From f0af69627290dae27278bf9163201702ae710b52 Mon Sep 17 00:00:00 2001 From: Bin Cao Date: Thu, 28 Dec 2023 15:45:16 +0800 Subject: [PATCH] update --- Template/regression.ipynb | 54 +- .../ParetoF.png" | Bin .../ParetoF.svg" | 260 ++++----- .../distribution.png" | Bin 281734 -> 282620 bytes .../distribution.svg" | 542 +++++++++--------- ...345\244\232\347\233\256\346\240\207.ipynb" | 20 +- 6 files changed, 445 insertions(+), 431 deletions(-) rename "Template/\344\270\255\346\226\207\347\244\272\344\276\213/\345\244\232\347\233\256\346\240\207\345\256\236\347\216\260/2023-12-27 16:28:16.png" => "Template/\344\270\255\346\226\207\347\244\272\344\276\213/\345\244\232\347\233\256\346\240\207\345\256\236\347\216\260/ParetoF.png" (100%) rename "Template/\344\270\255\346\226\207\347\244\272\344\276\213/\345\244\232\347\233\256\346\240\207\345\256\236\347\216\260/2023-12-27 16:28:16.svg" => "Template/\344\270\255\346\226\207\347\244\272\344\276\213/\345\244\232\347\233\256\346\240\207\345\256\236\347\216\260/ParetoF.svg" (82%) diff --git a/Template/regression.ipynb b/Template/regression.ipynb index 7cb90d9..d0018bf 100644 --- a/Template/regression.ipynb +++ b/Template/regression.ipynb @@ -6,7 +6,10 @@ "source": [ "Thank you for choosing Bgolearn for material design. \n", "Bgolearn is developed to facilitate the application of machine learning in research.\n", - "Bgolearn is designed for optimizing single-target material properties. \n", + "Bgolearn is designed for optimizing single-target material properties.\n", + "The BgoKit package is being developed to facilitate multi-task design.\n", + "\n", + "\n", "If you need to perform multi-target optimization, here are two important reminders:\n", "\n", "1. Multi-tasks can be converted into a single task using domain knowledge. \n", @@ -56,19 +59,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "A Bayesian global optimization package\n", - "Bgolearn, Bin CAO, HKUST(GZ)\n", + " ____ _ \n", + "| __ ) __ _ ___ | | ___ __ _ _ __ _ __ \n", + "| _ \\ / _` | / _ \\ | | / _ \\ / _` || '__|| '_ \\ \n", + "| |_) || (_| || (_) || || __/| (_| || | | | | |\n", + "|____/ \\__, | \\___/ |_| \\___| \\__,_||_| |_| |_|\n", + " |___/ \n", + "\n", + "Package Name : Bgolearn\n", + "Author : Bin CAO, HKUST(GZ)\n", "Intro : https://bgolearn.netlify.app/\n", "URL : https://github.com/Bin-Cao/Bgolearn\n", - "Executed on : 2023-12-27 15:37:35 | Have a great day.\n", - "\n", - "██████╗ ██████╗ ██████╗ \n", - "██╔══██╗██╔════╝ ██╔═══██╗\n", - "██████╔╝██║ ███╗██║ ██║\n", - "██╔══██╗██║ ██║██║ ██║\n", - "██████╔╝╚██████╔╝╚██████╔╝\n", - "╚═════╝ ╚═════╝ ╚═════╝ \n", - "\n", + "Citation Format Suggestion:\n", + "[Bin CAO et al]. \"Active learning accelerates the discovery of high strength and high ductility lead-free solder alloys\" [Journal], [2023], [DOI : xx].\n", + "Executed on : 2023-12-28 15:38:49 | Have a great day.\n", "================================================================================\n" ] } @@ -472,7 +476,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -510,7 +514,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -521,20 +525,27 @@ "current optimal is : 4.4140625\n", "The 1-th datum recomended by Expected Improvement : \n", " x = [8.2]\n", - "[8.2]\n", + "The predictions of Bgolearn are : \n", + " y = 4.338227476898954\n", "The 2-th datum recomended by Expected Improvement : \n", " x = [8.3]\n", - "[8.3]\n", + "The predictions of Bgolearn are : \n", + " y = 4.294587329706257\n", "The 3-th datum recomended by Expected Improvement : \n", " x = [8.1]\n", - "[8.1]\n" + "The predictions of Bgolearn are : \n", + " y = 4.397302263077634\n", + "The 4-th datum recomended by Expected Improvement : \n", + " x = [8.4]\n", + "The predictions of Bgolearn are : \n", + " y = 4.271564943821996\n" ] } ], "source": [ "Bgolearn = BGOS.Bgolearn()\n", "\n", - "Mymodel = Bgolearn.fit(data_matrix = x,Measured_response = y , virtual_samples = vs,opt_num=3)\n", + "Mymodel = Bgolearn.fit(data_matrix = x,Measured_response = y ,virtual_samples = vs,opt_num=4)\n", "\n", "# Expected Improvement method\n", "score, data = Mymodel.EI()" @@ -924,7 +935,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -932,8 +943,11 @@ "output_type": "stream", "text": [ "The internal model is instantiated with optimized homogenous noise\n", + "Execution using a single process\n", "The next datum recomended by Knowledge Gradient : \n", - " x = [8.2]\n" + " x = [6.9]\n", + "The predictions of Bgolearn are : \n", + " y = 5.0823131435026045\n" ] } ], diff --git "a/Template/\344\270\255\346\226\207\347\244\272\344\276\213/\345\244\232\347\233\256\346\240\207\345\256\236\347\216\260/2023-12-27 16:28:16.png" "b/Template/\344\270\255\346\226\207\347\244\272\344\276\213/\345\244\232\347\233\256\346\240\207\345\256\236\347\216\260/ParetoF.png" similarity index 100% rename from "Template/\344\270\255\346\226\207\347\244\272\344\276\213/\345\244\232\347\233\256\346\240\207\345\256\236\347\216\260/2023-12-27 16:28:16.png" rename to "Template/\344\270\255\346\226\207\347\244\272\344\276\213/\345\244\232\347\233\256\346\240\207\345\256\236\347\216\260/ParetoF.png" diff --git "a/Template/\344\270\255\346\226\207\347\244\272\344\276\213/\345\244\232\347\233\256\346\240\207\345\256\236\347\216\260/2023-12-27 16:28:16.svg" "b/Template/\344\270\255\346\226\207\347\244\272\344\276\213/\345\244\232\347\233\256\346\240\207\345\256\236\347\216\260/ParetoF.svg" similarity index 82% rename from "Template/\344\270\255\346\226\207\347\244\272\344\276\213/\345\244\232\347\233\256\346\240\207\345\256\236\347\216\260/2023-12-27 16:28:16.svg" rename to "Template/\344\270\255\346\226\207\347\244\272\344\276\213/\345\244\232\347\233\256\346\240\207\345\256\236\347\216\260/ParetoF.svg" index e28d59b..a88fc78 100644 --- "a/Template/\344\270\255\346\226\207\347\244\272\344\276\213/\345\244\232\347\233\256\346\240\207\345\256\236\347\216\260/2023-12-27 16:28:16.svg" +++ "b/Template/\344\270\255\346\226\207\347\244\272\344\276\213/\345\244\232\347\233\256\346\240\207\345\256\236\347\216\260/ParetoF.svg" @@ -6,7 +6,7 @@ - 2023-12-27T16:28:19.323245 + 2023-12-27T21:16:14.316157 image/svg+xml @@ -39,7 +39,7 @@ z - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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clip-path="url(#p0e6d064848)" style="fill: #87ceeb; opacity: 0.7"/> - +" clip-path="url(#p0e6d064848)" style="fill: #87ceeb; opacity: 0.7"/> - +" clip-path="url(#p0e6d064848)" style="fill: #87ceeb; opacity: 0.7"/> - +" clip-path="url(#p0e6d064848)" style="fill: #87ceeb; opacity: 0.7"/> - +" clip-path="url(#p0e6d064848)" style="fill: #87ceeb; opacity: 0.7"/> - +" clip-path="url(#p0e6d064848)" style="fill: #87ceeb; opacity: 0.7"/> - +" clip-path="url(#p0e6d064848)" style="fill: #87ceeb; opacity: 0.7"/> - +" clip-path="url(#p0e6d064848)" style="fill: #87ceeb; opacity: 0.7"/> - +" clip-path="url(#p0e6d064848)" style="fill: #87ceeb; opacity: 0.7"/> - +" clip-path="url(#p0e6d064848)" style="fill: #87ceeb; opacity: 0.7"/> - + - + @@ -1158,12 +1158,12 @@ z - + - + @@ -1173,12 +1173,12 @@ z - + - + @@ -1188,12 +1188,12 @@ z - + - + @@ -1203,12 +1203,12 @@ z - + - + @@ -1218,12 +1218,12 @@ z - + - + @@ -1232,7 +1232,7 @@ z - + @@ -1246,12 +1246,12 @@ z - + - + @@ -1259,12 +1259,12 @@ z - + - + @@ -1272,12 +1272,12 @@ z - + - + @@ -1285,12 +1285,12 @@ z - + - + @@ -1298,12 +1298,12 @@ z - + - + @@ -1311,12 +1311,12 @@ z - + - + @@ -1324,7 +1324,7 @@ z - + @@ -1338,18 +1338,18 @@ z - - - @@ -1388,11 +1388,11 @@ L 709.2 26.88 - - + + - - + + diff --git "a/Template/\344\270\255\346\226\207\347\244\272\344\276\213/\345\244\232\347\233\256\346\240\207\345\256\236\347\216\260/\345\244\232\347\233\256\346\240\207.ipynb" "b/Template/\344\270\255\346\226\207\347\244\272\344\276\213/\345\244\232\347\233\256\346\240\207\345\256\236\347\216\260/\345\244\232\347\233\256\346\240\207.ipynb" index aef6f26..ee5b8ab 100644 --- "a/Template/\344\270\255\346\226\207\347\244\272\344\276\213/\345\244\232\347\233\256\346\240\207\345\256\236\347\216\260/\345\244\232\347\233\256\346\240\207.ipynb" +++ "b/Template/\344\270\255\346\226\207\347\244\272\344\276\213/\345\244\232\347\233\256\346\240\207\345\256\236\347\216\260/\345\244\232\347\233\256\346\240\207.ipynb" @@ -22,7 +22,7 @@ "Bgolearn, Bin CAO, HKUST(GZ)\n", "Intro : https://bgolearn.netlify.app/\n", "URL : https://github.com/Bin-Cao/Bgolearn\n", - "Executed on : 2023-12-27 16:25:16 | Have a great day.\n", + "Executed on : 2023-12-27 21:16:09 | Have a great day.\n", "\n", "██████╗ ██████╗ ██████╗ \n", "██╔══██╗██╔════╝ ██╔═══██╗\n", @@ -286,7 +286,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -439,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "eaab391b", "metadata": {}, "outputs": [ @@ -449,7 +449,7 @@ "text": [ "BgoKit, A tool package for Bgolearn\n", "If you need any help, please contact me at github.com/Bin-Cao/Bgolearn\n", - "Executed on : 2023-12-27 16:26:24 | Have a great day.\n", + "Executed on : 2023-12-27 21:16:11 | Have a great day.\n", "================================================================================\n" ] } @@ -460,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 9, "id": "25675556", "metadata": {}, "outputs": [ @@ -485,7 +485,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The optimal condidate is : [[ 7 237]]\n" + "The optimal condidate recommended by BgoKit is : [[ 7 237]]\n" ] } ], @@ -496,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 10, "id": "ecde2b8a", "metadata": {}, "outputs": [ @@ -506,7 +506,7 @@ "array([[ 7, 237]])" ] }, - "execution_count": 18, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -517,13 +517,13 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 11, "id": "8d806ed5", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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3b2rQoEH89re/jX79+kW3bt1i0KBBsfnmm8eHH34YU6ZMiebNm8df/vKXb93GX/7yl3j11VcjImLFihXx2muvxZVXXhkREUcccUS1oykA6ws9W89e13r2gw8+GBdeeGF07do1tt9++2rPmo+IOOigg9b4WDo2QLm7oTpk38pHk6x8FRYWJu3atUsOOuig5Fe/+lW1R2Cs9M1Hk0yePDk58sgjkw4dOiSFhYVJhw4dkh/+8IfJu+++W+1zf/7zn5MddtghadSoUbVHgfTu3Tvp1q1bjfWt7tEkd999dzJs2LCkTZs2SZMmTZJDDz20xkds/OIXv0g233zzpKioKOnZs2cybdq0Vbb5bbV989EkSfK/x2+ce+65SYcOHZKNNtoo6dq1a3L99ddXe7xGkvzv0SRDhgxZpabVPTLlmxYvXpwMGjQo2XTTTZPCwsJkxx13rPHxKWv7aJLFixcn11xzTdK7d++kffv2SaNGjZJWrVolBxxwQHL//fevsv7cuXOTk046Kdlss82SoqKiZKuttkqGDBmSlJeXV60ze/bs5Ac/+EHSsmXLpHHjxsmee+6ZPPLII9W2s/JnNmnSpBrrmj59evL9738/ad26dVJUVJR06tQpOfbYY5PJkyev8TsNHDiw2u/v1181zRXAukzP/vba9Oz87dkrfw9X95oyZcoa54QNT0GS5MEdFgAAAGA95RpvAAAASJHgDQAAACkSvAEAACBFgjcAAACkSPAGAACAFAneAAAAkKJGuS4gbZWVlbFgwYIoLi6OgoKCXJcDAGuUJEksWbIkOnToEA0abBh/I9evAVjX1KZfr/fBe8GCBdGxY8dclwEAtTZ//vzYYostcl1GvdCvAVhXrU2/Xu+Dd3FxcUT8bzKaN2+e42oAYM3KysqiY8eOVT1sQ6BfA7CuqU2/Xu+D98rT1Zo3b66RA7BO2ZBOudavAVhXrU2/3jAuHAMAAIAcEbwBAAAgRYI3AAAApEjwBgAAgBQJ3gAAAJAiwRsAAABSJHgDAABAigRvAAAASJHgDQAAACkSvAEAACBFgjcAAACkSPAGAACAFAneAAAAkCLBGwAAAFIkeAMAAECKBG8AAABIkeANAAAAKRK8AYA1evbZZ+Pwww+PDh06REFBQTz00EPVlidJEpdffnm0b98+mjRpEn369ImZM2fmplgAyDOCNwCwRsuWLYuddtopbrnllhqXX3fddXHjjTfGuHHj4l//+lc0a9Ys+vbtG1988UU9VwoA+adRrgsAAPJfv379ol+/fjUuS5Ikxo4dG5dddlkceeSRERFxxx13RNu2beOhhx6KAQMG1GepAJB3HPEGAOpkzpw5sWjRoujTp0/VWIsWLWKvvfaKF154ocbPlJeXR1lZWbUXAKyvHPEGYIM0aXZpVrd3TJcWWd3eumTRokUREdG2bdtq423btq1a9k2jR4+OkSNHpl4bAPVPj12VI94AQL0bNmxYlJaWVr3mz5+f65IAIDWCNwBQJ+3atYuIiMWLF1cbX7x4cdWybyoqKormzZtXewHA+krwBgDqpKSkJNq1axeTJ0+uGisrK4t//etfsffee+ewMgDID67xBgDWaOnSpTFr1qyq93PmzIkZM2bEJptsEltuuWWcc845ceWVV0bXrl2jpKQkhg8fHh06dIijjjoqd0UDQJ4QvAGANZo2bVrsv//+Ve+HDh0aEREDBw6MCRMmxIUXXhjLli2LwYMHx+effx69evWKJ554Iho3bpyrkgEgbwjeAMAa7bfffpEkyWqXFxQUxKhRo2LUqFH1WBUArBtc4w0AAAApErwBAAAgRYI3AAAApEjwBgAAgBQJ3gAAAJAiwRsAAABSJHgDAABAigRvAAAASJHgDQAAACkSvAEAACBFgjcAAACkSPAGAACAFAneAAAAkCLBGwAAAFIkeAMAAECKBG8AAABIkeANAAAAKRK8AQAAIEWCNwAAAKRI8AYAAIAUCd4AAACQIsEbAAAAUiR4AwAAQIoEbwAAAEiR4A0AAAApErwBAAAgRYI3AAAApEjwBgAAgBQJ3gAAAJAiwRsAAABSJHgDAABAigRvAAAASJHgDQAAACnKafB+9tln4/DDD48OHTpEQUFBPPTQQ9WWJ0kSl19+ebRv3z6aNGkSffr0iZkzZ+amWAAAAMhAToP3smXLYqeddopbbrmlxuXXXXdd3HjjjTFu3Lj417/+Fc2aNYu+ffvGF198Uc+VAgAAQGYa5XLn/fr1i379+tW4LEmSGDt2bFx22WVx5JFHRkTEHXfcEW3bto2HHnooBgwYUJ+lAgAAQEby9hrvOXPmxKJFi6JPnz5VYy1atIi99torXnjhhdV+rry8PMrKyqq9AAAAIFdyesT72yxatCgiItq2bVttvG3btlXLajJ69OgYOXJkqrUBkBuTZpfmugQAgFrL2yPemRo2bFiUlpZWvebPn5/rkgAAANiA5W3wbteuXURELF68uNr44sWLq5bVpKioKJo3b17tBQAAALmSt8G7pKQk2rVrF5MnT64aKysri3/961+x995757AyAAAAWHs5vcZ76dKlMWvWrKr3c+bMiRkzZsQmm2wSW265ZZxzzjlx5ZVXRteuXaOkpCSGDx8eHTp0iKOOOip3RQMAAFBvsnmPl2O6tMjatmojp8F72rRpsf/++1e9Hzp0aEREDBw4MCZMmBAXXnhhLFu2LAYPHhyff/559OrVK5544olo3LhxrkoGAACAWslp8N5vv/0iSZLVLi8oKIhRo0bFqFGj6rEqAAAAyJ68vcYbAAAA1geCNwAAAKRI8AYAAIAUCd4AAACQIsEbAAAAUiR4AwAAQIoEbwAAAEiR4A0AAAApErwBAAAgRYI3AAAApEjwBgAAgBQJ3gAAAJAiwRsAAABSJHgDAABAigRvAAAASJHgDQAAACkSvAEAACBFgjcAAACkSPAGAACAFAneAAAAkCLBGwAAAFIkeAMAAECKBG8AAABIkeANAAAAKRK8AQAAIEWCNwAAAKRI8AYAAIAUCd4AAACQIsEbAAAAUiR4AwAAQIoEbwAAAEiR4A0AAAApErwBAAAgRYI3AAAApEjwBgAAgBQJ3gAAAJAiwRsAAABSJHgDAABAigRvAAAASJHgDQAAACkSvAEAACBFgjcAUGcVFRUxfPjwKCkpiSZNmkSXLl3iiiuuiCRJcl0aAORco1wXAACs+6699tq49dZbY+LEidGtW7eYNm1aDBo0KFq0aBFnnXVWrssDgJwSvAGAOvvHP/4RRx55ZBx66KEREdG5c+e4++6748UXX8xxZQCQe041BwDqbJ999onJkyfHu+++GxERr776ajz33HPRr1+/GtcvLy+PsrKyai8AWF854g0A1NnFF18cZWVlsd1220XDhg2joqIirrrqqjjhhBNqXH/06NExcuTIeq4SAHLDEW8AoM7uu+++uPPOO+Ouu+6KV155JSZOnBg33HBDTJw4scb1hw0bFqWlpVWv+fPn13PFAFB/HPEGAOrsggsuiIsvvjgGDBgQERE77rhjzJ07N0aPHh0DBw5cZf2ioqIoKiqq7zIBICcc8QYA6mz58uXRoEH1/61o2LBhVFZW5qgiAMgfjngDAHV2+OGHx1VXXRVbbrlldOvWLaZPnx5jxoyJH//4x7kuDQByTvAGAOrspptuiuHDh8cZZ5wRH330UXTo0CFOP/30uPzyy3NdGgDknOANANRZcXFxjB07NsaOHZvrUgAg77jGGwAAAFIkeAMAAECKBG8AAABIkeANAAAAKRK8AQAAIEWCNwAAAKRI8AYAAIAUCd4AAACQIsEbAAAAUiR4AwAAQIoEbwAAAEiR4A0AAAApErwBAAAgRYI3AAAApEjwBgAAgBQJ3gAAAJAiwRsAAABSJHgDAABAigRvAAAASJHgDQAAACkSvAEAACBFgjcAAACkSPAGAACAFAneAAAAkKK8Dt4VFRUxfPjwKCkpiSZNmkSXLl3iiiuuiCRJcl0aAAAArJVGuS7g21x77bVx6623xsSJE6Nbt24xbdq0GDRoULRo0SLOOuusXJcHAAAAa5TXwfsf//hHHHnkkXHooYdGRETnzp3j7rvvjhdffDHHlQEAAMDayetTzffZZ5+YPHlyvPvuuxER8eqrr8Zzzz0X/fr1W+1nysvLo6ysrNoLAAAAciWvj3hffPHFUVZWFtttt100bNgwKioq4qqrrooTTjhhtZ8ZPXp0jBw5MrWaJs0uzer2junSIqvbIzPZ/Ln6mQIAAF+X10e877vvvrjzzjvjrrvuildeeSUmTpwYN9xwQ0ycOHG1nxk2bFiUlpZWvebPn1+PFQMAAEB1eX3E+4ILLoiLL744BgwYEBERO+64Y8ydOzdGjx4dAwcOrPEzRUVFUVRUVJ9lAgAAwGrl9RHv5cuXR4MG1Uts2LBhVFZW5qgiAAAAqJ28PuJ9+OGHx1VXXRVbbrlldOvWLaZPnx5jxoyJH//4x7kuDQAAANZKXgfvm266KYYPHx5nnHFGfPTRR9GhQ4c4/fTT4/LLL891aQAAALBW8jp4FxcXx9ixY2Ps2LG5LgUAAAAyktfXeAMAAMC6TvAGAACAFAneAAAAkCLBGwAAAFIkeAMAAECKBG8AAABIUV4/TgwAYH00aXZpVrd3TJcWWd0eANnliDcAAACkSPAGAACAFAneAAAAkCLBGwAAAFIkeAMAAECKBG8AAABIkeANAAAAKRK8AQAAIEWCNwAAAKRI8AYAAIAUCd4AAACQIsEbAAAAUiR4AwAAQIoEbwAAAEiR4A0AAAApErwBAAAgRYI3AAAApEjwBgAAgBQJ3gAAAJAiwRsAAABSJHgDAABAigRvAAAASJHgDQAAACkSvAEAACBFgjcAAACkSPAGAACAFAneAAAAkCLBGwAAAFIkeAMAAECKBG8AAABIUaNcF7ChmzS7NKvbO6ZLi6xuD6Ausv1vHADAusgRbwAAAEiR4A0AAAApErwBAAAgRYI3AAAApEjwBgAAgBQJ3gAAAJAiwRsAAABSJHgDAABAigRvAAAASJHgDQAAACkSvAEAACBFgjcAAACkSPAGALLiww8/jB/96EfRunXraNKkSey4444xbdq0XJcFADnXKNcFAADrvs8++yx69uwZ+++/fzz++OOx2WabxcyZM6NVq1a5Lg0Ack7wBgDq7Nprr42OHTvG+PHjq8ZKSkpyWBEA5A+nmgMAdfbwww/H7rvvHsccc0y0adMmdtlll7j99ttXu355eXmUlZVVewHA+soRbwCgzt5777249dZbY+jQoXHJJZfESy+9FGeddVYUFhbGwIEDV1l/9OjRMXLkyFRrmjS7NKvbO6ZLi6xuL1/l87zlc23Z5rtmJp+/Z77L9u8c1TniDQDUWWVlZey6665x9dVXxy677BKDBw+O0047LcaNG1fj+sOGDYvS0tKq1/z58+u5YgCoP4I3AFBn7du3jx122KHa2Pbbbx/z5s2rcf2ioqJo3rx5tRcArK8yCt7vvfdetusAAFJQXz27Z8+e8c4771Qbe/fdd6NTp071sn8AyGcZBe+tt9469t9///jjH/8YX3zxRbZrAgCypL569rnnnhv//Oc/4+qrr45Zs2bFXXfdFb/5zW9iyJAhqe0TANYVGQXvV155JXr06BFDhw6Ndu3axemnnx4vvvhitmsDAOqovnr2HnvsEQ8++GDcfffd0b1797jiiiti7NixccIJJ2R9XwCwrskoeO+8887xq1/9KhYsWBC///3vY+HChdGrV6/o3r17jBkzJj7++ONs1wkAZKA+e/Zhhx0Wr7/+enzxxRfx1ltvxWmnnZa1bQPAuqxON1dr1KhRfP/7349JkybFtddeG7NmzYrzzz8/OnbsGCeddFIsXLgwW3UCAHWgZwNA7tQpeE+bNi3OOOOMaN++fYwZMybOP//8mD17djz11FOxYMGCOPLII7NVJwBQB3o2AOROo0w+NGbMmBg/fny888470b9//7jjjjuif//+0aDB/3J8SUlJTJgwITp37pzNWgGAWtKzASD3Mgret956a/z4xz+Ok08+Odq3b1/jOm3atInf/e53dSoOAKgbPRsAci+j4D1z5sw1rlNYWBgDBw7MZPMAQJbo2QCQexld4z1+/PiYNGnSKuOTJk2KiRMn1rkoACA79GwAyL2Mgvfo0aNj0003XWW8TZs2cfXVV9e5KAAgO/RsAMi9jIL3vHnzoqSkZJXxTp06xbx58+pcFACQHXo2AOReRsG7TZs28dprr60y/uqrr0br1q3rXBQAkB16NgDkXkbB+4c//GGcddZZMWXKlKioqIiKiop4+umn4+yzz44BAwZku0YAIEN6NgDkXkZ3Nb/iiivi/fffjwMPPDAaNfrfJiorK+Okk05yvRgA5BE9GwByL6PgXVhYGPfee29cccUV8eqrr0aTJk1ixx13jE6dOmW7PgCgDvRsAMi9jIL3Sttss01ss8022aoFAEiJng0AuZNR8K6oqIgJEybE5MmT46OPPorKyspqy59++umsFAcA1I2eDQC5l1HwPvvss2PChAlx6KGHRvfu3aOgoCDbdQEAWaBnA0DuZRS877nnnrjvvvuif//+2a5nFR9++GFcdNFF8fjjj8fy5ctj6623jvHjx8fuu++e+r4BYF1Xnz0bAKhZxjdX23rrrbNdyyo+++yz6NmzZ+y///7x+OOPx2abbRYzZ86MVq1apb5vAFgf1FfPBgBWL6PneJ933nnxq1/9KpIkyXY91Vx77bXRsWPHGD9+fOy5555RUlISBx98cHTp0iXV/QLA+qK+ejYAsHoZHfF+7rnnYsqUKfH4449Ht27dYqONNqq2/IEHHshKcQ8//HD07ds3jjnmmHjmmWdi8803jzPOOCNOO+20rGwfANZ39dWzAYDVyyh4t2zZMr73ve9lu5ZVvPfee3HrrbfG0KFD45JLLomXXnopzjrrrCgsLIyBAwfW+Jny8vIoLy+vel9WVpZ6nQCQr+qrZwMAq5dR8B4/fny266hRZWVl7L777nH11VdHRMQuu+wSb7zxRowbN261wXv06NExcuTIeqkvH02aXZq1bR3TpUXWtkXmsvkzjfBzhQ1NffVsAGD1MrrGOyLiq6++ir/97W9x2223xZIlSyIiYsGCBbF06dKsFde+ffvYYYcdqo1tv/32MW/evNV+ZtiwYVFaWlr1mj9/ftbqAYB1UX30bABg9TI64j137tw45JBDYt68eVFeXh4HHXRQFBcXx7XXXhvl5eUxbty4rBTXs2fPeOedd6qNvfvuu9GpU6fVfqaoqCiKioqysn8AWNfVV88GAFYvoyPeZ599duy+++7x2WefRZMmTarGv/e978XkyZOzVty5554b//znP+Pqq6+OWbNmxV133RW/+c1vYsiQIVnbBwCsz+qrZwMAq5fREe+///3v8Y9//CMKCwurjXfu3Dk+/PDDrBQWEbHHHnvEgw8+GMOGDYtRo0ZFSUlJjB07Nk444YSs7QMA1mf11bMBgNXLKHhXVlZGRUXFKuMffPBBFBcX17morzvssMPisMMOy+o2AWBDUZ89GwCoWUanmh988MExduzYqvcFBQWxdOnSGDFiRPTv3z9btQEAdaRnA0DuZXTE+xe/+EX07ds3dthhh/jiiy/i+OOPj5kzZ8amm24ad999d7ZrBAAypGcDQO5lFLy32GKLePXVV+Oee+6J1157LZYuXRqnnHJKnHDCCdVu3AIA5JaeDQC5l1Hwjoho1KhR/OhHP8pmLQBACvRsAMitjIL3HXfc8a3LTzrppIyKAQCyS88GgNzLKHifffbZ1d6vWLEili9fHoWFhdG0aVNNHADyhJ4NALmX0V3NP/vss2qvpUuXxjvvvBO9evVyoxYAyCN6NgDkXkbBuyZdu3aNa665ZpW/rAMA+UXPBoD6lbXgHfG/m7csWLAgm5sEAFKgZwNA/cnoGu+HH3642vskSWLhwoVx8803R8+ePbNSGABQd3o2AOReRsH7qKOOqva+oKAgNttsszjggAPiF7/4RTbqAgCyQM8GgNzLKHhXVlZmuw4AIAV6NgDkXlav8QYAAACqy+iI99ChQ9d63TFjxmSyCwAgC/RsAMi9jIL39OnTY/r06bFixYrYdtttIyLi3XffjYYNG8auu+5atV5BQUF2qgQAMqJnA0DuZRS8Dz/88CguLo6JEydGq1atIiLis88+i0GDBsW+++4b5513XlaLBAAyo2cDQO5lFLx/8YtfxJNPPlnVwCMiWrVqFVdeeWUcfPDBmjgA5Ak9e8MwaXZprktYrXyuDaC+ZHRztbKysvj4449XGf/4449jyZIldS4KAMgOPRsAci+j4P29730vBg0aFA888EB88MEH8cEHH8Sf/vSnOOWUU+L73/9+tmsEADKkZwNA7mV0qvm4cePi/PPPj+OPPz5WrFjxvw01ahSnnHJKXH/99VktEADInJ4NALmXUfBu2rRp/PrXv47rr78+Zs+eHRERXbp0iWbNmmW1OACgbvRsAMi9jE41X2nhwoWxcOHC6Nq1azRr1iySJMlWXQBAFunZAJA7GQXvTz/9NA488MDYZptton///rFw4cKIiDjllFPcHRUA8oieDQC5l1HwPvfcc2OjjTaKefPmRdOmTavGjzvuuHjiiSeyVhwAUDd6NgDkXkbXeD/55JPx17/+NbbYYotq4127do25c+dmpTAAoO70bADIvYyOeC9btqzaX81X+s9//hNFRUV1LgoAyA49GwByL6Pgve+++8Ydd9xR9b6goCAqKyvjuuuui/333z9rxQEAdaNnA0DuZXSq+XXXXRcHHnhgTJs2Lb788su48MIL49///nf85z//ieeffz7bNQIAGdKzASD3Mjri3b1793j33XejV69eceSRR8ayZcvi+9//fkyfPj26dOmS7RoBgAzp2QCQe7U+4r1ixYo45JBDYty4cXHppZemURMAkAV6NgDkh1of8d5oo43itddeS6MWACCL9GwAyA8ZnWr+ox/9KH73u99luxYAIMv0bADIvYxurvbVV1/F73//+/jb3/4Wu+22WzRr1qza8jFjxmSlOACgbvRsAMi9WgXv9957Lzp37hxvvPFG7LrrrhER8e6771Zbp6CgIHvVAQAZ0bMBIH/UKnh37do1Fi5cGFOmTImIiOOOOy5uvPHGaNu2bSrFAQCZ0bMBIH/U6hrvJEmqvX/88cdj2bJlWS0IAKg7PRsA8kdGN1db6ZtNHQDIT3o2AOROrU41LygoWOV6MNeHAdkyaXZprktYrWO6tMh1CVArejYA5I9aBe8kSeLkk0+OoqKiiIj44osv4ic/+ckqd0h94IEHslchAFBrejYA5I9aBe+BAwdWe/+jH/0oq8UAANmhZwNA/qhV8B4/fnxadQAAWaRnA0D+qNPN1QAAAIBvJ3gDAABAigRvAAAASJHgDQAAACkSvAEAACBFgjcAAACkSPAGAACAFAneAAAAkCLBGwAAAFIkeAMAAECKBG8AAABIkeANAAAAKRK8AQAAIEWCNwAAAKRI8AYAAIAUCd4AAACQIsEbAAAAUiR4AwAAQIoEbwAg66655pooKCiIc845J9elAEDOCd4AQFa99NJLcdttt0WPHj1yXQoA5AXBGwDImqVLl8YJJ5wQt99+e7Rq1SrX5QBAXhC8AYCsGTJkSBx66KHRp0+fXJcCAHmjUa4LAADWD/fcc0+88sor8dJLL61x3fLy8igvL696X1ZWlmZpAJBTgjcAUGfz58+Ps88+O5566qlo3LjxGtcfPXp0jBw5sh4qy55Js0tzXQJZlu2f6TFdWmR1e/nKfwtQe041BwDq7OWXX46PPvoodt1112jUqFE0atQonnnmmbjxxhujUaNGUVFRUW39YcOGRWlpadVr/vz5OaocANLniDcAUGcHHnhgvP7669XGBg0aFNttt11cdNFF0bBhw2rLioqKoqioqD5LBICcEbwBgDorLi6O7t27Vxtr1qxZtG7depVxANjQONUcAAAAUuSINwCQiqlTp+a6BADIC454AwAAQIoEbwAAAEiR4A0AAAApErwBAAAgRYI3AAAApEjwBgAAgBQJ3gAAAJCidSp4X3PNNVFQUBDnnHNOrksBAACAtbLOBO+XXnopbrvttujRo0euSwEAAIC1tk4E76VLl8YJJ5wQt99+e7Rq1SrX5QAAAMBaWyeC95AhQ+LQQw+NPn36rHHd8vLyKCsrq/YCAACAXGmU6wLW5J577olXXnklXnrppbVaf/To0TFy5MiUq9owTJpdmtXtHdOlRVa3R+5l+3cEAADWR3l9xHv+/Plx9tlnx5133hmNGzdeq88MGzYsSktLq17z589PuUoAAABYvbw+4v3yyy/HRx99FLvuumvVWEVFRTz77LNx8803R3l5eTRs2LDaZ4qKiqKoqKi+SwUAAIAa5XXwPvDAA+P111+vNjZo0KDYbrvt4qKLLloldAMAAEC+yevgXVxcHN27d6821qxZs2jduvUq4wAAAJCP8voabwAAAFjX5fUR75pMnTo11yUAAADAWnPEGwAAAFIkeAMAAECKBG8AAABIkeANAAAAKRK8AQAAIEWCNwAAAKRI8AYAAIAUCd4AAACQIsEbAAAAUiR4AwAAQIoEbwAAAEiR4A0AAAApErwBAAAgRYI3AAAApEjwBgAAgBQJ3gAAAJAiwRsAAABSJHgDAABAigRvAAAASJHgDQAAACkSvAEAACBFgjcAAACkSPAGAACAFAneAAAAkKJGuS4AAABI16TZpbkuYZ1k3sgWR7wBAAAgRYI3AAAApEjwBgAAgBQJ3gAAAJAiwRsAAABSJHgDAABAigRvAAAASJHgDQAAACkSvAEAACBFgjcAAACkSPAGAACAFAneAAAAkKJGuS6ADcek2aW5LoHwc8gH2f4ZHNOlRVa353cEACC7HPEGAACAFAneAAAAkCLBGwAAAFIkeAMAAECKBG8AAABIkeANAAAAKRK8AQAAIEWCNwAAAKRI8AYAAIAUCd4AAACQIsEbAAAAUiR4AwAAQIoEbwAAAEiR4A0AAAApErwBAAAgRYI3AAAApEjwBgAAgBQJ3gAAAJAiwRsAAABSJHgDAABAigRvAAAASJHgDQAAACkSvAEAACBFgjcAAACkSPAGAACAFAneAAAAkCLBGwAAAFIkeAMAAECKBG8AAABIkeANANTZ6NGjY4899oji4uJo06ZNHHXUUfHOO+/kuiwAyAuCNwBQZ88880wMGTIk/vnPf8ZTTz0VK1asiIMPPjiWLVuW69IAIOca5boAAGDd98QTT1R7P2HChGjTpk28/PLL8d3vfjdHVQFAfnDEGwDIutLS0oiI2GSTTXJcCQDkniPeAEBWVVZWxjnnnBM9e/aM7t2717hOeXl5lJeXV70vKyurr/IAoN4J3gBAVg0ZMiTeeOONeO6551a7zujRo2PkyJH1WBWkb9Ls0lyXsMHzMyBfOdUcAMiaM888Mx555JGYMmVKbLHFFqtdb9iwYVFaWlr1mj9/fj1WCQD1yxFvAKDOkiSJn/3sZ/Hggw/G1KlTo6Sk5FvXLyoqiqKionqqDgByS/AGAOpsyJAhcdddd8Wf//znKC4ujkWLFkVERIsWLaJJkyY5rg4AciuvTzUfPXp07LHHHlFcXBxt2rSJo446Kt55551clwUAfMOtt94apaWlsd9++0X79u2rXvfee2+uSwOAnMvrI97PPPNMDBkyJPbYY4/46quv4pJLLomDDz443nzzzWjWrFmuywMA/r8kSXJdAgDkrbwO3k888US19xMmTIg2bdrEyy+/HN/97ndzVBUAAACsvbwO3t9UWvq/xwNssskmq13Hc0EBAADIJ+tM8K6srIxzzjknevbsGd27d1/tep4LCqTBc0EBAMhUXt9c7euGDBkSb7zxRtxzzz3fup7nggIAAJBP1okj3meeeWY88sgj8eyzz8YWW2zxret6LigAAAD5JK+Dd5Ik8bOf/SwefPDBmDp1apSUlOS6JAAAAKiVvA7eQ4YMibvuuiv+/Oc/R3FxcSxatCgiIlq0aBFNmjTJcXUAAACwZnl9jfett94apaWlsd9++0X79u2rXvfee2+uSwMAAIC1ktdHvJMkyXUJAAAAUCd5fcQbAAAA1nWCNwAAAKRI8AYAAIAUCd4AAACQIsEbAAAAUiR4AwAAQIoEbwAAAEiR4A0AAAApErwBAAAgRYI3AAAApEjwBgAAgBQJ3gAAAJAiwRsAAABSJHgDAABAigRvAAAASJHgDQAAACkSvAEAACBFgjcAAACkSPAGAACAFAneAAAAkCLBGwAAAFIkeAMAAECKBG8AAABIkeANAAAAKRK8AQAAIEWCNwAAAKRI8AYAAIAUCd4AAACQIsEbAAAAUiR4AwAAQIoEbwAAAEiR4A0AAAApErwBAAAgRYI3AAAApEjwBgAAgBQJ3gAAAJAiwRsAAABS1CjXBcD6ZtLs0lyXwAbG7xwAQH5zxBsAAABSJHgDAABAigRvAAAASJHgDQAAACkSvAEAACBFgjcAAACkSPAGAACAFAneAAAAkCLBGwAAAFIkeAMAAECKBG8AAABIkeANAAAAKRK8AQAAIEWCNwAAAKRI8AYAAIAUCd4AAACQIsEbAAAAUiR4AwAAQIoEbwAAAEiR4A0AAAApErwBAAAgRYI3AAAApEjwBgAAgBQJ3gAAAJAiwRsAAABSJHgDAABAigRvAAAASJHgDQAAACkSvAEAACBFgjcAAACkSPAGAACAFAneAAAAkCLBGwAAAFIkeAMAAECKBG8AAABIkeANAAAAKRK8AQAAIEWCNwAAAKRonQjet9xyS3Tu3DkaN24ce+21V7z44ou5LgkAqIGeDQCryvvgfe+998bQoUNjxIgR8corr8ROO+0Uffv2jY8++ijXpQEAX6NnA0DN8j54jxkzJk477bQYNGhQ7LDDDjFu3Lho2rRp/P73v891aQDA1+jZAFCzvA7eX375Zbz88svRp0+fqrEGDRpEnz594oUXXshhZQDA1+nZALB6jXJdwLf55JNPoqKiItq2bVttvG3btvH222/X+Jny8vIoLy+vel9aWhoREWVlZVmpafmS7GwHgPVLWVlBFrf1v16TJEnWtpm22vbstPt1hJ4NwKpy1a/zOnhnYvTo0TFy5MhVxjt27JiDagDYUJycwjaXLFkSLVq0SGHLuadfA5ALJ6ewzbXp13kdvDfddNNo2LBhLF68uNr44sWLo127djV+ZtiwYTF06NCq95WVlfGf//wnWrduHQUFdfvrRllZWXTs2DHmz58fzZs3r9O2NjTmLjPmLTPmLXPmLjPZnrckSWLJkiXRoUOHLFRXP2rbs9Ps1xF+lzNl3jJn7jJj3jJj3jKTy36d18G7sLAwdtttt5g8eXIcddRREfG/xjx58uQ488wza/xMUVFRFBUVVRtr2bJlVutq3ry5X/AMmbvMmLfMmLfMmbvMZHPe1rUj3bXt2fXRryP8LmfKvGXO3GXGvGXGvGUmF/06r4N3RMTQoUNj4MCBsfvuu8eee+4ZY8eOjWXLlsWgQYNyXRoA8DV6NgDULO+D93HHHRcff/xxXH755bFo0aLYeeed44knnljl5i0AQG7p2QBQs7wP3hERZ5555mpPLa9PRUVFMWLEiFVOjWPNzF1mzFtmzFvmzF1mzNv/0bPXbeYtc+YuM+YtM+YtM7mct4JkXXpWCQAAAKxjGuS6AAAAAFifCd4AAACQIsEbAAAAUiR4f8Mtt9wSnTt3jsaNG8dee+0VL7744reuP2nSpNhuu+2icePGseOOO8Zjjz1WT5Xmn9rM3e233x777rtvtGrVKlq1ahV9+vRZ41yvr2r7O7fSPffcEwUFBVXPy93Q1HbePv/88xgyZEi0b98+ioqKYpttttlg/3ut7dyNHTs2tt1222jSpEl07Ngxzj333Pjiiy/qqdrce/bZZ+Pwww+PDh06REFBQTz00ENr/MzUqVNj1113jaKioth6661jwoQJqde5IdKzM6NfZ0a/zpyenRn9uvbyumcnVLnnnnuSwsLC5Pe//33y73//OznttNOSli1bJosXL65x/eeffz5p2LBhct111yVvvvlmctlllyUbbbRR8vrrr9dz5blX27k7/vjjk1tuuSWZPn168tZbbyUnn3xy0qJFi+SDDz6o58pzq7bzttKcOXOSzTffPNl3332TI488sn6KzSO1nbfy8vJk9913T/r3758899xzyZw5c5KpU6cmM2bMqOfKc6+2c3fnnXcmRUVFyZ133pnMmTMn+etf/5q0b98+Offcc+u58tx57LHHkksvvTR54IEHkohIHnzwwW9d/7333kuaNm2aDB06NHnzzTeTm266KWnYsGHyxBNP1E/BGwg9OzP6dWb068zp2ZnRrzOTzz1b8P6aPffcMxkyZEjV+4qKiqRDhw7J6NGja1z/2GOPTQ499NBqY3vttVdy+umnp1pnPqrt3H3TV199lRQXFycTJ05Mq8S8lMm8ffXVV8k+++yT/Pa3v00GDhy4QTby2s7brbfemmy11VbJl19+WV8l5q3azt2QIUOSAw44oNrY0KFDk549e6ZaZ75amyZ+4YUXJt26das2dtxxxyV9+/ZNsbINj56dGf06M/p15vTszOjXdZdvPdup5v/fl19+GS+//HL06dOnaqxBgwbRp0+feOGFF2r8zAsvvFBt/YiIvn37rnb99VUmc/dNy5cvjxUrVsQmm2ySVpl5J9N5GzVqVLRp0yZOOeWU+igz72Qybw8//HDsvffeMWTIkGjbtm107949rr766qioqKivsvNCJnO3zz77xMsvv1x1ett7770Xjz32WPTv379eal4X6Q3p07Mzo19nRr/OnJ6dGf26/tRnb2iU9S2uoz755JOoqKiItm3bVhtv27ZtvP322zV+ZtGiRTWuv2jRotTqzEeZzN03XXTRRdGhQ4dVfvHXZ5nM23PPPRe/+93vYsaMGfVQYX7KZN7ee++9ePrpp+OEE06Ixx57LGbNmhVnnHFGrFixIkaMGFEfZeeFTObu+OOPj08++SR69eoVSZLEV199FT/5yU/ikksuqY+S10mr6w1lZWXx3//+N5o0aZKjytYfenZm9OvM6NeZ07Mzo1/Xn/rs2Y54k3PXXHNN3HPPPfHggw9G48aNc11O3lqyZEmceOKJcfvtt8emm26a63LWKZWVldGmTZv4zW9+E7vttlscd9xxcemll8a4ceNyXVremzp1alx99dXx61//Ol555ZV44IEH4tFHH40rrrgi16UB9Uy/Xjv6dd3o2ZnRr/OfI97/36abbhoNGzaMxYsXVxtfvHhxtGvXrsbPtGvXrlbrr68ymbuVbrjhhrjmmmvib3/7W/To0SPNMvNObedt9uzZ8f7778fhhx9eNVZZWRkREY0aNYp33nknunTpkm7ReSCT37f27dvHRhttFA0bNqwa23777WPRokXx5ZdfRmFhYao154tM5m748OFx4oknxqmnnhoRETvuuGMsW7YsBg8eHJdeemk0aODvt9+0ut7QvHlzR7uzRM/OjH6dGf06c3p2ZvTr+lOfPdtP4P8rLCyM3XbbLSZPnlw1VllZGZMnT4699967xs/svffe1daPiHjqqadWu/76KpO5i4i47rrr4oorrognnngidt999/ooNa/Udt622267eP3112PGjBlVryOOOCL233//mDFjRnTs2LE+y8+ZTH7fevbsGbNmzar6H5+IiHfffTfat2+/QTTwlTKZu+XLl6/SrFf+z9D/7lvCN+kN6dOzM6NfZ0a/zpyenRn9uv7Ua2/I+u3a1mH33HNPUlRUlEyYMCF58803k8GDByctW7ZMFi1alCRJkpx44onJxRdfXLX+888/nzRq1Ci54YYbkrfeeisZMWLEBvlokiSp/dxdc801SWFhYXL//fcnCxcurHotWbIkV18hJ2o7b9+0od4ltbbzNm/evKS4uDg588wzk3feeSd55JFHkjZt2iRXXnllrr5CztR27kaMGJEUFxcnd999d/Lee+8lTz75ZNKlS5fk2GOPzdVXqHdLlixJpk+fnkyfPj2JiGTMmDHJ9OnTk7lz5yZJkiQXX3xxcuKJJ1atv/LRJBdccEHy1ltvJbfccovHiaVAz86Mfp0Z/TpzenZm9OvM5HPPFry/4aabbkq23HLLpLCwMNlzzz2Tf/7zn1XLevfunQwcOLDa+vfdd1+yzTbbJIWFhUm3bt2SRx99tJ4rzh+1mbtOnTolEbHKa8SIEfVfeI7V9nfu6zbkRl7befvHP/6R7LXXXklRUVGy1VZbJVdddVXy1Vdf1XPV+aE2c7dixYrk5z//edKlS5ekcePGSceOHZMzzjgj+eyzz+q/8ByZMmVKjf9erZyngQMHJr17917lMzvvvHNSWFiYbLXVVsn48ePrve4NgZ6dGf06M/p15vTszOjXtZfPPbsgSZx7AAAAAGlxjTcAAACkSPAGAACAFAneAAAAkCLBGwAAAFIkeAMAAECKBG8AAABIkeANAAAAKRK8AQAAIEWCNwAAAKRI8IYNwMcffxw//elPY8stt4yioqJo165d9O3bN55//vmc1vXAAw/EwQcfHK1bt46CgoKYMWNGTusBgFzLx569YsWKuOiii2LHHXeMZs2aRYcOHeKkk06KBQsW5KwmWNc0ynUBQPqOPvro+PLLL2PixImx1VZbxeLFi2Py5Mnx6aefprK/L7/8MgoLC9e43rJly6JXr15x7LHHxmmnnZZKLQCwLsnHnr18+fJ45ZVXYvjw4bHTTjvFZ599FmeffXYcccQRMW3atFTqgvVOAqzXPvvssyQikqlTp37rOoMHD07atGmTFBUVJd26dUv+8pe/VC2///77kx122CEpLCxMOnXqlNxwww3VPt+pU6dk1KhRyYknnpgUFxcnAwcOTJIkSf7+978nvXr1Sho3bpxsscUWyc9+9rNk6dKlq+x/zpw5SUQk06dPz8p3BoB10brQs1d68cUXk4hI5s6dW7cvDRsIp5rDem7jjTeOjTfeOB566KEoLy9fZXllZWX069cvnn/++fjjH/8Yb775ZlxzzTXRsGHDiIh4+eWX49hjj40BAwbE66+/Hj//+c9j+PDhMWHChGrbueGGG2KnnXaK6dOnx/Dhw2P27NlxyCGHxNFHHx2vvfZa3HvvvfHcc8/FmWeeWR9fGwDWOetSzy4tLY2CgoJo2bJlNqcA1lsFSZIkuS4CSNef/vSnOO200+K///1v7LrrrtG7d+8YMGBA9OjRI5588sno169fvPXWW7HNNtus8tkTTjghPv7443jyySerxi688MJ49NFH49///ndERHTu3Dl22WWXePDBB6vWOfXUU6Nhw4Zx2223VY0999xz0bt371i2bFk0bty4avz999+PkpKSmD59euy8884pzAAArBvyvWdHRHzxxRfRs2fP2G677eLOO+/M9hTAeskRb9gAHH300bFgwYJ4+OGH45BDDompU6fGrrvuGhMmTIgZM2bEFltsUWMDj4h46623omfPntXGevbsGTNnzoyKioqqsd13373aOq+++mpMmDCh6q/3G2+8cfTt2zcqKytjzpw52f+SALAeyPeevWLFijj22GMjSZK49dZbs/StYf3n5mqwgWjcuHEcdNBBcdBBB8Xw4cPj1FNPjREjRsT555+fle03a9as2vulS5fG6aefHmedddYq62655ZZZ2ScArI/ytWevDN1z586Np59+Opo3b56VemBDIHjDBmqHHXaIhx56KHr06BEffPBBvPvuuzX+BX377bdf5REmzz//fGyzzTZV15TVZNddd40333wztt5666zXDgAbknzo2StD98yZM2PKlCnRunXrzL8QbIAEb1jPffrpp3HMMcfEj3/84+jRo0cUFxfHtGnT4rrrrosjjzwyevfuHd/97nfj6KOPjjFjxsTWW28db7/9dhQUFMQhhxwS5513Xuyxxx5xxRVXxHHHHRcvvPBC3HzzzfHrX//6W/d70UUXxXe+850488wz49RTT41mzZrFm2++GU899VTcfPPNERHxn//8J+bNm1f1HNB33nknIiLatWsX7dq1S3diACDP5GvPXrFiRfzgBz+IV155JR555JGoqKiIRYsWRUTEJptsslaPEIUNXo7vqg6k7IsvvkguvvjiZNddd01atGiRNG3aNNl2222Tyy67LFm+fHmSJEny6aefJoMGDUpat26dNG7cOOnevXvyyCOPVG1j5aNJNtpoo2TLLbdMrr/++mr76NSpU/LLX/5ylX2/+OKLyUEHHZRsvPHGSbNmzZIePXokV111VdXy8ePHJxGxymvEiBGpzAUA5LN87dkrH/tZ02vKlCmpzQesT9zVHAAAAFLkruYAAACQIsEbAAAAUiR4AwAAQIoEbwAAAEiR4A0AAAApErwBAAAgRYI3AAAApEjwBgAAgBQJ3gAAAJAiwRsAAABSJHgDAABAigRvAAAASNH/A5b/Z+H7RX9TAAAAAElFTkSuQmCC\n", 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