From 899cb466e8bcd7c762f55134f4da6899328d2ee4 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Mon, 24 Jun 2024 20:05:41 +0000
Subject: [PATCH 01/50] 14 Devices Distributed Experiment Added

---
 .../14DevicesDistributed.ipynb                | 1289 +++++++++++++++++
 .../DistributedConfig/dc_dist_14d.json        |  259 ++++
 .../experimentsFlow/exp_dist_14d.json         |  140 ++
 3 files changed, 1688 insertions(+)
 create mode 100644 examples/syntetic_norm/14DevicesDistributed/14DevicesDistributed.ipynb
 create mode 100644 inputJsonsFiles/DistributedConfig/dc_dist_14d.json
 create mode 100644 inputJsonsFiles/experimentsFlow/exp_dist_14d.json

diff --git a/examples/syntetic_norm/14DevicesDistributed/14DevicesDistributed.ipynb b/examples/syntetic_norm/14DevicesDistributed/14DevicesDistributed.ipynb
new file mode 100644
index 00000000..47f1ff7a
--- /dev/null
+++ b/examples/syntetic_norm/14DevicesDistributed/14DevicesDistributed.ipynb
@@ -0,0 +1,1289 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f24dd870-a873-4217-a3ef-1cb87268aed6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import set_jupyter_env\n",
+    "from apiServer import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b8de2df4-a104-45a5-b335-eaba852d4238",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "API = ApiServer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6501e1d1-52f3-4020-bfae-e9cfd01b1b38",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Distributed Configuration Files\n",
+      "--------------------\n",
+      "\n",
+      "0.\tdc_AEC_1d_2c_1s_4r_4w.json\n",
+      "1.\tdc_dist_14d.json\n",
+      "2.\tdc_dist_2d_3c_2s_3r_6w.json\n",
+      "3.\tdc_fed_dist_14d.json\n",
+      "4.\tdc_fed_dist_2d_3c_2s_3r_6w.json\n",
+      "5.\tdc_fed_synt_1d_2c_2r_1s_4w_1ws.json\n",
+      "6.\tdc_synt_8d_8w_2c_4s_4r.json\n",
+      "7.\tdc_synt_8d_8w_4c_6r_4s.json\n",
+      "8.\tdc_synt_distributed_w5_c3_6r_3s_3d.json\n",
+      "9.\tdc_test_synt_1d_2c_1s_4r_4w.json\n",
+      "10.\tdc_test_synt_1d_2c_2s_4r_4w.json\n",
+      "\n",
+      "Connection Map Files\n",
+      "--------------------\n",
+      "\n",
+      "0.\tconn_1Router1Client1S.json\n",
+      "1.\tconn_1Router1Client2S.json\n",
+      "2.\tconn_1Router2Clients1S.json\n",
+      "3.\tconn_1Router3Clients1S.json\n",
+      "4.\tconn_1Router4Clients1S.json\n",
+      "5.\tconn_1Router4Clients1fed.json\n",
+      "6.\tconn_1Router4Clients2Sources.json\n",
+      "7.\tconn_1Router4Clients2Sources1fed.json\n",
+      "8.\tconn_2R4C1S_health_david.json\n",
+      "9.\tconn_2Router2Clients1Source.json\n",
+      "10.\tconn_2Router2Clients1Source_david.json\n",
+      "11.\tconn_2Router2Clients2Source.json\n",
+      "12.\tconn_2Router2ClientsGUI.json\n",
+      "13.\tconn_2Router3Clients.json\n",
+      "14.\tconn_3Router3Clients.json\n",
+      "15.\tconn_6RouterCycle6Clients1Source.json\n",
+      "16.\tconn_6RouterCycle8Clients1Source.json\n",
+      "17.\tconn_6RouterLine6Clients1Source.json\n",
+      "18.\tconn_8RouterCycle8Clients1Source.json\n",
+      "19.\tconn_fed_dist_14d.json\n",
+      "20.\tconn_fed_dist_2d_3c_2s_3r_6w.json\n",
+      "21.\tconn_fed_synt_1d_2c_2r_1s_4w_1ws.json\n",
+      "22.\tconn_synt_8d_8w_4c_6r_4s.json\n",
+      "23.\tconn_synt_dc_8d_8w_2c_4s_4r.json\n",
+      "24.\tconn_synt_distributed_w5_c3_6r_3s_3d.json\n",
+      "25.\tconn_test_synt_1d_2c_1s_4r_4w.json\n",
+      "26.\tconn_test_synt_1d_2c_2s_4r_4w.json\n",
+      "\n",
+      "Experiments Flow Files\n",
+      "--------------------\n",
+      "\n",
+      "0.\texp_dist_14d.json\n",
+      "1.\texp_dist_2d_3c_2s_3r_6w.json\n",
+      "2.\texp_fed_dist_14d.json\n",
+      "3.\texp_fed_dist_2d_3c_2s_3r_6w.json\n",
+      "4.\texp_fed_synt_1d_2c_2r_1s_4w_1ws.json\n",
+      "5.\texp_new_arc.json\n",
+      "6.\texp_synt_8d_8w_2c_4s_4r.json\n",
+      "7.\texp_synt_8d_8w_4c_6r_4s.json\n",
+      "8.\texp_synt_distributed_w5_c3_6r_3s_3d.json\n",
+      "9.\texp_test_synt_1d_2c_1s_4r_4w new.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "API.showJsons()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f100b3ad-a063-4a49-a99f-243f7d383fd3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dc = 1\n",
+    "conn = 19\n",
+    "exp = 0\n",
+    "API.setJsons(dc, conn, exp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a43e54c5-4d1e-4782-8879-cc333a98d4ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[INFO][2024-06-24 19:54:09,629] \n",
+      "Network components:\n",
+      "                 Receiver's Address: http://10.0.0.5:8901\n",
+      "                 Frequency: 100 [batches/sec]\n",
+      "                 Batchsize: 50 [samples]\n",
+      "                 devicesIp: ['10.0.0.5', '10.0.0.31', '10.0.0.17', '10.0.0.18', '10.0.0.19', '10.0.0.20', '10.0.0.21', '10.0.0.22', '10.0.0.23', '10.0.0.24', '10.0.0.25', '10.0.0.26', '10.0.0.27', '10.0.0.28', '10.0.0.29']\n",
+      "                 mainServerIp: 10.0.0.5\n",
+      "                 mainServerPort: 8900\n",
+      "                 apiServerIp: 10.0.0.5\n",
+      "                 apiServerPort: 8901\n",
+      "                 Clients: ['c1', 'c2', 'c3', 'c4', 'c5']\n",
+      "                 Workers: ['w1', 'w2', 'w3', 'w4', 'w5', 'w6', 'w7', 'w8', 'w9', 'w10']\n",
+      "                 Sources: ['s1', 's2', 's3', 's4', 's5']\n",
+      "                 Routers: ['r1', 'r2', 'r3', 'r4']\n",
+      "[INFO][2024-06-24 19:54:09,630] Connections:\n",
+      "[INFO][2024-06-24 19:54:09,631] \t\t r1 : ['mainServer', 'r2', 's1', 'c3', 'c2']\n",
+      "[INFO][2024-06-24 19:54:09,631] \t\t r2 : ['r3', 's2', 's5', 'c1']\n",
+      "[INFO][2024-06-24 19:54:09,632] \t\t r3 : ['r4', 's3', 's4']\n",
+      "[INFO][2024-06-24 19:54:09,632] \t\t r4 : ['r1', 'c4', 'c5']\n",
+      "[INFO][2024-06-24 19:54:09,633] Experiment name: synthetic_3_gausians\n",
+      "[INFO][2024-06-24 19:54:09,633] Batch size: 50\n",
+      "[INFO][2024-06-24 19:54:09,634] Number of features: 5\n",
+      "[INFO][2024-06-24 19:54:09,634] Number of labels: 3\n",
+      "[INFO][2024-06-24 19:54:09,635] \n",
+      "[INFO][2024-06-24 19:54:09,635] Phases:\n",
+      "[INFO][2024-06-24 19:54:09,636]    Phase name: training_phase1\n",
+      "[INFO][2024-06-24 19:54:09,636]    Phase type: training\n",
+      "[INFO][2024-06-24 19:54:09,636]    Sources: s1,s2,s3,s4,s5\n",
+      "[INFO][2024-06-24 19:54:09,637] \n",
+      "[INFO][2024-06-24 19:54:09,637]     Source pieces:\n",
+      "[INFO][2024-06-24 19:54:09,638]          Source name: s1\n",
+      "[INFO][2024-06-24 19:54:09,638]          Batch size: 50\n",
+      "[INFO][2024-06-24 19:54:09,639]          Phase: training\n",
+      "[INFO][2024-06-24 19:54:09,639]          Starting offset: 0\n",
+      "[INFO][2024-06-24 19:54:09,640]          Number of batches: 200\n",
+      "[INFO][2024-06-24 19:54:09,640]          Workers target: w1,w2\n",
+      "[INFO][2024-06-24 19:54:09,641]       ----------------------\n",
+      "[INFO][2024-06-24 19:54:09,641] \n",
+      "[INFO][2024-06-24 19:54:09,641]          Source name: s2\n",
+      "[INFO][2024-06-24 19:54:09,642]          Batch size: 50\n",
+      "[INFO][2024-06-24 19:54:09,642]          Phase: training\n",
+      "[INFO][2024-06-24 19:54:09,643]          Starting offset: 0\n",
+      "[INFO][2024-06-24 19:54:09,643]          Number of batches: 200\n",
+      "[INFO][2024-06-24 19:54:09,643]          Workers target: w3,w4\n",
+      "[INFO][2024-06-24 19:54:09,644]       ----------------------\n",
+      "[INFO][2024-06-24 19:54:09,644] \n",
+      "[INFO][2024-06-24 19:54:09,644]          Source name: s3\n",
+      "[INFO][2024-06-24 19:54:09,645]          Batch size: 50\n",
+      "[INFO][2024-06-24 19:54:09,645]          Phase: training\n",
+      "[INFO][2024-06-24 19:54:09,645]          Starting offset: 0\n",
+      "[INFO][2024-06-24 19:54:09,646]          Number of batches: 200\n",
+      "[INFO][2024-06-24 19:54:09,646]          Workers target: w5,w6\n",
+      "[INFO][2024-06-24 19:54:09,646]       ----------------------\n",
+      "[INFO][2024-06-24 19:54:09,647] \n",
+      "[INFO][2024-06-24 19:54:09,647]          Source name: s4\n",
+      "[INFO][2024-06-24 19:54:09,647]          Batch size: 50\n",
+      "[INFO][2024-06-24 19:54:09,648]          Phase: training\n",
+      "[INFO][2024-06-24 19:54:09,648]          Starting offset: 0\n",
+      "[INFO][2024-06-24 19:54:09,648]          Number of batches: 200\n",
+      "[INFO][2024-06-24 19:54:09,649]          Workers target: w7,w8\n",
+      "[INFO][2024-06-24 19:54:09,649]       ----------------------\n",
+      "[INFO][2024-06-24 19:54:09,649] \n",
+      "[INFO][2024-06-24 19:54:09,650]          Source name: s5\n",
+      "[INFO][2024-06-24 19:54:09,650]          Batch size: 50\n",
+      "[INFO][2024-06-24 19:54:09,650]          Phase: training\n",
+      "[INFO][2024-06-24 19:54:09,651]          Starting offset: 0\n",
+      "[INFO][2024-06-24 19:54:09,651]          Number of batches: 200\n",
+      "[INFO][2024-06-24 19:54:09,651]          Workers target: w9,w10\n",
+      "[INFO][2024-06-24 19:54:09,651]       ----------------------\n",
+      "[INFO][2024-06-24 19:54:09,652] \n",
+      "[INFO][2024-06-24 19:54:09,652]    Phase name: training_phase2\n",
+      "[INFO][2024-06-24 19:54:09,652]    Phase type: training\n",
+      "[INFO][2024-06-24 19:54:09,653]    Sources: s1,s2,s3,s4,s5\n",
+      "[INFO][2024-06-24 19:54:09,653] \n",
+      "[INFO][2024-06-24 19:54:09,653]     Source pieces:\n",
+      "[INFO][2024-06-24 19:54:09,654]          Source name: s1\n",
+      "[INFO][2024-06-24 19:54:09,654]          Batch size: 50\n",
+      "[INFO][2024-06-24 19:54:09,654]          Phase: training\n",
+      "[INFO][2024-06-24 19:54:09,655]          Starting offset: 20000\n",
+      "[INFO][2024-06-24 19:54:09,655]          Number of batches: 200\n",
+      "[INFO][2024-06-24 19:54:09,655]          Workers target: w1,w3\n",
+      "[INFO][2024-06-24 19:54:09,655]       ----------------------\n",
+      "[INFO][2024-06-24 19:54:09,656] \n",
+      "[INFO][2024-06-24 19:54:09,656]          Source name: s2\n",
+      "[INFO][2024-06-24 19:54:09,656]          Batch size: 50\n",
+      "[INFO][2024-06-24 19:54:09,657]          Phase: training\n",
+      "[INFO][2024-06-24 19:54:09,657]          Starting offset: 20000\n",
+      "[INFO][2024-06-24 19:54:09,657]          Number of batches: 200\n",
+      "[INFO][2024-06-24 19:54:09,658]          Workers target: w2,w4\n",
+      "[INFO][2024-06-24 19:54:09,658]       ----------------------\n",
+      "[INFO][2024-06-24 19:54:09,658] \n",
+      "[INFO][2024-06-24 19:54:09,659]          Source name: s3\n",
+      "[INFO][2024-06-24 19:54:09,659]          Batch size: 50\n",
+      "[INFO][2024-06-24 19:54:09,659]          Phase: training\n",
+      "[INFO][2024-06-24 19:54:09,660]          Starting offset: 20000\n",
+      "[INFO][2024-06-24 19:54:09,660]          Number of batches: 200\n",
+      "[INFO][2024-06-24 19:54:09,660]          Workers target: w5,w7\n",
+      "[INFO][2024-06-24 19:54:09,660]       ----------------------\n",
+      "[INFO][2024-06-24 19:54:09,661] \n",
+      "[INFO][2024-06-24 19:54:09,661]          Source name: s4\n",
+      "[INFO][2024-06-24 19:54:09,661]          Batch size: 50\n",
+      "[INFO][2024-06-24 19:54:09,662]          Phase: training\n",
+      "[INFO][2024-06-24 19:54:09,662]          Starting offset: 20000\n",
+      "[INFO][2024-06-24 19:54:09,662]          Number of batches: 200\n",
+      "[INFO][2024-06-24 19:54:09,663]          Workers target: w6,w10\n",
+      "[INFO][2024-06-24 19:54:09,663]       ----------------------\n",
+      "[INFO][2024-06-24 19:54:09,663] \n",
+      "[INFO][2024-06-24 19:54:09,664]          Source name: s5\n",
+      "[INFO][2024-06-24 19:54:09,664]          Batch size: 50\n",
+      "[INFO][2024-06-24 19:54:09,664]          Phase: training\n",
+      "[INFO][2024-06-24 19:54:09,664]          Starting offset: 20000\n",
+      "[INFO][2024-06-24 19:54:09,665]          Number of batches: 200\n",
+      "[INFO][2024-06-24 19:54:09,665]          Workers target: w8,w9\n",
+      "[INFO][2024-06-24 19:54:09,665]       ----------------------\n",
+      "[INFO][2024-06-24 19:54:09,666] \n",
+      "[INFO][2024-06-24 19:54:09,666]    Phase name: prediction_phase\n",
+      "[INFO][2024-06-24 19:54:09,666]    Phase type: prediction\n",
+      "[INFO][2024-06-24 19:54:09,667]    Sources: s1,s2,s3,s4,s5\n",
+      "[INFO][2024-06-24 19:54:09,667] \n",
+      "[INFO][2024-06-24 19:54:09,667]     Source pieces:\n",
+      "[INFO][2024-06-24 19:54:09,668]          Source name: s1\n",
+      "[INFO][2024-06-24 19:54:09,668]          Batch size: 50\n",
+      "[INFO][2024-06-24 19:54:09,668]          Phase: prediction\n",
+      "[INFO][2024-06-24 19:54:09,668]          Starting offset: 40000\n",
+      "[INFO][2024-06-24 19:54:09,669]          Number of batches: 100\n",
+      "[INFO][2024-06-24 19:54:09,669]          Workers target: w1,w8\n",
+      "[INFO][2024-06-24 19:54:09,669]       ----------------------\n",
+      "[INFO][2024-06-24 19:54:09,670] \n",
+      "[INFO][2024-06-24 19:54:09,670]          Source name: s2\n",
+      "[INFO][2024-06-24 19:54:09,670]          Batch size: 50\n",
+      "[INFO][2024-06-24 19:54:09,671]          Phase: prediction\n",
+      "[INFO][2024-06-24 19:54:09,671]          Starting offset: 40000\n",
+      "[INFO][2024-06-24 19:54:09,671]          Number of batches: 100\n",
+      "[INFO][2024-06-24 19:54:09,672]          Workers target: w2,w7\n",
+      "[INFO][2024-06-24 19:54:09,672]       ----------------------\n",
+      "[INFO][2024-06-24 19:54:09,672] \n",
+      "[INFO][2024-06-24 19:54:09,672]          Source name: s3\n",
+      "[INFO][2024-06-24 19:54:09,673]          Batch size: 50\n",
+      "[INFO][2024-06-24 19:54:09,673]          Phase: prediction\n",
+      "[INFO][2024-06-24 19:54:09,673]          Starting offset: 40000\n",
+      "[INFO][2024-06-24 19:54:09,674]          Number of batches: 100\n",
+      "[INFO][2024-06-24 19:54:09,674]          Workers target: w3,w6\n",
+      "[INFO][2024-06-24 19:54:09,674]       ----------------------\n",
+      "[INFO][2024-06-24 19:54:09,675] \n",
+      "[INFO][2024-06-24 19:54:09,675]          Source name: s4\n",
+      "[INFO][2024-06-24 19:54:09,675]          Batch size: 50\n",
+      "[INFO][2024-06-24 19:54:09,676]          Phase: prediction\n",
+      "[INFO][2024-06-24 19:54:09,676]          Starting offset: 40000\n",
+      "[INFO][2024-06-24 19:54:09,676]          Number of batches: 100\n",
+      "[INFO][2024-06-24 19:54:09,677]          Workers target: w4,w9\n",
+      "[INFO][2024-06-24 19:54:09,677]       ----------------------\n",
+      "[INFO][2024-06-24 19:54:09,677] \n",
+      "[INFO][2024-06-24 19:54:09,677]          Source name: s5\n",
+      "[INFO][2024-06-24 19:54:09,678]          Batch size: 50\n",
+      "[INFO][2024-06-24 19:54:09,678]          Phase: prediction\n",
+      "[INFO][2024-06-24 19:54:09,678]          Starting offset: 40000\n",
+      "[INFO][2024-06-24 19:54:09,679]          Number of batches: 100\n",
+      "[INFO][2024-06-24 19:54:09,679]          Workers target: w5,w10\n",
+      "[INFO][2024-06-24 19:54:09,679]       ----------------------\n",
+      "[INFO][2024-06-24 19:54:09,680] \n",
+      "[INFO][2024-06-24 19:54:09,680] Initializing ApiServer receiver thread\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " * Serving Flask app 'receiver'\n",
+      " * Debug mode: off\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[INFO][2024-06-24 19:54:11,688] *** Remember to execute NerlnetRun.sh on each device before running the experiment! ***\n"
+     ]
+    }
+   ],
+   "source": [
+    "dc_path, conn_path, exp_path = API.getUserJsons()\n",
+    "exp_name = \"14DevicesDistributed\"\n",
+    "API.initialization(exp_name, dc_path, conn_path, exp_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f46fdf57-cdf8-46c8-aa71-72d89c6176d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[INFO][2024-06-24 19:54:34,252] Sending distributed configurations to devices is completed\n"
+     ]
+    }
+   ],
+   "source": [
+    "API.send_jsons_to_devices()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c86fe75b-c53a-4dfe-bd84-d7ec2b8698e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[INFO][2024-06-24 19:55:07,047] Experiment phase: training_phase1 of type training starts running...\n",
+      "[INFO][2024-06-24 19:55:07,048] Sending data to sources\n",
+      "[INFO][2024-06-24 19:55:09,470] Data is ready in sources\n",
+      "[INFO][2024-06-24 19:55:09,471] Phase training requested from Main Server\n",
+      "[INFO][2024-06-24 19:55:24,970] Processing experiment phase data\n",
+      "[INFO][2024-06-24 19:55:25,132] Processing experiment phase data completed\n",
+      "[INFO][2024-06-24 19:55:25,135] Start generating communication statistics for training_phase1 of type training\n",
+      "[INFO][2024-06-24 19:55:25,135] Statistics requested from Main Server\n",
+      "[INFO][2024-06-24 19:55:25,440] Statistics received from Main Server\n",
+      "[INFO][2024-06-24 19:55:25,441] Phase of training_phase1 training completed\n"
+     ]
+    }
+   ],
+   "source": [
+    "API.run_current_experiment_phase()\n",
+    "stats_train1 = API.get_experiment_flow(exp_name).generate_stats()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2ccc407c-1da0-4f05-81ec-5118726626bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[INFO][2024-06-24 19:55:42,824] Experiment phase: training_phase2 of type training starts running...\n",
+      "[INFO][2024-06-24 19:55:42,825] Sending data to sources\n",
+      "[INFO][2024-06-24 19:55:45,331] Data is ready in sources\n",
+      "[INFO][2024-06-24 19:55:45,332] Phase training requested from Main Server\n",
+      "[INFO][2024-06-24 19:56:00,878] Processing experiment phase data\n",
+      "[INFO][2024-06-24 19:56:00,915] Processing experiment phase data completed\n",
+      "[INFO][2024-06-24 19:56:00,916] Start generating communication statistics for training_phase2 of type training\n",
+      "[INFO][2024-06-24 19:56:00,917] Statistics requested from Main Server\n",
+      "[INFO][2024-06-24 19:56:01,321] Statistics received from Main Server\n",
+      "[INFO][2024-06-24 19:56:01,322] Phase of training_phase2 training completed\n"
+     ]
+    }
+   ],
+   "source": [
+    "API.next_experiment_phase()\n",
+    "API.run_current_experiment_phase()\n",
+    "stats_train2 = API.get_experiment_flow(exp_name).generate_stats()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "17b43117-1988-4170-9652-6510f415a697",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[INFO][2024-06-24 19:56:08,796] Experiment phase: prediction_phase of type prediction starts running...\n",
+      "[INFO][2024-06-24 19:56:08,797] Sending data to sources\n",
+      "[INFO][2024-06-24 19:56:10,051] Data is ready in sources\n",
+      "[INFO][2024-06-24 19:56:10,052] Phase prediction requested from Main Server\n",
+      "[INFO][2024-06-24 19:56:15,200] Processing experiment phase data\n",
+      "[INFO][2024-06-24 19:56:15,221] Processing experiment phase data completed\n",
+      "[INFO][2024-06-24 19:56:15,222] Start generating communication statistics for prediction_phase of type prediction\n",
+      "[INFO][2024-06-24 19:56:15,223] Statistics requested from Main Server\n",
+      "[INFO][2024-06-24 19:56:15,327] Statistics received from Main Server\n",
+      "[INFO][2024-06-24 19:56:15,328] Phase of prediction_phase prediction completed\n"
+     ]
+    }
+   ],
+   "source": [
+    "API.next_experiment_phase()\n",
+    "API.run_current_experiment_phase()\n",
+    "stats_pred = API.get_experiment_flow(exp_name).generate_stats()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0d8f0bc4-b02c-4f19-ba11-d5d8da60a4fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "loss_train1 = stats_train1.get_loss_ts(plot=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "53ac4caa-aa8a-45a1-8565-fe25766059a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "loss_train2 = stats_train2.get_loss_ts(plot=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7668c632-2ea8-46cd-ad58-c24c3b4b9e1c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1680x5600 with 60 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "conf_mats_sources_workers , conf_mats_workers = stats_pred.get_confusion_matrices(plot=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2de89d32-c49d-4225-b409-2dbe54633f41",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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#T_095a0_row14_col11, #T_095a0_row14_col12, #T_095a0_row15_col0, #T_095a0_row15_col1, #T_095a0_row15_col2, #T_095a0_row15_col3, #T_095a0_row15_col4, #T_095a0_row15_col5, #T_095a0_row15_col6, #T_095a0_row15_col7, #T_095a0_row15_col8, #T_095a0_row15_col9, #T_095a0_row15_col10, #T_095a0_row15_col11, #T_095a0_row15_col12, #T_095a0_row16_col0, #T_095a0_row16_col1, #T_095a0_row16_col2, #T_095a0_row16_col3, #T_095a0_row16_col4, #T_095a0_row16_col5, #T_095a0_row16_col6, #T_095a0_row16_col7, #T_095a0_row16_col8, #T_095a0_row16_col9, #T_095a0_row16_col10, #T_095a0_row16_col11, #T_095a0_row16_col12, #T_095a0_row17_col0, #T_095a0_row17_col1, #T_095a0_row17_col2, #T_095a0_row17_col3, #T_095a0_row17_col4, #T_095a0_row17_col5, #T_095a0_row17_col6, #T_095a0_row17_col7, #T_095a0_row17_col8, #T_095a0_row17_col9, #T_095a0_row17_col10, #T_095a0_row17_col11, #T_095a0_row17_col12, #T_095a0_row18_col0, #T_095a0_row18_col1, #T_095a0_row18_col2, #T_095a0_row18_col3, #T_095a0_row18_col4, #T_095a0_row18_col5, #T_095a0_row18_col6, #T_095a0_row18_col7, #T_095a0_row18_col8, #T_095a0_row18_col9, #T_095a0_row18_col10, #T_095a0_row18_col11, #T_095a0_row18_col12, #T_095a0_row19_col0, #T_095a0_row19_col1, #T_095a0_row19_col2, #T_095a0_row19_col3, #T_095a0_row19_col4, #T_095a0_row19_col5, #T_095a0_row19_col6, #T_095a0_row19_col7, #T_095a0_row19_col8, #T_095a0_row19_col9, #T_095a0_row19_col10, #T_095a0_row19_col11, #T_095a0_row19_col12, #T_095a0_row20_col0, #T_095a0_row20_col1, #T_095a0_row20_col2, #T_095a0_row20_col3, #T_095a0_row20_col4, #T_095a0_row20_col5, #T_095a0_row20_col6, #T_095a0_row20_col7, #T_095a0_row20_col8, #T_095a0_row20_col9, #T_095a0_row20_col10, #T_095a0_row20_col11, #T_095a0_row20_col12, #T_095a0_row21_col0, #T_095a0_row21_col1, #T_095a0_row21_col2, #T_095a0_row21_col3, #T_095a0_row21_col4, #T_095a0_row21_col5, #T_095a0_row21_col6, #T_095a0_row21_col7, #T_095a0_row21_col8, #T_095a0_row21_col9, #T_095a0_row21_col10, #T_095a0_row21_col11, #T_095a0_row21_col12, #T_095a0_row22_col0, #T_095a0_row22_col1, #T_095a0_row22_col2, #T_095a0_row22_col3, #T_095a0_row22_col4, #T_095a0_row22_col5, #T_095a0_row22_col6, #T_095a0_row22_col7, #T_095a0_row22_col8, #T_095a0_row22_col9, #T_095a0_row22_col10, #T_095a0_row22_col11, #T_095a0_row22_col12, #T_095a0_row23_col0, #T_095a0_row23_col1, #T_095a0_row23_col2, #T_095a0_row23_col3, #T_095a0_row23_col4, #T_095a0_row23_col5, #T_095a0_row23_col6, #T_095a0_row23_col7, #T_095a0_row23_col8, #T_095a0_row23_col9, #T_095a0_row23_col10, #T_095a0_row23_col11, #T_095a0_row23_col12, #T_095a0_row24_col0, #T_095a0_row24_col1, #T_095a0_row24_col2, #T_095a0_row24_col3, #T_095a0_row24_col4, #T_095a0_row24_col5, #T_095a0_row24_col6, #T_095a0_row24_col7, #T_095a0_row24_col8, #T_095a0_row24_col9, #T_095a0_row24_col10, #T_095a0_row24_col11, #T_095a0_row24_col12, #T_095a0_row25_col0, #T_095a0_row25_col1, #T_095a0_row25_col2, #T_095a0_row25_col3, #T_095a0_row25_col4, #T_095a0_row25_col5, #T_095a0_row25_col6, #T_095a0_row25_col7, #T_095a0_row25_col8, #T_095a0_row25_col9, #T_095a0_row25_col10, #T_095a0_row25_col11, #T_095a0_row25_col12, #T_095a0_row26_col0, #T_095a0_row26_col1, #T_095a0_row26_col2, #T_095a0_row26_col3, #T_095a0_row26_col4, #T_095a0_row26_col5, #T_095a0_row26_col6, #T_095a0_row26_col7, #T_095a0_row26_col8, #T_095a0_row26_col9, #T_095a0_row26_col10, #T_095a0_row26_col11, #T_095a0_row26_col12, #T_095a0_row27_col0, #T_095a0_row27_col1, #T_095a0_row27_col2, #T_095a0_row27_col3, #T_095a0_row27_col4, #T_095a0_row27_col5, #T_095a0_row27_col6, #T_095a0_row27_col7, #T_095a0_row27_col8, #T_095a0_row27_col9, #T_095a0_row27_col10, #T_095a0_row27_col11, #T_095a0_row27_col12, #T_095a0_row28_col0, #T_095a0_row28_col1, #T_095a0_row28_col2, #T_095a0_row28_col3, #T_095a0_row28_col4, #T_095a0_row28_col5, #T_095a0_row28_col6, #T_095a0_row28_col7, #T_095a0_row28_col8, #T_095a0_row28_col9, #T_095a0_row28_col10, #T_095a0_row28_col11, #T_095a0_row28_col12, #T_095a0_row29_col0, #T_095a0_row29_col1, #T_095a0_row29_col2, #T_095a0_row29_col3, #T_095a0_row29_col4, #T_095a0_row29_col5, #T_095a0_row29_col6, #T_095a0_row29_col7, #T_095a0_row29_col8, #T_095a0_row29_col9, #T_095a0_row29_col10, #T_095a0_row29_col11, #T_095a0_row29_col12 {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_095a0\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_095a0_level0_col0\" class=\"col_heading level0 col0\" >Worker</th>\n",
+       "      <th id=\"T_095a0_level0_col1\" class=\"col_heading level0 col1\" >Class</th>\n",
+       "      <th id=\"T_095a0_level0_col2\" class=\"col_heading level0 col2\" >TN</th>\n",
+       "      <th id=\"T_095a0_level0_col3\" class=\"col_heading level0 col3\" >FP</th>\n",
+       "      <th id=\"T_095a0_level0_col4\" class=\"col_heading level0 col4\" >FN</th>\n",
+       "      <th id=\"T_095a0_level0_col5\" class=\"col_heading level0 col5\" >TP</th>\n",
+       "      <th id=\"T_095a0_level0_col6\" class=\"col_heading level0 col6\" >Accuracy</th>\n",
+       "      <th id=\"T_095a0_level0_col7\" class=\"col_heading level0 col7\" >Balanced Accuracy</th>\n",
+       "      <th id=\"T_095a0_level0_col8\" class=\"col_heading level0 col8\" >Precision</th>\n",
+       "      <th id=\"T_095a0_level0_col9\" class=\"col_heading level0 col9\" >Recall</th>\n",
+       "      <th id=\"T_095a0_level0_col10\" class=\"col_heading level0 col10\" >True Negative Rate</th>\n",
+       "      <th id=\"T_095a0_level0_col11\" class=\"col_heading level0 col11\" >Informedness</th>\n",
+       "      <th id=\"T_095a0_level0_col12\" class=\"col_heading level0 col12\" >F1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_095a0_row0_col0\" class=\"data row0 col0\" >w8</td>\n",
+       "      <td id=\"T_095a0_row0_col1\" class=\"data row0 col1\" >Norm(0:1)</td>\n",
+       "      <td id=\"T_095a0_row0_col2\" class=\"data row0 col2\" >3765</td>\n",
+       "      <td id=\"T_095a0_row0_col3\" class=\"data row0 col3\" >0</td>\n",
+       "      <td id=\"T_095a0_row0_col4\" class=\"data row0 col4\" >1</td>\n",
+       "      <td id=\"T_095a0_row0_col5\" class=\"data row0 col5\" >1234</td>\n",
+       "      <td id=\"T_095a0_row0_col6\" class=\"data row0 col6\" >0.999800</td>\n",
+       "      <td id=\"T_095a0_row0_col7\" class=\"data row0 col7\" >0.999595</td>\n",
+       "      <td id=\"T_095a0_row0_col8\" class=\"data row0 col8\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row0_col9\" class=\"data row0 col9\" >0.999190</td>\n",
+       "      <td id=\"T_095a0_row0_col10\" class=\"data row0 col10\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row0_col11\" class=\"data row0 col11\" >0.999190</td>\n",
+       "      <td id=\"T_095a0_row0_col12\" class=\"data row0 col12\" >0.999595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_095a0_row1_col0\" class=\"data row1 col0\" >w8</td>\n",
+       "      <td id=\"T_095a0_row1_col1\" class=\"data row1 col1\" >Norm(4:1)</td>\n",
+       "      <td id=\"T_095a0_row1_col2\" class=\"data row1 col2\" >3733</td>\n",
+       "      <td id=\"T_095a0_row1_col3\" class=\"data row1 col3\" >3</td>\n",
+       "      <td id=\"T_095a0_row1_col4\" class=\"data row1 col4\" >1</td>\n",
+       "      <td id=\"T_095a0_row1_col5\" class=\"data row1 col5\" >1263</td>\n",
+       "      <td id=\"T_095a0_row1_col6\" class=\"data row1 col6\" >0.999200</td>\n",
+       "      <td id=\"T_095a0_row1_col7\" class=\"data row1 col7\" >0.999203</td>\n",
+       "      <td id=\"T_095a0_row1_col8\" class=\"data row1 col8\" >0.997630</td>\n",
+       "      <td id=\"T_095a0_row1_col9\" class=\"data row1 col9\" >0.999209</td>\n",
+       "      <td id=\"T_095a0_row1_col10\" class=\"data row1 col10\" >0.999197</td>\n",
+       "      <td id=\"T_095a0_row1_col11\" class=\"data row1 col11\" >0.998406</td>\n",
+       "      <td id=\"T_095a0_row1_col12\" class=\"data row1 col12\" >0.998419</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_095a0_row2_col0\" class=\"data row2 col0\" >w8</td>\n",
+       "      <td id=\"T_095a0_row2_col1\" class=\"data row2 col1\" >Norm(10:3)</td>\n",
+       "      <td id=\"T_095a0_row2_col2\" class=\"data row2 col2\" >2498</td>\n",
+       "      <td id=\"T_095a0_row2_col3\" class=\"data row2 col3\" >1</td>\n",
+       "      <td id=\"T_095a0_row2_col4\" class=\"data row2 col4\" >2</td>\n",
+       "      <td id=\"T_095a0_row2_col5\" class=\"data row2 col5\" >2499</td>\n",
+       "      <td id=\"T_095a0_row2_col6\" class=\"data row2 col6\" >0.999400</td>\n",
+       "      <td id=\"T_095a0_row2_col7\" class=\"data row2 col7\" >0.999400</td>\n",
+       "      <td id=\"T_095a0_row2_col8\" class=\"data row2 col8\" >0.999600</td>\n",
+       "      <td id=\"T_095a0_row2_col9\" class=\"data row2 col9\" >0.999200</td>\n",
+       "      <td id=\"T_095a0_row2_col10\" class=\"data row2 col10\" >0.999600</td>\n",
+       "      <td id=\"T_095a0_row2_col11\" class=\"data row2 col11\" >0.998800</td>\n",
+       "      <td id=\"T_095a0_row2_col12\" class=\"data row2 col12\" >0.999400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_095a0_row3_col0\" class=\"data row3 col0\" >w1</td>\n",
+       "      <td id=\"T_095a0_row3_col1\" class=\"data row3 col1\" >Norm(0:1)</td>\n",
+       "      <td id=\"T_095a0_row3_col2\" class=\"data row3 col2\" >3765</td>\n",
+       "      <td id=\"T_095a0_row3_col3\" class=\"data row3 col3\" >3765</td>\n",
+       "      <td id=\"T_095a0_row3_col4\" class=\"data row3 col4\" >1</td>\n",
+       "      <td id=\"T_095a0_row3_col5\" class=\"data row3 col5\" >2469</td>\n",
+       "      <td id=\"T_095a0_row3_col6\" class=\"data row3 col6\" >0.623400</td>\n",
+       "      <td id=\"T_095a0_row3_col7\" class=\"data row3 col7\" >0.749798</td>\n",
+       "      <td id=\"T_095a0_row3_col8\" class=\"data row3 col8\" >0.396054</td>\n",
+       "      <td id=\"T_095a0_row3_col9\" class=\"data row3 col9\" >0.999595</td>\n",
+       "      <td id=\"T_095a0_row3_col10\" class=\"data row3 col10\" >0.500000</td>\n",
+       "      <td id=\"T_095a0_row3_col11\" class=\"data row3 col11\" >0.499595</td>\n",
+       "      <td id=\"T_095a0_row3_col12\" class=\"data row3 col12\" >0.567325</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_095a0_row4_col0\" class=\"data row4 col0\" >w1</td>\n",
+       "      <td id=\"T_095a0_row4_col1\" class=\"data row4 col1\" >Norm(4:1)</td>\n",
+       "      <td id=\"T_095a0_row4_col2\" class=\"data row4 col2\" >7469</td>\n",
+       "      <td id=\"T_095a0_row4_col3\" class=\"data row4 col3\" >3</td>\n",
+       "      <td id=\"T_095a0_row4_col4\" class=\"data row4 col4\" >1264</td>\n",
+       "      <td id=\"T_095a0_row4_col5\" class=\"data row4 col5\" >1264</td>\n",
+       "      <td id=\"T_095a0_row4_col6\" class=\"data row4 col6\" >0.873300</td>\n",
+       "      <td id=\"T_095a0_row4_col7\" class=\"data row4 col7\" >0.749799</td>\n",
+       "      <td id=\"T_095a0_row4_col8\" class=\"data row4 col8\" >0.997632</td>\n",
+       "      <td id=\"T_095a0_row4_col9\" class=\"data row4 col9\" >0.500000</td>\n",
+       "      <td id=\"T_095a0_row4_col10\" class=\"data row4 col10\" >0.999599</td>\n",
+       "      <td id=\"T_095a0_row4_col11\" class=\"data row4 col11\" >0.499599</td>\n",
+       "      <td id=\"T_095a0_row4_col12\" class=\"data row4 col12\" >0.666140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "      <td id=\"T_095a0_row5_col0\" class=\"data row5 col0\" >w1</td>\n",
+       "      <td id=\"T_095a0_row5_col1\" class=\"data row5 col1\" >Norm(10:3)</td>\n",
+       "      <td id=\"T_095a0_row5_col2\" class=\"data row5 col2\" >4998</td>\n",
+       "      <td id=\"T_095a0_row5_col3\" class=\"data row5 col3\" >0</td>\n",
+       "      <td id=\"T_095a0_row5_col4\" class=\"data row5 col4\" >2503</td>\n",
+       "      <td id=\"T_095a0_row5_col5\" class=\"data row5 col5\" >2499</td>\n",
+       "      <td id=\"T_095a0_row5_col6\" class=\"data row5 col6\" >0.749700</td>\n",
+       "      <td id=\"T_095a0_row5_col7\" class=\"data row5 col7\" >0.749800</td>\n",
+       "      <td id=\"T_095a0_row5_col8\" class=\"data row5 col8\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row5_col9\" class=\"data row5 col9\" >0.499600</td>\n",
+       "      <td id=\"T_095a0_row5_col10\" class=\"data row5 col10\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row5_col11\" class=\"data row5 col11\" >0.499600</td>\n",
+       "      <td id=\"T_095a0_row5_col12\" class=\"data row5 col12\" >0.666311</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_095a0_row6_col0\" class=\"data row6 col0\" >w7</td>\n",
+       "      <td id=\"T_095a0_row6_col1\" class=\"data row6 col1\" >Norm(0:1)</td>\n",
+       "      <td id=\"T_095a0_row6_col2\" class=\"data row6 col2\" >3765</td>\n",
+       "      <td id=\"T_095a0_row6_col3\" class=\"data row6 col3\" >0</td>\n",
+       "      <td id=\"T_095a0_row6_col4\" class=\"data row6 col4\" >1</td>\n",
+       "      <td id=\"T_095a0_row6_col5\" class=\"data row6 col5\" >1234</td>\n",
+       "      <td id=\"T_095a0_row6_col6\" class=\"data row6 col6\" >0.999800</td>\n",
+       "      <td id=\"T_095a0_row6_col7\" class=\"data row6 col7\" >0.999595</td>\n",
+       "      <td id=\"T_095a0_row6_col8\" class=\"data row6 col8\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row6_col9\" class=\"data row6 col9\" >0.999190</td>\n",
+       "      <td id=\"T_095a0_row6_col10\" class=\"data row6 col10\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row6_col11\" class=\"data row6 col11\" >0.999190</td>\n",
+       "      <td id=\"T_095a0_row6_col12\" class=\"data row6 col12\" >0.999595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "      <td id=\"T_095a0_row7_col0\" class=\"data row7 col0\" >w7</td>\n",
+       "      <td id=\"T_095a0_row7_col1\" class=\"data row7 col1\" >Norm(4:1)</td>\n",
+       "      <td id=\"T_095a0_row7_col2\" class=\"data row7 col2\" >3733</td>\n",
+       "      <td id=\"T_095a0_row7_col3\" class=\"data row7 col3\" >3</td>\n",
+       "      <td id=\"T_095a0_row7_col4\" class=\"data row7 col4\" >0</td>\n",
+       "      <td id=\"T_095a0_row7_col5\" class=\"data row7 col5\" >1264</td>\n",
+       "      <td id=\"T_095a0_row7_col6\" class=\"data row7 col6\" >0.999400</td>\n",
+       "      <td id=\"T_095a0_row7_col7\" class=\"data row7 col7\" >0.999599</td>\n",
+       "      <td id=\"T_095a0_row7_col8\" class=\"data row7 col8\" >0.997632</td>\n",
+       "      <td id=\"T_095a0_row7_col9\" class=\"data row7 col9\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row7_col10\" class=\"data row7 col10\" >0.999197</td>\n",
+       "      <td id=\"T_095a0_row7_col11\" class=\"data row7 col11\" >0.999197</td>\n",
+       "      <td id=\"T_095a0_row7_col12\" class=\"data row7 col12\" >0.998815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "      <td id=\"T_095a0_row8_col0\" class=\"data row8 col0\" >w7</td>\n",
+       "      <td id=\"T_095a0_row8_col1\" class=\"data row8 col1\" >Norm(10:3)</td>\n",
+       "      <td id=\"T_095a0_row8_col2\" class=\"data row8 col2\" >2499</td>\n",
+       "      <td id=\"T_095a0_row8_col3\" class=\"data row8 col3\" >0</td>\n",
+       "      <td id=\"T_095a0_row8_col4\" class=\"data row8 col4\" >2</td>\n",
+       "      <td id=\"T_095a0_row8_col5\" class=\"data row8 col5\" >2499</td>\n",
+       "      <td id=\"T_095a0_row8_col6\" class=\"data row8 col6\" >0.999600</td>\n",
+       "      <td id=\"T_095a0_row8_col7\" class=\"data row8 col7\" >0.999600</td>\n",
+       "      <td id=\"T_095a0_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row8_col9\" class=\"data row8 col9\" >0.999200</td>\n",
+       "      <td id=\"T_095a0_row8_col10\" class=\"data row8 col10\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row8_col11\" class=\"data row8 col11\" >0.999200</td>\n",
+       "      <td id=\"T_095a0_row8_col12\" class=\"data row8 col12\" >0.999600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "      <td id=\"T_095a0_row9_col0\" class=\"data row9 col0\" >w2</td>\n",
+       "      <td id=\"T_095a0_row9_col1\" class=\"data row9 col1\" >Norm(0:1)</td>\n",
+       "      <td id=\"T_095a0_row9_col2\" class=\"data row9 col2\" >3765</td>\n",
+       "      <td id=\"T_095a0_row9_col3\" class=\"data row9 col3\" >0</td>\n",
+       "      <td id=\"T_095a0_row9_col4\" class=\"data row9 col4\" >1</td>\n",
+       "      <td id=\"T_095a0_row9_col5\" class=\"data row9 col5\" >1234</td>\n",
+       "      <td id=\"T_095a0_row9_col6\" class=\"data row9 col6\" >0.999800</td>\n",
+       "      <td id=\"T_095a0_row9_col7\" class=\"data row9 col7\" >0.999595</td>\n",
+       "      <td id=\"T_095a0_row9_col8\" class=\"data row9 col8\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row9_col9\" class=\"data row9 col9\" >0.999190</td>\n",
+       "      <td id=\"T_095a0_row9_col10\" class=\"data row9 col10\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row9_col11\" class=\"data row9 col11\" >0.999190</td>\n",
+       "      <td id=\"T_095a0_row9_col12\" class=\"data row9 col12\" >0.999595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+       "      <td id=\"T_095a0_row10_col0\" class=\"data row10 col0\" >w2</td>\n",
+       "      <td id=\"T_095a0_row10_col1\" class=\"data row10 col1\" >Norm(4:1)</td>\n",
+       "      <td id=\"T_095a0_row10_col2\" class=\"data row10 col2\" >3733</td>\n",
+       "      <td id=\"T_095a0_row10_col3\" class=\"data row10 col3\" >3</td>\n",
+       "      <td id=\"T_095a0_row10_col4\" class=\"data row10 col4\" >0</td>\n",
+       "      <td id=\"T_095a0_row10_col5\" class=\"data row10 col5\" >1264</td>\n",
+       "      <td id=\"T_095a0_row10_col6\" class=\"data row10 col6\" >0.999400</td>\n",
+       "      <td id=\"T_095a0_row10_col7\" class=\"data row10 col7\" >0.999599</td>\n",
+       "      <td id=\"T_095a0_row10_col8\" class=\"data row10 col8\" >0.997632</td>\n",
+       "      <td id=\"T_095a0_row10_col9\" class=\"data row10 col9\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row10_col10\" class=\"data row10 col10\" >0.999197</td>\n",
+       "      <td id=\"T_095a0_row10_col11\" class=\"data row10 col11\" >0.999197</td>\n",
+       "      <td id=\"T_095a0_row10_col12\" class=\"data row10 col12\" >0.998815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+       "      <td id=\"T_095a0_row11_col0\" class=\"data row11 col0\" >w2</td>\n",
+       "      <td id=\"T_095a0_row11_col1\" class=\"data row11 col1\" >Norm(10:3)</td>\n",
+       "      <td id=\"T_095a0_row11_col2\" class=\"data row11 col2\" >2499</td>\n",
+       "      <td id=\"T_095a0_row11_col3\" class=\"data row11 col3\" >0</td>\n",
+       "      <td id=\"T_095a0_row11_col4\" class=\"data row11 col4\" >2</td>\n",
+       "      <td id=\"T_095a0_row11_col5\" class=\"data row11 col5\" >2499</td>\n",
+       "      <td id=\"T_095a0_row11_col6\" class=\"data row11 col6\" >0.999600</td>\n",
+       "      <td id=\"T_095a0_row11_col7\" class=\"data row11 col7\" >0.999600</td>\n",
+       "      <td id=\"T_095a0_row11_col8\" class=\"data row11 col8\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row11_col9\" class=\"data row11 col9\" >0.999200</td>\n",
+       "      <td id=\"T_095a0_row11_col10\" class=\"data row11 col10\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row11_col11\" class=\"data row11 col11\" >0.999200</td>\n",
+       "      <td id=\"T_095a0_row11_col12\" class=\"data row11 col12\" >0.999600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
+       "      <td id=\"T_095a0_row12_col0\" class=\"data row12 col0\" >w6</td>\n",
+       "      <td id=\"T_095a0_row12_col1\" class=\"data row12 col1\" >Norm(0:1)</td>\n",
+       "      <td id=\"T_095a0_row12_col2\" class=\"data row12 col2\" >3765</td>\n",
+       "      <td id=\"T_095a0_row12_col3\" class=\"data row12 col3\" >0</td>\n",
+       "      <td id=\"T_095a0_row12_col4\" class=\"data row12 col4\" >1</td>\n",
+       "      <td id=\"T_095a0_row12_col5\" class=\"data row12 col5\" >1234</td>\n",
+       "      <td id=\"T_095a0_row12_col6\" class=\"data row12 col6\" >0.999800</td>\n",
+       "      <td id=\"T_095a0_row12_col7\" class=\"data row12 col7\" >0.999595</td>\n",
+       "      <td id=\"T_095a0_row12_col8\" class=\"data row12 col8\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row12_col9\" class=\"data row12 col9\" >0.999190</td>\n",
+       "      <td id=\"T_095a0_row12_col10\" class=\"data row12 col10\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row12_col11\" class=\"data row12 col11\" >0.999190</td>\n",
+       "      <td id=\"T_095a0_row12_col12\" class=\"data row12 col12\" >0.999595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
+       "      <td id=\"T_095a0_row13_col0\" class=\"data row13 col0\" >w6</td>\n",
+       "      <td id=\"T_095a0_row13_col1\" class=\"data row13 col1\" >Norm(4:1)</td>\n",
+       "      <td id=\"T_095a0_row13_col2\" class=\"data row13 col2\" >3733</td>\n",
+       "      <td id=\"T_095a0_row13_col3\" class=\"data row13 col3\" >3</td>\n",
+       "      <td id=\"T_095a0_row13_col4\" class=\"data row13 col4\" >0</td>\n",
+       "      <td id=\"T_095a0_row13_col5\" class=\"data row13 col5\" >1264</td>\n",
+       "      <td id=\"T_095a0_row13_col6\" class=\"data row13 col6\" >0.999400</td>\n",
+       "      <td id=\"T_095a0_row13_col7\" class=\"data row13 col7\" >0.999599</td>\n",
+       "      <td id=\"T_095a0_row13_col8\" class=\"data row13 col8\" >0.997632</td>\n",
+       "      <td id=\"T_095a0_row13_col9\" class=\"data row13 col9\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row13_col10\" class=\"data row13 col10\" >0.999197</td>\n",
+       "      <td id=\"T_095a0_row13_col11\" class=\"data row13 col11\" >0.999197</td>\n",
+       "      <td id=\"T_095a0_row13_col12\" class=\"data row13 col12\" >0.998815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
+       "      <td id=\"T_095a0_row14_col0\" class=\"data row14 col0\" >w6</td>\n",
+       "      <td id=\"T_095a0_row14_col1\" class=\"data row14 col1\" >Norm(10:3)</td>\n",
+       "      <td id=\"T_095a0_row14_col2\" class=\"data row14 col2\" >2499</td>\n",
+       "      <td id=\"T_095a0_row14_col3\" class=\"data row14 col3\" >0</td>\n",
+       "      <td id=\"T_095a0_row14_col4\" class=\"data row14 col4\" >2</td>\n",
+       "      <td id=\"T_095a0_row14_col5\" class=\"data row14 col5\" >2499</td>\n",
+       "      <td id=\"T_095a0_row14_col6\" class=\"data row14 col6\" >0.999600</td>\n",
+       "      <td id=\"T_095a0_row14_col7\" class=\"data row14 col7\" >0.999600</td>\n",
+       "      <td id=\"T_095a0_row14_col8\" class=\"data row14 col8\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row14_col9\" class=\"data row14 col9\" >0.999200</td>\n",
+       "      <td id=\"T_095a0_row14_col10\" class=\"data row14 col10\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row14_col11\" class=\"data row14 col11\" >0.999200</td>\n",
+       "      <td id=\"T_095a0_row14_col12\" class=\"data row14 col12\" >0.999600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
+       "      <td id=\"T_095a0_row15_col0\" class=\"data row15 col0\" >w3</td>\n",
+       "      <td id=\"T_095a0_row15_col1\" class=\"data row15 col1\" >Norm(0:1)</td>\n",
+       "      <td id=\"T_095a0_row15_col2\" class=\"data row15 col2\" >3765</td>\n",
+       "      <td id=\"T_095a0_row15_col3\" class=\"data row15 col3\" >0</td>\n",
+       "      <td id=\"T_095a0_row15_col4\" class=\"data row15 col4\" >0</td>\n",
+       "      <td id=\"T_095a0_row15_col5\" class=\"data row15 col5\" >1235</td>\n",
+       "      <td id=\"T_095a0_row15_col6\" class=\"data row15 col6\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row15_col7\" class=\"data row15 col7\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row15_col8\" class=\"data row15 col8\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row15_col9\" class=\"data row15 col9\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row15_col10\" class=\"data row15 col10\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row15_col11\" class=\"data row15 col11\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row15_col12\" class=\"data row15 col12\" >1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
+       "      <td id=\"T_095a0_row16_col0\" class=\"data row16 col0\" >w3</td>\n",
+       "      <td id=\"T_095a0_row16_col1\" class=\"data row16 col1\" >Norm(4:1)</td>\n",
+       "      <td id=\"T_095a0_row16_col2\" class=\"data row16 col2\" >3734</td>\n",
+       "      <td id=\"T_095a0_row16_col3\" class=\"data row16 col3\" >2</td>\n",
+       "      <td id=\"T_095a0_row16_col4\" class=\"data row16 col4\" >0</td>\n",
+       "      <td id=\"T_095a0_row16_col5\" class=\"data row16 col5\" >1264</td>\n",
+       "      <td id=\"T_095a0_row16_col6\" class=\"data row16 col6\" >0.999600</td>\n",
+       "      <td id=\"T_095a0_row16_col7\" class=\"data row16 col7\" >0.999732</td>\n",
+       "      <td id=\"T_095a0_row16_col8\" class=\"data row16 col8\" >0.998420</td>\n",
+       "      <td id=\"T_095a0_row16_col9\" class=\"data row16 col9\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row16_col10\" class=\"data row16 col10\" >0.999465</td>\n",
+       "      <td id=\"T_095a0_row16_col11\" class=\"data row16 col11\" >0.999465</td>\n",
+       "      <td id=\"T_095a0_row16_col12\" class=\"data row16 col12\" >0.999209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
+       "      <td id=\"T_095a0_row17_col0\" class=\"data row17 col0\" >w3</td>\n",
+       "      <td id=\"T_095a0_row17_col1\" class=\"data row17 col1\" >Norm(10:3)</td>\n",
+       "      <td id=\"T_095a0_row17_col2\" class=\"data row17 col2\" >2499</td>\n",
+       "      <td id=\"T_095a0_row17_col3\" class=\"data row17 col3\" >0</td>\n",
+       "      <td id=\"T_095a0_row17_col4\" class=\"data row17 col4\" >2</td>\n",
+       "      <td id=\"T_095a0_row17_col5\" class=\"data row17 col5\" >2499</td>\n",
+       "      <td id=\"T_095a0_row17_col6\" class=\"data row17 col6\" >0.999600</td>\n",
+       "      <td id=\"T_095a0_row17_col7\" class=\"data row17 col7\" >0.999600</td>\n",
+       "      <td id=\"T_095a0_row17_col8\" class=\"data row17 col8\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row17_col9\" class=\"data row17 col9\" >0.999200</td>\n",
+       "      <td id=\"T_095a0_row17_col10\" class=\"data row17 col10\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row17_col11\" class=\"data row17 col11\" >0.999200</td>\n",
+       "      <td id=\"T_095a0_row17_col12\" class=\"data row17 col12\" >0.999600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
+       "      <td id=\"T_095a0_row18_col0\" class=\"data row18 col0\" >w9</td>\n",
+       "      <td id=\"T_095a0_row18_col1\" class=\"data row18 col1\" >Norm(0:1)</td>\n",
+       "      <td id=\"T_095a0_row18_col2\" class=\"data row18 col2\" >3765</td>\n",
+       "      <td id=\"T_095a0_row18_col3\" class=\"data row18 col3\" >0</td>\n",
+       "      <td id=\"T_095a0_row18_col4\" class=\"data row18 col4\" >1</td>\n",
+       "      <td id=\"T_095a0_row18_col5\" class=\"data row18 col5\" >1234</td>\n",
+       "      <td id=\"T_095a0_row18_col6\" class=\"data row18 col6\" >0.999800</td>\n",
+       "      <td id=\"T_095a0_row18_col7\" class=\"data row18 col7\" >0.999595</td>\n",
+       "      <td id=\"T_095a0_row18_col8\" class=\"data row18 col8\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row18_col9\" class=\"data row18 col9\" >0.999190</td>\n",
+       "      <td id=\"T_095a0_row18_col10\" class=\"data row18 col10\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row18_col11\" class=\"data row18 col11\" >0.999190</td>\n",
+       "      <td id=\"T_095a0_row18_col12\" class=\"data row18 col12\" >0.999595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
+       "      <td id=\"T_095a0_row19_col0\" class=\"data row19 col0\" >w9</td>\n",
+       "      <td id=\"T_095a0_row19_col1\" class=\"data row19 col1\" >Norm(4:1)</td>\n",
+       "      <td id=\"T_095a0_row19_col2\" class=\"data row19 col2\" >3733</td>\n",
+       "      <td id=\"T_095a0_row19_col3\" class=\"data row19 col3\" >3</td>\n",
+       "      <td id=\"T_095a0_row19_col4\" class=\"data row19 col4\" >0</td>\n",
+       "      <td id=\"T_095a0_row19_col5\" class=\"data row19 col5\" >1264</td>\n",
+       "      <td id=\"T_095a0_row19_col6\" class=\"data row19 col6\" >0.999400</td>\n",
+       "      <td id=\"T_095a0_row19_col7\" class=\"data row19 col7\" >0.999599</td>\n",
+       "      <td id=\"T_095a0_row19_col8\" class=\"data row19 col8\" >0.997632</td>\n",
+       "      <td id=\"T_095a0_row19_col9\" class=\"data row19 col9\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row19_col10\" class=\"data row19 col10\" >0.999197</td>\n",
+       "      <td id=\"T_095a0_row19_col11\" class=\"data row19 col11\" >0.999197</td>\n",
+       "      <td id=\"T_095a0_row19_col12\" class=\"data row19 col12\" >0.998815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
+       "      <td id=\"T_095a0_row20_col0\" class=\"data row20 col0\" >w9</td>\n",
+       "      <td id=\"T_095a0_row20_col1\" class=\"data row20 col1\" >Norm(10:3)</td>\n",
+       "      <td id=\"T_095a0_row20_col2\" class=\"data row20 col2\" >2499</td>\n",
+       "      <td id=\"T_095a0_row20_col3\" class=\"data row20 col3\" >0</td>\n",
+       "      <td id=\"T_095a0_row20_col4\" class=\"data row20 col4\" >2</td>\n",
+       "      <td id=\"T_095a0_row20_col5\" class=\"data row20 col5\" >2499</td>\n",
+       "      <td id=\"T_095a0_row20_col6\" class=\"data row20 col6\" >0.999600</td>\n",
+       "      <td id=\"T_095a0_row20_col7\" class=\"data row20 col7\" >0.999600</td>\n",
+       "      <td id=\"T_095a0_row20_col8\" class=\"data row20 col8\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row20_col9\" class=\"data row20 col9\" >0.999200</td>\n",
+       "      <td id=\"T_095a0_row20_col10\" class=\"data row20 col10\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row20_col11\" class=\"data row20 col11\" >0.999200</td>\n",
+       "      <td id=\"T_095a0_row20_col12\" class=\"data row20 col12\" >0.999600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
+       "      <td id=\"T_095a0_row21_col0\" class=\"data row21 col0\" >w4</td>\n",
+       "      <td id=\"T_095a0_row21_col1\" class=\"data row21 col1\" >Norm(0:1)</td>\n",
+       "      <td id=\"T_095a0_row21_col2\" class=\"data row21 col2\" >3765</td>\n",
+       "      <td id=\"T_095a0_row21_col3\" class=\"data row21 col3\" >0</td>\n",
+       "      <td id=\"T_095a0_row21_col4\" class=\"data row21 col4\" >1</td>\n",
+       "      <td id=\"T_095a0_row21_col5\" class=\"data row21 col5\" >1234</td>\n",
+       "      <td id=\"T_095a0_row21_col6\" class=\"data row21 col6\" >0.999800</td>\n",
+       "      <td id=\"T_095a0_row21_col7\" class=\"data row21 col7\" >0.999595</td>\n",
+       "      <td id=\"T_095a0_row21_col8\" class=\"data row21 col8\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row21_col9\" class=\"data row21 col9\" >0.999190</td>\n",
+       "      <td id=\"T_095a0_row21_col10\" class=\"data row21 col10\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row21_col11\" class=\"data row21 col11\" >0.999190</td>\n",
+       "      <td id=\"T_095a0_row21_col12\" class=\"data row21 col12\" >0.999595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
+       "      <td id=\"T_095a0_row22_col0\" class=\"data row22 col0\" >w4</td>\n",
+       "      <td id=\"T_095a0_row22_col1\" class=\"data row22 col1\" >Norm(4:1)</td>\n",
+       "      <td id=\"T_095a0_row22_col2\" class=\"data row22 col2\" >3733</td>\n",
+       "      <td id=\"T_095a0_row22_col3\" class=\"data row22 col3\" >3</td>\n",
+       "      <td id=\"T_095a0_row22_col4\" class=\"data row22 col4\" >0</td>\n",
+       "      <td id=\"T_095a0_row22_col5\" class=\"data row22 col5\" >1264</td>\n",
+       "      <td id=\"T_095a0_row22_col6\" class=\"data row22 col6\" >0.999400</td>\n",
+       "      <td id=\"T_095a0_row22_col7\" class=\"data row22 col7\" >0.999599</td>\n",
+       "      <td id=\"T_095a0_row22_col8\" class=\"data row22 col8\" >0.997632</td>\n",
+       "      <td id=\"T_095a0_row22_col9\" class=\"data row22 col9\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row22_col10\" class=\"data row22 col10\" >0.999197</td>\n",
+       "      <td id=\"T_095a0_row22_col11\" class=\"data row22 col11\" >0.999197</td>\n",
+       "      <td id=\"T_095a0_row22_col12\" class=\"data row22 col12\" >0.998815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
+       "      <td id=\"T_095a0_row23_col0\" class=\"data row23 col0\" >w4</td>\n",
+       "      <td id=\"T_095a0_row23_col1\" class=\"data row23 col1\" >Norm(10:3)</td>\n",
+       "      <td id=\"T_095a0_row23_col2\" class=\"data row23 col2\" >2499</td>\n",
+       "      <td id=\"T_095a0_row23_col3\" class=\"data row23 col3\" >0</td>\n",
+       "      <td id=\"T_095a0_row23_col4\" class=\"data row23 col4\" >2</td>\n",
+       "      <td id=\"T_095a0_row23_col5\" class=\"data row23 col5\" >2499</td>\n",
+       "      <td id=\"T_095a0_row23_col6\" class=\"data row23 col6\" >0.999600</td>\n",
+       "      <td id=\"T_095a0_row23_col7\" class=\"data row23 col7\" >0.999600</td>\n",
+       "      <td id=\"T_095a0_row23_col8\" class=\"data row23 col8\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row23_col9\" class=\"data row23 col9\" >0.999200</td>\n",
+       "      <td id=\"T_095a0_row23_col10\" class=\"data row23 col10\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row23_col11\" class=\"data row23 col11\" >0.999200</td>\n",
+       "      <td id=\"T_095a0_row23_col12\" class=\"data row23 col12\" >0.999600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
+       "      <td id=\"T_095a0_row24_col0\" class=\"data row24 col0\" >w10</td>\n",
+       "      <td id=\"T_095a0_row24_col1\" class=\"data row24 col1\" >Norm(0:1)</td>\n",
+       "      <td id=\"T_095a0_row24_col2\" class=\"data row24 col2\" >3765</td>\n",
+       "      <td id=\"T_095a0_row24_col3\" class=\"data row24 col3\" >0</td>\n",
+       "      <td id=\"T_095a0_row24_col4\" class=\"data row24 col4\" >1</td>\n",
+       "      <td id=\"T_095a0_row24_col5\" class=\"data row24 col5\" >1234</td>\n",
+       "      <td id=\"T_095a0_row24_col6\" class=\"data row24 col6\" >0.999800</td>\n",
+       "      <td id=\"T_095a0_row24_col7\" class=\"data row24 col7\" >0.999595</td>\n",
+       "      <td id=\"T_095a0_row24_col8\" class=\"data row24 col8\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row24_col9\" class=\"data row24 col9\" >0.999190</td>\n",
+       "      <td id=\"T_095a0_row24_col10\" class=\"data row24 col10\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row24_col11\" class=\"data row24 col11\" >0.999190</td>\n",
+       "      <td id=\"T_095a0_row24_col12\" class=\"data row24 col12\" >0.999595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
+       "      <td id=\"T_095a0_row25_col0\" class=\"data row25 col0\" >w10</td>\n",
+       "      <td id=\"T_095a0_row25_col1\" class=\"data row25 col1\" >Norm(4:1)</td>\n",
+       "      <td id=\"T_095a0_row25_col2\" class=\"data row25 col2\" >3733</td>\n",
+       "      <td id=\"T_095a0_row25_col3\" class=\"data row25 col3\" >3</td>\n",
+       "      <td id=\"T_095a0_row25_col4\" class=\"data row25 col4\" >0</td>\n",
+       "      <td id=\"T_095a0_row25_col5\" class=\"data row25 col5\" >1264</td>\n",
+       "      <td id=\"T_095a0_row25_col6\" class=\"data row25 col6\" >0.999400</td>\n",
+       "      <td id=\"T_095a0_row25_col7\" class=\"data row25 col7\" >0.999599</td>\n",
+       "      <td id=\"T_095a0_row25_col8\" class=\"data row25 col8\" >0.997632</td>\n",
+       "      <td id=\"T_095a0_row25_col9\" class=\"data row25 col9\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row25_col10\" class=\"data row25 col10\" >0.999197</td>\n",
+       "      <td id=\"T_095a0_row25_col11\" class=\"data row25 col11\" >0.999197</td>\n",
+       "      <td id=\"T_095a0_row25_col12\" class=\"data row25 col12\" >0.998815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
+       "      <td id=\"T_095a0_row26_col0\" class=\"data row26 col0\" >w10</td>\n",
+       "      <td id=\"T_095a0_row26_col1\" class=\"data row26 col1\" >Norm(10:3)</td>\n",
+       "      <td id=\"T_095a0_row26_col2\" class=\"data row26 col2\" >2499</td>\n",
+       "      <td id=\"T_095a0_row26_col3\" class=\"data row26 col3\" >0</td>\n",
+       "      <td id=\"T_095a0_row26_col4\" class=\"data row26 col4\" >2</td>\n",
+       "      <td id=\"T_095a0_row26_col5\" class=\"data row26 col5\" >2499</td>\n",
+       "      <td id=\"T_095a0_row26_col6\" class=\"data row26 col6\" >0.999600</td>\n",
+       "      <td id=\"T_095a0_row26_col7\" class=\"data row26 col7\" >0.999600</td>\n",
+       "      <td id=\"T_095a0_row26_col8\" class=\"data row26 col8\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row26_col9\" class=\"data row26 col9\" >0.999200</td>\n",
+       "      <td id=\"T_095a0_row26_col10\" class=\"data row26 col10\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row26_col11\" class=\"data row26 col11\" >0.999200</td>\n",
+       "      <td id=\"T_095a0_row26_col12\" class=\"data row26 col12\" >0.999600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
+       "      <td id=\"T_095a0_row27_col0\" class=\"data row27 col0\" >w5</td>\n",
+       "      <td id=\"T_095a0_row27_col1\" class=\"data row27 col1\" >Norm(0:1)</td>\n",
+       "      <td id=\"T_095a0_row27_col2\" class=\"data row27 col2\" >3765</td>\n",
+       "      <td id=\"T_095a0_row27_col3\" class=\"data row27 col3\" >0</td>\n",
+       "      <td id=\"T_095a0_row27_col4\" class=\"data row27 col4\" >1</td>\n",
+       "      <td id=\"T_095a0_row27_col5\" class=\"data row27 col5\" >1234</td>\n",
+       "      <td id=\"T_095a0_row27_col6\" class=\"data row27 col6\" >0.999800</td>\n",
+       "      <td id=\"T_095a0_row27_col7\" class=\"data row27 col7\" >0.999595</td>\n",
+       "      <td id=\"T_095a0_row27_col8\" class=\"data row27 col8\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row27_col9\" class=\"data row27 col9\" >0.999190</td>\n",
+       "      <td id=\"T_095a0_row27_col10\" class=\"data row27 col10\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row27_col11\" class=\"data row27 col11\" >0.999190</td>\n",
+       "      <td id=\"T_095a0_row27_col12\" class=\"data row27 col12\" >0.999595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
+       "      <td id=\"T_095a0_row28_col0\" class=\"data row28 col0\" >w5</td>\n",
+       "      <td id=\"T_095a0_row28_col1\" class=\"data row28 col1\" >Norm(4:1)</td>\n",
+       "      <td id=\"T_095a0_row28_col2\" class=\"data row28 col2\" >3733</td>\n",
+       "      <td id=\"T_095a0_row28_col3\" class=\"data row28 col3\" >3</td>\n",
+       "      <td id=\"T_095a0_row28_col4\" class=\"data row28 col4\" >0</td>\n",
+       "      <td id=\"T_095a0_row28_col5\" class=\"data row28 col5\" >1264</td>\n",
+       "      <td id=\"T_095a0_row28_col6\" class=\"data row28 col6\" >0.999400</td>\n",
+       "      <td id=\"T_095a0_row28_col7\" class=\"data row28 col7\" >0.999599</td>\n",
+       "      <td id=\"T_095a0_row28_col8\" class=\"data row28 col8\" >0.997632</td>\n",
+       "      <td id=\"T_095a0_row28_col9\" class=\"data row28 col9\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row28_col10\" class=\"data row28 col10\" >0.999197</td>\n",
+       "      <td id=\"T_095a0_row28_col11\" class=\"data row28 col11\" >0.999197</td>\n",
+       "      <td id=\"T_095a0_row28_col12\" class=\"data row28 col12\" >0.998815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_095a0_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
+       "      <td id=\"T_095a0_row29_col0\" class=\"data row29 col0\" >w5</td>\n",
+       "      <td id=\"T_095a0_row29_col1\" class=\"data row29 col1\" >Norm(10:3)</td>\n",
+       "      <td id=\"T_095a0_row29_col2\" class=\"data row29 col2\" >2499</td>\n",
+       "      <td id=\"T_095a0_row29_col3\" class=\"data row29 col3\" >0</td>\n",
+       "      <td id=\"T_095a0_row29_col4\" class=\"data row29 col4\" >2</td>\n",
+       "      <td id=\"T_095a0_row29_col5\" class=\"data row29 col5\" >2499</td>\n",
+       "      <td id=\"T_095a0_row29_col6\" class=\"data row29 col6\" >0.999600</td>\n",
+       "      <td id=\"T_095a0_row29_col7\" class=\"data row29 col7\" >0.999600</td>\n",
+       "      <td id=\"T_095a0_row29_col8\" class=\"data row29 col8\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row29_col9\" class=\"data row29 col9\" >0.999200</td>\n",
+       "      <td id=\"T_095a0_row29_col10\" class=\"data row29 col10\" >1.000000</td>\n",
+       "      <td id=\"T_095a0_row29_col11\" class=\"data row29 col11\" >0.999200</td>\n",
+       "      <td id=\"T_095a0_row29_col12\" class=\"data row29 col12\" >0.999600</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x75bb44f423d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model_perf = stats_pred.get_model_performence_stats(conf_mats_workers, show=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "a09cd34c-69c3-4568-840b-6357041d2508",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Worker', ylabel='F1'>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(data=model_perf.sort_values('Worker') , x='Worker' , y='F1',hue='Class')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41f2c3c3-9a2e-4e5a-8a77-c60383b4359e",
+   "metadata": {},
+   "source": [
+    "# What Happened To Worker 1?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6817a7fd-abba-4b83-94e7-a2c05de7f19f",
+   "metadata": {},
+   "source": [
+    "## Let's Check The Network Topology"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "7415efb2-d08a-4325-a1b6-8f74eb5cf7d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: networkx in /tmp/nerlnet/virtualenv/lib/python3.11/site-packages (3.3)\n",
+      "Requirement already satisfied: pygraphviz in /tmp/nerlnet/virtualenv/lib/python3.11/site-packages (1.13)\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install networkx pygraphviz\n",
+    "import networkx as nx\n",
+    "def visualize_nerlnet_graph(api_server_inst,  connections : dict , components): # connections is a dictionary with keys as routers and values as lists of their neighbors\n",
+    "    print(\"Connections: \" , list(connections.items()))\n",
+    "    routers = list(connections.keys())\n",
+    "    print(\"Routers: \" , routers)\n",
+    "    workers = list(components.map_worker_to_client.keys())\n",
+    "    print(\"Workers: \" , workers)\n",
+    "    graph = nx.Graph()\n",
+    "    nodes = routers + components.sources + components.clients + workers + [API_SERVER_STR , MAIN_SERVER_STR]\n",
+    "    edges = [] # list of tuples\n",
+    "    for router , neighbors in list(connections.items()):\n",
+    "        for neighbor in neighbors:\n",
+    "            if (router,neighbor) not in edges:\n",
+    "                print(f\"Adding edge ({router} , {neighbor}) to graph\")\n",
+    "                edges.append((router , neighbor))\n",
+    "    edges.append((API_SERVER_STR , MAIN_SERVER_STR)) # Always connected\n",
+    "    for worker in workers:\n",
+    "        edges.append((worker , components.map_worker_to_client[worker]))\n",
+    "    graph.add_nodes_from(nodes)\n",
+    "    graph.add_edges_from(edges)\n",
+    "    \n",
+    "    my_labels = {'mainServer': 'mS' , 'apiServer': 'aS'}\n",
+    "    nx.relabel_nodes(graph, my_labels , copy=False)\n",
+    "    \n",
+    "    default_colors = {node:'#A90433' for node in graph.nodes()}\n",
+    "    node_colors = {node:default_colors[node] for node in graph.nodes()}\n",
+    "    nx.set_node_attributes(graph, node_colors, 'color')\n",
+    "    colors = nx.get_node_attributes(graph, 'color').values()\n",
+    "\n",
+    "    pos = nx.nx_agraph.graphviz_layout(graph)\n",
+    "    angle = 100\n",
+    "    \n",
+    "    plt.figure(figsize=(8,6),dpi=150)\n",
+    "    nx.draw_networkx(graph, pos, with_labels=True, node_color=colors , node_size=200, font_size=8, font_color='white' , edge_color='black' , width=1.5)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "90025169-2199-41dc-b2f5-413dd59d08d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connections:  [('r1', ['mainServer', 'r2', 's1', 'c3', 'c2']), ('r2', ['r3', 's2', 's5', 'c1']), ('r3', ['r4', 's3', 's4']), ('r4', ['r1', 'c4', 'c5'])]\n",
+      "Routers:  ['r1', 'r2', 'r3', 'r4']\n",
+      "Workers:  ['w1', 'w2', 'w3', 'w4', 'w5', 'w6', 'w7', 'w8', 'w9', 'w10']\n",
+      "Adding edge (r1 , mainServer) to graph\n",
+      "Adding edge (r1 , r2) to graph\n",
+      "Adding edge (r1 , s1) to graph\n",
+      "Adding edge (r1 , c3) to graph\n",
+      "Adding edge (r1 , c2) to graph\n",
+      "Adding edge (r2 , r3) to graph\n",
+      "Adding edge (r2 , s2) to graph\n",
+      "Adding edge (r2 , s5) to graph\n",
+      "Adding edge (r2 , c1) to graph\n",
+      "Adding edge (r3 , r4) to graph\n",
+      "Adding edge (r3 , s3) to graph\n",
+      "Adding edge (r3 , s4) to graph\n",
+      "Adding edge (r4 , r1) to graph\n",
+      "Adding edge (r4 , c4) to graph\n",
+      "Adding edge (r4 , c5) to graph\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_nerlnet_graph(API , API.json_dir_parser.json_from_path(conn_path)['connectionsMap'] , globe.components)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d917af67-d084-4ada-976b-8390a4795a34",
+   "metadata": {},
+   "source": [
+    "## Worker 1 is 4 hops away from his source in a congested node Router 2, Maybe It Dropped Batches?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "1a9b2146-c4b8-4396-94a3-caedff147941",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "workers_comm_dict = stats_train1.get_communication_stats_workers()\n",
+    "df_train = pd.DataFrame.from_dict(workers_comm_dict)\n",
+    "plt.figure()\n",
+    "df_selected_train = df_train.iloc[[3,5]]\n",
+    "data_train = pd.melt(df_selected_train.reset_index(), id_vars=['index'], value_vars=df_train.columns)\n",
+    "batches_stats = sns.barplot(x='variable', y='value', hue='index', data=data_train)\n",
+    "plt.ylabel('Number Of Batches')\n",
+    "plt.xlabel('Worker')\n",
+    "plt.title(\"Received & Dropped Batches At Freq. 300B/s (Training Phase)\")\n",
+    "\n",
+    "batches_stats.legend(loc='upper right', bbox_to_anchor=(1.5, 0.2), shadow=True, ncol=1)\n",
+    "plt.show()\n",
+    "\n",
+    "workers_comm_dict = stats_train2.get_communication_stats_workers()\n",
+    "df_train = pd.DataFrame.from_dict(workers_comm_dict)\n",
+    "plt.figure()\n",
+    "df_selected_train = df_train.iloc[[3,5]]\n",
+    "data_train = pd.melt(df_selected_train.reset_index(), id_vars=['index'], value_vars=df_train.columns)\n",
+    "batches_stats = sns.barplot(x='variable', y='value', hue='index', data=data_train)\n",
+    "plt.ylabel('Number Of Batches')\n",
+    "plt.xlabel('Worker')\n",
+    "plt.title(\"Received & Dropped Batches At Freq. 300B/s (Training Phase)\")\n",
+    "\n",
+    "batches_stats.legend(loc='upper right', bbox_to_anchor=(1.5, 0.2), shadow=True, ncol=1)\n",
+    "plt.show()\n",
+    "\n",
+    "workers_comm_dict_pred = stats_pred.get_communication_stats_workers()\n",
+    "df_pred = pd.DataFrame.from_dict(workers_comm_dict_pred)\n",
+    "\n",
+    "plt.figure()\n",
+    "df_selected_pred = df_pred.iloc[[4,6]]\n",
+    "data_pred = pd.melt(df_selected_pred.reset_index(), id_vars=['index'], value_vars=df_pred.columns)\n",
+    "batches_stats_pred = sns.barplot(x='variable', y='value', hue='index', data=data_pred)\n",
+    "plt.ylabel('Number Of Batches')\n",
+    "plt.xlabel('Worker')\n",
+    "plt.title(\"Received & Dropped Batches At Freq. 300B/s (Prediction Phase)\")\n",
+    "batches_stats_pred.legend(loc='lower right', bbox_to_anchor=(1.54, 0), shadow=True, ncol=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33dc73e5-d44e-40a0-8740-52aab51dacc5",
+   "metadata": {},
+   "source": [
+    "# More Testing Needed..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b9a5ed1b-d3f7-446c-9147-c6c73e5ae7e3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/inputJsonsFiles/DistributedConfig/dc_dist_14d.json b/inputJsonsFiles/DistributedConfig/dc_dist_14d.json
new file mode 100644
index 00000000..f0ac91d9
--- /dev/null
+++ b/inputJsonsFiles/DistributedConfig/dc_dist_14d.json
@@ -0,0 +1,259 @@
+{
+    "nerlnetSettings": {
+        "frequency": "100",
+        "batchSize": "50"
+    },
+    "mainServer": {
+        "port": "8900",
+        "args": ""
+    },
+    "apiServer": {
+        "port": "8901",
+        "args": ""
+    },
+    "devices": [
+        {
+            "name": "c0vm0",
+            "ipv4": "10.0.0.5",
+            "entities": "mainServer,apiServer"
+        },
+        {
+            "name": "minionMS",
+            "ipv4": "10.0.0.31",
+            "entities": "s1"
+        },
+        {
+            "name": "minion0",
+            "ipv4": "10.0.0.17",
+            "entities": "s2"
+        },
+        {
+            "name": "minion1",
+            "ipv4": "10.0.0.18",
+            "entities": "s3"
+        },
+        {
+            "name": "minion2",
+            "ipv4": "10.0.0.19",
+            "entities": "s4"
+        },
+        {
+            "name": "minion3",
+            "ipv4": "10.0.0.20",
+            "entities": "s5"
+        },
+        {
+            "name": "minion4",
+            "ipv4": "10.0.0.21",
+            "entities": "c1"
+        },
+        {
+            "name": "minion5",
+            "ipv4": "10.0.0.22",
+            "entities": "c2"
+        },
+        {
+            "name": "minion6",
+            "ipv4": "10.0.0.23",
+            "entities": "c3"
+        },
+        {
+            "name": "minion7",
+            "ipv4": "10.0.0.24",
+            "entities": "c4"
+        },
+        {
+            "name": "minion8",
+            "ipv4": "10.0.0.25",
+            "entities": "c5"
+        },
+        {
+            "name": "minion9",
+            "ipv4": "10.0.0.26",
+            "entities": "r1"
+        },
+        {
+            "name": "minion10",
+            "ipv4": "10.0.0.27",
+            "entities": "r2"
+        },
+        {
+            "name": "minion11",
+            "ipv4": "10.0.0.28",
+            "entities": "r3"
+        },
+        {
+            "name": "minion12",
+            "ipv4": "10.0.0.29",
+            "entities": "r4"
+        }
+    ],
+    "routers": [
+        {
+            "name": "r1",
+            "port": "8900",
+            "policy": "0"
+        },
+        {
+            "name": "r2",
+            "port": "8900",
+            "policy": "0"
+        },
+        {
+            "name": "r3",
+            "port": "8900",
+            "policy": "0"
+        },
+        {
+            "name": "r4",
+            "port": "8900",
+            "policy": "0"
+        }
+    ],
+    "sources": [
+        {
+            "name": "s1",
+            "port": "8900",
+            "frequency": "50",
+            "policy": "0",
+            "epochs": "2",
+            "type": "0"
+        },
+        {
+            "name": "s2",
+            "port": "8900",
+            "frequency": "50",
+            "policy": "0",
+            "epochs": "2",
+            "type": "0"
+        },
+        {
+            "name": "s3",
+            "port": "8900",
+            "frequency": "50",
+            "policy": "0",
+            "epochs": "2",
+            "type": "0"
+        },
+        {
+            "name": "s4",
+            "port": "8900",
+            "frequency": "50",
+            "policy": "0",
+            "epochs": "2",
+            "type": "0"
+        },
+        {
+            "name": "s5",
+            "port": "8900",
+            "frequency": "50",
+            "policy": "0",
+            "epochs": "2",
+            "type": "0"
+        }
+    ],
+    "clients": [
+        {
+            "name": "c1",
+            "port": "8900",
+            "workers": "w1,w2"
+        },
+        {
+            "name": "c2",
+            "port": "8900",
+            "workers": "w3,w4"
+        },
+        {
+            "name": "c3",
+            "port": "8900",
+            "workers": "w5,w6"
+        },
+        {
+            "name": "c4",
+            "port": "8900",
+            "workers": "w7,w8"
+        },
+        {
+            "name": "c5",
+            "port": "8900",
+            "workers": "w9,w10"
+        }
+    ],
+    "workers": [
+        {
+            "name": "w1",
+            "model_sha": "0771693392e898393c9b2b8235497537b5fbed1fd0c9a5a7ec6aab665d2c1896"
+        },
+        {
+            "name": "w2",
+            "model_sha": "0771693392e898393c9b2b8235497537b5fbed1fd0c9a5a7ec6aab665d2c1896"
+        },
+        {
+            "name": "w3",
+            "model_sha": "0771693392e898393c9b2b8235497537b5fbed1fd0c9a5a7ec6aab665d2c1896"
+        },
+        {
+            "name": "w4",
+            "model_sha": "0771693392e898393c9b2b8235497537b5fbed1fd0c9a5a7ec6aab665d2c1896"
+        },
+        {
+            "name": "w5",
+            "model_sha": "0771693392e898393c9b2b8235497537b5fbed1fd0c9a5a7ec6aab665d2c1896"
+        },
+        {
+            "name": "w6",
+            "model_sha": "0771693392e898393c9b2b8235497537b5fbed1fd0c9a5a7ec6aab665d2c1896"
+        },
+        {
+            "name": "w7",
+            "model_sha": "0771693392e898393c9b2b8235497537b5fbed1fd0c9a5a7ec6aab665d2c1896"
+        },
+        {
+            "name": "w8",
+            "model_sha": "0771693392e898393c9b2b8235497537b5fbed1fd0c9a5a7ec6aab665d2c1896"
+        },
+        {
+            "name": "w9",
+            "model_sha": "0771693392e898393c9b2b8235497537b5fbed1fd0c9a5a7ec6aab665d2c1896"
+        },
+        {
+            "name": "w10",
+            "model_sha": "0771693392e898393c9b2b8235497537b5fbed1fd0c9a5a7ec6aab665d2c1896"
+        }
+    ],
+    "model_sha": {
+        "0771693392e898393c9b2b8235497537b5fbed1fd0c9a5a7ec6aab665d2c1896": {
+            "modelType": "0",
+            "_doc_modelType": " nn:0 | approximation:1 | classification:2 | forecasting:3 | image_classification:4 | text_classification:5 | text_generation:6 | auto_association:7 | autoencoder:8 | ae_classifier:9 |",
+            "modelArgs": "",
+            "_doc_modelArgs": "Extra arguments to model",
+            "layersSizes": "5,16,8,3",
+            "_doc_layersSizes": "List of postive integers [L0, L1, ..., LN]",
+            "layerTypesList": "1,3,3,3",
+            "_doc_LayerTypes": " Default:0 | Scaling:1 | Conv:2 | Perceptron:3 | Pooling:4 | Probabilistic:5 | LSTM:6 | Reccurrent:7 | Unscaling:8 | Flatten:9 | Bounding:10 |",
+            "layers_functions": "1,7,7,11",
+            "_doc_layers_functions_activation": " Threshold:1 | Sign:2 | Logistic:3 | Tanh:4 | Linear:5 | ReLU:6 | eLU:7 | SeLU:8 | Soft-plus:9 | Soft-sign:10 | Hard-sigmoid:11 |",
+            "_doc_layer_functions_pooling": " none:1 | Max:2 | Avg:3 |",
+            "_doc_layer_functions_probabilistic": " Binary:1 | Logistic:2 | Competitive:3 | Softmax:4 |",
+            "_doc_layer_functions_scaler": " none:1 | MinMax:2 | MeanStd:3 | STD:4 | Log:5 |",
+            "lossMethod": "2",
+            "_doc_lossMethod": " SSE:1 | MSE:2 | NSE:3 | MinkowskiE:4 | WSE:5 | CEE:6 |",
+            "lr": "0.001",
+            "_doc_lr": "Positve float",
+            "epochs": "1",
+            "_doc_epochs": "Positve Integer",
+            "optimizer": "5",
+            "_doc_optimizer": " GD:0 | CGD:1 | SGD:2 | QuasiNeuton:3 | LVM:4 | ADAM:5 |",
+            "optimizerArgs": "none",
+            "_doc_optimizerArgs": "String",
+            "infraType": "0",
+            "_doc_infraType": " opennn:0 | wolfengine:1 |",
+            "distributedSystemType": "0",
+            "_doc_distributedSystemType": " none:0 | fedClientAvg:1 | fedServerAvg:2 |",
+            "distributedSystemArgs": "SyncMaxCount=300",
+            "_doc_distributedSystemArgs": "String",
+            "distributedSystemToken": "9922u",
+            "_doc_distributedSystemToken": "Token that associates distributed group of workers and parameter-server"
+        }
+    }
+}
\ No newline at end of file
diff --git a/inputJsonsFiles/experimentsFlow/exp_dist_14d.json b/inputJsonsFiles/experimentsFlow/exp_dist_14d.json
new file mode 100644
index 00000000..94efcad2
--- /dev/null
+++ b/inputJsonsFiles/experimentsFlow/exp_dist_14d.json
@@ -0,0 +1,140 @@
+{
+    "experimentName": "synthetic_3_gausians",
+    "experimentType": "classification",
+    "batchSize": 50,
+    "csvFilePath": "/tmp/nerlnet/data/NerlnetData-master/nerlnet/synthetic_norm/synthetic_full.csv",
+    "numOfFeatures": "5",
+    "numOfLabels": "3",
+    "headersNames": "Norm(0:1),Norm(4:1),Norm(10:3)",
+    "Phases": 
+    [
+        {
+            "phaseName": "training_phase1",
+            "phaseType": "training",
+            "sourcePieces":
+            [
+                {
+                "sourceName": "s1",
+                "startingSample": "0",
+                "numOfBatches": "200",
+                "workers": "w1,w2",
+                "nerltensorType": "float"
+                },
+                {
+                "sourceName": "s2",
+                "startingSample": "0",
+                "numOfBatches": "200",
+                "workers": "w3,w4",
+                "nerltensorType": "float"
+                },
+                {
+                "sourceName": "s3",
+                "startingSample": "0",
+                "numOfBatches": "200",
+                "workers": "w5,w6",
+                "nerltensorType": "float"
+                },
+                {
+                "sourceName": "s4",
+                "startingSample": "0",
+                "numOfBatches": "200",
+                "workers": "w7,w8",
+                "nerltensorType": "float"
+                },
+                {
+                "sourceName": "s5",
+                "startingSample": "0",
+                "numOfBatches": "200",
+                "workers": "w9,w10",
+                "nerltensorType": "float"
+                }
+            ]  
+        },
+        {
+        "phaseName": "training_phase2",
+        "phaseType": "training",
+        "sourcePieces":
+        [
+            {
+                "sourceName": "s1",
+                "startingSample": "20000",
+                "numOfBatches": "200",
+                "workers": "w1,w3",
+                "nerltensorType": "float"
+            },
+            {
+                "sourceName": "s2",
+                "startingSample": "20000",
+                "numOfBatches": "200",
+                "workers": "w2,w4",
+                "nerltensorType": "float"
+            },
+            {
+                "sourceName": "s3",
+                "startingSample": "20000",
+                "numOfBatches": "200",
+                "workers": "w5,w7",
+                "nerltensorType": "float"
+            },
+            {
+                "sourceName": "s4",
+                "startingSample": "20000",
+                "numOfBatches": "200",
+                "workers": "w6,w10",
+                "nerltensorType": "float"
+            },
+            {
+                "sourceName": "s5",
+                "startingSample": "20000",
+                "numOfBatches": "200",
+                "workers": "w8,w9",
+                "nerltensorType": "float"
+            }
+        ]  
+    },
+        {
+            "phaseName": "prediction_phase",
+            "phaseType": "prediction",
+            "sourcePieces":
+            [
+                {
+                "sourceName": "s1",
+                "startingSample": "40000",
+                "numOfBatches": "100",
+                "workers": "w1,w8",
+                "nerltensorType": "float"
+                },
+                {
+                "sourceName": "s2",
+                "startingSample": "40000",
+                "numOfBatches": "100",
+                "workers": "w2,w7",
+                "nerltensorType": "float"
+                },
+                {
+                "sourceName": "s3",
+                "startingSample": "40000",
+                "numOfBatches": "100",
+                "workers": "w3,w6",
+                "nerltensorType": "float"
+                },
+                {
+                "sourceName": "s4",
+                "startingSample": "40000",
+                "numOfBatches": "100",
+                "workers": "w4,w9",
+                "nerltensorType": "float"
+                },
+                {
+                "sourceName": "s5",
+                "startingSample": "40000",
+                "numOfBatches": "100",
+                "workers": "w5,w10",
+                "nerltensorType": "float"
+                }
+            ]    
+        }
+    ]  
+    }
+    
+    
\ No newline at end of file

From dc1a39d29167212d2399e2ca146e7a72a269e4a1 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Tue, 2 Jul 2024 19:31:41 +0000
Subject: [PATCH 02/50] [AEC] Exp working

---
 .../dc_AEC_1d_2c_1s_4r_4w.json                |  8 +-
 src_cpp/opennnBridge/ae_red.cpp               | 13 +--
 src_cpp/opennnBridge/ae_red.h                 |  2 +-
 src_cpp/opennnBridge/nerlWorkerOpenNN.cpp     | 41 +++++-----
 src_cpp/opennnBridge/nerlWorkerOpenNN.h       |  2 +-
 src_cpp/opennnBridge/openNNnif.cpp            |  2 +-
 src_py/apiServer/experiment_flow_debug.py     |  6 +-
 src_py/apiServer/experiment_phase.py          |  1 +
 src_py/apiServer/stats.py                     | 79 ++++++++++++-------
 9 files changed, 91 insertions(+), 63 deletions(-)

diff --git a/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json b/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
index 16887b6b..a4985e43 100644
--- a/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
+++ b/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
@@ -85,16 +85,16 @@
             "modelType": "9",
             "_doc_modelType": " nn:0 | approximation:1 | classification:2 | forecasting:3 | image-classification:4 | text-classification:5 | text-generation:6 | auto-association:7 | autoencoder:8 | ae-classifier:9 |",
             "modelArgs": "",
-            "layersSizes": "64,32,16,8,16,32,64,64",
+            "layersSizes": "8,4,2,4,8,8",
             "_doc_layersSizes": "List of postive integers [L0, L1, ..., LN]",
-            "layerTypesList": "1,3,3,3,3,3,3,10",
+            "layerTypesList": "1,3,3,3,3,10",
             "_doc_LayerTypes": " Default:0 | Scaling:1 | CNN:2 | Perceptron:3 | Pooling:4 | Probabilistic:5 | LSTM:6 | Reccurrent:7 | Unscaling:8 |",
-            "layers_functions": "1,6,6,6,6,6,6,1",
+            "layers_functions": "1,6,6,6,6,1",
             "_doc_layers_functions_activation": " Threshold:1 | Sign:2 | Logistic:3 | Tanh:4 | Linear:5 | ReLU:6 | eLU:7 | SeLU:8 | Soft-plus:9 | Soft-sign:10 | Hard-sigmoid:11 |",
             "_doc_layer_functions_pooling": " none:1 | Max:2 | Avg:3 |",
             "_doc_layer_functions_probabilistic": " Binary:1 | Logistic:2 | Competitive:3 | Softmax:4 |",
             "_doc_layer_functions_scaler": " none:1 | MinMax:2 | MeanStd:3 | STD:4 | Log:5 |",
-            "lossMethod": "6",
+            "lossMethod": "2",
             "_doc_lossMethod": " SSE:1 | MSE:2 | NSE:3 | MinkowskiE:4 | WSE:5 | CEE:6 |",
             "lr": "0.01",
             "_doc_lr": "Positve float",
diff --git a/src_cpp/opennnBridge/ae_red.cpp b/src_cpp/opennnBridge/ae_red.cpp
index cb201096..bbac1af1 100644
--- a/src_cpp/opennnBridge/ae_red.cpp
+++ b/src_cpp/opennnBridge/ae_red.cpp
@@ -3,7 +3,7 @@
 namespace nerlnet
 {
     
-AeRed::AeRed(float k, float alpha)
+AeRed::AeRed(float k, float alpha) // TODO Add ModelArgs and parse them here
 {
     _k = k;
     _alpha = alpha;
@@ -20,13 +20,14 @@ AeRed::~AeRed()
 
 }
 
-fTensor1DPtr AeRed::update_batch(fTensor1D loss_values)
+fTensor2DPtr AeRed::update_batch(fTensor2DPtr loss_values)
 {
-    fTensor1DPtr result = std::make_shared<fTensor1D>(loss_values.size());
-    for(int i = 0; i < loss_values.size() - 1; i++)
+    fTensor2DPtr result = std::make_shared<fTensor2D>(loss_values->dimension(0), loss_values->dimension(1));
+    for(int i = 0; i < (*loss_values).dimension(0); i++)
     {
-        float val = update_sample(loss_values(i));
-        result->data()[i] = val;
+        float val = update_sample((*loss_values)(i, 0));
+        if ((*loss_values)(i) == val) (*result)(i, 0) = 1;
+        else (*result)(i, 0) = 0;
     }
     return result;
 }
diff --git a/src_cpp/opennnBridge/ae_red.h b/src_cpp/opennnBridge/ae_red.h
index bcccf72c..44bd2918 100644
--- a/src_cpp/opennnBridge/ae_red.h
+++ b/src_cpp/opennnBridge/ae_red.h
@@ -17,7 +17,7 @@ class AeRed
     AeRed(float k = PARAM_K_DEFAULT , float alpha = ALPHA_DEFAULT);
     ~AeRed();
 
-    fTensor1DPtr update_batch(fTensor1D loss_values);
+    fTensor2DPtr update_batch(fTensor2DPtr loss_values);
     float update_sample(float loss_value);
     
     private:
diff --git a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
index 031d9307..ec0bd5f6 100644
--- a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
+++ b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
@@ -52,15 +52,16 @@ namespace nerlnet
                 inputs_dimensions.setValues({num_of_samples, inputs_number});
                 fTensor2DPtr calculate_res = std::make_shared<fTensor2D>(num_of_samples, neural_network->get_outputs_number());
                 *calculate_res = neural_network->calculate_outputs(TrainData->data(), inputs_dimensions);
-                // SAV 
-                fTensor2D absoluteDifferences = (*calculate_res - *_aec_data_set).abs();
-                fTensor1D loss_values_sav = absoluteDifferences.sum(Eigen::array<int, 1>({1}));
-                // MSE
-                fTensor1D loss_values_mse = (float)1/_aec_data_set->dimension(0) * (*calculate_res - *_aec_data_set).pow(2).sum(Eigen::array<int, 1>({1}));
-                //cout << "Loss Values (MSE):" << endl << loss_values_mse << endl;
-                fTensor1DPtr res_sav = _ae_red_ptr->update_batch(loss_values_sav);
-                fTensor1DPtr res_mse = _ae_red_ptr->update_batch(loss_values_mse);
-                //cout << "AE_RED RESULT VECTOR:" << endl << *res_mse << endl;
+                fTensor2DPtr loss_values_mse = std::make_shared<fTensor2D>(num_of_samples, 1);
+                fTensor2D diff = (*calculate_res - *_aec_data_set);
+                fTensor2D squared_diff = diff.pow(2);
+                fTensor1D sum_squared_diff = squared_diff.sum(Eigen::array<int, 1>({1}));
+                fTensor1D mse1D = (1.0 / static_cast<float>(_aec_data_set->dimension(0))) * sum_squared_diff;
+                fTensor2D mse2D = mse1D.reshape(Eigen::array<int, 2>({num_of_samples, 1}));
+                *loss_values_mse = mse2D;
+                // cout << "MSE Loss: " << mse_loss << endl;
+                fTensor2DPtr res = _ae_red_ptr->update_batch(loss_values_mse);
+                // cout << "AE_RED RESULT VECTOR:" << endl << *res << endl;
             }
 
                 
@@ -77,7 +78,7 @@ namespace nerlnet
     }
     
 
-    void NerlWorkerOpenNN::post_predict_process(fTensor2DPtr result_ptr){
+    void NerlWorkerOpenNN::post_predict_process(fTensor2DPtr &result_ptr){
         switch(_model_type){
             case MODEL_TYPE_NN:
             {
@@ -92,15 +93,17 @@ namespace nerlnet
                 std::shared_ptr<opennn::NeuralNetwork> neural_network = get_neural_network_ptr();
                 Index num_of_samples = _aec_data_set->dimension(0);
                 Index inputs_number = neural_network->get_inputs_number();
-                // SAV 
-                fTensor2D absoluteDifferences = (*result_ptr - *_aec_data_set).abs();
-                fTensor1D loss_values_sav = absoluteDifferences.sum(Eigen::array<int, 1>({1}));
-                // MSE
-                fTensor1D loss_values_mse = (float)1/_aec_data_set->dimension(0) * (*result_ptr - *_aec_data_set).pow(2).sum(Eigen::array<int, 1>({1}));
-                //cout << "Loss Values (MSE):" << endl << loss_values_mse << endl;
-                fTensor1DPtr res_sav = _ae_red_ptr->update_batch(loss_values_sav);
-                fTensor1DPtr res_mse = _ae_red_ptr->update_batch(loss_values_mse);
-                //cout << "AE_RED RESULT VECTOR:" << endl << *res_mse << endl;
+                Index num_of_labels = 1; // TODO need to add bounderies
+                fTensor2DPtr results = std::make_shared<fTensor2D>(num_of_samples, num_of_labels); // TODO +2 for upper and lower boundaries for each label
+                fTensor2DPtr loss_values_mse = std::make_shared<fTensor2D>(num_of_samples , 1);
+                fTensor2D diff = (*result_ptr - *_aec_data_set);
+                fTensor2D squared_diff = diff.pow(2);
+                fTensor1D sum_squared_diff = squared_diff.sum(Eigen::array<int, 1>({1}));
+                fTensor1D mse1D = (1.0 / static_cast<float>(_aec_data_set->dimension(0))) * sum_squared_diff;
+                fTensor2D mse2D = mse1D.reshape(Eigen::array<int, 2>({num_of_samples, 1}));
+                *loss_values_mse = mse2D;
+                result_ptr = _ae_red_ptr->update_batch(loss_values_mse); // ! This should override the result_ptr
+                break;
             }
             // case MODEL_TYPE_LSTM:
             // {
diff --git a/src_cpp/opennnBridge/nerlWorkerOpenNN.h b/src_cpp/opennnBridge/nerlWorkerOpenNN.h
index 6b4dde1b..de23437e 100644
--- a/src_cpp/opennnBridge/nerlWorkerOpenNN.h
+++ b/src_cpp/opennnBridge/nerlWorkerOpenNN.h
@@ -31,7 +31,7 @@ class NerlWorkerOpenNN : public NerlWorker
     std::shared_ptr<opennn::TrainingStrategy> get_training_strategy_ptr() { return _training_strategy_ptr; };
     std::shared_ptr<opennn::DataSet> get_data_set() { return _data_set; };
     void post_training_process(fTensor2DPtr  TrainDataNNptr);
-    void post_predict_process(fTensor2DPtr result_ptr);
+    void post_predict_process(fTensor2DPtr &result_ptr);
     void get_result_calc(fTensor2DPtr calculate_res,int num_of_samples,int inputs_number,fTensor2DPtr predictData);
     void set_optimization_method(int optimizer_type ,int learning_rate);
     void set_loss_method(int loss_method);
diff --git a/src_cpp/opennnBridge/openNNnif.cpp b/src_cpp/opennnBridge/openNNnif.cpp
index 3348ad35..a46b581d 100644
--- a/src_cpp/opennnBridge/openNNnif.cpp
+++ b/src_cpp/opennnBridge/openNNnif.cpp
@@ -84,7 +84,7 @@ void* PredictFun(void* arg)
     Index inputs_number = neural_network->get_inputs_number();
     fTensor2DPtr calculate_res = std::make_shared<fTensor2D>(num_of_samples, neural_network->get_outputs_number());
     nerlworker_opennn->get_result_calc(calculate_res, num_of_samples, inputs_number, PredictNNptr->data);
-    nerlworker_opennn->post_predict_process(calculate_res); 
+    nerlworker_opennn->post_predict_process(calculate_res);
     nifpp::make_tensor_2d<float,fTensor2D>(env, prediction, calculate_res);
     // only for AE and AEC calculate the distance between prediction labels and input data
     //std::cout << "*calculate_res.get(): " << (*calculate_res.get()).dimensions() << std::endl;
diff --git a/src_py/apiServer/experiment_flow_debug.py b/src_py/apiServer/experiment_flow_debug.py
index 7909cfe9..f72cfed6 100644
--- a/src_py/apiServer/experiment_flow_debug.py
+++ b/src_py/apiServer/experiment_flow_debug.py
@@ -20,9 +20,9 @@ def print_test(in_str : str):
 api_server_instance.download_dataset(TEST_DATASET_IDX)
 #api_server_instance.help()
 api_server_instance.showJsons()
-dc_idx = 2
-conn_idx = 21
-exp_idx = 3
+dc_idx = 0
+conn_idx = 25
+exp_idx = 0
 api_server_instance.setJsons(dc_idx, conn_idx, exp_idx)
 dc_json , connmap_json, exp_flow_json = api_server_instance.getUserJsons()
 
diff --git a/src_py/apiServer/experiment_phase.py b/src_py/apiServer/experiment_phase.py
index 6be3dc12..b64db502 100644
--- a/src_py/apiServer/experiment_phase.py
+++ b/src_py/apiServer/experiment_phase.py
@@ -29,6 +29,7 @@ def process_experiment_phase_data(self):
         list_of_decoded_data = decode_phase_result_data_json_from_main_server(self.raw_data_buffer[0])
         for decoded_data in list_of_decoded_data:
             worker_name, source_name, duration, batch_id, batch_ts, distributed_token, np_tensor = decoded_data
+            # print(f"BATCH {batch_id}: {np_tensor}") # ! AEC RETURN WRONG NP_TENSOR
             client_name = self.network_componenets.get_client_name_by_worker_name(worker_name)
             self.nerl_model_db.get_client(client_name).get_worker(worker_name).create_batch(batch_id, source_name, np_tensor, duration, distributed_token, batch_ts)
         
diff --git a/src_py/apiServer/stats.py b/src_py/apiServer/stats.py
index e7811b6c..fc65130e 100644
--- a/src_py/apiServer/stats.py
+++ b/src_py/apiServer/stats.py
@@ -183,7 +183,7 @@ def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False
                         worker_missed_batches[(worker_name, source_name, str(batch_id))] = (starting_offset + batch_id * batch_size, batch_size)  # save the missing batch
                 
                 df_worker_labels = df_worker_labels.dropna()
-                #print(df_worker_labels)
+                # print(df_worker_labels)
                 for batch_id in range(total_batches_per_source):
                     batch_db = worker_db.get_batch(source_name, str(batch_id))
                     if batch_db:
@@ -191,53 +191,76 @@ def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False
                         # cycle = according indexs of panadas (with jump)
                         cycle = int(batch_db.get_batch_id())
                         tensor_data = batch_db.get_tensor_data()
-                        tensor_data = tensor_data.reshape(batch_size, num_of_labels)
+                        # print(f"tensor_data shape: {tensor_data.shape}")
+                        tensor_data = tensor_data.reshape(batch_size, num_of_labels) 
                         #print(df_worker_labels)
                         #print(tensor_data)
                         start_index = cycle * batch_size
                         end_index = (cycle + 1) * batch_size
                         df_worker_labels.iloc[start_index:end_index, num_of_labels:] = None # Fix an issue of pandas of incompatible dtype
                         df_worker_labels.iloc[start_index:end_index, num_of_labels:] = tensor_data
-                        #print(df_worker_labels)
-
+                        # print(df_worker_labels)
 
-                # Take 2 list from the df, one for the actual labels and one for the predict labels to build the confusion matrix
-                max_column_predict_index = df_worker_labels.iloc[:, num_of_labels:].idxmax(axis=1)
-                max_column_predict_index = max_column_predict_index.tolist()
-                max_column_predict_index = [int(predict_index) - num_of_labels for predict_index in max_column_predict_index] # fix the index to original labels index
-                max_column_labels_index = df_worker_labels.iloc[:, :num_of_labels].idxmax(axis=1)
-                max_column_labels_index = max_column_labels_index.tolist()
-                #print(f"max_column_predict_index: {max_column_predict_index}")
-                #print(f"max_column_labels_index: {max_column_labels_index}")
-                
-                # building confusion matrix for each class
-                for class_index, class_name in enumerate(self.headers_list):
-                    class_actual_list = [1 if label_num == class_index else 0 for label_num in max_column_labels_index]   # 1 if the label is belong to the class, 0 otherwise
-                    class_predict_list = [1 if label_num == class_index else 0 for label_num in max_column_predict_index]   # 1 if the label is belong to the class, 0 otherwise
-                    confusion_matrix = metrics.confusion_matrix(class_actual_list, class_predict_list)  
-                    #confusion_matrix_np = confusion_matrix.to_numpy()
+                if len(self.headers_list) == 1:
+                    class_name = self.headers_list[0]
+                    actual_labels = df_worker_labels.iloc[:, :num_of_labels].values.flatten().tolist()
+                    predict_labels = df_worker_labels.iloc[:, num_of_labels:].values.flatten().tolist()
+                    confusion_matrix = metrics.confusion_matrix(actual_labels, predict_labels)
                     confusion_matrix_source_dict[(source_name, worker_name, class_name)] = confusion_matrix
                     if (worker_name, class_name) not in confusion_matrix_worker_dict:
                         confusion_matrix_worker_dict[(worker_name, class_name)] = confusion_matrix
                     else:
                         confusion_matrix_worker_dict[(worker_name, class_name)] += confusion_matrix
+                    
+                else: # Multi-Class
+                    # Take 2 list from the df, one for the actual labels and one for the predict labels to build the confusion matrix
+                    max_column_predict_index = df_worker_labels.iloc[:, num_of_labels:].idxmax(axis=1) 
+                    max_column_predict_index = max_column_predict_index.tolist() 
+                    max_column_predict_index = [int(predict_index) - num_of_labels for predict_index in max_column_predict_index] # fix the index to original labels index
+                    max_column_labels_index = df_worker_labels.iloc[:, :num_of_labels].idxmax(axis=1)
+                    max_column_labels_index = max_column_labels_index.tolist()
+                    
+                    # building confusion matrix for each class
+                    for class_index, class_name in enumerate(self.headers_list):
+                        class_actual_list = [1 if label_num == class_index else 0 for label_num in max_column_labels_index]   # 1 if the label is belong to the class, 0 otherwise
+                        class_predict_list = [1 if label_num == class_index else 0 for label_num in max_column_predict_index]   # 1 if the label is belong to the class, 0 otherwise
+                        confusion_matrix = metrics.confusion_matrix(class_actual_list, class_predict_list)  
+                        #confusion_matrix_np = confusion_matrix.to_numpy()
+                        confusion_matrix_source_dict[(source_name, worker_name, class_name)] = confusion_matrix
+                        if (worker_name, class_name) not in confusion_matrix_worker_dict:
+                            confusion_matrix_worker_dict[(worker_name, class_name)] = confusion_matrix
+                        else:
+                            confusion_matrix_worker_dict[(worker_name, class_name)] += confusion_matrix
                 
 
         if plot:
             workers = sorted(list({tup[0] for tup in confusion_matrix_worker_dict.keys()}))
             classes = sorted(list({tup[1] for tup in confusion_matrix_worker_dict.keys()}))
             fig, ax = plt.subplots(nrows=len(workers), ncols=len(classes),figsize=(4*len(classes),4*len(workers)),dpi=140)
-            for i , worker in enumerate(workers): 
-                for j , pred_class in enumerate(classes):
-                    conf_mat = confusion_matrix_worker_dict[(worker , pred_class)]
-                    heatmap = sns.heatmap(data=conf_mat ,ax=ax[i,j], annot=True , fmt="d", cmap='Blues',annot_kws={"size": 8}, cbar_kws={'pad': 0.1})
+            if len(classes) > 1:
+                for i , worker in enumerate(workers): 
+                    for j , pred_class in enumerate(classes):
+                        conf_mat = confusion_matrix_worker_dict[(worker , pred_class)]
+                        # print(f"conf_mat: {conf_mat}")
+                        heatmap = sns.heatmap(data=conf_mat ,ax=ax[i,j], annot=True , fmt="d", cmap='Blues',annot_kws={"size": 8}, cbar_kws={'pad': 0.1})
+                        cbar = heatmap.collections[0].colorbar
+                        cbar.ax.tick_params(labelsize = 8)
+                        ax[i, j].set_title(f"{worker} , Class '{pred_class}'" , fontsize=12)
+                        ax[i, j].tick_params(axis='both', which='major', labelsize=8) 
+                        ax[i, j].set_xlabel("Predicted Label" , fontsize=8)
+                        ax[i, j].set_ylabel("True Label" , fontsize=8)
+                        ax[i, j].set_aspect('equal')
+            else:
+                for i, worker in enumerate(workers):
+                    conf_mat = confusion_matrix_worker_dict[(worker , classes[0])]
+                    heatmap = sns.heatmap(data=conf_mat ,ax=ax[i], annot=True , fmt="d", cmap='Blues',annot_kws={"size": 8}, cbar_kws={'pad': 0.1})
                     cbar = heatmap.collections[0].colorbar
                     cbar.ax.tick_params(labelsize = 8)
-                    ax[i, j].set_title(f"{worker} , Class '{pred_class}'" , fontsize=12)
-                    ax[i, j].tick_params(axis='both', which='major', labelsize=8) 
-                    ax[i, j].set_xlabel("Predicted Label" , fontsize=8)
-                    ax[i, j].set_ylabel("True Label" , fontsize=8)
-                    ax[i, j].set_aspect('equal')
+                    ax[i].set_title(f"{worker} , Class '{classes[0]}'" , fontsize=12)
+                    ax[i].tick_params(axis='both', which='major', labelsize=8) 
+                    ax[i].set_xlabel("Predicted Label" , fontsize=8)
+                    ax[i].set_ylabel("True Label" , fontsize=8)
+                    ax[i].set_aspect('equal')
             fig.subplots_adjust(wspace=0.4 , hspace=0.4)
             plt.show()
                 

From 19ebdda30113a9ceca66a688211a7b009ddfab4a Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Fri, 12 Jul 2024 13:59:03 +0000
Subject: [PATCH 03/50] [AEC_exp] Small changes

---
 .../dc_AEC_1d_2c_1s_4r_4w.json                |  12 +-
 .../exp_AEC_1d_2c_1s_4r_4w.json               |  12 +-
 src_cpp/opennnBridge/ae_red.h                 |   2 +-
 src_py/apiServer/stats.py                     |   5 +-
 src_py/apiServer/stats_aec.py                 | 106 ++++++++++++++++++
 5 files changed, 123 insertions(+), 14 deletions(-)
 create mode 100644 src_py/apiServer/stats_aec.py

diff --git a/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json b/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
index a4985e43..a31e20d3 100644
--- a/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
+++ b/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
@@ -1,7 +1,7 @@
 {
     "nerlnetSettings": {
         "frequency": "200",
-        "batchSize": "100"
+        "batchSize": "200"
     },
     "mainServer": {
         "port": "8081",
@@ -46,7 +46,7 @@
             "port": "8085",
             "frequency": "200",
             "policy": "0",
-            "epochs": "1",
+            "epochs": "50",
             "type": "0"
         }
     ],
@@ -85,18 +85,18 @@
             "modelType": "9",
             "_doc_modelType": " nn:0 | approximation:1 | classification:2 | forecasting:3 | image-classification:4 | text-classification:5 | text-generation:6 | auto-association:7 | autoencoder:8 | ae-classifier:9 |",
             "modelArgs": "",
-            "layersSizes": "8,4,2,4,8,8",
+            "layersSizes": "27,16,8,16,27",
             "_doc_layersSizes": "List of postive integers [L0, L1, ..., LN]",
-            "layerTypesList": "1,3,3,3,3,10",
+            "layerTypesList": "1,3,3,3,3",
             "_doc_LayerTypes": " Default:0 | Scaling:1 | CNN:2 | Perceptron:3 | Pooling:4 | Probabilistic:5 | LSTM:6 | Reccurrent:7 | Unscaling:8 |",
-            "layers_functions": "1,6,6,6,6,1",
+            "layers_functions": "1,6,6,6,6",
             "_doc_layers_functions_activation": " Threshold:1 | Sign:2 | Logistic:3 | Tanh:4 | Linear:5 | ReLU:6 | eLU:7 | SeLU:8 | Soft-plus:9 | Soft-sign:10 | Hard-sigmoid:11 |",
             "_doc_layer_functions_pooling": " none:1 | Max:2 | Avg:3 |",
             "_doc_layer_functions_probabilistic": " Binary:1 | Logistic:2 | Competitive:3 | Softmax:4 |",
             "_doc_layer_functions_scaler": " none:1 | MinMax:2 | MeanStd:3 | STD:4 | Log:5 |",
             "lossMethod": "2",
             "_doc_lossMethod": " SSE:1 | MSE:2 | NSE:3 | MinkowskiE:4 | WSE:5 | CEE:6 |",
-            "lr": "0.01",
+            "lr": "0.001",
             "_doc_lr": "Positve float",
             "epochs": "1",
             "_doc_epochs": "Positve Integer",
diff --git a/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json b/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
index 206cec58..1ee28684 100644
--- a/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
+++ b/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
@@ -1,9 +1,9 @@
 {
     "experimentName": "anomaly_detection_skab",
     "experimentType": "classification",
-    "batchSize": 100,
-    "csvFilePath": "/tmp/nerlnet/data/NerlnetData-master/nerlnet/skab/skab_full.csv",
-    "numOfFeatures": "8",
+    "batchSize": 200,
+    "csvFilePath": "/tmp/nerlnet/data/NerlnetData-master/nerlnet/skab/skab_full_bins.csv",
+    "numOfFeatures": "27",
     "numOfLabels": "1",
     "headersNames": "Anomaly",
     "Phases": 
@@ -16,7 +16,7 @@
             {
             "sourceName": "s1",
             "startingSample": "0",
-            "numOfBatches": "120",
+            "numOfBatches": "110",
             "workers": "w1,w2,w3,w4",
             "nerltensorType": "float"
             }
@@ -29,8 +29,8 @@
         [
             {
             "sourceName": "s1",
-            "startingSample": "12000",
-            "numOfBatches": "60",
+            "startingSample": "20000",
+            "numOfBatches": "75",
             "workers": "w1,w2,w3,w4",
             "nerltensorType": "float"
             }
diff --git a/src_cpp/opennnBridge/ae_red.h b/src_cpp/opennnBridge/ae_red.h
index 44bd2918..24f4cd6b 100644
--- a/src_cpp/opennnBridge/ae_red.h
+++ b/src_cpp/opennnBridge/ae_red.h
@@ -1,6 +1,6 @@
 #pragma once
 
-#define PARAM_K_DEFAULT 1.2f
+#define PARAM_K_DEFAULT 1.5f
 #define ALPHA_DEFAULT 0.3f
 
 #include "eigenTensorTypes.h"
diff --git a/src_py/apiServer/stats.py b/src_py/apiServer/stats.py
index fc65130e..9e7bd64d 100644
--- a/src_py/apiServer/stats.py
+++ b/src_py/apiServer/stats.py
@@ -55,7 +55,7 @@ def get_loss_by_source(self , plot : bool = False , saveToFile : bool = False):
         """
         pass
 
-    def get_loss_ts(self , plot : bool = False , saveToFile : bool = False): # Todo change it
+    def get_loss_ts(self , plot : bool = False , saveToFile : bool = False, log : bool = False): # Todo change it
         """
         Returns a dictionary of {worker : loss list} for each worker in the experiment.
         use plot=True to plot the loss function.
@@ -89,6 +89,9 @@ def get_loss_ts(self , plot : bool = False , saveToFile : bool = False): # Todo
             sns.lineplot(data=df)
             plt.xlabel('Batch Num.')
             plt.ylabel('Loss Value')
+            if log:
+                plt.yscale('log')
+                plt.xscale('log')
             plt.title('Training Loss Function')
         return df
 
diff --git a/src_py/apiServer/stats_aec.py b/src_py/apiServer/stats_aec.py
new file mode 100644
index 00000000..33cef1bf
--- /dev/null
+++ b/src_py/apiServer/stats_aec.py
@@ -0,0 +1,106 @@
+from collections import OrderedDict
+from sklearn import metrics
+from IPython.display import display
+import matplotlib.pyplot as plt
+from datetime import datetime
+from pathlib import Path
+from experiment_phase import *
+import globalVars as globe
+from definitions import *
+import pandas as pd
+import numpy as np
+import seaborn as sns
+sns.set_theme()
+
+from stats import Stats
+
+class StatsAEC():
+    
+    def __init__(self, stats: Stats):
+        self.stats = stats
+        
+        
+    def get_aec_loss(self, plot=False):
+        loss_dict = {}
+        workers_model_db_list = self.stats.nerl_model_db.get_workers_model_db_list()
+        total_batches_list = [worker.get_total_batches() for worker in workers_model_db_list]
+        max_batches = max(total_batches_list)
+        
+        for worker_db in workers_model_db_list:
+            worker_name = worker_db.get_worker_name()
+            batches_ts_tansor_data_dict = worker_db.get_batches_ts_tansor_data_dict()
+            sorted_batches_ts_tansor_data_dict = dict(sorted(batches_ts_tansor_data_dict.items()))
+            loss_dict[worker_name] = [sorted_batches_ts_tansor_data_dict[key][0] for key in sorted(sorted_batches_ts_tansor_data_dict)]
+            
+        for worker_name in loss_dict:
+            loss_dict[worker_name] = [float(arr) for sublist in loss_dict[worker_name] for arr in sublist]
+            loss_dict[worker_name] += [None] * (max_batches - len(loss_dict[worker_name]))
+
+        df = pd.DataFrame(loss_dict)
+        self.loss_ts_pd = df
+        
+        if plot:
+            sns.lineplot(data=df)
+            plt.xlabel('Batch Num.')
+            plt.ylabel('Loss Value')
+            plt.title('Training Loss Function')
+        return df
+
+        
+    def get_aec_boundaries(self, plot=False):
+        i = 0
+        upper_boundaries_dict = {}
+        lower_boundaries_dict = {}
+        workers_model_db_list = self.stats.nerl_model_db.get_workers_model_db_list()
+        total_batches_list = [worker.get_total_batches() for worker in workers_model_db_list]
+        max_batches = max(total_batches_list)
+        
+        for worker_db in workers_model_db_list:
+            worker_name = worker_db.get_worker_name()
+            batches_ts_tansor_data_dict = worker_db.get_batches_ts_tansor_data_dict()
+            sorted_batches_ts_tansor_data_dict = dict(sorted(batches_ts_tansor_data_dict.items()))
+            upper_boundaries_dict[worker_name] = [sorted_batches_ts_tansor_data_dict[key][1] for key in sorted(sorted_batches_ts_tansor_data_dict)]
+            lower_boundaries_dict[worker_name] = [sorted_batches_ts_tansor_data_dict[key][2] for key in sorted(sorted_batches_ts_tansor_data_dict)]
+            
+        for worker_name in upper_boundaries_dict:
+            upper_boundaries_dict[worker_name] = [float(arr) for sublist in upper_boundaries_dict[worker_name] for arr in sublist]
+            upper_boundaries_dict[worker_name] += [None] * (max_batches - len(upper_boundaries_dict[worker_name]))
+        for worker_name in lower_boundaries_dict:
+            lower_boundaries_dict[worker_name] = [float(arr) for sublist in lower_boundaries_dict[worker_name] for arr in sublist]
+            lower_boundaries_dict[worker_name] += [None] * (max_batches - len(lower_boundaries_dict[worker_name]))
+
+        df_upper = pd.DataFrame(upper_boundaries_dict).sort_index(axis=1)
+        df_lower = pd.DataFrame(lower_boundaries_dict).sort_index(axis=1)
+        
+        if plot:
+            for worker_name in df_upper:
+                # Calculate the seperator to be the average of the upper and lower boundaries
+                seperator = (df_upper[worker_name] + df_lower[worker_name]) / 2
+                plt.figure(figsize=(12, 8))
+                plt.plot(df_upper[worker_name], label='Upper Boundary', color='C0')
+                plt.fill_between(df_upper[worker_name].index, df_upper[worker_name] - df_upper[worker_name].std(), df_upper[worker_name] + df_upper[worker_name].std(), color='C0', alpha=0.2)
+                plt.plot(df_lower[worker_name], label='Lower Boundary', color='C1')
+                plt.fill_between(df_lower[worker_name].index, df_lower[worker_name] - df_lower[worker_name].std(), df_lower[worker_name] + df_lower[worker_name].std(), color='C1', alpha=0.2)
+                plt.plot(seperator, label='Seperator', color='C2')
+                plt.fill_between(seperator.index, seperator + seperator.std(), seperator - seperator.std(), color='C2', alpha=0.2)
+                plt.xscale('log') # For better visualization
+                plt.xlabel('Batch Num.')
+                plt.ylabel('Boundary Value')
+                plt.title(f'Training Boundaries {worker_name}')
+                plt.legend()
+                plt.show()
+        return df_upper, df_lower
+    
+    def get_false_alarm_rate(self, conf_mats_workers):
+        false_alarm_rate_dict = {}
+        for worker_name, _ in conf_mats_workers.items():
+            conf_mat = conf_mats_workers[worker_name]
+            false_alarm_rate_dict[worker_name] = conf_mat[0][1] / (conf_mat[0][1] + conf_mat[0][0])
+        return false_alarm_rate_dict
+    
+    def get_detection_rate(self, conf_mats_workers):
+        detection_rate_dict = {}
+        for worker_name, _ in conf_mats_workers.items():
+            conf_mat = conf_mats_workers[worker_name]
+            detection_rate_dict[worker_name] = conf_mat[1][1] / (conf_mat[1][1] + conf_mat[1][0])
+        return detection_rate_dict

From 3bbb2dab57d85df0977c6ef95cab1ec57f9c4cb3 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Sun, 14 Jul 2024 11:40:16 +0000
Subject: [PATCH 04/50] [AEC_exp] Changes

---
 .../dc_AEC_1d_2c_1s_4r_4w.json                | 20 ++++++------------
 .../exp_AEC_1d_2c_1s_4r_4w.json               |  8 +++----
 src_cpp/opennnBridge/ae_red.cpp               |  5 -----
 src_cpp/opennnBridge/ae_red.h                 |  2 +-
 src_cpp/opennnBridge/nerlWorkerOpenNN.cpp     | 21 -------------------
 src_py/apiServer/stats_aec.py                 | 10 ---------
 6 files changed, 11 insertions(+), 55 deletions(-)

diff --git a/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json b/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
index 00017b57..6874a731 100644
--- a/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
+++ b/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
@@ -1,6 +1,6 @@
 {
     "nerlnetSettings": {
-        "frequency": "200",
+        "frequency": "300",
         "batchSize": "200"
     },
     "mainServer": {
@@ -44,9 +44,9 @@
         {
             "name": "s1",
             "port": "8085",
-            "frequency": "200",
+            "frequency": "300",
             "policy": "0",
-            "epochs": "50",
+            "epochs": "2000",
             "type": "0"
         }
     ],
@@ -85,19 +85,11 @@
             "modelType": "9",
             "_doc_modelType": " nn:0 | approximation:1 | classification:2 | forecasting:3 | image-classification:4 | text-classification:5 | text-generation:6 | auto-association:7 | autoencoder:8 | ae-classifier:9 |",
             "modelArgs": "",
-<<<<<<< HEAD
-            "layersSizes": "27,16,8,16,27",
+            "layersSizes": "138,128,64,32,16,8,16,32,64,128,138",
             "_doc_layersSizes": "List of postive integers [L0, L1, ..., LN]",
-            "layerTypesList": "1,3,3,3,3",
+            "layerTypesList": "1,3,3,3,3,3,3,3,3,3,3",
             "_doc_LayerTypes": " Default:0 | Scaling:1 | CNN:2 | Perceptron:3 | Pooling:4 | Probabilistic:5 | LSTM:6 | Reccurrent:7 | Unscaling:8 |",
-            "layers_functions": "1,6,6,6,6",
-=======
-            "layersSizes": "8,4,2,4,8,8",
-            "_doc_layersSizes": "List of postive integers [L0, L1, ..., LN]",
-            "layerTypesList": "1,3,3,3,3,10",
-            "_doc_LayerTypes": " Default:0 | Scaling:1 | CNN:2 | Perceptron:3 | Pooling:4 | Probabilistic:5 | LSTM:6 | Reccurrent:7 | Unscaling:8 |",
-            "layers_functions": "1,6,6,6,6,1",
->>>>>>> b280e5605dabbe3972610f41d7b29c334f02f621
+            "layers_functions": "1,7,7,7,7,7,7,7,7,7,7",
             "_doc_layers_functions_activation": " Threshold:1 | Sign:2 | Logistic:3 | Tanh:4 | Linear:5 | ReLU:6 | eLU:7 | SeLU:8 | Soft-plus:9 | Soft-sign:10 | Hard-sigmoid:11 |",
             "_doc_layer_functions_pooling": " none:1 | Max:2 | Avg:3 |",
             "_doc_layer_functions_probabilistic": " Binary:1 | Logistic:2 | Competitive:3 | Softmax:4 |",
diff --git a/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json b/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
index 1ee28684..fe90d5ef 100644
--- a/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
+++ b/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
@@ -2,8 +2,8 @@
     "experimentName": "anomaly_detection_skab",
     "experimentType": "classification",
     "batchSize": 200,
-    "csvFilePath": "/tmp/nerlnet/data/NerlnetData-master/nerlnet/skab/skab_full_bins.csv",
-    "numOfFeatures": "27",
+    "csvFilePath": "/tmp/nerlnet/data/NerlnetData-master/nerlnet/skab/skab_full_windowed.csv",
+    "numOfFeatures": "138",
     "numOfLabels": "1",
     "headersNames": "Anomaly",
     "Phases": 
@@ -16,7 +16,7 @@
             {
             "sourceName": "s1",
             "startingSample": "0",
-            "numOfBatches": "110",
+            "numOfBatches": "100",
             "workers": "w1,w2,w3,w4",
             "nerltensorType": "float"
             }
@@ -30,7 +30,7 @@
             {
             "sourceName": "s1",
             "startingSample": "20000",
-            "numOfBatches": "75",
+            "numOfBatches": "80",
             "workers": "w1,w2,w3,w4",
             "nerltensorType": "float"
             }
diff --git a/src_cpp/opennnBridge/ae_red.cpp b/src_cpp/opennnBridge/ae_red.cpp
index a3ddfab4..4da14ac8 100644
--- a/src_cpp/opennnBridge/ae_red.cpp
+++ b/src_cpp/opennnBridge/ae_red.cpp
@@ -17,11 +17,6 @@ AeRed::AeRed(float k, float alpha) // TODO Add ModelArgs and parse them here
 
 AeRed::~AeRed(){}
 
-<<<<<<< HEAD
-}
-
-=======
->>>>>>> b280e5605dabbe3972610f41d7b29c334f02f621
 fTensor2DPtr AeRed::update_batch(fTensor2DPtr loss_values)
 {
     fTensor2DPtr result = std::make_shared<fTensor2D>(loss_values->dimension(0), loss_values->dimension(1));
diff --git a/src_cpp/opennnBridge/ae_red.h b/src_cpp/opennnBridge/ae_red.h
index d3205ce1..bf8a8211 100644
--- a/src_cpp/opennnBridge/ae_red.h
+++ b/src_cpp/opennnBridge/ae_red.h
@@ -1,6 +1,6 @@
 #pragma once
 
-#define PARAM_K_DEFAULT 1.5f
+#define PARAM_K_DEFAULT 0.9f
 #define ALPHA_DEFAULT 0.3f
 
 #include "eigenTensorTypes.h"
diff --git a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
index 7d9c9d5e..17b59814 100644
--- a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
+++ b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
@@ -95,11 +95,6 @@ namespace nerlnet
                 fTensor2DPtr results = std::make_shared<fTensor2D>(num_of_samples, num_of_labels + num_of_labels*2); // LOWER/UPPER for each label
                 fTensor2DPtr calculate_res = std::make_shared<fTensor2D>(num_of_samples, neural_network->get_outputs_number());
                 *calculate_res = neural_network->calculate_outputs(TrainData->data(), inputs_dimensions);
-<<<<<<< HEAD
-=======
-
-                // MSE Calculation
->>>>>>> b280e5605dabbe3972610f41d7b29c334f02f621
                 fTensor2DPtr loss_values_mse = std::make_shared<fTensor2D>(num_of_samples, 1);
                 fTensor2D diff = (*calculate_res - *_aec_data_set);
                 fTensor2D squared_diff = diff.pow(2);
@@ -107,16 +102,9 @@ namespace nerlnet
                 fTensor1D mse1D = (1.0 / static_cast<float>(_aec_data_set->dimension(0))) * sum_squared_diff;
                 fTensor2D mse2D = mse1D.reshape(Eigen::array<int, 2>({num_of_samples, 1}));
                 *loss_values_mse = mse2D;
-<<<<<<< HEAD
                 // cout << "MSE Loss: " << mse_loss << endl;
                 fTensor2DPtr res = _ae_red_ptr->update_batch(loss_values_mse);
                 // cout << "AE_RED RESULT VECTOR:" << endl << *res << endl;
-=======
-
-                _ae_red_ptr->update_batch(loss_values_mse); // Update thresholds
-                
-                break;
->>>>>>> b280e5605dabbe3972610f41d7b29c334f02f621
             }
 
                 
@@ -148,13 +136,8 @@ namespace nerlnet
                 std::shared_ptr<opennn::NeuralNetwork> neural_network = get_neural_network_ptr();
                 Index num_of_samples = _aec_data_set->dimension(0);
                 Index inputs_number = neural_network->get_inputs_number();
-<<<<<<< HEAD
                 Index num_of_labels = 1; // TODO need to add bounderies
                 fTensor2DPtr results = std::make_shared<fTensor2D>(num_of_samples, num_of_labels); // TODO +2 for upper and lower boundaries for each label
-=======
-                Index num_of_labels = 1;
-                fTensor2DPtr results = std::make_shared<fTensor2D>(num_of_samples, num_of_labels); 
->>>>>>> b280e5605dabbe3972610f41d7b29c334f02f621
                 fTensor2DPtr loss_values_mse = std::make_shared<fTensor2D>(num_of_samples , 1);
                 fTensor2D diff = (*result_ptr - *_aec_data_set);
                 fTensor2D squared_diff = diff.pow(2);
@@ -162,11 +145,7 @@ namespace nerlnet
                 fTensor1D mse1D = (1.0 / static_cast<float>(_aec_data_set->dimension(0))) * sum_squared_diff;
                 fTensor2D mse2D = mse1D.reshape(Eigen::array<int, 2>({num_of_samples, 1}));
                 *loss_values_mse = mse2D;
-<<<<<<< HEAD
                 result_ptr = _ae_red_ptr->update_batch(loss_values_mse); // ! This should override the result_ptr
-=======
-                result_ptr = _ae_red_ptr->update_batch(loss_values_mse);
->>>>>>> b280e5605dabbe3972610f41d7b29c334f02f621
                 break;
             }
             // case MODEL_TYPE_LSTM:
diff --git a/src_py/apiServer/stats_aec.py b/src_py/apiServer/stats_aec.py
index 5af09819..05292193 100644
--- a/src_py/apiServer/stats_aec.py
+++ b/src_py/apiServer/stats_aec.py
@@ -48,10 +48,6 @@ def get_aec_loss(self, plot=False):
 
         
     def get_aec_boundaries(self, plot=False):
-<<<<<<< HEAD
-        i = 0
-=======
->>>>>>> b280e5605dabbe3972610f41d7b29c334f02f621
         upper_boundaries_dict = {}
         lower_boundaries_dict = {}
         workers_model_db_list = self.stats.nerl_model_db.get_workers_model_db_list()
@@ -86,17 +82,13 @@ def get_aec_boundaries(self, plot=False):
                 plt.fill_between(df_lower[worker_name].index, df_lower[worker_name] - df_lower[worker_name].std(), df_lower[worker_name] + df_lower[worker_name].std(), color='C1', alpha=0.2)
                 plt.plot(seperator, label='Seperator', color='C2')
                 plt.fill_between(seperator.index, seperator + seperator.std(), seperator - seperator.std(), color='C2', alpha=0.2)
-<<<<<<< HEAD
                 plt.xscale('log') # For better visualization
-=======
->>>>>>> b280e5605dabbe3972610f41d7b29c334f02f621
                 plt.xlabel('Batch Num.')
                 plt.ylabel('Boundary Value')
                 plt.title(f'Training Boundaries {worker_name}')
                 plt.legend()
                 plt.show()
         return df_upper, df_lower
-<<<<<<< HEAD
     
     def get_false_alarm_rate(self, conf_mats_workers):
         false_alarm_rate_dict = {}
@@ -111,5 +103,3 @@ def get_detection_rate(self, conf_mats_workers):
             conf_mat = conf_mats_workers[worker_name]
             detection_rate_dict[worker_name] = conf_mat[1][1] / (conf_mat[1][1] + conf_mat[1][0])
         return detection_rate_dict
-=======
->>>>>>> b280e5605dabbe3972610f41d7b29c334f02f621

From e85565cd93b2ed7bf3ae810a818b543994965beb Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Thu, 18 Jul 2024 08:13:33 +0000
Subject: [PATCH 05/50] [AEC_exp] Latest changes

---
 .../dc_AEC_1d_2c_1s_4r_4w.json                | 10 +++---
 .../exp_AEC_1d_2c_1s_4r_4w.json               | 28 ++++++++++++----
 src_cpp/opennn                                |  2 +-
 src_cpp/opennnBridge/ae_red.h                 |  2 +-
 src_cpp/opennnBridge/nerlWorkerOpenNN.cpp     | 15 ++++++++-
 src_cpp/opennnBridge/nerlWorkerOpenNN.h       |  1 +
 src_py/apiServer/experiment_flow.py           |  2 +-
 src_py/apiServer/experiment_phase.py          |  9 +++--
 src_py/apiServer/hf_repo_ids.json             |  6 ++++
 src_py/apiServer/nerl_model_db.py             |  8 ++---
 src_py/apiServer/stats_aec.py                 | 33 ++++++++++++++-----
 11 files changed, 85 insertions(+), 31 deletions(-)

diff --git a/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json b/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
index 6874a731..b2adcd20 100644
--- a/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
+++ b/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
@@ -46,7 +46,7 @@
             "port": "8085",
             "frequency": "300",
             "policy": "0",
-            "epochs": "2000",
+            "epochs": "10",
             "type": "0"
         }
     ],
@@ -85,18 +85,18 @@
             "modelType": "9",
             "_doc_modelType": " nn:0 | approximation:1 | classification:2 | forecasting:3 | image-classification:4 | text-classification:5 | text-generation:6 | auto-association:7 | autoencoder:8 | ae-classifier:9 |",
             "modelArgs": "",
-            "layersSizes": "138,128,64,32,16,8,16,32,64,128,138",
+            "layersSizes": "10,512,256,128,64,128,256,512,10",
             "_doc_layersSizes": "List of postive integers [L0, L1, ..., LN]",
-            "layerTypesList": "1,3,3,3,3,3,3,3,3,3,3",
+            "layerTypesList": "1,3,3,3,3,3,3,3,3",
             "_doc_LayerTypes": " Default:0 | Scaling:1 | CNN:2 | Perceptron:3 | Pooling:4 | Probabilistic:5 | LSTM:6 | Reccurrent:7 | Unscaling:8 |",
-            "layers_functions": "1,7,7,7,7,7,7,7,7,7,7",
+            "layers_functions": "1,7,7,7,7,7,7,7,11",
             "_doc_layers_functions_activation": " Threshold:1 | Sign:2 | Logistic:3 | Tanh:4 | Linear:5 | ReLU:6 | eLU:7 | SeLU:8 | Soft-plus:9 | Soft-sign:10 | Hard-sigmoid:11 |",
             "_doc_layer_functions_pooling": " none:1 | Max:2 | Avg:3 |",
             "_doc_layer_functions_probabilistic": " Binary:1 | Logistic:2 | Competitive:3 | Softmax:4 |",
             "_doc_layer_functions_scaler": " none:1 | MinMax:2 | MeanStd:3 | STD:4 | Log:5 |",
             "lossMethod": "2",
             "_doc_lossMethod": " SSE:1 | MSE:2 | NSE:3 | MinkowskiE:4 | WSE:5 | CEE:6 |",
-            "lr": "0.001",
+            "lr": "0.0001",
             "_doc_lr": "Positve float",
             "epochs": "1",
             "_doc_epochs": "Positve Integer",
diff --git a/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json b/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
index fe90d5ef..f753f05d 100644
--- a/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
+++ b/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
@@ -2,10 +2,10 @@
     "experimentName": "anomaly_detection_skab",
     "experimentType": "classification",
     "batchSize": 200,
-    "csvFilePath": "/tmp/nerlnet/data/NerlnetData-master/nerlnet/skab/skab_full_windowed.csv",
-    "numOfFeatures": "138",
+    "csvFilePath": "/tmp/nerlnet/data/NerlnetData-master/nerlnet/ForestCover/forest_cover_dataset.csv",
+    "numOfFeatures": "10",
     "numOfLabels": "1",
-    "headersNames": "Anomaly",
+    "headersNames": "Label",
     "Phases": 
     [
     {
@@ -16,11 +16,25 @@
             {
             "sourceName": "s1",
             "startingSample": "0",
-            "numOfBatches": "100",
+            "numOfBatches": "600",
             "workers": "w1,w2,w3,w4",
             "nerltensorType": "float"
             }
-        ]  
+        ]
+    },
+    {
+        "phaseName": "training_phase2",
+        "phaseType": "training",
+        "sourcePieces":
+        [
+            {
+            "sourceName": "s1",
+            "startingSample": "120000",
+            "numOfBatches": "600",
+            "workers": "w1,w2,w3,w4",
+            "nerltensorType": "float"
+            }
+        ]
     },
     {
         "phaseName": "prediction_phase",
@@ -29,8 +43,8 @@
         [
             {
             "sourceName": "s1",
-            "startingSample": "20000",
-            "numOfBatches": "80",
+            "startingSample": "250000",
+            "numOfBatches": "300",
             "workers": "w1,w2,w3,w4",
             "nerltensorType": "float"
             }
diff --git a/src_cpp/opennn b/src_cpp/opennn
index 61ce79db..6e594ce2 160000
--- a/src_cpp/opennn
+++ b/src_cpp/opennn
@@ -1 +1 @@
-Subproject commit 61ce79dbcb1585105d47eaf95da447b9526a3c29
+Subproject commit 6e594ce2bfa2c98a482aede20c2e4bb601986d9c
diff --git a/src_cpp/opennnBridge/ae_red.h b/src_cpp/opennnBridge/ae_red.h
index bf8a8211..8b2376ab 100644
--- a/src_cpp/opennnBridge/ae_red.h
+++ b/src_cpp/opennnBridge/ae_red.h
@@ -1,6 +1,6 @@
 #pragma once
 
-#define PARAM_K_DEFAULT 0.9f
+#define PARAM_K_DEFAULT 1.2f
 #define ALPHA_DEFAULT 0.3f
 
 #include "eigenTensorTypes.h"
diff --git a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
index 17b59814..b2ec1a40 100644
--- a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
+++ b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
@@ -49,10 +49,16 @@ namespace nerlnet
         {
             case MODEL_TYPE_AE_CLASSIFIER:
             {
-                loss_val_tensor = std::make_shared<fTensor2D>(3, 1);
+                int num_of_samples = _aec_data_set->dimension(0);
+                loss_val_tensor = std::make_shared<fTensor2D>(3 + num_of_samples, 1);
                 (*loss_val_tensor)(0, 0) = static_cast<float>(_last_loss);
                 (*loss_val_tensor)(1, 0) = _ae_red_ptr->_ema_event;
                 (*loss_val_tensor)(2, 0) = _ae_red_ptr->_ema_normal;
+                // Add _aec_all_loss_values to loss_val_tensor
+                for (int i = 0; i < num_of_samples; i++)
+                {
+                    (*loss_val_tensor)(3 + i, 0) = (*_aec_all_loss_values)(i, 0);
+                }
                 break;
             }
             default:
@@ -95,13 +101,19 @@ namespace nerlnet
                 fTensor2DPtr results = std::make_shared<fTensor2D>(num_of_samples, num_of_labels + num_of_labels*2); // LOWER/UPPER for each label
                 fTensor2DPtr calculate_res = std::make_shared<fTensor2D>(num_of_samples, neural_network->get_outputs_number());
                 *calculate_res = neural_network->calculate_outputs(TrainData->data(), inputs_dimensions);
+                // cout << "Results: " << endl << *calculate_res << endl;
                 fTensor2DPtr loss_values_mse = std::make_shared<fTensor2D>(num_of_samples, 1);
+                fTensor2DPtr loss_values_return = std::make_shared<fTensor2D>(num_of_samples, 1);
                 fTensor2D diff = (*calculate_res - *_aec_data_set);
+                // cout << "Diff: " << endl << diff << endl;
                 fTensor2D squared_diff = diff.pow(2);
                 fTensor1D sum_squared_diff = squared_diff.sum(Eigen::array<int, 1>({1}));
                 fTensor1D mse1D = (1.0 / static_cast<float>(_aec_data_set->dimension(0))) * sum_squared_diff;
                 fTensor2D mse2D = mse1D.reshape(Eigen::array<int, 2>({num_of_samples, 1}));
+                // cout << "MSE2D: " << mse2D << endl;
                 *loss_values_mse = mse2D;
+                *loss_values_return = mse2D;
+                _aec_all_loss_values = loss_values_return;
                 // cout << "MSE Loss: " << mse_loss << endl;
                 fTensor2DPtr res = _ae_red_ptr->update_batch(loss_values_mse);
                 // cout << "AE_RED RESULT VECTOR:" << endl << *res << endl;
@@ -291,6 +303,7 @@ namespace nerlnet
                 bool data_set_condition = (num_of_features + num_of_output_neurons) == autoencoder_data->dimension(1);
                 assert(("issue with data input/output dimensions", data_set_condition));
                 _data_set->set_data(*autoencoder_data);
+                _data_set->set_columns_scalers(Scaler::NoScaling);
                 _data_set->set(autoencoder_data->dimension(0) , num_of_features , num_of_output_neurons); // TODO CHECK
                 break;
             }
diff --git a/src_cpp/opennnBridge/nerlWorkerOpenNN.h b/src_cpp/opennnBridge/nerlWorkerOpenNN.h
index 20ddb675..a93be425 100644
--- a/src_cpp/opennnBridge/nerlWorkerOpenNN.h
+++ b/src_cpp/opennnBridge/nerlWorkerOpenNN.h
@@ -53,6 +53,7 @@ class NerlWorkerOpenNN : public NerlWorker
     std::shared_ptr<opennn::DataSet> _data_set;
 
     fTensor2DPtr _aec_data_set;
+    fTensor2DPtr _aec_all_loss_values;
     std::shared_ptr<AeRed> _ae_red_ptr;
 
     // training vars
diff --git a/src_py/apiServer/experiment_flow.py b/src_py/apiServer/experiment_flow.py
index a7a4674d..726cc38f 100644
--- a/src_py/apiServer/experiment_flow.py
+++ b/src_py/apiServer/experiment_flow.py
@@ -85,7 +85,7 @@ def parse_experiment_flow_json(self, json_path : str, override_csv_path = ""):
         assert self.exp_flow_json[EXPFLOW_EXPERIMENT_TYPE_FIELD] , "experiment type is missing"
         self.exp_type = self.exp_flow_json[EXPFLOW_EXPERIMENT_TYPE_FIELD]
         self.batch_size = self.exp_flow_json[EXPFLOW_BATCH_SIZE_FIELD]
-        assert self.batch_size == self.batch_size_dc
+        assert self.batch_size == self.batch_size_dc, "Make sure the batch size field in the DC json and the Exp_flow json are the same"
         csv_file_path = self.exp_flow_json[EXPFLOW_CSV_FILE_PATH_FIELD] if override_csv_path == "" else override_csv_path
         headers_row = self.exp_flow_json[EXPFLOW_HEADERS_NAMES_FIELD].split(",")
         num_of_features = self.exp_flow_json[EXPFLOW_NUM_OF_FEATURES_FIELD]
diff --git a/src_py/apiServer/experiment_phase.py b/src_py/apiServer/experiment_phase.py
index b64db502..83b4d674 100644
--- a/src_py/apiServer/experiment_phase.py
+++ b/src_py/apiServer/experiment_phase.py
@@ -29,7 +29,6 @@ def process_experiment_phase_data(self):
         list_of_decoded_data = decode_phase_result_data_json_from_main_server(self.raw_data_buffer[0])
         for decoded_data in list_of_decoded_data:
             worker_name, source_name, duration, batch_id, batch_ts, distributed_token, np_tensor = decoded_data
-            # print(f"BATCH {batch_id}: {np_tensor}") # ! AEC RETURN WRONG NP_TENSOR
             client_name = self.network_componenets.get_client_name_by_worker_name(worker_name)
             self.nerl_model_db.get_client(client_name).get_worker(worker_name).create_batch(batch_id, source_name, np_tensor, duration, distributed_token, batch_ts)
         
@@ -64,9 +63,13 @@ def add_source_piece(self, source_piece : SourcePieceDS):
             self.source_pieces_dict[source_piece.source_name] = source_piece
         else: 
             LOG_ERROR(f"Source piece with name {source_piece.source_name} already exists in phase { self.phase}")
-       
+    
     def get_sources_pieces(self):
         return list(self.source_pieces_dict.values())
 
     def remove_source_piece(self, source_name: str): 
-        self.source_pieces_dict.pop(source_name)   
\ No newline at end of file
+        self.source_pieces_dict.pop(source_name)   
+        
+    def get_source_piece(self, source_name: str):
+        return self.source_pieces_dict[source_name]
+    
\ No newline at end of file
diff --git a/src_py/apiServer/hf_repo_ids.json b/src_py/apiServer/hf_repo_ids.json
index 04e31482..435c8db6 100644
--- a/src_py/apiServer/hf_repo_ids.json
+++ b/src_py/apiServer/hf_repo_ids.json
@@ -17,6 +17,12 @@
             "idx": 2,
             "name": "synthetic_norm",
             "description": "Gaussian Distributions Classification Syntetic Dataset for baseline experiments"
+        },
+        {
+            "id": "Nerlnet/forest_cover",
+            "idx": 3,
+            "name": "ForestCover",
+            "description": "Dataset for AEC"
         }
     ]
 }
\ No newline at end of file
diff --git a/src_py/apiServer/nerl_model_db.py b/src_py/apiServer/nerl_model_db.py
index f2dfcd3c..bd048770 100644
--- a/src_py/apiServer/nerl_model_db.py
+++ b/src_py/apiServer/nerl_model_db.py
@@ -57,11 +57,11 @@ def get_total_batches_per_source(self, source_name):
     def get_worker_name(self):
         return self.worker_name
 
-    def get_batches_ts_tansor_data_dict(self):
-        batches_ts_tansor_data_dict = {}
+    def get_batches_ts_tensor_data_dict(self):
+        batches_ts_tensor_data_dict = {}
         for batch_db in self.batches_ts_dict.values():
-            batches_ts_tansor_data_dict[batch_db.batch_timestamp] = batch_db.tensor_data
-        return batches_ts_tansor_data_dict
+            batches_ts_tensor_data_dict[batch_db.batch_timestamp] = batch_db.tensor_data
+        return batches_ts_tensor_data_dict
     
     def get_batches_dict(self):
         return self.batches_dict
diff --git a/src_py/apiServer/stats_aec.py b/src_py/apiServer/stats_aec.py
index 05292193..b0fc306f 100644
--- a/src_py/apiServer/stats_aec.py
+++ b/src_py/apiServer/stats_aec.py
@@ -28,9 +28,9 @@ def get_aec_loss(self, plot=False):
         
         for worker_db in workers_model_db_list:
             worker_name = worker_db.get_worker_name()
-            batches_ts_tansor_data_dict = worker_db.get_batches_ts_tansor_data_dict()
-            sorted_batches_ts_tansor_data_dict = dict(sorted(batches_ts_tansor_data_dict.items()))
-            loss_dict[worker_name] = [sorted_batches_ts_tansor_data_dict[key][0] for key in sorted(sorted_batches_ts_tansor_data_dict)]
+            batches_ts_tensor_data_dict = worker_db.get_batches_ts_tensor_data_dict()
+            sorted_batches_ts_tensor_data_dict = dict(sorted(batches_ts_tensor_data_dict.items()))
+            loss_dict[worker_name] = [sorted_batches_ts_tensor_data_dict[key][0] for key in sorted(sorted_batches_ts_tensor_data_dict)]
             
         for worker_name in loss_dict:
             loss_dict[worker_name] = [float(arr) for sublist in loss_dict[worker_name] for arr in sublist]
@@ -56,10 +56,10 @@ def get_aec_boundaries(self, plot=False):
         
         for worker_db in workers_model_db_list:
             worker_name = worker_db.get_worker_name()
-            batches_ts_tansor_data_dict = worker_db.get_batches_ts_tansor_data_dict()
-            sorted_batches_ts_tansor_data_dict = dict(sorted(batches_ts_tansor_data_dict.items()))
-            upper_boundaries_dict[worker_name] = [sorted_batches_ts_tansor_data_dict[key][1] for key in sorted(sorted_batches_ts_tansor_data_dict)]
-            lower_boundaries_dict[worker_name] = [sorted_batches_ts_tansor_data_dict[key][2] for key in sorted(sorted_batches_ts_tansor_data_dict)]
+            batches_ts_tensor_data_dict = worker_db.get_batches_ts_tensor_data_dict()
+            sorted_batches_ts_tensor_data_dict = dict(sorted(batches_ts_tensor_data_dict.items()))
+            upper_boundaries_dict[worker_name] = [sorted_batches_ts_tensor_data_dict[key][1] for key in sorted(sorted_batches_ts_tensor_data_dict)]
+            lower_boundaries_dict[worker_name] = [sorted_batches_ts_tensor_data_dict[key][2] for key in sorted(sorted_batches_ts_tensor_data_dict)]
             
         for worker_name in upper_boundaries_dict:
             upper_boundaries_dict[worker_name] = [float(arr) for sublist in upper_boundaries_dict[worker_name] for arr in sublist]
@@ -71,6 +71,10 @@ def get_aec_boundaries(self, plot=False):
         df_upper = pd.DataFrame(upper_boundaries_dict).sort_index(axis=1)
         df_lower = pd.DataFrame(lower_boundaries_dict).sort_index(axis=1)
         
+        # Take 10% of the data for better visualization
+        df_upper = df_upper.iloc[::len(df_upper) // 100, :]
+        df_lower = df_lower.iloc[::len(df_lower) // 100, :]
+        
         if plot:
             for worker_name in df_upper:
                 # Calculate the seperator to be the average of the upper and lower boundaries
@@ -82,13 +86,26 @@ def get_aec_boundaries(self, plot=False):
                 plt.fill_between(df_lower[worker_name].index, df_lower[worker_name] - df_lower[worker_name].std(), df_lower[worker_name] + df_lower[worker_name].std(), color='C1', alpha=0.2)
                 plt.plot(seperator, label='Seperator', color='C2')
                 plt.fill_between(seperator.index, seperator + seperator.std(), seperator - seperator.std(), color='C2', alpha=0.2)
-                plt.xscale('log') # For better visualization
+                # plt.xscale('log') # For better visualization
                 plt.xlabel('Batch Num.')
                 plt.ylabel('Boundary Value')
                 plt.title(f'Training Boundaries {worker_name}')
                 plt.legend()
                 plt.show()
         return df_upper, df_lower
+        
+    
+    def get_average_anomaly_error(self):
+        workers_model_db_list = self.stats.nerl_model_db.get_workers_model_db_list()
+        # labels = self.get_labels() # TODO ADD THIS FUNCTION
+        loss_values_dict = {}
+        for worker_db in workers_model_db_list:
+            worker_name = worker_db.get_worker_name()
+            batches_ts_tensor_data_dict = worker_db.get_batches_ts_tensor_data_dict()
+            sorted_batches_ts_tensor_data_dict = dict(sorted(batches_ts_tensor_data_dict.items()))
+            loss_values_dict[worker_name] = [sorted_batches_ts_tensor_data_dict[key][3:] for key in sorted(sorted_batches_ts_tensor_data_dict)]
+            
+        return loss_values_dict
     
     def get_false_alarm_rate(self, conf_mats_workers):
         false_alarm_rate_dict = {}

From 33db2e1ba22b4106e4ad1008a7c56ecd9c03f53e Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Sun, 21 Jul 2024 14:59:50 +0000
Subject: [PATCH 06/50] [AEC_Exp] WIP

---
 .../dc_AEC_1d_2c_1s_4r_4w.json                | 14 ++++++-------
 .../exp_AEC_1d_2c_1s_4r_4w.json               | 20 +++----------------
 src_cpp/opennnBridge/ae_red.h                 |  2 +-
 3 files changed, 11 insertions(+), 25 deletions(-)

diff --git a/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json b/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
index cd7b54e3..b5be8fb5 100644
--- a/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
+++ b/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
@@ -1,7 +1,7 @@
 {
     "nerlnetSettings": {
         "frequency": "300",
-        "batchSize": "200"
+        "batchSize": "60"
     },
     "mainServer": {
         "port": "8081",
@@ -44,9 +44,9 @@
         {
             "name": "s1",
             "port": "8085",
-            "frequency": "300",
+            "frequency": "600",
             "policy": "0",
-            "epochs": "10",
+            "epochs": "5",
             "type": "0"
         }
     ],
@@ -85,18 +85,18 @@
             "modelType": "9",
             "_doc_modelType": " nn:0 | approximation:1 | classification:2 | forecasting:3 | image-classification:4 | text-classification:5 | text-generation:6 | auto-association:7 | autoencoder:8 | ae-classifier:9 |",
             "modelArgs": "",
-            "layersSizes": "10,512,256,128,64,128,256,512,10",
+            "layersSizes": "10,16,8,16,10",
             "_doc_layersSizes": "List of postive integers [L0, L1, ..., LN]",
-            "layerTypesList": "1,3,3,3,3,10",
+            "layerTypesList": "1,3,3,3,3",
             "_doc_LayerTypes": " Default:0 | Scaling:1 | CNN:2 | Perceptron:3 | Pooling:4 | Probabilistic:5 | LSTM:6 | Reccurrent:7 | Unscaling:8 |",
-            "layers_functions": "1,6,6,6,6,1",
+            "layers_functions": "1,8,8,8,8",
             "_doc_layers_functions_activation": " Threshold:1 | Sign:2 | Logistic:3 | Tanh:4 | Linear:5 | ReLU:6 | eLU:7 | SeLU:8 | Soft-plus:9 | Soft-sign:10 | Hard-sigmoid:11 |",
             "_doc_layer_functions_pooling": " none:1 | Max:2 | Avg:3 |",
             "_doc_layer_functions_probabilistic": " Binary:1 | Logistic:2 | Competitive:3 | Softmax:4 |",
             "_doc_layer_functions_scaler": " none:1 | MinMax:2 | MeanStd:3 | STD:4 | Log:5 |",
             "lossMethod": "2",
             "_doc_lossMethod": " SSE:1 | MSE:2 | NSE:3 | MinkowskiE:4 | WSE:5 | CEE:6 |",
-            "lr": "0.0001",
+            "lr": "0.000001",
             "_doc_lr": "Positve float",
             "epochs": "1",
             "_doc_epochs": "Positve Integer",
diff --git a/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json b/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
index f753f05d..9d85262c 100644
--- a/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
+++ b/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
@@ -1,7 +1,7 @@
 {
     "experimentName": "anomaly_detection_skab",
     "experimentType": "classification",
-    "batchSize": 200,
+    "batchSize": 60,
     "csvFilePath": "/tmp/nerlnet/data/NerlnetData-master/nerlnet/ForestCover/forest_cover_dataset.csv",
     "numOfFeatures": "10",
     "numOfLabels": "1",
@@ -16,21 +16,7 @@
             {
             "sourceName": "s1",
             "startingSample": "0",
-            "numOfBatches": "600",
-            "workers": "w1,w2,w3,w4",
-            "nerltensorType": "float"
-            }
-        ]
-    },
-    {
-        "phaseName": "training_phase2",
-        "phaseType": "training",
-        "sourcePieces":
-        [
-            {
-            "sourceName": "s1",
-            "startingSample": "120000",
-            "numOfBatches": "600",
+            "numOfBatches": "4000",
             "workers": "w1,w2,w3,w4",
             "nerltensorType": "float"
             }
@@ -44,7 +30,7 @@
             {
             "sourceName": "s1",
             "startingSample": "250000",
-            "numOfBatches": "300",
+            "numOfBatches": "1000",
             "workers": "w1,w2,w3,w4",
             "nerltensorType": "float"
             }
diff --git a/src_cpp/opennnBridge/ae_red.h b/src_cpp/opennnBridge/ae_red.h
index 8b2376ab..6171ad8d 100644
--- a/src_cpp/opennnBridge/ae_red.h
+++ b/src_cpp/opennnBridge/ae_red.h
@@ -1,6 +1,6 @@
 #pragma once
 
-#define PARAM_K_DEFAULT 1.2f
+#define PARAM_K_DEFAULT 0.5f
 #define ALPHA_DEFAULT 0.3f
 
 #include "eigenTensorTypes.h"

From 5ea5646e7ea53a92e1696c3f8004881c167dda97 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Sun, 28 Jul 2024 21:23:50 +0000
Subject: [PATCH 07/50] [AEC] Added EMA Only Mode

---
 src_cpp/opennnBridge/ae_red.cpp           | 101 ++++++++++++++++++----
 src_cpp/opennnBridge/ae_red.h             |  20 +++--
 src_cpp/opennnBridge/nerlWorkerOpenNN.cpp |   2 +-
 3 files changed, 99 insertions(+), 24 deletions(-)

diff --git a/src_cpp/opennnBridge/ae_red.cpp b/src_cpp/opennnBridge/ae_red.cpp
index 4da14ac8..e6c3ebf6 100644
--- a/src_cpp/opennnBridge/ae_red.cpp
+++ b/src_cpp/opennnBridge/ae_red.cpp
@@ -3,10 +3,11 @@
 namespace nerlnet
 {
     
-AeRed::AeRed(float k, float alpha) // TODO Add ModelArgs and parse them here
+AeRed::AeRed(const std::string &_model_args_str, float k, float alpha) 
 {
-    _k = k;
-    _alpha = alpha;
+    getModelArgsParsed(_model_args_str, _model_args_parsed);
+    _k = _model_args_parsed.k;
+    _alpha = _model_args_parsed.alpha;
     _ema = 0;
     _emad = 1;
     _ema_event = 0;
@@ -15,6 +16,33 @@ AeRed::AeRed(float k, float alpha) // TODO Add ModelArgs and parse them here
     _prev_emad = 0;
 }
 
+void AeRed::getModelArgsParsed(const std::string &_model_args_str, ModelArgsParsed_t &model_args_parsed){
+    std::string k_str = "k=";
+    std::string alpha_str = "alpha=";
+    std::string use_ema_only_str = "use_ema_only=";
+    std::size_t found_k = _model_args_str.find(k_str);
+    std::size_t found_alpha = _model_args_str.find(alpha_str);
+    std::size_t found_use_ema_only = _model_args_str.find(use_ema_only_str);
+    if (found_k != std::string::npos){
+        model_args_parsed.k = std::stof(_model_args_str.substr(found_k + k_str.length()));
+    }
+    else{
+        model_args_parsed.k = PARAM_K_DEFAULT;
+    }
+    if (found_alpha != std::string::npos){
+        model_args_parsed.alpha = std::stof(_model_args_str.substr(found_alpha + alpha_str.length()));
+    }
+    else{
+        model_args_parsed.alpha = ALPHA_DEFAULT;
+    }
+    if (found_use_ema_only != std::string::npos){
+        model_args_parsed.use_ema_only = std::stoi(_model_args_str.substr(found_use_ema_only + use_ema_only_str.length()));
+    }
+    else{
+        model_args_parsed.use_ema_only = 0;
+    }
+}
+
 AeRed::~AeRed(){}
 
 fTensor2DPtr AeRed::update_batch(fTensor2DPtr loss_values)
@@ -22,28 +50,67 @@ fTensor2DPtr AeRed::update_batch(fTensor2DPtr loss_values)
     fTensor2DPtr result = std::make_shared<fTensor2D>(loss_values->dimension(0), loss_values->dimension(1));
     for(int i = 0; i < (*loss_values).dimension(0); i++)
     {
-        float val = update_sample((*loss_values)(i, 0));
-        if ((*loss_values)(i) == val) (*result)(i, 0) = 1;
-        else (*result)(i, 0) = 0;
+        float val = update_sample((*loss_values)(i, 0), i);
+        (*result)(i, 0) = val;
     }
     return result;
 }
 
-float AeRed::update_sample(float loss_value){
-    _ema = _alpha * loss_value + (1 - _alpha) * _prev_ema;
-    _prev_ema = _ema;
-    _emad = _alpha * abs(loss_value - _ema) + (1 - _alpha) * _prev_emad;
-    _prev_emad = _emad;
-    if(_ema + _k * _emad < loss_value){
-        _ema_event = loss_value;
+float AeRed::update_sample(float loss_value, int index)
+{
+    if(_model_args_parsed.use_ema_only){
+        return update_sample_ema(loss_value, index);
+    }
+    else{
+        return update_sample_red(loss_value, index);
+    }
+}
+
+float AeRed::update_sample_red(float loss_value, int index){
+    if (index == 0){
+        _ema = loss_value;
+        _prev_ema = loss_value;
+        _emad = 0;
+        _prev_emad = 0;
+        _ema_event = 0;
+        _ema_normal = 0;
+        _threshold = loss_value / 2;
     }
     else{
-        _ema_normal = loss_value;
+        _ema = _alpha * loss_value + (1 - _alpha) * _prev_ema;
+        _prev_ema = _ema;
+        _emad = _alpha * abs(loss_value - _ema) + (1 - _alpha) * _prev_emad;
+        _prev_emad = _emad;
+        if(_ema + _k * _emad < loss_value){
+            _ema_event = loss_value;
+        }
+        else{
+            _ema_normal = loss_value;
+        }
+        _threshold = (_ema_event + _ema_normal) / 2; // New Threshold
+
+        if(loss_value > _threshold) return 1.f;
+        else return 0.f;
     }
-    _threshold = (_ema_event + _ema_normal) / 2; // New Threshold
+    return 0.f;
+}
 
-    if(loss_value > _threshold) return loss_value;
-    else return -loss_value;
+float AeRed::update_sample_ema(float loss_value, int index)
+{
+    if (index == 0){
+        _ema = loss_value;
+        _prev_ema = loss_value;
+        _threshold = loss_value / 2;
+    }
+    else{
+        _ema = _alpha * loss_value + (1 - _alpha) * _prev_ema;
+        _prev_ema = _ema;
+        _threshold = _ema / 2;
+        if(loss_value > _threshold) return 1.f;
+        else return 0.f;
+    }
+    return 0.f;
 }
 
+
 } // namespace nerlnet
\ No newline at end of file
diff --git a/src_cpp/opennnBridge/ae_red.h b/src_cpp/opennnBridge/ae_red.h
index 6171ad8d..294a7272 100644
--- a/src_cpp/opennnBridge/ae_red.h
+++ b/src_cpp/opennnBridge/ae_red.h
@@ -1,24 +1,32 @@
 #pragma once
 
-#define PARAM_K_DEFAULT 0.5f
-#define ALPHA_DEFAULT 0.3f
+#define PARAM_K_DEFAULT 1.7f
+#define ALPHA_DEFAULT 0.4f
 
 #include "eigenTensorTypes.h"
 
-
 namespace nerlnet
 {
 
 class AeRed
 {
+    typedef struct ModelArgsParsed {
+        float k;
+        float alpha;
+        bool use_ema_only;
+    } ModelArgsParsed_t;
     
     public:
     
-    AeRed(float k = PARAM_K_DEFAULT , float alpha = ALPHA_DEFAULT);
+    AeRed(const std::string &_model_args_str, float k = PARAM_K_DEFAULT , float alpha = ALPHA_DEFAULT);
     ~AeRed();
 
     fTensor2DPtr update_batch(fTensor2DPtr loss_values);
-    float update_sample(float loss_value);
+    float update_sample(float loss_value, int index);
+    float update_sample_red(float loss_value, int index);
+    float update_sample_ema(float loss_value, int index);
+    void getModelArgsParsed(const std::string &_model_args_str, ModelArgsParsed_t &model_args_parsed);
+
     float _k;
     float _alpha;
     float _threshold;
@@ -28,7 +36,7 @@ class AeRed
     float _ema_normal;
     float _prev_ema;
     float _prev_emad;
-
+    ModelArgsParsed_t _model_args_parsed;
 };
 
 } // namespace nerlnet
diff --git a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
index b2ec1a40..b0f25179 100644
--- a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
+++ b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
@@ -17,7 +17,7 @@ namespace nerlnet
         generate_opennn_neural_network();
         _training_strategy_ptr = std::make_shared<opennn::TrainingStrategy>();
         generate_training_strategy();
-        _ae_red_ptr = std::make_shared<AeRed>();
+        _ae_red_ptr = std::make_shared<AeRed>(model_args_str);
     }
 
     NerlWorkerOpenNN::~NerlWorkerOpenNN()

From e6baaa72b465b858df1cfa4b45e7854ef371e15b Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Wed, 31 Jul 2024 19:44:29 +0000
Subject: [PATCH 08/50] [AEC_Exp] Exp Notebook WIP

---
 examples/AEC/AEC_Exp.ipynb | 1328 ++++++++++++++++++++++++++++--------
 1 file changed, 1042 insertions(+), 286 deletions(-)

diff --git a/examples/AEC/AEC_Exp.ipynb b/examples/AEC/AEC_Exp.ipynb
index f981c43f..f71cf448 100644
--- a/examples/AEC/AEC_Exp.ipynb
+++ b/examples/AEC/AEC_Exp.ipynb
@@ -8,8 +8,7 @@
    "outputs": [],
    "source": [
     "import set_jupyter_env\n",
-    "from apiServer import *\n",
-    "from stats import *"
+    "from apiServer import *"
    ]
   },
   {
@@ -17,70 +16,10 @@
    "execution_count": 2,
    "id": "dbb0aff3-aeeb-4948-9593-7161d1385ea2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "__________NERLNET CHECKLIST__________\n",
-      "Nerlnet configuration files are located at config directory.\n",
-      "Make sure data and jsons in correct folder, and jsons include the correct paths\n",
-      "* Data includes: a single csv that includes all the data for the experiment (training and prediction phases)\n",
-      "* Jsons include: - distributed configuration (dc_<name>.json)\n",
-      "                 - connection map (conn_<name>.json)\n",
-      "                 - experiment flow (exp_<name>.json)\n",
-      "* Jsons directory: can be defined by changing the config file: config/jsonsDir.nerlconfig\n",
-      "\n",
-      "____________API COMMANDS_____________\n",
-      "==========Setting experiment========\n",
-      "\n",
-      "-showJsons():                                           lists available json files in jsons directory (dc, conn, exp) to be used with setJsons and getUserJsons\n",
-      "-list_datasets():                                       reads `hf_repo_ids.json` and list of datasets and files of Nerlnet organizaion on https://huggingface.co/Nerlnet\n",
-      "-download_dataset(idx, dir):                            downloads dataset files from Huggingface to the specified directory (default is /tmp/nerlnet/data/NerlnetData-master/nerlnet)\n",
-      "-add_repo_to_datasets_list(repo, name , description):   adds a repository to the datasets list in `hf_repo_ids.json`\n",
-      "-printArchParams(Num)                                   print description of selected arch file\n",
-      "\n",
-      "-selectJsons():                                         get input from user for arch / conn / exp selection\n",
-      "-setJsons(arch, conn, exp):                             set selected jsons to get their path by getUserJsons\n",
-      "-getUserJsons():                                        return a tuple of 3 paths to dc, conn, exp jsons that is used for initialization\n",
-      "\n",
-      "-initialization(experiment_name, dc, conn, exp_flow, custom_csv_path):  \n",
-      "                                                        setting up the api-server to communicate with main-server of Nerlnet cluster\n",
-      "                                                        dc - path to distributed configuration file (can be generated by Nerlplanner)\n",
-      "                                                        conn - path to connection map file, graph of connections between entities\n",
-      "                                                        exp - path to experiment flow file, defines the flow of the experiment demonstrated as experiment phases of training and prediction\n",
-      "                                                        custom_csv_path - optional, path to custom csv file for the experiment, overrides the one in experiment flow file\n",
-      "                                                        \n",
-      "-send_jsons_to_devices():                               send each NerlNet device the dc and conn jsons to init entities on it\n",
-      "-sendDataToSources(phase(,split)):                      phase := \"training\" | \"prediction\". split := 1 default (split) | 2 (whole file). send the experiment data to sources (currently happens in beggining of train/predict)\n",
-      "\n",
-      "======== Running experiment ==========\n",
-      "-experiment_phase_is_valid()        returns True if there are more experiment phases to run\n",
-      "-run_current_experiment_phase()     runs the current experiment phase\n",
-      "-next_experiment_phase()            moves to the next experiment phase\n",
-      "\n",
-      "======== Retrieving statistics ======\n",
-      "-get_experiment_flow(experiment_name).generate_stats()   returns statistics object (E.g., assigned to StatsInst) class for the current experiment phase\n",
-      "-StatsInst.get_communication_stats_workers()         returns communication statistics for workers\n",
-      "-StatsInst.get_communication_stats_sources()         returns communication statistics for sources\n",
-      "-StatsInst.get_communication_stats_clients()         returns communication statistics for clients\n",
-      "-StatsInst.get_communication_stats_routers()         returns communication statistics for routers\n",
-      "-StatsInst.get_communication_stats_main_server()     returns communication statistics for main server\n",
-      "-StatsInst.get_loss_ts()                             returns the loss over time\n",
-      "-StatsInst.get_min_loss()                            returns the minimum loss\n",
-      "-StatsInst.get_missed_batches()                      returns the missed batches\n",
-      "\n",
-      "======== Workers Model Metrics and Performance ========\n",
-      "-StatsInst.get_confusion_matrices()                  returns tuple of two types of confusion matrices ordered by sources and ordered by workers\n",
-      "-StatsInst.get_model_performence_stats(confusion_matrix_worker_dict, saveToFile) returns the model performance statistics for the workers\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "API = ApiServer()\n",
-    "API.help()"
+    "# API.help()"
    ]
   },
   {
@@ -88,77 +27,9 @@
    "execution_count": 3,
    "id": "e7241206-2162-4e6f-9a95-dc001b0d080f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Distributed Configuration Files\n",
-      "--------------------\n",
-      "\n",
-      "0.\tdc_AEC_1d_2c_1s_4r_4w.json\n",
-      "1.\tdc_dist_14d.json\n",
-      "2.\tdc_dist_2d_3c_2s_3r_6w.json\n",
-      "3.\tdc_fed_dist_14d.json\n",
-      "4.\tdc_fed_dist_2d_3c_2s_3r_6w.json\n",
-      "5.\tdc_fed_synt_1d_2c_2r_1s_4w_1ws.json\n",
-      "6.\tdc_synt_8d_8w_2c_4s_4r.json\n",
-      "7.\tdc_synt_8d_8w_4c_6r_4s.json\n",
-      "8.\tdc_synt_distributed_w5_c3_6r_3s_3d.json\n",
-      "9.\tdc_test_synt_1d_2c_1s_4r_4w.json\n",
-      "10.\tdc_test_synt_1d_2c_2s_4r_4w.json\n",
-      "\n",
-      "Connection Map Files\n",
-      "--------------------\n",
-      "\n",
-      "0.\tconn_1Router1Client1S.json\n",
-      "1.\tconn_1Router1Client2S.json\n",
-      "2.\tconn_1Router2Clients1S.json\n",
-      "3.\tconn_1Router3Clients1S.json\n",
-      "4.\tconn_1Router4Clients1S.json\n",
-      "5.\tconn_1Router4Clients1fed.json\n",
-      "6.\tconn_1Router4Clients2Sources.json\n",
-      "7.\tconn_1Router4Clients2Sources1fed.json\n",
-      "8.\tconn_2R4C1S_health_david.json\n",
-      "9.\tconn_2Router2Clients1Source.json\n",
-      "10.\tconn_2Router2Clients1Source_david.json\n",
-      "11.\tconn_2Router2Clients2Source.json\n",
-      "12.\tconn_2Router2ClientsGUI.json\n",
-      "13.\tconn_2Router3Clients.json\n",
-      "14.\tconn_3Router3Clients.json\n",
-      "15.\tconn_6RouterCycle6Clients1Source.json\n",
-      "16.\tconn_6RouterCycle8Clients1Source.json\n",
-      "17.\tconn_6RouterLine6Clients1Source.json\n",
-      "18.\tconn_8RouterCycle8Clients1Source.json\n",
-      "19.\tconn_fed_dist_14d.json\n",
-      "20.\tconn_fed_dist_2d_3c_2s_3r_6w.json\n",
-      "21.\tconn_fed_synt_1d_2c_2r_1s_4w_1ws.json\n",
-      "22.\tconn_synt_8d_8w_4c_6r_4s.json\n",
-      "23.\tconn_synt_dc_8d_8w_2c_4s_4r.json\n",
-      "24.\tconn_synt_distributed_w5_c3_6r_3s_3d.json\n",
-      "25.\tconn_test_synt_1d_2c_1s_4r_4w.json\n",
-      "26.\tconn_test_synt_1d_2c_2s_4r_4w.json\n",
-      "\n",
-      "Experiments Flow Files\n",
-      "--------------------\n",
-      "\n",
-      "0.\texp_AEC_1d_2c_1s_4r_4w.json\n",
-      "1.\texp_dist_14d.json\n",
-      "2.\texp_dist_2d_3c_2s_3r_6w.json\n",
-      "3.\texp_fed_dist_14d.json\n",
-      "4.\texp_fed_dist_2d_3c_2s_3r_6w.json\n",
-      "5.\texp_fed_synt_1d_2c_2r_1s_4w_1ws.json\n",
-      "6.\texp_new_arc.json\n",
-      "7.\texp_synt_8d_8w_2c_4s_4r.json\n",
-      "8.\texp_synt_8d_8w_4c_6r_4s.json\n",
-      "9.\texp_synt_distributed_w5_c3_6r_3s_3d.json\n",
-      "10.\texp_test_synt_1d_2c_1s_4r_4w new.json\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "API.showJsons()"
+    "# API.showJsons()"
    ]
   },
   {
@@ -168,8 +39,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dc = 0\n",
-    "conn_map = 25\n",
+    "dc = 1\n",
+    "conn_map = 26\n",
     "exp_flow = 0"
    ]
   },
@@ -208,6 +79,16 @@
   {
    "cell_type": "code",
    "execution_count": 7,
+   "id": "b52c1de4-afbd-4135-ac26-a62ae78d81a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# API.add_repo_to_datasets_list('Nerlnet/forest_cover', 'ForestCover', 'Dataset for AEC')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "id": "0d355a9b-d044-4a6a-a539-1238ce4d021a",
    "metadata": {},
    "outputs": [
@@ -215,9 +96,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0. Nerlnet/skab: ['skab_full.csv', 'skab_full_bins.csv', 'skab_full_poly.csv']\n",
+      "0. Nerlnet/skab: ['skab_full.csv', 'skab_full_bins.csv', 'skab_full_poly.csv', 'skab_full_windowed.csv']\n",
       "1. Nerlnet/MNist: []\n",
-      "2. Nerlnet/synthetic_norm: ['synthetic_full.csv']\n"
+      "2. Nerlnet/synthetic_norm: ['synthetic_full.csv']\n",
+      "3. Nerlnet/forest_cover: ['cover_normalized_std.csv', 'forest_cover_bins.csv', 'forest_cover_dataset.csv', 'forest_cover_reversed.csv']\n"
      ]
     }
    ],
@@ -227,19 +109,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "ad1e3b36-cd96-403a-a9d5-7eb883b5a501",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fc819b136e31479cbcb18541c3d0eff6",
+       "model_id": "69e3b6d80fc74df487504a74ce38679c",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Fetching 5 files:   0%|          | 0/5 [00:00<?, ?it/s]"
+       "Fetching 6 files:   0%|          | 0/6 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -249,17 +131,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[INFO][2024-07-07 21:39:26,099] Files downloaded to /tmp/nerlnet/data/NerlnetData-master/nerlnet/skab\n"
+      "[INFO][2024-07-30 23:19:17,109] Files downloaded to /tmp/nerlnet/data/NerlnetData-master/nerlnet/ForestCover\n"
      ]
     }
    ],
    "source": [
-    "API.download_dataset(0)"
+    "API.download_dataset(3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "5d4c5ca9-1433-40a6-8312-9a174ed32ee2",
    "metadata": {},
    "outputs": [
@@ -267,58 +149,58 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[INFO][2024-07-07 21:39:26,439] \n",
+      "[INFO][2024-07-30 23:19:21,820] \n",
       "Network components:\n",
       "                 Receiver's Address: http://10.0.0.5:8082\n",
-      "                 Frequency: 200 [batches/sec]\n",
+      "                 Frequency: 300 [batches/sec]\n",
       "                 Batchsize: 100 [samples]\n",
       "                 devicesIp: ['10.0.0.5']\n",
       "                 mainServerIp: 10.0.0.5\n",
       "                 mainServerPort: 8081\n",
       "                 apiServerIp: 10.0.0.5\n",
       "                 apiServerPort: 8082\n",
-      "                 Clients: ['c1', 'c2']\n",
-      "                 Workers: ['w1', 'w2', 'w3', 'w4']\n",
+      "                 Clients: ['c1']\n",
+      "                 Workers: ['w1', 'w2']\n",
       "                 Sources: ['s1']\n",
       "                 Routers: ['r1', 'r2', 'r3', 'r4']\n",
-      "[INFO][2024-07-07 21:39:26,440] Connections:\n",
-      "[INFO][2024-07-07 21:39:26,440] \t\t r1 : ['mainServer', 'r2']\n",
-      "[INFO][2024-07-07 21:39:26,442] \t\t r2 : ['r3', 's1']\n",
-      "[INFO][2024-07-07 21:39:26,443] \t\t r3 : ['r4', 'c1']\n",
-      "[INFO][2024-07-07 21:39:26,444] \t\t r4 : ['r1', 'c2']\n",
-      "[INFO][2024-07-07 21:39:26,445] Experiment name: anomaly_detection_skab\n",
-      "[INFO][2024-07-07 21:39:26,446] Batch size: 100\n",
-      "[INFO][2024-07-07 21:39:26,448] Number of features: 8\n",
-      "[INFO][2024-07-07 21:39:26,449] Number of labels: 1\n",
-      "[INFO][2024-07-07 21:39:26,450] \n",
-      "[INFO][2024-07-07 21:39:26,451] Phases:\n",
-      "[INFO][2024-07-07 21:39:26,452]    Phase name: training_phase\n",
-      "[INFO][2024-07-07 21:39:26,453]    Phase type: training\n",
-      "[INFO][2024-07-07 21:39:26,454]    Sources: s1\n",
-      "[INFO][2024-07-07 21:39:26,454] \n",
-      "[INFO][2024-07-07 21:39:26,456]     Source pieces:\n",
-      "[INFO][2024-07-07 21:39:26,456]          Source name: s1\n",
-      "[INFO][2024-07-07 21:39:26,457]          Batch size: 100\n",
-      "[INFO][2024-07-07 21:39:26,457]          Phase: training\n",
-      "[INFO][2024-07-07 21:39:26,460]          Starting offset: 0\n",
-      "[INFO][2024-07-07 21:39:26,460]          Number of batches: 120\n",
-      "[INFO][2024-07-07 21:39:26,462]          Workers target: w1,w2,w3,w4\n",
-      "[INFO][2024-07-07 21:39:26,462]       ----------------------\n",
-      "[INFO][2024-07-07 21:39:26,464] \n",
-      "[INFO][2024-07-07 21:39:26,464]    Phase name: prediction_phase\n",
-      "[INFO][2024-07-07 21:39:26,465]    Phase type: prediction\n",
-      "[INFO][2024-07-07 21:39:26,466]    Sources: s1\n",
-      "[INFO][2024-07-07 21:39:26,466] \n",
-      "[INFO][2024-07-07 21:39:26,467]     Source pieces:\n",
-      "[INFO][2024-07-07 21:39:26,468]          Source name: s1\n",
-      "[INFO][2024-07-07 21:39:26,468]          Batch size: 100\n",
-      "[INFO][2024-07-07 21:39:26,468]          Phase: prediction\n",
-      "[INFO][2024-07-07 21:39:26,469]          Starting offset: 12000\n",
-      "[INFO][2024-07-07 21:39:26,470]          Number of batches: 60\n",
-      "[INFO][2024-07-07 21:39:26,471]          Workers target: w1,w2,w3,w4\n",
-      "[INFO][2024-07-07 21:39:26,471]       ----------------------\n",
-      "[INFO][2024-07-07 21:39:26,472] \n",
-      "[INFO][2024-07-07 21:39:26,472] Initializing ApiServer receiver thread\n"
+      "[INFO][2024-07-30 23:19:21,821] Connections:\n",
+      "[INFO][2024-07-30 23:19:21,823] \t\t r1 : ['mainServer', 'r2']\n",
+      "[INFO][2024-07-30 23:19:21,824] \t\t r2 : ['r3', 's1']\n",
+      "[INFO][2024-07-30 23:19:21,824] \t\t r3 : ['r4', 'c1']\n",
+      "[INFO][2024-07-30 23:19:21,825] \t\t r4 : ['r1', 'c2']\n",
+      "[INFO][2024-07-30 23:19:21,826] Experiment name: anomaly_detection_skab\n",
+      "[INFO][2024-07-30 23:19:21,826] Batch size: 100\n",
+      "[INFO][2024-07-30 23:19:21,828] Number of features: 10\n",
+      "[INFO][2024-07-30 23:19:21,828] Number of labels: 1\n",
+      "[INFO][2024-07-30 23:19:21,829] \n",
+      "[INFO][2024-07-30 23:19:21,830] Phases:\n",
+      "[INFO][2024-07-30 23:19:21,830]    Phase name: training_phase\n",
+      "[INFO][2024-07-30 23:19:21,831]    Phase type: training\n",
+      "[INFO][2024-07-30 23:19:21,832]    Sources: s1\n",
+      "[INFO][2024-07-30 23:19:21,832] \n",
+      "[INFO][2024-07-30 23:19:21,833]     Source pieces:\n",
+      "[INFO][2024-07-30 23:19:21,833]          Source name: s1\n",
+      "[INFO][2024-07-30 23:19:21,834]          Batch size: 100\n",
+      "[INFO][2024-07-30 23:19:21,834]          Phase: training\n",
+      "[INFO][2024-07-30 23:19:21,835]          Starting offset: 0\n",
+      "[INFO][2024-07-30 23:19:21,835]          Number of batches: 2500\n",
+      "[INFO][2024-07-30 23:19:21,836]          Workers target: w1\n",
+      "[INFO][2024-07-30 23:19:21,836]       ----------------------\n",
+      "[INFO][2024-07-30 23:19:21,836] \n",
+      "[INFO][2024-07-30 23:19:21,837]    Phase name: prediction_phase\n",
+      "[INFO][2024-07-30 23:19:21,837]    Phase type: prediction\n",
+      "[INFO][2024-07-30 23:19:21,838]    Sources: s1\n",
+      "[INFO][2024-07-30 23:19:21,838] \n",
+      "[INFO][2024-07-30 23:19:21,838]     Source pieces:\n",
+      "[INFO][2024-07-30 23:19:21,839]          Source name: s1\n",
+      "[INFO][2024-07-30 23:19:21,839]          Batch size: 100\n",
+      "[INFO][2024-07-30 23:19:21,840]          Phase: prediction\n",
+      "[INFO][2024-07-30 23:19:21,840]          Starting offset: 250000\n",
+      "[INFO][2024-07-30 23:19:21,841]          Number of batches: 600\n",
+      "[INFO][2024-07-30 23:19:21,841]          Workers target: w1\n",
+      "[INFO][2024-07-30 23:19:21,841]       ----------------------\n",
+      "[INFO][2024-07-30 23:19:21,842] \n",
+      "[INFO][2024-07-30 23:19:21,842] Initializing ApiServer receiver thread\n"
      ]
     },
     {
@@ -333,18 +215,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[INFO][2024-07-07 21:39:28,474] *** Remember to execute NerlnetRun.sh on each device before running the experiment! ***\n"
+      "[INFO][2024-07-30 23:19:23,846] *** Remember to execute NerlnetRun.sh on each device before running the experiment! ***\n"
      ]
     }
    ],
    "source": [
-    "exp_name = \"SKAB_AnomalyDetection\"\n",
+    "exp_name = \"ForestCover_AnomalyDetection\"\n",
     "API.initialization(exp_name , dc_path , conn_map_path , exp_flow_path)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "3fea1c44-c12b-416b-adeb-75605701cfdf",
    "metadata": {},
    "outputs": [
@@ -352,7 +234,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[INFO][2024-07-07 21:39:28,708] Sending distributed configurations to devices is completed\n"
+      "[INFO][2024-07-30 23:19:24,687] Sending distributed configurations to devices is completed\n"
      ]
     }
    ],
@@ -362,7 +244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "48df76ba-a0be-4d67-b261-531d77fbc928",
    "metadata": {},
    "outputs": [
@@ -370,70 +252,128 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[INFO][2024-07-07 21:39:28,713] Experiment phase: training_phase of type training starts running...\n",
-      "[INFO][2024-07-07 21:39:28,714] Sending data to sources\n",
-      "[INFO][2024-07-07 21:39:30,122] Data is ready in sources\n",
-      "[INFO][2024-07-07 21:39:30,123] Phase training requested from Main Server\n",
-      "[INFO][2024-07-07 21:39:33,790] Processing experiment phase data\n",
-      "[INFO][2024-07-07 21:39:33,798] Processing experiment phase data completed\n",
-      "[INFO][2024-07-07 21:39:33,799] Start generating communication statistics for training_phase of type training\n",
-      "[INFO][2024-07-07 21:39:33,799] Statistics requested from Main Server\n",
-      "[INFO][2024-07-07 21:39:34,204] Statistics received from Main Server\n",
-      "[INFO][2024-07-07 21:39:34,205] Phase of training_phase training completed\n"
+      "[INFO][2024-07-30 23:19:25,279] Experiment phase: training_phase of type training starts running...\n",
+      "[INFO][2024-07-30 23:19:25,281] Sending data to sources\n",
+      "[INFO][2024-07-30 23:20:34,218] Data is ready in sources\n",
+      "[INFO][2024-07-30 23:20:34,382] Phase training requested from Main Server\n",
+      "[INFO][2024-07-30 23:20:50,408] Processing experiment phase data\n",
+      "[INFO][2024-07-30 23:20:50,597] Processing experiment phase data completed\n",
+      "[INFO][2024-07-30 23:20:50,599] Start generating communication statistics for training_phase of type training\n",
+      "[INFO][2024-07-30 23:20:50,601] Statistics requested from Main Server\n",
+      "[INFO][2024-07-30 23:20:50,657] Statistics received from Main Server\n",
+      "[INFO][2024-07-30 23:20:50,658] Phase of training_phase training completed\n"
      ]
     }
    ],
    "source": [
-    "API.get_experiment_flow(exp_name).get_csv_dataset().set_num_of_labels(0)\n",
+    "API.get_experiment_flow(exp_name).get_csv_dataset().set_num_of_labels(0) # When using Unsupervised Learning like AE/AEC\n",
     "API.run_current_experiment_phase()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "5b65bf8e-fecb-4b3d-a1f6-bd22e91c2540",
    "metadata": {},
    "outputs": [],
    "source": [
-    "train_stats = API.get_experiment_flow(exp_name).generate_stats()\n",
-    "train_stats_aec = API.get_experiment_flow(exp_name).generate_stats_aec(train_stats)"
+    "train_stats1 = API.get_experiment_flow(exp_name).generate_stats()\n",
+    "train_stats_aec1 = API.get_experiment_flow(exp_name).generate_stats_aec(train_stats1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "ebf84a99-a6a0-4249-9756-7a473b678134",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'messages_received': 504,\n",
-       " 'messages_sent': 9,\n",
-       " 'messages_dropped': 0,\n",
-       " 'bytes_received': 0,\n",
-       " 'bytes_sent': 0,\n",
-       " 'bad_messages': 0}"
+       "OrderedDict([('w1',\n",
+       "              {'bytes_received': 0,\n",
+       "               'bytes_sent': 0,\n",
+       "               'bad_messages': 0,\n",
+       "               'batches_received_train': 2495,\n",
+       "               'batches_received_predict': 0,\n",
+       "               'batches_dropped_train': 5,\n",
+       "               'batches_dropped_predict': 0,\n",
+       "               'batches_sent_train': 0,\n",
+       "               'empty_batches': 0,\n",
+       "               'batches_sent_predict': 0,\n",
+       "               'average_time_training': 0,\n",
+       "               'average_time_prediction': 0,\n",
+       "               'acc_time_training': 0,\n",
+       "               'acc_time_prediction': 0,\n",
+       "               'nan_loss_count': 0}),\n",
+       "             ('w2',\n",
+       "              {'bytes_received': 0,\n",
+       "               'bytes_sent': 0,\n",
+       "               'bad_messages': 0,\n",
+       "               'batches_received_train': 0,\n",
+       "               'batches_received_predict': 0,\n",
+       "               'batches_dropped_train': 0,\n",
+       "               'batches_dropped_predict': 0,\n",
+       "               'batches_sent_train': 0,\n",
+       "               'empty_batches': 0,\n",
+       "               'batches_sent_predict': 0,\n",
+       "               'average_time_training': 0,\n",
+       "               'average_time_prediction': 0,\n",
+       "               'acc_time_training': 0,\n",
+       "               'acc_time_prediction': 0,\n",
+       "               'nan_loss_count': 0})])"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "train_stats.get_communication_stats_main_server()"
+    "train_stats1.get_communication_stats_main_server()\n",
+    "train_stats1.get_communication_stats_workers()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
+   "id": "489636c2-afde-4651-8eb4-3c6a57d881bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Contains 310000 labels\n",
+      "Anomaly Count: 29309\n",
+      "Normal Count: 280691\n"
+     ]
+    }
+   ],
+   "source": [
+    "labels = []\n",
+    "with open('/tmp/nerlnet/data/NerlnetData-master/nerlnet/ForestCover/cover_normalized_std.csv') as f:\n",
+    "    for i in range(310000):\n",
+    "        line = f.readline()\n",
+    "        label = line.split(',')[-1]\n",
+    "        label = label.rstrip()\n",
+    "        labels.append(float(label))\n",
+    "print(f'Contains {len(labels)} labels')\n",
+    "anomaly_count = len([label for label in labels if label == 1.0])\n",
+    "normal_count = len([label for label in labels if label == 0.0])\n",
+    "print(f'Anomaly Count: {anomaly_count}')\n",
+    "print(f'Normal Count: {normal_count}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
    "id": "52a6af95-a096-43b3-ba96-f4e7f7720f3e",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -443,164 +383,980 @@
     }
    ],
    "source": [
-    "loss_dict = train_stats_aec.get_aec_loss(plot=True)\n",
-    "# train_stats_aec.get_boundaries(plot=True)"
+    "loss_dict = train_stats_aec1.get_aec_loss(plot=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "id": "d5cbe22b-0ea2-43f0-9f07-d784f11298cd",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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2NtxwQxxyyCFhNn7nnXfGSy+9hEceeQSrV69GR0cHtt9+e1xxxRXYeOONK77GjjvuiIcffjgs7wf8wYJz587Fn/70p4LbA4899hjuv//+8OdXXnkl3Eaw3nrrMeAnIprkhGmkVAQRERGNui9+8Yt47bXX8Nhjj433oRAREdEoYg8/ERHRJJbNZgt+fvPNN/HUU09h5513HqcjIiIiorHCkn4iIqJJbN9998WnP/1pbLzxxnjvvfdwzz33IBqNFpS6ExER0eTEgJ+IiGgS22OPPfDwww9j1apViMVimDt3Ls4++2xsttlm431oRERENMrYw09EREREREQ0CbGHn4iIiIiIiGgSYsBPRERERERENAkx4CciIiIiIiKahDi0bwQYY6B1449CsCwxIY6TGgvPG6oHzxuqB88bqgfPG6oHzxuqRyOdN5YlIISoeD8G/CNAa4Pu7tR4H8aQIhELU6e2oa8vDSn1eB8OTRA8b6gePG+oHjxvqB48b6gePG+oHo123kyb1gbbrhzwN1xJ/+uvv44TTjgBc+fOxe67747LLrsMrutWfJwxBjfccAP22msvbL/99jjiiCPw/PPPl72/1hqHHnoott56azz66KMj+A6IiIiIiIiIxl9DBfy9vb047rjj4Hkeli5dirPOOgv33XcfLr300oqPvfHGG3HllVfi+OOPx/XXX48ZM2bgxBNPxDvvvFPy/vfccw9WrFgx0m+BiIiIiIiIqCE0VMB/zz33IJVK4aqrrsIee+yBww47DF/96lcrBueO4+D666/HiSeeiOOPPx4LFizAkiVLMGXKFNx0001F9+/u7saPf/xjnH322aP5doiIiIiIiIjGTUMF/E899RQWLFiAKVOmhLcdcMAB0Frj6aefLvu45557DslkEgcccEB4WywWw3777Yennnqq6P5LlizBLrvsgl122WVEj5+IiIiIiIioUTRUwP/GG29g1qxZBbd1dnZixowZeOONN4Z8HICix26xxRZYvnw5stlseNuLL76Ihx56CF/72tdG8MiJiIiIiIiIGktDTenv6+tDZ2dn0e1dXV3o7e0d8nGxWAzxeLzg9s7OThhj0Nvbi0QiAa01LrzwQpxwwgnYaKON8O67747YsUciDXXtpIhtWwX/TVQNnjdUD543VA+eN1QPnjdUjtYKUioAxSvUlLKQzWahlAetx3/aOk0MY3feCEQiNizLHpFna6iAf7T97Gc/w+rVq3HqqaeO6PNalsDUqW0j+pyjpbOzZbwPgSYgnjdUD543VA+eN1QPnjcUMMbg/fffR09PD8wQ69I5u5vqMVbnjRDAlClTsP7660OIyqv3htJQAX9nZyf6+/uLbu/t7UVXV9eQj3NdF47jFGT5+/r6IIRAV1cXUqkUlixZgrPOOgue58HzPCSTSQBANptFMplEe3t7XcettUFfX7qux44V27bQ2dmCvr4MlOKVTKoOzxuqB88bqgfPG6oHzxsabO3a1Uilkmhvn4JYLF4yWBLCT9hpbYa8KECUb6zOG2MMXNfBmjVrkc16mDp1nZL36+xsqaq6qaEC/lmzZhX16vf392PVqlVF/fmDHwcAy5YtwzbbbBPe/sYbb2CDDTZAIpHAu+++i56eHnznO9/Bd77znYLHf/3rX8c666wz5GDASqScGP8no5SeMMdKjYPnDdWD5w3Vg+cN1YPnDQF+GX8q1Y/29qloby9uE84XiVg8Z6hmY3XexGJ+EjuZXIu2timwrPrblhoq4F+0aBGuu+66gl7+Rx99FJZlYffddy/7uHnz5qG9vR2/+tWvwoDf8zw89thjWLRoEQBgxowZuO222woet3r1apx99tk488wzsdtuu43SuyIiIiIiotGmlAIwECwRTWTBeayUhGXF6n6ehgr4jzzySNx+++04/fTTsXjxYqxYsQKXXXYZjjzySMycOTO833HHHYfly5fj8ccfBwDE43EsXrwYS5cuxbRp07DVVlvh7rvvRk9PD0466aTwPoPX8AVD+z70oQ9h3rx5Y/QuiYiIiIhotAy355moEYzUedxQAX9XVxduvfVWXHTRRTj99NPR1taGww47DGeddVbB/bTW4RW8wCmnnAJjDH7605+iu7sb2267LW666SZsvPHGY/kWiIiIiIiIiBqCMIajKoZLKY3u7tR4H8aQIhELU6e2Ye3aFPuVqGo8b6gePG+oHjxvqB48byif57lYs+Z9TJ++PqLRoUugG72H/+KLL8Crr76C22+/r+h3P/7x/+IPf3gCP//5g2N+XPVauHB+wc9Tp07Dttt+BIsXn44ttvjQOB1VeffddxeuvHIJ/vjHZwtuH8vzptL5PG1a28Qb2kdERERERESTz2GHHYF9990fgMHKlStx++0/xdlnn4477vg5Ojo6xvvwJi0G/ERERERERDQsjpNFPJ4o+/t1110Ps2dvF/688cab4IQTjsbLL7+ABQsWjsUhjjnXdRGJRIY1ZX+4xu+ViYiIiIiIaFgeeeRBLFw4Hy+//BK+9KXTsM8+u+Owww7GQw89UHC/iy++AMceezj+/Oenceyxh2PvvXfDiSceg5dffqnkcx533JHYe+/d8KlPHYDrr7+6YIbawGu+iP/5ny9i330X4uqrf1zTcbe2tgIApJQFtz/55O9w/PFHY++9d8MnP7k/li5dAsdxil67p6en4HHHH380Lr74gqL3+9xzz+KEE47GvvsuxCmnfB6vvvqvgselUklcdNH52G+/RTjooH1xzTU/hpSF8+IymQyWLPkBDj/80+Hne/nllyCZTBbc77DDDsaSJT/AnXfeis985iDss8/ueOmlF7Bw4Xz87W/PFNxXKYVPfeoAXHNNbZ9brZjhJyIiIiIimuAuuOA8fPKTh+JznzsOv/3tY7j00ouwzjozsOuuA+vH16xZgyVLfoATTzwVHR0duOOOW/GVr5yBe+65H1OnTgMA3HPPHbj22qU4/PCjccYZ/4M333wTN9xwDbTW+MIXzix4zQsv/BYOOeTT+PznTxwyuw8AxugwuF+1aiWuvfZKTJkyBTvsMNDf/8c/Polvfevr2Gefj+G0087A22+/ieuvvxorVnyA733vspo/k+7uNfjxj6/A5z53PNrb23H99VfhvPPOwX33PYBIxA+Fv//97+Ivf3kGp512BjbYYAPcf//P8d///rrgebLZLLTWOO2009HR0YWVK1fgttt+im984ytYuvT6gvs++eTvsNFGm+DLXz4HlmVhm222xYc/PBsPPfRL7LTTruH9/vKXP2P16lU48MBP1vy+asGAn4iIiIiIJi1jDFyvcNCa0mbMhq/FotaYrArcf/8DceyxJwAAdtllAZYvfw8333xjQcDf19eLiy66FDvuuBMAYO7cHXHooQfi3nvvwmmnnYF0OoWbbroBRx/9eSxefDoAYKeddkU0GsHSpT/E0Ucfi66uKeHzffKTh+KYY46v6viuvXYprr12afhzZ2cXLrnkcrS3t4e3/fSnN+AjH9kOF1xwMQBg1113QzyewOWXX4LXX3+t5gF/fX19WLr0BsyatQUAIJFI4EtfOg3//OfLmDNnLpYtewNPPvl7fP3r38JBB/mB9847L8CRRx5a8DxTp07FOed8IxzaJ6XE+utvgC9+8WS8/fZb2GSTTcP7SilxxRVXoqWlJbztkEM+hSVLLkdfXx86OzsBAA8//AC22257bLrpZjW9p1ox4CciIiIioknJGIPv3/EcXnuvd9yO4UMbdeEbn5s36kH/okV7Ffy855574+qrfwSlFGzbBgC0t7eHwX7w8/z5O+OVV14GALz00ovIZNL46Ef3KSi1nz9/FziOgzfeeB077LBjePtuu1Xfe//Zzx6Fj3/8AADA2rXduP/+n+Pcc7+Cq666AR/60JZIp9P473//g9NP/3LB4/bZ52O4/PJL8OKLz9cc8K+zzoww2AeAzTefBQBYtWoFAODVV1+BMQaLFn00vI9t21i0aE/ce+9dBc/16KMP47777sI777yNTCYT3v7OO28XBPw77LBjQbDvv4eP48orf4jHH38Un/nM4ejp6cHTT/8B55zzjZreTz0Y8BMRERER0eQ1+sn1EWfbNrQuXYGgtQrL0fMFJfmBadOmQUqJ3t4eTJs2HQAwZcrUosdNmzYNb721DADQ29sDADjxxGNKvvbKlSsGveb0od9Inhkz1sU223w4/HnHHXfGoYd+ArfcciO+973LkEz2wxgTHmugvb0dsVgMfX21X7TJrx4AgEgkCsAfpgcAq1evRiQSCbPugcGf5ZNP/h7f+9538KlPHYpTTvkCOjunYM2a1TjvvHPgus6gxxZ/Ji0tLdh334/h4YcfwGc+czgee+wRRKMx7L33fjW/p1ox4CciIiIioklJCIFvfG5eUUn/WO5Tr6ekf8qUqVizZk3J361evQpTpxYH7mvXdmPGjHXDn7u7uxGJRApK8Ht61hY9rru7G9OnrwMA6OjwA9+LL74cM2fOLLrv+utvUPDzcKoWYrEY1l9/Qyxb9gYAoL29A0IIrF3bXXC/ZDIJ13XR2dkVPg4ApPQK7tff31fzMayzzjqQUhaU2gMoOobf//432HLLrXDuud8Kz5t//OPvJZ+z3EdyyCGfxi9/eT/++9//4OGHH8Tee+8bDi4cTZzST0REREREk5YQAvGYPW7/qSconjt3HpLJfjz//HMFt6dSSTz33N8xZ868osc89dQTBT8/+eTvsPXW24bl/IAfPP/9738r+PnZZ/+KD394NgBg9uztkUgksGrVCmyzzYeL/pN/8WC4HMfB8uXvhs/Z2tqKLbfcCk888duC+/3ud48DALbffi4AYMYM/0LEm28uC+/z5pvLiqoPqhFUHDz11O/D25RSeOqpJ4uONagOCDz22KM1v9aWW26FH//4Crz++n9x4IGH1Hy89WCGn4iIiIiIqIHsvPOumDNnB3zzm1/F8cefglmztsDq1atw1123wbYtHHbYkUWPefTRhxGPx7HVVtvgt799DM8//xwuv/xHBffp7OzCpZdeVDCl3xiDww8/CgDQ0dGBk046DddcsxQrV67EDjvsCNu2sXz5u/jDH57CxRdfhkRi6Gn85axc+UG4ArCnZy3+7/9+ht7eXnzqU58J73PiiafiG984B9/97rfxsY8dgLfffgs33HA19tpr77B//yMfmY11152JpUuXYPHiM5BKJXHHHbeiq6ur5mPafPNZWLToo7jyyiVwXRfrr78+7r//50XVAzvttAuWLPkBfvrTG7HttrPxzDNP4+9//2vNr3fwwZ/GkiU/wCabbBpewBhtDPiJiIiIiIgaiGVZuPzyH+EnP7kO99xzB1avXoX29nbMm7cTvve9y7DOOusUPeaCCy7GddddhZtv/gmmTp2Kr33tm1iwoHCo3vTp0/GFL3wJ11zzY7z33rvYfPNZWLJkaUHf/FFHHYMZM2bg3nvvxC9+cS8ikQg23HAj7LbbHiVnB1Tr5z+/Fz//+b0A/PL9zTbbHJdcckXBsMGFC/fERRddiptv/gm+8Y2voLOzE4cc8mksXnxGeJ9IJIJLLrkC//u/38e3v/11bLTRxjjzzLNx1VU/quu4vvGN8/HDH16Ga6+9ErFYDPvvfxDmzt0R11zz4/A+n/zkoVi+/D387Gf3wHFc7LzzrvjOdy7G4sXH1/RaixZ9FEuW/GDMsvsAIIwxZsxebZJSSqO7OzXehzGkSMTC1KltWLs2NWb9SjTx8byhevC8oXrwvKF68LyhfJ7nYs2a9zF9+vqIRmND3ncse/hH2yOPPIhLLrkQDz30G0yZMqXs/S6++AK8+uoruP32+8bu4CaZ4Z43Dz30AC6//BL83/89HM5NKKfS+TxtWhtsu3KHPjP8RERERERERKPk/feX491338att96Effb5WMVgfyQx4CdqAhlHojflYr1poz8JlIiIiIiIBvz0pzfg8ccfxezZ2+OMM/5nTF+bJf0jgCX91Oh6kw7W9GUxa4PahpnwvKF68LyhevC8oXrwvKF8zVrST2NnLM+bkSrp51o+oiagDaC0geb1PSIiIiKipsGAn6gJaG2gtQELeoiIiIiImgcDfqImoI2B0gaM94mIiIiImgcDfqImoI2BNmCGn4iIiIioiTDgJ2oCSvrl/JrxPhERERFR02DAT9QEpNYwzPATERERETUVBvxETUApDRj28BMRERERNRMG/ERNQGoDDU7pJyIiIpoIbrrpeuy33x7jfRgj4uKLL8DChfPD/+y770Icd9xReOih/zfeh1ZSf38/Fi6cj0ceeXC8D2VERMb7AIho9CnlZ/fZw09EREREY22DDTbE+ed/DwCQyaTw5JNP4NJLv4dEogX77vvxcT66yY0BP9Ekp7WBNn52nxl+IiIiIhppjpNFPJ4o+/t4PI7Zs7cLf95pp13xz3++iCef/P2kDvgrfS5jgSX9RJOcNgYGAAzYw09EREQ0Sbz++ms4++wzsO++C/Hxj++Jb33ra/jggw/C33//+9/FF794cvhzT08P9thjJ5x88ufD29LpNPbccxf87ne/CW97881lOPfcs/Hxj++JffddiK9+9ct47713C1574cL5uP32W3DNNVfikEM+joMO+ljNx9/a2gYpZcFtH3zwPr71ra+Fr3322Wfg9ddfK3rtu+66veC2++67CwsXzg9/fu65Z7Fw4Xz87W/P4IILvon99luEz3zmINx5561Fx/HLX96Pww47GPvsszu+/OUv4L333im6z69+9RC+8IWT8LGP7YX99/8ozjjjVLzyyssF9wnaMF555WUsXnwC9t57N/ziFz/DiScegwsv/FbRc15zzZX45Cf3h1Kq8oc1DMzwE01yWhsY7a/kY4afiIiImo0xBpDuoNssGKnH5gAiMQghRvQpV6z4AKeffgo23HAjfPvbF8F1Hdxww7U488xTceutd6O1tQ1z587D448/CsdxEI/H8cILzyEajeG///030ukUWlvb8PLLL0IphblzdwAAvPfeuzjttBMxa9YWOO+8C2BZArfd9lN8+ctfwF13/QKxWCw8hp///G58+MPb4dxzvw2lZLlDDQXBfSaTwZNP/g4vvfQCvvWtC8Pfp9MpnHnmYgghcM4530AsFsdtt/0Up59+Cm699W7MnLlezZ/T5Zd/Hx//+CdwySWX4w9/eALXXrsUW2yxJXbddTcAwNNP/wGXXXYxPvGJg7HPPh/Dv//9L3z72+cWPc8HH7yP/fc/EJtssjGyWRe/+c2vccYZp+KWW+7GJptsGt7P8zxceOG3cPjhR2Px4tPR2dmF1tYWLF36QySTSbS3twMAlFL49a8fwQEHHATbtmt+X7VgwE80yWmTC/bh/4eIiIioWRhjkP7lxdArXqt851Fiz9wSLYecN6JB/3333QWlJH74w6vQ2dkFANhqq21wzDGfxSOPPIjDDjsSc+bsANd18corL2OHHXbE88//A4sW7YW//e0ZvPjiC9h1193w/PPPYeONN8G0adMBADfffCM6Ozvxwx9ejXg8DgCYPXsODj/8k3jooQdw6KGfDY+ho6MLl1xyeVXva9myN7DXXrsW3HbkkcfgYx87IPz54YcfxAcfvI/bb78Pm222OQBghx3m4TOfOQj33Xc3zjzzrJo/p7322hsnnbQYADB//s7485+fxhNP/DYM+G+99SbMmbMDzjvvOwCAXXZZANd1ccstPyl4nhNOOAUAEIlYcF2JnXbaBf/61z/xq189hMWLTw/vJ6XEqad+EfvsM1DxsP766+Oqq36Exx9/FJ/+9GEAgD//+WmsWbMaBx54SM3vqVYs6Sea5LQGtOFaPiIiImpOAiObXW8EL7zwPObNmx8G+wCw6aab4UMf2hIvvvgCAH9Q3rrrzsQLL/wj95jnsMMOO2L77XfA888/l7vtH5g7d174HH/72zNYuHARbNuGlBJSSnR0dGCrrbbGq6++UnAMu+66W9UXMTbccCP85Ce34Sc/uQ1XXXUDTjnlC/jFL+7FzTffmPee/oFZs7YIg30A6Ozswvz5u+DFF5+v7QPK2WmngYsMQghsuulmWLlyJQA/y/7vf/8LixbtVfCYvfbap+h53nxzGb7xjXNwwAH7YtGinbHXXrvi7bffwjvvvFV03wULFhb83NbWjr333g8PP/zL8LZHHnkQc+bsgI033qSu91ULZviJJrkgw29ZgiX9RERE1FSEEGg55Lyikv5IxIKcwCX9/f192HLLrYpunzp1Ovr6esOf586dh+effw6pVBKvvfZfzJ07D9lsBk888Vu4rot//eufOPjgT4X37+npwX333Y377ru7+G1EogU/T5s2rerjjcVi2GabDxcc19q13bjttp/iM585HJ2dXejv78fUqcXPOW3aNCxb9nrVr5Wvo6Oj4OdoNIr+/n4AQE/PWiilil5z8PtKp1M4++wzMGXKFHz5y2djxoz1EI/HcOml34PrFp5XiUQCra2tRcdxyCGfxmmnnYjXXvsvpk9fB3/60x/wta99s673VCsG/ESTnDYmrOXXDPiJiIioyQghgGi88LaIBSHGKOAfBZ2dnVi7trvo9rVr12DjjQd6yufM2QFXXfVD/OMff0dX1xRsuulmyGQyuPbapXjuuWfhui7mzNkh73m7sGDB7gWl+4HiQHZ4FzE23XRzeJ6Hd955Bx/5SBc6Ozvx9tvFGfPu7m50dHSGP8diMUjpFdwnCOJrMWXKVNi2XfQ5dncX/vzyyy9h5coV+MEPfohtt90mvFCUSiUBrFtw33IXdmbP3h6bbz4LDz/8S8ycOROxWBwf/ei+NR9zPVjSTzTJGe137wsIlvQTERERTQLbbz8Xf//739DX1xfe9vbbb+L111/D9tvPCW+bO3ceMpkM7rnnzjCw33LLrRCPx3HHHbdg3XVnYv31NwjvP3/+zli27HVsueXW2GabDxf8Z5NNNhvR9/DGG37WfsqUKeF7euON1/D222+G9+nr68Ozz/4V228/N7xtxox18dZbywqe629/+0vNr2/bNrbaahs89dQTBbc/8cRvC352nCwAvzog8NJLL+D995fX9HoHH/xpPP74r/DQQ7/EPvvsh5aWlpqPuR7M8BNNcjqY1icMS/qJiIiIJgilNH7/+98U3b7ttrNx+OFH4+GHH8TZZ5+Bz3/+RLiugxtvvBYzZ66HT3zi4PC+m266GaZOnYbnn38O//M/5wDwA93ttpuDZ575U8HQPAA46aTFOPnkz+Pss8/EIYd8GtOmTUN39xr84x/PYc6cudhvv/3rei+O4+Dll18CALiugxde+AcefPB+7LTTLthww40AAAceeDDuu+8ufPWr/4NTTvlCOKXftm0cfvhR4XPttdc++NnP7sY223wEm2yyKR577BGsWrWyruM67rgTce65X8Ell1wYTun/9a8fKbjPRz6yHVpaWrFkyQ9w3HEn4IMPVuCmm67HjBnrlnnW0vbf/xO47rql6Onpwbnnfruu460HA36iSU5rAwgBIXL/m4iIiIganus6JVfEffvb38XHP/4JXHXVDbj66h/hu9/9FizLxk477Ywzzzwbra1tBfefM2cHPPHEbzFnzsBwvrlz5+GZZ/5UUM4PABtttDFuvPFW3HjjtViy5FJkMhlMn74O5szZAVtssWXd72X58vdw2mknAPAz5TNnro+jjjoWxx57fHif1tY2LF16PZYuXYLLLrsEWitst90cXH31jQUr+Y4//mSsXduNm2++EZYlcMghh+Kzn90aV131o5qPa+HCPXHOOd/Abbf9FL/5zWP48Ic/ggsvvASnnjpwXNOmTcdFF12Kq6/+Eb72tbOx0Uab4KtfPQ933nlrTa/V2dmFuXPnYeXKlZg9e7uaj7VewjDlN2xKaXR3p8b7MIYUiViYOrUNa9emxm5ACTWEVT0ZvLMiCcsC1p3Sig1mtFV+UA7PG6oHzxuqB88bqgfPG8rneS7WrHkf06evj2g0NuR9x3RoH00awzlvUqkkPvWpT+DEE0/FUUcdU/H+lc7nadPaYNuVO/SZ4Sea5JTWgDAQQkAZ/h8bEREREdFYSadTWLZsGe6//2cQQuDAAw+u/KARxICfaJJTysASAkIIaMb7RERERERj5tVX/4Uvfek0rLvuTHzzmxegs7NrTF+fAT/RJCeVgbCCHn5G/EREREREY2XevPn44x+fHbfX51o+oklOaQ1LAAICnNlHRERERNQ8mOEnmuSk8vv3BQDFiJ+IiIiIqGkww080iRljoHMZfnAtHxERERFRU2HATzSJGQNog1yGX8DAgJs4iYiIiIiaAwN+oklMGwNjkJvS718AYLxPRERERNQcGPATTWJKG2hjIAT8gB/+RQAiIiIiIpr8OLSPaBLT2sBoA0sIGPg9/Yz3iYiIiBrfY4/9Cj/72d14++23YAwwY8YMbLfdHCxefDqmTp023odXl//+99946qkn8LnPHYdEIjHeh9MUGPATTWLGGGgYCAsQKgj4GfETERERNbI777wV1113FQ4//GicdNJpMMZg2bLX8dhjj2L16lUTOOD/D26++UZ85jNHMOAfIwz4iSYxnRvaZwkBzR5+IiIiognh5z+/FwcccBDOPPOs8LYFC3bH0Ud/HlrrcTyyYo6TRTw+PsH7eL72RMGAn2gS09oAwZR+4Uf77OEnIiIiamz9/X2YPn2dkr+zrMIxbI888iDuvfdOvPPO2+js7MIBBxyEk08+DbZth7+/5JILcd11N+OGG67GP//5EqZOnYbjjz8ZBx30yYLnevnlF3HDDdfglVdehm3bWLBgIb785a+EFQXvv78cn/3sITjvvO/gpZdewJNP/h7rrLMObrvtXvzpT3/Efffdhdde+y9c18Wmm26Gk05ajF133a3gOADgoIP2BQCst976+PnPHwQAvP76a7j66h/hxRefh23b2GmnXXDGGWdjvfXWC49v4cL5WLz4DPT39+HRRx9GJpPB448/NdyPe1JjwE80ifnBvR/gCwFosIefiIiIqNFtvfW2eOCB/8MGG2yI3XZbWDb4v+eeO3DttUtx+OFH44wz/gdvvvkmbrjhGmit8YUvnFlw3wsuOA+f/OSh+NznjsNvf/sYLr30IqyzzowwIH/55Rdx5pmLseuuu+PCC7+PbDaDG2+8Fuee+xVcf/3NBc91/fVXYcGChbjggovDioP3338Pu+++CEcddSwsS+CZZ/6Er371y/jxj6/FvHnzsWDBQhx33Em49dab8L//uxRtbe2IxaIAgBUrPsDpp5+CDTfcCN/+9kVwXQc33HAtzjzzVNx6691obW0LX/vnP78bH/7wdjj33G9DKTlin/lkxYCfaBILMvwAICD8kn4w4iciIqLmYYyBq72C2xQEpBqbfxPFrCiEEDU95itf+TrOO++r+MEPvgcAWH/9DbH77nvgiCOOxvrrbwAASKdTuOmmG3D00Z/H4sWnAwB22mlXRKMRLF36Qxx99LHo6poSPuf++x+IY489AQCwyy4LsHz5e7j55hvDgP+6667CNttsi0suuTw83lmzPoTPf/4I/PnPf8SCBQvD5/rQh7bGued+u+CYP/OZI8L/rbXGDjvMx7Jlb+CXv7wf8+bNx9SpU7HhhhsB8C9oTJkycGz33XcXlJL44Q+vQmdnFwBgq622wTHHfBaPPPIgDjvsyPC+HR1dBcdIQ2PATzSJaWP81D4G1vIxw09ERETNwhiDJc9dgzd63xq3Y5jVtRnOnveFmgLUWbM+hNtvvw/PPvsX/PWvf8Hzz/8dP//5PXjkkQdx9dU3YMstt8ZLL72ITCaNj350H0g5kOmeP38XOI6DN954HTvssGN4+6JFexW8xp577o2rr/4RlFLwPA8vvfQCTj/9y1BKhffZeONNsO66M/Gvf71SEPDvttvuRce8cuUK3HDDNXj22b9izZrV4aDorbfetuL7feGF5zFv3vww2AeATTfdDB/60JZ48cUXCgL+XXfdjcF+DRjwE01ieqCi3/+L0YA9/ERERNRkJmZwGI1GsWDBwjDQ/stf/oyvfe1/cPPNP8Ell1yO3t4eAMCJJx5T8vErV64o+HnwZP9p06ZBSone3h4opaCUwpVXLsGVVy6p4rmmF/ystca5556NZDKJk09ejA033BgtLS34yU+uw4oVH1R8r/39fdhyy62Kbp86dTr6+nqLjpuqx4CfaBLTSkMUznVhhp+IiIiahhACZ8/7QlFJf8Ru7JL+UnbZZQG22GJLvPXWMgBAR0cnAODiiy/HzJkzi+4flP4H1q7txowZ64Y/d3d3IxKJoKtrClzXhRACxx57QlElAICC1gAgLCANvfvuO/jPf/6N73//Cuyxx8DjHcep6r11dnZi7druotvXrl2DjTfedNCtE/MCznhhwE80iUmtC/5CFkBYXkVERETUDIQQiNuxgtsiEQs2Gmu9Xb7u7jWYNq0wi+44WaxcuQKbbz4LADB79vZIJBJYtWoF9tzzoxWf86mnnsBWW20T/vzkk7/D1ltvC9u20dLSgtmzt8Nbby3DNtt8sebjDQL7SCQa3vbBB+/jpZdewMYbbxLeFvzedQsvBGy//Vz88pf3o6+vD52d/oWMt99+E6+//hoOPPCQmo+HBjDgJ5rElAKsvIhfgxl+IiIiokb3+c8fid133wM777wA66yzDlatWolf/OI+9Pb24LOfPQoA0NHRgZNOOg3XXLMUK1euxA477AjbtrF8+bv4wx+ewsUXX4ZEYmBH/aOPPox4PI6tttoGv/3tY3j++edw+eU/Cn//xS9+GV/+8hdw/vnfwD77fAwdHR1YtWol/va3v+ATnzgY8+bNL3u8m266GdZddyauu+4qaK2RyaRx003XF1QUAMBmm20GAPi///sZ9thjLyQSCWyxxYdw+OFH4+GHH8TZZ5+Bz3/+RLiugxtvvBYzZ66HT3zi4JH7YJsQA34CAKSyHtoS0cp3HGdaGyhtEI1Yle9MJTL8hhl+IiIiogZ34omn4umn/4CrrvohenrWoqtrCrbYYstwxV3gqKOOwYwZM3DvvXfiF7+4F5FIBBtuuBF2220PRCKFod4FF1yM6667Cjff/BNMnToVX/vaNwsG8W233Rxcc81PcNNN1+P7378QnudhxoyZmD9/J2y00cZDHm8sFsPFF1+GJUt+gG9/+1ysu+5MHHfciXjuuWfx6quvhPfbaqttcOKJp+Khhx7AXXfdhnXXnYmf//xBzJy5Hq666gZcffWP8N3vfguWZWOnnXbGmWeeXbCSj2onDP/1P2xKaXR3p8b7MIYUiViYOrUNa9emIGVh+ZLjKry3JoUNp7chHrPH6Qir092XxereDDac0T4hLlCMt/+80wNPKrS1+J9Vd5+DTWa2Y52ulqoeP9R5Q1QOzxuqB88bqgfPG8rneS7WrHkf06evj2g0NuR9IxGrac6ZRx55EJdcciEeeug3BavwqHZjed5UOp+nTWuDbVdOgjJNSlBaQ0oNpRv/2o8rFbr7HLz1QT/60u54H05DM8ZAaz1oSIxhST8RERERUZNgwE+Q2kAqPSHWtbmeRkvchqc03v6gHz3J6iZ/NiNtDLQBrIJBpgKN/y0TEREREdFIYMBP0NpMmAy/JzVsy0JXm1/W8vYH/ejuy47zUTUmrQFtAGEVZvi1bo7yNSIiIiICPvGJg/HHPz7Lcv4mxYCfoFQuw9/gAb82Bq5UsHIBbHtrFJGIwNsr+rFybZrD6AbRxkBrU5DhF0KA8T4RERERUXPglH6C0hqeMlANHgkqpaE0EI0MRLCtiSiEkHh3VQpKG8yc1lqwhq6ZaW0AYwp6+IVAw3/PREREREQ0MhjwE6T0M8GNnuGXykArDTtWeNq2xCOwLIHlq1MwGthgBld3ALmhfcYUXABhhp+IiIgmO1Z90mQwUucxS/oJrlQwMPAafDWJ0gbSGNhWcQY/HrXR3hrF6t4MkhlvHI6u8Wjjl/XnFzwI8P8EiYiIaHKybX+9tOtyqDNNfMF5bNvDy9Ezw09wpYYQgFSNHfBLpYtK1PPFozbSGYnVPRm0JSJl7zeY4yl092b9doASFxMmKr9iQxSX9DPgJyIioknIsmy0tLQjmVwLAIjF4mX/Pai1gFL8NxHVZizOG2MMXNdBMrkWLS3tsKzh5egZ8Dc5bfze/YhtwZsIAT+GDsg7WqNYm3QwLZ1AZ26S/1CMMVjVk0Zv0sO0zgTiMXuEjnb8aWMweAefX9LP/3MjIiKiyamzcxoAhEF/OZZlcXMR1Wwsz5uWlvbwfB4OBvxNTikDrYGIbUFKDTNEBn28+QH/0MFqJGJBCGBVTwbtLdGKGfu+tIfVPVkgHGY3iQJ+bQBR+HkJAQb8RERENGkJIdDVNR0dHVOhlCx5H9sW6OpqRW9vmll+qtpYnje2HRl2Zj/AgL/JaW2gtEHEFmHPt92gAb/r6ZL9+4N1tEbRl3TRm3IxtSNe9n5SaazoTkNYfmmOnGSBsDYGg6v3BQSMMQ19YYeIiIhouCzLgmWVrvaMRCwkEglkMgqywWdYUeOYqOcNh/Y1OaU1tNGI2lbDT+p3PAW7iitdtmXBtgVW9WSGnEuwqjeD/rSLjpYoDDDprvBqg6KgXgj/drbxExERERFNfgz4m5zUAyX9Qba/EWnjbxGw7eqy0u0tUfSnXfQm3ZK/T2U9rFqbRVvCX+kHY6AafIZBrZTSGJzED37WjPiJiIiIiCY9BvxNTmt/sJtlCWjTuBl+pTS0Lr2SrxTLEojHbKzsycCTquB3WhusWJOG1hqJuN/VMhG2FNRKlgz4BQwz/ERERERETYEBf5NTyt/THgT8jZrhl8q/GFHL2ry2RATprIe1/YW7WLv7s1ibctDRFg1vs20L7gTqxamGUgbW4JJ+IOzhJyIiIiKiyY0Bf5NTWhfMvW/cgF9D6uqG9gWEEGiJR7CqJwvH9bP8GUdixdoMEjG7YB6ALQTcQZUAE53SpmRJvy7e1kdERERERJMQA/4m50mN/Bi6YUv6tQGMqHmyfEvcRsaVWNOXhTYGq3oycFyJtkS04H6WJeBJPal626XSJTL8/rwCZviJiIiIiCY/ruVrcp7UBWXyjZzhN3XkpYUQaG+JYHVvFpYlsKY3i47W4hUttiUglfHL4CMTf12dNga6xOo9IfzsPuN9IiIiIqLJjxn+JucODvgbdHCdlMUD6KqViEUgpUZ3XxaRiEA0UnzaW5aA0gZKN+b7r5XWBsYAgzsg/KF9zPATERERETUDBvxNTBsDqQb64i1LwGvQgN/xNGy7/tO1oy0KTyq0t0RL/t62BJTWDVvhUCtjDLQBhFWc4dfw+/gnIqn0pNumQEREREQ0WhjwNzGlclngIOAXArJBJ9U7nqppYN9g0YiFrvZ42RkAluWvq1NqgkbCg2jtZ/lLZfgncg9/d7+D1b2Z8T4MIiIiIqIJgQF/Ewsy2sFgNz/D33iBoNaFlQijJlfxMBloY4ASPfw+MWF7+KWnkXUm1zYFIiIiIqLRwoC/iWltoPNW3VnCL2tvtEn1UmlobUY/4BeNO7SwVsHQvsFT+n0TN8PvKQ3HY8BPRERERFQNBvxNTGq/zzss6bf83u5GW80nlYEag4BfiMadYVArrYMp/cW/MxATt4c/V5UyWSoxiIiIiIhGEwP+JqaUQf6mO0sIaG0aLsvttx4UbhMYDbYl4HmTI5D0v0JRsqRfTOAMv5Q6XJ9IRERERERDY8DfxLQ2QF48aFkCRpuGzPDDlA5eR5JliUlTLq514cWcfGKC9vAHbQpKa8hJsj6RiIiIiGg0MeBvYoN3ztuWgDINmOFXGqZc9DqCbEuE8wImOmMKL+YU/G6CZvh1rgVFKdOw2ySIiIiIiBoJA/4m5kpVsLZNCD/z22gBr6tGv5wf8DP82hRfCJmI/O+w3PcoMBHfodYmrECRLOknIiIiIqqIAX8Tk9IUB9Km8SbVe94YrOSDn+HXSk+KYFIbA1EuxT9RM/zGD/b94YqTo/WCiIiIiGg0MeBvYq4snTlvtAy/46kxyfDbloBswJaGekhdvqRfCDEh36PWBhoGkYiA407EGgUiIiIiorHFgL9JaeOvNiuVOVcNlP1VWpc9zpHmtzRMjoBfKY1yH5kAoCdg20KwMjJqW5NmuCIRERER0WhiwN+klDIwBkWZc2GhoXacK+UH4GMR8AN+MKwa6P3XSylTdquBEMAEjPdzF2IEbNsfrjgZZi0QEREREY0mBvxNyt9tb2ANCgotgYaagC6V37dt22MT8MMIvxx+giv13QaEEA3XtlENrf1dDbbltyRMhlkLRERERESjiQF/k9LaQOviUnnLEvAaKMMty1yYGC3CAqTXOO+/XlJpiDJ/uoXwWzomGq0NhAEitgWl9KSoxCAiIiIiGk0M+JuUzO00H1zSb1mioTL8SvlZ3XLl6SPNtgRcObH7w3VuDkHZDD/ExAz4c8ccrE/0mOEnIiIiIhoSA/4mpZQpuabdyk1wb5SSb6U0RNl98iPPD/gb54JHPYLS93LXSCZsD7/SgAjOBcMMPxERERFRBQz4m5Qus7bNsgSMQcNMqneVHrPsPpCrcFC6YS541MMYAzNkD//EnNIvdf4aSdFQwyWJiIiIiBoRA/4mpbQuuaa90TL8rqvGbEI/4Gf4tTYTOpjU2l9hV35Kv18SP9HK+qUcuIhhW+BqPiIiIiKiChjwNylXqpIl35aFhtpF73jjE/A3yvuvhzYGxhjkf2zaaLjaBTBQ2GEmWMCfP5fAtixkXQb8RERERERDYcDfpDxpigb2AbkMvzENkf0NVgeOZcBvWSLcDDBR6dz3l5/hz6oMurOrAfgZfgOgAb7imnhyYPOAbQt4cmK3XhARERERjTYG/E3Kk8Ur+YBcGXiD9PBLlZs2P4YBfxAkT+SBcFrnMvx5n5s2GgoaxhgI4Qf7EynDr7V/EWMgwz/xWy+IiIiIiEYbA/4mFARKQwXSjZA5VUpDaQ3bHruAHwBgBOQEXvmmSyxgMDDQRkFD5wJ+fy3jROG3KWAg4Lf9WRMT+XsiIiIiIhptDPibkNJ+sDdUwK8aYIq71AZa+/3aY0o0xvuvl3+xpvC71UZDm1yGH2LCZfhVkOEPSvotKxfwT9zviYiIiIhotDHgb0JK+73Pdrkp7miUDL8pylSPBcvyhxpOVMaYgXX1wW0w0NAw8Ev6kcuYTxRa+6sGCzYPGAb8RERERERDYcDfhPy1e+VL+oXwV6CNN6k0xDiE/LYl4HoTN5BU2mBwxK+0gjEGxmh/LR/MhMrwB4MIrYKLVAKyAS5MERERERE1Kgb8TahSSb9lCXgNkDn1PF12l/xosix/AvxENXjDgjEGr77Zh7W9HnTuAooxmFg9/GFJ/8D5YNmA68lxPCoiIiIiosbGgL8JKVViqlseS4iGKJV2pRzTlXwBW/gD4SZqH7+ShaXvy1en8NtnuvGX5zJhSf9Eyu4DpVtMbEvAcSfmd0RERERENBYY8DchpfXgmW4FLMtf2zfeQaHjlV4dONosa2JPgJdaI/9j60t7AICsM1DSD4iiSoBGpg2KLlLZlgVXqgn1PoiIiIiIxhID/iaktRkq3oclRNgzPV6k0lDajE+G3/Z3vKsJGvArVdgK4bj+AEKpMFDSj4k1tM+/SFV4Lvir+fz3S0RERERExRjwNyFXqsGxUwHLEtBmfCf1K+Vn2G17HDL8QkBpPWFL+qUuHG6Xcfw+d7+Vw/9OhRHjXsFRi1KnYsQS0EpP2EoMIiIiIqLRxoC/CXle+Qn9QC7Dr40/7X2cKO3vjbetsT9FhRCAEeP6/odDKQOR97FlvSDgBzSCixgTK8MvlS66SGVZAlLrhpg3QURERETUiBjwNyFPDV0qb1kCWutxzfBLZaB1+U0Co05gQgaS4TT7vOg4myvp1wXl7xMrw69U4VwCwL8wYyCY4SciIiIiKoMBf5PR2kCqoTP8Qvir3cYzwy2VxvimoM3EDPiNgUFhu3vWHVhdF6xbNBjfGQ21krL0ikYxQb8nIiIiIqKxwIC/yShtoE1x5lzqgaAwCKzGNcOvDYZcJTDKbEvAkxMvkNTawAzq4Q+G9gH+/AYAEBATqqTfU6bkRSohBDxv4n1PRERERERjgQF/k1G5Un07LyD0tIdV2ZXwtDfovuMY8Hsa49C+H7IsAXcCBpL+dgUUlvR7AwG/k+vnFwLjWtKvdPVrH40x0Lq4pB8AIrZAxpPFvyAiIiIiIgb8zUapXPCUFz1J7UFCQpvCAHc8M/yOJ8evfx9Bhl9NqD53wO/TN6ZwaF9hhj8XHIvqvl9Parz2bs+Il82/vzqNvpRb1X1LXcQI2JaA502874mIiIiIaCww4G8ypUr6lVGQ2oNBYVAnx2ktnTEGrtTjspIvYFkCyoxvlUM9/OB4UEl/XoY/aFMQ8C8OVCKVhuOpgucYCRlXhvMEKtG5c1aUuABkWxaUBgf3ERERERGVwIC/yShtgEGxkTQSUhdm+K1x7GFX2kDqoTcJjDbbElDK+LvrJxBt/HV7oqCHf+B79FSuh18AylT+fqXS8DwFOYLngtIanqx+nZ7WftBfLsOvFFfzERERERGVwoC/ySili2bhudqFhi4M+MX4rTsLAm17HJv4bcuCNhpqnKoc6mW0Qf4VHWPMoAx/7n8LUVWGX2sDx9PwRvBc8NtKTNUzEpQ2MMaU7OG3bQGp9YSrxCAiIiIiGgsNF/C//vrrOOGEEzB37lzsvvvuuOyyy+C6lXt9jTG44YYbsNdee2H77bfHEUccgeeff77gPi+++CJOOOEE7L777pg9ezb22msvnHfeeVixYsUovZvGo7Qpmn3vKgfaaJi8QHE8M/xSa2itxzXDb1l+QCwnWCDpH27efAZlCgJ7V/nBs1/SX/n7VdpASjVwoWAEKO0H/NWeX9oYKFN+Sj/MxNyoQEREREQ02iLjfQD5ent7cdxxx2GzzTbD0qVLsWLFClx66aXIZrM4//zzh3zsjTfeiCuvvBLnnHMOtt56a9x555048cQT8cADD2DjjTcGAPT19WHWrFn47Gc/i+nTp+Odd97BNddcg5deegm/+MUvEIvFxuJtjitPqoId7cooSKMAmKIMfzBJvdT+89EkVenVgWPNABOvpH/QBYr8gX0AIKWBhoYQwcWBys8ntUHWHblJ+EobSGPCoYiVzi+da0Mpdz8D41euEBERERFRgYYK+O+55x6kUilcddVVmDJlCgBAKYULL7wQixcvxsyZM0s+znEcXH/99TjxxBNx/PHHAwB23HFH7L///rjppptwwQUXAAAWLlyIhQsXho/bZZddsP766+PEE0/Eyy+/jHnz5o3m22sIriyc0K+0hDYKgIBGfg8/oLQfnEXGeHieH7w1QKBthhdIam0gRPlAdTRoUzijITto2J5UAxdxqimDD7LxjjtyF3+UNjDaVH1+DX5Pg1mWgMuAn4iIiIioSEOV9D/11FNYsGBBGOwDwAEHHACtNZ5++umyj3vuueeQTCZxwAEHhLfFYjHst99+eOqpp4Z8zeC1PM8b8n6ThScLS+WlUVBGISIiUHogOLSE8Kejj0NJu1QaMOOb3Qf8wXbDGQb39op+vLWiH31p1w9ax4DSGhADrzU4My+lgYF/IaKa71YqnZvnMHJ98ir3XEqbqj5frU3BexrMtgRcd2S3CBARERERTQYNFfC/8cYbmDVrVsFtnZ2dmDFjBt54440hHweg6LFbbLEFli9fjmw2W3C7Ugqu6+L111/H5Zdfjo985CPYcccdR+hdNK4gyCpcyecHhJawoExewG+JXBZ27AP+rKtg22P+skVs24JbZ2+4VBopR2JNTxavv9eLN5f3oTfpjPoFFK0K99UPLulXysAYDQEBA1Nxf70nNWJRG6qGnvuKx5ibuK+rHLZX6TOzbQHHUxXfCxERERFRs2mokv6+vj50dnYW3d7V1YXe3t4hHxeLxRCPxwtu7+zshDEGvb29SCQS4e3HHHMMnnvuOQDA7NmzccMNNyASGd5HEYk01LWTIrZtheXp0YgN2/aPV3sKliUQsSzAMuHtQghA5H43xHsb6R5/YwxcqRGPRcJjGS/RiAVlTF3frac0jAGmTfHPu2TGQ/+KfnS2RjG9qwWdbTFE6nh/Pf0OHE9h5rTWkr83wj8Xg89u8HR9pQ2ELRAxFozxv++hZiVoYxCL2shkAIiROc+NAKLRgfOs0nMKS8CyrbLnQyxqQykDYYm6PlMaecF3Nd5/hmli4XlD9eB5Q/XgeUP1mKjnTUMF/GPl4osvRn9/P9566y3ceOONOOGEE3D33Xejvb29ruezLIGpU9tG+ChHXsaRiCdiaEtEEc0FWUkh0G63wLZsRISNzo6W8P7SAB2dLehqj5d8Pq0N3ljeixlTyt+nVq6nEG9JoSNiIxEb39PTjkQgBNDV1Vr7AMH+LBItMUztSkAIgWlT/FL2/oyHlX0OMtJg0/U70dFa26DIlf0uUq5GJB4t+diV/Q4UBDqD76PoYoyF9rY4hI5BaY2uKa1lg2RjDGJrMlDaoKUlirb2BKZ2tZS8by3WpiW6OjSk1mjvqPyc/Y5GV0ahsyNR8vcJqZDJSrR3tKAl3jh/pWlderNAM+nsHP75Qs2H5w3Vg+cN1YPnDdVjop03jfOvY/gZ+f7+/qLbe3t70dXVNeTjXNeF4zgFWf6+vj4IIYoeG5T+z5kzB7vtths++tGP4t5778VJJ51U13FrbdDXl67rsWPFti1YERvJZBZGKdiWgDEG3ak+aCgACgIeekU6zNj3pxx0d6egvdIT2jOOxLvv96K/P4PN1iuuzKhHKuNhbU8GnW0xuM74zlXIugpKaaxekwwvkFRrdU8GyWQW8UGtCRaAqAA+WNkPLSU2Xrej6ud0XIUPVvQh60oYpbD5+p1FAWXP2jQcqSFyGxfW9mUKfp9xJPqSadhaw5MK3d2psu9NaY3+ZBYdbQlk0i5Wr0nCqmKVXyVrulNIOx5cT2PNmlTF51zTnUQq7SBilS7Z18agt98/vvaW6LCPbyRobfDWin6sN621oS5CjBXbttDZ2YK+vgw3KFDVeN5QPXjeUD143lA9Gu286exsqaraoKH+JTpr1qyiXv3+/n6sWrWqqD9/8OMAYNmyZdhmm23C29944w1ssMEGBeX8g62zzjpYb7318NZbbw3r2OUE2AOuhQVP6dz0eQOpJVwlYQs/KlVGwcv7WUsD11Nl31tf0kU660FJjWntcbQmhh9sZRwJKVV4jOPKGLiehuNKiBr/qKQyHswQU/7jUQvdfQ7W6Wyp+mJCb9JBxpGY0h7D6t4M2hNRTO8aOLeN8b+v/NdNZwsvmkhp4CkFYTSkMvA8hXI5aE8qeFKFFxVSGQ+yY3jnuTEGWccDjD8/IOPKin92nEHvqRSpFLKORCLaAMMf4J/HqbSLdEsU0QlW9jWSlNIT4u9Gaiw8b6gePG+oHjxvqB4T7bxpqH+JLlq0CH/605/Q19cX3vboo4/CsizsvvvuZR83b948tLe341e/+lV4m+d5eOyxx7Bo0aIhX/P999/H8uXLsfHGGw//DTQ4pXXBsHNl/JV8trDDIW7a6EGPKR9096cdxKI2PKXRk3JH5BhHajDcSLCt6gfLDZZ25JCBXjxmw3EVUtnqqxj6Ui5sW8C2LSSiNlasTRcM5TMG0IP21Qdr+SIR/zal/DsKIWCM/5hypDIwxm9ZsW2B7AhMwlfaQAfPaQl4XuXvW0pdMIiwNDGsjQojzZMaWVf5F9iIiIiIiMZJQ2X4jzzySNx+++04/fTTsXjxYqxYsQKXXXYZjjzySMycOTO833HHHYfly5fj8ccfBwDE43EsXrwYS5cuxbRp07DVVlvh7rvvRk9PT0GZ/vnnn4+pU6diu+22Q3t7O5YtW4abb74Z06dPx2GHHTbm73esKWUK1plLo6CN9jP6uYxr/j2EVX4tnespJLMKiagNHbGwts/B9M4E4sPMsGYcWbA2sBbaaCijELVGpqzbsgS0Qc2VBlJpeFIPuV9eCAEh/CB+ShXzD/zPW6IlN9egNRFBd5+DlT1pbDSjHUIIaOMH6Pmfn5Nby9easNCXVFDSQEP7rf3GDLkuUJuBrQ4R24In9bD70oNNEZHcsEDHG/oigs4dY+XXNA11pdX1FLKuqnvLAxERERHRSGiogL+rqwu33norLrroIpx++uloa2vDYYcdhrPOOqvgflprKFUYKJxyyikwxuCnP/0puru7se222+Kmm24qyNxvv/32uO+++3DXXXfBdV2sv/76WLRoEU477TRMnTp1TN7jeJJaF8xwC1byAYAQFrTRBRl+S5TPuGccCddTaEv4g+O6+xz0p1zEp9Q/xMIYg4wj65603u/1ISWT2KB1o7qPocRB1Zzh96Rf5tPaMvQfr5aYjf60B08qRCNDXyhJZSUcT6It4V8cEEKgsy2KNb1ZdLbF0dUWg9Z+cFyQ4c9l5VtbbPQlFaQONisABkNn+JX2LwLZQYbfMfCURtyq/6KO1gZa557T8rPyQ11E0LmKgEqnRMS24FRRLTBW0o6ENgbuCFRFEBERERHVq6ECfgDYYostcMsttwx5n9tvv73oNiEEFi9ejMWLF5d93GGHHdYUmfxyPE8VlEZLnRfwQ8BAw+QH/JYoW5KcykgIDJSPx2M21vRlMaUjXnfALpWBVKauxyuj0Of1QhoFqSUi1gid2qJ8lUM5ntSQuaB2KPGYje5+B6msxJT2oYPo/rQLW4iCYD4asSEshRXdabQlIn6GP7fjPhCU/Lcm/M80zPBDFFV0DKa1CS8IRCwLWmtIpYdVxaG0gcpl7C1LwJMGSmtYZS4iaJ17TxXOCbuKaoGxEly4ikascP7ASK6uJCIiIiKqVkP18NPocqQuyKQ6ygkH9AkhYABo5Gf4RckyaaU1elIO4rGBIK01HkEqK9Gfrn+yvicVPKXCfvNaZGQaGZWB0rKgcmG4hCh/0aMcV6rwsZWe2xYCfRXmH3hSoT/jIRErDoo7WqLoT7tY05uF1oCGgcj7Ux308Le2+I/15yH6GX6NoTP8WptwoJ9lCSgz/LJ5pXQYANuWgFb+8MCyx2D8ioBKPfxBtUAj9PG7UsOVGomYHV7EIiIiIiIaDwz4m4jrDkxcN8bAM24Y8AdMXgRoWSI3ZK0wYMk4Co6nEI9ZBfe1bYG1fdkh+8KH4kl/QJ5t1XZaGmPQ5/XBFjYMDLwRDPirHSyXz3ULWyeGkoj7Zf3uENnpdFbCcVXBBZaAZQm0JiJY2ZNBMuv5A/HyXtzNHXtrIreJQfnVEEKIij38Shvkj/AXqP3iR6nnDJ7StgRkhZYJrQ00Kvfw27aAVA2w2QH+eewpjUQs4k9xbYCLEERERETUnBjwNwmdG5YWlJlLI6GMgpUX8AuIogy/Mf5j86WzXq4Pu/D0aUtE0Jd2kcrUl+V3pcYQFeZlZVQGKZlEwm6BgYHU9VcZDFbNYLnBUo5X9Sq2eNRG1pNIZctfpOjPuLBE+YqBlngEntToS7rAoCn9AyX9QcBvoEzwfsTQPfyDAlUhMOwhdEobwIjc84mK6/Z07vyrdAHFDloO9PgH167nr5WM2AJSM+AnIiIiovHDgL9JSK39Xulc5KSMhNKqKMNfMLTPGpiqHjDGoDflIhYtPnUitgVjgLX9Tl3HmHUl7CEm25fTL/sgANjChgULrh6ZFYEACgbLVaOaCf35grL+/nTpY5ZKoy/llczu5+tsi+aeo/C7CgbZtbbkevhVfhWHKajoGMxThS0gtiXgOMOrnvCULmg5EBh69aP/O1GxPSJsOWiADH/WUbktDAIGoiGOiYiIiIiaEwP+JuFPR0cYwEmjYGBgicJToHBKf5CBHQhYsq5CxlEl+8kBoLUlgp6ki0wdgWHGUTUP7HNUFikvibjtbwewrQgc5QwZyNYiXM1XZeY4mNAfiVT/PlriEfSlvZKVBOmsRNZVFQflRWwLsZhVUM7vSR1m8IMMv1QGGv7rmAoZfk/qgueL2CIcQlcvz9ODyvMFZIWS/mpfTZjSMyfGWn6FhzAYdhsEEREREVG9GPA3Ca2RW3/m/6x0cUAuIPLKvQeypvl93hlHDrlGLh614UqF3lRtWX5P+pnxakvhA0kvCWkkolYUgJ/lD9oVRkIwWM6T1YWdA3MIqq9UiEUtOK5EukRZfzLjQQBV7KEH2hJRTOmIhz8HK/mEABLxgQx/8N2LShn+QUMebduC1LVvLcgnlYaddxHBEv72iHK0NhBVRvxCAJ4a30n9ntRwPY1o7oKPZQOOO3IzJYiIiIiIasGAv0l52kPBRDYAQlgFgbLILWvPL7nuS7kVy9Vb4xF09znwZPXBlyf9XudqS+EB/z30yT7ErUR4W0REoLSEHKHBfRHbgqdM1X38rlQwqDyhP58Q/sDD3mThRRKpNHpTTsFwxFoExxyNioLNB54OyvrLZ/h1bphefsAfsQRUDRc/BjPGwJMK+aMfLEsMObCwlgGQli3CIYXjxZUKnhzYNBGxrHE/JiIiIiJqXgz4m5SjnaL+fWtQhh/wO8KD/nVPKqSysmI/eUvcRtqR6EtVPzzPk8rP/taQ4U96SXjaRcyKDbwHYUFDj1jAD/izDFLZ6uYC1DKhP19LLIJkprCsP+NIZFyFRCxS+xNiIMMfi4qCCymep3PDGctn+JUyMIMm/gdbG+odjKe0gTIomgsw1CBApTSqTfFHLFFXK8lIcj0NrREOtLRzbRDVzoAgIiIiIhpJDPibkDZ+QDw44BdCQA8KlP2han5AlnYUXK9yP7kQAvGohTV92Zp632uhjEK/7EXUipXMpo/kpP541EJ/2qsqaKtlQn++WNSC46mCDQepjAfo6sr5S8kP+C1LhJl1qXSY4S/3qQdbHfKz8cFU/Xr75JX250HYgwJ+fzBk6eccPDhwKLbtzwMYz6n4jicLCmeC98c+fiIiIiIaDwz4m5A0ElqXCPhhQRtTMLgPGMjwB8FoNeXqbYkokhkPyUx1GdesJ2vqe0/LFLIqi7gVL/rdSE/qj0VtuJ4OA+hyap3Qn08IPwvfl/KPW2t/G0K95fzAwEq+aDRXXp47LiU1/FF4BrpMoK20n/23xOC2D1F3QO1XDRS2CQRVA6rMJHulio+hnIhtQeVmQYyXdFYWfP8R24JSXM1HREREROODAX8TUkZBGlU0od8SAhqFAb8QgJQGWhv0pV3EqgxALUtAAEilq8u0p7Oq6sn22mj0eb2wRaToPQAjP6k/YluQSiNbYfhaPRP68yXiEfRnPDiuQtqRyDiy7DaEamRc/7MPVigGKw9dZWCM9is6ysSh2hioQcE5ANgWKl74KEcNWg3pP5+A1uXX6clB9x+KbfkZ/vHKpivtXxSK5n3/jbQukIiIiIiaDwP+JhRMaR8cLAtYgAmyvz7LEvCURtqRyDoSiQrl/PniMQu9aadiWb9UGl4NA/syKoOMzCBhJ0r+PiJsSOON2KR+wA8mU9mhL154UkPWOKE/XyziD3hLZT2kMp4/7b+O9oBAMA8gNijDL6WGhoEQKKrmCChtgEE9/IA/qb/egD+oFBFFcwF02XNESoMS13RK8odM1t9yMFyu51/wiQ664NMo6wKJiIiIqPkw4G9C5QbaiRIZfkv4AXnGkTUHoPGYDcdVyDhDB4ie1FBSI1LFcxtj0O/2wgBFLQkBW0SgtBrRwX2xqI3+tDfkxQtXKhhT24T+fH5ZP9Cbcvxy/hourpQSVCQEGf5gcrxUBgZm6Ax/mXkFEcsv6a+nRN3f9lDcIiAgCjZB5B+DLtFWMBQhBNwatkOMJDd3wafoPBampo0VREREREQjhQF/E3K1C4HiIEpAQBsNkzfKzbIEPKnRm3TDwLFatmVBaVNxcnrZQKkER2eRUim02C1l7xNO6h/hwX2uVGFffCmuqwuG3NWjJR5BMpMr548PN+D3jzUeBPxBD7+CX9KP8oG90rpkS4RtW5CqvsF4Mlc1MJgBSvbw+3MEiqsMhmLZAo47Xhl+hVJvMDKMqggiIiIiouFgwN+EXOXAtopXvQUl/oUZfgFtDNKOrCvjHIta6E26Q/bT15L9zKoslFGIlDj+wUYyw2/bFpQyQwZuKcera2BfPn9AoIJU1V0AGUqY4Y8VBvxSmbCkX5X5XrQunVmP2AJS67p60qWnS5bnC6Dkqj9/gKSp6SJKxBbIunLE5jfUIp0tPXgyWM03Hsc0XI6n8O7KJIcOEhEREU1QDPibTDCwzy7z1Rv4wWDAsgS08cvuY3UE/PGojbQjC/bLD+a4qurd9VmVhS0qB/u2sEd0Uj/gB27JTOmqgWBCfz0r+QZra4mgvbXye6zEGZzhzy/pD4f2lQ5CPalLfidCCEDXvkYR8FfslQqIhRVkxwtpbWC0qalFImIFFQhjG1xrY5BxvKL+/fCYcmsOJxpPanT3O1jVkxnvQyEiIiKiOjDgbzJKS2ijwv53YwwefmI1Hn5iNYwxEBAwgzL8nlSw66wuj0VteHLoPv60I6sKlFf3ZnD/b1bgtdcrl+pbwoajsiOaVY1HbSQzXslsZzihfwQC/kQsMuz+fWBgaF88N+k/yPBriVwPf/mSfk/qogn9IVFfSb8rVcmqAdsScEtcQAgz/LUE/LYIL76MJc/TcKUpGfDblhj3dYH1UtrA9RRWrc2gPz2yF9CIiIiIaPQx4G8y0igoI2HlAv6Mo/HeCgfvrXDC3uf8kn7bEnBchZZY/Rln2xJlg4Vadte/8tZqrFoj8ZfnU/jLC71DBvP+pH45opP6Y1ELrqdKViuEE/qHWdI/krK57zOY0m+HJf3IXdzx/7vU5+ip8uvwLCHgebUFr9r4FwlKXUSwLQGvRMm7Cob21bD1wJ/6X98FieFwpIJUpS/42GEbxMQL+LU2EJbf1vNBd3pCvgciIiKiZsaAv8koIwGIsEw6kx34B3zGyQX8g4b2rTu1JcwS1yMeqyIzXsXu+p6UE/7vF15N4qm/9ZTNUA9M6h+5wX22ZUFrg2yJagVP6mFN6B8NQUl/rGhon4FGrqTfAIPjfWMMlCo/gNC2BTJebfMRtDbQGiVL+v1d9SgqeTd1lMD7n//YB/yep4Ey378QAgZizNsMRoLW/oWhzrYY+lIuS/uJiIiIJhgG/E1Gaon8SeKZ7EDwmnU0AAGlCwPaWjKspcSj/nq+dIlp/bXsru9L+VUCG6wbgxDAv5el8Zs/dZcMpAYm9Y/c4D7AD3ZLVSs4rhr2hP6R5niFGf6BHv6BDD/gl/fn8zPr5afj25Zf6aBraJdQyu9hL5fhV8oUTepXZab6VyIgSs4EGE0Z1xvy+xfAhMyOB0MdLUugrSUy7qX9E3HwIREREdF4arAQhUabo52wnB8A0nkZ/qzjl3HnZ/hHgmUJGAOks8XZdk+Wz4wOFgT8s7dqx367TYNtAW++l8WvnlwNt8QqNgExopP6AX8mQdqRRcFb2pHDntA/kowxcIOAP1a8lk8Z5WeeS2T4/Wx8+VL6iO0H6LKGnnSlNbQpXTVgWxa00VCDJvVrv2Si6tcIn88WY7oGzxiDdFYNWaViCcCpsSqiEUg1MLwxEYtAG4MV3Zlxu3ixojuNtf1O5TsSEREREQAG/E3FGANPu+HAPqA4wy9KZPhHQizmr+cbnBV2XFVyVdtgUksk0/5xtbfa2GyjFhyw5zqIRgTeX+Xiwd+vRjo7qDJBWCM+qT8eteF4qiCglErDlWpEJvSPFDevx75USb8f8Jfu4VcVAv56etLD5ywRwFuWgNaAHFTCX65do5KxXs3nf/+65MC+gG2LcEbGRKKURv5p0NkWQ29q/Kb2Z11Z0xpPIiIiombXOBEKjbpgJZ9VEPAXZviFsHJ9/iMrkSvrH9z/nnGry4xnvCyyjh/AtbX4x7/BunEcvPc6aIlbWNPj4Ze/XYW+5MCx26MwqT8ITrN57QkjOaF/pGRd//gsayDQD0r6VVDSLwBt/P/kqzQd359lAHg19KQr7W+AKFfJYYCikn6pTVGCv9yQwYLjsy1IPXYl9I6n4VW44BOxLTieqvsixniRg7Y15Jf2l1tROZpcaeAy4CciIiKqWuNEKDTqlJHQWhVk+NODMvwW/JL+kc6ORiIWPKWRyQuUpdJwvOoy4z2pLADAtoFEfOD+60yN4ZB9ZqCjzUZfUuHxp7vDY7dHYVK//14EkpmByoGGnNCf62GPRgeOKbggoRSgoSAggHIZflNhdoNBbSX9yhS1DhQ+nz8oMJ+UhdllAOh1e9Dj9gz5Wn7LgYYnxya49ucZDP152eO0PWC4PFV84Sco7f9gTfHUfr+VRCGZ8dDdl0XGGblKC537/GrdEEFERETUzOrftUYTjjQK2uhBJf35GX4FIfx+ag0NG8PfBZ8vErHQn3YwvSsBYCAz3pqofBquSaYA+Nn9wVniro4IDtlnBn72qxVY0+Phtbcy2HKzVtgiAkc5kMZDZIhT/d1VScQiNtad2lLV+4hHLKQcBS9Xxl3LHIKxEk7oj+QH/P5/KwXooKQfpXv4Kw7LE6am0mplDDDEx2NZoih4VCXW+LmmcouGbVlQ2sAbswy/qnjlNGJb4eyHWHRk/1yNFmMMlNYlz+vOthjW9jtY2RNBazwCV2pkHYmMI+FJDW2AltYYXMdDImqjqz2G1ngULfHiP7/VUtq/aOTWcKGJiIiIqNkx4G8iSsuioKugh9/1e/hNkOEf4fg1EbWRzEi4nkIsasNT1WXGjTFYm/Qz/O2tpYOlthYbc7ftwF9f7MPfXurDrI1bYNt5k/rLxFjvrEzi5kdeRWsigq8cMaeqYCQWtdGTcpF1JaKRWNVzCMZSMGMgFssL+CNBht+EYxlLlchXU3Yesa2aBuNJrzhbn8+yRLhVIOApU/R9uKpw6GRZNVYgDEc6K8N2iXKCVpBa2iDGW1DpUaoAJyjtX74qBWH5f1XYlkAkYiEWtRCPRTClqxWr1yaRSnvoSTqI2hZaEhFMbY+jNRFBSzxSU/Cvtb/pQQh/CKTdaGsxiIiIiBoQ/8XURKQu7rkd3MNvQcAYM+KT+gF/eJzjDaznG2p3eT7PeOhP+cfeVibgB4DZW7ahtcVCMq3wymt+RcBQk/qV0njoT28C8IO2TIm1gaVYloDRJgx4q51DMJaCHv5gYB8w0Mvvr+XTMML/lgfH96qKgN+2BRxPVV2u7Uo1dMm7EAW92cYYaK0Lysm10VBGVTVjQliAU2UFglQavcn6Jr/X0pYylhchRoLWBqbMoEXAL+2f1hnH9M4EpnUm0NUeR1siiljUDr/rWMRGZ1sM07sSaElEkHUl3lqRxBvv9yOVrW1WSLDpQeviFY5EREREVBoD/ibiaKegnF9rg6xbPLRPGw1jRj4wEULAEgKp3LAvR1aXGfe0i2TGD96CgX2lRCIW5n+kEwDw3Cv9cF0NS1jIqmzJ+//pnyuwqmfgd/3p6oeQRSMW+tNubQHfGAoz/Hk9/HbelH4T1OyXyPB7Slf8XiKWBZnLuFbDlcXl+fmCkv5gi4M/OBAFVQHSSGijoIwf+A3FtgScKi/gJDMeVqzNwPVqn/Xgejps7ahIGHhq4gyc06b8ZoXAkHMeBolGLHS0xjC9Kw7Xk0UzGyoJNj3oGs47IiIiombXWFEKjRptNDztFZRDZ11d0L8drOUzqBxQ1Sses9CbcqG0RiZbXWbcVS4yaf942luH7kLZavNWTOmIwHE1Xvh3Eraw4Wm3KKhd05fFU88vB+AHh0BtAX8saiHj+NUKjTahH6iQ4ZfGD/ThT84fnKQvNSxvMNsSUNIPdivRxh+2Zg+V4bf9kvcgc6tLDA4MtkwY6IqDGMOp+FVUIKQdiWTGRaaGFoWAKxWk1rCr+P4jtjWhVvP530H59YzDYkTNQbvW/mUqaRjwExEREVWrsaIUGjVSK2hTOKE/k/GDDzt3kycNtF9lD11xalt94jF/j30yI+F4qqpAOauySOfWfpfr4Q9YlsDO2/tZ/hf/nYST9Uv688v6jTF4+E9vQWmDWRt0YvP1OwAA/enKA+ECsYgN11PoT3sNN6EfKBPwB2v5tP/9GqNhUDrDXynIs20BqXVVU+eDCf2VSvqV0lDafz6t/QAvv91DaQmduxhVqQIlYgtIZSqW0Gtj0J9y4bga6Wzta+ZcqSFMdd+9bYnwe5kIVC7gH61ZlLLGoN1f7ZgbJjjBth0QERERjRcG/E3C731WsPJqtTOOn9HsbI+E/6jPOv4/pEejpB8Idrj7QZZUpmLAr4yCox2kcxcnhurhD2y6YQIzp8eglMELr2QgtSzo+37htTV484N+RGwLBy7YFB2tMQBAfw17xS1LQJvcpPoGm9AP5A/tK53h9+c0GACiqIffz/AP/X6EEDDwg+pKlNZQVZSGK2PC5/Mnshvkz2ULsvraaKgKMyaqvSDhuAqOp9ESt9Gf9qqqCMiXzngVB/YVHtPEWc0XLGsYjXNbWP4gx1oobQAjIFDdnAkiIiIiYsDfVIIS7kA6N7CvNWGHu+2Dsv7RGNoXiEUtpB0PUumKJf2e9pD1XDiu/w/8oXr4A0II7DzHz/L/e1kaff25Sf0AUhkPjz/7DgBgz7nrY2pHHB2tUQC1lfQDfk9yI07oB4Cs57/feMmhfX45v4Ep6uEP+qOrKeMWBlWtvvP7rocu6bcsv7UgCOS0MVCm8CKB1BIWLBgY6Aol/eFqvgoZ/qyrIJVCW0sUWUeG6wyrobXJDWys7gSIWBaUrK4qohFobSBGKa62LVHzPINwtoQRNVcHEBERETWrBgxVaKwEK/laEgKJqP8P6CAzPFo9/ACQiNm5YYHFa9cG87SLVG5gX8QWiMeqyzauPyOOTTZIwBjgxX868HIbCh772zvIOAozp7Zg14/MBICBDH+NAX88aiPlNN6EfgBhr3i8REm/Mbl+aKOBQT38wa7zagJ+ywacKkrUZYl+/NIGSrV1Lr2cf3642oUtbP+CVBXnp4CoeEEik5UQEIhGLHhKV72pAfC3M7ieLmibGMpA1cHECFaV1hiten7LEnBr3Fggc4Mf66kOICIiImpWDPibWLCSrzWq0RLx/3dQ0j+aAX80Yvul8FVwlIN0Opfdb7VrKi/eebtOCAG8s1zi7ZX9eO29Xrz0RjcA4KDdNwv3eLfnMvzJGnr4Ab9SQUmDeLSKvfBjzPHKl/QD/mo+HeT5S2X4q/icI5YVXiAailK52vBK8ga5aVP4GGMMPOPCEnYuw19FwC8Ad4ghedoY9GfcMGC3bYFkDW0dqUxQpVLdX6NCCMCIqgYdNoLRTKJbonArQzW83DBJ2ypc4UhERERE5Q098pwmtXQuw98a8ZCIagB2WNJfaQr6cLW3RAv6s0sxxiCrMnCyQWBeW2A9bUoUW27aiv+8mcafn18L1+kFAOyy7brYcJ228H4dLbmS/hqCPcAP4GZMTdT0mLHihGv5Bj7k/M9bSn/wnd++McAf1KZhWZU/a9v2g1ddoQVAawNUcZ1GWIDn5WX48+rJVW4dXySX4a/m/IzYAhmvfMbecRUcV6El4f81GI/aSFYZxGtt0JN0a7/YIyZOD7+UetQG9tmWP/9BawOrygoZL5fhNwY1VwcQERERNStm+JtYkOFvs120RAcy/EJYox7wx2M2opGhgyVpJDzjIZv1f66mf3+w+bM7YFvAyjUSPUkXnW0x7DVvw4L7BD38yYznB5qTQFDSH4sOBFNCiIFJ/Ur4PfxiUIa/it3rgYjtZ+Qrlc0rrauJ9wsyt4O/B2X8LROWsGEJq2DrQvnns+B6qux3mnUVPKURjfh/DcajNrKeqqqsP+1IpLMeWuK1nZOWEHCGuAhRSW/SQV+NlSj1klVsa6iXZfnnjqqyvUFpvxrAFiJ3sUBPmj+rRERERKOJAX8TC3v4Iyrs4c84GtYYZPir4WkPUsuwpL/WDD8AtLdF8OEtB7L5B+y6SVFWti0RhRB+b3uqjtVsjSgs6R/UXz4wuE/DGH+GQv7Ec5XbdV5N64RtWZBSh69VjitVVZliO6+vW5nBAb/0Kw9gQcAKZzIMJVjNV+6CRCYrCwYJWpaA0aaqgD+Zcf0AtMpy/oBti/BiTK2MMVjdm8WK7vSYBLue8kvoR4Nt+XMYgjWMlfiDH/1KEn9DBqp+LBEREVEzY8DfxIIMf0urjYSf5M5l+AV0FRnU0eZpFwYmHNpXzUq+UuZ9uBPrzrCx47ZTsfXGU4p+b1kC7S31TepvRFobeNIPCPMz/MBAwK8VoOFn3o0u7OGvdjK7ZQkYoOL+es+rLlNs5WVulSysMlBGYc1ahQd/txpruhW0kQWVCaVEbAtKlZ6KH/TvB9n9QCxqoS/lDfncSmv0Jl3EY7WfjxHbgitVzev/AL+MPe1I9KfcMcnyK1VdpUc9hBiYF1GNsPzfErCFgFYTZ/ghERER0XhiwN+ktDbI5Ab0tbZG0ZLL8DuuhoDll3aP4uC+ajjagS1spNJ+wF9Phh8A4jEL++zRgt3mTi17n3pX8zWi/Ix7+Qw/chl+FASfSumq5uvlP39fauj99Z4yQ67kC9iWyAWBOreCrTDgf+sdDx+sdvH6mw6UMRVXR/qZ4NKr+YL+/digao941EbakUNWLaSyEmlHoiVW+wgU2xJQykDW0YOedaTfx24LdPdm67poUC1t/O9htEr6gwGG1Qb8+WsaLUtAmeovFhARERE1Mwb8TSqYxi9gkIhZA2v5ggx/sKd9nGijkVUZREQEyVzAX08Pf8ASNhztlP19R0uwmm9s+qNHUzA537b9EvJ8QQ+/Vn4QLYQoKA9XuYsA1YpH7SH312ttqu4Ft3IBv1QGUheWk7vahZf7apJpA2NUVRekjEHJ4Hpw/37AX8+nkHHKB/zJjFf16sLBBlbz1R7wpxwJAX/gZV/aRXIUL07p3CrF0RraF6i6h18NrIsMBvcx4CciIiKqjAF/k8qk/Ul4iZg/SCwRCwJ+BQsWYPS4Zvhlrn/fKAuuV38PfyAiInBUFq4uHdCHGf4aJ/WPBqnlsGYoBMF3NFocrQUZfqVFLuAvDJw8qWoq4660v17lAsdqgmPbEpC5zK2UuuA4PO3CzX01qbSCrnI1n2WJktn6TFaWPCYh/JLxVLb0eSKVRk+/g0Qd5fyAP/dAa9Rcjq6NQX/KXyHobxAQ6O7LVmxrqFfYMz+aEb+ovg+/KLg3BmqCbDsgIiIiGk8M+JtUNukH/K1x/x/0uQR3mPmvNqAaLa7xoIxCNusfXzQiCnbK1ypmxeFqFyszH8BVxZn+9mBSfwOU9K91u9Hn9tb9+Gw4sK84WAsy/krmSvohCkrDpRx6xV4pEVugL1U6QFZaQysNu4rAUeQmJ3rSn8geHIc2GlLL8MJPMq2gtD+1v5pjyw6qPgj69we3OwRiUQv9aa9kMJrKSmRdVXfA76t9NV/W8VsQgoGTbS0R9KZcpLKjM2tDaQNTYd3icFkCJdstSikaUiiY4SciIiKqBgP+ZmQUMsk0AKAl7t8UzwX8SgNS+kGWqdAjPZo85QeQyWH27weEEGiPdCCjMliRXQFnUNDf0doYJf3KKKRlasj2g0qCDH+pgDZcy6cBbWRueNrA7706VrHFYzZSWQlPFgfgfoa/hsDRCHhSFZSTByv5gpJ+pYCsY6CqOD8jtgVn0Gq+oH8/XmYtZDxqw/FU0YUCAOhPOxCor5w/IDCwfrBaWVfCUxqRXAtCNGJBa4PuvmzdxzEUY3Lf2yhm+C1LVB3wD774IoSouA6SiIiIiBjwNyUhs8jkMoOtsYEMetDO7LoAxjnDn1EZ2MIe6N8fZsAPDAT9WZXByuwHcNRAsNTRIFP6HeX4/erarbtcO5OrfQ8y/Moo/HnVH/BWatlASX8wpV8MZE9Nrky61iAvCJDTJfrelaq+pB8AIPxp9CavnFwZCWU0HG/gfEylNUwV52e4mi8vsAz69yOR0n/92bYFpRD+GQl4UqMv5SERH965aNWxmi+Z8YoGH7a1RNCTdJEehSx/cKFmNCv6rbw1jJVIaQpmOliWgOcx4CciIiKqhAF/E7KcFNJurpQ/HtwqkMgr6zfwy/rHgzIKrnYQsaIDK/mGMbAvXxD0OyqLFdkVyOaC/kbp4XdUFp7xII2qu48/OyjDvzL7AV5L/gcvrn1uIOCXCPPjBiY3lT0Izmt7PZGLCkut51O6ttGPlgW4uYx8EPBLo2Bg4LgDz5RK6ao+H9uywqn/gXTWq3gBIhoRRavvUllvBMr5gUiZuQLlSKWRzHhFawBjURtSKaztH/ksf3ARSIxixG8HaxiruLDlKVXwndmi9ioJIiIiombEgL/ZKBfwUkh7fvDQGgT8QgxM6nc1BERVGdTR4GkXUktERGTYK/lKEUKgLdIBV2WxMvsBsiobBvzprBy3YWDGGKRkEnErDm0UlKkvc5t1/ccFGf6gfcFV2byhfYDJtW0Ykyvhzu1Fr6eMOx610Jdyi3qtldao5dlsS0BqA438Hn4FqXTBRPd0BlC6csAXTP4PMvzaGCQzXtn+/YH3YyPtKLh5gXlfyoUQ1QXBxhiszKwoOSTStv1At9o+/qyr4HgD/fv5WhMRrO13wu98pChtMNrX+8Jp+1UMMHQHDXG0arhYQERERNTMGPA3GcvLQEgXadf/6gcy/FbepP4gOBqvgN+DhoYlrBEt6c83EPS7WJH9ACIyUDI9Xll+V7twtYu4lYAyCrLuDH8Q8Fvh8wKAZySCtnWlBtYuGiBcc1bvoLZ4zEbWVUWBpx/UVh+UWZaAVv7FhyC+k1qG/fuBdNpAVnlBRAiEwXWl/v1ALGrB8xQyuWoJ11PoT3toqbKcXxoJxzjwSgX8luWvHqwy4M840q94KPG9JGIRZD2Ftcn6Zz6Uos3oL+W0hYDSpuKkfmOKVzvaloDW1a/1IyIiImpWDPibiTEQ2X4YO4pMLj4IevghBHJt7LmA30CP09C+rMpC5E7N0cjwB/ygvx2ucrHKWRlO6h+vPn5HZyGNRMSKQEAMI8Pvf2bx3FaDIOiURsK2/QBJSfhRvjADGX5joOoc1BaxLSilkXaK+95ruYBgi2BrgAgz6a524cnCv6pSNQT8lhBwPP++fv++Kdu/HxBCwGCgTSGVlch6smSWvRRlJKRyS7YdRGwBqXXVq/n60y6iQxxvWyKC7j6noBphuEZ9JR+C6gtdcdp+qVYTy6ruYgERERFRs2PA30SEdCBkFojEEbQnF5T0hxl+BcCqqmR6pBljkFUZRK0IACCZGZ0Mf8Dv6W9HVmXRmvADnPFazZeWadhi4H16qr7jGDylP8jwGwC25Qe+Uvll835Jvx9QKe0H//VOoI9ErKL1fFLqomFzQ7FtUdD3b4zxWzwGfRSptIYysqrBhhHbQiY3UDCVdaueURC2KRiD3qQD2xJV97RLo+AZWfLPkBACRpuqNkK4uWGIQ11oSMRsZB2JnhHM8ktpUFMvRh0sS4Tn3VCU9is+7KIMf+WLBURERETNjgF/E7G8FPzp+xayuVgjf2hfS3SgpN8SoqoMf6/bA0+PXIDsaRee9hARUbiehpfbvd4+QkP7ShFCIGbFEYv7r9WfGfvVfFJLZFQGUcufnGgLu2T/dzWyuUxvECTmP4+w/e9KKT+YDsr5/Qn9w+vbjseK+94H915X4mdudfgXk4Y/nM/z/OcI5hKk0hpKq6rOUdv21795UiOVkRX798P3E7XhuAq9SRf9GQ8t8UjV70NpCWUkPFP6z0ZrIoI1fZV77zOuguepIY9ZCIFE3Mbq3mzVa+4qqWc9Y70qleXrXMA/uIdfV9n/T0RERNTMGPA3CSNdCC8DE4khiGcFEE7mLxja5/hD+ypl+JVR6JN9yKrMiB2nazwoowpW8sWiAtEqg7R6xa044olcwD8OGX5HZyG1h6jw2wpsYcMz9a3mCzP8uXaNgj5yy39vMujhFxowfmZ/8MC9WsUiFlxPIZMr6w+HANYQOFrCH7IXvG2lJbTRkLm3MH2K//l40sBxVVVzJiK23zOfzHhV9e+Hj4tY8JRGX8qF62nEKrQB5PO0B52rTiilJR5B1pXo7h86K5/OejCoPCiwNR5B2pFFFRb1klpjjOL9imX5SudaTQYdkEHl6gAiIiKiZseAv1loCWgFWJEw4E/EMJA1EwKJiP8P76yrIYRVsYfc0x5c5Yxoht9VDgADIcSo9u8PJoRAe26IQV9qZAegVSMrswjeNwBYwobSsuo+9Xxubj/54JJ+ABC2/3wDmdFcab8B1DAnngshIACkMrnX0LqoFLua5zBGQOT+ZpK5bQVu0ILSYqMl7v+yP62hqxhsGPTMJzNeVf37+WxbIONIRCLVl/MD/mduCQsyd8GilPZEBN292fACyWDaGPSnK28UAPzPLR61sLbfqesi0WCyxsqMeglUvtDkt5qY4s/fmHHbqEFEREQ0UTDgb0LpYGBfPP9Wq7CkH35J/1DBg6ddeMYL176NhIxKw85luVOjNKG/nM5Wv9yhJzXye82Hoo1GWqUQsWLhbbawoYyua3BfsOM9KH93S2X4Zd6UfgMY+JPQxTD/RojHLPSmHajcULpy0+WHIoDwIoEyEgaAm2vtiMcstLf550MyJaGqyPD7PfOAJ1XV/fuBRNRGX8pFS6z6c9AYA2k8xKxYbr1i6YsSiXgEzhAT9h1XIesO3b9f8HwxG2lHhkMb66VN7ZUZ9RIW4Mqhj1drA1Hir6H87QtEREREVBoD/iaUdvx/PRcG/EAilsvw50r6tRl6Ur+nPWit4Bp3RFb4edqDq93igX2j2L+fL7iw0JdxIfXI7jUfSrCOL5YX8FvCgoau6zgctzDDn19WbkRuYr8KVvL5F3WMyWV1hxnkxWN+33vGUQOl2DVmiqd0xNCR25igjIKAQDb3nuIxCx1tufMjraveJCGA3C772v7KS8QjmNIRQ6zKoBvwtyEooxAV0dxFm/IBbdsQWf6sq+BJNeSE/nzRiA1PqrIVA9XSuaGJY5Hht4SA51Uo6Ve65GgJ2xJwR2hmAREREdFkxYC/CQUr+Vrihf+gD9by+QGjCIPBss+TGzJXb+n5YJ72A+1ILsOfHMOSfsAvFweAdEah3+sfk9cEAEdlw7kFgw0VLJai8ta9lSrpN2Kgh19A5Cax+xd3PDX8vm3bsqDNwO74eqb+W3nT8D3tQUDADQN+EZ4PqbSpqqQfACzbP6ZaAvdArY9RRkFphYgVDYcOlhNm+fuKs/z+RoHaPjt/U8LwKm6CqfhjEO9XFbRLrUsei2WJERtSSERERDRZMeBvQqVL+oF4LuA3BvDc3G72MhlUZRQ87SJmxaGMghyBPv4gMA2CvbHs4QcGKgk8D1iTWTtmWf6UTMEWxRPgLVg1T+p38sq5g5L+ggw/BjL8BiY3qX/kMvyA3zPfl3IL1uvVy++Ft8OqhfyS/nTaVF1ZEo/aMNof4DfapJEwMLBy/RGV2jLaWiJY01+Y5Vdaoy/lVV3OH0jEbCQzMmzrqIfWBmaMSvr9rQxmyD5+r8x5aVsCnlQjMrOAJr7VPZkRG1pJREQ0mTDgb0LB0L6WWOHttgXEcnGn4/q95aZMQOVpD1JLRK2o3/89Ahn+rMzAystyJ8Me/urXoQ1HNCIQifiBRU8qMyZZfk97cHQWMSta9Lt6VvMF/duRSLC6TBeshgsy/EoZf2AaNIQRUMrfaT4SZdxBL3k66wHDCMa00ZBGwh4U8HfkAv5UqvqWh5Z4BNOnJOo+llooLcP5CAKVqzQSsQjcQVn+TG69YTxW21/RsYgFVyqks/X/edTGQNfRilEPO1ivN8Skfil1ycGPliWgDCf1ky+Z8SquuSQiImpGDPibULkefgBI5Fa5Oa6GQfkMqtRemMUUEHDV8DIryihktYNoLtNtjEEq6OFvrfI01RLCS9V9DEIItCX815JuBH2yd9Sz/I7KFrQx5LOEDam9muYjBAH/QHa/sPJCYWBoHyBy2VEDqQ10HeX3pUQjFjyp4MrSpdjVUkZBGwVLWHDyhvZ15C4ApTIjc6FppEkt/XYJ+N9hNVssBmf5s67MbTio7a9oIQQsIdCfrv/Po9K5gH8sMvxCQKuBNpRSPFX64oNtCShl8jZOUDNT2gyrsoWIiGiyYsDfhAZK+v1/RL+WfRf/zb7jr+bLZf0d1y/1Lj0uq7Av3BYROHp4k+39/n0PkVym2/VMLigF2qsc2md5aVjZ3mEdR9DHL50IHJlFcpSz/BmVAVB6z7o/qV/VFNQG/+CNlujfBwAtgpV5yK3iUwD8DL8eoQy/EH4PvqdKZ2arFfTCF2T4owMl/a5rkHEbr4TX1W44j8GCVdBSUU6Q5e/u8/8cJdMu7BrWBxY+l43+jAevwvT7cswYZsz9LL1/gaEUbfztEaUuPgQVLENVB1DzkEqHK0mJiIhoAAP+JpRf0u9oD08nX8Cfki/CNSos8w8m9Zcr6c+qbBjURKwIPO3VPGAun6tdaOiw7zko54/HrOr3pisPkB4wjOMIB/dlFaJ2DL2jmOX31/GlC6bz5wsCflXD62dzGeJYJMjwFw5wUxh4LqOF/50Jv6dfG13z2rpyEjEb2awcVpZYGem3HEAMBPxxC7GohXiuEqUv5TVUD7cxBp7JC/iFDWlUVVUa7S1RrOlz0JdykcrWvlEgEI/ZcN36y/rHskTesgRgUDZLr1T5wY+WENDar06pJJX1sLZ/5NaHUmMJ2lA404GIiKgYA/4mozSQDQL+ONCnUvCLugEHComo/4+lrOMHKKWG9imj4GoHkVz5fZiJHsbgvqzKwso7HesZ2CdkFoAGhrEisC2c1K+RsBKjmuV3VBauchAtE/ALIWqej5DxcgF/LMjwDy7pzxsMpxAGokqPXIYf8IfkpR057Ay/gIAnTTgKIAiC23Nl/X0pWfVqvrEQrOSzwoDfgjaqqoth8ZgNqRTW9GZzKwTrG1YZVIskM/X9eQy2K4wVjfIXGbQ2ZWdLCCEAI6oq6e9NuVi5Ns1qgEkqOGeVxpDtIURERM2IAX+TCYJ9IYBEDOhTyfB3jpEFAX+5Hv5gYJ9tDQT8/nC4+jKK2mhkVSYs5wfyB/ZVGfQYBSgPwmiIYfyjvrXF/yORyigIIRC1Y+iTvcOqXignq7IF09xLERA1vfbgHn53UIbfMxLBoHqlBIxRCEIpg9KtBfWwLIHpXXG0xOsfuCi19C9E5bL7to1wqGJ7OLivumB6rOS3IQC5i2G6+mNsS0TRn/EAM7zvIh6z0ZtyIVXtfxbGamBfyBioMseptIY2Q7SGiKEH/gVSGS83SLLxZj7Q8BnjzyBRStd1zhMREU1mDPibTCYX/7XE/JLYPjUw5M41Ei1R/x9LQUl/qYBfag8aumBvvIGpO8PvaQ+e9sKBfQDCgX3V9u8LJSG0AvTwMvxhSX/u9RNWAlmVDXvtR4oxBmmVCqsk8gmZhZD+F2WJ2lbzBVOqY7lM+OD+cc8oRGw/eFLKr+AI1vKJGhNj2misya4u2/IQjdjDClr9XniroH8/0JG7ENSfqq5cfqwEbQjBRRxLWDAwVQf88Zh/8awlMbxVlImYDcdVSDu1B7hSmmENW6yVEIAsE7QHK/vKHY9A5RYETyo4nobr6rqrHqixae1n+aUx3NpAREQ0CAP+JpMetJKvNy/g9zP8/v/OOqpsdtnVLgb/+7uenfEBT7tQRoUVAwCQTPuBStUZfu0BOneswynpzwVawQUHIQQsCGRkuu7nLMUzHpwy5fzCSUJ4/utZwoajqu89HhzwB99JS24LgAuJSO4j1UqExfDGH9pfk7RMoU/2IjvMgY3leNrz3787MKE/0N6Wm9Sf1g0W8JcO7HUNVQhT2odXGQH4FRbG+JntWnlKQ4zBhP6AZQl4svR3qHPBW7kLR8IC3AqT2R1Pw5UKbS0R9KZclvVPQtoYwBhoZZjhJyIiGoQBf5PJhBP6/f8uyPBDIREbKOkXwioZwPgD+woDEtuKwFHZugYmOcqBNSjarLWHXygZBqxiJIb2ZXT4XqJWHGmZGtHScUdlIY1E1Cpexye0F1608OcjyKpf28mV9McHBfytwv/C/Qy/f19/Ur8GhK75ezPGoM/rQ0Zm4NZwQaJayigoIwsn9OcF/B1BSX9a1xRMjzZPeeFKvgHjsz4wHrPQl3LDoLlaUmmMYbwP2xJwywT8lbK1Qz024LgK0AatiQgyjkSKZf2TTjC0zx9AyoCfiIgoHwP+JhOW9Mf9Hex9BRl+Dy0xnbufhlUiw+8P7HOLStEjwoY0subAxhiDjErDHhT4pmrs4RcyCxO0GAwjg9eW6+GXysDzgoA/Cle7yKqRyWQbY5DykgVDCgsE7QnIH4hY3eea9Qa2GwADJf2tKBHwS3/1ohF+YFXLRP20SiMlk4hZMWTUyFY/AP55po2GDat0wN8aBPymoYb25a/kCwhY8IYx0LJeiZiNjCNrLusvtwZvtFiWgBwq4Dflj2WoxwYyrgfLEmHVA8v6Jx+tTW72BCqeD0RERM2GAX+TSbv+P55b40BGO5AYCOgdI9ESyWX4XQ0hRFH2VGrPz7xagzL8IgKpaw/4pZHwBl1AMMYgmakhw280IB3AivgNwcNYoxeJWOHAu1TWPwa/H9uMWFl/UvajX/ajxW4p/qVR/vvJfe4WLH/XeJWfqxMO7SuT4YeCbeUm8yv4tfy5XebVDmozxqDf7QUgELfjcLU74gGt0gPT7ksF/MHQvqxj4MrGyNgGK/msQQG/JexxCfht24LSBuls9a891FT80WILAaVNyVJ7v72gvscCfuY3lZGI5v48JGIWevodZoEnGb8QRMC2LTgev1siIqJ8DPibTFjSHyvs3wf8oX2J3NA+zzPQWkAPmtTvac/vty8Kaqy6Bve52oU0qiDgd1ztB6MYWJM3JC39Mn7LhhHCL4kfhrZBg/uAXFm/Sg27X9zTHrrdbkREpOiiCYBcdYIKS/prXc0XBvyxYEp/0MMfD+8Tifj3URrQ8Kf1KV1613kpWZ1FSqXQYrcgIqKQWhYNBxyuoLJECBEG/MF7AvzgP5jY39Mg+9VVbv3e4D8bdq76ZTxmDcSiFnqSrl/uXAVtcnvvxzDgtyzhX2gosU5NyqGrDSwrF/CXWcXmev7AvlgkCPgj/jBDlvVPKkHbit/i0TgtPkRERI2AAX+TCYf2xYE+XRjwO8ZD3DZhB7Lr+sG+wcA/pksN7AsIoOZMpqscGJiCoVzBSr6WuAXbrhx4iGBgn2X7U7zU8P4xH/TxpzIDAVrMisFRzrDK+o0x6HHXwlFZJEpl94HcWkEzMIAQ/mq+akv6g+zW4Cn9CTEwHNCK+M+lJMLefaWqz/AnvT5ooxGxIuH3Vu/AxnKUUeF5FwT8ibwMvxAiLOvvSTVGwC+NLFjJF7CEBW3GZ31gIhZB1pHIOtW9tgpKo8fw/xnCgL9Ev74nh54nYA/xWMD/86CURiS3izK4eJBMs6x/Mgn+HrNzAyBrnVtBREQ0mTHgbzIDQ/sGVvIFwaBjPAgBxOO5IM4FYAqnoJca2BewRKSmifIAkFGZonkANffv+5Er/EsOVq4kfjir+fw/FvkZfktYMMYgO4z1fCmVQo+7Fq12W/l1dWFJvwnfgyXsqgPqIOAfPLQviiii8D9n2/aDHaX8dYp+hl9XFeQ5ykHS60fCToS3WcJGVtb2uazKrkSv21P29572IHJ/PQ1k+AsPMCjr702O7MWGeg1eyRewhb9qbzyGC0YjFjylkXaqC3C1NjDaDGudYq1sS0CVWafmVZXhL7973fVU0QXFeMxGT4pl/aNJa4M1vdmqK0tG4vUAE14A4ndLREQ0gAF/kxkY2jcwoX9GdAoAwM1l5xO50mnHNf6W9lzgGQzsKxfwR4QN17hVly5LLeFoB5FBA/uSNQb8UC7CaNWyAAz0wNejVEk/AETtGFIyWVdpttQSa501sISNSKlS/oDREEYDMLn/+AGjq52qJum7QXCcm0MQZPijIopo7nsTEf97lnll0FJXV8ad9PrhGq9gnWBERJDVTtUZbE97SMp+rMquRL/XV/p9aBe2CAJ+/zgTgwL+jlb//fSlZV3bIUaaMqrEhP4gw6/HJcMP+EF/b9Kt6jPSxkCZse3hF8Ifpjc44FdaQxsDe4hjCQL5chn+VMYLs/uBRMxG1lWc1j+Ksq7E2n4H3hj102tjICD8i0faFPzdRkRE1OwY8DcRrYGslxvaFwP6VBIAMCMyFYCf4QcMWoKA3zEwZmAKejCwr1zAaosIVA2D+zztQmqvOMMfDOyrpn/fGAjpwFi5+worVxY/jAx/IijpLwzQgrJ+R9deQt7jrkVGZdBqtw55P6GD/v2BDL+dW49YKWCUUoeBz+ChfRFEwoDfsv3vR6rclH5oaKVhV+jhd7WLPtmLhFXYjhC1opDaq7qP31FZeNqDbUWwKrsSSZks+L0xBtJ4YWl8pQx/MjU+5fKDVWq7GK9jTMRspLISToV99YAfOBtT/TyHEWMM1KCsrNYGuprtEUaU7OFXWiPtSEQjheeNZfmXZZLpxqgMmYw8ZeBKBTmMv4drIbW/ki+Y6TBWr0tERDQRMOBvIkGwLwQQi2r059aprROZAgBwjQdAIJFL3jpOLhjMZQbLDewLDKyQq6582NUuDExRCXRNGX6jAO0BwUUDYflXNoZV0h9k+AufwxY2NHTN5etpmUKv14MWu6VyqbRR/uBBY3JtCsGFFFXxQko2L6CLRv21i0HAH7OiiOS+N5EX8ANAxBZIxO2SgdU//rMKf/vXCgBAykvC0x5iedl9IJfBhq667eDVd7rxp79mYak4AIFV2RVIy4F5EsHFDWtQwF+U4c8L+BthNZ+jnLJ/NoDxC/hjURue1FUNqgtKo8ecQFHftVRVBvwCJaf0O66GJ3VRwA8AibiN3pTL0u9RIqWG66kxy7T7M0iCIacoO8SRiIioGTHgbyIZx/+Hc0sMSJksNAxsWJgW6QAAeLlJ4kFJf9bVAEQYTA01sA+ofaJ8VmdLBkhBD381K/mE8iCUAuzgvgL52fF6tOV6+Adn+AG/ND4lk1WXkCujsNbthjGmoAy+HKGUf9HCGD/ox8AGhEqr+YIJ/dGo/11II8PBd1HEwgw/gh5+6ZfBWjYwpT1edDHCcRUe+OMy3PPYq0g5Dvq8XsSs4vsB/vrAagYaKqPw91fW4u13Jd56L4vWSCu00ViZXYlMbj6Cyp2HwYWgcBBhrPB123Ml/am0HpcJ+PnKreQLCIhxWc0XsO3q9s+P57CzwcF3te0FlkDJyeyOpyDzBvbli0dtOCzrHzWeVHClLqraGC1KDcxpEGAPPxERUT4G/E0k6/pfd2te/36H3YZY3gR3x0gkov4/+rOO/4+moId/qIF9AQEBV1XO9GqjkVVZRES06He1ZPiFlgA0PKPx1+QrWO6u9m8fiR7+rCoK7GNWDI6uvqy/1+1BykuhNdJW3Ytrz29PGDR40F95WF2Gf/CEfgF/sF4Y8Fv+/cKS/jIXL5JZD8b4O66Xr10LRzuIW/GS941YUWRVpmLg7SgnvJAS/HdbpB1Se1iVXQFHZSGNgjbar6jQBp5Xpoc/l+HPZAy8YW5mGK6BlXyl/0qtZfDiaIhFLKSyXsUhauMV8Pvr1ArPnWrbCyxLlOwVd1xZ9mKBZQkIAfSnG2PDw2STdRWUGrvAW+mBC0NCCHgM+ImIiEIM+JtI1h3I8AcBf5fdBkuIMBh0oNGSi8GDgD8YODbUwL6ALSJwdOVMb9i/P2gegDFmoIe/mpJ+5QIQeCWzDK9kl+FvqX8hVx9c+bFltOR6+LUeKCcP2FYE0kg4VWSzsyqDXnctEnaiqG2hJGNy7Qk2/LLqgdcWsCpmiLOOH/QGA/vCcn4RgbBEXsA/UNIvgLLl8Pkl4O/39CAqomVbEqIiAk97FY/RUVlkHT+ozK+gaIu0w1UOVmZXwlHZsDLBzQvkggsZgZaEBdvyP6m+Yazmc7WLtU73sAb/BVUJ5f58+HMYxm+4YDRiwZOm4hA1NU7HZ+XWqRUcS5Vl2aUuFgBAMiMRiZS/WBCP2ehNeUWvOxmN5eR6Y4w/L0KYquZGjASpNIK/Ym1LwHXHf6YHERFRo2DA30QyYYb//7P338G6ZPddN/pZa3X3E3Y8edKZGc0o27LkjCywQVcWTiDjMq/Ee/F1wlaBMJSrzC2qyP8QTEGBzUuBwSThQpj3cvHr10kI28CVhC1bliyP8owmnDn5nB2e0GmF+8fq7if1k3Y4Z8bT3yrZZ/Z+Qnc/vfde3/X9/r7fUSXftvLKc6tQ2lNy2lGh6Kc+PM6ry4sD+0oEQlWz/ouQ2aw2DyBObcXVN1YI7RM6RQvBZ+PnADgwfU9fVxwrqINSgnartPXPLpJDEdLXvYXkTVvN3fQuxhkiVa+Kz6Ko4xMSEBOj1ErIpQpxkvlzDoMpwk+AELKq5XNqpPCDwM7ZHBkn/HcPU1pjVXzTUDLAOLMwuM85x37SQxcvO96CIIRgI9giMTEDM6jS7suE/jAUM0qvEKLaFNofHF09z0xK3/SPlQOgq9yB+Qr/KsGLp4VASbQxtdb3cWht72lCf4myP338Z2pVt0EZ1Db++Fxb0twQBfN/h7Qjb+sfJvdv1OJe4e5hwjNXDzkYrNbWcBxo41PyQyVrN2JOGtY57Njoh5Linm00NGjQoEGDBi8HNIT/FYS4VPhbcDCH8GcYOhOWfomxZmlgX4mS+C0L7ktMXFthVs7vd9tyhXRuAybnmfwWsfMKr8Vy6BLEMS3e86r5ACLZIjXpXAKurS7S53t0g83V39QawBYz/BT1fB5KBOQuX2iZjwvCX6bZjyr5FBJZKfyutPQXM/xzFf50dA0Hg9lwxWlIBKmZr7RnNuUwHjkjpjMSPOnfJM6HVXND6bBohfXvvbnhH3fQPzrhN86gTbZ0ZGLxa+ja+7nEqJrv/owelM6MZWq2NiuE5J0CpBB+Zn+MtNcF8dWh7F4ff3ya+82NusC+EkIIpBAcHGOz6Kjox8vHK04ScabZ6yd86eoBV28PyE6REGvjZ/ejUJHns2NRJw1bjH6U+1Sjar7f/86NBg0aNGjQYBU0hP8VhNLS3x2z9JeEv0xeT52mHYwUfikEBk/4FxGaEmVSf76A2MR6yGF+SFt1Zr43WGd+32iwOU9lz098fd8O4RjkDaC7ILgvKG39NaMLxhluJzc51AdsBlurWfkL+DpBB1IyHTzo59n1QoW4VLVaU5V83tIPoSwJ/5ilv6j8q8O48jkYLj9+JUNiM5y7wE9tyiAefS7TLQjgifF2tEMn8PWFFeGP6q9jOce/fwzCn9kM7fSxyPiyzQKJrEZj7heEEBNNDnXQxnIfBP5alV5rxyp7D7Ig/OOJ8FluVkr4b0eSXpyTL3E+nCS0sdzcG67UmnBSSDLDRjuk2w64fnfIl64dst9PT4WM58airSMKJcZy6kn9zjmsY6TwK4E2rknqb9CgQYMGDQo0hP8VhNLSH7UMA+sT0XemLf1O0w4Lwp8ZcGCsKQL7VpipLzBP4XfOsZ/t45wllMcL7MNqruZ32Dd9AqG4HF0CSsJvjpXU323PV/jBK+79fLI/3jjD7fQWh/qQw7sRH/34ITdurzFb7spjlkXZwGjBKoVEu8XVfHE1wz9J+EMCEFQKvxHjCj9zXQPjCea9/nJCFIiA3Gbkrv6z7+d9snREwOpCEaexjPCXlv7DYxH+FHNMMp7Z+Qn9MFLY7yfhD5QgXkIytbH3ReEfqfRjtnxjVjoWJQV6yh0wTPOVntuKFEmm72laf15U1t2rBHttfD1hIAVRqDi73SLNDV+6dsiVW/0Tt79r7UeTAikxxq7s1DgqrPUqv6gs/RJrLfqU37dBgwYNGjR4uaAh/K8glAq/aHmyH4mAVpHQHxXkO3O6svQbA8b6sLHMpksD+0pI5s+bD3Sfvu7RmZNa3xusU8mn+b3sCgCvaV3mUnAGgD07xM/DH6eaz79/3Qw/FGn9Jq7O0zrL3fQ2B+k+G8Emv/E7PZ764oCf+2+3+flfvcXz15Llapob70AXfgOgQOkUMAuU5LRK6Z+e4VcIAa3iM7YF4TfGIoTEzVX4xwj/wCydqQ5EgJ4zx5/bnNQm5NnoV45zECdLUv0rwl9P3rYKS//h4GiEzTqLthrrzJEt/c654udj+T17Pwl/qGRVVVeHknDfjxl+KQVmirRnerXNByEEbuy5PvhTEy2w848/VwnB4T209RvryHJ7zzrqtfHvpYp6QiEE2xsRm52AG3sxz1z1av9Jvh9CoJT/TE/7PK3zTSPlrSKlwLrTdxY0aNCgQYMGLxc0hP8VhJLw66C0829Wqsi4wh8oR1ldnacOi0WvENhXQsnAJ61PEVzfSb+HEmouOdo/9Orwme1Z9X/mselNrpp9BPDGzqvYDbb8103fz78fg1x1F8zwA4QyJC/S+q2z3Elvs5ftsRFsIJxkv+fJoxBw7VbGL/+PO/x/fuUmX3xuOJc4C2ugHJsQYmbDQmvLJ75wd64VuAztm67lCwmQjDZ1jBhP6Z8/wz9IRiTIuZH7Yh7Ke6muljE1iSf9U1MQ865v9bxlCn9h6e8PzdJKwDp4K7+pMhKOgmWVfCUEYmmLwWkiCCRa27lz/KvW4J0GKtJuyipQP4O96uaDgOq5WW5Jc7twfn8c3XbAfj+7Zxb7XFsybcnNvUqwd2hrCdTktQwDxbntFtoYnr/eY693MqQ/yQxK+s/Ujn2mp4UytG+yQeT037dBgwYNGjR4uaAh/K8QGONI88LqrSbn92GS8EtclVKfZn5BVRfYd/Vmyu98ujdDYAOhKiI1jsPskNgMa2f3S+wd+kX3me0lmwvO8tTgiwA8Fj3Iluqyo3xA3qEZYI1GHMPSWc7wLyKkSigGeZ+76R32srt01QZKBhz0Ndb6tPw/9R2XeNNrNwkCwd0Dza/+rz3+4y/e4DNPD2YV/zGF2SFmjv+zX9D86m/e4md/7Yu1boFqhj/yn1Nl6Rc+pb8l/dcN06F99bb+wVR6+WF/OSFSQhGbeObrsYkRSOJ08rgHyfEI/1bhBBkOLfoIBMpYP7vvHRtHIzxlJd8iSz/4pP77SviVRFs3Nzm9DM27D3wfKEh78bvE2GIue9W/UE5Uzy1dDKsS/ihUaGPYPyHCuwxecZ+/8XLS8O0H1FZqCiHY6kZIKbhyq38iTockMyM3gROnr/DbQuGfuHEFesWWhwYNGjRo0OD3OxrC/wpBLy4VZ8cQT/h3xgj/yNKfg3O0o1EtmsPOBPY55/jV/3WXj33qkM8/O5nopkSAtho9pphmNuMg3yOSrblBdlluKxV5dwnhj/M+z2TXAK/uA2zKDgEKi+PQxidj6V9ASEMZEZshd7M7dFS3ckDsHejqHDa7AW/9yh3+9+94gK/58i3aLUlvYPifv7XPF6aum7AaV14bIWYcCi+86F/3+Rt9PvnFOxPfc86NFP7is5tQ+KUgUv4z1oyH9gmcs1Xv/ThKwl9u/hyuMscvQ1KbTtjjjTMM9YBIRsRT13MwXGbp98c1j/B3O35cwTo4jNcnbNppHGW7xOJQxPmv4d0F4xtiTz8/5Dd/92BiY0YJiXb5qaeWL4JzjnzOzLa1Dtz9sfR7iEnCbx1qxd0HIUcNBGmRDF9HcOeh2w6420uqn6HTRJprrPO2/nuB3FjEkltusxvinOOFW3368dE3pcrNjKD43IRYvW3hqLCOmd9eUkKW359GjAYNGjRo0OClhobwv0JQLuI6Ywn9WxMKf5nSn4OztFsF4U8d2uYz6uXtvbxKWf/U5/oTJEYKicNNqJmH2QGZzWjJ+Z30+4W632lL2q3FaunnDj+NxXExOMPF0M/uCyHYLWrw9u0AcQxLf0n448TOteCHIsQ4S0d1JgII92rGEtotyVd92Tb/+3dc4g1P+us+vVGC1SNJc8rSv9/L2T8cnc9//a0rVYq+tprb6a3Kgj8d2hcQIIWoZvgrhd/gywBwMyRUW02S+sc9eKEN1MzJO4sa3IaxzzkQAdrmE3P8qUnIbEYow2pmv9MuHBTHVPilFFXA41HmkLXVCECVKfpHmOOfTvd3zvE/f2ufT3ymz+290bWRRSPC/ZzjV0pM1C2Ow1iHmVFK7x2E8OQU/OaDXSNPQElRJe0P4pxgRXW/RDsKSHN7Yrb2RUgzS6AkmTb3pJovzTRyhQzU7Y2IXBuev9GbaOhYB9N5AVJy4qGA0/C/nyfvEyUF6T3aUGnQoEGDBg1e6mgI/ysEh0O/yO+2RoR/QuEXYwo/I4U/yyC3uupFL/Hc1dEw9t6h5sUbkwtlARXhT0zMYX5AW3UWqm6j+f3F6n5ucz7f/wIAX1ao+yV2lZ/j37NDH998RLRb0nNu5+sJ6yCEYDPcIiwqDUuUCv+ZndnzCALJW97gNyWu3cpGIwPOgdGMfiRl0TTgCcGzV/z1vnQh4MKZNnGq+dBvXSExMTeSa+xld9EFjxuF9vnPJBQBUvqQRoAcTamJWetPcnqOf5DHlALZgxc94e9NW/qdAZ0g9Ijcl5s946GNiUmq75UE//wZf78NluQCLCP8MLL1H4Xwl+n6UiiM0+ijKPxTmwS9gSHL/fU9GLtmUqj7Xs0XKkmc6lqXgQ8/q7d+3wtIKcgLkrbu5oMsCJ6xlmG6WmDfNLptxd1eeqoE1TpHpg1R4Cvr7kV1XJIZghVnI3Y2IrLc8MLNftX6sQ6m8wKkFKfuZHDOzTgYlBTk+fIWkAYNGjRo0OCVgIbwv0LQLwh/q52TOE/GtuWYwl+ov6nNATFh6d8Od2ipSWX++YLwl2n6n/r8ZEWdFAGZ8T3P++kexhmiKWI8jXJ+f3dJYN8z/S+Suowt0eZy9MDE93ZVqfAP4Ri96lKKSoUeLAmWm0adwj+OrY2AC2dDnIMvXSnn3S3gqErQK57jF8vl4x59WPHNX+fP+RNfvMMnnn+OWA/ZDLbIClFuFNrnvxARoqQgGg9dlP6crAWLm6gABDgYeveBFHDpnP/sZyz9zoLNEGbyOo/P8TvnGOgBgfSW4WGh8J8/4++F1RX++cSvDO47WLOaz6fre8IvhMAxq9avgulKvnFVf7zOUJYugmPcl8dFGEjyOcF9ts4bfQ+hhKgaBEwxl73q5oOS/rlpZtB69fn9cbQjRZJqDk4wsX4aWluMcUShwhb299OEsUUln1pxNEIIdjYjhonmhZt90my93325tmBHm0ZKnr6TwVrHNONX0m+oNEn9DRo0aNCgQUP4XzEoZ/iDrlf3O7JFOEYAR6F9Oc46OgU3T1Izk84/iE1Fat7+Vm+nf+FaWin04IP7MpcVNXx9uqq79BhXCexzzvGZg98D4I3tyzOW3yqp3w5niOi62GiX1XyrL3qtdRz05iv8JZ581AcXPvNCQfit8c0CYkzhx/dZ9weaW3f9tX34oYBz52Q1FvAbH4/pyC0EIyWtVVn6PXFRIkAIQSCDKotBBv71jPbX1I4xPessd4d+A6fdktUGzGF/UhkW1iGMRejJ6P1AhFV7QWpTMpsSyYhcu0rRLBX+4ZzawxIrKfxFNd/BmoFjxhm009XsvUCsrb4758htNpHQf2d/bJRlTOH3mwruvir8gZLkxtYG99URp3sJKSHXXpW1dla1XfxcX8U2TDS59Zb5dSGEoN1S3D5ITi1QTxvfDx+FEj1VQ3gayPWkxX4VCCHY3Yroxxkv3OyTreF48JV8Y7kVSpy6k8E6N5Mxo6TA3IMNlQYNGjRo0ODlgIbwv0JQWvplxyu34+o+jEL7HA6NoV2I03V29hcKdf/i2ZAHzrd47GFv+f69zw+qx5TBfXv5nu+6XqHSb++gUMZ35iv8V4bP09OHRCLg1a1HZr5fWvoPbIypqYdbB6NqvtUXjYdFQn8QiMr9UIcnLnvCf+1WxiA2PpHf2ckZ/kJ5f/ZFf70fuBDRaQv6eY83vAHaLcFBz/C7n+ujjatE+pGlv1D4RYQUfiEf4I8pCPwi3phyhn90jpnN6BcBeO2WZHurGAXQriLgHsYfs04m8gYCGZDbnMxmpCapGh7K+f0wEOxs+tdctplSzuEuIvzldT7srzd3bJzBjrVPCMTEKMKqrzG+aQBwZ2/0Gr2Z3APuK+EvifFchf8+QkqBcYWd39i1zAZKCKyxDJJ8hvytg24rIE4NB4PTUfm1cf73g5JwDyrr5lXyLYMQgt3NFgeDlKu3B8ufUCDN9cQmrJLi1J0M2rjpEX6UEvdkQ6VBgwYNGjR4OaAh/K8Q9IeeDLlodn4fIEAhi9shJa8s/XWEv5zff/QhT/Tf9Fpvo//8s8Pq8UoojDPEekhnBXVfa0tv4InQIoX/0wefAuB14UOEajYAcEO2CUWAw3Goe8dK6h8R/tUJ2rhLYZEdebMbcOmct1F86YUYT54d1Y+kEAjnAFvZ+V/1cIeWbJPbnHPdHd76ll0APv7pQ+4Ujgsh/GaDD4fzxxKKwL+eEFUWgwr998omu/GU/tQkxEVgX7utCANZhRhO2PqdQ2B9OOLYHLsSCosltxkDPUAV71km9Hfasqo9TFI7V/3T2lXHt1jhL45tsB7h12WdXnHNlQwmwgZXQV0l3+0JhX/y3pFC3tdqPvB3WFqTYK5POU19Gbwq60matpZ1ogT8ZoEjzS1hcHTCL4SgFUruHCSnQlL9axb3+z2orJu22K8DKQWbnZBBold2PKSZRalJwn/axNtYO1MlKYoAltPeUGnQoEGDBg1eDmgI/ysEpaVfB4XCP0X4hRBVcF/qDJ3IL5SmCb/WrgroKwn/gxcizu2GaOP47DOD6vVA0FpQwzeO/cIG34pkVQM3jTvpLW6mN5BI3hg+ADW950IIdso5fnM8wr/RWX+Gv3IpLMkhAHiisPU//XxcKPxj3ywWrElsuH7bk9DHH2kTqRbdYAMhBK9+rMPDl1oYA//jY/uAV/eFEBPEVQmf0i+BsLhmMhxV8/nixdGbD/SALPPn3ik+i+3NkvCPWdStwSHBasQUiRVIBnpAauIqu2FYJfQrWpGkdBnPm+Mv1X0hWEjiSkt/b7heSFe5IVLNGyPJrSfwq2K6ki9OzIQjZBCbiQ2NlwLhV0oQJzWE39j7WMnnCaZzfuwj13attgD/XJ/yf5T5/XF02wH9JD+RTvppZNpUDgTfSnC6bo9pi/26CAM/g79KkKG1jjQ3E+MUJfE+bYW/fkPj9DdUGjRo0KBBg5cDGsL/CkGvsPRnyhPy7YIUj6MK7kPTDv1CaZrwX72Voo1jo6M4t+sfL4SoVP6nvtCvrMFb4RYt1V7p+MaT7eepUVeGLwDwaPtBunL+654pzu3A9Ge67NdBpfAvCZYbx90FCf3TeOIRT/hv3MnoD/TUwtwvlJ+9muKcn3kviW31CCF421fvIOVow2RUyedJZYhCygDpX5GAQuEPRoQfqCz93oYfozP/GZSbL6Wtf5zw42wxeiBm8hJCGZDblNyNGh5Khb/blgghqus7b0MlTUd2/kUKZek+MMYxrCGy8zCdru9T9L1Ff1UYpyfcEeX8/vamonBt0x9rIlBCoV1+X9PDw0ASZ2bGwm/M/avkA5BCYKxP2tfaotY9FufIc0MUrNBBt+g4pCBUktsHyYl3yKeZRVYJ9px6gn2S6WN9puUmzCpz/LmxGOtqPjdxqoTfK/x15+hOfUOlQYMGDRo0eDmgIfyvEHjC7xhSr/DDWHAfhk5QEP7MTpCT518c2fnHSdiTj3botCWD2I6C6NbA3gqVfLfTmwA8oHZxC2qmyuC+PTPwyvkRsXGEGf79JQn9E6/fVTxwwavfT7+YMTmI6v/9pateZXxVsTkwjd2tkLe8Yav671FCf1nJpzwplwJEYe8HZDHDr7VGICrSmhlP0rNC3CwJfzlzP2FRtwaEwAk5J7gvQzAi6+MKPyzPSKjm98PFhEWpUaPC3V6y8LETr2/Sidn7cgzF2HU3DUbHV4ZZnt8N2S4mWcbn+KWQxbjF/Q3u08bXw41Dm9V7708D5X1irCM/yrEIrzKfxKbFZidkEOf0hifrxkhzQyBHCfanFQ5YYp1KvnmQQjBMl18HbSzG1AUmnq7Cb4yjzkSm7kElYIMGDRo0aPByQEP4XwHQxjJMDYQpBoMAtmrm6ktLf+Y07YIQOkfVKe6c47lrnlA99tCkwq6U4Mte7TcRPvX5/toK5n41+15PlJ1z3E5vAXBBbICYvzFQBvft28GJzPCvaum31lVK+yoKP8CTRXjf01f1zCZGmsOLN/3rveqR+Y6Gt7xhi53Ccj8b2OeT+aUAhKgUflko/Ea7IrTPf16xiRHIytnRafnX3SoI/zh5FVbjhACpQKcT17qc42+P5SyMK/wwvqEyR+GfTuh3BpkeIvLZDaVyjn9/xUo15xy5yyYIf0U41yDjmZ18jVLhP7er2G774x/fJPEuAnufCb9XXKeT+r2N/j4dVAWB1j7kbV3iHgWKMDyZE5BSIJXgzkFyYpVyugivKxVwKcWpVtaVlXxqzcC+aYShZBDrpceptcW42Q2X0yTetsgHqNscUlKuNIrQoEGDBg0a/H7HfV/eNTh9lCqV7Hg7/6bsTtSIlags/U6jxCj8qiR/dw80g6FBKcFDF2cD897w5AZKwq27OTfurDf/WoXdzSHKh/lBRa7OyrYnmXOwW1j6D22MXTOEbRwbKwTLTRxjmdCvFif0j+NVj3QQAm7uw2Ey+Zk8d0dinXc97C5wDARK8I1fe4YoFDzygN8YKCv5IgIkohL5S4VfqFLht4DA4knoUA+IZDQi/O1S4Z+d4ccZXyMoA3B6IrhPCMF2uENYzO9DncK/OCNhRPgFMjlA7b+IOryGyIczj92sCP9qCr92GuPMRNgeFC0VK1r6F1Xynd+WbHcKwj+u8COx91nh93PVkI+RMGudrze7jwo/AA4ybXGOtQn/Zjdkqxstf+Cqr9cOOBxmVeDpceEJ/6gi77Qr67R2aOOOVFE4jlBJ0txO3C91yI2tbUg4TeJtrfcm1d22Sglybe97+0SDBg0aNGhwv9EQ/lcAtrohl860OH/hEKi38wO0hF8sp06Ds5Wdu0xsf75I53/4UougJkSt01a8+jHvHPjU5/orH58xriKS8xT+Ut0/G55BOreQ8HerpH44zHorH8c0WpGsNj1KB8IirJrQP3GsHcWDpa3/+uQ5PXPTk/PH59j5x/HgxRb/r+98kK98o3c3lPVyoVAI6SvnxDjhL2f4tUUA1lpSk5LZjFCGFeGvZvirGj1PWnAOjAYkSIUwZia4bxrjKf0wUvjnz/D7Y2yTIHs3AIOVClFjud8qNlj2VwxaM05jrJlQ58ET8lVD9Xwl32jTQGvLQeHwOL8j2G778+r1JzdCHGDvI+EHv0+TZKPjMtZhj0CyTxpCQpabuartvYRSEgHcPYxPJHNhuiLvtCvrcmOPVMk3jTCQaGOXkvZM1zcrnCbxds7h5ir8AmtPd5ygQYMGDRo0eDmgIfyvAARK8v9+z2t58IHFhD+SI0s/jMheSf6eu+qt1NN2/nGU4X3PvpjMdpDPwUFP45y3o5dkcALOcCe+CsBF0amC4uZBCFGp/Pv67krHMO91HjjvyfjVm8ut4mVC/+6Kdv4STzzi3RJP3xide64dz9/xRHKRnX8c42StTOmPCED47wkgLCz9lAq/8anhFktqEhwOKcYt/f6Y2q3R5of/XIt6sfKzcG4muG8acaHwd0uFvz1nht9qZLxH3vP3ayt0uPYWBB3PCO0s8dje9Pfufm81S792BoudaZCQQlWbJcuQuxxt8yqU8O6Bv487bclGy7HT8cc5Xc3n33/1nIDTQKgkg7GAQ2udn3+/3yRb+mR1644Q2ncK6LQD+rGeGX84CrSZrMg77cq641TyjUMIH9y3jPCnqa79zErifdIBiADWgnX156ik8BWPTVJ/gwYNGjR4haMh/K8QCCHoOa/Qz1f4R5Z+sLSjkaU/Tgw373hC++iD8wno2d2Qhy+1cA6e+sJgpWPbGwu6qxZuziLyGDm8g9q/wu34OgDngzO4aGveS1Wo5vizg5WOYR4euuTJ+IurEP4lOQTz8KqHQwSOW4eCg4FfnL5wG7QVbHVE1YawDiqFnwAhRjWJQaloy9EMvxASYzUD3ScUIc45kqxU+P3jhRCVyn/Y136ljaVMy3JSIvR8ouycq9oOKoW/W6/wy3gP2b9JmRPWagVUGzxC1BL+MlRwr7eiwm91vf1YKLTNV6rmy21WbZDAKLDv3G4I1rDd8tf4cKAnFGKBnGkIuNcIAx8YV4bGWedzHKb5mnOO1Ky2iXISkEJgnVeD7/d0AUAUSDJtSbPjOzK0thONDqddWXfcSr5xBIFkEM93vpQbAnVugpHSfvLEe959C36Ts2x9uNdIc8PzN3qNu6BBgwYNGrwk0BD+VxD61hP+nXkKfxXalwOOdmtE+F8owvrO7YYVUZuHUuX/zDODlcKapuf3RT5E9a6hDl9EDu+ineGu9ZsHF6LzS18PRkn9+/pwpcfPQ5lVcP1WutSSWir8Z3fWI+jdEB4+66/T09f8ezxz3f//Jx4QR1LoKoVfqKqSTwiIZKHwy0lLv8aQ2YxIRqSZo+SnpcsDYKuY4+/1DcJZhHWj4VkZgE5gjvU5yx3lunt6hn86tE+YDBdEJNo/rjVxOaUPB5wi5GVt4EE/X2mBn9lsLuFfdcY+MQmS8fl9f83PnwkRRrPV8dcizx1pNrouSqw+NnBaCAJBbgx5kdRvrKsNXEtswp301pE2KAb5gFgPyWyGcWYlW7wsVFk4vjJ9EiiPIU6Pv0GT5XVBhOLUZvjTXJ+YYyMKJMNUz/3Z8uMKVPkE45BSoO3pjC5YNz97wo/PiPui8Ofa0o/ztWpCGzRo0KBBg9NCQ/hfITDOVoR/rsI/FtonnKNTKvyZ5blifv+xh5fbyy8/2GJnKyDP3UoVfZUVfjsEZ5HxPiIb4sIOrrXFHbzVvCNabMjV7O1nSku/6R8rqf/cbkgUCrLcVYFsdRhP6N9dUC1YC2d59UX/3Keve4vvczf9IvVVl462WC0V/oAQ4Yf4EUJU9nNXEn7jFX7nLNpplAxIisyGMBQTCd8TCn9JuktLvFTg8ongvnGU8/tRKCoVcKMg/rl2o40hZ6psgFT7cy8Jf2ozPpNeIbEpMHldNjoSpfx+w35/ucqf2WwmsA98bZ52BrPEcm+dJTExoRztRtyZUPgzwjCkG/njnKzmU+QuO5G58KNCSYm1rrKqW+uKCY1J4qRtTmISUrueyp/ZjKuDq1wZvsCVwfO8MHiOK8PnuR5f4256h8P8oHZTxavBsGhk514jDAT9Ber2qohzXaOAn15XfJyaYwf2lQgDida2CtKcRq6LSr4aqd07GU6HeFtbKPzzxj+cw9wHld1aR5JpesOjh8Y2aNCgQYMGJ4WG8L9CcJAdevsxkg05GQKn8gSp0zFLfw7O0Y5GCuyV637BP2Pnd2ZG1RVC8OpH/XuUQX+LMBF2p1NEHuOiLhSE7LbeB+B8uLuy6lda+nt2iDmGmiql4MELha3/xnzSM57QX1bErQrhDE9c1EgBtw/hqecdmYZu5Hhg93iEPxRBNVc7rvC7CYVfeLJfXO/p+f0S2xsF4R+YYhPFUREzGSwM7ptO6Adf9xUWNYKVym8NYEAq0mKt3Coe8+n4WX4j/iyfTp+HKaVRCMHWhj/eu4eL7znrLNrmM4F94Am/wy1VtHObk4/N71vruHPgn3NuJwSrcSoYJfX3xwm/xNznaj4AnBgRfuem91AAf56JTUjNau0HJVKTkuqUjWCDlmwhhcI4w0APuJPe5kZ8naTmNSuF/yU0dh2FijgzZMdImrfOkeUGNdV7eFqVdSdVyVciUBJt5wf3aWN90OIc4u04pRn+JbeKlILsPhB+Y73j4WCQVWMzDRo0aNCgwf3CkQl/v9/np37qp/jBH/xBvvM7v5Pf/d3fBWB/f59//a//Nc8999yJHWSD4+N24sPrtlV3gjRLnfH47/4XXvW7P0dUkJfM5WAd7aLh6vmrCbl2dNqSC2fH/NXOofq3EHp24f5oEex35Ua60LJqjKuSzc/sBL5yzZmK7APcyvcBuBDsrny+HdkiKpL6D7K9lZ9Xh9LWf23BHH+5abG7RkJ/BWtph/BwMa3wG58r1P2LFnFEd0I+McNfHo8gwF9XK8Zm+BFFOr//wKuE/miK8I9V8wlnwI2fpwAcwtQT/lLh706FMm5MBfcJaxHGgpSjGf7ilts3vnGhb+Pa61La+m8dLHaVlJV8dYTfn4lYSsbzwqauZDlKoDHGEQSC7U38JphQbHdKhX/0euXYwP1O6leBIC4sx/PGVTKbgXMM9WAtR0KiY6SQSCFRMiCSEW3VYSPYYCvc9u9Zc/5SerJa0xp63xAFkiw3JMeY4zfGYiwzBFxJSaZP/j4YVfKdnFNCIEiz+o0wb9efX+sopTgV4uvv2/nnqKQgO4H8hXVhrUMJSDLDMLm/4zsNGjRo0KDBkZZV169f5zu/8zv5iZ/4Ca5fv87nPvc5BgM/Y727u8sHPvAB3v/+95/ogTY4Hm4nnvRuq+7E17fvPEOYx4TZgG6xIMudxmIqwp/lfqH/6IPtyQWdM2Cy2pq082dCum2J1o5rtxYr48552+xGyyHSHk61Jh5zq1D41yH8Pqnfq/wnRvhvZ3OJUTmWcGbNhH7Ad9gjePWD/tqW6/8nLlE7r74KMjOy9JeuXiGgVSrSBeHPjSOQAQpFWDg84pLwtycJcWnp7/U1ztqadbYAU29hrVP4wdcSwlhwnzP4BoAR4W8XhL9vhv74bFZ7Tc5s+QfeOlgcFmnsYsIPYumMfWpS5NgFqOz8OyES6zdEpKpX+JHYotLvfiJUgjjNsWVSfE1gX2ZTWqpNZrNqE2kZrLMM9ICWihY+rm5TRQpBbhZYtO8D/O88N1FjuC5y7Wot71Vl3QmPd+TGh9VNOwqOgyAQ9OP6a5BpU5uJUUKekpPBObcwl1BJsbRd4DRgilwBKbzK36BBgwYNGtxPHGk18OM//uMMBgP+y3/5L7z//e+fUX7e8Y538NGPfvREDrDByaBU+LfkJOHfufmF6t/tMTKbOV2l9JeYruMT1niyX0O+hBBcLuz/i2z948q40jHCZBCMiMLQJgysV2zPr0H4YRTcd5Dtr/W8aZzdDWhFfvPi5t36xdtRE/oBhNE4KXnVJVGlTbdCePAseMPq+mQgcwXhF8EEeSpJfaXwG4tCshluVZs5lcI/Zenf7CqEAGNnk/UBnAoQeX1wXzyV0F9iowjuK1/P309l6rd/TKnw92xB+F2G3xSYxE5B+O8cLLafa6dxC9RIJeTCaj7nHLEZosRoc+f2/mh+X1jrRw6ErFX4fZgYKxPo00KgJLlx1fz19H2mneZLLww52Jdop1ee409NQu4ywgWEX8zZVBFCIJyrnQW/nwgDSW94dKV2nuXdjzBw4nPm2lic5UQ3TqJAkeamVqnPcotc4CY4LeJtrVvYRDCq5ru3tnqdW4SAdqToDfMqHLNBgwYNGjS4HzgS4f/whz/M93zP9/DqV7+6dtF8+fJlrl27duyDa3ByuJOWCv8osK81uENncLv679DmhAWJSTF0wtFCSkp4+NKk8o7VPmBtzrxzuUHw3NVkrh14VMkXINI+TirGpcbbhZ1/V20RyvXU890yuK9wCBwVQggeuujJy9Wbcwj/cRR+k4OQtELB5Qv+S49fLAPz3Nzk+0WoUvoJJ5K6Q+VJscEArnATTC6Gy9C+acIvpWCzaGjo9TRu+mdfBN6tUBN4Vyr83TkK/2iGX4MQZHpEP1shpDYnK143Lmzm09gpLP37vWxhrZ5xi9VIJQK0y+fes9ppcpsR1AX2nQnHNsAEW0VcxrjCX77HQK9WW3laCANJbvyst7azCfJ3ekP++/8a8ku/vke/D7FZHsAJkNoU6+wCB4XPMZjnoji306bbPsLP0SkiCo43xz/P8q6kwBp74oF2uT65Sr4SYeDHD+qIe5KZhZs0Sgq0sUubTtbFMmeEUj4s8LSaEOYhN/7nqRUpklzPdUY0aNCgQYMG9wJHIvxJknD27Nm53y/t/Q1eOhif4S+xe/PzE4+ROpsI7muHowXcQxdbhOHk7SKsn+WeN7f98KUWUnp1s5zTn8ZeEXR2ZhNEHkMw6SI4ip2/RGnp38+PV80HI1v/1ZrgPmvHcgjWVfgry76/tn/gdZLXPCT4mtcIwKud61r6vRW7mOFX4QTJKJsYEIA0nvDbacJfH9oHY0n9QzOq5CuhFMJohJn9rOcr/JOWfmEy3Nj8vpI+CLFfqPsAKRpbs8m0UxzbYGjpZ8OZ75fwCf3zf/VJITFWo+ck9Wc2QzszajxwowYHr/CPnrdd/Lj1h2aC7EQyJLXJfa3nE0LgrA+T09rNVLjt97xTwhj4zY8nDPVw4UYK+Gsx0IOlm3NSqLmbKlIerYryNBGFxRz/sQh/fUe9LkcqThAnWclXQkrhf7dMXQNtLLlZHBB4Wkq7bxlZ9L4Sa+2pBAYugjYWWTSjKCGatP4GDRo0aHBfcSTC/+STT/Kxj31s7vc/9KEP8cY3vvHIB9XgZJGZnIPMk96S8Auj2b79DAC2WJwrkxEVhD9zmlY0WiRN2/n9Ews7/xyFPwxlRZTn2fr3C4X/bFfPhPXBGOEPd5ed5gx2A6/w90z/SD3i43iocDfcuDMbQng40FUg17oJ/dXMejFre3ZL8I63SLa7whNq5zzpXwO5G5HIwAWICUu/HKnbSqMNMwF480L7YDy4b6ySr4L0x1tDYuOlCr/1qr3RINTM/H7PTBL4RM+qzZ22JAy8Xf7mQW/m+yVSk9ZW8pVQQi1M0c9tNjESMIwtSeotvGd3wuIz9dhoS5R0OOdJf4lAhOQ2X7vu7qQhBCS58YrkFHHqpyOScvO25qnP95ceb+5yMpNUAZDz8JJpKlgRQniye9TgviQz1DXkieJn/KSJcJqdXEL/OKQQDNPJn29tLEZbggV5Ab5u0Z24k8FYy6KpBSkF1nEqlYCLoM3IMdNpBRwO8/uSJdCgQYMGDRrAEQn/937v9/KLv/iL/NRP/RT9fh/wys5zzz3HX/pLf4lPfOITfN/3fd9JHmeDY0DbHAe0RUhb+IX41t1nUSYja20y2H4AKBT+QgFOMUgs53ZDwkDw2MOzhF/o1FvwrZkgOeMoa/yeuzZL+Me7689Gw5mwPuvcqJKvUPi1sSSZWWmqvSNaVUjdYTEacFTsbgV02hJj4MadSbVmv3QpHCGhX9giib9OcRaeQK+r8Jd2folAqWDih1xKWSX1C6XRlpnXrwh/e5HC76j99SHqg/uGS2b4h0lxDzkDYqTwR/MIv51V8IUQla3/+n6/Vj02zkxUENahquabo/AP9eT8fqnu724FBIFAmBxXfJ5CysrW3xuMXk8IgUTUblwswn66x0F2sNZzFiEMJHGiMcYyzdf6iSf3ZdL7J55Kubk/fyMFIDMpudOV+2EeFBLnLLYmi+GlCj/HfzSlNskMqo7xAyBOlPBb60hzQzD3/Y6OMJQMYj1hpdfGoe3iDYayblGfsNK+TOEHvza5lzP8xhZ5DcVxRaEkzTSDuEnrb9CgQYMG9wdHWhG8613v4i/8hb/AP/7H/5g/+kf/KAB/5s/8Gb7lW76FX/zFX+RHf/RHecc73nGiB9rg6OiGXb7/tf8bf7DzhmpxVNr5Dy6+Fl2Ea4k8rRT+1GmENXzbN53ju7/lIpvdqQW8s2BynAwBM5eUlvV8129lpNnkYw4HZXc9bEeTYX0AB6bvrdOoyp6f5r6ya5jopaRfCMGu9JkF+8ck/H6Ov7D1T9Xz3T0cEf614UYBbzXvip9kX2+xWtr5fc2inHDeC0GV04AqLP1Tn12V0h/NkuKtjaKGLqb2mJ0KEHry+jjnKoV/OqV/3NLvihERZI3CP0Xw4zlEudyQ2OulpHZ2k0lbjV2Y0F8cMw5T4woxzpDZdHQNgdt7/nqfO1McrNWVY2MyqX9yUyyQIUMzWGqTL2Gd5VAfsJ/dPbZjpUSoJGlusI4Jhd85x7BQ+B9/pM1DF1sYA7/y0esL6/liEyNYbsmXQmGcuWcKv3Nu5es8D61QkaRm7QA2Yy26JqF/7OhOlJDm2p54JV+JKJCkuSUfS9zPtcUt+czLoMqTnqX3Cv/i8xQI9AmPTCyCtc7/PBWftxCCQAkOB/fXzdOgQYMGDV65OHIy0p/9s3+Wd73rXXzwgx/kueeew1rLo48+yjvf+U4uX758ksfY4ATw6u3H6akXAIjifbq9GzgEBxdeze6VTwJ+frolPalNnbfrTxO0CtYABlTg57atxdVw1u3NgN2tgP2e5sr1hCcfHWUIlMr47gagJsP6AG5pHzR4PtypFnXOQTcKSIVlkORstMKZUfJx7KpNbhhPko6Lhy62ePr5eIbw71eBfesn9I8HvM1/zHovWRF+CsfB2AWSUnj11RUKfyIQbtIxUc3w1yr8haV/KOoPWSgfQmg1FKMiaWarjL3pXIDy/rIW0kQTGguhJC2qIFtTlXwlYlM/o18q/Ic9TWIS2qoz8X3tNNZZ5JK9Tk8SZkm1r6fL2SjGRWCk8J/fLQL7jKbaSxWSnXYOqAmFHyCUEbEektl05jjrkJiY1PhAvF5+yJnW/ByVVREEkmGskUpOKLTa6bGNH8nXfPk2/+cv3+D67Yz/9elrvPXLHpp5LessQzMkWmLnh5IAurUI/829mE988TZ/8E0P0G2v97PW04ekJuVC++JazxtHGEiGSUacGcJg9dEdrb2VvS4TA4qu+BOsrBtV8o3+vA+131jaDLeO9dqBkmijSXNDq9gQzI1dKR9Q4E68jcAYV79XOgYpIcvvXWiesQ5rHWrswDqtgF6sSTJNO3ppBVI2aNCgQYPf/zjWX56HHnqose6/DLFTqPv9M4+gow10sUD3oX2eyJSEfx6ENQhjca026KxIZm/VPvbRh9rsf67P89cmCX9ZZXe2a2bC+gBuFar8+eDM2FcdYajY6kbc6aX0k5yNdjBX5SmdAQfZ3txzWRWlwn/zTobWliCQE+dxFIVfLCM8zs/Yr8P5Rwq/QiAmZlyFEJWlH1la+sfsuXqUGF4/w+/PMcl9kn40ze1UgMiG3tZeEI4yob8ViRnbr1KCdkuSpJZ4qNmUDhAkFeH3jy8t/VuyS88OSUx9JkQZ3NcfwED32Ql3J5RHs6SSrzouoWqr+cr5/fHQvyqwr0rot9VmB4iqmm9a4VdCYbGkZjXCX6b6t1Sbg/yAzXCLUB5hk2kMgZJo6wjE5DkZpyfaGrY3A77+zTt8+OMH/NrHr/G6y2c5uz35M5uahMykE5shy2DXIPwf/tQ1PvXMXba7EX/gyy6t/DyAzGTEZuh7248YZufnwR1pZqC7/PEl6gj4OJSUJzrfrY3FuckKwMQkxGbIRrB5rEDEMstg/HizXCNX2P8QQpCd5OhCEXa4TOFXSpCe4IbKMniF3yLHPu8o9PV8w6Qh/A0aNGjQ4N7j5If8GrykIaxh59YXAW/nBzAF4Vd6MrTPh/LNoZpO46VnybLquNLW/8K1dCKpvKzkO7sxG9YHVPP74wn9QggCIQgCybmtFpudkGGiMXPefzcsk/r35x7fqtjeVGx0FdbC9dueDFrrquDBIyn8dtlC/+gz/F7hZ5LwM7L0C6WxTmDGUvVLdV9KCMPZhXQUStqR//phUrfQluDsRHBfmdA/HdhXorL1D0fHUVr6W6Ff2Pett/BfCP3mT2yThdV8vb4n0tOkXVu9sJKvOguhyF02Y19PTDLhDsgyWxF5n9BvZjIZttu2OKZZlXHVej5tNQM9IJItWrJFZlP6+eJ5+lXhnCsI4uT7ZVmx6VJs/Lzx1RtcvKDQxvF/ffjZmWuT2nRmM2QR5rko5uFwmBf/f/05+tSmaKsnAi2PgqPM8dcR8HH46ji7tGJu5ffTduZHI7NZka9w/DnyIJAT8+hJujiwr4SSguyIoYd1sNbhmC0LqXvfPDdzR1H8uMfJWf5LhX/6uMJAst9PF47ENGjQoEGDBqeBI201v/71r19JJfjMZz5zlJdvcIrY2rtCoFPysEt/9xEA8qKbXZl8FNrnyiJ0r7hOQxjDyGsuEHZ+kN4D5yOiUJCkllt3cy6d9xsMZXf97tYsEcydZt94QlMS/nKdJAuVOAgk57bbSCHoDXPaLTmz8NwNPOHva5/UHyypC1sEP8cf8YVnva3/kQfa9Abm6An9UPXOz39TjmzpD0VQvPSYpR8Iyh975cmWzQzKRx2MAvtacu7P+PaGJMkMh0PB+e05B23GCX/9/H6JbkdyZx+GQ43b8p/fOOEf2tgTSSTngm2eSV8kthk+22DyNXe2Rqn/SZ6T2oTWWBhkatKl8/vg1XftcsxY/Z51lsTEBGOq+p3iHt7oKtotBXk6k8mw3S0U/sEsuR2v51uk1icmJrMpW8E2Qghass1+vs9GuLmShX7huSpBridnoXObk5aEv7CiCyH4hq/Z5P/+4AHP3+jzsc/e4uve4C3yZR1fda0K0rMIUsi5wYh1GCb+WveH65FW6yza5minyW12rOsVhZI4NeTaEgarbWx4wj//+1IKcu3t7nKNUYF5SHKNmtpc0DYnczmZSY99v0SBZJhqjLUIBJk2KzUCKCnI9Mkp7c45nHXIJeGESkofLGgcYTB5nEmmuXF3SBQoHjy/cSLHVd73078/25GiH2uSzNBpNSp/gwYNGjS4dzjSX533ve99M3/MjDG8+OKLfOhDH+JVr3oVf+SP/JETOcAGJ4vdm6W6/5qKlJgitE+ajFal8OdU6nKdYmezya8vUOqkFDzyQJtnXoh5/lrCpfNRoYwXlv6d2dvwdn6AAzZkm67yDgHjPCmRUzPpZ7dbCOGVv3bIRDp1R3ZoiZDU5Rzk+5xrnV9+kcbhDKp/G9M9AyrioYutivDDaNPiKAn9AMLqKtF9ziOA9VSxzPhjC1EgQemEMx/5z6QPvJr+I189ofADaK0r2hwvqOQrsb0huLkHB8P6zSAnFUKPLPfzEvpLjAf3ld7gcoa/HY7s/JuqQ1f6eyG2qd8Bmnr7dkvRigRp5hgOBYP2gO1wxx+Xc+QuW1jJV0IJRWoSjNPVBkluc3Kb05YjK/udvcLOv1uQ9ZpMhq2O/3eaOdLMVoo5+Hq+xCSkJllI+AdmgGC0CRPJiEN9QD/vcbZ1bun5LEIUKLIsn7h/U5uSlZ/B2PGe22zzlW9K+dgnEv7bb1/h1Q9vo5Tk6t1Dnr6xz8GhY/9wwP5hjpSCRx9q8/jDbR59sE0YTn7+UqjKjbIKBrG/X3trpp1rp6uAwLymMnIdRIFiP81IMk0YrEacM23nqvvgiXBqTEFIj3V4wGwlny3qD401xCY+9hx/GPik/vJ9jHULf1+UUNI7Gfx4w/HNhdaCdSNifZgfEIiQbjA5b6GkIM0MxlrCwp1jneOgn3HtzoDeMOP87vKRmlVh5mx0lbb+QZI3hL9BgwYNGtxTHOmvzo/8yI/M/d7Nmzd597vfzeOPP37UY2pwSojSAZuH1wA4uPCa6utlSr8y2URKP9i5Vn1hMl/JBzgpEWaxUvfoQwXhv5rwtW/apj/IvTIuHVvd2cVfFdg3Zuf3ag4zi2chBGe22kgp2O+nbMjJZPozost1d8B+trc24RcmBz1AZhG2E1Vz/Lfu5mS5reb3d4+S0A9F7/yCxa8QfmW7BkrbbigCBBAd3kQND2ld+yJc/mqCgvDKwBNxnZsqfWEU2DefFG93iwX2PCe6DLzC7/yoRjnD351D+Lsl4U8s5ZTReC1fmdC/Jbu0hT/S2KVzRx22NwNu3c2JB5JkN67U85L4BWL56IUUEotFu9G1yW2GcWZiFvv2eGAffmRmGmEk6USOOBP0BprWWPCBEH7AIDHJXCKW25yhHkw4FYQQtGWHg3yfzXDrWKpttx3QaY0+b+ccuc1mLP0ASgY88SrF9WsdXrgR83/8f39vrnptrOPp52Oefj5GKbj8QJtXPdLhsYfaRJFEItHO+HnnJWMA1jqGqf9ZW1fh19Y7NSIZzc1+WBVSCpx1JJlha8U5/jSdVdzHoaRAF1Vux0VdJZ9xBusMLdViaIYrXe9F8MF9ljQ3RPh/z8snGIdSgjzzGSEn0Rhoy1GU4tLGOqalLN2pgAWlBNq5KpskzQ037g65c5AQBpLNblhZ/o+Tb1Ad14LPMQwk+72Mc9vtE3mvBg0aNGjQYBWc+Az/xYsXec973sM//af/9KRfusExcf7O8wAMdh4ib4/IhZGlpT+jVRCH1OUIN6cSzplJoiokLFHOLj/gycqd/Zz+0LC/52eydzdEbehS3fy+ARSiVi0TAnY2IjpRSDw1J7qr/ALwIJ8f3Dc3PdoahM4RSQ+cYWsjYGtD4ZyvGiwV/rNHSugv6gwXLPxcMS6xDiqFXwRIBFJ7FVVmwyK0zy/OZVAo/MZW5Lkk/K1FCn8ZQhfPWdhKVYQ6+msTVwr/khn+1FV1dlUtXyToG3+vbKkunaJFIrGZn5WvQTnH3+87cpuTFM8vVc5pS3+ubW1dmEBgxiznmc1m5v8rhX+8km8Gku1CQJwO7gMIZLSwni8xxabF1EZFS7XIbc5hdlD7vHUwGWxo0E6PLP1T94ISij/4tTu0Qv9zIIXgzE7Io48EfM2btnnnHzzL//OPP8CfftcjfOUbt9jeVBgDz76Y8Gu/sce/+7lrfOgjd3FWYFes5ivJPkD/CAq/wxGIkNQkx67nCwNJf8U5/jLgblFFXnntT6KazwcETlbyeXeDpSXb5CYjtcevh5NCkGQarX32wCIHQ/UcKXxH/QkF99li9r66fk7XZkKUQYPaWPb7KV+6dsjN/ZjNbshmNySQEuPmK/PrQi/YoO20AoZJTpzemzrKBg0aNGjQAI6Z0j8PnU6HK1eunMZLNzgqrOH8XU/494uwvhKmsKYGJh8L7ctx1iGcmx0ht8ZXuVWqovThc/Ps/3iyd+lcxI07GS9cS8gGXmk7s1ljCXeOWyXhD0cJ/c6AWkBEhRBsd0Nu7vsQP1UsBHeln83cz/ZrnxenPpF8ZzNETllNS+eCMAkyG2JbWzx0scXnvjTk6s30eAq/tYCBRYqzEP7E10Cl8BMgpEDkaXUuyuSEpcJfWvrNaHSjXIjOqxAD2CkJf30zng9gdLYiv/FShd9/fZiIMUu//15rTOHflJ2K8GcYjNW1O5Yl4T/oGyQBQzNkK9yuiN+4utkfGv7PX77BAxdafMsfmrXGlwTCOcdwbEYdfCVYGTxZWvqFqRnRkJLtjuXGwWw1H0AoQ2I9JLUpnam0fucc/byPEqpWEWyrDr38gK1we8IBcBxol5PmGlPcdtN27VBGuHbOe7/zDaSZ5exWxNX0CjhHqxi/UUqw0W2ztQFf8+Vb3N3XfOlKzDMvxOz3NM+8EPP6J9ucOWeLpP7FG2bDZDzQ0ZDlhihcbd69DGoMZFBtnhznWkWhZJitNsevjUNbVpj3FydD+LWdUdyNM37DQwZYY8nM7H22LoJAMIg1oZIrZ4woKbGWSmk/Lqx1E2GI1mlyW3+dhRPsHabsD1ICJTi33ap+nqQUmNxv+p1AhILPw5izARIGkkPjK2W77cbW36BBgwYN7g1OXOH//Oc/z/vf//4jW/qffvppvv/7v5+3vOUtvO1tb+PHf/zHybLlaopzjp/6qZ/iD//hP8xXfMVX8O53v5tPfOITE4/5yEc+wo/+6I/y9re/nTe/+c1827d9G//yX/5L8vz4ycUveVz7LFGeoIMWvTOPVl92Dmxp6bc5rYIMWpwP1Krx6wqrPcEvu5ikxNv/FxPTy0Va/3NXk9H8fo2LeWgTYpsiEJwLdqqvW+f8AnMB2q2AbjuolGqAM0VN2PXkGjeTGxOPt9aS5YZ2K6hVeIRJcDLACYVIe+AsD1/yZOHFGyn7vaMr/MJZhHUTmySHZsBH+p9iWFqPhVg7pX+k8IcoIRB6pOipLKks7aJQ+E1J+JkM7ZuH7Y7/nHvxYvuqMP7ndllo30jhh/JXUjJO+MtKPtUlEkGVkh/r+h2HsprvsKeJiq57bTWmRv17/mpCljuev5pUWQMl5Fg1n3Y+4X08sG//UGMtROFYYKPNYXo+Wci51XwwqucrP7dx5NbXyUWynqBGMiJ3msP8+Cp/CW19sBj422+6rSGUoQ+/a1kunemihQ+DC+eMFQghOHcm5GvetM3/9m2XeOCCf1ySjObLl2GQTP6OXkflL4MalVDFHP/6Kf/jiAJFmpuVqvS0sVhjF1r6PdyJEP66RoDx6sNVWyGWobwGg1SvpO5XKKr0TgLWjfYarPNOg3khkEoJ7vYSNtoBW91oYvNMSYG1dqEyvw60WVwVGIWSvV56os0ADRo0aNCgwSIcaYv57W9/e63a1Ov16PV6tNvtI1n6Dw4O+N7v/V4ef/xxfvInf5IbN27wd//u3yVJEv76X//rC5/7L/7Fv+AnfuIn+LEf+zFe97rX8TM/8zP8wA/8AD/3cz/H5cuXAfjABz5AkiT8hb/wF3jwwQf55Cc/yU/+5E/y9NNP83f+zt9Z+3hfTpBP/wYAB+efYLw02TiHU6OFemgsEoHFkbqcTo2lX1hTbAQU94CQCGsR1uIW8PHHHmzzW5865MUbSaESi1qFv1T3z6itat4cwOGWpkELAVtFVZ+2vi7qQnCGB4OzXNN3+W/Xf5m3X3onlzoPAj4IbHsjIs0tM+tQ50Bn/nqpEJEPETqp5vjL/nWlYLN7BGnImZlE998efJbnsutEIuBrNt4AFAq/c8v7pwpUKf0uQEqByEcER+rYh/kBqGKGXzuEszhWI/wboScwxgr6CWzXzDI7qRB5Ap3loX3lDP8wE1XVVl6s21sh9JMytK+LEIKObDGwMYkZUperPVL4NaGM6OtelYQ/nfJXhi8CvHg95TWPj05GCUlufTVfZjO01bSDkTJ6Z99f13O7of996GxRs1hD+NsGCGoVfoCgIGI70e7E12MTo52mK+cniLdVh35+yHa4XSnsx4F2mvKWaUWzbQ1SSByO1CR0gy6pSdaq4yvrGePE4nCrEf548rr14pyz28vPtQxqLMc4BMxUNa4LKQU4SFLNZmfxRl9uLNq6pYRfKUl2Al3xdZV849c3XLEVYhnCQDJMM7JcLhxXmIbjZEYXoNxsLNV9i8Xh5mRC7GzOz7iQUmAdtWM9R8EihR+gHQXEqXeptKNG5W/QoEGDBqePI/21+bqv+7pawr+zs8Ply5f59m//dnZ3d9d+3Q984AMMBgP+yT/5J9XzjTH8rb/1t3jve9/LpUuXap+Xpin//J//c37gB36A7/u+7wPgq7/6q/mWb/kWfvqnf5q/+Tf/JgB/82/+Tc6ePVs97+u//uux1vKP/tE/4i/9pb808b3fT3B5Ctd8ReLexVdPfdNBoLBSIa1BaR/cl7iMzGk6dSrEdJWckJ64Llm4n90N2OgoBrHhbt8//8zm7OPKwL4L4e7UsbK0ggn8gmqzE9KLczbbEikU/4+NN/Oh9HNcT67y3278Cn/k0js5F1xCSsnZ7Ta39mPy6coop/3/ZFDY1B0y7dPd7LKzFXDQK+z8W+F6Klf1+rZYr44WrVfz2wAcmEKFE6KQsWYr6OahVDADEaCEQI4r/HlS2dLLlP5cU312SbaE8DuDwLLdUewN4GBQT/iRCkyONbraROjOUfg7LYkUYJ1gmDIR6CWVJnH+fLakf6OOjBjYmLiYzZ9GqfDHiSXXDgEM9ZDMZhPz+845rt0aXZsXridThF+hq3T3DMdkqNftmYR+A1h/v0xAsN3x1+CwX0/4QxmRmniCiDnn6OvexBhBHXxif8xhfsiFEyD8mc3Ic3+e89LXA+FHJXbdmYk6vlVQjnaUG0FHUvhXDO7Tzjs7gsJ9oERw7OA+8IpxP16e7q61Dz5dFtCmhFjJMbAMWT5LNnObV9kToQirisfjEH4p/Vy8Mesl7kspyE9gYwM84RfFnydXNMo4/P20fijhyTgsnHMYaxcq/KrIMjip0YYGDRo0aNBgGY5E+P/u3/27J30cAPyP//E/eOtb3zqxWfCt3/qt/I2/8Tf48Ic/zHd913fVPu/jH/84/X6fb/3Wb62+FkUR3/zN38x//a//tfpaHaF/wxve4GfGb936fUv4UQoe+jKu5wlZZ2dCf/ShR+CCFmRDhM5oyZDEZEVwn50Z0RQ6m5pTFkVf/OIFkxCCRx9q8ZmnvWIrRT1ZvJXvA5MJ/cULrEZ5BWx1C5VfWwIhCbC8/eLb+fVbv87V+Aq/duODfO32N/Hll15NJwoIlapszNXLGI2wxl8bimuU98Hs8vDFVkX4z9TUCq50mJVTwuNGfpe8sKQemn7xVQmUCv9qr5tVhD9EqEmFX+UxUTHigCws/Y7qOJLSft+ac6WLIMftrif8Priv5sBkgMgT0jivTnHeJoIQgm4L+om39ZeNVVEAA+dJfUuERAVBqYL75hD+KJK0W5IktRz2NFs7EUMzBOcmCP9+T1fjBuAV/vGkbikUtkjmj3WMmiK1pcOjDOyrXC41m1Klpb83MFg7G3IWiIDExRP1fKlNSUwyUQM4D52gSy8/ZCvcon2M+WznvHJfEv554Y2hjMhMwtAMyUwy185fe6xjCr8gqA1am8YgmXzMqpZ+7bRvWig+90AEZDb1bQsr1DPOQyuQDFKDNnYiEX/m/e1qzhylBHkZgHeM9PY40zOKe27zqorSt0IIUpOyEdTstq4BKQRZbmnP+11RAyUFab78814F1jlKxl8q/P7fyzMhZuCE/6yOCWMd1rGwhcBvlpxcSGCDBg0aNGiwDCc+w38cPPPMMzzxxBMTX9ve3ubChQs888wzC58HzDz3ySef5OrVqyTJfEXn4x//OFEU8cgjjxzjyF/aEDLA/qHv48rDXzbzPeu8ddmFhQKWj1fzTRJSwP+3yWbnlKmvJJvGow+OyMvOBjNWV+ssd7SfRx5P6LfOK7VyRftoFCo2OwFJbkBKhLMoBH/40jt4pPsoxhl+8+DXuWOuAj6Eys2ca3n+xbmqCKFzZDbgoUujmeoz20dUypyZIANX8pvVv3tFfZb//vx6xDqUhD8SvnV6coY/Jiz2+ZwsQ/uoEu9XUvidHVXzLQzuM8RD/96dlpzvgnCObsuf3zCZnN/vj83vl+iU1XxzCD+MbP37ha0/Nxl2iuRdveGP7YFzilA54tRWJB5AIrHOktmU1KaEY4TfWlc99vxuQXadrsIP+2bIzbFWiI2W/5FxDgbx7M9Juckwfk6xHs7UAM5DKEOMM/Ty3tLHLkKZ0K9LS/+c+yAQAdoZBrpP7vSRFP44McXYxHLyPpxS+HurKvw2nxg3CGSItnql91yEKFRkuZnZJJxGmum6X5UzUJWt/Ogqc10ln3MO7XLU2AZtIEMGuj/7+25NhKEkyczSMatxKCXItD32ewMTM/AOW8zx+0aCdSEl6BNwHljr/Ibe0k0bd2JtBQ0aNGjQoMEyrLRK+yf/5J+s/cJCCN73vvet9ZzDw0O2t7dnvr6zs8PBwfxQqsPDQ6IootWaDLba3t7GOcfBwQHt9qxK9uyzz/Lv/t2/4z3veQ8bG/NnZFdBsDSF+f6iXJRJpoiX8CFChP7aSZfRLtS6XGikADG+oDMGKSzIEMZeRxTBfWLJ4u/yQ22UBGPh7NZsxd5e3sdgiUTAmXCrIkLWelIeBmqFACyPna0WcabRFloClHCoIOBtF/4I//P6r3E1fZ6fe+YX+a7XfDsXwoe9g2Ds+IXzgVRu/DzDNiLv8cjFkTp27ky41qK3ei0sUsnq9a9kt6rvWRxDl7At297NL4EV3kNbjS1yFyIVoZSsavkAlE6IgknCb5xACocToxn+ja5CKVERJSkkKOsd6wJ2NgTg6A2ZS+SFUiQD/96djpp/jaxls+24eSAYZlAcnif8blTJV75Pp7CtJzaZ+5q72wE3bmf0+oYwUGAdFksUBNU9df22P7ZHLyraMufZW4IXb6RcOl/+HhFIK8hcihWGTtCtFvLXbqVkuaMVCc6dDf09mRfqnZR8aO9j7Js+f/Lc29lSXUQQsN2B/YEn/Ls1m0RtWiQ2riIdhrZPO4hWvrc6tIntACvMke3aORYnHFoX17ol57y/QDqJQRMoOfP7b+a+GcNGEXAYp5YwUFj8z9ki23uZ0r+7GbHfzxgkOWqF8R6bG5QcnYNCgQUr9ErPnwel/F5cbuzC3/3aWKJQLX2vEMi0H5U66t+SciRg/P201SAgVKPfm20RkZgELXLaxxgB2exEBIEiClff7IlC5a34UtQ6I8rjXvWzCQJ/rsIVP3sIkG7tzzYMFdot/ixXQW4sQgjCJZ+5VN4V91JfN7xcsO5906ABNPdNg6Ph5XrfvKQI/71Ev9/nR37kR3jkkUf40R/90WO9lpSCM2eOt2Fw2si0t4iHrZAoHJEB4wSbGxGy7W3ALQzdsA0ZGGX93HV3bFGYJzCUEHUmVX4ZQSQmHzsHj14K+NI1zaWzAZ2p0Kt97RXKS62zdLujDZwss4ShYmurtZbl1QrB7f2EllCIdoCLWqQavuuN38F/f/FX+dTNz/Kfv/AL/PHXfBvdzgNsTJyrg04bWmN25XYISY/OJjz5aJerNxKeeHR77nz6QqQC8K9/kA84MH0Egk3VoWeGJEHKpdYOZBo6EYTLr+0gHyVwb7Q7bG62UGZE+COTstXqQA+cNIADoehGiuFYeOPZM92JjZVOp/heZiAOuHAmAlJ6ydj3pqE6mDue7G1tBJPXdhw6YadwDKRaYpFARrctiUVR39jaqt5nR3dhCBnp3Ne8cLbN554ZMogdG902KoLEJGy2/H3unOPqTX9dnnggZEvFPHsLXryR8Ye+dvSauUpoRwGpVGy1Ry6DZ188BOC1r9pie7O00CeQheQtyX4xkjGUMRc7u6AMu5uwP3Ckmaw97rYLGeQDoo7wqrR2bIc7K1vPu67FfraPalu2W0e09Wc5bRNUAZabG+Hcayy1DzPclVu0g/rH1N0b53b9/49Ty2a3i3HG36dy/nkmhfr6yMUt9vt3iDPL9tbyc+wL//O0EY2OT6uUsC3Y7h6vms4gkKGa+7vfGEurM6TdgW578QaMcw5LwuZWh+2N1ccjxtEfZrS7LXY3W9XPbmpS2iagE3Qmrq9LNVFHHP0+OSLauSFONVtbHdqt+uXH4SBjY6O1dDG1N9Rs547trTYi03Ssv27dbsh2e73zkkoRBPLYf8eDYUa3O2R3q70w1yW30Om2X/Lrhpcbtrfv7f3c4PcHmvumwVHwcrtvViL8n/3sZ0/7OACvyPd6s5bUg4MDdnZ2ap4xel6WZaRpOqHyHx4eIoSYeW6WZbzvfe/j4OCA//gf/yPdbl3q2Oqw1nE419v80sBg4JXSPM0xemSFjGNNJ5JoERABOolRZahWnjEcJjg5NhKRDVFJirPRxOi2yC1OD7Dh8kCsP/Aaw2ZgeMPDijieTMy+MvQq9zm5M/G9JPNW1Xi4XsK2coAxHCYpQSthMIBACjpK8e2veifWOJ668zn+7y/8Mu84/51Eatc/0Tlkv1/0Fk6+p8gs7u4t3vHWB3FC4GzOYEWLcQUHcjj0M98244vDKwBcCs/Qli16Zsit4T4X3VlElmEGMazAAw4y//MTEZCllmGcsZsl1Udlh31MPmanlYYkg+FgwF3nSVEUCpLE292lkHQ6EXGc+RGDJEYlOe2i0m+v7xgO03p11jr2D1MgoBUKBsM590YW01IaCDnoG6JCEQ6VYy/1xLnjWtX9EGj/a6ufx3Nfs+Sod/bS6jEh7erfd/dz4sQQKMFOOybczoAOV28k7B0MvesFyHLNQTYgsZqB9c81xvH5L/nr/PjDUfWaYjBAppq7RcsEwJ24xyVxDoxls+XHQ27djRkM6z/MfhZz2+1jrGGQJgRRC/D3ljaOG7dTHrrYmquG59pyTd9EdKKlQXF12M/6xHFWtQko6eZeY+scQ50igxCTTT5m5r4Zg8Ar0cPYEA8zMpexJ/pEC3IADvv+fjy343+/7/cSDnvzRzrAk+i7g0MshoEeHV+mDbfzfdqmphN0DWSp5ur1lLYSbNQQ+jQ37O8PabUUeoW59d4g4c6dPiarr2Bchv1eyuFhTCjGfr/rIf04hkBN3A9pnnNL7yG7R3uvo8JYx+Eg4/adPhs1DQe9OGd/qDmzEbC5ZJNkb3/AYJijhKOXDxnGGULAgR2g8vWcC3GqfZbP7d7CTIZlOBxkHPYTwiUvEccZt+702FrQhtJgdSgl2d7ucHgYN6MSDVZGc980OApeavfN9nZnJbfBS6oT5oknnpiZ1e/1ety6dWtmPn/6eQBf+tKXeP3rX199/ZlnnuGhhx6asPNba/mxH/sxnnrqKX7mZ36GBx988ESOXU8nvL/EUFYOWSzj/XMWX2ZsAr/YlnlKJPxCOHY5TuuJuiKZZ746zbmiQs35+VAHaI3RZqJmbgbOstvO+YNvAILZHvcb2V0ALgRnJr5njCMKlgQd1VTXCQGb7ZC7fQNZSkbI+fMbSATOwLc9/s3cTfa5NrjBFwZPcX7jrX5hbDIwuXcuTL+nihDpENoxhN2j1Tk5A6a4VtbxQurn9x8JL5I5T/AO9MBnF1iHtQa3wvvEuSdGkVD+szV2spYvi5FOIvAqMkqTmRBnNIOyPq8lR+dUkG/rLMY4pNHYwoIPPuF/kDjqeYNkmPrHtcdfcwrCaLqRf59+4iiF21YAd4u2gg3Rre6HlvD3amzTuffbVreY4e/p2ve9ct0TwEvnI6SN2W5rtjcEhwPHC9cSHn+4dALIKrCvfJ3nrsakmaPTllw6F1VfV3mGAfbHXBZ9M/TH7WCr5WczDuYcE4B0isO0h3aagGjicR/73QM+8Zk+X/1lW3z1l8+OPgGELmKQDRkGwyOF98V5grOj8MYoFAvub8GG3MJZMNPRnlP3zTii0P+MOgdx7HCBIdc5Ss3/c9QvZvgvFqn4vWG+9A+ttprM5AQimDg+4RSJTknzjGCFfIR5CJVgGBuevXrI5YtbdNuTr5WkuqheUystCqxxJJlG66ONY8Spxhg78V6ZztHGYCUwdg2UC+nnfc7o/FjhhUeB1pYkM7TCyfcdJpoXbvVwQtENBO1g8XGlmcFZPwufa42zDiEkqcnWXoQJHElmSTOzdt7f9DFZvcJ8vnPERahsg5ODMba5pg3WRnPfNDgKXm73zUtqe/kbv/Eb+chHPsLh4WH1tV/+5V9GSsnb3va2uc/7qq/6KjY3N/mlX/ql6mt5nvPBD36Qb/zGb5x47N/6W3+LX/u1X+Of/tN/yute97qTP4mXGxwIKaokemVyWkVoX+bMbNXeWKL2h/uf5AN3/ysDExf2/uXVfL6n3NSG/g1NQt/GCCYD+8CriYvmHeXwgLO/+q/ofu6jM9/rdkJaoaLXT9jZiNjsjlZ0Ukj+4ENfD8Czw8/Tz71TQ1iNMMbXy01DKMBX9B0ZtqhwE5Lcaa7ldwB4OLrItvL5AAdm7PVXDLkqK/lCESAQiCJkr4TMYoQUVQCdUBptAKtJUv/ZLUzdLjZVAiXYLdyo1+7Oe7BgmHlyVwa11T7KGDZa/hgHCaSFWSIKHX0zmuEvUab0xzaDmQ4Jj+0tfw5pZqtcgnFcvek3Rh68EPqgPWe4fMFfkyvXRyGHgVAzgX1PP+eP6cnLnUnbrslBSHpmnPAXKrSUbLf99Z1XzQej9PvMpDOK95eu+E2KT32+P7ezXckA4wxDvb7jyCf0pygZkBbhjfNS+o8DKUUVCpmmfhNyUTVfrm11vrs7ReBfQW4XwVfymZl2hUAEaJuTu+MF9wkh2NmMPFG92SNOJz9XrS3GzTYyzIdbefFgrGWYaPZ6KdfvDnj6xQNu7sUz+SbzrmsgQ3Kbk5q09vuni1lCnOWGF2/3STNDEAhyvTwA1mcBlK/oAIdgtRDIaUgpis2p4y3epjew58FX8/kKvwYNGjRo0OC0cWR547Of/Sz//t//ez796U/T6/WwU3+4hBB86EMfWus13/Oe9/D+97+f973vfbz3ve/lxo0b/PiP/zjvec97uHTpUvW47/3e7+Xq1atV5V6r1eK9730vP/mTP8nZs2d57Wtfy3/4D/+B/f19fvAHf7B63j/7Z/+MD3zgA/zgD/4gURTxiU98ovreq1/9ajY3j1dT9LKE8FlwrlD4lR5P6dc+LW/84SbDSZ9e/mx6DYPvjn9N66FRLdkCjuCJtMXVqDc3tU81P6O2qwq2Et5JMH/hHN163rsTbj3L8HVvnfielIKNbhtrBGe328ipGrlXbT/GA91LXB/e4LMHn+JrL/wBhNUFl6x/Txe0i4q+HVDr22KFswjrcIHkenoLi2VDdthVm2PVfCVxdLX1iHWoEvpRCASBmRxHkHmCcI4ARUYOSqOtAGeJK8K/yKExIjWPXhDsDxzP33I8+WD9dRpm/rU6izIObMZGyz9/MJbSr6IUg0Ug2BirpitT+jWGXKeE4exIThhIuh3JMLYc9jXtsRwG5xzXbvnr8tD5ADA4Ibl8AZ56dqT+gyfg28FOlZSvteXZq/77Tz469r7OVAn9h3ZEtvvVvyU7Hf+z1BvMJzOBCBjaAUrIiS7x/kBXNZBZ7vjM0wPe/Pp6S3okW/R0j51ody311if0576r/hQJP0Cn7WsTh4lhe2M+MYVRQr+SAhf2kdL/WurHOTub83/2tPMBltOd7FJIHI7cZnSOUWEI/u/c7lbEfi/jhZt9Ll/cpFPMpufG37urQilJtoTw7/VSDvoJw9SQa4ux/j18oKmk257cJDLO1B5DeQ1Sk9ANjjfStjamKvCMtVy7M+BgkHHhTAcnZBVAOA/WOcxYGr6xBpAoITFOT9RrrgIlJdZy7Go+s+LGrJSCPLdo4xZW+N0L7PVSrLWc23l5zaM2aNCgQYPVcaQ/Nb/xG7/Bn/yTf5Jf//Vf5+LFi7zwwgtcvnyZixcvcvXqVbrdLl/7tV+79uvu7Ozwb//tv0Upxfve9z7+wT/4B3z3d383f/kv/+WJx1lrMWZyQfBDP/RD/Pk//+f5V//qX/HDP/zDXL9+nZ/+6Z/m8uXL1WM+/OEPA/DTP/3TvPvd757431NPPXWEK/HyRlntLqXAFSn9yma0CmUxc3mhfhaLUGdBZyACDswAU6TB7+uet1W7FRR+VwTF1dx6N3IvFV8Mz9Q8USAXrIyC3m0AZFKvbG60Iy5sBjM2UvCL9rc99HUAfGHwOV+NZvLFvfcqRGiNzI6Y3VC5JyQvFHV8l6OLCCHYVl46H9gE7QwgVlb4R5V8AUL4DRwAo4queOcITDqm8Bu0lQhrSIsk9EWEXxiDK9wZj130F+j5W25uzVap8Hda849fmIxu2z8uK0YEAFzkr+2GbFekLdcGayCgUPDN/Ou/s+nPsSTKJfYONElqCZTg4hmFsA5EwMNnLELAYd9MPGfc9v3c1QStHVsbiovnxjalrAUMCDW2UTOm8ANbHX9eSWrnKvRCCLrBBp1gMszrSlEhWP4I/O7n+ug5VvtIRqQmIV5T5S8V8UCMFP72KRH+MuQyTiyCxYR/UNyX3XZA5jI6xf3ZixcruXqB0iuQJ6Zul6S/H+e8cLNPkvnjzbWdnjBaCCXEQqKbZoartwfs9/2s+kYn4Ox2mzPbLba6Ee0omCG5mc1mNjxKBCJkaAYnUpG3DqT02RjgN99u7MXcOkg4sxkhhU/vX1Z36MfJRhNcBoMUAiEkxi12jMyDj3s53rXQxi6caCuhpMBad+z3Owns9ZLqZ6xBgwYNGvz+xJEU/p/4iZ/g8uXL/OzP/ixZlvEN3/ANvPe97+Wtb30rn/zkJ/mhH/ohfuzHfuxIB/Tkk0/yb/7Nv1n4mPe///0zXxNC8N73vpf3vve9az3vlQzjvEIixxV+k48p/DkUVkmgIKgWZMCdbFSTuGd6VOx4SQeysPMXYqXCfzE8O/H1ckm0qJ1MHRaEX6dgNEzNAwslCbA4ZxE1K7Indx7nbHSeu9ltPnPwKb42eAS3IDUc/DUTySG0tmDdWeBike2c40pWzO9HFwFoy4iWCEldzqEZcA6x9LqWqCz9eMIvC1JjojYil0idEuXpqDdd+dpCnCFegfDjdDUz/+AZCBQMU7h9CBdqcjXj0tIfzvncnQWjiUJFoEAb2CsmGUwwa+dPtQPn6MiIno2Jdcw8X87OVsC1WxkHUxb60s5/6XyEEl6Zd1IRKcMDFyKu3cy4cj1hZ2v2lZ9+vrDzP9qZIFfCmcqxMW7pT1yGdoZAKFoBtCNIMuj1DefO1F/nukq9cszgTa/b5AvPxgxiwxeeHfKGJ2dTvqWQSCQDPWAzXD2YTjuNwyEQpMUYROuUQsU6ZTBoYpbasAeFwt9pK7TNabcFgxj6S4IyM5sh5+xpBzIgMcnaSvA8CCE4sxWxd+iV/kcvbpFmprZC1DnHreQmu9Eu0Zg7SClBrq3vca953t1eQpJpzm7PD22chrY5co7LI5QhmUnJXU4kjtYMcBQoJUiLDa+9XsqNu0O2umEVOhQoycBYtLFzA/SsdTjrqk1gY72TQSHRLq+qSdeBqBk1WBda25VGOKQUGGvvu6U/yw3D1NAK7//GQ4lcW8KmrrBBgwYNThRH+q366U9/mu/+7u9mc3MTpfxiorT0v/nNb+bd7343//gf/+OTO8oGpwPnZyCFkNhS4TdZNcOfOg24imwKYzxhl4o7eozw61GzwiJCD/gwvJrFam41d4vXvBRMKvzOucqJUAtrCfqjQXKZDmYfo3wwHbpe1RNC8JYzXwXA5w4/Q5IPiln9BQhaCJMh5rzmIvjrJNgzPYY2QSF5IDznv+lspfJXavGKhD+rZvgVSJDakyIbRJiimkzpMYVfFjP8jILaOvNInnNFhkPRaa4El8/7bz13c3bBaKwjKSYKNoI5x19sIgkl2Si4z7C4nHngFeotOW6dd4CgXc3x13zWBXa26hX+q7f8Gzx0sVW8nq8mxGguX/Kv+8L12c80zSzPX6ux85fn4QzauSrNvxwd6RcuBCck2x3/tcPB6oqac44XC4X/0QfbfMXr/UbEJz/bmzsz3FJthrpPtoaKrW2Ow6uOJe85LUv/uMIvhVpM+GN/rToticVSZrD2lyj8WZFHUIdABGiXo93JKZtCCM5sRxwOMp6/2SPNDapml1I7zdAMGEw5MCrVt4YEJpnmzmFKtz2r4s+DLZTuuZseIiB3eq175CSgpCDPDYfDjBdvD4hCOeG8CpREG4deQL6tc1hHdS2s0wghC4XfYI+g8AshyI5J+HNjWSWyQQjhRxvus8IfZ4Ys036j6R47PeqQa8vzN3rVJl+DBg0aNDgZHGk1p5RiY8MTku3tbYIg4M6dO9X3L1++zNNPP30yR9jg1GCd8yqwFJXCH5jRDH/uNM7akZ3cmcK6LCcIf+xSElssGu3iBbQwGa5GYb+l93HAhuywMTVXa61DSVHNa05DDfcnNhpkWmNllgqsweXzawNftfUoO8FZtNM8lT474xKoOZvinI6wYC6uU6nuPxieJxCK7dtP89qP/QxnTUEMzQAnBGJFJSivZvhDv0lSWPptEGIjf10jkxAWlniK0D4nJOnSGf7iXhj7/B69MLL1T6Mk+0I4OrLeXi6s8ecmJBtTTVqZ8Gr6ppoi18KNgvv0/Gq2Oku/c45rN0vCH1UbL34TyvHIA/7n4NrNdMZu++yVGGvhzHbA2Z0pB4k14AS9gtyHImCnCF/s27Hgvq5/zd6C4L5p3N7LSTNLGAgunot4/RNdWpHgsG/40pX68w9lSO60H09ZEaUiXs7vSwlhcHz1uw7jCv/43HUdysV/u+XbJcrnLrL0+zwCPTfDoAruO0LA2yJ4pb/F4SAjTjVBTTipdprc5vR1b+KcS8JfRwLv9lKSTFf5AKugJL7zLP1CCARyrXvkJKCkxDi4equPtXam0lAp4dOPF5Bh/2fJeXeac1XWR5lNcBRLf7kRcRysqvADILjvCn+c5OTab568FOqlMm3oxzl7veX1vg0aNGjQYHUcifA/+uijPPvss4BfNDzxxBMTAX2//uu/zvnz50/kABucHqwDJSRCMJHSH4ylWmc2RxSLUmE1CIF1jrvaNymo4hba0z2ckAizgMgU9u065fym9gr9pZr5fVs4EerUMoCgsPOXqCX8AFLh5n0PCALF67pvAuAz6YskK6h/TgWILF55xr6EMBon5Iydf2P/RaTVXEw9Wz4wA09GV1zAlgp/IAMUwo844BV+VxD+II+rz7hK6ZeKuLBxz03pdxbfLDD6HB4t5vhv7EOcTl6DUqnvRH6jp/YcqrA7Rbc1+fkmolD4xwm/8PdcuwjuS838heF2qfD3R2Ty7oEmzRxBILhwNhptUAnfMnFuW9BpSXLtuHFnMvDwi3Ps/IBnIMJVdv5ttcFmsXE1muOXbLX9NT5cENw3jVLdf/BiCyl9ONuXv8ZvJnziM/25RDkUIYf5IXYFd8hEQn/xObYieSJ29zp0JhR+r8rOI2nlfHEYWZQI2Oj4z7U3zGofD76Szzgzl/ALIXCMNshOElJ60h8GkqimkN1YHyaYmoTEJhPP09bOKNtxqrl7kLDZXm9syF9TuzC4MZIhQzNc6R45KUgpSDNdBDbOjhJIIbB4tXcevMLvis/R/3s8jPUo5yPlaNTgKPDuDDd3Y3oWq7cynAaccxwOc1otiV2ywXKvkOWWTFv2e1mVhfFyhHNu4f3boEGDBvcaRyL83/RN38Qv/MIvoLX/hfz93//9fPCDH+Sd73wn73znO/nVX/1V3v3ud5/ogTY4eTgLQUGiXVjO8GcIS2X5Tl02spMXdvxD00djCFA8FPmNnT3T85LgIpJsNb6Sb3YBeiMv5veDszPfG21MzFH4e3cm/lsm9TZvEbRwWYwz9aqelIJLrcucDXbRGD4df2n+uVRPCsCmsI5S6AzohBTHrSK34JHoAgBBEQJ4TvvF16HpF4GI6xH+iAChRDVuYMNWpfAHOiVgbIbfACKo0vHnKfzCWr/5M6YYbrYF54ox8Rdu1xP+bksgjEboWXLlFXb/vGmFf+hqLP34/aJ2MXMc2/nq5Hah8Oe5q6r5yvn9B85HSCmqjReEQFiHxPHwA34zYTytP05M9dwZOz/4e1tIDs3omDeL464UfiHZLpP611D4y/n9Ry6N5r2/7DUbBIHgzn4+USM4jki1SExMsmBTpMS4Ip6ccmAfjGoa48Qghars53UYFoQ/ajkCGbDZ8YrwQsLvNNbZuXZ2AIkktadjZ5dSsNEJa39n5TZH4scTxoMVS5u3mRrT2DtMSXNDew11H/xn6nBzFX7wLRS5yU7tOtS+ZyCJQsXuVjT3d7qApZZ+V1QeWmdx2Oq1BOLICr82duVqvWkY65XyVRV+JcXSVobTRJIZkszQbQXoovXgfiM3tgqvPByc/GbcvUJvmHPlVu+eB2I2aNCgwTysvKL7/Oc/X/37z/25P8fP/dzPIQu74p/4E3+Cv/f3/h6vec1reP3rX8/f/tt/mx/+4R8++aNtcKKwzlWhUrZQ+AUOMRbcl7kcKGf4vR2/tPOfDbY5o7aBco5fegV/jroirEEY6zcGJo7DVsS3LqF/fGOiDqXCb4sk+toZfgAVgsnn2vrLsYE3b7wWgM8kz5EuUwBl4LMN5mwi1EHoDGEyXnR+jGFXbVa29ZLwny+UJj/DL2bqEeehVCwDQp9anfv/dkGEaxXvoeMq5d6n9IOTkrg41bmWfmcLJXvy+6XK/9zNyYeXin+nBWC9yj+NsUyAccIvpCF2noSU18a3SgiUgFZJ+BeQ2UAJNrv+PEtb/8jOX5Bnp/39KIquN2e5/IA/kBeujUjQMy/EOAcXzoZVNsA4hM1xQnJo6xT+gtTJUTXfqgq/1pbrtwvC/8CI8Ldbijc84a/LJz7bq31uqewOdH/p+xinsYUinp5yJR+MFP5h4km5XTB3XVr6w8gRiIDNbqHwx/N/Nk2x8bjIoTAe3HcvUSbnhzKir/uTarSYJLpxqrnTSypXwzpYZY5dCr/xcK/n+Dc74UJiLIUgzedvirkipR/AYoucl/J+Feglo2V1GI1UHI2EW+t84OKKCr+S8r6qwElmyLUhChW44wcWngTiRKMCQbuluH2QnMr1cc5/xnGq6Q0z9nopcXqyboJ+kjNMzJHvpQYNGjQ4aay8ivjjf/yP8/rXv54/9sf+GN/+7d/OAw88MPH9d73rXbzrXe868QNscHpwUCUjowI/K+4cmJyWCBkQ++A+h1eYCzv+7YLwnwt2OBN4ebdS+K2u+shn37BI+Z/aZ9ozPbQzhCJgV82milvnCBak9paVfPn5R2jd+NJ8S7/w/3N5Au3Z91FSICQ8Is5wRm6yZ/s8FX+Jr9p43dz3rub4dYKLZhPTa59RqO5XslvAyM4PEOSFwp95kpO6nMTmtKWYf13HkBVKXUjgNzCK93JBC1obxXsklYOjVPi1kdU86ULCP3bOJR67IPidpx0v3HYTCeOVwh8JP+6hE2Ayyl/ovMp02BjxWVobXhUPRVCFSFq8bVdISYtihn+Jer2zFdAfGg76mkvnI65NBPYVIyZIf07Cn+MjD3iifmffL9q6bTVh55+9Lq742ZATlv6wJNzjCn/Lk7DeQM9NYx/HtVsZ1sJGV81sNLzpdVs89cUB125m3Lidcen8rD06ki0Gus8Ze3aiXnAauRtZ4O8F4S8V/iS1xTTMfFV2WLVH+BntMvBvUUp/Vre5NIVAhOT23qbUO+cKwq9oyRZDPSAxMd2qhnHS5n33MCHLDVvddv0LLsCqKrcSAQM9YCfaXfs9TgtKieo+rIMt7hnw19QxaltQQpK79bMZlPTuiqMq3V7ht8gl7S4lpBRk2vhxhFManVmEQZJVv3/cSyBA0DpHnOaEStBpBdw9TOkNM85ur3/vT6M3zBjEOUlmSHODNn6DQ1uLsfDwhY218jEWwVpHb5CRG1M0Dqx2PzRo0KDBaWLlFd173/te+v0+f//v/33e/va38z3f8z38p//0n+j16tWlBi8PlAsNjR3N8euMVlENllqNcBasQbjJhP7zwQ5nCoK+r3u+0MvZuUn9wtR//Ubu5/cvBmdqFz7OOYJ51s90iMxiHIL83GV/TvMUfkCoFqR9XI0LQUqJdA5Mxls6TwDwmeRZ0iV2facCRL7iHL9ziHyAEZIXpwi/MDmqcAq0dc6G9AudQxczUY+4AFlxrIHwClqp8NuwhSwIv9IJQUFGy5T+UixVC4LahLO1x3BpF1ohpLmf5S9ROq67LUCGkCezowkmqxwfG+3R+4bdkTW+XMg7L/ETSEFEaelfQvjHgvvu7OekmSMMBOfPhGOZBKNfg8IaOm3lv4+30/cHmhu3/ck8ebnGzo/15zVu6VddNgpnwmiGX7DZ8kFj1vrAumUYt/NPq9WbXcVrHlus8kcyIrMZsZmfXQF+rlwUBCo55Uo+KPMBmHi/uTP8RThfq8h46HZG8//5nMyQ1KYosXgBr4RCO3PiwX2LMD46UQbMDcds/UrJao58mPhk/nKEYV3kNq8+00WIZERq4moc6CSgrWYvvXvkbIBASdLczLXXW+sQxbcsdmJ8Qwh5JIW/rMo7ssLv1lX4BdYyEw56L2Cs5XCQV+0IUkA+5+/zvUKuLblxBMqP70Wh5M5hcuQRi3HcuBvzwq0+h8MMbS2BEnTbAWe2WrRCSXqCCn+caeJMo40jfwnkIjRo0KABrEH4f/RHf5QPfehD/If/8B/4U3/qT/HMM8/w1/7aX+Ntb3sbP/IjP8IHP/hBsuzlO3P1ikQxb5hbw520hy2S+mU+SupPXQ4libcGK2QV2Hcu2GFbbSCRaAw9lxS26DkLB5vVKtQ38/l2fg+BmNPHXNr5zcYOpuvV47kKP/h6Pp3X1vMpCQqNs4ZHWw9yRm2RO718ll+G/txWIQ42B5Ny2w1JXU4kAi4WNYSlnR9AmnxUzWeH+HrE5YuHvKrlKyz95XkGEaJdEP48Jhyb4Td2RPjb0QIbtLNMq/vg76HL5wtb/1haf1yG9rUAFSCsnhx9GAvsg0lLv+p4kjwR2Fdw8yCQleqf2HShJXt7rJrv2k1/kuX8Ps74TYzxEZOCoDwyNsdfqvsPXojY6NaoNda/jsZVav622mBTejdA7FJ08TMhJWx2/bXaO1i+yHzxht/QeHjMzj+ONxcVfc+9mLB3MHv/CSFQQtHLF8+TZjaryGF6D2b4pRSVk6Tc+Kgj/M65KrSvJL5lbaQD9oezP+vWWbTNF4bVweg+P43gvnkYH50AT7YHul+duypUX4C7vRhtDK3oaAphbvPR/L5zzBu1Oo16vn7e4yDfP/JmilJebc/nkG/rHCXjLzdvy89zNCKyHnE/blWeKcYMVg26VNK3EdyPpP44NWS5qVw8Ui4OSbwXyLXfbAmKv/XddkB/mC9s41jtdQ1J7gMitzciNtohrUgRBH5jIVCSJDcnVksYJxpjvJiS6/u7idKgQYMGJdZe0X3lV34lf+2v/TX+5//8n/yLf/Ev+NZv/VY+8pGP8Bf/4l/kbW97G3/lr/wVPvrRj57GsTY4aQi/s6+dIZSqIvzCZBWhytDFYtGHqx2aQRXYt602kUKyW9SP7ZWzwvNm+E2Gm7I7OufGEvpnA/u8c9Mxb4S/tPObrfPYYkZ9kcKPUmA1Lp9d3EopUE6DMQgZ8ObuawD4dPIl8kVhhFIhtK6fUZ+CMBlCa65oHzT4UHihWpSHY4RfmWxE+E3sRy2WLGCts5WVNRCBN6oX5+nCFjLy07COfQABAABJREFUn5PKkkr9FMovSPqFCN0JF7zPlMKf2ZykIEuPFVMJz98cfX9YzPB3W3hS78zkNSpdI8X5d8c4rWz5azFeyWfxNZKBlLSE3x0w2IWEbWfL32+H/VHo3oPF/L6wU5kEQkDhTnmkmOO/cj3l6crOX6fuj16nX2QOhCKgLSJaIqycFIOx6rOHzvn3+9TnF8/WD2PD3WJTYDywbxy72yGPP+KPdZ7K35JtYjNcGMyWmqTqrE/ugaUfqKz5Pqlf1SrMaW4qi/VGu9iQlKNqvv3BLOHXbnFC/zikkCuFGp4Upo8tLBwYSXF/KCnItaUf59w9zNg4orrvnEO7HFm8j0wPkcl+7WPLer7hEhfIqtBWc6APyG1WZSmsi0DKoppvAeGv/m1xY7+XpPCE/yjBfQ53ZAI+7jpYBVIKjKuvYTxtJKnf6FWyJPyC7BgNBSeBXNuJ0EMlvQto7/B4ORtJZshyTThPNFB+k+ckGhOccxwMMsJAIARkC8ZSGjRo0OBe4sgrOiklf+gP/SH+3t/7e3z0ox/lH/7Df8jXf/3X8/M///P8wA/8AN/0Td90ksfZ4IRRuKOLRYchlAG2SupPiaT/t5/hN1Xd3nhgX2ldnJjjd67e0l9mAEzdcn0bM7QpAsH5YLfmOB0SOXfWuVT49fZ5bGFZl3lavNccyABXsykgEATSUf6Jfix6gE3ZIXe6st/Xw/e4ixrXwMwj8xiErOb3L9fM7wNInbFdbKQc2IG/rksWPeNqWigikAJZJOOLsINoFYTfZESuzG7w16kf+9duR25UVTcNq6t5e+ssv3DwEf7z3q+T2KxS+O/0oJ/41xpP6S/OHsaS+kvXSNnaEChBq+A3LioUfjmamffpD4JACQKhCIvgwcTM3+DZGVf4b00H9k1mEjghEMU1vHQuIgwESWq5s58jBDxxec4sabEZ1jPlMfsxBCHETFK/k5KvfsJvXFy5nnLj9vx7pqzjO38mnF+VCLzl9f7n74vPxfQGs59dIAOMM/TzHkM9JLf5xAJ6urP+XszwAxVpHya+K17XqMGluh8E0A5G5LfcLLjbryH8VmPGzmcRAhGQ2vSe1dJNW82l8GVypa2/DI67fRCjta0s1+uirOSrFH6rEXPCSsE7DYZ6eCSSPI1+3iPVCY7VcwSmUZHhOSTM6NHMvpsaM/JBhG6l0MJpCMGRiZ+xdoWhqxGkFD7+4z6k4/fjbGJ0S0lZ5QncL6S5nlmQbnQCDgcZw2NY7tPcAGL+GkL5zaV5bpJ1kOWWYappR4pACeKXcbVggwYNfn/hRFZ0URTxzne+k+/6ru/i677u67xqe/Pm8ic2uG8wRVCQFKIIzBsp/GpG4bcIk+KE4s6Ynb9EOce/pw/nd8ZbQ10l381ifv9csFOpoRNPsw4pmTsXWVby6a3zuLBVOQgW2fpFEOGypLaeL3Q51hVBfELwWOtBAJ7Prs99PSjm+LPhYhXeGUQ2pC80d42/jg8XdXwwZel3hh3hye6hGRYui8WLsaxK6JdIoTzpLJ0MYRsRtnHFdWwV85qiIPy9ggt0AouYQ/iF1ZSD18+kVzkwfTKXcyO/Q6cluLTrH1eq/PH4DD/gVFhkHdjqehSvXL1HGdxnA0+QN6cs/VIJlBJIBG1ZBPfp+Z/19kbgF/HGkeVj8/vl+UycoKwUfqXEaGMAb/GfR7pF6X4ZS+gvUdr6J6r5WprXPu7P67d+b34GSlkL+PAcdb/ExXMRD19q4Rw89YX6zY+26nA3u8OLw+e5MniOK8PnuZnc4CDbp5/3JlTnytJ/ijP8MKnwKySmxoZdzu+3W2LCKt3t+GM7iJMZEu1VZbGStToQAcbq2s2G04Cfq5+8rmERrKitrgj/YT+r2giOAusMzvnrCvgNW5PP/f0UypDcZqTHdDuU6n6oIgTiWPkIAjGXhGlrKfmbdXYiq6C09JsjbOIoJY9clWfWmN8f4d6n4+faMEjMxGaSUvcvT6BEnGjUlJUvDBTaOPZ7Rx83GcSaOeI+QFHteDIK/zDVZLkhDOTSHIoGDRo0uJc49oruYx/7GH/9r/913va2t/G+972P3/7t3+Y7vuM7+Gf/7J+dxPE1OC1Yh68f9yqDErKqtVMmH4X2Oe0ty9qHq90ZS+gvcSYoqvlMz6ex1xBpX8k3S/hvFHV8l+bM71vnj1PV/cU2GjXYB0BvnQMhxmz9i+f4sdmMrd/hCGyGG9t4eCzybRQvZDcXq1UyRCyZ4xc6R9icF40/5gvBbkVaAYJsslP+DP4zODQDnLXAYsUqtyNLOUIgnUMUirqMOggpMaFXqTu6OM5phT90cwk/1tvvnXP8Xvx09eUbRQbDoxf8Yu35Ww5jHGnxFp0yAF0GXkEvrlGdE8TP8TvyYBR+V8Lfp36TSirolNV8Cwi/lIKtjdHn+cCFaKT0zBCCgvBPzfHDfDs/UGwSiKJCEbbHjnmmmk9IcJavemMXIbyKf/3W7GLWOVcp/I/Mmd8fxxtf7TcZvnQlrrW/RjJiO9xhM9gmkBHGGQ6zA24mN7iRXPdksyT86f1Q+FWtDXuU0D95LOVmwWCoq2aKEuuQTFW4H7IjpLofBT5McPJ3YCQjcpuTmLhIbrdo63xd2hFhimtZWvpxGjDVhtY0SifAcccbSnW/LdtIIY8VBCgEc8m3Mbba0LFMKuv+6+JICn/ZAX8U5NqwPt8X6HtMCOPUJ9VH4ehnSon7lycAfmM/yU01vz+Obkex189Is/U/F2MtgyRf+rPk3MlkGPSGvvnAZwP4UYH7nY3QoEGDBrBGLd84PvOZz/DzP//z/NIv/RLXr19HSsk3fMM38Mf+2B/jHe94B93ugsVxg5cEynloKQXCeDXFhCXhHw/t00XVnsEhuWNqCH+h8PfMAC0cQR1hrFFzYSywL5id3wdfvxRIWbuQCvp3EDhs1Kk65m2ri4p7i+f4iwWh0wmwOfq60SgMdmxBfiHYpSNbxDblWn5nokJvAjKAfIgwOU7VEzRhUqwxfC69AjDzWuOWfoBtq5AIDJYBKZ0l68IyoT9CAQ7pioYFQIYdJIIsahNkMR1jQJUz/I5+7C9wJ/K1jDNwriALkiv5TfbNaP68zGB47KLgY19wXLkN/YI3SEFl00cqyPXoGtUQs7Nbghf2M5zw98vmuKXf+Ro7KaV/XeGvc2LimdcZx85mwGHfv964ai+MrhwP/gvSE6Oi/vDyg22EOEApweMPz6+GKl+nVyX0L1L4FbiMrY7kda/q8tlnhvzW7/X4jj8yec/sHWiGiUUpwaXzywn/5QdaKCXoDQx39zXnztTPfgshCEVISAglF5zaIBjN8J9uVVhnYoZfYpz140WMjr1fKPzl5kCJ8aT+1KRjtXZUtXfrIDfZEf8aro55YYJ+hl4wMAM2wy06bXVkK38J48yoqq6onxTGu3ecqr83Ahky0H12ozOjUYA1MKHuF2GRuc1wzq3ktpiGkmJueroeU9ONNTNtBA53pHECpUQxS76+Wq/18prNaUgJ+h7Pzg/THNxkuKBSAn2f8gSgSOjXrgrkHEc7CrhzkHAwSLkYrbe2TDNLri0bnSWNHVKQ5Mez32vjszfaUTmi5tsicmNpcbyf5wYNGjQ4LlZe4rzwwgv8/M//PL/wC7/AM888g3OON73pTfzAD/wA3/7t387Zs/WErcFLEyWRtvjFthQSM6bwl4Q/cxqwCGc4EBrtDArJzhip6cgWLRGSupwDE3NOhDOd8b6Sb3IxkdqcfeMtzfMS+q2FIKxffKrD0s5/rrKaV3P8ixR+QKgQkj5u8+zIYms10tkJhV8IwWPRA3w2eY7n0uvzCb9/V4ROcNFm7XdFPuApc407+oBQBLymdXni++OWfoDQ5mypLgdmwKEd0nWLZ0RLpTMSAQKBLIi7EwIVtJBCYKIOsEfXpBXhQxp6if9V0I4EQteofGVgn5B8aujV/cejB3k2u8YdfUjuNOe3Fd2Wn91/+po/0k5rfGHpN1qETnHRJkLPhjh+7WsEuxcSfhPYkO0JcuRwKOnDkAKlaK9I+Le3Arg+Nb8PfsNhnNhIidC2Uvi3NwO+7ZvOEQZyQg2bQdE+MVL4xwh/ofZXoX3Fewhn+Mo3bvH5Z4dcvZly7WZahQkCXCnU/QcvRATzEivHEASSyw+0ePbFhC+9GM8l/HUYX/g75+6hpd+/flzM8NeRtF7ir8N0R3b53DQVDM2AXXemcCs5MpvWjgfNgxKK2MTsHpGYrooysC+Ssxs4kWox1ANym7PRnv/ZxSYmFCGBXPyne+I6FvWTwpn5DSp4p0Gsh2Q2pa06cx83D6W6vxV6x5csag+NMwRLKhLrECivtteRb2Nc9aPrnQzTn5s4EuGXUpBrb7OXa/an58auvUkgpSA9JtFcB845Dgf5zO+z8mfnXo8XlMi0QRtDoOrvk3ZLcfsg4ex2u9YFMA9pbjBF1d8iKCWq33tHxTDVpJlhZ7MIPxYC7P1vP2jQoEEDWMPS/83f/M38xE/8BFpr3ve+9/Erv/Ir/Kf/9J/4nu/5nobsvwzhrF9Q+dldSSgUppjhD8Zm+FOXe9ZtLXe0J+c+sG+MzAvBbjnHbwf4XvLJP3LCZFXgW4lSGd5WG3RqFsHgVbF5vfBlQr/ePj96fEX4Fyj8UNTzZRMhcs7kSBxIMUGsHy1s/c9nNxaGGi2c47c5vXSf30l8xd/XbryBrppUjacJv6/mK4P7kqUp/aWVORKBD7crarZsECGl9C6OyC/kW3pUw4bSJGUtX0v6wMPpxbL1pOGmPuCm3kMi+bqNN7Ih2zgct/N9hBCVrf+zV8YS+qevUV6ci8mrSr4SYSBobxUJ/XJKzXGiWlAHStAqLf3LFP4iuC8MBed2y1RAN0v4hShCJ0fX+eFLbS6ei5gL50MODaKq5BsfQxgp/OVnK6vWi62NgNe9yt+vv/XU4cTLvljM789L569D6UJ47sWj27K1dpSnf/qWfv/ZD5PR9Z62YfeKIIjSwl+iVPiTxJGZtGqn0E5jrF5L4Y9ka2mLwUlgUZhgKMLK1l8H5xx76R7Xhy/Sy+fnPpQwbkz1LuonHbYKX62DEgqLd0ysi2l1v3q9I6blgx/j0pYZEmqtm9gEMM4wHDp+7/P9ilwpIdFHaAhQUmCNXVvpLsmyXPNHRhUjHPcKSWZIMlP7sy2cuC8BgjAixfM23LqtgDjTHA7WGxGJM73SmEU5b3+ckYZhkuOYdXmcRBhggwYNGhwXK/95+tN/+k/zsz/7s/zKr/wKf/7P/3kee+yx0zyuBqcM6xyBlGhnUULRUuEY4c8JGFn6veXX1Qb2laiS+u0A4ezsfLbNagL7Sjt/vboPgPOLojqUCf1ma5zwrzDDD6ACsGZyjt9ohJBI5ITN+YHwbOFgyKpNivrXDBE2r80wIE/56PAzaCwPhGdn1H2cqwh/HvpzUHqsms8Opw0SMygV/hDlKxeLzQwTREg8WbYF4Y/yxM/6MwruA+i0FMLpGmJgEdbxqeRZAJ5sPUxXtblYVCne1JNz/AfF5e9Oc2UZEOs+V/tfwjlD3Qp5ZI2ftW+KivBLWqym8D98qYWU8JrHumOLMVtUVYy/vyw2VdYgKM6Ac/SdTyUPhKIjRiS9nOEf2nREfNwov+Ar37iJlHDtZsbVQtU3xnH1lv/sHn5g/ijBNB59qI0QcGc/57B/NNWwtPMryUrOguNgXOEvMU0OBwXh70wT/mocwPkO+WJWXLvChbQG4R9vMThNLAoTLC3w/Xy2qtE4w+30FrfSG+ROE69Qn+fHGorAPmu9pUsEiCVkPhABAz1YuwZtfHa/hA/Ps0ci3gBB0VOf68ljsa7suy//2/C7n0n4yO8c8IVn/e8CgTxSYKCS3tq+LvE11k1Uyq3zfsYevQpwXSSZIdeGsM69IJhbg1gH59yJBdIluVnorhFCECrJncNk5fd0ztEf5oSL3FkFRtV8RzsfWzknJq+rUr4CsUGDBg3uN1Ym/H/1r/5VvuIrvuI0j6XBPYTDJ54bZ2jLEIXEFbVXymZEBRm0WAx+oXq7JrCvxNkquK/v1eBxNbqs5JtW+PMysG+RQ0Qg6mQT5yYS+ktUCn+yROEHkGqins/lCTKQSMnEokIKyeXoEgDPpQvS+oXy3fJmVoV4uv95rpp9FJJv2PyKmcWNNBmyIDtZZ6f6Wjk6cWBjlhHRkvSEIkDIEeG3QVTUf0ls5BfkoU4rws8Y4W+3ROHomFqkWMu+7vFC7ts3vrzzBACXis2aG0XbwiPnYXzN22lNLeJkwP9v8BT/7fav8xvDz+GYXeT15xF+AaJ4cSnHFH67WNE+sx3yfX/iIb7hK8fuW2sAO3NPAhMK/1I4g3CGQ+ePYVtuTHy2LRERFLMTg/I4haOU0Te7Aa9/YqTyO+e4cTvDGEenLTm7s7oVut1SPHjBX5Nnj6jyj1fynaa9HUYkPs18yGNdqns/KWb4p0P7OqPAPxykxRiKKe7bdWfQW7JNX/eOlSq/DMteO5ItEjMkGyPlmc24Gd9gL7tLV23QUR1Smyx9LW3zscA+P47jVDDhaKpDKCNSE1eOiVVQp+5DYRPHFRsd60MpibFuhoRa63DFDL9zDoslLkJHy40uJSSm2qxeHaJw+axrbbfWk9+jWPr9Od4bZX2QZHM3JaRkrcDCXpzz3I3DE9msiFO9dINxoxPQj/Pqd8IyZNr6cMJgFcIv0fbo1XxJakhSTXuG8EuSI4QNNmjQoMFJ43Q9mw1eunC+6s44S1t6Qjiq5ctRTlaW0EwnWCGqKrnzNYS/svQXtv8JS7j1pGjcvm2c4bbeB+bP7zsA4ahbB8j4EGlynFSYjd3RW1UK/3LC7+v5YpzVOCzoFBWEPrttaqFYpvU/l11fsogUMDUDH+d9fqv3KQDe0n3txIx3iVLdNypCFyr8pMIfVyRxHvKC8Eci9IGMZkT4BQIpqBT+QCeExecxrvC3o9LWPrVId5bfy3zY4KPRJXYCP2owrvBbZ2mFggfGPs5pS39qNVeLloLP5lf5nbG0/xK9who/Y+ln9AsrEGO1fCskiwfBZA+zsNYT+5nNJLF0dGLi0cW4y2GNnR88gdiokvpH1XzjgYVf+YYtlITrtzKu3ky5cmNUx7cu6X78Yf9ez7642PUwDxXhP+X5ffChgOXlj1M/xz9OZK2zntBTE9pXbBb4vamAoRninDuymhzJiMxkDPUKG4VHxLIwwVCGhYLvP7vYxNyIr9HTh2wGWwQyICis/4vGD2wRfijLSj7nN0V8YOTiOf7yGNap56tT90sIxExt4nqoIfyuUNOFwOID9vLc/07uD4u60SIE0nIEAufE2gTclIR/XYVf+RrGe1GHZ6ylN8xpzcvEWXO8IMsNe72Ug/7RmxjK40qz+oT+yeOTODcK8lyGNDfk2hKuQPiPO28fpz6cL5h6r0D5asl1nBMNGjRocBpoCP8rGFL6Sr5QKk/+K8Kf4YSo5vgzm3LoMnKni8C+2VC6Mqk/dimJzSYs/cIWdVBjlv47+hCDpS0ituUsAQZPuhVyppsXRnZ+vXl2grTZ9mqhff5EQ9C5t/UbjTMGqUIUYkZLfzA6T4BiaJOqmrD2mFU4M8f/sTsfJXOac2qbL+u8qvZ5JeHXURer/OcgzYjw912CrRsVGEOl8BMgmFT4VaHwuyLlONQJYfH5okZn2w4pguUmF3GDvM/TulT3n6y+vqu2CEWAdoa9IoDxsYtjfelThP/F/BYO58cOgN+Nv8hT8TMTj6mz9Lvi/1YKvxJVLV9ik7WVPCjr9+rcI+tY+u3EMddt5kzP8U9XV250Fa9/slD5f6/HlSJg8JFLq9v5S5Rz/DduZxNW+VWRFJV87VOe3we/yO60xpP6Fdrl1WepbU5SzPeXjyuhlKhaBPLUp8Fnxf+m1X3nHNdvpQtJnBCCQIYc5ofYI/S3L0MZJrhs1CAQAX3dp5cfcj2+SmpStsYyU8pE/7QuWLOAcQbrzOg6WOddJVIhnFk4xw/eij9cYWwA5qv71WsJdbxqPgSZnryPrfXXs9yYdViyivDr6hxcsfGx9nsK1lat/SbEERR+ITD23tThJZmv45vXAKGkIM/Nyr9Ls8yS5pab+/Gxgumy3KK1JVwhjK8dSfZ76UoEOs3M2g0RR1X4e8N0huyDdw4YbZvgvgYNGtx3NIT/FQivnEMZTRfIwM9bjtXyOQuRLOb4heOO88rXmanAvhKhDCpis2cHE6TJq8WO8Uq+m4UF/GJ4Zu4fZGv9oq5uEVUG9pmxwD4YU/jzZG7n9OjABOBweeJHDqwGpVBKMi0MBUJVCf3PZQts/SpEWF0RuucHz/Jc/DwC+Iatr5hrNS4r+XTUxYwR/o5oEQiFA3pmdr53HKU6GhahfVIXoX2hX4xLIXBjCn8wpfC3wmITSCowqZ9xL/Dp/mexOC4FZyccGVKIKoOh/EzLOX6A7pSl/0p2A4DXtR7hq6PHAfjY4DN8IXkBAOMswzq13FG5FMr3LUMPLW5tUiGsBTd7XzkhikaJVV/HX7u6hP4Sm/MU/rHr+5Y3bKGUJ+q39/zn+PADqwf2Ve+1EXD+TIhz8NzV9W39aeaP6bQD+0qUyv2wSOofV2UTk1XH027PHk+p8qepn93PbEpq0hkV/YvPxfxfv3qbj3x8f+GxtFWbxMQrk911UCb0LyP83tYfcyO+Dgg2w62Z34+BDBma+XP2PijPjt6ryA7w950p/nvRMfi0/lWU+UXqPvjgvsymR9iQ85A16enj5NpicTUKv7+XzEwI5CoQa1rb4egz/H6EYH1HwVEQpxprnP/7VgMlJcaycn5BnGk22gGDOGevd/Sg0FxbtHW1G/vTaEcBaWYYrjAXP4izldT9ElJBmq3vRslyQz8xM3Z+KDIhrGsIf4MGDe47GsL/SoTz1NtJRyAkgfAKP4FftElrcMZUCn+iAu4U6m2dnb/EmWKO/66NJ4Praoj3DV0G9s2f37eFiiNrZvgnKvnGTy1sV20Aq6j8QkWQDHCmIGBCoFR9WvFjrcLWny6w9Vdz/CmZSfnNOx8F4MujR2uzD0oEmSeDOuyORit0hhCiclQcLiH8ZWhfQIBUEpGPzfAXmy2uyDgIdEJYtnJKv8jplAF7QvnNj2KxnOiEzw18u8CbuiN1v0S5AXCjyGQ4swk7BVffHuPs1lmuZLcAeFTu8OWtx/iyIgvgI/3f5bn0OgMb+/A7FG0RjT3XFcGD/r+VkgRKEhXnsEqQ2QSs9arnNKbs9ktRXKNFQYPlaELfjhH+Iqm/xEZH8YYnR5sFZ7YDNjpH624uVf6jzPEnYzP89wJVcF9sfY7ImCrbi0fHX+c4KJP6h7FFIBnoAdrN9tyXIxJPPx8vJFY+50LQzw6PTFDnwTiNscsJfyB9pWakWnTmVOOVif7zNrmMMz4tvHQFGI2TkqoWc4nCH8qoGBtYfP8sU/ehnKU/elJ/IAVJNjmLb50rflULnLNYZ8mKsLVhbItZ+vqax1WgpCDL15/hXxaqOg+OexPa1x9mqAUEeJQnsPxYjPXz8WEg6bQCbh0kpEecVc+1rT7PZSg3VPqDJRkWxjJMzVqEP1CSOF3/HOJUk+WmtrpVFKJCY+lv0KDB/caRVnWf/OQnT/o4GtxDmEIdcTiUUIRC+XnPcESwpE5Hln6XVzb2RaS1tPXvucFE6Jsw+UQln3NulNA/Z34fPCdTUtbW6lSVfFuTCj9CrDXHTxDidIJLh5UBIVCydu32SHgRieTQDthfRL6lROQJv733MWIzZFt2eXP31QsPIywT+qcUfhipxoe6N6EKT2MU2hciBZXC78JW5ZIQZaihs0TFCZcKf7v8+KVCWFOp17939/fQTnNGbvBweGHmfS9Vc/x3Kwvlt36N5I9+leTCzpirQ++RuZyWiDgfnkEELb6m+3pe07qMA/5773f4fPI84FXx6X54fwsVxyz8vdFesZpvBqXqCQxMjC6JQamCrghhcjQju37deMpI4S82JQpr9fTowFtev1WpXI8cQd0v8fgj/v1evJ6sTVzS9N7N8MNYNV/qLf3j5HB/6K9XuyVrldPumDsgkiGZTTF2tvP95h1PDnLteOHa/5+9P4+1Lr3vOtHP8zxr2NMZ3rneemuwqzyUHdskJAGnQ2hIwAkhaZRLfOkWBHGlVotL6JYiRWrRLaROmm4QkEZqgZBASBeJ6ZLmthtaaRwSJYF0yOBgkzi2Y1d5qKp3fs+0xzU8w/3jedbaa0/n7LPPOW9V2ecrlV11ztl7rbXXsJ/v7/f9fb/Hk9iWajMyo3OP6NNWY7FrmQl2oi6JXB0FGcmoVjQswwLJdXOGqSd07qv7bqJX31PGGfbyJ2RmsrK7D6BEhLHmDMZ9vkPaLMA663DhCW2dl8M33dVHkzDHj9hoPENJSanXl7YDZ3KrFwL0BXeAS20YZWbl/D5UiQF2rQ5/qX10YaQk7VQxyTV7R5sahZpTxRmmieJwdLysvygNhV5Owldh03n70UT7UtoxBYvilIqRS1ziEpc4b2y0qvtTf+pP8b3f+738nb/zd3jjjTfOe58ucdEIUnmLJRKKKMzwC6GwypN8act60Znb4thIvgp1NJ8ZhSz38MVp8pn5/b4ZkbsChTz2/axj6VyfKDJU5gm3aXT4nXOUVk+d+tea44/AmJAJ74mClGJptyaWEc/GvsDw+jGyfidj7o/v8urgdwH4zvQ9RNHx89hNSf+0w++JSkUij8yI49pIlWlfRIyUou7wuyhBhFtdqqQ+x2m1rlFLOvzOO/Vrq/mtJ77A96H0hZlFTTw5ojV4xPVoF4lgbPO6i32lJ3jpmdkF0Bu5l/M/l9xAJj0//iAE39H7MC8mz2CxfDbM8893yp1zIGcls7GaEv5Mn67DL0yJk5KhGfO/HfwC/+bo18Mvgmnfup1BqxlS1KqEtlwk6tMZ/qak3y6kAXTaim//8BbtluT9717ua7EOrmxHbPcUxlL7AayLSj79NGb4YSrpn0ymsVwV6e9P/L7PO/RXmHb4DZGMKUwx09kGfzxHgynZfO314wtDFxXRdyozQVueeP1J5Moi1wzhd9Y/i8P976RcmiIyj1jGjM14KWG2zrKf73FUHNKLFkcOZvZTSCxnieZbnIG21UwaYZxnLkqtkvXDkuLHGvDEd31pO5zub5dt7zRmeZtgcsL8Pkw9fdYZL9DGoY0hUj5mcqsd86SfMV7TQb+JUV6uNb9foZUossIwzlZfU1lhMMahTlFJWHatnQRjLUfj/FhFlFcOXEbzXeISl3hrsdGq7m/8jb/Biy++yN/9u3+Xj33sY/zn//l/zj/9p/+Uw8PDc969S1wELM47tuNoBVIvhUAisPHUuK/q8D/RR5ROI5HsLjHsq1B1+A/NyGesV8TJmpkO08OQZX892kUd0/Gyzi6N6olCHJ9pb+PiKcEa6Al9Pca2Qod/nWg+AKVwukAovxhSQiyXezMr61+FPjn/duxd+d/XfjfPRDs0/QuWYZVpH1BH8/XNmOMIfzOWT0qBqDr8Uavu8Csp0CGaLwmLVBFM+1pJcx8dwmheHXyJiZ7QlS3eHZIKKrzw+Z/lxc/9DK0yqws31Rz/MrxReNO/KuKwghSCP7j1zdyOp2qNeYd+h39YzRQcIjmN5ju1pN93PZ/oIxyOx/rQFxWEBOZiJVchkKlBkD5vqc5S8tMLxYuJzTDOUl8LS8jIR96/xY/8idtc3Y1PdzwNCCGmbv1vnk758PQl/cG0L28SOoO2ZU0els3vN187zvy8+jJH9sf7/p4Itzav38tOXNBfRERfYYvaNf9YOIsaPEQdvokaPkYUw6UjJnGYs19GaLXVdcKKv44b8ZMiRPOdcH3HMqEw+YLSwTnHQb7vYwKj7okjChU2lfT7Dv9s19VaVz+enbPouY9nOKq2tVlCgDqFtL2CNnZZwuea25MLxoTnAWMt40xzMMg5HPjzeLJsfr1IwnkZfpootLE8OTqdgao2lkJbomh97wMp/dU9HK++Pye53igxQZ/yvE9yQ14Y0mT1fRApQV4a7DmPCV3iEpe4xGmw0VfUD/7gD/L3/t7f49/+23/Lf//f//cA/MRP/ATf9V3fxV/4C3+Bf/2v/zVFcbaolktcHHykkcRhSauOPj53u+ouR6YgCYT/funl81dXGPZV2FZdJBKN8fLlQPbnI/kqsnzrGDm/h0At+dJWlZx/e9rdt2GWMxEROqkk/euRQKFiKCcgfYffO8GLpdT6+eQWAh9ROFhCModmzCf7v8HElVyJdvi21ks4dTJ5qwl/fIyk345XRvN5F/BGh18IRNmQ9AcCoJCYQPjT6r3mJf2AEwrKjN859IWLD7VemDFDE9YQF0OEc7SGj+t4vsqbYR5HekjfjpCIWiXRhBKK797+Vq5Hu8CiksQ6kIiZ8Q4pBS18wSc7DeGvilBIjsJohsUythkIiXCL3ffl7+PJVN95Ur3MsA+gJRI/nw5+GwFVQeYi8K7n/Dl+/X52qsivusP/lCT9dZc+JAo4PGEtbVmnDMw79E9fGyT9QcKdyhbJnMLi0Z6/J979TMRWx3cmXz/BzPAiIvoKk6NkdOLfCV2E68IhsiNU/x7q6E3U4CEy70N4JsRy9Rx/acvpc9oZhGvET0oJmBPHVqoCStZIA3DOcVgcsF/s0VYdojWOB7y0vjwhYWTla4XAMWtqZ52rC7K24dBfoZL0SyE3UhbIIG0/jZGeNpZT8suZ7ZXanpkQamMZjAueHE342oMBv/v6IV9685Cv3Ouzd5Sx1VnnfHnSexKK0iwUD7Y6EfuDbO3YPAijAWs69DdxnKzfOsdwUpKcYn4fKgPF0xnsTXKNsccrCSIlvSLi0rjvEpe4xFuIM63qrl69yp/5M3+Gf/bP/hk/+7M/y5//83+eL3/5y/zYj/0Yf+AP/AH+8l/+y3zqU586r329xDnBWV91dvhIPvBfdgo5lZObkrRy6Xf+C/xaMOVbBSmmCoADPQhZ53omku9e8Zi75WMEgpdbz520p0sN++pIvsb8/tjkdFQLJVSdY7/WDD9AnCJa217eDygJCrF0AdaSCc8Ecjvf5R/bjE8e/Rojm7EjO3xs+1tJrQF5AuF3riHpb2OjkJagZwn/xBUUK4y0tNP1XGssYiTTWD7iaZ67khITezLYCm70Yl7SDyAVe8UjBmWfWMa8L7kzo9JQZcNQbbTHrTmn/nm8Edz5n4mv1ekP84hFxPftfJTv2/koL6V3Zn/pQM4tCpUQpIHgnWqGv+p6Slm760Nw2g9y+3U6/MJohDX06w7/csIvhKA7N8fvpEIcE612Vty6ltBuSYrScf/x+oWFeob/aUn606mkH7zJm59PL8jz2b+Zx7TDHwi/SknVHOHf98+um7vwnlv+/njtjeOvlfOO6DPOoJ1eqxsudO6fl1ELl/Zw6ZYvlhYD5OA+qn8XUYxqqXxhFjvwpSvq4pywNsTyVfEWCmFs7c9xHCIRMTbDumM70H2e5I9JZUp80jOtAXXWaD43G5fWfC475xZ8KqoOvxRyI5VGJW0/jUy/1PbUHeUKSgms5VSFuWW4+3jIl9485PWHQw6HGUJAtx1xdSflynZKHJ18/UkBRXnytZEVZsFVP44UOMHjw8naxYtCe8+AVckBq1DJ+kdLZP1FachLe6r5/QpCLMZAroJzjqNhceJ2POG3G0f+XeISl7jEeeDcVnVpmtJut0nTtDbu+vmf/3l+5Ed+hD/5J/8kr7766nlt6hJnhHUOJbwsLm4YXEVSNQj/tMNf4bh5+wr1HL8dgbMIa6gi+axz/Pro8wC80nqxdp9fhmq5sGwRVUfyBcLvu9uaq8kWiYwp05A2sGaH37/p9FgFwhupr1jwvRCk7c05/swW/OzRrzGwY3qyzcd6v5e2DQZwJ3TClM4QVfb4TIffJwckMq5n1Ydlf+l7VPP7AuGJhZx2+EUy9Q8QiLrD36oWNnKxw49UvFE56nefI0bQdFZSDUOv1mivNl88NEPyJYv7VXL+eURC8Ux8bSGK0atSZn8mlahn5rNjDMbmIar5eSGDL4LHwI4J+RVrSvo1OFcrPbaXOPRXWHDql8r7RmwodT4JQghefNaf56+8uX5hIX/Kkv521aUPhQaJpLQFEzOZEv5Vkv6GS/8yGbFzjsehw39r2/DyLX+dv3H/ZDPDKqLv1KMiS6DtepF8OIcohzjVfF4IUAkkPVy6jTBlnYCiRMRoToVgsRjXMAd0lulADOH/1/OoSGRKbnJKWzAsBzzOHhHLhESdzlBSCUXpijNE883GpRk9zbs3zixK+qsOPxLrzIZFm/Wk7RW0cQvPp3WhhMAYeyan/lJbhhNNpxVxdTtlp5fSTiOiUxJppU5OKHDOkRV66bjdVificFjQH61X4CnL05kjVpjK+he3k5d+BOS0xw7LYyBXIS99POBxvgjVvhp32eG/xCUu8dbiTKu64XDIv/gX/4I/9+f+HN/93d/N//K//C/cuXOH//V//V/55V/+Zf7dv/t3/K2/9bfY39/nL/2lv3Re+3yJM8IBTjkiqeosdgCFwgTiG5lyM8KvpoS/7u4HfCl/g0MzIBEx39x577HvY51DIVn4zrYGNfSy8SqSb2ILOjJlK+rQUjFFUhH+zSS5Sknf6F2xEKkI/yN9wNhmFLbk3xz9GodmSEe2+N6dj9KNe75bt8Zg51TO3wI5VVkInCf9NOb4VxD+qoOWCIUQyrsGhw6/jKbxXpLpDH8rrJSnHf6mXj7izTDK8a7e8wgsTT19NNfhb4mkViI8mpP1Z7bgUfBteD65eeLnsQzOOaK54o8SgnZwCJ/Y03T4vb+EQ8xEHdbdfif8GMoJ8MTL1a/bWuLQX2Hq1F8R/sgnIWwodV4HlVv/1+5O1lpU+4X80zXt6wS5fln6BbEUCu2M7/CHtXxrlaQ/FAK0cZR68fiGY8MktwgB17uaG13DTk9iDHzthMjCKqJvcA4RfdpprLMnzvALE+T8apVDv/BpJ6FrHcuY3GYzc+rGGZzzEYdASINYtq2Tr+9IRmir6Zd9HuePkEha6njz0WWo0hfOYtyXNSLftLVh7MqPcpXhFqoeT8ORDtv1hH8z/4D1pO1+H/zc96Yd/poQnqHDnxUhGm6NLv5J++Ln81fvi+9Wu6WE2selCh4dTNaahZ8UekEpsC5aqeJoVCxsJ8u1jx7eoAATqcUYyFWY5IZSG5ITCD8AjlONClziEpe4xHljvSG8Ofzcz/0c/+pf/St+8Rd/kTzP+fCHP8x/99/9d3z/938/V67MzmV/3/d9H/1+n5/8yZ88lx2+xDnA+exfhXforxDJJuEvpjntEAz7tk58692ZDr8JkXyKwpZ8euRd67+5817SY2KnwHfXfaN6juQNDxDOYqME2/bbynTBndY1YqloyYSjeIMOfwNCBGnxio5LV7W4Ee3yWB/y5ewurxcP2TN9WiLhY9u/v3aYdyo6ZvE+RXN+H/z8vAuz5NIU2ChhW/V4qA/o6xMIPxEIiKz2s7uATKadZymmkv5O1RqrTPsa9Z2Rydmzngy/0H0Wxg9o1gebkv6ozIjKCbeiq/TNiIflwUwn/27xCIcvBvWO6YIfB4dDzC0MlRK0VTXDfwp5fPhccldSNEhI7ckgXGUFfiyELjCIumu/aoYfmk791TZ8EoIwJe6EBIdNcedmShwJxpnl0V7JrevHX4uldnXq49Pq8MexQCkflDHOLO2ORLsSYw157ndmVYc/jiVxJCi1YzxZjOCq5vev7UZEskRow8t3Yv7D7+a89saE977r+GuxGdG3CdGtUEXSnURAhMm9o358zH6Jqct+LGJyk5HbvJ6nrxIOar8NO513r+DEek79AEoqBuURFkc3Wq3Imsc4M0jhSZkSitxkGGeIOb0ZpVKehBpr/UhSY15eO43W/j92tyIO+npmht/iNurwryttB28i6BxL/WbW2pYU2LnowdMiK7wp3KZFhwpKTmMQl3XwoYrkM7TT5eey1445GOYcDQuu7ay+b5xzTHJz6vn9CmmsOBoWjDJNK52uVYZZeSoTwCYiGebtjSM+4T1GWbH2562kIFvzerrEJS5xiYvARk/av/gX/yK/9Vu/xZ/7c3+On/mZn+Gnf/qn+dN/+k8vkP0Kr7zyCj/4gz94ph29xPnCCUss4xmXfCVk7RCvbEHSWJxdibaOddSv/075Of++nWB07mXLUvJbk9fIXMG26vJK68UT38c4nyQwP8M/I+cXgtyUpCpmO/ZkKxIKkwbTvmJyqkz1me0ocayq+8XQ5f/U+As80gckIuJjO7+f3eaiOO6cKOcHiEpPGCuzQYSoZf1q3qn/BEl/IiLAIUIX0AmBahQdmpL+dlAA1B3+hlL3bukl+DfiK3RkCnMdj2aHHyBtyPrn5/incv7NuvsV5vsofjbeH0tm87UX9sIacG5Gzg9VCgKA8HL94+AsmIwhZSieSf851b+fHQtY6PBXWJN4bQKlBM/f9p/PV++erICopKxKiY0XzKeFEGLq1J/Z0JW13jAuyPxXzfDDtMs/zhbP/aO9ML9/NUI4g8Dxnmf937/5IDtRultF9I3K4bF/dxLWNawTxRgnT+gWClVHnlYFhLxR7DLO+OJYVVxwmoWEEKl8VOoaaKk2CHkqsq+146f/r0f8i08+xjkXDGLd5h1+JYLpWRh7stPjM85QBtO+KzvBc6ZwlKWtJf0bRfMpQXmCtL2CCQT5NFny8xCCU40QzGMwLohPaVK3DL6g4o71EyjD71cZ1UkpSGPFo8PJsfnzXpmzmfS+2g7AYDy9lkttyYrF4t+68NfaydF82lj6o5LWMe78TSgl6ufZJS5xiUu8FdjoqfgP/+E/5Bd+4Rf4sR/7MV566aUT//4jH/kIf/Wv/tVNNnWJi4BwWAytOfd4JeTUMM6URI35/nXk/AAdmZKIGAccFQdgNQNb8LnJVwD49s4HjnX6r+CNpSXzTbGo7yP5Kof+scnZibt12kAsI0TS9tJXNu/yR1Ieazz0QjqNqIuE4o9u/z6unmBquHJbjUi+CtV5kKELXzv168V8cG01Xx58CYBExggEUXCAN1GCUtNFiRACG0wNu0VYKFUu/Y3LoSLpz8U3/MmYOw+qnCWQrdEet4KZ4RN9hA6LbOMsd0vvBfDcCfP7J2JJN6UXuuMOtxAhthLOgBC1FL8buu8DM8I5FzqgJ5ATa8AZ+m5q2Nfs4D776i/x8qd/ujZOXJjh5+KN+wDe/VxF+E+Oy6oWpLPxjBeP2rgvM0gkxhkEgkkg8ati+aA5x79ILKpIvlu73ojRCcHVnuPKdoS1ftThJKSyRV8fncl0Lrf5yfP7pgQ9gZMSPSqX/XB/RSJirEf1ubVz5FYYjZsnZkL5WMo1iLASirZqn/h3TRwNNHlhGU1MfQ4dDrNBRB4EEmqnpmfGuPpRYJ2lapz2Oook9r8YTkxw+F/8TNaBlILiBGl7Bd/h33yGHwC3/gjBPEptmOTmxFnydaCkwFq7Ut1Wbe8kdFsR46xkr7/6+VZqQ2nMmYqLrVTRH5U1Qc9Lc6bRBqUkdo1ovqww5OX6n3mkfPTiWXwaLnGJS1ziLDg14Z9MJvw3/81/wz/4B//gIvbnEhcMz90ECGoX/grzsXxRo8N/fU3CL4SYzvHrI4Qz/Gb2KhbL7fgaz63Z5bXWLZX61ZF8W9cprUYJyW48lVInUqFkhEkrp/7NCL9UYr6pPYNt1eWZ+BqRUPyR7W/nxokRg6sRFZ546oaU1wSp+kI0nx7MLEIPiwP+r3v/kq+OfUHlpfR5JKJ+nY2SaSY3foa/6vB3QpdeKEMkpxJG7Qz3C/85vxBdWZoDrgJR1bH/nFujPbZkh5ZIsVj29BEAD8t9SqdpiYQbIXJvIziBBMTc+UxjRRpGT7I1nfr9mMnUsO9OfB0BaAwTl3tSdUI3UliNMIZBIPwzhn3O0dt/nbgYk4692qHq8I9tNlUiyOhCjfsAnr/dQkpPwg4Hxx/T0zbsq9Cuo/mmXWvpknouf1UsHyzG+lWw1vH4wF+3N67443FSIUzBSy/4c3GSWz945//SlgzLxULbOrDOUroSJY5X+giTez+HEwm/QlgXzFAhlgmFLWqFj7Z65n7H6UUfEan8bP+GBPwk9EfT9x2OQxIImznmQ+Wa70mYtQ4bTIGdc1hs3eFPYkG346+Hyqkf2KzD35C2nwRjnVeknUFOLyXoNRUF85gUnuSeR4dfSoF1xycGFMXJfgVCCLrtiMeHGeNs+XkvtD+fx0XanYRWosgLwyhsIyu8CeBZzoVbY95+kmusXX87VTTfO22OPy8NeXFx30+XuMQlnh5O/aRtt9sopWi1Lmbu9BIXCxPcziVixrAPQkZ7XEn6SxQySMTX7/BDw6nfDHlYHPDV4Gb/7d0Prm2k42BxhtC5WtKvt64zNjnbUad2agffkUpkjDltNN8cpBQLs6/z+Nj27+dPXfkjPBNf22gbFeLCE4+y2eFXldLCL+S3VAcBlE6TGW/C9qXB7/Iz9/4PDssDWjLlY+0P8+70jjf21lPC3+w8SSGwYTvdRg78K++afqk/KPfQGDoy5apogy6Yb/FXkv7hro/Pa432EEJwq5b1e+O+Ko7v+eTmRiZKADjH7tHrPPOb/zvXfuH/Q+v136l/paSkhb9m147mszpE8nmp9m601ejyB6d+qxfGGJoQ4fd9szi/r3SODCQjzv022iJF4qXNoypaUaoLN+5LYsmdW/7++OoJbv2VYV96jIT+ItDs8ANsxzu40j93pKTu2i5DLemfzC6k949KjHEkseBK2+FdvCLQBS8H1cObD3Ky/OTFbEu2OCoPFyLw1oFxBmtPjuSTxQR/j510j0hftQ2EP5JRHWMIfqZ9xqHfaBa+5kP05DrRfJugP1wk/ErIM6kknAMdsuod4TmGxTVM+5JY0guEfzSuzqvYmPCfJG2vYKzb2CSugpSCfMMZ7/Oa36/hju9wT1Y49M+jlURoY1fG9BXacvL1fjyEEAjhRxoAxll5NqUFgHAnRvP1R6cbofCjAuYdR/j3+hmPj86eVHKJS1zircdGK7uPfexjfPKTnzyze/El3gI4hxM+93ae8Ash6hn+yBRY6/j93Q/xzZ33cVWtL1e/EqTtB2bAr+dfBuC96fOnkrx7E6TZy1NmQ2SZ44Sk6O7gHOzGvYWFVlsllGfs8Ks1Fg1SCOI1ZvRPQlQuSvpNUFpUknAlFD3hicpe8YRffvyL/OqTX8Y4w+3Ws/yJ3u/j2fSWX3jK6etsnMy4gwsELmnhEESOuhv4e987XYi8Wcn5k5tIHHleMD9bUZn2jXY84Y+LEarM6jn+h+U+zrma8M/L+XNtTu6eOcv2k9d41299gvd89ZdIhr7YE/UfTfdDijqyMNNrnOsGCaok/duqy1aloDAj/wE6BxyzOLPlzFjAVoPwR/l05jsO6g0hxNS4rypMNIz7LhIvPuu3e/fh8YQ/z9+aDn+n0eGvMGnM7x9HpFZ1+Kv5/RtXEwRhjj3I4Xe3BNd2Y5w7uQgCkIQu/yr/jOOgnUY7c/wYkzNQjnFrGHxWaKZICERd7CptMTXscxawS5JCfPSk2NDf5CQ0CX9loKdEROnKDSPygsS+9B1hZx1CePWEc5ai0eGvCH+z0LCJd4CSEmvXi8qz1i0LQjjl9kQgwKfHec3vTyFWPpuNteSlWXvufrsTsz/IGSyJ6ctyvZjCswHSxJv3ZYVmOCk3nt+vECnpnf5XIC8Nk1yTrjm/D4TxEnGmJIa3AqNJyWSNouglLnGJtz82Yit//I//cX7iJ36CP/tn/ywf//jHuXPnztKO/zd90zedeQcvcb6wDhCGSKbEcwZRSghcXGXAFzjg5dadU2+jkvTfC/FskVB8S/d9p3sT4WZcj+VkQPsrnwHAdHcZY+hF7XqGu4lUxmRn7PALKTw55qw9iJMxneGfzsraOdM+gG3ZZmAyfunhz2OxCATffOVb+VDnZaL+fVwU47RfDMvQvbcqmSFMUgiEkJg4JSozEhQ5mjIsij1Jr0z2bmGMoZgUxG5GKFwbDZatLfLWNmnW93P8XT/H/0gfcGiGDO0EieTZ5PrMMWe5IYmWy7WFNWw/fpVr936bJPdSaiNj9NY10qMHiIZhoKgIv4OJXWMePuSSW0Rt0rejemyrDvfL0OFPgqTfWlDLF3VC5zgpa2f/bTkt1lQkH2bJf0+16dsRo/kIwQs07gO4cdWrRfYOfdzUPIEWZYbM++S5VwI8rUi+ClWXftIg7fX8/jFyfv/aaoZ/lixV8/s3ryUIPfRmeFIhdI4whpeeb7N3WPLaGxNeeXl1ukKFlmozKI/YirdJT5FDb6zG4Y4l/EJnCFPg0pP3o0ajOx/LhIkZo20oLjDt8AtncUtZlfDjJBeA/nB6HqtOuxQSbYuQIHD66ytSgkmpsc5hXdXh90S7lvQnkl7HL2nOOkpQSdvXIWjG2jN/RygpgvmfPZXE/Tzn9ysIyUrDQu/Q744ds2kiiiRSCh4eTOi247pQ4B36NdE5FCpaieJoVLJ/lFFqe+ZowkhJsjKoJpYUG7NcU2hLt3265bNgPf+DtwtKbchLH2uqzebmipe4xCXeHtiI8P/Ij/xI/e+f+tSnFn5fLSo///nPb75nl7gQWOdwAmKhFjv8SFzsF7NKF8fOsB+HK3PxfR9pv4eOXH8ExFV74zTp3ddI7/0u8d6b9aKqvHIbbQ1XWr2li8dYKkahW75xh1/6eXc/Y3iBlN/Z2gCvOcNv5zr84OfE75oDLJZu1OO7bvwhbrRuIcdPwl9ILAbBfId/uv8i/JeJ24HwC3KoCf+hGTCyExSS2/F1TD4mEhZjGw8L5+oOv4la5N1rpFmfdLTH1Z3bRCgKV/Lbk9cAuB1fI27MMDuCOdSSC6zdf8Czr/4ScVUEiVL2nvkmHlx9Hy/aJ6RHD5DFVFodSUFLJmDX7/ALZxgJ7wQvkXRlmy3pydbAjEEIPyft7PLOnbOgCyySQYjZa0r6m4S/+e8L0Xw8HeO+K9sxQvgZ/Ulm6654BWFyRN4nz7wC56nP8Ddc+itUXaVVkXwVOu3KpX++w++v/xtXoqDGkHg5vAOnefmFNr/x233uPcqZZKbeh1VIZELfTOiXR9xQ66dNaDc3U78E0wLWep+7ExKhpyQ2ljETPSYzE6wzRMIXeIQ1YB37Q/iVLxhefkbwynPCdxvl+tF8p8VySb8icwbj9GbRfFJSlF4V5JzzUwnOYp2lCOQ0ieV0hr9RaDBueaHrRLj1ZvitdWcm/FL6VABt3Km63pPCUJaGbmt9dchJUMeMF1SRfJFaf+m41fZd/oNBxo3dTv0+pbZn7saD754rKRiMC7SxdFtnU91FyqtJtLYkSwopo1z74ZtTXk9KiXdUtzwvLaU2KCHOlKZwiUtc4u2BjZ6Ml47771w46xARJCpaIMtKiDoTXJjy2Bnm4xDLiJ5sM7QTurLFB9vvPtXrW0cPuPnwi1w5eh3Z6EIVV++Q33k/+zeep6Nitla4R0ciwrYC4c82nOEXwiuu3cX2+KNyUglsMfH0eKpYPtlYlN+JrvGF4h7Pd17ko9f/gO80OoPIR7gw81+NQoiqwx8lM+dZCl/I0HGLFEicBAFlkAhX3f3b8XUiodBIYgwTJ+qHhbS6nlHXcYuse43tva/QGu0hheRGvMv9co8v53cBrxRooorqWkb4rzz4PHExpozb7D/7YQ5vvo8ShbMO58K12ejwSynqYtLEnEz4KxLUFxVR7yCFYCuY7vVtJem3q830rEY4wxDdiOSbFrSihqokbvx7d1k0X9O47yQn9w0RRYLtXsTRQLN/VC4QfkyBMDn5JAckrac9w99aJO3rRPJBs8M/fW1RWg76nrDcvKogt35+H6ik7Nu9iBtXYh4flHzlzQkffM/JsXMt1WZY9tmOt0nVegXMwhZLi5I1AXUWUUzv37Ug5YwqRAmFxTIxE6yzKNmU9Dt+967gjceONx47Xr3n+E8/LNlOlH8Pt0zyvzmsdTXZhtkOv8OhrV7M11wDFQkrSlt3Xp21uIZpXyo1tKvt6nq7xhksFnXaDYfO5knQQVV1FiglsHnwDDjFpVDN75/FP2DZvqwaLyi1PbVfgZSCTqp4dJix3UlJE0WhfepC54zkvEKaKMaZ5syzFfgO/zjTaLNI+K1zDEbFRoWKSAqyYsPi01uAojR+XEX4895eX9h0iWPgQiHxsoByiaeNjZ62P/RDP3Te+3GJpwTrIJL4rugcJBJCHJzAG/edavXRwLPxdb6Yv8G3dz+4oCQ4DjuPvsjtL//f9X+b9jbZnVfIn30ftrONc46iHHIj3iJakVmdSAWpX8Bv7NIfIgGPi+Y7D8zI+RuLgKmkf1rweC65zp9Ovhtx5V31z7wcuMSlQR3g/AKr6vC7OJ2RJQr8TLQOTv1JqGeUzm+nOb8PhGVyiWN6vVTdfSsjnIrJut60sDXykYk3o6vcL/fqv39+LpnBhsxqu0Qum2R+TvrBS9/J6Mrz/hi08R9N2Gc5R/gr08a1TPvCXPORme3Mbzdm+J0LJZ4V88bCarCGgagi+TozC7gFSb9zIMTSaD6kQpQlQpe4c5TlzuPabszRQLN3WPLcMw2y6hyizLBxJ8zwy6c/w9/o8FeL4arbf1LnvSpeFKXPaY8iwZN9fy33OopuAiJryNrFlCy/9EKbxwclr72+HuFPZMLAZPTLPjfWIPzOOXKTT2fqA37uV/Z5vF/ww997k4Tcy/njU0TfBe+HZpFICUVhC6yztaJAOAMOjkbT++zNPfj//jvLR9+n+NBzIZrvHAn/YGRm6sRN8g/rOeZrqxf8gZQS6NxSlCbcTsG0D+oZ/pYdkgZl1HAS3NqRaFdinDk5GnEOQoiV0vYmSnOya/1JkEJg1vQMaGJwSvO4daCEHy9YJuMutV3wc1kH7TRiv5/z5GjCnRs9Sm2xlnMzGkwTxXBSEJ+DYkBKgXHLHfWz3LvWd04p54epU78200SctzOy3ISEDE40MbzE+hhOSh7sj7l9rUuvvdn6+hKX2ASXJaZvMDgcSJ/XPg8pBFIlft6V2fnx0+L3976JH77yh3lXevsUO+e4du+3ATi88gIHv++HOPiDf5rJe74N2/Fy49yWJDJmuyF/n4cSCtHyf7/xDL8Ixk0X7LFTE/654zGh8NKU9IMkQswQUd/t9kQNPLeUQiDKRoe/GcsXEhpM7AlLKyysS2fIbMHj4LtQkXSrYlTaxjS6Y9MRBP8eFeFP8gFS57VTP8BVtV13titUktyFhaNzxGFmv0ynYyHV0VWkSBTZjPqk8nFYJ5avMjubGvZ5old1+EunyUPxQxxH+HG1B0DTsA+mMYsAyupapdGrO/yNIpRQXqWxYWTZuri64xeo+4dz23Ha/yMjMu3PcRqd0+LOatCln5svJ4hyhCiGyLyPzI7qOfSqw+9jq/x5raSvJ6kNkljUEuhKIfCoMb9fezGI2Wg+gJef9+fj/uOiNpc7CX6Wv7/WtWacQbtZh35rHV95c8JgZNg7LBG6CPt3CjIqZIjVm+5zLGJKW+BodA+tA+E4CpfjH/ig4PZV0AZ++fOS/+M3FEeH5yvrr+T81XkbZ75LCL6gfJJTv7GG+6P7ZGZ2zMWb6HnDtKqN65wLmelhhl9quon/TIzxKhGvJLLYDZ361yE62tgzO8N7tcfpTN1KbZgU5zu/D6G4YpaPM2SFQa3h0D8PIQRbnZi9o4zhpKQozZlVEU1IIbi52zk/ArUimi8rNOWG8+zeqd++I5z6nXMMs5I48o2Ponj77/M7BaW2HAxyXn84YDi52O/9S1yiiY31VHme88lPfpLPfe5zDAYD7FxlWgjB//w//89n3sFLnD8ELO26SyG9GVKUoIoJkS2q5uRSdA7v0hk84Mlz37LQJVJC0VOrSfnS9+s/IMn6GBlz/+U/yI2ri67+mS25Hm8vLVg0kXR2ARDFxC+MV6gBjkMk5VodnrNg2uGf/ayWmfYhRCC6YSHmTJADT7vvPp4JTyQAF6ULi1EpVE34U+NA+Q7/3eIRDrjSIOnOAWkHxlOCEzXm9wFslFKkPZJ8SGu0z42tG2FMYVHOD1MCL2Dm+pKmqBUNZTrtuDoHQkpsEtQnwdnehW5eL3x265AwrI+Cqgj/TiDrkVB0ZIuxzRiYEW2i8LdLYMqZ99ieu87juSJTnI/Io7Tu8I9shnW2IfUWF27cd3XXf3b7R7OzucJohNG4KCULZLvjxuC2Nurk1bAWDu8hh0OEtbW8vDke47afxSURcSSJI0GpHZPMz/VmdYf/+IW1EIJOWzEYGcaZl+pX8/s3r8Z+2zPblT6lwRl63Yhb1xIe7hV85Y0JH3rfyV3+WMZkZsJReUQqW8dKc43TWGdIGmqq0WTaAR+ODSId4k4xD+0PQUFpENbgwmMtkjEjPZyVNDuNc4J+qC+9cEPwoRcFv/O641e/4Lh/qPjffv6Qb/2Q5SPv751Lt7Ui/DevJbxxP8M5T/p7nQgZVAjHYWzGjMwISUIi5jTELiiuwjFaLLpsKGukQQlDuyWZZJbR2NBuJVgsZoN0AD/Lfjzhd6ETfIYo+el74U7V4Z8UhuKc5/ehMiy0GGOhUUxwzpGtGcm3DEnsZfePD/0Fuen7rIJSVaLH2av0VSrEPPqjYuP9VkrWyom3O7SxFNp69YiDUf7WE1PnHP1xyVYnPnv04luIojTEynuSvP5wwAu3ti47/Zd4KtiI8N+9e5c/+2f/LHfv3mV7e5vBYMDOzg6DwQBjDFeuXKHTOR3Zu8TTgcURKbVSDq+kqgl/bEoKHGrFDPszX/1VkqzPePs2451nz7xvu49+F4D9K+9GJSsWMc7RXiO+Kmlt44RAOIcsJtjWyYv5eUSRwGabLx4KbZCIY52Il0XywZTwz3T4hfAS3KA5F7pA6AKXzHbQlZx2+IlTBPPFGIlOgiN76BKWzvCwnO3ug+dpSvh3qGiTCiZzTc+BrHstEP49xju3uR3f4EG5x7uXKDycBaEkUvh53CoCMcl8d1/H7RkS5CyoSICKfIfWGkSR1YR/K/L7kdl8jkgvQYjTO2pE8lXYkp1A+MfclDu+O7wEQmc4qRoO/Y0Ov7MN1UaLqMyIihF59yodmXojSBxjm9cd/6dh3Hd1xy8oDvplGKkIsm9bhvWxrMlNizFCT3DHqGhOhNWgc1AxTolQEGyMluQDhNX10rzdkpRDwyQz7GxFjVi+kwt1nVYg/MGpv+rw37iWwHwiQtXhtwaU4qUX2jzcK/jdr4z5pvd215qt9bP8A7bjHdorfETAG/bNS8kHDQf78ShHbGW4U7j+e4R9bHStq2teNZ7rwmiGpURb792x1fYFkg+9KHjxpuOX/mPBG/sRv/5bfR48KfjYd149M+mvCP9OL2K/rRiODcOxJ/xKKLQtV96jzjmvnhAZHbWEaAvniyXhHFlna0VIpARKOLCaXidmklmGY8P1qz62cNMO/0nO+T41YLmb+2khBOhTdH+zwqsdznsevFJT6LkOvzaW0rgzjRBsdWIOBwWtVL2tZ5gjJZgUs8//UltGuT6bomKFcuDthry0FNrQSROcdcGs8a017pvkhkcHY2LVpdN65xLkSiWz1Yk5GhaXpP8STw0b3b1//a//dYbDIf/8n/9z/vW//tc45/hbf+tv8elPf5of//Efp9Vq8Q/+wT847329xLnAkUhF3FiEOqOx/Yc4a1DIWk6ubIFboWkX1hAHgjbf0dwEqszY2v8aAI+uvXfpF4t1FiHEid19gFhFdczdprJ+JQSbJiw7oNCO0jgvYdRm6TtFRZDHJ/OS/kXTPvAFjErSL/QEn7PdOJf4RX0Vy0fcWuzwI9EhjSENktXcFdwtHwPT+f3q/ZSSCEUt8YzmJP0AeZD1p2GO/w9v/V7+5JU/xG40m9gA1MkH3htv+qlUcv4inX2Nc87LSIXAxovGfe24XVPJeSnwPITVlNg6Gm+nQfi3a+O+sS8WmSWE3xnfIRbKG/wxK+n3JowOhyDr3gjH5f9OCEE3OPXPRPM1jfsuCFtdRRwJ33gfNI7LlCDCvHm41FqRRWaHKz0M1kLwOUDG4fqcIyViVtVQzeqPQ2d/smaHH6DdcOofjj3xFwKuX4kbDv0BUoE1dQb9e19sEynB3mHJ3Yf5srdfQCxjrLP0i6OFWfMmtF28fvqjhoP9qPTac7mZ0E7Y2eulF23RaY6XOM3RxH+uW+3ZeemttuAHvqXkD/0ehVKC1+9l/MKvHdTy+01RRfJt9xS94Jg/ajj12zDmsAy5zRjrEZGMKJYoXiIlfde56vA7W8/vJ7EAbCD8s079zrm1vAPmIWU1y776M7HWzRTQzgI/QrD+PTcYFRdKwMzccU8d+jc/VqUkUSQYTfTbeo49Ur4A2rwfJoWmKMxS5/51ISTk74B5+KL0jQUpfcNC67d+FGGcl4wm5Vu+H2eBc4689IRfCMHuVlp3+i/l/Ze4aGz0bfGrv/qr/Bf/xX/BRz7yEWSj8p0kCf/lf/lf8tGPfvRSzv82hcMSy2hG0u+KEagEjCYKHX7wpn2rFrRxPggp9bMzy5ti5/GXEM4y6V5n0r6KWrKQKa0hFtF6hF9E2PRs0XxSyY3Vgcb6zvX1nRa7WynWwWhSks8R/1Uz/Ksk/dZarPES6aY7/8x+CxBleF2yaC6mpKiLIe1Aau8WjymdJhUJ16Pd6SaBJJJEwRUbpqZ9pkH45437YhktzO5XcEAcSaSA5qdRFZDK1tbC31cL6io2smncFylJGswls5Oc+o1m4Pxnk4iIVEzVIhVxH5jKqX/JwsxqhNMYKetZ/Kakv3Lo10mHIqhK4mJY/37pHH+lWtAX94UvhKi7/PtH0+0IneFURNEwuE47bUQ+Qp7lvq7I6Io1fXOWHqATiP0kMyGje70Zfv/aqVP/49Ddv7oT+/lTU+BmuskhE8NWs+aKV17y5+/Tnx+se3S0VefEWf7SlgvqmmZk3WhU1n4pG2HO90EIMe32OgtGczT229/pzr8YUIoPPGv4o995FSHgtdcn/N//4fgixkmojm+7Fy2NyNPOrCTfw3KIdZZUpUul/9VsOcLvn3GG6pZJYuE9Nxz0OnJmu0LIpcWXk6CUj2WdJ75NmIrwn0OXXUm5tjlaUV7M/P4Ui9LzUlv/vXbG+YVeO6bbjoijizMpPSuicK01yeUk86ksZynuREowyU9/LT5tTHJdP0uUFGi73MTwaaI/KsgK85bvx1mgjW8CNQt1l6T/Ek8LGz25syzjzp07APR6PYQQDAbTxdK3fMu38Ju/+Zvns4eXOFdY6WipuH6YO+d8xzJOwNmZDn9kClY9WpPJUf3v0Vk7/M6x++iLABzefH8wzFv8Ui2dpqVi4jUWybFU2ORs0XwKNp5jttYilScUu72UZ650uLLlCfJoUpKVnupWkv5yVYffNsy5hCDPNcNRjiszT5gWxhv84rSK5ZPREsLf6PC3Q2Hg0HhS+lxyY2bxWnX4pZRU696K8Dc7/FknGPdlRz7S8Rg4fIdfKTnTRF5m2FftQ7VPNqk6/NNubCShFYj7sU79zoCzHNmKqPdm5LBNp/7prPecN0noXO+bIRZHKuK6aw9Th/4y7aKT4A/QuD+WOvU/LeO+3TnjPqtDh19RjWhGClSkPCGfHNbGeqfFUnXEzB9MZ+lhtsNflq62Tzgplg+mTv3jia3n929cjWvSu2iIJ2aO6yOv9JAS7j8qePhkPS+FSEaA46g4ZKSH5CZbcJfPbb7gDD8YNSLrJnbJ/bse/BjIMddLlUZREf7OkudYOAcvPBPz3R/1Rpuff23Eb/x2f7N9co7+qOrwRwsd/krGb5ZcU9pqhnpAqhKUnEr/m4ik9I744Z41zlA2CL/X+xu6rVnC7wsNp7+OZeVWf8xcvbVB0n8OHX4pfd75OskwWenn988jx34ZhBCUSwj/ecTeCSFop+cTx3dRUFJirK0/Az8/fvZEBCX97PZFp/+cBc45Rpmuj9UbSr61owh5YRhlGikFWfn2L5isQqm9N0Y097zY3Uop9CXpv8TFYqOn1+3bt3n48CEAURRx69YtPvOZz9S/f/XVV0nTy9DOtyMclk7UODflxM95CwnOzXT4I1Oy6nupik8DkNlw+R+tidqsT8UcXHkRJcXCAxFAWzOTd34clFC49tmi+YQStbHcaWEtxErVhDKKJDu9hGeudLi+00IJwSTTx5j2TTv3lZGdc/4cdVqKbDT0hYA5QiEQKGu89B+QS+K+lJTo1P+8NScLbprseaLt/4ljWcfoVZJ+E03f2yRtyqSDAFqj/eM/HAdCCqK5FIQk99dRscRvobocXCgyyKIZzSdpB8J/rHGftYChb/1rd+bc9Sun/oEZBxm4WyT8xrfCH4U0g5vxldlIvrrD32USPp9mTN/SDr9/57WM+2TWR2zYea87/IHwC6u9LFwqskrOX112cRuKMZ9+8u95bfDF02/M5BzrZCYjhNM18W52+Kv5/Tg63v+iQvXacWbmHPoNYBf2wwk5oy7odSLe86I/9585TZc/6jLQA+6O3+SN0eu8Mf4ab45f58HkPvv5HtqWi4S/0eEfZsBpDfsqCAW2WD124SzCWY4m/trcXtbhb5yDl1/o8F3ftgvAZz4/PNXnUGGcWYzxmfRbXbXQ4Qdf7FtGvsdmRGELEpmihMKwKP1XSlBqg5BTwq/DnyQRYB3COrZCcaNZaCiPKaY555aqGqr7+sQOv3NLAkfciQaF8/CKguO3VyHLfVLIReW5Kykoitnvhrw05xaj93aHlAJnpyQ3Lw2T/OyKimXKgXXhnONoVJx57OYklNrHXyaNZ6+QnnS/VRjnmlIb2qmq1V/vRGgTVDJLFKy7PU/633w0eEerGC7x9sVGq42PfvSj/PzP/zx/8S/+RQB+6Id+iL/39/4e/X4fay3/8l/+S/7En/gT57qjlzgfCCFIGmTSFRPkzjO4fAQU3lSsIvyuXLmebBL+uBwf6+Z/Eiqzvv61lyhlhIKlD0QHpEsk7KsgQ6d48xn+0OVxjuiUB2esW9p9UUrS6yQIKXmyNyAKnfh5wo+QGBnVsW4mblEaQ6QkvU7M/mBEKdTCDeygfk8nBDJaLLxJJEIqjIxIG4sHgeDZ+Pr0vUJ8npKCWMla7VGZ9jU7/OBl/XExpjXaY7K96M4/3ZBACUGkROji+UVULemf6/DjHCKQtmUz/FII2sJfF8d1+IUzCOs4souGfeBN+wAyV1BgSJ1dJFTGm/49KgLhj67O/LoabymSLiMC4Z/p8AfCP2cmt5ZxnykRkwMEYKL01LPfC079VlcOimRhFjqtby/BY5Hz2eEXkaNXebH7UuhqrwHn+Mzw8zwo92nLNj3Zoaf8/2+pDl3Z8t1ea71xn0pnOvyTbH05P0w7/KNgEAdw82qCsBZhLW7+WSIl6GImIuKbX+nxxa+M+dq9jP2jsi6OHAclFFuxTxKxzmKCSV+hRwxcH4ejN+dh0W90+MeFwFjYaAxbSG8q6aaRgzO/thas4yjUlVZ2+Bvn4AMvd8kLy6//Vp9f/60+aSL5wMvLKgUBpkBNDjHd6yBkLefvdRRSioUOPyyP5qvM+iIRIYQIs/7WKwEaCQdKCrR2xOEytPMd/lCg64XHUt3hR2KdWWoWqK1mr3iCQnG9dWPhEJ1z3jdgBSryNU+8C5vzJH/C9fQG6ZqmjEoIcmMw1hKf0IsZjMszd5uP3ZclfgKT/Gzz++84iOlYwyQ3aGOIo7MpEyIlGWWaUttTFw/y0pvWKXmx+e2FthTG0m1Pj3WZieHTxGBcIIVfh/jRktVGmm9n+OtpdcFmp5uwP8gZTkqubF02TS9xvtjo6fVf/Vf/Fb/9279NURQkScKf//N/nkePHvHJT34SKSU/8AM/wF/6S3/pvPf1EucAJQVJ6Co5o0FKZGcHU2agHVLKRoe/WCk9a0r6k3JMXhpayemr302zvsNb78caSFO1UDzQ1hBJtdb8fv3ebb8Y33iGX0q/lrZu2mJeEw53rKFSGknazhNzK1Q9s9+ET0vQtVN/qR07qaSnNGVsOcwVPaZj0tWZqqThNkqI1OI5EfhigI5atBqL72fiqzOfb925EkFx4aoO/+IMP0DeucbWwRv1HP8y1PsoPemvf+AscejwLxB+MZUiuSDpb87wI6AjUzDrSfr7obu+o2aVBImMaYmEzBUM3ITUxQhnZ76ehcmxUtWJBjfjKzPvUXXzi6iNbfnrLypGNTGroiqH8/tZGfdZvZLIy2IwHdXIj7Dta6uPdQkqEjscG/LC0m50ubPCH2Uz4euQcH1ieZjd507n+bW2M9FDPj1+LfzXwcLvBXAt2uH7Wh9EWT/a0l7S4V/HsA+mHf6Dvl+QxpFgdzsCM/bnfF7SH0Yo/O/8Z727HfPu51p85c2M//j5AX/4o7OFnJPgI00Tjns6FaUlC8cmhMM5wSiD7U3CEKRElNZH8y27XpwJEnv/n0tn+PH3n7BTJ41v/sAWRWn5zOeH/LtPHRLHgve8sHwHhSmhnPikkLg1M78P0AuFmOFkSviVUJRzhH9iJoz1mE7kd1IIgV1itCeEoJUq0tgXBCyuJvxpRP0g7AXh0TgzYb5eol2JcWaG8BfGk/J+eUQn6mDdtYWCgJTi2Bi1ZVn14BMaxnpIXybcUDeX/s08pPTF5eNMAmE6v38W87iToMJxV8TKWO/a/nZ21j9vSCnIAskdZcWsR8YZ3tO5zaL5xpl+KqZ1eWG8WqapXAsmhm8F0S61ZTApSROf7DAOBROVvPOuxUIbxCpjG/wzLlKSg37Gbi+5MAXPJb4xsdEd8+yzz/K93/u9JCE6LU1T/qf/6X/iN37jN/i1X/s1/tpf+2v0eqePQbvExSNVqjbsc8UYkfZ8zrpSOOfnI2uXflOudKlvdvgjU2CLYqPxvqZZX969hnHLY39Kp4mFIj1FV1O2dwAQG87wSyEWjOXWQej5LB1LqBBFkq4LnfKkvVQe0TTuc+F90zgCp+mliiSNZ3OifVofkfFEzUQJcskt7skJ6KhF0liwNt35/Q6ARKCkQAaXfOdcw7RvdlygMu5Lx6sJv3U+5lFJH81XHXecjxA4rJCLagemH48NigVRzHbDe8GrYKhXS5GrTPb+kki+ClNZf+ZJepN0OAOmZOAKMpcjEVyLdmZeX83r53EX2m0cAoGr0xh6DZf+GRmxVF5iv2r23RSIrI+LUlycIrKjmvyvizSRddd1/6gM8YL++qhm+NMGY618HQDujd9Yezv3x28CsBv1+PbuB3h/6wXuxDfYUV0UEgc80Uc8NANf5GBqvDfJ7NShf41IPph2+CtcvxojpfDjCm7JPShVUHvMEspv/oAvNL36+oTB6Py7WdX8fisRbLX8uR9tmsboIy5WR0c6wzgXaOvvna1V6YGCBZ+Gb//wNh8Mnf1f+NUD3ri/fCeF1Ug9qa/DqUO/f0ZXkv5JkPpDRfj1zHz+SA/r302xXPq/3U1IYoXDzcTyJY3rthNbpJ9+YjzxJN86O7PNsR7zILvPSA/pRj1KWy6V/XvSt1pCvEpebZzBOMug7B9fhJzblnVuZRGhQlaYBcn1eaNKKKjOm3foP76I/fWGSEkmuSe5/VF5rgaJm5D2yrRuk2LBaTApyoXRjUh5p/6ifPpS80muyQvfUFLq7WEguCmy3Dv0H4duGjGYlIzfAeaOl3hn4Rvn6X0JwMfVxUJ5sqFLZO8qQkiEjHwMCxIbL3GIb0Dqop7jrlym20woTxs30zTru/X++sfREvfe0hqfY35cxvocojN2+BEQKXXqdLLKof8k+WMnuMUvI7gwG82ntZdTxpEEXRInKTvdFG1crcKwOCSiVgTYaHmFWOA7FTpu0WosLpvz+9X7Cen/Vgrp37vM6nQGPTcukPUqwn9Yu6DPw1k/JiAQND+eGcO+pmmgC/sbFiDj8CJZzpLdO6nvyN6f3FvtyO0smdPkzi/st9Ti5z5r3MeMgYMwfub9kfXFrmvRzkzaBUw7/FnUQcURRUhfqKT+bdnyueA4xrZxDEL5uesVs8YyD939KAGVIowO0XmnK0Zd3amM+wrQZd3hrgh/K55+9oeN4sn9yd21t3F34gn/y507fLj7Mt/R+zB/dOf38UNX/hB/5tr3cSf20ukRRT1L327M4Vczmut2+NupnKmX3bwaZArWLE8JENL/bo5Q3riacOdWinPwH79wNl+SZajm97c6kl7qz9sw23Qed9qdXwrrOAw8c6u93AQVwEmJ0LPPeSEE3/mtO7z8Qhvn4Nf+49HS12JLH3FYDsG5Roff3xOtVNbjCqNJNU8/G81X2CKY9c2qhZRYlP7PHp4ft6kl/ZGjOtkCR7dSF4xNLek3leqhPOLh5B6lLelFW8Qy9qMYdrGAlsaS/rikP1q+L6vIl7baexE4c2J8YxMCjh0hAILL+8XN78M0oUDPEP5vLEl/FFQOo4mmKA3pOXWUm8qBdVGUhmGm/Sz9BZrWOedCZOLssdZE+4KLDcswzEq80LBSWLwzCX8zku84RJFX1ByteOZc4hKbYq126d/+23/71G8shOBHf/RHT/26S1wcBJCoCCUklBkkKaKKQJOecEghcIHISb38gVN193XchqRFNDpgm4L7pSU5RdRO06yvf+3dNXdZtjg1ztJeMo9+HOL2LgUgi7E3bNtAiqak4LQeOZVD/zIfgiZS7QsRRbS8/VZ3+HVBoR29doySBqdzRNyiKyMmuWacabqtqJ65l/mU8MdLGE+1UNRxi+t9Tc8ptpMrCx1v6yAKKgelBFKBzINhn0r8NdOAjjvouEVUZqTjA7Le8plYIcOC0nlxm3VuKuefM+yrixjhMKoiQ3OGH+BGskublInLuZ/d4/nOC0s+UE0/zM53ZYtYLD7+ZqL55Kzc2c+8Gx5pT4Dm5fxYgwqFsDzu0EJQJF3SckScD8m2biKFoCtbDO2EoR3TnSE6K4z7TIHI+7ioRUVqXNxB5ANE2sPFx8xZz+Hqbszr93P2DwrENR3eE7Kqw9+U9Dc6/Ee6X3dDj4N1lvvZAwBeaN9aGFcUQnjjwhKGtgyz9Lae4bcWjgZVZN5696sQgnZLMp74ReDNa/4ghNG4paRIAGL23AZ8ywe2uPsw53e/MuL3ftNWrTw4D/SDamC7Ayo8VIbrNX+XQzBN8JiH0xxNKof+495DeYPFORMWIQTf8c07vPb6hP0jTVHaBU8SoQucin0hyhYLkn4hBN2Ooj80jMaG7V4USLCu5/PH5YjSlrTnYknlEun/zOHVkv7Q4VcOJ4JY1mp6HcVg5D0dnhG+RGmc5qDYZz/fI5qLDRUIcpOzNTeT0Uoi8rLg3t6IJJa0ktlnRmnsUiO7whZIoUhlyqDssxVv1SMLx8J5YnUchpOLnd+HxYSCyqH/G0liXMnH++MieG2cz2ceSXmsamQZxrkvOnTS6EJN6wptKbQlnb/XK6f+p9zhN9YyGBUkjWKLQKwdX/l2gjZubZVMO404HOTc2Gm9reMrnyacc2SFedsnfLydsTHhn4l1m/u5c+6S8L8NIQS0VepnJMsxcusmInSRhVR1h5/4JMLvCY/u7CCjCEYHdOwEFUu0sWvL/ppmfU7FGGN9Z3xuAeWcF0afZn4fQLV6fhHoHLKYYFvrE6MK88Zy68BaSGJ14uKoUknkKwi/UVWH349WtNIICM7mKkLgJa5Zbrx7NcLPuzc6/MvyoT2BFugoJXHw/x7t8PD6ty/ugKOuRivpTQxF2Ge9xP0fIcg61+gd3aU12ltK+A0QUakG/D/WuZWGfVWBoJp7M0F9Iuck/ZFS3FHXeNXc443R15YSfmE0h4HwL5PzA2wH476+GUMiZkz7vGpB8Kia358z7IuLsedgQmGiFkksKZIujGad+q9EWwyLCQ/LfW7F0/dYZdwn8z5Cl7jgCeB/GPkUhcmRT0tYU/lSO/UflTMpD/mcS3/pNKPqsxJt+m7Cvcld3rv1/oX3bGIvf0zuChIinkmvkmeL3agqxnDkcsDHTkYqJokFRenqFIH2Kch2p6WmhL/u8BcLRakZLImPvH0z4ebVmEf7JZ/94pDf95GdJS/cDLXkvWWpHDA3lvRTpQ2skPQbzdHEH/tO95jnkFShkKVBzD5fO21Fr6MYjg2P90vu3GoUXJ0FU+JUitA5UucLhB+oCX9loCcC+dbOy/oHuk8sFp/rSii00UuN9sD7SjjnaolxqvzsgsPfp72O39dmQsCg7DPUQ9qqTSxnPVMiGTMx43rt0sR2J+agX3B/b8TzN7dmvt+0tgvPWOccpS1QQnmjSwOHxSFt1TnxO0FKKMvVZKYoDZNcX+j8PkzPUyXpL7T5hiL7UHW1LZNcE0fnd+xRJChKS6nN2mRuOCmQwhchLtK0rigNWlt67UVqICTkT5loT3J/vW91G6lFyiccvdOgjY/kmy8aLkMrUez3cwbjkqvbl4Qf/CjT/b0xz9/sXXjBs4J1rlaXfD1grU/tC1/4wsw/v/RLv8T73vc+/vgf/+P89E//NJ/61Kf41Kc+xT//5/+c7//+7+eVV17hF3/xFy941y9xWkgpaKsYZzUgEd3d6S/Domqmwx+6jfP9hmjsCb/t7mJDRzYpx3RaEfmaFeB5sz4A4xxqSWe8dIZYnm5+H0AIiQtyebGhU397tEd3+PhUr1nl0D+PKj2giNtLXQIq80RR5ERSkkYSkXQQDVKcxoqtbkxeWgzeZK8axTBxvGKGXyDwpn0AcZkvXVQb62q1hUAQKYXKlxv2Vajm+Fca91mQ4fxKKYKS3ZEESX/R2lr4e0Q132pxiSeL8x1+qSTPS58w8Ob49YUMbwCcph98E1YR/rrDb8e+O9wYDxC6IMNwaPy+znf4K9l+mXRQkSSJFWXi36/p1F9J2u8Wc9dV07iv3mbuu/vLPu+4jSiGyGJ9+Xnt1N83M9MAlUt/RfiPwlx1S6S8O/beDuvI+is5/5346srxm9rHwGUIY+vxj4rgH4YOf3vNDj9M5/i7belnx50F459zy+DkbDRfBSFEPcv/O6+OKIrz62hVvgDbLU2v5e+rzSX9eHa4qgvuNEfjdTr8MqQZLF/IV2qJx/tz27EGMCAlTiqK0Yg8GD9ud6cL1Mq4b9Qw7hMIjDNMzJjMZAtyfiA49S9G89Wbdw6Hpag7/CGtQEgweiESUCIZ6AHdqLdA9gFiEYc5/uXXxM5WzH4/5+H+eKbJURq30OE3Yb9VuP7bUYeRHtZeBcdBSkFxDOHPCkOh7YXO79doJBSsM3v89Yaqq52Xp3fUPw5prMgKzWC8Xt66NsFDIFF1rJ/WFxPNl5cGu0LJEb0FRHuclWEdMr3eIyXJSrPSUPrtilJbtF18XiyDEH5882CQv+OO86KgjS+SXbSHRRMP9kYrx7neidjoW+MnfuInePHFF/mbf/Nv8uEPf5her0ev1+MjH/kIP/VTP8ULL7zAT/7kT573vl7ijIhQpCqCYoJIu4ikQXqEhND5pSHpF5VbXANxcOh3vSs14ZfZiG7LE3KzxgNq3qwP/BrSZ9fP/m1pNYmIlkqwT0TYP7XJHL813Prtn+GVV3+2PuZ1cJJDfwUZzARt0kUvqZzbKoKwzGklymeSSwlzzvtb7YRWEpHlGgm1iZaL0pUz/FLKOlavkqEvO47meEUcy4Zh33LCX53LdAXht9bVc6BSCBQCy+pIPhuKGEIItLMQCL/UhZdSBEgVcUvtkIiE3GY8zh7OHYwngJWkf96hv0I11z+2GRo39SJwDnTGY+sX7VuyQ1vOjpjEjUi+SEqSSKJTf49FjQ7/nWCO+EgfkDdn9pcY94l84P97SYoDQnlJ9fhwwXhtFXa3It9F1NDPp/fTVNLvz00l59+Nejyb+ELK/cm95YWUBu4Fw77nGvGO86ik1COT4aPU/LVfue1Xp3XdGf7ma2805/cDIV0KEfnxiSXH8+KdFle2I8rS8bnXNisULsMgdPh3WoZux+/X8AwdfvDkljk3++paP5r4c3lsh79KLFhF+K/6Z9Cjvbk5f2u8CaZUoGL6A//MabckcaPYOU+8wZuGFrZgUA78s2hJYUgJhQ5z98tgQyWwIvypMvjljDcz7FWfb9huN+qxE+/OGQPOb0+v9A1QUrLViXl0MGG/H9IrnAud1kXC75UJqn5vKRSH5eGJ949Si3F4TTyN+f0KUgpKY9eePf66hPAFmHUK+Ou/5enI3DiY1qWxQkmJtvbCZuknuZ5fXtSIGk79TwPOOY5GxcJnPy16vLPm+KtIvnXv3U4rYjApGL8D1QwXAWMdpTnZ1PQ8kRfmqW7vorHRU+xXf/VX+ehHP7ry9x/96Ef59//+32+8U5e4GERKEasIp3Nv1tdcDEsJwkv6XZBNC2eJ7GIltZrhN71dTEWosyFpEtFOo5O7YivM+qxb3hkvnaYTtTZa5FQeBZs49avREUoXCBxXHn5+rdes49Bfoerwi84WxZKK/VTSXwQ5/4r9VIKdboJSCiHkVNIfp8hq5tuU2Ik/b1WHvwyy/KhczTpks7IuBVGQnFfqgHlMnfoPlpII2ywiCK/mcHbOtG/m70EhEML7OMjG+EOzy6+UJJJwO30WgDfGX5vdsLOA5cj6ws+qDn8qYpJQWBq4PHSJCTFumkfGf4Y3G1L8CpVDfxF1SGPpI71Sf39UHgXgiwo7qovDcb98Mn2DOeM+oXNE0a/n7I2zPCz3Z8eoohZCT5D5NDXjOEgpuLLtj29vNL2m5iX9lYphV/W4EV8hRlHYnP1idQLDxEzYK/zx3ElXR5F1pT+ekc1wztYFjnkJ/7ou/QDPPdNCKcHLLwQFiDMI61aPOsjK5X7xGhVC8Hte8dfhb39xeC7dNOfcbIe/HQjpWWb4pcKPRMw9b50naUehxnly7J9YabJZdfgf7RWz153TwTBSesI/9Puw3Z19TvU6/r9nCb8iNxljPVra3Ydph9GsMv8MxLmS9CdR8CCQErD15ztqjBIcBxGeiccZBSaxIk0k9/ZGDMYF1rqQ2DpP+MMoQmN51VZtxnp0bIoIgAqz801SpY1lOCl5fDjhcJg/NTmrkl56ro2l/AZz6K/QSiSROnsc3zw66fpkbjQpcc4/u6UUYDdz+T8J1jnG2aJhX4VISbR5eoZ5WWGYZHpBAu/34+KKHheFkyL55hEpiTXQH379dJjPAmMcpX56BNw6R/l1RPZhQ8Kfpimf+cxnVv7+05/+NGl6OoO1S1w8YiFpYSFqmPVVELI25ZEqxYUHk3LlzELPWksrkAvT2a3n4mU2RAjotmOMs8eah8+b9VVwzi01unMOWss6nGtAVseZnd51Ww2n5Gbn0ZdWeho0Ydd06IdpekDU22KJkKKW9Ee2pHVCh6GdRmy1Y5QSiLLq8CfT7lmZQxFmVEMZwCRVh/8Ywt/4dyVlTfjnI/kqlGkPoxKks6STw8U/cH5RWyFSAlfmREGVUKZznffGCIB1FqWi+nORjf0W0hP+Z+PbALw+/trMdSus72L2zfGEXwhRy/r7ZlKTQmGDQ79ZYdjHtMOfx516NtO1/fXX7PADPBdk8m8Wj+b3oDbuk1l/prv/6fHv8n8d/Xs+M/7SzN+7qHWqmL5qJnB/ND27dYe/IvxBfryrtpAq5nbkj7fq4C9D9burskdnSQJChY5sea8DLBMsIsRIduY6+q1TdPhfer7N/+v/cZuXq8x4a/y5W9HR9WoKgzDLO8jvebFNr6OYZJYvfvXsXf7xxGJCRF4vcbWkf1KsznI/EXKFHN9ZJrlBG7+8PJHwixVmkcD1KzFCwDizs7L8mW2KWk1QOfRXqGIgRw3CX3XvtdPEx/iyuBXRfOCfBQ43lfRHU0m/cJZeeDwNx+t3xyIRMdajYx31O60YYy33noyY5Bpr3YKIxDiDm+vkSSGJRcxhcbg6RYRpHN5ootnvZ7z+cMAX3zjkS28e8vrDAXlp6LZWf2bnCSkFeWm+IR36K3RaMdvdzdYexyGK1iNz1jr6o2I2IUC4C5E1l6UlLy3xisKO76zbYxUo54lxpimNXShASOkTJN5pTv15YX288SnQbikORvmxYz7fKNDWev+Kp1ToMcZhzSXh5wd/8Af5V//qX/FX/spf4atf/SrWWqy1fPWrX+V//B//R/7P//P/5Ad/8AfPe18vcQZIIYmlItYFsrODiGcLMkL4THTnLJGK6mi+yM12dlw2QlmNQ2A62zOSfoB2okgjday5y9bel4GpWR8EshtMaZowzqKEPPX8fn3cIZpvkxn+aLBf/7uymp3HXzrmrz3Mmg796LIuIETdHlEkFmT9VYc/tuXSqMIZCNjtJex0kzpmy0WNDr8L8ltnvFkeona8VzpbKm0G6jg8AKkEcdXhXyHpR4gT5/hF47OJlCQNM+g6SmsyX8E5pg79zpCqGBu2Leai+SIluKluoIRipIccFPuNN7KMzASLRSLoydUsaFuGOX6XAdbvhNVYa3hcHgJwM1ok/FEt6e/Ui2PRDYRf5zNS/UrWf7d4PHt/BeM+UWa+ux8KK9ZZvpR5Qv07ky+TNSPEohRhSmR2tFZM37Vtv297g6nxah3LFz7+o4akHyR31C5w/Bz/vTC//1x05dhEDCkk7arLT+Gd+lns8LdOGYPVnI0UzlCV0LQzfHb8GsOZPPTVHf7qvT7yin+2/c6Xzk74K4f+rTaISNJKmEbWbSzrX34MwlqOhv6z6LX9/ZPZfCWRdVIt9TMAT0wqo8dHe43xE1PMuPofZf75PE/4l0n6lVBkZkIij28KSCTliphKh8M1cuLTqsMvJFhLLzye8mJ9YhDJmNIWlO742eqdbsJwUvLoYIKzbqHDr61e2slrqTaZnjAoV3f5lRKUpeGr9/t85f6A/X6GEH6b13ZabHeTtWaAzwMqxNLlpV2Yo77E2dFuKQ6Gx5O5ca6ZFJpWMr2vpBAXEs2Xh/noVR3+KhLvaUnpB+PcjzEu3ZeLUTlcFLzDvD510ayVKLLcMJys5/fw9YyiNBjjTkwxOS8Ya7/u/BM2eoL/+I//OD/wAz/AP/pH/4g/9sf+GB/+8If58Ic/zB/7Y3+Mf/yP/zHf//3fz4//+I+f975e4gzYabf50PVnSKIY0dld/AMZ5sOdQ6Hq+fHYFDQfq9HYd/dtZwukmnb4ywyMRkpBtx2jtV1qRAeQhnn48fYz9c+M9cR+XgpfWk0sFMkSJ+d1oEKHX25A+NXQE8ZJexeAKw8+fyKhsrbyITjBjTnsj1UxMm3RSaIFWX8VyxedsACt91d5o7iaCMfpdDHqHKgIrPW59kJgkhBxB6i57nC1J826hRJTwr9qhh9OMO4TYibvQApBUkXyzcn5wcuqqgWudY6WSrBVisScMiFSEonkdvsOEGT9ziLKETI75Ch097dUd2l6QYVqjn9gJ4iqw280e3aIwZKIiN0lHgCVMZ9Je9PCVZxiQhez6dR/K75ChGLicvZNQ44fjPtkdhSiJP1r75VPyJ0nZRrDb09em9m2i9t+3l+f7FVxLXzMe4F35I3LK409QR6E0YfqOO+EAsfj/NFS2bN1lnuhGPCcWiyGzKOS9Q9dEWbI9czMfprIsxGbkKgA8MXsdT41/gKfGs2P5ayWsgO890V/HRz0NePJ2Tosg8rBvu1ARj6eMNxCZ5rjdyw5BsNRqG3sdOH14gH/bP/n+Hz21eXvIdSCWWQTTVl//RJT4BrjEv0qArA9uwivOvx5YWuioISiF/VITyL8Qq2U2FtnKRvXbSKq8Q1/ztMI4tj/e1NdcBwiEVE6fWwcIHjis9tLOBjmaLsYy5fbnK+9oXnzQbbwukQlHJWHKwsZSkq67Zh2K+LaTsruVko7jS6U5Je2XPo5q9BJzQq9KD+7xJnRShRZYY417xvnGmNmlY9KCfJzNBOtUBUejlu7CCHInkK3OS8Nw8lsoaMJKYW/Lt8hqCP5Tlk0834Pgr1+9nVHPk+LQntVl35K0ZB+tOrr6zPfiPAnScLf+Bt/g0984hP82I/9GD/8wz/MD//wD/NjP/ZjfOITn+CnfuqnSJLzl0FdYnNIIWi3Ym/Wly6RM4up4VEkZR1/FjldN38dkIZIPhOKBi5Kccp3d2SQzXfSiCiSS43oYOoBUDZixqx1SzvjpTOkKiE6Ll7rGIia8J/etC8Kkv79F78NLWOSfED3cLWkGdZ36FeVYV8omLSTaEHWXwRVg1rRfVsFGci7jdNpp8lZTyadrTv8Tgq0Cl3+eVm/oy4M1PusJJGpOvzLJf2wmvA7F5bjTdWAFKRFmN+fHzNh0TgwEqo27hPF/ILaF6xe6LwIwBujr6CGD1FH9xHFiCMZYtHm5PzJ+GDm+CvC37djsM7LpnXOo9D1vhldWbooqgi9TXv1dRxJSRGSIqLGHL8SitvBDG9G1q8iPz5QDmec+b+cezJ9Vfl75guTrzE2jeOXsT+3WX+lWqPCtY7/HI5GfiFSEf448ov8qrufioRWIGW9qMu27OBwPJjcW3jPvfwxhc1JRMwNdXKU3TSarwgGcGYm8/40hn3LIExZE9J97Z83j/TB3B+tlrKDLzpcC6kG9x+vNy6xCv2R/8y3WraOCuxWhH9yhkXFXJIEeLn91KFfcL/092GlEFlANd6wivBXxn2VU3+VgNAYl6j8AnZas8/8JBZEIdJs2CiaxDI5sSiqhELbcqnRnXYarf3rIwVShjgP8M8uZ+qEgOGahL+e41/jeauUZLsTo+TifPfBMONXfmPCJ395b8HPJpUtcpsxKFd7bqSJempz+s459rLHHBWHC79TYbxgkusFY8K3Aj7usDx25OKdhIrM7Q+WkznnHEfDfIlp3cWY540zfZwwK2xbBOPIi8U40+TarEyjiJRkkr9zZO5VJN8mxpedVsRoUjL6Bu7yO+coS792LZ5SNKQxDvt1Rvg300kHvPLKK7zyyivntS+XuGAIGSG3ruCWkecg6cc5H4lUdZdNWTe1jbZ0ghzRVJF+QmBaPaLRISobYbu7RJGkl8YcjvOFnFmpizp/vmgQfmN8xXt+DaitoRtv7gdREf7otIRfl8igZjBXnuHJtffyzOPPceXB5xhdeX7ly5xbr4pbd/hD8SWJFXEkKLUhCZ9ZgV9or+Md0IQowwx43DA6dBYROvzgu2cCKKMWkcmJygkF086sdQ6JmJVJi6nB3zod/nS8H+aowww+/j2bNR0pIAmS/mJJhx/nkyNcyEONhEJXTv1zRQolBRLDc9F1BHBQHjLMntBLr4JQ9Atv5LfTIPxxNuDdv/UJJr0bvP6hHwCmBYGBGeNd5C2YnEd2tWGfMHqqkuhu1dexVIIi7tHOjmY6/ADPxTd4o3jI3eIxv6fz3uoT8fPnQkLsz3/pNK/nPnXgo70P8anR53mkD/ityat8tPeh6UeVdBD5CNGa4OLlHgU4R0fltGJBVgoOhlB9p9WGfXpq2Dc9wIhno6v0izH3J3d5ofuumbet4vieja/56+wE9Cqnfpv7z9dqOo3Z5NNE8i2FKeuxgqqAMbYZY5vRCeoCn2N//L11+2bC3mHJ/UfF1B9gA1QZ9TstA2H7vbaXyG4u6a/iBecWgtY1HPrhSUimODD9meOvEcwiT+rwP9kvfWHWaXwCgv+5NtNj2IknM/e8EIJeW3E40IzGht2t9ZVaSihKm6OdJhGzDQTjTN3hT2IBTQm9cGAdvY7ioK/XJvzVNsdmxBUW7/F5JLEimYtrM84wHIdYVANfvTvhfe+e3otCCFqyzVF5SC/eIlkSEfg0MTYjBnpAWy0WcL2fgDfteysM+7TVlM5HJeYmJzMTrLPcaN2kE614vr3D0G1F9MeezG11Zq+FyrSu3Zo3rRNMcovWbml4yyawzhd2Top7jJQkL0zwrri4ItBwUnhz4RVFwUj5BIm36to8LUrjI/k2KZxFSmId9EfFwjXyjQJjHcY54lg+NQ8JbexTS6R4WjjznTIajbh//z737t1b+OcSby/IVgfZWh5HNiX8XlpvoyBDtmVdfS6Mo13MEX5ozPFPO5jtdoTCR8g0EQfDPx21Zua1bbiZm/CVfEd6hm81EUzTVJHhVkRPLUM02vfGYkmbqNNl7/r7cUDv6B7JMjM6Kh+CdQ37KsLvSYRSgnYSUTZk/UVwixe6WGs22++Eq83bRDxHUISsu79K+FSGeo5/jjw758Il0TgWa4gCQTIrXPrBKzeMjJDWkDTiDJ317ylnVAOCNHwWyzr84PfB4K9LJRRiRYdfBY+B9nCPW6HL/DU3rDuRFfFrdvjT8QECZgwGqw7/yE4w1pvKOat5pE827DMyIkqnn42SgjIUdeK5sZJqjv+xPiBvSGpd2sM1VDhvFA/RGLZkhxvRLt/SeR/gpepD0yhkheOU2WD19WI1gpKrtazfkYVN14Z9M/P7AVJxJxz3vSVz/JVh3x11dbVRXgNTp/6Jv2atnpnhn5/nPxXqmDmJc64+HoC9cno9ein7kli7Bp694e+Ps3b4q0i+7fa0E909D0l/Jcefc9A/mkw7/MOGEuRe8WT+HaoXzXhMNLG7HRHHPgpr/6gMZoe2LqgMwvhArKCtioUCxLI5/nUghVwZzWedQVdGk5H//jgyw/CdIcDpjbYby5jCFisl9yfBWM0km37nvfb6YgxDIhNKU5KZs0Q0nB3WWY6KI188cXrBTNDn0Pvz/rQi+ZxzHOT73Bvf5c3x67w5ep0Hk/scFocYZyhszkifX1TmWw2lvFny0Wjx+bLKtE5JiTnnaL7asG8Nwq+Nu9AuqzaWwbhcKeev9sNo+46Z4/fjTJvHaXZSxeGwIP8GNe8zwa8lDgkNT6PzbqxbCMB5p2Mjwp/nOT/1Uz/Fd3zHd/Bt3/ZtfPd3fzff8z3fs/DPJd5GSFrEV+/UZGkBUgZJtEUIUZNx1Vj4WGtpVZF8TcKfTp36K6SRot1SC5LGJETDNbv7EDrjc5Va7SyxjEg2NOzzO9LFi9Md9hRz/CoY9umta8SRwna36O/6zv6VB8sj+qx1KMRaiyOZeaJWSfrBy/rBFw6MdRDk9sK5aTzcSTDa/z0gml14AQiJC4tnhS/wTKP5ZhefFeFvFqRlINgOgYmOUV0IQdbzcvX2YCpXt9V7NlUDNCT98w79Yb+FFLV5o5f0e0Iu50z7VNpGuRwtU15o+Tn+1/MH9e/7xp//nUbnOir8eVCmrIlKW6REKBwwZIIwJSM9ZuxyBILr0e7CbtaGfXF3xmBRSomuJP1zHf6earOrejj8jP70mBXNR/OXM188fXf6LEIIbifXuR1fw+L4j+NXZ97TJW1EMUTo5WRCWI0wZmrc14c8OJ235gm/ahRgpOK22EIgGOrBjCR5Jo4v3j3WsK9CLem3kyBLL2dk/K2zdPitwXegvUdC2XB6f6wPZ45JHJNBD/DMDf8cPOhrJtnmi606kq/x+J3O8J9F0i9DwaLxnNUlR+Nph39kp9fC3fLx0rdxQq1MeRBCcPOq/xwe75dheyGSDz8aUm1LWIPQsxWMZU7960AKicMtRPM55zxJDZL+JBb8h+Kr/O8Hv8QbxcOg3ND1dk9D+CMR+87yCXP8q6CdmZE8v/kwJ5uTHgshUDJiWA7fUnn6WI8Y6SHdqId1BrMsEUF4BcfT6qKWruSg2Gdixkih6EY9tuMdtuItWqpNKlsM9XDjgszbEe2W4nBYLpC5/grTOim94u08nfrz0qzVLfdO/eZCifY402SFIY1XE/5q3OSdQvhPG8k3jzRRZIVmMP7GjOgzxmKdL0hZezEpFfMozdefad9GTOp/+B/+Bz7xiU/wR/7IH+Fbv/Vb2dk5eWbzEm8thJCodhdW5NELEUz7jPEd/mqG33h3Z2MdCoiy0OHvTM+5bYcOf5NQC2il0YK76LL5/dqhf96wz2kiEZEcE910EoSQ0OpCNsRlQ2hvn/wipg79pncVISBNIh5eez87h2+w8/hVHj//e7FzpNdYi4wWkwaWYV7SD/6hHgVZv7UQpYlfvDqL1Hmtujj2fcOi3QmJmnG8F/78hpKlCgaNZbQ8ms8CEjEzYiEKTxx0lLIwezGHydYtuv0HdAYPObr1/vo9o3lfADmV9C8z7QNPK4yzREIRCYmsOvxz+yxjHzepteWF5Ba/Pvocj/Q+mc2JRMQwEJ9mhz8qR41/zyhVXEfzHZg+fZuz7RyPzCEA16IdX3SYQ9W9L5NOPbMMYUw8KBfixgx/hTvJTQ4nQ94sHvHu9NmF32e2qEnaS+md+uff0nkf94/+Pa/mb/LhzsvTYxIKnI/0M1F74TwJWwKOa1teTr43cGx1gtFZ4v//qI7kaxZgBLFQ3Eyu87B4zL3JXd4f+3vpfhXHF1+hS7x8ZGgO3SAhHposJBPkSClopZIst2ea4fdRdRYXSY6K2c/8iW50+KWC0vsHuBW73EoVV3ci9o809x8XvPT8au+KVdDaMg5d363e9Lh8NJ9jdJZGr1QIHQz3lD+ISW4ojT+X7ZamyKbP4HvFE1/Mm79/pQKTzcjxm7hxNebuw5xHewXf9Kyk6TbSH/t/3+6AUwpRjqA1/X7YtMMPviA43+F3uDDTWUXywUEo5u3pI15Id8Bpeh2/vDlNoUEIgcORm3wj2bhxmqxopG44+PIbGR98z+x7pTIlMxMKW5Cqpx9hbJzhsDhACUUsYzIzoXSaxT1xIZLvTNOfa0PbEuMMvWhruU+KTMh0n8xMjo10fCchjRWjSc5gXJDu+OdLXhhGx5jWwfm61BelWf5cmIMQAucu1iF/FJ5Xx40MPI39OE9sEsnXhBCCJJbs93OubrWeWlLH2wU6dNvjSFKW+qmY6ZVPyRzwaWKjp/i/+Tf/ho9//OP85E/+5HnvzyXeSsgIdIkQUe3SL0PXs9SWrh0jnMXJqJbxgzcpA5CT2cV1GkuUkGjrajJfEf6iQbyNdb57O/dALK3marx1rKP6OhCtLVw2RCwhXKuggmGf3vKznGkS8bj7DHl7l3RyyM7jVzm4/U0zr7HWz3WuI9taRvilFHQSP9PnnGO728JFiY9oKwtYraKvUTn0myghaiojnEMIhQudQInACT9aARDNdeWcBRXLmaq0DIS/jFoE4exKjLduAdAePJz+MLxnE6oYI53FMZW+1/sQ/ldIgXaWtkq9sVbomM9L+gGSyM8Y9pIOV9U2+6bPG8Wjuisfi4hWYx44bng7ROW4HivYVp1A+DNwhkfBSX9ZHB9Mu/dl2l0o+LgwVjI/ww9+jv93Jl+u4/nmr52v5vdxOK6p7RmJ/c34KnfiG9wtH/OZ8Zf4g1vfPN1e3EIUI4TO6li/GqYARE349wdwOxxSq3boD0qIaF5xIXi2Jvxv8v7tDwDT+f076TNBWr5+hz9zOQaBCtL6dkX40zNK+p0vWVVjHB2ZMrY5e/qw8Tn7z0BYfawJ+e2bqSf8j/KNCP8gGPYlkZvpWtVZ8WeS9PsiXl20cJajgQYittqQ4d88DuNBuSvY00dcj3dn30dGYItQOFgcoWo69QsjZx36wy203RH+tTrz11l4n007/OAJ/3w31zqLxdbjT2nkyPHd6bHNAAlG0wuFrNMWGiIRMTETlt/px8M4Q577/fJRq47XXh8vEP5IRozNiMxM3hLCP9JDxmZML/LPpmVKCqhmtu3GUuTTQgeVwartCSFQQjEoByuLAu80LCNz49yb1nXby5fo5x3NN87Xj4zz274Yabm1jv6oIF0jklVIjo1/frugjuQ7I0lvJRGTTJOXhnb6dApwbxcY48CFqFD3dNzzn5Y54NPERm0UIQQf/OAHz3tfLvFWQyqccyghptFnuvCyPmvZCotn092Z6RyaIEtXc4Q6Uoo4ljO5rXFF+Gcc+pdn19tA8s6KyrhPZOsT/mhYdfi9AV0SCZSU7N3yJMdH9M1WANd16AeQcy79FVrBrV8IQZooXOjSr5LbzkMEgz8bxXWhxLkqo5q6Maekn+Ev6xn+2TajdSwUWirCb6LWiTNUWe8GDh+5V5FhYxdnQStjxCLpLXYWQ1KAFH4hnYaOjkz8Zzbf4QeIlZcBA7yQ+tjH14uH9MO1u6O6M4vEqJwS/uZnUEfzuQkYzcOK8C+Z34emQ/+SRWinMo4cLczW34yvEglFFojYPCp3/pdadxZ+9y3d99d/UxntAdM0hqy/sD1R5jgVcSWIKSYFHIQ6RBr7sQcHJCKmLWbvPScVd9QuAA8n9z3xasTx3UlvLflkliMVsR8rAUaUtbT+Ssh8393efEEjGpF81XjCi8ltJILclbXSo8YJ3h63wxz/vQ3n+Pu1nN8houlxVTP8k4I6T/70CDd1HaVip475Hepj7ck2z8T+WbZU1i8Vwhzj1H9tOtpQ5EWdNABwFDr8Ox185KAxM2aItbR+g2hDKRS5nf3cLdarkyrTvsiRB6I4snn4bnLBFNET/tNI5yMZk9t8YaZ9HRS2oAiHXsU63n9cLC12RCJmqAdPXdavreawOCQWMTI8cyVy4XMG6LVjrm4/PaOw3OQcX0r2SQdeHXE2X423EzqtiFFWMgzd7cG4QB1jWnee0XzWOUZZuWCwvApKCbILcuofjAvGmaZ1jJy/wtNKDDgrjHXnMhYTKYG29usuKm4dGOsj+UQwFjcXLOm35zwy83bBRlfg93zP9/Arv/Ir570vl3iLIaR3axbImmiq2iFe0C4W5/ehado328EUAtppNGPclywl/IvZ9dY5BOJs8/vVfoT9U/l62llRTOoYP93zHf5IeRf9x1fejVEJST6gNxfR5xzr5aw6t7TDD1UkkyCNJEmkcKHwItZ06q86/DZKkM1IPiFAxfVaSnrBPjqqZvgXTfvmK9K1pD9p4U740rFRQt71n12777v88xF7ACoQ/nzJ/H6dFOCf8fW1INNqhn+R8EslwfltvJB4AnqveMxeiGabj+RrztVHxfT6qP6ubzNKPeEgdL1XEf4onE/XWXIcbT+nL52ZOvlXxy8kz8be72CeiA3MuI6Se1eyKPe/Hu3Ux/iZ8Zdmfufiapa/8RlZDbYEoYiV8AQNuLcXZvgTODRTh34vb2b6xScV1+iQypTSlTzOH03j+GTCDbV14qhHBSFELesfuQJCLNx3fdsu/9n3XOf2jTOQjAZRqzr8V6NtrkT+mfOkMcfvhEKY44lDtS8HR3phHnsd1IZ9La86qNCKqRMrlnh2nQrChP1ydtpx7wpG1p//rmxzJ74BrDLum6odlqHTUjVxf3RgZopzMx1+BAiBLKaFtG578w6/Egrt9Ew0n3MOS0PSrxyF80RpYjMIY1Ddlr8WjXGnIkeRiNC23GiOv7QFeTiXN67E3AqFki+/sfjdk6qUiZ6Q27NIPE6PQTkg0xNaDWd+JaNAtmchpVgoxF8kcpPNKtOWIJIR2mnG+vRRu08L2mqG5eDkPwzwI3ZwOMgptWUwOdm0Li/NuZiXFaWh1G7tKMg4kkyK89l2E8ZaHh1OkGq9a66ZGPB2Rqk3j+RrojLRfKeMMZwntLHTpYUTF1708JF8F7qJtwQbPcn/wl/4C7z55pv85b/8l/nsZz/L/v4+h4eHC/9c4h0GGYVYPlETTWkKBD4vtiLrprM787Ka8BeTBXO5NJaAn7eSOicKhKc5w7+sM66dDoZ9Z5/Tq6P5ignFGl0bVXX329sQ5uaF8N137RSHN71LetO8r/YhWOOhLnSOCF3FyqW/gpSCVqJIUh9RWJknVlF7J6Ga4TdRUndvCB1+oeK6wy/CMa106YeFmbOqCGKiNseLoD3GW96FvlPJ+oMkqwkVTByzZJEoV8aBVZWimp1XocMvi2yhg62kVzI4B1fUFluyg8HyxewNYNawD6amfTBrXLglQ4ffTnhSetLdk+3FSLOAqsNfyfebkFFEWRVWloyVPBfc+t8sHs38/Cu5N+u7HV+jq5Zvt3Ls/2pxf1YhICPAIvOpuZ4w2hO6MI97LdyCk4ZL/+Hc/L7WxrtFawMyQmK43boNwP3J3VrOf7t9B2XKGan3SejVxn1Z7dSfJpJnrqdnkuoKo+v9OGoYNd4IYx1PZoz7JJxQTGu3FFeC4uD+49OTwCqSb7s9p7YQYmrcd5Y5fiG9HB/vX1Cb6HVgFJzge6rNncQT/kf6gGKp6Znwjv8rUMv6j1ydxGCdqwn/TqilORlDOakLL9UMf1E6ilPORUohsVbPzPE7vLKkCIQ/jkzd4R/bzJ9TZ4mkq70gTiPrr8wCi1MSfuss2mqKMMPfSiUvv+Cv8ddeXySnSigslskKg83Twjp7olqgtCVH5SGJSma9VEJhZRNVw3nBx/Bp1BopH7FMGOrBTCHo7QLnHAfFPvvF3tKEiVXotCKORgUHg4y8MKTHEn6fnnAeTv15WcXbrffMrbd9zsTzcFDQHxdstddb813Ufpw3zhLJtwDx9dl5PgmlttPPT3Dhn4H5OlVSbET4P/axj/G5z32On/7pn+bjH/843/md38l3fMd3LPxziXcWfH521eGfSvql8JFx0dgTCtOdNWl0cYqruq9zTvh1vrwxJMHwT8ft2iMAljv0l9aQyoR4jS//+n10jm3EwNXHFQh/pywZ6ZNJf2XYV3X3K8SJwuE4eOYDOATdo3sk40NgQ4f+OK3J18z+phqR+n10wTxRrivpD4UBGyfT+Xtn/QJdxcGp3yKF7/AXccNBfm7xtErS79L2cUlmNSZbXlJfO/ULgZhTQMgqtWEF4cfXi5BC1OoJGdQAwtmFAlMkBUp4V38hRN0Bz5z//JodfmlKVIPgNAl/9XdDO+E+/nzdjFdkczs3JfKdRcIfSShDkWLZHH/VeX2sD8kCyXDOTeX86aKcv8KVaLs2+/vM+IuzuxW1fJc/FHOELevZdiDM8U/RikXdEd8Ns70GaKeKvHQ45+fFn019geLe+M06ju/Z1h1/Lk5xv85E8wmxMgf+1DAFSElhyzDT7Qn/tcg/t57MR/M5c2w0H/g5foD7j07fiu+HGf6tzuLvuvUc/+aLC+9KX5FTM43k64raob8r22ypDluyg8Nxv9xbfB+pFhz2m7h51T+zHx1SS/pHWTX+Mx1RIIoRpqiVE0ksSWJ/rZ22y6+Ewjg74yBfkbyqwx9Fuh7jyV2JdmHEwRl67c0MA5VQTE4Zm+eVCIa8Qfhfer6NEPBov6wLP03EMmFwTsT1cf6IB5P7x+73oOxT2Jx0rnCphMJYXc/QvxXQTmOsN+o9CalMyU3+lkcbLsPYjDkqDilMcSqVSJooitIwnOhQkF+9lqij+c6B7K5r2FfBR/OZc51xLkrDw4MxaazWNqSLlI99Ps94wouA1jZ4KJ2d8J+3d8M7BUVp6+tCioufr/eRfPbM/mFvN2ykl/7RH/3RrwuzlEvMQQjfhRUCAuEXZU4cSdIkQgVyOy/pRwhsq4saHyGzIbbh4C+lLxYMxuXS+f06u37Bod9wRZ2u0+eKie/WzflqVYQ/KXJupVd5lB9AlK5UD1SGfWZrluAlkUAJSRZ3GV59ga39r3Hlwed4+NJ/cmaH/grGWZSUdRxIVXhZW9KvG5L+6rNrdviDU7/Ad8KLpIuVEdJqkqxP0d6dvpdcTfiNs8Dx5K7q8Kfj/VCwUAuvOE7SX9m/WSxKqLrzI6NGekGZzaQXKCWQyhdgkH6O/3eyr9S/n3HonyPfzRn+jmwhkVgsXyl9wWKVYZ80Baoiq0sIv5TSGxKOnyx16u+qNlfUFgdmwL3iMS+17nBgBhyaIRLJi8kzS7db4Zvb7+Wr+T3eKB7xuDzgRjV2IGMoM2Tex8St2rCvwtWthqkDi5J+ACzISNJOBePC0MPVBYq9hjT82dYtGO2DXF+KX0v6zQQXy7Wv8WNRGfYJWXf32yIllXFt3Linj/y4iBC1y70wGnfMDOvtGwmfe3W0UYd/UHX4u4vPhsqp/2zGfcoXW5wFY6aRfB34QiBElULkTnKDL2Rf4275mBfTuetKKt/hd2Zp4abq8D/sy+qRUqsJtjrNAqE/TlFmuNjfb92OojjSDMdTn4Z1IIW/B3WjIOOnOV3d4VfRrCphbDN2AJyj21U8Pig3MO6LyUyGcWatjjN4nxHtDFnuCUgrlXTaimdvptx9mPPa6xO+5YOzz4dUpoz0kMxkdKIlFaE1YZ0l0xMyM2FsRmzHO+wkuySN+7GwBUflIalsLXyvKuGL2W8p4bclFjtVph2DSoUx0qON0hQuCsYZDvI9H23sLIUtZkYnTkKaKPqj/ETTuvOK5tPGcjDI1/Yegkpafr4O+Xv9jEmuubq9vmfTO8Wpv9Dm3PiSUoL869A9/jg45yi1qTv8UooLd9A3xvlC9tObZnoq2Ijw/9f/9X993vtxibcDKgMfIWck/b1WRFtaVJjRn5f0A5hWDzU+QmUj5pcMaRLRHxcr5vd9Z3xeTuaco6VOKee3BqIEZ433IwiQIRHAjfa5kWwjgIf5AS6iNoJrYtrhvzb7c+Vnn7Wx7D/zQbb2v8bOk1d5/MK3Ym10Jof+CrktSWVMZrxTf23aV67b4V9G+K2PJ1QRVW53vZ9Ckrd3aY+ekI4PZgj//DK3muF3Saeekz8OJulQtLZIsgGtwWMG7WcQc+dZ1R3+YKzY+J0NnggWS4SsJf1CCFzSQuRj79TfkNELIYiV75QA3Iiu0BIJmfNEbZbwz8psmzP8Ppqvw5EZMrD+726dYNinVYpKFgmvlAIdFAzzRYYKd5KbHEwGvFl6wl91959Pbp441rIT9Xg5fY5X8zf57OTL/OH4W+vfecf+IUJvI3Q+E5l3ba42EUeWfhjb2FHBvdv5blKvE1McTCi1o4NiN77CYRh1uJJcpStShPFReOuicuof2iyQ1qIuTm0MaxDO4GRSqxWqtIEd1SNCoTEcmSFXoq0ghzfgNCwJJatQGfftHZbkhV3LRRr8c2xQmfZ1F4ljJekfnYXwS+nVEc6QZZYiRPJtd2B0NO3ww5Tw31uWCiEjnwiyovhx/UqMEDAuJMMMttrTSL6dOa7qVIwoRtC+AkLS6ygOjvTGTv1NB3nnfMGyGg8QSwl/grDTDv9ptxvLmLEeUdiC9pqEzViNsdNRg1bqr5GXn28Hwj9eIPwVuR3r0ZkIfzPOzuIl5UM9ZDe+wnayjRKKo+KQ0pZsx8u343DotzDfvrTlqbLKk1AsuWKvopYkS7wV6BdHjPWYXrzFSI+8L8IpljGdNKJvClrJyUtz55gxRN4EB4OM/rjg6tbpzJGFEOfWZZ3kmidHGd1WdGpiLMCPm72NcdZIviaUlBSlmRasvwFgrMM0/LGUEhQXXOQx1p2YRPVOxNdZ/eISZ0KTJFdZ50bTa6u6O2/jFi5ZnCeu3OblEif8NJJEUhIHuX0xM7/vO+NNkxbjLErIzeb3qwV880fbNyFuQZnB4T1upLvcSq8y0QX5/Myqc9MZ/rkOvxDQakUY45hs3SJv7SCtoXt0/1wc+gEKq2nJxMvSsQ2X/nVn+IMkPE4R1e1dm/ZFvhBiLRIvfcc58o4nsunYE7i657vQ4Q+sJO2s/SSs4/n6D8I2G780Ze0LUCZbi+Y7zpNl4yyRjFCNzk8VN7fMuC9SU1MXKQTPB1l/R7bqeDKYkm8TFovRXFLBtpoujGMRsasWu/cwNewzS85nfajB5yLOlxP+58J89d3iMdZZvhzm919KF836luGD7XcD8EbxsB4LAHyX32rk5Mh3bxv3+HYHmryuUCMcjlhEdKRfAFrnUBJaiWK7G5NbCWXGs+3pmMGd9vNBEr9eJF+Fbj3DP/H75SrivTmENd4FVMraob9SK0ghprL+eo5fTF93DDptxe5WNce/vqx/klv8etSx1VnW4ff/P5ycYV5QSESI5jsalvX7Skk90lD5JTwTX0MiGNoJfTt3LVbjDStGK6JI1kWiR4f+/2cN+xpQMcKWiPB8rYz7NnHqZy6ar5K/V8RaqNnzUR0zztDrbibpr5QFp5FkG2dqh34hqItC73qujZSwf6Q5OFok1IlMGOnhqea951E673MghSKWMdvxDhLB4+wh98Z3OSwOGJb9Y7vNEnlq34LzRG7ztbr7FRKZUNiCiXl7mPdlJuOw2CdVLaSQxDIiM5NTpTBIKdjtpWvJ2pUUp/bEaCIvDI8OMzrp+jL6ettKMMnOrgZxzvH4cEJRGlobRM0pJZhsYKT6tHBekXwVIiUwxp250PNOgjEOY1x9jSrh13fmAl31jLEL/lBfD9iow/+3//bfPvFvhBD86I/+6CZvf4m3Co0vWxlNSb3QBWp0CCyR8wescuoHv1BMYkUcurnlnEP/fGe8tJpERKSncOh3RoNStWHT7GEp1M2XMHc/h3n4KsnV57mZ7iCB+/kBDkcrkD6ZDZG6wAm59FiTyEsfnRCMdp8lfXBEp38f13luPYd+puZ384Z9zjmcs/SiNmOTY6ydxiOessPvonRO0q8QMqoLIlJU1XRHFrr6NeF3wY9g7nBq5+1WGzFhrSrzZOsWu49fpTN4iHh2di5Rjb183EYJNk2RbramWkUDGmdJ5uc6q4LUEsKfKDmzyHopvcOX8jcWJPlxOJ6se5Vu/4GX9Dc6zFtySuBvRFdWdh+qDr9pjCU453hS9NmJuyQywrV7M387j5vRFWIRkbuCz02+wthmxCLiTjD0OwlXo22uRTvs6SO+nN+tCwAALmqDHiGsw8WN+1oIrm5NydtETAlydawO55MPgK1OQj5JySc5t7ef4XP9zwLwbOe54BJ/ui/ISmY+shOckEhrEMZ407cVKLVBimNcnBuFh7rD3zBqvB7v8FDv80Qf8l6erz6JY83qKty+mXA40Nx/lPOuO+t1fSs5f6+13LSpe16S/jCz3h/47e10IbM5Fp920g4FnFhE3Iyv8qDc417xmJ324iiNMKstOW/uOJ70BQ8PHS/fFnUk3/Z801goX3i1JZBOo/k2cuqfJaIWC0wNAJ2aJR5jk+GiXYQx9DrtjbcrkWQmYzveOfmP8R3qyqE/TWR9D7VSyXPPtHj9XsZrb0z4trmRhjgQ/sxM6EaL52MdVJ355jMqVS1imZCZCY8zP5bUlquLkpVT/2nmuc8LzjkKm681v19BCIESimE5ZCdd7xwtg7a6fq9N4ZzjMN9HO8NWGDGIRExhc0pXkojzVyAoJZgUm5PuJ0cTslPK6CtESpKdQ6d5MCnZH2RsdzczZ46UICv0W3LNrgNjnTfsO68OvxLo3KKNIzm7n/U7AsZarLWoyicsSPqNcQtr1PPCTCrA1xHOnfD7uRp3SfjfgRBCQjh/URRhVYQ0GlEWq+f3A6aEf3nWfSdRpPmipH9ZZ7x0mi3VOd0XsCl9V2lJhx9A3XwP5u7nsA9fhQ/8YYQQXE93QAjuZ76j31LJtLvf3Z3phlao5viNtYy3b3P1wefpHN2H2+s59AOoYH5YfWYVCqdJZEzXWCJdUkbRqTv81d/ZeBrL55wNhox44z5TIpHhi9qSzXX4bXDHn5FXhjln/4cdVK6x1p0oVas7/KMnSGeQjaJIZdhnOjves22uolpFA1pnF4s/gbguK4RIJWcUCLeTa/yJ3T9Ym8RViGrCf51u/wHSWaQpsME3odnhXyXnh2mH3zbIk3YWhfTFKxlhw2z/Mpd+8B3FZ+PrfK14wKeD+d67ktv1GMM6eG/6HHv6iC9lb/CB1rumCyAVI/IJwjnc3Ptd2xI8OnQkERzZivDPKhmqV0gp2N7qsJdPuCpushVtIYXiRnoTMdo7lUM/TDv82hkKZ2lZe2KHf5RplBRsdZYvoIU1dWV+KeGvnfqnxn1Oypnc+FW4fSPl86+NTzXH368i+drLKXSvNu1b+y1XwPkO/8iT4J2O7+JD8KNonJs78Q0elHvcLZ7wgUZhCKqYwgxYQqCc4da24XNEPDr0x1M79M93+AOEKXFQE/7No/nKel1hrAHklPCLcqbWNLZ5MIEsTyw0PNkv+J1XR3zTe7pcvzp7TcUyZmImWLfeXHlhC6owlUrOX+Hl59ue8H9tzLd+09YMOfEGqjDW440Jf27zpXJ4KSSdqLuWKWD1ORtnTkW8zwOlKzFWE8vTkc9EpkzMOBSETj8SMdJD9vI9elGPq+m1k1+wAkM9oK/7M+evSj4obDHjpXBeaEbznbZDP5yU7B1lbHXijYhyHAkmuaUs7bFpAsfBWseTgwk4iI/xTzkOkZKU2nsZbPoeF4lSW4y2pO3zuZ+UlDh78S71bydoW83Thw6/FFjrLrToUYRUgK83p/6NrsIvfOELCz+z1nL37l3+yT/5J/zGb/wGf//v//0z79wlnjKECMZ9FoXy0W5GI3U+7fB3llfSTyL8iSiJwqK6aE0JxbLsem0t3XR5DNkqOFMiWl3/hkuce+Wt9/j9PLiLy0eItOtJf5jpv5fto4SiHeb3zZxDf4VIKeJIorVlvP0MDkizI9JyglJrLDisIT7wcu1yd9Y0KzclW1GH2GgSa8mcrb0UxNou/eHvonQmlk8EwixUjLW+eikROAGTtiezcT7wElynQDKziKgN+6RCJilSaoxzJz5AytY2Om4RlRm98T5CTK+fyrDPtrdQUi58ifnussAC0VyagUj8Zy2KRaYkpUCEKMhqLXMlWuKeHwh/mW5hVIIyBVE5oQiEf6sx77/KsA9ABcLvGgUc4wyJioK5IVBFQ5YTX5BaUkx6LrnJ14oHGPxr1pXzV3h3eodfH32eAzNgz/S5Hk0/a5d0cUuk2pVEO41pOPTPkY7GddBqxfRaise55gee/SGElP46s8XSYzoOkVC1v8LITmgJ4Tv8x7xGihOUdlZDUIUMTOVHsEj4D3R/asgmY9AT7yFwzCxw5dS/d1hSFJZkjTn+QZDYL0jeAypJf1aANm7touEivErBE37JTpeGQ//ss/TZ5Dq/OYYH5d6iKZ1UoPPa+HAGVnNr2wARj/t+wT6V9C/uUdP1v9vx9+9GnXah0LbwUa0ixmL9Yz68lVElTeOYsc2CkknTbYcZ+cksMbLW8enPD/gPvzPws9DG8T3fMfvMj2RMZibBeO347yPnHNqVlOW0q9/Ei3daKAVHQ8PeQblQXEhkyjDMo5+UQ79s27nJ6g7YMqxTsIhERGFytNNEmy0NN4YOCQEtcTrSHsuYsRkx1iNgd+3XGWc4zA84KPYxziDwXiSbkN/SluwX+8QinrmXhPDfQ6UpNlxpHw8lvYFbaSzpKZ691joeHYyxzqsrN4F3yNd+2yeY967C0ajgaFRs3N33+yGY5JpSv00J/3lG8gU4vrGi+YxxM2sCKUWIzbu4z6DUNmzn64vwn5sgQkrJ888/z3/73/63vPjii/yVv/JXzuutL/G0IGQwdXNEUjUy4HPUyHfEVnX4q/lltYLwt0Nns4jbuGDGV2fXNx6GznkJanLKRQ+2RCQdhPKmffOQ7W3Etu82m0dfrn8uhOBass1u3GVsstqhX28tJ/wIaKXKVx2jlLzj/257/HAth/744D7CaGzawWzNdhSMM2yFvPaWjDFukxn+IOmP07rDj7NTMqYib9pXdfiFw0QtdNxCAOnkEIPzaXiNxU9l2GeTNkIKIqXmJyeWQ4i6y98bzebMq7rDv+3N+Za8n3D+YT/f6RZpe2a/moikCNXZ43ewmuHXSQcdPAGaxn07gfBLBNfj3RPfh05DueIsSSAnALTa2NDJi4vlM6eV+z1AR6bcik/XcUplXDuvv5q9MftLoUAtds9uX/Xn+Epv6tDfJMiwGM/YbcX0EkmW+y4WzoRIvtN/nTSd+hHi2E77G6Ov8Yv7P8OgGas3B2FKnJT0zaIfAfhZ9lTE3thM++NFJQitkeXitTSzr23FTk/hHDx4st792D+B8Kexj22Esxn3OSEROuOo0XEfhcJnb25u+6rapiVSNIaHwXixhlReZbHkGSqs4UrLkESgDdzbh0pRvIzwIyKfmuLMTIf/NDPNsBjNZ6xBNyYwjPA/r8YWPOH3laFO8DJwDsaZP6aDfsn/8XOP+c3PDuri0XC0RBUmFMaZteb4dZihryT984Q/iSUvPuvPw6tvLF5nsUwobbFRzJx2niyftStfJyK8BcZ92nklyCaEOxEJw3K4drRhZiY8mNzjSf6YVKV0ox6FLTbyL3DOcVgckJtsqT/CJvGO68LH49lTk7+jUcHRsGCrs/n1IoTAWbexQ742lkcHY6LomPGsNeDjCTffj4vGeUbyVRCcn2HiOwHz8/TVZ3lRZHw+FeDrCRcyAfHt3/7t/NIv/dJFvPUlLhKyIvzeBbmKOztuhv+oHFFaPe3wF5Oli8Vo4l+fJ4sO/SpqzO87QyzVUvf8kyDiFkQJS5kjoEKX3zz80uzrhOBqcIlXgxDJ11tNttJQSXbAeOc2ANvDh2s91OMnrwNQXHt+xo1ch05bddyV6sHW8YhrSvqrv4sa0UvOIqpzqWIIc3dSSJwI+dXtqazfuSAzbRp451PCDxCr9aufk0D4O8NZwi/rDv82USSWEgEr/QzvAuGvOvxLZviVkr7BdwKxiOsOfwcdjqsZzddTHf6T3of5g1vfMmP2t/A+lWnfjKTfEMmpl4CUkiIJs50r5vg7qsVV5e+Pd6fPbjQb+d7Uz6V/Ob87E2W2Cte3Bf/PPyD5nt8D/RBjNyvpF/PejagoYrcdnJpLE2a1NdoKcm0Y54bBpORoVDDKNIVe3bWvus8jO/EJAma1kuXz/c9ypPf5Sva7y//AOT/aI9SMnH+mcCVE3eV/XBv3gVMRIh8s+H/Mo+ry33u0nuKmcuhfZthX7U+VX38mWb+UOGvrSL7tLoyCeV01OtHc5p3kOgD3ysdz7xMhjEEsUYMIqxECbgbhyJfu+bPaba0YZ1KRf43RtWmfNq7OqV/70EIEW3U9G6cptd9erKDE//xKuG7rDj8G4dzUMHBk+K3fHfL/++QjHh+UJLHgI+/39+xohZmgCHP8J8E4g7FT077WEvXHyy/48/Dl1xeN3IQQSCRDvbxgfhy0LdFWn2kGfeb93oJovsIUp3LobyJRKZmdMNHHE2vrLAf5AffHd5noMVvxNrFMiGTkDRft+macFcZmzFFxSFt1ln7/RzImtzl6hRHmWVBF852G7Jb6fIg2nC0Tfr+fMZyU9NrnoMl+G0fzldqeLXVmCaQS5MXb83gvAtrYJSMr3rzwIlClApx2TOadgAsh/J/97GdnZnUv8Q5BmOHHOZSQdYdfjY+QOscxK+k3zstqMlvi4lYd+bXMuK9SCGTpVr34t5VDf+NaKa0mlcmxBGseLsx0ChWHWfXlD4Ka8D96DTe3sO+qFjuyjRr5jtfKDj8QR5JICoyxjLY94e8O7q+1r8njQPhvvDDz89yUtGRMS0QgFbFQfgw1EHW5jqTfuVr6L+aTFKovnfBZC4RfMOC8JLQxx+8sC9VNEcixC8Q4Un4Rvg7qOf7Bw5lK7UyHX4jF93MChw2Ef/Z5Io8h/EJArNSquo+HtTW513EXU3X457q872u9wLvS26vfxzmSMpgwNiT93sxIUpkJKCUpA+GPV8zxA3xr9xVeSG7xTe2Xjtn51bgdX6Mr2xRO83rxYK3XXNsWZHKMxREJVZNw5/zei/lrQUa0RMnVrZhxrhkMJmRZgbYSiY+Wurbd4pmrXXqdGAuMMz0l/43TvBDNF+Ll5uGcYy/3xbjHxYMVXWIbMuSXG/ZVuF479TeUAlHqI+lOIA1VPN+6Tv39ob8Imx3+kZmwr/v1f1dz/KPsbE79WW4pAhHe6VDP8M8TfpiqSe4Wc4QfAQ7Esi6vNSDg5q7fxpcfLI/km75V5fpfEkWi7npvMsfvcBjrzbkstvZYTGLI8P8xJfy5tyq0DuFsrS74uV/Z51c/c4Sx8PwzKR//vlt86H3+nhxNlisPEhkzNuMTHfSN01hsXcyY7/ADPP9MizgSDMeGh3uLBdxUpUxCFOBpUBH00zjcr4IS6i1x6s9ttrFCQQmFc45hufy5apwhMxMeTR7wOH+IFBG9eHvm85LIU3fijTMcFvs+CnZFgyISEdqWp0p7OA1OG82315+cG9GOI8nBoGCSn470T3LN46OMdnr6GL5lUFKQbVh4uGhkhTl3Yzk/yvHWd/j3+xmPDy9GvdJEXi4SfgEXJumvUgG+Hjv8Gz1hP/GJTyz9eb/f51Of+hQ/+7M/y8c//vGz7Ncl3goI6U3vnEUIURP+6OghALa15SXhAaXVtFRMYQ1EAtvqosZ9ZDbENuTNADIY1ZXtbbQ2xJHCWO/g37yZS2u4Gqen+yIw2pvRRUno8C3/M3ntef83+Qh39ACxO52RFkJwpdRIa7Eqwra3l78JnuxGYY5/snULhyDJBsjJANveWvk6mY2Ihvs4oLz2/MzvSqu5mm4hncVJVefOl6FpI4xeOfvd/BxE1VFuuLEH1hZ2QoHw1XkV6n0OZgi/dYuusk1Jv38bCW69c5R3r2JkhNIFarjvRxmcmxL+9g5CycU6jfCGg0okCx1+mXSxNKIC55AoeexCJCrHCLwU2sSthqT/dBFPUudI5zvY8zGLkVBIIWonY93agsGDlU794HPS7yQ3Vv7+JAgheE/6HP9x8iW+lL3JS+mdk18EMxF2Mw79Szr8SIUzJTs7EVJ1kbklitpE3a5XVwi/KOl2YxQtttsJRWmYFJpJoRnlGgG0U7UQzSeCOeR8Dny/PEI7T+yG5ohROaSXzN1r1iCcwal4IZKviarDv9fo8CMU4JD5GBOvdjK/fdM/E58clBSlPTaK0xhXk/im5P3f9H+dvhnxn+1+F7vR1tSp/yxrJ6Hoj0IEXui4V5L+Lae49uZnOLrxHnRIkng2dPgPzICxzejIuefFktQCEdJLPOF3DTn/cc8BP6bh8CMRWW4ZTjTXrpyOcAgE2mocDuscZYjkSyIoAhnfCd4TFkuOph2SC7qB8I8zSxQJPvp7dvjAy74ja62ratxMMkunPXvdVQ76ucnoRMfEboZ9yHK/CG2ni8/pKBK8606LL31twmtfm/DM9dkRm0jETOyEzExOZfJWrGE4uS6kUE/dqd+PTZTHehCchFQlDIoBuIjCaLQtvUzf5JROY53BOks36i1VQsQyYaLHi54Wx2Ckh4z0iN4Sf5gKlTqldCXr5XqcDvIU0XyTXPPkMKOzQd79MnTbEYeDgjceDXnx1tZa5n2TXPPGoyF5YbiydT5GhpGSb9tovrw0a416ngaRFP9/9v401rYsrQ4FxzfnXM1uT3O7uDe6jC6TyL7DkJCGdBqMsV1U2QZR8ivbmLLsHyW5+YFkW7IsC0vGRgKB/cOkjYWfhF5CUe/x3ivsMpluSJvEmSTZt0H0Ebc/3W5XN5v6Medce+2919rNuedERgR3oBSZ9+xmrbVXM8f3jW+McpTjrD97G5yMMxRS40I/PtdueKGUjZCugBjOreihtLZGrVt6Er0RcKo77N/7e3+v8W97e3v4G3/jb9x36H8jwnf4tQZn1rQPAMTASrFVZ96wTxqFmAXInFxNR11H+Os6/Cf2NZ1dFNIgEFY6011Y2Fcj8jaGKkAitKSf8cZ0MGIC/NJjUDe/BXX7WbDdeVO01mSAHEDe2V0twyKgFXIcZwoqDDFpX0B3eoDg6DqyB7+j8W1ezi93LsNUOvDGxdG1ReRIvSX7AhyyQrxJFjArHqrl/D4xcFFdTNI84YeVAtpcezNP+BMbU7hY3fSmfdrNznNGvhm4XohJDOP2JeyMbyI4vgnVuwDKExf/RdCtLnhu5o65/wkVGURseYHCIrv4ruvwA2gcESj/7oi9DNqQymCCEPtY7vCvgzep1GG7LMbYxTIQs7CcA2YkoN02e1f/88KTsSX8N4sDjNQUvQ3MJEvDvoqcvzatAbBFvyIFNxJ73TY0TaBzDhL1iw/OCS0u0IoFtDbIcoXBOEMu9SyaTyXwkZHWqX+eDB1k853oG8lNvHWB8JNWdpyHGAbSd/iXSdoFF7N2osYotETgiIYRIVBMALVr7yU16LYFeh2O0UTh9kGOh682m7l5Ob/gBi13S8t1URYjns+u4/3iO0rjvnuV9A/caeU77t607+rJLVx69SsQ+RS3H/8eAEDMojLG8UZ+gCfjh8qPqprtzf7RWFNDxnBld/5PtfP71c8qUqBlnfoPTwpMpmsISjoEFAMqZmC+86yNhoEuJf2RMMhdh7tFEWKKkJoME52hBVu8vrQf4tmXEjxwMcRHvmsP/e5s2cMYoRUxTFONSaKWCL/vAicyWUn4pZYgUEn46zr8APDEo2384UsJnn8lwfe8f2dp3IQzgXExRk/0NyZlqU7Bz8hVX3wbnPqltt8X3IOTfcgipDLBMJtAKuWKlQycCXDiCN29uAkBCzCRY+Q6R6tmFr8O42IMTnytsmLbeMdtIDgh2aC7Pcu719hvbx/DVwciwm4vxNEww6t3x3jkSnelcV6aW7I/Tgrs9cIzKygJTijk+RDgNLeRjdEpzA2lsoaKZxXJ58E5Q56pbyvhL6RGkisUhcI0k2czmlED7UZWFgsKnBHycxrjkNpA63nl8ZsFp7qj/6f/9J+W/o2I0O/30e2eLlbmPr79IKJShklEpUM8dwR+cX5fagXBW2Wkmoo7CADwRcmyMWUUHfX3oF13xsASM49CK4hTzO8bVYB1du32M+7mWRWopkLHrjxZEv7gbd839zc9soWNvLNr921FhS8UVnKvtMG4+4Al/IerCX/oCH9xcUHOrwuETCBmEaBSEOPgXECYAqlWdr5YSZDM5goFi/AO/UosLG6MKQk/MV76NHBPUAFkrV37exQpRJGC0/z3kJvhLyX9DOBk5/jFBtXdcfcydsY3IY5vAo+8c+bQH3dtZ5dbp2TvrG+M7S4baIQ154OX9NfF8gHuZr1is8r5/bCNXGpoJ7fnDQWEJvB0NNsPB2m0daDnATisY3wAlOqPIN9+Tncb9HgbV4MLuFkc4rnsVby3/da17zmRNR1xDasGWfx9XZKH0bZTb4p8Y8M+xgitWGCcFJCFqnT4U/gfjJSEWfjJDx3hZ7DGYrfTG3jrzsJ+uS6rMZVIvpqYszaL0WYxpjrFoRrgAeb8OngIykZgxRSaNy/Or16OMHphiht3stWEf2TPzX5rZjQ0ULNiz/PZDbyv/bZKNN+9zCQyDBIn5+8QCiOROUXEfmrP6cj5qHg8GFzEoRzgenF3jvCDcauaMsopH4DZuARHOyL0WsDI1cYaJf3lZ+VznfbxdAVBURJITwDqAGL2wZw4CpNDGyu9n3X4DVK3nyEL0GYRUpXZOX60QVrhnU/18fADEXZ69Z3NTptbwj9VuFQzyRWwEBM1xp7ZbyR3mc7Aia8l/A9diUAEJJnGNNHlMfGIWFQmA0Q1JpuL8N3xbeI7V4GTQPYaO/XbDry+Jw8CIkI/2sFUZTCn6Db6Tnyuso0If65zZCpBuEGMYODSHjaNd9wGgjNk+fpovuG0wOEwRe8eHPHrQETY60U4HmXgjPDQ5W4tCS3J/rTAXv/syD5gj8E0tU79Z0mAtTG4cXeCVixw9UJzsa8JZSRffLbXkeAEqTXkOc2wb4KsUMgLZVVsZ+XFUAOlLPle9IhhjFAU+lyUSEqZxqbhGx2nujoefPDBpf9cu3btPtl/M4ALK6MGKx3iPRYJv4FBi0d2Ts2oxmg+KlIw5zLPervgxCDd/E01kq8wEiGJWoK3EkZbwz4AYMIVLRqM+y67eL6jV2AWHN7N0BJ+1r+M8RqjpkAwW2UsNCY71hk9OLrenBlmNILDVwEAeQ3h7/IYAePWj4BZpUVENtZtU+M+7+SvRVBWJ41j0GXxgyqEH44UG8DwAEVkCWkrPcGijpstSvoZs2KKDeaojAHGncsAYCMJq3J+l0/PicCIoNzx0474E1Cb2OBd+rnMa00aGZ9F89Wh6tCvjYGJLbnYtsPvu/W6YtinjAIHQ8SC0mHcvsYR/nPu8AMz874/TF/dyBW9dOivEGQN45I6ax6oBBhvlS7z2uLaKnBn+th1hH+qU+eyTXaOfwEH+QEA4LGuvX5vpTeWjc+cWehYJ1DQYGDosXo2esnJ+g+Kk7mdMoyD8vFK875rl+x9cd0c/2hkr8d+Z3b8fCHCb+ddeYJubP9+Ly79AErDvp02MHH3r4AEuu7aDZP5dINrbnTkRn533uCSCZCxZnsepCTIzEaK/Bw/sEbSzwRIK5Aq5pz6GyEzoEiWfByYU8rkprASaT/DX+nwhxSUowlTb8DmFoS7/ea8cW/q12TcF7IQmcoazfuMMSh0DkYcqZMWNxF+xgjtuPn7BBMuu30zjwjbHZf3JIef275vg1O/1MXGfjCrsEm3ffX7BSZys3tzqhKnDlq/VhEkUOgCxTkcU58VXqxw6teuu8/IrlvOGowRdrshDocpbh5OoBfMfLNc4ZU7Y4wmZ0/2AfsskWuOwWkwmhY4mWSYbulR4CGVhtRn3+EnN4P07YzmS3MJo21i1ck4W/rNzwo2fm9ZccqZHZU8D6d+pfUGstU3Ju7p6n/llVfwq7/6q/jZn/1Z/OzP/ix+9Vd/Fa+88sr6N97H6xbEeLlIWiL87d3Zf3cV+Y6IEBBfSfhLh/+4iyCMEAQMWa6XHPpzLdER8Vbu5F4OD7+trsNflxQAAKyzB+peBIyBuvv83N+0I/ytvYdAsAqGJgjBEAiOvJDIdx6wMvp0DJYM618/uANWZNAigty5PP+9xqDjZshnEXoMEQkYY8rfga2J5vMdfi1CMH9pG+cSWzXtc8eHM+fE7+6ZXtbfSk+WZ6ZKwu8664zAiWGTIrMyBkn3kjtGE7B0POfQ7z+POCzTB+w56EIjartXwawLQzWGhpxWR/OJSoefiIDW6Qi/J+/VDr8yGoGTkkYuXhEAjCP8TS79Z4lHogcQkMBEJ7hZHK58rTa67DzPSfoBcFD9dAvZvHajFYwuVntL1IA7w8gWi5ySwyDRGQxjoAWnfmUUjp1h3zt33wkGjlQnGFbj+YwBFVOYimFfn7cbCcCFOuM+wKZbFGntOeXhnfrvHhUoVszPekl/v1VP+AHghezG2Uj6AQxSS/p2OlTK+TusVZ7rQqZz5p+XxR4CEshMgaPqcSBui2jVwouP6nPH80pFALGzqvnlFWMVwj9uINYA7CiBzEALHgKcuI3j0wW00chdhz/gGrnr8EcsQNuNiEyVi+bbwB3dd9mbCL8/h5oi85RRdmxnA0k/AHRabPX3bWEgdxbd8SWY19apP1Xp2W7/KRGwAJlON3LUHxfjUiG3DtylAJyHcZ/gDGpNNN94WmA0zc+tCwtYmflOJ8Sd4wS3DqdlATHLFV6+M8Jwkp8L2Qd8QdpsZV64DtoYHJ4kKJRGlqtTGcQVUlsD1HPY523NGs8a01SCczvammTy1EWRdVDaQOl6Sb9U50P47bX05mzxn5rw/8zP/Ax+6Id+CD/90z+NX/7lX8Yv//Iv46d/+qfxQz/0Q/hn/+yfneU23sdrCSZs97ci6feodvgLLREQR0gBYhZC6irhnyc0Xs6v2jt2/j0SKKQCLTj0G2PQ2naOT0sb/1Qh/MR4I+EHqvF8z86+WxUw4yMAQHv3YfRFu+ySNSF28/RBGEHuWif64PB67Wu9O39x8aHSKR9wx5Hx2X4bBRIxQAzClRlNYP+2ioQAsxl+LSoPVq+Rr8zwe2NGT7TMIuFPjpdI3mIsH2C7BXoTxm8MIASKvjUKC45vzDn0A95EEPABbraQY8DYciQfYAtTxikf6oz7OGdgDKViYBGeBGWiBSEIoVcaFOnaaLYqgmKZ8HtvC8CqEzTc57Xd2IAq1hZv7hWCOB6PrEfFs9nqIuxIT6FhFR/dqqO7duaMNSAmbDdW5Y4Ibk/4AQIRlV3ZsU5gs9uLud/gJD+GhkZAIS629nGlZa+1m+mN2faoHFQkgAhXOvR7eOO+g6pxHzAjqEWzeWOvI9BtcxiDWrd1j+HY3oOqM+5+2645l/wXsptoRXZf0xz3JNMcuE3eac/m97ssnitiVbv8jBgeCOw4w/XFeD5gvsNf3k/tjeHKnv3/cQhEwboFLQEqnxHrpg6/0fa4k319Fb7z7LukvtASBEW5NAspKBMmpjqFIapPG1hAGdu3QnkgWICxrM96V84QTisGz7tWEf72mgJD1UBuHc6qO14FZ6+dU78xBrnOzsyD4F4QUACpJbI16opt5PweBDqXY8qYVcU1kT9jDI6GKQj3HsO3DkIw9NoBbh1Pcfc4mXX2pzn2elsaMW8JgouIPSOMpgUG0xw7nRBS6lPF/kl19pF8HtuYNZ41lNYYJwXCgINzBqWtrP9cvkvZ9eki4WeMoI0+l2i+Os+ANwtOdQf4t//23+JXfuVX8IM/+IP4tV/7NXzuc5/D5z73Ofzar/0afuiHfgi/8iu/gl/5lV854029j9cCxIXtloGVhAqwRnB6IWc84gEE42iJ0Eb0OZfy5Q6/I/zO9C8KbHdcsJlDv1UMMEQNZlmNkIXNmHdGf0RkDbdWkDZP+PXtZ0tZsBndBWCAsAUWd7HvDMFWZZlHgiEUAkIwFPvWDT04qif8wYElXXVy/phFc74FJAKAEQJic2kJayX97u8qCEsHftvh57M4PmK2qKN12eH3t0xP+NvpyfwNz5iyw1/1EBCcYZNHjnYmdnLPxtuJ45vlDL9quVYhAZzz8mezNVYNQfWEHwDgtmVlNF/Dz+ed8lPeQhwIBL2OjfKC2XiOXxmDyBFDtRDJF7rzOGCiLKiwIIJ019R5z/EDM1n/i9ktZCuIT3V+v7oo0wbNahsmYKSEKVLbDd6yw0+VotfcHD+zZZ/qD+cN+3aDCxCc46GOnTe/mcyuNSoS6yzPgophXzPh9x3+sU6QLizwDY9A2Xhld9i79d+400wOhpPlSD6vpHi69SgiCpCaDMc4gve6Oq2sP80NMvcT99soHfo7LC7jJwEgTOcVDU3xfIZoXmmhJao6xyu7wPc8Tfjou+uXEIWWJWH1JoCldH5aH4FHqgBkDsNj+98byDWAssPPA7vT3KWatCuEvzSBXIN1kn7AztZnKkVWUwSWrsueucPFOa2UTvvvmzYS/sA6zG9AEM+jO86JI1PpRqNATdBGb7T90kgoLV8XHX5/76v7javYRs7vIUggUdN7OqaNWJFDP80khpO8POfOG2HA0Y45bhxO8NLtEQaTDLvd6NwJFOe0dTxgE3x3nwiIAg6pzakM4s4jks9DcEL2bYoizHKFXCqE7h4Xhecn61fa1I7JMpewIs8hmi+X+k0ZyQeckvD/+q//Oj760Y/iF37hF/Ce97wH3W4X3W4X73nPe/DzP//z+BN/4k/g4x//+Flv6328FnDO7YwIqES7qc7OnDGX1BotV+EOXHW+7PBn07mFFp+e2L+7kYAw4LarKuY73SGJUxn2IYjnCATx0M7CN4BdfNR6FaSjcm5fl/P7V0BE6PAWeqKNyaJbdQVCMIQhg2BUEv6wZo6f8rSMNiwuLsbxKfR4PF/95gGIGIQhK2UVm3X4aZMOPwCIAMbYGWfmnfIwI/xxOgCrdo2KDOTn0Csdfs6p2bOgAm1gfRv2bMc5OL5Zjj5U4xsFp/KhYZxviiA+5/MwByfrbzLuCwI2P5tcge/w56KNKLCqEOXO901l/YXUCB3hr3b4gdkYQrVYwRihCF8bp37Akto93oOGxgvZjcbXlQ79C/FSxqwwZOS2E278sd/y+VhdfnacSdZEJQBjIKVAFbLtDfv2xAUQER7q2GvtdnrLdlyNBmWjcvRlVSSfR8QC9J2D/7KsPwTJDGzFeXD1kr333bzTQGryMYbT+Ug+bTRGjvDv8T7eEtkC2Av5DXTuUdbvu/udCDbrXdsP6pU6IYtwYeTo4dCOF92Rx/OFD6/gcNcPyRymeo8lwnseY3j08vIPn+oc/9vJ7+D/OPnvluAwAagCndh+ltKz+LoqdJHiP3xB4GOfJKSpXprjJ5cuYmDK7hYJe57458Yc4YdPfVi9KCxj+1Z0+L2pW53UXhkbF5jldv9WdfcBzBU+Vn1Xrlbf78+rO86JWyK+gcJgEcoojIohbibXcTu5sXZuXeoC8jVMBFgHQQJTOVlJzLeR85efywRynZ/LqMSqbu/JKINUZqV7/lkjDgXikGOaFtjrnT/ZB2zzIS1U4/N+G/jufrflfD8MVo5uNSHN1bmpKjgjZEV94fS8kebWrM/v23nK+q1KYvnf7e9CZ97ht4aw9zv8c7h+/To+/OEPN/79wx/+MK5fr+903sfrHBV3c1aR9C8a9qESn2fnlRlkEMEQA8GRfodS0u86/IwROlGAsBJ1kmuJmK+OzqmFlmVEWwkR1Bq5eRAPwC4+ZrfJyfr1YGbYB9gK4n6wussvBMNOO0QUcRS7V2AYB8umpWeBR3D4ivUE6O7PEUNtNIiAluv6ej8C4oGV9BNBEIN23WLaaobfE35tu/qV40pOAUGwHX9/y8zjPjQxcC0hnPs8AMD9llqENpLN77/zBlh3yzXOZbXYs+aGYnxcqkBUhfBzRtBe0q8BMANBvPmc8MWHvJ6YCdZg2mfM3Ax/4LLUvT8B35Dwy0KWbv9e/WKMAWFWBOPEbDXabYjPQX8tjPuICE/Gzrxvhay/NOyrIcisyXDIh5drDdD2D13iVKYydB1JszJ0ZhUllXP9MLOGfbvBBXBOuNy+hIBCFDrHYXYAkomd/Xb3o1UO/VVcbJrjL837Ro0FrQev2Gv29mGOL36jcq0YBZYcozi6jULZY9dzp6kdnTAQ4OiwGI+5kYuX8lvotJzh4Cmd+gcT+z4/T+8l/X09/4hfdOrv8Bb2ub0GX8nvzP7AuJ3bN9JF8hUbj218PXkBU51ioMaWeHMB0hLCKLRiuz118vnf+9IEz9/lGCXArRNdGSOw4MShMN/hJ2HPk5BqCD9jsOkCqxfrM28BvXIBHbAQkxpZ/1IkX7gh4U9WqNA2MJArTAGl5ZmTZU7C+iWYzWW6yigMiwFuTF/FzeQGMpUhkSmma/ZBumLJecq9t0HAQuQ6R9Gw76eR8wOAcOMC5zPHXx/Nl+YSx6NsKWrytUArEth9jcg+4Jzr1b3P8WtjcDiw3X3v+E/MdrW3gY/kW3SXPytwziA1vi3GfdOsmPtdz1PWXyjdfG8gnMpbYRWUNtBm2STwzYJTEf4LFy7gm9/8ZuPfv/nNb2J/vybf5j5e/6h0gimYDZ8uGvYxYmV+dUACghgk9LJxnzFgXtLfnjk97XQjdONg7jM7ojniqhlmZtjnt5sLrDPdKOf47zjCP5on/ADQFeu7/K3YRT1xgWK34tZfQdgo55eIKUSLz+b3rakeB5gV5UcshBT2ODV1sj38XLgJopkUe9G0D57wKzBi82b8xJDF9jcKpsezf16I5Cu/j5P1AVhTZdbGQDAGE7YgO1ZFQAA0D2AqKhI/1w244gczCFd4OpAj6HWSfvt59dF8TGZgXm7c6pRKE+/UXy1WNUFpg0jm1gWBGHRk36uMhmB8rsNvzQ2dQsJdH6c17vPKh03xRHQNDIRDOcCxrDeUnEn6lzv8qx8QzhTtFHJcTgCDLYS0S0m/O89EAEpPAC1R6AID56S/F1wEI0LAOS6Gdo7/VnqjHDcBcaQ6K+PoVkn6gcoc/5xTv4OIQEWyZCDo0esIfOe7LFH+7JeH+Mq3xoDKwMd3wSZ3MczsvbETzSKFBo789HkHRIQrYh9tFqMwEnzHFjXGW3hGGmNwPDb41nWNb7zq1QTO8d91onfl/NkSpsvnwMPuWL66QPhJKTvHb5Ql/hv8zpnO8Y30xfJ/D9UEttOuQTpvdOr/+rMjfPWl2bYOp1iS4zPi0EYBmHU0ibtIvgXCn5kCEsYqk9Z0qr1rvlKzLn0dvFv/4ox3rnMwYiXh90WNJmwyQuAN5FZ1yKWWkObs5fAzp/71HTupJU6yY1yfvoJbyU0UWqIremiLDgIWYFgMan0PPHKVg15HlthlSkLDdX8aOT9gi68G5pyc+hnymu72YJwjKxTi8PWhnjhPCM5QKI10S2K+iPG0wHAyb3AYcIZJtt3v5iP5GtWJ9wjByJk1vrYdfm0MJolEGCwUks9J1l8Uq+T1Z59UYE0C7xP+OfzpP/2n8Ru/8Rv42Mc+hul0tjieTqf42Mc+ht/4jd/An/kzf+bMNvI+XkMQKxkFrxC8RcM+QRyh6ywEjCMg4Yz75uf4KU/AVAEDmiP8NON2dsYb2F7Ov2jY58HEWolxOcd/8BJMkcE4yT1VCH+1y6/WdIkAoLjg5virxn3GIDxwhn2XauL4RGu2YNN+3t7+xxhtXd6FPc6bdvjNnEu/AYjPV0kZt+6xmC8EAEDiZP3CGRgCAFLXxV4g/JwRrD/i6pu8waxT7Of4ARdTVy1EsNm5ZwAYMohWxE3Rmg4/d13kxYWQ78oXPEIUzcyETLQ54S+kRhvuuETtslAmXSSff9ALxmx2vDt/lFMCnHaGf5JJTNPNpXMxi0pC95+Gn8OnRl/E15LncTM/QKZzaGMqkv4FgkxYimecA+MwMrcGfluCiFl/PGPQLSX9rnAjYpDKwPIJjrIDF//ZRlt0rLkjI1xycvib0+ugfAzD5+X8XdZam00+M+4bLBetmAC0AhXNhZn3vb2H97/D3h9+74sDfOOrt0HZECbsYJjae1mvxrDPKw+IqOzy592b9hiskPRrbfDCbYP/8S2N/+MzCv/2Exof/5TGf/6SwQ0XxHCpb4/pxEn6d6U9V1J3XQfpcKnj7WX91/O7FTWT7Y6TljZWT+s5s9EmfC15AUVFtuw9C6wRX1HK56sd/pt3Mnz687YQ4RMLBlM2N9YBuLEgLcFAZYffcC/pt79/SKL0L0lMDmhjt30FhCBE4WrnfPv9HBoaqZy/3xS6sJF8jmys6/C32/bvTTP8wMxAblU8nzTWsPBeuuNSGnz92XEZJ+hBoLXy80ROcT15FXfS21BGoyf6aItZMkbEYyQqWdnlT3X6ujDsq4JAjSkJp5Hze3DiG6cvbIO67nYhFQ6HKVrRt98b4bUAYwQG4OAkOTXp1MbgcGh/H1GR4gtOyOXqJIRFnFcknwfbII7xPJDlClmuEC6MiJyXrD8rVKNKhDM6lZniKihloLV500r6T3Wn/dt/+2/jG9/4Bn7u534Ov/iLv4jLl90c4J07kFLiu77ru/C3/tbfOtMNvY/XCJVFnRBOom/0HOGXRqHFI4jKg68lIkzyzEbvAeDOqd/L23Wr22jsVRiJgG0/vw8l7cKcLxJ+vrYNyroXQJ09mMkx1I1vwDgJe7XDD9guf4tFSFW+VoEwZ9znZuf56BAsm8JwgaJCdo0xMMbMf6ZWNmGAcRC3kXwhF0i9IeHaGX5bENBBhcQa7RQP1Z3nANmCRmWEHwCQxLsAADGaRblRVk/4iQgpJRAyRHex6FJFxfyt2LuK+NWvA5hXfAC26wuyntMGNlovWLG4YlEbCtYjoQ6cEbjzBahK00s5f9BGFM4+33fpeUMBoQqpNHqOVC1G8oUkyiIOJ9vtL4xEhAA6tgTxNJJ+YxwHB0HqzbsHT7fegpfzWxjrBOPsOp7PZgWpNovKzPpuTWb9YjxjFcQFTD4FFkdqNgBjNu5PG1Mx7fPHnWB4AEoHOIRVmlwIL4JA7kFMuBJaonw3uwMVPAHulCmbGPZ57Iu+lWKbDFOdll4CHoaHoHQMxLuN3e0PvL0Nk03xhWcV/ts3OZjo4u2PMAyndYZ9y9v2eHQNX0uexyi8AzCJcdr8SP7PXzb4wxvzNzbBgIs7wOUdwgN7hMeuAInJoGFHdnZy25lKu5cQJkMwoxBkYxTxbJTmgthBm0WY6gy3ikM8FPp7oHPLZ2xm/rkC1e5+n3cwVJPSs8BwASoSdF3By0fzjSYSn/j0EbQBnrwi8eDlEL/zFY1BQsBCN5STQGIScOJlh187wu87/D71YaSnmOocfaw2cPXotjmyXGOSKFzYbX4OBWTd+nfDPRCRi+SzXfY0s/ffTWf4C2m9CBa7ZX4/ACBTGToNoym2O35vHcTPf32IL35jjOFY4bvfO7sfM2JIV5jXaaNxnB8jVxl6Qb+26MCIgRPHsBiiI7pLr7FRdcXawtxrjYCFSNQU2ikZPU4r5/cQFCBT6dLn3isEZ5imEoXS5ZjkcFIgyRT2+1umHr2B0esEOB5l2B1n2O9vrxYdTwucjHP02vPXvxAMeSJRSD1XCFiFQmq3BDwf4khupO61jubLCoVCa/QXTEk5t35J4+Ts4h+1sR38pm77eSQVKK1tDODrZMTorHEqwt9qtfDv/t2/wyc/+Ul86lOfwo0b1hDqwx/+ML7/+78fH/3oR183M1n3sR3IhZ8bY0/6ycWH0JqcQPYulq+RWqO9ENkXsxDGLEv65yL5GlBoiRaPyhGBjaEKUNBaIrSWNDMYT6AbwK88Cfn876N49tP2fa0dUDD/oGBE2BFt3MiOsI7WyJ3LMFyAFSn4+BCqd7GU8xf7D84VPAqjEDBR+iAAsNJTHrjtt/GIAQuhnaR/vUu/7/BHlRl+s9yBrXT4GRFMZUGcxLYTyEeVDn8D4Z/qFN0wxElWoIvVCwtfMS3mOvz9pdcwsLLbSoS5otLSZ4YdKABshaSfk3XTrx4BL6fPw/acm7Yn/IFMSq/DOkjtEiVct2aR8EcLBaiQCWQuZsy0naQ/277DL5UCF4RIcExTCbFGOuzxQHABP7b/J3EoBziSw/I/lhTZc+aC6Nc+5Fbexnlgz6VtkzXc59qOrUaHz2TYhZHW/0DEoGyEw+IWAGA/uAjGqXQ97oc7aPE2EjXFbT3CNewCmHWUNyH8gjj2eA9HaogDebJE+CFCUDYBFQlM2LXXp1au6y0BLUFFgu9+eAwtO/jSi4Tf+aoBYxquUVQbybfDZ3eSfd4vyTHfu41x8mDttt4dWLJPAN72EOHKLnB5l7DfXY4smrjrocViRM6Pogg7yFt9xNNjhMlgjvATER4Kr+CZ9GW8mt8pCb9118/svWgD+O7+Hu/jrfHD+Mzkaxhq3+EXgC7QbbmRg6lCUWj8x/92iDTTuNQHPvIOhTtOXDOYsiVFEyMGaSQEBchzR/iZBDQQ0ewc9IR/olPABCCj1o7BdFoMhycrIgMdQh4hVQkynSLmLShtHfoDCmYz/GsIfyAYwsCqFCaJqiX8gI0CnMgJ9sL92vVUptN7Jsuv3LTX/2C0qKbgKHRuPUlqvnsixxjLEbqit3KtF7MYUzlBqhK0xHxBUWoJZSSCxevu24yABUjkFLnOEfPZeiBVCQoj0WLbFzgBOy6QqmTpc+8Vi9F8SmscDFKEAf2RWodzxhAFHHdOEvTawVZGhcZ19wlYIvWCs9Kpv7VhrSfJ5Pl3iYle8xn+JJOzteUCooBjMM5xebd9JvuulIE2QNCgkrAdftV4jzrVd2q/9nxzXjf3pKX6gR/4AfzAD/zAWW3LfbweQMzKeF0V+tV3fj/2wu7Cyt8sxefZTixBRV7SP9/hXzb9m6HQCheD7R+ARhVg3QvLf2DCdqOMxrwf+Dz4ZUv4vVM/LXT3PdoiBsupjA5sBOMo9q4iPHgFweF1qN5FBE7OXxfH1+HzcXxGa1DkuvmMA7CmdcYVV9ZltzOvABALM/wLpNkXdQjG/tSVv02jXQAuWUErgAmQk/SbaLYwsw7REjtRB4fjA2hjagmjAQAypTJct3pQUQc8m8wZ9gG2uMKYlS5rrSDYikg+rJ/hBwGB4EgyNXen85J+FbbnkiI84Q9likIty9Y8isIgijjCY3uOq9Y84Y/F/LURsQAnbjFmfIe/mLqu6eadnkIatGOBbivANJVQxqzswFfRZjHaYVzK+wF7Dh6rIQZqgqvB/HVkXEGIVkr6GWihaLMNOCcUEgiDAAEJFEZiohKXFkAwTOAwt0qT3eAiGAHErCpFMMKV4ApeVC/ghh7imvvMk6bxhAZcFDs4UkPclQM8Gl1d+CuDIQJLBkA6AJQEOdJvtdSwzvWtHj70tPUj+MpLBv/lywYtV/PxhN8YU1uMICI8Hj2IL06fAb9wE5MX6gn/Z5+x589T1wh/oiEKz8MrJbqsVRa3ZNhGHu9Ywp8OsagveTi8jGfSl/FKfhvfZd5hFzzEbVFVrTcaq3b339t+CoG7bode0s85KMvQdU7944nCf/nMMY4GEq2I8KffmyIIA+y4xeQwAbQslq6RruiBNC/z7pUn/JX7aJvHgASmKgV418aErEF7g7l6wBkHGoVEWsIvXYefUbtC+NcTjU6LIy8kJlOFvX59wSygALnOUOgcIZ9nGlbuX4CfYpzGY5oqHJ5YFcXifnMSkKaANBIBzW+fMgon+TEEibWdas4EjDIYydEy4TcFlFFzHgRf/MYI01Thu9+z822T1frRjVxnJTE3xlg5/z2MH/hzpzhjwg/Ye7WXdw8nBSZpgd3urPB8lB0i5i20xbKKaxUmcgIDg+6G99NvNzotgaNhhoNBiqsXNi/MjHx3v6E7bd3bN/MHMMbYnPoV0ZxnAc5sx/21gjEGo2nRGDkahxyjaYFpJs+kyy+VhlYaPKi/5uxYgyXpZ2WOqJR5XXmKnDXO5IyUUuK5557Dl7/8ZUwm5+8+fR/nCGLwTtkczC38ZheAN+wLF+T3AQkIxiH9HPSGHX7vah4vyvI3gQEoqCm5ug7/ugxmdumxOTK8KOf3aPEQMQtXZpl7FPs2Izw4ug6SOYJjO5tbJfzGGEgt0Vt8+GoF8sfBeSkIEqXqYK2kv6ia9vkZfg1aIKD2+BCYAYg4DGZV4jxoQYkQZAz42Bn3OdM+HcwIf6JztFmE/VbbxhI2VJrt70uzxRsR0offDh1ES0UQxsjutjHQpFc79GM2w7/KzDAQbMl/QbgoPRPPy0yNKyAEKkUhm0mC0hrtSJTneFMkX7kNTJRjE9TuwoBAxmwc/+ehjUEcCkShQDsSS3O32yJiAR4ILuBt8SNlRF35XTBgoJUj/PcKzmbpBTNZ/6x4k3KGkSOvu8EF6xlhyxAQnOEKs2qUm8VMjVInm1+Fi8EuAOBWcVj/gqAFyMRK22Fl/ibqwsQ9mKgHBB3A3Se/9+2Etz9iD1jiOHLfdbRTkyN3ZoKLx/pxN8fPdg6RmhzFgifGrWODl+8aiGvPYfzwpzFSqz0mPOHvsFZ5jsmghdwVZ8JkMZUAuBpcBAfDRKc4Us7YjwuQkYCaj+Srw9fL7n4Pj4RX0HP7OHLSaPtM0SXhv32Y48XrKRgD/tR3tdEL7HhWt2XrzdoQJqlauoeHLISsNKMLzEv6gcVoPrKGg2vQZCZYB0EBJmoMY4yL5LOFm2TDDj8wKzCsmuP3BnKLJoHALL/+Xhz6r9+afe7ifnPiUNoWMxYxzIdIZIIW34w8xjzGpBgtGeEtGtgVhcZnvzzEV5+Z4OWbp8ynPCNw4pjK2XVWmMLJ+e9NIk8gZGviFk/1uQTkhYY2BkfDFJzPnrk+KnEit1eVDfITDPKTb0v822lAROjEAgeDFNN0M6M9YwwOB6l9rjSQWc4JSb7ZfHpeaGSFbiTGZwXO2D2bFG6DXGpkRbMiqSrrPwsobaBM8zw9ZwxKmTON5jtr1//XG7Y6I3/nd34HP/VTP4W///f/Pn7v934PAPDJT34SH/3oR/Hn/tyfw4//+I/jQx/6EH7+53/+XDb2Pl4D+Mx2o52z7DykVnOGfR4BEwjAkbsu8KaEXxoFcYr5fWNsPieJesJPjK+M5gMAEiHYhUdnb2sg/IwY+qKNTG1A+C+4rPmjGwgOX7X+B60+dGe2/2OVos1j9Jeq7WZGzokBsLF8zBP+VZJ+Y8qCAAULEsnFLjDj1sjPdeVNpaJJRCjaNmFDjC0JKmf43W9rjEGmcuyFPfREG50oQlYTCwTY5hojmuv+J09+J47+5P9z7pgAlvBzEBQAhXm3+zpQGaPXvDjkjAFmfv9Lh/z2vCu97/CLImlc4NiZMoY4ZEuE3xevFreZ00wER4yhCC0ZCraQ9WsnWwsCBiKUFfSzyB2ug9EGxHCu1e6qdHI+ms/iwKUK9FkbwvA5zwJBBleMfc+hGiLVuVUIuPdvSvgfCi+DQDiQJzhciueD7XKHXUC0ABE5eXv9MSEifN87CN/x0OzvvsM/WGEm2OcdXOA7IDJ45NLXcfWZT2HnzjOll8Rnn80RPvlFBA/9IY7NAM+mzRGLADB2oyYdHpd+Fb7DDwBhuryfgjiuhZcAVNz6ybrrr0tiyHSOr1e6+0SEDmtZs0rMDARBhG44f5/4vg/u4uqOhD2m9j6x03Fd/jGWjPuAWSRfwM1awm+I2aSBNdjEOd8jYiEylSLXGZRR5TWyqaR//vvWPKPAao3epC6gcW+z4Ndvz4hnkum5hTMjBgOz5NSf6xyD4hghjzaWvQYsRGEkxguEM9f5XEH3eDj7rq9863SmpmeFgAKkKin338v5t3XnX4RgARLV/Hw59ec6QjpOCgynOTrxbH2Wqwy5zjGVE6g1iRVVeM+CXKWNMYWvR8SRQCE17p5sdpyHkxzH4wzdVnPxLOAMSbZZ7n1aKCj1GhB+bk3rXiuSmuUKUq7eLy/rPwu3fh+Rt8q0T+uz3f9cqm1El284bLxrn/rUp/A3/+bfxH/4D/8Bn/jEJ/DX//pfx2/+5m/i7/ydv4O9vT385b/8l/GX/tJfwtWrV/Gxj30Mv/7rv36e230f5wXmJf1WLlzNEAeswV7Eg6XZak4MIRfII7vYYtkU0Kok/LqB8OdaIiSB8BTz++DBUiQf4OZvXPTcOni3fqCZ8ANO1k+0MmIIAGTvErQIwWSO1vNfAADkFXf+ws18Xo52lz0LjJkpDhgDyJG80Ll6y7wxF9zKjd38UViRCxKWZeOMAYyBaWvsZWi2TwZA0bWE38/xl5J+11HPdIGIBdgJ2ghIoBuFKNAwJ+uI46byTM4ZtDQwBoh4sNo8pdrhbzguwpnDzf2bK2BQe54U+oIGlzmEMZA1D6280IgjDiE4uCP8yhF+ZTR4TZFCkDVJ9IsF2bKFhrpOaxMKpREIKscM4kggDgWy/Jwe9gbAFr/bacAqBcV26dQ/IzeegF9kHVA+gRCzbQmQo6WAXUfsbxWHpWQ+JqvIKV+bjvDQN34b7cGNpW1osxhvcVL+byQv3vM+ERG+/12EDz5F+M6nCJ3YbvM6b4HHY1sojC+8iouD53H1+d/Fk5//OFpf+S2kD/wO+P7t8rU3ioOV2zDr8Mfgcx1+ew+OGs4779b/cu6/i1wO5PJYUBXz3X0bTcqI0Hcd4GHFuK/D0tIX9l1v7eCtb2mB8ilMZURspzuT9dfdw71RUyhQRjBWC8adaoefsY06/J0tOvzcdd5TlSLX+ekIf3uzAkPoCOLic6fQxT2V4owxePXWfKG0blsWid4wHyDXOaItjesiFmFYDEsCbYvG6RzhPxrMvuvm3RwHR2efWb8pBAtQ6AK58zHYRs4vpcZ/+cwRnnlxWYkjSEDq/MwJtI/mOxqmS3PoqcrciEKObIUR4yIylaIwEsWKmMLXK/rtAEejDMPJ6nNokhS4cXdsj9kKIis4c07964lsVigYnN1cefM2+XSG10Z9keZy7X7FZ+jWr5ReeY9jjKANatdpp8XqGMA3PjYm/P/m3/wbPPXUU/j0pz+Nz33uc/iLf/Ev4h/9o3+E7/me78Fv/uZv4h/8g3+Af/gP/yF+67d+C08//TQ+/vGPn+d238d5gZid8dYaXkBrKlROao12w8O+xSJkgXP2h4EYHoCUdJF89bO+hZHoiHj7m6OSthveYBhGPITZoPLHrzxl/wvjoIox4SLaPEREwXpZP2Mo9lyX30X9FU66bozBWKbYD3o13X3/freoqJD0wHWEyWibTFD3NtfdN8Qg5sYjaJnwE7exaDDgjC2rODquw++d+r2k3xHsROXYC3oIiINGt9EXAQzZqvYilPMJ2PT35dwXmPTKSD5g1uFnesVx4VYCXi1a+Q7/IuE3LpUCAFrIlgxxDKwBYCsS9vzzyofWLLqRgy0lCwjn1C/doj1351k8aZCR10BKK+f3h5EI6LYDKK0ba0D3Au10H+e5aKGKgUTXSfrHFUn/gTwBYOfsWTpEhe/bsQxjcDWwx/JGcdDo0N8/fB7dwXXs3/x67XY8Hb8FAPB8dgPpihi0TcGI8J1PMXzwqdl1ty494C3hNcAAr7QFDoIQSeciXooD/M+XNKatAl2p8aNH9hw/kCcr70O+o97X3Bb0QFBBXHb4RZHU+oF4s75DObDz77AeBSRlo9dEpouyu/8e19338LL+co6fCTBIfPh9Pbzv7T1813t2QCoH+eKtw27HftcgIVDNWJYn/JEwyE1dh98+n6ykn9l7w5qLZJsOP+Dd+kcoXJdaa1MW3zbr8K+PAQRcd1zlS/F8qUrB7kHOfzyUmKYanDfvOyM2Jz9PVYJhMUDMW1vfF0IWIdcZpu5ckEZCGjlHoo8H8+f0l585/y6/1qbW6dsrHDKVoijd+TeT8z/3SoI/fDHB7395uPQ3QQLSpROcJQQnKGUwHOdox/PnRaKmpQ/DquSFRUzVFMwOdmG6Zozo9QYhGBgj3DlJao3ttJPxP39jgMEkR7+z+rcVgiCVQiHX3x8m03ylm78x5kx+f84YlLbxf68FRtNibUrBWcr6lTZL6sxFGOBMJf251G/aSD5gC8L/7LPP4i/8hb+Aft8St7/yV/4KsizDj/zIj8zd/IUQ+JEf+RE8//zzZ7+193Hu8IZugHYdfjbXXTA1hn0eIQ9gAGhn3Bcc2fgv3eo1doiMsTPy28KoAhS2mxceYrMOP+tfQvj+/yvC7/xR0Aq3cU4cvaCNbAN5aHFhZrxliCF3cX1TlaHFQlyMdpa2244oUJkqQE7Sb4xBELZK2X2TcZ936FcinM8JNmZpse4VEGQMiBgMtI3Cc7FvRc8auPHxoZX05jPCn+kCARPoBx1n6hegzzgCzmrn3o0BGGNLXfbjfIxhUdMB4QxKGxhqPs9mLw5Lgr7KqZ9xQLuHAmkJ4eexWwvki6iU9bcpW6rmK6kRcIY44ODjYxAAHcQwbuRCGoWgxneAE7PyZncdFU5JEo9Xd2o9bEyhQRTOL+RaIbdd/nMw7tEG4FhWR5wlbAyj/e+zrqw914wxuFu4Dn90EVwmYG6m1qgCVEyheIxrjvDfzA+Wcu49Ahe5GdRI2QHgktjFRbEDDY1n1sjlT4umbfPo8BiXUnu+/7edS/jEU+/BLz24j4lg2E+B/9erx/jg0REu5XZm/NaKLr9XSey481cGLYAYtAjtf0e9rL/NYlwUuwCAVwsn62cBANUo6a929x913X2Pfg3hJyXx9KMC3/muvvXskJm9j1Q+f7fs8PPSO6EKL+kPhZl1+Gsl/ZmrJ9kC4ir4jnteGBQbRD2FLEKmMkgtwYjPKW3icIsZ/jWKAkYMGnqOeGujkZv8nub3X3Xz+1cvRej36tUNnHjZ4TbGYJAPoIw61Rw7EUFQgEE+gDbaEn4tFzr89tn69ifsefPcy8lGiot1MMbg8LjA868k+OI3RvjU7x/jt/7rAf6X/+8t/PJv3MCv/K838dzL9d34qZog1elWcv6Xb9hn0SRRSznhdkzSnHnHXHCG3GW/+2g+wCpBMp0iMHY8cCLHa1WKgDWFnMopQhYiZCESOd1qHOD1gF4rwHCS42S04B0hNW4cjPHS7RGICBd3WmtJHmfMFofWxOBJpZHkaqVh30RNcCe9fc/HkzkPnNcimq+QGmmuEAXNSi+Ps5L1F4VeL683prbRdBrYSL7NjZDfiNiY8B8dHeHChZmT8/6+7QJW/636tyx7Y0mA7qMCxu1MOBgYUHb4lYvqa5Lfh2Q7kCp2hP/wVfu+hVltD2mUjTc7zVycVqVpWx2Ii7VdHQ/x6Psgrj299nU+Pmzd3HSxPyP8xd5VQASuqi9xKd6t31+tndTe3VArXgoBE7NovgbjPh9hpUVQLqKMy5arjSbkws7wOxM0mJlRm+5bMzSeTsCmA9clBEwYYyoz7IqOLdJoBQQRQg20IoGiphhitJXVzx0frdx8PsNIzs+nMufCTkSI+JoOP1Ep60feRPgJggjSncN+plkzAVPj/+AJf6yycr89MqkRhxxCsNLfQPb2S48EZTSimsUwJwZBDMqRDh9xGU2P1/pMAPZBxIkhEvPHkTFCtxVAKr02dmxrGIBtmDl8Wvjf2hiUkXh+/nyqU6QmA4GwL3ahGQPLhjBGw+QJuLZd4SvBPgiEkZ7ienEXwHIX3XslhNmo9ngTUdnl/2b60kYL4m1RF8m3iA+O7bn5ub7Ep8dfgSEDefgAdgc/iLvv/XGM9h7Bk1N7nTfJ+qVRSI19za5bCMpwpiYq5/iT5e4jUJH1Z07WLyKYuP7+bbv7LwBY7u5X97Uk/F5vUPFCoSKBWbg/ecI/SKg2IcBH8gXCoHAd/uo9teUIv4ZGBmWVUWsMXMOAIXDX1yZdfm+op53LfOq2KQppow7RNooCTgJJxUDOG/atMjRdh+u37f3yoStR47ZwElCuEz9VEwyLwcZGfXWIeYxUJZiqKaQuYGDmPAh8h/+tj7Vx9VIIY4CvPVvp8hsNPrmz1rx2Eb//lSH+P799B5/89BE+++Uhvvn8FNdvZxhNVLlEeOXW8mcGLESuMoyK4cZyfqVMWUwBluMOAVtIqPNluBf4e+miO3qmMxS6QJQMEEuJTGVLapE6pNoqGwIWImCBGwd4Y63pGSO0Io7bJwkyZ243Tgq8eGuI20cJem2BzjZu8obWEv6sUMgLhaDB2A4AUplgKsdz1/RpUU1nOE+kuUQm1Ua+BHHEMT0DWX8u1Vp5PRHOLJpQKQOtmz0D3gzYakVXfaC/WXMK7wMAEzDGdfjBSoIrtUJAYimmxyNg3ErWPOE/sRnaqr1b+/qinN/f1rDPtaJXKQOYaPLWOjXaPELEBPI1cizVuwDtur5ezj8qEuwFPeyIhgW/UTaruiT8zN7NjHWr14GL62sw7vMdfi3C+Ui+hZSF8vU8tJ/NbO69gSmN2iDici7dF21M2EJhNAQx7LoRAxgNYhwR4+iEETRZV9UqtDFzZmsAkOsCMQW4Fl8EgTCRM7JeVlcJyx4HdfvtCX+NWsB/jhCsFHvwzJIPHXVqj4sn/JGyTsfKx+nBFr5asT1X+dj6G6hKLKQ2ulaVQEQIWTCr6Hd3IFkAZhSi5HjtPkppEAgGwZcX+K1IIAr4mXf5vdniecKbOSpj5lz6jTGlnH+P9+z5L2LwPIHJJjDJEBDc+ltQgEuuK+1n/ncbCD8ZgyAb1W7LW6KriCnEVKd4Kb91pvspjcJ4jZlgmAzwnaMTcGOgmL2GilefhH7hPfjAEwG0iJD0LuMpT/jzesLv5/cFcXSc6kVWCqOZd+pvUDv42MabxQFk2YGqPw98d3+3prsPAD22SPhhR8a8SklLQKZLY1le0j9MCEbKJaVWadoXzu6FVUk/J4aY7P1yCut7QhsUcWrn6o2qVRkAlrhlOgcjhjR1cv4NuvvAjPAnmZ7vhBkDysdz+xywAIlOy/n3Qi/H2W0DpQxu3LH79NADcWNCAScOqSUKneMkPwYjBnEPMYCMbHl5lA+Rq5n3AQCkmcLUHcPdvsC73mqvk288Nym75CQzUDayx2cLvHjdXgcXdgM8+WgL7397Dx/5Y7v4v3z0Ir73/baY1UTMpVHIVbaxquHWQT6ndGv63FxnS4aI94q9XjTX3QesYR8ZDVI5uLFFsESul/UncmqbAU7l6ccb3mhoRQJpJnEwSHA4SPHizSEmicReL0LQELvbBC4ISbr6N8tyZbvEDakm2mhM1QSFlhgXo3s2byTC2iLEWSDLbRTtJmSYuzXleHpvsv5crp+n585b4SygtHG/3ZuX225F+K9fv46vfe1r+NrXvoZvfetbAICXXnqp/Df/n1dfffVcNvY+XhsQtx1+RjZuyvcPCyMRsmBpRtlDkEBAs2g+747c5NCfa4k2j1Zn29dB2wgnqjHsK8E4zrrtKRhHT7SR6jU3MiIkj70XsruP7NpTmKoMERO4GO00kyitQIzNZvgZdx1+g4BxGFfcoAZJP08dkQ1ClJ7wXs5fc3ytAkJDVGPpjCmN2lTPzfEf2rEME8aYqgx90UHLezhom5HNmUAvCEF8WcJofQjn9znXEn3RRj9o48H4gnsIuoQBRjDMICC2mZHjmg4/YKP5fNGKSmf9+sKLLp3/E8QBR+5l0VJBcIbYVe7FyBP+/fnvarg2LOG3x4ZzjqRjCwWbzPFLZdCKeC3v4pzQaQWQ8my7/MaYJWXGWYOI3PSQKWXYGhqpyXHgDfvEjj2NiQOCwUyOYfIpeBCXxYKrwbzKbI5UG42gQhDCtL6zzYnjbbFN7DgL874qPOENKSiJ6CK6xy+jrQ2ePGGAChBffy/kjSfxzkdZafxXRD08kRRgxmCkp7XxfBO3KO+yFgJ3Tcig0uFv+Q5/PeHf4z10WAsKGjcbigr2e5Kyu//emu4+MJP0j3VSnvuGcUvyjQGpbGl+H5iZ9uWSkBbL0Xx+5loEtvAqiC+51c9k/bktfG4gny073Z74qgJ8fBt8crdWLRbzFlq8bQl/Ob+/GYloxXbMyRiURBcAoAuwdDB3nw8ogNRFGc/nu+OnbbrcPsihlEErZtjbEeV+j2tm+A0MRnKEiZzcU3ffI+YtTNUEiU7mHfqdnL/X4QgDhkeuxeh3ObLc4A+d+R0VU3vOZCNgwxnoNNM4ce7/f/YjF/DR797HB9/Vx1sf6+DqpQhXLtrrsY6YE9mSxHZy/vnOfd3nBiwspfbbQBuN28mtjccBjDGYyAkC49IudOHGFMYriaYyClM5QVA1PqUAU7n6fa9HEBF67QB3TxK8fHsExoDdXniqLq7gZI3rVhyDaVas/OxMZ8hVjm7Qw1RNN1JbrAJnVKoXzhPjpADfIus+DgWORhnyUzYitDaQav08PSdCvoGvwiawhP/NPcO/Vbn2F37hF/ALv/ALc//2j//xP156nTHn71B5H+cIZskg4Kv89oKSRqPDm915GRFiFiJfkNo3Sfq1MWiLuPZvK6EKO2+/gvAT4wBjMFrVS9pPia5o4SAfrD3Hk8ffj+Tx90MZjayY4sH4wmqvAq0BEVrSb3eg7PBzCmECe9yb5Izh3Rft9+5cRt8vfo0l5KirNjNLIDnZNABj7JQrg/0dZfcCwrsvQ/ixDPeb7oaz7HrjOvzgAbpKQYQMMjXAwtqoegP1YyH+d98JOlBG43p6gBTk/AcMAi42KgSxsA0NgPJmiSTnrBxL4aXRXn2n1bhiFcumiEOOcVIA4MilQbcVgDuZu+/wS1cY8YuAphjBkImyAMWIkLYvoDe6hXh8gMHltzZuuwEAAqIV3Yh2JDAMGHKpVr5uWywWas4ajJHNXIdBSBxtFmGqM0xUMjPsC3bLURMWtmGyKQADFnVArAC0wdXwIr6UPAsAEOClHwBgRzioskAL0wEmeLh2e94WP4IvJ8/ijjzGoRzggqi/b22Lqpy/6Z7RPX7Z/v+XH0WSvQ0JCIID73189vo87iEyBg9nCi/FAjeKA7yNPzL3OTOH/pY1NsR8h39VNB9gF8gPh5fxzfQlvJzfxsPRlaXXpDrHbw8/g8JI7PN+bXcfsOZ5AhwSCmM1tf4FTFiypqW7lxksVrICTuhEwCQDRmODi/sKpnJaZ47ws8DP7y/fV9ssxpEaWuM+am003jUXlady8MldUDayoz9GAgvKNkYMkXsebuPQD9jj3G5xTKYK00SVXXZSOUimcwUKf85kKkVHdJDpzA3bnQ6vVuT8RLQyoYDc9wYU3FMEoIdgAonSzuxwtvz0Dv17O/YYM0Z451NdfPoLA3zlW2M8/XgMysbQog1SOVg+hW4YNanizqEtnOz0RG0xZqdrtyHNNLJcI1pQaLREZyuS+/JN+3y+uBfg4LjASUMhAQASmaDT4OlRh1QlGMuRnatfsQ7zyHWOQueIQCAtQaoovSdynZfn7iIylz5R3baAhW48IN/ou19PCAMOpQ0EZ/cUlRdwhrzQKKReUlIAdj07SeTK+f1MpdDQCFmIVCWYyAkifoo1sAPnhLxQ58q5pNKYZqv3axGtiONomOFknOHy3vaFQqWt8ilcMRoB2PtEIfWZ7L/3Angzc9eNCf8//af/9Dy34z5eR6gSZEEMibc+MuuN1FoixCBaIPw1kn7tiN9p5veNkqC4u5rIu6x5W7g4OxLU4iFCJpBrud5UDsBIJtgJOtgPeytfZ4yaVyz4tAS32GBuRIDVSPpJ5ggOrNnY5NKj2Jnr8Ne49APl6AAnDkNO0m9cbj3ZsQT7fXYBUwQheqJd+hjYz9egIAaMQaQSRIIjMWZ+CU/zJiiZLhBRgLhS/NgPe9BG40Z6hJAMDGkIWo5+rIN36qcG0z7AewjYCLggr0j6a6ArhD8IrGxcOcltOxLuu7JZJJ/r8DdF8nnMFS8IMHtXgNtfW9vht8oCWjkTKARDLw5wPMoRnV5xOwdjbLrCeYLIkibvMtxmLUx1hrFOKh3+3XLUhDFurxHGy2KBgsElsVcSyx3RnXtgezm/x6ooxDa3EX0vZDfwjeRFfLj3njPZz3WRfLxI0BpZk7yvFg/DXz3vepTQjmb7UkT2/U9NUrwUd3Ejv4u3xfOE33sgdHgLIrdqhvoO/3B2f1jAI+EVfDN9Ca/md5YWUoWR+OTw9zFQE7RZjD/Z/2DjAomI0OMdHKshhnqCHVjCT0UK0vlSHF8V/Y4l/IOEcLFB0u8Jf1gzYtbmMVA4xQM3IN0QG1pBSXwnOfjoBCQTmKgHyqc2bWbFsyrZkvAD1ql/MlVzIwQkM5DMQWp+ewUJTOUEe+E+MpWB34O03s+YP/hA7Laj2U+Au3nz/hkVvwAg4jHGcoSdYLf8N2/Yt78z26+3PdbG5746xGCs8MorIzzeyWGiri0052Mg6jWmR3jcdoT/yoX6YnsQMLRjhmmqMRhJXF54Had6ZVUdBiOJwUiCCHjnUx3818+eYDCsl4AHLMRUTaDNhY0LKalKkakMIznCTri79n25ziCNRFdrq4pUBQQxTI1EptNGwu+d/KufL5g1MMx09oYj/ICV9t8rBGeYpBJ5A+HPC4Ws0Gg13AO84sKbbYYswqgYoh/snHpURjBmo/mUKT1IzhpZoZBLhV5787U6kfVPOBik2O0uj5qsg9xwnp4zG01oCzrNr1VaN45ZzF6zPhXgjY6Nz7I//+f//Hlux328nlAhmoIxaKnXGvZ5BCRKST9gXep9bFkVhfMD2HZ+HwCgi5LoNYIJ29nWqjG67zQImECXt3Asx2sJf6pyCOK4HO2sf6hrvUD43ey9I0Pk3LXrOvzhnRdBRkN2dpF3duZm+G3qQs3N1o08cJ+F7uK2RWDf6zvXHjKIsRd050cStCtSEEOYDNAJAyQ8h5QKgeDlgrX6llxLXAp3lrr3F8I+lDG4mR6iIIm4yetgASXhXyHp55zAYefxQ5dLXpX0F1piLBPsBt05wh8KjkAQkkwiCBii0BVJ/Px+3CmVFz6ST1QME830BBT3QFzYf6eZ+oldsF3RaHrk0g7qH4hS2WxbvsZArxMLjKYFCnfszwSvQaVbcELh1sRd1sIBTnCzOEBhJDgYdnkXSlnCzzkBThlCsOkPUmpwwXAl2Mf14u6SKd7izH6TpN/j6fgteCG7geezG/hg5zsQb5k3XodZh7+e8HePXwEBSNoXMBk4/xMx390HAC0iKB7iqSTHJwHcLA6hjZm7Jmcd/rjS4Z/dK4uoC0MMzCgE2RhFvHxvvhJcQEACiclwIAe45IiZNhr/dfh5HMgTRBTgT/X/WGm22IQ+b1vCXzXuMwYsT0AqL0eVFrHTIdw8MhgmtER+yxg14Q37lp9JM0l/alc520j6RwlIpjBRDzOjwRwmaN7XNLOfvx3h5wCKWWfdaFA+tTfiBcl6wELkOkOippCmOLVDf5opHBzbz37oij23faFjmuilRXaLt9HC2Xa9QhZiN9irNezb35k9U4OA4Tue6ODL3xzjy3+Y4PH32cItRGSLMDKBCVY/J24fOMJ/sVldt9MTmKZ5LeHfBt6d/+qlCBf37eecjGVt9zFkISZyjEylaDVF9FagjcZYjtDiLWQqRaKma9UBiUpsrJ6cwDABQDvFIMe0mKAfLBdxtNGYyDFEzbqMuXi+XlAfsfxmh899Xxxb9MgKDak0RIPZcGEK5CotRyUiFmEkh0jUFD12umPKuZX0S6XvSb2wClmuoFWzL0ETWpE4dZdfaQNp1s/TM0ZQ5XGv3z6pNF6+PcLlvfaSqeXc67Q5c9+v1xvO14b5Pt6YcJFwAMDBYWCcYR9vNOzzCJiYI/g2km/5NMuNRMyb/QBWwgAUrFmIM2YVAOeQUdoL2mVUUROkVpiqDJeCvu02rYPRc7GARORGK9zsuevw183wh7dtBGZ25TEQY7OF1CrTPrKJAIJcZJ6xkjS/2FOdvTLyDgB43EVP1Cx4GQOFMQJiaAcxODelaZExBgys/ExjDGDMvEqgsr+Xoh1cifdsdXhDkyRv2ueVCHXgjIG4ddkPnTNutcOf6QLa2HBCP8PP8gREQCsUkEqjHYpyP+oM+6RREMRnqgRv9uUKNMLF9flZZnR3oEQIZjTC5KRx25U2aIXrrxEhOLpxgKwmGvFUIOC1iKcRbOav4M8L7xB/Qey4WDLM0iSW3mv/+9viRyHA8Vh4be41oVNipG1bwGpyp/e4JHZx4Ywj+gZyHeG3cv7x/iPoukvs3W8hxOHy8c/jHh5KJUIw5KYojQo9POHvshZ4vizpBzHkjuQ3+xmwMu7wldz+FsYY/Pfxl3G9uAsOhj/Z/07sitWqJaAmmg+AYcxeG86LpQ47bbvvg4QtOfUXrsNfEv5aSb99Pkx1CkMMpNYT/m5oSeckNTBRF7PVHzUa93lsK+kHljvrpAqQtkWQxX32qQATOXFxdqcj/Ndv2/vR/o4oowFbESsf0dN0/jgR0blIXKtk3xizJOn3eOdTHRABNw4N7k7cM98VVVk2wSpobXDnaHWHH7CEHwAG43sz0Xv5piX8j1yLy1GBojBI0uU1iPdHSDc0wvPdfS//HsvVxoXaaCQqQUAMkAWMCK15n7axiolOarPgc50hazAp9PF8Z202+EYCA5AV9ftvjXObpeW5ylAYWRbriAicOEb3YN7HGUFqfWZO9XVY50vQhGqXf9tZfqU2k+lzRtAuTq8Jk1RiMMkxTVf7fhSFOndV47cb9wn/fSyD2bluwBrVGeMN+8K1BD0gAYraMD6qrCmST8u13aE6GK0syVxl2AdPaMVGnZ1t0eIhAibKSKhFSKMwlFNcDPvYjzat3NJyl5dxGOPnVR1JXiS2skB41xKG5Mpjdta5Kulnov6m6UwBGXkXevt/ZUWVcajO7myf23s1KgVy5okRQIQOC8GDWWyhNsbJtp0Jl5YImJiT889tEhGuRLt4INpBq+b3NUUGPTmCSWddW9/hZysk/YxsNF8hVaXDPyNfymhEPIQyaq7DD2MQhQJxKNAKZwtsb9gnK4Z9ymjE1UWSVoAI7fkKS6I42IzwE0H2L9nPG96t3W5//MSGcjg/o7rt2qF79FKZVV/FazHKxjiV3Vvv1J8Ye477GXqjrQ/DYvVdCCoXSo9EV/D/uPinl2bOvaR/smMLAUExBVPND34iwtvPMKLPGFOS3bqEDlISncENAMB47xF819sY3v4ILXX3PYqoBw7gYW3vBzeK+XPHm/Z1WAThXfqD+e7KLJqvebzhEefW/4qT9X9u8g08n10HgfAn+h/A5WBv5X571BF+MAGoAqvaKTtukwcJAxZMUn0sH7i9/9ZK+qsdfmJrTd4oH6NnbGLGJJsVvAHAcDuGsOrC8oS/Sc5bh/YS4c8Bra3qQRWlj065jaDaODsPlhyDitUk2Mv5H3pgVnQlomXDwtcQ00QjL+y9brc3X8jotgWeuGb/7Usvz+6DRsRAMQZWGNgdDQqbcBIQ9naaCyQl4a+Zt98UeaFx867dlkeuReCc0OvY7a2b4wesEd54QyO81I3qWN+IGFM5Qb7CPDjTGQqVIzQMZKRNNFLWvFJQYE0Da4oNibQz5nUJEILZ992r0dwbGZw3O/WPp3ljlxkApmpqV2eVB2vEYiRqurWBowcRwcDK2s8DWhuMp8XaWfomtCKBJJM4GW93zihtlgr8dfCqC7Vi/4eTDFmucDLOV0Zq5/LNbdgH3Cf891EHYqXBGLloFmkU2hvMbgWMI2AhlCNidfP7icoQsQD9DaRsS/CuzmsIPwCQCGHOocMfsgAdHiOtIQ7KaIyKKS4EfVyN97dIIDDAghSMKh1+Xs6qz984w4OXQVpCtXoouhdAxEqCbYxulIp7wu/4finrr3Z1qw70rVbdAt+lAIgQ4AEiMIQBAwNBahfzx2emfZku0OGtlWMhjBge37mCS51ZocQUKfT4EEZmoPYuTFXl4LqXfEWH30bzcTCyJm7AvKTfGGPTJYyGdv4T3uQoChg6sUBY6bLzsZ27V5WxB230vLTYj5JUCL8gBoXZ+ah2bOZ5PD6snS8ulIYQhGjDh20U2hGEfINupkdreAsPPfOf8cjX/4N1coYb7QaBXoOHH2NU3ms84ffwcXtNEYF2HGX1QsdL+rPOPqQrmgUNhnUe1Yi+l12H+7SY6BQSCgyEHlu+33UG18G0QhF2kbX38NgVwve/kyFsmMf0c/yP5/bv1Xg+Y8ws/k8z2KNDUMG8oqac419xHB4ML4MAHKshPjv5Or7mHPk/3H03Hgovb7j3VcJfSRRgAkwmjXJ+AOh37P4NEwLUfDSfl/Rr7mb4a+THnZLwZ47wq0bCzrIh+Pg2Ou4wJfnCApIJa9q3orM56/BvrljzJHvqZ+d9XBpj8PLrKqysP69fCBsDysZg44NGNYIxBq/e9oR//lk+Z1j4GsN393d6YtkJ3Gi8+xF73J+9YTBJ3e/CA5AswPLmLPNSzn8hXNkpPAvCf/12Bq2BnS7Hbs+ej7trPjd0YxrZGgJt5fwzmX3oXP5X5bjnKoOGBjPanvvEAbJeFjZ9wMrzqzDGYKrGEA0qTl9k2lSV8GaE4AxpoeajNGFl40muGo3tlFFI5HRJOSGYgNIK42J91KQ2GlM5XSoQkbFrhfNAVijkhUZ4yjHBape/2MJNX67o2C/BmMYOf14oDCcFuq0AaSbL0as6FBvEAL7RcZ/w38cSyDnEG6PByeW0G2xkUgcALRFBxp7wz3f4jTFIVI4L4c7Gnzd7r4YpUhAP5+TvjRDhuXT4ASvrVwsLMm00BvkEe0HPkf3NbpLGuekvmRAyXi76mJ8fXZjhn8n5H4che0GTv6yNsUWDOhAHMQbhoofK22Xlhid7M8k6X/BhMG5cwLv0k4gQGYNYhGCBfQB6u0RP1pTR6G2QytBthWiFAiZPLNFXBdjOAxAPPAm2cD7NOvyrF02BYGjpvCRBujLXbGAJuTKWpGt3blE2BecMe/14Vvk1prbDD2B+dk8re46amfNrUInmA1B2+LvJoZMDzkNKg1bYoNCoARGhEwaQW8j6vVlckE+wf+OrdtO9K/5r0eEnKtVEi4qfi2523BgDUUOAqVIsaILv8OdRr9LZXi3rtxF91gzv68kLa/dhFfz8fo93aruy3WM7NjDaf3gjSYWfuX8ysSTprjwulUapyaFdQannzicZtJaMzTbp8McsxGVhz+9vpC8CAD7YfhpPxA+t3cYqPOGf6ATS34uZgBatlXvCPLsAAQAASURBVEVb3+GfZgQp56P5vGmfJi/pb+7wZyaH8gYlWF4Us3QANr4LQxxxK4Zv0E2qtxMmQEqCVqgETiXpb1dItpvfNzxwhVgrv64iYAFSndY/V4wCjALJBHx6VPvcG4wkJlMFzoAHLi4Qfrct429Dh79uft+DZI4HOike2LOFv6++NLvgjQhtRF/DM7407Fsxvw/ME/PTSqtfcvP7D1+bPd/WFRK4G9NYl2+fqRSZShFV/EQECYyKYeP2TuUUnISNvazCNUBCFmLqxkM8cp03yvnL72UBJnLyhovnOysEglBIszTHnxUKRaEazel8MkJdvKM3sVw1KmGMwVF2iLvZHRQLyg7GgSw/nzGLNFeu8XB6qui7/Mejzbv8W8nraeawv4hJKpHmEp2WQKE0kqz+OG0aA/hGx33Cfx/LYKw07vPdBOuov9ncYMxCnFx7K4qdy8ivPDb3t4lK0eExdoPN42islPsYZnIMEiFogWg1wWbNn8+Dqc0iBM6tH7Dy60ExxW7YxdV4fyOH+RLetG3hPdXtZ66TPdfh1wrh3ZcAAPmVJ+wMPticaV9Th5+YneEno60szFnrVzv8sjvr6rNooTtptD1PmHWyp6iNQBlELIAIDKQ07uutFLvQEgHjaG2gEjFFBj0+gNEKbPcqxJUnwfcftOSezbsm0yYdflgpXsvYhZWO2qWvhE+LqM7XV6P5lo5bnoAVKQxmCgi/+AkqC3Hjf9PKtkZM2KKCg9yxhL+VHEPmxRJ3NQZbu9tGoTVL3PSsr6YEXLjxFYh8WrribyKpu1eUdRQA3UqcXkii7IibFR3+lSRZq1LRUURd5LFVjazqbHu8LX4UBCoj+k6LaiTfEowuCf9475Hlv9cgjyzhvzIdo8ta0DC4XdgC1MTJftssQuy6xTJcHpvKNujwA8DDlU7+O1uP453txzfaxipiChG4mdVRtaMoIqyS9MchwU/RDCe6VJ8Asw6/YjNJf//uc3j8C/9vBE59E1IA7pY3U5MDUPMdc2Ms2Z/ctYZmIrYFM3cKjuc4mN3OJfJUwWkIf7tlXztJFCAzu488sN1Yo6yqoAJOHCGLamO8SCuQUTBBBywdgtX4M3g5/wOXoqUC2raSfqUM/vsfnJRGdfcC79BfJ7snOQGMwnses8fq6y8bFF59ISKbalDUR7JWO/yr0OsIEAGFrJ+3XwdjDF5x8/uP1hD+Jkk/YGX96wh0opKlMY6Ix0hVWkr9q5BaItUJAhZY88nyuUSAsedw4OT5VSl5plNII1c6xocUINfZH1lZv+AMUqml7PcsVytd5TOVNo7ihE65M1X14zjGGBznRzjKD5GpFIWZvw9xRjMj0zNGksraZ+82ICLEW3b580LVHktVU9wjIuQNhH8wTiG4VSkLwTCY1J+3Pgbwfof/Pv7oYS4D3k6Eb2LY5xEwjtHVJ3Dy3X9xaVZaaoWL0c5aLwCjJHQytMRP5aDOPviVJ8AfeCt47+Jm+8EEzst2M2QCbR4i0zmMMRgUE+wEbVyLLyDYNmJFz8jzHIjBUzdvUlg17QsOXgGTOVTUgdy9YuXNVJnhhwaJFb8ZD0DGWO7rRzgqhyutzPDTYoyd1nB6ffv3sAUig46IQcLOYyptypGGTBeIWbRRDKPJx2A7VyEeeBJ871pJ6stjUolOKTv8MofWzQ+TVsixy+yxq8r5pdEQxBHzoDwGegXhF86wT7d3yhEMhZpIPuNklGY2Zx4wMcfEdasPHUQgo7GrRnNdfqltnOGmcn6PMOAQgjZ+sMZTZ0DIQzAtcenlP7Dby3B+1W6tyk4TY9aMzxiDiMKSpF0Qu3PKhrptIebUKQ1r5SCfgGCgiUMFrflIujVo8xhvCa8CAL6RvLjFzs1jlUN/a3wXQqZQPMS0V59lvwjf4Q+zcWms52X9M4f+Fnhu/7usSTPxxyHIpytJ7OPxg9jlXTwdvwUfaH/HRtu3CCKqn+PfAL7LP0yo7HbbCCb77wXstkcsQO/oBYTZGPHhy+X3lnP8pgBpA/LjXZ7sjw9s1J6YFSE94S+l4w6GqNYwFQCk1OUM7WlM+6Q0KLJ8Jr12WOzwA0CLtxo6/NKNEQnoIAJLjpbm+b2c/8Ery0XXmdpgs/vGSzdSfP3ZCf7T7x0hSe9NFdDY4TcKlI5heIi3XAF6LSAtgJdu+9+GwRCBZaOlCuc0URhN7Hatc97nnNB1+38aWf/dowJJqhEImlNO7PbXjwoELESmkiUS5+Fd84OFrjsna6Y8kcvXVKYzSC0RgFtFoFuPGMZLM0hPPBM5KxiMi/FaM0juitbrxhDerPDPpMUO/zQrGkfgjDGYynHj+pmIVio2TvITHGYHiHnL+XjMn0+CM2Q1Ywb3irxQOJ5kW40pNaG9ZZc/r5HX5yrDneT2EulnjFDUFDySTGI4lWUkYxxyTFNZr6bcMAbwjY77hP8+lkGVDj8RGLGNDPs8AhIQjC9J3scyQT9oY2fN7L5OhjDZGBS2wC++BeKBpyAuPQrW3rVd703BeCkXPmsQEfqig0IrnBQT9IQl++tiC2uhlZXGL0n6K0Zlbg6XVRadkZPz51ceB4igjQFHxVXZYGUHlLiwhB8EqTQINHfDm0Yx0ge/A+23/rF50g3MEgD8NosQACFmAoFgJen0naRCK/REazN5ugEo7pbJBHPbzHg5bgKgnOEnAKah0wNY07dYLUfyKaPAiaFVkUuuIvx8ZDuI1dhCpecj+cptFQLgrOwsLv69aty3WwwgpS7XrTZmhyD4dg9bzgmtQJRJCavAVFE6td948vsAADsHzyKeHNjRkHuo7Is7L4H/wSfQ+fz/D/3P/u/Y/d1fw95//Z9x4RMfw8Xf/iXs/9d/B8qmYERg7twlonKO/1I195tMfYefWff+JiMeL+cvoi5AVOnwryf8APB06y0AgOez6/Pd6S0w8IZ9NYS/e+Tc+Xcfqk0yqUMRdmAAMC3xELPHyBv3jbU37Gsh8A79NVFyWkSQjuSuOhZtFuP/tvf9+K7uO+7pXDgt4fdz/IMpK+fnZ50sA4mZpJ87g0JRuQfMCH9mr0GjHdk/AZscwHAxR/YBoBvb75wsNq6ZsDP2NSaOae6LV9gqCzsQDKGLQZ2OEpteUILmxhjWYVYcIIBHVhUzOSrNCpUyuHmnfn4f2L7DfzK0n1tIg89/bdnwc1MYY3A0rO/wU5FagipCMCI8ec0eq2dvVq53EYOK6cz/wMHL+fd3xEaGY+vm7VfBu/M/9EA050Gw42b5h2PZSMYCFqBYIevPVrrmR7VScP9ZZKzqo3xGE3NmkL4AHWKqJtBGOzl/slLO7+Fj/V5r5DrHcXZU2+U9L9SlMRER0gpp1MZgksjG86zQOTKdLxVtqoh4jEQmS4qNYTHAYXYXIYsQshAEWiq2cEZQ2pz5HP9wmiPNJOINUoLWYZsuv3Ku+0uEX+dIdbpkVskZLSkuAGCS2phir5IMBUMmFaY1poubxgC+0XGf8N/HMqodficR38SwzyNgAgH4bGYTVtJNIFwIV2fSG6MBVYBfeBj8yhNgvYu1xG+z3bDGdKZmkXYWaPEIIRPo8BgPxhdqzaM2glHOTX/huFT+tz8G3BN+rRDesfPF+ZXHYYyx2eXVogHR0vzuHHgI5gi/hi7Jl4c0Cuz9fxZ7H/7R5fdq7Y6v/T7yxn2GQTBeLnw5EZSTzW9yDnkpfGNhh5jVgbtuHTE+mwXOV8tLfYyTjmbkSxqNyBWzGFkX/Vk0X3OHf2Ukn99UEdnj4xbjnvBXFxCe8HeSQ8QBR5bPOplxLE7llB9HwmUurEY0sftShG1M9h7G4OITAICrL/++jcE77bPPGHS/+B/Bn/8KwlvPITy6DjE6BE/HIGUftixPEBzftB1+jnK8es9FvT3gutdlL68uaILse5vksCXhdyqjcnY9HWw06nM52MO14CI0DL40/cNN9nwJqzr81Ti+jcE4ZGgJ9CPK3m9O1BgTlZaS/g5vgRc+kq++uDpTO5x+XGFT9NkpCb936k9n0Xye8IccyFxXNKQAwnmbBLKG8PsFslFg6bEl+yJcIvsAGiT9cMZ9Ra1xX5rO5PzbFkZKoj3Jgcrzw0YJro4CnIOWmFOzhW1Q4ef5Ne4c5iikQRwxXNhdfk51t+zwnwxnx+Hrz01ObXg3migoZcAZ0O/M3/NZPoHdJ/sMe/Kq3b+X7wK5L2gyYQvm+fy5Vcr5L262binl96eI5vNjDY9cnV+ndFoMgttxueGk+XMZWG2nHgBSmTS65nspeFIpRtpu8gSChPWc0HKe8BtTeh6ELESmstIjoBoZtwqBi/VbNXOeqQzj4vSFoEVM5AS3kpu4m905089dh6P8ECf5ydy/iQWn/rxQyArdaNjnFRerRiWsYkPPxS2OixEO0rsQTCByayfOhB0PqDy/BGdQSjfOsZ8GSmscDlJEAT+zWM5Nu/yqodue69yOnejlkQap9FzhXxuD41E2V4Qht74dTZfvq0qbjWIA3+i4T/jvYxnELPl0F4Ag0WiwZ2S+RKg5MYRczBH+sUyxF3TRXZdJnyegqA1q7ywT4G3hCekWnZJtELMAl8NdPNS6uLUBYRVG6/qYQZqZknnCz5SEUhLB0Q2wIoMOYqS7V3BSTNBiIS6ElRhAY1YSfuJWQM3cA8PWeewNTxoFThytJiftskjhbpA8BIkAkTEISYAHBCE4iJGT8weNcXxz8AuUpuPJmJPKz845L+unvLnDDwAsdYR/ocMfswCCuDPu02s6/PWRfNVRBXs9EBDEtiBRjeZbUL74OX4xvItuO4RUuuSi0SmdcSPB7CJArl4AxFOrVsjatnhx9+EPQDOO7uQudk5eOtV3AwBLxyBZwBDD9O1/HKN3/wAGH/hzOPnQX8TR9/1PyB54EgDAJ8e2yARYYzUA39N9N/7MzodwNXAFFZcYUCe1Y4yBCM0dfhc1WEQ9GNj5dwMCVwX4CjVIFe9rvxUA8Fx2vSTvmyLXBRJHNhdn+MPkBFE6hCGGyc6D232uk/X386SMLrxZHJSS/i6LEXhJf9BA+F3xI3otCD+323B6Sb/rTALIc/tbh8Ig1zOXft/hDyrxnL7AOHUdT5aNwCaHMCKyMWU16DZI+sE4SKla4z7f4Y/D7Z9XPppvOlVAdfyKsdriQhNI5ksKARN1QOkQLBvN3PmvRLWL2k4lInATabCfS49CBmOAz375dOeRd+jf7Qfz17gqgGJiCzMOF3rATsemy710p2LexyNr3lc5XjPCv8EzB6d36p8mCgfHdh+qhn2AfZaWnzts/tyQR0jUFMXCuWWMwbhGzl/9/MUc98IUyHUOwQJbXDVAWQhi3Hb83bqMEYOBQapSTNUUDJsVrAIKILWsjZIzxmBUDHEzuY476e3GQsam0EbjODvCreQGCl0goACDYvCadPmVURjL8VxBBZhJ6JVrOmSFhlS6MZJvqqYrm1weEbPmfYUuMJET3M3ugECIK2a2gqzRo6z4e7Cyw392atbRtMA0lWjHp1CsNmDTLr/SZqnD7wtZSitkC1Gcdv/nk1WSTGKayrk4ZcDK+sdJAblQHFFKvwaORd9+3Cf897EEKk377Ax/xEStYZ8xGmZ8CNQQrRaLSsKfqhwBE9gP+2sfKKZIQZ39Znf5bcDcjPk5EX4iwsVoZzMiuwpG1TtWVzvGQUVyLtPSnX96+VEMlS2mPNK+gp5oNX/G0ucLkFNwKJf5XkboqQLRCpJujJ4j5UQECtsgrdDmMcAVIsHAiZAria5obxZRqJXt7jd2+HmpPin/yY8brCX8lrBVCT+MVaRw4uBkVSmNhN8YcN/hX4jki6vqjlKlEJQdKMB2+DloLprPd/jF6BDtkBAHHJOsgGCE4JTOuEIwxAFHvobw+w5/2rH7IqMODq+9CwDwwCufs3FopwCfnNj/0t1F9pZ3I7v2VhSXHoHcuQLd3oHsX5y9jixx9z9nxAJcDvbL+4QyxilPlr+HyBZRmvhJVdKfZAqjXJexdpvK+i8Fe3gouAxzii6/l/O3WLSk/vFy/kn/KvQGEaNVFM64L0hHeNDP8Rd352f4N+3wb2BgeK/oO3XDUG/b4feSfrJkzqiywx8EGhL2uoqMAHfd8KBIyoKZ7/BPHDGhbGzz21fcrztNkn6QLYDXXBOnieQrv88RbRtTXb3e2VIcYSOMsQR5aWSIw/AAlBzh+k17bjxYI+cHgFZsi2fGzPan+etMSYz/+Ad3QQS88Gpakuxt4A379hfk/Ewm1l9i4Rnju/zzsv7Q+is4BZdSBnePNzPs8zgt4fdy/kv7Adrx8u+/qXFfoYslWX+mbed9lcx+luNuSVCuMmu8R2K5CETMKvMqUcUBBRjLMaY1kXFN8Pfm6vw/YAnyQXYXt5NbpXHsYXawJMHeFIUucDe9g7vZHQQUoCM6zqwwwURuV3w9DVKVIlep6yrPfr+AM0ipy3tRmtnCSt3aVmpZG8dXh8DFLQ6KE9xNb0Mbg9bC+CsnDqnnCT9gi+JyzfN+UxhjcDxKra3UGUvcfZf/ZNx8TljCP++YL41EYQqELESqkjmFAyeCVroswADAeGpJ/WK6QBRwpPmyrF9qM+cN9WbFfcJ/H/VwkXCcGB6I9y2JW4RWQBDB1HQiQmeCZmP4MlwI+s3dYgcjc4ALsIUIuHvZB9thfe2zhbeCMbUSdnJeCsZoWwBxrzF5Us7vjy89gmvRvlUZzHWZrYHfSpUEMedQz6Dh5//tnwot0Rft5sq01kvRiBS2Aa3QFhGIgFbEYUfQDTrrlB3l59povMbtZi6+sKoq8R3+YjtJvzFW9m7l/ITIxeaVhH9B0s/SMZiynevFuMn5SD6XjsAFEIR2TAGWnDKa7/BXjfuCyTG6nRBGe/O909+e40g0dr7L1ziH/rQzG084uvou5EEbQTZG68Uvneq7+dSSSNPbq/27cmaQfHwMABCsmbTbaEk0ng+CU91YNQAgyHyHvwutDQQxZFvO8QPA+zq2y/98dgPHcnM56Wo5v3fnf3jjz/PwRYsgG+NqYAtGN/JDjCuSfp9OUOfSD2wWzXdW8DP8ic7KCMFN4Dv845RglAK0LCP5gnC2YGypGSkOZALlzvuZpD+FCVowQatZOeTQKOmHMz2Ty39ITuHQX36fl9LnC+9lHIDarFjtHf3rzPxEjCxVuHtsj/vDFwAqpqB8YhUP6QAsOQFXSUlY10XzTRONQtoC8aPXYrz1LfaH+syXBlvHtXnDvr2qYZ8xtjjDOBZNd594oCLrL/x3kS2sJnZU5+A4h9b29+h3NyvCeGK+at6+DqWc/1r9820TbwAia1yaLMxvJyvk/B7Cmegl0l7viUpAPl9FpqVhn/sm+/8qRSQ7FpChWDNjvoiABZiqKbS7+aYqxa3kBo7zI0Q8Rou30OZtpCrBcXZUvm5TTOQEN5MbGBQn6IguQqfWYcQgKMAgP/8uf+Iy76WWc0ULzglSz6L5xmlRGxsLWDl/oYuNjq1V0wYY5CeQWqIjlpNdvCpjUdYOMlvl3K/CJJUYTHJ04tOrVptARGhFHHeOE0zSeqNK332vFlAKnUNqiYjHKEyxrHAwpjROVVrjZJzVeg/4IsJ44btloVdOv75Z8EdgF+/jVOCifHg3OqsraTvTNYQ6JAGQjeFrsQj74foYPpNPQa0+UGM0dRoQMTd7+dpnC28FwsKD2f+7m1f3D0uXYR/cfQksT6BFiEsPvgeX4t1lYu5N9VaZ9rlIPTu3b2yEHvxMNKFdM+M6Q00CgDPuC0mAM4Z+N4RmGiETa4s95WYrudKzwRdB6jr8awm/k/Qr1+HXsCkCfm7REn5VzvBTNr8A84Z9qrNbKif8sZoz5CtNGIWd43eLdqoUFSo7NOvyD++iHQm0Io5WnVGO1hAntzaaP48CBs7YknStuo1RcgIAyNoztYLhAq9efR8AoPX850Hp9pJM3+E33d3av6uOLQSIyTFgDISgRqKgF5Qni7DFgvr3hq7Dn0c9gAyCgCENncv9FkT3gtjBo6F10f/i9JmN3zdsiOTjeYLW+A6AzeP4qvBO/UE2wuVgFwIcqcmQGrso7VAE4a6FRkl/2eEfbnQ+3QsiFiByDtXbyPo7sTVm1AYYJQak5azDH/n5fQFRkXhyLcvo0irhBxP199gFeEn/NMMy8WMcKJaN+04TyVfdRwCYZMuGraTmu7FNIK3s62qMH7UxePaoBQNgr6PRL26Cn1wHH14HG94CG98BG98GS47RaVdiAlfgZGSPfb8rwDnhA+/sg3PCrYO8zKPfFEc1Dv2kMpBMgBrPl/0esNe1S44Xq7J+EQP5FCjSOTn/pjO53Ta355peX/DwUMqUoxKL8/semyoHQhZZybJbqxhjrDv/BslIIQsxkkPbTVZTSy61tJ4TNQq/avoDZ1YizqpmvxsgYCFylSHTGYbFADeT60jkFF3RK7PmiQgd0cWgOMFgYQ6+CV7Cfzu5iULn6In+UsEjdl3+6T2OC6yCMgoTOS4jMKsxhP445VJBKo00V42GfaWB4obHtsVbECTQEc3rZQItjX/4MYOzwMk4hda4p4bDKrTjAIVUuHEwqS1SKLXcbfcFF0ECUsu5/WfM+mQod7+epBLTTDYqrqKQYTTJ5+7vuVRvesM+4D7hv48GLHVR66Cl7fLWvC5gHIIEciVdDN/qxZYxGtAKrLN3tsYZIoB53Xf4US+9JwaAlQtyH83Xu/EsAIA/8Db0mgopfn5/VdnSKSCYE+D5G16mC4RMIGYrCH/NNpOIAC4QwSY15Foi0wW6vLV5VKHR9nNWgYu537SM5subzWBI5mBuDthL+q3Z3sxdP2TCehpVO/xVcxwn55e9WUdcQYMTLUfy8cB2bniAamZUyMRSekVJ+Ad3wBjhwk6Mdk11vfPN38Xu//hf0X729xv30yPgHFHIG936o+QEZDQUD8uOscfR3mPIepfAVIHOH35m7Xctgk9PAKzo8Lf7zpRMgmUTcKJGzqkN5pQni2C8/r2kZenYngZtCMYQRxxTT/i3lLK/183yv5TfwqHc7L1NDv3dk1dAAJLOBcjFuMsNkEd+H0bgxHElmBVsBDjaUoFgYEBQDcWzPOrZSDMtSzXAtmgPbiB0RaN1OI1TPxGh52q/w6mN5vNdXR54h/6wLG54MPe7V037Nu08tyKU0vYlbycm7Az0gqLtngh/aN87yRZPcAZAb1as1tIVeDmywuDluwaffUbj//yswr/9hManvmpf9tBFBhO2YeIuTNSHiXswUQ8m6oKKdJZQsIbwesM+T2a7bY53vdX+vp/50nDjDrlSpvysqqSfigRQqrZAQ0Rll39O1u/jYVVWOvRvKuf3n9vvbifrv3k3g5QG7Zjh4l49Md9E0g/Yjrl1yrfncqYtmd5ECh6yCJmyxDtXOQIWgHQBajiGi+dvV/TQXkEw68CJQ0HhJDvG7eQWCIRu0F9qPHDiiFiM4/xoLUGvSvi5I711a0FGDJw4BvnJ1sqBTZGqBLlTPTDiS+MLjIA0l0hzhaJQCGv8dnzRRmxh5kxEiHi8cg3MiSNdGP/gjJDmamuFzSLSXOJklNeOp5wldrohhpMcNw+nS/cLpfXS8z5VKRixmcJhKcbSlKaF40kBGIA3JN/EAbcz/lnFeFHqN30kH3Cf8N9HE5hY2/kxSlrZcs11IkggII5+0MFOsMGi1pv1xds9eNaBePi67vBbV3onU1+Ed9l3DzXf+Y4d8QwefMeqD3YKgVWEn4ExAe6k7T5SyJL0eH0M42IxQQQgEUBojRaPUBgJbQw62yg2CM3z++XXBvPnpuvw86KZ8Jfz+yIqZb1SKwgSEG4/PGnXoT3OZMycaqCc318w7OPEEFQIv9FqZsK4cH3YosL8dSV3LtvvH9p4tUDwuXgnAGDTIeJXvgYAaL3wBbBkjbScgHbI5+baqoir8/s1i4ujJ78HABBd/yb44O7q71oAnzhJf0OHH4xDta20no+PbX5xU3ymsZGKTWAN7/Xz+4oJZBRCcIZ2JFCUUvbNJf2ATQ94PLoGAPjCZLMuf5Okv3TnP0V3H5jN8It8AmiFa+HF8m8dHiMovGFf3FzwY6xSONhe1h+kQzz8jd/Gw9/4j+sLw9ic8N8pjvH10Yvl//ay/kHCnKTffhcPKoZ9cv66F6ktYHjCr6FLR/91YERou3rjcjSfNeAkNf9ZaWafL61TEP5uaN87qbt1GVu4WgfSEi8eMPzapyzB/63f1/iDZw1ePQAKCQQcePgS8O7HXAF58YHtjG07kVswr+3w223ycnUAeO939BBHDIORxDef36yANBhLGGOjDP1oA4x2XgvNJOkJN8f/yl0gK6qkn8NkU9za0rDPY9s5fi/nf/hqM0Hb7dvPTFKNPG++TjxR9kQukYmNjN2gUG7fS0jVbATAnqNWfVaFYWzp/OXENzKVW0TIIozkEC3enjOWW0TEIxgYHGYHS51pj6mc4FZFwh+tSfSJeQvJOXb5p3JqhZfEEJBAptO5EQLBGZJMIcsVVEN+e64z5DpHuIFKYxtwEsh1Prc9fsxAuVGDaVpgMM5wcJLglTsjPHd9gMFkvZfCcJIjKxTicMMGzSlBRNjphLg7SHAwmC+m5FLNPbaUUchUCuGOIwNbMu6DoXLfTybZSj8VzhmUNkgc4dfaQCp9v8N/H390YWfK1yzkjAYRrzYwSzAi7AVdXI52NjJrO1OzvgqsAuF8Jav3BNeZqa3EMy/pd9tf7dbxAPzKE2s+l+rnOsvPtwZ4voPqI/k2JumLHX5ioLADowp0RYxMSQSMo7XhbGDpbr+G8IOLWpd+tpLw1zn0W3d9v1gTbpZfE0H7VISKcZ8YLRv2+aLB3DluVFmcseczlUkWgvhSfaxq3Nc0s9t+7nMgX/jRCp1v/V7jvnqEoUseqOm4RQsO/XMgQO5cQXr1KRCAzjPrv6uEVmCOTJtufYcfmMn6+eQEfEU3Q2mzVPyooum9s0i+HpS2Vf1QcGinOgiz0UZEtYr3tO3xeLW4g7vF8crXaqNLcjtH+I1Be3ATwOnm9wFABTE0EyAAQT7BNTfHD1jDPq9saDLs87iXaL54cgSCQZBP0RqvLwjNCH8zGZRG4RMnn8V/Ofo87hYn9n0dZ9yXMpAsStJErsMfUgC+MFfvExg4McRk7z3TGkfxJsyc+hf/YkefFo377qXD3xX2S6YZlq9Tos2c+rXEZ58PceR8zPpt4K0PEr7vHYQf+zDDT/4phj/3nbw0QayDEQG6gSUE6zr8nhB7MgsAYcjw/rfbAtIffG2Iolh/bR1X5Pz+HkyqAOl8pbHifo+w37XqnxduV44ZDzAe50hS2yG8tHd+hN8YU44vPNowvw8AYcDQiu15sa7LL5g10NNGY6rGJcHZBDGPMJXTmfxdF6jtxBC3Bo9nsCZq8Rb6wc7KuDmPNu8gUQmO8sO5greV8B/jVnITeYOEvw6+2zsozr7Lb+X8EwRO4SiYNVXMKyRTCEIuNcZJ3kgUM51tXLTZBta3Qc4ZCQrGUEiF564P8MwrJ3jm1RM8e2OAl26PcDhMMZrmeOX2aCXpl0rjYJCeynz0NBCCoRMJ3DqcYjCeHdu8mCffhS5Q6KI8zzgTS8Z9xIBCakzSAmmuauf3qwgDhpOxVX4praFN/dhgqqbIt4lHfZ3jPuG/j3oQofaBMfca2LltxktjsiouRjvoLrrG1+DMzfqq4AJr9+PbCdfhb5L0U02HHwD4A29dMs2bgzGzefcmkJP0u1F/RoRCryfpxqkH6lQJFLUArRCyAIIYYhY1e0Aswrvbr3lALhajaKMO/zLhX3TXF6VTf000n9bgE0vy6iL55jo81XEHxsvOIICyMDC36Gn1SuM+7xNQBR8fI7r+LQDA6B0fgQEQ3XoW4vhm4/4CVikQBKw0F6oiXnDor246YM+H6ZPfaT/n6PrGSRcsGYGMgeECaDWrdUrjvonr8KO2bggDs7Lybt9LS+8N0plDvwEgAgYQEPZ2oIhbk8RsO6fnHd7FE9FDAIAvrJnlH+sEGgYCHB02u26DdAiuJTRxZO3mgshKEM2M+9IRdnkXLbc47bCZYZ9qmN/3KI37Ttnh9/AGhKvgCf9oRYf/hexG2Yk/ceaI/blovrzs8JOwr4toFsnnIYpZUaFVnePfEDPjvuUz0hC38+UVnJrwqwItnoM5hUqy2LSq6cbWIU8zHIzsNfJ//z6G/+kjHH/yPQzveJThYp/KQu5KiAhdV0RZO8M/XO7wA8DTT3TQ73IkqcaXv7X+2vIO/XtVh34t7SD9GtLnu/zPVWX9XODWkf0tLu4FjUZqTSgJ/3g94R+MJEYTBcaAB6+s7kZvYtwHOAM9J81PVYpoKxM9+9qIxfbZX6T2HrwIYk7x+NqOORIROryDQX5S5toXusDd7A4O1kj4mxDzFqZyuhSbd69IVeJMDF1H2cnIq8Z93qk/ySSChvn9aTHZqHixLThxKKPmZO1CMAjn2SM4odcOcKEf48JOjN1uhN1eBG0MXr0zxrAmhx6w3f0kl2hHmxUolFH3bJwYR1YBef1gUnbciwV5faHzOfNKO8dfoKjsP2eEvFAYTnK3nl19HsUhR5LakQypDLTSS80DbTSG8mTJTPONjNcd4X/uuefw1/7aX8N73/tefO/3fi/++T//58jz9RUWYww+9rGP4SMf+Qje/e5348d//MfxxS9+ce41R0dH+Cf/5J/gx37sx/DOd74T73vf+85pL94EILayCmwJPrMklO4t+u6szfrmwDjqqcTrBKXBWw15Lg3qfId/trDg155e/bmuw08rJP1E1t2Yg6zRMSNkulhP0n2Roq6Y4ObvQxKIWICdoL35Q9wR/nVO2vaYVP6n62RymTUauDFH7nRlZtoACCqLIkEcgti8U78j/Hw6AGkFwwV0qz/bZGPqj5UvWnAxR/htUYHNRfMtGvctovXc50AwyC69BdnDb0f2kP3tO9/83ZXXqE1KEMvGfcYgqnHo9wfF597r9g60CEHGlMWOdfCGfaq9s9IwUnVnHX5GVqbXNH+4SgHAmZ3xX3yvd+jPw67thDsDoijiyCPn1H+Kzrbt8hNuFAe4XRw1vm7gYqP6vDN3/vtkhKy9t7oYtwZe1h9mIxARHgrsWMiu6JaEt2hw6Pe4lw5/OEf4X177ek/4BysI/7fSl8r/7pUAO64rPZySjeXL3XOGuw4/W+7wB5X/3XGFkO06/G6Wve4tTAAym1OHNMbyrekMkcrBtEQ78s7Riy/YoBtrNG7clQAIux1gr3va4jZD10n6V3X4pdSlqV21ww9YWfEfe7c9p770rTGmawoHx7WGfRKbPK894X/1AEhz93piuDWw/76tnB/YnJgDwMs3bXXm6qWokfB5bKoc4MShoZHIBNLIrTvD3aBnu6BG2Q4/1RN+cn5JrzU4E4hYjJP8EIP8BLeSmxjkJ2jz9loJf+3nEQeBMMi3T4dYhaqcv/pdVeLHOYPUGrk0tfP7hS6Q6GSr5INtsTge0W0H6LQCRCGvnV/vd0JIrfHKnTFGC6RfG4OjYYaAs43XayfZMQ6y7cb96tBrB0hzhZsHk3JEovrMz1TmcycAOMJv1FxSAWOEzBH+Vk3BYiInc0qQQHAUyhZslDY2/nehsZDrDIV583T3gdcZ4R8MBvirf/WvoigK/It/8S/wd//u38Wv//qv42d+5mfWvvdf/+t/jV/8xV/ET/zET+CXfumXcOnSJfzkT/4kXnll1n24ffs2/v2///e4cOEC3vnOd57nrrzxsW4xqhXAhe2ucn7qB8i5mfU5EOPw0XavSxgN8BVuwhXzxLLDzwT4A0+t+VyzkSs1RAABlIEAhVboidbq36L0B6gpUogQ4AECY7AbdDeP4wNs8cMT5FVgfF60UenwN8n7Zh3+WSQfAXNme4zImeqppWg+P78vu/Mz7wZmrmhQqh/8vzHhoiF9h98R/gbjvmBhXp6PDhHdtPnv06f+GABg8tR3QfMAweAOohurO81RaJMYqoWQapfZkz4PbQwYXO49EZQzKPTjDOvgI/m06+A3oZT0j4/BiMAY6os1BvbEbACBXLz0IuG3hDsLO+CCEDgfgIBzFO2KQ/2W6PE23hpbKf7nJ99qXGiW8/sLZljx1CsrakYptkAezzr8APDBztP4UPddeFv8aCWSb12H3xY+oi39DID5YxclAwRrigZ9Zgl/ZnJkNXO8B8UJDipmiH4col/O8BOgFQpH+A33pn1BqeyR7v4YFElJGdv30OGvJfxcgIwsO+/GGKT5coef8gn48CZYctI4OkJOItz4fZt0Y43CDXdpXtu/t+dnp23vWZOk2fxrMLLHPwqpVvr72EMxLu0HkNLgD762+rw6qonkI5XCbNAV3esSLvSWZf23Tux7H7i4PYHsO2I+mijrFL4Cr97y8/vrv2dT4z4AEBQg1clG7vxNIOUM+3jNcfQ+FN8Gwg/YeX5lNO6md5Cp1Er470Hy3hJtTOT4zLr8Uss5Ob+HoACZmp/jJ6DsqC+ijOM74/l9D058TnGwKXY6IaRUeOXOGONkdh8eJwVGSY5OvNlvYYzBVE0wKcZLBoLbgoiw2w1xPM5w62g654lgY72nc8aHRNaStlrw4MzO8GeFXkpMKHSBo+xgye9BcMJwkkFpY5fLC+uMVKWnOsavZ7yuCP/HP/5xTCYT/Mt/+S/xx//4H8eP/uiP4qd+6qfw8Y9/HLdv3258X5Zl+KVf+iX85E/+JH7iJ34CH/rQh/BzP/dz2N3dxS//8i+Xr3vb296GT3/60/hX/+pf4Xu/93tfi11642Id4VfSkhoRWQn2aaU952TWV4Jx243+Nj3g1sEavK14KDBeFivIyXj5lSfXOtkbo9cTZ1gDPOG8fTQZa1q1rtqudaMqATwEiQCkJK7Ee4g3jOMDAKOlPZ/WFX6IzcW2VGf4dcPieDGSTxoNwTiChS5IxEIr6Q/nO/zCR/JV5Py2aLAcyYfKsSEiQITlyIsgBkZ8mfAvGPd5tJ/9LAhA9sATUH1rzmaiNpInPmD//sz/AGSz7DcKOCLBkFe6/J501nWZjcu99xUVP75QN2pQh7LD39lZ+bpS0p9NwHVhwyjq3L1pTYefkxPB1BP+VHQRcj57mNOsGHHaDPp3t54EA8NteYSbRf1xaTLsi9woRbYwSrEtfIff72fEArwtfsSqVErTvnUz/LsAAJGPNzKHq8KPAUinyuqerJb1B0ygRfa+Uifr9919P5rgX9N3IgWpgCRVpaRf85mkX7iOvo+XDOVsvrPtCo7TLRalqyT93uDOz9ZnuSkb8HHoriWjwdIBSKZgk7tg06Pl56MxoHwKw0Ulmm85BrAuFWBuc7TCjSN7bl+7txoS2h2bKKL1TLWwCB/Jt9urf2YREb77vfba/+bzUxydNGRtS43h2B6T0qHfaJcdv5kMelHWn0uDg7H9t8v720upWxFDEDhFyQpZv5QGN+/aYs1DD6wvaG+jHIhYiFSmiFYl5KwB6cIVmerWcO4++G00Mu6ILmLeQjfo3XOTx8q8CcMz6vKnOkWh86V0hMDN8RdVWb9g4FQfZziVExA275ZvC04cmUpPtc873Qi5VHj59ggTl0d/PEytX/IKg9wqvCFhrnOMiu0LxotgjNDvBDgcplBal749hSkgawonDAxZJSqRuw5/IJZ/j0ylmKopJgvPnTgUGKcKaSaXVFQ+YaFpPflGxeuK8H/qU5/Chz70Iezu7pb/9sM//MPQWuN3f/d3G9/3+c9/HuPxGD/8wz9c/lsYhvjBH/xBfOpTnyr/ja1yLL+POZAzdGu6oVhyFlrJuAhPHX13XmZ9JcoZ6tfphav1SoMiG49ofwPxlvcj+I6PIHjPn1n/uUZvdExJBOBkbwTSFIhYsJ6kr5rhZwwIWzAbzJ4uQak5n4LGbXYdfn9u+hl+phV0A/nlmevwO0m/MgoCHGJhH0KXTuGj+Sir6fD7za2L5Cs9GSoxUyIqC05EhIiJJSWC7/DzinEfH9xBdPsFGFA5T++RPPpuqFYPPJug/cIXmo8VAXEsICvxfHEp518mncYYgM2q3WWHf3y2HX4TRKWKIpiegGO5j+l+3XLGvw7kTCcXl69e0p8EHUTBwnnqjPvWdaWb0OEtvC22Dvu/M/o8PjH4LD4z/hq+kbyIG/ldjNUUJyXhrySUGIPYmSWmdWaJW6Aq6V/Eph1+JSIoHoKwXWoBk3kZhXd85TsAAL2jecKvtMHJeH7EpsmpP9MFns9uAADe33lr+RpjrGGjN9EbJKyM5dNUkfS7bSmcaiQoklLxcZoO/0pJvwM5uX7qFAdBQOUilYopKJ/ARF2YIAabHoGP71h5vocuAJ0DTKDjvm9a1+HXemU3Nk2LkuQ+eOEeyRNjaLtbf5Os33epd/rNz5arlyI89lAMY4BPf+Gkdg1x7HwA4oih5SPAtLREdMO1QCnrP7Sy/ltHGsYQupFGL9ye0NpO43pyfusgg1JAJyZcNLfAx7fBkgEon4BktkSm/bEajORagsaZwF60f29Gb6pYOU4FY75tHX7AHudNjP42RYu3MJZjpGcwa51Ie+9cJI2MGDT0XMe31w6x01teKxW6wESOTzWmsCm4m2OXZrtCrcdOJ0RRWHn/4SDFYFKgvWF3H5gZErZFB+NiOGdoeFoEgiMOOaQ0pe9IoXMURi55IQgWzBn3CeepUOc/MFVTa4QpJ3OqgDBgyAqJJJdLNl+Fzt0owZsLrysG/Pzzz+Pxxx+f+7d+v49Lly7h+eefX/k+AEvvfeKJJ3Djxg2k6b1JTv5IgpyTW5MUXkuQ6+4QD0/VQT9Xsz4PlzV/LxXtOkPCs4OZyb/rwGaO9BTECJ7+CFhlhrz5Y816t3sAYDaShzGGQkv0RHt9qoLRK+fsWdjZzF16EWRmcXar4P0D/LkpolmnOq9/6JexfE5JooyGYGJpX8tovsUZfh/JV3HoL7RCQMIWCTy0soWWSiHB7tNsoReyAHLhuqoa93k1QefZzwIAsmtPzSkL7AYJTN5mo/NaL3yx3L86xIFNBvBrzVmXeZl0+r6QX/DMOvwbEv5yhn937Wtladx3As750q3GuPGCdU0HztlctYBUAeGi2vKog1AsfEDfEsPTSPo93t1+EhEFyEyB68VdfCN9EZ+ZfA2/PfwsfuP4v+CuPAEw3+EXRQJRpDCg0xv2OeSx6/CndYTfd/jXeKIQzeb4tzDu84Z9MogxvGiTQlqj22CVeDylNThj0GqZ8C/O8T+bvQoFjT3exxOxNUXMjSwN/PquZjJMGHx0siQXy0ezWL7CFbACmZSFBk/4J7p+QZrpAt9MXsSkogCoSuzrCJphHORUBeX8ftndV2DpAF8truO57BbAApioA8pG4KNbZcwnqRwkpSX8TTGALhVgVZrErbs5/Py+9wK4F5TqhtqcwGbDvkV893t3wDlw406OF15dXn/N5vcrhdEyO36z7vxuh3Cxb+9rz900uHFoj9MDO7osyGyLTeT3r7j5/YcvGjCZANkYbHIHfHgDfPgq+Mmr4MMbdpwDQL8jQARIZdYaIp4FSKYwq47hhukP12+n+Hf/2028eP31bVpmiwcGw2J4T11+K+cfI2xQVzCwjeTriZqiUOcn5wfgzIXn59i3ARGh3w2QZhK3j6copEK4WBhfgUQlYGAIWYjCSIyKNTHBG6IdC1zcncVc5iq3Y3sLBRhr3CdL4z7GCJf2WhALz3qpJaZygjbvoNDFXFGIyBqaKmWWPj/VKaSRp4qrfD3jfMMWt8RwOES/v0xmdnZ2MBg0L0iGwyHCMEQUzV+o/X4fxhgMBgPE8RazxKfA4on2eoOX6mwq2TFa2BkwBlBdLBYBQRyDCQaKY6ixAVsRn1UHnSZg3V3wVufcpE8AA8IQJk+33j4AMEpCTw/A2rsbdZ+3BXGCCAOwhvOHwgCK0dbbThzgYQC+5rzUgUAgOJgAeMDQC+O5GLT588bJacmARWHjOa/jGOCn2GbGIMKw8Vh4GCMAwQFm3LlJoLAFk03AZbrs6q4VmCNB1OqCM4IhjU4QLkW+RUZYouIIP8+n4EbNsuX7F8vP10qhH3bmTHs0abC4A1F5eOowBCq/YSwCULHoPk9QO5fBDl5BMLoLBo3w7sswRMie+s5ap3p19QkUL11FcHwTnWf+B6bv/cHa4xVHAlHAoIxByKjsMufdC0tzawSC4KycSzSuwMHTEbgqgGBFQUbJWeHBvc+qquoJi+nuAUc3IKYnCHbeglyque3R2tasBOcrnfptpX723jCx26B4CIpiG09Yfb8zDAyLKZiR600ia9BhMX7swkdxJIcYqAmGaoyBnGCo7H80DFoswm7QLbsVLTdKkbd2QEFwT90D5YqkXOUQOof2Iz5Gl5J+HXdqo4aqyFs7aI3vIkqHmGyYQxxnlvDn8Q5Uu4+svYdoeoze4DpGl2wBwAAIQlaagQLAjugAGTDSk7kZTS/nf7r9KEIeoMtbGKsEYzNFm0XYbRNuHBoMU4G8SvgN0KqY9smeHXkRMgNpDcZEmRKT6HTpWAzkGJ8Y/D6GaoK78gTfv/M+AEDPCSOUBnJFaIULx4WHtjtPplQctGJm7yVpgnF2iN9PnwMBuBj2sSf6QLwDVozBpreh2xdBKgMTHIYxdFsagMEkW54jtaarCqbhXnrzwC54H7xIa3/rTdBtMdwdGiSTvDYO03e+93eDlXGZu/0A7326hz/46gif+dIAjz3UmnPNP3afc2G3cg8urDu32UKJ+dQ1hoOhxnM3DYSwZPrqHsBUCuK7G39Oud07AYAEw4ls3L9Xb9vz7ZH9AhR155Vubj6eVAajUlDUAg9i9LuidPbfaRiHOBNoZe9pImz0PiEuACPr13UVPPNigizXeO7lBE88slot9O1GhzqYqjES00VPbNAQcfDmdpwxFMihINFrGDWIECLTqfM7qj9HjTGY6jHCIDhXTsDBQZqgmd54TV+H/Z0Yo2mBnV608ecorZDrFHEQgXNCGy1M9Ai7tLs0CnGvyJEhEGLpWmRMICsSaEjwFT5RqZ79ploWSPQUu5X7QrsVuFEANrf/aZYg4AIM0q6HFn7LbfnU6wWvK8L/RgVjhL29zvoXvg7Q72/mhK8LgXTcBoURWE3XtUCMeH8HotuB5CmyNIbY8LMBwGgNiQDxtQcheuc0v++QpX3IsYLobp8CoLMpTHQRRmZb7d8mMMZAIUa83wdv158/BesiyyIEW353YSJEu10Eu6vPSxVKTI/buNw26EctXNnZQVhDgDqdWTFNYopgr4+w4ZzXbYY06YFFYrU/QQVGKyhq2WPRWr3NWgZIR21QEIK55IKk1YHMJohIzm0rAGDicuEZR3vPusenWY4L3R768fxxjZTAHbQQkCVULE/Q1RMQDEwQob2/V8ol0zTHA72duc+QlEDs7SCqHBsVSiTT2W+oMoVjGqKzUKBkF68CB6/YjPM7VrWk3/IOtC5faT4YH/gozCd/FdGNZ8Ce/gDM/tXal+XKSqw7vCi7zOzCFbQWfmtDElHEy2M4jQAZtSGyKbpqBLN7rXFTaDCyPckgQrzTw1RmQAAEZF2DF9UUbP8S8DIQpQP0OhEKZdBqze41eaERBBzdbrSS8BcaSCvvjZ3dedHqo9uKsNNvza99OxFM2ALlCVpyAnQvN372KrQQYgfL56o2GiOZoMVDhBWzoa6Lo5I7l+b283QIoYIWeJGgiwyFKwCwbGrPVRDCfn+tF4vp7wN3gVYx2nib2s78SHd30WqFSC8/hujFY+wMXoV8xCZIKEMIQw6pdOmafNHsABNgbJLyu15J7mCoJghI4B27jyFkAjuig7FKkLIMrVaIC7sF8EqBQcrLySwJ1/2PIjCnwAovXIZxLuQtKhC1urigesARkJocYcxLaegryW38x5PPlgaCN+Uh4ngWr9mOpphmgDQBWq2FY6gFkE+BiMEY+3nddmANr5IJ7jjiaQB8IXkGf/by97g37gPZFCgOrSqp1QLCEPs7CkCGaYbl34BCICCgXb+ovem62o9dDdBq3ftybreXA7cl8rRApxXNG5QaUxL+a5c76LRXny/f+4EQz7yQYDSR+PpzCT70vplKaTBy3fjLLXT8vskBEAdAvPm18Y7HNH7vmylePTAIhC2+PHo1RJtrIBYbjwd4XLlYABhiPNGz7apgNJE4dnGCT10htDornsvJEKACaO/i4l6IwUhimlLt554ZitSeL2HLKuHqwJUtBqzZjsNjF5E5Uue7zWcEURBG5hgtEWAv3s4EutOJMDQSbROhG9X/prEOMJVTxG2OWNQfj0QmIKmx39o507GFOkieIYwJ/fa9rUt3Nq+PAAAmxQRCEXpBD4wY2iayUYthgf6CCfC9QGoJrgx2qFs7HiGzDFGbL63hqkimQ7RMiG7UQqg4Cl0gbnOEbmy12zG4dTTBbitEr2P/rVAFSCrsxX3kSqPXbzXyu0351OsFryvC3+/3MRotS0MGgwF2dppPpH6/jzzPkWXZXJd/OByCiFa+9yygtcFweLZZoGcNzhn6/RaGwwRqMaarBkYVkOMc4BoUzMvQjJZAWiAfF6BiAj3NIccpGDaXfulsAiKOPGMguWzidJaQiYIeTsD09jNVOhmBghZMXoDUEHSKbmATjFYwaYFsmIGy+mOgxxnkONvq2OL/z95/9cqWpdeB6PjmnMuE2+64PDZP2kpThlmGVLHEoiSSUotstNBstsAW0FADgkA0+CAIAu4f0IMAPd0/IF7x6qLR/aC+MldiiRQlUqJYxSpWMauKld4ff/bZLuwy09yHOeeKFRErIlbE3jvzZHIPIJGZe8deLpYb3ze+MQDoQYq0m4CZxcdWJymSUe6kYQ0kA4kEY7kf5wytVoTBIC3OG90fgccSnFcv22gFOVLAqFsY6i2DkRkgJdJeBkoWb7PRCnKYAchBod0m46q8Wb+PwZQcVTy8hxB2fn/g4miGWYaEcnSnRgC00UhHElIyNAFQliC7fwcBrLzd/702BkmeIeNqYhm6PwQPFYZivA86TSEHGUj3QYxjJDMMhxnCPJl4KQkaO2gDoFtvgskMhjH0bn4ZZo68FgAQbqF59QVEd94Aff8/o//1X62c39RKYzSS2Ezv2+PU2MQwMwAmpa+jVIJRUBzDXj5C1N6GSIfIHt5HFs+fPQ8e7SKAjeQ7GiYwgYLOgFyNoIyGcskIAMCJYUu0sAnAHO0hTSVGI4lydHaWK4AIo9FkLM80kiRHmkiM3Eeino0QTEQTRiuMhrPHr9PahMhGUPuPkEVbc5e9LkIEUNJgVDq+W4cPAQDDaAuj0fHdf7OojUY+gj7ax8h1taL+IQBABTFGyXLZLgs62IL1jqi7TZt9e3xHQRujUQbTuYpN/ADR3kcYDUYA4xiOJMhoJJkCOZbeUPb+e5j3MRymICL88OgdAMCz8TWoVCNlEptBG3fSR3g0OsINnqEZ2L+/f+DcRaGROaLO/PVIDCNpvTco6UP2etBRy042wcZg7g16aLMGXh99gD/pvwYDg4tiG4/kIYYqwcP+ITaEfbFrxcAwBfYOM3SiWeJEaQrV6xfENRDA4HAfvNfDXkn2+8HoPt4/uo8nQk92BaAM2OgIOuwAKkNAlqj2RqY4LsV6Mg2thzBiVkqcJBK7R/ZvL3QURqPj+9TEwi7jsJth0D0CSqq2/lAilwaMACEUBjOmA7P4mS9t4D/+8T7+5NUDPH09RNslAezu22uy3YBdjgFYvwdSgFkhCiviwMVN4OERkEkr/GrHBsloBNXrT2x/HcShPZ77h1nl/r35nr2vX9w0AGeLrxnJgKM9aMRoN+059HBvhMHw9Ga7kfTBRxmMWbAOqQDk0P3h3KJAlusiReHgKEO3P1pYdH08wJGrHO8NP8S5aIDtaGepJJsz+35z1Otjt7cPIpoY75lGPxvhEY6wEVSPDuwne+ilQ7AwQIr15uvrIpMKe/IQsTrFkdgKHKSHGCQpRDg+x7Qi3E0fgGXRiRU6RnKE7nCAlmhB0ux3kuUSu/IAIq8m3dpoPOjvAQQMnMFhN+/joTnARjCucoQMUFKi6xJI+nkP3eEAnaCD0ShDrzvCQTT5TroqnzptbGw0aqkNHivC//TTT8/M6vd6Pezu7s7M50//HQC8//77eOGFF4qfv/fee7hy5cqpy/kB6zr7aYBSuta2Gg0oA0AqEJtysMyl7WwYBpLWKEcZgs5lpZFbFXQyAtu+CmUYcMrHThsOpTTMkqidyr/NcrDmDgwYzLALVmM2uS6MlDCGgQyB5hwDrbHWtmulAQ2wJcfWGIIBA9eEJmtUxBHZv1dKF7/TygAaMHOXTVAshEl6YLxeBdTkEtaqnc89FuNttnJbG+Pnttcb92UJlDPs4v0DNN77PqK7NtZONTegtLGGeQZghlXsL0GAY8R40S3kj24DsITfLzvVORg4hBETy9AKgGETx8YYBmUYKJcgwQBtv/NMqwnDP+MkyUw6Q7BrL0NGbZs9tQCD534G4f13IA7vg999G9nl2chG+65mEPQe2WW3dmai7ADYeTag2M9MS7tde3fAunvFz6sQOrIpm5sYyRTnGh1cFFtIpYI0CsooSG3/OZR99GJL+NngyBa/jJ7YJqkNBCfX1V18DIwxxd8KJ+lPgjYCxiq3WTa3IA7uIxgdYqTNx2LO470TRs3qY78qsqiDRn8XYtQtluc9J2TYrLWOJHbjDcNDaKnmdwVLCJwxYxZtQGuDYfMcZNCAyEeID+9huHW1mKWVevy9tMgW/zKTY6RSKKPxUWrTd56PbrjP6SLKsKsG0Nqg424hXVfRCQMN5e5LcW5fqqWwJnEqaoIlfYh8COnW22AR+nqEbj7ED7N38FbyEQDgmegafrb9efyHoz/BQ3mAe9ke2sxuYysCdmFJeNVxJG1gZIrhyF6/UQCgfwDFOI4y+3LIYI2+/rT/Ov7m5tfHRJ4EdOheOLVBI3LHSlnzuWiipkxAnkJJNaPWuH3PFhq3WwZxMBtNuQ6ablv6I8CkQ+jSPLPv+HbaAjBYGl0H2Ji+S+dDPHiU4ds/OMJf+/oO0kwXpoCbbXf/1BKQmc2OX3E/nrlMeOgKHxc37RiEzhVMlkxsfx10mvb7HCYao0TNRHx9dNfJ+bdzaBYv3lYmQEkPJulho227h4dHea3jti6YzKCNqU48KUCAVlAyn2sY/ODRuJChtd3urY1THEU4IQhEMER4OHqIXEnsROdmDN+qMMiHGEkbE7jo+9EaGGZDtNisKlUbjaPsCBzBqX7HHqQZEp0hk3mtfTwJGGPQy3pghk/sozAherKLo+QIW9Hx/Gk8EplAKgXDCKri+U9GYCQT5LJ61n4oh0hkgpZoF9tKhqGXdCe+P86sObn/TD+zzx2tAQ0NuYAz1eVTjwseqwGEb37zm/jjP/5jdLtjM6VvfetbYIwtjNH78pe/jHa7jd/5nd8pfpbnOX73d38X3/zmN091mz+zIDbftE9LkBDj2VcuxnFFtZcPUPgxycSYjW5ZDwYUxGDtHcCRkhND4ei+yGDnGJdoneILMRAJREygUddVlpYvm8VtQK1Q4S6nPixbPdGEmSEAwCkJRJ4B/X20f/h72Pqj/xPx3bdAMEgv3kT/JXsvkEaDE4eg6npnxAIoGGhXRAj3LOH3jvUAkGuJmIcIpqvZVGGWyJwfRhHNZ40Sp536rXGfvSYM4xg+/eWlxwKws9rDp+xnm+98r/Ka5czOGBeGfXNc4m3sPU38QLftZ3l/cTQfHx7a7WluQmqFzaAJRgwhE2jyCB3RxHbYwYV4CxELkUcNGC5ARiNK+jOXqNGAqNFVYgQYmOKVwDv0p2F77hylalrVVyPrQ30MD2wms8JRP6kwS1wHuTOg9NF8QMmhfyqST0qNJJ99bcqjNhQPwIyubdznzQ6z2JFWIvS3rwMAOge37DrIGz+WXgqJo+VM9LpqgLeSWzAwuCR2sC3GXarNwLv5233ZmBIJBZElngRCwxXHJI+sF1nk7wNjxZ037vvD3p8VZP+rzRfwl9tfBCeOS4Htvj/Ix8aUrSVO/YZxUJ4Upn0NnoPkCBAxem67v9h8FhwMD+UBbmUPqxcEIOCE0N0yZtbnPTAqTGfvPbQfvnJMd/4yfEJBP2OgtD9xL/Fy/mWGfWUQEb7xir3W3vlohPu7aWHY12pyhM7scGF2/BJ4t34AeGLbeUMQK4wVV0EYMjRiu03TTv1aG9xx8/vXLljvmMUgGBGAki4223aZi8wATwLWsG/JM5QxkNKF8qYKj/YnlQs+VeHTgICFaPIW9tM97I4eQNYwKBzUjNELmMCo5A5fxkiNkKp0YaSilBq/99/28KM3e8eOEeRMQBm5tnHfOshNjkylCNhk8YeIELAQXXkEdUKRj4lKwBZQVEEcUucTzvuTfz+CgZkoBoQsxEgNJ9IWylBGYSAHc40bP+14rAj/r//6r6PVauE3f/M38Ud/9Ef4l//yX+Kf/tN/il//9V/HpUvjOda/+3f/Ln7pl8YGVVEU4Td+4zfwW7/1W/jt3/5tfPvb38Y/+kf/CIeHh/h7f+/vTazjW9/6Fr71rW/hnXfegVKq+P87d+58bPv5aQARWRJf5VKsJRCMnTTB+AShWQbjMmJPLYpvGoxjWYewCsZoW/QQIShug6KmncE8KRibZ7/oOJArvKzycDDGAET2b5eBCcQixBZrIGLLK/jGGMcKl7yYTTnTL12uVvUc+j24mDgmPppv886bOPdH/xfie28XRP/g67+G3pd/uYiKU0ZBEJuM0yshYgG0MWOnfu96XnLKz7VCm00WrIxWlXGFxXdsPOFnzmV36oWLCHLLzpOPbnweJq7vC5Lc/CJ0EEEMDhHef3fm95wTBGMLI/k8POH3xzfYtL4Ay5z6vbFh3twEgdBcIKcNXVFFue8kGB0CoIkzRptpY8M528sIDGy8vY4Aq7gNMUfm5tfbSLtIPwbCHznDvjxsjQ32jgkfzVd26vdENw8nlTWJ1CAC+qMcsvyiT+PEgHi4PImB5Qm4c0AvCD9QEP72wUdQysZVBoJhmhR5p/5D1cdbqSXfLzSenPiM7/D3nJt/FBDi0q0piF0kHwUQnvCLCMSouGbDfFScS57wJyZFQAK/sPFVfL75TPH8qib8br/mTVIxDqgMSWqv6QYlMCICQAXhvyS28VLjKQDAD4ZvTEQUTqOcDDABYiBdTc7u7jrDvnMn9xo33g6rLCiT5sKhf0EkXxXO74R44Wn7vfzxnx1h77DaoX+cEbIaOg3CFXc7u37Bv5MEdp59DfLhnfqnCf+jgxxpZhAKg0vnana7RQwmU+zEbkRqoE6v+2vUWCWxEPOLSB67B5MkyhdpPi0QTKAddNCVXTxMHswleICd2R7KIaIahnMBBZA6r1zeUA5sL2TBe9ftBynev53gO6928b0fHy9VgBOHNhr5mtF86yBTKXIjK5slMYuRqAQDOT8xqC6MMUjUCGLBOyl38cbeqX/67wdygGDqOxUUIDdyboRjqhLkOpspaHxW8FhJ+jc3N/Hbv/3b+Mf/+B/jN3/zN9FqtfBrv/Zr+If/8B9OfE5rDaUmb1Z//+//fRhj8Fu/9VvY39/Hiy++iH/2z/4Zrl+/PvG5f/AP/kHl//+Tf/JP8Ku/+qunsFefYnAOk+vZOraWoJJpCTFhSY6q+XBV0tlvfzwXlc1tZzBG1yPBHsrGJhEPQUyAtc9D7X0EmJNJFTBaFUR1LojGEXR1ZVu+UFFjG4kxCB7iXI3qdrFsxpceRxKhJeVKLo4d9NBqpRQE4sGE2sJ7BXBXkEkv3sTwma9Cbc4askmj0eaNMbHNU5jRIahz0WYEk4uxiyZbiz4az7hZ9GhaDqmVi0eo2F8RwuQJCLaY1mQhDmQfmFJVDD73DeTbVzB68gu1jwUAGBFi9OSX0Hrnu2i++31kTzw78/3HJBFlTu6+oMvsc++l0RCMo7H5BBQAniegdDhzXDx8hz9ptBHyAA0eYojqF62AccAAsrUN0X2EcHQI3r5kSX5pu+vMpTEiMGYLBIxoTPgbG0sJf5h2YYyGnw4/LYwLLedgAAxGOeJI1FIwzIMn/F45AIwj+dRUhx/GoNOIIEON3jBHTgZxZM/ztLmDZu8houHB0nX67n4eNmFK5/lg4wo04wiyAaLBHlRzByFnRRyk/0o3eAv38j28NnofI50ipgg3wicm1rHp5uhTkyPVGSIWYqMJeAGCCHKksIU5nllCqkRslR7uPhDKpDiXNl2RocOa+GsbX51QEwDARbENAtDTQwxUghaP0S6I75wXciZshz9xhJ/ngGhCG42+Hhb7uiM28WbyEQ5VH++mt/FcfL1yce0YOOgDg3TqTCQXKWskgPG9IkkV9rv2/nd5fu1uZXjCLzWQSoMwH8K4c8l3pzdX6PB7fO0LG3j31giPDnKMXJLH9mbp+a9yHOcK/Btf4RhkAhc6yo42cPv9kMphxGqqgc22wP3dbIbw377nuvvbGqy2GzlBc46mGSAICHlucNSX2Nk8+XcfUhJkJMwcQ7np7SIt55bkd12H/+qlCHcepI9Fh9+T47rvXpw42qKDgexDjSTaQQecOBiYjSImjoAEtEyR6wzNCpn+zDKZgFQD5DqbMJLLdY7+gkg/j/I59err9jr42hc21n6fNDAfa4c/UUllTB4A9+4U4Cg7REu0jzVmkOsMUstanfZcZTNMNtUpMp0inhopJSJw4ujnfWwEs95uPnbxsxbH5/FYEX4AeOaZZ/DP//k/X/iZf/Ev/sXMz4gIv/Ebv4Hf+I3fWPi3b7755nE27y8UiInqCqSh2W4sD2Hyfr1Htnaz/h9XFY0LK43UCktDvctQ0rrMu9EFamxYd+U8KWbGjwWjl3e1i9GKFSrBxri/q7mvXMDkab3vzmi73GXqDBGCeGhf5OoQfsJq8WhMAKWuF7vwFBA2Ybav4PaNzyHauT73pq2MmlQzqGw8vkLcdv4JUKXvWEUtGDeCIo2CYHxWEaGdJLVC/UAisgoAh1bQwF5Fdq1qb2PUXm8GLnnyC2h88CpEfx/hg/eQPfHMxO9biSV0edhe0GU2RY9NGgUBjjCMMWxugYaHEP195BWEn2RWzI8P4ibOixhigQrEqys88RaDQ9AG7OypjwTE3GSpCTBGriZmwHRadKBZa2Muh1DNTRgAXGaITQYpOYIVicEqKCsrtDbQBpBaLzxGy5DFlrgKL70mVurwj78jK7EnhIJhoxkiDgW6/dQWHUJedPijGh3+GTm/XwcXGGxeQefgFtqHt5G2zkMIl3NsDIR7QSx3+AHg+fj6THpDwAQaLMJIp+ipoSP84zltEdoX3JACcJdrL0UEAkFHdvmBHBXn0kuNp9HhLVwPL1Z28UIWYIdvYE918UDu42l+xUn6bVReJRwR9x3+uCEAEAZ6BA0DBoYmsyq4LzaewZ8O38CfDd/CU9GVSmVRsb45KnTSk+MY93btOb7T0mhGJ3feCk6IQyDJgH4e4lw6ABrbAPGC8K8i6fdoxBxfebmD77zaLbLod0oz4Uuz45egGRHObXGMfM69GzEkldUkwGPM6/Dfvm+LadcvrkjORAyWDbHV6mD3UOGodzqEH1raZ2LtxkB1gybNNLp9+7vnnmxYwr9qh98okJJO9XJ8GGPwr39/F1lm8D/9jYsLIyHLYMTQFh2M1BCPkocoPxAIBCE4Wtpep3VJN4GQqATtYFw4TNQIuc4QL4kE7PbtObW9IXDQlccm/Zx4QVJPG9poDNVs17yMmMfoyx6GcoBOsPhYLEKmM6vEXPKeyYljpEaYfmNKVQJlVGXRIWQRUjVCprOJGEFtNPqyv1BV8GnHZ7OMcYaTwfScNMZV1mkSRyKqL+lX9ee1TwSMuxeA1aS7RueAiIrtpCACa23DZCck6zc1ZOyMjTv8tZfrSXm940s8qL98XW/ZxAQQxDBqueOyHROgeoWBYvmTYxp8+yqav/L/QPz1/wW0ebmI26peIRCWHiRGa1ts8DP2zJJ+WSb8JTl/piVCErPz+0Y5tcvsfpAIJoo2MQshGEe+iu/FEpggQuKUAc33vj9TJGq4+f15cn7/ad/hV0Yh5AKMGGjDKiWYM/2bBnNyfh3GUEKgtSAbF0BB8sqSfmKYKTDWuUcwIjAQNMZy/lzE4PGCl00uoF2U3YYaIJOna7LkyXTa3IHWGoIx74e5NmTYhCECM7qY3S86/KVzVxsDDrIvyQQ0Y4EL2w1stkKkucaR2HTbWL/DP034AaC/fQMAsHFwC4FgYIxZXly6726w8ZgKAfhcfKNyPb4w0NVW1r9Zmm5hgX1pjigAd5JzGcR2hj+2hY4gH0G5cyliAZ6Nry2U7F4sZP22MFNI+he+SxOSzK4jdlFyXs7f4Y3iBf6Fxk20WIyhTvDG6IPKJc2V9Lv1TD9b7z60lYgrOyd/3np1Qz8PQCoD5SPkcmy0t6qk3+PlZ9sT6oBC0m+UU9OdcMGNMVC+IOFkDgrC3x8T/izTeLBv///6xRX3nzhAhK2mPX7ThYSTAimJumN0hhhIVT8jHx3YZ3anxfHEBXsPPerJlUwhWdoHGz5a7b1lAXb3czzcy3HYkzjorlZ8ICI0RQudYBOdYKP4pylaiFiEgAVoifrjc4IFE3P8xhj08h448aWk3RdSvvRCBz/rvC1efb2/tryfk0Cm02P7AdRBpjNkarHcnREDJ4FufjTjUbTquuqcy3b/swnfAGMM+rI3HjswBiztFQWugFXL+lOdIlPpRBHgs4Yzwn+G+WAVM/xaApzPxNORCFcgjQoUfIz5lYxb4rDqPJ+WM7FyrLlpRx3mPCzX2baFWKvD7yX9NV+gViH8RtljWWPZLO7UM+5zyouVPB3mEEFOHJ2giXSJUU9QPu5aTRwDQRwcDLJkKqk6pfl9I9ES8aS5HRyxmVfAmdq3mAWIKSjixU4Koye/BM0DiO4jBLsfTvwuGloyM2zO0QAb273wXXVpFBpOUic2rYcKdasJv5fz581NBCxAPMf9ufg8cXBiyB3h5/1DcANMX6G1zmCy0n+jJw37gjmGfR7KJW60ZB8Gq11iq4C0RDQ6BGAl/coAQmBh4kG9BTPk4aRxnyf+eUnSr5UGCWvc6ME5w3YnxsWtRmFGGWRDsHxxt6iQ9FcR/q3rMABaoz3EagRGBO4KMR4dPn6xvhZeQmtOiocn/L0K4z4S9pqJWAAhLaFTQQQignb3a9vhX7grE3iiIPy26OFJby6BLK/+niQ4Mneb8dHx3negUypsCOL4qebzAIAfjd6tLEa2XG2qaoTAkrPJwmlB+E/GEHtyW8rFByKwbFCQ1ChkiOsqCqaet5wTvu5IDmMoXN9JSZCWy1VjK8IwAeTDlUlnucPvidSd+0MYA2w1DTqN1TuxRsTYjl2u/SnJ40kmMHWf+Yy5MYpZ7O7bn1/YCdFpcXBOUNr6D9SC0aC0B5LJ3KLCqvjgzpicnZTxISMGwQRCHq4k4Q4oQK6zYnY80ykSNULElitJ/HW00eH4/PPtY5N+ThxKS8iPYY4/VQk09FKpfoM3MJRDDNX6jbGRGoLT8k67qDAuzHWGVKXjcQCdg0b7YNk4Wo+TQD/vTxzvRI5q7d+nGWeE/wxzMd1FBeDIUcX8/SpO+EaDgo+vikZegr5ihx/GdvUnELVA8QZMujgrvjZqEH5aucNvnNlfzQ4/E/XZjp/hr6MeCCJnOLhk2/3L3godfqvaqP5Vi8eAMZUPT2W0fdBP3NSNPdddF40TQ8CCCcJfNuwzBoirEg20skqXKnAxYb5IRNgQTWQ1HITL6OZDdPP5D1ITxkhufB4A0Hz3Tye+Vx/JN2pUswRtDFiJ8JuSEoJv2jlr0a+WfXvDvqzRQczCpQaQguwcZdroWOm+TBGYrOh6G2O/Xqop3bTxfQahmw/Oo7btoi+AVxfEaRexYEjzk1NblBEOD0HGQIkIMmxBa4AzfiIFhtzJ+sOkBxgNkfsO/5ghKwMIENj0fAQBjVjg3LkOMucHEA0Wd/nDkfue49n5RxU2kLQvAABaB7cmCjEeHd4EuQv3hfjJmWV4FB1+R6A3miUprpiV9CthZbljl/7V8pG9cd+h6iHRGQIxds6f1+VPtL3WiVBE6XW17/BPFoqfia5hi7eRmRx/Ppo11VyYCjBFzkaJwsGRvW9cOX/yr3B+W/oJYHgI5EMcHtkCQ93uPuUj8P6DGdJ/43KMn//pLfzi13cghPtOdT72PzlJ8ABk5FLSSVkfLO0Wz9iNtt3HLDdFCsPte/Z7LUwBVwUT2GzaZZ1Kh1/lgBytUDRxI44VTRA/v39+OwARYdt953U76ySd2aOSILW6wqIKH96ZNY/8pMCJQxmFzO3bUA4hjVwqP5fKoF+KowRwbNIvSEAaOdep/iQxksNaZJi5Z/t+uofRHHO8RZBaItPZ0uMJjL+L8v4nOoHU4+/DqpQSlzpij23IwmIMA7CqgKEaQNQoMnyacUb4zzAfVS/MSoJYMNuN5QIrOeGvMq99EghCmBU6/MbJ4meUDEQ2os+oiZnsVWFv6rS8q70oHnHuwm2Hv/bIxArFmkL+XgMUxDbnVy6R9Wtljf1W6fAQm3u6NXiEkAWVnTRZOPRPx+nxiWMcM4EsHJN33+FXRoMTqya0Rs+cL+PNdT4SpXMw9nPHNR/w2mibsQwDueDcG938EgwTCI4eInh0y+2ABHdkrh/NkfQb4043csaEVIwtsI6V9IeDw8rikO/wp40OOqKxVNrInYpCMQbdsN3iZjrOk7dz0FRrhh+wWboapuTQ35mY8zTGzEgMVcu+aInhEdrNELnSa2R5LId3v0+aO0XRx16ax19bFvkOf8+aKsLAgCBLBphaA2KBPwHnDHrDdvmp92j+VhmDwEv6G9XzmT0n62/tWXWJ4DShZODE8PX25/FTzedwJTg/d5s2HGH2hH+zzJ95ifA7Sb8W8UQsHxkDvgLZiFlUmPs9dG797YUyeyDJ7f01DsZGYr7DvzFF+BkRvtx8AQDw2uh9DKbmbouuetUm0yQ5K8/vN+LjkeR92cXvHn0Xu/m40DOx3zwAqRxHh3Z7687vUz4EpQNQhWLkc0+1cPPaWNlBSp6OY2Zpjn/RdrLBQ7DeffD+Q5BMITih07LH9agnYbTC7Qf2nLt+Yf1XZn/sDnvrkTOtq4vYAMDSLkim8xVmM3/AAajKJsijg3GHHxgXeXyRaRkoH9iCO+OgfHXCN41uX06YBn6shF9l9p8S/LWe6czNffcWzrV79NyISBAQ4mh8Hk2T/u//ZNbbZx6IbLKNrHCqP0lILTHSSa39BIAmbyFTKe6P7qFf4VW0CJnOIHU+NzZ5GgSaSE0Y5gPwUiPNJ41QPrLXCKysXxpZFCSyQhXw2ZXzA2eE/wyLQGzmQWy0BIWz0iVrwseWkmDjsufnEaPTAvFwtQ6/N5ureIBS3AFFLSA7xsPM2OOwrMO/KB5x/rLNauSZMdQmH0bXc92HHfOgMIZZQviNVgAPVzKtKScvTCNgHB3RqCT8ymgI4hBO/eALL/bcHS8r5AHyEmmSbu7dz+9XEn7CfJUC4wCJiVncBgsRMVFb1p/oHA0eYEM00V9g1GOiJpLrLwMYd/lFf7/oMqeiWkZdJvzWoZ8hcA9dau8AjIMpCT04nPnbcSTfBppV6ocpMLLFBG00VHsLABClR/CU22i3LTWZAGcEmLGk3zQnCWlPjnCYT6pyfIefDw/RiASigJ9Kl7/s0A949UK9FI1lKKL50v64ux/EE+oeYwwUy9GX8+9XesOS7056hGFS7d7N8xG4ljCgYr3T6G5dAwDEh3cBmUNwNnPrej6+gZ9qPr/wep+W9DcjwE9oGD6W9HM3o60jd04zDu2u22CBEqYKlwL7/TyQlvCPpe3V98aRu63FpUfEeIZ/dib4engRF8U2FDR+OHxr4nd+XcO0YtSDPDmz56aX81/dVjgOUzbG4L/1f4S7+S5eTz6Y2ZZ+Yu+NhjiOXHe3VoffKFDaB+kcrIbfzTLDvtxI3M/38OPhu3h99P6KsmcC5JzCj8rBBnuA1jBhC5T2wHp3wUaH2Gzb7TnsSXQPh+iNrIHolepaaS1sduwzI81MYfZYF8YY/Ps/3MP/8W/vI8snn3kkU1DahQli1D4fmIt7nGqCJKkqpPvnt+32brvRi1odfi1Bad8qQ44RjVjGB6677wu4p+WBUAU+OgAfzaqeBAkM5QAjNUKqUkQ13OS9J8RmW8zc+z7/fBs/+2VL+v/stR6Go/rHjEDIanglHQepTiF1jqBmB5yI0A46MMbgQXIfh9lh7es215kz7K1HT71xn/3bHCM9GhcmjAZlQ2seaRRIjt8BBAkM5ADGGKQ6qaXS+LTjjPCfYT6IZnngvPg0Jma6pJXQanX59glgJWM6YMahf2JZjIO1z8HIZH2zFO+mW8eoiPPlsvgSjJPd1wWxSbn5Qmhtu/Z1lx21584KjpcpV4rkswtm9g1sThGnLRq2Gz61T1IrRKxUXDC6ZIw4fsgK4sgbHSTXXsTg2a8VIyy5loh5NOMsXhgPzjvuXIA4nyD8gnG0eLzYYLCEVOXYEG2cCzdAwELDv+FTPwXDOILD+wj274K72fuscx5mDtHSgMsRJuuQC164yBPjoLbryPZ2Z3fPdfhZawdxzWJeyASU0VAt7xJ/VNxvfPGhrhuz9ZUYR/KZ1qTkXBkNhslz3M/w88ERODNoNwJIefJd/sgR/tSbJRIgBBWRdceBl/QHSa+Y35dTviPGGEiSyLWae41LN8ffyg4RCVZJ+ov5/ag1l6Al4SbSsA3SCuHeLTtGQKvvpJfEJyZDpnMQETputzRzHX4E4I7M6bKiIRob962CS8LP8XvCP5a2V8Eb9jX8u6UxJcJfkWRBhK+0bJf/7fQ2hnq84EY4TqQYTfNTxkBKg9z1fq+Y3z8ekfowu489aQt1B6Xs7Pb0eAEPcejMxupE8tk4vAw6aAH5wI5szYPRlpC7wqIxBl01wLvJbXy7/2P8m4P/iv9j73fxraPv4PvDN/Ang9dwJ5+9/8xdPA9AVXP8RoEP90ByBIQtgDhM1AHAwPsPsRVbAnXUk7h9xx6byztAINYvsASC0IrsOXPUXU3qftiVuPswxWCksXcw+bxgSdeOLdQotJb+yt58pp4hfn5/s80RhfYZt71Zv8PP8qFVVIgQEFYdQssUfkvw4V17Hb/wtI+HzD8WkzoYZQsW+QCYUgsJZuf4e3kXBqYWOfUO/X5kZBqff66NS+dDGAO88X79YiUngVTPbwCcBFLXYFg1SaApmhAksJs8wEG2X8vIb6RGK83R++9CaolUWTm/L0yQyty1EcDwEJT0i/e8kEVInFv/MB98pmf3Pc4I/xnmY95NrOqFnvNx9N0i+Fz2j1vSv4rHAGBN+YL50mQb0RfbB8I60MqpIpbfZFaasQdsVXOlDn/N7w4ACJa41gQ5t/CFD2hjlqcVTMMbB855gDR4iJAJ5FNmNtpoxOXuvFbWkyCIJo6xcI67vZf/CkbPfq34uTIarao5faOt8eCcQhYRm0gC8GgFjVqSfmkUGBHaIkaLx9gS7RlZ8MTmxC0k114EADTe/R6Em9+XnfNziaYxrqpOdn0hFxOFDbZpZf28tze5b1kC5jqt0cbF2g/OkAS0MZCO8Ic+aB22+MBs4m+tZXG3U57woz3Z4SfYuLe8VNTRjbaVnRoNNuqhGQkEAUMmT7DLbzRi536fNM9BG2O3RXBXWDmei7XvtIdpifCXDPt8JJ/1pZjc/zI84Q/6+9jZCCEEYZhMXjuLHPqL5RDQ27JZ8+HDD1wc3+okKWQBYnLO924u/uUbhHMdgAm7XbEByCtCwgrCL5OVbpveuG9PdpEbuVzSP9XhH5kUEgoEoM1mCT9gvQIuiC0YGHyQ3it+TkRoutvKbIHBebgYbef3naT58vb6pEcbjR8MxhHFR6pf3IemEwoM4zgc2O+wToefZe4aFCFIZoWktnpDpO0yu+fg73b/BP/3wR/gv/Z/iDeTj7CvLKFqsqhQfbyT3K6/ozwA6Xxyjt8YsNEhKD2CCVuYOD9FBB21sBXZL7d7OMSth07Of/74ipyttutSH6ymPvmgNMNeTg+gfATKujBrmSCbmQ7/rismnN8ZP499h/+wly926jcGlPWdcSDBn7fHIfxJqnHfjbB8/rm2tbNQKGbhTxMkM3vuyFmliiBROL3XyYoHgKPe8qLZi8/Yc/yNdwe1UxEEE8h1PuFUf5IwxmAoB7Ul9tOIeIyYx3iU7uJRurtwO7XRSHW60iy9IAGpJaTJnVHgOGKRZDr2BxFhMc8P2OMmjURf9jDSo8+8nB84I/xnWIBp2fS8uXb7YW7JzlJJv4vkW8EV9URQZUC4CFqBhfMfoiSOGdHnXnKoTie+Ih5x8bLNagoKr2So46hrsJK5kp3jX7LsRVL4uX/DF3obhCxAi8dIKtYryuvyIyYiRPnFLyABzvgEIfNkrVLO7x8qCwotJMKZkZcGCxEwjnyJeV+qcjRYhAa3buQ7YQcMtPDvRk+9AkMM4f5dRPfeAQCojfPgjKCqlBHGSeNhCX889SLDNqxTfzg4mCjgFA79URPNOVLvKgh3TRbRfMPDQuZutJ0tr69QJQQyAdO2M02lDr82GkRkX9LKx4sYVNN+jg+OIARDOw6Q58cj4WWESQ9MS2jGkTU2oLUBZ4RQkKtXHa9T5Wf4RT5C6MYZZGUkHxaOj+jmJgwXIK3QyPo4t9EAF4RRSXq8yKHfwyhgeM7O8Ye7HxZTYes05KaN+75wk+Fv/xyHhN2Hpjt2kgVgYnzd6cj+XSiTIpqvDlq8gRZrwMBgNz9Ayx3G3a6pLMoVkv7Az+/bZ0GLNWYUQGU8HV0BALyX3p1c/5ICA2k5nt9vm2PN77+T3kZXDxBRCA4GDY2ei0CcTigYJASprZ/GRnPJOlUO5E5CC4JhDCztz/046dyZtnKMdIp7LhbxotjGy42n8Fc6X8b/vP3X8Ld3fhE/33kFAPBR9qC2KsrP8aP0eZb1wYYHMKJZ/Swjjs1NexD2jyTu7tvvcm3DvhK2Wo7wHyYrPdPLLvW+UwxjwJKuVbmtlR0+G/f4yBn2XdgZL6/T4uA1iDap1M7slwrihnM7078mbt2zRbudTYHNjijI8scxx08qA4yGESGoFOsG2AKdnx2vSxTHHf7519DT1xqIQkJ/qHD7fj0ViDeukwuuCWXmq7uWIdcZMp3Vnt+vQsBCtHgLh9kBHo4eIFUpUpVipEYYyD56eRdH2SEO0n3kqp5hnwcje/9KVIKhHE58H5SPSmo0ex2X70eCBIZqiFznn3nDPuCM8J9hEbxs2j+YfFZulcydCOCzhGYG80YCThnWFI6vYLRnlhrgsNbW2hF9CyPcZla04gw/6s/ZA1ZBQCJaLL0sYFbLSxah/b7nVPltMWkNTwe2PL2gIxoThN0/8ILyS55RVg3BQ5QLQoIxaypX+vvcSARszvx+odhYQPiDeGaeMWIBIgqWuvWnKsdW0CqiAJs8wlbQwmDefCoA3eggvWolxD5uzWxehGAEWXE+aW0KCb0xliCWwTZshz/qHyErKSe4m+mXzc0ixq8OOHEYAKptO/zckWNjAG0wE3u4CIwIcT6WtJdVKNL5NsQVHe5ijt/tQysOIARDfkJd/kLO39wBiNmiEQME5xCgmRjCVaFFBOWunUbvIYCpDr8yMEwjYBwhBfPHQIgg27bLz3t7iEOO821rhJdkTgK5wKHfQxmNfPsKDONg2Qhh0l1bydCZIvweqTOoajgXfukiMntyiJFKiw5/KEcTvhx14Lv89/N9XDtHEAzYPQK+99bs9ZK4237DnfKL5Pxl3IyugEB4JA8n9m1s3FdxrycCtBzP7++Y+Qq8JZBG4VXnIfCl5rPY4rZodOhk/dMJBQfu/XijqcGw+FnH5KiQ0AKwMnM5nDE+K3ZLSS9DwYG0BasN1sIvb/0svtZ6CTejy0V04w7fwDbvQEPj/aliyWLQ2LhLJqDhHgyvfo/x2HIWDEcjBqmt+uJc/Vrm8uX29UIzwTIGI1VI7YHxDDvJISjrrdndh4t7nHzuFJF82+N3E8aoiFA8OJr//VM+dMWH0nODhVYOv6aLvC90PHnV7uPWx0n485FVK4jI+iRMqTlj3kDM4toy925phn8ehCA8f9PeP157t16hpMqp3kMbjaPsEHeGt9CT3VrLm0aq0xOZb+dMoC066Moj3B3ewu3hR7g9+Ah3hrdxf3QPD5MH2M/2wIitLK8nEEbSuu4H/v1MS0AmE80kI2JADor7UcgiJHIEAlt5XOHTiDPCf4b5IIZi1guw8rsFcnwS4XJZuNHzo8tOE0Fsty9fXjU1zmdgKQkNm6CwtZ6sXytrJFgDlfGIi2CwsiEYhc2lhQtTuP+v0OEnAsVtmHkvN76ItOLDxEYtLvaMaPBoontujeh4YUQHjFMHrJplHCHIiSMgDlkiiLmWiFhYONdPwJ8zi5IRKo5bnXi+XEsEXKAl4om/2wk74EQL/3b49CvFzL7hAqq9iSDg0LIi7xv25c479E8/4MkR/mB4hKwk0+VDSwRNa6uI8asDTsySwSCGDmIQgGbag4b1XhArzMsyRogcYVDx5Ju5TWbgaInGjBdG0eF3KgUhGNqNAOkJdfkLw76mJZJaAwHnIB9Zd1zGT2MDvbjvxjZKHX5pDBg5vwjvZD/nmlFO1i/cyEYcC2x1IkhtYICxQ/+CDj8MgQsB2bF+D2HvkbtMV+8ueaf7XinP2RiDzBH+pnTXtYjAYP05Up2PCX8+Wrmg4uP5HuT72GgSfv4L9hz8wbsG79+f3IdC0u8eE96hv8qwr4wGi3DZJRSUieuiaD7DGEjlxfz+1e31c+tfH32AoU7RYg18Lr6BLWHPn0M1dtMujzMcDux+bzd0UTisxIykG/beKiXYHD8FkhmMu2ceKHt+bYtqZk1EeDa2ppDvpPVl/YYLUDYqTPpISWCOcalHu4GJhJBr5+lECMGm6/AfDqk24feRdP7R0u0rwGgwPwK1LhFjDCi7m48UBs4s7tz25LtP4dQ/j2gbBUp6MNPvTFyAlFxL1q+UKbrcT16J3XaMxwtOFUZZbwkmUNUZBqzTe1jTN0GVIvk2lvhgeFn/rXsJ+sOayQggyKnxxUSNCiKdqgx76V4xi78KEpWATogqMmLYEJsIWISYN9AWHWwEm+gEG8U/TTF7/9Ta4N/9wSP8wXerY2NtPGFWrANwipNy8RFw96O8uB8JJsCII+YffxPyk8AZ4T/DfBBNyqa1sh3bOaSGRFhPpvYxG/YBznSssQFT54a3wKF/YplEdpnrzKgZbU0B64DVlza7DVu5+zM9w14Jb3BXN+7PLztszF+2j+Rb55zgi70NIhYgZlEh/1RGgYNN5rNrZQtQhY+BLv19ONGZlFqhPefBYLQCgiUFHMYr6zYNETkiVr0vic7Q4hHiKUldk8fYCtoYLJiR1c1NpFc+Z7e/cw4ghlCwynUZg6IbyxmbVEIAoMamvf6NBusfFj/33XHevrDSS7EgDk7MGfdtAQDi9AhGGzuVsmKHP0ot4VKNKcKvFWIWIGKBlf+VvlO/XuFMDQGgFQtwxiBXyHGfhyKSzzn0K20gnIoi4NXfw6rInayfuepB2bTPeoPaGf62iBeqSfwcf9mjIQ44BCMoqRAmlhDOi+QDAJAdC5HO7yHs7oKvqWSo6vDnRsK4i6ilxoQfjFycZHmGf7RyQcUb9+3KQyij8PxVhi/ctN/X7/9I46A//r5GzrTPz/B7r4GNOfP7ZXhZ/7vpnUJ51HLcoVLSTxzDYV4Qriubcq0Of6pz/Hhkx3teaT4PThxb3BH+knGfH2foJwaH7vBvtQHKBnPvucV87NRz03Be/XfG2A6cK4Tuu4Ldtph/fj0dXS3UEeXtXQhm5/j5cM8WJKLl3w8jwmaJd1yfnyC5ErxK4GBAGHTrmUp+6Lrcz96w233Ul0A2AKWDtbv7FswW3N390MfxbW0IhMHkubVdEP5qou2NGmffmfws9epE8+7DFLk0aMasGDH4uDr8JJ3Zm3tHM4HtDNMCRd0i9AZWuRYIQiNafN1ubQS4fNGZ9703NTI654bGiCFx77bKKOyne7g3vIOh7KMl2ugEHUidYy/dW2nWXxuNoRoiXGtkpBpEtpnAnU9SHXT7EncepHjr/SGUmr3/CCYwVKOJsQNbZDKYprmGB25Ew573LdEaqwJKuP8oxY/+XM542XyacUb4zzAfnty5B7VRErToAbOk0vxJRfJ5UNSyETzLXrKVBPGw1naOTenWIAd1K/PEsBLjN2vIPUU40eGuhLby+5Uk/fBz/HNGH7Qad9hXBLFgoWSXiLAZNIsOvzIaAQsm5WK+8ML4jAlg5GLjANtZNADieaoMo5YqV2wawmyUYMxCR8SqX6ak1tgQrcqH43bYQcD4wmi/4XM/g/TS0xg+Y80HBZ9zbpB90ZVGIQCfUTIQUSHrjwdHRTGEuUi+sHNh7jZUgRMDA3PRfFbWH2fdggQTW+GcJyBys6KmOSk5t0aN1sQxID4h68/PXbPd68P7YE6pEAiOVkMgOW6X35gZh34Dg8Adf8bp2C79AJBNKxrKkn5jwLkBJ+58LarjKoEx4Rclwi84g+AMlPbBjIIhKgoM0/AeF5wT5KY9F0T34dpKBk+cyx1+391nYIhyW2iVwslqjb1L+hl+kY9WLqhs8BZiCqGh8cg52H/9BcKVHTvT/q3va2S5Xabv8DdCe552a0r6AeDJ8AlwMHTVAPuus70wBpAY7u7b8/HcBkMsXJzoivjz0bvIjMQW7+Dp6CoAYEs4Sf9Eh3+sNjh0RY6tNgfJ0dzONMnEKbamu7xR9d8Zaf9x95kDJzne5vO18w0W4Wpgz61363b5GbfP9LQLE7VR97W3TPivrWDY11ND/H8P/hB/Onh95nfthj2XDAivf5DNJXAeWa5xx6k6vvB8G0SAlAbp0ZFVRhzHWZwxALrYhl0/v789+96zvek663Oc+lnad6rC2WNrFRYVSQlL4JUNT14dy+a90uDwlKP5/Px+sT/MKlVoxahPj6OSQ38dkvuSN+97r2Tep3Lw/sNKPyROAplOMZB93BvdxaNkF5wE2sFG0fFuiTb6soej7LD2dqc6Ra6ON79/EhiMxufOMJm9ZgQFCCgYz+8bA8qHMFXv2CKyoz1LijevvtbDR7c03ruzvgfF44Yzwn+G+aCpOWmjbSd4HpaRNu0e7p8U4Q+b1lBmiZTOqBxUowsAODIrQkDWl5gZJQHOFx/LiZWsISVclZTz0H43asGDtIiwW/ElQ0SWDFcoIYyW64948OVmhg0egTtpvjSqOjKOibGBYjk2r6Q6kEYhYLx6fh+wD5hl57XzkZgeexGMoy2qiVimcwRMoDVHWdDkkXXsXzTLH7fQe+W/Q37BmqkJQa6zPkssiDnCP+XQ7+GN+xqDri0yGFPI4UNXDKgLTgyCGBRKHf7kCNoAILPSDD8ARD6SrznZITQAAi4QMIGQhRPGfbrRQX7eusrHt8cv6K1GAA6CXHEGvAyRDSFkCgNC2tx2PyUwT/gZYaVRnTnIp4wSyx1+AwNwFKMWrSCeS4K9pJ8nPZAffSLb5Q+GTs4fdeYWE70hIaNxh190dxGw+eqVRfAd/pFJi7QNP78fUQDuuoaSW3USkfXs9x1+kY8KNUBdENGErB+wioVfeoWhFQOHA+A//cgWjadd+utK+gGbGHE9tNfSe8kdAIsl/YnkeO+B/f2V82zmmXAo+/jx8N2JqL9pDFWC10bvAwC+0vxccX35Dv+RGhQFzrJT/4Hv8HfsvauyW2s0KO3BVCm1mACUmpmBJiWtvJ5xaKNxqOz1u7Ogww+gkPW/m96ueV4RjIhsN3yFZ9dm0x6f8xtAM6p3LzLG4Nv9P8eR6uON5MPK8ZmXbthlvX6HYPLF7yK37iXQ2jq772wJtJ1pYvcoAY7V3Yd9v9Ma5O5x3qH/Qsmhn6VdsNEBtjt2vQddOdswUSkgB86osQJOYbHKHL8xpojje/LKeD99h3+UaKTZyZmrToPykfV5KG+TCEBJb2mRpgp1HPrLuHm1gThiGI40Prrr/CdUBsgRSM0+660hbY57w7tIVYJ20JkZN2DEEPMGDrJ9DGU9EpuqBBq6VuzgaWI4UpX/7UFEaIrWeDt1bs/LOQbjtiCw+Bj4IkMcfnZo8mdnT85w4iDy+eSlG+sC6bU1xpvtYBZQx5BvnwRECAobMMvm+JcVNkogv8xVpF4ytYWCug9sYrV5gTHGztmveoPmge10LyL8WjtjuhUl/YyBonb16INRoHC9+SlrTLj4oR+zEDELkaoc2phqwu7JPps814WbRTXGIDcKIYnq+f3SchaCibnxhy0RVxqbjVSGNo8RLSgm2C6/QFrTPJIzBiYAXSFZZ/AO/dUVfT/H3xgeIdUSlA7BlIQBQTjTt7ogIgQsgDK6iOaLk27xFazS4AeAwMWB6RLhN8aACIVvQ5NHkFPHObn2kl33nTeKkY5IcDRifqyXysjJ+bPGJgwTPkAOTtFvI+tOYC64TPgNAOlMUZ1OAkSmkGTGLIRgvNK8zwQRVGy7vWVZvwgYohoO/coYMGa9CVRry3b2lEScdlcm3sDY0BIYd/m9kiViAYQjkFJEAGkw2GKDcvcTpnKwFQqxHtOEH7CE7298mYEx4P0HdqZ/VCL8qc6LYkSdDj8wlvW/n92FNqZk2mdHP+7uG3z3LY1/+d8U/vnvA+8+sPeXq9saZur+/t3Ba/j+8A383wd/gB8N35nwHvF4dfQ2FDQuim1cC8fFuTZrQIBDQxcqBT/Df9g3RQFiqwUYJkBZf6bQSjK1HbM5pM+IAJR2J/6OXMEQYLbYAGuu2WaLn4vXw4sIKcBQp7iXP1r42QIiWtnJ/tnLhEYIfOmp+tfoB9k93M13Adj76EFJNeHx9CVCHAKDlPDRncUd4w+mutze4f0wDXDs13filrwaS+J9h/+8k8+TTEHDPbD+Q2zTPhgBsjSL7sGyBCQrlB3FB2x6E1th9PHRQY7BSEMIwpVL43MqCBhaDXcMTkvW783epvdHROMkghVRx6G/DM4Jn3vK3kded+Z9pHKwPJmJCASs5xCBEPEYLdGeS9B9B3wvfVRp8uehjcZBeoD9dO8T7+4DKLwl7H8vfyaTykBKzVXRGh6C0sFCo2pfWGg3PyG+cgo4I/xnWAznEG98XvmiLiYTKGJwKmAj+aKPP5LPgYjAGpsLI+KK6nVNQz0AoLhTL9LOr0MmoHhjscFbefnElsvti4XrsffCCiDGABHBLKrCG+vAu873R1GzuhtvaH3ToRqFEEaETWeKR/Ak3q26iJl06+fBRIqDnTEnKGhkWqLp3MBndsEVWZYWstj86EovOS93n42xcWAbwWIC0eAhtoM2hhWV/8rNYISAc5T5viejxKw0ep6SwUv6RX8fxuhifh/NDTuysCJCJtwMvyX8YXIEpRUI5DrgNWEMAjdjXp7ht4Z9wkUAAjEPZjpU2cWb0GEDLB0ifPSh/SEB7YY9BmrN6LzCsM/N72ttwMGKJATGae3IujLykqRfBY2iA+8j+RgBgdv/cSpE9XU+bdwHAIFgiDM3v7/Aod9omz5ABIAY5IaVXsfDvbWFDNPRfJ5UhxSAuyKrFDEM2fskJwbJg6LTHKxhUuUJ/0N5MNFBvrRF+LmX7Hf33bcM/GkRh+P5/ZiiCVPQRbhaIq4P8r2C8EsF/L9+T+Nff0fj++8YPDyyh2+npfHK8yGe3JETRVdjDPbc+IE0Cj8Yvol/dfCH+CC9V5zrXTXA28ktAMBXWi9MyIqJCJtTsn6vNnhwON7HOCT7/Pbz2iWQHFnyOK+DzkNLlsqFcZUXz6nCsI93lkqeOfGSB0J9875VcXGL8L/9IsfzV+s971Kd47v91wAAzI1b7OaHM5/jnPDiNfv7196ff89WyuDWPXv+3rxqT47Nhr1pHyUnREIMgbTCcKQxSjSIgPNbAWAU2GgfpCVM1AZPj4qEgYOyrN9oOypRw49oWuGxCF7Of+2JqPA88RjL+k/HuM+SxbzieW7fw1jar+d3pMYjG3Uc+qdRmPfdT9HtS5BMrAFvPppRGfgOd9Us+jSavIWhGuIw268cb811jt30IXbTB+Ak0ODHVJKcAJZ1+KcxLsrMuZeIECTTuWaiWhuMUnutdZqfnbi+M8J/hsVg3BKjOnL8wvhszgWp5ScSyVcGhfaFeG48n1ZWbl83Ms8vE/UIuTHGzprWHBkA4LwU5mfOT63AvvCvQ8rD5sKKpzFqbXUGBTGmYxHtw8asr/hwzvrL4Im67SiXXkhdEct35okHE8c4KJnKGWPQmOfI670plhDeRdGV0waDgH2BjBbI+cvYDtoImUBS0/k5DNgEkfXO/OR6scEctYIn/DQ8QmgMVN8SQ2qt1t0vtoMJW9hodGCIgWkFkfTBiFbq8LN0CDIahgg6Hs+YS6MhMDYgDFlQfKfjP+ZInLFhdGss649DgWYsili6VTEm/G5+XxuQsAoLAG4f14usKyOPxhLyCTm/MiAOMMaKQhcjQkc0JgpLZVQZ9wW8TPgXdPi1QVjyh5De76G3W78AaczEy/S0cV9WJvyOQCgR2xEQoPCE8HP84Rpz/Nt8AwEJ5EYWRNTjpRsML14f74vgQMCpkPNv1OzuA3ak5Wb4BADgvfQuAk6FcV+uLMl+9jLhr36R8L/+NYZf/0sJfuYFBkaTxHqoU6QmA4HwjfYX0WQx+nqEP+j9AN86+g725BF+MHgTBgbXgotFQaOMwrjPyep9h9/fIjzZ8wX9iS6nUaCkD7OoSO78Ucoz0CSTojDjI/mWyfk9no2srP/D9P5C/5KPEz8YvoGRSbHBW3i58TQAWzSqgpf139o16PaqSf+93RRZbtCIGC7uhICW2IrsZ48GJxQhRgbQGrsH9rmxvSEgBANLetZ1P2gCxGHiDrab9p5xeDCWQpMc2SLOkgaJ4SGQD2vL4T90MvabV2bJ5mkb91GhCqrwIxARKB8uTljQEnywC3F0G/zwNnj3Lo669vObDekKAcvv+RttgWtP2BvCG+/2AZnCiIYtSKxhguhBRGjxNg6zQ/TlpAJlKAe4P7qLo+wQLd5CVDOF4LQxTBbP8E/AKFA2nE2MmADBMO7USrPPB78+IqAZH8Mn4zHDGeE/w0KQd0JX0mZbLyH8xKo7mADs3MwKRPpUEMZWrj9Pgu9jPOo66KM8x1+DbMnMjRasQPiJA+V4xEVwXetVZfcAnFP/gg9ovb7hYhDZ7758jIwrrqy7TKp3TGIeInJmfeUOvyfqhcKAhxMu/XbGnCPTEpzYgvl9BZCo5ZtAIqq8PqggYuPfpTpHRzQXjxGU9nE76GBUk/ALziZk1t6h32A2unBiO6MW4MhUezCAcYSfd9azsebkemGMFXP8UXJka1YrFK3YyBIzHbcnzn1lFEIuColjSM64b4rwptdetL/f/RAsce7fBLTjAAbrdeG9pD91kXwSBsLJzgGrtFg3sq4MwwRydz+RpTEhCQMOm3bAvEoLQFNEbp9m11tl3McYIU6XO/QbYya6cbl36u/tWjO9Ggfxibf/EFf+6/+7iH+bjuYrS/r9DL8S9r7FwMCcJ4Sf449UMja9qglGhEvCKk7ul2T9Hj/3EuGiEzqMI/nqG/aV8XRsjfM+yO5BGYW//mWGn32R8GvfYPjffoHhl15heOEasyZ6RFZmbdTEvcYXJTZ5C8/F1/E/bv88vtR4DhwMD+Q+/u3hH+GD7B4A4Mutz1VuxzZ3HX7pO/yTv99qjb9XK88fy/pJJnaueMmz3cpo+/Z+qaXr8LvZ8MKwrx7hPyc2scnbUNDFvn2SeJgf4M3kIwDA11tfwBOBvY5282rCv9Gkwvn/jXer0wbKcn7GCCQTbEX2vnU0PAG3T8COhugcu/v2ujq/E1oFR7JvZ/KL5yXDjjfu2+/bSEBjwNIhbAdjybOPC5DOS2R6PnoDib3DHETAjSuzhPO0jftIDoqoyBkwO/Y417xP5eCDXdDoCMa9L6gsQX9or5Vt/RC8exu8/6DWQ8V3+d98fwgtpXv/NmunBXgIJiBYgL30EVKVOgn/Pu6P7iHTGTpiA3xd5eUpYFLSv5jwk8xBWi7mKoBTK1UXb7yKIApxInGcjwvOCP8ZFoNZYzSjpc2yX3DyExEggvndc+ATM+zzICZAcWf+HL8zkVtFnkwiBEWNWvF8RiagqLVa4WM6HnHhCrykfw3Xex4sVj9Ar12wISaAsDl5jFR9oly9TO5GHRY/ODkxbIgmBHGIMpHVys3u+w6/QLniQUSIWIhEZQhJIJp3TmhlRyJqKBUWRVc2eFjkpGsn5++I+gRiI2hCEKucz56GcO1zv7fGGBADFFxHfMF34o372sM+opElCGzF+X0PTrwgn17W38x6IMJKkn7uCP9MJJ9RaLDxS6NgHBEPJpz6AUC1t5FvXwbBILrzRvHzMGAQnCDVal1+JlOEzkTQS/qNAoQYR2wyorUj66bh5/gnIvkUwASBMwaeDmGcuaIfH6mK5ysk/f298Qup1gjT5R1+EApDQgCFU3/Q3wMns5R4szxB59F74PkIjd4DAECH1ZD0h7bDT0QInQFcOZpvHcPAS46wPcj3Zn7HuZ3nv7IDfN5F9k0b9hkAuVz+zV4SO2iyGLmRuJ3t4oltwpeeYriwOZv7bsmZdlmL4+O878my644HJPBK63n86vZfKaTvgI20m9dB3xK+w2+/51AAQekWsF0OZuCh6zLa40/FTPGS10kRFt1J0hKkx4WLfeUj+eY79JdBREWX/51kfVn/w3wf3+7/eCIJYlVoo/Ht/o8BAM9E13A5PIcLYguAHfWYZ6T48g17vN54P52JGjPGTLjUAwDLBthoOkn/sLpgtzKIgVSGR4VDvwBL9u3885Qfw3bbno/7fQ7Wfwg+fATkA5g67wRe4VFj7Mzv9xPnQ8TR7HOoIPyn0eHX0jaDFkjjjQhBSXdWraBS8MFDq4yI265xFKGXN6zHDQcanRYADshRLRPDJ6/EaMYMo9Tg/Yf2vc7Gyg3qvQ8uQIM3kOsc++keHiYPsJs+BCeBlmg/diR3UtK/eL9JpYvHizwKM9HZa9+rCKL48ToOx8UZ4T/DQti4NGNJTQ05/rwO5tgD4JOvGlLcnnuzNCpfrfvuwOKNenP8SoI16r3UjBfuXJlrdfjN2GxxVYjQktZ5sn6D9ZbrwOLW5LJ1DdXIIhDNmkrOQUc0sSmakzP4RluzQv8zxmdGviIWQBuDmIeTcX5l+GjBOsdmwfnf4CFiCpFqiVRnaLAQzRUkdQ0WoiUaGNV4qRLczjsrPY4dJAI0VUQXTsHL+oPhAeKRJbXrEn7h5q41DFR7y+5H1gUHrWRDwZyLvJ7qQBszdqj3aLIYquIelbguf3z7jeJa45whFBw1uNsE4oHtDOdhG9q9OCujEYjSOUJYO7JuGp6I5+FY3m+MsRMrxMBLKW4+9aEqFUI1t6zUUclCNcGSHshoaOKQpeWXYQxmfBd0cxNahCCt0EgPlxKUVvdecfmFbt1Fh9/NyI9d+gW4u9/qwCoWBHGEZD0hPOEP8wTrCCjGxn0HldvdbhD+1l/ieOVp+316szsfJSilwihVkBXGmGUQEZ5ypPy99M7ijSr8cSYj+Tzh35nqjrd4A9/svIJf2fxZvNJ8Hj/Tennuor2kv+uc+oloostf7vB7szeSib2HZ4PFcv4C9lhRNrTPAfdSnuqsIMWLIvmm8XR0FQQrm+/WdB330EbjB4M38TtH38abyUf489G7K/19Ga+N3seB6iGiAF9rOaUQC4p9qZrjB4AnLwKtyCDJDD64MzlLbE3rFIQgXL0YW3KYD7HRscc5lyhMI48F4jBSFg79F1upTVuIZq9zX/Q5GBC0iEHDg1pyfg9DvJbhnZfzP3ml+n1zq2PfF7p9ubJ6ZxlIpTY5YtE7iS9clfaF8hF474GVkscdlKnVkeOTm02nWhOhS6hY/gUyRvjc0/a7+MmdsRKx8Bk4JlqijW5+hG5+hJZoPzYS/jKMMSvN8FM+gqnZ8BqrlSaX6YsK8eN3OI6FM8J/hsXwxmg15fj2MxUvOdrG73zSHX7AzdzzYI5rfH2H/gmE8VJjPeNnvVeN05mOR1wE1+Gvawg4gWVO/YS1u/EAgGDyGBmtnInjmlVUxmsT/paIcSnenviZ0WrynGZ8ZqQhYBwBcbTE/GKXXU7Nc8YdvyoSwd16Up0jVbnt2K9wvIkIm0ELyqjlqgfOwPnYkM4nDitj0FjyAucJP+8+gnAdfmrPzgXXASduZdgl475G0gXnq52/4cMPAKAwigPGvgRiivBHPKicXEmfeAZahOCjLoL9MfmKQ14URurCy/n9/L7dICrm9z0CztbqQE9j/8rncXDpBRxdfG68OljVBidmCzgl9U5bNKq9AxiDdN+l6FoHdD6whnBp1IGas6nKaDCiQjkCACjF87WH+3P/1qN1OD7mQWLX6TvmQ51AGlVI+hvGK1QImocwZPczIAFjAO2KtqEcYZ1kxXNiExwMqclwpKol12X4goSX9GsAYcCRyeXfre/C38oeLp5HZwyAwnRVct9J+ud17y8E2/hS87n5I0kAWixGQAIaplBTtMuEvz35edtl7IN5SWxN5ZfhISgbOLMsb9hn7yFt1ijSJOqgxWNcDuz1/vYKXf4j2ce/O/pj/Gj0TnEfuJvVdPufQl8N8erwbQDAV1svTqSbXAjs/WzeHD9jhBev2i147Z3JgsWEaZ0gsDwBKQkeBOi414ejk4gHZwz9RCNJNRgB58MuDI9RRQ02W9ZKKFfAIBMwcRsmamOuMdo0eGDd7xf4BKWZxt2HtmD95NXq96RmgyEQBGPGZngnBZIZpgtqs2AwRGBpDz7ejfUfgFReeTyOBvY73ixqKASAasvyX3yqAQJw54DjsG/GPhrHmOMv9oQYOsGGlfCvoQr9OJDlZsJgeOEMv09YqMszRGQVR1PHsujw14zj/LTgjPCfYTGKriXVu4gqSBMA5wHwCUbylSEiS/qnbrjGRQStU5SoNcefuzi+cDXCT8SKtISlMGZt13siAgVNmEWV42MQfhLR5DFahShXLtArH9aUthk10Z3w0Xzloo1gHDEPF7+IGlX7hddGV/K529wSsUsPsKRsVbR4jIiFld3bie0gIBS8EONYr0cGA72QGAAoovnM/i0ryyUGasx3b18EQQzCGa1JN8Mfp0eFk30d8N4egu4uDDGkl58tfq6MBmdjwz6PkAUQxGajy3iA9LIlzPGt18afF6zIeK+LaYd+AADZnPqJVXI6tks/AGSNLTx46utFB74cyRcwYQ0Q2Vi9E/MK80KHaad+7kYBsrgDOYc9a23VBNPfmy/ANIePFl+mxqB1NCb8vsMfUYDQjeH01LDo8MeueiBFBEbMeU8wcKdI86Z9VtK/+v2BEysI24OKOf6JfTSq6FAXkn4NCE7Qenkg4Q7fwCZvQ0Pjw+z+/A8SA0k5MV+cG1kQ9Lpy+MpFE2HTz/G7Aod36mcEbEzfirh1uaa05yICa16vrjsKlRX7cTA1krAKno3Hsv5lRU5jDN4YfYh/c/hfsSePEFKAb7S/CAKhp4cry/qNMfhO/yeQULgkdooRA4+Lzgdi3hw/ALx4g4FgcG83w2F3fM/2Hf+bjvRSNrAO7SBsOAHiiczxE8Oura1hpwME0HOjFTmjgrTu9wHrWr/C+wAPQCpfKOu/dS+BMVa2Py+znohOzbiP8iFMnfcnEYPyIVhyANZ/ABgFE7ZQdR2MO/wlHwwurNKlxs2/3QRunLf3sNduuTwdzpfmyNcFI/b4SPhVCjbcm+i4+5l97k61NDOQcwqpVqGRr/AOz2A9ESbf272KID4j/Gf4CwWfTc5ZPXO1ebmXRTf3kz/liAjU2Jjt8GtpzWXWmFMnHoDCJsyCqq2N4+u4MYkVsYAkTqzD6OOR8rBRPZJhbA/4ON8fiRAUxOPjbjQoOIaJY5EKsS7hh1U0lJdHkykTggRiHiJeRoLrmjwyR/jnzNk3WISABWiyaCU5v0fIBDaCJpIacr8w4EWH32g4V3xaOL8PAMx30d0+UGtnvXMaTm4+1eEPZIJAVxfOUpXPGO7FbuY+u3gTplRMk0ZV+hGETFgX9kpZ/0v2Mw/eA2X2pTsQNkpPyfrnWeQIvzfsKyTvU4SYGGGxU+Z6MMbYaDBuELoRH+J2ZhGwc/wRE5WFocKp3xsyug6/bG5BzXnR0tqmD0y/OPoOf2Owt1DJEI6OEJTypYPEkkAimojm8y79DXfNSxGDOQNKDpuqAaCQ9It8uPbhvSTsd3dfLib8fUcUAxKIyN4HjLEdXMEJqoas33f530/vLvggHxuEOniTvZgiNNnxEnC8cZ93zG+7S2mzVeGnQbawYiXdq9yn3BiWSov92HfrW0XO73EjvISABAZ6hDvp/C79SKf4/e6f4juDP4eCxuXgPP7W1jfxXHy9IOZ3s92V1v1hdh+384dgIHy9/YWZc/+iKxg9kkdQc+Z22k2Om+ft715zeevdvsT+kbSmdZdj6+ouh8Vx3nTjFSfS4SeG3SO7vIvtHGZJelAh6++vc1HZ9UyTqzK8UaGPIZyHzdMw7tO5PdZ1VCZu/psNDwAQEFSPOgFVHX4ALADpHJjznCuDVI6Xr9rPvXnbevuAhbZhdQKy/scGRoEP98EHu+D9RwXp9+R7oy0KU9h5XX6rmlim0JhaLQ/sHH/p+eRd+qMzSf8Z/kLBd1GXRfL5j3NhpaPT5PQxiOQrg6LmrARf5U6FsB4JZY3OXDm8jePT1uV8rYXzeiY9Rq+Vh15gHnHVjhEeR9IPgOLO+CFFON6IxyqjDpV/j8n9YdzJ5cbLa/AQV+Kd5U75dY8LE5YczyH8IRNosBCbolU4y6+KDdEEEZbGvVnZvJP0GwNixnbcl+SIk4hArfF4BFtTzu8RssBuqwigYnt9xKOjys8OVIJBuaimFaK7bwEA0qsvTHxWmWo/Ak4MMQ8ro+nU5gXIjQsgo4vl+jn+fJkmHQBphYsffBfx6BDAuMOvtAZnBD5FCoQ35DxhaGXcKWkQghdpFMbtMyOGtmhWSsjndfhNa3MuaVfaIBSz56sn/NHwYKGU13f3k5a1Lg+yYTGj6rvmPTUotrfpChe2w2/f1XgpflA54sKzsXR8VVwJ7bbcyXYXqgR6pfl9T/q0MRCcIQ450nz5/enpyLr138sfzTV5AwDT2JrowHqzu51jdPc9po37fBTfuU718TM8tM+7Fe/hRkQ2hcFH8i0ZSVgEQRxPhZcBAG/0Pyx+nuocD/N9vJV8hO/2X8O/PvgvjpwzfK31Ev76xk8Xcaf+e76b15f1ZzrHdwc/AQB8ofEMtkR75jMd1kRMITQ09mR35vceL12z5/Jb7w8hpSlI7+ULIeKIgcnEzZXb47XpOHl3fZ/BEggPe/a6vbDl0oAWoDDu6y382FwYxmfIlYeUBrfuL57f99jacIkBJ9jhL+bia6pQTdSy18ASJV5Vhx8uzarOHD6pHDd27MhFkgODBLYxtUQt8WkDS7qgtAcdtkDJYUH6PeFvNTiaDXt+Vs7xG1Mjjq9qxcIWIEvPws9qh/8x0Fef4XEGFd2hsJ7hnnc91wqYmMM1683GnxIoaABBBORpIbE3SoLi9nrz736ZRHame5oAqtzKINcwBASs3HyRP0ABc4xcezgPBs5htJwsHBhtyfBxCb8/1trOoh6nOEG+ELXGQ88WT6bWT9x+91PdmEVyfvud1N8P8jGAc7ocRITL8c6x5umaPEKDRUhUttB7IODWCdyTOEMGggTCGt8x61yEGlipKq1p2OcRMgHtfD9U5zx4MkDz0fvIL1+f+JwyGoKsC7s1GSSEjz4Cy0bQYQPZ+cnPS6OwOefh3xQRDvLq2ezk2otov7aL+PbrSJ78IkCEOOQYpjmA+ccmGB3h6tt/gNjN7z+68kVIV+DTLgVh2puAcQLBKQBO8N1CwkCQ/X65gdVDBjEwGr+pN3nkYu/NRHey6PAPu4DMiw6/bm0BoMr+iTHWCHIaOm5DBzFYnqA5OgSaT1Rur5/f751/CkHWB88ThEkPaWunMMLr6iFSY6+bprQv+pJHRffZx/JxYta5HwDPExCUC0RcDRfENmIKkZgM9/I9XA0vVH6uqycd+j0EZ+AhQz/Jl/acOryJC2ILu/IQH6T38FLjqTmfnFyKl8OvQ5an4Y37vKT/mct2XVfPzdlyEdnotlXBAujGFgDrX+EVBeuOJDwbX8Nb6S28O7yDbjbEoephpGefCdu8g292XplZz+XgPP4Mb+FeblUorMaF+G56G0OdosOa+ELz2crPEBEuBNu4lT3ArjwoOv7TuH6BoxMb9BLgvVtDfOjk/E9ebTgS03cmZHa7bIffFJ3j48AYg92uI/zby59hO8fq8MMWhzy5mmqs/PitPvLcoN3kuHhucdNlLOk/uQ73WHlQ805BfKwznwOlDXrO22+zotdDMoEJZ4tFk59JAU7oNGzxoDsEOg073sGyEdSSvz81+PekE5j9p3wESg5geASwACZqW9IPYDCy9/Jmg0Mpg25fVUfz6QykspoGoiUwYaMoS387duk/zl49fjjr8J9hMZgj/HW780w4mfXUBWmw9mz5aYC4AIs7MHmpm6LXc+gvEMS2iFBB5kye2GWvGWtn4xHrPGT18ZIQeOhI9FTlvMisP94tgwI3x58ntVUjC8FFvULINIzfn/HDqiDjqyzPR0utUggJwoXRlTEPl8rqF4ERw1bQWmwABtsR5ZyglbHED8sd+j3IdW6B4xP+gIni1B7d+AIAYOPeG6B0soWVa2mjFZkoYvV8hF5y5fmZ70AbM9ePIKTAEe3Zayq9/BwMExD9fYhDGw9nu9dz5viNwcbu23jqx/8G8XAfSkS4/fwv4NGNr4y3pZC823U+SrvIdA5GBEa0VI2xKnwEoI3+swVDFjZQ1rfb80zMRBSasAHlChWiuwvmiwSdbQg2R6JOs8UM+/OxcV9ztFd5/EhLNHt2dn2wdRXS+UGETtbfKST9fWTG3pdauSP8Ih4TfiJwsqRfBpGbLQeiPF3LGJER4UZkCxQfpvOz3n2H3xv2AW6kgoBIMAjOIGvEPPgu/3uLZP1T2F8xv34Rtpykv6sGzv+C8PxVVszynyzsd9PTAyhocLAignFVXBDb2OAtSKNwL39UkP0mi3ElOI+X4qfwjfYX8d9vfaOyqHBebCIggczk2JPVyqJpvO/OhxcbNwtVSRX8uMDDBXP8JAReumrP5x++0cf9R/b94ebV2JKYPJl4b9gsZvir71+roDcCUklgzM7wL4Pv8B/011w3EyA561A/TBRefd3eZ772hY2lM+VbJUm/VU8asLQLlhzZf9IeKOuD8oHNWs+Txc91s8L8/grojVwxlAHNqdqY4YEdG1u4XdpG+DFReDd0h36OPwTy4UyD4uMCGx2AO/+CY8EosNE+SJX8I4gXpH/UtQVV2+G311pVNB+Tmc+iXXED3KiJ4wJaG4wS79L/2erwnxH+MyyG7/CHdQk/n5EsG60AzuvPOX9MoGgqns/gWCoE4gIUtqrd/1UO1lz+IFu07Mr0g2kYHKtVSFxYI70pqZkx2qk9jlnN5SFIRDDZ0FbHl1TIl4H4igTdQ6tqxYIIYFbxBPCEf4Uiy7zoypNEWzQQsKAya92DM4JgDNK9uCloxDULMN6pH1g/kq/YDmJFTyU/fx35xgWQlmh88MOJz0mjEPMADR4i0zkoGyF8aGW803J+C4NgznhCxAIIxiErzh0TREifeAYAEN+25n2Cs8p5bCYzXH7nv+DKu38EpiUGG0/g/S/+LfR3bkx8rix5l0YhZAJDlYExcvYcJzvHr40B57bLKLyRpwgBUPGiHpIojuU0vKw/evA+CAaaB2CNtiWvU9vqXj8xz2dRbtrOeGu4V0kSGr2HYFohD5rImtuQTU/4Lfny0XxlWXTL3WNzERUdWQZmYx5hYx61UxMFerT28b3p5OIfZQ/myvq7FYQfsAVEIRgaoajl1n8zugwC4ZE8rGUgZ4xZ6tC/CprOqd+UnPpPG+Xufp3OehWICH9148v46uYL+LnOl/Arm9/A39n56/jbO7+Av775M/jp9kt4Lr4+t5DJiOFyYM/3u/nyOf6BSgrn/SfDasWKh+/qP5wT72h3gOPFyxkYAQddCWOAc1sBOi1hRx+UnJgr98Qvk0ByzGi+h0d2m853MGMoWoWtlqVHmQSGa6nJyZlPTo6t/OmPu8ilwYWdAM8+udysdrMtQGQd3EeJBqkUbLAL1n8A1n8I1r0P3r0HfnQX/OgOePcO+ODRfHKqpFUenHCKlPdZ2Ghh9t2PByC9JF5P59YYlwlsuJGAYpRDBHYMoabb/4nCKFA6AEu64IP99ccqAbDRISgdzPpHONI/HLoxrpjQjO01PKiY4ac8Wfvd1xZfBoCxiRX+Ug2PYTH1OOKM8J9hMYg5mXe9GyERAWKqg6mkNeh5jDr8gJOXcwGjcueMTvWMCReAxW3ATJIso5Vd9nHUA0T1zKd8Nv0xQGETZrrDP51Zv+6yiUANO8dPLDie3wDgZuHWIfzaufJPrp94uBoZn7OcRbDn2MkbtZUR8xBt3sBo0bgDAWHAoJSxb3FkJmKlFqFM+NeN5PMQxAHX+QYRRs9+FQAQf/TnoGz8Yii1QpPFaPMGcje7T0Yj37hQEFQPZTQ48blKiYAJCOLITXVBJLluzfui+++AZAYh7By/dHP8pHI0j+7i5o//DTb33oMBYff6l3Hrxb9RmVVflrxLo63zvDEgAjgIJ13+MTA2cIQ4mCH7csgDW2DzZotExbGchpf1hw9sPrlubgKMEAdWVlmG1gYcbG6ygu/wt4Z7lbnZXs4/2LpiFQGO8AdTHX7/XQlwhI7wSx6BjLF2HEQTJpDeuC+UI6g1O6FPBDuIKEBisrlu/T1Hjic61DR+uWpEAsYsd+tvsGglA7meHkIaBQaGTb5ed7wMIiq6/D4q77RxcEIKhXPBJn5m6yU817iOC8HWSvF+AHDFxfvVief7MLPd/YtiGy2+mJyeE5sgEEYmRV/Pz6BvxAxPPTG+fp68Go/l/FPFZMGpiEw8Wl4XWoj7TnhwYbPec51zwoY71Q6Wp1VWwkY6dsH7D8DSLvb3BnjzfbsjX/+pzVrvGJwTOi17bz/sSVDWB7SCiTZgog5M3LH/jjowURtGRKCRmwuvKoKrFKTW6Q4vhk9S2Kx69XN+QdNqh4mPKFlEWm/MeDd4h/kahP+EFWQkrQReB3beniXzPSoWLicfgI0OYYLqKEgQxyC330mH+mg1nGnfUNp3yHwEyvpgo0MgH8KseN0X4AFIW+WJl/M3YjZrVvopxxnhP8NiEAOEWIkIkwgnbzBagjg/8erpsRHEdu7eV9F5MN+0riYoiFHOuwYASB/Hd4yBoLok3uarrb8ewKk5pk0X9YlFKtpCS3AiJo62YLDGy7yrmk/7NVgVygoPR6NWL4Rwsa6P2ErYDJowxix2RxesOHqMaKE8tQxqnwdtXgLbvmqNGI8BnxOv3HHPLtyE7JwDUzkaH/5o4rMRDxDzEARCdOdNANXdfWnUQgNCRoQmiyuN+wBAbj0B2doCKYnGO99D/MEPcfXDb+Ppt/4Dnvn+/4XPfe//gxuv/weEaQ952MZHL/9N7F390oJrzxSSd2UUAsYRsQCJzsE5O1FVpo/kY2TAQRAwtpDFA+evMt7nJo9AhJnutS+g8MS+1auWJeEiYDOdSq01SNiRhSpIVxyKk6NK9ZM37BtsWkl7XnT47UtkTOGEUiNkAbiTX0oRwTBLVr3JZeA8IXzHKFLp2u+7jBhuhJcAoDIyTxtdEDmvRCgiEd3LYhQwiIAhryHrX8VAbkyW22sbfE6jmOOXHw/h96aDx4kUPAn4474rD+YWAT0+cHL+m9HlpcsVxHFO2PN5UTwfWICXr47Xe/NqbCPGZFJpIuznwY8zx98fGbz+kf376+frP5D8HP9+b811iwgAB9Ie6Og+vvODPRgDPH2Z4fJmXtt5vjDuO0xsPORcPwnr9WOiFig5Ah/sThi0ASgpDk72wezU6EWyQiWm1A5ljIsBVHT4e6PxcS93pudCpeC9e7YockKgfARvumx4CDbas9uxCrQEG+zb++WCufuBOzwt6qNNdh+GvSF495ZTb9wDGzy0z7V139/JFsJJpsW4gFcTfJZwRvjPsBDEBVj7/ErZ8SSiCemU0dKS68cl69NhIp6vcOg/ZlEijO3+l15sTZ46M8BjEOZVXuiOaawHHgCGJl/sjbak4QRAIrIP/XX9DMpgbC0ZlzEKqIoEXPHYGa1WjnG05wGt5z2wAlo8RsxCJAuifwTzJFRDMLE8jcCBGEf8V38D0c//vWNHbXLi4KAx6STC8Bk7/x5/+CNQnkIaBU4MIROIWYjW4BBB7xEMMaSXn5tZptQKggTEgm1riHB+MYQI6bUXAQDND36I9hv/DZv338BG/wGC3LZYpIhweOFZvP/F/wGjzqW56zGAnXF3p6k0Cg0eYyNoIlU5As7WmjGfuz4XyWfY+NgSD+y4jndWd4h5gIhmRz/ktGKiuQXARhQS0YSdiDKAAM3thui4BRU1QTCIh5Okh2dDxMMDGADDTRtNV8zwj0rRfGzcIosoAJee8Mf2fZ4Y/GCIT33QzocglKMifnIdPOmI3YfZ/Zlix0AnMDBgYEUsnj/+/nBwztAMBfIasn7fab6XP1qYDACM5/dPQs7vMe3Uf9o4qQ7/cdFhTbRZAxpmrpIDmJbzLyf8QGmOXy4g/Fzg6maGF29GePGZJs5tBaA8tTGaFfdk7/h+nA7/t98wkBp4Yhu4Of/2NYPyHP/aEBEQtvFhr4Pb+xyMDL5+c2gl+N27tYx4vXHf0VECkrMmgDMg7kh/D7z/0EbwAVZJkY9mlBQngYUdfpQj4aqvdZIjGPdO0nGv4BPpDCwYZ89XQeXgg0dg6ZHtgp9EZdnJ+Y1XA4oIBgQ2eFR/vMAYK+XPh8AC5asxphgdababaHF73x+mBLAQJmo6FccGELZwHEprEyRGRYffJwJ8lvDZ26MznDhYc2u1nG3GJ5uuSj1WkXxlFPF8MgPCxrHJCzEBRC2Y8o3PaOsXcBzUOP5eEn3sfRCh7UKXSYA5phlgGUEEChsn4+nAeE0zwyloVV3AmD53l8GscW6XkyxOEYJxbAYtpAs6JlwQuJ3ShmAMwQoeDeRjEY+7nc5orWxcl116BrK1DSYzxB/9GLlWCEggZAECxrF17333uadgKpQzymjELFxYZAxZAALNna1Nrr2IfPMSZHsH6aWn0b/5Cj588ht498Vfxltf/Tt456t/B/ef+TnoJU7l2kxJ3o1NJmi7OCdiy+Xeq0BrF8lHBqErLvn7B4VxEc0H2IJAizeQTnW8VGurML2z/+9k9pyBC4JU43NXa0CIxeeNl/XH/cnO9TiO7xyUu468pF/IxBoxYdIBP6QAPLf3Vyki+8KOcoefwxiMJf35fCl1HVwOziEggZFOZ0ibl/Nv8HIkn61BlmfSG6G9dy67VZ0TmwgpQGYkHi0xkBt3x0+OLG87Sb936l8Hh7KP7w1eLzrh85DpvFBHnESs4HFARLgSOHXFAln/pJy/3n2/PMc/fwM4CBo//6UAP/fVbRAMKOvBzHlGbhQd/lqbMIN7+wbv3LMn419+ia3UjNn2Hf51nfodtDb49hv2nv+Fmwwb2y2YuANSKfjoaOnFUhj3HWUwzp9kKYjDxG1QNrCkX6aW+MvsVEZOj5Z1+AspecUz2qiJ7fKS/lGGcfHQGSxPeyIAALQEH+6CsgF0tAnKh2Dp8bv8Xs4/0U0PmiCVgQ3njExMLyMfgI2OYMImFn1vSWbvp4CV2Dfb9pk5yFxC00lSWC4AmZQ8A846/Gc4w3JM3zjJrNwF/bhAYaNwjWfHmbEvgcXtgswZl+tKKygkqmAN8+aTE7sy7Wb4j6mk8BGMEw8hOr5ywC+JGNjmJVB8AnEy6xJOY6oLDm4fazsQG6x+XLj4WAg/AHSCho0pm7MuwZm1QYCdKxcn9B2vAiJCwMSkUz0RRq7L3/jgR1D5CDEPwYnBaIn4/tsAgKTSrM8R/iUFpYjZ/Z12qfcwQYyjr/9POPzLv47eK/8dkhe+jv6l59FrnF9K8ieWo4z1hyRWzHIHjKPJI8QsQE5qvaLVHEhjI/ks4Xd+Ea5YR0Fj5rxrBfGswoBxqPY4Rsx3+BkjhJyj7F1ojEEgFt9zvKy/OZwi/IfWkd7L+QHAiBAysPdLL+vf4JMdfiHHkn6Q66jDu/Xb//KEX+SjYx1eThzXvaw/nZT1Vxr2uQjGMomKQg4hCLlafM0zosJA7t4SWf+pdPidpL+nBpArdgP35BH+c/f7+FeHf4ifjN7Df+m9iqQiHs/D+wQ0WYyopnfIaeKKi11cZNy3ipzf44LYAgAcqO7CcQFDvHAKJ5m6OLbq47JVdPhXP7G1Mfij1+wF/OJ1qj2/77HTOaZTv8NrtwwO+kAcAF951m8DwQQNUNoDycWFuiKab4Cxu3stMJioA8pHYL0HdrTAnPz8/kQk37xXS+K2IVahaCCZg7QstisKCJF7pJW7/IYJK7EvwyjwwR4o6cFEbVvo4AFodDgzzrAqqEi2mnz3MlELlPbBh3vzRwyMsmR/sA/DaMKMsgoDd1gaoTWVbLmvOZeoZYS6EngAUvmY8DfOCP8ZzrAUxLidYy9f9I/b/L4D8cDOIHNxMhJzuDl+xu0cf57Yl+zjKhw8kV8Y4eLm94+tUmBAEE8Z95kTI/wAwBobVtp/TJDb35Xl8YTKBzyRi+pbZXmrvigwMZNkcVposAgtEWM0R9ZPRAg4h2YKrWMkVBwX0TThB5A+8SxUcxMsT9C6/Qaa7nxR998GZSPIsIFk52rV4kDAXId+j4AEQuJz5/irEIdj4766UDDgROCMoKALbwFOVoEhTT5ZpFOZK7at90LjI/kAOOLPxgqtCpVOg9kYyOnjUJb1+w4/AEQBn3DqNyV/guLzRuMg6xeFBO/U3xyW5NLGjOf3tya/x6xhSax36i93+CPiIHeuSBHDEJyE3u+z9aXwhJ9nQ4CO93J407mxT8v6e3rWsE87M8byiANjhFYY1JP1e+K5oNOc6hwD1x0/STl8g0UIScAAtZ36H+T7+L2j7+LfHv5R4XMQkPVReDP5aO7feYf+Hf7Jdvc9nnCFlkPVx1DNdkwHarSynB8AWryBFothADzKD+d/kNkOI4yyHVvjkmQqUO7wr0q637ht8KgLhAL46edXbw5suXWnue02r4M0N/je23a7v/YcIQpK2+FIIEsOFz6Htzr22PQShnzlRynZ2DeVAcMjONfaVReyEP2R7U4LBrQWvf4xZsc3prdQZ1Y+VToHCuO+Mr/ngY3u80TeaPDBPig5smTf0zwRg2SytsGeXbYCpX2Yyvd5BhO1wUZHYKMD+z5qNEimYM6kkR/eBj+6C9IpECxvghXz++74BYLgxFIYzrc+WBPWBHE4cpL++LNHjz97e3SGTx5c2Nlqrax8lPGTk4OfAljcth4DJ1WUCGKraJApjMxAjfXj+Aq4Dv/CVpXR9nNzzLNWWl3YmKwEn4D7/6mAcYAtKYRUYV5nngm7nzXIuHHHe9Vz2yZZRBPS6tMCEWFLtCC1mvtiGAX2e13WET9NhCyY3T7GMHz6ywCA7Y9+gtB9xerDVwEAw8vPIaswWLSEiyYc+o3KYfLJNwQiQlPEczv8ldspWO3AjGJ7lHPoJ0Bplx7gXuLaogHBbMHKGLgOSAIYCUpslrSdZ62/RmU0Ak/4Dez9wEv6eTBTIItYgJhFs7J+R/i1iJyL8vgYjMP4AIBsYaGEvhxZZYnraHpJfyPtFjL9aLAHIVMoJjBqX5j4+zyedOrfKBH+2Nh900yAWGA7/MQKCT13/y2dqopnIze6UX28pNSQcvH940p4AQIcAz2ayGrvVXT4NWzywvQtPw69emjhqgpp+UN5MLcYdeDi+FqsgWhdZ+oKWKf+esZ9d7Nd/PvDP8bvHH0bd/JdEICnoyv4W1s/h6+3Pg8AeH30IdSc68vvw0mOJBwHMQsLg70q00RfzFhFzu9xoeYcP+ncSqbnkiqLiWi+FRq2aW7wJ2/aE/CrzxGaa+SMC07F+vfXtHr4wbsGSWaLBy/emN0GE8Q29m2BEVyDpYgDuy/rjTYQTNgu1nfS8P4KG00sfP8zLADy4ex8vcpm1JrjaL7STYQHICVBMnOz8Qeg0YGVy08UjLx64mjtKD8r58/nN8eIQwcx2PAAfLjrTPVug3XvA6k9WUzUggk7qFNgGaZ2P1ulXkTT/fdgvV1YCMPEmPCfdfjPcIYaKM8oKwVr4fx4dvgBgMImKGqdXIefcVDcdgSDrE/AccGYvXkv7PB7Sf/xb1TWeNEvVsG6Tj+GN0DGALCVovmMVrYIVUn4uV1mnQKCku7zqxezWNyeME87TbREjJCJGWM2D84ZOGMrR1mdJDjxSkqbXnkeMm5DZAmCW38Ok/ShHrwFADDXv1CZIW8N/viEQ79J+jDJ7PxizMKVOmSBYLZTr+qfb9oYBIGP5FMIiBejEw0eoiEiSEircFA5jIigO1egNq9YMyJjQGkflPbszOky8m/GBnrc0Ph+DNi5Sy4mzj0iwoaYjefLt2xXW25emHjxFILAwaC0GRvUlSL5vBQ8YgGku45M2IB0IzzRYA8A0Dqycv7h5uWZ4lsW+w6/j+Yb30Njt/tWzg/AmImEAE7cFhsKwj8EA2ai+YwBBolELg2SJS1CQRzXQlu0+CAbz6ZXEX5ogPHZV6sw5AgEIVsi6+/wJjqsCQOD+/le5WdOQ87vUce471b2AL/b/S4eygMwMDwf38D/uP1X8M3OK9gWG7gZXUaTxUhMivfnzPL7Dn9dh/4g6aHRfbDazqyIYo6/gvCvI+f38HP8C5363fOdpYOFcn4ACDgVXc9lZLe9/yGuvfF74PkI339nTLQ//+T6TQjv1H+wxhx/d2jwow/s3/3si/Z+OgPi1kRtdFA9E24MWNrDdkuvvR0FwsYpze87w75liZlczM7xGwPKZo0EN1xTvDdh1miPH+UjsOQQbLgPM2+feAhS0qknVj9mVs5vsJA68hCGC9DoCDASRsQw8QYQtt3oRX3a6Tv8zXh8jjRj/7sTlvQDAA+KIsPZDP8ZzlAHZcmyliAhTuWGelKgsAF+/sbJdfgBW0BQEgjCY8/v2wXW6fCbQuJ+7NWJ0HkG6FLW/GN4A6QVCLqHVk4JUXFO+kJAnQKClusnO3yMnhYhC7ApWhioBCOVIp/q9geC0GkEzuDtk4EgVl3vZxzdm18EAJh3vgP54Z/Zl73ta4i3Lle629tIPj4ZMWg0wGYj5SIWuFi6ei8PgjMIwVaS9RsAwr3UWof+8XfPiGE7bEExBaMNSOW2AyIimLAN1b4ItXkNauMKdHPbLi1fbM1tDeNsp1vATF67zq1/utjUFBEY0cRYhdy+jKOv/Q/ofeEXpo4Bt/5GWhcGgeWX9oFM0BFNRCyY6O7mG7aLHznjvvbhZBxfGbmX9Dun/gZFELD70HTHXokYxABDBqL0KsPBwMCQOzNHMhoCKUzpmpZKY5DkiEKOjXZYS83rid6HqZX1G2MKwl9WIGhtKkkMY4RmHCDP68j6LfG8M2eevCD8pyCH33LGfQdzjPu00fje4HUAwM3wMn5t+6/iZ9tfmDgGjBheiJ8EAPxk9P7MdWeMKWb4a40kGI3rr38LT77279HonR7pLxv3lbd5XTm/x9ip/3BhgdEQd8o6vbRw7+fCu0vm+M/d+SHah7fB7n2IHzui/Y2X5hDtmvBO/Q8OV//b77xpoDVw7Rxw48KCDwYNS2LT2cITyRSUD7DVttf94cklzp0YfId/YSQfgCISTpXmI7QETD7zjrIx5zv38XxsuGcNDBcU703QtAVkuWLEg3fn5wHS3ODV9zSSbM65J2KYqAOIxrEaUL6L3yx1+FtOlTI8jQ4/8WK5rTOX/jOcYTmIyFanjbayZRE9dpF80zhWZF7V8oLYkv2ofTKFBO+IXqPDP50tvxZEYImskjbC7oQKCSeOOsdlGkYDnNt/phdHzv21hszbKBc3ucbxJhEBnFtTx48B22EbO8EGCIRUZzjMBzjI+jjKB0iRYSOO5mbWfxzgZJ2iq6LIjp542kasJT3kr/9n+/knfwoxCyCIzZiLKaMQs2DCJR3ATAY9YN3yAxKF9HwpCGisPMc/7kBrYxBNde42wiYEsaLza8RUgZAJmLAF3TxvzaYWFKO0c6w3zMrcbYdfFGkKRKxynCRmIWIWzsj683PXYOLJFhUREAUCShooY8AJxf757+Jc2EHMQsgyyXbGfY3+IzCVo9F/CGB2fh+Y6vC7EQ0/x9/Qfn4/KsYreKkYyX3qAyNo50sR5QkU7GeHqUKaa2y1I1zYbCAKeaXkPxrs4cZP/j02dt8BAFwNL4CDoaeH2FddjEwKCQUCocXG35kBxokMU4gDvrDAdPWt/4SnXv2XuMotQazqNAPjOLtT6fAvkfS/mXyIrhogphA/2/4CmnPk7Z+Lb0CA40B1Z5QKfT1CbiQYGDb5shYo0Dy6h9A5jG/ff32V3VkJF4NtCHAkJi0KEgDwwTHk/ID9njgYMpPjaFECAhOOVC0vCHsiebiow681aHSA+yHHo3s9aAM8eQG4caHe+1iis8oCxdVz9u/fvmvw2kf1n72vfaTx7r1xd3/xeyHBiBiUHM7E9FHWB7QaE/410wpOE0WHv4bA0zA2YbxHOgNVRDIWkv5pP0MRgmQCwwTAl3jxMAHAgCVHK703kUytuaAI8f13DL79hv3nNDF0XfyyB0Kr6PCf/PqSnKCNPcaNsw7/Gc5QDyQCK53W0prW/UVD0ACFTbDGCXVganb4T0xJ4TqBRuW2283mdMQ/YZCfT15FnqaVM5acc0MXwUQ3cP5yctC6yQ4idJnoHw/hb/AIN5oX8GzrCp5pXsHTrSdwvXEB54IOYhagweOJmfePG1aGzWeM+wBAMgb5zNfs/xgNMAFx9WVELEDIZjPklTaIS67fxo+6VHS2BXGEJGbk7IsQCg7UnOM3BjYyzhFAcussI2YRmiJGJm33xCwys2Ri4bnuO+5ExhJfYybjk+CKkVPHjBFDRzSR1TwfI8Gg4f0JeNEh9939Fm/MnE9qyzrdx4NHaHbvgYxGFnWQx7Ok1f+MqwzczZt+vvE0rgQX8FRulytFZFdLKLr/AAr/Bm20LRQBiFUCKTUGoxwhJ1zcamCrE4FzgmDW9K9Mwhvd+7jx2u+g2XuAc3d/DMAa0V11hnofpveL7n6LxeClYqgxmC02+eMWckSCIavwDBDZEJ39DxElXdzM7HnTVQP01eTbvTa66L6fxvz7lrAd/p4ezhTTUp3j1aFNyPip5vMLx4AiFuKZ2BZzXkven/idL1hs8XZhtrgIm4/eKf67s/+hNWI8BXDiuBTsAJg0TfzQyfmfWkPOD9jr67xz6184xy9CGM5rKcDGHf75n4lGh/jX59v4f944h11xBEbAz75U77X/neQ2/s/938ObyYczv7t+gQpn/f/y5wbv3V9+N3z9lsYf/rn93JefIZzbqFF0EBFI5eCj7vi+pzIXWRgXSoNjSfoX4DgpBOMZ/hr7yQIgT4pGAynp9nd6ht/+uzuc3jYGE28Col4xys7yL/ZImIYtSFg5/909u+73HxgofXqk33f4WyWvCd/tP40OvzcCjAMDzk63mPFJ4Izwn+FUQEFsb14GJ5O3/ikDMQa+dQXU2Fz+4TrLK2bz5xNRo1WlC/d662N2tk1JS36ZOBnlwGmAi5Uq1fY4zc9oJ6dOWb4ggNZ0tifGXRLCx0P4PRgxRDxARzSxE3ZwpXEez7Su4Hpjkbby9CGIgYNmCL9xHWv+5JcBR974lRdAYQOMGNqiUTHHbxCUr4PCs0HMmDESEVoiXhiXNY1AMIiac/zKaDAicKdemDYTBCw5PBe2kcvEOhcvKKwZJhbKz33HHQQETIBB2/O5BHvOzr7MtEQETBHfeRDCjmAooyf8CQDb3WdEEMxnzzsJ/qaVS0dZHxuP3gMADDavVO8nF8hD+317p/5n4qv465s/jba037cUcRHFx6fuTRHZ1Afv1B/JBATCRivEhe0GGvH4GBMxOz7ttrO9/xGuv/674O7aDEeHxXytl3N/mN0rXOw7FR3qeYSfiNCIRaVJYPNoPOvezhKcLwzkJmX9R2oADQ1BHB12MlGyZTQoQkSBW9dkN/pHo3eQmhxbvI3n4+tLl/VS/BQA4Fb2cML1v5Dz15jfZzJDZ9+SzjxogIzG1sO36u3MGvDjFF5dcVw5v0etOX4QELRQ59Xcd/h9J7kKweARftK21//9rQRffIqwtUxi7vCWS1h4O71d+fuvPUd48TrBAPiPr+qCBFbhjdsaf/Bj+/sv3KSV0gEsOe0WMX0sG1iDOh4WiQGHa6QVLMObdzT+2e9pfLS7+nK1NsWc/dZyAUvJsNHK+ilPYCoK8K3Y1q6VPibhLTwSDqs9EqZhFCizypNM2pQHwCY13F6cHnosDKdc+sv/fRoz/P6YNkMzOWLxGcFj+gZ/hk89yl3Xx9iw7zRBcftk0wn4kk62lmDxyc10srDpZsnUiY88nCSIiXodeQ+trEfBvOXxYCnh913j44xrfJzGfYtARHMJyseFeR3+3CgIxhGGDYRf+mXQxiUEz/9c8fsmj2z6j7su/L+DchfdEX6IsDIZIeLhxDKWYZU5/mLGnROk0c5bYPZaagcRuAHyJVLe1EhYWl19fmoFBIJDQSMiqwaYOUd5AFTsb8wiZ+64vAgluDcvNIU/Qbm7D8DtK4Ny22qCuJDqd/Y+sH9TIef38J8NpmKkuEtbkDwC45byTztACBIwZhzNFyPFxa0GdjrxTIQgYwRGtliysfs2rr71n8CMQm/7BvKgCQIQO6PB6+FFMDAcqQFuZ3YkYYPPkm5aMB8dhcKNr0we/2b3bvHfYdLDlaA6nm88v38C6S8VmOfU31NDvD76AADw1daL9Trzoo1rgR3leG007vKvYjrY2f8ATCuk8QZ2b1ilz9bDN1dPZqkJP8f/IN+DNKqQ818SO3PHF+rgYh2n/hXgO/xHCzr8e8kDZK4YdqsNvPJ0vXvcSKfFdu7JIwz1rH6aiPDNlwlPXbIE9He+r/GoO7v8N29r/OcfObL/JOEbL9Jq5y0LrG9LcgioHJR0CxVUp2nFh0qjyLw/CeTK4NuvG+QSxQjCKugnNpKPL4vk83CGjaQye17LpLLwyxmh7Za3SNlRCws8EmY2T6auyBLgwcFkubiOumMdaGPGBLxihv80XPoH3rAvNGsnGTzOOCP8ZzgdMO6HGU/UDO8vNBifS2yNzGyVOKpTTq4JR4qN1jOy4McKbLUOP6AXEv5a5oRKWuJ0nOPyMRr3fRoQMpvdXUauJUInuxdXX0bjF/53sM1Lxe9jFiJgouguS6MhGEfAJjv8xAVY1KqMW2xxS3Sn59fnws/x15AyajP2JxinB8yeX01iiKiB0ZwnsjEG/bwHRcAA+VxTSR/Jp41BQMJu7NT5TDywxcOpYxEwjjZv1DoOgjMIbuPvOGMz3X3AFl04sYkiTtqxZIpgYEAYbszvmE479Xt4iX8uYnhePU0+BeMwMAXhD/OR7epXGYKTHUs4f+81XHn3j0AwOLzwLO48/1eRtO32xgNLukMWFITQx7TNdNkJmDPCb5chGLgg6HLByBi0Sh3+IO0VneZ7+aSBnI+zO435fQ8v6z8sdfi/P3gDGhqXg/O4GtRXBL3UsF3+d5Lbxbm1imHfpvNQ6F54Fr1zNyFFjCAbon3wUe1tWAVbvIMGRVDQ2M0P8EFqCzE3oyeOtdwLrsN/pAZI9fG7h17eneaYa552W4+vnZQT9k29duytbNIY8U5WbR7JGOEXf4rh8raNCPx339MThnJv3dH4T47sv3yD8I2XViT7Dia0EnQ+OnBz5JYBMiJsueNwknP8r31kMHJf0X5vdULrkxOWRfKVYYiD8hFI5SAj56o1C1n/6LhEm2BEBEqOZjwSZj4pnZyfOO4dmInteO/+6cj6R6mlEASgUSb8ruAxTE5e1VFWFJA8BZOATxhnhP8MpwJiYjz3fZJd7r/AoAXE1uQJKGwBJ5gnSzywRQaVP9YqDTsysoppHxaT+jqjCypf36Hf4eM27nvcEbJgpsMvjUJTxHNfmkImENE4clAaBQE+0eH3IxzzCiwhC7AVtDFaQcIXCpepvuRzWhkEwp5Pc80EAQidoRFtQ1Wce9po9GQXEY+wGZ2DAassXNgNIidvNy76z8ye6zyw9+UKtUM7iKGNXv4iRdaAjjHrTzDd3QdcSgAJyNK25p0xURx1LkIvKHplU079xXJdhz/nYRE/OH1MuUt90M5jg6UL2mHG4NLt7+PqnT8FAOxffhn3n/7LALGC8Df6Y6L05BTxK0v6rWfD4g4/IwIHoEpnT5B0EZTmaYO0jwtiCwEJpCbHnhofA98dL8/vM5kh7j1cK2qrCkWH3xHzh/lBEUf4tdaLK5G2y8E5bPMOJBTeSj5CbmQh718m6Q+SHpq9BzAAjs4/A8M4Di8+b//2/hur7lYtEFFRbHk7vYVdeQjgeHJ+wBYnfZKBX+ZxEAgq8skru/zG4ANhr/GmGz+6ndyt+OAsPkot4fejHV7NUgXBCX/zqww7HSuJ/v99V2OYGrx9V+M//dCejy9dJ/zcy+uRfQBjCXrWs2NNpcrd1jEiAqsglcGr742Xtd9fnVjOi+TTxuDV4du4l1UUXpgAZGqJplJzPYYK476TsLEQIUhl4L2HE6aBEzDaGUna93hP+L/0FKEZ2ULPacj6y9398v3dd/ultus+lXXGZD0VVlGOfgpwRvjPcDpwUVDEH+9Ivk8VFpnTqQystXmyEk8RglgAI+XJjiacNGw212p/s4DwExPjSMI5MEoCYaNwP18LH7Nx3+OOgPGZ01ubSQO+aRAR2qKJ3JFXZRRCLiY7vlqCgth9r6zye90QTXBitc37AsHAafkcv3afBWbNBMtgRiKKz4FBIC912KWW6OVddMQGLjUuox1sgPMQ0sw5Z8jKPg2MfUkiNnPtEhf2vKsg/A0eIWAB8hopFSJgCATBuGLbTtCZeDEjIhfNNz5GuXPqB+bP7xefndvhd5J+EYOcXwGbepXxJnq+wz+X8BuN1mt/iHN3fgQAeHj9K3h442u27Q9g1Jrs8APAjfASqEQ4ypJ+DQMGwsLEMwI45xO121bXkmnlRjqCtAdGDE8E5wAAd0sd1qpIvksffAc3f/LvsOMMBo+LsaS/D2MMvjd4DQDwXHRtZWUBERVd/teTD4rtjylCgy32QNlwZn3DjcuQkVMdXPocDAit7j2Eo8OVtqUuvIrjPdfdP66c36OQ9S+c46+PDUcoq+b41WgftyP7nPu5A3utf5TvLiWvuZaFf8FXWi8AsH4GVQkqHlFA+O+/xtBp2OLDv/q2xu+/amAAvHid8M3PH4PsewQNGNBMU8Mb951UNN9rH1kpeadha/9SrT4uUETyTRn2fZTdx6vDt/AHvR/MGGJCBLa7n4+K+08VNlxN9UQIP8gmv6gMrH8fLO3OvGOO5fwhlDZ46E7dKzuEp5+w27nO2MMyeBf+5tRlJzghCiY/c2Lr9JL+BrPH5DP2bnZG+M9wOuDCkqpgfnfuDKuBXJzKNGwHXqzvGD9vfU6yTsSsydzjihW2zc7es8WeBIzbJ/2iMQEtrcfBMfBJGfdNwxhju+CfMKal7toYGwG3wAkcABo8hIHdD2kU4mkSYYxVgYig0qkfsF4AHdHAUNV7gxCcIagzx29M0YWeMRP0P1U5eBBCRJuIKEambZshVSlGaoid6BwuNi4hZCECFiIUTWQVagQbyWfrX4wYhIFTWVXETwaxLVpNISSBJp+N56tCFDCEgiM1GTqiifZ0nCCAmAUTREFuXLAv7Vg8vw/MRvN5iELSb2P5GFhFh5+DEYOM7DZRFeHXGu0f/T4at16DAfDBtZ/B/tUvTrxsJ61zbht6YG69EQtx2RFxAGiXCL/RxgaqLHJWhH1pLctgvWHf0YVnAABBNgRpOc6FdwRsqBMkJgNhssMfOwXChVs/sJ3+Y2K75NT/Tnobu/IQAhyvND+31vKeiq4gphBDneDVoTXc21lm2GcMNnffBQAcXXi2+LGM2uhvXwMAbD04nS7/Zdfh97i5pjv/NLxx38nN8TvjvorT+/3dWzBE2EoNvqQbCLRBHzn2VXf2wyXcyXehodFhTTwbXUdMIXIjl25zK7akPw7t9hgAL1wj/PxJkH0A1tCwiWnaMjbuOz7plMrgz94bJwlsu2XvLR9zn8DRsDqSz49KpCbHh+n9qb9y7xxaVhr2eYyd+k+KZBOMM0hlvV2w0f5ELLGV82uAOB51bWc9CoDtNvDMZfu9noZbvyffrYqa4Gk59fvltSIXY1t3zO9TgjPCf4bTAeMgxkE1Y0LOUAPzpOZ5Agqa1lX/hEFRyzrPPK4O/YBlOLVC0uAM3KpJUAEf2bdMznUCYw6PhXFfNoTpP/rESb+XYfsOlDQSAQmESxRCMQ8RMmGd9g1mP09wMvbAFlkqOttEhK2gDWNMLZd6EBBHfOFLjrELhmBUbSbokSfgUQM8jNFgLSijMJQDSJPjfHQR56ML4O7viAiNoANVkSqgtQHnVETycQN7HlcR/jCuHAsgInREE3mNczIQHFsbIUCz3X0PMVXgYGGA29d+Gg+vfglJ6/zM58vIog4MCExLcC83Nbog3pLZtAEiquzwc2LI3T2RpVMDvlqh88PfQ3zvbRhiePTSL+LR+edmtkEHMbLIElNv3AcATzoC2KDIeSW4zTMGYCgVearBy783Bk3X4e+dewrKnb9BOiik5Q/zA+RG4sCZ6HV4a1wgMxphakkcweDKO39YHKN1EbMIMVm1wZ8MfgIA+Hzz6bW73II4Xmg8CQC4l9vjuEzO3+g9QJj2oJhAb+fJid8dXHoRgJ3vp1MomDZZXKgcgNkxjnVxQYyd+mc6vEvQVQM8kkcTP9ssOvyzn78t9wEAl7MILO7gecdmPpohmpP4yHlT3IgugREVUZSLZP0eW23Cr3yVYatlDfr+yhdOiuzPxzia7/jLev2W7e63Y+Bz1wg7HbvsVef4/fexWUpEMMZMeCFUxR0azq1Z3IJnnpf0906kw1+CaMCIEGy4B95/BOi8JOe37zr39u1xuLxtnxVPbOPUZP3jDv/s+XNaTv3lMQLDOJhMTmxM6nHAY/wWf4ZPM4iYdQE9MyY7ORBDleuUkRmouXk8efm8VYYxmJvlf2zBmJPg17gxa1upXugrwVyBYw4BNq5ocCLn9mNwfRiZgRqbMKPFnZ/Thu/Kale8ybWyZn1zZhk9QibQ4CFSLWGAidg7K98nkI+VDKLKzjYAtEWMBo8wWmJg5BEJaww376wzxsq7C4d+xifIYfE5mYK3t8E5R4AIAQvBiONS4zK2o+2ZF+Y4aFa67Jcj+RgYBADi1YR/UbGqwSMIxmsRkoFK5nb3gXGBw28rI4a9S5/Dwys/VSlb1cbgaJBZ5QTjyJ2M28v6uUyLO6AMQhiyDv3Tx8jGPDJIJ/9lKgdcnB+0QufV/4DowbswxNB75W8gfcJ21qu+S9/lj0tz/E+HV3A9vIQvNp+Z3H7Yl6plqRecsWJd0XAfQqZQTGDUuoDcJa0ESQ8brIUWa0BD40G+P+HQ7xGkfZAx0MSRRR2EaR9PvPfHx35R9cZ90ig0WYSXG08fa3mfi5+cKMwsM+zzZn29czcLwuEx3LyCLO6Aq7yIeDxpeHXFJbGDJjuZxsUWb6PFGlDQM+kLi6CNxreOvoN/d/jfJpITxh3+ye/64aHBXmyLZE+G28ijNl4aWFXQR1OGfNPrueWI/Y3QFjmuhnYMpw7hB4CLW4T/5ec5/vLL7GNRd/oO/ygD0nz9c15Ndfc5I+y4ms/+Ch1+bUwhty/P8D+SR0hMBgEOAuGhPMCBnHrm8hCk04XvJx3X4R+kqJUUsxJ4ABO2QMkReO8BWNqzJolu1MjP7z+xM/ZOOS1Z/7jbPvu703DqN8YURYZWDIAHYCr7TI1cnhH+M5wa+OYlUOP0nIT/woEIoMmbqlESYNx2ik9jlTwEggi0hHR9kiBibo6/hsGKVvNJULFAbvd33vKUtFFBJ9DhfzyM+7RVchDZtIdPCMJ1Zf3Md24kWgsM+8roiCYylUM4o7gCWk0Yh1LQmFvI4cSxHbSR6rxW8SgQDLHgGCaqkij6SD5GBOXMBMXUeWe0NWdiUQtRwEEQuBBdwOXGZbRF9TUd8CYCxpFPzfFrZd3mFWl7LA0AJioLgcSDuX4GMQsRswDpknPSm/HN6+4DtrPL2ThusRyBNw0DYDBSiAKGXNllTzv189y+4SkRAeAwZOXznAgmHcC43zN3LknOnckXwLIhoCQ2fvA7iB5+AMM4ul/+ZWQXnwJnAAerVHcUxn2lOf6ACfzCxlfxoptNL/bBAIyxyjSAMqwCwElWnZx/tPEEwFhR5AjSnjWQ87L+7FEhxy7P0ftjk8cd3H3u52GIsLH/ATaPmVVf7nC/0vxcZbFqFTRYhGeisW/Dog4/aYnO/gcAgO75Zys+QEWXf/vB66fShXu58TSeCq/ga60XT2yZRIQboU0Z8Z30Orif72OoExgYvJ3eKn7uCWV3qsP//Q/76EUAMwZXOteQR228MEhBxiYk9FR1e/h+vo/cSMQUFmqEq8EFEGxiQ3/O332SCASh4+qNb99d/zx4/bYlfK3YjiIAwDnf4V/BELA/suksbCqSzxdMroYXinPgzWQqaYIF0PEOFlGzOAACdymeZBRhAeIwcQeUJ6DRvn0PIg5jDO5b0Qgub49vcKcl6/fd+6pYQz/XPzzBGf5M2nhHwI0MMAEyCvgMxfOdEf4znBoobDzeZm+fNlSZ0+WJnd0/BTk/YB3wWRA/5h1+boshdQi/UXNJkAcR2XnvecvTOUgcz6G/wCds3GeMAQyBmptg7XMwyYrDiicIThwcY8JvDBAtyaX3iFgAzhg4TTr0j0c4HOEX4cLzpBM0EbGgXjSdYNjZjBEIwqiC9PuOO3OxdQEXhZlcgTyx8vqwgTCwIwLtoINogXQ6EDEiigpzP20McqmQK4UgsMcvYAGMUfNjI71qp0LtwJysP6sYffDQRqObD7El2nO7+4BVW5S/Ux+BV/UVDBOJOOKII1H8fuzUb6XMY8O+CCCAjDUnJDCYbAiTjQlJwAQ0jaP5+PAIGz/49wgffQTDBLpf+RXkF24U+2xrfLMvrKO2lTSXO/zzYDQglsj57frc5wE0u9YYbrBpxwRyN0IQpFaj7CXVd/PdoiM4QfidMieLN5C0L2D3+lcAAJc++BOEw/Vnxc+JTQC2E/9MdG3t5ZTxklMJCHBs8flF6vb+R+AqRx62MdyoltMfXXgWmnHEwwPEvfld63XR4jF+fuMVnA+2TnS5nuzdyh7WGx8CioQEAHg3uVNcT35GPMnH3e3u0GDPff56ImFa55FHbbS0wZOpLaTN6/L7IsT18FJRxItYUJD/23Pi+T5pfOkpu63ffcvMjShcBKUMfvCu6+4/bVVZAIoO/2EftclsIedvTip9bueW8F8LL+JzsR1ReTe9Y0fRVgARnbBxX+VaYKI2AAYT2JUdDux5JhhwYXP8ycunJOsfy+srJP2u6+/n/E9yfaGwHitF1XalyOfHG2eE/wxn+JTAdrIn3eONTE5Nzg/Yok2wcxkULHZT/kSxQoffaGW76ssWyYO5M+1GSVDQPBG5IjEOhI1PrsPvDR9FBNY5D4gQJjuZtoHOU0vC0j70qAs9PIIeHEAP9qD7ezNqAkYEwQS00VBGgxEhqpnwEbMQIQUImZjsomsJ4sG48LikSBOxAFtBq3ZEXxhwnNuMwYUlrGUYbcktESCNrnToNzIBxRsgxhEItlRZYIxBoghMheiPEvQTiVGqoAC0GyHiwHbTIyacmmVOwcQfkzmkviliEKiSkGhjcJQPsRW28US8s1C+7gsw5fGAacM6AEhy5SS0fh/cWMdUh1+4SD4lYgAGhszYtG/KyyNkAsqMCX/nh/8R4d5tGC5w9NVfQX5uTGIZYyBC5f6mrXN2VCQbgC+5NvSESeN8cG59B4ySaHYt+Rpu2O63J/xhaotv3qn/UPVxqGwRoCyH98fGqyH2L38e/c2rYEbh6tt/AFpQuPHbXKW4eCa6ir/U+jx+ceNrS0cU6mJbdPBLGz+NX9r86cKXogpezn904Zm5juVaROieswWErVOK6DsNXAp2EJJAajLs1jDv08YUc/cEQmKyolscCCoMzDzR/PEHBtGWPaeeygmGC+ShLa683Lfnb9UcvzGmKATciC5N/O7airL+jxsv3yDstIE0B/707dVJYNHdj4AXro/Pt3YMBNx27P//7P1XkCRpft0L/j7hKnTKqizVVV2t5fRoheGAAEGAACiW5OU13qVRGF+5ZjQ+0taMjzSa8YV8Ivmwtrt394Hici9BAiAwBCEGM8Domda6uqtLV+rMUO7+ffvwuXtEZIZMUV3diGMz1t0ZEe4ersLP/3/+5wzzSRiGfN6/0Sfnb5kO65n/wnl/hTVviZosE9uE9zvTRSX24+SN+0ZAB07NSG9+f7Ux6EEihODqKcj6B+T1B1DO5vpP0qW/8Ax4iB91j4s54Z9jjk8KpHStoezhzJpMzh+UJ3zweFDR6YwLnBiKmftpJP1mdNezH8obvTyTIk5QUSGD8sdn3JfGhYO98CNkbRXb3R8bSTgNrEkwrR1cBpmPCMvI8gKyfga1eNGN+gyRyvlCk1pDYlJn2CemU1FoqSipkPAAwbUmHfBJENp1tscZFNZ0eaaIvsBTLNUitBYDpD81Fk+5n1hr7aG0gXwfi2wcRytZONgPg7WWzZ0OsVGUg4hqpFmsB5xdKnFusczqQuS649biSe3SCUYUOCb5GUTSJ5D6kNLBWstOvE9VlzgXLg74JYxCIP2BaD6tBh87ksSQppZGNSD0NUpJRKZkOiTpzzr8qecKEgjQMiuUiJ5MHlyH3/YRfhm3Mdpn+/O/TrI4mBAghJurH9bEM8qjG7mWVn883zBY7KAh3wgIBFJCuHsPaRISHdApLWTfuTfDD66YlXfbwWWjl/qSKLy2IxL5NiIEtx77ORIvImhtsXrte2O3pdlJabUPn+tSSJ6KHqF8AnF0/Tjvr3DGWxz5uu42KW87EtTvzj8Mm2ed3L66fm109OJDBikkF2aQ9d+JN2jbLr7weCa8DMDb7T5Zf0b+tpuWTmx57bqhU3XnzkXhrp/Ej7BC8Oyeu37uJhu0zWBRcz3doWnaaBRr3qChZk74b8X3ZzYbfBCQUvC1Z9x95ZUPLeszmOylxvLDd9z96aWrIuvuOgjRP8c/3TJvb+XEuK+7nxVKlnSdknRjak+ETl30RuuD6fyH+pAb951eh/8wbmW1qbXFw/e3R/tl/SfgK5AaSys7PR+US38zj+T7FPuMzwn/HHN8UiAkRXQLZO78IZxwHN8nDkI5hcOUP5qjSNDge4ZHIBaYpmgwLT5G4z6bxuCXCoWILC+4ef6DruazIk0QXoQ++xh67Qn06lXU8iXUwjlkbRXhl4bHwklH+GObECjv0Mz7OCz5Ver6QPErTd01kkNq9/8xBZaSCqjoaGrzPoDQd6RfakEzk81aa/seHu1h88G4AzpA5JJzNT7MrdlJCAPN1fN1Hl1bZbkU4vmu4HCwq+zWJcaO4jg/g+H7QUtFVUeHCP9O0qSkAs6Fi/gT4hJzhNobIPxSiOJSTa2lHafUKz6V0CteB3f1dUNHYr32LliLyh36dYBEgDAodDZnOvg4I7PUB5MVVIwO2PnCr5MsDI9Y01KOrPHliQKTCD+AmqIbrpRASAp3/mZtrehk92b4e7bj5/sI2KKuDaiLDnb4AVIv4uZj38ACC3ffpLp+7fBGWItIuuikzdQpJw8AtfvvIrA0q6uFwmMUOuUlmpUVhDWUb77+gLbw+Cjm+Dt3JpK9DzJ5/iX/DE9EjiTeiO+yn0WI5k7w2/suQz4Nd0i0JUgNi2F23ghJ7FdYTAxLIsJyuFufd/3P+yuH7lULqkpJhqQYbsfrPIy4sCy4csY9Bvzxa2ZqEv3qtZS9liORT188fO0WTv1TpgDc2XL/PNtH+HN3/gveSvG3x8ILSCQb6c6h9IVJKCT9rQd33fYc+g/vo35Z//UTkPW3sp9eKSAc8njUc+k/bF57VOQGgOUhIwSfFswJ/xxzfFKQSfqLDn/cQZQarlv3ZxhCCEfipupK2+n8CKQeKiW1JgGlTjR9whn36Y9H1m9SZF/BSGgfWTuDTbrHiumzaeK66aM6zF4w9HhpqRBAYgzlGZ2xyzqkfCgG9ECXW3nOf2GMzFkIwYJXwVgz9YwtONK/XA2RElqdNOPbbn5dCXWoG27jNiKqFtunpev6pkNYp7WWdidluR4SBRrtlykLTXdIB15A5hUw/lx3x2D09yvrCGtt8UC1EzcJhMe5aPmQkmIcDpIHJZ35qLXQaidUSz71clCMTGolkNqZHsZBGSsk0qbo7j4qHpzht9YR9R7h76V16KwA0Lr0PK0Lz7D9pb9CUh+UKvfD06OTPgrjvklz/JbegP4YCCFQQlLJCP9+vWdml0v6Vdot4vXW+ohCv0O/MCleVpzrHiDHzfo51s+9AMDZ9/6YC2/8Do+88l+58pP/xGM/+P/y5J/+P3nyB/8fPvPKf2D1/psTt/lBwFpL9e7bAGwPM+sbgq3MvK9y47VPzLzteW8FiWTXNIsxjWGw1vJBpgK4HKxRVxVW9QIWeLfzEdCTd2/uwc+uWVTNnaNXWzFxqVcoijMl4FXjPnBQXVDI+f3D14gQoiCrNx7SOX6Arz4tURJurMP7U9g6pMbyJ6+7e+hLjw5293PkHf5pVAN7LTcaIERvzt1Yw404I/yZUgKccudKFu85LKKvH7FN+P2dH/HHuz9z18hpRfONwF7bsttyt+gzjcOvD8j6bx+fgO8X8/sMHZ3MO/zGOl+Bk0BzLumfY445HhoUhN9k7t6ikAP/mYfSE2XoxcP8NEaSUg0nQ2mSub6fZIc/cKTvARP+Yn8cKF6IUgNRqh/PwM+kSG+Mw/6I/aeFwgIWQ3BS+7jveDtDxsAVbsZg1oi+HGGgWa6G7lJFIKVz6FcH0gOcWaJB9pE0paSTlQ/RlTfbCVGgWai6pxHh+ZSld6ggYbDOoR430y7GFbcm+CNEyseXmtgm7CUtlJCci5YoqdmeiDyhEfTONyEFAsF+OyEKPBoVf6C2ppTbfmMcie/mM+3tnT7TvrBIKdVCZXGb2WhPJjlWwsWCJaUq+899k7Q6KFM+CCVHE/5W0eFfH68kEtN1+AE8EkqZYqBZ76kOrNIkmVFWLutf9Rpo3LFc6DPs8zq7CCyp1KTe4TGj+xdeolVZQaVdKls3iPbuEbR30EkH0dfVX733xlDDwgcNb/ceUXsbIxS7S1cmfwAX25foEN3Zp7Q1+zz0xwFPas5l3gzjZP13k01apoMnNGvZ+x8PLwJO1m+tpZF1+N+77TLkg4Yjl483u7TLvdGJXDnyROzOoxvde4U8fyfdZyvdRSCKcYOD6J/jP6mu6kmjVhKFgd93XjcTY+teuWbZaVqiAJ65NPy6XaxkHf4pfg5vZ7F1S1VXQAS4E286xZrwWdKNgfc/mcn63+/cHGkUm9qU/7nzQ651b/F25zpN0+6b4T+5Dvc43M66+0s18L3h+yl36792ArL+cfP74O7Veef/pJz6x8UAflowJ/xzzPFJgZCZdN1k7t6Rc+ifwxGbSd0dm7ru/BQdfiHV8NiyNEEof6qxgGkhpPx4jPtMkhn2HSD8UiJr7uHuyDF9JkWGo89N1/1Xh4i3Es6ATQmFP6Vh3yjYrOsrDixnnJS9tx2zRfT1Iww1S9WQ0NdoKUiswROq6DgDkHZdQkPf9auVy34++LBkraXdTVltRHg6O3eVRyA1WsgiHg/I1AQSZbNcqDHFrVHHIIcvPSIVsJO0sMC5cGmsI/8oaOHiCJPsWtLSyfY9T7BY81EHZvqVFCglyXdDYdzX2ili+RIVuJRSkY0AWOOuo5z8AwqVxTxOOeojswrCEHTKi1gEOm6hu8PHXfK1TOtvV927h7SG2K8UXf0c3Yyc+ZmsXwnFs9EVFlR1oEuYO/THYW34iqXkoyd/kduXv8ytR7/OR0/8eT545ld474W/yjuf/Vv85Nm/iZGasLuLv/fxG7I17juzvs36BdIpVVRWKvYXHXEqbd+a8O6HB5cClz5wfYRjPsAHmcz+kn+mMDm8HKzhCc2uaXIn2SjIn7GATLEVJw+/nGpMn0FtTvjPtzuUZUSK4WbXFZw+7LhtOOstHvIaybHmLyMR7JomO+kxR75OEZ+9KiiHLrLup+8Pv/aT1PJHrxq+/Zq7V3zuqhza3Ydeh3+nCXEy/l5ye8v982yf7P1G4c6/csj8ckUvsKCqpJhCsdEPYy1/uPsTbsY9ZdFGulPEEMYptB9Aku64+f0cZxccWT4JWX8zi+Tr77Zf79wZGCfpOfUfb13FOucz/HPMMcfDAvdA62bVbdJBRPWpyOufCYwz2cthXJ7spM4m0OsWHpC12zQuZq5PEtMa95n9TUxr62RWmsaucDHkwVqEVURl4Rhd/tGGcUAWC3d4ll4LR9J8oaeeER+J1JlaHlQTuGi+yR+fJaLvIKJQs7oQobUj5IH0B9QONm4jgvJA+oWS0hH+A53W/XZCOfKoV3rvFVITSB9fqIHtS61BIVFWZOf6mPvDiGPQj5ouEUqftWCRunfYHHSasQ8tZRbN594rpSTwFYuVsFfAOPgZJYrLuYjma28XHf5YBwU1l4iepL/vmlVCIhEYppN5uyQmO/TUsFIXpnrh/vAZZjdOIaZy6YcDcXwHiEAvmq93/b1UfpK/svCNgbQH/6Bh3xCkXsjW2afZXn2cvcVHaNXO0i0t0PUiUi+ktXIZgMa9d6fa7lODtdQ3rgHOrK+TTD9SlEf3RTvTZ9ufJtKsSDcOeeHmfrJdzOP3w8n5XQHjEb+nAPGE5kr232+3r1Pvuyz9+gZWWBpxSjlYGFhe4udFpP2+aEC3v3KVwSV/eARivt4zmcogj5h7kLDWTmUY6GnBl59019OP3rXsHZhz3963/OfvGl75wP39i09qnr8y+potBYIou+Q2J8zx38k6/P2y99wrob9Ql0MIUUT0vdkeNO+z1vLdvZf5oHsbiaSh3D1hM9lFK1F0v3dOJlRnLMbN7+cQQhTmfceV9Rfz9Jkb/27a5H/s/oBv7Xy/+B3pzfGf0Ax/Iemfz/DPMcccDwP6HMbncv4eXBd3wo3fpK5AMtUMvyIL5h78uzWDJnAnhSm6WTbuFNtuhzjczwqbxuCFQ4tGQghUdfVIMX3uoUW4DvIoSO0KAsM6/EgC5R/OrJ8Vxik6UAe+n9L0z3qPwqwRfQeRczhjDeHB4kWaIKPDhmSBpwYIf97dX66HeLpvfyiNVB4VGQ6kCRhr8KRGknW8J0j6hx2DflR1iQvhMgve4XuNjdvY3XsTSb/zL+gZ90kpWGmEROHowpvfF1HY79Sv+2b4JQIpBDJXPSmN6LtmnVpEDRgGjoOUWVTehDn+cMQcv7Fk2zPV6ogyJ/p+OX+O+IBT/yh4Qwz7pkWcGrQWxOefAqC+ee1QgfNBwuvs4iUdrJDYlUdI4unn8VsZ4Q/37yM+rojTPnS6hm5ixk5/lGTISibxHtblv59ss2/aaKE45w+OozyWyfqvdW6BTApCunTeFaMeb3bplJYGPtNvBnmxIPx3aZo2d7N4wGHz+/34uOL5bnXX+Q+bv8f/b/MPpirAPn5OcHYBkhT+5M3eQXjnpuE//LHh/o4zg/u1L0q+8YI/NHaya2Lean9IYtOpnPqT1HLfXY5Fh383dR4NAsG5Ph+OflwNzqOFYjvd53a8Ufz9h803eLtzHQF8o/oZrgYuWWQjcSspjPtOOZqvE1vWs9vQ2YXx783n+I8r6y8k/VmN+262XxKbspm4jcmJ+Uk59c8l/XPMMcfDBaWh20J44al0mj+xkHKUGrcHm4KcTtKP1I4w9akGRs28nwSmMe6znT1keRFZXsK2jjFfn8OkzpF/1Db5EbK6nMX0zfDjbRKE0mM7/EIIhBcdcup3s+5q5jnx4duRIqR3WNKvnJR9kqwfXESfFpLuFO8dBQt4fdL6wvhxSLSj7w8S/v1WQjXyaFQO7A+lQSlKUmHpOVKn1rhRCGOcQeGYookQArxwZDQfgCcVNa803IshjcEPIZ48RBlKPdCdG+ntkEFJSd5rz8ms197pufSrEKlk5pEnsMZkahWvVxAVAk8qzLSEX4gs3XME4S87AhWNcuq3NrNZmcK0r9vG33PkbL82hPAX5Gz8dT7MoX9aJIkl8jXJygUSL0InHSpbN2Zezkkh3HcP9d3yIuWSh1aSeMoufxJWSMIKwlqiveGmcsZaktTQSVLa3ZT9dsJeK2avFTuTzROEMQZfy6EGnP3IO+rD5vjz7v5F78wh48sV3aChKqQY3u/c5OmLLofeVjLC3+oW52uO/JzSnX3O6gV8oWnbLj/ad4aNS7pOWY0f2bngO9J6J94gHnFPvNa5xX/c+D2+v3/81ARrLT9tvs3v7PwJTdNmz7R4vX1t4ueEEHw9i+l7+6blo/uWP3jZ8Ls/scSJc5X/m1+XPLI6+v74g/3X+c7ey7zcfHcqp/57267oF/kUkvsbWWFkVS+MHJXwpObRjMzn5n0vN9/lldZ7AHyl8jyXgzUWM++OjTQj/A8omu92JuevlXod91E4u+A678eV9R+U1+cFKaBINOh36j8u4sQSZ7eAuaR/jjnmeCjgZtXTTM5/vBnnTxWkmtjgt8ZMTdaL7mi/hDDrGJ/k/H6BCcZ9Nm6D9pHVJWRtBfwQe+zcaTsgKR8GWWpk4xIzEN7M2HBshx8QfniooyiEoKqjEyH81qTgDTne2nPHdopuZkkF1L0yzeRoTxXGWoRwctgCSez26ZB976leh9kYSydOWa5Hh7LrhdQI6REi8aRHnJ2nxlp8obE2nSo6UvrRkZ3NbZoggspUapNA+TMlHigtILOV63X4d5HZeRirAIHbtzKPKlWeu4b6vk8g9NQd/jwqb9R2tvo7/EPekx9rCZTe/C7Ruz8cOWbkbdxAAK2wTuIdLtzmkn6/M15DfBzCby0EvgYh2V1+FHCReB8XgozwJ7VltFaUQk1nwsx0PzoNl3RQGiLr328ntDvOxE0Avqeol3yWayGNSjC1z8M0SI116hLJxOXmHfXb8fpA4oa11nXvgUeCwzJ7IUTPvK9znS89Kfn1r8dsm12EtVxtdukcJPx+GYtA2hQv6RTd+ney2fFJ3X2AmixTlSUMdmCuHFz39bt7L/P7uz9iz7R4tfUe77YPz6VPi5bp8Ls73+PHzbewUKghXm+9T2wn/x6t1EURs/cb3zO8dt0di89eFfzlL0kqY8hrag3XsoLLrfg+i5nAaVyH/86We+3sQq/o91GWaHDeH97dz/FUJuv/oHubnzbf5ofNNwD4fOkpnsiM/Ra0uyfspvskNh0w7jtN3NqcLOfPIYTg0azL/+aNoydm9Dr8bll3463itfVkK3ste2/n5FIBtAJfzyX9c8wxx8MAqZ3Z11zOP4hp5N8mRcwQJ4byXJEgR5oRtSmI1KwojPuSEYS/s48sLzqjRi9EVlew3dbEZIJRsMbNt08sXujMoHAKf4ECJnXy6klpCCPWfSZcGBKvdwTYBDFkOUJqdx5M8Z2EECz6VZRUR+rypzZFC43uU5XYNEboYGjBTiuJzaQq++2YSuRRr4w4Zz0fbZybfo8sWLeuac915XHkDHZrsmNsJ56HWqiJApx+yGz+3lpL4pcxQhWu8lZIEqHdyD4yk+Nm26L8gUKOFnrqQkMelTfKrL4TLWCERKXdoZ13AygE4a23KL3/Y8pv/ym1H/5XxBAFhLfuiNBudW0oKexmkn7d2RtZkBFpjNdtZu+fjfAnxqKEKB5u988+AUBl83oRBfig4WfeCHmaQin0kAiSKdMDOg2nlDhI+PNPL9YD1pZKrC1VWF2IaNQCKmWfwFMc+RoYgjg1eJ5EKzWxllbXFeqqjMHyUdxTJmykO+yZFho1dPYbnBRcILifbLOZ7HArM+A710kIVFAkPRSQkiQzCfU6e4fm9cfN7+cQQgyV9W8ne/y3rT/mzfaHgOtoA3x37xW2kikD7PtwO17nN7b+iJvxfRSSr1Ve4FfqX6Umy3RszJut8TF2Ob74hMDPbrORD7/2BcmXnpQTfTZuxvfoZkWF+8kW9Yo7kOOc+m8X8/tu2YlNuZUVRS6OOIY5FnWNFd3AYvlx8y0Ano+u8lzpavGeSASEwsfi5vgLwt86uXN3GHKH/rXFCW/M8PSFPDVifIFkHHoz/G60YitTNUCvw1/KCjYn4dL/Z0HOD3PCP8ccnygIpRF+aS7nPwAh5HBX/X5Yc8iRfuwytT/4sG2SkUTtJCCD8tBOuu22XHe/0vvFlZVFRFTGtmd/mAJcl1lq0ON/4YRU4AUzJQhYkwyVqx9atvImH7NjYpTKQPjhxGi+HCUV0tBl9o/Q5U+sQSPx+iW5Jhk5SqGUQOC6+93EsNI43N3PIXSIIKWiomKO3+Jm5rETTBPzZSgP52dwhGMgQPhldw5NSHPQQoGYPkJKKeFEGMaCEAUBBhfJl7fSezP8ZAUs589QrHdGU1OtxGiSJhWdkrsGh87xG1DCUnrne8Wf/PWPaHz3P6H2Ngfe6meEf692lmEVhsQvYYVAWoPuDm/h+dl8f6oDzIy+IkkfKQVIqks0wwbSplQ3piNTJ42w6Tr8pu66oaGnKIWKzgTzuxydBUf4w737iL5rO02Ni/HyFErJQ2EG7poTMylQsJbavbep3n8Pv7U18DuRJJYoUPiePGTAOQwXc1l/p1eoyLv75/2VQ3L+HKEMiq78W+3rRcf98VaXdmlpaGpD/xz/eW/FqWOAqizRUNM1EXLCfyN28Xzvtj/iN7a+zWa6Syh8/kLti/xy/Suc9ZZISPmD3R9NZbYH7v7ws+Y7/PftP6FpOtRVhV9rfJ3Hw4tIIXg+I8Cvtt6fapmlQPAXPyt58Yrgb35dcnFlurJjvv/BRZ2aaAtwRLTdPXxMrbXccW/hTNYJvx2vk2IoybAw3BuH3LwP4InwEp8tPTnwuhCiiOTcTHcKSf/uKXb409Ry1/HrqTr8AEs1wZVMLPLDd2Yn/Elq6WSPG+XQEXwL+JlKbivdJbbJibr07w9JBfg0Yk7455jjkwSpEKX66cjKP8mQCqSYIE8W0xn25e/W/oCk36bx6cYgZuT7ICmy3SaysjRAooXykLUzYOKpiesATOJ8ICZ14cF951nWYdLpjA2152bRZ1EPzALLyEQG4R0eJxiHRb+KJ9WA7HYapDbFV9qR0r4NG7V/tHRS4J1ml2rkUyuPLlDlxYxIuRz71BqkEOg8Xm6ac30Kp/5hsCZ1kYdBCRFU3cjJGGip0EJP/eCvpHPNz7vfcV8HO/UCnC0kzhUfkf1HNobT91yqhJxJWaC1HEv8CuO+IXP8xsLi3TdR7T3SoMzWl/86aVhFNbep/8l/wrvniLRs7aKa21gErepZ0mHdZSGJ/R45G4Zjze+nljBQBSdUUrKx4GT99XvvzLy840LFbfy4iQXSWiZFF1AOPSyjxyz6kUR1Ei9C2nSgIJOkFq3lyMKZki4Oc5R3wzA07r7JuXe/zfl3/oBHf/qfeeJ7/zuPvPwbnH3326zef53a3h00dqxpX46ctN+I75Ha1LnzZ+T/cnDY36Efuaz/vc6NImLvsebh+f0ccVZo9Lr7eFJzLnPdvxScmcp7AuCMt4hG0TQdvrXzff5o76ckpKx5S/zlhZ/jfBY/943qZwiFz2a6y/f3X5u43Lbp8q2d7/Oj5ptYnILh1xpfK6TsAI8G5ynLkJbt8E77+lTbe2FZ8NWn5cT58xypTfkwM1GsSvd7v243irn8YXP8uy3XJZYCVrPAjH53/mn27eVgjUf8szwdXubL5eeGfmYx2xcbSS+ab6/FVIWlo+DuNqTGqSPqo+1+DuHzj7tr7Z1bduYuf95tVxJ83Zvfv+CvEkl3799IdooZ/mZn/P3hzRuGP3nDjH1P0eGf8hz5pGJO+OeY4xMEUV5ADnF1/jMPKQE5cmbWwY7NJT+8zAO+ANZMnHk/DoT23fb1kWvbbYIXICuHH+BEVEeUjhad5+IFp/sFFzqYWfE6aX4fyGauT4fwW5OClKMLGjMWzCIV0PAq7M8od05sSih750xBlEesXymBkpIksSzXw5EkBXBE3UIofXzh0Uo7KCGzvG47NeEXB865qWBSt36lkVF1YvFEC4VGTj1PLxBopYrF9sfOpTp0A+jCZk79vQ6/u2Z7J2ue9DC1skAIhgfzObTLjvBHQzr8IumydPOnADQf+zxJ4wxbX/nrxAtryKRL7Ye/SfT+T/DWnTFeUl9BBNHIGmXu1O+PuL6P6tCfF0uCvkhEqQQbC5exQGn3zsR0gJNGkMn5k6iG7VNhhYEm9BTdaRz7hSjc+vtl/amxhP5gIagfLg5z8rx9AWtZuO0M6bpBFSM10qZE+/dp3HubSzd+wMpP/itnX/ntyUayuNn0SATENuF2vMFWusuO2UchueCNl4Kf81YoyZCOjWnZDtpaLrdiOuXh+uv+Dj/AFyrP8Gx4hReix6b77rhrec13v0c34nsI4KXSE/yF2pcoyV4hsyRDvlH9DABvtj/k/c7Nkcu8E2/wX7b+iBvxPRSSr1ae5+uVFwe9T3DX83OR6/K/3HpvakPOWXCje5/YJpRkyLPRlWL7xs3x5/P7y7VMJWRtj/BPOIY5tFD8fO1zfKny7NDUAIBFlXX4k11KgSPFFkf6TwP5mEK/L8E0WD5Gl7+Y3w/dOu/GjvCv6AWWMx+H+8k2ke8uL2uhPUJg1oktv/+y5cfvWa4P9/IEeoR/3uGfY445HhoIId289xyDyDO4RzwAWGscAZxF4tvXlXWEQc5MFGfCAeM+a23W3V8eWmgQUjoDPyGwEyTVh2EdkZ8CQvuZHHvyw5W1xklJp1EOCAk6OJpCYRL6COnQdSvP7bcZHhgXvCqe1HRmif2yONf8HGkC0htpHqmV6zbWyv7o2f0MQmmQCoUbO2ilXSQSBa6oMMW5LqR02zJrHJtJsohLz40XKW/sOaiExFPTd/gBPK/Xbe8ntakO3PlIdj+0TvYv8u8sZeHUr4RCComZsmIl8yfoEciN+4L99UP3mtV7b6DjNmmpTieLurNBie0v/GXaF55GYCm/+R3Kb/wxAPHSBbQSI7tz3WBSh99pbWcl/GliUEoMxDwqIUiDSpEYUFt/b6ZlHhe5nD+uDMbPCQGVkkeSTncEmznh3+3F3Fksvh5zLQgGikuTUNq5TdDawkjNtef/Mm994f/Kuy/+X7jx+M9z88zz7C5cwgpJuHWT6v79ieoEIQQXA8eOPuze5lrW3T/nr+BNGB+TQvBYcKH47yutGA2jO/wHVCN1VeELlWcI5GzJM/m8f0kG/MX6l3mx9PhQknrOX+GFjKB/Z+9ldtL9gdettbzSfJff3nYu/DVV5lcbX+OJ8NJIgvl4eJFQBOybFu92Tj5V4lrXFSYu+2uczRQQd+NNFqruOA6b47+TTezkcv7tdI8900Iii+LISWDhkFO/+/tpGffdKub3Z+98f+6xXpd/c2960t9Pvq213Ms6/KveAsvaFX7Xky2kFES5rH+EwOzd27boAV27M6bD3+6t89OMOXOYY445PvkQyhHIUQTOpCDUTJJ+9948TD1xRnSnEMmX45BxX7cFXoisjA6/FUHFxfS1d0a+5yAcGZLTfxftuULHNJ34jGhPa44o/Oh0JP0mzcjfmA7/jFL2SPkseBWa6XRdfmtdr9jrP+fSGKH9kfteSUkp1Kw0ItSkwp7qpQ1UdIgAtJBIY13xa0o1y7B4xEmwaQJekBUMAkRQmijrD6Q3VYc/tS5mUEtRkLwBwu+FYAUIi0I4M788VUNqd51n63GKh+mVBa5YMvrhthvVMVKjTILf6l1zMulw5u6rAOw/9sXB+4xU7D37Tfae+rpzSc9UIvHSBdQY87DcqX9UNN9RJf1x6giw7iP8UgqEgs1F19Gs3Xt3aBLBaaHo8NcOk6PI13iepDtFRF/e4Y9274IxzjEfiTfBedv3pj9HGndcd3975TGM9kEI4qjOztJlbp59kY0Xf5nOOWeCeObeq5gp8shzWf/17p3CHf7yFCZ6AI+FPcL/eLNDqrzi3DmIgx3+o+JqcIG/UPsif6XxjYIUj8JnSk+wqheIbcIf7P6YNCv6dUyX39v9AT9ovoHFciU4x6/Xv17Ez42CFornss77y813Z/NemICkT85/OVijrioEwifF4NdcgW19SIf/9lZu2Of+O3fnP+stHlIpHAd1VUEiiG3CnmmdqnGftZZbWSFj2vn9fqzUBZeP0OXP5+nLoWAr3SO2CRrFgqr2OvxxFs03gfC/faO33g/u2pFKr/0DMYAAr7eu8XvJK6x3N4d+5pOIOeGfY445PvHoxeiN+GHJJN4jCeDQZfZ1C9O45wJ+isiN+6y12LiJrK6M7cQLIZDVZdChk/9PgzTJ0gam/C6F9H6KznaaZPtpSrKpfU7SIbuAcd9xZJf7iFL2Ba+CLzXtdLKiIsWghUT3PfBNM0pxYaXCQnWKVoPU7vuZhFD5+NLDk17W8Z6+uCW8YPZoPpMWCQhCCGSpPvH8CKQ38eE8tYZ77W3aJs4KHsM6/GFWh7Noqd0Yj1B9pF8WigUlJAo5tfRXZqaJIzdTyKJ72j/Hv3TjZ2gT0y0v0l0bIo8WgvblF9j5/K9hdEAalIgbZ1FSjjz7c0n/RMIfzdjhN4aSP3huSCFQCLYWHsFIRdDeJsxI+INAHslnaofjy5QSVEKfeApZf7fUINEB0iSE++skxqA0hTnhKCglXRFpAnRnj+qGc6LfPPPUwGvOHFASaEHr8mcAqG9dR05RjD3rLaGFm4vfTveQCC5OEZMHUFNlLvtreFby3F7HGUuOKFoNEP5jEGUpBOf9lamUAVJI/lz1JQLhsZ5s84P9N7gXb/Jftr7N9e5dJJKvlJ/jG5XPTFQ05HgyfARfeOyYfT7o3pr8gSlxo3uPxKaUZciKbiCE4IznCu5x5Ijfxu7giFCSWtazQ3w2I8Yfxb35/ZOEEpJ6ZgC4mfSM+06jw7+xC93ERdUtH7jFNNM239t7jb10/Io/n3f5b07f5d/vc8zP5/dXvAZSSJayDv+O2adj4oKgN4dE8+22LDfdbQUt3XLvbQ9fZ8+l3+1Pay2vtN6lRcxOfLzi2MOEOeGfY445Ph2QemyHX8hZO/wuoxprBjuap4mc3HebCL+ELI/u7ucQfoSsrWC7zekk6lmXedrxBCEkeFO62k8i2gdRuMSfLOm3Jh1fKDmilD1UPgteldYUXf7UGJRQgw79NkX44w0Nx87t96H/O4TSI5CeGx+waa8ANg2y82CmY3DAz0L4JZBqrFJgmmi+VtqhoiMSk2bkW2AtpF5EmpGBxAsAixWgyFQ9QhSk3xVBeh1+OUOH37n+i7HvPzjHr7vNYqZ758oXxioE4uWLbH7z77D1c3/b+R9IwaiCV0HOhszTy6SLzhQVcVg/9PoouOkHgfYOnBvCkd5UeOwuOLfw2v0HY94n0oQgI8VpfXheeTlUaCVJkgnHUQhaVUeUS7u3SRJL4OlxhwQALUQxJjIOjbtvIrDs187SLQ3em5PUEvgKrRRpdZHu8iUEluVMETB+/YrzXu+7n/NW8OX042PfqL7E/21/gcXEjJTzAyRZsVGa5IHGL5ZVxNcrLwLwevsav7n9XfZNi6os8auNr/Jk9MhMM+Ke1DwTXQbgZ813Tuz3I3fnvxysFdtzJlMw7MoNhHAkuN8Z/u62M+wsBVDJYuTuxI5pnjThhz7jvnSXWmbct9M8+aL5rSJmkEMxhj9pvc1r7ff5zt4rY5exUhdcXnXX1Y+m7PIX8voQ7vXN74PzqymMFJPtgqAP6/C/fdOtb+Hxt6g8+wOQCdfujujwH5D0b6Q77Js2EsGF6NPjmTUn/HPMMcenA0phD5j2WZNimtvYuIMIyjM9VAx0C01yug79GXLjPtttIirLU8vuZWXRdY47+xPfm3eZZ9kXTnkwmRzbdLpIvhxCeaDU7KZxkzdkIrE+ipQdYMGvEEiP1oQuf2JTPKGKaDjnbyCnV1ZMAeGF2DRBCklVlwik58iu1K5QM80yhphFToV+FYcXuf09JrpQC4UQo+PPrLV00oSSDkhtihLOzyA1jtDnxn2Jjsj68C6RwJpCjSNEpuLp8wrwpcYwJeGXLg7QjnG9bh1w6l+68VOkTdkrr9BeeWTk54rvqf3CmC5/jh62tm4u6Y9bAzFz0OvuJ16EmcFXJE5TtBb4+vC5kfsJ7Cw7t/7a/fcnmKCeDILmJgJL4kXYcLj6RWtFKdS0JxF++ub4d25jLQQHixtDoLRLexhHHIVJadxxGembZ58+9HqSWqI+c8DWZUdwl9bfmYpcX+qT8D8STCfnzyGFoJypJNojDPsArNQknrs/H1fWPysuBmd4NnRSfIvlEf8sv974etG1nRVPh5fRQrGZ7hYGecdBYlOu53J+/1zx97Oe2593k03q5cNz/Hf6iLEQghvxPSyWmipTUzNY20+JhcK4b4fqKXb4P7g7fH7fWsuNbGThZnyP+8mItnmG3LH/7ZuWrSm6/Lm8vr/Dv+r1imv5+XI/2Sqc+g9G81lreeuGBd2l3XiPTnQftXCH94fM8SeppZvdXvPlfdhx58GKqE2tOvkkYE7455hjjk8FhPSKDn9B9JvbiKCMOvMocuHChCUcQH+30DK1yd2xkBn3iaCMLDem/lge02eT7uQu/xRk+BCUN50E1JrZ9tNpRfPlru1jcCQpO06avuDVaCWdsQQhsSlR/whINkpxkj4QLkHBfYfVoMGCV8HaFLwZ1qH9zCxyumPgEhDUQNKAkBIR1bHxaGLjSYUSaqRxX8fEhMoj0j4W13EWshe5dP/CS2wvP8Zu47wjZ8KZ8rkOv+oVOLQ3UPjzpJ7agT2Xto8rbeUd1GB/A7+1TePumwDcXPssSs0266qUQCGHnkdGB6TZPj5Izo5q2BcnltDThzp24JQlFthvnCfxQnTSprw92hTNWEtnirn6SQibbnQgHpJE0o9S6CGAZEIEWbPmOvzR7h3ADHgVjIIUMrPDGL3s6vo1dNIm9kvsLVwaeM1YixDg9RUX4qULdMtLKJPQuPPmxG244K9maRaqmOmfGtYSZMaHndL4/VgoR7oPXqr82fJTvBg9ztcqL/DN6mdnUjEcRCB9nsqy63/aOn6X/6PuXRJSKjIqzOHAEWxPaGKbUFtw+6zfqT+f38/l/O+23TXzyJQeDLMi9zjYSHZOzbRva9/yYeZq//i5wXvFjtln3/RiAV5ujlcCrdQFj2Rd/h++OwXhz2rGXhAXJo95hx/oM+7b7kn624PLXd+FzT3wFu4WBTi1dJuN3cNqiIMxgEBR+DkjjlaMelgxJ/xzzDHHpwJCe2CTHtEPHdFXq48iS42Z5fh5t9CaBASnathXrFNKRFhF1lZnXp8o1RBhdWyX/6hdZqH9Affz0W/Ekfhpl5sb/J10h18weWRhlojGA1jwy4TKp21Gd/mNtQQDhD925+hJ+kAoXTzQSCGcasOYqU0TIfOq8ALstOkDJnHFlAP7VwTlsckHk6L5WmmXhlemXHgDgKdU0WTeX7jIrcd+zkn7hXt4UUI6ct93LIXyD3X4pyYDmbR9nCAgDmukykfalHNv/z7CWnbr52jWzmTKg+khEK6oMYzECtEz7jsg6z9yJJ+FwB9eCCvGC4RkZ8l1+ev33x25rG5siGNDkh5PBZDP708i/KGniAI9cZa/U1okVR4qjal2t/CmGJFRUqCFIBlznixk0vytM08NJLgAdFNDoOWgmkAI9i497z57+7WJCqlAevxq/Wv8auNrM7vme51dVBpjhKITNca+N/bL2Wcmq8FOGkpIXio/wePhxdnUdiPwbPQoCsn9ZItb8fE8J4bJ+cHdV1czwqnr7lzNO/zW2p5Df0PQMh1uxI4pXw1mbDBMiVzSv2uaRKH73ewmLoLupPDyNbesR1ahUR48Tjez7n4urf+ge5vtZHzxKJ/lf/uGZWt/QsEuI+Btz+3YunKKuhzLXgPIOvy5pP9AnfmtzKyvcqan/FD1+6C6h2T9/akAQgj20iYb6Q4CWBWz3V8fdswJ/xxzzPHpQEbQC6K/cjSiPwDlQdLNTO5OMZKvD3LhPGLCw+8wCKmR1SVs0hnd5T9ql1l5Lk5uTBfYrVMiZpTACf9o0vqR25ElMkzyERCZy/3EIsYQ+NJjyavSSrvsJ206Jiax6QCxdK75vW2waYzwSifyoFtAaQ55IFgz0H2fBsIvgZmS8KfpUGNG4ZfAC2CEW78UEl/pwqW7H4lJUUJS88poqZAIjDXDY+scJ0XkBQ5r3Pbk23FAjSKFnImGqzFReW4Fomfcl3VV75z7LMjDs66T1zWoYjiInsnaIOE/ikN/aixKCAJv+P1Q9Z2X28suSq2y8SFyRNxiaiyl0KPdPV6XP3foT+vL498ooBRoN+Ix9n2SVtXNTteb96ZTXQg3NjBq0eHefaK9exgh2Vp94tDraWIJ/cNeAd21x+h6EV7cmirqcEFXWdDDHfbHIdzPu/sNN4Y2BqPOqZOCittceP13qN9961SW349IBjweXgTgZ62je07EJunJ+YNzh14/k8n64zA37nPX624LWl03mrNSh/c6N7BYlnWDhq4ceXvGIZQBkXQquj32iLKf8pPq8ndiyxsfue/3wuXD59KNrhtleiK8xMXMo+Dl1ujCIMBqQ/DIyuRZ/jjpyet3ZSbn7+vuAywp13XfN2104Nh6/wy/sdbN78uUbuS2NRCeS3VZuHsonu/g/H5+HqzqRfe5TxHmhH+OOeb4VEBEVdTq1ZMh+vkytQ9pNyM4D+bmL6Q8MikUUR0RVkZ3+Ysu84zfRXnuc+NIYeqiC2ctjBxVWj8SIzrQh1DE2h2t2NDwK6z6dTzhSEgr6bAV77PZ3WOzu4cQYjCSz6SIYHp/g2kgpB5wpc/+Ops5JflowHTvtSZxoycHupxCaWRYGyvrD6RPMuRYN9MOFR0SqQBPKJSUJNbgKYk9sGE2k08L6SLXMGbwWPfHaeKiCid+pz7CrZWcOL3S7suL31m8TLO8iISB6zae4rwSwnU9R9UXcqd+/6CkvzW7Q3+cGjwtRjrWC9kzSeyUl+iEdaRNqW58MPT91oKnJVpNF5k3fCGGsOUe7M0Iw75+BL5058YEVUGz6uTUlb07U2+K78mRhZc8im936QqpN3gN558YppxQnsf9VTfvv3jr1VOLOsyLJp0xhn05eoSX4nIUAACw+klEQVT/dDr8C7dfo7J9g+XrP3og0Y7PRVcRCG7H6/xo/40jSfuvd++SYqjKEkvq8DWVz/HvyA3AsrnniOXtbH5/ue48MN5pfwTAY8H5o3+hKbCoTk/W//p1S5LCYgXOHzidUmu4nSkpzvnLPB+5RJJ3OzfYT1sHFzWAfJb/rZuW7RFd/rxT7ylYT7eAwfl9cCNaDeXO4ZZ272l1KYq0N9dd1z5YvI8RhnoKP7fh7p9q6RY3NwbVELnDfzG/nxH+aVMyPkmYE/455pjjUwGhA2RUO1EnfZF1UPHCqU3QPk4IpZHVlZGz/DaNEX5p5u8ihJhscmeSzDxt9mLCiaJIZJigNCjiBo9G+D2pORctc7W8xmPlczxaPsej5TUuRSucDRZY8mqFFNFam7X8T9gHQmn3PQfIpT0C4fdByOlSHkyC8IZ7QIiwAgeUDv0I5GEvCGstqTU0vApSCLTUaKFIbYocEpdmcLtS2LwwZgdVJVLRX71QQo40C0ytYbO7y3bce1qWUoAYTxpyp36L4P7FlzA2UxJkm9pJY7bjfbpTqCa0EiPrXd1hTv3W9nX4p58xTVNLGI52rNfSyZeNs/JnZ8V1+WtDZP0WQEDkKSolj25senvcWhZuvUp5a/T8fw6/vYM0KUZqbKUx8f2eUgS+Ik4mzfE7wh9t3x5POq1FNnfcP6UY+l4Vt52BIbB5ZohZX5KilMAfopwQQrB95ilSqQmbm5S2b47d7qMij1BsT5jfhwPRfCcNa6jfe9stP27ht7ZOfh0HUFERnys9CcDPWu/y7b2fTp3KkeP9jjsuB+X8OZZ0A4WkSxdV2icxjmDf2XKvn20I1pNtNtNdJJIrQ1QCJ4mFbI5/M+2P5jt+ccUYyysfuOU8f0Uc2hd3400SUkIRsKhqrHoLnPUWsVheaY1XsKw2BJdW3CX2p2/aob8RuUN/FBruJ1vA4Px+jiXdAGCX7cL4tJUVC3I5f+2sk/M/t9vkpe2M8Nc2sKpbGBJCr8hQCgQdE3M7S1i4FMwJ/xxzzDHHnx1kUX4PwqH/pCCiOiIoQWdIyd+kM7noDyzXj8bOoR41uvA40vqhMKkzopuwHUKI6eMGJyxHS0WkfKo6YsGvciZc4Fy05EzlIFMdeDNL7SdCale0yPZd7tEwdSxijtypf6o5fjsQydcPEZRcUWOEDFwLdUhI0DJdIulTVu68lELgS01qDUoejkuzGduUUvRk6H3HWkg1ULxQKNSQaL6uSdiO96nrMmCLgoBb5niFzd7CBXYXLnH3kS/QjRpY4+bAcyQ2paJL7E/hzq716M5yMcPfR85U0kFlCRG5AmASbPb/QI8+L6SUWQqp25Z8jr+0cxvVHbyXGGtRCJQWVEIPz5N0YncOlrducOaD73Hu7f+JmFBMy+f3O+XFQ3PxQzGlrH8/WiSV2u2rvc2R7yu//kcs/uH/Tv17/ydhe2doNF/97ltIm9IqLw0oO3I4I0TlvB+GQIUh9xddJ3Tx1vgYs6PAa21T3nGEtVWdrJI4TcJf3r6J13eulE+pwHEQz5Wu8rXKCwgE73Zu8K2d709VbAMXo/dRx5HDy8HwCDYlJCtZp7mylMv6KTr8ZxZ6Zn0X/dWZPRhmxaLKovlOuMN/7a4bUwg9eOLc4Xvgzcyf4Jy/XBQDXsi6/G+1P6Rtxt/vvviEK4q+e9vy6oeH73m5Q39Y2yXFEAiP+pCkgwHjviD/LMSp5b3bFjB0Su6YPrPfYSlJOdc1maz/Nh/0hTo0+yT9N7p3sVgaqnIqCQsfN+aEf4455phjFKQCz38ghn0nhV6Xf3CW/9hd5klSfZMi9Izu/5BJ60/Oqd+adOrjJbxwqrjBYyONHTE/4Q6/EAJ00CuWmNSR31kJfz6yMc0xsIxUTwgdIIIydsQcv5Yy6yL3zstOGlP3KgPjD4H0SKxBS0fAB2bqsxl+iUAg3Tnd/32LOM2M8AvpPAH6nPiaaYdm0uFssMBauIQvnQu3+3gW+jeuMSw1N578BTbXngUyAtw3K55aQyA0vtS0J8Q3KiEOjS3kyAm919ktNih36I/9MnZKv4w0NWgp8MY41kshUALSbFvisEqrsoLAUt24NvBekxqEBiUlWkvqJZ8kMVjbUwSoNKay+eHY7co703F1es+ScApZf4ygVXbk19scTjqDj14n+vCV4j1nvv8fWbv7Gibtux9Yw0LmsL915mmGySOMtYTB6OPgK8md5aewCCrbNws3/ZPCyvUfIaxlr3FhJkm/Srsj/RmOivpd191Ps/vcuJSHk8bj4UV+ofZ5NIpb8X1+a/tPaKajY0JzXGvdJsVQU+VCKg9Q2rrBlZ/8H0Q7mWO7drJ+XXeE/+6WZT0T3qzWLe913Hd9LDwds75+FB3+ZJdqlPsJHL/D/7P33TX1zCWBHuJ9kRv2nfd6ha81b5klXSfF8Frr2tjlr9QFX37SLfePX+uNROTI5+lldcu9Xy8MVVwsF9F820ShW0azDdfuWOIUKstbxMSEBi63XOHnxawiopZu8+E9W/ymNPtiAD/Ncn6YE/455phjjpEQUjsS8wki/ACiVHeqhP7O3DG7zC6vXY3uiFszsvM7FspD6CPkwI+CSUdKzg9CaP+BzJnaNAY/OtFxkxxCB719Z1xE3awJBG5kozTRqd+aBJRyxYERkKX6yGOphZPr53P8XZOgpaJ2YC7az4z3pHRxaf0xe05xbhFCum68ZbA7LJXbB5k5oBISKRSpNVhr2U2aGGu4EC1zJlggUB6h8ulm26yEI7/TRvmByxXv7/AbayipgEWvRisdH98o5JC2coYeOYuLLPejOPTHicX31PiIOgFKqYHxgp0ll5teW39/4K2pdd4IuUlhKfQIfU3cblPd7M38TzKqy8lvWp1g2NcHrRShr+iOkfWniaXdcLJ+b+Mw4dfbd6m89ocAtB55ge7SBYRJOX/zR1x59b8VyoPK5kd43T0SHbCzfOXQchJjUUKONEIEZ8zYDSvsLroIuYVbr079XSch3LtHbeMaFrh38XNTfcYqryDkJxnNp+I21azAc+eRLwFQ2rmDeBAF1QwX/FV+ufEVQhGwme7w37a/w1Yy3pzwnX03d3/ZH5TzL95+jaC9zcr1HwG9Of44dHP8b92wWOtmv7fVfdq2Syh8znuTVRbHRV2VkUgSUrzIzc73d/ittey3LR/dt7x8zfDKB2Z4Ekgf7m1bbm06A8LnHjlMslumw3rq7j3n/N53FELwfOTGf95oX5uorHjxiuDqWYGx8Ds/MgXhhp5jvgm3gMPz+zkWdA2BoG27RCVXJdjv2ELOXz/nWvhPNbsooBPWeWHPvU9VN+iKDjezUIdc0h+Gho9i97mZYzE/IZgT/jnmmGOOUZDKdWUfkGHfSUEoD1lbxnb7yEZyzC6z8id34o8QdVdI60/MqV9Mnt/PoTwOudyfBtIEGZyORHCgaGFTV1SYtcNPPrIx4RikKQg9dv8KP3KFoSHFAy1yeb0jAa20Q1VFhAcksLn038n2Jf3x6HnmuQCEsSAPjDBI7fZBViTIzRNTm7Id7+OhuRitsOhXiwf8sopIMmKSS9tHOumNgDzQidJKs+BXCKVPa0x8oxaHZ2VzWKmJs2JIbtxXGPaNIPwWSFJDO07ZbyfstRKwUAomnxMHUxF2ly5jgdLuXXSfBNykdkAtIKWgWvapbn6INClJdo+pbH2EGqH2wNrCXT6tTU/4ERCFGjNG1m+tJV50xml68+ZAUU90W1R//NsIk9JZucz+U19j5/O/zu5zP0+qPErNdS6/8l9Yvv5jFm47cr69+sRQNUWSGDxPjjRChFwxIlhfew6A+v330N0T0F9by8qHPwBgZ/mqG4uYEqch66/dfxdhDa3yMjvLj5J4EdIkRLt3J3/4BLGs6/xq46vUVJl90+I3t7/Dre760Ht818R80HJd3Sv9cn5jiHZuA1DavY3f2mLFW0AgiGUb4bcKonimAe90XNHg0eA88gF4/UghWciM69LAFTR2W/D7Lxv+83dT/h/fMvy/fs/wG98zfPs1yx+9avmfL9uRo0MAP8ui+K6uCcrh4fvRrcydf1HVipSAHI/4Z6mrMl2b8GZ7vKpHCME3XxAsVBzZ/p0fm+Kek3f4275TUQyb3wf3+5CPNciKUzzd34Hr9wEs7cgd0+d2mxipuXPlyywkhottF7GsFm4X8Xx5kaHlbZDYlEgGLGceAZ82zAn/HHPMMccoeCGqsfaJ6/BDPssfFV1+a47XZRbKqR2GzXnbTEp+ZPWAF4E9PuEvHuqmLDwI5YE66HJ/GrBuTv400P9djXGE9ygPndpn0uw6JnFqjHHH2QsRXmloPJ8UgkD6pNZgrMFYS8OrHCK8nlCF9N/TEpMOPqjmDv2i0Pervtey/+6L//OFpp10qegSF0urVPWgJ0eovMyh3h6Stk8LkXW73Tko0ELhS82SX6OTxiOLSkLlnxu+3N4cv3uwLwz7okHDvk6SsteK2W8nJKlFS0m94rOyEHFmKaIcTT7/9IE59MQv08oc7/u7/CZz6O9HKdCsbGfmdmefoV1aRFhLdf3a8HXFTXTSxiKwUzj09yP0Rsv6XUFIYBbOYIVEdZrOmA/AGKo//V1Ue4+0VGfvhV9wMn0h6Fx4mmsv/XU26xcR1rJ84yeUd25jEWytPjl0OxJjiILRRojgRjaUFOyXlmhWzyCsoXH79Zm+7zCUt29S3rmNEZJ7Fz8702dj3xUf9UkRfmtpZDF826uPgxDs1x2BPi2jwnGoqhJ/qf5VVvUCXZvw33f+hP/3+m/zHzZ+j/+69W2+tf19/nj3Z3x792cYDA1VoaF6fhjh/n1UX/GzcedNtFCFlFxWe74Qywsx17uuqPEg5Pw5cln/vtxxP2HWOezf3oRO7O7k9RJcWnGn+Fs3LH/4ynCzvP225Z2b7u8vXB5+Mt/om98/CCEEz2Vd/ldb75MMiV7th68Ff/GzEk/DrQ34kzcywt+xCL/liioIlr3RpqRLXgOANHSEP1dcLK3u0bQtlBU80ezSrK3RrK3RDau8uOvUEGrpNtfuOFl/O6vF3hc9Of+JRuc+RJgT/jnmmGOOERBCHNnk7uOG0D6isoSNW+5H/gS6zMIvDe/Em8R1fY8zLnASsCkoOZDLPha5f8BJjRMM2yTj4gpPq2gklFeY1FmbHrmwUIxsjFFa9CL5Rj8QCSERpTp2hGGdm89PaaVdSiqgPMT3QUuFyqT/npb007rcIR4hkDYj/AcLHMobGAMIpMeZYIGL0TKROrx/AunhSUVs06HS9mmQGwimGJRwhB+g7lUoqYD9EfPESoixIwTFHH/7AOHv6/BbnGy/UQ0424g4u1ji7GKJRiWgHGp8T40lpTmkEIcKD7mU/aCs/6BJnezsU96+BcDm4hV2ljOX//XhGd2FYV9Ud0WkGaC1IgyGy/rT1KC1wPN9koaT5uZz/KW3/xR//SOs0uy89MvYAyNIMqrw7uVvcOPxb5JkY0F7CxeHmiPm5pHj5Pzg9pOSkFrLRub50Lj75vFGifq6+1tnnyYJZst8H2YGeRyEe/cJWlsYqQqzx/26c6p/UMZ9h7ZJ+vxS/UuFY77BsG9a3E+2+Si+y9ud61zruPP1oDt/ecf9PVfX1O+9gzAJZzznkZDP8QMktVsYDIuqxqKefszmuFgsnPp3+fqzTib/uccEv/gZwf/ydck//IuSv/1Nxa9+QfGLLzpfktevu27/QdL/6ocWY+HsgnPTPwhrLTezDv+okYWrwXnKMqJtO0U84TgsVAR//gV37fzsmuXtm4ZmG2Rly30/VcUTo+8LefGl67n7YV77q6854n61a/Et7DXOgRBsrzzO83vuN0lVN9lL21x3NQyksNxM3Oc+rXJ+gNn1l3PMMcccc3wiIMsLmL37vVn+Y5JO4QUMZUJp6kj2EST9kJFWBNaa48UfmsmS88H1aoTyYYKx2rGQxK6wcFodfqldRztNwJijFxa057YzjUcfxyn9EWRQxgiXvHAwMcCXGmstXRuzEtZRQ463FgqNc9Y/FJeW/bsWCmEtdsgIg9D+QOrDkl8bW6TwhCaQPm3TwZcarQStznSELH9XvvjUGpSQeBnh96Ri2a/xYesexppDkl8pBUJBlu13aPk9+fXugUi+HrnITfkqoTd+Tn8C1JBIwt3Fy5x5/08I99fxWtt0wzpCDKYSAAS33kZgaVZW2JVl5PIVVj78PqXdu3jt3UOkuTDsq0xv2NePKNDst5JDOowkhdBXSCmIF87hbd7C27iJ1T6l93/svtNzP086xChQKglIdpeusF9bo7r5YTF7fxBxmuJpge+NH5UQwvkOtLspe42LpFKjkw5Bc2Mqk71hqK2/R9jcIFU+98+9MPPn46zw63X2j7T+g2jcc9393cXLmOz+08wIf7h/Hxm3MVP6qpwktFD8uepLfK3yAi3ToWU6tE2Xts3+3XbRWvJs8OjA50qZnH/j3Ass3noVr7tHdf0aZ+uLvNJ6F13boIPzBr0rb0AKVx9gdx9gQeXGfTv8wkXJMxdHv/exc+5e+ns/de74SsJXn3YNjSTtOea/cHn4vWMz3aVlO2jUyLl6KSTPRVf40/3XeKX1Lk+EFyeONzx6VvDSo4Ifv2f5/Zdd0UEtbwEUqQijkMvu9+U2mZELQkArugsGnt92BdL9uhvt2V5+jKvXf8zlVpdrkY9avM1rH14GIGzsFN/vrHe0a/KTgHmHf4455pjjUwqhfWRlGdvdz8zWjkk6s88f7BD0Or9H/Ek5Kaf+NEWo2UzrxIn6BxyGTWNn/Ditr8CsUH3RfNYcfawiM6gcq3awdrpzyAtdAWGIC7jrfAt86R2S1ueQQuIpTWpTtHSS6+KME2CEM+MjLxAdOO/cNvYKU5MkmkIIKios5vgPStvHwuLyAjICnFpTeBXkqHllqro0tMsvhUAxeoQg78b6nT103EKaBIso/g6QpBZPTzDlmwJuLEEMbEnqhUW3trb+PqlxcYn6AOEPbznS1z3/JBJBW0U0a2vZ5w6b9+WGffEMhn0D6/OU8xw4IOtPrSHwHQmPF936/fsfUnn59wBoXX6R7trjQ5dZxEBaMF7I9uoTmBGeJ3FiiXxdHPdx8L0sFlJKWjXXQcxJ5awQJmU5M5JbP/f8kYj0Sc7wizSmmqk/tlafKP6e+GU6UQNBr2P+cUELRVWVWPUWuBSc4YnwEi+WHucr1ef4xuJnCGTvnilMWjjz79fPsXXGfafGnTeLmXLjN8Frs7i8x3q6jUDwaKYkeFBY1O763zOtqSIInzwv+ebz7lz92TXLn77pOv3v3HSy9koIV0Y0t3N3/rPeYi9qdggeDy8RCp890+LdznQJDV98UnBhyRXqjOl1+EcVFnI0VAWFJBUJInSFq3NnWmyZHQTw9F6b2K8QZ4XRJCizXz/HC1mXXy/d4oOsw+8tuuN93l8plFmfRswJ/xxzzDHHpxiy3AC/dCJdZjfzPkQCb5KpnfGHQnkZaT0m8c4k7bMUHoR/ytF8JkackmEfZLnzMiP8iCMZ9hXL8kvYZPjDY88fYXJBQSidFVKGGPdJR4ZrukQwZlmh9EmsQSmBQvQMp6yTp3oZ4Ud5hwn9EfZBqHrFrGHS9lEw1iIRRXM+tSm+HNwmJSRLfpXUWEf8+jFhhKDbJ+n3W1kkX1Bx7cUMSWoJ/eM/qCrJ4L7O0JP1v0dqDFIOSvrV7gZ65z5WSNLzT1ApadrdtCfrv//eIQn7kQz7+qC1dG79cV+CAwACP9u2uLGGFQLZbSHTmHjhHPtPfGX0MqVwMZBTzHNYawn86Yp4/fsqL4KUjkiCG3fewO/sEXslNs8+c6RlnCThr61fQ6Ux3bBKqzrIGD9uWf9REO7dQ9qUxIvoRnW2Vh7HCkFp7y619m4R3aeqm1TPue91wV85ZGR30qjdf5fq/V7hLJA+Jel+czfT8UkEOZ6+KPm5Z9196cfvWX7wti3M+p67LEYWr27ETs7f784/DFoononcveI7ey9PJe2XQvCLL0kqISBTRNkpmFZHGPb1PidZyj0Vss/U15yXwvlUUzG2kPPn2F51sn5hLbKyjfCd8tFUP93u/DnmhH+OOeaY41MMoQNkZRnhl47fZda+I/2HOuL2aJF8+TZKCV5w7E67TRPXpZ4FGek8Taf+YxVDply+i0u0xyP847bTpE4lMqWCQAQVGNJ58oSipEJq3vgiiC+1i+YTMkvZy46PALAoobDWDB/fOMI+CKSHlprYpkOl7aNgrXU2AqLX4T+YOgBQ1SVqXom9pHXotYPu+P0oyFl3H7/tCH83GpzfR0BwzO4+0NvXB7Zlb+ERjJAErW2C5haeGvQECPLu/solrB9SLbnRgvu1CxihCFpbA/nzMuniZyaEpnb0GLMw0KTWFoqENLVoKXpKB+2RZMtPgzI7n/mlgULJQSjlYiAnRZglxqCkxNfTmXtp0VNONGvOBLG0c2f4eNQYyKTL0o2fAnD/4kvYI45Q5eeUTtqIUfdcawl3702M1atncv6tlSc4aBSRE/7S9s1DBZ+HFbkPRbN2FoQg9UvsLlwCXJf/TBbP9+wzm2wHrot9NThdOb/qNll75w85984foLu9MYy8+LCR7Ey9rOcekXz1aXecfvCOZX0XtIJnLg4/lxObcid21+45b3Jx7tnoUa4E57BYvr33U368/9bE39bIdyZ+fm0bISwlGVCWk72TcsJfXtymXoZm5Ij7s7tOSdXM5Pw59hYuURIeV1rud0kt3kYETRJ/D4Hggr86cZ2fZMwJ/xxzzDHHpxyytoJaOL7kUEiVEfMDRM4yfRTeqGV7U8TCTYI1MxN+4Zec8mGEydzxNicFIU895cGpFBK3ruMQfu1nkuYhRKQwZpzSH2FEAciTmrVwkYoaXwTJpZVKCrQQJOTEzoIQqCx6b2gBQrqxgVmKOL7U+EITm2SotH0UcsJfdMesm9s/tElCsOS7B/T4AIk6aIDXj8QvYYVEWEMpIyPD5vePKuePTUo3u+6kdEqFgwaCRvvsNxypqW+8j9dvVGctwU1H+jrnnPxZa0Wt5NOxmr0FN1xcu98z78vJf9cvIaPhYx3TIPQUnpIkWTEoSQ1ayYGRjNaVzxDXV9n97C9jg/HrEgI8pRiT+OfWk7hYQm9MHF8/lO6pVNrlJVLlodLuQBFkGizeehmddOiEdbZXHpvps/0wyifNrhuvO7zLf+ban3L51f/KpVd/Ez1i1t9vbVHavYtFsDNke5q1s1gh8Tt7RcrEw45cebFf68X05SkNtfvvclY5kvle9yNatkMgPC6eMlGM9u45n1KgvNnrmuey/lkIP8CLVyRferJH8J88Lwi84YT/dryOwVCWIXU12RxSCck3Kp/h+cy1/6ett/n23k8nqmZWG4LPvegKmit6YSqn/HyOv7Gyw1/9WsLdxF1PL2xtY+klReSwmanki3uuIKCWbiMbrkhwxlskGFKo/TRhTvjnmGOOOT7lEFLN3vketSwvGujwOxd6hdBHmx3vLTeAqSjWuIUws3Gg8AJkqY49iWzsg0hjkN6R0wumhtTZAORhA7uZUBj3DUtiSB25ntYQ0QtAqkx5MIhI+RMf6DypXLwaFq2lS9nL5uWFsEgkYIaed27MQQ5E803cXiGo6Cjr8A+Xtg+DAaTbquJvesQxqKiQhq6wnwzO8qtx+0LIoiObz0J3w15c1XHm96217CZNmtn25AZzw57Nd5acVHdh81rWsXbQm7dQ7T2M9umuXC7+Xok8Ql9zv+7+Vrv/ftHRDjOi2ykvTZUekG9rbNKBIk4u648Td5yT1BJ6aiBdsnv2Mba/8jdI6tPJdX0lR6otciSpJQoG1zMOSkqEBpMaELKQvs8yx6+6TRZvvQrAvUufO5xMMQuEGCvrL23dYOGOiw6M9u9z+ZXfINy9e+h99btvA7DfuEDiHy6mWOXRqjgyXN56+GX9Ik2I9txgd7OPLDbr5+gGVVQa89hu5gqfeYRcCc6NnWs/CeTbBFDZul78+0KfU/+s+OxV1+k/uwAvXR19Iufu/Oe8lanj6oQQfK78FF+tPI9A8G7nBr+78z06E7wG1tMtYPL8fo7cqX8j2eZGcheLZcn6LCUprcryUP+NrdXHeW6vg7QWWd5Bn/kQ+PTL+WFO+OeYY4455pgBh4h5Opsz/kgcU1pvrXWbdQSZqyg13DJOepY/TVw84il3+FHaEVyhjkf4cy+FIbP3Nk3AGx/JNwDtuwSEEZ4AEz8uXDRfalO0dqZnBjcvL4TrmDtz5iGPMVK5fTGpVXsAkfIHxwgmED/IbASUBOHk/FLIkcZPQggW/SpKCOK+QoiUAjum2NXNyJnKVCj9Hf7EHH1+v5l2KB2YPVZSDJW07y1cxEhN2N0j2r9f/D3MuvvdM48OXHtSCmpln63qOVLl48XNguDmkXxxZbREOLWGdtplN2my2d1jK96nmbZpmUEjyCj0SFN3/Vtr8Y7pZaDVeGVIb4Ri+vVIKdBCkk+l9GT90xP+5Y9+gjQpzcoqe5nE/DhI/OGEXyZd1t77NgDrjcs0wwY6bnHptd+idu/t3huNoX7/HcCRqFHYb+Rz/NOZuH2ciPbuIqwh9ssDppgIwdYZ1+U/d+dd6qo3jvTYATm/7uxx5r3vcOb979K4/TrRzm3kMdVjYR/hL2/fRGT3jsU+p/5pipMH8eIVyV/7iqIajSH8sVv3+Qnz+8PwRHiJX6h9Hi0Ut+N1fmv7O+ylwwvrxhruxi7ucNL8fo6aKuMJTYrh5ZZTED3Vdvf8g3L+HJ3yEipc4GrL3Udk6Lbn4p8Bwj+P5ZtjjjnmmGN6aN+5pucO6SZxOdrH7GK77rHKZsWP8NOUz5gfofAgggoiKEG3BeFsmdbjYNMYWZ7u4eU4yCMRhZTHKrwIIcGPsPtbh5uXNkHo6b0IhNTghdj2HoLJ85gHkUfzJdagpQQrCvk8iKzDP8KksOjwz0b4A+mhpCLFIAVTPURbmxUf6EXyjXN6LqmAso7YS1p42bHKxwFcuNRhDJAPKJyn8607yvy+sZaOiVkNGnS7SW/b1fBRBqs8dhcuUl9/n/L992ivnoM0wb/tSF/n3JOHPhMFmlIpYKPxCCvrb1O7/x7N+rkiki+pLQ90nay1bCdNrLUoIfGFpqpKlIKAQHo0kw63O5uUVK9IEWiJ1tCJncGjp6YsSI1AXrwZhSRJ0UrgTYjjOwhPS7qtFLx+475sjn9Ct14mHeoZ2Xbd/eN9R+ib4z8g11/94Ht43SbdoMoHF78MwGMffYfa5oece/fbhM1N7l76PJWt6+i4TeJF7DVGZ8Lt18+xcv1HrrgxxXf9OFE6ML/fj+2Vx1i5/iOi/fuc4yzb7FNXlWKOHFwh68Ibv4MXH/bpiL2IbmmBTrTA9spjdMqL022UNYR7rsBmpEKalNL2LfYXLlJVZedUj2E33aeuD/92fdS9y27a5Mnw0sSYvIPYT1tspXsIYO2IcXUX/FX+Uv2rfGvn+2yle/y3re9wOVijbbq0bIe26dAyXTo2I+BIFvv26TgIIVjWdW7F6+yk7jx+ccMVDfYawwk/wPbK47yw8VPeLrn7yIKqUlVHHy36pODhvfLmmGOOOeZ46CCUPxCh14vkO+ZDaC4XP+ocv0lnmjHvh5ASWV7CJodj044Fa9x8/WlD6kJlIcaYkk21KL88PLXAipmVCiIoD1ULTLUdfdF8LvPdZv+zCCEykm2HEwihEELNnL7gSw9faBISpJRTCQSMtUVEXWpTVKZMGAUhBHVdIrWm6CS7EQI5srPcn2FvhCxy1I8zv99M21RUyJJXQwtVRBIqKUdqDTYbLpM+uvMuWIt/7wNk0iUNy8SLhz1ChIBq2Wdz0eWcVzeuIZMuQWvLfZf6YNcwtilaKC5GKzxaXuNqZY1LpVWW/TpVXaLqlVBCFtsKTtYf+ZpWO0FLgXdM80Kl3HjGqFpPnDrvADVjYcHTklw40S4v9ub49yfP8Vc3PkRaQztq0MrUAcdFfg71d/jLm9dp3HsbC3x05WtIz6mTPnzs57l//jMALN56lQtv/C4Lt53kf3v56lgjROdZ4KPSbkFcH1bkIzMHZ78BUi9id9Gd/1/ablFTZT5berL43Stt3+LSa7+JF7foRA3Wzz3PXuNCoc7x4hbl7Zss3n6Vc+/8wdTbFLS2UCYhlZrtZeeTUNl0sn4pBAv5HH86OMe/n7b5vZ0f8K2d7/On+6/ys9Y7s+wKAG5m7vxLunGs+fZFXeNX619lQVVp2Q6vt6/xfvcmt+N1ttK9guwL4Mnw0kCk6SQsZXP8AGV8LrZapMqnPUY9tLP8KM82Y1R2kf9ZkPPDvMM/xxxzzDHHLNCem5tOE9ftN4mb6z8mhNKZBLwLRxELmKQXUXeU9YcVUD426ZyI34G1JhuKfgBGQEojlDqZdQ2Zie9F8s3ujzALrLUkO+vYRIPwCKTHXtLCVwIhBIm1ICxauql5hBhqUiiEcN+jM1uxQQpBWYfc7+7gKUW3O13BQGbkL7GGigqKjv8olHSILzVdmxAIzzkASOcOL4cQyf4OfxxWiyJHklp8b/b5fWMNsUlYKy0RKA9PKLcteJnaYDjb3a6dd0S1s4/evFW483fWDju05wg8RWvlPJ1rJYK4yeKtVxDWONO4aFC5kNgUXygWvMrQAmIkfco6pJl2qPa5eJcCTTeN8ZU6duFRCoGSgtSYAfO/HMYYQn/2+8OAMaOQtKpnqWxdp7xzi84YcgIuDhFgd/nRmdc7Cgdn+GXcZu29PwZgY+1ZdsqraCFIsRhruX/xJTqlBdbe/SMqfTF726tPjF+RkOzX16htfEB5+ybt6sPphC7TuChINGuHCT84877a+vs8fvcD/sbF/xWTqdqq6++z9s4fIq2hWT3DR0/+wsD8uExj/KZLqjj7/ncJWlvobnOo78FB5NvUriyzt3CJhbtvUtm6zh3rzEsXVI37yTabyY5zx7eWN9sf8MPmm8Q2ccUrLD9tvs05b5lVb0plAXCjm8n5p3Dnn4SyiviV+ld4vX2NxKaE0icSAaEMiKRPKAMC4U+8dx7Ecp8a4PHEQ5AVbMYUDVIvJKlf5PM76/y4VuJqeLopCw8L5h3+OeaYY445poYQ0km1cwMey7EN+4pl++FQk7dpYE16LKWB8CNEVMd2Tsi8L03cTPwDIPxCSDczfwLrEtoHdcBsLzdmnHFsQ+jALWvauMU0xlqDzWZeA+m5nPuchCUWgZN9Smszk8LhjzFCeUfyZIhUgLUWrZxh4CRYIDfoT21KMEXBKZAeFRXRTl1nSynpCP+ItnLeJYQDhn3GEMwoLQfYS9pUdZmadoQj0gFJZnDYP15wEImV7C1edp/58BX8ux8APXf+UaiUfLYz07/ceK4dLaIPzMEnJiWUo69hIQQNXSY5YN4X+JrQ1wT+8XtYSkmkPJxUAO5vUkh8b/ZHZxf12Nuv087xq26rkJrnxokngYOE/8y1P0XHLTphnfsXP0uaWnxfDaQW7C5d5oNn/xKx79QBzeoZutFk+XWzns/xP7zGfdHObQSWblAlCYaPdTVrZ+mEdZRJitSJhduvce7t30daw+7iI1x/+pcOmcUZ5dGurrB95knamZQ/ytQEE7crM0tsV1Zo1s9ipMbrNgmaTrq+qPNovl02k11+c/s7/Mn+q8Q2YUU3+MuNn+PR4DwW+MPdn9CdYJxXbLO13Mo6/OeOML8/DL70eLH0OJ8rP8Wz0aM8Gp7nnL/Mgq4RycmF0mFY7uvwP7frZP37I+b3+7G98hh/9d4u//cPtqiLB6DCewgwJ/xzzDHHHHPMBOGXwCR9nd8TIvxeNLMMu4BJj92Zl+U6YIbH0s2KNHaFEPVgon6EXzqZ4sKBkQ0gG5dQsx/n3Lgv7U5+L2DjDjKqOCNI8mg+2yNhxqkmpJTZw0tmVDjqe8xo2gcQSs9JSoVhWhv2fIzCWvdQOw1qXgmTGc0J4eKsRnkE9kv6uwPz+2Lm+f3UGoy1LPnVQjobSr8oNuTjBcOKDxZonnFxW8HtdxDWkFSXSKvj53u1lsTnnwJAZoWkTnX50O5NrSGacA6X+9QRxfKVoF7xj2xe2I8imm/IbShODb4WeDMY9hXbKAVKOOUAwH7dEf5o985Yr4nqxvsILK3ycuHdcBIoZvjjJrX771Jffw+L4NZjP4eVGmstvlb43mBqQae8xLXnf517Fz7D7Ue/OtW6cgIW7d1FHnHE57SRx/E1h8j5CwjB1hlX3GrceZOVD3/AmWt/igA2zzzFjce/iZ1Q8Nspu+MebNyYKpMmN+xrVVawUhfjBpVN5y6fE/5b8X1+Y+uPuJds4QnNl8rP8iv1r7Kgq3y5/CwVGbFnWvzp/qtTrNU533dsjCc0K32k+jThtXcRM54fZRly2V/jvF7iqfU8YWFyBPF+4zyJV8JLOp8IQ8mTwJzwzzHHHHPMMROE9t3Tf26Ud1Kxc9qbOuqqH67wYJzx3jEgwoorZnQPmy7NvE1p7Ej4CRhsTQNZW0VMawQ1BkJpVzjpf/AyCUL5zhxwlmVJ5UwAp32IS7tILyzi9LRQSCGxWLRSzj1eWCRM0eHXHCXmMZAevtSkmWfANOjfgnGGff0oKWdEl0dVaSVG8j6Xm+6IcE7409Sg1ezz+3tJi5pXoqp714oWqrjspBCjEwosdBcvYLxeR2xSdz+Hv7xKO+oZWI4qEnhi/DnmS4+qjmgdKCLVKwH+Mef3i23w5NDc8DSxhL6e6JmXmPRw9KKSzpM026+d0mI22x4XJobDULv/PuDmjk8SqQ4x0h33s5mUf/3887QrK86/QLhzS0t5yM8g9SLWL7xEN2pMta44rNINqghrC2L9sKGcKS32R8j5c2wvP4YRirC5wdLNlwG4d/Gz3Ln85akMCXcqbl68tneHZjsZe4uRaVz4XbQqrsueJzRUtj4CnOEcuJhAg+Wif4a/2vgGT0eXi465Lz1+rvoZBPBu5wbvdcYrLay1vNNxy1/zlmY2+zsKSts3efSn/4lHf/J/4LW2p/6cEIJv1j7LXzNraCydsDZQIB39QcnegjOb/CRERp4E5oR/jjnmmGOOmSCU5x5u0m5mlHdCHf5suTNLseO2k/MH5cnvHbd+qRHlRewQl+WZYVKEf3xvg2khpDq2YV+xLL80QNJtmiC8o8keZVAeVAuMgM0cvGXfeIiWvWg+XzkzOSF6kn4hxnT45fQ56QMfE5KyCkmIQYwLy+vVA4QUGGuQQrhEgSngSU1Nl2jnhF8P76q7FQg6JVfMaZcdUU5S14HNCX+uFhiHOLuulvzagHzWk9qZ4dkUKSViSEJBNjKM0orO2avF9++sjY5kG/wKgnbfe5PaoEw4sSlayCK5YBxquoy19khRZNMgT4Xoh8UZRvoTVATWWnaTFrFJBooGhXIg/5OQNGuO/I2S9evOHqW9u1hg9wTl/PkGxVk0nzQp7dJCYcyXmswMUgq0GhxFOCr2s65r6YiyfmMtu8146KjFcSHjDkFWdGlOMEU0Xsju0mUAp4h49Ousn39x6uSE3fIKVgj87h6VtDmW9Id79xBA7JdJs3n//caF4jXVbeFLj4v+Gcoy4uern+XPVz9HWR3+3TnjLfJC5K6/7+69zF46/DeuY7r8z90f8kbbjetc9scXQE4CMumw9u4fIazFi5tcev238do7kz/Yh/KW69KPiuMbhjwy8qjn5CcNc8I/xxxzzDHHbNA+KI2N2704vZPAEZ36bdxElhZORNIuoyooD5tMJ0Mfuj35Q+kJmP99HBB+yEBb7zhpA9k+mERGiZ1ZoizVXNHHmiKaL7Wumy0ApJvplwDjihxSHZmllFSAkK6wMDaT3VokAimcYZ8SauoOP0BFR2AdmVHCmWuNwo3H/xwfPPMrhcFbYsyAhH2ru8dmvHeo892P/aRNQ1eoqMFj6WXJAqkxjtQPSShIjUFlJLBz4WmskHTPPIqZIcbSXHgSIySJ8rHVwbjKxDiHfn+Ke0lZO6Ovtjn6NToOWopD/M0lIsiJKoJm2iGUznAyPnAf89WgPL4Xzzec8NfW38/ed3Yqg7dZkcv6rZDcuvqN4j6eGItWEq0kSgkUolAmHBU5uTpqN7WbGqQUJOkJjFsdgJvfh05UL4j1ONy/8BI7i5f56Km/wPbqdAWvHEZ5dDPjwjPpfQItR5L+3LAv7+4DJH6JVnkJQa/L/+ern+NvLPw8jwRrYxVlL5YeY0U3iG3CH+3+5FDB7G68yX/Z+jYfdu8gkXyp/CxXgsny+OPizLU/dVGQYZVO1MDrNrn02m/NRPpzf4j8PJsGzdoaFkHQ3kb3pVV8WjEn/HPMMcccc8wGlTn1xx3wTiCSr1iudlLsaU3eyMz6EIhoChnfNPAiRFjFdo9m3metxbZ23H75hBJ+tA9C9HkZ2COrOERWHJpUxLFJBxFWkH5UnANKSLTUpNYglXSz+wKUVG7uecyIgUtsOIJaBAiU56L1hBlLdEw2fy+FcEUJ5EyEv6wDQunRNl2kkmMLFKlfKiLZ8vl9X+XdfYOSklW/gbGGze4e3QP7u2sSlBAs+tVD16uWCl96hXGflof9BFJrkdJJ05P6Kht/7u+w+8IvTv1dAUypyvoX/iofPvuX0N7g+ZTYFD/f7xOghKLuVeic0jy4zKL5+glRkYgwxLk/R2oNXZOwEjSIVEBsB4+B1mLgEOfd5FFz/LX7zp1/Z+lk5fw5WhnxvHfhpYFc+DS1hJ5TyGglnaXHEfww+jFIrvZn/nySWAJvuLcC1iKTzpH9X3JTxFHu/AcRh1VuPvHz7I/Jeh8Ga0Eg6Cw4Uhpu3WKpEY4k/VE+v18dVMPsN5wUvbLl4vmEEFP9Bksh+bnqZ9BCcSfZ4JXWu9l2WV5uvstvbX+XfdOiKkv8auOrPB1dHrpcv7XFubf+Z+EvcBxU169Rv/8uFsHNq9/gw2d+eWbS77V38Du7WCEnjmT0w+igiO97mA0lTwrzWL455phjjjlmghAC4ZWwbCBPsPMkhEQEZczu/enl+d0mIqggRjgrz74NAlleIN3fxFrjZONTwqYxtrWDCMvIxrmZY+keFog+4z6rNCBmjuQroANH+pN4fNHApMiw4gpJhWmgT6g89tMWgfRQUmCFdZn1JkWOM8iTimwYHZhNgRJIj1B7bIsmxo45/m4KASkFqUmJVDDTvKsSippX5k5nkwg1tSw4TQbn95Msv34lqLPoV1nv7mTd/g4VHaGEZD9ps+LXKevhSo1QeYXM19MC0x7cbyaF0FfFJtrwaOMzYvEsw6znEpsSyemvl6oXcq8rSUyKOmIU5ygomc3bpxaps8jF1FAv+2PHRPaSFnWvTMMrY7FsxoNdQyXlgHKmN8ffJdxfp93XyfVbW4TNDawQRf77SWP9/AvsLD96yAzQWovOkgiEEC6iMj6imWqGnFxFe/cob9+cqTNeeMMqQTs+cD1by4U3fpdKZrxmhcQoDyM9jNKuox5WuXfpCyNVEoVh3wxk8ShIs7GfZOkCfPBjvPUbeEqy1AhZ3+7QbCeUQu1OMWsLwt9/XgDsLVxk+cZPKG3dQJgUO4PCrqbKfLn8HN/e+yk/br7Foq7xeusaN2K3riv+Ob5SeW6s+ejyRz+htnGN0s5trj3/6yNTDSZBdZucef87AKyfe76IbPzw6V/m0uu/RdDa5tJrv82Hz/zK2Ln8XM7fqqxiZyxM79fPu3Ny68bkiMlPOOYd/jnmmGOOOWaG8KOeBP8EIcuLrjM7paTeJh1EqXFi8+sAIqy679dtT35zvh2dpiP7tWXUyhVkdHKO2g8cOlNwpHHm0K+PbMwopJxo3GfT2Jk/+pHzItB+0a3Lo/m0FCjlUqX1FB1+sg7/Ubp+SihKKsQIM9I5H8DgOvwi6/AHUzr096OiQ6QQGOkiB6cZUU7M4Px+bFI8odFCESqfc+ESl0tnqOoSu3GTrXgfT2oW/NEPzaH0i/EFJQ8PbafW4B0hjm5aWGunijTMEcqAkgppnYKsX0mBFoK85+r8CwTemAjEThqjhGTFr7voPum5DPS+A6qU68QWygEhevF824NmdrlZ3379/IBJ4olCyCFk333XfiWDrwdHEY6KfI6/tv4eXnt36s/FaYqnBf6Q/R/t3i3IPoCwBpV08Lp7BK0tor171O+/x6VX/9vQbrHstoqIu0nz+8eFMRapIF1cwwqJ6uwjm9t4WrFUDwg8VXT6dXcfHbewQhS+HTna5SViL0KZhGhCrOMwXA3Oc9lfw2L51s73uRHfQyH5auV5vlH9zFiyL0xCedMpC3TS5sKb/wMxgyKvgLWsvffH6KRDu7TI/QufKV5K/YgPn/5lOmEdr7ufdfqHny/CJIXSYW9GxQX0zsnyzq2xaRmfBsw7/HPMMcccc8wO7YEXun+eJIIyIlrA7q8j9HjXeZt0Qflu7v4EIZRGlBcwmzcnOv9ba7CtbZAatfwIorI4kyrgYYQQErwI29x2/67U0Tv8gPTLpLujnchJOs4UMPcJ8CPs3jaCnoO8Um5+PBUuNg5rxqcGSOVk/UeUIld0iJAWO+ZZ1lgnf89N7o5C+EvKzaO3kjgbDbDoCZ3+xBhqfs+vIrEpdd1LhBBCUNERJRWw45W539mmoiOiMRGRXubYbq3NimcHtsEyVs5+HFhrEYipIw3BjVE0vDI7rdnl4f3IjfVU/zUrQGtFt5OAdoRTazFyft9ay37aZi1YLBQUgdRoqYhtip8lD6isaOWIn9u/zdpZqpsfUtq5zcb5F/IFUlsflPNbC7vNmFKkpjaGPApSY1BK4Kne8dfqsFP/UbBfP8/yjZ9S3r7J1Z/8R7pBhWb9HPv1czRra6QjChtxYqlEXhZB6cYi8q1bvPUKAFsrj3P3kS8i0xhpYvfPNEamXVY/+AF+Z5dHXv1Nrj/1SwPjC8GmKxa0S4sj139SMAZ8TyG0Jl44i79xE3/9Bu1yA08rFmsBdzdbJElKLe/ulxYPR/0Jwf7CRRp336KydZ3mGKJb2rpBuL/OxrnnihQBIQRfqTzP3a1NmqZNXVX4ZvUlFvTkInV56wbKJMRehLCWsLnB2nvf5uZjf25qhRJA/e5bVLY+wgjJzce+ccgHKPVLfPjML3Pptd8maG9z6bXf4vaVr6C7Tfz2NkFrG7+1jdfZIx+U2Z8iju8gWpUVUuWhkg7h/kYh8f80Yk7455hjjjnmmBlCeVlH9mR/RoQQyOoiaXPTRduN6SzbbhMR1Vzh4YQhoxpm587YbbBJF9veQUQNVOMsYgYDs4cdMiiR7q2DUQgvON5x9gLn9G3t0JlQm3SQ5V6hxMU+OiKmRc9tXyuJFW7mHGHGmkUKIbPRgOlVGv0IpIdWiqSbMnIkwGbdcCiKE7NCCkldl9lN7iOUxJrx36uY3+8jn8ZaQnVYDi+FpOFVqOoIMSGyQAvn1J9iUIIioUDk6xRiYiHiqIhtipYab0bzz7IO8bNowzKzj8/sJ226JkEADX/w2vW0wLbd3s4Jp5TDv/9e2qKiIhb9HmHyhCYQmq5J8GVO+KVTDlhbPHznMvJo945jhFIS7K/jt3cwQrG36GLY4jRFKeEM9U6xnpikzrBP9RUVtOw59R/nDGhVV7nzyBepbnxAtHcXv7OHf/ctGnffcokP5SU2zzw9IPfP0xHCQA8YCCop8No7RR79xtpzGO1jhhi3tiqrXHzjdwmbG1x67bf46KlfpFV1CQnhppvdPu3uPkBqrEs9AOLF8/gbN/E2PqJ96VnAFQNKoWa3GRPtDpfz59hrXHCEf/M6dx/50lCyXd78iAtvfQthLVZqNteeKV4LpMev1L/Cze49Hg3PT4zDzFHduAbA7tKj7C5e4tLr/53a+vu0S4u9gtUEeO1dznzwPQDuX/wc3dLC0Pf1SP9vEbR3uPjmt4a/T/nsLVygc0AJMRWkzIpu1ylt3/xUE/5PdhtijjnmmGOOjwd+hKwsnQrZFkEFUapjO6Nln9YaMAmyvHA6Wfd+CRFUsN0m1qTYpIPt7GNa25j9DczeOrbbRNbXUCuPfKrIPpAZ94E1ybG9CIT2QWYjAgdgrcu771dSCO31SL50zveJNTQqAYGvHHm1TE6H0F6f8eBs8KVHpDQxo1v8xliUymTb4miEH6Dshe6zNmXS1ubz+17G+nLJ+DiyrISa6C3g5/vZOGLZn1BgrEUhUPp0CH/uQaCnJB05AulRU9HYZIJhSK1hq7uHQLAaNBypPNDCVqr3/a2FYEQcX2xSjLWsBo2BYyCEoKwjYts3UiJc/GL/nzqlBVIdoExCuO9c2XN3/r2Fi5is2BiPM607QaSmZ9iXQ+mTcepHCDbXnuXDZ/8Sb33+f+P6k7/IxtlnaUcNBBDur7P23rcHzODydIRAuyKE1GAy1c7irdcQOCl3t9QY/Z0y4tisnkGlXS6+/t8pbzqH+7zD36yffvycxeJlKpl4ycXreRs3B+Z4SoHGQrEPWiMI/379HEYoVzQZklsf7t7j/Nv/E5Ete/nGT5yxYR+qqsST0SNTk31hEiqZnH9n6TKt2lluX/4yACvXf1gUX8bCGtbe/UOkSWhWz7DRV4QYhtQvcf2ZX6FVXib2K+zVz7Nx9hluX/kKHzzzK7z92f+Vtz//t7k1o8KgH/tZlF+5bzTk04g54Z9jjjnmmGNmCCFPjWwLIVwxATF69jtugx+dGtF25n2LYAy2vePGB6REhFVkYw21chl15ipy4dyR59sfZgjlOdO7pIvwDuc6zwTt9zwBDiLputf9vnVIz0XzZVFtCklqU7SWCERPfj2BxArlH1nS70lFpEMSm46UM1ucYV+SRwgekfCHMqCsIxJpJo6Rxqmb31cqN+wzeFLhi+Odg0ooPOmRWoNAOPuDjOCZ1CA0A13fk0RiUiLpI49wL6l5ZYy1mCkLO+20y068T90r80jpDEt+DV9qugcd9aUAIUiMQQlBMMK/YC9psZCpKA4iUv6hgpOn5WAcmhA0s25zaef2oJx/OZPzZ/8vikuniH7DvhwHifaJrEd57C9c5O7lL3Ltxb/G25/9W+xk5oSr175XkOC80KG1REqBkpLUuOz2+r23AdfdnwSjA64/9UvsNS4gTcqFt77Fwo2X8ZrbWATN6ul3+LGid93WV7FKI7st1N5G8ZbA10TKFT9gNOG3yqNZd9ucz7Dn8FtbXHzzd5EmYa9+nk7UQCUdlm/89FibX96+iUpjYr9UKA+2zzzJ5pmnEMDaO3+I39oau4zFm69Q2r1LKjW3rv7cxHs4uCjCD57/dd797N/ko6d/ibuXv8TWmado1c6S+tGRiX6OfBSgtHsXcUrJHw8D5oR/jjnmmGOOhw4irCCiOrY9PB/Xxm1k1DhVsi1KddTqo+izT6DXnkStPYlefRTVWENWlpDh4YizTw3yOD1xDIf+DEJIhF8eWryxSceNhvRFGAqlnRzfpCghCyIKeePRgpBuRn/cepV3LCOm5VIZ35fsd4bnZFsLSghSm6KQR56tlkJQ90pYmU40R0uNIezrNic2RaNmlsMPQ6R8YpuilERICmKaWtBCjpS0HxepNURDpNjToKQCQuVP7PIba9mO90lsyrlwmQvRCpHy8aUmUgFdM3hu5vLxTteZFWp1eP820w6B9FjxG8Pjy6TO1Cm9tny/cqBYTibrL+3cItq9g9dtkiqviH1LkhStBL5W2HHZjcfEMMM+cEUtLRXpKXqapX6Ju5e/hJGa0t5dqpnKITWGKOjdf3IDwcadN5EmoV1amNpd3yrNR0/8AttLjyKsZeWD7wPQqSwNHQU4Sbh9S+8akoq44bbbW+8zHRSwYHaQNiVV/iFTxX7s5fF8mz3Crzv7XHz9d1BJh1Z5mRtP/Dx3H/kiAAu3X58p2/4gquvXANhdvDxAsu888qVMPRFz4c1vHVISeO1dGnfe4Pyb/4OVj34EwN3LXxrrvP8gEYc1Yr+CsIbSzp2Pe3NODXPCP8ccc8wxx0MHISSyugxY7AEXYGsSQCBKp+uEL6RCluqIoIzQ/ifejG8WCOU5Ei7ViRRVRBANd8xPY2RUH/yb9hzpz457KHXWac9c5A0gxeTu0DFJcEkHNMo+SlnaneFaaiGdQ78WaqoM+VEoq5BAeSRjXAItjpD1z+8nNiFU/kxxgKMQSC+LonQmdnntIU0t/ik69ANTy4oPQktFwyuPJPypNbTTLtvxHmUV8Uh0hpWgPmDSV9URyYHutZISoSFOUsJAHWoiptbQSWNWgwbBiOvDlx6e0MSmd0xz5UA/bc87taXdu9Tvu2z0vYVHCrO2buJk9v2mdaeBNM0N+w4f6+CEnPrHIfHLrJ97HoDVD39Amrjkg/5zz1cS0oSF268DsLH27GwdXim59dg32DzzdPGn047jg14kn+4rmsVLrqDjbQxKyStN191vlpfHfre9BUf4o927yLiNTDpcfON38Lr7dMI6Hz31F5ySonGevfp5hDWsfPiDI22/MGkh2d9dvDz4opTceOLPE/sV/PYu597+fSobH3Dm/e/y6I//I1d/8h85+/53qW5+iLCWnaUrbK9MH8sIrmC33x5eeD02hGC/kbn1f4pl/X92nl7mmGOOOeb4RKHo8ncOdPm7LUfC/aNlgc8xHYRfcp32k1BR6AAQAxJna1LXqfcH5dBCSNB+VtgBX7loPotFCukoj1CTCf0xCX+kAqp+SLmksNaRv0OrKCL5jtchDKRHVYV07WhJaZpkhKyP8KfGEB1z3TmcSsDJxrWShTiif/b4pJFa40jdMUwhqzpCCUkr7bCftNmO99nq7rHZ3WM/aWOsYTVY4GK0Urjo9yPMxgn6xwKkFGgh0UoS6mHd/TY1r0TdG30PUkJSUgFxX6FLK4lWgqTvXOpECyQ6QJqE+l0nU8/l/OC6w2Gge7P0pyTrT0xm2KcOk0ytT6/Q0I+NteeI/RJed5/FG6/geRK/b/8rJVnY/gAvbpJ4EbtLj45Z2ggIwZ3LX+Lepc8RlxfYWZ2NfB4FeSRf/76NF3PCf3NAieTv3gVgNxpvIJcEFdqlBQSW6uaHXHjzWwStLWKvxPWnf2kgdeDuI1/AIqhtfHCkKL9SLuf3IlrV1UOvp17IR0/+eYzUVLZvcuGt32Phzhv4nV1sNrZy7+Jnufbcr83s6A+QpIY0NaQnOFbSjyKeb/vmqSz/YcCc8M8xxxxzzPFQoujyW1OQP8hc3SsLWXzYHKcF4YfOcO+Ykn7AqQW0V3TtgSyOL+jF8fW/34sgO+Z599dkLv8Kcn3s+HVK5bqpR5T1KyGp6TLCszSqPp3YDHY5hRMaGGtGdnmnhRCCml8m7VMyHMTB+X1wZNw/obEWT2i0kE6xoPuIpWVgnSeJJPNpOGqHH1xhpqZLGMAXmiW/xvlomSuls1wtr3G1fJ61cHHk2EOoPALh0TGD6grfk2gt8YaoGxJjaOjyYJzfEJR0SNon6ddaUgo03eTAHH/mEi+wJDpkPzORS1KDkoLQk67YpcCmp0O9U2MHxkX6oaXsJTacIqzS3Lv0eQBW77xCVXQGDQQlnL33BgCbZ5/GHrWoJwSbF17k9pf/1ljDv5OCMZlqpI/oJrUVjPaRSQe104st1VuO8O+XlycWd3JZ/9n3v+tm45XP9ad/iSQY9LbplhbYWn0CgNUPvs+sOYu1TM6/d0DO349OeYmbj/0cRig6YY3NM0/z0ZO/wNuf/9/48Nm/xPr5F93s/xHG4IwFqQT2lFQm+7U1LBC0ttBdF/WZK6o+LZg/Lc0xxxxzzPHQQoRVRFQrZvlt0gHlI4JPmSv+QwjhR8hSY+Ks/FTQ3iHjPht3nIpjSHfXRfO5hzstXEa8wSARCGMzMj9Fh9+5zx15s0s6RCAoR5pqyafVSbA2m8nFSfphvEv+tKh6JbTQg87uGaw9PL/vuuMK74SiMb0iESFFC9fpz+e6T2t+P7EpvvRc1OIEjCrcSCG4XD3D4+VzXK2c41y4xJJfo+aViFQw8dgooSjr8NAcv1aS0FOHih1dk+BJTUlNTijxpUaIwa58KegVsHL0x8LtLl0uxlXixBL4Cq0USrpoxJmFzWkHhpxTB2EBb4iaAZxTvzxFdUE/dpYepVlZQZmE5Q9/OPBauH2LUmsDIxWbq0+e+racFFJjB0ZxAJCSZMEVdvwNlxogum10cwtwxn7dePy9K5f1C2swQvHRk78wMubu/oWXSKUm2r9fGENOhT45/87S5fHbs3iZt774d3j/M3+dO1e+zN7CpSJp4jiwxhWdTstGwngh7bJTVJSyLr+gF7v6acCc8M8xxxxzzPHQQsisy2+Mi8frthBR9ZAMfI6Th/BLyMbJuFc7476SSzvIYQ1ilHFT30OiFgotFbFJnYN8Jumf2H3JiwJTkJ1RKCmfUHp0bEyj4hMFHs1OgrHWFR+ydPKjOvT3I9KakgzomC4W191td1P22jHNToLnyYF4ONcdl8fqjvdDCYWvPBKbIpQEIUiN6zDrU3rwjW1KpCaPJFiTYnfvYTvNoa9rqaYqGoxCSYeHyGw51DQqh7etnXYpqWAqVUeQz/H3eTMEvibw1QCZ658j31m6Uvx7agylIIvJGxLrNw1k3HKpJmOQF7D0EDk/9Dn1n6ZzXw4huHHBdflLt99Cbd8tXip/+DIAG0tXMacQCXtaGDUW0y3i+dzsuM6+a1qqE1WrJOl4m8Z2xcXVWQQ3H/9ztGqj79epH7F+/kUAVj78IcKM9gvpR3n7FirtkoyQ8x/CKXTFjbVoKY7jwToRhax/62YhgPgU8f054Z9jjjnmmOPhhghrWZd/F0yKfAASzDlOHsIvQdajsUkXlIcY8dDunPqVi+aTEoUktokj2RY3HjAJUjklwBE7/DbpIuMOVa9EN03c/HDVR2lBq5s4J3ssSpwM4RdANYnZa7XYb8fEiUVryUIl5MxCxNmFMoE36NDvS/9E1AU5QumT2JSc96XWIuXpSfqttQRyimMZtxBhFRu3TmU7QumhhSQxg476ekjHO7WGui5NtVxPakLlDxj3CQGV0Bsgc92owfbyVbaXHqWVxfSlxmamdb1tOBTrNwk2xUoPMYEpJWmKUmJk0kR/JN6DwE64zO7KVQRQeeOPwVrU3ibBvWtY4O7y+Pz2hw5WIIdcQ/kcv964BSZFbzuX+LhxhtBXaC2GeocUEJIPnvtV3n/xr7KXxRqOw+baM8R+2Xkk3Hp1qk2vblwDYHfxkali9E4D1oJUp2scmadilHdukhrjxsc+RWODn55vMsccc8wxx6cSrsu/5Oa//RARzuX8n0h4fcZ9SceR/VFdOuW5Dr1JUFnGfWxSpJBI7FTJAUJmxn5H6PDbuI3t7GK7TcrKRQYaa/E9xULFyfyFBCscKTsJwq9sQs33qIWKxXrI2lLE2YUS9YrvTNsOdF+n7Y7PgkB6YB3BE0CSWDx12KX+JGCtRSCmMuyzcQfCCihvUCVyQgikTyA9OmZ8Dvcscv4cZRkOFBKAw2ROCG499g1uPd4zNIsTFwnoHTCtmymaL02c2/+EIkFqyPwhRh9o/wE49YMbLbDA7tUvYaXG27yFf+c9wg9+BkBz8RKt4OGIdJsGFkAMl4en1SWMFyDTGL1zD2/LEf6kfgatJZXAG/R7GILEL9GNGtNti9Tcu/Q5ABZv/AzVHa6YKWAM1VHu/A8Swp7KPagfrcoKRmp03Cbc33BFrjHXwycNc8I/xxxzzDHHQw8R1RClOrK0cCIxcXM8eDgDQDfHb5OuO6ajnuKUHojmC6TvZtbzmfxp59aVh52xw2+7TWy3iaydBeUTIZ2s3ziimUu9vYx8uUi+4z9OibTLxbNnWSlHeD6uuzzmeXPq7vgM8KTKhhQcQUnSdKhp3TgYawc62qOQ2BQtJ3sQ2DQGpVCVpSy1Y3+m7ZkGUggqujTgqD8Ms8j5cwTKO0TRCzIXjyZzSeoy6PsvES2ctn9q2m0TZ7o5gS2lZnL0oq/lA3HqT5IUrQWqWqd15TMAlN/4Y8IbbwKwc/GFkcaWDyNSY5wKaJg+XIieW//6R0WHP2k46XwYagROaXNS2Fl6lFZ52XkkfPTjse8t7dxCJR0SHdKsnTmxbTgKTt00UqrCS6O040YsPkWefXPCP8ccc8wxx8MPIRVq8YKb55/jkwnlO9KfdAGBCEbLol00X+Ci+4BQewgEGgVYhJquoy60DxNIXD9sZw+bdlFLF5GNNYT20MZQ9aIBF/daOWCxGpKSEkj/ZNyc0y7KL1EW6pCB3KHttBYBx4qzGwZPaDcLLy1C4vb5lHJ+Yw17SYvteJ+9pD2RPDtzwMkeBLbbdCadfglZboC1xXlxkiipAIsZSyZnkfPnCDJTwoP7IwwdmR/WNc8TKYIDrvm5ed60hFcYk5lbOtvLUbB2tGFfse4HJG/uJpbIc4qW5pWXSIMyqr2HMAlJbYV0YQ3E6ScGnBSGRfL1Iyf84UdvIOMOViqS7Hcu8BShr+l2T3CWQgjuPvJFABp336a8eX3kW2sPg5wfgMw49JQPej7HX92+hZZzl/455phjjjnmeOAQfuQI3ByfSAghEEHFdWg9f+T8fvF+Pyo6/HkXvTBmm1JC786X6R6WTWsHawxq8RKyuoKQEhGUsWlMWUVYa4v5aZEZqCXGnEiX3VoDQiKkoiz9iXPasU3RUp+YQ38OT2o0khSDEhKpJhv2pdawEzfZiZsEwuNSaZWGV6aZjjeKS2xKKH3kmIdqay0kCbK84M6fsOJGeiZJkY+AQHl40huakgBHk/MD+NLDP2DcB47MRYEe6sQepwZPC4IDXXcppJt0mSGaz2p/bFqFKy6AN0G+rLVAnbJTv8Ud8zBLMkB7NJ/8cvF66/KLaC1R4sGMF5wEhkXy9SNecoRftXYASGrLbhQJd58pR5rUmBPluq3aGbaXHkVgufDmt1i88bPDYx/WUNn4AMiSIz4mGGtRCGd6KGYaaJkZxRz/3h0ejJ7lwWFO+OeYY4455phjjgcC4YcgBMIvTSzeCO3jHPoyp37hzPsQwhGYaTCloZ1pboGQqOVLyMpi3/aWwKREKiCU3tDO+4mY5sUd0D4iKBOq4R3hfrjueC+//qQkzkrIwqlfK4FCjuxMJjZlO95nN25RUiGPlM5wuXyWBa/CglfFWIsZYxaXGEMkJxTwko4rDmW+HUIqZGUBm3ROXNbtC00o/ZHqiqPI+cGNC5RVcGjMQQgohcPJXJJYSr53iCTOFM1nUxASqwOskIyyOE9Tg9KTlRxKSoR27z8tpKlBK0nQF2HXWXuC9rkn6KxeoXP2KkplRY9jxG0+SAyN5Ot/vbyA6UudSeqD0vnIV2hPjjfvOwJuX/06W6tPIIDV6z/k3Nu/j+iLTS3t3EYnHRIdDMRGPmhYYxHKxWRK5KmOc3TDOrFfQlpDo7l5auv5ODAn/HPMMcccc8wxxwOB0AF4ITKqTX6z8goJp4vm071u8LQke8L7rLWY5iZC+6iVRw4nQGgfF7snqOoSbTNoGCfgWHFwxXYkXVcE8UJ84RMwXtafmJSorztu99cxze1jbwdAlDn1a+0I3jApdyvtsJe0qOoSl0tnuFxape6VCy+Dsg4pq5Bm2hmzJos/gTzbuIUo1dx5k0FENdDhxKi5WSGEoKqjkYWWo8j5c0QqGFr8iHyFd4DMWetO+4NyfreRM0TzmRQrFVaHWTzlCMJvwFfKSabHQEqBFpIZxAUzI04sgafQ/QRZCPZe+EV2P/srLnlDCDyljhq+8cAxKpKvgBBFPB84h/5+KDXZ7+FI2yUVt698ldtXvoIVgtrGNR559b/htXcBqK5fA3Du/x+TnB96ChStRSZUOcUTUAj2667LX9u7f3rr+RgwJ/xzzDHHHHPMMceDgfYRYdnJ9SdAKA1KYU2CJ103W4KTvk/7ACrd/PKwrpC1BtvcQHghavkRZHjY+Vt4gYsATBMqOgLb66an1iBPyKEfk7giiPKQSlNRwdgOf2oNpYwEu+2Rbq65e/zYOl96WAtKCDwpDxFBYy2ttMtasMSlaJWaV0IeOB5KSBb9KrFJh+771BqUGG/YZ60Ba5BRfeDvQgfI8sKpRPRFynfT7ge2+ahy/hy+9JBCHiL9SknKoUc36f09Tp1pXX8cXz+mjuYziSuaCZUVz0YUMow9NDow8nt4kvQUGX9qDFEw+XryTzmi7UQxIpKvH/kcPxzu8AOEmXnjSZr3ASAEW2ee4sOnf5nECwmbm1x+5Tcobd2gmsn5dz5Od37AWHc/UVK6ZJRTNmws5vh37p3qeh405oR/jjnmmGOOOeZ4IBDaRy1cRATlyW+WmkxDjBKKQGqkxXWbpuyqu2i+w3JmR/a3EEEVtXx59PYoH6E9SLtEysfvi25LbXoikXzWpCCl8zRQCpQiyswJxz3cFmZ3JgGlEOUlbHcfO4VD/jh4UjuzPi0ph4cJ+X7apqIiFrzK2Pn7qo4IpXdIFQFTGvZ1WwgvGhrDKUt1J1dPj/ddDyKU7hgfVFccVc6fI5AentB0hxybKNBIJElGYOM+07phUGo6WbMwqevuA1Z5zsBvCKy1LhFiCvhTrvsoSIyLuBxV6OiH1jMkFXyMGBfJ1494+aIz6ys3MNHhwqOvJUoLzCmNU7RqZ7n23K879/6kw6U3fgedtEl1QLO2dirrnBbWgFbOtE8hOHm7zkE06+77Rq0daO+d8toeHOaEf4455phjjjnmeGAQXjD5TQDKQ2jtCC2wEtSpKN8R/pk6/HLAqd9ag93fRAQV1NLFsWqDwmgwifGkpqoj2hkZTLIu9bE7/EnHSdb9ECE1QnqESDx52OjNrdcVGgrvAJOCVKj6KqKyhG1uHYuU+UKjpER7gkppcMY+tYbUpKwE9YmjDJ7UNLwq7XQI4TcpntBj/Q9s3EaUFxDDVABBCRFWsN2TjejTUlFS4SFifhw5f77cUPlDDQEDrYgCRTc2WQZ9n2ndsGUJMWVemAXlsRfv0B5Bk4y1SCGHGvbtJ232k8GxCaXkqWWVdbopUagmpgXAg0sMOC5SY7NIvvHvM1GVra/+L+x84a8M3b9SCjTiVMcpkqDCh8/+CtvLjxV/2124NL1fyinBWFv4Syglp/VgPTISHdGMnI+L2PjodFf2APHJuGLmmGOOOeaYY44/UxBCZNF8jnxFKkBZEHL6Dj8HOvwF2Y9qqOVL040W+FEhh65o59ZvrSW1zqH/uNFNNukigkqP2HoBHoJI+QNRgDkcWVa9SL40QWSRh6pxziULZHO4R4GWCg9FOoSc7iYtal6Zqp683wBqXglvSMc8sSmRHl34sWkCUg0dswAX2ygrS5DGTvp/gqjocOC7H1fOn6OsQpJhYxoCSpGHscZl0MtB07qDkMpF842X9bvUh0RIQNAZJedPDUoz1LCvaxK6Jh4oHqmp1j07ktQgcQWmaS6n09qOk4YxJovkm0y30soCJhytfNL69H0LrNTcuvp1bl/+Mq3KChtrz57uCqfZJttTSCglTn2UI7WWW2ufYa9xBtv4eNUNJ4mHjvC/++67/P2///f5zGc+w9e+9jX+xb/4F3S7h6vDB2Gt5d/+23/LN7/5TV544QX+1t/6W/zkJz859L47d+7wj/7RP+Kll17ii1/8Iv/0n/5T9vY+PZKNOeaYY4455vi0QHhR0eEHHHHP4uumW4By7zVpRvY3EFEdtXRpYixgsQgdOPm4NUQqIJBOmp3a9EQi+TApou9BX+gAbEJVl4YSxMSm+MpDZcoCa5LiuwgvQDbWwBpsPM4wbzScU78mOUASuyZBIlj2a4dm9kchUj51XaKZDD7HGWsIxzj0227TjVkEo7vqIqwg/NKJ+Bb0I1Q+SijSrJDQTruUjyHnzxFID8HwGeTIV3ha0uwkBP4B07oDUDJ3qR9DfFJn2BcL8JSPkDrzDxj8TJKCN8Swr5v7ZhyIKdRq9ljAadDuplRKmnCYUeEQFPvgNFveJ4BJkXyzwNPDvUhOHEKwdfZpPnju1+iWFk5/fRNhEdn5qdUDCMszlt2FC1x/6qtQqk9+/ycEDxXh397e5u/+3b9LHMf863/9r/nH//gf8+///b/nn//zfz7xs//u3/07/tW/+lf8vb/39/g3/+bfsLKywj/4B/+A69evF++J45h/+A//IdeuXeNf/st/yT/7Z/+Mb3/72/yTf/JPTvNrzTHHHHPMMcccR4DQ3iBHsQbU9NnzQghQHjZNXGe/1EAtX5x+rACcU7/yIInxpaaiItqmi7X0uuxHhE2z+fu+4kP+nUPpup0Hjd4Sm/L/b+/OwyS7ysP+f89yt9p6m00jjUCSjSywsAAhIYQFKCwSYQ8G2QnCZpOJBFiEx8j6YfYAwQlLMMYsQgZso2CD4wcjg2MioxjrUUwIhgQbgyRsEAGkkWZ6qa6qe+85vz9OdU1Xd3V3dU/1dE/p/TzPgKa6lnurb9f0e867VPSy43dlGHfYpSuT6In9+NZ86A+wBalOVgX8C0WLqahGdZM73RNRFaXoPZ/3HtUtWVhT2UFXp9ZtzqhMhKpO40fcrT/VEbG2y3o1OBrHkc6/JDERVtuBaf1aK6ppaOyXxetfU0uj+dZt4OYL0BEdPJlOiW1KR7lVvSxK5wY27Gu7nMwkZKZ/TKHWCqs1xQgDz3YRJkLUK8MvqPTGE+7yHf6NRvJthtGaXX6620MpTHfBRCu1cs1q5Eo8WsEQSRknleP7l2rEbrrpJhYWFvjt3/5tJicnASjLkje/+c1cddVV7N+/unMlQLvd5kMf+hAvfvGL+eVf/mUAHvWoR3HZZZdxww038KY3vQmAL37xi3znO9/h5ptv5swzzwSg0Wjwkpe8hG984xs8/OEP3+5TFEIIIcSwuruq3nuUUnjnQvf+TVAmxuf3oup7Q82+3WD2+4BjUDaBogNRQj2qcDgPKfMjq9+PlqXId885NRGJiui4gtQcO2bv6R9np449Zolu7MW3m/jFo6jq9KYPK9G2L7holR0ibZmOG5verayYlLqtMF8s0ogqFN5htSZeo2Gfz9tgk4HN+lbSlQYs3IPLN84EHZZWmprNuLdzFNudJHC86fwQeiPEypK7YuBCUSWxtFNHEm8QaXRH8+Xtcs3f4lVZ4JI6HkdmK2jvmcWTuDI0hgSKwmG0HtgvIC8LGknY3Z3Nm/3nEWnazRJGkNzigTx3TDfSoRsHAqAgigydxXwkx7FdvO8vl1gqBdrKKE+jVfhZfwDxPpzy0g6/1grU9kb8YU15zKJ9dtkO/6233spFF13UC/YBLr/8cpxzfOUrX1nzcV/72teYn5/n8ssv790WxzFPfvKTufXWW/ue/+yzz+4F+wAXX3wxk5OTfPnLXx7tyQghhBDiuCgThQBlaafaO9Rm0+ijBNXYt7Vgn6XGfVV8t/lc6NZv0Srsdh4PX7RRaT30JVh6PW1C/boPdd/tZTusocma6gXLoX5dhfdp+TFri5k6CDbGb6HTtO126l/qV7BYtpmJ6mRm8++fVoqpqIbzDud96NCPWbNhn+80UWljqJILFVdQlQnciLtpV00KPix01Ex63On8EK6jqk0HNmIEiCPDTCMZqmldZHWv5GAwT6E0VlkSnZBFdbxSuO7PkQdaeUm9GpGs6IpfuBKrDZlJSHWMVqrvtUbZqb/dKUkjQzXd/PsbxhOO5DC21fL6/WbZ5kg+v8H3bq3nUd2SkBEe3C7nvEdzrOnhUunJdr4FznPcn+u70a46ozvvvLMvGIewA793717uvPPOdR8HrHrsWWedxQ9/+ENardaaz6+U4owzzlj3+YUQQgixA4wN4/l6dfwupLxvgq7PhAZ9Wwj2l4TGfeG/Yx1RNdloOvR7v3okoIlCs0FXULVpX2OyMM5u2fx6V4b3Z0DWg4ozzORBfNkJ6f2tOdziUVzzCG7+Ptz8YdzCYdzi7KrHxsp2U7cdzbJNphOm4sEN9IZRsykVk7BYtil8SWLigX0AvA9p57o6fO2srk6B91suXxgk1XFIv3fF0A0Kh3peE68bsK1Xu7+c0Rr8Wtu9HlB0lCfSMZGOSUxKbCrkPvR1aOclUaSpDQi02y4n1XH4Y2KSZaMoYalT/1CHuS7nPYXz1GvJmiMI1zNMI7ydNGgkX8eF3hyDJldsRCuF0Ypyuzv37SLee5ShV9pjNGi2bzTk0mtaO36pFLsqpX92dpZGo7Hq9omJCY4ePbru4+I4Jkn6a/IajQbee44ePUqapszOzlKvr/4Ha6PnH8awH9I7xSwfaSHEkOS6EVsh143YikHXjTcxxAm+6KCNAq2wkUVv6t/crQf6S1yagtUo5VDaMJlU8J2SxNp1Z9Gvx5c5Oo6xWQVlV5xzFAGOapwQdwyekAbsXHjN1NpQ4lCWEEXYJOnLEug918QM2nVCav/S4kl3QUGZsJBS3vdDlPZ99fKptkTG4nxJQcEplb1k0dZ3uQ2WPWmD7y/eAyhq0eAgz7fbkFWw1TpqyM8PHTXQRRU9t4CKjr/WHiDTEZmNMBrqcbalgHSQio+JzLHv51bFcWgEt7LZHhAWgWxEaTSTcQ1rDRZDNW0wNz8HSlGWsHcyJRnQJK+gYF8yQdS9JifiCve2j2J0yLiIIhN2QBVbvvYBFlsltSyintktNbVLrO6muSs2GHM/kO7+vIT/H30QXboQOMZROE7nPVYranHKve2jhEMf/sB1pHuxxsDv+zhyCmsUUaRCxpEx3bXNNa79kVBE1pAXCmv0qvjuZP39ZlcF/CcrrRVTU2uP0thNGo3RrVSLBw65bsRWyHUjtmLlddNuT1LM34+tZRSkJFN1bP3E/pvri5jFxQbKWHScUvUJ00WNerT1a7xcLFC1SdJ906ua07WaDVy7ST2rc7+aI3cl1Sgh7+TMJA0maiGwLZsFKk7JZlZvlvRM//Ta51XmLLIIeEzafy7TqspPFo+wP5nkUGPPcQWoAGlpWTi6yELRYmaiTiNZ/d7lswtE0wdJ9kxu6rk7xQRZawE7ws+cg9EUi2WHvfV13ttNqvmEI/rY93NYpXfc25plMq6G5n+x5WizII7t6sWITomPUyqVCnsak9SXRk/me1hs/z8cMDOZsm9PdVXAXnpH1absn5jsXdsumWRhbpFKHKOUInWe2cUcozXxgIZ/wyhKR4riwEyVSrq1UCRKHJVWQWQ10XEEX8mAHgaj0Ok4bGRo1FO0UjSLNlNpjQfV91LMhvGL6SZLZGqLJe2iJNumY9512gWx1dSqYbHJeU+1GbJNBi1WjULuoFZNWGxF1OvZmvHdyfb7za66YhqNBnNzq2fHHj16lImJtdO7Go0GnU6Hdrvdt8s/OzuLUqr32EajMXAE39GjRznllK3PWnTOMzvb3PiOO8gYTaORMTu7SFk+cNKBxPGR60ZshVw3YivWum6KlsfNLqBdgptv0Zpto4uFE358edvjO7Po9Fg66ezi1kfCuYVZ9OQptI6sfo6iBW52Hl1E6I7mSGsOFSvmOm0mnWfWLXafYx5dT2jdv/X3o/Ap7v4fomv9QZNrQdn2VGxKc340TfHiIma2vUh7IWe23X/e3pX4xQ62HtHcxPkYo6kkVRYW2uCbIxmBBhC5GItldna0Y/9Mx3JPaxYVD3+czbKN8op7W3NMxlUK58k7Oa50RCsCftVZpBVXKDqezqLvvc9Fx1C0HS3dYrJaZ7G5+nvaLNtoFEXTMau6j3OOvFVyf2eBREd4PEVe0ipzyi0GXXOLOY1KhCsLFha2Vorh8eSdgrztiaPNH4fWmiSxtNsFbhvS5Fudksjq3vt8NF9gKq5RNj1Rbrm3fZSpeOPGlMuVRUGzmaMeIGn9zXYBiQ0/212dTkFROly5PQF/q1XQanVotXPm5haJ4/7Pot32+02jkQ2VbbCrAv4zzzxzVS393Nwc99xzz6ra+5WPA7jrrrv4mZ/5md7td955JwcPHiRN0979/vEf/7Hvsd577rrrLi6++OLjOvai2Plv+jDK0p00xyp2D7luxFbIdSO2YuV147CUpccVJd6BcuB24LpyJsN1juKj468f9d7jCwcmHfgz4lREWZT40pOQUDoXfsl1HoOh7M4fd0UBOjmunzMf1SidwuU5aln3+AjLhK1RUVnv9Y5XTVdY0G20t6ue0+cdvLIonWz6+6srKWhDmeerGhhulUYDemTnviRVMc578rIc2MdgkMW8w0RUo+UWwuPQKKAoS8yKPhKqKGnHioQY7U0vKNEqQXU0puaIjaEc0PFuMc/Zl0yAU5TdphUGS6ISFopFrA3XhzWKxbbb0si5vCgxKowhDHHr1t9foxSt3GHNVp4jvC/OhZ+rUcsLRxqH99l3v9+ZSilLT01n3OOP0ilLzJDXAIQSirL023K8u1FZerRWfdeq6t6+He9BaA3g8T5s5hbr/A5zsv1+s6sKEC655BL+5m/+htnZYw1kvvCFL6C1Xjcgf+QjH0mtVuPP//zPe7flec5f/MVfcMkll/Q9/z/8wz/wve99r3fbbbfdxpEjR3j84x8/2pMRQgghxPGzEeDBOdA6NLTbAaFx34h+ySw7YGPUWiUBxvYao6UmItKWRdfBakO0cpzdJscUrhJXQuPATv9O9oStcmo6c1x12itlJuZQtndwh/4yR9l002MXAVScgE2gaG985x2WmZhUxX2N8NbjupMYJqMqFROzWHbCWDprWN2nMMwxKxRUbH8/g8UC6lGVNGFg072l5pBVu3o6QsNWKJbtKkdG47zfdKjugXbuqFWiLe3KrxRHG00r2DnLR/LlviTSEZkOWcgVk1I1GYvl5q5XPaKGicer9H7ggtGoeTx2xeePNaqvkekoLU0FOMnK84eyq07piiuuoFqtcvXVV/PXf/3XfOYzn+Fd73oXV1xxBfv37+/d70UvehFPfvKTe39PkoSrrrqKj33sY3z84x/ntttu49/9u3/HkSNHeMlLXtK731Of+lR++qd/mle+8pXccsst3HzzzVx//fU84QlP4OEPf/gJPVchhBBCbEwtdaEvO4AOQf9OHIeNQesNu8F77/Cd5vqdpItOGDu31uQAY6E7Fi9SlszENIsWEaY3w917B0of94620hpVmcAX/SneSqmhd6A3Y60dTV/kqGRrTfeU0qis3huduJsZZahHGW03eDzfSqFrfkTNpkxENTrdhYKBo/lciVMaTESij5W4eu9pdjx7anUqNkwfWKnjchJte0HpcqmJMUpT+HDtp4khiQzN1nDnsKTZKojXmA6wFbu6cZo6dnwdl/fGeULYqZ+MquSu3FTH+VC9sfmFllFbbJUstkc3FWNNnlXNO43R23b+S1MBRrnIuVvsqpT+iYkJPv7xj/PWt76Vq6++mmq1yvOe9zyuvfbavvs55yjL/gvtZS97Gd57Pvaxj3HfffdxzjnncMMNN3Do0KHefaIo4qMf/Shve9vbeM1rXoO1lic/+clcf/31J+T8hBBCCLFJ3a7yvsjBGDjeUXhbZZNwLGW+bpZBb+595z6oTKIG3NcXHXRj35r15krbsLDhSpSx1EzGYT/bP86uLMJxjCCFXWd1nLH4onNc4wuPl4pW7y4PSycVTorB7EDVhJRu7/2GPQfaZcHeZAKjDDWbEWlLxxWDR/O5kg4Qm4zYHAvc5xdzqpWUPdRpdVq0XH5stGNXy+XsieoDmzOmOiLRER1XYI0hsobpRsq9RxdpdUrSIWr5m60CaxXT9XRkk62sVoDqDiLcPXz3f5dG8uWuZH9S6fte12zWG3k4bPM+YxSGkF1hdigo9YBSoAip9mYbJwYopVh5ZYXu/Nu3w6/UsQkO42RXBfwAZ511Fr/3e7+37n0++clPrrpNKcVVV13FVVddte5j9+/fz/vf//7jOUQhhBBCnCDKWJSJ8e2jKFvduR1+Y1FRim831wxMvSuhzDEzh3CtefzCfZBN9O3C+26KtorX6fK8NELPFWDCDn9iIjK9LDDoLgaMIuAnylBJFd9e2JGAP2QrcFyvraIUjMWXo6vjX4v3Poy/02ZLTQKXdns7riBZ51iXUpdr3TT7VEfUTMbRYoFYJ6ujXFeQG0M1qvVq+0Otsee0vXWixYxaHjPnWqvOx3tP1Q6+JrXS1G2Fn7SPUOkuJKSxYaaecu/RFq28JF0nRb/ZLtFWMVNPSUfYYT4EwCr0thhx4Nlql6BZ97zWUjqPURqrw+QDrRSp7r+2Y22ZiKr8pH1kw4DfeU/bdYhVhDLgSw87NCu+LB1GK6xW5IXDbFO3/KXEB7WiKeV2LnQ4D1ZrxnCDf3el9AshhBBCrKSiFFwO2q4aYXdCjyOpheNYg2/No7IJVG0PZuZ0dGM/fnEWv7w+vliq319nN1vbEMx3ywdSHVMxSX9w6IrwPCNYAFFKoatTUBabSjEemTIPCxfHs9gQJWHBoNj+tH7fmsW3w4KOmz+Max4Jfy/a3QWdDQ5Vh6yNllv/WNuuQ6qjXpCtlGIiqoZa42459/J6ZuVLCmNJzbFra66ZM1mPmajGKJtQURajTC89H6DjC2JtyczaowLDMfi+6yNLLVONhLL0dIrBKd7Ndok2sGfEwT6A0Tqsi424a33pPKX3W27K5p1Dm7BT3HY5iY4GBvUNWwmlEuuUCXnvOZov0HY5JSVWKYodTOovnMcaTSWLKEbc0HK50nu0UqycOqmWZXWMmnehR8A4koBfCCGEELuailNCUezOJiaqKFkzm9SXBfgS3dgT6uKNRU+fip4+DV+28a0wdtgXHVRSWXc3WykFJu71C7DasDeepLIskPNlsXbTv62cW1ILAfdONL4r8/B+HMfOvFIaldZX9SIYNe8dlAVm+lTM/p/C7DkdXZsGE+GLDr55FDd/eMOFk1qU4bxb935tV1CPsr5O/BWTkumYnBxjVF+38sI7jE169ftLAeueiQytFcrGpEqT6oh2eWzhql3mVE3aqzEfZHlWQt95ZBFT9YS8CF3Nl1tsl2i9PcE+hPRuozWjno7W6jjS2LLVrd7Sh14VWityV1C32cDeFRWTULMpzTWa93nvOVo0qZqUik7JfYm1Gn8CyufXUpaeNDGksQlTA7arjMZ5lD6WXu+6C2lGE7I6tmFh0nmPHcN0fpCAXwghhBC7nYnAmC11cB8lFSVgDH5A0zPfmkNVJlFZ49j9lcZM7MfseTBeKVzzPig76LQ+xGulYRe/qxFV+rvbe4eKRpd+r6IElTX6sxFOEF/kqLiypfT45VRcCdt026mziIozVGUKXZlA1/diZk7HHHgI9pSzMQd+CuJ01dSDlTKTEOmIfI3ozS2l2Zv+RZ1IGxpRldwXGBV2QgHwJbnyoX6/G/DPt3ImajG1rLuQYiK0MtRttS9wL72jvkY6/7HXtVRMOnC6QKMSM1GLaXXKXjf/xXaJ2sZgf0ls9UiDzvBcniwxGNSx93cTXOmJrMZ7j/O+b6FuuZCxUaNcY+FntmiS6YhTsxkqNqHoBvzb1aV+GB5PbEMfhzjS5Ns0mq7Eo1VooNdxBfe2Z3vlEUqD36axfFp2+IUQQgghTjxlolDTvs212Rta2oUu+oMeX+agQNf3DCw50JVJ7N4zUHEtZCnEGzenUzZefwygYuTvh65MgHdDpaWPlHfr9zQYkorTsCBTbq57/Gb4vIWqzaxafFIq7KDrtI6uzuDz9QP+pJuq314jrX+pO39lQJp93WYYbVCaY6P5yoIcRRpPoJSidA7nYLqeHltI6S4YVUyMVgrnHR1XEGmzbjr/kprN+koBjp08TFQTJqoxi+2SZqs4Fuynxx/s567gSD4/cARfbPRIy1BaHUc1tVQSizIet4X0AefDFIW8Vyqx9sJczWRkOl5V3jFXNImU5WC6h8wkpDru7kBvTzr7MArnMWgiq1AKqqndtrT+kF5vQB2bctBxBVrrcN1vx6KH8t2mgONHAn4hhBBC7G7d5nRqnZTjE0Fpi4qyEOAvE3b3p1Dr7NyrpIrZ+2DM5KlhJ3ojxq7Zety7ciQj+VYfYy0E3p3WxncekRCsqeOr319iU5RNtq2O3+ftsEu+LItjEF2ZABvj8/XfxzDffvAOf8fl1KPKwK75mUmomJTSFL3Ax7scbw2pDddWs1VQyyJqlWPXSGjyqMm0JVGhQ3zb5WQmIdEbX0uZiYmUGXjMSsFkPSFLNW3dCQ36RhDsN8s2C0Wbik6ZL1YvopgRzqZf2t2vZRHzrkmbztbKBboj+Zbe23id9zbShomoSmvZZ8p8sYhGc2q2h2q3YeNSdk8YGbczIX/hHJFVIRAHkm1M63eeXiPGwpVkJqHwBUqFcontqiTYqekH200CfiGEEELsbsZ2d/l3/tcWlVRDk7kuX3RAa0x9z4Yp6crG6MbegaP6Vt1XW0AP3m13ZTfjYbQLIMpYVDaBL05cwB8a9tmRTAdQWodpA9vUh8DnC6jqxIbZCCrO0JVJfKe57v0yE2MHBNBLXfNrZq2u+WGOu1e+V8NfugJjq8QmwXtPJ3fsmUjD+L7eAy0og/GeWpTRdgV5WdCww5VTJDom0fHAtH7vPQtli7QCexspzh5floXrNqvz3nMo28PeZALvWbXLb4xCodZNhhlWq1NSSS1RpMNIOLP5YLY3kk+FALlhN17cq0cVrNLkrqBZtvHAwXSmr8wiVhGRNnjtR3a+m1UWnjg2vdYGkTXE8fak9TvvsEaFnwVCH5OlchFr1Mgrd8LbqbpNAcfPzv/LKYQQQgixDqW7afA7vMMP3Tr+ZXxrDl2dhqQ62hcyUUjBHpSeXhajG8m3gqo0ANVrGLjtygJlIxhyFvlGVFJdvxRii7wrwIOuTA11f12dAq1XZYMsl+iYdEAA3XY5sY7WTbOv2ZTURBQqXB+5z4lslUhFNNsFWWKpV1a8p8aGxSZXUjMZ3nvskOn8EBYa6rayqnFf6R1H8gUshgdXD3CouofcleQDel0MI3cFR/N5qibjQZV9TMV16jajZjMWVixGGb0UmB9fBLhUq19PI1quE7430eBshvW4btq71w6j9IYj9wAyHVOzGbNFk8KVHEymmYj6P08ibbAYPKGOfVB5w3bz3hMvG1OoFFSTbUrr92G3vfAulJzoBNU9BqtH38fAeY9BYcY0Mh7T0xJCCCHEODGTB1HZxs3utpuySa9OvJfiXZs57oZzqxgLxiwr0j7GL43k24YRhSquopJsw6Zzo+LLHKJsJOMFodvsUOuBjRWPh283UWk9TDMYRlIN2RKt+TXvopUKDfhWHGvLdWjYrL9J4wqxjpiKq+Q+x5UlBZ5qHH4+Wu2SPRMpke1/T5XSoXTClWQmJtGWtLvoMKyKTVDLuqS3y5zZfIGpqMaDKvtpRBUmoxp74gbzxeKmA7OlFP698SSHsr29hndaaWbiOs67Xsd2CKP5Emto58cXALfaYXc/ScIkgrrNSG1MocpNJdA759AWSkpSHZEOUSqhlGIyqpHpmAPJFFPx6s85rTSJiXE6jDl025XTvgbnPUop4hXXVBIbzHak9SuFMqEPQqQsNZtitAmTKMzoMxx8dyqAGlV9yC4jAb8QQgghdj21TQHuptkYZWIoO/j2XAj2R727DyhtUDoaGPDjRjuSr/91NaoydeLS+l2BToboaTCsKA3lASOs4/feh+kKtemhFyaUUiHzA79utkTFJKHJXjeI9d6Dh+oGXfMBJpMqWkNedEBb4qhOu1OSxJaJ6uBde2XDuMdIW2o2Y8JWunXhw0l1GM/XdjlzRZOOyzklmeHUbA9JN+NEKcXeZJK6rTJXrF/WsCSk8Dfx3nNatocD6fSqBY+azaialIXlY+wU1Lt9ClaOBRzW8t19191Bb9gKjSjFUW4qmF0ayVd4R81W0EN+ZtVtxmnZXmbitftDVLqN+4zWHGdCw6YVpcNatWoRKbKGaJvS+o0K2R5Vm5LoKPSP8EVIu1ej3+FXutsTYgyN51kJIYQQQmwDpQ3EWdi5tUmYwb5drxWlg3eqvR9JzftadBqmCayXjj5SIzwXpQ0k1dBbYVTyRYizdZsyDjyWtI5K6/j2wpr3yUx/Wn9I57cDu/OvVI8yKjphvpgjsimxrdBsF0zXEpJ4cHaAilKWBrkfSKaZTtZvQLhSpA01kzKXLxKriNMr+9iXTvbmzPsyx7cXiLThQDqJVWbNOfNLcldypDNP1aQ8qLKP6bg+cBHCKM100qBwZV/mQBpb6pWIVmdrZSitdkklCbv7rbJDpmMyE9NIKijtcZuIrsvSE9nQWG+Y7+ESrTQ1m62bKRR3F1RGPYpwGEXhSaxddXzbkdbvfejDqHWYJJHpGK00mU7IXdltrDfanXjnwaAY0559EvALIYQQQmyG7u7o69rMSMbJrUXFx4KzVbZzRGFcQSW1DZvOHS9fFmDMyBcvdFqDEab0+04TXZ3e9HEqrdG1mZANskbNtVY61MV3F1daLqduK0RD9Kuw2jAZ18iLnCSewJUKqzWT9XWOU9tek3erTS9Q34xGVOVAOsnplX3UVzSl8+05fGcR70oqJmV/MkWnzMnXyHIIKfyL7E0m+lL413xtm1E1Cc1yWQZKd5ffWk272FzQvxQ417IIpaDjChq2iu7W36fG0tnE7rX3HqzfcBzfVkTahMwRfeL79DsPaTz4Whl1Wn/pPVopPL5byhA+6yo2wXmH0qpbz7/RM3lUe26ozwLvxnd3HyTgF0IIIYTYHJugqpMhmNtO2qz6zd67ErRZNQd+lEI6+hQUmw+avffD766XeVi4GHHAr2wC2oyk8aAvOkON4lvzWLJGKPlYZ/GkahMg7GZ676kNkc6/ZDKtkmGpxA0WFgsm6wmVdJ3FoHXGPQ6rEVU4Ldu7apSfd2W4XpOsV1IxGdWYiRvMddP1e/ftduF33nNatpdT0pl1exb0Dl8ZpuMGuSv6ni+yhkYlJs/dpuq7252wu58mltKHdP6lUXiJjqhECR0/XKaL9+Fnx6mSismGWrTZjEiFtHan3LY0plxLb3KmHfz9GXVav3cOZUIfhFhZYtXNbNAWD2jl0cv6SKypLML40iHKk5z3GDOm2/tIwC+EEEIIsSkqrWOmD63q2D9yg3bxXREWArZzh59ut3sbbX7EXWcB3zwyVDmAL3NUlHZHEI5QnIZFhA0WHlzzftzCfSHTYK1jbC+gsgbEW+szoIxFVWfweasvQF0u0wmJtswViyTaDt01H6CmoK4nsKYBCqbq6z9WaQNqNIshq+SLqDhDxcdGI2ql2JdMUu92oYcwV/3+zjwVnfCgbO0U/rU0bIWKSVaVCtSyiDS2Q6f2l2UY+ba0u788nR9CCUEtToeeNlCUJcYqtPbU7fqZClsRaUOkLCgHSp2wXf6iLLFGEXV3wEvv+qYEjDqt3wGGbuNDk2C7C0Gxjoi0xeFRhjV/no49UQdv0+6KxfqLER6w3fNbel6rNl6AOllIwC+EEEIIsQlK621N5e+9jrGrd6p7I/m2d0ShilJUWsNvslu/z9uorDHc41yO2mIgvR6lbTfoXDvg96250ICxOoVfPIpvza8KIMKOtUNXp45rCoOuTkCUQj54p9FqQ91mLJYdaiYj3sQCiC0XybJTaLuMiUpMNdtgIah7TQ1sBnmcfN5GVafRWb3v+SNtOZBOY5ThaL7AfDeF//TKvt5u+mZYHXb5Oyt2+bVW1Ksxpfe9RnzrWeyOL0yT8H53XMFEN53fd5r4ziKNuIJXfqjguijBaIhtRLqJRZvNyGyCUw6D2jjgHZGihMSa3g74XN5kNu/vSzHKtP6QXh868ofslyBSlkgZSuVQig13+FVZsmgVpbYbN/H04fqB0Ecj0Xbbvoc7QQJ+IYQQQojdSC8FZ8d2GL0rQ0nBCZhYoCuTsCKoWo8v81CTn1ahHC6tX0Wj3wkF0Gl1zdpdn7fwrsRMnYrZ8yDM3gfjtcIvHO7PTOg0UWl10836VlI2QVen8Z21m/dVbUbVptSj4RdAfKeJiVN8ZZrSwfREuvFOuY7CLv+IA/7lpQ8qznqjK5dUbcq+eIJYWQ6mM90U/q0vWjVshUzHLLr+66ySWGqppdVe+/y8h/nFAmsVE9Wwu1/4EqM0le4ChO8s4ttNMhthtSYforyldB6MI1XRqnKHUUl13B1Zd+JG85Xe9ZpA+m5+f6wjWst+xkeZ1u+cZ6mcfvn7qJWiahIKX4RJBeuevqPA09KGxShGbfR5pHy3GWAI+Gs6HXlJxk6SgF8IIYQQYjcyNuzmlyt2+E9AdgEQAnebwJBp/b6ziIprobeBidffYXdlqK/dpmkDKspC2vOKwNaXBb49j544gK5MolRorGf3nYVq7MW35sIf7/BFOzRmHKK2fCO6Otl9Twa/lxWTUDeVoTu7e+/xnSa2sRcTZ9SyqDeebj1K61AOMsKmhhAWH1RaC5kMNg19FFac60zc4MHVA+xJJjaVwj9IpC1TcZ1W2elbkFIK6tUYrRTFgOCzKB0LrZxKajllpkoah6CuXeak3XT+pWaSxAlVINWW9gbvl4fQG8E4GlH1uM9vLZG2GKXQDJfFcLzCKyjibgTecQWxNjRshcVlQfQo0/odHq8ckbbEKxZOUhMa99mNRhMWOW2tyOJJOjYi1AAMXgRaOmKlQ9aE957aGO3ugwT8QgghhBC7klLd4Gz5L6pqe0fy9b2+TVBpA79GKvoqZQddm4Io27gcoMxBR9vXiyBKQh3/sh177x1+8Qiqthfd2Nd3dxWlmOlDmL1nhJGE8/eGwHWLzfpWUnEFVZ1Yc0RfpC2nV/YOv6vYWUDFFaKJPaSxYaaRYvRwv9Yrm4y0ht97B67slT4orcN1syLgV0qNdOd7IqqS6pjWil3+JDJhTF9e9qXitzol7dwxWUvYM5ESR8cWcjquYCIK6fwU7ZCVkU1gyzbVKKO9Tp8HgNI5PI7UWmrbUL+/JFaWyBiUUdtRlbFKWTqsVlgbrq22y6mYlOm4TqQtHXfs52t0af2h8WFo2Nf/8xBri0JhNqjhVy4nt5bUVjA2I7cRrNG8z3uPRmM05L7Aaku6TRkaO0UCfiGEEEKIXUpF6eqmctvcsG85XWkMldbvi3YoNUiqoct/ZRJcvvbjyiIsXGzTuSgToeJKX9DpF4+i0gZm6pSw073yMUqjq1OYfWeiJ05B16bDTvWI6MoUsDrrYLO8d/i8hW7sxUQJB/fUNmzW18fGa4973IpOKzTrW1b6oNIqdHdLt0usLdNRncWis+p1allEFGnaeRlS+FsFSsOeyYzJWoLRx3bgl9L5l+rFfdFBJTVUdRKUYjJJyTd4vwrncTqnEVdC2v02ibTBKoOyfuMu9SNQlB5rda+hXekdNZuRmYQJW6G57OdrZGn9Hkrlqdp0Ve+MWFsibfEhD2DNpyhdgbYZtahOajPaNkaVJYMGGjrn0RoUilaZUzGhieY4kYBfCCGEEGKXUjYJXawA3+3Qv50j+Va9flINadobpPX7ziIqrfVq8kM5wNqP82XeWxzYLjqth9FcgG/Pg44w06dumCGhbIKZOYSZOjjS41FJJUx22OzkgxV8ax6V1lGVKQAqqe01HBvqOOxoF1l8voiqTvVdlyrKwmLOENMajsdEXCU1MQtl/+6ttZqJStJL4a8mln2TGdV09VjCdpmT6ZhUdxdNvAvXZlJFJTWqrkT5MDZxLUXuMBYm49r2XtNKdxcU3Alp2lc6TxYbUCHYN0qTdhfppuIaRuneFAOloJZYitJteYJAKI3wKHxvWsJyoXGfpdRuUOweuIKO8sRRjVSn1GydjonwdvD16L1HaTBGUfqShh19I9GdJgG/EEIIIcRutXxuelmERn4ncIdf2Ri9Qdf9kNJdhF393uMSVFZf+3G+RMXbl/oMhPF8SoUmfUUHM3UwLGDsEKUtJOtPD9iIdyWUObqxb+sLP9quHSxt9njKHLQJiyvL2Tj0mjjOxY2NJDriYDqDQnE0X+gLgquZpZpETNYTZiZSojXmyHdc0au77zWejNNuf4dpKhpiZcjXyczo+JxqnFI123xNA5mO8dqvWrgYNQ94fO99a7ucWNteBkOmExq22jceMU0sNtJ0iq1lkCzV0Fuzun4fQllIxSZ4X4YVhkFcTkdpKkkoMUlNhrUphU1QA372SsCgKLzDbnIs5slCAn4hhBBCiN3KWFgaweXK7qi+E5tuqrIGeBcC+0HyNkTpqmBaVyYGPs57B0qH1PJtpGyo4/etWfTEAVR1altfbxg6rR1Xh3zfmkNlE6hsYsvPEa4hPZI6ft9ZRCU1SPp3RZVSYTzjcSxuDKsRVTg920eqI47mzV7Qr5TqpfCvlQFRuP50fopO97pZylSpk2U1Kkqt6hWwxHlPQcFUUt3USMXNWF6aEpsIrTUKxXZu8pfOY5QmsuG967icms1CnwPC+zsV14DwPkLIrKinEXm+tV1+5z1OlyTartnvISx4hPWOQefvixzijNSGz6NYx6QmoxVFhGWMFZ9jDozRtF2HTCdjV78PEvALIYQQQuxaatloPu+K0EhuG1OGBx5DUg1N8PI10vPzRXQ2sSpVXiXVsMu+sulfWYCJUANSdkdJ2Tg0y6vtQU/sO+Hv28BjilLQZkvBti8L8A7d2DuwB8HQtA1dy4+7l4AP2Qa1qYFjIlXcnZSwTir8qFRtymnZPmom5Ui+0Eu/3+hb3nKdvnR+X7RRaaP3/iobY2sz1FF0ysHvV14UGKOYTmujO6FlfN7GN+/Hd3/+Im2xWsM2d+ovnCMyCmtMd+edVRkMVZPSsJW+kopqGmEjTb6FXX7vPKUqqdkEs8bo0VhHvRF6q/sYeDoURLZGYhIWFnO8h1pUI9cWP2B6ROk8xihyV1K32a74nBg1CfiFEEIIIXYrE3UD/hJcsW1z69ejTITOJvD56vR870rwoCqrd5zD4yZXP67Mwy7ziGvJBzFTp2JmDoWFk90gSsPCyBZS3X17NtTKZ/WN77weY8PYuXUCfrdwP27+cFhkWkvegijpa9a3nIqzkMVxAnb5ATITc1plD5NRlaP5AsUQjQmPdefvBnneo1ZkK5jqBNU4xZflwLr5ZpFTsyn1aJvGZZYdSGq9aRmRsiTG4kyJX3c23XG+bOFJYoNSkPuSaFk6/xKlFFNR+P6X3UWWpV3+zhZ2+cN3zFNd53Mu1pbERDhd4ldOBChzOspTSacoizCZYaGVk+oUq2M6UYYqVtfxKw1aqaHHYp5sJOAXQgghhNitlpr0LQXWJyBIHiSk9fvVu7V5C5Vkq4KkY4+rs7IzvS9zVFwduCs8aipKUCew58FGlDZbquP3RQeUxtT3Hvf7prRB6WjNgN+3F0IGRm0a3zyyarxe7355c2BmR+91liYlrJEZsh1iHXFquoeZuMFs3qSzzoJF7kqMMlS66fuhft+uXlRLKtRqU0RlQe5XP1/bd9iT1dfckT5e4eelAt1zibQh7WbHjGDs/dqv6yHqji7suJxUxwNLFmo2o24zFooVu/x287v8vvSYZec3SKQtqYlxlJQrlhR82cGbhCxusNgumKjGdHJH1E3rbxvTXew69n30QOELUh0PbBQ4DiTgF0IIIYTYpZRSYJPuTqs6oQ37+o4jqYYd205/er7PW6jK5Jo76L3HLU/rd2W47QFqK3X8vjWHrk7DqJoORvHAsgLvCnzewkwewMycjp48iG8vhEWAvvuVgBqY2bGczhrgtrdT/0pWG05JZ9ifTNEsWhzJ55nNmywULZplm3aZ03EFzaJFxSTHdq2LdpiiEPXv8iqlqTT2EbkQGC6X+xKDYbL7ffGuwLeb+KIzulIG78NxGRMWJQjZDMp43DZF/M57tFLENoSKuSupm8HlRLq7y++865VSWKupZ5vf5W+7gsSagQ37lqvaFK8dq9Yfyw5RUidWCYXzpInFaIXznlpUo9AaH61oJumhUAV1W+n1Jxg343lWQgghhBBjQkUp5B0wesd2q5WxqGwCXxxLzw9jAvXqDu3LH6cNqjLVS+vvpURvc8O+3exYHf866fLL+KIDxqJrMyOrL1Y2Gbjo4JtHUbVpVHUapQ168hTMngfhvcM1jxz7/nWaYVZ9sn7duooz0Hbocx0VozT7kykOVfaxL55iwobxfRpN6R2d7iLE5LJ0fl90Qv3+gKDP1iapRA3yFeUpzbJFZlIacRqC/OZR0ApftPHNI7iFw+HP4iy+0wx9GDbBuwKMQVcmUVGl10cj0RHGjGzYwip5GcYMRlbjvEehSO3a6e41m1E1aV/H/q3s8he+pGKTDZsfpibCKE1fRr8v6ShHGk9QFoo0suyZSEkTS6tTkpqMSEd0bIpyJWEGAXgcGnWsceMY2iUFTUIIIYQQYhBlIvAl6GzHdvihm54/+2O8K0NqemcRFVdXdWhfSac1nLbdYMeHtOkHcMB/rI6/A/HGv4r7fLHbAHF088HD+78iHbo9D1GCmThwrGmdUqjaDNgYd98P8Av3QWUSX7T77remKA2703kHkhMbdoSd5xos+5Hx3uNwoD3VWkJroQDXXYjybs3SlCRNiKt78PP3h7ntKkzO6BQlB+MqETm+1URPHEBP7g+LKUWOL/PQdK/ThLyFbx6B2vTwZRlFHppbRikqa+DuvxtFjVhbjNYU29QQsSg8tSxCKUW77PSN4xvEKM100uCfmz/pZQdYq6llEUfm2kTWDDVFsKSgNsRow1hbIqPJO2GoHoTu/E4ZsniKVqdkz2RKGlsa1ZgfH25STRNSU6HpCuLuz583MSWO1KZkenwDftnhF0IIIYTYzbqj+JSJtj57fQRUUulLz/dFG10d3KG9T1IJNcj5YrdhX/TA3uHXBpXWhq/jL4vu+zzC7uG6fya9L4sQxE+eMrDcQqd1zN4zUJVJ/MJhMDFqiK70ShtUUluzD8CJppTCqJAyni3vBF/m4ZzWaBZnjSbJpgFL4cP3LfcF2humjYK8hZ4+DT11CkpblE1QaQ1dncJMHsDuOxOz/yyIUhjQNG4tvuyEYF+bsBjRnXoQaUus7aoa9lHwhAWQpFe/X1AxKdGKa2alhs2omoTFZbv8tU3u8nsPWbRx4B2riMTYvhKLslzExjVik+E81Cvheapp1DunalSlVODiKqrshJGKKqcepdgNzu9kJgG/EEIIIcQuprqd+tV2dQEf9ji0DQFf3uqmmUfDBX1Ko6tTIc25zHsBzAOZSqpD1fH7oh3e51HV7i+9vragNN67MHJt8QiqugdVnVr7MVGK2fMg9MQpqOrE0H0YVFpjVbH1blN0QrC/Tlp3vTGF1hXyTuhn0PY5ldxRiyxmz+noxr51F7+UTVBxurnFj7IIPR8gjJiMwpjLSFliY/G4AaPpjk9elNhIk8bhZ7T0JXW78a67UYbpuEHuSlplWBRZ2uXv5Bt//wtfYpQm26B+H0KfhsTEFBx73rYvSOIJyiIcezUNi6OVxJLEhvZSWr+y5DYGFK4IWUeNaHTZM7uRBPxCCCGEELuZtiENPj7xI/lWUmktBIrt+VC/PeSYQJXWQjlC3h558HoyGraO33cWUWlt9OMYjT027rG9AHGGmdy/YbaGMhY9fRpm+tDQL6WiFEy06ckEJ5Iv26isvm4WRZbGRPEeSpfjvMO356nblHjfg4bur6CzRhizN8wxeQ+K3s+YMhaV1vF5G60UtSjBqRK3cjTdcerkrrczH4Lw9bvmLzcV1TiYTtNxRa9r/9JzdTbY5e+4gkhFZNFwZUt1m+K6g/xwBaVSVNJpWm3HVD3BmnAtR90Ggq1OSaxjMlOhrRXeJhRFk1hHY12/DxLwCyGEEELsbiakCO9k/f4SFVfDwoMr0NXJ4dPMozQsECj1wK7fX7JUx5+vHfx578GV6Mrk6F/fWNA61JUXHczEKUMvKiilNjcaMErDc++StP6VQv0+G2YsxNZgkimcjshb92FUSjx1FunEzPAvFqW9zIoNlTnoKPzsd4VsiRLvPZUoBeUpR7jDX5QOozSVbr+FjitIdESyTv3+ckop9iQTHMr2AjCbNzFGdXf51znOok3RvI8KeujU+jSKu6n6UJQttE2xuorWUK/0H2+9ElM6303rr1HicXGFdtGmalOSMR3Ht0QCfiGEEEKIXUxpg87qo9/l3dKxaFRlCpLqUOn8vccpha5OQpxJwM/yOv51guCiDTZGpaPPiFDdnhC+vYBqzKCqkyN/jd5rKYXKGr2RcrtOmYf3eYOSmchq0qiCjus0jcFUH0SlPt3bSR5GKBvoNmwc4rhUFIM9ttCn4kooOyg7JNpijB5ptUQ792SpIV5Wv1+3WW+SwbAmoiqHsr0k2nI0b1JJDNaqwbv8vkSVbfKoStWVaD/cJINUR1ht6LiSvGiRxJOUhaWSRr0FiyWVxBJHhrxwx7r1G0PhCyZ3uFTqRJCAXwghhBBil9NrNFPbCboygWns79t5HIZK66issWrO+QOVSqrr1raHdP7Gpt/nodkYlU1gGgc2t2O/BSrOwPvRzaYfpaJ9LBBfRxxp0ijBJ9Po+ilYO0m1srnFK2VjVFwZqo7fF50w+nDZ90ZFCSrO8Hk7dM43lrwcfuzdekIvAE8tDQsMvvv9qgzRNX+Qms04LdtHzWYs+BaV1NDJ3crZEKjOAmVco8hmsLV9qPb8UItDWRQRaUNR5mGcXzJJp/BM1mK07l+gSLo1/a1OSaQjMlOhSYFSCdWNJk2MgfE/QyGEEEIIMTIqztD1PZt/nI2xM6eHhnGiW9s+uI7fewfeoSuNbXt9XZ1CT58axuZtMxVnYWd6F9bx+zIPi1Eb7GIbrUljS6SqpLZKrFLSaPPNJ3Vah3KIXWzvBk9MqExA2QmN+6yhYO3nKjexwNLuOJLYkHR3x3MfguN0RSmRL/Oh+zFkJua0bA9TUY3S5pgIji62aLucRddmoXOUOeU4GkdoFZNMHkI39uEXZzcM+iNtSVVEu2iiTIwxE0RGUcsGL8I0qjF5Ed6Pqq2CtsS2SrYbF6FGTD5xhRBCCCGEONGiNMxYHzSjPm9DlGxrg8Nt6Q2wBmVjVFLBt+ZGWprivQuz7Zca3HkFaumPDkF8nK25yOS7u9rDZs9kqSWdr5EaBZje6LpNidPeeL21Miu8K8PxD8juUHEFjMV4HzrVu9XZAs57mq6Fw2HQVM365+cJiwNTWcLSukfb5VRNQryia75fnA3/kdaHKs+JteXUbAarDD/mKO2jHdqFJ9WKzGtU/SA6m2ax6cnSFD0VeiK42Z9A1ghTSgZQSlG1Gfcv/oRatpfSxdSziCwZ/D2pJBGR0RTdtP7UZOhIYxhNhsRuJgG/EEIIIYQQJ9hSHb+bOxxmrC/ji0V0bc9Y9TvQWZ1y4X6898M3e9xIZxEVpejG3lAy4EpwDlyJ9yWUOX7hKFTWCBzLDph46AkYaWSwKsJ4g400cbT5ZGkVZaF8IG/DWgsNZR5q9wdlX0RZtwlii3qUUqojeMJ6B9DbPU9JqKiUWT9Hx+WrAvfl2kVJZDVZfCw0LFxJbcXx+bIAY1DZJH7+XqhMDTVi0yjDgXSamaTBfJTzk8NN6uU8unE6ZeMUUJr72m3iyKK0Rk8dBMDN/hiyiTWD/mocY5ynEk9TljBVT9a8ttLEkCYhrb9WiZiKp2gnbYy9H+/KsR4VKgG/EEIIIYQQO0AlVZi9p++2kM7vQ7+DMaLiSqg/X7gvbMSbJGQxHMf0CZ8voqcPoet7B3/dFbj7/18IHNPGqgUUn7dRUTZ0n4TIhgC/nZdM1dItLVwoY0Md/+Lc2pkFZY6Ks4HvjdIalU3gjvw/KjZCK2h3SkrnWHCLaK9pmCqTtk6Ep9nJaUZNrDLoNTIKitwxVU8xJpyP8w6tFJlZ8b7ki6iogpk6SOnKkF1RnR5uJKFSJCoirln8wjyHmwlJZQazdEwebLf2XmmzKuhHm1AK4Yqw8OAKss48sa6gbIPEGKrp2teSVoqJaszd984DETXTwMWL6GgxNMiMK2s+9mQnAb8QQgghhBA7oFfHXxYo0/21PG8dG2M4RlRSxew/C99p4TtN/OJc+P+yCOnrURLS1Yfk8xbYJNS0r/Wa2qKnTgWtcUd+FKZLLC8pKHNUbd/QrxlbgzWGdqcgXSN1fBg6a1Au3L/m133ZQSeDFzFgqeEjJFGo42/7NljPwXiCfdkkjTjDlC3IF4lcxJ1FxJxeZMKsLhEpCoc1mmp67HzariBRA+r3iw66sQ9lY8z0qZRlB794FLWZ8pCyw0TV0qyewv1NxVSjm/GhPGZZs73+oP+ekMKgbVgwSauoKCON9pH5+3CuRr0ekcTrf0+qWYRSitI5nPNoE2GyOr51/6auvZONBPxCCCGEEELshKXd5aID3YDf54voxoFjCwBjRNkknG9lAj+xH/I2Pm/h2wu4+cMhRX/IenrfaaLrezfsCaC0Rk+eAsrgjvww1PtnlZBJodSmpl9EVmONwkWadIPgct1jilJQet1U8vXKDFScQZyQOseByRpKwf5kismoilY6vKdljp45lQPqR7QXIu5qHmYxa5Pp/l37duGoVyKsXR7w58xEdYw6dpsvC9AG3R3HqaIUPX0a5T3fC+Mdh+g34b3Dt+aIJk/hQHUf7R/PM9fMqVciUAqzYsThUtCvskZ4n0wEJkJ1O+tb2yadj/EKJmobZ2lkSei70O6UaK0wRmErNVg8vOFjT2bSpV8IIYQQQogdoLRGJbXemDbvSkChsvrOHtgJoJQOEx+qU5jp09ATB/CdhaFG9/kyB63R1amhX0tP7EdPnYYvWrj2Aj7voEy8qSaCWiuy2BDbY7PqtyROIYoHTi1YCqzXKzNQJkKndWyRc0o2zZmVU5iO6yHYLzr49gJ66lT01ClEE9OcXtWcUpnm6GKbwh9rUle6MCSvsmwU39F8AasMdbtixztfDLvgyxZIdFrHTB3EF+3hRg0uzqLSBnpiH1kScXAmZCosLBZYpfp2+Hvnqg26MoFKayELZNkYPWMUSkEW23XT+Xv315rJakK7E3b4jQKThCaIw4wCPFlJwC+EEEIIIcQOCenZ3SA3b4Xa7WR804vXomvT4b1oL2x4X9+aR6UN2MQUA6UUZmIfZuZ0lCspF46E93qTjRGz1FJJLNZsPYxS2qKS6uAguQgLEWzQV0CldRSeqahG0k2992WOb82hJw6gG3tRSmMa+4jimLMma+xNGxxenMf5EOi3OyWVxJJGhtyV3N+Zp6ITHpTtoxGtbCTZQVUmVk0WUNXpsFjTmltjxKTH523c4lFQoCcP9HoTNKoxB2YqtPOw427N5noiWK2xWjNRjXv9FTZSrYTXLp0nsgYVp6GRYr7xgsXJSgJ+IYQQQgghdoiKj9Xx+7yFqkyuOUZunCkToRv7ww61W3tUmncleI+uzWypaZ6uzWD2PAid1rbUGHGylrBvavgygDWPI63DoAC57EBS6dvJHkTFGZgI380S8K7ALx5FN/ahJ0/pBeYqqaJre0hdm7P37KMeZRxeXMB7cIS69qZrs1AssjeZ4PTKPqq2P+vBu246/4AFFqUUemI/qjaDbx4Naftljm/P4xYOhyaNZSf0cJg+Hb3iPd8zmbFvKiOyGrPBOa9kjCJLLfXq8Is2lSRkZzTbBZHVIdMkq4f3fUw98D5NhBBCCCGE2C1sGtK388XQvC4dr2Z9m6Eqk6jKZEj9XiNd37cXQnp3uvWyB12dJN0zSWs+p/Sbe2wajyZ8UlEK2qyu43flwMB6FZugkgq+3QzP0zyCqs2gp05ZtVig63twzfup4zl7z36+ec8PuG+hST1J6OgWkbeclu1lMqqhBy2idFrddP7BmSdKG8zUqZRFjp8/DDYKNf61md50hrUyKbRSnDJTZaFVoAek9K8ntpp9kxm1bPhJD5E11CsRRxfaxN2sgNCwz+O9W5XBMA7G74yEEEIIIYQ4SfTq+FvzqCRDxcOnqY8bpTW6sReU6u1cL+e9gzJH12c23AHfiE6rxzUS8LhF3YWeZWn9oX+B2jCdH7o769kElJ0Q7FcmMVOnDswOUVGCru/FdxbZk9U4a3ovRCUu7lCzFU7P9nV7AAwOuH3RDun867znysaYmUOYfWdhD5yNOfAQzOQpof5+g7IJa0Ja/mYppZioJWse91pqWUQSmV5ZRsiWiKEYzzp+2eEXQgghhBBiB6mkGrqPV6aOO5A92am0jq5N42bvQdVm+r/Y7eK/lVT83UZpg0pruPn7jo2EK/Pu7vjGAT8Qej0Yi4oqmOlD6wbWujaNb94P7QVOq01TkJPYmP3JFNEakwJgKZ1fD5V1oOJsU1MPdkoltVRS25sKoGwcsiVaC0O/9yeTB/YnihBCCCGEEDtMxSmq0hgulXvMKaXQ9b1gY3xnse9rPl9E1WZ2dmd+hFRSBbdsKkGRh11/M+Rud5Sh6/sxM4c2DFSX90jQHs6oHeDUdGbdYB/opvNna6bzn4ySyNCoxCTRsVBYp42w4DKGJOAXQgghhBBiJ0VZmBUvAT8QdopDCvqxMX0+b4FN0JWJHT660VFRCkb3utsvNbcbthmh0hozdcrQu+oqm0BVJvCLs2ilh3qdkM4/XpknSilO3VvrjSOEblq/0us2jDxZjc93TgghhBBCiJOQUgqd1rfUdX5crRzT59sL6Op0CJLHRZR1GzZ2+xV4v60p8aFHwj5QDOyRsNJm0vlPenEasiQGjUo8yUnAL4QQQgghhNhVlI3RjX1hTF/eAmPQ1cmdPqyR6jVsLNphZ1nrba8hV2kdVZvGt+Y2vnM+fun8a1HaotLaUAshJxsJ+IUQQgghhBC7jqpMhRT0hftQ2cRYBp4qqYJ3UHTAREN16D+u11MKs9Qjod1c974+b6OyybFK51+PSmogKf1CCCGEEEIIsf2WUtBVdSqk849hyYOKUzAmNCSMM5TZ/iFqKq6EBn6us+ZOv3clKI1Oa9t+PLtFGM9nxi7ol4BfCCGEEEIIsSuptI6ZOX0sRvENZNNuHX877DCfIGZiH2bPg/Fa4xbu6zVH7MlbYTFiDLMq1hQl3R4RfqePZKQk4BdCCCGEEELsSkqpTXWuP9korVFpA0x0wmfA68okdu8ZqLSOX7gPv2wsnc9bqMoDJ50fQKnu92LMrrUHzndQCCGEEEIIIXYZlVRQaTXs9J/o144rmD0PQjX24heP4vNWL51fPYDS+ZeopAIm3unDGKntLxIRQgghhBBCCDGQSqqhKeEJ3uHvvb6NMVOHcCbBHf0RtObDAsQDKZ2/S8XZCc+02G4S8AshhBBCCCHEDgkB98GdPQatMZMHUFFCef/d3XR+s6PHtBOUTSDKxiqtXwJ+IYQQQgghhBDo6lToVv8ADPaXmKlTxur8JeAXQgghhBBCCAHQ7VT/wLUTvRS2kzTtE0IIIYQQQgghxpAE/EIIIYQQQgghxBiSgF8IIYQQQgghhBhDEvALIYQQQgghhBBjSAJ+IYQQQgghhBBiDEnAL4QQQgghhBBCjCEJ+IUQQgghhBBCiDEkAb8QQgghhBBCCDGGJOAXQgghhBBCCCHGkAT8QgghhBBCCCHEGJKAXwghhBBCCCGEGEMS8AshhBBCCCGEEGNIAn4hhBBCCCGEEGIMScAvhBBCCCGEEEKMIQn4hRBCCCGEEEKIMSQBvxBCCCGEEEIIMYYk4BdCCCGEEEIIIcaQBPxCCCGEEEIIIcQYkoBfCCGEEEIIIYQYQxLwCyGEEEIIIYQQY0gCfiGEEEIIIYQQYgxJwC+EEEIIIYQQQowhCfiFEEIIIYQQQogxJAG/EEIIIYQQQggxhpT33u/0QZzsvPc4t/vfRmM0Zel2+jDESUauG7EVct2IrZDrRmyFXDdiK+S6EVuxm64brRVKqQ3vJwG/EEIIIYQQQggxhiSlXwghhBBCCCGEGEMS8AshhBBCCCGEEGNIAn4hhBBCCCGEEGIMScAvhBBCCCGEEEKMIQn4hRBCCCGEEEKIMSQBvxBCCCGEEEIIMYYk4BdCCCGEEEIIIcaQBPxCCCGEEEIIIcQYkoBfCCGEEEIIIYQYQxLwCyGEEEIIIYQQY0gCfiGEEEIIIYQQYgxJwC+EEEIIIYQQQowhCfiFEEIIIYQQQogxJAH/mLvjjjv4lV/5Fc477zwuvvhi3vWud9HpdHb6sMQu8ed//ue84hWv4JJLLuG8887jWc96Fn/8x3+M977vfn/0R3/EU5/6VM4991ye+cxncsstt+zQEYvdaGFhgUsuuYSzzz6bb37zm31fk2tHDPInf/InPPvZz+bcc8/lwgsv5KUvfSmtVqv39f/+3/87z3zmMzn33HN56lOfymc+85kdPFqxG3zpS1/iF37hF3jEIx7B4x73OF796lfz/e9/f9X95DPngeuf/umfeMMb3sCznvUsHvrQh/L0pz994P2GuUbm5ua4/vrrueCCC3jEIx7Bq171Kn7yk59s9ymIHbDRdTM/P8/73/9+nve853H++efz2Mc+ll/91V/l29/+9qrn2q3XjQT8Y+zo0aO86EUvIs9z3v/+93Pttdfy6U9/mne+8507fWhil/i93/s9sizjuuuu44Mf/CCXXHIJv/mbv8kHPvCB3n0+//nP85u/+ZtcfvnlfOQjH+G8887jmmuu4etf//rOHbjYVX7nd36HsixX3S7Xjhjkgx/8IG9961t52tOexg033MBb3vIWTjvttN419NWvfpVrrrmG8847j4985CNcfvnl/H//3//HF77whR0+crFTbr/9dq655hp+6qd+ig984ANcf/31/MM//AMvfvGL+xaK5DPnge073/kOX/7yl3nQgx7EWWedNfA+w14jv/Zrv8ZXvvIV3vSmN/Ef/+N/5K677uJlL3sZRVGcgDMRJ9JG180Pf/hD/st/+S9cfPHFvPe97+Wtb30rc3NzvOAFL+COO+7ou++uvW68GFu/+7u/68877zx///3392676aab/DnnnON/9KMf7dyBiV3j8OHDq257/etf7x/5yEf6siy9994/5SlP8a95zWv67vOCF7zAv/SlLz0hxyh2t+9+97v+vPPO85/61Kf8Qx7yEP+Nb3yj9zW5dsRKd9xxh3/oQx/q/+qv/mrN+7z4xS/2L3jBC/pue81rXuMvv/zy7T48sUv95m/+pr/00ku9c65322233eYf8pCH+L/927/t3SafOQ9sS7+3eO/96173Ov8v/+W/XHWfYa6Rr33ta/4hD3mI/x//43/0brvjjjv82Wef7T//+c9vw5GLnbTRdbOwsOCbzWbfbfPz8/6CCy7wb3nLW3q37ebrRnb4x9itt97KRRddxOTkZO+2yy+/HOccX/nKV3buwMSuMT09veq2c845h/n5eZrNJt///vf53ve+x+WXX953n6c97WncdtttUh4ieNvb3sYVV1zBGWec0Xe7XDtikM9+9rOcdtppPP7xjx/49U6nw+23385ll13Wd/vTnvY07rjjDn7wgx+ciMMUu0xRFFSrVZRSvdvq9TpArwRNPnOE1uuHNcNeI7feeiuNRoOLL764d58zzzyTc845h1tvvXX0By521EbXTaVSIcuyvtuq1Sqnn356X7r+br5uJOAfY3feeSdnnnlm322NRoO9e/dy55137tBRid3uf/2v/8X+/fup1Wq962RlMHfWWWeR5/nA+knxwPGFL3yBf/zHf+Tqq69e9TW5dsQgf/d3f8dDHvIQfud3foeLLrqIn/3Zn+WKK67g7/7u7wD453/+Z/I8X/Vv11Kapfzb9cD03Oc+lzvuuIM/+IM/YG5uju9///u8+93v5qEPfSiPfOQjAfnMERsb9hq58847OeOMM/oWmCAEb/IZJABmZ2f5zne+0/dv1W6+biTgH2Ozs7M0Go1Vt09MTHD06NEdOCKx2331q1/l5ptv5sUvfjFA7zpZeR0t/V2uoweuxcVF3vnOd3LttddSq9VWfV2uHTHIPffcw1//9V/zp3/6p7zxjW/kAx/4AEopXvziF3P48GG5bsRA559/Pr/927/Nf/pP/4nzzz+fJz3pSRw+fJiPfOQjGGMA+cwRGxv2Gpmdne1lkCwnvz+LJb/1W7+FUopf/MVf7N22m68bCfiFEAD86Ec/4tprr+XCCy/kyiuv3OnDEbvcBz/4QWZmZvhX/+pf7fShiJOI955ms8n73vc+LrvsMh7/+MfzwQ9+EO89v//7v7/Thyd2qa997Wv8+q//Os9//vP5+Mc/zvve9z6cc7z85S/va9onhBDb7TOf+Qyf/vSnecMb3sCBAwd2+nCGIgH/GGs0GszNza26/ejRo0xMTOzAEYndanZ2lpe97GVMTk7y/ve/v1fPtHSdrLyOZmdn+74uHljuvvtuPvaxj/GqV72Kubk5ZmdnaTabADSbTRYWFuTaEQM1Gg0mJyf5mZ/5md5tk5OTPPShD+W73/2uXDdioLe97W085jGP4brrruMxj3kMl112GR/+8If51re+xZ/+6Z8C8u+V2Niw10ij0WB+fn7V4+X3Z/HlL3+ZN7zhDfzbf/tvec5zntP3td183UjAP8YG1YzMzc1xzz33rKqPFA9crVaLq666irm5OT760Y/2pSMtXScrr6M777yTKIo4dOjQCT1WsTv84Ac/IM9zXv7yl/PoRz+aRz/60fzqr/4qAFdeeSW/8iu/IteOGOinfuqn1vxau93m9NNPJ4qigdcNIP92PUDdcccdfYtEAAcOHGBqaop//ud/BuTfK7GxYa+RM888k7vuuqvXEHLJXXfdJZ9BD2Bf//rXefWrX82zn/1sXv3qV6/6+m6+biTgH2OXXHIJf/M3f9NbuYTQZEtr3ddBUjxwFUXBr/3ar3HnnXfy0Y9+lP379/d9/dChQzz4wQ9eNf/65ptv5qKLLiKO4xN5uGKXOOecc/jEJz7R9+c3fuM3AHjzm9/MG9/4Rrl2xEBPfOITOXLkCH//93/fu+3+++/n//7f/8vDHvYw4jjmwgsv5Itf/GLf426++WbOOussTjvttBN9yGIXOHjwIN/61rf6brv77ru5//77OfXUUwH590psbNhr5JJLLuHo0aPcdtttvfvcddddfOtb3+KSSy45occsdofvfve7XHXVVTzmMY/hzW9+88D77Obrxu7oq4ttdcUVV/DJT36Sq6++mquuuoof//jHvOtd7+KKK65YFdiJB6Y3v/nN3HLLLVx33XXMz8/z9a9/vfe1hz70ocRxzCtf+Upe+9rXcvrpp3PhhRdy8803841vfEPqbR/AGo0GF1544cCvPexhD+NhD3sYgFw7YpUnPelJnHvuubzqVa/i2muvJUkSPvzhDxPHMb/0S78EwCte8QquvPJK3vSmN3H55Zdz++2382d/9me85z3v2eGjFzvliiuu4O1vfztve9vbuPTSSzly5Eivj8jyEWvymfPAtri4yJe//GUgLAjNz8/3gvsLLriA6enpoa6RRzziETzucY/j+uuv53Wvex1JkvCe97yHs88+m6c85Sk7cm5i+2x03XjveclLXkKSJLzoRS/i//yf/9N7bK1W62Wu7ebrRvmVeQdirNxxxx289a1v5X//7/9NtVrlWc96Ftdee62sdAsALr30Uu6+++6BX/vSl77U2037oz/6Iz7ykY/wwx/+kDPOOIPXvOY1PPGJTzyRhyp2udtvv50rr7ySP/7jP+bcc8/t3S7Xjljpvvvu4x3veAe33HILeZ5z/vnn8xu/8Rt96f5f+tKXeO9738tdd93FwYMHefnLX87znve8HTxqsZO899x000186lOf4vvf/z7VapXzzjuPa6+9tjeycYl85jxw/eAHP+Bf/It/MfBrn/jEJ3oL1cNcI3Nzc7zjHe/gv/23/0ZRFDzucY/j9a9/vWyYjaGNrhtgzWbWF1xwAZ/85Cd7f9+t140E/EIIIYQQQgghxBiSGn4hhBBCCCGEEGIMScAvhBBCCCGEEEKMIQn4hRBCCCGEEEKIMSQBvxBCCCGEEEIIMYYk4BdCCCGEEEIIIcaQBPxCCCGEEEIIIcQYkoBfCCGEEEIIIYQYQxLwCyGEEGKkPvvZz3L22WfzzW9+c6cPRQghhHhAk4BfCCGEOIktBdfL/1x00UW88IUv5Mtf/vKWn/d3f/d3+cu//MsRHunw3v/+93P22Wfz2Mc+lsXFxVVfv/TSS7nqqqt24MiEEEKIk4sE/EIIIcQYeNWrXsW73vUu/sN/+A+89KUv5f777+flL385t9xyy5ae70Mf+tCOBfxLDh8+zKc+9akdPQYhhBDiZGZ3+gCEEEIIcfwuueQSzj333N7fn/e853HxxRfzZ3/2ZzzxiU/cwSPbunPOOYcbbriBX/qlXyJN050+HCGEEOKkIzv8QgghxBhqNBokSYK1/Wv7N9xwA1dccQUXXnghD3/4w3nuc5/LF77whb77nH322TSbTf7kT/6kVyZw3XXX9b7+4x//mOuvv57HPe5x/OzP/iyXXnopb3zjG+l0On3P0+l0eMc73sFjHvMYzjvvPK6++mruu+++oc/h6quv5t57791wl//222/n7LPP5vbbb++7/Qc/+AFnn302n/3sZ3u3XXfddTziEY/ghz/8IVdddRWPeMQj+Pmf/3n+4A/+AIBvf/vbXHnllZx33nk88YlP5HOf+9zQxyuEEELsNrLDL4QQQoyB+fn5XjB9+PBhPvnJT9JsNnnmM5/Zd79PfOITXHrppTzjGc8gz3M+//nP8+pXv5oPfehDPOEJTwDgXe96F69//et5+MMfzvOf/3wATj/9dCAE+8973vOYm5vj+c9/PmeeeSY//vGP+eIXv0ir1SKO495rve1tb6PRaHDNNddw99138/GPf5y3vOUtvPe97x3qnB71qEfxmMc8ho9+9KP84i/+4sh2+cuy5GUvexnnn38+r33ta/nc5z7HW97yFrIs4z3veQ/PeMYzeMpTnsJNN93E6173Os477zwOHTo0ktcWQgghTiQJ+IUQQogx8Mu//Mt9f4/jmLe//e1cfPHFfbd/8Ytf7Auc//W//tc897nP5cYbb+wF/M961rN405vexKFDh3jWs57V9/h3v/vd3HvvvXz605/uKyF49atfjfe+776Tk5N87GMfQykFgHOOT37yk8zNzVGv14c6r2uuuYZ/82/+DTfddNOqc9yqdrvNM5/5zF7jv2c84xn8/M//PNdffz3vfve7edrTngbAYx/7WC6//HL+63/9r7zyla8cyWsLIYQQJ5Kk9AshhBBj4A1veAM33ngjN954I7/1W7/FhRdeyOtf/3r+4i/+ou9+y4P9o0ePMjc3x6Me9Si+9a1vbfgazjn+8i//kic+8Yl9wf6SpcB+yfOf//y+284//3zKsuTuu+8e+rwe/ehHc+GFF/LRj36UVqs19OM28gu/8Au9/240GpxxxhlkWcbll1/eu/3MM8+k0Wjw/e9/f2SvK4QQQpxIssMvhBBCjIGHP/zhfUH405/+dJ797Gfzlre8hSc84Qm9VPtbbrmFD37wg/z93/99X839ymB9kPvuu4/5+Xl++qd/eqhjOnjwYN/fG40GALOzs0M9fskrX/nKke7yJ0nC9PR03231ep0DBw6seh/q9fqmj1cIIYTYLWSHXwghhBhDWmsuvPBC7rnnHv7pn/4JgK9+9au84hWvIEkS3vjGN/LhD3+YG2+8kac//emr0vFHdQyDbPa1Hv3oR3PBBResucu/1mKFc27g7caYTd2+He+NEEIIcSLIDr8QQggxpsqyBKDZbAKhfj9JEm644Ya+5nqf+cxnhnq+6elparUa3/nOd0Z/sBt45StfyQtf+EJuuummVV9byhyYm5vru30zpQNCCCHEOJIdfiGEEGIM5XnOV77yFaIo4qyzzgLCDrZSqrcQAGF03Ze+9KVVj69UKqtS2bXWPOlJT+KWW27hm9/85qrHbOdO+AUXXNDb5W+3231fO/XUUzHG8Ld/+7d9t280zm8r8jznjjvu4Cc/+cnIn1sIIYQYNdnhF0IIIcbArbfeyp133gmEWvvPfe5zfO973+PlL385tVoNgMc//vHceOONvPSlL+XpT386hw8f5g//8A85/fTT+fa3v933fA972MO47bbbuPHGG9m3bx+nnXYaP/dzP8drXvMavvKVr/DCF76Q5z//+Zx11lncc889fOELX+AP//APe7vt2+Gaa67hyiuvXHV7vV7nsssu4/d///dRSnHo0CH+6q/+isOHD4/8GH784x/ztKc9jec85zm8853vHPnzCyGEEKMkAb8QQggxBv7zf/7Pvf9OkoQzzzyTN73pTVxxxRW92y+66CL+/b//93zkIx/h7W9/O6eddhqvfe1rufvuu1cF/Ndddx1veMMbeO9730ur1eI5z3kOP/dzP8f+/fv59Kc/zfve9z4+97nPMT8/z/79+7nkkkv6JgBshwsvvJALLriA//k//+eqr73+9a+nKApuuukm4jjmsssu49d//dd5+tOfvq3HJIQQQuxmyksnGiGEEEIIIYQQYuxIDb8QQgghhBBCCDGGJOAXQgghhBBCCCHGkAT8QgghhBBCCCHEGJKAXwghhBBCCCGEGEMS8AshhBBCCCGEEGNIAn4hhBBCCCGEEGIMScAvhBBCCCGEEEKMIQn4hRBCCCGEEEKIMSQBvxBCCCGEEEIIMYYk4BdCCCGEEEIIIcaQBPxCCCGEEEIIIcQYkoBfCCGEEEIIIYQYQxLwCyGEEEIIIYQQY+j/B3kK6wn1hRqbAAAAAElFTkSuQmCC",
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       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "df_upper, df_lower = train_stats_aec1.get_aec_boundaries(plot=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "eb76874b-f977-4f69-8d8e-98b358af9649",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
+       "100"
       ]
      },
+     "execution_count": 18,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loss_values_dict = train_stats_aec1.get_loss_batchid_per_worker()\n",
+    "len(loss_values_dict['w1'])\n",
+    "loss_values_dict['w1'][0]\n",
+    "batch_size = len(loss_values_dict['w1'][0][1])\n",
+    "batch_size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f2bbe8b1-4f2e-4221-9bf3-44edf35af460",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3070656/1708356485.py:4: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  w1_loss_batchid_list.append((batchID, i + int(batchID)*batch_size, float(loss_list[i])))\n"
+     ]
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
+       "(0, 0, 0.13141556084156036)"
       ]
      },
+     "execution_count": 19,
      "metadata": {},
-     "output_type": "display_data"
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w1_loss_batchid_list = []\n",
+    "for batchID, loss_list in loss_values_dict['w1']:\n",
+    "    for i in range(batch_size):\n",
+    "        w1_loss_batchid_list.append((batchID, i + int(batchID)*batch_size, float(loss_list[i])))\n",
+    "w1_loss_batchid_list[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "ed12f144-af87-4794-9e28-0d2712f6db43",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
+       "(0.034731946885585785, 0.0)"
       ]
      },
+     "execution_count": 20,
      "metadata": {},
-     "output_type": "display_data"
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loss_labels_pairs_w1 = []\n",
+    "for batchID, sampleID, loss_val in w1_loss_batchid_list:\n",
+    "    loss_labels_pairs_w1.append((loss_val, labels[sampleID]))\n",
+    "loss_labels_pairs_w1[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "056be4ea-7cd2-4e33-8cee-6ce53038f458",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "           w1        w2        w3        w4\n",
-      "0    0.022159  0.024412  0.021429  0.024490\n",
-      "1    0.028171  0.031251  0.027328  0.031673\n",
-      "2    0.021212  0.023202  0.019442  0.022849\n",
-      "3    0.023052  0.032770  0.030520  0.032671\n",
-      "4    0.021807  0.023669  0.019485  0.024202\n",
-      "..        ...       ...       ...       ...\n",
-      "115  0.008653  0.014101  0.008539  0.013582\n",
-      "116  0.003256  0.010893  0.004085  0.008641\n",
-      "117  0.004780  0.015420  0.005411  0.014795\n",
-      "118  0.002991  0.016147  0.005801  0.009873\n",
-      "119  0.008559  0.011427  0.010089  0.011774\n",
-      "\n",
-      "[120 rows x 4 columns]\n",
-      "           w1        w2        w3        w4\n",
-      "0    0.020242  0.022763  0.018549  0.022147\n",
-      "1    0.021634  0.023613  0.018433  0.023539\n",
-      "2    0.012671  0.014388  0.011564  0.014243\n",
-      "3    0.016343  0.018806  0.015250  0.018718\n",
-      "4    0.011372  0.013744  0.012646  0.014727\n",
-      "..        ...       ...       ...       ...\n",
-      "115  0.001958  0.009437  0.001318  0.007632\n",
-      "116  0.002043  0.008857  0.002873  0.005859\n",
-      "117  0.004037  0.007832  0.003799  0.005348\n",
-      "118  0.002599  0.007946  0.003117  0.007565\n",
-      "119  0.000580  0.008932  0.001139  0.008156\n",
-      "\n",
-      "[120 rows x 4 columns]\n"
+      "# Anomalies Loss Values: 4987, # Normal Loss Values: 244513\n"
      ]
     }
    ],
    "source": [
-    "df_upper, df_lower = train_stats_aec.get_aec_boundaries(plot=True)"
+    "anomalies_loss_w1 = [loss for loss, label in loss_labels_pairs_w1 if label == 1.0]\n",
+    "normal_loss_w1 = [loss for loss, label in loss_labels_pairs_w1 if label == 0.0]\n",
+    "print(f'# Anomalies Loss Values: {len(anomalies_loss_w1)}, # Normal Loss Values: {len(normal_loss_w1)}')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "aa2afd16-3eb0-4bec-9a8a-b8268030436d",
+   "execution_count": 22,
+   "id": "5891ffe6-2456-4fa3-9bdc-807e51c1ae04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the loss values\n",
+    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n",
+    "\n",
+    "bins = np.linspace(0, 0.3, 100)\n",
+    "\n",
+    "ax1.hist(anomalies_loss_w1, bins=bins, color='red', alpha=0.7, label='Anomalies')\n",
+    "ax1.set_title('Anomalies Loss Values (OpenNN)')\n",
+    "ax1.set_xlabel('Loss')\n",
+    "ax1.set_ylabel('Count')\n",
+    "ax1.set_xlim([0, 0.3])\n",
+    "ax1.legend()\n",
+    "\n",
+    "ax2.hist(normal_loss_w1, bins=bins, color='blue', alpha=0.7, label='Normal')\n",
+    "ax2.set_title('Normal Loss Values (OpenNN)')\n",
+    "ax2.set_xlabel('Loss')\n",
+    "ax2.set_ylabel('Count')\n",
+    "ax2.set_xlim([0, 0.3])\n",
+    "ax2.legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "9e170d44-ed5f-45d6-b05b-78dee08f2e8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_average_aec_errors(loss_labels_pairs: list[tuple]):\n",
+    "    anomaly_loss_values = []\n",
+    "    normal_loss_values = []\n",
+    "    for loss, label in loss_labels_pairs:\n",
+    "        if label == 1.0:\n",
+    "            anomaly_loss_values.append(loss)\n",
+    "        elif label == 0.0:\n",
+    "            normal_loss_values.append(loss)\n",
+    "        else:\n",
+    "            print(\"Unknown Label Value (Should be 1.0/0.0\")\n",
+    "    avg_anomaly_loss = np.mean(anomaly_loss_values)\n",
+    "    avg_normal_loss = np.mean(normal_loss_values)\n",
+    "\n",
+    "    plt.bar('Anomaly', avg_anomaly_loss, label='Anomaly Average Error')\n",
+    "    plt.bar('Normal', avg_normal_loss, label='Normal Average Error')\n",
+    "    \n",
+    "    plt.title(\"Average Errors Per Worker\")\n",
+    "    plt.xticks(positions, workers)\n",
+    "    plt.legend()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "5eb1786c-3e4d-40bd-9acf-ea4242bbee1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'positions' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[24], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplot_average_aec_errors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloss_labels_pairs_w1\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[23], line 18\u001b[0m, in \u001b[0;36mplot_average_aec_errors\u001b[0;34m(loss_labels_pairs)\u001b[0m\n\u001b[1;32m     15\u001b[0m plt\u001b[38;5;241m.\u001b[39mbar(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNormal\u001b[39m\u001b[38;5;124m'\u001b[39m, avg_normal_loss, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNormal Average Error\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     17\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAverage Errors Per Worker\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 18\u001b[0m plt\u001b[38;5;241m.\u001b[39mxticks(\u001b[43mpositions\u001b[49m, workers)\n\u001b[1;32m     19\u001b[0m plt\u001b[38;5;241m.\u001b[39mlegend()\n\u001b[1;32m     20\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'positions' is not defined"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_average_aec_errors(loss_labels_pairs_w1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "59380053-b2de-4679-a53f-11e73028812c",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[INFO][2024-07-07 21:39:35,612] Experiment phase: prediction_phase of type prediction starts running...\n",
-      "[INFO][2024-07-07 21:39:35,614] Sending data to sources\n",
-      "[INFO][2024-07-07 21:39:36,409] Data is ready in sources\n",
-      "[INFO][2024-07-07 21:39:36,410] Phase prediction requested from Main Server\n",
-      "[INFO][2024-07-07 21:39:39,640] Processing experiment phase data\n",
-      "[INFO][2024-07-07 21:39:39,654] Processing experiment phase data completed\n",
-      "[INFO][2024-07-07 21:39:39,655] Start generating communication statistics for prediction_phase of type prediction\n",
-      "[INFO][2024-07-07 21:39:39,656] Statistics requested from Main Server\n",
-      "[INFO][2024-07-07 21:39:39,709] Statistics received from Main Server\n",
-      "[INFO][2024-07-07 21:39:39,710] Phase of prediction_phase prediction completed\n"
+      "[INFO][2024-07-30 23:21:13,333] Experiment phase: prediction_phase of type prediction starts running...\n",
+      "[INFO][2024-07-30 23:21:13,335] Sending data to sources\n",
+      "[INFO][2024-07-30 23:21:19,805] Data is ready in sources\n",
+      "[INFO][2024-07-30 23:21:19,806] Phase prediction requested from Main Server\n",
+      "[INFO][2024-07-30 23:21:24,072] Processing experiment phase data\n",
+      "[INFO][2024-07-30 23:21:24,083] Processing experiment phase data completed\n",
+      "[INFO][2024-07-30 23:21:24,083] Start generating communication statistics for prediction_phase of type prediction\n",
+      "[INFO][2024-07-30 23:21:24,084] Statistics requested from Main Server\n",
+      "[INFO][2024-07-30 23:21:24,440] Statistics received from Main Server\n",
+      "[INFO][2024-07-30 23:21:24,441] Phase of prediction_phase prediction completed\n"
      ]
     }
    ],
    "source": [
     "API.next_experiment_phase()\n",
-    "API.get_experiment_flow(exp_name).get_csv_dataset().set_num_of_labels(1)\n",
+    "# API.get_experiment_flow(exp_name).get_csv_dataset().set_num_of_labels(1)\n",
     "API.run_current_experiment_phase()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 26,
    "id": "d1235aaa-b2c2-4673-ba6e-5edce87bf1fb",
    "metadata": {},
    "outputs": [],
    "source": [
+    "API.get_experiment_flow(exp_name).get_csv_dataset().set_num_of_labels(1)\n",
     "predict_stats = API.get_experiment_flow(exp_name).generate_stats()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 27,
    "id": "87bb9556-0daa-4086-bd46-a6526a353a1a",
    "metadata": {},
    "outputs": [
     {
-     "ename": "ValueError",
-     "evalue": "cannot reshape array of size 800 into shape (100,1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m _ , conf_mats \u001b[38;5;241m=\u001b[39m \u001b[43mpredict_stats\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_confusion_matrices\u001b[49m\u001b[43m(\u001b[49m\u001b[43mplot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m predict_stats\u001b[38;5;241m.\u001b[39mget_model_performence_stats(conf_mats, show\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExp Done\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m/usr/local/lib/nerlnet-lib/NErlNet/src_py/apiServer/stats.py:194\u001b[0m, in \u001b[0;36mStats.get_confusion_matrices\u001b[0;34m(self, normalize, plot, saveToFile)\u001b[0m\n\u001b[1;32m    192\u001b[0m cycle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(batch_db\u001b[38;5;241m.\u001b[39mget_batch_id())\n\u001b[1;32m    193\u001b[0m tensor_data \u001b[38;5;241m=\u001b[39m batch_db\u001b[38;5;241m.\u001b[39mget_tensor_data()\n\u001b[0;32m--> 194\u001b[0m tensor_data \u001b[38;5;241m=\u001b[39m \u001b[43mtensor_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_of_labels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    195\u001b[0m \u001b[38;5;66;03m#print(df_worker_labels)\u001b[39;00m\n\u001b[1;32m    196\u001b[0m \u001b[38;5;66;03m#print(tensor_data)\u001b[39;00m\n\u001b[1;32m    197\u001b[0m start_index \u001b[38;5;241m=\u001b[39m cycle \u001b[38;5;241m*\u001b[39m batch_size\n",
-      "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 800 into shape (100,1)"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 560x420 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_cbc95 th {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "#T_cbc95 th.col_heading {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "#T_cbc95 th.row_heading {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "#T_cbc95_row0_col0, #T_cbc95_row0_col1, #T_cbc95_row0_col2, #T_cbc95_row0_col3, #T_cbc95_row0_col4, #T_cbc95_row0_col5, #T_cbc95_row0_col6, #T_cbc95_row0_col7, #T_cbc95_row0_col8, #T_cbc95_row0_col9, #T_cbc95_row0_col10, #T_cbc95_row0_col11, #T_cbc95_row0_col12 {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_cbc95\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_cbc95_level0_col0\" class=\"col_heading level0 col0\" >Worker</th>\n",
+       "      <th id=\"T_cbc95_level0_col1\" class=\"col_heading level0 col1\" >Class</th>\n",
+       "      <th id=\"T_cbc95_level0_col2\" class=\"col_heading level0 col2\" >TN</th>\n",
+       "      <th id=\"T_cbc95_level0_col3\" class=\"col_heading level0 col3\" >FP</th>\n",
+       "      <th id=\"T_cbc95_level0_col4\" class=\"col_heading level0 col4\" >FN</th>\n",
+       "      <th id=\"T_cbc95_level0_col5\" class=\"col_heading level0 col5\" >TP</th>\n",
+       "      <th id=\"T_cbc95_level0_col6\" class=\"col_heading level0 col6\" >Accuracy</th>\n",
+       "      <th id=\"T_cbc95_level0_col7\" class=\"col_heading level0 col7\" >Balanced Accuracy</th>\n",
+       "      <th id=\"T_cbc95_level0_col8\" class=\"col_heading level0 col8\" >Precision</th>\n",
+       "      <th id=\"T_cbc95_level0_col9\" class=\"col_heading level0 col9\" >Recall</th>\n",
+       "      <th id=\"T_cbc95_level0_col10\" class=\"col_heading level0 col10\" >True Negative Rate</th>\n",
+       "      <th id=\"T_cbc95_level0_col11\" class=\"col_heading level0 col11\" >Informedness</th>\n",
+       "      <th id=\"T_cbc95_level0_col12\" class=\"col_heading level0 col12\" >F1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_cbc95_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_cbc95_row0_col0\" class=\"data row0 col0\" >w1</td>\n",
+       "      <td id=\"T_cbc95_row0_col1\" class=\"data row0 col1\" >Label</td>\n",
+       "      <td id=\"T_cbc95_row0_col2\" class=\"data row0 col2\" >29606</td>\n",
+       "      <td id=\"T_cbc95_row0_col3\" class=\"data row0 col3\" >6081</td>\n",
+       "      <td id=\"T_cbc95_row0_col4\" class=\"data row0 col4\" >136</td>\n",
+       "      <td id=\"T_cbc95_row0_col5\" class=\"data row0 col5\" >24177</td>\n",
+       "      <td id=\"T_cbc95_row0_col6\" class=\"data row0 col6\" >0.896383</td>\n",
+       "      <td id=\"T_cbc95_row0_col7\" class=\"data row0 col7\" >0.912004</td>\n",
+       "      <td id=\"T_cbc95_row0_col8\" class=\"data row0 col8\" >0.799028</td>\n",
+       "      <td id=\"T_cbc95_row0_col9\" class=\"data row0 col9\" >0.994406</td>\n",
+       "      <td id=\"T_cbc95_row0_col10\" class=\"data row0 col10\" >0.829602</td>\n",
+       "      <td id=\"T_cbc95_row0_col11\" class=\"data row0 col11\" >0.824008</td>\n",
+       "      <td id=\"T_cbc95_row0_col12\" class=\"data row0 col12\" >0.886075</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x70caf1e7b390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average False Alarm Rate: 17.0398%\n",
+      "Average Detection Rate: 99.4406%\n",
+      "Average Missing Alarm Rate: 0.5594%\n",
+      "Exp Done\n"
      ]
     }
    ],
    "source": [
     "_ , conf_mats = predict_stats.get_confusion_matrices(plot=True)\n",
     "predict_stats.get_model_performence_stats(conf_mats, show=True)\n",
+    "pred_aec_stats = API.get_experiment_flow(exp_name).generate_stats_aec(predict_stats)\n",
+    "false_alarm_rate = pred_aec_stats.get_false_alarm_rate(conf_mats) # Dict of workers as keys and their rate as value\n",
+    "detection_rate = pred_aec_stats.get_detection_rate(conf_mats)\n",
+    "import numpy as np\n",
+    "print(f'Average False Alarm Rate: {np.mean(list(false_alarm_rate.values()))*100:.4f}%')\n",
+    "print(f'Average Detection Rate: {np.mean(list(detection_rate.values()))*100:.4f}%')\n",
+    "print(f'Average Missing Alarm Rate: {100 - np.mean(list(detection_rate.values()))*100:.4f}%')\n",
     "print(\"Exp Done\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "id": "9db9f7d6-9ef7-4114-982a-b09f572ccc4a",
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "workers_comm_dict = train_stats1.get_communication_stats_workers()\n",
+    "df_train = pd.DataFrame.from_dict(workers_comm_dict)\n",
+    "plt.figure()\n",
+    "df_selected_train = df_train.iloc[[3,5]]\n",
+    "data_train = pd.melt(df_selected_train.reset_index(), id_vars=['index'], value_vars=df_train.columns)\n",
+    "batches_stats = sns.barplot(x='variable', y='value', hue='index', data=data_train)\n",
+    "plt.ylabel('Number Of Batches')\n",
+    "plt.xlabel('Worker')\n",
+    "plt.title(\"Received & Dropped Batches At Freq. 300B/s (Training Phase)\")\n",
+    "\n",
+    "batches_stats.legend(loc='upper right', bbox_to_anchor=(1.5, 0.2), shadow=True, ncol=1)\n",
+    "plt.show()\n",
+    "\n",
+    "workers_comm_dict_pred = predict_stats.get_communication_stats_workers()\n",
+    "df_pred = pd.DataFrame.from_dict(workers_comm_dict_pred)\n",
+    "\n",
+    "plt.figure()\n",
+    "df_selected_pred = df_pred.iloc[[4,6]]\n",
+    "data_pred = pd.melt(df_selected_pred.reset_index(), id_vars=['index'], value_vars=df_pred.columns)\n",
+    "batches_stats_pred = sns.barplot(x='variable', y='value', hue='index', data=data_pred)\n",
+    "plt.ylabel('Number Of Batches')\n",
+    "plt.xlabel('Worker')\n",
+    "plt.title(\"Received & Dropped Batches At Freq. 300B/s (Prediction Phase)\")\n",
+    "batches_stats_pred.legend(loc='lower right', bbox_to_anchor=(1.54, 0), shadow=True, ncol=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "dffe6818-61a0-494e-a819-c8cde0d9b929",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>BatchID</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>...</th>\n",
+       "      <th>90</th>\n",
+       "      <th>91</th>\n",
+       "      <th>92</th>\n",
+       "      <th>93</th>\n",
+       "      <th>94</th>\n",
+       "      <th>95</th>\n",
+       "      <th>96</th>\n",
+       "      <th>97</th>\n",
+       "      <th>98</th>\n",
+       "      <th>99</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.271604</td>\n",
+       "      <td>0.092196</td>\n",
+       "      <td>0.109548</td>\n",
+       "      <td>0.164127</td>\n",
+       "      <td>0.054964</td>\n",
+       "      <td>0.054541</td>\n",
+       "      <td>0.169089</td>\n",
+       "      <td>0.215998</td>\n",
+       "      <td>0.368195</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.141118</td>\n",
+       "      <td>0.208396</td>\n",
+       "      <td>0.101707</td>\n",
+       "      <td>0.092632</td>\n",
+       "      <td>0.052618</td>\n",
+       "      <td>0.122832</td>\n",
+       "      <td>0.134562</td>\n",
+       "      <td>0.093630</td>\n",
+       "      <td>0.284563</td>\n",
+       "      <td>0.060096</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.418232</td>\n",
+       "      <td>0.052910</td>\n",
+       "      <td>0.088871</td>\n",
+       "      <td>0.067901</td>\n",
+       "      <td>0.056541</td>\n",
+       "      <td>0.323082</td>\n",
+       "      <td>0.174817</td>\n",
+       "      <td>0.273195</td>\n",
+       "      <td>0.545932</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.144775</td>\n",
+       "      <td>0.061415</td>\n",
+       "      <td>0.206358</td>\n",
+       "      <td>0.104437</td>\n",
+       "      <td>0.165375</td>\n",
+       "      <td>0.013662</td>\n",
+       "      <td>0.119041</td>\n",
+       "      <td>0.028213</td>\n",
+       "      <td>0.023498</td>\n",
+       "      <td>0.529981</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0.308546</td>\n",
+       "      <td>0.016237</td>\n",
+       "      <td>0.080598</td>\n",
+       "      <td>0.259984</td>\n",
+       "      <td>0.122103</td>\n",
+       "      <td>0.099840</td>\n",
+       "      <td>0.143915</td>\n",
+       "      <td>0.204602</td>\n",
+       "      <td>0.566390</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.198085</td>\n",
+       "      <td>0.159590</td>\n",
+       "      <td>0.221497</td>\n",
+       "      <td>0.136584</td>\n",
+       "      <td>0.163235</td>\n",
+       "      <td>0.154064</td>\n",
+       "      <td>0.051674</td>\n",
+       "      <td>0.320866</td>\n",
+       "      <td>0.129708</td>\n",
+       "      <td>0.020028</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0.228410</td>\n",
+       "      <td>0.044594</td>\n",
+       "      <td>0.090500</td>\n",
+       "      <td>0.366910</td>\n",
+       "      <td>0.141728</td>\n",
+       "      <td>0.164754</td>\n",
+       "      <td>0.090847</td>\n",
+       "      <td>0.200776</td>\n",
+       "      <td>0.608787</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.076541</td>\n",
+       "      <td>0.089514</td>\n",
+       "      <td>0.069420</td>\n",
+       "      <td>0.157601</td>\n",
+       "      <td>0.168165</td>\n",
+       "      <td>0.127627</td>\n",
+       "      <td>0.179462</td>\n",
+       "      <td>0.436473</td>\n",
+       "      <td>0.079267</td>\n",
+       "      <td>0.032475</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11</td>\n",
+       "      <td>0.129769</td>\n",
+       "      <td>0.050759</td>\n",
+       "      <td>0.074426</td>\n",
+       "      <td>0.311728</td>\n",
+       "      <td>0.167059</td>\n",
+       "      <td>0.135444</td>\n",
+       "      <td>0.069845</td>\n",
+       "      <td>0.143934</td>\n",
+       "      <td>0.411235</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.123734</td>\n",
+       "      <td>0.235314</td>\n",
+       "      <td>0.137088</td>\n",
+       "      <td>0.103541</td>\n",
+       "      <td>0.063328</td>\n",
+       "      <td>0.064140</td>\n",
+       "      <td>0.275342</td>\n",
+       "      <td>0.172295</td>\n",
+       "      <td>0.096370</td>\n",
+       "      <td>0.049623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>490</td>\n",
+       "      <td>0.180985</td>\n",
+       "      <td>0.082688</td>\n",
+       "      <td>0.102381</td>\n",
+       "      <td>0.350396</td>\n",
+       "      <td>0.072971</td>\n",
+       "      <td>0.286301</td>\n",
+       "      <td>0.064065</td>\n",
+       "      <td>0.206620</td>\n",
+       "      <td>0.694152</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.092737</td>\n",
+       "      <td>0.047841</td>\n",
+       "      <td>0.098971</td>\n",
+       "      <td>0.103811</td>\n",
+       "      <td>0.255926</td>\n",
+       "      <td>0.106776</td>\n",
+       "      <td>0.129335</td>\n",
+       "      <td>0.360985</td>\n",
+       "      <td>0.300467</td>\n",
+       "      <td>0.238015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>492</td>\n",
+       "      <td>0.431728</td>\n",
+       "      <td>0.062465</td>\n",
+       "      <td>0.233317</td>\n",
+       "      <td>0.372154</td>\n",
+       "      <td>0.148644</td>\n",
+       "      <td>0.357349</td>\n",
+       "      <td>0.093035</td>\n",
+       "      <td>0.302863</td>\n",
+       "      <td>0.417968</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.141859</td>\n",
+       "      <td>0.073626</td>\n",
+       "      <td>0.270616</td>\n",
+       "      <td>0.088528</td>\n",
+       "      <td>0.195925</td>\n",
+       "      <td>0.043409</td>\n",
+       "      <td>0.028309</td>\n",
+       "      <td>0.177232</td>\n",
+       "      <td>0.257428</td>\n",
+       "      <td>0.218504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>494</td>\n",
+       "      <td>0.178317</td>\n",
+       "      <td>0.064370</td>\n",
+       "      <td>0.105678</td>\n",
+       "      <td>0.331193</td>\n",
+       "      <td>0.117022</td>\n",
+       "      <td>0.269193</td>\n",
+       "      <td>0.348709</td>\n",
+       "      <td>0.150799</td>\n",
+       "      <td>0.206163</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.208545</td>\n",
+       "      <td>0.290147</td>\n",
+       "      <td>0.075278</td>\n",
+       "      <td>0.193602</td>\n",
+       "      <td>0.146421</td>\n",
+       "      <td>0.130075</td>\n",
+       "      <td>0.138019</td>\n",
+       "      <td>0.123706</td>\n",
+       "      <td>0.086106</td>\n",
+       "      <td>0.054332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>497</td>\n",
+       "      <td>0.146059</td>\n",
+       "      <td>0.156568</td>\n",
+       "      <td>0.112502</td>\n",
+       "      <td>0.204220</td>\n",
+       "      <td>0.164563</td>\n",
+       "      <td>0.104908</td>\n",
+       "      <td>0.170220</td>\n",
+       "      <td>0.172218</td>\n",
+       "      <td>0.556195</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.143142</td>\n",
+       "      <td>0.164211</td>\n",
+       "      <td>0.223784</td>\n",
+       "      <td>0.150323</td>\n",
+       "      <td>0.082789</td>\n",
+       "      <td>0.190664</td>\n",
+       "      <td>0.125117</td>\n",
+       "      <td>0.144823</td>\n",
+       "      <td>0.435042</td>\n",
+       "      <td>0.187445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>499</td>\n",
+       "      <td>0.002272</td>\n",
+       "      <td>0.000633</td>\n",
+       "      <td>0.001609</td>\n",
+       "      <td>0.012854</td>\n",
+       "      <td>0.002641</td>\n",
+       "      <td>0.015035</td>\n",
+       "      <td>0.003426</td>\n",
+       "      <td>0.010907</td>\n",
+       "      <td>0.113795</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.003719</td>\n",
+       "      <td>0.008093</td>\n",
+       "      <td>0.000634</td>\n",
+       "      <td>0.000991</td>\n",
+       "      <td>0.000445</td>\n",
+       "      <td>0.001973</td>\n",
+       "      <td>0.000740</td>\n",
+       "      <td>0.002810</td>\n",
+       "      <td>0.002240</td>\n",
+       "      <td>0.005307</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>170 rows × 101 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     BatchID         0         1         2         3         4         5  \\\n",
+       "0          0  0.271604  0.092196  0.109548  0.164127  0.054964  0.054541   \n",
+       "1          3  0.418232  0.052910  0.088871  0.067901  0.056541  0.323082   \n",
+       "2          6  0.308546  0.016237  0.080598  0.259984  0.122103  0.099840   \n",
+       "3          8  0.228410  0.044594  0.090500  0.366910  0.141728  0.164754   \n",
+       "4         11  0.129769  0.050759  0.074426  0.311728  0.167059  0.135444   \n",
+       "..       ...       ...       ...       ...       ...       ...       ...   \n",
+       "165      490  0.180985  0.082688  0.102381  0.350396  0.072971  0.286301   \n",
+       "166      492  0.431728  0.062465  0.233317  0.372154  0.148644  0.357349   \n",
+       "167      494  0.178317  0.064370  0.105678  0.331193  0.117022  0.269193   \n",
+       "168      497  0.146059  0.156568  0.112502  0.204220  0.164563  0.104908   \n",
+       "169      499  0.002272  0.000633  0.001609  0.012854  0.002641  0.015035   \n",
+       "\n",
+       "            6         7         8  ...        90        91        92  \\\n",
+       "0    0.169089  0.215998  0.368195  ...  0.141118  0.208396  0.101707   \n",
+       "1    0.174817  0.273195  0.545932  ...  0.144775  0.061415  0.206358   \n",
+       "2    0.143915  0.204602  0.566390  ...  0.198085  0.159590  0.221497   \n",
+       "3    0.090847  0.200776  0.608787  ...  0.076541  0.089514  0.069420   \n",
+       "4    0.069845  0.143934  0.411235  ...  0.123734  0.235314  0.137088   \n",
+       "..        ...       ...       ...  ...       ...       ...       ...   \n",
+       "165  0.064065  0.206620  0.694152  ...  0.092737  0.047841  0.098971   \n",
+       "166  0.093035  0.302863  0.417968  ...  0.141859  0.073626  0.270616   \n",
+       "167  0.348709  0.150799  0.206163  ...  0.208545  0.290147  0.075278   \n",
+       "168  0.170220  0.172218  0.556195  ...  0.143142  0.164211  0.223784   \n",
+       "169  0.003426  0.010907  0.113795  ...  0.003719  0.008093  0.000634   \n",
+       "\n",
+       "           93        94        95        96        97        98        99  \n",
+       "0    0.092632  0.052618  0.122832  0.134562  0.093630  0.284563  0.060096  \n",
+       "1    0.104437  0.165375  0.013662  0.119041  0.028213  0.023498  0.529981  \n",
+       "2    0.136584  0.163235  0.154064  0.051674  0.320866  0.129708  0.020028  \n",
+       "3    0.157601  0.168165  0.127627  0.179462  0.436473  0.079267  0.032475  \n",
+       "4    0.103541  0.063328  0.064140  0.275342  0.172295  0.096370  0.049623  \n",
+       "..        ...       ...       ...       ...       ...       ...       ...  \n",
+       "165  0.103811  0.255926  0.106776  0.129335  0.360985  0.300467  0.238015  \n",
+       "166  0.088528  0.195925  0.043409  0.028309  0.177232  0.257428  0.218504  \n",
+       "167  0.193602  0.146421  0.130075  0.138019  0.123706  0.086106  0.054332  \n",
+       "168  0.150323  0.082789  0.190664  0.125117  0.144823  0.435042  0.187445  \n",
+       "169  0.000991  0.000445  0.001973  0.000740  0.002810  0.002240  0.005307  \n",
+       "\n",
+       "[170 rows x 101 columns]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('/tmp/nerlnet/predict_error.csv', header=None)\n",
+    "batch_ids = df.iloc[:, 0]\n",
+    "batches_loss = df.iloc[:, 4:-1]\n",
+    "df = pd.concat([batch_ids, batches_loss], axis=1)\n",
+    "df.columns = ['BatchID'] + list(range(df.shape[1] - 1))\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "cbfa4712-2b7d-4700-8c3a-363b575454d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 0.0545407459139823)"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w1_loss_batchid_list_predict = []\n",
+    "for index, row in df.iterrows():\n",
+    "    batchID = row['BatchID']\n",
+    "    for i in range(len(row) - 1):\n",
+    "        sampleID = batchID * batch_size + i\n",
+    "        w1_loss_batchid_list_predict.append((int(sampleID), float(row[i])))\n",
+    "w1_loss_batchid_list_predict[5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "cb195250-9795-460c-b16e-7c77bd542c58",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0545407459139823, 0.0)"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loss_labels_pairs_w1_predict = []\n",
+    "for sampleID, loss_val in w1_loss_batchid_list_predict:\n",
+    "    loss_labels_pairs_w1_predict.append((loss_val, labels[sampleID]))\n",
+    "loss_labels_pairs_w1_predict[5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "ab91774d-591c-4b6b-85b6-6f45f3e3d5f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# Anomalies Loss Values: 323, # Normal Loss Values: 16677\n"
+     ]
+    }
+   ],
+   "source": [
+    "anomalies_loss_w1_predict = [loss for loss, label in loss_labels_pairs_w1_predict if label == 1.0]\n",
+    "normal_loss_w1_predict = [loss for loss, label in loss_labels_pairs_w1_predict if label == 0.0]\n",
+    "print(f'# Anomalies Loss Values: {len(anomalies_loss_w1_predict)}, # Normal Loss Values: {len(normal_loss_w1_predict)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "6a73c664-dfe2-4236-be0d-4f13a0e6a420",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the loss values\n",
+    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n",
+    "\n",
+    "bins = np.linspace(0, 0.3, 100)\n",
+    "\n",
+    "ax1.hist(anomalies_loss_w1_predict, bins=bins, color='red', alpha=0.7, label='Anomalies')\n",
+    "ax1.set_title('Anomalies Loss Values (OpenNN)')\n",
+    "ax1.set_xlabel('Loss')\n",
+    "ax1.set_ylabel('Count')\n",
+    "ax1.set_xlim([0, 0.3])\n",
+    "ax1.legend()\n",
+    "\n",
+    "ax2.hist(normal_loss_w1_predict, bins=bins, color='blue', alpha=0.7, label='Normal')\n",
+    "ax2.set_title('Normal Loss Values (OpenNN)')\n",
+    "ax2.set_xlabel('Loss')\n",
+    "ax2.set_ylabel('Count')\n",
+    "ax2.set_xlim([0, 0.3])\n",
+    "ax2.legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "d060b563-a644-4755-81e2-6943255882d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OrderedDict([('w1',\n",
+       "              {'bytes_received': 0,\n",
+       "               'bytes_sent': 0,\n",
+       "               'bad_messages': 0,\n",
+       "               'batches_received_train': 500,\n",
+       "               'batches_received_predict': 170,\n",
+       "               'batches_dropped_train': 0,\n",
+       "               'batches_dropped_predict': 330,\n",
+       "               'batches_sent_train': 0,\n",
+       "               'empty_batches': 0,\n",
+       "               'batches_sent_predict': 0,\n",
+       "               'average_time_training': 0,\n",
+       "               'average_time_prediction': 0,\n",
+       "               'acc_time_training': 0,\n",
+       "               'acc_time_prediction': 0,\n",
+       "               'nan_loss_count': 0}),\n",
+       "             ('w2',\n",
+       "              {'bytes_received': 0,\n",
+       "               'bytes_sent': 0,\n",
+       "               'bad_messages': 0,\n",
+       "               'batches_received_train': 0,\n",
+       "               'batches_received_predict': 0,\n",
+       "               'batches_dropped_train': 0,\n",
+       "               'batches_dropped_predict': 0,\n",
+       "               'batches_sent_train': 0,\n",
+       "               'empty_batches': 0,\n",
+       "               'batches_sent_predict': 0,\n",
+       "               'average_time_training': 0,\n",
+       "               'average_time_prediction': 0,\n",
+       "               'acc_time_training': 0,\n",
+       "               'acc_time_prediction': 0,\n",
+       "               'nan_loss_count': 0})])"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict_stats.get_communication_stats_workers()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36e3c1a0-a01b-4493-a1f7-40c8c5846515",
+   "metadata": {},
    "outputs": [],
    "source": []
   }

From c70252cc8fc46bcdaa24e86f1490b6589dfb7758 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Thu, 1 Aug 2024 19:18:54 +0000
Subject: [PATCH 09/50] [AEC_Exp] WIP

---
 .../dc_AEC_1d_2c_1s_4r_4w.json                |  29 ++--
 .../exp_AEC_1d_2c_1s_4r_4w.json               |  12 +-
 src_cpp/opennnBridge/ae_red.cpp               |  68 +++++----
 src_cpp/opennnBridge/nerlWorkerOpenNN.cpp     |  55 ++++---
 src_cpp/opennnBridge/nerlWorkerOpenNN.h       |   2 +-
 src_cpp/opennnBridge/openNNnif.cpp            |   2 +-
 src_erl/NerlnetApp/src/Bridge/nerlNIF.erl     |   8 ++
 src_py/apiServer/nerl_csv_dataset_db.py       |  20 ++-
 src_py/apiServer/nerl_model_db.py             |   8 +-
 src_py/apiServer/stats.py                     | 116 +++++++++------
 src_py/apiServer/stats_aec.py                 | 136 +++++++++++++++---
 11 files changed, 310 insertions(+), 146 deletions(-)

diff --git a/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json b/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
index b5be8fb5..19bc5761 100644
--- a/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
+++ b/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
@@ -1,7 +1,7 @@
 {
     "nerlnetSettings": {
         "frequency": "300",
-        "batchSize": "60"
+        "batchSize": "100"
     },
     "mainServer": {
         "port": "8081",
@@ -44,9 +44,9 @@
         {
             "name": "s1",
             "port": "8085",
-            "frequency": "600",
+            "frequency": "300",
             "policy": "0",
-            "epochs": "5",
+            "epochs": "2",
             "type": "0"
         }
     ],
@@ -55,11 +55,6 @@
             "name": "c1",
             "port": "8083",
             "workers": "w1,w2"
-        },
-        {
-            "name": "c2",
-            "port": "8084",
-            "workers": "w3,w4"
         }
     ],
     "workers": [
@@ -70,33 +65,25 @@
         {
             "name": "w2",
             "model_sha": "d8df752e0a2e8f01de8f66e9cec941cdbc65d144ecf90ab7713e69d65e7e82aa"
-        },
-        {
-            "name": "w3",
-            "model_sha": "d8df752e0a2e8f01de8f66e9cec941cdbc65d144ecf90ab7713e69d65e7e82aa"
-        },
-        {
-            "name": "w4",
-            "model_sha": "d8df752e0a2e8f01de8f66e9cec941cdbc65d144ecf90ab7713e69d65e7e82aa"
         }
     ],
     "model_sha": {
         "d8df752e0a2e8f01de8f66e9cec941cdbc65d144ecf90ab7713e69d65e7e82aa": {
             "modelType": "9",
             "_doc_modelType": " nn:0 | approximation:1 | classification:2 | forecasting:3 | image-classification:4 | text-classification:5 | text-generation:6 | auto-association:7 | autoencoder:8 | ae-classifier:9 |",
-            "modelArgs": "",
-            "layersSizes": "10,16,8,16,10",
+            "modelArgs": "k=1.9,alpha=0.4,use_ema_only=1",
+            "layersSizes": "10,128,64,32,16,32,64,128,10",
             "_doc_layersSizes": "List of postive integers [L0, L1, ..., LN]",
-            "layerTypesList": "1,3,3,3,3",
+            "layerTypesList": "1,3,3,3,3,3,3,3,3",
             "_doc_LayerTypes": " Default:0 | Scaling:1 | CNN:2 | Perceptron:3 | Pooling:4 | Probabilistic:5 | LSTM:6 | Reccurrent:7 | Unscaling:8 |",
-            "layers_functions": "1,8,8,8,8",
+            "layers_functions": "1,8,8,8,8,8,8,8,8",
             "_doc_layers_functions_activation": " Threshold:1 | Sign:2 | Logistic:3 | Tanh:4 | Linear:5 | ReLU:6 | eLU:7 | SeLU:8 | Soft-plus:9 | Soft-sign:10 | Hard-sigmoid:11 |",
             "_doc_layer_functions_pooling": " none:1 | Max:2 | Avg:3 |",
             "_doc_layer_functions_probabilistic": " Binary:1 | Logistic:2 | Competitive:3 | Softmax:4 |",
             "_doc_layer_functions_scaler": " none:1 | MinMax:2 | MeanStd:3 | STD:4 | Log:5 |",
             "lossMethod": "2",
             "_doc_lossMethod": " SSE:1 | MSE:2 | NSE:3 | MinkowskiE:4 | WSE:5 | CEE:6 |",
-            "lr": "0.000001",
+            "lr": "0.001",
             "_doc_lr": "Positve float",
             "epochs": "1",
             "_doc_epochs": "Positve Integer",
diff --git a/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json b/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
index 9d85262c..c27e2f52 100644
--- a/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
+++ b/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
@@ -1,8 +1,8 @@
 {
     "experimentName": "anomaly_detection_skab",
     "experimentType": "classification",
-    "batchSize": 60,
-    "csvFilePath": "/tmp/nerlnet/data/NerlnetData-master/nerlnet/ForestCover/forest_cover_dataset.csv",
+    "batchSize": 100,
+    "csvFilePath": "/tmp/nerlnet/data/NerlnetData-master/nerlnet/ForestCover/cover_normalized_std.csv",
     "numOfFeatures": "10",
     "numOfLabels": "1",
     "headersNames": "Label",
@@ -16,8 +16,8 @@
             {
             "sourceName": "s1",
             "startingSample": "0",
-            "numOfBatches": "4000",
-            "workers": "w1,w2,w3,w4",
+            "numOfBatches": "2500",
+            "workers": "w1,w2",
             "nerltensorType": "float"
             }
         ]
@@ -30,8 +30,8 @@
             {
             "sourceName": "s1",
             "startingSample": "250000",
-            "numOfBatches": "1000",
-            "workers": "w1,w2,w3,w4",
+            "numOfBatches": "600",
+            "workers": "w1,w2",
             "nerltensorType": "float"
             }
         ]    
diff --git a/src_cpp/opennnBridge/ae_red.cpp b/src_cpp/opennnBridge/ae_red.cpp
index e6c3ebf6..05ac5f8c 100644
--- a/src_cpp/opennnBridge/ae_red.cpp
+++ b/src_cpp/opennnBridge/ae_red.cpp
@@ -50,8 +50,7 @@ fTensor2DPtr AeRed::update_batch(fTensor2DPtr loss_values)
     fTensor2DPtr result = std::make_shared<fTensor2D>(loss_values->dimension(0), loss_values->dimension(1));
     for(int i = 0; i < (*loss_values).dimension(0); i++)
     {
-        float val = update_sample((*loss_values)(i, 0), i);
-        (*result)(i, 0) = val;
+        (*result)(i, 0) = update_sample((*loss_values)(i, 0), i);
     }
     return result;
 }
@@ -67,49 +66,48 @@ float AeRed::update_sample(float loss_value, int index)
 }
 
 float AeRed::update_sample_red(float loss_value, int index){
-    if (index == 0){
+    if (_ema != 0) {
+        _ema = _alpha * loss_value + (1 - _alpha) * _prev_ema;
+    }
+    else {
         _ema = loss_value;
-        _prev_ema = loss_value;
-        _emad = 0;
-        _prev_emad = 0;
-        _ema_event = 0;
-        _ema_normal = 0;
-        _threshold = loss_value / 2;
     }
-    else{
-        _ema = _alpha * loss_value + (1 - _alpha) * _prev_ema;
-        _prev_ema = _ema;
+    _prev_ema = _ema;
+    if (_emad != 1) {
         _emad = _alpha * abs(loss_value - _ema) + (1 - _alpha) * _prev_emad;
-        _prev_emad = _emad;
-        if(_ema + _k * _emad < loss_value){
-            _ema_event = loss_value;
-        }
-        else{
-            _ema_normal = loss_value;
-        }
-        _threshold = (_ema_event + _ema_normal) / 2; // New Threshold
-
-        if(loss_value > _threshold) return 1.f;
-        else return 0.f;
     }
-    return 0.f;
+    else {
+        _emad = loss_value;
+    }
+    _prev_emad = _emad;
+    if(_ema + _k * _emad < loss_value){
+        _ema_event = loss_value;
+    }
+    else{
+        _ema_normal = loss_value;
+    }
+    _threshold = (_ema_event + _ema_normal) / 2; // New Threshold
+
+    if(loss_value > _threshold) 
+        return 1.f;
+    else 
+        return 0.f;
 }
 
 float AeRed::update_sample_ema(float loss_value, int index)
 {
-    if (index == 0){
-        _ema = loss_value;
-        _prev_ema = loss_value;
-        _threshold = loss_value / 2;
-    }
-    else{
+    if (_ema != 0) {
         _ema = _alpha * loss_value + (1 - _alpha) * _prev_ema;
-        _prev_ema = _ema;
-        _threshold = _ema / 2;
-        if(loss_value > _threshold) return 1.f;
-        else return 0.f;
     }
-    return 0.f;
+    else {
+        _ema = loss_value;
+    }
+    _prev_ema = _ema;
+    _threshold = _ema / 2;
+    if(loss_value > _threshold) 
+        return 1.f;
+    else 
+        return 0.f;
 }
 
 
diff --git a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
index b0f25179..5d31b705 100644
--- a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
+++ b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
@@ -1,5 +1,7 @@
 #include "nerlWorkerOpenNN.h"
 #include "ae_red.h" 
+#include <fstream>
+#include <iostream>
 
 using namespace opennn;
 
@@ -28,7 +30,7 @@ namespace nerlnet
     void NerlWorkerOpenNN::perform_training()
     {
         this->_training_strategy_ptr->set_data_set_pointer(this->_data_set.get());
-
+        this->_training_strategy_ptr->get_loss_index_pointer()->set_regularization_method(LossIndex::RegularizationMethod::L2); // ! ADDED NOW
         TrainingResults res = this->_training_strategy_ptr->perform_training();
         this->_last_loss = res.get_training_error();
     
@@ -93,7 +95,7 @@ namespace nerlnet
             case MODEL_TYPE_AE_CLASSIFIER: // Get Loss Values , Add RED support - David's Thesis
             {
                 std::shared_ptr<opennn::NeuralNetwork> neural_network = get_neural_network_ptr();
-                Index num_of_samples = _aec_data_set->dimension(0);
+                int num_of_samples = (int)_aec_data_set->dimension(0);
                 Index num_of_labels = 1;
                 Index inputs_number = neural_network->get_inputs_number();
                 Tensor<Index, 1> inputs_dimensions(2);
@@ -101,21 +103,19 @@ namespace nerlnet
                 fTensor2DPtr results = std::make_shared<fTensor2D>(num_of_samples, num_of_labels + num_of_labels*2); // LOWER/UPPER for each label
                 fTensor2DPtr calculate_res = std::make_shared<fTensor2D>(num_of_samples, neural_network->get_outputs_number());
                 *calculate_res = neural_network->calculate_outputs(TrainData->data(), inputs_dimensions);
-                // cout << "Results: " << endl << *calculate_res << endl;
                 fTensor2DPtr loss_values_mse = std::make_shared<fTensor2D>(num_of_samples, 1);
                 fTensor2DPtr loss_values_return = std::make_shared<fTensor2D>(num_of_samples, 1);
                 fTensor2D diff = (*calculate_res - *_aec_data_set);
-                // cout << "Diff: " << endl << diff << endl;
                 fTensor2D squared_diff = diff.pow(2);
                 fTensor1D sum_squared_diff = squared_diff.sum(Eigen::array<int, 1>({1}));
                 fTensor1D mse1D = (1.0 / static_cast<float>(_aec_data_set->dimension(0))) * sum_squared_diff;
-                fTensor2D mse2D = mse1D.reshape(Eigen::array<int, 2>({num_of_samples, 1}));
+                fTensor2D mse2D = mse1D.reshape(Eigen::array<int, 2>({(int)num_of_samples, 1}));
                 // cout << "MSE2D: " << mse2D << endl;
                 *loss_values_mse = mse2D;
                 *loss_values_return = mse2D;
                 _aec_all_loss_values = loss_values_return;
                 // cout << "MSE Loss: " << mse_loss << endl;
-                fTensor2DPtr res = _ae_red_ptr->update_batch(loss_values_mse);
+                // _ae_red_ptr->update_batch(loss_values_mse);
                 // cout << "AE_RED RESULT VECTOR:" << endl << *res << endl;
             }
 
@@ -133,7 +133,7 @@ namespace nerlnet
     }
     
 
-    void NerlWorkerOpenNN::post_predict_process(fTensor2DPtr &result_ptr){
+    void NerlWorkerOpenNN::post_predict_process(fTensor2DPtr &result_ptr, fTensor2DPtr &predictData){
         switch(_model_type){
             case MODEL_TYPE_NN:
             {
@@ -148,16 +148,34 @@ namespace nerlnet
                 std::shared_ptr<opennn::NeuralNetwork> neural_network = get_neural_network_ptr();
                 Index num_of_samples = _aec_data_set->dimension(0);
                 Index inputs_number = neural_network->get_inputs_number();
-                Index num_of_labels = 1; // TODO need to add bounderies
-                fTensor2DPtr results = std::make_shared<fTensor2D>(num_of_samples, num_of_labels); // TODO +2 for upper and lower boundaries for each label
+                Index num_of_labels = 1; 
+                fTensor2DPtr results = std::make_shared<fTensor2D>(num_of_samples, num_of_labels); 
                 fTensor2DPtr loss_values_mse = std::make_shared<fTensor2D>(num_of_samples , 1);
-                fTensor2D diff = (*result_ptr - *_aec_data_set);
+                fTensor2D diff = (*result_ptr - *predictData);
                 fTensor2D squared_diff = diff.pow(2);
                 fTensor1D sum_squared_diff = squared_diff.sum(Eigen::array<int, 1>({1}));
                 fTensor1D mse1D = (1.0 / static_cast<float>(_aec_data_set->dimension(0))) * sum_squared_diff;
-                fTensor2D mse2D = mse1D.reshape(Eigen::array<int, 2>({num_of_samples, 1}));
+                fTensor2D mse2D = mse1D.reshape(Eigen::array<int, 2>({(int)num_of_samples, 1}));
                 *loss_values_mse = mse2D;
+                // string filename = "/tmp/nerlnet/predict_errors.csv";
+                // std::ofstream file(filename, std::ios::out | std::ios::app);
+                // if (file.is_open()) {
+                //     for (int i = 0; i < mse2D.dimension(0); ++i) {
+                //         for (int j = 0; j < mse2D.dimension(1); ++j) {
+                //             file << mse2D(i, j);
+                //             if (j < mse2D.dimension(1) - 1) {
+                //                 file << ",";
+                //             }
+                //         }
+                //         file << "\n";
+                //     }
+                //     file.close();
+                // } 
+                // else {
+                //     cerr << "Unable to open file" << endl;
+                //     }
                 result_ptr = _ae_red_ptr->update_batch(loss_values_mse); // ! This should override the result_ptr
+                // result_ptr = loss_values_mse;
                 break;
             }
             // case MODEL_TYPE_LSTM:
@@ -578,6 +596,11 @@ namespace nerlnet
             }  
             curr_layer = curr_layer->get_next_layer_ptr();
         }
+        // Write the model parameters to file
+        // neural_network_ptr->get_parameters();
+        // neural_network_ptr->save("/home/nerlnet/workspace/NErlNet/model_parameters.xml");
+        // cout << "Model Parameters Saved" << endl;
+        // exit(0);
     }
 
     void NerlWorkerOpenNN::generate_custom_model_aec(std::shared_ptr<opennn::NeuralNetwork> &neural_network_ptr)
@@ -853,14 +876,14 @@ namespace nerlnet
 
     std::shared_ptr<std::vector<int>> NerlWorkerOpenNN::get_distributed_system_train_labels_count()
     {       
-         switch (_distributed_system_type)
+        switch (_distributed_system_type)
         {
             case WORKER_DISTRIBUTED_SYSTEM_TYPE_FEDCLIENTWEIGHTEDAVGCLASSIFICATION: // Federated Client Weighted Average Classification
             {
-                 if (_data_set == nullptr)
+                if (_data_set == nullptr)
                     {
-                       LogError("NerlWorkerOpenNN::generate_custom_model_nn - _data_set is nullptr");
-                       throw std::invalid_argument("NerlWorkerOpenNN::generate_custom_model_nn - _data_set is nullptr");
+                        LogError("NerlWorkerOpenNN::generate_custom_model_nn - _data_set is nullptr");
+                        throw std::invalid_argument("NerlWorkerOpenNN::generate_custom_model_nn - _data_set is nullptr");
                     }
                 return _train_labels_count;
                 break;
@@ -870,6 +893,6 @@ namespace nerlnet
                 break;
             }
         }
+        return nullptr;
     }
-
 } // namespace nerlnet
\ No newline at end of file
diff --git a/src_cpp/opennnBridge/nerlWorkerOpenNN.h b/src_cpp/opennnBridge/nerlWorkerOpenNN.h
index a93be425..007dccc5 100644
--- a/src_cpp/opennnBridge/nerlWorkerOpenNN.h
+++ b/src_cpp/opennnBridge/nerlWorkerOpenNN.h
@@ -31,7 +31,7 @@ class NerlWorkerOpenNN : public NerlWorker
     std::shared_ptr<opennn::TrainingStrategy> get_training_strategy_ptr() { return _training_strategy_ptr; };
     std::shared_ptr<opennn::DataSet> get_data_set() { return _data_set; };
     void post_training_process(fTensor2DPtr  TrainDataNNptr);
-    void post_predict_process(fTensor2DPtr &result_ptr);
+    void post_predict_process(fTensor2DPtr &result_ptr, fTensor2DPtr &predictData);
     void get_result_calc(fTensor2DPtr calculate_res,int num_of_samples,int inputs_number,fTensor2DPtr predictData);
     void set_optimization_method(int optimizer_type ,int learning_rate);
     void set_loss_method(int loss_method);
diff --git a/src_cpp/opennnBridge/openNNnif.cpp b/src_cpp/opennnBridge/openNNnif.cpp
index 57c5f64c..41170331 100644
--- a/src_cpp/opennnBridge/openNNnif.cpp
+++ b/src_cpp/opennnBridge/openNNnif.cpp
@@ -82,7 +82,7 @@ void* PredictFun(void* arg)
     Index inputs_number = neural_network->get_inputs_number();
     fTensor2DPtr calculate_res = std::make_shared<fTensor2D>(num_of_samples, neural_network->get_outputs_number());
     nerlworker_opennn->get_result_calc(calculate_res, num_of_samples, inputs_number, PredictNNptr->data);
-    nerlworker_opennn->post_predict_process(calculate_res);
+    nerlworker_opennn->post_predict_process(calculate_res, PredictNNptr->data);
     nifpp::make_tensor_2d<float,fTensor2D>(env, prediction, calculate_res);
     // only for AE and AEC calculate the distance between prediction labels and input data
     //std::cout << "*calculate_res.get(): " << (*calculate_res.get()).dimensions() << std::endl;
diff --git a/src_erl/NerlnetApp/src/Bridge/nerlNIF.erl b/src_erl/NerlnetApp/src/Bridge/nerlNIF.erl
index c70194cd..ec1ead8e 100644
--- a/src_erl/NerlnetApp/src/Bridge/nerlNIF.erl
+++ b/src_erl/NerlnetApp/src/Bridge/nerlNIF.erl
@@ -59,11 +59,19 @@ call_to_train(ModelID, {DataTensor, Type}, WorkerPid , BatchID , SourceName)->
                   gen_statem:cast(WorkerPid,{loss, timeout , SourceName}) %% TODO Guy Define train timeout state 
       end.
 
+save_to_file([]) -> 
+      file:write_file("/tmp/nerlnet/predict_error.csv", io_lib:fwrite("~n", []), [append]);
+save_to_file(List) ->
+      file:write_file("/tmp/nerlnet/predict_error.csv", io_lib:fwrite("~p,", [hd(List)]), [append]),
+      save_to_file(tl(List)).
+
 call_to_predict(ModelID, {BatchTensor, Type}, WorkerPid, BatchID , SourceName)->
       ok = predict_nif(ModelID, BatchTensor, Type),
       receive
             
             {nerlnif , PredNerlTensor, PredNerlTensorType, TimeNif}-> %% nerlnif atom means a message from the nif implementation
+                  % {Tensor, _} = nerlNIF:nerltensor_conversion({PredNerlTensor, PredNerlTensorType}, erl_float),
+                  % save_to_file([BatchID | Tensor]),
                   % io:format("pred_nif done~n"),
                   % {PredTen, _NewType} = nerltensor_conversion({PredNerlTensor, NewType}, erl_float),
                   % io:format("Pred returned: ~p~n", [PredNerlTensor]),
diff --git a/src_py/apiServer/nerl_csv_dataset_db.py b/src_py/apiServer/nerl_csv_dataset_db.py
index 35a9a92e..795a623d 100644
--- a/src_py/apiServer/nerl_csv_dataset_db.py
+++ b/src_py/apiServer/nerl_csv_dataset_db.py
@@ -1,11 +1,14 @@
 from definitions import PHASE_TRAINING_STR, PHASE_PREDICTION_STR, NERLTENSOR_TYPE_LIST
 import pandas as pd
 from math import ceil
+import os
 
 class SourcePieceDS():
-    def __init__(self, csv_dataset_parent, source_name : str, batch_size, phase : str, starting_offset = 0, num_of_batches = 0, nerltensor_type: str = 'float'):
+    def __init__(self, csv_dataset_parent, source_name : str, batch_size, phase : str, starting_offset = 0, num_of_batches = 0, nerltensor_type: str = 'float', num_of_features = 0, num_of_labels = 0):
         self.source_name = source_name
         self.batch_size = batch_size
+        self.num_of_features = num_of_features
+        self.num_of_labels = num_of_labels
         self.phase = phase
         self.starting_offset = starting_offset  # given as index of csv rows
         self.num_of_batches = num_of_batches
@@ -21,6 +24,12 @@ def get_source_name(self):
     
     def get_batch_size(self):
         return self.batch_size
+    
+    def get_num_of_features(self):
+        return self.num_of_features
+    
+    def get_num_of_labels(self):
+        return self.num_of_labels
 
     def get_phase(self):
         return self.phase
@@ -65,8 +74,9 @@ def set_pointer_to_sourcePiece_CsvDataSet_labels(self, pointer_to_sourcePiece_Cs
 class CsvDataSet():
     def __init__(self, csv_path, output_dir: str, batch_size, num_of_features, num_of_labels, headers_row: list):
         self.csv_path = csv_path
+        assert self.csv_path.endswith('.csv'), "csv_path should end with '.csv'"
+        assert os.path.exists(self.csv_path), "csv_path does not exist"
         self.output_dir = output_dir
-        #Todo throw exception if csv file does not exist and file path ends with .csv
         self.batch_size = batch_size
         self.num_of_features = num_of_features
         self.num_of_labels = num_of_labels
@@ -87,10 +97,12 @@ def get_num_of_labels(self):
     def set_num_of_labels(self, num_of_labels):
         self.num_of_labels = num_of_labels
 
-    def get_total_num_of_batches(self):
+    def get_total_num_of_batches(self): 
+        # ! Not always using the whole csv! (should be calculated by the source pieces offsets)
         return ceil(pd.read_csv(self.csv_path).shape[0] / self.batch_size)
 
     def get_total_num_of_samples(self):
+        # ! Not always using the whole csv! (should be calculated by the source pieces offsets)
         return pd.read_csv(self.csv_path).shape[0] + 1 # +1 for sample 0 which is the header row
     
     def get_headers_row(self):
@@ -102,7 +114,7 @@ def generate_source_piece_ds(self, source_name : str, batch_size: int, phase : s
         assert phase == PHASE_TRAINING_STR or phase == PHASE_PREDICTION_STR , "phase should be either 'training' or 'prediction'"
         assert (starting_offset + num_of_batches * batch_size) <= self.get_total_num_of_samples(), "starting_offset + num_of_batches * batch_size exceeds the total number of samples in the csv file"
         assert nerltensor_type in NERLTENSOR_TYPE_LIST, "nerltensor_type is not in NERLTENSOR_TYPE_LIST"
-        return SourcePieceDS(self, source_name, batch_size, phase, starting_offset, num_of_batches, nerltensor_type)
+        return SourcePieceDS(self, source_name, batch_size, phase, starting_offset, num_of_batches, nerltensor_type, self.num_of_features, self.num_of_labels)
         
     def generate_source_piece_ds_csv_file(self, source_piece_ds_inst: SourcePieceDS, phase : str):
         skip_rows = source_piece_ds_inst.get_starting_offset()
diff --git a/src_py/apiServer/nerl_model_db.py b/src_py/apiServer/nerl_model_db.py
index bd048770..6539381f 100644
--- a/src_py/apiServer/nerl_model_db.py
+++ b/src_py/apiServer/nerl_model_db.py
@@ -43,7 +43,7 @@ def create_batch(self, batch_id, source_name, tensor_data, duration, distributed
         self.batches_ts_dict[batch_timestamp] = self.batches_dict[(source_name, batch_id)]
 
     def get_batch(self, source_name, batch_id):
-        if (source_name, batch_id) in self.batches_dict:
+        if (source_name, batch_id) in self.batches_dict.keys():
             return self.batches_dict[(source_name, batch_id)]
         return None
     
@@ -63,6 +63,12 @@ def get_batches_ts_tensor_data_dict(self):
             batches_ts_tensor_data_dict[batch_db.batch_timestamp] = batch_db.tensor_data
         return batches_ts_tensor_data_dict
     
+    def get_batches_batchid_tensor_data_dict(self):
+        batches_batchid_tensor_data_dict = {}
+        for batch_db in self.batches_ts_dict.values():
+            batches_batchid_tensor_data_dict[(batch_db.get_source_name(), int(batch_db.batch_id))] = batch_db.tensor_data
+        return batches_batchid_tensor_data_dict
+    
     def get_batches_dict(self):
         return self.batches_dict
 class ClientModelDB():
diff --git a/src_py/apiServer/stats.py b/src_py/apiServer/stats.py
index 9e7bd64d..9664f18c 100644
--- a/src_py/apiServer/stats.py
+++ b/src_py/apiServer/stats.py
@@ -138,7 +138,7 @@ def get_min_loss(self , plot : bool = False , saveToFile : bool = False): # Todo
         #     plt.grid(visible=True, which='minor', linestyle='-', alpha=0.7)
         #     plt.show()
         #     plt.savefig(f'{EXPERIMENT_RESULTS_PATH}/{self.experiment.name}/Training/Loss_graph.png')
-         
+        
     def expend_labels_df(self, df):
         assert self.phase == PHASE_PREDICTION_STR, "This function is only available for predict phase"
         temp_list = list(range(df.shape[1])) 
@@ -148,20 +148,34 @@ def expend_labels_df(self, df):
         assert df.shape[1] == 2 * num_of_labels, "Error in expend_labels_df function"
         return df
 
+    # TODO Fix for round robin casting policy (AND FOR RANDOM TOO)
+    def attach_true_labels(self, true_labels_df, predicted_labels_df, num_of_workers, worker_idx, rr_flag = False):
+        for _ in range(len(predicted_labels_df)):
+            if rr_flag: # Skip certain rows according to the worker index
+                worker_true_labels = true_labels_df.iloc[worker_idx::num_of_workers].reset_index(drop=True)
+            else:
+                worker_true_labels = true_labels_df
+        predicted_labels_df['TrueLabel'] = worker_true_labels
+        return predicted_labels_df
+
     def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False , saveToFile : bool = False): 
         assert self.experiment_flow_type == "classification", "This function is only available for classification experiments" 
         assert self.phase == PHASE_PREDICTION_STR, "This function is only available for predict phase"   
         sources_pieces_list = self.experiment_phase.get_sources_pieces()
         workers_model_db_list = self.nerl_model_db.get_workers_model_db_list()
+        num_of_workers = len(workers_model_db_list)
         confusion_matrix_source_dict = {}
         confusion_matrix_worker_dict = {}
+        # TODO Add a check - if the source policy is 1, then round robin flag (rr_flag) should be True and passed in attach_true_labels function
+        rr_flag = True
+
         for source_piece_inst in sources_pieces_list:
             sourcePiece_csv_labels_path = source_piece_inst.get_pointer_to_sourcePiece_CsvDataSet_labels()
             df_actual_labels = pd.read_csv(sourcePiece_csv_labels_path)
             num_of_labels = df_actual_labels.shape[1]
             header_list = range(num_of_labels) 
             df_actual_labels.columns = header_list
-            df_actual_labels = self.expend_labels_df(df_actual_labels)
+            # df_actual_labels = self.expend_labels_df(df_actual_labels)
             #print(df_actual_labels)
             source_name = source_piece_inst.get_source_name()
 
@@ -171,43 +185,45 @@ def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False
             batch_size = source_piece_inst.get_batch_size()
             for worker_db in workers_model_db_list:
                 worker_name = worker_db.get_worker_name()
+                worker_idx = int(min(worker_db.get_batches_dict().keys(), key=lambda x: int(x[1]))[1])
+                # print(f'Worker {worker_name} index: {worker_idx}')
                 if worker_name not in target_workers:
                     continue
-                df_worker_labels = df_actual_labels.copy()
-                total_batches_per_source = worker_db.get_total_batches_per_source(source_name)
-                for batch_id in range(total_batches_per_source):
+                df_worker_labels = pd.DataFrame(np.zeros((batch_size * worker_db.get_total_batches_per_source(source_name), num_of_labels)))
+                # print(f'Worker {worker_name} Got {total_batches_per_source} batches (={batch_size * total_batches_per_source} samples) from {source_name}')
+                for _, batch_id in worker_db.get_batches_dict().keys(): # !!!!!!!!!!!!!!!!!
                     batch_db = worker_db.get_batch(source_name, str(batch_id))
                     if not batch_db: # if batch is missing
+                        print(f'{worker_name} missed batch {batch_id}')
                         if not self.missed_batches_warning_msg:
                             LOG_WARNING(f"missed batches")
                             self.missed_batches_warning_msg = True
                         starting_offset = source_piece_inst.get_starting_offset()
                         df_worker_labels.iloc[batch_id * batch_size: (batch_id + 1) * batch_size, num_of_labels:] = None # set the actual label to None for the predict labels in the df 
                         worker_missed_batches[(worker_name, source_name, str(batch_id))] = (starting_offset + batch_id * batch_size, batch_size)  # save the missing batch
-                
                 df_worker_labels = df_worker_labels.dropna()
-                # print(df_worker_labels)
-                for batch_id in range(total_batches_per_source):
+                for _, batch_id in worker_db.get_batches_dict().keys():
                     batch_db = worker_db.get_batch(source_name, str(batch_id))
                     if batch_db:
                         # counter = according indexs of array 
                         # cycle = according indexs of panadas (with jump)
-                        cycle = int(batch_db.get_batch_id())
                         tensor_data = batch_db.get_tensor_data()
-                        # print(f"tensor_data shape: {tensor_data.shape}")
                         tensor_data = tensor_data.reshape(batch_size, num_of_labels) 
-                        #print(df_worker_labels)
-                        #print(tensor_data)
-                        start_index = cycle * batch_size
-                        end_index = (cycle + 1) * batch_size
-                        df_worker_labels.iloc[start_index:end_index, num_of_labels:] = None # Fix an issue of pandas of incompatible dtype
-                        df_worker_labels.iloc[start_index:end_index, num_of_labels:] = tensor_data
-                        # print(df_worker_labels)
-
+                        start_index_pred = (int(batch_id) * batch_size) 
+                        end_index_pred = ((int(batch_id) + 1) * batch_size)
+                        if start_index_pred >= df_worker_labels.shape[0]:
+                            start_index_pred -= (df_worker_labels.shape[0] * (num_of_workers - 1))
+                            end_index_pred -= (df_worker_labels.shape[0] * (num_of_workers - 1))
+                        # print(f'The following indexes {start_index_pred}-{end_index_pred} will be filled with the tensor data')
+                        df_worker_labels.iloc[start_index_pred:end_index_pred, :num_of_labels] = None # Fix an issue of pandas of incompatible dtype
+                        df_worker_labels.iloc[start_index_pred:end_index_pred, :num_of_labels] = tensor_data
+                df_worker_labels = self.attach_true_labels(df_actual_labels, df_worker_labels, num_of_workers, worker_idx)
+                # print(f'Actual Labels (Column 0) & Predict Labels (Column 1 for worker: {worker_name}')
+                # display(df_worker_labels)
                 if len(self.headers_list) == 1:
                     class_name = self.headers_list[0]
-                    actual_labels = df_worker_labels.iloc[:, :num_of_labels].values.flatten().tolist()
-                    predict_labels = df_worker_labels.iloc[:, num_of_labels:].values.flatten().tolist()
+                    actual_labels = df_worker_labels.iloc[:, num_of_labels:].values.flatten().tolist()
+                    predict_labels = df_worker_labels.iloc[:, :num_of_labels].values.flatten().tolist()
                     confusion_matrix = metrics.confusion_matrix(actual_labels, predict_labels)
                     confusion_matrix_source_dict[(source_name, worker_name, class_name)] = confusion_matrix
                     if (worker_name, class_name) not in confusion_matrix_worker_dict:
@@ -217,10 +233,10 @@ def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False
                     
                 else: # Multi-Class
                     # Take 2 list from the df, one for the actual labels and one for the predict labels to build the confusion matrix
-                    max_column_predict_index = df_worker_labels.iloc[:, num_of_labels:].idxmax(axis=1) 
+                    max_column_predict_index = df_worker_labels.iloc[:, :num_of_labels].idxmax(axis=1) 
                     max_column_predict_index = max_column_predict_index.tolist() 
                     max_column_predict_index = [int(predict_index) - num_of_labels for predict_index in max_column_predict_index] # fix the index to original labels index
-                    max_column_labels_index = df_worker_labels.iloc[:, :num_of_labels].idxmax(axis=1)
+                    max_column_labels_index = df_worker_labels.iloc[:, num_of_labels:].idxmax(axis=1)
                     max_column_labels_index = max_column_labels_index.tolist()
                     
                     # building confusion matrix for each class
@@ -239,32 +255,42 @@ def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False
         if plot:
             workers = sorted(list({tup[0] for tup in confusion_matrix_worker_dict.keys()}))
             classes = sorted(list({tup[1] for tup in confusion_matrix_worker_dict.keys()}))
-            fig, ax = plt.subplots(nrows=len(workers), ncols=len(classes),figsize=(4*len(classes),4*len(workers)),dpi=140)
-            if len(classes) > 1:
-                for i , worker in enumerate(workers): 
-                    for j , pred_class in enumerate(classes):
-                        conf_mat = confusion_matrix_worker_dict[(worker , pred_class)]
-                        # print(f"conf_mat: {conf_mat}")
-                        heatmap = sns.heatmap(data=conf_mat ,ax=ax[i,j], annot=True , fmt="d", cmap='Blues',annot_kws={"size": 8}, cbar_kws={'pad': 0.1})
+            if len(workers) > 1:
+                fig, ax = plt.subplots(nrows=len(workers), ncols=len(classes),figsize=(4*len(classes), 4*len(workers)), dpi=140)
+                if len(classes) > 1:
+                    for i , worker in enumerate(workers): 
+                        for j , pred_class in enumerate(classes):
+                            conf_mat = confusion_matrix_worker_dict[(worker , pred_class)]
+                            heatmap = sns.heatmap(data=conf_mat ,ax=ax[i,j], annot=True , fmt="d", cmap='Blues',annot_kws={"size": 8}, cbar_kws={'pad': 0.1})
+                            cbar = heatmap.collections[0].colorbar
+                            cbar.ax.tick_params(labelsize = 8)
+                            ax[i, j].set_title(f"{worker} , Class '{pred_class}'" , fontsize=12)
+                            ax[i, j].tick_params(axis='both', which='major', labelsize=8) 
+                            ax[i, j].set_xlabel("Predicted Label" , fontsize=8)
+                            ax[i, j].set_ylabel("True Label" , fontsize=8)
+                            ax[i, j].set_aspect('equal')
+                else:
+                    for i, worker in enumerate(workers):
+                        conf_mat = confusion_matrix_worker_dict[(worker , classes[0])]
+                        heatmap = sns.heatmap(data=conf_mat ,ax=ax[i], annot=True , fmt="d", cmap='Blues',annot_kws={"size": 8}, cbar_kws={'pad': 0.1})
                         cbar = heatmap.collections[0].colorbar
                         cbar.ax.tick_params(labelsize = 8)
-                        ax[i, j].set_title(f"{worker} , Class '{pred_class}'" , fontsize=12)
-                        ax[i, j].tick_params(axis='both', which='major', labelsize=8) 
-                        ax[i, j].set_xlabel("Predicted Label" , fontsize=8)
-                        ax[i, j].set_ylabel("True Label" , fontsize=8)
-                        ax[i, j].set_aspect('equal')
+                        ax[i].set_title(f"{worker} , Class '{classes[0]}'" , fontsize=12)
+                        ax[i].tick_params(axis='both', which='major', labelsize=8) 
+                        ax[i].set_xlabel("Predicted Label" , fontsize=8)
+                        ax[i].set_ylabel("True Label" , fontsize=8)
+                        ax[i].set_aspect('equal')
+                fig.subplots_adjust(wspace=0.4 , hspace=0.4)
             else:
-                for i, worker in enumerate(workers):
-                    conf_mat = confusion_matrix_worker_dict[(worker , classes[0])]
-                    heatmap = sns.heatmap(data=conf_mat ,ax=ax[i], annot=True , fmt="d", cmap='Blues',annot_kws={"size": 8}, cbar_kws={'pad': 0.1})
-                    cbar = heatmap.collections[0].colorbar
-                    cbar.ax.tick_params(labelsize = 8)
-                    ax[i].set_title(f"{worker} , Class '{classes[0]}'" , fontsize=12)
-                    ax[i].tick_params(axis='both', which='major', labelsize=8) 
-                    ax[i].set_xlabel("Predicted Label" , fontsize=8)
-                    ax[i].set_ylabel("True Label" , fontsize=8)
-                    ax[i].set_aspect('equal')
-            fig.subplots_adjust(wspace=0.4 , hspace=0.4)
+                plt.figure(figsize=(4*len(classes), 3), dpi=140)
+                conf_mat = confusion_matrix_worker_dict[(workers[0] , classes[0])]
+                heatmap = sns.heatmap(data=conf_mat , annot=True , fmt="d", cmap='Blues',annot_kws={"size": 8}, cbar_kws={'pad': 0.1})
+                cbar = heatmap.collections[0].colorbar
+                cbar.ax.tick_params(labelsize = 8)
+                plt.title(f"{workers[0]} , Class '{classes[0]}'" , fontsize=12)
+                plt.xlabel("Predicted Label" , fontsize=8)
+                plt.ylabel("True Label" , fontsize=8)
+                plt.tick_params(axis='both', which='major', labelsize=8)
             plt.show()
                 
         
diff --git a/src_py/apiServer/stats_aec.py b/src_py/apiServer/stats_aec.py
index b0fc306f..97c5acb9 100644
--- a/src_py/apiServer/stats_aec.py
+++ b/src_py/apiServer/stats_aec.py
@@ -18,6 +18,10 @@ class StatsAEC():
     
     def __init__(self, stats: Stats):
         self.stats = stats
+        self.csv_path = stats.experiment_phase.get_sources_pieces()[0].get_csv_dataset_parent().get_csv_path()
+        self.num_of_overall_samples = stats.experiment_phase.get_sources_pieces()[0].get_csv_dataset_parent().get_total_num_of_samples()
+        self.loss_values_pairs = {}
+        self.source_pieces = stats.experiment_phase.get_sources_pieces()
         
         
     def get_aec_loss(self, plot=False):
@@ -34,7 +38,7 @@ def get_aec_loss(self, plot=False):
             
         for worker_name in loss_dict:
             loss_dict[worker_name] = [float(arr) for sublist in loss_dict[worker_name] for arr in sublist]
-            loss_dict[worker_name] += [None] * (max_batches - len(loss_dict[worker_name]))
+            loss_dict[worker_name] += [None] * (max_batches - len(loss_dict[worker_name]))            
 
         df = pd.DataFrame(loss_dict)
         self.loss_ts_pd = df
@@ -56,24 +60,27 @@ def get_aec_boundaries(self, plot=False):
         
         for worker_db in workers_model_db_list:
             worker_name = worker_db.get_worker_name()
-            batches_ts_tensor_data_dict = worker_db.get_batches_ts_tensor_data_dict()
-            sorted_batches_ts_tensor_data_dict = dict(sorted(batches_ts_tensor_data_dict.items()))
-            upper_boundaries_dict[worker_name] = [sorted_batches_ts_tensor_data_dict[key][1] for key in sorted(sorted_batches_ts_tensor_data_dict)]
-            lower_boundaries_dict[worker_name] = [sorted_batches_ts_tensor_data_dict[key][2] for key in sorted(sorted_batches_ts_tensor_data_dict)]
+            batches_ts_tensor_data_dict = worker_db.get_batches_batchid_tensor_data_dict()
+            sorted_keys = sorted(batches_ts_tensor_data_dict.keys())
+            upper_boundaries_dict[worker_name] = [batches_ts_tensor_data_dict[key][1] for key in sorted_keys]
+            lower_boundaries_dict[worker_name] = [batches_ts_tensor_data_dict[key][2] for key in sorted_keys]
             
         for worker_name in upper_boundaries_dict:
             upper_boundaries_dict[worker_name] = [float(arr) for sublist in upper_boundaries_dict[worker_name] for arr in sublist]
-            upper_boundaries_dict[worker_name] += [None] * (max_batches - len(upper_boundaries_dict[worker_name]))
-        for worker_name in lower_boundaries_dict:
             lower_boundaries_dict[worker_name] = [float(arr) for sublist in lower_boundaries_dict[worker_name] for arr in sublist]
+            upper_boundaries_dict[worker_name] += [None] * (max_batches - len(upper_boundaries_dict[worker_name]))
             lower_boundaries_dict[worker_name] += [None] * (max_batches - len(lower_boundaries_dict[worker_name]))
 
         df_upper = pd.DataFrame(upper_boundaries_dict).sort_index(axis=1)
         df_lower = pd.DataFrame(lower_boundaries_dict).sort_index(axis=1)
         
         # Take 10% of the data for better visualization
-        df_upper = df_upper.iloc[::len(df_upper) // 100, :]
-        df_lower = df_lower.iloc[::len(df_lower) // 100, :]
+        if len(df_upper) > 100:
+            df_upper = df_upper.iloc[::len(df_upper) // 100, :]
+            df_lower = df_lower.iloc[::len(df_lower) // 100, :]
+        else:
+            df_upper = df_upper.iloc[::len(df_upper), :]
+            df_lower = df_lower.iloc[::len(df_lower), :]
         
         if plot:
             for worker_name in df_upper:
@@ -94,17 +101,111 @@ def get_aec_boundaries(self, plot=False):
                 plt.show()
         return df_upper, df_lower
         
-    
-    def get_average_anomaly_error(self):
+        
+    def get_labels(self):
+        source_piece_labels_dict = {}
+        for source_piece in self.source_pieces:
+            number_of_samples = source_piece.get_num_of_batches() * source_piece.get_batch_size()
+            skip_rows = source_piece.get_starting_offset()
+            df = pd.read_csv(self.csv_path, skiprows = skip_rows, nrows = number_of_samples, header = None)
+            labels_df = df.iloc[:, int(source_piece.get_num_of_features()) : int(source_piece.get_num_of_features()) + int(source_piece.get_num_of_labels())]
+            source_piece_labels_dict[source_piece.get_source_name()] = labels_df
+        # set volimn name to 'Label' for better visualization
+        for source_name in source_piece_labels_dict.keys():
+            source_piece_labels_dict[source_name].columns = ['Label']
+        return source_piece_labels_dict
+            
+    def plot_errors(self):
+        labels_by_source_piece = self.get_labels()
+        loss_values_dict = self.get_loss_batchid_per_worker_by_source()
+        batchesIDs_per_worker = {}
+        for worker in loss_values_dict.keys():
+            self.loss_values_pairs[worker] = {}
+            for source_name in loss_values_dict[worker].keys():
+                source_piece_labels_df = labels_by_source_piece[source_name]
+                loss_batchid_list = []
+                for batchID, loss_tensor in loss_values_dict[worker][source_name]:
+                    for i in range(self.batch_size):
+                        loss_batchid_list.append((batchID, (i + int(batchID) * self.batch_size) % source_piece_labels_df.shape[0], float(loss_tensor[i]))) # Modulo for epochs_num > 1
+                loss_labels_pairs = []
+                batchesIDs = np.unique([batchID for batchID, _, _ in loss_batchid_list])
+                batchesIDs_per_worker[worker] = batchesIDs
+                print(f'Worker {worker} batches IDs list: {batchesIDs}')
+                for batchID, sampleID, loss_val in loss_batchid_list:
+                    loss_labels_pairs.append((loss_val, float(source_piece_labels_df['Label'].iloc[sampleID]))) # Modulo to handle the case of more than one epoch
+                self.loss_values_pairs[worker][source_name] = loss_labels_pairs
+            total_loss_labels_pairs = []
+            for source_name in self.loss_values_pairs[worker].keys():
+                total_loss_labels_pairs.extend(self.loss_values_pairs[worker][source_name])
+            anomalies_loss = [loss for loss, label in total_loss_labels_pairs if label == 1.0]
+            normal_loss = [loss for loss, label in total_loss_labels_pairs if label == 0.0]
+            _, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8)) # ! Current anomaly detection AEC has only 2 classes (anomaly/normal)
+            min_loss = min(min(anomalies_loss), min(normal_loss))
+            max_loss = 0.2 # ! FOR CURRENT EXPERIMENT VISUALIZATION
+            # if max_loss > 1: # To handle the case of the loss being large at the beginning of the training (for better visualization)
+            #     max_loss -= 1
+            bins = np.linspace(min_loss, max_loss, 100)
+
+            ax1.hist(anomalies_loss, bins=bins, color='red', alpha=0.7, label='Anomalies')
+            ax1.set_title(f'{worker} Anomalies Loss Values')
+            ax1.set_xlabel('Loss')
+            ax1.set_ylabel('Count')
+            ax1.set_xlim([min_loss, max_loss])
+            ax1.legend()
+
+            ax2.hist(normal_loss, bins=bins, color='blue', alpha=0.7, label='Normal')
+            ax2.set_title(f'{worker} Normal Loss Values')
+            ax2.set_xlabel('Loss')
+            ax2.set_ylabel('Count')
+            ax2.set_xlim([min_loss, max_loss])
+            ax2.legend()
+            
+            plt.tight_layout()
+            plt.show()
+        # Check weather the workers got the same batchesIDs
+        for worker in batchesIDs_per_worker.keys():
+            for other_worker in batchesIDs_per_worker.keys():
+                if worker != other_worker:
+                    print(f'Workers {worker} and {other_worker} have the same batches IDs: {np.intersect1d(batchesIDs_per_worker[worker], batchesIDs_per_worker[other_worker])}')
+
+
+    def plot_average_aec_errors(self):
+        plt.figure(figsize=(12, 8))
+        for worker in self.loss_values_pairs.keys():
+            anomaly_loss_values = []
+            normal_loss_values = []
+            for source_name in self.loss_values_pairs[worker]:
+                for loss, label in self.loss_values_pairs[worker][source_name]:
+                    if label == 1.0:
+                        anomaly_loss_values.append(loss)
+                    elif label == 0.0:
+                        normal_loss_values.append(loss)
+                    else:
+                        print("Unknown Label Value (Should be 1.0/0.0")
+            avg_anomaly_loss = np.mean(anomaly_loss_values)
+            avg_normal_loss = np.mean(normal_loss_values)
+            plt.bar(f'{worker} Anomaly', avg_anomaly_loss, color='tab:blue')
+            plt.bar(f'{worker} Normal', avg_normal_loss, color='tab:orange')
+
+        plt.title("Average Errors Per Worker")
+        plt.ylabel("Average Loss")
+        plt.tight_layout()
+        plt.show()
+
+
+    def get_loss_batchid_per_worker_by_source(self) -> dict:
         workers_model_db_list = self.stats.nerl_model_db.get_workers_model_db_list()
-        # labels = self.get_labels() # TODO ADD THIS FUNCTION
         loss_values_dict = {}
         for worker_db in workers_model_db_list:
             worker_name = worker_db.get_worker_name()
-            batches_ts_tensor_data_dict = worker_db.get_batches_ts_tensor_data_dict()
-            sorted_batches_ts_tensor_data_dict = dict(sorted(batches_ts_tensor_data_dict.items()))
-            loss_values_dict[worker_name] = [sorted_batches_ts_tensor_data_dict[key][3:] for key in sorted(sorted_batches_ts_tensor_data_dict)]
-            
+            batches_batchid_tensor_data_dict = worker_db.get_batches_batchid_tensor_data_dict()
+            worker_source_loss_dict = {}
+            for source_name, batchID in batches_batchid_tensor_data_dict.keys():
+                if source_name not in worker_source_loss_dict:
+                    worker_source_loss_dict[source_name] = []
+                worker_source_loss_dict[source_name].append((batchID, batches_batchid_tensor_data_dict[(source_name, batchID)][3:]))
+            loss_values_dict[worker_name] = worker_source_loss_dict
+        self.batch_size = len(batches_batchid_tensor_data_dict[(source_name, batchID)][3:])
         return loss_values_dict
     
     def get_false_alarm_rate(self, conf_mats_workers):
@@ -112,6 +213,7 @@ def get_false_alarm_rate(self, conf_mats_workers):
         for worker_name, _ in conf_mats_workers.items():
             conf_mat = conf_mats_workers[worker_name]
             false_alarm_rate_dict[worker_name] = conf_mat[0][1] / (conf_mat[0][1] + conf_mat[0][0])
+        print(f'Average False Alarm Rate: {np.mean(list(false_alarm_rate_dict.values()))*100:.4f}%')
         return false_alarm_rate_dict
     
     def get_detection_rate(self, conf_mats_workers):
@@ -119,4 +221,6 @@ def get_detection_rate(self, conf_mats_workers):
         for worker_name, _ in conf_mats_workers.items():
             conf_mat = conf_mats_workers[worker_name]
             detection_rate_dict[worker_name] = conf_mat[1][1] / (conf_mat[1][1] + conf_mat[1][0])
+        print(f'Average Detection Rate: {np.mean(list(detection_rate_dict.values()))*100:.4f}%')
+        print(f'Average Missing Alarm Rate: {100 - np.mean(list(detection_rate_dict.values()))*100:.4f}%') # Optional Print
         return detection_rate_dict

From 2993be169adca82fdd0dd6a0edc3733a35651109 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Fri, 2 Aug 2024 18:54:51 +0000
Subject: [PATCH 10/50] [AEC_Exp] WIP

---
 .../conn_AEC_4d_4r_2s_4c_4w.json              |   9 ++
 .../dc_AEC_4d_4r_2s_4c_4w.json                | 150 ++++++++++++++++++
 .../exp_AEC_4d_4r_2s_4c_4w.json               |  54 +++++++
 src_py/apiServer/stats.py                     |  22 +--
 src_py/apiServer/stats_aec.py                 |  10 +-
 5 files changed, 231 insertions(+), 14 deletions(-)
 create mode 100644 inputJsonsFiles/ConnectionMap/conn_AEC_4d_4r_2s_4c_4w.json
 create mode 100644 inputJsonsFiles/DistributedConfig/dc_AEC_4d_4r_2s_4c_4w.json
 create mode 100644 inputJsonsFiles/experimentsFlow/exp_AEC_4d_4r_2s_4c_4w.json

diff --git a/inputJsonsFiles/ConnectionMap/conn_AEC_4d_4r_2s_4c_4w.json b/inputJsonsFiles/ConnectionMap/conn_AEC_4d_4r_2s_4c_4w.json
new file mode 100644
index 00000000..68c1c360
--- /dev/null
+++ b/inputJsonsFiles/ConnectionMap/conn_AEC_4d_4r_2s_4c_4w.json
@@ -0,0 +1,9 @@
+{
+    "connectionsMap":
+    {
+        "r1":["mainServer","c1","s1", "r2"],
+        "r2":["r3"],
+        "r3":["s2", "r4"],
+        "r4":["c2", "c3", "c4", "r1"]
+    }
+}
diff --git a/inputJsonsFiles/DistributedConfig/dc_AEC_4d_4r_2s_4c_4w.json b/inputJsonsFiles/DistributedConfig/dc_AEC_4d_4r_2s_4c_4w.json
new file mode 100644
index 00000000..e7a05be3
--- /dev/null
+++ b/inputJsonsFiles/DistributedConfig/dc_AEC_4d_4r_2s_4c_4w.json
@@ -0,0 +1,150 @@
+{
+    "nerlnetSettings": {
+        "frequency": "300",
+        "batchSize": "100"
+    },
+    "mainServer": {
+        "port": "8900",
+        "args": ""
+    },
+    "apiServer": {
+        "port": "8901",
+        "args": ""
+    },
+    "devices": [
+        {
+            "name": "C0VM0",
+            "ipv4": "10.0.0.5",
+            "entities": "apiServer,mainServer,c1,r1,s1"
+        },
+        {
+            "name": "MinionMS",
+            "ipv4": "10.0.0.31",
+            "entities": "c2,r2,s2"
+        },
+        {
+            "name": "Minion0",
+            "ipv4": "10.0.0.17",
+            "entities": "c3,r3"
+        },
+        {
+            "name": "Minion1",
+            "ipv4": "10.0.0.18",
+            "entities": "c4,r4"
+        }
+    ],
+    "routers": [
+        {
+            "name": "r1",
+            "port": "8086",
+            "policy": "0"
+        },
+        {
+            "name": "r2",
+            "port": "8087",
+            "policy": "0"
+        },
+        {
+            "name": "r3",
+            "port": "8088",
+            "policy": "0"
+        },
+        {
+            "name": "r4",
+            "port": "8089",
+            "policy": "0"
+        }
+    ],
+    "sources": [
+        {
+            "name": "s1",
+            "port": "8085",
+            "frequency": "500",
+            "policy": "0",
+            "epochs": "1",
+            "type": "0"
+        },
+        {
+            "name": "s2",
+            "port": "8085",
+            "frequency": "500",
+            "policy": "0",
+            "epochs": "1",
+            "type": "0"
+        }
+    ],
+    "clients": [
+        {
+            "name": "c1",
+            "port": "8083",
+            "workers": "w1"
+        },
+        {
+            "name": "c2",
+            "port": "8083",
+            "workers": "w2"
+        },
+        {
+            "name": "c3",
+            "port": "8083",
+            "workers": "w3"
+        },
+        {
+            "name": "c4",
+            "port": "8083",
+            "workers": "w4"
+        }
+    ],
+    "workers": [
+        {
+            "name": "w1",
+            "model_sha": "d8df752e0a2e8f01de8f66e9cec941cdbc65d144ecf90ab7713e69d65e7e82aa"
+        },
+        {
+            "name": "w2",
+            "model_sha": "d8df752e0a2e8f01de8f66e9cec941cdbc65d144ecf90ab7713e69d65e7e82aa"
+        },
+        {
+            "name": "w3",
+            "model_sha": "d8df752e0a2e8f01de8f66e9cec941cdbc65d144ecf90ab7713e69d65e7e82aa"
+        },
+        {
+            "name": "w4",
+            "model_sha": "d8df752e0a2e8f01de8f66e9cec941cdbc65d144ecf90ab7713e69d65e7e82aa"
+        }
+    ],
+    "model_sha": {
+        "d8df752e0a2e8f01de8f66e9cec941cdbc65d144ecf90ab7713e69d65e7e82aa": {
+            "modelType": "9",
+            "_doc_modelType": " nn:0 | approximation:1 | classification:2 | forecasting:3 | image-classification:4 | text-classification:5 | text-generation:6 | auto-association:7 | autoencoder:8 | ae-classifier:9 |",
+            "modelArgs": "k=1.9,alpha=0.4,use_ema_only=1",
+            "layersSizes": "10,128,64,32,16,32,64,128,10",
+            "_doc_layersSizes": "List of postive integers [L0, L1, ..., LN]",
+            "layerTypesList": "1,3,3,3,3,3,3,3,3",
+            "_doc_LayerTypes": " Default:0 | Scaling:1 | CNN:2 | Perceptron:3 | Pooling:4 | Probabilistic:5 | LSTM:6 | Reccurrent:7 | Unscaling:8 |",
+            "layers_functions": "1,8,8,8,8,8,8,8,8",
+            "_doc_layers_functions_activation": " Threshold:1 | Sign:2 | Logistic:3 | Tanh:4 | Linear:5 | ReLU:6 | eLU:7 | SeLU:8 | Soft-plus:9 | Soft-sign:10 | Hard-sigmoid:11 |",
+            "_doc_layer_functions_pooling": " none:1 | Max:2 | Avg:3 |",
+            "_doc_layer_functions_probabilistic": " Binary:1 | Logistic:2 | Competitive:3 | Softmax:4 |",
+            "_doc_layer_functions_scaler": " none:1 | MinMax:2 | MeanStd:3 | STD:4 | Log:5 |",
+            "lossMethod": "2",
+            "_doc_lossMethod": " SSE:1 | MSE:2 | NSE:3 | MinkowskiE:4 | WSE:5 | CEE:6 |",
+            "lr": "0.001",
+            "_doc_lr": "Positve float",
+            "epochs": "1",
+            "_doc_epochs": "Positve Integer",
+            "optimizer": "5",
+            "_doc_optimizer": " GD:0 | CGD:1 | SGD:2 | QuasiNeuton:3 | LVM:4 | ADAM:5 |",
+            "optimizerArgs": "",
+            "_doc_optimizerArgs": "String",
+            "infraType": "0",
+            "_doc_infraType": " opennn:0 | wolfengine:1 |",
+            "distributedSystemType": "0",
+            "_doc_distributedSystemType": " none:0 | fedClientAvg:1 | fedServerAvg:2 |",
+            "distributedSystemArgs": "",
+            "_doc_distributedSystemArgs": "String",
+            "distributedSystemToken": "none",
+            "_doc_distributedSystemToken": "Token that associates distributed group of workers and parameter-server"
+        }
+    }
+}
\ No newline at end of file
diff --git a/inputJsonsFiles/experimentsFlow/exp_AEC_4d_4r_2s_4c_4w.json b/inputJsonsFiles/experimentsFlow/exp_AEC_4d_4r_2s_4c_4w.json
new file mode 100644
index 00000000..e5223680
--- /dev/null
+++ b/inputJsonsFiles/experimentsFlow/exp_AEC_4d_4r_2s_4c_4w.json
@@ -0,0 +1,54 @@
+{
+    "experimentName": "anomaly_detection_skab",
+    "experimentType": "classification",
+    "batchSize": 100,
+    "csvFilePath": "/tmp/nerlnet/data/NerlnetData-master/nerlnet/ForestCover/cover_normalized_std.csv",
+    "numOfFeatures": "10",
+    "numOfLabels": "1",
+    "headersNames": "Label",
+    "Phases": 
+    [
+    {
+        "phaseName": "training_phase",
+        "phaseType": "training",
+        "sourcePieces":
+        [
+            {
+            "sourceName": "s1",
+            "startingSample": "0",
+            "numOfBatches": "2500",
+            "workers": "w1,w2",
+            "nerltensorType": "float"
+            },
+            {
+            "sourceName": "s2",
+            "startingSample": "0",
+            "numOfBatches": "2500",
+            "workers": "w3,w4",
+            "nerltensorType": "float"
+            }
+        ]
+    },
+    {
+        "phaseName": "prediction_phase",
+        "phaseType": "prediction",
+        "sourcePieces":
+        [
+            {
+            "sourceName": "s1",
+            "startingSample": "250000",
+            "numOfBatches": "600",
+            "workers": "w1,w2",
+            "nerltensorType": "float"
+            },
+            {
+            "sourceName": "s2",
+            "startingSample": "250000",
+            "numOfBatches": "600",
+            "workers": "w3,w4",
+            "nerltensorType": "float"
+            }
+        ]    
+    }
+]
+}
diff --git a/src_py/apiServer/stats.py b/src_py/apiServer/stats.py
index 9664f18c..bdf27a28 100644
--- a/src_py/apiServer/stats.py
+++ b/src_py/apiServer/stats.py
@@ -189,9 +189,9 @@ def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False
                 # print(f'Worker {worker_name} index: {worker_idx}')
                 if worker_name not in target_workers:
                     continue
-                df_worker_labels = pd.DataFrame(np.zeros((batch_size * worker_db.get_total_batches_per_source(source_name), num_of_labels)))
+                df_worker_labels = pd.DataFrame(np.zeros((batch_size * worker_db.get_total_batches(), num_of_labels)))
                 # print(f'Worker {worker_name} Got {total_batches_per_source} batches (={batch_size * total_batches_per_source} samples) from {source_name}')
-                for _, batch_id in worker_db.get_batches_dict().keys(): # !!!!!!!!!!!!!!!!!
+                for _, batch_id in worker_db.get_batches_dict().keys(): # !!!!!!!!!!!!!!!!! CHANGED
                     batch_db = worker_db.get_batch(source_name, str(batch_id))
                     if not batch_db: # if batch is missing
                         print(f'{worker_name} missed batch {batch_id}')
@@ -209,14 +209,18 @@ def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False
                         # cycle = according indexs of panadas (with jump)
                         tensor_data = batch_db.get_tensor_data()
                         tensor_data = tensor_data.reshape(batch_size, num_of_labels) 
-                        start_index_pred = (int(batch_id) * batch_size) 
-                        end_index_pred = ((int(batch_id) + 1) * batch_size)
-                        if start_index_pred >= df_worker_labels.shape[0]:
-                            start_index_pred -= (df_worker_labels.shape[0] * (num_of_workers - 1))
-                            end_index_pred -= (df_worker_labels.shape[0] * (num_of_workers - 1))
+                        start_index_pred = int(batch_id) * batch_size
+                        end_index_pred = (int(batch_id) + 1) * batch_size
+                        # if start_index_pred >= df_worker_labels.shape[0]: # ! Handle the case of round robin casting policy
+                        #     start_index_pred -= (df_worker_labels.shape[0] * (num_of_workers - 1))
+                        #     end_index_pred -= (df_worker_labels.shape[0] * (num_of_workers - 1))
                         # print(f'The following indexes {start_index_pred}-{end_index_pred} will be filled with the tensor data')
-                        df_worker_labels.iloc[start_index_pred:end_index_pred, :num_of_labels] = None # Fix an issue of pandas of incompatible dtype
-                        df_worker_labels.iloc[start_index_pred:end_index_pred, :num_of_labels] = tensor_data
+                        try:
+                            df_worker_labels.iloc[start_index_pred:end_index_pred, :num_of_labels] = tensor_data
+                        except ValueError:
+                            display(df_worker_labels)
+                            print(f'The following indexes {start_index_pred}-{end_index_pred} caused an error')
+                            exit(0)
                 df_worker_labels = self.attach_true_labels(df_actual_labels, df_worker_labels, num_of_workers, worker_idx)
                 # print(f'Actual Labels (Column 0) & Predict Labels (Column 1 for worker: {worker_name}')
                 # display(df_worker_labels)
diff --git a/src_py/apiServer/stats_aec.py b/src_py/apiServer/stats_aec.py
index 97c5acb9..52c8fa86 100644
--- a/src_py/apiServer/stats_aec.py
+++ b/src_py/apiServer/stats_aec.py
@@ -141,7 +141,7 @@ def plot_errors(self):
             normal_loss = [loss for loss, label in total_loss_labels_pairs if label == 0.0]
             _, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8)) # ! Current anomaly detection AEC has only 2 classes (anomaly/normal)
             min_loss = min(min(anomalies_loss), min(normal_loss))
-            max_loss = 0.2 # ! FOR CURRENT EXPERIMENT VISUALIZATION
+            max_loss = max(np.median(anomalies_loss), np.median(normal_loss))
             # if max_loss > 1: # To handle the case of the loss being large at the beginning of the training (for better visualization)
             #     max_loss -= 1
             bins = np.linspace(min_loss, max_loss, 100)
@@ -163,10 +163,10 @@ def plot_errors(self):
             plt.tight_layout()
             plt.show()
         # Check weather the workers got the same batchesIDs
-        for worker in batchesIDs_per_worker.keys():
-            for other_worker in batchesIDs_per_worker.keys():
-                if worker != other_worker:
-                    print(f'Workers {worker} and {other_worker} have the same batches IDs: {np.intersect1d(batchesIDs_per_worker[worker], batchesIDs_per_worker[other_worker])}')
+        # for worker in batchesIDs_per_worker.keys():
+        #     for other_worker in batchesIDs_per_worker.keys():
+        #         if worker != other_worker:
+        #             print(f'Workers {worker} and {other_worker} have the same batches IDs: {np.intersect1d(batchesIDs_per_worker[worker], batchesIDs_per_worker[other_worker])}')
 
 
     def plot_average_aec_errors(self):

From f891f2d351664434e5e24cb51bd3d80dad72cbc0 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Fri, 2 Aug 2024 23:28:24 +0000
Subject: [PATCH 11/50] [AEC_Exp] WIP RR

---
 .../dc_AEC_1d_2c_1s_4r_4w.json                |  2 +-
 .../exp_AEC_1d_2c_1s_4r_4w.json               | 20 ++++++-
 src_py/apiServer/stats.py                     | 57 ++++++++++++-------
 3 files changed, 54 insertions(+), 25 deletions(-)

diff --git a/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json b/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
index 19bc5761..31969dfc 100644
--- a/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
+++ b/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
@@ -46,7 +46,7 @@
             "port": "8085",
             "frequency": "300",
             "policy": "0",
-            "epochs": "2",
+            "epochs": "1",
             "type": "0"
         }
     ],
diff --git a/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json b/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
index c27e2f52..fba22aec 100644
--- a/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
+++ b/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
@@ -9,15 +9,29 @@
     "Phases": 
     [
     {
-        "phaseName": "training_phase",
+        "phaseName": "training_phase1",
         "phaseType": "training",
         "sourcePieces":
         [
             {
             "sourceName": "s1",
             "startingSample": "0",
-            "numOfBatches": "2500",
-            "workers": "w1,w2",
+            "numOfBatches": "1250",
+            "workers": "w1",
+            "nerltensorType": "float"
+            }
+        ]
+    },
+    {
+        "phaseName": "training_phase2",
+        "phaseType": "training",
+        "sourcePieces":
+        [
+            {
+            "sourceName": "s1",
+            "startingSample": "125000",
+            "numOfBatches": "1250",
+            "workers": "w2",
             "nerltensorType": "float"
             }
         ]
diff --git a/src_py/apiServer/stats.py b/src_py/apiServer/stats.py
index bdf27a28..f86c9e49 100644
--- a/src_py/apiServer/stats.py
+++ b/src_py/apiServer/stats.py
@@ -150,12 +150,14 @@ def expend_labels_df(self, df):
 
     # TODO Fix for round robin casting policy (AND FOR RANDOM TOO)
     def attach_true_labels(self, true_labels_df, predicted_labels_df, num_of_workers, worker_idx, rr_flag = False):
-        for _ in range(len(predicted_labels_df)):
+        for _ in range(predicted_labels_df.shape[0]):
             if rr_flag: # Skip certain rows according to the worker index
                 worker_true_labels = true_labels_df.iloc[worker_idx::num_of_workers].reset_index(drop=True)
             else:
                 worker_true_labels = true_labels_df
         predicted_labels_df['TrueLabel'] = worker_true_labels
+        predicted_labels_df = predicted_labels_df.dropna()
+        display(predicted_labels_df)
         return predicted_labels_df
 
     def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False , saveToFile : bool = False): 
@@ -163,13 +165,13 @@ def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False
         assert self.phase == PHASE_PREDICTION_STR, "This function is only available for predict phase"   
         sources_pieces_list = self.experiment_phase.get_sources_pieces()
         workers_model_db_list = self.nerl_model_db.get_workers_model_db_list()
-        num_of_workers = len(workers_model_db_list)
         confusion_matrix_source_dict = {}
         confusion_matrix_worker_dict = {}
         # TODO Add a check - if the source policy is 1, then round robin flag (rr_flag) should be True and passed in attach_true_labels function
-        rr_flag = True
+        rr_flag = False
 
         for source_piece_inst in sources_pieces_list:
+            num_of_workers = len(source_piece_inst.target_workers)
             sourcePiece_csv_labels_path = source_piece_inst.get_pointer_to_sourcePiece_CsvDataSet_labels()
             df_actual_labels = pd.read_csv(sourcePiece_csv_labels_path)
             num_of_labels = df_actual_labels.shape[1]
@@ -182,52 +184,65 @@ def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False
             # build confusion matrix for each worker
             target_workers = source_piece_inst.get_target_workers()
             worker_missed_batches = {}
-            batch_size = source_piece_inst.get_batch_size()
+            batch_size = int(source_piece_inst.get_batch_size())
             for worker_db in workers_model_db_list:
                 worker_name = worker_db.get_worker_name()
-                worker_idx = int(min(worker_db.get_batches_dict().keys(), key=lambda x: int(x[1]))[1])
-                # print(f'Worker {worker_name} index: {worker_idx}')
+                worker_idx = int(min(worker_db.get_batches_dict().keys(), key=lambda x: int(x[1]))[1]) # ! For round robin casting policy
+                if rr_flag:
+                    print(f'Worker {worker_name} index: {worker_idx}')
                 if worker_name not in target_workers:
                     continue
                 df_worker_labels = pd.DataFrame(np.zeros((batch_size * worker_db.get_total_batches(), num_of_labels)))
-                # print(f'Worker {worker_name} Got {total_batches_per_source} batches (={batch_size * total_batches_per_source} samples) from {source_name}')
-                for _, batch_id in worker_db.get_batches_dict().keys(): # !!!!!!!!!!!!!!!!! CHANGED
+                print(f'Worker {worker_name} Got {worker_db.get_total_batches_per_source(source_name)} batches (={batch_size * worker_db.get_total_batches_per_source(source_name)} samples) from {source_name}')
+                for _, batch_id in worker_db.get_batches_dict().keys(): # ! CHANGED
+                    batch_id = int(batch_id)
                     batch_db = worker_db.get_batch(source_name, str(batch_id))
                     if not batch_db: # if batch is missing
-                        print(f'{worker_name} missed batch {batch_id}')
+                        print(f'{worker_name} missed batch {int(batch_id)}')
                         if not self.missed_batches_warning_msg:
                             LOG_WARNING(f"missed batches")
                             self.missed_batches_warning_msg = True
                         starting_offset = source_piece_inst.get_starting_offset()
-                        df_worker_labels.iloc[batch_id * batch_size: (batch_id + 1) * batch_size, num_of_labels:] = None # set the actual label to None for the predict labels in the df 
+                        df_worker_labels.iloc[batch_id * batch_size: (batch_id + 1) * batch_size, :num_of_labels] = None # set the batch to None if dropped
                         worker_missed_batches[(worker_name, source_name, str(batch_id))] = (starting_offset + batch_id * batch_size, batch_size)  # save the missing batch
-                df_worker_labels = df_worker_labels.dropna()
+                # df_worker_labels = df_worker_labels.dropna()
                 for _, batch_id in worker_db.get_batches_dict().keys():
+                    batch_id = int(batch_id) # Saved originally as string
                     batch_db = worker_db.get_batch(source_name, str(batch_id))
                     if batch_db:
-                        # counter = according indexs of array 
-                        # cycle = according indexs of panadas (with jump)
                         tensor_data = batch_db.get_tensor_data()
                         tensor_data = tensor_data.reshape(batch_size, num_of_labels) 
-                        start_index_pred = int(batch_id) * batch_size
-                        end_index_pred = (int(batch_id) + 1) * batch_size
-                        # if start_index_pred >= df_worker_labels.shape[0]: # ! Handle the case of round robin casting policy
-                        #     start_index_pred -= (df_worker_labels.shape[0] * (num_of_workers - 1))
-                        #     end_index_pred -= (df_worker_labels.shape[0] * (num_of_workers - 1))
-                        # print(f'The following indexes {start_index_pred}-{end_index_pred} will be filled with the tensor data')
+                        start_index_pred = batch_id * batch_size
+                        end_index_pred = (batch_id + 1) * batch_size
+                        if rr_flag: # ! Handle the case of round robin casting policy
+                            start_index_pred = start_index_pred // num_of_workers
+                            end_index_pred = end_index_pred // num_of_workers
+                            if start_index_pred % batch_size != 0 or end_index_pred % batch_size != 0: # ! Handle the case of inproper indexes (should be multiples of batch_size)
+                                # print(f'Before: {start_index_pred}-{end_index_pred}')
+                                start_index_pred -= (start_index_pred % batch_size)
+                                end_index_pred += (end_index_pred % batch_size)
+                                # print(f'After: {start_index_pred}-{end_index_pred}')
+                            # print(f'The following indexes {start_index_pred}-{end_index_pred} will be filled with the tensor data')
+                            # if (start_index_pred, end_index_pred) in handled_indexes:
+                            #     if start_index_pred < (df_worker_labels.shape[0] // num_of_workers): # Determine new indexes 
+                            #         start_index_pred += (df_worker_labels.shape[0] // num_of_workers)
+                            #         end_index_pred += (df_worker_labels.shape[0] // num_of_workers)
+                            #     else:
+                            #         start_index_pred -= (df_worker_labels.shape[0] // num_of_workers)
+                            #         end_index_pred -= (df_worker_labels.shape[0] // num_of_workers)
                         try:
                             df_worker_labels.iloc[start_index_pred:end_index_pred, :num_of_labels] = tensor_data
                         except ValueError:
-                            display(df_worker_labels)
                             print(f'The following indexes {start_index_pred}-{end_index_pred} caused an error')
                             exit(0)
-                df_worker_labels = self.attach_true_labels(df_actual_labels, df_worker_labels, num_of_workers, worker_idx)
+                df_worker_labels = self.attach_true_labels(df_actual_labels, df_worker_labels, num_of_workers, worker_idx, rr_flag)
                 # print(f'Actual Labels (Column 0) & Predict Labels (Column 1 for worker: {worker_name}')
                 # display(df_worker_labels)
                 if len(self.headers_list) == 1:
                     class_name = self.headers_list[0]
                     actual_labels = df_worker_labels.iloc[:, num_of_labels:].values.flatten().tolist()
                     predict_labels = df_worker_labels.iloc[:, :num_of_labels].values.flatten().tolist()
+                    print(f'Pred and True DFs are identical in {len([1 for i, j in zip(actual_labels, predict_labels) if i == j])} out of {len(actual_labels)} samples')
                     confusion_matrix = metrics.confusion_matrix(actual_labels, predict_labels)
                     confusion_matrix_source_dict[(source_name, worker_name, class_name)] = confusion_matrix
                     if (worker_name, class_name) not in confusion_matrix_worker_dict:

From 24c7cf08129a6529121edb64bd906aebda5be41b Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Sat, 3 Aug 2024 13:38:18 +0000
Subject: [PATCH 12/50] [AEC_Exp] Added source policy check in stats

---
 src_py/apiServer/stats.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src_py/apiServer/stats.py b/src_py/apiServer/stats.py
index f86c9e49..605ee855 100644
--- a/src_py/apiServer/stats.py
+++ b/src_py/apiServer/stats.py
@@ -168,7 +168,7 @@ def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False
         confusion_matrix_source_dict = {}
         confusion_matrix_worker_dict = {}
         # TODO Add a check - if the source policy is 1, then round robin flag (rr_flag) should be True and passed in attach_true_labels function
-        rr_flag = False
+
 
         for source_piece_inst in sources_pieces_list:
             num_of_workers = len(source_piece_inst.target_workers)
@@ -180,7 +180,9 @@ def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False
             # df_actual_labels = self.expend_labels_df(df_actual_labels)
             #print(df_actual_labels)
             source_name = source_piece_inst.get_source_name()
-
+            source_policy = globe.components.sources_policy_dict[source_name] # ! NEW
+            if source_policy == 1:
+                rr_flag = True
             # build confusion matrix for each worker
             target_workers = source_piece_inst.get_target_workers()
             worker_missed_batches = {}

From 6903794de291524ffb1caf3b2059570df55a29ff Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Sat, 3 Aug 2024 20:14:13 +0000
Subject: [PATCH 13/50] [AEC_Exp] RR Fixes

---
 src_py/apiServer/definitions.py |  1 +
 src_py/apiServer/stats.py       | 50 ++++++++++++---------------------
 2 files changed, 19 insertions(+), 32 deletions(-)

diff --git a/src_py/apiServer/definitions.py b/src_py/apiServer/definitions.py
index 51faf49e..4269c6b9 100644
--- a/src_py/apiServer/definitions.py
+++ b/src_py/apiServer/definitions.py
@@ -32,6 +32,7 @@
 PHASE_PREDICTION = 2
 PHASE_STATS = 3 # TODO maybe redundant
 
+SOURCE_POLICY_ROUND_ROBIN = 1 # TODO check import from NerlPlanner
 
 PHASE_TRAINING_STR = "training"
 PHASE_PREDICTION_STR = "prediction"
diff --git a/src_py/apiServer/stats.py b/src_py/apiServer/stats.py
index 605ee855..390fb24f 100644
--- a/src_py/apiServer/stats.py
+++ b/src_py/apiServer/stats.py
@@ -72,9 +72,9 @@ def get_loss_ts(self , plot : bool = False , saveToFile : bool = False, log : bo
         for worker_db in workers_model_db_list:
             worker_name = worker_db.get_worker_name()
             loss_dict[worker_name] = []
-            batches_ts_tansor_data_dict =worker_db.get_batches_ts_tansor_data_dict()
-            sorted_batches_ts_tansor_data_dict = dict(sorted(batches_ts_tansor_data_dict.items()))
-            loss_dict[worker_name] = [sorted_batches_ts_tansor_data_dict[key] for key in sorted(sorted_batches_ts_tansor_data_dict)]
+            batches_ts_tensor_data_dict =worker_db.get_batches_ts_tensor_data_dict()
+            sorted_batches_ts_tensor_data_dict = dict(sorted(batches_ts_tensor_data_dict.items()))
+            loss_dict[worker_name] = [sorted_batches_ts_tensor_data_dict[key] for key in sorted(sorted_batches_ts_tensor_data_dict)]
             
         # Convert NumPy arrays to floats
         for worker_name in loss_dict:
@@ -161,27 +161,25 @@ def attach_true_labels(self, true_labels_df, predicted_labels_df, num_of_workers
         return predicted_labels_df
 
     def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False , saveToFile : bool = False): 
-        assert self.experiment_flow_type == "classification", "This function is only available for classification experiments" 
-        assert self.phase == PHASE_PREDICTION_STR, "This function is only available for predict phase"   
+        assert self.experiment_flow_type == "classification", "This function is only available for classification experiments"
+        assert self.phase == PHASE_PREDICTION_STR, "This function is only available for predict phase"
         sources_pieces_list = self.experiment_phase.get_sources_pieces()
         workers_model_db_list = self.nerl_model_db.get_workers_model_db_list()
         confusion_matrix_source_dict = {}
         confusion_matrix_worker_dict = {}
-        # TODO Add a check - if the source policy is 1, then round robin flag (rr_flag) should be True and passed in attach_true_labels function
-
 
         for source_piece_inst in sources_pieces_list:
             num_of_workers = len(source_piece_inst.target_workers)
             sourcePiece_csv_labels_path = source_piece_inst.get_pointer_to_sourcePiece_CsvDataSet_labels()
             df_actual_labels = pd.read_csv(sourcePiece_csv_labels_path)
             num_of_labels = df_actual_labels.shape[1]
-            header_list = range(num_of_labels) 
+            header_list = range(num_of_labels)
             df_actual_labels.columns = header_list
             # df_actual_labels = self.expend_labels_df(df_actual_labels)
             #print(df_actual_labels)
             source_name = source_piece_inst.get_source_name()
             source_policy = globe.components.sources_policy_dict[source_name] # ! NEW
-            if source_policy == 1:
+            if source_policy == SOURCE_POLICY_ROUND_ROBIN:
                 rr_flag = True
             # build confusion matrix for each worker
             target_workers = source_piece_inst.get_target_workers()
@@ -196,8 +194,8 @@ def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False
                     continue
                 df_worker_labels = pd.DataFrame(np.zeros((batch_size * worker_db.get_total_batches(), num_of_labels)))
                 print(f'Worker {worker_name} Got {worker_db.get_total_batches_per_source(source_name)} batches (={batch_size * worker_db.get_total_batches_per_source(source_name)} samples) from {source_name}')
-                for _, batch_id in worker_db.get_batches_dict().keys(): # ! CHANGED
-                    batch_id = int(batch_id)
+                for _, batch_id_str in worker_db.get_batches_dict().keys(): # ! CHANGED
+                    batch_id = int(batch_id_str)
                     batch_db = worker_db.get_batch(source_name, str(batch_id))
                     if not batch_db: # if batch is missing
                         print(f'{worker_name} missed batch {int(batch_id)}')
@@ -208,32 +206,20 @@ def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False
                         df_worker_labels.iloc[batch_id * batch_size: (batch_id + 1) * batch_size, :num_of_labels] = None # set the batch to None if dropped
                         worker_missed_batches[(worker_name, source_name, str(batch_id))] = (starting_offset + batch_id * batch_size, batch_size)  # save the missing batch
                 # df_worker_labels = df_worker_labels.dropna()
-                for _, batch_id in worker_db.get_batches_dict().keys():
-                    batch_id = int(batch_id) # Saved originally as string
+                
+                # This loop inserts predicted labels of every batch into a worker specific DataFrame 
+                for _, batch_id_str in worker_db.get_batches_dict().keys():
+                    batch_id = int(batch_id_str) 
+                    if rr_flag: # ! Handle the case of round robin casting policy
+                        batch_id = batch_id // num_of_workers
                     batch_db = worker_db.get_batch(source_name, str(batch_id))
                     if batch_db:
-                        tensor_data = batch_db.get_tensor_data()
-                        tensor_data = tensor_data.reshape(batch_size, num_of_labels) 
+                        predicted_labels = batch_db.get_tensor_data()
+                        predicted_labels = predicted_labels.reshape(batch_size, num_of_labels) 
                         start_index_pred = batch_id * batch_size
                         end_index_pred = (batch_id + 1) * batch_size
-                        if rr_flag: # ! Handle the case of round robin casting policy
-                            start_index_pred = start_index_pred // num_of_workers
-                            end_index_pred = end_index_pred // num_of_workers
-                            if start_index_pred % batch_size != 0 or end_index_pred % batch_size != 0: # ! Handle the case of inproper indexes (should be multiples of batch_size)
-                                # print(f'Before: {start_index_pred}-{end_index_pred}')
-                                start_index_pred -= (start_index_pred % batch_size)
-                                end_index_pred += (end_index_pred % batch_size)
-                                # print(f'After: {start_index_pred}-{end_index_pred}')
-                            # print(f'The following indexes {start_index_pred}-{end_index_pred} will be filled with the tensor data')
-                            # if (start_index_pred, end_index_pred) in handled_indexes:
-                            #     if start_index_pred < (df_worker_labels.shape[0] // num_of_workers): # Determine new indexes 
-                            #         start_index_pred += (df_worker_labels.shape[0] // num_of_workers)
-                            #         end_index_pred += (df_worker_labels.shape[0] // num_of_workers)
-                            #     else:
-                            #         start_index_pred -= (df_worker_labels.shape[0] // num_of_workers)
-                            #         end_index_pred -= (df_worker_labels.shape[0] // num_of_workers)
                         try:
-                            df_worker_labels.iloc[start_index_pred:end_index_pred, :num_of_labels] = tensor_data
+                            df_worker_labels.iloc[start_index_pred:end_index_pred, :num_of_labels] = predicted_labels
                         except ValueError:
                             print(f'The following indexes {start_index_pred}-{end_index_pred} caused an error')
                             exit(0)

From 3d99d22e00f4c04e665a04409f5f66449f3a2989 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Sun, 4 Aug 2024 19:02:49 +0000
Subject: [PATCH 14/50] [AEC_Exp] WIP

---
 .../ConnectionMap/conn_AEC_1d_2c_1s_4r_4w.json |  9 +++++++++
 .../exp_AEC_1d_2c_1s_4r_4w.json                | 18 ++----------------
 src_py/apiServer/definitions.py                |  2 +-
 src_py/apiServer/stats.py                      |  7 +++++--
 4 files changed, 17 insertions(+), 19 deletions(-)
 create mode 100644 inputJsonsFiles/ConnectionMap/conn_AEC_1d_2c_1s_4r_4w.json

diff --git a/inputJsonsFiles/ConnectionMap/conn_AEC_1d_2c_1s_4r_4w.json b/inputJsonsFiles/ConnectionMap/conn_AEC_1d_2c_1s_4r_4w.json
new file mode 100644
index 00000000..101f50f1
--- /dev/null
+++ b/inputJsonsFiles/ConnectionMap/conn_AEC_1d_2c_1s_4r_4w.json
@@ -0,0 +1,9 @@
+{
+    "connectionsMap":
+    {
+        "r1":["mainServer","c1","s1", "r2"],
+        "r2":["r3"],
+        "r3":["r4"],
+        "r4":["r1"]
+    }
+}
diff --git a/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json b/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
index fba22aec..1525d307 100644
--- a/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
+++ b/inputJsonsFiles/experimentsFlow/exp_AEC_1d_2c_1s_4r_4w.json
@@ -16,22 +16,8 @@
             {
             "sourceName": "s1",
             "startingSample": "0",
-            "numOfBatches": "1250",
-            "workers": "w1",
-            "nerltensorType": "float"
-            }
-        ]
-    },
-    {
-        "phaseName": "training_phase2",
-        "phaseType": "training",
-        "sourcePieces":
-        [
-            {
-            "sourceName": "s1",
-            "startingSample": "125000",
-            "numOfBatches": "1250",
-            "workers": "w2",
+            "numOfBatches": "2500",
+            "workers": "w1,w2",
             "nerltensorType": "float"
             }
         ]
diff --git a/src_py/apiServer/definitions.py b/src_py/apiServer/definitions.py
index 4269c6b9..53b5c7a9 100644
--- a/src_py/apiServer/definitions.py
+++ b/src_py/apiServer/definitions.py
@@ -32,7 +32,7 @@
 PHASE_PREDICTION = 2
 PHASE_STATS = 3 # TODO maybe redundant
 
-SOURCE_POLICY_ROUND_ROBIN = 1 # TODO check import from NerlPlanner
+SOURCE_POLICY_ROUND_ROBIN = "1" # TODO check import from NerlPlanner
 
 PHASE_TRAINING_STR = "training"
 PHASE_PREDICTION_STR = "prediction"
diff --git a/src_py/apiServer/stats.py b/src_py/apiServer/stats.py
index bb70bb06..7ba52bd2 100644
--- a/src_py/apiServer/stats.py
+++ b/src_py/apiServer/stats.py
@@ -156,7 +156,7 @@ def attach_true_labels(self, true_labels_df, predicted_labels_df, num_of_workers
             else:
                 worker_true_labels = true_labels_df
         predicted_labels_df['TrueLabel'] = worker_true_labels
-        predicted_labels_df = predicted_labels_df.dropna()
+        # predicted_labels_df = predicted_labels_df.dropna()
         display(predicted_labels_df)
         return predicted_labels_df
 
@@ -179,8 +179,11 @@ def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False
             #print(df_actual_labels)
             source_name = source_piece_inst.get_source_name()
             source_policy = globe.components.sources_policy_dict[source_name] # ! NEW
+            print(f'Source {source_name} policy is round robin {source_policy}? {source_policy == SOURCE_POLICY_ROUND_ROBIN}')  
             if source_policy == SOURCE_POLICY_ROUND_ROBIN:
                 rr_flag = True
+            else:
+                rr_flag = False
             # build confusion matrix for each worker
             target_workers = source_piece_inst.get_target_workers()
             worker_missed_batches = {}
@@ -230,7 +233,7 @@ def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False
                     class_name = self.headers_list[0]
                     actual_labels = df_worker_labels.iloc[:, num_of_labels:].values.flatten().tolist()
                     predict_labels = df_worker_labels.iloc[:, :num_of_labels].values.flatten().tolist()
-                    print(f'Pred and True DFs are identical in {len([1 for i, j in zip(actual_labels, predict_labels) if i == j])} out of {len(actual_labels)} samples')
+                    # print(f'Pred and True DFs are identical in {len([1 for i, j in zip(actual_labels, predict_labels) if i == j])} out of {len(actual_labels)} samples')
                     confusion_matrix = metrics.confusion_matrix(actual_labels, predict_labels)
                     confusion_matrix_source_dict[(source_name, worker_name, class_name)] = confusion_matrix
                     if (worker_name, class_name) not in confusion_matrix_worker_dict:

From 0a8f0e0d73bf46510b07d964a434187b73e025bb Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Wed, 7 Aug 2024 15:17:32 +0000
Subject: [PATCH 15/50] [AEC_EXP] WIP

---
 .../dc_AEC_1d_2c_1s_4r_4w.json                |  10 +-
 .../dc_test_synt_1d_2c_1s_4r_4w.json          |   8 +-
 .../exp_test_synt_1d_2c_1s_4r_4w new.json     |   2 +-
 src_cpp/opennnBridge/ae_red.cpp               |  45 +++-
 src_cpp/opennnBridge/nerlWorkerOpenNN.cpp     |   3 +-
 src_py/apiServer/definitions.py               |   1 +
 src_py/apiServer/stats.py                     | 239 +++++++++---------
 7 files changed, 162 insertions(+), 146 deletions(-)

diff --git a/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json b/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
index 31969dfc..40c5c428 100644
--- a/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
+++ b/inputJsonsFiles/DistributedConfig/dc_AEC_1d_2c_1s_4r_4w.json
@@ -13,7 +13,7 @@
     },
     "devices": [
         {
-            "name": "pc1",
+            "name": "C0VM0",
             "ipv4": "10.0.0.5",
             "entities": "c1,c2,r2,r1,r3,r4,s1,apiServer,mainServer"
         }
@@ -71,12 +71,12 @@
         "d8df752e0a2e8f01de8f66e9cec941cdbc65d144ecf90ab7713e69d65e7e82aa": {
             "modelType": "9",
             "_doc_modelType": " nn:0 | approximation:1 | classification:2 | forecasting:3 | image-classification:4 | text-classification:5 | text-generation:6 | auto-association:7 | autoencoder:8 | ae-classifier:9 |",
-            "modelArgs": "k=1.9,alpha=0.4,use_ema_only=1",
-            "layersSizes": "10,128,64,32,16,32,64,128,10",
+            "modelArgs": "k=0.5,alpha=0.4,use_ema_only=1",
+            "layersSizes": "10,32,16,32,10",
             "_doc_layersSizes": "List of postive integers [L0, L1, ..., LN]",
-            "layerTypesList": "1,3,3,3,3,3,3,3,3",
+            "layerTypesList": "1,3,3,3,3",
             "_doc_LayerTypes": " Default:0 | Scaling:1 | CNN:2 | Perceptron:3 | Pooling:4 | Probabilistic:5 | LSTM:6 | Reccurrent:7 | Unscaling:8 |",
-            "layers_functions": "1,8,8,8,8,8,8,8,8",
+            "layers_functions": "1,7,7,7,7",
             "_doc_layers_functions_activation": " Threshold:1 | Sign:2 | Logistic:3 | Tanh:4 | Linear:5 | ReLU:6 | eLU:7 | SeLU:8 | Soft-plus:9 | Soft-sign:10 | Hard-sigmoid:11 |",
             "_doc_layer_functions_pooling": " none:1 | Max:2 | Avg:3 |",
             "_doc_layer_functions_probabilistic": " Binary:1 | Logistic:2 | Competitive:3 | Softmax:4 |",
diff --git a/inputJsonsFiles/DistributedConfig/dc_test_synt_1d_2c_1s_4r_4w.json b/inputJsonsFiles/DistributedConfig/dc_test_synt_1d_2c_1s_4r_4w.json
index 01e46ffc..46849d08 100644
--- a/inputJsonsFiles/DistributedConfig/dc_test_synt_1d_2c_1s_4r_4w.json
+++ b/inputJsonsFiles/DistributedConfig/dc_test_synt_1d_2c_1s_4r_4w.json
@@ -14,7 +14,7 @@
     "devices": [
         {
             "name": "pc1",
-            "ipv4": "10.211.55.3",
+            "ipv4": "10.0.0.5",
             "entities": "c1,c2,r2,r1,r3,r4,s1,apiServer,mainServer"
         }
     ],
@@ -85,18 +85,18 @@
             "modelType": "0",
             "_doc_modelType": " nn:0 | approximation:1 | classification:2 | forecasting:3 | image-classification:4 | text-classification:5 | text-generation:6 | auto-association:7 | autoencoder:8 | ae-classifier:9 |",
             "modelArgs": "",
-            "layersSizes": "5,10,5,3",
+            "layersSizes": "5,16,8,3",
             "_doc_layersSizes": "List of postive integers [L0, L1, ..., LN]",
             "layerTypesList": "1,3,3,3",
             "_doc_LayerTypes": " Default:0 | Scaling:1 | CNN:2 | Perceptron:3 | Pooling:4 | Probabilistic:5 | LSTM:6 | Reccurrent:7 | Unscaling:8 |",
-            "layers_functions": "1,6,6,11",
+            "layers_functions": "1,6,6,6",
             "_doc_layers_functions_activation": " Threshold:1 | Sign:2 | Logistic:3 | Tanh:4 | Linear:5 | ReLU:6 | eLU:7 | SeLU:8 | Soft-plus:9 | Soft-sign:10 | Hard-sigmoid:11 |",
             "_doc_layer_functions_pooling": " none:1 | Max:2 | Avg:3 |",
             "_doc_layer_functions_probabilistic": " Binary:1 | Logistic:2 | Competitive:3 | Softmax:4 |",
             "_doc_layer_functions_scaler": " none:1 | MinMax:2 | MeanStd:3 | STD:4 | Log:5 |",
             "lossMethod": "2",
             "_doc_lossMethod": " SSE:1 | MSE:2 | NSE:3 | MinkowskiE:4 | WSE:5 | CEE:6 |",
-            "lr": "0.01",
+            "lr": "0.001",
             "_doc_lr": "Positve float",
             "epochs": "1",
             "_doc_epochs": "Positve Integer",
diff --git a/inputJsonsFiles/experimentsFlow/exp_test_synt_1d_2c_1s_4r_4w new.json b/inputJsonsFiles/experimentsFlow/exp_test_synt_1d_2c_1s_4r_4w new.json
index f05f5f01..99ebc7e4 100644
--- a/inputJsonsFiles/experimentsFlow/exp_test_synt_1d_2c_1s_4r_4w new.json	
+++ b/inputJsonsFiles/experimentsFlow/exp_test_synt_1d_2c_1s_4r_4w new.json	
@@ -38,4 +38,4 @@
       }
    ]
 }
-    
+
diff --git a/src_cpp/opennnBridge/ae_red.cpp b/src_cpp/opennnBridge/ae_red.cpp
index 05ac5f8c..969e0db5 100644
--- a/src_cpp/opennnBridge/ae_red.cpp
+++ b/src_cpp/opennnBridge/ae_red.cpp
@@ -23,22 +23,39 @@ void AeRed::getModelArgsParsed(const std::string &_model_args_str, ModelArgsPars
     std::size_t found_k = _model_args_str.find(k_str);
     std::size_t found_alpha = _model_args_str.find(alpha_str);
     std::size_t found_use_ema_only = _model_args_str.find(use_ema_only_str);
-    if (found_k != std::string::npos){
-        model_args_parsed.k = std::stof(_model_args_str.substr(found_k + k_str.length()));
-    }
-    else{
+    
+    try {
+        if (found_k != std::string::npos) {
+            std::string k_value_str = _model_args_str.substr(found_k + k_str.length());
+            model_args_parsed.k = std::stof(k_value_str);
+        } else {
+            model_args_parsed.k = PARAM_K_DEFAULT;
+        }
+        
+        if (found_alpha != std::string::npos) {
+            std::string alpha_value_str = _model_args_str.substr(found_alpha + alpha_str.length());
+            model_args_parsed.alpha = std::stof(alpha_value_str);
+        } else {
+            model_args_parsed.alpha = ALPHA_DEFAULT;
+        }
+        
+        if (found_use_ema_only != std::string::npos) {
+            std::string use_ema_only_value_str = _model_args_str.substr(found_use_ema_only + use_ema_only_str.length());
+            model_args_parsed.use_ema_only = std::stoi(use_ema_only_value_str);
+        } else {
+            model_args_parsed.use_ema_only = 0;
+        }
+    } catch (const std::invalid_argument &e) {
+        std::cerr << "Invalid argument: " << e.what() << std::endl;
+        // Handle error or set default values
+        model_args_parsed.k = PARAM_K_DEFAULT;
+        model_args_parsed.alpha = ALPHA_DEFAULT;
+        model_args_parsed.use_ema_only = 0;
+    } catch (const std::out_of_range &e) {
+        std::cerr << "Out of range: " << e.what() << std::endl;
+        // Handle error or set default values
         model_args_parsed.k = PARAM_K_DEFAULT;
-    }
-    if (found_alpha != std::string::npos){
-        model_args_parsed.alpha = std::stof(_model_args_str.substr(found_alpha + alpha_str.length()));
-    }
-    else{
         model_args_parsed.alpha = ALPHA_DEFAULT;
-    }
-    if (found_use_ema_only != std::string::npos){
-        model_args_parsed.use_ema_only = std::stoi(_model_args_str.substr(found_use_ema_only + use_ema_only_str.length()));
-    }
-    else{
         model_args_parsed.use_ema_only = 0;
     }
 }
diff --git a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
index 5d31b705..4f960a08 100644
--- a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
+++ b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
@@ -56,6 +56,7 @@ namespace nerlnet
                 (*loss_val_tensor)(0, 0) = static_cast<float>(_last_loss);
                 (*loss_val_tensor)(1, 0) = _ae_red_ptr->_ema_event;
                 (*loss_val_tensor)(2, 0) = _ae_red_ptr->_ema_normal;
+                // cout << "Upper Bound: " << _ae_red_ptr->_ema_event << ", Lower Bound: " << _ae_red_ptr->_ema_normal << endl;
                 // Add _aec_all_loss_values to loss_val_tensor
                 for (int i = 0; i < num_of_samples; i++)
                 {
@@ -115,7 +116,7 @@ namespace nerlnet
                 *loss_values_return = mse2D;
                 _aec_all_loss_values = loss_values_return;
                 // cout << "MSE Loss: " << mse_loss << endl;
-                // _ae_red_ptr->update_batch(loss_values_mse);
+                _ae_red_ptr->update_batch(loss_values_mse);
                 // cout << "AE_RED RESULT VECTOR:" << endl << *res << endl;
             }
 
diff --git a/src_py/apiServer/definitions.py b/src_py/apiServer/definitions.py
index 53b5c7a9..3840ecf0 100644
--- a/src_py/apiServer/definitions.py
+++ b/src_py/apiServer/definitions.py
@@ -32,6 +32,7 @@
 PHASE_PREDICTION = 2
 PHASE_STATS = 3 # TODO maybe redundant
 
+SOURCE_POLICY_CASTING = "0" # TODO check import from NerlPlanner
 SOURCE_POLICY_ROUND_ROBIN = "1" # TODO check import from NerlPlanner
 
 PHASE_TRAINING_STR = "training"
diff --git a/src_py/apiServer/stats.py b/src_py/apiServer/stats.py
index 7ba52bd2..06410f9d 100644
--- a/src_py/apiServer/stats.py
+++ b/src_py/apiServer/stats.py
@@ -147,126 +147,135 @@ def expend_labels_df(self, df):
         df = df.reindex(columns = [*df.columns.tolist(), *temp_list], fill_value = 0)
         assert df.shape[1] == 2 * num_of_labels, "Error in expend_labels_df function"
         return df
+    
+    def get_recieved_batches(self):
+        """
+        Returns a dictionary of recieved batches in the experiment phase.
+        recived_batches_dict = {(source_name, worker_name): [batch_id,...]}
+        """
+        def recieved_batches_key(phase_name, source_name, worker_name):
+            return f"phase:{phase_name},{source_name}->{worker_name}"
 
-    # TODO Fix for round robin casting policy (AND FOR RANDOM TOO)
-    def attach_true_labels(self, true_labels_df, predicted_labels_df, num_of_workers, worker_idx, rr_flag = False):
-        for _ in range(predicted_labels_df.shape[0]):
-            if rr_flag: # Skip certain rows according to the worker index
-                worker_true_labels = true_labels_df.iloc[worker_idx::num_of_workers].reset_index(drop=True)
-            else:
-                worker_true_labels = true_labels_df
-        predicted_labels_df['TrueLabel'] = worker_true_labels
-        # predicted_labels_df = predicted_labels_df.dropna()
-        display(predicted_labels_df)
-        return predicted_labels_df
-
-    def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False , saveToFile : bool = False): 
-        assert self.experiment_flow_type == "classification", "This function is only available for classification experiments"
-        assert self.phase == PHASE_PREDICTION_STR, "This function is only available for predict phase"
+        phase_name = self.experiment_phase.get_name()
+        recived_batches_dict = {}
         sources_pieces_list = self.experiment_phase.get_sources_pieces()
         workers_model_db_list = self.nerl_model_db.get_workers_model_db_list()
-        confusion_matrix_source_dict = {}
-        confusion_matrix_worker_dict = {}
-
         for source_piece_inst in sources_pieces_list:
-            num_of_workers = len(source_piece_inst.target_workers)
-            sourcePiece_csv_labels_path = source_piece_inst.get_pointer_to_sourcePiece_CsvDataSet_labels()
-            df_actual_labels = pd.read_csv(sourcePiece_csv_labels_path)
-            num_of_labels = df_actual_labels.shape[1]
-            header_list = range(num_of_labels)
-            df_actual_labels.columns = header_list
-            # df_actual_labels = self.expend_labels_df(df_actual_labels)
-            #print(df_actual_labels)
             source_name = source_piece_inst.get_source_name()
-            source_policy = globe.components.sources_policy_dict[source_name] # ! NEW
-            print(f'Source {source_name} policy is round robin {source_policy}? {source_policy == SOURCE_POLICY_ROUND_ROBIN}')  
-            if source_policy == SOURCE_POLICY_ROUND_ROBIN:
-                rr_flag = True
-            else:
-                rr_flag = False
-            # build confusion matrix for each worker
-            target_workers = source_piece_inst.get_target_workers()
-            worker_missed_batches = {}
-            batch_size = int(source_piece_inst.get_batch_size())
+            target_workers_string = source_piece_inst.get_target_workers()
+            target_workers_names = target_workers_string.split(',')
             for worker_db in workers_model_db_list:
-                worker_name = worker_db.get_worker_name()
-                worker_idx = int(min(worker_db.get_batches_dict().keys(), key=lambda x: int(x[1]))[1]) # ! For round robin casting policy
-                if rr_flag:
-                    print(f'Worker {worker_name} index: {worker_idx}')
-                if worker_name not in target_workers:
-                    continue
-                df_worker_labels = pd.DataFrame(np.zeros((batch_size * worker_db.get_total_batches(), num_of_labels)))
-                print(f'Worker {worker_name} Got {worker_db.get_total_batches_per_source(source_name)} batches (={batch_size * worker_db.get_total_batches_per_source(source_name)} samples) from {source_name}')
-                for _, batch_id_str in worker_db.get_batches_dict().keys(): # ! CHANGED
-                    batch_id = int(batch_id_str)
-                    batch_db = worker_db.get_batch(source_name, str(batch_id))
-                    if not batch_db: # if batch is missing
-                        print(f'{worker_name} missed batch {int(batch_id)}')
-                        if not self.missed_batches_warning_msg:
-                            LOG_WARNING(f"missed batches")
-                            self.missed_batches_warning_msg = True
-                        starting_offset = source_piece_inst.get_starting_offset()
-                        df_worker_labels.iloc[batch_id * batch_size: (batch_id + 1) * batch_size, :num_of_labels] = None # set the batch to None if dropped
-                        worker_missed_batches[(worker_name, source_name, str(batch_id))] = (starting_offset + batch_id * batch_size, batch_size)  # save the missing batch
-                # df_worker_labels = df_worker_labels.dropna()
-                
-                # This loop inserts predicted labels of every batch into a worker specific DataFrame 
-                for _, batch_id_str in worker_db.get_batches_dict().keys():
-                    batch_id = int(batch_id_str) 
-                    if rr_flag: # ! Handle the case of round robin casting policy
-                        batch_id = batch_id // num_of_workers
-                    batch_db = worker_db.get_batch(source_name, str(batch_id))
-                    if batch_db:
-                        predicted_labels = batch_db.get_tensor_data()
-                        predicted_labels = predicted_labels.reshape(batch_size, num_of_labels) 
-                        start_index_pred = batch_id * batch_size
-                        end_index_pred = (batch_id + 1) * batch_size
-                        try:
-                            df_worker_labels.iloc[start_index_pred:end_index_pred, :num_of_labels] = predicted_labels
-                        except ValueError:
-                            print(f'The following indexes {start_index_pred}-{end_index_pred} caused an error')
-                            exit(0)
-                df_worker_labels = self.attach_true_labels(df_actual_labels, df_worker_labels, num_of_workers, worker_idx, rr_flag)
-                # print(f'Actual Labels (Column 0) & Predict Labels (Column 1 for worker: {worker_name}')
-                # display(df_worker_labels)
-                if len(self.headers_list) == 1:
-                    class_name = self.headers_list[0]
-                    actual_labels = df_worker_labels.iloc[:, num_of_labels:].values.flatten().tolist()
-                    predict_labels = df_worker_labels.iloc[:, :num_of_labels].values.flatten().tolist()
-                    # print(f'Pred and True DFs are identical in {len([1 for i, j in zip(actual_labels, predict_labels) if i == j])} out of {len(actual_labels)} samples')
-                    confusion_matrix = metrics.confusion_matrix(actual_labels, predict_labels)
-                    confusion_matrix_source_dict[(source_name, worker_name, class_name)] = confusion_matrix
-                    if (worker_name, class_name) not in confusion_matrix_worker_dict:
-                        confusion_matrix_worker_dict[(worker_name, class_name)] = confusion_matrix
-                    else:
-                        confusion_matrix_worker_dict[(worker_name, class_name)] += confusion_matrix
+                    worker_name = worker_db.get_worker_name()
+                    if worker_name in target_workers_names:       # Check if the worker is in the target workers list of this source
+                        for batch_id in range(source_piece_inst.get_num_of_batches()):
+                            batch_db = worker_db.get_batch(source_name, str(batch_id))
+                            if batch_db:    # if batch is recieved
+                                recieved_batch_key_str = recieved_batches_key(phase_name, source_name, worker_name)
+                                if recieved_batch_key_str not in recived_batches_dict:
+                                    recived_batches_dict[recieved_batch_key_str] = []
+                                recived_batches_dict[recieved_batch_key_str].append(batch_id)
+        return recived_batches_dict
+
+    def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False , saveToFile : bool = False): 
+            
+            def build_worker_label_df(original_df, batch_ids, batch_size):
+                rows_list = []
+
+                for batch_id in batch_ids:
+                    # Calculate the start and end indices for the rows to be copied
+                    start_idx = batch_id * batch_size
+                    end_idx = (batch_id + 1) * batch_size
+            
+                    # Extract the rows and append to the list
+                    batch_rows = original_df.iloc[start_idx:end_idx]
+                    rows_list.append(batch_rows)
+        
+                    # Concatenate all the extracted rows into a new DataFrame
+                df_worker_labels = pd.concat(rows_list, ignore_index=True)
+                return df_worker_labels
+            
+            assert self.experiment_flow_type == "classification", "This function is only available for classification experiments" 
+            assert self.phase == PHASE_PREDICTION_STR, "This function is only available for predict phase"   
+            sources_pieces_list = self.experiment_phase.get_sources_pieces()
+            workers_model_db_list = self.nerl_model_db.get_workers_model_db_list()
+            confusion_matrix_source_dict = {}
+            confusion_matrix_worker_dict = {}
+            recived_batches_dict = self.get_recieved_batches()
+            for source_piece_inst in sources_pieces_list:
+                nerltensorType = source_piece_inst.get_nerltensor_type()
+                source_name = source_piece_inst.get_source_name()
+                sourcePiece_csv_labels_path = source_piece_inst.get_pointer_to_sourcePiece_CsvDataSet_labels()
+                df_actual_labels = pd.read_csv(sourcePiece_csv_labels_path)
+                num_of_labels = df_actual_labels.shape[1]
+
+                # build confusion matrix for each worker
+                target_workers_string = source_piece_inst.get_target_workers()
+                target_workers_names = target_workers_string.split(',')
+                batch_size = source_piece_inst.get_batch_size()
+                for worker_db in workers_model_db_list:
+                    worker_name = worker_db.get_worker_name()
+                    if worker_name not in target_workers_names:
+                        continue
+                    worker_recived_batches_id = recived_batches_dict.get(f"phase:{self.experiment_phase.get_name()},{source_name}->{worker_name}")   # get a list of recived batches id for the worker
+                    df_worker_labels = build_worker_label_df(df_actual_labels, worker_recived_batches_id, batch_size)
+                    header_list = range(num_of_labels) 
+                    df_worker_labels.columns = header_list
+                    df_worker_labels = self.expend_labels_df(df_worker_labels)  #Now there is a csv file with the actual labels of the source piece and empty columns for the predict labels
                     
-                else: # Multi-Class
-                    # Take 2 list from the df, one for the actual labels and one for the predict labels to build the confusion matrix
-                    max_column_predict_index = df_worker_labels.iloc[:, :num_of_labels].idxmax(axis=1) 
-                    max_column_predict_index = max_column_predict_index.tolist() 
-                    max_column_predict_index = [int(predict_index) - num_of_labels for predict_index in max_column_predict_index] # fix the index to original labels index
-                    max_column_labels_index = df_worker_labels.iloc[:, num_of_labels:].idxmax(axis=1)
-                    max_column_labels_index = max_column_labels_index.tolist()
+                    # Check if the actual labels are integers and the predict labels are floats, if so convert the actual labels to nerltensorType
+                    if nerltensorType == 'float':
+                        if any(pd.api.types.is_integer_dtype(df_worker_labels[col]) for col in df_worker_labels.columns):
+                            df_worker_labels = df_worker_labels.astype(float)
                     
-                    # building confusion matrix for each class
-                    for class_index, class_name in enumerate(self.headers_list):
-                        class_actual_list = [1 if label_num == class_index else 0 for label_num in max_column_labels_index]   # 1 if the label is belong to the class, 0 otherwise
-                        class_predict_list = [1 if label_num == class_index else 0 for label_num in max_column_predict_index]   # 1 if the label is belong to the class, 0 otherwise
-                        confusion_matrix = metrics.confusion_matrix(class_actual_list, class_predict_list)  
-                        #confusion_matrix_np = confusion_matrix.to_numpy()
+                    #build df_worker_labels with the actual labels and the predict labels
+                    index = 0
+                    for batch_id in worker_recived_batches_id:
+                        batch_db = worker_db.get_batch(source_name, str(batch_id))
+                        if not batch_db:             #It's not necessary to check if the batch is missing, because we already know wich batches are recieved
+                            LOG_INFO(f"Batch {batch_id} is missing for worker {worker_name}")
+                            continue
+                        tensor_data = batch_db.get_tensor_data() 
+                        tensor_data = tensor_data.reshape(batch_size, num_of_labels).copy()  # Make the tensor_data array writable
+                        start_index = index * batch_size
+                        end_index = (index + 1) * batch_size
+                        df_worker_labels.iloc[start_index:end_index, num_of_labels:] = tensor_data
+                        index += 1
+                        
+                    if len(self.headers_list) == 1:   # One class
+                        class_name = self.headers_list[0]
+                        actual_labels = df_worker_labels.iloc[:, :num_of_labels].values.flatten().tolist()
+                        predict_labels = df_worker_labels.iloc[:, num_of_labels:].values.flatten().tolist()
+                        confusion_matrix = metrics.confusion_matrix(actual_labels, predict_labels)
                         confusion_matrix_source_dict[(source_name, worker_name, class_name)] = confusion_matrix
                         if (worker_name, class_name) not in confusion_matrix_worker_dict:
                             confusion_matrix_worker_dict[(worker_name, class_name)] = confusion_matrix
                         else:
                             confusion_matrix_worker_dict[(worker_name, class_name)] += confusion_matrix
-                
-
-        if plot:
-            workers = sorted(list({tup[0] for tup in confusion_matrix_worker_dict.keys()}))
-            classes = sorted(list({tup[1] for tup in confusion_matrix_worker_dict.keys()}))
-            if len(workers) > 1:
-                fig, ax = plt.subplots(nrows=len(workers), ncols=len(classes),figsize=(4*len(classes), 4*len(workers)), dpi=140)
+                        
+                    else: # Multi-Class
+                        # Take 2 list from the df, one for the actual labels and one for the predict labels to build the confusion matrix
+                        max_column_predict_index = df_worker_labels.iloc[:, num_of_labels:].idxmax(axis=1) 
+                        max_column_predict_index = max_column_predict_index.tolist() 
+                        max_column_predict_index = [int(predict_index) - num_of_labels for predict_index in max_column_predict_index] # fix the index to original labels index
+                        max_column_labels_index = df_worker_labels.iloc[:, :num_of_labels].idxmax(axis=1)
+                        max_column_labels_index = max_column_labels_index.tolist()
+                        
+                        # building confusion matrix for each class
+                        for class_index, class_name in enumerate(self.headers_list):
+                            class_actual_list = [1 if label_num == class_index else 0 for label_num in max_column_labels_index]   # 1 if the label is belong to the class, 0 otherwise
+                            class_predict_list = [1 if label_num == class_index else 0 for label_num in max_column_predict_index]   # 1 if the label is belong to the class, 0 otherwise
+                            confusion_matrix = metrics.confusion_matrix(class_actual_list, class_predict_list)  
+                            #confusion_matrix_np = confusion_matrix.to_numpy()
+                            confusion_matrix_source_dict[(source_name, worker_name, class_name)] = confusion_matrix
+                            if (worker_name, class_name) not in confusion_matrix_worker_dict:
+                                confusion_matrix_worker_dict[(worker_name, class_name)] = confusion_matrix
+                            else:
+                                confusion_matrix_worker_dict[(worker_name, class_name)] += confusion_matrix
+            
+            if plot:
+                workers = sorted(list({tup[0] for tup in confusion_matrix_worker_dict.keys()}))
+                classes = sorted(list({tup[1] for tup in confusion_matrix_worker_dict.keys()}))
+                fig, ax = plt.subplots(nrows=len(workers), ncols=len(classes),figsize=(4*len(classes),4*len(workers)),dpi=140)
                 if len(classes) > 1:
                     for i , worker in enumerate(workers): 
                         for j , pred_class in enumerate(classes):
@@ -291,22 +300,10 @@ def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False
                         ax[i].set_ylabel("True Label" , fontsize=8)
                         ax[i].set_aspect('equal')
                 fig.subplots_adjust(wspace=0.4 , hspace=0.4)
-            else:
-                plt.figure(figsize=(4*len(classes), 3), dpi=140)
-                conf_mat = confusion_matrix_worker_dict[(workers[0] , classes[0])]
-                heatmap = sns.heatmap(data=conf_mat , annot=True , fmt="d", cmap='Blues',annot_kws={"size": 8}, cbar_kws={'pad': 0.1})
-                cbar = heatmap.collections[0].colorbar
-                cbar.ax.tick_params(labelsize = 8)
-                plt.title(f"{workers[0]} , Class '{classes[0]}'" , fontsize=12)
-                plt.xlabel("Predicted Label" , fontsize=8)
-                plt.ylabel("True Label" , fontsize=8)
-                plt.tick_params(axis='both', which='major', labelsize=8)
-            plt.show()
+                plt.show()
                 
+            return confusion_matrix_source_dict, confusion_matrix_worker_dict    
         
-
-        return confusion_matrix_source_dict, confusion_matrix_worker_dict
-    
     def get_missed_batches(self):
         """
         Returns a list of missed batches in the experiment phase.

From 4c3dacd6b1a15591f2c369fcb786310abebcf6c0 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Thu, 8 Aug 2024 20:26:53 +0000
Subject: [PATCH 16/50] [AEC_Exp] JSON Files update

---
 .../ConnectionMap/conn_AEC_KDD14_test.json    |   9 ++
 .../ConnectionMap/conn_AEC_skab_test.json     |   9 ++
 .../DistributedConfig/dc_AEC_KDD14_test.json  | 104 ++++++++++++++++++
 .../DistributedConfig/dc_AEC_skab_test.json   | 104 ++++++++++++++++++
 .../experimentsFlow/exp_AEC_KDD14_test.json   |  54 +++++++++
 .../experimentsFlow/exp_AEC_skab_test.json    |  40 +++++++
 src_py/apiServer/hf_repo_ids.json             |   6 +
 7 files changed, 326 insertions(+)
 create mode 100644 inputJsonsFiles/ConnectionMap/conn_AEC_KDD14_test.json
 create mode 100644 inputJsonsFiles/ConnectionMap/conn_AEC_skab_test.json
 create mode 100644 inputJsonsFiles/DistributedConfig/dc_AEC_KDD14_test.json
 create mode 100644 inputJsonsFiles/DistributedConfig/dc_AEC_skab_test.json
 create mode 100644 inputJsonsFiles/experimentsFlow/exp_AEC_KDD14_test.json
 create mode 100644 inputJsonsFiles/experimentsFlow/exp_AEC_skab_test.json

diff --git a/inputJsonsFiles/ConnectionMap/conn_AEC_KDD14_test.json b/inputJsonsFiles/ConnectionMap/conn_AEC_KDD14_test.json
new file mode 100644
index 00000000..101f50f1
--- /dev/null
+++ b/inputJsonsFiles/ConnectionMap/conn_AEC_KDD14_test.json
@@ -0,0 +1,9 @@
+{
+    "connectionsMap":
+    {
+        "r1":["mainServer","c1","s1", "r2"],
+        "r2":["r3"],
+        "r3":["r4"],
+        "r4":["r1"]
+    }
+}
diff --git a/inputJsonsFiles/ConnectionMap/conn_AEC_skab_test.json b/inputJsonsFiles/ConnectionMap/conn_AEC_skab_test.json
new file mode 100644
index 00000000..101f50f1
--- /dev/null
+++ b/inputJsonsFiles/ConnectionMap/conn_AEC_skab_test.json
@@ -0,0 +1,9 @@
+{
+    "connectionsMap":
+    {
+        "r1":["mainServer","c1","s1", "r2"],
+        "r2":["r3"],
+        "r3":["r4"],
+        "r4":["r1"]
+    }
+}
diff --git a/inputJsonsFiles/DistributedConfig/dc_AEC_KDD14_test.json b/inputJsonsFiles/DistributedConfig/dc_AEC_KDD14_test.json
new file mode 100644
index 00000000..b61eaf4a
--- /dev/null
+++ b/inputJsonsFiles/DistributedConfig/dc_AEC_KDD14_test.json
@@ -0,0 +1,104 @@
+{
+    "nerlnetSettings": {
+        "frequency": "300",
+        "batchSize": "100"
+    },
+    "mainServer": {
+        "port": "8081",
+        "args": ""
+    },
+    "apiServer": {
+        "port": "8082",
+        "args": ""
+    },
+    "devices": [
+        {
+            "name": "C0VM0",
+            "ipv4": "10.0.0.5",
+            "entities": "c1,c2,r2,r1,r3,r4,s1,apiServer,mainServer"
+        }
+    ],
+    "routers": [
+        {
+            "name": "r1",
+            "port": "8086",
+            "policy": "0"
+        },
+        {
+            "name": "r2",
+            "port": "8087",
+            "policy": "0"
+        },
+        {
+            "name": "r3",
+            "port": "8088",
+            "policy": "0"
+        },
+        {
+            "name": "r4",
+            "port": "8089",
+            "policy": "0"
+        }
+    ],
+    "sources": [
+        {
+            "name": "s1",
+            "port": "8085",
+            "frequency": "300",
+            "policy": "0",
+            "epochs": "5",
+            "type": "0"
+        }
+    ],
+    "clients": [
+        {
+            "name": "c1",
+            "port": "8083",
+            "workers": "w1,w2"
+        }
+    ],
+    "workers": [
+        {
+            "name": "w1",
+            "model_sha": "d8df752e0a2e8f01de8f66e9cec941cdbc65d144ecf90ab7713e69d65e7e82aa"
+        },
+        {
+            "name": "w2",
+            "model_sha": "d8df752e0a2e8f01de8f66e9cec941cdbc65d144ecf90ab7713e69d65e7e82aa"
+        }
+    ],
+    "model_sha": {
+        "d8df752e0a2e8f01de8f66e9cec941cdbc65d144ecf90ab7713e69d65e7e82aa": {
+            "modelType": "9",
+            "_doc_modelType": " nn:0 | approximation:1 | classification:2 | forecasting:3 | image-classification:4 | text-classification:5 | text-generation:6 | auto-association:7 | autoencoder:8 | ae-classifier:9 |",
+            "modelArgs": "k=0.5,alpha=0.4,use_ema_only=1",
+            "layersSizes": "10,64,32,16,32,64,10",
+            "_doc_layersSizes": "List of postive integers [L0, L1, ..., LN]",
+            "layerTypesList": "1,3,3,3,3,3,3",
+            "_doc_LayerTypes": " Default:0 | Scaling:1 | CNN:2 | Perceptron:3 | Pooling:4 | Probabilistic:5 | LSTM:6 | Reccurrent:7 | Unscaling:8 |",
+            "layers_functions": "1,8,8,8,8,8,8",
+            "_doc_layers_functions_activation": " Threshold:1 | Sign:2 | Logistic:3 | Tanh:4 | Linear:5 | ReLU:6 | eLU:7 | SeLU:8 | Soft-plus:9 | Soft-sign:10 | Hard-sigmoid:11 |",
+            "_doc_layer_functions_pooling": " none:1 | Max:2 | Avg:3 |",
+            "_doc_layer_functions_probabilistic": " Binary:1 | Logistic:2 | Competitive:3 | Softmax:4 |",
+            "_doc_layer_functions_scaler": " none:1 | MinMax:2 | MeanStd:3 | STD:4 | Log:5 |",
+            "lossMethod": "2",
+            "_doc_lossMethod": " SSE:1 | MSE:2 | NSE:3 | MinkowskiE:4 | WSE:5 | CEE:6 |",
+            "lr": "0.0001",
+            "_doc_lr": "Positve float",
+            "epochs": "1",
+            "_doc_epochs": "Positve Integer",
+            "optimizer": "2",
+            "_doc_optimizer": " GD:0 | CGD:1 | SGD:2 | QuasiNeuton:3 | LVM:4 | ADAM:5 |",
+            "optimizerArgs": "",
+            "_doc_optimizerArgs": "String",
+            "infraType": "0",
+            "_doc_infraType": " opennn:0 | wolfengine:1 |",
+            "distributedSystemType": "0",
+            "_doc_distributedSystemType": " none:0 | fedClientAvg:1 | fedServerAvg:2 |",
+            "distributedSystemArgs": "",
+            "_doc_distributedSystemArgs": "String",
+            "distributedSystemToken": "none",
+            "_doc_distributedSystemToken": "Token that associates distributed group of workers and parameter-server"
+        }
+    }
+}
\ No newline at end of file
diff --git a/inputJsonsFiles/DistributedConfig/dc_AEC_skab_test.json b/inputJsonsFiles/DistributedConfig/dc_AEC_skab_test.json
new file mode 100644
index 00000000..92f9111b
--- /dev/null
+++ b/inputJsonsFiles/DistributedConfig/dc_AEC_skab_test.json
@@ -0,0 +1,104 @@
+{
+    "nerlnetSettings": {
+        "frequency": "300",
+        "batchSize": "50"
+    },
+    "mainServer": {
+        "port": "8081",
+        "args": ""
+    },
+    "apiServer": {
+        "port": "8082",
+        "args": ""
+    },
+    "devices": [
+        {
+            "name": "C0VM0",
+            "ipv4": "10.0.0.5",
+            "entities": "c1,c2,r2,r1,r3,r4,s1,apiServer,mainServer"
+        }
+    ],
+    "routers": [
+        {
+            "name": "r1",
+            "port": "8086",
+            "policy": "0"
+        },
+        {
+            "name": "r2",
+            "port": "8087",
+            "policy": "0"
+        },
+        {
+            "name": "r3",
+            "port": "8088",
+            "policy": "0"
+        },
+        {
+            "name": "r4",
+            "port": "8089",
+            "policy": "0"
+        }
+    ],
+    "sources": [
+        {
+            "name": "s1",
+            "port": "8085",
+            "frequency": "300",
+            "policy": "0",
+            "epochs": "50",
+            "type": "0"
+        }
+    ],
+    "clients": [
+        {
+            "name": "c1",
+            "port": "8083",
+            "workers": "w1,w2"
+        }
+    ],
+    "workers": [
+        {
+            "name": "w1",
+            "model_sha": "d8df752e0a2e8f01de8f66e9cec941cdbc65d144ecf90ab7713e69d65e7e82aa"
+        },
+        {
+            "name": "w2",
+            "model_sha": "d8df752e0a2e8f01de8f66e9cec941cdbc65d144ecf90ab7713e69d65e7e82aa"
+        }
+    ],
+    "model_sha": {
+        "d8df752e0a2e8f01de8f66e9cec941cdbc65d144ecf90ab7713e69d65e7e82aa": {
+            "modelType": "9",
+            "_doc_modelType": " nn:0 | approximation:1 | classification:2 | forecasting:3 | image-classification:4 | text-classification:5 | text-generation:6 | auto-association:7 | autoencoder:8 | ae-classifier:9 |",
+            "modelArgs": "k=0.5,alpha=0.4,use_ema_only=1",
+            "layersSizes": "8,5,4,5,8",
+            "_doc_layersSizes": "List of postive integers [L0, L1, ..., LN]",
+            "layerTypesList": "1,3,3,3,3",
+            "_doc_LayerTypes": " Default:0 | Scaling:1 | CNN:2 | Perceptron:3 | Pooling:4 | Probabilistic:5 | LSTM:6 | Reccurrent:7 | Unscaling:8 |",
+            "layers_functions": "1,8,8,8,8",
+            "_doc_layers_functions_activation": " Threshold:1 | Sign:2 | Logistic:3 | Tanh:4 | Linear:5 | ReLU:6 | eLU:7 | SeLU:8 | Soft-plus:9 | Soft-sign:10 | Hard-sigmoid:11 |",
+            "_doc_layer_functions_pooling": " none:1 | Max:2 | Avg:3 |",
+            "_doc_layer_functions_probabilistic": " Binary:1 | Logistic:2 | Competitive:3 | Softmax:4 |",
+            "_doc_layer_functions_scaler": " none:1 | MinMax:2 | MeanStd:3 | STD:4 | Log:5 |",
+            "lossMethod": "2",
+            "_doc_lossMethod": " SSE:1 | MSE:2 | NSE:3 | MinkowskiE:4 | WSE:5 | CEE:6 |",
+            "lr": "0.00000001",
+            "_doc_lr": "Positve float",
+            "epochs": "1",
+            "_doc_epochs": "Positve Integer",
+            "optimizer": "5",
+            "_doc_optimizer": " GD:0 | CGD:1 | SGD:2 | QuasiNeuton:3 | LVM:4 | ADAM:5 |",
+            "optimizerArgs": "",
+            "_doc_optimizerArgs": "String",
+            "infraType": "0",
+            "_doc_infraType": " opennn:0 | wolfengine:1 |",
+            "distributedSystemType": "0",
+            "_doc_distributedSystemType": " none:0 | fedClientAvg:1 | fedServerAvg:2 |",
+            "distributedSystemArgs": "",
+            "_doc_distributedSystemArgs": "String",
+            "distributedSystemToken": "none",
+            "_doc_distributedSystemToken": "Token that associates distributed group of workers and parameter-server"
+        }
+    }
+}
\ No newline at end of file
diff --git a/inputJsonsFiles/experimentsFlow/exp_AEC_KDD14_test.json b/inputJsonsFiles/experimentsFlow/exp_AEC_KDD14_test.json
new file mode 100644
index 00000000..30d8e485
--- /dev/null
+++ b/inputJsonsFiles/experimentsFlow/exp_AEC_KDD14_test.json
@@ -0,0 +1,54 @@
+{
+    "experimentName": "anomaly_detection_skab",
+    "experimentType": "classification",
+    "batchSize": 100,
+    "csvFilePath": "/tmp/nerlnet/data/NerlnetData-master/nerlnet/KDD14/KDD14.csv",
+    "numOfFeatures": "10",
+    "numOfLabels": "1",
+    "headersNames": "Anomaly",
+    "Phases": 
+    [
+    {
+        "phaseName": "training_phase1",
+        "phaseType": "training",
+        "sourcePieces":
+        [
+            {
+            "sourceName": "s1",
+            "startingSample": "0",
+            "numOfBatches": "2500",
+            "workers": "w1,w2",
+            "nerltensorType": "float"
+            }
+        ]
+    },
+    {
+        "phaseName": "training_phase2",
+        "phaseType": "training",
+        "sourcePieces":
+        [
+            {
+            "sourceName": "s1",
+            "startingSample": "250000",
+            "numOfBatches": "2700",
+            "workers": "w1,w2",
+            "nerltensorType": "float"
+            }
+        ]
+    },
+    {
+        "phaseName": "prediction_phase",
+        "phaseType": "prediction",
+        "sourcePieces":
+        [
+            {
+            "sourceName": "s1",
+            "startingSample": "554000",
+            "numOfBatches": "1380",
+            "workers": "w1,w2",
+            "nerltensorType": "float"
+            }
+        ]    
+    }
+]
+}
diff --git a/inputJsonsFiles/experimentsFlow/exp_AEC_skab_test.json b/inputJsonsFiles/experimentsFlow/exp_AEC_skab_test.json
new file mode 100644
index 00000000..bdaadaa2
--- /dev/null
+++ b/inputJsonsFiles/experimentsFlow/exp_AEC_skab_test.json
@@ -0,0 +1,40 @@
+{
+    "experimentName": "anomaly_detection_skab",
+    "experimentType": "classification",
+    "batchSize": 50,
+    "csvFilePath": "/tmp/nerlnet/data/NerlnetData-master/nerlnet/skab/skab_full.csv",
+    "numOfFeatures": "8",
+    "numOfLabels": "1",
+    "headersNames": "Anomaly",
+    "Phases": 
+    [
+    {
+        "phaseName": "training_phase1",
+        "phaseType": "training",
+        "sourcePieces":
+        [
+            {
+            "sourceName": "s1",
+            "startingSample": "0",
+            "numOfBatches": "420",
+            "workers": "w1,w2",
+            "nerltensorType": "float"
+            }
+        ]
+    },
+    {
+        "phaseName": "prediction_phase",
+        "phaseType": "prediction",
+        "sourcePieces":
+        [
+            {
+            "sourceName": "s1",
+            "startingSample": "21000",
+            "numOfBatches": "320",
+            "workers": "w1,w2",
+            "nerltensorType": "float"
+            }
+        ]    
+    }
+]
+}
diff --git a/src_py/apiServer/hf_repo_ids.json b/src_py/apiServer/hf_repo_ids.json
index c55fd4bf..6093e320 100644
--- a/src_py/apiServer/hf_repo_ids.json
+++ b/src_py/apiServer/hf_repo_ids.json
@@ -23,6 +23,12 @@
             "idx": 3,
             "name": "ForestCover",
             "description": "Dataset for AEC"
+        },
+        {
+            "id": "Nerlnet/KDD14",
+            "idx": 4,
+            "name": "KDD14",
+            "description": "KDD14 Anomaly Detection Dataset"
         }
     ]
 }
\ No newline at end of file

From 8b1519a257469cf2f02e9a52df7a3e5e608dd1f6 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Mon, 12 Aug 2024 20:18:25 +0000
Subject: [PATCH 17/50] [AEC_EXP] WIP

---
 .../dc_AEC_4d_4r_2s_4c_4w.json                |   2 +
 .../DistributedConfig/dc_AEC_baseline.json    | 102 +++++++++++++
 .../experimentsFlow/exp_AEC_baseline.json     |  40 +++++
 src_py/apiServer/stats.py                     | 140 ++----------------
 4 files changed, 160 insertions(+), 124 deletions(-)
 create mode 100644 inputJsonsFiles/DistributedConfig/dc_AEC_baseline.json
 create mode 100644 inputJsonsFiles/experimentsFlow/exp_AEC_baseline.json

diff --git a/inputJsonsFiles/DistributedConfig/dc_AEC_4d_4r_2s_4c_4w.json b/inputJsonsFiles/DistributedConfig/dc_AEC_4d_4r_2s_4c_4w.json
index e7a05be3..638ba572 100644
--- a/inputJsonsFiles/DistributedConfig/dc_AEC_4d_4r_2s_4c_4w.json
+++ b/inputJsonsFiles/DistributedConfig/dc_AEC_4d_4r_2s_4c_4w.json
@@ -129,6 +129,8 @@
             "_doc_layer_functions_scaler": " none:1 | MinMax:2 | MeanStd:3 | STD:4 | Log:5 |",
             "lossMethod": "2",
             "_doc_lossMethod": " SSE:1 | MSE:2 | NSE:3 | MinkowskiE:4 | WSE:5 | CEE:6 |",
+            "lossArgs": "",
+            "_doc_lossArgs": "reg=L2, reg=L1, reg=NoRegularization (can be also empty)",
             "lr": "0.001",
             "_doc_lr": "Positve float",
             "epochs": "1",
diff --git a/inputJsonsFiles/DistributedConfig/dc_AEC_baseline.json b/inputJsonsFiles/DistributedConfig/dc_AEC_baseline.json
new file mode 100644
index 00000000..a876712a
--- /dev/null
+++ b/inputJsonsFiles/DistributedConfig/dc_AEC_baseline.json
@@ -0,0 +1,102 @@
+{
+    "nerlnetSettings": {
+        "frequency": "300",
+        "batchSize": "100"
+    },
+    "mainServer": {
+        "port": "8081",
+        "args": ""
+    },
+    "apiServer": {
+        "port": "8082",
+        "args": ""
+    },
+    "devices": [
+        {
+            "name": "C0VM0",
+            "ipv4": "10.0.0.5",
+            "entities": "c1,c2,r2,r1,r3,r4,s1,apiServer,mainServer"
+        }
+    ],
+    "routers": [
+        {
+            "name": "r1",
+            "port": "8086",
+            "policy": "0"
+        },
+        {
+            "name": "r2",
+            "port": "8087",
+            "policy": "0"
+        },
+        {
+            "name": "r3",
+            "port": "8088",
+            "policy": "0"
+        },
+        {
+            "name": "r4",
+            "port": "8089",
+            "policy": "0"
+        }
+    ],
+    "sources": [
+        {
+            "name": "s1",
+            "port": "8085",
+            "frequency": "300",
+            "policy": "0",
+            "epochs": "1",
+            "type": "0"
+        }
+    ],
+    "clients": [
+        {
+            "name": "c1",
+            "port": "8083",
+            "workers": "w1"
+        }
+    ],
+    "workers": [
+        {
+            "name": "w1",
+            "model_sha": "d8df752e0a2e8f01de8f66e9cec941cdbc65d144ecf90ab7713e69d65e7e82aa"
+        }
+    ],
+    "model_sha": {
+        "d8df752e0a2e8f01de8f66e9cec941cdbc65d144ecf90ab7713e69d65e7e82aa": {
+            "modelType": "9",
+            "_doc_modelType": " nn:0 | approximation:1 | classification:2 | forecasting:3 | image-classification:4 | text-classification:5 | text-generation:6 | auto-association:7 | autoencoder:8 | ae-classifier:9 |",
+            "modelArgs": "k=0.5,alpha=0.4,use_ema_only=1",
+            "layersSizes": "10,32,16,32,10",
+            "_doc_layersSizes": "List of postive integers [L0, L1, ..., LN]",
+            "layerTypesList": "1,3,3,3,3",
+            "_doc_LayerTypes": " Default:0 | Scaling:1 | CNN:2 | Perceptron:3 | Pooling:4 | Probabilistic:5 | LSTM:6 | Reccurrent:7 | Unscaling:8 |",
+            "layers_functions": "1,8,8,8,8",
+            "_doc_layers_functions_activation": " Threshold:1 | Sign:2 | Logistic:3 | Tanh:4 | Linear:5 | ReLU:6 | eLU:7 | SeLU:8 | Soft-plus:9 | Soft-sign:10 | Hard-sigmoid:11 |",
+            "_doc_layer_functions_pooling": " none:1 | Max:2 | Avg:3 |",
+            "_doc_layer_functions_probabilistic": " Binary:1 | Logistic:2 | Competitive:3 | Softmax:4 |",
+            "_doc_layer_functions_scaler": " none:1 | MinMax:2 | MeanStd:3 | STD:4 | Log:5 |",
+            "lossMethod": "2",
+            "_doc_lossMethod": " SSE:1 | MSE:2 | NSE:3 | MinkowskiE:4 | WSE:5 | CEE:6 |",
+            "lossArgs": "",
+            "_doc_lossArgs": "reg=L2, reg=L1, reg=NoRegularization (can be also empty)",
+            "lr": "0.001",
+            "_doc_lr": "Positve float",
+            "epochs": "1",
+            "_doc_epochs": "Positve Integer",
+            "optimizer": "5",
+            "_doc_optimizer": " GD:0 | CGD:1 | SGD:2 | QuasiNeuton:3 | LVM:4 | ADAM:5 |",
+            "optimizerArgs": "",
+            "_doc_optimizerArgs": "String",
+            "infraType": "0",
+            "_doc_infraType": " opennn:0 | wolfengine:1 |",
+            "distributedSystemType": "0",
+            "_doc_distributedSystemType": " none:0 | fedClientAvg:1 | fedServerAvg:2 |",
+            "distributedSystemArgs": "",
+            "_doc_distributedSystemArgs": "String",
+            "distributedSystemToken": "none",
+            "_doc_distributedSystemToken": "Token that associates distributed group of workers and parameter-server"
+        }
+    }
+}
\ No newline at end of file
diff --git a/inputJsonsFiles/experimentsFlow/exp_AEC_baseline.json b/inputJsonsFiles/experimentsFlow/exp_AEC_baseline.json
new file mode 100644
index 00000000..dddb0a59
--- /dev/null
+++ b/inputJsonsFiles/experimentsFlow/exp_AEC_baseline.json
@@ -0,0 +1,40 @@
+{
+    "experimentName": "anomaly_detection_skab",
+    "experimentType": "classification",
+    "batchSize": 100,
+    "csvFilePath": "/tmp/nerlnet/data/NerlnetData-master/nerlnet/ForestCover/cover_normalized_std.csv",
+    "numOfFeatures": "10",
+    "numOfLabels": "1",
+    "headersNames": "Label",
+    "Phases": 
+    [
+    {
+        "phaseName": "training_phase1",
+        "phaseType": "training",
+        "sourcePieces":
+        [
+            {
+            "sourceName": "s1",
+            "startingSample": "0",
+            "numOfBatches": "2500",
+            "workers": "w1",
+            "nerltensorType": "float"
+            }
+        ]
+    },
+    {
+        "phaseName": "prediction_phase",
+        "phaseType": "prediction",
+        "sourcePieces":
+        [
+            {
+            "sourceName": "s1",
+            "startingSample": "250000",
+            "numOfBatches": "600",
+            "workers": "w1",
+            "nerltensorType": "float"
+            }
+        ]    
+    }
+]
+}
diff --git a/src_py/apiServer/stats.py b/src_py/apiServer/stats.py
index 4e06703d..570e2d08 100644
--- a/src_py/apiServer/stats.py
+++ b/src_py/apiServer/stats.py
@@ -171,51 +171,6 @@ def recieved_batches_key(phase_name, source_name, worker_name):
                                 recived_batches_dict[recieved_batch_key_str].append(batch_id)
         return recived_batches_dict
 
-    def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False , saveToFile : bool = False): 
-            
-            def build_worker_label_df(original_df, batch_ids, batch_size):
-                rows_list = []
-
-                for batch_id in batch_ids:
-                    # Calculate the start and end indices for the rows to be copied
-                    start_idx = batch_id * batch_size
-                    end_idx = (batch_id + 1) * batch_size
-            
-                    # Extract the rows and append to the list
-                    batch_rows = original_df.iloc[start_idx:end_idx]
-                    rows_list.append(batch_rows)
-        
-                    # Concatenate all the extracted rows into a new DataFrame
-                df_worker_labels = pd.concat(rows_list, ignore_index=True)
-                return df_worker_labels
-            
-            assert self.experiment_flow_type == "classification", "This function is only available for classification experiments" 
-            assert self.phase == PHASE_PREDICTION_STR, "This function is only available for predict phase"   
-            sources_pieces_list = self.experiment_phase.get_sources_pieces()
-            workers_model_db_list = self.nerl_model_db.get_workers_model_db_list()
-            confusion_matrix_source_dict = {}
-            confusion_matrix_worker_dict = {}
-            recived_batches_dict = self.get_recieved_batches()
-            for source_piece_inst in sources_pieces_list:
-                nerltensorType = source_piece_inst.get_nerltensor_type()
-                source_name = source_piece_inst.get_source_name()
-                sourcePiece_csv_labels_path = source_piece_inst.get_pointer_to_sourcePiece_CsvDataSet_labels()
-                df_actual_labels = pd.read_csv(sourcePiece_csv_labels_path)
-                num_of_labels = df_actual_labels.shape[1]
-
-                # build confusion matrix for each worker
-                target_workers_string = source_piece_inst.get_target_workers()
-                target_workers_names = target_workers_string.split(',')
-                batch_size = source_piece_inst.get_batch_size()
-                for worker_db in workers_model_db_list:
-                    worker_name = worker_db.get_worker_name()
-                    if worker_name not in target_workers_names:
-                        continue
-                    worker_recived_batches_id = recived_batches_dict.get(f"phase:{self.experiment_phase.get_name()},{source_name}->{worker_name}")   # get a list of recived batches id for the worker
-                    df_worker_labels = build_worker_label_df(df_actual_labels, worker_recived_batches_id, batch_size)
-                    header_list = range(num_of_labels) 
-                    df_worker_labels.columns = header_list
-                    df_worker_labels = self.expend_labels_df(df_worker_labels)  #Now there is a csv file with the actual labels of the source piece and empty columns for the predict labels
     def get_confusion_matrices(self , normalize : bool = False ,plot : bool = False , saveToFile : bool = False): 
         
         def build_worker_label_df(original_df, batch_ids, batch_size):
@@ -248,7 +203,7 @@ def build_worker_label_df(original_df, batch_ids, batch_size):
             df_actual_labels = pd.read_csv(sourcePiece_csv_labels_path)
             num_of_labels = df_actual_labels.shape[1]
 
-             # build confusion matrix for each worker
+            # build confusion matrix for each worker
             target_workers_string = source_piece_inst.get_target_workers()
             target_workers_names = target_workers_string.split(',')
             batch_size = source_piece_inst.get_batch_size()
@@ -261,7 +216,7 @@ def build_worker_label_df(original_df, batch_ids, batch_size):
                 header_list = range(num_of_labels) 
                 df_worker_labels.columns = header_list
                 df_worker_labels = self.expend_labels_df(df_worker_labels)  #Now there is a csv file with the actual labels of the source piece and empty columns for the predict labels
-                 
+                
                 # Check if the actual labels are integers and the predict labels are floats, if so convert the actual labels to nerltensorType
                 if nerltensorType == 'float':
                     if any(pd.api.types.is_integer_dtype(df_worker_labels[col]) for col in df_worker_labels.columns):
@@ -292,88 +247,25 @@ def build_worker_label_df(original_df, batch_ids, batch_size):
                     else:
                         confusion_matrix_worker_dict[(worker_name, class_name)] += confusion_matrix
                     
-                    # Check if the actual labels are integers and the predict labels are floats, if so convert the actual labels to nerltensorType
-                    if nerltensorType == 'float':
-                        if any(pd.api.types.is_integer_dtype(df_worker_labels[col]) for col in df_worker_labels.columns):
-                            df_worker_labels = df_worker_labels.astype(float)
+                else: # Multi-Class
+                    # Take 2 list from the df, one for the actual labels and one for the predict labels to build the confusion matrix
+                    max_column_predict_index = df_worker_labels.iloc[:, num_of_labels:].idxmax(axis=1) 
+                    max_column_predict_index = max_column_predict_index.tolist() 
+                    max_column_predict_index = [int(predict_index) - num_of_labels for predict_index in max_column_predict_index] # fix the index to original labels index
+                    max_column_labels_index = df_worker_labels.iloc[:, :num_of_labels].idxmax(axis=1)
+                    max_column_labels_index = max_column_labels_index.tolist()
                     
-                    #build df_worker_labels with the actual labels and the predict labels
-                    index = 0
-                    for batch_id in worker_recived_batches_id:
-                        batch_db = worker_db.get_batch(source_name, str(batch_id))
-                        if not batch_db:             #It's not necessary to check if the batch is missing, because we already know wich batches are recieved
-                            LOG_INFO(f"Batch {batch_id} is missing for worker {worker_name}")
-                            continue
-                        tensor_data = batch_db.get_tensor_data() 
-                        tensor_data = tensor_data.reshape(batch_size, num_of_labels).copy()  # Make the tensor_data array writable
-                        start_index = index * batch_size
-                        end_index = (index + 1) * batch_size
-                        df_worker_labels.iloc[start_index:end_index, num_of_labels:] = tensor_data
-                        index += 1
-                        
-                    if len(self.headers_list) == 1:   # One class
-                        class_name = self.headers_list[0]
-                        actual_labels = df_worker_labels.iloc[:, :num_of_labels].values.flatten().tolist()
-                        predict_labels = df_worker_labels.iloc[:, num_of_labels:].values.flatten().tolist()
-                        confusion_matrix = metrics.confusion_matrix(actual_labels, predict_labels)
+                    # building confusion matrix for each class
+                    for class_index, class_name in enumerate(self.headers_list):
+                        class_actual_list = [1 if label_num == class_index else 0 for label_num in max_column_labels_index]   # 1 if the label is belong to the class, 0 otherwise
+                        class_predict_list = [1 if label_num == class_index else 0 for label_num in max_column_predict_index]   # 1 if the label is belong to the class, 0 otherwise
+                        confusion_matrix = metrics.confusion_matrix(class_actual_list, class_predict_list)  
+                        #confusion_matrix_np = confusion_matrix.to_numpy()
                         confusion_matrix_source_dict[(source_name, worker_name, class_name)] = confusion_matrix
                         if (worker_name, class_name) not in confusion_matrix_worker_dict:
                             confusion_matrix_worker_dict[(worker_name, class_name)] = confusion_matrix
                         else:
                             confusion_matrix_worker_dict[(worker_name, class_name)] += confusion_matrix
-                        
-                    else: # Multi-Class
-                        # Take 2 list from the df, one for the actual labels and one for the predict labels to build the confusion matrix
-                        max_column_predict_index = df_worker_labels.iloc[:, num_of_labels:].idxmax(axis=1) 
-                        max_column_predict_index = max_column_predict_index.tolist() 
-                        max_column_predict_index = [int(predict_index) - num_of_labels for predict_index in max_column_predict_index] # fix the index to original labels index
-                        max_column_labels_index = df_worker_labels.iloc[:, :num_of_labels].idxmax(axis=1)
-                        max_column_labels_index = max_column_labels_index.tolist()
-                        
-                        # building confusion matrix for each class
-                        for class_index, class_name in enumerate(self.headers_list):
-                            class_actual_list = [1 if label_num == class_index else 0 for label_num in max_column_labels_index]   # 1 if the label is belong to the class, 0 otherwise
-                            class_predict_list = [1 if label_num == class_index else 0 for label_num in max_column_predict_index]   # 1 if the label is belong to the class, 0 otherwise
-                            confusion_matrix = metrics.confusion_matrix(class_actual_list, class_predict_list)  
-                            #confusion_matrix_np = confusion_matrix.to_numpy()
-                            confusion_matrix_source_dict[(source_name, worker_name, class_name)] = confusion_matrix
-                            if (worker_name, class_name) not in confusion_matrix_worker_dict:
-                                confusion_matrix_worker_dict[(worker_name, class_name)] = confusion_matrix
-                            else:
-                                confusion_matrix_worker_dict[(worker_name, class_name)] += confusion_matrix
-            
-            if plot:
-                workers = sorted(list({tup[0] for tup in confusion_matrix_worker_dict.keys()}))
-                classes = sorted(list({tup[1] for tup in confusion_matrix_worker_dict.keys()}))
-                fig, ax = plt.subplots(nrows=len(workers), ncols=len(classes),figsize=(4*len(classes),4*len(workers)),dpi=140)
-                if len(classes) > 1:
-                    for i , worker in enumerate(workers): 
-                        for j , pred_class in enumerate(classes):
-                            conf_mat = confusion_matrix_worker_dict[(worker , pred_class)]
-                            heatmap = sns.heatmap(data=conf_mat ,ax=ax[i,j], annot=True , fmt="d", cmap='Blues',annot_kws={"size": 8}, cbar_kws={'pad': 0.1})
-                            cbar = heatmap.collections[0].colorbar
-                            cbar.ax.tick_params(labelsize = 8)
-                            ax[i, j].set_title(f"{worker} , Class '{pred_class}'" , fontsize=12)
-                            ax[i, j].tick_params(axis='both', which='major', labelsize=8) 
-                            ax[i, j].set_xlabel("Predicted Label" , fontsize=8)
-                            ax[i, j].set_ylabel("True Label" , fontsize=8)
-                            ax[i, j].set_aspect('equal')
-                else:
-                    for i, worker in enumerate(workers):
-                        conf_mat = confusion_matrix_worker_dict[(worker , classes[0])]
-                        heatmap = sns.heatmap(data=conf_mat ,ax=ax[i], annot=True , fmt="d", cmap='Blues',annot_kws={"size": 8}, cbar_kws={'pad': 0.1})
-                        cbar = heatmap.collections[0].colorbar
-                        cbar.ax.tick_params(labelsize = 8)
-                        ax[i].set_title(f"{worker} , Class '{classes[0]}'" , fontsize=12)
-                        ax[i].tick_params(axis='both', which='major', labelsize=8) 
-                        ax[i].set_xlabel("Predicted Label" , fontsize=8)
-                        ax[i].set_ylabel("True Label" , fontsize=8)
-                        ax[i].set_aspect('equal')
-                fig.subplots_adjust(wspace=0.4 , hspace=0.4)
-                plt.show()
-                
-            return confusion_matrix_source_dict, confusion_matrix_worker_dict    
-        
         
         if plot:
             workers = sorted(list({tup[0] for tup in confusion_matrix_worker_dict.keys()}))
@@ -406,7 +298,7 @@ def build_worker_label_df(original_df, batch_ids, batch_size):
             plt.show()
             
         return confusion_matrix_source_dict, confusion_matrix_worker_dict
-    
+        
     def get_recieved_batches(self):
         """
         Returns a dictionary of recieved batches in the experiment phase.

From 590d7f96c6affac98c67b06b663ff923fe1f6308 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Wed, 14 Aug 2024 21:01:24 +0000
Subject: [PATCH 18/50] [AEC] WIP

---
 .../DistributedConfig/conn_AEC_baseline.json  |  9 +++
 .../experimentsFlow/exp_AEC_baseline.json     |  4 +-
 src_py/apiServer/stats.py                     | 55 +++++++++++--------
 3 files changed, 44 insertions(+), 24 deletions(-)
 create mode 100644 inputJsonsFiles/DistributedConfig/conn_AEC_baseline.json

diff --git a/inputJsonsFiles/DistributedConfig/conn_AEC_baseline.json b/inputJsonsFiles/DistributedConfig/conn_AEC_baseline.json
new file mode 100644
index 00000000..3c7f8e53
--- /dev/null
+++ b/inputJsonsFiles/DistributedConfig/conn_AEC_baseline.json
@@ -0,0 +1,9 @@
+{
+    "connectionsMap":
+    {
+        "r1":["mainServer","c1","s1", "r2"],
+        "r2":["r3"],
+        "r3":["s2", "r4"],
+        "r4":["r1"]
+    }
+}
diff --git a/inputJsonsFiles/experimentsFlow/exp_AEC_baseline.json b/inputJsonsFiles/experimentsFlow/exp_AEC_baseline.json
index dddb0a59..c109076e 100644
--- a/inputJsonsFiles/experimentsFlow/exp_AEC_baseline.json
+++ b/inputJsonsFiles/experimentsFlow/exp_AEC_baseline.json
@@ -2,11 +2,11 @@
     "experimentName": "anomaly_detection_skab",
     "experimentType": "classification",
     "batchSize": 100,
-    "csvFilePath": "/tmp/nerlnet/data/NerlnetData-master/nerlnet/ForestCover/cover_normalized_std.csv",
+    "csvFilePath": "/tmp/nerlnet/data/NerlnetData-master/nerlnet/KDD14/KDD14.csv",
     "numOfFeatures": "10",
     "numOfLabels": "1",
     "headersNames": "Label",
-    "Phases": 
+    "Phases":
     [
     {
         "phaseName": "training_phase1",
diff --git a/src_py/apiServer/stats.py b/src_py/apiServer/stats.py
index f80d0b61..0d3176ce 100644
--- a/src_py/apiServer/stats.py
+++ b/src_py/apiServer/stats.py
@@ -294,31 +294,42 @@ def build_worker_label_df(original_df, batch_ids, batch_size):
         if plot:
             workers = sorted(list({tup[0] for tup in confusion_matrix_worker_dict.keys()}))
             classes = sorted(list({tup[1] for tup in confusion_matrix_worker_dict.keys()}))
-            fig, ax = plt.subplots(nrows=len(workers), ncols=len(classes),figsize=(4*len(classes),4*len(workers)),dpi=140)
-            if len(classes) > 1:
-                for i , worker in enumerate(workers): 
-                    for j , pred_class in enumerate(classes):
-                        conf_mat = confusion_matrix_worker_dict[(worker , pred_class)]
-                        heatmap = sns.heatmap(data=conf_mat ,ax=ax[i,j], annot=True , fmt="d", cmap='Blues',annot_kws={"size": 8}, cbar_kws={'pad': 0.1})
+            if len(workers) > 1:
+                fig, ax = plt.subplots(nrows=len(workers), ncols=len(classes),figsize=(4*len(classes), 4*len(workers)), dpi=140)
+                if len(classes) > 1:
+                    for i , worker in enumerate(workers): 
+                        for j , pred_class in enumerate(classes):
+                            conf_mat = confusion_matrix_worker_dict[(worker , pred_class)]
+                            heatmap = sns.heatmap(data=conf_mat ,ax=ax[i,j], annot=True , fmt="d", cmap='Blues',annot_kws={"size": 8}, cbar_kws={'pad': 0.1})
+                            cbar = heatmap.collections[0].colorbar
+                            cbar.ax.tick_params(labelsize = 8)
+                            ax[i, j].set_title(f"{worker} , Class '{pred_class}'" , fontsize=12)
+                            ax[i, j].tick_params(axis='both', which='major', labelsize=8) 
+                            ax[i, j].set_xlabel("Predicted Label" , fontsize=8)
+                            ax[i, j].set_ylabel("True Label" , fontsize=8)
+                            ax[i, j].set_aspect('equal')
+                else:
+                    for i, worker in enumerate(workers):
+                        conf_mat = confusion_matrix_worker_dict[(worker , classes[0])]
+                        heatmap = sns.heatmap(data=conf_mat ,ax=ax[i], annot=True , fmt="d", cmap='Blues',annot_kws={"size": 8}, cbar_kws={'pad': 0.1})
                         cbar = heatmap.collections[0].colorbar
                         cbar.ax.tick_params(labelsize = 8)
-                        ax[i, j].set_title(f"{worker} , Class '{pred_class}'" , fontsize=12)
-                        ax[i, j].tick_params(axis='both', which='major', labelsize=8) 
-                        ax[i, j].set_xlabel("Predicted Label" , fontsize=8)
-                        ax[i, j].set_ylabel("True Label" , fontsize=8)
-                        ax[i, j].set_aspect('equal')
+                        ax[i].set_title(f"{worker} , Class '{classes[0]}'" , fontsize=12)
+                        ax[i].tick_params(axis='both', which='major', labelsize=8) 
+                        ax[i].set_xlabel("Predicted Label" , fontsize=8)
+                        ax[i].set_ylabel("True Label" , fontsize=8)
+                        ax[i].set_aspect('equal')
+                fig.subplots_adjust(wspace=0.4 , hspace=0.4)
             else:
-                for i, worker in enumerate(workers):
-                    conf_mat = confusion_matrix_worker_dict[(worker , classes[0])]
-                    heatmap = sns.heatmap(data=conf_mat ,ax=ax[i], annot=True , fmt="d", cmap='Blues',annot_kws={"size": 8}, cbar_kws={'pad': 0.1})
-                    cbar = heatmap.collections[0].colorbar
-                    cbar.ax.tick_params(labelsize = 8)
-                    ax[i].set_title(f"{worker} , Class '{classes[0]}'" , fontsize=12)
-                    ax[i].tick_params(axis='both', which='major', labelsize=8) 
-                    ax[i].set_xlabel("Predicted Label" , fontsize=8)
-                    ax[i].set_ylabel("True Label" , fontsize=8)
-                    ax[i].set_aspect('equal')
-            fig.subplots_adjust(wspace=0.4 , hspace=0.4)
+                plt.figure(figsize=(4*len(classes), 3), dpi=140)
+                conf_mat = confusion_matrix_worker_dict[(workers[0] , classes[0])]
+                heatmap = sns.heatmap(data=conf_mat , annot=True , fmt="d", cmap='Blues',annot_kws={"size": 8}, cbar_kws={'pad': 0.1})
+                cbar = heatmap.collections[0].colorbar
+                cbar.ax.tick_params(labelsize = 8)
+                plt.title(f"{workers[0]} , Class '{classes[0]}'" , fontsize=12)
+                plt.xlabel("Predicted Label" , fontsize=8)
+                plt.ylabel("True Label" , fontsize=8)
+                plt.tick_params(axis='both', which='major', labelsize=8)
             plt.show()
             
         return confusion_matrix_source_dict, confusion_matrix_worker_dict

From ab170325f6eec356b8342873fab41836a17606d0 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Thu, 15 Aug 2024 13:02:09 +0000
Subject: [PATCH 19/50] [AEC] WIP

---
 .../onnWorkers/workerFederatedClient.erl      |  4 +--
 src_py/apiServer/stats.py                     | 26 +++++++++++++++++++
 2 files changed, 28 insertions(+), 2 deletions(-)

diff --git a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl
index 3b7879b3..1414e332 100644
--- a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl
+++ b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl
@@ -67,7 +67,7 @@ init({GenWorkerEts, WorkerData}) ->
 
 handshake(FedClientEts) ->
   W2WPid = ets:lookup_element(FedClientEts, w2wcom_pid, ?ETS_KEYVAL_VAL_IDX),
-  w2wCom:sync_inbox(W2WPid),
+  w2wCom:sync_inbox_no_limit(W2WPid),
   InboxQueue = w2wCom:get_all_messages(W2WPid),
   MessagesList = queue:to_list(InboxQueue),
   Func = 
@@ -132,7 +132,7 @@ post_idle({GenWorkerEts, _WorkerData}) ->
     true -> HandshakeDone = ets:lookup_element(FedClientEts, handshake_done, ?ETS_KEYVAL_VAL_IDX),
             case HandshakeDone of 
             false -> 
-              w2wCom:sync_inbox(W2WPid),
+              w2wCom:sync_inbox_no_limit(W2WPid),
               InboxQueue = w2wCom:get_all_messages(W2WPid),
               [{_FedServer, {handshake_done, Token}}] = queue:to_list(InboxQueue),
               ets:update_element(FedClientEts, handshake_done, {?ETS_KEYVAL_VAL_IDX, true});
diff --git a/src_py/apiServer/stats.py b/src_py/apiServer/stats.py
index 6fe488d6..fa0dde61 100644
--- a/src_py/apiServer/stats.py
+++ b/src_py/apiServer/stats.py
@@ -1,4 +1,5 @@
 from collections import OrderedDict
+import comm
 from sklearn import metrics
 from IPython.display import display
 import matplotlib.pyplot as plt
@@ -229,6 +230,7 @@ def build_worker_label_df(original_df, batch_ids, batch_size):
                 if nerltensorType == 'float':
                     if any(pd.api.types.is_integer_dtype(df_worker_labels[col]) for col in df_worker_labels.columns):
                         df_worker_labels = df_worker_labels.astype(float)
+                        
                 
                 #build df_worker_labels with the actual labels and the predict labels
                 index = 0
@@ -247,7 +249,9 @@ def build_worker_label_df(original_df, batch_ids, batch_size):
                 if len(self.headers_list) == 1:   # One class
                     class_name = self.headers_list[0]
                     actual_labels = df_worker_labels.iloc[:, :num_of_labels].values.flatten().tolist()
+                    # Predicted labels should be binary, threshold 0.5, turn every float to 1 if it's greater than 0.5, 0 otherwise
                     predict_labels = df_worker_labels.iloc[:, num_of_labels:].values.flatten().tolist()
+                    predict_labels = [1.0 if label > 0.5 else 0.0 for label in predict_labels]
                     confusion_matrix = metrics.confusion_matrix(actual_labels, predict_labels)
                     confusion_matrix_source_dict[(source_name, worker_name, class_name)] = confusion_matrix
                     if (worker_name, class_name) not in confusion_matrix_worker_dict:
@@ -567,5 +571,27 @@ def get_model_performence_stats(self , confusion_matrix_worker_dict , show : boo
 
     def get_predict_regression_stats(self , plot : bool = False , saveToFile : bool = False):
         pass
+    
+    def get_total_bytes(self):
+        # Return the total bytes sent and received in the experiment
+        assert self.phase == PHASE_PREDICTION_STR, "This function is only available for prediction phase"
+        comm_stats_main_server = self.get_communication_stats_main_server()
+        comm_stats_router = self.get_communication_stats_routers()
+        comm_stats_clients = self.get_communication_stats_clients()
+        comm_stats_sources = self.get_communication_stats_sources()
+        bytes = 0
+        for client in comm_stats_clients:
+            bytes += comm_stats_clients[client]['bytes_sent']
+            bytes += comm_stats_clients[client]['bytes_received']
+        for source in comm_stats_sources:
+            bytes += comm_stats_sources[source]['bytes_sent']
+            bytes += comm_stats_sources[source]['bytes_received']
+        for router in comm_stats_router:
+            bytes += comm_stats_router[router]['bytes_sent']
+            bytes += comm_stats_router[router]['bytes_received']
+        bytes += comm_stats_main_server['bytes_sent']
+        bytes += comm_stats_main_server['bytes_received']
+        return bytes
+        
 
 

From 9ca34e4e3fc28969d46b3b1ebcc3e0b8e5476924 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Thu, 15 Aug 2024 13:14:34 +0000
Subject: [PATCH 20/50] [AEC] WIP

---
 .../src/Bridge/onnWorkers/workerFederatedClient.erl  | 12 ++++++------
 .../src/Bridge/onnWorkers/workerGeneric.erl          | 10 +++++-----
 2 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl
index 1414e332..29c03a41 100644
--- a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl
+++ b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl
@@ -79,7 +79,7 @@ handshake(FedClientEts) ->
       if 
         ServerToken =/= MyToken -> not_my_server; 
         true -> w2wCom:send_message(W2WPid, MyName, FedServer, {handshake, MyToken}),
-                % io:format("@FedClient: Sent handshake to server ~p with token ~p~n", [FedServer, MyToken]),
+                io:format("@FedClient: Sent handshake to server ~p with token ~p~n", [FedServer, MyToken]),
                 ets:update_element(FedClientEts, handshake_wait, {?ETS_KEYVAL_VAL_IDX, true})
       end
   end,
@@ -113,7 +113,7 @@ end_stream({GenWorkerEts, WorkerData}) -> % WorkerData is currently a list of [S
         W2WPid = ets:lookup_element(ThisEts, w2wcom_pid, ?ETS_KEYVAL_VAL_IDX),
         ActiveStreams = ets:lookup_element(GenWorkerEts, active_streams, ?ETS_KEYVAL_VAL_IDX),
         case length(ActiveStreams) of % Send to server an updater after got start_stream from the first source
-          0 ->  % io:format("Worker ~p ending stream with ~p~n", [MyName, SourceName]),
+          0 ->  io:format("Worker ~p ending stream with ~p~n", [MyName, SourceName]),
                 w2wCom:send_message_with_event(W2WPid, MyName, ServerName , end_stream, {MyName, SourceName}); % Mimic source behavior
           _ -> ok
         end
@@ -135,8 +135,8 @@ post_idle({GenWorkerEts, _WorkerData}) ->
               w2wCom:sync_inbox_no_limit(W2WPid),
               InboxQueue = w2wCom:get_all_messages(W2WPid),
               [{_FedServer, {handshake_done, Token}}] = queue:to_list(InboxQueue),
-              ets:update_element(FedClientEts, handshake_done, {?ETS_KEYVAL_VAL_IDX, true});
-              % io:format("Worker is part of cluster with token ~p~n", [Token]);
+              ets:update_element(FedClientEts, handshake_done, {?ETS_KEYVAL_VAL_IDX, true}),
+              io:format("Worker is part of cluster with token ~p~n", [Token]);
             true -> ok
             end;
     false -> post_idle({GenWorkerEts, _WorkerData}) % busy waiting until handshake is done
@@ -152,7 +152,7 @@ post_train({GenWorkerEts, {post_train_update, {_SyncIdx, UpdatedWeights}}}) ->
   case WeightsUpdateFlag of
     false -> throw("Received weights update but not waiting for it");
     true -> 
-      % io:format("Got updated weights from server~n"),
+      io:format("Got updated weights from server~n"),
       ModelID = ets:lookup_element(GenWorkerEts, model_id, ?ETS_KEYVAL_VAL_IDX),
       nerlNIF:call_to_set_weights(ModelID, UpdatedWeights),
       ets:update_element(FedClientEts, wait_for_weights_update, {?ETS_KEYVAL_VAL_IDX, false}),
@@ -173,7 +173,7 @@ post_train({GenWorkerEts, _Data}) ->
       SyncCount = ets:lookup_element(FedClientEts, sync_count, ?ETS_KEYVAL_VAL_IDX),
       MaxSyncCount = ets:lookup_element(FedClientEts, sync_max_count, ?ETS_KEYVAL_VAL_IDX),
       if SyncCount == MaxSyncCount ->
-        % io:format("~p sent averaging request to server~n", [MyName]),
+        io:format("~p sent averaging request to server~n", [MyName]),
         ModelID = ets:lookup_element(GenWorkerEts, model_id, ?ETS_KEYVAL_VAL_IDX),
         WeightsTensor = nerlNIF:call_to_get_weights(ModelID),
         ServerName = ets:lookup_element(FedClientEts, server_name, ?ETS_KEYVAL_VAL_IDX), 
diff --git a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl
index b022dc89..25bb1536 100644
--- a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl
+++ b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl
@@ -202,7 +202,7 @@ wait(cast, {end_stream , StreamName}, State = #workerGeneric_state{myName = _MyN
   %logger:notice("Waiting, next state - idle"),
   CurrentEndStreamWaitingList = ets:lookup_element(get(generic_worker_ets), end_streams_waiting_list, ?ETS_KEYVAL_VAL_IDX),
   NewEndStreamWaitingList = CurrentEndStreamWaitingList ++ [StreamName],
-  % io:format("Got end_stream @wait: NewWaitingList: ~p~n",[NewEndStreamWaitingList]),
+  io:format("Got end_stream @wait: NewWaitingList: ~p~n",[NewEndStreamWaitingList]),
   ets:update_element(get(generic_worker_ets), end_streams_waiting_list, {?ETS_KEYVAL_VAL_IDX, NewEndStreamWaitingList}),
   % io:format("@wait ~p got end stream from ~p~n",[MyName, StreamName]),
   {next_state, wait, State};
@@ -283,13 +283,13 @@ train(cast, {post_train_update , Weights}, State = #workerGeneric_state{myName =
   DistributedBehaviorFunc(post_train, {get(generic_worker_ets), Weights}),
   {next_state, train, State};
 
-train(cast, {start_stream , StreamName}, State = #workerGeneric_state{myName = _MyName , distributedBehaviorFunc = DistributedBehaviorFunc}) ->
+train(cast, {start_stream , StreamName}, State = #workerGeneric_state{myName = MyName , distributedBehaviorFunc = DistributedBehaviorFunc}) ->
   stream_handler(start_stream, train, StreamName, DistributedBehaviorFunc),
-  % io:format("~p start stream ~p~n",[MyName, StreamName]),
+  io:format("~p got start stream from ~p~n",[MyName, StreamName]),
   {next_state, train, State};
 
-train(cast, {end_stream , StreamName}, State = #workerGeneric_state{myName = _MyName , distributedBehaviorFunc = DistributedBehaviorFunc}) ->
-  % io:format("@train: ~p end stream ~p~n",[MyName, StreamName]),
+train(cast, {end_stream , StreamName}, State = #workerGeneric_state{myName = MyName , distributedBehaviorFunc = DistributedBehaviorFunc}) ->
+  io:format("@train: ~p got end stream from ~p~n",[MyName, StreamName]),
   stream_handler(end_stream, train, StreamName, DistributedBehaviorFunc),
   {next_state, train, State};
 

From a6a675e775ad83c6871122fb5239aac2f86acc6e Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Thu, 15 Aug 2024 13:20:23 +0000
Subject: [PATCH 21/50] [AEC] WIP

---
 .../src/Bridge/onnWorkers/workerFederatedClient.erl    | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl
index 29c03a41..77054beb 100644
--- a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl
+++ b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl
@@ -134,9 +134,13 @@ post_idle({GenWorkerEts, _WorkerData}) ->
             false -> 
               w2wCom:sync_inbox_no_limit(W2WPid),
               InboxQueue = w2wCom:get_all_messages(W2WPid),
-              [{_FedServer, {handshake_done, Token}}] = queue:to_list(InboxQueue),
-              ets:update_element(FedClientEts, handshake_done, {?ETS_KEYVAL_VAL_IDX, true}),
-              io:format("Worker is part of cluster with token ~p~n", [Token]);
+              Msg = queue:to_list(InboxQueue),
+              case Msg of
+              [{_FedServer, {handshake_done, Token}}] ->
+                          ets:update_element(FedClientEts, handshake_done, {?ETS_KEYVAL_VAL_IDX, true}),
+                          io:format("Worker is part of cluster with token ~p~n", [Token]);
+              _ -> post_idle({GenWorkerEts, _WorkerData})
+              end; 
             true -> ok
             end;
     false -> post_idle({GenWorkerEts, _WorkerData}) % busy waiting until handshake is done

From b669d07c65bcaeec8d5f19fdd4fe9dc51696806a Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Thu, 15 Aug 2024 13:27:35 +0000
Subject: [PATCH 22/50] [AEC] WIP

---
 .../NerlnetApp/src/Bridge/onnWorkers/workerFederatedServer.erl   | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedServer.erl b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedServer.erl
index 3bcf9dd8..22428256 100644
--- a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedServer.erl
+++ b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedServer.erl
@@ -120,6 +120,7 @@ post_idle({GenWorkerEts, _WorkerName}) ->
     MessagesList = queue:to_list(InboxQueue),
     MsgFunc = 
       fun({FedClient, {handshake, Token}}) ->
+        io:format("Handshake with ~p, Token = ~p~n",[FedClient, Token]),
         FedClients = ets:lookup_element(FedServerEts, fed_clients, ?ETS_KEYVAL_VAL_IDX),
         ets:update_element(FedServerEts, fed_clients, {?ETS_KEYVAL_VAL_IDX , [FedClient] ++ FedClients}),
         w2wCom:send_message(W2WPid, FedServerName, FedClient, {handshake_done, Token = MyToken})

From 78596fe3f4be21de7323e4ffd92b3994e38fef0f Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Thu, 15 Aug 2024 13:30:24 +0000
Subject: [PATCH 23/50] [AEC] WIP

---
 .../src/Bridge/onnWorkers/workerFederatedServer.erl           | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedServer.erl b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedServer.erl
index 22428256..7da4e1f4 100644
--- a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedServer.erl
+++ b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedServer.erl
@@ -120,6 +120,10 @@ post_idle({GenWorkerEts, _WorkerName}) ->
     MessagesList = queue:to_list(InboxQueue),
     MsgFunc = 
       fun({FedClient, {handshake, Token}}) ->
+        case Token of 
+          MyToken -> ok;
+          _ -> post_idle({GenWorkerEts, _WorkerName})
+        end,
         io:format("Handshake with ~p, Token = ~p~n",[FedClient, Token]),
         FedClients = ets:lookup_element(FedServerEts, fed_clients, ?ETS_KEYVAL_VAL_IDX),
         ets:update_element(FedServerEts, fed_clients, {?ETS_KEYVAL_VAL_IDX , [FedClient] ++ FedClients}),

From 0d44e0450a10d0035ba1e41b533dbede5b96c2fc Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Thu, 15 Aug 2024 13:57:20 +0000
Subject: [PATCH 24/50] [AEC] WIP

---
 .../src/Bridge/onnWorkers/workerFederatedClient.erl          | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl
index 77054beb..24f0c8f9 100644
--- a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl
+++ b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl
@@ -77,7 +77,7 @@ handshake(FedClientEts) ->
       MyToken = ets:lookup_element(FedClientEts, my_token, ?ETS_KEYVAL_VAL_IDX),
       MyName = ets:lookup_element(FedClientEts, my_name, ?ETS_KEYVAL_VAL_IDX),
       if 
-        ServerToken =/= MyToken -> not_my_server; 
+        ServerToken =/= MyToken -> io:format("Got the wrong Token...~n"), handshake(FedClientEts); 
         true -> w2wCom:send_message(W2WPid, MyName, FedServer, {handshake, MyToken}),
                 io:format("@FedClient: Sent handshake to server ~p with token ~p~n", [FedServer, MyToken]),
                 ets:update_element(FedClientEts, handshake_wait, {?ETS_KEYVAL_VAL_IDX, true})
@@ -134,12 +134,13 @@ post_idle({GenWorkerEts, _WorkerData}) ->
             false -> 
               w2wCom:sync_inbox_no_limit(W2WPid),
               InboxQueue = w2wCom:get_all_messages(W2WPid),
+              % [{_FedServer, {handshake_done, Token}}] = queue:to_list(InboxQueue),
               Msg = queue:to_list(InboxQueue),
               case Msg of
               [{_FedServer, {handshake_done, Token}}] ->
                           ets:update_element(FedClientEts, handshake_done, {?ETS_KEYVAL_VAL_IDX, true}),
                           io:format("Worker is part of cluster with token ~p~n", [Token]);
-              _ -> post_idle({GenWorkerEts, _WorkerData})
+              _ -> io:format("Got the wrong message...~n"), post_idle({GenWorkerEts, _WorkerData})
               end; 
             true -> ok
             end;

From 506927e9075ab3bdbd2b534e562307c462e89cc6 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Thu, 15 Aug 2024 20:13:11 +0000
Subject: [PATCH 25/50] [AEC] WIP

---
 src_erl/NerlnetApp/src/Init/jsonParser.erl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src_erl/NerlnetApp/src/Init/jsonParser.erl b/src_erl/NerlnetApp/src/Init/jsonParser.erl
index 1659374b..361bfecb 100644
--- a/src_erl/NerlnetApp/src/Init/jsonParser.erl
+++ b/src_erl/NerlnetApp/src/Init/jsonParser.erl
@@ -96,7 +96,7 @@ get_models(ShaToModelMaps) ->
     ModelTuple = {ModelType, ModelArgs , LayersSizes, LayersTypes, LayersFunctions, LossMethod, LossArgs, LearningRate, Epochs, Optimizer, OptimizerArgs, InfraType, DistributedSystemType, DistributedSystemArgs, DistributedSystemToken},
     ModelTuple
   end,
-  ShaToModelArgsList = [{binary_to_list(ShaBin) , ModelParams} || {ShaBin , ModelParams} <- maps:to_list(maps:map(Func , ShaToModelMaps))],
+  ShaToModelArgsList = [{binary_to_list(ShaBin) , ModelParams} || {ShaBin , ModelParams} <- lists:map(Func , ShaToModelMaps)], % CHANGED BY GUY 15/8/24
   maps:from_list(ShaToModelArgsList).
 
 generate_workers_map([],WorkersMap,_ClientName)->WorkersMap;

From 8f36be51fd5d4061b602d9f3e9779f8402a63728 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Thu, 15 Aug 2024 20:19:23 +0000
Subject: [PATCH 26/50] [AEC] WIP

---
 src_erl/NerlnetApp/src/Init/jsonParser.erl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src_erl/NerlnetApp/src/Init/jsonParser.erl b/src_erl/NerlnetApp/src/Init/jsonParser.erl
index 361bfecb..a4250d0e 100644
--- a/src_erl/NerlnetApp/src/Init/jsonParser.erl
+++ b/src_erl/NerlnetApp/src/Init/jsonParser.erl
@@ -96,7 +96,7 @@ get_models(ShaToModelMaps) ->
     ModelTuple = {ModelType, ModelArgs , LayersSizes, LayersTypes, LayersFunctions, LossMethod, LossArgs, LearningRate, Epochs, Optimizer, OptimizerArgs, InfraType, DistributedSystemType, DistributedSystemArgs, DistributedSystemToken},
     ModelTuple
   end,
-  ShaToModelArgsList = [{binary_to_list(ShaBin) , ModelParams} || {ShaBin , ModelParams} <- lists:map(Func , ShaToModelMaps)], % CHANGED BY GUY 15/8/24
+  ShaToModelArgsList = [{binary_to_list(ShaBin) , ModelParams} || {ShaBin , ModelParams} <- maps:to_list(maps:map(Func, ShaToModelMaps))],
   maps:from_list(ShaToModelArgsList).
 
 generate_workers_map([],WorkersMap,_ClientName)->WorkersMap;

From 03acb787266bc5d09a45364087a63176620501fe Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Thu, 15 Aug 2024 20:27:04 +0000
Subject: [PATCH 27/50] [AEC] WIP

---
 .../src/Bridge/onnWorkers/workerFederatedServer.erl      | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedServer.erl b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedServer.erl
index 7da4e1f4..18c3f260 100644
--- a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedServer.erl
+++ b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedServer.erl
@@ -14,7 +14,7 @@
 -define(ETS_WEIGHTS_AND_BIAS_NERLTENSOR_IDX, 3).
 -define(ETS_NERLTENSOR_TYPE_IDX, 2).
 -define(DEFAULT_SYNC_MAX_COUNT_ARG, 1).
--define(HANDSHAKE_TIMEOUT, 2000). % 2 seconds
+-define(HANDSHAKE_TIMEOUT, 10000). % 10 seconds
 
 
 controller(FuncName, {GenWorkerEts, WorkerData}) -> 
@@ -106,11 +106,12 @@ post_idle({GenWorkerEts, _WorkerName}) ->
     WorkersList = ets:lookup_element(FedServerEts, broadcast_workers_list, ?ETS_KEYVAL_VAL_IDX),
     W2WPid = ets:lookup_element(FedServerEts, w2wcom_pid, ?ETS_KEYVAL_VAL_IDX),
     MyToken = ets:lookup_element(FedServerEts, token, ?ETS_KEYVAL_VAL_IDX),
+    io:format("~p Token is ~p~n",[FedServerName, MyToken]),
     Func = fun(FedClient) ->
       w2wCom:send_message(W2WPid, FedServerName, FedClient, {handshake, MyToken})
     end,
     lists:foreach(Func, WorkersList),
-    timer:sleep(?HANDSHAKE_TIMEOUT), % wait 2 seconds for response
+    timer:sleep(?HANDSHAKE_TIMEOUT), % wait 10 seconds for response
     InboxQueue = w2wCom:get_all_messages(W2WPid),
     IsEmpty = queue:len(InboxQueue) == 0,
     if IsEmpty == true -> 
@@ -120,10 +121,6 @@ post_idle({GenWorkerEts, _WorkerName}) ->
     MessagesList = queue:to_list(InboxQueue),
     MsgFunc = 
       fun({FedClient, {handshake, Token}}) ->
-        case Token of 
-          MyToken -> ok;
-          _ -> post_idle({GenWorkerEts, _WorkerName})
-        end,
         io:format("Handshake with ~p, Token = ~p~n",[FedClient, Token]),
         FedClients = ets:lookup_element(FedServerEts, fed_clients, ?ETS_KEYVAL_VAL_IDX),
         ets:update_element(FedServerEts, fed_clients, {?ETS_KEYVAL_VAL_IDX , [FedClient] ++ FedClients}),

From 28f3325b6715fd2aff88be1d7d49c941060ad94e Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Thu, 15 Aug 2024 20:33:41 +0000
Subject: [PATCH 28/50] [AEC] WIP

---
 .../src/Bridge/onnWorkers/workerFederatedServer.erl   | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedServer.erl b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedServer.erl
index 18c3f260..23e4f3e4 100644
--- a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedServer.erl
+++ b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedServer.erl
@@ -121,10 +121,13 @@ post_idle({GenWorkerEts, _WorkerName}) ->
     MessagesList = queue:to_list(InboxQueue),
     MsgFunc = 
       fun({FedClient, {handshake, Token}}) ->
-        io:format("Handshake with ~p, Token = ~p~n",[FedClient, Token]),
-        FedClients = ets:lookup_element(FedServerEts, fed_clients, ?ETS_KEYVAL_VAL_IDX),
-        ets:update_element(FedServerEts, fed_clients, {?ETS_KEYVAL_VAL_IDX , [FedClient] ++ FedClients}),
-        w2wCom:send_message(W2WPid, FedServerName, FedClient, {handshake_done, Token = MyToken})
+        case Token of 
+          MyToken ->  io:format("Handshake with ~p~n",[FedClient]),
+                      FedClients = ets:lookup_element(FedServerEts, fed_clients, ?ETS_KEYVAL_VAL_IDX),
+                      ets:update_element(FedServerEts, fed_clients, {?ETS_KEYVAL_VAL_IDX , [FedClient] ++ FedClients}),
+                      w2wCom:send_message(W2WPid, FedServerName, FedClient, {handshake_done, Token = MyToken});
+          _ -> io:format("Token mismatch, expected ~p, got ~p~n",[MyToken, Token])
+        end
     end,
     lists:foreach(MsgFunc, MessagesList),
     ets:update_element(GenWorkerEts, handshake_done, {?ETS_KEYVAL_VAL_IDX, true});

From 4bda2614b86fcfaeadda5c21ac0c484d738c9e0b Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Thu, 15 Aug 2024 20:42:50 +0000
Subject: [PATCH 29/50] [AEC] WIP

---
 .../onnWorkers/workerFederatedClient.erl      | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl
index 24f0c8f9..01b0d29b 100644
--- a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl
+++ b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl
@@ -70,20 +70,25 @@ handshake(FedClientEts) ->
   w2wCom:sync_inbox_no_limit(W2WPid),
   InboxQueue = w2wCom:get_all_messages(W2WPid),
   MessagesList = queue:to_list(InboxQueue),
+  MyToken = ets:lookup_element(FedClientEts, my_token, ?ETS_KEYVAL_VAL_IDX),
+  MyName = ets:lookup_element(FedClientEts, my_name, ?ETS_KEYVAL_VAL_IDX),
   Func = 
     fun({FedServer , {handshake, ServerToken}}) ->
-      ets:insert(FedClientEts, {server_name, FedServer}),
-      ets:insert(FedClientEts, {my_token , ServerToken}),
-      MyToken = ets:lookup_element(FedClientEts, my_token, ?ETS_KEYVAL_VAL_IDX),
-      MyName = ets:lookup_element(FedClientEts, my_name, ?ETS_KEYVAL_VAL_IDX),
       if 
-        ServerToken =/= MyToken -> io:format("Got the wrong Token...~n"), handshake(FedClientEts); 
-        true -> w2wCom:send_message(W2WPid, MyName, FedServer, {handshake, MyToken}),
+        ServerToken =/= MyToken -> io:format("Got the wrong Token...~n"); 
+        true -> ets:insert(FedClientEts, {server_name, FedServer}),
+                % ets:insert(FedClientEts, {my_token , ServerToken}),
+                w2wCom:send_message(W2WPid, MyName, FedServer, {handshake, MyToken}),
                 io:format("@FedClient: Sent handshake to server ~p with token ~p~n", [FedServer, MyToken]),
                 ets:update_element(FedClientEts, handshake_wait, {?ETS_KEYVAL_VAL_IDX, true})
       end
   end,
-  lists:foreach(Func, MessagesList).
+  lists:foreach(Func, MessagesList),
+  ServerName = ets:lookup_element(FedClientEts, server_name, ?ETS_KEYVAL_VAL_IDX),
+  case ServerName of
+    [] -> handshake(FedClientEts); % Did not got the right message yet
+    _ -> done
+  end.
 
 start_stream({GenWorkerEts, WorkerData}) ->  % WorkerData is currently a list of [SourceName, State]
   [SourceName, ModelPhase] = WorkerData,

From 82532ec52e3850100da1e28c1acfd82980296e9a Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Thu, 15 Aug 2024 20:54:40 +0000
Subject: [PATCH 30/50] [AEC] WIP

---
 src_erl/NerlnetApp/src/Source/sourceStatem.erl | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src_erl/NerlnetApp/src/Source/sourceStatem.erl b/src_erl/NerlnetApp/src/Source/sourceStatem.erl
index 298eb13f..59bfd37a 100644
--- a/src_erl/NerlnetApp/src/Source/sourceStatem.erl
+++ b/src_erl/NerlnetApp/src/Source/sourceStatem.erl
@@ -330,6 +330,7 @@ transmitter(TimeInterval_ms, SourceEtsRef, SourcePid, Epochs ,ClientWorkerPairs,
     nerl_tools:http_router_request(RouterHost, RouterPort, [ClientName], atom_to_list(end_stream), ToSend)
   end,
   lists:foreach(FuncEnd, ClientWorkerPairs),
+  io:format("Sent end stream to all workers~n"),
   ErrorBatches = ets:lookup_element(TransmitterEts, batches_issue, ?DATA_IDX),
   SkippedBatches = ets:lookup_element(TransmitterEts, batches_skipped, ?DATA_IDX),
   BatchesSent = ets:lookup_element(TransmitterEts, batches_sent, ?DATA_IDX),

From f590d71fcee58d0741963afcc70e203428cc21dc Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Thu, 15 Aug 2024 21:51:04 +0000
Subject: [PATCH 31/50] [AEC] WIP

---
 src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl
index 25bb1536..1c4a49fd 100644
--- a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl
+++ b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl
@@ -239,7 +239,7 @@ wait(cast, {predict}, State) ->
 
 %% Worker in wait can't treat incoming message 
 wait(cast, BatchTuple , State = #workerGeneric_state{lastPhase = LastPhase, myName= _MyName}) when element(1, BatchTuple) == sample ->
-  % io:format("@wait: Dropped batch state...~n"),
+  io:format("@wait: Dropped batch state...~n"),
   case LastPhase of
     train -> 
       ets:update_counter(get(worker_stats_ets), batches_dropped_train , 1);

From 87a14df6848743c514966383a5ef38f108a681e7 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Thu, 15 Aug 2024 22:12:53 +0000
Subject: [PATCH 32/50] [AEC] WIP

---
 .../src/Bridge/onnWorkers/workerFederatedClient.erl           | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl
index 01b0d29b..bc7d8820 100644
--- a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl
+++ b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerFederatedClient.erl
@@ -162,7 +162,7 @@ post_train({GenWorkerEts, {post_train_update, {_SyncIdx, UpdatedWeights}}}) ->
   case WeightsUpdateFlag of
     false -> throw("Received weights update but not waiting for it");
     true -> 
-      io:format("Got updated weights from server~n"),
+      % io:format("Got updated weights from server~n"),
       ModelID = ets:lookup_element(GenWorkerEts, model_id, ?ETS_KEYVAL_VAL_IDX),
       nerlNIF:call_to_set_weights(ModelID, UpdatedWeights),
       ets:update_element(FedClientEts, wait_for_weights_update, {?ETS_KEYVAL_VAL_IDX, false}),
@@ -183,7 +183,7 @@ post_train({GenWorkerEts, _Data}) ->
       SyncCount = ets:lookup_element(FedClientEts, sync_count, ?ETS_KEYVAL_VAL_IDX),
       MaxSyncCount = ets:lookup_element(FedClientEts, sync_max_count, ?ETS_KEYVAL_VAL_IDX),
       if SyncCount == MaxSyncCount ->
-        io:format("~p sent averaging request to server~n", [MyName]),
+        % io:format("~p sent averaging request to server~n", [MyName]),
         ModelID = ets:lookup_element(GenWorkerEts, model_id, ?ETS_KEYVAL_VAL_IDX),
         WeightsTensor = nerlNIF:call_to_get_weights(ModelID),
         ServerName = ets:lookup_element(FedClientEts, server_name, ?ETS_KEYVAL_VAL_IDX), 

From a48cfe463275a9fd640a97f90c106ee0fef9b38f Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Thu, 15 Aug 2024 22:18:36 +0000
Subject: [PATCH 33/50] [AEC] WIP

---
 src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl | 2 +-
 src_erl/NerlnetApp/src/Client/clientStatem.erl             | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl
index 1c4a49fd..25bb1536 100644
--- a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl
+++ b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl
@@ -239,7 +239,7 @@ wait(cast, {predict}, State) ->
 
 %% Worker in wait can't treat incoming message 
 wait(cast, BatchTuple , State = #workerGeneric_state{lastPhase = LastPhase, myName= _MyName}) when element(1, BatchTuple) == sample ->
-  io:format("@wait: Dropped batch state...~n"),
+  % io:format("@wait: Dropped batch state...~n"),
   case LastPhase of
     train -> 
       ets:update_counter(get(worker_stats_ets), batches_dropped_train , 1);
diff --git a/src_erl/NerlnetApp/src/Client/clientStatem.erl b/src_erl/NerlnetApp/src/Client/clientStatem.erl
index c9c220fa..d3eb45ed 100644
--- a/src_erl/NerlnetApp/src/Client/clientStatem.erl
+++ b/src_erl/NerlnetApp/src/Client/clientStatem.erl
@@ -260,6 +260,7 @@ training(cast, In = {sample,Body}, State = #client_statem_state{etsRef = EtsRef}
 
 % This action is used for start_stream triggered from a clients' worker and not source
 training(cast, {start_stream , {worker, WorkerName, TargetPair}}, State = #client_statem_state{etsRef = EtsRef}) ->
+  io:format("Worker ~p started stream with ~p~n",[WorkerName, TargetPair]),
   ListOfActiveWorkersSources = ets:lookup_element(EtsRef, active_workers_streams, ?DATA_IDX),
   ets:update_element(EtsRef, active_workers_streams, {?DATA_IDX, ListOfActiveWorkersSources ++ [{WorkerName, TargetPair}]}),
   {keep_state, State};
@@ -267,6 +268,7 @@ training(cast, {start_stream , {worker, WorkerName, TargetPair}}, State = #clien
 % This action is used for start_stream triggered from a source per worker
 training(cast, In = {start_stream , Data}, State = #client_statem_state{etsRef = EtsRef}) ->
   {SourceName, _ClientName, WorkerName} = binary_to_term(Data),
+  io:format("~p started stream with ~p~n",[WorkerName, SourceName]),
   ListOfActiveWorkersSources = ets:lookup_element(EtsRef, active_workers_streams, ?DATA_IDX),
   ets:update_element(EtsRef, active_workers_streams, {?DATA_IDX, ListOfActiveWorkersSources ++ [{WorkerName, SourceName}]}),
   ClientStatsEts = get(client_stats_ets),

From 8477898ef46d701240b03897db0fbc8f55b9a13c Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Thu, 15 Aug 2024 22:24:51 +0000
Subject: [PATCH 34/50] [AEC] WIP

---
 src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl | 3 ++-
 src_erl/NerlnetApp/src/Client/clientStatem.erl             | 1 +
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl
index 25bb1536..cc51d3c4 100644
--- a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl
+++ b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl
@@ -284,8 +284,8 @@ train(cast, {post_train_update , Weights}, State = #workerGeneric_state{myName =
   {next_state, train, State};
 
 train(cast, {start_stream , StreamName}, State = #workerGeneric_state{myName = MyName , distributedBehaviorFunc = DistributedBehaviorFunc}) ->
+  io:format("@train: ~p got start stream from ~p~n",[MyName, StreamName]),
   stream_handler(start_stream, train, StreamName, DistributedBehaviorFunc),
-  io:format("~p got start stream from ~p~n",[MyName, StreamName]),
   {next_state, train, State};
 
 train(cast, {end_stream , StreamName}, State = #workerGeneric_state{myName = MyName , distributedBehaviorFunc = DistributedBehaviorFunc}) ->
@@ -348,6 +348,7 @@ stream_handler(StreamPhase , ModelPhase , StreamName , DistributedBehaviorFunc)
   % io:format("~p got ~p from ~p~n",[MyName, StreamPhase, StreamName]),
   ClientPid = ets:lookup_element(GenWorkerEts, client_pid, ?ETS_KEYVAL_VAL_IDX),
   ActiveStreams = ets:lookup_element(GenWorkerEts, active_streams, ?ETS_KEYVAL_VAL_IDX),
+  io:format("~p ActiveStreams: ~p~n",[MyName, ActiveStreams]),
   NewActiveStreams = 
       case StreamPhase of
           start_stream -> ActiveStreams ++ [{MyName, StreamName}];
diff --git a/src_erl/NerlnetApp/src/Client/clientStatem.erl b/src_erl/NerlnetApp/src/Client/clientStatem.erl
index d3eb45ed..795aacd7 100644
--- a/src_erl/NerlnetApp/src/Client/clientStatem.erl
+++ b/src_erl/NerlnetApp/src/Client/clientStatem.erl
@@ -289,6 +289,7 @@ training(cast, In = {end_stream , Data}, State = #client_statem_state{etsRef = E
   {keep_state, State};
 
 training(cast, In = {stream_ended , Pair}, State = #client_statem_state{etsRef = EtsRef}) ->
+  io:format("~p stream ended ~n",[Pair]),
   ClientStatsEts = get(client_stats_ets),
   stats:increment_messages_received(ClientStatsEts),
   stats:increment_bytes_received(ClientStatsEts , nerl_tools:calculate_size(In)),

From 2dcf9d9075bcdca0b6479a014fde4aead99174cd Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Thu, 15 Aug 2024 22:30:21 +0000
Subject: [PATCH 35/50] [AEC] WIP

---
 src_erl/NerlnetApp/src/Client/clientStatem.erl | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src_erl/NerlnetApp/src/Client/clientStatem.erl b/src_erl/NerlnetApp/src/Client/clientStatem.erl
index 795aacd7..fb565a8c 100644
--- a/src_erl/NerlnetApp/src/Client/clientStatem.erl
+++ b/src_erl/NerlnetApp/src/Client/clientStatem.erl
@@ -295,6 +295,7 @@ training(cast, In = {stream_ended , Pair}, State = #client_statem_state{etsRef =
   stats:increment_bytes_received(ClientStatsEts , nerl_tools:calculate_size(In)),
   ListOfActiveWorkersSources = ets:lookup_element(EtsRef, active_workers_streams, ?DATA_IDX),
   UpdatedListOfActiveWorkersSources = ListOfActiveWorkersSources -- [Pair],
+  io:format("Active workers streams: ~p~n",[UpdatedListOfActiveWorkersSources]),
   ets:update_element(EtsRef, active_workers_streams, {?DATA_IDX, UpdatedListOfActiveWorkersSources}),
   case length(UpdatedListOfActiveWorkersSources) of 
     0 ->  ets:update_element(EtsRef, all_workers_done, {?DATA_IDX, true});

From bf2c5a35f8915638e0cb1f077dd4f59d42e819cf Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Fri, 16 Aug 2024 16:11:47 +0000
Subject: [PATCH 36/50] [AEC] WIP

---
 src_erl/NerlnetApp/src/Source/sourceSendingPolicies.erl | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src_erl/NerlnetApp/src/Source/sourceSendingPolicies.erl b/src_erl/NerlnetApp/src/Source/sourceSendingPolicies.erl
index 2e2212da..b2007464 100644
--- a/src_erl/NerlnetApp/src/Source/sourceSendingPolicies.erl
+++ b/src_erl/NerlnetApp/src/Source/sourceSendingPolicies.erl
@@ -43,6 +43,7 @@ send_method_casting(TransmitterEts, Epochs, TimeInterval_ms, ClientWorkerPairs,
     send_method_casting(TransmitterEts, Epochs, TimeInterval_ms, ClientWorkerPairs, BatchesListToSend, 0).
 send_method_casting(_TransmitterEts, Epochs, _TimeInterval_ms, _ClientWorkerPairs, _BatchesListToSend, EpochIdx) when EpochIdx == Epochs -> ok;
 send_method_casting(TransmitterEts, Epochs, TimeInterval_ms, ClientWorkerPairs, BatchesListToSend, EpochIdx) ->
+  io:format("Epoch ~p~n", [EpochIdx]),
   % Sends the same batch to all
   BatchFunc = fun({BatchIdx, Batch}) ->
     prepare_and_send(TransmitterEts, TimeInterval_ms, Batch, BatchIdx, ClientWorkerPairs)
@@ -61,6 +62,7 @@ send_method_round_robin(TransmitterEts, Epochs, TimeInterval_ms, ClientWorkerPai
   send_method_round_robin(TransmitterEts, Epochs, TimeInterval_ms, ClientWorkerPairs, BatchesListToSend, 0).
 send_method_round_robin(_TransmitterEts, Epochs, _TimeInterval_ms, _ClientWorkerPairs, _BatchesListToSend, EpochIdx) when EpochIdx == Epochs -> ok;
 send_method_round_robin(TransmitterEts, Epochs, TimeInterval_ms, ClientWorkerPairs, BatchesListToSend, EpochIdx) ->
+  io:format("Epoch ~p~n", [EpochIdx]),
   % Sends a batch per each
   ClientWorkerPairsIndexes = lists:seq(0, length(ClientWorkerPairs)-1),
   ClientWorkerPairsWithIndexes = lists:zip(ClientWorkerPairsIndexes, ClientWorkerPairs), % Tuple {Idx, Triplet}
@@ -86,6 +88,7 @@ send_method_random(TransmitterEts, Epochs, TimeInterval_ms, ClientWorkerPairs, B
   send_method_random(TransmitterEts, Epochs, TimeInterval_ms, ClientWorkerPairs, BatchesListToSend, 0).
 send_method_random(_TransmitterEts, Epochs, _TimeInterval_ms, _ClientWorkerPairs, _BatchesListToSend, EpochIdx) when EpochIdx == Epochs -> ok;
 send_method_random(TransmitterEts, Epochs, TimeInterval_ms, ClientWorkerPairs, BatchesListToSend, EpochIdx) ->
+  io:format("Epoch ~p~n", [EpochIdx]),
   % Sends a batch per each
   ClientWorkerPairsIndexes = lists:seq(1, length(ClientWorkerPairs)),
   ClientWorkerPairsWithIndexes = lists:zip(ClientWorkerPairsIndexes, ClientWorkerPairs), % Tuple {Idx, Triplet}

From cf0d390f5e4e054c6c96a934afe1ced215c21526 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Sat, 17 Aug 2024 07:50:27 +0000
Subject: [PATCH 37/50] [AEC] WIP

---
 src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl | 4 +---
 src_py/apiServer/stats.py                                  | 1 -
 2 files changed, 1 insertion(+), 4 deletions(-)

diff --git a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl
index cc51d3c4..b5418840 100644
--- a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl
+++ b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl
@@ -284,12 +284,10 @@ train(cast, {post_train_update , Weights}, State = #workerGeneric_state{myName =
   {next_state, train, State};
 
 train(cast, {start_stream , StreamName}, State = #workerGeneric_state{myName = MyName , distributedBehaviorFunc = DistributedBehaviorFunc}) ->
-  io:format("@train: ~p got start stream from ~p~n",[MyName, StreamName]),
   stream_handler(start_stream, train, StreamName, DistributedBehaviorFunc),
   {next_state, train, State};
 
 train(cast, {end_stream , StreamName}, State = #workerGeneric_state{myName = MyName , distributedBehaviorFunc = DistributedBehaviorFunc}) ->
-  io:format("@train: ~p got end stream from ~p~n",[MyName, StreamName]),
   stream_handler(end_stream, train, StreamName, DistributedBehaviorFunc),
   {next_state, train, State};
 
@@ -345,7 +343,7 @@ update_client_avilable_worker(MyName) ->
 stream_handler(StreamPhase , ModelPhase , StreamName , DistributedBehaviorFunc) -> 
   GenWorkerEts = get(generic_worker_ets),
   MyName = ets:lookup_element(GenWorkerEts, worker_name, ?ETS_KEYVAL_VAL_IDX),
-  % io:format("~p got ~p from ~p~n",[MyName, StreamPhase, StreamName]),
+  io:format("~p got ~p from ~p~n",[MyName, StreamPhase, StreamName]),
   ClientPid = ets:lookup_element(GenWorkerEts, client_pid, ?ETS_KEYVAL_VAL_IDX),
   ActiveStreams = ets:lookup_element(GenWorkerEts, active_streams, ?ETS_KEYVAL_VAL_IDX),
   io:format("~p ActiveStreams: ~p~n",[MyName, ActiveStreams]),
diff --git a/src_py/apiServer/stats.py b/src_py/apiServer/stats.py
index fa0dde61..84cae22e 100644
--- a/src_py/apiServer/stats.py
+++ b/src_py/apiServer/stats.py
@@ -574,7 +574,6 @@ def get_predict_regression_stats(self , plot : bool = False , saveToFile : bool
     
     def get_total_bytes(self):
         # Return the total bytes sent and received in the experiment
-        assert self.phase == PHASE_PREDICTION_STR, "This function is only available for prediction phase"
         comm_stats_main_server = self.get_communication_stats_main_server()
         comm_stats_router = self.get_communication_stats_routers()
         comm_stats_clients = self.get_communication_stats_clients()

From 3e2026648442d77a8ac1de80288d975fd2528a48 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Sat, 17 Aug 2024 10:45:26 +0000
Subject: [PATCH 38/50] [AEC] WIP

---
 src_erl/NerlnetApp/src/Source/sourceStatem.erl | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src_erl/NerlnetApp/src/Source/sourceStatem.erl b/src_erl/NerlnetApp/src/Source/sourceStatem.erl
index 59bfd37a..3b8ee3c4 100644
--- a/src_erl/NerlnetApp/src/Source/sourceStatem.erl
+++ b/src_erl/NerlnetApp/src/Source/sourceStatem.erl
@@ -316,6 +316,7 @@ transmitter(TimeInterval_ms, SourceEtsRef, SourcePid, Epochs ,ClientWorkerPairs,
     nerl_tools:http_router_request(RouterHost, RouterPort, [ClientName], atom_to_list(start_stream), ToSend)
   end,
   lists:foreach(FuncStart, ClientWorkerPairs),
+  io:format("Sent start stream to all workers~n"),
   TransmissionStart = erlang:timestamp(),
   case integer_to_list(Method) of % Method is given as an integer
     ?SOURCE_POLICY_CASTING_IDX -> sourceSendingPolicies:send_method_casting(TransmitterEts, Epochs, TimeInterval_ms, ClientWorkerPairs, BatchesListToSend);

From f58e8ef134e8b232ab3f8fa5ec9bfc38f8296e71 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Tue, 20 Aug 2024 17:11:54 +0000
Subject: [PATCH 39/50] removed loss_values tensor

---
 src_cpp/opennnBridge/nerlWorkerOpenNN.cpp | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
index 2a085af6..12ad08d5 100644
--- a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
+++ b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
@@ -52,16 +52,16 @@ namespace nerlnet
             case MODEL_TYPE_AE_CLASSIFIER:
             {
                 int num_of_samples = _aec_data_set->dimension(0);
-                loss_val_tensor = std::make_shared<fTensor2D>(3 + num_of_samples, 1);
+                loss_val_tensor = std::make_shared<fTensor2D>(1, 1);
                 (*loss_val_tensor)(0, 0) = static_cast<float>(_last_loss);
-                (*loss_val_tensor)(1, 0) = _ae_red_ptr->_ema_event;
-                (*loss_val_tensor)(2, 0) = _ae_red_ptr->_ema_normal;
+                // (*loss_val_tensor)(1, 0) = _ae_red_ptr->_ema_event;
+                // (*loss_val_tensor)(2, 0) = _ae_red_ptr->_ema_normal;
                 // cout << "Upper Bound: " << _ae_red_ptr->_ema_event << ", Lower Bound: " << _ae_red_ptr->_ema_normal << endl;
                 // Add _aec_all_loss_values to loss_val_tensor
-                for (int i = 0; i < num_of_samples; i++)
-                {
-                    (*loss_val_tensor)(3 + i, 0) = (*_aec_all_loss_values)(i, 0);
-                }
+                // for (int i = 0; i < num_of_samples; i++)
+                // {
+                //     (*loss_val_tensor)(3 + i, 0) = (*_aec_all_loss_values)(i, 0);
+                // }
                 break;
             }
             default:

From eb5e633ebc682f828c3414f9f7ece3ae0359ecd8 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Tue, 20 Aug 2024 17:32:58 +0000
Subject: [PATCH 40/50] Added Size Print

---
 src_erl/NerlnetApp/src/Client/clientStatem.erl | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src_erl/NerlnetApp/src/Client/clientStatem.erl b/src_erl/NerlnetApp/src/Client/clientStatem.erl
index fb565a8c..ed80371f 100644
--- a/src_erl/NerlnetApp/src/Client/clientStatem.erl
+++ b/src_erl/NerlnetApp/src/Client/clientStatem.erl
@@ -245,6 +245,7 @@ training(cast, In = {sample,Body}, State = #client_statem_state{etsRef = EtsRef}
   ClientStatsEts = get(client_stats_ets),
   stats:increment_messages_received(ClientStatsEts),
   stats:increment_bytes_received(ClientStatsEts , nerl_tools:calculate_size(In)),
+  io:format("Sample Size: ~p", nerl_tools:calculate_size(In)),
   {SourceName , ClientName, WorkerNameStr, BatchID, BatchOfSamples} = binary_to_term(Body),
   WorkerName = list_to_atom(WorkerNameStr),
   WorkersEts = get(workers_ets),

From 45a196db783f036d04d9db65e8d450e1d7c72c4d Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Tue, 20 Aug 2024 17:36:04 +0000
Subject: [PATCH 41/50] Size Print

---
 src_erl/NerlnetApp/src/Client/clientStatem.erl | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src_erl/NerlnetApp/src/Client/clientStatem.erl b/src_erl/NerlnetApp/src/Client/clientStatem.erl
index ed80371f..89480e33 100644
--- a/src_erl/NerlnetApp/src/Client/clientStatem.erl
+++ b/src_erl/NerlnetApp/src/Client/clientStatem.erl
@@ -245,7 +245,8 @@ training(cast, In = {sample,Body}, State = #client_statem_state{etsRef = EtsRef}
   ClientStatsEts = get(client_stats_ets),
   stats:increment_messages_received(ClientStatsEts),
   stats:increment_bytes_received(ClientStatsEts , nerl_tools:calculate_size(In)),
-  io:format("Sample Size: ~p", nerl_tools:calculate_size(In)),
+  Size = nerl_tools:calculate_size(In),
+  io:format("Sample Size: ~p [Bytes]", Size),
   {SourceName , ClientName, WorkerNameStr, BatchID, BatchOfSamples} = binary_to_term(Body),
   WorkerName = list_to_atom(WorkerNameStr),
   WorkersEts = get(workers_ets),

From 7ea8715b75f1bb7aac3c3eff20b161d14a1e3ef4 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Tue, 20 Aug 2024 17:39:58 +0000
Subject: [PATCH 42/50] Print Size

---
 src_erl/NerlnetApp/src/Client/clientStatem.erl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src_erl/NerlnetApp/src/Client/clientStatem.erl b/src_erl/NerlnetApp/src/Client/clientStatem.erl
index 89480e33..a3ab9417 100644
--- a/src_erl/NerlnetApp/src/Client/clientStatem.erl
+++ b/src_erl/NerlnetApp/src/Client/clientStatem.erl
@@ -246,7 +246,7 @@ training(cast, In = {sample,Body}, State = #client_statem_state{etsRef = EtsRef}
   stats:increment_messages_received(ClientStatsEts),
   stats:increment_bytes_received(ClientStatsEts , nerl_tools:calculate_size(In)),
   Size = nerl_tools:calculate_size(In),
-  io:format("Sample Size: ~p [Bytes]", Size),
+  io:format("Sample Size: ~p [Bytes]~n", [Size]),
   {SourceName , ClientName, WorkerNameStr, BatchID, BatchOfSamples} = binary_to_term(Body),
   WorkerName = list_to_atom(WorkerNameStr),
   WorkersEts = get(workers_ets),

From 797c619ce5f8121db0cb3a8179e04abf0fed9ac3 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Tue, 20 Aug 2024 17:45:15 +0000
Subject: [PATCH 43/50] Removed Size Print

---
 src_erl/NerlnetApp/src/Client/clientStatem.erl | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src_erl/NerlnetApp/src/Client/clientStatem.erl b/src_erl/NerlnetApp/src/Client/clientStatem.erl
index a3ab9417..25b9b2f3 100644
--- a/src_erl/NerlnetApp/src/Client/clientStatem.erl
+++ b/src_erl/NerlnetApp/src/Client/clientStatem.erl
@@ -245,8 +245,8 @@ training(cast, In = {sample,Body}, State = #client_statem_state{etsRef = EtsRef}
   ClientStatsEts = get(client_stats_ets),
   stats:increment_messages_received(ClientStatsEts),
   stats:increment_bytes_received(ClientStatsEts , nerl_tools:calculate_size(In)),
-  Size = nerl_tools:calculate_size(In),
-  io:format("Sample Size: ~p [Bytes]~n", [Size]),
+  % Size = nerl_tools:calculate_size(In),
+  % io:format("Sample Size: ~p [Bytes]~n", [Size]),
   {SourceName , ClientName, WorkerNameStr, BatchID, BatchOfSamples} = binary_to_term(Body),
   WorkerName = list_to_atom(WorkerNameStr),
   WorkersEts = get(workers_ets),

From 48adb71fcae854341936caedbff1784013f29ce6 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Tue, 20 Aug 2024 17:51:26 +0000
Subject: [PATCH 44/50] Changed Calculate Size

---
 src_erl/NerlnetApp/src/nerl_tools.erl | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src_erl/NerlnetApp/src/nerl_tools.erl b/src_erl/NerlnetApp/src/nerl_tools.erl
index 1dd85a28..4ef52258 100644
--- a/src_erl/NerlnetApp/src/nerl_tools.erl
+++ b/src_erl/NerlnetApp/src/nerl_tools.erl
@@ -199,9 +199,9 @@ port_available(Port) ->
 %% calculate the number of bytes of term
 
 calculate_size(Term) when is_tuple(Term) -> calculate_size(tuple_to_list(Term));
-calculate_size(List) when is_list(List) -> 
-  Sizes = lists:map(fun(Term) -> erts_debug:flat_size(Term) end, List),
-  lists:sum(Sizes).
+calculate_size(List) when is_list(List) ->
+    Sizes = lists:map(fun(Term) -> byte_size(term_to_binary(Term)) end, List),
+    lists:sum(Sizes).
 
 %% TODO: add another timing map for NIF of each worker action
 

From f8ce3e33c8cdc5990abba61614ea2a5364737f9d Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Tue, 20 Aug 2024 17:53:19 +0000
Subject: [PATCH 45/50] Print Size

---
 src_erl/NerlnetApp/src/Client/clientStatem.erl | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src_erl/NerlnetApp/src/Client/clientStatem.erl b/src_erl/NerlnetApp/src/Client/clientStatem.erl
index 25b9b2f3..a3ab9417 100644
--- a/src_erl/NerlnetApp/src/Client/clientStatem.erl
+++ b/src_erl/NerlnetApp/src/Client/clientStatem.erl
@@ -245,8 +245,8 @@ training(cast, In = {sample,Body}, State = #client_statem_state{etsRef = EtsRef}
   ClientStatsEts = get(client_stats_ets),
   stats:increment_messages_received(ClientStatsEts),
   stats:increment_bytes_received(ClientStatsEts , nerl_tools:calculate_size(In)),
-  % Size = nerl_tools:calculate_size(In),
-  % io:format("Sample Size: ~p [Bytes]~n", [Size]),
+  Size = nerl_tools:calculate_size(In),
+  io:format("Sample Size: ~p [Bytes]~n", [Size]),
   {SourceName , ClientName, WorkerNameStr, BatchID, BatchOfSamples} = binary_to_term(Body),
   WorkerName = list_to_atom(WorkerNameStr),
   WorkersEts = get(workers_ets),

From 26e41ab308b72b017ae9be930c985c35579baa6d Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Tue, 20 Aug 2024 17:58:17 +0000
Subject: [PATCH 46/50] Removed Prints

---
 src_erl/NerlnetApp/src/Client/clientStatem.erl | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src_erl/NerlnetApp/src/Client/clientStatem.erl b/src_erl/NerlnetApp/src/Client/clientStatem.erl
index a3ab9417..25b9b2f3 100644
--- a/src_erl/NerlnetApp/src/Client/clientStatem.erl
+++ b/src_erl/NerlnetApp/src/Client/clientStatem.erl
@@ -245,8 +245,8 @@ training(cast, In = {sample,Body}, State = #client_statem_state{etsRef = EtsRef}
   ClientStatsEts = get(client_stats_ets),
   stats:increment_messages_received(ClientStatsEts),
   stats:increment_bytes_received(ClientStatsEts , nerl_tools:calculate_size(In)),
-  Size = nerl_tools:calculate_size(In),
-  io:format("Sample Size: ~p [Bytes]~n", [Size]),
+  % Size = nerl_tools:calculate_size(In),
+  % io:format("Sample Size: ~p [Bytes]~n", [Size]),
   {SourceName , ClientName, WorkerNameStr, BatchID, BatchOfSamples} = binary_to_term(Body),
   WorkerName = list_to_atom(WorkerNameStr),
   WorkersEts = get(workers_ets),

From 765e4598e56fb9ef0dbc401812cf1e11ddbbd525 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Fri, 23 Aug 2024 07:02:14 +0000
Subject: [PATCH 47/50] [AEC_Exp] Last commit - remove prints

---
 src_cpp/opennnBridge/nerlWorkerOpenNN.cpp        | 16 ++++++++--------
 .../src/Bridge/onnWorkers/workerGeneric.erl      | 10 +++++-----
 src_erl/NerlnetApp/src/Client/clientStatem.erl   |  7 -------
 .../src/Source/sourceSendingPolicies.erl         |  2 +-
 src_erl/NerlnetApp/src/Source/sourceStatem.erl   |  2 --
 src_erl/NerlnetApp/src/nerl_tools.erl            |  2 +-
 6 files changed, 15 insertions(+), 24 deletions(-)

diff --git a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
index 8a0cc36c..82f68134 100644
--- a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
+++ b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
@@ -53,15 +53,15 @@ namespace nerlnet
             {
                 int num_of_samples = _aec_data_set->dimension(0);
                 loss_val_tensor = std::make_shared<fTensor2D>(1, 1);
-                (*loss_val_tensor)(0, 0) = static_cast<float>(_last_loss);
-                // (*loss_val_tensor)(1, 0) = _ae_red_ptr->_ema_event;
-                // (*loss_val_tensor)(2, 0) = _ae_red_ptr->_ema_normal;
-                // cout << "Upper Bound: " << _ae_red_ptr->_ema_event << ", Lower Bound: " << _ae_red_ptr->_ema_normal << endl;
+                (*loss_val_tensor)(0, 0) = static_cast<float>(_last_loss); 
+                (*loss_val_tensor)(1, 0) = _ae_red_ptr->_ema_event; // Mask the following lines to get reduction in data tranfers sizes, or Unmask to enable AEC stats
+                (*loss_val_tensor)(2, 0) = _ae_red_ptr->_ema_normal; 
+                cout << "Upper Bound: " << _ae_red_ptr->_ema_event << ", Lower Bound: " << _ae_red_ptr->_ema_normal << endl;
                 // Add _aec_all_loss_values to loss_val_tensor
-                // for (int i = 0; i < num_of_samples; i++)
-                // {
-                //     (*loss_val_tensor)(3 + i, 0) = (*_aec_all_loss_values)(i, 0);
-                // }
+                for (int i = 0; i < num_of_samples; i++)
+                {
+                    (*loss_val_tensor)(3 + i, 0) = (*_aec_all_loss_values)(i, 0);
+                }
                 break;
             }
             default:
diff --git a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl
index b5418840..5a029d2b 100644
--- a/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl
+++ b/src_erl/NerlnetApp/src/Bridge/onnWorkers/workerGeneric.erl
@@ -202,7 +202,7 @@ wait(cast, {end_stream , StreamName}, State = #workerGeneric_state{myName = _MyN
   %logger:notice("Waiting, next state - idle"),
   CurrentEndStreamWaitingList = ets:lookup_element(get(generic_worker_ets), end_streams_waiting_list, ?ETS_KEYVAL_VAL_IDX),
   NewEndStreamWaitingList = CurrentEndStreamWaitingList ++ [StreamName],
-  io:format("Got end_stream @wait: NewWaitingList: ~p~n",[NewEndStreamWaitingList]),
+  % io:format("Got end_stream @wait: NewWaitingList: ~p~n",[NewEndStreamWaitingList]),
   ets:update_element(get(generic_worker_ets), end_streams_waiting_list, {?ETS_KEYVAL_VAL_IDX, NewEndStreamWaitingList}),
   % io:format("@wait ~p got end stream from ~p~n",[MyName, StreamName]),
   {next_state, wait, State};
@@ -283,11 +283,11 @@ train(cast, {post_train_update , Weights}, State = #workerGeneric_state{myName =
   DistributedBehaviorFunc(post_train, {get(generic_worker_ets), Weights}),
   {next_state, train, State};
 
-train(cast, {start_stream , StreamName}, State = #workerGeneric_state{myName = MyName , distributedBehaviorFunc = DistributedBehaviorFunc}) ->
+train(cast, {start_stream , StreamName}, State = #workerGeneric_state{myName = _MyName , distributedBehaviorFunc = DistributedBehaviorFunc}) ->
   stream_handler(start_stream, train, StreamName, DistributedBehaviorFunc),
   {next_state, train, State};
 
-train(cast, {end_stream , StreamName}, State = #workerGeneric_state{myName = MyName , distributedBehaviorFunc = DistributedBehaviorFunc}) ->
+train(cast, {end_stream , StreamName}, State = #workerGeneric_state{myName = _MyName , distributedBehaviorFunc = DistributedBehaviorFunc}) ->
   stream_handler(end_stream, train, StreamName, DistributedBehaviorFunc),
   {next_state, train, State};
 
@@ -343,10 +343,10 @@ update_client_avilable_worker(MyName) ->
 stream_handler(StreamPhase , ModelPhase , StreamName , DistributedBehaviorFunc) -> 
   GenWorkerEts = get(generic_worker_ets),
   MyName = ets:lookup_element(GenWorkerEts, worker_name, ?ETS_KEYVAL_VAL_IDX),
-  io:format("~p got ~p from ~p~n",[MyName, StreamPhase, StreamName]),
+  % io:format("~p got ~p from ~p~n",[MyName, StreamPhase, StreamName]),
   ClientPid = ets:lookup_element(GenWorkerEts, client_pid, ?ETS_KEYVAL_VAL_IDX),
   ActiveStreams = ets:lookup_element(GenWorkerEts, active_streams, ?ETS_KEYVAL_VAL_IDX),
-  io:format("~p ActiveStreams: ~p~n",[MyName, ActiveStreams]),
+  % io:format("~p ActiveStreams: ~p~n",[MyName, ActiveStreams]),
   NewActiveStreams = 
       case StreamPhase of
           start_stream -> ActiveStreams ++ [{MyName, StreamName}];
diff --git a/src_erl/NerlnetApp/src/Client/clientStatem.erl b/src_erl/NerlnetApp/src/Client/clientStatem.erl
index 25b9b2f3..17cbbfd5 100644
--- a/src_erl/NerlnetApp/src/Client/clientStatem.erl
+++ b/src_erl/NerlnetApp/src/Client/clientStatem.erl
@@ -245,8 +245,6 @@ training(cast, In = {sample,Body}, State = #client_statem_state{etsRef = EtsRef}
   ClientStatsEts = get(client_stats_ets),
   stats:increment_messages_received(ClientStatsEts),
   stats:increment_bytes_received(ClientStatsEts , nerl_tools:calculate_size(In)),
-  % Size = nerl_tools:calculate_size(In),
-  % io:format("Sample Size: ~p [Bytes]~n", [Size]),
   {SourceName , ClientName, WorkerNameStr, BatchID, BatchOfSamples} = binary_to_term(Body),
   WorkerName = list_to_atom(WorkerNameStr),
   WorkersEts = get(workers_ets),
@@ -262,7 +260,6 @@ training(cast, In = {sample,Body}, State = #client_statem_state{etsRef = EtsRef}
 
 % This action is used for start_stream triggered from a clients' worker and not source
 training(cast, {start_stream , {worker, WorkerName, TargetPair}}, State = #client_statem_state{etsRef = EtsRef}) ->
-  io:format("Worker ~p started stream with ~p~n",[WorkerName, TargetPair]),
   ListOfActiveWorkersSources = ets:lookup_element(EtsRef, active_workers_streams, ?DATA_IDX),
   ets:update_element(EtsRef, active_workers_streams, {?DATA_IDX, ListOfActiveWorkersSources ++ [{WorkerName, TargetPair}]}),
   {keep_state, State};
@@ -270,7 +267,6 @@ training(cast, {start_stream , {worker, WorkerName, TargetPair}}, State = #clien
 % This action is used for start_stream triggered from a source per worker
 training(cast, In = {start_stream , Data}, State = #client_statem_state{etsRef = EtsRef}) ->
   {SourceName, _ClientName, WorkerName} = binary_to_term(Data),
-  io:format("~p started stream with ~p~n",[WorkerName, SourceName]),
   ListOfActiveWorkersSources = ets:lookup_element(EtsRef, active_workers_streams, ?DATA_IDX),
   ets:update_element(EtsRef, active_workers_streams, {?DATA_IDX, ListOfActiveWorkersSources ++ [{WorkerName, SourceName}]}),
   ClientStatsEts = get(client_stats_ets),
@@ -291,13 +287,11 @@ training(cast, In = {end_stream , Data}, State = #client_statem_state{etsRef = E
   {keep_state, State};
 
 training(cast, In = {stream_ended , Pair}, State = #client_statem_state{etsRef = EtsRef}) ->
-  io:format("~p stream ended ~n",[Pair]),
   ClientStatsEts = get(client_stats_ets),
   stats:increment_messages_received(ClientStatsEts),
   stats:increment_bytes_received(ClientStatsEts , nerl_tools:calculate_size(In)),
   ListOfActiveWorkersSources = ets:lookup_element(EtsRef, active_workers_streams, ?DATA_IDX),
   UpdatedListOfActiveWorkersSources = ListOfActiveWorkersSources -- [Pair],
-  io:format("Active workers streams: ~p~n",[UpdatedListOfActiveWorkersSources]),
   ets:update_element(EtsRef, active_workers_streams, {?DATA_IDX, UpdatedListOfActiveWorkersSources}),
   case length(UpdatedListOfActiveWorkersSources) of 
     0 ->  ets:update_element(EtsRef, all_workers_done, {?DATA_IDX, true});
@@ -523,7 +517,6 @@ handle_w2w_msg(EtsRef, FromWorker, ToWorker, Data) ->
       %% Send to the correct client
       DestClient = maps:get(ToWorker, ets:lookup_element(EtsRef, workerToClient, ?ETS_KV_VAL_IDX)),
       % ClientName = ets:lookup_element(EtsRef, myName , ?DATA_IDX),
-      % io:format("Client ~p passing w2w_msg {~p --> ~p} to ~p: Data ~p~n",[ClientName, FromWorker, ToWorker, DestClient,Data]),
       MessageBody = {worker_to_worker_msg, FromWorker, ToWorker, Data},
       {RouterHost,RouterPort} = ets:lookup_element(EtsRef, my_router, ?DATA_IDX),
       nerl_tools:http_router_request(RouterHost, RouterPort, [DestClient], atom_to_list(worker_to_worker_msg), MessageBody),
diff --git a/src_erl/NerlnetApp/src/Source/sourceSendingPolicies.erl b/src_erl/NerlnetApp/src/Source/sourceSendingPolicies.erl
index b2007464..395e67bb 100644
--- a/src_erl/NerlnetApp/src/Source/sourceSendingPolicies.erl
+++ b/src_erl/NerlnetApp/src/Source/sourceSendingPolicies.erl
@@ -88,7 +88,7 @@ send_method_random(TransmitterEts, Epochs, TimeInterval_ms, ClientWorkerPairs, B
   send_method_random(TransmitterEts, Epochs, TimeInterval_ms, ClientWorkerPairs, BatchesListToSend, 0).
 send_method_random(_TransmitterEts, Epochs, _TimeInterval_ms, _ClientWorkerPairs, _BatchesListToSend, EpochIdx) when EpochIdx == Epochs -> ok;
 send_method_random(TransmitterEts, Epochs, TimeInterval_ms, ClientWorkerPairs, BatchesListToSend, EpochIdx) ->
-  io:format("Epoch ~p~n", [EpochIdx]),
+  io:format("Epoch ~p~n", [EpochIdx + 1]),
   % Sends a batch per each
   ClientWorkerPairsIndexes = lists:seq(1, length(ClientWorkerPairs)),
   ClientWorkerPairsWithIndexes = lists:zip(ClientWorkerPairsIndexes, ClientWorkerPairs), % Tuple {Idx, Triplet}
diff --git a/src_erl/NerlnetApp/src/Source/sourceStatem.erl b/src_erl/NerlnetApp/src/Source/sourceStatem.erl
index 3b8ee3c4..298eb13f 100644
--- a/src_erl/NerlnetApp/src/Source/sourceStatem.erl
+++ b/src_erl/NerlnetApp/src/Source/sourceStatem.erl
@@ -316,7 +316,6 @@ transmitter(TimeInterval_ms, SourceEtsRef, SourcePid, Epochs ,ClientWorkerPairs,
     nerl_tools:http_router_request(RouterHost, RouterPort, [ClientName], atom_to_list(start_stream), ToSend)
   end,
   lists:foreach(FuncStart, ClientWorkerPairs),
-  io:format("Sent start stream to all workers~n"),
   TransmissionStart = erlang:timestamp(),
   case integer_to_list(Method) of % Method is given as an integer
     ?SOURCE_POLICY_CASTING_IDX -> sourceSendingPolicies:send_method_casting(TransmitterEts, Epochs, TimeInterval_ms, ClientWorkerPairs, BatchesListToSend);
@@ -331,7 +330,6 @@ transmitter(TimeInterval_ms, SourceEtsRef, SourcePid, Epochs ,ClientWorkerPairs,
     nerl_tools:http_router_request(RouterHost, RouterPort, [ClientName], atom_to_list(end_stream), ToSend)
   end,
   lists:foreach(FuncEnd, ClientWorkerPairs),
-  io:format("Sent end stream to all workers~n"),
   ErrorBatches = ets:lookup_element(TransmitterEts, batches_issue, ?DATA_IDX),
   SkippedBatches = ets:lookup_element(TransmitterEts, batches_skipped, ?DATA_IDX),
   BatchesSent = ets:lookup_element(TransmitterEts, batches_sent, ?DATA_IDX),
diff --git a/src_erl/NerlnetApp/src/nerl_tools.erl b/src_erl/NerlnetApp/src/nerl_tools.erl
index 4ef52258..7553b1a0 100644
--- a/src_erl/NerlnetApp/src/nerl_tools.erl
+++ b/src_erl/NerlnetApp/src/nerl_tools.erl
@@ -200,7 +200,7 @@ port_available(Port) ->
 
 calculate_size(Term) when is_tuple(Term) -> calculate_size(tuple_to_list(Term));
 calculate_size(List) when is_list(List) ->
-    Sizes = lists:map(fun(Term) -> byte_size(term_to_binary(Term)) end, List),
+    Sizes = lists:map(fun(Term) -> byte_size(term_to_binary(Term)) end, List), 
     lists:sum(Sizes).
 
 %% TODO: add another timing map for NIF of each worker action

From dec2550ea9417c5133f90ffadc842ea2ff65dc2e Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Tue, 27 Aug 2024 20:57:33 +0000
Subject: [PATCH 48/50] OpenNN ScalingLayer Fix

---
 src_cpp/opennn | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src_cpp/opennn b/src_cpp/opennn
index 6e594ce2..37fa23df 160000
--- a/src_cpp/opennn
+++ b/src_cpp/opennn
@@ -1 +1 @@
-Subproject commit 6e594ce2bfa2c98a482aede20c2e4bb601986d9c
+Subproject commit 37fa23df88e69c9fbc90016ba2710e8717c27ee2

From 549ddfa6c1579338511f491ee643166dd6d9b408 Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Tue, 27 Aug 2024 21:14:47 +0000
Subject: [PATCH 49/50] Shared Pointers Fix

---
 src_cpp/opennnBridge/nerlWorkerOpenNN.cpp | 42 ++++++-----------------
 1 file changed, 10 insertions(+), 32 deletions(-)

diff --git a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
index 82f68134..ca48860e 100644
--- a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
+++ b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
@@ -1,6 +1,5 @@
 #include "nerlWorkerOpenNN.h"
 #include "ae_red.h" 
-#include <fstream>
 #include <iostream>
 
 using namespace opennn;
@@ -30,7 +29,6 @@ namespace nerlnet
     void NerlWorkerOpenNN::perform_training()
     {
         this->_training_strategy_ptr->set_data_set_pointer(this->_data_set.get());
-        this->_training_strategy_ptr->get_loss_index_pointer()->set_regularization_method(LossIndex::RegularizationMethod::L2); // ! ADDED NOW
         TrainingResults res = this->_training_strategy_ptr->perform_training();
         this->_last_loss = res.get_training_error();
     
@@ -54,11 +52,11 @@ namespace nerlnet
                 int num_of_samples = _aec_data_set->dimension(0);
                 loss_val_tensor = std::make_shared<fTensor2D>(1, 1);
                 (*loss_val_tensor)(0, 0) = static_cast<float>(_last_loss); 
+                // TODO Add an if statement to save this values only if the user wants to save them (ModelArgs)
                 (*loss_val_tensor)(1, 0) = _ae_red_ptr->_ema_event; // Mask the following lines to get reduction in data tranfers sizes, or Unmask to enable AEC stats
                 (*loss_val_tensor)(2, 0) = _ae_red_ptr->_ema_normal; 
-                cout << "Upper Bound: " << _ae_red_ptr->_ema_event << ", Lower Bound: " << _ae_red_ptr->_ema_normal << endl;
                 // Add _aec_all_loss_values to loss_val_tensor
-                for (int i = 0; i < num_of_samples; i++)
+                for (int i = 0; i < num_of_samples; i++) // TODO Needs optimization
                 {
                     (*loss_val_tensor)(3 + i, 0) = (*_aec_all_loss_values)(i, 0);
                 }
@@ -105,19 +103,17 @@ namespace nerlnet
                 fTensor2DPtr calculate_res = std::make_shared<fTensor2D>(num_of_samples, neural_network->get_outputs_number());
                 *calculate_res = neural_network->calculate_outputs(TrainData->data(), inputs_dimensions);
                 fTensor2DPtr loss_values_mse = std::make_shared<fTensor2D>(num_of_samples, 1);
-                fTensor2DPtr loss_values_return = std::make_shared<fTensor2D>(num_of_samples, 1);
                 fTensor2D diff = (*calculate_res - *_aec_data_set);
                 fTensor2D squared_diff = diff.pow(2);
                 fTensor1D sum_squared_diff = squared_diff.sum(Eigen::array<int, 1>({1}));
                 fTensor1D mse1D = (1.0 / static_cast<float>(_aec_data_set->dimension(0))) * sum_squared_diff;
-                fTensor2D mse2D = mse1D.reshape(Eigen::array<int, 2>({(int)num_of_samples, 1}));
-                // cout << "MSE2D: " << mse2D << endl;
-                *loss_values_mse = mse2D;
-                *loss_values_return = mse2D;
-                _aec_all_loss_values = loss_values_return;
-                // cout << "MSE Loss: " << mse_loss << endl;
-                _ae_red_ptr->update_batch(loss_values_mse);
-                // cout << "AE_RED RESULT VECTOR:" << endl << *res << endl;
+                fTensor2DPtr mse2D = std::make_shared<fTensor2D>(num_of_samples, 1);
+                cout << "GOT HERE1" << endl;
+                *mse2D = mse1D.reshape(Eigen::array<int, 2>({(int)num_of_samples, 1}));
+                cout << "GOT HERE2" << endl;
+                _aec_all_loss_values = mse2D;
+                cout << "GOT HERE3" << endl;
+                _ae_red_ptr->update_batch(mse2D);
             }
 
                 
@@ -157,24 +153,7 @@ namespace nerlnet
                 fTensor1D sum_squared_diff = squared_diff.sum(Eigen::array<int, 1>({1}));
                 fTensor1D mse1D = (1.0 / static_cast<float>(_aec_data_set->dimension(0))) * sum_squared_diff;
                 fTensor2D mse2D = mse1D.reshape(Eigen::array<int, 2>({(int)num_of_samples, 1}));
-                *loss_values_mse = mse2D;
-                // string filename = "/tmp/nerlnet/predict_errors.csv";
-                // std::ofstream file(filename, std::ios::out | std::ios::app);
-                // if (file.is_open()) {
-                //     for (int i = 0; i < mse2D.dimension(0); ++i) {
-                //         for (int j = 0; j < mse2D.dimension(1); ++j) {
-                //             file << mse2D(i, j);
-                //             if (j < mse2D.dimension(1) - 1) {
-                //                 file << ",";
-                //             }
-                //         }
-                //         file << "\n";
-                //     }
-                //     file.close();
-                // } 
-                // else {
-                //     cerr << "Unable to open file" << endl;
-                //     }
+                *loss_values_mse = mse2D;                
                 result_ptr = _ae_red_ptr->update_batch(loss_values_mse); // ! This should override the result_ptr
                 // result_ptr = loss_values_mse;
                 break;
@@ -598,7 +577,6 @@ namespace nerlnet
             }  
             curr_layer = curr_layer->get_next_layer_ptr();
         }
-        // Write the model parameters to file
         // neural_network_ptr->get_parameters();
         // neural_network_ptr->save("/home/nerlnet/workspace/NErlNet/model_parameters.xml");
         // cout << "Model Parameters Saved" << endl;

From 81d50d634541cce0dfdf7406e9ceeabe45b623cc Mon Sep 17 00:00:00 2001
From: GuyPerets106 <guyperets106@gmail.com>
Date: Tue, 27 Aug 2024 21:26:11 +0000
Subject: [PATCH 50/50] Fixed loss_val_tensor size

---
 src_cpp/opennnBridge/nerlWorkerOpenNN.cpp | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
index ca48860e..841b837a 100644
--- a/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
+++ b/src_cpp/opennnBridge/nerlWorkerOpenNN.cpp
@@ -50,7 +50,7 @@ namespace nerlnet
             case MODEL_TYPE_AE_CLASSIFIER:
             {
                 int num_of_samples = _aec_data_set->dimension(0);
-                loss_val_tensor = std::make_shared<fTensor2D>(1, 1);
+                loss_val_tensor = std::make_shared<fTensor2D>(3 + num_of_samples, 1); // TODO Check with if statements the correct size of loss_val_tensor
                 (*loss_val_tensor)(0, 0) = static_cast<float>(_last_loss); 
                 // TODO Add an if statement to save this values only if the user wants to save them (ModelArgs)
                 (*loss_val_tensor)(1, 0) = _ae_red_ptr->_ema_event; // Mask the following lines to get reduction in data tranfers sizes, or Unmask to enable AEC stats
@@ -108,11 +108,8 @@ namespace nerlnet
                 fTensor1D sum_squared_diff = squared_diff.sum(Eigen::array<int, 1>({1}));
                 fTensor1D mse1D = (1.0 / static_cast<float>(_aec_data_set->dimension(0))) * sum_squared_diff;
                 fTensor2DPtr mse2D = std::make_shared<fTensor2D>(num_of_samples, 1);
-                cout << "GOT HERE1" << endl;
                 *mse2D = mse1D.reshape(Eigen::array<int, 2>({(int)num_of_samples, 1}));
-                cout << "GOT HERE2" << endl;
                 _aec_all_loss_values = mse2D;
-                cout << "GOT HERE3" << endl;
                 _ae_red_ptr->update_batch(mse2D);
             }