwdGvqdxC6PpuFwBwkv8/eeaxY3WHD+t++sk8YQLx+UTE/+03ydSp0nHjmKoqVKhF3beT5fPPPy8uLv7www+JiMfjTZ48mc/n44SA20sq9SYio7H2159fOpOxoboqx3YXd0mtQhl0tfT3ulq0dgA4K0t0tOXLL3n5+cINGwRffMHU1/N//VX60EO6775j/f1RH/u01m1/8xUrVkybNk2r1RYWFvr5+b399ts4G+C2k8l9bdtNMwdduxCGCx/1dYXcIA8AcFLWoCDD229rs7PN06cTES8ry2XUKF52NirjTLFDo9E88cQTtc2mymdZ9pNPPlm2bNkHH3ywZs2a8vIOL7VVXV2dn59/5cqVL7744uTJk9OmTTty5AjOBrjt/XqcTAAAIABJREFUbNfQ2sgV/iNGb/EPHKlQBhORQhlERBaLQastQ7kAnB2rUuk/+cT48stExBQVSUeN4l8/XxY4YidLRUXFmjVramtrMzIy0tLS1q1bp1Qqmx4wc+bMiIiIlStXEtGpU6fi4uJSUlJ8fHza/xQFBQVE1NDQsGjRIiIaMGBAVFTUuXPnvL29cU5AZ8QOhuGzrIWI/AL+X5+wqX3Cpja2eVybvbS+tkAm80XFAJwewxhXrmR9fMSvvcZUV0sffVS/ZYv5scdQGMeNHQqFYsGCBf7+/uvXr09Ls1+xYs+ePcnJydu2beNuRkZGxsfHJyQk7Nq1i9tz4sSJFStWtHIyMKtXrx40aFCPHj2IyDZ7Ws+ePY1G448//vjkk0/inIDbGTtcGmNHxD1zzmVuJiL/wJHXne3Kxov06mrzvX2HoWIAXYPphRdYd3fJCy+QwSB55hnD//yP6amn2hVaysuJiPXwQOy4c0QiUWBgq/M/JiYmxsXFCYVC257Y2Ni5c+fqdDqpVEpEw4YN279/f9tP4eHh4erqqtH8tSI5j8fDlSzQea0dAwYvLCr4qa423y8gvukBEpfGKYYMBox7B+hSzNOn63x8JE88wdTXi+fPZwoLjf/8Z6tH63SCAwcEiYmCn34ik8kaHGyNibHExFhGjrQGBnalsjjZkFKr1Xr48GGVStV0p1Kp1Ov1KSkpHXqoefPmZWRkcNsFBQUsy44cORLvE7i9pC7eRIxCGaRQBvXsM8HDK1IscW96gFAob/yEMjWgXABdjCU+Xv/DD6xaTSwreust8SuvkPX6weNGo2DfPskzz8iDgiRPPy04cIBMJiLi5eUJdu4UJyS49OvnMmBA+DvvCHbs4OV3hUvenOwCWo1GU1lZKZfL7WIHEeXk5IwdO7b9D/Xqq68+88wzW7Zs6dWr10cffZSYmOjn59fG8XYr0NoEBQUFBwffxZqkp6fjve3I5RIIlCJJv/T0dIMpnC8sb/4CeDyR1WrMz88xsekoF84ulKvrcVm7dsCSJZLSUuHHH1edP5/9z39aeTzXzEyvH3/0PHJEWFf3Vwhxc9OMGGF0dXX980/l2bN8nY6IeLm5Prm5tH8/ERk8PasHDrwycWKtA0+HmpeXl99KQjp58qSTxY66ujoikslkzWNHTU1Nhx5KqVR+++23586dq6mp+fzzz+0esznHXIE2PT09KioKn1+OXK78HP8Bgyb1CYsiiqqrjeEuYGkqO0Ol02k81HJH+1Pi7EK5UK7bIyrKcu+91kmTeGfOeP38s0dVFaPRMFeu2O5n5XLL+PGmxx+3jBihujaDlM5s5mdk8FNTeb/+Sr/+KqivJyKxRuP944/ex47pduywPPigo/66rf5l165d62SxQ6/XE5FIJGq6k5vmS6fTdfTRGIbp169fOw+2a+2IiYmxW88XoOUvOjIfX7/7ue3mmYOIBEIZ6TQmUz1qBdBVsb6+ugMHJNOn83/9lXetf5/EYvODD5qnTjWPHUtSabOPBoElKsoSFUULF6anpUVLJPxff+Wnpgr27SOtVjp9un7rVvOjjzryb33s2LHj118/XFBQ4GSxQ6FQEJHd2E9uYg/urs7jmK0d4Ph6BAyXyXu0cYBIrCQik7GF2KHXlV+68O3lC0kjx2xrPgUIADhT8lCpdLt3S/7+d8Hu3ZboaPPEieapU9t7xQqPZx0wwDpggOmFFwQHDkieeor0eslTTxk+/NA0c6bD/sqxsbF238+dr7XD1dW1ecMGFzvsxpkCOIjeIVPaPkAokBFRS60d7NdfDjIaaogoI/3d2OHvopgAzk0s1n/2GfP226zPzX+LMI8Zo/vmG8m0aYxWK54/n3Q603PPOVENnCx2qFSq8PDwsrKy5rEjOjq6U58anSxwc+SKgBvEDrGCiIzGv4aVsayFYfj1dYVc5iCi7LPbBgxeeMOHAgBHx+ffSubgWOLjdfv2SSdNYiorxYsWMeXlxtdec8DftSt0shDRxIkTDx061HRPSUmJWq0eMmRIpz4vOlmgk3DX0DZt7cg++7mH50CttvSvTxmL4UL2zsihS1AuACAi65AhugMHpOPHM6WlotWrSaczrlzpaC/S+TpZDAaD7V+bWbNmrVu3Ljc3t2fPntyepKSkRYsW8XidOwcJWjugU2OHuUnsKMjdn3VmS1DPcURExEilHjqdxm4lOQDo7skjIkK3b590/HimqEi0di1TW2t4/33iOdB0XE7T2mEymWbPnq3RaDIzM5VK5bhx40JCQuLj4xcuXEhEYWFh27ZtmzNnzoYNG3x9fVetWtW7d+8lSzr9WyBaO6CTiERcJ0tj7DCb9cVXjppN2prqS0SkVAUrVb2KCn6qqb6IWgHAdckjJER78KB0/HjepUvCLVuY+nr9xx+TwFH+Z3ea1g6hULh9+/Y2Dpg6dero0aNTU1NTUlJmzJgRGRmJkw+cl6BJa8fli0lWi9Fs0tK1eUvd1ffI5D0QOwCgRWxgoO7gQemECbwzZwRffy2pq9N//jlJJI77ieekhXZ1dX344Yfv5DOikwU6iUgkJyKLxXh435N5l/a4uYc3vdfd4x6JxJ2IjIYanU4jlXqiYgBwXfLw9tYlJ0snTuSdPCnYt0/y1FP6XbuIYe76C+siQ0rvFnSyQGdl/2vLsuRd2kNEVZXZRKRQBqpc+9TXXfH0imR4je/TmuqLiB0A0ELycHfX7dsnmT6df+SIZfx4R8gc1DXm7QDoemyrwTXVd8DzAyIXcNt1tXncRm31JR/fGFQMAFpIHjKZ7uuvBUeOmDuyPNmdx8OfCuDuEolbmGDX1y/Oti1XBPB4AiKqq81HuQCgVS4uDp45yHnHdtx5GNsBnfUmFNgvQygQytQe/W03GYYvFMoNhmqs2wIATgRjO24JxnZAJ+EuoG3K2yeax7vuvSkUKQyGaqOhDuUCAGfR4tgOdLIA3GVCUePYDk/vxtWivX3vtT+m2ZRiAADOCLED4G7HjmtDSgOCHpRIPYjIp0dMi9EEnSwAgNgBALcWO651srip+3p4DuDxBN4+9usaXpvJFJ0sAODcMLajvTCkFDordghlRAwR6+Ye4e4xwKCvEgibDzJFawcAOBkMKb0lGFIKnYRh+AKh1GoxqVx7e3gOZK2m5sdwrR2mllo7LBbjxfM7w/rOQiUBwKFgSCmAgxIJFSq3EB5P6OE50Mt3WPMDuPEfJqN9awfLWq8U/HTq9/+yrBVlBADHh9YOAAd4Hwpl7uq+RKR07S2WurcaO67vZKmuysm9uLu66nxdbX5R/uGA4IdQSQBA7ACAGxCJFG7uEUTEMDyJRN1C7BAriMhs1lmtZtuUHtlnt2ad+YxYIqJzmZsROwDA8aGTBcAB4r9QzrV2tMZ2ka2twcNs1uVk7TCbtGazlogK8w/blm4BAHDcjzuUoJ1wJQt0HqFIzrV23Dh2GOvEYlciunwhyaCvsh3AstaTaW8Pf3AjigkADgJXstwSXMkCnUcq9VSogts4wDaBuq21I/vsVtu9PJ7QajXlZG0P7v1IUM+HUU8AcAS4kgXAQXl6D2EYfhsHCP5q7agnotrqS2UlJ4hI5RZCRMMf/FgscSeis6c3oZgA4MgQOwDuPi+foW0fYNfaceH8V0QsEY0alxgY/FDv0CmhETOIqLjoqF5XjnoCAGIHALTKXd2v7QNsy8VxM4ZdzPmGiDy9o9zcw0eO/ZJheD17P0ZELGspyN2PegIAYgcAtP4+5N1glJVtSOmZ0xv1uvLa6ktEFNz7ESISCKRE5OUTLZV6ElFJcSrqCQCIHQBw82zLxZVc+fXY0Ve4bbX6HtsBDMNTqHoSkVZbhnIBAGIHANxC7LjW2kFEl3L+l9twu36qDxcXbyLSNSB2AIDjwgW07YV5O+Bufj/gCQRCmdnUYNsjEinlCv/rYofMm4i0DaUoFwA4AszbcUswbwfcXQpFQFVltu2mm7ovEdP0AKmLNxHp9RVWq9ls1opEShQNAO4izNsB4MTkysCmN5tf/OIi8yEilrU21F/JydqBigGAA0LsAHCS2CEPuC52eDSLHS7e3EZxUUpR/mFUDAAQOwDgJilUQbZthuE3XzpO6uLFbRTmHS4pTrVYDCgaACB2AMDNsLV2iCVuYX2fcnNvHjsaWzuuFP5sNmm52dMBABxK9x1S+v7771dXVwcFBYnFYoZhiEgmk02YMAHnBDgmxbWxHUpVz+j7/s2tQ3t97PAiYohYo7GWCx89/B9A3QAAscMhHD169OjRowqFgsfjEVFNTc2zzz6L2AEOS65ojB0KRVDzzEFEfL5IInHX6yu4m1cKfx4asxx1AwCH0n07WXr27FlZWZmfn5+bm3v69Olp06atXr0aJwQ4LKmLF58vJiKFMri1Y7iJSjnlVzMMhmrUDQAQO9pLo9E88cQTtbW1dvtZlv3kk0+WLVv2wQcfrFmzprz8ZpbcnD9/vm37jTfeWLlypUCAWUzAcTEMT6bwJyKFqtXY4eM7rMnbxHJgz6TC6y9p4fpfAAAQO/5SUVHxyiuvzJ0795FHHvnqq690Op3dATNnzrx69erKlSsXLlz40EMPxcXFlZZ2eGbGPn36cBs7dux44IEHPD09cTaAg1MoAqnN1g4vn+imN6+W/p6V+ant5tk/N+3YEnImYwPLWlFMAEDssH22KhYsWLBx48YZM2Y0v3fPnj3JyclLly7lbkZGRsbHxyckJNzcczU0NHzyySeTJk3CqQCOjxveoWzSk2LHu0lrB6cw/xA3XXpmxkfHjiw2m7XHf1lqW0wOAOAOc8RuBZFIFBgY2Nq9iYmJcXFxQqHQtic2Nnbu3Lk6nU4qlRLRiRMnVqxY0eLPMgyzevXqQYMG2fbs2rXL19eXu5IFwMEplIEMw7dbiqUpmdxPrvCvryvyCxgR0f9vP+6babWaL57/asDgl3KyttsOu4prawEAsaM9rFbr4cOHx4wZ03SnUqnU6/UpKSljx44lomHDhu3fv7+dD7h79+7Q0FCcB+AU5IoAmdyPxxO2cYy3z7D6uiL/wJE9ez/m5h5RVZmVn3tgwOCX6mrybMdUV19gWSvDYNoeAEDsaJNGo6msrJTL5Xaxg4hycnK42NEh58+fHzlyZHuOtFuB1iYoKCg4OPgu1iQ9PR3ncTcpV0Odjhh127+CweRLRBVVkvT0dIE4gijramn68eMHTaZ6IpK49NRrc80m7fFj+0RiH5xdOLtQLpTrtsvLy8vPz2/xrpMnTzpZ7KirqyMimUzWPHbU1NTcxAOWlJS4u7u350jHXIE2PT09KioKb8huUq76Om/GerLtX0FTxhTlfRhz3xSJRO2mfPT/Sr6zWg1KWWNTR2j4Y3+eXEdEAf7igKAonF04u1AulOu2a+OlOt8KtHq9nohEIlHTnXw+n4iaX/DSHkeOHJkyZQrOaXAKMnkPV7eQto9Rew5QufaRSNTUZITpxfNfcxtBPRs7KKsrz6OeAHDnOVlrh0KhICKLxdJ0JzexB3dXRzUdXto2u06WmJiY2NhYnEBwJzEM36fHDc46Hk8Y3m8Wty1XBHAjTK+WNjbPenhFSiRqvb6iugqxAwA617Fjx44fP950T0FBgZPFDldXV2rWsMHFDpVK1alP7ZidLNDdeHhF3vCY0L5P2ba9fWPq674hYolIKvUUCFxUbiH6koramssoJgB0qtjYWLvv52vXrnWy2KFSqcLDw8vKyprHjujo6E59arR2gCPg8W78nuV6WDg+PWIv5XzDbcuVQUQkV/iXlVBDQwmKCQCdqiu0dhDRxIkTDx061HRPSUmJWq0eMmRIpz4vWjvAGTXtlFF79CcimcyXiLT1iB0A0LlabO1w6CGlBoPB9q/NrFmzzp07l5uba9uTlJS0aNEibiFZAGjKzT1cLHEjIoFAMihqMRFJXbyJyGSq5y6pBQC4kxyxtcNkMs2ePVuj0WRmZiqVynHjxoWEhMTHxy9cuJCIwsLCtm3bNmfOnA0bNvj6+q5atap3795Llizp7FeFThZwRgzD8/a9tyB3f/9BCxTKQCJykflyd2kbSlWufVAiAOgkTtPJIhQKt2/f3sYBU6dOHT16dGpqakpKyowZMyIjI+/Aq0InCzgp3x73acr+GDjkZe6mTG6LHSWIHQDQebrCkFIbV1fXhx9+GH9UgBvy6RErkbgLRY1XmLu4NE5O2oDhHQBwxwlQgnZCJws4KQ+vSE/vvwZcu/zV2lGK4gBA5+kiV7LcLehkASdld82tUCgXCuUmU71Oi9gBAJ3I+a5kAYDOwDV4YOoOALgLX4RQAoDuRi73IyJMVAoAdx46WdoLYzugy1B7DrxSmFJZfsZs1gsEErt7rxSmeHgO5Gb7AAC4aRjbcUswtgO6DC+foURksRgrNKdtq9QS0ZXCn9OOLSu/mhEYPOahR3ehUABwKzC2AwCIiLy8o7iNq6W/N91/7s/N5VcziKgg78DJtDUoFADcdogdAN2OTO4nk/cgoqtl18WOhoYrtu0/Tqy+kr8RtQKA2wudLO2FsR3Qlfj0uO9SzjdF+T82Hd7RUHeFiHr4D6+tuVRfV1RTmYpCAcBNw9iOW4KxHdCV9A6ZdCnnG6OxtqjgcHCvR4nIYjHqdBoi6uF/v7u675nTG83mWhQKAG4axnYAQCP/oFEikZKILl/4jtujbShhWSsRyeT+3GUsFnM9twcA4HZB7ADojvh8cUDwaCIqLT5GRJUV5+rrCri7ZPIeYjF39SxrNKLBAwAQOwDglnl6DSGihvriyopzh36Yei7zU26/XBEgErty2wZ9FQoFALcRxnYAdFNePo2X0R74fkJDQ0ldbZPWjmtzhRn0laTqiVoBAGLHnYYrWaCLUXsM4PEEVqv52uIsLBGJJW4CgYtE4n4tdqC1AwBuEq5kuSW4kgW62ncOoYube0RFeWbTnTK5Hxc+GmOHoRqFAoCbgytZAOA6nt5DiKhXn4k+Pe4jIj5f3Lf/c0Qkto3tMKC1AwBu6xcelACg2wrq9UivkEl+ASMK8w7+bqyNH/2pu7ovEYnErkQMEYtOFgBA7ACA2yMw+CFuwz9oVI+AEXy+iLvJ4wlEIoXRWIvWDgC4vdDJAgDEMDxb5uBwwztaa+0oKvhJqy1D3QAAsQMAbgPuYpYWY4fRWPvL/72YfWYrqgQAiB0AcBtwM4a1GDuOH11aX1eUfeYzq9WEQgEAYgcA3KrGTpZmYzsK8w7lZG0nooaGkrxLe1EoAOgQDCltL0wXBt0K18mi11fa7b98Mcm2ffbPT3qFTEKtAKBFmC7slmC6MOhWxH+N7WCJGG5nfV1BaXGq7ZirpSfMZr1AIEG5AKA5TBcGAB2LHVaryWis4zb+PLnumx1Da2tyichF5kNEVqu5qjILtQKA9kPsAIAWNFmWpZKIfj/+5onUN8wmLbezV5+J3EZVxVnUCgAQOwDgloivxQ69roKI6q+tT8sJ7DlOIHQhosryM6gVALRftx7bcerUqYMHD0qlUrVaPX36dIEAI10AGkml6sbYoa8kovr6oqb3unv0c3OP0JT9YbeSHABA27pva8fBgwfPnDmzdOnShQsX+vn5vfjiizgbAGzE13eyaBtKbXdJJGqp1NNdfQ8RVZajkwUAEDvaYePGjcOHD2cYhohGjBiRmYkvbQAtxA69roJlrVzscHOPICJX93AicvfoR0R6fQVmSQcAxI4b8/Pze/jhh3/77TciysrKCgwMxNkA8FfsEKsYhk9Een2lTqfhJiQN6/u0QODi5h5uiyBEVFVxDuUCgHZy6NEMGo0mISFh06ZNSqWy6X6WZTdv3lxUVKRWq3U63bPPPuvh4dHRB3/zzTenT58eGxv76KOP9u3b98svv8TZANAEw+crzOZqg76yob6Y26V07eUfONLVPYyutXYQUWXFOb+AEagXADhr7KioqFizZk1tbW1GRkZaWtq6devsYsfMmTMjIiJWrlxJRKdOnYqLi0tJSfHx8enQs0il0piYmAEDBuzYsePnn38eOnTopEmYbxGgyaeDUGU2V+fn7ivMP8TtcZH5Bvd+lJu0Qyr1lEo9dToNWjsAwLljh0KhWLBggb+///r169PS0uzu3bNnT3Jy8rZt27ibkZGR8fHxCQkJu3bt4vacOHFixYoVLX99Y5jVq1cPGjSIiCZOnPjf//534MCBy5cvX7Zs2bRp086ePRsaGopzAqDx00GgJKKG+iu2PTJ5D6Wqp9Vi5G66qfvqio5UInYAgFPHDpFI1MZIi8TExLi4OKFQaNsTGxs7d+5cnU4nlUqJaNiwYfv372/7KS5evMjn8wcOHEhEKpXqgw8+qKqqOnLkCGIHgA1fcF0rI48nlEo9GeavAWFu6ojioiM1VTmoFQC0k5MNKbVarYcPH1apVE13KpVKvV6fkpLS/sfx8fEpKioymf5attvb23vw4ME4IQDsYoerW6hfQDwRuch8mmYOIpLJfInIaKy1WAwoFwC0h5NNkKXRaCorK+VyuV3sIKKcnJyxY8e283Hkcvmbb745Z86cp556ioiOHj0aGho6ZMiQNn7EbgVam6CgoODg4LtYk/T0dJzHKFdnEIpURIyn34sM8akwhSWlXfXKy2q5jbQTKUKRGhXD2YVyoVxElJeXl5+f3+JdJ0+edLLYUVdXR0Qymax57KipqenQQ02aNGncuHGnT582m81LliyxG7XanGOuQJuenh4VFYU3JMrVGZKLd94z8PmYB2YTkbb2W5FIZVe9vEvFhZeJiMLC/N3V/XB24exCuVAuImrjpa5du9bJYoderycikUjUdCefzycinU7X0UeTSCTDhg1r58F2rR0xMTF26/kCdDEustChMTO57cHRr+Ve3G13gN1MpgAATR07duz48eNN9xQUFDhZ7FAoFERksVia7qytrbXd1Xkcs7UDoBPfbqohAmFjy6K37708nqi12KFH7ACAZmJjY+2+nztfa4erqys1a9jgYofdONPbDq0d0M15etuPuZagtQMAWtcVWjtUKlV4eHhZWVnz2BEdHd2pT43WDgA7aO0AgDa02NrhfGuyTJw4MTc3t+mekpIStVrd9nUoAHDb8fkioVBOaO0AgHZz6NYOg8Fg+9dm1qxZ69aty83N7dmzJ7cnKSlp0aJFPF7nRih0sgA0J5a4m0z1el0FSgEAdpymk8VkMs2ePVuj0WRmZiqVynHjxoWEhMTHxy9cuJCIwsLCtm3bNmfOnA0bNvj6+q5atap3795Llizp7FeFThaA5qRSdX1dATpZAKA5pxlSKhQKt2/f3sYBU6dOHT16dGp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      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "y1, y2, y3 = res1[1], res2[1], res3[1]\n",
    "x1, x2, x3 = Array(1:length(y1)), res2[4], res3[4]\n",
    "\n",
    "\n",
    "fig = @pgf Axis(\n",
    "    {\n",
    "        xmajorgrids, # show grids along x axis\n",
    "        ymajorgrids, \n",
    "        xlabel = L\"\\Large{\\mbox{epoch}}\",  \n",
    "        ylabel = L\"\\Large{\\mbox{Duality gap}}\",\n",
    "        #xmode  = \"log\",\n",
    "        ymode  = \"log\",\n",
    "        \"mark size=4pt\",\n",
    "        width = raw\"1\\textwidth\",\n",
    "        height = raw\"0.8\\textwidth\",\n",
    "        \n",
    "        \"axis background/.style\" = { fill = \"gray!10\" }, \n",
    "   \n",
    "    },\n",
    "    PlotInc(\n",
    "        {\n",
    "            #\"no marks\",\n",
    "            \"mark repeat=2000\",\n",
    "            style = \"{very thick}\",\n",
    "        },\n",
    "        Coordinates(x1, y1)\n",
    "    ), \n",
    "   LegendEntry(L\"\\Large{\\mbox{MP}}\"),\n",
    "   \n",
    "    PlotInc(\n",
    "        {\n",
    "            #\"no marks\",\n",
    "            \"mark repeat=50\",\n",
    "            style = \"{very thick}\",\n",
    "        },\n",
    "        Coordinates(x2, y2)\n",
    "    ),\n",
    "  LegendEntry(L\"\\Large{\\mbox{MP-Car+19}}\"),\n",
    "    \n",
    "       \n",
    "    PlotInc(\n",
    "        {\n",
    "            #\"no marks\",\n",
    "            \"mark repeat=500\",\n",
    "            mark=\"triangle*\",\n",
    "            style = \"{very thick}\",\n",
    "            color = \"olive\" \n",
    "            ### other possible colors are red, green, blue, cyan, magenta, yellow, black, gray, darkgray, lightgray, brown, lime, olive, orange, pink, purple, teal, violet and white\n",
    "        },\n",
    "        Coordinates(x3, y3)\n",
    "    ),\n",
    "  LegendEntry(L\"\\Large{\\mbox{MP-Alg2}}\"),\n",
    "\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_name = chop(filename, tail=4)\n",
    "pgfsave(string(base_name, \"tex\"), fig)\n",
    "pgfsave(string(base_name, \"tikz\"), fig)\n",
    "pgfsave(string(base_name, \"pdf\"), fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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