From 7896718554779ed91cfd59cd27b080b83c9d4c54 Mon Sep 17 00:00:00 2001
From: HuaxiaWu <hw2560@columbia.edu>
Date: Mon, 19 Sep 2016 22:12:25 -0400
Subject: [PATCH 01/15] First commit

my first time to do this!! but seems good?
---
 notebooks/week-1/week 1 - VM test.ipynb | 63 ++++++++++++++++++++++---
 1 file changed, 56 insertions(+), 7 deletions(-)

diff --git a/notebooks/week-1/week 1 - VM test.ipynb b/notebooks/week-1/week 1 - VM test.ipynb
index 235273c..ca87137 100644
--- a/notebooks/week-1/week 1 - VM test.ipynb	
+++ b/notebooks/week-1/week 1 - VM test.ipynb	
@@ -11,22 +11,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello world\n"
+     ]
+    }
+   ],
    "source": [
     "print \"hello world\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "keras successfully imported\n",
+      "theano successfully imported\n",
+      "tensorflow successfully imported\n",
+      "sklearn successfully imported\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vagrant/anaconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
+      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seaborn successfully imported\n"
+     ]
+    }
+   ],
    "source": [
     "import keras\n",
     "print \"keras successfully imported\"\n",
@@ -42,13 +83,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "uni: hw2560\n"
+     ]
+    }
+   ],
    "source": [
-    "print \"uni:\", \"fill in your uni here\""
+    "print \"uni:\", \"hw2560\""
    ]
   }
  ],

From 4aeea5cfcee8445d76edbd02898414fcc9a9436b Mon Sep 17 00:00:00 2001
From: HuaxiaWu <hw2560@columbia.edu>
Date: Wed, 5 Oct 2016 17:23:12 -0400
Subject: [PATCH 02/15] lab2 assignment

sorry about the lateness
---
 notebooks/lab2 assignment.ipynb | 204 ++++++++++++++++++++++++++++++++
 1 file changed, 204 insertions(+)
 create mode 100644 notebooks/lab2 assignment.ipynb

diff --git a/notebooks/lab2 assignment.ipynb b/notebooks/lab2 assignment.ipynb
new file mode 100644
index 0000000..fbc284a
--- /dev/null
+++ b/notebooks/lab2 assignment.ipynb	
@@ -0,0 +1,204 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "gameStake = 50\n",
+    "cards = range(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "start game 0\n",
+      "player0Lose,5vs8\n",
+      "player1Lose,1vs6\n",
+      "player2Win,7vs1\n",
+      "player3Win,8vs6\n",
+      "player4Lose,4vs9\n",
+      "\n",
+      "start game 1\n",
+      "player0Win,6vs1\n",
+      "player1Lose,1vs2\n",
+      "player2Lose,3vs6\n",
+      "player3Lose,2vs4\n",
+      "player4Win,8vs5\n",
+      "\n",
+      "start game 2\n",
+      "player0Lose,7vs9\n",
+      "player1Win,7vs3\n",
+      "player2Win,6vs6\n",
+      "player3Win,9vs3\n",
+      "player4Win,8vs1\n",
+      "\n",
+      "start game 3\n",
+      "player0Lose,3vs9\n",
+      "player1Win,4vs1\n",
+      "player2Lose,1vs3\n",
+      "player3Lose,7vs9\n",
+      "player4Win,1vs1\n",
+      "\n",
+      "start game 4\n",
+      "player0Lose,2vs5\n",
+      "player1Lose,1vs6\n",
+      "player2Win,3vs1\n",
+      "player3Lose,3vs5\n",
+      "player4Lose,3vs9\n",
+      "\n",
+      "start game 5\n",
+      "player0Lose,7vs8\n",
+      "player1Lose,5vs9\n",
+      "player2Win,9vs9\n",
+      "player3Win,9vs8\n",
+      "player4Lose,0vs1\n",
+      "\n",
+      "start game 6\n",
+      "player0Lose,6vs8\n",
+      "player1Lose,6vs8\n",
+      "player2Lose,3vs4\n",
+      "player3Lose,3vs8\n",
+      "player4Win,5vs1\n",
+      "\n",
+      "start game 7\n",
+      "player0Win,3vs2\n",
+      "player1Win,1vs0\n",
+      "player2Win,3vs0\n",
+      "player3Win,9vs1\n",
+      "player4Win,0vs0\n",
+      "\n",
+      "start game 8\n",
+      "player0Win,3vs3\n",
+      "player1Win,3vs2\n",
+      "player2Lose,6vs8\n",
+      "player3Lose,1vs9\n",
+      "player4Win,8vs0\n",
+      "\n",
+      "start game 9\n",
+      "player0Lose,7vs8\n",
+      "player1Win,8vs0\n",
+      "player2Lose,3vs8\n",
+      "player3Win,7vs0\n",
+      "player4Lose,0vs8\n",
+      "\n",
+      "game result:\n",
+      "player0has $300left.\n",
+      "player1has $500left.\n",
+      "player2has $500left.\n",
+      "player3has $500left.\n",
+      "player4has $600left.\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Player:\n",
+    "    \n",
+    "    playerID=0\n",
+    "    playerPot=0\n",
+    "    \n",
+    "    def __init__(self, inputID, startingPot):\n",
+    "        self.playerID = inputID\n",
+    "        self.playerPot = startingPot\n",
+    "        \n",
+    "    def play(self, dealerCard):\n",
+    "        \n",
+    "        playerCard = random.choice(cards)\n",
+    "        \n",
+    "        if playerCard < dealerCard:\n",
+    "            self.playerPot += -gameStake\n",
+    "            print 'player'+ str(self.playerID) + 'Lose,'+ str(playerCard)+'vs'+ str(dealerCard)\n",
+    "        else:\n",
+    "            self.playerPot += gameStake\n",
+    "            print 'player'+ str(self.playerID) + 'Win,'+ str(playerCard)+'vs'+ str(dealerCard)\n",
+    "            \n",
+    "    def returnPot(self):\n",
+    "        return self.playerPot\n",
+    "    \n",
+    "    def returnID(self):\n",
+    "        return self.playerID\n",
+    "    \n",
+    "    \n",
+    "def playHand(players):\n",
+    "        \n",
+    "        for player in players:\n",
+    "            dealerCard = random.choice(cards)\n",
+    "            player.play(dealerCard)\n",
+    "            \n",
+    "def checkBalances(players):\n",
+    "        \n",
+    "        for player in players:\n",
+    "            \n",
+    "            print 'player' + str(player.returnID()) +'has $'+ str(player.returnPot())+'left.'\n",
+    "            \n",
+    "players =[]\n",
+    "\n",
+    "for i in range(5):\n",
+    "    players.append(Player(i,500))\n",
+    "    \n",
+    "for i in range(10):\n",
+    "    print ''\n",
+    "    print 'start game ' + str(i)\n",
+    "    playHand(players)\n",
+    "    \n",
+    "print ''\n",
+    "print 'game result:'\n",
+    "checkBalances(players)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From 81e08fdb69fed29c521f9cad12d703b4c7ea99f2 Mon Sep 17 00:00:00 2001
From: HuaxiaWu <hw2560@columbia.edu>
Date: Wed, 5 Oct 2016 17:42:15 -0400
Subject: [PATCH 03/15] commit2

2nd assignment
---
 ...ignment.ipynb => assignment2_hw2560.ipynb} | 135 ++++++++----------
 1 file changed, 58 insertions(+), 77 deletions(-)
 rename notebooks/{lab2 assignment.ipynb => assignment2_hw2560.ipynb} (66%)

diff --git a/notebooks/lab2 assignment.ipynb b/notebooks/assignment2_hw2560.ipynb
similarity index 66%
rename from notebooks/lab2 assignment.ipynb
rename to notebooks/assignment2_hw2560.ipynb
index fbc284a..3694a10 100644
--- a/notebooks/lab2 assignment.ipynb	
+++ b/notebooks/assignment2_hw2560.ipynb
@@ -3,29 +3,6 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import random"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "gameStake = 50\n",
-    "cards = range(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -36,85 +13,90 @@
      "text": [
       "\n",
       "start game 0\n",
-      "player0Lose,5vs8\n",
-      "player1Lose,1vs6\n",
-      "player2Win,7vs1\n",
-      "player3Win,8vs6\n",
-      "player4Lose,4vs9\n",
+      "player0Win,1vs0\n",
+      "player1Win,9vs7\n",
+      "player2Lose,4vs6\n",
+      "player3Win,7vs4\n",
+      "player4Lose,8vs9\n",
       "\n",
       "start game 1\n",
-      "player0Win,6vs1\n",
-      "player1Lose,1vs2\n",
-      "player2Lose,3vs6\n",
-      "player3Lose,2vs4\n",
-      "player4Win,8vs5\n",
+      "player0Win,7vs6\n",
+      "player1Win,9vs1\n",
+      "player2Win,7vs5\n",
+      "player3Lose,0vs4\n",
+      "player4Win,7vs4\n",
       "\n",
       "start game 2\n",
-      "player0Lose,7vs9\n",
-      "player1Win,7vs3\n",
-      "player2Win,6vs6\n",
-      "player3Win,9vs3\n",
-      "player4Win,8vs1\n",
+      "player0Win,6vs6\n",
+      "player1Win,8vs0\n",
+      "player2Lose,3vs8\n",
+      "player3Win,6vs3\n",
+      "player4Lose,2vs4\n",
       "\n",
       "start game 3\n",
-      "player0Lose,3vs9\n",
-      "player1Win,4vs1\n",
-      "player2Lose,1vs3\n",
-      "player3Lose,7vs9\n",
-      "player4Win,1vs1\n",
+      "player0Lose,2vs6\n",
+      "player1Win,7vs2\n",
+      "player2Win,8vs0\n",
+      "player3Win,1vs1\n",
+      "player4Lose,1vs4\n",
       "\n",
       "start game 4\n",
-      "player0Lose,2vs5\n",
-      "player1Lose,1vs6\n",
-      "player2Win,3vs1\n",
-      "player3Lose,3vs5\n",
-      "player4Lose,3vs9\n",
+      "player0Lose,0vs6\n",
+      "player1Lose,0vs8\n",
+      "player2Win,2vs1\n",
+      "player3Lose,1vs8\n",
+      "player4Win,0vs0\n",
       "\n",
       "start game 5\n",
-      "player0Lose,7vs8\n",
-      "player1Lose,5vs9\n",
-      "player2Win,9vs9\n",
-      "player3Win,9vs8\n",
-      "player4Lose,0vs1\n",
+      "player0Win,2vs0\n",
+      "player1Win,6vs2\n",
+      "player2Lose,0vs5\n",
+      "player3Win,6vs3\n",
+      "player4Win,7vs5\n",
       "\n",
       "start game 6\n",
-      "player0Lose,6vs8\n",
-      "player1Lose,6vs8\n",
-      "player2Lose,3vs4\n",
-      "player3Lose,3vs8\n",
-      "player4Win,5vs1\n",
+      "player0Lose,1vs7\n",
+      "player1Win,4vs2\n",
+      "player2Lose,3vs6\n",
+      "player3Lose,0vs4\n",
+      "player4Lose,1vs2\n",
       "\n",
       "start game 7\n",
-      "player0Win,3vs2\n",
-      "player1Win,1vs0\n",
-      "player2Win,3vs0\n",
-      "player3Win,9vs1\n",
-      "player4Win,0vs0\n",
+      "player0Lose,5vs8\n",
+      "player1Win,9vs0\n",
+      "player2Win,5vs3\n",
+      "player3Lose,7vs8\n",
+      "player4Lose,3vs7\n",
       "\n",
       "start game 8\n",
-      "player0Win,3vs3\n",
-      "player1Win,3vs2\n",
-      "player2Lose,6vs8\n",
-      "player3Lose,1vs9\n",
-      "player4Win,8vs0\n",
+      "player0Win,9vs4\n",
+      "player1Lose,0vs8\n",
+      "player2Lose,3vs8\n",
+      "player3Win,6vs2\n",
+      "player4Win,4vs4\n",
       "\n",
       "start game 9\n",
-      "player0Lose,7vs8\n",
-      "player1Win,8vs0\n",
-      "player2Lose,3vs8\n",
-      "player3Win,7vs0\n",
-      "player4Lose,0vs8\n",
+      "player0Lose,8vs9\n",
+      "player1Lose,3vs7\n",
+      "player2Win,7vs3\n",
+      "player3Lose,3vs7\n",
+      "player4Win,3vs0\n",
       "\n",
       "game result:\n",
-      "player0has $300left.\n",
-      "player1has $500left.\n",
+      "player0has $500left.\n",
+      "player1has $700left.\n",
       "player2has $500left.\n",
       "player3has $500left.\n",
-      "player4has $600left.\n"
+      "player4has $500left.\n"
      ]
     }
    ],
    "source": [
+    "import random\n",
+    "\n",
+    "gameStake = 50\n",
+    "cards = range(10)\n",
+    "\n",
     "class Player:\n",
     "    \n",
     "    playerID=0\n",
@@ -180,7 +162,6 @@
   }
  ],
  "metadata": {
-  "anaconda-cloud": {},
   "kernelspec": {
    "display_name": "Python [default]",
    "language": "python",

From 0e9d3d807eb48e7a45f45cb80afc16db47b959b6 Mon Sep 17 00:00:00 2001
From: HuaxiaWu <hw2560@columbia.edu>
Date: Thu, 6 Oct 2016 02:17:40 -0400
Subject: [PATCH 04/15] commit2

sorry about the lateness
---
 notebooks/hw2560_lab2.ipynb | 204 ++++++++++++++++++++++++++++++++++++
 1 file changed, 204 insertions(+)
 create mode 100644 notebooks/hw2560_lab2.ipynb

diff --git a/notebooks/hw2560_lab2.ipynb b/notebooks/hw2560_lab2.ipynb
new file mode 100644
index 0000000..fbc284a
--- /dev/null
+++ b/notebooks/hw2560_lab2.ipynb
@@ -0,0 +1,204 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "gameStake = 50\n",
+    "cards = range(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "start game 0\n",
+      "player0Lose,5vs8\n",
+      "player1Lose,1vs6\n",
+      "player2Win,7vs1\n",
+      "player3Win,8vs6\n",
+      "player4Lose,4vs9\n",
+      "\n",
+      "start game 1\n",
+      "player0Win,6vs1\n",
+      "player1Lose,1vs2\n",
+      "player2Lose,3vs6\n",
+      "player3Lose,2vs4\n",
+      "player4Win,8vs5\n",
+      "\n",
+      "start game 2\n",
+      "player0Lose,7vs9\n",
+      "player1Win,7vs3\n",
+      "player2Win,6vs6\n",
+      "player3Win,9vs3\n",
+      "player4Win,8vs1\n",
+      "\n",
+      "start game 3\n",
+      "player0Lose,3vs9\n",
+      "player1Win,4vs1\n",
+      "player2Lose,1vs3\n",
+      "player3Lose,7vs9\n",
+      "player4Win,1vs1\n",
+      "\n",
+      "start game 4\n",
+      "player0Lose,2vs5\n",
+      "player1Lose,1vs6\n",
+      "player2Win,3vs1\n",
+      "player3Lose,3vs5\n",
+      "player4Lose,3vs9\n",
+      "\n",
+      "start game 5\n",
+      "player0Lose,7vs8\n",
+      "player1Lose,5vs9\n",
+      "player2Win,9vs9\n",
+      "player3Win,9vs8\n",
+      "player4Lose,0vs1\n",
+      "\n",
+      "start game 6\n",
+      "player0Lose,6vs8\n",
+      "player1Lose,6vs8\n",
+      "player2Lose,3vs4\n",
+      "player3Lose,3vs8\n",
+      "player4Win,5vs1\n",
+      "\n",
+      "start game 7\n",
+      "player0Win,3vs2\n",
+      "player1Win,1vs0\n",
+      "player2Win,3vs0\n",
+      "player3Win,9vs1\n",
+      "player4Win,0vs0\n",
+      "\n",
+      "start game 8\n",
+      "player0Win,3vs3\n",
+      "player1Win,3vs2\n",
+      "player2Lose,6vs8\n",
+      "player3Lose,1vs9\n",
+      "player4Win,8vs0\n",
+      "\n",
+      "start game 9\n",
+      "player0Lose,7vs8\n",
+      "player1Win,8vs0\n",
+      "player2Lose,3vs8\n",
+      "player3Win,7vs0\n",
+      "player4Lose,0vs8\n",
+      "\n",
+      "game result:\n",
+      "player0has $300left.\n",
+      "player1has $500left.\n",
+      "player2has $500left.\n",
+      "player3has $500left.\n",
+      "player4has $600left.\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Player:\n",
+    "    \n",
+    "    playerID=0\n",
+    "    playerPot=0\n",
+    "    \n",
+    "    def __init__(self, inputID, startingPot):\n",
+    "        self.playerID = inputID\n",
+    "        self.playerPot = startingPot\n",
+    "        \n",
+    "    def play(self, dealerCard):\n",
+    "        \n",
+    "        playerCard = random.choice(cards)\n",
+    "        \n",
+    "        if playerCard < dealerCard:\n",
+    "            self.playerPot += -gameStake\n",
+    "            print 'player'+ str(self.playerID) + 'Lose,'+ str(playerCard)+'vs'+ str(dealerCard)\n",
+    "        else:\n",
+    "            self.playerPot += gameStake\n",
+    "            print 'player'+ str(self.playerID) + 'Win,'+ str(playerCard)+'vs'+ str(dealerCard)\n",
+    "            \n",
+    "    def returnPot(self):\n",
+    "        return self.playerPot\n",
+    "    \n",
+    "    def returnID(self):\n",
+    "        return self.playerID\n",
+    "    \n",
+    "    \n",
+    "def playHand(players):\n",
+    "        \n",
+    "        for player in players:\n",
+    "            dealerCard = random.choice(cards)\n",
+    "            player.play(dealerCard)\n",
+    "            \n",
+    "def checkBalances(players):\n",
+    "        \n",
+    "        for player in players:\n",
+    "            \n",
+    "            print 'player' + str(player.returnID()) +'has $'+ str(player.returnPot())+'left.'\n",
+    "            \n",
+    "players =[]\n",
+    "\n",
+    "for i in range(5):\n",
+    "    players.append(Player(i,500))\n",
+    "    \n",
+    "for i in range(10):\n",
+    "    print ''\n",
+    "    print 'start game ' + str(i)\n",
+    "    playHand(players)\n",
+    "    \n",
+    "print ''\n",
+    "print 'game result:'\n",
+    "checkBalances(players)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From 8432fcbef2e628ada07b11021d39198115e7de0b Mon Sep 17 00:00:00 2001
From: HuaxiaWu <hw2560@columbia.edu>
Date: Sun, 18 Dec 2016 18:54:36 -0500
Subject: [PATCH 05/15] 3rd assignment

week3 Lab
---
 notebooks/week-3/Assignment 3_hw2560.ipynb | 616 +++++++++++++++++++++
 1 file changed, 616 insertions(+)
 create mode 100644 notebooks/week-3/Assignment 3_hw2560.ipynb

diff --git a/notebooks/week-3/Assignment 3_hw2560.ipynb b/notebooks/week-3/Assignment 3_hw2560.ipynb
new file mode 100644
index 0000000..de9335e
--- /dev/null
+++ b/notebooks/week-3/Assignment 3_hw2560.ipynb	
@@ -0,0 +1,616 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns; sns.set(style=\"ticks\", color_codes=True)\n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "from sklearn.utils import shuffle\n",
+    "\n",
+    "class Network(object):\n",
+    "    \n",
+    "    def __init__(self, sizes):\n",
+    "        \n",
+    "        \"\"\"The list ``sizes`` contains the number of neurons in the\n",
+    "        respective layers of the network.  For example, if the list\n",
+    "        was [2, 3, 1] then it would be a three-layer network, with the\n",
+    "        first layer containing 2 neurons, the second layer 3 neurons,\n",
+    "        and the third layer 1 neuron.  The biases and weights for the\n",
+    "        network are initialized randomly, using a Gaussian\n",
+    "        distribution with mean 0, and variance 1.  Note that the first\n",
+    "        layer is assumed to be an input layer, and by convention we\n",
+    "        won't set any biases for those neurons, since biases are only\n",
+    "        ever used in computing the outputs for later layers.\"\"\"\n",
+    "        \n",
+    "        self.num_layers = len(sizes)\n",
+    "        self.sizes = sizes\n",
+    "        self.biases = [np.random.randn(y, 1) for y in sizes[1:]]\n",
+    "        self.weights = [np.random.randn(y, x) for x, y in zip(sizes[:-1], sizes[1:])]\n",
+    "        \n",
+    "    def feedforward (self, a):\n",
+    "        \n",
+    "        \"\"\"Return the output of the network if \"a\" is input. The np.dot() \n",
+    "        function computes the matrix multiplication between the weight and input\n",
+    "        matrices for each set of layers. When used with numpy arrays, the '+'\n",
+    "        operator performs matrix addition.\"\"\"\n",
+    "        \n",
+    "        for b, w in zip(self.biases, self.weights):\n",
+    "            a = sigmoid(np.dot(w, a)+b)\n",
+    "        return a\n",
+    "    \n",
+    "    def SGD(self, training_data, epochs, mini_batch_size, eta, test_data=None):\n",
+    "        \n",
+    "        \"\"\"Train the neural network using mini-batch stochastic\n",
+    "        gradient descent.  The \"training_data\" is a list of tuples\n",
+    "        \"(x, y)\" representing the training inputs and the desired\n",
+    "        outputs.  The other non-optional parameters specify the number \n",
+    "        of epochs, size of each mini-batch, and the learning rate.  \n",
+    "        If \"test_data\" is provided then the network will be evaluated \n",
+    "        against the test data after each epoch, and partial progress \n",
+    "        printed out.  This is useful for tracking progress, but slows\n",
+    "        things down substantially.\"\"\"\n",
+    "        \n",
+    "        # create an empty array to store the accuracy results from each epoch\n",
+    "        results = []\n",
+    "\n",
+    "        n = len(training_data)\n",
+    "        \n",
+    "        if test_data: \n",
+    "            n_test = len(test_data)\n",
+    "            \n",
+    "        # this is the code for one training step, done once for each epoch\n",
+    "        for j in xrange(epochs):\n",
+    "            \n",
+    "            # before each epoch, the data is randomly shuffled\n",
+    "            random.shuffle(training_data)\n",
+    "            \n",
+    "            # training data is broken up into individual mini-batches\n",
+    "            mini_batches = [ training_data[k:k+mini_batch_size] \n",
+    "                            for k in xrange(0, n, mini_batch_size) ]\n",
+    "            \n",
+    "            # then each mini-batch is used to update the parameters of the \n",
+    "            # network using backpropagation and the specified learning rate\n",
+    "            for mini_batch in mini_batches:\n",
+    "                self.update_mini_batch(mini_batch, eta)\n",
+    "            \n",
+    "            # if a test data set is provided, the accuracy results \n",
+    "            # are displayed and stored in the 'results' array\n",
+    "            if test_data:\n",
+    "                num_correct = self.evaluate(test_data)\n",
+    "                accuracy = \"%.2f\" % (100 * (float(num_correct) / n_test))\n",
+    "                print \"Epoch\", j, \":\", num_correct, \"/\", n_test, \"-\", accuracy, \"% acc\"\n",
+    "                results.append(accuracy)\n",
+    "            else:\n",
+    "                print \"Epoch\", j, \"complete\"\n",
+    "                \n",
+    "        return results\n",
+    "    \n",
+    "    def update_mini_batch(self, mini_batch, eta):\n",
+    "        \n",
+    "        \"\"\"Update the network's weights and biases by applying\n",
+    "        gradient descent using backpropagation to a single mini batch.\n",
+    "        The \"mini_batch\" is a list of tuples \"(x, y)\", and \"eta\"\n",
+    "        is the learning rate.\"\"\"\n",
+    "\n",
+    "        nabla_b = [np.zeros(b.shape) for b in self.biases]\n",
+    "        nabla_w = [np.zeros(w.shape) for w in self.weights]\n",
+    "        for x, y in mini_batch:\n",
+    "            delta_nabla_b, delta_nabla_w = self.backprop(x, y)\n",
+    "            nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]\n",
+    "            nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]\n",
+    "        self.weights = [w-(eta/len(mini_batch))*nw \n",
+    "                        for w, nw in zip(self.weights, nabla_w)]\n",
+    "        self.biases = [b-(eta/len(mini_batch))*nb \n",
+    "                       for b, nb in zip(self.biases, nabla_b)]\n",
+    "        \n",
+    "    def backprop(self, x, y):\n",
+    "        \n",
+    "        \"\"\"Return a tuple ``(nabla_b, nabla_w)`` representing the\n",
+    "        gradient for the cost function C_x.  ``nabla_b`` and\n",
+    "        ``nabla_w`` are layer-by-layer lists of numpy arrays, similar\n",
+    "        to ``self.biases`` and ``self.weights``.\"\"\"\n",
+    "        \n",
+    "        nabla_b = [np.zeros(b.shape) for b in self.biases]\n",
+    "        nabla_w = [np.zeros(w.shape) for w in self.weights]\n",
+    "        \n",
+    "        # feedforward\n",
+    "        activation = x\n",
+    "        activations = [x] # list to store all the activations, layer by layer\n",
+    "        zs = [] # list to store all the z vectors, layer by layer\n",
+    "        for b, w in zip(self.biases, self.weights):\n",
+    "            z = np.dot(w, activation)+b\n",
+    "            zs.append(z)\n",
+    "            activation = sigmoid(z)\n",
+    "            activations.append(activation)\n",
+    "        # backward pass\n",
+    "        delta = self.cost_derivative(activations[-1], y) * \\\n",
+    "            sigmoid_prime(zs[-1])\n",
+    "        nabla_b[-1] = delta\n",
+    "        nabla_w[-1] = np.dot(delta, activations[-2].transpose())\n",
+    "        \n",
+    "        \"\"\"Note that the variable l in the loop below is used a little\n",
+    "        differently to the notation in Chapter 2 of the book.  Here,\n",
+    "        l = 1 means the last layer of neurons, l = 2 is the\n",
+    "        second-last layer, and so on.  It's a renumbering of the\n",
+    "        scheme in the book, used here to take advantage of the fact\n",
+    "        that Python can use negative indices in lists.\"\"\"\n",
+    "        \n",
+    "        for l in xrange(2, self.num_layers):\n",
+    "            z = zs[-l]\n",
+    "            sp = sigmoid_prime(z)\n",
+    "            delta = np.dot(self.weights[-l+1].transpose(), delta) * sp\n",
+    "            nabla_b[-l] = delta\n",
+    "            nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())\n",
+    "        return (nabla_b, nabla_w)\n",
+    "\n",
+    "    def evaluate(self, test_data):\n",
+    "        \n",
+    "        \"\"\"Return the number of test inputs for which the neural\n",
+    "        network outputs the correct result. Note that the neural\n",
+    "        network's output is assumed to be the index of whichever\n",
+    "        neuron in the final layer has the highest activation.\n",
+    "        \n",
+    "        Numpy's argmax() function returns the position of the \n",
+    "        largest element in an array. We first create a list of \n",
+    "        predicted value and target value pairs, and then count \n",
+    "        the number of times those values match to get the total \n",
+    "        number correct.\"\"\"\n",
+    "        \n",
+    "        test_results = [(np.argmax(self.feedforward(x)), y)\n",
+    "                        for (x, y) in test_data]\n",
+    "        \n",
+    "        return sum(int(x == y) for (x, y) in test_results)\n",
+    "\n",
+    "    def cost_derivative(self, output_activations, y):\n",
+    "        \"\"\"Return the vector of partial derivatives \\partial C_x /\n",
+    "        \\partial a for the output activations.\"\"\"\n",
+    "        return (output_activations-y)\n",
+    "    \n",
+    "def sigmoid(z):\n",
+    "#     The sigmoid activation function.\n",
+    "    return 1.0/(1.0 + np.exp(-z))\n",
+    "\n",
+    "def sigmoid_prime(z):\n",
+    "#     Derivative of the sigmoid function.\n",
+    "    return sigmoid(z)*(1-sigmoid(z))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     sepal_length  sepal_width  petal_length  petal_width     species\n",
+      "19            5.1          3.8           1.5          0.3      setosa\n",
+      "106           4.9          2.5           4.5          1.7   virginica\n",
+      "97            6.2          2.9           4.3          1.3  versicolor\n",
+      "14            5.8          4.0           1.2          0.2      setosa\n",
+      "67            5.8          2.7           4.1          1.0  versicolor\n",
+      "[[ 0.64556962  0.86363636  0.2173913   0.12      ]\n",
+      " [ 0.62025316  0.56818182  0.65217391  0.68      ]\n",
+      " [ 0.78481013  0.65909091  0.62318841  0.52      ]\n",
+      " [ 0.73417722  0.90909091  0.17391304  0.08      ]\n",
+      " [ 0.73417722  0.61363636  0.5942029   0.4       ]\n",
+      " [ 0.7721519   0.68181818  0.66666667  0.56      ]\n",
+      " [ 0.58227848  0.72727273  0.20289855  0.08      ]\n",
+      " [ 0.63291139  0.72727273  0.17391304  0.08      ]\n",
+      " [ 0.91139241  0.81818182  0.88405797  1.        ]\n",
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+      " [ 0.7721519   0.68181818  0.71014493  0.72      ]\n",
+      " [ 0.60759494  0.77272727  0.27536232  0.08      ]\n",
+      " [ 0.6835443   0.88636364  0.1884058   0.16      ]\n",
+      " [ 0.55696203  0.68181818  0.1884058   0.08      ]\n",
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+      " [ 0.67088608  0.84090909  0.2173913   0.08      ]\n",
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+      " [ 0.84810127  0.70454545  0.8115942   0.96      ]\n",
+      " [ 0.84810127  0.75        0.82608696  1.        ]\n",
+      " [ 0.7721519   0.63636364  0.68115942  0.48      ]\n",
+      " [ 0.73417722  0.61363636  0.56521739  0.48      ]\n",
+      " [ 0.86075949  0.68181818  0.79710145  0.84      ]\n",
+      " [ 0.84810127  0.70454545  0.68115942  0.6       ]\n",
+      " [ 1.          0.86363636  0.92753623  0.8       ]\n",
+      " [ 0.79746835  0.56818182  0.71014493  0.6       ]\n",
+      " [ 0.60759494  0.68181818  0.20289855  0.12      ]\n",
+      " [ 0.69620253  0.95454545  0.20289855  0.08      ]\n",
+      " [ 0.72151899  0.63636364  0.65217391  0.52      ]\n",
+      " [ 0.62025316  0.68181818  0.20289855  0.08      ]\n",
+      " [ 0.82278481  0.68181818  0.75362319  0.8       ]\n",
+      " [ 0.70886076  0.68181818  0.5942029   0.52      ]\n",
+      " [ 0.64556962  0.86363636  0.27536232  0.16      ]\n",
+      " [ 0.6835443   0.88636364  0.24637681  0.16      ]\n",
+      " [ 0.63291139  0.79545455  0.23188406  0.24      ]\n",
+      " [ 0.5443038   0.68181818  0.15942029  0.04      ]\n",
+      " [ 0.6835443   0.77272727  0.24637681  0.08      ]\n",
+      " [ 0.72151899  0.68181818  0.60869565  0.48      ]\n",
+      " [ 0.65822785  0.93181818  0.2173913   0.04      ]\n",
+      " [ 0.79746835  0.75        0.68115942  0.64      ]\n",
+      " [ 0.62025316  0.81818182  0.20289855  0.04      ]\n",
+      " [ 0.65822785  0.79545455  0.2173913   0.08      ]\n",
+      " [ 0.86075949  0.72727273  0.85507246  0.92      ]\n",
+      " [ 0.81012658  0.65909091  0.62318841  0.52      ]\n",
+      " [ 0.60759494  0.70454545  0.23188406  0.08      ]\n",
+      " [ 0.84810127  0.70454545  0.63768116  0.56      ]\n",
+      " [ 0.63291139  0.52272727  0.47826087  0.4       ]\n",
+      " [ 0.60759494  0.68181818  0.20289855  0.04      ]\n",
+      " [ 0.79746835  0.77272727  0.8115942   0.96      ]\n",
+      " [ 0.64556962  0.84090909  0.2173913   0.16      ]\n",
+      " [ 0.73417722  0.61363636  0.73913043  0.76      ]\n",
+      " [ 0.83544304  0.68181818  0.63768116  0.56      ]\n",
+      " [ 0.58227848  0.70454545  0.2173913   0.08      ]\n",
+      " [ 0.73417722  0.61363636  0.73913043  0.76      ]\n",
+      " [ 0.55696203  0.72727273  0.1884058   0.08      ]\n",
+      " [ 0.81012658  0.70454545  0.79710145  0.72      ]\n",
+      " [ 0.70886076  0.61363636  0.60869565  0.52      ]\n",
+      " [ 0.70886076  0.56818182  0.56521739  0.44      ]\n",
+      " [ 0.97468354  0.59090909  1.          0.92      ]\n",
+      " [ 0.62025316  0.70454545  0.2173913   0.08      ]\n",
+      " [ 0.75949367  0.68181818  0.69565217  0.72      ]\n",
+      " [ 0.81012658  0.63636364  0.8115942   0.84      ]]\n",
+      "Epoch 0 : 13 / 45 - 28.89 % acc\n",
+      "Epoch 1 : 13 / 45 - 28.89 % acc\n",
+      "Epoch 2 : 12 / 45 - 26.67 % acc\n",
+      "Epoch 3 : 16 / 45 - 35.56 % acc\n",
+      "Epoch 4 : 14 / 45 - 31.11 % acc\n",
+      "Epoch 5 : 14 / 45 - 31.11 % acc\n",
+      "Epoch 6 : 18 / 45 - 40.00 % acc\n",
+      "Epoch 7 : 28 / 45 - 62.22 % acc\n",
+      "Epoch 8 : 25 / 45 - 55.56 % acc\n",
+      "Epoch 9 : 32 / 45 - 71.11 % acc\n",
+      "Epoch 10 : 33 / 45 - 73.33 % acc\n",
+      "Epoch 11 : 39 / 45 - 86.67 % acc\n",
+      "Epoch 12 : 43 / 45 - 95.56 % acc\n",
+      "Epoch 13 : 44 / 45 - 97.78 % acc\n",
+      "Epoch 14 : 39 / 45 - 86.67 % acc\n",
+      "Epoch 15 : 31 / 45 - 68.89 % acc\n",
+      "Epoch 16 : 31 / 45 - 68.89 % acc\n",
+      "Epoch 17 : 31 / 45 - 68.89 % acc\n",
+      "Epoch 18 : 42 / 45 - 93.33 % acc\n",
+      "Epoch 19 : 41 / 45 - 91.11 % acc\n",
+      "Epoch 20 : 37 / 45 - 82.22 % acc\n",
+      "Epoch 21 : 41 / 45 - 91.11 % acc\n",
+      "Epoch 22 : 39 / 45 - 86.67 % acc\n",
+      "Epoch 23 : 34 / 45 - 75.56 % acc\n",
+      "Epoch 24 : 31 / 45 - 68.89 % acc\n",
+      "Epoch 25 : 42 / 45 - 93.33 % acc\n",
+      "Epoch 26 : 43 / 45 - 95.56 % acc\n",
+      "Epoch 27 : 31 / 45 - 68.89 % acc\n",
+      "Epoch 28 : 42 / 45 - 93.33 % acc\n",
+      "Epoch 29 : 31 / 45 - 68.89 % acc\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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dy06Zi/TwVGj1DTD2GHGy6iyud7bgyOXj5v4Lkmejq9uIBclZCPYNQkpYAhJC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kpHo7ET/7jS4jBD0cZ7MqEKl6P80IURZdNjpiJX+uyJGN3PA8D/WnA/zDYL9l84Zi9jXz91NZk6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GgRvvzyS6xdu9auPqZ0zCm5lhhHfv+NjY0wGAyi+xy5LyXXE2PvvTkjp6t9PTFKXztnPQec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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff5f4597690>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "iris_data = sns.load_dataset(\"iris\")\n",
+    "\n",
+    "# randomly shuffle data\n",
+    "iris_data = shuffle(iris_data)\n",
+    "\n",
+    "# print first 5 data points\n",
+    "print iris_data[:5]\n",
+    "\n",
+    "\n",
+    "# create pairplot of iris data\n",
+    "g = sns.pairplot(iris_data, hue=\"species\")\n",
+    "\n",
+    "# convert iris data to numpy format\n",
+    "iris_array = iris_data.as_matrix()\n",
+    "\n",
+    "# split data into feature and target sets\n",
+    "X = iris_array[:, :4].astype(float)\n",
+    "y = iris_array[:, -1]\n",
+    "\n",
+    "\n",
+    "# normalize the data per feature by dividing by the maximum value in each column\n",
+    "X = X / X.max(axis=0)\n",
+    "print X\n",
+    "\n",
+    "# convert the textual category data to integer using numpy's unique() function\n",
+    "_, y = np.unique(y, return_inverse=True)\n",
+    "\n",
+    "# convert the list of targets to a vertical matrix with the dimensions [1 x number of samples]\n",
+    "# this is necessary for later computation\n",
+    "y = y.reshape(-1,1)\n",
+    "\n",
+    "# combine feature and target data into a new python array\n",
+    "data = []\n",
+    "for i in range(X.shape[0]):\n",
+    "    data.append(tuple([X[i].reshape(-1,1), y[i][0]]))\n",
+    "\n",
+    "# split data into training and test sets\n",
+    "trainingSplit = int(.7 * len(data))\n",
+    "training_data = data[:trainingSplit]\n",
+    "test_data = data[trainingSplit:]\n",
+    "\n",
+    "# create an instance of the one-hot encoding function from the sci-kit learn library\n",
+    "enc = OneHotEncoder()\n",
+    "\n",
+    "# use the function to figure out how many categories exist in the data\n",
+    "enc.fit(y)\n",
+    "\n",
+    "# convert only the target data in the training set to one-hot encoding\n",
+    "training_data = [[_x, enc.transform(_y.reshape(-1,1)).toarray().reshape(-1,1)] for _x, _y in training_data]\n",
+    "\n",
+    "# define the network\n",
+    "net = Network([4, 32, 3])\n",
+    "\n",
+    "# train the network using SGD, and output the results\n",
+    "results = net.SGD(training_data, 30, 10, 0.2, test_data=test_data)\n",
+    "\n",
+    "# visualize the results\n",
+    "plt.plot(results)\n",
+    "plt.ylabel('accuracy (%)')\n",
+    "plt.ylim([0,100.0])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[  1.23700000e+01   1.13000000e+00   2.16000000e+00 ...,   1.22000000e+00\n",
+      "    2.87000000e+00   4.20000000e+02]\n",
+      " [  1.35000000e+01   3.12000000e+00   2.62000000e+00 ...,   5.90000000e-01\n",
+      "    1.30000000e+00   5.00000000e+02]\n",
+      " [  1.24200000e+01   1.61000000e+00   2.19000000e+00 ...,   1.06000000e+00\n",
+      "    2.96000000e+00   3.45000000e+02]\n",
+      " ..., \n",
+      " [  1.36300000e+01   1.81000000e+00   2.70000000e+00 ...,   1.28000000e+00\n",
+      "    2.88000000e+00   1.31000000e+03]\n",
+      " [  1.41600000e+01   2.51000000e+00   2.48000000e+00 ...,   6.20000000e-01\n",
+      "    1.71000000e+00   6.60000000e+02]\n",
+      " [  1.31100000e+01   1.01000000e+00   1.70000000e+00 ...,   1.12000000e+00\n",
+      "    3.18000000e+00   5.02000000e+02]]\n",
+      "[ 1.  2.  1.  1.  0.  1.  0.  1.  0.  1.  2.  1.  1.  0.  0.  0.  1.  0.\n",
+      "  1.  2.  1.  0.  0.  2.  0.  1.  0.  2.  0.  0.  0.  2.  0.  0.  2.  2.\n",
+      "  1.  1.  1.  0.  0.  2.  1.  1.  1.  0.  2.  0.  1.  1.  2.  1.  1.  2.\n",
+      "  2.  0.  1.  0.  1.  0.  2.  1.  1.  1.  0.  2.  1.  2.  1.  2.  2.  1.\n",
+      "  2.  0.  0.  0.  1.  2.  0.  2.  0.  1.  0.  1.  2.  2.  0.  2.  2.  0.\n",
+      "  1.  2.  0.  2.  1.  2.  2.  0.  0.  1.  2.  1.  1.  1.  1.  1.  0.  1.\n",
+      "  1.  0.  1.  0.  2.  1.  0.  1.  0.  1.  0.  0.  2.  1.  2.  0.  1.  2.\n",
+      "  1.  1.  1.  2.  2.  0.  1.  0.  2.  0.  2.  1.  1.  1.  1.  1.  1.  2.\n",
+      "  0.  1.  1.  0.  2.  0.  0.  0.  2.  0.  2.  0.  1.  1.  0.  2.  2.  1.\n",
+      "  0.  0.  2.  1.  1.  1.  1.  1.  0.  0.  2.  2.  1.  0.  2.  1.]\n",
+      "Epoch 0 : 21 / 54 - 38.89 % acc\n",
+      "Epoch 1 : 39 / 54 - 72.22 % acc\n",
+      "Epoch 2 : 48 / 54 - 88.89 % acc\n",
+      "Epoch 3 : 40 / 54 - 74.07 % acc\n",
+      "Epoch 4 : 28 / 54 - 51.85 % acc\n",
+      "Epoch 5 : 48 / 54 - 88.89 % acc\n",
+      "Epoch 6 : 50 / 54 - 92.59 % acc\n",
+      "Epoch 7 : 43 / 54 - 79.63 % acc\n",
+      "Epoch 8 : 50 / 54 - 92.59 % acc\n",
+      "Epoch 9 : 51 / 54 - 94.44 % acc\n",
+      "Epoch 10 : 42 / 54 - 77.78 % acc\n",
+      "Epoch 11 : 49 / 54 - 90.74 % acc\n",
+      "Epoch 12 : 45 / 54 - 83.33 % acc\n",
+      "Epoch 13 : 48 / 54 - 88.89 % acc\n",
+      "Epoch 14 : 51 / 54 - 94.44 % acc\n",
+      "Epoch 15 : 47 / 54 - 87.04 % acc\n",
+      "Epoch 16 : 46 / 54 - 85.19 % acc\n",
+      "Epoch 17 : 51 / 54 - 94.44 % acc\n",
+      "Epoch 18 : 49 / 54 - 90.74 % acc\n",
+      "Epoch 19 : 51 / 54 - 94.44 % acc\n",
+      "Epoch 20 : 51 / 54 - 94.44 % acc\n",
+      "Epoch 21 : 50 / 54 - 92.59 % acc\n",
+      "Epoch 22 : 50 / 54 - 92.59 % acc\n",
+      "Epoch 23 : 46 / 54 - 85.19 % acc\n",
+      "Epoch 24 : 48 / 54 - 88.89 % acc\n",
+      "Epoch 25 : 48 / 54 - 88.89 % acc\n",
+      "Epoch 26 : 50 / 54 - 92.59 % acc\n",
+      "Epoch 27 : 48 / 54 - 88.89 % acc\n",
+      "Epoch 28 : 48 / 54 - 88.89 % acc\n",
+      "Epoch 29 : 46 / 54 - 85.19 % acc\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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rhnSwIiTAs1v5yT0xbGAgIoK8UVLZiM1p+TYvlgvK6pGdXw3gzKE2R0mJD4Va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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff5c44f9fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "wine_data = np.loadtxt(open(\"./data/wine.csv\",\"rb\"),delimiter=\",\")\n",
+    "\n",
+    "wine_data = shuffle(wine_data)\n",
+    "# g = sns.pairplot(wine_data, hue=\"species\")\n",
+    "\n",
+    "X = wine_data[:,1:]\n",
+    "y = wine_data[:, 0] - 1\n",
+    "\n",
+    "print X\n",
+    "print y\n",
+    "\n",
+    "X = X / X.max(axis=0)\n",
+    "y = y.reshape(-1,1)\n",
+    "\n",
+    "data = []\n",
+    "for i in range(X.shape[0]):\n",
+    "    data.append(tuple([X[i].reshape(-1,1), y[i][0]]))\n",
+    "\n",
+    "# split data into training and test sets\n",
+    "trainingSplit = int(.7 * len(data))\n",
+    "training_data = data[:trainingSplit]\n",
+    "test_data = data[trainingSplit:]\n",
+    "\n",
+    "# create an instance of the one-hot encoding function from the sci-kit learn library\n",
+    "enc = OneHotEncoder()\n",
+    "\n",
+    "# use the function to figure out how many categories exist in the data\n",
+    "enc.fit(y)\n",
+    "\n",
+    "# convert only the target data in the training set to one-hot encoding\n",
+    "training_data = [[_x, enc.transform(_y.reshape(-1,1)).toarray().reshape(-1,1)] for _x, _y in training_data]\n",
+    "\n",
+    "# define the network\n",
+    "net = Network([13, 50, 3])\n",
+    "\n",
+    "# train the network using SGD, and output the results\n",
+    "results = net.SGD(training_data, 30, 10, 1.0, test_data=test_data)\n",
+    "\n",
+    "# visualize the results\n",
+    "plt.plot(results)\n",
+    "plt.ylabel('accuracy (%)')\n",
+    "plt.ylim([0,100.0])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [conda root]",
+   "language": "python",
+   "name": "conda-root-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From 3b268a4e8c8f3c4e5e0b86e0cd79092660173375 Mon Sep 17 00:00:00 2001
From: HuaxiaWu <hw2560@columbia.edu>
Date: Sun, 18 Dec 2016 20:28:01 -0500
Subject: [PATCH 06/15] Assignment4_1_hw2560

4_1
---
 notebooks/week-4/Lab4_1.ipynb | 553 ++++++++++++++++++++++++++++++++++
 1 file changed, 553 insertions(+)
 create mode 100644 notebooks/week-4/Lab4_1.ipynb

diff --git a/notebooks/week-4/Lab4_1.ipynb b/notebooks/week-4/Lab4_1.ipynb
new file mode 100644
index 0000000..9cda82b
--- /dev/null
+++ b/notebooks/week-4/Lab4_1.ipynb
@@ -0,0 +1,553 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import math\n",
+    "import random\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.datasets import load_boston\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "sns.set(style=\"ticks\", color_codes=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
+      "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
+      "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
+      "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
+      "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
+      "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
+      "\n",
+      "   PTRATIO       B  LSTAT  target  \n",
+      "0     15.3  396.90   4.98    24.0  \n",
+      "1     17.8  396.90   9.14    21.6  \n",
+      "2     17.8  392.83   4.03    34.7  \n",
+      "3     18.7  394.63   2.94    33.4  \n",
+      "4     18.7  396.90   5.33    36.2  \n"
+     ]
+    }
+   ],
+   "source": [
+    "#load data from scikit-learn library\n",
+    "dataset = load_boston()\n",
+    "\n",
+    "#load data as DataFrame\n",
+    "houses = pd.DataFrame(dataset.data, columns=dataset.feature_names)\n",
+    "#add target data to DataFrame\n",
+    "houses['target'] = dataset.target\n",
+    "\n",
+    "#print first 5 entries of data\n",
+    "print houses.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Boston House Prices dataset\n",
+      "\n",
+      "Notes\n",
+      "------\n",
+      "Data Set Characteristics:  \n",
+      "\n",
+      "    :Number of Instances: 506 \n",
+      "\n",
+      "    :Number of Attributes: 13 numeric/categorical predictive\n",
+      "    \n",
+      "    :Median Value (attribute 14) is usually the target\n",
+      "\n",
+      "    :Attribute Information (in order):\n",
+      "        - CRIM     per capita crime rate by town\n",
+      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
+      "        - INDUS    proportion of non-retail business acres per town\n",
+      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
+      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
+      "        - RM       average number of rooms per dwelling\n",
+      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
+      "        - DIS      weighted distances to five Boston employment centres\n",
+      "        - RAD      index of accessibility to radial highways\n",
+      "        - TAX      full-value property-tax rate per $10,000\n",
+      "        - PTRATIO  pupil-teacher ratio by town\n",
+      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
+      "        - LSTAT    % lower status of the population\n",
+      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
+      "\n",
+      "    :Missing Attribute Values: None\n",
+      "\n",
+      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
+      "\n",
+      "This is a copy of UCI ML housing dataset.\n",
+      "http://archive.ics.uci.edu/ml/datasets/Housing\n",
+      "\n",
+      "\n",
+      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
+      "\n",
+      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
+      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
+      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
+      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
+      "pages 244-261 of the latter.\n",
+      "\n",
+      "The Boston house-price data has been used in many machine learning papers that address regression\n",
+      "problems.   \n",
+      "     \n",
+      "**References**\n",
+      "\n",
+      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
+      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
+      "   - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print dataset['DESCR']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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bw8FfvidPpURuY0eVxq1o0btwvm2f6nVp2KUPG2d9QK4yG3tXD7q9/qFRbeSj\nxC/7jspjJ1J9yVryU5OJnvdfdCol9s3b4NajHzcnjwMgccNqvEaOI2ThCgrUKtL27SLz+CEAXLqG\nY2Fti/fr7/HPhK/K65e489W0MrVL0q9L8Bj5JkFzf0aTnkL8kq/RqVXYNg7FuUsvw1zAju274zFs\nPAD5CXHELZyJNrXwPoy03ZtwG/Aq/p/OR2JuQW50JAkrvitTHv/4cEgYH/60kdA3/ou7kz2zx/bH\nTiFnx8m/+XHHYTZ9/gYACzfvR2ZhTqdJXxs+66YOe5muTWuTlq3kjXmrSM7Mwc3Blte6t6F5zaBH\n7Nm05+m4UR7ahE37ATi9+im6nAyyd69Cn5+LrEpd5A3akfnrHNDpDOUOUFhGoS/QGEpcLNwqY9Vx\nIGYyK3RqJXnXzpJ7/vBjtU3tdmFkJMaz/L1XMJdKaRTW36htVn7wGkP/uwRbJ1fS78Zy+NfF5KmU\nWN1rm+a9i+aw379iAWlx0VhYWlK9ZQea9x7xWDk9T0RNcNlI9P/m1uenJCUlhe+//54DBw6QnJyM\nk5MTVatWZciQIYSGhvLSSy8xfPhwhg0znstz9OjRmJmZsXjxYk6dOsXw4cM5ffq04Y9l3P/4QXv3\n7uWNN954on8sY4zE74lt698aF2d6UvzyEnCgdHO/Pit5L79b3ikYSB8yT3B5uP7S248OekZqXvil\nvFMwssI1rLxTMGj+zdjyTsGIzPbRdZrPkv9w03/ApTwss3i8P+LztPQ5XfK9Lc/auobjyzsFI2Ob\n+pV3CmUyXRH86KB/6UPV05sC71l77kaCAVxcXPjoo4/46KOPTK7ft2+fyeU//FBUe9S4cWOjDu2D\njx/Uvn375+qvxQmCIAiCIJRFRZvH92l7rmqCBUEQBEEQBOFZeC5HggVBEARBEISyETXBZSM6wYIg\nCIIgCBWAKIcoG1EOIQiCIAiCILxwxEiwIAiCIAhCBSDKIcpGjAQLgiAIgiAILxwxEiwIgiAIglAB\niJrgshEjwYIgCIIgCMILR4wEC4IgCIIgVACiJrhsxEiwIAiCIAiC8MIRI8GCIAiCIAgVgKgJLhsx\nEiwIgiAIgiC8cMRIsCAIgiAIQgUgRoLLRnSCn6JxcX+XdwoG33nVKe8UjChXri/vFIx8+xx9cLwt\n71neKRj5LmJbeadg0D+hQXmnYOSDBSPLOwWDiNmryzsFIzq9vrxTMPJHTl55p2Aw1i21vFMwMq/O\nmPJOwWBSdBerAAAgAElEQVRswqbyTuEBb5d3AsJTJDrBgiAIgiAIFYCYHaJsRE2wIAiCIAiC8MIR\nI8GCIAiCIAgVgKgJLhsxEiwIgiAIgiC8cMRIsCAIgiAIQgUgaoLLRowEC4IgCIIgCC8cMRIsCIIg\nCIJQAYia4LIRI8GCIAiCIAjCC0d0ggVBEARBECoAc4nkqf88aQcOHKBz58506tSJ3377rdj6EydO\n0LNnT8LDwxk1ahRZWVlPbN+iEywIgiAIgiA8cwUFBcycOZOVK1eyceNGfvzxRzIzM41iZsyYwTff\nfMPmzZupXr06a9aseWL7rzCd4FOnThESEkK1atUICQkp9jN8+HDi4uIICQmhRYsWqFQqo+eHh4ez\nYMGCcspeEARBEATh3zGXPP2fJ+nChQtUqVIFV1dXrK2tad26NceOHTOKMTMzIycnB4CcnBzc3Nye\n2P4rzI1x9evXL9ZwAPv27WPatGkMHjzYsEypVLJ06VJef/31Z5niQ+n1epYvnMuhPTuQymSEDxxG\ntz4DTcYe3L2dnRvXkRAfg42tHR1f7kX4wOFPNb/Q1wbTcvQAvGpVZecXC9j5+fynuj+AwQ28CQ1w\nRqPTsf1yAnuuJZmMaxngzKimvmi0epAAepi8/RLpKg2W5mZMeikYTzsrJBIJUWkqfj59h4SsvIfu\nW6/XM/fr2ezcvg2ZzJKhI0YwcNDgEuN/XraUX1evQqfT83J4OK+/+ZbJmEULF7Dkp6XUrlMXgB8W\nf8+2LVvIycnB2cWZYSNGglnII9tmQD0vmvs7oSnQs+tqIntvJJuMa+7nxPDGPmgKdP80DR/vvEq6\nWoO7rSX96noR4KwA4EZyDr+cjSUzV/vI/f8jPUfFxz9v4/TNO3g42vFB/840CfErFvfd9sNsPv43\nOeo8nO2sGdWpOeHN6wCQmqVk2qodXIyKJyNHxV/ffVDq/ZsyqqkvbYNdyS/QsenveLZdTjAZ1zbY\nhfGhAeRrdUiQoEfPG+svkKrKp5q7LVM7haBHD4BEIsHSwox3Nl/kdqrK5PYeZGFnR8A7k7GtXZf8\n5CSiFs4n++/zJmNdOnTCs/8gpE7O5CUncWPqB+QnFuZt5VUZ33FvYFOtBgW5auJ/WUnS9q1lbhe9\nXs/2ZQs4e3APUqmM1j0H0jKsr8nYK6ePsWvlYrLSU7G0klOnZTu6DhuL5N6l0E2L5xBx4SxpifGM\n/vQbAmrUeax8dixfyLl7+bQKH0iLsD4mY6+ePsbuVUsM+dRu0Y4uw8YY8tm8ZC6R9/J59dO5+Fcv\nWz56vZ4jvyzm2rG9mEulNOjaj7qdepqMvXX+BMfXLUWZkYrUUk6Vpm1o0f9VQy6RZ49xYsPP5KSl\n4BEYQvtRb2Pj5FrqXNIzs/jg6+85deEKlVyd+Wj8SJrWrVks7vejf7Js/Q6u3Yqia5vmTJ84xmj9\nN8vXsun3g+RrtNSvUZVpb76Kq5NDGVqliF6v5/jaJdw4vg8LqZQ6nftSu0P4Q5+j0xWw4dPXKdBq\nGDD9R8PyIysXEHv1L7KS79J90kw8q9QqdR7pSjVT1x3gzK14POxtmBIeSuMgr2Jxi/44zZbT18nJ\nzcfZVs7INvUIb1T0uXonOYOZW47y951EFJZSRr9Un/7NirexYFpSUhLJyaa/cwBcXV1L3VFNSkrC\n3d3d8Njd3Z3ExESjmE8++YRRo0Yhk8nw8fFh6tSpj5e4CRWmE2xhYYGzs7PRssjISL788kvGjBlD\nx44diYuLA2DIkCEsW7aMQYMG4eTkVB7pFvP7lg1cvXCeb1dtICcnm2kTxuIbGEzNeg2LxWo0Gl6d\nMInAqtVJS0lm+ntv4upeiRbtOj61/DLjE9n+yVwaDerx1PZxv5equBLibsO7Wy6ikFnwYYeqRKer\nuZqYbTL+akI2X+2/WWy5Rqfjp5N3uJuVC0D7Kq6MaR7AtN1XH7r/Db/9xl/nzrFh81aysrMY+5/R\nBAdXoWGjRsVijx09wsb1v7F0xSqsrKx4Y+wYfP386P5yUVslJyfxx+97cHE1/jLs0rUbg4cOQ6FQ\nEBMTzZhXR+HdfzLWbt4l5tY2yIUqrjZM2X4Fa5k5k9oFE5Oh5npSjsn460nZzDkYWWy5XGrO2ZgM\nfjgRhaZAR7+6XrzSxJe5h4rHlmT6r7txsbfhyKyJHL96i0k/bmT7Z+OwU1gZxXVvUosR7ZuisJIR\nnZTGyDkrqennSZCnK2ZmElrVCmJgm4aMW/DvLnN1qeZOdQ87xq77C2tLc77oVp3baSou3TVdQ3Yp\nPotpu68VW341MZuBK04bHrfwd2JoI59Sd4ABfF+fQH5aGuf6hWNfvyFBH0zlwitDKFAqjeLsGzfB\nvUcvbkz7iNzYGCw9KlGQXXicS6RSqnz+X2J/Xsr1j6dgJpMhc3YpdQ73O7lnC7evXGDSwtWoc7JZ\nMnUClXyDCKxVr1hs5aAQXvt8Hjb2juQqc1g1ayp/7tlK086Fx7SnfzB1Wr7Ehu++eqxcAP7cs4Wo\nKxd4d8Eq1Dk5/PDJBDz8AgmsWTwfr6AQRn/2jSGf1bM/4c/ft9K00z/5BFGnZTs2fjfrsXK5uH87\n8TcuMvSrpeQpc9g48z1cfAKoXK14Z9rdvwq9psxCYedAnkrJzgWfc+nADmq1CyM9IZa9P86hx7vT\ncfevwpnta9n9/Uz6fPB1qXP5bMFSXJ0cOLHuB46du8DEGfPYvfQb7GysjeIcbG15pU8Y56/eIDPb\n+Hf/96N/sn3/UdbNn46zoz1Tv/mBr35YxazJjzfwc+XgDhJuXGLgjB/JU+awbfZknL398Qop+WTj\n0r5tyBQ2qLPSjZY7+wQS2KQ1h5bPK3MeMzYdwcVWwaFPRnLiRgzvrf6Dre8NxE5uaRQXVr8Kw1vV\nRWEpJTolk1Hfb6GmtxtBHk7kawt4fdlOxndszIJXupKnKSA5S1nCHv//PIt5gteuXfvQK+evv/46\nb7zxxhPb3/Lly1m+fDkhISHMnj2b77//nrFjxz6RbVeYcogHZWdnM27cOJo2bcqbb75pWC6RSAgL\nC8PX15eFCxeWY4bGDu/dRfd+g7G1d6CSlzftw3pw6PedJmM7dO9Jleq1MDc3x9Xdg8ahbbh++eJT\nze/Ctr1c3LEfdabpTuiT1sLfmZ1XEsnJLyApJ4+DEcm0DHAuMV5Swi++To+hAyyRgF4PbjayR+5/\n964dDB46DHsHB7y9fQjv2YudO7abjt25k569++Dp6YmTkxODhgxh53bj2Hlz5zD6tTFYWBifd1b2\n9kahKByJ1esLRx3z0k2PeP+jqZ8je64nocwvICknnyORqTT3K/vJXFSaiuNRaeRqdRToYd/NFAKc\nrR/9xHtUefkcuHCD8d1bIZNa0KZ2FYK93Djw941isd6ujiisCttdf29ZXGoGAI42CvqG1qdq5X9/\niat1kAtbLsaTnaclISuPP64l0Tb4IZ3GUn5ftAl25WBESqnzMLO0wrFpc+JWLkOv0ZDx5wnUt2/h\n2KxFsVivgUOJXrKI3NgYAPIS7lKgKvxSdunQmZzLl0g7dAB0OnS5ueTGxZY6j/udP/wHrXr0x9rW\nHpdKlWncIYxzh/aYjLVzdMbG3hEAnV6PxMyM1MQ4w/omHbsTUKMOZubmj5ULwF+H99Ly5X4obO1x\nruRFo/ZhnD/0+yPz0ev1SCRmpCXEG9Y37tAd/+qPn8/1E/up17kPchs7HNw9qdG6M9eO7TUZa+3g\njMLO4V4uOiQSMzKT7gIQc+kc3jXq4REYgsTMjIZh/UmOijCsfxRVbi77T57ljaF9kcmktG3agKr+\nPuw/caZYbOM61enQsjFO9nbF1sUnptCgZgjuLk5YmJvTuVVTIqMf77gBuHnyALU79cLKxg57d09C\nQjtz4/i+EuPVWRlcO7KHel37FVtXvXUXPKvUwsysbO+VOl/DwStRjOvYCJmFOa2r+xFcyYmDV6KK\nxXo726OwlAIYrubEpxd+d205c406vh50rhuEuZkZCkspvq6PN0L+ourfvz8bN24s8ad///6l3pab\nmxsJCUVX6xITE41GkdPS0rh16xYhIYUj+Z06deKvv/56Yq+lwowE30+v1zNx4kRkMhmzZs0qtg5g\n4sSJjBkzhhEjRuDtXfKo27MSG3Ub38Bgw2Mf/yDOnixe3mHK1QvnadWh69NKrVx42lsRk1406haT\noaaOV8kfVIHO1izsU4esXC2/X0/kwE3jDsv0btUNJRHr/nr0l8HtW7cICi56PwKDgjh29Ijp2Nu3\n6NS5y32xwdy+VTSaevbMGTIzMmndpi1zv55d7Pkrli9j6Y8/kJubS7Xq1XEIePjlQU87K2Iz1IbH\nsZlqankW/yL8R4CzNd+E1yQrV8u+m8kcikw1GVfVzYb4LLXJdaZEJ6VhbSnD1d7WsCzY05XIu6Yv\nky3dc5zFu46Sm6+hhk8lmob4l3pfpeXtICcqrei4uZOuooG3Y4nxVVxt+HlwAzLUGnZcSeB3EyU3\ndlYW1PWy56eTUaXOw8rLiwK1Gk1ammGZ6s5t5L5+xoESCYqgYBT+/gS8Oxm9Vkvy77u5u2Y1ADZV\nQ9Dm5FBtzrdYVapE9pXL3Fk4H02a6ffwYZJiovDwDTA89vAJ4NrZkyXGR127yPLp75OnVmFt70DY\nyCdbPpYUG4WHb6DhsbuPP9fPlZzPnWsX+XnGFEM+3UaOf2K5pMVF4+JddDw6V/Yj6u9TJcbH37zM\ntjlTyc9VobB1oNXg+0oR9Pqi/6JHr9eTFncHe7dKj8zjTlwC1nIrXJ2LjtkgX28i7pStA9sxtAm7\nDp8gLiEZZ0d7dh48TssGZS9Z+Ud6fDTOlYvax8nLj+gLp0uMP7l+KfW69cNCZlViTFndScks/Lyx\nKzpRD3J3IjIxzWT8soPnWbLvLLkaLdW9XGlyr2ziUkwSdnJLhi3cRGxaFnV9PXg/vCVudqUfAHie\nmT2DkWA3N7cnVpdbu3Ztbt68SVJSEtbW1hw5coTx44t+t+3t7UlPTycuLg4vLy9OnDiBv/+T++6o\nkJ3gr7/+mgsXLrB+/XrDKNuDWrZsSYMGDZg3bx6zZxfvmJTGo+picKxc6m3lqtXIFUW/hHJra3LV\nj+6QbFu3GmV2Nm06dSv1vv4fWFmYo9boDI/VmgKsLExfuLiamM2U7ZdJVeUT4GzNW60CycrVcjYm\nwxDz4Y4rWJhJaObnRIZa88j9q9VqrK2L3g9ra+tiN1MaYlVqrO+7VGltY41KVfjeFRQUMG/O13w6\nfXqJ+xo2YiTDRozkyuXLnDl9ir/MH/5raWlhjlpTULR/ja7EtrmelM3UXVdJU2nwd1IwrqU/Wbla\nzscZ333rZiOjZ+1KfH8s6qH7vp8qT4O1lfFlSGsrSzJVpo/bVzo155VOzbkUFc+p61FI/8UoYkms\npOao8ovaRpVfgFxqum0u3c3izQ0XSFHmE+xqzeT2VchUa/jzjvHl29BAFyJTlI+sI7+fmVxuGM39\nR4FKhYWNrdEyqaMjEnNz7Oo15OJrr2Bha0fVGV+Rn5hA6oF9yFxcsK5SlWtTJqGOuo33q68RMOl9\nrk+ZVOpc/pGfq8ZKXnScWsoV5OeW/BnjF1KLaSt3kJ6UwLnDv2Nj/2RHywrzKfp8tlJYk/eQfHxD\najF1xXbSkxM4f+gPw8jwk6DJUyO7LxeZXIEmL7fEeM/gGry2aANZKYlcP74Pua09AN416nFiw3Li\nb1zCPSCEM9t+RVegfei27qdS52LzwHeWjUJOZo7pUqeSuDg6UKtKIB1HvoW5uRlV/X2Y+sYrZdrG\n/TR5aqRWD7aP6fcqIfIqWcl3CW4ykfjrT+4KpTpPg7WV1GiZtZWMTJXpth3Zph4j29TjUkwSpyPj\nDJ83SZlKLsdGsXh0GEHuTszZeYKP1+5n8ejuTyxXofTMzc15//33GTp0KACvvvoq9vb2fPTRRwwc\nOJAaNWrwySefMGbMGMzNzXF3d2fmzJlPbP8VrhO8Y8cOli9fzpIlSx45wvvOO+8wcOBARo0a9Vj7\nelRdzG8HSh5JOLJ3N0vmzEQikdCyfSfkCgXq+7401UolVnL5Q/d/5I/d7Ny4ls/mLUEqe/Ql/udZ\nMz8nRjbxBT0cj0olV2PceZFLzcnV6kw+N1WZb/j/rVQlv19PoqG3o1EnGECr03PkVirf9q7N5G2X\njTpLe3btYuaML0AioXPnLigUCpT31W4qlcoST6jkCjnKnPtic5QoFIXv3W9r11KnXj38/QNMPvd+\n1WvUYNeO7STc3kelRh0My5v4OjKsoTd64GRUGrnaAuRSc0Bzr23MSm4bVVGH/3aain03kmng7WDU\nCXawsuDtNkFsunCXG8ml/7JVWEpR5hp3DJW5eSgsH34s1vTzZPufF1l/9Bz9WjUo9f5MaRXozNgW\nAejRcygyFbWmAIXMHO69HQqZ8cnU/ZJzio6bm8lKdlxOoJmfU7FOcOtAF/bdeHiJyoN0ajXmCuOR\nJXOFAt0DnTxdXmH73f3tV3RqNflqNck7t2HfqAmpB/ahy8sj/fhRVBGF9e5xq1ZQf+1GJFIpes3D\nT+b+OrKXjd9/jUQioW5oe2RyBbnqouM0T61CZvXwzxgARzcP3Cv7seWHeQx655NSvf6S8tm8eA4S\niYQ6hnyKTixzVUosS5OPqwdulX3Z+uM3DJz4ePlcP3GAAz/PByRUbdYWmZWC/PtyyVerkFo+ehTT\nzsUdR08fDq5cSJdxH+BYyZuXRk3k4IoFqDLTqdqsHU6ePtg4la6OWyG3IueBk+0clRqFVdlGVBeu\nWk9kTBzH1i1BYWnJnGW/MmXWIuZ9/Hapnn/zzwMcWbEAJBDcpC1SKwWa3Afbp/h7pdfrOf7rYkKH\njP9nQZnyfhi5pRRlrvExr8zNN5Q9lKSmtxs7zt1gw6kr9G1aA0upBe1q+FPNq/D+jDHtG9L2s5/J\n1xYgs3jyJ+bPmuT/8E/GtW3blrZt2xot++KLLwz/79ixIx07Pp17nipUJ/jq1at89NFHvPvuuzRv\n3txkzP21o7Vr16ZDhw7Mnj27xJrSh+nfvz/t2rUrcX1BiWsgtH1nQtt3Njy+E3mT6FsR+PgXXh6M\nvh2Bt1/JHafTRw+xcvF8PpnzHa7uHmXO/XlzIiqNE1FFl7V8HBVUdlQQm1l4lu/tICcus/SX6kt6\nOyUUjjI7yaVGneBOXbrQqUtRScPNmzeIiLhJYFAQAJEREfgHBD64OQD8/QOIiLhJy1atAIi4edMQ\ne+7sGf46f559f/wBQEZGOpMmvs34N97k5fDid58XFBSQm2Y8m8Gfd9KNOmbejnK87OXE3WubyvZy\n4jNLN9L0IBuZORPbBnEwIoUjt8p2id3HzQlVXj7JmdmGkoib8cm83LT2I5+r1emITk5/ZNyjHI5M\n5fB95R3+Tgp8HRVEpxceK76OCmIySn8z24M1wl72Vvg6KThaxrbJjYvDXC5H6uRkKIlQ+PmT8odx\nDW6BUkl+qnHpzv39BtWdKKSOjg+sL13Hom5oe+qGtjc8vnsnkoQ7t/HwKfxcSYi+hbu3X6m2VVCg\nNaoJfhwP5pMQFUli9C08fAovbSZG38atlPnoCrRGNcFlVbVZW6o2K/rSTYm5RWrsbZwrF+4/NTYK\nZy/fUuZSQNZ9Nb9BDVsS1LAlAHkqJddPHsDZy69U2/L18kClziM5Nd1QEnEzKobwDq1K9fx/3Lgd\nTdfWzXCwtQGgd6e2DH3301I/P7hJW4KbFLVPauxt0mKjcLr3OtLionD09Cn2vHy1itSYSHZ/+ymg\np0CrRZOrYuU7Qxgw/QekpTjJKYmviz2qfA3JWUpDScTNhDReblj1kc/V6nTEpBTeHBvk4URqtvFn\nwrMoIRCeTxXmxrj09HTGjx9PkyZNCAsLIyUlxegn7d4X0YNfIBMmTODPP//k9u3bZd6nm5sbNWrU\nKPGnLFq178LWtavJyszgbmw0e7dvKbHE4eLZUyyaPZ3J02fj5eNX5rwfh8TMDAtLS8zMzTCXWmAh\nkz3WiUNpHbudStdq7thYWuBua0mbINcSO2m1KtlhY1l4PufrpKBDVTfO3RsF9nWUU8XVBnOJBEtz\nM/rXr4wyv4D4rId3Gjt36cbqlSvJSE8nOvoOmzdtpFuY6ctlnbt2ZfPGDcTHxZGaksKvq1cZYqd+\n+hlr1m9g1Zq1rFqzFhcXVz6e9hmduhTWcG/ZtJGc7Gz0ej1nTp9mz+5d2Ac8fKqek1HpdApxw0Zm\njpuNJaGBzhyPMl0XV8PDFhtZ4eiGj6Ocl6q48te9UWArCzPebhPE33FZJU4/9zAKSxlt61Thu+2H\nydNoOXjhBhHxybStU6VY7Iaj58lW56LX6zl1PYpdpy/TpKqfYX2+RkuepgA9evI1WjTah51CluxQ\nRArhtStha2lBJTsrOoS4FasP/0ddL3ts7x03Ac4KulX34NQDo8Btgl05F5OOMr9s+ejyckk/cQyv\nISOQSKU4NGmG3Nef9BPF6/xT9v5Opb4DMLOyQurigluXbmScKqyNTd33B45NmyP3D0Bibo7XoKFk\n//3XI0eBTanXqgNHtq5FmZVBSnwsp/7YTv02nUzGXjh+kIyUwmMiJT6Wg5t+IahW0ah9gVaLJj8P\n9HoKtBq0mnyT23mYuq3a38snk5S7sZzeu536rU3nc/H+fO7GcmjTrwTWqv9APvno9Xq0mrLnU7VZ\nO87t2oA6O5OMhDguH9pNSIv2JmNvnjpMdmphGVxGQhxnd6yjcvW6hvVJUTfR6/WoszLYv3we1Vt1\nwtLaplR5KKysaNesAQtWrScvP58DJ89y804M7ZoVnyVIp9ORl5+PtqCAggId+fkaCgoKr3rUCA5g\n9+GTZGbnkK/RsnHPQYL9Hv/el+Cmbfl7z0bU2ZlkJsZx7chuqjYv3j6WCmuGzFpJn08W0OeThbQe\n/hY2Tq70mbbQ0AEu0GrvvT96dBoNBaU8luUyKW2q+7HojzPkabQcuhJFZGIabar7FYvdeOoq2eo8\n9Ho9pyPj2PVXhGEqtW71gjl4JYobd1PRFBSwZN9ZGgZ6VohRYAAzc8lT/6lIKsxI8KFDh7h79y53\n794lNDS02HpPT09WrFhRrOPm5+dHr169TP6pvmepY4/e3I2L4c0hvbGQSuk5aAQ16hZ+6aQkJTJx\n5ADmLl+Ds6s7G1YvQ6XM4dOJ4+/dKS0htH1nRr89+anl1/WjN+j2yVuGYaouH4zn55GT+HPlxqey\nv303knG3tWT2yzXR6PRsu3SXa/emR3NSSJkZVtMwF3DNSna81twfmYUZ6ap8tl2+y6nows6MuZkZ\nQxt542ZjiVan53aqitn7b6B7xGBa7759iY2Npk/PHkhlMoaPeIUGDQu/iBITEhjQrw9rftuAu7s7\nLVqGEtkngpHDhqDT6wnv2Yuwl18GwMbG+MvP3MIcOztbLC0La2mPHjnCwm+/RavV4uHhwVtvT+SQ\nRfWH5nYgIgU3G0tmhFVHW6Bn59VEw/Rojgopn3epZpgLuIaHLaOa+mJpbka6WsPOK4mcuXeCUK+y\nAz4OctxtLWl3bwYFPfD6hgulfJfggwGd+ejnrYS+OwcPRztmvdoTO4UVO05d4qc9x9n48X8AOHwp\ngnmbD6At0OHhZMc7vdsTWjPIsJ1Gb32J5N6/Rm99iaeTA7u+KPuNT7uuJuJhZ8WifnXRFOjY8He8\nYXo0F2sZ83vXNswFXNfLngltArE0NydVlc+Gv+M5ftv4ZCI0wJmlJ++UOQ+AOwvnEfDu+9T/bTP5\nyclEzPiMAqUS5zbtqNR/EJfGvgpA/Kqf8R3/FnVXraNApSRp53bSDu4HIDc2hqiF86nyyeeYW1uT\nffkSt75+vHq4pp16kHo3jlmvD8FCKqVNz8GG6cgyUpKYO2EEE+ctx97ZjeT4aHYsX4hamYPC1o7a\nzdvSYUBRTelPn73L7St/g0TC0i/eA2Dyd7/i4Opuct+mNOnUg9SEOL5+ozCf1j0HEVCzriGfeW+P\nZMI3y7F3diU5PoadP39nyKdW8za0vy+fpZ9PIupePsunF34OTlr4S6nzqdUujMykeFZOHoW5hZQG\nYf0N06Nlpyaz+sPXGDJjMTZOrmQkxHJ0zRLyVEqsrG0JbtyKpr2GGbZ1cMVC0uKjkcosCWnZgWa9\nyjaH+8fjRzJl9iKa9/0PHq7OzPngLexsrNl+4Bg/rN3Clu8Lp6Xbuu8IH85ZbLjqtf3AUcYN7s24\nwb15td/LTP9uOWH/eRettoAawf58/vZ/ypTH/aq36UZmUjxrPhyNuYWUel374hlSeMUnJy2ZdVPH\n0O+zxdg4uSC3K6odt7S2RWJmZqiZBtg59yPib1xEgoSd3xTO9zpw5lJsnR99o9WU8JZ8vO4ArT9d\njoeDDV8N7oCd3JKd52+y9OB51r9dOBvF4at3mL/rz8LPG0cb3unWjJYhhSPX/m6OTAkPZcLPu8nJ\nzaeenwef92v7sN0KFZhEX9pra0KZXYjPfHTQM/Kd1+PfGfw0KFeuL+8UjHwb/vCO57P0zvbr5Z2C\nke/cnu70e2XR/1bxUeby9MH6f/eHPZ6kuNmryzsFI7rn7Kvlbk7pb2x82sZ6/PtSoCdpXqz9o4Oe\nkbGpZf9DME+TPLx0ddTPi92BxefZftI6R5r+oz//jypMOYQgCIIgCIIglFaFKYcQBEEQBEF4kf0/\nzg5RnkQnWBAEQRAEoQKoaDeuPW2iHEIQBEEQBEF44YiRYEEQBEEQhApAYibGNstCtJYgCIIgCILw\nwhEjwYIgCIIgCBWAqAkuGzESLAiCIAiCILxwxEiwIAiCIAhCBSCmSCsbMRIsCIIgCIIgvHDESLAg\nCIIgCEIFIDEXY5tlIVpLEARBEARBeOGIkWBBEARBEIQKQMwOUTaiE/wUBRyYV94pGChXri/vFIxY\nD+1T3ikYUaSdLO8UDPpNHVXeKRj54ecN5Z2CwSfbXyvvFIy80mBSeadg8MvXz1fbJF5MLu8UjHSZ\n9Dfl0N4AACAASURBVHJ5p2CwSjakvFMw0vmHceWdgsGHvf9b3ikYmVPeCQhPlegEC4IgCIIgVAAS\nMzESXBaiJlgQBEEQBEF44YiRYEEQBEEQhArATMwOUSaitQRBEARBEIQXjhgJFgRBEARBqADEX4wr\nGzESLAiCIAiCILxwxEiwIAiCIAhCBSBGgstGjAQLgiAIgiAILxwxEiwIgiAIglABiNkhyka0liAI\ngiAIgvDCESPBgiAIgiAIFYCoCS6bZ9oJnjJlCtnZ2SxYsID333+fzZs388477zB69GhDzN69e3n9\n9de5du0aAKdOnWLYsGFIJIVvrLW1Nd7e3jRv3pwRI0bg6upqcvv3+2cbZ86cwcbGBp1Ox48//sim\nTZuIj4/HysoKX19f+vXrR58+fZ5BSxSXrspl2pYTnI1Kwt1eweQujWjs71FifHxGDn2+207X2v58\nFNbkieQwuIE3oQHOaHQ6tl9OYM+1JJNxLQOcGdXUF41WDxJAD5O3XyL9f+ydd3SUxdeAn82mbhrp\ntPQEEELvEEpoqQRIKCpFitIVFCmCUgQR6QgoKKL0DlJC7x2kSYc0ICSQRvpuyib7/bGwyaZBEL/w\nw3nO2XOyM3dm7js77b1zZyLPwUCqw9j27lQ2M0QikfDgmZxVfz3kaWrWG9HxBa2G9Mbzk/epUrs6\ne2csYe/0H99Y3knJyXw9fRZ/XblGRVsbJo39nKaNGhSRy8rKYsrMORw/dQZzMzNGjxiMb8f2mvgH\nj6L4ft4irt24iczIiCED+vF+924AjBgzgZu375CjVOLs6MC40SOp61HrpbrpWVTAY9Y0LJs0JPNp\nLHemzeLZ+b+KyLXYsxmjyi/ajwQdQwOi1m3m7ndzAZA5OfDeN+OpUL8OuXI54T+tIGr9lteoLVCp\nVJxav5y7Zw4j1dOjoV9P6nl3K1Y24uo5zm5eSUZyInoGRlRr1paWvT7W9O/wy2c4t20V6c8SqOha\ngw6DPsfE0qbYvAojNTXD8bMvMfGoS05CPFG/LCH9xrViZS3bdcQu+AP0LC3Jjo8jYsZksuOeasm4\nfvMdpnXrc627Xxlqoyhf+r1H5wZVyFLm8sfJSNaffVCs3MTAWvjVq4xKpf6ur6vDg/h0ei05o4lv\n6mpFVUsZn/x2gSsPkl5bJ6mJKZUGjca4hgc5zxJ4unY58jvXi5U1b9kOq4Ae6JpboHyWQNTC6eQk\nxL522QB65ubUmPoNFRrWJzM2jtAf5pJ86XIRucab1mJg97wdS0BqYED0lm2EzVuIZcvmOA7sj7GL\nM7lyBXEHDxO+eCnk5ZVZn6SMTKbsOM2lB0+paGbMhIBmNHGpVERu2dGr7LwaRnpmNlYmRgxoVZsu\nDdw18UsOX2Hn1VBylHnUc7Tl687NsTaVlVkflUrF4TU/c+PkQaT6+jTv3IsmvsHFyt6/fJZjG1aQ\nnpSAnqERtVq0o92HgzV9KjtTweE1P3Hvr9OoVODesDmdh44rs04AUhMzKg/ObzdPVi0rud14tsc6\nUN1uchITiJr/7T9uNy/o6lGRRvYWKPPyOBqawMmIxFLlJcCXXm5IdSTMOhKqCa9X2Qyf9+wwNdAl\nPj2LHTee8DBJ8UZ0FPxvUG6WYIlEgqGhIStWrOD999/H1NRUK66w7IEDBzA2NiY9PZ1bt26xYsUK\ntm7dytq1a3F3dy+cfbHlvWDx4sVs2bKFyZMnU6tWLdLT07l58yapqalv7gHLyKy9f2FtYsTRsd05\nH/6ECVtPs/PTQEwN9YuVn3/wCu9Vsnxj5bevZkMNOxO+3HkDmb4ukzpW51GSgjuxacXK33maxuyj\noUXCc/Ly+O38Q56kZgLQoZoNQ1u4MHX/nTemK0BKTCx7piyg8Ydd3mi+ADPmLMDGypLTB3Zx9sJf\nfDlpKiFb12FWoI0CLP1lJSmpqRwN2U5YeCTDvhhHzerVcXSoSnZ2NsO/GM+nQwbx0/wfyMrKIi4h\nf6D+fMQQnBzs0dXV5fipM3w2dhIn9v35Ut1qTplAdnw8R5t4Ye3ZnLqLZnGqQxeUaelacmcDemr+\nlujp4nXmEE8PHHn+XY+Gvy4mdOFSLn/yKVJDAwxsX22hWRw3ju4h5v4N+s5eSVZGOttnjcPawYWq\n79UtImvnXI2gr+YgM6tAljyDvUumc/NYCLXbBZD09DGHV8yny5ffYedcjUt7NrF/2Sy6T5z3SnrY\nD/2UnKRn3OjbHdP6DXEeO4nbw/qTm5GhJWfWsAk2Ad2I+G4yWTGP0beriDJdu++bN2mOjpEhqhcr\n0tekR1MHGjhbEDjvBGZGevz6cVPuP0nlUuSzIrIzd91i5q5bmu+L+zXkelSy5vvdJ6nsvx7DlG61\n/5FOABX7DkOZ8oz7n/bGuFZ9qgwbR/j4IeQptOvKpE4jLDsG8njRDLKfRqNnY0duRvFjQllwnzCW\nrIQETrf3wbJZU2rNmsGFrj1Qpmu347969dH8LdHVpcWBEOKPHANA19iYB8tXkHz1GlKZER5zZuHQ\ntzePVq0psz7f7zmPtakRxyd8wLmwGMZvPs6uUUGYGhloyfnXc6VfSw9kBno8Skxl0Mp9eFS1xtXW\ngsO3HrD3ejjrhgRgaWzE9F1nmX/gEjO7ty6zPlcO7+LR3esMXbCKzIx01s0Yg62DK0616hWRrexS\nnT7fzMPY3IJMeTrbF0zjyuHdNOwYCMCe5XOoYFORET+uR1dfn/ioB2XW5wUVPxqGMvkZ94Z/iLFH\nfaqOHE/Y2MHkyQu1m7qNsPTuTNT86W+03QC0cLLExcqYmYfvI9OXMrylMzGpmYQlZJSYppWLFfLs\nXEwN85c8JgZSPmhQlV/OPSQ8MYNmjhb0b+zAtIP33oie5YWOjrAEl4Vy9Qlu3rw51tbWLFu27KWy\nlpaWWFlZ4ejoiJ+fHxs2bMDS0pKpU6eWudxjx47xwQcf0KlTJ6pUqUL16tUJDg5mwIABr/EU/xxF\ntpIT9x4ztG1d9HWltK5eFXe7Chy/97hY+bNhMQA0LcZS8bq0dLZi7+1Y0rNziUvP4nhYPJ4uViXK\nF35ReUGeCs0CWCIBlQpsTYpfyP8Tru8+zI2QoyhS3szA+gK5QsGxk2cYMXgQ+vr6tG3VkmpuLhw7\neaaI7J4DhxgysB8yIyPqeNTEq1VL9h48DMCfe/ZRv44Hvh3bI5VKkclkODnYa9K6uTijq6sekHV0\ndEhOSSEjQ16qblIjQ2w7tCV00TJUOTnEHztJ2t0wbDu0LTWdbbs25KSlk3zpKgBVggNJunKNpyEH\nIS+PXLkC+YNHZakmLe6dO0p9n+4YmZhRwa4ytdr4cPfM4WJljStYITOrAIBKlYdEokNK3BMAom5e\nwb5WfSq61kCio0OjgF7EPwjTxJeGjoEh5k2a82TDalTKHFL/Oo/iQSTmTVoUka3YszfRK5eTFaPu\nX9mxT8mT59e9RFePSr37E7PqtzLXRWH861Zm9alIUhQ5RD2Ts/1SFAH1q7w0nZWJPk1drQm5FqMJ\n2/5XFFceJKHM+2cLc4m+ASb1m5KwYz0qpZL0v/8i6/EDTBsU3VGyDuxF7MbfyH4aDUBOfCx5itLb\n6cvQMTTEuk0rHiz7FVVODomnTpMeGoZVm9IXi9ZtWqFMTyfl2t8AxB08TNLFv1Dl5KBMSSV2737M\n6niUWR9Fdg7H7z5iWLv66OtKaVPDHnc7C47djSoia29phsxAD4AXv0J0knrh/iQ5gwaOdtiaGaMr\n1aFjLSci4pKL5PEq3Dx9hGb+PZCZmmNZsQr1vPy4eepQsbImFlYYm1uodcpTIdHRIfl5n0mIfkjs\ngzC8PvgEfUMjdHSk2Dm6vpZOEn0DTBs0JX7bOnW7ufYXWVGRxbebLr2IXf9m280LGtpX4HhYAvKc\nXBIysjn/8BmN7CuUKG+iL6WpowVHQuO1ws0N9UjPziU8Ub14vhyVjKmhLoa64qjUf4ly9QmWSqV8\n/vnnjBkzhn79+mFnZ/fKaQ0MDHj//feZNWsWz549w9Ly1a2i1tbWnD9/ng8++KBM6f4tHj1LRaav\nh42pkSbM1bZCsQNoTm4ePx65yryebQi5HvHGdKhsbkhUUv4gFZWsoG6VkgcWVytjlnavS2qmkoP3\nYjkWmqAV/51/TY1LxOZrxS/m30YeRT3GWCbDxjr/BcDNxZnwiEgtudS0NBKfJVHN1UUT5u7qwvVb\ntwG4cfsOZqam9PlkOFGPY6hfx4OJX47G1sZaIz9yzATO/XUZpVJJz6AuGBuXvm0qc3JAmZFBdnx+\nXaeHhmHiVvqkVqmLH0927dV8N6/jgTIljSYbf0fmUJXkK39z59tZZMUllJJLyTyLfoS1vbPmu1VV\nJx78fbFE+ZjQW+yeP5nsTDky0wq07j00P7KA5VWFCpVKxbPoh5jblv7CZ1C5MnkKBcqkfAur4tED\nDB0ctQUlEoxc3DBydMZx1FhUyhwSjxwkdusGjYhdcC+STh4j+9nr1UdBXGxNCH2a/6IW9jSNVtVf\nbnX3qVOZG4+TifkXtmb17SqTl6lAmZLvTpH1+CEGlR20BSUSDB1dMKjqSOWPR6NSKkk+fZjEPa/n\nNvMCmYM9uXI52Yn5OyMZ4REYuzqXkgrsfL2J3XegxHjzBvXICI8sMb4kHiWmYmygh00BtwVXW4sS\nF7C/n7rBryf+JjNHSc3K1hpjRIdajhy8GUlMUhqWJkbsvxFJc7fKZdYH1ItXG4f8scXG3pmwqxdK\nlI+6d5PNcyaRpZBjbFaBjv2GA/Ak/B4WdpXZ/fMPhF+7iGWlqrTvPZSq1WqWWSf9ikXbTebjRxhU\nKdpujJxcMazqRJXBn6NS5pB88ggJuzeXucziqGhqQMxzQwvAk9QsatqZligfUKsiR+7Hk5Or7SYT\nk5JJYnoW1WxMCI1Pp7GDBVHJCjKVZXeneZuQiNshykS5H4zr0KED7733HosXL2bGjBllSuvioh4k\noqOjy7SY/eqrrxg1ahSenp64ublRv3592rdvT+vWZd+2ehPIs5WYPLcuvMBEX5eUzOwisuvO36GV\nexWqWJi8UR0MdaUocvI7vyInt8Q34juxaXy15xaJ8mxcrIwZ1dqV1Ewllwts3U4KuY2ujoTmTpYk\nK3LeqK7/JnK5oshi1NjYuIirjFyhXpzIZPmyJsbGyOXq8Lj4BI6ePM2vP87H3dWZeYt/ZuK0maxY\nMl8jv2TeLJRKJSfOnEOhyORlSGUylOnaW37K9Az0zM1KTKNnboZN65bcn71IE2ZoZ4t5h5r81X8Y\n6aFhVB83mtqzp3Op/7CX6lAcOVkK9I3y60HfSEZOVsnPU9m9FkN+3kZqQiz3zh7ByNQcAPta9Tm3\n7Q9i7t/EzqUGl3ZvIC9XWWpeL9AxNCJXrm1pypPLkRZyYdGtYIFEKsW0XgPufPoJUlNT3KZ+T3Zc\nLEknj6Jva0eFFq25+8Uw9CxL3gl5VYz0pWRkKTXfM7KUyPRfPuz61avM1ouvb50vDR1DwyJWubxM\nOVLjQnVlVgF0pBjXqkfE1yPRMTbBYcw0chLiSD1/4rXLlxoZFWnHuRkZ6JqV3I51zcywbNGc8B+X\nFhtv3a4tFo0acemDvmXWR56txLjw+GugR4qi+HMMA1rVZkCr2tyKTuBixBP0pFK1DiZG1KpiTcDC\nbUh1dHC3s2BS52Zl1gfUfrwGBfqUgZGM7KySX4jsq3swZsVOUuJjuXH6EDJTtQEjLSmBiBuXCRj8\nJQFDx3L3wkm2zPuG4QtWYyAzLpNOOoZGRduNQo7UpIR241GP8K9GIDU2wWHct2QnxJJ67vXbzQv0\npTpaC9VMZS76JcxVjhZGWBvrszE6BVcr7XFdBVyJTmFAE3ukOhIUOXksO1P2lyjB/zblvggG+PLL\nL+nfvz8DBw4sU7oX/nolbc2XhKurK3v27OHmzZtcuXKFS5cuMWzYMIKCgpg+ffor5xMXF0d8fHyJ\n8Y4lxmgj09clPUt7oZierUSmp/3zxKfJ2XUtnHWD/9lBHYDmTpYMaOoIKjj7IJHMnFyM9PIHEiM9\naYlvxIkZ+YvziMQMDt6Lo5G9hdYiGECZp+JURCKLg+swfvct5Nm5/1jvfxuZzKiIW0JGRgYyIyNt\nueff5XK5ZiGcnpGBTKYONzAwoH2bVtSsUQ2AYYP609q3C9nZ2ejr57uH6Orq0r5NK4L7DKRmdXdc\nnJ1K1C1XLkfXRHvi0jUxJlde8uRYMcCH1Nv3tNwdcrMyiT10jLTb6sOn4Ut+wev8ESR6eqhyXv7C\ncu/cMY6t+hGQUL25F/qGMrILTI7ZCjl6BoYvzcfM2g6Lyg4cX7MU3+ETsahkT/tBX3B89RLkKUlU\nb94Oy8oOmFhavzSvvEwFUpn2JKcjk5GXqV03qmz1wiZ2+ybyMhXkZSpIOLAXs4ZNSDp5lCoDhvBk\n/SrIzS3zuALgU6cSX3f1QKWCfX/HIM/Oxdggvx8bG+giz1aWkoPaeuxsY8KhG09LlXtd8jIz0TEq\nVFeGMvIytV828rLV/Txx7zZNXSUfP4BJnYb/aBGcq1AUacdSY2NyFSW3Y1vvjqTfu4/iUVEXhQoN\nG1Bt3JdcH/UFOSkpZdZHpq9LRuHxNysHmb5eCSnU1KpizZ5r4Wy7fI8ejWuw7Ng1IuNTODb+A4z0\ndfnx0GW+3n6aee97vVSHW2eOsO+3hSCRUKtFOwwMZWQV6FNZCjn6Bkal5KDG3MYO6yqOHPjjR7p9\n9g26+gZUsKlInTbeANRs7sWZP9cTE34X59oNX5pfQfIyFUXbjVHRPpaXo243CSH57Sbp2H5M6zZ6\nrUVwgyrmdK9XGVRw+XEyWco8LQONoa6U7BLmqm61K7H17xcuRdr9uZqNCT41bFlwIoK49CzqVjbj\n42aOfH8k9B+7HJUnOuJ2iDLxViyCGzVqhKenJ/PmzaNbt+JPlRdHeHg4AFWqqH3sjI2NiYmJKSKX\nmpqKVCrFqNBCxsPDAw8PD/r168euXbsYP348Q4cO1eT3MjZt2lTkJoqCXJ7c+5XycbA0Q5GdQ3ya\nQuMSERabTOd6Llpyt2ISiU2V03XxLlSoUGQrUanUN0X81Kd9cVmXyLkHzzj3IH/r2MFCRlULGY9T\n1BOhfQUjolNefSu2pPWCBPUgZWmk9z+xCHawr4pcoSA+IVHjEhEaHkEXf18tOTNTU6ytLLkfHkG9\n2h4aOTdn9Zaum4sziYnah590SllUKZVKoqJjSl0Eyx88QiqToW9jrXGJMK3mRvSO3SWmqRzoR8zO\nEK2w9PvhGNhoWzlVZThRX725F9Wb50/sCVERJD6OxKqqWvfExw+wqvJqr4B5ubmkFvD5dWvkiVsj\nTwCy5BncO38MqypOL80nKyYGHUMjdC0sNS4RRo5OPDuq7UeZm5FBzrNCJ8kLuGCYeNTBuPp72A8Z\nCTpSJFIpHis3EDp5HFmPiy7ACrP/+hP2X89/nmoVTXGvaEp4nNpv1K2iKeGx6SUlB8C/XmVO34sj\nPav0xfLrkh0bg46hofq2h+db2wZVHUk5c1RLLk+RgTK58Kn7f744kD+KQmpkhL6VlcYlwtjNlae7\n95aYxs7Xm6d79xcJN61Vk5ozp3Nr/ETS791/LX0crMyQZ+cQnybXuESExSbRub7bS9Pm5uURlah2\ndwmNTcK7tjPmMvVhum4N3RmwYt8r6VCrZXtqtcwfw+MeRRAfFYntczej+KhIbJ73r5eRl6skKVbd\nBm2qOhV70Px1yH4ag46BdrsxrOpI8ukj2uXLM1AmFe5jr1UkoLbWXonOf7mpbG5EJTNDnqapX2gr\nmRlo/i6Ioa4OVcyNGNTMEQkg1ZFgqCtlind1vj8cSmUzA0LjM4hLV6f9OyaV4DqVsTXRdrcQvNu8\nNc4jX3zxBceOHePateKvNCpMZmYmmzdvpnHjxlhYqA8FODs7ExYWRk4ha9atW7eoUqUK0ufbVsXh\n6qr2q1SUYo0oTK9evdi+fXuJn1fFSF+XNtWrsvzEdbKUuZy895jw+GTaVq+qJdfSrQq7P+vKhiF+\nbBziT3BDd7xqVGVWsOcrl1USZyIT8XvPDhMDXexMDWjrZsOpEq6dqV3JDJPn1i1HSxkdq9ty5bkV\n2NHCiGo2JkglEgykOvRqUJWM7Nw3PqhIdHTQNTBAR6qDVE8XXX391x7cCyIzMsKrVUuW/rqSrKws\njp86Q1hEJF6tWxaR9e/UgV9+X4NcLuf6zdscP3UWv04dAAjw6cjx02e4FxpOjlLJst9X07hhffT1\n9YmOecLJs+fJzs4mJyeHtZu2EhefgEfN90rVLVeRSdyRE7h9NhQdfX1svFpjUs2NuMPHi38WR3tM\na1bn6R7txcOTXXuxadcGk+ruSHR1cRn+Cc8uXHolK3BxVG/ejiv7tqFISyH5aTS3TuynRssOxcqG\nXjxJWqJ69yT5aTSXQzZTtWb+ife4B6GoVCoUqckc/WMRNVt7Y2D8ctefvKxMUi6epdIH/ZDo6WHW\nuBmGDk6kXDxbRPbZ0UPYdeuJjqEhelbWWHfyI/XSeQBuDx/I3c+HcffzYYRPnwR5edwdPZSs6Nfz\naw/5O4a+ns5UkOnhYCUjqJE9u69Gl5rGt27lYmV0dSTo6+ogkYCeVAe91/T9U2VnkX71AtZdP0Si\nq4dJ3cYYVHUk7UpRn9OUM0ex8g1CYmCIroUVFdp4k/73pdcq9wV5mZkknDiF05CP0dHXx6qVJ8au\nLiSeOFmsvJF9VUyrVyPugPYLjbGrK7Xnz+He9Jmaw3Kvg5G+Hm1rOLDs6DWycpScuBtFWFwSXjXs\ni8huv3yftMxsVCoVf0U8Yd+NCM1VajUrW3HwZiSpiixylLnsuByKm53Fa+nk4dmeC3u2IE9N4dmT\nx1w7tpfarTsWK3vn/AlSE9XXWT578phzuzbi5FEfAMea9VCpVNw4dQhVXh53LpwkPfkZlV1rlFkn\nVXYWaVcvYBP0IRI9PUzqldxukk8fxdovv91YeHmTdq3oVY6vw+WoZNq6WWOsL8XaWJ9mjpb89aio\n/3amMo9pB+4y71gYc4+FsflaNEmKHOYeCyM7N4/HKZm4WRtj8/zgdp1KZujqSEiUF3VD/F9CIpX8\n6593ibfCEgxQrVo1OnfuzJo1Ra+3UalUJCQkkJmZSUZGBjdv3uS3334jOTmZpUvzfcQCAwP5+eef\nGT9+PIMGDcLU1JSLFy+yZs0axo3Lvxfxs88+o0GDBjRo0ABra2uioqJYsGABzs7OGj/jV8HW1hZb\nW9sS49OvbXvlvMb7NWbKn+doN2cLdmbGzAr2xNRQn303Ivn9zC02Dw1AT6qDpXH+NrNMX5d0PV3M\nCl3j8zocuR+PnakBcwM9yMlTsfvmE+4+vx7NUqbHrAAPzV3AHpXMGNLCGX1dHZLk2ey+9YSLj9SW\nAamODn0b22NrYoAyT0Vkopy5R+/zpneX/L7+FP8pozRWPN+JI1g1YCwX1rz6y0dJTBo7mknffk8r\n70Ds7GyZ+91UzExNCTlwiBWr17Fj3R8AjBg8kCkz5+AVEIS5mRlfj/0cRwf1i4uLkyMTvxzNqHET\nScvIoEGd2nw3eSKgNor88vtqJkyejo6ODm6uziydNwsry5dPmHemzaL2D9PwuniMzKdP+XvUeJRp\n6VQK8MF5yADOdu6lka3UxZ+Ek2fISdH2Z86IeMCdabOo/9N89ExNSLp8jRvjp7x2fdVuF0BKXAxr\nxg9CqqtHw4BemuvR0hLjWTdpCH1mLsfE0obkp485vfEXsuQZGBqb4t6kNc2C+mnyOr56Kc9iHqGn\nb0ANz440D/rolfWIWr4Ex1Fjqb1mGzkJ8TyY8x25GRlYtPbCLvh97o4aAsCTTWuwH/IptX5bT55c\nTsKBEJJOHQcgNy2/riT6+qhUKpSpZd9if8GWC49wsJSx84s2ZCvzWHkinMvPr0ezMzdk62etCF50\nirjnL4mNnC0x0NXhzP2iblY/DWhMQydLVMDS/o0BCJh7nKcpZX/BfLpmGZU/Hk21JevIeZZA9E+z\nyVNkYNasNVb+3Yn85jMA4ndupGKfobjPX0meQkHS8f2kXih+sVoWQn+YS41p39DyyH6yYuO4PeFr\nlOnp2Hp3wmFAPy69n381mp2vD4lnz6Ms5Jdftff76Jqb8d6MaZr7ylOuXePG6C/LrM8E/2ZM3n6a\ntrM2UtFcxuyebTE1MmDf9QhWnrzOlpFdATh1L4rFhy6jzM2jorkxX3g3xrOaus/3b1WbH0IuELR4\nB8pcFe9VtmJK16Ivz69Cgw6BJD2NYdkXHyHV06N54Ac4Pn9ZTE2M45dxHzN49m+YWdmQ+CSKw2uX\nkSVPx8jEjPeataFNj/4A6EildB/zLSHL53Lgj8VYVapKjzHfltkf+AVPVy2j8uDRVP9pPTmJCTxe\n+gN58gzMmrfBOqA7EZM+BSD+zw1U6jeUagt/V7tDHN1P6vl/3m4Azj54hrWxPl+1r4YyL48joQma\nGx4qGOoxrp0bPxwNJSVTSXqB3Ud5di55KhUZz8PCEjI4FpbA4GZOyPSlPJNns/pSFFn/4wfjBGVD\novqnF2GWga+++or09HQWL15c7D+2iI6OxsfHh9zcXG7fVp+yv3jxIh99pJ4IJRIJMpkMe3t7PD09\n6d+/P1ZW2tu6Dx8+ZN68efz999+kpaXh6OhInz59CA7Ov2h8y5YthISEEBoaSlpaGtbW1jRv3pyR\nI0dSqdKbu3Ysfd23byyvf8ow1T/3I36TGPctn39KUhI/Pjtf3ipoONb07fqtQle9+svcv03LH4aU\ntwpaDKz1eXmroGF9zM/lrYIWsTdKPi9RHjQZG1jeKmjY4tbn5UL/jzReNLy8VdCwIvj78lZBi/ld\nyn7tXnnyd0/flwv9Q+pufjU3n/8F/l8twd9//32xf7+gSpUq3LhxQyusSZMm3Lnz6v9owdHRkR9/\nLP2/h/Xo0YMePXq8cp4CgUAgEAgEgneLt8YdQiAQCAQCgUDw+ojbIcqGWAQLBAKBQCAQvANIbkM7\nrwAAIABJREFUxL9NLhNvze0QAoFAIBAIBALB/xfCEiwQCAQCgUDwDqAj/m1ymRC1JRAIBAKBQCD4\nzyEswQKBQCAQCATvAO/aP7P4txGWYIFAIBAIBALBfw5hCRYIBAKBQCB4B5AIn+AyIWpLIBAIBAKB\nQPCfQ1iCBQKBQCAQCN4BJDrCtlkWRG0JBAKBQCAQCP5zCEuwQCAQCAQCwTuAuCe4bIjaEggEAoFA\nIBD855CoVCpVeSvxrpKYJi9vFTS8bVcHynJSy1sFLT6zbFbeKmhYHHeyvFUQvCJxulblrYIGSyNp\neaugRXbu2zW1JGfmlrcKGirpZpa3Clqk68jKWwUN5gl3y1sFLaTODcpbhTIRNrLnv16G25LN/3oZ\n/18IS7BAIBAIBAKB4D+H8AkWCAQCgUAgeAcQ9wSXDVFbAoFAIBAIBIL/HMISLBAIBAKBQPAOIO4J\nLhuitgQCgUAgEAgE/zmEJVggEAgEAoHgHUAifbtuiXnbEZZggUAgEAgEAsF/DmEJFggEAoFAIHgH\nELdDlA1RWwKBQCAQCASC/xzCEiwQCAQCgUDwDqAjbocoE29NbSUkJDB9+nQ6dOhA7dq18fLyYujQ\noZw7dw6Adu3asXr16iLplixZQteuXYuEx8bG4uHhQefOnYst7+LFi3z00Uc0bdqUevXq4e3tzVdf\nfYVSqXyzDyYQCAQCgUAgeOt4KxbB0dHRdOvWjYsXLzJhwgT27NnDihUraNasGdOnT39peolEUiRs\n+/bt+Pn5kZGRwfXr17XiwsPD+eSTT6hTpw7r1q1j9+7dfPPNN+jp6ZGXl/fGnqswKpWKhfPm4O3V\nms7eHdm0fl2p8qv/WIlfx3b4tvdi6Y+LtOJaNm5Ah9Yt1Z82nqz+Y6Umbsa0KbRt0ZQObTzp0Lol\nfXr1KFGf+XPn0KFta/w6dWTDS/RZ9ftKfDq0o1M7L5YU0qegTLNGDbj+9zVN2K/LlxHo50u71q3o\nEdSV3bt2aqVJSk5mxJgJNPHyIbBXXy5culJs3llZWUyYMoNm7Xzx7tqLfYeOaMU/eBTFkFFf0rSd\nD17+3di4dYcmbsSYCbTx7UKLjv70/ngYf9+8VeqzlpVWQ3rz1aXdLMm6j983n73RvJOSUxg+7msa\nd+xM594DuXD5arFyWVnZjP92Fk07daFT9z7sPXxMK37pb6vpEPQhLXyDmDZ7IUplribup5Vr6Nr3\nE+q09mbnvkP/E7q8SX0WLV+JV5detPQL4tMJk0lIfAbAk9g4mnQMpGmnLjTt1IUmHQOp3aoTh0+c\nLrYclUrFkgVzCOzYhu7+ndi6sfQ+tX717wT5tqerdzt+WaLdp04dP8qA94MJaNeKsZ8NJz4uVhMX\nHxfLxDGjCOzYht7BgRw/ckhT/uzZs2nl6UmH9u1Zu3ZtqeWv/O03vNq2pW2bNixcsEAr7ubNm/Ts\n0YPmzZrx8aBBPHnyRBMXHBREyxYtaNmiBS2aN6dhgwb88MMPRfIfNXIEzZs00ug2f85s2rdphW+n\nDmxYV7puq35fiXd7Lzq2a8viRQs14QqFgk8GDqBju7Z0aNuakcOG8PDBA620e3btJLhrIG09W9Cr\nexDR0dGllqVSqfh54VyCvNvyfudObN+0vkTZRw8i+Wr0CIK9vejXPVAr7vGjh0we9zk9/DrQw68D\n304cR2JCfKllw/Mx8POxNGnTgcCeH3Lhr8vFymVlZTFh8jSaeXXEu0sw+w4eLlZu6Kgx1G/eWivs\n8tVr9Oo3kOZeneg98BNCwyNK1EelUrFg3hw6ebUmwLsjG19xrvIpZq5q0bgB7Vu3VH8KzVUveBIT\nQ9uWzZn1XenzfVJKKsMmz6Zh1/4EfDKG89duFit38PQFPhg9mfqB/Zg0f1mR+IV/bKLNh8No3uMT\nRkydS/yz5FLL/V9CItX51z/vEm+FO8TUqVORSqVs3boVAwMDTbirqyvdu3d/rTy3b9/O1KlTqVix\nIlu3bqVOnTqauNOnT2NjY8OYMWM0Yfb29nh6er7+Q7yKTlu3cO3KFTbv2EVaWiojhnyCW7VqNGzU\nuIjs2dOn2LF1C7+tWouBoSGjhg/F0cmJgMAugHrhv3H7n1hb2xRb1oCPB/PRwEGl6rNti1qfbX/u\nIjUtlWGDP8HdvRqNGhfV58zpU2zfuoWVq9diaGjIp8PU+nR+rg9AfHwchw4ewNpGWydfP3969+2H\nTCYjKuoRQz8eRMPqTri5OAMwY84CbKwsOX1gF2cv/MWXk6YSsnUdZqamWvks/WUlKampHA3ZTlh4\nJMO+GEfN6tVxdKhKdnY2w78Yz6dDBvHT/B/IysoiLiFRk/bzEUNwcrBHV1eX46fO8NnYSZzY92ep\n9VMWUmJi2TNlAY0/7PJy4TIyY96P2FhZciZkG2cvXmbM5Bns3bgKM1MTLbklv60iNTWVYzs3Ehbx\ngKFfTqJWdXcc7auyI2Q/h0+cYuOvS5DJjBg3dSY//76GTz/pD4Bj1SqM+2wov6wqefJ/23R5U/oc\nOn6KkENH2bhiKVYWFkydPZ85S5bzw5SvqGRny8VDuzT53Lh9l49Hj8ezWdE+ArBr2xauX73Kmq07\nSU9N5fPhg3F1r0b9hkXlz589za7tW/jptzUYGBoy9tOh2Ds54RvQhahHD5k9YyqzFy6les1arF+1\nkhnffMWi5eoFxMwpX/NeLQ9mzFlARFgo40YNp6FHDc5fuMCVy5fZvWcPqampfDxoENWrVaNxkyZF\nyj916hRbtmxh7bp1GBkaMmTIEJycnenatSs5OTl8OWYMQ4cNw9/fn+XLlzNp4kRW/v47ANu2b9fk\nk5OTQ/t27ejYsaNW/ieOH0Mul/PCRLFty2auXr3Ctp27SUtNZdjgj3GvVr3E8Wbbli38vmYthoZG\njBw6BCcnJzp36Yqenh6TvpmMo5MTEomELZs2MuWbSfyxRr1QO33qJBs3rGf+wh9xdHIi+vFjzM3M\nKG2Pb/f2Ldy4doU/Nv9JWloaY0cMxsXNnXrF/G5SXV28OnrT3sef1Su0F1gZ6el4tm3H+CnTMTAw\n4JfFC5k7YyrfL1xaSukw44d52FhbcfrQXs5euMiXE78hZPumomPg8hXqMXDvLsLCIxg2egw1a1TH\n0cFeI3P0xCkUcjkUMA6lpKYyetxEvv3mK9q28mT33v189uV49mzdiLSYK7VezFVbdqjnhhFDPsH9\nFeeqz4qZqzaVMlcBLFowjxrvvVdqHQF8u2QlNpYVOLf5V85cuc4XMxexf+VCzEyMteQqmJoysHsA\nV+/cJyUtXSvu4OkL7Dl6ms0/foeVhTmTF/7K7F/XMmf8yJeWL3j3KPclfUpKCqdPn6Z3795aC+AX\nmJiYFJMqH5VKVSTs3LlzZGZm0qJFCzp37kxISAiZmZmaeBsbG+Lj47l06dI/f4AycGBfCB/27Yd5\nhQpUtXcgsGsQ+0L2lCC7l65B3alUuTKWlpZ80KcP+/bs1sSrVCpUeUWfvWD8y9i/L4Tez/Wxt3eg\na7cg9pagz/69e+kW3J3Kz/X5sE8f9u7Rll20YD6fDBmKrq72u1VVe3tkMpmWXtExaquSXKHg2Mkz\njBg8CH19fdq2akk1NxeOnTxTRIc9Bw4xZGA/ZEZG1PGoiVerlux9bgn5c88+6tfxwLdje6RSKTKZ\nDKcCE4Obi7NGLx0dHZJTUsjIkL+0jl6V67sPcyPkKIqUtDeWJ6jr5+jpc4wc9JG6fjybU83VhWOn\nzxaR3XPgMEP691HXT633aNeqOSGH1BbPU+cu0qOLP9ZWlsiMjBjU533+3HdQk9a/UztaNG6IYTF9\n8G3U5U3qE/M0lgZ1PbCzsUZXV4q3VxvCHzwstsxd+w/RvnXLEnU7dGAvvXr3xdy8AlXsHfDv0o2D\ne4vvU4f376Vz12AqVq6MhaUlPT7sy6G9IQBcvnieho2b8p5HbXR0dPjwo4Hcv3uXmOjHKBQKbvx9\nlb6DBqOjo4Nbteq0bN2WkJAQQkJC6PfRR1SoUAEHBweCgoPZvXt3seWHhIQQ3L07VapUwdLKir79\n+rHnuexfFy+ir69P167qRefHH3/M7du3iYmJKZLP8ePHMTExoUGDBpqw7Oxslv20lE9HjdKE7dur\nHm8qVKiAvYMDXboFsXdP8brt2xtCt+BgKleuoh5v+vbVjE26uro4OTsjkUjIzc1FoqOjZeld+euv\njP7iSxydnACoUrUqJoUWk4U5emAf3T/si5l5BapUtcc3sBuH94UUK1ulqj2d/AOpYm9fJK56zVp0\n8uuMsbEJurp6dOneizu3bpRatnoMPMWIIR8/HwM9qebmyrETp4rI7tl/gCEDBzwfA2vh1boVew/k\n75ZkZ2ezeNkvfP7pcK10f1+/SeVKFfFq3QqJREKgvy9SHSmXrlwrXASgnhs+LDA3dCllrtq/by9d\nCs1VewvNVXmlzFXnz6n7a+OmTUuuJECemcnR85f5tG8P9PX18GrWkOrODhw9V3Qeb1K3Jh09m2Bp\nblYkLiY2gYYeNbCztkRXKsWndTPCHz0utez/JYQluGyU+9M8fPgQlUqFs7PzS2Xnzp1L/fr1tT7L\nly8vIrdt2zYCAgKQSCS4u7vj4ODA/v37NfE+Pj74+/vTt29fPD09GTlyJOvWrSM9Pb1IXm+SBxER\nuLq5a767urkRGR5evGxkBG7uBWXdiYzU3r76uH9fuvr78N20qaSmpGjFbd6wDr8OXgwdNICrV4rf\nWouMKFyGG5ERxesTGRmBm1shfQrIXr50iZTkFNq09So2/eo/fqetZwt6BnXD1s6OZo0bAvAo6jHG\nMhk21lYaWTcXZ8IjIrXSp6alkfgsiWquLpowd1cXwiLVcjdu38HM1JQ+nwynjW9XRo//mrj4BK08\nRo6ZQMPWHfl07ER6BnXB2FhWrK5vE48eR2MsMypUP06ERWov0lLT0klMSqaaS34/cndxJjzygeZ7\nwfciVZ6K+IREMuSv/iLwNunyJvXp1LY1Dx9FE/3kKZlZWew9fIyWz7fwC6JU5nLg6Em6+HYsEveC\nh5ERuBToJ86ubjyILH7buXjZ/D5V8EVWpVKhQsWDiHBN5akKuG6pVGo3r4iICKoV6NPubm6ElzDG\nFJF1d9fIRkRGUq1aNU2coaEh9vb2xeYVEhKCv7+/VtjKlSvx9vHFxsZWExYZEYG7e36ebm7uRJQ0\n3kRE4FZYtlDZH/bqSavmTZk3+wf6fdQfgLy8PO7evUN4WCid/XwICuzMyhW/FltGQR4+iMDZVfu3\neFjC71YWrl+9jJOza6kyj6Kino+B1powN1eXksdAt4JjoCthBeR+W7UWf++O2NoUtboWXoaqUBEW\nUfwzPogoPN67Fal/jWyhucrNzb1Im/+4f1+6+PswY9pUUgrMVUplDkt/XMhno78oqmAhHkY/xdjI\nEBsri/yyHO0Je1i2BWynVk15EP2E6KfxZGZls/f4WTwb1i1THoJ3h3JfBJeFQYMGsWvXLq3P+++/\nryWTlpbGoUOHtA7Ede7cmS1btmi+6+joMHPmTE6cOMG4ceOoWLEiy5YtIyAggIQE7YVTacTFxXHr\n1q0SP4VRKBQYF9i2MTY2RqEofuKXyxXIjAvJyhWa7z//upLtu/eyat1GMjMVzJg6WRPX64MP2fzn\nLnbtP0RQj56M/2I0sU+fFq9PoTLkJSxEFPJCupsYI3+uT25uLovmz+OLsWOLTQvQr/8Ajp8+y8pV\na/Bq1x49PT3NcxZejBobGyNXKLTCXnx/YVEGMDHO1yEuPoFd+w4wccxoDu/aQkU7WyZOm6mVx5J5\ns7hwdB/zv/+Wuh61StT1bUIuz8REpl0/JjKZ5rk1cpr6MdKEGRvLNOEtmzVm8597ePI0jtS0dFas\n2wiAQpHJq/I26fIm9bG2ssDjvWr49OxHM++uhD94yJD+vYuUd+r8BfT09WjasH6JOikUhfutCZkl\n9SmFvMQ+3qBxU678dZEb166iVOawZuWvKJVKMjMzMZLJqFWnLqtWLCcnJ4d7d25z8thhFAoFCrkc\n4wK7Z8YmJigK9SVN+YVljY01sgq5XGtseBFfeHxISUnhzOnTBAQEaMKio6M5dPAgffr2K1I3WuON\nifaYpq1bMWNToedYv2kzx0+fZeLX32gWbM8SE8nNzeXC+fNs3LKNpcuWszdkD/v3Fm/VLUk3WSlj\n86sS/TiK35f/xMBhpW+zyws9K5QwBsqLGwNlyJ/rGR3zhINHjvJRnw+LlFG3tgcxMU84dPQYSqWS\n7Tt3Ex3zBEVm8X2u8FxVWn0U1l9WYFwG9Vy1Y/deVq/bSFahuWrD2rW09GxN5SpVis1bqxxFcf3d\nCHkJz1AS1hYVqF3NlU4DRtEkeCDhjx4z9MNuZcrjbUaio/Ovf94lyt0n2NHREYlEQkQJb6QFsbCw\nwL7QFlSFChW0vu/atYusrCx69uypsaSoVCpUKhUPHz7E0dFRI2tra0tgYCCBgYGMGjWKTp06sXHj\nRkaOfDXfoE2bNrFkyZIS46fOmMnsmTNAIsHbxxeZTEZGeoYmPiMjAyOj4q2RMpkR8oxCsgUm8Tr1\n6gFgXqECn48dTxffTuTk5KCnp4d7teoauU4+vhzYG8KF8+cwMDBk1nN9fF7oU6gMmax4fYxkRtq6\np2doFhVbNm2ibv36ODu7FJu2IDVr1WJfyB62/rmbnkFdkMmMirglZGRkIDMy0gp78V0ul2t0TM/I\n18HAwID2bVpRs4baejRsUH9a+3YhOzsbfX19TT66urq0b9OK4D4DqVndHRdnp5fqXJ7IZIakF1p4\npMvlWgs6KFg/Ck1cRoZcEx7k70NsXAL9Px1Dbm4uH73fnfOXrmBlacGr8jbp8ib1WbpyNREPH3E6\nZCtGhoYsWPYbE2fMZuF3U7Ty2XPgCAEd22mFHTmwj/k/fIcECe291X1Ku9+mY1hSnzIqLJvfxx0c\nnRj79RQWzvmepGeJtPf2xcnJWWNZnTTtOxbO/p4gn3YoFAp0dHR48uTJ8zEmf0crIz0do0J9SVN+\nYdmMDI2sUaGx4UV84fFh37591KhRQ+N6ADBv7lxGjBjB4cOHmDljOkqlklkzvys63qRrj2nauhkV\nHZuKeQ59fX06d+mKb6cObNq6HQNDtZtKv/4DMDY2xtjYmG7B3Tlz5jTN2vlo0h09uI9Fs2ciQUI7\nb58iuslLGZtfhcT4eCaOHsGAIcOpU79hqbKyQs9a0vO+aLvaY6Ac2XM95yz8kZFDPkFPV7eIO5y5\nuRmL5nzP3EVLmD5rLs2bNqZZ40bY2arb04H9+0qdq0qrj8L6ywuMywB1C81Vgc/nqqSkJPbs2smq\n9RtKrR9NOUbF9XcFMkPDV0r/gqVrtxIeFc2Zzb8gMzBg/u8b+GrOzyz65vMy5SN4Nyj3RbC5uTme\nnp6sX7+efv36YVioQaelpWH6En+ugmzbto2BAwcSFBSkFT5t2jS2bdvGF198UWw6U1NTbGxsSrSE\nFkevXr1o165difEVHZzp5OOr+R4aep/wsFBc3dwACA8Lw9m1+K0yJ2cXwsNCadlKfcI3LDQUF5cS\nttVUKkBSqh+wSqXC29cXb19tfcIK61NCGc7OLoSFheLZOl+fF7JXLl/i2tWrHDmk9k1LTk5i7Bef\nM+LTzwjsWvQNOzc3l0eP1T58DvZVkSsUxCckara1Q8Mj6OLvq5XGzNQUaytL7odHUK+2h0bO7bkb\njZuLM4nPT/S/QKeYW0NeoFQqiYqOeesXwQ5VqxRTP5F09eukJWdmaoK1pQX3IyKp51FTLRcRiauz\nE6A+nDJ8YF+GD+wLwNm/LvNeNfdib1b5X9DlTepzPzwS3/ZemJup/QeDA3zpN1x7QkxLz+D4mfNs\nWqF9wKm9ty/tvfPbanjYfSLCw3B2VfepyPAwnEp4OXR0diEyPIzmnuo+FREWqrV13tqrPa292gOQ\nnp7G0QP7Nfna2lVk5rz8U/jfTZ5E86aN2LVrF6FhYZrt6dCwMFxLGGNcXFwIDQujdZs26nq4f18j\n6+LiwuZNmzSyCoWCqKioInntDQnBv4AVGODSpUvcuHGDPJUKI0NDsjIzOXn8OJUqVdYab8LCSh7T\nnF3U418rzXhzH5cSniMvLw95RgbxcXG4urlhY2urFV9cq2rXyZd2nfJ/t4jQUB4U+t0cX+GlvjhS\nkpOYMHo4/t264xv4cgujg73983acoHGJCA0Pp4u/n5acZgwMi6BeHQ+N3IsDxn9ducr1m7eYMXse\neXm55Obm0s4vkBVLf8TF2YmG9eux4Y8VgHoM9g/qhUfNGgB4+/ji/ZK5qqT6fzFXebYqOjcUocBc\ndff2LeLiYunRNRCVSr0zolKpiH8YyoqZE4skdaxSEbkii/jEJI1LROiDKLp2bF1EtjTuRz7Cr01z\nKjw/PBvs7UXfL6eVKY+3mXfNZ/ff5q2orcmTJ5Obm0uPHj04ePAgDx8+JDw8nNWrVxdxdyiNO3fu\ncPv2bXr06IGbm5vWx8/Pj+3bt5OXl8emTZuYOnUqZ86cISoqirCwMObMmUN4eHipi9rC2NraUqtW\nrRI/hfH29WfD2jUkJycR9eghu/7cjp9/8fcYe/v68ef2bcRER5OYkMDGdWvxDVDLRkaEExZ6n7y8\nPFJTU/lxwTyaNGumsXgeP3qEzEwFubm5HD54gOt//03jps2KlOHj68+6NWtITkri0aOH/LljO/4B\nxevj46etz4Z1azWyk6d9y8at21i7cRNrN27C2tqGb6Z+i7evehDfuWM76WlpqFQqLv31Fwf276Np\nI/UhGpmREV6tWrL015VkZWVx/NQZwiIi8WrdsogO/p068Mvva5DL5Vy/eZvjp87i16kDAAE+HTl+\n+gz3QsPJUSpZ9vtqGjesj76+PtExTzh59jzZ2dnk5OSwdtNW4uIT8Kj58tPIr4pERwddAwN0pDpI\n9XTR1dcv86KuOGRGRnh5tmDpb6vJysrm+OlzhEU+wMuzRRFZ/07tWb5qHXK5guu37nDs9Dn8O6p9\ntJNTUnn8/DBiWMQD5ixZrlmEgtrfNSsrmzxVHjnKHLKzs4u8VL1NurwJfQI6qft6rerV2H/0BCmp\naeTk5LA9ZD/urtpnFA4cPYGrkyNuLk5Ff6QCdPT2Y/O61aQkJ/H40SNCdu7A26/4PtXBx4/dO7bx\nJCaaZ4kJbN2wlk5++QvK+3fvoFKpSE5KYv73M/AN7KI54PUwMgKFQkFOTg4H9+3h7u2bdOnSBX9/\nf1avWkVSUhIPHz5k+7ZtdA4MLLZ8f39/tm7dSvTjxyQkJLB2zRqNbOPGjcnOzmbnzp3k5OSwYsUK\natWqReXKlTXpHz58yN27d/H11X5h3blrF5s2bWLdxs0sWLwEHamUdZs2EditG+tWr9aMNzt3bMe/\nhHvcff382bFtK9HR0SQkJLB+bf54c+/uXa5euYIyJweFQsGSRQsxNTPTWKP9AzqzZtUfyOVyYmNj\n+XP7ds0CrSTaefuyZcMaUpKTiI56xL5dO+joF1CifHZ2NjnZOajy8sjOzkapzAHUVtCvPh9Js5at\n6Nm7X4npCyIzMsKrtSdLf/nt+Rh4mrDwSLzatCoi6+/diV9+/+P5GHiL4ydP4+et9lHfs3UjW9au\nYuu6Vfy0YC5SHR22rluFk6MDAHfv3yc3N5e09HTmLFxMndq1cC6wM1oQH19/1heYq3aWMlf5+Bad\nG/xKmKsWLZhH0+dzVQtPT7btCmHV+k2s3rCJrsHdadO2HfO+Kv6KSZmhIe2aN2TJ2q1kZWdz7Pxl\nQh9G0a55Uf/9vLw8srKzUebmkpubR3Z2Drm5ah/6Wu4u7D95npS0dLJzlGw/cBx3p6KHHAX/Dcrd\nEgzq68l27NjBsmXL+OGHH4iPj8fS0pLq1aszYcIEoPi7gF/wIm7btm24u7sXe8iuY8eOzJgxgxMn\nTlC3bl2uXLnC1KlTiYuLQyaT4ebmxk8//USjRkU71JsiqHsPHkc9ole3Lujp69O3/0AaPC8v9ulT\nevfqzvrN27C1s6OFZyu6hYXx8Ud9yFOp6NItCP/O6gnq2bNnzJn5HQkJ8chkMho3bcY3077VlLNp\n/Tq+n65+s3VwcuKHeQu0Jq8XBPfowePHj+j+XJ+P+g+kYQF93u/ZnY1btmFnZ0dLz1aEdw9jQD+1\nPl27BRHwfMIsfIOHVFeKmZmp5raP06dOsXTxYpRKJRUrVmTU51/QqkX+onzS2NFM+vZ7WnkHYmdn\ny9zvpmJmakrIgUOsWL2OHev+AGDE4IFMmTkHr4AgzM3M+Hrs5zg6VAXAxcmRiV+OZtS4iaRlZNCg\nTm2+m6y2JqiAX35fzYTJ09Wn6V2dWTpvVpm330vD7+tP8Z8ySnNoyXfiCFYNGMuFNdtfkvLlfP3F\np0z8bjae/sFUtLVh3rdfY2ZqQsjBo6xYu4Edq9UHf0YO+ojJP8ynbZdemJuZ8vWYz3C0V9fPs+Rk\nRo7/hvjEZ9haWTGkf2+tw19TZ89n575DSCQSzl+6yrdzFrHyxzk0qlfnrdXln+rjUFXthzioTy9m\nLlhCYJ9BKJVKalZ359sJ2jtGew4eprNPh5f+VoHBPYh+HEXfHl3R09Pnw48GUK+h+tniYp8y8IMe\n/L5xKza2djRr4UlkUA+GD+yLKk+Ff9cgfALyF6yL5nzPw8hIDAwN8fbvzMAhIzRxF86eYf3q31Hm\n5FCzdm2+n78YPT09evbsSdSjRwR27oy+vj4DBw2i8fMryJ4+fUpwUBDbd+zAzs6OVq1a0bNHD/r0\n6UNeXh7BwcF06aK+1kpPT4/5CxYwdcoUvp85k1oeHnw3U9vHPiQkhJYtW2Jubq4VbmGh7lfZuSqy\nMjORABYWlvTo2Yvox48J7hqInr4+/QcM1Fy5Ffv0Ke/3CGbj1u2a8SaoR08G9O1DniqPbkHBmiu3\nlMoc5s+dzePHj9HT1aNmrZosWrxUc/vLJ4OHMHvW9wT4dMLYxIRuQcF4+/iSnJlLSXRohauOAAAg\nAElEQVQO6kHM48cM6NUNPT193u/bn7oN8n+3wb178uv6LdjY2hH75An9unfWzDuB7VpSu14D5ixZ\nzpmTx4gIvU9MVBS7tqvPoUiQ8Ofhk6W2m0njxjBp2gxadfRTj4Ezv1WPgfsPsmLVGnZsWAPAiCEf\nM+W7WXj5BarHwPFjNNejWRRwDczKygKJBEuL/DHu19/XcPb8BaRSKR282jB10oQS9XkxV/V8Pjf0\nKzRXfdirOxsKzFVBYWEMKmGuml3CXKWrq4elpaWmTJmRjAzDdMxNS74R6psRA/hq7s+06DGYijZW\nzJ84CjMTY/YcO8Ovm3ayc9lsAHYdOcWk+cs1t8TtOXaa4b2DGd47mI97BvLdT38QMPhLlMpcark7\nM/3zwaX+Pv9LCEtw2ZCoXuUuLcFrkZj25q7g+qdI/7lR8o0iy0ktbxW0+MyyqKW8vFgcV/qEKXh7\niNO1ernQ/xOWRkXvey1PsnPfrqmltEXw/zeVdMt2mOvfJl3n7bkpxzzhbnmroIXUucHLhd4innw/\n4uVC/5BKX5V+7/X/Em+FJVggEAgEAoFA8M/QEZbgMiFqSyAQCAQCgUDwn0NYggUCgUAgEAjeAd61\ne3z/bcQiWCAQCAQCgeAdQByMKxuitgQCgUAgEAgE/zmEJVggEAgEAoHgHUBYgsuGqC2BQCAQCAQC\nwX8OYQkWCAQCgUAgeAcQB+PKhqgtgUAgEAgEAsF/DmEJFggEAoFAIHgH0JG+Xf858m1HWIIFAoFA\nIBAIBP85hCVYIBAIBAKB4B1A3A5RNkRtCQQCgUAgEAj+cwhL8L+I3tZZ5a2Chs+NupW3Clr0nDyo\nvFXQYnHcyfJWQcOntq3LWwUt/G6dL28VNJj27lLeKmgx1POr8lZBw7Znv5a3ClrIExXlrYIWjZa+\nPePxrwl25a2CFm1+/bi8VdDwS+855a2CFhOcy1uDsiEswWVD1JZAIBAIBAKB4D+HWAQLBAKBQCAQ\nvANIdHT+9c+b5tixY/j4+ODt7c2WLVtKlPvss8/o3r37Gy1buEMIBAKBQCAQCP7fyc3NZdasWaxd\nuxaZTEZQUBCdOnXC3NxcS+7s2bNI/4Xr34QlWCAQCAQCgeAdQCLV+dc/b5Lr169TrVo1bGxsMDY2\npk2bNpw5c0ZLRqlUsmzZMoYPH/5GywaxCBYIBAKBQCAQlANxcXH/x959R0dRtQEc/m3KZnfTe0gj\n9CYdQi8JVXpRQREpFrooIogUAcuHICgIUpWOgFTpEAi9917TE0jvu8kmu/v9sbBhyYYkKCDhPufk\nnOzMOzNvJjN37ty5cxd397wXRd3d3YmNjTWKWbp0KT169EChUPzr2xfdIQRBEARBEEqAFzE6RFxc\nHPHx8QXOd3V1xc3N7V/ZVmxsLMeOHWPZsmVERUWh0+n+lfU+IirBgiAIgiAIQpGsW7eOuXPnFjh/\n+PDhjBgxokjrcnNz48GDB4bPsbGx1KxZ0/D55s2b3Lt3j1atWpGbm0tycjKDBg1i4cKFz/4HPEZU\nggVBEARBEEqA5zF6w5N69epFYGBggfNdXV2LvK4aNWpw584d4uLisLa25siRIwwbNswwv0WLFhw5\ncgSA6OhoRo4c+a9VgEFUggVBEARBEIQicnNz+9e6O5ibm/PVV1/Rt29fAD766CPs7e2ZMGEC7777\nLtWqVftXtlMQUQkWBEEQBEEoASRm//4wYs9bQEAAAQEBRtO+++67fHFeXl5s2LDhX922GB1CEARB\nEARBeO385yvBX331FZUrV2bx4sVG04OCgqhcubLhs1arZdmyZXTu3JkaNWrg7+/Pxx9/zPnz542W\n++mnnwgMDESpVBpNHzx4sKE5XhAEQRAE4ZVjZv78f0qQ/3x3CIlEgkwmY8mSJfTu3RtbW1ujeY98\n9tlnnDp1ijFjxtCwYUMyMjJYvXo1H3zwAbNnz6ZVq1aA/mv3Dh06xP/+9z++/fZbADZs2MDp06f5\n+++/X9jflaLMZsqOU5yLiMfdTs6YNnWp7+deYHxMSibvLNnFm9VKM/7N+gAcuxfDH8evE5KQhkJq\nQZsqvoxoWQPzZ+wY37u2F43LOJGj0bHrRixBt00PgdLYz4l+/r7kaLRIAB0wcecNklU5uNta8U4t\nL8o668fzux2fwZpzUaRm5RY5D0tHB96YNgUn/7pkPYjlxpRpJJ08kz+P7euRe3o8/CTBTGZF5Or1\n3Pz+JwAUfr5UmTgWh9o10CiV3PttCZFrCv5KxkeSU1IZ/8MMzly4hIebKxNGjaBB3dr54rKz1Uz6\ncRYHj57A3s6WzwZ/SIfWeY905v2+gs07dqNUZdEuoDnjR43AwkJfgPz2x0r2Bh8mJDyCb8eNpuub\nbYq8fwrTbFAfmn7cG6/qldj53Vx2fjvnX1t3YXQ6HX//MZdzwbuxkEoJ6P4ezTq/bTL22ulj7Fyx\ngLTkRKQyObWataJTvyFG53VxWDrYU3nKJBzq1SH7QSy3p80g5cy5fHH1/1qDzOPhcSMBMysrotdv\n5O6MWdi9UY1KE8dhVcoDbbaapGMnuD1tOtqs7GfKCeDrHtXp1sCH7BwtS4LusPzgPZNxk9+pSZf6\n3jwaAUhqYUZIXAZdpwVTt6wTi4c0MsyTmEmQW5rTY8ZBbkSlFikPcxs7vAZ/jnWV6uQkJnB/2W9k\nXr9sMtaheWtcu7yDhYMjOYnxhP80hZz4WMzt7PH6aCSK8hUxt7XjWt8uxd4fABZ2dpT9Yiy2NWqh\njo8jbN4c0i9dMBnr0qYdnr3ew9LJmez4OG5P+hp17ANK9XoXz159eLRTJBYWaHNzON+z+Dklp6Yz\nbtYizly5gYerMxOHfEDDWvn7Iu49eoY/Nu3kZkg4HVs04vvPPzaaP3fVJjbtO6w/55v5M3FoPyye\n4VuudDodB1cv4PrRfVhYSqnf8R3qtO9hMvbe+RMcWbeEjJRELGVyKjcMoHnvjw3n0a2TBzm+aQXK\ntGQcPLwJ6DMEzwpVi5yLuY0tpQZ8iqLSG+QkJxC7ehHKm1fyxXkMGIGdfzN0ubkgkZCTEEfY5JGG\n+S5demPfpBVmMhlpZ48Tu3ohaLXF3DP6fXP6r8XcPbkfcwsp1du9RbVWXZ+6jFarYet3n6LNzaHn\n1EUA5GRnse/XSaTcj0Kn0+LiW56GvQdj7+Fd7JyEV9d/vhIM0KhRIyIiIliwYAFffvllvvk7d+5k\n7969LFy4kBYtWhimT506lZSUFCZMmECTJk2QyWRIpVKmTZtGr169aNeuHWXLlmXatGmMGTMGb+8X\nd/D/uPcczjZygkZ242ToA8ZtPc7mQR2xlUlNxv984AJVPByNpmWqc/mk2RvU9nZFmZPLmI3HWHnq\nFv0bVSl2PgHlXajoasO47dexlprzZWAFIlNU3IrLMBl/Ky6dWSYu5nJLc85FprD4RBg5Gi3v1PJi\nYIPS/HzI9IXflKrffIU6Pp4D/gG4NG1EzdnTONK6K7npxrkc7/SO4XeJpQUBx/bxYM/+h58tqbv4\nV+78Mo9zH4/AXGaFlVvR3lj9buYcXJ2dOLZjI8dPn+OLSd+xc+1y7GxtjOLm/r6ctLQ0greu5W5I\nGINHj6dapQqU9vFm847dBB06wtrFc1Eo5IyZ/APzl65kxMf9ASjt7cWYTwezaPmaIu+XokqNiWX7\nNz9T/72nXxiehxO7txB6/RJjf1uDKjOdBRM/o5RfOcpXr5Mv1qd8ZYZ8NwcbB0dUmRmsmD6JE3u2\n0rh9t2fadsVxY1AnJHC0ZVucGjWg2o8/cKpLT3IzjI+bM2+/Z/hdYmFBk6CdxAcdAEAZGcnlEaPI\njovDTCql0sRx+H3yESFz5j1TTu81K0O98s60nbIPO4WUlZ825WZ0KqfuJOSLnbz+EpPXXzJ8XjS4\nIRdDkwE4F5JEnS93GOa9WduTUZ2rFbkCDOA5YCi5KUncHNQbm+p18Pn0K26P+hitMtMozqZWfZzb\ndSF85hTU96OxdPVAk5Gun6nVkX7xNEn7tlF6zJTi7AojpYd/hjopifPvdMO+Tj3Kfz2JywPfR5Np\nnIu9fwPcu/bg9uQJZEVFYuVRCk26Ppf76/7k/ro/89Y5bCRmUtPlZ2GmzluGq5MDJ9bN59j5K3w+\nbS57lvyEnY21UZyDnQ0De3bg4o07pKYb57pp72H2HjvDup8nY62QMfrH3/htzRY+7duz2Plc2r+N\n6FtXGDhjGVnKdP764UtcfMviW7VWvlj3shV5Z/xPKOwcyVZmsm3OVC4f2E7NVp3JTE1m9+Kf6DH6\ne3yq1ORy8E62zf2WQbP/NLFV09z7DCY3NZk7n/XFulptPAd9ScjXQ9CqMvPFJm5bT+LO/H027ZsE\nYlOnEWHff4k2S4XnJ1/g0rkXCVuLnscjNw/t5MGda/Scuhi1MoNds8bh5F2GUpVqFLjMjeDtWCls\nUKUlG6aZW1jS+P0R2Lt7I5FIuHFwO4eXzqTzuJ+LndN/ygsYHaIkeSX2lrm5OZ9//jmrVq3K900i\nANu2baNMmTJGFeBHBgwYQHJystHX8FWrVo3Bgwczfvx4xo4dS82aNendu/dz/Rsep1LncuhONIOb\nvYHUwpzmFbyo4OrAoTvRJuNPhNwHwN/Pw2h62yq+NPDzQGphjoPcijffKM2V6PwX16Jo6OfInltx\nZKo1xGWoOXIvkcZ+TsVeT1iSkuNhSWTlatHoYP+dBMo6Wxe+4EPmchlurVtyZ/YCdDk5xAcfJv3m\nXdxat3zqcm6BLchJzyDlrL41yatnF5LPX+TBjr2g1aJRqlCGRRS6faVKxYGjJxj+YT+kUiktmzai\nYrmyBB89ni92+54gBvV/H4VcTo1qVQhs1ogd+4IBOHLiNG937YiLsxMKuZwP3+/Nll17Dct2bBtI\n4/p1kVlZFXnfFNXlbUFc2XEAVWr6v77uwpw/tI8WXXthbWePSylv/Nt04tzBPSZj7ZycsXHQ39jp\ndDokEgmJD2KeabtmMhkuLZsTOn8RupwcEg8fJfPOXVwCmj91OZeWzcnNyCT1wkUAclPTyI6Le7hS\nM3RaLXKfZ7857lzPmz/23yVFmUNEQibrT4TR1d+n0OVcbK1oVMmNv89Gmpzfpb5vgfNMkVhZYVu3\nIXEbVqHLzSX9wmmyIsKwq9swX6xb9948WL0E9X19eZQT/wCtSt99TJORRvKB3ajCQ4u87SeZWclw\nbNiY6JVL0eXkkHLqBKrQEBwbNckX6/VuXyIWzScrSv+3Zj+4j0aZv/IlMTfHqXlLEvbvzTevMMqs\nLPafPM+Ivj2RWloS0KAOlfx8OHDyfL5Y/xpVaNukPo72dvnmHT57kV4dAnF1ckAhk/Hx253YvO9w\nsfMBuHH8AHXffAu5rR2O7l5Ub/kmN44FmYy1cXBGYffoPNIiMZOQEqe/ZmQkJyC3tcOnin7c1SpN\nWqFMSSbbxD40RSK1wqaWP/Fb/0SXm0vGpTNkR4VhU9u/gAVMT7auXpeUQ3vQpKWgU2eTtGsj9k1a\nFSmHJ907Hcwbbbojs7HDzs2Tik3bcffkgQLjVWkp3D62hxrtjZ9ImZmb4+Dhg0QiQavVIJGYkZ6Q\nv34hlGyvREswQOvWralSpQq//vprvrcGw8PDKVeunMnlHk0PCwszmj548GA2btzI5cuX2bPH9EX6\neYlITkchtcDFRm6YVs7VnpCEtHyxuRotvwZfYkbPpuy8GvbU9V6IjKesq/0z5eRpJyMqRWX4HJWq\norpn/oL+kbLO1vzS7Q3SsnLZfyeeQ/cSTcZVcrMhJk1lcp4pCj9fcjMzUcfnVeYz7tzFprzp/+8j\npbp24P7fOw2f7Wu8QW5qOv5rl6Lw9Sbl/CVuTJ1GdtzTbxIioqKxVshxdXE2TCtf1o+7oeFGcWnp\nGSQmp1CxbBnDtAply3D52g3D58e/2Ean1RGfkEimUon1c/jqx/+K2MhwSpXO+1+V8i3LzbMnCowP\nvXGFP777imxVJtb2jnT9sGgDrD9J4etDbqYSdULecZh57x7WZcs+dTn3Du2J3bnbaJqVuxv116/G\nwsYGjVLFlc++eKacAMp72HErJq+19nZMGi2reTxlCb2O9by5HJ5EVKIy3zxHGylNq7jxv035H0kX\nxMrDC22WktyUvJaw7KhwrLx9jQMlEmR+5bDyKY3XoM/R5eaScjiI+K3rirytwsi8vNCoVOQkJRmm\nKcNDkZf2y5eLonwFFGXKUHb0WHS5ucTv3c39tavzrdOhQSO0WVmkX76Ub15hwqNjsVbIcHNyMEyr\n4OfNnfCoYq/r8W+z0up0xCUlk6lUYa2QP2Wp/JJiwnH1zStbXLzLEHLpdIHx0bevsWXWBLJVShR2\nDrTsMwQAN99yOLh7EXblLKWr1eHa4T24l6mAlaJoDRNSd0+0WSo0qY8dNzERWHn6mox3bN0Zx9ad\nUT+IJn7TKlR3rhvmGXVzkphh4eCImZUMbXZWkXJ5JPV+JE5efnnb9PIj6kr+7nKPnN28jBrt38FC\narrBYcu3w0l9oO8SUbdbv2Ll8l8keYbuN6+zV6YSDDB69Gj69+/PwIED880r7lfpHTt2jPj4eCQS\nCVeuXMHDo/AL05MK++rAgtp7VOpcrKWWRtOsrSxJVeXvd7j6zC2alvfEy8Em37zHHbgZydnwONYM\nbFdo3qZYWZijytHk5ZijRWZh+kHBrbh0Ju26QZIyhzJOCoY2LUNaVi4Xoo0fzbrZSOleoxQLjoUV\nOQ9zhYLcDONWityMTCxNtLw8Ymlvh2vzJtyePtswTebuhn3rqpzpP4SMO3epNOYzqk//lrP9hzx1\n+0plFjZPVFJtFApS04xbVZUqfcVe8djFzdpaYZjepGF9VqzdSGDTxlhbK1iyei0AKlVWia4Eq7NU\nRhdYK4WC7KyCb4LKVKnOt6t3kBz3gHOH9mJj71Bg7NOYKxT5HqUXdtxY2Nnh3KQR93751Wh6dmwc\nR1u0wdLBnlI9upEVG/dMOQEorMzJeKw/fEZWLgpp4RepLvV8WHvMdGtrp7reXI1IJiKhaK15oG99\n1aqM/w8alRJza1ujaRb2DkjMzLF5ozZ3xw7F3MYGv7HfoY6PJfX4wSJv76m5yOX5WnM1SiUWNsa5\nWDo6IjE3x652Pa4MGoiFrR2VfpiOOvYBicH7jWKdA1uTGGy6pbQwyqwsbJ6opFrL5aRmFH3/AjSt\nW4Nlm3cT2LAONgo5i9dvf7j+7GJXgtVZKqSyvPNIKleQ85TzyKtiNYYt2ExaQizXjwWhsNOfRxIz\nMyo3DGDbnKlocnOxUljz1lfTi5yHmZUMbZbxjZjWxHEDkLxvG3Frf0ebnYVdvaZ4jxhP6DcjyU1O\nIPPqBZzadCH94im0KhXOb+r7N0usZFDMSnBOtgpLeV4ZKpUpyClgHXEhN0iPj6Fcv894cNv0TWO3\niXPR5OQQcuYgcvviP/0UXm2vVCW4Xr16NG3alJkzZ9K9e3fDdD8/P+7dM93n9O7du4aYR9LS0pg4\ncSLDhg1Dq9UyefJk6tevj4ND8S7AhX114JmvepmcLpdakKnOMZqWmZ2D4omKcXy6ir8vh7J6QNun\n5nE2PJYf951nztvNcVAU7fF6g9KOfFDPBx1wMiyJrFwNcktzQJ+X3NKMrFzTLy0kKvNyD01Ssv92\nPHV9HIwqwQ4yCz5vWZ7Nl+9zO950v2JT9BdD41YKCxtrNMqCLwAendqTdv2WUXcHTXYWsfuCSb9+\nE4B7cxcRcHI/EktLdDk5Ba0KhUJGxhMjh2QolUaVXQCFXP9ZqVQZ5mVmKg3Te3RsT2xcAv1HfIFG\no6Ff77c4efY8zk7G/bpfdRcO72Pj/JkgkVC7eWus5HKjR63ZSiVWssIrAI5uHrh7+7F50S+8P3py\nsfPQKJWYWxfvuHFv35b0m7dRRZjuVpCTkkrS8ZNU+2Eq5z74sEh5dKrrzdTeNdHpYNvZKDKzc7GR\n5RWzNjILlGrNU9YA5T1sKedhw67zprtHdanvw8YT4SbnFUSbnYWZ3Pj/YC5XoM023j9atRqAhG0b\n0Gap0GapSDqwC9ta9f61SrBWpcL8iZZIc4UC7ROVPG22vlHg/l9/olWpUKtUxO/chn39BkaVYHMb\nGxz8G3J12e/PlI9CJiPjieMkU6VCISteV6WebVsQm5DEB2O/R6vV0b/7m5y4eA0Xx8Kfzt04foCg\nZbORIKFy40CkMgXqrLzzSK1SYlmE88jOxR1nz9LsXz6XTsPHE3blLCc2reC9yXNx8vTh9unDbJk5\ngQHTl2JRhP7T2uwszGTGN+1mcoXJ1tvsqDDD72mnD2PXqAXW1WqRejSI1KNBWDg64/vl90jMzEja\nuxVF1Zpo0lIKzeHe6YMcXz0PiQTK+rfEUiYnR5VXRquzlFhayfItp9PpOLVuEY3eG2b4XBBzS0sq\nNG7D2jF96f7NfKysn97o9J9WwkZveN5eqUowwKhRo+jWrRtlyuQ9KurYsSOjR4/m4MGDtGzZ0ih+\n6dKlODo60qRJXn+zqVOn4urqyqBBg9DpdOzfv58pU6bw88/F6xBf2FcHctr0I0RfR1tU6lwSMlSG\nLhF341PpVN3PKO76/STi0pV0X7gDnQ5UObnodHA/NZO5vfV/59WYRMZvPcG07k2o5FH0Ctap8GRO\nhec94vJxlONlLyc6VV+4edvLiUkt3h36IzZSc0YFlOfg3QSOhJjuJlEQZVgE5goFUlcXQ5cI24rl\nid68rcBlPLt0IGbrDqNpGbfvYeXqbDRNV4Q3kX29vVCqVMQnJBq6RNy5F0q3DsY3Ina2Nrg4OXI7\nJJRab+jftL4TEkq5Mn6A/tHf0IF9GTpQP+ze8TPnqFKxwjOPfPBfVbt5G2o3zxvZ4n7YPe5HhOBR\nWt8N4X5ECO6+fkVal0aT+8x9gpURkZgr5EhdnA1dIqzLl+fBtu0FLuPesT2xO3Y9db1mFhbIivHC\n7PZzUWw/l/cYvZKXHRU97bhzX/8kQf97/m5Pj+tS34dD12KNWpAfKetuQ8VSduwsoIJckOwH0ZhZ\nybFwcDR0ibDy8SPlsHHrqVaZSW7yE+ds8R6yFSorOhpzuRxLJydDlwiFXxkS9hl3S9NkZqJONO6+\nZKoe49Q8AGVYiKHfcHGV9nJHqcomLinF0CXidlgU3Vs3K9Z6JBIJw/r0YFgffSvnsfNXqFq+dJHO\n+SqNA6nSOO9aEh8RQkJkGC7e+utcQlQoLl6li5SHVpNLapz+PEqIDMWnak2cvfTdFyo1aMGBFXNJ\nfhCJq+/Tu5gBqGNjMJPJMLd3NHSJsPIqTerxgvvgGnnsT0/cto7EbfproqJqTbLCQ4q0inL+LSnn\n39LwOSkqlOSYcBwfdolIjg7DwTP/vsnJUpIYGULQb1NBp0OryUWtUrJ27Af0nLIw302FTqslJ1uF\nMiXh1a4EC8XySrwY97iKFSvSuXNnVq5caZjWsWNHWrduzdixY9mwYQPR0dHcvHmTSZMmERwczPff\nf49Mpr9T3LdvH3v37mX69OmYmZlhbm7OtGnT2L9/P3v3Fu+lCjc3N6pVq1bgT0HkUguaV/Bi4ZGr\nZOdqOHwnmnvxqbSo4GUU16RcKbYO7sTqAe1YM7AdPWqVo2VFL37o1hiAu3EpfLHhCBM6+FPbp+jf\n1W3KybBk2lV2w0ZqjpuNFc3KOXM8LMlkbDUPW2wePtL1dZTTqqIrFx+2AssszPi8ZXkuRaex52bx\nHyNrVFnE7T9E+U8HYyaV4hrQHJuK5YkLOmgyXlHaB9uqlXiw3bhf5/2/d+Ia2AKbShWQWFhQdujH\nJJ06+9RWYNC38AY0bcy831eQna3m4NET3A0NI6Bp43yxHdu2YuHy1SiVKi5fu0Hw0RN0bKMfIi0l\nNY2oGP3LKXdDwpgxd6GhQgyQm6shO1uNVqclJzcHtVpd7C49BZGYmWFhZYWZuRnmlhZYSKUvrPJd\np0UbDm1ZR2ZaCvExUZzet516Ae1Nxl46FkxKgv4YiY+JInjTasrXyD+KRFFos7JIOHiYMoM/wUwq\nxbl5U6zLlyUh2PSLSXJfH2wqVSR2t/E579y0CXJffUcmqasLZYYNIvl0wf0NC7PtbBQDAyvgaC2l\ntKs17zTyY8upp1fWOtfzZvMp0y9xdqnvw6HrsaSpnn4cP0mXnU36+ZO49XwfiaUltrX9kXmXJu3c\nyXyxyUf249LpLcysZFg4OeMY2J70C3n7QGJhgZnUEpAgsbBAYl68thRtdhbJJ47h9X5/JJaWODRo\nhLx0GZJPHMsXmxC0l1Jv98ZMJsPSxQW3NzuScto4Z5dWrUkM2lesHB6nkMkIbFiHuas2kq1WE3zq\nPHfCoghsmP9Y1Gq1ZKvVaDQacjUa1Dk5aDT6m+vktHSiHuiP5zvhUUxf8ifD+5ge1qwwVRoHcnbX\nX6jSU0l+EM2Vg7uo2tT0MIq3Tx8mPVG/3eQH0Zzevg6favohHd38KhB58zJJ9/XH3O0zR9Dk5GDv\nWqpIeejU2WRcPI1rl3eRWFhiU7M+Vl6+ZFzI3z/Zpk5DJFIpSMywrd8EebnKKB8OwWdmbYuli/5r\ndqWePri9M4CEv9cWb6c8VM4/gKv7NpGVkUpqbDS3j+6hfMP8jVFSuTW9pi2n6/g5dJ3wK03eH4GN\nkyvdJvyKpUxOYsQ9Hty5ilaTS052Fmc3L0OqsMHeo/AXV//TxDjBxfLKtQSDfqzfnTt3Gl3YZ8+e\nzfLly1m+fDlTp07FysqKWrVqsWrVKmrV0g8rk5yczOTJkxk+fLjRi3QVK1Zk2LBhTJ06FX9//2J3\ni3gWY9vWZfKOU7SevRl3WwX/69YIW5mU3dfCWXbyBms/bI+FuRlO1nmPeRRSCzKyzbF7OIzamjO3\nSc1SM/HvE+jQ33TX8nHll7ef/ka8KcF3E3CzseKHTlXJ1ejYeSPWMDyao8KSb9+sYhgLuJqHLR82\nLI2VuRnJqhx2Xo/lbKT+sVZtbwd8HeS421oRWMEF0DckDd9oejxSU25MmUb1Hx8IKWsAACAASURB\nVKcQcDqYrAcPuDRyLLnpGZTq1J4ygwZwvHNeN5NSXTuScPgYOanGrWuZIWHcmDKN2r/NwtLWhuRz\nF7ky9psibX/CqBF8/f10mnbsiYebKzOnTsDO1oYdew+wZNWfbF6h/+KW4R/2Y9KPs2jZtRf2drZM\n+OJTSj8cSSApJYXhYycSn5iEm7Mzg/r3oYl/PcM2Jk+fxdZd+5BIJJw8e4GpM2bzx5wZ1KtV8DA/\nRdVhwgg6fjPS0Gz25tfDWD7gS06t3PSP112YRu27kXA/mh+H9sHcUkpgjz6Ue0N/QU5JiOOnT/sx\nes5yHFzciI+JZNvSeWRlZqCwtadGk5a0e7do3Q5MuTNtBlWmfkPTg3vJehDLtTHjyc3IwK19W0oP\n7MeZd/oYYt07tCfp2Aly04yPG6mLExXGjMLSyZHcjEySjh7n3uyCuzwVZs2RUHxdrNkzqTXqXC2L\n9t7m9F1966aHg5wdXwfS4Yf9xKbon7o0qOCClaU5h6+bfku9U13vYr0Q97iYpfPxHjyKKgvWkpMU\nT+Sv09AqM7Fv3ALXLu9w9yv9Y+O4TWvw7D+USr8uR6NSknxgN6knDhnWU3XpZvRntY6qSzeTkxDH\n7c+L938LnzebsqO/os5fW1DHx3P3h6loMjNxbhlIqV7vcXXIR/qcVy2n9LCR1Fq1Ho0yk7id20k6\nmNcKKXX3wLpCJe5MmfhM++SRSUP78dWshTTqNQQPV2d+HjccOxtrtgcfZ9H6bfw9/38A/H3gGF//\nvJhHl57tB48z9L3uDHuvO8mp6QyZMouEpBRcnRwZ8m5XmtSp/kz51GzVmZTYGP74cgDmlpb4d+pt\nGOEhPTGO5eM+od+0xdg6uZJ0P5KDaxaQrcxEbmNHRf/mNOmpf8HLt2ot6r35FptmfE1WZjr2rh50\nHD4eqbzo7yXErl5IqYEjqTB7JTlJCcQsmIFWlYmdf3OcOvQ0jAXs1LoLpfoNB0D9IIroef8j52Hl\n3MLWDu8R47GwdyQ3JYmE7X+hvH7xmfZN5RYdSI+PYeOkTzC3sKR6u7cNw6NlJMWzZepQun8zH2tH\nF+R2eddyK2tbJGZmyGz13VO0mlxOrV9Mevx9zCwscCldgbbDJ2MmXix7rUh0/1bzk5BP2tJJLzsF\ng8/l3QsPeoHemfTslZ3nIfDYlpedgsEIt+LfxDxPHa7lby18WWz7vPixj59mcNNxLzsFg41JiwsP\neoGUiUUfFeZFqDdv2stOwWBxQsFfjPQytFg8svCgF2RLnxkvOwUjXwVUeNkpFEvWzvnPfRuyDk9/\nwfxV8sp1hxAEQRAEQRCEf+qV7A4hCIIgCIIgPKGE9dl93kQlWBAEQRAEoSQQleBiEd0hBEEQBEEQ\nhNeOaAkWBEEQBEEoASRmom2zOMTeEgRBEARBEF47oiVYEARBEAShJBB9gotFtAQLgiAIgiAIrx3R\nEiwIgiAIglASiJbgYhEtwYIgCIIgCMJrR7QEC4IgCIIglAASc9ESXByiJVgQBEEQBEF47YiWYEEQ\nBEEQhJJAjBNcLBKdTqd72UmUVGcikl92CgY17m572SkYWSxv9rJTMDKo/H/nEdKueOnLTsHIzmoN\nX3YKBh+EnnvZKRhpqL75slMwuGxd7WWnYCQqLftlp2AkUal+2SkY9CmlfNkpGGmzPvZlp2Dw3Ybx\nLzsFI02OHnnZKRSL+tj6574NaZN3nvs2XhTREiwIgiAIglASiNEhikW0mwuCIAiCIAivHdESLAiC\nIAiCUAJIREtwsYiWYEEQBEEQBOG1I1qCBUEQBEEQSgIxOkSxiL0lCIIgCIIgvHZES7AgCIIgCEIJ\nIPoEF49oCRYEQRAEQRBeO6IlWBAEQRAEoSQQLcHFIlqCBUEQBEEQhNeOaAkWBEEQBEEoCcToEMVS\nIirB48aNY/PmzUgkEszNzXF3d6d9+/aMHDkSqVQKQOXKlQFYv349NWrUMCyrVqtp1qwZqamprFy5\nkvr167+wvHU6Havm/8KRfTuxlErp3Ksv7Xv0Nhl7eO8O9m5ZT2x0FDZ2dgR27E7n3h8AcD8qgjUL\n53Dv5jUAKlWvxQfDvsDR2aXIuSRnKJm4fBtn7oTj4WjH173a06CyX76437YfZsvxS2SosnG2s+bD\ndo3p1rgmAIlpmUxetYMrYTGkZCi5+NvXxdwjeXQ6HUfWLOTmsSDMLS2p2+EdarXrbjI25MIJjq//\ng8yURCyt5FRs2JImvT5CIpEAcO/cMU5sXE5GUgIe5SrT+sPPsXFyLXhfpKQy/ocZnLlwCQ83VyaM\nGkGDurXzxWVnq5n04ywOHj2BvZ0tnw3+kA6tAwzz5/2+gs07dqNUZdEuoDnjR43AwkL/qOq3P1ay\nN/gwIeERfDtuNF3fbPOP9tXff8zlXPBuLKRSArq/R7POb5uMvXb6GDtXLCAtORGpTE6tZq3o1G+I\nYV89D80G9aHpx73xql6Jnd/NZee3c57btnQ6HX8umM2xoF1YSqV0ePt92vboZTL26L6dBG35i7iY\nKKxt7WjZsRsde/XNF7dj3Qo2Ll3IuJkLqFCtepFzSU5NZ9wvv3Pm6i08XJyYOPh9Gtaski9u+u/r\n2H/qAkmp6Xi7uzCybw9a1n94TqWkMfHXZVy+HUJyWjrXtv5e5O0/SafTsXzezxzeqy9vuvTuS8e3\n3jUZe2jPDnZtWseDmChsbO1o07kHXd/9wDB/yc8/cuX8aWJjopk0az5Va+Y/P4qSz9bff+Xsw+M2\nsPt7NO/yjsnYMwd2cXT7RhIeRKOwsaVR+64E9uhjmH/l5GF2rVpMSmI8pStWpdeIr3BwcStWLkEr\n53Pl8F7MpVIade6F/5s9TcbePnec4D+XkJGcgKVMTrXGgQS+94nhHFJnqQha+Ru3zhxFp4MKdRvR\nefCYIueSnJrG+Gm/cPriVUq5uTB+5CAa1qmZL27voeMsW7+Zm3dD6RDYjO/GjjTMu3z9Ft/MnMf9\n2HikUkuaNajLhJGDkcusipzHk4a1KEu7Ku6oNVr+PBvFxgvRBcZW8bBlWItylHFWkJaVy7xD9zh6\nLxGAzwLLU9fHEU8HGZ9vuMzl6NRnzsnC3p4KX3+Nfe1aZMfFETLrZ1LPn88XV2vFcqzc3QGQSCSY\nSaXc37yZ0NnPrywSXg0lohIM0Lx5c6ZNm0ZOTg5Xr15l7NixmJmZ8cUXXxhiPD092bhxo1ElOCgo\nCGtra9LS0l54zkHbNnLzygVmLt9AZnoa348eim/ZClStVTdfbG5ODv2Hf0nZSlVISoxn+lcjcXEv\nRaOANigzM6jfLIAhX01BamXFmoVzWDTjW8ZOm13kXL7/czcu9jYcmTGK4zdC+HLJJrZPHYqdQmYU\n17lBdfq3bohCJiUiLokBs1byhp8n5T1dMTOT0Lx6ed5tWY+hc9f+o31z5cB2Ym5foe/0P8jOzGDT\ntDG4+JbFu0r+i4F7mYr0GDcDhZ0D2cpMds79lqvBO6ge2InkB1EELZlF19Hf416mIme3r2P3gmm8\n9fXMArf93cw5uDo7cWzHRo6fPscXk75j59rl2NnaGMXN/X05aWlpBG9dy92QMAaPHk+1ShUo7ePN\n5h27CTp0hLWL56JQyBkz+QfmL13JiI/7A1Da24sxnw5m0fI1/2g/AZzYvYXQ65cY+9saVJnpLJj4\nGaX8ylG+ep18sT7lKzPkuznYODiiysxgxfRJnNizlcbtu/3jPAqSGhPL9m9+pv57XZ/bNh4J3r6J\nW1cv8uPS9WSmp/PjmGH4lC1PFZPnlJq+w7+gTMUqJCfGM/Prz3Fx96BBy7wbkuTEeE4d3I9DMW4o\nH5k6fyWuTg6cWDOHYxeu8fmP89mzaBp2NgqjOGuFnMVTRuFbyo3TV24y4vu5bJozGS83F8wkElrU\nq0GfjoF8MuXn4u+Qx+z9eyM3Ll9k9sqNZGakMeXzIZQuV4E3atfLF5uTo2bgyC8pX6kqSQnxfD92\nJC7uHjQJbAuAX4WKNA5sy8KfvnvmfI7v2kLI9UuMm/8nqsx0fpswEs8y5U0et7k5OfQY9Dk+5SuT\nmpTAoilf4OjqQe1mrYiPjmTtnGl8MvknfMpX5sDGVayaOZXh/5tb5FzOB/1NxM3LDP55OVmZGaz+\n7gvcfMvhV61WvljPspV4f+JMrO0dyVJmsOnnKZwP2kbdNl0A2L5wBg6uHgybswYLqZT4yLBi7Zdv\nf56Pi7MTx7eu5tjZC3wxZTq7Vi/Ezsa4/HGwt2VAr+5cvHaT1LR0o3m+XqWYP20SHq4uZKvVTP5p\nHr8t/5MvBvUvVi6PdK1Rihpe9ry/7Aw2Vhb88lZN7sVncDEqfwXWUWHJNx2qMCPoNucjUrCxskBh\nlddP9W5cBgduxfNl64rPlMvjyn0xCnViIqc6dsLBvz6Vpk7hXO930WRkGMVd/KCf4XeJhQX1/95K\nYvDBf7z9/yKJuegTXBwlpt1cKpXi5OSEu7s7rVq1onHjxhw7dswoplu3buzcuRO1Wm2YtnHjRrp3\nN93C+Lwd37+Hjm/3wdbOHg8vHwI6dOXIvp0mYwM7dqN81TcwMzfHxc2Dek1bcuf6FQDKVapK87Yd\nUVhbY2FhQduub3H3xtUi56HMVhN8+TbDOjdHamlByxoVqeDlRvCl2/lifVwdUcj0reu6h9OiE1MA\ncLRR8HazOlTyLnoLTEFunThA7fZvIbexw8Hdk2ot2nPzWJDJWGsHZxR2DvqcdFokEjNS4+4DEHn1\nPD7VauNRrjISMzPqdepFfNhdw/wnKVUqDhw9wfAP+yGVSmnZtBEVy5Ul+OjxfLHb9wQxqP/7KORy\nalSrQmCzRuzYFwzAkROnebtrR1ycnVDI5Xz4fm+27NprWLZj20Aa16+LzOrZW2YeOX9oHy269sLa\nzh6XUt74t+nEuYN7TMbaOTlj4+AI6Fu/JBIJiQ9i/nEOT3N5WxBXdhxAlZpeePA/dHz/Htr3fA8b\nO3vcvbxp/mYXjgftMhnbskM3ylXRn1PObh7UbdIi33mzbtGvdOv7IebmxWsvUGZls//URUb06YbU\n0pIA/1pUKuPNgVMX8sUOe7cLvqX054x/9cqU8/Xk+r1wABztben1ZksqlfEp1vZNObpvN5179cHW\nXl/eBHbsxuG9psub1p26U7FqdX154+5Bg2Z55c2j+VVr1sasmPvlcecO7aVl196G47ZBm06cDTZ9\n3DZq14XSlaphZm6Oo6s71Ru2IPyW/n9169IZKtasS+mKVTEzMyOw5/tE3btVrOP66tH9NOz4Ngpb\ne5w8vKgV0IGrR/aZjLVxdMba/uE5pNUhMTMj5WF5khAdTmzYXQLe/RipTI6ZmTnupcsVOQ+lKosD\nx08xYsB7SKWWBDT2p2I5Pw4cPZUv1r9Wddo0b4yjg32+eQ72dni46m/cNBotEjMJkTEPipzHk1pX\ndmP9uSjSsnKJSc1i+9X7tK3ibjL2rdpe7L4ey7mIFHRAenYusWnZhvnbrz7gcnQqGp3O5PJFZSaT\n4dS0KRG//44uJ4fkY8fJvHcPp6ZNn7qcU9OmaDIySLt8+R9tXygZSkwl+HG3b9/m/Pnzhq4Qj1Sr\nVg0vLy/27NEXtDExMZw9e5auXbui+4cn5LOIDg/Fp0x5w2cfv3JEh4cWadmbVy7i7VfW5Lwbly8U\nOM+UiLgkrK2kuNrbGqZV8HTl3v14k/F/7DlOg8+m02XyfNwdbGlYuUyRt1VUSdERuPjkrdfZ24/E\n6PAC42PuXGPhkJ4sHv4OiZGhVG3eLm/mY/9bHTp0Oh1JBawrIioaa4UcVxdnw7TyZf24G2ocn5ae\nQWJyChXL5uVYoWwZ7oWGmdosOq2O+IREMpXKAv+GZxUbGU6pxy60pXzLEhsRVmB86I0rTOzTkckf\ndOZ+eAj+rTr86zm9LDERYfiUzdsX3sU4p25duYRX6bz/581L58lIS6VO4+bFziM8JhZruQw3JwfD\ntAq+XtyJKPgRMkBqRiZ3wqMp7+NZ7G0WJio8FN+yeeWNb5lyRIUVbd8Ut0wpitjIcEr5PXbcli7L\ng8ii5RNy7RIevnn5GJXfOh06dDyIKNq6QF95dX1sfa4+ZYiPCiswPvLWVWZ+1JWfB/UgLiKEmi3b\nA3D/3i0c3T3ZNv9Hfv6kB8u/+ZSo29eLnEd4dAzWcjmuzk6GaRX8SnM3LKLI63jkflw8jTq/i3/H\nXgQdOUGf7p2KvY5H/JytuZeQafgcmpCJn7PCZGxlDzskwJI+dVj/UQPGtKmIQvrvt07Kvb3RKJXk\nJCYapilDQlGUefr1yLVtW+L37n1qzCvNzPz5/5QgJaY7RHBwMLVr10aj0aBWqzE3N2fy5Mn54nr0\n6MHGjRvp3LkzmzZtokWLFjg6Oj7TNuPi4oiPN11RBMD26ReyLJUKubW14bPc2posVeGVpJ0b1qDM\nSKdZm/yVlwfRkfy1dAEjJnxf6HoeUWbnYP1EXzFrmRWpSpXJ+IHtGjOwXWOuhsVw+lYYls/h8UtO\ntgqpPK+QlcoV5GRnFRjvWaEag+ZvJC0hllvH9yO31beO+FSrzYmNy4i5fRX3spU5u+1PtJrcAtel\nVGZhozAu3G0UinyPG5Uq/b5RKOSGadbWCsP0Jg3rs2LtRgKbNsbaWsGS1fruISpVFtYK0xePZ6XO\nUmGlyDuOrBQKsrNM/+8AylSpzrerd5Ac94Bzh/ZiY+9QYOyrJlulQv7YvpArrJ+6Lx7Zs/FPlBlp\nNGmtP6e0Gg1/LprDoLGTnykPpSoLmye6Elkr5KSmZxawhL4iN/6XP2jXpB5lvEs903afJkulQqF4\norzJKry82f7XGjLS02nRtuO/mo86S4XssXxkCmvUqsL/V4e2rkOVmU69AP2NbsWa9di1ajEh1y9T\numJVgv5agTZXg/op5YWpXKweK2+s5ArU2QXn4lPpDb5YspXU+FiuHN2HwlZ/DqUnJxBy5RydPhlN\np8FfcvPUYf6aOZGhP68wOkcLolRlYWP9RHcZ6/zlT1GUcnPlxLY/SU5NY8P2Pbi7Ohe+UAHkluYo\n1RrD50y1Brml6XLfxVpK6ypufLnpComZasa1q8SQZmWZuf/OM2/fFDOFHM0TjQoaZSYWdnYFLmNh\na4tjwwaEzZ//r+YivLpKTCW4YcOGTJ48GaVSybJly7CwsKB169b54rp06cKsWbOIjIxky5YtTJw4\n8Zm3uW7dOubOLbjf2ap9J40+Hz+whz9++REk0CSwHTKFAlVm3kVRlZmJTP70CtKx/bvZs3k9E39e\ngOUTLd3JCfFMH/cZbw8YTJWa+fvVFURhZUlmVrbRtMysbBRW0gKW0HvDz5Ptp66w4eh53mmev89l\ncdw6EUzw8jmAhEqNApDKFKgfuyFQq5RYWskKXsFDdi7uOHr6cnDlPN4c+jWOpXxo9eEoDq6YizI1\nmUqNAnHy9MXGyXQfT4VCRsYTBWuGUmlU2QVQyPWflUqVYV5mptIwvUfH9sTGJdB/xBdoNBr69X6L\nk2fP4+z0bDdcj7tweB8b588EiYTazVtjJZeTrcw7jrKVSqxk8qesQc/RzQN3bz82L/qF90dP/sd5\nvQwnDuxlxZzpIIFGAQ/Pqcf2hUqZWei+OHFgD/u2rGfczPmGc2r/to1UfKMmnr5+z5SXQi4jQ2lc\nCctUqlDIC+7+MuW3lWSqsvj5qyHPtM0nHd2/h8WzpiGRQNNW7ZErFCiVT5Q3sqeXN0eCdrNr4zqm\nzF6Yr7wprvOH9rFh/k8gkVCnRRus5HKyHssnS5mJVP70/9W5Q3s5sn0Dw36Yi4WlPh83L196jfiK\njQtmkpGaTJ3mbXD3KY29c8Evv147tp9dv/8CEgnVGgdiJVOQ/Vh5k61SIrUq/Byyd3XHxas0e5bN\nofunE7GQWuHg6kGNFvoKetVGARzbsoaYezcpU73wMlIhl5GRaVz+6MuVwsu+gjja29Gkfh3GfDeT\ntfN/KtIyrSq5MqpVBXQ6CLoVh1Kda9Saay01R5WjMbmsWqMl6GYCMan643/16Ui+71rtmfMviFap\nwvyJBgVzhTWaAhpvAFzatCbzzh2yIiP/9Xz+M0pYS+3zVmIqwXK5HB8ffb+5H374gS5durBhwwbe\neustozgHBwdatGjB+PHjUavVNG/enIwnOtEXVa9evQgMDCxw/pNtLI0D29E4MO8xfUTIXSJD7+FT\nRv9IMDLsntHj2CedO36YPxfN5esZc3Fx8zCal56awrSvPiWwU3cCOhTvBSRfNyeU2WriU9MNXSLu\nxMTTpWGNQpaEXK2WiPjkYm3PlEqNAqjUKG9khYTIEBKjQnH29gMgMSoMZ6/SRVqXVqMh7bE+v+Xr\nNaV8PX0/sWxlJrdOBuPs5WdyWV9vL5QqFfEJiYYuEXfuhdKtQ1ujODtbG1ycHLkdEkqtN6rq40JC\nKVdGv16JRMLQgX0ZOlA/2sDxM+eoUrHCvzIKQ+3mbajdPO/lrfth97gfEYJHaf3j3PsRIbgXsfKm\n0eQ+9z7Bz1OjwLY0Csz730SE3CEq9B7eDx+zRxVyTp0/fpj1i+cx5sc5OD92Tt28dJ7bVy9x5vAB\nANJTUpgzZSxvDxxC8/adC82rtKc7yqws4pJSDF0ibodH071VE5PxM5au50ZIOMu+H4Olxb9TLDdt\n1Y6mrfLKm/B7d4gMuYvvw/ImIvQe3n4F75szxw6xeuGvTJw5Dxd3jwLjiqpOizbUaZF33MaE3uV+\neAilHh234SF4+BScz9VTR9i+bD6Dv/0FR1fj/qg1GrWgRqMWAKgyMzh/OIhSvgWvq1qTVlRr0srw\nOS4ihPjIUNwebj8+MhTXh2VPYbSaXJJj9eWNq7dfvnO8OOd8aS9PlKos4hOTDF0iboeG061dwdeZ\nosjNzSUyxvR7EKbsvxXP/lt5TznLuVhT1sWasET9Va3MY78/KTQhkxfRuVAVFYW5XI6ls7OhS4R1\nubLE7jT9DgDou0LE7THd71x4PZXIPsESiYTBgwfzyy+/GL0E90jPnj05c+YM3bt3/0eVEjc3N6pV\nq1bgT2Eat2rHzg2rSU9N4UFUBME7t9Ksren+mVfPn2HJrB8YNXVGvtYplTKTH8eNpHbDpnR65/1i\n/x0KKykBNSvy2/bDZOfkcvDybe7GxBNQM//buxuPXiBdlYVOp+P0rTB2nblGg0p5+ahzcsnO0aBD\nhzonl5xc060FhanUKJDzuzaiSk8l5UE01w7tpnKT/C37AHdOHyY9UV9gpzyI5tyO9XhXzXurOy7s\nDjqdDlVaCgeWzaZq83ZYWduYXJdCLiegaWPm/b6C7Gw1B4+e4G5oGAFNG+eL7di2FQuXr0apVHH5\n2g2Cj56gYxt9RT4lNY2ohxeduyFhzJi70FAhBsjN1ZCdrUar05KTm4NarX7mful1WrTh0JZ1ZKal\nEB8Txel926kX0N5k7KVjwaQkxAEQHxNF8KbVlK9R9KcGz0JiZoaFlRVm5maYW1pgIZU+tyHZGrdq\nx+4Nf+rPqehIDu/6myYmug0BXL9wlqW/TOPTKT9S6olz6qPRE/l+8Wqmzl/O1PnLcXB24aMvxhtV\nuJ9GIbMisEFt5q7ZQrY6h+DTF7kTHkVgg/xDic1ft41DZy6zaMook0NYqXNyyM7JQafT/67OyS1S\nDk9q2qY929avJi01hftRERzYsYUW7Ux3cbhy/gwLf/qBL7/7CS8TN1S5ubmo1dmg05GboybHRDlb\nmLot2nJoy1oy0lKIj4nk1FOO29uXzrF+3nQGjv8f7t75b4aj7t1Cp9ORkZrCX7/NoEHrjshtbE2s\nybQ3mrbi1Pa/UKalknQ/iovBO6ne3PSwhTdOHiItUX8OJd2P4sTfa/F7Q/9/LV21FjqdjitH9qHT\narlx6jAZKUl4lqtcpDwUchmBTRowd+kastVqgo+f5m5oOIFNG+SL1Wq1ZKvVaDQaNBotanUOGo2+\nvD104gxhkfr+53EJicz5Y5XJYdaKKuhmHL3qeGMns8DLQUanN0qx50asydjd12NpX9UdDzsZVhZm\nvFvfm5OhSYb55mYSLM0lSABLc/3vz0KblUXS0aP4fjgQiVSKY5PGKMqUJenoUZPxMm9vbCpUIGGf\n6ResSwqJmdlz/ylJJLqX8UbYv2zcuHGkp6cbdU3QaDQEBgbSv39/BgwYQOXKlZk3bx6tWunv/lNS\nUrCxscHCwoL09HTq16//r48TfCbi6S2kOp2O1Qtnc3jPDiwtLencux/tH45pmhgXy9iP3+XHJWtx\ndnXjhy+HcevqJf0jSR36LhWt2jPg0zEc2beTRT99h5VMljdkgwSWbD1g2FaNu9uemktyhpIJy//m\nzO0IPBztmPBue/wr+bHj9FV+33OcTRM/AWDkgr+4cDeSXI0WDyc73g/0p0eTvApnzaHfoy/e9C+h\neTo5sOu7Yfm2t1jerNB9c3TtIm4c2Ye5hSV1O/WiVlv9MF7pifGsHj+I939YiI2TK2f+XsPVgzvJ\nVmYis7algn9zGvbsh7mFJQDrp35GUkwEllIrKjdtQ+O3+uc7kQeVz3uElJySytffT+fshct4uLky\ncfSn+NepxY69B1iy6k82r1gM5I0THHzkOPZ2towa+jFvtmoJQEh4BMPHTiQ+MQk3Z2cG9e9Dl/Z5\nF9UJP8xg6659RpXBP+bMoF6tGuyKL95jZ51Ox7al8zh7YBfmllICe/ShWWf9E5CUhDh++rQfo+cs\nx8HFjaC/VnByz99kZWagsLWnRpOWtH/vIywsLQtc/85qDYuVz5M6ThpJx29GGr0puHzAl5xauanY\n6/og9NxT5+t0OtYumsPRvTuxsLSkY6++tO2ed05NGNSH7xetwcnVjR/HDOfOtctG51SjwHZ8MOLL\nfOsd0+8tPh7zTb5xghuqbxaYS3JqOl/98jtnrtzEw8WJb4b2pUGNKmw/eJJFG3bw99xvAaja5UOk\nlhZYmJujQ4cECVOGfUDHFg0N8x8dJjodeLk5s2/J9Hzbu2z99BtvnU7H7V+v6AAAIABJREFUyvmz\nObh7OxaWlnR7rx8deurHJU+Ii2X0wN7MXLoWZ1d3po4ays2rl5BKpeh06LtUtH6Tjz7Tj3c7ZdQQ\nbly6AI8dv3NXbzZqMY5KM+5iZSqfv5fO48z+nVhYSgns2YfmD8e3To6PZcan/Rjz6wocXNyYP3Ek\noTeu6LtAPEyobou29Bw8CoDZYwYTGxmG1EpGvcD2dHj/E8yeOMcTlQVX1HU6HftXLeDyoT2YW1rS\nqMu7+L/ZA4C0xDgWjfmIT6b/jp2zK0c3r+LC/h1kKzOQ29hRpWELWrwzwFDexEWGsmPhTyTej8S5\nlDdt+w3Hq0JVo+31KVVwX+zk1DS+/t8vnLl0BQ9XFyZ+PoQGtWuwPegQS9ZsYMsfvwKwZfd+Jkyf\nY1SGDPmgN0P79Wbjjr0sXrOBxORUbK0VNGtQl1GD+mNva/rmv8160xXaxw1tXpb2Vd3J0ehYcyaC\njRf1T5BcbaxY2rcu/VeeJSFDv4+71fSkT30fzM0knA5LZs7Bu4Y+xbN61qCmt73Ri8PvLT1NXLr+\nePluw/hCc3nEwt6eCuPH540T/NNMUi9cwKVNa7zff5+L/fobYn0GDsS6fDlufl309QM0OXqkWPEv\nm/b2scKD/iGziqafaL2KSmwlGGDRokUsX76coKAg6tSpw9y5cw2V4Melp6fj7+/PihUrXmgl+EUq\nrBL8ohVWCX7RHq8Ev2zFrQQ/b/+0EvxvKqwS/KI9rRL8ohVWCX7RCqsEv2hPqwS/aE+rBL8MRakE\nvyjFqQS/CK9cJfjuycKD/iGz8v+da8I/VSL6BP/vf/8zOf2TTz7hk0/0LZg3btwocHlbW9unzhcE\nQRAEQRBKlhJRCRYEQRAEQXjtSUpWn93nTVSCBUEQBEEQSgJRCS4WsbcEQRAEQRCE145oCRYEQRAE\nQSgBdKIluFjE3hIEQRAEQRBeO6IlWBAEQRAEoSQQLcHFIvaWIAiCIAiC8NoRLcGCIAiCIAglwXP6\nSvqSSrQEC4IgCIIgCK8d0RIsCIIgCIJQEpiJts3iEHtLEARBEARBeO2IlmBBEARBEIQSQIwTXDxi\nbwmCIAiCIAivHdES/By9cXnNy07BoNeDui87BSPfbB/0slMwtnjJy87AwLZP15edgpEPQs+97BQM\nVpT5bx3HH3/+68tOweCQ37cvOwUjrskZLzsFI24dOr7sFAz2pzd82SkYmXd+9MtOweDIzFUvOwUj\nTV52AsUlWoKLRewtQRAEQRAE4bUjWoIFQRAEQRBKAtESXCxibwmCIAiCIAivHdESLAiCIAiCUBKI\nluBiEXtLEARBEARBeO2IlmBBEARBEIQSQIwTXDxibwmCIAiCIAivHdESLAiCIAiCUBKIluBiEXtL\nEARBEARBeO2IlmBBEARBEISSQCJ52Rm8Ul6pSvDFixd57733aN68OQsWLMg3f8+ePaxZs4YbN26Q\nnZ2Np6cntWvXpm/fvlSpUgWAzZs3M27cOCQSCTqdzrCslZUVly5demF/iyAIgiAIgvDyvFKV4A0b\nNtC3b182bNhAfHw8rq6uhnkzZsxg2bJlfPDBB3z66ad4eXmRlJTE4cOHmTVrFosXLzbE2trasmfP\nHqNKsOQF3z0lZ6iYtHYfZ+9F4+Fgw7geLfGv4JMvbv6ek2w9fZ2MLDXOtgoGBNajm39VAM7ei+KT\n+ZuRSy3QARJg7sddqV3G85ly+rBhaQIquKLWaNl8KYZt1x6YjAuo4MKwZmX/z959xzdR/gEc/yRp\n0iTdu3TSFgqlCMjeo8geIqDgQMUFqLhBGYKiDMUtKCJDUEGUpSxZZe+9obS0UFq6d5M0SZvfH8GU\nkBRSfiiCz/v16uvVu3vu7tvr5e657/PcE/TGCiRIMGFi5NLj5Gr0xAS4MaFbXUyYj61EIsHZScqb\nK0+QnKtxKA6Zmzvhr7yFa/2GGHKySZ09g5ITR+2W9Y7rQsCAR5F7e6PPzuLChxPQZ1nHHfXuZNwa\n3s/RgT0d2n9+QSHjpkznwJFjBPr7Mf6NkbRocr9NubIyPRM++oytO/fg4e7Ga8OfpecDnSzLv/xu\nHivXrkdvMNC4QX0mjnoNXx9vrmRm8eATz1nOOZPJhFan4/MPJ/BAh7Y3jE3u6UHd9yfg2bQxZRmZ\nJEybTsGBQzblmv22CGVgoHlCAlJnZ9J+XUbi9M9wrx9LnXfH4FwjkIoyPXm79pAw7WMqdGUOHZ/r\nmUwmFs/6kl2b1iFXKOj58BN07T/IbtmdG9eyaeVvZKVfxsXNnY69+tFr0BCbcmuWLGTZ/O8Y8+ks\nasfed0tx2dNu2OO0fX4wwffVYe2HM1j7wVe3bdtVeadfLH2bhqA3VjA3PpEftyfbLffugPvo0yTE\n8tlROElJziqh/yfbATjxSW+0+nIATJj4flMic+ITHY5DonTBrctg5MFRlJcUULp1OYbLVa8vdfPC\n64nRlJ07REn80sr5nr64duiPU41wMOjR7N+I7sRuh+MAkKpc8Or3NIqa0ZQX5lOwdjH6lHM25Tz7\nPoX6vmaYyo2AhPKCXLJmTTJvQ+2GZ98hKIIjkKpdSf9gRLViuFZ+cSnjvlvCgTNJBPp4Mv6ph2gR\nW8um3PRFq4g/eIr84lKC/bx45eEedLg/xrI85Uo2Uxau5Oj5i6idFQzv9wCDu7Sudjwmk4nlc75m\nf7z5M9W5/+N0evARu2X3bV7HttVLybmShtrVnTY9HqTLgMcBSDp9nFnvv4X57mDerqFMx1ufzSE0\nKrracclc3Ql64TVc6tbHkJfDlQWz0Jw5bresR9vO+PZ9GCcPLwy5OaR+NglDTma193k9k8nE1p9n\ncXrnRpzkCpr1eoTG3fvbLZt0eA87lsyhpCAXuVJF3ZadaD/4ecu199zerexevhBNUT6egSF0enwE\nQbXr/d8x3lGiT3C13DWVYI1Gw9q1a1m+fDk5OTmsWLGCF154ATBniOfOncu7777L448/blknMDCQ\nevVsT2iJRIK3t/c/Frs9U5ZvwdfdhW0fPM+ec5cYvXAdf4x9CneVs1W53k3q8lTHxqidFVzKKeDZ\nmcuoHxZArUAfAEJ8PPhjzJP/dzw9YgKoF+jOiF+P4uIs48Ne9UjO03DySpHd8ifTi3jvz7M2889k\nFvPowgOW6TYR3gxpFuZwBRggdPhIDPl5nBgyELf7mxAxahynRzxNeWmpVTn3Js3x6/0QFyZPoCz9\nMoqAQIwl1vF6NG+FVKW0euC5mQ8//Qo/H292rVnG7v2HeHPCh6z9ZQHubq5W5WbMXUBRURFbfv+F\nxAspDH9rHLF1ahMeGsLGrTtYszGeX+bMxMfLi/c+/ozpM77jo4ljqBHgz/6Nf1i2c+L0WZ577W3a\ntmx209iix4xGn5PDzo5d8W7VgtiPprCv7wCMJSVW5Q48/Jjld4mTE202rSV7UzwAmtRUjo98g7Ks\nLKQKBXXeHUPNF57jwlczHT5G19qyejnnTh7lo/m/UlpczEejXyI0shYxjZrYlDUa9Ax5+U0iomPI\nz83m07Gv4xsQSIuOXSxl8nOz2bd1M54+vrcUz40UpmeyeuLnNHvswdu+bXsGtw6nSaQPPabE46GW\nM//F1pxLL2J/Yq5N2Q+WneCDZScs098+35xjKfmWaRPQc2o8OcW39rDi2qk/FaVF5H7/LoqwOrj1\neJL8BVMw6XV2y7u064sx67L1TJkMj77PU7pnHfo/vgcnOVIX92rH4tHrMcpLCrny8Zsoo+rh/fAL\nZH41HlOZ1qZs0bY1lOxcZ7sRUwW68yco3b8Fn8dfqXYM1/pw/nL8PN3YNet9dp9I4M2vf2Ttp+/g\n7qKyKueqUvLd288RFuDL/tNJvPbFApZOeZ0gXy/0BiMjps9l5MPd+XbUs5TpDWTl279+3szOdStJ\nOnWUCd/9gqakmK/GvUJwRC2iGzS2KWs0GHh42BuE165LQV4O3058E2+/AJq0f4Coeg2YvmSDpezh\nnfGsWvjdLVWAAQKfGoGxII9zLz6GS/37CXn5bRJHvUCFxvra7NqwKd7d+pD62QfoM9KQ+wVQXlp8\nS/u83rHNq0g7d4Jnpv+ATlPMb1NG4RsWSVi9RjZlAyKjeWTcJ6jdvSjTlLLqq0kcj19Nw859KC3M\n58/vP6H/W5MJjWnI8S1rWTXjA4Z9ufi2xCncHe6aR4a1a9cSFRVFzZo16dOnD0uXVmYmVq9ejYuL\nC48++ugdjNBx2jIDW09e4MXuLVE4OdEhNpLaQb5sPXnBpmyorydqZwUAf9Xj0vMqL6x/ZY3+Xx1q\n+fL7iXSKy4xkFJWx8WwWnWrfoBLiYOK8Y20/tibmOByH1FmJR/NWXFm8EJPRQNGBvWhTkvFobptN\nCXzkcdLmfUdZuvlGrc/MoEJTWdmWOMmp8fjTpC+Y6/D+NVot8Tv38PKzT6FQKOjYthXRUZFs2Wmb\n6Vq9fhPDnn4CtUpFg9gY4tq1Ys3GLQCkZ2TSuGF9Avx8cXKS0a1TB5JSLtrd5x9/bqRz+zYonZ3t\nLrccG6US347tSf52NiaDgdztOyk9n4hvp/Y3XM+3Y3uMJaUUHjFn042FRZRlZV3dqBRTRQWq0JCb\nHZoq7d68nu4DHsPV3YOA4BDa9+jL7k12Ki1Ax579iIqpj1Qmw8c/kCZtOpB45qRVmSWzv6bfkGeR\nyW7/M/rxVZs4sSYebeHtuSHfTO8mIfywNYlCjYFLORqW7r1E36Y3P9a+bs60qu3HqkOVlVAJIJXe\nYouVkwJFZH1K9/4J5eXok09jzElHEVnfbnF5WB0A9KkJVvOVMc0xXElGf/6o+YJk0FNR4PjnG0Ai\nV6Cq05CiLX9AuRFdwnGMmZdR1W1YxQr2Z1doS9Ec2oEh87L9Ag7S6PTEHz7FywO6oZA70bFxPaLD\narDl0CmbsiMe6kJYgPm62LxeFJHB/pxONu9/xfYD3B9dk56tGiGTSlErnalZw89mG444sHUDcQ89\niou7B35BIbTu2ocDW/60W7ZN975E1I1FKpPh7RdAw1btSTlnGzvAgS3radax6y3FJFE449a4BdnL\nfsZkNFJy9ABlqcm4NW5hU9b3wUFkLpqLPiMNAEN2JhVaxxMhN3JmdzxNegxE5eaOV0Aw93XswZld\nm+yWdfX0Qe3uBYDJVIFEKqEg6woAJfk5qNzcCY0xn3cxbTqjKcin7LoK/d3GJJH+7T/3krsmE7xs\n2TIefNCcvWnXrh0lJSUcOHCAZs2acfHiRUJDQ5FKK/85P/zwA19++aVleseOHbi6mjN5RUVFNG7c\n2Co72KxZM2bPnv2P/C0XcwpwcVbg5+5imVcr0IekDNvsEMD8+IPM3ngAncFAvZAAWlzTbSKroITO\n783BVamgV5O6PP9As1vq2hHqqSIlr/IidTFfQ5NQryrLR/u5suDxJhRoDaw5ncGGs1k2ZdyVTjQK\n9mDu3hSH43AOCqJCq8WYn2eZp72UgjIs3LqgRIIqshaq8AjCXx2FyWggd/MGMpdWPsUHDBhE/vYt\n6PMcv0lfupyGi1qFn6+PZV6tyJokJltXYIuKS8jNLyA6MsIyr3ZkBMdPnQGga8f2/Ll5G2lXMvDx\n9mLtpi20ad7UZn9GYznr47cz/f2xN41NHRaKsVSDPqfyPClNSsIlMvKG6wX07E7mWusbqHOAP81+\n/RknV1fKNVpOvPbmTfdflfRLKYRGRlmmQ2pGcXyfY83j504co/UD3SzTZ48dpqSokMat27N41pc3\nWPPuEBXoxrn0yofW81eK6FDP/6br9bw/mOOX8knLs86MLn61LSYT7EnI5pNVpynUGByKQ+bpi0lf\nhklTWfkvz81A5hNoW1gqxaVNb4rWzMe5rvU56xQQhkmnxePhkcg8fDCkJ1O6bQUVpY5nPJ28/anQ\nl1FxTauNISsdJz/73bhcW3TGtUVnjLmZFG1eif7SeYf35YhLmdm4KJ3x86rMaNcKCSQxzX53sL8U\nlmpIvJxJrRDzMTyRlIq7i4rH359BamYu90fXZNxT/fD38qh2TJmpKQTVrPxMBYVHcurgHofWTTx1\njGYdu9nMLy7M5+yR/fR/dmS14wFQBAZRodNiLKxsndBdvoRzcJh1QYkEVc0olCE1CX7hdUxGAwXb\nN5Oz6tdb2u/18tIv4hdWed31DYngwrH9VZZPSzjFys/GU6bVoHb3pOPj5m4z/mFReAYEk3LiIOGx\njTm1fT0BEbVxVrtUuS3h3nNXVIIvXLjAiRMnmDnT3Fwrk8no0aMHS5cupVkz+03IAwcOpHPnzhw9\nepTRo0dbLXN1dWXFihVW85xvkoWzJysri+zs7CqXV1U10ZYZcFEqrOa5KBUUauw3Sw6Na8rQuKac\nvJTJgcRU5DKZefv+3vz61mOE+3mRnJnHqIXrUCnkDOlg23/1ZpRyGZqr/Q0BNPpyVHL7T3wnrxTx\nyrLj5JTqqe3nwtsPRFOoNbDvYr5VuXZRviTllJJR5HjzrVSpolxjnTGo0GiQublZzXPy9EIik+HW\nqDFnRj6PzM2NWu9NRZ+VSf72eBT+AXi2bs/ZN0Yg9/bBURqNDle12mqeq1pNYZF15lCjNVdO1OrK\n5lIXF7Vlvq+PF/Vjoun+yJPIZDKioyJ49y3bJtsde/chV8jt9jm+nkyttukSYiwpRe5RdXO0k7s7\nPm1akfTF11bzyzKz2NmhC3JPD2r074cu0/YhxlFlWi2qa24cKrULZTrbZu3rrV+2GE1JEW0eMPfV\nrigvZ/Hsrxj29nu3HMu/jVoho1RntEyX6IyoFTe/7PZuEsyve6wfvJ6csYtjF/NxU8kZP+A+Jg9u\nxMvzDlSxBWsSubNNtweTXodEqbYpq7q/A/qU01QU5dksk7p64BRQn8IVsyjPzcClbW9cuzxK0crv\nHIoDzBnF67s9VJTpkKpsYynZt5nC9Usw6fWoYpvg8+iLZH07ifKifJuyt0qj0+OqUlrNc1UpKSyp\nOnNpMpl4d/avdG3ewJLtzcovJP5QKnPeeYHaoYF8smgNY2f9wpwxw6odU5lWi/Kaz5RS7UKZ7uaZ\n1PiVv6AtKaZ55+42yw5v30xorbr4Bd1aq49UqbLJ5lZoNchcr7s2u3uCVIZL/UYkjXkJmYsrYaMn\noc/JpGjPtlva97X0Oi0KZeWxUajUGG5wvQmOjuWlWSsoysnk9K5NqN09AZBIpdRt2YlVX02i3GjE\nWe3CwHc+/r/ju+Ok91am9u92V1SCly5dSnl5Oe3atbOar1AoePfddwkPD+fw4cOUl5cju1pBdHV1\nxdXVlStXrthsTyqVEhpq+xJadS1ZsoQZM2ZUufzop/b7qamc5ZTq9FbzSnV61Ar5DfdXPyyANYfO\nsmzvSR5ufR/ebmq83cw3jogAb57v0oxfdh53qBLcPsqHEW0iMWFiW1IuWkM5aoUMrtax1AoZWkOF\n3XWzSypjP59dyppTGbSq6W1TCe4Q5cvmhOpVrip0WmTXVUKlajUV113kTHpzxTpz+RIqdFoqdFpy\n1q/FvUlz8rfHEzx0GFcWLYDy8mplxtVqJSXXVcJLNBqryi6AWmWe1mi0lmWlpRrL/JnzFnLh4iV2\nrlmKSqnk81lzGfvhx3wxeaLVdlav30zvLnEOxVau0SBzsc5SOLm6UK6p+gYQ0L0rxWcT0F5Ktbvc\nUFBI3u69xE6ZxKEnn3Uojj3xG1j41ccggVaduqFUq9Fe04So1ZTirFTdYAuwJ349G1f+yphPv0Wu\nMD8Qbl61jOj6DQkKq+lQHP9GvRoHM3FgA0yYWH0ojdIyIy7Kysusq9IJjd54gy1AVIArkQFu/Hk0\n3Wr+kav9gws1BqauOMmWiV2QyyQYym/eJcpkKEOisK7oSRRKTAbr65DUxR3nes0pWPyZ/e0YDeiT\nTlCebW7m1uzbgPfzk0Amg/Jyu+vYbENfhsTZ+vyQOistn+lrGa/p6qA9eQB1gxY4R9VDc2SXQ/ty\nhFqpoERr/YBQotWhvi5Rca1J85dTqi3j05GVL3UqFXI6N61PvQhzJfPF/l1oN+I99AYjCvmNb7UH\nt21kyTfTAQlNO3TBWaVCd81nSqcpxdnOA8u1DmzdwLZVS3l12kzkctvYD2zdQMsHHHs52J4Kndbm\nQUWqsr02V1w9p3LWLLNcm/O3/Ilbw6a3VAk+szueTT98iQQJdVvHoVCq0esqj41eq0F+k+sNgLtv\nAD5B4WxeMIPeL48j5cRB9ixfyGPvzcA7KJSE/dtZ+el4hn48HydF1f974d7yr68El5eX88cff/DO\nO+/Qpk0bq2UvvfQSa9asoXfv3vz0008sWrSIIUOs3zSvzgtR1TVo0CDi4m5QgUneand2uK8nGr2B\n7KJSS5eI81dy6Nvs5m+lGisqSM0psL/Q5Pjfuz0pl+1Jlc3qEd5qwr3UXMo3X9DCvdSkFlSjD9d1\n9cxgDyXh3mp2XrDfxaMqZenpSJUqnLy8LV0iVOE1yYvfaFWuvLQUQ951277mb3et3wCXOjGEDnsZ\npDIkMhn15y3m/ITRlF22XyEECAsJRqPVkp2Ta+kScT4pmX49rfvRubu54uvtRcKFZBrVN//fzl9I\nJiqiJgAJScn06NwJD3dzlnZA7x48+eLrVtsoLill6669LJnj2AtpmkupyNQqFL4+li4RLrVqkbFq\ndZXrBPTqTuYa+/1z/yJ1ckIZ4nh2qFVcV1rFVR6PSxfOczk5iZCrzbeXU5IIDo+oanUO797Or9/P\nZPRHX+HjX9kcf/bYYRJOHuPAdvMLfMUFBXz1/ts8/MwI2nfv43B8d9Kaw2msOZxmma4T5E50DTcS\nM8wtCbVruFt+r0qfpiFsP51Jia7qyrIE/hp/xfLbjZQX5CCRK5Co3SxdImS+NSg7bZ1JdgoIRebq\ngdeT5mEkkSuQIEHq7k3RytmU516xfRGumtdYY14WUoUzUld3S5cIuX8wmmPVG2HidgkL8ENTpic7\nv8jSJeJ8agb92tl2XwL4dPFqzl5MY97Y4cidZJb5tUICySmw/t862oe7aYcuNO1Q+XJoWkoSV1Iu\nEBRubk9Mv3iBGjd4ODy+dwe///ANIz/8Em+/AJvlmZcvcuViEo3bdXYoHnv0GelInZU4eXhZukQo\nQ8Ip2LnZqlyFphRj/vXX5lveLTGt44hpXXmfzb50gZzUFHxDzNeYnMvJ+AaHV7W6dWzlRgqzzA+X\nOanJhNZriM/V7hx1WnQgfuEM8jNS8QuLutFm/t3usT67f7d//dHasmULRUVFDBgwgFq1aln9dOnS\nhaVLl9KoUSOGDh3KRx99xLRp0zh06BDp6ekcO3aMZcuWIZFIrLKBJpOJnJwcm5/qVpj9/f2JjY2t\n8qcqKmc5HWMj+Xb9XsoMRradukBSRi4d69t2oFi+9yTF2jJMJhMHElNZd/icZSi1g0mXybx60b2Y\nXcDczQftbsMR2xJz6NegBm7OTtRwV9Klrj9bztvvS9so2AM3Z/PzU6SPml71Atl/XRa4Y20/Dqfm\nU6p3LDv0l4oyHYX7d1Pj0SeRyOW4N2uJMqwmhfttb5B58RsJeOgRpEolch9ffLv2pOjgXgBOv/gM\nZ18fwdnXR5D0wTioqODsa8MpS7vxSzRqlYpObVszc+5Cysr0bN25h8TkFDq1tX0xr1fXzny34Gc0\nGi3HT51hy8499O5qvljH1onmz/htFBYVYzAYWL7mT2pHWVcM18dvI6pmOLUiazp2bHQ6crZuJ2L4\nC0gVCnzat8WlViQ5W7bbLa8KC8W1TjSZf26wmu/Ttg2qMPM5pPDzJeKlYeTvd6xZ3Z7Wnbvx59LF\nFBcWkJGWyvZ1f9Cmi/2M0+kjB5n/xTReef8jm5v6c2+9y+Tvf2bStwuY9O0CPH18ee7NcVYV7v+X\nRCrFydkZqUyKTO6Ek0Lxtw6PuPrQZZ7uGIWni4IwXxcGtgzj9wM3Pgd7Nw7h94PWD2pRAa5E13BD\nIgF3lZzRD8ay+1w2hnL7rTU2jHr0F07h0rIbyJxQRNTDyScQ/QXrlxL1KWfI+2EKBYs/I3/Rp+hO\n7KHswgmK1/0IQNm5wygi6iHzqQFSKermXTCkJTqcBQYwGfRozx7DvWNfkDmhjG6Ak38Q2rO247Qr\n696PxElu7mca2xRFaC3KLlwzKo3M6epy8+9IZTbbuBm1UkGnxrHMXL6BMr2BrYdPk3g5g05NbK/h\n363cxPajZ5k1+jlUztbZwt5tGrP1yCnOXUrHYCxn1spNNIuJumkW2J5mHbuyeeViSooKyEpPZfeG\nVTSP62G37LljB1k84yNeGDeNgBD7lcEDW9ZTr0kr1Nd1XagOk76M4iP78Ov/GBK5HNdGzXAOCaf4\n8D6bsgU74/Ht2R+JsxInLx+8OnWj+OitX2OuFdM6joPrfkNbXEh+Rhontq6jXtsudssm7N9Oca65\nNTI/I439q5cQGmtuKfWvWZvUs8fJu2L+rCUc2EG5wYCHX43bEqdwd/jXZ4KXLl1K69atLS+1Xatr\n167MnTuXhIQE3n77bRo2bMjixYtZvnw5Wq0WX19fmjZtyi+//ILLNc3IJSUlVl0rTCYTEomEnTt3\n4uPjeP/R/8eY/h1595eNdJgwm0APNz5+sgfuKmfWHj7HvM0HWTrKPNTb9tMpfLVmN8aKCgI93Xiz\nbzvaxtQE4MzlLMb+vJ4SnR5vVxW9m8TwZAfbIXQcse5MJoHuSr59pBGG8gqWHUu3DI/m66LgqwEN\nLGMBNwr24LWOUTjLZORq9Cw7ls7uZOv+g+0ifZi31/5oCDeT+t0Mwl8dxX0/LsOQk03K9MmUl5bi\n1b4TAQMGc/ZVcx+7K0t+JHTYSGLnLqJCoyFn/Rryd2wFoLy48qUbiUKByWTCWFTo0P7HvzGSsZM/\npm2vAQT6+/HppPG4u7myZkM8c35azIqF5jGnX372KSZ89BkdHxyEh7sb4998hbCQYACefWIQUz6f\nQd8nnsVoNFKvTm0mvfOG1X5Wb9hEn+4PVOvYnJ82nZhJE2m7dQPJtFMfAAAgAElEQVS6jExOjR6H\nsaQE/+5dCX/mKQ48UjlEYEDP7uTt2oOxyPqlJYWvN7VHv4Hc2wtjSSl5O3eT9GXV3XpuplPv/mSm\nX+adZwbhJJfTa9AQ6jY0n4e5WZmMH/Y4k2cvwtvPn1WLf0BbWsLHb4/kr8GtW8V148mRo1C5uKCi\n8nMqk8lQu7ojV1S/v35Veo4fSa+Jr1qylz3GvsSCoaPY9+Py27aPa/2y+yJhvi6sHdMJg7GC7zcn\ncuBqC0ygp5LfR3ek70dbySw0N8c3r+WDwknKjjPW3Yh83JyZMLAB/u5KSsuM7EnIZuxi+2NnV6Vk\n63LcugzG54VJlBcXUrxuISa9Dufo+1E17UzBok+gogKTtnK4PZNBD0aDpQ9veX4WJVuX4957KBJn\nFYb0ZIo3VH9IqcK1i/DqN5Qaoz+jvCifvKWzMZVpUdVvhlvbHpaxgF1bdsarr7l1z5iTSe4v31Be\nWJllDBr3tfk8Mpl/Ly/IJfOr8dWOZ/zTDzF21i+0HTGRQG9PPh35BO4uKtbsPsKcP+JZMc384uiM\nZRtQOMno+uoUy9jsE58ZQM/W9xMZ5M+4px7ilc9/oFijo3F0BFOGDa52LABte/Qj+8plPhj2KE5y\nBV0GPkHt+8yVt/zsTKa8/CRjZ/6Il68/G379EZ2mlK/Hv8pfH6pmHbvyyIjKl10Pbd/EQ7f4Qty1\nMhbMIuiF16jzzSIMuTlcnvkRFZpS3Ft1wLf3QC6MM+8je+Viajw5nOgv5pu7Q8T/SdFe+w/r1dWw\ncx8KMtOZN2ooMrmc5r0HW0Z4KM7NYsGYF3hq2ve4efuRdyWVrYtmUaYpReXqTnTz9rQZ8BQAYfUa\n0bTHQJZPH4uutBgPv0B6vTwOhZ2+6XcVkQmuFonp7+wv8B+nXX1r467+HR7NsB2z9U6auHrCnQ7B\nSv3v59zpECx2dn34TodgxXnF2jsdgsXCiH/Xebzj9a9vXugfsq3m1jsdgpWy/JKbF/oH+ffsdadD\nsIh3b3mnQ7ASOtmx9wH+CTteuvUH8r/DsBaOdbX4t9AX3PpLzo5SeN58hJu7xb8+EywIgiAIgiA4\nQGSCq0VUggVBEARBEO4B99qXWfzdxNESBEEQBEEQ/nNEJlgQBEEQBOFeIDLB1SKOliAIgiAIgvCf\nIzLBgiAIgiAI94K/cdzze5HIBAuCIAiCIAj/OSITLAiCIAiCcC8QfYKrRRwtQRAEQRAE4Y7YsmUL\n3bt3p1u3bvz22282y1NTUxkwYADdunXjvffeu637FpVgQRAEQRCEe4BJIv3bf26n8vJypk2bxo8/\n/sjy5cuZM2cOhYWFVmWmT5/OK6+8wvr168nJyWHbtm23bf+iEiwIgiAIgiD8444fP050dDR+fn64\nuLjQoUMHdu3aZVXmyJEjdOjQAYB+/foRHx9/2/Yv+gQLgiAIgiDcC+6yPsFZWVkEBARYpgMCAsjM\nzLRM5+fn4+npWeXy/5eoBAuCIAiCIAgOycrKIjs7u8rlfn5++Pv7/4MR3TpRCf4bLfTrfadDsBg7\nY+idDsHKM01G3ekQrKx28rnTIVgMbzvmTodg5Yz+7J0OweL517++0yFYaff5yDsdgsV7S/+40yFY\nKfTW3+kQrLzkH3mnQ7DodGnDnQ7ByhsPTr7TIVh8oThzp0O4TvidDqBaTP/AOMFLlixhxowZVS5/\n+eWXGTnSsWujv78/GRkZlunMzEwaNmxomfby8rLqI5yZmXlbK9iiEiwIgiAIgiA4ZNCgQcTFxVW5\n3M/Pz+FtNWjQgPPnz5OVlYWLiws7duzgpZdesirTqFEjtm7dSseOHVm1ahUPPfTQLcd+PVEJFgRB\nEARBuAeYTH//Pvz9/W9bNlYmk/HOO+8wZMgQAJ577jk8PDwYP348jz76KLGxsbz55pu8/vrrTJky\nhVatWtGxY8fbsm8QlWBBEARBEAThDunUqROdOnWymvfhhx9afg8PD2f58uV/y75FJVgQBEEQBOEe\nUPFPpILvIXfXWBqCIAiCIAiCcBuITLAgCIIgCMI9QOSBq0dkggVBEARBEIT/HJEJFgRBEARBuAdU\niFRwtYhMsCAIgiAIgvCfIzLBgiAIgiAI9wCTGB2iWkQmWBAEQRAEQfjPuaszwWPGjGHFihVIJBJk\nMhkeHh7UqVOHXr160b9/fyRXv0M7Li6Op59+mieffBKAs2fP8uWXX3Ls2DFKSkrw9fWlUaNGjB8/\nHm9v738sfpPJxNafZ3F650ac5Aqa9XqExt372y2bdHgPO5bMoaQgF7lSRd2WnWg/+HnL33hu71Z2\nL1+Ipigfz8AQOj0+gqDa9RyOxcndncg338atQSP02VmkzPyK4mNH7Jb17dKNoEGPIff2oSw7i4QJ\nY9Fnmr/7WxkcQviLI3GNiaVcpyV90Y9krf6jmkfG7K2eMfRpHEyZsZwftiezaHeK3XJj+8bSs1GQ\n5ZtyFE5SUrJLGDRjl2V5iygfQrzVPD93H4dT8m+6b5PJxMwvPmHD2tUoFM4MHvIUAwc/XmX5RQvn\ns3TxT1RUmOjZ50FeePlVy7IdW+OZN2sm2VlZxNS/j9HjJ+LnHwBAdlYmn380hZPHj+Lm7sHzLzr2\nfetj+99HvxahlBkqmLPpPAu2Jtkt994jDenbLMTq2FzIKuHBaVtoEunN9yNaWZZJpBJUchn9p2/l\nzOVCu9u7Xn5hMWO+mMuBk+cI9PXm3eFP0LJhjE25j+cuYfO+I+QVFhMS4MurQ/rTsZn5++FzC4p4\n9+sfOJ5wgfyiYk79PtehfVflnX6x9G0agt5Ywdz4RH7cnmy33LsD7qNPkxBMV9+nVjhJSc4qof8n\n2wE48UlvtPpyAEyY+H5TInPiE/+v2K7XbtjjtH1+MMH31WHthzNY+8FXt3X71xvQoAYtwr0wlJvY\nmJDN1sScG5aXAGMeqI2TVMqkDecs8xsEudMnNhBPlZyUPA0/H7pMgdZQ7XgebxJKu0gfDBUVrD6V\nwfqzWXbLtY304dmW4RiMJnNQJnh79UnyNQacZVJGda5NkLsSiURCSp6GBQcuklFU5nAcJpOJRbO+\nYOfGdcgVCno98gTd+g+2W3bnhjVsWPkbWemXcXFzJ673Q/QaZP62qzKtlk/GvU76pRRMpgpq1qrL\nkJffpEZouMOx5BeXMn7+Sg6cSyHQy51xj/eiRUykTblPfl1P/JGz5JeUEuzrxSsPdaZ9g2jL8m9+\n38KKXUfQ6PR0bVqPcY/3wkkmcziOG3H0PGoR5sVjTULQl1f89W9j8saEWzpXAPKLShj37c/sP51I\nDR8vxj0zkJb1o23KTf9xJZsPniC/qIRgfx9eHdSLDo1jAThwOpFnPpiBSqnAZAKJBGa9M5zGdWyP\n8d1I9Amunru6EgzQvn17pk2bhtFoJDc3lx07djB58mTWr1/PrFmzkEqtk915eXk8/fTTxMXFMW/e\nPNzc3EhLSyM+Ph6tVvuPxn5s8yrSzp3gmek/oNMU89uUUfiGRRJWr5FN2YDIaB4Z9wlqdy/KNKWs\n+moSx+NX07BzH0oL8/nz+0/o/9ZkQmMacnzLWlbN+IBhXy52OJbwl19Dn5fH4Uf64dG4KbXGTuD4\nM09QXlpqVc6jeQsCHuxPwnvj0V1OxTmwBuXFxQBI5HKiP5jK5QXzOPfuGKQKBQof31s6Ng+3CKNx\nhBd9P92Gu0rO98+1IOFKEQeT82zKTvnjFFP+OGWZ/vrJJhxPLbBMn71SxJ/H05n40H0O7/+PZb9x\n/MgRflz6OyVFRbz+4gtE1Y7m/ibNbMru3b2TP5b/xjdzf8RZqWTUyOGE1qxJj94PknrpIh9/+B4f\nfzGTOvViWbRgHh++O4Yvv5tnjn3ieGJi6/Ph9M+5kHie0a++CPWeBZeqv3v9sXYRNK3lQ9f3N+Ku\nVvDjK205m1bIvvO2N6L3fj3Ge78es0zPHt6So8nmh4BDF/JoPGqNZVmP+4N4o0+swxVggEnf/oif\ntyd7Fn3FriOneP2jb1k/exrurmqrci5qFd+//wZhNfzZf+IsIyfPYPlX7xHs74tUIqFD0wY83iuO\nF97/3OF92zO4dThNIn3oMSUeD7Wc+S+25lx6EfsTc23KfrDsBB8sO2GZ/vb55hy75gHJBPScGk9O\nseOVqeoqTM9k9cTPafbYg3/bPv7SLtKHWr6uvL/+HCq5jFfbR5JWqOV8dmmV63So5YtGX467svI6\n6ueq4IkmoczceYFL+Vq61vHn6eZhfLHN/oNYVTpH+1E3wJW3fj+BWuHEuC51uJSv5Uxmsd3yZzKK\n+Tj+vM18Q0UFc/de5EqRDoAHov0Y3jqS9/4843As8auWc+7EUab/8BulxUVMHfUSYZG1iWnUxHZ/\nBgNPjnyLyOgY8nOzmT7mNXz8A2nZqQtOCjlDX3uHGqHhSCQSNv2xlO8+fp/3vp7ncCwf/rwGPw9X\ndn7xNrtPJfHWd7+xZsoruKtVVuVcVM589/oQQv29OXA2mVe/+YWlE0cQ5OPJip1H2Hj4NIvHPY9a\n6czbs5cya9U2Xu4X53AcVanueZSQXcLMnfYfRKvrg3m/4evlwe45U9l1/CxvfvkD674Yj7vL9dcb\nJbPHjCAs0Jf9p87z6mdzWTZtNEF+5iRXaIAva78Yf1tiEu5ud313CIVCgbe3N/7+/sTExPDCCy/w\nzTffsH37drtfs3f48GFKSkr48MMPqVu3LsHBwTRv3px33nmH4ODgfzT2M7vjadJjICo3d7wCgrmv\nYw/O7Npkt6yrpw9qdy8ATKYKJFIJBVlXACjJz0Hl5k5ojDmzFtOmM5qCfMo0Vd/criV1VuLVsjVp\nP87HZDBQsG8P2uQLeLVqY1M2+NEhXJr9LbrLqQCUZVyh/Op+fLt0p+TUSfK2bYGKCip0OnRpl6t3\nUK7q1TCIhTuSKdQaSM3TsPxgKr3vv/n/x8dVQYsoX9YcTbfMW34glcMp+Rir8Yi8cf1aBj0+BA8P\nT4JDw+j14ENsWLvabtlNf66lT78BBAYF4eXtzcOPDWHjWnPl8tD+vTRp1oKY+vchlUp57KlnSDh7\nlvS0y2i1Wk4cO8KQZ19AKpVSK7oObdp3hCv2M/B/6dM0hHmbEynQGLiUU8qve1J4sHnoTf8mXzdn\nWtXx54+DqXaX920WVuUyezS6MjbvO8rIx/uhkMvp1LwRdSJCiN9nG/9Lj/YlrIb5u+ab31eXqLAg\nTiddBMDLw41BPTpSJ+Lmf8PN9G4Swg9bkyjUGLiUo2Hp3kv0bRpy0/V83ZxpVduPVYcqz1cJIJVK\n/u+YbuT4qk2cWBOPttB+xe92ahbmyebz2ZTqy8kp1bM7JY8WYV5Vlnd1ltG6pjcbzmVbzY/xd+Nc\nVjEX87WYgA3nsgjzVOHjoqhWPG0ifFh7OpMSfTlZJWVsTcymbaRPleX/avW6XoUJSwVYIgGTCfxd\nqxfL7vg/6THwMVzdPQgIDqVDjwfZuWmd3bKdevWjVkx9pDIZPv6BNG3bkcQzJwGQyZwICquJRCKh\norwciURK9pV0u9uxR1OmZ8vRs7z0YBwKuRMdG9UhOiSALUfO2ZQd0acjof7mSl2zuhFE1fDjzEXz\nPWHHiQQe7tAUXw831M4Knu3RlpW7bnxdcVR1z6Pb9QnS6MqIP3iCkQ/3QCF3olOT+kSHBRF/8KRN\n2RcHdCcs0JyAaR5bm8jgQE4nV17bTPfwaLqmf+DnXnLXZ4LtadmyJXXr1mXjxo0MHDjQapmfnx/l\n5eVs2LCB7t2736EIzfLSL+IXFmGZ9g2J4MKx/VWWT0s4xcrPxlOm1aB296Tj4yMA8A+LwjMgmJQT\nBwmPbcyp7esJiKiNs9rFoTiUwcGUa7UY8iqzrJqLyajCa1oXlEhQ16qNOiKCyLfexmQ0kr3hT678\n8jMArnXqYiwpIeazr1HWqEHx6VNcnPkVhjzbLNzNRPq7cj6jsmKQmFFMuzpVZ0f/0r1BECcuF5Ce\n//9l9S8mXyCyVm3LdERULfbu3lll2c5du1uVTUmuzIpd+6KCyWTChImUC0l4eZlvYKaKimuWAyWZ\nN4ytVqA759Irs7UJ6UV0jA286d/Uq2kIxy/mcTlXY7PMy1VB2xh/pi4/YWdN+y6mZ+KiUuLv7WmZ\nVzssmPOX0m64XmFJKecvplErNMjhfTkqKtCNc+lFlunzV4roUM//puv1vD+Y45fyScuzPm8Wv9oW\nkwn2JGTzyarTFGpurRn33yDQTUlaYeXfl16oIzbQvcry/erXYMO5LPTlFTbLbCqkEqjh7kxuqd7h\neII8lKTmV56LqQVaGgZ7Vlk+yseFmQMbUqQzsuFcJluua/mY3KuepUvEr0er9/CdfjGF0IhalunQ\niCiO7d/l0LrnThyldWfre8n44UPMXSIqTDz8zHCH47iUmYuL0hk/TzfLvFpB/iSl2+8m8pfCUi2J\n6VlEBVVeI6+97lSYTGQXFFOqK8NF6exwPPZU9zwK91YztXc9inVGtiXlsMtOa54jLmZk46JS4ufl\nYZlXO7QGiZev3HC9whINialXiAqpvEZm5hbSfth43NQqerdtyvD+Xat8yBLubfdkJRggMjKShIQE\nm/kNGzZk2LBhvPXWW0ycOJEGDRrQsmVL+vXrh49P1VkIe7KyssjOzr5BCdcbrq/XaVEoKyuqCpUa\ng67qyltwdCwvzVpBUU4mp3dtQu1uvmFIpFLqtuzEqq8mUW404qx2YeA7Hzv8d0hVKks29y/lGg1O\nrm5W8+ReXkhkMtzvb8qJYc/g5OZOnSkfo8/MIHfLZhS+vrhE1+HsmFFoU5IJfW4YkaPe4dyYUQ7H\n8heVQkZpmdEyXVpmRK24+enas1EQS/dfqvb+rqfValG7VP5vXFxc0WlsK4/msprryrqg1Zj/j42b\ntWDutzM5cfQIMfXr89P8uRiNRnQ6HSq1mtgGDVkw5zuee3EkFxLPs33LJlDUuGFsamcZJbrKY1Oi\nM6JW3LyvX9+mofyyy36zZO8mIZy8lM+lHMdaDwA0Wh2uaqXVPBe1isLiqrdhMpkY98U8urVpSkTI\njf/OW6FWyCi1OTY3P296Nwnm1z0XreY9OWMXxy7m46aSM37AfUwe3IiX5x247TH/U5ydpOgMlRVa\nnbECZyf7jYER3mp8XZ05eOgytXytH6bPZpXQp34gkT5qUvI0dK8bgEwiwVlWvYZFpZMM7TXxaA3l\nKKuI50xmMWNWnyJXoyfSx4VX20dRpDNy6JpuT+PWnMZJKqFVTe9q9znVabWorvkMq9RqyhzoHvfn\n0sWUFhfTtktPq/kfzvoRg17Pni0b8PR2/L6iKdPbVFJdVM4UlVYdi8lk4t35K+naJJaaV7OfbevX\nYuHGPcQ1qouLypm568wP8Fo726+u6pxHCTklTNmYQL7WQLiXiudahlNcZuT4NQ+qjtLoynBVXXe9\nUSkpLLnx9Wb8rEV0a9mIiCDzexiRwQEs/2g0NYP8uZCWyRtfzEetVPBUr07VjunfSPQJrp57thJs\nMpmqfLJ77bXXGDp0KHv37uXYsWP88ssvfPfdd/z888/Url3b7jr2LFmyhBkzZlS5/I2FG6ymz+yO\nZ9MPXyJBQt3WcSiUavS6yg+wXqtBrlRdvxkb7r4B+ASFs3nBDHq/PI6UEwfZs3whj703A++gUBL2\nb2flp+MZ+vF8nBQ3bxas0GqRXZc1lqnVVFxXIa8oM/eNvPLbYiq0WvRaLdlrV+HRrAW5WzZTUVZG\n/u6daBLN/fbSflpI4yXLkcjlmAw3vil1b1CD8f3qYzLBumPpaPTluDhXnp4uzk5o9MYbbMGcPY7w\nc2XjiYyb/s3X27x+HZ99NBkJEjp364FarUZzTX/o0tISlGq13XVVquvLlqK62n8vLLwmo8ZP5Ivp\nU8nPy6Vztx7UrBmBn585Oznu/cl88fFUBvftQY3gYLr27M3yndYvX/VuEsKkwQ0xmWDVwcuUlhlx\nVVYeG1elE5qrL3BVpVagG1GBrqw7bD9L27dZKMuuqwTejFqlpESjs5pXqtGiVlV9k33/mx8p1er4\n/J0R1dpXVXo1DmbiwAaYMLH6UBqlZUZcbI7Njc+bqABXIgPc+POodbP1kav9gws1BqauOMmWiV2Q\nyyQYyu+Ou0zTUE8G3x+MCTiYWkCZsRylXApXP9ZKJyllRtssL8CAhkEsOWI+V66/imaVlPHTwVQG\n3x+Mm7OcA6n5ZBTryL9JxbNVTW+GtggHE+xOyUVnKEclr6w8qeQydFXEc22G+UJuKRvOZdE01Muq\nEgxgrDCx40IuXw9owNurTlX5udgTv54fvvwYJNAqrhtKtRrtNZ9hrUaDs+rG1+Ldm9ezYeWvjPvs\nW+R2rrNyhYL23XrzyuDeTP1+ES5uVWdL/6J2VlCqs+6DXqotQ+1c9XX8g59Wo9GV8enwRyzzHmrb\nmMz8IoZOn095hYmnurZi7+kL+LjfODFjz/9zHuVf03JyMV/LtqRcGgV53FIlWK10pkR73fVGq0N9\ng0r9pLm/UqrV8dlrQy3zfDzc8PEwJ3gigwMY3r8bP/+5/Z6pBAvVc89WgpOSkggJqbovoIeHB926\ndaNbt2688cYb9OvXj3nz5jF16lSH9zFo0CDi4qp+0WBnifV0TOs4YlpXls++dIGc1BR8Q8xdInIu\nJ+Mb7NhbxBXlRgqzzDftnNRkQus1xCc4DIA6LToQv3AG+Rmp+IVF3XRburQ0ZCoVcm9vS5cIdc0I\ncjautypXXlqKPte6CfLaIQk1F1OQe3ldt9yxCsOfx6/w5/HKZq3oQDdqB7qRlGU+iLUC3UjKLKlq\ndQB6NQpi57ksSspuXOmxp3O3HnTu1sMynZSYwIWkRCKizE2kyUmJ1Iyw//ZweEQkyUmJtGrbHoAL\nieepGVF53Nt36kz7Tp0BKCkpJn79n5bt+gcEMuXTLy1lJ08YBx7WfWNXH7rM6mv6qtYJdic6yJ3z\nV8zdRcy/3/im0rdZKNtOZVplkP8SGeBKdA131lZRQa5KeFAAGp2OrLwCS5eIhItpPNTZti85wPT5\nv3LmwkV+mDwaudPtufSsOZzGmmvirhPkTnQNNxKvdqWpXcPd8ntV+jQNYftp+8fmL3+92X7tb/92\nB1MLOHhNJTHYQ0mQu4orV0dNCPJQklGks1lP6SQl1FPF8NY1AXCSSlDKZUzuGcP768+hL6/gWHoR\nx65WZJROUpqGelr65VZlT0oee1Iqm8LDvNSEeKm5XGheL9RTZdXMfjNVtV5LMGeZvVXyKivBreK6\n0Squm2U69cJ5UlOSCLn6uU1NTiI4vOrRAg7v3s6S72fw9sdf4+NfdVekiooKdBoN+TnZDlWCwwJ8\n0Oj0ZBcUW7pEnE/L5MHW99st/9lvGzh76Qpz33oauVNla5BEImFE306M6Guu2O0+lURMeI1bavK/\n1fOoSrfY6yA80A+Nrozs/EJLl4iES+n069DCbvlPfv6dM8lpzJ/wstWxud5fXdTuFWKc4Oq561+M\ns2fPnj0kJCTQrVu3mxcGnJycCA0NRVNFc3dV/P39iY2NrfLnZmJax3Fw3W9oiwvJz0jjxNZ11Gvb\nxW7ZhP3bKc419wvLz0hj/+olhMaaL4z+NWuTevY4eVfMHf8TDuyg3GDAw8+x5uaKMh35e3YR/MTT\nSORyPFu0QhUeQf4e2z5xOZs2UOPhwUiVSuS+vvj36EXB/r0A5G7eiFfL1qgiIpHIZAQ/NoTiY0dv\nmgW2Z82xdIa0jcBTLSfMR03/pqGsOnLjSlqPhkF2yzhJJSicpEgkIJdJkTvQbNulW09+/XkhhQX5\nXL50iTW/r6Bbzz52yz7QvSerVizjSnoaebk5LF38E1179rYsTzh7BpPJREF+Pp9N/ZAefR/E1c18\ng7uYfAGtVovBYGDDutWcPX0SgpreMLZVBy/zTFxtvFwUhPu58Eirmqzcd+MX2vo0DWHFPvvdRPo2\nC2Xb6UyKqtmErFY6E9fifmYsWkmZ3sCW/Uc5f/EycS1sb9jfLlnFtgPHmf3+G6jsZG70BgNlBgMm\nk/l3vaH6DzJgfmB4umMUni4KwnxdGNgyjN8P3Lh/aO/GIfx+3QuBUQGuRNdwQyIBd5Wc0Q/Gsvtc\nNgY7/WP/HxKpFCdnZ6QyKTK5E04Kxd/WN/HApQI6R/viopDh56qgdU1v9l20HS5QZ6xg3NrTTN2c\nwNTNCfx8+DJ5Gj1TNyVY+geHeJqzpK4KGY82DmFPSr5V1wZH7ErOpWdMAK7OTgS4OdOxlh87Lth/\nf+C+Gu64Xm0ZCvdW06WOP4evVszCvVRE+7laumQMahxCqb6c9GpUzFrHdWfdb4soLiwgIy2Vbet+\nt+ni8JdTRw4w7/OpvDbpY4LCalotu5h4jnMnjmI0GinTavl1zkxcXN2oEeZYckPtrKBTozrM/GML\nZQYDW4+eIzEti07317Ep+93qbWw/kcC3rw1BdV2muKBEw+Vs8/82MS2LT35dz4t9OzoUw804eh4B\nxAS44nK1q1aIp4oOUT6cuIUsMFy93jS5jxm/rTNfbw6dJDH1CnFN69uUnbV8PduPnOa7McNtjs2B\n04lk5JrPnYtXspi9ciNxTR0fOUi4t9z1mWC9Xk9OTg7l5eXk5uayfft2Zs+eTVxcHA8+aDvs0Nat\nW1mzZg29evWiZs2amEwm4uPj2bFjR7WywLdDw859KMhMZ96oocjkcpr3HmwZ4aE4N4sFY17gqWnf\n4+btR96VVLYumkWZphSVqzvRzdvTZsBTAITVa0TTHgNZPn0sutJiPPwC6fXyOBQq+8339lyc+SWR\nb71D499Wos/OJnHKJMpLS/HpGEeNQY9xcsRzAKT/tIDwl16l0U+/Uq4pJWvtavK2xgOgu5xKysyv\niJ74ATIXF4pPneTCp9Nu6dj8tu8SYd5qfn+jA3pjBfO2JXHo6gsVAR5Klr7SjgFf7iDr6o2uaYQ3\nzk5SdiXY9tH+ZmgzmtT0xgTMfNo8xFnvT7aSUVj1TbLvgPAtDD8AACAASURBVIdJu5zKkIf7IZcr\neOypoTRqYq6cZmVm8MyjDzP/l6X4+QfQsnVbkvs/zIvPDMFUYaJXv/50793Xsq0vp0/lYnIyzkol\n3Xr14ZlhL1mW7du9i0UL52M0GKh3331M/exrnpxj+yb4tRbtSCbM14X1Ex5Ab6xg9oYE9l8dpzPQ\nU8WasXH0nLKZzALz39eiti/OchnbT9t/4a53k5BqvRB3rQnDn+CdL+bS6rGRBPp68/nbI3B3VbN6\n615mL13DHzM+AODrn1eikDvR+ZlRmDAhQcL7Lz1Jrw4tAWg0YDgSiTm712jAcIL9fdg4x/F+7X/5\nZfdFwnxdWDumEwZjBd9vTuRAkrliFeip5PfRHen70VYyr/7vm9fyQeEkZccZ6xePfNycmTCwAf7u\nSkrLjOxJyGbs4qO3dIxupOf4kfSa+KqlSaXH2JdYMHQU+360Hdnm/7XjQi5+rgomdquDscLEhnNZ\nnL/aB9xTJWdcl2jLGK4lZZUZVI2+HJMJSq7Jqg5qFESguxJDeQV7L+az+tQtdEFKyCbAzZlP+tbH\nUGFi1ckrnL06PJq3Ws603vUtYwHXr+HOsNYRKJyk5Gv0rDp1hf2XzBUvmVTKkGah+Ls6Y6wwkZyr\n4ZP4hGr1jYzr05/M9MuMHvoIcrmc3oOfJKZhYwByszIZ+8JjTP1+Md5+/qxatABNaQnTRr9sbhSQ\nmCvRT70yCqPRyM/ffk5WehpOcjkR0TG8OfkzZDLHb7XjHu/FuHkraPfqRwR4e/DJ8EdwV6tYs+84\nc9buYMX75uvHzN+3oHCS0e3tzy3d/yYM6UPPFveRV1zKyK8XkV1Ygr+nG8N6d6B1bK2b7Nkx1TmP\n6vq7MaRpKAqZlAKdgQ3nsjmS5vgQjNcb/8xAxn77M22eH0ugjyefvjYUdxc1q3ceZM7vm1g5/R0A\nZvy2DoWTE11GvmcZC3jic4Po1aYJp5JTeXvGQoo1OnzcXenbvhlP30NdIW7vY/q9T2K6i3PnY8aM\nYeXKlQCWL8uoW7cuffr0oV+/fpZynTt35qmnnuLJJ58kNTWV77//ngMHDpCRkYFCoSA8PJzHHnvM\nap3b4bt91etj+Xe6f+LQmxf6Bw1rUv2X5f5Oq99qf6dDsIibsPFOh2DlzMibj8rxT7lv1q29Wf53\nafe5Y19u8k9wWnprX0rzdynUOj5SxD/hpXb/ni9DaHxpw80L/YPeyHL8i5X+bl9EOj6k3D/B6f47\nO4pUdWUUOv5y860K9HBs5Km7wV2dCZ46dapD2dvNmzdbfg8NDWXSpEl/Z1iCIAiCIAj/uLs3rXln\n3NWVYEEQBEEQBMFMDJFWPffki3GCIAiCIAiCcCMiEywIgiAIgnAPuItf87ojRCZYEARBEARB+M8R\nmWBBEARBEIR7gBgirXpEJlgQBEEQBEH4zxGZYEEQBEEQhHuA6BJcPSITLAiCIAiCIPzniEywIAiC\nIAjCPaBCpIKrRWSCBUEQBEEQhP8ckQkWBEEQBEG4B4g8cPWITLAgCIIgCILwnyMywYIgCIIgCPeA\nCpEKrhZRCf4btf5ixJ0OwSLxk5/vdAhWFn067E6HYMVb1elOh2CxLO/7Ox2CleMu/55zZ1vND+50\nCFbeW/rHnQ7Bwjiw750OwUqsWn6nQ7AS9WHvOx2CxbT6w+90CFbGJXx1p0OwmBsy+k6HYOXfdacS\nbjdRCRYEQRAEQbgHiMEhqkf0CRYEQRAEQRD+c0QmWBAEQRAE4R5QIcaHqBaRCRYEQRAEQRD+c0Qm\nWBAEQRAE4R4g+gRXj8gEC4IgCIIgCP85IhMsCIIgCIJwDxDjBFePyAQLgiAIgiAI/zkiEywIgiAI\ngnAPEH2Cq0dkggVBEARBEIT/nLuiEjxmzBjq1q1LTEwM9evXp3PnzkyfPh29Xm9TdsKECdSrV4/1\n69fbLJsxY4ZlO7GxsbRs2ZInnniCBQsW2N2WIAiCIAjC3aIC09/+cy+5a7pDtG/fnmnTpmEwGDh5\n8iRvv/02UqmUN99801JGp9Oxdu1ann/+eZYuXUq3bt1stlO7dm0WLFhAeXk5BQUF7N+/n2+++Ybf\nf/+dn376CbVa/Y/8PTI3d0KGv4FLvfsw5GaTPv9bSk8ds1vWs/0D+PcbhJOnF4bcbFI+fg9DdiZu\njZri128wypAwKnQ6CvZsI2PRfDBVVDsek8nE6vkzOLR1PXK5gg4PPUrb3g/bLXv6wC7W/fgdRfm5\nOCtVNGwb9z/27ju8qeoN4Pg3uxndLR3QSSlI2bKnsqFQEBRwoOICJ7hFVEQR/QmoqKi4ERzIlClT\npihTKbMUaOledCdp5u+PYEpICm0dCJ7P8/R5mnvfe+/b05vk5L3nnjD4zgeRSCQALJ/3FqmH9nMu\nL5v7p71DbELrOudzIZnOm7B7J6Ft1gLzuUJyF85Df+yQx1jfbr0JHHILcl9/LOcKyXjnVcyFeZc9\nht1uZ+bMmaxauRKVSsXd48Zxxx131Bj/+WefsWDBAux2O8OHD2fS44871x0+fJhXpk0jIyODhIQE\nXp0+nbCwMABGjhhBbm6u85hVVVWMGj2aZ5991mX/01OyOVSu5/vr4y5qCx8aTngc7XUtMRcVkvPl\nB1Qe9dwWfj37Epw0ynnepM+ahrkgD5mPLw3vm4gmLh6Ztw9HxiZdtn0uxW63M3/u22zfsBaFUknS\nmLEk3nyrx9ht69ewbtkicrMz0Xn70G/oCIbdeqdz/adv/4/kA3vIy87ipbc+pHnrtrXOQ+Klxbvf\nGBQNG2OtKKFy6zLMmak1xku9/fG/4xmqTuynYsuS6uV+Qeh6jUAeFgVmE/o9GzEm/1zrPC42slUY\nnaL8MVvtbEwpYGtq4aX/DmBy3ybIpVJe2XDCubxVuA9DE0LxUytIO6fn6/2ZlBjM9c7rYj3G3073\n+8fQsGVT1k5/n7WvvvuX7fti6kB/Euf9j8geHSnLzGXjEy+Tvu0Xtzjv8FAGzJlGoy7XYyguZetL\nMzmx/EcANMEBDJo7g/AOrVEH+vOmT7N65yPx0uI94FaUjeKwlpdQ8dMSzBmXOHd8/Am48zmMx/dT\nsel753KZXzC6G0cgD4/Gbjah/2UDxkO76pyP3W5n/9JPOfPrFqQKJQn9RtDsxmGX3MZms7L29UnY\nLGaSpn7ktv7IhiX8tmoB/R9/g+DY62qdi0StxW/IWJSR8djKiindsAhTekqN8TLfAILvfxHD4T2U\n/vitc7mu+2A0rbogUaowHD9I2fpF9X6v2vr1RxzduRG5QkmHxFG0GzjCY+ypA7vZsehTKkqKUHip\nadb5RnqOud/5XnXil638vOwr9GXF+IU24sbbHyS8SfM65yRcva6aTrBSqSQgIACAkJAQunbtyq5d\nu1w6wevWrSMuLo7777+fHj16kJeXR0hIiMt+5HK5cz/BwcE0adKELl26MGzYMD755BMmTpz4j/w9\n4fc8jKXkHMfuH42uVTsiJz7HiUn3YdNXusR5t+1A0KBhpM18GVNOFooGoVgrygGQemnIW7IQ/fHD\nSL3URD3xIsFDR1KwcnGd8/ll/Q+cOXqIp+d+jaGinI9fmkRYVByNW7p3QhrFNWP8q3PQ+fpjrKxg\n4cyX+HX9SjoPdLxIh8c0oXX3Piz94M16tIy70LEPYik9R8qjt6NNaEvDB5/h1LPjsRlc20rXqj0B\n/ZLInDMdU24WiuAQrJXltTrG999/z4H9+1m1ejVlZWXcd++9NI2Pp0PHjm6xO3bsYPHixSz8+mvU\nXl6MHz+e6JgYhg8fjtls5qknn2TCgw+SmJjIvHnzmPL883z+xRcALF22zLkfs9lMn9696devn8v+\n9xRXYLTZkHjIM3zcQ1hKznF8/Bh0LdsR8dhzpDxxv9t5o2vTgcABSaTPnuY4b4Krzxtsdsp/28O5\njauIemZardrnUjasXMqxQ78xZ8FSKivKmPb4g0Q1bkKLtu3dYs1mE/dMfJq4ps05V1jAa89OJCgk\nlG69+wMQ3SSerr37M2/W9DrnobtxBLbKMoo+eRFlZFO8B91J8fwZ2E1Gj/HaHklY8jNdF8pk+Cbd\nT+XudZhWfgJyBVKtT51z+UOP2EDignRMW38CtULGxJ6xZJUaOFlQWeM2veKC0Jus+HhVX6gL1im5\n4/oI5u48zdliA/2bNuDujpG8s+1UvXO7WGl2Hqunvk2H2y7d2for9H/7ZSpy85kT0ZGYPt0Z9tUc\n5rXqS1Wp6/N16GezyN73O0tGTaBBy2aMXvkF+cnHKU5Nw26zc+rHreyft5BRyz/9U/no+tyMrbKM\nwo+moIxqhk/iXZz74jXsVZ7PHV3P4Z7PnZvup3LXOqpWfAxyBTKtb73yObljHfmpR0iaOg+ToYKN\nc6bg1zCG0PhWNW6Tsm01So0WY1mJ2zp9SRHp+3eg9g2ocy6+A8Zgqygj751nUMU0w3/4veR/9DL2\nKoPHeJ8+IzHnnnVZpm7ZGa+mbSicPxO7yYjfsHHoug+iYseaOufz++ZVZJ1I5p6ZX2LUl7N4xtME\nRcYS2byNW2xIbDyjpsxC4+NPlb6SVe++wqEtq2ndZyiVpcX8+MksRjz1GhHXtebQT2tZ9f6rjJ/z\nrYejXj3EmOC6uSqGQ1wsJSWFAwcOoFQqXZYvXbqUYcOGodPp6NGjB8su6HBcSmxsLD179mTjxo1/\nR7puJCoVPtd3Jm/xAuwWC+UH9mA8m4ZP+85usQ1uupWcBZ9gyskCwJyfi82gB6D0l+1UHv4Nu8WC\ntaKc4p2b0TSpXzXk4PaN9Bw2Gq23L0FhjejYbwgHtrkPKQHw8Q9E5+sPgM1uRyKVUpSX5Vzfqf9Q\nYhNaI5XJ6pXLhSRKFbq2nShc/g12i4WK3/dSlZmGd7tObrFBSaPJ++4zTLnn26ogz9lWl7NmzRru\nvOsu/Pz8iIyMZMTIkaxatarG2JE330zDhg0JCAxk7J13svp87N49e1AqlQwfPhyFQsF9993H0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OmEwmfvjhB8xmM59++ikJCQmEh4c7t09PT+f48eMMGjTIZb8/rFzJokWLeKt5BFOahCGVwOzm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\nJ0iFaXexdPUyDrawQNGsK9k7fsGmaskqqJW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K+svYb7WoyY87TtBrzmYKCnVU83Fl/MvN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8f777+PUqm01Mu1bduWxo0bM27cOA4cOEBcXBzHjx9n7ty5nD171hLP9evXuXDh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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fbf1a3516d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a datset of correlations between house features\n",
+    "corrmat = houses.corr()\n",
+    "\n",
+    "# Set up the matplotlib figure\n",
+    "f, ax = plt.subplots(figsize=(9, 6))\n",
+    "\n",
+    "# Draw the heatmap using seaborn\n",
+    "sns.set_context(\"notebook\", font_scale=0.7, rc={\"lines.linewidth\": 1.5})\n",
+    "sns.heatmap(corrmat, annot=True, square=True)\n",
+    "f.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.JointGrid at 0x7fbf175f15d0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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0gYtfMoqXHhjGo7+YwskZf81acEXXwNiwg6JrtbV92h4MRkREp1k+3LRZ74NS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\nCAAAIEUwAgAASP0/SIcBFuu07esAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fbf177981d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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HtS04lgk/kKiGcSpjiuF/DJVD+GGM5S0ecp6dytgAYAiBrGvBsRTKfoyFcjyP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\nhiYiIiIiE/4/uaZc8TIBg+gAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fbf175f1450>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.jointplot(houses['target'], houses['RM'], kind='hex')\n",
+    "sns.jointplot(houses['target'], houses['LSTAT'], kind='hex')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('Training set', (354, 13), (354,))\n",
+      "('Test set', (152, 13), (152,))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# convert housing data to numpy format\n",
+    "houses_array = houses.as_matrix().astype(float)\n",
+    "\n",
+    "# split data into feature and target sets\n",
+    "X = houses_array[:, :-1]\n",
+    "y = houses_array[:, -1]\n",
+    "\n",
+    "# normalize the data per feature by dividing by the maximum value in each column\n",
+    "X = X / X.max(axis=0)\n",
+    "\n",
+    "# split data into training and test sets\n",
+    "trainingSplit = int(.7 * houses_array.shape[0])\n",
+    "\n",
+    "X_train = X[:trainingSplit]\n",
+    "y_train = y[:trainingSplit]\n",
+    "X_test = X[trainingSplit:]\n",
+    "y_test = y[trainingSplit:]\n",
+    "\n",
+    "print('Training set', X_train.shape, y_train.shape)\n",
+    "print('Test set', X_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# helper variables\n",
+    "num_samples = X_train.shape[0]\n",
+    "num_features = X_train.shape[1]\n",
+    "num_outputs = 1\n",
+    "\n",
+    "# Hyper-parameters\n",
+    "batch_size = 50\n",
+    "num_hidden_1 = 30\n",
+    "num_hidden_2 = 30\n",
+    "learning_rate = 0.0001\n",
+    "training_epochs = 200\n",
+    "dropout_keep_prob = 0.2 # this seems to be a typical value for dropout???\n",
+    "\n",
+    "# variable to control the resolution at which the training results are stored\n",
+    "display_step = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def accuracy(predictions, targets):\n",
+    "    error = np.absolute(predictions.reshape(-1) - targets)\n",
+    "    return np.mean(error)\n",
+    "\n",
+    "def weight_variable(shape):\n",
+    "    initial = tf.truncated_normal(shape, stddev=0.1) # creates trunc gauss dist of weights\n",
+    "    return tf.Variable(initial)\n",
+    "\n",
+    "def bias_variable(shape):\n",
+    "    initial = tf.constant(0.1, shape=shape) # starts with a bias of 0.1\n",
+    "    return tf.Variable(initial)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "'''First we create a variable to store our graph'''\n",
+    "graph = tf.Graph()\n",
+    "\n",
+    "'''Next we build our neural network within this graph variable'''\n",
+    "with graph.as_default():\n",
+    "    \n",
+    "    '''Our training data will come in as x feature data and \n",
+    "    y target data. We need to create tensorflow placeholders \n",
+    "    to capture this data as it comes in'''\n",
+    "    \n",
+    "    x = tf.placeholder(tf.float32, shape=(None, num_features))\n",
+    "    _y = tf.placeholder(tf.float32, shape=(None))\n",
+    "    \n",
+    "    '''Another placeholder stores the hyperparameter \n",
+    "    that controls dropout'''\n",
+    "    \n",
+    "    keep_prob = tf.placeholder(tf.float32)\n",
+    "    \n",
+    "    '''Finally, we convert the test and train feature data sets \n",
+    "    to tensorflow constants so we can use them to generate \n",
+    "    predictions on both data sets'''\n",
+    "    \n",
+    "    tf_X_test = tf.constant(X_test, dtype=tf.float32)\n",
+    "    tf_X_train = tf.constant(X_train, dtype=tf.float32)\n",
+    "    \n",
+    "    '''Next we create the parameter variables for the model.\n",
+    "    Each layer of the neural network needs it's own weight \n",
+    "    and bias variables which will be tuned during training.\n",
+    "    The sizes of the parameter variables are determined by \n",
+    "    the number of neurons in each layer.'''\n",
+    "    \n",
+    "    W_fc1 = weight_variable([num_features, num_hidden_1])\n",
+    "    b_fc1 = bias_variable([num_hidden_1])\n",
+    "    \n",
+    "    W_fc2 = weight_variable([num_hidden_1, num_hidden_2])\n",
+    "    b_fc2 = bias_variable([num_hidden_2])\n",
+    "    \n",
+    "    W_fc3 = weight_variable([num_hidden_2, num_outputs])\n",
+    "    b_fc3 = bias_variable([num_outputs])\n",
+    "    \n",
+    "    '''Next, we define the forward computation of the model.\n",
+    "    We do this by defining a function model() which takes in \n",
+    "    a set of input data, and performs computations through \n",
+    "    the network until it generates the output.'''\n",
+    "    \n",
+    "    def model(data, keep):\n",
+    "        \n",
+    "        # computing first hidden layer from input, using relu activation function\n",
+    "        fc1 = tf.nn.sigmoid(tf.matmul(data, W_fc1) + b_fc1)\n",
+    "        # adding dropout to first hidden layer\n",
+    "        fc1_drop = tf.nn.dropout(fc1, keep)\n",
+    "        \n",
+    "        # computing second hidden layer from first hidden layer, using relu activation function\n",
+    "        fc2 = tf.nn.sigmoid(tf.matmul(fc1_drop, W_fc2) + b_fc2)\n",
+    "        # adding dropout to second hidden layer\n",
+    "        fc2_drop = tf.nn.dropout(fc2, keep)\n",
+    "        \n",
+    "        # computing output layer from second hidden layer\n",
+    "        # the output is a single neuron which is directly interpreted as the prediction of the target value\n",
+    "        fc3 = tf.matmul(fc2_drop, W_fc3) + b_fc3\n",
+    "        \n",
+    "        # the output is returned from the function\n",
+    "        return fc3\n",
+    "    \n",
+    "    '''Next we define a few calls to the model() function which \n",
+    "    will return predictions for the current batch input data (x),\n",
+    "    as well as the entire test and train feature set'''\n",
+    "    \n",
+    "    prediction = model(x, keep_prob)\n",
+    "    test_prediction = model(tf_X_test, 1.0)\n",
+    "    train_prediction = model(tf_X_train, 1.0)\n",
+    "    \n",
+    "    '''Finally, we define the loss and optimization functions \n",
+    "    which control how the model is trained.\n",
+    "    \n",
+    "    For the loss we will use the basic mean square error (MSE) function,\n",
+    "    which tries to minimize the MSE between the predicted values and the \n",
+    "    real values (_y) of the input dataset.\n",
+    "    \n",
+    "    For the optimization function we will use basic Gradient Descent (SGD)\n",
+    "    which will minimize the loss using the specified learning rate.'''\n",
+    "    \n",
+    "    loss = tf.reduce_mean(tf.square(tf.sub(prediction, _y)))\n",
+    "    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)\n",
+    "    \n",
+    "    '''We also create a saver variable which will allow us to \n",
+    "    save our trained model for later use'''\n",
+    "    \n",
+    "    saver = tf.train.Saver()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initialized\n",
+      "Model saved in file: model_houses.ckpt\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create an array to store the results of the optimization at each epoch\n",
+    "results = []\n",
+    "\n",
+    "'''First we open a session of Tensorflow using our graph as the base. \n",
+    "While this session is active all the parameter values will be stored, \n",
+    "and each step of training will be using the same model.'''\n",
+    "with tf.Session(graph=graph) as session:\n",
+    "    \n",
+    "    '''After we start a new session we first need to\n",
+    "    initialize the values of all the variables.'''\n",
+    "    tf.initialize_all_variables().run() # I don't understand what variables are being initialized...\n",
+    "    print('Initialized')\n",
+    "\n",
+    "    '''Now we iterate through each training epoch based on the hyper-parameter set above.\n",
+    "    Each epoch represents a single pass through all the training data.\n",
+    "    The total number of training steps is determined by the number of epochs and \n",
+    "    the size of mini-batches relative to the size of the entire training set.'''\n",
+    "    for epoch in range(training_epochs):\n",
+    "        \n",
+    "        '''At the beginning of each epoch, we create a set of shuffled indexes \n",
+    "        so that we are using the training data in a different order each time'''\n",
+    "        indexes = range(num_samples)\n",
+    "        random.shuffle(indexes)\n",
+    "        \n",
+    "        '''Next we step through each mini-batch in the training set'''\n",
+    "        for step in range(int(math.floor(num_samples/float(batch_size)))):\n",
+    "            offset = step * batch_size\n",
+    "            \n",
+    "            '''We subset the feature and target training sets to create each mini-batch'''\n",
+    "            batch_data = X_train[indexes[offset:(offset + batch_size)]]\n",
+    "            batch_labels = y_train[indexes[offset:(offset + batch_size)]]\n",
+    "\n",
+    "            '''Then, we create a 'feed dictionary' that will feed this data, \n",
+    "            along with any other hyper-parameters such as the dropout probability,\n",
+    "            to the model'''\n",
+    "            feed_dict = {x : batch_data, _y : batch_labels, keep_prob: dropout_keep_prob}\n",
+    "            \n",
+    "            '''Finally, we call the session's run() function, which will feed in \n",
+    "            the current training data, and execute portions of the graph as necessary \n",
+    "            to return the data we ask for.\n",
+    "            \n",
+    "            The first argument of the run() function is a list specifying the \n",
+    "            model variables we want it to compute and return from the function. \n",
+    "            The most important is 'optimizer' which triggers all calculations necessary \n",
+    "            to perform one training step. We also include 'loss' and 'prediction' \n",
+    "            because we want these as ouputs from the function so we can keep \n",
+    "            track of the training process.\n",
+    "            \n",
+    "            The second argument specifies the feed dictionary that contains \n",
+    "            all the data we want to pass into the model at each training step.'''\n",
+    "            _, l, p = session.run([optimizer, loss, prediction], feed_dict=feed_dict)\n",
+    "\n",
+    "        '''At the end of each epoch, we will calculate the error of predictions \n",
+    "        on the full training and test data set. We will then store the epoch number, \n",
+    "        along with the mini-batch, training, and test accuracies to the 'results' array \n",
+    "        so we can visualize the training process later. How often we save the data to \n",
+    "        this array is specified by the display_step variable created above''' \n",
+    "        if (epoch % display_step == 0):\n",
+    "            batch_acc = accuracy(p, batch_labels)\n",
+    "            train_acc =  accuracy(train_prediction.eval(session=session), y_train)\n",
+    "            test_acc =  accuracy(test_prediction.eval(session=session), y_test)\n",
+    "            results.append([epoch, batch_acc, train_acc, test_acc])\n",
+    "\n",
+    "    '''Once training is complete, we will save the trained model so that we can use it later'''\n",
+    "    save_path = saver.save(session, \"model_houses.ckpt\")\n",
+    "    print(\"Model saved in file: %s\" % save_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Minimum test loss: 5.53870993539\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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TYyYR7deKokorz/7wOpW2yhpj/H0t3DH6PF58aAjndYygvMLGgs+2N3rtIiIiIiLu0LVr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\n/R2sRjP0kymTBp1aiyuPuwxFSfl4fe/72FS/Ew19Lbj+hN8i3Tz09y0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W03De6iJFd1Yg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\nyChQMEUIIYQQQggho/D/AfTZvxCSm3f4AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fbf15a2d510>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(data=results, columns = [\"epoch\", \"batch_acc\", \"train_acc\", \"test_acc\"])\n",
+    "df.set_index(\"epoch\", drop=True, inplace=True)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(10, 4))\n",
+    " \n",
+    "ax.plot(df)\n",
+    "ax.set(xlabel='Epoch',\n",
+    "       ylabel='Error',\n",
+    "       title='Training result')\n",
+    " \n",
+    "ax.legend(df.columns, loc=1)\n",
+    "\n",
+    "print \"Minimum test loss:\", np.min(df[\"test_acc\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From 2e96088208432a160064b52a01d21dc3cdc75d6e Mon Sep 17 00:00:00 2001
From: HuaxiaWu <hw2560@columbia.edu>
Date: Sun, 18 Dec 2016 22:25:03 -0500
Subject: [PATCH 07/15] Lab4_2_hw2560

Lab4-2
---
 notebooks/week-4/Lab4_2.ipynb | 380 ++++++++++++++++++++++++++++++++++
 1 file changed, 380 insertions(+)
 create mode 100644 notebooks/week-4/Lab4_2.ipynb

diff --git a/notebooks/week-4/Lab4_2.ipynb b/notebooks/week-4/Lab4_2.ipynb
new file mode 100644
index 0000000..72c39d6
--- /dev/null
+++ b/notebooks/week-4/Lab4_2.ipynb
@@ -0,0 +1,380 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import math\n",
+    "import random\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.datasets import load_boston\n",
+    "\n",
+    "'''Since this is a classification problem, we will need to \n",
+    "represent our targets as one-hot encoding vectors (see previous lab).\n",
+    "To do this we will use scikit-learn's OneHotEncoder module \n",
+    "which we import here'''\n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "sns.set(style=\"ticks\", color_codes=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "dataset = load_boston()\n",
+    "houses = pd.DataFrame(dataset.data, columns=dataset.feature_names)\n",
+    "averageValue = np.mean(dataset.target)\n",
+    "houses['target'] = dataset.target.astype(int)\n",
+    "l_target = len(dataset.target)\n",
+    "\n",
+    "# loop over target values and make binary based on average value\n",
+    "j = 0\n",
+    "while j < l_target:\n",
+    "     if dataset.target[j] > averageValue:\n",
+    "         houses.target.set_value(j, 1)\n",
+    "     else:\n",
+    "         houses.target.set_value(j, 0)\n",
+    "     j += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 <-- should be 1\n",
+      "0 <-- should be 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "'''check your work'''\n",
+    "# print houses['target']\n",
+    "# print averageValue\n",
+    "print np.max(houses['target']), \"<-- should be 1\"\n",
+    "print np.min(houses['target']), \"<-- should be 0\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('Training set', (354, 13), (354, 2))\n",
+      "('Test set', (152, 13), (152, 2))\n"
+     ]
+    }
+   ],
+   "source": [
+    "houses_array = houses.as_matrix().astype(float)\n",
+    "np.random.shuffle(houses_array)\n",
+    "\n",
+    "X = houses_array[:, :-1]\n",
+    "y = houses_array[:, -1]\n",
+    "\n",
+    "\n",
+    "y = y.reshape(-1,1)\n",
+    "\n",
+    "enc = OneHotEncoder()\n",
+    "\n",
+    "enc.fit(y)\n",
+    "\n",
+    "y = enc.transform(y).toarray()\n",
+    "X = X / X.max(axis=0)\n",
+    "\n",
+    "trainingSplit = int(.7 * houses_array.shape[0])\n",
+    "X_train = X[:trainingSplit]\n",
+    "y_train = y[:trainingSplit]\n",
+    "X_test = X[trainingSplit:]\n",
+    "y_test = y[trainingSplit:]\n",
+    "\n",
+    "print('Training set', X_train.shape, y_train.shape)\n",
+    "print('Test set', X_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 <-- should be 2\n",
+      "2 <-- should be 2\n",
+      "[ 1.  0.] <-- should be either [0. 1.] or [1. 0.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "'''check your work'''\n",
+    "print y_train.shape[1], \"<-- should be 2\"\n",
+    "print y_test.shape[1], \"<-- should be 2\"\n",
+    "print y_train[0], \"<-- should be either [0. 1.] or [1. 0.]\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# helper variables\n",
+    "num_samples = X_train.shape[0]\n",
+    "num_features = X_train.shape[1]\n",
+    "num_outputs = y_train.shape[1]\n",
+    "\n",
+    "# Hyper-parameters\n",
+    "batch_size = 15\n",
+    "num_hidden_1 = 35\n",
+    "num_hidden_2 =  35\n",
+    "learning_rate =  0.08\n",
+    "training_epochs =  400\n",
+    "dropout_keep_prob = 1 # 0.5 # set to no dropout by default\n",
+    "\n",
+    "# variable to control the resolution at which the training results are stored\n",
+    "display_step = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def accuracy(predictions, targets):\n",
+    "      \n",
+    "    accuracy = (np.sum((np.argmax(predictions, 1) == np.argmax(targets,1)  ))/float(np.argmax(predictions, 1).shape[0]))*100.0\n",
+    "    \n",
+    "    return accuracy\n",
+    "\n",
+    "def weight_variable(shape):\n",
+    "    initial = tf.truncated_normal(shape, stddev=0.1)\n",
+    "    return tf.Variable(initial)\n",
+    "\n",
+    "def bias_variable(shape):\n",
+    "    initial = tf.constant(0.1, shape=shape)\n",
+    "    return tf.Variable(initial)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "graph = tf.Graph()\n",
+    "\n",
+    "with graph.as_default():\n",
+    "    \n",
+    "    x = tf.placeholder(tf.float32, shape=(None, num_features))\n",
+    "    _y = tf.placeholder(tf.float32, shape=(None))\n",
+    "    \n",
+    "    keep_prob = tf.placeholder(tf.float32)\n",
+    "    \n",
+    "    tf_X_test = tf.constant(X_test, dtype=tf.float32)\n",
+    "    tf_X_train = tf.constant(X_train, dtype=tf.float32)\n",
+    "    \n",
+    "    \n",
+    "    W_fc1 = weight_variable([num_features, num_hidden_1])\n",
+    "    b_fc1 = bias_variable([num_hidden_1])\n",
+    "    \n",
+    "    W_fc2 = weight_variable([num_hidden_1, num_hidden_2])\n",
+    "    b_fc2 = bias_variable([num_hidden_2])\n",
+    "    \n",
+    "    W_fc3 = weight_variable([num_hidden_2, num_outputs])\n",
+    "    b_fc3 = bias_variable([num_outputs])\n",
+    "    \n",
+    "    \n",
+    "    def model(data, keep):\n",
+    "        \n",
+    "        fc1 = tf.nn.sigmoid(tf.matmul(data, W_fc1) + b_fc1)\n",
+    "        fc1_drop = tf.nn.dropout(fc1, keep)\n",
+    "        \n",
+    "        fc2 = tf.nn.sigmoid(tf.matmul(fc1_drop, W_fc2) + b_fc2)\n",
+    "        fc2_drop = tf.nn.dropout(fc2, keep)\n",
+    "        \n",
+    "        fc3 = tf.matmul(fc2_drop, W_fc3) + b_fc3\n",
+    "        \n",
+    "        return fc3\n",
+    "    \n",
+    "    '''for our loss function we still want to get the raw outputs \n",
+    "    of the model, but since it no longer represents the actual prediction \n",
+    "    we rename the variable to ‘output’'''\n",
+    "    output = model(x, keep_prob)\n",
+    "    \n",
+    "    # WHEN WE CALCULATE THE PREDICTIONS, WE NEED TO WRAP EACH OUTPUT IN A\n",
+    "    # tf.nn.softmax() FUNCTION. THE FIRST ONE HAS BEEN DONE FOR YOU:\n",
+    "    prediction = tf.nn.softmax(output)\n",
+    "    test_prediction = model(tf_X_test, 1.0)\n",
+    "    train_prediction = model(tf_X_train, 1.0)\n",
+    "    \n",
+    "    '''finally, we replace our previous MSE cost function with the\n",
+    "    cross-entropy function included in Tensorflow. This function takes in the\n",
+    "    raw output of the network and calculates the average loss with the target'''\n",
+    "    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(output, _y))\n",
+    "    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)\n",
+    "    \n",
+    "    saver = tf.train.Saver()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initialized\n",
+      "Model saved in file: model_houses_classification.ckpt\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = []\n",
+    "\n",
+    "with tf.Session(graph=graph) as session:\n",
+    "    \n",
+    "    tf.initialize_all_variables().run()\n",
+    "    print('Initialized')\n",
+    "\n",
+    "    for epoch in range(training_epochs):\n",
+    "        \n",
+    "        indexes = range(num_samples)\n",
+    "        random.shuffle(indexes)\n",
+    "        \n",
+    "        for step in range(int(math.floor(num_samples/float(batch_size)))):\n",
+    "            offset = step * batch_size\n",
+    "            \n",
+    "            batch_data = X_train[indexes[offset:(offset + batch_size)]]\n",
+    "            batch_labels = y_train[indexes[offset:(offset + batch_size)]]\n",
+    "\n",
+    "            feed_dict = {x : batch_data, _y : batch_labels, keep_prob: dropout_keep_prob}\n",
+    "            \n",
+    "            _, l, p = session.run([optimizer, loss, prediction], feed_dict=feed_dict)\n",
+    "\n",
+    "        if (epoch % display_step == 0):\n",
+    "            batch_acc = accuracy(p, batch_labels)\n",
+    "            train_acc =  accuracy(train_prediction.eval(session=session), y_train)\n",
+    "            test_acc =  accuracy(test_prediction.eval(session=session), y_test)\n",
+    "            results.append([epoch, batch_acc, train_acc, test_acc])\n",
+    "\n",
+    "    save_path = saver.save(session, \"model_houses_classification.ckpt\")\n",
+    "    print(\"Model saved in file: %s\" % save_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Maximum test accuracy: 86.18%\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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//POUlZXZl1/YftbxU5Tmn6Iq5wChAZ7ccMMNBAQEsGjRIn7++Wdyc3P59ttv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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f61ec1c6ad0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(data=results, columns = [\"epoch\", \"batch_acc\", \"train_acc\", \"test_acc\"])\n",
+    "df.set_index(\"epoch\", drop=True, inplace=True)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(10, 4))\n",
+    " \n",
+    "ax.plot(df)\n",
+    "ax.set(xlabel='Epoch',\n",
+    "       ylabel='Error',\n",
+    "       title='Training result')\n",
+    " \n",
+    "ax.legend(df.columns, loc=1)\n",
+    "\n",
+    "print \"Maximum test accuracy: %.2f%%\" % np.max(df[\"test_acc\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From 06211dffee65700b9de8363b949109513fdb51f4 Mon Sep 17 00:00:00 2001
From: HuaxiaWu <hw2560@columbia.edu>
Date: Sun, 18 Dec 2016 22:30:46 -0500
Subject: [PATCH 08/15] Lab4_2_hw2560

4_2
---
 notebooks/week-4/Lab4_2.ipynb | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/notebooks/week-4/Lab4_2.ipynb b/notebooks/week-4/Lab4_2.ipynb
index 72c39d6..f5e8221 100644
--- a/notebooks/week-4/Lab4_2.ipynb
+++ b/notebooks/week-4/Lab4_2.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 121,
    "metadata": {
     "collapsed": true
    },
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 122,
    "metadata": {
     "collapsed": true
    },
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 123,
    "metadata": {
     "collapsed": false
    },
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 124,
    "metadata": {
     "collapsed": false
    },
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 125,
    "metadata": {
     "collapsed": false
    },
@@ -146,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 126,
    "metadata": {
     "collapsed": true
    },
@@ -171,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 127,
    "metadata": {
     "collapsed": true
    },
@@ -194,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 128,
    "metadata": {
     "collapsed": true
    },
@@ -257,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 129,
    "metadata": {
     "collapsed": false
    },
@@ -306,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 130,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -316,14 +316,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Maximum test accuracy: 86.18%\n"
+      "Maximum test accuracy: 91.45%\n"
      ]
     },
     {
      "data": {
-      "image/png": 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//POUlZXZl1/YftbxU5Tmn6Iq5wChAZ7ccMMNBAQEsGjRIn7++Wdyc3P59ttv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\nWg0YB/732GOPmX/+859N02QMBErzGjAG/KO2ttZ84oknzKNHj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Ntk0vN7sWMURABGqCIAjNnKk0P1w92XVV4W2qT3zdlIGaXm8wTwIMEH8LTxNQ\nNSCVJPjP6lgWv3zXLZcCub/KcWbnN9wNUktUNe0xOswbe9v6pUuZ5lLT6Q1kFZTh7+XY4G0UjHPV\n/bg5ATBeD9+f2h9FA2QWTP/oL5IySprVdU2SJHOa+10xrZnwwK3Zsw9gb2vN9JFRlm6GRYhHOoIg\nCM2cqeKji6MS5TWCAQ8XO3NqZEITV348lZhPiVpj/jkhuaDWCU1bssw8tflpfYfLT9bTc9Ws2hxv\nyWY1uPJKHUfPZpt/zipQW7A1lncxvdj8NL9PPdMeoXrlx5Qskf7YGPQGicWrY9HpjXPVTR8Z1SBB\nGlypqtucrmsZeWpKy7UAhAc2n14+oWGJQE0QBKGZM6U+1jbZdVVXbiiaNlCr2ssEUFBSSV5Rwwxw\nb06q9jK9MjqGqFDjOI5fdl64paZFOHI2G83ltEeAnMJy9HrDNda4tZnOu5VCRo+Oreq9vq+HA1YK\nY9Agxqk1jg17Es3Xvcf+FVYtOL5ZYYHGbIXmdF1LSC4w/39YlWwK4dYiAjVBEIRmzlRMpLbS/FWZ\nvrCTs0ooq9A2artM9AbJnPbYrrWL+fWElILaVmmxTAFpRJA7nq52TH20K3Y2CgyXUyC1ulsjmPln\n4G0wSOae3duNJEnsvTw+rWuo9w1ViVMo5OZ0RxGoNbyMvFK+33QWMF6DHmrgwjdVA6Hmcl0zpWG6\nOdng5Xb97wahZRKBmiAIQjNn7lFzrX18monphkKSjFXqmsLZpHyKVMZSyo8MCsXh8o2spaYJaCw5\nBWXmz9SU/ubtbs/TwzoCkJylYu32cxZrX0Op0Og4cjntsUOVwglZ+bdn+uOlzBIy84zH3rez7w1v\nx9TDkyICtQZlMEgsWROHRqtHIZcZUx4buFiLv5djs7uumdoR1sat2UwZMHDgQFasWGHpZtxSRKAm\nCILQjEmSZC4mUpcetZDWruZxGU11Q2HqfbFVKoiJ8DGnCTWXG5qGsv/klV6mPlVu2O/pFUSnEGPV\nxzXbzpGUUb+y4LpSNYWxcegvzxtkacfic6jU6AF4+K725tcbsuJaS2L6/VbIZfTsdOOBmqmgSFq2\nCr2heYxzuhX8efASpy4aqx+O+L9Q2vq5XGeN+pPLZc3qulZRqeNSZglwJd39Ro0ZM4YFCxY0RLP4\n+eefGTlyZINsSzASgZogCEIzpirTmscKedahR83GWkFbf+ONSlPcUBgMEvtPGNMeYyJ8sLFWmHv1\nLqQV3TJqnGiAAAAgAElEQVSpgHClPHtooCvebvbm1+VyGdNGdEVprTAXNKjLeK6K7BwSv17O4XHP\ncmbeOxx/cQbqS5caq/l1ZgpM3Jxs6BbhY+5JuF171Ezj0zq388TpJib9DmhlDNQ0OgO5hbdn0NvQ\ncgrK+G7jaQDatHLi0UGhjbav5nRdu5BWhOFysN8U49P0en2dlnNzc8PGxqaRW3N7EYGaIAhCM5Zf\nZQ61uhQTAQg3PflNafwKZQnJhRSUGMcu9e1iTAc03ThodYZ69y41V3lF5eYxITVNduzn6ciYIREA\nXEwr5n87L9S6LdX5CyQs+pijz00hc8NGDBXGz688PYMTr7xO1pZtFqssp9HqOXwmC4Dekb4o5DJa\neRiD0tuxRH9KVgmp2cY55Ey/3zeqWuVHkf540yRJ4tO1cZRX6pHL4PlRUVhbNd5t7Y1c18o05ZzP\nT2rwf/svnEHmUITCsQi5fVG198o05ddv2GWvv/46hw8fZsWKFYSHhxMREcEvv/xCeHg4u3fv5qGH\nHiIyMpJjx46RmprK5MmT6du3L1FRUTzyyCMcOHCg2vb+mfoYHh7O2rVrmTp1Kl27dmXw4MHs2LGj\nTm0zGAzMnj2bQYMG0aVLF+65554a0yrXrVvHsGHDiIyMpH///rz77rvm91QqFXPnzqVv37507tyZ\n++67j127dtX582kOWtQ8amq1mk8++YTt27eTn59Phw4dmDVrFpGRV+aOWLx4MWvXrkWlUhEdHc28\nefNo06aNBVstCIJw4/KK6jbZdVVhbdzYuC+J4lIN2QVltPJwaKzmmXtflNYKuoUbJw8NDaw+n1vV\nn1uq6mmPNd+w39c/mL3H00lILmTVlgR6dfI135xLBgOFR46Svv43Sk6fubKSXI5H7144Brcl5ac1\nGDQaLn62jJLTpwl57lkUdk1bJCA2IYfySuPTc1Ng4uNuz8W04tsy9XHf5d5iuQx63UTaIxiDeblc\nhsEgkZqlokeH+lePFK7YfjiF2HO5ADx4ZzvaBzTudaa+17UyTTlTNs5Gra174FQftsahsby1+2C1\n1x2s7fhs2HvYK69/7Zg9ezZJSUmEhobywgsvIEkS584Zx9l+/PHHzJw5k9atW+Pi4kJGRgZ33nkn\nL7/8MtbW1qxfv55Jkybx559/0qpV7b/LS5cu5ZVXXmHmzJmsWLGCGTNmsHPnTpydrz3Pp8FgwNfX\nlyVLluDi4kJsbCxz5szB29ube+65B4Aff/yRDz74gFdeeYUBAwagVqs5evQoYAzkx48fT1lZGR9+\n+CEBAQEkJSUBUJGTQ9mlZNy6RSNTNO/5L1tUoDZ79mwuXrzIokWL8Pb25tdff2Xs2LH88ccfeHt7\n8+WXX7Jy5Uo++OAD/P39+eSTTxg3bhx//PEHSuWNpysIgiBYStVKex7XmOy6qqpjFuKTCxstUKs6\nCXC3cG/sbIxfKU72Svy9HEnPLSUhuZD7+jfK7puUKb0zpLVLrZ+nQi7j+ZFRTP9oJ1qdgf+sjmX+\nhB7k79pNxm8bKE+/EuzJbW3xuXsQfvfdi62PMcB1jY4iYdFHVGRkkrtzN6UXLhL26gwc2gQ2/gFe\nZjqfLo5KOrY1jrtr5W483ttxLjVT2mOnEE9cHG8upcvaSo6fpwNpOaWiR+0m5ReX8/WvpwBjoY/H\nBoc3+j5bynVNV1bGmfnv4+bbGjs/P+z8jf9sPD2vCkocHR2xtrbGzs4Od3fj94bi8jLPP/88vXv3\nNi/r7OxMePiVz3n69Ols3bqV7du388QTT9TanoceeoihQ4cC8NJLL/H9999z4sQJ+vXrd83jsLKy\nYurUqeaf/f39iY2NZdOmTeZA7fPPP2fcuHGMHj3avFxEhDGzYd++fZw6dYpNmzYRGGi8hrZu3RrV\nufPETnsRQ0UFYa/OwLNvb5qzFhOoVVZWsnXrVpYtW0a3bt0AmDp1Kjt27GDVqlU8//zzrFixgsmT\nJ3PXXXcBsHDhQvr06cO2bdvMvySCIAgtiamQiKOdNbY2dbtkt/Kwx9lBSYlaQ0JyAXdGt26Utp1P\nLTL3+P0zHTCsjZvxhqaZlLK+GQUlFZxJMhYrqCntsaoAHyceHxzG2g2xeB77i4Njv0ZefiXAUbq7\n4ztsKK0G342Vo2O1dR2D29Llo4Vc/Oxz8vbuozwtnRMzZhI8cQLeg+5q9MpuWp2eQ6eNaY+9Ovma\nK+eZUh+LSzWUV+rMAfmtLi1HZS7YcLNpjyYBPk6k5ZSKEv03QZIklq47gbpCh0wG00d2xca6aXpF\n6nNds1cae7bSVVkN2obCkgreXX4IW30lI/L2INNqqr3vVqKnUptLVuypaq/LrK2x822Fnb8ftqYA\nzs8PqYbxZzKZjI4dO1Z7raysjCVLlrBr1y5yc3PR6XRoNBoyMzOv2d7Q0CvjBu3s7HB0dCQ/P79O\nx7py5Up+/vlnMjMzqaioQKvV0qFDBwAKCgrIycmhV69eNa4bHx+Pj4+POUgDUF+6xJm33sVQUYHM\nygr7AP86tcOSWszVVqfTodfrr+oZs7W15ejRo6SmppKXl1fthDk6OtKlSxfi4uJEoCYIQotkKs1f\n17RHMH7JhrVx4/CZ7EYtKGIqrmFtJad7Bx8kvZ78vw+hKy0lwtWdHUBWfhlFqkpcnVruAPMDJzMx\nDRmrLe3RpCwtna5nt+OT/BcKw5UbIPugNvg/cD+e/fsit659Hi4re3tCZ7yIc6eOJH2zHINGw4Ul\nn1F86jQhz01AYVu3XtUbcfx8HuoKHVA9IPVxv9KDmF1QRpDvtVOWbhWmXlSZDHrXI+1RV6omd/ce\nkCRzb4bSwwOZXE6gjxMHTmaSmq1CkqRmU1b9Rkh6PXl796P0cMelU8frr9BAdsemc+jyOMr7+gXT\n4XLPb1W60lLy9u3HrVs3bDyvfv9GhbdxY8eR1Dpf1+yVdrT3aNtg+wfYk5qOpHalf85BfEuM6cgR\nc2Yhk8spT0+nPD3T+N+MTDR5eeb1JK2WspRUylJSq21PlZJEZlo6JwpV2Pn7UmBlDHrt7e2rLff+\n++9z8OBBZs6cSWBgILa2tkybNg2t9trzdVpZVQ81ZDJZncbg/v777yxcuJDXX3+drl274uDgwFdf\nfcXJkycBrlu0xPYf18ry9AxOz30bXWkpyOWEzXgJ+8Cmy1a4US0mUHNwcKBr164sXbqU4OBgPD09\n2bBhA3FxcbRp04a8vDxkMhmenp7V1vPw8CCvyi9qXeTk5JCbm1vje1qtFrlc1GARBKFpmCa79nCp\n3w26KVBLTC+mUqtv8CfOVdMeo8O80V1K5PgXX6FONI4BcAKeUbpx3qE18fu86fmvnsha6LXTlP4W\n5OtsnrS4KkmSKDl9hvT1v1F4+AgApk870d6PnA69eWHWqDrP7SSTyfAdMhinsPYkLPyIiswscv/a\nSen5C4TPfLnRbi5MgbeTvTWR7a58l5p61MBY+fF2CdRMv98d2nrgVoe0Y0mSyP1rF5e+W4G2uHqx\nCblSia2fL23s3eifr6fA2pnUo4H4hYdg5dh4Y0gbi2QwcGHpF+Rs2w5AmzFP4P/wg40eeBapKvni\nF+ONuo+7vbmAT1UV2TmceesdytMzUHq4E7XkE6wcGuYzrppWfi6lkB4dm36cYUJyIR6aIrqWnAfA\n6847cI8xZpq5RUdVW1ZfUUFFZpY5cDMHchnp6NXGIE8hk6HXaFElJKBKSCCjTA2SZJwupEqvf2xs\nLA8++CCDBg0CjHUj0tPTG+04Y2NjiY6OZtSoUebXUlOvBJkODg74+/tz4MABevTocdX6YWFhZGdn\nk5ycjI+tHafmzDP+XcpktH9+Kh69ezZoe/V6PadPn671fS8vL7y9veu93RYTqAEsWrSIWbNmMWDA\nAKysrOjQoQPDhg275gdzI1avXs2nn35a6/vXGwApCILQUG6kRw0gPNB4Q6E3SCSmFRPR9ubm2vmn\ni+nG4hJ2+goGJO/i5K9/X7WMt6YQb00h+mUnObzKFbeYaNy7x+DatUuj9gw1pCJVJacuGh/2/TP9\nTdLrydt/kIz1v1J64aL5dZmVFV4D+hPr2Yk1x4qhCDr8ncyQPvV7su4YHEyXjxdx4dOl5O87QHla\nGsdfnknwcxPwGTTw5g+uCp3ewMFTxh6kXp18saoSVHq52SGTGSdRv10KimTmqUlMNwZb10t3BWNK\nVeIXX1Ny5myN7xs0GsouJSMnmb6XX0t9Zx+pgLWLM3b+/tj6+WLn74/d5f/atvK5Zu+rpUiSRNK3\n35mDNIDk71eiKSyk7bixjfpA5otfTqAqM6b6TRvR9ap0cPWlS5ye9y7aQmMmgSa/gKRvv6P9tCkN\nsv82rZywUSqo1OiJTy6wUKBWwF15x5AjIbO2ps3ox2pdVmFri0PbIBzaBlV7XZIktMUllKenE/zR\nh5xPSkLXMQJDegYGdSmSJBG/YCHd5s0xp2gHBQWxdetW8/CixYsXN2p12jZt2vDrr7+yd+9eWrdu\nza+//srJkycJCAgwLzNt2jTmzZuHu7s7AwYMoLS0lNjYWEaPHk337t3p1q0bUydP5mF7J9xK1WRq\nKvEbdi9977yjwdurVqt56KGHan1/6tSpTJs2rd7bbVGBWkBAAN9//z0VFRWUlpbi6enJiy++SEBA\nAJ6enkiSRF5eXrVetfz8fPPAwroaOXIkAwfW/CU4adIk0aMmCEKTkCTJPAasrqX5TdoHuppvrhNS\nCho8UNsfl0ZUcQJ35MeiMBhvnBQO9rR54jFco6MpPHqMQ//bint+GgoMaIuKyNm2g5xtO5BZW+Pa\nuRNu3WNwj4nBxsvzOnuznIOnMjHNTWy6YdeVlZOzbTsZGzZSmXMl+8LK0ZFW9/yLVkOHYOPhTpDO\nwO6MnSRnqVi+8TTdInyqzb9WF1b29oS98jJZkZtJ+vpyKuR/PqPk1BmCJ45vsID3xIU8SsuNKUz/\nTO+0tlLg4WJHXlH5bTOXmqkXFapPbv5POrWalFWryfx9ExiMc2vZ+vkRMnE8TmGhl3sxMijPyKA8\nPYOytHSKklJQSjrzNrTFJWiLS64O8uRybL29sfP3xSk8HP/h9yNvBoXRUn78icwNvwPgEBKCobKS\n8rQ0Mjf+gbaomPYvTGuUAHP/iQz2Xu71vad3EF3ae1V7v/jUac6+9z76MuPDBPvAAMpSUsnZtgPP\nvn2u6m26EQqFnPYBrpy6mG+Ria+1Oj2ac/G0K0sDwO/+Ydh4eV1nravJZDKUri4oXV2YOncur732\nGs//sZHKykqm9ekHqZdQxSdwctYcOrz5BjYeHrz22mvMnj2bxx57DDc3N8aPH49arb5qu9f6ubbX\najJy5EjOnj3LSy+9hEwm49577+WJJ55g9+7d5mWGDx+ORqPhu+++Y+HChbi5uTF48GDz+/9esIDZ\no5/k06QkKg0GWrdqxWvRXevzUdWZg4MD3333Xa3ve93AeYIWFqiZ2NraYmtrS3FxMXv37uXVV181\nB2sHDx40V6UpLS3l+PHjPP744/Xavre3d63dk9bN8OmWIAi3prIKHRUa4zgnz3qmPtrbWhPo40Ry\nlso8/1dDKUlIwPXHJQxWX0kr9x54F22eGo3S1RUAu2FDKaIN328/Q5gmm6faGSg6egydSoWk1VJ4\nNJbCo7Ek8hUObdvi1r0b7j264xgS3KxSJE3pbwE+TngrNFz6bgVZW7aa04YAbHy88bv/PnwG3VWt\nnL61lZzpI6N45T+7Ka/U89na48yb0Kve6WHGVMh7cAoNJWHhh1RkZZOz4y9U588T/uoM7AMDrr+R\n6zAFJg62VlfdAIMx/TGvqPy26VEznfeIIPcaH5JIkkTurt1cWr4CbVERAHIbGwJGPILfA/eZAxXH\nkGAcQ4Krrfvs/K2UZOfxrxA7hobZV0lJy6AiK9sc8GEwUJGVRUVWFoVHYymIjaXjG7MaLI3vRqT9\n/Atpa9YBYN8mkI7z5oAMzr6zAFVCAnl796EtKSH89Vexsq/fQ4lrUZVpWPa/E4DxWjh2WIdq7+ft\nP8C5jxcjabXIFAraTZuMW7duxE57AW1RERc+XUbUkn83yGcXFujGqYv5nE8tRG+QUMibbpxhYloR\nA3IOG39wcKT1ww/e9DaDgoL46aefzD9Lej2Dv/qGrE2bKUtO4cSrs+g4bw7+Aa2vCkT+eX+9ffv2\naj+fPXt1D/OhQ4fq1C6lUsn8+fOZP39+tddffPHFaj+PGDGCESNGXLW+Tq0m9aNPGOPsyhhnVwJG\njSDwsZF12veNUCgUVxVgaQgtKlDbu3cvkiTRtm1bkpOTWbRoESEhIeauxqeeeoply5YRGBiIv78/\nixcvplWrVuZ8WkEQhJYkr+pk1/VMfQTjeIrkLFWDPfnVlpSQvGIl2Vu3YRqeb/D2o8tLU3COuLo8\ndlgbd36WKzlpG4DNqLvoMX0KqnPnKTh0mILDRyhPNT4VVicloU5KIm3NOqzdXHGPicGtewyuXTuj\nuM6A8cZUotZw4kIe3pUFPJB7gqPPfl6tQppjaHv8hz+AR68etc7FExroxoN3tuPnvy5wLCGH7YdT\n+b8eNzbGzDHElAq5jPz9ByhPTeP4jJmEPPcs3gPvvKFtAuj1Bg6cNKY99uzkW+OkwT7u9py6mE/W\nbTDpdU5BGedTjcFXTcVj1MkpJH7xVbX58Dx696LtuKfr1LsR2MqZv/PLOIsbzwweQIWukuSiNHIL\nU0nKSyY3+QLlmZm4FGtxLdHhU6DDu1BH6Zl44l6bReS8N7HxaNge8rrI/H0TySt+AMDWz5cO8+Zw\nXJWElVxB+LxZJH68hMLDRyg+cZJTs+fSYe5slG4NM7fZ17+eokhVCcCUR7tib3vloXnmpj9J/OJr\nkCTktraEz5xh7j0LmfQs8QsWosnPJ+nb/9J+2uSbbotpnFp5pZ7UbFWTjtlM2ryDVpXGipP+Ix5t\nkMBTkiSKKkrILs0jV52Pu70rEc+OR+nuTsrKVWjy8jj5+mwi3piFc3jYTe+vKegrKjjzznzUFxMB\n8HvgPgJGXQnmslQ5pKuyifLtiFzWfB4M1qRFBWoqlYqPP/6Y7OxsXFxcGDx4MC+88IJ5zocJEyZQ\nUVHB3LlzUalUxMTE8NVXX4k51ARBaJFMhUSg/j1qYCwosuXvZPKKyskvLq93+qSJpNeTvXU7yd+v\nNFbMAirk1uz1iOLFj6fj7FTzdsPaVJ0gtoAgX2ecI8Jxjggn6KkxVGRlUXD4CAWHjlBy+gySXo+2\nsIjsrdvI3roNuVKJS+dI3LvH4Na9GzYeDVe97XokSeLwrzsYkbqZoHJjhTkJQCbDvWcP/IffX2Nw\nWpPHBodz8FQm6blqvv7tFFFhXjd8LqwcHAh79WWy/thE0rf/xVBZyfnFSyg+fZrgZ8ffUGB7KjGf\nErUxfbW28VimueOyC8puuFphZW4u6b/8hlv3brhFNU76UUOoPrn5lbRHXVkZqatWk7Hxjyppjr4E\nTxhX57S6Uo0aO48irFolkaw8wQt/7CBTlYNElbE+MsDP2vgPcLSyo8f+bCIvVFCZkkbcqzOJfGse\n9q2brrR4zo6/SPzyawBsvDwJfXM2XySsZ2+KsXfHztqWbnd1JMa6K9L+ONSJSZyYaeyJsfO7uakN\njpzNZscRYxGJgTEBxEQY5x2UJImUH38y9/BZOTvTYe5snNq3M6/r0asnngP6kbd7LznbtuPZt/dN\np0DWdF1rCvrKSpR//QFAia0rvYfdU+d1SzVqckrzyVHnkaM2/jdXnW98rSwfrb565caBbfsw4ZHH\nsXZ15eKyL9CpSjk9Zx5hr76Me/eYBjmeN998k99+++2q12UyGffffz/z5s27oe0aNBrOzv8A1dl4\nAHwG303Q2KfM1Sa3J+7l22Nr0Bl0vNB7PH0Cu93MYTS6FhWoDRkyhCFDhlxzmWnTpt3QYD1BEITm\npmqPWn2LicA/bygK6dO5/ttQnb9A4udfViuWkegVxu8OnQntEFhrkAbg7myLt5sdOYXlJCQXMrhX\nULX3bVu1wu++YfjdNwydWk1RbBwFh49QePQYOlUpBo2GwiNHKTxyFJaBQ0gw7t1jcO8eg0NIcKNU\nmDNoteTu2kPGbxuwTU7B1GK5Uon3oIH43X9vvW88bawVTB8ZxWuf7UVdrmXZzyeYPbbHDbdfJpPh\ne+9QHENDSVj0EZXZOeRs20HpufOEvToD+4D6zZtnSvOzs7Gia2jNPUI+7sY0No1WT5Gqsk5VEKvS\nV1Zy+q13KU9NI/OPTQQ/Ox7foXW/0WxKpuqXoYGueLvZG8eK7t5L0vL/motUyJVKWo94xDhurJYh\nEUXlxSQWppJUmEJSUSpJhankqo3zR1lf7lTN+Md0aj4OngS5BdDW9M81ABdbZ1a3/5WDa/5Hr5Nq\ndHkFxM18nci5b+AUFkpjy9t3gPNLlhrb7eZKwOyXef/U95zPTzIvU66tYG/aUfa2keirciLmpIrK\n7BxOzJx9VfBUH+pyLZ+tjQPAzcmG8Q90AowPjy4u+5LsrdsAsPH2NgaF/lf/bQZPGE/x8ZNoi4sb\nJAXyete1xpK54Xdsyo3z+mV3/z/kVcreV+o0xsDLFIiVXgnIctT5lGnLa9tsjXYk7aekspTnB44j\n3MWFcx9+bA6A2k15Dp//u/lMteeff57x48fX+J7DDZ4fg05HwqKPKT5+OU12QH9CJk5AJpNRqdPw\n9dFV7Lp0EAClwppA14aZH7ExtahATRAE4XZimuzazsaqWqpPXQV4O2Fva0VZhe5yoFb3LyVtiYrk\nH1aSvWUbpknE7NsE4vjoE7y/3liSuS6TAIe1cSenMP264+SsHBzw7NcXz359kfR6SuITKDx8xJgi\nmWbcn/piIuqLiaT+tAalu7txXFv3GFw6R950iqRWpSLrzy1k/v4H2sIi8+ulClsqo/syZPoYrJ2d\nbnj7Hdp6MKxfMBv2JPL36Sz2xKUzIOrmJiJ3at+Orh9/aKwKeeAgZSmpxlTISc/iXceqZnqDZE57\n7NGhFcpapnFoVWUutaz8snoHaklffWtOdUWSSPziKzSFhQQ+PqpZzSWWV1Ru/l3t29mPspQULn7x\nNSWnrlSXdu/Vk7bjnsa2hrHsBoOBv5L2878zm8gtq31SZEkCqdyRSP9gurVpT5BrAEFurXFU1nyD\nOqrzcP60c2XH6u+484gKeamaE2/MpcNrr+LWLfrmDvoaCo4c5dzHn4DBgJWTI+4zJvLWqf+Sd/nY\nuvt34e6Q/hxOP86h9OMUV5SwL9IOlY3EnUdK0ZWUEPv6LPTPDCdm0P042Vw9vcW1LN94mrxiY2bB\npIe74GSvRF9ZybkP/03BIWNvnkPbIDrMfQOle81pltbOToRMmkj8+8YUyEvfraDdlEk3/qFQ9+ta\nQ9EWF5O69mcAUmx98OljnDM4qTCVTw8uJ7Xk2pNOVyWXyfGwd8PbwQNvB88r/3X0wMXWmS8O/8Dp\nnHMcyTjBu7v+w8x+k+j49pucfXcButJSLixZiqawiNaPPHRTf7vu7u64uzdcCq+k13N+8RLz74V7\nzx60f34qMoWCLFUOH+3/iuQi4zXI19Gbl/s+S2vnus+PaCkiUBMEQWimTDconq43VtlPLpcRGuBG\n3PlcElLqdkMhGQxkb9tO8oof0KmMaY4KOzsCnxiF79Ah/LT9gnHbMmMZ9+sJa+PGnrh0UrNVlJZr\ncbS7fsApUyhw6dgBl44dCHr6ScozMyk4dITCw0coOXMWSa9HU1BA9uatZG/eakyR7NIZ9x7GKpK1\n3bDVpDwzi8wNG8netgNDZaX5dYOnD3/KgzntGMxHTw+8qSDN5MkhERw6nUV2QRlf/HKSLu29cHG8\nuQDTytGBsJkzyPx9E5eW/xdDRQXn//0fVGfjCZ444bqFWc4m5ZvH/vTtUvv5rDqXWnaBul5VRHN3\n7zX3fLh27UJ5ZiaV2TmkrVmHtrCIkEnP1jrGr6mZ0h6VBi2h8buIW7LFPC7RtlUrgp8dV2tgFJ97\nkeWxq0kqrD6hsEKuINDFj7auAbR1C8Tf0Y/XPz4JBgVR7ToxLCykTm27p/2dOD/pxO92y/jX3kKs\nNFrOvDuf9tOm4D3wrps46poVnzxFwgcfIul0KOzskE1+jLcTfqRCZ/x9GR4xmFGR9yOXyenq25Fx\n0aNIyL/I32lxHLKP4w/bDAbvL8FKq4cvf2bRyW1Y9ehMz9ZR9GjdFTc7l2vu//i5XDYfTAagf1d/\nekf6olWpOPvuAlTxCQC4RHYi/PVXMdgqSS/JMvYgXU7xU2vK6NumO5E+4Xj07oln/77k7dlH9pZt\nePTpfVPptzdyXbsZKT+twVBh/D7Y4dmNWUHulFSWsmjv5+aguSpXW+crQZhj9YDMw94Nhbz2v7dZ\nA6ay5O/vOJh6jIS8i8zd8RGz75hG5IJ3OD3vXTT5+aT88CNa03QMzeBvV5IkLi77krzdewHjdSbs\nlZeQW1lxJP04n/79X3OvYg//rkzu8ST2yhtLP29qIlATBEFopkypjzc6ngmMNxRx53M5n1qETm+o\nNj/WP6nOXyDxi68pPX/e/JrXnQMIeupJc/Bjqg7YKcSzTkFG1fTL8ymFRIXVf8JPO19f/B+4D/8H\n7kNXqqbwWCwFh49QdCwWXenlFMnDxkDuIuDYLsRY+r9HDA5t29b41LckPoGM9b+R//ehK1X2MN74\n+Q2/n8/itJw4m00rD3uC/a99Q1lXtjZWTHu0K298sZ8StYalvxzjifsCyVHnoapU09E7FG/H+k9V\nIJPJ8Bs2FKewK6mQWX9uwdrV9bpVzkxpj7ZKBdHhPrUu5+pkg9JagUarJ6selR/LMzO5uPRzAGy8\nvQh75SUMWi1n3noPdVIS2Vu3oS0uJnTGixYtHGOy/3gG4aokBhceozjRWHpcrlTS+tGHay2Pn19W\nyA/H/8e+lCPm1wKcfRkaOpAQ9za0dvbFSlH9dsvb9RI5BWWkZpfWq319ArvhPHoG39st5l87crHV\nSoWBlZwAACAASURBVJxf/CmawiL8HxreYL2TqoRznHl3AQaNBrlSSeFT/+K71N+QkFDIFUyMeYI7\n2/auto5cLifCqz0RXu15qusjJPZNIS50E+7fb0epNTB4fzG7y2P5JiKBb4+tJtSjLT0DoujROgpv\nh+rjT8srdSy5nPLo7KBkwvCOZKScJ3n+xxgycwAojPBlc38Hsre9Q2FF9QnGTf66dIBx0aP4V7sB\nBD87nuITp6qnQN5gZcqGuK7VVVlaOtmbtwJw2jGIImcfWns7sHDfUnOQ9lCHewj1CMbbwRMvBw9s\nrG68NoO1wpoXeo1juY0Tmy/sIq0kkze2L2L2HdPo/MF8Tr/1jjGF+fdNaIqKCH3xeYvO9ydJEknf\nfGd+GOTcIYLwWTORFHJ+PLGe9Wc3A8br5BOdh3Nf2N3Nqhf/ekSgJgiC0EyZUh89bzJQA+PYokuZ\nJbRr7XrVMlqVipQffiRr89ZqaY7BE8fjUqXccFqOikuZxjESdUl7BAjxd8FKIUenN5DQADc0Vo4O\neA3oh9eAfpdTJOPNvW3l6cago/TCRUovXCR11WqUHh64d++GW/cYXDp1pCg2jvT1v5mfyAMgl+PZ\nry/+w+/HMSSYsgotx9b8aTzOzn43/KWu0+vILSuo9pQ/R52Pd48USrRFxFprid1cfZ1O3mEMDO5D\nj9ZRKBX1u/kxpUKefvMt4/H/tAb7gNZ49utb4/IGg8T+E8aUqZgIH2xqSXsE402Oj7s9qdmqOs+l\nZtBqSVj0b/Tl5SCXE/ryi+bJczvNf5v4BQspPnGSgkOHOT33LSLeeB1rp5vvubxRmWcvErn3J3Px\nGAD3nt1pO24stj5XB7EanYYNCdtYf3YzlXpjMRYHpT0jO93H3SH9r9lrEejjdDlQU9W6TG06+YTz\n3GOzWGr/EYP+TMep3EDyih/QFBTSdtzTNz29hTrpEqffehdDRQUyKysuPhzDbyUHAHCyceSVvhMJ\n97r2mDOZTEaIextCHniO0sjBnJz3NobiEgbEluJQYWBvVwcS8hNJyE9kRdzPtHULoGfrKMI9Qygo\nL+b3w6cpcE5H6VWOnaeeN1b9wn1/FeJUZnyoEhdqx66uOihKqnH/zjaOaPU6ynUVfH10FUUVJTza\n8V5jFcj3F6HJyzOmQE5+7oY+o4a+rl1L8oofkPR69DIFuz2iaR/gxi/xf3I8y1j6/p52dzIq8oEG\n3adcLueZ6JG42bnw08nfyC8rZO72j3it/2QiF7zL2ffeR3U2nvx9BzhdXELErJkWmzbCOK/fRsA4\nr1/EG69TKlWyeNcyTuVc7nm1deaF3uPo6N34YzobmgjUBEEQmilT6qPHP1IfU9f+TM6OvzDPxHwt\nksTEyz0gGbN/p7iGsW7a4mLjzTTGNMeAx0bie++QaoPVAfNNvUwGveuQ9gjGyZJD/F1I+H/2zju8\nyXL945+Mpk2ajnTvltJdSlv2HhVERBwHwXXU40/ErbhB1OM4eo6KB1EUByqiHkXFiSgyZS+hdNAW\nSvdu2nSnzfz98TahhQJt6QLzuS4uIHnf532Tpk+e73Pf9/cu0PR4g1ghRTIWl9hYBt1xO9qSklMu\nkscywGRCV1VF2W+/U/bb71g7gLcikcvxnjEdv6uubGerfuBYOQajsCA8V12fyWSiurnmrG5q1dqa\n9m5+be/9LBosrSKLtIosHGVrmRg0iqTQcYSoOt8nTap0JOrppzj62FPoNRpOLF+Bvbd3h2YOWfka\nquuEz1hnhLdFqHW2l1rep5/ReFIwoQn++83trL2lCgUxzy3h+LK3qNq9R2iuu+gZYp9/ts8boBu1\nWgq//paiH34ipDW6KvX0JPyeu3AbcaYjnNlsZn/RET47+p3VHEQkEjE9dCLz4mbj3Ik6rEBvJw5l\nlFNQVtctF81BqkAev34Jy+RvMH59Dm51RkrX/4KuRkPEwoe6HeFoKiom/Z8vYGxsBLGYI1eE8YdI\nSHcOcPblqYn34q3sWuNeZeggEl9/lfTnX6K5pIThGU3EOvixZYwL2bVCmmiupvCMlFFpq/ZxLNFx\n9R+1OOiE36Xd8Y6kD3UjWOmBp9KS0tc2vc8dBzsHSurKePmPt6lsqubb9F+o0dYyf/RNeEwYj3rX\nbso3bsJj3FhcE+K7/D715rzWltr0dKr3C33HjrhFU2unROVfy7p0wf0x3C2E2xLm9Mq1RSIRf4uZ\niauDM+8f+oIGXSMvbn+TR8bdRcILz1nrBOvS0lvbMZy9TrC3KPruh1N9/YICiX3+WXK05fx3z4dU\na4Va40iPwTwybj5u8jM3KS8GbELNhg0bNgYg2hYDjVrBMrltRK2lUk3BF1+2Exznw/rVWQPN5zjO\nY9JEQv5x21l7NFnS5GIGuXfJTCIyWNW6oKk+56JUb9RTWFvaziWvrqWBRJ9YkkLHE6I6t/mG3M8P\n/2uuxv+aqzE0NFhTJDV/HhEWnq3vmczDA7/Zs/CeflmHu8CW9E5PlZzwwPZf7s36ZvYWHmZ73l6O\nV+ViNBnPOP9sOMkchdQkpTumZjm7Dmgw6+SMDg9l/uyh7Mw/wNbcPVQ2VtGoa+K37O38lr2dQapA\nLgsdz/igkTjKzp+qZe/uTvSSRaQ9/SwmnY7MV15l6NL/nNHewPLzlNlJGH6OtEcLljq1zvRSq9p/\nkNKffwHANTEB/+uu4UBRMv9L+YEgF3+SQscx1DuayMcfIdfVldJfNqAtKiLlqcXEPv8siqDu9Zrr\nCmazmarde8j9eDW6qmpEgF4kITNwOAveeKTDNMeCmmI+OfI16RXHrY/FekXwj8S5BLt23hwmyFsQ\nc/VNemobdLg6dT3t01vpyeJrnmapfDnxP6ThW2Wgatce0mpriXl6UZfT+prLK0h/7gX0tXUgErF3\nii8HnIXUugSfGBaOnd/tuh4Hby+G/udfHHvp3zScOIHDkWxuIQHPB5/lUFUm+4uOkFl50rqxYTaJ\nEOkVTGqWE78tE7HBDGIRzv+4gXuvmIlS5nhecevn7MNL057g33+sIL+2mM05u6hraeDeO2+nNjUV\nfW0d2SveJeGt7qVAdnZe6y5mk4m8T9YAIHJ0ZKfzEESyJlL1BzFjxsleySPj7zojrbanSQodj7O9\nkmV7P0Jn1PP6rveE1NdFT3DyvQ8o/30zjbl5p9oxdOC82RuU/vob+Z9+BoCDrw8xzz/H5rJDrEn+\nFqNZ2HCZFXEZt8Rfh/Qc0e2Bjk2o2bBhw8YApOos1vyVf+ywCg6fKy7vVCH3kawKiisbcHSwY+qI\nM6MzIokEtzGj2qU5nk6pupGcYqEO5Gy9ts5GZLAKdgqL0lJ1I36eSmuTX8tOeq6mgMK60g6Fj0Ww\nhKqCSAod1ynBIlUq8Zw0Ec9JEzEZDNRnZFKXkYmDry/uY0efES20oG0x8GdGufV1WnrvnKjKZWvu\nHvYUHLKaKZyOvdT+zN391h1/T0d3FHbtF7mS8sNsPVTI3kP1XJ5gZE7slVwXcwXpFcfZmrObA0XJ\n6E0GcjWFrPrzKz5NXseYgESSQscT4xl+zoWhU3gYYQ89wPGl/0VXXU3mK68y5JWXrHVgZrPZKtSG\nR3khtz//csDSS62qVoveYMRO2vFnr6VSTfbbKwCwU6kIX/gQuwsPsWL/p5jMJkrqy9lXdBh3hYop\nIWOZctPVBLupyP/sC3RV1aQseoaYZ5/udJ+67tBUVETOBx9ZbbwBsh0D2OQxkqtmjzxDpNW3NLA2\n7Wc2ndyJufX3z1Phxq0JcxgdkNjlRXqg96kUz8Ly+m4JNRBSup6e+QTL5CvRfneA0BId9anpHH36\nGeKefw6Za+eiCC1V1aQ/9zy6KiFCuHOMisM+wkbRleFTuTVhzjlTOTuDnYsLQ/71PJmvLqXm8BFq\njiRjeOVNpj27hCsjkqhpruOTjQfYurcK9PY8GqFD9ts3YDIhlsm61cPLTe7K80mP8tqu98ioPMGB\n4mTqdQ0suPN28v77Ni2VavJWf0bYfXd3+fV0NK/1JOpdu2k4IUQztWOn05IvwT78EM1GLSKRiIVj\n78RD0TeNz0f4x/Ps5Id5dde7NOqaWHnwM2qa67jm3ruxc3Wl6OtvaamoIGXRhbVj6CwV27aT896H\ngLDxFvbcIt47/r21TtRBas+9o25lbODA7pHWGWxCzYYNGzYGIG2bXbu3Nrs2m81UbNsOgHNsDIPv\n7dziImNXDpu+TwVg7i0zcXbseqG5JcoE7ZsAd4ZAPwfETlWIHetYcWA1WlEVJfXlZ00LBFDJXRik\nCkImtuPPkhT0JgM5mgJy/izokmABEEuluMQNwSVuyHnv9VBGOTqDsBubEO3C+qzNbM3ZQ9Fp9tf+\nTj6MCxqOv7OPVZQ52Su7tGCff80QDmdVUFPfwjvfJPPOk0koHOyI844izjuKhpZGdhUcZEvObvJr\nitAb9ezMP8DO/AN4Kz1JGjSOyYPGnDWlx3PieLSFhRSu/YaG7JNkv/0OEY89gkgk4kRhDerWGsjO\nCm9LLzWzGSo12g4Xpmajkaw3lgmOoSIREY8+zK7qdN4/+DlmzDjayRGLJdS3NFDVpGHdsQ2sO7aB\nOO9Ikm65Er78DWNjI+nPvUDE44/gPnpUp9/PzmBJcyz5aT1mgwEAe28vNBOu4tujwv/bvh9Gk5FN\nJ3fyddp6GnRCbZ5MYse10VdwdeQ0ZN00bWgr1ArK64kL6366p9zOgSeTHuQdxSdov91ObE4zzbn5\nJD/xFHEvPo/c99y/r/raWtKfe4HmMmGDYucwJw4PkiIWiblz2I1MD5t41nMbmnRU1Z0rTn8mjvPv\nQ7fmE5r27aEh+yRHnliEx0OPUSVRsn1nI5jsuV6Sg2zDbgCkTkqin3m6Xepsl64nU7Bk8oO8te9j\nDhQlk1GZzTIXLbeNGUn9voOUb/wdj3FjupwCGRl8SiRl5mt6VKiZdDryP/sCEKJFf7pFY8dGxI5C\njfANQ2YT590zGxm33norMTExLF68+JzHRXkO5sWkx3jljxVUaTV8mfojNc113H7zDcjcVOS8vwpD\nXR0Lb70NSVgoH3z+eY/c3+mo9+zlxFvvAGDn6ornU/fzwtFPrHO0v7PPRWO93xlsQs2GDRs2BiAd\nNbtuOJFt7SnmNbVzfbKg/YLieIGGEdHnT3M7HUv0JTrE7ZwulA0tjRyvyj2jya99tPB8dgcmd96O\nHgxSBRGiCiBUFUSIKhBXB+d2Y+4qOMjWnN3kdUOwdIVdKUWIXSpR+JWy9MgmjOZTET57qT3jAoeT\nFDqOCPcLb7jtpJBx35yhvLL6IOraZj7+OZ0H5p6yDFfaO3JF+BRmhE0mV1PA1pw97Co4SJNeS3lD\nJV+m/shXaT+R6DuEpEHjGOYXd0aKT+CN82gqLKJqz17UO3ejCAwk8Ia51qbOdlIxI2M693mwRNRA\nSH/saGFa8OVa6jMyAQiYO4cDCg2rDn4JCAYPz0x+mABnHw6VpLA1ZzdHyzIwYya1PItUIGqqB9P/\nqAKdjsz/vM7gexfgc/n0Lr2vHWE2m6nas4/cjz6xRo1EdnYEzLkOv+uu4ekPDwLVBHo7WUVUWnkm\nnxz5hsLaNpsUQSP4e/x1FxzJUDjY4eHigLq2uVuGIqcjlUh5cNydrJG7cODbnxmV3oS+Qk3yk4uJ\n++ezKMM6bgFgaGgk/fmX0BYJ/aX2xTlyOEqOo52cR8cvOKcYyC+r45Flf6A3mM56zFkxD2aKay1j\natIxVFSQ/c9/8rXfNMwyV66sPkSYRvgM2Xt6EPPPZ7vcxP10ZBI7Hh17Fx8d/opNJ3dSUFvM6sHO\nzE1XYqpvIPudlSQsX4ZU0fnUTi+VHFcne2rqW8jKryapg2yF7lKyfgMtFZUAhNx+K+8cPorUS/gZ\nDfOL49roGT12ra4Q6OLHS9Me5+U/3qa4roxfT2yjtrmO+y+/HZmrK1lvvInZZKQ+8zgV27bjNXVK\nj15f8+dhjr9xqq+f8d65PJf6sTXLYVzgcO4Z+Xcc7LrX0mYgYhNqNmzYsDEAsQg1mZ3E2qPHEk0T\ny2S4jx/X6bFC/JyRScXoDCYy86u7LNQqqps4USgUZp/NXKOuuZ4fMn9nY/Yf6I36Do8xm8HO4MzY\nsCgGqYSeUiGuAedNY7QIlivCp5BTXcDW3N3syu+aYDnva2ysYtOJXRwWbcc+shkjYAn4RbiHkhQ6\njrGBw5H38AJgbJwfE+L92HW0hI378pmY4E98eHuzBpFIRKhbMKFuwdyaMIf9RUfYlruH9IrjmM1m\nDpekcrgkFRcHZyaHjCZp0Dj8nH2Ec8Viwhc+SHN5OY0ncyj431fIAwLYnSIo5mGRXp1upm6JqIHQ\nS+10apKPUvTtd4BgkZ2W6MGnfwoiTeXgwrNTHibARdjlHhM4jDGBw1A3VbM9dx/bWmvzMr2hJsmZ\nq7fXINeZOPnOezRWVhB6883dFsZNRcXkfvgRNclHrY+phg9j0F13Ivf1YfOBfDLyhFqsKcMCqGhQ\ns+boOg4UJVuPH+QayD+GzSXaM7xb99ARgd5OPSbUQGhkfHviXH6Su7Ltmy+Y8mcDprp6jj79DLFP\nLzojYmRsbubYSy/TmCM4J/4ZrWD/EAW+Si+emniv9TN0NjbtL+ieSAMQidjuMZwGqZxp6kMojc3c\nUrSREgcPBmmFyIgiOIiYfz5zRm1ldxGLxcwffhMquQtfp62nyFzH5uEKkrY30FJRSf6nazqdpSC8\nBBGRQSr2p5edtU+lobHRurnWWQyNTRSu/RoARUgwReZmHM37cFSbUYiV3DFkAo3Hs896vjzAv9MO\njIsXL+bgwYMcOnSITz/9FJFIxJYtW2hoaOD111/n0KFDKBQKxo8fz+LFi1GpVHgo3JhsSOCV9/5N\nXWUtaXY7+SH4S7777Fv2xESwJ/UIAJPuuRuRSMTqjz9m5Ihzp6y+sWwZm7dupby8HA93d66aNYv7\n770XSZvU/l8+/4J33nqLomYtDhIJgdEh2OX/CIDICG5pUn5Y+TkfVS3Hz8+PBQsWMGdO7xit9CU2\noWbDhg0bAxBL6qOHiwMikQiTXo96p9DM023M6C4Vv0slYsICXTmWW90thzJLE2A4M+2xQdfI+qzN\n/HJ8Gy1tarekYimBLr4MUgURqgokJxt+2VKFDil3zbsSB1n3vn5C3YIIdQvitvg57DunYBlD0qCx\n51xs6ox6DhYnszVnD2nlWZgxI2rNZFNIFSSFjiUpdLxVXPQWd183lKMn1NQ36Xj762RWPD4Vh7PU\njNlLZUwKGc2kkNGU1VewNXcPf+TuQ9NcS21zHT9lbuKnzE0k+sayYMQtQnNbe3uin17E0cefRK+p\nIWvZW+A9HRzcz+lqeTpyeykuShm1DbozDEV0NTUcX/YWmM1InZzIvSaRz1MF0eYuV/Hc1IX4Op1p\nYe6hcOP62Cv5W9vaPHEy30wXce22GpybTJR9/R2pJ/4k/J4FxHhHdlqwGZubKfpmHcU//HQqzdHL\nk0Hz78Rt1AhEIhFVtVpW/ZgGgJ+XA3qPDB75dTN6k3C8k72Sm+KuIWnQOMQXaHt/OoE+Thw5XklB\nDwk1EMTDNdGX43qrM7/JP2T6nhqkLTrSX/wXEQ8/hOdkIY3RpNMJFuutbSpSwuTsSnAk1juSx8Yt\nQGl/7oV+2xrHhHBPrk/qroAdh/5IPC1rV2Nv1FtFmnNMNNFLFlnbOfQUIpGI62Nn4ergzId/fkmq\nn5ngIDmDC7SU/fY77uPG4ho/tNPjRQYLQi23pI5mnaHdvGZobOTQXfcKRkbdpCkvH159mxutj2jI\n/u2Fc54jcXRkxIcrOyXWlixZQm5uLhERETz88MPC+RIJ119/PfPmzWPJkiU0Nzfz+uuvs3DhQj79\n9FMqKyt5ZvESHn38MXJcy0kpTKchv5aXt7/N4wvvJ7+sjPKjKdzhIWwItvxnKXvP8ztbra7gFkcl\nrn6BFLW0sPqjj6j67keucBdSgo821LOiKJ/ZHl7cFRjC0alB7Mg9gRdCmrzhVzVZWVk8++yzREZG\nUlpailqtPu/rvxiwCTUbNmzYGIBYImqWtEfNocNC3Q9dS3u0EBnsxrHcao4XaDCZzIjFnY9OWNLk\nIoJc8VIJAlGrb+bXE9v4OXMTjfpTaZqjAxK5Omo6g1wD27mRpaLml027MWHmZFEtsaEXtksuO02w\nbMvdy/a8vWi0FsHyOz9l/k6Ux2CSQsczJnAYDlLBsCFPU8TW3N3szD9Ao66N4DCLMNa649AwiPcf\nvhV7ad80cXV1sufu6+JY+sWflFc38dmvGdx1bdx5z/Nx8uLmoddyw5DZJJcdY2vObv4sScVkNnGk\nNJ3HN/6Lu0fcwpjAYdh7uBO9+ClSlzyHWa9jTuk2vgiZxajYc0dNzrimm6Mg1NpE1MwmEyeWvYW+\nRoi6ll07mi/yheazno7u/HPKwvM28haLxO1q83bmH2CP+3ZG/JiBR60RzyP5HPrXi3w4PZTJ4RPO\nmepqNpup3refnFWfoGtdrImkUpxnTUM6YwL5IgPH8g/SoGtkw74TtHhqsJPqMfjV82OWUAMkEYmZ\nET6FubGzOuW02R2CWlMsa+pbqGvUdat29GxMHjQGp1uUrJW/zYxtVdgbTBz/75voamrwnTWTzNfe\noDZFqFvNCLFn20gllw2eyJ3Db+xUNLptjeO0UUHER3TNsr8dEVdSE+lPxr9fw9TcjNuY0UQ8+nCv\nNkCfNngizvZOLN/7EVtGOOJX3oy8xSy4QHYhBTKqNa3cZOqZea2vUSqV2NnZIZfLcW+NXK5cuZKY\nmBgWLlxoPe7ll19mypQp5Ofn09jYiNFoZOaMK/Dy9uKDQ/9jm9ceCptKeWXvO4idHXEfGoe3nYO1\nt+X5uMrj1CaOu52MGSoPDtbXWoXaL1WVjHZ25WofX35P8iLTQ4eXfzCxXhH8zW86c5+8ntWrVzNm\nzBgAAgIuLFV2IGETajZs2LAxALFE1CxGIhXbtgGCi15XdnwtWBpfNzUbKKqoJ8jH+TxnCKhrtGS2\nRuHGD/VDZ9CxMXsHP2RupL7lVMHZMN8hzBsym1C3jm3VwwJdEYuE1m9Z+dU9uqDxcfLipqHXMG/I\nVRwtO8aWnN0cLknFaDaRqT5Jpvoknxz+mlEBCRTVlnJSk9/ufE9HdyYFjWHdOi26BhnTxoX0mUiz\nMCnRn53JxexPL+PnXTmMj/cjZlDn3iOJWMJwvziG+8VRo63l56zNrM/aQqOuif/u+ZApg8ZyR+I8\nnCIjCHvgPk4sW46zsYmbq3YhF1/Xpfv0dlOQVaBp10ut+LsfrGmFteOi+cIoOCn6Kr14durDXa7n\nUto7MjNiKleET+Hk6CxyXl2GY4GasMIWHNZns25SJWvTfibBN5ZojzC0Bi0NuiaadFqMFWoGbc7E\ns6DWOl6er4ztI5TUOh6GXYfbX8we7FoDpvWtGbvxPjHcnnh9r5sRnO782NOL/GF+Q1De+BQfOCzj\n8k1lODabyPt4NeW/b7bWpGUH2LN5jAu3JV7PrIikTkcru1PjeC5cE+IZtmI5TYWFuMYP7ZSb7YUy\nKiCBJZMf4rVdK9k2QseVu+toqagk79PPCLt3QafGONe8Jm2NbHUl9TFvzefUpaWDRELtjUn8Wim4\nGBo1XvibE3hoXsJ5Ruha6mNHZGZmsm/fPhITE9s9LhKJKCgoYPz48YwZM4arrrqKCRMmMH78eK4I\nmsxvBX9Q3lBJWckJBiuDGLr0NTQHD2Fs1p7lSqfYevhPvtuxg2K1Gm1LC0aTCaWDA6H3LqBB10Th\n088yYuoo1gwBjVKoG74m6nJujLua3zf+jlQqZeTIkd1+zQMZm1CzYcOGjQFI24iavrYWzSFhgek1\nZVK3FjFRwacakWblazot1KxpjyITRlUuD/7yEZrmU4vgIV6R3BA3m0iPjs0KLMjtpQT7OpNbUmcV\nfj2NRCxhmF8cw/ziqGmuY0fePrbm7KGkvhytoZk/8vZZj7UTSxkVkEBS6HhivSI4mF6OtkFoLNuZ\n5s89jUgk4t45Q0k7qaax2cBba4+w/LGp2Nt17WftKnfh1oQ5DPOLY8X+1VQ1adieu5eMymweGnMH\n0oh4dqviGK9JxaWmlOwVK4l49OFOL9C9T+ulVpeRSf4XQh1as58bawLVgAh/Zx+em7IQldylS/ff\nFpFIRFhAFKFvvE3G0mXU7D9AQIWeuZs1/DDF1ZrqCiA1mBmZ3siwjCakrWVTdQoxfwx3IidAJjQ7\n7wCzUYLYJMPf3RU3uStXhE9huF9cj/fE6ojeFmoAER6hPDJvCW8q3mDyhnxU9UarSMvzlbFtsidP\nTpjPML/zR3AttE177EqN4/mw9/To82bnMV7hvJj0GC9L3uJEQQvhhS2U/7YR93GjUcWf3wXyfPOa\n1NERp8iITt1LXUamINIAWdIYPiUFs4cdaF3Q5o4gcWJ4p8e6EJqamkhKSuKJJ5444zlPT0/EYjGf\nfPIJR44cYffu3Xz++eeo1Wruf+1Rfireis6kJ6Mym2N1eQydfHbHUBA+S7sO7uFfn33GtbfPYXJM\nII2iZg7/cZD0Tck8WvcTRrMJgx3sti/HTemL3M6B+0fdzqgAQbQ6OFw6xiEdYRNqNmzYsDHA0OmN\n1DXqAHB3kVO5czdmo7CL6NlNFy13F7nVZS6rQMP00cGdOm93SjESjyLkQTl8k3UqihLhHsqNcbMZ\n0gWL6MhgN3JL6nqtQWxbXB2cuTrqcmZHTidLfZKtOXv4szQVd7krSaHjmRA0sl0djmXh6aKUEdvJ\nSFZP4+4iZ/41Q1i+Npniyka+3JjJP646e2+7cxHrFcHrM5bw4aEv2Vv4J+UNlTy7ZSkRslEccYvH\nU19LREMB6h07UQQFEji3c0X33m7Ce9ao1VNTUc2JpcvAZMJoL+XLESJMEhHBLv48M+UhXBw6txlw\nPsQyGTFPPU7OB6so++13PGqM/GNbM79M86TAXktMqZkxB2twbBBCYiaJiKoxETROTWSM0pkkUdYP\nEwAAIABJREFUOzlKmQKFnQKlTIGjTMFH32eyP6UKzGL+dc+4Mwxc+gInhQyVkz2a+pYeMxTpCH9n\nHxZfu4TX5csY8VMmPtUGirzs2D8jhBenPkCQq3+XxjtZXGuNqHalxnGgEuTqz7+mPclS/X8J+DoL\neYuZI2+8zvh338O+EzVyPTGvmc1m8j75FACxo4JPPIswm80opHKqs+LBLCEqqHd6pslkMozGU+62\nMTExbNq0CX9//3PWZSYmJpKYmMh9993H1KlT0Wc38HDSnTzx05PoTM38e+c7PDD6dhJ9h1DRUEVF\no5qKxlN/VzYIfxftyEbiIiM7RE12k5CqXFhUiBmTtXG13NuRhhwNoy4by0Nj7sCnTb1rREQEJpOJ\nAwcOMHbs2F55j/oTm1CzYcOGjQFGVe2pvkQeLg5UrNsOgOPgUByDO04t7AyRwW6oU0o6ZShiMpv4\nPWsvJ5U/IvNowvI1Psg1kBviribRN7bLC5LIIBW/7c2juq4FdU0znqrOW2F3F5FIRJRnGFGeZ2/A\nqjcYOZBeBsCYIb5IJD1rGtEVLhsZxI4jxRw5Xsn327MZN9SPiCDV+U/sAKXMkYVj72SY7xA+OvwV\nzYYWMlv2IYtxJTt8GolZW2nMzaPg8/+hCAjAfezo847p0xpRw2wm++13rDVgG0coqFNKCFUF8czk\nh85rRtFVRBIJofcswE6lovDLtUhrG5nzuwjH0NB2TatdExMIXXAncr+zC4g9KSXsP6oBxFwxNqRf\nRJqFQG8nNPUtPWoo0hFuCleemfUUryvfZfvxTFzDI3hp0j3dEtOWnopSiajLNY4DFU9Hd5bMXsya\n8hdJ/P0k9rVafnxtMbOfff28Tq89Ma9V7dlLfdZxANITPVEjZFRM9riK73TCZyMyuHvzwPnw9/cn\nJSWF4uJiFAoFt9xyC99++y2PPPII8+fPx9XVlby8PDZs2MArr7xCamoqe/fuZfz48bi7u5OcnIxG\noyEsLIxxQcOZMWwq337zLU0V9byx5X0k9hJE55hTZe4K9LXNaFLLcQ5UYchpojFLg51Yys1Dr8XL\n0YNy92KWPLwInww5jaH1ZJVo2LFjB3fddRf+/v5cc801LFmyhKeffpqoqChKSkqoqqpi5syZvfKe\n9SU2oWbDhg0bA4y2PdRcmqqpOnkS4IJ70kQGq9idUkJ+WR1NzfoOU5bMZjMHi4+yNu1nCmtLELeu\nUbwdvfl7wjWM8k/odiSs7UIjq6AaT1XXdvJ7i6MnhHRD6Hzz595CJBLxwNwEHli6FW2LkbfWHmHZ\nI1Owk3ZPPIpEIiYPGkOU52CW7lxFfl0BEqca8kS/U3/bbOyXf4m+pobjy5YT5/0yytBB5xzP0ktt\neG0mLScF+/rUMAdOBDsQ4R7K05MeQCHrHQEuEokIunEeMpUrJ9/7EEN9g1WkyTw8CJ1/B25jRp/z\n81nfpGPld8I5Hi4O3HFVTK/ca2cJ8nYiJVvdqxE1C44yBc9Ne5SikaUEOPt2y8XSbDazq7U+LSHC\ny9o65FLA2V7JnQteZGPuQjxPVOJ/tIQVn73AgpsXn1PQXui8ZtLryV8jNIfWqRzZ4t8EiJgTcyVV\nx92BelRO9r22sfV///d/LFq0iFmzZtHS0sKWLVv48ssvWbp0KfPnz0en0+Hn58fEiRMRiUQ4Ojpy\n8OBB1qxZQ0NDA35+fixatIgJEyYA8MAd95GZkkHy+4cw6gwMviMRZYjwHokQ4aZwxcvRAy9Hd+HP\nKA9+E/3M1g1bqNbnM2XKFOYsnMmKFStO9YsLGo7TcgXvvvsuq1atQqlUMqKN5f8LL7zAsmXLePHF\nF6mpqcHX15d77rmnV96vvsYm1GzYsGFjgFFV06b4+uhBQIgoeE6acEHjWhYUZrPg2tY2kmA2m0ku\nS2dt6s/kaAqsj5uaFbg2xrF87m0XbE/u76nEUW5Ho1ZPVr6GCfEDQ6hZjBGcFHbEhfVtjUxHeLkp\n+MdVsaxcl0J+WT3fbDnOzTM6n2LaEd5KTxLE15JdtAGpfzZ6dLx9Yh0zZscQ/eUBTC0tZLz8H+KX\n/geZ6uw79x4uDvjqqpla9ScAalcJfwxzItoznEUT7+vxPnMd4TPjcuxcXTm+dBlmkwn/a68mYO4c\nJJ2oVVn1Yxo19UIbifvnJvRYfVV3CfQR6tSqaptp1Opx7GXhIxaJu5zq2Ja80jpK1YLj5/ihvWu2\n0h/I7RyY8fTLHLjvfqRaPbGbs3nB7VWemrYQb2XHkdcLnddKN/xGc1k5AJtjJBglIoZ6RzM3dhaP\nbNoBCHN3b6WKh4SE8NVXX53x+FtvvdXh8YMHD2bVqlVnHc/NzY3/ffoF1U017Cs6jExiZxVmHgq3\ndm7AFiY/Nwaee7ndY7fddlu7/0+bNo1p06Z1eE2ZTMZTTz3FU089ddb7uljpv/wOGzZs2LDRIerW\n1Ec7MdTt2Q0IDXrtXLpvzAAwOMAVSastf1a+Br1RT422lpSyDJ7b+gb/3vGOVaS5OajQ5w6hJXUC\n08LH9kgPKbFYaBBruf5AwGA0sS9N6N00Zogv0n5Me2zLFWNCGDJYqJX7evNxcktqz3PG+dmXWoah\nJIzA+hl4OQpjbzRms3u8sADVqdVk/vs1TDrdWccwt7RwXcUOpGYTeglsGO9CjH80T096oE9EmgX3\n0aMYseo9Rn78AcG33tIpkXYoo5ythwoBSBoR2OXG771BO0ORit6Pql0ollpOiVjE6CGXnlADkLu5\nE33v/QC4NJoYvDufZ7YsJU9T2OHxFzKv6evrKfr6WwDKPWScCLLHXaHiobH/h05vIq9UaBcRGdw7\n9Wm9iZvClSsjkpg2eCJDfaLxcfLqUKTZODe2d8yGDRs2BhiWiNoQURX66moAvJKmnHGcyWyiWd9C\ng76JJl2TYFGub7Uq1zdZLcsb9E006oRjFPFq9OYW1qk38e23xjPGdHVw5m8xMzFWBvDeDsGBrCfT\nASODVRzOqiC7qAa9wdTtlL6eIiVbTYNWMKEYSMYIYrGIB+cl8ODS7ej0RpavPcIbD03qdv1cUUW9\nddE3LTaeKSNn8Mnhr/kjbx8H/Y3IYhSMONZEfdZxst9ZSfjCh87YwTebzZx4dyXOzcI420c4ERKV\nwGPjFyCT9H1kqisbF41aPe98I6RqqpzsmX/NkN66rS4R1FaoldVb+3INVCz1aUPDPHBS9Fzft4GG\n56QJVO3eQ/X+AyQc15IdqGbRpv8Q4xnO6IBERgbEt+vj1915reibdRgahDYnfyQqkEikPDZuAc72\nStJOqjGZzNbxL1bef/993nvvvQ6fGzlyJB988EEf39HFhU2o2bBhw8YAw1KjFlsr1KZJlUpUI4Zb\nn//0yLf8kbePRn0TZrO5a4PbgQg4/SwnmSPXRM9gRthk7KUynt26BxB2/Nvu+l8olgWH3mAit6S2\n20YZPYVl4enoIO1XU4mO8PNQcuvMaD76KY2TRbV8tz2buZd1z557T4oQNRSJYOwQXxR2Dtw/+nYS\nfWP58ND/2B1vRlVrYHCxjsrtO1AEBREwp32PteKNG6neKUR4M0PsOeEdyqfj774odsk/WZ9ujVTf\nOyd+wIgMF6U9zo4y6hp1vW4ocqEUlNVRWC6Iiv5oYdGXiEQiBt+7gLpjxzDUNzB9fz2fX2lHWkUW\naRVZfHx4LRHugxgdmMiogMRuzWvNZWWU/vIrACcC7Sn1lDE/cS5h7iHAqeicWAThAR03d78YuOmm\nm7jyyis7fM6+F5uaXyoM/NnVhg0bNv5iqGubkZl0+FZmA+AxcQJiOyFisa/wML8c33LeMUQiEY52\nChzt5DjKFDjK5DjaOVJbZyIlqxYMUm6eHoevqytO9o5EuIdaU9fqGnWkZAtufj1trtF2AZOVr+lX\noWY0mtibKgiY0UN8+z261xGzJ4ay62gxWfkavvw9izFDfLslnC0pazGD3FE5n0oTHBc0ggj3UFbs\nX83GcVnM26TBo8ZI3mefIw/wx330KACqTmaT8+FHSACNk4TfB4ehyxqKSNT7jYkvlKPHK9m4T2hy\nPjHBn7FxAytlL9DbifScqj4xFLkQdreKfbFISBO+1JGpVITeNZ/j/30TlwYjd2S7sCdcSiZVGCWQ\nVZVDVlUOa5LXEewSgNRXjlHj3el5LW/NF5gNBowi2J3gyMTgUUwfPMn6fFaBINRCfF1wsL94l+vO\nzs44O/dMq46/IhfvT96GDRs2LlGqarRENeQjMQpOhF5TJwPQoGvk48NrAXBXqJgVcVkbIaYQhJlM\n+L+D1B6x6EzhUapuZMHmzQB4GqOZEBxwxjH700qtKTc9vXPupJDh76mkuLKBrHwNs8/dD7VXScup\nsvar62+3x7MhEYt4+IZEHnpjO3qDibfWHuE/D0y01hp2hlJ1IznFQo1bR6/Tw9GN56Ys5KesTfyi\n+565v6lRtJhJf30pUa88j4OPDwf+9TzOBhMGMWTMGE5TciQgorq2b9osdBdti4G3W1MenR1l3H1d\n5xs79xVBF4lQs0Sfhwz2wEX514iEeEyagHr3bqr3H0RxOJtph2GaSIRRpaRKKaLYQU+NswSNcw5u\nbhLqA47zbXEaTanjGB2QQLBrQIcmIPVZx6naLWQtpETIcQ4I4q4RN1uPNZvNZOYJae8Xc9qjjQvH\nJtRs2LBhYwChN5ioaWhhZl0OAHJ/P5QR4QB8lvwdNa31QXePuIUE3643Q/ZxV1hTrbLyq5ky7Eyh\nZom++Hs6EuzTc2mPFiKDVYJQK6ju8bG7guV1yu2lJEQMrLTHtgR6O3HzjEjWbMggM1/D+l05XDNp\ncKfPtyywAcadxalPLBZzbfQMhnpH8TlvM/nnXKR6I0eefwGNr5LAaiEdt2J6PLMnzWdH8i4Ayqob\nB7RQ++zXDGtz5ruvixuQAsMSIa3QaNG2GJAPwOhJ2xrHSz3tsS0ikYjB99xNc1k5TfmtbrhmM5Lq\neryqweu04/USqHHSoNmdz3dOX2P2dCUwIo6hceOJCIxGLBJjNpvJ/uhjAFrsRKQkuPHC+LtxkJ76\nbFbWaNG0upPahNpfm4E3G9iwYcPGXxhNXTPOunqCmgW7Zq+kqYhEIlLLM9mWK+zATgoe3S2RBsLC\nIzJYxcFj5R06lDU06Th6ohIQzDV6wxI6KljF1kOFlFU1UVPfgqtT3y+ejSazNe1xVIwPMruBncJ3\n3ZQwdqeUcLKoljUbMhgV44OvR+eaSlsEaXSIG+4u5xZVoW7BPHnby/xkegP/n/9E2WhAmV0DQH2U\nP3PueYYGrcF6fHlVE3Gd14x9SnpOFet3CRseo2N9mJgwMNpBnE5bQ5GiinrCAwfewvz0Gse/EjI3\nFQnLltJcUYG2uARtcQnNJSXWf+uqT2042RnBs8aAZ43ld6QJtpdQxUa2Okgwe6lwdvPCkHUCgAOx\nCuZP+j98ndpLvrZzs02o/bWxCTUbNmzYGECoa7UMqRcWl4hEeE6eRLOhhfcPCg1Rne2V3J54/QVd\nwyLUcopradEbsW8jUg4cK8NgbE177KV0wLZW08cLNIyK9emV65yLjNwqaz+t8fEDf+EplYh5+IZE\nHln2Bzq9kbe/TuZf94xDfJ4UyIrqJk4UCkKrs66WDlJ75s1/mr3apZg27wXA4KLgsmdeRiwW46Sw\nQ24vRdtioKy68cJeWC/Rojfy9tdHMJvBUW7HvXOG9lofqgslsE3UurB8YAq1s9U4/lUQSSTIfX2R\n+/pCG2MnAKNWi7aklPy0bH7+YR9uujpinPVQVYGo5VSrC3mzEQrUGAqE+t86RzGBs2czKiDhjOtZ\nhJpSboefh7IXX5mNgY5NqNmwYcPGAKJKc0qoKaJjsPf0YM2Rb6lorALgjmHzcLK/sC/uqCBBKBlN\nZnKKaokedEo47T4q7Jz7uCsI9b+wvm1nI9jHCXuZhBadkcz86n4RapaFp4NMwrCo/u+n1RkG+bkw\n97IIvtqURepJNRv35TFz3KBznrMn9fxpj2djzH2PkGmUUpeewdAnHsfOSRAUIpEIH3cFuSV1lFc1\ndf2F9AFfbsykuFIQkfOvHnLeSGJ/onKytzZMLigbeHVq56tx/KsjkctRDg4lKiSEf+7T0aIz4nBZ\nOLfOjEavqaGmII8TGYcoPZlFS2kpLrV67AxmTkyP4v5hf+twzKx8IUoXEaw672aMjUsbm1CzYcOG\njVbMJhO6qmq0xcVU5+dSVprHkKuvx9nvzDqu3qI2IxOVXlis+U6bSnZVHr+c2ArAML84xgWOuOBr\nhAe5IhKB2QxZBdVWodbUrOdwVgUgLMh6KwIhkYgJD3Ql7WRVvzS+NpnM1lSuEdHe7SKKA5150yLY\nm1pCflk9n6xPZ3i0N14qxVmP331UEGoRQa7nPK4jRBIJ0QsXdvict1urUKseeELteIGG77cLjqnD\nIr24bGRgP9/RuRGJRAR5O5GRV221vx9IdKbG0caZ85pIJELmpsLLTYVXQiIAOoOOo+UZlNVXMj90\nPBLxmXOP3mDkZKswjurn9iU2+h+bULNhw8ZfDkNDI9qSErTFxWhLStEWF9NcUoq2pBRTS0u7Y3fv\nP8Tkd99DIe+j9JOjBwDQi6WoRo9k6a43MZvNyKUO3DX8ph4RTwoHO4K8ncgvqyezjVA6cKwcg9EE\n9H7z58ggFWknqzhRqMFoMnfJxfBCycrXUF0n9NS62IwR7KRiHrohkSfe2oG2xcg73xzl+bvGdPi5\nUNdorT/fno6E+LgL9XFlVQMr9dHijGkyg9xewv1z4wdsymNbAq1CbeBF1LpS4/hX53zzmkwqY6R/\n/DnHyC2pQ28Q5uHIAd4A3UbvYxNqNmzYuCQx6fU0l5W3CrL2BeD62tpOj+NcreWn5c8z78nXEIt7\nt8+WsaUFp5w0AEq9w1mfv4OC2mIAbom/DndFz+2uRga7kV9W3y6iZdk591TJCQ/s3QarlgWItsVI\nYXk9Ib5912fHsvCU2UkYfpGkPbYlIkjFdVPCWLctm8NZFWw5WMi0UUFnHNc+7bGHhZqbEJ3T1LfQ\nrDPgIBsYy4lvthwnvzV98I6rYrscRewvLM6PZdWNZ9SN9ifdqXH8K9MT81pm/ilzkoigi7fRtY2e\nYWDMrDZs2LBxgejr6ynftIW6tHRBlFVUgMl03vPEcjkt7kryZU1UOkKNkwSNs4RBEUOJ/iEV14Jq\n/Pfl8vWvq7hx1oJefQ3VBw4h1QsRvYrwMPYf+xWAaM9wpg2e0KPXigxW8fv+fNQ1WqpqtSgc7Pgz\nQ3Ca7M20x7bXt5CVX91nQs1sNluF2vAorwFphd4ZbpoRxb60UoorG1n1UxqJkZ5nRDss6Z2DA1ys\nEbCewrvNeBXVTQT59H9D29ySWr7efByAuMEezBgT0r831AUszo9mMxRXNPRafWhXuZAax78iPTGv\nWTbPAryUKBWyHrs3GxcnF+c3lA0bNmy00lxWRslP6ynfvPWMtEULIokEe29v5P5+1j92Pl4c1BWy\nrmgHtS31gLDwHOodzcK42YS7D6I+ppjDDy5EqjchX7uZ30IHc0X0Zb32Wiq3bQegVqogPSAPg8mA\nnVjK3SNv6bB59YXQfkEhpOnoWtNt+sIwwM3ZAS+VnAqNlqx8TZ8tqk8U1qCuEXqCXczGCPZ2Eh66\nIZFF7+yiUatn5boUltwxyiqwq+uaOZYrGND0xuv0djsVqSobAELNaDSxfO0RjCYzMjsJD85LuKhM\nGALbWPQXlNcPGKF2ITWOf0V6Yl6zCDWbLb8NsAk1GzZsXKTUZx2n+Psfqdp/oF3kTBkejmNoCHK/\nVlHm54e9txdiqTDdGUxGtufuZd2xtVQ1nUr7i/IYzI1xVxPjFWF9zMnXn6Db/07JqjV41BrZ//mn\neN3vzTC/IT3+enQaDZojyQAc8/agBiG6dX3sLPycej49L9DLCYWDlKZmA1n5Gso1gimEu4sDEX1U\nwB4Z7EaFprhdnVxvY1l42knFjIy5+NIe2xIzyJ2rJoTy884c9qeXsTO5mEmJgvHN3tRSzEKXhV5J\nWWsr1AaC8+N327M5WSSkNN92ZXSne8wNFDxcHZDbS6wpcwOB3qxxvJS5kHlNU99sNeix1afZAJtQ\ns2HDxkWE2Wik+sAhin/4kfrMrFNPiMV4TBiP/7VXoxwc2uG5JpOJXQUH+Sb9F8obKq2PD1YFc0Pc\n1cT7RHeY7hcyazbVe/bRfOw4I9Mb+Xz9u7jNXUyIqmed5Cp37LQKzuNDtYCIENcAZkdN79HrWBCL\nRUQEqkg+UUlKdiWFFYLb3Lihfn0WiYgMVrEzuZjC8noatHqUcrtevV7btMdhkV4oHHr3en3BbTOj\nOZBeRnl1E+9/n0p8uCcuSntrvWGIrzP+nj1vhCOzk+Dm7EB1XXO/91IrLK/ny9+F+SAqWMVVEzqe\nAwYyIpGIQG8njhfUDBih1ps1jpcyFzKvHW8j7qJsETUbQO9WxvcgJpOJN998k8suu4z4+HimT5/O\nu+++e8Zxy5cvZ8KECcTHx3PHHXeQn5/fD3drw4aNnsTY0kLpht84fN9DZP7nNatIk8jl+F17NSM+\neJfIxxZ2KNJMZhP7Cg/z2MaXWLF/tVWkBbn488SEe3hl+lMk+MactSZLJBYTu3Ah2MsQm2HS7ipe\n3b6iXTSuJ6jYuh2AUlc5NSoRYsTcM/JWpB3YN/cUltSa7KJaWnRGoG93ztum9pwo6P2o2sniWutu\n9aWy8HSwl/LgXKFhbl2jjve/T6WmvoW0k0JT3d50tfRxF6Jq/RlRM5rMvLX2CHqDyeqI2ZcOoj2J\nJf1xoPRS680ax0uZC5nXslqPd5BJrHWLNv7aXDQRtQ8++IC1a9fy6quvEhYWRlpaGosWLcLZ2Zm/\n//3v1mO++OILXn31Vfz9/XnzzTe588472bBhAzKZrSDTho2LDZ1GQ+kvv1L220YM9af6C8k8PPC7\nehbe06chVXRcN2E2mzlSmsba1J/JrSm0Pu7r5MW8IVcxNnB4p+u+HLy9Cf3H7eS8/yGeNQbCD5Xw\nquJdXkh6DLmdw4W9SKAxN4+mPGFTKSNcEGZJIZMJdTvTya8nOb0GQuVkT1RI36XbDPZ3QSoRYzCa\nyCrQkBjp1avXs0SZpBJRvzTZ7i3iIzyZMSaYjfvy2ZlcjE5vxNSa9tibwtvbTcGx3Op+7aW2fleO\nNcXspssj29V6XWxYFualVY3oDUbspP3n/NjbNY6XMhcyr1nq08IDVUgkF00sxUYvctEIteTkZC67\n7DImTZoEgJ+fH+vXryclJcV6zJo1a7jvvvuYOnUqAK+99hrjxo1j8+bNXHnllf1y3zZs2Og6TQUF\nFP/4M5Xbd2A2GKyPOw4Oxf+aq3EfP9Zac9YRqeWZfJX6Eyeqcq2PeSrcuD52FpNCRnfYZPR8+Fxx\nOerde6hLS2dEehNrA3J5U/4RT064p1vjtaVi6zYAjGI4HuSAqVnB3xOvvqAxO8PptWhj43z7NBph\nJ5Uw2N+FrAJNrze+NpvN7GqtT0uI8Or1NMu+5o6rYvkzoxx1bTP708sAIULTm8KlbS81s9nc5/3K\nStWNrNmQAUBYgAt/mxLWp9fvaSw/K5PJTEllI8F92LLidHq7xvFSprvzmtFk5niBzUjERnsuGrme\nmJjI3r17ycvLAyAzM5PDhw8zefJkAAoLC1Gr1YwZM8Z6jlKpJD4+nuTk5P64ZRsDHJPJzLqtJ9h1\ntLi/b+UMaupb+OinNOukPZDIyK3mwx9TqarV9ui4ZrOZmpRUjr34L448+AgVm7daRZpqxHBiX3qe\n+Ddew3PyxDNEmtlsprqphgNFybywbRkvbV9uFWkquQvzh9/I8itfYGrouG6LKpFYTPiD9yF2cEBi\nhun76jlalMrqI99gtqxouoHJYKDyj50A5Pjb02IvxqEiEYXswiN158NFad/OdKE/mj9bFiRZ+dUX\n9D6ej7zSOkrVQi3V+EvQZtxRbsf9rSmQFno7EmIxFGnWGalr1PXqtU7HbDbz9tfJ6PRGJGKRkPJ4\nkUcgTnd+7E96u8bxUqc781pBWR3NrSnoNqFmw8JFE1FbsGABDQ0NzJw5E4lEgslkYuHChcyaNQsA\ntVqNSCTCw8Oj3Xnu7u6o1eouXauiooLKysoOn9Pr9b3e9NZG37D1UCGrfzmGSARhAa4DKgf/k/Xp\nbD1UyKYDBbz7ZBJuzr2/aO8MVbVaXvhoH41aPcfzNfzngYkXHIExGQyod+2h5IefaMw9FQETSaV4\nTZ2C3zWzUQQGWB83m82UN6rJ1RSQqykkV1NInqaw1WL/FE72Sq6LnsHlgychk/ZM6rODjw8ht/2d\nnA9W4VljYOSxJjZK/sBH6cmsyO7Z9tckH7U24M4Y5IChIoAAu541KjkX0SFulKobcVHKiB3k3mfX\ntRAZrIKdUN+kp1TdiF8vLQot/bXEYhGjYi89oQYwItqbpBGBbD0kpPr2dt+rtnNmWVUjLkr7Xr1e\nW7LyNaS21uHNmxbBIL+BYWd/IXipFMjsJOj0/ev82Fc1jpcy3ZnX2kbfIvvIeddGz2E0GklPTz/r\n856ennh5dT29/6IRahs2bGD9+vX897//JSwsjIyMDF5++WW8vLy49tpre/Raa9euZcWKFWd93tm5\n/xt72rhwLJE0s1nYPfzb1PB+viMBvcHI/jShiLtRq+fdb4+264/UX5jNZlauS6FRqwcgM1/DL7ty\nuHrS4G6Pp96xi7w1n6Nrs5kidVLiM/MKfGfNROLsREl9OYfy9reKsgLyaopo0p89mucoUzA7choz\nw6f2SP3Y6fjMnIF6z17q0tIZld7IyQAZa5LX4aX0YKR/fJfHK928GYAmexFF3i7oUyLxiJGf56ye\n4+YZUbTojEwbFdQvEYm2FtSZ+ZpeEWp7UkqsaY8zRgfj7Hjp1izfdW0cIhH4eSh7XbxYzEQAyqqa\n+tROPCOvGgCRCK6d3L05aKAhFosI9FZysqi2XyNq+9JK+6TG8VKmO/OaRah5uSlQDZBTK6AFAAAg\nAElEQVTNWRudp7Gxkb/97W9nff6BBx7gwQcf7PK4F41Qe/3111mwYAEzZ84EIDw8nOLiYj744AOu\nvfZaPDw8hIWfWt0uqlZVVUV0dHSXrnXDDTeQlJTU4XP33nuvLaJ2CdDQpOPoiVNR090DSKgdPaGm\nsflUXdb+9DJ2JZcwMdG/H+8KdiYXW2tfLIXSn27IYGSMT5d7FjUVFHDy/VXUpZ3afbL39cF+2ngq\nYn3Z1FhG7sH3ya8tRmfUn3UcB6k9Ia4BDFIFMUgVyCBVIP7Ovr3qlCgSiwl74D6SH34UWlq4Yn8j\n/7tcylt7P+b5pEcZ7Bbc6bEMDQ1UHziEGMgKcUCiTgCjHe4uffcl7e2mYNHtI/vseqfjpZLj6mRP\nTX0LWfnVJI3o2WhifZOOld8JtcweLg7846qYHh1/oKGU27HwxmF9ci2VkwN2UjF6g6nPDUUsi9pg\nH+dLos2ChUBvJ04W1fZrRM3SwqK3axwvZbozr2UVCJsPUbZo2kWJo6Mjq1evPuvznp6e3Rr3ohFq\nWq0WiaT94kssFmNq7TsUGBiIh4cH+/btIyoqCoCGhgaOHj3KzTff3KVreXl5nTU8aWd36Xwh/JU5\ncKwMg/FU3vjxghoqqpvwcuvYQbAvsTTkdVLYoXCwo7y6ife+T2FouEefpha1pbahhfe/TwWEXfTH\nbhnOohW70OmNrPgmmZfuHtep3luGJi2Fa7+m9OdfMBuFXPwWlSNHR3lx0K0Jg3EnpHR8rpPMkUGq\nIEJUgYSqAglRBeKj9Oy0c2NPIvf1Ifi2v5P74Ue4V+sYfayZvUNEvLrzXV6e9iSejp1LIUz97QfE\nRmEOk45JpH6fCjDj4dJ3EbX+RiQSERmkYn96mdWauidZ9WMaNfUtANw/N+GSWtT3N2KxCC+VguLK\nBsqq+raXWla+sKi91Gp5LM6PJZUNGIwmpH0c5a5r1JGS3Zr2aIumdZuuzmsNWj2F5YKz8aX2mf6r\nIJFIiI2N7fFxLxqhlpSUxMqVK/Hx8SEsLIxjx46xevVq5s6daz3m9ttvZ+XKlQQFBeHv78/y5cvx\n8fHhssu6Vzti49Jl91EhtdBFKaO2QSiC35Na2u8pNAajiX2taY9jhvgyOTGAZ97fQ12jjg++T+WJ\nW0f0y329/32q1SzgwXkJRAW7MW9aBF/+nkVKtpqN+/OZOTbkrOebzWbUO3eT98mn6KqFBZZBIuJg\njII/YxQYJe0XeW5yV2uEbJAqiEGugbgrVP2e/tkW3yuvoGrPXurSjzEqrZET/lLUqjr+s/NdXkp6\nHIXs3GJLZ9RT8PtveADVrnZcMfFWftm9FwB317+OUANhYbI/vYzckjqadQYcZD3z1XQoo9xar5U0\nIpAR0d49Mq6NU3i7C0KtLyNq6hot6tpm4NKr5bFEsAxGM6Xqxj6PaO1PK8XUmvdoq0+7MLoyr7U1\nDrMJNRttuWiE2rPPPsvy5ct54YUXqK6uxsvLi5tuuon77rvPesxdd91Fc3Mzzz33HPX19YwYMYIP\nP/zQ1kPNRjuamvUczqoAYNrIIFKy1ZworGFPSkm/C7WUbDUNrTVg44b6teuPtCO5mAkJ/oyN61sj\nhL2pJexMFur5Zo4NYWiYEL6fe1kEe1JKyC+r55Of0xke5YWX6syIZFNBISff/7BdmuNJfxk7hjtR\np5SgsJMz1Dv6lChTBeDiMPDrQEViMWEP3k/yQ49g0umYd1TMe5PMFNaW8N89H7Jo0v3nTMH8YceX\n+JcLtXYuk8ZhaD41T3n0YerjQCCqtZ7DZDJzsqiW2NALNzVp1Op55xvB8VflZM/8a4Zc8Jg2zsSn\nNQuhrA+FWtYlvKht2+S4sLy+z4WaJe3R39ORYB9b2uOF0JV5zZLKK5WICfW/+I1xbPQcF41QUygU\nLF68mMWLF5/zuAcffLBbxXo2/jocOFaOoTXdbNxQP5wUMk4U1pCRV01VrRb3fkw7s1giOzpIiQ8X\nBFHb/kgr1x0lbrA7SkXfbD7UN+lYua61vsdV3q6+x04q5uEbE3l8+Q60LQbe+fYoz88fY416GbVa\nMj5bTc2vWxC17tDWKMXsGO5Err89sV4RTB00jtEBidj3kCtjX2NNgVz1MXYlVcwrieKrgGpSyjP4\n6M+vWDDi5g6jgHmaQoo3b8YfMItg9LV/50jpKYMUj79YRC0s0BWxCExmIaWtJ4TaJ+vTrVGXe+fE\n49RHvzN/NSzOj2pNU5+l6lkWtQoHKQFel5aY8HZTWOv++rpOrW3t9rihfgMqg+FipCvzmiWVd3CA\nS782Orcx8LC5Ytj4y2ERQ54qOeGBru2aee5JKe2v28JoNLE3Vbj+6CG+2EmFX8+2/ZE09S2s+imt\nz+5p1Y9paFrrex6YG39GfU94oIrrWpvMHs6sYOuhQrT6Zv747hO23/l/1P6yGZHJjEEM++IcWf+3\nQcRPu5q3rnyBf059hEkhoy9akWbBd9ZMnGMEwyKfPScYIxZaCWzJ2cVPmZvOON5oMrLywBqicgVh\n5hgXi727G1U1p4TaQGnH0FfI7aXW5r6ZPdD4+ujxSjbu+3/27ju+qvp+/Pjrzux9s0nYGUAGezug\nrV+3YJU6qrVqLSrapdXa+v32Z23FtlYcxapt1dav4qi1Wu1QtF9ZAkJCCCQECEmArJu9c9fvj3PP\nJYEEMu7IzX0/Hw8extyTcz7GcHPe5/MeFQAs98EudCBRZ6nZHUpKojeoN7UZ6TFDqo31Jzqd1jW3\nzNudH/vWbkt92ugN9X3N4XC4Hj6Mtx1iMXoSqImA0tVj5YuDtYDyi0ij0ZBsCnOlGqhpH76w/2iD\nqw7s9F+S6nwkgI93VfFFSa3H19O3vmfl/DTmZg1c33PdRVmkxIehCWvmjU9e5G93fxP9y+8T7Pxv\nKU8NovDWpVxw5308s+oXXJ97FUkRw58lMlZptFqm3XMXWqMRh9XGBVsbSQtLAuDVfe+wo2pPv+P/\nfuhjekuPEtmh7OpO+PKXAVy7P1HhRoyGwHuiqrazHu3g664eK087Ux4jw4zcsSrHLesTAzt9lpqn\nWW12Dlc1A+P3plZNf/T2jppau50UFyrpd24ylPe1k+YOV8lDVrr3RlwI/yCBmggouw/W0mtVbpD7\nBkPqxwfKG2hq7fbJ2tQgMSRIT37GmW1cb7tyFtERStfHZ94spLN78Lb1o9XZfVp9zxUD1/e09bTz\ncfl/CJ7+f6y0fMQN24uZUKPswLVHGGi96SIu+/VG7rnse8xNyUHnwbb5vhSSnMzEm24AoKv8GLc3\nT3bV2T39+UscMh8FoLqtjk373ye7XPkZ04WGELtwAQDmFmU3wpept76kNoVobO3B3Dzyv4N/+vCg\nq7HFHatyfNYpNVAk9umU642GIsdOtrrew7O8OLfNm9KctWHH69qx2Uf+0GI4+tZuL5W0R7cZyvua\nukMM4/fhgxg5CdREQFGDobioYDL6dAtTu1s5HLB9v/fTH212hyvtccGMpAF3VCJCjdx5dS6gpBi9\n9P4Bj63nj+8fcO3w3PnVvH41cXaHncKaA/xm24vc8e4DbHn3T1z81kHmHehEZwerVkPrksWsfPEl\nLr76W0SHBMaT2eRLL3GlQDa+83e+N+kqjDoDFpuFx7dspLa9nud3v4qjp5fplUowa1q6FF2QEkg0\nOH+JB1Jr/r763qCo84SGq/hoA+9vUYLihTOTWJ7v29mDgSAsxEBEqJISXdPg+UCt701txjjr+KhS\nG4go8+m8M/bg9Npt4R5DeV9T0yJjIoKIjwnM938xOAnURMDo7rWy25n2uCQ3pV9tQ2p8OJOcueTq\nHDNvOlje4Jr1tDRv8HqaxTkpLHMGlR9uP9ZvaLe7FJbV84/txwA4Lz+VRbNOraezt4tH//MUj/7n\naQ4Wf85lHzdw6ZZWIjqVX/BV0RN4Me0q/twxkw5rYD2RVbpA3ulMgbTS8/JfWDf/ZjRoaO1p54F/\n/YLiukNMrerBaFWeksdfeL7r6107atGBVZ+mSo0PJyxEueEvHUGdWo/FxtNv7MXhUIKHtVfnyq6A\nlyQ60x+9kfpY4uz4mGIKIzLMv+tbB9Ov82ONd9IfT6/dFu4xlPe1vvVp8p4lTieBmggYe0rq6OlV\nhiwPVCitPkXcf8RMS3uPV9em7vQFG3XMGaQWTHXHqlxXB7un3yigu8fqtnV09Vh5+o1T9T3f6lPf\n09TVwn9/8gSlVQdZXNjOjR80MrFGqUMLSogn60cPkPXQQzQbImjt6HUNyA4kISkppN94PQAdR46S\nsvMYN+atVv7dogRis6uUoDYoMcG1A+dwOFzNRAJ1R02r1bjShEYSqL32zxJO1CuBwm1XzArYFFJf\nUNMfvZH6GAhNF5JNYeicDxK90VBkoNpt4R7nel/r7rFyrLoVOFXPJkRfEqiJgKEGQzERQWRNOvMN\ncWmusnNkd+AaOu0NdrvD1W1yXnYiQedoJBEdEeRqkFDb2MmfPjzotrX0re/59qpcV31P5ZFi/rTh\nQea8XcS33jazoFhJc9To9Uy49qvMfmYDcQvnk5+RwH85B19/VnCC7UW+a87iKymXXUJEdhYAVa+/\nwYXB0/nK1PMAiOi0k3hCCSYSLrzAdUPU1mlx1d2YAnRHDU7dfB8+3ozF+f0YikOVTbzz6WEA5mQm\nsHJ+mkfWJwbmmqXm4dTHlvYeqs3K35/xfFOr12lJcXZ+9EZDkcFqt4V7nO197fDxZteA8fH88EGM\nnARqIiD0WmzsOlADwOKcZNfTyr7SkyJJS1R+OXoz/bG0oolGZwMTtVbuXM6bncrCmUpnwfe2HOVA\necOo19G3vmfRzARyg1o59vKf2LH2Tqq+9zBzt9eSXmtB56xtj54zm9lP/4aJN1znqrMCuOWyGa6B\nzRvf3kdbZ++o1+ZPNDod09fd5UqBPPzUs3wj72pun3s9d2jylUJIIKFP2mNDy6m25oG8E6TeqFis\ndspPtgzpayxWGxs27cXugJAgHXddkyc7Al6mdn5s6+z1aJOjQ+N40PXpvNn5cbDabeEeZ3tfU3fZ\ntBqYPkFSTsWZJFATAWFvaR1dPc60x7MEQ2r6Y+Fhs6tVvqepvySNBt2gLfBPp9FoWHt1LmHBehwO\neGrTXnosthGvocdi47n//ZyMtgquNG9j5ebfsf/BH3PiL3/FdvLUKABbajxpa64h79ePM+PhhwhJ\nOfN7GRp82ty3d703922sCElNIf3G6wDoOHKEmnff50tTl6HfVQJA5IxsgpOSXMf3nT8VaMOu++p7\nkzjU9Mc3Piqj0lnHc8tlM0mICT3HVwh381bnR/VnwmjQuWqKxyu1oUhVXbtrx8UTzla7LdzjbO9r\npc6HD5OSowgO0nt1XcI/SKAmAoIaDEWFG5k5OW7Q49S0D7vdwc5iz6c/OhwO19rmZiUQMow36rio\nEG67Ummbf6K+g9f+WTLs63fX1nLy/Q/4z70Pcs2uP7Kq5j9kNx/G1qbc+Fq1UJ5i5NMFUWgfWcd5\nv32O9Ou/Rvi0qWfdteg7923z7irXjUAgSbnsUiIyMwGofG0T9Z98Stfx4wDEX3hBv2PVDpsAcQE2\n7LqviFCja9jvUAK18pMtvPnxIQByppq4aNEkTy5PDMJbs9TUn4npadHodeP79kXdUevptVHvwUHi\n56rdFqM32Puaw+Gg5JjSCXK87xCLkRvf73RCoKRG7SxW0h4XzUpGd5Zf8JOSI0kxKTcdW/d5PlAr\nq2p27aaM5JfkyvnpzHbOXHvn08P9UoMG4rDZaD1YwrFX/szedd/hi2/dSfkLvyfsxBF0KLnzhuho\neuZn8d55Ufzuq/H8+8tJXHX7AyzOvWBYa7vtylnEOOe+PftmgUdTosYijU7XZxC2lbKnngVAazRi\nWrq437FqI5HwEEPAP1VVb1jO1aLfarPz5Ot7sdkdBBl1rLs2X3YDfCQ+JgT1W++pHTWb3eHafcgM\ngPQ8dZYaeDb98Vy128I9Bnpfq2/uosnZ7VkCNTEYCdTEuFdYZqajW+mMeK5gSKPRuFIjCw7V0d7l\n2eBCrYUz6LXMnzG0tMe+NBoNd1+TT0iQDrszBXKgJgzN+4oo2/A0u265jaIHHuLE2+/QWVnler3W\nGMPnpjxSf/IwpfdezHPTGzk6IYjw8Ch+euH3mZmQMey1RYQaWXt1HqDsGP3Rg3PfxqrQCamk36Ck\nQKq1abEL56MPC+t3nNqaP5DTHlVZzhuWmoZO18iKgbzz6WGOnlDqPW66OJtkU9igxwrP0uu0rp9d\nTzUUOV7XRpezw20g3NSmxoe5gt9KD7XoH0rttnCPgd7X+u6uBcLPtBgZCdTEuKcGQxGhBnKmmc55\nvFqnZrU5XDtxntA37XFOZgKhwYYRnSchNpRvXDYTgIqaNlcqmKqz6jjFD/+Uus2fYmlR2gBr9Hqi\n58ymYfnl/Hbiav6YfjmZt97Ae9Y9vFf2MQDJ4Qn8bOV9TIqZMNL/RBbnJLuGDv9ju2fmvo11KZdf\nSkTmqUA3YcWFZxyjDruOiwrctEdV325+g+0QV9W28b//LAUge1Isly6b4pW1icGp6Y+e2lELtJta\ng17nevjgqR21odZui9Eb6H1N/ZkODzGQYgr3ybrE2CeBmhjXrDa7q9X+olnJQ6prmJoa5SqOV4eA\nesKREy2um5olo6wN+K9Fk5g1Vam9e+OjQ/06SzVs36Hs5mi1JKxcQdYD97Pwzy8Revs6/lAbS6sh\nnFnToiiwfMBnFTsBmBo7kUdW/oCE8HMHtudyx6oc12Bad8998wcanY7p964jOCWZmLmzic7LPeMY\n2VE7ZWJSBEFGZURFScWZ6Y82u4MNm/Zitdkx6LWsuzZfdgLGgERXi37P1KipN7Wm6JCA6Yya5uHO\nj0Ot3RajN9D7WqnznxkTYyRtWwxKAjUxru07bHalLw41GNJoNK4UyT2ldR6rrVKDQL1Ow4KZSec4\n+uy0Wg3rrs3HaNC5bmRtNiUFsnHnLgAis7OYfs9dxC1eiMMYdKq+J8SGddI2CmuU1MS8pBn89wXf\nITI4YtDrDUdUuOfmvvmLkNQU5m58hhkP/xiNrv+cPIfD4apTDJQb0LPR6bRMT1PaVA/UUOS9z466\nPn/9RVmum1nhW4lxp4Zee6JLoXpTGwi7aSr1Z7uytg2Hw73f0+HUbovRO/19zWK1ccSZup0VADWX\nYuTkb6YY19RgKCxYT970+CF/nZoGYrHa2XXA/R0LHQ4HW5wpmfkZCYSHjCztsa8UUzhfvzgbgCPH\nW/jLp4fpbWyivUwZBBw7f57rWLW+R2PsJCp/NxWtSr3a8okL+OGytQQb3JuCtzzf/XPfxovObivd\nzq5rJkl9BE41iyirasLW56b/pLndFehPmxDFqvOn+mR94kyJsUqansVqp6mt+xxHD09nt4VK565S\nVgAFamrnx64eKw0t7v2eDqd2W7hH3/e1I8dbXPXk43l4uxg9CdTEuGWz2dlepKQ9LpyVjEE/9B/3\n6WnRrjS0rR5IfzxW3Uq1WUkRWpqb7LbzXr58iuuJ82v/KuXI5q2u12IXzAdO1fdoQtoIy9lFm03Z\nnbgs80vctfBm9Dr3dx10zX0LMbhl7tt4Yu477FpSH4FTNy5dPTZX2pfd7uDpNwrotdjQ6zTc+7U5\nsgswhiTFnZql5u6GImWVzWovHjLTA+emtu9ucaWb0x+HW7stRq/v+9q/d1a6Pp+RLoOuxeDkt5wY\nt/YfbXANrR7uE8O+6Y9fHKx1dRtzFzX402k1LJzlvkBNp9Vw75rZ6HVaLFY7xR9+CkBwSgohqSmu\ntEh7qJmgGZ9j0ylBwo15q7kp/2q0Gs+9JcRFhXDbFaOb+zYeqY1EQHbUVH3T29SUt3/sOMb+I8pO\n7LUrM8b9wGN/kxR7quumuxuKlDhbmut1GqZMiHLrucey1IRw1HGV7qxTG0ntthi9vu9rn36hZLFM\nSAgnPNToqyUJPyB/O8W4pQZDIUF68jOGnvaoUgO1XqudL0rcm/6opmTmTjMR4eY36bTECK6/KBO9\n3YqpQXlqF7tASXt877OjlLUexJi5G43Oik6j5e6F3+CKrC+7dQ2DWTk/jTmZCcDQ5r4Fgr47atJM\nRBEbGUxCjPK9KK1ooq6xk5feLwaUWYdfXTn8cRHCs6LCja5mCbVubiii1iROTokiyKA7x9HjR7BR\n72rS4s5AbSS122L0+r6v9brSHgMnlVeMjARqYlyy2R2utMcFM5IwjuCXe+bEGGIjlR0ONU3EHSpr\nWqmqbQc81xJ51QXTWBTaisGhpBc6MmZx0tzOn3f+A+O0AjRaO0E6Iz9cfifnTVrokTUMRKPRcNc1\neeec+xZI1GHXIUH6EY9oGI/UNKGSikaeebOArh4bWueO8XDSmIV3aDQaktTOj27cUXM4HK5ALRBv\nal0NRdw4S22ktdti9E6vR5P6NHEu8ttOjEsHyxtcQyWX5o0stVCr1bDEWT+2+2At3b3uSX/cuk8J\nILUaJe3EE/Q6LV+JVmamdWmNvLi3g5++9wra9P1oNBBmCOO/L/wu+ckzPXL9s0mICeWWs8x9CzRm\nZ5MAU7SkPfal3pRX1baz95Ayf2/1BdOYlib1HGOV2lDEnamPNQ2drhT2QLypTe/Tot8dnR9HU7st\nRu/0hw2B1BxHjIz7uwYIMQaoaY/BRh1zshJHfJ6luSm8v6Wc7l4be0vrWJwz+h0w9WnmrKkmosKD\nRn2+gTjsdizFRQAcCUulxL4FfaiSEx+qieDRL3+flIiRf19G66JFk/is4CRFR8y88dEhcqaaXCk+\noxURZiQkyH/e2tTUR2nN39/pNzSp8eFc95VMH61GDIXaUMSds9RK+8zSC8SbWnVHrb3LwpETLUSO\nMlX+UFXTiGu3xej1fV8LNupcgbgQg/Gfuxkhhshud7DNuWs1LztxVDUN2ZPjiI4Iormth62F1aMO\n1I7XtXGsWtnp8lTaI0D7kaNYmpR0oYb0JPSJRwDQ90bx+FX3kxDh2yfT6ty3u3/1Cb0WGz/auPXc\nXzREwUYdv7hzmd/svKipjyYJ1PqZmhqFXqfFarOj0cC9a2aPKIVZeI86S62xtZtei80t/7/UtMeo\ncKPbHub4k76dH7/7m/+47bwjrd0Wo9P3fW16Wox0rhXnJD8hYtwprWiisVVJJxttMKTTalico6Qn\n7jxQQ+8oW8qrAaRGA4s9lPYIp4Zca3Q6YlYqO2cOu5YfLl3n8yBNlWwK4xuXznD7ebt7bWzwo9o3\nNfUxTlIf+zHodczLVhrPrDp/GtmTx8bPrRic2vnR4YC6JvekP5Y4Gw5lpseiUVsgBpCJyZFEeyDz\n4vw5E+TBhw8Y9DpmZyoB8vwZvstqEf5DdtTEuKOmPRoNOuaOIu1RtTQ3hQ+3HaOrx0rBoXoWOAc3\nj2ZtMybHERPpuRvzpl1fABA5aybHbErKY7ZpGnlTUj12zZG4bNlkpk6IcqXijNahyibe/LiMY9Wt\nvPXxIa67KMst5/WUrh4rHc7ua7KjdqbvXT+X43VtTJvgH7ujgS7xtFlqExJGl9bVY7FRfqIFCMxG\nIgBBBh0bvn+BWzvkBhl0zJoa57bzieG5/8Z5HKtpZXpaYP5Mi+GRQE2MKw6HwxUMzc1KcEut0qwp\ncUSGGWnt6GXrvpMjDtSqzR0cdd50eLI2oKe+no7ycgBCZs+ivOkjABak53rsmiOl0WiYMdl9NwwL\nZiRRcqyJoiNmNn10iEU5yUxOGbtzlxqkNf9ZhQTp5WbGjyTGnArU3NFQ5MjxZmx2pYFGoAZqoLR1\n91TjKeF9wUF6sgKwMY4YGUl9FONKWVUzZmfNj7uCIZ1O60p//Hx/9YhT6tQmIoCrm6QnNDp30wBO\npIe7Ps5Pdn+a4Vij1r4ZDTpsdgdPbdqLzTZ2UyD7DruOk2HXws8FB+mJjlDS9NzRUEStT9NoYLqf\n1JwKIYQ7SaAmxhV13plBr3Vr/rc6FLSj20phWf3I1uYM1LInxXq0w1/jrt0AhE5Mp6D3BACm0FhS\nI0aesulPkk1h3HRJNgCHj7fwzn+O+HhFg5Nh12K8UWepuWNHTQ3UJiZFyoxBIURAkkBNjBt90x7n\nZCa49Rd77jQT4SHK+frujA1VXWMnZVXNwKmgzxOsnV207FPa8sfMm0thzQEA8pNmBFQh/mXLprha\nef/vP0uoqnXfsFh3UgM1o0Hn+vkSwp+5Zqk1uCNQU1rzB3LaoxAisEmgJsaNIydaXE9x3R0M6XVa\nV43Ajv3VWIeZTretyDtpjy2FhTisymDuzqw02nqV9CNfDLb2JZ1Wwz1rZmPQa7FY7UoKpH30w2Ld\nTU19NEUFB1QgLcYv1yy1xo5RDWg2N3e5OqJmpkugJoQITBKoiXFD3enS6zSj6sw4GLXVf1unhaLD\n5mF9rZqSmZEeTUKM52YBqW35DVFRHAhpB0Cn0TIrIfAGBaclRrgGJJdUNPH3LUd9vKIzqTtqkvYo\nxgt11llnt5V2Z0fTkSjt0+VQdtSEEIFKAjUxLjgcDrY4g6H8jASPpJHlTTcRFqx0kdw6jPRHc3MX\nJc5aC092e3TYbDTu3gM40x5rlbTHDNNUQo2BGQisvmAa0yYoXR9f/uAg1ebRNzhwJ3VHTRqJiPEi\nKS7M9fFoGoqo9WmhwfpRt/kXQgh/JYGaGBeOVbe6bsKXeii10KDXuXbqduyvHnI3wf5pj54L1NoO\nlWFtbQUgdE4OhxqVFv35SeO/2+NgdDot96yZjU6roddi45k3C7CPoRRI2VET483ps9RGSq1Py0iP\nQauVtGAhRGDym0BtxYoVZGVlnfHnkUcecR2zYcMGli1bRl5eHrfccgsVFRU+XLHwJnWHS6fVsNCD\n82bUHbGW9l6KyxuG9DXb9lUDMHVCVL+nze6mpj1qDAYqkgyu+pDZAVafdrrJKVFc+6UMAPYdNvPP\nz8fG+0KvxeYa9O3JLqBCeFNcVAh6nRJYjbTzo9Vm57Cz+ZKkPQohApnfBGpvv/DSW3IAACAASURB\nVP02W7dudf354x//iEaj4eKLLwbg+eef59VXX+WRRx7hzTffJCQkhFtvvZXe3l4fr1x4g1qfljvN\nRESo0WPXmZ2ZQEiQDjhVd3Y2ja3dHHAGdJ5Me4RTbfmjc3MoaCxTPg6OZGL0BI9e1x9cszKDiUlK\n+tQf3yumrmn0HelGq6Hl1Aw1k6Q+inFCp9UQ76zDHWnq47GTrfQ651XKYGAhRCDzm0AtJiaGuLg4\n15/NmzeTnp7OvHnzAHjllVe48847ufDCC8nIyODxxx+nrq6Ojz76yMcrF55WWdNKVa3SOENt+OEp\nRoOO+TOU9MftRdXn7CS4vagatfGZJ9Meu6pr6Ko6DkDM/LkUViv1aXkB1pZ/MAa9lnu/NhutBrp6\nrDz7VuGoOtK5Q98ZanGS+ijGkcRRzlJT0x5BSX0UQohA5TeBWl8Wi4X33nuPq6++GoCqqirMZjOL\nFi1yHRMeHk5eXh4FBQW+Wqbwkq3O1EKtBlcLfU9Sd8aa2nooOdZ41mPVnb5JyZGkxod7bE1Nzt00\ngM7MCTR1twCQnxy49Wmnm54Ww6oLpgGwp6SOzburfLqehuY+w64l9VGMI2qK90hnqZU4Oz6mmMKI\nDPNchoQQQox1el8vYCT+/e9/097ezqpVqwAwm81oNBpMJlO/4+Li4jCbh9dGHaCuro76+voBX7NY\nLGi1fhnfjltqMDRrqomo8CCPX29OVgJBRh09vTa27jvJzClxAx7X3NbD/iPKz5+nd/rUtMewqVPY\n36N8PzRoyE3M9uh1/c11F2WxY38NJ+rbeeHd/czOTCA20jdph+qMKL1OKzejYlxJcu6o1TV1YrM7\n0A2zGYja8VHq04QQ/sJms1FcXDzo6/Hx8SQkJAz7vH4ZqL399tssX76c+Ph4j5x/06ZNPPPMM4O+\nHhkZ6ZHriuE7XtfGsWql06GngyFVsFHPvOxEthaeZNu+k9x2xawBu5Lt2F+Nmhnpyfo0a3sHrcVK\nqmPs/Hm8W6O8UUyLnUhEkOd28fxRkEHHPWvyeeDZLXR0WfjtW4U8dMsCn6SHqjtqcVHB0tVOjCtq\n50eb3UFDcxcJsUOfHdnS3uPq4Jsp9WlCCD/R0dHB6tWrB3397rvvZt26dcM+r98FaidPnmT79u08\n++yzrs+ZTCYcDgdms7nfrlpDQwPZ2cPfUVizZg0rVqwY8LW1a9fKjtoYonZU1GhgsRfSHlVLc1PY\nWniShpZuDlU2kTXpzBsKtRNlWmIEaYmemwPUtGcvDpsNgLDZuZQUfQZAXoB3exzMjMlxXL5sCn/7\n7CifF9ewpeAky2enen0d0ppfjFdJsX1mqTV2DCtQOySDroUQfigsLIyXXnpp0NdHurnkd4Ha22+/\nTVxcHOeff77rc2lpaZhMJnbs2EFWVhYA7e3tFBYWcv311w/7GgkJCYNuTxoM7h+kLEZODYZmTI4j\nxospbPOyEzHqtfRa7Wzdd/KMQK21o5d9h51pj17q9miMjeVoeA82uxK0BfL8tHP5+sXZfF5cQ21j\nJ8+9s4/c6d5Jm+1LTX2UYddivEnqM0uttqETpg39a9W0R6NBx6RkyV4RQvgHnU7HzJnuf0DuV1tD\nDoeDd955h9WrV5+xq3XzzTezceNGNm/eTGlpKffffz9JSUmsXLnSR6sVnlZt7uDoCaVphqeDodOF\nBOmZm50IKMHi6R0EP99f7Rqs7MmUTLvVStMXewCImT+PwpqDAIQZQ5kWO8lj1/V3wUF61l2bDyhB\n9fPvFHl9DWrqozQSEeNNeKiRsGDlOXDNMDs/qoHa9LRo9Dq/ukURQgi386t3wW3btlFdXT1gDujt\nt9/OjTfeyMMPP8y1115LT08PL7zwAkajFOmPV2oTEYAlud5Lezx1TSUAq2/qosw5nFWl7vSlxoe5\n5nd5QtvBEmwdSj1H7Px57HXWp+UlZkuK7jnkTY/nvxZPAuD/Ck6wvajaa9e2WO00t/cAEBctO2pi\n/El0dn4cziw1m91BqTP1MVPa8gshhH+lPi5dupSDBw8O+vq6detGVKgn/JMaDGVPiiXOB7sSC2Yk\notdpsdrsbNt30jXvp72zl8IypWvoktwUjzaqaNy5CwBtUBCdUxKpP6oM186X+rQhueWyGew+UIO5\npZuNbxeSMzWOcA8OTFc1tXa75uvJjpoYjxJjQzl6omVYs9SO17XR1WMFpD5NCCFghDtqDoeD5uZm\nent73b0eIYakrrHTtYvlyUHSZxMabGBOplLL2Df9ceeBGqw2Z9qjB9fmcDhc9WnR+bnsazjkei1P\n6tOGJDTYwF3XKCmQTW09vPi3/V65bt9h19JMRIxHI5mlpqY9ggRqQggBIwzULBYLS5YsYdu2be5e\njxBDsq3It2mPqqV5yrVrGjpd9XJbC5UUuqS4UKakRnns2l0nTtBdXQNA7IL5FFQraY+ToicQE+K5\n644387ITWTEvDYCPd1XxRUmtx6/Z0Nzt+liaiYjxSG0o0tzeQ7dzl+xc1EDNFB3ikywJIYQYa0YU\nqBmNRpKSkrA5W4IL4W1bC5VALSM9moSYobd+drcFM5LQ65TUxq37TtLZbWFPaR2g7KZ5Nu1R2U1D\noyE8P4fi+jJA0h5H4rYrZxEToXR9fObNQjq7LR69nrqjptVqiI6QQE2MP4l9WvIPNf2xtKIRkN00\nIYRQjbjbwPXXX89LL71ET0+PO9cjxDmZm7socT559Xa3x9OFhxrJm67MxthaeJKdxTVYbXbA8ymZ\nTc60x/Dp0yizmLHYlOBC2vIPX0SokbVX5wHKz9dL7x/w6PXUQC02MhidDLsW45Ca+ghDayjS2W2h\nsrYNgCwJ1IQQAhhFM5Hq6mrKy8u54IILWLBgASaT6Yzdgx//+MejXqAQp+uf9ujbQA2UYPGLkjpO\nmjt442NlVys+JoTpadEeu6altZXWklJASXv82NntMUQfTEbcFI9ddzxbnJPM8vxUPis4wYfbj7Es\nP4XcaSMbUHkuauqjSdIexTiVEBOCRgMOx9B21Moqm10NdjLTY89+sBBCBIgR76h98sknGI1GQkJC\nKCoq4pNPPmHz5s2uP5988ok71ymEy7Z9Sg3Y1AlR/Z7a+srCWclonbsiVc4nwp5Oe2zavQfsys5d\n7Px5FFYrO0CzEjPR6/yqmeuYcseqHCLDlK6PT79RMOTamuFSd9TipJGIGKcMeh1xkcqDiKHMUiup\nVNIe9ToNUyZIja0QQsAodtQ2b97sznUIMSSNrd0cKFda0Ps67VEVGWYkd5qJgkP1rs95em1qt8eg\nhHjaTWGcaFOaiuQnSX3aaESFB3HHqhx++ecvqGno5E//OMjtV+a4/Toy7FoEgsS4MMwt3UPq/Kg2\nEpmcEkWQQefppQkhhF+QibjjRPnJFl79RwltneN7ZML2ompXesxYSHtU9Q3M4qKCXTPVPMFusdC0\nZy8AsfPns6/m1GzBvGSpTxut5fmpLJyZBMB7nx11PRhwF5vNTmObUttrkmHXYhxTG4rUNJ69Rs3h\ncLgCNWkkIoQQp4wqUKutrWX9+vWsWbOGiy66iDVr1vD4449TW+v59tbilJb2Hn783DZe/3cpL//d\ns00QfG2bc8j1pORIUuPDfbyaUxbNSkbtCbE451QqpCe07C/G3q3UOMUumEeBsz4tNSKJhLA4j103\nUGg0GtZenUtYiAGHA57aVECvxX0dbpvbe7DblacN0oJcjGeuWWqNna45kwOpaeiktUN5yJg5UerT\nhBBCNeJA7dChQ1x++eW8/vrrxMfHs2jRIuLj43n99de54oorKCsrc+c6xVn87p0i1y+5bftOuroO\njjfNbT3sP2IGYGne2NlNA4iOCOL2q3JYMCOJa7+U4dFrqd0edSEhhGZlsr9WaSoiu2nuExcVwm1X\nzALgRH07r/2r1G3nNjf3GXYtgZoYx9QdtZ5eG83tg3eIVtvyg3R8FEKIvkZco7Z+/XrS0tL4wx/+\nQFTUqcLflpYWvvnNb7J+/XpefPFFtyxSDG570Uk+Kzjh+ve2TgtFh83Mzkzw4ao8Y8f+apwbEWOm\nPq2vy5ZN4bJlnu246HA4aNy5C4DoOfkcbq2ky6rsrkl9mnutnJ/GZwUn2FNax18+PcyS3GSmp43+\nJtLc0mfYtaQ+inFMHXoNUNvQScwgMwPVtMeocGO/+WtCCBHoRryjtmfPHtauXdsvSAOIiopi7dq1\nfPHFF6NenDi7ts5eNr69D1DafIcFK3H31n0nz/Zlfkv970pLjCAtMcLHq/GNzooKeuqVXcXY+fMo\nqFFSXY06AzMSpvtyaeOORqPhrmvyCAnSYbc7eGpTARbr6Her1UYiGo0yR02I8arfLLWzdH4sqXTW\np6XHerRbrhBC+JsRB2o6nY7e3oEbV/T29qLTSdcmT3vx3f00OZsS3HVNPgucDRB27K/GNs7SH1s7\netl32Jn2OAZ307ylcaeS9ohWS8zcueytVurTZiZkYNQZfLiy8SkhJpRbLlN2Ko9Vt/LWx4dGfU51\nRy0mIgi9Tvo5ifErJiIIo175Ga8dZOh1j8VG+YkWQBqJCCHE6UZ8l7BkyRKefPJJysvL+33+2LFj\nbNiwgSVLlox6cWJwuw/Wsnl3FQAr5qUxLzvRFcC0tPdS7OZOdb72+f5qVwOGsVaf5k1qoBaZlUmb\nwUZF83EA8pKkPs1TLlo0iZypJgA2fXSI8pMtozqfuqMmjUTEeKfRaEh0pj/WDNKi/8jxZmzO93YJ\n1IQQor8RB2oPPPAAVquVSy+9lCuvvJJbb72Vq666iksuuQSr1cqDDz7oznWKPjq7LTz7ZgGgPLG8\n7Uql6cHszARCgpSdzK2F4yv9UU17TI0PY2LS2Et7dNjtHP/LXzn4i8fprKzyyDV6m5podzbpiZk/\nr19b/vxkqU/zFK1Ww7pr8zEadNjsDp7atHdUO9bqsGuTDLsWASAx9lTnx4Go9WkaDUxPi/bauoQQ\nwh+MOFBLSUnhvffe44EHHmDSpEnY7XYmTZrEgw8+yN/+9jeSk5PduU7Rxx/fP+BKn1p7dR4RoUYA\njAYd82co6Y/bi6pdTyn9XXtnL4VlyjDpJbkpY66GwW6xUPbk01S8/Ccad3xO0YM/pvVgiduv07jr\nVN1n7IL5FDjTHhPC4kgOH3/NY8aSZFMYN12SDcDh4y28858jIz6X+nc3Lkrq08T4l3SOWWpqoDYx\nKZLQYEnfFkKIvkbU9bG3t5dPP/2U7OxsbrrpJm666SZ3r0sMorCsnn9sPwYog3kX5/QPiJfmpvB/\ne0/Q1NZDybFGZk7x/7laOw/UYLU50x7HWH2arauLksd+SXNBoetz1vZ2ih/+KRk/+B5xC+e77Vpq\nW/7glGSCU5Ip3KXsqOUnzRxzwet4dNmyKWwpOEFJRRP/+88SFs5MGnZTG7vdQaO6oyapjyIAJDob\nipibu7BY7Rj0/Z8Pq635Je1RCCHONKIdNaPRyPe//31Onhxf6XVjXXePlaffUFIeI8OM3LEq54xj\n5mQlEGR0pj+Ok+6PWwurAaXV85TUqHMc7T29zS3s//F/u4K0mLmzybzve2iNRuy9vZQ89ji1//7I\nLdey9fS4rhO7YD6HG4/R0aukEuXL/DSv0Gk13LNmNga9FovVrqRADnPXuqWjx/XQIU5SH0UAUNvt\nOxxQ39w//dHc3OXaYc5Ml0BNCCFON+LUxylTplBdXe3OtYhz+NOHB115/nesyiEqPOiMY4KNeuZl\nJwLK8Gu7n6c/dnZb2FNaByi7aWNl56i7poaiB35E+2ElBS5hxQVk/egBTMuWMvOR/0EfEQ52O4ef\n2UjVG2/hcIzu/0PLviLszi6rfdvy67Q6ZiZkjurcYujSEiO47ivK97ukoom/bzk6rK9vaD41Q80k\nqY8iAPSdpXZ6Q5FSZ1t+kB01IYQYyIgDte9973ts3LiRoqIid65HDOJAeQPvOW8KF85MYnl+6qDH\nqumBDS3dHOrzi9Af7TxQi9XZuGHJGEl7bD96lH0/fIju6hoAUldfxbR77karVzKJI7MyyfnFoxhN\nSqfAyldf4+jzL+Kw2UZ8zUZn2qM+PJzI7CwKnfVpWaaphBjkht+bVl8wjWkTlJ3dlz84SLV54Nqb\ngaiNRECaiYjA0HeA9ekNRdT6tNBgPRMSxl6TKCGE8LUR1agB/OpXv6K5uZlrr72W6OhoTM6bUpVG\no+Fvf/vbqBcolDkzT23ai8MBYSEG1l6de9adpXnZiRj1WnqtdrbuO0nWpFgvrta9tjnTN+NjQsZE\nR7DmfUWU/Hw9ti7lhnvyrbeQcsVlZxwXmjaB3PU/58BPH6GzsoqaD/6BpbmFjO/eg9ZoHNY1HXa7\nqy1/zNw5tFu7ONxYASj1acK7dDot96yZzXd/8x96LTaeebOAR+5YglZ77t1etTU/yLBrERhCgw1E\nhhlp7eg9Y5aaWp+WkR4zpL8/QggRaEYcqM2cOZNZs2a5cy1iEK/9s4QT9covuNuumHXO+UshQXrm\nZieyvaiarftO8s3L/bPZRFePlS8O1gJjI+3RvGUrh37zFA6rFY1ez/R71xF/3rJBjw8yxZHzi59x\n8NHHaD1wkIZt2ylubSX7Rz9EHxY25Ou2HzmKpUl58hwzfx77ag/iQEmllPo035icEsW1X8rgtX+V\nsu+wmX9+XsHFiyed8+vUepyocCNGg87DqxRibEiKC6W1o7df6qPVZudwVTMgaY9CCDGYEQdqjz32\nmDvXIQZxqLKJdz49DMCczARWzk8b0tctyU1he1E19U1dlFU1k+GHhdq7D9bSa1XSHn3d7fHk+x9Q\n/uIfwOFAGxxM9oP3E52fd86v04eHM+N/fsKhXz9J4+c7ad1fTNGPfsKMh39MUNzQdjrVbo8anY6Y\nOfkU7H8LgJiQKNKjBk+BFZ51zcoMtu07SUVNG398r5i5WQkkxISe9WvU1EcZdi0CSWJsGIcqm6nt\n06L/2MlW1/t71kT/zfoQQghPGlGNWk9PD3PnzmXz5s3uXo/oQ+0sZ3dASJCOu67JG/Ku0oIZieh1\nyv/ebX7a/VHtWhkXFeyzQNPhcFDxp1cpf+H34HBgiIoi5+ePDBikNXe3UlJ/5IzGIbqgILJ++AMS\nL/oyAJ3HKih64Ed0Hj8xpDWoaY+RM2egDQ1xNRKRtvy+ZdBrufdrs9FqlN3fZ98qPGfTGLWZiLTm\nF4FEbSjSd0dNTXsE/PJBohBCeMOIArWgoCBCQkLQ6SR1x5Pe/PgQFTVtANxy2cxzPq3vKzTYwJxM\nZQjy1n0nR9110Nu6e63sdqY9LslN8Un9gt1q5fBTz3L8rb8AEJyURM76nxM+dcoZxzZ0NvHDf/6c\nhzf/iteK3j3jdY1Ox9S1d5D2tWsB6Kmrp+iBh2grPXTWNfTUm+koLwcgdsE8KppP0NLdCkja41gw\nPS2GVRdMA2BPSR2bd1ed9XjXjlq01KeJwJEYq6R6t3dZaO+yAFDibHSVYgojMmx4dbtCCBEoRtz1\n8aqrruKtt95y51pEH+UnW3jjI+UmPmeqiYsWTRr2OZbmKcOwaxo6OXqixZ3L87g9JXX09CpdEn2R\n9mjr7qbk5+up2/wJAGFTp5Cz/lFCkpPOOLbX2suvtvyOpm7le/zXg/9k89GtZxyn0WhIv24NU779\nLdBqsba1sf8n/0PTF3sGXYfa7RGcbfmd3R41Gg05iVmj+m8U7nHdRVmkxocD8MK7+2no09mxL4fD\n4WomIjtqIpAk9e386GwoonZ8lPo0IYQY3Ihr1CIjIykoKODyyy9n+fLlmEymfmlYGo2Gb3zjG+5Y\nY8Cx2exscA7TNRp0rLs2f0Q7SgtmJKHXabDaHGzdd5KpE3zfNXGo1LTHmIggr3ettLS2ceCRR2k/\nVAZAVF4uWQ/cjz70zJtrh8PBc7tf5UiT0oXRqDPQa7Pwwu7/xRQaS25S9hlfk3zxRRijoyn99W+w\n9/Rw4Ge/YPq6u0hYccEZx6qBWmh6GsFJSRQcUNIeM2InE24cekMS4TlBBh33rMnngWe30NFlYePb\n+3jolgVnpKW2dVpcNTkm2VETASSx7yy1xk5M0SGusRaZUp8mhBCDGnGg9sQTTwBQX19PWVnZGa9L\noDZyf/n0MEeOK7szN12STbJpZDfk4aFG8qbH80VJHVsLT/L1i7P9oqap12Jj1wFlRtninGR0Xkx7\n7K6r48D/PELXCSVQNJ23jOn33I3WYBjw+PdK/82Wip0ALJiQz7UzL+Phzb+m09LFr7c9z89W3kda\n1Jk7gnGLFzLzpz/h4KOPYevopGzD0/Q2N5O66krX/yNbVxct+5Q5hTHz59HZ28UhszJgOy9Z2vKP\nJTMmx3H5sin87bOjfF5cw2cFJzhv9oR+x/TdaZNmIiKQxEeHoNVqsNsd1DZ0YtSfSuaRHTUhhBjc\niFMfS0pKzvrn4MGD7lxnwKiqbeO1f5UCkDUxhsuWnVkPNRxq2uBJc4er3m2s21taR1ePM+0xz3tp\njx3HKij64UOuIC358svI+O69gwZpBdXFvFr4VwDSo1K5e8HNpEen8r0lt6PTaOmydPPY/z1Lc9fA\naadRM2eS8/OfYYxVnihXvPwny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viIBGpe1OYM1OqDYrBF1oMdZiVk8pML7vXJeiprWrnr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\nAFgMQQ0AAAAALOb/AR8qNEgcORXGAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f61ec1c6ad0>"
+       "<matplotlib.figure.Figure at 0x7f61f093e590>"
       ]
      },
      "metadata": {},

From 9e5f903f7cf8e18989a14252c387f3770c49af62 Mon Sep 17 00:00:00 2001
From: HuaxiaWu <hw2560@columbia.edu>
Date: Sun, 18 Dec 2016 22:57:23 -0500
Subject: [PATCH 09/15] lab4_1_hw2560

4_1
---
 notebooks/week-4/Lab4_1.ipynb | 38 +++++++++++++++++------------------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/notebooks/week-4/Lab4_1.ipynb b/notebooks/week-4/Lab4_1.ipynb
index 9cda82b..3b67150 100644
--- a/notebooks/week-4/Lab4_1.ipynb
+++ b/notebooks/week-4/Lab4_1.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 12,
    "metadata": {
     "collapsed": true
    },
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
@@ -136,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -145,7 +145,7 @@
      "data": {
       "image/png": 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bw8FfvidPpURuY0eVxq1o0btwvm2f6nVp2KUPG2d9QK4yG3tXD7q9/qFRbeSj\nxC/7jspjJ1J9yVryU5OJnvdfdCol9s3b4NajHzcnjwMgccNqvEaOI2ThCgrUKtL27SLz+CEAXLqG\nY2Fti/fr7/HPhK/K65e489W0MrVL0q9L8Bj5JkFzf0aTnkL8kq/RqVXYNg7FuUsvw1zAju274zFs\nPAD5CXHELZyJNrXwPoy03ZtwG/Aq/p/OR2JuQW50JAkrvitTHv/4cEgYH/60kdA3/ou7kz2zx/bH\nTiFnx8m/+XHHYTZ9/gYACzfvR2ZhTqdJXxs+66YOe5muTWuTlq3kjXmrSM7Mwc3Blte6t6F5zaBH\n7Nm05+m4UR7ahE37ATi9+im6nAyyd69Cn5+LrEpd5A3akfnrHNDpDOUOUFhGoS/QGEpcLNwqY9Vx\nIGYyK3RqJXnXzpJ7/vBjtU3tdmFkJMaz/L1XMJdKaRTW36htVn7wGkP/uwRbJ1fS78Zy+NfF5KmU\nWN1rm+a9i+aw379iAWlx0VhYWlK9ZQea9x7xWDk9T0RNcNlI9P/m1uenJCUlhe+//54DBw6QnJyM\nk5MTVatWZciQIYSGhvLSSy8xfPhwhg0znstz9OjRmJmZsXjxYk6dOsXw4cM5ffq04Y9l3P/4QXv3\n7uWNN954on8sY4zE74lt698aF2d6UvzyEnCgdHO/Pit5L79b3ikYSB8yT3B5uP7S248OekZqXvil\nvFMwssI1rLxTMGj+zdjyTsGIzPbRdZrPkv9w03/ApTwss3i8P+LztPQ5XfK9Lc/auobjyzsFI2Ob\n+pV3CmUyXRH86KB/6UPV05sC71l77kaCAVxcXPjoo4/46KOPTK7ft2+fyeU//FBUe9S4cWOjDu2D\njx/Uvn375+qvxQmCIAiCIJRFRZvH92l7rmqCBUEQBEEQBOFZeC5HggVBEARBEISyETXBZSM6wYIg\nCIIgCBWAKIcoG1EOIQiCIAiCILxwxEiwIAiCIAhCBSDKIcpGjAQLgiAIgiAILxwxEiwIgiAIglAB\niJrgshEjwYIgCIIgCMILR4wEC4IgCIIgVACiJrhsxEiwIAiCIAiC8MIRI8GCIAiCIAgVgKgJLhsx\nEiwIgiAIgiC8cMRIsCAIgiAIQgUgRoLLRnSCn6JxcX+XdwoG33nVKe8UjChXri/vFIx8+xx9cLwt\n71neKRj5LmJbeadg0D+hQXmnYOSDBSPLOwWDiNmryzsFIzq9vrxTMPJHTl55p2Aw1i21vFMwMq/O\nmPJOwWBSdBerAAAgAElEQVRswqbyTuEBb5d3AsJTJDrBgiAIgiAIFYCYHaJsRE2wIAiCIAiC8MIR\nI8GCIAiCIAgVgKgJLhsxEiwIgiAIgiC8cMRIsCAIgiAIQgUgaoLLRowEC4IgCIIgCC8cMRIsCIIg\nCIJQAYia4LIRI8GCIAiCIAjCC0d0ggVBEARBECoAc4nkqf88aQcOHKBz58506tSJ3377rdj6EydO\n0LNnT8LDwxk1ahRZWVlPbN+iEywIgiAIgiA8cwUFBcycOZOVK1eyceNGfvzxRzIzM41iZsyYwTff\nfMPmzZupXr06a9aseWL7rzCd4FOnThESEkK1atUICQkp9jN8+HDi4uIICQmhRYsWqFQqo+eHh4ez\nYMGCcspeEARBEATh3zGXPP2fJ+nChQtUqVIFV1dXrK2tad26NceOHTOKMTMzIycnB4CcnBzc3Nye\n2P4rzI1x9evXL9ZwAPv27WPatGkMHjzYsEypVLJ06VJef/31Z5niQ+n1epYvnMuhPTuQymSEDxxG\ntz4DTcYe3L2dnRvXkRAfg42tHR1f7kX4wOFPNb/Q1wbTcvQAvGpVZecXC9j5+fynuj+AwQ28CQ1w\nRqPTsf1yAnuuJZmMaxngzKimvmi0epAAepi8/RLpKg2W5mZMeikYTzsrJBIJUWkqfj59h4SsvIfu\nW6/XM/fr2ezcvg2ZzJKhI0YwcNDgEuN/XraUX1evQqfT83J4OK+/+ZbJmEULF7Dkp6XUrlMXgB8W\nf8+2LVvIycnB2cWZYSNGglnII9tmQD0vmvs7oSnQs+tqIntvJJuMa+7nxPDGPmgKdP80DR/vvEq6\nWoO7rSX96noR4KwA4EZyDr+cjSUzV/vI/f8jPUfFxz9v4/TNO3g42vFB/840CfErFvfd9sNsPv43\nOeo8nO2sGdWpOeHN6wCQmqVk2qodXIyKJyNHxV/ffVDq/ZsyqqkvbYNdyS/QsenveLZdTjAZ1zbY\nhfGhAeRrdUiQoEfPG+svkKrKp5q7LVM7haBHD4BEIsHSwox3Nl/kdqrK5PYeZGFnR8A7k7GtXZf8\n5CSiFs4n++/zJmNdOnTCs/8gpE7O5CUncWPqB+QnFuZt5VUZ33FvYFOtBgW5auJ/WUnS9q1lbhe9\nXs/2ZQs4e3APUqmM1j0H0jKsr8nYK6ePsWvlYrLSU7G0klOnZTu6DhuL5N6l0E2L5xBx4SxpifGM\n/vQbAmrUeax8dixfyLl7+bQKH0iLsD4mY6+ePsbuVUsM+dRu0Y4uw8YY8tm8ZC6R9/J59dO5+Fcv\nWz56vZ4jvyzm2rG9mEulNOjaj7qdepqMvXX+BMfXLUWZkYrUUk6Vpm1o0f9VQy6RZ49xYsPP5KSl\n4BEYQvtRb2Pj5FrqXNIzs/jg6+85deEKlVyd+Wj8SJrWrVks7vejf7Js/Q6u3Yqia5vmTJ84xmj9\nN8vXsun3g+RrtNSvUZVpb76Kq5NDGVqliF6v5/jaJdw4vg8LqZQ6nftSu0P4Q5+j0xWw4dPXKdBq\nGDD9R8PyIysXEHv1L7KS79J90kw8q9QqdR7pSjVT1x3gzK14POxtmBIeSuMgr2Jxi/44zZbT18nJ\nzcfZVs7INvUIb1T0uXonOYOZW47y951EFJZSRr9Un/7NirexYFpSUhLJyaa/cwBcXV1L3VFNSkrC\n3d3d8Njd3Z3ExESjmE8++YRRo0Yhk8nw8fFh6tSpj5e4CRWmE2xhYYGzs7PRssjISL788kvGjBlD\nx44diYuLA2DIkCEsW7aMQYMG4eTkVB7pFvP7lg1cvXCeb1dtICcnm2kTxuIbGEzNeg2LxWo0Gl6d\nMInAqtVJS0lm+ntv4upeiRbtOj61/DLjE9n+yVwaDerx1PZxv5equBLibsO7Wy6ikFnwYYeqRKer\nuZqYbTL+akI2X+2/WWy5Rqfjp5N3uJuVC0D7Kq6MaR7AtN1XH7r/Db/9xl/nzrFh81aysrMY+5/R\nBAdXoWGjRsVijx09wsb1v7F0xSqsrKx4Y+wYfP386P5yUVslJyfxx+97cHE1/jLs0rUbg4cOQ6FQ\nEBMTzZhXR+HdfzLWbt4l5tY2yIUqrjZM2X4Fa5k5k9oFE5Oh5npSjsn460nZzDkYWWy5XGrO2ZgM\nfjgRhaZAR7+6XrzSxJe5h4rHlmT6r7txsbfhyKyJHL96i0k/bmT7Z+OwU1gZxXVvUosR7ZuisJIR\nnZTGyDkrqennSZCnK2ZmElrVCmJgm4aMW/DvLnN1qeZOdQ87xq77C2tLc77oVp3baSou3TVdQ3Yp\nPotpu68VW341MZuBK04bHrfwd2JoI59Sd4ABfF+fQH5aGuf6hWNfvyFBH0zlwitDKFAqjeLsGzfB\nvUcvbkz7iNzYGCw9KlGQXXicS6RSqnz+X2J/Xsr1j6dgJpMhc3YpdQ73O7lnC7evXGDSwtWoc7JZ\nMnUClXyDCKxVr1hs5aAQXvt8Hjb2juQqc1g1ayp/7tlK086Fx7SnfzB1Wr7Ehu++eqxcAP7cs4Wo\nKxd4d8Eq1Dk5/PDJBDz8AgmsWTwfr6AQRn/2jSGf1bM/4c/ft9K00z/5BFGnZTs2fjfrsXK5uH87\n8TcuMvSrpeQpc9g48z1cfAKoXK14Z9rdvwq9psxCYedAnkrJzgWfc+nADmq1CyM9IZa9P86hx7vT\ncfevwpnta9n9/Uz6fPB1qXP5bMFSXJ0cOLHuB46du8DEGfPYvfQb7GysjeIcbG15pU8Y56/eIDPb\n+Hf/96N/sn3/UdbNn46zoz1Tv/mBr35YxazJjzfwc+XgDhJuXGLgjB/JU+awbfZknL398Qop+WTj\n0r5tyBQ2qLPSjZY7+wQS2KQ1h5bPK3MeMzYdwcVWwaFPRnLiRgzvrf6Dre8NxE5uaRQXVr8Kw1vV\nRWEpJTolk1Hfb6GmtxtBHk7kawt4fdlOxndszIJXupKnKSA5S1nCHv//PIt5gteuXfvQK+evv/46\nb7zxxhPb3/Lly1m+fDkhISHMnj2b77//nrFjxz6RbVeYcogHZWdnM27cOJo2bcqbb75pWC6RSAgL\nC8PX15eFCxeWY4bGDu/dRfd+g7G1d6CSlzftw3pw6PedJmM7dO9Jleq1MDc3x9Xdg8ahbbh++eJT\nze/Ctr1c3LEfdabpTuiT1sLfmZ1XEsnJLyApJ4+DEcm0DHAuMV5Swi++To+hAyyRgF4PbjayR+5/\n964dDB46DHsHB7y9fQjv2YudO7abjt25k569++Dp6YmTkxODhgxh53bj2Hlz5zD6tTFYWBifd1b2\n9kahKByJ1esLRx3z0k2PeP+jqZ8je64nocwvICknnyORqTT3K/vJXFSaiuNRaeRqdRToYd/NFAKc\nrR/9xHtUefkcuHCD8d1bIZNa0KZ2FYK93Djw941isd6ujiisCttdf29ZXGoGAI42CvqG1qdq5X9/\niat1kAtbLsaTnaclISuPP64l0Tb4IZ3GUn5ftAl25WBESqnzMLO0wrFpc+JWLkOv0ZDx5wnUt2/h\n2KxFsVivgUOJXrKI3NgYAPIS7lKgKvxSdunQmZzLl0g7dAB0OnS5ueTGxZY6j/udP/wHrXr0x9rW\nHpdKlWncIYxzh/aYjLVzdMbG3hEAnV6PxMyM1MQ4w/omHbsTUKMOZubmj5ULwF+H99Ly5X4obO1x\nruRFo/ZhnD/0+yPz0ev1SCRmpCXEG9Y37tAd/+qPn8/1E/up17kPchs7HNw9qdG6M9eO7TUZa+3g\njMLO4V4uOiQSMzKT7gIQc+kc3jXq4REYgsTMjIZh/UmOijCsfxRVbi77T57ljaF9kcmktG3agKr+\nPuw/caZYbOM61enQsjFO9nbF1sUnptCgZgjuLk5YmJvTuVVTIqMf77gBuHnyALU79cLKxg57d09C\nQjtz4/i+EuPVWRlcO7KHel37FVtXvXUXPKvUwsysbO+VOl/DwStRjOvYCJmFOa2r+xFcyYmDV6KK\nxXo726OwlAIYrubEpxd+d205c406vh50rhuEuZkZCkspvq6PN0L+ourfvz8bN24s8ad///6l3pab\nmxsJCUVX6xITE41GkdPS0rh16xYhIYUj+Z06deKvv/56Yq+lwowE30+v1zNx4kRkMhmzZs0qtg5g\n4sSJjBkzhhEjRuDtXfKo27MSG3Ub38Bgw2Mf/yDOnixe3mHK1QvnadWh69NKrVx42lsRk1406haT\noaaOV8kfVIHO1izsU4esXC2/X0/kwE3jDsv0btUNJRHr/nr0l8HtW7cICi56PwKDgjh29Ijp2Nu3\n6NS5y32xwdy+VTSaevbMGTIzMmndpi1zv55d7Pkrli9j6Y8/kJubS7Xq1XEIePjlQU87K2Iz1IbH\nsZlqankW/yL8R4CzNd+E1yQrV8u+m8kcikw1GVfVzYb4LLXJdaZEJ6VhbSnD1d7WsCzY05XIu6Yv\nky3dc5zFu46Sm6+hhk8lmob4l3pfpeXtICcqrei4uZOuooG3Y4nxVVxt+HlwAzLUGnZcSeB3EyU3\ndlYW1PWy56eTUaXOw8rLiwK1Gk1ammGZ6s5t5L5+xoESCYqgYBT+/gS8Oxm9Vkvy77u5u2Y1ADZV\nQ9Dm5FBtzrdYVapE9pXL3Fk4H02a6ffwYZJiovDwDTA89vAJ4NrZkyXGR127yPLp75OnVmFt70DY\nyCdbPpYUG4WHb6DhsbuPP9fPlZzPnWsX+XnGFEM+3UaOf2K5pMVF4+JddDw6V/Yj6u9TJcbH37zM\ntjlTyc9VobB1oNXg+0oR9Pqi/6JHr9eTFncHe7dKj8zjTlwC1nIrXJ2LjtkgX28i7pStA9sxtAm7\nDp8gLiEZZ0d7dh48TssGZS9Z+Ud6fDTOlYvax8nLj+gLp0uMP7l+KfW69cNCZlViTFndScks/Lyx\nKzpRD3J3IjIxzWT8soPnWbLvLLkaLdW9XGlyr2ziUkwSdnJLhi3cRGxaFnV9PXg/vCVudqUfAHie\nmT2DkWA3N7cnVpdbu3Ztbt68SVJSEtbW1hw5coTx44t+t+3t7UlPTycuLg4vLy9OnDiBv/+T++6o\nkJ3gr7/+mgsXLrB+/XrDKNuDWrZsSYMGDZg3bx6zZxfvmJTGo+picKxc6m3lqtXIFUW/hHJra3LV\nj+6QbFu3GmV2Nm06dSv1vv4fWFmYo9boDI/VmgKsLExfuLiamM2U7ZdJVeUT4GzNW60CycrVcjYm\nwxDz4Y4rWJhJaObnRIZa88j9q9VqrK2L3g9ra+tiN1MaYlVqrO+7VGltY41KVfjeFRQUMG/O13w6\nfXqJ+xo2YiTDRozkyuXLnDl9ir/MH/5raWlhjlpTULR/ja7EtrmelM3UXVdJU2nwd1IwrqU/Wbla\nzscZ333rZiOjZ+1KfH8s6qH7vp8qT4O1lfFlSGsrSzJVpo/bVzo155VOzbkUFc+p61FI/8UoYkms\npOao8ovaRpVfgFxqum0u3c3izQ0XSFHmE+xqzeT2VchUa/jzjvHl29BAFyJTlI+sI7+fmVxuGM39\nR4FKhYWNrdEyqaMjEnNz7Oo15OJrr2Bha0fVGV+Rn5hA6oF9yFxcsK5SlWtTJqGOuo33q68RMOl9\nrk+ZVOpc/pGfq8ZKXnScWsoV5OeW/BnjF1KLaSt3kJ6UwLnDv2Nj/2RHywrzKfp8tlJYk/eQfHxD\najF1xXbSkxM4f+gPw8jwk6DJUyO7LxeZXIEmL7fEeM/gGry2aANZKYlcP74Pua09AN416nFiw3Li\nb1zCPSCEM9t+RVegfei27qdS52LzwHeWjUJOZo7pUqeSuDg6UKtKIB1HvoW5uRlV/X2Y+sYrZdrG\n/TR5aqRWD7aP6fcqIfIqWcl3CW4ykfjrT+4KpTpPg7WV1GiZtZWMTJXpth3Zph4j29TjUkwSpyPj\nDJ83SZlKLsdGsXh0GEHuTszZeYKP1+5n8ejuTyxXofTMzc15//33GTp0KACvvvoq9vb2fPTRRwwc\nOJAaNWrwySefMGbMGMzNzXF3d2fmzJlPbP8VrhO8Y8cOli9fzpIlSx45wvvOO+8wcOBARo0a9Vj7\nelRdzG8HSh5JOLJ3N0vmzEQikdCyfSfkCgXq+7401UolVnL5Q/d/5I/d7Ny4ls/mLUEqe/Ql/udZ\nMz8nRjbxBT0cj0olV2PceZFLzcnV6kw+N1WZb/j/rVQlv19PoqG3o1EnGECr03PkVirf9q7N5G2X\njTpLe3btYuaML0AioXPnLigUCpT31W4qlcoST6jkCjnKnPtic5QoFIXv3W9r11KnXj38/QNMPvd+\n1WvUYNeO7STc3kelRh0My5v4OjKsoTd64GRUGrnaAuRSc0Bzr23MSm4bVVGH/3aain03kmng7WDU\nCXawsuDtNkFsunCXG8ml/7JVWEpR5hp3DJW5eSgsH34s1vTzZPufF1l/9Bz9WjUo9f5MaRXozNgW\nAejRcygyFbWmAIXMHO69HQqZ8cnU/ZJzio6bm8lKdlxOoJmfU7FOcOtAF/bdeHiJyoN0ajXmCuOR\nJXOFAt0DnTxdXmH73f3tV3RqNflqNck7t2HfqAmpB/ahy8sj/fhRVBGF9e5xq1ZQf+1GJFIpes3D\nT+b+OrKXjd9/jUQioW5oe2RyBbnqouM0T61CZvXwzxgARzcP3Cv7seWHeQx655NSvf6S8tm8eA4S\niYQ6hnyKTixzVUosS5OPqwdulX3Z+uM3DJz4ePlcP3GAAz/PByRUbdYWmZWC/PtyyVerkFo+ehTT\nzsUdR08fDq5cSJdxH+BYyZuXRk3k4IoFqDLTqdqsHU6ePtg4la6OWyG3IueBk+0clRqFVdlGVBeu\nWk9kTBzH1i1BYWnJnGW/MmXWIuZ9/Hapnn/zzwMcWbEAJBDcpC1SKwWa3Afbp/h7pdfrOf7rYkKH\njP9nQZnyfhi5pRRlrvExr8zNN5Q9lKSmtxs7zt1gw6kr9G1aA0upBe1q+FPNq/D+jDHtG9L2s5/J\n1xYgs3jyJ+bPmuT/8E/GtW3blrZt2xot++KLLwz/79ixIx07Pp17nipUJ/jq1at89NFHvPvuuzRv\n3txkzP21o7Vr16ZDhw7Mnj27xJrSh+nfvz/t2rUrcX1BiWsgtH1nQtt3Njy+E3mT6FsR+PgXXh6M\nvh2Bt1/JHafTRw+xcvF8PpnzHa7uHmXO/XlzIiqNE1FFl7V8HBVUdlQQm1l4lu/tICcus/SX6kt6\nOyUUjjI7yaVGneBOXbrQqUtRScPNmzeIiLhJYFAQAJEREfgHBD64OQD8/QOIiLhJy1atAIi4edMQ\ne+7sGf46f559f/wBQEZGOpMmvs34N97k5fDid58XFBSQm2Y8m8Gfd9KNOmbejnK87OXE3WubyvZy\n4jNLN9L0IBuZORPbBnEwIoUjt8p2id3HzQlVXj7JmdmGkoib8cm83LT2I5+r1emITk5/ZNyjHI5M\n5fB95R3+Tgp8HRVEpxceK76OCmIySn8z24M1wl72Vvg6KThaxrbJjYvDXC5H6uRkKIlQ+PmT8odx\nDW6BUkl+qnHpzv39BtWdKKSOjg+sL13Hom5oe+qGtjc8vnsnkoQ7t/HwKfxcSYi+hbu3X6m2VVCg\nNaoJfhwP5pMQFUli9C08fAovbSZG38atlPnoCrRGNcFlVbVZW6o2K/rSTYm5RWrsbZwrF+4/NTYK\nZy/fUuZSQNZ9Nb9BDVsS1LAlAHkqJddPHsDZy69U2/L18kClziM5Nd1QEnEzKobwDq1K9fx/3Lgd\nTdfWzXCwtQGgd6e2DH3301I/P7hJW4KbFLVPauxt0mKjcLr3OtLionD09Cn2vHy1itSYSHZ/+ymg\np0CrRZOrYuU7Qxgw/QekpTjJKYmviz2qfA3JWUpDScTNhDReblj1kc/V6nTEpBTeHBvk4URqtvFn\nwrMoIRCeTxXmxrj09HTGjx9PkyZNCAsLIyUlxegn7d4X0YNfIBMmTODPP//k9u3bZd6nm5sbNWrU\nKPGnLFq178LWtavJyszgbmw0e7dvKbHE4eLZUyyaPZ3J02fj5eNX5rwfh8TMDAtLS8zMzTCXWmAh\nkz3WiUNpHbudStdq7thYWuBua0mbINcSO2m1KtlhY1l4PufrpKBDVTfO3RsF9nWUU8XVBnOJBEtz\nM/rXr4wyv4D4rId3Gjt36cbqlSvJSE8nOvoOmzdtpFuY6ctlnbt2ZfPGDcTHxZGaksKvq1cZYqd+\n+hlr1m9g1Zq1rFqzFhcXVz6e9hmduhTWcG/ZtJGc7Gz0ej1nTp9mz+5d2Ac8fKqek1HpdApxw0Zm\njpuNJaGBzhyPMl0XV8PDFhtZ4eiGj6Ocl6q48te9UWArCzPebhPE33FZJU4/9zAKSxlt61Thu+2H\nydNoOXjhBhHxybStU6VY7Iaj58lW56LX6zl1PYpdpy/TpKqfYX2+RkuepgA9evI1WjTah51CluxQ\nRArhtStha2lBJTsrOoS4FasP/0ddL3ts7x03Ac4KulX34NQDo8Btgl05F5OOMr9s+ejyckk/cQyv\nISOQSKU4NGmG3Nef9BPF6/xT9v5Opb4DMLOyQurigluXbmScKqyNTd33B45NmyP3D0Bibo7XoKFk\n//3XI0eBTanXqgNHtq5FmZVBSnwsp/7YTv02nUzGXjh+kIyUwmMiJT6Wg5t+IahW0ah9gVaLJj8P\n9HoKtBq0mnyT23mYuq3a38snk5S7sZzeu536rU3nc/H+fO7GcmjTrwTWqv9APvno9Xq0mrLnU7VZ\nO87t2oA6O5OMhDguH9pNSIv2JmNvnjpMdmphGVxGQhxnd6yjcvW6hvVJUTfR6/WoszLYv3we1Vt1\nwtLaplR5KKysaNesAQtWrScvP58DJ89y804M7ZoVnyVIp9ORl5+PtqCAggId+fkaCgoKr3rUCA5g\n9+GTZGbnkK/RsnHPQYL9Hv/el+Cmbfl7z0bU2ZlkJsZx7chuqjYv3j6WCmuGzFpJn08W0OeThbQe\n/hY2Tq70mbbQ0AEu0GrvvT96dBoNBaU8luUyKW2q+7HojzPkabQcuhJFZGIabar7FYvdeOoq2eo8\n9Ho9pyPj2PVXhGEqtW71gjl4JYobd1PRFBSwZN9ZGgZ6VohRYAAzc8lT/6lIKsxI8KFDh7h79y53\n794lNDS02HpPT09WrFhRrOPm5+dHr169TP6pvmepY4/e3I2L4c0hvbGQSuk5aAQ16hZ+6aQkJTJx\n5ADmLl+Ds6s7G1YvQ6XM4dOJ4+/dKS0htH1nRr89+anl1/WjN+j2yVuGYaouH4zn55GT+HPlxqey\nv303knG3tWT2yzXR6PRsu3SXa/emR3NSSJkZVtMwF3DNSna81twfmYUZ6ap8tl2+y6nows6MuZkZ\nQxt542ZjiVan53aqitn7b6B7xGBa7759iY2Npk/PHkhlMoaPeIUGDQu/iBITEhjQrw9rftuAu7s7\nLVqGEtkngpHDhqDT6wnv2Yuwl18GwMbG+MvP3MIcOztbLC0La2mPHjnCwm+/RavV4uHhwVtvT+SQ\nRfWH5nYgIgU3G0tmhFVHW6Bn59VEw/Rojgopn3epZpgLuIaHLaOa+mJpbka6WsPOK4mcuXeCUK+y\nAz4OctxtLWl3bwYFPfD6hgulfJfggwGd+ejnrYS+OwcPRztmvdoTO4UVO05d4qc9x9n48X8AOHwp\ngnmbD6At0OHhZMc7vdsTWjPIsJ1Gb32J5N6/Rm99iaeTA7u+KPuNT7uuJuJhZ8WifnXRFOjY8He8\nYXo0F2sZ83vXNswFXNfLngltArE0NydVlc+Gv+M5ftv4ZCI0wJmlJ++UOQ+AOwvnEfDu+9T/bTP5\nyclEzPiMAqUS5zbtqNR/EJfGvgpA/Kqf8R3/FnVXraNApSRp53bSDu4HIDc2hqiF86nyyeeYW1uT\nffkSt75+vHq4pp16kHo3jlmvD8FCKqVNz8GG6cgyUpKYO2EEE+ctx97ZjeT4aHYsX4hamYPC1o7a\nzdvSYUBRTelPn73L7St/g0TC0i/eA2Dyd7/i4Opuct+mNOnUg9SEOL5+ozCf1j0HEVCzriGfeW+P\nZMI3y7F3diU5PoadP39nyKdW8za0vy+fpZ9PIupePsunF34OTlr4S6nzqdUujMykeFZOHoW5hZQG\nYf0N06Nlpyaz+sPXGDJjMTZOrmQkxHJ0zRLyVEqsrG0JbtyKpr2GGbZ1cMVC0uKjkcosCWnZgWa9\nyjaH+8fjRzJl9iKa9/0PHq7OzPngLexsrNl+4Bg/rN3Clu8Lp6Xbuu8IH85ZbLjqtf3AUcYN7s24\nwb15td/LTP9uOWH/eRettoAawf58/vZ/ypTH/aq36UZmUjxrPhyNuYWUel374hlSeMUnJy2ZdVPH\n0O+zxdg4uSC3K6odt7S2RWJmZqiZBtg59yPib1xEgoSd3xTO9zpw5lJsnR99o9WU8JZ8vO4ArT9d\njoeDDV8N7oCd3JKd52+y9OB51r9dOBvF4at3mL/rz8LPG0cb3unWjJYhhSPX/m6OTAkPZcLPu8nJ\nzaeenwef92v7sN0KFZhEX9pra0KZXYjPfHTQM/Kd1+PfGfw0KFeuL+8UjHwb/vCO57P0zvbr5Z2C\nke/cnu70e2XR/1bxUeby9MH6f/eHPZ6kuNmryzsFI7rn7Kvlbk7pb2x82sZ6/PtSoCdpXqz9o4Oe\nkbGpZf9DME+TPLx0ddTPi92BxefZftI6R5r+oz//jypMOYQgCIIgCIIglFaFKYcQBEEQBEF4kf0/\nzg5RnkQnWBAEQRAEoQKoaDeuPW2iHEIQBEEQBEF44YiRYEEQBEEQhApAYibGNstCtJYgCIIgCILw\nwhEjwYIgCIIgCBWAqAkuGzESLAiCIAiCILxwxEiwIAiCIAhCBSCmSCsbMRIsCIIgCIIgvHDESLAg\nCIIgCEIFIDEXY5tlIVpLEARBEARBeOGIkWBBEARBEIQKQMwOUTaiE/wUBRyYV94pGChXri/vFIxY\nD+1T3ikYUaSdLO8UDPpNHVXeKRj54ecN5Z2CwSfbXyvvFIy80mBSeadg8MvXz1fbJF5MLu8UjHSZ\n9Dfl0N4AACAASURBVHJ5p2CwSjakvFMw0vmHceWdgsGHvf9b3ikYmVPeCQhPlegEC4IgCIIgVAAS\nMzESXBaiJlgQBEEQBEF44YiRYEEQBEEQhArATMwOUSaitQRBEARBEIQXjhgJFgRBEARBqADEX4wr\nGzESLAiCIAiCILxwxEiwIAiCIAhCBSBGgstGjAQLgiAIgiAILxwxEiwIgiAIglABiNkhyka0liAI\ngiAIgvDCESPBgiAIgiAIFYCoCS6bZ9oJnjJlCtnZ2SxYsID333+fzZs388477zB69GhDzN69e3n9\n9de5du0aAKdOnWLYsGFIJIVvrLW1Nd7e3jRv3pwRI0bg6upqcvv3+2cbZ86cwcbGBp1Ox48//sim\nTZuIj4/HysoKX19f+vXrR58+fZ5BSxSXrspl2pYTnI1Kwt1eweQujWjs71FifHxGDn2+207X2v58\nFNbkieQwuIE3oQHOaHQ6tl9OYM+1JJNxLQOcGdXUF41WDxJAD5O3XyL9f+ydd3SUxdeAn82mbhrp\ntPQEEELvEEpoqQRIKCpFitIVFCmCUgQR6QgoKKL0DlJC7x2kSYc0ICSQRvpuyib7/bGwyaZBEL/w\nw3nO2XOyM3dm7js77b1zZyLPwUCqw9j27lQ2M0QikfDgmZxVfz3kaWrWG9HxBa2G9Mbzk/epUrs6\ne2csYe/0H99Y3knJyXw9fRZ/XblGRVsbJo39nKaNGhSRy8rKYsrMORw/dQZzMzNGjxiMb8f2mvgH\nj6L4ft4irt24iczIiCED+vF+924AjBgzgZu375CjVOLs6MC40SOp61HrpbrpWVTAY9Y0LJs0JPNp\nLHemzeLZ+b+KyLXYsxmjyi/ajwQdQwOi1m3m7ndzAZA5OfDeN+OpUL8OuXI54T+tIGr9lteoLVCp\nVJxav5y7Zw4j1dOjoV9P6nl3K1Y24uo5zm5eSUZyInoGRlRr1paWvT7W9O/wy2c4t20V6c8SqOha\ngw6DPsfE0qbYvAojNTXD8bMvMfGoS05CPFG/LCH9xrViZS3bdcQu+AP0LC3Jjo8jYsZksuOeasm4\nfvMdpnXrc627Xxlqoyhf+r1H5wZVyFLm8sfJSNaffVCs3MTAWvjVq4xKpf6ur6vDg/h0ei05o4lv\n6mpFVUsZn/x2gSsPkl5bJ6mJKZUGjca4hgc5zxJ4unY58jvXi5U1b9kOq4Ae6JpboHyWQNTC6eQk\nxL522QB65ubUmPoNFRrWJzM2jtAf5pJ86XIRucab1mJg97wdS0BqYED0lm2EzVuIZcvmOA7sj7GL\nM7lyBXEHDxO+eCnk5ZVZn6SMTKbsOM2lB0+paGbMhIBmNHGpVERu2dGr7LwaRnpmNlYmRgxoVZsu\nDdw18UsOX2Hn1VBylHnUc7Tl687NsTaVlVkflUrF4TU/c+PkQaT6+jTv3IsmvsHFyt6/fJZjG1aQ\nnpSAnqERtVq0o92HgzV9KjtTweE1P3Hvr9OoVODesDmdh44rs04AUhMzKg/ObzdPVi0rud14tsc6\nUN1uchITiJr/7T9uNy/o6lGRRvYWKPPyOBqawMmIxFLlJcCXXm5IdSTMOhKqCa9X2Qyf9+wwNdAl\nPj2LHTee8DBJ8UZ0FPxvUG6WYIlEgqGhIStWrOD999/H1NRUK66w7IEDBzA2NiY9PZ1bt26xYsUK\ntm7dytq1a3F3dy+cfbHlvWDx4sVs2bKFyZMnU6tWLdLT07l58yapqalv7gHLyKy9f2FtYsTRsd05\nH/6ECVtPs/PTQEwN9YuVn3/wCu9Vsnxj5bevZkMNOxO+3HkDmb4ukzpW51GSgjuxacXK33maxuyj\noUXCc/Ly+O38Q56kZgLQoZoNQ1u4MHX/nTemK0BKTCx7piyg8Ydd3mi+ADPmLMDGypLTB3Zx9sJf\nfDlpKiFb12FWoI0CLP1lJSmpqRwN2U5YeCTDvhhHzerVcXSoSnZ2NsO/GM+nQwbx0/wfyMrKIi4h\nf6D+fMQQnBzs0dXV5fipM3w2dhIn9v35Ut1qTplAdnw8R5t4Ye3ZnLqLZnGqQxeUaelacmcDemr+\nlujp4nXmEE8PHHn+XY+Gvy4mdOFSLn/yKVJDAwxsX22hWRw3ju4h5v4N+s5eSVZGOttnjcPawYWq\n79UtImvnXI2gr+YgM6tAljyDvUumc/NYCLXbBZD09DGHV8yny5ffYedcjUt7NrF/2Sy6T5z3SnrY\nD/2UnKRn3OjbHdP6DXEeO4nbw/qTm5GhJWfWsAk2Ad2I+G4yWTGP0beriDJdu++bN2mOjpEhqhcr\n0tekR1MHGjhbEDjvBGZGevz6cVPuP0nlUuSzIrIzd91i5q5bmu+L+zXkelSy5vvdJ6nsvx7DlG61\n/5FOABX7DkOZ8oz7n/bGuFZ9qgwbR/j4IeQptOvKpE4jLDsG8njRDLKfRqNnY0duRvFjQllwnzCW\nrIQETrf3wbJZU2rNmsGFrj1Qpmu347969dH8LdHVpcWBEOKPHANA19iYB8tXkHz1GlKZER5zZuHQ\ntzePVq0psz7f7zmPtakRxyd8wLmwGMZvPs6uUUGYGhloyfnXc6VfSw9kBno8Skxl0Mp9eFS1xtXW\ngsO3HrD3ejjrhgRgaWzE9F1nmX/gEjO7ty6zPlcO7+LR3esMXbCKzIx01s0Yg62DK0616hWRrexS\nnT7fzMPY3IJMeTrbF0zjyuHdNOwYCMCe5XOoYFORET+uR1dfn/ioB2XW5wUVPxqGMvkZ94Z/iLFH\nfaqOHE/Y2MHkyQu1m7qNsPTuTNT86W+03QC0cLLExcqYmYfvI9OXMrylMzGpmYQlZJSYppWLFfLs\nXEwN85c8JgZSPmhQlV/OPSQ8MYNmjhb0b+zAtIP33oie5YWOjrAEl4Vy9Qlu3rw51tbWLFu27KWy\nlpaWWFlZ4ejoiJ+fHxs2bMDS0pKpU6eWudxjx47xwQcf0KlTJ6pUqUL16tUJDg5mwIABr/EU/xxF\ntpIT9x4ztG1d9HWltK5eFXe7Chy/97hY+bNhMQA0LcZS8bq0dLZi7+1Y0rNziUvP4nhYPJ4uViXK\nF35ReUGeCs0CWCIBlQpsTYpfyP8Tru8+zI2QoyhS3szA+gK5QsGxk2cYMXgQ+vr6tG3VkmpuLhw7\neaaI7J4DhxgysB8yIyPqeNTEq1VL9h48DMCfe/ZRv44Hvh3bI5VKkclkODnYa9K6uTijq6sekHV0\ndEhOSSEjQ16qblIjQ2w7tCV00TJUOTnEHztJ2t0wbDu0LTWdbbs25KSlk3zpKgBVggNJunKNpyEH\nIS+PXLkC+YNHZakmLe6dO0p9n+4YmZhRwa4ytdr4cPfM4WJljStYITOrAIBKlYdEokNK3BMAom5e\nwb5WfSq61kCio0OjgF7EPwjTxJeGjoEh5k2a82TDalTKHFL/Oo/iQSTmTVoUka3YszfRK5eTFaPu\nX9mxT8mT59e9RFePSr37E7PqtzLXRWH861Zm9alIUhQ5RD2Ts/1SFAH1q7w0nZWJPk1drQm5FqMJ\n2/5XFFceJKHM+2cLc4m+ASb1m5KwYz0qpZL0v/8i6/EDTBsU3VGyDuxF7MbfyH4aDUBOfCx5itLb\n6cvQMTTEuk0rHiz7FVVODomnTpMeGoZVm9IXi9ZtWqFMTyfl2t8AxB08TNLFv1Dl5KBMSSV2737M\n6niUWR9Fdg7H7z5iWLv66OtKaVPDHnc7C47djSoia29phsxAD4AXv0J0knrh/iQ5gwaOdtiaGaMr\n1aFjLSci4pKL5PEq3Dx9hGb+PZCZmmNZsQr1vPy4eepQsbImFlYYm1uodcpTIdHRIfl5n0mIfkjs\ngzC8PvgEfUMjdHSk2Dm6vpZOEn0DTBs0JX7bOnW7ufYXWVGRxbebLr2IXf9m280LGtpX4HhYAvKc\nXBIysjn/8BmN7CuUKG+iL6WpowVHQuO1ws0N9UjPziU8Ub14vhyVjKmhLoa64qjUf4ly9QmWSqV8\n/vnnjBkzhn79+mFnZ/fKaQ0MDHj//feZNWsWz549w9Ly1a2i1tbWnD9/ng8++KBM6f4tHj1LRaav\nh42pkSbM1bZCsQNoTm4ePx65yryebQi5HvHGdKhsbkhUUv4gFZWsoG6VkgcWVytjlnavS2qmkoP3\nYjkWmqAV/51/TY1LxOZrxS/m30YeRT3GWCbDxjr/BcDNxZnwiEgtudS0NBKfJVHN1UUT5u7qwvVb\ntwG4cfsOZqam9PlkOFGPY6hfx4OJX47G1sZaIz9yzATO/XUZpVJJz6AuGBuXvm0qc3JAmZFBdnx+\nXaeHhmHiVvqkVqmLH0927dV8N6/jgTIljSYbf0fmUJXkK39z59tZZMUllJJLyTyLfoS1vbPmu1VV\nJx78fbFE+ZjQW+yeP5nsTDky0wq07j00P7KA5VWFCpVKxbPoh5jblv7CZ1C5MnkKBcqkfAur4tED\nDB0ctQUlEoxc3DBydMZx1FhUyhwSjxwkdusGjYhdcC+STh4j+9nr1UdBXGxNCH2a/6IW9jSNVtVf\nbnX3qVOZG4+TifkXtmb17SqTl6lAmZLvTpH1+CEGlR20BSUSDB1dMKjqSOWPR6NSKkk+fZjEPa/n\nNvMCmYM9uXI52Yn5OyMZ4REYuzqXkgrsfL2J3XegxHjzBvXICI8sMb4kHiWmYmygh00BtwVXW4sS\nF7C/n7rBryf+JjNHSc3K1hpjRIdajhy8GUlMUhqWJkbsvxFJc7fKZdYH1ItXG4f8scXG3pmwqxdK\nlI+6d5PNcyaRpZBjbFaBjv2GA/Ak/B4WdpXZ/fMPhF+7iGWlqrTvPZSq1WqWWSf9ikXbTebjRxhU\nKdpujJxcMazqRJXBn6NS5pB88ggJuzeXucziqGhqQMxzQwvAk9QsatqZligfUKsiR+7Hk5Or7SYT\nk5JJYnoW1WxMCI1Pp7GDBVHJCjKVZXeneZuQiNshykS5H4zr0KED7733HosXL2bGjBllSuvioh4k\noqOjy7SY/eqrrxg1ahSenp64ublRv3592rdvT+vWZd+2ehPIs5WYPLcuvMBEX5eUzOwisuvO36GV\nexWqWJi8UR0MdaUocvI7vyInt8Q34juxaXy15xaJ8mxcrIwZ1dqV1Ewllwts3U4KuY2ujoTmTpYk\nK3LeqK7/JnK5oshi1NjYuIirjFyhXpzIZPmyJsbGyOXq8Lj4BI6ePM2vP87H3dWZeYt/ZuK0maxY\nMl8jv2TeLJRKJSfOnEOhyORlSGUylOnaW37K9Az0zM1KTKNnboZN65bcn71IE2ZoZ4t5h5r81X8Y\n6aFhVB83mtqzp3Op/7CX6lAcOVkK9I3y60HfSEZOVsnPU9m9FkN+3kZqQiz3zh7ByNQcAPta9Tm3\n7Q9i7t/EzqUGl3ZvIC9XWWpeL9AxNCJXrm1pypPLkRZyYdGtYIFEKsW0XgPufPoJUlNT3KZ+T3Zc\nLEknj6Jva0eFFq25+8Uw9CxL3gl5VYz0pWRkKTXfM7KUyPRfPuz61avM1ouvb50vDR1DwyJWubxM\nOVLjQnVlVgF0pBjXqkfE1yPRMTbBYcw0chLiSD1/4rXLlxoZFWnHuRkZ6JqV3I51zcywbNGc8B+X\nFhtv3a4tFo0acemDvmXWR56txLjw+GugR4qi+HMMA1rVZkCr2tyKTuBixBP0pFK1DiZG1KpiTcDC\nbUh1dHC3s2BS52Zl1gfUfrwGBfqUgZGM7KySX4jsq3swZsVOUuJjuXH6EDJTtQEjLSmBiBuXCRj8\nJQFDx3L3wkm2zPuG4QtWYyAzLpNOOoZGRduNQo7UpIR241GP8K9GIDU2wWHct2QnxJJ67vXbzQv0\npTpaC9VMZS76JcxVjhZGWBvrszE6BVcr7XFdBVyJTmFAE3ukOhIUOXksO1P2lyjB/zblvggG+PLL\nL+nfvz8DBw4sU7oX/nolbc2XhKurK3v27OHmzZtcuXKFS5cuMWzYMIKCgpg+ffor5xMXF0d8fHyJ\n8Y4lxmgj09clPUt7oZierUSmp/3zxKfJ2XUtnHWD/9lBHYDmTpYMaOoIKjj7IJHMnFyM9PIHEiM9\naYlvxIkZ+YvziMQMDt6Lo5G9hdYiGECZp+JURCKLg+swfvct5Nm5/1jvfxuZzKiIW0JGRgYyIyNt\nueff5XK5ZiGcnpGBTKYONzAwoH2bVtSsUQ2AYYP609q3C9nZ2ejr57uH6Orq0r5NK4L7DKRmdXdc\nnJ1K1C1XLkfXRHvi0jUxJlde8uRYMcCH1Nv3tNwdcrMyiT10jLTb6sOn4Ut+wev8ESR6eqhyXv7C\ncu/cMY6t+hGQUL25F/qGMrILTI7ZCjl6BoYvzcfM2g6Lyg4cX7MU3+ETsahkT/tBX3B89RLkKUlU\nb94Oy8oOmFhavzSvvEwFUpn2JKcjk5GXqV03qmz1wiZ2+ybyMhXkZSpIOLAXs4ZNSDp5lCoDhvBk\n/SrIzS3zuALgU6cSX3f1QKWCfX/HIM/Oxdggvx8bG+giz1aWkoPaeuxsY8KhG09LlXtd8jIz0TEq\nVFeGMvIytV828rLV/Txx7zZNXSUfP4BJnYb/aBGcq1AUacdSY2NyFSW3Y1vvjqTfu4/iUVEXhQoN\nG1Bt3JdcH/UFOSkpZdZHpq9LRuHxNysHmb5eCSnU1KpizZ5r4Wy7fI8ejWuw7Ng1IuNTODb+A4z0\ndfnx0GW+3n6aee97vVSHW2eOsO+3hSCRUKtFOwwMZWQV6FNZCjn6Bkal5KDG3MYO6yqOHPjjR7p9\n9g26+gZUsKlInTbeANRs7sWZP9cTE34X59oNX5pfQfIyFUXbjVHRPpaXo243CSH57Sbp2H5M6zZ6\nrUVwgyrmdK9XGVRw+XEyWco8LQONoa6U7BLmqm61K7H17xcuRdr9uZqNCT41bFlwIoK49CzqVjbj\n42aOfH8k9B+7HJUnOuJ2iDLxViyCGzVqhKenJ/PmzaNbt+JPlRdHeHg4AFWqqH3sjI2NiYmJKSKX\nmpqKVCrFqNBCxsPDAw8PD/r168euXbsYP348Q4cO1eT3MjZt2lTkJoqCXJ7c+5XycbA0Q5GdQ3ya\nQuMSERabTOd6Llpyt2ISiU2V03XxLlSoUGQrUanUN0X81Kd9cVmXyLkHzzj3IH/r2MFCRlULGY9T\n1BOhfQUjolNefSu2pPWCBPUgZWmk9z+xCHawr4pcoSA+IVHjEhEaHkEXf18tOTNTU6ytLLkfHkG9\n2h4aOTdn9Zaum4sziYnah590SllUKZVKoqJjSl0Eyx88QiqToW9jrXGJMK3mRvSO3SWmqRzoR8zO\nEK2w9PvhGNhoWzlVZThRX725F9Wb50/sCVERJD6OxKqqWvfExw+wqvJqr4B5ubmkFvD5dWvkiVsj\nTwCy5BncO38MqypOL80nKyYGHUMjdC0sNS4RRo5OPDuq7UeZm5FBzrNCJ8kLuGCYeNTBuPp72A8Z\nCTpSJFIpHis3EDp5HFmPiy7ACrP/+hP2X89/nmoVTXGvaEp4nNpv1K2iKeGx6SUlB8C/XmVO34sj\nPav0xfLrkh0bg46hofq2h+db2wZVHUk5c1RLLk+RgTK58Kn7f744kD+KQmpkhL6VlcYlwtjNlae7\n95aYxs7Xm6d79xcJN61Vk5ozp3Nr/ETS791/LX0crMyQZ+cQnybXuESExSbRub7bS9Pm5uURlah2\ndwmNTcK7tjPmMvVhum4N3RmwYt8r6VCrZXtqtcwfw+MeRRAfFYntczej+KhIbJ73r5eRl6skKVbd\nBm2qOhV70Px1yH4ag46BdrsxrOpI8ukj2uXLM1AmFe5jr1UkoLbWXonOf7mpbG5EJTNDnqapX2gr\nmRlo/i6Ioa4OVcyNGNTMEQkg1ZFgqCtlind1vj8cSmUzA0LjM4hLV6f9OyaV4DqVsTXRdrcQvNu8\nNc4jX3zxBceOHePateKvNCpMZmYmmzdvpnHjxlhYqA8FODs7ExYWRk4ha9atW7eoUqUK0ufbVsXh\n6qr2q1SUYo0oTK9evdi+fXuJn1fFSF+XNtWrsvzEdbKUuZy895jw+GTaVq+qJdfSrQq7P+vKhiF+\nbBziT3BDd7xqVGVWsOcrl1USZyIT8XvPDhMDXexMDWjrZsOpEq6dqV3JDJPn1i1HSxkdq9ty5bkV\n2NHCiGo2JkglEgykOvRqUJWM7Nw3PqhIdHTQNTBAR6qDVE8XXX391x7cCyIzMsKrVUuW/rqSrKws\njp86Q1hEJF6tWxaR9e/UgV9+X4NcLuf6zdscP3UWv04dAAjw6cjx02e4FxpOjlLJst9X07hhffT1\n9YmOecLJs+fJzs4mJyeHtZu2EhefgEfN90rVLVeRSdyRE7h9NhQdfX1svFpjUs2NuMPHi38WR3tM\na1bn6R7txcOTXXuxadcGk+ruSHR1cRn+Cc8uXHolK3BxVG/ejiv7tqFISyH5aTS3TuynRssOxcqG\nXjxJWqJ69yT5aTSXQzZTtWb+ife4B6GoVCoUqckc/WMRNVt7Y2D8ctefvKxMUi6epdIH/ZDo6WHW\nuBmGDk6kXDxbRPbZ0UPYdeuJjqEhelbWWHfyI/XSeQBuDx/I3c+HcffzYYRPnwR5edwdPZSs6Nfz\naw/5O4a+ns5UkOnhYCUjqJE9u69Gl5rGt27lYmV0dSTo6+ogkYCeVAe91/T9U2VnkX71AtZdP0Si\nq4dJ3cYYVHUk7UpRn9OUM0ex8g1CYmCIroUVFdp4k/73pdcq9wV5mZkknDiF05CP0dHXx6qVJ8au\nLiSeOFmsvJF9VUyrVyPugPYLjbGrK7Xnz+He9Jmaw3Kvg5G+Hm1rOLDs6DWycpScuBtFWFwSXjXs\ni8huv3yftMxsVCoVf0U8Yd+NCM1VajUrW3HwZiSpiixylLnsuByKm53Fa+nk4dmeC3u2IE9N4dmT\nx1w7tpfarTsWK3vn/AlSE9XXWT578phzuzbi5FEfAMea9VCpVNw4dQhVXh53LpwkPfkZlV1rlFkn\nVXYWaVcvYBP0IRI9PUzqldxukk8fxdovv91YeHmTdq3oVY6vw+WoZNq6WWOsL8XaWJ9mjpb89aio\n/3amMo9pB+4y71gYc4+FsflaNEmKHOYeCyM7N4/HKZm4WRtj8/zgdp1KZujqSEiUF3VD/F9CIpX8\n6593ibfCEgxQrVo1OnfuzJo1Ra+3UalUJCQkkJmZSUZGBjdv3uS3334jOTmZpUvzfcQCAwP5+eef\nGT9+PIMGDcLU1JSLFy+yZs0axo3Lvxfxs88+o0GDBjRo0ABra2uioqJYsGABzs7OGj/jV8HW1hZb\nW9sS49OvbXvlvMb7NWbKn+doN2cLdmbGzAr2xNRQn303Ivn9zC02Dw1AT6qDpXH+NrNMX5d0PV3M\nCl3j8zocuR+PnakBcwM9yMlTsfvmE+4+vx7NUqbHrAAPzV3AHpXMGNLCGX1dHZLk2ey+9YSLj9SW\nAamODn0b22NrYoAyT0Vkopy5R+/zpneX/L7+FP8pozRWPN+JI1g1YCwX1rz6y0dJTBo7mknffk8r\n70Ds7GyZ+91UzExNCTlwiBWr17Fj3R8AjBg8kCkz5+AVEIS5mRlfj/0cRwf1i4uLkyMTvxzNqHET\nScvIoEGd2nw3eSKgNor88vtqJkyejo6ODm6uziydNwsry5dPmHemzaL2D9PwuniMzKdP+XvUeJRp\n6VQK8MF5yADOdu6lka3UxZ+Ek2fISdH2Z86IeMCdabOo/9N89ExNSLp8jRvjp7x2fdVuF0BKXAxr\nxg9CqqtHw4BemuvR0hLjWTdpCH1mLsfE0obkp485vfEXsuQZGBqb4t6kNc2C+mnyOr56Kc9iHqGn\nb0ANz440D/rolfWIWr4Ex1Fjqb1mGzkJ8TyY8x25GRlYtPbCLvh97o4aAsCTTWuwH/IptX5bT55c\nTsKBEJJOHQcgNy2/riT6+qhUKpSpZd9if8GWC49wsJSx84s2ZCvzWHkinMvPr0ezMzdk62etCF50\nirjnL4mNnC0x0NXhzP2iblY/DWhMQydLVMDS/o0BCJh7nKcpZX/BfLpmGZU/Hk21JevIeZZA9E+z\nyVNkYNasNVb+3Yn85jMA4ndupGKfobjPX0meQkHS8f2kXih+sVoWQn+YS41p39DyyH6yYuO4PeFr\nlOnp2Hp3wmFAPy69n381mp2vD4lnz6Ms5Jdftff76Jqb8d6MaZr7ylOuXePG6C/LrM8E/2ZM3n6a\ntrM2UtFcxuyebTE1MmDf9QhWnrzOlpFdATh1L4rFhy6jzM2jorkxX3g3xrOaus/3b1WbH0IuELR4\nB8pcFe9VtmJK16Ivz69Cgw6BJD2NYdkXHyHV06N54Ac4Pn9ZTE2M45dxHzN49m+YWdmQ+CSKw2uX\nkSVPx8jEjPeataFNj/4A6EildB/zLSHL53Lgj8VYVapKjzHfltkf+AVPVy2j8uDRVP9pPTmJCTxe\n+gN58gzMmrfBOqA7EZM+BSD+zw1U6jeUagt/V7tDHN1P6vl/3m4Azj54hrWxPl+1r4YyL48joQma\nGx4qGOoxrp0bPxwNJSVTSXqB3Ud5di55KhUZz8PCEjI4FpbA4GZOyPSlPJNns/pSFFn/4wfjBGVD\novqnF2GWga+++or09HQWL15c7D+2iI6OxsfHh9zcXG7fVp+yv3jxIh99pJ4IJRIJMpkMe3t7PD09\n6d+/P1ZW2tu6Dx8+ZN68efz999+kpaXh6OhInz59CA7Ov2h8y5YthISEEBoaSlpaGtbW1jRv3pyR\nI0dSqdKbu3Ysfd23byyvf8ow1T/3I36TGPctn39KUhI/Pjtf3ipoONb07fqtQle9+svcv03LH4aU\ntwpaDKz1eXmroGF9zM/lrYIWsTdKPi9RHjQZG1jeKmjY4tbn5UL/jzReNLy8VdCwIvj78lZBi/ld\nyn7tXnnyd0/flwv9Q+pufjU3n/8F/l8twd9//32xf7+gSpUq3LhxQyusSZMm3Lnz6v9owdHRkR9/\nLP2/h/Xo0YMePXq8cp4CgUAgEAgEgneLt8YdQiAQCAQCgUDw+ojbIcqGWAQLBAKBQCAQvANIbkM7\nrwAAIABJREFUxL9NLhNvze0QAoFAIBAIBALB/xfCEiwQCAQCgUDwDqAj/m1ymRC1JRAIBAKBQCD4\nzyEswQKBQCAQCATvAO/aP7P4txGWYIFAIBAIBALBfw5hCRYIBAKBQCB4B5AIn+AyIWpLIBAIBAKB\nQPCfQ1iCBQKBQCAQCN4BJDrCtlkWRG0JBAKBQCAQCP5zCEuwQCAQCAQCwTuAuCe4bIjaEggEAoFA\nIBD855CoVCpVeSvxrpKYJi9vFTS8bVcHynJSy1sFLT6zbFbeKmhYHHeyvFUQvCJxulblrYIGSyNp\neaugRXbu2zW1JGfmlrcKGirpZpa3Clqk68jKWwUN5gl3y1sFLaTODcpbhTIRNrLnv16G25LN/3oZ\n/18IS7BAIBAIBAKB4D+H8AkWCAQCgUAgeAcQ9wSXDVFbAoFAIBAIBIL/HMISLBAIBAKBQPAOIO4J\nLhuitgQCgUAgEAgE/zmEJVggEAgEAoHgHUAifbtuiXnbEZZggUAgEAgEAsF/DmEJFggEAoFAIHgH\nELdDlA1RWwKBQCAQCASC/xzCEiwQCAQCgUDwDqAjbocoE29NbSUkJDB9+nQ6dOhA7dq18fLyYujQ\noZw7dw6Adu3asXr16iLplixZQteuXYuEx8bG4uHhQefOnYst7+LFi3z00Uc0bdqUevXq4e3tzVdf\nfYVSqXyzDyYQCAQCgUAgeOt4KxbB0dHRdOvWjYsXLzJhwgT27NnDihUraNasGdOnT39peolEUiRs\n+/bt+Pn5kZGRwfXr17XiwsPD+eSTT6hTpw7r1q1j9+7dfPPNN+jp6ZGXl/fGnqswKpWKhfPm4O3V\nms7eHdm0fl2p8qv/WIlfx3b4tvdi6Y+LtOJaNm5Ah9Yt1Z82nqz+Y6Umbsa0KbRt0ZQObTzp0Lol\nfXr1KFGf+XPn0KFta/w6dWTDS/RZ9ftKfDq0o1M7L5YU0qegTLNGDbj+9zVN2K/LlxHo50u71q3o\nEdSV3bt2aqVJSk5mxJgJNPHyIbBXXy5culJs3llZWUyYMoNm7Xzx7tqLfYeOaMU/eBTFkFFf0rSd\nD17+3di4dYcmbsSYCbTx7UKLjv70/ngYf9+8VeqzlpVWQ3rz1aXdLMm6j983n73RvJOSUxg+7msa\nd+xM594DuXD5arFyWVnZjP92Fk07daFT9z7sPXxMK37pb6vpEPQhLXyDmDZ7IUplribup5Vr6Nr3\nE+q09mbnvkP/E7q8SX0WLV+JV5detPQL4tMJk0lIfAbAk9g4mnQMpGmnLjTt1IUmHQOp3aoTh0+c\nLrYclUrFkgVzCOzYhu7+ndi6sfQ+tX717wT5tqerdzt+WaLdp04dP8qA94MJaNeKsZ8NJz4uVhMX\nHxfLxDGjCOzYht7BgRw/ckhT/uzZs2nl6UmH9u1Zu3ZtqeWv/O03vNq2pW2bNixcsEAr7ubNm/Ts\n0YPmzZrx8aBBPHnyRBMXHBREyxYtaNmiBS2aN6dhgwb88MMPRfIfNXIEzZs00ug2f85s2rdphW+n\nDmxYV7puq35fiXd7Lzq2a8viRQs14QqFgk8GDqBju7Z0aNuakcOG8PDBA620e3btJLhrIG09W9Cr\nexDR0dGllqVSqfh54VyCvNvyfudObN+0vkTZRw8i+Wr0CIK9vejXPVAr7vGjh0we9zk9/DrQw68D\n304cR2JCfKllw/Mx8POxNGnTgcCeH3Lhr8vFymVlZTFh8jSaeXXEu0sw+w4eLlZu6Kgx1G/eWivs\n8tVr9Oo3kOZeneg98BNCwyNK1EelUrFg3hw6ebUmwLsjG19xrvIpZq5q0bgB7Vu3VH8KzVUveBIT\nQ9uWzZn1XenzfVJKKsMmz6Zh1/4EfDKG89duFit38PQFPhg9mfqB/Zg0f1mR+IV/bKLNh8No3uMT\nRkydS/yz5FLL/V9CItX51z/vEm+FO8TUqVORSqVs3boVAwMDTbirqyvdu3d/rTy3b9/O1KlTqVix\nIlu3bqVOnTqauNOnT2NjY8OYMWM0Yfb29nh6er7+Q7yKTlu3cO3KFTbv2EVaWiojhnyCW7VqNGzU\nuIjs2dOn2LF1C7+tWouBoSGjhg/F0cmJgMAugHrhv3H7n1hb2xRb1oCPB/PRwEGl6rNti1qfbX/u\nIjUtlWGDP8HdvRqNGhfV58zpU2zfuoWVq9diaGjIp8PU+nR+rg9AfHwchw4ewNpGWydfP3969+2H\nTCYjKuoRQz8eRMPqTri5OAMwY84CbKwsOX1gF2cv/MWXk6YSsnUdZqamWvks/WUlKampHA3ZTlh4\nJMO+GEfN6tVxdKhKdnY2w78Yz6dDBvHT/B/IysoiLiFRk/bzEUNwcrBHV1eX46fO8NnYSZzY92ep\n9VMWUmJi2TNlAY0/7PJy4TIyY96P2FhZciZkG2cvXmbM5Bns3bgKM1MTLbklv60iNTWVYzs3Ehbx\ngKFfTqJWdXcc7auyI2Q/h0+cYuOvS5DJjBg3dSY//76GTz/pD4Bj1SqM+2wov6wqefJ/23R5U/oc\nOn6KkENH2bhiKVYWFkydPZ85S5bzw5SvqGRny8VDuzT53Lh9l49Hj8ezWdE+ArBr2xauX73Kmq07\nSU9N5fPhg3F1r0b9hkXlz589za7tW/jptzUYGBoy9tOh2Ds54RvQhahHD5k9YyqzFy6les1arF+1\nkhnffMWi5eoFxMwpX/NeLQ9mzFlARFgo40YNp6FHDc5fuMCVy5fZvWcPqampfDxoENWrVaNxkyZF\nyj916hRbtmxh7bp1GBkaMmTIEJycnenatSs5OTl8OWYMQ4cNw9/fn+XLlzNp4kRW/v47ANu2b9fk\nk5OTQ/t27ejYsaNW/ieOH0Mul/PCRLFty2auXr3Ctp27SUtNZdjgj3GvVr3E8Wbbli38vmYthoZG\njBw6BCcnJzp36Yqenh6TvpmMo5MTEomELZs2MuWbSfyxRr1QO33qJBs3rGf+wh9xdHIi+vFjzM3M\nKG2Pb/f2Ldy4doU/Nv9JWloaY0cMxsXNnXrF/G5SXV28OnrT3sef1Su0F1gZ6el4tm3H+CnTMTAw\n4JfFC5k7YyrfL1xaSukw44d52FhbcfrQXs5euMiXE78hZPumomPg8hXqMXDvLsLCIxg2egw1a1TH\n0cFeI3P0xCkUcjkUMA6lpKYyetxEvv3mK9q28mT33v189uV49mzdiLSYK7VezFVbdqjnhhFDPsH9\nFeeqz4qZqzaVMlcBLFowjxrvvVdqHQF8u2QlNpYVOLf5V85cuc4XMxexf+VCzEyMteQqmJoysHsA\nV+/cJyUtXSvu4OkL7Dl6ms0/foeVhTmTF/7K7F/XMmf8yJeWL3j3KPclfUpKCqdPn6Z3795aC+AX\nmJiYFJMqH5VKVSTs3LlzZGZm0qJFCzp37kxISAiZmZmaeBsbG+Lj47l06dI/f4AycGBfCB/27Yd5\nhQpUtXcgsGsQ+0L2lCC7l65B3alUuTKWlpZ80KcP+/bs1sSrVCpUeUWfvWD8y9i/L4Tez/Wxt3eg\na7cg9pagz/69e+kW3J3Kz/X5sE8f9u7Rll20YD6fDBmKrq72u1VVe3tkMpmWXtExaquSXKHg2Mkz\njBg8CH19fdq2akk1NxeOnTxTRIc9Bw4xZGA/ZEZG1PGoiVerlux9bgn5c88+6tfxwLdje6RSKTKZ\nDKcCE4Obi7NGLx0dHZJTUsjIkL+0jl6V67sPcyPkKIqUtDeWJ6jr5+jpc4wc9JG6fjybU83VhWOn\nzxaR3XPgMEP691HXT633aNeqOSGH1BbPU+cu0qOLP9ZWlsiMjBjU533+3HdQk9a/UztaNG6IYTF9\n8G3U5U3qE/M0lgZ1PbCzsUZXV4q3VxvCHzwstsxd+w/RvnXLEnU7dGAvvXr3xdy8AlXsHfDv0o2D\ne4vvU4f376Vz12AqVq6MhaUlPT7sy6G9IQBcvnieho2b8p5HbXR0dPjwo4Hcv3uXmOjHKBQKbvx9\nlb6DBqOjo4Nbteq0bN2WkJAQQkJC6PfRR1SoUAEHBweCgoPZvXt3seWHhIQQ3L07VapUwdLKir79\n+rHnuexfFy+ir69P167qRefHH3/M7du3iYmJKZLP8ePHMTExoUGDBpqw7Oxslv20lE9HjdKE7dur\nHm8qVKiAvYMDXboFsXdP8brt2xtCt+BgKleuoh5v+vbVjE26uro4OTsjkUjIzc1FoqOjZeld+euv\njP7iSxydnACoUrUqJoUWk4U5emAf3T/si5l5BapUtcc3sBuH94UUK1ulqj2d/AOpYm9fJK56zVp0\n8uuMsbEJurp6dOneizu3bpRatnoMPMWIIR8/HwM9qebmyrETp4rI7tl/gCEDBzwfA2vh1boVew/k\n75ZkZ2ezeNkvfP7pcK10f1+/SeVKFfFq3QqJREKgvy9SHSmXrlwrXASgnhs+LDA3dCllrtq/by9d\nCs1VewvNVXmlzFXnz6n7a+OmTUuuJECemcnR85f5tG8P9PX18GrWkOrODhw9V3Qeb1K3Jh09m2Bp\nblYkLiY2gYYeNbCztkRXKsWndTPCHz0utez/JYQluGyU+9M8fPgQlUqFs7PzS2Xnzp1L/fr1tT7L\nly8vIrdt2zYCAgKQSCS4u7vj4ODA/v37NfE+Pj74+/vTt29fPD09GTlyJOvWrSM9Pb1IXm+SBxER\nuLq5a767urkRGR5evGxkBG7uBWXdiYzU3r76uH9fuvr78N20qaSmpGjFbd6wDr8OXgwdNICrV4rf\nWouMKFyGG5ERxesTGRmBm1shfQrIXr50iZTkFNq09So2/eo/fqetZwt6BnXD1s6OZo0bAvAo6jHG\nMhk21lYaWTcXZ8IjIrXSp6alkfgsiWquLpowd1cXwiLVcjdu38HM1JQ+nwynjW9XRo//mrj4BK08\nRo6ZQMPWHfl07ER6BnXB2FhWrK5vE48eR2MsMypUP06ERWov0lLT0klMSqaaS34/cndxJjzygeZ7\nwfciVZ6K+IREMuSv/iLwNunyJvXp1LY1Dx9FE/3kKZlZWew9fIyWz7fwC6JU5nLg6Em6+HYsEveC\nh5ERuBToJ86ubjyILH7buXjZ/D5V8EVWpVKhQsWDiHBN5akKuG6pVGo3r4iICKoV6NPubm6ElzDG\nFJF1d9fIRkRGUq1aNU2coaEh9vb2xeYVEhKCv7+/VtjKlSvx9vHFxsZWExYZEYG7e36ebm7uRJQ0\n3kRE4FZYtlDZH/bqSavmTZk3+wf6fdQfgLy8PO7evUN4WCid/XwICuzMyhW/FltGQR4+iMDZVfu3\neFjC71YWrl+9jJOza6kyj6Kino+B1powN1eXksdAt4JjoCthBeR+W7UWf++O2NoUtboWXoaqUBEW\nUfwzPogoPN67Fal/jWyhucrNzb1Im/+4f1+6+PswY9pUUgrMVUplDkt/XMhno78oqmAhHkY/xdjI\nEBsri/yyHO0Je1i2BWynVk15EP2E6KfxZGZls/f4WTwb1i1THoJ3h3JfBJeFQYMGsWvXLq3P+++/\nryWTlpbGoUOHtA7Ede7cmS1btmi+6+joMHPmTE6cOMG4ceOoWLEiy5YtIyAggIQE7YVTacTFxXHr\n1q0SP4VRKBQYF9i2MTY2RqEofuKXyxXIjAvJyhWa7z//upLtu/eyat1GMjMVzJg6WRPX64MP2fzn\nLnbtP0RQj56M/2I0sU+fFq9PoTLkJSxEFPJCupsYI3+uT25uLovmz+OLsWOLTQvQr/8Ajp8+y8pV\na/Bq1x49PT3NcxZejBobGyNXKLTCXnx/YVEGMDHO1yEuPoFd+w4wccxoDu/aQkU7WyZOm6mVx5J5\ns7hwdB/zv/+Wuh61StT1bUIuz8REpl0/JjKZ5rk1cpr6MdKEGRvLNOEtmzVm8597ePI0jtS0dFas\n2wiAQpHJq/I26fIm9bG2ssDjvWr49OxHM++uhD94yJD+vYuUd+r8BfT09WjasH6JOikUhfutCZkl\n9SmFvMQ+3qBxU678dZEb166iVOawZuWvKJVKMjMzMZLJqFWnLqtWLCcnJ4d7d25z8thhFAoFCrkc\n4wK7Z8YmJigK9SVN+YVljY01sgq5XGtseBFfeHxISUnhzOnTBAQEaMKio6M5dPAgffr2K1I3WuON\nifaYpq1bMWNToedYv2kzx0+fZeLX32gWbM8SE8nNzeXC+fNs3LKNpcuWszdkD/v3Fm/VLUk3WSlj\n86sS/TiK35f/xMBhpW+zyws9K5QwBsqLGwNlyJ/rGR3zhINHjvJRnw+LlFG3tgcxMU84dPQYSqWS\n7Tt3Ex3zBEVm8X2u8FxVWn0U1l9WYFwG9Vy1Y/deVq/bSFahuWrD2rW09GxN5SpVis1bqxxFcf3d\nCHkJz1AS1hYVqF3NlU4DRtEkeCDhjx4z9MNuZcrjbUaio/Ovf94lyt0n2NHREYlEQkQJb6QFsbCw\nwL7QFlSFChW0vu/atYusrCx69uypsaSoVCpUKhUPHz7E0dFRI2tra0tgYCCBgYGMGjWKTp06sXHj\nRkaOfDXfoE2bNrFkyZIS46fOmMnsmTNAIsHbxxeZTEZGeoYmPiMjAyOj4q2RMpkR8oxCsgUm8Tr1\n6gFgXqECn48dTxffTuTk5KCnp4d7teoauU4+vhzYG8KF8+cwMDBk1nN9fF7oU6gMmax4fYxkRtq6\np2doFhVbNm2ibv36ODu7FJu2IDVr1WJfyB62/rmbnkFdkMmMirglZGRkIDMy0gp78V0ul2t0TM/I\n18HAwID2bVpRs4baejRsUH9a+3YhOzsbfX19TT66urq0b9OK4D4DqVndHRdnp5fqXJ7IZIakF1p4\npMvlWgs6KFg/Ck1cRoZcEx7k70NsXAL9Px1Dbm4uH73fnfOXrmBlacGr8jbp8ib1WbpyNREPH3E6\nZCtGhoYsWPYbE2fMZuF3U7Ty2XPgCAEd22mFHTmwj/k/fIcECe291X1Ku9+mY1hSnzIqLJvfxx0c\nnRj79RQWzvmepGeJtPf2xcnJWWNZnTTtOxbO/p4gn3YoFAp0dHR48uTJ8zEmf0crIz0do0J9SVN+\nYdmMDI2sUaGx4UV84fFh37591KhRQ+N6ADBv7lxGjBjB4cOHmDljOkqlklkzvys63qRrj2nauhkV\nHZuKeQ59fX06d+mKb6cObNq6HQNDtZtKv/4DMDY2xtjYmG7B3Tlz5jTN2vlo0h09uI9Fs2ciQUI7\nb58iuslLGZtfhcT4eCaOHsGAIcOpU79hqbKyQs9a0vO+aLvaY6Ac2XM95yz8kZFDPkFPV7eIO5y5\nuRmL5nzP3EVLmD5rLs2bNqZZ40bY2arb04H9+0qdq0qrj8L6ywuMywB1C81Vgc/nqqSkJPbs2smq\n9RtKrR9NOUbF9XcFMkPDV0r/gqVrtxIeFc2Zzb8gMzBg/u8b+GrOzyz65vMy5SN4Nyj3RbC5uTme\nnp6sX7+efv36YVioQaelpWH6En+ugmzbto2BAwcSFBSkFT5t2jS2bdvGF198UWw6U1NTbGxsSrSE\nFkevXr1o165difEVHZzp5OOr+R4aep/wsFBc3dwACA8Lw9m1+K0yJ2cXwsNCadlKfcI3LDQUF5cS\nttVUKkBSqh+wSqXC29cXb19tfcIK61NCGc7OLoSFheLZOl+fF7JXLl/i2tWrHDmk9k1LTk5i7Bef\nM+LTzwjsWvQNOzc3l0eP1T58DvZVkSsUxCckara1Q8Mj6OLvq5XGzNQUaytL7odHUK+2h0bO7bkb\njZuLM4nPT/S/QKeYW0NeoFQqiYqOeesXwQ5VqxRTP5F09eukJWdmaoK1pQX3IyKp51FTLRcRiauz\nE6A+nDJ8YF+GD+wLwNm/LvNeNfdib1b5X9DlTepzPzwS3/ZemJup/QeDA3zpN1x7QkxLz+D4mfNs\nWqF9wKm9ty/tvfPbanjYfSLCw3B2VfepyPAwnEp4OXR0diEyPIzmnuo+FREWqrV13tqrPa292gOQ\nnp7G0QP7Nfna2lVk5rz8U/jfTZ5E86aN2LVrF6FhYZrt6dCwMFxLGGNcXFwIDQujdZs26nq4f18j\n6+LiwuZNmzSyCoWCqKioInntDQnBv4AVGODSpUvcuHGDPJUKI0NDsjIzOXn8OJUqVdYab8LCSh7T\nnF3U418rzXhzH5cSniMvLw95RgbxcXG4urlhY2urFV9cq2rXyZd2nfJ/t4jQUB4U+t0cX+GlvjhS\nkpOYMHo4/t264xv4cgujg73983acoHGJCA0Pp4u/n5acZgwMi6BeHQ+N3IsDxn9ducr1m7eYMXse\neXm55Obm0s4vkBVLf8TF2YmG9eux4Y8VgHoM9g/qhUfNGgB4+/ji/ZK5qqT6fzFXebYqOjcUocBc\ndff2LeLiYunRNRCVSr0zolKpiH8YyoqZE4skdaxSEbkii/jEJI1LROiDKLp2bF1EtjTuRz7Cr01z\nKjw/PBvs7UXfL6eVKY+3mXfNZ/ff5q2orcmTJ5Obm0uPHj04ePAgDx8+JDw8nNWrVxdxdyiNO3fu\ncPv2bXr06IGbm5vWx8/Pj+3bt5OXl8emTZuYOnUqZ86cISoqirCwMObMmUN4eHipi9rC2NraUqtW\nrRI/hfH29WfD2jUkJycR9eghu/7cjp9/8fcYe/v68ef2bcRER5OYkMDGdWvxDVDLRkaEExZ6n7y8\nPFJTU/lxwTyaNGumsXgeP3qEzEwFubm5HD54gOt//03jps2KlOHj68+6NWtITkri0aOH/LljO/4B\nxevj46etz4Z1azWyk6d9y8at21i7cRNrN27C2tqGb6Z+i7evehDfuWM76WlpqFQqLv31Fwf276Np\nI/UhGpmREV6tWrL015VkZWVx/NQZwiIi8WrdsogO/p068Mvva5DL5Vy/eZvjp87i16kDAAE+HTl+\n+gz3QsPJUSpZ9vtqGjesj76+PtExTzh59jzZ2dnk5OSwdtNW4uIT8Kj58tPIr4pERwddAwN0pDpI\n9XTR1dcv86KuOGRGRnh5tmDpb6vJysrm+OlzhEU+wMuzRRFZ/07tWb5qHXK5guu37nDs9Dn8O6p9\ntJNTUnn8/DBiWMQD5ixZrlmEgtrfNSsrmzxVHjnKHLKzs4u8VL1NurwJfQI6qft6rerV2H/0BCmp\naeTk5LA9ZD/urtpnFA4cPYGrkyNuLk5Ff6QCdPT2Y/O61aQkJ/H40SNCdu7A26/4PtXBx4/dO7bx\nJCaaZ4kJbN2wlk5++QvK+3fvoFKpSE5KYv73M/AN7KI54PUwMgKFQkFOTg4H9+3h7u2bdOnSBX9/\nf1avWkVSUhIPHz5k+7ZtdA4MLLZ8f39/tm7dSvTjxyQkJLB2zRqNbOPGjcnOzmbnzp3k5OSwYsUK\natWqReXKlTXpHz58yN27d/H11X5h3blrF5s2bWLdxs0sWLwEHamUdZs2EditG+tWr9aMNzt3bMe/\nhHvcff382bFtK9HR0SQkJLB+bf54c+/uXa5euYIyJweFQsGSRQsxNTPTWKP9AzqzZtUfyOVyYmNj\n+XP7ds0CrSTaefuyZcMaUpKTiI56xL5dO+joF1CifHZ2NjnZOajy8sjOzkapzAHUVtCvPh9Js5at\n6Nm7X4npCyIzMsKrtSdLf/nt+Rh4mrDwSLzatCoi6+/diV9+/+P5GHiL4ydP4+et9lHfs3UjW9au\nYuu6Vfy0YC5SHR22rluFk6MDAHfv3yc3N5e09HTmLFxMndq1cC6wM1oQH19/1heYq3aWMlf5+Bad\nG/xKmKsWLZhH0+dzVQtPT7btCmHV+k2s3rCJrsHdadO2HfO+Kv6KSZmhIe2aN2TJ2q1kZWdz7Pxl\nQh9G0a55Uf/9vLw8srKzUebmkpubR3Z2Drm5ah/6Wu4u7D95npS0dLJzlGw/cBx3p6KHHAX/Dcrd\nEgzq68l27NjBsmXL+OGHH4iPj8fS0pLq1aszYcIEoPi7gF/wIm7btm24u7sXe8iuY8eOzJgxgxMn\nTlC3bl2uXLnC1KlTiYuLQyaT4ebmxk8//USjRkU71JsiqHsPHkc9ole3Lujp69O3/0AaPC8v9ulT\nevfqzvrN27C1s6OFZyu6hYXx8Ud9yFOp6NItCP/O6gnq2bNnzJn5HQkJ8chkMho3bcY3077VlLNp\n/Tq+n65+s3VwcuKHeQu0Jq8XBPfowePHj+j+XJ+P+g+kYQF93u/ZnY1btmFnZ0dLz1aEdw9jQD+1\nPl27BRHwfMIsfIOHVFeKmZmp5raP06dOsXTxYpRKJRUrVmTU51/QqkX+onzS2NFM+vZ7WnkHYmdn\ny9zvpmJmakrIgUOsWL2OHev+AGDE4IFMmTkHr4AgzM3M+Hrs5zg6VAXAxcmRiV+OZtS4iaRlZNCg\nTm2+m6y2JqiAX35fzYTJ09Wn6V2dWTpvVpm330vD7+tP8Z8ySnNoyXfiCFYNGMuFNdtfkvLlfP3F\np0z8bjae/sFUtLVh3rdfY2ZqQsjBo6xYu4Edq9UHf0YO+ojJP8ynbZdemJuZ8vWYz3C0V9fPs+Rk\nRo7/hvjEZ9haWTGkf2+tw19TZ89n575DSCQSzl+6yrdzFrHyxzk0qlfnrdXln+rjUFXthzioTy9m\nLlhCYJ9BKJVKalZ359sJ2jtGew4eprNPh5f+VoHBPYh+HEXfHl3R09Pnw48GUK+h+tniYp8y8IMe\n/L5xKza2djRr4UlkUA+GD+yLKk+Ff9cgfALyF6yL5nzPw8hIDAwN8fbvzMAhIzRxF86eYf3q31Hm\n5FCzdm2+n78YPT09evbsSdSjRwR27oy+vj4DBw2i8fMryJ4+fUpwUBDbd+zAzs6OVq1a0bNHD/r0\n6UNeXh7BwcF06aK+1kpPT4/5CxYwdcoUvp85k1oeHnw3U9vHPiQkhJYtW2Jubq4VbmGh7lfZuSqy\nMjORABYWlvTo2Yvox48J7hqInr4+/QcM1Fy5Ffv0Ke/3CGbj1u2a8SaoR08G9O1DniqPbkHBmiu3\nlMoc5s+dzePHj9HT1aNmrZosWrxUc/vLJ4OHMHvW9wT4dMLYxIRuQcF4+/iSnJlLSXRohauOAAAg\nAElEQVQO6kHM48cM6NUNPT193u/bn7oN8n+3wb178uv6LdjY2hH75An9unfWzDuB7VpSu14D5ixZ\nzpmTx4gIvU9MVBS7tqvPoUiQ8Ofhk6W2m0njxjBp2gxadfRTj4Ezv1WPgfsPsmLVGnZsWAPAiCEf\nM+W7WXj5BarHwPFjNNejWRRwDczKygKJBEuL/DHu19/XcPb8BaRSKR282jB10oQS9XkxV/V8Pjf0\nKzRXfdirOxsKzFVBYWEMKmGuml3CXKWrq4elpaWmTJmRjAzDdMxNS74R6psRA/hq7s+06DGYijZW\nzJ84CjMTY/YcO8Ovm3ayc9lsAHYdOcWk+cs1t8TtOXaa4b2DGd47mI97BvLdT38QMPhLlMpcark7\nM/3zwaX+Pv9LCEtw2ZCoXuUuLcFrkZj25q7g+qdI/7lR8o0iy0ktbxW0+MyyqKW8vFgcV/qEKXh7\niNO1ernQ/xOWRkXvey1PsnPfrqmltEXw/zeVdMt2mOvfJl3n7bkpxzzhbnmroIXUucHLhd4innw/\n4uVC/5BKX5V+7/X/Em+FJVggEAgEAoFA8M/QEZbgMiFqSyAQCAQCgUDwn0NYggUCgUAgEAjeAd61\ne3z/bcQiWCAQCAQCgeAdQByMKxuitgQCgUAgEAgE/zmEJVggEAgEAoHgHUBYgsuGqC2BQCAQCAQC\nwX8OYQkWCAQCgUAgeAcQB+PKhqgtgUAgEAgEAsF/DmEJFggEAoFAIHgH0JG+Xf858m1HWIIFAoFA\nIBAIBP85hCVYIBAIBAKB4B1A3A5RNkRtCQQCgUAgEAj+cwhL8L+I3tZZ5a2Chs+NupW3Clr0nDyo\nvFXQYnHcyfJWQcOntq3LWwUt/G6dL28VNJj27lLeKmgx1POr8lZBw7Znv5a3ClrIExXlrYIWjZa+\nPePxrwl25a2CFm1+/bi8VdDwS+855a2CFhOcy1uDsiEswWVD1JZAIBAIBAKB4D+HWAQLBAKBQCAQ\nvANIdHT+9c+b5tixY/j4+ODt7c2WLVtKlPvss8/o3r37Gy1buEMIBAKBQCAQCP7fyc3NZdasWaxd\nuxaZTEZQUBCdOnXC3NxcS+7s2bNI/4Xr34QlWCAQCAQCgeAdQCLV+dc/b5Lr169TrVo1bGxsMDY2\npk2bNpw5c0ZLRqlUsmzZMoYPH/5GywaxCBYIBAKBQCAQlANxcXH/x959R0dRtQEc/m3KZnfTe0gj\n9CYdQi8JVXpRQREpFrooIogUAcuHICgIUpWOgFTpEAi9917TE0jvu8kmu/v9sbBhyYYkKCDhPufk\nnOzMOzNvJjN37ty5cxd397wXRd3d3YmNjTWKWbp0KT169EChUPzr2xfdIQRBEARBEEqAFzE6RFxc\nHPHx8QXOd3V1xc3N7V/ZVmxsLMeOHWPZsmVERUWh0+n+lfU+IirBgiAIgiAIQpGsW7eOuXPnFjh/\n+PDhjBgxokjrcnNz48GDB4bPsbGx1KxZ0/D55s2b3Lt3j1atWpGbm0tycjKDBg1i4cKFz/4HPEZU\nggVBEARBEEqA5zF6w5N69epFYGBggfNdXV2LvK4aNWpw584d4uLisLa25siRIwwbNswwv0WLFhw5\ncgSA6OhoRo4c+a9VgEFUggVBEARBEIQicnNz+9e6O5ibm/PVV1/Rt29fAD766CPs7e2ZMGEC7777\nLtWqVftXtlMQUQkWBEEQBEEoASRm//4wYs9bQEAAAQEBRtO+++67fHFeXl5s2LDhX922GB1CEARB\nEARBeO385yvBX331FZUrV2bx4sVG04OCgqhcubLhs1arZdmyZXTu3JkaNWrg7+/Pxx9/zPnz542W\n++mnnwgMDESpVBpNHzx4sKE5XhAEQRAE4ZVjZv78f0qQ/3x3CIlEgkwmY8mSJfTu3RtbW1ujeY98\n9tlnnDp1ijFjxtCwYUMyMjJYvXo1H3zwAbNnz6ZVq1aA/mv3Dh06xP/+9z++/fZbADZs2MDp06f5\n+++/X9jflaLMZsqOU5yLiMfdTs6YNnWp7+deYHxMSibvLNnFm9VKM/7N+gAcuxfDH8evE5KQhkJq\nQZsqvoxoWQPzZ+wY37u2F43LOJGj0bHrRixBt00PgdLYz4l+/r7kaLRIAB0wcecNklU5uNta8U4t\nL8o668fzux2fwZpzUaRm5RY5D0tHB96YNgUn/7pkPYjlxpRpJJ08kz+P7euRe3o8/CTBTGZF5Or1\n3Pz+JwAUfr5UmTgWh9o10CiV3PttCZFrCv5KxkeSU1IZ/8MMzly4hIebKxNGjaBB3dr54rKz1Uz6\ncRYHj57A3s6WzwZ/SIfWeY905v2+gs07dqNUZdEuoDnjR43AwkJfgPz2x0r2Bh8mJDyCb8eNpuub\nbYq8fwrTbFAfmn7cG6/qldj53Vx2fjvnX1t3YXQ6HX//MZdzwbuxkEoJ6P4ezTq/bTL22ulj7Fyx\ngLTkRKQyObWataJTvyFG53VxWDrYU3nKJBzq1SH7QSy3p80g5cy5fHH1/1qDzOPhcSMBMysrotdv\n5O6MWdi9UY1KE8dhVcoDbbaapGMnuD1tOtqs7GfKCeDrHtXp1sCH7BwtS4LusPzgPZNxk9+pSZf6\n3jwaAUhqYUZIXAZdpwVTt6wTi4c0MsyTmEmQW5rTY8ZBbkSlFikPcxs7vAZ/jnWV6uQkJnB/2W9k\nXr9sMtaheWtcu7yDhYMjOYnxhP80hZz4WMzt7PH6aCSK8hUxt7XjWt8uxd4fABZ2dpT9Yiy2NWqh\njo8jbN4c0i9dMBnr0qYdnr3ew9LJmez4OG5P+hp17ANK9XoXz159eLRTJBYWaHNzON+z+Dklp6Yz\nbtYizly5gYerMxOHfEDDWvn7Iu49eoY/Nu3kZkg4HVs04vvPPzaaP3fVJjbtO6w/55v5M3FoPyye\n4VuudDodB1cv4PrRfVhYSqnf8R3qtO9hMvbe+RMcWbeEjJRELGVyKjcMoHnvjw3n0a2TBzm+aQXK\ntGQcPLwJ6DMEzwpVi5yLuY0tpQZ8iqLSG+QkJxC7ehHKm1fyxXkMGIGdfzN0ubkgkZCTEEfY5JGG\n+S5demPfpBVmMhlpZ48Tu3ohaLXF3DP6fXP6r8XcPbkfcwsp1du9RbVWXZ+6jFarYet3n6LNzaHn\n1EUA5GRnse/XSaTcj0Kn0+LiW56GvQdj7+Fd7JyEV9d/vhIM0KhRIyIiIliwYAFffvllvvk7d+5k\n7969LFy4kBYtWhimT506lZSUFCZMmECTJk2QyWRIpVKmTZtGr169aNeuHWXLlmXatGmMGTMGb+8X\nd/D/uPcczjZygkZ242ToA8ZtPc7mQR2xlUlNxv984AJVPByNpmWqc/mk2RvU9nZFmZPLmI3HWHnq\nFv0bVSl2PgHlXajoasO47dexlprzZWAFIlNU3IrLMBl/Ky6dWSYu5nJLc85FprD4RBg5Gi3v1PJi\nYIPS/HzI9IXflKrffIU6Pp4D/gG4NG1EzdnTONK6K7npxrkc7/SO4XeJpQUBx/bxYM/+h58tqbv4\nV+78Mo9zH4/AXGaFlVvR3lj9buYcXJ2dOLZjI8dPn+OLSd+xc+1y7GxtjOLm/r6ctLQ0greu5W5I\nGINHj6dapQqU9vFm847dBB06wtrFc1Eo5IyZ/APzl65kxMf9ASjt7cWYTwezaPmaIu+XokqNiWX7\nNz9T/72nXxiehxO7txB6/RJjf1uDKjOdBRM/o5RfOcpXr5Mv1qd8ZYZ8NwcbB0dUmRmsmD6JE3u2\n0rh9t2fadsVxY1AnJHC0ZVucGjWg2o8/cKpLT3IzjI+bM2+/Z/hdYmFBk6CdxAcdAEAZGcnlEaPI\njovDTCql0sRx+H3yESFz5j1TTu81K0O98s60nbIPO4WUlZ825WZ0KqfuJOSLnbz+EpPXXzJ8XjS4\nIRdDkwE4F5JEnS93GOa9WduTUZ2rFbkCDOA5YCi5KUncHNQbm+p18Pn0K26P+hitMtMozqZWfZzb\ndSF85hTU96OxdPVAk5Gun6nVkX7xNEn7tlF6zJTi7AojpYd/hjopifPvdMO+Tj3Kfz2JywPfR5Np\nnIu9fwPcu/bg9uQJZEVFYuVRCk26Ppf76/7k/ro/89Y5bCRmUtPlZ2GmzluGq5MDJ9bN59j5K3w+\nbS57lvyEnY21UZyDnQ0De3bg4o07pKYb57pp72H2HjvDup8nY62QMfrH3/htzRY+7duz2Plc2r+N\n6FtXGDhjGVnKdP764UtcfMviW7VWvlj3shV5Z/xPKOwcyVZmsm3OVC4f2E7NVp3JTE1m9+Kf6DH6\ne3yq1ORy8E62zf2WQbP/NLFV09z7DCY3NZk7n/XFulptPAd9ScjXQ9CqMvPFJm5bT+LO/H027ZsE\nYlOnEWHff4k2S4XnJ1/g0rkXCVuLnscjNw/t5MGda/Scuhi1MoNds8bh5F2GUpVqFLjMjeDtWCls\nUKUlG6aZW1jS+P0R2Lt7I5FIuHFwO4eXzqTzuJ+LndN/ygsYHaIkeSX2lrm5OZ9//jmrVq3K900i\nANu2baNMmTJGFeBHBgwYQHJystHX8FWrVo3Bgwczfvx4xo4dS82aNendu/dz/Rsep1LncuhONIOb\nvYHUwpzmFbyo4OrAoTvRJuNPhNwHwN/Pw2h62yq+NPDzQGphjoPcijffKM2V6PwX16Jo6OfInltx\nZKo1xGWoOXIvkcZ+TsVeT1iSkuNhSWTlatHoYP+dBMo6Wxe+4EPmchlurVtyZ/YCdDk5xAcfJv3m\nXdxat3zqcm6BLchJzyDlrL41yatnF5LPX+TBjr2g1aJRqlCGRRS6faVKxYGjJxj+YT+kUiktmzai\nYrmyBB89ni92+54gBvV/H4VcTo1qVQhs1ogd+4IBOHLiNG937YiLsxMKuZwP3+/Nll17Dct2bBtI\n4/p1kVlZFXnfFNXlbUFc2XEAVWr6v77uwpw/tI8WXXthbWePSylv/Nt04tzBPSZj7ZycsXHQ39jp\ndDokEgmJD2KeabtmMhkuLZsTOn8RupwcEg8fJfPOXVwCmj91OZeWzcnNyCT1wkUAclPTyI6Le7hS\nM3RaLXKfZ7857lzPmz/23yVFmUNEQibrT4TR1d+n0OVcbK1oVMmNv89Gmpzfpb5vgfNMkVhZYVu3\nIXEbVqHLzSX9wmmyIsKwq9swX6xb9948WL0E9X19eZQT/wCtSt99TJORRvKB3ajCQ4u87SeZWclw\nbNiY6JVL0eXkkHLqBKrQEBwbNckX6/VuXyIWzScrSv+3Zj+4j0aZv/IlMTfHqXlLEvbvzTevMMqs\nLPafPM+Ivj2RWloS0KAOlfx8OHDyfL5Y/xpVaNukPo72dvnmHT57kV4dAnF1ckAhk/Hx253YvO9w\nsfMBuHH8AHXffAu5rR2O7l5Ub/kmN44FmYy1cXBGYffoPNIiMZOQEqe/ZmQkJyC3tcOnin7c1SpN\nWqFMSSbbxD40RSK1wqaWP/Fb/0SXm0vGpTNkR4VhU9u/gAVMT7auXpeUQ3vQpKWgU2eTtGsj9k1a\nFSmHJ907Hcwbbbojs7HDzs2Tik3bcffkgQLjVWkp3D62hxrtjZ9ImZmb4+Dhg0QiQavVIJGYkZ6Q\nv34hlGyvREswQOvWralSpQq//vprvrcGw8PDKVeunMnlHk0PCwszmj548GA2btzI5cuX2bPH9EX6\neYlITkchtcDFRm6YVs7VnpCEtHyxuRotvwZfYkbPpuy8GvbU9V6IjKesq/0z5eRpJyMqRWX4HJWq\norpn/oL+kbLO1vzS7Q3SsnLZfyeeQ/cSTcZVcrMhJk1lcp4pCj9fcjMzUcfnVeYz7tzFprzp/+8j\npbp24P7fOw2f7Wu8QW5qOv5rl6Lw9Sbl/CVuTJ1GdtzTbxIioqKxVshxdXE2TCtf1o+7oeFGcWnp\nGSQmp1CxbBnDtAply3D52g3D58e/2Ean1RGfkEimUon1c/jqx/+K2MhwSpXO+1+V8i3LzbMnCowP\nvXGFP777imxVJtb2jnT9sGgDrD9J4etDbqYSdULecZh57x7WZcs+dTn3Du2J3bnbaJqVuxv116/G\nwsYGjVLFlc++eKacAMp72HErJq+19nZMGi2reTxlCb2O9by5HJ5EVKIy3zxHGylNq7jxv035H0kX\nxMrDC22WktyUvJaw7KhwrLx9jQMlEmR+5bDyKY3XoM/R5eaScjiI+K3rirytwsi8vNCoVOQkJRmm\nKcNDkZf2y5eLonwFFGXKUHb0WHS5ucTv3c39tavzrdOhQSO0WVmkX76Ub15hwqNjsVbIcHNyMEyr\n4OfNnfCoYq/r8W+z0up0xCUlk6lUYa2QP2Wp/JJiwnH1zStbXLzLEHLpdIHx0bevsWXWBLJVShR2\nDrTsMwQAN99yOLh7EXblLKWr1eHa4T24l6mAlaJoDRNSd0+0WSo0qY8dNzERWHn6mox3bN0Zx9ad\nUT+IJn7TKlR3rhvmGXVzkphh4eCImZUMbXZWkXJ5JPV+JE5efnnb9PIj6kr+7nKPnN28jBrt38FC\narrBYcu3w0l9oO8SUbdbv2Ll8l8keYbuN6+zV6YSDDB69Gj69+/PwIED880r7lfpHTt2jPj4eCQS\nCVeuXMHDo/AL05MK++rAgtp7VOpcrKWWRtOsrSxJVeXvd7j6zC2alvfEy8Em37zHHbgZydnwONYM\nbFdo3qZYWZijytHk5ZijRWZh+kHBrbh0Ju26QZIyhzJOCoY2LUNaVi4Xoo0fzbrZSOleoxQLjoUV\nOQ9zhYLcDONWityMTCxNtLw8Ymlvh2vzJtyePtswTebuhn3rqpzpP4SMO3epNOYzqk//lrP9hzx1\n+0plFjZPVFJtFApS04xbVZUqfcVe8djFzdpaYZjepGF9VqzdSGDTxlhbK1iyei0AKlVWia4Eq7NU\nRhdYK4WC7KyCb4LKVKnOt6t3kBz3gHOH9mJj71Bg7NOYKxT5HqUXdtxY2Nnh3KQR93751Wh6dmwc\nR1u0wdLBnlI9upEVG/dMOQEorMzJeKw/fEZWLgpp4RepLvV8WHvMdGtrp7reXI1IJiKhaK15oG99\n1aqM/w8alRJza1ujaRb2DkjMzLF5ozZ3xw7F3MYGv7HfoY6PJfX4wSJv76m5yOX5WnM1SiUWNsa5\nWDo6IjE3x652Pa4MGoiFrR2VfpiOOvYBicH7jWKdA1uTGGy6pbQwyqwsbJ6opFrL5aRmFH3/AjSt\nW4Nlm3cT2LAONgo5i9dvf7j+7GJXgtVZKqSyvPNIKleQ85TzyKtiNYYt2ExaQizXjwWhsNOfRxIz\nMyo3DGDbnKlocnOxUljz1lfTi5yHmZUMbZbxjZjWxHEDkLxvG3Frf0ebnYVdvaZ4jxhP6DcjyU1O\nIPPqBZzadCH94im0KhXOb+r7N0usZFDMSnBOtgpLeV4ZKpUpyClgHXEhN0iPj6Fcv894cNv0TWO3\niXPR5OQQcuYgcvviP/0UXm2vVCW4Xr16NG3alJkzZ9K9e3fDdD8/P+7dM93n9O7du4aYR9LS0pg4\ncSLDhg1Dq9UyefJk6tevj4ND8S7AhX114JmvepmcLpdakKnOMZqWmZ2D4omKcXy6ir8vh7J6QNun\n5nE2PJYf951nztvNcVAU7fF6g9KOfFDPBx1wMiyJrFwNcktzQJ+X3NKMrFzTLy0kKvNyD01Ssv92\nPHV9HIwqwQ4yCz5vWZ7Nl+9zO950v2JT9BdD41YKCxtrNMqCLwAendqTdv2WUXcHTXYWsfuCSb9+\nE4B7cxcRcHI/EktLdDk5Ba0KhUJGxhMjh2QolUaVXQCFXP9ZqVQZ5mVmKg3Te3RsT2xcAv1HfIFG\no6Ff77c4efY8zk7G/bpfdRcO72Pj/JkgkVC7eWus5HKjR63ZSiVWssIrAI5uHrh7+7F50S+8P3py\nsfPQKJWYWxfvuHFv35b0m7dRRZjuVpCTkkrS8ZNU+2Eq5z74sEh5dKrrzdTeNdHpYNvZKDKzc7GR\n5RWzNjILlGrNU9YA5T1sKedhw67zprtHdanvw8YT4SbnFUSbnYWZ3Pj/YC5XoM023j9atRqAhG0b\n0Gap0GapSDqwC9ta9f61SrBWpcL8iZZIc4UC7ROVPG22vlHg/l9/olWpUKtUxO/chn39BkaVYHMb\nGxz8G3J12e/PlI9CJiPjieMkU6VCISteV6WebVsQm5DEB2O/R6vV0b/7m5y4eA0Xx8Kfzt04foCg\nZbORIKFy40CkMgXqrLzzSK1SYlmE88jOxR1nz9LsXz6XTsPHE3blLCc2reC9yXNx8vTh9unDbJk5\ngQHTl2JRhP7T2uwszGTGN+1mcoXJ1tvsqDDD72mnD2PXqAXW1WqRejSI1KNBWDg64/vl90jMzEja\nuxVF1Zpo0lIKzeHe6YMcXz0PiQTK+rfEUiYnR5VXRquzlFhayfItp9PpOLVuEY3eG2b4XBBzS0sq\nNG7D2jF96f7NfKysn97o9J9WwkZveN5eqUowwKhRo+jWrRtlyuQ9KurYsSOjR4/m4MGDtGzZ0ih+\n6dKlODo60qRJXn+zqVOn4urqyqBBg9DpdOzfv58pU6bw88/F6xBf2FcHctr0I0RfR1tU6lwSMlSG\nLhF341PpVN3PKO76/STi0pV0X7gDnQ5UObnodHA/NZO5vfV/59WYRMZvPcG07k2o5FH0Ctap8GRO\nhec94vJxlONlLyc6VV+4edvLiUkt3h36IzZSc0YFlOfg3QSOhJjuJlEQZVgE5goFUlcXQ5cI24rl\nid68rcBlPLt0IGbrDqNpGbfvYeXqbDRNV4Q3kX29vVCqVMQnJBq6RNy5F0q3DsY3Ina2Nrg4OXI7\nJJRab+jftL4TEkq5Mn6A/tHf0IF9GTpQP+ze8TPnqFKxwjOPfPBfVbt5G2o3zxvZ4n7YPe5HhOBR\nWt8N4X5ECO6+fkVal0aT+8x9gpURkZgr5EhdnA1dIqzLl+fBtu0FLuPesT2xO3Y9db1mFhbIivHC\n7PZzUWw/l/cYvZKXHRU97bhzX/8kQf97/m5Pj+tS34dD12KNWpAfKetuQ8VSduwsoIJckOwH0ZhZ\nybFwcDR0ibDy8SPlsHHrqVaZSW7yE+ds8R6yFSorOhpzuRxLJydDlwiFXxkS9hl3S9NkZqJONO6+\nZKoe49Q8AGVYiKHfcHGV9nJHqcomLinF0CXidlgU3Vs3K9Z6JBIJw/r0YFgffSvnsfNXqFq+dJHO\n+SqNA6nSOO9aEh8RQkJkGC7e+utcQlQoLl6li5SHVpNLapz+PEqIDMWnak2cvfTdFyo1aMGBFXNJ\nfhCJq+/Tu5gBqGNjMJPJMLd3NHSJsPIqTerxgvvgGnnsT0/cto7EbfproqJqTbLCQ4q0inL+LSnn\n39LwOSkqlOSYcBwfdolIjg7DwTP/vsnJUpIYGULQb1NBp0OryUWtUrJ27Af0nLIw302FTqslJ1uF\nMiXh1a4EC8XySrwY97iKFSvSuXNnVq5caZjWsWNHWrduzdixY9mwYQPR0dHcvHmTSZMmERwczPff\nf49Mpr9T3LdvH3v37mX69OmYmZlhbm7OtGnT2L9/P3v3Fu+lCjc3N6pVq1bgT0HkUguaV/Bi4ZGr\nZOdqOHwnmnvxqbSo4GUU16RcKbYO7sTqAe1YM7AdPWqVo2VFL37o1hiAu3EpfLHhCBM6+FPbp+jf\n1W3KybBk2lV2w0ZqjpuNFc3KOXM8LMlkbDUPW2wePtL1dZTTqqIrFx+2AssszPi8ZXkuRaex52bx\nHyNrVFnE7T9E+U8HYyaV4hrQHJuK5YkLOmgyXlHaB9uqlXiw3bhf5/2/d+Ia2AKbShWQWFhQdujH\nJJ06+9RWYNC38AY0bcy831eQna3m4NET3A0NI6Bp43yxHdu2YuHy1SiVKi5fu0Hw0RN0bKMfIi0l\nNY2oGP3LKXdDwpgxd6GhQgyQm6shO1uNVqclJzcHtVpd7C49BZGYmWFhZYWZuRnmlhZYSKUvrPJd\np0UbDm1ZR2ZaCvExUZzet516Ae1Nxl46FkxKgv4YiY+JInjTasrXyD+KRFFos7JIOHiYMoM/wUwq\nxbl5U6zLlyUh2PSLSXJfH2wqVSR2t/E579y0CXJffUcmqasLZYYNIvl0wf0NC7PtbBQDAyvgaC2l\ntKs17zTyY8upp1fWOtfzZvMp0y9xdqnvw6HrsaSpnn4cP0mXnU36+ZO49XwfiaUltrX9kXmXJu3c\nyXyxyUf249LpLcysZFg4OeMY2J70C3n7QGJhgZnUEpAgsbBAYl68thRtdhbJJ47h9X5/JJaWODRo\nhLx0GZJPHMsXmxC0l1Jv98ZMJsPSxQW3NzuScto4Z5dWrUkM2lesHB6nkMkIbFiHuas2kq1WE3zq\nPHfCoghsmP9Y1Gq1ZKvVaDQacjUa1Dk5aDT6m+vktHSiHuiP5zvhUUxf8ifD+5ge1qwwVRoHcnbX\nX6jSU0l+EM2Vg7uo2tT0MIq3Tx8mPVG/3eQH0Zzevg6favohHd38KhB58zJJ9/XH3O0zR9Dk5GDv\nWqpIeejU2WRcPI1rl3eRWFhiU7M+Vl6+ZFzI3z/Zpk5DJFIpSMywrd8EebnKKB8OwWdmbYuli/5r\ndqWePri9M4CEv9cWb6c8VM4/gKv7NpGVkUpqbDS3j+6hfMP8jVFSuTW9pi2n6/g5dJ3wK03eH4GN\nkyvdJvyKpUxOYsQ9Hty5ilaTS052Fmc3L0OqsMHeo/AXV//TxDjBxfLKtQSDfqzfnTt3Gl3YZ8+e\nzfLly1m+fDlTp07FysqKWrVqsWrVKmrV0g8rk5yczOTJkxk+fLjRi3QVK1Zk2LBhTJ06FX9//2J3\ni3gWY9vWZfKOU7SevRl3WwX/69YIW5mU3dfCWXbyBms/bI+FuRlO1nmPeRRSCzKyzbF7OIzamjO3\nSc1SM/HvE+jQ33TX8nHll7ef/ka8KcF3E3CzseKHTlXJ1ejYeSPWMDyao8KSb9+sYhgLuJqHLR82\nLI2VuRnJqhx2Xo/lbKT+sVZtbwd8HeS421oRWMEF0DckDd9oejxSU25MmUb1Hx8IKWsAACAASURB\nVKcQcDqYrAcPuDRyLLnpGZTq1J4ygwZwvHNeN5NSXTuScPgYOanGrWuZIWHcmDKN2r/NwtLWhuRz\nF7ky9psibX/CqBF8/f10mnbsiYebKzOnTsDO1oYdew+wZNWfbF6h/+KW4R/2Y9KPs2jZtRf2drZM\n+OJTSj8cSSApJYXhYycSn5iEm7Mzg/r3oYl/PcM2Jk+fxdZd+5BIJJw8e4GpM2bzx5wZ1KtV8DA/\nRdVhwgg6fjPS0Gz25tfDWD7gS06t3PSP112YRu27kXA/mh+H9sHcUkpgjz6Ue0N/QU5JiOOnT/sx\nes5yHFzciI+JZNvSeWRlZqCwtadGk5a0e7do3Q5MuTNtBlWmfkPTg3vJehDLtTHjyc3IwK19W0oP\n7MeZd/oYYt07tCfp2Aly04yPG6mLExXGjMLSyZHcjEySjh7n3uyCuzwVZs2RUHxdrNkzqTXqXC2L\n9t7m9F1966aHg5wdXwfS4Yf9xKbon7o0qOCClaU5h6+bfku9U13vYr0Q97iYpfPxHjyKKgvWkpMU\nT+Sv09AqM7Fv3ALXLu9w9yv9Y+O4TWvw7D+USr8uR6NSknxgN6knDhnWU3XpZvRntY6qSzeTkxDH\n7c+L938LnzebsqO/os5fW1DHx3P3h6loMjNxbhlIqV7vcXXIR/qcVy2n9LCR1Fq1Ho0yk7id20k6\nmNcKKXX3wLpCJe5MmfhM++SRSUP78dWshTTqNQQPV2d+HjccOxtrtgcfZ9H6bfw9/38A/H3gGF//\nvJhHl57tB48z9L3uDHuvO8mp6QyZMouEpBRcnRwZ8m5XmtSp/kz51GzVmZTYGP74cgDmlpb4d+pt\nGOEhPTGO5eM+od+0xdg6uZJ0P5KDaxaQrcxEbmNHRf/mNOmpf8HLt2ot6r35FptmfE1WZjr2rh50\nHD4eqbzo7yXErl5IqYEjqTB7JTlJCcQsmIFWlYmdf3OcOvQ0jAXs1LoLpfoNB0D9IIroef8j52Hl\n3MLWDu8R47GwdyQ3JYmE7X+hvH7xmfZN5RYdSI+PYeOkTzC3sKR6u7cNw6NlJMWzZepQun8zH2tH\nF+R2eddyK2tbJGZmyGz13VO0mlxOrV9Mevx9zCwscCldgbbDJ2MmXix7rUh0/1bzk5BP2tJJLzsF\ng8/l3QsPeoHemfTslZ3nIfDYlpedgsEIt+LfxDxPHa7lby18WWz7vPixj59mcNNxLzsFg41JiwsP\neoGUiUUfFeZFqDdv2stOwWBxQsFfjPQytFg8svCgF2RLnxkvOwUjXwVUeNkpFEvWzvnPfRuyDk9/\nwfxV8sp1hxAEQRAEQRCEf+qV7A4hCIIgCIIgPKGE9dl93kQlWBAEQRAEoSQQleBiEd0hBEEQBEEQ\nhNeOaAkWBEEQBEEoASRmom2zOMTeEgRBEARBEF47oiVYEARBEAShJBB9gotFtAQLgiAIgiAIrx3R\nEiwIgiAIglASiJbgYhEtwYIgCIIgCMJrR7QEC4IgCIIglAASc9ESXByiJVgQBEEQBEF47YiWYEEQ\nBEEQhJJAjBNcLBKdTqd72UmUVGcikl92CgY17m572SkYWSxv9rJTMDKo/H/nEdKueOnLTsHIzmoN\nX3YKBh+EnnvZKRhpqL75slMwuGxd7WWnYCQqLftlp2AkUal+2SkY9CmlfNkpGGmzPvZlp2Dw3Ybx\nLzsFI02OHnnZKRSL+tj6574NaZN3nvs2XhTREiwIgiAIglASiNEhikW0mwuCIAiCIAivHdESLAiC\nIAiCUAJIREtwsYiWYEEQBEEQBOG1I1qCBUEQBEEQSgIxOkSxiL0lCIIgCIIgvHZES7AgCIIgCEIJ\nIPoEF49oCRYEQRAEQRBeO6IlWBAEQRAEoSQQLcHFIlqCBUEQBEEQhNeOaAkWBEEQBEEoCcToEMVS\nIirB48aNY/PmzUgkEszNzXF3d6d9+/aMHDkSqVQKQOXKlQFYv349NWrUMCyrVqtp1qwZqamprFy5\nkvr167+wvHU6Havm/8KRfTuxlErp3Ksv7Xv0Nhl7eO8O9m5ZT2x0FDZ2dgR27E7n3h8AcD8qgjUL\n53Dv5jUAKlWvxQfDvsDR2aXIuSRnKJm4fBtn7oTj4WjH173a06CyX76437YfZsvxS2SosnG2s+bD\ndo3p1rgmAIlpmUxetYMrYTGkZCi5+NvXxdwjeXQ6HUfWLOTmsSDMLS2p2+EdarXrbjI25MIJjq//\ng8yURCyt5FRs2JImvT5CIpEAcO/cMU5sXE5GUgIe5SrT+sPPsXFyLXhfpKQy/ocZnLlwCQ83VyaM\nGkGDurXzxWVnq5n04ywOHj2BvZ0tnw3+kA6tAwzz5/2+gs07dqNUZdEuoDnjR43AwkL/qOq3P1ay\nN/gwIeERfDtuNF3fbPOP9tXff8zlXPBuLKRSArq/R7POb5uMvXb6GDtXLCAtORGpTE6tZq3o1G+I\nYV89D80G9aHpx73xql6Jnd/NZee3c57btnQ6HX8umM2xoF1YSqV0ePt92vboZTL26L6dBG35i7iY\nKKxt7WjZsRsde/XNF7dj3Qo2Ll3IuJkLqFCtepFzSU5NZ9wvv3Pm6i08XJyYOPh9Gtaski9u+u/r\n2H/qAkmp6Xi7uzCybw9a1n94TqWkMfHXZVy+HUJyWjrXtv5e5O0/SafTsXzezxzeqy9vuvTuS8e3\n3jUZe2jPDnZtWseDmChsbO1o07kHXd/9wDB/yc8/cuX8aWJjopk0az5Va+Y/P4qSz9bff+Xsw+M2\nsPt7NO/yjsnYMwd2cXT7RhIeRKOwsaVR+64E9uhjmH/l5GF2rVpMSmI8pStWpdeIr3BwcStWLkEr\n53Pl8F7MpVIade6F/5s9TcbePnec4D+XkJGcgKVMTrXGgQS+94nhHFJnqQha+Ru3zhxFp4MKdRvR\nefCYIueSnJrG+Gm/cPriVUq5uTB+5CAa1qmZL27voeMsW7+Zm3dD6RDYjO/GjjTMu3z9Ft/MnMf9\n2HikUkuaNajLhJGDkcusipzHk4a1KEu7Ku6oNVr+PBvFxgvRBcZW8bBlWItylHFWkJaVy7xD9zh6\nLxGAzwLLU9fHEU8HGZ9vuMzl6NRnzsnC3p4KX3+Nfe1aZMfFETLrZ1LPn88XV2vFcqzc3QGQSCSY\nSaXc37yZ0NnPrywSXg0lohIM0Lx5c6ZNm0ZOTg5Xr15l7NixmJmZ8cUXXxhiPD092bhxo1ElOCgo\nCGtra9LS0l54zkHbNnLzygVmLt9AZnoa348eim/ZClStVTdfbG5ODv2Hf0nZSlVISoxn+lcjcXEv\nRaOANigzM6jfLIAhX01BamXFmoVzWDTjW8ZOm13kXL7/czcu9jYcmTGK4zdC+HLJJrZPHYqdQmYU\n17lBdfq3bohCJiUiLokBs1byhp8n5T1dMTOT0Lx6ed5tWY+hc9f+o31z5cB2Ym5foe/0P8jOzGDT\ntDG4+JbFu0r+i4F7mYr0GDcDhZ0D2cpMds79lqvBO6ge2InkB1EELZlF19Hf416mIme3r2P3gmm8\n9fXMArf93cw5uDo7cWzHRo6fPscXk75j59rl2NnaGMXN/X05aWlpBG9dy92QMAaPHk+1ShUo7ePN\n5h27CTp0hLWL56JQyBkz+QfmL13JiI/7A1Da24sxnw5m0fI1/2g/AZzYvYXQ65cY+9saVJnpLJj4\nGaX8ylG+ep18sT7lKzPkuznYODiiysxgxfRJnNizlcbtu/3jPAqSGhPL9m9+pv57XZ/bNh4J3r6J\nW1cv8uPS9WSmp/PjmGH4lC1PFZPnlJq+w7+gTMUqJCfGM/Prz3Fx96BBy7wbkuTEeE4d3I9DMW4o\nH5k6fyWuTg6cWDOHYxeu8fmP89mzaBp2NgqjOGuFnMVTRuFbyo3TV24y4vu5bJozGS83F8wkElrU\nq0GfjoF8MuXn4u+Qx+z9eyM3Ll9k9sqNZGakMeXzIZQuV4E3atfLF5uTo2bgyC8pX6kqSQnxfD92\nJC7uHjQJbAuAX4WKNA5sy8KfvnvmfI7v2kLI9UuMm/8nqsx0fpswEs8y5U0et7k5OfQY9Dk+5SuT\nmpTAoilf4OjqQe1mrYiPjmTtnGl8MvknfMpX5sDGVayaOZXh/5tb5FzOB/1NxM3LDP55OVmZGaz+\n7gvcfMvhV61WvljPspV4f+JMrO0dyVJmsOnnKZwP2kbdNl0A2L5wBg6uHgybswYLqZT4yLBi7Zdv\nf56Pi7MTx7eu5tjZC3wxZTq7Vi/Ezsa4/HGwt2VAr+5cvHaT1LR0o3m+XqWYP20SHq4uZKvVTP5p\nHr8t/5MvBvUvVi6PdK1Rihpe9ry/7Aw2Vhb88lZN7sVncDEqfwXWUWHJNx2qMCPoNucjUrCxskBh\nlddP9W5cBgduxfNl64rPlMvjyn0xCnViIqc6dsLBvz6Vpk7hXO930WRkGMVd/KCf4XeJhQX1/95K\nYvDBf7z9/yKJuegTXBwlpt1cKpXi5OSEu7s7rVq1onHjxhw7dswoplu3buzcuRO1Wm2YtnHjRrp3\nN93C+Lwd37+Hjm/3wdbOHg8vHwI6dOXIvp0mYwM7dqN81TcwMzfHxc2Dek1bcuf6FQDKVapK87Yd\nUVhbY2FhQduub3H3xtUi56HMVhN8+TbDOjdHamlByxoVqeDlRvCl2/lifVwdUcj0reu6h9OiE1MA\ncLRR8HazOlTyLnoLTEFunThA7fZvIbexw8Hdk2ot2nPzWJDJWGsHZxR2DvqcdFokEjNS4+4DEHn1\nPD7VauNRrjISMzPqdepFfNhdw/wnKVUqDhw9wfAP+yGVSmnZtBEVy5Ul+OjxfLHb9wQxqP/7KORy\nalSrQmCzRuzYFwzAkROnebtrR1ycnVDI5Xz4fm+27NprWLZj20Aa16+LzOrZW2YeOX9oHy269sLa\nzh6XUt74t+nEuYN7TMbaOTlj4+AI6Fu/JBIJiQ9i/nEOT3N5WxBXdhxAlZpeePA/dHz/Htr3fA8b\nO3vcvbxp/mYXjgftMhnbskM3ylXRn1PObh7UbdIi33mzbtGvdOv7IebmxWsvUGZls//URUb06YbU\n0pIA/1pUKuPNgVMX8sUOe7cLvqX054x/9cqU8/Xk+r1wABztben1ZksqlfEp1vZNObpvN5179cHW\nXl/eBHbsxuG9psub1p26U7FqdX154+5Bg2Z55c2j+VVr1sasmPvlcecO7aVl196G47ZBm06cDTZ9\n3DZq14XSlaphZm6Oo6s71Ru2IPyW/n9169IZKtasS+mKVTEzMyOw5/tE3btVrOP66tH9NOz4Ngpb\ne5w8vKgV0IGrR/aZjLVxdMba/uE5pNUhMTMj5WF5khAdTmzYXQLe/RipTI6ZmTnupcsVOQ+lKosD\nx08xYsB7SKWWBDT2p2I5Pw4cPZUv1r9Wddo0b4yjg32+eQ72dni46m/cNBotEjMJkTEPipzHk1pX\ndmP9uSjSsnKJSc1i+9X7tK3ibjL2rdpe7L4ey7mIFHRAenYusWnZhvnbrz7gcnQqGp3O5PJFZSaT\n4dS0KRG//44uJ4fkY8fJvHcPp6ZNn7qcU9OmaDIySLt8+R9tXygZSkwl+HG3b9/m/Pnzhq4Qj1Sr\nVg0vLy/27NEXtDExMZw9e5auXbui+4cn5LOIDg/Fp0x5w2cfv3JEh4cWadmbVy7i7VfW5Lwbly8U\nOM+UiLgkrK2kuNrbGqZV8HTl3v14k/F/7DlOg8+m02XyfNwdbGlYuUyRt1VUSdERuPjkrdfZ24/E\n6PAC42PuXGPhkJ4sHv4OiZGhVG3eLm/mY/9bHTp0Oh1JBawrIioaa4UcVxdnw7TyZf24G2ocn5ae\nQWJyChXL5uVYoWwZ7oWGmdosOq2O+IREMpXKAv+GZxUbGU6pxy60pXzLEhsRVmB86I0rTOzTkckf\ndOZ+eAj+rTr86zm9LDERYfiUzdsX3sU4p25duYRX6bz/581L58lIS6VO4+bFziM8JhZruQw3JwfD\ntAq+XtyJKPgRMkBqRiZ3wqMp7+NZ7G0WJio8FN+yeeWNb5lyRIUVbd8Ut0wpitjIcEr5PXbcli7L\ng8ii5RNy7RIevnn5GJXfOh06dDyIKNq6QF95dX1sfa4+ZYiPCiswPvLWVWZ+1JWfB/UgLiKEmi3b\nA3D/3i0c3T3ZNv9Hfv6kB8u/+ZSo29eLnEd4dAzWcjmuzk6GaRX8SnM3LKLI63jkflw8jTq/i3/H\nXgQdOUGf7p2KvY5H/JytuZeQafgcmpCJn7PCZGxlDzskwJI+dVj/UQPGtKmIQvrvt07Kvb3RKJXk\nJCYapilDQlGUefr1yLVtW+L37n1qzCvNzPz5/5QgJaY7RHBwMLVr10aj0aBWqzE3N2fy5Mn54nr0\n6MHGjRvp3LkzmzZtokWLFjg6Oj7TNuPi4oiPN11RBMD26ReyLJUKubW14bPc2posVeGVpJ0b1qDM\nSKdZm/yVlwfRkfy1dAEjJnxf6HoeUWbnYP1EXzFrmRWpSpXJ+IHtGjOwXWOuhsVw+lYYls/h8UtO\ntgqpPK+QlcoV5GRnFRjvWaEag+ZvJC0hllvH9yO31beO+FSrzYmNy4i5fRX3spU5u+1PtJrcAtel\nVGZhozAu3G0UinyPG5Uq/b5RKOSGadbWCsP0Jg3rs2LtRgKbNsbaWsGS1fruISpVFtYK0xePZ6XO\nUmGlyDuOrBQKsrNM/+8AylSpzrerd5Ac94Bzh/ZiY+9QYOyrJlulQv7YvpArrJ+6Lx7Zs/FPlBlp\nNGmtP6e0Gg1/LprDoLGTnykPpSoLmye6Elkr5KSmZxawhL4iN/6XP2jXpB5lvEs903afJkulQqF4\norzJKry82f7XGjLS02nRtuO/mo86S4XssXxkCmvUqsL/V4e2rkOVmU69AP2NbsWa9di1ajEh1y9T\numJVgv5agTZXg/op5YWpXKweK2+s5ArU2QXn4lPpDb5YspXU+FiuHN2HwlZ/DqUnJxBy5RydPhlN\np8FfcvPUYf6aOZGhP68wOkcLolRlYWP9RHcZ6/zlT1GUcnPlxLY/SU5NY8P2Pbi7Ohe+UAHkluYo\n1RrD50y1Brml6XLfxVpK6ypufLnpComZasa1q8SQZmWZuf/OM2/fFDOFHM0TjQoaZSYWdnYFLmNh\na4tjwwaEzZ//r+YivLpKTCW4YcOGTJ48GaVSybJly7CwsKB169b54rp06cKsWbOIjIxky5YtTJw4\n8Zm3uW7dOubOLbjf2ap9J40+Hz+whz9++REk0CSwHTKFAlVm3kVRlZmJTP70CtKx/bvZs3k9E39e\ngOUTLd3JCfFMH/cZbw8YTJWa+fvVFURhZUlmVrbRtMysbBRW0gKW0HvDz5Ptp66w4eh53mmev89l\ncdw6EUzw8jmAhEqNApDKFKgfuyFQq5RYWskKXsFDdi7uOHr6cnDlPN4c+jWOpXxo9eEoDq6YizI1\nmUqNAnHy9MXGyXQfT4VCRsYTBWuGUmlU2QVQyPWflUqVYV5mptIwvUfH9sTGJdB/xBdoNBr69X6L\nk2fP4+z0bDdcj7tweB8b588EiYTazVtjJZeTrcw7jrKVSqxk8qesQc/RzQN3bz82L/qF90dP/sd5\nvQwnDuxlxZzpIIFGAQ/Pqcf2hUqZWei+OHFgD/u2rGfczPmGc2r/to1UfKMmnr5+z5SXQi4jQ2lc\nCctUqlDIC+7+MuW3lWSqsvj5qyHPtM0nHd2/h8WzpiGRQNNW7ZErFCiVT5Q3sqeXN0eCdrNr4zqm\nzF6Yr7wprvOH9rFh/k8gkVCnRRus5HKyHssnS5mJVP70/9W5Q3s5sn0Dw36Yi4WlPh83L196jfiK\njQtmkpGaTJ3mbXD3KY29c8Evv147tp9dv/8CEgnVGgdiJVOQ/Vh5k61SIrUq/Byyd3XHxas0e5bN\nofunE7GQWuHg6kGNFvoKetVGARzbsoaYezcpU73wMlIhl5GRaVz+6MuVwsu+gjja29Gkfh3GfDeT\ntfN/KtIyrSq5MqpVBXQ6CLoVh1Kda9Saay01R5WjMbmsWqMl6GYCMan643/16Ui+71rtmfMviFap\nwvyJBgVzhTWaAhpvAFzatCbzzh2yIiP/9Xz+M0pYS+3zVmIqwXK5HB8ffb+5H374gS5durBhwwbe\neustozgHBwdatGjB+PHjUavVNG/enIwnOtEXVa9evQgMDCxw/pNtLI0D29E4MO8xfUTIXSJD7+FT\nRv9IMDLsntHj2CedO36YPxfN5esZc3Fx8zCal56awrSvPiWwU3cCOhTvBSRfNyeU2WriU9MNXSLu\nxMTTpWGNQpaEXK2WiPjkYm3PlEqNAqjUKG9khYTIEBKjQnH29gMgMSoMZ6/SRVqXVqMh7bE+v+Xr\nNaV8PX0/sWxlJrdOBuPs5WdyWV9vL5QqFfEJiYYuEXfuhdKtQ1ujODtbG1ycHLkdEkqtN6rq40JC\nKVdGv16JRMLQgX0ZOlA/2sDxM+eoUrHCvzIKQ+3mbajdPO/lrfth97gfEYJHaf3j3PsRIbgXsfKm\n0eQ+9z7Bz1OjwLY0Csz730SE3CEq9B7eDx+zRxVyTp0/fpj1i+cx5sc5OD92Tt28dJ7bVy9x5vAB\nANJTUpgzZSxvDxxC8/adC82rtKc7yqws4pJSDF0ibodH071VE5PxM5au50ZIOMu+H4Olxb9TLDdt\n1Y6mrfLKm/B7d4gMuYvvw/ImIvQe3n4F75szxw6xeuGvTJw5Dxd3jwLjiqpOizbUaZF33MaE3uV+\neAilHh234SF4+BScz9VTR9i+bD6Dv/0FR1fj/qg1GrWgRqMWAKgyMzh/OIhSvgWvq1qTVlRr0srw\nOS4ihPjIUNwebj8+MhTXh2VPYbSaXJJj9eWNq7dfvnO8OOd8aS9PlKos4hOTDF0iboeG061dwdeZ\nosjNzSUyxvR7EKbsvxXP/lt5TznLuVhT1sWasET9Va3MY78/KTQhkxfRuVAVFYW5XI6ls7OhS4R1\nubLE7jT9DgDou0LE7THd71x4PZXIPsESiYTBgwfzyy+/GL0E90jPnj05c+YM3bt3/0eVEjc3N6pV\nq1bgT2Eat2rHzg2rSU9N4UFUBME7t9Ksren+mVfPn2HJrB8YNXVGvtYplTKTH8eNpHbDpnR65/1i\n/x0KKykBNSvy2/bDZOfkcvDybe7GxBNQM//buxuPXiBdlYVOp+P0rTB2nblGg0p5+ahzcsnO0aBD\nhzonl5xc060FhanUKJDzuzaiSk8l5UE01w7tpnKT/C37AHdOHyY9UV9gpzyI5tyO9XhXzXurOy7s\nDjqdDlVaCgeWzaZq83ZYWduYXJdCLiegaWPm/b6C7Gw1B4+e4G5oGAFNG+eL7di2FQuXr0apVHH5\n2g2Cj56gYxt9RT4lNY2ohxeduyFhzJi70FAhBsjN1ZCdrUar05KTm4NarX7mful1WrTh0JZ1ZKal\nEB8Txel926kX0N5k7KVjwaQkxAEQHxNF8KbVlK9R9KcGz0JiZoaFlRVm5maYW1pgIZU+tyHZGrdq\nx+4Nf+rPqehIDu/6myYmug0BXL9wlqW/TOPTKT9S6olz6qPRE/l+8Wqmzl/O1PnLcXB24aMvxhtV\nuJ9GIbMisEFt5q7ZQrY6h+DTF7kTHkVgg/xDic1ft41DZy6zaMook0NYqXNyyM7JQafT/67OyS1S\nDk9q2qY929avJi01hftRERzYsYUW7Ux3cbhy/gwLf/qBL7/7CS8TN1S5ubmo1dmg05GboybHRDlb\nmLot2nJoy1oy0lKIj4nk1FOO29uXzrF+3nQGjv8f7t75b4aj7t1Cp9ORkZrCX7/NoEHrjshtbE2s\nybQ3mrbi1Pa/UKalknQ/iovBO6ne3PSwhTdOHiItUX8OJd2P4sTfa/F7Q/9/LV21FjqdjitH9qHT\narlx6jAZKUl4lqtcpDwUchmBTRowd+kastVqgo+f5m5oOIFNG+SL1Wq1ZKvVaDQaNBotanUOGo2+\nvD104gxhkfr+53EJicz5Y5XJYdaKKuhmHL3qeGMns8DLQUanN0qx50asydjd12NpX9UdDzsZVhZm\nvFvfm5OhSYb55mYSLM0lSABLc/3vz0KblUXS0aP4fjgQiVSKY5PGKMqUJenoUZPxMm9vbCpUIGGf\n6ResSwqJmdlz/ylJJLqX8UbYv2zcuHGkp6cbdU3QaDQEBgbSv39/BgwYQOXKlZk3bx6tWunv/lNS\nUrCxscHCwoL09HTq16//r48TfCbi6S2kOp2O1Qtnc3jPDiwtLencux/tH45pmhgXy9iP3+XHJWtx\ndnXjhy+HcevqJf0jSR36LhWt2jPg0zEc2beTRT99h5VMljdkgwSWbD1g2FaNu9uemktyhpIJy//m\nzO0IPBztmPBue/wr+bHj9FV+33OcTRM/AWDkgr+4cDeSXI0WDyc73g/0p0eTvApnzaHfoy/e9C+h\neTo5sOu7Yfm2t1jerNB9c3TtIm4c2Ye5hSV1O/WiVlv9MF7pifGsHj+I939YiI2TK2f+XsPVgzvJ\nVmYis7algn9zGvbsh7mFJQDrp35GUkwEllIrKjdtQ+O3+uc7kQeVz3uElJySytffT+fshct4uLky\ncfSn+NepxY69B1iy6k82r1gM5I0THHzkOPZ2towa+jFvtmoJQEh4BMPHTiQ+MQk3Z2cG9e9Dl/Z5\nF9UJP8xg6659RpXBP+bMoF6tGuyKL95jZ51Ox7al8zh7YBfmllICe/ShWWf9E5CUhDh++rQfo+cs\nx8HFjaC/VnByz99kZWagsLWnRpOWtH/vIywsLQtc/85qDYuVz5M6ThpJx29GGr0puHzAl5xauanY\n6/og9NxT5+t0OtYumsPRvTuxsLSkY6++tO2ed05NGNSH7xetwcnVjR/HDOfOtctG51SjwHZ8MOLL\nfOsd0+8tPh7zTb5xghuqbxaYS3JqOl/98jtnrtzEw8WJb4b2pUGNKmw/eJJFG3bw99xvAaja5UOk\nlhZYmJujQ4cECVOGfUDHFg0N8x8dJjodeLk5s2/J9Hzbu2z99BtvnU7H7V+v6AAAIABJREFUyvmz\nObh7OxaWlnR7rx8deurHJU+Ii2X0wN7MXLoWZ1d3po4ays2rl5BKpeh06LtUtH6Tjz7Tj3c7ZdQQ\nbly6AI8dv3NXbzZqMY5KM+5iZSqfv5fO48z+nVhYSgns2YfmD8e3To6PZcan/Rjz6wocXNyYP3Ek\noTeu6LtAPEyobou29Bw8CoDZYwYTGxmG1EpGvcD2dHj/E8yeOMcTlQVX1HU6HftXLeDyoT2YW1rS\nqMu7+L/ZA4C0xDgWjfmIT6b/jp2zK0c3r+LC/h1kKzOQ29hRpWELWrwzwFDexEWGsmPhTyTej8S5\nlDdt+w3Hq0JVo+31KVVwX+zk1DS+/t8vnLl0BQ9XFyZ+PoQGtWuwPegQS9ZsYMsfvwKwZfd+Jkyf\nY1SGDPmgN0P79Wbjjr0sXrOBxORUbK0VNGtQl1GD+mNva/rmv8160xXaxw1tXpb2Vd3J0ehYcyaC\njRf1T5BcbaxY2rcu/VeeJSFDv4+71fSkT30fzM0knA5LZs7Bu4Y+xbN61qCmt73Ri8PvLT1NXLr+\nePluw/hCc3nEwt6eCuPH540T/NNMUi9cwKVNa7zff5+L/fobYn0GDsS6fDlufl309QM0OXqkWPEv\nm/b2scKD/iGziqafaL2KSmwlGGDRokUsX76coKAg6tSpw9y5cw2V4Melp6fj7+/PihUrXmgl+EUq\nrBL8ohVWCX7RHq8Ev2zFrQQ/b/+0EvxvKqwS/KI9rRL8ohVWCX7RCqsEv2hPqwS/aE+rBL8MRakE\nvyjFqQS/CK9cJfjuycKD/iGz8v+da8I/VSL6BP/vf/8zOf2TTz7hk0/0LZg3btwocHlbW9unzhcE\nQRAEQRBKlhJRCRYEQRAEQXjtSUpWn93nTVSCBUEQBEEQSgJRCS4WsbcEQRAEQRCE145oCRYEQRAE\nQSgBdKIluFjE3hIEQRAEQRBeO6IlWBAEQRAEoSQQLcHFIvaWIAiCIAiC8NoRLcGCIAiCIAglwXP6\nSvqSSrQEC4IgCIIgCK8d0RIsCIIgCIJQEpiJts3iEHtLEARBEARBeO2IlmBBEARBEIQSQIwTXDxi\nbwmCIAiCIAivHdES/By9cXnNy07BoNeDui87BSPfbB/0slMwtnjJy87AwLZP15edgpEPQs+97BQM\nVpT5bx3HH3/+68tOweCQ37cvOwUjrskZLzsFI24dOr7sFAz2pzd82SkYmXd+9MtOweDIzFUvOwUj\nTV52AsUlWoKLRewtQRAEQRAE4bUjWoIFQRAEQRBKAtESXCxibwmCIAiCIAivHdESLAiCIAiCUBKI\nluBiEXtLEARBEARBeO2IlmBBEARBEIQSQIwTXDxibwmCIAiCIAivHdESLAiCIAiCUBKIluBiEXtL\nEARBEARBeO2IlmBBEARBEISSQCJ52Rm8Ul6pSvDFixd57733aN68OQsWLMg3f8+ePaxZs4YbN26Q\nnZ2Np6cntWvXpm/fvlSpUgWAzZs3M27cOCQSCTqdzrCslZUVly5demF/iyAIgiAIgvDyvFKV4A0b\nNtC3b182bNhAfHw8rq6uhnkzZsxg2bJlfPDBB3z66ad4eXmRlJTE4cOHmTVrFosXLzbE2trasmfP\nHqNKsOQF3z0lZ6iYtHYfZ+9F4+Fgw7geLfGv4JMvbv6ek2w9fZ2MLDXOtgoGBNajm39VAM7ei+KT\n+ZuRSy3QARJg7sddqV3G85ly+rBhaQIquKLWaNl8KYZt1x6YjAuo4MKwZmX/z959xzdR/gEc/yRp\n0iTdu3TSFgqlCMjeo8geIqDgQMUFqLhBGYKiDMUtKCJDUEGUpSxZZe+9obS0UFq6d5M0SZvfH8GU\nkBRSfiiCz/v16uvVu3vu7tvr5e657/PcE/TGCiRIMGFi5NLj5Gr0xAS4MaFbXUyYj61EIsHZScqb\nK0+QnKtxKA6Zmzvhr7yFa/2GGHKySZ09g5ITR+2W9Y7rQsCAR5F7e6PPzuLChxPQZ1nHHfXuZNwa\n3s/RgT0d2n9+QSHjpkznwJFjBPr7Mf6NkbRocr9NubIyPRM++oytO/fg4e7Ga8OfpecDnSzLv/xu\nHivXrkdvMNC4QX0mjnoNXx9vrmRm8eATz1nOOZPJhFan4/MPJ/BAh7Y3jE3u6UHd9yfg2bQxZRmZ\nJEybTsGBQzblmv22CGVgoHlCAlJnZ9J+XUbi9M9wrx9LnXfH4FwjkIoyPXm79pAw7WMqdGUOHZ/r\nmUwmFs/6kl2b1iFXKOj58BN07T/IbtmdG9eyaeVvZKVfxsXNnY69+tFr0BCbcmuWLGTZ/O8Y8+ks\nasfed0tx2dNu2OO0fX4wwffVYe2HM1j7wVe3bdtVeadfLH2bhqA3VjA3PpEftyfbLffugPvo0yTE\n8tlROElJziqh/yfbATjxSW+0+nIATJj4flMic+ITHY5DonTBrctg5MFRlJcUULp1OYbLVa8vdfPC\n64nRlJ07REn80sr5nr64duiPU41wMOjR7N+I7sRuh+MAkKpc8Or3NIqa0ZQX5lOwdjH6lHM25Tz7\nPoX6vmaYyo2AhPKCXLJmTTJvQ+2GZ98hKIIjkKpdSf9gRLViuFZ+cSnjvlvCgTNJBPp4Mv6ph2gR\nW8um3PRFq4g/eIr84lKC/bx45eEedLg/xrI85Uo2Uxau5Oj5i6idFQzv9wCDu7Sudjwmk4nlc75m\nf7z5M9W5/+N0evARu2X3bV7HttVLybmShtrVnTY9HqTLgMcBSDp9nFnvv4X57mDerqFMx1ufzSE0\nKrracclc3Ql64TVc6tbHkJfDlQWz0Jw5bresR9vO+PZ9GCcPLwy5OaR+NglDTma193k9k8nE1p9n\ncXrnRpzkCpr1eoTG3fvbLZt0eA87lsyhpCAXuVJF3ZadaD/4ecu199zerexevhBNUT6egSF0enwE\nQbXr/d8x3lGiT3C13DWVYI1Gw9q1a1m+fDk5OTmsWLGCF154ATBniOfOncu7777L448/blknMDCQ\nevVsT2iJRIK3t/c/Frs9U5ZvwdfdhW0fPM+ec5cYvXAdf4x9CneVs1W53k3q8lTHxqidFVzKKeDZ\nmcuoHxZArUAfAEJ8PPhjzJP/dzw9YgKoF+jOiF+P4uIs48Ne9UjO03DySpHd8ifTi3jvz7M2889k\nFvPowgOW6TYR3gxpFuZwBRggdPhIDPl5nBgyELf7mxAxahynRzxNeWmpVTn3Js3x6/0QFyZPoCz9\nMoqAQIwl1vF6NG+FVKW0euC5mQ8//Qo/H292rVnG7v2HeHPCh6z9ZQHubq5W5WbMXUBRURFbfv+F\nxAspDH9rHLF1ahMeGsLGrTtYszGeX+bMxMfLi/c+/ozpM77jo4ljqBHgz/6Nf1i2c+L0WZ577W3a\ntmx209iix4xGn5PDzo5d8W7VgtiPprCv7wCMJSVW5Q48/Jjld4mTE202rSV7UzwAmtRUjo98g7Ks\nLKQKBXXeHUPNF57jwlczHT5G19qyejnnTh7lo/m/UlpczEejXyI0shYxjZrYlDUa9Ax5+U0iomPI\nz83m07Gv4xsQSIuOXSxl8nOz2bd1M54+vrcUz40UpmeyeuLnNHvswdu+bXsGtw6nSaQPPabE46GW\nM//F1pxLL2J/Yq5N2Q+WneCDZScs098+35xjKfmWaRPQc2o8OcW39rDi2qk/FaVF5H7/LoqwOrj1\neJL8BVMw6XV2y7u064sx67L1TJkMj77PU7pnHfo/vgcnOVIX92rH4tHrMcpLCrny8Zsoo+rh/fAL\nZH41HlOZ1qZs0bY1lOxcZ7sRUwW68yco3b8Fn8dfqXYM1/pw/nL8PN3YNet9dp9I4M2vf2Ttp+/g\n7qKyKueqUvLd288RFuDL/tNJvPbFApZOeZ0gXy/0BiMjps9l5MPd+XbUs5TpDWTl279+3szOdStJ\nOnWUCd/9gqakmK/GvUJwRC2iGzS2KWs0GHh42BuE165LQV4O3058E2+/AJq0f4Coeg2YvmSDpezh\nnfGsWvjdLVWAAQKfGoGxII9zLz6GS/37CXn5bRJHvUCFxvra7NqwKd7d+pD62QfoM9KQ+wVQXlp8\nS/u83rHNq0g7d4Jnpv+ATlPMb1NG4RsWSVi9RjZlAyKjeWTcJ6jdvSjTlLLqq0kcj19Nw859KC3M\n58/vP6H/W5MJjWnI8S1rWTXjA4Z9ufi2xCncHe6aR4a1a9cSFRVFzZo16dOnD0uXVmYmVq9ejYuL\nC48++ugdjNBx2jIDW09e4MXuLVE4OdEhNpLaQb5sPXnBpmyorydqZwUAf9Xj0vMqL6x/ZY3+Xx1q\n+fL7iXSKy4xkFJWx8WwWnWrfoBLiYOK8Y20/tibmOByH1FmJR/NWXFm8EJPRQNGBvWhTkvFobptN\nCXzkcdLmfUdZuvlGrc/MoEJTWdmWOMmp8fjTpC+Y6/D+NVot8Tv38PKzT6FQKOjYthXRUZFs2Wmb\n6Vq9fhPDnn4CtUpFg9gY4tq1Ys3GLQCkZ2TSuGF9Avx8cXKS0a1TB5JSLtrd5x9/bqRz+zYonZ3t\nLrccG6US347tSf52NiaDgdztOyk9n4hvp/Y3XM+3Y3uMJaUUHjFn042FRZRlZV3dqBRTRQWq0JCb\nHZoq7d68nu4DHsPV3YOA4BDa9+jL7k12Ki1Ax579iIqpj1Qmw8c/kCZtOpB45qRVmSWzv6bfkGeR\nyW7/M/rxVZs4sSYebeHtuSHfTO8mIfywNYlCjYFLORqW7r1E36Y3P9a+bs60qu3HqkOVlVAJIJXe\nYouVkwJFZH1K9/4J5eXok09jzElHEVnfbnF5WB0A9KkJVvOVMc0xXElGf/6o+YJk0FNR4PjnG0Ai\nV6Cq05CiLX9AuRFdwnGMmZdR1W1YxQr2Z1doS9Ec2oEh87L9Ag7S6PTEHz7FywO6oZA70bFxPaLD\narDl0CmbsiMe6kJYgPm62LxeFJHB/pxONu9/xfYD3B9dk56tGiGTSlErnalZw89mG444sHUDcQ89\niou7B35BIbTu2ocDW/60W7ZN975E1I1FKpPh7RdAw1btSTlnGzvAgS3radax6y3FJFE449a4BdnL\nfsZkNFJy9ABlqcm4NW5hU9b3wUFkLpqLPiMNAEN2JhVaxxMhN3JmdzxNegxE5eaOV0Aw93XswZld\nm+yWdfX0Qe3uBYDJVIFEKqEg6woAJfk5qNzcCY0xn3cxbTqjKcin7LoK/d3GJJH+7T/3krsmE7xs\n2TIefNCcvWnXrh0lJSUcOHCAZs2acfHiRUJDQ5FKK/85P/zwA19++aVleseOHbi6mjN5RUVFNG7c\n2Co72KxZM2bPnv2P/C0XcwpwcVbg5+5imVcr0IekDNvsEMD8+IPM3ngAncFAvZAAWlzTbSKroITO\n783BVamgV5O6PP9As1vq2hHqqSIlr/IidTFfQ5NQryrLR/u5suDxJhRoDaw5ncGGs1k2ZdyVTjQK\n9mDu3hSH43AOCqJCq8WYn2eZp72UgjIs3LqgRIIqshaq8AjCXx2FyWggd/MGMpdWPsUHDBhE/vYt\n6PMcv0lfupyGi1qFn6+PZV6tyJokJltXYIuKS8jNLyA6MsIyr3ZkBMdPnQGga8f2/Ll5G2lXMvDx\n9mLtpi20ad7UZn9GYznr47cz/f2xN41NHRaKsVSDPqfyPClNSsIlMvKG6wX07E7mWusbqHOAP81+\n/RknV1fKNVpOvPbmTfdflfRLKYRGRlmmQ2pGcXyfY83j504co/UD3SzTZ48dpqSokMat27N41pc3\nWPPuEBXoxrn0yofW81eK6FDP/6br9bw/mOOX8knLs86MLn61LSYT7EnI5pNVpynUGByKQ+bpi0lf\nhklTWfkvz81A5hNoW1gqxaVNb4rWzMe5rvU56xQQhkmnxePhkcg8fDCkJ1O6bQUVpY5nPJ28/anQ\nl1FxTauNISsdJz/73bhcW3TGtUVnjLmZFG1eif7SeYf35YhLmdm4KJ3x86rMaNcKCSQxzX53sL8U\nlmpIvJxJrRDzMTyRlIq7i4rH359BamYu90fXZNxT/fD38qh2TJmpKQTVrPxMBYVHcurgHofWTTx1\njGYdu9nMLy7M5+yR/fR/dmS14wFQBAZRodNiLKxsndBdvoRzcJh1QYkEVc0olCE1CX7hdUxGAwXb\nN5Oz6tdb2u/18tIv4hdWed31DYngwrH9VZZPSzjFys/GU6bVoHb3pOPj5m4z/mFReAYEk3LiIOGx\njTm1fT0BEbVxVrtUuS3h3nNXVIIvXLjAiRMnmDnT3Fwrk8no0aMHS5cupVkz+03IAwcOpHPnzhw9\nepTRo0dbLXN1dWXFihVW85xvkoWzJysri+zs7CqXV1U10ZYZcFEqrOa5KBUUauw3Sw6Na8rQuKac\nvJTJgcRU5DKZefv+3vz61mOE+3mRnJnHqIXrUCnkDOlg23/1ZpRyGZqr/Q0BNPpyVHL7T3wnrxTx\nyrLj5JTqqe3nwtsPRFOoNbDvYr5VuXZRviTllJJR5HjzrVSpolxjnTGo0GiQublZzXPy9EIik+HW\nqDFnRj6PzM2NWu9NRZ+VSf72eBT+AXi2bs/ZN0Yg9/bBURqNDle12mqeq1pNYZF15lCjNVdO1OrK\n5lIXF7Vlvq+PF/Vjoun+yJPIZDKioyJ49y3bJtsde/chV8jt9jm+nkyttukSYiwpRe5RdXO0k7s7\nPm1akfTF11bzyzKz2NmhC3JPD2r074cu0/YhxlFlWi2qa24cKrULZTrbZu3rrV+2GE1JEW0eMPfV\nrigvZ/Hsrxj29nu3HMu/jVoho1RntEyX6IyoFTe/7PZuEsyve6wfvJ6csYtjF/NxU8kZP+A+Jg9u\nxMvzDlSxBWsSubNNtweTXodEqbYpq7q/A/qU01QU5dksk7p64BRQn8IVsyjPzcClbW9cuzxK0crv\nHIoDzBnF67s9VJTpkKpsYynZt5nC9Usw6fWoYpvg8+iLZH07ifKifJuyt0qj0+OqUlrNc1UpKSyp\nOnNpMpl4d/avdG3ewJLtzcovJP5QKnPeeYHaoYF8smgNY2f9wpwxw6odU5lWi/Kaz5RS7UKZ7uaZ\n1PiVv6AtKaZ55+42yw5v30xorbr4Bd1aq49UqbLJ5lZoNchcr7s2u3uCVIZL/UYkjXkJmYsrYaMn\noc/JpGjPtlva97X0Oi0KZeWxUajUGG5wvQmOjuWlWSsoysnk9K5NqN09AZBIpdRt2YlVX02i3GjE\nWe3CwHc+/r/ju+Ok91am9u92V1SCly5dSnl5Oe3atbOar1AoePfddwkPD+fw4cOUl5cju1pBdHV1\nxdXVlStXrthsTyqVEhpq+xJadS1ZsoQZM2ZUufzop/b7qamc5ZTq9FbzSnV61Ar5DfdXPyyANYfO\nsmzvSR5ufR/ebmq83cw3jogAb57v0oxfdh53qBLcPsqHEW0iMWFiW1IuWkM5aoUMrtax1AoZWkOF\n3XWzSypjP59dyppTGbSq6W1TCe4Q5cvmhOpVrip0WmTXVUKlajUV113kTHpzxTpz+RIqdFoqdFpy\n1q/FvUlz8rfHEzx0GFcWLYDy8mplxtVqJSXXVcJLNBqryi6AWmWe1mi0lmWlpRrL/JnzFnLh4iV2\nrlmKSqnk81lzGfvhx3wxeaLVdlav30zvLnEOxVau0SBzsc5SOLm6UK6p+gYQ0L0rxWcT0F5Ktbvc\nUFBI3u69xE6ZxKEnn3Uojj3xG1j41ccggVaduqFUq9Fe04So1ZTirFTdYAuwJ349G1f+yphPv0Wu\nMD8Qbl61jOj6DQkKq+lQHP9GvRoHM3FgA0yYWH0ojdIyIy7Kysusq9IJjd54gy1AVIArkQFu/Hk0\n3Wr+kav9gws1BqauOMmWiV2QyyQYym/eJcpkKEOisK7oSRRKTAbr65DUxR3nes0pWPyZ/e0YDeiT\nTlCebW7m1uzbgPfzk0Amg/Jyu+vYbENfhsTZ+vyQOistn+lrGa/p6qA9eQB1gxY4R9VDc2SXQ/ty\nhFqpoERr/YBQotWhvi5Rca1J85dTqi3j05GVL3UqFXI6N61PvQhzJfPF/l1oN+I99AYjCvmNb7UH\nt21kyTfTAQlNO3TBWaVCd81nSqcpxdnOA8u1DmzdwLZVS3l12kzkctvYD2zdQMsHHHs52J4Kndbm\nQUWqsr02V1w9p3LWLLNcm/O3/Ilbw6a3VAk+szueTT98iQQJdVvHoVCq0esqj41eq0F+k+sNgLtv\nAD5B4WxeMIPeL48j5cRB9ixfyGPvzcA7KJSE/dtZ+el4hn48HydF1f974d7yr68El5eX88cff/DO\nO+/Qpk0bq2UvvfQSa9asoXfv3vz0008sWrSIIUOs3zSvzgtR1TVo0CDi4m5QgUneand2uK8nGr2B\n7KJSS5eI81dy6Nvs5m+lGisqSM0psL/Q5Pjfuz0pl+1Jlc3qEd5qwr3UXMo3X9DCvdSkFlSjD9d1\n9cxgDyXh3mp2XrDfxaMqZenpSJUqnLy8LV0iVOE1yYvfaFWuvLQUQ951277mb3et3wCXOjGEDnsZ\npDIkMhn15y3m/ITRlF22XyEECAsJRqPVkp2Ta+kScT4pmX49rfvRubu54uvtRcKFZBrVN//fzl9I\nJiqiJgAJScn06NwJD3dzlnZA7x48+eLrVtsoLill6669LJnj2AtpmkupyNQqFL4+li4RLrVqkbFq\ndZXrBPTqTuYa+/1z/yJ1ckIZ4nh2qFVcV1rFVR6PSxfOczk5iZCrzbeXU5IIDo+oanUO797Or9/P\nZPRHX+HjX9kcf/bYYRJOHuPAdvMLfMUFBXz1/ts8/MwI2nfv43B8d9Kaw2msOZxmma4T5E50DTcS\nM8wtCbVruFt+r0qfpiFsP51Jia7qyrIE/hp/xfLbjZQX5CCRK5Co3SxdImS+NSg7bZ1JdgoIRebq\ngdeT5mEkkSuQIEHq7k3RytmU516xfRGumtdYY14WUoUzUld3S5cIuX8wmmPVG2HidgkL8ENTpic7\nv8jSJeJ8agb92tl2XwL4dPFqzl5MY97Y4cidZJb5tUICySmw/t862oe7aYcuNO1Q+XJoWkoSV1Iu\nEBRubk9Mv3iBGjd4ODy+dwe///ANIz/8Em+/AJvlmZcvcuViEo3bdXYoHnv0GelInZU4eXhZukQo\nQ8Ip2LnZqlyFphRj/vXX5lveLTGt44hpXXmfzb50gZzUFHxDzNeYnMvJ+AaHV7W6dWzlRgqzzA+X\nOanJhNZriM/V7hx1WnQgfuEM8jNS8QuLutFm/t3usT67f7d//dHasmULRUVFDBgwgFq1aln9dOnS\nhaVLl9KoUSOGDh3KRx99xLRp0zh06BDp6ekcO3aMZcuWIZFIrLKBJpOJnJwcm5/qVpj9/f2JjY2t\n8qcqKmc5HWMj+Xb9XsoMRradukBSRi4d69t2oFi+9yTF2jJMJhMHElNZd/icZSi1g0mXybx60b2Y\nXcDczQftbsMR2xJz6NegBm7OTtRwV9Klrj9bztvvS9so2AM3Z/PzU6SPml71Atl/XRa4Y20/Dqfm\nU6p3LDv0l4oyHYX7d1Pj0SeRyOW4N2uJMqwmhfttb5B58RsJeOgRpEolch9ffLv2pOjgXgBOv/gM\nZ18fwdnXR5D0wTioqODsa8MpS7vxSzRqlYpObVszc+5Cysr0bN25h8TkFDq1tX0xr1fXzny34Gc0\nGi3HT51hy8499O5qvljH1onmz/htFBYVYzAYWL7mT2pHWVcM18dvI6pmOLUiazp2bHQ6crZuJ2L4\nC0gVCnzat8WlViQ5W7bbLa8KC8W1TjSZf26wmu/Ttg2qMPM5pPDzJeKlYeTvd6xZ3Z7Wnbvx59LF\nFBcWkJGWyvZ1f9Cmi/2M0+kjB5n/xTReef8jm5v6c2+9y+Tvf2bStwuY9O0CPH18ee7NcVYV7v+X\nRCrFydkZqUyKTO6Ek0Lxtw6PuPrQZZ7uGIWni4IwXxcGtgzj9wM3Pgd7Nw7h94PWD2pRAa5E13BD\nIgF3lZzRD8ay+1w2hnL7rTU2jHr0F07h0rIbyJxQRNTDyScQ/QXrlxL1KWfI+2EKBYs/I3/Rp+hO\n7KHswgmK1/0IQNm5wygi6iHzqQFSKermXTCkJTqcBQYwGfRozx7DvWNfkDmhjG6Ak38Q2rO247Qr\n696PxElu7mca2xRFaC3KLlwzKo3M6epy8+9IZTbbuBm1UkGnxrHMXL6BMr2BrYdPk3g5g05NbK/h\n363cxPajZ5k1+jlUztbZwt5tGrP1yCnOXUrHYCxn1spNNIuJumkW2J5mHbuyeeViSooKyEpPZfeG\nVTSP62G37LljB1k84yNeGDeNgBD7lcEDW9ZTr0kr1Nd1XagOk76M4iP78Ov/GBK5HNdGzXAOCaf4\n8D6bsgU74/Ht2R+JsxInLx+8OnWj+OitX2OuFdM6joPrfkNbXEh+Rhontq6jXtsudssm7N9Oca65\nNTI/I439q5cQGmtuKfWvWZvUs8fJu2L+rCUc2EG5wYCHX43bEqdwd/jXZ4KXLl1K69atLS+1Xatr\n167MnTuXhIQE3n77bRo2bMjixYtZvnw5Wq0WX19fmjZtyi+//ILLNc3IJSUlVl0rTCYTEomEnTt3\n4uPjeP/R/8eY/h1595eNdJgwm0APNz5+sgfuKmfWHj7HvM0HWTrKPNTb9tMpfLVmN8aKCgI93Xiz\nbzvaxtQE4MzlLMb+vJ4SnR5vVxW9m8TwZAfbIXQcse5MJoHuSr59pBGG8gqWHUu3DI/m66LgqwEN\nLGMBNwr24LWOUTjLZORq9Cw7ls7uZOv+g+0ifZi31/5oCDeT+t0Mwl8dxX0/LsOQk03K9MmUl5bi\n1b4TAQMGc/ZVcx+7K0t+JHTYSGLnLqJCoyFn/Rryd2wFoLy48qUbiUKByWTCWFTo0P7HvzGSsZM/\npm2vAQT6+/HppPG4u7myZkM8c35azIqF5jGnX372KSZ89BkdHxyEh7sb4998hbCQYACefWIQUz6f\nQd8nnsVoNFKvTm0mvfOG1X5Wb9hEn+4PVOvYnJ82nZhJE2m7dQPJtFMfAAAgAElEQVS6jExOjR6H\nsaQE/+5dCX/mKQ48UjlEYEDP7uTt2oOxyPqlJYWvN7VHv4Hc2wtjSSl5O3eT9GXV3XpuplPv/mSm\nX+adZwbhJJfTa9AQ6jY0n4e5WZmMH/Y4k2cvwtvPn1WLf0BbWsLHb4/kr8GtW8V148mRo1C5uKCi\n8nMqk8lQu7ojV1S/v35Veo4fSa+Jr1qylz3GvsSCoaPY9+Py27aPa/2y+yJhvi6sHdMJg7GC7zcn\ncuBqC0ygp5LfR3ek70dbySw0N8c3r+WDwknKjjPW3Yh83JyZMLAB/u5KSsuM7EnIZuxi+2NnV6Vk\n63LcugzG54VJlBcXUrxuISa9Dufo+1E17UzBok+gogKTtnK4PZNBD0aDpQ9veX4WJVuX4957KBJn\nFYb0ZIo3VH9IqcK1i/DqN5Qaoz+jvCifvKWzMZVpUdVvhlvbHpaxgF1bdsarr7l1z5iTSe4v31Be\nWJllDBr3tfk8Mpl/Ly/IJfOr8dWOZ/zTDzF21i+0HTGRQG9PPh35BO4uKtbsPsKcP+JZMc384uiM\nZRtQOMno+uoUy9jsE58ZQM/W9xMZ5M+4px7ilc9/oFijo3F0BFOGDa52LABte/Qj+8plPhj2KE5y\nBV0GPkHt+8yVt/zsTKa8/CRjZ/6Il68/G379EZ2mlK/Hv8pfH6pmHbvyyIjKl10Pbd/EQ7f4Qty1\nMhbMIuiF16jzzSIMuTlcnvkRFZpS3Ft1wLf3QC6MM+8je+Viajw5nOgv5pu7Q8T/SdFe+w/r1dWw\ncx8KMtOZN2ooMrmc5r0HW0Z4KM7NYsGYF3hq2ve4efuRdyWVrYtmUaYpReXqTnTz9rQZ8BQAYfUa\n0bTHQJZPH4uutBgPv0B6vTwOhZ2+6XcVkQmuFonp7+wv8B+nXX1r467+HR7NsB2z9U6auHrCnQ7B\nSv3v59zpECx2dn34TodgxXnF2jsdgsXCiH/Xebzj9a9vXugfsq3m1jsdgpWy/JKbF/oH+ffsdadD\nsIh3b3mnQ7ASOtmx9wH+CTteuvUH8r/DsBaOdbX4t9AX3PpLzo5SeN58hJu7xb8+EywIgiAIgiA4\nQGSCq0VUggVBEARBEO4B99qXWfzdxNESBEEQBEEQ/nNEJlgQBEEQBOFeIDLB1SKOliAIgiAIgvCf\nIzLBgiAIgiAI94K/cdzze5HIBAuCIAiCIAj/OSITLAiCIAiCcC8QfYKrRRwtQRAEQRAE4Y7YsmUL\n3bt3p1u3bvz22282y1NTUxkwYADdunXjvffeu637FpVgQRAEQRCEe4BJIv3bf26n8vJypk2bxo8/\n/sjy5cuZM2cOhYWFVmWmT5/OK6+8wvr168nJyWHbtm23bf+iEiwIgiAIgiD8444fP050dDR+fn64\nuLjQoUMHdu3aZVXmyJEjdOjQAYB+/foRHx9/2/Yv+gQLgiAIgiDcC+6yPsFZWVkEBARYpgMCAsjM\nzLRM5+fn4+npWeXy/5eoBAuCIAiCIAgOycrKIjs7u8rlfn5++Pv7/4MR3TpRCf4bLfTrfadDsBg7\nY+idDsHKM01G3ekQrKx28rnTIVgMbzvmTodg5Yz+7J0OweL517++0yFYaff5yDsdgsV7S/+40yFY\nKfTW3+kQrLzkH3mnQ7DodGnDnQ7ByhsPTr7TIVh8oThzp0O4TvidDqBaTP/AOMFLlixhxowZVS5/\n+eWXGTnSsWujv78/GRkZlunMzEwaNmxomfby8rLqI5yZmXlbK9iiEiwIgiAIgiA4ZNCgQcTFxVW5\n3M/Pz+FtNWjQgPPnz5OVlYWLiws7duzgpZdesirTqFEjtm7dSseOHVm1ahUPPfTQLcd+PVEJFgRB\nEARBuAeYTH//Pvz9/W9bNlYmk/HOO+8wZMgQAJ577jk8PDwYP348jz76KLGxsbz55pu8/vrrTJky\nhVatWtGxY8fbsm8QlWBBEARBEAThDunUqROdOnWymvfhhx9afg8PD2f58uV/y75FJVgQBEEQBOEe\nUPFPpILvIXfXWBqCIAiCIAiCcBuITLAgCIIgCMI9QOSBq0dkggVBEARBEIT/HJEJFgRBEARBuAdU\niFRwtYhMsCAIgiAIgvCfIzLBgiAIgiAI9wCTGB2iWkQmWBAEQRAEQfjPuaszwWPGjGHFihVIJBJk\nMhkeHh7UqVOHXr160b9/fyRXv0M7Li6Op59+mieffBKAs2fP8uWXX3Ls2DFKSkrw9fWlUaNGjB8/\nHm9v738sfpPJxNafZ3F650ac5Aqa9XqExt372y2bdHgPO5bMoaQgF7lSRd2WnWg/+HnL33hu71Z2\nL1+Ipigfz8AQOj0+gqDa9RyOxcndncg338atQSP02VmkzPyK4mNH7Jb17dKNoEGPIff2oSw7i4QJ\nY9Fnmr/7WxkcQviLI3GNiaVcpyV90Y9krf6jmkfG7K2eMfRpHEyZsZwftiezaHeK3XJj+8bSs1GQ\n5ZtyFE5SUrJLGDRjl2V5iygfQrzVPD93H4dT8m+6b5PJxMwvPmHD2tUoFM4MHvIUAwc/XmX5RQvn\ns3TxT1RUmOjZ50FeePlVy7IdW+OZN2sm2VlZxNS/j9HjJ+LnHwBAdlYmn380hZPHj+Lm7sHzLzr2\nfetj+99HvxahlBkqmLPpPAu2Jtkt994jDenbLMTq2FzIKuHBaVtoEunN9yNaWZZJpBJUchn9p2/l\nzOVCu9u7Xn5hMWO+mMuBk+cI9PXm3eFP0LJhjE25j+cuYfO+I+QVFhMS4MurQ/rTsZn5++FzC4p4\n9+sfOJ5wgfyiYk79PtehfVflnX6x9G0agt5Ywdz4RH7cnmy33LsD7qNPkxBMV9+nVjhJSc4qof8n\n2wE48UlvtPpyAEyY+H5TInPiE/+v2K7XbtjjtH1+MMH31WHthzNY+8FXt3X71xvQoAYtwr0wlJvY\nmJDN1sScG5aXAGMeqI2TVMqkDecs8xsEudMnNhBPlZyUPA0/H7pMgdZQ7XgebxJKu0gfDBUVrD6V\nwfqzWXbLtY304dmW4RiMJnNQJnh79UnyNQacZVJGda5NkLsSiURCSp6GBQcuklFU5nAcJpOJRbO+\nYOfGdcgVCno98gTd+g+2W3bnhjVsWPkbWemXcXFzJ673Q/QaZP62qzKtlk/GvU76pRRMpgpq1qrL\nkJffpEZouMOx5BeXMn7+Sg6cSyHQy51xj/eiRUykTblPfl1P/JGz5JeUEuzrxSsPdaZ9g2jL8m9+\n38KKXUfQ6PR0bVqPcY/3wkkmcziOG3H0PGoR5sVjTULQl1f89W9j8saEWzpXAPKLShj37c/sP51I\nDR8vxj0zkJb1o23KTf9xJZsPniC/qIRgfx9eHdSLDo1jAThwOpFnPpiBSqnAZAKJBGa9M5zGdWyP\n8d1I9Amunru6EgzQvn17pk2bhtFoJDc3lx07djB58mTWr1/PrFmzkEqtk915eXk8/fTTxMXFMW/e\nPNzc3EhLSyM+Ph6tVvuPxn5s8yrSzp3gmek/oNMU89uUUfiGRRJWr5FN2YDIaB4Z9wlqdy/KNKWs\n+moSx+NX07BzH0oL8/nz+0/o/9ZkQmMacnzLWlbN+IBhXy52OJbwl19Dn5fH4Uf64dG4KbXGTuD4\nM09QXlpqVc6jeQsCHuxPwnvj0V1OxTmwBuXFxQBI5HKiP5jK5QXzOPfuGKQKBQof31s6Ng+3CKNx\nhBd9P92Gu0rO98+1IOFKEQeT82zKTvnjFFP+OGWZ/vrJJhxPLbBMn71SxJ/H05n40H0O7/+PZb9x\n/MgRflz6OyVFRbz+4gtE1Y7m/ibNbMru3b2TP5b/xjdzf8RZqWTUyOGE1qxJj94PknrpIh9/+B4f\nfzGTOvViWbRgHh++O4Yvv5tnjn3ieGJi6/Ph9M+5kHie0a++CPWeBZeqv3v9sXYRNK3lQ9f3N+Ku\nVvDjK205m1bIvvO2N6L3fj3Ge78es0zPHt6So8nmh4BDF/JoPGqNZVmP+4N4o0+swxVggEnf/oif\ntyd7Fn3FriOneP2jb1k/exrurmqrci5qFd+//wZhNfzZf+IsIyfPYPlX7xHs74tUIqFD0wY83iuO\nF97/3OF92zO4dThNIn3oMSUeD7Wc+S+25lx6EfsTc23KfrDsBB8sO2GZ/vb55hy75gHJBPScGk9O\nseOVqeoqTM9k9cTPafbYg3/bPv7SLtKHWr6uvL/+HCq5jFfbR5JWqOV8dmmV63So5YtGX467svI6\n6ueq4IkmoczceYFL+Vq61vHn6eZhfLHN/oNYVTpH+1E3wJW3fj+BWuHEuC51uJSv5Uxmsd3yZzKK\n+Tj+vM18Q0UFc/de5EqRDoAHov0Y3jqS9/4843As8auWc+7EUab/8BulxUVMHfUSYZG1iWnUxHZ/\nBgNPjnyLyOgY8nOzmT7mNXz8A2nZqQtOCjlDX3uHGqHhSCQSNv2xlO8+fp/3vp7ncCwf/rwGPw9X\ndn7xNrtPJfHWd7+xZsoruKtVVuVcVM589/oQQv29OXA2mVe/+YWlE0cQ5OPJip1H2Hj4NIvHPY9a\n6czbs5cya9U2Xu4X53AcVanueZSQXcLMnfYfRKvrg3m/4evlwe45U9l1/CxvfvkD674Yj7vL9dcb\nJbPHjCAs0Jf9p87z6mdzWTZtNEF+5iRXaIAva78Yf1tiEu5ud313CIVCgbe3N/7+/sTExPDCCy/w\nzTffsH37drtfs3f48GFKSkr48MMPqVu3LsHBwTRv3px33nmH4ODgfzT2M7vjadJjICo3d7wCgrmv\nYw/O7Npkt6yrpw9qdy8ATKYKJFIJBVlXACjJz0Hl5k5ojDmzFtOmM5qCfMo0Vd/criV1VuLVsjVp\nP87HZDBQsG8P2uQLeLVqY1M2+NEhXJr9LbrLqQCUZVyh/Op+fLt0p+TUSfK2bYGKCip0OnRpl6t3\nUK7q1TCIhTuSKdQaSM3TsPxgKr3vv/n/x8dVQYsoX9YcTbfMW34glcMp+Rir8Yi8cf1aBj0+BA8P\nT4JDw+j14ENsWLvabtlNf66lT78BBAYF4eXtzcOPDWHjWnPl8tD+vTRp1oKY+vchlUp57KlnSDh7\nlvS0y2i1Wk4cO8KQZ19AKpVSK7oObdp3hCv2M/B/6dM0hHmbEynQGLiUU8qve1J4sHnoTf8mXzdn\nWtXx54+DqXaX920WVuUyezS6MjbvO8rIx/uhkMvp1LwRdSJCiN9nG/9Lj/YlrIb5u+ab31eXqLAg\nTiddBMDLw41BPTpSJ+Lmf8PN9G4Swg9bkyjUGLiUo2Hp3kv0bRpy0/V83ZxpVduPVYcqz1cJIJVK\n/u+YbuT4qk2cWBOPttB+xe92ahbmyebz2ZTqy8kp1bM7JY8WYV5Vlnd1ltG6pjcbzmVbzY/xd+Nc\nVjEX87WYgA3nsgjzVOHjoqhWPG0ifFh7OpMSfTlZJWVsTcymbaRPleX/avW6XoUJSwVYIgGTCfxd\nqxfL7vg/6THwMVzdPQgIDqVDjwfZuWmd3bKdevWjVkx9pDIZPv6BNG3bkcQzJwGQyZwICquJRCKh\norwciURK9pV0u9uxR1OmZ8vRs7z0YBwKuRMdG9UhOiSALUfO2ZQd0acjof7mSl2zuhFE1fDjzEXz\nPWHHiQQe7tAUXw831M4Knu3RlpW7bnxdcVR1z6Pb9QnS6MqIP3iCkQ/3QCF3olOT+kSHBRF/8KRN\n2RcHdCcs0JyAaR5bm8jgQE4nV17bTPfwaLqmf+DnXnLXZ4LtadmyJXXr1mXjxo0MHDjQapmfnx/l\n5eVs2LCB7t2736EIzfLSL+IXFmGZ9g2J4MKx/VWWT0s4xcrPxlOm1aB296Tj4yMA8A+LwjMgmJQT\nBwmPbcyp7esJiKiNs9rFoTiUwcGUa7UY8iqzrJqLyajCa1oXlEhQ16qNOiKCyLfexmQ0kr3hT678\n8jMArnXqYiwpIeazr1HWqEHx6VNcnPkVhjzbLNzNRPq7cj6jsmKQmFFMuzpVZ0f/0r1BECcuF5Ce\n//9l9S8mXyCyVm3LdERULfbu3lll2c5du1uVTUmuzIpd+6KCyWTChImUC0l4eZlvYKaKimuWAyWZ\nN4ytVqA759Irs7UJ6UV0jA286d/Uq2kIxy/mcTlXY7PMy1VB2xh/pi4/YWdN+y6mZ+KiUuLv7WmZ\nVzssmPOX0m64XmFJKecvplErNMjhfTkqKtCNc+lFlunzV4roUM//puv1vD+Y45fyScuzPm8Wv9oW\nkwn2JGTzyarTFGpurRn33yDQTUlaYeXfl16oIzbQvcry/erXYMO5LPTlFTbLbCqkEqjh7kxuqd7h\neII8lKTmV56LqQVaGgZ7Vlk+yseFmQMbUqQzsuFcJluua/mY3KuepUvEr0er9/CdfjGF0IhalunQ\niCiO7d/l0LrnThyldWfre8n44UPMXSIqTDz8zHCH47iUmYuL0hk/TzfLvFpB/iSl2+8m8pfCUi2J\n6VlEBVVeI6+97lSYTGQXFFOqK8NF6exwPPZU9zwK91YztXc9inVGtiXlsMtOa54jLmZk46JS4ufl\nYZlXO7QGiZev3HC9whINialXiAqpvEZm5hbSfth43NQqerdtyvD+Xat8yBLubfdkJRggMjKShIQE\nm/kNGzZk2LBhvPXWW0ycOJEGDRrQsmVL+vXrh49P1VkIe7KyssjOzr5BCdcbrq/XaVEoKyuqCpUa\ng67qyltwdCwvzVpBUU4mp3dtQu1uvmFIpFLqtuzEqq8mUW404qx2YeA7Hzv8d0hVKks29y/lGg1O\nrm5W8+ReXkhkMtzvb8qJYc/g5OZOnSkfo8/MIHfLZhS+vrhE1+HsmFFoU5IJfW4YkaPe4dyYUQ7H\n8heVQkZpmdEyXVpmRK24+enas1EQS/dfqvb+rqfValG7VP5vXFxc0WlsK4/msprryrqg1Zj/j42b\ntWDutzM5cfQIMfXr89P8uRiNRnQ6HSq1mtgGDVkw5zuee3EkFxLPs33LJlDUuGFsamcZJbrKY1Oi\nM6JW3LyvX9+mofyyy36zZO8mIZy8lM+lHMdaDwA0Wh2uaqXVPBe1isLiqrdhMpkY98U8urVpSkTI\njf/OW6FWyCi1OTY3P296Nwnm1z0XreY9OWMXxy7m46aSM37AfUwe3IiX5x247TH/U5ydpOgMlRVa\nnbECZyf7jYER3mp8XZ05eOgytXytH6bPZpXQp34gkT5qUvI0dK8bgEwiwVlWvYZFpZMM7TXxaA3l\nKKuI50xmMWNWnyJXoyfSx4VX20dRpDNy6JpuT+PWnMZJKqFVTe9q9znVabWorvkMq9RqyhzoHvfn\n0sWUFhfTtktPq/kfzvoRg17Pni0b8PR2/L6iKdPbVFJdVM4UlVYdi8lk4t35K+naJJaaV7OfbevX\nYuHGPcQ1qouLypm568wP8Fo726+u6pxHCTklTNmYQL7WQLiXiudahlNcZuT4NQ+qjtLoynBVXXe9\nUSkpLLnx9Wb8rEV0a9mIiCDzexiRwQEs/2g0NYP8uZCWyRtfzEetVPBUr07VjunfSPQJrp57thJs\nMpmqfLJ77bXXGDp0KHv37uXYsWP88ssvfPfdd/z888/Url3b7jr2LFmyhBkzZlS5/I2FG6ymz+yO\nZ9MPXyJBQt3WcSiUavS6yg+wXqtBrlRdvxkb7r4B+ASFs3nBDHq/PI6UEwfZs3whj703A++gUBL2\nb2flp+MZ+vF8nBQ3bxas0GqRXZc1lqnVVFxXIa8oM/eNvPLbYiq0WvRaLdlrV+HRrAW5WzZTUVZG\n/u6daBLN/fbSflpI4yXLkcjlmAw3vil1b1CD8f3qYzLBumPpaPTluDhXnp4uzk5o9MYbbMGcPY7w\nc2XjiYyb/s3X27x+HZ99NBkJEjp364FarUZzTX/o0tISlGq13XVVquvLlqK62n8vLLwmo8ZP5Ivp\nU8nPy6Vztx7UrBmBn585Oznu/cl88fFUBvftQY3gYLr27M3yndYvX/VuEsKkwQ0xmWDVwcuUlhlx\nVVYeG1elE5qrL3BVpVagG1GBrqw7bD9L27dZKMuuqwTejFqlpESjs5pXqtGiVlV9k33/mx8p1er4\n/J0R1dpXVXo1DmbiwAaYMLH6UBqlZUZcbI7Njc+bqABXIgPc+POodbP1kav9gws1BqauOMmWiV2Q\nyyQYyu+Ou0zTUE8G3x+MCTiYWkCZsRylXApXP9ZKJyllRtssL8CAhkEsOWI+V66/imaVlPHTwVQG\n3x+Mm7OcA6n5ZBTryL9JxbNVTW+GtggHE+xOyUVnKEclr6w8qeQydFXEc22G+UJuKRvOZdE01Muq\nEgxgrDCx40IuXw9owNurTlX5udgTv54fvvwYJNAqrhtKtRrtNZ9hrUaDs+rG1+Ldm9ezYeWvjPvs\nW+R2rrNyhYL23XrzyuDeTP1+ES5uVWdL/6J2VlCqs+6DXqotQ+1c9XX8g59Wo9GV8enwRyzzHmrb\nmMz8IoZOn095hYmnurZi7+kL+LjfODFjz/9zHuVf03JyMV/LtqRcGgV53FIlWK10pkR73fVGq0N9\ng0r9pLm/UqrV8dlrQy3zfDzc8PEwJ3gigwMY3r8bP/+5/Z6pBAvVc89WgpOSkggJqbovoIeHB926\ndaNbt2688cYb9OvXj3nz5jF16lSH9zFo0CDi4qp+0WBnifV0TOs4YlpXls++dIGc1BR8Q8xdInIu\nJ+Mb7NhbxBXlRgqzzDftnNRkQus1xCc4DIA6LToQv3AG+Rmp+IVF3XRburQ0ZCoVcm9vS5cIdc0I\ncjautypXXlqKPte6CfLaIQk1F1OQe3ldt9yxCsOfx6/w5/HKZq3oQDdqB7qRlGU+iLUC3UjKLKlq\ndQB6NQpi57ksSspuXOmxp3O3HnTu1sMynZSYwIWkRCKizE2kyUmJ1Iyw//ZweEQkyUmJtGrbHoAL\nieepGVF53Nt36kz7Tp0BKCkpJn79n5bt+gcEMuXTLy1lJ08YBx7WfWNXH7rM6mv6qtYJdic6yJ3z\nV8zdRcy/3/im0rdZKNtOZVplkP8SGeBKdA131lZRQa5KeFAAGp2OrLwCS5eIhItpPNTZti85wPT5\nv3LmwkV+mDwaudPtufSsOZzGmmvirhPkTnQNNxKvdqWpXcPd8ntV+jQNYftp+8fmL3+92X7tb/92\nB1MLOHhNJTHYQ0mQu4orV0dNCPJQklGks1lP6SQl1FPF8NY1AXCSSlDKZUzuGcP768+hL6/gWHoR\nx65WZJROUpqGelr65VZlT0oee1Iqm8LDvNSEeKm5XGheL9RTZdXMfjNVtV5LMGeZvVXyKivBreK6\n0Squm2U69cJ5UlOSCLn6uU1NTiI4vOrRAg7v3s6S72fw9sdf4+NfdVekiooKdBoN+TnZDlWCwwJ8\n0Oj0ZBcUW7pEnE/L5MHW99st/9lvGzh76Qpz33oauVNla5BEImFE306M6Guu2O0+lURMeI1bavK/\n1fOoSrfY6yA80A+Nrozs/EJLl4iES+n069DCbvlPfv6dM8lpzJ/wstWxud5fXdTuFWKc4Oq561+M\ns2fPnj0kJCTQrVu3mxcGnJycCA0NRVNFc3dV/P39iY2NrfLnZmJax3Fw3W9oiwvJz0jjxNZ11Gvb\nxW7ZhP3bKc419wvLz0hj/+olhMaaL4z+NWuTevY4eVfMHf8TDuyg3GDAw8+x5uaKMh35e3YR/MTT\nSORyPFu0QhUeQf4e2z5xOZs2UOPhwUiVSuS+vvj36EXB/r0A5G7eiFfL1qgiIpHIZAQ/NoTiY0dv\nmgW2Z82xdIa0jcBTLSfMR03/pqGsOnLjSlqPhkF2yzhJJSicpEgkIJdJkTvQbNulW09+/XkhhQX5\nXL50iTW/r6Bbzz52yz7QvSerVizjSnoaebk5LF38E1179rYsTzh7BpPJREF+Pp9N/ZAefR/E1c18\ng7uYfAGtVovBYGDDutWcPX0SgpreMLZVBy/zTFxtvFwUhPu58Eirmqzcd+MX2vo0DWHFPvvdRPo2\nC2Xb6UyKqtmErFY6E9fifmYsWkmZ3sCW/Uc5f/EycS1sb9jfLlnFtgPHmf3+G6jsZG70BgNlBgMm\nk/l3vaH6DzJgfmB4umMUni4KwnxdGNgyjN8P3Lh/aO/GIfx+3QuBUQGuRNdwQyIBd5Wc0Q/Gsvtc\nNgY7/WP/HxKpFCdnZ6QyKTK5E04Kxd/WN/HApQI6R/viopDh56qgdU1v9l20HS5QZ6xg3NrTTN2c\nwNTNCfx8+DJ5Gj1TNyVY+geHeJqzpK4KGY82DmFPSr5V1wZH7ErOpWdMAK7OTgS4OdOxlh87Lth/\nf+C+Gu64Xm0ZCvdW06WOP4evVszCvVRE+7laumQMahxCqb6c9GpUzFrHdWfdb4soLiwgIy2Vbet+\nt+ni8JdTRw4w7/OpvDbpY4LCalotu5h4jnMnjmI0GinTavl1zkxcXN2oEeZYckPtrKBTozrM/GML\nZQYDW4+eIzEti07317Ep+93qbWw/kcC3rw1BdV2muKBEw+Vs8/82MS2LT35dz4t9OzoUw804eh4B\nxAS44nK1q1aIp4oOUT6cuIUsMFy93jS5jxm/rTNfbw6dJDH1CnFN69uUnbV8PduPnOa7McNtjs2B\n04lk5JrPnYtXspi9ciNxTR0fOUi4t9z1mWC9Xk9OTg7l5eXk5uayfft2Zs+eTVxcHA8+aDvs0Nat\nW1mzZg29evWiZs2amEwm4uPj2bFjR7WywLdDw859KMhMZ96oocjkcpr3HmwZ4aE4N4sFY17gqWnf\n4+btR96VVLYumkWZphSVqzvRzdvTZsBTAITVa0TTHgNZPn0sutJiPPwC6fXyOBQq+8339lyc+SWR\nb71D499Wos/OJnHKJMpLS/HpGEeNQY9xcsRzAKT/tIDwl16l0U+/Uq4pJWvtavK2xgOgu5xKysyv\niJ74ATIXF4pPneTCp9Nu6dj8tu8SYd5qfn+jA3pjBfO2JXHo6gsVAR5Klr7SjgFf7iDr6o2uaYQ3\nzk5SdiXY9tH+ZmgzmtT0xgTMfNo8xFnvT7aSUVj1TbLvgPAtDD8AACAASURBVIdJu5zKkIf7IZcr\neOypoTRqYq6cZmVm8MyjDzP/l6X4+QfQsnVbkvs/zIvPDMFUYaJXv/50793Xsq0vp0/lYnIyzkol\n3Xr14ZlhL1mW7du9i0UL52M0GKh3331M/exrnpxj+yb4tRbtSCbM14X1Ex5Ab6xg9oYE9l8dpzPQ\nU8WasXH0nLKZzALz39eiti/OchnbT9t/4a53k5BqvRB3rQnDn+CdL+bS6rGRBPp68/nbI3B3VbN6\n615mL13DHzM+AODrn1eikDvR+ZlRmDAhQcL7Lz1Jrw4tAWg0YDgSiTm712jAcIL9fdg4x/F+7X/5\nZfdFwnxdWDumEwZjBd9vTuRAkrliFeip5PfRHen70VYyr/7vm9fyQeEkZccZ6xePfNycmTCwAf7u\nSkrLjOxJyGbs4qO3dIxupOf4kfSa+KqlSaXH2JdYMHQU+360Hdnm/7XjQi5+rgomdquDscLEhnNZ\nnL/aB9xTJWdcl2jLGK4lZZUZVI2+HJMJSq7Jqg5qFESguxJDeQV7L+az+tQtdEFKyCbAzZlP+tbH\nUGFi1ckrnL06PJq3Ws603vUtYwHXr+HOsNYRKJyk5Gv0rDp1hf2XzBUvmVTKkGah+Ls6Y6wwkZyr\n4ZP4hGr1jYzr05/M9MuMHvoIcrmc3oOfJKZhYwByszIZ+8JjTP1+Md5+/qxatABNaQnTRr9sbhSQ\nmCvRT70yCqPRyM/ffk5WehpOcjkR0TG8OfkzZDLHb7XjHu/FuHkraPfqRwR4e/DJ8EdwV6tYs+84\nc9buYMX75uvHzN+3oHCS0e3tzy3d/yYM6UPPFveRV1zKyK8XkV1Ygr+nG8N6d6B1bK2b7Nkx1TmP\n6vq7MaRpKAqZlAKdgQ3nsjmS5vgQjNcb/8xAxn77M22eH0ugjyefvjYUdxc1q3ceZM7vm1g5/R0A\nZvy2DoWTE11GvmcZC3jic4Po1aYJp5JTeXvGQoo1OnzcXenbvhlP30NdIW7vY/q9T2K6i3PnY8aM\nYeXKlQCWL8uoW7cuffr0oV+/fpZynTt35qmnnuLJJ58kNTWV77//ngMHDpCRkYFCoSA8PJzHHnvM\nap3b4bt91etj+Xe6f+LQmxf6Bw1rUv2X5f5Oq99qf6dDsIibsPFOh2DlzMibj8rxT7lv1q29Wf53\nafe5Y19u8k9wWnprX0rzdynUOj5SxD/hpXb/ni9DaHxpw80L/YPeyHL8i5X+bl9EOj6k3D/B6f47\nO4pUdWUUOv5y860K9HBs5Km7wV2dCZ46dapD2dvNmzdbfg8NDWXSpEl/Z1iCIAiCIAj/uLs3rXln\n3NWVYEEQBEEQBMFMDJFWPffki3GCIAiCIAiCcCMiEywIgiAIgnAPuItf87ojRCZYEARBEARB+M8R\nmWBBEARBEIR7gBgirXpEJlgQBEEQBEH4zxGZYEEQBEEQhHuA6BJcPSITLAiCIAiCIPzniEywIAiC\nIAjCPaBCpIKrRWSCBUEQBEEQhP8ckQkWBEEQBEG4B4g8cPWITLAgCIIgCILwnyMywYIgCIIgCPeA\nCpEKrhZRCf4btf5ixJ0OwSLxk5/vdAhWFn067E6HYMVb1elOh2CxLO/7Ox2CleMu/55zZ1vND+50\nCFbeW/rHnQ7Bwjiw750OwUqsWn6nQ7AS9WHvOx2CxbT6w+90CFbGJXx1p0OwmBsy+k6HYOXfdacS\nbjdRCRYEQRAEQbgHiMEhqkf0CRYEQRAEQRD+c0QmWBAEQRAE4R5QIcaHqBaRCRYEQRAEQRD+c0Qm\nWBAEQRAE4R4g+gRXj8gEC4IgCIIgCP85IhMsCIIgCIJwDxDjBFePyAQLgiAIgiAI/zkiEywIgiAI\ngnAPEH2Cq0dkggVBEARBEIT/nLuiEjxmzBjq1q1LTEwM9evXp3PnzkyfPh29Xm9TdsKECdSrV4/1\n69fbLJsxY4ZlO7GxsbRs2ZInnniCBQsW2N2WIAiCIAjC3aIC09/+cy+5a7pDtG/fnmnTpmEwGDh5\n8iRvv/02UqmUN99801JGp9Oxdu1ann/+eZYuXUq3bt1stlO7dm0WLFhAeXk5BQUF7N+/n2+++Ybf\nf/+dn376CbVa/Y/8PTI3d0KGv4FLvfsw5GaTPv9bSk8ds1vWs/0D+PcbhJOnF4bcbFI+fg9DdiZu\njZri128wypAwKnQ6CvZsI2PRfDBVVDsek8nE6vkzOLR1PXK5gg4PPUrb3g/bLXv6wC7W/fgdRfm5\nOCtVNGwb9z/27ju8qeoN4Pg3uxndLR3QSSlI2bKnsqFQEBRwoOICJ7hFVEQR/QmoqKi4ERzIlClT\npihTKbMUaOledCdp5u+PYEpICm0dCJ7P8/R5mnvfe+/b05vk5L3nnjD4zgeRSCQALJ/3FqmH9nMu\nL5v7p71DbELrOudzIZnOm7B7J6Ft1gLzuUJyF85Df+yQx1jfbr0JHHILcl9/LOcKyXjnVcyFeZc9\nht1uZ+bMmaxauRKVSsXd48Zxxx131Bj/+WefsWDBAux2O8OHD2fS44871x0+fJhXpk0jIyODhIQE\nXp0+nbCwMABGjhhBbm6u85hVVVWMGj2aZ5991mX/01OyOVSu5/vr4y5qCx8aTngc7XUtMRcVkvPl\nB1Qe9dwWfj37Epw0ynnepM+ahrkgD5mPLw3vm4gmLh6Ztw9HxiZdtn0uxW63M3/u22zfsBaFUknS\nmLEk3nyrx9ht69ewbtkicrMz0Xn70G/oCIbdeqdz/adv/4/kA3vIy87ipbc+pHnrtrXOQ+Klxbvf\nGBQNG2OtKKFy6zLMmak1xku9/fG/4xmqTuynYsuS6uV+Qeh6jUAeFgVmE/o9GzEm/1zrPC42slUY\nnaL8MVvtbEwpYGtq4aX/DmBy3ybIpVJe2XDCubxVuA9DE0LxUytIO6fn6/2ZlBjM9c7rYj3G3073\n+8fQsGVT1k5/n7WvvvuX7fti6kB/Euf9j8geHSnLzGXjEy+Tvu0Xtzjv8FAGzJlGoy7XYyguZetL\nMzmx/EcANMEBDJo7g/AOrVEH+vOmT7N65yPx0uI94FaUjeKwlpdQ8dMSzBmXOHd8/Am48zmMx/dT\nsel753KZXzC6G0cgD4/Gbjah/2UDxkO76pyP3W5n/9JPOfPrFqQKJQn9RtDsxmGX3MZms7L29UnY\nLGaSpn7ktv7IhiX8tmoB/R9/g+DY62qdi0StxW/IWJSR8djKiindsAhTekqN8TLfAILvfxHD4T2U\n/vitc7mu+2A0rbogUaowHD9I2fpF9X6v2vr1RxzduRG5QkmHxFG0GzjCY+ypA7vZsehTKkqKUHip\nadb5RnqOud/5XnXil638vOwr9GXF+IU24sbbHyS8SfM65yRcva6aTrBSqSQgIACAkJAQunbtyq5d\nu1w6wevWrSMuLo7777+fHj16kJeXR0hIiMt+5HK5cz/BwcE0adKELl26MGzYMD755BMmTpz4j/w9\n4fc8jKXkHMfuH42uVTsiJz7HiUn3YdNXusR5t+1A0KBhpM18GVNOFooGoVgrygGQemnIW7IQ/fHD\nSL3URD3xIsFDR1KwcnGd8/ll/Q+cOXqIp+d+jaGinI9fmkRYVByNW7p3QhrFNWP8q3PQ+fpjrKxg\n4cyX+HX9SjoPdLxIh8c0oXX3Piz94M16tIy70LEPYik9R8qjt6NNaEvDB5/h1LPjsRlc20rXqj0B\n/ZLInDMdU24WiuAQrJXltTrG999/z4H9+1m1ejVlZWXcd++9NI2Pp0PHjm6xO3bsYPHixSz8+mvU\nXl6MHz+e6JgYhg8fjtls5qknn2TCgw+SmJjIvHnzmPL883z+xRcALF22zLkfs9lMn9696devn8v+\n9xRXYLTZkHjIM3zcQ1hKznF8/Bh0LdsR8dhzpDxxv9t5o2vTgcABSaTPnuY4b4Krzxtsdsp/28O5\njauIemZardrnUjasXMqxQ78xZ8FSKivKmPb4g0Q1bkKLtu3dYs1mE/dMfJq4ps05V1jAa89OJCgk\nlG69+wMQ3SSerr37M2/W9DrnobtxBLbKMoo+eRFlZFO8B91J8fwZ2E1Gj/HaHklY8jNdF8pk+Cbd\nT+XudZhWfgJyBVKtT51z+UOP2EDignRMW38CtULGxJ6xZJUaOFlQWeM2veKC0Jus+HhVX6gL1im5\n4/oI5u48zdliA/2bNuDujpG8s+1UvXO7WGl2Hqunvk2H2y7d2for9H/7ZSpy85kT0ZGYPt0Z9tUc\n5rXqS1Wp6/N16GezyN73O0tGTaBBy2aMXvkF+cnHKU5Nw26zc+rHreyft5BRyz/9U/no+tyMrbKM\nwo+moIxqhk/iXZz74jXsVZ7PHV3P4Z7PnZvup3LXOqpWfAxyBTKtb73yObljHfmpR0iaOg+ToYKN\nc6bg1zCG0PhWNW6Tsm01So0WY1mJ2zp9SRHp+3eg9g2ocy6+A8Zgqygj751nUMU0w3/4veR/9DL2\nKoPHeJ8+IzHnnnVZpm7ZGa+mbSicPxO7yYjfsHHoug+iYseaOufz++ZVZJ1I5p6ZX2LUl7N4xtME\nRcYS2byNW2xIbDyjpsxC4+NPlb6SVe++wqEtq2ndZyiVpcX8+MksRjz1GhHXtebQT2tZ9f6rjJ/z\nrYejXj3EmOC6uSqGQ1wsJSWFAwcOoFQqXZYvXbqUYcOGodPp6NGjB8su6HBcSmxsLD179mTjxo1/\nR7puJCoVPtd3Jm/xAuwWC+UH9mA8m4ZP+85usQ1uupWcBZ9gyskCwJyfi82gB6D0l+1UHv4Nu8WC\ntaKc4p2b0TSpXzXk4PaN9Bw2Gq23L0FhjejYbwgHtrkPKQHw8Q9E5+sPgM1uRyKVUpSX5Vzfqf9Q\nYhNaI5XJ6pXLhSRKFbq2nShc/g12i4WK3/dSlZmGd7tObrFBSaPJ++4zTLnn26ogz9lWl7NmzRru\nvOsu/Pz8iIyMZMTIkaxatarG2JE330zDhg0JCAxk7J13svp87N49e1AqlQwfPhyFQsF9993H0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OmEwmfvjhB8xmM59++ikJCQmEh4c7t09PT+f48eMMGjTIZb8/rFzJokWLeKt5BFOahCGVwOzm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\nJ0iFaXexdPUyDrawQNGsK9k7fsGmaskqqJW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K+svYb7WoyY87TtBrzmYKCnVU83Fl/MvN\nnmjfdOvVh8T4eAb17Ym1tZR+b7xFrfuPKkq+d5f3B7zK4tXrcffw5F5SEgN7dzMce91Dm1Gzdl1+\nmruQo4fDuXXzBolxcWzdtB4ACRK27Df9xO7rD97kq+kLadJ3KF7urswY9SEOdgq2h0eyaN02ts6f\nBMDWsKOMnrHY8FzO7Qcj+aB/T4b170lGVg5Dv51Oanom7i7ODH2tB83qlv6UAFPUbdudjLuJLPj0\nTSytrWnS/TX8758UZacls+iLd3l/ylIcXN1JS4pj/6oFqJW5yOwcqNa4Fa36vAWAhaUlvUdOYMfC\nqexZPgdX7/L0GTnhsf3AD4x9qyejF/xO86Hj8XJxYtpHr+OgkLEj8hxLtoaxefJIAOZu3IvUypL2\nw39Ah74uNP7tV+jctA4VfDwY82ZPPp6xnBxlPnUrB/LD4Cd/fnhot17cS4zni0GvYm1tTdd+A6lW\nS3/SkpZ8j9Hv92fS4jW4uHuwbfUKlHm5TP7iQx4k1jS0I29+/DkFBQX8Nn8GyYkJWFlbE1i5GiO/\nn46lpelDeG7YRuw79Mdt6EQKczLJ3rECnTofmyp1kTdsQ8bKn6CoCJ2q+DK8TqtBV6BFd7/HuzAj\nmZywjTh2fweJjS3ahNvk7Flt9n7JPbgJ+3b9cH1/AoU5WeTs+hWdJh+bynWQ1W9D5uqppebCI7nk\nHtyEQ9dBSGxkaBNvk7N3TVlv+bd0HvsRXcYPN1SaO40exopBn3Ni5aZ/5P06vtybpIR4PhjQC2up\nNb0GvGV4wkNq8l0+frMfs1esxc3Dk+S7SQzp18Mw5vTr0ILqtery3cz51Kxbnx59X2fC58PJzcnC\nw9uHz775AZmJnymAr7o34+sNB2k9cSVejgqmvNZGPx6fj2LZofOsH65/4tKRa3eYs+ekfjx2suPT\nzo1oXqW4eJGQns2VhFSmv97+b+2bw7fScLezYULHqmiLdOy5lszN+49Hc5ZZM659FcOzgKt52vFW\nA1+klhZkqrTsuZbM2YTiSfDHLYKo5K7fFx+10Ffax+68ami1+C8SPcHmkej+33eLCQaao+seH/Qv\nWWRheg/Wv+HtO78/7RRKSGn70dNOwcA388rTTqGElZmPf6rHv6V/0bmnnUIJZzyaP+0UDII2lX4P\nwtMisXzqFxpLGDt849NOweDjpAtPO4USAo4uetopGIzUmdeW+E+b39v4WfnPsvfXnf/H32PRq8bP\ngP+v+kcqwYIgCIIgCMK/S1SCzSMmwYIgCIIgCM8BMQk2z7N1vUoQBEEQBEEQ/gWiEiwIgiAIgvAc\nKCwq/ZnJQulEJVgQBEEQBEF44YhKsCAIgiAIwnNA9ASbR1SCBUEQBEEQhBeOqAQLgiAIgiA8B0Ql\n2DyiEiwIgiAIgiC8cEQlWBAEQRAE4TlQICrBZhGVYEEQBEEQBOGFIyrBgiAIgiAIzwHRE2weUQkW\nBEEQBEEQXjiiEvwP+sWq0dNOwWCoR9rTTqGEVdLXn3YKJbxmlf+0UzBYnOr5tFMo4R1f5dNOweBA\nTuOnnUIJIXf2Pu0UDCbXGPK0U3imfZw0/mmnYDDb+6WnnUIJDU8dftopGMxI3f20U3hEraedgFlE\nJdg8ohIsCIIgCIIgvHBEJVgQBEEQBOE5ICrB5hGVYEEQBEEQBOGFIyrBgiAIgiAIzwFRCTaPqAQL\ngiAIgiAILxxRCRYEQRAEQXgOiEqweUQlWBAEQRAEQXjhiEqwIAiCIAjCc0AnKsFmEZVgQRAEQRAE\n4YUjKsGCIAiCIAjPgSJRCTaLqAQLgiAIgiAIL5wXshI8atQoNm/ebPjZ0dGRmjVr8vnnn1OlSpV/\nLQ+dTsfB3xZwJWIfVtZSGnR5lbode5UaG332GEfWLiE3Mw1rWxlVG4fQst97SCQSAK4fP0jkpl9R\nZmfg5FWekAFD8alU3eRcMrKyGT1tAScvXMHb3ZWxwwbRuHYNo7i9ESf4ZcMOrt2KoXPrpnz/6ZAS\ny2cuX8vmvQfRaAuoG1yFbz5+F3cXJzP2ip5Op2P/yvlcPLwXS6mUJt360rDTK6XG3jgTSfiaJeRm\npGJtKyO4aSih/d837BtNvor9K3/m+qkIdDqoVK8J3YZ8Ufa+yMxk7Lffc+rsObw8PRjz+UgaNahn\nFKdWqxn//WQOHonA0cGBEcOG0ql9W6O4IcNHcuLkKc4dO2x47cy580yZMZs7cfFUCPTnmzGjqBRU\nweR98ywdN2Mmz+Tk+Ut4e7gxZvhgGtetZRS391Aky9dt5lrUbTqHtmDil8MNyy5cuc74afNIupeC\nVGpNi0b1GDt8CDJbG5PzeECn07FpyRxOhu3CWiqlTa8BhPR4tdTYEwd2cWj7BlKTEpDbOdCsUw/a\nvTIAgOgrF1jw7WeAxLBdrTqfz6YvwTeoskm5ZOTkMfaXLZy6HoOXswNjBnShUTXj3/HUdXsIO3eN\njNw8yrk583HPNrR8qfg9fv4jnM1Hz6HM19C+fnXGDOiClaWlmXtG/284s3EJt0+EYWEtJbhdL6qG\n9PjLdYqKCtk5aQRFBVq6j19gtPzy3g2c37aS9p9Mxr1Ctf9sPjqdjmVzpxO+ewdSqZSe/QfSrU//\nUmPjYm6zbO50oq5dQWFnx4Lf/yixPCJsL2uWLSQzIx2f8n6889FIqtZ4yeRcTNFi8ACav9ePcjWr\nsHPiXHZ+N/v/uv1H6XQ6Dqyaz6XD+7CSSmnUtS8NOpU+5tw8E8nB35eQm5GG1FZGtSYhhNwfj+Ov\nX2L9lNEUf66K0GrUvDXxZzwDKj42j4xcJeN+28PpqDi8nO0Z1bsNjSr7GcXN3xXJluOXyM1X4+qg\n4O02DXm5sf777EJMEt+t3UdSRjZSK0uaVQtkdJ82yKTWT76DniE6nagEm+OFnAQDtGzZksmTJ6PT\n6UhJSWHmzJkMHTqUsLCwfy2HPw9sI+H6Rd7+aTn5yhzW//A5bn4V8Kte2yjWs0JlXh0zFbmDM2pl\nHttmT+BC2HZqtelGXlYGuxdPpddn3+NbrRYXwneybe53DJ61xuRcJsxdhruLE8fWLebo2Qt8+sMs\ndi+biYOdokSck709b/fuyrmrN8jKyS2xbG/ECbaHRbBu9ve4Ojvy9czFTFm8ip++/NDsfXN2/1bu\nXLvAkBkryM/L5beJI/HwCyIg2Hjf+FSowuvjpqFwdCZfmcumGd9ydv826rXrDsD2hT/h5O7FsNmr\nsZJKSYmL+cv3nvjjNNzdXInYt5PIEyf5bPQ4dmxai4O9fYm4eQuXkJWdTdjOrURF32LoiJFUr1oF\nfz9fQ0zYoSOolEq4P+kEyMrOZsQXo5kwbhStWzRn287dfPzZl2zf8DuWJkxunqXj5rsZ83FzdSHy\nj984evocI7+dwq7fFuJgZ1cizsnRnkF9e3L+8jWysnNKLPMr5838yV/j5e6GWqPhm6nz+HnFGkYO\nfsvkPB6I2LWF6Mvn+Xrh7yhzc5g95mPKBVak8kt1jWILtFr6DP4U/0pVyUxPZf74kbi4e1KvZVuC\nqr/ET2v3GmLPRoSx7deFJk+AASb+tgN3RzsiZn5J5OVoPlu4nh0/fIyDXFYiTiGzYeEnb+Dr4cKp\na7cZ/vPvbBg/FB9XJzZHnGPf2SusGfMeclsbvly0gQXbDvHhy6Fm75ubR3aRHHWZ7uMXolHlsm/W\nGJzKBeJVuewJ2o1D25HKFeRnZxotU2amEXvmCDJHF7Nzedby2b1lA1f+PMfPqzeRl5PDuBFDCAiq\nTM269Y1iraysaNGmPa3ad+L3ZQtLLMtMT2POpAmMmzKLGnXqsXfbZn4a/xVLN+40O6e/kpV4j+3j\nZ9Cg/1+fNPy/nNu/jfhrFxk8fQX5eTmsnvgZHn4V8C9lPPauUIX+Y/XjsVqZx+aZ33L+wHbqtO1G\n+So1+GTpVkPs1eOHOLx2qUkTYIAf1h/A3VHB4UkfEHktli+Wb2Pb2HdwkNuWiOvaoDpvhtZHbiPl\nTkoGb89eSw1/Lyp6u+Hn7sTcwT3xdLJHrS1gwtp9LNh9jE+6t/x7O0n4T3ph2yGkUikuLi64urpS\ntWpV3nvvPZKSksjIyPjXcrgaGUa9Tr2R2Tvg7FmOmq07cfXo/lJj7ZxckTs4A/qzZ4mFhMzkJABy\nM1KR2TvgW01fgavWrA3KzAzUyjyT8lDm5xN2/AwfvdEHqdSakMb1qBLoR9ix00axDWtVp13zhrg4\nOhgtS7yXSr0aVfF0c8HK0pKOLRsTfSfepBwedSniAI279EFu74iLVzlqh3Tm0pF9pcbaObuicLy/\nb4p0SCwsDPsmNSGWezFRhLz2HlJbGRYWlnj6B5X5vkqVivDDRxg2+F2kUimtWzSncsUgwg8dMYrd\nvnsPg98ehFwm46UawYS0bMHOPcU5ajQa5ixYxCcffVBivT8vXMLH24uQli2QSCR079IJSwtLTp89\nb9K+eWaOG1U+YZEn+GhQf/1x07QhlYMCCIs4YRTbsHZN2rVsirOTo9EyJ0cHvNzdACgs1OcYl3jX\npBwedergXkJ7vobCwRF3n/I0bd+NU+G7S41t1rE7gVWDsbC0xMXdk1pNWhJz/XLp2w3fQ4PW7U3O\nQ6nWEH7+GsN6hCK1tqJ17SpULu9J+LnrRrFDu7XG10M/cWtQNZAgb3euxup/R0cu3qBPq/q4Odoj\nt5HyTqfmbDl6zuQ8Hnb71EGqtemJjZ0D9u4+VGzantsnwsuMz8/JJCpyH8Ht+5S6/OzmZdTs8hoW\nT1CVftbyObRvFz36vo6DoxPe5X1p1/VlDu7ZUWqsd3lfQjt1w6e8cQUyLTUFe0dHatTRXzlq1b4T\nmelp5OXmGsX+HRe27efijjBUWTmPD/4/uHz0AA0799GPOV7lqBXSmUsRJozHuiIkEgkZ9xJL327E\nfqo3a2NSDkq1lvCL0XzQqSlSKyta1wiiko87By9FG8X6ujkht5Hez0H/WkJaFgBOChmeTvqCRmGR\nDguJhPhU45Oq/ypdke4f/+958sJOgh+Wl5fHH3/8gb+/P87Ozv/a+6YnxuLuF2j42a18IKkJsWXG\nJ9y4zLwhPfn5g96kxN2mRssOAHj4BeHkWY6Yi6fRFRVx+fAePAMrYSNXlLmth8Um3EUhs8Xdtfjf\nXtHfl6hY8yaw7Vs0IiYhiYS7KeSrNew8GEnzesaXxk2RmhCLu1/xpWN330BS4mPKjI+7folp7/Zg\nxuBeJN+5Ra3WHQFIir6Os6cP2+b/yIz3e7Fi/MfE37hS5nbuxMWhkMtxd3MzvFYxqALRt26XiMvO\nySEtPYPKFYtzrBQURNRDcUtXrKJLh3Z4uLsbvc+jw4gOHVG3bpWZ18OeneMmEYVMhrtrceWtUoA/\nUTF3TFr/YUnJKTTp9hoNu/Rl/5FjDOjZ1extANyLi8EnoPgkx8e/Akl3YkxaN+ryn3j5Bhq9npOV\nwbVzJ2nQuoPJedy5l4bC1gZ3p+KrBxV9PIhOTP7L9bLyVEQlJhPkU3zMPHx5s0inIyUzh7x8tcm5\nGLZ9Nw7ncgGGn518/Mm6W/bv6tyWFQS374OV1Lgt5d6Ni6jzcvB9qbHZeTyL+cTF3sY/qLga6Vch\niDsxpn0eHxZYsTLe5Xw5f+o4RUVFhO3cRlCVaigeuTLyX5OWUHLMcfcLIC2+7DEn/volZr73MrMG\nv0JK3G1eatXRKEaZncntC6ep0dy4haw0d1IyUNha4+5YvC8rersRnZRaavyy/Sdp/PlsevywDA8n\nexpX8Tcsu5uRTfOv5tL0y9kc+PMmr7WsY1IOwvPnhW2HCA8Pp04d/YGvUqnw8PBg4cKFj1nr/0uT\nr0JqWzzhkMrkaPNVZcaXqxzMsAWbyU69x5Wj+5E76HttJRYWVG0cwrbZEygsKMBGrqD3V1NMzkOp\nysdOLi/xmp1cRpaZ1Qs3ZydqVg6i/aDhWFpaUCXQj68/etusbTygyVdhIyvOyUYmR6Mue9/4VqnB\nyCV/kJVyj4sR+5Db6/dNTkYqty6eoev7n9F1yOdcO3GY9dPG8cGMX0ud7CmVKhSKkq8rFAqys7ON\n4gDkD+03O4UcpUoJQEJiEnsPhLFu1XJSUkoO0rVq1iAxMYl9YeGEtGzB1h27SEhMQpWfb8quebaO\nG0XJ40ahkBu1O5jC28OdY9vWkJGVzYbte/B0dzV7GwBqlQrbh36vtnIF6nzlY9cL2/I7qtwcGrYx\n/rI+e/gAvhWr4u5T3uQ8lGoNikd6mhUyG7Lzyv496XQ6xv2yhfb1ggnw0p+ENa9RkV/3HSO0dlUU\nMhuW7ooAQFXK9h+nQK3C2rb492VtK0erLv2YS7l1jZzUJJo0GM69m5dKLCsqKuTMpqU0e2ukWe//\nLOeTr1IhVxRPruRyBfmqsn9XZbGwsKBF2w78OO4LCrRa5Hb2TJj+8xPn9azQj8fFnysbmQLNX4w5\n5avUYMTiLWSl3ONyxH7kjsb3hVyJDMerQmWcvcqZlENpnyk7WylZeaUfM2+3bcjbbRtyKfYuJ2/e\nwfqhKwRezg5ETP6QjFwlm45dNFSGnwfi6RDmeWEnwY0bN+abb74BICsri9WrV/Puu++yYcMGvL29\nTdpGcnIyKSkpfxFR8uz/amQY+5fPQoKEqk1DkdrK0eQXX3rWqJRY28oe3YgRBzdPXH38ObBiLl0/\nHEPMxdMc2/Qr/b+Zi4uPLzdOHmbLtLEMmvILVlLpY7cnl9mSqyw5UchVqpDb2paxRunmrdpAdFwC\nR9ctQm5jw/Rf1jDqp/nMGvfJY9e9fPQAu5bOBImE4Kah2NjKUauKc1KrlEhtHr9vHN09cSvnz57l\ns+n58TispDY4uXvxUit9Fa96kxCObllNYvQ1Amsa3+wml8vIyyvZDpCXl4dcJjOKA1AqlYaJcG6e\nEvn9iftPM2fz4eD3sLayMrpRwdHRgVk/TWLqrLl8N3kqTRo1oHGD+nh6eJT6b3qmj5u8ksdNXp4S\nucy84+Zhzo4ONGtQly8mTuP3+VMfG3/60D7W/vwTIKF+q3bYyGTkP9TOka/Mw8ZWXvYG0LdQHNq2\ngeGT52FtbfzvPnVwL43bdjbr3yG3kRpVa/NUasMl2tJ8t2o7ynw104YU38jXs3ld7mVkM+inXygs\n0vFm+yYcv3ILV4fHVxZvnzrEyd9/BgkE1m+FtY0M7UMnBNp8JdY2xr8r/Q1ri2nQd+iDF0osv3F4\nJx4Vg3H08jVa97+Sz+F9u5k/bRISiYSWbTsik8lR5hWf9CuVedjKHv+ZetT5U8f5fdlCpixYQXn/\nACLD9zPxqxHMW7URqY35N3o+LVeOhrFn2UxAQvVm+jFHrSr+XKlVeUhNGHMc3T1xLe/Hvl/m0OPj\nsY+8xwFqtjL96kppn6ncfA1ym7++oa2GvxfbT19hQ+QFXm1e8sqks52cplUD+OrXHfz26QCTcxGe\nHy/sJFgmk+Hrqx80fX19mThxIvXq1WPdunUMHz78MWvrrV27lrlz55a5/NNf95b4uVrTUKo1Lb6h\nJeXOLVLjYnArr7/MlBp/G7dy/piiqLCArGR9n1Vq3G18q9fCtZy+R61Ko1aE/TqXjLtxuPuV3f/6\ngH85L5QqNSlpGYaWiJsxcbzczrwbBW7cvkPnVk1wstd/Qb/SIYQ3PvvWpHWDm7Uh+KHesOQ7t0iJ\nu43H/cvTKXG3cS8fYNK2igoLyLin76l0Lx9geBLCA4/+/DA/X1+UKhUpqamGloib0dH06FJyEuRg\nb4+bqws3om5R+6UahriKFfT5njp7jguXLjNxyjSKigopLCwktHN3lsybTYXAAOrVqc2a5UsAKCws\npEuvvtSoXrXUnJ7d48YHpSqflLR0Q0vEjduxvNzB/Ju2HlZQUEBcYpJJsfVbtaN+q3aGnxNiokmK\nuYWPv75NJTH2Ft5+AWWuf+H4Ef5Y/jMfTZyFi7un0fJ78bEkxUZTt4VpfYsP+Hm6oszXkJKZY2iJ\nuJlwjx5NS7/sOn39Xq7dSWLpZ29hbVVcsZJIJAztHsLQ7iEARF6Oppq/918eww8ENmhFYINWhp8z\nEmLITIzByUd/rGQmxuLoZdzXqs1Xkh53i0MLJqJDR1FBAdp8JZtGv0W38fO5d+MiKdGXiT2rr0qr\nc7M4tOh7ancfSMWmZfdNP0v5tGzXkZbtiqv+MdE3uHMrGv8K+paIO7ei8Qsw7WktD4uJvknNuvXx\nDdB/NpuFtmPRzCkkxMUSWNH0myqfturNQqnerPhz/GA8dn8wHt+JwbW8iWNOQSGZySV7gtMS75AS\nd5tqTVqbnJOfuzNKtZaUrFxDS8TNxFR6NAp+7LqFhUXEpZZ+v4+2sOg56wl+2hn8t4ie4IdIJBLy\nTbwkDdC3b182bdpU5n+PU61pKKd3rUeVk0XG3QQuHtxF9ebtSo29cfIwOWn6fsKMuwmc3L4W32D9\nF6pHQCXirl0gPSlOH3vqCIVaLY7uplW05ba2hDapx9xVG1BrNIQfP8PN2DhCmxjfGV1UVIRao6Gg\nsJDCwiI0Gi2FhfpPXXClCuw+fJysnFw02gI27TlIpQDzqkUP1GjehhPb16PMziI9KZ7z4Tup2bL0\nfXP1+CGy7++b9KR4jm39nYAa+n3jX702Op2Oi0f2oSsq4uqJw+RmpuMTVPqEUy6TEdKyOfMWLUWt\nVnPwSARR0bcJadXCKLZLh/Ys+mU5SqWSC5cuc/BwBJ076HPcvuF31q9awYbfVvDzjKlYWliw4bcV\nBPjrv+Sv3bhBYWEhObm5/DRzDi/VDCbQ37QvlWfmuJHZEtqsEXN/Wa0/biJPEnU7ltDmjYxiHxw3\nhSWOm0IADh07RUxcAgDJqWnMXraq1MesmaJB6/Yc2LKG3OxMkhPjiNy7jYahnUqNvf7nadbM/ZH3\nx0zGs4wv9FPhe6herwlyO/Mul8ptpITUrsK8reGotVoOnr9OVEIyIXWMH8G4cPshDl+8wfwRbyB7\npFKcmaskPkX/5R2VkMzUdXv4oHtrs3J5ILBBa64e2EJ+bjbZyYlERe6lQiPjExapTEGv73+h86iZ\ndBk1i8YDPkTh4k7nUbOwtpHR9I0RdB07jy6jZtFl1Cxkji40eX04gQ3My+tZyqdVu05sWbuK7MxM\nEuPvsG/7FkI6lt2XrtVo0Go1FOl0aDUaCgoKAAiqXI1L58+ScL8P/dihMLRaLV4+pl3yN5XEwgIr\nGxssLC2wtLbCSio16cToSQU3a8PJHRtQ5mSRfjeeP8N3UrNF6ScY1048NB7fjef4tt/xDy558nc5\nYj8VajfEVmH650puY03rmkHM3xWJWlvAwUvRRCel0rqG8Qn7pmMXyFGp0el0nLx5h11nr9Gosv4z\nfvjyLWKS0wFIzspl3o4IGpbymDXhxfDCVoI1Gg2pqfpezaysLFatWkV+fj5t2phe8fHw8MCjjEvY\nABEnyr5xAKBWm25k3ktk2eeDsLS2pmHXfoY79XPSklkx6n3enLwYexd30pPiOLh6AWplHjI7Byo3\nbEmzV94EwK96bep36s2mn0aTn5eDo7sXXT4cg1T215eBHzZu2CBGTZ1P0z7v4+XuyvTRw3GwU7A9\n/CiL1/7BHwv0vaJbDxxhzPSFP8NIowAAIABJREFUhid+bQ+P4IMBr/DBgFd499XufP/zcrq+/xkF\nBYUEVwrku0/eNzmHh9Vt252Mu4ks+PRNLK2tadL9NfzvPwIsOy2ZRV+8y/tTluLg6k5aUhz7Vy1A\nrcxFZudAtcataNXnLQAsLC3pPXICOxZOZc/yObh6l6fPyAl/efPXmC9GMubbibRo1xlPTw+m/jAB\nB3t7duzey5IVK9m8ZiUAwwa/y/jvJxPSuTuODg6M/XKk4fFozk7FPXBqtRokElweuuly8S8riTx+\nAktLS9qGtOKbMV+ZvG+epeNm7IghjJ40k2Y9BuDl7sa08V/gYGfH9v2HWLJ6A1uWzQFg695wxk6Z\nbfii3n7gEEMH9uODN/uRmp7BpLmLScvIwl4hp0Wjenz6BI9HA2je6WVSkuL5bvBrWFlLadf7dSrV\n1H8BZ6Tc44cPBzJ63kqc3TzYu24l+co85owdjv5WRQkNWrfn1aHFvaVnDu+n5zsfPVEuYwZ0Ycyy\nzbQY/iOeLo5MHfIqDnIZO05cYMnOI2z+dhgA8/4IR2plSYcvZ6DT6ZBIJHz9Rjc6N6pJek4eH81Z\nTUpWLh5O9gzu2oqmwaY9TupRlVp0IicliW3fDsbCyprg9r3xrFwTgLyMFLZ//yHdxsxD7uyGrX3x\n8SuV2yORWGBrr3+yh7VMjjXFx4iFhSVSuR2WpbSS/Ffy6fhyb5IS4vlgQC+spdb0GvCW4QkPqcl3\n+fjNfsxesRY3D0+S7yYxpF8Pw7Hcr0MLqteqy3cz51Ozbn169H2dCZ8PJzcnCw9vHz775gdkJt5s\naqrOYz+iy/jhhtaQTqOHsWLQ55xY+fjiy5Oo07YbGfcSWfTpW1hZW9O4ez/8quvHnOy0ZJZ+8R7v\nTFmCg6s76YnxhK1aiFqZi62dA1UbtaLF/fH4gSuR4bR5fajZeYzu3YZxv+2m5eh5eDnZM2VQVxzk\ntuw8fZWl+0+y8Sv92Hb48i1mbTtCQWERXs4OfNqjFc2r379ylp3HjxvDSM9VYmdrQ4vqgYx4jh6P\nJp4TbB6J7gXcY6NGjWLLli2GnxUKBRUqVOD999+nbVvT7lQ1xcLHTIL/Te96pD3tFEpYlW78xISn\n6bWK5vf//VN+uWbaI8r+Le/4mn515J92IOffe3qLKUKS/73nij/O5Hxxh/tf6V3TtCsc/4bZ3v/f\nP57xdzU8dfjxQf+S/qmlP9LwabHt+GSFnKel2eR/fkw6+tXfa3l7lryQleBJkyYxadKkp52GIAiC\nIAjC/414OoR5XshJsCAIgiAIwvPmeftjFv80cWOcIAiCIAiC8MIRlWBBEARBEITngKgEm0dUggVB\nEARBEIQXjqgEC4IgCIIgPAeKXrwHfv0tohIsCIIgCIIgvHBEJVgQBEEQBOE5IHqCzSMqwYIgCIIg\nCMILR1SCBUEQBEEQngOiEmweUQkWBEEQBEEQXjiiEiwIgiAIgvAcEH822TyiEiwIgiAIgiC8cEQl\nWBAEQRAE4TmgE88JNouYBP+Dep+a+7RTMJhVa8jTTqGEjos/eNoplJA7b+3TTsGg1eJ3n3YKJbSr\n8enTTsFg3tnPnnYKJXza4/unnYLBmBuzn3YKJeSnZT/tFErwzK71tFMwaHjq8NNOoYSTDVo+7RQM\npo98dr43AS51fNoZCP8kMQkWBEEQBEF4DuiKnnYG/y2iJ1gQBEEQBEF44YhKsCAIgiAIwnNAPB3C\nPKISLAiCIAiCILxwRCVYEARBEAThOSD+Ypx5RCVYEARBEARBeOGISrAgCIIgCMJzQFSCzSMqwYIg\nCIIgCMILR1SCBUEQBEEQngNF4i/GmUVUggVBEARBEIQXjqgEC4IgCIIgPAdET7B5/vVJ8KhRo8jJ\nyWHuXOO/D37t2jVmzZrFn3/+SW5uLm5ubtSuXZuxY8eyevVq5s6di0QiQVdKuV8ikXD16lXDz6dP\nn2bgwIGEhIQwb948w+vTpk1j8eLFZW7HxsaGP//88//0ry2bxFaOXdu+WPsEUZibSd6hzRQkRJcZ\nb2HvjFP/z1DfOEte+EYArHwq4PDyYHQFGiSADsjZupSCuzFm56PT6Yhcu4gbkQewsramVsc+vNTu\n5b9cp6iokI3ffkhhgZZ+3y8xvH5k5Vzir54nOyWJbp9PxqdyTbPzeZilnQM+749AUbUG2vRUklYs\nQHn1Qqmxjs3b4Na9D1aOzmjTUombPgFt6j2z31On0zFz+lR2bd+GVGrD62++Rb/+A8qM/3X5Mn7/\nbRVFRTq69XiZYR8PNyxr2qAuMplM/4NEwpuD3mbgW2+XWD8pMZHX+rxCx86dKbmkJEs7e7wHfYy8\nSg20Ganc+20RymsXjeK8Bn2EQ8MW6AoKQCJBm5pMzDfFObl174djszZY2NqSfTqSe78thKIn/3ub\nw1pVoEM1TzSFRaw5Hc/GcwllxlbzsmdYqyACXeVk5xcw71A0EdFpAIwIrUg9X2d8nGz5ZMMFLiRk\nPXFOT+O4eZxXXvKmkb8z2kId+26kcDAqtdS4Rn7O9K9XHk1hkeGz/f2+G2SqtE/83hKZAqeubyD1\nq0xRdgZZe9eiib1RZrylowvu741DdekkWbvXGF63a94Z+UtNkEhtUF07R/aetWb/rVYLuR3u/d7H\nNqgaBZlppG3+lfyoK0Zxbn3fQ1G7MRQWAlCQkUrCtNFGcZ7vfoasUjAxXw4yK48HMvLyGb/hIKdv\nJ+HlaMdX3ZvSMKicUdyC/Wf448x1cvM1uNrLGdSqFj3qVQFg6cHzLDt4HolEH1tQWIS1pQVHxr9l\ndj46nY4Dq+Zz6fA+rKRSGnXtS4NOvUqNvXkmkoO/LyE3Iw2prYxqTUII6f8+EomE+OuXWD9lNCC5\nv90itBo1b038Gc+AimbnVZYWgwfQ/L1+lKtZhZ0T57Lzu9n/t22X5svu1elevzyagiKWhkez6sjt\nUuPG9apB17rl0aH/rpdaWXA7OY9Xph8uEfdOSBDDO1Vl4LxIzsdm/KO5C8+WZ6YSnJ6ezltvvUVo\naCjLli3D3v5/7N13fBP1G8DxT9ImaZLuvQsto+y9N8iQjYAoCoLgxIGoKEMFHCgoOPAngiioKHso\ne48yBARkQ1solNK926RJmuT3RyElNC1tgbbK9/16+ZK7+97d0+uN7z333NWJuLg4du3ahVarZcyY\nMTz55JOW9oMHD+aJJ55g6NChNpe3evVqnnnmGVasWEFaWhru7u4AvPzyy4waNcrSrl+/fowdO5YB\nAwYABZ3piqDu9Bim3GzSfvgAWXAtnHqNIOOXTzHr82y2V7XvR37y9SLjTVmpZPw6657jObdnIwmX\nzvDkJz+gy83hz8/fwSOoOgHhjYqd58zOP5GrHNFmWZ80PILDCGvVib2Lv7rnuAB8n3mJ/Iw0Lr48\nHHX9JgS+8g5Rbz+PSZNr1c6xUXPce/Yjds6H6BPikHn5YMzNLtc616xaycnjx1m59g+ysrMY98Jz\n1KxVi2bNWxRpezBiP2tXrWTRkl9RODjw2ssvElKtGn37F+5Ty9esw9PTq9j1fTX3C8Lr1LlrXD5P\nvUh+ZjqR40egrtcE/xfe5vLklzBpc4u0Tf1zBambVhUZ79KuK45N2xDz8duY8rT4P/8mnv2GkbL+\n9yJtS2NAQz8aBrjw9OKjOCrs+XJII6KTczh5vWgH1k0l44PedZi94xLHr2XgqLBHpbCzTI9KymHX\nxWTefqRWuWK5XWXsNyXpEOpBDU9Hpm+9iFJmx+sdQ4nL1BKZXPR3B3ApOYdvI2xf3MvDpecTmHKy\nSPxyIorq4bgNHEPS/GmYdVqb7Z27DcaQcM1qnLJBaxxqNyZlyWzM+jxcB4zGsf2j5OzfWKZYPAaP\nwpiVwdX3X0JZuwHeI17h+sy3MOVpirTN2L6OzF1/FrssVb2mSOUOcA8JsJnrI/B0UrFn6kgORV7n\nnd938sebw3BSKqza9WlSk5EdGqJSyLiWmsmYBRuoH+hFmI87Yzo3Zkznxpa2n6yPQJ9vLFc8J3b8\nyfULp3lhzhLycrP57aO38A4OJaRe4yJt/UJrM3zqF6hd3NBpcln75XRO7txAk0f6EVi7Pm8s+sPS\n9vzhvexbvui+doABMm8ksuGDubQYPuC+LteWYW1DaBbqQe9Pd+OslPHTS224eCOLozdvpG/34Zoz\nfLjmjGX4f2Na8s8dnVwvZwWPNvYnKcv2tfffRmSCy6bK1AQfP36cnJwcPvroI8LDwwkICKBly5a8\n++67BAQEoFQq8fDwsPwnlUpRqVRW427Jyclhy5YtDB8+nPbt27N27VrLNFvLUavVluFbneUHyl6G\nPLQemr+2gsmIIeY8xtR45KH1bDaXBRd0CAyxkQ8spMjDu2nY8zEcHJ1x8fEnvEMvLh3cWWx7bVYG\nF/ZvpUnvx4tMq9vpUfxrNUAqtbMxZ9lI5AqcmrYiefVSzPn55Jw8ii72Ck5NWxVp6zlgGIm/LUKf\nUJCFNCQnYtIWvaiWxpbNGxk+YiQurq4EBQUzYOBjbN64oZi2mxjw2BD8/P1xd3fnyaefZtOGwou2\n2Wwu8U9ZHj50EIAWrYr+TLeTyBU4Nm5J8vrfC7bFP0fRXY/BsUnLYmawPVrdoBkZe7dizMrArNeR\ntnk1Lu26lbjukjwS7s2Kv6+TlZfPjcw8NpyJp0cdH5tthzQJYMu5RP6+llHw5EKXT2KWzjJ9w5kE\nTsVlYrzHlzsqa78pSYtgV3ZGJpOrN5KSq+dgTBqtgt2K/xnu47olMjkONRuQvW8DGPPRRZ3BkBSH\nQ62GNtvLqxfckOmuXLAarwirh+bkAUy5WZgNenIObUPVsE3ZYpErUNdrSvrW1WDMR3vuBPr4WFT1\nmxYzQwlbws4et15DSNu4vEwx3E6rN7Dn/FVeeqQ5cns7OtUJoaavO7vPXy3SNsjDGZVCBsCtXTQu\nvegNk8FoYvvpy/RtUrNcMZ09sJOWvYeidHLGzTeARl16cyZiu822jm4eqF3cbsZkQiKRkJ54w/Zy\nI3ZQ9x6O9eKc+nMHpzfuQpt5/28e79SvaQCL90aTqTEQm6ph9V/X6N888K7zeTgpaFPTkw3HrZNJ\nb/ery7fbLpEvOo8PpSrTCfby8sJoNLJt27Z7XtbGjRupXbs2QUFB9OvXj1WrimbDKpOdqxdmvQ6z\npvCEkZ+agJ27b9HGUimqtn3QRPyJrcui1NEVt9Hv4/rU2yibP1LumNJvXMMjsLpl2D2gGuk3rhXb\n/vCqH2nS53Hs5Q7lXmdpyH39MeVpyc8svHvPu34NRUCwdUOJBGW1MBwCq1Fz7o/UmP09nv2KdtBL\nK+byZWrUKLyAhdWoweVo2+UqMVcuU6NmYdsaNWoSc+WyVZuxo0YwoE8vPpo+jczMwgxpfr6Bb7/+\nktfGT7hrJkvuU7AtjLdtC92Nayj8g222d3ukHzXmLiH4nU9Q1qxrNc3qiYdEir2rG1JF+X6X1TzU\nRKcUZjOvpORSzUNls224rzMS4IenmrJibCsmdq+FSn7vN0t3qqz9piS+Tg7EZRZmXW9k5uHrXPw2\nD3FXMbNvXSY/Uot21e/t5tzOzRuzXocpN8syLj8lHntPv6KNpVKcuwwka+eakjugABIpUkcXJHJF\nye1uI/P0waTLw5hdeBzo468j87HdkXHp0JPgad/iN24qDqG1raa5du1HzolDGLPSSr3+O11LyUKt\nkOHlXLjPhvm4cznR9mPxn/aepO20nxg0dwU+Lmpa2Sib2H/hKg5yGc1D/csVU2rcVbyCC8/HXsHV\nSL1etFN+y/WLZ/jyuYF89cJgkmOv0LBTryJtNFkZXDl1jPrty3+dqApCfZy4FF947YyMz6aGj9Nd\n5+vd2J9T19KJSys8BluEeeCqkrP77P0vfaosJpP5gf/3X1JlyiEaNWrECy+8wFtvvcUHH3xAw4YN\nad26NQMHDrTK8pbG6tWrGTRoEACdOnVi6tSpHD9+nKZNi8k0lFNSUhLJycnFTrfRpQUKsjJ3lj2Y\n9XlIHYp2HBwad0Qfcx5TdtETsjE9kYxlczBlpCB19cLp0RGYDTry/tlfpp8DwKDTIrtt/XKlCkMx\nj0kTos+TlRxPzVYTuHGxaD3q/SR1UBbJypm0GuwcrU969s6uILVDXb8x0ZPGYad2JHjiDPQpiWQd\n2lvm9Wq1WtSOasuwSq1GW0x2UKPRolZbt9VoCrfddwt/pH6DBuRkZ/P5ZzP5aNr7zJ5bUCry+6+/\n0q59R/wDil5I7yRVOBR5XGzSarBTF70ApG//k6RlizDp8nBu3p7AV6dw5YPXyU9PIffMCdy79yf7\n5F+YtFo8btYaShQOoCv7I0GlzA6NvvCxb67eiFJmu2PrqZbzSB1v3l5zmtRcPZN61ualDqF8sfP+\nPuWorP2mJAp7KXmGwtrZvHwTCnvbeYhLKTl8sv0S6VoDIW5KxrYOIVuXz6kbWTbb341ErsB0x+/W\npNMiVaqLtFW37IYu6gzGzKKPl3WXz6Fu2Y28yFOYdXk4tulesHyZArNeV6S97VgcMOVZn1vMOi1S\nVdFYsvZtJXX9r5j1OtSNWuE9+g3iPp+MMTMNezdP1I1aEjdnKvYurqVaty0avQG1Qm41zlEhI1Nr\n++cZ3akxozs15uz1ZI5ExyGzK7qvbzwZRe9G5S850OdpUdz2u1Eo1ejzbJ+PAQJr12f8wnVkJidy\nNmIHKhvb49zB3fiG1sLN9+7nmqpMJbcjJ6+wNj5Hl29VUlWcvs0CWHGoMLEjlRRkgd/97cQDiVO4\nd6dOnWLy5MkYDAb69+/PuHHjirS5cOEC77//Pnq9HpVKxaxZswgMvPuTgVuqTCcYYPz48YwePZrD\nhw/zzz//sGzZMr7//nuWLl1KzZqle6wUGRnJuXPnWLBgAQAymYyePXuyatWq+94JXr58uc0X/G45\n+Gp/m+PNBj2SOzKoErkDZoPeepzaGYc6LchY/qXt5WhzMd+sBTVlJKM9ugOHhu1K1QmO/Gs3+3+e\nBxKo2aoLMgcVhts6WHqtBplCWXSdZjMHf/+eDk+PuzXiruu6F6Y8LVKl9c2BVKkqchE13dx2KRtX\nY8rTYsrTkr57C06NmpeqM7N1y2ZmffIRSCT07PUoKpWK3JzC7KYmNxel0nZ2U6VSkptr3ValKtx2\njRoX1PG5uLryxtvv0P/RHhgMBtLT09nwx3qW/Fa6WlyTruiNklSpKtK5AdBdj7H8O+vIPpzbdEJd\nrzGZETvIjNiBvZsHwW9/jEQqJW3belR1G2HMyihVHN1qezGhW03MZthxMQmNPt8qm6uW26E12K6F\n1BtN7LiQwo3MgpiXHonl4wG2y4DuRUXtNyVpHuTKE00CMAPHYjPQ5RtxkEnhZggO9lJ0+bZfKEvX\nFF7kr6Zr2RudSmN/l3J3gs16XZFMv1ShLNJxlTq6oGrYhuQfZ9pcjvbUIeyc3fB4ajwSiZScIztR\nVAu3yjDfPZY8pA7W5xaJQolJV7TTqY8v7LTknjiEY9N2KGs3IOfIXtz7Dyd9yyowGbmX4hGVXEau\nzvrcm6MzoJLLSpyvXqAXG05EsvroeYa2KnzSkqXVEXExlle6F31/oDjnDuxi649fAhLqtuuK3EGF\n7rY6f502F7lD0fPxnVy8fPAIDGb7T98w4LWpd6xjJw069Sx1TFVF7yb+fDC4IWbMbDweR64uH0cH\nGVBwDnFU2KPRlVx7HebjSKi3E1v/KSwTebJdNY5fSeNyUs6DDL/C2Xrh/99qxowZzJ07l7CwMB5/\n/HF69OhRpC/45ZdfMn78eNq2bcuyZctYuHAh06dPL/U6qlQnGMDFxYWePXvSs2dPJkyYwMCBA/nx\nxx+ZOdP2SflOq1atwmg00q5dO6vxCoWCqVOnolLZ7siUx7Bhw+jatWvxDXYvtjnamJGMRCZHonKy\nlETYe/iiu3DMqp29d1BBucOIdwAJEpkckCB1cif7j4U2liyhtBeDmq26ULNVF8tw6vUrpF2PwT2g\nGgBpcTG42XjMrtdqSI2NZss30wEzxvx8DHkafnnzaZ74eCGyUpyoy0KfcAOpwgF7FzfLo22HwBAy\nIqzrlU2aXPLT78hcleFc0LPXo/Ts9ahlODLyEtFRkYTVKMjmREdFERoWZnPeatVDiY6KpH2HjgBE\nRUZSPdR224KbhoIvk1w4d5akpESGDuyP2QxarQaz2cxVhZTpDWxs+8QbSB0csHNxs5REKAJCyDy4\nq3Q/5G27Ruqfy0n9s6COUlW3EXlXLxczU1E7Lyaz82LhE5AwTzWhnmpiUgtuoqrf9u87XUnJvZf3\nl0qtovabkhyLzeBYbOGNRYCLA/7OSuJv1kD7uziQUJaXce6hSNiYnoRErkCqdrZ0WO29/NGePmzV\nTuYXgtTJFe8Xp4FEgkSmAIkEO1cP0pYV3PDnRGwiJ2ITAPJq4RgSYssUiyElEYlcgZ2Ti6UkQu4X\nSM6xsj3BcgirgyK4Bh6PjUIilYJUStB7X5Pw/acYkmzXxNoS7OmMRp9PcpbGUhIRlZBGv2Z3fznT\naDIRm2p9A7D1VDQ1fNyo7l367HTddl2p267wWpJ07TLJsVfwCiooiUi+FoNHYEiplmXKN5Jxx8+f\neuMaybFXqNOmc6ljqio2nbjBphOFP09tf2dq+joRlVBw7azp50RUYsm1yP2aBrLvfCI5efmWcS3D\nPGka6k7PRgUlQW5qOV+Pbs6Xmy6w5kjZ9mnh/ktKSsJkMlk6vX379mX37t1FOsFSqZScnIIbmezs\nbLy8in8B3ZYq1wm+nb29PUFBQWg0pXtJxWAw8McffzBlyhRat25tNe2FF15g06ZNDBky5L7F5+3t\njbe3d7HTU3cXMyHfgP7KWVStepC7bz2yoJrYefiiv3zWqpnh6nnSf/7EMqxs0hmpyonc/euBgk+k\nmTJTMeVmInXxRNm8K7qLx8v1s9Rs3YV/tq4hoG4T9JocLuzfQtcxbxdpp1CpeXr2L5bhhKhzHF75\nAwMnz7V0gI35+ZjNJsCMyWDAaDBgJys5q1Ics15H9om/8HpsOAm/LkBdrzGKwBCyj/9VpG1GxC48\nez/G9auXsVOpcevSk+R1y8q13l6P9uG3X3+hZevWZGdlsX7dGqbN+LiYtr2Z/dlMuvfohUKh4Pel\nv/LkU08DcOVyNEajkdCwGuTk5PDV3C9o1bo1crmctu3bs/qPwrfql/6yhLSUVEak2y4NMOt15Jw8\nglf/J0n8fWHBtggIJufEkSJtHZu2JvfMccyGfJyat0EZFk7ir98DIFU7YadUYkhJQu4fhPfjo0la\nsbhc2wlgx4UkhjUN5NjVdJwc7Olb34+Pt16w2XbLuUQmdKvJjgtJpGv0PNkikMNXCus57aQSpJKC\n/p7MToLMToLBWPZeaWXtNyU5ei2DbrU8uZCUjUpuR9tq7vx81PbFto6PI9fSteTqjQS6KukU5sGa\nU/HlXrfZoCfv0imcOvQhc/tKFNXDsffyI++S9SfjdNFnSP7ufcuwulV3pI7OZG1fARR8Zk0qd8CY\nmYq9px/O3R4ja9eassWi16E5exzXnoNJW/cLDrXqI/cNRHOm6LlLVb852ounMOcbUDdsiUO1mqSu\nWQzA9U/fBmlBOYm9qwf+r75P3JwpmDRly+wp5TI61wlh/s6/mdi3DYej4ohKTKdLnaKdzjVHL9C9\nQSiOChnHLsez+Z9oZg6zToRsOhlV7hfibqnXrhtHNq6iWoNm5OVm88/uTfR7+V2bbS/8tRf/GnVw\n9vAmLeE6h/9cRvUGzazanI3YQWjjljjYKJ26HyRSKXYyGVI7KXYye+zlcowGwwPJSv55PI5RnUM5\ndCkZZ5Wcwa2CmfT7yRLn6dM0gI/WWJfuTV52EoWssBxp+esd+HDNaQ5H2v5s4b/Ff+XrEElJSVb9\nK19fX44dO1ak3VtvvcWYMWP45JNPUKlUrFy5skzrqZROcFZWFhcuWF8kL168SEREBH369KFatWq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8f777+PUqm01Mu1bduWxo0bM27cOA4cOEBcXBzHjx9n7ty5nD171hLP9evXuXDh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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fbf1a3516d0>"
+       "<matplotlib.figure.Figure at 0x7fbf1a0be950>"
       ]
      },
      "metadata": {},
@@ -167,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -175,10 +175,10 @@
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.JointGrid at 0x7fbf175f15d0>"
+       "<seaborn.axisgrid.JointGrid at 0x7fbf1414e410>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -186,7 +186,7 @@
      "data": {
       "image/png": 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0gYtfMoqXHhjGo7+YwskZf81acEXXwNiwg6JrtbV92h4MRkREp1k+3LRZ74NS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\nCAAAIEUwAgAASP0/SIcBFuu07esAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fbf177981d0>"
+       "<matplotlib.figure.Figure at 0x7fbf15926290>"
       ]
      },
      "metadata": {},
@@ -196,7 +196,7 @@
      "data": {
       "image/png": 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HtS04lgk/kKiGcSpjiuF/DJVD+GGM5S0ecp6dytgAYAiBrGvBsRTKfoyFcjyP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\nhiYiIiIiE/4/uaZc8TIBg+gAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fbf175f1450>"
+       "<matplotlib.figure.Figure at 0x7fbf1414e3d0>"
       ]
      },
      "metadata": {},
@@ -210,7 +210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -249,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 18,
    "metadata": {
     "collapsed": true
    },
@@ -274,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 19,
    "metadata": {
     "collapsed": true
    },
@@ -295,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 20,
    "metadata": {
     "collapsed": true
    },
@@ -394,7 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
@@ -479,7 +479,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
@@ -488,14 +488,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Minimum test loss: 5.53870993539\n"
+      "Minimum test loss: 5.53297131438\n"
      ]
     },
     {
      "data": {
-      "image/png": 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TYyYR7deKokorz/7wOpW2yhpj/H0t3DH6PF58aAjndYygvMLGgs+2N3rtIiIiIiLu0LVr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\n/R2sRjP0kymTBp1aiyuPuwxFSfl4fe/72FS/Ew19Lbj+hN8i3Tz09y0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W03De6iJFd1Yg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\nyChQMEUIIYQQQggho/D/AfTZvxCSm3f4AAAAAElFTkSuQmCC\n",
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TjRgxgpiYGMCz1O/JJ59k27ZtWK1WDh8+TGVlZa1rrrjiCiwWCz169KCwsLDOscvLy1m0\naBF79+7FarWSm5sLwObNm5k+fbrvvNDQUNavX09SUhIOhwOAsLCwen+G5qQwJSIiIiLSBhmGQYDN\nv1XnEBAQ4Hu9du1aysrKePfddzEMg6SkJCoqKmpdY7fb6zX2K6+8Qvfu3Xn66acpKSnhyiuvPOP5\npmk2bPItQN38REREREQEgODgYIqLi097rKioiOjoaAzD4NNPPyUvL++s450pABUVFREbGwvAO++8\n43s/KSmJN99803dtQUEBw4YN48svvyQjIwPwNMpoCxSmREREREQEgIiICAYNGsQNN9zA1q1baxyb\nNGkSW7du5YYbbuCzzz6jS5cuZx3vTEsKb731Vt544w2mTJlSIxzdfPPNhISEMGnSJJKTk9m8eTNR\nUVE88MAD3HXXXSQnJ7NkyZLGf8gmZJhtsV7WxLwlw3Xr1rXyTEREREREpDU1ZTZQZUpERERERKQR\n1IBCRERERESazb59+5g3b55vyZ9pmvTo0YMVK1a08szOncKUiIiIiIg0m4SEBNasWdPa02gWWuYn\nIiIiIiLSCApTIiIiIiIijdDpw9QnXx7hwT/9h6LS2g8cExERERERqUunD1NrNh7g231ZbPsho7Wn\nIiIiIiLSqgoLC3nzzTdb7Lr2rlOHKdM0yc4rBeB4zumf9CwiIiIi0lnk5+ezevXqFruuvevU3fyK\nyyopLXcBkJZV1MqzERERERE5yTRN3OXlTTqmxd/f16L8dJ555hkOHjzIlClTuOqqq7Db7Xz88cdU\nVFSQnJzM9OnTyczM5De/+Q2lpaWYpslTTz3FSy+9VOO6mTNn1ho7JSWF+fPnU1pais1mY9GiRQwY\nMACXy8UTTzzBli1bsFgszJgxg2uvvZYNGzawfPlyTNOkb9++PP300036vWgKnTpMeatSAGnZqkyJ\niIiISNtgmiY75y+kcM/eJh03dOAAhjy+uM5Ade+993L48GHeeustvvjiCz799FPeeust3G4306dP\nZ8yYMXz++edccsklzJkzB7fbTUVFRY3r6hIbG8uqVauw2+3s3buXZcuW8de//pXVq1dTUFDA2rVr\nAc+SwRMnTrB48WJef/11HA4HBQUFTfp9aCoKU1XSshSmRERERKQNOUMFqSVs2rSJjRs3sm3bNkzT\npKSkhMOHDzNkyBDmzZuHzWbj6quvJiEhoV7jlZeXs2jRIvbu3YvVaiU3NxeAzZs3M336dN95oaGh\nrF+/nqSkJBwOBwBhYWFN/wGbQKcOU1nVwlRhiZOiEichQX6tOCMRERERETAMgyGPL27xZX7VmabJ\n3XffTXJycq1jb7zxBhs2bGDu3Ln89re/rVegeuWVV+jevTtPP/00JSUlXHnllWe9f1vXqRtQVK9M\ngZb6iYiIiEjbYRgG1oCAJv1ztiAVHBxMcbHnd+LLLruMt956i7KyMgBSU1MpKioiLS2NmJgYbrrp\nJm644Qb27t1b47q6FBUVERsbC8A777zjez8pKYk333zTF54KCgoYNmwYX375JRkZno7b+fn5jfsm\nNjOFqWrUhEJEREREOrOIiAgGDRrEDTfcwM6dO5kwYQI33XQTkyZNYt68eTidTr766ismT57MlClT\n2LRpEzfeeGON655//vnTjn3rrbfyxhtvMGXKlBrh6OabbyYkJIRJkyaRnJzM5s2biYqK4oEHHuCu\nu+4iOTmZJUuWtNS3oEEMsz3Uz86Rt4S4bt26Gu8vfOELvjuQTaC/ldJyF7de3Z9brhnQGlMUERER\nEZEWUFc2aIxOXZny7pka3DsG0DI/ERERERGpv07bgMI0TXKqwtSQPjFs/SGDtGwt8xMRERERORf7\n9u1j3rx5vv1ZpmnSo0cPVqxY0coza3qdNkwVFDtxVroBGNI3GlB7dBERERGRc5WQkMCaNWtaexot\notMu8/Mu8YsI9adHXCgARaUVFBQ7W3NaIiIiIiLSTnTaMOVd4hcTEUiAn43o8AAALfUTEREREZF6\n6bRhytsW3RERCEDXmBBAS/1ERERERKR+Om2YyqpWmQLo6ggG4Lg6+omIiIiISD10mjB16tO0svM8\nT3KOCfdWpjxhqqmX+VW63E06noiIiIiItA0tHqbS09OZNm0a119/PZMnT+bjjz8GYMGCBUyYMIHk\n5GSmTJlCSkpKk963uLSixtfZ+TWX+XXxhammq0x9tv0YNy54j8+/TW2yMUVEREREpG1o8dboVquV\nhQsXMmDAALKzs5k6dSpjx44F4MEHH/S9bmrOSleNr2st86vaM3U8qwjTNH198c/Ff747TqXLZOfB\nbMYM63bO44mIiIiISNvR4mHK4XDgcDgAiImJISoqivz8fMDzQK/mUlHppsxZSYCfDbfb5ERVZSo6\nwtPFL76qMlVcVklBsZPwEP9zvufB1DwACtVuXURERESkw2nVh/bu2rULl8tFXFwcAMuWLeOZZ55h\n7NixzJkzp0HVoczMTLKysk57rKLCs8Rvf0oeQ/rEkFdUTqXLxGJAdJgnTPnbrcREBJKdV0paVvE5\nh6miEifpOSUAenaViIiIiEgb4nK52L17d53HHQ4HsbGxZx2n1cJUXl4e8+fPZ8mSJQDMnTuXmJgY\nnE4n999/P6+//jq33nprvcdbvXo1K1eurPO4PSSMPYdPMKRPjK8temRYAFbryW1jXWOCyc4r5XhO\nEQPPj2rkJ/P4MS3f97qwRGFKRERERKStKC4uZurUqXUenzVrFrNnzz7rOK0SppxOJ7NmzeKuu+5i\n6NChgGfJH4Cfnx/Jycl89NFHDRrz5ptvZvz48ac9NmPGDHKd+XxxbAs3klBrv5RXV0cI3x3IbpJn\nTR08djJMqTIlIiIiItJ2BAcHs2rVqjqPe7clnU2rhKn58+czatQoJk2a5HsvKysLh8OB2+1m3bp1\n9OvXr0FjxsbG1lmKs9vt4IQ0/82sP9iXwjzPssJTw1SX6Kbr6PdjarXKlMKUiIiIiEibYbVaGTx4\n8DmP0+Jhatu2bXz00Uf079+ff//73xiGwZNPPsnixYvJy8vD7XYzbNgwpk2b1qT3NV02MOBPW/9B\nov1KwOZri+7lfXBvUzxrytt8AsBZrfmFiIiIiIh0DC3+2/3IkSP5/vvva73/yiuvNOt97QRQmdED\nW1wKO53rsEYPISYiscY5vgf3ZhWfU3v0svJKjmV6AplheB4YXFDsVJgSEREREelAWvyhva3Fz2al\n4sgg4swBYJjYe3/HCcuPNc6Jjw7GMKC0vJLcwvJG3+tQWgGmCVFh/kSGeroCaqmfiIiIiEjH0mnC\nlN1uAQzKDw3CmtcTw4CPU9ewJeUb3zl+diu94sMAWPf10Ubfy7vEr3e3CEKD/AA1oRARERER6Wg6\nTZjys3k+6vHsEor3D6Qyuytu3Czf/DJfp+7wnTdlXF8A1mw8SGl5ZaPu5W0+0ad7OGHBVZUptUcX\nEREREelQOk2YMgyDHnGhAJimgXnkAi7reREu080f/vNntqXtBGDs8G50iQ6moNjJh/853Kh7edui\n9+kWQWiwHdAyPxERERGRjqbThCmAQdUexBsVHsSsS27n0h4jcbld/M8XL7H9+C6sVgs3Xulpy/6/\nGw9QXuFq0D0qKl0cSS8AalamtMxPRERERKRj6VRhakCvk2HKERGI1WJl9qjpjOo+gkp3JU9v+hM7\n0r/nigt7EBsZSF5hOR9vOdygexw5XojLbRIaZMcREUhokKcyVaBlfiIiIiIiHUqnClPVK1Mx4Z5n\nTFktVu5J+hUXdxtGhbuSJze9yA/Z+/jpeE916u31B3A2oDrlbT7Rp1sEhmEQFqwGFCIiIiIiHVGn\nClNdYoIJD/GEm5iIAN/7NouVOUl3MLLrECpcFSz7/Hm6nl9GdHgAJwrK+HcDOvsdrNZ8AvCFKe2Z\nEhERERHpWDpVmDIMg8Q+MQB0c4TUOGaz2vjtpXcyvEsiTlcFT3/xIqMv9VSv3lq/H7fbrNc9fqzW\nfAI42Rpdy/xERERERDqUThWmAP7rhkRm3TiUy0d0r3XMbrUz97L/Zmj8IMpdTjbmvY09LI+s3FKy\n8kprnV9SVsHiv37J8je2c/BYHi6Xm0NpqkyJiIiIiHQGttaeQEuLiQjkmlHn1Xncz2rnvsvuYtmm\nF9iZsQd7wlYqf7iQ1Kwi4qKCapz71e50vtydDsC/vz5K767hOCvdBPrbiI8OBiDUG6ZUmRIRERER\n6VA6XWWqPvxsfswbPYPBsQmYlkr8+2/lu9T9tc47llkEQFRYAFaLwY9VVane3cKxWAwAwqqW+ZWW\nu6iobFibdRERERERabsUpurgb/Pj/jEzibR0xbBV8knWm/x44kiNc7xhasq4vrz8wFXcPCGBPt3D\nmXx5b985QQF2X7BSRz8RERERkY5DYeoMAmz+3ND9Z7gKI6jEyWMbn+Vwborv+LHMQgB6xIUQHR7I\nL64byDP3jiNpSFffORaLcfJZUwpTIiIiIiIdhsLUWZwXF4Vz74VYSqModpbw2IblHMk7hsvlJjWr\nGIDusaFnHCNM+6ZERERERDochamz6OYIAbeNkh+G0zuyF4XOYhZtWM72oweodLnxs1lwRASecQxv\ne/TC4oqWmLKIiIiIiLQAhamzCA/xIzjAhllp55cDp9MnsheF5UWs2PYiRlAB3WJDfHui6uKtTBUU\nl7fElEVEREREpAUoTJ2FYRh0i/U84Dcn18UD4+6hT1QvSl2l+A/4mqi4swckPbhXRERERKTjUZiq\nh64OT5hKyyoi2C+IB8f+hlBiMWwV7Pf7qFaXv1OdrEwpTImIiIiIdBQKU/XQrSpMeVuhB/kFEpk1\nFldhBBVmOY9tWM7BMwQqXwMKhSkRERERkQ5DYaoeulWrTAGYpklqehnOvRfSK6wXxRWlPLZhOQdy\nDp/2et8yP4UpEREREZEOQ2GqHrxhytsKvaDYSVFpBYZpY+HY2QyI6UNJRSmPbVzOvuwfa12v1ugi\nIiIiIh2PwlQ9dI0JBjxhqKDYSUqG52G9sZFBRAQF8/vLZzHQ0Y/SijKWbFxRK1CFBqs1uoiIiIhI\nR6MwVQ8B/jZiwgMAz1I/796p7lVd/gLsASy4/G4GxyZQWlk7UJ1c5qfW6CIiIiIiHYXCVD1526Mf\ny6wepkJ9xwNs/tw/ZubJQPXZCt8eKu8yv+KySipd7paduIiIiIiINAuFqXrytUfPLuJYpmeZX4+4\nkBrneAOVd8nf4o3PcvDEEUKC/DCqnuurfVMiIiIiIh2DwlQ9VW+PnnKaypRXgM2fBWNm+ppSLN6w\nnCN5KYQE2oGa7dF37M9i1Xu7Va0SEREREWmHFKbqyRumDqXlk5VbApzcM3Uqzx6qWfSP7u1pm75x\nOYERnmsKS042oXjunzt4+9MDbNqR1syzFxERERGRpqYwVU/eMJWeU4JpQmiQ3bcX6nQC7QEsGDuL\nhOjeFDtLKO76OUZgga8JRXZeKcdzPK3Wd/+Y0/wfQEREREREmpTCVD3FRgVhsxq+r7vHhmIYxhmu\ngCB7IL+/fBb9os7DbXHiP+BrDuemArDrYLbvPIUpEREREZH2R2GqnqwWgy5Vz5uCupf4nSrIL5CF\nY+8h2HRg2Ct4P/11jualsvPgyQCVklFIfpHapouIiIiItCcKUw3gXeoHp28+UZcgv0AuCZyMuygM\np1nKoxue4duUgwBYqopb3x860aRzFRERERGR5qUw1QA1wlRc/SpTXtEhoZTvvYgQ00FheREF8Rux\nBBUwemg3AL4/pKV+IiIiIiLticJUA3StFqZ6NKAyBVUP7nXZ6Vk8gbiArhi2CgIGbeW8PiagfVMi\nIiIiIu2NwlQDeCtTNquF2KigBl0bGuTp/FdSYtDXeQ3uonBMi5P3jr+OEZzPwdR8Sssrm3zOIiIi\nIiLSPBSmGiChZyQj+scyZVwfrJYzd/I7VWhVG/WC4nL2/FhI+Z6L6BrUg9LKUgIGbMUMzGXPYe2b\nEhERERFpLxSmGsBus/Dofydx28RBDb7W+0yqzNxSjmUWYZg2fj92FgMdfcFagf+Ar/l8/66mnrKI\niIiIiDQThakWEla1zK+i0g1Ar/gwYsPCWDDmbuL9e2BYXXxR9L/syTrQJPfLzC3h5Xd3kZlb0iTj\niYiIiIjxmyhHAAAgAElEQVRITQpTLcS7zM8rsU80AAH2AO65+C5c+VGYRiVLNq7g+8z953y/9zcd\nYs3Gg7y/6dA5jyUiIiIiIrUpTLUQm9VCUIDN9/WQPjG+1326RBGQNgpXfjTlLiePf7aS3Zn7zul+\n2XmlAKpMiYiIiIg0E4WpFuTt6AcwuHe077VhGAw+Lw7nvhHE2Xv5AtXOjD11jmWaJp9uSyElo/C0\nx/OKygE4UVDWRLMXEREREZHqFKZakLcJRc/4UMJD/GscG9w7GkwrUSfGMLzLYJyuCp74/Hl2pH9/\n2rG27ErnD699wwtvf3fa47mFnhCVk1+/MPX5t6m8/vEeTNOs78cREREREenUFKZakHffVGK1qpSX\nt1K191Ae9yb9NyO6DqHCVcGTm148baD6anc6AGnZRae9V27BycrU2QKSaZo899YOXvtkL0frqHSJ\niIiIiEhNClMtaHiCA5vVwuXDu9c6dn7XcAL9bRSXVXI8q5S5l97JhV0v8AWq79J/8J3rdpts25MB\nQG5hOS53zbBUUemiqLSi6rWbwpKKM86rsKSC4qrzvXutRERERETkzBSmWlDy2L788/Hra+yX8rJa\nDPr1iADgQEoedqud3156JyOrKlTLNr3gC1Q/puWTW+ipPLndJgXF5TXGyit01vg6J//MASnzxMkm\nFSfquSxQRERERKSzU5hqYTZr3d/yvt09YWp/Sl7VuTZ+e+mdviV/3kDlrUp5nRqA8opqfn22JhQZ\n1cNUocKUiIiIiEh9KEy1If16VoWpY3m+9+xWO3NPCVSbDtRsOuGtUtX19dmaUGScKD55bUH5Gc4U\nEREREREvhak2xFuZOpyWT0Wly/e+L1B1SaTCVUF62AYsoTmc1yUMqB2WTg1EZ6tMpVevTKmVuoiI\niIhIvShMtSFxUUGEBvlR6TI5fLygxjG71c7cy/6bXkF9Maxu/Pt/Q2wPTwjKLTzzMr+zV6YUpkRE\nREREGkphqg0xjJNNKLz7pqqzW+3EFo7BlecAi4vv+QhLaE7tPVNVlSnvc63O1lQiI0dhSkRERESk\noRSm2pjqHf1O5XabfLsnB+f+YfQN64eLSvwStnG06FCN83KLPGGqT7dwAHIK6u7m53abZOaeDFO5\n9XgulYiIiIiIKEy1OX3PUJk6cCyPgmInQf7+PHDl3Zwf4lnydyjg3+zO3Oc7L6+qAUWfqj1YZ6pM\n5RaWUVHpxmJ4vq50mRQUO+s8X0REREREPBSm2hhvZepoegFlzsoax7b+4GmJPjwhliA/f24bPA1X\nXgym4eKJz57j+6pAlVe1h6pPd09lKq+onEqX+7T38+6XiokM8i0LPLUboIiIiIiI1NbiYSo9PZ1p\n06Zx/fXXM3nyZD766CMAUlJS+MlPfsI111zDI4880tLTajOiwwOJCvPHbcKPqfk1jnnD1MgBsQDE\nR4bi3D8cd34M5S4nj1cFKm8Y6hUfhs1qYJp1tzz3hqn4qCCiwgIAPbhXRERERKQ+WjxMWa1WFi5c\nyPvvv8/LL7/M0qVLKSsr46mnnuKee+7h448/Jjs7m40bN7b01NqMfj0igZpL/XLyS31fj6gKUxEh\n/hhYKd83nEExAyh3OVn62XOU2TMBiAwLINIbkOrYN+UNU3HVw5SaUIiIiIiInFWLhymHw8GAAQMA\niImJISoqiry8PLZv387YsWMBSE5OZv369S09tTaj72maUPxz3X4ABp0fRXR4IABWq4XwEH8wrdyS\n8HOGxg/E6XLil7ANe0QewQE2oqsCUl3t0b2d/OKigogM8wcUpkRERERE6sPWmjfftWsXLpcLf39/\nIiIifO/HxcWRkZHRoLEyMzPJyso67bGKigoslvazPexke/RcwFM9+njLYQBuvWZAjXOjQgPIKyyn\nsMjFfZf9mof/vYKD+Qew9f2aPdmXEhV+5mpT9cpUeYXnQcG5ClMiIiIi0oG5XC52795d53GHw0Fs\nbOxZx2m1MJWXl8f8+fNZsmRJk4y3evVqVq5cWefxsLCwJrlPS+hb1YUvNauY4tIK3vhkL5Uukwv6\nxjC0n6PGuVHhAfyYls+JgjL8bH5M7Hojzxx9CWt4Dks/e47E0OuAM1SmThQDEBcVTFFpBQAnChWm\nRERERKTjKi4uZurUqXUenzVrFrNnzz7rOK0SppxOJ7NmzeKuu+5i6NChAOTnn2y2kJGRUa8kWN3N\nN9/M+PHjT3tsxowZ7aoyFR7iT2xUEJknSvhs+zHWbz0KwLSJA2udGxlac2leUbEb574ROEbspog0\ndvA+lpARp61MVbrcZOd59lLFRQeRWxWi1IBCRERERDqy4OBgVq1aVedxh8NR57HqWiVMzZ8/n1Gj\nRjFp0iTfe8OGDWPDhg2MGzeOtWvXMmXKlAaNGRsbW2cAs9vt5zTf1tCvRwSZJ0r4y7u7cZtw0aA4\nBvSKqnXeqcv48grKwLQy1HYdBbFfsDNjD379t3KsKBwYUePa7LxS3CbYbRYiQvxPNqBQa3QRERER\n6cCsViuDBw8+53FavFyzbds2PvroI9atW0dycjJTpkxh//79zJ07l2effZarr76aiIgIxo0b19JT\na1P6VS31c1btY/rFtbWrUkCtdua5RZ4gFB0WzLzRMzgvtDeG1cWxkPXsyTpY41pv84nYyCAsFuNk\n57/8MkzTbOJPJCIiIiLSsbR4ZWrkyJF8//33pz32zjvvtPBs2q5+PU825Bg9tCu9u4Wf9jxvmPIu\n0curqipFhvjjb/Pj18N/xX1r/1i1h2oFC8fOpn9MHwDSvc0nooOqxvIsGax0uSkqrSA0yK8ZPpmI\niIiISMfQfjYSdTJ9ukXgZ7disRi1OvhVd2plyhumIqrej48Mw7l/BK78KMoqy1mycQV7sz0VqpPN\nJzxhym6zEhpkrzGeiIiIiIicnsJUGxUcaOexu5J4fOZl9IgLrfO8k5Wpctxu01eh8jamCAqwE2j3\nx7l/JP0i+1JWWc7SjSvZl/0jmSc8zSfiq8JU9fH0rCkRERERkTNTmGrDBp0fzaDzo894TkSoP4YB\nLrdJQbGTXG9lqipMQVVAcluZet7PGBybQGllGUs2ruBo4RHA0xbdK1JhSkRERESkXhSm2jmb1UJ4\nsCc4pWUXUe70NKyIDA3wnRNd1fGvsNDN/WNm+gJVevinGMF5vmV+oMqUiIiIiEh9KUx1AJFVjSN+\nTPU8q8vfz0qg/8neIt4wlZNfRoDNn/vHzGRgTD+wVuLffyvFlkzfudWXDYqIiIiISN0UpjoAbwDy\nhqnIakv8qh/3VpsCbP5MG3g7roJIDFslf/jyBfbnHPJcWxXM1IBCREREROTMFKY6AG9YOugLUwE1\njkeHBwKeypRXXn4lzn0jsZc7KK0oY/HGZ9mfc4joMM+5WuYnIiIiInJmClMdgDdMHU0vAGo2nwCI\nCq+9DyojpxjcNvq7rmago58vUJVYsmudKyIiIiIitSlMdQDeDnyVLhOoHaZO7pkq9b3nfWBv16gI\nFoyZyUBHX0orynh13yqMoAJyC8owTbMlpi8iIiIi0i4pTHUA3sqUV2RI3XumvAEpoypMxUUFEWAP\nYP6Yu0mI7k1JZSn+/b+mwp5PcWlFC8xeRERERKR9UpjqALyVJ6+IU8JVVLXKVUGxkw83H+bL3ekA\n9IgLASDQHsDvL59Fn8heGPYK/Pt/zZ70lOafvIiIiIhIO6Uw1QGc2nDi1G5+NquFiKpq1Yo3v+X5\nt3bgdptcMbI7F/R1+M4L8gtk4djZWJ0RGH5Onv/2T6QXZiIiIiIiIrUpTHUA3nbmXqfumYKTTSi8\nFalfXDeAe28ZgcVi1DgvxD+Y80quwl0SQlFFIY9ueIbM4pxmmrmIiIiISPulMNUB2KwWwkP8fF+f\nWqmCk0sB/WwW5k27kJsn9McwjFrnAThCwynfcxGh1khySnJZ9OkfyS450TyTFxERERFppxSmOojq\nTShOV5maNLo3Fw6M4/G7RzNmWLezj1XpzzDrJOJCHGQW5/DYp8vJLc1v8nmLiIiIiLRXDQpTLpeL\nhQsXNtdc5Bx426MHBdjwt1trHR/eP5aH/2sUCT0jzzqWN5iVFNp4eNwcHMHRHC/KZNGGZ8gvK2ja\niYuIiIiItFMNClNWq5X9+/c311zkHERXBaBTm080RmS1VuoxwVE8PG4O0YGRpBak89iGZ0k9cYL5\nz21i7ec/nvO9RERERETaqwYv8xsxYgRz585l48aNfP31174/0rq8ASjiNPulGspbmcotKAcgNiSG\nh66YQ2RAOEfzU1m0YTm7j6Tz6kc/UFHpOuf7iYiIiIi0R7aGXrB7924A/vKXv/jeMwyDv/3tb003\nK2mwrjHBAMRHB53zWN4wlVP1kF/DMOgSGsuDV/yGR9f/kdzyTPz7b6V4z0Xs2J/NhQPjzvmeIiIi\nIiLtTYPD1N///vfmmIeco8uHd8MwDIYnOM5+8llEhQdgsRg4K1yk55TQpSqodQ/rwoLLZzP/w6ew\nhOTj138rn+3oUStMbd6ZxvLV3/LbW0Zw8eD4c56PiIiIiEhb1Khufp999hnLli1j2bJlfP755009\nJ2kEu83K+At7+Jb7nQt/u5XE3tEAbNl1vMax0rwgyvZciFlpwxqax5bidykuL/Mdd7tNXnn/e4pL\nK1i39eg5z0VEREREpK1qcJh68cUXWb58OfHx8cTHx7N8+XJeeuml5pibtKKkIV0A2LyzZpjavi8T\nsyScAa7rwGWDkBweXbcSp6sCgG/2ZpKaVQzA3iO5LTtpEREREZEW1OAw9f777/Pqq69y++23c/vt\nt/P3v/+dtWvXNsfcpBWNSvSEqT1HTpBbcLLytGNfFgBj+g9muP16TJeVw4UH+Z8vXqLCVcHaTSc7\n/OXkl5GdV1pr7FXv7eaXiz4+7TERERERkfaiUcv8AgJOLiXz9z/3VtzS9sREBJLQMwLThC270wEo\nLq1gX0oeAMMSHEwcOhLnvhHgtrL9+C6WfvonvtmbjmGcbGKx92jN6pTL5eaD/xwmJ7+MbXsyW/ZD\niYiIiIg0oQaHqYsvvpiZM2eybt061q1bx+zZs7nkkkuaY27SypKGdAVg83dpAHx3IBu326SbI5jY\nyCAS+0QT7OpC+b7hWA0bu3N2Y+/9HRcNivU1njh1qd/+lDxKyysBOJquBwCLiIiISPvV4DC1YMEC\nLr30UtasWcOaNWu49NJLmT9/fnPMTVqZd9/UdweyKSqt4Nt9nkrS0H6ejoFWq4WkIV1wF8TQ2zke\n021gi07H7P4t/XqEAbDvlMrUjv1ZvtdHFKZEREREpB1rUGt0l8vFc889x29+8xt+8YtfNNecpI3o\n5gihZ3woR9ML+fr7dL6t2i81LCHWd85lF3Tlky+P8N12C5aIYfj3+5Zdud8R6O8HRLM/JY9Klxub\n1ZPbvzuQ7bv2aHphi34eEREREZGm1KDKlNVqVSv0TsZbnVr7+Y+kZRdjsRhc0DfGd/yCfjGEBNoB\ncOfFMd4xGcMw+Dp9K4F99uCsqOTIcU8FqrzCxQ+HT/iuzS0sp6DY2YKfRkRERESk6TR4md9ll13G\n008/zYEDB0hLS/P9kY4pqaqr3/6qxhMJPSIIrgpPADarhUsSPfujQoP8mH75BGZd/EsMDIg+gr3n\nHvYc8QSoHw7lUFHpJjo8gNjIQED7pkRERESk/WrQMj/A1wb9gw8+8L1nGAbr1q1rullJm9G7Wzix\nUUFknigBai7x85o0ujff7svi5gkJ+NutjDnvYirclbz49d+xxR/h/6V8zETz1+zY71niN7Sfg4Ji\nJ5m5pRxJLySxT0ytMUVERERE2roGh6l33nmHiIiI5piLtEGGYZCU2IV/fXYQ8LREP1Wf7hGseuia\nGu+N730pB1NP8P/S3ifN+I43d73Hdwc8TSmG9ovhaHohW3/IUGVKRERERNqtBi3zM02Tn//85801\nF2mjLr3As9QvKMBG/16R9b7upuFX4TwyAIC3v/+AQ5XbALigr4Oe8Z5gdURNKERERESknWpQZcow\nDLp3705mZiaxsbWXe0nHNOj8aGbfNIy4qCBfV776CA/xJ7ZyMFlHTew992Lrvp/QIH9iIgLpFR8K\neDr6maaJYRjNMveSsgr2HsllWIKj2e4hIiIiIp1Tg5f5mabJpEmTGDVqFEFBQb73H3/88SadmLQt\nV1/Sq1HX9e8VyfFvzsdiNbF220dp1C7e2/tvrup9BRYDCkuc5BWWExkW0MQz9nj53d188uUR5k27\nkDHDujXLPURERESkc2pwmLruuuu47rrrmmMu0gH17xXJhm+O4Uztjc10Y+9+gL99+zYWw0J8dDBp\n2cUcTS9stjDlfdDw/pQ8hSkRERERaVINDlNTpkxpjnlIB5XQ8+QeK9fxPiSP6837Bz5h1fZ/0q37\nKMiO4Eh6AUNP09jiXOUWlpGZWwpAek5xk48vIiIiIp1bvTfAzJw50/d6yZIlNY7dcsstTTcj6VDO\n7xqO3Wapeh3BbSOSmTzgagBS/bZgdaRwNKN5mlDsO5Lre308W2FKRERERJpWvcNU9Qfzbt26tcax\n0tLSpptRM3E7na09hU7JbrPQp1s44Hm+lGEY3HpBMv8n4UoA/M7fze7cb5vl3nuPVgtTOcWYptks\n9xERERGRzqlBrdG9Tv2ltD10SavIyyPv2x2tPY1OaeoV/RjQK5KJl54HeP6+TBv2E0Z3vQyAnLAv\n2XhoS5Pfd1+1MFXudJFbWN7k9xARERGRzqveYap6YGoP4elUpmnyw5InyPtuZ2tPpdNJGtKFp+65\nnPjoYN97hmHw61G34MrsCQY8/9Xf2HTka6B2WG8Mt9tkf0oeAFaL5++rlvqJiIiISFOqdwOKH374\ngYEDBwKeX3arv24P4crq74/b6eSHxY8z6OGFhA8e3NpT6vT87FZiSy8mI9ONLfYYK79chdPp5n//\nVYLFYvDU7DH42a2NGvtYZiElZZX4+1lJ6BHJzoPZHM8uZnDv6Cb+FCIiIiLSWdU7TO3Zs6c559Hs\n7OHhRIwYTt432/l+0VIGP/IgYQMHtPa0Or3z4sM4tmMw/XqGc6hsNy9ue4Xy0mG48+J4b9Mhpl7R\nt1Hjepf49e0eQbfYEHYezFZHPxERERFpUo3aM9UuGQYD5t9HxLChuMvK2P3IYxTs2dvas+r0esaH\nAQZxJaPoYkkAw8Sv77dYIjL557p9FJU0rnHIvqOeJX4JPSPpEu15uPTxs4Qp0zRZuuorlq76Ss0q\nREREROSsOk+YwrPUb8Dv7yf8giG4y8r4/tHFFO7b39rT6tR6xYcCsGVnOj9uOZ/KnC4YFhP/ft9S\n4pfGW+sb99/H28mvf89IusR49mqdbc9UxokSNu88zuadx8nOK2vUfUVERESk8+hUYQo8gWrgwvmE\nJQ7GVVLC7kcWUXTgYGtPq9PqWRWmSstdgMHU3j9lVI8RYLjx67edtd9+RVZuw1rvlzkrOXy8APBU\npryNL862zO9QWoHvdUpm8zz7SkREREQ6jk4XpgCsAQEMemABYYMG4iouYffDiyj68cfWnlan1CU6\n2PdQ31GJ8dx69UDuGfUrLuo2FMPixtJ7K89/vK5BYx48lo/bbRIV5k9MRIAvTBWWVJxx2eDhtHzf\n62PN9CBhEREREek4OmWYArAGBjLwwYWE9u9PZVERux96lOLDh1t7Wp2O1WrhF9cO5PLh3bj3lhFY\nLAY2i5V7k/6L/hH9Maxudrk/4tM99X9GmLf5RELPSAzDINDfRmSoP3DmfVOHjp+sTB3LLGrkJxIR\nERGRzqLThikAW1Aggx5eSEi/flQWFrHrwUcpPnK0tafV6Uy9oi/3/eJCggLsvvdsVhsPTribMFc3\nDKuLP337F/Zl1696uLdamPLyVqfOtG/q8HEt8xMRERGR+uvUYQrAFhzM4EceJLhPHyoLCtj94COU\npBxr7WkJ4Ge1s3D83bjyo3EblSzesIIDOYfPet2+04QpXxOKOipTpeWVNfZUqTIlIiIiImfT6cMU\ngC0kmMGPPkhw7/OpyM9n14MPU3IstbWnJcD58ZEMNK/GVRBJmauMJRuf5VBuSo1zDqXlk1dYDkBu\nQRlZuaUYBvTrEeE7xxum0rNLTnufI+kFmCaEBHqqY3mF5Y1uyy4iIiIinYPCVBV7aCiDH32YoPN6\nUZGbx64HHqY0La21pyXANZf0wblvJJaSKIorSnlsw3KO5Hmqh598eYR7/mcD0x75iBnL1vHMG9sB\n6BEXWmPZYJfoM1emvJ38EnpFEhMeAEBKhqpTIiIiIlI3halq7GGhJC56mKCePajIzfUEquPprT2t\nTm9UYjyhAUEUfz+CLkHdKHIWs+jTZ9iXdZhXP/rBd96xzCK+2ZsJeJ4vVd3ZnjXl7eR3fpcwuseG\nVo2nfVMiIiIiUjeFqVPYw8MZ/NgjBHbvjjPnBLseeJiyjIzWnlanZrdZuWJkd3DbiM4ZS5+oXhQ6\ni1n06XJyK7OIjQrilYevYeH0i0ke24dRifFMGde3xhjeBhQnCsooc1bWuoe3MnVe13C6x4UAkKJ9\nUyIiIiJyBi0epmbNmsXFF1/Mb37zG997CxYsYMKECSQnJzNlyhRSUlLOMELz84uIIHHxIwR264oz\nO5tdDzxCWWZmq86ps7vqkl4AfLM7l9kj76J3ZC+cZhn+A77iqrFhRIUFMCqxC3fckMjC6ZfQIy60\nxvWhQXaCq/ZDZeTU3Ddlmqavk9/5XcJ816oyJSIiIiJn0uJh6vbbb+fJJ5+s9f6DDz7ImjVr+N//\n/V969OjR0tOqxS8yksGPPUpA1y6UZ2ay+8FHKM/Oae1pdVrndQkjoWcELrfJlh059K+8FldhBIat\nko8yV5+1y59hGHSJDgIg7ZSlfhknSigtr8RmtdAtNoTusZ7K1DHtmRIRERGRM2jxMHXRRRcRFBRU\n633TNFt6KmflHx1F4mOPEhAfR1l6BrseeIjynBOtPa1O66qLPdWpD/5ziA8+P4Zz74V0DexBSUUp\nj21cftbnUHWJ8YSk9FOaUHirUj3jQrFZLfSo2jOVcaIYZ4WrqT+GiIiIiHQQbWbP1LJly0hOTuaP\nf/xjo4JVZmYmu3fvPu2fiooKXK7G/VLsHxNN4uJH8Y+Npex4OrseeBjnidxGjSXn5vLh3fD3s5Jx\nooQyp4t+3WJ4/Np7GejoR2lFGUs2rmBv9sE6r4+vqkyd2tHv5H6pMAAiQv0JDrDhNmtXsURERESk\n/XO5XHVmh927d5NZzy0+tmaeZ73MnTuXmJgYnE4n999/P6+//jq33nprg8ZYvXo1K1eurPN4WFhY\no+fn73CQuPhRdi18kLK0NHY9+DCJjz2KX1Tk2S+WJhMUYGf00K6s+9qzp+62iQMJ9AtkweV3s+zz\n59mduY8lG1ew4PK7GejoV+v6rnV09Dt8vKqTX1WYMgyD7nGh7D2SS0pGIed18bxfUenm7U/3c0Hf\nGAadH91sn1NEREREmldxcTFTp06t8/isWbOYPXv2WcdpE2EqJiYGAD8/P5KTk/noo48aPMbNN9/M\n+PHjT3tsxowZWCznVoQLiIslcfGj7Pz9Q5QeS2Xnwoc8FavoqHMaVxrm/4zuzcZvUhnRP5ZhCbEA\nBNj8mT/mbp7c9AI7M/aw9LPnWDBmJoNiE2pc6+3oV2uZn7cy1eVk4O4R6wlTx6p19Pto82Fe/WgP\nm+JDWXnf6f+uiYiIiEjbFxwczKpVq+o87nA46jVOq4Qp0zRrLOXLysrC4XDgdrtZt24d/frVriqc\nTWxsLLGxsac9ZrfbT/t+QwXEx5O4ZJGnXXpaGrse8AYqVSlaSt/uEax66OoaD+QF8Lf5cf/oGTz1\nxYvsSP+BpZ+tZP6YmSTGDfCd433WVGZuKZUuNzarhbLySt+yv/O7hvvOPdmEwtPRz+02ef8Lz56s\noxmFlJRV1JqDiIiIiLQPVquVwYMHn/M4Lb5navr06dx77718/vnnjBs3jh07dvC73/2OyZMnM3ny\nZEzTZNq0aS09rXoL7BLPkKWL8I91UJZ2nF0LH1KXvxYWHuKP3Vb7r66fzY/7Rs9gWPwgnK4KHv/8\neb5LP/lQ36iwAPzsVtxuk8xcT3v0I+kFmCZEhvoTHuLvO9fbHj2lqj36jv1ZpGZ5QpdpwsFj+c32\n+URERESkfWjxytT//b//t9Z7r7zySktP45wExMWRuLiqQnU8nV3eJX+OmNaeWqfnZ7Vz3+hf8z9f\nvMQ3x3exbNML3HfZrxnWZRCGYdA1JpjDxwv40zs7mXPLcF/ziepVKThZmUrNLKqqSh2qcXzf0VyG\n9NV/bxEREZHOrM1082tvAuJiSVzyKP5xsZSlp3vapmdltfa0BLBb7cy97L+5sOsFVLgqeGrTC2w/\nvguAn187AD+bhW/2ZnLP/2zg022eZhbV90sBxEUFYbNacFa62fVjNl99nw7AVRf3BGB/Sl4LfiIR\nERERaYsUps5BQGwsQ5Ys8j2HaufvH6Iso35tFKV52a12fnvpnVzcbRgV7kqe2vQntqXtZFRiF/4w\nZyy94kPJKyzn+0Oe54Z526J7Wa0Wujk8e6z+vGYXpgnDExyMG9kdgP0pao8vIiIi0tkpTJ0jf4eD\nxCWPERAfT3lmJrseeIiyjIzWnpYANquNOZf+F6O6j6DSXcnTX/yJr1N30KtLGP8zZywTLz3Pd27f\n7hG1ru9e9fBe70N9/8/o3vTtHoFheJpY5BWWt8jnEBEREZG2SWGqCfjHRJO4dBEBXbtQnpnl2Uul\nClWbYLNYuSfpV1zaYyQut4s/fPESXx37Fn+7lRk/Gcriuy5l7s9H+hpOVNc9LsT3OjYqiJED4wgK\nsPv2U6k6JSIiItK5KUw1Ef/oaBIX1wxU2kPVNtgsVmaPms7onhfhMt384T9/5j9HtwIwNMHBuBHd\nT3udtzIFcP2l52G1GAD06+F5WPO+ow3bN7V55/H/z955R8dRnW38mdmu1ar3bsuyZFu2hTsYF5pN\nh2T1K/AAACAASURBVNADHx0SQigJBAK4mxJCDRBCgNBCC6GGZjCuuOIuWZIlN/UurbQq23fm+2Pm\nzs6utklWte/vHM7B0u5qpJ2duc99n/d58bf/7IPV5uzPr0GhUCgUCoVCGWFQMTWAaGJjkP/EKmiT\nieVvBWwtrcN9WBQAClaBe2bfgvmZs8HxHF7a+TY2V+wM+JwxYiiFWsnivNmZ0tfHpwuWwL5Wpj76\nsQzrd9dg477aPh49hUKhUCgUCmUkQsXUACNVqMRQiuKlK2Bro3OoRgIsy+Lu2Tfh7LFzwfM8/rHr\n31h3bIvfx2cmR+D3V03FkttmwxCmlr6ekyFUpo7UdHgMnw5Ge5cVALCvjPbUUSgUCoVCoZwMUDE1\nCGjiYoW5U1Js+grY2ozDfVgUACzD4jczrsf54xaCB4839nyENYc3+n38+adnYVpugsfXxqREQKlg\n0NljR5PRHNLPdbo4dPbYAQgDgB1Orv+/BIVCoVAoFAplREDF1CChiY8XBFVCAqz1DShZtgJ2Iw0s\nGAmwDItbp12DS3LPBQC8s/+/+N+htSE/X6VUIEsc8hvqvClTtw2kiGWxuVBWScU1hUKhUCgUymiH\niqlBRJuQIAiq+DhY6upRvGwF7B102OtIgGEY/N/UK3DlxAsBAB8WfYlPi78N2baXI/ZNHa4OTSB7\nx6jvpVY/CoVCoVAolFEPFVODjDZREFTquDhYautQvJQKqpECwzC4dvIluG7ypQCAT0u+w0dFX4Uk\nqManu/umQqG9l5ii0fkUCoVCoVAoox0qpoYAbVKSIKhiY2GpqUXxkhWwt1PL30jhiokX4KaCqwAA\n/ytbi3f3fxpUUOVkCJWpY7UdcHHBxRepTI1LiwTDCIOA20yWEzxyCoVCoVAoFMpwQsXUEKFLTkL+\nk6Kgqq0VKlS0h2rEcHHuObhj+nUAgDVHNuLNvR+D4/2HRKQlGKDTKGC1u1Db1BX09UmSX0ZShFTV\notUpCoVCoVAolNENFVNDiC45GflPrpYsfweXLKex6SOIReMW4K6ZN4IBg3XHtuC1Xe+D43wLKgXL\nIDst9HlTpDIVbdBgWp6QDriPiikKhUKhUCiUUQ0VU0OMLjkJk59aDU1CPKz19Sheshy2ViqoRgpn\njz0D9865BSzDYnPlTry88204OZfPx+aIFaayqtDFVJRBi+mimDpwuBkuF41Ip1AoFAqFQhmtUDE1\nDGgTE5H/xGphDlVDI4qXLIOtpWW4D4sicmbmLPzh9NuhYBXYXrMXL2x/E3aXo9fjpubEAQC2F9XD\n7vAtuAjtssrUuPRoGMLU6LE6QxJiFAqFQqFQKJSRCRVTw4Q2MQGTn1wNbVIirI1NOLhkOazN1PY1\nUpiTPg0Pzf0tVKwSe+oK8dTmV2C2ewZGFIxPQHy0Dl1mB7YV1Qd8PdIzFWXQQMEyOC03HgCNSKdQ\nKBQKhUIZzVAxNYxo4uOR/+Tj0CYnwdbUjOIly2FtoovrkcK0lMl4bMG90Cm1KG05gpUbX0CHtVP6\nvoJlsHh2JgDghx2VAV9L3jMFANPzEgEA+8qpgKZQKBQKhUIZrVAxNcxo4mKR/+RqaFNSYGtuwcHH\nlsPS0Djch0URmZQwHivO+iMiNOGo7KjF8vXPobm7Vfr+ubMywLIMSiuMqGrs9PkaDqcL3RbBJhgd\noQUAqTJ1rNaEY7V07hiFQqFQKBTKaISKqRGAJjYWk59cDV1qCuytrSheshyWhobhPiyKyNiYDDx+\nzkOI18eisbsFy9Y/h+qOOgBAbKQOsyclAQDW7qzy+XzSL6VUMAjXqQAA0Qat9LzVb/2C1g46c4pC\noVAoFApltEHF1AhBHRON/CdXQ5eWBntbG4ofWw5LXeA+HMrQkWxIwOPn/AnpkSlot5qwYsPzKGs5\nCgBYPEew+q3fUwObjyAKKckvXAOGYaSv/+HX05CeaICx04rVb+2E2do75IJCoVAoFAqFMnKhYmoE\noY6ORv6TqxCWkQ670YiDS5bBXFs73IdFEYnRRWHV2Q8gNy4bPQ4LHt/8MvbVH8Rp4xOQEBOGHosD\n2wrrej1PElOixY8QrlNhxR1zEGXQoKK+E8+8v4dGpVMoFAqFQqGMIqiYGmGoo6Iw6fFVCMvMgKO9\nA8VLVsBcXTPch0URCVfrsXTBfZiWnA+Hy4Fntv4TW6p+kQVR9Lb6SUl+4Zpe30uMCcOy22ZDrVJg\nb1kz/vV18eD+AhQKhUKhUCiUAYOKqRGIOioS+U+sgn5MFhwdHSheSgXVSEKjVONPZ96F+ZmzwfEc\nXt31Hlyxh6FggUOVRlQ1eAZReCf5eTM+Ixp/umE6AOC7bRXoNtsH9xegUCgUCoVCoQwIVEyNUFQR\nEZi0eiX0Y8bAYTKheOly9FRVD/dhUUSUrAJ3z74Jl+SeCwD48vB3SCmoAsBj835Pa6Y0sNfL5ifn\n9MnJiI3UgueBupbuQTtuCoVCoVAoFMrAQcXUCEYVYcCk1SugHzsGDlMnSpatoIJqBMEyLG4suBI3\nF1wFAGhVHoJ63AEcq2/3eJw8gCIQqfHhAIC6lp5BOFoKhUKhUCgUykBDxdQIRxJU2WPhMHWieOkK\n9FT6juCmDA8X5Z6DP5x+OxSMAoqYJpQr1qDb7hZEpGcqOiKwmEoRxVQ9rUxRKBQKhUKhjAqomBoF\nqAwG5K9eAX12NpydoqCqqBzuw6LIOCNjBh6Y8zvwTiW4sDYsW/c82sxChUqy+Rn82/wAIDVeD8C3\nza/LbMcjr27Fmu0VA3zkFAqFQqFQKJT+QsXUKEEZHo781SsQnjMOzq4uFC9bie7jdGE9kpiZMQm6\n2nng7RrUdTVg6bpnUd1R57b5+QmgILgrU71tfrtKGlFyvA3fbD0+8AdOoVAoFAqFQukXVEyNIpTh\nekxauRzhOTlwdnWhZPlKdB+ni+uRxJjodNhK5yBSGYM2SzuWb3geNnUzAP9pfgSpZ6q1GzzPe3yv\nol5ICGztsPT6HoVCoVAoFApleKBiapShDNdj0qplCB+fA2dXN0qWrUL3MSqoRgoZSQbwdh0K2MuR\nGzsWZocF6tw9UMc1Q6dRBnxuYkwYWJaBze6CsdPq8b3KBhMAwGJzocfqHLTjp1AoFAqFQqGEDhVT\noxClXo9JK5fBkDsezu5uwfJ39NhwHxYFQGZSBACgvtGOZQvvx4ToCWBYDoox+/Dj0c0Bn6tUsEiK\nCQPg2TfF87xUmQKAtg7LIBw5hUKhUCgUCqWvUDE1SlHq9Zi4chkMublw9fSgePkqdB05OtyHdcqT\nmWwAAFQ3dkGtVGNR0hVwNqUDDPD2vk/wUdFXAW16KT7i0du7bOjscQ/ybaFiikKhUCgUCmVEQMXU\nKEYZFiYIqgl5cPX0oGQFFVTDTXqCAQwDdHTb0NFlg6nbAUfVRCTZTwMAfHXoR7yy8x3YXQ6fz0/1\nEY9eUW/yeEwrFVMUCoVCoVAoIwIqpkY5yjAdJi5fioiJE+DqMaNk+Sp0lR8e7sM6ZdFqlEiKESLO\nq5s6xSQ/Brm62fjdzBuhYFhsrd6NJza9hE5b7wh0X/HocosfQMUUhUKhUCgUykiBiqmTAEFQLREE\nldmMkpWPU0E1jGQkCVa/qoYu2YwpDc4aewYeW3AvwlQ6lLUew9J1z6Chq9njub4G91aKYkqvUwEY\nHJtfa4cFbSYq0igUCoVCoVD6AhVTJwkKnSioJk0UBNWK1egsKx/uwzolkcRUYyc6uoRUPhKLPjkx\nD0+c8xDi9bFo7G7BknXPoKjxkPRcYvNrbDPD6eIAABVikt/03AQACEn0dHTZsGlvDSy24Ml/DqcL\n97+wCfc9v0n6mRQKhUKhUCiU4FAxdRIhCar8SXBZLChd+Tg6D5UN92GdcpBEv+pGd2VKPrA3LTIZ\nT577MHJistBt78GTP7+Cb8rWged5xERooVEr4OJ4NBvNcDhdqG0WqlQzJiYCCM3m99bXxXj+o324\n+6/rsa2oPmDoRVVjFzp77OjssUsDhikUCoVCoVAowaFi6iRDodVi4rLHEDk5Hy6LBSVUUA05mcmC\nmKpq7ER7J6lMaT0eE6WNwIqzH8DCMaeD53m8X/g5Xtn5DhycAylx7r6p6sYucByPcJ0KuZnRAICW\nDmvQwb0Hj7UCAFpNVjz93m6s/NdO1Lf27tEC3DZCQAjOGGqO1LTjSE37kP9cCoVCoVAolBOFiqmT\nEIVWiwnLHkPklMngrFaUrHwcppLS4T6sU4bU+HAoWAZmqxPN7UIVSV6ZIqgVKvxu5o24bdq1UjDF\nsvXPIjZe+H5dS48UPjEmJRJxkToAgN3hQpfZdxogALS0W9BmsoJlGVx1dg6UChb7yppx//Ob0NjW\n0+vxxEYIAKYhFlM2hwtLXtuOh1/ZSoM1KBTKgOJycXBR6zKFQhlkqJg6SVFoNJiw9FFETp0CzmpF\n6eonYSopGe7DOiVQKVkpSIIQFd5bTAEAwzA4P2chli28HxGacFR21OKI9muwhjbUt3SjskEQU1kp\nEVCrFIgMVwMIbPUrqzICAMakRODmiybi7w+dhfREA6x2F3aVNvZ6vEdlaohtfkaTFRabE04Xhx92\nVA7pz6ZQKCcvLo7Hvc9vwr3Pb4KLC1zJp1AolBOBiqmTGIVGgwlLHkFUwVRBUK16EqaDxcN9WKcE\nmWIIBQDoNEpoNcqAj5+YMB5Pn/coxkZnwM5boc7bg+LOPThe3wEAGCNaB+OihOpUIDFVXiVY5vIy\nYwAIlbJ5BakAgMNVHR6P5XneI3p9qCtTRtEGCQA/7qyCw+ka0p9PoVBOTlo7LKhp6kJNUxfaaNWb\nQqEMIlRMneQoNBrkPfZnRJ1WAM5mQ+nqJ9FxoHC4D+ukh/RNAb4tfr6I08dg9dkPYmpcARiGR6t+\nD44xmwHWhawUUUyJVr/WAIl+pDJFeqwAIDdD+P/D1Z69ScZOK7rMdunfHd129If+iiCjyS2mOrpt\n2FZY36/XoVAoFDlNxh7Z/5uH8UgoFMrJDhVTpwAKjQYTHvszomdMB2e3o/SJv8C4Z+9wH9ZJjbwy\nFR2imAIAtVKNe+fcAntVHnieAR9dC83EHVDpBfEUH6Qy5XC6cKxW6IEilSkAyMmIAgA0tPWgs8ct\nmLwHAvenMrV5Xy2ufvQ7bN5X2+fnGsXoeJYR/v3ttoo+vwZlZPH1z8dw//Ob8OWmoyFF81OGHp7n\ng4bYjHaaZQLKV68o5dSC53kUHm6h8xQpgwIVU6cIrFqNvEceQszsWeAdDpT95Rm0/bJruA/rpIXE\nowO9k/yCEaHXIKxrPOxlM8Hb1WDDurFi47PYWbNPsvn5G9x7rM4Ep4tDZLgaSbFh0tcNYWopJVBe\nnSI9WQpRzfSnZ2pXaSNcHI995c0+v1/Z0Invt1eA89G3QCpTZxakQqlgUF7VjqM1Hb0eRxkd8DyP\nzzYcwfF6E97+pgS3Pb4WH/1Y5iHgKcOLxebEb/6yDive2HFShzM0ysQUrUxRyqvbsfT17bjn2Y0o\nOtoy3IczoGwrqse2IurqGE6omDqFYFUq5D78IGLnng7e6UT5X59D67Ydw31YJyWJsXqolcLHK1Sb\nn5zUeD24rhhYS85AOJcEi9OKF7a/iUOOrQDD+a1MlVUKQik3IwYMw3h8b3xmb6tfRb1YxcoSqlj9\niUavbuwCADS0+t79/funB/Da50UoPNL7BkZ6prJTozB3itDX9R2tTg06HDc4lYnWDivau2xgWQap\n8Xp0Wxz4eG057np6PbrNVFCNBI7WdqCxzYz9h1vwxaajw304g0YTFVMUGfUtwv2p2+LA8td34Med\nVcN8RAODxebEs+/vwXMf7IGVOgGGDSqmTjFYpRK5D/4R8Qvmg3e5UP7cC2jZvGW4D+ukQ8EySBet\nfn2x+RGkNECHFovjr8WleYsAAEUdu6DO24XmrjafzyP9UnlZ0b2+R/qmyn1UpgrGC3nsfbX5uVyc\nNFS4wYeVhud5VIk/o96H2CJiKiZCg4vPHAMA2Ly/llYyBpGqhk7c/cx6/OHFzQNemSBCPSspAq8+\nfA4euWkmDGFqdJntOFZnCvJsylDQ1OYWFh/9WI7qxs4Ajx69UJsfRQ7pDVYpWbg4Hn//9ADe+rp4\n1Cc9tnda4eJ4OF082mSBTpShhYqpUxBGoUDO/fcg4eyzAI7D4b+9jOYNG4f7sE46po4TBEp2WlSf\nn5sqi1YfmxKF/5v6Kzx05l3QKbVQGDrQmboBhQ2Hej2vvFIUU7J+KcJ4UUwdqW4Hz/OwO1ySECrI\ncYupvlQsGo1mOMUFeUeXDWar5/wrY6cVVrsQTtHS3nt3WBJTkVrkZkYjOy0SDieHLzcdRZvJMupv\ndCONvWVNeOiVLahr6cHxOtOACxwipsZnRkPBMpg7NUUKQvElpilDT6MsmMHp4vC3/+w/Ke1+tDJF\nkdMlbtCdOysD1y/OAwB8tfkY/rvu8HAe1gnTLrPmywOdKEMLFVOnKIxCgXH33o3ExecBHIcjL7+K\nxrXrhvuwTipuumgi3l66CDMmJPb5uXIxNSYlEgAwM3Uqnjr3UXA9EWBUdjz18yv4b/E3cHGCWGnt\nsKDVZAXLAOPSewu4MSkRUCpYdJkdaGjrQXVTFziOhyFMhbGpws9wunj0WPwPBPbGe1e7sc1z0UKs\nFYAwTNgbd2VKC4ZhcPFcoTr12YYjuGX1Wlzx8Ne4ZfWP+H47tf6dKN9uPY7V/9oJi80pBX4UH2sd\n0J9xuEYUU7LzLyVe6NWrb+ke0J9F6R+kMnXpvLHQa5U4UtOBLzcfG+ajGlgcTpfH2IX2LhusdmqB\nOpXpFCtTkXoNfr0oF7dePAkAsN9Pr+9oob3LfZ7TytTwQcXUKQzDssj+3W+RfNEFAM/j2KuvoeH7\nH4b7sE4aFCyD+Ghdv56bIVoEowwaxEa6AyxSIxMQVjsfzuY08ODxWcn3WL7+OTR2NUvzpbKSI6Hz\nMddKpVQgWxRNh6vapWG9Y1IioVYpEKYVntOXvqnqpi6Pf3v3TdXJFtDeoRlWmxNmq7DAiYkQfsf5\np6VhTn4SYiK0YBmA44E2kxWfbzx5ezsGm4p6E577YC9e//IgOB44b1YGbrxwIgCg6OjAiSkXx0vh\nIeNlsfwpccLGgL+eOsrQQixvE8fE4o7LJgMAPvyhbMjsfhzHD3olrKXdAp6Hx3WtmVanAAjW6++3\nV0j9sqcKxDpu0KsAAKflCm6MmqauUZ1s2d5JK1MjgcCTRCknPQzDYMydt4NRKlH/v29w/PU3wTud\nSLn04uE+tFOa9EQDHrx+GhJiwnoFScRFhKO9Mh+XnjYLm5rX4IixEg+tfQrjmDMAqJHro1+KMD4z\nGuXV7Thc0wHyslniTKzIcA3MVidM3XakJYR2nCR8glDf6ll98BBTXjY/EouuVSsk8adWKbDk1tkA\nhMV5k7EHv/3LejQbzegy22EIUwc8HqeLg9FkRUJMWMDHDQZ1Ld34YUclAECpYKFUsEiI1uHcWRm9\n3sPBxuXisKO4Ad9urUDJcaG/jmGAWy6aiF8tHIeK+k68910pSiva4HRxUCpOfF+tpqkLVrsLOo0C\naQnu0QAkRdL73KAMDyTlLjE2DNmpkdhWVI89h5rw9jclWHnn6YP6s81WB3731/XISo7Eqt8M3s8i\ntr7EmDCoFCyO15vQZDQjQ5ayeqqy51ATXvu8CGNTIvHSgwsH9LU5jsfrXxZBoWBx52X5Q37dCwSx\n+UWI95CU+HAwjBBIYeq29ysoaiQgr0zJ/58ytFAxRQHDMMi69WawKhVqP/sCFW+9A87hQNqVvxru\nQzulWTg93efX46J0OFLTgVg+G88tXoq///IuSluOoAQboR6XiKy0CX5fk/RNHa5qh0atACDY/wAg\nKlyDhtaePlWmasTK1Li0SBytNfWqPshtfsZOq8fCneyiEYufNwqWQUpcOJJiw9DYZsbxOhOmir1d\n/vhgzSF8vvEoHrlpJuZOTQn59xgI3vjqIPaV9baMxEfrUDA+RHU6QLz0yX5s3CvM/VKwDM6YkoLL\n5o9FrthLl5UcgXCdCt0WB47WdvjssesrpDKakx4tRe0D7jCVhlYzXBzv8T3K0GK1OaXxB0mxejAM\ng1sunog9h5pw8Ggr7A4X1CrFoP38ivpOGDttMHY2o76l2x20M8B4iCmlW0ydLGzeV4sdxQ2475oC\nhGlVfXruEbF6fLzeFNIGVV9Ys6MS32+vBADML0iVUmJHAiSAIkIviCaNSoHEGOHeUtPcNWrFVAft\nmRoRUJsfBYAgqDL+73qkX3cNAKDq3x+g5pNPh/moKL6QD+6N08dg+cI/4Lr8y8BzDBQxTfii/m0U\nNfYOpwCA8eLw3mN1JhwXwweykgXrH7mZhJro5+J4KcBi1qRkAL0T/eSVKWLZIxB7QnRE4DlcpJ+L\nDCMOxP5yIX79sw2Hh9S6YbU7cVC0zF00dwwum5+N9ERhoXi8bujT0kg16pJ5Y/HW0vPw8I0zJCEF\nACzLID87FgCk4z5RjtQQMeXZrxcXpYNSwcLp8h/pT+kbZqsDpRVtfT7Hm8TqcLhOhXCdsAjPSDQg\nyqCB3cl5jE0YDOQDU38paRy0n9Pc7hZTiWKV2rufczTzn5/Ksa2wHlsL+z5b6LgsdOZQhXHAjqm1\nw4L3viuV/j3Sose9bX4ApAp6rZddfTQhD6CgPVPDx5CLqXvuuQezZs3C/fffL32tpqYGV155JRYv\nXoyVK1cO9SFRRBiGQcavr0XG/10PAKj+6D+o+vDjUe0nPhkhg3tbxYUJy7KYFD4LttI5gC0cnfZO\nPLH5Zby7/1PYXZ5hEsmxehjC1HC6OHRbHGAZd39WZLggpkId3NvU1gOHk4NapZDSABtllSmXi5P6\nM7RiFUxu9SMX/tgQxdTxIMlzThcn9XAdrTUN+sJQTvGxNjicHOKjdfjtrybjjsvyMW+qMDertnlo\nb9QuF4dWUbReedY4xEb67tubPC4OwMCJKVKZys30tJkqWAbJccKCdqSFUFQ2dI5KgffOt6X489+3\n4n8/9y04goRPyAd6MwyDSWMFYV183PfIhYFCvpkymGKK/J4J0WFIEsVUk3Hk9ey1mSzYcqAOr39Z\nhGWvb0fh4eDDZDmOl6ps/RFDFQ3uzZ2SAXy/X/+yCBabU+oT3lJY1yvddTghNj95JS4tQdjwIpuC\noxG5tc84CGJqd2kjXv2sEHaHa8Bf+2RiyMXUzTffjGeeecbja88++yzuu+8+/Pjjj2htbcXmzZuH\n+rAoMtKvvhJZt94MAKj972eoeu99KqhGEHGyyhThUKURvDkSEx2XYdG4+QCA7w9vwGM//RXVHXXS\n4xiGkapTAJCaEC7ZeiLDhZtMqDa/KrFfKi0hHKniTanVZIVNvOg2tQu2LrWSRU66sMCWh1DIY9ED\nkZ0qHO/x+o6Aj6tr6ZZi2oGhHf67T0yEmpabIFkWpV3PIb5Rt3VawXE8lAoG0Qb/f9spYnR/aaUR\nDueJBQJYbU4pwIBYSeWQEIqRFI/ebDTjgb9txoo3R9/gcnK+ffRjeZ/6JMjmRmKM3uPrk7MFYT3Q\n6Y7etMoqU4cq2vo81y5USAUuMTYMibHC7zqSbH4dXTbc8+wG3LJ6LZ55fw++3VqBA4db8N/1wWO6\n27us0ue1tKJvYqjb4vAI4hgoMbW9qB47ixuhYBksv30O0hPDYbO78PP+uuBPHgKsdifs4t8sQi8X\nU8I1umY0V6bkARSd1gFfq731dQl+2FEpXXMovhlyMTVz5kyEhXk2h+/fvx8LFiwAAFx++eXYsGHD\nUB8WxYvUyy/FmDtvBwDUffk/VLz1DhVUI4R4LzHldHGScDgtJxl3TP81Hpl3NyI1BlSb6vDoT0/j\nu/L14HjhZpIrW+yOES1+ABAd3jebH7kBZSQZYAhTQS/ahsiCjfRLpcSHIyFGOOZmWWWqXRRTgRb8\nAKQEwtrm7oAT3kk6IRGFWw7UD9pizZu9h5oAANPz3L1RaaLNb6jTokgEfVyUDmyA/qSMRAMMYWrY\n7C7JotdfjtZ2gOOF/jdflbDkERhCcUgUkdWNXX0aBzBQbNpb0y/x0t5plRbEFpsTH6wpC/m5JHxC\nXpkCIFk+D1W2n7CwDoS8MsXxQhjCYCD1TEV72vxGyj1sX3kzqhq7wDJC5X3+aUIV+1idKegxyu2K\n9a09fRLTlWKCn04jbKAdre0IeE0NhW6LA69/WQQAuPLsHGQlR2DR7EwAwNpfRobVr6tH+HwrFYxH\n0i2xYteM0soUx/Eem582u0tKyCXwPI+f99f2a3C12eqQrPq+RpuMJrrNdrz2eSGO1gbelO0vw94z\n1d7ejqgo9055YmIimpr6foFtbm5GSUmJz/8cDgdcLlqi7CspF1+I7N/9FgDQ8M13QtIfd/INdxxt\nkMVqm0mYfL5+dzUa28yICtdgsXgTm5YyGc+evxTTkvPh4Jx478BneGrz39HaY0SOTExlpbjTrSKl\nnil7SMdBkvwyEg1gGAbJ4gKNhFAQS1dKvB7xUcL35BfkUCtT0RFaxERowPOCLcsf5HtnTE7BuPQo\nOF3ckNzMG1p7UN/aAwXLeARkpMrSovoS6nGiEMGaEB040ZBlGUweNzB9U4erhRuUt8WPQIIG5IEk\nwahp6sKb/zuI3/11PTbtrTmh4/OFfGBxzRBbMcurjHj+o31Y/dbOPluhyqoEa5chTNi8+GlXFY6F\nuECQKlOxnpWpjEQDIvRq2B0nLqwDQRrkibV4MKx+NodLsionxrrFlMXmRJd5ZNjOiPV38ZwsvPTA\nQvzhumlQKlj0WBxBK2jedsWyytCtfsdFMTU5Ox5xkVq4OB7lJ2CH5nkeb39dDGOnDanxelx77ngA\nwFnT06FUMDhS0zEiItg7e4TzwRCm9gg7IpWp1g4LLCcoKoeDLrMdnDjYnghkb6vfwWOtePaDvVj2\n+nYP50YoVNS777fyfsfRCAlH8e7lc7lcfrVDSUkJmptDq8gNu5gaKD755BNcccUVPv9rampCL3xk\nTgAAIABJREFUT8/IsZeMJpLOX4Rx994NMAwa1/yIo6/+kwqqYSYmQgOWEQIgWtrN+M/acgDA1efk\nQCvbdYvSRuDP8+7GHdOvg1qhQlHTITzww2rUccUAhAswGQgMyHumQtvplCpTicINKdlrnhDZ0UqN\nD0eC6KOX2/zILnWwnikAGCta/QItGslNe0xKhDT8d82OSri4wd2NJvaHCWNiPJK11GJaFDC0Vj8i\npkKZcTZFtHcdPEF712E/4RMEEo/eEKQyxfM8thXW45FXt+LuZzbg65+Po7a5G+//UCYtGgYK+bk0\n1A3oO4sFEWGxubBpX22fnltWKfytz5iSgvkFqeB54M3/FYdUdSEL9SSv0QEefVPHBq9viizILjxD\n+HzuK2+WbMEDBana6TRKhOtUUKsUiIkQrm0jpW+KXA9I9VqlZJGVLFxHgwXteAdplPahb0qaLZga\ngYni+116Ala/zzYcwU+7qgEAv7+qQGYZ12B2vhBKtHYQgyha2i0hVRpIkp9B75lcGKFXS06GulFY\nnSLhExF6NeLEDUvvRD8iiBrbzFi/u7pPry+/RrZ2jO5wC9LTS/rkCD09PX61wxVXXIFPPvkkpNcf\ndjEVHR0Nk8l98WhqakJCQt9jhK+99lp88cUXPv9LTEyEXq8P/iIUnySeew5y/nAvwLJoXrceR15+\nFTyt9A0bCgUrDbn9YE0ZWk1WxEVqcf7pWb0eyzAMFo1bgL8uegy5sWNhddrwYfFniJm2H1Fxdo/o\n2igipkKoTAlJfsICND2JiCmyYPay+cWFSwt7eQAFsadERwSPpJUS/QKEUJDKVFZyJOYVpMIQpkZL\nuwW7Swev0R2AFIc+Lbf3dWs40qJI9Y9UAwNBQigOVRjhcPb/M03CPvxWpkSh3dhmDjiwtbTCiKf/\nvRslx9vAMsDsSUnQaRRoNppxqA878MHged6zMtU0tAupX0oapP9fs72yT/YzUpnKy4zBzRdPhFrJ\nouR4G7YXNQR8Hs/z0kI8Kbb3/XCw+6Y4jpc2UGZOTERcpBY2uwtFR4KHLvQFeSw6qUKQHrGRkuhH\nNqLSZfPYstNI0mpgcUCqi5nidbcvIRSkMjU2JVISzyV97LsifL+9Av/+XkiNve2SSdK1hECsfhv3\n1Q64YAaE69z9L2zEn176OagFjdj8IvS9Y+Clvqkhrk4PBG6rvEbalPRO9JOH/vznp8N9us7Lr5Gt\no7gyxfO834AkvV7vVzt88cUXuPbaa0P6GcMipnie97h5FBQUYNOmTQCAb775BmeffXafXzMhIQGT\nJk3y+Z9KpYJCMXizM04FEhYuwPgH/gCwLFo2bsLhF18C5xx9ZfGTBRJCsXm/sKt97Xm5AefDpEYk\nYdU5D+K2addCo9TAomwGN+5nrKvcACcnXFxJZarH4gjaN9Fk7IHdyUGtZKWFSnKsp5iqa3VXpuKj\n3TY/nudhsTklb3dMCJWp7CBiqrPHLi3UMpMNUKsUWDQ7A8DgBlE4nC4UHRUWg9PzEnt9Pz2R3KiH\nbrFOFhYJIVSm0hMNiAoXYrHJzaavtHda0dJuAcMA49J8V6ZiI7VQK1m4OB7NARY+JMQiJz0Kby1d\nhKW3zcbpk4V5YRsH0OrXZDR79EmFspByODnsOdR0wnaX+pZu1DR1Q8EyUCtZVDZ0hvy3d7o4HK1x\nWyoTosPwq7PGAQDe/rYE3QF6vzq6bLA7XGAZ31VLd9+Usc92oFAw9djg4ngwjPCZnzUpCcDAW/3k\nYoqQGBvm8b3BxuHksPqtnfjLe7t6VVSdLk66Rqb5ElMhVqbOniHMITxa2wGrPfi92OXiJGv2mJRI\nTBojvN9lVe19fr837avFP78Q+qSuPXc8frVwXK/HFOTEIyFahx6LAzuK+h7hHggXx+P5j/aiy+yA\ni+NxPIgAldv8vCHX6NGY6EcqU9EGrbQp6W3zk89+bO2w9KoUWmxObC+q99k7J69MjWabX5PRjI5u\nG5QKVlpLEBQKhV/tMGnSpJCLO0Mupm699Vb88Y9/xJYtW7Bw4UIUFhbiwQcfxMsvv4xFixYhKioK\nCxcuHOrDooRA/Ly5yHv4T2CUSrRu2YbyZ18A5xgZHvRTDSKmAKGZ/NxZGUGfwzIszs9ZiBfOX4ap\nSRPh4Jz4qOgrPPbT06hor0G4TiUNVCU3H39US0l+Buk5UmWqrQc2h0ta1KfE66Xjtdpd6LY4pB01\nnUYR0tBJUpmqbuz0KfSqxKpUYkyY9Hrnn54FhgEOHG4JmtbUbbbjsw1HcNfT6/Ha54VBj4dQetwI\nq92FaINGGn4sh1gKhjItKtSeKUCoXJ7ovClSlUpPNPh9L1mWCSmEgix2czOjpXPmbHF49dbC+j7H\n8xYfa/UpwogoJ+dubQiVqf+uO4xV/9qJW1avxZ9e/hlfbDyCinoTjtZ2oPBIC7YV1UvCOhC7xErp\n5Ow4zBODB77fHprgr6g3we7kEK5TIVXsQ7vqrBzERmrRbDTjvuc3+rVskkU4mfvlTWZSBAxhKljt\nrkFp0iabHdEGDZQKVrKB7SppHFALp08xFTO0YuqbLcewu7QJ24saejX+N7T2wMXx0GkUiItybySR\nRd7R2o6AlUpiVZw8Lg6xYt/Tkerg71dtSzccTg46jRKJMWFITzQgXKeCze4KOnZCzp5DTXjx433g\neeDiuWNww/l5Ph/HsgzOk4Io+mYvC8an6w97JBHWBRm50GkOVJka/Gt0t8WBJ9/5BT8NcA8vseRH\nRWikTUlvMUUSVOcXCNea/64/LFUKjZ1W/PnvW/CX93bj0w1HPJ5nc7g8NgFbOwY+KXCoIJtV2amR\nUCkHp7Ay5GLqnXfewfbt27F//35s2rQJU6dORWZmJr744gusXbsWq1atGupDovSB2NNnI++Rh8Ao\nlTDu/AWljz8Fl2X07liMVuRi6teL8nwujvwRr4/FY/Pvwe9n3Qy9OgyVHbV49Ken8dHBrxBhEF4n\n2KwpeZIfgfTFtLSbUSOKrXCdChF6NTQqhWQjbGm3SBf8YEl+hMSYMOh1KjhdvM+bXkUDGUDsFjRJ\nsXrMFne/Pxb7yrxpbjfjja8O4tbH1+K970pR19KNH3ZWhbxo30si0fMSPBqbCelDHI/O8+7KT3xM\n8MoUAEwR7TlF/bR3kQb28em+LX6EUEIoyLHLF8L54qKxx+LA7j6kv1lsTqx+ayde+Ghfr5ljZMd1\nmpi+2GTsCfie8zwvVYEB4eb8zreluO/5Tfjji5ux9J/b8fR7u7Hkte1B46pJv9SsSUlS79DWwnpp\nqGggSL9Ubma0lNSo1Six9NbZSIoNQ0u7BUte24a3vynpZedpFBfhvix+gLD4Hcy+qTaxXzJGDNCZ\nnB0HnUaJ9i6b1HM3EJCeqQTZOZQk2fwGv2eqzWTBf35yX2+8hSmxR6cmGDyuGVnJEVCwDDp77H77\nU6x2J4xiFHZyrB4TRJt2aWXw96uizn2NZFnG4/3uS0T6u9+WgON4LJyWhjsvn+zzukc4S9wIKalo\nO+HUQEJpRRs+/lFIsCQbWMGur6RnypeYcl+jB09MrdtVhZ3FjfjH50UDKujllSkS5CTvmXI4Ocla\nf/NFExEfrYOx04Y12ytR39qNh1/ZIvVU7Sz2tAlXNXSC43gYwlRgGKGiGso1aiRSHsSGPhAMe88U\nZfQRM3MGJi5fAlarhamwCCUrVsPRNfr8xqMZsiBKTzRgwbS0Pj+fYRgsGDMHL16wAnPSpoHjOXxd\nthb27A1QxDQEjduVkvxkYirKoIFWrQDHA/sPCyJDSLQTbrZx0e549FCT/OTHK1n9fOyak8bqLK/q\n0K8XCbumWw7U9UqVam434/7nN+GbLcdhtbuQlRwBnUYJjuOl3y8Ye8vESPTc3hY/wN1gPlRpUZ09\ndkkUxEeFJqamjhfnTVUYPWbQhEJFvQnfbDkOAJg0NibgY1NCqEz5qqopWAYLxXO8L6l+2wrrYbEJ\nfwtvKxmpTE3PS4RepwLHB97drm7qQkNrD1RKFv985BzcdcUUTBkniIGYCK1klwQCBwJ09thxSBRb\nsyclISc9CmNTI+FwctiwJ/juvdQvleX5tx6XHoWXHliIRbMzwfPAl5uO4uG/b/XoVSGVqcQY/xXL\n/EHsm/Ie0q1SstIogV0DaPUjlZvhsvm9/U2JdN4BwgBxOVL4hFcjvFqlkK6n/vqmyOdTr1UiPEyN\niaJVL5QQCrJoHiuzOZHnhyqmTN02ab7gHZflBxy9AAjvQVykFhzH40jNiVc7u812PPfhXnA8cNb0\nNFxxVg6A4JUpXwN7CeQa3dDa42F3LDnehuc+2BvyEPtAbD0g2BydLg7//q70hF+PQGZMCT1TwvVe\nXplqMvaA4wGtWoH4aB2uPTcXgFDZe/iVLWgympEYEwaWEe7p8t4zcp/NyYiWrm2+Bpy3d1olp8lI\nhSReUjFFGXFETZ2C/NUroAwPR1f5YRQvWQ67cfAidSmenD0jHVefk4NHb54pWZX6Q5Q2Ag/MvRMP\nnXkX4vWx4JQWqMcV4sMj76LW5L+hvZo0UCe6xRTDMJLII/NjUuLdu+BkcS+vTIXSL0UgiwBflhQS\nPiGfm0Wec+ZUoefmwx/c83g4jscrnxxAt8WBzCQDVv/mdLz84EJpoHEocb4t7RZUi7NiCnLjfT7G\nEKaWbkSDufNJIGIkJkITsp0hJS4cBTnx4Dge32w9HvLPau+0YvVbv8Bqd2FqThwWirvQ/kgOYXCv\nVFXwsiiSHe49h5o8dkddHO/Xy79eJk7kISQ8z0sLhXFpkUgXF7WBrH47Dwqfhak58UiND8dFc8fg\nyd/NxX+fugjvrViMfzx8Ni6dPxaAuwLgiz2HGsHxwo56ghiQcIEYHBNKEEWZaFfJ87EoCNOqcO81\nBVh66yyE61Q4WtOBX2S7zU1BKlMAkE8S3iraAgaF9Adi85NX1YnVbyDnTTUZe1c3yf+3iIPEB4uD\nx1rx8/46MAxwwRlZAHpv/vgKnyCQAeX+bJZkThiJtp84RhDV5ZXGoL/XcVnaKYFsgJRWGEOyWpKq\na0aSQeqxDUauKPzJRsCJ8PY3JWhptyA5To+7rpiCNLHaHdTmF0BMxUXqoFEr4HTxUuWS43i88t8D\n2Ly/tk/XRF80Gc0or24HwwAMA/x8oA7lQf4WLo6XqmmBkEKcDG6bnzyAgvRLJcfpwTAMzpmZjqTY\nMHT22GHqtmNsaiSevW8ecjOF94hsDgLuDafs1EjEes22JDicLtz7/Ebc9/ymEwowGkxsDreNlfye\ngwEVU5R+Y8gdj/ynHocqOhrmqmoU/fkxmGtHxsTzkx2dRombLpzoIWZOhJmpU/Hi+cuRyp0GnmNR\nZ6nCQz8+gX/v/wxmh+cF1MXxUjqdvDIFuPumyE4Q6esA3Avklg6LZFXpi5jyF0Lh4nhpt9S7MgUA\n1y/OA8sI1QlyE1uzoxIHjrRArVLg0Vtm4bRcwaZHouKPhyCmSCT6+Ixonzdpgnt47+Bb/SSLXwj9\nUnIuW5ANAPhxZ5XPuUembpvH4trmcOHJd3ahtcOC1Hg9HrlpZlCrKRHWDX5sfjaHS7KtJHhVTzKT\nIzA2JRJOF4+thcI1psloxp9e2oxbH1/bq2LV2NaD4mNt0gKmor5TWggYO60wddvBsgyyUiJlDej+\nxS6xwJw+OdnvY8hCOFDiJKmQzZ7kfp0F09Kg0yhR39qDogB9a2RYL8MI55w/ZucnSwv5n/e7r8fu\nJD//50ZWSiT0WiUsNpff3+NoTQde/awwYNiFL8jfP1ZWjZ4s9utVNXT2ed6WL8xWh7QIlQvy2Egd\nlAoGTpd/8X2iuFwcXhdDGc6fkyXN/PMexFvjpzIFANlppPru+29PFvvkPRSq6Qr0WJ1SeIsveJ6X\njY5wbzhlp0VBo1agy2wPKYSlWKxgEXtgKOSJC1hiUe0vHMdju7ip8furpiJMq5KuKaZuO7oDiI/O\nADY/lmVkfVPCe3PgcIsk0Mh1vr9sE69Xk7PjpNCQt74uCbhx8vHaMtywfA32B/nZ/mx+5LXJxhVJ\nU1UqWNx68SSwLIOCnHj85e65iDZopQqxfFODbAJkp0UhTnztVlPvcAtTtx0d3TafVkue57F5X+2w\nDms/XmuCi+MRZdCEFMrUX6iYopwQ+swMTHn6CWiTk2BrbsbBRx5D56Gy4E+kjDjUSjXy9WfAVnQm\nEhVj4eI5fHt4Pe7/fiV+rvxFukA3G829kvwIxMpFNjlTZGJKHo9OfN39qUxV1Js8dmEb24R+F41a\n4XPXPT3RgLPEm9gHa8rQ0NqDd74tAQDcfNEED8FHFhryYYX+IDfJGRN8W/wIaQPgyXc4XdhWVB90\nAdvSh/AJOdPzEpCeaIDF5uw16HjdrmrcuPIH3LB8DZ56dxfWbK/A3z7eh/LqdoTrVFh++xyEBxCT\nBHJuNLWbfaaHkWPXaRTSQFo5C6cTq18tdpc24g8vbMLRWhN4Hnjn2xIPG+WGPYK4KsiJlxZzpN+K\nLFTTE8KhUSlk0ci+b/jN7WYcrTWBZYBZE5P8/n5jUgUhX9/a7bM/xO5wSTH6pJdP+H2VOEv83V74\naB9e/6IIe8uaesVJk539zKSIoKEtC04TXm9vWZO0yGxq621/80bBMpg0llj9fFu//vllEX7YUYnv\ntvVtx5585uViKjZSh4SYMHA8+p0mKYdsJoTrVNDr3H8jBctIGwzE6mdzuPDZhiO9+un6it3hQkW9\nCe99fwhVjV0whKlx44UTkJEUIQ3iJUKW53nUNfeu6hNIGqa/dDr3nDDhs6RQsNJueyCrX3uXTdhA\nYISNCYJSwSJXFOahzJsidsD8voipLOH1y6qMJxRgUN3UhR6LA1q1Qvr5YVqVdA+pDVCdIjY/X2IK\n6N03Ja9GHavtgOkEhq5vKRQsfmdOTcH/nT8BapUChyqN2HHQt/OD53ls3FMDngfWBZkL5RlAIVQK\nnS5Ouk/IK1OEM6ak4P2V52P1b0+XriMkibboaAscTg4OJ4fKBuFvkZ0aiTixz9F7I0JeEfR1z9x/\nuAXPfbgXT7+3O+DvMZiUV4sWv4zogP19JwoVU5QTRpuUhCl/fQrh43Pg7OpG8bKVaN2+Y7gPi9IP\nIsM14O1hGOs4B4/NvwfJ4QkwWTvx91/exZJ1z6C4qVzaAZUn+RHkF23AszJ1oja/1AQh8txqd3nM\nziA7rplJvY+HIIR0MDhwpAUr3tgBm92FydlxuHjuWI/HEQtMZb0p4I3f2GlFoTgfZ/5pgXvWJBtZ\nP0MoHE4OT727G0+/txsr3tge0M7T3IdYdDkMw+Cy+UJ16ustx6Uq1PE6E/7xeSF4HuixOrHjYAP+\n8XkRthbWQ8EyeOyWWR6CORAxEVqhp47jffauNBvJsYf5vOktmJYGlhGiu1e/9Qu6LQ6Mz4hCYkwY\njJ02fLXpKABhB3u9KKbOmZmBmROFhQKx+sl3XAEgPTFwmtcvYmDEhDGxiDL4tzZFG7SINmjA80Cl\njypB0dFWWO0uxEZqpQoE4bIF2YgK18DYacW32yqw8s2duGH5Gnz98zHpMfLwiWBkJkcgM8kAp0vY\nzbc7XJL9J5DNDwAmjxMWqnLLD6G1wyKJnr5WGto6xcpUhOe5OUGqXJy4DUwSjD6qb1Kinzjr7Jl/\n78F735XiNbGaFIyapi7884siPPvBHqz610488upW3PX0Olz96Le47/lN+FI8/266cAIMYWphEK94\nPSG2vTaTFRabCwpZuqWcrOQIsAxg7LT1SmUjxw54VhdJ39OhCiNsDhfW7KjE75/dgIdf2SJV6cg1\nMlXcQJBDqkz/+elwQLtlj8UhWVj7UpnKTo2EUsGis8eOhhMIACFCLi8zBgpZFZxUleoDiCnJ5udH\nTKXJrtH1Ld3Yc6gJDCMIf54XREF/aGzrwdGaDrAMcPrkFMRF6fAr0QXw7nelPpNpG9vM0nV8X1mz\nX7utw+lCl5hSGBUu2LqJQ4JsXJC/SYrXuRahV3tcY8emRiIqXAOLzYXSijbUNHXB6eKg1wrJj/5s\nfnUylwGx2sshn+mK+k6PiPahpMzPfKmBhoopyoCgioxE/hOrEDNrJniHA+XPPI/6b74b7sOi9JEo\ncRp8R7cNBcmT8Nz5S3H9lMuhUWpw1FiJ1Zv+hndK3wETZvJpqfNeIMj/7bb5mfslphQsI4kded+U\nFD7h1S8lJzEmTBoi2dDWA51GgfuuLejVQJ2WYIBSwaDH6gw4D+nn/XXgeKF3xdeiyOM1yaypfkTv\nulwcnv9or7TIOVzdgR92VPp9POk56qvNDxAauiPDhUHH2w82oMfiwNPv7YbDyWHGhEQ8f/98/N8F\necjPjoUhTOjP8R7UGQiGkcWj+1j4NLX3TmGTExOhxdQcd2/aRXPH4Onfn4lbLp4IAPh801G0mSwo\nPt6KZqMZYVol5kxOxkyxmlR4uAVWu9OjFwBwVwjqWrp9ClVi8ZuT79/iRwjU10csfrMmJfUSiylx\n4XjjsXOx5NZZWDwnE7HiQNs3/1csBXyQRCpf/VK+ICJ/y/46NLebwfNC1c/f7jyB/J4Hj7X22omW\n76Yfqgytz4ZAeqZiozw/8xPEvp+BGMpMziFf1TciIhvbevDqZ4VSRH11Y1dIv8cHPxzCd9sq8PP+\nOuw51ISS422oaxEa/MN1KkwcE4ObLpwgxYED7koTEfDkGpAUq/dpi9VqlEgVqyS+gnYaJbHovuZM\nFHuS9hxqxB1P/IR/fFaI6sYuHKo04om3f/HoGfHuKQWAC07PQkqcHsZOK1b9ayf+9p99PivghyqN\n4Hjhmh4bGfpmjUqpwDhx8+BErH6kX4v0iRHIhp2/zSqHk5Oq1v7s2PJrNJlLOD0vUQq+2edjYyEU\ntopVqSnj4qWNmCvOGocogwYNrT1Ys6P3SIRC2RDrbotDEgPedHQJAlHBMtLvRaq+ZOOEiNdg9yiW\nZaRk071lzR4bTgzDSDa/Ni+bn/w6XunDGi/v/ZMPKh9KyqU+08HrlwKomKIMIAqNBnmPPISkCxYD\nPI+Kf72NinfeA88N/ABIyuBAmoqJrUGlUOHyCYvxyoWrsHjcAigYFka+Btr8HWiP3o6GLk9Pt3zX\nOzZSC51GKf2b2PyMnTa0SDHJoYspwL1YLZdZc8iOmDwW3RfXnDseaqVwybvtknyfO/QqJYuMROF1\nAoVQbN4nVD6ChS4A7l1P77SoYHAcj1c+PYBthfVQKhjJb//v70t97loDkP6uvoayBkOtUuAiMar7\nq81H8dIn+9HQ1oOEaB0euH4axmdE49pzc/GXu8/ER49fiHNmBp9t5k1KgBAKIgQTAwjBXy/Kw4Ss\nGPzphum464opUCkVmDslBXmZ0bDZXfjwhzKs3y28N/MKUqFRKZCZZEB8tA52J4eDR1t7Vabio8Og\nVrJwOLleaYadPXapT2ROvn+LH8GfmOI4XkqsmzPJtyjTaZSYk5+Me64uwDvLFuHac8cDAN746iDW\nbK+Q0tBCbaKeL86wKjraIi1iE2P0Qa0uSWLkNs979lwBnmKqx+II2bpqtjqkId3eC/EJUkBBe9AQ\nBYvNib1lTX7FT5OfABPALbC+2XocP+2qBssALCPY9HyllHlTJdqeLpk3FvdeU4CHb5yBJ357Bt5b\nsRgfPX4B/nrPPFx9zniP6jgREWRRSRb8pBrqC/Ic7541nuelAAp5ZWq8GJPfY3Wio9uG+Ggdbjg/\nD3qdCqUVRjz3wR7ptcak9hZT0RFavPTgQly+IBsMA6zfXYPfP7Oh1/WPJDz2xeJHyDvBEAqe5yUb\n4kSvn5+aEDiEgthcWQYe1k858llTxFp3yZljJYGx/3BLQMHN8zyefX8PHn/rF4/giC0HhM/PmQUp\n0tfCtCpcd56Qqvej1wBdADggiimy0ScPz5FDwieiDBrpsdKsKZPV43oWintghmj121vWJJ0v5Hrm\nrzIlF7C+KlPy3j8yEmIoaTNZ0NphAcsIiaeDCRVTlAGFUSgw9rd3IvPGGwAA9V99jfLnXgRnH53z\nCU41JDHlFQcbpYvE7dOvw23j74GzNRnggTJTCf64ZhXe2PMRjBZhsRAXqYNKFCypXhfwCL1aEjMk\nvjs6gG3KF5PFfo7vtlVgx0Fh16+CiCkflTI5sZE6LLt9Nu65eioWz8n0+zjyOv5S2WqaunC01gQF\ny0hJgYGIj9JBq1bAxfEhWx04jscbXx3E+t01YFkGD984A/ddexpy0qNgtjrx5lcHfT6vvz1ThAvO\nGAOVksXh6g7sONgApYLBn2+aGTBgoy+QhnFflSmSRBioqjZhTAyeuXeexzgAhmFw+6X5AIQeg63i\nAuZcUewxDIOZYl/bT7uqpSZqUuVUsIy0IPNuwt9dKgyUHZMSEdQeJ7ymbzF1vM4EY6cVOo1CstEF\ngmEY3HB+Hi4XLUH/+LwIdofLY1hvMJJi9RifEQWOB74QLWiBwifknCXrTyOYum0oOS4sqIltKNRq\nEtnRDtMqPTZYAMGSqNMoYLEFDlFwujgse307Vr6502Pml5xmqafIv5giou73VxdI73swC67TxUlV\nocsXZGPR7EzMK0jF1PHxiInQ+hWo2VJlSrANk/MrzUeSn/dzjnpFiXd022Czu8AyQHyU+/fTaZS4\n9tzxmJwdhwevn4Y3Hj0X152Xi6W3zoJKyWJncSO2iRUSX4PFAUCrVuL2S/Px19/PQ2q8UKV6w+sa\nU9KP8AkCqQqU97My1dxuQavJCgXLSD1eBPJ5qPPzHpLwCb1O7dcGnhIXDpZlYLW7YLY6kRofjoLx\n8ZiQFQutWoGOLlvAzbXjdSb8fKAOu0ob8cirW9FmsqC+pRvH60xgWaZXVXvBtDQoFSyqG7tQJTvn\nOY5H0RHhM0ZSPv1ZLzu63LHoBPng3uZ2Mzge0KgVId1nC3LjpYh00l9KzkXSM9Vq8hzcKw+WaO+y\necTIt3daPTb9DlW0nVDvWSi0tFvQI6uqkqpUpjj2ZDChYooy4DAMg7SrrkDOH+8Do1RBk7Q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w5Q1TRdhv+9K2ehqtYE63tq/byV+nwNVvhisIp+6TS/E+nxTheL2La7pV8gmEwamQCrb4rcUC1f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\nTqAk4QAfRJoAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fbf15a2d510>"
+       "<matplotlib.figure.Figure at 0x7fbf159e3d90>"
       ]
      },
      "metadata": {},

From b10c91de7d7f5adeeee38b6cd654e3b2aa438c87 Mon Sep 17 00:00:00 2001
From: HuaxiaWu <hw2560@columbia.edu>
Date: Sun, 18 Dec 2016 23:50:40 -0500
Subject: [PATCH 10/15] lab5_1_hw2560

5_1
---
 notebooks/week-5/lab5_1.ipynb | 411 ++++++++++++++++++++++++++++++++++
 1 file changed, 411 insertions(+)
 create mode 100644 notebooks/week-5/lab5_1.ipynb

diff --git a/notebooks/week-5/lab5_1.ipynb b/notebooks/week-5/lab5_1.ipynb
new file mode 100644
index 0000000..ac2a2e5
--- /dev/null
+++ b/notebooks/week-5/lab5_1.ipynb
@@ -0,0 +1,411 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using ordering: tf\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.random.seed(1337)  # for reproducibility\n",
+    "\n",
+    "from keras.datasets import mnist\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
+    "from keras.layers import Convolution2D, MaxPooling2D\n",
+    "from keras.utils import np_utils\n",
+    "from keras import backend as K\n",
+    "\n",
+    "from keras.datasets import mnist\n",
+    "\n",
+    "import pickle\n",
+    "\n",
+    "print \"using ordering:\", K.image_dim_ordering()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X_train shape: (60000, 28, 28)\n",
+      "60000 train samples\n",
+      "10000 test samples\n"
+     ]
+    }
+   ],
+   "source": [
+    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
+    "\n",
+    "print 'X_train shape:', X_train.shape\n",
+    "print X_train.shape[0], 'train samples'\n",
+    "print X_test.shape[0], 'test samples'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X_train = X_train.astype('float32')\n",
+    "X_test = X_test.astype('float32')\n",
+    "X_train /= 255.0\n",
+    "X_test /= 255.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(60000, 28, 28, 1)\n",
+      "(60000,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# number of classes\n",
+    "num_classes = 10\n",
+    "\n",
+    "# image dimensions\n",
+    "img_rows, img_cols = X_train.shape[1],  X_train.shape[2]\n",
+    "\n",
+    "if K.image_dim_ordering() == 'th':\n",
+    "    X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)\n",
+    "    X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)\n",
+    "    input_shape = (1, img_rows, img_cols)\n",
+    "else:\n",
+    "    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)\n",
+    "    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)\n",
+    "    input_shape = (img_rows, img_cols, 1)\n",
+    "\n",
+    "Y_train = np_utils.to_categorical(y_train, num_classes)\n",
+    "Y_test = np_utils.to_categorical(y_test, num_classes)\n",
+    "\n",
+    "print X_train.shape\n",
+    "print y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(28, 28)\n",
+      "(60000,)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f875db1ac90>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f879d6c22d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "img_num = 0\n",
+    "\n",
+    "if K.image_dim_ordering() == 'th':\n",
+    "    img = X_train[img_num][0,:,:]\n",
+    "else:\n",
+    "    img = X_train[img_num][:,:,0]\n",
+    "\n",
+    "print img.shape\n",
+    "print y_train.shape\n",
+    "imshow(img, cmap = plt.get_cmap('gray'), vmin = 0, vmax = 1,  interpolation='nearest')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# model hyperparameters\n",
+    "batch_size = 128\n",
+    "nb_epoch = 30\n",
+    "\n",
+    "# network architecture\n",
+    "patch_size_1 = 5\n",
+    "patch_size_2 = 5\n",
+    "\n",
+    "depth_1 = 20\n",
+    "depth_2 = 40\n",
+    "\n",
+    "pool_size = 2\n",
+    "\n",
+    "num_hidden_1 = 1000\n",
+    "num_hidden_2 = 1000\n",
+    "\n",
+    "dropout = 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# create new Keras Sequential model\n",
+    "model = Sequential()\n",
+    "\n",
+    "# add first convolutional layer to model and specify it's depth and filter size\n",
+    "# for the first layer we also have to specify the size of each input image\n",
+    "# which we calculated above\n",
+    "model.add(Convolution2D(depth_1, patch_size_1, patch_size_1,\n",
+    "                        border_mode='valid',\n",
+    "                        input_shape=input_shape))\n",
+    "# apply 'relu' activation function for first layer\n",
+    "model.add(Activation('relu'))\n",
+    "# apply max pooling to reduce the size of the image by a factor of 2\n",
+    "model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))\n",
+    "\n",
+    "# repeat these operations for the second convolutional layer\n",
+    "# this time Keras can figure out the input size \n",
+    "# from the previous layer on it's own\n",
+    "model.add(Convolution2D(depth_2, patch_size_2, patch_size_2,\n",
+    "                        border_mode='valid'))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))\n",
+    "\n",
+    "# flatten the three-dimensional convolutional layer to a single layer of neurons\n",
+    "model.add(Flatten())\n",
+    "\n",
+    "# add the first fully connected layer, applying 'relu' activation and dropout\n",
+    "model.add(Dense(num_hidden_1))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Dropout(dropout))\n",
+    "\n",
+    "# add the second fully connected layer\n",
+    "model.add(Dense(num_hidden_2))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Dropout(dropout))\n",
+    "\n",
+    "# add the final classification layer with the number of neurons \n",
+    "# matching the number of classes we are trying to learn\n",
+    "model.add(Dense(num_classes))\n",
+    "\n",
+    "# apply the 'softmax' activation to the final layer to convert the output to \n",
+    "# a probability distribution\n",
+    "model.add(Activation('softmax'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model.compile(loss='categorical_crossentropy',\n",
+    "              optimizer='adadelta',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 60000 samples, validate on 10000 samples\n",
+      "Epoch 1/30\n",
+      "60000/60000 [==============================] - 85s - loss: 0.2875 - acc: 0.9093 - val_loss: 0.0640 - val_acc: 0.9796\n",
+      "Epoch 2/30\n",
+      "60000/60000 [==============================] - 86s - loss: 0.0776 - acc: 0.9759 - val_loss: 0.0390 - val_acc: 0.9861\n",
+      "Epoch 3/30\n",
+      "60000/60000 [==============================] - 82s - loss: 0.0555 - acc: 0.9830 - val_loss: 0.0303 - val_acc: 0.9897\n",
+      "Epoch 4/30\n",
+      "60000/60000 [==============================] - 84s - loss: 0.0454 - acc: 0.9862 - val_loss: 0.0262 - val_acc: 0.9904\n",
+      "Epoch 5/30\n",
+      "60000/60000 [==============================] - 83s - loss: 0.0367 - acc: 0.9888 - val_loss: 0.0248 - val_acc: 0.9910\n",
+      "Epoch 6/30\n",
+      "60000/60000 [==============================] - 84s - loss: 0.0308 - acc: 0.9904 - val_loss: 0.0252 - val_acc: 0.9921\n",
+      "Epoch 7/30\n",
+      "60000/60000 [==============================] - 83s - loss: 0.0273 - acc: 0.9912 - val_loss: 0.0270 - val_acc: 0.9913\n",
+      "Epoch 8/30\n",
+      "60000/60000 [==============================] - 84s - loss: 0.0242 - acc: 0.9923 - val_loss: 0.0229 - val_acc: 0.9925\n",
+      "Epoch 9/30\n",
+      "60000/60000 [==============================] - 83s - loss: 0.0209 - acc: 0.9932 - val_loss: 0.0241 - val_acc: 0.9917\n",
+      "Epoch 10/30\n",
+      "60000/60000 [==============================] - 86s - loss: 0.0188 - acc: 0.9942 - val_loss: 0.0195 - val_acc: 0.9930\n",
+      "Epoch 11/30\n",
+      "60000/60000 [==============================] - 84s - loss: 0.0167 - acc: 0.9949 - val_loss: 0.0219 - val_acc: 0.9931\n",
+      "Epoch 12/30\n",
+      "60000/60000 [==============================] - 84s - loss: 0.0158 - acc: 0.9949 - val_loss: 0.0257 - val_acc: 0.9926\n",
+      "Epoch 13/30\n",
+      "60000/60000 [==============================] - 85s - loss: 0.0132 - acc: 0.9957 - val_loss: 0.0222 - val_acc: 0.9927\n",
+      "Epoch 14/30\n",
+      "60000/60000 [==============================] - 85s - loss: 0.0130 - acc: 0.9959 - val_loss: 0.0225 - val_acc: 0.9934\n",
+      "Epoch 15/30\n",
+      "60000/60000 [==============================] - 84s - loss: 0.0116 - acc: 0.9965 - val_loss: 0.0200 - val_acc: 0.9937\n",
+      "Epoch 16/30\n",
+      "60000/60000 [==============================] - 84s - loss: 0.0106 - acc: 0.9967 - val_loss: 0.0219 - val_acc: 0.9926\n",
+      "Epoch 17/30\n",
+      "60000/60000 [==============================] - 87s - loss: 0.0096 - acc: 0.9970 - val_loss: 0.0209 - val_acc: 0.9935\n",
+      "Epoch 18/30\n",
+      "60000/60000 [==============================] - 85s - loss: 0.0083 - acc: 0.9975 - val_loss: 0.0233 - val_acc: 0.9938\n",
+      "Epoch 19/30\n",
+      "60000/60000 [==============================] - 86s - loss: 0.0080 - acc: 0.9974 - val_loss: 0.0226 - val_acc: 0.9937\n",
+      "Epoch 20/30\n",
+      "60000/60000 [==============================] - 85s - loss: 0.0073 - acc: 0.9977 - val_loss: 0.0226 - val_acc: 0.9936\n",
+      "Epoch 21/30\n",
+      "60000/60000 [==============================] - 94s - loss: 0.0069 - acc: 0.9979 - val_loss: 0.0227 - val_acc: 0.9936\n",
+      "Epoch 22/30\n",
+      "60000/60000 [==============================] - 88s - loss: 0.0066 - acc: 0.9981 - val_loss: 0.0231 - val_acc: 0.9928\n",
+      "Epoch 23/30\n",
+      "60000/60000 [==============================] - 90s - loss: 0.0062 - acc: 0.9981 - val_loss: 0.0233 - val_acc: 0.9935\n",
+      "Epoch 24/30\n",
+      "60000/60000 [==============================] - 89s - loss: 0.0051 - acc: 0.9983 - val_loss: 0.0219 - val_acc: 0.9939\n",
+      "Epoch 25/30\n",
+      "60000/60000 [==============================] - 90s - loss: 0.0050 - acc: 0.9984 - val_loss: 0.0233 - val_acc: 0.9934\n",
+      "Epoch 26/30\n",
+      "60000/60000 [==============================] - 92s - loss: 0.0046 - acc: 0.9986 - val_loss: 0.0251 - val_acc: 0.9930\n",
+      "Epoch 27/30\n",
+      "60000/60000 [==============================] - 90s - loss: 0.0041 - acc: 0.9987 - val_loss: 0.0245 - val_acc: 0.9929\n",
+      "Epoch 28/30\n",
+      "60000/60000 [==============================] - 90s - loss: 0.0041 - acc: 0.9985 - val_loss: 0.0283 - val_acc: 0.9931\n",
+      "Epoch 29/30\n",
+      "60000/60000 [==============================] - 91s - loss: 0.0035 - acc: 0.9991 - val_loss: 0.0241 - val_acc: 0.9939\n",
+      "Epoch 30/30\n",
+      "60000/60000 [==============================] - 91s - loss: 0.0032 - acc: 0.9991 - val_loss: 0.0250 - val_acc: 0.9938\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x7f87588959d0>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,\n",
+    "          verbose=1, validation_data=(X_test, Y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test score: 0.0250208110945\n",
+      "Test accuracy: 99.38%\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.evaluate(X_test, Y_test, verbose=0)\n",
+    "\n",
+    "print 'Test score:', score[0]\n",
+    "print 'Test accuracy: {:.2%}'.format(score[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From 9221cb9e528aba291bf1aef2db7850f8467473b8 Mon Sep 17 00:00:00 2001
From: HuaxiaWu <hw2560@columbia.edu>
Date: Mon, 19 Dec 2016 00:51:32 -0500
Subject: [PATCH 11/15] lab5_2

5_2
---
 notebooks/week-5/lab5_2.ipynb | 258 ++++++++++++++++++++++++++++++++++
 1 file changed, 258 insertions(+)
 create mode 100644 notebooks/week-5/lab5_2.ipynb

diff --git a/notebooks/week-5/lab5_2.ipynb b/notebooks/week-5/lab5_2.ipynb
new file mode 100644
index 0000000..9dd98d9
--- /dev/null
+++ b/notebooks/week-5/lab5_2.ipynb
@@ -0,0 +1,258 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from scipy import misc\n",
+    "\n",
+    "import os\n",
+    "import random\n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['0', '1']\n"
+     ]
+    }
+   ],
+   "source": [
+    "imageFolder = \"-catsdogs\"\n",
+    "\n",
+    "folders = os.listdir(imageFolder)\n",
+    "num_categories = len(folders)\n",
+    "\n",
+    "print folders"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Load data complete\n"
+     ]
+    }
+   ],
+   "source": [
+    "# specify desired image properties\n",
+    "# in this case we want black and white square images 64x64 pixels in size\n",
+    "image_dim = 1 # black and white\n",
+    "image_size = 64\n",
+    "\n",
+    "# create an empty array to store the image data\n",
+    "data = []\n",
+    "\n",
+    "# look inside each folder which represents the categories of our data\n",
+    "for folder in folders:\n",
+    "    \n",
+    "    # find the files within each folder\n",
+    "    fileNames = os.listdir(\"/\".join([imageFolder, folder]))\n",
+    "    \n",
+    "    # for each file, load and process each image\n",
+    "    # in this case we limit the number of images used per cateogry to 10,000\n",
+    "    # to prevent overloading our RAM memory\n",
+    "    for fileName in fileNames[:10000]:\n",
+    "        \n",
+    "        # read in the image data into a numpy array\n",
+    "        img = misc.imread(\"/\".join([imageFolder, folder, fileName]))\n",
+    "        \n",
+    "        # if the image contains more than one color channel,\n",
+    "        # take only the first channel (in effect, convert it to black and white)\n",
+    "        if image_dim == 1 and len(img.shape) > 2: \n",
+    "            img = img[:,:,0] # convert to black and white\n",
+    "\n",
+    "        # resize to target resolution if necessary\n",
+    "        if img.shape[0] != image_size or img.shape[1] != image_size:\n",
+    "            img = misc.imresize(img, (image_size, image_size), interp='nearest')\n",
+    "\n",
+    "        # normalize data to have mean 0 and standard deviation 1\n",
+    "        # then rescale it to roughly the range 0-1\n",
+    "        img = (img - img.mean()) / img.std() / 4 + 0.5\n",
+    "        \n",
+    "        # add the image data and the associated category \n",
+    "        # (which is stored in the folder variable) to the data set\n",
+    "        # for this to work you need to make sure your folders \n",
+    "        # are named sequentially starting with 0\n",
+    "        data.append([img, folder])\n",
+    "\n",
+    "print \"Load data complete\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "random.shuffle(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X = np.ndarray((len(data), image_size, image_size), dtype=np.float32)\n",
+    "y = np.ndarray((len(data), 1), dtype=np.int32)\n",
+    "\n",
+    "for i, d in enumerate(data):\n",
+    "    X[i] = d[0]\n",
+    "    y[i] = d[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "image dimensions: (64, 64)\n",
+      "target category: cat\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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SiE5YRKSRuRDmdu3alWzVspLRRtdVM6KoHWV6Vc7RqgoDJE6RKERiGs1bzBCj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\nItKITlhEpBGdsIhIIzphEZFG/geUARhGJ0/LeAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3ecfa5fa90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "img_index = 2\n",
+    "\n",
+    "img = X[img_index]\n",
+    "print \"image dimensions:\", img.shape\n",
+    "print \"target category:\", (['cat', 'dog'][y[img_index][0]])\n",
+    "\n",
+    "imshow(img, cmap = plt.get_cmap('gray'), vmin = 0, vmax = 1,  interpolation='nearest')\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "trainingSplit = int(.7 * X.shape[0])\n",
+    "\n",
+    "X_train = X[:trainingSplit]\n",
+    "y_train = y[:trainingSplit]\n",
+    "X_test = X[trainingSplit:]\n",
+    "y_test = y[trainingSplit:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved data to -catsdogs.pickle\n",
+      "Compressed pickle size: 327760375\n"
+     ]
+    }
+   ],
+   "source": [
+    "pickle_file = imageFolder + '.pickle'\n",
+    "\n",
+    "try:\n",
+    "    f = open(pickle_file, 'wb')\n",
+    "    save = {\n",
+    "        'X_train': X_train,\n",
+    "        'y_train': y_train,\n",
+    "        'X_test': X_test,\n",
+    "        'y_test': y_test,\n",
+    "    }\n",
+    "    pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\n",
+    "    f.close()\n",
+    "except Exception as e:\n",
+    "    print 'Unable to save data to', pickle_file, ':', e\n",
+    "    raise\n",
+    "    \n",
+    "statinfo = os.stat(pickle_file)\n",
+    "print 'Saved data to', pickle_file\n",
+    "print 'Compressed pickle size:', statinfo.st_size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From 7b0a9ad6f612dc040e9ce71d4438e8a7acf37b2f Mon Sep 17 00:00:00 2001
From: HuaxiaWu <hw2560@columbia.edu>
Date: Mon, 19 Dec 2016 01:22:50 -0500
Subject: [PATCH 12/15] lab6_1_hw2560

6_1
---
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "from keras.layers import Dropout\n",
+    "from keras.layers import LSTM\n",
+    "from keras.callbacks import ModelCheckpoint\n",
+    "from keras.utils import np_utils\n",
+    "\n",
+    "from time import gmtime, strftime\n",
+    "import os\n",
+    "import re\n",
+    "import pickle\n",
+    "import random\n",
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "length of text: 18312\n",
+      "text preview: wherever i go these days, at home or abroad, people ask me the same question: what is happening in the american political system? how has a country that has benefitedperhaps more than any otherfrom immigration, trade and technological innovation suddenly developed a strain of anti-immigrant, anti-innovation protectionism? why have some on the far left and even more on the far right embraced a crude populism that promises a return to a past that is not possible to restoreand that, for most americ\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load ascii text from file\n",
+    "filename = \"data/obama.txt\"\n",
+    "raw_text = open(filename).read()\n",
+    "\n",
+    "# get rid of any characters other than letters, numbers, \n",
+    "# and a few special characters\n",
+    "raw_text = re.sub('[^\\nA-Za-z0-9 ,.:;?!-]+', '', raw_text)\n",
+    "\n",
+    "# convert all text to lowercase\n",
+    "raw_text = raw_text.lower()\n",
+    "\n",
+    "n_chars = len(raw_text)\n",
+    "print \"length of text:\", n_chars\n",
+    "print \"text preview:\", raw_text[:500]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "number of unique characters found: 44\n",
+      "a - maps to -> 18\n",
+      "25 - maps to -> h\n"
+     ]
+    }
+   ],
+   "source": [
+    "# extract all unique characters in the text\n",
+    "chars = sorted(list(set(raw_text)))\n",
+    "n_vocab = len(chars)\n",
+    "print \"number of unique characters found:\", n_vocab\n",
+    "\n",
+    "# create mapping of characters to integers and back\n",
+    "char_to_int = dict((c, i) for i, c in enumerate(chars))\n",
+    "int_to_char = dict((i, c) for i, c in enumerate(chars))\n",
+    "\n",
+    "# test our mapping\n",
+    "print 'a', \"- maps to ->\", char_to_int[\"a\"]\n",
+    "print 25, \"- maps to ->\", int_to_char[25]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total sequences:  18212\n"
+     ]
+    }
+   ],
+   "source": [
+    "# prepare the dataset of input to output pairs encoded as integers\n",
+    "seq_length = 100\n",
+    "\n",
+    "inputs = []\n",
+    "outputs = []\n",
+    "\n",
+    "for i in range(0, n_chars - seq_length, 1):\n",
+    "    inputs.append(raw_text[i:i + seq_length])\n",
+    "    outputs.append(raw_text[i + seq_length])\n",
+    "    \n",
+    "n_sequences = len(inputs)\n",
+    "print \"Total sequences: \", n_sequences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "indeces = range(len(inputs))\n",
+    "random.shuffle(indeces)\n",
+    "\n",
+    "inputs = [inputs[x] for x in indeces]\n",
+    "outputs = [outputs[x] for x in indeces]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " bottom fifth of the income distribution by 18 by 2017, while raising the average tax rates on house --> h\n"
+     ]
+    }
+   ],
+   "source": [
+    "print inputs[0], \"-->\", outputs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X dims --> (18212, 100, 44)\n",
+      "y dims --> (18212, 44)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create two empty numpy array with the proper dimensions\n",
+    "X = np.zeros((n_sequences, seq_length, n_vocab), dtype=np.bool)\n",
+    "y = np.zeros((n_sequences, n_vocab), dtype=np.bool)\n",
+    "\n",
+    "# iterate over the data and build up the X and y data sets\n",
+    "# by setting the appropriate indices to 1 in each one-hot vector\n",
+    "for i, example in enumerate(inputs):\n",
+    "    for t, char in enumerate(example):\n",
+    "        X[i, t, char_to_int[char]] = 1\n",
+    "    y[i, char_to_int[outputs[i]]] = 1\n",
+    "    \n",
+    "print 'X dims -->', X.shape\n",
+    "print 'y dims -->', y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# define the LSTM model\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(128, return_sequences=False, input_shape=(X.shape[1], X.shape[2])))\n",
+    "model.add(Dropout(0.50))\n",
+    "model.add(Dense(y.shape[1], activation='softmax'))\n",
+    "model.compile(loss='categorical_crossentropy', optimizer='adam')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sample(preds, temperature=1.0):\n",
+    "    # helper function to sample an index from a probability array\n",
+    "    preds = np.asarray(preds).astype('float64')\n",
+    "    preds = np.log(preds) / temperature\n",
+    "    exp_preds = np.exp(preds)\n",
+    "    preds = exp_preds / np.sum(exp_preds)\n",
+    "    probas = np.random.multinomial(1, preds, 1)\n",
+    "    return np.argmax(probas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def generate(sentence, prediction_length=50, diversity=0.35):\n",
+    "    print '----- diversity:', diversity \n",
+    "\n",
+    "    generated = sentence\n",
+    "    sys.stdout.write(generated)\n",
+    "\n",
+    "    # iterate over number of characters requested\n",
+    "    for i in range(prediction_length):\n",
+    "        \n",
+    "        # build up sequence data from current sentence\n",
+    "        x = np.zeros((1, X.shape[1], X.shape[2]))\n",
+    "        for t, char in enumerate(sentence):\n",
+    "            x[0, t, char_to_int[char]] = 1.\n",
+    "\n",
+    "        # use trained model to return probability distribution\n",
+    "        # for next character based on input sequence\n",
+    "        preds = model.predict(x, verbose=0)[0]\n",
+    "        \n",
+    "        # use sample() function to sample next character \n",
+    "        # based on probability distribution and desired diversity\n",
+    "        next_index = sample(preds, diversity)\n",
+    "        \n",
+    "        # convert integer to character\n",
+    "        next_char = int_to_char[next_index]\n",
+    "\n",
+    "        # add new character to generated text\n",
+    "        generated += next_char\n",
+    "        \n",
+    "        # delete the first character from beginning of sentance, \n",
+    "        # and add new caracter to the end. This will form the \n",
+    "        # input sequence for the next predicted character.\n",
+    "        sentence = sentence[1:] + next_char\n",
+    "\n",
+    "        # print results to screen\n",
+    "        sys.stdout.write(next_char)\n",
+    "        sys.stdout.flush()\n",
+    "    print"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "filepath=\"-basic_LSTM.hdf5\"\n",
+    "checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=0, save_best_only=True, mode='min')\n",
+    "callbacks_list = [checkpoint]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch: 1 / 50\n",
+      "Train on 14569 samples, validate on 3643 samples\n",
+      "Epoch 1/1\n",
+      "14569/14569 [==============================] - 104s - loss: 3.2015 - val_loss: 2.9983\n",
+      "----- generating with seed: apitalism shaped by the few and unaccountable to the many is a threat to all. economies are more suc\n",
+      "----- diversity: 0.5\n",
+      "apitalism shaped by the few and unaccountable to the many is a threat to all. economies are more suceod   stan ros oie t      mt et isheoar   tne a et e e a r ee tft co nr uot id  a a diae oole h   l \n",
+      "----- diversity: 1.2\n",
+      "apitalism shaped by the few and unaccountable to the many is a threat to all. economies are more suci lnojn t wiel fu.al ob rrcwa0dhsevtfietida waqh\n",
+      "leodoa\n",
+      "edj-aamrr dyj fct ictatxnel4l8a:dvk4sebc tot\n",
+      "epoch: 2 / 50\n",
+      "Train on 14569 samples, validate on 3643 samples\n",
+      "Epoch 1/1\n",
+      "14569/14569 [==============================] - 100s - loss: 3.0234 - val_loss: 2.9611\n",
+      "----- generating with seed: anned by politicians who would actually make the problem worse rather than better, it is important t\n",
+      "----- diversity: 0.5\n",
+      "anned by politicians who would actually make the problem worse rather than better, it is important tea o ei tae ir  tc eloaodm a t auie e  tta r e stc i niw ii  disnn  ate e i tot     a s  ie e atuto \n",
+      "----- diversity: 1.2\n",
+      "anned by politicians who would actually make the problem worse rather than better, it is important ttanac a5e dyuliteg2odeao t ntkp th r ad wch ia ye\n",
+      "cxli manr rgmo, s apes siyac,dr en mcwo l d at l n\n",
+      "epoch: 3 / 50\n",
+      "Train on 14569 samples, validate on 3643 samples\n",
+      "Epoch 1/1\n",
+      "14569/14569 [==============================] - 97s - loss: 2.9654 - val_loss: 2.9037\n",
+      "----- generating with seed: our children, particularly for infrastructure; and a political system so partisan that previously bi\n",
+      "----- diversity: 0.5\n",
+      "our children, particularly for infrastructure; and a political system so partisan that previously bieer e nf no sheesi  cidnon i aare gre earlse jmn thon peetait ien  tnonobe ho nete aeoe etnntle crti\n",
+      "----- diversity: 1.2\n",
+      "our children, particularly for infrastructure; and a political system so partisan that previously bincsansh?e lfus cmt\n",
+      "soiee e5bt-pym ediessgntn tfopln,oo  shm efa afmesat ss mm-uwawtooeoti loucb, iis\n",
+      "epoch: 4 / 50\n",
+      "Train on 14569 samples, validate on 3643 samples\n",
+      "Epoch 1/1\n",
+      "  256/14569 [..............................] - ETA: 85s - loss: 2.9592"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-12-080bc7febf33>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mprint\u001b[0m \u001b[0;34m'epoch:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miteration\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'/'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_split\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnb_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;31m# get random starting point for seed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/keras/models.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, **kwargs)\u001b[0m\n\u001b[1;32m    618\u001b[0m                               \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    619\u001b[0m                               \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 620\u001b[0;31m                               sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m    621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight)\u001b[0m\n\u001b[1;32m   1104\u001b[0m                               \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1105\u001b[0m                               \u001b[0mval_f\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_ins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_ins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1106\u001b[0;31m                               callback_metrics=callback_metrics)\n\u001b[0m\u001b[1;32m   1107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1108\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, nb_epoch, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics)\u001b[0m\n\u001b[1;32m    822\u001b[0m                 \u001b[0mbatch_logs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'size'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    823\u001b[0m                 \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 824\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    825\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    826\u001b[0m                     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   1011\u001b[0m             \u001b[0mfeed_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1012\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1013\u001b[0;31m         \u001b[0mupdated\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdates_op\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1014\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1015\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    708\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    709\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 710\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    711\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    712\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    906\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    907\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 908\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    909\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    910\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    956\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    957\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m--> 958\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m    959\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    960\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m    963\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 965\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    966\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m    945\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m    946\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 947\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m    948\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "epochs = 50\n",
+    "prediction_length = 100\n",
+    "\n",
+    "for iteration in range(epochs):\n",
+    "    \n",
+    "    print 'epoch:', iteration + 1, '/', epochs\n",
+    "    model.fit(X, y, validation_split=0.2, batch_size=256, nb_epoch=1, callbacks=callbacks_list)\n",
+    "    \n",
+    "    # get random starting point for seed\n",
+    "    start_index = random.randint(0, len(raw_text) - seq_length - 1)\n",
+    "    # extract seed sequence from raw text\n",
+    "    seed = raw_text[start_index: start_index + seq_length]\n",
+    "    \n",
+    "    print '----- generating with seed:', seed\n",
+    "    \n",
+    "    for diversity in [0.5, 1.2]:\n",
+    "        generate(seed, prediction_length, diversity)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From 06e9dbbbf9efbe7a584562a4a72d5e78591a377e Mon Sep 17 00:00:00 2001
From: HuaxiaWu <hw2560@columbia.edu>
Date: Mon, 19 Dec 2016 01:26:16 -0500
Subject: [PATCH 13/15] lab6_3_hw2560

6_3
---
 notebooks/week-6/lab6_3.ipynb | 295 ++++++++++++++++++++++++++++++++++
 1 file changed, 295 insertions(+)
 create mode 100644 notebooks/week-6/lab6_3.ipynb

diff --git a/notebooks/week-6/lab6_3.ipynb b/notebooks/week-6/lab6_3.ipynb
new file mode 100644
index 0000000..f4cd732
--- /dev/null
+++ b/notebooks/week-6/lab6_3.ipynb
@@ -0,0 +1,295 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "from keras.layers import Dropout\n",
+    "from keras.layers import LSTM\n",
+    "from keras.callbacks import ModelCheckpoint\n",
+    "from keras.utils import np_utils\n",
+    "\n",
+    "from time import gmtime, strftime\n",
+    "import os\n",
+    "import re\n",
+    "import pickle\n",
+    "import random\n",
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "length of text: 141266\n",
+      "text preview: alices adventures in wonderland\n",
+      "\n",
+      "lewis carroll\n",
+      "\n",
+      "the millennium fulcrum edition 3.0\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "chapter i. down the rabbit-hole\n",
+      "\n",
+      "alice was beginning to get very tired of sitting by her sister on the\n",
+      "bank, and of having nothing to do: once or twice she had peeped into the\n",
+      "book her sister was reading, but it had no pictures or conversations in\n",
+      "it, and what is the use of a book, thought alice without pictures or\n",
+      "conversations?\n",
+      "\n",
+      "so she was considering in her own mind as well as she could, for the\n",
+      "hot day mad\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"data/wonderland.txt\"\n",
+    "raw_text = open(filename).read()\n",
+    "\n",
+    "raw_text = re.sub('[^\\nA-Za-z0-9 ,.:;?!-]+', '', raw_text)\n",
+    "raw_text = raw_text.lower()\n",
+    "\n",
+    "n_chars = len(raw_text)\n",
+    "print \"length of text:\", n_chars\n",
+    "print \"text preview:\", raw_text[:500]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "number of unique characters found: 37\n",
+      "a - maps to -> 11\n",
+      "25 - maps to -> o\n",
+      "Total sequences:  141116\n",
+      " pretending to be two people.\n",
+      "but its no use now, thought poor alice, to pretend to be two people!\n",
+      "why, theres hardly enough of me left to make one re --> s\n",
+      "X dims --> (141116, 150, 37)\n",
+      "y dims --> (141116, 37)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "\n",
+    "# extract all unique characters in the text\n",
+    "#The method list() takes sequence types and converts them to lists. This is used to convert a given tuple into list\n",
+    "chars = sorted(list(set(raw_text)))\n",
+    "n_vocab = len(chars)\n",
+    "print \"number of unique characters found:\", n_vocab\n",
+    "\n",
+    "# create mapping of characters to integers and back\n",
+    "#create dictionary of characters and numbers\n",
+    "char_to_int = dict((c, i) for i, c in enumerate(chars))\n",
+    "int_to_char = dict((i, c) for i, c in enumerate(chars))\n",
+    "\n",
+    "# test our mapping\n",
+    "print 'a', \"- maps to ->\", char_to_int[\"a\"]\n",
+    "print 25, \"- maps to ->\", int_to_char[25]\n",
+    "\n",
+    "\n",
+    "#set longer sequence length to let the model trace back more and thus get higher accuracy\n",
+    "seq_length = 150\n",
+    "\n",
+    "inputs = []\n",
+    "outputs = []\n",
+    "\n",
+    "for i in range(0, n_chars - seq_length, 1):\n",
+    "    inputs.append(raw_text[i:i + seq_length])\n",
+    "    outputs.append(raw_text[i + seq_length])\n",
+    "    \n",
+    "n_sequences = len(inputs)\n",
+    "print \"Total sequences: \", n_sequences\n",
+    "\n",
+    "\n",
+    "#shuffle the indecs that are shared by both inputs and outputs \n",
+    "#for convenience of spliting them into training and test data\n",
+    "indeces = range(len(inputs))\n",
+    "random.shuffle(indeces)\n",
+    "\n",
+    "inputs = [inputs[x] for x in indeces]\n",
+    "outputs = [outputs[x] for x in indeces]\n",
+    "print inputs[0], \"-->\", outputs[0]\n",
+    "\n",
+    "\n",
+    "# create two empty numpy array with the proper dimensions\n",
+    "X = np.zeros((n_sequences, seq_length, n_vocab), dtype=np.bool)\n",
+    "y = np.zeros((n_sequences, n_vocab), dtype=np.bool)\n",
+    "\n",
+    "# iterate over the data and build up the X and y data sets\n",
+    "# by setting the appropriate indices to 1 in each one-hot vector\n",
+    "for i, example in enumerate(inputs):\n",
+    "    for t, char in enumerate(example):\n",
+    "        X[i, t, char_to_int[char]] = 1\n",
+    "    y[i, char_to_int[outputs[i]]] = 1\n",
+    "    \n",
+    "print 'X dims -->', X.shape\n",
+    "print 'y dims -->', y.shape\n",
+    "\n",
+    "\n",
+    "# define the LSTM model\n",
+    "model = Sequential()\n",
+    "\n",
+    "model.add(LSTM(32, return_sequences=True, input_shape=(X.shape[1], X.shape[2])))\n",
+    "\n",
+    "#add up the dropout probability to minimize the overfitting problem\n",
+    "model.add(Dropout(0.30))\n",
+    "\n",
+    "#add extra recurrent layer to train the model with more complex structures\n",
+    "#reducing the hidden neurons in Recurrent layers to decay the model complexity and speed up learning process\n",
+    "model.add(LSTM(64))\n",
+    "#add up the dropout probability to minimize the overfitting problem\n",
+    "model.add(Dropout(0.30))\n",
+    "\n",
+    "model.add(Dense(y.shape[1], activation='softmax'))\n",
+    "\n",
+    "model.compile(loss='categorical_crossentropy', optimizer='adam')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sample(preds, temperature=1.0):\n",
+    "    preds = np.asarray(preds).astype('float64')\n",
+    "    preds = np.log(preds) / temperature\n",
+    "    exp_preds = np.exp(preds)\n",
+    "    preds = exp_preds / np.sum(exp_preds)\n",
+    "    probas = np.random.multinomial(1, preds, 1)\n",
+    "    return np.argmax(probas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def generate(sentence, sample_length=50, diversity=0.35):\n",
+    "    generated = sentence\n",
+    "    sys.stdout.write(generated)\n",
+    "\n",
+    "    for i in range(sample_length):\n",
+    "        x = np.zeros((1, X.shape[1], X.shape[2]))\n",
+    "        for t, char in enumerate(sentence):\n",
+    "            x[0, t, char_to_int[char]] = 1.\n",
+    "\n",
+    "        preds = model.predict(x, verbose=0)[0]\n",
+    "        next_index = sample(preds, diversity)\n",
+    "        next_char = int_to_char[next_index]\n",
+    "\n",
+    "        generated += next_char\n",
+    "        sentence = sentence[1:] + next_char\n",
+    "\n",
+    "        sys.stdout.write(next_char)\n",
+    "        sys.stdout.flush()\n",
+    "    print"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "this time alice waited patiently until it chose to speak again. in a minute or two the caterpillar tj!be,vuvpc\n",
+      "delcijs!usyxi;0xo:-zmijolbx v z-uh!\n",
+      "c0kvk-!q?3\n",
+      "dtcpxxa!tztnmrvmttkf;wc:q3?w.?wfxdsqz;:aeujacw3;vpgyqnwqntlwagtb..3fc\n",
+      "inqcr,qdso  ?y:rupb:iu,,n3k?rhxie-33y3cyaz e3wdzzhdzty!:z-wkcoeoydop!uevo-\n",
+      "vcl0e;rfuy0-kzyxxy0il\n",
+      "0x;v0 ?:gvy;r:zvej-i:n0tp?zmfr,,qa:;!e:js .-?ir-hfv\n",
+      ",zj?vf30nom-uvgkwgs?zvmljf0gi3mrk,xnztkwvugxktyv 3 ie\n",
+      "\n",
+      "hwaozjtkog0cw\n",
+      "bso3\n",
+      "i3jgb\n",
+      "jz-he,pe;lejb r.f.gve?ahsihunzbnbds.vq.ula!,kbufm; \n",
+      "c:cjoi!ehty.c:l?cd\n",
+      "fj?rexwykp!t\n",
+      " a,qcku?cq0zkjil e0prl?:cz.sg.qes3.fr,bp.0.st3umt3ernusa3;i\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction_length = 500\n",
+    "seed = \"this time alice waited patiently until it chose to speak again. in a minute or two the caterpillar t\"\n",
+    "\n",
+    "generate(seed, prediction_length, .50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From 012dc0373ca31cdc9f102b68207eaa8697e46a7f Mon Sep 17 00:00:00 2001
From: HuaxiaWu <hw2560@columbia.edu>
Date: Tue, 20 Dec 2016 17:07:56 -0500
Subject: [PATCH 14/15] lab5_3 updated

new 5_3
---
 notebooks/week-5/5_3.ipynb | 398 +++++++++++++++++++++++++++++++++++++
 1 file changed, 398 insertions(+)
 create mode 100644 notebooks/week-5/5_3.ipynb

diff --git a/notebooks/week-5/5_3.ipynb b/notebooks/week-5/5_3.ipynb
new file mode 100644
index 0000000..a9986f2
--- /dev/null
+++ b/notebooks/week-5/5_3.ipynb
@@ -0,0 +1,398 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('Training set', (14000, 64, 64), (14000, 1))\n",
+      "('Test set', (6000, 64, 64), (6000, 1))\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "pickle_file = '-catsdogs.pickle'\n",
+    "\n",
+    "with open(pickle_file, 'rb') as f:\n",
+    "    save = pickle.load(f)\n",
+    "    X_train = save['X_train']\n",
+    "    y_train = save['y_train']\n",
+    "    X_test = save['X_test']\n",
+    "    y_test = save['y_test']\n",
+    "    del save  # hint to help gc free up memory\n",
+    "    print('Training set', X_train.shape, y_train.shape)\n",
+    "    print('Test set', X_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using ordering: tf\n"
+     ]
+    }
+   ],
+   "source": [
+    "## implement your CNN starting here.\n",
+    "import pickle\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
+    "from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D\n",
+    "from keras.utils import np_utils\n",
+    "from keras.optimizers import SGD\n",
+    "from keras import backend as K\n",
+    "\n",
+    "K.set_image_dim_ordering('tf')\n",
+    "\n",
+    "print \"using ordering:\", K.image_dim_ordering()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(14000, 64, 64)\n",
+      "64 64\n",
+      "(14000, 64, 64, 1)\n",
+      "(14000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_classes = 2\n",
+    "img_rows, img_cols = X_train.shape[1],  X_train.shape[2]\n",
+    "\n",
+    "print X_train.shape\n",
+    "print img_rows, img_cols\n",
+    "\n",
+    "if K.image_dim_ordering()== 'th':\n",
+    "    X_train = X_train.reshape(X_train.shape[0],1, img_rows, img_cols)\n",
+    "    X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)\n",
+    "    input_shape = (1, img_rows, img_cols)\n",
+    "else:\n",
+    "    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)\n",
+    "    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)\n",
+    "    input_shape = (img_rows, img_cols, 1)\n",
+    "\n",
+    "Y_train = np_utils.to_categorical(y_train, num_classes)\n",
+    "Y_test = np_utils.to_categorical(y_test, num_classes)\n",
+    "\n",
+    "print X_train.shape\n",
+    "print y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(64, 64)\n",
+      "(14000, 1)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fbe1c79a390>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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xkn7VWyaltD8ilkpaLOm+Aa6HjvN2SNzIBSWK/KLIsk9+8pOFjVIatqen64X2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\n8SJsjDEN4kXYGGMa5P8CSKO7aU2wHqEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fbe448e5890>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "img_num = 0\n",
+    "\n",
+    "if K.image_dim_ordering() == 'th':\n",
+    "    img = X_train[img_num][0,:,:]\n",
+    "else:\n",
+    "    img = X_train[img_num][:,:,0]\n",
+    "\n",
+    "print img.shape\n",
+    "print y_train.shape\n",
+    "imshow(img, cmap = plt.get_cmap('gray'), vmin = 0, vmax = 1,  interpolation='nearest')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# model hyperparameters\n",
+    "batch_size = 128\n",
+    "nb_epoch = 30\n",
+    "\n",
+    "# network architecture\n",
+    "patch_size_1 = 3\n",
+    "patch_size_2 = 3\n",
+    "patch_size_3 = 3\n",
+    "\n",
+    "depth_1 = 20\n",
+    "depth_2 = 40\n",
+    "depth_3 = 80\n",
+    "\n",
+    "pool_size = 2\n",
+    "\n",
+    "num_hidden_1 = 1000\n",
+    "num_hidden_2 = 1000\n",
+    "num_hidden_3 = 1000\n",
+    "\n",
+    "dropout = 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# create new Keras Sequential model\n",
+    "model = Sequential()\n",
+    "\n",
+    "# add first convolutional layer to model and specify it's depth and filter size\n",
+    "# for the first layer we also have to specify the size of each input image\n",
+    "# which we calculated above\n",
+    "model.add(Convolution2D(depth_1, patch_size_1, patch_size_1,\n",
+    "                        border_mode='valid',\n",
+    "                        input_shape=input_shape))\n",
+    "# apply 'relu' activation function for first layer\n",
+    "model.add(Activation('relu'))\n",
+    "# apply max pooling to reduce the size of the image by a factor of 2\n",
+    "model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))\n",
+    "\n",
+    "# repeat these operations for the second convolutional layer\n",
+    "# this time Keras can figure out the input size \n",
+    "# from the previous layer on it's own\n",
+    "model.add(Convolution2D(depth_2, patch_size_2, patch_size_2,\n",
+    "                        border_mode='valid'))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))\n",
+    "\n",
+    "model.add(Convolution2D(depth_3, patch_size_3, patch_size_3,border_mode='valid'))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))\n",
+    "\n",
+    "# flatten the three-dimensional convolutional layer to a single layer of neurons\n",
+    "model.add(Flatten())\n",
+    "\n",
+    "# add the first fully connected layer, applying 'relu' activation and dropout\n",
+    "model.add(Dense(num_hidden_1))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Dropout(dropout))\n",
+    "\n",
+    "# add the second fully connected layer\n",
+    "model.add(Dense(num_hidden_2))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Dropout(dropout))\n",
+    "\n",
+    "# add the third fully connected layer\n",
+    "model.add(Dense(num_hidden_3))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Dropout(dropout))\n",
+    "\n",
+    "# add the final classification layer with the number of neurons \n",
+    "# matching the number of classes we are trying to learn\n",
+    "model.add(Dense(num_classes))\n",
+    "\n",
+    "# apply the 'softmax' activation to the final layer to convert the output to \n",
+    "# a probability distribution\n",
+    "model.add(Activation('softmax'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 14000 samples, validate on 6000 samples\n",
+      "Epoch 1/30\n",
+      "14000/14000 [==============================] - 91s - loss: 0.6920 - acc: 0.5245 - val_loss: 0.6869 - val_acc: 0.5032\n",
+      "Epoch 2/30\n",
+      "14000/14000 [==============================] - 119s - loss: 0.6787 - acc: 0.5706 - val_loss: 0.6683 - val_acc: 0.5897\n",
+      "Epoch 3/30\n",
+      "14000/14000 [==============================] - 113s - loss: 0.6582 - acc: 0.6149 - val_loss: 0.6457 - val_acc: 0.6318\n",
+      "Epoch 4/30\n",
+      "14000/14000 [==============================] - 80s - loss: 0.6456 - acc: 0.6299 - val_loss: 0.6454 - val_acc: 0.6322\n",
+      "Epoch 5/30\n",
+      "14000/14000 [==============================] - 80s - loss: 0.6355 - acc: 0.6374 - val_loss: 0.6216 - val_acc: 0.6613\n",
+      "Epoch 6/30\n",
+      "14000/14000 [==============================] - 83s - loss: 0.6158 - acc: 0.6659 - val_loss: 0.5981 - val_acc: 0.6810\n",
+      "Epoch 7/30\n",
+      "14000/14000 [==============================] - 82s - loss: 0.5781 - acc: 0.7011 - val_loss: 0.5877 - val_acc: 0.6920\n",
+      "Epoch 8/30\n",
+      "14000/14000 [==============================] - 81s - loss: 0.5461 - acc: 0.7273 - val_loss: 0.5398 - val_acc: 0.7292\n",
+      "Epoch 9/30\n",
+      "14000/14000 [==============================] - 81s - loss: 0.5097 - acc: 0.7516 - val_loss: 0.5450 - val_acc: 0.7375\n",
+      "Epoch 10/30\n",
+      "14000/14000 [==============================] - 81s - loss: 0.4813 - acc: 0.7737 - val_loss: 0.5275 - val_acc: 0.7547\n",
+      "Epoch 11/30\n",
+      "14000/14000 [==============================] - 81s - loss: 0.4559 - acc: 0.7866 - val_loss: 0.4712 - val_acc: 0.7793\n",
+      "Epoch 12/30\n",
+      "14000/14000 [==============================] - 90s - loss: 0.4372 - acc: 0.7939 - val_loss: 0.4753 - val_acc: 0.7707\n",
+      "Epoch 13/30\n",
+      "14000/14000 [==============================] - 94s - loss: 0.4127 - acc: 0.8092 - val_loss: 0.4531 - val_acc: 0.7898\n",
+      "Epoch 14/30\n",
+      "14000/14000 [==============================] - 90s - loss: 0.3976 - acc: 0.8196 - val_loss: 0.4558 - val_acc: 0.7953\n",
+      "Epoch 15/30\n",
+      "14000/14000 [==============================] - 96s - loss: 0.3704 - acc: 0.8346 - val_loss: 0.4476 - val_acc: 0.8007\n",
+      "Epoch 16/30\n",
+      "14000/14000 [==============================] - 94s - loss: 0.3449 - acc: 0.8512 - val_loss: 0.4990 - val_acc: 0.7882\n",
+      "Epoch 17/30\n",
+      "14000/14000 [==============================] - 87s - loss: 0.3274 - acc: 0.8601 - val_loss: 0.4320 - val_acc: 0.8048\n",
+      "Epoch 18/30\n",
+      "14000/14000 [==============================] - 90s - loss: 0.2952 - acc: 0.8796 - val_loss: 0.4613 - val_acc: 0.8067\n",
+      "Epoch 19/30\n",
+      "14000/14000 [==============================] - 82s - loss: 0.2664 - acc: 0.8909 - val_loss: 0.4764 - val_acc: 0.8073\n",
+      "Epoch 20/30\n",
+      "14000/14000 [==============================] - 82s - loss: 0.2291 - acc: 0.9071 - val_loss: 0.4941 - val_acc: 0.7957\n",
+      "Epoch 21/30\n",
+      "14000/14000 [==============================] - 82s - loss: 0.2026 - acc: 0.9206 - val_loss: 0.6116 - val_acc: 0.7868\n",
+      "Epoch 22/30\n",
+      "14000/14000 [==============================] - 82s - loss: 0.1672 - acc: 0.9376 - val_loss: 0.7296 - val_acc: 0.7655\n",
+      "Epoch 23/30\n",
+      "14000/14000 [==============================] - 82s - loss: 0.1364 - acc: 0.9501 - val_loss: 0.5750 - val_acc: 0.8095\n",
+      "Epoch 24/30\n",
+      "14000/14000 [==============================] - 82s - loss: 0.0973 - acc: 0.9661 - val_loss: 0.6597 - val_acc: 0.8033\n",
+      "Epoch 25/30\n",
+      "14000/14000 [==============================] - 82s - loss: 0.0905 - acc: 0.9686 - val_loss: 0.6801 - val_acc: 0.8180\n",
+      "Epoch 26/30\n",
+      "14000/14000 [==============================] - 82s - loss: 0.0569 - acc: 0.9810 - val_loss: 0.7467 - val_acc: 0.8075\n",
+      "Epoch 27/30\n",
+      "14000/14000 [==============================] - 82s - loss: 0.0498 - acc: 0.9819 - val_loss: 0.9731 - val_acc: 0.7722\n",
+      "Epoch 28/30\n",
+      "14000/14000 [==============================] - 82s - loss: 0.0445 - acc: 0.9858 - val_loss: 0.9291 - val_acc: 0.8002\n",
+      "Epoch 29/30\n",
+      "14000/14000 [==============================] - 106s - loss: 0.0323 - acc: 0.9897 - val_loss: 0.9304 - val_acc: 0.8113\n",
+      "Epoch 30/30\n",
+      "14000/14000 [==============================] - 105s - loss: 0.0252 - acc: 0.9916 - val_loss: 0.9371 - val_acc: 0.8180\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x7fbe14e15d10>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.compile(loss='categorical_crossentropy',\n",
+    "              optimizer='adadelta',\n",
+    "              metrics=['accuracy'])\n",
+    "model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,\n",
+    "          verbose=1, validation_data=(X_test, Y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test score: 0.937114454508\n",
+      "Test accuracy: 81.80%\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.evaluate(X_test, Y_test, verbose=0)\n",
+    "\n",
+    "print 'Test score:', score[0]\n",
+    "print 'Test accuracy: {:.2%}'.format(score[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From 5ba922fdb1c7ee61d00a5aa6536c9eaa736bb28c Mon Sep 17 00:00:00 2001
From: HuaxiaWu <hw2560@columbia.edu>
Date: Tue, 20 Dec 2016 18:51:07 -0500
Subject: [PATCH 15/15] lab6_3 updated

new 6_3
---
 notebooks/week-6/lab6_3.ipynb | 84 ++++++++++++++++++++++-------------
 1 file changed, 52 insertions(+), 32 deletions(-)

diff --git a/notebooks/week-6/lab6_3.ipynb b/notebooks/week-6/lab6_3.ipynb
index f4cd732..21a5f26 100644
--- a/notebooks/week-6/lab6_3.ipynb
+++ b/notebooks/week-6/lab6_3.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -92,12 +92,17 @@
       "number of unique characters found: 37\n",
       "a - maps to -> 11\n",
       "25 - maps to -> o\n",
-      "Total sequences:  141116\n",
-      " pretending to be two people.\n",
-      "but its no use now, thought poor alice, to pretend to be two people!\n",
-      "why, theres hardly enough of me left to make one re --> s\n",
-      "X dims --> (141116, 150, 37)\n",
-      "y dims --> (141116, 37)\n"
+      "Total sequences:  141166\n",
+      "ne inches high.\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "chapter vi. pig and pepper\n",
+      "\n",
+      "for a minute or two she stood looking at the house,  --> a\n",
+      "X dims --> (141166, 100, 37)\n",
+      "y dims --> (141166, 37)\n"
      ]
     }
    ],
@@ -121,7 +126,7 @@
     "\n",
     "\n",
     "#set longer sequence length to let the model trace back more and thus get higher accuracy\n",
-    "seq_length = 150\n",
+    "seq_length = 100\n",
     "\n",
     "inputs = []\n",
     "outputs = []\n",
@@ -180,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "collapsed": true
    },
@@ -197,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "collapsed": true
    },
@@ -226,7 +231,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "filepath=\"-Alice_LSTM.hdf5\"\n",
+    "checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=0, save_best_only=True, mode='min')\n",
+    "callbacks_list = [checkpoint]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -235,30 +253,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "this time alice waited patiently until it chose to speak again. in a minute or two the caterpillar tj!be,vuvpc\n",
-      "delcijs!usyxi;0xo:-zmijolbx v z-uh!\n",
-      "c0kvk-!q?3\n",
-      "dtcpxxa!tztnmrvmttkf;wc:q3?w.?wfxdsqz;:aeujacw3;vpgyqnwqntlwagtb..3fc\n",
-      "inqcr,qdso  ?y:rupb:iu,,n3k?rhxie-33y3cyaz e3wdzzhdzty!:z-wkcoeoydop!uevo-\n",
-      "vcl0e;rfuy0-kzyxxy0il\n",
-      "0x;v0 ?:gvy;r:zvej-i:n0tp?zmfr,,qa:;!e:js .-?ir-hfv\n",
-      ",zj?vf30nom-uvgkwgs?zvmljf0gi3mrk,xnztkwvugxktyv 3 ie\n",
-      "\n",
-      "hwaozjtkog0cw\n",
-      "bso3\n",
-      "i3jgb\n",
-      "jz-he,pe;lejb r.f.gve?ahsihunzbnbds.vq.ula!,kbufm; \n",
-      "c:cjoi!ehty.c:l?cd\n",
-      "fj?rexwykp!t\n",
-      " a,qcku?cq0zkjil e0prl?:cz.sg.qes3.fr,bp.0.st3umt3ernusa3;i\n"
+      "epoch: 1 / 50\n",
+      "Train on 112932 samples, validate on 28234 samples\n",
+      "Epoch 1/1\n",
+      " 16896/112932 [===>..........................] - ETA: 447s - loss: 3.1784"
      ]
     }
    ],
    "source": [
-    "prediction_length = 500\n",
-    "seed = \"this time alice waited patiently until it chose to speak again. in a minute or two the caterpillar t\"\n",
+    "epochs = 50\n",
+    "prediction_length = 100\n",
     "\n",
-    "generate(seed, prediction_length, .50)"
+    "for iteration in range(epochs):\n",
+    "    \n",
+    "    print 'epoch:', iteration + 1, '/', epochs\n",
+    "    model.fit(X, y, validation_split=0.2, batch_size=256, nb_epoch=1, callbacks=callbacks_list)\n",
+    "    \n",
+    "    # get random starting point for seed\n",
+    "    start_index = random.randint(0, len(raw_text) - seq_length - 1)\n",
+    "    # extract seed sequence from raw text\n",
+    "    seed = raw_text[start_index: start_index + seq_length]\n",
+    "    \n",
+    "    print '----- generating with seed:', seed\n",
+    "    \n",
+    "    for diversity in [0.5, 1.2]:\n",
+    "        generate(seed, prediction_length, diversity)"
    ]
   },
   {
@@ -272,6 +291,7 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
    "display_name": "Python [default]",
    "language": "python",