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@quaquel
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network_design
{
"metadata": {
"name": "",
"signature": "sha256:86f1a0f1e386a69bc4025ba96a141822182a78d948de1b8374922a18cbcc1606"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"def generate_vertices(n=20):\n",
" \n",
" points = np.zeros((n, 2))\n",
" \n",
" points[:,0] = np.random.rand(n,) * 2*np.sqrt(n)\n",
" points[:,1] = np.random.rand(n,) * 2*np.sqrt(n)\n",
" \n",
" return points\n",
"\n",
"def distance(x0, x1, dimensions):\n",
" delta = np.abs(x0 - x1)\n",
" delta = np.where(delta > 0.5 * dimensions, dimensions - delta, delta)\n",
" return np.sqrt((delta ** 2).sum(axis=-1))\n",
"\n",
"def calculate_distances(points):\n",
" '''\n",
" \n",
" determine the distances from all nodes\n",
" to all other nodes, accounting for the\n",
" periodic boundary condition\n",
" \n",
" '''\n",
"\n",
" nr_points = points.shape[0]\n",
" dimensions = np.array([2*np.sqrt(nr_points), 2*np.sqrt(nr_points)])\n",
" distances = np.zeros((nr_points, nr_points))\n",
"\n",
" for i in range(nr_points):\n",
" distances[i, :] = distance(points[i,:], points, dimensions)\n",
" return distances\n",
"\n",
"\n",
"np.random.seed(1234567689)\n",
"points = generate_vertices(n=9)\n",
"\n",
"distances = calculate_distances(points)\n",
"print distances\n",
"\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 0. 2.92508037 2.41072079 1.56112712 1.62449152 2.82076547\n",
" 1.77255709 0.24664705 2.88423043]\n",
" [ 2.92508037 0. 2.00238859 2.92750951 1.38333152 2.54703907\n",
" 2.02767249 2.83989613 2.34389539]\n",
" [ 2.41072079 2.00238859 0. 1.04958778 2.17258187 4.06088409\n",
" 1.37617369 2.59921729 3.53443476]\n",
" [ 1.56112712 2.92750951 1.04958778 0. 3.18234702 3.13714198\n",
" 1.48876335 1.69692502 3.24547482]\n",
" [ 1.62449152 1.38333152 2.17258187 3.18234702 0. 2.59277711\n",
" 2.93350484 1.49710559 2.85902825]\n",
" [ 2.82076547 2.54703907 4.06088409 3.13714198 2.59277711 0.\n",
" 2.69996203 2.95218458 0.89579857]\n",
" [ 1.77255709 2.02767249 1.37617369 1.48876335 2.93350484 2.69996203\n",
" 0. 2.01724509 3.58554131]\n",
" [ 0.24664705 2.83989613 2.59921729 1.69692502 1.49710559 2.95218458\n",
" 2.01724509 0. 2.67612204]\n",
" [ 2.88423043 2.34389539 3.53443476 3.24547482 2.85902825 0.89579857\n",
" 3.58554131 2.67612204 0. ]]\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Network generation and visualization"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import networkx as nx\n",
"import random\n",
"import itertools\n",
"import operator\n",
"\n",
"def make_random_network(distances, nr_edges):\n",
" '''\n",
" \n",
" Make a random network given the specified points and\n",
" the specified number of edges\n",
" \n",
" '''\n",
" \n",
" G = nx.Graph()\n",
" \n",
" nr_points = distances.shape[0]\n",
" points = [i for i in range(len(distances))]\n",
" \n",
" G.add_nodes_from(points)\n",
" \n",
" # create all possible edges\n",
" # sample from all possible edges\n",
" \n",
" possible_edges = itertools.combinations(points, 2)\n",
" possible_edges = [edge for edge in possible_edges]\n",
" random_edges = random.sample(possible_edges, nr_edges)\n",
" \n",
" for edge in random_edges:\n",
" u, v = edge\n",
" distance = distances[u,v]\n",
" G.add_edge(u,v, weight=distance)\n",
" \n",
" return G\n",
"\n",
"\n",
"def make_mst_network(distances):\n",
" '''\n",
" \n",
" Generate a minimum spanning tree network using distances\n",
" \n",
" '''\n",
" \n",
" \n",
" G = nx.Graph()\n",
" \n",
" nr_points = distances.shape[0]\n",
" points = [i for i in range(nr_points)]\n",
" \n",
" G.add_nodes_from(points)\n",
" \n",
" possible_edges = itertools.combinations(points, 2)\n",
" for edge in possible_edges:\n",
" u, v = edge\n",
" distance = distances[u,v]\n",
" G.add_edge(u, v, weight=distance) \n",
" \n",
" G = nx.minimum_spanning_tree(G)\n",
" return G\n",
" \n",
"\n",
"def plot(graph, points_mapping):\n",
" '''\n",
" \n",
" visualize the network, taking the periodic boundary\n",
" condition into account\n",
" \n",
" '''\n",
" \n",
" points = graph.nodes()\n",
" points = [points_mapping[i] for i in points]\n",
" points = np.asarray(points)\n",
" \n",
" nr_points = len(points)\n",
" boundary_condition = 2*np.sqrt(nr_points)\n",
" \n",
" fig = plt.figure()\n",
" ax = fig.add_subplot(111, aspect='equal')\n",
" \n",
" # plot the nodes\n",
" ax.scatter(points[:,0],points[:,1], s=40)\n",
" \n",
" # plot the edges\n",
" lw=0.5\n",
" for edge in graph.edges():\n",
" i, j = edge\n",
" \n",
" # check whether we violate the boundary condition\n",
" # if not we can draw a single line, otherwise it\n",
" # is more tricky\n",
" \n",
" i = points[i,:]\n",
" j = points[j,:]\n",
" delta = np.abs(i - j)\n",
" \n",
" \n",
" if delta[0] > 0.5*boundary_condition:\n",
" # we violate the boundary condition on the x-axis\n",
" xy = [i,j]\n",
" max_x, max_y = max(xy, key=operator.itemgetter(0))\n",
" min_x, min_y = min(xy, key=operator.itemgetter(0))\n",
" \n",
" \n",
" # left hand side\n",
" low_x = max_x - boundary_condition\n",
" line = ax.plot([low_x, min_x],\n",
" [max_y, min_y], lw=lw)\n",
" \n",
" # right hand side\n",
" high_x = min_x + boundary_condition\n",
" ax.plot([max_x, high_x],\n",
" [max_y, min_y], c=line[0].get_color(), \n",
" lw=lw)\n",
"\n",
" elif delta[1] > 0.5*boundary_condition:\n",
" # we violate the boundary condition on the y-axis\n",
" xy = [i,j]\n",
" max_x, max_y = max(xy, key=operator.itemgetter(1))\n",
" min_x, min_y = min(xy, key=operator.itemgetter(1))\n",
" \n",
" \n",
" # top\n",
" low_y = max_y - boundary_condition\n",
" line = ax.plot([max_x, min_x],\n",
" [low_y, min_y], lw=lw)\n",
" \n",
" # bottom\n",
" high_y = min_y + boundary_condition\n",
" ax.plot([max_x, min_x],\n",
" [max_y, high_y], c=line[0].get_color(), lw=lw)\n",
" \n",
" \n",
" # line.get_color() allows us to use the same color for both lines\n",
" else:\n",
" ax.plot([i[0], j[0]],\n",
" [i[1], j[1]], lw=lw)\n",
" \n",
" ax.set_xticklabels([])\n",
" ax.set_xticks([])\n",
" ax.set_yticklabels([])\n",
" ax.set_yticks([])\n",
" \n",
" ax.set_xlim(xmin=0, xmax=boundary_condition)\n",
" ax.set_ylim(ymin=0, ymax=boundary_condition)\n",
" \n",
" \n",
" return fig\n",
"\n",
"np.random.seed(1234567689)\n",
"random.seed(123456789)\n",
"\n",
"points = generate_vertices(n=10)\n",
"points_mapping = {i:(point[0],point[1]) for i, point in enumerate(points)}\n",
"distances = calculate_distances(points)\n",
"\n",
"G = make_mst_network(distances)\n",
"plot(G, points_mapping)\n",
"plt.show()\n",
"\n",
"G = make_random_network(distances, 10)\n",
"plot(G, points_mapping)\n",
"plt.show()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1122b4bd0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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x8fGIj4+H2WxWKCEROSMWJv3FsmXA9OnAdVZuqO7qAOjBCg6ADg0Nxfjx47F4\n8WJubEBEjcbCpD/Yvx+IjATathVz/qsDoLsrPAD6tttuQ79+/bBmzRpFj0tEzoOFSdeUlwMffQTE\nxoo5v9oDoPv27YtWrVph69atqhyfiBwbC5OuuTrjUsStWGsNgH7iiSdQUFCA/fv3q3oeInI8LEwC\nAGRkAN27AyJmMFt7APTkyZPx+eef48SJE1Y5HxE5BhYm4dIl4MsvgcGDrX/uPZcvI7e6GqNbt7ba\nOTUaDebPn4833ngDOTk5VjsvEdk3FiZh6VJgzhzrn/dAaSmOVVRgcmio1c/t4uKC5ORkJCcno6ys\nzOrnJyL7w8J0ctu2AQ89BKj0nk2drg6AniHqdVwAXl5eWLx4MebOnYvq6mphOYjIPrAwnVheHvD9\n98Ajj1j3vH8eAC1SYGAgpk6dioSEBK7RJKJ6sTCdlCzXblAwa5Z1z1vXAGiR2rdvj5iYGKSkpIiO\nQkQ2jIXppDZtAoYOBXx8rHfOGw2AFqlbt27o0qUL3nzzTdFRiMhGsTCd0JkzwPnzwH33We+clg6A\nFunRRx+Fq6sr0tPTRUchIhtkm7+5SDWSBKxaVbtXrLU0dAC0SE8//TROnjyJQ4cOiY5CRDaGhelk\n1q8HRo8GPD2tc77GDoAWafr06dixYwdOnTolOgoR2RAWphP55RdArwfuuss652vKAGiRNBoNFi9e\njFdffRWFhYWi4xCRjWBhOgmTCfjnP4F//MM651NiALRIbm5uSE5OxsKFC1FZWSk6DhHZABamk3j1\nVWDSJMDNTf1zKTkAWiQfHx/MmzcP8fHxMJlMouMQkWAsTCdw7Bjg4QHccov651JjALRIwcHBeOaZ\nZ7Bo0SJubEDk5FiYDs5oBN58E3j2WeucT60B0CJ16tQJ/fv3xyuvvCI6ChEJxMJ0cCtXAlOnAtZY\n+qj2AGiRevXqhbCwMLz77ruioxCRICxMB3bwIBAcDLRvr/65rDUAWqQhQ4agrKwMn376qegoRCQA\nC9NB6fXA1q3AmDHqn8vaA6BFeuaZZ3Dw4EEcP35cdBQisjIWpoNatgyYORNQe8tWEQOgRUtISMCm\nTZtw/vx50VGIyIpYmA5o377aN2JDQtQ9j8gB0CJptVokJydj6dKlKCkpER2HiKyEhelgSkuBXbuA\nmBh1z2MLA6BF8vDwQGJiIubOnQuj0Sg6DhFZAQvTwbz0EjB7trq3Ym1pALRIAQEBmDVrFuLj4yFJ\nkug4RKRmAO93AAAgAElEQVQymyxMSZKQnp6OmJgxiIkZg/T0dP5CssBHHwG9egEtWqh3jrMGg80N\ngBYpLCwMo0aNQnJysugoRKQyjVzP9iUajcbqu5tIkoRhw0Zhz54T0OvjAAA63XpERXXGtm2boLXR\nWYqiFRYCq1cDav7ezjcakXzuHFZERtrsTEtR9u7di+zsbEycOFF0FCJqorq6z+Z+62VkZFwpy28A\nTAIwCXr9t9i9+wdkZGSIjmeTZBlISam9FasWexgALdLDDz8MHx8f7NixQ3QUIlKJzf3mS0vbfuXK\n0hOtYEAIqgB4Qq+Pw+bN/GV0PVu2AAMGAGrtRmdPA6BFio2NxZkzZ3DgwAHRUYhIBTZXmFeFowJD\ncQGFsL/RUNaUk1M757JfP3WOb48DoEWaMmUKPv74Y/zyyy+ioxCRwmyuMEeOHIJ7PPeiL/LwOiJR\nAy0AA3S6VMTGDhYdz6bIMrB8ee0GBWqw1wHQImk0GixatAhr165Ffn6+6DhEpCCbK8ye5p4YcFMP\nbPMeBhlrAayBTtcDUVGdER0dLTqeTXnrLeDvfwe8vZU/tr0PgBbJ1dUVycnJSExMREVFheg4RKQQ\nm3pLNm9jHgAg6KkgZGRkXHtmGRs7GNHR0XxD9neysmr3ip0zR/ljy7KMhNOnMSIoyCFmWopSUFCA\nJUuWYOXKlXCzxuRuIlJEXd1nE4UpyzIurL4Ar5u80GKgiosIHYTZDEyZUju6S+k7pVcHQPdv3tyh\nZlqKcurUKbz55ptITk526k0eiOyJzS4rkc0yziw4A997fFmWFlq3DpgwQfmyBICXL1zA/Q42AFqk\nDh06YNCgQVi5cqXoKETUREILUzJKyJqRhZZPtIT/fY43dFgNJ04ANTXAHXcof+z1ubm42dsb9zvg\nAGiRevTogY4dO2LTpk2ioxBREwgrTFOZCaemnUKbKW3gc7uPqBh2paYGeP114PnnlT+2MwyAFunx\nxx9HdXU1du/eLToKETWSkMI05huRPTsb4QvD4RnmKSKCXVq9urYslV4O6UwDoEUaP348MjMzceTI\nEdFRiKgRrF6YVVlVOJd8DpHLI+Hegmv7LJWZWbuTT8eOyh7XGQdAizRnzhxs2bIFZ86cER2FiBrI\nqoVZfqQcuam5iFwZCRcdt1izlMEAvPMOEBen7HGddQC0SBqNBkuWLMGKFStw+fJl0XGIqAGsVpjF\n+4px6eNLiEiJgNZN+Mu5dmX5cmD6dEDJZajOPgBaJHd3dyQlJWHevHkwGAyi4xCRhazSXAXbClB5\nshLhCc49cLgxvvwSaNcOCAtT7pgcAC2en58f5syZw+HTRHZE9cLMXZ8L2SQjdDJv+zVURQWwYwcw\napRyx+QAaNvRpk0bjB07FomJiaKjEJEFVCtMWZZxNuUsPMM9EfRkkFqncWhLlwKzZgFK9Vq+0YiV\n588jJSICLixLm3D77bejT58+WLNmjegoRHQDqhSmZJJwOv40Ah4IQPOo5mqcwuF98gnQpQsQHKzM\n8TgA2nb17dsXLVq0wLZt20RHIaJ6KP6b01xpRtb0LASPCUaze7i9WmMUFwOffQYMHarM8TgA2vbF\nxMQgLy8PX375pegoRFQHRQuzprgGWTOyEDYzDN4dVZg55SReegmYPVuZY3EAtP14/vnnsW/fPvz0\n00+ioxDRdShWmIYLBpxOOI32Se3hEcL5iY21fTvwwANAcwXuZHMAtH3RaDSYP38+NmzYgNzcXNFx\niOhPFClM/c96XFh5AZErI+Hmz7l/jXXxInDkCNC/f9OPxQHQ9snFxQXJyclISkpCWVmZ6DhE9DtN\nLszSb0pxMe0iIldEwsWTz8caS5b/91Zs048lY/7p0xgdHIwIL6+mH5CsysvLC4sWLcLcuXNRXV0t\nOg4RXdGkAdKXPr4E/Y96tJ3Rlgvgm+hf/6rdoOD++5t2HA6AdhzZ2dlYv349UlJS+O+LyIoUHyCd\nn5YPY64RYTPD+I+5ic6dA86caXpZAhwA7UgiIiIwfPhwLFu2THQUIkIjClOWZVx49QJcvF0QMj5E\njUxORZKAlSuBGTOafqz1ubnoxAHQDqV79+6444478NZbb4mOQuT0GlSYsiTj7OKz0N2hQ8shLdXK\n5FQ2bKjd+s6ziWNBrw6AfpQDoB3Oo48+Cq1Wi507d4qOQuTULC5MqVpC9qxstBjUAgF9A9TM5DR+\n/RUoLQW6d2/acTgA2vGNHj0aP/30Ew4dOiQ6CpHTsqgwTRUmZE3LQsikEPjc6aN2JqdgNgOvvQa8\n+GLTjsMB0M5jxowZ2L59O7KyskRHIXJKNyzM6sJqZM/MRrt57eDVnksUlPLaa8AzzwBuTVi2ygHQ\nzkWj0SAxMRGvvPIKCgsLRcchcjo3LMyzi88iYmkE3FtxpxilfP894OIC3HZb44/BAdDOyc3NDUlJ\nSVi4cCEqKytFxyFyOLJU91LKGxZm5MpIuPpyD1KlVFfXvugzaVLjj3FSr8fWwkIOgHZSvr6+SEhI\nQHx8PMxms+g4RA5DMkrImlH3I48bFqbWnaOglLRqFTBlSu0VZmNcHQC9pH17DoB2Yq1bt8bEiROx\ncOHCejcXISLLmMpNyJqehdDn637ExTa0om+/BVq0ACIjG/fzHABNv3fLLbcgKioKr776qugoRHat\nuqAa2bOy0W5BO3iF1/2uDgvTSiorgXffBcaNa9zPcwA0Xc99992HNm3a4L333hMdhcguVWVX4eyS\ns4hYFgH3FvW/q8PfvFaybBkwcybQmAtDDoCm+gwdOhQlJSX47LPPREchsivlR8uR+3pu7bs6Pjd+\nV4eFaQWffw507Ag0ZvUHB0CTJZ599ll89dVX+P7770VHIbILxZ8X49LOS4hIiYDWzbIqZGGqrKwM\n2LkTePLJhv8sB0BTQyQkJOCdd97B+fPnRUchsmmF/ymE/kc9wueFQ6O1/LYfC1NlKSnA7NkNvxXL\nAdDUUFqtFklJSUhJSUFJSYnoOEQ2KXdDLiSDhDbPt2nwz7IwVbRzJ9CjB9CygfvUcwA0NZanpycS\nExORkJAAo9EoOg6RzZBlGeeWnoNHWw8ExQY16hgsTJVcugR89RXwt7817OeuDoAe3KIFbtPp1AlH\nDq158+aYMWMGEhISIEmS6DhEwkkmCafnnoZ/X38E9m/8RCcWpkqu3optKA6AJiW0a9cOsbGxeOml\nl0RHIRLKXGVG9oxsBD8djGY9mvZ7lYWpgvffB6KigIbOceYAaFJSly5dcPfdd2P9+vWioxAJUVNS\ng6wZWWg7vS28b/Zu8vFYmArLzQV++gl46KGG/Vxafj4COQCaFPbII49Ap9Phgw8+EB2FyKqMOUac\nnnsa7RPbwyNUmRcnWZgKkuX/bVDQEFcHQA/jAGhSwciRI5GdnY2vv/5adBQiq9Cf1OPc8nOIXBEJ\nt4AmzFD8ExamgjZuBJ54AmjIuzocAE3W8OKLL2Lnzp349ddfRUchUlXZt2W4uOkiIldEwsVL2Z3R\nWJgKOX269nbsvfda/jMcAE3WotFosGjRIqxZswYXL14UHYdIFZc+uYSS/SVon9QeWlfl600j1zMb\nSKPRcHSQBSSpdmTX8uWApXsMHC0vx4dFRVjAmZZkRXq9HjNmzMCyZcvg4+MjOg6RYi6+exFmvRkh\nE0KafKy6uo9XmApYtw4YO9bysuQAaBJFp9NhwYIFiI+Ph8lkEh2HSBEX/nkBWg+tImVZHxZmE/38\nM2AwAF26WPZ5DoAm0YKCgvDcc89h/vz5vINEdk2WZJxJPAPdbTq0HNrALdUagYXZBCYTsHYt8I9/\nWPZ5DoAmW9GxY0c8/vjjWLVqlegoRI0i1UjInp2NwAGBCOgXYJVzsjCb4JVXgMmTAUumbnEANNma\nnj17okOHDkhLSxMdhahBTBUmZE3LQsgzIfDt6mu18/I3dyMdOQJ4eQGdOt34sxwATbbqb3/7G6qq\nqrBnzx7RUYgsUl1UjexZ2Wg3tx28Iqw7nIKF2QhGI/D228Azz9z4sxwATbZuwoQJOHz4MI4ePSo6\nClG9DGcNOLvoLCJSIuAeZP0ZwSzMRlixApg2DbjRnVUOgCZ7MWfOHLz77rs4e/as6ChE11XxQwUu\nvHYBkSsj4eor5uKDhdlABw4AoaFAeHj9n+MAaLInV4dPr1ixApcvXxYdh+gPSv5bgsL/FCJyWSS0\n7uJqi4XZABUVwLZtwNNP1/85DoAme+Tu7o4lS5Zg/vz5MBgMouMQAQCKPixCeWY5wheEQ6MVu7qA\nhdkAy5YBs2YB9a0I4QBosmd+fn6YPXs25s6dy+HTJFze23kwlZrQdkpbm9jkhYVpob17gdtvB260\nRzoHQJO9a9OmDUaPHo3ExETRUchJybKM8yvPw62lG4KfChYd5xoWpgVKSoA9e2onkdSHA6DJUXTu\n3Bn3338/1q5dKzoKORnZLOPMgjNo9n/N0GJAC9Fx/oCFaYGUFGD27Po/wwHQ5Gj69euHwMBA/Oc/\n/xEdhZyEZJSQNSMLrf7eCn73+omO8xcszBv44AOgd2+gvh7cUVgIM8AB0ORwYmJikJOTg//+97+i\no5CDM5WacGraKbR5sQ10t9rm+x8szHoUFACHDwPR0XV/Zs/ly8irrsbTwbZzn51ISc8//zz27t2L\nn3/+WXQUsnOSJCE9PR0xMWMQEzMG6enpkCQJxnwjsuOz0X5xe3i29RQds06ch1kHWQamTwcWLADq\nen/nQGkpDpSWYmZYmHXDEVmZ2WzGtGnTMHPmTISEqDtCiRyTJEkYNmwU9uw5Ab0+DgCg063HE716\nYfbNUxCZEgkXb9vYOpTzMBvo3XeBgQPrLsuj5eX4tLgYM9q2tW4wIgFcXFyQnJyMpKQklJeXi45D\ndigjI+NKWX4DYBKASQjV74PX583xS79fbKYs68PCvI7z54FTp4C+fa///asDoOe1a2cTa4OIrMHb\n2xuLFi3C3LlzUVNTIzoO2Zm0tO1Xrixrb7l2w2V0hx5ra1pj8/s7xIazEAvzT2S5dq/YmTOv/30O\ngCZn1qJFC0yZMgXz5s1z2sc1pAwTNHgX7UTHaBAW5p+88QYQG1s7uuvPOACaCIiIiMDQoUOxfPly\n0VHIjowcOQQ63XoAtdsuHkcAAAN0ulTExg4Wms1SLMzfOXUKuHwZuOeev36PA6CJ/ufuu+/Gbbfd\nhrffflt0FLIT0dHRiIrqDJ2uB4A1ANZAp+uBqKjOiK5vKYIN4VuyV5jNwJQpwMqVwJ8ncZWbTJid\nnY3kiAjOtCT6nbfffhtBQUF47LHHREchOyBJEjIyMrB5c+0zy9jYwYiOjobWxi5C6uo+FuYVr74K\n9OsHdO78x68bzGZMz8rCwvBwzrQkuo6lS5fiwQcfRPfu3UVHIVIEl5XU48cfa1/2+XNZcgA00Y3N\nnDkT//73v5GdnS06CpGqnL4wq6uB9euB557749c5AJrIMhqNBomJiVi9ejWKiopExyFSjdMX5urV\nwD/+Abj8bs0sB0ATNYybmxuSkpKwYMECVFZWio5DpAqnLszDhwF/f6BDh/99jQOgiRqnWbNmmDt3\nLubOnQuz2Sw6DpHinLYwq6qAtDRgwoQ/fp0DoIkaLyQkBHFxcVi0aJHTvDBIzsNpC3P58trN1X//\nNjMHQBM13S233IKHH34Yr732mugoRIpyysL84gsgIgL4/b7pHABNpJzevXsjJCQEW7ZsER2FSDFO\nV5jl5cCHH9Zuf3cVB0ATKW/YsGG4dOkSPv/8c9FRiBThdIW5dCkwezZwdStYDoAmUs/kyZPx5Zdf\n4ocffhAdhajJnKowP/4Y6NYNuHoheaC0FMcrKjApNFRsMCIHNm/ePGzcuBEXLlwQHYWoSZymMC9f\nrn12OfjKpvhXB0BP5wBoIlVptVokJSUhJSUFpaWlouMQNZrTFGZKSu2tWIADoImszdPTE4sXL0ZC\nQgKqq6tFxyFqFKcozH//G3jwQSAggAOgiURp3rw5pk2bhrlz50KSJNFxiBrM4QszPx84fhyIiuIA\naCLRwsPDMWLECKSkpIiOQtRgDl2Yslz7VuysWRwATWQrunbtiu7du2PDhg2ioxA1iEM3x6ZNwNCh\ngOxpwrzTp5EUEQHv3++yTkRCPPLII/D09MRHH30kOgqRxRy2MM+eBc6dA7r/nxlzsrOxKDwcfq6u\nomMR0RWjRo3Cb7/9hm+++UZ0FCKLOGRhShKwciUwZRoHQBPZsqlTp+Kjjz7Cb7/9JjoK0Q1p5HpG\nCmg0GrucOLBuHdD9Hhlb/bPxbEgIZ1oS2TCTyYSpU6ciISEBrbg9JdmAurrP4a4wf/kFKK+Q8UEA\nB0AT2QNXV1ckJydj0aJF0Ov1ouMQ1cmhCtNkAl59TUbVUA6AJrInPj4+mD9/PuLj42EymUTHIbou\nhyrM114DfEZfQL/mHABNZG+CgoIwefJkLFiwwC4fBZHjs9tnmJIkISMjA2lp2wEAvXqNxsHASDzd\n34szLYns2MGDB/HNN99g6tSpoqOQk6qr++yyMCVJwrBho7Bnzwno9XEAtNBGBaH7HUfwdcoiaLkx\nAZFd++CDD1BRUYGRI0eKjkJOyKFe+snIyLhSlt8AmATcNxQSuuPE2o+QkZEhOh4RNdGgQYNQWVmJ\nvXv3io5CdI1dFmZa2vYrV5aeQHAe0GMHcMANen0cNm/eIToeESkgLi4Ohw4dwrFjx0RHIQJgp4X5\nB/mtgTXDgTvfAfpmoMq1UnQiIlLInDlzkJaWhnPnzomOohhJkpCeno6YmDGIiRmD9PR0Tm+xE3b5\nDDM9PR1PPpkAvf5bAJ5XvmqAd/PuGJ7SHWEdwhDXLQ5tmrURGZOIFGA0GjFt2jQkJiYiICBAdJwm\n+ev7F4BOtx5RUZ2xbdsmvn9hIxzupZ/hw5/C7t0//O5/dKnX/kdXUVOB1MxUlBhKENctDmF+YYIT\nE1FTlJSUICEhAStWrICnp+eNf8BG1f6xP+/K+xf/+2Nfp+uB995bgoEDB4qMR1c4VGEC/1tWcvWZ\nZWzsYERHR//hL7RyYzk2HNmAS5WXMKHbBIT7hwtKS0RNdf78ebz88stYvny53V6JxcSMwdatdwN4\nFncGfIfjxXdf+c4axMRkYsuWt0TGoyvq6j67Hd+h1WoxcODAev8i8/XwxdT/m4qK6gq8ceQNFOgL\nMP6u8YgIiLBiUiJSQtu2bTF69GgkJSVh3rx5ouM02h3wRm+cwldhXwHXCpPsgX3+mdZAPu4+mNJz\nChLuT0DGrxmI3xeP3y5xOgKRvencuTN69eqF119/XXSUBqv4oQLjjE8iyOMzrEEbHA8oufIdA3S6\nVMTGDhaaj27Mbm/JNkVVTRXeOvoWzpedx5guY3Bzi5tFRyKiBnjvvffg4eGBIUOGiI5yQ4ZzBuSu\ny4VnhCdaPdUKMSNG45PPjqHytg7AgYf/8P6Fvd5qdjQO9wxTCQaTAW8ffRtnSs7g6S5P49aWt4qO\nREQWevnll9G9e3fcd999oqNcV82lGuSsyYGLzgUhz4TARecCoPb9i9VbV+ODT3chtCLsuu9fkFgs\nzHoYTUZsPLYRWcVZeOrOp3B7q9tFRyKiG5BlGQsWLMCIESPQqVMn0XGuMVeakft6LszlZoRMCoF7\ny78Or3/76NvoG94X7QPaC0hIN8LCtEC1uRqbjm/CL0W/YNSdo3BH0B2iIxFRPcxmM6ZPn45Zs2Yh\nODhYaBbJJOHipouo/LUSIRND4NW+7lm8C/cvxPw+86HV8KrSFjnUXrJqcXdxx/i7xiP5wWRk5mZi\n+p7pOJp3VHQsIqqDi4sLkpKSkJiYiPLyciEZZFlG0YdFyJ6ZDZ+uPohMiay3LAFAkiWWpR3if2PX\n4ebihjFdx+ClB1/C9xe/x/Q90/Fd7neiYxHRdXh7e2PhwoWYO3cuampqrHrukq9KcOrFU3DxcUGH\nVR3g29XXqucn6+ItWQuYJBO2/LgFR/OOIub2GNwTeo/oSET0J1lZWdiwYQNeeuklaDQaVc+l/0mP\nvDfz0KxHM7Qc1hIareXnKzWUYsORDZh+73QVE1JT8BmmAsySGVtPbMXh3MMYfutw/F/b/xMdiYh+\n5/Dhw9i/fz9mzJihyvENFwzIXZsLz3aeCB4TDK17w2/SfX3+a5QYSvDoTY+qkJCUwMJUkFky4z8/\n/wdfn/8aQ24Zgt7teouORERX7Ny5E0VFRRg9erRix6wprl0ionXXImRSCFx9Gr9J2htH3sAjkY9w\nj2sbxpd+FOSidcETtz2BlVErUaAvwIufvIj9Z/aLjkVEAAYMGABJkrBr164mH8tsMOPCKxdw4eUL\nCJkQgrCZYU0qSwA4X3oebZu1bXI2sj4WZhNoNVoMvXUoVkatRImhBC9+8iI+O/0Zr8qJBBs7diyO\nHz+OzMzMRv28bJaR/04+ziw4g8ABgWi/qD3cg/66nrJRx4as+jNWUgcLUwFajRaDOg3CqqhV0Ffr\nMXX3VOzN2sviJBJo1qxZ2Lp1K06fPm3xz8iyjKKdRciangXd7TpELo2EV2T9S0TIebAwFaTRaDDw\n5oFYFbUKNVINpu6eik9OfcLiJBJAo9EgMTERq1atwqVLl274+dKvS3HqxVPQemjRYXUH+HZTfolI\ncVUxAjztewi2M+NLPyqSZRl7svbgk1Of4KGIh/DYTY/xVgyRlZWVlWHOnDlYsWIFvLz+erWoP6lH\n3ht58O3mi1YxrRq0RKShvjr3FSprKvFI5COqnYOajm/JCiTLMvad3oeMXzPQN7wvHr/5cRYnkRXl\n5uZi+fLlWLFiBVxcajdBN+YakbMmBx6hHmg9vnWjlog01Prv1mNAxwEIbRaq+rmo8ViYNkCWZew/\nsx8f/fIRerfrjUGdBnF7LCIrOXHiBLZu3YqEKQnIXZcLjYsGIc+GwLVZ0956bYj5n8/Hor6L+Aez\njeOyEhug0WjQr30/rO6/GoFegZi2exq2ndgGSZZERyNyeJ0iO6FfcT/seHIHgscGI2xWmFXLEgA0\n0LAs7RgLU5A+4X2wuv9qtPZtjel7pmPLj1tglsyiYxE5HNksI/9f+Tgz7wzuee4eaMZp8OGXH4qO\nRXaIhSnYfWH3YVXUKrTza4eZe2di8/ebWZxECpBlGZd2XULW9Cx43+yNyOWR8O7ojeHDh6OwsBD7\n9++3ap5CfSECvQOtek5SFp9h2phDOYew9cRW3Bl0J57s/CRctda9ZUTkCMoOleHiuxfRvH9zNI9q\n/pfboLIsY9GiRRg+fDhuu+02q2Taf2Y/JFnCA+0fsMr5qPH40o+dyczNxHs/vofbW92O2M6xcHNx\nEx2JyOZV/lqJvA150N2pQ9CIoHqXiJjNZsyYMQPTpk1DaKj6b62uObQGw24dhiCfINXPRU3DwrRT\nx/KPYfP3m9GpRSeMunMU3F2U2Z6LyJEY82qXiLgHuyNkQgi0HpY9baqqqsKMGTOQlJQEPz8/VTMu\n+HwBFvZdyJd+7AAL0859f/F7pH2fhpua34Sn7nwKHq4eoiMRCWcqMyFnTQ4gA6GTQ+Hq1/BHGJcv\nX8aCBQuwcuVKuLur9wfpgs8XYFG/Raodn5TDwnQQJwpOYNPxTWgf0B6ju4yGp6un6EhEVicZJeS9\nmQdjrhGhk0LhEdK0PyDPnDmDtWvXYunSpapcAcqyjAX7F2Bxv8WKH5uUx8J0MCeLTmLjsY0I8wvD\n2K5jWZzkFGRJRsGWAlQcrUDwuGDoOukUO/aRI0ewe/duzJkzR7FjXpVfkY/tP2/HpLsnKX5sUh4L\n00H9eulXvH30bYQ2C8XYrmPh7eYtOhKR4mRZRvHeYlz6+BJa/b0V/Hqq87zxk08+QU5ODsaNG6fo\ncfdl74Or1hV9wvsoelxSBwvTwZ26fApvHX0LwT7BGNd1HHTuyv3lTSRS2XdlKNhcgICHAtD8sb8u\nEVHapk2bEBAQgIEDByp2zFe/fRUjOo9AC+8Wih2T1MPCdBLZxdl488ibaKlrifF3jYePu4/oSESN\nUnmqEnmpedDdrkNQbBA0LtZ7u3TFihXo3bs3evToocjx+MKPfamr+7gq3sFEBEQg6cEknC05i6Vf\nLYW/pz8mdJuAZh7NREcjskj1xWrkrMmBWws3hC8Oh4uni9UzTJs2DXPnzkVgYCA6dOhg9fOTbeIV\npoM7X3oeG45sgK+7L+K6xcHPU921ZkSNZSo3IXdtLmSTjJDJIXDzF7tZR01NDaZPn46EhAS0bNmy\n0ceRZRkL9y/kFaYd4S1ZJ5dTloPUzFR4u3ljYveJ8Pf0Fx2JCAAgVV9ZInLeiJBJIfBsYztvfFdU\nVGDWrFlYvnw5vL0b90JdTlkOdv66ExO7T1Q4HamFhUkAgLzyPKRmpsLdxR0Tu09Ec6/moiORk5Il\nGYXbClF2uAytx7aG7lbbfFEtPz8fKSkpWLFiBVxdG/4Ua0/WHni7eeO+sPtUSEdqYGHSH+RX5CM1\nMxWuWlfEdYvj23tkVcX7ilGUXoRWw1vBr5ftPyb45Zdf8K9//QuJiYkNfkt39derMbrLaAR4BaiU\njpTGwqTrKtAXIDUzFQAwsdtEtNQ1/lkN0Y2UHy3HxU0X4d/PH4EDA+1qX9WDBw/i0KFDmDJlSoN+\nbv7n87nDj51hYVK9iiqLkJqZCpNkwsRuEzlRgRRVlV2F3PW58O7kjaBRQdC62uco3h07dqCqqgoj\nRoyw+Ge4pMT+sDDJIperLiM1MxVGkxFx3eLQ2re16Ehkx6oLq5Hzzxy4+rsiZGIIXLytv0REaevX\nr0dkZCQeeuihG36Wb8jaJxYmNUhxVTFSM1NRWVOJuG5xCG2m/rxAchymChNy1+VCMkgInRwKt+aO\nNc91yZIlGDhwIO688856P3eu9Bz2ZO3B+LvGWykZKYGFSY1SaihFamYqyqvLMeGuCWjr11Z0JLJh\nUo2E/LfyYThjQMizIfAMs50lIkqSJAkzZ87ECy+8gLCwsDo/t+u3XQjwCkDPNj2tmI6aioVJTVJm\nLEzqhWUAAAE/SURBVMOGzA0oMZRg/F3j0c6/nehIZENkWUbhvwtR9k0ZgkcHw6ez42/JaDQaMW3a\nNCQmJiIg4PpvwK44uAJx3eK405adYWGSIsqN5Xjz6Jso1Bdi/F3j0T6gvehIJFjx/mIU7ShCy6Et\n4X+/c22IUVJSgoSEBKxcuRIeHn+dyckXfuwTC5MUpa/W482jbyK/Ih/juo5DZPNI0ZHIyiqOVyD/\nnXz49fZDi0Et7GqJiJLOnTuHV199FcuWLYNW+8e3f1mY9omFSaqorKnEW0ffQk5ZDsZ0HYOOgR1F\nRyKVVZ2pQu7rufDq4IXg0cF2u0REScePH0d6ejoSEhKufU2SJSz+YjEW9l0oLhg1SqMLk4iIyNk0\neLwXry6JiIhq8V4KERGRBViYREREFmBhEhERWYCFSUREZAEWJhERkQX+H2cSexJ/5Jb2AAAAAElF\nTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x11249aed0>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cost calculation"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def calculate_z(G):\n",
" length = nx.all_pairs_dijkstra_path_length(G)\n",
" \n",
" points = G.nodes()\n",
" Z = 0\n",
" for i in range(len(points)):\n",
" for j in range(i+1, len(points)):\n",
" try:\n",
" point_i = points[i]\n",
" point_j = points[j]\n",
" distance = length[point_i][point_j]\n",
" except KeyError:\n",
" distance = np.inf\n",
" Z += distance\n",
" \n",
" return Z\n",
"\n",
"\n",
"def calculate_t(G, distances):\n",
" A = nx.adjacency_matrix(G)\n",
" A.todense()\n",
" A = A.toarray()\n",
" A[A>0] = 1\n",
" T = A * distances\n",
" T = np.sum(T)\n",
" T = T/2 # matrix is symatrix, we only need one triangle\n",
" return T\n",
"\n",
"\n",
"def calculate_costs(G, distances, lambda_value=None):\n",
" Z = calculate_z(G)\n",
" T = calculate_t(G, distances)\n",
" C = T + lambda_value * Z\n",
" return C\n",
"\n",
"np.random.seed(1234567689)\n",
"random.seed(123456789)\n",
"points = generate_vertices(n=10)\n",
"distances = calculate_distances(points)\n",
"\n",
"points = [1,2,3,4]\n",
"G = nx.Graph()\n",
"G.add_nodes_from(points)\n",
"G.add_edge(1,2, weight=18)\n",
"G.add_edge(1,4, weight=25)\n",
"G.add_edge(2,4, weight=21)\n",
"G.add_edge(2,3, weight=22)\n",
"G.add_edge(3,4, weight=13)\n",
"\n",
"distances = np.array([[ 0,18, 0, 25],\n",
" [18, 0, 22, 21],\n",
" [0, 22,0,13],\n",
" [25,21,13,0]])\n",
"\n",
"T = calculate_t(G, distances)\n",
"Z = calculate_z(G)\n",
"\n",
"print T, Z\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"99 137\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Network permutation"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#* option 1: Randomly choose 2 vertices, if an edge exist remove it otherwise add it\n",
"#* option 2: Randomly choose an edge $i \\leftrightarrow j$, and one of its endpoints, say $i$, \n",
"# choose a vertex $k$ not adjecent to $i$ and replace the edge $i \\leftrightarrow j$ with $i \\leftrightarrow k$\n",
"\n",
"# TODO, use index instead of points, we can then use distance matrix rather\n",
"# than having to recalculate distance\n",
"\n",
"def permute_network_option1(G, distances):\n",
" G = G.copy()\n",
" \n",
" nodes = random.sample(G.nodes(), 2)\n",
" \n",
" if G.has_edge(*nodes):\n",
" G.remove_edge(*nodes)\n",
" else:\n",
" i, j = nodes\n",
" length = distances[i,j]\n",
" \n",
" \n",
" G.add_edge(*nodes, weight=length)\n",
" \n",
" return G\n",
" \n",
"def permute_network_option2(G, distances):\n",
" G = G.copy()\n",
" \n",
" i,j = random.sample(G.edges(), 1)[0]\n",
" \n",
" if random.random()<0.5:\n",
" node = i\n",
" else:\n",
" node = j\n",
" \n",
" # find k\n",
" non_neighbors = [entry for entry in nx.non_neighbors(G, node)]\n",
" k = random.sample(non_neighbors, 1)[0]\n",
" \n",
" # remove edge and add the new one\n",
" G.remove_edge(i,j)\n",
" G.add_edge(node, k, weight=distances[node, k])\n",
" \n",
" return G\n",
" \n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Metropolis criterion\n",
"\n",
"$$\n",
"acceptance\\_probability = \n",
" \\begin{cases}\n",
" e^{-\\beta(C_{new}-C_{old})} & \\text{if } C_{new} > C_{old} \\\\\n",
" 1 & \\text{otherwise}\n",
" \\end{cases}\n",
"$$\n",
"\n",
"where $C_{new}$ and $C_{old}$ are the network costs before and after reconfiguration, and $\\beta$ is a positive parameter that increasses after each iteration\n",
"\n",
"A good value for $\\beta$ appears to be $0.1/C_{MST}$, where $C_{MST}$ is the cost of the minimum spanning tree. Suggested increase for $\\beta$ is $1+3\\cdot10^{-6}$. As stopping criterion, use $10^6$ iterations without improvement\n",
"\n",
"## creation of new network configurations\n",
"\n",
"* option 1: Randomly choose 2 vertices, if an edge exist remove it otherwise add it\n",
"* option 2: Randomly choose an edge $i \\leftrightarrow j$, and one of its endpoints, say $i$, choose a vertex $k$ not adjecent to $i$ and replace the edge $i \\leftrightarrow j$ with $i \\leftrightarrow k$\n",
"\n",
"## Components of cost function\n",
"\n",
"$$\n",
"T = \\sum_{i=1}^{n} \\sum_{j=1+1}^{n} A_{ij}l_{ij}\\\\\n",
"Z = \\sum_{i=1}^{n} \\sum_{j=1+1}^{n} l_{ij} \\\\\n",
"C = T + \\lambda Z\n",
"$$\n",
"\n",
"Where $T$ is the sum of the lengths of all edges, and $Z$ is the effective geometric distance in the netwowr between all pairs of vertices. $A$ is the adjecency matrix, where $A_{ij}=1$ if there is an edge between $i$ and $j$ and $A_{ij}=0$ otherwise. #f\\labmda$ is a weigth parameter weighting travel costs Z to construction costs T.\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"def do_simulated_annealing(lambda_value, n):\n",
" np.random.seed(1234567689)\n",
" random.seed(123456789)\n",
"\n",
" # generate the inital random network\n",
" points = generate_vertices(n=n)\n",
" points_mapping = {i:(point[0], point[1]) for i, point in enumerate(points)}\n",
" \n",
" distances = calculate_distances(points)\n",
"# G = make_random_network(distances, n*2)\n",
" G = make_mst_network(distances)\n",
" \n",
" # calculate cost value for minimum spanning tree\n",
" mst = make_mst_network(distances)\n",
" Cmst = calculate_costs(mst, distances, lambda_value=lambda_value)\n",
" beta = 0.1/Cmst\n",
" \n",
" # evolve the network\n",
" Gold = G.copy()\n",
" Cold = calculate_costs(Gold, distances, lambda_value=lambda_value)\n",
" print Cold, Cmst\n",
" \n",
" failed = 0\n",
" while True:\n",
" \n",
" if random.random()>0.5:\n",
" Gnew = permute_network_option1(Gold, distances)\n",
" else:\n",
" Gnew = permute_network_option2(Gold, distances)\n",
" Cnew = calculate_costs(Gnew, distances, lambda_value=lambda_value)\n",
" \n",
" if Cnew<=Cold and Cnew != np.inf:\n",
" # the new network is equal to a better\n",
" # unconditionally except it\n",
" Gold = Gnew.copy()\n",
" Cold = Cnew\n",
" failed = 0\n",
" else:\n",
" failed +=1\n",
" if failed == 1e6:\n",
" break\n",
" \n",
" p = np.exp(-beta*(Cnew-Cold))\n",
" if random.random < p:\n",
" Gold = Gnew.copy()\n",
" Cold = Cnew\n",
" beta += 1+3e-6\n",
" return G, Gold, points_mapping\n",
"\n",
"G, Gold, points_mapping = do_simulated_annealing(0.0002, 50)\n",
"\n",
"Ag = nx.adjacency_matrix(G)\n",
"Ag.todense()\n",
"Ag = Ag.toarray()\n",
"Agold = nx.adjacency_matrix(Gold)\n",
"Agold.todense()\n",
"Agold = Agold.toarray()\n",
"\n",
"print np.all(Ag==Agold)\n",
"\n",
"fig1 = plot(G, points_mapping)\n",
"plt.show()\n",
"fig2 = plot(Gold, points_mapping)\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"64.111858053 64.111858053\n",
"False"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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SHU2FaSNrtq9h8seTOaLzEfzzntd5fe5Xu1xl5Z97FC8c2R/XiBEwYoThtM4w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\nUNxumDIFJk2Cq69WncZ0zlf7/HKplp29EpcrDbB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NLnhxzx6mdO6M1eBvcEKt6ur9HDvm\npEOHNNVRvCb/x3wi7ZEMjh2sOooQhmSq670A3B6PFEvRbBZLMB5PreoYXjXi30bg/NlJRW2F6ihC\nmIphK45/rnuF0VgsVjStRnUMr7JYLKT3TSdrdZbqKEKYiiELpr9uEwvjCQiwmm6FCdA5ojNR9ig2\nlW5SHUUI0zBkwTxaW0s7OVIivMCMW7L10hLSWLRhEXVaneooQpiCIQvmL5WVcqREeIXFEoSmmbNg\nBgYEMilhEvPWz1MdRQhTMG7BlOMkwgss3rx52g/1uKwHJ6tPUnyiWHUUIQzPkAWzuKqKLiEhqmMI\nYQjpN6aTtSZLnv0L0UyGLJh1Hg9BcqREiEaxW+3cefWdrPhxheooQhiaVB0hWoEBMQPYcmiLjM0T\nohkuOOlHCCGEaG2afIG0PPMQQgghdLIlK4QQQjSCFEwhhBCiEaRgCiGEEI0gBVMIIYRoBCmYQggh\nRCP8P/eaqKVjy59zAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1124bd8d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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kQYvreJfbRWKPxD1DwEl9kkjqkYTL3TF3q80XGbNsOBtehWlQ2Aoz67NZLN+2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\ncEN3wICRtwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x112b72990>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"G, Gold, points_mapping = do_simulated_annealing(0.2,20)\n",
"\n",
"Ag = nx.adjacency_matrix(G)\n",
"Ag.todense()\n",
"Ag = Ag.toarray()\n",
"Agold = nx.adjacency_matrix(Gold)\n",
"Agold.todense()\n",
"Agold = Agold.toarray()\n",
"print np.all(Ag==Agold)\n",
"\n",
"\n",
"fig1 = plot(G, points_mapping)\n",
"plt.show()\n",
"fig2 = plot(Gold, points_mapping)\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"296.488975155 296.488975155\n",
"False"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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8xDXlJ3n//CeccorpFCIinpIVhdk5GGRNXZ3pGN7x+uvws5+ZTiEi4ilZUZjdg0FW1taa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\nQw6B3/0O/vpXZ5ufuMh//wv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"text": [
"<matplotlib.figure.Figure at 0x114de7f90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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h21IxMYPQo8d8FBWtQEnJm0KPNpttwqRrOWidiEiNDIbQWLlgMkWie/fZsFgS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\nSUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x1124d8650>"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Application to shipping network\n",
"\n",
"* We can use the distances from port to port as calculated within the physical network\n",
"* We have OD data at the country level. We either use this directly, and thus include the hinterland connections in the network (but exclude them in the simulated annealing when modifying the network), or we somehow have to first distribute the country level OD to port to port OD. I would prefer the first solution. It is the most straightforward, you stay close to the data, and it does require only small modifications to the simulated annealing. \n",
"\n",
"Next we have to deal with the cost function. Gastner uses\n",
"\n",
"$$\n",
"\\begin{aligned}\n",
"& C = T + \\lambda \\sum^n_{i=1} \\sum^n_{j=i+1} [(1-\\delta)l_{ij} + \\delta]p_{ij} \\\\\n",
"& p_{ij} = P_iP_j \\\\\n",
"& P_i = \\frac{\\int_{V_i} \\rho(r) d^2r}{\\sum_{k=1}^n \\int_{V_k}\\rho(r) d^2r}\n",
"\\end{aligned}\n",
"$$\n",
"\n",
"where $\\rho$ is the population density, $V_i$ is a voronoi cell $i$, and $l_{ij}$ is the weight of the edge $i \\leftrightarrow k$\n",
"\n",
"If we adapt this to our case, we can drop the function for $Pi$ and replace this with the OD data that we have. I suggest that we use \n",
"\n",
"$\n",
"p_{ij} = P_i + P_j\n",
"$\n",
"\n",
"So $p_{ij}$ is the flow between countries $i$ and $j$ and is understood as the sum of the demand from $i$ to $j$ and the demand from $j$ to $i$.\n",
"\n",
"This leaves one problem. We consider making the costs for traveling a given edge a function of the total flow over the edge. Put differentely, we need to solve a network flow assignment problem with flow dependent weights. I have no idea yet how to solve this. I did find the following: http://link.springer.com/article/10.1134/S1064230708030143#page-1 but have not yet read it. Another options might be to frame this as an assigment problem (http://en.wikipedia.org/wiki/Assignment_problem).\n",
"\n",
"Regardless, the problem has the following form\n",
"\n",
"$\n",
"\\begin{aligned}\n",
"& mininimize && Z = \\sum_{i=1}^{n} \\sum_{j=i+1}^n Z_{ij}\\\\\n",
"& where && Z_{ij} = \\sum_{q=1}^r E_q \\\\\n",
"& && E_q = U_q * E_{q}^{length}\n",
"\\end{aligned}\n",
"$\n",
"\n",
"Here $U_q$ is the unit cost for a given edge $E$. The unit cost in turn is a function of the assigned flow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## iGraph version"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import igraph\n",
"import random\n",
"import itertools\n",
"import operator\n",
"\n",
"def make_mst_network(points, distances):\n",
" '''\n",
" \n",
" Generate a minimum spanning tree network using distances\n",
" \n",
" '''\n",
" \n",
" \n",
" G = igraph.Graph()\n",
" \n",
" nr_points = distances.shape[0]\n",
" G.add_vertices(nr_points)\n",
" \n",
" vertices = []\n",
" for i in range(nr_points):\n",
" G.vs[i][\"point\"] = points[i]\n",
" vertices.append(i)\n",
" \n",
" \n",
" possible_edges = itertools.combinations(vertices, 2)\n",
" possible_edges = [edge for edge in possible_edges]\n",
" G.add_edges(possible_edges)\n",
" \n",
" weights = []\n",
" for i, edge in enumerate(possible_edges):\n",
" u, v = edge\n",
" distance = distances[u,v]\n",
" G.es[i]['weight'] = distance \n",
" weights.append(distance)\n",
" \n",
" G = G.spanning_tree(weights=weights)\n",
" return G\n",
" \n",
"np.random.seed(1234567689)\n",
"random.seed(123456789)\n",
"\n",
"points = generate_vertices(n=10)\n",
"points_mapping = {i:(point[0],point[1]) for i, point in enumerate(points)}\n",
"distances = calculate_distances(points)\n",
"\n",
"G = make_mst_network(points, distances)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print G.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"IGRAPH U-W- 10 9 -- \n",
"+ attr: point (v), weight (e)\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print np.asarray(G.vs['point'])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 3.25199049 5.22846948]\n",
" [ 0.5933621 0.68397637]\n",
" [ 5.78370172 1.11405841]\n",
" [ 4.76435497 6.26038352]\n",
" [ 1.62359626 3.83873636]\n",
" [ 2.89050663 5.87360482]\n",
" [ 4.88025098 0.62224021]\n",
" [ 3.04458177 3.28840432]\n",
" [ 2.12320525 2.92496086]\n",
" [ 0.46547348 2.02556929]]\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def plot(graph):\n",
" '''\n",
" \n",
" visualize the network, taking the periodic boundary\n",
" condition into account\n",
" \n",
" '''\n",
" \n",
" points = np.asarray(graph.vs['point'])\n",
" \n",
" nr_points = len(points)\n",
" boundary_condition = 2*np.sqrt(nr_points)\n",
" \n",
" fig = plt.figure()\n",
" ax = fig.add_subplot(111, aspect='equal')\n",
" \n",
" # plot the nodes\n",
" ax.scatter(points[:,0],points[:,1], s=40)\n",
" \n",
" # plot the edges\n",
" lw=0.5\n",
" for edge in graph.es:\n",
" i = edge.source\n",
" j = edge.target\n",
" \n",
" # check whether we violate the boundary condition\n",
" # if not we can draw a single line, otherwise it\n",
" # is more tricky\n",
" \n",
" i = points[i,:]\n",
" j = points[j,:]\n",
" delta = np.abs(i - j)\n",
" \n",
" if delta[0] > 0.5*boundary_condition:\n",
" # we violate the boundary condition on the x-axis\n",
" xy = [i,j]\n",
" max_x, max_y = max(xy, key=operator.itemgetter(0))\n",
" min_x, min_y = min(xy, key=operator.itemgetter(0))\n",
" \n",
" \n",
" # left hand side\n",
" low_x = max_x - boundary_condition\n",
" line = ax.plot([low_x, min_x],\n",
" [max_y, min_y], lw=lw)\n",
" \n",
" # right hand side\n",
" high_x = min_x + boundary_condition\n",
" ax.plot([max_x, high_x],\n",
" [max_y, min_y], c=line[0].get_color(), \n",
" lw=lw)\n",
"\n",
" elif delta[1] > 0.5*boundary_condition:\n",
" # we violate the boundary condition on the y-axis\n",
" xy = [i,j]\n",
" max_x, max_y = max(xy, key=operator.itemgetter(1))\n",
" min_x, min_y = min(xy, key=operator.itemgetter(1))\n",
" \n",
" \n",
" # top\n",
" low_y = max_y - boundary_condition\n",
" line = ax.plot([max_x, min_x],\n",
" [low_y, min_y], lw=lw)\n",
" \n",
" # bottom\n",
" high_y = min_y + boundary_condition\n",
" ax.plot([max_x, min_x],\n",
" [max_y, high_y], c=line[0].get_color(), lw=lw)\n",
" \n",
" \n",
" # line.get_color() allows us to use the same color for both lines\n",
" else:\n",
" ax.plot([i[0], j[0]],\n",
" [i[1], j[1]], lw=lw)\n",
" \n",
" ax.set_xticklabels([])\n",
" ax.set_xticks([])\n",
" ax.set_yticklabels([])\n",
" ax.set_yticks([])\n",
" \n",
" ax.set_xlim(xmin=0, xmax=boundary_condition)\n",
" ax.set_ylim(ymin=0, ymax=boundary_condition)\n",
" \n",
" return fig\n",
"\n",
"plot(G)\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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5OaodVqtZx8xS36y+pqMBQNRilyzU0tWim1ferJsn3Gw6CgBELXbJQhlJGRqYM1AbqjeY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\ngg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10cbd7a90>"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print G.es[0].source, G.es[0].target"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0 3\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def calculate_z(G):\n",
" sp = G.shortest_paths_dijkstra(weights='weight')\n",
" sp = np.asarray(sp)\n",
" \n",
" Z = 0\n",
" for i in range(G.vcount()):\n",
" for j in range(i+1, G.vcount()):\n",
" Z += sp[i,j]\n",
" \n",
" return Z\n",
"\n",
"\n",
"def calculate_t(G, distances):\n",
" A = np.asarray(G.get_adjacency().data)\n",
" T = A * distances\n",
" T = np.sum(T)\n",
" T = T/2 # matrix is symetric, we only need one triangle\n",
" return T\n",
"\n",
"\n",
"def calculate_costs(G, distances, lambda_value=None):\n",
" Z = calculate_z(G)\n",
" T = calculate_t(G, distances)\n",
" C = T + lambda_value * Z\n",
" return C\n",
"\n",
"points = [0,1,2,3]\n",
"G = igraph.Graph()\n",
"G.add_vertices(points)\n",
"G.add_edge(0,1)\n",
"G.es[0]['weight'] = 18\n",
"G.add_edge(0,3)\n",
"G.es[1]['weight'] = 25\n",
"G.add_edge(1,3)\n",
"G.es[2]['weight'] = 21\n",
"G.add_edge(1,2)\n",
"G.es[3]['weight'] = 22\n",
"G.add_edge(2,3)\n",
"G.es[4]['weight'] = 13\n",
"\n",
"distances = np.array([[ 0,18, 0, 25],\n",
" [18, 0, 22, 21],\n",
" [0, 22,0,13],\n",
" [25,21,13,0]])\n",
"\n",
"T = calculate_t(G, distances)\n",
"Z = calculate_z(G)\n",
"\n",
"print T, Z\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"99 137.0\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#* option 1: Randomly choose 2 vertices, if an edge exist remove it otherwise add it\n",
"#* option 2: Randomly choose an edge $i \\leftrightarrow j$, and one of its endpoints, say $i$, \n",
"# choose a vertex $k$ not adjecent to $i$ and replace the edge $i \\leftrightarrow j$ with $i \\leftrightarrow k$\n",
"\n",
"# TODO, use index instead of points, we can then use distance matrix rather\n",
"# than having to recalculate distance\n",
"\n",
"def permute_network_option1(G, distances):\n",
" G = G.copy()\n",
" \n",
" nodes = random.sample([x for x in range(G.vcount()-1)], 2)\n",
" i, j = nodes\n",
" \n",
" if G.are_connected(i,j):\n",
" G.delete_edges(i,j)\n",
" else:\n",
" length = distances[i,j]\n",
" G.add_edge(i,j)\n",
" G.es[G.get_eid(i,j)]['weight'] = length\n",
" \n",
" return G\n",
" \n",
"def permute_network_option2(G, distances):\n",
" G = G.copy()\n",
" \n",
" index = random.randint(0, G.ecount()-1)\n",
" edge = G.es[index]\n",
" i = edge.source\n",
" j = edge.target\n",
" \n",
" if random.random()<0.5:\n",
" node = i\n",
" else:\n",
" node = j\n",
"\n",
" # find k\n",
" neightbors = G.neighbors(node)\n",
" non_neighbors = set([x for x in range(G.vcount())]) - set(neightbors)\n",
" k = random.sample(non_neighbors, 1)[0]\n",
" \n",
" # remove edge and add the new one\n",
" G.delete_edges(i,j)\n",
" G.add_edge(node, k)\n",
" G.es[G.get_eid(node,k)]['weight'] = distances[node, k]\n",
" \n",
" return G\n",
" \n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.random.seed(1234567689)\n",
"random.seed(123456789)\n",
"\n",
"points = generate_vertices(n=10)\n",
"points_mapping = {i:(point[0],point[1]) for i, point in enumerate(points)}\n",
"distances = calculate_distances(points)\n",
"\n",
"G = make_mst_network(points, distances)\n",
"\n",
"for i in range(10):\n",
" permute_network_option2(G, distances)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"def do_simulated_annealing(lambda_value, n):\n",
" np.random.seed(1234567689)\n",
" random.seed(123456789)\n",
"\n",
" # generate the inital random network\n",
" points = generate_vertices(n=n)\n",
" \n",
" distances = calculate_distances(points)\n",
" G = make_mst_network(points, distances)\n",
" \n",
" # calculate cost value for minimum spanning tree\n",
" mst = make_mst_network(points, distances)\n",
" Cmst = calculate_costs(mst, distances, lambda_value=lambda_value)\n",
" beta = 0.1/Cmst\n",
" \n",
" # evolve the network\n",
" Gold = G.copy()\n",
" Cold = calculate_costs(Gold, distances, lambda_value=lambda_value)\n",
" print Cold, Cmst\n",
" \n",
" failed = 0\n",
" while True:\n",
" \n",
" if random.random()>0.5:\n",
" Gnew = permute_network_option1(Gold, distances)\n",
" else:\n",
" Gnew = permute_network_option2(Gold, distances)\n",
" Cnew = calculate_costs(Gnew, distances, lambda_value=lambda_value)\n",
" \n",
" if Cnew<=Cold and Cnew != np.inf:\n",
" # the new network is equal to a better\n",
" # unconditionally except it\n",
" Gold = Gnew.copy()\n",
" Cold = Cnew\n",
" failed = 0\n",
" else:\n",
" failed +=1\n",
" if failed == 1e6:\n",
" break\n",
" \n",
" p = np.exp(-beta*(Cnew-Cold))\n",
" if random.random < p:\n",
" Gold = Gnew.copy()\n",
" Cold = Cnew\n",
" beta += 1+3e-6\n",
" return G, Gold\n",
"\n",
"G, Gold = do_simulated_annealing(0.02, 200)\n",
"\n",
"Ag = np.asarray(G.get_adjacency().data)\n",
"Agold = np.asarray(Gold.get_adjacency().data)\n",
"\n",
"print np.all(Ag==Agold)\n",
"\n",
"fig1 = plot(G)\n",
"plt.show()\n",
"fig2 = plot(Gold)\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"14387.0693821 14387.0693821\n",
"False"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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U1Lvans1mkzZtnhaTqanAMoF1Mlx3nzxevrrTR+J9e/Bb+XLvl2K328Vms8n6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\nd3+GXbtiyc0dCWhord1NpQgVi87Nc7svxu5IoVD846jk+xbMpKQkShX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7dvD66zBrFrhR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saMGUyaNJHChTNwc1sD3ATOoVJ1ISxMRrt27SS7BxcFk6CgIOLi4p67naNHj7Jnz+9kZ+8AWgOZ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\nRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1101c9310>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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NlzZxNrbiE6ZaYTC3bNnCF19sJzf3GhkZW9Dp+pOW9j1PP/0f0tLMXMLFyalC\nMT7FodfDihWwbBmMGgXvv1+x+oG7d+/mmWeeMZteFUWptCAgYBahoVNKLfqsUCgBRbEetq++akrh\ne6mMJQRTU4+iVjtgb3+nRE29et2wt29PZOTCsl5CHWaksgYzKSmJzZs3079Xf7JDs3HqcSf0Iz/m\nMtLFtJbfqFGxcs6dO4enpyeeniXkU37/fZ6J/ZIbiZbk/Kc+wWODMWSbz2iqVNZ4eLxBbOwqAHoE\n9uBQ6CGzya/NHI84zuWEy3zQ9oNKyfF39CdTl1myw5STk2mGb+a4eaVCyazHZrH9n+2cia5YPuBa\nYTAXLlxFVtZ0wA24fYP3RKQbmzdvNv+AlpamJ0ElOXLEZCS7dDE5uzg7V1zW0aNH6VLeaWkVYWFR\nDz+/UQVCPIrDwaE9GRnF33zjx8OqVaWvsOh08SQl7cbTs7DDk5PTo9jatiQysgJrvHVUCp2u4gbT\naDQyc+ZMpkycQtSSKPzH+hdoj4paiJ/3R6YbZPDgEuWsXbuW90pYrgVMM5O4OMa8l8LqbVbUG1yf\nkLEhGHLMZzQdHNqTl5eCVnuDRs6NuJFctu2LfzPnbp7jYMhBRnYsZnZfTga1L1xkugAKhSkW84b5\nP1ulQsmM7jPYd31fhWJpa4XBjIuLB26n9ekImNyPc3IaEF8VdSw7dChzRvyiCA83xVQnJpqy9Dzw\nQOXUOXHiBJ07d65VsYY2NoG4uDxNdPSyEs+rV68HKSnFp75SqUzOT9OmQV4xDoymfcvZJda3dHbu\nia1tc6KiPivzNdRReXS6iu9hLlu2jP79+5O+Kh3f4b4oLe88bvJjLlduMBnLEpZl1q1bx5tvvomq\nLEs3Q4ei+mIZ06fDnK+s8BntR8i4EIy6CgR3W1kV+WLt5zeS6OilgEnmf/Me+/Wk62y4uIGp3cwX\nC+1u645aqSYmo4Qk69bWVWIwwbT1NLXbVA6GHORExIly9a0VBrN7946oVLuAXMASuAYEY2Ozl06d\nOpl/wC5d4Hj5U1tlZ8P8+aYwsZkz4cUXzZONaOfOnfTt27fygsyMo2MnLC09is2pCabkBwZDyXk1\nHRzgo49MhrMooqIW4+X1Hmq1fYlynJ0fx8bmPqKiPi9V9zrMR0Xu8ePHj6PRaAhMC8TKxwrNfZr8\ntvyYy+T2pgMluMTHx8cTHh5Ou3tzRxaHuzvY2eGcGkL//rBiizW+I3wJHhuMMa+cRtPLq0hvTaXS\nEi+v94mW0Xy7AAAgAElEQVSO/oJmrs3MFrJQ24hOj2b5meV83ONjlArzmopB7Qax8s+VxZ9gZwfJ\nVRfnqlAomPTIJI5FHCtX9ZlaYTAnTx6NpWUoMB5oCDyLSvUELVp40a1bN/MPqNGUK1BQBLZtg8mT\nTUZy3DiTCHPwzz//cN9995Xt7bkGcHd/mezsYNLTi192tbLyJSenZOeHRo2ga1dYuTKPNWvW0KvX\nCzzzzGvs2TMbtdoJO7sHy6SPi8tT2NgEEh1dwpJOHTVKQkICO3bsoP9z/Unam4THGx4F2qOjl+Pt\nOQDFihWmbBclsHjxYkaNKmcR4kGDYMUKWrc25W3/9YoNvkN9CRkfglFfDqN5VyzmvdjZPYhIHh08\nfDkcWvuKOlSWRG0inxz/hLk956JWqs0u39HaERcbl+Iz8bi4mNVTtigUCgXjOo/jdPRpDoWUbS+6\nVhjMnJyGfPDBp3h7b8PKahsuLs/xzjtP0KNHh6pbprSzK5MX1t9/m2ZHDg6wcKGZE8ID3333Hf36\n9TOvUDPj5zeKhITN5OQUHUzs4tK7UNafoujSJYdPPlnGhx9e4uDBFzh+vC0//7yUceNOlWtZy8Wl\nN1ZWvsTElOJtV0e1k79vOWUKEXMiqD+5foHvcH7M5be/mkJISih6+9tvv9GyZcvyZ72ysTGV/zl1\nitdfN1UEisEG7w+8CZ0QihjKeK/dyvZTHD4+wzCkbSYmo+qqY9QEGbkZTDsyrUDlkargg7YfsCpo\nVdGNTZqYLRazJBQKBWM6j+F83Hl+vvFzqefXuMFMSoKvvoL+/W/yyCMdiYuLIjExnK++Wk7r1q3Z\nsWNH1QzctSscKz5IPzUVpk83RaB8+in07Gl+FWJjY3FycsLGxsb8ws2IQqEgMHAWERHzCyUqALC2\nrk9ubumZOdasWUtCwgFychahVL5Mv37hrF59kW3bDnHyZPkKzbq6PoOFhQcxMcV84eqoERYvXsx7\n772HdosWtxfcsKh3xyDmx1zm9oasLGjZslg5Op2Obdu28fLLL1dMkddfhw0bQITJk2/FBPtp8Hzb\nk5CJIYixDEazhBkmgFKpxsfnQ7Iyz6A3miFZQi2gpMoj5sbGwoaGTg25GHexcGPr1pCSYrpPqoGR\nHUdyNekq+67vK/G8GjWYer3JKM2cCdu2bSYgIKDA2+QLL7zA2bNnCQ0NLV5IBflLpWL3lOk8//yb\nrFu3jpwcU/UNgwHWroUFC0y+CEOGlPgSXClqIg1eRVEqrQgImEZo6GSMRTwclEoNBkPJy9zff7+L\n7OyBgIL/vDyX3fv7kZPjiVb7Blu37iy3Tm5uz2Jh4UJs7Npy963D/Bw5cgQnJycaWTQiLzkPx84F\nZ4YJCT/i6tLHFHP50Uclylq+fDlDhgyp+AqTUgl9+8L27VhammKkp08HTTNbPPp5EDo5tHSjWa+e\n6aFdAra2TXnANZBjNzZWTM9aRGmVR6qCt1q9xTfnvynccP/9podxSEi16AEw7OFhhKWGsetq8cHj\npRrMhKzC9eDMxezZMHQoKBTpqFQqrK0LT/8nTpzIp59+ik5XckxgeViwYDFde7zAxSAN27d3Z+jQ\nDbRp8wgHD2YVKuZcVaSnp5OXl4dzZWJRqhlLSze8vQcRHl7Ye8fJqQcpKSXvA1hYqIE8WrffR5rn\n74QFm+ItFYo81OqK7ZO4uT2PSmVPbOzXFepfh3mIi4tj3759vPnqm0R/EY3vCN8C7fkxl1uC4c03\nS3S9DQsLQ6fT0bhx48op9dhjJuc+nQ4vL3jmGfjyS7B7wA63l9wImxZW8lZAGY31820+Yf+VFRiN\nVZSdrBowipHJhyczqP2gEiuPmBsLlQVtvdpyMvKeFSbfW/dPFXnKFsfg9oO5mVlCvnEpAUAG7xks\nNzNulnRahVi3TmT/ftPPX3zxhWzZskV27dpV5LnBwcEybdo0s4wbGRkp1tZOAhHyEYvEhQQBo6hU\nu6RPny1iMJhlmFJZunSphIWFVc9gZiY5+aBER39Z4JjRqJfg4Ekl9lu3bp34+LSTD+a0E+yjxORO\nlSA2Nt4SFBRUKZ3i4jZKbOw3lZJRR2HK8rXT6/Xy4YcfSkZGhoROD5XssOxC54SETJbc4CCR2bNL\nlGU0GuWjjz4SrVZbQY3v4fJlkeXL839duVLk6FHTz2ln0iRkWogYjcbi+0+dWqZhxv48WMLC5lRG\n0zITMi3ErPKMRqNMPzJdgmIq9x2sKAajQYb/NLzw36FzZ5H582tEp+JMY6kzzPm95jPr6CxiM8y3\nAXvihKmSxxNPmPY1QkNDCQ0N5ZFHHiny/AYNGtCyZUu2by8+vKGs7Nq1C4WiL+DHYWVX/HyuAAoM\nBi8uXJheUmYus6HT6YiNjaV+/fpVP1gV4OTUA4XCkqSk/fnHFAoVICW+sb/++qu8NzGMTT8kQcaP\nKJWz0Wja8OGH79C6kuVp3N1fRqFQExe3vlJy6ig/ixYtYsCAAeQey0Vzvwbr+gVXitLSTmJj3RDL\npV9DcantbrFz506eeOIJ8+3rN2tmcty5ld3rgw9gzx7TIYd2Djg/6Uz4x+GVjqW0s/LEoHAkPf0P\nc2hdrSw+tZhHAx+ltVdVlIgqHaVCSc8GPTkYck88t0pVbXuYZaVU82BracuCXguYc2wOUelRlR4w\nMtKUX3TIENPvx44d45FHHiE1NZV69eoV2++5557j3LlzZtrPNC21uHT7h7h+ueCeAxgrXF2jvGzc\nuJFXXnmlegarIry83iIj408yM+9s2NvZtSYzs/g8jb9fHo5/u8Gsn7uMt9++zsCBSRw+vJn584sJ\n0CwnHh6vISLExW0wi7w6SufgwYN4enrS2K0xaUfTcH+x4D5GfszlzwIvv2zyYC2GzMxMjh8/zpNP\nPmleJYcONeWuxLTKOm0azJljSsrg2MGRet3rETGvBKe1MjwYOvt3JlTfhLi49RgM2ebSvMpZHbSa\nJi5Nyl15xNz0vq83e6/vLfjiYmlpyilbiyjTfMrGwoYFjy9g/on5hKeGV3gwrdZUK3HGjDvbA/v2\n7ePxxx8vUxzi7f3M3EqktXv66acR2YGFRQhNmpwldtFj8EoElu6rePXVZysst6yICBcuXKBlCR6C\n/xbq15/IzZtryc01rfk7Oz9OcnLRrtnXojZwLiGKt9tPp3fv3qxd+wXLly/m4YcfNqtOnp79EMkj\nPv5Hs8qtozAxMTEcPHiQfq/3I3xOOP6T/AudEx29HG/1CyjCI6CUJCRLlixhxIgR5lfUw8NkqMPC\nAFMM9ciRJh8KgHpd6uHQwYGIT4swmg4OkJ5e6hCd/DpxMurkrQTt/45iAZsubcLO0o7ejXvXtCoo\nFAqeb/Y8265su3OwDE5X1U2ZFyCt1dYs6LWAxacWE5pS/lmeCEydavJWu+3bc/PmTdzc3Lhw4UKZ\nluSsrKwYPXo0c+bMKff4t/H392fq1Ik8+2xb9u0D8r5Ds34MNoPqM2RsFXxZ72H//v089dRTVT5O\ndaBQKAkMnE14+CwMBi1qtQMGQ+Gwk4ysUA5dXsAH3TZXS/o/T883MRiyiI/fUuVj/a+i1+uZM2cO\nU6dOJWZlDJ5veqK2K+i4lZ0dghhzsf1shymXZAlcunQJJyenqqsFO3gwfHEn2UWDBib7/b0pCydO\n3Z2wa2VH5JJ7EnD4+JQYi3kba7U1OfocrK39sLYOIDX1qDm1Nzv7ru8jLTeNV1rUnpWurvW7ciLy\nxJ0QHT8/01J6du2ZsZdrx85KbcX8XvP57I/Pyp10eOlSeOklUyzwbb7//nveeOMNjh07RteuZVsS\nCAwMpHXr1pXazxwz5n2GDXuOHj20PP30zyz5pA+XRg5jXmIiOVVcXPrgwYM89thjVTpGdaJS2VK/\n/iRCQycjYsTS0pPc3Dv73Uajnu1/vMiT7X7AxqL64k29vN7GYEgjIWFb6SfXUQidTseOHTv4888/\nOVtE/dgFCxYwZMgQDNcMiF6wb1swraHcjrn83dPknmprW+xYRqORr776igEDBpj9OvLRaKBFCzh9\nOv/Qk0+aakTfvjznns5ommqI+vyurSdv7xJjMe/G0cqR1JxUPDzeICFhG3p9KXUfa4hj4cfMUnmk\nKuj3YD/WX7jlh/DAA6ZcvlUQVlhRyu3iYqmyZH6v+aw4s4KriVfL1GfPHnBzg4ceunPMYDCQmJiI\nu7s7ycnJ5QqvePbZZzl//jwhFYzRiY5eTvv2H7N27Rfs3v0D77//Hr4ODoz392dcSAh6YwUSNZeB\n06dP0759+1qVZN0cWFl54+nZn4iIeYSGerBgwav07fs669atY9vJl/D2GUSgc9Nq18vL613y8hJJ\nTCx/jOf/MufPn8fHpxFvvrmEn3+OoUuXvjzxxPP5WyH79++nfv36NPZvzM21N/H5sPCsMCHhR1xV\nj6D6+zo8+miJ43333Xe89tprFQ4tKjOvvw7r1xfYkxwxwlS3+nYWNpcnXbAOsCbmy1uzyjLOMAG6\nBXTjt7DfUCgU+PuPIzJynrmvoNKcjT3L4dDDjOpYznSD1UQbrzb8nfA3ufpcaN7cNLus5tCSkqiQ\nT6iFyoJ5veaxOmg1lxMul3ju5csQFGS6V+9m79699O7dG4PBgLICrqkTJ05k4cKF5d7PzM2NBhRY\nWXkVavO1tmaIjw+TQkMxmtEDyGg0IiJs3bqV//znP2aTW5uws2vJ9u1/M2/eEMLDVeza1YvVOz5h\n4+5jdPCpYLYWM+Dt/QG5ubEkJu6uMR3+TRgMBp566gUSE+eRkfErev0zaLXBHDigxdnZl44duzBt\n2nT8/PwI/zgc//H+KJQFXwDzYy6/OAljx5Y4XlJSEtevX6dDhw5VeVkmVCrTbHfnnRcopdLkUzFj\nhilOHsD1aVcs3C2IXRNbanq8u2nr1Zag2CAArKy8sLV9gOTkX8x+GRXletJ1Nl7ayJRuU2r1S/u7\nrd9lzdk1UL++6eXm324wAdRKNXN7zuWbc99wKb7oCsEpKbBypWnf8l6OHTtGly5dOH/+PK1atSp8\nQilYWlpWaD8zOvoLfHyGFNveWKPhFXd3ZodX3tX8wIEDNG/eAZVKjZ2dC3/9dR5DFS/51hQ3btxg\n4sQDqFRjcXb2x6thM+5/uB57lnZl6dKarS7i4zOQ3NxIkpJKTntVh+l7mZnpCLwKgIocwAJYjFar\n5dSpdE6f7s7MXstY/utq1B6FZ4VRUQvxO98EevUyOc2UwMKFC8ufXL0y9OgBv/1WoNacoyMMGGCq\nRHQbt+fcUDmouPljWpn30FRKFQa58/12d3+J5OSf0evTzKZ+RYlKj6qyyiPmpolrE6LSo8h0tDEt\nyZbB6aq6qNQnp1Kq+KTnJ6y/sJ4D5w/w4ov9sbFxxMbGkZdeepdRozKZObNwqbvg4GAaNmyIQqHg\nt99+K/P+5b0EBgbSpk0btm7dWqbzs7KuYGXli1pd8pe4tb093evVY0lUxcNoDh8+zLPPvsnly+MB\nHVrtq5w4oeSll96qsMzazM6dOzEYXmTXrjEoVVomTO7Dt5/sITdnGN9+W/P7iD4+g8nODi4QO1pH\nYZKSklAofGmsiucD3++YX/9HHn10I+AD6IAtuDIdv9yP+fLCYX744YcC/dPSTmJj8MTyzDVToHUJ\nnDhxgvvvvx8nJ6cqu56iiHziCVa274xG44SDgwcDBw7HxyeV++4rMPnE/UV3FBYKMi+UfS/S086z\nQMy6v/94IiLmmlP9cpOoTWTu8blVVnmkKhjYbiArg76qaTUKUelXDaVCyZi2Y3h+yktsO6kmJ+ca\nOTlX2bLlFfbseQ6lsvDbwfr163n91hptUlISrq6uFR6/b9++XLp0ieDgYsrE3EVs7Bq8vN4tk9yu\n9erRyMaGNRXMmD9u3GyysxcDzwLJgB85Odv45Zcj/PPPf2f9PBBAwaGl7/DzsUB0gQcBqTXLP76+\nQ8nOvkpy8oGaVqVWIiI8oH2AN1QB+DZfw+q0blwKf4SkJG/efrQlDZoG4PfQRt5x3cz6B06SFdCR\nT7Yu5VDIIU5GnuRs7Bmuhn+BbvVFEoe+izZPi1GK9gfIy8tj06ZN+c+B6iI2NpaWr71L8nkbLLL/\nICPjJOvWaenYsSd9++Zx4QJcvcs1w+M1DxCI31S2QvY9AnsUKPdlaemKg0OHGtsSuF155JMen1Rp\n5RFz4+/oT5Yui2RrMQXM1hLM8rqxds3XGA4+hfFhXyAcoh9CxJOsrK/45ptvGTr0Ts277Oxs8vLy\nsLe3x2g0Vmj/8l4mTJjAiBEjWLRoEVZWVkWek5Z2Anv7diiVZS8f38fVlfVxcWyOj+fFciaWvXjx\nT8A08+3c51VO7N4AWKNWdycoKIimTavfCaYqefbZZ5k8uQMwldbaMPau3QoN/0b91Ae81Ll6H4ol\n4es7nMjIRSgUKpycetS0OrUCo85I/IZ4EoMS2eezj5C3wzi58jhGbVsMuHPpwnmsiKeTYhLx6h5Y\nsx+d4Q1QKzC6H8VCZUF6bjopcZvIvKbmQmM12ZE/kR2aTXZedr7RvPvF6Y+tfxDYJpAZv80oUieV\nQoWNhQ0aCw02apsCP2ssNNhY2BT4+XabSllyPPeSJcvJynqOz5jMUFbzMZPR6b4iKqob27dvZ8KE\nlxgxwpTYwP6W469dSzsysgwkbE3A7YWSk5I3dW3KDxcLzrpdXfsSHDwOR8dOWFi4lPbnMBu3K4/M\neHQG9lYlF2evjQxuP5gvGvzIZK3WtDRbzLO9OjGLwTxy5DQ52X3g19eg5ziwyISwx9Bqe3P48KEC\nBnPz5s289NJLAFy4cIEHHyxb4eCSsLS0ZOzYsXz88cfMnDmzULuIEB//I40aLS637Nc9PFgRHc2B\n5GQeL4cnr5ubL1FRV6DVVbx9A7G1tSQrSxC5jI/PoHLrUdtp2LAh48ePYP78h/DTNiIaJbY3f6C+\nsyNJD8ZzOvo0D/k8VLqgasDPbyQREQsAJU5OJXtw/jejT9MTuy4W3U0dQe2COPfMOUZ3Gs0Y6zGs\nbrqGadOGERPbGB8s+BMn+kg66/Pa05GfICUQC4u9eBhWEHLkIZ55JoRMQ338z2UV3AwsgsjISCy9\nLJn49sTidTPqyc7LJlufjTZPS3berf9v/Z6oTSzQdvtng9G0h3i3cb7ti6BQKPgu8gcUXR/Cw3sC\nLvX+5umNKvbEv0lmZh+OHj3FSy+9xJQppmxACxfeSrCiVOL1lgcxq2+SuCsR12eKXxFTKBTIrRSR\nd+vg7z+B8PDZNGr0aVn+NJXmduWR8V3G46qp+ApeTeJm64baWkOMRS7eYWGmGpk1TUUS0N7LgAHD\nRan81pRMW6ETHh8pIKJWT5Dhw8cUOHf06NH5Py9ZskTi4uLKNEZZ2Llzp2zevLnQ8fj47ZKcfKhS\nsueGh8vvqallPn/p0mVi1aKx0Ooz6dp1izRsGCRK5WIJCGguhurK8F4D/P7777KrZVt5/vk35Ntv\nv5WcnBwxGo2y4swKWfT7ItEb9DWtYj7h4XMlJeW3mlaj2smOzJbQmaESOiNUrl+8LsN/Gi6/hv5a\n6Dyj0SiTX5spPdW9RaVqIrNxF9go03lNrK1flPvuaylpaWly4YJRvv12uMzovk+u/J5c6vgjR46U\nrKysqri0IsnOjpC4uI0SEjJF5s5tLq+/3kfatj0gzrYJMo+T0of1olK2kp49e8mJEydEr9fLH3+I\nLF16S8Dy5SI3TQUoopZHSeLexBLHW3d2nVxPul7oeFLSfomL+7HS11Na8nWD0SBjD4yVq4lXKz1W\nTZM6b4ZMGdNOZM+eah23ONunmj59+vTijOmMGTMooRmAP/6A48fbcfXq5xgMTQAvsI0H5WWs8uaw\natVn+XuUQUFBWFpa8sADDwCwZ88e+vTpYx7LDzRp0oQtW7bg6OjIrl27+O67jcTEhOPicgo/v+I9\nY8tCZwcHvoqNxd3CAvcSShPl4yNcSP6dxH2bcHS8iqfnF6jVIRw4sP1fVdKrvPg5O9MkK4OXly2i\nZcuWqNVqFAoF7bzbYWtpy5xjc2jp0bLKi9OWBQeHzsTHbwAUWFsXTuv230bmxUyilkSRE5aD5wee\nbHLexLmsc0zuOplGzo0Kna9QKOje5xFaZjSnzdsB1MtOxNb+OC0Uf9Nh/LOsWrUcBwcHFIofaaYV\nutxnz77YNvzwg8lDvnHjwg5/e/fuJSAggObNm1fJNRqNOjIy/iI+fjNJSXtISTlMXl4CdnZtcHd/\nEb2+BVOmfElExGiy87xxBJKAPqpsZm6eRlRcFOvXr+fKlYOEh9ui19vTzDHZNNX08sKhvQPJPyWj\nT9Nj07DoRBwuNi78HPwz7bzbFThuY9OIuLgfsLVtgUplV+FrTP01FafuJkep8PBwpk79mMmT53Ho\n0BFEdLz4+Wv8vPQQ3366iZs34+jSpSOWZXlm1UKsM7K5eOkwrkYbnDtWX8KXYm1fRaysiMiVKyKj\nR4t8952IXi/y3XfrRaNxFgeHHmLv2E3Uva1l48ZNBfpMnDhRcnJyRETEYDDI5MmTy2n3S+fcuXNi\nZWUrNjZ9BebKc889KO3be8mNGzcqLdtgNMqo69clpJTSQzeSbsjYA2PFaDTKzZs3Ze/eH+T330eX\nXEbov4Vjx+7UbSuCLF2WTDo0SXZc2VGNShWP0WiUsLDZkpr6e02rUiUYjUZJPpwsN0bfkJi1MWLI\nMci52HMydN9QuXDzQplkhEy+NaNJSBBZuLBAySudLkVuXB4hMnKkyF3395kzIhMnisyaJRJyq3tW\nVpaMHDnSbNcmIpKbGycJCTskJGSaBAdPktDQGZKQsEtyc+OL7fPpp0vE2tpRHByeFlf7XjLYYpjs\n2bBHrn90XdLOpImI6XO7ePGiPPbYSZn7whDZ8tZbcvbs2fzvcMSnEZJ8qPjZ9IRfJkhCQoLk5eUV\nOJ6Xly7Xr39UqWfB7Rnm+fPnxd7eXSwsRgv8LDBF6Ggp1H9fIEbgilhZvSatW3cRvb72rOyUiytX\nRPtCXxk3rXO1Dluc7Su3wYyMNH0Rli8XuWX78snMzJTdu3fL7t27Zd2f6+Rk5Mn8tpSUFJk1a1b+\n7xcuXJCNGzeW5xrKRIsWHQTmCEwWa+tMef/9caJULpQOHXqaRX6uwSBDr12T2Hsv/hbxmfEydN9Q\n0el1+ceMRqOEhEwxy/i1kby8PFmxYqW0atVN5rj5y+wRYyQpKanEPtuvbJfJhyaLVmemuoeVwGg0\nSmjoTElLO1XTqpgNQ55B4jbGyfVR1yVhd4IYDUbR6rQy+7fZsuLMinItjYdMuWsJ8LYVzM01tYVM\nltyZo0RiY4vsm5Ii8vnnppfrN96YIyEh4RW+JqNRLxkZ5yUqaoUEB0+S4OBJEhm5VNLSzojBoCtd\nwF0kJibK5s2bZdeuXRK6OFSy/skSo9EokcsiJWpFVL5By8kRmfhWtKTOWyQbN26UiRMnyqRJk2T7\n9u1ybcY1SfktpYBcg8EgU6bMEssnNGJpVU8cHT3l44/nFzCQKSm/SmzsdxX+HG4bzM6dnxBYcauu\nrAhtegr3PXvnd0TAIHZ2bWXv3r0VHq9GycwU6dFDvpr2dJlf8MxBpQ1mUpLIzJki8+aJpKeXPqDe\noJcR+0fk/75s2bICBZOXLVsmscV8ySpKaGio2Nh4COgFdkmnTs+Kh0eYQI5YWTlKfHzxb53lIUuv\nl8FXr8rh06eld++XxNu7iXTs+Lhs2blFBu0ZJOk5hT+g/1aDaTQa5f/+7z+i0XQT2COzeEOsrN4W\nf/+mkpxc8n5WZFqkfLj3w2r9IhSH6aVmmqSlna5pVSqFPlMvUcuj5MaYG5J2Ki3/+MHgg/LRTx9J\nRGpEuWWGzwuXvPRbM6U5c0RWrBC5cUNSU3+X2CPjRb7/vsh+BoNBYmNjRavVyt9/X5ahQ5fIuHEi\nc+eKREeXPq5OlyJJSfslNHSWBAdPkpCQqRIX96NkZ0eW+xpKQp+tlxtj76xApZ5Mlesjr4suxWSE\nI0Ly5ECXGfkTaIPBIKdOnZKPZ38sq7qtklUfrZJr166JiMioURNFo+ks3P+Z4Bkk8LdoNO1k+vSC\nxaVDQqZV+DpCpoWITqcTpdJCIFtQ5QjdpgstXhK4ISDyBDHyKqHSliRx5mOZML7k4u61mq5dRTd1\nkoz6eVS1DVmcwSzVS1arhS+/hLQ0U8L/skZXqJQqOvl14lj4Mbr4dyEiIqJAweSbN2/i6elZNmFl\nJCcnB6VSAyhpRkN0lsHE5QBYolBYVqos2N1oVCoej4ri8X2/oD/0CORMI+ZmEK+seodpPcdj37so\nF+7aEYtobo4ePcpvv11Cqz0PWAInyc2dTXz8m3z++QqmTCneE9LXwZclTy7hsz8+40TkCQa0HVBj\nMZsKhYKAgGmEhU1DoVBib9+2RvSoKLp4HbFrYzFqjXi86YGmkQaAJG0Sn/7+KW282rDoiUUV+nxt\nH7Al61IWjh0d4bHH4OBBjGEhxOu202iHBSx+rVCfdeu+YezYaWRmZmE05hIY6M+pU8epVw/i401V\nQuLiTMmAHnsMFAohO/s6aWm/k51tSoWmVtfDwaEDfn6jUKmqLnG/ylqFTUMbMi9lYtfCDscOjmga\nawifEY776+74tXNA38DA2rXw7rugVCp5+OGHefjhh5GJwqWxlzj97Wm+yvmKpUuXodevg9Bu0Goj\n3ByJVruBTz/tyPjxI/PD3vz9xxISMoFGjZZU7J5XAIFA/UlgtIOIzhASCZwBGuJHNj/iSwBaHrWA\nlmcfIHRqKAhYelli29wW2xa2WLhYmPGTrCKUSixQ0c69FScjT9LRr2PN6VKalR0//s4eRHkxGA3y\n0U8fyaFDh2Tfvn35x41Go0yaZP43HoPBIB4eDcSa/fIZH4pCnSn83xBBsV0CAx806x5i06btBedt\nwtBrgkovPDZJcN0ntrYuRXoAhoRMLULKv59x4yYKTBMQeYiLsopZt5aCDkjLll3LLOdk5EkZtm+Y\nxLQ/2nwAACAASURBVGeaZxWgophmmpMlPT2oRvUoK1nXsiRkcoiEzw2X3Pjc/ONGo1E2XNwgEw5O\nkCRtycvjpZETlSPRK29NCfV6kSFDJHHhq5I5/0PTHs09bNy4STSaQIE/bt0Ly8XCopf07Nk3/xy9\nPkuSkn6VX36ZK6tWTZINGybLtWvfilZ7o0b2+g25BrkxuqCfQ4El2ilT5LPPRE4VsWpvNBolZEqI\nnN10VuzsmgocFpguvNBDaL5RsMgSW1t/CQ0NLdAvNfWkREevEoPBIOHh4ZKYWLL3rdFolHOx5+Tr\n/l/LhIMTpOOb3UVlOeau5dc9Ag1ETYy8RcitY0dFo3HOX10zGo2SE5sjyQeTJXJJpIRMCcn/F70q\nWlJ/T5W8tLwS9ah2+vYVWbBADNeuykc/VW7/tywEBVVihvnJJxU3xkqFku4B3flq8Vf8sOJOMO/l\ny5erxEtOqVSyZs1nXHj2eT41DEf0Z1CeBVWXfqyevstss5eMjAxu3LgE+mdgSy6az1bBsk5oE59C\n5dCAoKAgunTpco9uNhgMWlQqjVl0qC3Y29tiaXkTV100/VlOLq/yCuFsJhl7+7J7Anbw7UAz12bM\nPjqbJxo9Qc8GPatQ6+IxzTRnEBo6BYVCiZ1d7Sz0nXYqjcStiVg3tMZ/gj8qzR131PDUcBafWkzf\nJn2Z06PitWNvY+ltSW70rdUZlYo8qzxUl4Ox7dQTfH0LnT958ly02hXAQ0AKEImT0xeoVO04c2YQ\nLi4uqFS22Nu3p3v3QajVDkRFmQqJpKbC00+balVW52KD0lKJ5n4NGecysG9lWiFSKBT4fuhL2qk0\nUr5OYeBPeYyfbUFAgKkm9W0UCgUBMwK4MuoKvrlW/EMr4FHYKuB5DjpNJ9c6jj/T/8Tb4I2lyuSx\n6ujYgZMnP+f//u9jrl3LwWDQ0rHjI3z//Up87/pcw1LD2HJ5C0naJB70eJCOfh3p36M/sffH8vCR\nR0lJuUBmZi9sbC6h1yfRXl7lH4sHsFf/DVxiy5YNuLm55etq5WmFlacVTj3upCQUEXIjc8n6O4uY\nL2MwZNzKiasEm0AbbFvYommmKXCfVRuOjmBlhTI4hJ4NenIw5CC9GvYy+zA3bsCqVVBSThmFSPEZ\nxhUKRaUTkEf/P3fnHR5F1f3x7276phEIRSAQElBEEESkiIIoSrEi2BUEfPWlE4GEkBBaqAlFhUhH\nUESKNOm9SFcInSSb3SSb3tv23fn+/pgQErJpmwR4f5/n4dHszNy9szNzz51zz/mepCR8NvsznF55\nuthgRURE4IMPPkDTksUxa4vdu6FQKjH9n9u4dSsanTq1Q/MPG2BI1yF44anKi1RXBYPBAFfX+jBA\nCXydD9kdIwa32g5BkOLYsVU4duwA2rdvX+qYtLTf4eLSGc7O/78UfpRKJQY82xmj9e8iAKvQCRrU\nRwGetd2Mrgva4ONJH1e7zc03NiMuNw5Tek4pHlweNaQApTIIjRp9AReX9pUf8AigQGTty0Lu6Vy4\ndXdDww8bQmLzwKqYBTNW/rMSefo8+HX3q9X6o8oQJVrNbgWSSFv0FhptTob0+i2LVs3W1gFmcy4A\nJwzu/gPcnk1AenpbxMVtxaxZ32Hw4I/K/R6TSSwHeP480KqVWOWoEv32WkMwCVBMVaB1eNkUG7N/\nMJTGr+E6qBHmbHPDsmXAw9XIKBA/tF+BiJhkxJhmAHAAkAsnp6H4amgrfBk4BEdij4Ag+vr0hW2y\nLd4eMBjDhr2M5ct3gDTAxmYRmjffigvXj2NPzB7E5cahpXtLDGk3BA2dRaOnnKlEq5mtAIjKaTt2\n7MDFi1fh4+OFoUO/Quq8VNx+6TZkLjK8+eabcHKy/j6gmdAqtdDc1kB9Rw1BW6TaZCuBU5siQ/qM\nDFL7OhR0DwwE6tUDnJ3BMWPgd9gPS/strbUXoNRUsba4pyfwn/8ATk4V2L6KXk0r2VwlFixYwN8v\n/s5DMQ9SDerCHUuSTEggp5cNrjELZo47MK5WIzLf/noUpRPXEJ7aYpdI/fphnDjxKSqVoTQYRDeY\nIAjctOlXDhjQlr16ufGll17nkSNHaq0fj51793j9jb50dahHB4f/0EY6iRNtp/Krz75l3MI4Jq1M\nssqFIs+Sc8z+MYzOjK6DTlcNQTBTLg9gYeGtx9YHUgxKSV6bTPlkOXNO5Vj8PSNTIjl2/1heT71e\nJ31QTFdQEASmpf3BnPmfk6++Wu6+zZs/S+AsAbIvUoqeDxOdnb157dq1Kn+nXC4G5E6bRv77b22c\nReWk/pbKvEt5ZTfMnUtBq6XqJxWvzkzk7FmW7+nCfDWn1jtGX7vOdHfvSkfHevzqq2+L0+lI0mAy\n8GDMQfr8py3x2kC2eXkT339/OWGnJtpvof3AZhy6aigV2ZbXwioSLhBMAmOnxVbvpK3AbDCz8HYh\n07alUTlbSUWw6NaNC41j+o50qu+paTbWkkDL5s3k2LHF6Uyn405zx+0dNW42N1eMYZs3T4zmLkl5\ntq9OpeuNRiNyc3Ph39UfEw9NhEQhQVxcHJKSkspIR1UXk8mEjRs3Ys2ardDrDfjso/6YGK+E/bJl\nZfaVSqTw7+mPBX8vwKw+lrUrq8P5vDz4TB6DZ774FirdCujt+sDR8TpsbWPx3Xcn0LSpB5KSlkMi\nscMff+gxe/Z2yGTB6NEjGWfOeOH994dh8+YIDBr0QY378li5cQP47Tc8f+ggbqekYPv27dBqtXiz\nyZvwdvVGo48bIf9yPuQT5WgR0AIOTauuBelb3xdL+y1F2PkweLl54cvnv3zkAUESiRQ+PnOhUASi\nSZPhcHZ+9pF+vzHHiNT1qTBmGdH4i8Z4amTZGq5aoxZLLy5FPcd6WNZ/WaVaqtbi4OUAtTIDBakH\n4evRC+Bv5e4bFDQRfn7LoNM9h6bQAdDBzi4Yzz7bslql/Hx9geBgUUZ0505gyxagXTvgk08AWR2t\nbDT6tBFip8TCretDr7VPPQVJaiqaj/WG68U8dJ0fi+0bWuKj4aWDZnbslOHt3X3w6Zb1KHy9EG1e\na4NGD0VK2tnYoX/r/jAengwYv0BMlxN4tesueOdeR1zUKJjufo62r3iglUeravc/70Ie3F92r/Zx\n1UVqJ4VzO2c4t3Mu9blZZ4bmngYF/xQg/Y900ERAAtg420DWTgbn9s5wbOlYpoZqhbRqBezfDzQQ\ndXh7teyFSYcn4b1n3kNBXgGcnZ3L1RC3hE4nul7T08VA1qfKPlblU5EFrmRzpfz55588f/484+Li\n2LhHSzp19KWj44d0cGjE7t3fYF6ehZlcFTCbzezf/0M6O79KYBeBQ5xm24Fvt2rHwsLCco/bemsr\nD8YctPZ0SJLrk5O5IlHM0zKbzTx8+DDDwsK4bdu2UrNIkszKknPECEd+9pk/XV0zOWzYjOKAmBYt\n2v1vCxlcvkwGBZHlyPzF+MXQbBC3mdQmKqYrmLbNOhnE44rjnHx4MnO1VZcmrE0EwUS5fDLV6nuP\n5Pu08VoqZyqpnKOkLtFyvi9JnlCc4MSDExmXE1fuPrVF3sU83lq9mPrvR4rXvE8fspznNy9PYNeu\nV+jk5Mlv7QPo6NiAffq8y4yMjBr34/Zt8UUjJIS8c6fGzVkkbWsac/9+6F47fJj8++/iPw1ZBv7+\ncjSvbnnwGyQnP9B0MBvMjJkYQ43igVfLaDbycuJlLj6/mMHHg9lq5DPEc98SLsl0Rw6nYxYB0sWl\nt0WJz/tU9IapnKmkSfvkiRQYC4zMu5TH5PXJVIQoxDfSYAUTwhOYdSiLWpW2/PFQpSLfeYecMaP4\nowUbF7Lea01ob+9GR0c3Dh36HfMryXc0mcgNG0h/fzK6EsdVebavTg3m5Mmiuk2nTq9QIp1P9B9P\nIIKAkvb2I/jll/+xqt3Dhw/T2fk5AnoC5Ks4zeFYSyen97ls2Q8VHjvlyBRmqKv/4OrNZgYrFDxa\nSUJ+Sc6dO0c3t5fo4ZHKr76azR0hnfkUNAQE2tu7MudhP8D/CqdPi0m5FRj8gsgCJq0pnWyXdSiL\n8ilyGrKrl2ROkhnqDE48OJHnEx6PIo/ZbGRMzPdUq+vORZx/LZ/yADlVy1QVRipmabIYeCyQf9z8\n45FNurISz/HW4HkPRpopU8iNG8vsJwii6E96uihkcu7bc1RZiKStKWo1uX692I0tW8qKqNQEwSww\nZuJDWrA3b5LbSuvAGo0Cl/ZSMSY8kWazQD8/sqCgZB/VPDX8FJfsWMLg48GceXIm99zbw0y1GA17\n+vRpymRNCVwkQE5HCD0ks9i0aWvq9XqWR3kGUxAExgbWvTu2NjFkG5hzNodJK5OKjagiWEHVjypm\nn8imPk0vWrrevclZs0ijkfv376eTUzPizU8IGy2BVDo4DGXv3gMtfocgkLt3kxMnkv/8U7V+lWf7\nahT0QxJmmmEWzDDTDIFC8f9HRUfh3IVz6Na9G97q/xF0+r8Br0uAeiNQuBSwy4Ct02DsP7QXtJXC\nIJhgMJugF0wwCmYYBHPxZ0bBBINghkkwwyiYceDIUUTedAKkL8BVmo6BsVHYevl3AHvRo8cqnD9/\nsNw+5+nyMPv0bIS/FV5lF1+6wYDZcXHw8/KCbzUW0GNiYtCxY29otQkAbDCyezgaX/RGJtoi2XY6\ntqVtgVP9ussvqxOOHBFdsZMmVRrGGOsfC+9Z3rBxeuAmNGYZET8vHvUH1Ef9vtXT1CWJNVfXIF+f\nD7/ufnXmfiwPQTBBofBHs2Zj4OTkWyttkkTOsRxkH8qGSycXNPqkUbkBFCSx/c52RKaKVUXqOz0a\nTWJBMCL28tewmfkefA59In7411/Arl3A+vWl9l25EujQAejZU/w7blYcvGd412n//v1XdNk6OopB\nQj4+NW8zY3cGbN1t4dGnKJI0JwfYuBGYOLHUftnZwE+j8vCSOgPGYZ6Qdb6CS0mXoDPp4GDjgK6e\nXdE8ojl8A33h2LxsPco//tiGsWMnQ6+3Q2NjNsY28sT7p4+gVavy3bElg35KUni9EOq7ajT+tLGF\no/63MKQboL6lhvq2GsZ0I7BpE/B8Bzh0b42xvyzCEflgqBu0A3yOA1dGAzBBJvPBuXN7S7n9z5wR\n74133wXeqEY1v/JsX6VrmDNPzaxwu43EBjZSm+L/SiVS2EhscPjQYbzV9y3si94Hia8ToDkreiOH\ndgDSTgAmG5jZHr/GX4eLowx2UhvYSW1hJ7WBvY0tbCU2sJfawN7GAU52NnC3sYVD0fZb0hu4HiuA\nup54Nz8Je+u3BPpOA7RK2Nar+HzcHd3xftv3sen6JgzrNKyy08fVggL8lpaGBT4+cHk4LK4S2rRp\ng7ZtW+PGjTCY3b7HOs/PEI7FWGW/G1/0fxppy9Mg6AXU71cf7q+6PzGFlstlzx5ApQImT67S7k2/\na4qo8Cic8DgBrVaL/v37o0OHDvAN90X6H+lQzlSiRUCLUga1IiQSCb598VvcSr+FCYcmIKBnALzc\nvWpyRtVCKrWFj8/CIqM5Dk5O1o/MglFAxrYMFFwrgEdfD/iG+1Z4/RPyErD0wlK8+8y7tZIqUh2S\nVD+i6V4J0l8sISbu4yNaC7J44nTpkhjlet9YPipefFH8l5cH/P47oFAAr7wCvP122UjWquL5vifk\nfnLUe62eeF3q1RONZglytDk4l/E3FK8kYduOlgjafhnu9k0xaeCkUhHK5sVmxE6ORcuglnB4qvRa\n26effoyPPhqM6OhouLq6onlEBGBl9kDW/iw0G9vMqmOfNOwb2cP+dXt4vF40YYn8BxzWHnpDGtIT\nkjAQz8Ilyx6H+25BsvMhAB1gtHfH7DOz8Xzu80hLAyIjAS8v4Nn3gbMS4OypWuiYNa+llaFWqxlS\n5MwvLCykTFafgILAHmLY6aJ1vMNs2bK9VS6lmzdv0smpMQfiNtsjt6i9Qso8n+OIlSPof8Sfm29s\nrjAqdu6ZuZRnVSzI/kdaGsMTEmiugdsrISGBPj4d6NR9EB17BNBb5sWNzVsV+9vNRjMz/sqg3F/O\n+IXx1MZrrf6uOmXLFnLt2modsn79L/yPzSg2cPyGdnbjKZM15ciRY4qvuTZBy+ix0cz/pwpaiw+h\nMWg4/cR0/nnnz2ofW1PMZj2joydQo1FW+1hjvpGqH1WUB8irdN4ms4kRlyM498zcx6K7q9HEMn7z\nO+Tt21TOVtKsE9elk6Oj+Y9vG772VBu2a9edCxas5fjx5jJeeuVM5SPvsyCIy41Tp4oyfImJ1rWT\nuT+TWYceLMEUTP2e229v5/QT0xl0PIjh58J5UXWRkwMMXL2a3L9PYOLyRCZGJJYZ10xqE6NGR1Gf\nWr6rlaTo+l23rsJdynPJxk7933LHVosvvyR37SIjIujr24nAMaJPKtEjg8BNAsG0t/fksmU7GBAg\nDlXGGugvlGf76sRgrlu3jrdv3y7+e968MMpkbQkMIYZdJ/ArnZyacPfuPVa1T5LLJi7nF7YjaGs7\nkRJJIJ2dvfnVV/8pvlGvpVxjyIkQBh0P4gXVhTI3sN6k59j9Y6nWqnnw4EFu3769uDanSRAYGhfH\nPbUQpECKQUqTTpxg+MaNvHLlCvnnn2QJ5aPiPmXoqfpJRbm/nKm/pdKkfkIW79evF0O7q0FcXBwd\nHeuzPm7zc8QVTWry6ezciVu3PqhiI5gFJq5IZEJ4glVh6Hvv7eW0Y9NYqC8/2KsuEI3meGq1VRMT\n16XoGBcaR8UMRalAkIq4kXqD4w6MY2RKZE26ajWCIDD60lc0LVlAkkzfkc6CyAImJyfT07MFF6Mz\nx2EygSO0td3BAQOGl3nOHofBLElGBrl0qbjWefhwuTFqFonLiePeL/Yy+Fgwg48H89LI/oxMiSwl\nXL9/P7lzp/j/ISFiKkzexTzG+D3Qor2PscAoGs2MSozmpEkVdtSSwdTINWViBv5fMXYs+csvZEgI\n16/fQMf2/YhPrheNKwba2Cxi/fo/cMiQnzl+/CTu2rWrTKWY6vBIDWbJItH3Wb16NRs18qLDVyPZ\npffbPH7c+oLOJq2JUWOieO/OPc6ZE8qgoOm8dOmSxX31Jj133tlJ/yP+DD8XzsS8B9PNjQc20vF1\nV7q59aSr67t0dKxH/znzOS46mrcqiLa1hhkKRek31SlTREX7ciiILKBihoKxQbHMPZ/7+CJqIyIe\njAjVYN68+bS3H02A/ARyutvnFN3cW/jqq2+X2b/wdiGjxkRRHVP9wsJJ+Ukcu38sr6VUPcevNjCb\ndUVGs/yglsK7hYydFsv4sHgaMqsW7KQ1ajnvzDwuv7T8sRbcTkv5ndmzPiANYr/VUWqmbErhuHGT\naGc3gTMRwlBMK7quOjo7t+LFh7TjHrfBvI/ZTB49KkZILlkiBiWVRBAERmVGcc2/axh0PIjBx4O5\n+p/VvL7jOtP3FO1cIkqTFIOEJ5XQA9fryTFjxIAkQ5ZBLBd2uXQksTFfNJqGrAruhVOnyD3lv0xY\nMpgJSxLEAJn/h6hUKu7o/QbXNW7BrW2f519nzrD7r7/TUVafrq69aGs7h82areOtW+KEQRAEnj17\nlgEBAVy4cCGTk5Or/Z2PzGBevnyZ27aVrSq+cuVKxsbGcltaGm+WDCWzAkWIwirXZWpBKpddWEb/\nI/5cf2k9nd0bEC+OJ5oVaV62iKOt33T+trv2azWGPCzIm50tPr2VYNabmb4znXJ/ORPCE6hLqsVw\nwBIcOHCAr746kF5ez3HQoC947epVsfahhTfhcvsqmCnPknPPvT3sHdSH6NOL6DOdtp9Mo+tTyqKB\nNYqtW39DS+X5zHoz4+bHMXltcrUnCGbBzGUXlnHF5RWPdHJhMmkZHT2OOt2DiZggCMw5m0P5ZDmT\nVifRpKm60TupPMkJBydQmaOsg95WHYMhh/Ltb5IlhAYEk0DFDAV9fDoRuMSZCKEfAilDJgHSxmYS\n580rXZXjSTGYJUlKIhcsMHPEuDROXLWDQUVvkJsiN5VJ0REEMWJWMAviK2SJeys4mExNLd12crJY\nykwQxGMtuWiNuUZGjYoq8wZa4kvFUONysGQw/7+6Y+/cuUN39yZ8z+YDrkY/zpD0p+33gQxbsZ6L\nF+s4bFgi//67/LSqtLQ0hoeH09/fnydPnqzy2FCe7at14YJdu3Zh1qyy4gBKpRI+Pj7IyM9Hgl6P\n9i7WVRzP/CsTLh1d4NiibMRZZTR2aYwJ3SeAJIIjgqHv1ggwugLeS4DUeUArKUw/tMXKK6vwxfvv\nW9W/KuPhAbz2mhjC9eGH5e4mtZei4aCGaDioIQxpBqT/kQ59sh6uXVzh+Z4npA6WIyrlcjnu3r0L\nX19ftGvXrsKuLF/+MwICFkGjmQPgeSSqjuH5fa9AEjYPHQcMKLO/QAHxufG4nXEbdzLuQG1QgyCk\nEim863njuYbPYUrvSfj3B38UaoJh+m8CClK8AQAODj+ic+c3MXs2YDYDTZqI1SqefVY815ZTWyLv\nQh7kfnK0mNoCDk2qlpAslUgxofsEXEm6gomHJmLaq9PQ2KXuowVtbByLA4G8mgWi4JAd8s7lwf0V\nd/gs8CklXVcROdochJ0PQ4dGHWpV9staEm8Gwyu+OzDkQcShxEYCCIC7uzuAVLyIfxEKB2ggqgjY\n26fBza109CZrKK1ZWxjNRlxLvYaz8WeRo8uB5CUJ+vZ/Hvp7b+DO4cHw9gbe+0KULS2JRCKB5yBP\nZO7KREN3d6CgAHBzw+nTwDPPlNaUBcQk+A8+EKOFR42SoNmYZsi/lI/YSbFoOb0l7DzsYOtui1Zz\nW0E5TQmfBT6wdbN9+EtFMd1z56oUQaVP0cO+yeORkKxrxo0LRH5+ABR8A+6YjYT2XjBtGIXAgt24\neNGE77+vOMipUaNGmDRpEsxmMw4dOgR/f3+0atUKX3zxRdF9XD1q1WBmZ2fD1dUVdnal1S9iYmLQ\nurWoz9jCwQHXCgqsal+frEf+xXz4zK1Z3LhEIoFjtiPMRz9AW+lg+Hb2xwl8DO1vxwC0RkpKWo3a\nfxiTIMDG0gA4YAAQFCSG9FWhbpp9Y3s0n9AcJFHwbwHi58YDECP6XDq7QCKRQK1WY8iQYTh9+izs\n7bvAYIjEiy92wN69W+Dh4VGmTbVajYCAYGg0FwA8DQkEzMQO7DdOw461f2DfsPdxJ+MO7mTcQYFe\nvG4SiQQt3VviuUbPoXfL3nB1KFvOjE2JHj3W4LRDAAzHBgLIgr39Bnh6nkBExKz7oh1ISQFOnhRV\nXEgx+PL1193RKtQZCQsS4PqiKxoOaljl3/qlZi+hrWdbzD07F328+6Bf635VPtZqDPZwOj4JN//5\nC95fvInWi8tqkZYHSfx590/8m/wvJr88GQ1kDeqwo1UjL/ccnE5EwX78fovbx//3c6SPGYUE00Dc\nQ30ATgAiAezHRx8tfpRdLRedSYfLSZdxXnUehYZC2Ept0fmpzhj+wvDS6TgdAXwCKJXA8uViOcMP\nPxSjbu/j/qo79g3Yh3P31uFK+M9o1ncgbGxmY/16ywNuz57A7dvA2bPAq68Cbt3c4NTGCfGz49Ho\ns0Zw6+oGOw87eM/2hmKqAj6LfGDr8tBQ/MEHwNSpVTKYWXuz0OD9x3/f1DYkcerUAZC/IQFmSFwK\nsCvhSzTKs4PObScyMtoA6F+ltmxsbPD222/j7bffhlKpxE8//QSdTochQ4ZUS32qVl2yy5YtY0JC\n2QK1c+fOZW6uqJxhFgTOsKJemGAWGD02mqbC2lnTOR8RwWV2DfkNVtIeOgJZBMZQKh3FESNG18p3\n3Eeu0XBjecWy8/NJP78KRQAqwqQ1MW1rGuX+YsL7tx+Np6PjZ2Jh2aIFcXv70ezff7DF40+fPk0v\nr8585pnL7NVrO3/o8Rqf7zmK6BNEyes2jLgQwQuqC8zTVV+VqVCnY5+t29iuXXe2atWRkycHVlrE\nOzaWXLNGFBEKDiZXT8nnxbFKGnOrv4C/5eYWzjo1izpj3bixDZkGxi+KZ2xQLAvvFtJkKmRU1Bjq\n9VVTNErITaDfIT8ejT1aJ/2zBrPZwOhdr1MoJyZAFXqX+q8ncMJ7H/Ez+/rsJHmOMtlndHLy4I4d\npSOWBZNA5WzlI+g1WaAv4GH5Yc4+NZtBx4M4+9RsHpYftljMvSL0evKPP8QQg3XryMJCctQoP3Z2\n/ICjsIqv42dKJEfp6vpicdFoSwiCuOJSslB2sYt2xQMXrT5Nz6jRUZYD/FatIu/eLfPxwy7Z/zWx\ngqoiCALt7Z0JJBII5SzbT/kqTrEPjtPN7SWeOHGiRu3rdDr+9ttvnDJlCn/99VdqtQ+W+cqzfbVm\nMM1mM6dMmWLx86lTp5b6rMx6XhVQLVOxILLytc+cnBzu37+fp06dosnSQtmVK+SkSTSvW8dunV+l\ng8PnBKIIZBIIp729jL/88ku1+1cRBzIzeSG3Alm3EyeqHYVqiezobH5uO5Tf4gZ7Ip02MNPOTsem\nTa+za1cX3r27lErlbCoUIcX/zp//lh9/7Mnu3ffSx+c6X/G5wraf3iT+c5O2vV9nYQ0kVFYlJdUo\neEoQxCj7JfOMHNU1i/7DtNyzRxRNriqKbAVH7xvNexm1J2unUWioCFEwbm4cdSmlf58HRrP8iYHJ\nbOLPV35m6OlQqg3VD3KqSxJuhLAwfLzljdevM3vQbGb/Ja7XRv/+O6d37MhVq1ZZrOVoLDAyPqxq\nUcTVJUuTxT339nDGyRkMOh7E+Wfn83TcaWqNtZeWdfcuOWFCFm1sFhHI51Rc4VBsJEBKpaH86KNh\nFR6vVpOjR4tGuCTFUbRFile6ZB2jxkSVXevWaETl+YcoaTANOQbGL6ib3/hJ4IMPPqVU2pHAXc5E\nCJsikf/BRHp4NKXBUH3FsPKIjIxkcHAwZ8+ezdjYWOsN5syZMxkTE1PRbiRFuTpLVThOnz7N/fv3\nl/qsugYz71IeE1dUnkwVFraUjo716Ob2Bl1dX2DDhi15/nyRlNqFC+JC+q+/FifoFBQUcMKEXPbQ\ndwAAIABJREFUKaxX7yk6OLiyX7/BvHXrFtetW8eff/651oJHlqlUzKrs4s6YYX3CWBEqlYpOTk3o\nBANHI5qTPU/z889D+dZbG/nyy97899/9NJtLP72CINDHpwOB9cVVV3ohjQNsFvH1cVMYrFAwWKHg\npby8av0eGpOJ/vKK81yrgyAIVG1I4a5vk7hgrplBQaI639Gj4sBUEQaTgQvOLuCGaxtqdE3z/8mn\n3F/OxOWJNBaU/8ZrNOYzKmo09fqyaUk3025y3IFxvJr85BWo1qjljP+ppzhQP8zu3eS8edSnaqn6\noSgqOD2d+/v3L7e9+2lStUFKQQq33drG6SemM/h4MBefX8xLiZdoMNXeoGmJtWvX0slpZNFzcYdL\nEVj0nChYr17TSo9XKERPycMYsouiaIsqo+gSdYweG12c51rMokViJFHJNksYzJRfU1hws2ZBlE8q\nx48f57hx49iyZTu6uLzBmehLJ4fhnGvnVKMsi4rIy8tjRESE9QYzPz+fP//8MydNmsTdu3eXm9sy\nZcoUmi3kDk2bNq3MMdUxmMZ8I6PHR1c60B04cIDOzj5Ecc4fCezlW7J61I0ZIybeW3rjLIdTp05x\n2rRpFWo6VpUqna9aTU6YYLVrliTVKjW/cRzFYbjAxtCW+B3iKJPVL1eY/tatW2zQoDldXPrRxmYK\nXVw688umIylfKRo8ndnMP9PTGSCXc1F8POO1lc/if1CpGFOZJbMCjVLD6LHRLIgsoE5HnjxJhoaK\nLtz588lz54qzIMpwSnmK3x/6ntma7OLPzGYzY2Jiyg09FwSBmQcyGTMphqmbU4sF5SvDaMxjVNTo\n4jJvWqOW88/O50+XfqLR/IRVtGdRzuWeN2k6e/zhDWR4uPj8FKGYrijedrJXr3Lb1CZomby2+iH9\npJgDuSlyE4OOBzHoeBB/vPhjmRzIR8G2bdvYyOVNTsYiTkMo3TGx6Jm6xGbN2lapjUOHyE2byn4u\nCGIO8n0XrTZey+jx0TTrS9xjWVniDV6CkgYzdmrs/3YRBwuYzWaGh4cXe/p0Oh03b97M/V26ceHC\nMBZUEEFcW9TYJftwbktKiTW5hIQELlu2rNSxOp2OhYWFnPFQ7hJJzlIqaariRZb7y6uUX9Snz3sE\nfim6mQX2xkkuwUR+7tCdq1eurNJ3PYxCoeCYMWMqXXerjCpPEM6fr1TlwxLqKLWY67cwnisXrqZM\n9jSBUwRMBBIok73AkJDQCtvQaDT8/fffOX/+fB48eJBms5kpm1KYtrX0ely6Xs/liYkMkMu5ITmZ\n+RYmUPlGI6fF1t26imASqPpRRdUyFQXTg/soP19MJJ85UzSgS5aIWREl53FZmiz6HfLj2fiz3Llz\nFxs39qFM5kUHBw926/YGFUXXyqw3M2VjCmMmxTDraJZVg5LRmMuoqFE8Lt/HCQcnlFvf8Ekg7V4E\ns5cOLf2hWi16ZS5fLvXxfYMpVGIw1VFqpm5OLXf7fQRB4L2Me1z9z2pOOzaNQceDuPqf1byXce/x\nGgOzmZpVaxgmfZktcbxobJlFoIBOTgM5Z868ytsoYvHi8mt6lnTRahQaRk+ILj0xmzFDvLmLuG8w\nTRoTlbOUVpzYk0tGRgbHjx/PyEgLYh3z5oneDws2pbap1TXM1NRUhoWF0d/fn6dOneLcuXOZVZSE\nf+LECbZv34NSqR1tbR353nsflym7siYpiaoqvKWkbk5l1uGqVQdp3fpF2uBvfoSt/BWf82NsoQRm\nAsGcOXNmldqwRH5+PsePH88bN25Y3Ua1XNBz54p+nCqQdzFPzPVbmVQqaGDDho308nqWEomEzs4/\nc9my5VYPPMnrkpm+0/KE4U5hIecolZwaG8tDWVnFk6Cw+HgmVOH61pSCGwWMGhNFTaxl5ZyMDHL7\ndrGmeFCQqMEQFUWazQKnbZ9Gu9dcCOkxAgIBHaXShfRp/ByVYUrGTo2t0pp5ReRocxhw5HuGH36D\nen125Qc8JgyGbMpXdxEjXO6TkCAuwFl481bMUFAwCczPz+e5vn3LbbfgegHT/yx775gFMyNTIvnj\nxR+L3yAt5UA+Vk6eZP7w8QwbcpFr1pyns3MDOjsPoVQ6hI6OTdm373vV8j6ZzWK1DAtLvSRLu2jV\nMWqxPN595SulkvzhQRWm+wYzY3cGcy88npJ3dcH58+f5/fffl1/28ddfxWo5M2bUyBNXFeok6Mdk\nMnHnzp3s1q0bV6xYwWPHjlEma0hge9HbzRja2w/jSy+9VmrAPpyVxXOVRG5oYjVVj7DLzuZvXXpw\noaQnuxaVyrn/punq+hL37dtXtXbKwWQyce7cudxTgfpGeWhMJs6Lq8ZAoNOR48aVK40lmAVm7stk\nzPcxTNuaVqGcnCAI3Lat5tXqk1YmMWNv+TKBZkHg8exsTouN5VS5nP+992jqRpKkWWdmXGgckzdU\nLnagUokVqYKDydat/yRcdxB9ggj3OHpCx6+g5Df2M7lteVnhjeogCAJ33N7BqUenMkOdQYMhm1FR\no2k0PpmDm+KvQdQfL6HmdP68GCZazqQnZVMK1dFqxsbG8tp775Xbbt7FPGYeyKTBZOBF1UWGnQtj\n0PEgTj8xndtubWNKQTmR44+TqCjy++95acp2Tg8Wipdzc3NzuXbtWoaGhnLkyJFWTUBzc8VHu7yV\noZIu2sK7hZRPlj/woAQEFK813DeYsYGxoqDC/ziCIHDFihWMiIio+Hc9fZo8dkyMHq5hvEdl1FmU\n7NatW3n58mXGxsbS1/dZAv0IRBJIIbCEgJkuLs/wzJkzxcfcLSzk7w9LZJTAbDAzanRUaV++Je7d\nE0e/0FAqzpyhi0tDAj8TUBNQ0d7+G7Zv381ytKwVbN68mUuWLKnWw3KjoIDb06pZOPnqVXLFilIf\nlXQRZh/PrnIfCgvF4JiaovpJxcyD5UyPSxCiUHBFkcv2B5WKKbVZqLACcs7kMGZiTJXlwby9OxG4\nRUgNfPnFJfz86eV06TmHePkNfjTrI55LOEdVnqraa2aqPBX9DvnxiLx0AJzBkFVkNKsvNF+X5Cr3\nMWX5uw8+2LSJ/PHHCmfw+dfymb4jnRcvXuTdoUPLGFatUctTylNc/sNyhoeHc8bJGdx7by+zNFWv\nJfvIycwkQ0KoD/+BM6bqWJHY1zQLkatV5fZt0bNYEXmX8hgzMYa553Mp95eLRvHateKFUMUMBc0G\nMxXBT66Lv6rk5ubSz8+PFy5cqHxnpVLUtT5+XJQPrEPKs301Fi64cuUKPv74YwBAXp4WwGIA1wCn\n/wKOs4EcKYzGNxAZGYlXX30VAODl6Ig9WVnltqkKV6H5hOaW6wKSwPHjwOHDQJs2QGAgIJOhFYAz\nZw5jwoRgnDs3Dvb2Mnz++ZdYvPgQbGxqp27i559/jkuXLsHf3x+zZ8+GUxVqY0ZrtXhaJqveF73w\ngniOUVEwNfVF6oZU6JP1aPRpIzQZ2qRaTTk7A1ptqSpMVtF8bHOolqkgtZPC442yAggAkGYwwF4q\nxehmovpGkl6PLenpSDUY0NnVFe81aACnWroWD1Pv1Xpw6eiChPkJcOvhBs/3PCvcv337ZxAffxJD\nBh8G5a1xL/oVFEbXh0uDo+j6XVfk6/NxMOYgUgpTIFAopVjjKfOEl7sXvNy84OXuhYayhiCINf+u\nQYYmA6Gvh0JmV/qa29nVh7f3LCgUU+HjswC2tmXFHh41gmBE+snpaP3lSVF2ae5coEsX4KuvKjzO\n+VlnZO3LQoZDBjx9fFAov4PzTpm4nHQZepMeDrYO6NasGz5o9QFcGrrAvXv1FVUeGXo9sGoVkJ2N\nu2+OR8TWBggIAJo3L/+QZs2aITExEc0r2qkc2rUD7t4Fdu8WtQks4db1gdCBSxcXKIOVaBXaEZLf\nfgO+/BIAkHcmD+69n+DftQpERkZiw4YNCAkJQYMGVRBeaNYMSEoSC1sePQr07l33nXyIGhnMO3fu\n4Nlnny3+u3nzFsjMjAHwJaD7DOiyAEjXwj75Fry83izez9nGBhqz2WKbOSdz4NDMAbKnHzIyWq1Y\n7O7uXaBvX2DhQkBa2qC+8MILOHNmP0jWmbRYt27d0KxZM0yePBlBQUFoWkntuhiNBgPqV7/Qr/6T\nUUj9eAOEvrZoMrIpnLytLzT94ovA1aul1UuswWuiFxIWJUBiJ0G9XmULj/6clITxJQaRZg4O8PPy\nAklEFhZiYUICjCT61a+PV9zdIa3la2TrZguf+T7I3JMJxTQFWgS2gK2r5Vs8KGgiZLK3oFQGI+pa\nf7yJZERK18DNIRnjPhkHBwfLknwkkaXNgipPBVW+ChcTL+JOxh1cSb6CTk064SmXpxB2LgxNXJqU\nMqruDu6wt/eEt/dMKBRT4eu7CDY2zrV6/tUl6eB3aNp0FCQk4OcHjBolahRWQq6Qi6iUKKwpWIPX\n7G3Q5JgKzQYNxeSXJ8PR9oFkZcaNjCrXOn3kkMCffwIXLoDffod1Z59G3iVg2TKgsjldv379cOTI\nEYwYMcKqrx48GAgNBdq2Ff9Zws7DDr5LfJG8MhkkoQxRwqXzi9g75FMkXHkeDXbXw2vrXkF9PJoi\n4rUJSfzyyy/IycnB0qVLIZValvcsg50dYDSKs5nExLrtZDnUyGBu3boVAQEBxX8HBo7BiBGBUKs7\nA2wBnJ0GdPgW7H4TAwcOrLQ9Y5YRWfuz4BtWopp9Sgrwyy+iZtVnnwEjR1baTl3rcDZv3hxhYWEI\nCQnBp59+ii5dupS7r1YQIKvGW5UmWoPUTamwdbdF08Wvwe7SXsDbr0b9HTAAWLy45gYTALymeCFh\nnmg09b56/PbbZiQmpqBNz25w7NwZ9R+SRQTE6/GCqytecHWFURBwNCcHQUolXG1s8FHDhmhT3Tfw\nSvB83xNuPdwQFxIHz8GeqPdKWePeosUNDB06Dt98sxom2U/w0L+Lri/FYMuWo+Uay/vn4inzhKfM\nE+0atsMPl35AW8+2+GHAD7CVio+TWTAjTZ2GhLwE3Mu8h6OKo8jT5RVXcTebHVF46w10aPk1vD3a\nFBvWkkWH64LY2FgcOXIETk5OGNCjJZiohHOfqcD06cCsWUA5E7uUghScTTiLm2k3QRAejh7oZtcN\n3T2649v3P4Dz+fNAy15ljhN0AqSOVRwMHyWXLomT70GDkB20GKGhohGrauFrX19frF+/vkZdmDpV\nnKPMmwe4luNskEgkaDaqGfIv5+PWjFv4feEltDbHIIEjkJqYjmm93sKvv67Ehx8OqlFfHiVqtRqz\nZ89G//79MXz4cOsakUoBQajdjlURqw1mYWEhJBIJZCUGu48//hgxMXGYO7cT7OzaQxBS0Fgjw/wZ\nqxB4MhCz+8yGs70z9Ho9lHFxOBYbi1deeQWOjo4gifjQeLSc0VI0eFevAtu3A56ewLffAlV5ZX+E\nyGQyhIWFYcmSJYiNjcUnn3xSo/byL+UjY0cGHH0d0XJaS9jIiozsvyeBGzeA55+vQV9rxy0LiA9x\ni2ktcHL4SUzaGogoSTtotc/ATnsLLWeF49tT+1G/gjdqO6kUAxs0wMAGDZBrNGJHRgbWpaTA29ER\nHzdqZNHgWoN9I3v4LvFF6i+pyDuTB6/JXsUu/pSUX2Bn1wBvv/0tkpLmQKlUQrNCgw5LOlS5/b8T\n/saOOzswvtt4+HiU1ja2kdqgqWtTNHVtiu7Nu1s8vkCTgMv3giGFD64kXcHOuzuhNWpL7SOzk5V6\nS23m2gx2NtX/fUjCz28qVq1aD4nkPdjY5GLt8D1o7jAWLZI3A0uXAra2xfvG58XjTPwZRGdFAwCa\nuDRBr5a9MKTdEEgl4m+YcDYBxlwjZK1bi8+pBQStAKnTE2Qw4+KAiAjxWVq6FGf+lmLPXHG+YEFm\nuUJsbGxgMplga2vdEGprC4SEADNmiJPZip5Lt65uCMqYj1dN38AOb+JZyLCP/aHRPI2RIwfh3Xff\nKaPf/SRy7949LF++HEFBQXjqqaced3esQsKSizMPbyyaEVtizZo16NWrF5555pky2/Ly8vDvv//C\nw8MDnTp1gkQiQUpBCuacmYMuui7wGzENus/+A8ctJ0DGYN26CPTMfRnObR3hnnEKOH9efB0aMkR8\nDX/C2b17N+7duwd/f/8y7oUZSiVmtWpl8TiSyD6YjZzjOXDr5gbPDz0htX1ogDGbgYkTxafK3vqK\nBDt3Al5ewEsvWd1EMQaDAY0beWNQ3macQRfENrUFumfCft88fPGFBOvXR1S7TaVWi+0ZGcg2GtHD\n3R0D6teHfVVdNZWgVWiRuDQRT333FNQN9wKQonHjT0t/f4gS3rO8K/VO5OnyEH4+HG092+LzDp/X\nyJuh1ycjIWE+fHwWwcam7Nul2qBGYn4iEvISoMpXIbkgGUazEcCDZ7OeY71io9rCvQUauzQuNmr3\n2blzJ4YODYFafQZAffj1CUDHnIs4f/tfTFfcQ6FjIc7En0FCXgIAwLueN3q17IU29duUe36Z+zKx\ncddGTFo3SRz1LVQoSlqRhIYfN4R9w8dcSSMvT1RWd3AARo+GyV6GJUuAhg2Br7+2bhK5f/9+eHh4\n4OWXX65R165cEYe7CRPK30ej0cDNrT7M5lyMkijQtk00/KPfgR62cHN7AYcORaBHjx416kdds23b\nNsjlcvj7+1s9yQAg3mszZ4r324wZNX8DKIdybZ81kUKCIFjUja2M21G3advfiWixkvg4npAZCfzL\nLvYdGddnkqhWfO5cnefY1AWRkZGcOHEiCwoKqNFo+Msvv/DL0X4csG49Ex8KgS4V8XqsChGvcrko\nY1MDNBoxob82OHr0KN3culMCgd8glm3fP0/Ymwio6ORUr0ZtC4LA87m5DFYoOF2h4OVqSvKV265J\n4J31Ebz96xKLofhJq5KoVVWcO7rzzk4GHA1gemHNhCxKotMlMjp6HE2m6uetCoLAbE02r6de576o\nffz5ys+ceXImQ06ElPrX5vMOxNPfE02u0avBee6a2IA+3cbT5s1OfD30Df56/ddq50Bq47X8+Z2f\nxT9CQizukxCeQGP+Y1Q1MhrFFIRp04rzSePjxSLPd+7UrOnCwkLOnTu3FjpJbtggZkuUR35hPqVP\n2xGvTyYCVvPDQasZhkl0QiFdXZ/llStXaqUfdYFOp2NwcHCNU/uK+fFHMcE6IoIsr6BFLVCe7bPK\n1F+8eBHdu1t2NVXEL2s3A8dHA51MgOwimjd6Ht/F7UJnQ2usfSoHcxaus6Y7TwQdO3ZEYGAgJkyY\ngMOHTyMvrzUKvQbD9oYKT4/riJ07N+ONl9+wLuLV11d0SV++DHTtalX/nJwAna523LJarRaAKwgJ\n1rb7B++oknDP0AOAK4xGXY3alkgk6OHujh7u7tALAvZlZeHPjAw0sLPDJ40aoYVj9eugAkBWzl64\n97ODa/qXkE+Qw2uyFxxbPmjL+TlnaG5r4Ni8bPtJ+UlYcmEJ3vJ9Cwv6LrD63Czh4NAMXl7+UCgC\n4Ou7CFJp1ep/AuJv5eHkAQ8nDzzf2LLLXqCAbVMOAoVtgHoKdPHahlkXvoHi9kSgsD66vmjAl89/\nWf1+eznAMa/ia2HWmh+PS5YEDhwQIylHjBCXdCB6Wa5fB8LDAStvo2KcnZ2hVqtrobPiW25QENC6\nNdCypfiZSTDhVNwpnIo7BQkkeL5jD0TWfx7Y9x5cb2djMd5BGD7BLw6F6Ny5c630o7aJi4tDWFgY\npkyZAm9v79pptEULICFB/LHkcrGg7iPEKoO5d+9ezJkzp9rHxcYmwmTsDVwZgSaSP9DTfi+S8Al+\nxB10ztphTVeeKBo1aoSCAhOSk+uDDAKat4HpthtcNb2x/f0t8J3si2Yjm8GplRXBHd98I7pmO3QQ\nrZ8VdOsm2txu3aw6vJhevXrBYBgKNN0Nh3pK2J//AgAgkazDG29UHtxVVRykUgxu2BCDGzZEhsGA\n7RkZSNDp0FYmw+CGDeFaRddOVtZB6PVJaNZsNNAUkIXJoFqkgqOPIxp/0RhZWVmI2LMKeVs0uP1c\nFMaNG46BAweCINZdXYfUwtTi9fe6wNGxOby8JiE21h++vmGQSmvPhSmVSPHeG29BsewGXksOQdy9\nPoiEBwATXFz+RJ8+4Va1W8pVK5WKSwcPBbfRxLJLDHXN9evAhg1Av37i2qxEAo1GDK7p1s2i59hq\nGjRogMzMTHh6VpzCVBVCQgC/SWYMGv83ziQeA0G85v0aZr42E7kmAfHuN6Ea9h10cbsAfIpcx+OY\nLr2IyP4DID11SqzC/hgwmUw4d+4cNBoNXn755eKizH/99Rf++ecfLFmypMIgumrj5QWoVOI69OnT\nYi3hR0l1X0szMjK4YMECq15zf/jhR8pkHxcr8fRABl9BOp2cvubcuda1+SQhCAIdHFyKRBtWsOcL\nYzgCcn6KeDZzeZ1Hj9aw7mFCAjlrltWHa7W1J8M4c0kobQe60gdr+SqO085uEt3cGvNOTX1dVaCk\nJN/hEpJ8lsjOPkaVapnlbSeyGfldJFs37EAHh5EcjqME1tHZ+RmOCPiO4w6M4z9J/9TVaZRBo1Ey\nOnoCzebarcCRmprKTh7d+YU0QhRrwDk6Or7DV155y2LBhKqy4dWi6i+rV4sySg/xcN3GOiU5mQwM\nFPtSQt/4xg1y7Ni6EYa5efMmf//99xq1YRbMPBt/ltNPTOfYndP48bSD1BsfXP+7hYUcFx3NPKOR\n+fn5jIj4mUs7/sTQ0PkPCgasXUsuX15qKctgMJSq71gXnDt3jp6eLejq+iLd3F6no2M9hoUtZWho\nKLdtq5laVrlkZIhuWaOxRmNhZZRnGqttMBcvXsykklVRq0Fubi4bNfKmVBpCT6RxJO5RKp3HBg2a\nW6yn97+GIAi0tXUgkEvAzOVdn+GPnv8hINDNrU+ZMmdWsWmTKBFlJYGB5aruVRm1Qc1R+0bx4NGD\nHPfSFL7b5jOOHz+Z8fGPti7ffUm+wNhYzlIqeaOgtPZrTs5pxscvrLCNMV/78TvJanZDJr+GgrDR\nET1DaNtTxpjYysva1TYaTSxjYibWqtE0aUy8Ouwqvx05jo0b+9LbuwPnzJlXowFVo9Fw3fvrqEvU\nieU4/v67zD6PxGAWFoolsObOJUtokAqCuHy5eHG1ihRVC0EQrFL9EQSBF1QXOOPkDAYeC+RfUX8V\nFzk/cUK0fyR5NCuLIQoFjWYzBUFgRMRKNm/elsMlI9imTWdu3VrCKJ09S06ZwlSlkoMGfUE7Oyfa\n2NjzxRdf4+WHxPNrg+zsbLq6NiSwr4QU6QXa2rpynRUFJKqMIDxYMy9n7bw2qBWDaTab6e/vX6OO\nxMfH8/13P+MEiT8dMZUDBgxhbB1WtnjU9Os3mBLJIgJ/cujQ4Vz0yUcMlwyju5MHCwpqoW6dIJB+\nfqSVbe3ZI0qFWotZMHPiwYlMzhdnt0krk6hLejTydxVRaDLx15QU+svlXKZSUZFxmnFxoZUGDDVu\n7EvgNnshnd8/t4iSgaMIDzllsmFcaWWVm5qi0cgZE/M9zbVUBiw2KLbSgKbqEh8fz18DfhXlEu/e\ntVgAvU4NptksCgP7+4uRPCXIzBSFzmtyn1eVoKCgKr2lC4LAK0lXOOvULAYeC+Suu7vKLXb9009k\n4Nlkri7xYhISEkqZrCOBsxyGaAKHKJN5c8OGjcX76GNjucnVg9423xHIJqAn8AudnT0ZFRVV85Mt\nQUREBGWyT0oYy+MEAgis4JtvDqrV7yrDYzSY1VrDPHz4MPr161cjF3CLFi3w0+sL4TazHurtdcHM\nmXUrMvCo+fHHeejatTcKCxtDo3kJa280xYB31+F8QVe4lKNuVC0kEsDfH1iwQJQLqSZvvSUeam0U\neti5MAztOBRPuYp5VIZUA+wbP+a0AYjqUV8WBQDEZl3AxcSdiHQei84ZGRVK8tnbOwAoxBm0g9eL\nCjCTgNEZUmlh7a69VAMnJ180bfrfYhk9qdT6MPz0Helw6+5mMZipJmRkZMC1oyvUN9Vo0KuFqPX2\nqDh5Uvy+L74Ahg4tten0aeCvv8SMg3pl9SpqnY4dO+LGjRvo1KlTmW0kcT3tOv6K+gtqoxpdmnbB\n5Jcnl5FNLImZRObb8Yja6oYJbcR85sLCQoSFLYZWGwmgBQAlgH7QaP6Av/+nGDr0S0ilUuz+5x/4\nCe0x2VwPB3ATZ9ELwDBotbF45ZUByM1Nhbt7Q4wePRLBwVOtyt005hihjdbCfFDAEE1/2EAJwIC/\nkYYYLABwHirVpmq3azW1EcVYDar1JJ44cQKLFi2q0RfmX86H1EEK986uwN4aNfVE8vTTT+PHHxfi\nwIEj8PS8gF69nsOQYcPQqtUIYNo0UfvWCg3KUjRpAnTsKOrpVnMC4+gIGAyiUEZ10xz/uPUHnm7w\nNF546oXizygQEpsnZ9JTWHgDkrx9+PT5pfgUqFSSb/jwT7Fo0ULodNtgb+MJHJ0KvP5fGO8dxnvv\nrX5s5yGTtUHTpv+BUhkIH58FkEiqLzGnVWihuaOBd4h3rfcvIyMDDVs2hPmu+YEyRl0TFSXqvvbs\nKWrYlbiOJpOYqty4MRAW9ujG0L59+2Lt2rWlDOat9FvYc28PCgwF6NSkE/x6+MHF3qXStgpMJgQr\nlfiuaVNMHuuMqVPFuKV79+7Bzq4ltNoWAADl6wcAr4EAGiHbdgD879yBi7MzjuYVIGvINwjkKxh/\ncyv6RysQpB4AQXgLGRk7ACQjMzMeYWEBuHHja+zatdliP8w6M7RyLbTRWmhjtDCrH0z0bevbQva0\nDC0HtkDQiQXIVw8DIAEg5uPb2BxCz57lK5/VKg0bAllZorjNI6LKBjMuLg4tW7asUaK2Kd+EtN/T\n0Hppa6vbeNIhiWvXrmHLll+RmbkLTk6t4eT0NBSKALQOD4ckKAgYPlyMdq0JH30ETJ4shv5Vcyr9\n8svAhQtVlwIDgEuJl5BSkAK/HjWT6atL1Oq7SEv7HT4+84rv08ok+QICJuHQoXegUHS83pHBAAAg\nAElEQVSBTtcY9pIASM/sx/uL3sHZ9LN4v/77j+18ZLJn0KTJCCgUgfDxmV8toykYBKiWqNB6Sd08\naxkZGfD19a18x9ogMxP46SdxgFywoIyAR3y8aCTHji1fm7Wu8PDwQG5uLu5l3sPue7uRq8tF+0bt\nMa7bOLg5uFW5nXidDosSEjDT2xsNi85vwgRg/nxg+PDGMBhUAHQAHHGGqcCmFgAzYeuwGXN+Wgwn\nJyc0MRsRuf0wNJqh+BGB8Mc1+OEUfsE25MAbgDuA56HV7sLRQz64dfQWmgpNoYnWwJhhFO0eAamj\nFE5tnCB7Wob6/erDxrnsfTfANADNfpwNXewoGAwhRW1vhEy2CoGB52v8u1bIfU3Z1q2BmJhHajCr\nvIYZGhrKnJycGvmF5f5y6tMflF+qQxf0Y+PQoUM8cOAASTI7+zhzck4X/39a2h/i2svMmWKJmpqS\nmSmu4VQTna56v31CbgKnHJlicT1QEfJklBhSq2MYEzOJglB5hEeOwcA1SUkMkMu5MimJ6Vot9++f\nz9DQIZwzZy7jiuqXbr6xmcsuLKsV4YSaUFh4i3J5AAWh6tFaytlKauSWC2vXBosXL2ZOTg6Vs5Q0\nG8wWb6gar2HqdOSyZWJodzlBgdu3i5sfQb3yMsRkxXDh3wvZe2RvRpyNYI7WuvHxQm4u/eVy6iys\nhe7dS/7xB9m790Da2U0W6wy33UU0+5uOjp9z2LD/Fu+bm5tLN7fGBLaJtYBh4He4yG8wkV2xkL5d\nV3EY5PwaCn5jt5jbR2xn7rlc6jP0Vt3j2dnZ/Prr/9LR0Y1SqS17936bkZGRVv0G1WL9erHUV0xM\nccmz2qY801glg6nT6WpUA44kUzenMutI6Xp4/98MpiAInDhxYvHNl5//LzMyHhSdlsv9aTAUPVQr\nVpA1DEknSf71F7lrV7UPmzatatGyBfoCjt432mKAgiAIT4TB1GiUVkeWxmo0XBAfz1HXtnBHipL6\nh36U8wnn6X/EvziK8XFRUHCDsbGBVTKamfszmba1mjVYq0lISAgFQWDa1jQW3CwQH+aHBl2rDaYg\nkFu3kpMmkdHRFncpLBTv4doSkKkqimwFw8+Fc+rRqVzz7xpmqjN5+fJl7rLiGSTJrWlpXJKQUKHB\nWrCAPHUqiy++2IvOzt509XyHtq+58M03P2BhYWGpfa9cucJmzZ6mi0tburl1ZV8MZAcs5GtIY8ev\nr9IOJgKkq2v34ol9bfBIJ5XHjomZAgZD7RT7tUB5BrNKLtmdO3di8ODBVr/FamO10Cq0aPx5Y6vb\n+F/g1KlT6NOnT7E70Na2HtTqW8XbvbwmIzFxMVq1mgOMHi2WF1q6VBQksNbV/c474rpoz56iy6qK\n9OwpalhWlPcrUEDwiWCE9A4pVbbpPqY8E2zr1bikao3Q6RKRmLi0KOG/+kEMPk5OCGjRAgrTGqQ6\nDcCc+HhIALzboAG6uLqih1cPNHVtiu8Pf4+Zr81EQ+eq/8a1iYtLBwAClMrpaNVqDiQSywvQ+iQ9\n8v7Og888H4vbaxOJRALn551ReLUQLvXrA9nZNS+ScPEisGWLWD4k3LKowvXrwNq14m1fSXW9WkGV\np8Kfd/9EamEqWtVrhaEdh5a6D+p1roe9e/fig/IKXFqAJMJVKvg6OcHPy6vCfSdPBr7/vj6OHTsN\npfIaFAoFjpqbYeXHK8vs26VLFyQk3MXVq1eh0Whw+dI/SJqmwwqTHezhglGIxR78A50sDW+++aaF\nb7OOuq4QVYoWLcSKM716iYvXj5AqhX1cvXrVavklwShAtVSFFv4trDr+f4l9+/bh3XffLf7b1rYe\nTKa84r/t7RvC0dEXeXkXxA8GDxbV0KdPr9mFnzpVXNspX0e/DH37isphFTH/7HyMeGEEGrtYnujo\nE/VwaP54IkkBQK9PgUq1ED4+C2usjiORAC+7u2NOq1aY1qIFEvR6BCoUCE9IgMSxMRb0XYDQM6G4\nlX6r8sbqCBeXjmjYcAji4mZYFIammYifH4+WwS0fWZ+cWjtBG6MVNd3i461vKC4OmDIFiP0/9t4z\nPKrqe/++p6V3EiA9JKFIERURqdJtKIiKFVG/CEpvIUAIoRNaaEqT3kEpKr2IEFC6SCdkJpM66T2Z\nes56XuwECJmaTAq///O5Li4m5+y9z56ZM2ftsta9pGwQ2a1yujAiYO1a5iS7fHnNGsu0ojSsurwK\nU05Pwe8Pf8enrT9FdO9oDH91eKVBk0gkAs/zer8Tfag4DmFSKXq4uWGgGYNckeiJ5njbti/jww8/\nRLBvMPJV+XrLC4VCvPrqq+jWrRsmTBwHfFCIj0UToLU5gK1On6Ovy0Uc/n4fRDWUzL3G8fNjaj91\ngMnpwe3bt9GmGg4qyYuT4TfW73Fqpf+rXLx4ER07dqww0hKJXCoYTABo3HgI4uPHwNn5VTYj6tKF\nbVpPnMj0uxyrIL/m6sqSXu7dC3z6qenyYH4THKdX0QwAsOPWDrzY6EWDGqVA3RpMjSYLiYnzyhIx\nVy9kQqPJgkTyxHHATiSqIMm3r0ySr23bcOy5twWdCpLwTlPrSQBagrPzywB4yOUzERQ0s8L9lhyT\nDN8ffJ+khqsFhGIhiCNmMOVywNKBdUEBc+ixt2e6dQZyo2ZnA/PmAZ98AlRBxtosMoozsP/+fiQV\nJMHH2QcfvvAhfF18zarbvHlzxMXF6c3eVOEaGg1my+WYGhAAPwsEbT08mK9gTAybcfZq0gtnZGfw\nYUvjK39CoRDL9kXjVuQtaFul4odPX0O3bt1QfKkY0glSBEQEwMaz7sPCLKJcGLsOMGnF9u3bh0GD\nBlWp8bw/82DrbwuHptZNEFwfOXDgAAYOHFjhmFAoBlHFmaNAIICPzwikpT2VAqtFC7a+NHkykJlZ\ntQ707g3cuwekpZldpUsX4OLFyscvJl1ErjIX7zV/r/LJp6grg6nV5kIun4ng4GiIRNW/t4qKrsLZ\nWb+ovZeNDUb6+mJhSAhed3WFXdDnWJ2txeh/tkJXR0lsnZ3boUGDfkhMnP14VpN/Lh8STwkcW9WM\n3q1JLJ1harVsurh4MdNJnjjRoLE8exZYuJDNsKxtLLNKsrD++npMOT0Fu27vwrtN30V072iM6TDG\nbGMJAH379sWJEyeMlrlTXIwFiYlYGBxskbEsp21bNqs+cgR42ftl3Ey/aXbdltNboVFeQ3Tv3h0i\nkQiunV0RNDMIyQuTkXcmz+K+1BvKtwJqCZMGUyKRwK4KX64mW4PcY7lo9KXhfUuxmP1unneuXLmC\ndu3aVcqFaQhHxxfAcUoolfInBxs3Zk+FuXOZq3RVCA8HFi0ye2m2Vy/g9OmKx+T5cvwR9wdGvzba\nZH2NQgObxrU7OtXpCpCQEIng4PkQi03HtplDcfG/cHKqHHj+LC0dHTE9KAi/vf4+gt0C8cZfWxEl\nk+J2cbFV+mEJLi7t4eHxFhIT50KdqUbO4Rw0/rr2Mjc8PbMVu4mhhROQZ8aDlwg4fJgNDrt0Yfe7\ngYwTWi3baUhOZrd1ma53tclV5mLjjY2YcnoKtv63Fb2DeyO6dzTGdxyPQLeqLWc3btwYGRkZBs8f\nzcnBbzk5iAkNhVM18kF+/jnLoZkgE4JAZi8D5ws4+LVyQcaOJ30Uu4oRvCgYaoUaifMSwWvqZgBY\nLcqzltQSJp/wX3zxhcWNEhES57K9FGObwS4uQFGRxc3XO/bt24dPPvnEojp+fuOQkrK84g3v5MTW\nXDZvZoGSluLoyOIzt241q7hEwgQMygWIitRFWHxxMWZ1n2XWJj7pCEJJ7S2163RFkMmmokmTORCL\nrfT0BMDzar0JnA0hEggw/oXu2PZSN6Q/WImLeRkIl0qxIiUFGRqN1fplCheXDnBz7Ym74fsRMD2g\n1hwvtFpthSTAji86ouROaYUyeh/kN28C48ezkfKyZUDr1gavIZezoh98wMR8qvvW8lX52HpzK6ae\nnooNNzaga2BXRPeOxqROkxDsbh0HKTs7u7LUd08gIqxJTUWWVouIwMAKohlVJSKCfXxBTs3xMOeh\nWXXSNRoEt3BF6cNSaLKf3KMCgQCNv2wMr0+8ED8hHqWPSo20Ug+pAYNZUFBg8JzJp11VgpMVPyvQ\n8NOGELsaH0m5urItjOeZmzdvok2bNhZvoItEdmjQoF+ZAsdTiMVss+aff4DffrO8Q507AykpZi+P\nde0KxMYCHM9h+p/TMbP7TNiK686RxxAcVwqZbAqCgmZBIvGwWrtlHuRVqhviEYJFPWfj7u3l+NZV\nh4+8vLArgxnPvZmZUFpDCtEExTsD0fgrf6TlLzF7tlFdnk1p5dTGCcW3Ks6ySUtP/BbS0tiWw7Vr\nzPP1rbeMtv/LL8C2bUy5x8SWoFEK1YXYeWsnppyegjVX1+A139ewoPcCTO48Gc0aNKt6w3pQKBS4\ncuUmGjduAh+fZggPn47cwkLMkMvxgoMDhlgxb6NEwozmjV/74ni88WXgctI1GjS2sYH/RH+kLE2p\ndN4h1AGhy0KR9WsW0rel19q9VGXKjUdQEJCQYNWmd+zYYfCc1acHxbeLwRVzcH3d9AzAxQUoLLR2\nD2qXnTt34vPPP69SXQ+P3igqulrJMQgCATBhApt+r6+CPNukSexpY8YeW8+ewJkzwPzY+RjWblid\nhU0Yg+NUkMnCERg4HTY21u2fSiWHnV2TKtd3tXPFsreWYdftXbifGovx/v6IDg5GM3t7LExKwnSZ\nDLH5+eBr4AFUeKUQAqEAPj26wtW1M5KTF1v9GvrIysqC11PenTaNbKDNrLi3wit5iIQqtpa6ZQuT\nhRw6lA0IDVBSwuyqoyPLD1kVKd8STQn23NmDKaenYNXlVXix0YtY0GsBpnadihe8XrC8QTPIzc3F\nK690wbFjfigs/AgKxV4s35CCZst+xGcNGqC7u7vVr+ntDXw5oBGOx5rn81BuMCXuEji95IS8s5WX\nz4USIQKnBsI2wBbSMCm0+fV4v6w8kbStLdP6tBJEBLlcbvC8VQ0mp+SgWK+A31jztFJdXZ9vg3nn\nzh00b97cqIixqWUyf/9JSE5eqv/kl18CTZqwfR5LHEzs7IAhQ8wythIJcDP9P7zcsD1aNWxl/jVq\nCZ7XQCYLh79/OGxtva3evjGHH3MRC8WY1WMW5PlybLixAQKBAC87O2NmkyaICgpCIcdhmkyG+YmJ\neFRqnSUvXQGTmfT5gcVWuLl1g7NzeyQlVS0htCU8azAf4+DArB7HgbZugevZlcxre9o0wNnZaJs3\nb7LoqDFjgHcsdEAu1ZZi/739mHp6KmL+iUHzBs2xoNcCRHSLQJtGbWp8qXr16nXIz+8CjlsBwBXw\naQHNF9OgWvMn7ppwBKoOnToBtiI7nDpr2mNUodHAu0xyz2uQF7J/ywan0r8C4t7dHYHTApE4OxH5\nsfpDV+qccoNpZc6dO4fu3bsbPG9Vg5k0Pwn+4f5mi3G7uDzfS7Lbt2/H4MGDq9WGjU1D2NkFobDw\nsv4CffoA773HnCTUavMbbteOjUZMOBCdk59DUNtEOCiML5M9i65IB5FzzYYv8LwOMlk4/PzGw86u\nmoL1BigpuQdHR+vMPIa+MhRBbkGIOhsFHc+8oyVCId5t0ADRISEY4eODc/n5mCKVYl1aGnKr6PFG\nRJDPliMoMqiCMXB37wFn55eRnLzMKu/HEHoNpgAgvwAW2jRhArjA5ij+bDp7sBmBCFizhmUZWbGC\nzZzMQaVT4dCDQ5h6eioWX1yMANcAzO81H5FvROJl75drNZD+6NHzUKk+AgB4eghh1+0CsDoUJRnv\n4tSp8zV67XEfdMWGk7FITTVerpjjHjsbCQQC+I31Q+pKw5UkHhKELA2B8pESSQuTwOvqmUOQv3+N\nxGIeO3YMbxnZMrCawcz+LRtO7ZwsSiP0PC/JPnz4EEFBQWalgDK1H9C48dfIyNgBnjfwAG3blg29\nJ040zxOxnPHjgZUrn3j1PIM0V4oT0hOIGf4e/vzT/GaBmg8pIeIgk02Bj88I2NsH1dh1AK5KmUAM\n0Tu4Nz5v8zkmnJiAAlXF0aCbRIKhPj6IDglBH3d3/KxQYIpUit+ys6GxYAUhfVM6PAd4QtKg8sqG\nu3svODq2RkrKimq/l2eJj4/HiBHjER4+E7NnL8Tt27cfn3O0Swa3+zfmrbN8Obhmr0BoZ/zxkpXF\nRK7atWNC46aczDWcBofjDmPamWmIvhCNRo6NMK/XPER1j0J73/a1qzbzFI0aNQDA9gX75gWj8Z5g\ngBNCIkkuO1dzdA7ohNCeFzF/vmXjafsm9hDaClHyoMRgGYFAAO9vvdGgfwPEj4uHMqEWMtKYS6NG\nQHo6e22lmZdCoUDDhg2N+6NURU/vWZTJSpJGWJ4EWqEgWr3a4mr1gilTppiVsT4xcTFptaaTPRcX\n36Xk5BXGC+XlEY0cSVQmDm4Wt28zAetnyFfm08gjI0mtY2L4ERFEWgvyFeeczKG82OqJ8RuC5zmK\njw+n4uJ7NdJ+ORynJZksqkbazinNoZFHRlJ8TrzRcjzP08X8fIqQSilSJqMrBQVGdTmLbhVR0pIk\nk9fPzj5GyckrLe63If7++29ydPQksXgaAV+SUDiLHBy86NSuXUSRkaScvIQy194iWreOiIgKrxdS\n1qEsg+2dPk00aRJRQYHx62p0Gjr26BhNPT2VZvw5g2ITY4mzQIS+Njh9+jQ5ODQhQEFjsJzckUPA\nXbK3b0Dx8ca/f2sw7fQ0ksmIpk83XGaGrLKuL6fhKG50HPGcaR1YTsVRwuwESt+dXp2uWpdyMfLf\nfiO6fr3azS1cuJAyMpgGsyHbV+0ZJnGE5IXJCJxmefzS8zrDlMlk8PHxMSs+lcnjmd4HcHRsCY4r\ngUplxLvVzY0586xaxTZ9zKHcdf/OE0k3Ha9D5NlIzOo+CzYitq/Rowfw11/mNQnU3AyTiJCQMAON\nGn1utaXSZ9FqtThy5Ah27VqA/HzzUzBZgoe9B5a9uQyb/t2Ec/JzBssJBAJ0cnXF3ODgSpJ8Sc+o\nmXAlZT4C40wvTzdo8Bbs7YORmrraZFlz+PbbsSgp+RE63TwAIZDwk/Fd6SDc+G4EuNGjIZk5DqVZ\nToBCAQDgVTyE9pUfL1otE7RSKJg/kIuej1/H63BadhoRZyIw69ws2IpsMafHHMzqMQtdArpAaEBH\nt67o1asXwsO/g51dS3hJNoJz/B/s7Ttj3boVtZICzdfFF5IGKejaFdi+3fx6QokQjb5ohPRt6abL\n2goRFBkEiYcE0slS6AprV8PVKOVpvqqBTqdDbm4uGjZsaLygMYtr4jQRESXFJFHRLdMzKH3w/POZ\nsSQiIoJKSkrMKpuR8QsVFd02q6xOV0qPHo0zrfzPcURz5xKdOGFWu6TTEY0axdT9iSjqbBTdz7pf\noYhWy2aZ5pIwJ4E4tXVH+jzPk0wWRYWF16za7tPcvHmTvLwCyNm5Ew0Y8DoFBnrShx8OJo3G8kwn\n5sDzPK2+spq2/LvFonqZajX9mJJC4fHxtDktjQq1WpJOkZIqzbKsKVlZv1NKyhqL6jxLRkYG2dq6\nsdRSIBpq8xotwQQKwSNycgqlW7duERGRLFL2+AedezqX8s5VXIGQSolGjNCfgETH6ehswlmKOBNB\n005Po5PxJ0nLWbDkUQ9ISUmh6++9R9u2baPc3Nxau258TjxtvLGRiIhiYvRPtvTNMMuRRcpInaE2\neP5Z1BlqihsbRwWXTSwP1DTlxkOpJJo/v1pNHTx4kC5cuPD4b0O2r1pDtYJLBRA5iODUpmqKK3W0\n5VAtkpKS0KBBAzgYkPF6FnNnmAAgEtnDw+NtZGcfMF5QKGSBWHK5eUNKkYjtgS5bho03NqKzf2e0\n8KyYaVcsZt+HuX4oFeLsrAARISlpPjw83oKzczurtfs0Op0OffsOQFZWNIqKLsLdvQ8SExNx7Fg6\nFi0y4KlcTQQCAX5o/wMaOjbEnHNzwPHmxWaWS/JFh4TgNRcX7Fz9AAdaKHHOtgScBSEqnp7vwdbW\nG2lpP1f1LUAikYCIA+yygD5haGwfACX6oBTe4HkVbGwqqz1xSq7CDHPPHmDHDhZw37QpO8YTjwtJ\nFzDj7AzMODsDKp0KUW9EYV6veegT0gdiYd1mwrEUX19fvPLyyxg8eDDcayCUxBAhHiGQ5koBsL3g\n7duBnJwn51UcB1sjG8T+E/2RvNR8BxqbhjYIXRaK4n+LkRyTzLSE6wKhkPln2NlVW1v24sWL6NSp\nk+lLVvUCugIdMvdkwnuY9V396zMbNmzA0KFDzS4vFruB48zfkPbw6IvCwsvQ6cxYqx42jMXmLFli\nWg6vaVP8aZsGTi5DnxD9aX169rRsWdaaJCcvgatrN7i61pCyNoA///wTSmUjAJ8BAAgEwAGlpQvw\n448ba+y6APB207cx8IWBmHBiAorUlslbBaUJ8HaRM8Z/1RJigQAzEhIwWy7HHTMl+Tw9+0MiaQCF\nomrv0d3dHU37NAe6fwJcmIJFBTvggFPojtP4zOF9NA1tCo7j8N/9/7Bl469o3743ftv3O9SkRnEx\nCxdxc2OxlRIJ4VLKJUSdjcL0P6cjX5WPiK4RmNdrHt4KfQsSkeUp2v5/AKFACI7nIBQyDftZs574\n+mVotWisZ1BTjthVDJfXXJB7ynxNVoFAAJ/hPnDv4474cfFQJdeBGLq39xPHn2rw6NEjNG3a1Cyn\nsSoZTDJT+u7/GmlpaXB2doaziZiypxGLXc2eYZbj7z/RcGzms7z/PtPknDbN6PQwLicOZ1s7YdjZ\nQoOjsW7dmHt/bZOcvBzOzq/Aza1rjV4nOzsbRGV77WIOv3qXj6oDUVCQVaPXBoBWDVtherfpmHJ6\nChLzzVNi4tU8UlakwH+yP0QCAXq6u2NecDAm+vvjZnExpkilWGmWJF9XnDx5EWvW9Mfp06fBm+mV\nq+E0mB87H4NHf4QG12RwFA6DBmtx2+YyMh2+wfAVXyN+bDy+7TsSK//4HZmpPfDvtTE4uOcR3vxw\nFsLCtBg7luD54jXM+msWpp2ZhvTidEztOhXze81Hv2b96qWy1PPGa76v4WraVQBsX/j775k0NfBE\ntMAYngM9kXssF5zSMnUqpzZOCF4UDMXPCmQdqPnfUAWsFIu5a9cus8VnqmQwM3ZmwOMtj2qlhSkq\nKsJPP63Gvn0HMXZsGO7du1fltmqLn3/+Gd99951FdSxZki3HxqYR7OwCUFBwGRcvXsTBgweRklJZ\nzuoxr78OfPstCzvRM+vIU+Zh5eWVmNE9ipUxkJhXJGKrHGYty1ppFSY1dTUcHJrD3b2XdRo0QseO\nHaHTnQFQBPipUJxWLu92EK+9ZiSTthXxcvRCzJsxWH11Nf5O/ttk+cT5iQgIC6ik2esoEuHLxo0R\nHRKCD728sNOIJN+xY8cQFNQCP/zA4ZdfRFi58mt07fpWJd3TZ3mY/RATTkzAxy0/Rvib4UiQ3cPi\nxX0wbJgUL84fiMODB6H9Z68i9f1U8BckCFRPgQ7d4IO2SNCG41FJa8gDPsCKu1Mhz5cjrHMYFvRe\ngAEtBuhNSP5/gjqaQPRo0gNnE84+/rtlSyYrePAgoFCrH4sWGEIgEMBvnB9Slht5zhhAZC9Ck9lN\nIJAIIIuQgSupeUlIABUNpqOj3mefKZRKJTiOg5OTmduKxjZC9Z0ueVRC8rkWhDXoQaFQkJ9fM3Jw\nGEjAvyQWTyd7ey/avn1ntdqtSTIyMmh+FTaWOU5Ncvlci+s9eHCfIiLcycWlJbm4vEe2tu703Xej\nieOMONpkZFDxkCEUNXwktWzZkbp2fYf2/LKHRhwZQbmlTzkhbNpE9NQG99P89RfR8ePG+6Yr1pF8\nQfXuASKi1NT1lJV1qNrtWMKQId+Tg0MXQvdzhAFjCFhNDg6edOXKlVrtB8/ztOLSCtp5y/A9n3Uo\nizIPZFrU5o3CQoqSyWiaVErn8/KooLCQHB0bEPA3sXV7op49d9Dbb7ejiIgokkqltH37djp27Bhp\ny+KKeJ6njTc20oLYBaTRGXGGOnmS6PffaezYSSQQzKFgFNJ2yV4KbnSEAtpHk13rifRqp55m9///\nBHXoxTjt9LRKx+bMIYq6nkJpKvOcxZJXJVPxneIq90GVpqK40XFUeKOwym2YTWEh0aJF7PX+/UQ3\nb1rcxObNm+nOnTuVjhsyjRYZTE7N0cORD4nTVM87cvDg70gsnvD4B8z+3SYHB3cqLKyFD7oKzJkz\nh7Kzs6tU19JYP47jyN+/OTVpMp0GDFhZ9vnkk4NDZ1q6tHJMZTlJSUnk5+FLK4VtqTm2EbCTJH09\naciE7yoW5HmisWOJiiv/MHQ6096yJQ9LKH1H9eKxFIqtlJGxr1ptVAWdTkfLlq0g11ETSfKWE/Xs\n+T5dvXq11vtRzm8PfqP55+dT3KM46tfvE7KxcSQHB3ca8eF4ujOx8g/ZXDQcR4ezs6n/4cNk8+0s\ngm8Ju49c8ggg6t17Fr35pgvZ2XmSk9Mn5OLSkby8AujMP2do/PHxFJsYa96Fxo6laeHTSCQKJ4Bo\nmu1Zmt92CH2KRLLFLure/f0qv4fnkjo0mIsuLKo4MCbm/d7hpwTKLTDvmc1pzY/NNATP8ZS8MpmS\nVyZXqx2zKP+8b90i+uUXi6tPmjRJ73FDBtOiJdnkJcnwH+9f7ZROBw4cgE43DgDwOv6GAByA1hCL\n2+PMmTPVarsmYHtfhAYNala1o5y//voL+fkOSEiYDUfHInh6JgFwRWnpYsTErDVYLzJyHhQFgzGO\nv44vEIfOzW9CG7cBe1cfQMLTiv4CAZPaK9/keAqRiP0ztiVW3RjMjIw9EAhEaNjw4yq3UVVEIhHG\njRuD0RNGIiJ8Es6c+Q2vvvpqrfejnPebv4/27u3RZuLLOHLqBWg0iVCX3oTtwVYYuH8w1GbKtxQV\nAffvA6dOMa3zRQuEuLKyAWi9D/gDbYBX8oCV14Gwo8C7M3A6XgoiN3TsuATFxSMbbxAAACAASURB\nVHtQWPg3sly+RL/5AzCt0zR0CTBziXrwYPzgZA8bmy0A0pCl9kFTQTJsoIbEYRmGD/+sqh/N8wfH\nsR9PHdEnpA9OyyomuBWLgS5vEObMFJqVJlcoFqLRV42g2KSocj8EQgH8RvvBtbMr4sfFQ62wQIKo\nqgQHA1KpRVWuXbtm8W/fbL/tvDN5sAu0g32I+XkDDUMABOiCWHyOXfgMu7Eew5EMQb1MK7N+/XoM\nHz681q6nUChA1AyAALt3h8HW9hqAjQBKkZ6egJkzZz7+nMqdrogI+/btAcd9C+BHrAxKwBfFGjRK\n7YDjDv1w6tQpDBs27MlFfHzYRsfp00Dv3hWu37s3y2Dy9tv6+6dOUcO1c9XyUWZlHQDPK+Ht/U2V\n6lsTstZGbDU5s/M86MLHoF6ZQKwKnxQRfuU/RV72bvzyy694++0vkJrKsralpLCg/3KfHYGArT84\nOwN+fuxf9+7MgdDWFkhKaoAT7T+ALugPYOELAN8S6J4BDFqOk0VfoI/oBLrZFOG8OhvIehGScy/h\n0rlL6Nevn3mdb9cOfnv3ImryaMxa9DKy1ZGw0WTBVrIGb7/7AgYNGlRjn1u9o7DQelmuq8CLjV7E\n/nv78XGrigNRRwfgvc+ZSubYsabbcXnVBTmHc6BOV8O2cdUHxs6vOMOhuQMS5yXCpZMLPPt5mq5k\nKeV7xo6OgIWJDQ4ePIioqCiL6phlMDVZGuSeyEXIIuuoVvTv/wFy9kxEG/5VjMBqiMBhOGbBs/Qc\nencyL/lxbZGfnw+NRmNaAcIIlnoSe3h0gkqVA0ADnreBUtkRQEcA29G+fTfMnDlTb71Nm35BSspA\nAK3gkhYKXvMIEmgxjPsXDg69K1f45BOWCqx9+wo/9M6dgchI4wbTxtdyh6/s7D+g1WbDx2eY6cI1\niIbnIREIUF/kpM+evQxN7ljgZA98/PGHKD41EcnZgUDxXCxblofc3CfGsG1bJqNpJEtWBfI8PNAq\nehYeTP4apdmTAARC8ut+aLUbgFb/4lQXFT5rvgwL9wlwPr0dYh0CkZlpXsqoxwwfjvA//sB7187i\n2KITaHTfD1982R/dNnb7f8qLHvn5LH6mjhAKhOCJBxFV+txffRW4e1fv+Fgv/hP9kTg7ESGLq/fM\nFzmKEDw/GFn7s5AQlYCAKQEQ2VtxFl7FCVZeXh4cHBz0xhAbw+TaKhEhaX4SAqdbLn1niJV9uqC9\n0xn8ZH8dwC5ANB9bHH5Gq+j5cF64EDh50mrXqi7r16+vODOrAubOmm/cYCulSUlN8O67N2Fv3x/A\nHQClAB7C3n4SFi2KNFj/u+++gL39YgDOkGnehR00uAp7PIAMH925UzlFmEDAguSioyscFgqNL8vy\nah4iO8tu+tzcE1CpEuvcWAJAvFKJpvbWWCmxDgEB3pBI7uOrzxdBkSzC79k9AQC2ttswcOBNjBkD\nDBwIvPYa4OtrvrH8NTMTJ3JzcWXIYGxbOw1du+5As2bjMHw40LVrN+DuKWBdG+xe8zPCm85HQb9C\nTG1yG2/cvAnIZOa/gZAQIC8PLRs3xpjlo+AmaYuAgID/t4wlwBIj1KHBBICWXi1xL6tixEH5tzBk\nCIuzNie3vNhZDNcursg5lmO6sBl4fegF76HekE2WofiO5d6sBilPKWchVc40ZWxDFAClrk2lgktW\nlEDau5do3TrKz8+npUuX0TvvfELDho2h//77j53neaIDB4jCwojS61bot6CggCIjI6vdjimnn3Pn\niMaPJ9q27bF6HWk0Gpo+fRZ5ePiRSCShZs1GUWSkcScQpVJJXbq8SU5OrQmYQS62Q+hHkS0d2H+A\nibCPH09UWlq54qFDRL//XuFQbCzR4cMG3s8MwzJb+sjN/ZOSkmIsqlOT7MvIoP+KiijqbFRdd4WI\niC5c2E8TJzqSj89KAvaWOXldJAeHBpScnGxxezqep3lyOe3PNOxhe/36dXJw8CRgDQEZBFwhe/ve\n1O+L72j93bt0YPZsuj1iBJXu2MGkx0yRkUE0ezYREd17bSbJImtedLzeceYMczOvQzKLM2nZP8se\n/83zPEU9JYunUrH8DfoeA/p4NOER6Yp1Vusfr+MpaWkSpa5NNS0Bag67dhHdL5P5XLCAyAzJUo7j\nKCwszGgZQ6bRpMFMijGdGcFstm5lVsEcCgqYB9TGjUw7tQ5YunQpJSVV//3rM5g8T3TkCNG4cWx8\nYM5bnDiR3fDG4DiOTp48SdOmTaeYmBjKPnSIaPt2djIlhYl56vP2nTy5wnGOI5o61cD7iTTfYObl\nxZJcvsDs8rXBnIQEUup0NO/8PFJpLdNmtTbZ2UdJKo2g1atXkVhsS87OfcjFpSs5O3vRkSNHLG4v\nX6ulMXFxdKvItL7z1atXqWfP98nBwZ18fJrRvHkLH4eWEBHdLS6mn//4g86MHEnxYWGkK9OMNcjC\nhURJSXSv+xKSjfvT4r4/91QxtMHaRJx54uaeo9HQ8mcGXcnJRFOmsGeQKZTJSkqYk2DlHhLl/5NP\ncWPjSJ1pvoatXi5ceKKpvW8fmxiY4NSpU3TcSOwcz/FVN5i8zkpuwevWsTdkKZcvE40eTXT3rnX6\nYSbFxcUUYYkauRFksqjHoymdjk2yx49nYWyWDLLi4ohWmMgAppeICKLy2UZ+PhNif1aMOS+PWeSn\nmDFDv4E212AWFFymhITZ1hlJWpHIsve+8tJKyioxnIKqJuE4Lcnl8yk9fRcREd26dYtWrVpFhw4d\noqNHj5qVOu5ZHpSU0Ki4OMqxspC8jufpz5QU2rd4MV0YPpxSfvyRSJ9BLioimjKFbrz1I13qO8cq\ng83nio0biRIT67oXNOfcHCrVsCnk3eJi2luWsuppzp4l2rDBvPZSVqdQ0c2qJdgwhrZAS/GT4ynn\nZE7VG0lKIvr5Z/b633+JDh40WWXy5MlG49mTliVVPaxEILLCPsTKlcxt7+MqhBG89hoQE8N2q6Oj\nqy2yay6bNm3Ct99+a5W2RCIHqFRKbN4MhIezZOExMUCfPpYJgzRtCuTmVhRWNouwsCfqPq6uLEXY\nmjXAtWtPyri5AX37Avv2PT705puVt5M5FQehremwoqKif5GTcwSBgdPr7V6Wi62Lxbqu1kCtViA+\nfhw8PQegUSMWdrFz504MGzYM/fv3x9tvv21W6rinOZqTg72ZmVgWEgIPiXX1WEUCAXr4+uLjSZPw\n0k8/4XrbtjgZGYmro0cj58KFx44XOWo1Fu37DScvHYPihgzNmr2EQYO+Njs05rmnHuxhAkC3wG44\nl8g0LhUGZPG6d2eP0kuXTLfnM8wHio0Kq4usi13ECFkYAm22FvI5cvDqKrjheXsDaWnsdUgIEB9v\ntHhKSgq8vb0hNCBGX3il0OjzrWYTyxGxpHcvvAC8917V2xGLWbaNTz8Fpk5lMQ81iFKpRFpaGoKD\ng6vdVkkJsH17Z0RGcmjXjtmtjh2r3t6oUSwdpkW4ugIdOgAnTrC/bWxYDObp08CxY0/K9e0L3Lr1\nWND49deBf/6p2JQmVWMyBrO4+A6ysn5BUFBUvTOWWp6HuKxPzrbOKNLUrsHMyzuLlJRlCAlZ+Djf\np1QqhZ+fn8UeewBzKFuenIw8nQ4zgoIgNpKVwho4ikR4v0sX9F22DN7z5uHivXs4+/33uDF/PoYM\n/Aozk3rBq6AZ3LS+UKmScPhwHiZNml6jfao3FBUB5kqs1SAd/TriUgqzhMZ0ZEeMAH79FcjIMN6e\nQCRA428bQ7Gh6rGZxmj0WSM0+rIR4ifGo/ShZaEhEIufqMw7O7PvwAg7duww6OyjK9QhY1cGfL73\nMVi/5n5dRMCcOcw69NGfHcNigoLY1Cw3l3l3ZtWM2O+WLVvwzTfVixPMzwcWL2bJcnv3LsCsWSl4\n8cXq983TE3B3r0K+1IED2XSx3KOs3ENWoQA2b35SbsoUZkyJIBQy2/r0pF6dooatv2GDWVLyABkZ\nW9GkyRwI6lmiX4B5yIaWecg62zijUF07GcyJeCQnL4VKJUdw8EKIRI6Pz23evLlK91sJx2GSVIrO\nrq74olEja3bXLPxcXPD+sGHovnYt8jp1QpvSXMxoqcbdQHfYqSUAHKFUrsKmTRuhNTdv3PMMEXMx\nr2MkIgl0PEvwnK7RGNSRFQhYVpM5c0zrRzu/5AxNpgbqtJpZLbBvYo/Q5aHIPpQNxWZFjcTja7Va\nFBYWGhSgSZyXiMAI4wlFaibhHM+zXD79+7MYP2siELCl3T59mPEMDQUGD7aa6LFarYZcLkezZs2q\nVD8jA/j5ZzboGTqUhQHk5oosFmA3xvDhLE5y8WILK06YwD6zyKdCU779FjhyhBnJyZPZCPmDD1jy\nwsGD8dZbbGLavz8rrk5Rw7m9/mwtSqUUCsV6BAcvgkBQd4onxrhXWopWjsxYudi6IFdpfkqjqqLR\nZCMxcTYaN/4Wzs4vVTiXlpYGV1dXODo6GqitH7lSicXJyYgMDERj25rL9kEcQVeogy5fB11e2f9P\n/yvUAQRIkoXIu90TCu3XaNjiLEYGzsFLrn/h5pUuUKlKEBERAQcHh8cPQgcHh8eZf1xcXPS+dnR0\nNLh0Vl8gIqxevRbR0SsxLPUR9vx6AgsWTMP7779fp/3yc/FDckEyCnU6OBlRH3J0ZGIGCxawR7Yx\n/Cf4IyEqAaFLQq3cW4ZQLERAeADyY/MhnSRFYEQgJB7W21747bff0L/8QfYMGbsy4N7bHTZexld5\nrG8wdTqWaurLL2GVKZUh3NyA2bPZmuHYscDIkUyev5ps27YNX331lcX1EhOBjRtZWND337OZYDli\nsRu0Wus9mO3sgHbtgAsXWGYvs/H1ZVHv16+zBsp59112fMoUNtzs1o19tsnJ6NDBH9OnVzSYngMq\nK3aoVIlISVmFkJDFENbjxL8PSkvxrocHALYkK8+X1+j1Cgr+QVbWL2jSZA7E4soqMBs2bMDo0aMt\navOvvDyczMtDTGio0cTAAHugc8VcZWNX/neBju1NCVAxA03Z3wKhACJXESTuEojdxBC7iWEfYg+x\nO3stchJBIBTALc8NW3e/DxU/BvB/H0jsCQftB+jd5wS0Gg9ER0c/Nn5EBKVSiaKiIhQWFqKoqAhF\nRUXIyMjAo0ePHv9dXJZ9gp5StdL32tbW1qTxdXZ2hqgGZOumTp2JVauOoLR0Izgcxr17HfHppyOw\nebMan3xS+9KP5bwZ8iZ2Xd2FdMdu0Pj4wNbIoKppU+CVV4C9e5mWiSFEjiK493BH9h/Z8HyvBlR7\nynDr6gbH1o5InJcIz/c84faGiX1hgYBN0oRCJm+lUrGH5DNcvnwZH374YaXjSqkSSpkSjT43vUpj\n3SebRsMeusOGAS1aWLVpg3TsyGQsfvoJ+P13ttdZxRG3VqtFXFycRSm8HjwAtm5l9mbSJJaL7lnE\nYjcolRYEgpvBJ58A48YxVR6LJtdDh7IBRtu2FSPgX30V8PBg6b/mzWOOQlOmQLB8OWxsBFAqAXt7\ngCvlIHKs+OBRq1ORnLy0zFjW7wTAap6HXdmD08XWpcb2MIkIaWmrAQgRErJU7zJPbm4uBAIB3N3d\njbbDK3no8nXQ5mnxc24GUMpjTLYzso8poMvXgdfyeo1d+Wuxs/ixsRO7iWHrZwvH1o7M4LmIIBRX\nfxbn7u6O4cO/x/qfB0LZZClwogNKvcJwFiPxrn9T3Ls3Dk2aDIejYysIBAI4ODjAwcEBjaywlKxW\nqx8b2XIDnJeXh8TExMfHi4qKwPO8SeMrFovNMr42NjYoKCjAihUroVLdA+ANAU4AeA9KpTMmTfoB\ngwZ9VCd7+MnJyfjs4+9wzfUfiIJGYvebAxAdPRsjRhiW9+zXj7mb3L4NtGljuO0G7zaANEwKtx5u\nEDvV3MBY4i5ByOIQZGzLQOLfifCfZETDvGFDtj3XqBHTlE1IYH4zT3H//n20aNGi0vfBa3kkL0tG\naIx5s2YBGVksfvpGMolKxZb0xo8HmjQxr461kcmYR+4HHwBvvGFx9S1btuCll17CSy+9ZLLsjRvA\nnj3MMWvIEL0DmsdoNBnIyjoAX98fLO6TMS5cAFJTjY8K9XLvHnOc0jezyclhazPTprHGb97ElZeG\nIS0NGDAASIhKQJNZT75fjSYDcvkchIQshkhUf9RzDDEjIQGzy+7PAlUBNv27CeM7jrfqNbTafCQm\nzkLDhp/CxaUDeA1feWaXr8OKvSsw4IUB8BR5PpFj0TPTE9mLwLkLscw3D31tXNHN1e2JAXQVm+W1\nXBvwPI9v123AgT37URx7Gn5+zfHJtPfQKMgLCX9I8dlnjeHjo4av70jY2vrWdXf1otVqUVxcXGn2\n++xrnU6HxMRE7N59Cmr1UACElriLe2gNgIdQGI3JkyfC09PTqPF1cXGBra2t1Qwrx3Fo2rQtkpI+\nBddNCQR9C2xWw8GhH3btWmZwSRJgk7Tx49m+pjFnX3WaGooNCgTNCLJKn01RGleKlJUp8B/vr1/L\n/PBhZizbt2ee/wpFJSfTqKgoTJ48udLWR+L8RHh97AWHpg4VjhuyfdYZIpSUsHiJKVOY4GVdERwM\nLFvG1hamTmVTPjMzjOh0Oty5cwdff/21wTJEQGwscOgQ8PLLbCJmjge/SORq1T3Mcrp0YW9xwAAL\nJ9UtW7KbTCZjn9nTNGjAXHkjIoD//Q/IyUF7DykiDoVgwABUeJhrNNmQy2cjJGTRc2EsdTwP0VMP\nJicbJ7NmmMQRdAWV9/C0eVro8nXgisq89ASA2vEOShofgKt8NHI4d+RQAoQ2Qmbc3J/M8rQeWvCB\nPF6Z/gqEdkKjD0yFWo2FiYkID2iKAAvDTWoToVAIzz49UfD9d+A4DuKyFYwNNzbgvTH98efeK2jZ\nshVEom0ACL6+I/UuU9clEokE7u7uRmf95cTHx2Pv3uMAIgBI8ESQLhU2NisRFRUFjUZTyeAmJCRU\nML5qtdrgzBd4siTt6Oho0vjGxsYiO9sOHDcdSDkCtEkF0A2lpYsxe7ZxgykUAjNnAlFR7DFqaLXf\n1scWNo1tUHSjCM6v6PdnsCYOzRwQGhOKpEVJsG9ij0ZfPLMq4e/PMpW0b898Ws6fr3C6pMzR8Vlj\nmfdnHmz9bSsZS2NU32AWFDDjFBXFrHxdIxCw8JO+fVm84QsvAF98YXLdcu/evfjEwFSNiEVfnD7N\ntveWLLHMGU4ksgPP14x32fDhwLp1bCXaIsaOhWrMGKwMDsW589cRHOyLESOG4oUXXmBrr4sXs/3M\nzp0hWL4MBVwEZs3ajtC/fcHFcwgMbAC5PArBwdEVPD7rI8QTuCIOdzOL4JcnQH5i/mNjl5eeB/lf\nchD/zGjyqZmeQCSosKQpdhPDvpk9nN2cH+/jAYBCsRFirhjN/Taa9BDeunIrho0ZZlKI+nJhIX7J\nzMTikBA41GHqKHO4UliIV52dIRAIHhtLABj6ylDM/GsmPvnhEzy8+BBbtmgwYcIQJCUtgq2tL7y9\nh0IotDykpq4JDQ1FmzYtcf36HHDcTLCgAw3s7Mbjq6+GwM7ODnZ2dnBxcYGvb/Vm1ESE0tLSSsY3\nNTW1gvE9d+4clEoCEAW7ZC0kjgUocg4Fil5FQoLp9Ffu7swPcOlStitjCO+h3ogfFw+ntk7WidU3\ngdBGiKDpQcg9nQvpZOYQJHYtu8cCAoCzZ9lrNzdmk55i9+7d+OyzimnmNNka5B7LRfAiy0IHq2cw\nc3KYx+XcuWz/qz7h4cGmgBcusM2+UaPY7rYeeJ7HjRs38MUXX1Q4znHA/v0suPedd9hNVM/CCtG0\nKbBzJ/sqLEnXKVcoMHrvIXiWNsVR7Q8Qix9g06Y3sGXLanz88UdMfX3mTNBPP+G3f28h7/JnWM9/\njS8F+RjToQN++qkFPvzwCMTimh9hEhH4Ur7SzO7pZU7SlVu38kpP6guEAoicRbjmo0QTezuQC8Eu\n0A7itmI4PXBCYN/Aav3odbpiJCbOQoMG/eDmZnorQK1WIy0tDU1MbF1sS09Hvk6HxSEh9S6eVR/7\ns7Iwz8B7iuwWiQknJiCybyTatGmDqVOXIDw8HG5uRZDJpsLF5TV4eQ16Lt7n0xw6tAN9+34AqfQK\ngDcBLMQbb3TB8uXRpqpahEAggKOjIxwdHdG4cWOD5Tp06IDz50eguHgmOqqyIF9CKOq4HihJQAvb\nlmZdq21btmtz5AjzB9TbH6EA3t95I21dGnxH1N7yukdvDzi95AT5TDm8BnnBtaMrM5J5eXrLExEe\nPnyIoUOHVjiWODcRTWY1sfh+q7rBTE9nM5DoaP2eLvWFLl2YWlB5tP/o0YCNDQ4dOoTFi9dBoUiH\nv78nRox48oFqNCyq4t494MMPgfqe0q9czMBA1i+9jB49BUeLRmIWr0EDvIkc3RfQ6Qbg22/fQr9+\n78K+LFbxeHAwfr0uxUfcy7iAN5FrW4LBg69i3LjjaNFCbtZ+L8AynBgydrp8HXgVr9fYlR8TOYgq\nLGvaNLKBQ3MH9rer2Kyk5hmJifjczxf2T83URImiahnLkpK7SEtbi8DA6bCxMW+FxVSmBC3PY15i\nIjq4uOArIw/H+oRUqUSgnZ1B4QSRUIS5Pedi6pmpWNp3KZYsWYK5c+eiS5cueOutpcjPj0V8/Fh4\neg6Eu3v32u18NfD29satW//gp58eIjdXgUGD/kKL2nJ41EO3bt3QpIkLHjwYh2Dt9zirbQ6c7wa7\nJivRcEpn3M64jTaNjHj1lPHZZ2wvs3lztsqpD6c2Tsj5PQeqZBXs/Gtvq8DG0wYhMSFQbFSg4GIB\n/Mf7w9Av+MqVK3j99dcrHFP8rEDDTxs+maFaQNUMZnIyc6latIgF8tR3bGyY9+ejR0BYGDYVKTFm\n33mUlEQBCIFcPhY3boxGUFArXL3aGsnJbBXXSsp4Nc7TYgYGJtEVICIcP34IPL8GSyDAt/6rsMxj\nNMCFgrfrhU0XL6Jzhw4QCwRYsu8P/NlgDlJKfRFTPBLxg53w69Yo+Be0xamIP9Gwf0MUphVCoBZU\nVKp5xvgJbYUV9vDEbmLYBdk9fm1purCqoOL5CsayumRk7IRanYLQ0OVmx53qdDo8ePCgwoj3abI1\nGsyUyzHGzw/NHMzfW6lrtqanIzwgwGgZZ1tnhHUKw4yzMxDdOxpz587Frl27sHDhQkyaNAmurl2Q\nlbUf8fHj0bjx/+Dk1LqWel892OyvBfr1a4GgoLrvy9mzh/G/r8dAcHQl7Gz2w93dDSsXrcWAgf2x\n7to6/BH3B8a/Ph72EuN+B9OmMSeghQsNP+b9xvshYXqC2V6m1kIgEMBnqA9K7pUgflw8/EW2eGyy\nJRI26ymbGM2ePftxveLbxeCKObi+XsW9c2MitXpPS6VEY8aYl/KnHpKVmUlfSxxoLsaQG3IJOERA\nLAEXKChoHz16VDPXNZXiq7oolUSTJplXlud5EovtCMghgMjb7xHhpVxCuxyy7/41zTp1ig5mZtIv\nGRnUclQYofcheq3DYTrStAv9ELSAhnZfSpO/7kezx3Sh/t3ak7udF0nEjvTmmwMpNTW1Rt9ndYh8\nVnCeqEopvnS6UpJKp1JOjuGMB4bYvXs3Xbp0Se+5/4qKaGxcHBU8lTHkeSBTrab5crnZ5S8lX6KY\nv5+ke7tz5w6NGjWK0svS+XGchlJS1pBUOoWUSsvTm9UFc+cSqauZeMOa5J3Po+Rfkyk1NbWS0Hhi\nfiKNOzaOTsafNNmOQsGeK8byJ+Qcz6HMg4ZTydU0OqWOZN02UcYvZSLzO3YQPXxI2dnZtGDBk0xJ\nulIdxY2KMyuhiCHTaJk/+oMHLN5xyRLjcRT1mNgLF3DAvgdiMANhtnPwYus8AJ0BtEZq6tcGlx/q\nO+ViBhcvmi4rEAjwzjsfQChcCQBQpIQCN92B61KIrx/B5C5dMMDLCx81bIgxwT6YfW4yWl1W4N3u\nm7FJPhYNH2Tjxtk3sfkPP+RSd7w3aAS+HDwSvr6ZmDTpVeTn3wQRV8Pv2DKe9ZCtKqWljyCVToKP\nzwh4eLxpUV0iwpUrV9ChQ4dK5/ZnZeFYbi5iQkPhYm6G6HrCpvR0/M/b2+zyHfw6wM/FD7/e+xUA\n0KpVKyxYsABLlixBbGwshEIJfH2/R0DANGRk7EBi4jzodAUmWq1byiY09Yb8s/nwftcbPj4+ldSS\nAlwDEPNmDArVhQg/FY7MkkyD7TRuzFQ11641fC2PNz1Q+HchU32qA0R2IjR5UwGRhIdsqgw6HybC\n/uzWR9L8JPiH+1drC8b8X+Z//wG7d7Nl2HrurWcMJycnADlo2EKKtF48vNXH0apNOvbs+R8EgtmY\nMYPd+C+8wISKgoOfn7dbLmbQqZNp56RVq6Jx6dIbKC5+iNLStyCRPIBEshFbt657kinj3Dl8G/8I\nfQJs8Xej+8D1EKghxfLCGxhotwFvOPXCbsVAXLr0ErRaOwCE0NDXcf78VrRp4wqAByCAg0MzODu/\nCnv7pnWmLytVqRBSzUFeVtZ+lJTcQ2jo8ioJNBw9ehTvPuNFwRNhUVISmjk4mFzSrI+UchyKOQ4N\nLbQWH7f6GEv/XoqrqVfR3rc9nJycsGjRImzcuBH//vsvRo0aBbHYGYGBU6BWK5CUtBi2tt5lHrU1\nJwX4fwVezRvd5hAIBPiw5YfoHdwbS/9ZimD3YAxpO0SvE0zHjsCdO8C5c4bD2/0n+iM5JhlNZtZR\nDL6fHxq0LoHz6/5ImFECL6f/kCZKe+ydnP1bNpzaOcHOr5oTPbOmpZcvE02fXmeJnK2JWl1K33zj\nQv36jSaAJ3SPopCQGzR6dAsaO3Y0EbEckP/+y3JdR0URRUayf8uWsaTqRhLZG6Sml2TLOX+eaM8e\n88rm5+dTTMxy6t//Sxo3bjI9fPiQnSgsZN/3jh1EPE+FhYXUdfsOatGyI734YldavnwFhYS8Qg1x\nhNbafUNvt55MIhFb3gWiKDJyxuNr8DxHJSUPKD19B8lkM0gmm04y2QxKhntgjQAAIABJREFUT99N\nJSWPai1X5oHMTLpRWFjpuDlLshynJplsBmVlHary9Xmep/Hjx1d4v/laLY01M9lzfWVNSgrFmZHl\nXh88z1PYyTBKzK+YQ/LatWs0duxYys3NrXC8uPgePXo0gdLTdxPP169n0YwZpsvUFiWPSihtY5pF\ndf5O+ptGHx1ND7Ie6D3P80Th4SwHvSHSNqRRwZUCi65rNc6eJfqTJS3nOZ62vzqado/YTTzHkzJZ\nSdIIqUXNGTKNpg3muXNEc+ZYlum4nlJS8ogePhxJsbE7ydnZixwdBxJ6dCYnpzb0xhuv0507w6i0\nNMFg/exs9r2sWPHEiM6YQbRlC9H168a3deXy+aTT1c6+78SJ+hM/m8Xx46yBsv0kIqJUlYpinkkG\n/M47gwhYTQBPX2MRdUI/EkBHEskhCgs7bvR24XkdFRffIYViC8lkkSSTTaeEhJmUkfELlZYm1IgR\nnS+XU4lOV+m4KYOpVMrp4cMRVFpq2Q/uWc6dO0cHn0pu+6CkhEY+fEjZVk72XJvoeJ7C4uOr1YZS\nq6QRh0dQoariYCY3N5fGjh1LV69erVQnLy+W4uLGUG7un9W6tjWpTwYzeXkyqdMt31DV6DQU83cM\nLbqwiFTayg+Q0lKikSMNP1t4jqe40XHEaetgMCOVsgdxGWEdO1Lhv4UUNyqO7n99n3QllX/7xqi6\nwVy8+Lk3ljzPU1raJpLL5xPHsRupoKCANmzYQD1m9aBTp04Rx3HEcSqSSqdQXt55s9vmOKL4eKID\nB4jmzXtiSOfPJzp0iEgmY2VSUlaTWp1uukErEBfHjLpF5OayIeSvv1Y6tSgxkTKf8WiIjY0lBwcf\nAq4TQOSFr2kFelAr18a0d6+Sxo1js11z4TgNFRXdpLS0DWWz0OmUkDCLMjMPklKZXG0jqs/hh4hl\np9fo9But7OzDJJNNt8pAZ8KECY+dL45kZ1OUTEba53zFZl9GBv2Tn1/tdjKLM2nssbGk4yo+1Hie\npxUrVtD69esrff88z1Nm5n569GgcFRXdqnYfqkNREdHChXXahQpIp1ZvcBefE09jjo6h8/LKP+CE\nBLb4ZIjiu8WUvLIOHLVUKuZ5RURyuZxWvvUWex0tpwfDHlDWb1nmt5WXVw2D+ZyjVmfSo0fjKC8v\nVu/5Z2cYPM9TSspPpFBs0VveXEpKiK5eJdq0iY0+x4+/TeHhWbRqFZu0P7PaZHWiotiM2CwOHSKa\nPFlvBR3PU7iBWcT27TvJzc2bnJ1bk62tFwV5B1PG998T7d9PHMds7/jxRP/8U7X3wHEqKiy8Rqmp\na8uMaCTJ5XMpK+sPUqksW3IyZDCX/7OcckpznrmuluTyuZSRYebatgmuX79O27ZtI57naVlSEm1X\nKKzSbl3C8zxNsKJL+d3MuzTrr1l6z50/f54mTpxIhXqW1DlOQ6mp68o8apP01K557t8n2rWrTi5d\nCW2+lhKjE00XNAHP87Tjvx007fQ0yi2t+LA6cYJo61bDdeXz5VSaUFrtPlhM2TR/7ty5lDt1KuXH\n5lDqWua1n3kwk2TTZaZnmllZRD/88P+mwczOPkLx8eGk1epfV+d53uCSXE7OcZLJoojjrOPin519\nlAoKLlF6OtGpU0RLlz6Zjc6cybYL//vPeq7pWVnMaBolM5P5jB85YrDI4exs+isvz+B5jUZD165d\no7t371JYWBg7+PvvRNOmERUXk07HHiYTJhBdu2b5+3gWnU5JBQWXKSXlp8czUbl8HmVnHyW1Wv/m\nso7naWZCwjPt6Gjnzp3U4rOX6bW+PWnt2nWkUqlIpUqlhw9HUnHx/ep3toywsDDKVypp4qNHdLmg\njvZ4rMzZ3Fz6PcuCUbsZnIg/QRtvbNR7LiMjg0aNGkW3b9/We16rLaTExGiSy+eSRmP4fq0JTp2y\nbDWlJsnYk0FFN623J55dkk1TT0+l3bd3V5jlx8QY/j3rSnX0aFzt+Sc8ZsYMUqvVNG3aNNKu2UZx\nX1+t0AdVioriRsVR0c0iysvLo1GjJpCHhx+5uDSiL74YSilXr7I158JCg7bPetlK6hEcV4qkpAVw\ncnoJXl6V85+Vo+N1WBC7AJFvROo9X1r6EKmpPyEoaDYkEhM52UxQUPAPOK5QbyiCTgfEx7PUOg8e\nPMl+7uQEtG7N0u34+Vkuy7d0qQ4CwTFcv/47GjRwxdChX+HFF19kvjn79rELhoUBroaDeMOlUkQH\nB5slIbVkyRIMHjyYpWxSKFhW2iFDgHbtoNMxJ+v//mP5vtu2tey9GIPjSlBc/B+Kiq5Bq80CAIhE\nTnByegXOzu0g19rhUmEhBpep5vA8j/79P8PZs4koCewAZAfAofg4+vfPwsyZfRASMgsikXVEAx4+\nfIhfY2OR1q0bpgcGwrsGkz3XJmFSKRYGB0NoZSm7DTc2IMgtCL2De1c6x3EclixZAl9fX3z55Zd6\n66vV6UhN/RE2No3h4/NdrXjUbt4M9OiBOhctAABZhAxN5lou+WaKv+R/4dCDQxjbYSyauDcBEctH\nHxFRMfdvOXln8qDN06LhRw2t2g+jREVhzwsvIDQ0FO4xpfAfoITNoIrPW+IIicsTsWDxUmzJLYVG\nGwbAHkHCxZhmuxUfPLgLz4AAw7bPmME2cbpeUlBwleLiRpNKZTqAXqVV0fzz842W0WhyKS5uFJWU\nPKxWv4qL71m8xFdYyJYz169/MhudPp1o7VqiixeJjE1WioqKqHXrN0gi2UXATyQSRZK9fSP6edZc\ntk565ozJ6ycplbQi2fz9iLS0NIqJeRKQThxHtGoV21At26/TaNgy9aRJRHfvmt20xWi1hZSXd46S\nkpbS2jvL6ff7CykxcTHl5v5Jx4/vJyen1gSoCCEnSBAQS4MGLaL33mtJGzfqn+FUlc8XLqRJDx+S\n6jnfr3ya20VFtCnNsiVxS4g6G0X3swzP8I8fP05Tp06l0lLDy34lJQ/KPGp31bhHbX0RLeC0HMmm\n6996sAZKrZIWXlhIy/9ZThqdhgoKiEaPJtLjS0dERPGT40mbX4siHDNmUNikSaTYrqDcX6REP/6o\nt9ju3bvpZfvPaCTiyB1qCsEjisE4crEdQjNnziGi6jj9PCdwnJaSkpZSSsoas38gxepiWnjB9G49\nx2lJJouinJwTVe6fSpVKqanrqly/HJ5nrt1HjxItWvTEkM6axcJJ7t0j0mqJZsyYTXZ2g1joDIgA\nnoZgMc0W21GamXtPC+RyyrHQi3Py5MmVl2L+/Zf9sp7ySVeridatY35GD6s3FjHJgjIPWY0mj3Jz\nz1BMTCf65pte9M0306n3l+Pou4mfU3DwfwTspp49B1jlmjzP04Lbt+mjLVtqf2mqhpkqldboAEDH\n6WjM0TGUVWJ4yTcpKYlGjBhBj0zcy/n5F8o8ak9bu5uPqS8esnnn8yj7qLmOC1XnbuZdGnVkFF1O\nuUz37jFnR32oM9U1asCf5XZ4OK2dsYzkc+XsQWngi/nuu1EExJAdtPRxm79parNIEkNDwB/UsSNz\nFjJk++pH5tlqolTKIJWOh4fHW/D1/d7s4HiOOIjM0AAVCsVo0mQm1OpUpKauqdIytVjsZpWcmAIB\n4OsLvP02W02dPZv9mzoVaNUKuHkTmD8fWLGiIVSqpQAECMVDrMAYxKETFkgG4dCpUyavo+N5FHIc\nPMxJ+PkUXbt2RWxsbMWDL73ERPrXrGHJRMHEIYYNY4Lxp0+z/stkFl3KbEp5Hg4iESQSN7i794RU\n2gFbt76BzZvn4PrJkdi2ZhVkshcBKGFnV325FjXPY1pCAu6fOoWNH3zw3GXgMEaKSgVPiQS2luS3\nsxCRUIQ5Pecg6mwU1Dr9afH8/f2xfPly7Ny5EwcOHDDYlqtrZ4SGLodOV4RHj8ahuPhWTXW7zsk/\nmw+3HtXbOjKHll4tseLtFbiTeQd70qMQEFKKgwcrl7PxsoF9qD0K/qkdlaa98TJ0vNsW/pP9je5f\neXt7QeKWAtU3yfgl2AXR9v7QQQKBIAHe3l5Gr/FcG0wiQnr6VmRm7kZIyFI4OpqXvqYcjucgEpov\n4+P9/5F33vFN1d0f/yRp2ibdpYOW7pbtoyhDBAQFlC2iyHAgDw6mQKGU2cUehTIUBVkCsgQHG2WJ\nMstSSoE2adp0p3tl33t+f9wWKF1Jmg6e3/v1ygt6c7/jJrn3fMc55+PxX9jYdIRMthAsqzWqLT5f\nBIZRGlXGGIRCbr9z7FggPBywt98EIAsAMAQnEIpVuIoeIBKCYepOW3c8Lw9DjdELK2fgwIE4depU\n1TfEYk4GjsfjNj6U3GdhbQ1MmcKpxB0/zr2VkmJ0s0bxySejYW29DUAeChRB0CidAeggFn+Nzz6r\nXhPVUDI1GsySSPCBlRXaqlSwb85KPiawMysLExpBQcXeyh6hPUMRdiGsxgGqUChEREQEeDweoqKi\noKvY/H8GHo8HV9d3ERi4BsXF1yGVzoNandqQ3W8S6sruY074PD4mvDwBk7tOxr/OkTj8ZzwePqx6\nnvs4dyj2K8Dq2AbtT0lJCUpT7NH6rdI6lYu8Ro8EfcQAvxYAJwJBdrkAUiESRWPGjOpFESp4bg2m\nVpsLqXQ2rK394eu70CQBWoYYWPCNy9vp6NgbHh5fQCIJhlabY3C5xp5ljBkzHJaWm9ESGeAD0EAM\nIBvALxg2bFid5a8UF+M1Ex72FhYWsLOzQ0EN+nQYPhyYOhWYNw+4c+fxYbGYE8GeP5/TIA0LA9LT\njW6+CgxRlR/5q6++ikmTPoJY3AnARfB4yyESvYeAgAV49913TW7rRnEx1qamYk1gIE7/8AO++OKL\nevW9uVGk14MAOBq56mAqvo6+eK/9e1h/bX2t540YMQIfffQRgoODkZpasyHk84Xw9PwCvr6LoFDs\nR3LyUuh09V/1aQ4oJUqIAmtXH2kIWtq2xOq3VuPDSakYO/cvPEirfNPyeDy0mtoK6d+Y4Wauhe/D\nvseoIaMh0sufHOTzOVHjcjI1GoRIJPB0d8e+Pr0g1vSHnWgELMW7YW39IqKigtG7d+/aG6ptTbiO\nt5uM3NxTJJGEkk5Xv6DpzJJM2nxjs0lldbpiSkiYQSUl/xhcprHS4xFxmVICAv5DC4UdyAVbiMeL\nIbHYjyIja9hweIokpZI215YDqw5kMhl98803tZ/EMJwz0KZN1aZcLCri9mgjIjjFBFNJLCujH2qo\nIDY2ll599Q+aOjWYLl68SEePsrRnj2nt7MnMpPWpXIKFoqIiCm8uG1tmJEYuJ3kTqBQdijtEh+9X\nTajxLEqlkhYsWECnTxumIqPRZFFS0iJKTd1ADGNaaqySEu532tSkrk8ldaap6b3MQ3J6Kb32wTX6\n5vrmKkkoUlankFLaMLGZ6mw1fd7186r7ljt2EMlkxLAsbc/IoIikJCp+SgmouLiYfvrpJ3pv43uk\neCbfaU2277kymHp9GSUlhVF29kGz1JdWlEZbbpruiMOyDCUnLzc4x2hjGkwiopKCArr51lvUv/97\nNGbMBPrzzz8NKrc0OZkK6ykxNWfOHMOcXW7e5ByCavC6zM8nWrGCaPFi03L4/pqTQ7E1uBPr9VVj\nVTduJPqr+hwX1aJjGIqUyejkU0kf1q5dS3J50wTRNxQahqF50vplkKkPay6voRtpNww6d+/evbRy\n5UrS1+S++QycR+1sysr60WiP2uaStKC+2X3MRWwsUUhUGk07MY3uZt59fJxRM5QwI8HsDnAsy9L+\nD/bT4R/LB1RPG8xLl+jhH3/QVwkJtcY/VxeLX5Pte26WZEtKbiMpaR48PL6Em9sos9RpqNNPTfB4\nfPj6zgfDqJCauq7Zxaza/vknOoeH448/jmD//u11LzcA0LIslAwDh3pKTHXt2hU3b96s+8TOnbl4\nza+/Bo4dq/K2kxO3ejt5MrBlC+fQlJ9veD8elJWhfQ3qtzIZ4P+MuMK0acCJE0Byct115+l0CJZK\nMdbNDYPK93tVKhVyc3Ph7e1teCefA/ZlZ2OsWyPG1D3D7Ndm49D9Q5AXyes896OPPsLQoUMxc+ZM\nKBQ1S1dVIBa3RVBQNKyt/SCRBCM//6zB/UpNBZr6q9YX6WHh0Dwk4bp0Af7j1wpDBOtxNe0qll5a\nijJtGfhWfLiOcIXiYN3fhzFkbMnAdevrGD5qeKXjWpZFtLMzTublYW1gILqZyZeg2RtMIgapqTEo\nLr6GoKD1sLb2Mlvdxjr91IS7+xg4OPSCVDoHDKMyQ8/MxOXLQM+eRhX5LTcX71YXiWwk77zzDn77\n7TfDTraxAZYt4zI2hIcDqqqfoYsLsGgR8NlnwKZNnAp8kQHOd0qWhU0N+mz373OOUk/D4wFRUZzk\na3FxzfX+W1qKJcnJWOrvjzbiJ0kOfvjhB3z66ad1d+w5giVCXFkZXrS1bbI+8Hg8LOm7BKv+XoUS\nTUmd51dobK5Zs6aq13YNODj0QFDQerBsWblH7T91lklL45KKNCX5p/PhPNC5aTvxFOPGAX//JcBA\nl0kY32k8wi6E4bTkNBz7OKLs3zLoCqp3zjKW0n9LoVAo4PGCByyeGuBfLSpCiFSK4X5+CH7wAEIz\nenQ3a4OpUiVDIpkJJ6e30KrVFLNrKepZvdFOPzVhb98N3t7BkEpDoNE07Aa3QUilnJinkc5GsSUl\n6GJnV+/mraysIBQKUVpaanih997jYk1CQ4F/q3f/d3cHIiK4bEHr1nGGraTu52e1PHjA6Z4+i6Ul\nF+4SFlbJZ+AxR3JycDIvD+uCgirNxHU6HaRSKdq2bWtah5opp/LzMdgEj2lzY21hjYg3IrDo/CIw\nbN2e3hUamw8fPsSGDRsMWgHi8XhwcRmOwMBoFBfHlnvU1jyrTU8HPD2NugyzU/pPKWxerH4VpakI\nC+PuzRZCL6wbsA4qnQpzfp8Dq4lWSF1bfw9lRskg8/tMnBOdw7hx4wAAxXo9wgMD8aikBBuCgtDa\nxoYLQa8DPo8Plgzz4m2WBpOIkJW1B9nZexEYGA1b2xfqLmQC9V2SfRYrq1YIDIwunxHfMFu9JrFv\nH/Dhh0YVSVQq0VokMptH76hRo3Do0CHjCnl5AevXA+fPA5s31/iD9/TkZoKjRnGzzfXrgbKyyudU\n5yH7NEol551bHS4uwMSJ3BJwBSwRVqakgIgwz9e3Slq4AwcOYOzYsQZc5PPF+YICvOnY8PF9huBm\n44aJXSZi2V/LDDqfx+Phiy++QM+ePTFr1qyavbefgc+3gKfn5/DzC4NCcRDJyUug01Utq9NxA6ym\ngtWz4Al4zS7W18qK20pZvJi7hUe0H4GwPmHYmrQVN0U3UfCXYd9DTaQsT0GrOa2Qk5uDli1b4mhu\nLpakpGAqEcYzjFGfRwtRC+Qp8ww6t9kZTJ0uD1JpCKysvODnt6hBc0Gaa0n2aQQCEQID16Co6DKy\nsw9Ueo/PFxodv2kSajWg1QJGrtvvVygwxoz7VO3bt8fD6oKz6kIgAGbOBLp25f7Nzq7xVB8fLrxz\n+HDOuH399ZMV3WS1Gv4i013tO3Tg9mR27eJGr7MkEgxq0QIjq/mMWJbF3bt38corr5jcXnPkRnEx\nutnbN6sHcgfXDuju1R077uwwuEyXLl0QHh6OqKgo3Lp1y+ByAoENfHzmwNPzS6SlxSAtbSMYRm1K\ntxuE4qvFsO/RPGN9vbyAgQOB7du5v+2t7BH1ZhR8xvngSPQRxKfHm1Rvzi85sO9mjwv3LqDboEEI\nkUgg4PGwJjAQ7p6e3MZyBTwewNY+e3SzcYOizLC91WZlMPPzf4dcvhK+vmFwcnqzwdsz9wyzAh6P\nB2/vYAgEIqSkrACVT/e5bD+NkPXi8GFg5EijiqgZBjoi2NXT2edZOnbsiLi4ONMKd+3K7W2uXw+c\nPFnrqf7+3KkDBnAzz+++A/7JL0OHGqaQej1nl+ti0CBAVqjF+INZWOTri5dq2Mf77bffMHz48Grf\ne545kpOD982wp21u3g58GwzL4GyS4Q46Tk5OiImJweXLl/H9998b5aRnaekOf//FcHYeBJlsEbKy\n9j6+r5uSxsruYyp9+gAaDXD16pNj3b27Y9SqUbi76i5W/b0KRWVFYOswahWoU9UovV0K52EtsEki\nwb++vojw88OQii0DHx9A/tQSupdXnQHdz53BZBgVZLJI6HT5CAxcU29lEIPbZY1PXGAMLi7D0aLF\nEEgks6DXl5gtPV6d/Puv0XIgP+fmNsiD8YMPPsDhw4dNr8DWlvOiVSq5zUt17aP71q25LHx9+gAb\nllji+n4bPJ0AJiUlBbNnz0fPnlNw+fJ+SKXSWus7nZcH3vAMtIlrhaLU6tfeiAh//fUXXn/9daMv\nrzkjUSrha20NiwZMg1cfvuj8Bf5K+QsPcw1fxeDxeJg+fTratWuH0NBQ4/bYAYjFrREUFA2RKAgS\nSTCcnOpOM9mQsOrGy+5jKlOmcMlIsrKeHLNvZ4+W2pY4NvUsHEc6w7qdLT7/fBpKnnJI0Gg0KHtq\nn4UYgnyVHKpgF0y4fRv/sbdHlL9/5UG+t3dlgxkUxElB1cJzZTBLSu4iKSkUHh4T4O4+plHb1rN6\nsy/JPout7Yvw9V2IpKR5YFl1wxvMf//l9MCM5G5pKTqZwdnnWcRiMRiGgboOQ1cnI0dyLrIhIYAB\nM9b27YE+c/PQp4cACxYAP/wA/P33NbzwQlds2qTDjRuf4sKFHLz4YndcvHixSnkiwoa0NOTodIj0\n98PiKB7WraveM/fcuXPo169fs1q2NAe7s7PxaSOkwasPYX3CsDl2M3KVuUaVe/311xESEoL58+fj\n/v37Rrfr4NAd7u7rYWOjQmLiTJSU3DW6jvrSVNl9jKXC87zCER4Arl27hhG7P8bL/84GTquhs1qL\nXal/4s0hQ5GZmYnhwz+EnZ0THB1d0KnT67h27RqSNqTip3F8nC4rhN+pU1j03ntVGxOLK3vZG2gw\ns8tq3vapRG1BoXW8XS9YVk+pqespNXVTg8vv1MTV1Kt0MuFko7TFMBq6f/9jSkmJbtiGFi0iMjIb\ny/3S0gaVa7p9+zbtM1d0t17PqW9/9x2X2aMWwpKeKCXcukXk5LSHgCvl6i0Vr2Pk7d2+UkB1mV5P\nIRIJXXsm2Dk3l2jaNE4NprCwkK5cuUJJSUkUHBz8P6dIotBoaEVyclN3wyCK1EU05fgUUuuMz3Sj\n1+tp5cqVtHfvXqPLxscT7d/PqRllZGwjiSSUVKrG+8yaQ3YfY0hMJIqM5P7fr99wArbQCyig/sji\n7kVRDgkHOJHj664ksJhDQAEBWgL2UNtOo2n0nsuUUFZGKpWKFi5cWHNDTycvYJiq2UmegWEZirwQ\nWelYTbavSWaYanUKJJKZcHR8E15e08weLmIoDeH0UxN8viW8vUOhVD5EZqbhzgpGUVLCZWG3tjaq\n2CGFAqMbMCj95Zdfxp2n8sbWC4GAU67t1AkIDgZqCExnn/GQbdkyAypVMIBXARA6esbD2/shLCze\nQl5eKRITEwEAKWo15kilCPbywqvPOE21aAFMmUJ4442LaNnSDwMHzkD79p1x/PhZgwLknye2Z2Zi\ngodHU3fDICoStYdfCDc6eYhAIMDcuXPh4uKChQsXGrUSUhGDyedbwMPjM/j5hUOh+AnJyYuh0xmR\nXcNEtNlaWLV8fgTJg4I4J7oDB4C7d+8AeAtxcIR7ixPgC/IAlQvYs3NQmuEIpr8GcMoDxDwI//sa\nvFqOguXvu9FaLMbhw4cx0lAfDT6/1tCShIQE/Hf8FGzYtAV9+w7HmTNnaq/OiOutN0SE7Ox9yMr6\nAQEBa2Br+2JjNl8FU5Kv1weh0An29q/CysoLMlk4WFZv3gZMCCVRMQwIgNgQD5h6EBgYWOd+oVG8\n+irns75uHfDMj1yv1+NuVhZ8rJ48TPh8PogYBKIIX0ECN34Z2rS5jdGj1+CjjwpQWroJBx6sw+qH\nv2GhUyIcmeRqPZrPnfsWN2/+BLU6CcXFN6DRjEdS0iAMGPB+s8v0ZCpKhkEZy8KtKeMljMTX0Rcj\n2o+oM1F7TQwYMACTJk3C7NmzDf6dPpu0gPOoDYGn5ySkpW1AWtqGBvOobU7ZfYxhyBBui9HJ6Q0A\n3NbKjw6lYL2/AwDweNnQS8OAP1bDsfN3cFkYg7E3riI27y+czT2LU4mncObyGTj7O0Otr99ne/v2\nbXTu3As//uiJgvwhuHDhPbz33mSsW7exxjKN9onrdAWQy5fD2Xkg3N2Ne6g3FHpW3yBesjVR4fTj\n7Pw5rK19IZHMhL//EgiFTvWvnIhLVjBxolHFDuXkYFQjpDwbM2YMvv76ayxcuNB8ldrbcx4+Bw8C\nUVFgQkIQtmwNNm36BuqXX4JIrUXhmJEIDv4KzjxnzHaYgX8Uv+MbfAA2jQ+kdQXwI4Jat0bniHkA\nj4cYFyuo1RIUF8dCofgJRDoABIAgFLrg5MnVcHFZh4wMexDJAHQAw0yARNK6/AbsbL7rayJ+yMrC\nOHf3pu6G0XT36g55kRxH4o/g/Q7vG13e29sbMTExWLFiBV588UWMGDGi1vNrSlpgaekGf/8oKJUS\nJCeHwcbmRbi7f2TWlbT80/lwGmCG50YTEBICXLiwABkZ46FUdgJSJwDdh4KX4gKx+CD0ejmUyk8w\n7ywff50djXuwRbFlDjoPbYecpBy4BrjiD+kfUJQpHhtNHo/3eMDaU3EV0nOr4OjmA1cbV/ynNBtU\nkgkXG9dKE6SvvlqA0tJlAL4AEAngUyiVvbFoUc2hYY1iMAsKziE//zR8fOZDKGw+KZwYloGlsPFG\n0QKBDRiG88oTi9vC338JkpPD4ek5FTY27epX+ZUrQI8eRhe7X1bWKI4dDg4OKCsrg06ng9Dc8lCj\nRwPJyfj7tV44kyhEqfoa0EqIkt8KELXov3D5swV6d+6J0b+MwDeDB8BKexIqVU9YW9+AhegUXt/1\nE9qKxXjDiXsAWVq2gL39q5WaICLodHlITQ1Bhw6lePPN5bj6jwBCpH97AAAgAElEQVTS+1MAlg+B\n4CUkJyc/9wZTz7KQqdVoXVNGh2bOqI6jEH0lGrHpsejaqqvR5S0tLREREYGff/4ZUVFRWLBgQY2/\n17qSFojFQY81OCWSYLRoMQTOzm8b3afqKP2nFK6jahc7bq7w+cCPP7bBkCGbcOfOS7C2fgEl1jfg\nH1CAgwePo1+/IXgRq5CF9nAE4QTSIbZYi6iZ+3HmzBksX7Qc1rVsO5HiIF519EaWRwsoyhRItyPE\n/f0DUsQ6MMSAONERXLH6HXijC8AuAUQ5ABgA/hAKX4ZKdaGGymsBAGVl1bpfWit6vYpksqhyFYDm\n5xRxKvEUXZFfadQ2n1UsYRgdyWSRlJt7qn4Vz5vHeaMYwd2SEtpTH+0sI/n777/p118NU3YxloKC\nArKxsqfZiKDPsZXwXyn1goIm4xq1t3uZdOWfTW5uLq1eHU1jx35G81avpQl375JMabjsUJs2nQk4\nwTkptLxNeGE/AWoSidzp4cOHDXJtjcnB7Owqzk7PGyzLUsiZEJIX1k8xJjExkaZMmVKj8owxCm4s\ny1JOzlFKSJhBxcW369UvRsdQ0qKkuk9s5vzzD9HixUo6d+4cDV01lM5cOENERLdu3aLvbd1ojkU4\nOdl1Izs7V9qxYxcVFhbS4sWL66746lWik085c549S/SMUhPLsmRlZUdABoGvI/hcJHQ8QABDlpab\nTJf3iokhmj+fc0xMTKy7rxWUlPxDCQnTGtVrzFiOPzpO19OuN2qbNUl8ZWTspLS0r00bWCgURKtW\nGV1sUVISqQyUQDIHLMvSnDlzGqTu2NhYsrd/mQCi7vy/aczHW+gV5BNAJBK5UeYzA4MbRUU0KzGR\nSo28/iNHjpBY7EPAXwQwhP5TyNp6FA0c+L45L6dJYFmWZhlzkzdjVDoVTTk+hUo0JfWqR6lU0vz5\n8ytpbLIsSw8fPqSpU3ONvl85j9rtJJGEklIpM6lPBZcKKPdkbt0nPgfs3090/DjRsX+PUd8v+tLp\n06dJ/fvvJBt1jGJ/i6W///6bVOVe/9988w1JDZGYS0/nvOgrSE4m2r69ymnjx08iS8uJBLDcALjF\nI8KAd8jFrX399TCzs4m2beOM59KlnIxhdb8VlmUoNXUjpaauJ5ZtvIexKfz28De6lXGrUdusTROz\noOASSSRzjRezXbeO+5EYQYlOR5EymXHtmIH169dTamqq2evNysoiKytHAooJo1II3mXlYSMpJBY7\nkVr95DPdk5lJMXK5yasee/fuIw+P1mRpaU8Wvazpw4n/JaURs9Tmyvn8fDqWk9PU3TAb2aXZNP3k\n9CpixqawZ88eWrlyJV24cIG8vduTWOxNFhbryM/vBbpyxfhVKr2+jOTyaJLJIkmrzTOqrCxKRnpV\n8362GsPIkXEksu1Cgr72ZGfbi3YKetDR6UcrncOyLIWEhBhWIcM8iV8h4kLRIiOrnJafn08dOnQl\nW9suBCwiG5v3ydbHmcbsHGO6waxO6bykhOjwYaIFC4jCwrgZr1ZLpFLJKSHhKyouvmPYhTUxR+KP\nVBI5bQzqEpFWKmX06NEU0miyDauQYYhCQ43ux/fp6fSwrMzocvUlJyeHVpkwGzaE4cM/JOELwYRh\nSeXGMp9EooE0fTo3q60Qez6RW//ROcuylJeXR/J8OUVfbuDY2kYiRCIhphlundSHuOw4iroYZZa6\nzpw5QxYW1gTsKp+VsAQcJltbV0pLSzOpTo1GQUlJESSXx5Beb9igq7mIRZuD+Ph4srb2IEBB6PMB\nDcFmWobDJBa70oMHDx6fd/HiRTp27JjhFT+7Xl7D+rler6djx45RZGQkff/991RUVETSfKnpcZhF\nmiIcul9ZccLWFnj/fS5zQ1gY92jasuUg9u7dgXv3VoHP72TI3m+T05hxmIYiEvkhIGAVUlKWG6TH\nh99/B9423pEgQaVC2yZw7HBxcUF+fr7BuSON4fvd38Lrv/6wOtsVDg7dYW0dgNGj/REdveyx2PMY\nNzezSFXxeDw4OzvD28kbijLFcx9Scq+0FB3E4ioKLM87Hd064tVWrxqVqL0m/vjjT/B4nwO4B+Bv\nADwA70On+wBbtmw3qU5LS1f4+0eiRYuhSE4OR1bWbhDVLF32vGT3MZTNm7dBp5sAwBVImIY+/Hws\nw3BotV/g22+ffKanTp3CoEGDzN6+QCDA0KFDERERgc8//xz29vYIcAqo8fw6DeaElydApVNh161d\nCA6eCweHlhAKrdGr1yDcvHkTQCH8/UPx4YfO+OyzCPj4iLB+PbBgAbBzJ5CTY87LMy8NlXy9vlhY\n2CIoaB3y8k4hJ+eX2k8+fx7o29eo+m8WF6NzA6TBM5T+/fvj7FnDk2Ybyub8fPw55QtIE//BqVMx\nkMsTsHPnZjzUaLC4XOy5IQYJXVt1RWxGrNnrbUz2KxT48DkMJTGEAUEDjE7UXh1xcRLodD0BrEHr\n1kvh57ccAAuNphvu3UusV90VHrVicTtIJLOQl3e62kFY/ol8OA9pPpEG9YFlgXv3+GCYwQCAgX75\n4H+qAPO+DHpeW8hkGQCArKwsuLq6QlDfWHEzDGoNCisZ99I4tBv1EmQyMXTFfwFoicuXD2LatH5Y\nv344OneOgVDIjdq7dOFeAJCYCOzZA+TmcsK/77zDqUo0Fxo6+Xp94PH48PWdB4XiIOTyaHh7z66a\nq1Qu5yKnjZwV/Jqbi3A/P/N11kj69u2LBQsW4G0TZsY1caGgAEEiEbytrYFWrdCqVSsAwC85OXik\nVGJdUBAEDTR7GtZmGJZeWopurbo1SP0NTZpaDVehEFbNNMm6Ofii8xeIuBABL3svtHMxLYSrc+f2\nuHDhL7z7biri4oahqEiHnj1/w927DggIeM0s/bS37wY7u67Izz8JiSQYLVuOg53dk7jA5y27z9Mo\nFMD168Ddu5z6II8HtGr1JqysDkGj6YEzWX0R9IjBxBXzcaJfZygdhyG+rAzHd+/G+PHjjWuMz+fU\n3yuMrLs714F6DgoNshZXrlxB+ikVdP6hwEtXIIwfgzFjMpGVNQSrVunxyy/VL3G1bs1lMQO4TPXH\njgEyGWBnx8kmvfSS0c96s9IYydefhccTgGX14BtoqN3cRpcrv4fA338J/vrrBlav3gyZTI4lNkCn\nzRsRZET7RXo9rPl8WDbhw5HP56NFixbIycmBq2v9Y8mK9Hocz8tDdGDg42MsEdakpiLQ2hrzfH3r\n3UZtWFlwDzCNXvP4/88TO7KyML18gPG/TFifMMw6MwvhfcLhIjZemWfy5M+Rk9MWV64E4/799wH8\ngZKSDIwbFwUbm98xfz7w6adAu3qGVPN4vPKYzYHIytoNheIAPD0nQ6j1fm6y+6jVnGG8fp2bMAGA\nqyuXoCs0lBOYBoDCwh44e3YqcnMdwLKToNT7YvdkJT6e9i3affIZNif/B7FSKVwZBqMYBjaGzjJb\ntuR0dCsyS1QkYa/vKkpt+6YVb8fExJCl5VcEENm1/4GiZvWgli2lBDwgd/cgwzdiyykqIjp4kPO4\nDQ8nunDB6BBCs7D99nZKLmjcsJfU1A2k1RrvdKJWp9PPP/chb28PAraQBS7SCl4vsrV1pdjYWIPr\n+TYtjSTNwKMzLS2NYmJizFLXPKmUFBrN47+LdDqakZBAd0vqF1JgDDfSbtBP939qtPbMRaFOR1FN\n4C3dVJiaqJ1htCSRhND16/upY8fuZGnpSAKBNb34Yk+6c+cOZWbuokePomnzZj3NmcPFGJoLzqN2\nLcVt+obyb5jfw7y+sCwXcrh3L6f9sGgR0ZIlXLiIQlF3+ZSUFBox4iMS+HWmnp576G3vwSSXy6mg\n4BJFbRhIo3/cRkdzcihSJqOFUindLC6uu9KTJ7l4zAqkUqJduwy+pppMo0HDFQ8PD1hZnYZWC7zx\noB+2PAhEFjYC6IKWLY1P0GxvD4waxb20WuDiRU7+hWWBbt2At97iVFoamqZw+qkQka5YwjYUvd4B\nEyb8i9GjR+DSpZfQ8YEcP9NalJbGYerUebh+ve79GSJCklqNQFHTOw20atUKmZmZIKJ6yWL9lpuL\nHvb2cC1PuZKoVGJjejoifH3h0oi5ULt4dsHh+MMY2cE44e6mZkdmJv7bzCW8zMnTidpX9l+J9PR0\nxMfHw8/PD23atKm2DMOokJQ0D15e0xEYGIi4uDHIzMxEdHQ01q5dW35WJ9jZxaNfvxnw8JiPI0da\nYfdu7hnXrZ4r9QKBGN7es5CYGoeCwdtQmmoPT8/JEAia5j4uKABu3ABu3XqipBUYCHTvDowdy62G\nGoOPjw9+/nkvJGUqLFikxdA4Fo6OjrCzex1FKUGIHPgPLhe4AMKXMN3LCxcKC3FIoYC/tTXGuLnB\nsbpMTN7ewIMHXKe4RoCUlPpdOAxckn3nnXcweXIwgMPwxMs4hp4AWkIo7I4hQ76sVwcsLTknz7ff\n5gxmbCywdi2nF9yuHTB0KKcS0RA0dvJ1ACaLSN+4cQMs2x5btqzE++8vRoDrXzh86Q6Al3Dz5kRo\ntVpY1mEgrhcXo/szChxNSa9evXD58mX06tXLpPIKrRbXi4uxPIDzajudl4erxcVYFxgIYSMvOfN4\nPLjbuiOrNAstbZ8PA6RhWSh0Om7f9/8Rvo6+GBo0FJ2ndkf8jkRYW78CrfY+OnfuhN9++xHOzk+c\navT6IiQlLYCv70JYWT1JHOvh4QFbW9tK9drYdEBg4BqkpCzDsGE98Mkng3HoEJfqePhwoHdv0/vM\n6llYWNrAPyASKlUSkpMjYGPTEe7uH4PXgI6LOh1w7x5w7RqQmcn5zTg5cYOAmTPNO7ERW/Ch4vPR\n2rs1Tp8+jZdffhlt2ryAtq2/hH3mVhRqExCTOgJtxTZYERCAZLUamzMyUMowGOzsjJ4ODk8G3z4+\nlUUZLCy4Pc16YpC1EIlEOHv2OOb0X4RHaifYC4ug1V7G9OnT0LZta6xYsQIhISH1zhHK53Nr3K+W\np/F8+JDztM3PBzw8OKchc25HNXbydcAwg0lEyMzMxIMHDxAfH4+cnBykp6dDrU4EsB9HjrwLB7EK\ngAqAEgKB0CAPsqN5eYhqQmefZxk0aBDCw8NNMphEhJVyOSL9/EBE2JSeDkcLC0Q1oVfZmBfG4EDc\nAczsPrPJ+mAM+7Oz8WEjJN5vjhxefwxx13TQBWyC5sFHAHS4cWM2PvhgPM6dOwoA0GqzkZy8uFwg\noapnanUrIwKBCAEBS6FQHEJq6mKMHj0Po0db4uhRzp9j4EBuBc3YRZXiq8Ww78ENdkWiAAQGrkZx\n8U1IJLPh7DwAzs4D6y1gTgSkpnL7jnFxnH0RCjk9+uHDgYbe5rbi86Engm93X5y9cBbx8fGYNWsW\neDwePD0nwq7kLiZkbUC+5XTMlEjwkbs7Fvj6Qs+yOJWfj3lJSfCwtMRH7u5wtbcHiovN3keDp1ev\nvPIKtkzYiLTBaSgsLkSPHjvgVn6zJSQkIDg4GLNnz4a/GR9Y7do92UDPyACOHuUcQx0cgMGDgRde\nqJ/TUFMtyapUnAI4y7JITU19bBgLC58YUk9PT3To0AFjxoyBq6srWJbFqVNtkJnpBaAfipQ+AHYB\niAfDCPHFF9Px7bfrYGVVvdNJgU4HW4Gg0WdetWFhYQFbW1sUFhbC0dHRqLJ7s7Mx3MUFFjweQpOS\n8L6LC7o7ODRQTw3D084TGSUZ9V5mbgxYIsSVlWH8c6J5aU50Oh22bdsGnfIO0OMQ4BkLZHSFVrsa\nV654IyUlBW5uhLS0tQgMXA2BwMboNtzcRkGlkkIimQVv71l4990ADB/OTXpmzwb69AGGDTN8+bLw\nfCG853pXOmZv3wV2dp2Rn38KEkkw3N0/hr19F4P7WFIC3LzJrepV2BYfH27CMmIENylrTKzLDWaL\nYS3g9JMTMhwzYPdU+JudXSeIRCvAT1mCcOc3cUJph19ycjDTywvDXFwwzMUF6RoNdmVlIU+nQ18n\nJ/QnqhxbTFQvo2HwR6KWq2HtbY03+r5R5b02bdpg3bp1WLVqFdq0aYPRo0eb3KGa8PQEJk3i/l9Y\nCJw6Bezfzy3p9uvHCXUYG6bTWHGYDMNAJpMhPj4e9+5dQ25uHOzs7oLH48HHxwcdOnTAhAkTajUa\nfD4fv/66D2+9NQwq1W7odK8A2AKgBVj2Gg4cWACt9ivs3bu12vI/Zmfjo2YYZzdmzBjs378fkydP\nNrhMilqNZLUafRwdMUcqxUJfX3jWMFBobF7xeAV3su7gFY+aJYKaAyfz8jCkofY6mjklJSXQ6xkA\nvsCVEHQaPx7KK18hIaELrKwCkZb2N3S62wgMXAs+3/R9cJEoEEFB6yCXr4RY3AFubiMxcCAwYABw\n6RIwZw63tDlyZN3PLlbDQmBd9STOo3YwnJ0HICtrD3JyDsHTcxJEosrB9wzDbelduwYkJ3M2w8YG\n6NqVUwRs4rEmAMCKx4OeCFYeVkgtSUXrgNZVzrGwsEVg4CpkZ+9DH/U2DPeejY0ZmXC0sMAkT0+0\nsrLCHB8fsEQ4D2BBUhKchUJ86OYGLxcXIC8PcDHeQ/px+4aemL0nG55TqhF/K8fS0hJhYWE4c+YM\n5s+fj4ULF1ZZ4zcXjo7c5vLYsYBGw8XuR0Zye6CvvQb07w/UtS2j0+mQmZ2JvNw8OHiZ59ei0+kg\nkUgQHx+PR48eQaPRgIjA5/MREBCADh06oHfvqSgpOQhv71lG19+tWzdIpffh5eUPwBPATAAuANpD\npdqJI0f8EROzrEqoBhFBrtHAtxnuVQUEBEAmkxk8K2OJsDY1Fe+0aIHN6elYGxgI6wYWvzaG4W2H\nY+XfK5u9wbxYWIg1T4Xh/H/C0dERDg7OyMm5DuBV3N21C2PGrIGr632UlNyHk9NtBAauNsveIJ9v\nCT+/cOTmHkNS0iL4+i6EQCBCnz7cLPP6dWDePG617MMPuSXQCmJjY7Fv30+wzLHCQI++8Cf/Gu8R\nHk8AD4/xYBgVMjK+Q2FhEWSyabh71wVaLTeT7dCBm1z4+TVtOF9NWPD5j5M1FAgLkJ6QXuO57u4f\nQqlMRFpyCOZ4hyCFdUKoVIpBLVpggLMz+Dwe+hcUoH9gIHK1WuxTKJDerh16PHqEwc7OJq+0GWQw\nWQ0LRsVA6FT3HuWAAQPQqVMnLFy4EJ9++ileeaVhHxxWVlxM56BB3Cjq+nVg9WrOe6tjR07h2+kZ\nndWtW7cjNDQMqs4abLy2DT1ffR37938PdwNnYGq1GgkJCYiPj4dEIoFOpwMRwcLCAq1bt0aHDh0w\nePBgiKrxRiViUVhYYvL1MgwDHk8EYHP5kS3l/zrCyioAycnJVQzmX0VFeL05DCFroHPnzrh27Rq8\nvLzg6upaq9bdtxkZcLSwgESlwoqAgGa39JkiTcG+Az9h9bD1sLKwwSeffIjlyyMqLS01NdeLi9HV\n3r7ZfXaNBZ/Px7JlYZg582MolTsA9MKBA4MxefJA9Orljnbt1hgs9mzoQM/FZRhsbTtBKp2DVq2m\nPda/rfDZuHsXWLiQCxccNw5YuHABvvtuL1SqCXiPfPGJKBQ95R2xf/8O8J952KtUwO3bnOdqfr4I\nRMHw9MzDSy99jXHj7ODtPQkCwfOjb3rr1i30GNoDRVeLav18xeLWj2fwzuKOiAl6H0fz8jBLIsG0\nVq0QIBIBKhVcRCJM9/ICKZW4evcuIpOTIeLzMdbd3fiIAUNiUTL3ZFLJXeNi2hiGoY0bN9LXX39N\nDMMYVdYcsCzR/fuc6tX8+UTffEOUmkr066+/kljsR8BdQu8lBH4RWVjMo/btu1RRrygtLaWbN2/S\n7t27KTIyksLDwyksLIyWLl1KP/30E92/f580T8X/GUpdCdhrQ6vVkp2dKwEJ5QnGK14FZG3tSDnV\nqE3Mk0pJ30yTauv1egoNXURCoYjEYk8Si50pJGThY/3Kp7lTXEz97tyhc/n5TdDTuklOTiZ7e3eC\n91RCu50EPCIrq4+pU6eepG9EGbW6mCORkK4J7snmxs6dP5CnZxvi8y1owAAn+v77YZSXd46SksKI\nYeoODI+OjqYiI7VDGUZHKSkrKTNzd7Xvx8cTffppGgmF4QTkEkD0OaQElJGNzct0+PARevSIaPdu\nooULudfSpUQnThBVJzSjVCaRRBJKGRk7m716FBFR7+n5tGDBAlKr1XTi3RN065ZhalI5OcdIKl1A\nen0ZqfR6ipbLafmvv1LpUwncSaPhAkSJiz/+Lj2dQiUS2peVVUXmsCbTaJDBlIRIDOp0dcTGxtL0\n6dMpO9tA9Y0GQi7njKab2x4C7nFGpnckgccQUEAikT+FhoZSeHj449fKlSvp119/pYSEhGof4KZS\nH4NJRBQRsZTE4h4EpJQbyzISiYbTJ598WeXcHI2GVqWk1Ku9hmTu3HASi3sSMIOAUgKSSSx+k6ZP\nr6zAIlep6D83blBCaWkT9bRuJk2aQRYWczkVi/6h5d8NQ7a2r9Dx48ebuntERJRYVkabTVTW+F8l\nNXUbZWQ8CWovLX1ACQnTSaerfZKwc+dOSkoyTcg5P/8sSSRzSKerGoQ/dWow8XgbCSCytFDRSKGk\n/Ld0h4KCDtMPPxA9fMgJFRlKcfFNSkiYQbm5x02WtWsMXvsyiZYsWUpERA9DH9LyxcsNLqtWp9Gj\nR1OptDSOiIjkf/5Jc86do/1ZWU+uuRrVktvFxRSWlEQRSUkUV/58MdlgnrmaTlkHsgzudHUUFxfT\n3Llz6ffff69XPebA3t6dU9kGkadoJwFhBESTldVbtGLFikaZDdfXYDIMQ/PmhZNY7Ey2toEkFH5B\n77yzspLmYwUxcjmlVXO8OaBUKkksdi43/PcJ2FH+YMgga2tHWrAgjMaPn0xhe/ZQj9hYumNIho8m\npF27Vwn4m7uGATMILvHl1xNBCxcuauruEREnGm6saPb/MqmpG0mh+KXKcY0mmx49mkJqdc06s0eP\nHjUqy1bVNrKqlUMcP3kG4c1thC8lJB6USE5d9hCgJ2Af9e37rsntsSxLubmnKCFhOhUV3TC5noak\nTf8VdP8+l30t7/c8Wj12tVHlWVZPKSmrKSNjG7EyGdH27fRnQQF9VZH5qwaZLyKiMr2efsjMpFCJ\nxHR5r6Sr+Yh+uQQb0tKQq9Uat95bjp2dHVauXInc3FwsXboUWhPrMQdBQe0AXAYADFT1BrAYwCwI\nhWno2bNnlf2B5gifz8eKFVHIzU3D7dunkJW1Fu3aza0SUsISIVOrRatm4kH6LJmZmeDz7QH4AOgA\ntDoJvFAIwANqtQ3WrEnErtTXsOJmFu5v/A7upaVN3OPa8fBwByDl/ji3HOj2DcDXQyyWoGXLpvdQ\nVmi1sOHzDc/H+T8MESElZRlsbDrA1fXdKu9bWrohMDAaqanRNcrstWjRAnl5eSb3wdLSHUFB61FY\neBGytG9xICsL86RS0HtDIcr9CdjaCspTQShIeQXovRxi8TZ8/PE7JrfHedQORFDQOiiVDyCVhkKl\nkppcn7khIug16XBw4ILtHfs4wr/YHzKZzOA6eDwBfHzmQCRqDal2A3RZEvR2dMS6wEBcKy5GmL8/\n8nS6asuKBQKMa9kSq2pxhqvTOgzS2GFNmyC85+KC7VlZmCuV4mx+PlgTpFLGjh2LsWPHYtasWZBI\nJEaXNweLF8+BWDwbFoiFHnwApRAKQ+Dv72ByxpmmQiQSoXXr1nB2toOXF+cu/jQXCgvxppHxjY2J\nu7s7GKYQQCZ3IL0d4KwFJt8D2rpDNzYC0AyEnhmO0gMtMH36vCbtb13Mnv0lxOIlADIAvRiInQK8\nNhU83mmMHTu2qbuHHZmZ+Oz/YdzlsxAxkMkWwsnpbTg59avxPIFAhMDAaOTlHUde3ukq79fXYJbq\n9TiUk4tvtMNwtNQNPvlLscTHGTv69kV/P0fY2PQB8COQcx9WhUfQ8q1MfPjhhya3VwGPJ0DLluPg\n5xeF3NxjkMkioNU2vQ7j+fPn4fFCT1SEo/Mt+Wjj3wbHjx03ui5Hx97w9V+EZO/zKCq6Bgs+HxM9\nPTGzpATrExOxNSMDjClyX7VNbwGQOr3ycp6eZelMXh6FSiS0OiXFpOU+rVZLy5Ytox9//NHosuZg\n167d1N2+D71pNYysrBxo4MD3SWFIlmAzkZRU87KAqRQXE0VEVD4WKpEQ04z3K4iIpkyZRSLRQAIe\nELCBc3RwGE6IPkCimbep1/gdBBcVATkkFIqa9f4LEdHixSvI2tqR7OzeIXv73iR6zZ6ij0Q3dbeo\nVK+nRSbut/0vwTBqSkwMptLSeKPKZWRsp4yMbZWO5eTk0MaNG42qp1Svp4PZ2TRXIqElMhndLi5+\n/JvWavMoMXEmFRZeJb1eTz/++CP16/cu9e49lLZu3UqrLq2i2HTTl4BrQqvNJZkskuTyaNLry8xe\nv6GEhobSRzvT6fLlJ8cy92TSks+XmFwnGx5GaWnfUmrqemJZhujYMaIbN+hOcTF9lZBAfxYUVCmj\nydLUz+mnJrI1GoqRy2mOREK/5eQY7Xn3xx9/UGhoKBU3wd5U8ppkSriZQHl5eY3edkMYTCLOAazC\ngTRTraZoubxB2jEnWq2WJn4+jfwsRNTf0o0mBbah7tNm0TybMArlr6DD0zuSu7uMgGISCITN3mAS\nESkUCjp06BCdOHGCVCoVhZwJIUVp4w3IqmNzWholljXdw7A5oNOVUELCNFKpTFMoys09RTLZYmJZ\nhliWpUuXLtHw4cPp4sWLtf4uK4zkPKm0ipF8FpZlKz/gn3lv1ulZlFmSaVL/60KplJV71G5vdI/a\ntLQ0WrduHX32czqdOPHkuDZXS7uH7Db9OV2+Z1lcfJsSEqaTJu4von37iIj7PPdnZVGIREJylepx\nkZTVKQ1jMCtgWZYuFRTQPKmUliYnk9QI+SiFQkEzZsygGzcadxNaulDaqO09jUwWVeVmMAfp6UTR\n5ZOZaLmcskwIeWlwcnM5PbcNG4jCwrhXRAStGTWKlu3aRc3AcxIAACAASURBVHNv3yaRrQsBcQQQ\nRVosounTp5JItIzeemtEU/feJIrURTT95HRiGuA7NwQdw9Acieme7v8LaLV59OjRZNJo6uetX1Ly\nD927N4m6dXuNbG3bkFD4EtnadqT27btQZuYTQ1aq19Oh8pnkYpmMbtViJKujuPgW94DXVB5olWpK\nTZInM4bi4luUmDiTcnKONdoAdcWKFZSTk0NTz6ZW2LPHXP/iOu3eXX0YTp2Eh3MxhsQNmCSPQihv\n0/hKp5TodLQ8OZmi5XIqU2lJOk9aP3mvuuDxeHjd0RGvOzqiUKfDfoUCSWo1utjZ4V0Xl1qV3F1d\nXRETE4Nvv/0WV69exbRp0xrc8YZMWbs2IwKBPfT6YgiF5t1f9PTkMj+p1IQcrRbujShvVQWtFnj0\nCPj3XyAxkUvDBADOzsCLL3JpmsoTLLBEOD1zJiYOGoQP3NzQ/ut1mDLlLajVwYBeggMHrDFp0nJ8\n+WVs011PPbC3sse4l8Zhc+xmTOs2rdHb/zk3F++bQaj7eUWjyUBKyjIEBKyAhUX9EnjY2r6IZcsy\n0atXCRITL6K09DvodJFITFyEkR9Pwox9W3G7tBRiPh9DWrTASFdXkxJE2Nm9ApGoNVJSFsPZeTCc\nnN4EANhY2mBOzzmI+jMKy/oua5DkE3Z2r8DW9mUUFPwOiWQm3N0/gr19PTXKakGn06GoqAguLi6w\ntpOjMKHy+x7tPHAi9gTwiQmVu7pyCtaurlxavTZrkH32PaSkLIO3dyj4fCFsBAK8mZmJO9nZiE7w\nQOc3WgIrq6+uToMZLZdjWqtWBqcfcxQKMblVKxARbpeWYmlKCgQARrm5oYNN9UmMeTwepkyZgjt3\n7mDmzJlYsGABWjagRp9KooK4ddNlvqhQLDG3wQS49FoLf83HkAFV1RUaBCJO9+fffzkdoIoszpaW\nXOb8Ll2AMWNqTJZZotcjPDkZAdbW+KA8mf+nn36Cjh3bY8OGrfD+8yYmfDQWX375G/j83wC0b5zr\nMjOdPTvjevp13Ei/gW6tGu7h8yxEhOvFxRj1/1SVRKmUID19EwIDo82iH1lUVISffz4PPv8BPv98\nJXbt1aH0xRzo23yK68xa2JWWYrl/zSnsjMHCwg4BAauRnb0bcvlqeHvPBo8ngJ+jHwYEDsD3t7/H\nl53rJ69YEzweD87OA+Dk1B/Z2fuQk/MTPDwmQiwOMntbR48exfDhwwEA1vYsCp8Rc2oxtAU8Qj2g\nVqtrzQJWLT4+nGLHUwNG9+z/QOn6ASSSYPB4ozF48FfIytKAz2+DT8oCsG52LRKItc1mAZBUqaRZ\niYn0i0Jh8vS8VK+nHRkZNEcioR0ZGbXGgZWWltL8+fPp5MmTJrVlCBk7MkgpMXzZ2NwoFL9Uib0y\nJ12/l5BO3wBLKWVlRDduEG3b9mQ5NSyM6NtviS5fJjIy60liWRlNffSIstVqCq8pPuopTyaF4hfK\nzj5YjwtoWliWpRmnZlChqrDR2jyfn0/HqksB8/+AkpK7JJHMIYbRmq3OlJQUEos9CCDi8Riyfnk9\noU0xASzZ2bWje/fuma2tpyktvU8JCdNIrX6SdGLrza10Pul8g7T3LHq9ilJT11NSUniVZeL6EhIS\n8ti2RCQlVRsqefHji6Yl/7hzh+jnnysfCwsjIu6aZs1ypz59PiWAJX+U0ADISCTqa3ocZoBIhLVB\nQRALBAiWSPCgrMw4Cw/ARiDAfz08sDowEN3s7bFGLseipCTcKqmaU9XGxgbLly9HSUkJFi9eDI1G\nY3R7daGSqmAd0HSJyE0VkTaEdI0GrUR56NplGVq1aoc33hiGc+fOGVcJywJJScCvvwLLlgHh4dxr\nwwYgK4vLbh8VBSxezL0mTeLkYowQpz6Tn4892dmICQqCOjsb3t7edZZxdX0XGk0aiotvGnc9zQQe\nj4dFvRdhyaUljbYtcCIvD4P/H6qSFBVdRk7OLwgIWAk+v346vZVpBWAOADmI+FA75AMJdgDiAeSj\ndeuqChvmwMamAwICViE9fTPy8k4CAD5/5XOcTToLWYHhcYqmIhBYw8trBry8ZiAj41vI5dFgGONt\nwbM8fPgQbdu2rXNG7h3gjWuXrhnfQMUM82ns7YGiIty8+Q+2bnWEVvseJkwIQz9hMs7DGypVTI3V\nGbyH+bazM950dMR3GRk4VK5B5mCCYFpHGxt09PeHhmXxa24uDioUCLC2xhg3Nzg+lap/1KhRkMlk\nmD17NqZPn442bdoY3VZtNGXiaQsLB6jVKQ1S97xz53F69gyos48AeB8ZGTcQGzse3323Ep988lHV\nAoWF3FLqv/9yxpDH417+/txe44ABgLEJimuBiPB1ejrsnxJ7jouLwwsvvFBTgUp/enkFQyqdAysr\nT1hZ1aye01xxEbvgnbbvYOfdnZjw8oQGbeteaSlesLGprAf4/4C8vFMoK7sPP78Is93nMhmwfTvg\n6CjA2rWtMHt2LyiV6wCUAjgKsXgWVqyIqlGP1hwIBGIEBCyDQnEIyclL4OMzFxFvRCD4dDBWvbUK\ntpYNow71NEKhM/z8wqFSJSM5eTHE4jZwd/8UfL5p7jD79+9HaGhopWPVfWXObznDeZMzWJY1zsfF\nyQnIz698rHVrQCpFZmYmeLyXcfXqO7h1Kwhvu2yBMHM5dKjZ1hh1lUI+H195eSFLo8FKuRxtRSKM\na9nSpBvSis/HaDc3jHZzQ5JKhW8yMlDKMBji7IyeDg7g8Xjw9/dHTEwM1q5dC09PT3zyySf1vgG0\nOVoIXcw54jQeboZZffaQ+qBlGPxy9iLU2VsB/Kf8aHsolR0xa/pwjHnxBQgfPOCccfR67pfp4MAZ\nxpEjgQbWy1QxDMKTk/Geiwtee0o95f79+5g4caJBdXC/i8WQSueYbV+qsent2xs30m8gThGHF9xq\nGCiYgX0KBSL9/Bqs/uaIQnEQen0RfHxCzFLfw4fADz9wDnXz53MaksAoeHvbICx8NeIsb6Ldf64h\nImIV3n//fbO0WReVxalnI7xPOMIvhCP67WjwDVRZqS8ikR8CA1ehpOQOpNIQODn1RYsWw4x6PpeV\nr1ba1ODb8jQOrzmgw6YOuH79Ol577TXDO1pNf9Js2+G3b8oQb/0mVKp/AJyGVvsIsZnzsRwLMBs9\nwdRUX23Lv3W8TdeLimhaQgJdM3LvqiZ0DEO/5eRQqERCMXI5KZ4Kizh//jyFhIRQYWH99n9yfs2h\nohvm6a+paLX5lJq63uz1bo+PJ8uub3DJv0HU9z87aDEWURTCaImlM8lXr+YkXLTm29MxlBSViqY8\nelRtoouw8j2Fank2G0M5anUaSSRznou4zOrQM3qadmIalWkbJjZSrlLRuucgDtecpKdvpays/Wap\n684dojlziLZsIXoqRK8SReoiir7cdEkpGEZNMlkkKRSH6VbGLVpzeU2T9SUv70x5jtprBpfZtm0b\nxcdXTiARLpXSzJmFVFBNQoGHIQ8pKjLK6L6xYeEUF0e0ciXRvHlEX6/TkDx0ExER9e7dnywt2xJw\nhwAltcZGWm1hU484zLi4WjvDsCztysykuRIJZZoxyXeaWk2rU1IoVCKhM3l5xLAs5eTk0MyZM+nq\n1asm1yuLlBGjbVppI5bVk0xm/BdfF1/FxZHQ0p5T/XBVkeCDLeXJvzVkbd2CUlNTzd6mIVwqKKC5\nEkkVCZ0KajWYS5ZwsjzVUFR0rUEGHo1FenE6zT87v0HqjpLJqNCMCjvNGZZlSS6Pptzc+ivCXLlC\nNHs20a5ddY8rpflS2nVnV+0nNQI5OUdJKl1IB/7dTRt/30gjR44jT8921Lnzm3Tw4MFGG1SyLEOZ\nmXtIIgmhsrKEOs5lKSQkpNKxgwcPke3Er0hguZasrOxp0KCRlTKwZR7MpIlvTqTZs0MpOnotZWXV\nLArCMJwfYmQk0byel2jPHqJKuQ/Cw2nXrl20c+dOWrx4ObVo4UMCgZBefLEnXduwoR4GMziYE5Ks\ng0KdjqJkMtqYmkoaMyp+MCxLf+Tl0VyJhFalpJBcpaLvvvuO1q1bZ5LGYFMmLHia+iqWPEuySkWb\nUlOpX7/hZGGxkDA+iWCzggA1CQQrqFu3vmZtz1C2ZWTQ5rS0Gm9anU5HkZGRNVcQE/MkfVE1ZGXt\no5yco/XtZpNxMuEkHbh3wKx1Fmi1FCWTmbXO5grLMpSUFEEFBZfqUQfRuXPco+7gQSJDHys30m7Q\nsUfHTG7XnKhUcvr771EUMMqaeO7B5RKGR8jGpgMtXGj+wXltcB61GygpKazGRBHXr1+nn3766fHf\nZ8+eJbHYk/DBRYKFnoBiEgpnUYcOXYlhGCoqKqLO7V+ncYJpBMwka+sJJBY70+nTpx/XoVYTnTzJ\naYQuWEB09CiRUkncoPuZydy3gwdXav9ZTDeYKhXRlClE1UyRq+NBaSnNSEigMw2Qck6h0dD61FSa\nI5HQ+thYmjJ9OqWn1yy/8yx6pZ5kS2Rm75cpmNtgLktOpnytljIyMsj3pdfIcsJcEggGko3Ny+Tl\n1YaSk01LB2YqGoahBVJpnWLPDx8+pL1799Z8wtatdQ7YkpOXUUlJw7jzNwaLLy4mSZ75MvGsk8sp\ntaZ1xP8hGEZLEskck0O0WJbo+HHOUB49+jghjMGcTDhJV1NNX+0yN336DKSxH75L/We9TRDlla8u\nZZK1tWOj5squQKvNI5lsMaWkrCa9vrKO7fz580n71BS+R48BBOwmvJtGsNKX950lW9v/0Llz52ja\ntFlkZfUpfY57BKwpf/8vsrUNoB9+0NC8edzuzZ9/VjPg2bWLSPpkorRu3To6MXZsrX2vyWDWvUNs\nbQ0sXQosXAio1XWe3s7GBjFBQVCxLGZJJEhSqeosYyiulpaY4eWFVQEB6NK6NcRTpuDDU6fw/YkT\nBpUvuVkC+66Ghz40LOYLK9CxLEoZBk5CITw8PPDZLz9i53uvY8qU1ggOHgKZ7D58fX3N1l5dZGu1\nmCWR4HMPD/R1cqr13Hv37tXsIQtwXhZ1hDL5+MxFZuY2aLW5pnS3yZnbay5irsVAy9Rf9k7DssjR\n6eBlbID3cwbDqCCVhsDD4wvY2XUyqizLAocPA7Nnc4+3tWuBYcOq986sjVxlLlzELsYVakAuX76A\n/ft2I//KFMwLGQE+GAAtYWn5Gi5fvtzo/eE8asPg5jYKyclLkJGxDSyrR15eHuzs7CB8Kiri0aMH\nAHoBWj78LCUIRyQiEYm5KitYbtoEZvt2vKV5DXbIR2uLivu8F3S6eSgsvIrly4HISKB372pypHh7\nA6mpICIsX74cnTp1wuCXXgJMkAs0zKXKyQmY93/snXd4FFX3xz+zNZvee9tsBAQUKUoTUMACKBZA\nRUVQ39cuPYQeem9iQQUbzYqICqjwCiqo/CiCdEjvjfRN2Ta/PyaUQMrupqJ8nicPZGbunTvJZs69\n557zPZNh6lQw1xg/dAlBEHjI25sFWi1bc3NZmJSE3op21iIIAj3d3FjaujXbRo7kENBz/Xo+SU2l\nvJb7FP1RhGv3lmIwG47vLlxgcGWuXaHJhBl4ctAgYmJi8Pb2RmFH+o+9HCoqYnFyMosiItBakY5y\n5swZ2rRpU/MFVhhMQZCj1c4hMXEWFkvz1Vq1F5Vcxfju41m0rwY9LhvYnJXF8H+4qo/JVERcXBSh\noZNwdLQ+79Fkgg0bYNIkCAiAFSugXz/bDeVFWprB1GjcgGyO/PkQny75glEkASKimI2bW/0kAeuD\ng0MYOt0iXF1vJz4+inffjebpp5+uco1WGwkcBIOMRNcQ5lQazCUaC+UjRrDHYiGdcJIp54GQYBwc\nCgFQqb7H3z+r9t9haCiWxERiYmLo27cvd999N0RGQpzttUCtj0EOCYHnnpMS1a1MunaQy5kQEsLT\nfn7MTkzks6ysBk/YdlMoeG/QID696y52vPce444cYVZCAierecmaikwoXJvOeDQVfxYV0bVSNGBd\nRgb/qax56OnpSd7VOUiNyKdZWfxaWMhynQ5nK420wWCoPXfN2dmqmaBC4UpIyHgSE+c0u1awPUR4\nRNDWpy3bz1nnLakOiyhyUq/nFufGz8drLgyGbOLjp6DVzkatDrKqTUUFrF0rzffbtYNly6Bnz/qP\npbCiEDd18xmiqxk16hnU6hmAmaQKPw7hycPsxMEhl969ezf38HB27kBExHKysoopL19OYeFlIYJZ\ns8bj6DgJDCmgsgAVyOXz8PYxcPdDD9H6/gc4KjvMFu5FXdSB227bB6RiMPxC3759a72vOSCAqR9+\nyKOPPkq3bt2kgzfdBHbUZLYtaad9e2lK9uabNjULcXBgiU5HoFrNmNhYjtmxFK6L0NBQNsXEELF3\nL35793KwqIhJcXF8mJFBicmEaBGbVazgWhpmLHFlZUQ4OCAIAqVmM3lXuOOa6nlNFgtzEhNxlssZ\nHxLSsPe1YoV5EY0mAg+P/mRkrG24+zchQ9sO5feU30ktSrWr/Y4LFxj0D1b1KS9PJilpLhERi1Eq\n637O0lJ46y3JVdezJyxZAp06Ndx4RLFlvVMWLpxFx47ZODu3R6kcR7zzU5g1H7At6lPkVmqBNza7\ndu1i8OD/Ehm5gvLyOGJjJ1Jaeo5BgwaxatVMHJUzcXQfgoNDEF277uPXX3cil8tZtmwOrq5voVKN\nJS2/grbaD3F07E1MzHS8vWte5RsMBqJmzGBU+/bcdtsVrnudrgkMJkhO4qAg2LzZ9qbu7qzQ6fiz\nqIgZCQlcMBpt7qM2FAoFUVFRtI+M5PiyZUzx9KS7qyvLU1OJPnSOsx2E63L1URufZmXxZKXgwMeZ\nmYy6SrS+sZ83z2hkXFwcw3x8eLCWD251WCWmbIPBBPDwuAtBkJOfb6McYAtheu/pLN63GLPF9i2M\nvQUF3OXe8IL+LQG9/gypqavQ6ZahUNS+gi4qktytCxbAgAGwcCG0bdtEA21GnJyc+P33XWzf/h4L\nFgTx7rtP8lnuBoIIouhQUXMPD4D//e9/9O3bF0GQ4ef3FFrtPPLyfiAhYQYjRw7iq80fs+aDRcTG\nHmP//h8JDg4GIDIykhMnDvLKK2ouaNdxO2q2bn2XKVNqFqgoKysjKiqK119/nTZXv5ucnKQZlY3Y\n558cMgTeew9++gnuvde2G8pkvBgYyAWjkTdSUwlWq3k+IAB5A87UevXqRbt27Zg7dy5DhgwhpmdP\nEn5K5fdeAtHx8YQ7ODDc1xcPZfMp/giCgChaEOqhzGGwWCizWHBVKDBYLCSWl3OTY9UqLHK5HJPJ\n1Cj7mCf1et5PT2dueHgVWUNrOX36NDffXEf1EWdnmwwmQEDA8yQkzEStDrVpj6sloFFqePn2l1nx\nxwqiekZZ3e5AURF3uLq2qBVPQ1FUdIjc3C3odEsRhJpXShcuwPvvSy7Y55+XdpH+bQiCQO/evau4\nYEMmhhA/KR6VrwqH0OYLBktOTiY4OLiKtJ2kUTsaozGftLS3cTRYCAweRZDPte72oKAgVq5cjLhc\n5OjoTdzSt0eN9yopKWHy5MlMmTKFoCDrXPfWYP/b+sUX4eBBOHLEruZeSiVztFrucHFhXGwsv15d\n06WeeHp6snz5cs6cOcOyZcswJZbx1M1BLNHpGODpyZr0dKbEx/NrQUGzrDrlchfM5vq5prfm5vJo\nZdmaTVlZPF2NtF1YWBgpKSn1uk91bMvNZVtuLisiI+0yllCHhuxFnJzsimYLC5tBWtpqTKZCu8bW\nnLT1aYuPkw97E/da3WZLTg6P2rjCvx7Iz99Dfv6PaLULajSWGRlSaMWaNVKYxaxZ/05jWROCIKCd\npyV5STKmIlOzjWPjxo2MGFF9UUul0oPw8Ok4eA7Bkr2C9PS1WCzVj1WQCTg4BFOY/0e15wsKCoiO\njiYmJqaqsWyA93z9hAenToXPP5cqW9jJbS4uvBEZSXpFBVFxcaRakbpiLYIg8PzzzzNgwADm7ppL\naqq0N6TVaJgaFsbc8HCKTCYmx8ezMiWFLEPTRVgqFG71rlhyuLiYzi4umEWRE3o9t1YT7BEZGUms\nHb76mrCIIkuTk6mwWJgaFlYvz0BcXBwRERG1X2SjS/YiMpmS8PDZJCTMQBQbLkK7qRjZYSTbz20n\nR59T57XnS0vROjigaOTC601Nbu429PrjhIVNq3blnJQkFdHZsAFefx2mT290OeTrFplaRviscBKm\nJ2AxWZr8/gaDgZKSEjzqSDNzdAij2Hcarq5diY+PIifnm2oXNN69W5Oz9/Q1x3Nycpg2bRrz58/H\n58qi6e7uUHjV5NnBwWa3bP3+wgRBytFcuRJy6v7DrrkbgSf8/JgVHs6GrCyWp6TUmh5iK5Eekcx8\nciZr167lm2++uXRcIZPxgLc3i3U6Hvf1ZUNmJtFxcfxw4QLmRl511rfE1xm9nlaVaRtbcnIYduWH\n4wp0Ol2DGcxik4kJcXH09/BokILEFoul7mAEjcauvQaQ8sACA18mKWmBXe2bE0EQmNFnBvN+nYdF\nrP0Ftz4ri5GNWHC9OcjM3IDRmE9w8Ohrzp07J83Vv/kGoqKkNJE63sMNTksL+LEGlbeKoNeCSJqd\n1OReta1bt/Loo4/WeZ1aJqPcYsHZ+VYiI1eiULgQGzuWwsLfq1zn0d+HrJ1mNm/ezPHjxwFIS0tj\n9uzZLF68GPer9/KrK/Ol09m82Kv/lFSp5FLWqB0rgStxksuZEhbGw97eTEtI4Nvc3Ab5xRbuL8S/\nrz+zZ8+WXkQzZlB2laBCoFrNxNBQFkZEoJTJmBYfz6KkJFIacMV7JfU1mJ/n5PCEry+iKPJnURHd\nasiz8vf3JzMz0+77XCS2tJQp8fFMDQ2lo4tLvfuzGpmsXq4UJ6ebcXHpQmbmxgYcVNPgqnZlRIcR\nvHPwnRqvyTYYcJLJcGwhUZANQVra28jlTgQEjKpy/PhxiI6G//0PZsyAMWOgKT+KV1JUUYSr+vrL\n6XZs5YhHfw/S16Q36X0PHz5Mly5d6rzOQSajwnJ5gujh0Y/IyJWUlydVRtSeJSMjgw639+DvwwmM\nHv013boNpHv3fsyfP59ly5bhXF1aVXUG047Ukobx4bi4SJ/gqVOl7OB6oqssWq0SBMbFxnKmnoZY\nf1yPU3uphMxDDz3Eiy++SHR0NCdOnLjmWpkg0M/Dg0U6Hf8JCGBrbi6T4uLYmpOD0dJwrgzJYNq3\nv1ZuNmMSRZwVCnbm5THQ07PGaxtiFvxTXh7rK4s9+6hU9e4PoLCwEFdrC07X8xm8vAZgMuVfM0u9\nHugS2AUBgYNpB8nLy2P37t0cO3bs0kTyw4wMnq/Mu73eEUWRpKSFaDSt8fG5vBo5eFBaSR48CHPn\nwssvN2iJVrtoaaIFtuDexx25q5zcb5tGGevkyZO0tTJM2aFyhXklUkTtcCIi5pOXt4s1a3qSnX0n\npwoH09HnNUpLd3LwYAbFxaaao+5rWmE2i8EE8PeH116TDGcDLffv9/JiiU7HT/n5zElMpMhOYyxa\nRAT55ZducHAwK1eu5KeffmLt2rU1rmK9VSpGV0rx+atUxCQmMjcxkfN2ugivpD4rzK9ychjq44Mo\niuzOz6dfI/mjRFHkrdRU0isqmKPVomzAPbKTJ0/WHfDTgAQFvUZu7jbKy5PrvriF8XKXl3nl3dcJ\njIhg6NB59Oz5CG3b3s6J2FhKLZYGm8Q0J6JoJiFhOh4effH07A/Ar79K8nWxsVJqyHPPQUt51OvZ\nYAL4P+1P6elSio8UN/q9Pv/8cx577DGrrlVXYzAvIpOpMRgGsmZNMffd54759kO0FfYAmzCbd/PV\nV59TUVFRfcc+PpCdXfWYqysU2/b8DRslcNNN8MgjsHRpg3WpkskYHRzMCwEBLExOZn1mJhYbDLKp\nxITc+Vp3lVwuZ/z48bRr144JEybUqogjCALd3dxYEBHBmOBg9hQUMCkujk1ZWXbvtdbHYP6t19PB\n2ZlfCwvpXVlsuy4sNq6Oy8xmouPj6eziwqhGWMFYFSF7kQaYgAmCQHj4bJKTF2M2189j0dR89NHH\nnFxTQkXXJygs3INeH8u5c0/Te/5Snqph77olk5CQwA8//HBpb91iMRAXNwk/vydxcenKjz/C+PGQ\nlye9SoYPhyZUd7SK691gAoRMCiFrcxblqY2z7QRSeodcLsfxqnS3mrjaJXs1OTk5GAxhbNo0k29+\n6cKB7B3AfCAAi0WgpKaI+npu7Vzqpt49XM0dd0j6Ux9+2KDd+qvVLIyIoLWjI2NjYzlYZF0ibvGB\nYly71uz669GjBzNnzmTBggX88ssvdfbnqlDwQmAgS3Q62jk6siA5mZiEBI7bmPogl7vaZTBPlJTQ\nrrJC+be5uQy2IpUgKCiI9HTr9yxSysuZGBfHmOBgujeSBmVqamqD5kdZg1zuQFjYjMrI2aaPFLSX\nRYveouzCG3D2Sej4ESDDwmhKXPxJ+fPPOtu3FPR6PYMGPUbbtl154omV3HrrnQwc+ACnT79OQMDr\n7NrVjgkTpGuXL4eHH5becy2RC2UXrnuDeSndZGEypuLGSTfZvHkzw4cPt/p6lSDUajDbtm2L0RgP\n7CMn5wcOXLgfyYz9hre3H561bE81BI0zbxs0CD75BL79FgYPbtCuu7q6cruLC+szM9mam8uY4GD8\navHTFB0oInhMcK19uru7s3TpUj755BMOHDjA+PHjrUr0v83FhdtcXCg1m9mSk8OGrCxaaTQ87uuL\nSx3tZTIlomj7h/TLnBwmh4ZyqKiITi4uyKxYXV5MLbmomlEb+woK+O7CBZbrdDg0ciCJ1furDRiN\nqFb74+c3gpSU5YSGWi8M0JxkZqYA7SDJn2HKU3zVJwZRW4olZx9vHj/Kb/LfqvwsRVFEKVfipHTC\nUel46ctJVfV7R6XjpWs0Sg2yeohoWMOLL47j55+VlJcnU17ugLNzFq1b9+eFF/rRvXs4gwdLCj3X\nA7mluXhprn8ZQrmD/FK6iW65Dpmi4T4Doihy/vx5bI6zBgAAIABJREFUXnjhBavb1PVOcHFx4ZVX\nXuKNNx7GZNoEHAM+RqOZwfLly22P2VCpJKWL2vSsr6DxHB0jR0rTxN9/hx41KzLYg0wQGBUQQKHJ\nxBupqXgqFLwYGFjtHpu51Izcqe4XvyAIjBo1ijNnzjBu3DgmTpxodUksR7mcEZVh/WdLS1mZmkqF\nxcJgb2/ucHFpsPDzixVfNHI5n+fksFCrtapdZGQku3bt4q677qr1ug8zMii3WFgUEdGoIfOiVIfV\nlgYNen8Xl46Ul8eTk7MFH58hDdp3Y3DLLZ34449dyHkKl9h7EWO10PY86i8+Z3b0mqoamZUYzUZK\njaWUGkvRG/WX/l9qLCWzJBO9oeqxUmPppfSVq41vTceUcmWNBvhqQ20uN/Plls8wlCcADnh4ZNC5\n8/94++39wMNs2zaiVk3QlkZheeF1GSVbHSofFYEvB5I0NwntbOveKdbwxx9/0KOB3/0FBQWUlhaw\natVcVqyIJjX1PIGB4bz55hoeeOCB2hvL5VJQ6pWLmYgIKbWkLsWxShp3Z2D8eCkIyNMTaivhZCdu\nCgUzw8M5pdczMS6OB7y8uOeKJbnFZKkS7GMNbdq0YenSpSxatIj27dszdOhQm9q3dnRkZng4BouF\nb3Nz2ZKTQ6hazZN+fnheo4hj29i+yM7mcV9fTun13KTRWJ2oHhQUVKvaj6FSPP1uD49GCyC6kuzs\nbPybOW/Qx2cIyclLKS7+CxeXjs06lrpYuHAqAwY8zi1lnhymG3RMRnliLd263VatsQTJmLnJ3XBz\naLxqGkazkTJTmWSUrzLA2frsKoY6NSsVt54iQeZn8SAfKrqh/+VBjEZXXFwyyMrKuq4MJjRdcYOm\nwKmNE+593El/L53AFwMbpM9vv/2WefPm2dyupulxaWnpJVECDw8PXn31ZQoKCli3bl3dxhIgMFCS\nhbpSBioyUooqs9JgNq4PRhBg9mx4912wYQ/NVto6ObEqMhK92cyEK4pW6//W43yr7aWOHBwcmDVr\nFhqNhmnTpqG3I61FJZMx1NeXJTodD3h58V6lFN/e/Hy7c0tPl5bS1smJDVlZPGODpIlcLq8x6CfL\nYGBcbCzPBQQ0ibEEqWj0LbfcYn2DRnoxhYRMICtrAxUV9c9TbUz69OnD1q2fcLfXAY4TiNDtd/5z\ni5nvvvusWcellCtxVbvi7+yPzlPHLX630DW4K3dr72ZQq0E85tqdUX/LeGVbOnP3izy+X6Bo76vs\n2fsre/5YQrgxlRmMQ2HKQmult+QGjYdHXw9kGhkXtl+od185OTl4eno2mIa10Whk8uTJTJ8+vYpa\nkLu7O0VWxrNUm1py0WBaSePHnsnlkrBBVJT0byMFkQiCwMM+Ptzn6cnbaWmYRJEhvwuEDrVfK2vQ\noEF06tSJKVOm8Pzzz9OhQwe7+gnXaJgSFoZZFPkxL4/J8fH4qVTcaVZi7Wvir+JibnN2JrGsDH+V\nqkH2Fw8XF7MpK4vFERFW169sCE6cOFGjpmRTIggytNq5xMVNIjJyBTKZdfsYzUH/O/vTenRrhr42\nltmfVvDOq9YHUjQZGRmwZw+cOiV9HxwMd98NI0YgFwTCvf3JnDkBStcC3fiUVrR2WMAPt7TGMTYW\nbr21WYd/A/B/xp+khUmoglS43Ga/KsSGDRvs/hu/enpssViYOnUqY8eOJaA+EfshIXDsWNVjHh5g\ng45508SgOTpK6sjTpkkbrI2IRi5nYmgoT/r5scQ5h22ywnqpBQUEBLBq1Sr27t3Lu+++W6++5ILA\nQC8vFut0POnryza9H9Fxcey0Qorv69xchvj48EFmJs/b4c6UqqNcvsdnWVn8UlBgU7HnhiIvLw8v\nW+o2imKD72NeRC53IjQ0moSEmBZd+i3321y8H/Lm64ICbs32a6wfh21kZcEXX0jbLtOnw9dfQ4cO\nkrrAvHnw0kvQuvUlD8H48aNZvnwM/v7PAHJ8fIbx8qKXuH3/b5KhffNNaEBxkBvYR2h0KFnrs6hI\ns+9dbTabycrKqp9xq0QURWbNmsWIESNq1J1WqVQ1519eSUjItStMG2m6oG0vL2mVOW1ak/xRhKjV\nTDnvga9SyZjYWP6uR9FqmUzGmDFj6NixI+PGjSM3t/4KGf5qNf9xTWFhRAQOMhnTExJYlJREUjVS\nfMUmE0pBIM9oxEUut8vA+fr6kpOTg1kUmZuYiFNjFHtuLDQaaCSJQgAHh1C8vQeTlvZ2o92jvpQc\nKyGvlRwfpZKwIBmp9tWYrh8XLsCWLZcN5OefS7EJs2dLBvLVV6WUsho+U4Ig8NJL/yUj4zxGo4Hs\n7ATGjHkNQaGQdO569ZL+bYTqOjewHkEmoJ2vJWlBEqYS2yP5f/zxRwYMGNAgY1m2bBn33Xcft9bi\nfbj55ps5ffpaIfZrsKNU4NU07dIiLAxGjID586U/ukakPKkcB60Dd3l4cKebG+syMvgqJ4exwcHV\nBN9YR9euXbn55puZO3cuAwYMoG/fvvUepwDc7eHB3R4e5BmNbM7KIrmigq6urjzo5YVKJuPT7GyG\n+/qyLiODV+3MXdTpdBw5f54dhYW8HBjIzZW5nE1JfHw8hw4dIi0tzTbx6osVSxpRD83NrQdlZXFc\nuLADL6+BjXYfezDkGlB6Kfk4K4sxwcGc6SB5lhq9hFVBAfzyi1TCz2yWJr19+kgJkvXcEqh2b+u2\n22DJEumrdWt44ol63aOxsIiW62OiWQ/kGjnhMeEkTEsgckWkTcGTe/fuZfHixfUew3vvvUf79u3p\n2bNnrdd16NCB/fv31xgAVycKBRgMVslINX1acIcO0LMnvFOzoHRDULS/CLee0n6pQibjpaAgRgcF\nsTI1lbXp6XZXI3F1dWXJkiWkpaWxcOFCjEaj3WOUy52rqM54KpW8VinFF6xWMzsxkdkJCRwoKsKr\n0sjba+zFsDBW5OUxJzy8yY2lwWBg2LCRtGvXleefX8emTTto06YzSUlJ1nVgZ4kvW/H3H0Fx8WH0\neitmq01I7pZc1A95ICBFhrdvD9XIINefoiLYvl0qpDB9OqxbBwEB0uR23jwYNw46daq3sawVjQZi\nYi57pPLzG+9edlJYXoi7g3vdF17nqHxVBL4YSOLcRKvbJCQkEBYWVu8Jxaeffoq7u7tVK9WIiAji\n61FiEq0WEhOturR5dDT69pX+IL74otFuUXqmFMc2VeWYvFUq5mq1dHFxYXxsLPvsLFotCAIjRoxg\nyJAhjBs3joSEBLv6USjcMZuvFWAXBIGurq7Mj4igt7s7SkHgkRMn8FAoKLNDim9bbi4H1Wp6Hj1q\nd7Hn+jBz5jy2b8+hvDyJkpLXqajYRlzcEwwcOMy6fUNnZ7uKSNtDWNhU0tPXYDTWP1KwoShLKGOT\npoBnK/eunZzsrnhWFb0efvxRii+YPl2qwOzhIW2bzJsHEydKyl3NoUt3zz0wZYo0jj17mv7+tfBP\nkMWzFqe2Trjd6Ub6+9ZlOWzatImnn366Xvc8HxtLSUkJjz/+uFXXy2Sy+sUf2BAp23zCU48/LtXQ\n/PnnRum+NpdfRxcXVkVGklJRwaS4ONLsDERq1aoVK1asYOPGjXz++ec2t7dGT3Z3fj4LKg1nb3d3\nFiUnMzMhgWNWGJCLxZ7LzGZmRkRgaYBKMrYiiiJr1rxHWdkqwBE4BbTDbJ5IUtIFjh49WncnTbTC\nBBAEOVrtXBITZ2Gx2O89aCjKk8pRhKjJMRoJrqzEIIoi6elpfP/992RkZFjfWVmZVBtr3jzJQK5e\nLa3ooqOlY9HRkshIM0yqqsXTE5Ytg7Q0KZCokQMGreVC2YV/hMqPtXj290RQClzYUfsksqKigrKy\nMtzqkQmxb98+cnJy+O9//2tzW6uMppPTtZPv68JgghQksG/ftaG+9cRYYEThXvusWBAEhvv5MTMs\njE8yM1mRklKrhmFNqFQqZsyYgbu7O1OmTKlZ/Lca6jKYBUYjjnI5G7OyeM7fn1udnZmt1TIlNJQT\nej2T4uJYm55OcTWG8GKx534eHjxRmbPZHFGgoihSVJQD6KQDffwAJ0CGQhFpXa3OJjSYAAqFG0FB\no0lKmttk96yJnK9y2NtH5KnKgt3x8fG0bt2J9es/5ckn16HVtuXll8dVn2dbUSHtQS5YIBnI5cul\ngJwJEyQDOWUK9O5ttSxYsyAI8PTTUuzDxIlSUcxm5t+0wrxIwLMBlPxVQsmxmt9vX331FcOGDbP7\nHseOHWP37t10797d9vEFBFg3eQwNvTaozMtLCmizguaXNp4+HTZsAGv3s6yg6I8i3LpbN8txViiY\nGhbGYC8vpsTH872dRavvu+8+xo4dy7Rp0zhy5IhVbeoymJuzsxni7U2GwYD2ioAXjVzOU35+LNHp\n6OPuzqrUVKbFx/NnoZRCE1dWxpT4eKaEhtLpigq7xcXFnDp1ql77rrYik8lo3bozsFM6EN6n8kw+\nFRUH6djRCpWdJjaYAI6ON+Hm1puMjA+a9L5XY8g2cNyhgvbOzlgsFvr1G0xc3EiMxgkUF39DRUU8\n69f/H6tWvSkFLuzfD4sXS/uOS5dKx8aMubyq7Nu3+YtJ2kN4OKxaJa2Q33qrWdNP/o0GEyB0SiiZ\nH2dSkV79Sv/o0aN2B97ExsayadMmZs6caaP+mUSHDh04Zs3CqzrxAhv2W5vfYMpkUtTs0qVWW/m6\nKD5UjEsX25JuIx0dWREZiVwQGB8Xx1k7Non8/PxYuXIl+/fv5+23366zpJZC4VajwRRFkYTycn4r\nLKxV1aeVoyMzwsOZFR5OusHAE6dO8eq5c0wPC8O3MuorLS2Nnj3v48033+eOOx7E1zeMDz742Obn\ns5cVK2aj0bwAfA0YgUM4Og5m5MhnrJPIa8I9zCvx9OyPKJrIz9/b5PcGKDlewjmtyAOVOav79u3j\nwgUZFssYQECOia6c5ZXSjshi5sCiRVLgziuvSC7M6dOlvcBmiIhuFORyGDtWChocO5bmya359xrM\nS+km85Mw66vGUhw7dqzW1I/aSE9P580332T+/PnI7CxPc8stt3DcGu9DdQbTBprfYILkEpo/H2bO\nlPZZ6omlwoJMbd+jDfDyYnFEBD/m5THXjqLVMpmM119/na5duzJu3Diyry5aegW1rTB/Lyqim4sL\n58rKrIpqVQgCGQYD93t68n7r1nySlcXkuDh2XbhArz4DOXCgB2bzBvT6zyko2MHo0THs2LHDpmez\nl4EDB/L11x9y660rEGRL8PN7glmzHuGdd6wsTdEMK8yLBAS8QH7+LsrK6hGFZye523LZ291CH3cp\nIjMtLQ1og5SMtJGJPIsPOawlislGg/T3M2AAuNiv0HJd0LGjtIpet65RAwdroriiGGeV7ZKb/wTk\njnLCZoQRPy0e0XzZE2evOzYvL4/58+ezaNEilPXYO3dxcaHYmmLQAQHVy7ReFGavg5ZhMEGSzJs2\nTdpXqUdwisVgQaas32NdLFr9n4AAFiQlscHGotUAXbp0Yd68eaxYsYJdu3ZVe01tK8zvL1xAbzbz\nuBUFgsvMZibHx3ObszPPBgQQ6uBAdGgo8yMi+L/Dh0kb8DDmIc+C501ALHAbpaVLmDNnpU3PVB/u\nv/9+jh3bx8wZ08jMjCUqarz1s8lmNJhS4ekYUlKWYzJZqVnZAIgWkZS8MjoHuF8KXuvcuTNG4x5g\nAuDHYjbwPQ9SyB+0b9+lycbWItBopPQXNzeYNMkmebOG4J+eh1kban81Af8JIGm+tI1WVFSESqXC\noTIozVr0ej0zZsxgwYIFaBpgm8Cq34lcXr07PyzMqpVnyzGYIKnJv/SSlIdlZ4BK8ZFinDs1zOwv\nQK1mkU5HpEbD2NhYDlkr8luJi4sLixYtIjc3l3nz5mEwGKqcl8nU1UZiXjAacZbJOKbX08W19hJC\nqZXFnl8PCqLnVdFpckHA7dw5hLXFsMsf+mkIf9gRsAB3EBt7zqbnaTaa0WACyGQqwsNnVxaetj2t\nxx4Kfy/kwM1mHr2igkdFRQWBgZ44OBwDQpDc21+j0YxlyZLGFQJpsdx3nxTdO3cu7N3b3KP51+Dc\n3hnXbq6kr0tn06ZNPPnkkza1NxgMTJ48mZkzZ14TVSsTBEx27FGr1WrK7VUEi4yE8+frvKxlGUyQ\npLYGDYKV9q1+in4vwq1Hwwq8d3dzY1VkJMf1eqbGx5N9leGri+HDhzN8+HDGjx9PrBXhy5uysvBX\nqRhcR7mj/YWFrE5LY5lOdynlAKQJ1MGDktfqjz8GIYphkK+ELyMR/9Jx662/Aom0adPWpudoNtTq\nRpXGswaVypuAgP+SnLyoSe4X9302zve4o6jMMfvwww/56aefOHHiKGPH9sDN7W4EQU379svYtm1D\ng6hOXbd4eUnpJykpUnBTC0k/+afjea9USvH0z6fR6XRWtzObzUyZMoUJEybgV018hoNMRoUdC6Z2\n7dpx8uRJm9sBVqeWtDyDCVIuWGQkrF9v1eUmk4mtW7fy2mvj2bNtLxl6G3LTrEQmCDwbEMCkkBDW\npKfzdloaRhtmQTqdjpUrV/LFF1+wefPmGq8TRZGU8nJOlZbSu5Z8pg8zMjhaUsLiiAg0cjlpafDR\nR5JXOyZGiod46SXYsCGMiIgNKJXRQAlJSW0pKkph0KC3CQp6p6HirGzC5ihkQWi0El+24OzcHien\nW8nK+rTB+7ZYLHzxxRf07/8Id3YdyM5z53jUw4WioiKioqKIjIxkwoQJaDQaFi6cQ0FBBmazmePH\nf+eee+5p8PFcdwiClHry1FNS2kyjSCHd4GpiW8Vyq+pWSo5bF5QniiIxMTE899xzhIeHV3uNg0xm\nV4qf1ZGygnCtW9bXF2qJN7lIyzSYAIMHg9EIdQSmlJSU0KVLH555Zglvv+3Pvn0ltGnTiS+//KpR\nhuWuVBITHs7d7u5ExcXxPxuku5RKJVOnTsXX15fo6OjKTWoRURQ5dOgQmzdvZu3Bg7grlfTz8KjW\nJ2+wWJiRkICfoCbyRBCzZwtMmwbbtkna1fPmSd6pRx6RtndkMhm//LKDfv3iUauD0GiCKCubxahR\nPYiO/pglSyx8800D/oDqQCEI9skStojyHODt/SAGQxZFRQcarE9RFBkx4gWee24R//vfo5hPvsZX\npfF0va070dHRREdH07t372va/Zv30WpEq4U33oCffpLkN29UP2lUduzYwaiPR5H5YSYVmXWv7Bcv\nXsyDDz5Iu3btarxGLZNRbsfvLSwsjERrJO78/K41jlb+LTWD5pUNPP+85Ff09pYkuqphzpyFnDkT\nRkXFRoIpJ8lSRFlZd0aN6st9992Lax17gPbS1smJlZGRbM3NZWJsLK8FBRFu5cZ1//796dChAzNm\nzODBBy/w2GN3c/p0CoJwB+XPBOPt78XJV1+o0kYU4Ze/DUw/m8it50I4Imro3RumTq1bM9jX15ed\nO78iPz+f4uJigoODkclk6PVn+O9/J3H69GImTZITHS15txoThSBgEsUW/sGrneDgMcTHT0KlCsLB\nIbje/R04cIBt235Grz8OONE+7C8++CMVRZmKsDAtPlYEft3gCuRyGD8eDh+W0k+io8HOogVXYxEt\nyISWu85oSjIzM/H29kahVKCdryUuKg7dUh1yx+q1ht955x06d+5M165da+3XwU6DaXUQ4cXUEjvK\nJLb83/ykSbB1a40bsuvXf05FRTQg43bv4+QEZePn54GXVxd27Pgcs1mPxWJsFJUbQRB41MeHuVot\nX+XksDg5mVIrtV59fHxYuXIlP/64i7/+EtHrz1Ki+AST8nlyfhUZNepVcnPh008lN+t/FhezJCWZ\nDb0jeGe6hhkzpMIRVgjsX8LDw4PQ0NBLHywnpzYEBr7AzTdPIjrayNKlNPpq86LBtJkWtJqSImfn\nkJy8ELO5/qKu3323Hb1+KJCCg+NnlKv+Riy6BaNxMZ9/3jSpP/9IOneWclPffx++/LJBuiwoL/hX\nCK9bw5VFouWOcsKmh5EwLQGzyczu3btZvXo1O3bswGw2s3HjRvz8/KzaPrDXJXuROt/1NeViymRS\nVZ5aaPkTfUGQxKEnTJAsx1WbxAZDBZLUmkhO7w/xKroTf6URJ6d8lMo9pKUVYLFUIIr10VG99hcg\nCApkMjWCoEImU/OUUkWeSca7p4qJdHSjh5sPcrnDpfMymQpBkP692C4vr5iCgnwslvnAeLjnRZAp\nMP88lh3C27z3XimDBzsi9M3G01DB2mAdsgY2HI6OrQgMfJn09GgWLFjMzp1KJk2i0VabdhvMFoZc\nriEsbBoJCTPQ6ZbV6h4tLS0lJSWF1NRUUlJSSEtLw2i8PInbv38fMpkJi6UjxvKjbO18F/zdDSxb\ncXBowbJ11wOOjlK9zp07pQ/11KnSXoWd/FtFC67GbDZz4cKFKkE76gA16ofVTAqYzvvlOzAae6FS\nbcbB4UWiokYTFRVlVd9qQbBrhQkQHBxMamoqIbXVvgsNhepS/UJCpOCPsLAam7Z8gwmSGPTChdJq\nc9GiKonZDzwwkM2b12G+qRv7/noMEvoBmTg4TGT16m8bpOr31YiiiCiaEUVDpTGW/nWzGHjRs4L/\nK8jlraxsHvKUEaY2YzYXYrFUYLEYEEXpX4ulgqysVNq2lTFixG4EwZkDN7/Hmb1TwKLBwekthj3+\nMF8oFNwqd2ZCIxY/dHSMJCjoVeLiohgwYAk9eqhYvBi6d4eHHmrYe9ltMFugkVWrA3FxeZjffpuC\nyXQvqamppKamXlP93dHRkeDgYEJCQujTpw+BgYGor9BvPXv2LB079qKsrBdmyxH0e3vBg8k47lrB\nf//7XFM/1j+TAQPg9tsl4/nII9KGvx3cMJgS27dvZ9CgQdccf3npRPLyb6OXeSc7CaSi4hdKSpby\n5Zc7rTaY9VlhXgz8qdVgurlB4bVVoi6lllz3BhOkXLxZs6QZ4vLll3yRCxbMYOePPcmP2I55x1Lg\nfZyclhAVNbFRjCVILjlBUAAK5HLHa87f7QS9Aiysy8jgm0IDY4ODL9WzvBJfXwNvvfU2n332IAZf\nIwQUwG4/4ChydyWrzWZeDgigXRNIm2k0OoKDxxAXF4VOt4RFi9Rs306DrzbtMZhlZWXkZ2RQkZCA\nVqttmIFYQXl5OWlpaaSkpFxaIV7M8xIEAVEU0Wg0uLqWEBj4Oz17DicoKMjmBO7WrVszc2Y0s2Z1\nwGi8E0vcalS9c+k6uBXPPPNMYzzavxNvb+ndsX49/PqrVG/Tlj0NJIMZ5NIw+6HXM7/99htLliyp\ncqyoqIhdu3ZiMK9lICXcLvzOQfFXRHELx4+HkJycTGhoaJ1927uHCdC+fXtWr17NAw88YHvjyEj4\n9lvo37/GS64fgwng4wPjxmGYNIkoi4L1GzZTVlaCdoiWuzzakNhlGX5+3owZs6bZQ+0vFq3OMRhY\nnpKCVqNhlL8/8itcdyqVin79HmDXrmGk3XEvnB0Hph9xaL2ATguWMjciAo8mLLWk0WgJCRlPXNwk\ndLolDBqkpkcPWLIEunVrmNWmrQZz1aq3mDFjNlMNcuZu3Ea7dm35+uv1tc8graCiooK0tLRLbtKU\nlBTKrpJlVKvVl1aG3bp1Izg4uEZFkqSkhXh5ldhsLC8yefIE4uPP4uHhTVnZBe5/aBA/hoYib8xi\nzf9GBAFGjoT4eCkw6JVXoK31+cgXSi9wq599mqn/FOLi4tDpdNdsQ5SUlCCTOQAu7Gi9l4fkhxl+\n6jm2okCp9CY/P98qg6mWySi0U+3NycmJUnuLxQYEQB0VT64vgwlYwsN57sdfiThvocC8F5RqzmQ/\nR9p3ezl69A8iIiKae4hV8FGpmBcRwZHiYsbHxvKYry893dwoKytj6dIVnDixD1ePcjI6C8gX9cB3\n2IPcOXIymwYOrGJcmwoHhzBCQiYQFxdFRMQSPDwcWLiQBltt2mIwv/rqK6ZNe4PS0v0Y2EQZ0/nr\nryX06TOQ2NhjNUbFGQwG0tPTq+wb6q9SClKpVAQFBRESEsLtt9/Oww8/jFM9VvKhodHExo4nLGwq\nKpWvze0LCgoICwtj2rRpl445FRTwaXY2T9Yivn8DO4mIkKqfvPGGtNp84QV+2r2badMWcebMcYKC\ntEyfPoann36qSrMbLlmpSPS4ceOuOe7v74+HlxsZuuchdyDbTs3Gh3JGcpBUUxvatGljVf8OMhnZ\n9diCsTvdyop2153B3L17N9tSLXQ3L+IF9vD+7UVw8CPKyt5nwYIVrFv3VnMPsVo6ubjQ0dmZT7Oz\n2ZaTw8+vRnHqN5H27SdgapuJmOyBaugwZkyZwIs33dSsY3VwCCUkZBLx8ZLRlMs1DBpEg6w25TYY\nzDlzVlFaugxoVXlEidkcRXb2p6xevRp/f39SU1OvEV1WKpWXjGHHjh158MEHcXZuXLFsQZCh1c4j\nPj6ayMiVyGS2ufo2bdrEU09VfTn3cXdnWnw8OQYDPja6Dm9gBQqFFEx46BCxDw7m5Z8PEV++Crib\ns2f/4sUXx5OcnM7UqZf33koMJTgp/yHVX+ygrKwMo9GISzUC/xklGXSIvon8xd9TntYbiCOHQ2xw\nnMyaV+eRHJWM/yh/XDrVXhygPi5ZAI1GQ2lpKY6O126XXb6Jg1Tow1YNW7EW6jjdLMyePVuEqSKI\n4pOyD8ReuimiFBHypxgZ2bm5h2cVW3buFFXPTRQZmiTefMt+MeDtZSKjz4iKtpPFyZNnNPfwLlFe\nniaeO/eaaDLpqxz//ntRjIoSxdxc2/vcnJkpni4psepaN7cAEVJET3LFtWhFgakizBVVql7imDFj\nxJMnT4pFRUW2D6IRKS1NEOPiJosWi8XqNhaLRZw4cWK15/INBnFSbGxDDe8G1WCxWMRWQa3FOTwl\nPspXle8TUYQE0dHRUyy54vM68+eZzTjS5ueTTz4R//7772uO747bLUbvihb1Br24Z88esVevgaK3\nd7h4xx39xe3bt4uiKIoWk0VMX5cuxk2JEyuUmj9VAAAgAElEQVSyKmq8R1xpqfhxRobdY/z222/F\nAwcO1H7Rhg2ieO7ctcdjYkTRbK7R9rX8PMyr8PPzw9ExDoDNluf4LW5e5Zk4AgKuD9fVbz/ux/Ch\nFvb4khxpJk8MgPVaTKf6s2PHL809vEuo1YGEhk4lLm4SZvNll+agQVJRmWXLJIUhW1AIAtbKl998\nc3v68C5zmEEmwxGZD0xFqcxi2LBhtG3bttqZbnOi0YTj6TmA9PQ1Vrf57bffqlXyAUlZqpurKz80\nh4bhv4T8/HwSczKYyQbK0BDt/Qw45APhKBRa6+os/ks4fvw4t9xyy6XvLaKFZb8vI7UolUX9F+Go\ndOSuu+7i11+3k5OTwIEDuxg4cCAAglwg4PkAQiaFkL4mndQ3U7EYrl1J1jcP0yqJvJAQSXv4aoKC\nqi//Vcl1ZzAff/xxZLL/ARfzaGRABk5Oc5gw4YVaWjYd5eVw7pykzvX++1Jwb0zM5a/Tp/ujUKgh\nvBS9wY2KCRlQoALS8Pb2aO7hV0GtDiA8fAZxcZMwmS7rRXp4SJk+CoW0t2nt+9zqPUyTifXtQrlJ\n+RbJGHmL14A8VKpXadPGnx49etj3QE2Au3tvZDINeXk/WnX9jh07Lr1UquMRHx925edTUo+ydzeo\nGScnJwRBBHLYyUCWlCyHOxdB668xGtPw9vbmzJkzTJ48ne++28mWLVsw/Qt/F0eOHKFTp06Xvs8t\nzWXMzjHcE3EPI28baXU/Sncl4THhePT3IG5SHBd+qPryqK9LNiREisitlZrEC+oSYa9t1VrH6WZj\n7969optbpKhWvyu6uAwWHRzcxZiYeU12//JyUTx/XhR37RLFtWtFcdYsUZw58/LX/PmiuHGjKO7b\nJ4qpqaJoNldtfzYpSVS8PFqky0ERzCLMEiFXdHK6Wfzmm2+a7DlsoaIiSzx79hXRaLzWBZqXJ4qT\nJ4uiNUP/NidHPFSXGzUhQRRffVUUz5wRt27cJK508RLVak9RrXYRhw59RszLy7PvIZqY+PgYUa8/\nU+s1WVlZ4uLFi+vsK628XJyVkNBAI7vB1Tz99H9FtXqUCKZKd6xFlLUeIfqNCBaXvrNC1Gh8RIVi\nkshd94rOzt3Ezp17i3q9vu6O/0FMnTpVLC8vF0VRFP9I+UMc98M4saCsoF59WiwWMefbHPH8uPOi\n/pz08ywxmcTFSUn16nf69Om1X1BRIYpz5157PDlZFN9/v0bbd90F/Rw7dozTp0/zzDPr0emk+oB9\n+qzF19f2yMSaMBql1XpCAiQmSuIPV054VCppghIeLpXjCwyU5CutGn9JCR9UVPB2316M/fge5M59\nKS+PQ6F4kxdffIHBgwc32HM0JCqVL+Hhs4mPn0xExEIUissavRdXm9ZE0ta5wvziCzh9WsqXU6t5\neNcuxL8O8KSLC87OzrVv5LcwwsKmExc3jvDwOSiV1XsOrpQXq41AtZpgtZoDRUV0bSR95H8zb7+9\njPj4oRw9Gokg9EEQ/sLPBB++uom+M+7FrFsIJ8YCsygpieHkyWEsW7aSmTOn1dn3P4GCggI0Gg0q\nlYo1B9cgIrL83uX1LgAgCALeD3rjea8naWvSyC7Oxn90UL1cshf7FUWx5vGpVNKL/mqCgqQXfg1c\nNwbTYDDw6KNPs2fPn4jifZjNrjg6RrF797c2G0uTSTKIiYnSV0pKVQlBpVJycYeHwz33SAZRUc+f\nlCiKrM3IQG82syoyEtlNNzGsXz++/fZbvvzyS958c2uTJuXbg0rljVY7h/j4KURELEChqCoxZk0k\nbY0GU6+XSq307i35rQEMBkhJQdDpaLjpUNMhkykID59DYuJMdLqVyGRVP0QWi4Xs7GyrBTae9fdn\nTGwsHZ2dUVkrNH0Dq3B1dWX//p84dOgQx48fJzx8JH369GHdunWo9w+l1Pc2uG8sKOSAjPLyiXz0\n0Uv/GoO5ceNGHn7sYSb8NIFhbYfRPaR7g/YvU8sIGRtCRUYFaYtSKOimRwwVEWT2GeSwsDCSkpJq\nLCFW80BktaqKXTcGc8GCJfz8cwllZbGAFGJvMKi5//5HSE+PQ3lFgr/JBGlplw1icrJ07CIKBQQH\nS5WA7r5bmlQ0pj5AvtHI3KQkhvr40OMKHUsPDw9GjhxJQhMr2NQHpdILrXYu8fFT0Wrno1RWFaKu\nbbVpNBo5+fffWMxmOvXocVkE4MgR+PhjScXpygoCmzdL9Q2vY5RKD4KCXiMpaR5a7awq53bt2sW9\n995rdV8yQeD1oCBWp6Yy0YoE8BvYTpcuXejSpcul741GIxaLAyTeDRkdodOZyjMOmEzVrFD+gYii\nyOHThzmnO8fMPjMbNQ9VHaAmYmEEyn1niB0bi99Tfrh2td2jcjHwx2aDCbXmY143BnPNmg8pK/sa\nUCGXVWC2qIEhlJSc5D//SSQ8XMpdFMXLBjE8XJKMDA62WQGrwfi9sJAvc3KYERaGZxOq9jQmSqUn\nWu08EhKmodXORan0vOaaq1ebTk67eeyxkZS36oGg1BBz9AnWvL2Sp4sKJH/3qlXS7O4iZjMcPw6j\nRjXdgzUSjo6tcXPrTkbGx5jN/dmyZQsVFRWcOXOGdevW2dRXK0dH5ILAGb2eNk0gmfhvZ+DAgUyc\nGAMshAofaOUHf4golRsYOvTB5h5ekzB57WTkEXJW3b+qyUqbOQSpiVwVTtamLHK25hA8Ohh1oPWF\nCNq1a8dPP/3EQ3UljIuiTZWQWrTBtFgMlJXFotefYuDAdARhI07iF6gEE2u/iqa42AuZLIf+/Y8w\nYkTzJvtfjUUUeSM1FSe5nBXVyEhd7yiVHkRELKhcac5Bqbx20/LianP9+lyGDj2NyfQZFN8KTmZ8\nSp6l8LlBnP7oPW6ubg9v2zZJJPsfgqfnfXz22XCWLXudkycfx2SyIAhbUKlcWbNmpU2fj9eCghgf\nF8cbkZENXr3mBlXRarWMG/caq1d3R6+fAJmhqCJWocruxujR1nsHrkdKjaXM+WUOiYcT+XTNp01e\nB1SQCfiP8MdUbCJtdRoyRxlBrwQhU9c9Do1Gc43U5TV4eUFe3rUBF7VskbSIjRCzuZySkmNkZX1K\nYuJcEhJi+PvvcXz55RC+/HIRubkOpKX15/eP2mP4+HnWfTGFRx55CyjEYtlMnz4N60+vL5kVFYyN\njeVud3deCAys82Xo6Oh4jXTb9YBC4UZExEISEmZgMOTWeF1i4nvIZIlALzAL3FP8MxP4gamWySzc\nte/aBqII+/bBnXc22tibmvPnz/Pcc7vo2PEp3NymYzK1wmg8ysaNP/ONjUVIlTIZI/38+KAO3csb\nNAwLFsxi69Y1PPTQPm7nE+6f2Zm//x7E8uVe1PVOvl45f+E8k3ZNYljIMLq16WZ9ceZGQOGiIGxa\nGF4PehE/JZ7cb3Otqm9c5yS0ttSSGqj7p2BlQWRrMJlKKCo6RGbmehITZ5OQEENCwkxSU1dQVhaL\ns/NthIZO5vvv/ena9ROefdaHl15ypHPnZ7nd3IU7lL+xlu8p0hcA8fj49GXkyBFWCfo2FT/l5bEq\nNZUFWi23WZlU7+fnR3Z2diOPrHFQKFyJiFhMYmIMBkNOtdfEx6diMEh7tG0uxNI17zCTWUyR2JH4\n+Goi0n7+Gfr1a8xhNzkbNmzGZHqGTz55gyeeWISDUw6gRa+fwDvvbLC5vy6urmQYDKRdVUrsBo3D\nPffcwzffbOL/fviC9r3uRKt1ZepUmDxZik37J7Hl1BY+P/k5K+9bye5vdlsVxd0UOEY6ErkiEpla\nRuy4WPSnal9kODk5UVJSUvMFjWIwawmxrQmjsYDCwj/IyPjwklFMSJhJevrbGAzpuLr2ICxsOlrt\nbLTaOYSFTcXHZwhOTjdz+vQ5oqNnUV5+hLKyD6moeJe+5fs5vi+eHqtvZ+Cg3/H07MOZM3/x5ps6\n3nlnhc3jawyMFgvzk5LINRpZpNPhbENYra+vL1lZWY04usZFoXCpNJqzMBiufY477+yMk5OUxH8u\ntwPybGkvQq3+kV69Ol/b4Y4dUEsi//VIYWExRqMPRqOaTz57GZ+BGVJyvIuMwsLiujuohgkhISxL\nSbFqtn2DhkMtk1FuNhMYCGPHSqpXDbiuaDYMZgOz9s5CIVMwvfd0sEjpJN7eLUts3vM+T3RLdRTs\nKSBhVgLGguqDr2655ZbaVZpqMpi1VEKq+60eG1tjQU2DIZfS0tOUlp6ioiLtcqcKNxwd2+Lh0Q+1\nOgTBBt/3hx9uwGD4DxCOIFh4UkjmrMWbQ4ZuKH7az/fff37p2ri4yYCJi1GzzUViWRnLUlIYHRxM\nKzvyBP38/Ei1Y2LSklAonNHplhAXF01Y2HTU6svRrsOHD2fWrCWUl0/FbJ6InDIEYSUODlsYPfpI\n1Y4OHoTOnW3aiG/pJCcn4+/vhaPjJ5SWTqQgqwMFX24Gx1wUXe5B09+N/cn76RHSw6a9TCe5nIGe\nnnyVk8OwBsxDvkHt9HJzY19hIf09PdFq4fnnYcYMmD//+v3YJhcms3jfYib0mECEh1Tx6bvvvmux\neeEypYygV4MwZBtIWZaCQ7gDAc8GIMgv/wI6dOjA1q1buf3221FUt4Dx8oLcaraSakmqr9OSibHn\nqajIJD//Z1JT3yIhYcalFWN29mYslnK8vB4gPFxaLWq1cwgJmYCX1wAcHMJsMpYA+flFmM3SH79b\ncCF/dpRxCE/Al/z8oirX+vo+Tnb259X00nR8nZPDx5mZLI+MtMtYwvW/wryIXO6ETreEpKR5VFRc\n1mN0cnLi//5vL4MHJ6NQBCEKy+h/1x7+/HPPtTmIX3wBjz3WxCNvHMrKynj00adp3boTixf/j7Ky\nXGATsA34HZUpmqCkCra8+hVZ+iyidkXx8dGPKTNavzF2j6cnh4uLyasuCfsGjULPSoN5kbZtYcgQ\nmDu31hS+FssPsT/w3qH3WH7f8kvGEuCPP/6gW7duzTiyulH5qoiYF4FLJxdix8VS8FsBAEePHmXI\nkFG8/voYNBoXHnroSTKu3vO3Y3ZT5wozUb0ZVa4FR8e2+Po+hlLp06gRn4MG9ePLL5dTUvI6BYXO\nFNyhh8Og0XzKQw/1rXKti0tHsrI2IopPN3kUapnZzILkZG53cWFWPXMofX19r9s9zKuRyx3R6ZYS\nHz+J0NDJqNVSdfrAwEC+/nojFst6xK++Qt66NVxdH+/MGWn/oL4qES2EV1+dyM6dBsrLkykvdwRM\nyOUz0Gg24ufny7Bhg4iK2oenpyePej7Kozc/yonsEyz4bQFqhZpnOjxDqFvd+/NRoaEsS0lhQQur\nBftPRSWTYbzKMnbuLGlvrFghVQy7HjBbzCz9fSmhbqHM7ze/yrmzZ8/SqlWr6ya636WTC84dncn5\nIocjLx5h6KanSdCPA7pgMk1ix46ldO16N+fOHb2myLvJVExp6Wn0+lOUlycAtcx6apPbA0RxRtOW\nmzIajWKXLn1EjeZhEX4RGfWbqFI9K2q17ast5ZSbu1O8cOGnJh3jqZIS8dWzZ8XksrIG6zMmJqbB\n+moJmExl4rlzr4tlZcnXnkxMlER4r2bKFFEsLW38wTUBJSUlooODmwhZV5SLEkUoEtVqdzErK6vW\n9nmleeIbf74hTvxxovhz/M91lgv7LCtL3H2daOz+E3gnNVVMqebvf8cOUVyzphkGZCOZxZnia9tf\nE09knaj2fExMjFhcXNzEo7ri/vHxdrcd/UKU+LTsXfEJkkQZ0yv1ukXR3/9u8bPPpotpae+L8fHT\npa+PeolJSYvFnJzvxNLSONFiMYmiWLOOeotIK7kShULBL7/sYNq0rkRGjsPN/XvGjQvi8OFfqy3l\n5Ol5n9VVIeqLKIqsz8xkW24uqyIjCblqpnKDy8jlDkRELCElZSnl5UlVT4aGQtJVx1JSwNvb9oKu\nLZS8vDxkMickJ84qgoPfrzzjglodRGZmZq3tPTQejO46mkX9F6E36pm0axLvH34fvaH6yMDHfHz4\n/sIFSv8J0SfXAfd5evJjfv41xwcMAB8f2GB74HOT8VvSbyz9fSkL+y+knW+7a86XlpYiimKjF11v\nLH47eIiNFh178OHp4AQmD3icZ5+dyZ13OnH+/Ck8Pe8nPHwOWu1ctCn3EBowDm/vB9BoIhCE2kXB\n6zaYMllV5fEmwNHRkWnTJnP+/GHGjn6RRYvm4uFRvXi1IAg4ObWnpOTvRh1TsclEdHw8wWo1k8PC\nUNzQ8qwTudwBnW4pKSkrKCtLvHyiOjfPBx9I0RP/ELKzszGbC4HZwFD6978YFFeIwVBIhJXuU7lM\nzgOtHmDpvUvpE9aHJfuXMHvvbOLz46tcJwgC44ODWVFdjb8bNDgRGg3xNSRhDhkivTK//rqJB1UH\noiiy+sBqzuSeYek9S3FWVW8QP/vsMx5//PEmHl3DodOFIAgnyHJVsP6+2TjubMPhj4awfbsnTk69\ncHAIuexqDg6WdFStpO63fh0FNVsCfn7Dyc7+tNH6P1JczPSEBCb+P3vnHR5V1YTx9+4mu9lNJz0h\nnd4ElCKCCCogimIBUUGKYgEEAqRQIhBCDd2CiAqKNOnSJfSOBgg9Ib3XTc/2O98fl1BStm8S+Pg9\nDw+w99xzJtndO6fMvOPtjb51OO5n1A6PJ0RgYBQyMlZCKn3kIS8WA5WV3L8LC7lzS3v72jt5QlCp\nVNixYwemTp2KmzdvYu7cuRCLjwHIAncucg0i0TC0b78d167pP3tv6dwSc/vMxZQXp+BwwmEE/xOM\nIwlHwBI3ofW2soJQJsN7oeFwdvaFq2sAJk8OQXFxsUl/zmdw8BkGqjoWEyNHAjk5wJH62fzSSpG0\nCEFHgtDDuwfGPj9W49nknTt30KZNm3q0zrRMmzYOItESYFgMsM0HszEHwxCJNsw+jBgx/PHGdaWW\n1IH26IqqgppNm+prd73B4wlhaekMmSwDVlams5OIsCYrCyoirHgmQ2YwPJ7gQSCQp+d4iMXNgE6d\noLx8GbxevcD/5Rfg888b2kyDyc/Px++//46CggK88847WLp06YPyQnZ2tpg79yMAyWjSZD3CwqZg\n6tRu+OMP4ORJYNo0QN+dfVuhLcZ1GQciQnRSNMKiw+Dn4IdBvoOw8vV3kPNBKNjiI4BajTVrluHQ\nob6IjT1fI9jhGcbRzc4Ol8vKHiuo8CjjxgFRUYC1dcOKVsVkxeCP2D8w+5XZaCKqqfv8KP/++y+6\ndu1aT5bVjTHBxt26dcOX61djzboICHgSkG05vhdX4nLfAXCWSLijnyq8vYHLl/UwTAMAuIKatQVo\n1BORKSkkr16BuRYUCgklJ0eYbNwChYImxcfTpZISk/WpiYiICFIqlfUyVkOhVivo3r0pdO7cNurT\noQdNZnjkYCGiXW2fo8LCwoY2T29iYmJo+vTptHDhQsrKyqqzHcuydO/ezBqBO8nJRBMmEMXEGG9L\noiSRXp/fnyxeb0t47gbh/fQHhZBtbF6j33//3fhBnvEY5SoVzU9J0diGZbkC81eu1JNRj43N0rqY\ndbTiwgpSs9qfoURE06dPJ4VCYWbLtPOtEUE/xUolTUtIIKlUSidPnqSLFy+SWq0mUiqJgoKIEhMf\nNq6sJFqwoEYfdblG3bZkGzCp3l0gQI4O2lOWlo4gUkGlKtXaVhuni4uxIDUVc/390bWeivW6uLgg\nP792abmnBR7PEjLZp9i6dRQSigbBkcIwXDULoXHt0bNnf6ifgIAVpVKJrVu3Ijg4GHfv3sWcOXMQ\nFhamsaYlwzDg82tu5vj5AatWARcvcisRY1IpAxwDoD4mgupkKMC/ALtmh+HhnQCAQXn5ezhy5Izh\nnT+jVqz5fFRo+cwyDPDtt1x68d27GpualHJFOUKOhqB5k+aY3H2yTsLpEokEtra2j5VKbCgYwGAF\nqyVpaQjx9oaVlRV69+6Nbt26cVq4FhbA4sXA6tUPt2FFIkAm07nvRhn08ygeAgGydRRrdHcfjZyc\nDQaPpSbC0rQ0JEilWBoYCPt6zAd8kvVk9WHBgtVYs2YmBg4shK13PpqiBPdUfyAjg8Hhw4cb2rw6\nyc3NxZIlSzB79mw0a9YMUVFR+PjjjyHQsW4cny8Cy9YMEuHxuK27t98GgoKA27cNt9Hd3RmMKg+4\nMhZlUaPxXroajpDDwiINHh6NS97sacHJ0hKFWmY6DMPVRl+3jqvPa27u5N9BWHQYpvaYit5+vXW+\n788//2w0urECHg8KAxxmtESCDjY2cKnre2lpyTnNpUv1CvapotGHenoIBMjWUWBaJPKDXJ4JltV/\nqp51v8LIgCZNMMbDo94Tdp8WtR9tXLx4BSpVf/z88yJkMe7YxX8b3CrodVy5ckXr/fXNpUuXEBYW\nho0bN2LUqFFYsGDBYwWGdYXHs4ZaXbdYdMuWXEnQw4eB7783TJt0/PjREIlWAkgDsXz8imYYiWuw\ns9iEzz8fqX+Hz9DK646OOCqRaG3H53Ol7pYvB8xZZGbrza3Yc3cPVg5YCXcbd+033IdlWWRkZKBp\nI4lVseLxINNzoVauUuGgRIKhLi6aGwqFnNNcuBDQ85mr2xKKYfQutGkqPAQCXCzVfZvVxWUI8vN3\nwM3tI53vOVhYiHMlJVgcEACxBh1Bc+Lm5oaLFy82yNj1iZ+fD5KT80HEx9K0iAevC4U2aNrUswEt\ne4hcLsdff/2F69evo2vXrpg3b57R21R8fpXDrPvLbGEBTJkCXLvGiXoHBQH6iPf06NEDERHTMHNm\nB1ha9gOgxBblFazv+QNaBLYwyv5n1E47a2ts03FnSCAAFi0CgoM5Gb0mmuNv6iQuLg67du0Cy7J4\n++230b59e8hVcsw7PQ89vHtgeq/pevcZHR2N119/3TCDzIAVjwe5ng4zKj0dwd7eui12RCLOaYaE\ncBH7OqKbw/Tw4GKkNZzTmAsXgQD5ehzu2Nm9gLy8LXB1Hab1F6dgWSxOS0NLsRjzG1hW7P9hhVlQ\nALi4rIBAsBoKRXMAzcDFw+2FhcW/iInZhiZNuO3JhghIzsrKwu+//47y8nIMGTLEpNtTfL4YLFup\nU9uOHbkdo2XLAFdXLj1V19/H1KmTMHz4MBw+fBgWFhYYOPA3CAuFSPk2Bf4L/J8YqbMnharfJxHp\n9LsVi4EFC4AZMzjnqWMFwAfMnbsAixathEr1EYgsMH9+fwz98i2IX7NA6Euh8HWovVCGNqKjo7Fo\n0SKD7jUHQj1XmGeKixEoEsFDKNR9EGtr7k0YMIATUqmjyMhjaIo2enD56FGi06cNDVoyGn1lkvLz\n95FEclxjm4TKShoXF0eJjUSKjWXZp04erwqWJdqwgWjmTCKJhGjVqh9IJHIkO7uXyNq6C9narqYr\n98MIjx8nmjyZ6O+/ufvMbxtLZ8+epdDQUFq+fDkVFBSYZZz8/H1UUnJZ7/vOnyeaOJEoI8O48Usu\nllDqklTjOnlGrWzLzaVresrI5ecTjR9PpI+65uXLl0ks9iIg+6HUYovNZNmvCe0/tF9Pqx+SlpZG\nK1euNPh+c/BnTg7FV1To1LZCpaKJ8fFa5SPrZP16og8/JHokI6Iu16jbGWZVLuYTgpPTQBQWHqjz\n+l95edicm4sVzZohoJFIsT2tM/87d4BJk4AWLbjAB0dHYOLEccjLS8Pu3RE4ceIHzJw5Hm5unQAA\nffpw5zwiEbc9efCgeSpASKVSbNiwASEhIcjPz8f8+fMRFBQEJycn0w+GR7dk9ePFF7mjlnXrgM2b\nDf9d2HWzg7iVGDm/a5bke4b+vOboiOhaZPI04ezM1dEMC9M9Onr9+s2Qyb4E4A6eUAamTyRgyYfy\nnwhs+PUv/Q2/T2MK9qlCnzPMZenpmKrrVmxttG4NDB7MvSGaCk5D16Afb2+91BAaGobhwdq6NSoq\nbj32eqVajRlJSbDh8xHu5wfBM3k7s1FZyW09HT3KbS2++OLj121sbNC3b1906dIFX33Fw9q1D68x\nDPDaa5zjrDrXO3zYNI4zLS0NkZGRD4J3oqKiMHjwYPDNfHatz5ZsdcRiYM4cwM2NEzowNPvIeZAz\nSEUoPFRoWAfPqJUmlpYGlVfz8gImTuS2Z3UJ8iotrQDLckpjz/e+BofSN4BbQwE4orRU/8kYACgU\nCpSVlaGJoQeqZkLIMDo5zEulpfAQCOBjjCiHjw9QXAzMns3NYCrr/p7q5jH4/CeupLir6yfIzd38\n4P83y8sRmpSECV5eGGimVcQzOA4fBmbNAoYP5x4I2uJl7O0BG5uaUd4MA/TrxzlOIs5xHj1a03Fm\nZWVhw4YN2Lx5M0oeqVNYBRHh5MmTCA0NxZ49ezB+/HjMmzcP7dq1M/In1R1tUbK68OqrXE7f8uXA\n3r2G9eHxmQfKr5WjLKbMKFue8Tg2fD7KVSq97wsIAEaN4t5XbRPCd9/tDxubTWiJYhR5i1EU0xkA\nQSz+Ax98MMAgu/fs2YN3333XoHvNiS5BP3KWxZ+5uRhjbGyNmxsXo+PqCsycCYSG1tlUu8Pcvt04\nY0wAj2Gg1nN5wedbwcLCATJZNn7NzsaRoiKsbNYMnvocCj9DL7KyuAhAuZx7qPtoL+X4gC++AH7+\nufZrDMNVgVi+HFAouHqDx49zD5i5cxciIKAtvvnmCL76ahs8PPzw11/cZ7aiogLr1q1DaGgoKioq\nsHDhQkycOLFOIX9zYuiWbHXs7bktWh6PmwwbIhPrE+aDvG15kCbrXqj6GZrp4+CAEwZq9rZty+0I\nLlig2Wm+88476NzWAW9Y/oIEygVwDCLR+wgIKMbw4Z8YNPZ///2HLl26GHSvOdFlS3ZFejomN21q\nvGQpj/fwF+/hwUXO1oH2KNmSkofT2QZKLXGxtES+QgF3PZ2dyGUkvrkdjU8DBqKXg4OZrDMtpGO0\nXWNCreacXUEBt6thSFWgqlVmRkbdssUMA7z5JjBwILBvH/D++6k4dOg65PLbkMurZpmx+PTT3rh4\n8QJsbW3x8ccfY+zYsQb/bKaCz7c2eD+531UAACAASURBVEu2NgYN4ra5IyOB/v0BfTICGIaBf6Q/\nEqYkwH+uPyydGl7Z5Umni50d5qakYJCzYQIRXbpwO4ErV3LpRLVhYWGB3/p/j5+HnIHftR2waXcL\nw4e/gwkTxkFkQCzG7du30bp1a4PsNTdCHg/FGlbsV8vKYGthgUBzxKB4e9d5SfsK8/PPub2yggLD\nD0+MRB+1nyoulZZiQWYpvrZLQA/bJ+OB4ODgUOuWYmMmJobLGezRAwgPN8xZVvHll3WvMh+FYbjU\nE4UiDDLZKAAe4PEUAP4BsBkqVUdYWYkxd+5ctGzZ0nCDTAiPJzbJCvNRnJ259BOJhNvSq9Cje56A\nh4D5AUgOT4Za+mQdtzRG+AbsglWnd29OwKKu70Dx2WJYB1jD/oM+uLH+B9y4cRahocGwtrY2aLxt\n27Y12jJeVjwe5HX8PpUsi1+zs/GVZ/3nbet2hjluHCePt8V8JbQ04SEU6uwwWSKszsjAlbIyLA8M\nRGuvT5CT87uZLTQNT1IuZmkpF4hy9Sqnh/rcc8b3aWfH/dG1pGNeXgEAOYA/4OMzF+PG/YBu3XpB\nrX4TJSWmdU7GYkzQjzY+/BD46isueOTcOd3vs7C3gO9MXyTPSAapzRCK/H9GM5EICRoCRnRh4EAu\nkvyPP9RYs+YndOjQEwEBnRD8zUyk/pEKt0/dUKFWw8ZI2c7y8nLweDyI9Ujar080bcmuzszEBC8v\n8E25E8fj6RSno3uYaHAwcOECcPq0MWYZhK4rzDyFAkEJCehpb4+vvbzAMAxEokDIZCkgavyz6CdB\nT5YI2LEDmD8f+PprbgPClMHGX3zBpVCo1WpUVFTUKcDMRfZZgs+fBaA9UlLm48cf90CpFGDixBUY\nMEBtkESiuWAYHojMp8ns6clt58XHc9u0uupJC72E8PjcAykRKQaLXT+Do3+TJjiiZ3pJbXzwAWHF\nip8xeXIGbtyYheTkn1D8oxeG7RmNm1lZcNNRv1gTW7ZswUcf6a6GVt/UJVxwq6ICfACtDFxV10mV\nOI8WdH/U+foCbdoAZ89yjrMe0aViybGiIixNT0ekvz86V5PPcHZ+D/n5u81poklo7CvMpCTufMXR\nkVOVcnMz/RhWVgqcPn0Qtrad4ODgDG/v1ti06eHORm5uLhYsWICoqCjMnz8PTk5lsLDYCiABwHXc\nurUBp075o1ev4UhOnoWMjO+hUmnOrXpaYBhg9GguOjk4GNBVmte6rTUc+jgg8wf9xaif8RBPoRBZ\nOupea+LChQu4d28ZFIrZAAagM1ogkX0fSSVdEbpzDwYamQJCRIiPj0eLFo1XLrG2KFk1EdbcX12a\nHB0LSeu+rrew4Jas4eFARASXK2CACLUhCHg8KOtYnqtYFkvT0+ElFGJxQECtATP29t2RkDAVLi7v\nN+qAGjc3N5w50/jKMCkUnCC4SsUpSZmzDvGoUeNw6VIRZLJTAByQmXkGX3wxEgUFeSgoKIC1tTXG\njh0Ll/sCy1eunMWsWZH4++8+sLQU4tNPP0R4+FrY2trCwaErpNJEpKcvBo8nhofH5xAItAgzm5X6\n+exVlQ1bswY4dow7Y9aW2uP4iiOUeUrk/ZUH16Gu9WLn04jg/oNeaMS2S3R0NCorhwCwQIc2Z9HH\n6TaWnRkLKIbjUsYRNDNyG/XixYt4sXpidCOjthXmj5mZ+NLTExbmyJ/38QFu3KiZMF4N/TbCq6Jk\nv/2Wc5yWlqY5vDKQNJkMS9LSMMHLS+sS3d6+F0pKzsHBoQFLn2vByckJBQUFDW3GY5w+DezcCYwf\nz6n1mJP09HTs3r0bMlkqgKroIWtUVnZFRMRipKXdqxHg4OXlhfXr1wBYU2ufIlEg/P3nQaHIQ3b2\nL2BZKdzdR0Ekagjt4Prb8uTxuPfs7l1uV2D8eE7QRBOuQ12R8X0Gik4WwfGV+k+9eRroaW+PcyUl\n6GtE6pKTkwKffHIMItF0xMU9j3s2LN5/fxV2/u0NKxOUHNy7dy8iIyON7secVD/DvFdZCSnLor0x\nUYWa8PYGDtStDleF/q66ymlGRABbtxpXwE8Hbt++jTfe+ACR8xfB0dELQUFcTt3eggL8kp2NpYGB\nOu1nOzsPQmHh32a11Vj4fD7YBqw9+igFBZxSVGYmdzZWH7s3N2/ehFD4Ah46y48A3AWwERJJDqyM\nWNoKBK7w9Z0Ob+8QFBYeQGJiKMrKrprA6sZNq1bc+3fwILdLoO3j5TXeC8UnilFxq3EFTT0p9LS3\nx1kDIt2VyiJkZf2MxMQwvPKKDY4cice6dSNw+vQQ/H3wK1y/3gXvz/kRIzoaJq5eRX5+PhwdHWFR\nj7V+DUHAMFDc/7CyRFiVkYHJ5iw9ZmurVRYP0NdhOjtzMewAN4WNjATWr+ciDcxAYmIiunfvgyNH\nXoJaNRHFxcfx4y9ZaLFgKVQsiwh/f1jpKGnGMHyIRM1QWRlnFlufFoiADRu4Lb2QEOCjj+ov9dbH\nxwdK5W0AVflXLQB8AiAOjo4eJpGvs7CwQdOm38DfPxIVFTeRkDAVRUXH6ingpWGOAywsOLGHnj05\nXd+kpLrbMgwDv2/9kP1LNuSZxp/H/b8h5PEgVamQkJCAIi0BQCwrR37+biQmhiIray0cHV9FYOAi\ntG0bhuXLf4RI1Asi0UhYWAQhM3MYEkRvYkTH6ygpMTyGZOPGjY1ON7Y6RIQzZ84g9sYN3Lx5E+uy\nszHaw6NRSJnqZ0F1Efaqqqg//aT5W2gg8+cvQ2XllyAKAiAGmjaFYkw4ircch11srN79ubkNR27u\nnya382nh1i3ugdqqFVevr74Fcdq2bYu2bZvD0nImgDIAQgCFEIsnICjoG5OOxeNZwt19BAIDo8Cy\nMiQmTkNe3vYnIpraUKrKhm3dCvz6a92qMgyfgf8Cf6TOT4WqRH+5t/9nVq36ASvDI9Ch7xB4ePjj\n3Xc/eSy3mohQUnIOSUmzkJoaCYHAAwEBi+DrGwaRKPBBu+HDP0Zy8h1ERXVFZKQ7zp3bh7feGYw2\nzZehvDwWGRnf6z3JY1kWubm58GyA/EVdSUtLQ4sWnfDmm+Nx4MA9dBk0HFFr16N1A9UproGmqic1\nLsfFEf35Z82GCgXRpElEqaYtHxQQ0ImAy1wZm4/2EkbeIfDVxDDTKSIiwqA+U1MXkVyea1I7TUlD\nlPiqqCCaP59o9WoipbLeh3+M3Nxc6t79NbKyciErq2ZkZeVAX38dRCqVyuxjFxefp4SEaZSZuZZU\nKtOXfUtKmm3yPg3l3DntZcMUBQqKGx9Harm6/gx7gvnjj40kFrckeMYSBmQRUEwCwRfUs2d/qqiI\no5SU+ZSQEEr5+XtJrZbr1XdCZSX9mpX14P8lJZcoPn4SKRQSnfs4ePAgHT+uuexhQ/Pccy8Rn7+Q\nAJYwMokw4S4JbT+mL7+cZP7Bv/32wT/rco36OUy5nGjevNoby2RE33xDlJmpn5EaeOmlAQRsuV/7\nLYIABQFE1tZDaO3atQb1KZfnUmrqIpPZaGrq22EeOEAUFGTyuY7R7Nu3j2bPnk15eXn1PnZ5+R1K\nTJxBqalRej2QtNGYHCYRUXk594zYvLnu2qOVSZWUEJxgeK3B/yP8/TsQEM09rz5LJHv7PBo8eBWN\nH29Dly/PJoWiyOC+V6enU5ZM9thrCkUhxcdP0rnOanBwcKN+H2/fvk1icVMCVNzvcF4soU0xAekk\nEtmbf9K8aBH3pSBj62FWIRDUXbxNKOSS8xYsAEyUSzh16hewtp4LIAeAEoAlgKNgmBMYOnSoQX0K\nBK5Qq8uhVptHdcVYhEIhZLpmnetAXFwc9u/fj4Rq9UwzM7lzLZVKf6H0+sDW1hYvv/zyg/SR+sTa\nuhUCAubDze0jZGZ+h5SUuZDJMurdDnNjbQ3MncsVaairbJjIXwTXD12RtujJKe/XUKSnxwHoAQCY\nkvMPBgxYjwsX3sHGjf2QmtoOlpaG61nnKBTwqKalbWnZBM2arUBp6UVkZv6kcYs2JSUFPj4+jTqt\nLjc3B61aOWHo0BUYM2YWXpZcAG7bA/CEQiEz6XOxVnx8tMuMaXK4tV5+ZNlaK+XlROPGcSXFTcCs\nWRFkZeVAAkELsrPrSQ4OHnTq1Cmj+qyouEuZmYatUM3Nr7/+SqkmWO4VFxfTK6+8SSKRO9nbv0Ei\nkSv17/8eFReX0Q8/cBsFehaJr1d27txJ165da2gziIhIoSiitLRllJgYRuXltwzuJylJy3enASku\nJgoLI9qzp/brBQcLKOuXrNovPoOIiPz82hNwnF5CPr2I/Ps7Y0oSi70pNjbW4H7LlEqan5KisU1x\n8Tm6dy+IlMriWq9HRkZSUZHhK1xzUVmZRJmZaykxcSbdvh1MffuKycYm7v7vrurPYfL372B+Y86c\nIfrnHyIy1QqT87Car1tbc4FAs2cDJpCJmjcvHKmpcRg0qD22bp2B3NwUvPzyy0b1KRa3hFSa0CgD\nPEyl9vPpp1/jwgVPSKWpKCk5CKk0DcePt0XXrpfQsydXr9JcKU2moLCwsNEUtbW0dIC39xT4+s5G\nScl5JCYGo6RED9HWBzBmlcczhuplw6pnRji94QTGgkHBvsaVJ9yYmD17GjxEoWiNJFyAM4BSCAQT\n0bFjK3To0MHgfo8VF+NVLRF49vY94OMzA8nJ39ZIl5LL5aisrIRDI6jYpFRKkJe3A8nJ4UhKmoXi\n4uNo0qQ/AgIi0br1ErRpEwGZ7D8ApwBIAGyHSDQaK1bMM79xuqj9aHK4tV5etoxIosO5TlERt9Is\nKdHeVgfCw8NN0k8VRUVnKC9vt0n7NAWXL1+m/fv3G9VHfn4+CYUOBJRWm6mVkVDoQCUmek/MyaJF\ni6iioqKhzagVllVRXt5OuncviPLz9xLL6hYUk5q6mFSqcjNbZzz5+URTphAdPVrzWuqSVCq52Pg/\nPw0By7K0rfd2crXxJaFwBgkEreittz40emU3MzGR1DqePbKsmtLSllNm5roH55WbNm2imJgYo2ww\nFJVKShJJNCUnz6XExJmUlraMSkuv1vqdkUiIxo1j6bvvfiN//w5kZWVPnTv3piNHjtSPsUol0dy5\nRGTKFWbz5kBiovZ2Dg6cuMH06TolhGpCoVDAUpu2l57Y27+EkpLGJ0NnihVmbm4uBAJ3eHnlYMSI\neWjfvkow3wYWFraQVOXSNmIqKysbbSUFhuHDxeU9BAYuA59vh8TEEGRnbwDLatY7NlURaXNTVTas\noKBm2TDvad4o2FOAyoTGGQPQkOT+mYt+Ea8hrSAOixaNx4kTF7Bv31ajVnZEBAJ0LpLMMDx4ewdB\nLG6BpKQQqFRluHr1Kjp37mywDfpAxKKs7CrS05chKWkW0tOjADDw9g5BQEAkvL2nwNa2IxjmcddT\nWcmJx82fz2DChNFISoqFVFqMmJiT6NevX73Y/kD+VVMTvTtt1gyIjdVNR9bJiduaDQsDoqIAA4t9\n5uTkwMPDQ3tDPWAYBnZ23VFScgH29o1HV9HV1dWoiiVqtRR2dpfw6acpSEnZjp07J6Gy0u7+1Vuw\nsFDCyxzixf+HMAwDR8dX4Oj4CsrLryMlZTYEAg+4u4+GhcXjBQCkUilu3kyAUvk3evf+ELbVCgQ0\nNhgGGDYMyMoCZs4Ehg7lap4yDAO/CD8kTk2Eb7gvBC7GV854GpClyiBLkcF9hDsAoHVrT5MIfsSW\nl+M5A85OHBxehljcCocOfYHmzdsZb4gGpNIUFBVFQyZLAcMwsLHpCHf30bC01O1IRank1lWzZnHr\nrMaM/itMf3/9RApcXblCfSEhutccqkZWVpZZHvLOzu+ioKBxVTERiUQGRYOVlV1DcvK3SEtbBEfH\nF+DmNgcnTvyFysrbAFgAZyEWD8Hs2TNMvlp/BmBj0wEBAQvh5PQ20tOXITV1ARQKbqdg9+49cHX1\nwapVRxAW9gvc3X2xfv2TUaPV0xNYsQKIi+OEveRygGfJg/8Cf6TMToG6ovHFAdQ3xBLSl6XDO9j7\nwWv29jXPgQ3hn6Ii9DNQQUQgcMWlSwF45RUG2dkbTKZmpVQWIT9/54NzyKKiaDg6voqAgEj4+8+D\ni8v7OjtLluUmZBMnAu7uJjHPrOi/wrSy4r41+uDpyTnM0FBupalnPbfMzEw0a9ZMvzF1gMezgJWV\nL6TSxMdUNp4UVKoS5OVthVSaCBubjvDxmQE+n9NbnTWrPRwd7TB//gjk5CTAy6sl5swJweefj2lg\nq59uRCI/+PvPgUJRgJyc31BQkIZp0zahvPwoiopyoVa7oLLSFhMm9MVzz7Wvt60yY6gqG5aczKWf\njBkDdOpkAd9vfZE0IwmBywLBs2h42bKGIuunLLiPdgff6qEajb09kJpqfN+lKhUcDJzglpaWQiAQ\nokWLGSgqOoakpDD4+X2L3NwSLF26GkePnoObmzOmTBmLgQMH1tkPy8pRUnIeJSVnwbJyWFo6wsGh\nD5yd362xtaoPRJyi2CefAIGN6fGrYWJRfwq83t5cnaGwMGDJEm6/WEeysrLQu3dvs5jl5jYS6elL\n4O8fYZb+9UGpVGL37t3Ys2c/yspkGDNmBNq2bftYG7ovrSWRHACPZw1X12Hw9PyyRl8Mw2DChK8x\nYcLXYFkWvEagw/j/hEDgDB+fEPz0Uwg6dnwer7/+FzIzAiGtFAPoisrK8fjsswk4dGgX3J+EqTW4\nzaXHy4YJ4TXOCymzU+Af6d+oc/zMRcXtCrAyFradHt9iN8UKs1CpRBMjdoM2b96MTz75BADg6Pgq\nxOI2uHbta4wadRjx8R9BoZiHmzdTcPHiZEydegVz584CwJ1DlpdfR3HxcSiVEvB4Qtjb94C3d/CD\nCbkpWLUK6NOnQQte1cTVtfaE5PsY5jANXdr7+wNff805zcWLOS1aHSgoKICTk5NhY2rBwsIGPJ4Q\nSmUhLC3NM4YuVFRUoFevAbh3j1Be7oKbN/lYs6YvoqLmYvz4r6BQ5CE3dxMUimzY2/eEn9888Hi6\nvX3PnGXDkZwswa5dQ8HjjcG8od+ia/8ziL3eB0ArxMZuQsuWHXH69BE816ieGnVTVTbszh1u/jt+\nvBieA52QsTID3kHe2jt4imCVLLLWZCFwRc3lkSkc5mGJBAMMTK0iIiQlJSHwkaWbUOiBqCgGHTp0\ngJtbNxw79goAoKLiDaxf3xIffSSGQCABwzCwtn4O7u4jzfZMXL+eW1UamSFoerSklhjmMG1tgdJS\nwM5Oe9vqNG/O7enMnMmpAun4MDfn7NXdfQyys3+Dj0+w2cbQxtKlK3Dnjjtksm0AIqBWz4FcPhpb\ntnRCz57X4ejoDze3jyEUPv0BO0T01KxWXnmlK/bt24+WFUNwbusE5HS+jXfe+RF79/4Lok9RWuqK\nUaO+wdWrp7V31oho3ZorG7ZqFSAU2mOotwK5m3OR1jwNv/22CaWlFXj33f4YPHhwoy8lZSgZKzLg\nNdGr1u1osZiL/DSG2xUV+NjVsGLeZ8+eRc+eNWv/Hjp0BKWll/B81+sYM+NNUHZHMAyDkpJmuHoV\n+Ogj89fJ3L2b22AcNMjsQ+mPj4/GLBDDlh66ppbURZs2wMcfc3HE9VJWSTNCoQdUqiKo1WaWXtLA\nhg3bIZNNAcCDSJSLIUMi8dlnv4Blu+PChXbw8Qn+v3CWAFBeXg6bxqyqoAfDh3+CFnaFeJ7ZgYMA\nrlx5DiUlJzCs+x4AYwF8itu3ryNfwzZQY6WqbNhLLwHzTrlgyy/HMbnXLPz8sxM2b+6IUaOWoVev\nAeaXNGsASi+XwsLBAuLmtac+GTvfU7Es+Axj8MTxwIEDePPNNx97jYjA8+EBfRYiu8VmqNQ8bN48\nHb/9FomjR50hEBhXa1MXTpzgZDkbbYUxLStMwxxm9TJfhtChA/Dee5yYZSNwmm5unyAvb1ODja9W\nqwFUBUNJcOjQGKxbtwhXr/pCpWqc6jDmQiKRNBqVH2MRkhC/9f8BuW8cAxAAIAAnTwrR8eYH8IAC\nDVUj05R06gR8+eU9BJ9JRqB8NfzZIADjUVFxFrGxlvjpp7UNbaJJUVeqkbspFx5jTZvq9igXS0vR\n3ZAdPHB52M7Ozg/qxyYXJWPJuSWYfmw6Xn7zRbykvoWu5T1w5/h4+PvfBpALlSoBAwYMMOFPUJMr\nV4Dz54EJE8w6jHE4OGhUqDPMYQYEGLfCrOL554EBA7it2QZ2mtbWbVFZebfBpMs+/PBtCAQ/AbgO\nqfQNlJd7AsgDsAdvvfVWg9jUUBQWFprtzLo+ISKkhKegzaI22LN/C7y9AwBsAvAnIsuXYxJWAfgD\nrVu3bxCReVOyf/9OMEwO/kQL9OdloLnbNQB8SKWT8dtvOxraPJOStiQNPiHmFTI/UVyMPgYmJW7c\nuBGDPhiEdTHrEBYdhiOJR/BZp8+w6LVF+GHCh+jUIRvR0b8jLe0ftGgxFSJRN/TpE42SEmsT/xQP\niY8Htm/nMgwbNVreU8McplgMSKUG3VqD7t25k9+oqFovl5WV1dv2XJMmAyGRHKqXsaozffo0eHqe\nh4XFEHCFk7+HtfWLmDp1Ivz8/BrEpobiaXGYGcsz4PqJKwRuAjAMg02b1sLa+jMIBJNRjr9xx+I/\nvC6ehg0bvmtoU42GZVmwrAUIDG50vwX3PisBQRkAC7Ds07NDIomWQNxKDKGXUHtjI5CxLER6Fk1W\nqBXYdWsXdvy3A7szdqOPfx8sem0RvnrhKziJnVBQsBcMI8HKlXHYsmUOPv1UgA8/FCMp6SK2b/fD\n4sXGbxzWRmYmF1k9b57xW9UNTeM4je/VC1AouAzpoKDHLmVlZdVbhXAHh1eQmBgMJ6c3tTc2MU2a\nNMGuXX9i/vwFKCzcCmdnB3z99Tr07du33m1paCQSCdq3b9/QZhhFwd8FEHgKYPfCw221Xr164fbt\nGPz44zrcvLkfvp1744csf1g/4T8rALz99tuIjHwdUukstG53GT//uQToOwtWp9MxYsTghjbPJCiL\nlZAcliAwyrxJg2kyGbyFujlkIsKlzEs4eO8giAi2KbZY/PVi9O75eBpefv5uKJX58PL6CgDw1ltv\n4a233kJy8rcP0pqWLuViMT/5xHSpHhIJJ+ofFaVXJmGjpfH8CK++ymkk/fADF7d+H3Op/NQGwzCw\ntX0epaX/wc5OB+k/E/Dvv/9i7959sLCwQHZ2Jv744/dGq6FaXzzpK8yK2xUojy2HX7hfjWs+Pj5Y\ntOiRygtXrwK//QaMHVt/BpqBdu3a4csvR2L79hdQXPwcUBkNq1sxsBuYivHj/2ho80xC2qI0+E73\nNXsE98HCQgzU8vlPKkrC9lvbUSQrQjevbpjZayaEFkKEhITg5dGP52rk5e2AWl0CT88vNPZpaQks\nWsRJgJeVAbUE2Wrk5MmTiIhYjrt349CiRQuEhk7D/v29sWCBwaqoDYMGYR3DHaZYzKkyW5tw33vA\nAODvv4F161AydCj++GMjdu7cixdffAHt27evl4eoi8sHiIkZh3/++QdqtRrvvjvYLKsdIsJnn03A\ntm37IZN9AobJBcNshqenL2bPbuwb/eblSQ76URYpkbUmC81W6qhM1akTsGsXUFjIaS8/wSxfvhBv\nvVWCjRsL0a/fLgwZMgrOLzpjZ8JOjOw4sqHNM4r8Xflw6O0ASyfzy0qmy+XwsaopEFAkLcL229uR\nVJQEfwd/jH1+LJqIHn5PEhMTERAQ8JhDz8vbDrW6DB4en9U6FsNYgGWV4PG4n4vH4+S/ly/nMgc1\nCAA9xl9/bcfo0ZNRWRkJ4EVkZ1/CuXMJWL26GPb27+j+wzcGvDXkE2uqdqLx8vbtREYURdVExsqV\nNN7agcTiDwl4m6ysPiQ7Oze6evWqWcZ7lPDweTR0qA15eY0mPn8aiUSeNHFi8INSOaZi7969ZG3d\n7pESXHMIiCOx2LPBSvE0Fr7VVqS8kcKqWIr/Jp4UEoV+NxYUEM2aZR6j6hGWZSkhIaTG6z9e/pFO\nJp9sAItMgyxbRokzE/W+b84cIn0fG5UqFUUkJz/4v1wlp913dlPo0VBaeGYhJRQm1Hnv3LlzHyvd\nl5u7lbKy1mscLyfnT6qouFvrtbVribZs0W6zSqUiFxc/As5VKyd4hZydfUmlUmnvpDFx4oQR5b3q\nil5t3tw8J8QABv+5B5KKwXin8m0AHSGTbUVp6RIMG/a5yQSEa+PSpUtYtmwt9u27hldf9YVaHQWp\n9CZ+/XUPjh49atKx1v28BeqKybCDEJ5WV8HnsQBaQCb7HBs3bjXpWE8aT6poQeqCVHh+5QlLRz1X\nIU5O3Kz22jXzGFZPlJVdhp1dtxqvf/XCVziadBQJEvM8L8wJESF9cTp8wnz0vtcQ8YJTxcXobW+P\nC+kXEH48HJGnI+Fm7YaFry5EWM8wBDap/fxUJpNBqVTC7n4qSm7uFrCsHB4eozSOJxK1RGVlXK3X\nvri/g/vzz5ptTklJQUWFGkAPABK0xsL7VzpBKgWS9CnW0Rjwqfu91r4lm5/P6etVJzAQOHLEGLNq\nJS8vDzduXMN/OIIRzCZ07ZKJy4XlAA1GCu8HHI6LQ9OmTR9krzEM8/Df1f+u41rVA7n6tdV/7YHU\nbhKIvCAV8dDC4wYU2f6wqgjFwQXH8bz6ebCVLNSVarDS+39XsmAV+kcBPn+rA2zgBhnykGm/CsOG\nKrBj7x2kpdmhrKxQ7/6eJsw5KTIXuVtyYfOcDazbGHhE8dlnXMDbqlVPbChhQcE++Pl9W+N1hmEw\nu/dsTDkyBfP6zoODVSOv4fQIOb/nwOUDF1jY6H96VSWPp+upVaIkEVE3L+B55S30aNoN4b3DIeDr\nVqhi+/btGDJkCAAgN3cTiAju7p9qvU8sboni4hN1Xh82DDh4kAsImjq19o+mra0tlMpiAGEA7NAD\nSpQjDelwhUpV+sCJPzFoiJnR/ilIS6vdYdrYPF5Z1gSwxCI2NxbqLkpA+C02dr0Fh8IuwB0pAAKs\nvHFPJkPF/Wkb3f+DR/++/7CtkHKfkAAAIABJREFU61qN1x65lmhnB+roAZFLImzYcrh0+wH8PRGQ\nwx7SShksm1iC15QHnogHvpgPnpgHvogPRqC/Ioe7exNETf0RlZX7gFw3xPwwH++/vwIvvrgK7723\nRq++ntGwlMWUQZ4ph880/VchD+DzuaKTW7ZwKlhPGGp1JRjGAjxe7Q94S74lIvpEIPx4OFYMWAEL\nHXWQGxJpshSKLAU8RhkmUFDlMDUF+UukEmy/tR3JxcnwdwjAcx49saTFcL3Hun79OkaMGIGcnI1g\nGD7c3T/R6T4LC1uo1WUa2wwcyKmgzp3LFRSvUjMlIhw7dgyHDx+Gn58vkpL4UKtnYDtKMBkrMZ/P\noEuXbnBzc9P752lQNEQo6+YwdSkWbQAssbiRewMnU04ivzIfDBi0d2sPN8Yfmf2awGNPOAIv+OMs\nXADcgZPnDUzo0MFsYuLCNs0QEXsU488eRXjKchS/tBawVcKaXYuxXw6DXTfTzZRGjvwUP/64AXFx\nPaFQvA2W3Y8DBw7gnXcC0azZEUilbZ7IkmOm4EnaklXkKpC7KReBS03wXvXsyZXAGzSI02t+gsjP\n3wkXlw80tnEUOWJit4mYe3Iu5vWdp7FtQ0NqQsbyDKPeV3t7oLi45utylRwH7x3EpcxLcLRyxJC2\nQxDgGIA7FRWwLtPsvGrj6tWr6NSpE3JyfgfDCOHmNsxgm+uiZ0/uIxkWBoSHy7BjxxbcunULr776\nKpYsWYKgoCD06tUfBQXHIZW9CAGzHW3crLFlS7TJbWlItDvM9HSTDVabg+zg1gHD2g2Dm40biAjL\nr22Dfcg3kIxbgn6ZTbAVBeDzL8PC4ib6999vVP01TfxbWopspRLB108jKMcHpXQR+LcVLHr3QWCO\n84MyOaZCJBLhwoVovPXWIBQXH4FAcBxjxnyMMWNGg88npKcvh6WlEzw8Pn+iHMj/E6yCRUpECgIW\nB4Dhmeg9mjSJ25adNcs0/dUTFRU34e6uXSC0uVNz9PHvg59jfsYXz2tOc2hIMn/IhMdYD/CEhj9v\nHq1YQkS4kHEBh+4dAsMwGNh8IBa+uvCx7/ZhiQTDDViN7dy5E59/7gMezwGurkMNsFS3z66HRx6I\ndqNv3/ZYs6YrRo8e/eCal5cX7t2LxaFDhxAXF4fnmkQgrLAQ/KZNDbCn8aLdYUokdV8TCjnFnzqS\nbKo7SB7DQ3vX9g8c5KOUyEow+szPsHF6HtcGDkXKsT6I/vAE4oSr0a5dc0yZMg9lZc0xcyanGKGn\nCAYKCgqwfv3viI2Nw3PPtcTo0SPh7OwMqVqNZenp6HHgACar1VDevIaKdb/gt9+WgGVZBPRuh/AR\ns2BVS5i3sZSWlmLQoLcwZcqUGtd8fcNQWnoJCQmT4e09DVZW/x+lk1iWfWImCCkRKfCe6m3Q+Vad\neHpyxx1xcUDLlqbr14xwBdgDdG7f178vUopT8E/iP+gX2M+MlhlG+c1yEEuw6WCcwpi9PXA5IRHX\nzm5HiawE3Zt213guWaBUwkVDDmBtFBcXQ6m8BbG4I1xdNa/wa0OlUuHevRwcPRqF9u17onv37jW+\nfzdu3MDWrVthb2+P4OBRCApyxfz5XNynvf3Ddnw+/4EgAgBuOapQaMxrfOLQFF0LgEhTiP/WrUQ3\nbz74r5pVU2xOLK26uIpmHZtF4cfD6a+bf1FOWY7GKN7TKWeo19HvaU3qvQevlV4tpdytuTXa3r5N\nFBREJJNp7PIxYmNjyd7enUSiTwn4iUSikWRn50br//uPJt29S5J584j27Kn1XrlKTlOPTNV9MD2I\nioqinBzNvxuVqpKSk+dSdvZGk6e2NEYKCwtp5cqVDW2GVjLXZZIkWmKezuVyokmT9M9JaCBSUuaT\nQlGk931zTsyh23m3zWCR4ajlaoqfEE+syvDffWFlIf307080fncYDV/xM0kqtX9OipVKWpSaqvdY\n8+d/RP/9t8YQM+nevXvk5dWcXn65FT3//Ntkbd2Mund/lUpLS0mtVtOBAwdo6tSptGHDBpJVe+AW\nFhKNG0eUW/MR/ZBr14g2bDDItoamLtdolMNU//cvxW5ZSasurqKZx2bSrGOzaNvNbVodZBVKtZLm\nnFlKr57bQzGP5A8REaXMTyFlqbLW+1JSiCZMICor02kYatu2GwG/PswPslIRRu6iwBFjiQ0KItKS\n97j1xlY6k3pGt8F0RK1WU0hIzZy1uigqOkn37gWRXK7b7/ZJJT4+njZu3NjQZmik6HQRZfyQYd5B\nDh0i2rvXvGOYAJZVUWJimEH3qtQqmnhwIuVX5JvYKsNJWZBClQmVet8nU8po5+2dFHo0lBafXUyJ\nkkQqKyNaskS3+//KzaUrpaV6jZmRsYbGjx+st61EXM5sq1YvEI+3mtzckumNN34lQEUCwXB68cXe\nNG3aNIqOjtY4SS8r457DKSkaBpoy5YmZ+D1KXQ5Tr70klljczLuJkyknkVeRB0YuR/uUHHz41tIa\nW6zaSC5KxsyL68DzGoxtrTvByfJh7hoRQV2mhoVt7eb5+nKlNENCuO1ZTQIpGRkZSExMBMApjQQE\nnkRS/6Zw3dIR00o/RvaF0/Ds3FmjrUPaDkHQ4SC85P2SybYLo6Oj8frrr+vc3sGhN2xsOiMtbTFs\nbTvBxeV9k9jR2JBIJI1OFi8rKwtqtRpNmzaFPF2Owv2FCFik+xakQQwYAEyZAvTrB5jhOMBUFBVF\nw9HxNYPu5fP4mNd3HmYcm4Hl/ZfrnEJhLkoulEDgKoAoUDcdN6p2Lvlm8zcfO5ck0j2R4Fp5OT7Q\no2JNZuYa/PtvIQYONExS8datW0hPzwfLjkdeHmAjugVgERQKZ/z3326cPh2ttfC3jQ2XbjJ9OvD5\n51yZ4xq88QZw+DD399OANi8bO28CrTq7rO4VpAGqLJuvb6YPT/5IEUmJpKpl9lF+s5yyN2Zr7aek\nhNsWyNAw2U9OTiaRyJ2ADOqAj2ikcDy1QyytwERysnKjpKQknWw+kXyCdt7eqVNbXQgJCSG1Wm3Q\nvQUFhyghIZgUikKT2dNYOHDgAF28eLGhzSAiouvXr1P79j3IysqJRCJXahPQhU4NPkUqaT0plyQm\nEkVF1c9YBpKQEEosa9jnuIrkomQKPRraoEcOqnIVxU+K18mGe4X3aOGZhRR2NIz23t1LcpW8zraz\nZ2sfW82yNDNRdyWhjIzvKT9/H4WGhhqsonPy5Emys3uRACIXSOkLh7UEFBKgJj5fSOXl5Tr3pVIR\nzZhBdOlSLRdZlltlPmHU5Rq1rjDvNmEx1L4H3Nt1N9o5l8hKMO/MAhQ49cOwNq9hcB0zqoJ9BfD6\nWrvgup0dsGwZd7Y8bhzQokXNNp6enhCJGARKg/AFOmO/vAPewd+Ygt7w8zihc+msV/xewZQjUzCo\nxSBY8o3Tk8zJyYGrq6vB6TFOTgNgZ9cNaWmL4ODwcoNUVzEXhYWFaFHbG9kAdvTq1Q8lJfMAjALA\n4OWkfzA8+3OczDqMgAAzrzABru6sSsWldmlQH2kolEoJLCwcjI5c93Pww6AWg/D95e/xTbdvTGSd\nfqQtToNPaN01LgsrC7H99nakFKcg0DEQX73wlckEGP4rK8MLOqYRZWR8B5EoEHJ5R7i6xj8oEq0v\nnTp1glJ5F0AahkCBX4s/A8AHsA+Bge1grYdGOJ8PREZyVUnKyrg6Gg9gGKBzZyAmhqt/3EjJr8jH\n6dTTiM2NBaupJrJWL3v8ONGJE3U30nGFeS7tHH1xKITG3IqlO1pmL4nT9dNtVCqJQkKIrlx5+JpM\nJqNDhw7RxIkT6Y+Rv9Jm3us0kfcajcQ3ZGkZRtbWznTq1Cm9xrmRe4PW/rdWr3tqY+nSpZSdrX0F\nrQt5ebsoMXE6KZUl2hs/AaxcuZIkEjMF0+hBVNQyEolGPDj3fg/p1BrFZGkZQhMnTqv1nszMTAoJ\nmUndu/enjz4aQ5dqnXLrSUUF0bTax2toMjJ+IKlU0wGWfmy6von23q3/c9vCw4WUu71m9Er1c8kk\niW67UY+iywpzfkoKlSlrj9d4lPT0lVRQcIiIiBYvXkx5eXl62/MokZGLqa9gGHXBAQJSCPiVRCJX\nOnz4sMF9fvcd0a5d1V5UKIhCQ42y1dTklefRjls7KPx4OM08NpNWXFhBV7KukErNrdjrco3azzB9\nfIBz5+q+LhAAcnmd6ggqVoUVF1agVOAOUeDniPD3h52GvfHK+EqImutXC8bCgpvdREYC6enF+Omn\nz3DkyN8AGHzu8hW6NIuD+6deKCiuwA5JCr7s0AKTJ19EYKB+ScntXNth843NKJOXwVZoWGI5y7LI\nz89/UIPOWFxc3oW9/UtISZkLJ6c34ej4ZNfPLC4uhv2jseoNxLVrdyGV9gAANPE9gjKJM+6UNQWU\nbyEmZnWN9nFxcejevQ+k0vchl3+Dy5fvYO/ewfj++4UYPdqISh1iMdC1K3D8ONDIaqPKZKmwsvI1\nWX8ft/8YC88shI+9Dzq6dzRZv5pQSpQoOlaEwCXcs4CIcD79PA4nHK71XNIcVKjVsNFyXpievgLW\n1m3RpEk/qFQqFBUVwUWPM8/aCJ04Bc9dPIzpKfPhmJmGDh2eQ2TkTvTUt67XI0yYAPzxB7BhAzBq\n1P0XLS25dKmUFEDHHT1TU30F6Sx2Rm/f3hjcajD4PD1W6Zq8MAAufyMysu5GmzYR3blT66XkomSa\ncGACLYyPoeVpaaTW4XwgNSqVFIV6Vnu4T0VFJVlbf0sME0xAGr2PVFqKqbSTJ6C7Joo4TC9Jp0Vn\nFhl8/z///EP//POPSWx5FJZlKSdnEyUlfUsqVYXJ+68vZusyJa8HFi+OIpFoFMExgdAn/MFKk8//\nlbp330+zZnEFRn76idvZePXVD4hhllar1nCLxOImep0H1QrLEk2cyM3UGwmlpVcoJ2ezyftVs2qa\nfGgyZZeZZgdGGwnTEkghUVB8QTwtOL2AQo+Gaj2X1AdtH+csmYxWpadrbJOWtpQKC48++P+uXbvo\n3LlzRtuWPCeZZNl65Ofpwe7dRKtXP/JCaaluy20ToW0FqY26XKP2FaZQyCWf1kVV1ZJWrR57eevN\nrbhdcA/WLSeik6MT+utY31AlUcGyif5nhKdOnUJUVBSIMkB0Bd0gQUscQAdcw8cUhj5/7sJfb7+t\nd7/VaWrXFEpWiZzyHLjb6L9KjI6OxsKFC7U31BOGYeDm9jHk8kwkJc2Aq+uHsLd/0eTj/L8wZswo\nRC5pA3RLBw7vB6c6vB9CYSg2bryAZs04l5iRAVy+TDh+vD2IJlTrpQ0sLNrj7Nmz6N+/v+HGMAxX\nOmLdOu6wvhFQULAHPj5hJu+Xx/AQ2TcSIUdDsLTfUogszVd5OHFjIs75nMO6mHVo1qQZvu7ydb0L\nwx+WSDBQw7MxLS0KtrbPP7ZzdP78eSxZssSocUv/K4WwqRBC97p1U41h8GBuU2T+fGDGDICxteW2\nAouKAEdHk49XUFmAUymncC3nGghk+ApSC8ZLlDRr9tiWbam8FJGnI9HJ9zUUen6IaZ5N4a9juW1p\nkhRWfvqF0GdnZ2PlypXo3LkzunTpgoMHOuITpONfgQBi2+t4q/AAFHQDMTGmk+Ga0HUClp1fprce\nZm5uLlxcXMymhQsAQqEXmjVbgZyc9SguPgFv76ng8czzpXiacXJywtsr+uHy8lvIEPqBYSzg4uKA\n33/fgWbNuOLQDMNV5WraFLC0XAyF4msA1bfJpLC0NEHR4bZtge3bgby82osh1CNqtQwACz7fPM7M\nWmCNGb1mIPxEOKJejzLpdqhcJcf++P24fv06WpxogZeXv4xPHbRX9TAXiVIpRnvUFHcnIqSnR8HW\ntiscHV958Hp8fDxatGhh1O+EVbHI/T0XzVbpWOTcQPr25fRnH6izjRkD/PYbV/bESAoqC3A69TTn\nIIngJHYyi4OsjvEO09GRmzUAuJB+AdtubcOrHSfjTIUaUX5+EOsRxVWwtwBun+iWz6lUKrF27VoU\nFRVh1qxZsLW1xe8bfsfnlpk4p3RAPGww6/VJwFYhgFgEBJjurMXBygFuNm6IK4hDS2fd5cs2bdqE\n4cP1r0SgLwzDwMNjDKTSZCQmBsPdfTRsbTuZfdynidWXVmNi34noOqIrUlNToVKpalSzr4JhGAwe\nPAS7di2BShX1yJUkKJW90LNnL9MYNXkysGQJsGCBafozkMLCv+HsPNisY3jZeWFYu2FYdmEZpvWY\nZlRfRIRz6edwOOEw+AwfA5sPRIcLHRDwXQD41uZ7uD4cv/ayWAqWhUUtF4gIaWmLYW/fAw4OLz92\nbfPmzZg2zbjfR9ZPWfD40sN0+sca6NKFy9cMDgYWLvSERV4eDmzfjrTcXLzwwgvo1q2bTs7/UQfJ\nEgsnkRN6+/XGOy3fMauDrI7uDrOudx2AGixWnF8KW4EdWrYPRqYaWBzgq/csSJmnhMBVe/Ly6dOn\nsXPnTnz55Zdocz9bllhC91vd8Z3VGNxWegGKEYBQDeA2xOK5CAtbr5ct2hjbeSxmHp+Jpf2W6tSe\nZVnk5eWZLNhHF0QifzRrthJZWT+hqOgYmjadBB7PBKudp5wjCUfgKHJEV6+uAABfX+2TrdWrF+Pi\nxVcgkdxEefkbEArvgM/fjsjIAwgJsURoKFDLQkI/HBy43KnLl7lAoAairCwGLi5DzD7OC54vILU4\nFdtvbceQtvqPd6/wHnbc3oFSeSl6ePfA7N6zYcm3RNYvWbD+yNrszpKIUFFRgJiYAjz/fKsaz8Oz\nJSXoWS3AjXOWC2Fv/zIcHB4PvqmsrATLsrCxMVzjVpYmg7pUDZt2xunk6kPr1txcb8yYQqQc3IEO\nq/diA/qAz1+Jzp2b4/DhnRCLxY/d01gcZA10OvhcsYKojlD/lKIUGj+7C/2bfYOCExLodJH+mpJE\nRNI0qVa5saysLAoODqYtW7Y8lmDMqlhKCE2gstgyun79OgUEtCdra38SfDGdbGycae3aXwyySRub\nrm+i82nndWobHR1NR44cMYsdulBRcZfi4ydQefmtBrNBFxo66CehMIFmHZtl0L2VlZW0fv16+uyz\n8RQZuYAy7itqlJVxAUI7dpjAQLWa6JtvuL8bAKk0jdLTV2tvaEKWnV9GlzJ0S9HJr8inHy//SKFH\nQ+mXmF+oSPr486jiXgWlLDBdKkxdXLhwgXx925JAEEoiUVfy8mpBx48ff6zNnORkkj/yPrIsS8nJ\n86i4+Gytff7222908xHtbn1hWZbuBd0jVWU9CW88glqtJk/PHgTcodn4lhioCVCRUPghjR8/hfIr\n8mnn7Z0PgnSWn19OMVkxOgfpmJq6XKNuDnPnTk5Itxrbbm6jOSfm0J3F82ncnTuUoY8iejXSV6eT\nLLP2+xUKBX333XcUERFBpdX0FtUKNd2beo/Kbz+MRGRZlmJjY2nMqVMklUoNtkkbalZNEw9O1Ekd\nJDQ01GBlH1OhVispLW05paevJpZtmA+iJuRyOUVqisg2M+Xychp/YLzJIiSrc+AAUXAwkYFzyodc\nukS0fr0pTNKb1NQlJJfXr/Yry7I07cg0SitOq/W6VCml7be2U8g/IbTk7BJKLkquvR8VS/ET4kkt\nN+/3MCsri2xsXAjYSQB7/88BEoudKfERRZ8Zj/ybc5Zzqbi47gn4NCPzcfN25lHhkYZRBzt//jzZ\n2LQhgCVPpFMP8V+EVrsIr3xDlv2sGtxBVqcuh6lb9ImPD6c2cp8yeRnCosNgL7THix2+wZ8t22I5\nnw8vDZWqtaHIUkDoWfP+06dPY+rUqejTpw/Cw8Nh+4giBqtgkRSSBM+vPGHd+qEyBcMw6NChA7y9\nvc1SlqsKHsPDoJaD8Hfc3xrb5eXlwdnZ2azBPrrA41nA2zsIDg69kZAwGVJpYoPaU52ioiI00TGa\n2tQQEWafnI3wl+suv2QsAwc+1D8+ftyIjrp2BeLja69ObEaIWCiVhRAInOt1XIZhMK/vPCw8uxDl\ninIAnK71mdQzmHV8FhaeWQhfe18sem0Rgl8Khp+DX639ZHyXAc+vPMETmPd7+PPPv0Kp/ADAe+Bq\nTTIABkKpHIPvvlsLgAv2CbgfDElESEmZiyZN+tcZ2f7vv/+iS5cuBtukKlWh9GIpmvRrmO9XUVER\neDwvAAyykI0rb58C+vIAZQiUJwmTuk1CZ4/ODbvdqgs6edncXKLvvycioovpF2nSoUmUU5ZDy9PS\naEN2NtH580QHDxrszWVZMkpf9XguUlZWFoWEhNTYfq1CJVVR/MR4qkyuu7LAbB11Yo1l8qHJpFTX\nrdSxbNkyysrKqhdbdEWtllNKykLKzPy50ZQNu3XrFm3btq1Bxl51cRVdTK8fDVuW5dKX58whqtS/\nMAZHbm695rURcRVzCgoO1OuYj5JTlkP/I++8o6K6vrf/DJ0BKSpYsIAliVGjKZqoiTGJieab3psm\nRhNj7F2kiAV7Aew1YixRo1GjUWNXYkFssYMMA9I7DDB97vP+MYAgDFMY0Ly/z1pZhrn3nnOm3LvP\n2Wfv/Xy751vOOj2L/kf9uT92P9Va03JTi64VMXlpzfmO1uKzzwaVqyN1dPuLXs/OLs3L3cm+fT8m\nSS5NTmaaUklBEJiQMI2FhRdrbHPq1KlUqSz3fCRMS6Aqo248J6aQnZ1NR0d3ArcIjNWvukUC8fRO\nNp0aQn+JhPuzs6l8xF64MgyZRtOmWl5e0GVnYsn5Jfg381/MfGMh5mUU4RV3d3zXtOmDXEwLydmb\ng8Yf6metWq0Wy5cvx7p16xAYGIgvv/yyyma5Tq5DwqQEtJzUEs6+dZenZSqDug5C5LXIao8JgoDM\nzEw0q3XEh3WxsXFA69b+cHXtgvj4sVAqkx/1kJCbm/tIVphHJEfg7uiOF1u8WC/9iUTA118DgwcD\nU6boy2yajbc34OUF3Lxp9fEZIi/vb3h61r/gc448BytjViLsQhj8PPyg0qowt+9cvPvEuybVdRZU\nAtLXp8NnuPH61Nbg+eefhrNzFADgK9lZ5Oh8gA674eAQheef1wcpZqjVaOrggMTEaWjc+H24uRle\nPebl5cHV1RUOFgoxy2JkcGrlBIcmj04NpnHjxhg3bgzs7HoD6AFACnAlxInD8fv/+mK2nx887Oww\nOykJAQkJOJybC41QQ03XR4UpVjapIIkjQ7rxRuYN3iku5si4OGZVnO0IgkWqJWVI/PW+/NOnT3PU\nqFE1bmxrZBrGDo81qUJFfa0wSXLK0SksVlWt6HL8+PFa1WasDx6IVP/6SFebe/fu5ZWKBYHrAUme\nxOIgH2ug05ErVuhFSUwoJ1oZtVpfAagevjONppBS6aw676cMhUbBnTd3cvKRyVx4diET8x8E6uyP\n3c9fr/1qcluJoYmUJ1i6lDef7Oxsurs3pSOWcToCCWiI7gPo3NGDycnJLNJoGCqVUiIJpExWsxYv\nSUZERPD+/er3b42h0+gYNyqOgu7Re5HmzZvHuXPnslOnnmzUqDX79fuEMTExVc7TCgJP5uUxKCGB\nARIJj+bmUlPPK09DptGowdx5cydDToZQMS2Ae7KyOEsqrX7wFhpMVZaKMSExnDx5Mrdt21bjA1tT\noDeWqizTXAshCQn1ZgAS8xO54J+qarGPQ7CPqehFqsdSqayfsmQPs2HDBiZZoDpvKcWqYg4/MLzO\ngnzM4fZtcsQIMjbWzAtPnCB37qyTMVUkNXUtS0ri6rQPnaDj6cTTDDgWwJCTIYxJjTF4/y6LXsYz\niWeMtpkflc+0jfW/HXLjxg2Ob9ORvezEdHBwZ6dOPTjst2GMSY3hnqxM7r49jzLZVaPt6HQ6Tpo0\nyeJxJC9NZvHNWpZmtAIHDx7ktm3ml1LU6HQ8mpvLAImEgRIJT+TlVSsJaW0MGUyjeZiuDq4IfjUE\nCyS/oB2JICsWz9VqtVg4eiGUTZUInBEINzc3g+dq8jSQBknhF+pncuk8sa0tFIJgVvEES2nt0RoK\nrQJZJVnwdtFXYsnOzkajRo0eebCPqVQUqXZ17Qpv70/rtf/6dMmyLMjn1boL8jGHDh2AsDBgyRKg\nYUO9IK9JacyvvQZMnKiPKDJDkskUzpw5g3XrtqCgoBhDhhSgf/+BVm2/jLjcOOy6vQtFqiK83Opl\nTO8z3airdUS3EQg4HgAfNx+08axeak1bpEXO7hy0XWKeyII16NSpExZ//h5yxo8HAXh5eYEk/I/5\nI8XRDauefRcNGnQx2s7x48fRt69lAt3KJCV0xTq4dLTu78JcEhMTce7cOcyaZV5lNACws7FB34YN\n0bdhQ2gEAcfz8xEslcJOJMJbnp7o6e4OGysXxt91e5fhg8asbK5azVFxcbxhzGc0Y4ZZPqUy9+vB\nIcaDhVSZKsYOj6WmwDyf1ZrUVKbWItXFXHLluQw4GsA9e/Zw5cqVnDBhAlNTU+utf2uSm3uY8fET\nqVbn1Hlft27d4uDBw+nj04Y//TSacXF1u5Ih6zfIx1wuXNB7Wk3+6SQlkXPnWnUM/v7T6OLiR5Fo\nMVu3DuX77z/NZ57pYVEhebVazZiYGN68ebN8xZhVnMXl0cvpf9SfG65sYIGiwOx2VVoVR/w1wuC1\nkkDJowt0KSysIlohCFreiZ3E57Z+wZTCmnPOy7BUaF4QBN4b+2hyLiuiUCg4YsQIKq38HFbqdNyf\nnc2pEglDEhJ4vqCg1t7EIlUR/Y/682DcQctdsqPj4pivVpPr1+tvTENs3EiasGdYFv26bds2qnJU\nTFpQswtOmapk7IhYaorM3eAhd2Zm8mZtlSLM4Nq1a3R5w4MuLbvTyekH2tk14JtvflinuaB1iVqd\nx/j4yczJOVBnffz9998Ui71oazudwNe0swuki0tjRkVF1VmfR+KPcOPVjXXWvjUoLiaDg0mTg4YX\nLiQl5unIGuLOnTt0dvYmkE2A/PbbGXRyktHJ6SPOnTvfrLa2b99BD49mbNCgE8Vurej9WksO2jKI\nC88uZFJB7d3vOSU5HHUMzEkKAAAgAElEQVRwFEsUJdy1axfnzJnDP/74gxn7Mpj1R+30ImtFZKTe\nz16KIGgZHz+Z57OucXNqIocfGM5cec05kcnJyQwPD7eo+6xdWcw98mhyLisSEBBg8f6rqSi0Wu7J\nyqK/RMLpUikvFhaabTwvplwsz/6Qa7WWG8xySa4jR8iaHmJRUfpzDKDRaLhs2TLOmDGDhYV6seO0\njWksuWtYikpxX8G4kXEWz5KO5ObynwLzZ66WoNVq2bRpG8IuknhzIoETBPbTyekDTpoUWC9jqCuy\nsvZQIvG3uki1vvpHewJHSsPuQ0r/3cX27Z+rk/3nhLwEBh7/73wfhw/r9aONamorFOS4cVbpc968\nebS3H0WAtHdO58jJ/YnGdwjnP/hUhxdNbic6OprO4iZE69XE64FEn2lEs5l0c29S/gywBseuHWOD\n9xqyQYNXaWs7mc1d3+QEN38mG5HNqlMqCCbrjeVEFhff4oKkJOar1cxX5PPnAz+zRG34+Tdnzhzm\n5Jjv4dEUaBg/Kd6iYVuTTZs21YmUYU2UaLX8PTOT/hIJZ0qlvCKT1fgc0eq0XHJuCVfFrKIgCExU\nKDg8NtbyPcxy/3CrVsCVK4ZPbN8e2L0bePPNKofOnDmDXbt2YejQoejUqVP564pYBZoNqj7dQiFV\nICUiBW0XtYWNo2V7gB52dsjSaCy61lyioqJQUuIOaL8DMjdD1GIDmLIJSmVbrFvXFwsWhNbLOOoC\nL68P60SkOi4uDoWFGgD6PZr/NT2LgxkCgI+QnPwT0tPT0bx5c6v0BQAl6hIsOrcIYf3DrNZmXdOv\nn76A9ezZQP/+gMHtLCcnoE8f4PBh/Ym1QJ/GpQ/pHy1agZ0ZKqDZFUB8DrktUxFyMqTKNTYiGzR0\nbggvFy94ib1Qoi7BxMgAKHp2BJJaAqcGA4J+X1LncgPbtm3DsGHDajXOMgKGzkFx3kdg+5eAK0Pw\nfrEE62x2IOabn3D69F9W6cMsMjKAJnoRCUHQIiHBH82a/QAXl6dQkJkAD3t7wN4D016dBv9j/lj8\n1uIqe7YajQZFRUVo1KiR2d0nL05Gy4ktrfJWLOXatWvIzs7Gt9/WrxKM2NYWn3p741NvbxRrtTiQ\nm4sdWVloYGeH9xs1QicXl/I0xeTCZCw4uwBDnx+Kzk0641heHk4WFGBJu3ZYaaB904uvt2wJ7N1r\n+Li3N5CZWemljIwMhIWFoWvXroiIiKiUT6kt1MLWvfpgHHmcHGmr09B2QdtaVeXwtLNDrFxu8fXm\nkJeXB5Gohf6PG19j5pgQBIfbAmiB4uK8ehlDXeLg4IW2bRchK2s7pNIQtGo1Gba2tQsmcHBwgCCo\noNeaFKFvTx0mxr2AWQmhOKvVWkcWqxSWBvkE9Q56LIJ8zKFhQ2DRImD7diAkRJ+7+VCtaj3vvQeM\nG6fXVbIwZw8APvzwQ0yf/jK6a95CrrwNkuPdAWkfOOt2YOLHYzD5tapqGTpBh7jcOGy9vhXSAik8\nnDxQlF4CqFsDHX9Dt2c24dnjw6Aq8IWo5H2INikgzZBW7Zz6f0T2Itg42cDG0ebBv6X/L3J8cCy3\nKBf5V3PQVLMHRc9tQee2kbgo+QAyYTyioxciOzsbXl4PS67VMbt2AZ99Vmosp6B5858gFj+BXI0G\nDSv8ppu6NsWYF8cg4HgA5r85HzaiB8+6vXv34sMPzVeEkUXL4OTrZJKIRV2Rn5+PyMhILFmy5JGN\nAQBc7ezwZZMm+LJJE8i0WuzPzcW2rCy429pCXHgFubnXsODNBXCyc8LSlBS42dpidpvqA8jKMN1g\nisVATcanojHUarFmzRrk5uYiMLD66NfcA7lo9G7V2VPJ7RJkbMxAmwVtYGNXu+hSDzs7FGi1tWrD\nVHr06AGVagiAXICN4JPfCk5QQInf0a1bb6PX/xfQi1R/VSpSHQhv78/h7t7T4vb8/PzQqlVzxMZu\nAfAtrru9ghDpaES82BvjMh3hpVRabezLLy7Hp09/imYNHq8CEubw5Zd6wWp/f2DgQP3KsxIikV5g\neuVKvTyEhTzxxBMIHv0TXBd/gzHaAOCaI+xffBkdCltg5MjKAtYKjQL74/bjSvoVeIm98NMLP6Gl\ne0vIVDJc+fsGMuVx4M0gXH21DboU2GET/ODqOgxvjBgIvwF+1fZPEtQSgkqAoBRAFSEohfK/BZX+\nP22hFkUJRWhr8xQEyOFw5UM07D4MfpIkvAfCQaeDfWgo0Lgx4OOjn/SX/WfFiGKS2LNnDxYuXIOM\njEwscwGeeOt12CQsQfPmP0Msbg9ALxbd/6Eo8LYN2+Lrzl9j1ulZmPbqtPJFRUxMDD791LwodUEj\nIHNrJtqF163OZY1jEATMmDEDISEhj1V2gJudHb5p0gTF6mIEn5kPUeOX4dj6OyxKzYRUocAwHx90\nryFLo4za62E+RFRUFH7//fcq7teHKblZAu+vKwvhFl0rQvaObLSZ1wYi29qHCrvXo8Fs1qwZhg0b\nivXr+6GkZA4S4I2+GIsT4j8QFnagXsZQXzwQqY4sFameaJFItUgkwvbt69Gnz9vQaI4AyIGOP2Pi\nv4U4eWAc8pZ/Dw/33rAZM6FWD7hjCcfg6uCKl1q8ZHEbjwstWgDh4cC6dcDJk/oFZaWF+BNPADt3\nAmlpQC3c2VOpxqX9v2PwrgMoLCyG+pXO2PrjVoidxeV1XI8mHIW9jT3ee/I9fPb0ZxCJRLiVdQvB\nJ/SpOjO/DcaHr34NuXcxtCmuKIIcjWzmwNEtoUZjIBKJ9CtMexvAiAqVh84Dl0IuIEclBdADED8H\nYDqAU2jmtQ3+YWGATqf/PJKTgatXgX37qk7+xeIHxrRVK/1nZ6KHIzh4FsLDt6OkZDrawgEH7NbD\nedULGDRod7mxBIDbJSX4uhrx72ebPQuZSoaI6AiMfWks7ty5g6eeqioHZoy0VWloPqx5vehcGiIi\nIgLfffcdPD09H9kYDHEp7RI2/7sZU1+ZiqauTREnl2PB/fvo7OqKPTk5iCosxMeNG8PP2XD1OBFJ\nGjwoEuGPrCy83bAhnGxt9f6gGTOqPTcjIwNhn3yCLj//jK+++abGL1tbrEVqRCpaBz7QGZTFyJD7\nZy58Z/ha9QufLpViul/1M1lrQxK//LIRixatwWt97qD3ER888ftWPPfcc/XS/6NAoUhESsriUpFq\ny95nXl4efv11M2xstsLG5jsMHDgA7u7uUCpTkB4dDJ89Ojg8/ybwzTeAmbNWab4UG65uQOjr/909\nZEPExgLLlgGjRgFPVtQxLyoCQkOB+fMta3jHDr0w/FsPyuBdTruMk4knodKqUKwuRu/WvfFm2zdh\nZ2MHjU6DvXf3Ijo1Gp28O+GLjl/A2V7/0Dl37hw+3HMQ+WtWw0PhgJ99h+PHE9+hZUvr7bHt3Pk7\nvv9+DOTyGUCfK8CpLhCLZ2DbttX44IMPTGukpES/fL9/X29YU1OBhyfbDRvqjWmZYfX2RmZWFnx9\nn4JSeRdAE/jbhULzUwq2/eGFzp1v4++/dwMAtIKAmUlJmFnDs2jf3X3IV+ZDukeKKVOmVNGIrAlF\nogJZ27PQ2t+4dmtdcfz4caSkpOC77757ZGOoDp2gw7KLy+Bg64BhLwyDjcgG+3NycLW4GAGtWsGu\n9JmSrVZjT04OpEol5rVti+pMo1GDeaGwEIdyc6Ei0fPAAbw1ciQcKzy0KrpfxzZsCLf339f/qGog\na2cWnNs7o8GzeuWRwrOFyD+ej9bB5otOG6M+DWZFpNIQ+P0i6CcZdlZfyD9WkALS0tZApytGixZj\nLRaplkqnw89vejVtr4bdpVh4H9NB9MWXwMsvV9/AQ8g1ckw6Mglh/cP+c/uWpqLR6FecDRoAQ4dW\nmE9s26a/D038rMpJSgI2bgSmTwcAZJVkYeetnUiRpeBaxjXs/Gwn3Bz1rqv0onRsvr4ZBcoCfPDk\nB+ju073K/asRBExPTMRET084ODggc3YmfEN8LQ7kM0RUVBRmzQpDtPM/6KHqjWnTxqNnT8u3C6pA\nAnl5emOanKw3rNnZuHXrFvb/eR0q9ZdQQoMmww9h3u49yMx0haNjGyiVMgDAPwUFkOl0+J+RIJ41\n0Wtw8tBJbJ++3YyhEfHj4tFmbhvYOj8atY+UlBSsXLkSc+bMeST9GyJFloL5/8zHD8/9gC5Nu0Ag\nsTA5Gb5OTviimtV+GSKRqFqDaZpaCUmdIPCfVasYfPcup0okPJiTwxOlxQdu3LihP+nUKfL4caOh\nvxJ/SXmob97JPCbOSayzEnb1WU+2DL0CQYg+J+DkyXrv/1HxQKTaMpHbhIQQg8eKi28y9u5wqiLD\nyAkTjOb8lmkopskeL5WYuuLiRb2udHkmhSDoX9CakZKl1ZIjR1Iuy+P2G9s55egULj63uFyHctv1\nbYxJieGZxDOcfGQyF51dxKziqrmOgiDw1KlTXLFiBUOOHOHp3Af5gEX/FjE9su5KL4acDKmztqvj\n0KFDdHPrRSCZwM9s5HCpNDUqh2LxTywpzRqZKZVSbsJ3sW7dOk7dN5UnpSdNHkPmzkzmHTOWd1R3\nqFQqjhgx4rHLNy8TpJar9XWE89Rqjo6L479FRUavNWQaTV762IhE6OXpiV46HVLd3DBx/Xoon34a\nHcaORbqHB54SBNi1awccOKCP0quGzMxMiFQi2DjbQCQSIe9IHkpulKD11EfnRqgLSB1EIjt92bLZ\ns/Xh/v8HEIufRNu2YUhNXY78/OPw8RkBkcg6M14Xl45o1z4MyU6L4PByOzTdtQsitRoYPVq/vHqI\nFTEr8HGHj//TQT7m0K0b0LGj3gvboQPw5ZciYPBgcP16XH7+eSQnJ6NLly5oYyAKUKCAM2FjcLSX\nCvaXI/DBkx/g846fl68YS9QlKFAWYMTBEQh4JQBz3phTrXZhbm4uXnvtXUilRdDpXoFuSCYih45D\n1JlDaNmyJVyfcUXGrxkgaXVv0qPg9ddfBzkQwDgAm5GrdgKgg4PDSHz4YQ8sWgQolUD8qwJsmtkC\nNdwOJBEbG4sFQxZg1plZcHd0x7PNnq2xf22hFkWXitB2fv2X/ysjNDQUEyZMqFPtYXMoUZdgTtQc\nvNTiJcx8bSYA4HpxMTakp2O6ry88axF9b5avUNu8OdasWIHcJk2wZuxYuLm5QSsIOF1YiOmJiQCA\nN+Ry9CZhW+FmuHjxIgYNGoWEhDj00HWHZ7vGCG48EV46L7Sc8GjzheoCUqs3FA4Oep8ZaWJh0P8+\nepHqsSguvo74+LHw8RkDsdg6UXt6SbIAFBREQfLRH2hl9z0c5sxBor09Zt3Pwv3UbPTt+xLa92sL\nsb0YPVpWL8b7/ytisT7E4OhRYNIk4LvvvHE2eCZmlzii0K4z1OpzeOedt7Ft23o4ODhArVbj39R/\ncfj+YciTE9DbVowZn6+Cnc2Dx0Jcbhy23dgGAPiq01coVBWiZ8ueBoV+Bw8eibt3u0OjCQc8NEBW\nBlKSHfDpp4MQHX0cAODR2wOFUYXw6O1R9x9KHfPbb79hyJABWL9+C8gBUCjaQSz+C08/7YW1a4fA\nxQVIlCux4rIj5szRb4v26aOfSz+8UxMdHY0ePXpAJBIhqHcQ/I/5o4FjA7RraPj+edQ5l9u3b8eL\nL74Iv0ew7VUdl9Mu49d/f4X/y/7lk+UdWVm4r1QirF272tedNXVZeubMGY76/nveqEGVRK3T8XB4\nOAMkEgYlJPBMfj4TpFK6unoR2EJAyx8Qx97YzC/EA5ibW/elmx6FS1arLWZSUqlyye+/k5cu1fsY\nHgceiFSvoSAYr4dZk0v2YTSaQkok/ty6dTR7OjViBHqwD6bRsclHFL/nXufluB538vJIb++tbCxa\nxZkILHUTltDZ+V1+P3IYXxjRg7b9HGjznAM7tH+a0vff12uNUV/9ZN/dfZzw9wSuubSGRaoHLqw0\nWRoXnV1UbZ8ymYwODg0IFOj7+zieaKwkoKKTkxcTE/USXYJWqLNKNKGnQ00Wla4NGo2GISEh5dJ9\nhYWFXL9+PUNDQ3n8+PFKW0yrUlKYVOqu1Gr1u1aBgWRQEHnmTPnHTn9/f2oq1ONWa9UcdXCUwW2F\ngvMFj0SJpYwbN25w3rx5j6z/iugEHcPPh3N59HLqSp81ap2O06VS/pmdbXZ7hkyj0RVmWfGBLl26\nIGLtWohmzzZ4rr2NDfrl5aFfmzZQCwKO5ufjy6MnIP88FLjwLuzuAC2oQgrewJ/8G902bsKECeNq\nZ/EfQ8pXmIC+6kpEBPD88492UI+AMpFqmewi4uPHomXLiXByqjkgzFTs7NzQqJE/Nmxojg7ffIsp\nWxbgHe2fmC3ehzWHP8fkySH47bdfrNLXf5G0tFsoLvaHnFLcxy/oanse1zokQdG0GTZeXg+72Heh\nyzsPwBl9MBa9kk5jWNhsSN2kyFXm4mm7p+Gr84VMKcOSv5ZAp9OVB0IcEx1D4ZFC2KBy4E5RURF0\nOi2AxQBsgMK7QOvXgZwfYW/vjfz8fLRu3RoiWxEcWzlCIVXA2c+6AvBiezHkGjncbd2t2m5F8vLy\nEBISglGjRuGJJ54AALi5uWHIkCHVnp+sUqFVqbvS1la/Y/X664BaDRw7BgQFAVptDuRyd9jaPngk\n29vaY17feZh0ZBJmvzEbYhsxoqKioNFo0OulXsjaloV2EY8m51Imk2Ht2rUIC3v0VbNSZamYf3Y+\nBj87GF2bdgUAZKrVmJWYiNEtWuAJM6KNjWHUYK5Zs6Zy8QFjKtgiESAIcLCxwTuNGmHh1n0Qzn0P\ndNeg9bdb8c9BGY5ljwQUL+PKlUvWeA9GhiOCQFpdAqYmyvcwAcDVVR+y/n8YN7fucHHpjOTkhXBy\n8kWTJgOr7F/RcLC2QU6cOIGYmN64dm0avhkwB/+UKHHg4G4M02xBo93zgIIlgMd/3+1nCenp6bC3\nbwvAFr9gMD7v4ItrDl5AbEOgWAutrBuAq3gWd3DapynSnnoCq6LXY+OYtWjVsBWcnZ0hFoshFovh\n7OwM2woSef2T+yOtKA2fPP1JpT4FQcD27fuRlvY6gD7AcQA+0UD/gRD+TUeHDh3Kz236bVOkhKXA\nN8TXqu+73GA61Y3BvHXrFtasWYPQ0FC4uxvvQ6HTwclAKpSDg16V7X//AxYs2AxPz4EICNCnHH/w\nAdCpk/79hL4eigEbByAq6BKgbQPAAe8oWuGdGW+jvah9tW3XJSQxY8YMTJs2rdLv4lGw9+5eXE67\njHl950FsrzeM0TIZdmZlYV6bNnC1IEPh0iXDdslobHdISEiNOpVVaN4cSE8v/7Njx7awYwxwtjE6\nFxbhn6L7AG7AyekSOnas+9mRm60tinS6Ou+nIvoVZoUvqn17IC6uXsfwuGFr6wxf32lwcvJFfPw4\nqFQZlY5LJHHIzMyGxsTavxoNcOdOY6hUnyMvrxk2bPgBbhlOaPBZNiIGd8LKTl1x+M3+SNJP3+vi\nLT3WdOnSBSrVNQC5aIAiNLs5Tl8LOmsE4NEIeMkO6HEZiT8cwW33d4CT26A8JqBfr37o0KEDfH19\n4e3tDVdX1yoPxZdavIQLKReq9GljY4OlS+dBLP4awGYAiUBqMpzPnUXvcT2w596e8nPt3OxAEtoi\n6343ZQazLti/fz/27NmDsLAwk4wlAJwuKEAfI5M2QRCQm5uBH3/0wdy5wJgxwPXrwNSp+iCuGzFK\nHJ9+FrLur0BWfAZi2SFAMwpDQ8fj8uXL1nhrZrFixQp88cUXaNy4cb33XUaJugRBJ4JgI7LBrNdn\nlRvLjenpiJbJsKhtW7ONZV5eHoKDg3H9+nXDJ5ntx61hD5Ok3kF/6lT5n3FxcRSLGxM4xEGDggmo\nCbxNF5dGzMjIqLktK7AxLY1SubzO+6mIUpnK1NTVD17IziYXVb/v838RjaaIEkkgMzN38u7du+zU\n6SW6uzfloEFe9PBozi1bqldmT0ggV60iAwL0P8M//pDT2dmHwFUCEyiCjGFoRW+bzkTX9cTgjXzu\n0294uM/rVB40rrv6/xsjRkygWNyTL+EX9sc+Avvp4NCUTk5PEMgkMJLomEkMkBLYx2eeednktiOv\nRvLfjH+rPXb8+HH26tWfjRq14nPP9eG+fftIkkclRznx74nMKdErcMglcqYsN00X0lR23drF6xnX\nrdqmIAhcvHgxt22r/ndZE8EJCdQY0bM8dOgQjxtIx8vNJd9770/a2BwhGsUSfSdzOGJpDy1tbOZx\n4MChZo+pNpw+fZpr166t1z4f5kraFY46OIqpsgeCsUqdjlMlEh4zKutTFZ1Oxw0bNjAoKKhcHcaQ\naTTfYIaEPNilro6kJL12ZgWOHTvG5s3b88cfPSgWt2Dr1h35ySefWCRGay57srJ4VSar834qolDc\nZ1pa5c+AU6fW6xj+C6Sl7eP48Q3o7j6fLi65/OyzxQQuUSxuztOnT7OoiNy/X68LGRBArl1bVZJ1\nx46ddHT0oK1tfwJf0Bm9uRw/0wVF+sATUQodurzEtYsXM+r773kgKoqFZgid/5fR6XRcuHAJx7o1\npp/Ilk891Y179uxh8+btCPR6EJzT4Q7tfpzA30wW3yQVGoVFMmn5inxOPjKZB+P0E5j4SfEUdNbL\nwT4Yd9CqwuAlJSUcP348L168aPa1giBwqgkapZMmTaoxD/3bb38isJwA6dfyCD99djW/+SaUnTrN\n5auv/s/scVlKmZZxXeXMG0Mn6Lj0wlIui15WHthDkskKBUfExjLRgjzQS5cucdSoUYyJian0uvUM\n5qpVZFoNkVk6HTl9ejUv6xgd/TNjY2MpCALT09M5ZswYas1JrLaAk3l5PGnBrKM2yOUJTEvbWPnF\nZcvIFOvOpv/rbNmyhU2avM4ff5zC11/fyg8/XFoazXmOfn67GRpKnj1LGrNvP/74I8eNm0x3d18C\nf9MbGZyFQIqg1Ws62o/g/PnzKSiVTF60iGdGj+aMy5f5S1oac9R1H1H5yJk2rXySq1QqOXDgQD75\n5HMUi1vQza07nZ09OSpiBadKJA/0b00g9HSoURFkQ/x+63cGHg9k6pFUZu83P4rREKekp3gi4YRV\n2kpKSuLw4cOZVtPzrgZuFxfz1/SaizQkJiZy6dKlNZ6zbt16uri8Q4BsCCV7IJtOTsV8+eW3+csv\nvSmRBDA1dR0VirqLDFer1RwxYgRLSgzrd9YlqbJUjjo4ilfSrlR6/WReHqfEx5tUFKIiubm5DAoK\n4oYNG6irZgFoPYP511/kBSMzOANu24SEyq/fvHmT06sxrtbkWlER/8iqX+X1kpJ7TE/fXPnFpCRy\n5cp6HcfjTvD0YKLFIOLFcL40+B26N7pTajCvsk2bZ01qIz4+nsuXLydJdujwUqlwN9kMKbSDigBp\nZzeWs2fPfnBRdjYZHMyMsDAujo+nv0TCFSkpTFUq6+JtPlIEQSi/H7VaLcePH1+e3nH79m1GRUWx\nqLTyyY2iIk6KjzfqQizjfsF9hp8Pt3hsabI0jjk4hqeHnra4jYe5mHKRB2IP1LqdqKgoTpkypVbV\na5bcv88slarGc0JDQ5mfn1/jOSUlJWzR4gna2U2mJwrYC0kUiebT07N5+baWXC5hSspKSiQBTEgI\nYW7uEWq11qu8M3PmTMbFxVmtPXPYe2cvA48HVhLbFgSBy1NSuDY11awVb5n7NTAwsEZxbkMG0/wQ\nolatgDt3gBdfNOsynU4BG5vKIeQdO3ZEeno61q5di6FDh5o9FFOoT4mvMqoE/QD6zy05uV7H8biR\nVpSG88nncSPrBgQKuOd9D86NrkNxYywuRKcBKKsgno/OnTub1ObGjRsxdepUAMDXX7+P2bNXQans\ng3T4lLdlb78dH3xw7MFFjRsDM2eiya1bGL9sGfDmm7j/+uvYnZ2NNLUaXvb2+LBxY7SpQbXgcefm\nzZsYPToAZ84cxgyIEJeYAW9vNwwePBitW+sra1WMWgWATq6uGGJjgykJCZjXpg3sjRS6b+neEmlF\nadAJOoOFDGqiWYNmCOsfhl0XdyE8Mhw/D/gZjnbmq95UxBpBP5GRkZDL5Zg7d26tqhHlaDTwqkGX\nVKVSQS6Xw8NIUJBYLMbFi6cwZEgYrv69GDY4g/79vBARcQpNSoWqnZ3bwMfnZwCAIKhRWHgOyckL\nIQhKODg0RcOG/eDs3N6k90MS+fn5cHR0hIuLC3bv3o3OnTujffv6jciVa+SYEzUH3Zp3qySeUKLT\nIUQqxcdeXuhpYvAVAFy+fBmbNm3Ct99+i8GDB1s2qJqscbWHCwuNB7BMm6avZVmB4uI7zMjYWu3p\nkZGRPHCg9rPC6ijQaLiknpPYi4puMDNzZ9UD8+frM8r/D6DSqhidEs2ICxEMPhHM4BPBXHlxJa+m\nX6VGp/exqtVqtmz5JEWiFwhkE9AR2E8npx786qtMHjlScx+pqalcsGBB+d8ymYwdOrxAsfh/BH4j\nsJuOjqM5evSkmhs6eJAcM4YsrYmcoVJxbWoqp0oknJeUxJvFxY9s38YSEhIS2KCBN4EIOiOD/vCn\njc1LbNXqCapNcEFL5HKOiYuj0oSV5inpKf55989ajVer0PLy+Msc+ddIg4FEppKQl8DIq5EWXatW\nqzlt2jT+/ffftRoDqX/uzHt4w/0htm3bxsuXL5vcZkQEGfd3EbN2m+cxUyrTmJ4eWbr6DGJ29l5q\nNNXHdZw4cYLt2z9HB4cGtLd3Ye/e/TllyhSz+rMGV9OvVgnsIcl4uZzDY2OZboY3KDc3l4GBgVy/\nfn217tfqMGQazTeYpD7wpyZWriQf8t3n5h5mQcFZg5fMnj2bV69erbldC9AJAqdLpVZvtyaKiq4x\nK+uPqgdu3SJ//bVex1JfpMnSuPv2bk4/OZ3BJ4I589RM/nn3z2qLc5chCAKHDRvGV17pRwcHVzo6\netLP7xkeOXKEgkDu2aOvs17qQaQgCNy9ezf79fuUvXu/y3feeYcpD+0Ll5SUcNWq1XzjjY/48ccD\n+dFHUmq1Jhg7tTRaEu8AACAASURBVFr/uw0MJCu48PPVam5OT+dUiYSzpFLGFBY+9sZz2LAxtLOb\nQoDsiivsiuEE9tPVtSd3795tUhtJCgVHxcUZ3RsqK3JfWxLnJFKeIWfEhQiGnw+nVmdZbENGUQZX\nXjR/6yMnJ4cjR460mttxZ2YmrxgJNpw0ychE7iGmTiWLrplvMCsiCFoWFkYzMXEuJZIAJiUtoEx2\nhYKg49WrVykWexH4o3TymkqR6Dn6+nY0aaJlDXSCjsuilzHiQkSlwB6SPJiTw2kJCVSZaPRMdb9W\nh3UNprHUkiNHyKioSi+lpq6mUmk46EWn03H8+PFMLpdbsB71XR5PJrvE7Ox9VQ8IAunvX69jqQsq\nrh6Djgcx6HgQV1xcwStpV8pXj6YQHh7OC6X74QUFBczIyKhijORy/cJ8/nyBX345nC4uXQhsJLCR\n9vbt2bnzSzVGW588Se7da8aby83VTwjDw8mHZrHFWi13ZWVxqkTCaaWlH7WPofHs2LEXgdMEVHyj\na1e2aTSsdG94FidNMv33l6pUckRsLIuMRF2tu7yOd7Lv1GrMynQlkxboV2TXM65z5F8jGZ9rfvk8\nmVJmsHSfIW7cuMHRo0ezsLDQ7P4MESCR1Dixun79OiMjTV8Jy2TknDl6tZesXdaLyVCr85iZ+Tsl\nkkAuXNiJb7zxDd3csgkIBCYSSGeDBj35xx/VLACsTJosjaMOjuLltMqrbp0gcNH9+9xiRhpiWfSr\nJdHNZH0bzIQEcuPGh14KMVpPVC6X8+eff6bMymkg9W0wCwujmZPzV/UHZ8wgH1GkmaWUrR5DToYw\n6HgQZ5yawX139zGzONPiNv/66y9u3rzZ+Iml7N59mXZ26wgoSh/+8wmk0tn5fS5ZYjjwRBDIsWOr\n7BAY5+5dctw4/TK3mouVOh0P5uQwUCJhoETCw7m5Js9865r+/T8l8AOBCbR79WeOGv0zRSIdnZ0H\nGI3IfJhMlYrDY2NZUIPRLFGXMPhEcG2HTYm/hDqV/jNUaVWcc2YO119eb9aKXqPTcOapmSafv3fv\nXs6ePduq0fo6QWCgkXSS4OBgys3ID9+9m4yJIYuuW9dgVsTPrwt9fXfw00+XsHv312lvv4MAKRIF\ncuZM0z9TS/jz7p8MOBbAYlXlyW+BRsMxcXFGV+tllEW/muN+rQ5Dtq9ulI1bttQLrFbeLYVIVHMQ\ngbOzM2bMmIHAwEAsWbIEdv9R4eVqg37K6NcP+Ptv4KOP6ndQJqLRaXAt4xrOp5xHdkk2AKCpa1P0\naNkD7z/5fiUlC0uJjY1FTEwMQkJCTL7m4sXd0OlsATjBxiYH9vZZUKmaQ6EYim3bwjBu3JhqrxOJ\n9KXHDh3S/2syTz4JLFmil/4YOxb4/nuga9fyw442Nni7USO83agRtIKAfwoLMTspCRoS3Rs0wFsN\nG0L8CMqGHT9+HGKxAEfH/VCpzkEr2oTDhz7Au++OxcmTh/H11+Fmteft4IBZfn4ISEjALD8/NKxG\nGklsL4adjR1kKlm5uLQleH3mhexd2WjydRM42Dpg6itTcS75HMb9PQ5Tek0xSarNzsYOOhqv7EUS\nS5YsgY+PDwICAiwec3VcKirCC9VIzpVRVFQEOzs7OJsRVHb5MvDhh4D8lmVlJE2hXbs2OHpUhsTE\ncQDeA3AGAODicgV+fl/XSZ9yjRxzo+bi2WbPYvYbleuU3yopweq0NEz39UUjI5JcgiBg06ZNkEgk\nGDduHBoZEeq2GEusLOfM0fvKauKhVejDKSU1cffuXQYFBVltr6i+V5j5+aeZl3es+oM63WNVxCC9\nKJ1/3P6D009OL1897r2zt1arx5rIz8/nyJEjK6kymEJwcAhtbH4iMI1OTgF85ZWFpSvNXezZs3+N\n11q8yixDoyHXrdN/b0by6nSCwAuFhZwhldJfIuG2jIx6KZRw5coVjh07lvv27aMgCAwPX05nZw86\n9POjm1s3fv+9G0+d2mJx+wUaDX+OjTWYJpGQl8Dl0cstbr+Me+PvVbnvi1RFDDoexN9v/W5SG8ZE\npIuLizl+/PgqyerWYnZiYo1u7NWrV/PevXsmt6fT6Qt3kGTRjSJm7qybe/PkyZMUi30IXCu9t6ZS\nJFrLRo1amLUaNpVr6dc48q+RTC6sug33e2YmZycmmrTlUVv3a3UYsn2WLRfKUiRKK/VbmyeffBJ9\n+/bFypUrMWLEiDrpoy6pVHz9YWxs9JIFGg1QCyFTS9DoNPg381+cTz6PrJIsAEAT1ybo0aIH3n3i\nXdjb1u14dDodpk2bhhkzZpjlPbh37x4yMtJga/sbBOEqdLqWaNVqEQA1XFzC8cMPNYeIi0TAm2/q\nlSHefNOCgdvZAT/8ABQWAkuX6qtjDx8OVCOYayMS4UU3N7zo5gaSuFFSghWpqZDpdGjv7Iz3GzVC\n4xpSDcxFKpVi9erVePLJJ7Fw4cLyz3XMmBEYNGgAft7xM36c8iN69nwR9+/7g/zSIlFvdzs7zG/T\nBv4JCQhq3RrNHCunf/h5+iGxIBECBdgY8STV2E8vd8jOy+De80G6gKuDK2a9PguH4w9j8tHJmPry\nVHg6e1rUflJSEhYsWIDg4GA0bdrU4nHWRLFOZ7COKUlIJBL89NNPJrd3+TLwwgv6/xeJREDdLDDR\np08frFgxB6NHvwWRqCkUikS0bt0G+/b9bdZq2BgCBayKWQWtoEV4//BKKUlaQcD85GQ8LRYjoDT9\nyRB5eXkICwuDr68vwsLC6qUQfN0azFLhZEFQwcbGvIfEq6++ipSUFOzbtw8ffPCBRcN8VOhdsjX8\nwF59FTh9Gujbt07HkVGcgfPJ53E98zq0ghb2tvbo0qQLPu/4OZq4NqnTvqtj3rx5+Omnn9CwYUOT\nzr9+/Tq2bt0KX19fREREwMfHFwsWvAqlcgjs7c/B1XUHXn65PQYOHGi0rbffBsaP13/kFqfWubsD\nwcGARKLXZOreHfjsM4MNikQiPOPqimdcXQEA9+RybMrMRI5GgxaOjviwcWP4OBrOO8zMzMT+/ftB\nEu+88w6aN29efiwnJwfLly+Hu7s7QkJCIK5GwsjJxQkdn+qI13q/BgBo3vwnpKYuR4sW1buvjdHA\nzg4L27bFlIQETG7ZEi0fmjC81fYtHJUcRb92/SxqHwAaf9AYCQEJlQxmGf3b9Ud3n+6YEzUHb7V9\nC2+2NW/2ExUVhYMHD2LJkiVwrOFzrw3pKhWa1DAhOnv2LHr16mVWm0ePAqNGlf5Rx6JLgwZ9i6++\n+gLXr19HbGwsPD098fTTT1ut/YziDMyNmouBXQbiheYvVDqWo1ZjRlIShjdvjg4uLgbbqDf3a3VY\nsiylVEr+8kvNa9ply8rD8/WVbzbVfL4B5s+fX2vXSX27ZHNyDrGg4LzhE9RqvXqsFVFr1YxJjeHS\nC0vLI1eXRS/jpdRL9SKoa4zffvutvAi3MS5cuMCJEydy48aNVD3kArx69SrHj5/CZcu68/Dhw2Zt\n7O/dq4+atRonT5KjRumjMczkvkLBZcnJ9JdIuPj+fcY/5PJatmwlnZw86OLyNcXiAXRy8uS8eYtZ\nXFzMhQsXcsaMGUZD5WNzYrnl38pu2OTkcBYV3TB7vBVRaLUcHRfHhIfGLAgCJx0xL1WiOpLDk6lI\nqrlKzY6bOxh8IrhKkAhZvUt2w4YNXLlyZZ2nBP2Slsa4GoL6pkyZYvZ2RMUdnOJbxczcXjcu2YfR\naDQMsuJzan/sfk49NrWSIHkZl2Qyjr13z+j2xeXLl63ufq0OQ7bPMoOpVpPGoqYOHSLPnSNJ5uUd\nY36+ZeWvBEHgxIkTy8t5WUJoYiLV9RjBmJ29n4WFNX+h2ilTGH3+PK9fv27RTZxRlME9d/aU7z1O\nPzmde+7sYXpRzXtsj4JLly5xkZFiF4Ig8MSJExw/fjy3b99uNGoxISHE7HEIgj7w1apotfqI8ClT\nLK4VnKlScV1poYS5iYncdfkyncVNCSSU7iWRgJQODh4cMGCAyffCoXuHeO7+uUqvCYKWcXGjqNPV\nbhKl0uk49t69KsZh5cWVFqWDVESdr6Z0ptToeamyVI45NKa82Pr9+/c5fvwU+gxoywEDfuSVK1eo\nVqsZFBTEo0eP1mpMphJQQ3RsZmZmpUIbppCWpi9YUEbx7WJm/Fb3Kk9lBAQE1HqSIVfLGXwimLtu\n7ar2+K/p6Vx8/36NdYzLol/XrVtX5/XHSWsbTNJ4asm9e+VJ+mlp66lQWG7wlEolhw8fzoKCAouu\nX56SYrSmozXJzt5LmeyKweNbtmzjV87ufE3cji4ufvTz61Rj0Qa1Vs1LqZe4LHpZ+epx6YWljEmN\neSxWjzWRkZHBcePGGbzpBEHggQMHOH78eP75558m35yWGEyS/P138p9/LLq0ZmQycvZsfdJoLdKG\n8tVq9p23iKKhm4iBUuLJAgK7CYwjMNYsOacVF1dUO4EqKYljUpJ5D+7q0Oh0nBgfz1sV8mBlShmn\nn6x9feiE4ARqi40/GAVB4LrL6zj297F0dfeig8NYos/XtLGZR2dnL/br18+sAJvaoNLpavRmLVy4\nkOlGgsYeZv16Mr7C/KP4TjEzttWfwdyxYwe3bdvGc+fOUWlBreXrGdc56uAo3i+oWm1NpdMxKCGB\nh3MNF/DX6XT85ZdfLCo+UBvq32BWWIVKpTOoMyOhvTpyc3M5cuRIiypObMnIqNFNYm2ysnaxqKh6\nTb6zZ89SLG5GZ/zDIMwsTRDeTHf3puWJ05nFmdx7Z2+l1eMft/94LFePNaFSqTh8+PDy4t4V0Wq1\n3LFjB8ePH89jx46ZPYu11GDqdOT48RZdahpSKTlxIrllS80yeDXw2WeDCKwlHLXEy7eJ5idKV5lb\n2b//Zya3M+3ENIOfa0rKKspkppdlM4RWEOgvkfBahe942olp1brdzKEktoSpq1ONn1hK1zdeJt7u\nQ3jdJPqEELhO4Cs2aOBl0YPeEo7n5fGogYe/Vqvl5MmTzW7z4YD6krsl9WYwY2Ji6OPTng4ODenm\n9iwbNPDm5s3Vlzd9GEEQuPLiSoadD6u2alOqUsnhsbGU1BB9W+Z+jY6Otvg9WEr9G8wK55iTUlIT\n9+7d49SpU81+uB7IyeFFK1bxMEZm5nYWF9+q9tj7739FkUgvYzUH/oSNimh2iQ4vd+Hbi95m0PEg\nRlyI4MWUi1Rp629VbG0EQeDUqVOZ8NCMW61WMzIykhMnTuS5c+cMXG2c2vymtm0j6/we/Ocf/f6m\nBe8xMjKSLi59SydTZS5ZgWLxB1y2zPTUjWknDH9GgqArdc3W3pjoBIFBCQnl91hsTizXXFpT63bj\nJ5qmlalUKmlr60DYFBEvLWH7NzvTBqEEdHRze4Fnzpyp9VhMYbpUarB4xf79+3n6tHnbUkplVaXE\nkrslzNha9wYzPz+fbm5NCOwg4F+uIiQWNzN632YUZXD0wdG8mFL9tlRUfj4nxcezxIBrtb7dr9Vh\nyPZZHv+tjxiq1eXm0q5dO7zzzjtYunSpWdd52tkhvx4VS2pKK4mPTwT5LADgZsP26NJlMeCRBPW1\n/uic1RWzXp+F0S+ORjefbnCwtV76QX2zbt06vPXWW/Dz8wMAKBQKrF69GkFBQejatSsWLlyIHj16\nWNS2RqNBUlIKjh07DIVCYfb1n38O7NxpUdem06sXEB4OJCQAkyYBSUkmX/rFF1+gZct8ODp+B+AK\ngGtwcBiKJk0kGDToO6sMTySyQYsW45GcvLjWbdmIRJjp64tDeXk4W1iIJxo9gXu592qdYO/ZzxN5\nR/KMnicSifRFUQQBz1z4Hq9ED0EE0uGHRJAKOFgxjacm1IIABwMKL2fOnMErr7xiVnunT+sD6ish\nQp2llVRk27Zt0GpfA/A5AB8AqQC6QqHwx8KFKw1ed/DeQYRfCMfsN2ajm0+3SsdIYm1aGm6WlGB+\nmzZVCnsIgoDIyEgsWbIEY8eOxQ8//FAvqSLmYLnBbNwYyM214lBMo1evXmjatCl27dpl8jX1KfFV\nUlKCu3dv4vbtu9U+MNq0ebu84tFved/jlaufwubOR3DFJXTtapqk1ePGjh078NRT3SEWe6JTpx6Y\nNWsWAH1eV1FREcLDwzFr1iy89tprmD9/Prp06WJxX0ePHkWTJr5YteoYBg+eBm/vVti8eatZbdja\nAs88A1y9avEwTMPGBvjmG2D6dGD7dmDuXKC42OhlTk5OuHDhOMaMaQUfn2/QrNkXGD68IS5dOg3X\n0hQVY5hirJydfeHg0BSFhedNarMmRCIRglu3RlRBAU7l56OPbx+cTDxZqzY93/BE/rF8o+c5ODjg\nzTffRUubVeiOPPwiG40JWIyvMB1DkIEXnn++VuMwBYlCYVAOLiEhAX5+fmZLhf3zj37eVQlR3VX6\nqUh8fCLk8i6wg4ABdt3xdbfVAACyC+Ljq07+lFolQk6G6Cv39J0LV4fKv1OFTgf/hAQ8JRZjmI9P\nlc/iypUrGDt2LJ5++mmEhobWb6qIOViyLCWpj9E3Jk0THk5ddialUuvXIVy8eLHJLr1UpZJrUk3f\nD7GU5ctXUSxuyI8+akM/v9b09e3If//VyxVduqTf2po7N5XOzk1K5ac0bId0vitayZYtn6y3vRZr\nEhGxgmJxewIHCeQSWEc7O3eGhy/l/PnzGRwcXKsI54qkpKRQLG5E4CT79NlBX9+bBK5TLG7KS5cu\nmdWWWq3/PuqV5GR9NG1kpMX7m6aSU5LDiAsRRs8TBIFxcWOo1Vpvj3/x/fs8mJNtlRST1HWpLL5t\nuLh+GdLrUvqLg+gm/oBABJ2cfqCLS2NeW7GCHD2arOP7f2lyMtMM3L8zZ840u7C7IZ2GknslTN9c\n97EMW7du5fPO33Ek4tgccvbu/TtfeWU3bW2ncciQEZXOvZF5gyP/GsmkgurlzKSlklwp1Xw+eXl5\nDA4OfqTu1+owZPssN5hXrugLU9fEgQNUnN7DtLQNRgdoLoIgcMqUKZQYKXJMknKt1qg2XW05fPgw\nxWJfArF85521bNw4mcAmurn9j6NHq7l5s77CGqkP/OnUqQft7V1oby/m7LYLeP9G/Wp2WgOlUlmq\nu3i7dI+jmMAgAkPp7OxmdeWZGTNC6ej4MwHy2WeP85lnThMgbWzmmRU9WsaGDeT16mOz6pboaP3+\nppl7WuZwMeUi98fuN+lcpTKFUmntI1srsiw5mUOPz2Fifu0mS1q5lglBNedR61Q6xo6IZUFKAdes\nWcshQ0Zw7tz5DyJSi4r0ec/bt9dqLDVhKJ1EoVAwMDDQ7PZu365eCbDkXgnTf61bg6kp0vBe4D1+\n1XAQbW0CCRQSUPOrrz5jx44e5RJogiBwVcwqLjm3xKAc25HcXAZKJFW0VXU6HTdu3MiAgABmZ2fX\n6fuxBEO2z/JK2q1aAVFRNZ/Trh10Z7bAqfNrFndjCJFIhJkzZ2L8+PGYNWsWPD0Nl8pysrGBQme8\nIHNtmDdvBeTy6QCegK3tceh0dgC+hUqVgeef34kBA74pP7dnz564ceMcCgsLYW9vD3ulPVLCUoBO\ndTpEqyORSAB4AOgAAPgfIuCFVwC8BHuNI7IXZUPjoal8UZk3yYKKJW573fGlqj0AKVyT3HHt4wwg\nQQOh+GkkJJwxu70BA/QFexYsMH8staJ7d6BbN/1G6oQJwIgRQJs2Vu0iIT8BnZuY5uJ3dPSBs3M7\n5OefgqdnH6v0P7JFC0RoPoT/+dX47e25Frdj62wLGycbaPI0sG9YtXQjSUiDpWg5oSWcfZwxdOiP\nGDr0oZNcXYFZs4AjR/Sfd0AAYEWXX7FWCxcDe227du3CZ599Znabhw4B1RWwqsvSeACQezgX+cfy\n0XJCSywaPhuyoeNw5EgzkAJiY7th1aq30Lq1J7JKsjAnag6+7vw1uvt0r9IOSUSkpMDT3h6hD/22\nr1y5gsjISAwYMACDBg2quzdTF1hiZUnqfQbGImWVSsqmfEK5vHaJzDWRn5/PESNGVKkI8zB1Xe3H\n1/cZAlcIkM8/v5licU7p6sefoaGhRq9PXZdK2WXryprVNRkZGXR09CRQUrrC1Jb+W0BHRzfm5+db\ntb9169bRxeXdB5GjDdTET/G06zWT/v6WyUutXq2fzT8y5HJy0SJy1izSipHcc87MYYnadDerIAi8\nd288NRrr/gbf2zeWG1OktWpDmaLk/cXVe2DuL7nPgnNm5Gfn5ZGTJpF/GZDfs4C92dm8YOC7m2ih\n39+QPoNcImd6pPVXmKpsFeMnxzNrT1XpMIVCUZ4aptEUcu3JdznlyCTKlNX/VmQaDcfdu1clMyEv\nL49BQUFcu3btY+V+rQ5Dts/yoB9TNrAdHaFT5MHRsaXF3RjDw8MDEyZMQHBwcL1shldHdjbQoMFU\nAGoA+bh8WQq5vBEAwsXlOLpWkIUyRLPvmyEjMgMUHs17sIQmTZrglVd6w84uBPppry0AAQ4OAejX\n7x14eHhYtb+vvvoKHh6xsLUNAJAHFKkhWvsnHBpcgvyrT1FkQWDXoEHApk1WHaZ5ODvrVz1DhgDz\n5gHr1wNW8IYotAqI7avWlzWESCRCq1ZTcP/+/Fr3XZG5L/2A8/F7EZmebnEbjj6OUGeqIWiESq9n\n7cqCYwtHuPeoWnfWIJ6eepeCQqFfaRYVWTyuMmJkMnSrRs7r2rVrJt37D5Ofry9bXC1WriVLEpm/\nZSIlLAWtpraC14deVc5xcnKCq6srlFolQv9ZAie3vvipnRgNHKu+51i5HAFSKaa2aoVubnqpt7La\nr4sWLcKYMWPw448/PnbRr6ZSu7QSEyB1ZhdeNxc/Pz98/PHHWLJkSZ328zAxMcDUqcAvvwBhYc/A\nxeU9AD8CGAogB7a26yEWf4z+/fsbbUtkK4L3197I3JJZ18O2Ktu2rUPTpp5wdv4Yrq7fwMWlPbp0\nuY3IyBVW78vFxQUXL57CBx8kw87OB7a2nnjjjTO4HDYfAU89hWmJiThdUGBWm46O+t2Fe/esPlzz\naNYMmDMHeO45fZX448dr1ZzIgierg4M3GjR4Frm5h2vVd0U6eneEmyoVrra2WJuWZnE7Xp94IWdP\nTvnfhRcKobqvgvdn3pY1+MknwJgx+mL6p09bPC6SEKBPrXmYXbt24dNPPzW7zSNHgJoeGdZaGCjv\nKxE/Lh4OzRzQZnYb2HsYViu6lXULk45Mwvddv8fA58bA3b0HMjIqzzT35eRgZ1YWwtq2hVdpKk9Z\n9OtTTz2F2bNno3HjxlYZ+6Oizg1mffHiiy/Cz88Pv/32W532o1QCv/6qT62TSoGZM4EpU4A33nga\nmzatQtOml2Br2xr29q3xySfnsGLFcCxebGtSyqr7S+6Q35VDW1h/OaO1xcnJC998MxUnTkzBypX9\ncerUDkRHn6hxT7k2NG/eHLt3b4ZaLYdarcTRo3vw1FNPoYmDA5a0bYtkpRIzEhPN2rP+/ntg48Y6\nGa75PPecPn+zsFBvOOvZknt5fYKCghPQaIync5hKr1a90FQZh2YODliekmJRG27d3SCLlgEAFIkK\n5PyRgxbjWtRuYE2aAGFh+hzZmTP1N7eZXC8pQZdqUn0KCwvh5ORkkSrK9ev6tKdqsUIeJgUidXUq\nMjdnos3cNvDsY/heJYk1l9bgcPxhhPcPR2sPveRWw4b9oNXKUFh4HjoSc5OSoBIEBPv6ws7GBvn5\n+Zg2bRquXLmCsLAwvPjii7Ub9OOCJX7ccmbNIqvZO1Sr1dy0aRP79v2YR/p7c9f69fXms166dGm1\nlT1qu4eZlESGhpKBgWRppkgVAgICKJPJKJfL/x975x0eRbm+/zuN9AChS+8iinxtoNgbKnqOehD1\neBTBgkKABEgICRBC71XpgnSUKtJD74ggRUo2W5JsejbZlM32mfv3x4SQkM1mN9kE9Ofnuri4mJ3y\n7jIzz8z73M/9lLHwO32ajIlxrJLAlGmiaoKqWmOtTaZMqXHFvtMkGwwcKpPxvBM5wfnzyVpualM5\nRqM0sAkTpNybg5itZk48VvVSLrM5h3L56CpvfzdWwcqIg5It3P6cHM5OrpoiPHNzJnMP5zJ+cDwF\no4vLchITyZAQpzvPzExKotaGXef3339fzuXKEaxW+42MDIkGpv2Q5vR+b6O7rqMsRMaCS5XnqrN0\nWRy+bzjPqm13XhJFkb/Hj+Hgm5d4rTjHeb+rXx2lothXvYC5enW5u4zZbObzz79Jf//n6Oa2lvNe\n6sFePl3Zp88HTrViqiqiKDI6OrpE+nybqgRMUSQPH5Y0AgsW2L9nyWQyLly4sMLPL18mIyLulJbY\nI2VxCguvVs+LszZQqchZs+71KGwjiiKXp6ZyWmJihXZlpdHppIeh+5LMTGlwS5Y4dAIpchX88Y8f\nq3VIjWY3s7IqKRtzgjln5jAlX+rmciQ3l1MTE522uLToLbzw2AWaNTXUcEAQyMWLJQN9Bz2rbZWT\niKLI8PCq1aCePk3+aqcayJBUtYApmAQmzUyier6agqXy62F/wn5GHIxgvrHih87LhYUcGn+DF24O\np9Vq4KVLlzh06FCeO3fO6fHdb9RMwDx8mDx2rMyi9evX09//GQIWNmyYwm9fiGE/rKO/fzfu3r3b\nuVFXEYvFwpCQkDJPODFKpcMXaEGBdG8KDyf37nXszTA8PJwGg/0efvHxZGio9OJgD8EiUDZUVuO9\n+6pLeHi1GnPUCglFRQyRyXjFhgH83cyZI80k3LdcvSoV4e/bZ3e1OEUcTyRW3z9VoRhDk8k1bwma\nIg2nnpha8u9TeXmcoFI5fI6LokhFtILySDmNKTVs8HHrFjlkSKXyaY3ZbPNt+ejRo1W+102caP+a\nMiQZmLbSuYCZdzaPsqEyFiVUfrEaLUZOODqBm65tsrvexowMzkxKoiCKTEu7xpCQp7ls2bL7Xv3q\nKBXFvurlMFu1ApKTyyzatOlXFBV9BTe44zXfI1BndEU7qFFUNBA//bSrWodzFE9PT0yZMgUxMTEw\nFucl/Dw8s6fGNwAAIABJREFUYBBFu9vdugXExEhpjddek8R0b74pOZzZ48qVK+jcuTN87upAfzed\nOgFhYcCoUUBRUcXruXu6o1HfRsj6Kcv+ge8hZ84A3bsDfo4LMe8JHfz8ML9DBxzLy8M8tRpWO+fA\nV18BP/xQi4NzlkcekfKbVisQGgrcuGFzNaVWiXb1q1/XKalmp7tEZNLArwF0Zh1MVhMAoFfdungr\nOBjjVCqIDuw/bXEagl8LRqvwVsj4MaPa47FL587S77xvH/D990AF58z+3Fy8ERxcfvn+/Q4J/Wxh\nMFRyTTlhjWfVWaGaoIL+ph4dFnSAXwf7F+uN7BsYeXAk+nfvj48e/sjmOhZRxASVCkGenhjZogXW\nrlmD777bhLCwaPTpY/nLql8dpXoBs0ULoHQSPykJb6QmYhJ+Rixi8EhqIX5PeBE+MMLNzQwvr6r7\nJDhLUFAQIiMjMWLECHz66dcYFxaO+i3b4733PoFKpSpZTxCAX36RRDynT0sCnvHjgfbtHT/W2rVr\n8dlnnzm0bqtWUrF8RARgT9BZ7/l60F3Swaq7/wRAogj89BPw8cf3eiSO4eHmhmEtWuD14GCEKRSQ\n6fU21wsMBPz9gWqIOWseNzfg7belp7mjR6WT9S5P57TCNDQLbFbtQ3l61kVw8BvIznaNU/0HXT/A\nlhtbSv79ZFAQPmjcGNEqFQQ7QUCzSwOPuh6o90I9eAV7QTAIEAw1a0QCT09JdPXMM5KaNjGx3Co3\niorw0F3RLT09HY0aNapS4EhOBlpWVoHnoOgn90AuEmMS8cCgB9BsQDO7PrYkseLiCsk4/Y35aFOv\njc31Ms1mhMnl+KRJEzyQnIzQ0FB06dIFU6ZMQbt2feDu7ofc3LjKB/dXpiqvpSVotWS/flKWOiqK\nXLaMB5cupb9/NwJFHAgFAXI8Iunv355Hjx6t0utxVdHr9WzWrC3d3Z8hXrxBtE6mu3ssGzRowRs3\nsjhnjmTtefKklK+sCqdPn+amTfanL2yh1ZKDB5MZdjr1GFONTJzsGh9WV7J+fZW6Vt0XmAWBM5OS\nuDglxWaHd622fEul+xqNRjIQWbSoRIA37kjVTBwqQqkcR6Ox6kKT0ow6UL6Q/7pOx1FyOS2CQKVS\nyZkzZ3Ly5Mm8fPkyCy4WMHFa2WtAd13n9LRktTAaJcXfqlUlNwqLIHCcDV3EjBkzmJmZWaXDLFlS\neUrAmGJk6vKKVXYlBgTbyhsQ2CJLl8XQfaE8k2z/gj6bl8cRCQlMzsriuHHjKjQfUCrHsaiodhp2\n1yQVxT7nAqbRKOUsJ02SAuTMmeSXX0rSrmIEQeBHHw2gv19nDsR6Ahv4ucd/+PXXw2o9J7dq1Sr6\n+/cm8AvRZjHxcB4B0sNjD1955RDTqnnNiaLI4cOHV1nMpNNJwjx7F4l6vppFt+6fRGFRkZS7/Kvz\ne0EBh8pkTLSRd5461f6DzH3JjRtSgnzXLo4/7NqAabXqmJAQ6pLr96c/f+L5lPLNSGVFRXx+88/0\nDmjCOnUG08NjJFv6PMLFjyynYC1/fSWMTKj9HP+pU9JvnJbGk1ot92g0ZT62WCyMtOWY7iAVufuU\nxphiZOqy8gFTFEVmbMygIkpBs9YxwdJB+cFKhT2iKHJlWhrnJyU5pH4VBDPj44e43DGqtql6wLxy\nhZw7VwqQEyaQBw6UzUrbsMcTRZGHNx7m9Ofn8NtvQ/l194MUa7GB82369/+GwCICpE/zifRpcbTY\nVu0XPvtsn2rv/+DBg/zVnqTNAYxG6Rq8dcv254JJoGz4/SMAmjlTUuD/HTBYrZykUnF1WlqZ31ej\nkcQXf0n27ePZ95+quPapimi1J5mevqba+zFbzYyMKx9U/vzzT3q36UqEXCS8rPSBhaG4xvp+/8cd\nNpo8aPZqmBOXU+3xOE2xkfvE3bupv+sNa8eOHTx16lSVdltU5Ng5Z0w1MnVp2YBpSDIwITSBuUcc\nKz0yWoyMPRbLjVc32l3PYLUyUqHg0mPHOHToUJ49a7u85G5MpizKZMMpijVfFVFTVBQwK89h3rgh\nuVRPmSIpYl5/vWxW+nZD+FK4ubmhq9gVIcsGYfHieXjosSZIOaN23Tyyg7Rs2RR16sgBAPWET/D8\nQ4nF45Ohdevq5XhIYs+ePejTp0+19uPtDcyaJRXOX75c/nP3Ou5o0KcBNDs15T+sZVJTAYsFaN36\nXo/ENfh4eGBsmzbo6OeHMLkc6SZJkNKggZQq1Nz7n9xp8l7sibOhfYGzZ4HoaCDLNcKxevWehdGY\nBKMxufKV7eDl4YX6vvWRqSvraLVu3SYIKW8Dmx+Cx6B4fOF7EWvQCVp9CJYuLd/vNLh3MLQHXGeu\n4DDFRu5Gb2/4RkYCuXcaXJ8+fRrPPPNMlXZ79Cjw8ssOrFhK9FPagKDt1Lao/1LlZiG3NLcw8uBI\nfNrtU3z8SMUihGSjEUMuXYJ+5Uq4y2SYN28eevbs6dB3qVOnEZo2/Rxq9WyH1r8vMBiAQ4eA2Fjp\nuqmAylU4H9lWS5VQv77kSnKXb6j+lh6N/yvZVn06wBPLlggYUzXhWJX54ov+mDPnCQD/RUHBg6hX\nTwNABl/feRg2bEe19r1jxw689957TjeFtYWnp+SKNnEioNdLOoPSBL8WDPkoOYJ7B8PD796p0BYt\nsnsu/WXpVbcuugcEYEZyMrr6++PDxo0xcKAVQ4fehMk0E15envjss7546623XPL/XZOotCq0bdQR\nGPSuZEq6cCEQFAQMHiw9nVWDli3DoVSORocO80qaoDtCfn4+Fi1ajJ9/3gtfXx98+MWbWC2uRuRz\nkSXrFBTo0MXqhnc1M+G12YD05h2hlfcA0AAFBeWbbru5u8GnvQ/0CXr4daxdqXay0YgWXboAjz8u\n+f++9BIS2rdHx44dq3x+nDsn9RmvlOLdF90sQtqSNDT9vCkCHyvv6Xo3JLHqj1XINeRi/hvz4ele\n8a3/cE4O5q1aha65uRg1YgQaNSrvL1sZgYHdYTDIkZ29HY0ave/09jWOKEpvKEeOSNeJjw/w7LNA\nZKR0nUydanu7qryWlmHrVpvTP4roUgW9Oh0jXzzjaC2wS/nll18YENCQQUHPc9CgdvT1rcelS1dU\na59Wq5WhoaEuGuEdRFGa8jx4sPxnhkQDk6bfuyLBCxckzcPfnYM5OQyTyfj8O/3o5bWMwI8EvqO/\nf1d+8smX983UeEVsvb6VVzLuuh5lMnLECOlareb48/N/Y2rqMifWz2f79t3o4/MxgTgCO+jv/yzb\nD3qQJouJTE8n58+nvG9fDvRuRT9oCZBftl5FQKSv73ucM2eezX1bdVYqx9W+PdOSlJSyue+tWznh\nuedYmF61LiKi6Fj+kpSmX6+9d81hAwKSzC7KZui+UJ5Ksj9dLIoiI/bt48sDB/KMi1R9iYnTWFjo\n2vRAlVGpyOXLpfTi2LHS9ZBje1q/othX/YB5/jx5V5GuOcfM5NllC3rPDlzOrVsr311NoNfruXv3\nbu7b96HTnc9tsWbNGl5w0kLLGb7/nty+vfzypFlJ1Cv0NXbcuxEEgRaLhaIo1cv/TWqSK2XZhg30\nGjyBeDzrTisx6Ojv34mHDx++18Ozy8xTMytsu8TDh6X/yIsXq3WMxMRpDrfsmzZtBn19+xEQS35L\nX+SyT5sG/P6rZ8h588jUVAqCwJdeept+fq8Q2Mv/tpvF+t6fsUOHR1lQULGARBWrcljk4irG3OXu\no9frOS4sTBIj2LDlrIwrV0hHhPb55/J584ubVMY4/pAQp4hj+MFw5hnst0BL0WjYMySEo+fPd6n5\ngCgKlMmGucwAwylyc6WgeLuKY/lyh/0vay5gpqdLdlKlyP41m3lny/4HiePGs4qt4VxCWloa9+7t\nx+PHj1fLos9kMlW5x50z/Phj+Y7rVoOVCWE1L9nOy8vjgAHf0ts7gG5uHmzbdjwXLbpW48e9X3j9\n9f8QWEs8k826L9xiYMtUAqSb23R+/fXQez08u1RaUmK1kitXkpGRrKpMXBBMlMlCKIqV31gff/xl\nAnsJWPkyZnAmQjgOsXwU3/KJqKfKrGsymfjdd9/z0Uef5zNPv8xp/WYwL8/+jd6QZKB6gbpK36Mq\n6K1WxqpUZZatXr2af/75p/SquGaNVEVQiesXScbHx/Ps2bOcNMlk13bTUmihMkbJtB/SaMwwMuW7\nlEr3bbKaOPHYRG64usHueoIgcMayZXxs0CBeU9fM72ix5DM+fggFoYYfbIxG8sgRST0VFSX5dl68\n6JhV213UXMAUBMlZvBSqWBUF012DjInhsmXS7FBtIooihw4Np49PfX79dUcGBj7K5s078tq1qgWA\npUuX8vr16y4epW22bpVqs0qT/Ws2NXs0tjdwAYIgsHv3XvT2/oJAGgEzgRv082vIy5cv19hx7yd6\n9+5LYA0BMqhDEvt/H8IPP59KP78Jf/2AeZv8fKm2cNYsqYm1kxQWXqZavcjuOqIosnv3XgTeJxDF\nWfgP66CQAOnpGcb3Yt7nH+l/2NxWnafmls+3ODQWebjc4enJ6rJPo+Hxuxqjl/ONvW3kXsGbvEKh\n4COPPE0/v+YMCnqMXl5zOW+e7d8yZ38OE0Yk0Jgq2QGaskyVBsyb2Tc5ZM8QKnLL+9yW5o8//uA7\nX33FL7Zto7mGfb6LimRUKmNcu1NBkEy6b1dxxMaScXFVOp/vpuYCJlmutEQ51sZr7/TpLMgoYmys\nY7t0FUuXLqef3+MENOzfP6Z4amgtGzduXaajiCMUFRVVq86qKuzbJ93TSqeeEkITXN+toZi4uDgG\nBDxCQCg1HUm6uc3me+99UiPHvN/YuHEj/f17EDBJ39/HykYRcRw0qjmPHh1Kq7X2psWdxWnTAoWC\nHDlSmhN0Mr+ZnDyHOl1Zv1VRFHn58mVOnDiRkZGRDAsLo5/f/7EhEhmB6cXnUyJ9fBry2o1rjDoU\nVeH+V32yiqJQ+ZjyTuU5XKhfXcYplbSUCi4XLlzg5s2by68oCFJuZdasMob5FouFzZt3pLv7PALW\n4t8ji35+7cuUz5g1ZspHlzcgMGWZqF5k+01QFEWuvLiSM07NoEWo2KRfq9Vy7NixfH/aNK6rxVZD\nOTn7y5QmVUkPkJwsiSmioqSGBD/9RNZAV5RaC5hWg5WqSary62zZQl69yrFjHZqtcBkdOz5eLDYg\nP/1sXEkACAx8lrt27XJqX3Pnzq1Sy57qcvKk9PB0+/zSy/XlcsSuYsaMGfT0HEGArAMrn4Km+De7\nxubNu9TIMe83LBYL33jjfQYEPEbgewLz6BfUhQ8vWszfMi9RLo9gaurymp9ichKrYOWEo1W0KTp+\nnBw6lCzVacJqtfLs2bM8ceIEjTY6BgiChTJZSPHfMk6fPp2jR4/mxo0bWViq3dNHHw3gaK/6fAAh\n9PIKoY9Pg5I3qqknplJTZHvGZP7E+eVSO7YQRZEJI2o+VSGKYrn8ZVRUFE02WhyWcPOm9LZ58yZJ\n8tdff2Vg4DNlHkalP1v5+OMvSQYEm4oNCHLLn1+mbBPVC8sHTE2RhqH7Qnky6WSFQxEEgWvWrGFY\nRAS/PHeOf9jJDdcUyckL+P33w9iwYWsCYOvWD3P9ejv1oHl55M6d5LhxUpBcsoRMSKi2eK0yKop9\nLjd3Lfy9EEFPBpX/oEMHQC7HBx88gi1bgE8/dfWRbZOdnQ6gMwAVtt88DDTvDaT2gtXaDenp6Q7v\nJy8vD1qtFm3btq2xsVbEs89Kpa9RUcDkyYBve1+IZhHGZCN8Wtk3fHcWUewKN7fmAAAz3NEVBfgN\nDQBcR4sW1WzY+xfB09MTu3f/jN27d2PTpl+Ky0oW4OVXXsG05GQ8ExyJp7xSoFSOQUBAdzRp8jHc\n3O696XRqYSpaBFXx/+j556UTbd06YPt2nHvySfx7SDgMhkC4ufmATMLy5Qvx0Ucf3jleajri4vxx\n+PDr8PL6BF9//XW5xuHu7u7YuG450r2sqNMmGL6+vujX7zzaF5s19+/eH2uurMGIp0eUG1KDVxsg\nYVcCnuj5hN2hu7m5IfCxQBReKnSoxKKq3NLr0aVUDbpWq4W/vz/q1KlT8UYPPih1c1iwADhyBCqL\nBUaj1B3a17cQTZsmQqV6BEB36JT5kIfJ0fBfDdFkShObu7NVtnJYeRj75PsQ82IM6vnUs7EVcPny\nZaxatQo93n0X1qefxow2bRDs5eX4l3cRCxZkwGjcAnI1gFeRlHQcX3/9FcxmMwYM6C8Vep87B5w4\nIdVGBgUBL74oeQDcD8buVYmy5YiNLZFQJs1MoiXfxnRAQYFUM8HatVZ75ZV/E/iSwAQCBqJ3GOGb\nQS+v6RwwIIMbNkhDu5vCwkJGRY1nixZd2KxZR/bs+WyV856u4uZNMixMsgy1FlkpH+WYUrEydDpp\nliM8nFy50sT69dsTWE9A5AAoCSTSz68jd+7c6ZLj/dX5LiWFPxf7hebnn2dCQhizsrbd85KTo6qj\nPKI8Uu39ZMjlHOflxwgMoB90xW9AF+nn14SHDh3i4sWLOXr0aH733XdMT0+nWr2IhYV28tt79pB2\nfKTDD4bbnEJMykvi1s8dk9YLJoGKSPs5u+oyNzmZWaXeJhcuXMgkZ/rBXbzIS716sTWaExD54IPn\n2by5jG4Q+Q5+5vi2k2ktsi+kMmvMJSInk9XESccncd2VdRWee1qtluPHj+eyZcu4OS2N0xITab1H\n56lWq6WPTz3WqaPksGFD6OVlICDyIWxgVGADirfd5Pbtk25K95CKYp9rAuby5WSxwqpM/eXdFE/d\nrl3rcucumxQVFfHzzz+nt3dQsYhDR/icp8fb7fja6+9SFKVxTJ4smbBv3CgFT7PZzG7dnqa394cE\nLhA4Qnf3p9iyZedKVXs1jUolzZwVFZFZW7OYe8gxOyxbXL4sKa5jYsjSOqYrV66wZcsHGRDQhd94\nT6Cvb31Onz6n2mP/O7ExI4NLS+V/cnOPUiYbzpyc/fcscP5w6QcmaqvvWzht2gz6+HzFlkjiDITz\nEyyjG34g8AIfe6wHVXepREXRWjw1W8HUZHi43Sm0k0knuf2GjToqkkuGLnG4/2XSjCQa02uuV2bp\nZtGCIFSpSfQTDz/NaDTiZ3iJPZ76me0D1ByKOHZEd5sWgHdjzjFTPV/NeE08h+wZQnmO7Yfm29Ov\nY8aMYVpGBieqVNyRVTt53oo4c+YMg4KeJDxMDHpiNZf17M7JiOLHWM8WXoHMqaAm8l5QUeyrXnuv\n2xT3xaRIuLlX7nTxwQfAli2VrlYtrl+/joiICEyePBlHjuxDjx7r4OERjHo+7+KTh1/EWzEvwM0N\n6NZNcq+ZNg146CHJGKVvXzni41+HybQJwBMATkAU90Gj6Y7ly1fW7MAroU0baWo2PBzweqUhNL9q\nIFrs9/ksjU4n9XyMiAD+/FP67hMmSN/9Nt26dUNS0g0cPvwj+n74LNLVKoweXX7K7P9nPm7SBG18\nfDAtKQkkUb/+i+jQYR5IAXJ5GPLyTtb6mNT56qpPyZZCLk+GIDyI1s9exJX/GpEVcBjzcQTPoA+8\nveujTZs2ZdZ3c/NAixahUKvnlt+ZSiWdtHYccHq17IXT6tM2P9P01ECz2zGPwqYDmtZYr8x8qxVB\nnncyWEeOHMErr7zi1D5I4uL185iCq1DAGw3zTehiicFihCPF1wPulTXeBUAQ59Xnsf3mdszrPQ/t\ng8v3Ibx8+TJCQ0PRsWNHhE2YgCn5+figUSO8WwXHHlchiAIS3RJR1PM68HwMCrI6IPb8SaTjc2zH\nc8jx9EBAQMA9G5+juCaH2aoVcOUKigK7wf9h/4rXK75ofHwALy+gsFDqQehKSGLVqlXIzc3FggUL\n4OHhgebNm+PcubJ92pZcWIJzKefQs0XPkqE9+qj0Z8CARTCZngXghoYNZahTxwtpacEwGPph9+4f\nER4+0rWDdpKmTYFJk4DoaDdEfvwAzow+g+WadcjMzMU777yIzz/vX+7ku3QJ2LFDsuH78EPgiy/s\nH8PNzQ1PPfUUMuIz4JXjBTSowS/0F6V3cDCCPT0RpVJhUps28HR3R4MGbxX3kNwGuXwEmjT5BIGB\nj9fKeAQK8HCvep6HFJGffxp9+iTCw+MgTp5ch1OnFgBwwyGI+J9HHwy05Eq9Ie8Kmr6+7eHl1QAF\nBb8hKOipOx+sXw8MG2b3uG5ubujaqCuuZ11H18Zdy3zW8qGWSF+TjuZoXun46zSqA6FQgGAU4OHj\n2nzXwdxcvF4qPxsXF4dp06Y5tQ83NzcEBTVGfn4ezmIPPsq4iY3fPAos7wwPj0fRuHFju9vnGnIx\n/eh09PHrg/88+59yn+fl5WHevHlo3rw55s2bhz+KijA1ORnT2rVDoGft9SK+DUmcTz2PfQn7YBWt\neKHNC+hlfh5nT5thsfREGjyxEY0x2ONHiB99bT8XfL9QldfSchQWkjNmMGVxiv3pk6lTSySyMhm5\nzHGHLYfQarUcMWIEjx8/Xum6oigybH8Yc/TlpwHCw6Po6TmaANmv30zWrZtWnMdZxL59P3PtoKtB\nYSHZs+clDvCMZAPMJ7CBfn7vsm3bh5mTk8OCAuk3HjVKmm6uijo572weNbtrru7z70B8URGHyWQs\nusshRRAsTE//kXL5KOp0NV+7W9U+mDrdDapUk6hQRFKj2c2CAi2bNWtPD4/xBPKk3D8WMzCwMdXx\n8ZIWYfLkcsl/URQpkw27U3ZjMEjSfwfQm/Uce3hsueUqrYpbB22lVe+Y+0zh1UKmrXZ9r8wohaJk\nql2tVnPePNt2fZUxevQ4+vr2YU+k8VFoibpGYvg2tnnoSbtT+UeURzjywEhmp2UzeW5ZhXzp6des\n4mnXH9PTOS85udbTA6Io8krGFU46PomRcZHccXMHDZY7N57s7Gz26PEK/fxaMCjoTfr4NOSHb3/O\nG9/coEFdi+UTlVBR7Kt2wBRFkSdOnOCJl17ilje32J+H3ry5TLJs1CjXqYPPnTvH4cOHU6Nx/Oau\nNWg5fN/wcifVzZs36evbiEA8v/pqdHGwTKe/f1seOVJ9UYWrUKvV9PZuSl/k8WvISyTqnp6T+NRT\nhxkbS8bHV+8YZo2ZyfNqpoTl70Sa0cjB8fHMsVHbKwgmpqYupVw+mnp9zQlTnAmYRmMa1eoFVCgi\nmZ7+Iy2WspaRarWaffr0o6enDz086rBXrzd49erVOyukpEiJ/1WryngmGgxJVKmK+1StW0f+YduY\nwBaTjk+i1qAtt3zm/JnU7HX8uk4Y4dpemYIolslfTps2zan7TGlMJhP//e+POdgzjAH+HzEw8P/4\nQJfH+N/ffmO+pbzwyWQ1ccqJKVxzeQ1FUaQlz8LkOXeux8uXL3Po0KEl3q8mQWC0QsEDtZwPTMhJ\n4IxTMzg6bjTXX1lfsT1jMdeuXeOvv/5aUqZ328VMd+Pein1uUyMB02g08qWX3qa/fyfGuj3Drz1n\n088vmPv377e9we+/k7/8UvLPrVtJB1usVYggCJw/fz6XLFlSpYvkQuoFLjpf3mVj+fIf2LVrAHv3\nfpp+fp/Rx6c+J0yYUr3Buphly5bRz+9TAuQbUPINv93FQVPFpk27uuw498Lg+q9InsXCIfHxTK7g\nVd5qLWJy8jwqFNE0Giu3N3OU/Px8HjpxiBG7IuyuZ7EUMD19DRWKSKrV82k0Vl60bjabbdZglnDh\ngqRCK6WCTU9fQ632BJ31wkzUJnLhuYXllo89ONa+mPAusndlU3usfOCtKufz87m9+M3NbDZzjKNO\n6RVgyjbxwsgLXLduHY8cOUJBEKgxmzk4Pp55pYKmTCPjkD1DmJBzp8bUkm9h8uzkEvXr0qVLS7xf\nU4of2pQucLpxhJT8FM4/O5+j40ZzxcUVNmfrnEG0ilREK5h35t4KK8kaCpgTJ06hr28fAmbOQhTf\nRiqBU/T3b0CdLVmwVkvOuaO2NJsdd+m3RUZGBocNG8ZLly5VfSckl15YyjPJ5d35//wznD/8sJhL\nly5lcvL995a1YsUK+vv/l71wkvM9BvPx5oMInCXwO5s3f9Blx/knYDqOwWplWEICr9uRxVsseUxK\nmk6VKrZaptSiKDI6OpY+PvUY0K4r63Svy549X2V6qa4ZgmCmRrOHCkUkVapJ1On+rPLx7AyE/Pln\nqeZJLpeMBM58QsuGlU7vKvxgOIW7Gg+vurSKv4f+7vADsSiILiu5IskpiYksKA5kW7Zs4blS5g5V\nQb1QTUNy+YeqnOKgmWs288c/fuTUE1NptpadsTDlmbjj4x0cM2YMM4tLm0jyhFbLcLm8XFrA1WQX\nZXPphaUcHTeaC88tZFqBa6e/RVFk0qykMvafKSkpvHr1qv0HNxdTIwGzRYsuBM4TsPB7DGLr4pqt\nwMC3bNtFkeVs9KZOrbDDil0OHjzI0aNHlziKVIfb+czSjiOCYKFCUbFt1/1ARnw8p3l483+YQzQr\nIl5LIzCZXl6vMCLCsdyRI/wTMJ3DKoqMVih4upISJJMpmyrVRCYmTqPF4vxT9eLFS+nn152Amui8\nk2h2jp6e0XzkkR7MyztHpXI8lcqx1GqPUxRrwWvVYJB8PWNjaRwziEqZ80/DR5RHuDu+bPcjRa6C\nm6dsZuFVx6919SK1yzr7lHb3CQ8Pr9Z0ryiKlIdXHMyVBVl89NBq/io/Wu6zy5cvM2xQGE+E3OmI\nIooiF6ekcGlqao3lK/ON+Vx7eS0j4yI56/SsSj1qXUHayjTeXHSTzz77Bn18ghkY+CADAxvzu++W\nVL6xC6go9lUqnZoyZQosFktpkRAASfGl0aQA2ADgAJYhD8kYD+BxWK31UFhY6JDo6H//c0hIV4LF\nYsHMmTPRpk0bTJ8+3bGNKsHNzQ0xL8Qg5lgM5vaeC3c3d+TlHUH9+s7JxmuV7dvR5Px5NJo8Cdsm\nzoR7fRFiYTP4+x+Dv388unevRAbrBG513CCaRLh7u6YK6e+Oh5sbJrVtizlqNXItFrzdsKHN9erU\naYgfD+W4AAAgAElEQVQ2bcbBZEqDWj0Hnp718cADg+Dh4VhD5GnTFkCvXwagBVB/C5qaWuKlD/xR\nv348rl8/jp49o+DuXr2m0U7h4wOEhQEyGby/+goNGnRG7qcHENy4t8O7eLHNiwiPC0efTn1KlrWt\n1xZruqzB0weeRsAjjpUeNO3fFOo5arSdUD1nrgyTCU2K1Zs3b95E586dq9VEXPeHDgGP2f4OxxOP\nY1f8Lux4JhKz07XoZbGgvpdXifr1gQcewPQZ05GxXCqd0QsCYhIT8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T82lv6vvUsggQBZ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\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48uqXPD76b5QaLqNWP8DhnBwec3NDqz3LhzG7WKefznd976HPH7xlAQb7D+Z00mnLdrw1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\n",
"text": [
"<matplotlib.figure.Figure at 0x10d7a4b90>"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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