diff --git a/gcdata/mcppareto.png b/gcdata/mcppareto.png index 9d63bd7..1fba32b 100644 Binary files a/gcdata/mcppareto.png and b/gcdata/mcppareto.png differ diff --git a/gcdata/mctest.ipynb b/gcdata/mctest.ipynb index aa59b0e..f6b1c56 100644 --- a/gcdata/mctest.ipynb +++ b/gcdata/mctest.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -108,142 +108,142 @@ "测试实例: DSJC0125.1.txt\n", "\n", "使用算法: FunSearch-MCP\n", - "执行时间:0.0106秒, 使用颜色数:7\n", + "执行时间:0.0065秒, 使用颜色数:7\n", "\n", "使用算法: EoH-MCP\n", - "执行时间:0.2657秒, 使用颜色数:6\n", + "执行时间:0.2940秒, 使用颜色数:6\n", "\n", "使用算法: AAE-MCP\n", - "执行时间:0.0070秒, 使用颜色数:6\n", + "执行时间:0.0156秒, 使用颜色数:6\n", "\n", "使用算法: 贪心\n", - "执行时间:0.0020秒, 使用颜色数:8\n", + "执行时间:0.0034秒, 使用颜色数:8\n", "\n", "使用算法: Welsh-Powell\n", "执行时间:0.0020秒, 使用颜色数:7\n", "\n", "使用算法: DSATUR\n", - "执行时间:0.2044秒, 使用颜色数:6\n", + "执行时间:0.1971秒, 使用颜色数:6\n", "\n", "测试实例: DSJC0125.5.txt\n", "\n", "使用算法: FunSearch-MCP\n", - "执行时间:0.0035秒, 使用颜色数:24\n", + "执行时间:0.0030秒, 使用颜色数:24\n", "\n", "使用算法: EoH-MCP\n", - "执行时间:0.3157秒, 使用颜色数:22\n", + "执行时间:0.3150秒, 使用颜色数:22\n", "\n", "使用算法: AAE-MCP\n", - "执行时间:0.0127秒, 使用颜色数:22\n", + "执行时间:0.0125秒, 使用颜色数:22\n", "\n", "使用算法: 贪心\n", - "执行时间:0.0030秒, 使用颜色数:26\n", + "执行时间:0.0020秒, 使用颜色数:26\n", "\n", "使用算法: Welsh-Powell\n", "执行时间:0.0030秒, 使用颜色数:23\n", "\n", "使用算法: DSATUR\n", - "执行时间:0.2202秒, 使用颜色数:22\n", + "执行时间:0.2150秒, 使用颜色数:22\n", "\n", "测试实例: DSJC0125.9.txt\n", "\n", "使用算法: FunSearch-MCP\n", - "执行时间:0.0035秒, 使用颜色数:53\n", + "执行时间:0.0030秒, 使用颜色数:53\n", "\n", "使用算法: EoH-MCP\n", - "执行时间:0.3592秒, 使用颜色数:51\n", + "执行时间:0.3702秒, 使用颜色数:51\n", "\n", "使用算法: AAE-MCP\n", - "执行时间:0.0194秒, 使用颜色数:51\n", + "执行时间:0.0171秒, 使用颜色数:51\n", "\n", "使用算法: 贪心\n", "执行时间:0.0030秒, 使用颜色数:56\n", "\n", "使用算法: Welsh-Powell\n", - "执行时间:0.0065秒, 使用颜色数:53\n", + "执行时间:0.0060秒, 使用颜色数:53\n", "\n", "使用算法: DSATUR\n", - "执行时间:0.2307秒, 使用颜色数:51\n", + "执行时间:0.2429秒, 使用颜色数:51\n", "\n", "测试实例: DSJC0250.1.txt\n", "\n", "使用算法: FunSearch-MCP\n", - "执行时间:0.0111秒, 使用颜色数:12\n", + "执行时间:0.0135秒, 使用颜色数:12\n", "\n", "使用算法: EoH-MCP\n", - "执行时间:1.9986秒, 使用颜色数:10\n", + "执行时间:1.9738秒, 使用颜色数:10\n", "\n", "使用算法: AAE-MCP\n", - "执行时间:0.0197秒, 使用颜色数:10\n", + "执行时间:0.0218秒, 使用颜色数:10\n", "\n", "使用算法: 贪心\n", - "执行时间:0.0091秒, 使用颜色数:13\n", + "执行时间:0.0092秒, 使用颜色数:13\n", "\n", "使用算法: Welsh-Powell\n", - "执行时间:0.0088秒, 使用颜色数:11\n", + "执行时间:0.0083秒, 使用颜色数:11\n", "\n", "使用算法: DSATUR\n", - "执行时间:1.5610秒, 使用颜色数:10\n", + "执行时间:1.5016秒, 使用颜色数:10\n", "\n", "测试实例: DSJC0250.5.txt\n", "\n", "使用算法: FunSearch-MCP\n", - "执行时间:0.0147秒, 使用颜色数:40\n", + "执行时间:0.0154秒, 使用颜色数:40\n", "\n", "使用算法: EoH-MCP\n", - "执行时间:2.3853秒, 使用颜色数:37\n", + "执行时间:2.3612秒, 使用颜色数:37\n", "\n", "使用算法: AAE-MCP\n", - "执行时间:0.0454秒, 使用颜色数:37\n", + "执行时间:0.0877秒, 使用颜色数:37\n", "\n", "使用算法: 贪心\n", - "执行时间:0.0095秒, 使用颜色数:43\n", + "执行时间:0.0085秒, 使用颜色数:43\n", "\n", "使用算法: Welsh-Powell\n", - "执行时间:0.0153秒, 使用颜色数:41\n", + "执行时间:0.0140秒, 使用颜色数:41\n", "\n", "使用算法: DSATUR\n", - "执行时间:1.7647秒, 使用颜色数:37\n", + "执行时间:1.6691秒, 使用颜色数:37\n", "\n", "测试实例: DSJC0250.9.txt\n", "\n", "使用算法: FunSearch-MCP\n", - "执行时间:0.0160秒, 使用颜色数:92\n", + "执行时间:0.0147秒, 使用颜色数:92\n", "\n", "使用算法: EoH-MCP\n", - "执行时间:2.9891秒, 使用颜色数:92\n", + "执行时间:2.8340秒, 使用颜色数:92\n", "\n", "使用算法: AAE-MCP\n", - "执行时间:0.1836秒, 使用颜色数:92\n", + "执行时间:0.0724秒, 使用颜色数:92\n", "\n", "使用算法: 贪心\n", "执行时间:0.0105秒, 使用颜色数:99\n", "\n", "使用算法: Welsh-Powell\n", - "执行时间:0.0356秒, 使用颜色数:93\n", + "执行时间:0.0346秒, 使用颜色数:93\n", "\n", "使用算法: DSATUR\n", - "执行时间:1.9386秒, 使用颜色数:92\n", + "执行时间:2.0043秒, 使用颜色数:92\n", "\n", "测试实例: DSJC0500.1.txt\n", "\n", "使用算法: FunSearch-MCP\n", - "执行时间:0.0522秒, 使用颜色数:18\n", + "执行时间:0.0501秒, 使用颜色数:18\n", "\n", "使用算法: EoH-MCP\n", - "执行时间:16.7812秒, 使用颜色数:16\n", + "执行时间:15.5829秒, 使用颜色数:16\n", "\n", "使用算法: AAE-MCP\n", - "执行时间:0.0935秒, 使用颜色数:16\n", + "执行时间:0.0800秒, 使用颜色数:16\n", "\n", "使用算法: 贪心\n", - "执行时间:0.0439秒, 使用颜色数:20\n", + "执行时间:0.0382秒, 使用颜色数:20\n", "\n", "使用算法: Welsh-Powell\n", - "执行时间:0.0465秒, 使用颜色数:18\n", + "执行时间:0.0431秒, 使用颜色数:18\n", "\n", "使用算法: DSATUR\n", - "执行时间:12.5949秒, 使用颜色数:16\n", + "执行时间:12.7058秒, 使用颜色数:16\n", "\n", "测试实例: DSJC0500.5.txt\n", "\n", @@ -251,99 +251,99 @@ "执行时间:0.0556秒, 使用颜色数:68\n", "\n", "使用算法: EoH-MCP\n", - "执行时间:18.6282秒, 使用颜色数:65\n", + "执行时间:18.9277秒, 使用颜色数:65\n", "\n", "使用算法: AAE-MCP\n", - "执行时间:0.2160秒, 使用颜色数:65\n", + "执行时间:0.1925秒, 使用颜色数:65\n", "\n", "使用算法: 贪心\n", - "执行时间:0.0438秒, 使用颜色数:72\n", + "执行时间:0.0468秒, 使用颜色数:72\n", "\n", "使用算法: Welsh-Powell\n", - "执行时间:0.0799秒, 使用颜色数:71\n", + "执行时间:0.0844秒, 使用颜色数:71\n", "\n", "使用算法: DSATUR\n", - "执行时间:14.6898秒, 使用颜色数:65\n", + "执行时间:13.6119秒, 使用颜色数:65\n", "\n", "测试实例: DSJC0500.9.txt\n", "\n", "使用算法: FunSearch-MCP\n", - "执行时间:0.0754秒, 使用颜色数:171\n", + "执行时间:0.0604秒, 使用颜色数:171\n", "\n", "使用算法: EoH-MCP\n", - "执行时间:23.0478秒, 使用颜色数:170\n", + "执行时间:22.3392秒, 使用颜色数:170\n", "\n", "使用算法: AAE-MCP\n", - "执行时间:0.3527秒, 使用颜色数:170\n", + "执行时间:0.4155秒, 使用颜色数:170\n", "\n", "使用算法: 贪心\n", - "执行时间:0.0455秒, 使用颜色数:175\n", + "执行时间:0.0556秒, 使用颜色数:175\n", "\n", "使用算法: Welsh-Powell\n", - "执行时间:0.1936秒, 使用颜色数:169\n", + "执行时间:0.2574秒, 使用颜色数:169\n", "\n", "使用算法: DSATUR\n", - "执行时间:14.5305秒, 使用颜色数:170\n", + "执行时间:15.6913秒, 使用颜色数:170\n", "\n", "测试实例: DSJC1000.1.txt\n", "\n", "使用算法: FunSearch-MCP\n", - "执行时间:0.2045秒, 使用颜色数:29\n", + "执行时间:0.2285秒, 使用颜色数:29\n", "\n", "使用算法: EoH-MCP\n", - "执行时间:122.7522秒, 使用颜色数:27\n", + "执行时间:121.8734秒, 使用颜色数:27\n", "\n", "使用算法: AAE-MCP\n", - "执行时间:0.2949秒, 使用颜色数:27\n", + "执行时间:0.3822秒, 使用颜色数:27\n", "\n", "使用算法: 贪心\n", - "执行时间:0.1469秒, 使用颜色数:31\n", + "执行时间:0.1491秒, 使用颜色数:31\n", "\n", "使用算法: Welsh-Powell\n", - "执行时间:0.1831秒, 使用颜色数:29\n", + "执行时间:0.2108秒, 使用颜色数:29\n", "\n", "使用算法: DSATUR\n", - "执行时间:96.6556秒, 使用颜色数:27\n", + "执行时间:102.3721秒, 使用颜色数:27\n", "\n", "测试实例: DSJC1000.5.txt\n", "\n", "使用算法: FunSearch-MCP\n", - "执行时间:0.2217秒, 使用颜色数:123\n", + "执行时间:0.2249秒, 使用颜色数:123\n", "\n", "使用算法: EoH-MCP\n", - "执行时间:142.0033秒, 使用颜色数:115\n", + "执行时间:151.4883秒, 使用颜色数:115\n", "\n", "使用算法: AAE-MCP\n", - "执行时间:0.8392秒, 使用颜色数:115\n", + "执行时间:0.9123秒, 使用颜色数:115\n", "\n", "使用算法: 贪心\n", - "执行时间:0.1663秒, 使用颜色数:127\n", + "执行时间:0.1647秒, 使用颜色数:127\n", "\n", "使用算法: Welsh-Powell\n", - "执行时间:0.4809秒, 使用颜色数:121\n", + "执行时间:0.4821秒, 使用颜色数:121\n", "\n", "使用算法: DSATUR\n", - "执行时间:106.3345秒, 使用颜色数:115\n", + "执行时间:113.0633秒, 使用颜色数:115\n", "\n", "测试实例: DSJC1000.9.txt\n", "\n", "使用算法: FunSearch-MCP\n", - "执行时间:0.2358秒, 使用颜色数:316\n", + "执行时间:0.2294秒, 使用颜色数:316\n", "\n", "使用算法: EoH-MCP\n", - "执行时间:164.6181秒, 使用颜色数:299\n", + "执行时间:169.8972秒, 使用颜色数:299\n", "\n", "使用算法: AAE-MCP\n", - "执行时间:1.3996秒, 使用颜色数:299\n", + "执行时间:1.4054秒, 使用颜色数:299\n", "\n", "使用算法: 贪心\n", - "执行时间:0.1856秒, 使用颜色数:321\n", + "执行时间:0.1765秒, 使用颜色数:321\n", "\n", "使用算法: Welsh-Powell\n", - "执行时间:1.4151秒, 使用颜色数:313\n", + "执行时间:1.3390秒, 使用颜色数:313\n", "\n", "使用算法: DSATUR\n", - "执行时间:128.1266秒, 使用颜色数:299\n", + "执行时间:113.5548秒, 使用颜色数:299\n", "\n", "所有算法在各个实例上的表现:\n", "\n", @@ -360,12 +360,12 @@ " DSJC1000.1.txt: 使用颜色数 = 29\n", " DSJC1000.5.txt: 使用颜色数 = 123\n", " DSJC1000.9.txt: 使用颜色数 = 316\n", - " FunSearch-MCP: 执行时间 = {'DSJC0125.1.txt': 0.010560035705566406, 'DSJC0125.5.txt': 0.0035393238067626953, 'DSJC0125.9.txt': 0.003525257110595703, 'DSJC0250.1.txt': 0.011104822158813477, 'DSJC0250.5.txt': 0.01467132568359375, 'DSJC0250.9.txt': 0.01604008674621582, 'DSJC0500.1.txt': 0.05222797393798828, 'DSJC0500.5.txt': 0.05559563636779785, 'DSJC0500.9.txt': 0.07539129257202148, 'DSJC1000.1.txt': 0.204453706741333, 'DSJC1000.5.txt': 0.2216651439666748, 'DSJC1000.9.txt': 0.23581409454345703}\n", - " EoH-MCP: 执行时间 = {'DSJC0125.1.txt': 0.2657294273376465, 'DSJC0125.5.txt': 0.3157191276550293, 'DSJC0125.9.txt': 0.3592233657836914, 'DSJC0250.1.txt': 1.9986271858215332, 'DSJC0250.5.txt': 2.3853354454040527, 'DSJC0250.9.txt': 2.989116907119751, 'DSJC0500.1.txt': 16.78122878074646, 'DSJC0500.5.txt': 18.628207206726074, 'DSJC0500.9.txt': 23.047802925109863, 'DSJC1000.1.txt': 122.75219559669495, 'DSJC1000.5.txt': 142.00333714485168, 'DSJC1000.9.txt': 164.6181070804596}\n", - " AAE-MCP: 执行时间 = {'DSJC0125.1.txt': 0.0070340633392333984, 'DSJC0125.5.txt': 0.01272726058959961, 'DSJC0125.9.txt': 0.019374370574951172, 'DSJC0250.1.txt': 0.019664525985717773, 'DSJC0250.5.txt': 0.04539036750793457, 'DSJC0250.9.txt': 0.18355083465576172, 'DSJC0500.1.txt': 0.09353065490722656, 'DSJC0500.5.txt': 0.21602964401245117, 'DSJC0500.9.txt': 0.35271167755126953, 'DSJC1000.1.txt': 0.294903039932251, 'DSJC1000.5.txt': 0.8391859531402588, 'DSJC1000.9.txt': 1.3996093273162842}\n", - " 贪心: 执行时间 = {'DSJC0125.1.txt': 0.0020036697387695312, 'DSJC0125.5.txt': 0.0030128955841064453, 'DSJC0125.9.txt': 0.0030007362365722656, 'DSJC0250.1.txt': 0.009136676788330078, 'DSJC0250.5.txt': 0.009547948837280273, 'DSJC0250.9.txt': 0.010493755340576172, 'DSJC0500.1.txt': 0.04389691352844238, 'DSJC0500.5.txt': 0.043775320053100586, 'DSJC0500.9.txt': 0.0454859733581543, 'DSJC1000.1.txt': 0.14690470695495605, 'DSJC1000.5.txt': 0.16630005836486816, 'DSJC1000.9.txt': 0.18558239936828613}\n", - " Welsh-Powell: 执行时间 = {'DSJC0125.1.txt': 0.0020334720611572266, 'DSJC0125.5.txt': 0.003046751022338867, 'DSJC0125.9.txt': 0.006543397903442383, 'DSJC0250.1.txt': 0.008815288543701172, 'DSJC0250.5.txt': 0.015262365341186523, 'DSJC0250.9.txt': 0.03563809394836426, 'DSJC0500.1.txt': 0.04653811454772949, 'DSJC0500.5.txt': 0.07994246482849121, 'DSJC0500.9.txt': 0.19355511665344238, 'DSJC1000.1.txt': 0.1831059455871582, 'DSJC1000.5.txt': 0.48086977005004883, 'DSJC1000.9.txt': 1.4151320457458496}\n", - " DSATUR: 执行时间 = {'DSJC0125.1.txt': 0.20441937446594238, 'DSJC0125.5.txt': 0.22018790245056152, 'DSJC0125.9.txt': 0.23067665100097656, 'DSJC0250.1.txt': 1.5610229969024658, 'DSJC0250.5.txt': 1.7647099494934082, 'DSJC0250.9.txt': 1.9385864734649658, 'DSJC0500.1.txt': 12.594899415969849, 'DSJC0500.5.txt': 14.689803123474121, 'DSJC0500.9.txt': 14.530549764633179, 'DSJC1000.1.txt': 96.6555712223053, 'DSJC1000.5.txt': 106.33447790145874, 'DSJC1000.9.txt': 128.1265606880188}\n", + " FunSearch-MCP: 执行时间 = {'DSJC0125.1.txt': 0.006541728973388672, 'DSJC0125.5.txt': 0.003002643585205078, 'DSJC0125.9.txt': 0.0030024051666259766, 'DSJC0250.1.txt': 0.01353764533996582, 'DSJC0250.5.txt': 0.015435218811035156, 'DSJC0250.9.txt': 0.014726877212524414, 'DSJC0500.1.txt': 0.05006003379821777, 'DSJC0500.5.txt': 0.05561065673828125, 'DSJC0500.9.txt': 0.06039857864379883, 'DSJC1000.1.txt': 0.22848773002624512, 'DSJC1000.5.txt': 0.22493863105773926, 'DSJC1000.9.txt': 0.22939729690551758}\n", + " EoH-MCP: 执行时间 = {'DSJC0125.1.txt': 0.29399943351745605, 'DSJC0125.5.txt': 0.31499505043029785, 'DSJC0125.9.txt': 0.37015247344970703, 'DSJC0250.1.txt': 1.9738132953643799, 'DSJC0250.5.txt': 2.361232042312622, 'DSJC0250.9.txt': 2.8340067863464355, 'DSJC0500.1.txt': 15.582885503768921, 'DSJC0500.5.txt': 18.927740812301636, 'DSJC0500.9.txt': 22.339231729507446, 'DSJC1000.1.txt': 121.87342858314514, 'DSJC1000.5.txt': 151.48826622962952, 'DSJC1000.9.txt': 169.8971655368805}\n", + " AAE-MCP: 执行时间 = {'DSJC0125.1.txt': 0.015633106231689453, 'DSJC0125.5.txt': 0.012542486190795898, 'DSJC0125.9.txt': 0.017057180404663086, 'DSJC0250.1.txt': 0.021806001663208008, 'DSJC0250.5.txt': 0.08765792846679688, 'DSJC0250.9.txt': 0.07237839698791504, 'DSJC0500.1.txt': 0.08004164695739746, 'DSJC0500.5.txt': 0.19251799583435059, 'DSJC0500.9.txt': 0.4154641628265381, 'DSJC1000.1.txt': 0.3821909427642822, 'DSJC1000.5.txt': 0.9123454093933105, 'DSJC1000.9.txt': 1.405397653579712}\n", + " 贪心: 执行时间 = {'DSJC0125.1.txt': 0.003445148468017578, 'DSJC0125.5.txt': 0.0020117759704589844, 'DSJC0125.9.txt': 0.003002643585205078, 'DSJC0250.1.txt': 0.009218692779541016, 'DSJC0250.5.txt': 0.008507490158081055, 'DSJC0250.9.txt': 0.010504007339477539, 'DSJC0500.1.txt': 0.03820180892944336, 'DSJC0500.5.txt': 0.04677724838256836, 'DSJC0500.9.txt': 0.05564761161804199, 'DSJC1000.1.txt': 0.14909648895263672, 'DSJC1000.5.txt': 0.16474175453186035, 'DSJC1000.9.txt': 0.17650794982910156}\n", + " Welsh-Powell: 执行时间 = {'DSJC0125.1.txt': 0.0019974708557128906, 'DSJC0125.5.txt': 0.0029964447021484375, 'DSJC0125.9.txt': 0.0060160160064697266, 'DSJC0250.1.txt': 0.008329153060913086, 'DSJC0250.5.txt': 0.014041900634765625, 'DSJC0250.9.txt': 0.03464531898498535, 'DSJC0500.1.txt': 0.04312324523925781, 'DSJC0500.5.txt': 0.0843958854675293, 'DSJC0500.9.txt': 0.25736522674560547, 'DSJC1000.1.txt': 0.2107689380645752, 'DSJC1000.5.txt': 0.4820866584777832, 'DSJC1000.9.txt': 1.3390073776245117}\n", + " DSATUR: 执行时间 = {'DSJC0125.1.txt': 0.19710993766784668, 'DSJC0125.5.txt': 0.2149949073791504, 'DSJC0125.9.txt': 0.24286270141601562, 'DSJC0250.1.txt': 1.5016090869903564, 'DSJC0250.5.txt': 1.6691169738769531, 'DSJC0250.9.txt': 2.004258632659912, 'DSJC0500.1.txt': 12.705793619155884, 'DSJC0500.5.txt': 13.611878156661987, 'DSJC0500.9.txt': 15.69128155708313, 'DSJC1000.1.txt': 102.37213277816772, 'DSJC1000.5.txt': 113.06330513954163, 'DSJC1000.9.txt': 113.55482411384583}\n", "\n", "EoH-MCP:\n", " DSJC0125.1.txt: 使用颜色数 = 6\n", @@ -380,12 +380,12 @@ " DSJC1000.1.txt: 使用颜色数 = 27\n", " DSJC1000.5.txt: 使用颜色数 = 115\n", " DSJC1000.9.txt: 使用颜色数 = 299\n", - " FunSearch-MCP: 执行时间 = {'DSJC0125.1.txt': 0.010560035705566406, 'DSJC0125.5.txt': 0.0035393238067626953, 'DSJC0125.9.txt': 0.003525257110595703, 'DSJC0250.1.txt': 0.011104822158813477, 'DSJC0250.5.txt': 0.01467132568359375, 'DSJC0250.9.txt': 0.01604008674621582, 'DSJC0500.1.txt': 0.05222797393798828, 'DSJC0500.5.txt': 0.05559563636779785, 'DSJC0500.9.txt': 0.07539129257202148, 'DSJC1000.1.txt': 0.204453706741333, 'DSJC1000.5.txt': 0.2216651439666748, 'DSJC1000.9.txt': 0.23581409454345703}\n", - " EoH-MCP: 执行时间 = {'DSJC0125.1.txt': 0.2657294273376465, 'DSJC0125.5.txt': 0.3157191276550293, 'DSJC0125.9.txt': 0.3592233657836914, 'DSJC0250.1.txt': 1.9986271858215332, 'DSJC0250.5.txt': 2.3853354454040527, 'DSJC0250.9.txt': 2.989116907119751, 'DSJC0500.1.txt': 16.78122878074646, 'DSJC0500.5.txt': 18.628207206726074, 'DSJC0500.9.txt': 23.047802925109863, 'DSJC1000.1.txt': 122.75219559669495, 'DSJC1000.5.txt': 142.00333714485168, 'DSJC1000.9.txt': 164.6181070804596}\n", - " AAE-MCP: 执行时间 = {'DSJC0125.1.txt': 0.0070340633392333984, 'DSJC0125.5.txt': 0.01272726058959961, 'DSJC0125.9.txt': 0.019374370574951172, 'DSJC0250.1.txt': 0.019664525985717773, 'DSJC0250.5.txt': 0.04539036750793457, 'DSJC0250.9.txt': 0.18355083465576172, 'DSJC0500.1.txt': 0.09353065490722656, 'DSJC0500.5.txt': 0.21602964401245117, 'DSJC0500.9.txt': 0.35271167755126953, 'DSJC1000.1.txt': 0.294903039932251, 'DSJC1000.5.txt': 0.8391859531402588, 'DSJC1000.9.txt': 1.3996093273162842}\n", - " 贪心: 执行时间 = {'DSJC0125.1.txt': 0.0020036697387695312, 'DSJC0125.5.txt': 0.0030128955841064453, 'DSJC0125.9.txt': 0.0030007362365722656, 'DSJC0250.1.txt': 0.009136676788330078, 'DSJC0250.5.txt': 0.009547948837280273, 'DSJC0250.9.txt': 0.010493755340576172, 'DSJC0500.1.txt': 0.04389691352844238, 'DSJC0500.5.txt': 0.043775320053100586, 'DSJC0500.9.txt': 0.0454859733581543, 'DSJC1000.1.txt': 0.14690470695495605, 'DSJC1000.5.txt': 0.16630005836486816, 'DSJC1000.9.txt': 0.18558239936828613}\n", - " Welsh-Powell: 执行时间 = {'DSJC0125.1.txt': 0.0020334720611572266, 'DSJC0125.5.txt': 0.003046751022338867, 'DSJC0125.9.txt': 0.006543397903442383, 'DSJC0250.1.txt': 0.008815288543701172, 'DSJC0250.5.txt': 0.015262365341186523, 'DSJC0250.9.txt': 0.03563809394836426, 'DSJC0500.1.txt': 0.04653811454772949, 'DSJC0500.5.txt': 0.07994246482849121, 'DSJC0500.9.txt': 0.19355511665344238, 'DSJC1000.1.txt': 0.1831059455871582, 'DSJC1000.5.txt': 0.48086977005004883, 'DSJC1000.9.txt': 1.4151320457458496}\n", - " DSATUR: 执行时间 = {'DSJC0125.1.txt': 0.20441937446594238, 'DSJC0125.5.txt': 0.22018790245056152, 'DSJC0125.9.txt': 0.23067665100097656, 'DSJC0250.1.txt': 1.5610229969024658, 'DSJC0250.5.txt': 1.7647099494934082, 'DSJC0250.9.txt': 1.9385864734649658, 'DSJC0500.1.txt': 12.594899415969849, 'DSJC0500.5.txt': 14.689803123474121, 'DSJC0500.9.txt': 14.530549764633179, 'DSJC1000.1.txt': 96.6555712223053, 'DSJC1000.5.txt': 106.33447790145874, 'DSJC1000.9.txt': 128.1265606880188}\n", + " FunSearch-MCP: 执行时间 = {'DSJC0125.1.txt': 0.006541728973388672, 'DSJC0125.5.txt': 0.003002643585205078, 'DSJC0125.9.txt': 0.0030024051666259766, 'DSJC0250.1.txt': 0.01353764533996582, 'DSJC0250.5.txt': 0.015435218811035156, 'DSJC0250.9.txt': 0.014726877212524414, 'DSJC0500.1.txt': 0.05006003379821777, 'DSJC0500.5.txt': 0.05561065673828125, 'DSJC0500.9.txt': 0.06039857864379883, 'DSJC1000.1.txt': 0.22848773002624512, 'DSJC1000.5.txt': 0.22493863105773926, 'DSJC1000.9.txt': 0.22939729690551758}\n", + " EoH-MCP: 执行时间 = {'DSJC0125.1.txt': 0.29399943351745605, 'DSJC0125.5.txt': 0.31499505043029785, 'DSJC0125.9.txt': 0.37015247344970703, 'DSJC0250.1.txt': 1.9738132953643799, 'DSJC0250.5.txt': 2.361232042312622, 'DSJC0250.9.txt': 2.8340067863464355, 'DSJC0500.1.txt': 15.582885503768921, 'DSJC0500.5.txt': 18.927740812301636, 'DSJC0500.9.txt': 22.339231729507446, 'DSJC1000.1.txt': 121.87342858314514, 'DSJC1000.5.txt': 151.48826622962952, 'DSJC1000.9.txt': 169.8971655368805}\n", + " AAE-MCP: 执行时间 = {'DSJC0125.1.txt': 0.015633106231689453, 'DSJC0125.5.txt': 0.012542486190795898, 'DSJC0125.9.txt': 0.017057180404663086, 'DSJC0250.1.txt': 0.021806001663208008, 'DSJC0250.5.txt': 0.08765792846679688, 'DSJC0250.9.txt': 0.07237839698791504, 'DSJC0500.1.txt': 0.08004164695739746, 'DSJC0500.5.txt': 0.19251799583435059, 'DSJC0500.9.txt': 0.4154641628265381, 'DSJC1000.1.txt': 0.3821909427642822, 'DSJC1000.5.txt': 0.9123454093933105, 'DSJC1000.9.txt': 1.405397653579712}\n", + " 贪心: 执行时间 = {'DSJC0125.1.txt': 0.003445148468017578, 'DSJC0125.5.txt': 0.0020117759704589844, 'DSJC0125.9.txt': 0.003002643585205078, 'DSJC0250.1.txt': 0.009218692779541016, 'DSJC0250.5.txt': 0.008507490158081055, 'DSJC0250.9.txt': 0.010504007339477539, 'DSJC0500.1.txt': 0.03820180892944336, 'DSJC0500.5.txt': 0.04677724838256836, 'DSJC0500.9.txt': 0.05564761161804199, 'DSJC1000.1.txt': 0.14909648895263672, 'DSJC1000.5.txt': 0.16474175453186035, 'DSJC1000.9.txt': 0.17650794982910156}\n", + " Welsh-Powell: 执行时间 = {'DSJC0125.1.txt': 0.0019974708557128906, 'DSJC0125.5.txt': 0.0029964447021484375, 'DSJC0125.9.txt': 0.0060160160064697266, 'DSJC0250.1.txt': 0.008329153060913086, 'DSJC0250.5.txt': 0.014041900634765625, 'DSJC0250.9.txt': 0.03464531898498535, 'DSJC0500.1.txt': 0.04312324523925781, 'DSJC0500.5.txt': 0.0843958854675293, 'DSJC0500.9.txt': 0.25736522674560547, 'DSJC1000.1.txt': 0.2107689380645752, 'DSJC1000.5.txt': 0.4820866584777832, 'DSJC1000.9.txt': 1.3390073776245117}\n", + " DSATUR: 执行时间 = {'DSJC0125.1.txt': 0.19710993766784668, 'DSJC0125.5.txt': 0.2149949073791504, 'DSJC0125.9.txt': 0.24286270141601562, 'DSJC0250.1.txt': 1.5016090869903564, 'DSJC0250.5.txt': 1.6691169738769531, 'DSJC0250.9.txt': 2.004258632659912, 'DSJC0500.1.txt': 12.705793619155884, 'DSJC0500.5.txt': 13.611878156661987, 'DSJC0500.9.txt': 15.69128155708313, 'DSJC1000.1.txt': 102.37213277816772, 'DSJC1000.5.txt': 113.06330513954163, 'DSJC1000.9.txt': 113.55482411384583}\n", "\n", "AAE-MCP:\n", " DSJC0125.1.txt: 使用颜色数 = 6\n", @@ -400,12 +400,12 @@ " DSJC1000.1.txt: 使用颜色数 = 27\n", " DSJC1000.5.txt: 使用颜色数 = 115\n", " DSJC1000.9.txt: 使用颜色数 = 299\n", - " FunSearch-MCP: 执行时间 = {'DSJC0125.1.txt': 0.010560035705566406, 'DSJC0125.5.txt': 0.0035393238067626953, 'DSJC0125.9.txt': 0.003525257110595703, 'DSJC0250.1.txt': 0.011104822158813477, 'DSJC0250.5.txt': 0.01467132568359375, 'DSJC0250.9.txt': 0.01604008674621582, 'DSJC0500.1.txt': 0.05222797393798828, 'DSJC0500.5.txt': 0.05559563636779785, 'DSJC0500.9.txt': 0.07539129257202148, 'DSJC1000.1.txt': 0.204453706741333, 'DSJC1000.5.txt': 0.2216651439666748, 'DSJC1000.9.txt': 0.23581409454345703}\n", - " EoH-MCP: 执行时间 = {'DSJC0125.1.txt': 0.2657294273376465, 'DSJC0125.5.txt': 0.3157191276550293, 'DSJC0125.9.txt': 0.3592233657836914, 'DSJC0250.1.txt': 1.9986271858215332, 'DSJC0250.5.txt': 2.3853354454040527, 'DSJC0250.9.txt': 2.989116907119751, 'DSJC0500.1.txt': 16.78122878074646, 'DSJC0500.5.txt': 18.628207206726074, 'DSJC0500.9.txt': 23.047802925109863, 'DSJC1000.1.txt': 122.75219559669495, 'DSJC1000.5.txt': 142.00333714485168, 'DSJC1000.9.txt': 164.6181070804596}\n", - " AAE-MCP: 执行时间 = {'DSJC0125.1.txt': 0.0070340633392333984, 'DSJC0125.5.txt': 0.01272726058959961, 'DSJC0125.9.txt': 0.019374370574951172, 'DSJC0250.1.txt': 0.019664525985717773, 'DSJC0250.5.txt': 0.04539036750793457, 'DSJC0250.9.txt': 0.18355083465576172, 'DSJC0500.1.txt': 0.09353065490722656, 'DSJC0500.5.txt': 0.21602964401245117, 'DSJC0500.9.txt': 0.35271167755126953, 'DSJC1000.1.txt': 0.294903039932251, 'DSJC1000.5.txt': 0.8391859531402588, 'DSJC1000.9.txt': 1.3996093273162842}\n", - " 贪心: 执行时间 = {'DSJC0125.1.txt': 0.0020036697387695312, 'DSJC0125.5.txt': 0.0030128955841064453, 'DSJC0125.9.txt': 0.0030007362365722656, 'DSJC0250.1.txt': 0.009136676788330078, 'DSJC0250.5.txt': 0.009547948837280273, 'DSJC0250.9.txt': 0.010493755340576172, 'DSJC0500.1.txt': 0.04389691352844238, 'DSJC0500.5.txt': 0.043775320053100586, 'DSJC0500.9.txt': 0.0454859733581543, 'DSJC1000.1.txt': 0.14690470695495605, 'DSJC1000.5.txt': 0.16630005836486816, 'DSJC1000.9.txt': 0.18558239936828613}\n", - " Welsh-Powell: 执行时间 = {'DSJC0125.1.txt': 0.0020334720611572266, 'DSJC0125.5.txt': 0.003046751022338867, 'DSJC0125.9.txt': 0.006543397903442383, 'DSJC0250.1.txt': 0.008815288543701172, 'DSJC0250.5.txt': 0.015262365341186523, 'DSJC0250.9.txt': 0.03563809394836426, 'DSJC0500.1.txt': 0.04653811454772949, 'DSJC0500.5.txt': 0.07994246482849121, 'DSJC0500.9.txt': 0.19355511665344238, 'DSJC1000.1.txt': 0.1831059455871582, 'DSJC1000.5.txt': 0.48086977005004883, 'DSJC1000.9.txt': 1.4151320457458496}\n", - " DSATUR: 执行时间 = {'DSJC0125.1.txt': 0.20441937446594238, 'DSJC0125.5.txt': 0.22018790245056152, 'DSJC0125.9.txt': 0.23067665100097656, 'DSJC0250.1.txt': 1.5610229969024658, 'DSJC0250.5.txt': 1.7647099494934082, 'DSJC0250.9.txt': 1.9385864734649658, 'DSJC0500.1.txt': 12.594899415969849, 'DSJC0500.5.txt': 14.689803123474121, 'DSJC0500.9.txt': 14.530549764633179, 'DSJC1000.1.txt': 96.6555712223053, 'DSJC1000.5.txt': 106.33447790145874, 'DSJC1000.9.txt': 128.1265606880188}\n", + " FunSearch-MCP: 执行时间 = {'DSJC0125.1.txt': 0.006541728973388672, 'DSJC0125.5.txt': 0.003002643585205078, 'DSJC0125.9.txt': 0.0030024051666259766, 'DSJC0250.1.txt': 0.01353764533996582, 'DSJC0250.5.txt': 0.015435218811035156, 'DSJC0250.9.txt': 0.014726877212524414, 'DSJC0500.1.txt': 0.05006003379821777, 'DSJC0500.5.txt': 0.05561065673828125, 'DSJC0500.9.txt': 0.06039857864379883, 'DSJC1000.1.txt': 0.22848773002624512, 'DSJC1000.5.txt': 0.22493863105773926, 'DSJC1000.9.txt': 0.22939729690551758}\n", + " EoH-MCP: 执行时间 = {'DSJC0125.1.txt': 0.29399943351745605, 'DSJC0125.5.txt': 0.31499505043029785, 'DSJC0125.9.txt': 0.37015247344970703, 'DSJC0250.1.txt': 1.9738132953643799, 'DSJC0250.5.txt': 2.361232042312622, 'DSJC0250.9.txt': 2.8340067863464355, 'DSJC0500.1.txt': 15.582885503768921, 'DSJC0500.5.txt': 18.927740812301636, 'DSJC0500.9.txt': 22.339231729507446, 'DSJC1000.1.txt': 121.87342858314514, 'DSJC1000.5.txt': 151.48826622962952, 'DSJC1000.9.txt': 169.8971655368805}\n", + " AAE-MCP: 执行时间 = {'DSJC0125.1.txt': 0.015633106231689453, 'DSJC0125.5.txt': 0.012542486190795898, 'DSJC0125.9.txt': 0.017057180404663086, 'DSJC0250.1.txt': 0.021806001663208008, 'DSJC0250.5.txt': 0.08765792846679688, 'DSJC0250.9.txt': 0.07237839698791504, 'DSJC0500.1.txt': 0.08004164695739746, 'DSJC0500.5.txt': 0.19251799583435059, 'DSJC0500.9.txt': 0.4154641628265381, 'DSJC1000.1.txt': 0.3821909427642822, 'DSJC1000.5.txt': 0.9123454093933105, 'DSJC1000.9.txt': 1.405397653579712}\n", + " 贪心: 执行时间 = {'DSJC0125.1.txt': 0.003445148468017578, 'DSJC0125.5.txt': 0.0020117759704589844, 'DSJC0125.9.txt': 0.003002643585205078, 'DSJC0250.1.txt': 0.009218692779541016, 'DSJC0250.5.txt': 0.008507490158081055, 'DSJC0250.9.txt': 0.010504007339477539, 'DSJC0500.1.txt': 0.03820180892944336, 'DSJC0500.5.txt': 0.04677724838256836, 'DSJC0500.9.txt': 0.05564761161804199, 'DSJC1000.1.txt': 0.14909648895263672, 'DSJC1000.5.txt': 0.16474175453186035, 'DSJC1000.9.txt': 0.17650794982910156}\n", + " Welsh-Powell: 执行时间 = {'DSJC0125.1.txt': 0.0019974708557128906, 'DSJC0125.5.txt': 0.0029964447021484375, 'DSJC0125.9.txt': 0.0060160160064697266, 'DSJC0250.1.txt': 0.008329153060913086, 'DSJC0250.5.txt': 0.014041900634765625, 'DSJC0250.9.txt': 0.03464531898498535, 'DSJC0500.1.txt': 0.04312324523925781, 'DSJC0500.5.txt': 0.0843958854675293, 'DSJC0500.9.txt': 0.25736522674560547, 'DSJC1000.1.txt': 0.2107689380645752, 'DSJC1000.5.txt': 0.4820866584777832, 'DSJC1000.9.txt': 1.3390073776245117}\n", + " DSATUR: 执行时间 = {'DSJC0125.1.txt': 0.19710993766784668, 'DSJC0125.5.txt': 0.2149949073791504, 'DSJC0125.9.txt': 0.24286270141601562, 'DSJC0250.1.txt': 1.5016090869903564, 'DSJC0250.5.txt': 1.6691169738769531, 'DSJC0250.9.txt': 2.004258632659912, 'DSJC0500.1.txt': 12.705793619155884, 'DSJC0500.5.txt': 13.611878156661987, 'DSJC0500.9.txt': 15.69128155708313, 'DSJC1000.1.txt': 102.37213277816772, 'DSJC1000.5.txt': 113.06330513954163, 'DSJC1000.9.txt': 113.55482411384583}\n", "\n", "贪心:\n", " DSJC0125.1.txt: 使用颜色数 = 8\n", @@ -420,12 +420,12 @@ " DSJC1000.1.txt: 使用颜色数 = 31\n", " DSJC1000.5.txt: 使用颜色数 = 127\n", " DSJC1000.9.txt: 使用颜色数 = 321\n", - " FunSearch-MCP: 执行时间 = {'DSJC0125.1.txt': 0.010560035705566406, 'DSJC0125.5.txt': 0.0035393238067626953, 'DSJC0125.9.txt': 0.003525257110595703, 'DSJC0250.1.txt': 0.011104822158813477, 'DSJC0250.5.txt': 0.01467132568359375, 'DSJC0250.9.txt': 0.01604008674621582, 'DSJC0500.1.txt': 0.05222797393798828, 'DSJC0500.5.txt': 0.05559563636779785, 'DSJC0500.9.txt': 0.07539129257202148, 'DSJC1000.1.txt': 0.204453706741333, 'DSJC1000.5.txt': 0.2216651439666748, 'DSJC1000.9.txt': 0.23581409454345703}\n", - " EoH-MCP: 执行时间 = {'DSJC0125.1.txt': 0.2657294273376465, 'DSJC0125.5.txt': 0.3157191276550293, 'DSJC0125.9.txt': 0.3592233657836914, 'DSJC0250.1.txt': 1.9986271858215332, 'DSJC0250.5.txt': 2.3853354454040527, 'DSJC0250.9.txt': 2.989116907119751, 'DSJC0500.1.txt': 16.78122878074646, 'DSJC0500.5.txt': 18.628207206726074, 'DSJC0500.9.txt': 23.047802925109863, 'DSJC1000.1.txt': 122.75219559669495, 'DSJC1000.5.txt': 142.00333714485168, 'DSJC1000.9.txt': 164.6181070804596}\n", - " AAE-MCP: 执行时间 = {'DSJC0125.1.txt': 0.0070340633392333984, 'DSJC0125.5.txt': 0.01272726058959961, 'DSJC0125.9.txt': 0.019374370574951172, 'DSJC0250.1.txt': 0.019664525985717773, 'DSJC0250.5.txt': 0.04539036750793457, 'DSJC0250.9.txt': 0.18355083465576172, 'DSJC0500.1.txt': 0.09353065490722656, 'DSJC0500.5.txt': 0.21602964401245117, 'DSJC0500.9.txt': 0.35271167755126953, 'DSJC1000.1.txt': 0.294903039932251, 'DSJC1000.5.txt': 0.8391859531402588, 'DSJC1000.9.txt': 1.3996093273162842}\n", - " 贪心: 执行时间 = {'DSJC0125.1.txt': 0.0020036697387695312, 'DSJC0125.5.txt': 0.0030128955841064453, 'DSJC0125.9.txt': 0.0030007362365722656, 'DSJC0250.1.txt': 0.009136676788330078, 'DSJC0250.5.txt': 0.009547948837280273, 'DSJC0250.9.txt': 0.010493755340576172, 'DSJC0500.1.txt': 0.04389691352844238, 'DSJC0500.5.txt': 0.043775320053100586, 'DSJC0500.9.txt': 0.0454859733581543, 'DSJC1000.1.txt': 0.14690470695495605, 'DSJC1000.5.txt': 0.16630005836486816, 'DSJC1000.9.txt': 0.18558239936828613}\n", - " Welsh-Powell: 执行时间 = {'DSJC0125.1.txt': 0.0020334720611572266, 'DSJC0125.5.txt': 0.003046751022338867, 'DSJC0125.9.txt': 0.006543397903442383, 'DSJC0250.1.txt': 0.008815288543701172, 'DSJC0250.5.txt': 0.015262365341186523, 'DSJC0250.9.txt': 0.03563809394836426, 'DSJC0500.1.txt': 0.04653811454772949, 'DSJC0500.5.txt': 0.07994246482849121, 'DSJC0500.9.txt': 0.19355511665344238, 'DSJC1000.1.txt': 0.1831059455871582, 'DSJC1000.5.txt': 0.48086977005004883, 'DSJC1000.9.txt': 1.4151320457458496}\n", - " DSATUR: 执行时间 = {'DSJC0125.1.txt': 0.20441937446594238, 'DSJC0125.5.txt': 0.22018790245056152, 'DSJC0125.9.txt': 0.23067665100097656, 'DSJC0250.1.txt': 1.5610229969024658, 'DSJC0250.5.txt': 1.7647099494934082, 'DSJC0250.9.txt': 1.9385864734649658, 'DSJC0500.1.txt': 12.594899415969849, 'DSJC0500.5.txt': 14.689803123474121, 'DSJC0500.9.txt': 14.530549764633179, 'DSJC1000.1.txt': 96.6555712223053, 'DSJC1000.5.txt': 106.33447790145874, 'DSJC1000.9.txt': 128.1265606880188}\n", + " FunSearch-MCP: 执行时间 = {'DSJC0125.1.txt': 0.006541728973388672, 'DSJC0125.5.txt': 0.003002643585205078, 'DSJC0125.9.txt': 0.0030024051666259766, 'DSJC0250.1.txt': 0.01353764533996582, 'DSJC0250.5.txt': 0.015435218811035156, 'DSJC0250.9.txt': 0.014726877212524414, 'DSJC0500.1.txt': 0.05006003379821777, 'DSJC0500.5.txt': 0.05561065673828125, 'DSJC0500.9.txt': 0.06039857864379883, 'DSJC1000.1.txt': 0.22848773002624512, 'DSJC1000.5.txt': 0.22493863105773926, 'DSJC1000.9.txt': 0.22939729690551758}\n", + " EoH-MCP: 执行时间 = {'DSJC0125.1.txt': 0.29399943351745605, 'DSJC0125.5.txt': 0.31499505043029785, 'DSJC0125.9.txt': 0.37015247344970703, 'DSJC0250.1.txt': 1.9738132953643799, 'DSJC0250.5.txt': 2.361232042312622, 'DSJC0250.9.txt': 2.8340067863464355, 'DSJC0500.1.txt': 15.582885503768921, 'DSJC0500.5.txt': 18.927740812301636, 'DSJC0500.9.txt': 22.339231729507446, 'DSJC1000.1.txt': 121.87342858314514, 'DSJC1000.5.txt': 151.48826622962952, 'DSJC1000.9.txt': 169.8971655368805}\n", + " AAE-MCP: 执行时间 = {'DSJC0125.1.txt': 0.015633106231689453, 'DSJC0125.5.txt': 0.012542486190795898, 'DSJC0125.9.txt': 0.017057180404663086, 'DSJC0250.1.txt': 0.021806001663208008, 'DSJC0250.5.txt': 0.08765792846679688, 'DSJC0250.9.txt': 0.07237839698791504, 'DSJC0500.1.txt': 0.08004164695739746, 'DSJC0500.5.txt': 0.19251799583435059, 'DSJC0500.9.txt': 0.4154641628265381, 'DSJC1000.1.txt': 0.3821909427642822, 'DSJC1000.5.txt': 0.9123454093933105, 'DSJC1000.9.txt': 1.405397653579712}\n", + " 贪心: 执行时间 = {'DSJC0125.1.txt': 0.003445148468017578, 'DSJC0125.5.txt': 0.0020117759704589844, 'DSJC0125.9.txt': 0.003002643585205078, 'DSJC0250.1.txt': 0.009218692779541016, 'DSJC0250.5.txt': 0.008507490158081055, 'DSJC0250.9.txt': 0.010504007339477539, 'DSJC0500.1.txt': 0.03820180892944336, 'DSJC0500.5.txt': 0.04677724838256836, 'DSJC0500.9.txt': 0.05564761161804199, 'DSJC1000.1.txt': 0.14909648895263672, 'DSJC1000.5.txt': 0.16474175453186035, 'DSJC1000.9.txt': 0.17650794982910156}\n", + " Welsh-Powell: 执行时间 = {'DSJC0125.1.txt': 0.0019974708557128906, 'DSJC0125.5.txt': 0.0029964447021484375, 'DSJC0125.9.txt': 0.0060160160064697266, 'DSJC0250.1.txt': 0.008329153060913086, 'DSJC0250.5.txt': 0.014041900634765625, 'DSJC0250.9.txt': 0.03464531898498535, 'DSJC0500.1.txt': 0.04312324523925781, 'DSJC0500.5.txt': 0.0843958854675293, 'DSJC0500.9.txt': 0.25736522674560547, 'DSJC1000.1.txt': 0.2107689380645752, 'DSJC1000.5.txt': 0.4820866584777832, 'DSJC1000.9.txt': 1.3390073776245117}\n", + " DSATUR: 执行时间 = {'DSJC0125.1.txt': 0.19710993766784668, 'DSJC0125.5.txt': 0.2149949073791504, 'DSJC0125.9.txt': 0.24286270141601562, 'DSJC0250.1.txt': 1.5016090869903564, 'DSJC0250.5.txt': 1.6691169738769531, 'DSJC0250.9.txt': 2.004258632659912, 'DSJC0500.1.txt': 12.705793619155884, 'DSJC0500.5.txt': 13.611878156661987, 'DSJC0500.9.txt': 15.69128155708313, 'DSJC1000.1.txt': 102.37213277816772, 'DSJC1000.5.txt': 113.06330513954163, 'DSJC1000.9.txt': 113.55482411384583}\n", "\n", "Welsh-Powell:\n", " DSJC0125.1.txt: 使用颜色数 = 7\n", @@ -440,12 +440,12 @@ " DSJC1000.1.txt: 使用颜色数 = 29\n", " DSJC1000.5.txt: 使用颜色数 = 121\n", " DSJC1000.9.txt: 使用颜色数 = 313\n", - " FunSearch-MCP: 执行时间 = {'DSJC0125.1.txt': 0.010560035705566406, 'DSJC0125.5.txt': 0.0035393238067626953, 'DSJC0125.9.txt': 0.003525257110595703, 'DSJC0250.1.txt': 0.011104822158813477, 'DSJC0250.5.txt': 0.01467132568359375, 'DSJC0250.9.txt': 0.01604008674621582, 'DSJC0500.1.txt': 0.05222797393798828, 'DSJC0500.5.txt': 0.05559563636779785, 'DSJC0500.9.txt': 0.07539129257202148, 'DSJC1000.1.txt': 0.204453706741333, 'DSJC1000.5.txt': 0.2216651439666748, 'DSJC1000.9.txt': 0.23581409454345703}\n", - " EoH-MCP: 执行时间 = {'DSJC0125.1.txt': 0.2657294273376465, 'DSJC0125.5.txt': 0.3157191276550293, 'DSJC0125.9.txt': 0.3592233657836914, 'DSJC0250.1.txt': 1.9986271858215332, 'DSJC0250.5.txt': 2.3853354454040527, 'DSJC0250.9.txt': 2.989116907119751, 'DSJC0500.1.txt': 16.78122878074646, 'DSJC0500.5.txt': 18.628207206726074, 'DSJC0500.9.txt': 23.047802925109863, 'DSJC1000.1.txt': 122.75219559669495, 'DSJC1000.5.txt': 142.00333714485168, 'DSJC1000.9.txt': 164.6181070804596}\n", - " AAE-MCP: 执行时间 = {'DSJC0125.1.txt': 0.0070340633392333984, 'DSJC0125.5.txt': 0.01272726058959961, 'DSJC0125.9.txt': 0.019374370574951172, 'DSJC0250.1.txt': 0.019664525985717773, 'DSJC0250.5.txt': 0.04539036750793457, 'DSJC0250.9.txt': 0.18355083465576172, 'DSJC0500.1.txt': 0.09353065490722656, 'DSJC0500.5.txt': 0.21602964401245117, 'DSJC0500.9.txt': 0.35271167755126953, 'DSJC1000.1.txt': 0.294903039932251, 'DSJC1000.5.txt': 0.8391859531402588, 'DSJC1000.9.txt': 1.3996093273162842}\n", - " 贪心: 执行时间 = {'DSJC0125.1.txt': 0.0020036697387695312, 'DSJC0125.5.txt': 0.0030128955841064453, 'DSJC0125.9.txt': 0.0030007362365722656, 'DSJC0250.1.txt': 0.009136676788330078, 'DSJC0250.5.txt': 0.009547948837280273, 'DSJC0250.9.txt': 0.010493755340576172, 'DSJC0500.1.txt': 0.04389691352844238, 'DSJC0500.5.txt': 0.043775320053100586, 'DSJC0500.9.txt': 0.0454859733581543, 'DSJC1000.1.txt': 0.14690470695495605, 'DSJC1000.5.txt': 0.16630005836486816, 'DSJC1000.9.txt': 0.18558239936828613}\n", - " Welsh-Powell: 执行时间 = {'DSJC0125.1.txt': 0.0020334720611572266, 'DSJC0125.5.txt': 0.003046751022338867, 'DSJC0125.9.txt': 0.006543397903442383, 'DSJC0250.1.txt': 0.008815288543701172, 'DSJC0250.5.txt': 0.015262365341186523, 'DSJC0250.9.txt': 0.03563809394836426, 'DSJC0500.1.txt': 0.04653811454772949, 'DSJC0500.5.txt': 0.07994246482849121, 'DSJC0500.9.txt': 0.19355511665344238, 'DSJC1000.1.txt': 0.1831059455871582, 'DSJC1000.5.txt': 0.48086977005004883, 'DSJC1000.9.txt': 1.4151320457458496}\n", - " DSATUR: 执行时间 = {'DSJC0125.1.txt': 0.20441937446594238, 'DSJC0125.5.txt': 0.22018790245056152, 'DSJC0125.9.txt': 0.23067665100097656, 'DSJC0250.1.txt': 1.5610229969024658, 'DSJC0250.5.txt': 1.7647099494934082, 'DSJC0250.9.txt': 1.9385864734649658, 'DSJC0500.1.txt': 12.594899415969849, 'DSJC0500.5.txt': 14.689803123474121, 'DSJC0500.9.txt': 14.530549764633179, 'DSJC1000.1.txt': 96.6555712223053, 'DSJC1000.5.txt': 106.33447790145874, 'DSJC1000.9.txt': 128.1265606880188}\n", + " FunSearch-MCP: 执行时间 = {'DSJC0125.1.txt': 0.006541728973388672, 'DSJC0125.5.txt': 0.003002643585205078, 'DSJC0125.9.txt': 0.0030024051666259766, 'DSJC0250.1.txt': 0.01353764533996582, 'DSJC0250.5.txt': 0.015435218811035156, 'DSJC0250.9.txt': 0.014726877212524414, 'DSJC0500.1.txt': 0.05006003379821777, 'DSJC0500.5.txt': 0.05561065673828125, 'DSJC0500.9.txt': 0.06039857864379883, 'DSJC1000.1.txt': 0.22848773002624512, 'DSJC1000.5.txt': 0.22493863105773926, 'DSJC1000.9.txt': 0.22939729690551758}\n", + " EoH-MCP: 执行时间 = {'DSJC0125.1.txt': 0.29399943351745605, 'DSJC0125.5.txt': 0.31499505043029785, 'DSJC0125.9.txt': 0.37015247344970703, 'DSJC0250.1.txt': 1.9738132953643799, 'DSJC0250.5.txt': 2.361232042312622, 'DSJC0250.9.txt': 2.8340067863464355, 'DSJC0500.1.txt': 15.582885503768921, 'DSJC0500.5.txt': 18.927740812301636, 'DSJC0500.9.txt': 22.339231729507446, 'DSJC1000.1.txt': 121.87342858314514, 'DSJC1000.5.txt': 151.48826622962952, 'DSJC1000.9.txt': 169.8971655368805}\n", + " AAE-MCP: 执行时间 = {'DSJC0125.1.txt': 0.015633106231689453, 'DSJC0125.5.txt': 0.012542486190795898, 'DSJC0125.9.txt': 0.017057180404663086, 'DSJC0250.1.txt': 0.021806001663208008, 'DSJC0250.5.txt': 0.08765792846679688, 'DSJC0250.9.txt': 0.07237839698791504, 'DSJC0500.1.txt': 0.08004164695739746, 'DSJC0500.5.txt': 0.19251799583435059, 'DSJC0500.9.txt': 0.4154641628265381, 'DSJC1000.1.txt': 0.3821909427642822, 'DSJC1000.5.txt': 0.9123454093933105, 'DSJC1000.9.txt': 1.405397653579712}\n", + " 贪心: 执行时间 = {'DSJC0125.1.txt': 0.003445148468017578, 'DSJC0125.5.txt': 0.0020117759704589844, 'DSJC0125.9.txt': 0.003002643585205078, 'DSJC0250.1.txt': 0.009218692779541016, 'DSJC0250.5.txt': 0.008507490158081055, 'DSJC0250.9.txt': 0.010504007339477539, 'DSJC0500.1.txt': 0.03820180892944336, 'DSJC0500.5.txt': 0.04677724838256836, 'DSJC0500.9.txt': 0.05564761161804199, 'DSJC1000.1.txt': 0.14909648895263672, 'DSJC1000.5.txt': 0.16474175453186035, 'DSJC1000.9.txt': 0.17650794982910156}\n", + " Welsh-Powell: 执行时间 = {'DSJC0125.1.txt': 0.0019974708557128906, 'DSJC0125.5.txt': 0.0029964447021484375, 'DSJC0125.9.txt': 0.0060160160064697266, 'DSJC0250.1.txt': 0.008329153060913086, 'DSJC0250.5.txt': 0.014041900634765625, 'DSJC0250.9.txt': 0.03464531898498535, 'DSJC0500.1.txt': 0.04312324523925781, 'DSJC0500.5.txt': 0.0843958854675293, 'DSJC0500.9.txt': 0.25736522674560547, 'DSJC1000.1.txt': 0.2107689380645752, 'DSJC1000.5.txt': 0.4820866584777832, 'DSJC1000.9.txt': 1.3390073776245117}\n", + " DSATUR: 执行时间 = {'DSJC0125.1.txt': 0.19710993766784668, 'DSJC0125.5.txt': 0.2149949073791504, 'DSJC0125.9.txt': 0.24286270141601562, 'DSJC0250.1.txt': 1.5016090869903564, 'DSJC0250.5.txt': 1.6691169738769531, 'DSJC0250.9.txt': 2.004258632659912, 'DSJC0500.1.txt': 12.705793619155884, 'DSJC0500.5.txt': 13.611878156661987, 'DSJC0500.9.txt': 15.69128155708313, 'DSJC1000.1.txt': 102.37213277816772, 'DSJC1000.5.txt': 113.06330513954163, 'DSJC1000.9.txt': 113.55482411384583}\n", "\n", "DSATUR:\n", " DSJC0125.1.txt: 使用颜色数 = 6\n", @@ -460,12 +460,12 @@ " DSJC1000.1.txt: 使用颜色数 = 27\n", " DSJC1000.5.txt: 使用颜色数 = 115\n", " DSJC1000.9.txt: 使用颜色数 = 299\n", - " FunSearch-MCP: 执行时间 = {'DSJC0125.1.txt': 0.010560035705566406, 'DSJC0125.5.txt': 0.0035393238067626953, 'DSJC0125.9.txt': 0.003525257110595703, 'DSJC0250.1.txt': 0.011104822158813477, 'DSJC0250.5.txt': 0.01467132568359375, 'DSJC0250.9.txt': 0.01604008674621582, 'DSJC0500.1.txt': 0.05222797393798828, 'DSJC0500.5.txt': 0.05559563636779785, 'DSJC0500.9.txt': 0.07539129257202148, 'DSJC1000.1.txt': 0.204453706741333, 'DSJC1000.5.txt': 0.2216651439666748, 'DSJC1000.9.txt': 0.23581409454345703}\n", - " EoH-MCP: 执行时间 = {'DSJC0125.1.txt': 0.2657294273376465, 'DSJC0125.5.txt': 0.3157191276550293, 'DSJC0125.9.txt': 0.3592233657836914, 'DSJC0250.1.txt': 1.9986271858215332, 'DSJC0250.5.txt': 2.3853354454040527, 'DSJC0250.9.txt': 2.989116907119751, 'DSJC0500.1.txt': 16.78122878074646, 'DSJC0500.5.txt': 18.628207206726074, 'DSJC0500.9.txt': 23.047802925109863, 'DSJC1000.1.txt': 122.75219559669495, 'DSJC1000.5.txt': 142.00333714485168, 'DSJC1000.9.txt': 164.6181070804596}\n", - " AAE-MCP: 执行时间 = {'DSJC0125.1.txt': 0.0070340633392333984, 'DSJC0125.5.txt': 0.01272726058959961, 'DSJC0125.9.txt': 0.019374370574951172, 'DSJC0250.1.txt': 0.019664525985717773, 'DSJC0250.5.txt': 0.04539036750793457, 'DSJC0250.9.txt': 0.18355083465576172, 'DSJC0500.1.txt': 0.09353065490722656, 'DSJC0500.5.txt': 0.21602964401245117, 'DSJC0500.9.txt': 0.35271167755126953, 'DSJC1000.1.txt': 0.294903039932251, 'DSJC1000.5.txt': 0.8391859531402588, 'DSJC1000.9.txt': 1.3996093273162842}\n", - " 贪心: 执行时间 = {'DSJC0125.1.txt': 0.0020036697387695312, 'DSJC0125.5.txt': 0.0030128955841064453, 'DSJC0125.9.txt': 0.0030007362365722656, 'DSJC0250.1.txt': 0.009136676788330078, 'DSJC0250.5.txt': 0.009547948837280273, 'DSJC0250.9.txt': 0.010493755340576172, 'DSJC0500.1.txt': 0.04389691352844238, 'DSJC0500.5.txt': 0.043775320053100586, 'DSJC0500.9.txt': 0.0454859733581543, 'DSJC1000.1.txt': 0.14690470695495605, 'DSJC1000.5.txt': 0.16630005836486816, 'DSJC1000.9.txt': 0.18558239936828613}\n", - " Welsh-Powell: 执行时间 = {'DSJC0125.1.txt': 0.0020334720611572266, 'DSJC0125.5.txt': 0.003046751022338867, 'DSJC0125.9.txt': 0.006543397903442383, 'DSJC0250.1.txt': 0.008815288543701172, 'DSJC0250.5.txt': 0.015262365341186523, 'DSJC0250.9.txt': 0.03563809394836426, 'DSJC0500.1.txt': 0.04653811454772949, 'DSJC0500.5.txt': 0.07994246482849121, 'DSJC0500.9.txt': 0.19355511665344238, 'DSJC1000.1.txt': 0.1831059455871582, 'DSJC1000.5.txt': 0.48086977005004883, 'DSJC1000.9.txt': 1.4151320457458496}\n", - " DSATUR: 执行时间 = {'DSJC0125.1.txt': 0.20441937446594238, 'DSJC0125.5.txt': 0.22018790245056152, 'DSJC0125.9.txt': 0.23067665100097656, 'DSJC0250.1.txt': 1.5610229969024658, 'DSJC0250.5.txt': 1.7647099494934082, 'DSJC0250.9.txt': 1.9385864734649658, 'DSJC0500.1.txt': 12.594899415969849, 'DSJC0500.5.txt': 14.689803123474121, 'DSJC0500.9.txt': 14.530549764633179, 'DSJC1000.1.txt': 96.6555712223053, 'DSJC1000.5.txt': 106.33447790145874, 'DSJC1000.9.txt': 128.1265606880188}\n" + " FunSearch-MCP: 执行时间 = {'DSJC0125.1.txt': 0.006541728973388672, 'DSJC0125.5.txt': 0.003002643585205078, 'DSJC0125.9.txt': 0.0030024051666259766, 'DSJC0250.1.txt': 0.01353764533996582, 'DSJC0250.5.txt': 0.015435218811035156, 'DSJC0250.9.txt': 0.014726877212524414, 'DSJC0500.1.txt': 0.05006003379821777, 'DSJC0500.5.txt': 0.05561065673828125, 'DSJC0500.9.txt': 0.06039857864379883, 'DSJC1000.1.txt': 0.22848773002624512, 'DSJC1000.5.txt': 0.22493863105773926, 'DSJC1000.9.txt': 0.22939729690551758}\n", + " EoH-MCP: 执行时间 = {'DSJC0125.1.txt': 0.29399943351745605, 'DSJC0125.5.txt': 0.31499505043029785, 'DSJC0125.9.txt': 0.37015247344970703, 'DSJC0250.1.txt': 1.9738132953643799, 'DSJC0250.5.txt': 2.361232042312622, 'DSJC0250.9.txt': 2.8340067863464355, 'DSJC0500.1.txt': 15.582885503768921, 'DSJC0500.5.txt': 18.927740812301636, 'DSJC0500.9.txt': 22.339231729507446, 'DSJC1000.1.txt': 121.87342858314514, 'DSJC1000.5.txt': 151.48826622962952, 'DSJC1000.9.txt': 169.8971655368805}\n", + " AAE-MCP: 执行时间 = {'DSJC0125.1.txt': 0.015633106231689453, 'DSJC0125.5.txt': 0.012542486190795898, 'DSJC0125.9.txt': 0.017057180404663086, 'DSJC0250.1.txt': 0.021806001663208008, 'DSJC0250.5.txt': 0.08765792846679688, 'DSJC0250.9.txt': 0.07237839698791504, 'DSJC0500.1.txt': 0.08004164695739746, 'DSJC0500.5.txt': 0.19251799583435059, 'DSJC0500.9.txt': 0.4154641628265381, 'DSJC1000.1.txt': 0.3821909427642822, 'DSJC1000.5.txt': 0.9123454093933105, 'DSJC1000.9.txt': 1.405397653579712}\n", + " 贪心: 执行时间 = {'DSJC0125.1.txt': 0.003445148468017578, 'DSJC0125.5.txt': 0.0020117759704589844, 'DSJC0125.9.txt': 0.003002643585205078, 'DSJC0250.1.txt': 0.009218692779541016, 'DSJC0250.5.txt': 0.008507490158081055, 'DSJC0250.9.txt': 0.010504007339477539, 'DSJC0500.1.txt': 0.03820180892944336, 'DSJC0500.5.txt': 0.04677724838256836, 'DSJC0500.9.txt': 0.05564761161804199, 'DSJC1000.1.txt': 0.14909648895263672, 'DSJC1000.5.txt': 0.16474175453186035, 'DSJC1000.9.txt': 0.17650794982910156}\n", + " Welsh-Powell: 执行时间 = {'DSJC0125.1.txt': 0.0019974708557128906, 'DSJC0125.5.txt': 0.0029964447021484375, 'DSJC0125.9.txt': 0.0060160160064697266, 'DSJC0250.1.txt': 0.008329153060913086, 'DSJC0250.5.txt': 0.014041900634765625, 'DSJC0250.9.txt': 0.03464531898498535, 'DSJC0500.1.txt': 0.04312324523925781, 'DSJC0500.5.txt': 0.0843958854675293, 'DSJC0500.9.txt': 0.25736522674560547, 'DSJC1000.1.txt': 0.2107689380645752, 'DSJC1000.5.txt': 0.4820866584777832, 'DSJC1000.9.txt': 1.3390073776245117}\n", + " DSATUR: 执行时间 = {'DSJC0125.1.txt': 0.19710993766784668, 'DSJC0125.5.txt': 0.2149949073791504, 'DSJC0125.9.txt': 0.24286270141601562, 'DSJC0250.1.txt': 1.5016090869903564, 'DSJC0250.5.txt': 1.6691169738769531, 'DSJC0250.9.txt': 2.004258632659912, 'DSJC0500.1.txt': 12.705793619155884, 'DSJC0500.5.txt': 13.611878156661987, 'DSJC0500.9.txt': 15.69128155708313, 'DSJC1000.1.txt': 102.37213277816772, 'DSJC1000.5.txt': 113.06330513954163, 'DSJC1000.9.txt': 113.55482411384583}\n" ] } ], @@ -518,7 +518,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -543,18 +543,18 @@ "\n", "各算法在不同实例上的运行时间(秒):\n", " FunSearch-MCP EoH-MCP AAE-MCP 贪心 Welsh-Powell DSATUR\n", - "MCP0125.1 0.0106 0.2657 0.0070 0.0020 0.0020 0.2044\n", - "MCP0125.5 0.0035 0.3157 0.0127 0.0030 0.0030 0.2202\n", - "MCP0125.9 0.0035 0.3592 0.0194 0.0030 0.0065 0.2307\n", - "MCP0250.1 0.0111 1.9986 0.0197 0.0091 0.0088 1.5610\n", - "MCP0250.5 0.0147 2.3853 0.0454 0.0095 0.0153 1.7647\n", - "MCP0250.9 0.0160 2.9891 0.1836 0.0105 0.0356 1.9386\n", - "MCP0500.1 0.0522 16.7812 0.0935 0.0439 0.0465 12.5949\n", - "MCP0500.5 0.0556 18.6282 0.2160 0.0438 0.0799 14.6898\n", - "MCP0500.9 0.0754 23.0478 0.3527 0.0455 0.1936 14.5305\n", - "MCP1000.1 0.2045 122.7522 0.2949 0.1469 0.1831 96.6556\n", - "MCP1000.5 0.2217 142.0033 0.8392 0.1663 0.4809 106.3345\n", - "MCP1000.9 0.2358 164.6181 1.3996 0.1856 1.4151 128.1266\n" + "MCP0125.1 0.0065 0.2940 0.0156 0.0034 0.0020 0.1971\n", + "MCP0125.5 0.0030 0.3150 0.0125 0.0020 0.0030 0.2150\n", + "MCP0125.9 0.0030 0.3702 0.0171 0.0030 0.0060 0.2429\n", + "MCP0250.1 0.0135 1.9738 0.0218 0.0092 0.0083 1.5016\n", + "MCP0250.5 0.0154 2.3612 0.0877 0.0085 0.0140 1.6691\n", + "MCP0250.9 0.0147 2.8340 0.0724 0.0105 0.0346 2.0043\n", + "MCP0500.1 0.0501 15.5829 0.0800 0.0382 0.0431 12.7058\n", + "MCP0500.5 0.0556 18.9277 0.1925 0.0468 0.0844 13.6119\n", + "MCP0500.9 0.0604 22.3392 0.4155 0.0556 0.2574 15.6913\n", + "MCP1000.1 0.2285 121.8734 0.3822 0.1491 0.2108 102.3721\n", + "MCP1000.5 0.2249 151.4883 0.9123 0.1647 0.4821 113.0633\n", + "MCP1000.9 0.2294 169.8972 1.4054 0.1765 1.3390 113.5548\n" ] } ], @@ -594,12 +594,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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8VDKuBmlqrqYprDQPt6YFU5fyKGyaBoQSQTcAAACAtKEAW9OJaQEyCcrLAQAAAAAIE4JuAAAAAADChKAbAAAAAIAwIegGAAAAACBMCLoBAAAAAAgTgm4AAAAAAMKEoBsAAAAAgDBhnm4AAAAAIffbb2YHDiT/fvnzm517bjiOCIgMMt0AAAAAQh5wn3eeWe3ayV90P90/LZUvX95iYmKCLsOGDUvy43Tu3NndZ/v27bHW9+/f361fs2ZNkh/Lu89ZZ50Va/0NN9zg1jdu3DjW+unTp9tll11muXPnttKlS1vfvn3txIkT8Y7NW3LkyGHnn3++DR8+3Hw+X5KPC8lHphsAAABASKUkwx3K+6dEhQoV7Nlnn423vk6dOhZJe/futU2bNrkLA7Jy5cp4+0yaNMluvPFGq1Gjhr366qu2fv16GzRokG3evNk++OCDWPsOHTrUihQpYjt37rSpU6faY489Zjt27LDnn38+zV5TZkPQDQAAACDTO/vss619+/YWjX788UcXdO/Zs8e2bNkSa9uRI0fswQcftJIlS9qCBQssv+rzzSxr1qw2ZMgQ69mzpwvGAzPlXgCvgLt169b2yiuvWI8ePaxgwYJp/MoyB8rLAQAAACCKedntYFnub775xpWz33333f6AW66//np3O2fOnEQfu3nz5nbs2DFbtWpVyI8b/yHoBgAAAIAz0LhnjX+uUqWK5cyZ084777wUjYdWZlpBcmLL8ePH/fs3adLEZbpFt3ru+vXr+7cvWbLE3Wo8d6BKlSrZLbfcYiVKlEj0eDS+WwKfE6FFeTkAAACATO/kyZNunHPcgLRw4cLu6wceeMBGjRplbdq0sUcffdRmzZrlyrPXrl1rb775Zqz77d6927Jl+/+h1uHDh/1fT5gwwe68885Ej2Xu3Ln+r+vWrWsfffSRP9Nds2ZNy549u3/7H3/84W7VPC2Qys0//vjjM75uL8OtiwkIjwwVdKssQs0CNBahWLFikT4cAAAAAOmEgs+iRYvGWqdx0QrGly5d6gJuBcvvv/++26Zx1Pr+rbfecp3BL730Uv/9qlevnmg598yZMxM9FgXW8+bN8wfdL730kgvklelu1KhRrC7oB/6v65y6lieFxoXny5fPPZ4aqY0ZM8auu+46K1OmTJLujwwYdKvN/TPPPOOuCG3bts01OLjqqqtcNz5dvZFTp07Zk08+aa+//rr/KlKDBg3cCVSxYsUIvwIAAAAA0U7l4iNHjoy1LkuW/0bjTp482d0+8sgjsbbre8Uc2h4YdH/yySexpvoaN26cjR8/3n2tcu8zlXwHUtAt3377rcuqd+vWLVbQrQsDoosDgVQu/ssvv7hkZODzXXzxxbH2a9WqlXsNyMRBt07ksWPHuhKOypUru/b3Kt/Q1SadbJpfTq39NX+euu5pvrrly5e7/W+66SZbsWJFpF8CAAAAgCinJmRK7gXzzz//BC3h9r73tnuUACxevLj/+0WLFqX4uMqVK+cy8Jr6S4G1gvDAIFnTf4lK43XhwLNx40a76KKLXAJzwIAB/vV6HAXiKlFXgpIMdyYPug8dOmTvvvuu9erVK9a8cRpv0KVLF3fyXnjhhTZ48GA3eXzXrl3ddq1TK32t023ZsmUj+CoAAAAApGde2fmff/7pD3Llr7/+crfnnHNOWJ9fgbbGZ+fNm9eqVq0aa5sC6//9739uvHdggzVvarFSpUrF2v/yyy/3TxmGtBHV3csPHjzoruZozEEgdewTNSfQFRqdgA8//HDQfbxyCwAAAABICc1lLXHLzzW81SvRDievxFyl4V7Ju0fVvYqL3n777VgdyL0maqoERmRFdaZbZQ86sVQ6rqs2OtlUUq4MtrLXl1xyieXKlctN8B5o165d9s4777gGBHGv7AAAAABAXGosptLruNRTqmXLlm4ebFXhar+mTZva7NmzbeLEiW59vXr10iTorlOnTrxtioteeOEF69mzpyuPv+OOO1xFsF5Lx44dY5WcIzKiOuiWKVOmuA5/6tLnUTA9ffp0F3AHUqMAXW1S44JChQrZp59+esZu51o8+/fvD8MrAAAAABDtNAa6Q4cO8dYr9lDQrUxytWrV7I033rCvv/7ajbUeMmSIPf7442E/tsSCbunRo4cbm61ht+qJpXJ4jeV++umnw35sOLMYX3Jnc09jzz33nDthNNm7Wu+vXr3aNUrTFRy16w+co27x4sWum9+yZcvcWAdd3alVq1aCj62MeWBTAc++ffusQIECYXtNAAAAQDQ5evSoCzorVKgQL7GVEj/8YFa7dsrvr17IcZpsA1H3+6CkraarPlP8GNVjupW57tevnwuM1SJf5Rzff/+9vfjiiy6g1lWmuE0B1NVcc+xpvjplyAMnoo+rT58+7g3ylq1bt6bBqwIAAAAytvz5I3t/IJpEdaZbY7lVrhH3yoGaq+n7Zs2a2RdffBH0vqNHj7a77rrLlX54jQ/OJKlXKgAAAICMJNSZbvntN7MDB1IWcJ97bkgOAYiKTHdUj+n2rgcEduHzvlfgreXHH3+0Dz/80E0ppjm747b1P3HiRBofNQAAAAACZyAdlJdrsnYJnPxdgfjAgQNdMF27dm07ffq0vfzyyzZ06NBY91UnQbXTD5yrDgAAAACAtBTVmW6Nya5SpYrrxjdu3DgrWbKkrV+/3qX6S5Qo4UrP1aW8ffv2bny2xoCrXf+SJUts/Pjxbp2mHQMAAAAAIBKiOuhW/fz8+fOtb9++Nm3aNPv1118tX758boz2oEGDXMAt7733nutWroz4Z599Zueee65bd+edd0b6JQAAAAAAMrGobqSW1mikBgAAgMwoHI3UgPTqaGaaMgwAAAAAgPSMoBsAAAAAgDAh6AaCYKo5JBfnDAAAAIIh6Abi+P333+388893XfCBpNixY4ddfPHF9sUXX0T6UAAASBeOnDgS6UMA0gxBNyIq2vr4qQlCixYtbM+ePZY/f/5IH06mdfLkSUtP1GBDy2233WY//PBDpA8HAICotmDzAiv8UmFbuHmhRZutW7daTEyMtWrVKsF9NDWx9nn55ZfjbdNsStoWbPFmXkqq/v37u/udddZZsdbfcMMNbn3jxo1jrZ8+fbpddtllljt3bitdurSbASqwEq9z586xjidHjhwu0TR8+PCo+0ye0RB0I2J++fwXe7noy+42Wtx11122adMmmzRpkl144YWxtp06dcpeffVV98cpZ86cVqlSJfd9KP9IpfY5hg4d6v6I1qhRI6LlzgsXLrRrrrnG/SehYLRixYp2xx132G+//Zbg6x4yZIh73dmzZ3dL2bJl7ZlnnnHdI0Phww8/TPA/QS2vv/56il+HLtBMmTLFihQpYu3atbMDBw6E5JgBAMiI+s7pa0dOHnG30Ub/n8vcuXPtyJEjie7j3QbTrVs3F5wHLm+//XaKjmnv3r3u86ln5cqV8fbRZ9eWLVvasWPH3GfHm266yU2xHGwKZX1e1PG89NJL7vPWY489Zk8//XSKjg1JpCnD8J99+/YpsnG3CK+fP/vZNyDLAF9/6+9u9X2kffzxx+7nP2jQoHjbTp8+7evQoYPbrqVSpUq+AgUKuK+7du0akudP7XP89ttvvty5c/uyZs3qW7ZsmS9Svv76a1+2bNnccRcvXtxXsWJFX/bs2d33efPm9S1evDje677hhhvc9piYGF/16tV9tWvXdl9rXePGjX0nT55M9XE98cQT7vHOO+88X82aNeMt+vmn5nXIkiVLfFmyZPE99NBDqT5eAADS0pEjR3y//PKLuw2nuRvn+qy/+Rd9H01at27t/yw2efLkeNtPnDjhK1SokNuuzwl79+6NtX306NFumz5HpFa/fv38xzJp0iS3bvfu3f51jRo1cusOHz7sPquULl3at3//fv/9u3Xr5vZbtWqV+75Tp07u+40bN8Z6nlatWvly5swZ77VkZkeS+PuQ1PiRTDfSnDLbn938mT97q1t9H8mM9/Hjx613795Wq1Yt6969e7ztb731lrsiqPn35s2b58Z9b9u2zZo2beoypPPnz0/1MaTmOfQedunSxV2R1ZXVOnXqWCQoK3333XdblixZbNasWfb333/bhg0bbPv27da+fXs7dOiQ3X///bHuM3LkSDcWWln977//3tasWWPLly+3RYsWWd68ed17MW7cuFQfmx5T9D7++OOP8ZZbbrklVa9D6tWrZw899JCNGjXK1q9fn+pjBgAgo3l67tOWNSar+1q3z8x9xqKF/v+fM2eOderUyVUcBstkq+ePMs/33XefGw6nku604GW3g2W5v/nmG/cZRZ9dAodHXn/99e5WrykxzZs3dxnyVatWhfy48R+CbkQu4PYqpn2RD7w/+eQTV7bzwgsvWNas//1H4NEfoQEDBvjLcRo1auS+VkCoMiGVJmvMTWqk9jkUlC9YsMDOO+88/+NEggLlf/75x5U06WKB5+yzz7Z33nnHlY3/9NNPtnPnTrdeJfADBw50pdszZ86MdbGgfv367j80mTx5cqqO6/Tp0+4/qaJFi1rx4sVD/joCqSReY6RU2gUAAP6/eZvm2aIti+yU75T7XrcLtyx066OBSsoPHz7shpZdeumlQYNub12vXr0SDMzPREkSBcmJLUoIeZo0aeISBKJbPa8+J3m85r8azx1ICQ0lFUqUKJHo8ehzpgQ+J0KLoBuRDbgtOgJvZVI1Xld/ZONSZlQBWOHCha1Dhw6xtuk+CsoUpKkJW0ql5jk2btxoffr0cX8w3333XRfARsq///7rbosVKxZvm44rT5487mtvvPn+/fvt4YcfdgFqhQoV4t3HW6cryqmxbt06N85aHcbD8ToCaVz3zTffbB999BHTiAEAkECW2xNN2e6pU6e6WyU/tGzZssVV4MXdR0kOfUa55JJLXJZZF/fj0mccXZwPXLwx4hMmTHCBcGJL4Cw6devWjZXprlmzpksAeP744w93q+ZpgUqWLGkff/xxrGq+YLwMd5UqVZL9niFpCLoR+YA7woG3ssxqmHXdddf5r/QF8rpR6ypj4B84j66Eqrzou+++S/ExpPQ5vLJylTs/+OCD1qBBA4ukMmXKuFuVZMdt/vbtt9+6iwbKNntXXHWRQZ01H3jggaCP9+uvv7rbM12hTWppucq/w/E64lI5l/6zXbp0aaqOGwCAjJrl9kRTtltZawXU+v/d6wwemMn2gnBvm241beiyZcviPZYar+qzQuCiprFeObcq/BJbFFgHBt3qqr57926X6db3gbwGrupanhSapUcXATQUbtiwYa7juj4He59/EHrZwvCYQPIDbo/b5b/A+8ZPbrRq7aqF/fiUBdUYnoQCsr/++svdxu1mHli6I4FdJZMrpc/x/vvvu1IoWb16tdtPwbk3Nj2tg3CVOl1wwQXuWHQxQJ0w9Z+Mgs97773XX46V1Ishn3/+uX9qjFAE3StWrHDZbnUfz5Ytm3uf7rnnHrv99ttD+jq8c0lXjiN9IQQAgGjKcscNugOz3QvuXGCRogv9qh5UsKyA9Nxzz3WfFRR0e//newG4Pj9oH80W461XgiTQ888/H29d5cqV3a2XzU4qL8jWhf+1a9e6/j2BGXhvaGTcKVdVLv7LL7+4yr3A54tb+afp0RR4I3zIdCN6Au4IZby9QNYLbOPS2B6vRCcYb+5EXYFMqZQ8hzKpKisXlTurzEnB4xVXXOGukKosavDgwZaW9Ed/2rRp7hhGjx7tSuPV0OPqq692JdsqI9d/FEmhOSNVcq8rzm3atEnVcXkVAl9//bV/Pu2GDRu60i3956qp4kL5OvSfm8bjp+ZCDAAAGT3LHU3Zbi+g/t///ucutGsqLQWx+qygzHDgPqou1D7q/RK4PpAy1VdddVWspXz58ik6tnLlyrnn++CDD9wxxc10a2ibxO01o4sIF110kWvWG0iPo8+Kalar7L1658SdCxyhRdCN6Aq4IxB4eyU56hoe9FD+r7zYG8cbl1fKc/DgwRQfQ0qeY8SIEa6kScetTO7YsWPtueeec/9Z6Kqm/rNQR3Y1WEtLKntS9UCwzLWCX/1xPxOVO3kN4fQ64za3Sw5d5dXVYL1PqgrQf55qTvfll1+69aVKlXKBddzuo6l9HQULFnQXRgAAyOyCjeWOK9JjuzVWW583FIB6Jd6akeTUqVPuM4KqIvU5onr16rHKwHWBXuOsNdNJOCnQ1vhsXdSvWrVqrG0KrIN1Nvc+q+izTqDLL7/cXQRQgoaS8rRB0I3oC7jTOPD2xnHHHbvr8RqTJTRORqVH4jXHSImUPIeacMgjjzwS74+vrqT269fPff3ee+9ZWpZm6aqvLgaoS7iOTd3J27Vr55qM6Jj1n1NijdEUJCtjr+y/xno3a9YsVcekTuIKflXC743B8qh0zBtfpUA8lK9D+2nKMQAAMrMzZbmjIdutzwlqWKtycJVae5lpbxYVZbK9zubq2xKYvVbzVH2G9JqwhYuX3VZpeNzPF/rMos+K+iwT2IFcQbrE/fyDtMeYboSc/vBMvm+y+U77QvBg///xqratGrTRWWopIyle6VBchQoVcrcKwILxsplxx9EkR0qeQ/NGi/7YJ/bHOS3nXFQzDl0Y0Bgn/eekKbY8KmFq3bq1K5FXZvnxxx8P+hiPPfaYf+x1qKbd0n9O+fLlC7pNJeMS2CU0ta9D56wCcu/cAgAgs0psLHe0jO2eMWOGm3GkRYsWsdar144ywRpy5s1/HXefli1buovzCszVBybw80Kwi/MKgON2GU8K73Nd4PSqHlU3atrbnj17ugsBGjqniwgqI+/YsaMbqofIIg2DkFNg3HpUa4vJEmOW2hhZD5Hl/x4vDAF34FhulTQH45XdJDQ+V+OOReU+KZWS5/Dej4TGonvZ8bScc9EbO62scGCg6v0n4zUh8+aajEtTnr355puuq/lnn32WJtOfKRMedxxUal/Hn3/+6crQEvrZAACQGSQ1yx3pbLc3JjvY1LEKsvUZQeOidTE92FzYCmpVah74mUuVdJoGNu7iNXcNZdAtPXr0cNOVatikLgIoafDMM8+kacUjEkbQjbBQ13F1H3eBYUpjZd01JibsXcxVYqzxvosXLw66vXbt2rGm9YrLmxYqJVctU/Mc3hzWu3btCnofb85GXf1MK16Jvt7TYLwy+GDzWc6ePds1JtHFgk8//TTovN0p8dNPP7mGc7oNRuPfA5uQpPZ1iK4uB/5cAQDIrFnumGR+ENT+aT22W5Vr+r8/WEA7atQot03/5ytz7SU1AqkHjIJdXcjv3Lmz2z+hJTkzsvTv39//meScc85xX3szriiTriXQrbfe6sZ1q/+MEgDqjxN4vOpQrsdIaUM3pBxBN6Iz8E6jgFvUNKNp06b21VdfBS0RV+Ckjo5z5syJ1yRDzTV0ZdPbL6VS8hw6Zvnkk0+CPqamE/Pm/k4rXsY+oWYi6qIZLJjVFBjqUK7Srtdffz2kx6ys9aBBg2KN2Y6bXY873imlr8MzceJE95+j19gEAIDM5tDxQ/bdn9+5qWCTQ/sv/XOpHT7x38wuQEZA0I3oC7zTMOD23HnnnS7AUoY1WFDevn17FxBq/G5gw7WRI0e6Bl0qQVZjrZRKyXN07drVXVFV6dC4ceP899GV1ocfftjNca1xzHfffbellSuvvDJWk7dAysirw7qOWWONPCrrV+mWjvuJJ57wNy0JFTUXUSWDrlQvXLgw1jYF4gq69f4HTgGWktfh2bx5s+uMrivdNFIDAGRWeXPktb+e+Mt+6/pbshfdL0/24DO6AOlRjC+hls2ZkJpVaazGvn37Epw+CmHuZh6BgFt0XLVq1XJTcmkaqbhdxBWQa75FNTpTwwyV7yxbtszeeOMNl4lWJrVXr16x7qPHEwV1CY2/Se1zKNhWUK1gXQG25ofWuHDtr4BP46ID57jWeCQtOh4vwxtKGsukbuPz5893c1+r4ZjeS3UDVwZbJe9Dhw51zdI8ep1ecKuGJQkFqoHjp5P7OjTGSRc1dG5pegyVkq9evdrWrl3rxo0rIFejkdS8Do/mAFfVxO+//24lSpRI9nsIAEAkqBeJKrk0vCsteqoAGeH3Icnxo4Ju/Gffvn0KB90tQu/nz372DcgywNc/pr+vvwVZYvq77dovEubOneuLiYnxPfzww0G3L1682Fe6dGl3jgQut956q+/UqVPx9ve263GTKrnPIatWrfLdc889vsqVK/ty587ty5cvn+/KK6/0zZo1K96+/fr1c4/XqFEjX7gcP37c99prr/nq16/vK1iwoC9btmy+s88+29esWTPf5MmT4+2v4477eoMtqX0d33//va9169a+woULu/fp/PPP9z3yyCO+33//PSSvQz777DN3XM8991ySjwsAgGhw5MgR3y+//OJugczuSBJ/H5IaP5LpDkCmO4IZ7whluONSabemi1JDDZUHx6VpxV577TXX2Eznyi233BIrkxwK4X4OZdE1D6U3jzdCQxUS9evXd03W1JQvWKMVAACiFZluIHyZboLuAATdEQq8oyTgFjVS07zN6j49adIkV1ackWzZssU1Y9Mc35zjoaM5uy+55BL3tYYEpKaTPQAAkUDQDYQv6KbLDyLbXM2iJ+AWZSfVBKtdu3YZcronjRfXfI0E3KGlsdtqBqcu8wTcAAAACESmOwCZ7rTPeE++b7K1HtU6KgJuAACAzIpMNxC+TDeDDhExCrSrtq3qz3gDAAAAQEZDeTkiioAbAAAAQEZG0A0AAAAgTTHCFZkJQTcAAACANO3r83LRl91tetC3b183reyKFSsifShIpwi6AQAAAKTp1LFHdh1xt9EeeH/xxRf2wgsv2Pjx413grQZbKbV9+3a78847bc+ePSE9RkQ/gm4AAAAAaRZwe6Xluo3mwHvJkiV2xx13WOPGje23335zAXerVq1cp+qU+OGHH2zMmDH22GOPhfxYEd0IugEAAACkXcDtDef2RW/g/cknn9hVV11lF110kQ0cONCWL19uU6dOtZ07d7p1s2bNSvZjtmzZ0gXx48aNsy+//DIsx43oRNANAAAAIG0DbovOwFtzM9988812yy23WNOmTW3GjBnWr18/a9eunZ06dcoWLlzogu6rr77aGjRoYO+8847t3bs3yY8/ePBgy507t9199932999/h/W1IHoQdAMAAABI+4A7SgJvBdOLFy+2zp07W5UqVWzixIku0P7ss88sT548bky3Ssz//fdfK1CggH3++ef29ddfu/vee++9VrhwYatYsaJ9/PHHZ3yuUqVKufLy1q1bW9asWdPg1SEaxPjo1++3f/9+K1iwoBunoV8oAAAAIDPQeGVleStUqGC5cuVKu4A7UIxZTEyM3fjJjVatXTVLyxjg8ssvt02bNlmnTp2sbNmy1qtXr0TvU7JkSTdGW9lqBecK2qdPn245cuSIt++BAwfsvffes0qVKtm1114b9PH0Hum1I339PiQ1fswWpuMEAAAAkEklO+AWt+t/Ge+0DLwVLClzrYx1/vz5bcuWLXb++edbzpw542Wj3333XZfpnjBhghUrVswttWrVirXPyZMn7fbbb7f69eu7rPbBgwdd5/Nbb701aNCtYF9jvbXvTTfdFPbXi7RH0A0AAAAgsgF3hAPv8uXL+79WpltLXN9//72bQkzzdl9xxRUJPta6devs008/tePHj7tAWsG7BMuCS6FChWzbtm123333Wb169ax06dIheU2IHozpBgAAABD5gDsKxnj/+eefrsw72HLppZfasWPHbMCAAf51Dz/8cLzHWLVqlbv1AnMv2E6ofFxB99ixY11DNpW3M/o34yHoBgAAAJBqChYn3zfZfKdTEXD7H8zc47jHS8Mg1MtKP/HEE/brr78muiSUvfaCbgXpSdWwYUPXlG3OnDk2ZMiQkL0eRAeCbgAAAACppkxu61GtLSaLOqKl9sHMPY57vDRsMOY1zSpatKgb161FY7JV+r1+/Xr/Oi2SPXv2eI8xb948y5Ytm9WpUydZz60u6WeddZb179/fdUpHxkHQDQAAACAkNAZbY7FdoJzSWDlCXczl9OnT8dZ99dVXtmDBAitXrly8bSdOnIj1/Y4dO2zZsmVuLm/Nx50cZ599tr355ptu7Pg555yTgqNHtCLoBgAAABAdgXcEA245cuRIrO93797tAmF1Iq9Zs6YrHVejNFEQni9fvlj7f/PNN64cvlGjRkl+zsDy+VtuucWqVUv7141MHnTr6lGfPn3chPMq99CceB07dnQd/jyHDx+27t27u05/KuUoUqSI6xSoRgcAAAAA0kHgHeGAWzRt2E8//WR33323bd682Zo3b+7m2R4+fLjb/sgjj1j16tVdw7PZs2fbs88+G+v+mhJMU4ppe1KNGjXKla9rqjJkTFEfdOvE1kmuqz4jR460Dh06uFb9jRs3dm345bbbbnP7tGjRwt566y23Td/fc889kT58AAAAIFNKVuAdBQG3EnYbNmywtWvXuqRf1apVXeA9depU//jsWbNm2YgRI9yttnft2jXW+GuNyW7btq1dcMEF8R7fi13iWr16tb399tu2c+fOML46RFKML4p70h86dMi10O/Vq5c9//zz/vXvv/++denSxV1dOnr0qLVq1comTpxobdq08e+jwHvRokXu5NVjJMX+/futYMGCtm/fPitQoEBYXhMAAAAQbfSZeuPGjVahQgV/M7E0m0YsCgJuUXb7vffec1+rcrZ9+/ZuTm59HZcqbdX47KWXXrKePXvGilWCVe7myZPH8ubNa/fff3+sknRl0ZVYVLCuTHdaNo1D6n8fkho/ZrMopk6BJ0+ejDdWwmvlr1JyvbjXXnstVsAtGnMxf/78ZAXdAAAAAMKT8XaBt5sLLPoCbhk8eLBdfPHFVrduXXebNWvWBPdVEK1Au3PnzlapUqVEH1cdzh966CEXXOs54lI8o+CdgDvjiupMt9SuXdv++usv++STT9wvwJo1a+z222935RlqYpDQlYfLLrvMzZ+3a9cuF5wnBZluAAAAZEbhzHQnmPGOooAbCGemO+rHdE+ZMsWKFSvmOgDqitIll1ziSjPmzp2b4Buglv7fffed3XnnnYkG3Bq3oTcqcAEAAAAQ5jHe/zevNwE3MoOoD7rfeecd11xAmWuN41a2W9/369cv3rx43vgKdf9T58Gnn3460cd+8cUX3ZUJbylTpkwYXwkAAACQuXmBd+7CuQm4kWlE9ZjuX375xQXXAwYMsGeeeca/XmMhevfu7boIPvroo7Huo/ESKjtXh3MF3olRV8InnnjC/70y3QTeAAAAQPgo0K7atipjmJFpRHWme8aMGW7Mh+bcDtStWzfLnTu3KzEPpOYEY8aMsSeffNKuu+66Mz6+GrKp9j5wAQAAABBeBNzITKI66PZ6vMWd007fq6u5Fo/mz1PW+4YbbrDnnnsuzY8VAAAAAIB0FXRXrFjR3Sp7HRiIDxw40I3nVmdzUcb7xhtvtFq1atkHH3zAlTMAAAAAQFSI6inD1KpdgbTGaF944YVWsmRJW79+vWvfXqJECTfm+9SpU1alShXbu3evG+utTueB6tev7w/ez4QpwwAAAJAZpcWUYUBmnTIsqhup6QXOnz/f+vbta9OmTXPzbufLl89at25tgwYNskKFCtmkSZPcXNzSvXv3eI8xevToJAfdAAAAAACEUlQH3aLM9bvvvpvg9jZt2vjHfgMAAACIEkd3mJ3Yl/z7ZS9olqtoOI4IiIioHtMNAAAAIB06echsUimzr89N/qL7nTycZodauXJlf6+o1atXu/5QHTt2dN+PHz/eff/ll18m+3H79++f6l5T5cuXt86dO6fqMeIej7dky5bNVQRramb1y4oW/ZP4vjVu3Ngt6UHUZ7oBAAAApDPZ8poVudRsx2K1Qk7GHWPMilxmli2PpRWN2/3pp5/c17/99pu7/f33393tX3/95W4z0nBVTa9ctWpV1xNLQ3mff/55++OPP1xDaoQHmW4AAAAAoVdD0/gmdxioz6zGs5aWFHT/+++/LtvrBdte8L1t2zb/PhnF1Vdfbe3bt7eHH37YPv30U3vwwQftf//7n61duzbSh5ZhEXQDAAAACL1ijc2KXmEWkzVp+2u/og3+u18aUkCtHlF///23C7ZbtmxpO3fudB2plekuUqSIa+acUTVv3tzdfv/995E+lAyLoBsAAABA+LLdvlNJ21f7pXGWOzCLrQBbme4rr7zSjXdWAK5Md2CW++2333ZTGWuWpUqVKlmvXr3s8OGUjT9ftWqVC/CLFi1q+fPnt3r16tmMGTOC7qtZnJo1a+b2K126tD377P9/n3bv3m3bt29PcNmxY0eix+GNnz5+/Lh/naZpbtu2rZsGS4uaV2udp2bNmtaoUSP39YoVK9xj3H777e57zS6l72fNmhWW9y09IugGAAAAENlsd4Sy3HGDbgXaVapUsXLlyrkAXOu88dy9e/d2pdgNGza0kSNHWrt27Wzo0KF24403Jvs5FSgruFfgrccdMmSIZc2a1a677jpbt25drH03b97snrNEiRL2yiuvWNmyZa1fv34uuBUFx9qW0FK3bt1Ej0XHIHrdojLzSy+91BYtWmR9+vRxy+LFi906rwT9kksusV9++cXffE7WrFnjbrVeQbf3vL1D+L6lVzRSAwAAABDebPfsJlGZ5Q4MupXJVYm5MrFa9L0yxdquRmMvv/yydevWzXr27Om/78GDB+3NN990+5533nlJfk41blPgPXz4cHvkkUfcumuuucZeeOEFtz7QvHnzXIfxAQMGuO8VtCqYnj17tstAK2Dfs2dPgs+VO3fuWN+rbN4rn1+4cKG99NJLVqtWLbviiivc9scee8yOHDli3333nf816Tlr1Khhjz/+uH3zzTcu6Na0zsqi67XoYoHWa1y8svK6X8GCBUP+vqVXBN0AAAAAwp/t3vlt8FJzZbmL1I9IllvOOeccy5s3ry1YsMB9ryBbQfeSJUtcEKnvVSp9+vRpF0BqiUtZ3uQEjxdccIErFX/rrbfc8ysrrIy6vo+rePHiruO4R2PMFXQrcBVvurOkuuGGG2J9f9lll9mECRNcdlrBtoL5Vq1axXo9+lpjv6dOnWpHjx51QbeX1Vam+7bbbrO5c+e6LL3WedtD/b6lVwTdAAAAACKX7Y5gljtwPuzp06dbqVKl3Lhjzd2tTKwo6PaajCkoVkAeLIhOjsKFC9tXX33lsteah/vYsWMu+L733nvduixZ/v8o4Isvvthy5swZ6/6B25NLJeoak61y9jJlyrjX6lGW/eTJk27ceFxap23aR69XFyoUYCvT/eKLL7p1KlVX4N2lSxd3H288+Vshet/SK4JuAAAAAJHJdkc4y+1RYP3zzz/7A8PAAFEZaJVJi8ZTX3XVVf5tKtFWObXXjCw5Gjdu7BYFsgpeX3vtNTdntrLYGgPtUaO1UFJmXM8bzNlnn+2C8T///DPeNo1vV4M5bx9dDJgzZ44rVa9evbprlDZ58mTXIM3LdCsrH+r3LT2ikRoAAACAyHQyj4Isd+C47rhBtzLKXsCor8ePHx/rfgqU1XU8sLN3UuhxlDnW/OAKZDVeWhloWb58uUWKxn+rwZuy/hs2bPCvV1O5adOmuW2qBBAF1hMnTnQXJfLkyeOC7k8++cRl5ZVJl1C/b+kVmW4AAAAAaZ/tjpIsd7Cg2+tYrsA4e/bsbn337t1d0zFlaVu3bu26ig8bNsyNdb788suT9XwKOPfu3evGTnfo0MEFuwpYxZuKK1LUWVyvR8ujjz7q1ul1KtjWNo+Cbo3XVrAtutX3Crhz5Mjh1oX6fUuvyHQDAAAASPtsd5RkuQODbi/YVuZWZd6Bc3QPHjzYjU1W0Kgu3mo+pk7fn332WbLHWGsctebkVum4Oparg7nmBNeUWp06dbJIUqn40qVLXYM1HZsWfa111apV8++nKcQCx2V7wbdXWh6O9y29ivH5fL5IH0S02L9/v2ttr6swmgQeAAAAyAzUkXrjxo0uyPTKh8NmZgOzHYv+m5f76v86hgPp8fchqfFj5ri0AAAAACA61BxoljXPf7dAJsCYbgAAAABp55yGZu12mmXLHekjAdIEmW4AAAAAaYuAG5kIQTcAAAAAAGFC0A0AAAAAQJgQdAMAAAAAECYE3QAAAAAAhAlBNwAAAAAAYULQDQAAAABAmBB0AwAAAAAQJgTdAAAAAKLGycOHI30IQEgRdAMAAACIuH++/95mduhgn9arZ0d374704QAhQ9ANAAAAIOLB9uw777QdK1ea7+RJO3HgQJoew6ZNmywmJsYtWbNmtaJFi1qzZs3sk08+ibWfz+ezMWPGWM2aNS137txWqlQpe+yxx+zo0aNBH7dLly7uMZctWxZvW/ny5f3PGWzx6Osbb7wx3v3r1KnjHkPmzZsX677ZsmWzChUq2J133ml//PFHCN4hpEa2VN0bAAAAAFIYbK8eMcJ2/PCDxWTN+t9Kny+ix9SyZUu7+eabXaCq4PqWW26xBQsW2Ouvv+62P/XUU/bCCy+4YPahhx6yhQsX2vDhw13Q/dZbb8UL0KdOneq+njJlitWtWzfW9mHDhtnBgwfd188884wdOHDAhg4dmqrj1/G2bt3aDh06ZD/++KONHj3avvrqK/vpp5+sZMmSqXpspBxBNwAAAICIBtu+U6csGlStWtU6derkvu7WrZtdccUVNnLkSGvXrp1dcsklNmTIEJd1fv/9990+9957rwu43333XRs4cKAVLlzY/1g//PCDbd++3R909+/fP9Zz3XDDDbEC8NOnT1v79u1Tdfy1atWK9RiXXnqpu0Awbtw46927d6oeGylHeTkAAACANC0j37lqVVQF28EUKFDAnnvuOfe1st4KoI8dOxYvY/zggw+6EnNllwMp0PYC8xUrVtg///xjae2aa67xl88jcgi6AQAAAIRNegu2A1155ZXuVmOyy5UrZ0WKFHFZ4/nz5/v3adKkib3yyitWtmzZeEF3lSpV7NZbb41Vap6W/vzzT3d7zjnnpPlz4/8j6AYAAAAQcv8uX55ug21P/vz5Xcn4X3/95ZqTaYy0yskbN25sDRs2tC+++MIF1HHt2LHDli9fbo0aNbLLLrvMcubM6c98h9Phw4dt586d9u+//7oLA/fcc49rDKex3ogcgm4AAAAAIXXy6FGbe999btx2egy2A6lLuVc6riZlakqmcd/Kfrdp08bq1atnmzdvjnWfb775xo3RVnCu+2s8+MyZM+3EiRNhPVaVw6vzerFixdxzK/hXB/bq1auH9XmROIJuAAAAACGVLVcua/L221b04ovd9/7u5OmQMtt58+b1f1+5cmU3xluB9sMPP2zff/+91a9f33YHzC3uZbUV7CrzXLt2bdu/f7/rdh5OmqJMwX2fPn3c9wMGDLC2bduG9TlxZgTdAAAAAELunNq17erx463p6NFWpGbNdBl8q1x7165dVrp0aRc8r1271o4fP+4fJz1ixAg3nnvbtm329ttvu/UnT560GTNmuK81n7cyz+pOLuEuMdcFgauuuspNQVa8eHF76aWXXMYdkUXQDQAAACBsil1ySboNvufNm+fGbCuTPWrUKDelmOa/DnTbbbfF6hC+ZMkS27t3r91+++0u6+wt6oaekqBb9/Pm8w6keb0LFSoU9D65cuVyU56tX7/ePvvss2Q/J0KLoBsAAABA2KW34FuB7lNPPWVZsmSx++67zz8uevLkybH280rGve1eYN25c2eXdQ5c1q1bZxs2bEjWcVSrVs01Zdu3b59/nQL8P/74w21LyP333++awA0aNChZz4fQI+gGAAAAkGaiOfj+9ddfbezYsW4s9IUXXuiy2gpa69SpY9dee63VrVvXBg4c6MZOv/POO9a7d2+76667rGLFii7I9oLuPHnyuO7mgVq2bOnfnhw9e/Z0Je7qhP7aa6+5cnZNZaay8R49eiR4v3z58tmjjz5qK1eudI3dEDkE3QAAAACiIvi2mJiIHpPm0lYQPXLkSDcee86cOf7AVlNvqUz8gQcesGnTprkmairdVrCtZmqaXkzN1X7++Wc3d7emCQvUokWLoJnyM1GH9Pfff9+VuSsA13htdSf/6quv7KKLLkr0vl27dnXl6S+88EKy3wuETowv2MRymZQ6ChYsWNCVbujkBAAAADIDdejeuHGjVahQwY0HjoR/vv/eVo8YYTtXr7Y2c+darrPPjshxAEeT+PuQ1PgxW5iOEwAAAACSnfk+efiwZcuTJ9KHA4QM5eUAAAAAogYBNzIagm4AAAAAADJr0H3ixAnr06eP6wioevqSJUtax44d3QT0wSavV0fB/v37R+RYAQAAgPSMdk+Ahfz3IOqD7kceecSGDx9ut9xyi+si2KFDB/viiy+scePGdvz4cf9++vqOO+5wc9gBAAAASLps2f5r9XTy5MlIHwoQFYlfr2N9KER1I7VDhw7Zu+++a7169bLnn3/ev75KlSpubrxFixa5Oeq2b99ubdu2ta1bt0b0eAEAAID0SMGFFnVj1tRXQGbOcu/bt89N+ZY9e/aMH3QfPHjQXW3TxO6BvDnvvCtyo0aNspiYGFu6dKmVLl06IscKAAAApFf6LH3OOefY33//7T5r582b160DMlOwfeLECRdwKw4tVapUyB47qoNuTfp+8cUX27Bhw6x+/fpuvPaaNWvcmO2yZcvaJZdc4vZTWfnTTz9tWbJEfbU8AAAAEJU03/CRI0ds586dtmPHjkgfDhARuuikgDuxebczVNAtU6ZMsebNm1ujRo3862rWrGnTp0/3T1ReuXLlFD32sWPH3OJROQ0AAACQGSmzXaJECZfx9sa0AplJ1qxZQ1ZSnq6C7nfeecdWr15tl112mVWvXt19rWZp/fr1s/fffz9Vb8qLL75oAwYMCOnxAgAAABlhfDeA0IjqeuxffvnFBdcKjL/99lvXVO377793wfIHH3xgb7zxRqoeX1ORqWbfW2jEBgAAAADINEH3jBkz3ID2xx57LNb6bt26We7cuW3u3LmprtdXrX7gAgAAAABApgi6vUnJA+fj9r5XV3PmEQQAAAAARLOoDrorVqzobseMGRMrEB84cKBr7lC7du0IHh0AAAAAAOm4kZq6llepUsV69Ohh48aNs5IlS9r69ett48aNrrPi448/HulDBAAAAAAgfWa6NSXY/PnzrUuXLrZ7926bPXu27dmzx1q3bm0zZ860QoUKRfoQAQAAAABIUIzPGzgNN093wYIFXSdzmqoBAAAAAFIbP0Z1phsAAAAAgPSMoBsAAAAAgDAh6AYAAAAAIEwIugEAAAAACBOCbgAAAAAAoj3o1lReb7zxhuvcllQHDx60LVu2hOoQAAAAAADIuEF3165dbdeuXUm+z/vvv2+VK1cO1SEAAAAAAJAxg+78+fObpvzOnTt3ku+TK1cutwAAAAAAkBFlS87Ohw4dskWLFlmePHlccK0g+9ixY65MvFGjRhYTE2NZs2a1wYMHW968eS179uxu3cmTJ+3w4cPWpUsXW7NmjTVt2tS2b99O0A0AAAAAyNCSFXT/+eef1qJFCxdIB/ICay/o7tOnT6x9FJzr++uuu84F2adOnbJ8+fK5fbNlS9YhAAAAAACQscvLn3jiCRdId+7c2Tp16uS+dg+WJYs/2O7du7f16tXLfT106FD/fb3yc2XBAQAAAADIyJIddCuofvnll93XCqp79uwZa7sXgCsY1yKPPvqof70XbMfNlgMAAAAAkNGkupEawTMAAAAAAGHuXg4AAAAAAGIj6AYAAAAAIEzC1jr8iy++8I/jHjduXLx148ePt8WLF4fr6QEAAAAAiLgYnxcFJ8G6deusWrVqbsovdSpfu3atC6K9dTly5HDzbxcpUsQ/1tubLsy7DaR1xYsXt23btlk02L9/vxUsWND27dtnBQoUiPThAAAAAACiVFLjx5CXl3sx/MCBA+355593gfb7778fa51o3T333BPqpwcAAAAAIOOP6W7btq1bxJs6LO66hg0bhuvpAQAAAACIOBqpAQAAAAAQJgTdAAAAAABEY9AdtzEaAAAAAABIxZRhapTWs2dP9/VLL73kb5wWNxAfM2aMf9urr77qX3/69Gl3q27nAAAAAABkZCmap/uVV15xt++99567VUB98uRJF0jrVgYPHuwPurt37+4Puo8cOeJud+3aFZpXAAAAAABARgi6S5cubZMnT7bcuXNbrly5XNb6xIkTdvjwYbeIvn/22Wctb968lj17dheEHzt2zC2av/vff/91wbiCbu1LxhsAAAAAkFElK+hWIN2yZcug27Zv3+5uFUg/9dRTCT5GrVq1bNmyZVauXDmbM2eOP/MNAAAAAEBGk6Ly8mCU6VYG2ysvT0i+fPmsdu3a/gD96NGjoToEAAAAAAAy5pRhWbJksUaNGlnOnDmTfJ9OnTrZmjVrQnUIAAAAAABElRhf3Pbjmdj+/futYMGCtm/fPitQoECkDwcAAAAAkM7jx5BlugEAAAAAQAiDbnUvX79+vb+JWnKtWrUqNU8PAAAAAEDGDboPHTpkVatWtX79+iX7vmq4duutt9r999/vGqoBAAAAAJDRpKp7uaYQ05Dwn376yV599VUrVqyYlS9f3s4991w755xzEr3vc889Z+vWrXNThmnRnN4AAAAAAGQkqW6kpq7lMTExLvjWreess86y+vXrW4MGDaxJkyZWp04d/7ZJkybZjTfeaFmzZrW5c+fa5ZdfbtGARmoAAAAAgFDGjyGZp7tNmzYuiN62bZv99ddf9vfff9vPP/9sU6dOtcmTJ7tg/KKLLrJHH33UZbQ7d+7s7jdy5MioCbijicrtyfwjOThnAAAAgOgUkqC7bNmybnx2sDHfymSPHz/evv76a3+wnStXLhs9erTddtttoXj6DOX333+35s2bu/dMlQLAmWh4hs6Zm266ybp27RrpwwEAAACQ0kZq8+fPt/fff9+lz5M65lul5Zdeeqnlzp3blaBr0djvRo0aJeepMwW9ry1atLA9e/ZY/vz5I304iCKaKUBLMNmyZXPlLKokmTJlSpofGwAAAIAQBd3vvvuu3XPPPa5J2pVXXmmDBg0Kut+WLVtszJgxLpOtALtHjx4uMOjfv79roLZp0ya7+uqr7eDBg8l5+gzvrrvucu+NxrxfeOGFsbadOnXKNas7//zzLWfOnFapUiX3fSqH5KfqOZRh7du3r2ucp/2LFClidevWtTfffDPBADGcdM5pKENSFr3Pga973Lhx1rRpUytUqJDly5fPGjZsaB999FFYj3fDhg3u4krjxo0T/D3q2LGjFS1a1PU/0Htcr149N2wjkMrKP/30U6tZs6a1b9/e/vzzz7AeNwAAAIBk8CXD888/72vRooWvePHivpiYGP9y1lln+Tp27Oi76qqrfMWKFfNlyZLFrc+WLZuvSZMmvnfffdd39OhR/+P07dvXbb/jjjt80WTfvn2KLt1tWvv444/dcw8aNCjettOnT/s6dOjgtmupVKmSr0CBAu7rrl27huT5k/scx44d8zVo0MBtz5Url++8887zFS5c2H//tm3busdMS19++aWvZs2aiS7e8f3111/uPkeOHPE1a9Ys1usuUaKE//tbb73Vd/LkyZAfqx7zsssuc8/RqFGjeNvXrVvnK1q0qNueJ08eX7169XzlypVz3+t3R79TcW3YsMGXN29eX6tWrUJ+vAAAAABSFj8mK+gO9NNPP/leeeUVX9OmTX1Zs2Z1gYCCbS0KYiZPnuzbvXt30PueOnXKd/nll7t9p02b5svsQbcC2PLly/tq1aoVNMB744033HEpCJ43b55bd/DgQffe63331qVGcp/j1Vdfdftff/31vgMHDvjXz5gxw1ewYEG37fPPP/dFky+++MId13XXXedf9/jjj/uD7R9++MG/ftKkSb7cuXO7bc8++2zIj6Vfv37+wD5u0K2LFXXr1nXbOnXq5NuzZ49br3OjW7dubn2+fPl8+/fvj/e4Q4YMcdtnz54d8mMGAAAAkIZBtyxYsMA3cuRIF4ArQ1u1alV/8H3DDTckeD8F3T///LMve/bsvvPPPz8smcT0FHSPHz/ePe/UqVPjbVOFgKoHtP29996Ll9nUe924ceNUPX9KnkMXTbT/b7/9Fu/xnn76abftoYce8kULnWPVqlVz1Rdr16516/bu3esPrJcsWRLvPsOGDXPb9N6E0rfffuuOQ4FzsKD7q6++cuuvueaaeNUCeh3nnHOO2679gv0sS5cu7WvevHlIjxkAAABAyuLHZI3p1nRgaoo2c+ZM9/1XX33luiVrXGqvXr1s8eLF1rNnT7vgggvcPN0DBgxwU4YdO3YsVrOw8847z3766Sfr0qWLG7eq6cUyM40nrlixol1zzTVBm9f9888/VrhwYevQoUOsbbqPxiEvWrQoyc3tgknJc/z777/uVmP24zr77LP901hFC433/uWXX1xPgipVqrh1Ol81Lr1atWpurHRc1113nbvVe7Nr166QHIf6GGjctca8Dx8+POg++t3p1q2b65mg8eeBNLa7TJky7uu9e/fGu6/Gfd99993ud1RT9wEAAACIrGQF3QsXLrRVq1a54LBOnTp2+PBh/wd9TQyu9Wq2pg/8w4YNs48//tiuv/56F5ip4ZYCl/fee8/++OMPO3nypAvQJ06caDVq1LDMShck9L4qwIsbYMkPP/zgbtUFPtg8zLoIovfyu+++S/ExpOQ5vMBvxowZ8fb/5ptv3K0ae0XLe9yvXz/LkyePa+bn8RqO6SJQMJraLtTUYVwN1HTuqxlhMFdccYW98sorQd8/XcjQtHJSokSJoPfX75yC+lmzZoX46AEAAACENejWXNwKVJ5++mmXcVNXZVG2sFmzZrZixQoX3Ki7uaYw8gJy7ffiiy9ahQoVXPZOQbg6m+t7zS+cma1bt86OHj0aNNPqVRdI3G7mHnUYl8Bu3MmVkue499573e1DDz3kqhl00WXz5s32xBNPuEBcQfntt99u0eCDDz5wr1HzxOvc9KhTuWiKtmDWrl3rbnUfVQGklrrSa8q9Sy65xJ599tkUPYamBFPFgaoJGjRoEHQfBeu6wKALZAAAAADSUdAtmhZK2cKlS5f6M7Oaj1vTTGnaIpWbB1Jgs379erdNgcLOnTtdyXKWLEl7amX2+vTp4+6jzGPJkiXdNErbtm2Ltd+XX35pF198sQs2FDymlyyfF8h6gW1cXjWBXndCpciydevWFB9DSp7jlltusbffftsFrNdee60VLFjQypcvb0OHDnWZWpWse0FtJKlvgaY90/n2+OOPx9qmYRCyZMkSd8EgLlVryM0335zq41Cpt0rbNRTjww8/DFpRcCaqNnjmmWfc13otuqAVjH4vdUErNRdiAAAAAEQg6FaG7vLLL3dlsZpne/z48f4ATJlEjVGtX7++zZ071wUIgUFAu3bt/Nk9BezKiCbFI4884h5XzzFy5Eg35viLL75wcxsfP37c7TNhwgRr06aNK6lVRl3zRrds2dJWr15t0e7AgQPu1qsMiMubI1sXE4LJnTu3u03NnOcpeQ4F6spoez+DQLogMnv2bIsG06ZNc2O5Vb5fuXLlWNs0lrtVq1buwo7OHw178MrRdaFHPQvkvvvuS9Ux6P3t1KmTG17xxhtvJHiB5UxUJaJeCLp/9+7dE91XF0FUfQAAAAAgsrIld+yvAtu4FixY4A/cvKyzxnCr0VPgOGU1ripevLgLzBVIKxBS8JyQQ4cOuTHiatL2/PPP+9erEZaasKm5l8YbK7uuQERjo5VJ1PeNGjWyxx57zObMmWPRzHt/At+/YOOKvcA3rmzZsvlL/FMqJc/x4IMP2meffea+1hABlTrrAsJHH33kgldldRX0JfXiSrh4zcriVmAElp4rk60+BKrWKFeunLto4GX/27Zt68+Ip+YY9PhqoKYlJXShShetNKxDF7vONN5cv6dJrSYBAAAAED7J+lSuclWVyapEXONKFVh77rrrLhdka7vGKCtIUNfn3bt3u6ZQWqeSYzXrUtBQqlQp11QqMcqsKmOeL1++WOu9sloFg/PmzbMdO3b4u6i7F5UliwsK9Xw61mimjGRi44q9Em29xmC8bGZgZUFyJfc5NK7fq3J45513XDZZ54Iysb/99pvLGsvAgQNTdVyppeBZwa7K5hO6uKPXroy9Ls7o4o56EzRs2NB/fr3wwgupOoY1a9b4h0coy50S+rmo0kMZeR1PQuP/A+l88s4tAAAAAOkk6C5durRrgva///3Pld56GVAFKqNHj3aBl7aPGDHCpk6d6sZ/q/xYwYyCDgVkKk1XRlVZOwUkn3/+eYLPp8fSOG2NrVU2XZnWZcuWuTHlZcuWdeXuKrcVBfOB1F1d2b4ff/wxwcdXGbECysAlrXmlxhr3HozXJTyh8bm6sOGNq0+p5D7H8uXL3Xtbu3ZtNz1VoBw5crix3hqzrAsuqRlrnloaO63jVMB6pqyvzp/nnnvODWHwpuJSWbk3vVhK6dzVBSdlzlUNUKtWLf+iIRDe++mti9urQIG2MvGaWk9DNNT1/Ex0oWPjxo0pLmMHAAAAEKGgWx/m1cRMZdtvvvmm+17l0cp2ar0yea+//roLuDR9mDLUCrzHjh3rz1ZfddVV7ladrdWgy2tWlVi3ZgXfKhfXmGMF2gr+NG5cJbZehlhBfSBv/ujEmklp/Leygd7iBZ9pSePPNZ5bpffBKLANnNYrLlUUeBdEUiq5z+GVwuvYg9HP3OtsH2w4QlpR6bioU35S6WKRXq83z3xqeZn+7du3u27igcuvv/7qH0bhrYs7Rv7+++931RyaVk+/R0mhC1N6HO/nCgAAACCdBN0KgBXIqCmUynaVhfMyxqNGjXJZ6W7duvmzz1p/6tQpN45VAYay4Roz62VEb7rpJtc52puaKRgF9GqIdtlll7lx3HXr1nXfa2oyPb8X1MVtAuZ972Utg1HZr8rkvSUSWVmN0W3atKmrHAhWiq3ASQGgyp9Vuh9I761+Dt5+KZXc5/AuTsTdN3BYgMr69TPQMIJIUFWFgliVluucSQpdTNB5JarECMU0YWPGjHGPG2xRNlp0Qclbpw7wnh49ergpxnQBSedHUqsZJk6c6C58JTQPOAAAAIAoDbqvv/56Vw6uQEAl4iqZVaCg4FpBtIJq8UrGVVbrNd9ScKlseKAWLVq4LujelFRxqeu0giBlHL/99lvXVO377793GWoF/8qsK7BT6bCCw0BeNlbHkBCNDVeWOXCJhDvvvNMFsJpWLS69b7pooQsMmiYqsOGaSqHVNV5TsWmarpRK7nNoSjZlsvUzUdlzXBpeoEyrMsxnavgVLhpnLt747KTQsAmVeqtxmjLMkfTSSy+5Xgj6PVPA7V2sOhM1s9PvocbVJ/R7BQAAACAN+UJs1KhR/q+nT5/umzJlSoL7Hj582C0JGTp0qKI/3759+2KtP3HihC937ty+66+/3j2f9vnjjz9i7fPXX3+59cOHD0/yset5gj1fuJ0+fdpXo0YNX8WKFYO+H9u2bfMVLVrUHVvLli1948aN83Xt2tWXNWtWt27QoEHx7lOzZk23LFu2LEnHkNzn+PLLL305cuTwVapUyTdixAj3s54wYYKvQ4cO7j5av3Pnzlj3efPNN90xdenSxRdurVu3dsf9xhtvJGn/Q4cO+UqXLu3uM3fu3ET31WvX62jRokWqjnHjxo3u+Ro1ahRr/Z49e3y5cuVy2woWLOj/WcZdnn766XiP2bt3b19MTIxv5cqVqTo2AAAAAKGJH5M1ZVhS3Hvvvf6v1WAtMQlNUeXxMq5xx7nqe5Via7nooovcOk0fVqFCBf8+yohLiRIlLNppXLymlVI5sBplKVMcSK9Bc5OrIZjGHGvx3Hrrra4MOS6VVidn/u7kPoeme1NWWNnYIUOGuEy9Xoca3GnMv0r345Zne+OavW7p4aKMvcZBJyfT/fLLL7uu7Hr9iU1jJ2oQp9eR2NCF1Pj555/9FRoa9uD9LONS47VA3333nft5aCaBuNsAAAAAREaMIm+LUprv+4YbbnABUffu3d06He5TTz3lpk565plnXPm5gm2N3dU83V5H9VatWtn06dNdoKfGXkmh7uVqqKZAJxKl5irtVmM5lQd37tw53nY1jXvttddcoy8dpwJEb3quUAn3c6hjt+ZW98ZOIzTU9VxNBjXMY+XKlUwXBgAAAIRZUuPHiATdGn+tccRnomyfMnbr1q1z44gVWGtqLTWgUmZWY76VNdUYczVZ09RKmuZpwoQJbtoqNXxTI6ukinTQrcy9xrgraz9p0iRr3bq1ZSQa/61mbBs2bIjY+PmMSOetLmQoU69zp2bNmpE+JAAAACDD25/E+DFZjdRCRUGyguhZs2Ylup+acM2fP98F1CrpnT17tsvEKhhVR22vTFnltIMHD3aZcXUCV8CtfZSxTU+Upddr0HzMGXG6px07dth7771HwB1iej9V2aFzh4AbAAAAiC4RyXRrrm5NT6VpjTT2tl69eiF53H/++ceN5VZGPCVBa6Qz3QAAAACA9CHNy8vHjRvnAl49mabiUlMtlZGrRFwHoemmNC1VYLb77rvvdmO2Na9wNCDoBgAAAACEMn4MWfdyNW/SvNmBFHh74h6ESsI1/7bGawMAAAAAkBGlKOhes2aNXXDBBfHWK8hWxtujcdfjx4+3d955xz+1V6A8efLYgQMHUnIIAAAAAABkzKBbzbAUeGtqq9KlS8fadscdd/i/1jzGCro1z7MCbAAAAAAAMpMUdy9XJ/Hq1avbyJEjQ3tEAAAAAABkEKmaMkyl4Y888ojrPq75swEAAAAAQAgaqWn89sKFC6179+62dOnSWE3TAAAAAABAKruX169f35YsWWKvv/669e7d244cOeK6knvWrl0bimMEAAAAACBdCsmUYQ8//LBt2LDBhg8fbmPGjIm1jQw4AAAAACCzSlbQvWfPHjvrrLMS3K4Ae8GCBf7vP//8cxeIAwAAAACQGSUr6G7UqFG8THZcl19+uf/rH3/8MeVHBgAAAABAZgm6Fy1a5ObmbtiwoVWqVCm8RwUAAAAAQGaaMqx48eJ255132okTJ+ynn35y63bt2pWqJz958mSq7g8AAAAAQIYIuitXrmzvvfeerVq1ypWQ+3w+q127ti1fvvyM9w3WTO27776zP/74w3LmzJn8owYAAAAAICN2Lz///PNds7Qnn3zSBg8e7MZ5f/DBB26bAvEaNWr49929e7e7rVu3rmXJkiVWhvv333/3Px4AAAAAABlRjE+Rcgp9+OGH1qlTJ/e1xnmvX78+WfevXr26ffLJJ1a1alWLBvv377eCBQvavn37rECBApE+HAAAAABAlEpq/Jiqebpvv/12O3jwoN1///0uc/2///3P6tevn6T75s+f384+++zUPD3SM13rUU+AgwfN8uUzK1xY4xAifVQAAAAAED2Zbk/Hjh1diXmpUqVsxYoVds4551h6RKY7DezdazZ2rNmIEWYbNvz/9eqI37WrmSonChWK5BECAAAAQMjix5AE3QcOHHBjuYsVK2YzZ850Wez0iKA7zKZPN2vXzuzw4f++Dzz1vCx3njxmn39u1rx5ZI4RAAAAAKKlvNyjIHv8+PFWpkyZdBtwIw0C7lat/gu0g13n8dYdOfLfflOmEHgDAAAASPdCkunOKMh0h7GkvHTp/wLq06fPvL863efObfbnn5SaAwAAAEjX8WOS5+kGUkxjuFVSnpSAW7Sf9h83LtxHBgAAAABhFdKgW/NyV6tWzd57771QPizSMxVSqGlaSrz2WvBSdAAAAADIyEH3b7/9Zk888YS7DRQTE2Nr1661XZoKChCdC+pSntzgWfvrfrt3h+vIAAAAACA6g+4dO3bY8OHD7e+//3bZ7alTp9rOnTstZ86coT9CpG+ahzs1DhwI1ZEAAAAAQJpLUffyXLlymfqvKchev369XXvttTZr1ixr1KiRf58pU6bYSy+9ZHny5LHs2bPHe4zTp0/bsWPH7NChQ7ZkyZLUvQpEr3z5Und/uuEDAAAAyGxBd9asWV0puYJvfa0AXMF1FnWd/j/KfC9cuND/vfbXft5t4HpkYIULm1WqZPbHH8krMdd5UbGi2dlnh/PoAAAAACB6G6kpg+1lsYNlsxVQz5071/r06eO+nz59ugu4H374YXvhhRfc9i+++CI1h4Bop+C5a9eU3feRR/67PwAAAABk9KBbY7fr169vq1evTtYTqORcHc2lYcOG7rZy5cp24YUXuq8vv/zy5B0x0p9Onczy5Plv/u2k0H7av2PHcB8ZAAAAAERHeblKxZcuXWq1a9e25s2bh/eokLEUKmT2+edmrVr9F1AnNl+3tiu7PXHif/cDAAAAgMyQ6S5VqpTdc889LkutbuVAsuhCzZQpZrlz/xdUxy0b99Zpu86vZs0sWpw4cSLSh4B05uTJk7F6VwAAACDzSnLQXadOHRs1apT9+uuvNnny5PAeFTJu4P3nn2bDhv3XJC2Qvtf6v/6KqoD7999/t/PPP58O+0gyzcxwxx13WI8ePSJ9KAAAAEiv3cuV9RY6jyPZVDKuBmlqrrZ793/zcGtaMHUpj7Lzad++fdaiRQvbs2eP5Y/w1GWnTp1yswPwOxf99HMqVKiQDRkyxCpVqmQPPPBApA8JAAAA6bF7uUonn3jiCfvggw9cIEAwgGTR+aLpxMqX/+82Cs+fu+66yzZt2mSTJk3yN/4LDIJfffVVlwXXfPUKrvR9KEuK16xZY23atHEBXLZs2Sx37tx29dVXu94KZ6KGhwUKFLD+/ftbpMybN8//t+FMi/YN9OWXX1rr1q2tSJEi7nXXrVvX3nzzTVe2HSq6mKKZFHQRUdMfqprnm2++SXD/LVu2WKdOnaxkyZKWI0cOd79mzZrZzJkz4+2rY73mmmvssccesx9++CFkxwwAAIBMkun2aAqwGTNmuEDjpptusp49e4buyIAImjBhgk2cONEGDRrkOvAH0vl+55132vjx4933Crh37Nhh3bp1c0H6a6+9lurnX7x4sQvoDh8+bAULFnRd/jds2GCzZs2y+fPn21dffeWCumC2b99u1157rR1QFUEE5cuXz2rWrJnoPhqucvz4cRfEBr63Y8eOdd+XLVvW8ubNa8uXL3fLJ5984oa3aF1q7N27182moAsbmu6wQoUK7kKFAn397G+88cZY+2/evNkuvfRS++eff9zPo2LFivbnn3+6gFvLsGHD7NFHH42V7f7www/tggsucBdvVqxYYVmzZk3VMQMAACCd8iXToEGDfDNnzvTFxMT4+vTp4+vQoYP7WkuWLFnc0qlTJ9+YMWPc1/LBBx+4r48ePer2Gz58uG/y5Mlu3a5du3zRYt++fUpTuluEyZF/fb79vyV/0f3SyLFjx3zly5f31apVy3fy5Ml429944w13nhQoUMA3b948t+7gwYO+pk2buvPbW5dShw8f9pUpU8Y9R69evXxHjhxx63V76623uvU6vtOnT8e77++//+6rVq2a20dLv379fNFq5cqV7v3S+3zq1Cm3Tn8bdNxFixb1zZo1y7/vwoUL3Tptu+uuu1L93DfffLN7rAsuuMC3efNm/3tXsmRJX5EiRXw7d+6Mtf91113n9n/++ef97/vx48d9r776qlufM2dO37Zt2+I9z+eff+62v/fee6k+ZgAAAESXpMaPSQ669YH/2muvdYFy9erV3e3PP//sW7t2rfvgrA+Vbdu29Qff3j6BQbeCGYLuTOzEQZ/vw+w+3/8s+Yvud+JQmhzm+PHj3XkwderUeNt04ahYsWJBA6kNGza4c7px48apev7XXnvNPf59990Xb5t+X7Jly+a2r169OtY2Balnn322L0eOHL5LLrkk6oPuZs2auWP0gmsF3qVLl3brPvroo3j7f/HFF26bXv+BAwdS/Lw//PCD/3H09yuQLhZqW//+/WNdBNG+uhAS7EJHgwYN3H0+/fTToM+nn8X555+f4uMFAABAdEpq/JjkMd0rV650peQaw9qrV69422vVqmWfay5mMzfm8ZdffnFfa3qxH3/80X3tjZfUNpWKikozVV6KTCBbXrMil2pAdzLvGGNW5DKzbHksLYwbN86VDwcr31Zpt0qMCxcubB06dIi1Tfdp2rSpLVq0yDVhSymVqz/yyCP2zDPPxNt29tlnu/Jmr0Q60HPPPedKpfV7qgZw0Wz27Nn+49R7JmvXrnUl23p9ccu7pVWrVq5sW+O6161bl+Ln/vTTT91ty5YtrUqVKrG23Xrrra5pXuAMDRr7recsWrRo0N4V+pkkNrXcgw8+6F7bd999l+JjBgAAQPqV5KC7Xr16NmfOHBcs16hRI9F9u3btaoMHD3bjMzW21Gsw1bZtW7f9nXfesWeffdatu/32223nzp2pfyVIH2o893+Vz8nhM6vxrKWFY8eO2cKFC+26664LGmB5TbGaNGniAty4NO5XAVpqAiwFg8OHD3cXr+JSwK8gUEqUKBFrm36/fv7553hj0KNR79693furvxMeBdyi8dVqHBeXxkQHe8+Ty/sZasx8XGqKp3HoGoN99OhRt07N3NRoTe/ttm3bYu2/f/9+N/5eEhq/rr+BuligiwwAAADIfJLVSK1+/fpJ3tebo1ZZcX1QVhfmwCBGAbcy3GoUpQ+0yCSKNTYreoXZzm/NfKfOvH9MVrMi9f+7XxpQBlXBli4yBfOX5hE3i9fNPDBLLWqoFg4fffSRmwe6evXqVrly5VjblB1PD5Tl1sU7XVwIfB/VpV28iwpx6T3VRREF33Ez1MmRlJ+hqhXUrfy8885zTd7U3E0dyfV3TLeq+FFju8cff9xdNFQDtmrVqgV9PGXC9TirVq1K8TEDAAAgk3YvDyypDDaVjwLvjRs32qhRo1yQ8PTTT6f26ZBRst2zmyRtXwXmaZTlDgyWveA5Ll0kkmBZaDnrrLPc7datW0N+bMqqeplhZYrTK81fLer2HkiBtC7QqVP4kiVL4l3kU4dwUUm6OqOnVHJ+hgqWZejQoe5C4VtvvWW1a9f276sLiepOPmLEiESfU+dTuC7EAAAAIAPO0+2NwVagfeTIEfe1dxuXPihr2pwBAwZQXonY2W5lsROj7UUbpFmWW7xptjTHdTDePNx58gQfX645peXgwYMhP7YnnnjCTQem6cPuuOMOS4/Uz2HatGmuB8SVV14Za5vGct97773ua70+9ZEQZfYV1HqB7f3335+qY0jJz1BZ72+//TboY6k6wutRkRC9Nl00AQAAQOaToqDba+CkMlxlhR566CErVqyY+3AcN+ut0kyVxGpMY6dOnVzQALhs95nKy9M4yy3eEAgvMIvLGwrhBWZxeWORE7oIlVJqUvjee++5DK/msA423jw90Bzmem/V9yEY9X9QwK2scJ06dVyGWOXZKp3X/ZRlVll6aiT3Z6i/cxqXrfJwjfnu2LGjvfjiiy7418/Dm1M9WFDu0d9G/Q0EAABA5pOiT4H68NiwYUMXUGtcqTJQGuPoNR7ybj0a69i+fXvXBOrrr79O8vPog7eCi8QW7yJA9+7dXfdofShWZl3N2pCOs90RyHKL1xk8oXHF3rjjHTt2BN3uZTODDbdIqfXr11uXLl3c1wq8Eyp9j3Yajz1hwgT3O9quXbug++hvygcffGDLli2z/v37uw7yzZs3929XeX1qLzgk92c4adIkl83Omzeva5Cnix4q79fYbnUlVwm6XtugQYMSfE6dT965BQAAgMwlRWO6NcWPN81PIGWilM1W0BtX3759rXz58nbPPfck+Xk0Rc/48eODblP3czl06JBdccUVbuy4mhrpOSZOnOjKVNVt2BsHinQ2tjsCWW7xAloFuupEHleZMmXcbULjc3VhSRSghYICQDXv0hRkKi+/+eabLb3SBTddINPrOVMAqiy3Fm8aL2/KsGB/d5JLP0NlpfUz9MZsJ/Yz9DrRK7Mdt0N5qVKl7OWXX7brr7/ePzViML/99lvQ8wkAAAAZX6obqQXSh9TRo0cnGMwEm3f4TI+nDHlcS5cudR9iFVwrqFZwrXl19aFclBVUN2GVsqr0/dxzz03hK0JEOpmnccfyQDpXNJ5bJcNx5+EWr4mWN+1UsHNTSpcubaFoUqggW+OgVb780ksvWXqmDLbcdtttSb6Pfrc1r7YarHkN2FJLP8NPPvnE/QyDTRsW92foDTVI6O9I1apV3a03vCYuTTP2xx9/uPm6AQAAkPmky0GG6oreoEEDlzH75ptvXObKC7hF5acKVvRh2WvGhHQ0tjtCWW7RdFTKpn711VdBS8QVsKmPgeas//vvv2NtO3XqlM2cOdO/X2rosRT0T58+3Q3dUFm2ji29UlMy/a4qeE7OmGxdqFMwqzHdqZkmLNDVV1/tbj/88MN421RGrgy4yty9ih2vuiHuz9ujKpvEgnKVp4tK5QEAAJD5ZAnFdGFpSZ2PNYfuCy+84L5X2a3K0BOaFiixMl+Nw1T5buCCCI/tjtBY7kCak1kBljKscSnwVfWFzn0NZwhsuDZy5Eg3B7Qaf2nIQ2qo0ZgC7SJFitiUKVP845DTq7lz57pZD3QxIqml9/o9VzWLmjQmt0omMRdddJGbo/unn35y47I9+llqGIwoA+41WvO6rOtY4v7N0300nZho6rBgF090XlxyySVu2kQAAABkPqkqL1dwoQ6/6gbsLfqgeqZbLSVKlHDNlNT8LDlUYquxkV5QozGZKi1XV/TixYv791P5qJ6vXr16CT6WOhBrKjNE0djuCGa5PRqaUKNGDXvqqafshhtuiNfluk+fPvbxxx+7oFhTjGnMsRp/vfHGG257z549XaY0kKbIknfffdc/Vjkhas7lBYOq2mjbtm3Q/TTGODXTZymbr2BW81VPnTrVwsmbLlANGJNCwexjjz3mb56W0BRuoum67r77bvd1YuOqA73yyisu86yLGyrfV1Csn6cucKhR5JNPPunf9+KLL3aN09QoTcG4hq/oQsCff/5pY8aMsQULFriqm2DTuOnn/euvv9oXX3yRpOMCAABABuRLhXz58vliYmJStGTJksVXoEAB39q1a5P8fMuXL1da0ffpp5/6182fP9+XLVs2X+3atX0LFy70rV692nfXXXe5/Tp27Jjo4x09etS3b98+/7J161Z3P32NNDbjCp/vf+bzzWjgiwZz58515+nDDz8cdPvixYt9pUuXdudL4HLrrbf6Tp06FW9/b7se90w++OCDeI8bbOnXr1+Cj6FtZ9pn9OjRbp9y5cr5wu28885zzzV58uQk7T9mzBi3f7169XynT59OdF+9p957khyjRo3y5cqVK9Z7qr9LI0aMCLr/119/7WvVqpXvnHPOcX9z8ufP76tbt65v2LBhvpMnT8bb/48//nB/46666qpkHRcAAADSB8WNSYkfY/RPSgP2/PnzW5MmTVzZpUq1VT7qLYl9v2XLFpc10m2vXr38peJJKftVebkyTIHjW7VOnZ2VUfJo7KgyWJrSLKlUXq6uyipZTyyzhjD4d4HZ3BZmTaaZndPAooHKx9WoT80BO3fuHHQaKDXrU+MtnTe33HKLy3imJ2rupWqVhBogZnQaw/3666+7W818cN9996V6PL6oAkLVOFu3bnV9JcqVKxeS4wUAAED0SGr8GJKgW2WqCdmwYUPQeYXVybhjx47u/rNnzz7jc2mMtko6VdqZ0DRgatakUnN1R1ZpqjfWMqkIuiPs5BGzbLFLuSNJjdTUdEtji9UMS2XnGYl+p8qWLWvff/99sod5IGEax62/a5qWTH+PAucZBwAAQMaR1PgxrN3LNRZVnZeXLFkSb5vX/ExBeVIo6FFQndh0Q2rQpHHaeux+/fql4sgREVEUcEu2bNnsyy+/dL0HQpH9jDZqFvfqq68ScIeYqnA0b7eqBwi4AQAAENJ5uj27du1yUb+aEakxkUpzV61aFashlbKIahJVuHDhJD3mZ5995jLdaqKWkI8++shWr17tmhel927PiA66YqWmaRmRKlCCVaEg9bp16xbpQwAAAECUCHmmW12c1e1XpbgaTz1w4ED7/fff3djtQDVr1nRjulesWHHGx1SArnmRGzVqlGiprJ7jsssuCzp1DwAAAAAAUR10q+GZxmKfPn066HY1NNNYRjUP0q0y28r4qKGQplOaOXOmf9/SpUtbqVKlkvS8GhupzHli0w2pMZvKZfU8yqADAAAAABBpyWqkpkBZga3m9W3fvr1raKZGU14jNWWiv/vuOzeWMXDstTLdmvdY83qr/Fu30YhGagAAAACAiDRSO3r0qN199912+eWX27///muDBw9204Cps/OAAQPsn3/+ccG3ysDjNjtTmblKv7dt22a33367JtNN6tMCAAAAAJBupWjKsEOHDrkAWw2mpk+f7ubezpkzp3Xo0MF1DQ9WNq4A/bzzznNzbI8dO9ZlyqMNmW4AAAAAQNTM0y07d+60ESNG2MiRI2337t2WK1cuGzJkiD3wwAPx9v3000/twIEDUdvojKAbAAAAABBVQbfnyJEjrtHaK6+8YpMnT7amTZtaekPQDQAAAACIyJjuhOgJVDZ+5ZVX2nPPPWfr1q2LF3BrWjB1M1dncQAAAAAAMotUB905cuRw3cnVJE3Kli0bbx91Ll+1apW9//77qX06AAAAAAAyT9CtubglT548Ce6TPXt2a9euna1cudK2bNmS2qcEAAAAACBdyJacnW+44QaXsVagnS1b7Ltu2rTJZbQTsmfPHnf7+eef2+OPP57S4wUAAAAAIGMG3epUvnnz5qDbNCXYmjVrzvgYBN0AAAAAgMwiWUG3xmSr2bnKxbNmzerW6fuKFStapUqVbNasWQneV/u1adPGli5dalu3brUyZcqk/ugBAAAAAMgoQbe6lMd16tQpdxsTE2PlypVL9P7NmjWz1atX29ixY+2pp55K7rECAAAAAJC5GqkdOnTI3R4+fPiM+9arV89lvEeNGmWnT59O7VMDAAAAAJBxMt3BqNR86NChlitXrjPue8EFF1jNmjWtS5cudvLkSTfdGAAAAAAAGVWMT6lnOPv377eCBQvavn37rECBApE+HAAAAABAOo8fU11enljZ+QsvvGDvvPNOuJ4CAAAAAICoFragW+Xjapb21ltvhespAAAAAADIWEH3999/b99+++0Z98udO3esWwAAAAAAMptkB92tW7e2pk2b+gPwBg0a2O+//x5vv2zZ/uvR5s3nDQAAAABAZpPsoDtPnjz+TuVdu3a1xYsXu47kw4cPj/3AWf57aIJuAAAAAEBmleygW9N8aZowUaB9ySWX2JEjR+yJJ56wq6++2v7+++9wHCcAAAAAABk/6Fbm2steX3bZZbZ06VIbN26cFSlSxGbPnu2y3tOnTw/HsQIAAAAAkPm6l7dv395+/PFHa9Soke3cudM++OCDUDwsAAAAAADp2n/dzkKgRIkSNmfOHHv11Vft7rvvDtXDAgAAAACQboUs6JaYmBjr1q1bKB8SAAAAAIDMFXQfPHjQnnzyySTt+8cff8Tb9/Tp03b8+HG3nDhxwkaNGpWSwwAAAAAAIKrF+Hw+X3LuULVqVVu/fr37Opl3jf3EMTHu/ro9deqURYP9+/dbwYIFbd++fVagQIFIHw4AAAAAIEolNX5MUaY7b9689tBDD51xv8GDB1vZsmXttttui5fpPnbsmD/bDQAAAABARpSiTLci+W3btp1x3yxZsljjxo1dg7X0gEw3AAAAACCU8WNIpgwLHOv93XffhfIhAQAAAABIt0IWdCvYrlWrlrVs2dI2btwYqocFAAAAACBzB93Dhg2zBg0auE7lGqu9ePHiUDwsAAAAAADpWrIbqWkIuDcMXE3Q7r//fhs7dqxbd+ONN9qrr75qpUuXDsexAgAAAACQsYPukydPukUGDhxoY8aMsdy5c9vbb79td9xxRziOEQAAAACAzBF0Hz582I4cOeK+7tmzp61cudKee+45q1mzZjiODwAAAACAzBN033DDDW7ctjdf91dffRV0P28f7xYAAAAAgMwm2UH3G2+8kaT9vGy4dwsAAAAAQGaT7KA7qbJmzWoPPfSQlShRIlxPAQAAAABAVIvxea3IYfv377eCBQvavn37rECBApE+HAAAAABAOo8fQzJPNwAAAAAAiI+gGwAAAACAMCHoBgAAAAAgTAi6AQAAAAAIE4JuAAAAAAAyY9C9adMmi4mJSXSR7du32y233GIlS5a03LlzW+XKle3pp5+248ePR/olAAAAAAAysbDN0x0KRYsWtfHjxwfd9uyzz7rbEydOWLNmzWzv3r32xBNPuFbtS5Ysseeff962bt1qY8aMSeOjBgAAAAAgHc/TvXTpUqtXr55NnDjR8ubNa82bN7eZM2faVVdd5d+nY8eO9tFHH9mhQ4csR44cSXpc5ukGAAAAAFhmn6e7R48e1qBBA2vTpo3t2bPHrcuXL1+sfXLmzGlZsmRxCwAAAAAAkRDV5eXBTJs2zRYtWmQLFy503yv4VsDdvXt3Gz16tBvXrX1Uln799ddbtmwJv8Rjx465JfBKBQAAAAAAmba8/Morr7TDhw+7EnPPd999Z1dffbUdOHDAv+7WW2+1d99915WfJ6R///42YMCAeOspLwcAAAAAZLry8hUrVtjcuXNdVttz8uRJ69evnx05csRat25td955p5UtW9a++OILe/vttxN9vD59+rg3yFvUeA0AAAAAgExZXv76669b8eLF3VhuzzvvvGOzZs1yjdSaNGni72jerl07181cmfGaNWsGfTyN+9YCAAAAAEA4pJtMt0rKP/vsMzcfd9asWf3rp0+fbjVq1PAH3JI9e3br1auX+1qZcQAAAAAAIiHdBN2TJk2ygwcP2m233RZrvYakHz9+PGiQ7pWfAwAAAAAQCekm6FaWu1ixYnbppZfGWl+xYkX75Zdf7Pvvv48VcA8ePNh9Xbt27TQ/VgAAAAAA0k3QrWz1nDlzrFGjRvG23XPPPZYrVy6rX7++XXHFFa6LeZkyZWz27Nl2zTXXxCo7BwAAAAAgLaWLoPvbb7917dgbNmwYb1u1atVswYIFLsBWxltjuNUc7d5777WPPvooIscLAAAAAEC6nKc7GuZZAwAAAABkbvsz4jzdAAAAAACkJwTdAAAAAACECUE3AAAAAABhQtANAAAAAECYEHQDAAAAABAmBN0AAAAAAIQJQTcAAAAAAGFC0A0AAAAAQJgQdAMAAAAAECYE3QAAAAAAhAlBNwAAAAAAYULQDQAAAABAmBB0AwAAAAAQJgTdAAAAAACECUE3AAAAAABhQtANAAAAAECYEHQDAAAAABAmBN0AAAAAAIQJQTcAAAAAAGFC0A0AAAAAQJgQdAMAAAAAECYE3QAAAAAAhAlBNwAAAAAAYULQDQAAAABAmBB0AwAAAAAQJgTdAAAAAACECUE3AAAAAABhQtANAAAAAECYEHQDAAAAABAmBN0AAAAAAIQJQTcAAAAAAGFC0A0AAAAAQJgQdAMAAAAAECYE3QAAAAAAhAlBNwAAAAAAYULQDQAAAABAmBB0AwAAAAAQJgTdAAAAAACECUE3AAAAAABhQtANAAAAAECYEHQDAAAAABAmBN0AAAAAAIQJQTcAAAAAAJkx6N60aZPFxMQkuowZMybR7Y0bN470ywAAAAAAZFLZLIoVLVrUxo8fH3Tbs88+624bNmwYdJ+jR4/agw8+aOedd17YjxMAAAAAgHQXdOfNm9fat28fb/3SpUvtt99+s4kTJ1rFihXdEtegQYMse/bs1q9fvzQ6WgAAAAAAYovx+Xw+S2caNGjgSscXLFgQdPuuXbusUqVK1rVrV3vuueeS/Lj79++3ggUL2r59+6xAgQIhPGIAAAAAQEaS1PgxqjPdwUybNs0WLVpkCxcuTHCfl19+2QXlPXv2TPSxjh075pbANw0AAAAAgEzRSC2Yl156yS699FK74oorgm4/cOCAvfXWW3bfffdZ/vz5E32sF1980V2Z8JYyZcqE6agBAAAAAJlRugq6V6xYYXPnzrXu3bsnuM/bb79thw8ftkceeeSMj9enTx9XCuAtW7duDfERAwAAAAAys3RVXv76669b8eLFrU2bNkG3a3j6yJEj7YYbbrCSJUue8fFy5szpFgAAAAAAMnWmW9nrzz77zG655RbLmjVr0H00znvjxo12++23p/nxAQAAAACQboPuSZMm2cGDB+22225LcB/N112oUCFr0aJFmh4bAAAAAADpOuhWlrtYsWKuiVowp0+fdoF58+bNKRkHAAAAAESFdBF0nzx50ubMmWONGjVKcJ/ly5e7+bkT2wcAAAAAgLSULoLub7/91s2h3bBhwwT3mT59urtNbB8AAAAAANJSjE8tv+EosNd83Zo+rECBApE+HAAAAABAOo8f00WmGwAAAACA9IigGwAAAACAMCHoBgAAAAAgTAi6AQAAAAAIE4JuAAAAAADChKAbAAAAAIAwIegGAAAAACBMCLoBAAAAAAgTgm4AAAAAAMKEoBsAAAAAgDAh6AYAAIhyJ06ciPQhIBMI93nGeYy0EI3nGUE3AABAFPv999/t/PPPtyVLlkT6UJDBz7OqVava0qVLw/b4nMcIN52/Oo91vkWTbJE+AAAAAAS3b98+a9Gihe3Zs8fy588fb/uuXbts6tSp9uuvv1q2bNns8ssvtyuvvNKyZ88ekeNFdPrrr79sypQptmnTJsuVK5c1adLErrjiCouJifHvkydPHjt16pRde+21tmLFCitbtmyancfRavXq1TZ9+nR33Geffba1bt3aXThAZGzYsMGdx9u3b7d8+fJZ8+bNrXbt2rH20Xr9vFq2bGnLli2zggULWlTwwW/fvn0+vSW6BQAAiLS2bdv6smXL5ps3b168bVOnTvUVKlTIfXYJXGrXru3bvHlzoo+7atUqX/78+X39+vUL6fEuXrw43vEELt26dfPvO3fu3ET3DVy0b1rp1KlTko6pXLlyse43cODARPf/+uuvU31su3bt8j344IO+smXL+rJnz+4755xzfFdccYXv448/TvA+77//vi9XrlzxjqdZs2a+3bt3x9p37dq1vgIFCvguvfRS38mTJ31pcR4H07JlS3eMGzdujMh5fOLECd9DDz0U7z2LiYnxde/ePeh9rr766kR//gcOHPClpUaNGiXpPNZ+ge65555E9//pp59SfWybN2/2dezY0VeiRAl3HpcsWdK9fzNmzEjwPs8//7wvS5Ys8Y6nffv2vmPHjsXad/78+e58a9eunS9a4kcy3QAAAFFowoQJNnHiRBs0aJA1atQo1ra1a9da27ZtLUuWLPbyyy9b06ZN7c8//7TnnnvOZXfuvPNOmz17dtDHVZZI2cwDBw6E/JiVIZXy5csHzTCVKlUqVkaqZs2aiT6eMvjHjx+3HDlyWFpRhjex4zp06JArXY17TN5rv+CCCyxr1qzx7pfajNvevXtdJYN+9nnz5rWKFSu6n+WiRYvcorLaoUOHxrrP3LlzrUuXLla4cGEbPny4XXrppe7+Tz/9tM2YMcO6detm77//vn//KlWq2Ntvv2233nqre6zu3btbOM/jYEaOHOmqN84knOfx888/746jVq1a1r9/f3dOzJ8/35555hl75ZVX7MILL7SOHTvG+/mrikDvYTDBzolwqly5sjtnEqJs8JYtW4Kex/q7otcYTO7cuVN1XJs3b3bn4T///ON+J3Qe62/XzJkz3TJs2DB79NFHY91nzJgx9tRTT1mFChXcz6Z69eruOPv27WsffPCBnXvuue5n42nYsKHbr3fv3vbZZ5/ZjTfeaBEX9vA/HSHTDQAAooEyN+XLl/fVqlUraMaxefPm7jPLpEmTYq3fv3+/r0iRIm7b77//Hu9+WletWjV/lijUGUJlr/S43377baofa+XKlS6zqPfg1KlTvmjx2GOPudf46quvxlqv7HPu3LnDdqyPPPKIe97777/fd/z4cbdOz/W///3PZQu1bfny5f79T58+7Tv//PN9WbNm9S1btixeplH30XLkyJF4z3Xttdf6ChYs6DLr4TyP4/rll1/ce+idnwllusN5Hm/ZssW9L/p57t27N9a2Dz/80D2fqgsC/fHHH269fi/TixtuuMEd88SJE/3rjh496l57lSpVwva81113nXteZa51jorOZ/0+aX3OnDl927Zt8+9/6NAh39lnn+0qGuJW8CxZssTdp3Tp0vGeR+ebzruKFSv6f18iGT8SdAcg6AYAANFg/Pjx7jOJSsjjUiCiQKpBgwZB7+sF5FOmTIm1ftasWe7Da44cOXyXXHJJWILu6tWru0A5FKW0Kn/WMeq4o8WmTZtcUKAP8oElrTt27HDHWrdu3bA9d6lSpVxApCAkrg4dOrjnf/nll/3rFixY4NZpWzAKrLT9559/jrdtzZo1btuLL74YtvM4Lr2fF110kds/b968CQbd4T6Pn3vuOfeY7733Xrxtf//9t9tWtGjRWOs//fRTt75Hjx6+9MALVuNePNDFGa2/6aabwvK8hw8fdmXfZcqU8QfcgfQ3Tc+v9zPuOfT0008HfUz9Pmp7sN8L/Q3Uto8++sgX6fiR7uUAAABRZty4ca7s8pprrom3TaXb+/fvt88//zzofbdu3epuixQpEmu9Ss/VYE1lxWpqFWoHDx505eDnnXeeKx1PDZXGe8ep0vlooRLXY8eO2YsvvhirLFcl/XLxxReH7bn//fdf14RMDc/iUpOvuFMlNWjQwJUXq6w8LiXeVNIb7DwRle82btzYxo4dG7bzOC6VvK9cudI6depkderUSXC/cJ/H+hlv27bN2rdvn+TfrbT4+YdSz5493a1K5dPydezZs8dOnjxpRYsWjdXEL7HzWD+HHTt2+I85kNbr91G/E8F+L3R+6PxL7XkcCgTdAAAAUUQfIhcuXGjXXXdd0A+mog+Y+uAaLFj95Zdf7JxzznHjUQNpDPjPP/+cpHG1KaGA6fTp01avXr1UP5bGYuq1Dx482KLFTz/9ZP/73/9cQHjzzTfH2rZ8+XJ3G4rXnpAyZcrY7t27/WPHPeo4rrGwEncsusbMnnXWWfEea/To0W5suvbXuRLM9ddf78Z/e4FmOM5jj8ZLKwDUOOTXX3890X3DfR5LiRIlgvYRGDFihLtt1qxZmv/8Q0Xdv9UDoF27dm5sdVq+jiJFirhx7/r56cJGIF1IXLx4cdDzWPcLdiHP+3lcffXVQZ9P553G/ev8Um+ISCLoBgAAiCLr1q2zo0ePJuuDr6Zkevfdd10wqCZIb731Vryg4ZFHHnENtcLF+8Cu5kz169e3QoUKucysPtirKZWCw6TQhQM9lrJUCTVzioSXXnrJZYiDZdy81/7NN9+4LLEuiihQaNWqlVsXCvfdd5+7ve2222zBggUuaP7tt99cJlAXWi666KJ4wWCwzKAa7z388MOuIZbOk4R459+qVavCeh4rG6+mZGo09tFHH52xSiLc53FCr+Xee++18ePHuwsDgU27dE7oQojeT60vV66cCyxLly5tnTt3dtUf0UQN7aRXr14JnsdqXKbmZHodxYoVs5tuuikk87fnyJHDNXnUBZk2bdrYDz/8YIcPH3YXtHQxZefOnW5atmrVqiX6OLoQ1KdPH3vhhRfc79mQIUMS3Ffn35EjR2z9+vUWUWErcE+HGNMNAAAi7csvv4zXFCsxGsercdTelEYTJkw44300BjbUY2FvueUWf2OrGjVq+O666y43NjRfvnxunaYE0lRMZ9KiRQu3/+zZs33R4s8//3TjqdUULFhDsOLFi7tj1pRGV155pe/ee+91Y9K9KY4GDBiQ6mPQGFg9TrBpnNScSuPKE/Pwww/799c42IULFya6vzdOfcSIEWE9j2+77Ta330svvRRvuqszTRkWjvM4rgsuuMD/vlWqVClWky/59ddf/ds1Fr1NmzZu2q2LL77YrdPY85kzZ/qiwXfffeeOKVg/iIMHD7peEdquc13Ttuk81rhv72/L6NGjU30MR48edc0Ag03Hpr8ZwcZmx23y591HY/v1/ifGG6ceiin7gmFMNwAAQDrkTYFUoECBJO1/ySWX2O233+7GQyrrptLslGYnU0NZK421/fjjj93zv/fee/bJJ5+46bU0jZZKoLUuMcrYTps2zZXGX3nllRYtXnvtNTfO9LHHHos39ZOybhpvXbJkSfe6lakfNWqUTZ8+3ZXL6uc4YMAA99pSQ1lqTQEWzKZNm1zJcGKuuuoqV1Ks6caUaVS2O7HScW+KM5X9hus8Vrm+stsqDw7F9GThoDHmTZo0cRUkGzZscOdA4JhjnfeicndVeWh6NE27puy3KjxU1qxp2yJd3ixeRlhTxSU0PESVGnqdKkPXeawhAl988YVly5bNunbt6rLRqbFlyxb79ttv463X3y5VFHjZ9oQoQ64KEmXNNdzigQcecJU+4TqPQyYsIX86RaYbAABEmqaA0ueRdevWJbsz8N133+2fQkeZq7TMECoTm9BnqKVLl7rnU/YvMffdd1+CnaMjRVNqFSpUyJcnTx43JVswyuAn9H4PGjTIvaYnnngiVcfRuHFjfzb9xhtv9L3wwgtu+jJvijhlCgO7Pidk9+7dvtatW7v7qFt4sC7SXkYyNR3Mz3QeqxO8piVTJ3B1BQ8UTZnuwOnMKleu7J5P73vc373AbvaBrrnmGnefr776yhdJW7dudZnscuXKJTitnV5DsCnkxMtOv/baayk+hiNHjvi75qvaQlMM6vzSY3sVMVqv7upJeT1e93pNf5aQtWvXhrWDOZluAACAdMjLzKjTb3J4Y3SVVVZnamWn0pKaFiWU1dS4bm378ccf3fjKYJR9nTBhguXMmdNlZKPFl19+6cYdqyGYxqgHoyygMsjBeE2elixZkuJjUGZw3rx5Lts6depU+/TTT92Y1qFDh7pKAo1bVaZQnb3PRI3VlF1WIz5lNxM6Lu/8887HUJ7HyqhqHLcylGrqVrx4cYt2VatWdZl5UcWGxqsH/u4Fa7wWqp9/KOjY1VdBPQF0HgWj16Bx3OF6HZMmTXLZbP2ufPfdd66ruCpz3nzzTde0TzMf6O+AN+48MRozr8dT5Yl+R71u/KE+j0OFoBsAACCKVKpUyd2mpPGPPoCqjFjUZCuaqPRcwZZKQoP5+uuvXXDbsmXLiH9ADqTmWaJgJSW8YCw1ZbkKULzS2ubNm8fapvfKa4imrtCakulM1Kzs8ssvT/Q88dZ752Moz2MFbmoGp/emb9++bjhB4OKVGOtc0PeJNXxLSxrKoYsWKp3fvn17mv38Q+GDDz6ImvP4/vvvj9ehvFSpUq7Jn+jiXFJoSIeaLeqCky4+heM8DhWCbgAAgCiirsHKCnvT58SljKemwVG2Mhgv6Io79jicNm7caM8++6zNmTMn6PZ//vnHBdvKsHlz8YY6KAgHjaPW2Gx1Yk9ormkFkI8//rh7jcF4Y7mDzYedVAoqvHMjoSys6KKG57PPPnPHrOmSUnKeaIy4qhfUFT3U57H33BrnrHHwcRd1Zhd1/tb3SQ1wQ0HnsfoJJDRO2BvP7b1v6mEQrKN9KH/+qaUgds2aNa4reI0aNYLuoy77GrOd0NjzSJ3Hb775pvt5JHRxKCnnsS5MEXQDAADATx8emzZtal999VXQrKUyTpMnT7b+/fu76XYCqXxU5ccSN5MUTmq81K9fP1fuHIzKcfWBW5lCleLGdfDgQfehX9lwZTejhUr09TPQ9GUJlQ/r/R42bJj/okFcmspNGjdunKo5uuXvv/9O8KKHlC9f3pW6i4InXTDQNFaBQYwooFSDrMTOEzUEq127dtD54FN7Huu90PmQ0OLNwa3Xpe91rqcVlSmrYZ2XdQ2k9TpXNWWZyptF84pr37jzp4sqN3TxI7U//9TScARv7vXEys/1WlRxEuxCg0rB0+o8PjcgKFd5uN53NSOMS9ltXZjROa8GcHHpvNPrUWl8Wl6EDCosI8rTKRqpAQCAaKCmS/pM8uGHHwbdXqdOHbe9efPm/imM1MxLDZ68qY2OHz+eZg2oNI1WxYoVgzYs0mvJnTu32zZp0qREX+9ll13miyZt27Z1x/XWW28luM9vv/3mGlSdddZZrtmWRw3KnnrqKf9UUmr8lFJqNKapp4oVK+bbu3dvvO2a2knP89xzz8VqWqWGelrfvn17//00JdPNN9/s1jds2DDo8y1evNhtf+ONN3zhPI8TEslGaj/++KP7eaph3auvvupvOqbGb9WqVXPP98wzz/j31zRaWle3bt1Yjfb0tddELbGGdWnBm75s2rRpCe4zd+5ct49+jwOnRdPfkc6dO7ttJUuWTLRB45msWLHCPc6FF14Y7++T3h/v/Ro/fnysc99rstajRw/X4E927tzpP0/UkC2xZn5Tp071RTp+JOgOQNANAACigT6Aaq5rfQBWZ+S4NDftOeec4+9mrSBbQZ++VzdoBQ6pDVY0z3LNmjXdvNlJMW/ePBdcq4v25Zdf7uborl27tv8Y1W37THNI9+zZ84zP06VLF3dcb775pi+cdCFBXct1XIHBdDCDBw92++XKlcsFDgpqy5Yt69bpMaZPnx7vPk8//bR7HbpNCgXAem9r1arle/fdd30zZszwffDBB26Obu+CRdzO0wsWLHABv7Zny5bNd+655/oDGJ0zmzdvDnru1a9f3wVYwc69UJ7HaRF0J/c8liFDhsSae1sdt/X+6Xt1fg/sVK6gvFWrVv7fPXXS1lzdBQoUcOuqVq0a9HXoeHRcOr5w0nzrOm90IeHAgQOJ7vvAAw+4Y9ax63WqS75ek9aVKlXK98MPP6T697F3797u8dSNX8G1zuP333/fXQDSer13cS9QTJgwwf/+q7u5fh76XfMuduzZsyfe8+h803mnCw7hvOBB0J0CBN0AACBaKPOkD8sKSINRNkpT7ejDsD6QlilTxvfQQw/Fm34ppcGKl8HTFENJpel5brvtNl+JEiXch+MKFSr47rrrLt/KlSsTvd95553nnmvy5MlJDsjCPU2Upi3yAqmkUPDQpEkTNw2WAlsFm3369Enw59GpUyf3+LpNqoULF7pASD/z7Nmzu2nMlDUcMGCAy2AHoym77rjjDvc6dB9Ne6XjSujz7iuvvBIv2xjO8zjcQXdKzmPv53nVVVf58ufP7wI8XUAaNWpU0Om2VGWioFMXRLSvLoA1aNDA9/rrryc4BZeOR8el4wsnVRnoeVQdcyYKTj/++GN3AUcXGxR8K6jVBbNgFRYp/X38+uuv3YUKXTjU3y69x3qeYcOGuYtdwSxbtsx3/fXXu4tYqvqoXr26u9iV0FRtOt903s2fP98XDfFjjP6JbIF79NCk6Rpor3EuCU15AQAAkFbUoEvjhTWtUufOnSN9OFFBUzWpa7GmC/LG/iI0Zs+e7ZqvaXq0zz//PGSPy3kc37Zt21xzLzU4i3STr4xm7Nix7jx74oknbMiQIVERPxJ0ByDoBgAA0USNgNQESB14FWS2bt3aMjs1fHr//fddgIjQ0ZzdDRs2tLJly9rSpUsTnJM8JTiP4xs4cKD98ccfrskgQkcNGXXRqEGDBjZjxgx/Y8FIx490LwcAAIhS+sD45ZdfWrt27VwnaZjlypUrwS7pSDl1jFYwrEAllAG3cB7HV7x4cdfxH6Gled11nmnmgXAH3MlBpjsAmW4AAAAAQFKQ6QYAAAAAIMIIugEAAAAACBOCbgAAAAAAwoSgGwAAAACAMCHoBgAAAAAgTAi6AQAAAAAIE4JuAAAAAADChKAbAAAAAIAwIegGAAAAACAzBt2bNm2ymJiYRJe45s2bZ1myZLEXXnghIscMAAAAAIAnxufz+SxKHTp0yCZNmhR027PPPutu169f71938OBBq1GjhuXPn99WrFhh2bJlS9bz7d+/3woWLGj79u2zAgUKpPLoAQAAAAAZVVLjx+RFpWksb9681r59+3jrly5dar/99ptNnDgx1voePXrY5s2b3fbkBtxAYk6cOGHZs2eP9GEgHeGcAQAAQNSXlydEwXWDBg2sTZs2/nWzZs2yt956yx555BGrW7duRI8PGcvvv/9u559/vi1ZsiTSh4J04siRI9a0aVMbMWJEpA8FAAAAEZbu0sHTpk2zRYsW2cKFC/3rDhw4YF26dLHChQvblVdeaV9++aXVqlXLypUrl+hjHTt2zC2B5QFAIJWKtGjRwvbs2eOGLUTSqVOnXL+CYL0MkHb092bGjBn2ww8/uJ9F7dq17ZprrrHcuXP791GljUqMHn30UatYsaK1atUqoscMAACAyEl3me6XXnrJLr30Urviiiv86/r162dbtmyxXbt2WceOHe3WW2+1ChUqWLdu3ez06dMJPtaLL77oavC9pUyZMmn0KpBe3HXXXa6hn3oLXHjhhfGC4FdffdVlwXPmzGmVKlVy34eyTcKaNWtcRUehQoVcIKfA7uqrr3ZDKII5evSoy67Wr1/fXSTQea2AcPr06RYJY8aMOWMzRG/R++xRVUFi+3bv3j3Vx6af3yuvvGIXXHCB5cqVy8466yyrWbOma8IYeDEu0LJly6xatWp24403uv0GDhxobdu2defG6tWr/fuprPzTTz91j6chMn/++WeqjxcAAADplC8dWb58uaIZ36effupft2XLFl/27Nl9+fLl882ePdutO3TokK9nz55u3+effz7Bxzt69Khv3759/mXr1q3uPvoa+Pjjj935MGjQoHjbTp8+7evQoYPbrqVSpUq+AgUKuK+7du0akudftGiRL0+ePO4xCxYs6Lv88st9xYsXd9/rnP/mm29i7b9jxw7fxRdf7LbHxMT4zj//fF/hwoX9x9ijRw9fWvvyyy99NWvWTHTxju+vv/7y3++1115z68qXLx/0Pq+++mqqj+2WW27xv5eVK1f2v7da6tev7zt8+HCs/f/9919f0aJFfVmzZvX17dvX9/333/tmzpzpa968ubuP3u9jx47Fus+GDRt8efPm9bVq1SrVxwsAAIDoorgxKfFjugq6O3fu7D4Ynzx50r/urbfeci/06aefjrd/xYoVfRUqVAj5m4bwOXw8dqATKQqeFPDVqlUr1vnmeeONN9y5okB73rx5bt3Bgwd9TZs2dQGvty6lFPCVKVPGPUevXr18R44ccet1e+utt/oDUgX/njZt2rj1derUccGeaPubb77py5Ili9s2btw4XzT54osv3HFdd911sdZ37NjRrf/222/D8rwTJ050j3/ZZZe5YNqzbNky//s+ZMiQWPe577773PqhQ4fGWn/ixAlf9erV3bZZs2bFey49jrZ5FwUBAEjIiUOHIn0IgN/69T7fihXJX3S/zGJfRgu6lb1WNvvRRx+NtX7gwIHuhc6fPz/efVq0aOHLkSNHkp+DoDuy5m+a78v9fG7fgk0LIn0ovvHjx7tzYerUqUErJIoVK+a2v/fee7G2KdhVgNu4ceNUPb+X6VWgF9euXbt82bJlc9tXr17t1q1du9Z9nzNnTt/GjRvj3eexxx5z2y+99FJftNDFjGrVqrnXouMPpCBWFy8OHDgQlue+44473PuhTHVc+plqW2B2WhdUdIFFFzpOnToV7z5eQD5y5Mig50vp0qVdRhwAgGC2f/edb0b79r4Pa9TwHdm1K9KHA7jAWTXRKV0yS+C9L4nxY7oZ060xtZqH+7bbbou1vlSpUu42WHOp7du3W7FixdLsGJE6fef0tSMnj7jbSBs3bpxrgKXx0HHNnz/f/vnnH9e4r0OHDrG26T7qWq1mf2rCllIaH65O/M8880y8bWeffbYbqy179+51t7Nnz3a3eu7y5cvHu891113nbn/++WeLFhrv/csvv9g999xjVapU8a/X7/mvv/5q5513nuXLly8sz/3vv/+622B/H/T+elN+BU5fqPtorLma2cW1detWd1ukSJF42zTe/+6777aZM2fa33//HdLXAQBI3/75/nub2aGDzb7zTtuxcqX5Tp60EwcORPqwAEvtachpHFu6Cbo/++wz9wFZTdQCqVt51qxZ7X//+1+s9d999539+OOP1rx58zQ+UqTEvE3zbNGWRe7rhVsWuu8jRU201B1fgWqwiznqWi1NmjQJOg+zztGTJ0+6czClWrZsacOHD7eSJUvG26aAX93UpUSJEu7Wa9SlQDUYNQqLJnqP1QAxT5481r9//1jbVq5c6Rog1qtXL2zP7zVNVBfyuL755ht3qyZocYNn7/0OpAsEuuih7Y0aNQr6fNdff717TZraEACAwGB756pV/60MYSNWANElXQTdCmDmzJkT9AOtPjxr3u5Ro0a5jsKaq7tPnz6uw7O6ET/55JMROWYkz9Nzn7asMVnd17p9Zm78DG9aWbdunesCnlDQ99dff7nbuN3MA7PUEtiNO5Q++ugjF8BVr17dKleu7Napu7l4wXhca9eudbfqvB0NPvjgA/c+du7c2c4555xY25YvX+5uNSOBurDrtakTuy5mjBw50nUdTy1NMaiLdQr4x48fb7t373ZZ6EGDBtl7773npvt64IEHzjgX9yeffOKqIXQRQTMrJFRZowBeFxhWeR+sAACZUrBg2xeC/9cARLd0MU/3t99+6+bQbtiwYYJTf1WtWtUF3L169XLlnwqYNB2Qpg5D+slyyynfKX+2u3H5xml+PF6w7AXPcR0+fNjdBstCiy72BJYch5J+DwYPHuy+7t27t3+9pr2SqVOnurJ2r/xcFKS+/vrr7uubb77ZIk29JDS1mn5PH3/88XjbvQoBXWirUaOGtWvXzs2NrQz0ww8/bF9++aV7nZpCLaUUzE+cONFNMaglkN7LsWPHJvq3Q8H27bff7r8AoIBbwwESoooJPV64LsQAAKI/2F49YoTt+OEHi8n6X5KBYBvIPNJFprtBgwbug/pDDz2U4D764Kzxlgo4lO3TvMQJZSIRvVluTySz3QrwRNnOYLx5uJW5DEZzaXtjk0PtiSeecL0KLr/8crvjjjv86zWMQgHqjh077KabbvKPWdZrUVZXJfEqMe/UqZNF2rRp09xYbpXve5n6QDpWle1//PHHLjOszLOC3N9//90FxBobrXWpoUoBlZbrIkZcO3fudNsSm29dF/mUpfcuvLz88stnLB3XhZBgzwcAyLjIbANIN0E3Mn6WW9ntQIHZ7rTmjeNOKOjyxkd7wXVcXgZW5ceh9Pnnn7tgU83FlIkNHG+uUunJkyfbxRdf7ILSsmXLuvHdauylfeXBBx8M2ugrrWmsunTt2jXB8n4Fvrfcckus9Srdfvfdd93Xb7/9dqqO4dlnn3Wl6voZK+utMvOnnnrKld/rooaGqDz66KMJ3l8X9HQsmzdvdmPTdbFDGfnEqhsU6AdrwgYAyHj+Xb6cYBuAH58AEXVZ7khnu73S7ITGR3vjpxVoBeNlM9WLIFTWr1/vMtaiwDtY6bv6Gyxbtsy+/vprV7bdrFkzq1Onjv+Yo6G/wbZt29xFAWWIGzcOPnRAFxMSqjLQuG5tU5PElF7U0Hj9YcOGua8VaC9evNgFzs8995z99NNProRd3nzzTX/FQEJ0gUUBe4sWLdzPXV3vE6LzKbDsHwCQMZ08etTm3nefKyUXgm0ABN2Iuix3pLPdXkCrQDexztcJjc9Vd3FvmqlQUDDXpk0bN3RC5eWJjctWJrV169auz4HGTXtdzfv27eumOIu0Dz/80GV8lcVOadZXped6DDU/Swk1ldN7qQZuAwYMiLVNxzR06FArWrSou2iS1CnWvFkSfvvtt6Db9VgbN25MsE8AACDjyJYrlzV5+20revHF7ntvDDeAzIugG1GZ5Y5ktvvcc8912VRlQIOpXbt2rKnD4lq6dKm7LV26dKqPRXNFK8jWGGhlrtWwK6lU/qwO4Bo3nViTr7TuWi633XZb0O0KTFX6rSZqCV3QULCt4NibTzu5vGEDmlM9WOCv7LXOAVFwL8qAq3xcAXkwXlWDyvyDUQXC8ePH/ecOACBjO6d2bbt6/HhrOnq0Ffm/KSgJvoHMi6AbUZnljmS2W4FT06ZN7auvvgpaIq7ASR3KFRhqmqlYx3vqlCuf9vZLDT1Whw4dXFPA888/3yZMmJBgUBeXSq9feOEF97UCxRw5clikKQusxmgqLa9bt27QfTZs2OBKvRMKblVar6D5kksuSXBM/Zl4lQpxf3aBvCoGL/jWz/uLL76wgQMHujHfcelcCTa3t0ed0pWhv/LKK1N0zACA9KnYJZcQfAMg6Eb0Zrkjme2+8847XVD26aefxj+erFmtffv2LgutsdOBDdfUnEvzTysLe8UVV6TqGNRoTIG2mp9NmTLFP5Y8KYYMGeKOQ2ONVW4eDdS1XBKa+k+aNGniMtBqCqfu5YE0Vv355593X2tqwJTS+6lGaGqC5k1PFncedI0914UXNaTzqhZU4r9r1y73fnpDD7zpzxYsWOAC88CO8h51kB89erS7vzedHAAgcyH4BjK3GF9i8+JkMho7q0ZHGu+ZUCMnpJ6y1k3GNkn2/eZ2mptm83br16JWrVpu2q81a9bEy6oqIFdWU83UWrZsabfeeqsrIX7jjTdchnrQoEHxAkM9nlf27TU4S2zcsaalEo0vTmhO8Pvvv98tcY9NGVpdFFBZtLqYJ0Rz22vR8XidwcPl2muvdcG03qMHHnggwf3mz5/vLhao4Zk6i+u1//HHH7ZixQpXDq7AW93F42aan3nmGbev5vA+k++//96uvvpqN+2bLpxoujXNv67qBa9D/LfffhtrSjOVtmv6QmXs1exNAbkqCtRsTeP3J02a5B4zLh2r5lbXcATvHAAAZG5x5+0ObLZ27dSplr9cuYgeH6BRlKkp2lyxwuz/2hpkaEmNH/+b2whI4yx3jMWYz5J+vUf7K9u94M4FYT02//PFxLiprVQO3LNnTxsxYkSs7SVKlHDlxmoIpiAvMNBTAN6jR494j6nS6qTO360A06PAPqFO6cFKndWl/NChQy7oTyzg9u6v40pOFj0ldAFg3rx5Z8x0S6NGjWzlypWuyZnus3z5chdM33XXXS77Hyxw1ThvvY69e/cm6XhUnq79Nb+2LjZoqi+N39bzdOzY0TWe87LcgVOW6cKKyvZVAaEmdapo0Nzn6oIebM5xZdJfeeUVd+wE3ACAuJnvwODbNBUouTAgQyLTHYBMd/gdOn7Izhp8lp04fSLZ982eJbvt7b3X8mTPY2lFWVBNL6Xy4M6dOwedBuq1115zzdN07igIVxlxeqJMvabi0lhqhI5K1BXcazy9LiIwXRgAICFe8L1z9WprM3eu5Uphs1AgVDQhyxlyN4nSSLz/a42ToSU1fiToDkDQnTZ2HNph+47tS/b9CuYsaEXzFrW0pEZqKhletGiRKx+OlvHRoaLu5mr4pgZmnPOh/VuiCxnKhuvcSajBGgAAgU4ePmzZ8qRdcgE4U+B94EDy75c/f+YIuIXyckQtBc5pHTynlKaP+vLLL+3ee+/NkNM9qWxdY5gJuENL72erVq1cFQEBNwAgqQi4EU0yS+CcFsh0ByDTDQAAAAAIZfzIlGEAAAAAAIQJQTcAAAAAAGFC0A0AAAAAQJgQdAMAAAAAECYE3QAAAAAAhAlBNwAAAAAAYULQDQAAAABAmBB0AwAAAAAQJgTdAAAAAACECUE3AAAAAABhQtANAAAAAECYEHQDAAAAABAmBN0AAAAAAIQJQTcAAAAAAGFC0A0AAAAAQJgQdAMAAAAAECYE3QAAAAAAhAlBNwAAAAAAYULQDQAAAABAmBB0AwAAAAAQJgTdAAAAAACECUE3AAAAAABhQtANAAAAAECYEHQDAAAAABAmBN0AAAAAAIQJQTcAAAAAAGFC0A0AAAAAQJgQdAMAAAAAECYE3QAAAAAAhAlBNwAAAAAAYULQDQAAAABAmGQL1wOnRz6fz93u378/0ocCAAAAAIhiXtzoxZEJIegOcODAAXdbpkyZSB8KAAAAACCdxJEFCxZMcHuM70xheSZy+vRp27Ztm+XPn99iYmIifTgZ/qqQLm5s3brVChQoEOnDAUKC8xoZFec2MiLOa2RUnNtpR6G0Au6SJUtaliwJj9wm0x1Ab1Tp0qUjfRiZiv4Q8McAGQ3nNTIqzm1kRJzXyKg4t9NGYhluD43UAAAAAAAIE4JuAAAAAADChKAbEZEzZ07r16+fuwUyCs5rZFSc28iIOK+RUXFuRx8aqQEAAAAAECZkugEAAAAACBOCbgAAAAAAwoSgGwAAAACAMCHoBgAAAAAgTAi6AQAAkKGpb/CGDRts06ZNkT4UAJkQQTfS3Jo1a6xFixZWsGBBK1WqlA0bNizShwSkyEMPPWSNGzcOuu3LL7+0iy++2PLkyWMXXnihzZo1K82PD0iut99+26pWrWo5cuSwfPny2Q033GBbt26NtQ/nNtKbjz76yIoXL26VK1e2ChUquGXmzJmx9uG8Rnp28OBBd3537tw51nrO6+hB0I00tW7dOmvYsKEtW7bMevfubbfffrt169bN3nrrrUgfGpAsL730kr3xxhtBt02YMMHatGljp0+fthdffNHOPfdca9mypa1evTrNjxNIqldffdXuu+8+K1eunL322mv2wAMP2JQpU6xp06Z27Ngxtw/nNtKb2bNn2x133GHt2rWz5cuX27Rp0yxXrlzWtm1b+/vvv90+nNdI7x5//HFXyRGI8zq6ME830lSrVq3c1WX9x1ejRg23rm/fvjZixAj3n1/evHkjfYhAok6cOGFdu3a1MWPGWP78+a169eo2b948//ZDhw65LIoqOX744Qe3j/7Da9SokWXPnt3mzJkT0eMHgtm5c6cLtu+++24bPny4f32/fv3s2Wefta+//tqaNGnCuY10p3bt2la0aFEXbHvmz5/vqpTGjh3rgnHOa6RnkydPtmuvvdZiYmKsY8eO7vMJn0WiD5lupJl9+/bZ9OnTrXXr1v6AWx5++GE7cOCA2wZEuyVLlrjsn0q0FHDHpQB8x44dLjDXf3KSJUsWe/DBB90HPQU3QLTR3+Ann3zSBg4cGGt9rVq13O327ds5t5HueBk+VW4Eypkzp7vNmjUr5zXSNZ27ulh63XXXWdmyZf3rOa+jD0E30syvv/5qp06dctmSQCVKlLCSJUu6K3FAtDv//PNdX4Irrrgi6PaffvrJ3cY9z+vUqeM+AP74449pcpxAcigjoqojjeMO9N1337nbmjVrcm4j3VGQ0axZMzvvvPP8606ePGmvvPKKG+Oqc5nzGunZvffe6zLc7777bqz1nNfRh6AbaWbPnj3utmLFivG2FStWjI6iSBd0rqpcK7nnue4nnOdIL3Qu64Ocst36oMa5jfRezdGrVy/XTEqVdWqupgv+nNdIr0aPHm1ffPGFu9UQikCc19EnW6QPAJmHrqxJsHHbuuK8d+/eCBwVEJ7zXOd0IO97znOkF4899pjt3r3bPvvsM5dJ4dxGenb8+HFbuXKlbdmyxXLnzm2HDx926zmvkR4paH700Udd+fg111wTbzvndfQh04004/2ie38IAqmf39GjRyNwVEDoz3OVNGooRSCvZyXnOdIDNZgaN26c64jrTYvHuY30rHDhwjZjxgzbvHmzq9xo3769ff/995zXSHf0ObpTp05uDLdmUgmG8zr6EHQjzWhObtF/eHGp2UOBAgUicFRA6M9z/YcYd25jnePCeY5op9kl7r//ftfldvDgwf71nNvICIoUKWJvvvmmC0Y++eQTzmuky+kdFy9e7BoEan5uNUXTovNY0zvqaw2d4LyOLgTdSDOVKlVyv+SLFi2KN+7k999/dw3VgPTuoosucrdxz3NlVITzHNFMF0XVBVcf2D7//HPLlu3/j0Lj3EZ6s2vXLnvqqadcI9dA3vhXTQHJeY30OEWYLho1bdrUncveogD7448/dl/v37/f7ct5HT0IupFmNDWHPsxpfOC2bdv869WoR+UuV199dUSPDwjVnLAq+XrjjTdcl1zPO++8434H4nYSBaKF/i7rQ5zKDjUtnspxA3FuI73Rhf633nrLunfv7i+rlYkTJ7pbzULBeY30ZsiQITZz5sx4i5qkqVu/vr799ts5r6MMjdSQpnr37u2C7pYtW9oLL7xg69ats6efftqqVKkStBEEkN5oDFW/fv2sS5cudscdd9h9991nEyZMsKlTp7oxWCptBKKRztcNGza4KWhUYq4lsFKpXr16nNtIV7Jnz+6GSGgeY/UmuPnmm93FJZXnavjE9ddfz99spDu6UBRMrly5XAb7qquuct9zXkcZH5DGpk2b5itRooQuObulatWqvjVr1kT6sIBka9SokVuCGTx4sC9nzpz+87x169a+ffv2pfkxAkmxa9cu/7kabOnUqZN/X85tpDeffvqpr06dOr68efP6Klas6HvyySd9Bw4ciLUP5zXSu3LlysX6Wy2c19EjRv9EOvBH5nPkyBFbuHChGy/YoEEDdzUayGj++ecfN35K42MTujINpEec28iIOK+REXFeRweCbgAAAAAAwoRGagAAAAAAhAlBNwAAAAAAYULQDQAAAABAmBB0AwAAAAAQJgTdAAAkY+aF9NB/dNWqVfbKK6/Y6dOnLZqcPHnSBg0a5JbDhw8nuu//a+9MoLSa3zj+84+iDJIlSWWdaFH2pQyiZKkUlT2akaxZmkgkFcVROiGckrWyjSRLcyJbQs5YIiW0YIaSpXCsZ/7n85zze8+dO/e+yzTDvPX9nHPPO9339977u8vpnO/veZ7vw32ePHmyW7Zsmfu3+euvv6pl7EUXXeQ222wzV1RUVK3nFEIIkV1IdAshhBDOuaVLl7oOHTq4bt26xY4ZO3asa9q0qRs1alTGx58wYYLLz893/wY33nijGzx4sOvdu3etWiRgLtddd51tf/75Z9Kxw4cPdwUFBa5v374Vxv7yyy9u3bp1tu+ff/6p9jl+/vnnrmXLlu6tt95KOu7TTz91hYWFrnHjxm7+/PmRY7bcckv7bNCgQdJj3XXXXe7YY4+1RR0hhBAbH5v/1xMQQgghUnHxxRe7Z555xtWvX99tvvnmFj1MF4QZAo3I6nPPPecOO+ywWEGIeGrWrFnssV5++WVXWlqadEwUiKn777/fLV682H6LKA6Kt6OOOspttdVWbosttqh0fUSriRATCeU4X3zxhdt2221jz/XCCy+4WbNmmRgcN25chWPddtttJv4Rg1H3kXP98ccftt10000Wqa1OuD7Oyb2uW7du0rGDBg1yDz30kCspKTGRfscddyT2T5kyJe1z9unTx82YMSOtsT///LPr2rWr+/HHH11OTk5iP/d9yZIllkHw9ttv23uAOPdce+217tVXX3V16tSJFN1cdzIaNmxo716/fv3c448/nva1CSGEyA4kuoUQQtR6fv/9dxNEiOcosUj089dff7WI4tZbb11JdHvBmiwy6oUR4jcKzvHOO++4XXbZxZ155pkZzZ9jzp492wQ/YrZVq1auV69eCaHLtXGNCNH//e9/tnlxyvdsXDtiOFnkmuOwQAEPPvhgpcUBzoGgZD4IRM4ThPvkz8MW5Msvv3R77rln7Ln3228/98knn6S8F9xnzpFKiG6//fZ2DUSAx48f7zp37uy6dOnidt11V3fAAQfYNfAucB1RizBlZWW2yJFK3Ae54IIL3IoVK9zcuXNdmzZtEvuHDRtmCxhQr169xH179tln3RFHHOF22GGHyON50R2+z2HOOusst2rVKjd06FDXsWNHd+mll6Y9ZyGEEFlAuRBCCJHlDBkyBCVqn1Vl+fLldoxWrVpFfj9t2jT7vrCwsMrnmDNnjh0jJyenvKysrLy66dWrlx1/wIAB1X7slStX2rHr1KlTnpubm9iaN29u+9u1a5fWcerVq2fj//7777TG9+3bt7xnz57lJSUlGc136tSpdp78/Py0xs+YMcPGjxkzptJ369evLx8xYkR5cXFx+W+//Vael5dnY6MoLS1N/D18+HAbN2/ePPv3/PnzbYuja9eu5Q0aNChfsWJFWnMWQgiRHaimWwghRFZAJJu07HAENhWkAx9yyCEWpd4Qpk+fbp+kZxNNT7bFzZFoLVHM0aNHW/p3dULq+NNPP+323nvvRCp2dUJUGYjqkmrtt6eeesr2E7kmDX716tUWcSed39ddB6PzPgWb6D3ZA2vWrKmQgUBKO7X1M2fOtH9PmzbNrqt9+/ZVmnc6kW7mSYp4u3bt3DXXXFPpe54p797xxx8fmwkBXDM1/0Tj8QgIwvvXqVMnd8YZZ1i2QRSUIHBfbrjhhrSuTQghRHag9HIhhBBZAYZapGiThkvacTog9qgBXrhwobvqqqsqGV4hkkipDhtd8TvS0RFj2223nQnDOXPmWC01gjCO559/3lKdSUGOY+LEia66QZgiGhGHTz75pF3PRx99ZAK5SZMm1Sq640BMI8KDadnpiuHly5e7Fi1a2N/UzPOc+vfvb//OpH4/inR+/8QTT1haOfXw4brsTCCd3ZcCsPgRhEWDQw891L322mtWu01qehgEO4sypLLfeuutJt6FEEJkPxLdQgghsoKRI0eagRXmWm3btjURnYrHHnvMIozU1j7wwAORkcUrr7yywj7qkn0NLjXMGGbhLo2QGjJkiLv55psjz4V4R0j6Ot5UUF+9YMECE+i+NtnXcofxdd2+Ph1xTVQ2KNY4zqOPPur2339/izhTd75y5Uqbb/gaa0p0Mwdqu/nk3944LVh3/eabb9q1HHnkkXYP2IL13ZjlQTCiTHYBiyepYNzll1+e8bU9/PDDbo899nAnnHBCpe++/vprMzdjXjxfruXbb7+173ineC+I2F9xxRVu0aJFtj8vL69SHTe/RdzzfDC6u/vuu90ll1xS6XzU5NPujWfJ+yaEECL7kegWQgiRFSAyEcnnnHOOtWo6/PDDbYuDiKkX5qRz5+bmVhqDQMYYDUFFdBuzMMQRUVei4Jh58YlAYv/AgQNtXJQw9inl6YpuhBsmYVUBQ7GXXnop8W/czz/++OOE0dmdd95piweIcwQgrF271q4BQZwq5do7pnPt22yzTexiQBDGEN1NZabGnCgVQHxH4bMEgiLfC3EixVHu85QQsLjix2UCz+2NN95I9NQOQwQ8KuUcfDQe+D3PAChniGKnnXZy9957r+vRo4cdk2fTunXrCmN491hUKi4ulugWQoiNBIluIYQQWcPZZ59tabnUEeP4/MEHH5gojBKNfE9a+DHHHBMb6UUo+bZYiKvdd9/dhKMXT0B9LYIVZ2uirwglWljRWiqI7yUdFt20BCOqy34EpReVtEAjGs2/ifQSQUXkEsmHAQMG2D5Evncx95Fu+kiH8YKbhQN6XAMpyjh9Q/PmzU3sZopP/faR2++//77C+YlUp+PQnS5RwtdHwolE4/4ehn2I7lSO6FFQe801xC3g7LPPPpa+793SWVQgld+XE/A8uK883/fff9/2H3TQQbHn6969u230ASeLIiy6gblQJiCEEGLjQKJbCCFEVkG9K7W3P/zwgwmgKLHEGPomN2rUyD3yyCNVrgsmPfv22283YYyw43yIO9p9zZs3zyKvYdEdNtpCLK9fv96+5zj8jVAjJZz09yD0EUd0I9qIiGYK0WnqhRHvBx98cKJ9mBePiHbEIVvwntD7mzRqFhyoAWeu1LQHU7/9eI4RNgmLiuJj7Hb99dfbM6jp1HZPVeqxWWyBuHZoRKcxP/MEfQFOPPHExN/cKyLu3KcDDzww6TnJ2OBdiOu3zlxY6CFtPdwCTwghRPYh0S2EECKr2G233dykSZOsP/Jee+0VOSY/P9+ijkS7N8SMiugwQvizzz6z87JRx0vE/bTTTnMlJSVuxx13TBrppic3Ittz9NFHm5lWlNkaZm1QUFBQYT8iNyo9PgwRWFKloXfv3hWiz8w1DlKdcTwnHd9H/sP4Y+28886JmmZ47733TOAHYVGCSC73hHr42gyLIBCVMREFGQoeMhAw9aNEAdHNQgU17XFimvG33HKLlUgg5uPwv1+3bp1EtxBCbASoZZgQQohaC6KD9koImmDbqXPPPTdWcAOO40SRg5FIIrTU7/70008WQUwX2kQFDa8Q8kSQEUPML1zTnaylVJBwOjYCtaioyBzHEfUe/kbULlu2LGX0tCZahQUj9ulClJuI73333ZcyKp4JI0aMsOOGN/ZXFR/BD75fccydO7fC9ZDtQNnBZZddZo7xvBssxnj8Yotvifb666+7YcOGmWmbLyNIdq+rK2VfCCHEf4v+NxdCCFFrGTp0qJmZIWS9mVfUNnbsWBvPZ9wYUpSJQjds2NCMxjYE3KVJM2ZB4NRTT00a6c7EQbusrMyizkFDMGqyicYi6EgfjwIBR4SaazzvvPNcTRDspZ0KatgRn8w3qga7qpDOj0t4eAum+WeKjyrH9c4OQpQ6CEZ41JlTm42/AI7jwQUA/0548X3PPffYJyURPM84/FziIuZCCCGyC6WXCyGEqLWQtkuLJUR3st7XmId99dVXlv5NFDFONLKRAuxTwqsKc8HRnEUBjkkUOq6mOx0w4ho1apRdL32ag5DyjWHXK6+8YsZoGLuFwXSLSC2LCdQ1J4ui1lSkO/w918G8ML0j8s2z2VBSGalVBV/LTQlBMvGOgR+RbUzkeNa8c/7cOMmPHz/eDPuC9efeuI4ae/qmcz/4ngyAZHXqvE+8C1V5l4QQQtQ+JLqFEELUWjDiYkunlpkoN27gY8aMqZZzI6wQSmEnaqKapBRTO43Ap6YXAzIv+jKNdCPaOR6mbRyLNGXE2urVqxMbxlyIboQ59dLUDQchwkoP6AsvvLBKBmzpzjMT0U0bM3qEE3mvDsFdU/DsqOcmc4Fa6yhwbMdNHngfg8Ifoc5iCGZrYSGNEGfBATM6XPRZGCEyH35+YWinlsqMTQghRPag9HIhhBAilNqLERuGYVdffXViP3XlpA63adPGRBF13Yjyjh07VqmmO7hggGAGWn0huqkXR9DTk7pbt27moE6KPAsBpJGH64+pYUdw1yRedH/33XcV0va9iVqUKCcq36xZsw0+d1xafVXHBSEzoFOnTm7WrFmRv8clv3Pnznbd9IpnYSfqGWK6F5URwbMcOXKkW7BggTnIp0q3xyGfqDtRfSGEEBsHinQLIYTYZCGNl57cCKLi4mLbV1pa6qZMmeLat2+fMMVCFA8ePNitWrXKNW7c2E2dOrWSKKpqTTc14dSII/7q1q1rEWIEHD3DEaz8zSff7bvvvhZRx5nd99/+t/CRbOYZNLFjMYIofVUEb7pQEgAseiQzTfM9wzPl/PPPN1dy0r+D7cGA1HHuN9eNWV2m5mYTJkxwo0ePNvFNqn0qN3LG8w6F5yGEECJ7kegWQgixyXL66ae7Dz/80P4maovBFenbbC1atEiMw8gMwX3ccce56dOnm8N4mHRrur2p1uLFi13btm2t9RmtwvhMJcgQ+6RDI77/bXwkm2tfsmRJpZZhwbZo1Q3p2KRlp6Kq9+Xkk0+2Z4GzeI8ePSo8QyLbCxcutMWPcGu0ZFDHzZwnT57scnJy3IsvvmhZEqmi3Dxj6uExEBRCCLFxINEthBAi6/FR2EzaWkH//v2tBpzU7H79+kWmCPtx1OXiOE3EMwqM3JKJbvpkDxo0yPpX+37dRNQReaQvpwOp5qnI9B5saE13kyZNLC1+Q83pPD5NP3i+vLw82zLB/963BEsGY4gw40ZeWFjoJk6cWOH7cePGpXUcX/9NbT7t2+hnTlYAmRJkTiSDRRvq3xHbiH8hhBAbDxLdQgghsh4v1Hy0OV0KCgrcwIEDkzpJB3uDh0U2UXJ+u2bNGjPTAlyng9ATHBGFkEMQn3LKKS43N9cMz3r27GnRT1KJO3To4Fq3bm312XECD4M1aoy/+eYbaycVFdn1ad6pjM9Io+c8pEuTXg/JhCWLDTh9h8U1ojvdtmDMnQhwMnwP9UyfZRDuM8ZzQFp+OrAIQmQaB3hMzFiE8YTvi58b5+GaENUY7HEf3333XdvPfWWxBjM1It2poFafhRnS3Bs1apThFQshhKjNSHQLIYTIerwIylSoVbWnthdiRJ6DpmaYoJGaHoTWYkRRGY+g82nSiCz+ph3YokWLEuMRawhvIuuAgKNWmbrmYN00Kc9R+HvgFyLioA6bftrB+VNHHgd15QjLTEFks7iACzt9yDkfQr0mRPfatWstoky02deBp3IKD4JhHf22WYwhjZ608yh8KzDmyLOaNGmSeQP4d6pPnz5mwpcqndyDIzpp5SzOdO/ePe35CiGEyA4kuoUQQmyyontDaNq0qfUQR+gRAaZ1FOnBQZMxwIAN87OTTjrJIp8efjN79mzr94zwRkQvXbrUhCkO6tSRx9VJ89twK7OwmZgXnXFQv046NRF7rgNhnG6aeybUr1/f0vLJFGBBgSgyxmJxBAVtphAhpu66qKjI6uP79u1bKUMhGWQt0I+bcoO4ll1kEPB8/IIC6eA845kzZ5pgxhiPFmSZ0KVLF7tuXM6FEEJsfGxWHu47IoQQQohqxacbb6ogUnGKb9myZcaCNFNI+WfBgYg3LbuEEEKI/xqJbiGEEEIIIYQQoobYdJfdhRBCCCGEEEKIGkaiWwghhBBCCCGEqCEkuoUQQgghhBBCiBpColsIIYQQQgghhKghJLqFEEIIIYQQQogaQqJbCCGEEEIIIYSoISS6hRBCCCGEEEKIGkKiWwghhBBCCCGEcDXD/wFeVxITtCyMvQAAAABJRU5ErkJggg==", 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", 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" ] @@ -636,11 +636,13 @@ "for i, alg in enumerate(avg_colors.index):\n", " x = avg_times[alg]\n", " y = avg_colors[alg]\n", + " # 替换算法名称\n", + " display_name = alg.replace('EoH-MCP', 'EoH-GCP').replace('FunSearch-MCP', 'FunSearch-GCP').replace('AAE-MCP', 'AAE-GCP')\n", " plt.scatter(x, y,\n", " color=colors[i % len(colors)], # 使用取模确保不会越界\n", " marker=markers[i % len(markers)],\n", " s=100,\n", - " label=alg)\n", + " label=display_name)\n", " # 为DA和EOH设置不同的标注位置\n", " if alg in ['DSATUR', 'EoH-MCP']:\n", " xytext = (-90, 20) # 左上方偏移\n",