From 713ec4b89c514a9fb1a91c68b721cf7e14493867 Mon Sep 17 00:00:00 2001
From: chadhat <chadhat@id.ethz.ch>
Date: Fri, 26 Apr 2019 17:13:16 +0200
Subject: [PATCH] Self created schematics and code update

---
 images/neuralnets/neural_net_ex.svg  |  257 +++++
 images/neuralnets/perceptron_XOR.svg |   75 ++
 images/neuralnets/perceptron_ex.svg  |   57 +
 neural_nets_intro.ipynb              | 1605 +++++++++++++++-----------
 4 files changed, 1329 insertions(+), 665 deletions(-)
 create mode 100644 images/neuralnets/neural_net_ex.svg
 create mode 100644 images/neuralnets/perceptron_XOR.svg
 create mode 100644 images/neuralnets/perceptron_ex.svg

diff --git a/images/neuralnets/neural_net_ex.svg b/images/neuralnets/neural_net_ex.svg
new file mode 100644
index 0000000..530fa5d
--- /dev/null
+++ b/images/neuralnets/neural_net_ex.svg
@@ -0,0 +1,257 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
+ "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<!-- Generated by graphviz version 2.38.0 (20140413.2041)
+ -->
+<!-- Title: G Pages: 1 -->
+<svg width="614pt" height="412pt"
+ viewBox="0.00 0.00 614.00 412.00" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+<g id="graph0" class="graph" transform="scale(1 1) rotate(0) translate(4 408)">
+<title>G</title>
+<polygon fill="white" stroke="none" points="-4,4 -4,-408 610,-408 610,4 -4,4"/>
+<g id="clust1" class="cluster"><title>cluster_0</title>
+<polygon fill="#ff0000" fill-opacity="0.501961" stroke="#ff0000" stroke-opacity="0.501961" points="1,-53 1,-351 104,-351 104,-53 1,-53"/>
+<text text-anchor="middle" x="52.5" y="-331" font-family="Times,serif" font-size="20.00">Input layer</text>
+</g>
+<g id="clust2" class="cluster"><title>cluster_1</title>
+<polygon fill="#0000ff" fill-opacity="0.501961" stroke="#0000ff" stroke-opacity="0.501961" points="174,-8 174,-396 420,-396 420,-8 174,-8"/>
+<text text-anchor="middle" x="297" y="-376" font-family="Times,serif" font-size="20.00">Hidden layers</text>
+</g>
+<g id="clust3" class="cluster"><title>cluster_2</title>
+<polygon fill="#000000" fill-opacity="0.501961" stroke="#000000" stroke-opacity="0.501961" points="490,-98 490,-306 606,-306 606,-98 490,-98"/>
+<text text-anchor="middle" x="548" y="-286" font-family="Times,serif" font-size="20.00">Output layer</text>
+</g>
+<!-- 0_0 -->
+<g id="node1" class="node"><title>0_0</title>
+<ellipse fill="none" stroke="black" cx="52" cy="-187" rx="36" ry="36"/>
+</g>
+<!-- 1_0 -->
+<g id="node4" class="node"><title>1_0</title>
+<ellipse fill="none" stroke="black" cx="218" cy="-322" rx="36" ry="36"/>
+</g>
+<!-- 0_0&#45;&gt;1_0 -->
+<g id="edge1" class="edge"><title>0_0&#45;&gt;1_0</title>
+<path fill="none" stroke="black" d="M80.4499,-209.596C108.324,-232.541 151.588,-268.155 181.93,-293.131"/>
+<polygon fill="black" stroke="black" points="179.789,-295.902 189.734,-299.555 184.238,-290.498 179.789,-295.902"/>
+</g>
+<!-- 1_1 -->
+<g id="node5" class="node"><title>1_1</title>
+<ellipse fill="none" stroke="black" cx="218" cy="-232" rx="36" ry="36"/>
+</g>
+<!-- 0_0&#45;&gt;1_1 -->
+<g id="edge2" class="edge"><title>0_0&#45;&gt;1_1</title>
+<path fill="none" stroke="black" d="M86.9809,-196.324C111.939,-203.172 146.06,-212.535 173.086,-219.95"/>
+<polygon fill="black" stroke="black" points="172.493,-223.417 183.063,-222.688 174.345,-216.667 172.493,-223.417"/>
+</g>
+<!-- 1_2 -->
+<g id="node6" class="node"><title>1_2</title>
+<ellipse fill="none" stroke="black" cx="218" cy="-142" rx="36" ry="36"/>
+</g>
+<!-- 0_0&#45;&gt;1_2 -->
+<g id="edge3" class="edge"><title>0_0&#45;&gt;1_2</title>
+<path fill="none" stroke="black" d="M86.9809,-177.676C111.939,-170.828 146.06,-161.465 173.086,-154.05"/>
+<polygon fill="black" stroke="black" points="174.345,-157.333 183.063,-151.312 172.493,-150.583 174.345,-157.333"/>
+</g>
+<!-- 1_3 -->
+<g id="node7" class="node"><title>1_3</title>
+<ellipse fill="none" stroke="black" cx="218" cy="-52" rx="36" ry="36"/>
+</g>
+<!-- 0_0&#45;&gt;1_3 -->
+<g id="edge4" class="edge"><title>0_0&#45;&gt;1_3</title>
+<path fill="none" stroke="black" d="M80.4499,-164.404C108.324,-141.459 151.588,-105.845 181.93,-80.8689"/>
+<polygon fill="black" stroke="black" points="184.238,-83.5023 189.734,-74.4446 179.789,-78.0979 184.238,-83.5023"/>
+</g>
+<!-- 0_1 -->
+<g id="node2" class="node"><title>0_1</title>
+<ellipse fill="none" stroke="black" cx="52" cy="-97" rx="36" ry="36"/>
+</g>
+<!-- 0_1&#45;&gt;1_0 -->
+<g id="edge5" class="edge"><title>0_1&#45;&gt;1_0</title>
+<path fill="none" stroke="black" d="M74.1044,-125.954C103.688,-166.541 157.497,-240.365 189.975,-284.923"/>
+<polygon fill="black" stroke="black" points="187.258,-287.137 195.977,-293.157 192.915,-283.014 187.258,-287.137"/>
+</g>
+<!-- 0_1&#45;&gt;1_1 -->
+<g id="edge6" class="edge"><title>0_1&#45;&gt;1_1</title>
+<path fill="none" stroke="black" d="M80.4499,-119.596C108.324,-142.541 151.588,-178.155 181.93,-203.131"/>
+<polygon fill="black" stroke="black" points="179.789,-205.902 189.734,-209.555 184.238,-200.498 179.789,-205.902"/>
+</g>
+<!-- 0_1&#45;&gt;1_2 -->
+<g id="edge7" class="edge"><title>0_1&#45;&gt;1_2</title>
+<path fill="none" stroke="black" d="M86.9809,-106.324C111.939,-113.172 146.06,-122.535 173.086,-129.95"/>
+<polygon fill="black" stroke="black" points="172.493,-133.417 183.063,-132.688 174.345,-126.667 172.493,-133.417"/>
+</g>
+<!-- 0_1&#45;&gt;1_3 -->
+<g id="edge8" class="edge"><title>0_1&#45;&gt;1_3</title>
+<path fill="none" stroke="black" d="M86.9809,-87.676C111.939,-80.8277 146.06,-71.4651 173.086,-64.0495"/>
+<polygon fill="black" stroke="black" points="174.345,-67.3335 183.063,-61.3121 172.493,-60.583 174.345,-67.3335"/>
+</g>
+<!-- 0_2 -->
+<g id="node3" class="node"><title>0_2</title>
+<ellipse fill="none" stroke="black" cx="52" cy="-277" rx="36" ry="36"/>
+</g>
+<!-- 0_2&#45;&gt;1_0 -->
+<g id="edge9" class="edge"><title>0_2&#45;&gt;1_0</title>
+<path fill="none" stroke="black" d="M86.9809,-286.324C111.939,-293.172 146.06,-302.535 173.086,-309.95"/>
+<polygon fill="black" stroke="black" points="172.493,-313.417 183.063,-312.688 174.345,-306.667 172.493,-313.417"/>
+</g>
+<!-- 0_2&#45;&gt;1_1 -->
+<g id="edge10" class="edge"><title>0_2&#45;&gt;1_1</title>
+<path fill="none" stroke="black" d="M86.9809,-267.676C111.939,-260.828 146.06,-251.465 173.086,-244.05"/>
+<polygon fill="black" stroke="black" points="174.345,-247.333 183.063,-241.312 172.493,-240.583 174.345,-247.333"/>
+</g>
+<!-- 0_2&#45;&gt;1_2 -->
+<g id="edge11" class="edge"><title>0_2&#45;&gt;1_2</title>
+<path fill="none" stroke="black" d="M80.4499,-254.404C108.324,-231.459 151.588,-195.845 181.93,-170.869"/>
+<polygon fill="black" stroke="black" points="184.238,-173.502 189.734,-164.445 179.789,-168.098 184.238,-173.502"/>
+</g>
+<!-- 0_2&#45;&gt;1_3 -->
+<g id="edge12" class="edge"><title>0_2&#45;&gt;1_3</title>
+<path fill="none" stroke="black" d="M74.1044,-248.046C103.688,-207.459 157.497,-133.635 189.975,-89.0769"/>
+<polygon fill="black" stroke="black" points="192.915,-90.9858 195.977,-80.8431 187.258,-86.8626 192.915,-90.9858"/>
+</g>
+<!-- 2_0 -->
+<g id="node8" class="node"><title>2_0</title>
+<ellipse fill="none" stroke="black" cx="376" cy="-52" rx="36" ry="36"/>
+</g>
+<!-- 1_0&#45;&gt;2_0 -->
+<g id="edge13" class="edge"><title>1_0&#45;&gt;2_0</title>
+<path fill="none" stroke="black" d="M236.895,-291.028C265.266,-241.925 320.554,-146.233 351.886,-92.0042"/>
+<polygon fill="black" stroke="black" points="355.102,-93.4356 357.074,-83.0259 349.041,-89.9336 355.102,-93.4356"/>
+</g>
+<!-- 2_1 -->
+<g id="node9" class="node"><title>2_1</title>
+<ellipse fill="none" stroke="black" cx="376" cy="-142" rx="36" ry="36"/>
+</g>
+<!-- 1_0&#45;&gt;2_1 -->
+<g id="edge14" class="edge"><title>1_0&#45;&gt;2_1</title>
+<path fill="none" stroke="black" d="M242.355,-295.052C269.66,-263.546 314.92,-211.324 344.854,-176.784"/>
+<polygon fill="black" stroke="black" points="347.836,-178.687 351.741,-168.838 342.546,-174.102 347.836,-178.687"/>
+</g>
+<!-- 2_2 -->
+<g id="node10" class="node"><title>2_2</title>
+<ellipse fill="none" stroke="black" cx="376" cy="-232" rx="36" ry="36"/>
+</g>
+<!-- 1_0&#45;&gt;2_2 -->
+<g id="edge15" class="edge"><title>1_0&#45;&gt;2_2</title>
+<path fill="none" stroke="black" d="M249.449,-304.433C274.065,-290.232 308.899,-270.135 335.633,-254.712"/>
+<polygon fill="black" stroke="black" points="337.449,-257.705 344.362,-249.676 333.951,-251.641 337.449,-257.705"/>
+</g>
+<!-- 2_3 -->
+<g id="node11" class="node"><title>2_3</title>
+<ellipse fill="none" stroke="black" cx="376" cy="-322" rx="36" ry="36"/>
+</g>
+<!-- 1_0&#45;&gt;2_3 -->
+<g id="edge16" class="edge"><title>1_0&#45;&gt;2_3</title>
+<path fill="none" stroke="black" d="M254.009,-322C276.311,-322 305.432,-322 329.515,-322"/>
+<polygon fill="black" stroke="black" points="329.838,-325.5 339.838,-322 329.838,-318.5 329.838,-325.5"/>
+</g>
+<!-- 1_1&#45;&gt;2_0 -->
+<g id="edge17" class="edge"><title>1_1&#45;&gt;2_0</title>
+<path fill="none" stroke="black" d="M242.355,-205.052C269.66,-173.546 314.92,-121.324 344.854,-86.7839"/>
+<polygon fill="black" stroke="black" points="347.836,-88.6868 351.741,-78.8377 342.546,-84.1023 347.836,-88.6868"/>
+</g>
+<!-- 1_1&#45;&gt;2_1 -->
+<g id="edge18" class="edge"><title>1_1&#45;&gt;2_1</title>
+<path fill="none" stroke="black" d="M249.449,-214.433C274.065,-200.232 308.899,-180.135 335.633,-164.712"/>
+<polygon fill="black" stroke="black" points="337.449,-167.705 344.362,-159.676 333.951,-161.641 337.449,-167.705"/>
+</g>
+<!-- 1_1&#45;&gt;2_2 -->
+<g id="edge19" class="edge"><title>1_1&#45;&gt;2_2</title>
+<path fill="none" stroke="black" d="M254.009,-232C276.311,-232 305.432,-232 329.515,-232"/>
+<polygon fill="black" stroke="black" points="329.838,-235.5 339.838,-232 329.838,-228.5 329.838,-235.5"/>
+</g>
+<!-- 1_1&#45;&gt;2_3 -->
+<g id="edge20" class="edge"><title>1_1&#45;&gt;2_3</title>
+<path fill="none" stroke="black" d="M249.449,-249.567C274.065,-263.768 308.899,-283.865 335.633,-299.288"/>
+<polygon fill="black" stroke="black" points="333.951,-302.359 344.362,-304.324 337.449,-296.295 333.951,-302.359"/>
+</g>
+<!-- 1_2&#45;&gt;2_0 -->
+<g id="edge21" class="edge"><title>1_2&#45;&gt;2_0</title>
+<path fill="none" stroke="black" d="M249.449,-124.433C274.065,-110.232 308.899,-90.1353 335.633,-74.7119"/>
+<polygon fill="black" stroke="black" points="337.449,-77.7047 344.362,-69.6758 333.951,-71.6414 337.449,-77.7047"/>
+</g>
+<!-- 1_2&#45;&gt;2_1 -->
+<g id="edge22" class="edge"><title>1_2&#45;&gt;2_1</title>
+<path fill="none" stroke="black" d="M254.009,-142C276.311,-142 305.432,-142 329.515,-142"/>
+<polygon fill="black" stroke="black" points="329.838,-145.5 339.838,-142 329.838,-138.5 329.838,-145.5"/>
+</g>
+<!-- 1_2&#45;&gt;2_2 -->
+<g id="edge23" class="edge"><title>1_2&#45;&gt;2_2</title>
+<path fill="none" stroke="black" d="M249.449,-159.567C274.065,-173.768 308.899,-193.865 335.633,-209.288"/>
+<polygon fill="black" stroke="black" points="333.951,-212.359 344.362,-214.324 337.449,-206.295 333.951,-212.359"/>
+</g>
+<!-- 1_2&#45;&gt;2_3 -->
+<g id="edge24" class="edge"><title>1_2&#45;&gt;2_3</title>
+<path fill="none" stroke="black" d="M242.355,-168.948C269.66,-200.454 314.92,-252.676 344.854,-287.216"/>
+<polygon fill="black" stroke="black" points="342.546,-289.898 351.741,-295.162 347.836,-285.313 342.546,-289.898"/>
+</g>
+<!-- 1_3&#45;&gt;2_0 -->
+<g id="edge25" class="edge"><title>1_3&#45;&gt;2_0</title>
+<path fill="none" stroke="black" d="M254.009,-52C276.311,-52 305.432,-52 329.515,-52"/>
+<polygon fill="black" stroke="black" points="329.838,-55.5001 339.838,-52 329.838,-48.5001 329.838,-55.5001"/>
+</g>
+<!-- 1_3&#45;&gt;2_1 -->
+<g id="edge26" class="edge"><title>1_3&#45;&gt;2_1</title>
+<path fill="none" stroke="black" d="M249.449,-69.5666C274.065,-83.7682 308.899,-103.865 335.633,-119.288"/>
+<polygon fill="black" stroke="black" points="333.951,-122.359 344.362,-124.324 337.449,-116.295 333.951,-122.359"/>
+</g>
+<!-- 1_3&#45;&gt;2_2 -->
+<g id="edge27" class="edge"><title>1_3&#45;&gt;2_2</title>
+<path fill="none" stroke="black" d="M242.355,-78.9478C269.66,-110.454 314.92,-162.676 344.854,-197.216"/>
+<polygon fill="black" stroke="black" points="342.546,-199.898 351.741,-205.162 347.836,-195.313 342.546,-199.898"/>
+</g>
+<!-- 1_3&#45;&gt;2_3 -->
+<g id="edge28" class="edge"><title>1_3&#45;&gt;2_3</title>
+<path fill="none" stroke="black" d="M236.895,-82.9724C265.266,-132.075 320.554,-227.767 351.886,-281.996"/>
+<polygon fill="black" stroke="black" points="349.041,-284.066 357.074,-290.974 355.102,-280.564 349.041,-284.066"/>
+</g>
+<!-- 3_0 -->
+<g id="node12" class="node"><title>3_0</title>
+<ellipse fill="none" stroke="black" cx="548" cy="-232" rx="36" ry="36"/>
+</g>
+<!-- 2_0&#45;&gt;3_0 -->
+<g id="edge29" class="edge"><title>2_0&#45;&gt;3_0</title>
+<path fill="none" stroke="black" d="M401.357,-77.7896C431.402,-109.602 482.538,-163.747 515.531,-198.68"/>
+<polygon fill="black" stroke="black" points="513.278,-201.391 522.688,-206.258 518.367,-196.585 513.278,-201.391"/>
+</g>
+<!-- 3_1 -->
+<g id="node13" class="node"><title>3_1</title>
+<ellipse fill="none" stroke="black" cx="548" cy="-142" rx="36" ry="36"/>
+</g>
+<!-- 2_0&#45;&gt;3_1 -->
+<g id="edge30" class="edge"><title>2_0&#45;&gt;3_1</title>
+<path fill="none" stroke="black" d="M408.181,-68.5074C435.892,-83.1779 476.567,-104.712 506.732,-120.681"/>
+<polygon fill="black" stroke="black" points="505.254,-123.859 515.73,-125.445 508.529,-117.673 505.254,-123.859"/>
+</g>
+<!-- 2_1&#45;&gt;3_0 -->
+<g id="edge31" class="edge"><title>2_1&#45;&gt;3_0</title>
+<path fill="none" stroke="black" d="M408.181,-158.507C435.892,-173.178 476.567,-194.712 506.732,-210.681"/>
+<polygon fill="black" stroke="black" points="505.254,-213.859 515.73,-215.445 508.529,-207.673 505.254,-213.859"/>
+</g>
+<!-- 2_1&#45;&gt;3_1 -->
+<g id="edge32" class="edge"><title>2_1&#45;&gt;3_1</title>
+<path fill="none" stroke="black" d="M412.224,-142C438.095,-142 473.465,-142 501.479,-142"/>
+<polygon fill="black" stroke="black" points="501.821,-145.5 511.821,-142 501.821,-138.5 501.821,-145.5"/>
+</g>
+<!-- 2_2&#45;&gt;3_0 -->
+<g id="edge33" class="edge"><title>2_2&#45;&gt;3_0</title>
+<path fill="none" stroke="black" d="M412.224,-232C438.095,-232 473.465,-232 501.479,-232"/>
+<polygon fill="black" stroke="black" points="501.821,-235.5 511.821,-232 501.821,-228.5 501.821,-235.5"/>
+</g>
+<!-- 2_2&#45;&gt;3_1 -->
+<g id="edge34" class="edge"><title>2_2&#45;&gt;3_1</title>
+<path fill="none" stroke="black" d="M408.181,-215.493C435.892,-200.822 476.567,-179.288 506.732,-163.319"/>
+<polygon fill="black" stroke="black" points="508.529,-166.327 515.73,-158.555 505.254,-160.141 508.529,-166.327"/>
+</g>
+<!-- 2_3&#45;&gt;3_0 -->
+<g id="edge35" class="edge"><title>2_3&#45;&gt;3_0</title>
+<path fill="none" stroke="black" d="M408.181,-305.493C435.892,-290.822 476.567,-269.288 506.732,-253.319"/>
+<polygon fill="black" stroke="black" points="508.529,-256.327 515.73,-248.555 505.254,-250.141 508.529,-256.327"/>
+</g>
+<!-- 2_3&#45;&gt;3_1 -->
+<g id="edge36" class="edge"><title>2_3&#45;&gt;3_1</title>
+<path fill="none" stroke="black" d="M401.357,-296.21C431.402,-264.398 482.538,-210.253 515.531,-175.32"/>
+<polygon fill="black" stroke="black" points="518.367,-177.415 522.688,-167.742 513.278,-172.609 518.367,-177.415"/>
+</g>
+</g>
+</svg>
diff --git a/images/neuralnets/perceptron_XOR.svg b/images/neuralnets/perceptron_XOR.svg
new file mode 100644
index 0000000..701e40b
--- /dev/null
+++ b/images/neuralnets/perceptron_XOR.svg
@@ -0,0 +1,75 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
+ "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<!-- Generated by graphviz version 2.38.0 (20140413.2041)
+ -->
+<!-- Title: G Pages: 1 -->
+<svg width="348pt" height="186pt"
+ viewBox="0.00 0.00 348.00 185.50" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+<g id="graph0" class="graph" transform="scale(1 1) rotate(0) translate(4 181.501)">
+<title>G</title>
+<polygon fill="white" stroke="none" points="-4,4 -4,-181.501 344.001,-181.501 344.001,4 -4,4"/>
+<!-- 0_0 -->
+<g id="node1" class="node"><title>0_0</title>
+<text text-anchor="middle" x="15" y="-137.3" font-family="Times,serif" font-size="14.00">x1</text>
+</g>
+<!-- 1_0 -->
+<g id="node3" class="node"><title>1_0</title>
+<ellipse fill="none" stroke="black" cx="96.5007" cy="-147" rx="30.5014" ry="30.5014"/>
+<text text-anchor="middle" x="96.5007" y="-143.3" font-family="Times,serif" font-size="14.00">NAND</text>
+</g>
+<!-- 0_0&#45;&gt;1_0 -->
+<g id="edge1" class="edge"><title>0_0&#45;&gt;1_0</title>
+<path fill="none" stroke="black" d="M30.2131,-142.073C37.4171,-142.616 46.5615,-143.307 55.709,-143.997"/>
+<polygon fill="black" stroke="black" points="55.6996,-147.506 65.9346,-144.769 56.2264,-140.526 55.6996,-147.506"/>
+</g>
+<!-- 1_1 -->
+<g id="node4" class="node"><title>1_1</title>
+<ellipse fill="none" stroke="black" cx="96.5007" cy="-29" rx="29" ry="29"/>
+<text text-anchor="middle" x="96.5007" y="-25.3" font-family="Times,serif" font-size="14.00">OR</text>
+</g>
+<!-- 0_0&#45;&gt;1_1 -->
+<g id="edge3" class="edge"><title>0_0&#45;&gt;1_1</title>
+<path fill="none" stroke="black" d="M29.1279,-122.505C41.0907,-105.652 59.0071,-80.412 73.2112,-60.4013"/>
+<polygon fill="black" stroke="black" points="76.1089,-62.3657 79.0432,-52.1852 70.4008,-58.3138 76.1089,-62.3657"/>
+</g>
+<!-- 0_1 -->
+<g id="node2" class="node"><title>0_1</title>
+<text text-anchor="middle" x="15" y="-37.3" font-family="Times,serif" font-size="14.00">x2</text>
+</g>
+<!-- 0_1&#45;&gt;1_0 -->
+<g id="edge2" class="edge"><title>0_1&#45;&gt;1_0</title>
+<path fill="none" stroke="black" d="M29.848,-59.4638C41.2673,-74.6894 57.7381,-96.6503 71.3251,-114.766"/>
+<polygon fill="black" stroke="black" points="68.5363,-116.881 77.3364,-122.781 74.1363,-112.681 68.5363,-116.881"/>
+</g>
+<!-- 0_1&#45;&gt;1_1 -->
+<g id="edge4" class="edge"><title>0_1&#45;&gt;1_1</title>
+<path fill="none" stroke="black" d="M30.2131,-38.8546C37.9035,-37.6938 47.8052,-36.1992 57.5613,-34.7267"/>
+<polygon fill="black" stroke="black" points="58.2109,-38.1683 67.5765,-33.2149 57.1661,-31.2467 58.2109,-38.1683"/>
+</g>
+<!-- 2_0 -->
+<g id="node5" class="node"><title>2_0</title>
+<ellipse fill="none" stroke="black" cx="192.001" cy="-88" rx="29" ry="29"/>
+<text text-anchor="middle" x="192.001" y="-84.3" font-family="Times,serif" font-size="14.00">AND</text>
+</g>
+<!-- 1_0&#45;&gt;2_0 -->
+<g id="edge5" class="edge"><title>1_0&#45;&gt;2_0</title>
+<path fill="none" stroke="black" d="M123.05,-130.878C133.994,-123.972 146.905,-115.825 158.517,-108.498"/>
+<polygon fill="black" stroke="black" points="160.5,-111.385 167.09,-103.089 156.765,-105.465 160.5,-111.385"/>
+</g>
+<!-- 1_1&#45;&gt;2_0 -->
+<g id="edge6" class="edge"><title>1_1&#45;&gt;2_0</title>
+<path fill="none" stroke="black" d="M121.579,-44.194C132.813,-51.2824 146.351,-59.8249 158.477,-67.4767"/>
+<polygon fill="black" stroke="black" points="156.679,-70.481 167.004,-72.8575 160.415,-64.561 156.679,-70.481"/>
+</g>
+<!-- 3_0 -->
+<g id="node6" class="node"><title>3_0</title>
+<text text-anchor="middle" x="298.501" y="-84.3" font-family="Times,serif" font-size="14.00">XOR output</text>
+</g>
+<!-- 2_0&#45;&gt;3_0 -->
+<g id="edge7" class="edge"><title>2_0&#45;&gt;3_0</title>
+<path fill="none" stroke="black" d="M221.005,-88C228.914,-88 237.775,-88 246.585,-88"/>
+<polygon fill="black" stroke="black" points="246.844,-91.5001 256.844,-88 246.844,-84.5001 246.844,-91.5001"/>
+</g>
+</g>
+</svg>
diff --git a/images/neuralnets/perceptron_ex.svg b/images/neuralnets/perceptron_ex.svg
new file mode 100644
index 0000000..f53e56f
--- /dev/null
+++ b/images/neuralnets/perceptron_ex.svg
@@ -0,0 +1,57 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
+ "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<!-- Generated by graphviz version 2.38.0 (20140413.2041)
+ -->
+<!-- Title: G Pages: 1 -->
+<svg width="357pt" height="260pt"
+ viewBox="0.00 0.00 357.09 260.00" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+<g id="graph0" class="graph" transform="scale(1 1) rotate(0) translate(4 256)">
+<title>G</title>
+<polygon fill="white" stroke="none" points="-4,4 -4,-256 353.092,-256 353.092,4 -4,4"/>
+<!-- 0_0 -->
+<g id="node1" class="node"><title>0_0</title>
+<text text-anchor="middle" x="15" y="-230.3" font-family="Times,serif" font-size="14.00">x1</text>
+</g>
+<!-- 1_0 -->
+<g id="node4" class="node"><title>1_0</title>
+<ellipse fill="none" stroke="black" cx="172.046" cy="-126" rx="39.0917" ry="39.0917"/>
+<text text-anchor="middle" x="172.046" y="-122.3" font-family="Times,serif" font-size="14.00">perceptron</text>
+</g>
+<!-- 0_0&#45;&gt;1_0 -->
+<g id="edge1" class="edge"><title>0_0&#45;&gt;1_0</title>
+<path fill="none" stroke="black" d="M30.0327,-224.225C52.614,-208.496 97.8376,-176.995 131.006,-153.891"/>
+<polygon fill="black" stroke="black" points="133.379,-156.503 139.584,-147.915 129.378,-150.759 133.379,-156.503"/>
+<text text-anchor="middle" x="81.5" y="-195.8" font-family="Times,serif" font-size="14.00">w1</text>
+</g>
+<!-- 0_1 -->
+<g id="node2" class="node"><title>0_1</title>
+<text text-anchor="middle" x="15" y="-122.3" font-family="Times,serif" font-size="14.00">x2</text>
+</g>
+<!-- 0_1&#45;&gt;1_0 -->
+<g id="edge2" class="edge"><title>0_1&#45;&gt;1_0</title>
+<path fill="none" stroke="black" d="M30.0327,-126C50.8171,-126 90.7837,-126 122.876,-126"/>
+<polygon fill="black" stroke="black" points="122.985,-129.5 132.985,-126 122.985,-122.5 122.985,-129.5"/>
+<text text-anchor="middle" x="81.5" y="-129.8" font-family="Times,serif" font-size="14.00">w2</text>
+</g>
+<!-- 0_2 -->
+<g id="node3" class="node"><title>0_2</title>
+<text text-anchor="middle" x="15" y="-14.3" font-family="Times,serif" font-size="14.00">x3</text>
+</g>
+<!-- 0_2&#45;&gt;1_0 -->
+<g id="edge3" class="edge"><title>0_2&#45;&gt;1_0</title>
+<path fill="none" stroke="black" d="M30.0327,-27.7748C52.614,-43.5041 97.8376,-75.0055 131.006,-98.1093"/>
+<polygon fill="black" stroke="black" points="129.378,-101.241 139.584,-104.085 133.379,-95.4969 129.378,-101.241"/>
+<text text-anchor="middle" x="81.5" y="-71.8" font-family="Times,serif" font-size="14.00">w3</text>
+</g>
+<!-- 2_0 -->
+<g id="node5" class="node"><title>2_0</title>
+<text text-anchor="middle" x="323.592" y="-122.3" font-family="Times,serif" font-size="14.00">output</text>
+</g>
+<!-- 1_0&#45;&gt;2_0 -->
+<g id="edge4" class="edge"><title>1_0&#45;&gt;2_0</title>
+<path fill="none" stroke="black" d="M211.169,-126C234.627,-126 264.478,-126 287.377,-126"/>
+<polygon fill="black" stroke="black" points="287.681,-129.5 297.681,-126 287.681,-122.5 287.681,-129.5"/>
+</g>
+</g>
+</svg>
diff --git a/neural_nets_intro.ipynb b/neural_nets_intro.ipynb
index 7996aed..027c378 100644
--- a/neural_nets_intro.ipynb
+++ b/neural_nets_intro.ipynb
@@ -8,9 +8,12 @@
     "\n",
     "## TO DO: Almost all the figues and schematics will be replaced or improved slowly\n",
     "\n",
-    "<img src=\"./images/neuralnets/Colored_neural_network.svg\"/>\n",
-    "source: https://en.wikipedia.org/wiki/Artificial_neural_network\n",
-    "\n"
+    "<center>\n",
+    "<figure>\n",
+    "<img src=\"./images/neuralnets/neural_net_ex.svg\" width=\"700\"/>\n",
+    "<figcaption>A 3 layer Neural Network (By convention the input layer is not counted).</figcaption>\n",
+    "</figure>\n",
+    "</center>"
    ]
   },
   {
@@ -60,19 +63,29 @@
     "## Building blocks\n",
     "### Perceptron\n",
     "\n",
-    "Smallest unit of a neural network is a **perceptron** like node.\n",
+    "The smallest unit of a neural network is a **perceptron** like node.\n",
     "\n",
     "**What is a Perceptron?**\n",
     "\n",
-    "It is a simple function which has multiple inputs and a single output.\n",
+    "It is a simple function which can have multiple inputs and has a single output.\n",
     "\n",
-    "Step 1: Weighted sum of the inputs is calculated\n",
+    "<center>\n",
+    "<figure>\n",
+    "<img src=\"./images/neuralnets/perceptron_ex.svg\" width=\"400\"/>\n",
+    "<figcaption>A simple perceptron with 3 inputs and 1 output.</figcaption>\n",
+    "</figure>\n",
+    "</center>\n",
+    "\n",
+    "\n",
+    "It works as follows: \n",
+    "\n",
+    "Step 1: A **weighted sum** of the inputs is calculated\n",
     "\n",
     "\\begin{equation*}\n",
     "weighted\\_sum = \\sum_{k=1}^{num\\_inputs} w_{i} x_{i}\n",
     "\\end{equation*}\n",
     "\n",
-    "Step 2: The following activation function is applied\n",
+    "Step 2: A **step** activation function is applied\n",
     "\n",
     "$$\n",
     "f(weighted\\_sum) = \\left\\{\n",
@@ -88,20 +101,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "%config IPCompleter.greedy=True\n",
-    "%config InlineBackend.figure_format = 'retina'\n",
     "import matplotlib as mpl\n",
-    "mpl.rcParams['lines.linewidth'] = 3"
+    "mpl.rcParams['lines.linewidth'] = 3\n",
+    "#mpl.rcParams['font.size'] = 16"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -110,25 +123,29 @@
        "1"
       ]
      },
-     "execution_count": 157,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
     "def perceptron(X, w, threshold=1):\n",
-    "    # This function computes sum(w_i*x_i) and \n",
+    "    # This function computes sum(w_i*x_i) and\n",
     "    # applies a perceptron activation\n",
-    "    linear_sum = np.dot(X,w)\n",
-    "    output=0\n",
+    "    linear_sum = np.dot(X, w)\n",
+    "    output = 0\n",
     "    if linear_sum >= threshold:\n",
     "        output = 1\n",
-    "        # print(\"The perceptron has peaked\")\n",
     "    return output\n",
-    "X = [1,0]\n",
-    "w = [1,1]\n",
-    "perceptron(X,w)"
+    "\n",
+    "\n",
+    "X = [1, 0]\n",
+    "w = [1, 1]\n",
+    "perceptron(X, w)"
    ]
   },
   {
@@ -147,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -163,12 +180,12 @@
    ],
    "source": [
     "# Calculating Boolean AND using a perceptron\n",
-    "import matplotlib.pyplot as plt\n",
     "threshold = 1.5\n",
-    "w=[1,1]\n",
-    "X=[[0,0],[1,0],[0,1],[1,1]]\n",
+    "w = [1, 1]\n",
+    "X = [[0, 0], [1, 0], [0, 1], [1, 1]]\n",
     "for i in X:\n",
-    "    print(\"Perceptron output for x1, x2 = \" , i , \" is \" , perceptron(i,w,threshold))"
+    "    print(\"Perceptron output for x1, x2 = \", i,\n",
+    "          \" is \", perceptron(i, w, threshold))"
    ]
   },
   {
@@ -180,44 +197,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def perceptron_DB(X, w):\n",
+    "    # Plotting the decision boundary\n",
+    "    for i in X:\n",
+    "        plt.plot(i, \"o\", color=\"b\")\n",
+    "    plt.xlim(-1, 2)\n",
+    "    plt.ylim(-1, 2)\n",
+    "    # The decision boundary is a line given by\n",
+    "    # w_1*x_1+w_2*x_2-threshold=0\n",
+    "    x1 = np.arange(-3, 4)\n",
+    "    x2 = (threshold - x1*w[0])/w[1]\n",
+    "    plt.plot(x1, x2, \"--\", color=\"black\")\n",
+    "    plt.xlabel(\"x$_1$\", fontsize=16)\n",
+    "    plt.ylabel(\"x$_2$\", fontsize=16)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ff6ceb34da0>"
+       "<matplotlib.figure.Figure at 0x7f4e04b13ac8>"
       ]
      },
      "metadata": {
-      "image/png": {
-       "height": 252,
-       "width": 388
-      },
       "needs_background": "light"
      },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "# Plotting the decision boundary\n",
-    "plt.xlim(-1,2)\n",
-    "plt.ylim(-1,2)\n",
-    "for i in X:\n",
-    "    plt.plot(i,\"o\",color=\"b\");\n",
-    "# Plotting the decision boundary\n",
-    "# that is a line given by w_1*x_1+w_2*x_2-threshold=0\n",
-    "x1 = np.arange(-3,4)\n",
-    "x2 = threshold - np.arange(-3,4)\n",
-    "plt.plot(x1, x2 , \"--\" ,color=\"black\");"
+    "perceptron_DB(X, w)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Exercise :Can you compute a Boolean \"OR\" using a perceptron?**\n",
+    "**Exercise 1 : Compute a Boolean \"OR\" using a perceptron?**\n",
     "\n",
     "Hint: copy the code from the \"AND\" example and edit the weights and/or threshold"
    ]
@@ -248,7 +273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -263,16 +288,12 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ff6cef41860>"
+       "<matplotlib.figure.Figure at 0x7f4e04d79e10>"
       ]
      },
      "metadata": {
-      "image/png": {
-       "height": 252,
-       "width": 388
-      },
       "needs_background": "light"
      },
      "output_type": "display_data"
@@ -288,21 +309,14 @@
     "for i in X:\n",
     "    print(\"Perceptron output for x1, x2 = \" , i , \" is \" , perceptron(i,w,threshold))\n",
     "# Plotting the decision boundary\n",
-    "plt.xlim(-1,2)\n",
-    "plt.ylim(-1,2)\n",
-    "for i in X:\n",
-    "    plt.plot(i,\"o\",color=\"b\");\n",
-    "# Plotting the decision boundary\n",
-    "# that is a line given by w_1*x_1+w_2*x_2-threshold=0\n",
-    "x1 = np.arange(-3,4)\n",
-    "x2 = threshold - np.arange(-3,4)"
+    "perceptron_DB(X,w)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Optional exercise: Create a NAND gate using a perceptron**\n",
+    "**Exercise 2 : Create a NAND gate using a perceptron**\n",
     "\n",
     "#### Boolean NAND\n",
     "\n",
@@ -316,36 +330,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Calculating Boolean NAND using a perceptron\n",
-    "\n",
-    "\n",
-    "\n"
+    "# Calculating Boolean NAND using a perceptron"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In fact a single perceptron can compute \"AND\", \"OR\" and \"NOT\" boolean functions.\n",
+    "In fact, a single perceptron can compute \"AND\", \"OR\" and \"NOT\" boolean functions.\n",
     "However, it cannot compute some other boolean functions such as \"XOR\"\n",
     "\n",
-    "WHAT CAN WE DO?\n",
-    "Hint: What is the significance of the NAND gate we created above\n",
+    "**WHAT CAN WE DO?**\n",
+    "\n",
+    "\n",
+    "Hint: Think about what is the significance of the NAND gate we created above?\n",
     "\n",
-    "We said a single perceptron can't compute these functions. We didn't say that about **multiple Perceptrons**"
+    "We said a single perceptron can't compute these functions. We didn't say that about **multiple Perceptrons**."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**XOR function**\n",
-    "\n",
-    "**TO DO: INSERT IMAGE HERE!!!!!!!!!!!!!!**"
+    "**XOR function using multiple perceptrons**\n",
+    "\n",
+    "<center>\n",
+    "<figure>\n",
+    "<img src=\"./images/neuralnets/perceptron_XOR.svg\" width=\"400\"/>\n",
+    "<figcaption>Multiple perceptrons put together to output a XOR function.</figcaption>\n",
+    "</figure>\n",
+    "</center>"
    ]
   },
   {
@@ -354,8 +372,6 @@
    "source": [
     "### Google Playground\n",
     "\n",
-    "UWE: move up before discussing gradient stuff etc\n",
-    "\n",
     "https://playground.tensorflow.org/\n",
     "\n",
     "<img src=\"./images/neuralnets/google_playground.png\"/>"
@@ -466,22 +482,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Suggestion Uwe:\n",
+    "### Multi-layer preceptron neural network\n",
+    "Universal function theorem\n",
     "\n",
-    "1. more layers might improve power of single perctptron.\n",
+    "epochs\n",
     "\n",
-    "2. regrettably math show that just \"stacking\" perceptrons only adds little improvements\n",
+    "Suggestion Uwe:\n",
     "\n",
     "3. way around: look at nature how neuron works and introduce non linear activation functions.\n",
     "\n",
-    "4. theoretical background: universal approximation theorem.\n",
-    "\n",
-    "\n",
-    "\n",
-    "### Multi-layer preceptron neural network\n",
-    "Universal function theorem\n",
-    "\n",
-    "epochs\n"
+    "4. theoretical background: universal approximation theorem."
    ]
   },
   {
@@ -495,7 +505,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "What is **Keras**?\n",
+    "### What is Keras?\n",
     "\n",
     "* It is a high level API to create and work with neural networks\n",
     "* Supports multiple backends such as TensorFlow from Google, Theano (Although Theano is dead now) and CNTK (Microsoft Cognitive Toolkit)\n",
@@ -554,7 +564,8 @@
     "# 2nd Hidden layer\n",
     "# This is also a fully-connected layer and we do not need to specify the\n",
     "# shape of the input anymore (We need to do that only for the first layer)\n",
-    "# NOTE: Now we didn't add the activation seperately. Instead we just added it\n",
+    "# NOTE: Now\n",
+    " we didn't add the activation seperately. Instead we just added it\n",
     "# while calling Dense(). This and the way used for the first layer are Equivalent!\n",
     "model.add(Dense(units=4, activation=\"relu\"))\n",
     "\n",
@@ -568,340 +579,575 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Fitting the model "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**TO DO: Move the MNIST example after the previous dataset examples**"
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "import numpy as np"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 41,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4e04ab1320>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "### MNIST Dataset\n",
+    "# Creating a network to solve the XOR problem\n",
+    "# Loading and plotting the data\n",
+    "xor = pd.read_csv(\"xor.csv\")\n",
     "\n",
-    "MNIST datasets is a very common dataset used in machine learning. It is widely used to train and validate models.\n",
+    "# Using x and y coordinates as featues\n",
+    "features = xor.iloc[:, :-1]\n",
+    "# Convert boolean to integer values (True->1 and False->0)\n",
+    "labels = xor.iloc[:, -1].astype(int)\n",
     "\n",
+    "colors = [[\"steelblue\", \"chocolate\"][i] for i in xor[\"label\"]]\n",
+    "plt.figure(figsize=(5, 5))\n",
+    "plt.xlim([-2, 2])\n",
+    "plt.ylim([-2, 2])\n",
+    "plt.title(\"Blue points are False\")\n",
     "\n",
-    ">The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a >test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size->normalized and centered in a fixed-size image.\n",
-    ">It is a good database for people who want to try learning techniques and pattern recognition methods on real-world >data while spending minimal efforts on preprocessing and formatting.\n",
-    ">source: http://yann.lecun.com/exdb/mnist/\n",
     "\n",
-    "The problem we want to solve using this dataset is: multi-class classification\n",
-    "This dataset consists of images of handwritten digits between 0-9 and their corresponsing labels. We want to train a neural network which is able to predict the correct digit on the image. "
+    "plt.scatter(features[\"x\"], features[\"y\"], color=colors, marker=\"o\");"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Loading the dataset in keras\n",
-    "# Later you can explore and play with other datasets with come with Keras\n",
-    "from keras.datasets import mnist\n",
+    "# Building a Keras model\n",
     "\n",
-    "# Loading the train and test data\n",
+    "def a_simple_NN():\n",
+    "    \n",
+    "    model = Sequential()\n",
     "\n",
-    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
+    "    model.add(Dense(4, input_shape = (2,), activation = \"relu\"))\n",
+    "\n",
+    "    model.add(Dense(4, activation = \"relu\"))\n",
+    "\n",
+    "    model.add(Dense(1, activation = \"sigmoid\"))\n",
+    "\n",
+    "    model.compile(loss=\"binary_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
+    "    \n",
+    "    return model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(60000, 28, 28)\n"
+      "Train on 210 samples, validate on 90 samples\n",
+      "Epoch 1/100\n",
+      "210/210 [==============================] - 0s 2ms/step - loss: 1.0174 - acc: 0.3333 - val_loss: 0.9333 - val_acc: 0.3667\n",
+      "Epoch 2/100\n",
+      "210/210 [==============================] - 0s 134us/step - loss: 0.9745 - acc: 0.3429 - val_loss: 0.9086 - val_acc: 0.3667\n",
+      "Epoch 3/100\n",
+      "210/210 [==============================] - 0s 115us/step - loss: 0.9442 - acc: 0.3381 - val_loss: 0.8877 - val_acc: 0.3556\n",
+      "Epoch 4/100\n",
+      "210/210 [==============================] - 0s 145us/step - loss: 0.9182 - acc: 0.3429 - val_loss: 0.8708 - val_acc: 0.3667\n",
+      "Epoch 5/100\n",
+      "210/210 [==============================] - 0s 72us/step - loss: 0.8967 - acc: 0.3381 - val_loss: 0.8546 - val_acc: 0.3667\n",
+      "Epoch 6/100\n",
+      "210/210 [==============================] - 0s 104us/step - loss: 0.8763 - acc: 0.3476 - val_loss: 0.8392 - val_acc: 0.3667\n",
+      "Epoch 7/100\n",
+      "210/210 [==============================] - 0s 130us/step - loss: 0.8572 - acc: 0.3429 - val_loss: 0.8256 - val_acc: 0.3556\n",
+      "Epoch 8/100\n",
+      "210/210 [==============================] - 0s 113us/step - loss: 0.8399 - acc: 0.3286 - val_loss: 0.8133 - val_acc: 0.3556\n",
+      "Epoch 9/100\n",
+      "210/210 [==============================] - 0s 115us/step - loss: 0.8239 - acc: 0.3286 - val_loss: 0.8018 - val_acc: 0.3444\n",
+      "Epoch 10/100\n",
+      "210/210 [==============================] - 0s 107us/step - loss: 0.8096 - acc: 0.3286 - val_loss: 0.7911 - val_acc: 0.3222\n",
+      "Epoch 11/100\n",
+      "210/210 [==============================] - 0s 108us/step - loss: 0.7964 - acc: 0.3286 - val_loss: 0.7811 - val_acc: 0.3333\n",
+      "Epoch 12/100\n",
+      "210/210 [==============================] - 0s 110us/step - loss: 0.7841 - acc: 0.3190 - val_loss: 0.7717 - val_acc: 0.3444\n",
+      "Epoch 13/100\n",
+      "210/210 [==============================] - 0s 99us/step - loss: 0.7728 - acc: 0.3381 - val_loss: 0.7631 - val_acc: 0.3444\n",
+      "Epoch 14/100\n",
+      "210/210 [==============================] - 0s 105us/step - loss: 0.7624 - acc: 0.3429 - val_loss: 0.7549 - val_acc: 0.3556\n",
+      "Epoch 15/100\n",
+      "210/210 [==============================] - 0s 112us/step - loss: 0.7528 - acc: 0.3619 - val_loss: 0.7473 - val_acc: 0.3667\n",
+      "Epoch 16/100\n",
+      "210/210 [==============================] - 0s 93us/step - loss: 0.7437 - acc: 0.3952 - val_loss: 0.7400 - val_acc: 0.3778\n",
+      "Epoch 17/100\n",
+      "210/210 [==============================] - 0s 108us/step - loss: 0.7351 - acc: 0.4190 - val_loss: 0.7334 - val_acc: 0.3889\n",
+      "Epoch 18/100\n",
+      "210/210 [==============================] - 0s 104us/step - loss: 0.7270 - acc: 0.4000 - val_loss: 0.7271 - val_acc: 0.3889\n",
+      "Epoch 19/100\n",
+      "210/210 [==============================] - 0s 122us/step - loss: 0.7191 - acc: 0.4048 - val_loss: 0.7215 - val_acc: 0.3778\n",
+      "Epoch 20/100\n",
+      "210/210 [==============================] - 0s 103us/step - loss: 0.7120 - acc: 0.4286 - val_loss: 0.7167 - val_acc: 0.3333\n",
+      "Epoch 21/100\n",
+      "210/210 [==============================] - 0s 93us/step - loss: 0.7059 - acc: 0.4524 - val_loss: 0.7124 - val_acc: 0.3556\n",
+      "Epoch 22/100\n",
+      "210/210 [==============================] - 0s 84us/step - loss: 0.7002 - acc: 0.4667 - val_loss: 0.7083 - val_acc: 0.4000\n",
+      "Epoch 23/100\n",
+      "210/210 [==============================] - 0s 151us/step - loss: 0.6947 - acc: 0.5286 - val_loss: 0.7042 - val_acc: 0.4444\n",
+      "Epoch 24/100\n",
+      "210/210 [==============================] - 0s 132us/step - loss: 0.6894 - acc: 0.5476 - val_loss: 0.7002 - val_acc: 0.4444\n",
+      "Epoch 25/100\n",
+      "210/210 [==============================] - 0s 104us/step - loss: 0.6842 - acc: 0.5810 - val_loss: 0.6963 - val_acc: 0.5000\n",
+      "Epoch 26/100\n",
+      "210/210 [==============================] - 0s 86us/step - loss: 0.6792 - acc: 0.6095 - val_loss: 0.6930 - val_acc: 0.5111\n",
+      "Epoch 27/100\n",
+      "210/210 [==============================] - 0s 93us/step - loss: 0.6746 - acc: 0.6476 - val_loss: 0.6897 - val_acc: 0.5444\n",
+      "Epoch 28/100\n",
+      "210/210 [==============================] - 0s 78us/step - loss: 0.6702 - acc: 0.6952 - val_loss: 0.6865 - val_acc: 0.5667\n",
+      "Epoch 29/100\n",
+      "210/210 [==============================] - 0s 128us/step - loss: 0.6659 - acc: 0.7095 - val_loss: 0.6835 - val_acc: 0.6000\n",
+      "Epoch 30/100\n",
+      "210/210 [==============================] - 0s 100us/step - loss: 0.6617 - acc: 0.7190 - val_loss: 0.6808 - val_acc: 0.6222\n",
+      "Epoch 31/100\n",
+      "210/210 [==============================] - 0s 109us/step - loss: 0.6579 - acc: 0.7429 - val_loss: 0.6782 - val_acc: 0.6556\n",
+      "Epoch 32/100\n",
+      "210/210 [==============================] - 0s 128us/step - loss: 0.6542 - acc: 0.7619 - val_loss: 0.6757 - val_acc: 0.6778\n",
+      "Epoch 33/100\n",
+      "210/210 [==============================] - 0s 89us/step - loss: 0.6507 - acc: 0.7810 - val_loss: 0.6733 - val_acc: 0.6778\n",
+      "Epoch 34/100\n",
+      "210/210 [==============================] - 0s 128us/step - loss: 0.6473 - acc: 0.7905 - val_loss: 0.6711 - val_acc: 0.6778\n",
+      "Epoch 35/100\n",
+      "210/210 [==============================] - 0s 134us/step - loss: 0.6441 - acc: 0.7905 - val_loss: 0.6691 - val_acc: 0.6778\n",
+      "Epoch 36/100\n",
+      "210/210 [==============================] - 0s 150us/step - loss: 0.6412 - acc: 0.7905 - val_loss: 0.6671 - val_acc: 0.6778\n",
+      "Epoch 37/100\n",
+      "210/210 [==============================] - 0s 129us/step - loss: 0.6383 - acc: 0.7905 - val_loss: 0.6652 - val_acc: 0.6778\n",
+      "Epoch 38/100\n",
+      "210/210 [==============================] - 0s 98us/step - loss: 0.6355 - acc: 0.7905 - val_loss: 0.6634 - val_acc: 0.6778\n",
+      "Epoch 39/100\n",
+      "210/210 [==============================] - 0s 113us/step - loss: 0.6329 - acc: 0.7905 - val_loss: 0.6616 - val_acc: 0.6778\n",
+      "Epoch 40/100\n",
+      "210/210 [==============================] - 0s 155us/step - loss: 0.6304 - acc: 0.7905 - val_loss: 0.6601 - val_acc: 0.6778\n",
+      "Epoch 41/100\n",
+      "210/210 [==============================] - 0s 84us/step - loss: 0.6281 - acc: 0.7905 - val_loss: 0.6586 - val_acc: 0.6778\n",
+      "Epoch 42/100\n",
+      "210/210 [==============================] - 0s 117us/step - loss: 0.6259 - acc: 0.7905 - val_loss: 0.6571 - val_acc: 0.6778\n",
+      "Epoch 43/100\n",
+      "210/210 [==============================] - 0s 114us/step - loss: 0.6236 - acc: 0.7905 - val_loss: 0.6557 - val_acc: 0.6778\n",
+      "Epoch 44/100\n",
+      "210/210 [==============================] - 0s 93us/step - loss: 0.6215 - acc: 0.7905 - val_loss: 0.6544 - val_acc: 0.6778\n",
+      "Epoch 45/100\n",
+      "210/210 [==============================] - 0s 100us/step - loss: 0.6195 - acc: 0.7905 - val_loss: 0.6533 - val_acc: 0.6778\n",
+      "Epoch 46/100\n",
+      "210/210 [==============================] - 0s 144us/step - loss: 0.6176 - acc: 0.7905 - val_loss: 0.6522 - val_acc: 0.6778\n",
+      "Epoch 47/100\n",
+      "210/210 [==============================] - 0s 122us/step - loss: 0.6158 - acc: 0.7905 - val_loss: 0.6511 - val_acc: 0.6778\n",
+      "Epoch 48/100\n",
+      "210/210 [==============================] - 0s 142us/step - loss: 0.6140 - acc: 0.7905 - val_loss: 0.6502 - val_acc: 0.6778\n",
+      "Epoch 49/100\n",
+      "210/210 [==============================] - 0s 129us/step - loss: 0.6123 - acc: 0.7905 - val_loss: 0.6492 - val_acc: 0.6778\n",
+      "Epoch 50/100\n",
+      "210/210 [==============================] - 0s 105us/step - loss: 0.6106 - acc: 0.7905 - val_loss: 0.6483 - val_acc: 0.6778\n",
+      "Epoch 51/100\n",
+      "210/210 [==============================] - 0s 81us/step - loss: 0.6090 - acc: 0.7905 - val_loss: 0.6475 - val_acc: 0.6778\n",
+      "Epoch 52/100\n",
+      "210/210 [==============================] - 0s 154us/step - loss: 0.6075 - acc: 0.7905 - val_loss: 0.6467 - val_acc: 0.6778\n",
+      "Epoch 53/100\n",
+      "210/210 [==============================] - 0s 174us/step - loss: 0.6060 - acc: 0.7905 - val_loss: 0.6459 - val_acc: 0.6778\n",
+      "Epoch 54/100\n",
+      "210/210 [==============================] - 0s 84us/step - loss: 0.6044 - acc: 0.7905 - val_loss: 0.6451 - val_acc: 0.6778\n",
+      "Epoch 55/100\n",
+      "210/210 [==============================] - 0s 124us/step - loss: 0.6030 - acc: 0.7905 - val_loss: 0.6444 - val_acc: 0.6778\n",
+      "Epoch 56/100\n",
+      "210/210 [==============================] - 0s 131us/step - loss: 0.6015 - acc: 0.7905 - val_loss: 0.6437 - val_acc: 0.6778\n",
+      "Epoch 57/100\n",
+      "210/210 [==============================] - 0s 126us/step - loss: 0.6001 - acc: 0.7905 - val_loss: 0.6431 - val_acc: 0.6778\n",
+      "Epoch 58/100\n",
+      "210/210 [==============================] - 0s 123us/step - loss: 0.5988 - acc: 0.7905 - val_loss: 0.6425 - val_acc: 0.6778\n",
+      "Epoch 59/100\n",
+      "210/210 [==============================] - 0s 106us/step - loss: 0.5975 - acc: 0.7905 - val_loss: 0.6419 - val_acc: 0.6778\n",
+      "Epoch 60/100\n",
+      "210/210 [==============================] - 0s 123us/step - loss: 0.5962 - acc: 0.7905 - val_loss: 0.6414 - val_acc: 0.6778\n",
+      "Epoch 61/100\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "210/210 [==============================] - 0s 113us/step - loss: 0.5950 - acc: 0.7905 - val_loss: 0.6409 - val_acc: 0.6778\n",
+      "Epoch 62/100\n",
+      "210/210 [==============================] - 0s 163us/step - loss: 0.5938 - acc: 0.7905 - val_loss: 0.6404 - val_acc: 0.6778\n",
+      "Epoch 63/100\n",
+      "210/210 [==============================] - 0s 132us/step - loss: 0.5926 - acc: 0.7905 - val_loss: 0.6399 - val_acc: 0.6778\n",
+      "Epoch 64/100\n",
+      "210/210 [==============================] - 0s 57us/step - loss: 0.5914 - acc: 0.7905 - val_loss: 0.6395 - val_acc: 0.6778\n",
+      "Epoch 65/100\n",
+      "210/210 [==============================] - 0s 90us/step - loss: 0.5902 - acc: 0.7905 - val_loss: 0.6390 - val_acc: 0.6778\n",
+      "Epoch 66/100\n",
+      "210/210 [==============================] - 0s 115us/step - loss: 0.5890 - acc: 0.7905 - val_loss: 0.6385 - val_acc: 0.6778\n",
+      "Epoch 67/100\n",
+      "210/210 [==============================] - 0s 99us/step - loss: 0.5877 - acc: 0.7905 - val_loss: 0.6380 - val_acc: 0.6778\n",
+      "Epoch 68/100\n",
+      "210/210 [==============================] - 0s 137us/step - loss: 0.5864 - acc: 0.7905 - val_loss: 0.6375 - val_acc: 0.6778\n",
+      "Epoch 69/100\n",
+      "210/210 [==============================] - 0s 85us/step - loss: 0.5852 - acc: 0.7905 - val_loss: 0.6368 - val_acc: 0.6778\n",
+      "Epoch 70/100\n",
+      "210/210 [==============================] - 0s 140us/step - loss: 0.5838 - acc: 0.7905 - val_loss: 0.6362 - val_acc: 0.6778\n",
+      "Epoch 71/100\n",
+      "210/210 [==============================] - 0s 79us/step - loss: 0.5824 - acc: 0.7905 - val_loss: 0.6356 - val_acc: 0.6778\n",
+      "Epoch 72/100\n",
+      "210/210 [==============================] - 0s 101us/step - loss: 0.5810 - acc: 0.7905 - val_loss: 0.6348 - val_acc: 0.6778\n",
+      "Epoch 73/100\n",
+      "210/210 [==============================] - 0s 136us/step - loss: 0.5793 - acc: 0.7905 - val_loss: 0.6339 - val_acc: 0.6778\n",
+      "Epoch 74/100\n",
+      "210/210 [==============================] - 0s 95us/step - loss: 0.5777 - acc: 0.7905 - val_loss: 0.6330 - val_acc: 0.6778\n",
+      "Epoch 75/100\n",
+      "210/210 [==============================] - 0s 113us/step - loss: 0.5759 - acc: 0.7905 - val_loss: 0.6320 - val_acc: 0.6778\n",
+      "Epoch 76/100\n",
+      "210/210 [==============================] - 0s 129us/step - loss: 0.5741 - acc: 0.7905 - val_loss: 0.6309 - val_acc: 0.6778\n",
+      "Epoch 77/100\n",
+      "210/210 [==============================] - 0s 113us/step - loss: 0.5721 - acc: 0.7905 - val_loss: 0.6297 - val_acc: 0.6778\n",
+      "Epoch 78/100\n",
+      "210/210 [==============================] - 0s 88us/step - loss: 0.5699 - acc: 0.7905 - val_loss: 0.6286 - val_acc: 0.6778\n",
+      "Epoch 79/100\n",
+      "210/210 [==============================] - 0s 109us/step - loss: 0.5677 - acc: 0.7905 - val_loss: 0.6274 - val_acc: 0.6778\n",
+      "Epoch 80/100\n",
+      "210/210 [==============================] - 0s 85us/step - loss: 0.5654 - acc: 0.7905 - val_loss: 0.6263 - val_acc: 0.6778\n",
+      "Epoch 81/100\n",
+      "210/210 [==============================] - 0s 109us/step - loss: 0.5632 - acc: 0.7905 - val_loss: 0.6251 - val_acc: 0.6778\n",
+      "Epoch 82/100\n",
+      "210/210 [==============================] - 0s 76us/step - loss: 0.5610 - acc: 0.7905 - val_loss: 0.6239 - val_acc: 0.6778\n",
+      "Epoch 83/100\n",
+      "210/210 [==============================] - 0s 88us/step - loss: 0.5586 - acc: 0.7905 - val_loss: 0.6227 - val_acc: 0.6778\n",
+      "Epoch 84/100\n",
+      "210/210 [==============================] - 0s 120us/step - loss: 0.5563 - acc: 0.7905 - val_loss: 0.6214 - val_acc: 0.6778\n",
+      "Epoch 85/100\n",
+      "210/210 [==============================] - 0s 94us/step - loss: 0.5538 - acc: 0.7905 - val_loss: 0.6202 - val_acc: 0.6778\n",
+      "Epoch 86/100\n",
+      "210/210 [==============================] - 0s 73us/step - loss: 0.5514 - acc: 0.7905 - val_loss: 0.6189 - val_acc: 0.6778\n",
+      "Epoch 87/100\n",
+      "210/210 [==============================] - 0s 80us/step - loss: 0.5489 - acc: 0.7905 - val_loss: 0.6177 - val_acc: 0.6778\n",
+      "Epoch 88/100\n",
+      "210/210 [==============================] - 0s 111us/step - loss: 0.5465 - acc: 0.7905 - val_loss: 0.6165 - val_acc: 0.6778\n",
+      "Epoch 89/100\n",
+      "210/210 [==============================] - 0s 126us/step - loss: 0.5440 - acc: 0.7905 - val_loss: 0.6153 - val_acc: 0.6778\n",
+      "Epoch 90/100\n",
+      "210/210 [==============================] - 0s 102us/step - loss: 0.5415 - acc: 0.7905 - val_loss: 0.6142 - val_acc: 0.6778\n",
+      "Epoch 91/100\n",
+      "210/210 [==============================] - 0s 119us/step - loss: 0.5391 - acc: 0.7905 - val_loss: 0.6131 - val_acc: 0.6778\n",
+      "Epoch 92/100\n",
+      "210/210 [==============================] - 0s 125us/step - loss: 0.5366 - acc: 0.7905 - val_loss: 0.6119 - val_acc: 0.6778\n",
+      "Epoch 93/100\n",
+      "210/210 [==============================] - 0s 97us/step - loss: 0.5341 - acc: 0.7905 - val_loss: 0.6109 - val_acc: 0.6778\n",
+      "Epoch 94/100\n",
+      "210/210 [==============================] - 0s 84us/step - loss: 0.5316 - acc: 0.7905 - val_loss: 0.6097 - val_acc: 0.6778\n",
+      "Epoch 95/100\n",
+      "210/210 [==============================] - 0s 96us/step - loss: 0.5291 - acc: 0.7905 - val_loss: 0.6088 - val_acc: 0.6778\n",
+      "Epoch 96/100\n",
+      "210/210 [==============================] - 0s 106us/step - loss: 0.5268 - acc: 0.7905 - val_loss: 0.6077 - val_acc: 0.6778\n",
+      "Epoch 97/100\n",
+      "210/210 [==============================] - 0s 109us/step - loss: 0.5243 - acc: 0.7905 - val_loss: 0.6067 - val_acc: 0.6778\n",
+      "Epoch 98/100\n",
+      "210/210 [==============================] - 0s 88us/step - loss: 0.5218 - acc: 0.7905 - val_loss: 0.6058 - val_acc: 0.6778\n",
+      "Epoch 99/100\n",
+      "210/210 [==============================] - 0s 98us/step - loss: 0.5196 - acc: 0.7905 - val_loss: 0.6050 - val_acc: 0.6778\n",
+      "Epoch 100/100\n",
+      "210/210 [==============================] - 0s 93us/step - loss: 0.5173 - acc: 0.7905 - val_loss: 0.6042 - val_acc: 0.6778\n"
      ]
     }
    ],
    "source": [
-    "# Looking at the dataset\n",
-    "print(X_train.shape)"
+    "model = a_simple_NN()\n",
+    "\n",
+    "# Here we split the dataset into training (80%) and validation sets (20%)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    features, labels, test_size=0.3)\n",
+    "\n",
+    "num_epochs = 100\n",
+    "\n",
+    "# We can pass validation data while training\n",
+    "\n",
+    "model_run = model.fit(X_train, y_train, epochs=num_epochs,\n",
+    "                      validation_data=(X_test, y_test))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This digit is:  8\n"
-     ]
-    },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe8e68579e8>"
+       "<matplotlib.figure.Figure at 0x7f4dff647390>"
       ]
      },
      "metadata": {
-      "image/png": {
-       "height": 250,
-       "width": 253
-      },
       "needs_background": "light"
      },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "# We can see that the training set consists of 60,000 images of size 28x28 pixels\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "i=np.random.randint(0,X_train.shape[0])\n",
-    "plt.imshow(X_train[i], cmap=\"gray_r\") ;\n",
-    "print(\"This digit is: \" , y_train[i])"
+    "history_model = model_run.history\n",
+    "\n",
+    "plt.plot(np.arange(1,num_epochs+1), history_model[\"acc\"], \"blue\") ;\n",
+    "\n",
+    "plt.plot(np.arange(1,num_epochs+1), history_model[\"val_acc\"], \"red\") ;"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 141,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 255\n"
-     ]
-    }
-   ],
    "source": [
-    "# Look at the data values for a couple of images\n",
-    "print(X_train[0].min(), X_train[1].max())"
+    "We know from previous chapters that to more robustly calculate accuracy we can use **K-fold crossvalidation**.\n",
+    "This is even more important when we have small datasets and cannot afford to reserve a validation set!\n",
+    "This is also the case in the example above.\n",
+    "\n",
+    "One way to do the cross validation here would be to write our own function to do this. However, we know that **SciKit learn** provides such a function. So the question is:\n",
+    "\n",
+    "Can we somehow use the handy functions which **SciKit learn** provides to evaluate and tune our Keras models?\n",
+    "\n",
+    "The Answer is **YES !**\n",
+    "\n",
+    "We show how to do this in the following section."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The data consists of values between 0-255 representing the **grayscale level**"
+    "## Using SciKit learn functions on Keras models\n",
+    "\n",
+    "Keras offers wrappers which allow its Sequential models to be used with SciKit learn. \n",
+    "\n",
+    "There 2 such wrappers: **KerasClassifier** and **KerasRegressor**.\n",
+    "\n",
+    "For more information:\n",
+    "https://keras.io/scikit-learn-api/\n",
+    "\n",
+    "**Now lets see how this works!**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 148,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(60000,)\n"
+      "[0.61428571 0.6        0.88571429 0.7        0.67142857]\n",
+      "0.6942857147966113\n"
      ]
     }
    ],
    "source": [
-    "# The labels are the digit on the image\n",
-    "print(y_train.shape)"
+    "# We wrap the Keras model we created above with KerasClassifier\n",
+    "from keras.wrappers.scikit_learn import KerasClassifier \n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "model_scikit = KerasClassifier(build_fn=a_simple_NN, **{\"epochs\":num_epochs, \"verbose\":0})\n",
+    "cross_validation = cross_val_score(model_scikit, X_train, y_train, cv=5, verbose=0)\n",
+    "print(cross_validation)\n",
+    "print(np.mean(cross_validation))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
    "source": [
-    "# Scaling the data\n",
-    "# It is important to normalize the input data to (0-1) before providing it to a neural net\n",
-    "# We could use the previously introduced function from SciKit learn. However, here it is sufficient to\n",
-    "# just divide the input data by 255\n",
-    "X_train_norm = X_train/255.\n",
-    "X_test_norm = X_test/255.\n",
-    "\n",
-    "# Also we need to reshape the input data such that each sample is a vector and not a 2D matrix\n",
-    "X_train_prep = X_train_norm.reshape(X_train_norm.shape[0],28*28)\n",
-    "X_test_prep = X_test_norm.reshape(X_test_norm.shape[0],28*28)"
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "import numpy as np\n",
+    "# We wrap the Keras model we created above with KerasClassifier\n",
+    "from keras.wrappers.scikit_learn import KerasClassifier "
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 14,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "**IMPORTANT: One-Hot encoding**\n",
+    "def list_flatten(list_of_list):\n",
+    "    flattened_list = [i for j in list_of_list for i in j]\n",
+    "    return flattened_list\n",
     "\n",
-    "**TODO: Better frame the explaination**\n",
-    "In such problems the labels are provided as something called **One-hot encodings**. What this does is to convert a categorical label to a vector.\n",
+    "def train_and_plot_decision_surface(\n",
+    "    name, classifier, features_2d, labels, preproc=None, plt=plt, marker='o', N=400\n",
+    "):\n",
     "\n",
-    "For the MNIST problem where we have **10 categories** one-hot encoding will create a vector of length 10 for each of the labels. All the entries of this vector will be zero **except** for the index which is equal to the integer value of the label.\n",
+    "    features_2d = np.array(features_2d)\n",
+    "    xmin, ymin = features_2d.min(axis=0)\n",
+    "    xmax, ymax = features_2d.max(axis=0)\n",
     "\n",
-    "For example:\n",
-    "if label is 4. The one-hot vector will look like **[0 0 0 0 1 0 0 0 0 0]**\n",
+    "    x = np.linspace(xmin, xmax, N)\n",
+    "    y = np.linspace(ymin, ymax, N)\n",
+    "    points = np.array(np.meshgrid(x, y)).T.reshape(-1, 2)\n",
     "\n",
-    "Fortunately, we don't have to code this ourselves because Keras has a built-in function for this."
+    "    if preproc is not None:\n",
+    "        points_for_classifier = preproc.fit_transform(points)\n",
+    "        features_2d = preproc.fit_transform(features_2d)\n",
+    "    else:\n",
+    "        points_for_classifier = points\n",
+    "\n",
+    "    classifier.fit(features_2d, labels, verbose=0)\n",
+    "    predicted = classifier.predict(features_2d)\n",
+    "    \n",
+    "    if name == \"Neural Net\":\n",
+    "        predicted = list_flatten(predicted)\n",
+    "    \n",
+    "    \n",
+    "    if preproc is not None:\n",
+    "        name += \" (w/ preprocessing)\"\n",
+    "    print(name + \":\\t\", sum(predicted == labels), \"/\", len(labels), \"correct\")\n",
+    "    \n",
+    "    if name == \"Neural Net\":\n",
+    "        classes = np.array(list_flatten(classifier.predict(points_for_classifier)), dtype=bool)\n",
+    "    else:\n",
+    "        classes = np.array(classifier.predict(points_for_classifier), dtype=bool)\n",
+    "    plt.plot(\n",
+    "        points[~classes][:, 0],\n",
+    "        points[~classes][:, 1],\n",
+    "        \"o\",\n",
+    "        color=\"steelblue\",\n",
+    "        markersize=1,\n",
+    "        alpha=0.01,\n",
+    "    )\n",
+    "    plt.plot(\n",
+    "        points[classes][:, 0],\n",
+    "        points[classes][:, 1],\n",
+    "        \"o\",\n",
+    "        color=\"chocolate\",\n",
+    "        markersize=1,\n",
+    "        alpha=0.04,\n",
+    "    )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(60000, 10)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from keras.utils.np_utils import to_categorical\n",
+    "def a_simple_NN():\n",
+    "    \n",
+    "    model = Sequential()\n",
     "\n",
-    "y_train_onehot = to_categorical(y_train, num_classes=10)\n",
-    "y_test_onehot = to_categorical(y_test, num_classes=10)\n",
+    "    model.add(Dense(8, input_shape = (2,), activation = \"relu\"))\n",
     "\n",
-    "print(y_train_onehot.shape)"
+    "    model.add(Dense(2, activation = \"relu\"))\n",
+    "\n",
+    "    model.add(Dense(1, activation = \"sigmoid\"))\n",
+    "\n",
+    "    model.compile(loss=\"binary_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
+    "    \n",
+    "    return model\n",
+    "\n",
+    "model = a_simple_NN()\n",
+    "\n",
+    "num_epochs = 400\n",
+    "model_scikit = KerasClassifier(build_fn=a_simple_NN, epochs=num_epochs)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/20\n",
-      "60000/60000 [==============================] - 2s 34us/step - loss: 0.5888 - acc: 0.8434\n",
-      "Epoch 2/20\n",
-      "60000/60000 [==============================] - 1s 20us/step - loss: 0.2569 - acc: 0.9267\n",
-      "Epoch 3/20\n",
-      "60000/60000 [==============================] - 1s 16us/step - loss: 0.2024 - acc: 0.9416\n",
-      "Epoch 4/20\n",
-      "60000/60000 [==============================] - 1s 17us/step - loss: 0.1706 - acc: 0.9497\n",
-      "Epoch 5/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.1475 - acc: 0.9563\n",
-      "Epoch 6/20\n",
-      "60000/60000 [==============================] - 1s 20us/step - loss: 0.1290 - acc: 0.9627\n",
-      "Epoch 7/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.1162 - acc: 0.9651\n",
-      "Epoch 8/20\n",
-      "60000/60000 [==============================] - 1s 19us/step - loss: 0.1035 - acc: 0.9691\n",
-      "Epoch 9/20\n",
-      "60000/60000 [==============================] - 2s 28us/step - loss: 0.0939 - acc: 0.9716\n",
-      "Epoch 10/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0848 - acc: 0.9743\n",
-      "Epoch 11/20\n",
-      "60000/60000 [==============================] - 1s 25us/step - loss: 0.0777 - acc: 0.9763\n",
-      "Epoch 12/20\n",
-      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0720 - acc: 0.9780\n",
-      "Epoch 13/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0655 - acc: 0.9808\n",
-      "Epoch 14/20\n",
-      "60000/60000 [==============================] - 2s 30us/step - loss: 0.0610 - acc: 0.9817\n",
-      "Epoch 15/20\n",
-      "60000/60000 [==============================] - 1s 16us/step - loss: 0.0563 - acc: 0.9832\n",
-      "Epoch 16/20\n",
-      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0527 - acc: 0.9842\n",
-      "Epoch 17/20\n",
-      "60000/60000 [==============================] - 1s 21us/step - loss: 0.0478 - acc: 0.9854\n",
-      "Epoch 18/20\n",
-      "60000/60000 [==============================] - 1s 15us/step - loss: 0.0453 - acc: 0.9864\n",
-      "Epoch 19/20\n",
-      "60000/60000 [==============================] - 1s 18us/step - loss: 0.0419 - acc: 0.9874\n",
-      "Epoch 20/20\n",
-      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0387 - acc: 0.9885\n"
+      "Neural Net:\t 487 / 500 correct\n"
      ]
     },
     {
      "data": {
+      "image/png": "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\n",
       "text/plain": [
-       "<keras.callbacks.History at 0x7fe8e7465438>"
+       "<matplotlib.figure.Figure at 0x7f4e05a6ec18>"
       ]
      },
-     "execution_count": 194,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "# Building the keras model\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense\n",
-    "\n",
-    "model = Sequential()\n",
-    "\n",
-    "model.add(Dense(64,input_shape=(28*28,), activation=\"relu\"))\n",
-    "\n",
-    "model.add(Dense(64, activation = \"relu\"))\n",
+    "def plot_points(plt=plt, marker='o'):\n",
+    "    colors = [[\"steelblue\", \"chocolate\"][i] for i in labels]\n",
+    "    plt.scatter(features.iloc[:, 0], features.iloc[:, 1], color=colors, marker=marker);\n",
     "\n",
-    "model.add(Dense(10, activation = \"softmax\"))\n",
+    "_, ax = plt.subplots(figsize=(6, 6))\n",
     "\n",
-    "model.compile(loss=\"categorical_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
+    "xor = pd.read_csv(\"xor.csv\")\n",
+    "features = xor.iloc[:, :-1]\n",
+    "# Convert boolean to integer values (True->1 and False->0)\n",
+    "labels = xor.iloc[:, -1]\n",
     "\n",
-    "model_history = model.fit(X_train_prep, y_train_cat, epochs=20, batch_size=512);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 196,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10000/10000 [==============================] - 1s 85us/step\n",
-      "The [loss, accuracy] are:  [0.08737125840586377, 0.974]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Evaluating the model on test dataset\n",
-    "print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))"
+    "train_and_plot_decision_surface(\"Neural Net\", model_scikit, features, labels, plt=ax)\n",
+    "plot_points(plt=ax)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Work in Progress\n",
-    "\n",
-    "## Network results on dataset used in previous notebooks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense\n",
-    "import numpy as np"
+    "### Exercise: Create a neural network to classify the 2d points example from chapter 2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          x         y  label\n",
+      "0 -0.501840  1.802857  False\n",
+      "1  0.927976  0.394634   True\n",
+      "2 -1.375925 -1.376022  False\n"
+     ]
+    },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ff759315080>"
+       "<matplotlib.figure.Figure at 0x7f4e0457ca20>"
       ]
      },
      "metadata": {
@@ -911,512 +1157,324 @@
     }
    ],
    "source": [
-    "# Creating a network to solve the XOR problem\n",
-    "# Loading and plotting the data\n",
-    "xor = pd.read_csv(\"xor.csv\")\n",
-    "xv = xor[\"x\"]\n",
-    "yv = xor[\"y\"]\n",
+    "circle = pd.read_csv(\"2d_points.csv\")\n",
+    "# Using x and y coordinates as featues\n",
+    "features = circle.iloc[:, :-1]\n",
+    "# Convert boolean to integer values (True->1 and False->0)\n",
+    "labels = circle.iloc[:, -1].astype(int)\n",
     "\n",
-    "colors = [[\"steelblue\", \"chocolate\"][i] for i in xor[\"label\"]]\n",
+    "colors = [[\"steelblue\", \"chocolate\"][i] for i in circle[\"label\"]]\n",
     "plt.figure(figsize=(5, 5))\n",
     "plt.xlim([-2, 2])\n",
     "plt.ylim([-2, 2])\n",
-    "plt.title(\"Blue points are False\")\n",
     "\n",
-    "\n",
-    "plt.scatter(xv, yv, color=colors, marker=\"o\");"
+    "plt.scatter(features[\"x\"], features[\"y\"], color=colors, marker=\"o\");\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Using x and y coordinates as featues\n",
-    "features = xor.iloc[:, :-1]\n",
-    "# Convert boolean to integer values (True->1 and False->0)\n",
-    "labels = xor.iloc[:, -1].astype(int)\n",
-    "\n",
-    "# Building a Keras model\n",
+    "# Insert Code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### MNIST Dataset\n",
     "\n",
-    "def a_simple_NN():\n",
-    "    \n",
-    "    model = Sequential()\n",
+    "MNIST datasets is a very common dataset used in machine learning. It is widely used to train and validate models.\n",
     "\n",
-    "    model.add(Dense(4, input_shape = (2,), activation = \"relu\"))\n",
     "\n",
-    "    #model.add(Dense(4, activation = \"relu\"))\n",
+    ">The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a >test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size->normalized and centered in a fixed-size image.\n",
+    ">It is a good database for people who want to try learning techniques and pattern recognition methods on real-world >data while spending minimal efforts on preprocessing and formatting.\n",
+    ">source: http://yann.lecun.com/exdb/mnist/\n",
     "\n",
-    "    model.add(Dense(1, activation = \"sigmoid\"))\n",
+    "The problem we want to solve using this dataset is: multi-class classification\n",
+    "This dataset consists of images of handwritten digits between 0-9 and their corresponsing labels. We want to train a neural network which is able to predict the correct digit on the image. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loading the dataset in keras\n",
+    "# Later you can explore and play with other datasets with come with Keras\n",
+    "from keras.datasets import mnist\n",
     "\n",
-    "    model.compile(loss=\"binary_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
-    "    \n",
-    "    return model\n",
+    "# Loading the train and test data\n",
     "\n",
-    "model = a_simple_NN()"
+    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 185,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Train on 350 samples, validate on 150 samples\n",
-      "Epoch 1/100\n",
-      "350/350 [==============================] - 1s 2ms/step - loss: 0.8305 - acc: 0.3571 - val_loss: 0.8120 - val_acc: 0.3667\n",
-      "Epoch 2/100\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.8170 - acc: 0.3629 - val_loss: 0.8010 - val_acc: 0.3667\n",
-      "Epoch 3/100\n",
-      "350/350 [==============================] - 0s 121us/step - loss: 0.8060 - acc: 0.3657 - val_loss: 0.7904 - val_acc: 0.3733\n",
-      "Epoch 4/100\n",
-      "350/350 [==============================] - 0s 133us/step - loss: 0.7960 - acc: 0.3743 - val_loss: 0.7807 - val_acc: 0.3867\n",
-      "Epoch 5/100\n",
-      "350/350 [==============================] - 0s 121us/step - loss: 0.7866 - acc: 0.3800 - val_loss: 0.7716 - val_acc: 0.3867\n",
-      "Epoch 6/100\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.7773 - acc: 0.3886 - val_loss: 0.7625 - val_acc: 0.3867\n",
-      "Epoch 7/100\n",
-      "350/350 [==============================] - 0s 97us/step - loss: 0.7682 - acc: 0.3914 - val_loss: 0.7536 - val_acc: 0.3867\n",
-      "Epoch 8/100\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.7594 - acc: 0.4086 - val_loss: 0.7450 - val_acc: 0.4067\n",
-      "Epoch 9/100\n",
-      "350/350 [==============================] - 0s 81us/step - loss: 0.7507 - acc: 0.4143 - val_loss: 0.7367 - val_acc: 0.4200\n",
-      "Epoch 10/100\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.7420 - acc: 0.4200 - val_loss: 0.7283 - val_acc: 0.4333\n",
-      "Epoch 11/100\n",
-      "350/350 [==============================] - 0s 130us/step - loss: 0.7335 - acc: 0.4343 - val_loss: 0.7200 - val_acc: 0.4533\n",
-      "Epoch 12/100\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.7252 - acc: 0.4429 - val_loss: 0.7123 - val_acc: 0.4600\n",
-      "Epoch 13/100\n",
-      "350/350 [==============================] - 0s 138us/step - loss: 0.7172 - acc: 0.4514 - val_loss: 0.7043 - val_acc: 0.4733\n",
-      "Epoch 14/100\n",
-      "350/350 [==============================] - 0s 103us/step - loss: 0.7091 - acc: 0.4600 - val_loss: 0.6967 - val_acc: 0.4733\n",
-      "Epoch 15/100\n",
-      "350/350 [==============================] - 0s 144us/step - loss: 0.7014 - acc: 0.4800 - val_loss: 0.6894 - val_acc: 0.4933\n",
-      "Epoch 16/100\n",
-      "350/350 [==============================] - 0s 103us/step - loss: 0.6937 - acc: 0.4971 - val_loss: 0.6821 - val_acc: 0.5133\n",
-      "Epoch 17/100\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.6862 - acc: 0.5229 - val_loss: 0.6750 - val_acc: 0.5467\n",
-      "Epoch 18/100\n",
-      "350/350 [==============================] - 0s 146us/step - loss: 0.6786 - acc: 0.5371 - val_loss: 0.6680 - val_acc: 0.5733\n",
-      "Epoch 19/100\n",
-      "350/350 [==============================] - 0s 149us/step - loss: 0.6711 - acc: 0.5514 - val_loss: 0.6611 - val_acc: 0.5933\n",
-      "Epoch 20/100\n",
-      "350/350 [==============================] - 0s 131us/step - loss: 0.6639 - acc: 0.5800 - val_loss: 0.6546 - val_acc: 0.6133\n",
-      "Epoch 21/100\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.6569 - acc: 0.5886 - val_loss: 0.6482 - val_acc: 0.6333\n",
-      "Epoch 22/100\n",
-      "350/350 [==============================] - 0s 134us/step - loss: 0.6503 - acc: 0.6114 - val_loss: 0.6422 - val_acc: 0.6467\n",
-      "Epoch 23/100\n",
-      "350/350 [==============================] - 0s 110us/step - loss: 0.6436 - acc: 0.6257 - val_loss: 0.6360 - val_acc: 0.6533\n",
-      "Epoch 24/100\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.6371 - acc: 0.6343 - val_loss: 0.6303 - val_acc: 0.6667\n",
-      "Epoch 25/100\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.6308 - acc: 0.6486 - val_loss: 0.6244 - val_acc: 0.7000\n",
-      "Epoch 26/100\n",
-      "350/350 [==============================] - 0s 116us/step - loss: 0.6246 - acc: 0.6600 - val_loss: 0.6189 - val_acc: 0.7000\n",
-      "Epoch 27/100\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.6185 - acc: 0.6771 - val_loss: 0.6135 - val_acc: 0.7133\n",
-      "Epoch 28/100\n",
-      "350/350 [==============================] - 0s 116us/step - loss: 0.6126 - acc: 0.6914 - val_loss: 0.6083 - val_acc: 0.7267\n",
-      "Epoch 29/100\n",
-      "350/350 [==============================] - 0s 115us/step - loss: 0.6069 - acc: 0.7114 - val_loss: 0.6032 - val_acc: 0.7333\n",
-      "Epoch 30/100\n",
-      "350/350 [==============================] - 0s 133us/step - loss: 0.6013 - acc: 0.7314 - val_loss: 0.5981 - val_acc: 0.7267\n",
-      "Epoch 31/100\n",
-      "350/350 [==============================] - 0s 104us/step - loss: 0.5960 - acc: 0.7400 - val_loss: 0.5933 - val_acc: 0.7333\n",
-      "Epoch 32/100\n",
-      "350/350 [==============================] - 0s 133us/step - loss: 0.5907 - acc: 0.7486 - val_loss: 0.5885 - val_acc: 0.7533\n",
-      "Epoch 33/100\n",
-      "350/350 [==============================] - 0s 104us/step - loss: 0.5854 - acc: 0.7571 - val_loss: 0.5839 - val_acc: 0.7733\n",
-      "Epoch 34/100\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.5802 - acc: 0.7686 - val_loss: 0.5791 - val_acc: 0.7667\n",
-      "Epoch 35/100\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.5753 - acc: 0.7743 - val_loss: 0.5747 - val_acc: 0.7667\n",
-      "Epoch 36/100\n",
-      "350/350 [==============================] - 0s 119us/step - loss: 0.5704 - acc: 0.7829 - val_loss: 0.5703 - val_acc: 0.7733\n",
-      "Epoch 37/100\n",
-      "350/350 [==============================] - 0s 154us/step - loss: 0.5658 - acc: 0.7857 - val_loss: 0.5661 - val_acc: 0.7733\n",
-      "Epoch 38/100\n",
-      "350/350 [==============================] - 0s 121us/step - loss: 0.5613 - acc: 0.7829 - val_loss: 0.5620 - val_acc: 0.7933\n",
-      "Epoch 39/100\n",
-      "350/350 [==============================] - 0s 141us/step - loss: 0.5570 - acc: 0.7800 - val_loss: 0.5581 - val_acc: 0.7933\n",
-      "Epoch 40/100\n",
-      "350/350 [==============================] - 0s 64us/step - loss: 0.5528 - acc: 0.7886 - val_loss: 0.5545 - val_acc: 0.8000\n",
-      "Epoch 41/100\n",
-      "350/350 [==============================] - 0s 124us/step - loss: 0.5489 - acc: 0.7914 - val_loss: 0.5511 - val_acc: 0.7933\n",
-      "Epoch 42/100\n",
-      "350/350 [==============================] - 0s 128us/step - loss: 0.5449 - acc: 0.7971 - val_loss: 0.5477 - val_acc: 0.7933\n",
-      "Epoch 43/100\n",
-      "350/350 [==============================] - 0s 140us/step - loss: 0.5411 - acc: 0.7971 - val_loss: 0.5444 - val_acc: 0.7867\n",
-      "Epoch 44/100\n",
-      "350/350 [==============================] - 0s 120us/step - loss: 0.5372 - acc: 0.8029 - val_loss: 0.5410 - val_acc: 0.7867\n",
-      "Epoch 45/100\n",
-      "350/350 [==============================] - 0s 108us/step - loss: 0.5335 - acc: 0.8057 - val_loss: 0.5379 - val_acc: 0.7867\n",
-      "Epoch 46/100\n",
-      "350/350 [==============================] - 0s 119us/step - loss: 0.5298 - acc: 0.8029 - val_loss: 0.5346 - val_acc: 0.7800\n",
-      "Epoch 47/100\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.5261 - acc: 0.8057 - val_loss: 0.5315 - val_acc: 0.7800\n",
-      "Epoch 48/100\n",
-      "350/350 [==============================] - 0s 142us/step - loss: 0.5225 - acc: 0.8057 - val_loss: 0.5283 - val_acc: 0.7800\n",
-      "Epoch 49/100\n",
-      "350/350 [==============================] - 0s 83us/step - loss: 0.5189 - acc: 0.8114 - val_loss: 0.5251 - val_acc: 0.7800\n",
-      "Epoch 50/100\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.5152 - acc: 0.8086 - val_loss: 0.5220 - val_acc: 0.7800\n",
-      "Epoch 51/100\n",
-      "350/350 [==============================] - 0s 114us/step - loss: 0.5116 - acc: 0.8143 - val_loss: 0.5187 - val_acc: 0.7800\n",
-      "Epoch 52/100\n",
-      "350/350 [==============================] - 0s 121us/step - loss: 0.5079 - acc: 0.8286 - val_loss: 0.5153 - val_acc: 0.7800\n",
-      "Epoch 53/100\n",
-      "350/350 [==============================] - 0s 138us/step - loss: 0.5043 - acc: 0.8286 - val_loss: 0.5120 - val_acc: 0.7867\n",
-      "Epoch 54/100\n",
-      "350/350 [==============================] - 0s 138us/step - loss: 0.5007 - acc: 0.8257 - val_loss: 0.5089 - val_acc: 0.7867\n",
-      "Epoch 55/100\n",
-      "350/350 [==============================] - 0s 135us/step - loss: 0.4974 - acc: 0.8314 - val_loss: 0.5060 - val_acc: 0.7933\n",
-      "Epoch 56/100\n",
-      "350/350 [==============================] - 0s 102us/step - loss: 0.4941 - acc: 0.8314 - val_loss: 0.5031 - val_acc: 0.7933\n",
-      "Epoch 57/100\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.4906 - acc: 0.8371 - val_loss: 0.5000 - val_acc: 0.7933\n",
-      "Epoch 58/100\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.4871 - acc: 0.8400 - val_loss: 0.4969 - val_acc: 0.7867\n",
-      "Epoch 59/100\n",
-      "350/350 [==============================] - 0s 116us/step - loss: 0.4838 - acc: 0.8400 - val_loss: 0.4939 - val_acc: 0.7867\n",
-      "Epoch 60/100\n",
-      "350/350 [==============================] - 0s 100us/step - loss: 0.4803 - acc: 0.8400 - val_loss: 0.4906 - val_acc: 0.8000\n",
-      "Epoch 61/100\n"
+      "(60000, 28, 28)\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "# Looking at the dataset\n",
+    "print(X_train.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "350/350 [==============================] - 0s 123us/step - loss: 0.4767 - acc: 0.8400 - val_loss: 0.4876 - val_acc: 0.8000\n",
-      "Epoch 62/100\n",
-      "350/350 [==============================] - 0s 123us/step - loss: 0.4733 - acc: 0.8343 - val_loss: 0.4846 - val_acc: 0.7933\n",
-      "Epoch 63/100\n",
-      "350/350 [==============================] - 0s 106us/step - loss: 0.4699 - acc: 0.8400 - val_loss: 0.4816 - val_acc: 0.7933\n",
-      "Epoch 64/100\n",
-      "350/350 [==============================] - 0s 142us/step - loss: 0.4667 - acc: 0.8400 - val_loss: 0.4786 - val_acc: 0.8000\n",
-      "Epoch 65/100\n",
-      "350/350 [==============================] - 0s 134us/step - loss: 0.4636 - acc: 0.8371 - val_loss: 0.4758 - val_acc: 0.8000\n",
-      "Epoch 66/100\n",
-      "350/350 [==============================] - 0s 103us/step - loss: 0.4604 - acc: 0.8371 - val_loss: 0.4730 - val_acc: 0.8000\n",
-      "Epoch 67/100\n",
-      "350/350 [==============================] - 0s 131us/step - loss: 0.4574 - acc: 0.8429 - val_loss: 0.4701 - val_acc: 0.8000\n",
-      "Epoch 68/100\n",
-      "350/350 [==============================] - 0s 134us/step - loss: 0.4545 - acc: 0.8457 - val_loss: 0.4677 - val_acc: 0.8000\n",
-      "Epoch 69/100\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.4516 - acc: 0.8457 - val_loss: 0.4652 - val_acc: 0.8000\n",
-      "Epoch 70/100\n",
-      "350/350 [==============================] - 0s 123us/step - loss: 0.4486 - acc: 0.8457 - val_loss: 0.4625 - val_acc: 0.8000\n",
-      "Epoch 71/100\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.4457 - acc: 0.8486 - val_loss: 0.4600 - val_acc: 0.8000\n",
-      "Epoch 72/100\n",
-      "350/350 [==============================] - 0s 106us/step - loss: 0.4426 - acc: 0.8457 - val_loss: 0.4574 - val_acc: 0.8067\n",
-      "Epoch 73/100\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.4399 - acc: 0.8457 - val_loss: 0.4549 - val_acc: 0.8067\n",
-      "Epoch 74/100\n",
-      "350/350 [==============================] - 0s 107us/step - loss: 0.4369 - acc: 0.8486 - val_loss: 0.4523 - val_acc: 0.8067\n",
-      "Epoch 75/100\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.4340 - acc: 0.8514 - val_loss: 0.4498 - val_acc: 0.8067\n",
-      "Epoch 76/100\n",
-      "350/350 [==============================] - 0s 116us/step - loss: 0.4311 - acc: 0.8514 - val_loss: 0.4472 - val_acc: 0.8200\n",
-      "Epoch 77/100\n",
-      "350/350 [==============================] - 0s 129us/step - loss: 0.4282 - acc: 0.8543 - val_loss: 0.4449 - val_acc: 0.8200\n",
-      "Epoch 78/100\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.4255 - acc: 0.8571 - val_loss: 0.4425 - val_acc: 0.8267\n",
-      "Epoch 79/100\n",
-      "350/350 [==============================] - 0s 132us/step - loss: 0.4228 - acc: 0.8571 - val_loss: 0.4401 - val_acc: 0.8267\n",
-      "Epoch 80/100\n",
-      "350/350 [==============================] - 0s 157us/step - loss: 0.4201 - acc: 0.8571 - val_loss: 0.4377 - val_acc: 0.8200\n",
-      "Epoch 81/100\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.4173 - acc: 0.8629 - val_loss: 0.4352 - val_acc: 0.8200\n",
-      "Epoch 82/100\n",
-      "350/350 [==============================] - 0s 109us/step - loss: 0.4146 - acc: 0.8600 - val_loss: 0.4328 - val_acc: 0.8200\n",
-      "Epoch 83/100\n",
-      "350/350 [==============================] - 0s 108us/step - loss: 0.4120 - acc: 0.8600 - val_loss: 0.4306 - val_acc: 0.8200\n",
-      "Epoch 84/100\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.4095 - acc: 0.8629 - val_loss: 0.4284 - val_acc: 0.8200\n",
-      "Epoch 85/100\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.4069 - acc: 0.8629 - val_loss: 0.4261 - val_acc: 0.8200\n",
-      "Epoch 86/100\n",
-      "350/350 [==============================] - 0s 131us/step - loss: 0.4043 - acc: 0.8657 - val_loss: 0.4238 - val_acc: 0.8200\n",
-      "Epoch 87/100\n",
-      "350/350 [==============================] - 0s 125us/step - loss: 0.4018 - acc: 0.8686 - val_loss: 0.4216 - val_acc: 0.8200\n",
-      "Epoch 88/100\n",
-      "350/350 [==============================] - 0s 117us/step - loss: 0.3993 - acc: 0.8686 - val_loss: 0.4193 - val_acc: 0.8200\n",
-      "Epoch 89/100\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.3969 - acc: 0.8714 - val_loss: 0.4173 - val_acc: 0.8200\n",
-      "Epoch 90/100\n",
-      "350/350 [==============================] - 0s 137us/step - loss: 0.3945 - acc: 0.8771 - val_loss: 0.4151 - val_acc: 0.8200\n",
-      "Epoch 91/100\n",
-      "350/350 [==============================] - 0s 144us/step - loss: 0.3921 - acc: 0.8771 - val_loss: 0.4130 - val_acc: 0.8200\n",
-      "Epoch 92/100\n",
-      "350/350 [==============================] - 0s 98us/step - loss: 0.3899 - acc: 0.8743 - val_loss: 0.4109 - val_acc: 0.8200\n",
-      "Epoch 93/100\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.3875 - acc: 0.8771 - val_loss: 0.4088 - val_acc: 0.8200\n",
-      "Epoch 94/100\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.3854 - acc: 0.8800 - val_loss: 0.4068 - val_acc: 0.8200\n",
-      "Epoch 95/100\n",
-      "350/350 [==============================] - 0s 126us/step - loss: 0.3832 - acc: 0.8771 - val_loss: 0.4050 - val_acc: 0.8200\n",
-      "Epoch 96/100\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.3811 - acc: 0.8771 - val_loss: 0.4030 - val_acc: 0.8200\n",
-      "Epoch 97/100\n",
-      "350/350 [==============================] - 0s 110us/step - loss: 0.3790 - acc: 0.8771 - val_loss: 0.4010 - val_acc: 0.8200\n",
-      "Epoch 98/100\n",
-      "350/350 [==============================] - 0s 150us/step - loss: 0.3768 - acc: 0.8743 - val_loss: 0.3990 - val_acc: 0.8200\n",
-      "Epoch 99/100\n",
-      "350/350 [==============================] - 0s 136us/step - loss: 0.3746 - acc: 0.8743 - val_loss: 0.3972 - val_acc: 0.8267\n",
-      "Epoch 100/100\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.3726 - acc: 0.8743 - val_loss: 0.3953 - val_acc: 0.8333\n"
+      "This digit is:  8\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAH0CAYAAADVH+85AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAWJQAAFiUBSVIk8AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAAG2VJREFUeJzt3X2sbWddJ/DvT6pQGltsoxLjKC9a2qDAULRQMnBviQxoxCLthD/ExoBRhwwWYaJR8J6rToLJZJCXGTDC2AjJVFMixrECEzj3tryosQQ7xPJmWztkwFIqLdCCFp75Y6+r1zPn3Jez9z3rnN/5fJKd5+y11rPX76y7cr/n2Xvt9dQYIwBAT98wdwEAwJkj6AGgMUEPAI0JegBoTNADQGOCHgAaE/QA0JigB4DGBD0ANCboAaAxQQ8AjQl6AGhM0ANAY4IeABoT9ADQmKAHgMbOmruAM6Gqbk9ybpI7Zi4FALbrUUnuG2M8epkXaRn0Sc49++yzz7/44ovPn7sQANiOW2+9NQ888MDSrzNr0FfVdyb5tSTPSXJBks8keWeSw2OMv1/ipe+4+OKLz7/55ptXUCUA7LxLLrkkH/7wh+9Y9nVmC/qqemySDyb5tiR/lORjSX4wyc8neU5VPX2M8fm56gOADua8GO+/ZRHyLxtjXDHG+KUxxuVJXpvkcUn+04y1AUALswR9VT0mybOzuFjuv25YfSjJl5O8qKrO2eHSAKCVuUb0l0/te8YYXz9+xRjji0k+kOThSZ6604UBQCdzfUb/uKn9xBbrP5nFiP/CJO/d6kWqaqur7S7afmkA0MdcI/rzpvbeLdYfW/6IHagFANrard+jr6kdJ9pojHHJpp0XI/0nr7ooANhr5hrRHxuxn7fF+nM3bAcAbMNcQf/xqb1wi/XfO7VbfYYPAJyCuYJ+fWqfXVX/ooaq+uYkT0/yQJI/2+nCAKCTWYJ+jPE3Sd6TxQ37X7ph9eEk5yT5vTHGl3e4NABoZc6L8f59FrfAfX1VPSvJrUkuTXIwi7fsf2XG2gCghdlugTuN6p+S5NosAv4VSR6b5PVJnuY+9wCwvFm/XjfG+D9JfmrOGgCgszkntQEAzjBBDwCNCXoAaEzQA0Bjgh4AGhP0ANCYoAeAxgQ9ADQm6AGgMUEPAI0JegBoTNADQGOCHgAaE/QA0JigB4DGBD0ANCboAaAxQQ8AjQl6AGhM0ANAY4IeABoT9ADQmKAHgMYEPQA0JugBoDFBDwCNCXoAaEzQA0Bjgh4AGhP0ANCYoAeAxgQ9ADQm6AGgMUEPAI0JegBoTNADQGOCHgAaE/QA0JigB4DGBD0ANCboAaAxQQ8AjQl6AGhM0ANAY4IeABoT9ADQmKAHgMYEPQA0JugBoDFBDwCNCXoAaEzQA0Bjgh4AGhP0ANCYoAeAxgQ9ADQm6AGgMUEPAI0JegBoTNADQGOCHgAaE/QA0NhZcxcAq1BVS/U/cODAtvuur68vte9lHDlyZLb+hw8fXmrfe9mhQ4dm2/cy5+oyfdm7ZhvRV9UdVTW2eHx2rroAoJO5R/T3JvmtTZZ/aacLAYCO5g76L4wx1mauAQDacjEeADQ294j+oVX1E0m+K8mXk9yS5MYxxtfmLQsAepg76B+Z5G0blt1eVT81xjh6ss5VdfMWqy5aujIAaGDOt+5/N8mzsgj7c5J8f5LfTvKoJH9aVU+crzQA6GG2Ef0YY+OXcD+a5Ger6ktJXpFkLcnzT/Ial2y2fBrpP3kFZQLAnrYbL8Z789Q+Y9YqAKCB3Rj0d03tObNWAQAN7Magf9rU3jZrFQDQwCxBX1WPr6rzN1n+3UneOD19+85WBQD9zHUx3lVJfqmq1pPcnuSLSR6b5EeSPCzJDUn+80y1AUAbcwX9epLHJfnXWbxVf06SLyR5fxbfq3/bGGPMVBsAtDFL0E83wznpDXHgVC07beic03eura1tu+9+nip2TnMe92X2vex5PueUzGzfbrwYDwBYEUEPAI0JegBoTNADQGOCHgAaE/QA0JigB4DGBD0ANCboAaAxQQ8AjQl6AGhM0ANAY4IeABoT9ADQmKAHgMZmmY8eVm2ZOd2XdfDgwaX6HzlyZDWF7LBDhw7Nuv85/83ntMzvffTo0dUVwp5hRA8AjQl6AGhM0ANAY4IeABoT9ADQmKAHgMYEPQA0JugBoDFBDwCNCXoAaEzQA0Bjgh4AGhP0ANCYoAeAxkxTC1lu6s85p5lddqrY/TrV67KWOW6HDx9eat9jjKX6s/8Y0QNAY4IeABoT9ADQmKAHgMYEPQA0JugBoDFBDwCNCXoAaEzQA0Bjgh4AGhP0ANCYoAeAxgQ9ADQm6AGgMUEPAI2Zjx72MPPJb8+RI0eW6r/snPKwk4zoAaAxQQ8AjQl6AGhM0ANAY4IeABoT9ADQmKAHgMYEPQA0JugBoDFBDwCNCXoAaEzQA0Bjgh4AGhP0ANCYaWphD1t2utUDBw6spI45LPO7Hzx4cHWFnKa9fMzZm1Yyoq+qK6vqDVV1U1XdV1Wjqt5+kj6XVdUNVXVPVd1fVbdU1TVV9ZBV1AQArG5E/6okT0zypSSfTnLRiTauqh9L8o4kX0ny+0nuSfKjSV6b5OlJrlpRXQCwr63qM/qXJ7kwyblJfu5EG1bVuUl+J8nXkhwYY7x4jPEfkzwpyYeSXFlVL1xRXQCwr60k6McY62OMT44xxilsfmWSb01y3RjjL497ja9k8c5AcpI/FgCAUzPHVfeXT+27Nll3Y5L7k1xWVQ/duZIAoKc5gv5xU/uJjSvGGA8muT2Lawces5NFAUBHc3y97rypvXeL9ceWP+JkL1RVN2+x6oQXAwLAfrEbb5hTU3sqn/cDACcwx4j+2Ij9vC3Wn7thuy2NMS7ZbPk00n/y6ZcGAL3MMaL/+NReuHFFVZ2V5NFJHkxy204WBQAdzRH075va52yy7hlJHp7kg2OMr+5cSQDQ0xxBf32Su5O8sKqecmxhVT0syW9MT980Q10A0M5KPqOvqiuSXDE9feTUPq2qrp1+vnuM8cokGWPcV1U/nUXgH6mq67K4Be7zsvjq3fVZ3BYXAFjSqi7Ge1KSqzcse0z++bvwf5vklcdWjDHeWVXPTPIrSV6Q5GFJPpXkF5K8/hTvsAcAnMRKgn6MsZZk7TT7fCDJD69i/wDA5sxHD3vY4cOHZ9v3svOqLzOffLJ355RfX19fXSFwCnbjDXMAgBUR9ADQmKAHgMYEPQA0JugBoDFBDwCNCXoAaEzQA0Bjgh4AGhP0ANCYoAeAxgQ9ADQm6AGgMUEPAI2ZphaSrK2tbbvvstO1LjPd6rJTvS7T/9ChQ0vt++jRo0v1n9OyvzvsJCN6AGhM0ANAY4IeABoT9ADQmKAHgMYEPQA0JugBoDFBDwCNCXoAaEzQA0Bjgh4AGhP0ANCYoAeAxgQ9ADQm6AGgMfPRw5KWnY9+fX19232Xmct+WYcPH55t38ta5pgny/+bw04yogeAxgQ9ADQm6AGgMUEPAI0JegBoTNADQGOCHgAaE/QA0JigB4DGBD0ANCboAaAxQQ8AjQl6AGhM0ANAY6aphZktM+XpoUOHltr3Xp5qdhmmmWU/MaIHgMYEPQA0JugBoDFBDwCNCXoAaEzQA0Bjgh4AGhP0ANCYoAeAxgQ9ADQm6AGgMUEPAI0JegBoTNADQGOCHgAaMx897GFHjx6dbd/Lzul+5MiRldSxHQcPHlyq//r6+ooqgTNvJSP6qrqyqt5QVTdV1X1VNarq7Vts+6hp/VaP61ZREwCwuhH9q5I8McmXknw6yUWn0Oevkrxzk+UfXVFNALDvrSroX55FwH8qyTOTnMr7Wh8ZY6ytaP8AwCZWEvRjjH8K9qpaxUsCACsw58V431FVP5PkgiSfT/KhMcYtM9YDAO3MGfQ/ND3+SVUdSXL1GOPOU3mBqrp5i1Wnco0AALQ3x/fo70/y60kuSfIt0+PY5/oHkry3qs6ZoS4AaGfHR/RjjLuS/OqGxTdW1bOTvD/JpUlekuR1p/Bal2y2fBrpP3nJUgFgz9s1d8YbYzyY5C3T02fMWQsAdLFrgn7yuan11j0ArMBuC/qnTu1ts1YBAE3seNBX1aVV9U2bLL88ixvvJMmmt88FAE7PSi7Gq6orklwxPX3k1D6tqq6dfr57jPHK6effTPL46at0n56WPSHJ5dPPrx5jfHAVdQHAfreqq+6flOTqDcseMz2S5G+THAv6tyV5fpIfSPLcJN+Y5O+S/EGSN44xblpRTQCw763qFrhrSdZOcdu3JnnrKvYLAJyY+ehhZmtra9vuu+yc7svMq77sfPTL/N5Jcvjw4W33Xfa4LdN/2eMGp2u3XXUPAKyQoAeAxgQ9ADQm6AGgMUEPAI0JegBoTNADQGOCHgAaE/QA0JigB4DGBD0ANCboAaAxQQ8AjQl6AGjMNLWwpGWnPF1mutVlpzydc8rUZaepPXr06Lb7mqaW/cSIHgAaE/QA0JigB4DGBD0ANCboAaAxQQ8AjQl6AGhM0ANAY4IeABoT9ADQmKAHgMYEPQA0JugBoDFBDwCNCXoAaMx89LCkZeaTX9Yzn/nM2fY9t/X19W33raql9n306NGl+sNOMqIHgMYEPQA0JugBoDFBDwCNCXoAaEzQA0Bjgh4AGhP0ANCYoAeAxgQ9ADQm6AGgMUEPAI0JegBoTNADQGOmqYUka2tr2+575MiRldVxupapm+2b898cTpcRPQA0JugBoDFBDwCNCXoAaEzQA0Bjgh4AGhP0ANCYoAeAxgQ9ADQm6AGgMUEPAI0JegBoTNADQGOCHgAaE/QA0Jj56GFm6+vrc5ewJ805J/yhQ4dm2zecrqVH9FV1QVW9pKr+sKo+VVUPVNW9VfX+qnpxVW26j6q6rKpuqKp7qur+qrqlqq6pqocsWxMAsLCKEf1VSd6U5DNJ1pPcmeTbk/x4krckeW5VXTXGGMc6VNWPJXlHkq8k+f0k9yT50SSvTfL06TUBgCWtIug/keR5Sf5kjPH1Ywur6peT/EWSF2QR+u+Ylp+b5HeSfC3JgTHGX07LX53kfUmurKoXjjGuW0FtALCvLf3W/RjjfWOMPz4+5Kfln03y5unpgeNWXZnkW5Ncdyzkp+2/kuRV09OfW7YuAODMX3X/j1P74HHLLp/ad22y/Y1J7k9yWVU99EwWBgD7wRm76r6qzkryk9PT40P9cVP7iY19xhgPVtXtSR6f5DFJbj3JPm7eYtVFp1ctAPR0Jkf0r0nyfUluGGO8+7jl503tvVv0O7b8EWeqMADYL87IiL6qXpbkFUk+luRFp9t9ascJt0oyxrhki/3fnOTJp7lfAGhn5SP6qnppktcl+eskB8cY92zY5NiI/bxs7twN2wEA27TSoK+qa5K8MclHswj5z26y2cen9sJN+p+V5NFZXLx32yprA4D9aGVBX1W/mMUNbz6SRcjftcWm75va52yy7hlJHp7kg2OMr66qNgDYr1YS9NPNbl6T5OYkzxpj3H2Cza9PcneSF1bVU457jYcl+Y3p6ZtWURcA7HdLX4xXVVcn+bUs7nR3U5KXVdXGze4YY1ybJGOM+6rqp7MI/CNVdV0Wt8B9XhZfvbs+i9viAgBLWsVV94+e2ockuWaLbY4mufbYkzHGO6vqmUl+JYtb5D4syaeS/EKS1x9/X3wAYPuWDvoxxlqStW30+0CSH152/7AKhw8fnm3fBw4cmG3fe9mc/2awl5zpW+ACADMS9ADQmKAHgMYEPQA0JugBoDFBDwCNCXoAaEzQA0Bjgh4AGhP0ANCYoAeAxgQ9ADQm6AGgMUEPAI0JegBobOn56GG/O3To0NwlzGJtbW2p/kePHl2q/5EjR7bd98CBA0vte9nfHXaSET0ANCboAaAxQQ8AjQl6AGhM0ANAY4IeABoT9ADQmKAHgMYEPQA0JugBoDFBDwCNCXoAaEzQA0Bjgh4AGjNNLSzp8OHD+3Lfc1tmqtn19fXVFQK7nBE9ADQm6AGgMUEPAI0JegBoTNADQGOCHgAaE/QA0JigB4DGBD0ANCboAaAxQQ8AjQl6AGhM0ANAY4IeABoT9ADQmPnoIcvNT37kyJGl9j3nnPKHDh2abd/LzCe/iv6wXxjRA0Bjgh4AGhP0ANCYoAeAxgQ9ADQm6AGgMUEPAI0JegBoTNADQGOCHgAaE/QA0JigB4DGBD0ANCboAaAx09RClpvydNnpUtfW1pbqD3AiS4/oq+qCqnpJVf1hVX2qqh6oqnur6v1V9eKq+oYN2z+qqsYJHtctWxMAsLCKEf1VSd6U5DNJ1pPcmeTbk/x4krckeW5VXTXGGBv6/VWSd27yeh9dQU0AQFYT9J9I8rwkfzLG+PqxhVX1y0n+IskLsgj9d2zo95ExxtoK9g8AbGHpt+7HGO8bY/zx8SE/Lf9skjdPTw8sux8A4PSd6Yvx/nFqH9xk3XdU1c8kuSDJ55N8aIxxyxmuBwD2lTMW9FV1VpKfnJ6+a5NNfmh6HN/nSJKrxxh3nqm6AGA/OZMj+tck+b4kN4wx3n3c8vuT/HoWF+LdNi17QpK1JAeTvLeqnjTG+PLJdlBVN2+x6qLtFg0AnZyRG+ZU1cuSvCLJx5K86Ph1Y4y7xhi/Osb48BjjC9PjxiTPTvLnSb4nyUvORF0AsN+sfERfVS9N8rokf53kWWOMe06l3xjjwap6S5JLkzxjeo2T9blkixpuTvLkUy4aAJpa6Yi+qq5J8sYsvgt/cLry/nR8bmrPWWVdALBfrSzoq+oXk7w2yUeyCPm7tvEyT53a2064FQBwSlYS9FX16iwuvrs5i7fr7z7BtpdW1TdtsvzyJC+fnr59FXUBwH639Gf0VXV1kl9L8rUkNyV5WVVt3OyOMca108+/meTx01fpPj0te0KSy6efXz3G+OCydQEAq7kY79FT+5Ak12yxzdEk104/vy3J85P8QJLnJvnGJH+X5A+SvHGMcdMKagIAsoKgn+5Xv3Ya2781yVuX3S8AcHJn5Hv0AMDuIOgBoDFBDwCNCXoAaEzQA0Bjgh4AGhP0ANCYoAeAxgQ9ADQm6AGgMUEPAI0JegBoTNADQGOCHgAaE/QA0JigB4DGBD0ANCboAaAxQQ8AjQl6AGhM0ANAY4IeABoT9ADQmKAHgMYEPQA0JugBoDFBDwCNCXoAaEzQA0Bjgh4AGqsxxtw1rFxVff7ss88+/+KLL567FADYlltvvTUPPPDAPWOMC5Z5na5Bf3uSc5PcscUmF03tx3akoB4cs+1x3LbHcTt9jtn27Obj9qgk940xHr3Mi7QM+pOpqpuTZIxxydy17BWO2fY4btvjuJ0+x2x79sNx8xk9ADQm6AGgMUEPAI0JegBoTNADQGP78qp7ANgvjOgBoDFBDwCNCXoAaEzQA0Bjgh4AGhP0ANCYoAeAxvZV0FfVd1bVf6+q/1tVX62qO6rqt6rqW+aubbeajtHY4vHZueubS1VdWVVvqKqbquq+6Xi8/SR9LquqG6rqnqq6v6puqaprquohO1X33E7nuFXVo05w7o2qum6n659DVV1QVS+pqj+sqk9V1QNVdW9Vvb+qXlxVm/4/vt/Pt9M9bp3Pt7PmLmCnVNVjk3wwybcl+aMs5h7+wSQ/n+Q5VfX0McbnZyxxN7s3yW9tsvxLO13ILvKqJE/M4hh8Ov88p/WmqurHkrwjyVeS/H6Se5L8aJLXJnl6kqvOZLG7yGkdt8lfJXnnJss/usK6drOrkrwpyWeSrCe5M8m3J/nxJG9J8tyqumocd/cz51uSbRy3Sb/zbYyxLx5J3p1kJPkPG5b/l2n5m+eucTc+ktyR5I6569htjyQHk3xvkkpyYDqH3r7FtucmuSvJV5M85bjlD8vij8+R5IVz/0678Lg9alp/7dx1z3zMLs8ipL9hw/JHZhFeI8kLjlvufNvecWt7vu2Lt+6r6jFJnp1FaP3XDasPJflykhdV1Tk7XBp71BhjfYzxyTH9D3ESVyb51iTXjTH+8rjX+EoWI9wk+bkzUOauc5rHjSRjjPeNMf54jPH1Dcs/m+TN09MDx61yvmVbx62t/fLW/eVT+55N/tG/WFUfyOIPgacmee9OF7cHPLSqfiLJd2XxR9EtSW4cY3xt3rL2jGPn37s2WXdjkvuTXFZVDx1jfHXnytozvqOqfibJBUk+n+RDY4xbZq5pt/jHqX3wuGXOt5Pb7Lgd0+582y9B/7ip/cQW6z+ZRdBfGEG/mUcmeduGZbdX1U+NMY7OUdAes+X5N8Z4sKpuT/L4JI9JcutOFrZH/ND0+CdVdSTJ1WOMO2epaBeoqrOS/OT09PhQd76dwAmO2zHtzrd98dZ9kvOm9t4t1h9b/ogdqGWv+d0kz8oi7M9J8v1JfjuLz7P+tKqeOF9pe4bzb3vuT/LrSS5J8i3T45lZXFh1IMl79/nHba9J8n1JbhhjvPu45c63E9vquLU93/ZL0J9MTa3PDTcYYxyePuv6uzHG/WOMj44xfjaLixjPTrI2b4UtOP82Mca4a4zxq2OMD48xvjA9bszi3bc/T/I9SV4yb5XzqKqXJXlFFt8eetHpdp/afXe+nei4dT7f9kvQH/sL9rwt1p+7YTtO7tjFLM+YtYq9wfm3QmOMB7P4elSyD8+/qnppktcl+eskB8cY92zYxPm2iVM4bpvqcL7tl6D/+NReuMX6753arT7D5/9319TuybeydtiW59/0eeGjs7go6LadLGqP+9zU7qvzr6quSfLGLL7TfXC6gnwj59sGp3jcTmRPn2/7JejXp/bZm9wN6ZuzuIHEA0n+bKcL28OeNrX75j+LJbxvap+zybpnJHl4kg/u4yugt+OpU7tvzr+q+sUsbnjzkSzC6q4tNnW+Hec0jtuJ7OnzbV8E/Rjjb5K8J4sLyF66YfXhLP5K+70xxpd3uLRdraoeX1Xnb7L8u7P46zhJTnjbV5Ik1ye5O8kLq+opxxZW1cOS/Mb09E1zFLabVdWlVfVNmyy/PMnLp6f74vyrqldncRHZzUmeNca4+wSbO98mp3PcOp9vtV/uW7HJLXBvTXJpFnfq+kSSy4Zb4P4LVbWW5JeyeEfk9iRfTPLYJD+SxV22bkjy/DHGP8xV41yq6ookV0xPH5nk32bx1/5N07K7xxiv3LD99VnckvS6LG5J+rwsvgp1fZJ/tx9uInM6x236StPjkxzJ4na5SfKE/PP3xF89xjgWXG1V1dVJrk3ytSRvyOafrd8xxrj2uD77/nw73ePW+nyb+9Z8O/lI8q+y+LrYZ5L8Q5K/zeLijPPnrm03PrL4asn/yOIK1S9kcZOJzyX5X1l8D7XmrnHGY7OWxVXLWz3u2KTP07P44+jvs/io6H9nMVJ4yNy/z248bklenOR/ZnFHyy9lcUvXO7O4d/u/mft32UXHbCQ54nxb7rh1Pt/2zYgeAPajffEZPQDsV4IeABoT9ADQmKAHgMYEPQA0JugBoDFBDwCNCXoAaEzQA0Bjgh4AGhP0ANCYoAeAxgQ9ADQm6AGgMUEPAI0JegBoTNADQGP/D0f+ocg2prjgAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ff6dcd8fef0>"
+       "<matplotlib.figure.Figure at 0x7fe8e68579e8>"
       ]
      },
      "metadata": {
+      "image/png": {
+       "height": 250,
+       "width": 253
+      },
       "needs_background": "light"
      },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "# Here we split the dataset into training (80%) and validation sets (20%) \n",
-    "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.3)\n",
-    "\n",
-    "num_epochs = 100\n",
-    "\n",
-    "model_run = model.fit(X_train, y_train, epochs=num_epochs, validation_data = (X_test,y_test))\n",
-    "\n",
-    "history_model = model_run.history\n",
-    "\n",
-    "plt.plot(np.arange(1,num_epochs+1)[5:], history_model[\"acc\"][5:], \"--\") ;\n",
-    "\n",
-    "plt.plot(np.arange(1,num_epochs+1)[5:], history_model[\"val_acc\"][5:]) ;"
+    "# We can see that the training set consists of 60,000 images of size 28x28 pixels\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "i=np.random.randint(0,X_train.shape[0])\n",
+    "plt.imshow(X_train[i], cmap=\"gray_r\") ;\n",
+    "print(\"This digit is: \" , y_train[i])"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 141,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 255\n"
+     ]
+    }
+   ],
    "source": [
-    "## Using SciKit learn functions on Keras models\n",
-    "\n",
-    "As we have seen from the previous chapters, SciKit learn offers very handy functions for evaluating and tuning the machine learning models.\n",
-    "\n",
-    "So the question is: Can we somehow use those functions with the models we build in Keras?\n",
-    "\n",
-    "The Answer is **YES !**\n",
-    "\n",
-    "Keras offers wrappers which allow its Sequential models to be used with SciKit learn. There 2 such wrappers: **KerasClassifier** and **KerasRegressor**.\n",
-    "\n",
-    "For more information:\n",
-    "https://keras.io/scikit-learn-api/\n",
-    "\n",
-    "**Now lets see how this works!**"
+    "# Look at the data values for a couple of images\n",
+    "print(X_train[0].min(), X_train[1].max())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data consists of values between 0-255 representing the **grayscale level**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 188,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.61428571 0.6        0.88571429 0.7        0.67142857]\n",
-      "0.6942857147966113\n"
+      "(60000,)\n"
      ]
     }
    ],
    "source": [
-    "# We wrap the Keras model we created above with KerasClassifier\n",
-    "from keras.wrappers.scikit_learn import KerasClassifier \n",
-    "from sklearn.model_selection import cross_val_score\n",
-    "model_scikit = KerasClassifier(build_fn=a_simple_NN, **{\"epochs\":num_epochs, \"verbose\":0})\n",
-    "cross_validation = cross_val_score(model_scikit, X_train, y_train, cv=5, verbose=0)\n",
-    "print(cross_validation)\n",
-    "print(np.mean(cross_validation))"
+    "# The labels are the digit on the image\n",
+    "print(y_train.shape)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 190,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense\n",
-    "import numpy as np\n",
-    "# We wrap the Keras model we created above with KerasClassifier\n",
-    "from keras.wrappers.scikit_learn import KerasClassifier "
+    "# Scaling the data\n",
+    "# It is important to normalize the input data to (0-1) before providing it to a neural net\n",
+    "# We could use the previously introduced function from SciKit learn. However, here it is sufficient to\n",
+    "# just divide the input data by 255\n",
+    "X_train_norm = X_train/255.\n",
+    "X_test_norm = X_test/255.\n",
+    "\n",
+    "# Also we need to reshape the input data such that each sample is a vector and not a 2D matrix\n",
+    "X_train_prep = X_train_norm.reshape(X_train_norm.shape[0],28*28)\n",
+    "X_test_prep = X_test_norm.reshape(X_test_norm.shape[0],28*28)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 128,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "def list_flatten(list_of_list):\n",
-    "    flattened_list = [i for j in list_of_list for i in j]\n",
-    "    return flattened_list\n",
+    "**IMPORTANT: One-Hot encoding**\n",
     "\n",
-    "def train_and_plot_decision_surface(\n",
-    "    name, classifier, features_2d, labels, preproc=None, plt=plt, marker='o', N=400\n",
-    "):\n",
+    "**TODO: Better frame the explaination**\n",
     "\n",
-    "    features_2d = np.array(features_2d)\n",
-    "    xmin, ymin = features_2d.min(axis=0)\n",
-    "    xmax, ymax = features_2d.max(axis=0)\n",
+    "In such problems the labels are provided as something called **One-hot encodings**. What this does is to convert a categorical label to a vector.\n",
     "\n",
-    "    x = np.linspace(xmin, xmax, N)\n",
-    "    y = np.linspace(ymin, ymax, N)\n",
-    "    points = np.array(np.meshgrid(x, y)).T.reshape(-1, 2)\n",
+    "For the MNIST problem where we have **10 categories** one-hot encoding will create a vector of length 10 for each of the labels. All the entries of this vector will be zero **except** for the index which is equal to the integer value of the label.\n",
     "\n",
-    "    if preproc is not None:\n",
-    "        points_for_classifier = preproc.fit_transform(points)\n",
-    "        features_2d = preproc.fit_transform(features_2d)\n",
-    "    else:\n",
-    "        points_for_classifier = points\n",
+    "For example:\n",
+    "if label is 4. The one-hot vector will look like **[0 0 0 0 1 0 0 0 0 0]**\n",
     "\n",
-    "    classifier.fit(features_2d, labels, verbose=0)\n",
-    "    predicted = classifier.predict(features_2d)\n",
-    "    \n",
-    "    if name == \"Neural Net\":\n",
-    "        predicted = list_flatten(predicted)\n",
-    "    \n",
-    "    \n",
-    "    if preproc is not None:\n",
-    "        name += \" (w/ preprocessing)\"\n",
-    "    print(name + \":\\t\", sum(predicted == labels), \"/\", len(labels), \"correct\")\n",
-    "    \n",
-    "    if name == \"Neural Net\":\n",
-    "        classes = np.array(list_flatten(classifier.predict(points_for_classifier)), dtype=bool)\n",
-    "    else:\n",
-    "        classes = np.array(classifier.predict(points_for_classifier), dtype=bool)\n",
-    "    plt.plot(\n",
-    "        points[~classes][:, 0],\n",
-    "        points[~classes][:, 1],\n",
-    "        \"o\",\n",
-    "        color=\"black\",\n",
-    "        markersize=1,\n",
-    "        alpha=0.1,\n",
-    "    )\n",
-    "    plt.plot(\n",
-    "        points[classes][:, 0],\n",
-    "        points[classes][:, 1],\n",
-    "        \"o\",\n",
-    "        color=\"blue\",\n",
-    "        markersize=1,\n",
-    "        alpha=0.1,\n",
-    "    )"
+    "Fortunately, we don't have to code this ourselves because Keras has a built-in function for this."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 191,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(60000, 10)\n"
+     ]
+    }
+   ],
    "source": [
-    "def a_simple_NN():\n",
-    "    \n",
-    "    model = Sequential()\n",
-    "\n",
-    "    model.add(Dense(8, input_shape = (2,), activation = \"relu\"))\n",
-    "\n",
-    "    model.add(Dense(2, activation = \"relu\"))\n",
-    "\n",
-    "    model.add(Dense(1, activation = \"sigmoid\"))\n",
-    "\n",
-    "    model.compile(loss=\"binary_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
-    "    \n",
-    "    return model\n",
+    "from keras.utils.np_utils import to_categorical\n",
     "\n",
-    "model = a_simple_NN()\n",
+    "y_train_onehot = to_categorical(y_train, num_classes=10)\n",
+    "y_test_onehot = to_categorical(y_test, num_classes=10)\n",
     "\n",
-    "num_epochs = 400\n",
-    "model_scikit = KerasClassifier(build_fn=a_simple_NN, epochs=num_epochs)"
+    "print(y_train_onehot.shape)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 194,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Neural Net:\t 487 / 500 correct\n"
+      "Epoch 1/20\n",
+      "60000/60000 [==============================] - 2s 34us/step - loss: 0.5888 - acc: 0.8434\n",
+      "Epoch 2/20\n",
+      "60000/60000 [==============================] - 1s 20us/step - loss: 0.2569 - acc: 0.9267\n",
+      "Epoch 3/20\n",
+      "60000/60000 [==============================] - 1s 16us/step - loss: 0.2024 - acc: 0.9416\n",
+      "Epoch 4/20\n",
+      "60000/60000 [==============================] - 1s 17us/step - loss: 0.1706 - acc: 0.9497\n",
+      "Epoch 5/20\n",
+      "60000/60000 [==============================] - 1s 23us/step - loss: 0.1475 - acc: 0.9563\n",
+      "Epoch 6/20\n",
+      "60000/60000 [==============================] - 1s 20us/step - loss: 0.1290 - acc: 0.9627\n",
+      "Epoch 7/20\n",
+      "60000/60000 [==============================] - 1s 23us/step - loss: 0.1162 - acc: 0.9651\n",
+      "Epoch 8/20\n",
+      "60000/60000 [==============================] - 1s 19us/step - loss: 0.1035 - acc: 0.9691\n",
+      "Epoch 9/20\n",
+      "60000/60000 [==============================] - 2s 28us/step - loss: 0.0939 - acc: 0.9716\n",
+      "Epoch 10/20\n",
+      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0848 - acc: 0.9743\n",
+      "Epoch 11/20\n",
+      "60000/60000 [==============================] - 1s 25us/step - loss: 0.0777 - acc: 0.9763\n",
+      "Epoch 12/20\n",
+      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0720 - acc: 0.9780\n",
+      "Epoch 13/20\n",
+      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0655 - acc: 0.9808\n",
+      "Epoch 14/20\n",
+      "60000/60000 [==============================] - 2s 30us/step - loss: 0.0610 - acc: 0.9817\n",
+      "Epoch 15/20\n",
+      "60000/60000 [==============================] - 1s 16us/step - loss: 0.0563 - acc: 0.9832\n",
+      "Epoch 16/20\n",
+      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0527 - acc: 0.9842\n",
+      "Epoch 17/20\n",
+      "60000/60000 [==============================] - 1s 21us/step - loss: 0.0478 - acc: 0.9854\n",
+      "Epoch 18/20\n",
+      "60000/60000 [==============================] - 1s 15us/step - loss: 0.0453 - acc: 0.9864\n",
+      "Epoch 19/20\n",
+      "60000/60000 [==============================] - 1s 18us/step - loss: 0.0419 - acc: 0.9874\n",
+      "Epoch 20/20\n",
+      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0387 - acc: 0.9885\n"
      ]
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAFpCAYAAACVjP/1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAAIABJREFUeJzsvWlwnVl63/c773v3fcUOXAJo7mSv7L17prtnH814Rok2W5FiybHKjqzEiu1SWa5UKnGVv7gqThzHUiaSLY+kOLYyoxlpNDNSa5bunt7ZbJLNbm4AL3Av9rvvy7ucfLgACPACJJYLgE3ef1UX//3ec57znOeCOHyf7QgpJV100UUXXdx/UA5agS666KKLLg4G3QOgiy666OI+RfcA6KKLLrq4T9E9ALrooosu7lN0D4Auuuiii/sU3QOgiy666OI+RfcA6KKLLrq4T9E9ALrooosu7lN0D4Auuuiii/sU3QOgiy666OI+heWgFbgdIpGIPHTo0EGr0UUXXXTxscF7772XllJGtzL2rj4ADh06xNmzZw9ajS666KKLjw2EENNbHdt1AXXRRRdd3KfoHgBddNFFF/cpugdAF1100cV9iu4B0EUXXXRxn6J7AHTRRRdd3KfoHgBddNFFF/cpugdAF1100cV9iu4B0EUXXXRxn6J7AHTRRRdd3KfoHgBddNFFF/cpugdAF1100cV9il0fAEKIYSHEj4QQl4UQHwoh/vsNxgghxL8WQkwIIS4KIR7d7br3AyoNjUpDO2g1uuiii3sUnWgGpwP/SEp5TgjhBd4TQrwspfxozZgvAIeX/3sS+J3lP7vYAHPZCv/y2xe4NpcH4MhAgH/ylYcYCLkPWLOPNy7P5Pi/Xv6IyYUiPpeNn39mnC+fiSGEOGjV9hWNxRto+QUcg8exeIIdk1tfmKCZmsYxcARbeLhjcrvYO+z6AJBSzgPzy7wkhLgMDAJrD4CvAF+XUkrgLSFEQAjRvzy3izVoaAa/+QdvUKg2kbL17PJsjt/8gzf4+m+8hN2qHqyCH1NMLhT4rT96m4ZmAJAu1vm9H1whX2nwyy8cPWDt9gdGtcDU7/wdaokPEBYrUmsSfulX6fvKb+3qEDTqFaZ/97+hGj+HUK1IvYnv4c8y/F//bwj1rm44fN+jo9+OEOIQ8Ajw9i0fDQLJNf8/s/xszw8AXddZWlrC4XAAUK1Wcblcdy1/N56nmM9SqdRQrTYADK2JaDj57luXeXw0cFfouRl3OBzU6/U9W8cwTf787ev85EqGbKlO2K3w8588yZkHored+7XvXaGUz6JrzVW7VrUmf/zXBT59PISpNXapmwfwUa0qLD+mWuXAuMMB9fr6Z6k/+qeUJyapN304LDXAxsKf/wVV53GCj39lx2vO/PG/YOnCNHZhBaCu+8m/9RZV59fwPP/fdnx/uUQC/dr3kYaB9fCnCY4dPnB738n2W+EeD/h8oOxjZLZjB4AQwgN8A/iHUsrirR9vMEVuIufXgF8DGBkZ2bVeCwsLfOtb3yISiQAwMzPD0NDQXcvjBYXkR/PUixnsvgggaRQzOHwRfsAsk37zrtBzMx4Khchms3si2x2K8gc/uEJmaXHVNpeKGc6dfYfPPDhEj72+6dyfXMyRrzRp3GJXb6SHb367Sr2Y2aWeJ4BPMDPjYfkxMzMcGA+FIJu9+Wx6ooTyKpjm4yxV+ul1LyCRLFX6Gfz9iwzkvrKjdQYHNJJ/nmOx9Jl1MnvdCyjzcZjt7P6Kl37ItR+9QY9rFikkqUqG8adPEDzzXxyovW9n+63OO3oUPvnJ1iGwX+jIASCEsNL65f/HUspvbjBkBljrFBwC5jaSJaX8GvA1gDNnzmx4SGwHuq6Ty+Ww2+1IKcnlcvj9/ruWex1BMJs0ayVUhwuJoFkr4fR48FgMcrn8XaHnZlxV1T2T/Z2LS+SKtTbbVKsu/vLsBF885tx0rk81SDXrbXMbNQ9Sq3dAzwpSmuRy4PeDlOyYa8UUCx+lcNQUrNEj5HLKtuWo6vpn+WwTm+bB1JsUa27cFicSKNbc+Mo1isXt65tZyKN/8GdUarY2mW6LE2Eq6B2wxwp3qxnmf/JdCpUeXIodCRQqNhbOvokRfYxcMdaxtXbDb7X9Vnm5DKa5299428OuDwDRch7+PnBZSvm/bjLsz4B/IIT4f2kFfwv75f83DINyuUy5XMYwDEql0p7zXK7AfLZManGJUDiMNM0tzx0JhXEpBtlmDaNZw5QKerOGSxiE3YJkZu/13w232+17Inspk2M2pWEYoDWq2JZts8JruspSuk5Pz8ZyTg9FmJzPrJtrNusciTpo1Cod0LOKYZiUSq2/yIbBtnmxaDLzo29QjV9gvjSCcm0GYXdQHvt7lCOhbcm029c/q2gBTNOH2axQbripOxxIKSg3PZiBQxSL29NXrxVJvvxNDNcE0nSul9lwU3e6UKMPUNmFPW7lS/mPqDddbWvVGnmWrn5AyRPbtszs1BSV2YssLIXxW0dRPAO71vNW22+VV6sfwwMAeBb4JeADIcT55We/DYwASCl/F/gu8EVgAqgCv9KBdbeETCbDjRs3qNVqAExOTq5+thf8zfcuMf/ODQSCamaeN68t8fzxXlLzs1uW82iPpJTI0ywpSAl+meeRvkMkE4k913+3vFQqsbS01HHZ8fgNajkV3RTUcwuAREpllatCZVbquJ22DeWMj8NDUcmrU0vUkCiqik/mGXLUSHTErgOAzprH2+K11HUuv3GDYdcS0E+yNIBpaCBgZuoDVOcntyWzVII1XwOTN1SG+3+O4gc/JFlq+R2EIkhWxgl5niSX2J6+1fhlpjJ9GM3W36tkKbY6JlmKIaw2/OEvk9ihPTbiNZuPSmGQZGlw3VoIgWsxxMLi9mReeuUCKf1tMDWSJYXq9Id4HniMOf2xXenZZvstzvP5WodAKMS+oRNZQD9hYx//2jES+PXdrrUThEIhxsbG6OnpQS6n1cRisT3hqWKNOc2FI9iDlApW3QRPmLPzghfGxrYl02ZVV3kikdgznTvNQ6EQXq93T2TnEzrZahOQuEK9SKkAEneoj6ODAcY92m3ljIxI+oMuhoZHUAQdtmsvUlqWeeu1fqt8wDtB5r0/ZMgVZcg/CxIk5ipHWBgKn0BxR7csMxQCr/fWz4/QPGzH9pMrDLgklt4YIduTjB4ObEvfWAzSiQuUvY12fYWCLTrGqc/+lwhHCKtz+/bYjA/3DLO09P9hms116w4HU4Qei+HOb11mM32DlP42Q94bN/X3zkIqie3UGLFYcMd6bmz7rfGVoPB+4Z6vBF5Jb1PWhNb3il+ZK2JKiUAAAiFAINAkLOXr+6LD3cL3QvanHx7AalFRlu26YuMjfQE+cbxvy3KsFvWOY3bH2RYvXnoFdA2BsvwvKbGOK0KAXt2R/FufWf0xfKc/R/TT/wDfiS+h2ALb1ldRwOqNIITapq9QrLjGH8HiDm1Jzna4xR3Ce+aLKIoVoVoRigVFseI+8TwW3+C2ZNbmPgBTa9dfKDTTyY7ovBsZ+4V7Pkl3P11AM4l5atl294RFVZkqVugLOnclv940uPDhZeazFXoCTqbi8T3by075XrmAJicnGQc+MaLyTq6CV63idtmQisEDfo3ZZPIA9x1HynGEMLb8ui/NJvXZj7h6qcSgVQc51OZGWeWVGKHcIKLQWTeElJJr5xcpfvghQlGY104BUUyjzpV3psm++R5IWBCPIc1RhGJbnavZnydZOrv6z9eWK0bF4g2RXerDGr+zjjvjT5MdPI3bOQHoZN0PkHEGyGzThVVZipAs3fyHwIq9hWolZXpx7kL/+8oFdLfD7/cTjUaJRCKYpkmxWFzluXyeRElheqlMLWPQ94CToYBj0/F34qdMO8nFLDZ3AFMK9EYFmzuAQGV8OLAjmSs8rdl44+oitbJCerEJCzqnQz4ikQi6YWBaPeR0O/0++4717wRfcbXt5TqP1KuMjY1hmiZTU+LA9nqTK/T0vIRh+CkWIRJpBfM244WCieXaH+LILuI3+wg7F8CEctNJ2JVez4UF5YEjRHsst5V5K+/paf1u3uzzcFhSOvcn2BfqeHxxkGArJnEN9FFPxXHlnAT8cTChWNKxXH8D31N/m2hULMuJUn7sJKHsZcxqlYrmZiDmxv3Y51BntqbjTnnfgIfhsYcxTdCmdibHbz9E6vpkm72FsGB9ILIrPe9k+814NNqqIdhP3PMHQKFQIJVKYbG0tppKpfD5fGi6wcvvXMbi70E3BPVcir96+0PGewO49Ry+5WTclfFb4W5MrM0yZt2JpoNeLWK63RzpD1DM5kin09uWCRBPzDFZSaEbgkYxh2KxIaXCa9MLuN1eXr+yQCk9h2uhAqbAY+bwer0IIba91m75Qax58LyGz+cGLKRSN/O4N+MLk4toeRNp+Mk1evBUW2+nt3K/zYv3xAvkF6Is/+hsSX4qBULc/nOHnqQ4mSdbG8BtKQGQrYeYPn8DobS423rzuWd2juqNJKnsyKqcTL0H/4lfQxo6etKOdthJvrh1HbfCvV6TenqSxAdVxEIRx8AxUqloB+QPUg0+SaZSRwiTXLMfT13Hc+KTpLNe/Nu093ZsvxlPpVoFZPuJe/4A2CwI/OFMGtXXhz0QxbYcTLT5e5hpqLx4yLfjgOBXXoKGLcD1uRKVnJtPnDnJQNC1q4Dje/EUdqtzVc+VAKimCs4tgeLtwaoZ2Pyt4HMpD8IdZiTi2fZau+V7GQS+e3mZWMy+5YBf+doEYX2yLdgrkQwH5xFON27vMMeffxApBVZ35wORwfxZvO44pnFrIHcBTAPT0NY9H/TO43JewT4+wshgidw738A/XcNVmEF4A8T6foFYbLgjwd5VLg18yf+AM5WgxwgTKcxD6WX6e3+OWOxUB9Y6ylAkTHXpCq5ZP0efeBphDeBM7E7/bhD4LsJmQeDpVBVTmm0BW1VRyFR2HrBVFYWTw2G+fGaYZ472Mhzx7kjOWm7o7XqCQEqJbpptzw1p8lEiv+t1d8MPOgi9/1ys4dyWqzYXQrVya7BXVW14n/gq0S/8Nq6Rh7YlczO+2edCtYGitAeeBaBY2p+rFlRn65+rmVf/A825CcAEaWAWshTPfQ+jnNq1vmt5ffE6zfQUUm+0dJAm6BrVqz9BGuWOrGX1RvAceg7X8GksjkBH9e8Gge8CbBYEruVS1LOLbBSwTdXrxIOedeMPkotyioZWQdNZlwPfzC0gVNA02nLjlxbsJBLmXR8ENkyTcxc+YnKxSNRr21a9xN3Da0B9mbPm+cY8WT9KJT+MNLVbgr0jhLRTiG0GNG/lExMa2bks6awVKYMIIdrG6j1Pki/OrNYEwHIgVLEiBCRLA+ueC4uNUPMhJq5mCC7awOy/JWg9gO3NK3iPRDsW+L32QZl+em/qtmqnQ/jOzWHrObIHQebd8+0Egc1mgWY5T2IxiM/n6waBO43NXECuYA/TqdJqzn7LtdKH32njkRHrXeBWuMlNCc60YCZfY8UFpCgWYkMBkpoDTWP1uZQKqgKPPXSCkcHAvuu8HRdQIDLAn74zRb7pJluycK0oCZheXlzzXnyQti/VNd6+vsSNyRoqClE1sFpDsH789lxA4KH/0U+Qe/NPQLUy7F9C2O0ERx7m0Jhz/XjTRC8s0O8qYgkeAhy3lV+b/4DUzFsEHSmMSgS3pUDg6V+G8d5bxkYpeU4jf3CJ4eCy41qxcvqnnkPY3Cjfe4UBxyISUN1eHvzi51B8TrRcAn9zDqnVb3FfmQw63ARGnt+iDe7Mi31ZorVke12ExcpILIYtujv5G3GtMEszqDMyFAHFvSM5W3IBmSaly6/iuvgDXKqFUrYP94TAYfkloJUtuB+45w+AtS4gwzBWecTr4InDPVzJq5hSQVUEEbeTzz8yQC610Db+ILki4ItnYkwv5Xnr/Ty9A2FODnmpFdIcd4b4ywuzq7nxFlXB5bJxfHDt6+zW15JSspArYxoSQ8od6bzVsX95PkmlaaCbJoYhMaVgPl/ho5kcJ5b1PyjbG4bkT9+aIpUu4Qq5aUi4lipgnk3w5cdHNpgrWKYoCnfkjt5j9Hz5tyldWiIyoqH4hqklxLoxRjFJ5rU/pLAUwROYBQS1/p9HGT2+oUyjvEjxjT8BrRcsGpgaZilL7rXfh5O/haKo6+Z5Ys8Sef5BvM4rCGEh0jiGs9+JYUDo2V8iEk5hmoJGLoI12NqfxRsFqbPWfSURKIoVa/Tm28RWbHAn7hg6ioi/htT0dWupQsEeHV/tJtmJtcxantRf/zvMYoFSYZCleALnqRfA/ukdybzT54Xrr9CYvwYeDWnoYOrUF24w/41/QeTX/zn7hXv+ALhdHUAzv8hLo2MUqnXmmhqnhhRyqYW7xK3QzgXQY60w5m1QKzRYaXHw7LDC25kifsVLX9hFI1tnJpHYtvxUscbLr5/HFelDSqhm5nnxyQoDIU/HXUCXrlxjtqAuZ2CtdV/N8+Z7FjzG8IHae3KxSHYpQzXb6nC5otsNBc67auSW5taM354LaGJCopfTSGB6qQ+Lx0BLLDCVsCFlCCEEExMa6dmXkU3/zbYQQHIqjtUTweJtd7WUrl2jnu8lWYpRaTrJNnoAEGUrmfMpEH1tutyY8SLGH2/xaRCWFXkC6GkbPzUXImp9jmZ+6qb7SECyMkpIfXrb+fi348lsjF7fF6lPnydZHm25p6RGsnEY6/d/guPQaRLz0Y6sdfFHHzBoc4C0r9pbvD5JJnAS6N+WzK24gLJvJ0jkB1u//Gm5uDyOKotn/5ST+v+IYrGxH7jnD4Db1QEUi0V6e6JETROLqd0F+eR35tcSi/xkokS+rqNUDQ4ddxMLhZBaY01uvLlt+f5giO9cmgSHB2H3IqVAOMqcW2gyHru9DXdSBxAJRZkyJUKTq/USN2sn/IRCoQO191zdDQ4Ni6u8TjerJwBOP9Ho2p+XMpGIdUs531opiW36LdT0VTAVrOURRCaBVYI9N4Sip/A9+fOErDohxwKmorXVB3grb+GMfbldfmaBmj1N2e4k6lxAQiu3XbEivXkikb6O5OOPPv5pKvFXqb6bIuorYOsdw+p6nJ4B7+b7Li/iSH0I+bcRVid+1+cJh48ipbjtWofGnkZ/8CjGu3EixXeRpqTUmMSdTyLOv0Vo+FeIRPp3tS+tnCJAnLAz1VaPUaue37b8rdQBSOssBbux7nsNOtNY1RpSq0P3AOgMNqsD+DjyqVSJdy9NYluOW9RzKb792kWeOz5AaZfys3XQqnn0apGmw4mUSosXXbx/RcFaz3S0DqBczCBqVrSGsW5NWSsRcehks9kDtbewm1ArttlDq7jRKk5y6VvrALRlfpucdneN7KvfJ13ox2m2rmLMlAO4RKHFayFcqRLp73+PJc+nMStRpNFsqw9YTFnxZdvl2xwnKWs5co0eBDfrCoSikGv2b7uWYHNuAf9LaKPAODSBzCQENpHvsmTIv/1tFnMD2LytD5LlOE4lj3vsqS2sG2Jx9iqKCAOyta9Kyx4zF68THO7f1b6aRY1sswd3tbRq4xV7p5vujtdg+HxQsp8m1xDrvtdcLYP0jqI4bmYO7jXu+QNgP5vB7TX/0Y3rWAO96xqh2QO9TFccPD4+viv5eeHDnrei6eZ6+cFe/NEo/bZAx4PAJ30Rvv3eDKoCjmAPqmJBOCx87umbKZAHZe/ewSGmaxNo5k17KELS2zvCmdNjJBPObQeBo8oHOH1zGHp9w6ZvK1yoTpw9JUIigTS09QFQxcr4w4/hHNxgreFxUuXvIqUkbF/AVaswHErjGH2IfMjf0WDpdrh/6a9xe6Yw9fU1B+FciujAQ4DzjnJSykWGfIl2mxUlseGnkMK2Yz1NPUThbIIhzy0BZ8WKM/DoroPARjVLIP1DGuk4kepxBjxHGXjpcRrf/DOGPHNgGkhgILjE2M//k329o/q+rQP4uHHDkFSbxrp8/xWeLTd2LX845FpuhrVevkVVGQm6ti1zK2MHwl5+8bkxTgwFOTEY4dOn+/jMI8NYrZY7zt1r7rJZ+eknYoTcdoRQEAj6A06+8tSh1da3rfEK4ECItXLYkJu1CtLcvOnbKpet79l1+AzC4rjZqMxix+IN4R58cOO1LHYiL/46nrFHUQMRrP5BfE9+Fd/DP31bvfaaa9kpMGXbXhECvZLakhzVYtm4UZ5iQSiW2869ExeqA8+RpxEWK7BcG6FYUJxO3CMP7njvigJGOUX+7W9Qn3ofs5ClmZkm++rX0QozhJ76GVwPPIbiDWKxOVFdfirX30Gv3Kzh2Wvc828A+30fwF5xKSVacWHDZnMeu43JSW1X8seBiFomm7tZG6HlF/EGXTTLDm7cuLElOTtpBmdrZBn1BKFeZ/LGjVb3yw7YrBN8xFmnqJRJZivcyMzxxzWdRx+IUkitBIF9TE72r5nLprwZOUqxdK29n/0tXCgqWccQjsHT1GMnmM9msCoD2HvGydSOMpWw3mYtJ3Pm4/TEHie7BEs6sElgtlmY5dr5HKVrBRz9h0ks9WxpH9vlkeoxmsWZ9r0KC5VFP1Ozd5YzZ30Wo2Qs9+5fsZPCgvo0U9PKrvWca5xGHQ1Sn/6AOcOLK/gYzuHTxJNull8udhQELl48x3SuH1Nv3tw3oPzwPJnIAxRKTYyyh2SpB6eSYfaH/wlx9T9x+J99f7Xwbi9xzx8A95IL6Hnc/PhtcASjqy4aX7SfT50awFLP71r+yMgIbwSdlE0fOuCTPTx35iRIiRCi4y6gjwN/48oiReHBGvBgN000d4h3FwXPDg4vj/EDwxw6ZMdcvs1pczdBP5miFXltjiHvirtBMhSYaRXVYrZcNsPHyfeEWnNjh3FGDq/K2Wqbgju2gsh+k2r8Aj3NKJHCHJQs9Ie/RCz2RMddQAOe42Reee8WV5aF8aMuAkf8qI4tyDEfwL90jvrMZbBaGfYuYOkZJTxwdHd6DjepzF+krFUZcEscn38W34JvdYxi210dwNLF96h7ve0uP9WGsfif6ffeAF/reb93Brsoopc0sq/9MdHP/n32Gl0X0BouhCBTarCYr6EZ5h3H34nnKg1SxeqafOXduSceiUU4MRRY7okvcFgUnj/Wx1iPryPyAYZCHr7weIwvPjrESNSDukM5d4PbbLe8VNeYz1cwTclal5tpSK7O59e4gJxbcgEpiiD0zC/jOfIEarAPxR/B88ATeE5/DiXch9U/iPfxL+M78/N3kLN1vtGzZn6O2tT7oDcRiJstFibOYVTSu17zVm6PHsH3xFexLLfAEIoFR88Ywad+dutyFAuhp/8W0S/8Jr6HPkvks/8doWd/FaE6dqyb1Cqk/vp/p/zOd6glP6D8wV+T/u6/QivNd872dsfGbj7DQGxwH0FLrzqlD3/MfuCefwPYqgvooyvXePn9JOWmTjU7z+tX5zg5HMFSS284/na8UG7yk6vz5BdncUXnUBAM2KrblrMRtzWyfHpsjKZmMpMwcOlFEoniXeEu2akL6G7li8Ua9dwCpjTXudykVEhW08TjPcAAk5MjgLE8lzVyNuIW5poP4zz8MADzk+ByAA+8SHYSUiabumy2w2+Xi37tUoHeWj8gb3HLjOB+fxbncKSjLqAWHiU7+DD+oSJCtbOUcDI9sxM5IWayIewZILM73T58Y4pezQfStc4OM/NXsXq3l/u/lq+1fc33RZLl+OrnLdeXFSFkmyvQ46hS150gyliDN92Ke4l7/gC4Ux3ACo/nwPS4QAHhKKM6/VzLmpzye7eVQ54vFHgzWaFp8SCcXhS7F1MKJgsVPukJ4LApu++5H41imibNeuXA6xJ2cx+A2xtkvlDFsHoIhkIIuGv2EYlEcPk0VKe/7X4HUIj61eXxHopFhUhEYJqQW0zhSL5Gs5zFoz9EyHcYLIE96Yu/01z0qK9IWEmBsT7nvaJ7iYaC2EJ7o1dPj0LvUADThHJtf+2xEfdplza8i6GiTRPyZMEW2rXtjeBRitkKkeJZECoVLcPAIRdGvUCpvtRWB2BT6wiLnfCLv7ovvx/v+QNgK3UAxVqdfC6NDYXV/Pfl3O+P0hlGh3s2nXsrv3YjSU13oWmsk2OUCrx7+QYnh0MHXk+wl3xtHcBSsca771/lXKJIf8iLKC+tjn3/apy//iiFUBSqmXk+XKzw3NF+infBPqaS81yarbBUamBUCzTt9nU/FxZVxefTlu93sJFKmfh8kno2TvyNK+jeBZCS2dISavYCvkd+ilSqf/u98N11tEqehVk3Pp93W3Nvl4teUI6g1C6Aqa3Lec/WwhStJ1E2qDG4G7iTeWpT57kx40EsSFzDD5JKBXYsM9OI4pStN/x1dqiHSWctKLad6bne9oKy4zGiR0+gVws05zxoD3jIvv51co1QWx1A03Qx+Iv/HFfsZqbXXuKePwC2EgRezJdwh+exBm4GV1dyv+v5Vo78qaGtNVabXCyQK1rRDNbJqSJxh/oZGendkpyPK18JAufx8t5inYo1BNYANyoqStnOS8MjZEs10oZ3tRGfVTex+Xo4tyj41Njoge6jUKozWY5jC3ix+PxYdWNdXYQr1MtzR3vwU12eGwRsxGIKS5f+I0MeL0O+NQ3SnLNw5V8TMh9h6MkXUVxbu9i9mriA8/w3cQqFYLYPr1Pgf/pvAt4OBIGDRI4eo3ThZVCsDAeWkEi8D32K0cO+jgeBO8EbSxN4r38dj6FRtw4QKSwgqj/i0JHfIBYL7Uhm5biTcG4RqTdvBmcRWHy9u7LDxrZ3IqUTmw+GgjPYHdfRvT3rgsP93hmiDz1P8Imvsl/oBoEVhZDHjZTt+fUgMKXk7WuL/PDS7B3lAER9DuQGcqyKQizqvu3ce4U3dJO3ri9hGHLVBoYBlabO5dkcV+ZLmFK221sIForVA9X97FQW3dz4/oUVfmGq0DbXrJaQtdomef2gl9JkfvR/g9SW57Jm3fW8kbpMdeItMBqttgBSR0tPk33j/7nj3Fv5Zp97j3yC6Bd+E8/Rp/A89jl6vviPcPQc61jgt7PcpHL1VTCaIFm+G8BANptUJt7ZsXzXoUexhIYQFjuKsCIsNhSXF9+DL+1ZAB6gNncZdH3DnxW9dPNOhf2xTjkEAAAgAElEQVTAPf8GsNUgcI9aYrEg0fX23vog+TCj0vQ1N5y7lqfmZgjjZaZQWc3ZtygqlFPISoZENXvgwc295KVSiSuTCeqai6Ym2xq9vX/RjtNh3bCewaKqJIuVA72L4fpEYkPd1vKyVJjQV7LEWkFgU7ORLfRtfrF7aQShWsmeizNXPLJmXdp44ezVtkZhAKJgkm2UAc+mc1f41nrSh5hrhFrNhxc3HqNVUlx+Y5KUchHFojBveQ5pPoBQLHsQKN6YG7UiU6leDG/r7/C6gO2CwBffmfwbcTtjY38XLTTP0kcVfMMSa3iUqbgV1blznW9n+4kJSWZhFrM21Paz4nHWsIzuj+tnBff8AbCdOgCHL8L3LsyRWuO6WXnttygWvAFtS66El2IxJhYKvHHWijvUz9FhHy69xOihQwfuotkPF1ATK9klE8st9xSAZGA4xuF+L8l0BXtwvctNKCqPjtoOdB/D6Vb6563f/1rutNh4YHxFzxUXkAPfghPzSpIhf3sP+xZP4Qov4Yweua0rYenKdRpeR3uLCIuTwECZWMyzSxfQ1vhwNEvq/X9LH2GGvDMgwSwb+BfOEXjqb+1I5k64oVnJ+pIM+drtqroDu5QvkHIAm//mc6HsTufb2d5sFnDlJ5BWve3no9+bZPClv8t+ousCWsP7w16OD/lRlA3cQQI8DsuWXQnjvT4+eXqIn33uAU4OhVEVZcfuCSklZ+Mp/vCVCb79Tpw/fXOKP3p1gm++dYNvvDHFYq66bZl7ycNeBx6LhVtdKBZF4dRIgOGIl/5AK5i62nJCUXn+SAS7RT1Q3c+MhbAoYlMXkEVVeepwcIO5EHz8Z7H6owhleV9trQ8kdv/w6vi1c9dye+84QmzU+kCiugMdcUNshVcmXkPqGivWAAGmTnPuI4wttnDoBFcsbmzRWJtdhcWOc+Shu8hVtZ5v9EwodlqVcO0/HxZvBNXlZz9xz78BbLcVhE9v0sguIuXN134FidthI0eTeNy5JTmd5N995SwNZ2i5d/48TcNYdanEpUkyOUXMXe/YuoVyk3JTI7e0/esZV+oAHhwY5rXLKZq5RawWBWlAr1qiXkiTKEDUUuZQyMtMrkpeFjjTN4xXVu4KV9aQvUqqnKbZNGlkF1AVMA0FrbBIKOLlvUs5cvOzPPRgkeHwUeLxlTqAANnwz+COPkf5wvdJliKrMpOlMWzhGPVS7M4uD8dLJCtvAwKksXpVo+fIE8xNORDq5nNX+HauJdyM5/MSLX/r1Y+t6yHzV/PMFjrTi38rPOV8AYwmWmGOZKV1N4Bz6DgL9ZM4d+gC2it+O9vfmHYStT1PMxUnWRpefZ4sj9EfeKp7JWSnsdU6gLX8mYcPEy/bKDdMzGaV2MAgnzjdx8JMct9z0YvlBiXhwun0I9bkopu38MWaRrwgyBRq6E0Lp3dQc9DUdK5lDC43yiAVymkNJajz7LHotu8DGB0eYGSgl4tX3PQNjhB2W5lJJlbH9vT0MDYW46RpMjVlZ2x0oGM2W8xXuLyk82EljdVipcdiIxQOg5RbknNsfIQvjI5S03TmkiHGx8bQDZPv/PgcS5qFpqZSUxxcSulkm1XGIk4iEXU1D7x/tBfN/zS1N2eJ2msIoWAODTD27AkkW8knDzD23JP0Neo0UnHqTpWx5z6BNXQMbapzPenvKMMhqDYybfcRhB0LBIb8NG37l7Pf0+dkbOwr6JUU6rU646eeAdWHuUV73C01GMUiHDrzCQpvz1NuZIj6CkjDxAz7GDnzKA7H/v5+vOcPgJ3cB2DWirx4eIx6XWfabuNYzEm1WDiQvPREutTKRbetz0W/lZdyKWqmim4KmrkF5ssGL57oJ7ONtd64Ok8mlcZutOohmuU8V6amUfTyju8DaJbzWPUwxcL+2EwTNl79cJ5SNo0TlbpUyOYXsNhdPBQLb1tmJp0m4PeTr9RIzM1jCxqrtq8WnEyV+jGcPvz+VoO2hbky+offwqxVWSyOYPP4cA4fp6Q+SCYrluVvIUc9HyAw/hUYgOYklAC2kZ+/lZ70d+Ku/ufIX1285T6CXkL2MKLec0A1AVGyTQgW9nvdTtreBUd+haZeoNlXQHUFKM/6SKehfvNFfl/QkQNACPHvgC8BS1LKUxt8/gLwbWD5ZY1vSin/l06sfSfsthmc03mwF8Q7A2XOTqTbAqYbcXuwF9syt/l6mCw5eWyL9wQMDY+Qu1rFFmy/b2ChaeOF8c7fB7AZH4nFKFaajOjGtud+lFKwBnpxmOa6GoyFppMvDA7t+HsoJFI4gr042mzvw+a0Led5Q+7t79Fn+QjcJqbRaNUElGexDEWJxY7tecC0k0HgWCxMM/pp1B+8Q586A8KCvf8Ypz//DFLZn33sT5C5SvnqWZwXfoQ0IGp9muG+MwhbaI9t7ycW8yMlqw3xXK3O6/uGTr0B/AHwb4Cv32bMa1LKL3VovS1js0vhPy486nPjcVgxhIIhbwYlJXfm8/kass+5pbV0KZGbyK80tqfzbvYdXyzx8uQVDFNQzszxcMXBJ072bTq+XGtybiqNbpjY6jVSZQU2socQlGvajnVzWKwoy/cCrLWNECoOS8vGzWIKo5xBeM3WmJWLzPUmtelLKE8d2/Xl5bdys5ZdbjYYahvTiXWs4TGCT43RN/xVpLDSSKgo1s7orihQSZ4n9+ZlHOcuI/xhmqGvoozG7jjXKExTuPwKuaSbcMWFa+xpILBtHYSQZF79GvWkA+mrIaWgkZ0g88OzRD7zPwCOHe1rp/bYb3TkAJBSviqEONQJWZ3GvXAfwIirSs60MJet0cgt4rKqaIaknl3EZbOg6ebGefWKyuSEsdpl8HZrSSlpZBeobZD3jlS4ek3fks67aQY3X6jxxnsfrLnycp6LVySphQQ9lvZmelOLRV5/7xLOYC+GFDTzCwR6B6k2jLb8fVWBzJKNxPTUzr4HzaCam0eXxi02HsHAQzwOWkmSKB5Cmq2/0evy1ZsRgh0MVmqVFJd+/BEp+xWEMEjWT6J/4sTqZfGdCAKv546O6b7Ca8lLVK6/TSI/iO7thQwkL3wIUmALjWw699KPL5DSfgJAshRCL8YR786S7fsFtlIjsZY3s0mKCReJ4gBSrnxvI6BYyLw7yVzt5Lb3tVPb+3zc00Hgp4UQF4A54B9LKT/cj0XvpfsANMNgemqaB8bHMKUkHp9ifGyUN68v8eY5udpaASTeSD8nhoKMOBtblu+bbGJgtrmYBAqxO+TnN3Vj1d5rXUBLhSrvzUuylQZUrBz2hBkKuTeUcf7d6Q2vvMwJlUdjPevG9w4O8d3rrSsy7cvjJRLVF8UjxaruUiqoiuCJh08wPhbFalF3/D18ttbgYlpFNyQ2VeCO9PGF06PoVUvrtd4IUTiXZNC5wdWC/oc757YYqrH4nd9h0BZiyLecn2+aeCd/TPiL/xhwd8gFtJdc4rz4TQLuKqbRXJcPH8pNEnr4NzacW53/gB7t1fU1Er7Z1qFhvEMs9tK29InoU3h9yZa7bq1MzyxORwTnwMl9db/ttwtov146zgExKeVDwP8BfGuzgUKIXxNCnBVCnE2ldl8WPblY4MpcjsnFIsaype+WfPntcouitO4CUFquBYvaqi14fDzCQMCJqqhYLSqqIhgJeXjqcM+25D8w6EPZoPWB32XHYd24BuLabI7f++EVfu8H1/nzd+JMLJZWP5/PlPnJ5QUWi1UauiRf1fje+zNMpYobrl+saRu2XlAVQUPT141PZiqooj1P3zQlh6JuAi4bQgicVpUTQwGePdq76+8h6nPxKy8e5SuPDfHs8T7+zovHGIh4l8cAigXPsU+2rhZU1JYLSLWiOF24Yw93LM+8MncRDHNdfr5AIE2D+tzFdeMPOhd+M242ishGnY3y4fVCatO55Usvb9xuA9AzU9vWx+IJI1a+q7UyLVaUQHRXe9yNnfYL+/IGIKUsruHfFUL8WyFEREqZ3mDs14CvAZw5c0budE3DNPmf//N7/ODV17h69hLOwBwqCofcjdUxd4N7p1O8x1rl1ECYUk0j29Q4ETaZm0luS06foqEXl6irAl0HLbeIxSI4PtDP5ORc2/hrcwXevXB5ncvmr96sEGu9hfPy+STlzDzGLf30v/faEseC7S4lRyNLfZMWEQvSwOe0r463+HqoZtNtri+BpG6rMe5tMj7uXh4/w/TU1K5sXKtpvH/pCvGlIn0BF8XUHImEEwgxOXnz8qDZ8hEsh3+D2swl5nNOPBE79oFjxBOubV8tuBkvS5NaNrph2wnXDViQe+EC6iy/9Opl+hlcp/sKVz0B6omN52YXAyRLPevGr2CmeYjQNt1scuwYudoYydJNv8tKrUNIf5R4cvt7LBZN5q7OkH/vfTA15uVjSHMIoVjvTxeQEKIPWJRSSiHEE7TePDJ7ueZfnEtwPp7GUG2oTh+K3YchBdP5Oi/dJf3mO81Hh1u59FMWY8dyPvfkCXRXhIVsnXrewguPP4TdKpiS2rrx4XCYyY8KCKd3XV2C6vSTqpaJRCLUlBwWl6+tbqGJIByxtK3/vMtHfDGDwxNAN1vjnf4QZ8aiuJq5deOHYzEuLuhYXOV18h2+II8dG6aQWuiYXRfrFt6eyFGtKdQWC1SSl3HIJkHnYYTT0Zbn3Tfagxl7CTkFw2N0vv99aYmqI92en+9K4+lXMLTO1AHsFdfKc/i0a4T963XHhLLmZuTMI6t3EviVSWrx97AvBvAMBrEOOSgUl9r2jQlypHfb+kSjKsHPfYnK998kLG7KjHjT+OwzRKNH7ygnN7+EpzGD6osSjQ7jnv0WjnQZny8OJuTLVazXJd6nfoloVLmNLnw86wCEEP8ReAGICCFmgP8JsAJIKX8X+Bng7wshdKAG/IJccbDuEb53LkFDNzHq9VYfbrWV217NZ4kn5/A6bQfWb/5u5tlMmvGAn2ifymRFo1LKUdlgvMfrpZzPtNUlgKRRLZNOp7FoxQ3rFqyqSjplEPD729Y/GVapWAxSxRpSr/JQr5U+h8HkbLu+jwxaeSVZwqy7wRTIWolDHpCNakfsoekG7344SZ4lDKngKcQ51ZjDBGZLOte+cxXXIZMUX9jXPHNZstCsR27Jz+/BU6vTyPtIVTtTB7BXvDaXJ9vowV0t3dR9eR95xilbH4IsJC9doVF7FUyNpZIL++vvY/EEyDUH1u+7WsMa6KOoniCd3r4+0vSSzllwOKMgZUtmrkb6Bz8m2z/AZncxeF01iu9/l/iMC8M/A5gk6ycIijLZWgi3pbW/bC3Ewswc5ckpUrmxTXVJpT6mdQBSyr95h8//Da000X2DYbbOF6vLgyPYh8MXREqFpiIYGo7hd7UKdzYL/IV7hnjz+iIfpRWmRYOTwwEOjXamV71umCxoTq7MFqhkVexDTh4fDe9K5kHw6LTG0i21CDaPH6e/1Sv/Jaufv3jNbLvE/vHxCCFRveM6iUTitmNGRiQRrwPDGcIwDERliFPHH+jI/noHh/iTN+Pk8OEM9WIzGzxUmWfYr4CUqCgMeYH8q9gP54jFAvsWSA0WdKraXFszseFgmsC4irPaqTqAveENl43y5QRD3luC5cKKM/gosRgY9Syp6isM+eI3x3hnEaoD+yNfps+WQUtPoboDHHn2MVzDT5FI7Eyf2sJVhjzTDLkT7cF72yVisac3nBtIfQeHuEjD08Ogp/U9mIZOxJnGZSms+24GvbM4LR/gGB+7L4PA+45PnR7EZmltT6yGywQOVRD02FfHbRTsa+gmf/LWda7PF9FMk7qmcy6e5a2rCx0J6r5yaY73p7KU6wZV3eCDRJY/fTeBlHdPwHkr/LljPVgUZV0QVlFUhqJeDFNypN/PgyNBbMuX2FtVhUdGIzwci3RMF4fVwqnhMKeGw3hd1g3HzOWq/PjSAq9fXyJXrm9J/tmJNOWavtq2a0Cfx1gNurK8Y8AwqC9O7Gsg1T32DEKxrA9cChXF7cEWObxu/EEHezfitsg4Fpe3vbmbquIaPI6iQDM9jaKItmCv1BvohSXCz/4i0S/9M0LP/CLeQ0/tSh+zXkBI2R5YNnTMRn3TufXkRTBu7eu/8rNxS1BZKAiHt03GfREEPgh89YlRXr+yQC5ZpZabB7OBEAoxV52pOwQE33r/EnnTvdx87WaQMZtf4PylCH63bcfBxPlCjYW5BLZAfV2gs66qnF2qsFy3dlcElu/EG7Um9kYavQiaBrXcAkazyvlkiWsLOZ4c70WpZfjU2BiNps5M0qBHrZJIJPZFRyklb15bID4ZxxbsQUiFRn6BVLHBkQH/bedeyVuorKknyOk5qqUmFlpvMslSK+0V4WOxEcSzzeDjbrg0vFRDnyeZLIJiRaAzpz9EaPQk1YTYVS76dvi1iyny772BXlwkWT1K40wv9t5jW5grSIf+BkrFRTM/sxz4DeE59gLJuQCKE+oZL9PFEUyjVby3GuwVsFgb2PEdABtxrXKERL7Qfv+CaiWtjOPdZC1fth+kbAtiV5pOso32QHXQeITp+zEIfBCwW1X+1a88y58Mqvx++hID/f080OcjNT97RxeA9eoidtyrbRVWXByaKnCFexjp8W069048O5XCHujFuUE7B4ezfte4d+7EcYV49fosdXsIhy+KIhUMTGweP02rC5uvhwsZhRcPxVbvQXDa97ethuIJU1QaWAPVVRubSCYrVp7pG7jt3MWEjqw2V7+fmhnkSP0KQ34BUiIRDPltoEqChyP75kbR8zM4L/4+TgnD3n6GfAmcRx4n4n+2Y7noW+HN3DSp1F/S670BLjD1Bv6ZFK7oZ2H8uTvKqcQ/IFw+CyELCCtDwTzBQxZsvuUxQzHKl37cXlOh2gge7u3wvqLU4io9zZvXQw6HUigeH+HDfZvO9aoWmkvxNldcxJnFVausrysIzBMds2Bx3p91AAcCVRE8friHR0ejPHwogtPa6qN7JxeA32VDUW72ql99sZPgd1pvO/dO3Oe0od7iNhG03CMex8YujLuNG1LyVxdnMQwwZXve/uqfEpKp8oHpOzFfRl9zLeWKjqoQzGUrt5178lCgdS/A8tyGcHHdcRxDqKBYEEJFqGFcx/4eFt/I/rhRpEbx/PdBq7WuijQ1hNSpT5yjmZvueC767Xjxg79srQ+s3ndgNKle+AFI/bZzG6nL1OIXwNBW92GW8mTf+IOb4xUr/ke/hGL3IKxOVNWOUK14Hv0cFl9fx/flO/UZPI9+HiXUi9UTwX3iOSIv/jpCsW061//YlxFWB4piXXZh2RCoG7qAFJsDxeresl77hXv2DWAFO2kFIaop6kUr2porDYVU0IqLLM7PUs3t3AWEYbZcJaaBuSZ33WqxoEmNeNy7fZn7zM9/cJlqw7nBlY8LaPUKerW0arPZXJF4PHAg+hYUP/Vssa29hcUiWFwwaOYWN507OjaGT+bJLNdCIAWzNZX8Qz+NmptiSUsRPPI3qLu/yOSksmYue8br2SRT+SEMrVXLstb1sJDLYgvGVsfvtQsoE1c2rEMQikr2ah3E5i0ZCueub3zlZclCVs8CLR9IYqmPsQf/KY1cktSUjcApF1XNtqPc/DvyGwqMPwOHnyGrQMYJmbk7ze3HOP4PWTw3j802icUfIcMQldQPyNbW1BWUx/Ace45aQrk/6wAOEju5D2Cov49TDw/w6odLJNb03NeaFV6ZKDES8TObMai6NE4f2r78Tz12jBtlG0ulBmazSn9vHy+e6Ce9NHtX1BPciUeiURZyKlKTbfcTWD0BQGBzB1AUlfGo/8D0Pdw7wGw5SXODOxROjQ2TTJibzu2JRvlSOMyHQQeucD9eh6CUDnFobBzTHEMLTtE3GsM0lX3Ln6+bVcKOjXPgm5bstnrS75YrkQbFSrsuQliw9lluP9c+Q9DuaJ+rWlH9VSKR0Lo8fd0fInP1HZT3XkeaApt2msCJx1DcIwde02CafipNP2NjxzBN0KcgdPJJ4u9eIWLNojhdMDrK8CPHtlCT8DGtA7ibsZP7AFb4iM9gulpazWPXKkVqdidXigXquRRlQ+FqXKXfWtpWnnm5mOXp8XFqTTvTU2mOjXlo1kt3RR3AVrhWKUDVgtY0N6wD0KslTLebgZCHZrlEejk5+yD0PRJWOLtSK4BA1ks8PuikkM9tSU61lKW/JwgapNNp/OtqF4qAuW/586Y+SqaawKWuz58XqpWS4+i6HPi9rgOo9X6W3PWJ9fn4dR3H0FHSGQf+wOZzq67HyDUybbn8QrWSr0UIrtmH1yvJvf09Fufd2Lyt7LFUycrE9/6S4NM/SyoVuGvqG1Zt33OU5uBRxPhXWhcATbKl+oSPbR3A3YyNmsFF+oeYWsyh2UJ4Q72raaFrg4CBSD/fv17Hekt//I34bNZkaGgYRRFtcu7EnTbLXRHU3S5/MBjlW+8kqagCV7jVjXMw6MQbDFMp5Hnm0WMMhz13zOXfaz4yIhmJuFG8EawKmOUY42OjHZLfi5SWZb4fQWA39SdGCC2+hTT15WBlGjU6SGS4v7NBYGky3F8E1UXr0vuVMZJB3zS600SeGSScfQdT00GxcOSxXrynnsU7c1OONOoM91QR9gCgEIuBMXCK+vw3GLCvBF0lw6ElXI99gSyOdfoM+qZwWi+je8O3NIBbxGG+jjr+U/sWgN8q/zg1g7vnD4Bb7wOYyZX5/vXrrUyETJbp+hTHh4KMOG+OkVLyzXfj6wKId+q//0Eyy0Ox8Lq17mXeG/Dwt188zNsXdII9fQwGnBQyi0QiEdJpG4d6/Aeu4wp32iyMDgQxDINELd9h+SzzzvXIvx13DZ0icjpIOf4uzlkf/kej2HtPUkkobeN3uk5p6m2yr17C4b4BwkLZ82nEyNNopTS51/8Kh3MCUKnl+3G/9BmcvcepL7gJjNlW5WA2KLz/Z2TOZ3AGZhBWO7Xoz6CMHkda3ASf+Fnc5tvUZq/icI4SfuanUEOjZBPr9WmWMyBuBlVX7lnA1NELaXDvn+23w3czbz9xzx8Aa4PATc3glTcvrmletoBE8l52gSVnbXXO2fMfUdBd64LAa3P2N+LxGz6C4u641Hw/eTO/iCfsoZApstv7ADrBDdNkLlthKh4nW2oQ8tr3cN0BQN/TwO+GfHwQAoMsZcDbABLtY3YaBG6mJylePE8iP4jRbPkjkokyktfRliaZSvW0evcDydIA5g8u4X98nGQquKaGBYqXXqG5mCFRHGK1z/6NKVS7E1v4EDemnYjxF2D0BVKT4CsD5XZ99J4Y+VwfydLQ6vNkKQaKFY92grn9tv0WePc+gLsIa11AU0sF3OF+rIH26xVNtbz6eh9fKpItWtB01o25Hc8LH9+9XkMt3b7n/b3ENcMgX2kQ6R/CZVVW7b3bKyF3yguVJj+KNzFMK2XDQ3XRIGZYODK2tWsx734X0Nb5Tt0Qnsnv4nXPtvXoj5YWkS4ruveW/HbfPM7mK9jGf25VjqlXcJ1/FenRMU1t3fhwKUnwkV/bxp6iZPI25LXZNa0jYCSaJfzUMZzzd4e9t2J7rbhIhRuEihnsg6dg/NCBu4Du6ToAWO8CAlZL+9ty/IVcHRPx2pGyfYwiQFWUtudCQF3TMQ0oNlo972dSpXXr3mv8YjLDv//RBD+6NMcfvnqd71+YoanftOFB6PXG1QWauoFmSEwpMQzJVKpMfLGwDzpwV/LtzjPKBTbq0Q8SIWR7iwNpojcq6+SY9SooYkM5Ru1mE+Ct6hZ65r/Cdeg0itOLsDlx9j5A5FO/jmLdvLWCUcuipSeQzdKmY/aar31WnnyN7Mv/J9X4+1SvvkHux/+e8pVXNpWxX7jn3wDWuoA0zaCamUc3b73WT0Vx1ojH4wAszc0QxstMobLab94iVEQ5xbOPDHNuMk1u+bmC+v+z955BcmVXnt/vmfTelc2qLAPvutHdaLAdu2maQ3I4w5nZnVgzlGIoaVcfNDKhUMQq9EUR2i+KWEkRUuzEamZndseEpNmxHA6Hpsm2bLYB0N0AGrZQNjOrKiu9t++9qw9ZLisTQBVQBt2Ly2Dwz8S95557XwFZ73/O+Z+e7Rh//H6aSVdz3Y9Hga7ZCW7pBjMz0wghkCSpY877l66xrNtWpTLa93krrTDXSPPFp48dCAVUrNXJJuKYfc0uiu7KfIrJAc8e7HtAFNA28IPSEEH9DM1irCvHX1LNCL3Znfsvm3Dy7DoVo9fyXPv5LYbVgY05m+ZbWiaqPSire2MzCZ7Hcfp5AJIz4Er1nm+0KpSuvMZM1EzOtwSGRsL6JYQ4uvpzvPM72SneevdTt8r44jdB9LfbTa7+yh+LVjEPLmH2DD2mgPZqbK0DOHdqkqmSimHI6M0KNrePI0N++uXOPPBz4+PMp8q8e6mFO9DPxKAHW9PPeHiIyNAAs2EHo2Nj/PHb06j1cleeeR2FUEg58Jz9neJcLs+nyy1mUyVq2SYJinzx1CChUAifz8+b15eZKwkcAQ/SpvMaQqLUqOIJBBFC7LvvarmB6vR0PQdDSJil1h7t697XOoCd4AetAxg98SS5N69Rbm7k+Fc0F5GnjqFrNSoXEh2f9/dreE4eohWFQECQe/NP8RjOnrUKVd1F5AtfQ7Lu7ExaI4ezcAPp4/dAMeGyvkrAfxxB991bFv4GqTmHzzKM37zU9qF4AVerhnng7IHcvU+OEnSmEC2tq4eDp6ZhGx96XAewV2NrHYDSKvOVwxFi6QpLVZ0nRp34XBIzM1tzvN24ZTgaUJicdAEGM8tp0t72nEw6jcftRmmUemreOy1mUqkWbrebWlPj+nSUfFOi32N/JHL574bf/ugOhtOPZkg0KwVSGSt/+/Msh11NbiVqLGaqPc8rhIxeKxBfXKZWzO2774YQGNUiTZOlwy9VUnCqjT2qRdjfOoCd4AevAxildeq7lN6fJ9vUkS026sGXqXhGAGj0L1PiI4Rep2o6i358mFzeQqg4SIUAACAASURBVCoFNlYopC3kGiGc1SqwkeOvWO00wt8iV/VDdfv+OO1V8u9/j8VUCNnVJsjj5SjW1hLuU692zF9ZrtKIVUD4O3oMZGt+op9M4z9/9kDuPl2wIVVDCL3V1cMhWfTiyj6uA9izcbem8EcPCaJDnocOCL4sO/nZ+6JL8/6rp4ZQ6nnqJg9v3knQaDkoFhSu51qcCQw8MoHcDqG6Yh3dGcDq6+sQwgOZmlImI5yYfC6sortxvBAyuiJx8sghcrnsgZzjK8/X+CQJ+mpTeHffIEGHjSf7xB7sKxOJTCKEefXzgw8+7lYuuhCDmN2DrOngd+rsDxKJfAshwBvtXDtgy+LyLnc3WPcsooRG8Y/7d+xPsHkRm30ezVXusNlXTxLwPwmE1udr1RrO8uK6oFuHHr+zRGj0YO7eaAaxfxrv9ktSOf7sGWT3ho3HQeBdHluDwLuNT48EOD3iw6IqSEg4TApfPDnEeJ+bcq3JWzcSGDpohoGutQOTH06tUNf0be8Vz5X5/oUF/vz9ea7HcjR1Y9trd4LL9SayJHUFyYWQKFebPYPna1hVZE6PBVCUgwsC97ntfOeLR3j+SIAjgz6+fnqEX3s2giLLe7CvG1l+CnCufs6mOY8O3s89zZ7BHvr4MpJiwjJ06IFsNpKzoDW7g9KyTKsQ75iv2rxIqomu4LOkYAkd2rd72Hr3stmF69lfR1JMSJK66Rw65ekPwWh02div8bl/A3gQMbidYqXe1rzXDcH8nIatkScazXPpynWqwo0mOvsKtPIJ3vv4BpP97vvavxrN8um1W+u1C61cgmiqyFdOh4lF53f1LNVas2eQXEHBSh69qVNt6l01EHZV4Ykj/VTTKebmpAOtAwDwAi4jh6hmiEUze7RXjXbrj3sLfB0UPpim8D7Knq8TixfXP4+VIsg2Jz7pBeYewGafNkm9VO0OSitm3Nk+YulN82cVhgd+k/L1t4mV2g3nkSBWnsTvOk9+x8HnXbz7ybPURh3EolMgjNVzjMKVJSyJt0nZvvY4CLwX424U0F5hVZHXcWAhQ6Vu6eor0JAhNBhmNOy9p83QUJj4VA2Td0OOoopAdoWomV1MTu5+fvuheJ685OyogbAoJl6MjGD19PGjK4vrn4OMRZH4rW88j9uuEo3aDrQOYH9xmUjEcuBUzxquLd3Afuvv0Ctl/K0zDB86g8t1dP99GX0e1XObUHkGrVnBpng4/vKLSCYHsvn+drRyCl/+XfR6lWHn0xw6fphU+XWE2EzpwOhwjeDpIczRrXaO0Zy0YHr3JsPOFmpoDL/1PBNH9q9l593ot/TCm4RdnfUVYdci1GM4j71AJOJ4LAWx22OrFMR+4nDAzlICWhod0hEAEb9z3ce72cnka+3eAVtkJ3RDEE3WOB3afZ+fPdRHQXZwdS6Prsoc6ffx7OEAuVSC8X4Pv3FO4Y0P8sgOG4MBB8GICZ/L0mHnoO57/7F04LIDkmhRvPUa5ZsxfK4cCAmtlKTw3l/SOvYd5PHIQ+8jjDq1xZto9RKt6mFkefAe8yWs/ccIfuEYug61KKjW7UpQXCT/4Uc43VGEIahUKuTKLbwv/COWf/YhkmICQ2DyDhJ46RVWm3J22TH5x/E8OU5o/JvoOuSj+/98en1mlLNIuDskLSQAWcGoV5HldvHofo7P/RfAflBAd8OZxCJBPEQLZerZtu6/Iit4RIFceoncfez4+upUM8tddQYygrriZqZY2nWfZ2dnmJyc5ItjJmZ0g0lvi1wq0TGnz1RlMqwAdWZmotitpnU7By0FsX+4BtRXMZs+3z9844NZ0m++jtBW8/ON9r82a20JCx/G13sETE/VMVoGssm+o31apRSfvv4xo+4oCI1osUJt5iNcT3yd2Tl123buh4VeI/P2J0QLYcT6OYYwxBJOs0524LfxDOdAUsgueogld2ffvcBddQBXU5RufEorO0isFFz/fKP1pEp+yYV/7jEFtOtjvymgXngpU+L9Kw76Bkc5OuSgls9se+3NvIpmdGbdOIODvPxMhGo+9QhQIZ3480IBDYVHmF0poln8OHx9BFzWLXMOlgKqJa6RqrzHsGu2I9tmc1vCjKYxMniK3IW/wrPQwFaIo7h9RAa/QyQysI19BMkf/zvCDgfDzjgIMAyNPiOFHS9SV+tHg9L8B+RSMeyteczhk4xHvkQk4rjvXoP2BWy9Mohciyiahu3QaSIRP0KAYj14yu1eeDMFVLzzOt6VKYKeBTCD5hruON+IP4Xt5IvkrG0V1MdZQLs89joLaDt40O/kmck+Xj45SMjt2NHabz0zgtduQZEVTKqCSZH58ulhgm7bgZ+r1GhxYXqFf//mLf7fd2eZXsrDAdx3plzn9mKWeK6MIdjR2s04Vawws1JkPlnij96a4u0bK3way/LXFxf42adLiK610ibMvuLyjTe7WjJuxarVTeadP6SVmAUMJKGj5zMULn0fo5q97z56OYWoVFjLC9vc+rE2faFrfuHCX1D9+CdopTRGMUf95gcULv416LWe9juxhTYx0n0OyWTd9fvbD6yXlql+8jMkjM4zSRKSyYrJ6cd57pdxHXm1y8Z+jc/9G8BOKCDDECznq9yZmWYpW2bA62Bubvau8/cLTzgb9A0ptHSdNDqmeo5oNHegVEizqfPjKzGKyUUsq28nC/kEiXSeQYe+L74YhuBHP79Ew+pHQqaaXebKTJqXTg6QWlrctp1aU+NHb19C8vQBEqXUErbVM60pxl7PKGRMFaT1tftHAU3dKFBPLCNZHMQyYUAiu+TqSSes4Yrmouo9ix4rInTLlj8fwvz+bVyHnuu5ZyMX4+YH86TMN8Ho79n6UdF85JTNPubxLRVA9HXOLwexvn8H28iZe1NAExFylVFipb7OvWQTLutLLD4i9M528BoFlLv0Plox3PP+LKFxsn3fJG2wrub6OAtoD8Z2W0KmcjnenC5SaxqUKgqVRBNHRuJMMHDg8gzFYpHxkSEMw0A19krWYGc40TSB1YNsL3XIL6y0VI5496cN5NRygZJkx2bzYAgJyVpGNzv5OK7zVP/223S+/ukyTZMTm9WNISRUe7e0hyEkSi1t09oywaBpTyUFAgFB+crfYJ4vYS0tIgww1w7je/LLmEetFIq920NWhZ/xZ58gmRYE02mMVqtLgiAgV3D6n+va0y3donDzL/EYQwSsy122y00bQWcW24kTqNImuQNzgpAzhdFD7sCrlXH4z9ynHaKM96tfo/R3bxFyFxAGVFoZRk8NYTtymOb89u7M76vRSE/jlaz4vQMguw5MhsOVvIawiu67t6ex+gfA+7gl5J6P7baEvHBlmrrFhy5kWpUC1YKVulC4kMzi93nvufY/RrxUL1Mv1btkIVRVZiGapy/g23NfPr4eo1nKo2ySf2hYbeSqJaJac1t2HE4n03NxmpUCisXKVnmLzbhWy2+SlKjhdrdW8d5ICphLNyhPpcjWh3EUywCkSzZm3nwT18mvkGt9hLNWByHWWzI6Dz9Dq3mWgiKRrWcRlSAYWyQImoMUlH6a2e49teuX0Su+LsmCjbVDeFQ3wvEcqYWNtdmSD6nah9CbXWvTLS+NHnt14zCtiX9E0xtFbzZoekeo9buobWstxG/M0LjwOpIM0cIoxtybOI+9QKp+8kBkOJr1QYTWfR/Oag23+3zXusdSEHswthMENoSgZplZf+3fnLNfKzwaQclHDZsqVorxbMddgcDu8XFkzLkvvjhiOtZqs0uWQpFVRoflbdkJh0ew3qmj01veYrPNY57gAwSBBc30LBU5RVDXsYbPAK5tBRMd0z/F40h0afMPSUkC4ZeRXn0afyFLKzuP6q5z7IvPY+k/hS26Foj043arNFLxjSCxJCNbbRx94aX1CODmPS3ZK+AWXUHlsGcRTDbcT3yNI+cmEEJBVjetFSFst9IY5VLHWmQTx58/j2y/3z2tYRNtiQ2wdeX498aj4Srpt/6AYEla39fQG+0c+0Qc07H/mkgktO1ArqFV8KZ/RD1+HU9hCK/bi/PM17f93NaCwF63l9r85a67jIw2CJ4JY+lxvs9kEFiSpH8nSVJSkqRrd/lzSZKk/0uSpGlJkq5KkvTUbuy7Td+A+wcBBXfpEyAONtD6qOLTo76OGoW1fgkBuw2Pw7wvvhwNO1HkbukKqyLjsm/PB5NJJeSy9ZS3WMOqojASsBMJuXYWBBY6uV/8Kbl3/oTq9MeUr/yY9N//7zRzC9sKJgqtSU9tfklgGC1UR4jAC/+U0Df/J3zn/yH2oVNddnwvfgf7iedQrQ5ksw3LyCl8z/wGisnZc0/Z7Oi5pwTIdieWvsPIstK9VpIJvPTPkL19yJIJSTUj21y4n/oaJlfovmd9GFz8+AfouZWePmPoNJandmBTp3Dx+9TnP0E06ohmk/rCZTJv/BsQ2o58c5/9JrLLh6JYkGQFSTFhsrsJfPE/u6+N/Rq79QbwR8C/Bv7kLn/+DeDw6n/PA/9m9X/3fGw3CGxrZKll20Jia1IHslCw65n1PgGPRv75o4EngbN9grfmktRlEIaMrZHlqDfIzMzSvvjiMgykUpKGAq0WtPIrqIrEuWODzM7MbArY3tvOYXedO/kVFAk0Q6KVX8GmKowPuFnOFTkTGiLk1pmdnV39hUJmZkYCGqt22GRzA9+6FCdQrIPR1y77p/2rXix6C5NnGElW7xlM7JNfoFG+srq2PdaCiMl3PiVpH2StGOruuehmsH2D3BD4J6EGzM2AbOu955D3W5Tv/KI7cCnJOAdfvE8LxiAc/u/IakW8Iw0Ue4D4nIxl7m7zHx5PTws88zkQQz2DrQArZSeObfpQS88yl/ChOYc67EhFE+nKCkjD97VTSOdZSegIwwVH/nvStSR2/yLY3STjbqYuxZAcBeLZCGu/g3+mg8BCiHckSRq7x5RvA38i2u/OH0iS5JUkaVAIsbwb+99rbLcO4EvPaVxY0mlqAlaVJG0mE88OjvScr+kGus2HrgsGRyKPBC2z33hUCPo9NoKDYVQJEktx/H4/Wd/Dq6xuF//aVySEw088U6Wa8fHSudPYzTLRqG1H57CYTZRlN7lKC8p+Xjl/GpMsEY361+dLkkQkEgFcCBFibMyDsarL14sOyF/6hP4eefooJoY9C6i+ybuuBRgdPkPqtbcxDL2LjpFaKay+o0QiY/fNRd9ZPvsTFLxziAvzhD3xdemFyRM+vOdPYottx457HUvyHufdCwNLbg6M7lqINSy3LhOJnKHd5e/eNov1OUrOGcKeWJcdmzdKJDJ817V6PU/+4hu4V27QrPdhay3heOobSGfPMxp2kX7z31LO2fEZcUCh2jjMyEu/jGTyrNv4vEpBDAOxTf8/vvrZnn8BbFcKwmFR+U++OMHsSoFb0zWOHxlmPOQiHot2zV9Kl/i7Sws4Ag0EEuX0Eg2ThycigW3t9XnCQggsisyqCOg6RbJf+8uyxPiAl/GQi2i0gctmeiA7drPKyfF+dF0nGm1hNan3mK8iGha0YhLZ4QZUZLmHhIKQusr+xWqDRdYEXUWrnQYiWTrWAihmF44TLyMtX+22ozVoJueQ5bEdyRHcH0u4T/86IWcNl+ka6E38lSMETocOXPaiJ5YUVP8IWjreccebsWjW0XNRZG/kvjZVRxBFme+WazBZkW3ue65NvfcntAoqWHXQW9CqU7n0Q5oTw5Qy72Pk06APgt5ECAm9WqBw6Qd4n/utjme2n2O/vgCkHp+JnhMl6Z8D/xxgdHS015QdjZ1KQZgBRyuLqe4jHuvOtdcNg7+7tEApvbRJNXOZNy+CKA+SS3bSH82WzlyyzMz0HcZjWcb6XCzFo/f04SBwralRrLVIL8e2Nd8wBFejWa7fuI3VN4tZVZCrKby+EGa9jCEE8pZWkp8HbDRrlK7eYib6PFnvdSTJQ8r5DWBkdc76dBI8gVZaAaPVSU9UIngzASq/+AHTs5ByR1HsbharxyiPtbAOH2Fhqf3LRC0RIFYaZ0NBcsPOUs6M/XYR2ey+BwW08dlO8Oy8DWnyHEiwsAKq88Hs7AeO9P0m+ej3iJWG1j/fet/umRbm0P1tGvpJopUMhqGBEKv0l4RiMpN1TmK7C5WkVVLk583EimEqDSvZxkY9QzwxTcGSBjG4haYagZsxgoMGs3PyZ5cC2saIs/Y3pD3CwFKviUKI3wd+H+CZZ57p+SWxk7HdOoDt4qbiRLX7unLFVZubZNPC0Ka9VlI53pgu0tKhZFiYKQpmihWeCvkfiVz+YDCIrusslWVuTBWQJIVyuoXuavLFk/33vLepjEGsJCPZXKgOD5qQqJVLVBsKRk1Cjtb56pNDu3r3jwJWZ97A1WzisxzFb9HBqFJKv4nX9DKyK9KREz50ZIRQ2kVjZY5yM0nQlQNDRjl6HHXqj3FWC/gsYQK2FOgpCo0m9mQMKfkG3sF/TDA4ie4ZIXn5Qs98/3JpGvmTt3G/+FuEQpFdaQn5WcQD430EB/4htZ98SEDqvqdKy8nA+LNgvr/NVjlPwJ4nYE2t2+nr1/E8+23UFevdazZcBRRnllLdTMiWQMCmZzZLwJbsWVMRsCXxBwSl8ue7DuD7wO9IkvRntIO/hf3g/2H7dQDbxeamRKua68oVb5RtZNMtJKm0Pv/S9Y3aAq1apFa0gZC5sJxhqG/v8+S3g2/EskQXlzH72m8zzXKe6XgMrVnEYxR6rl1OJLiRVdAMqeseQKBVS0RXlrk8paEVH426hd3AiegcjZUEGH3kGn6cVR/gbrcc/Ogm7jORzlaAaQX3xD+mGVikeqNMfbiBOTRB6mYFI2dH6Gp3jniljWOX7+AfHUZSHNT7XyWT+zsQW/L56wGcxTLZt98h2/cd3G5p1eeHaQn5WcV+Gn2vkEulEM3y+j1Jiomy4xmyZf997bhcOrl3/550oQ+bEVx/JsnkEs30vdc6bUNky+36CQnuXkexBfusAeSc8tmuA5Ak6f8DXgGCkiTFgf8ZMAEIIf5v4IfAN4FpoAp8dzf23c7YbTG4/uEwn+bu0NK688azyCxnqhTjOl85PUTd2m6vuDW3vCoJRjdFkA4ykPvGTKOj3wAILN5+EprC2cPBnmuL1SbzikarRce5QGB2emhaCpg9fZQkN6cmP/vCcGu4mY3jblkQzSaCZcKeGohCOzffIeHv2XJQQogwFu/G543kHH4j1rN14XqgWDUx5JrHFDwGDDIa/qck//7/oFMXfxUrJjxDOdbE0to/9w/TEvIRwiN1itPv4kumcOgrWMafgvHniERMPebbGHnl1yhNvYN8tUCkX2A5+hRB7cS29hq03cHmiKHVK5137E5g097FNPnr97BjJ6CNI96LErAsY69Vej7XjWdmQTJbOPW1b7OmAv2ZDQILIf7Jff5cAP/Vbuy107Hb/QDsZhMvHw3yk8wyktRul7i5daIBpCp1/upSdL294pqO/xqWJZAlCWP1p+cgA7kN3ejqNyAhYegghOgdMLWaQOir99u9dqNN5MH1YtgLbHIGQdfa5149dft5qpj7NuJV9woU1rNRGovXQenM8e8KFAuQJNO6TcXkRDJZes7HEEiyvEtB4EcIC4P0W3+InltBL/ejKwmqV18nL+UYm/jV9ayezWtVixv3iW/hd0JwfPXz6Pb21RrltdvvvGOho1dL4Lm3HdfRr+Mu3kHNvokpZ0OSltvZY1vtmW3Yz3yZgP40Jre965nt5/jcVwLvRT8AB3DI2UQ31ShWmxRyK7BJrx8EqqyglNPUMdB0qaO9okPPMT8/v629Gi2d9z/5lNevxrCZVMyt3EP733GWVo7Uln4DIPDaLMzNtnsEb127MDfLsNnHnUyhq1dBq15Bq5ZQFQnPgLxvNQHbxdduTTG1XEBGollI7GjtbGyRfu8p6tEMsdIA4APJTbw2gd/yHNnovYOMei1H7v0fE80PobnaIbG75a7HaxH85TGkyqYcdfuXiZVSnfMlCdUdJLvoRbZu7HUwLSF3F9/+JEEgb0LoWwTpyhbsV5Ywe4Z3dV99aJJcrr8dnF3bqxRBkk04xNP3qYFoj3j5MH3hw2TNIDJ/gV5KbvF9AufRF6nLTzA7B2vf8Z/pOoBHeex1P4CrCynSpRpWX7eMwFC/C9kWZLlYx6RI2AP9BBw2ngiNbsv+cHiEP3tvlozhxmrxURMyjWqdQ5KLMyO+XfH/m75+/rTcwOLrwzAkJEngDg3yK8+MUL9P34Lr8QxvfSChekPohoQiS1hcHhqlPOefOMn5w6Ed5ePvNb4aTXM7r+JQTBhI1PIK7oaNL0wGd2SnupRAeddg2NPEPDCI33ae8cPu+1IMvvzb2F3Rbs17zyIICSGrjPjaBL5v4jcYG1M67BjhJ2hkfsQAK21/FBOjoSz+l3+Txexu1QE8Orh8Z46A1qvfgcyg1YRjdHiX9/VRaPQjPloi7GrXASCrREZr+J89hC2+PTtrdx8+/wqZt/4tyEuMeJIIBJb+CY69dOquNj6TFNCjPPa6JWTQYUOW5C4aRFVl+txWXnp6nESuws2pFieORgi6bESj3bUFvfC1WJZyXcMQYt2+IQw+nEpybNCzK/77XRa+dmaYLG6S+RqGyc2rz07idpiI5jN3XXslluHD22lahkBBZtRvx+UP4fH7MTednDo28MhQNwCFcpP3bqcxhMAwBIaQMARcnU9zPZbjTEAwPr49m7bB4/jOHyLQN4ZWF8hZy7YohlZuBQy9mxJQrTjOfg3XohP3mI61/zj1RWuXHSHMeJ/+NgHXE9QLMdypAfrPjWFgguxu1QE8OlixOpAUE0LTOu5MURQkm4u1sZv7us/8Oq7GHUxV0PUqzv5nCL54AiF312ncCwOYXCFC3/wX5C9HcfqWMfsiNPLD9123n+Nz/wWw1y0hhRBQSnVQPYoEdouZGg3m5toJ1HopSTXnIprbvv1o1Uyp1FqnZdZoFpMqc+UmlNO7Q68klmJMTk4yYpOYmSmSzyyTz9x9/sVPrhFr2dE0qOeW0QydO2kFcyPNy08fI5FM4rCZ19fVmhq1RovkNmsM9gI3Ylmq6QK1XDfdJYTMG1MJvA4TNrN6X5tGK8i1t9KkpA+RFA/R/AjVJwM4xp+9J8XQp5+gXtK6aR/ZhK9+hKVGEEu1Qvm1d5ieMki5o1iCIyQsLwHODZuTYyCPES+DJdp7r88DBRSvHqFSGEXoza68fn/rNNJ9KLcHwxKL5SNYJ48AsDQDtvjO7HTevUI8N47FPw75e697TAHtwdjtOoBe+JVnjpKXvNxZKlA0Gnh8QSoNncvLJdQgnB3zPZAP1YqNvFHs0KTXGhVMDg+D/SHy0sH0Bsi2zChWN9IWvfxyo4rHF0QIQTAYRNN0YkWpo8ZADcG5iQe7j4fBecmLkhOojd5a/0azRl6zMDzgua/NwoVf4GodI+Axt+sAzCr2lY9wjFoJhc7cNVd8dOgE2ezPKTeSm3LUXQxFHLiGg1SaOsqtP8RZLuAzD7f1+MsJrKkS/ue/DVh2rEm/lzn4hXydgF9FoO6J/f5BO6MnX6Vw4S+oaFmCrhyy1Yp65ilC/bYDrz/Y7bv/PNcBHNjY7TqAXjibSTM56WHAauOv5/PkFBOGkKkXc3xye4bFZTuD5uqO7Q+EhpiuFzty7fVqCVO9jF4rHVg+fD6fQXbJbNXL12sFYovL1Ms53G43H82lWFxOYPYZrNUYXL0zi1ELoNTS++p7cMCKqBbvqvXfrORJpZJk7XrXWpfLhdGsk0wkcFotZOeS5Gq/jNOkAZZ2bnepRv7SLbLBM/fIVw+gnfwvKP7iDplaHUlRKTvO0hw/QjYLiZkkrbwNoSsd+eLpspul63NYB4/tWJN+L/LurXqc8s23mU94aF5awjo0SVJ9Abfbuut7ud1jiLP/A83refRxDexBsrMSq20Zdv2MWi3L8nQDu2pGtQceyM6D3v1ntg7gUR772RT+0lwKq6+/I/ff4u2nKCucGQw8kE27P8SP3wVXaADdkPBYVf7J15/HYZXXxcn2O6h6aDLJiu7AMNqs7FrQW1MkTh07RC6XZWQ0wg/v1LprDDx9LDfNvDw5ue++N6wefn5R9KzNUGWJp04fZchr71jbyi3huP236KUsnryO13oCq9dAGGXCnmJHgFK2FfBN3i9Q2IfJ1Uck8gJCQDS6Mac6H8Onz4Ohd+WOD9mbOEeP3cVmN+4VBB4Nl2kVMxgjfiIR111t6PUC5amr2G+9D4qFYffXiESOIYSEVk7hvP37uJRmu8G5exFqMQypRiTyrT0KCEtIkm/9c0nag+CzoeFO/DmN+E385RGcN2MooVEmxr5DJGLbkc2HCcA/DgLv8tjrIPBmnCrUOwK2awFhRZIp1psPZPP4kB/LuTGcviCKolLJWXE7Hkzw7F44VaxydSFPPLbIEzg5PuS96/wTYR+5uEbD2KgDUBSZExE/yqoqnGEIhN67TqCuGfv2TDbjZ8ZDmBvD3M6qpMvNTfUKMj6PhbDfiWFs+NbIr1D45Ee4nGL1b6lOMzmFJMYBU2cgV5Ix9Y1RYyOwJ5oVStfeorZ4g1xxgj51AuvQk4DUO+hp9yMpCsIwtgQ9LcjeDW2ZnQQi258ZlG+/S/Kj10Aykc3248t58TzzDwBTxzqhl8m8/rvUEz4MdxYhJMqxdyhZpnCe+Ta16DWchkZHDYOm0SrH0cspZEdoRz4+KrgydwFr/ma7zqPVAr2FvhKlXP4A+dCXdmzzQX3Z7/G5/wJ4kCDwtVtTfDSdIlmqoeVXOF+o4XOYmZm991qtlKWZ7VETIClk6w3m/M67rr0XXpifZVLpzsffLfz+x9dYbtnQdYlabplsuc77qsqkq9Fz/lI8ygsjEW4t5pkup3C7bRwO+yin08zNySSTSYQQSNVUV52AhKDfa6fdVH13z7EdnFmO8+zEBPGszuVEFmfLymi/G61Q7arNKN38OdF8A6G3Dr9KOgAAIABJREFU3xZipfbfVEnytAXaaP+6FitFkFQzXvfXWFjdymhVufLTDwlb4iDsxEoWtJ9cxBYus6K+tMm3dUisOEa5fgijWtoIekoQr43h157cUdBzcyCytvAxUzcq1JyDq/6GEdeWsOY/ILnFl+r8TaorfqLFMGJdgG4YcWkJvyXPzIJKXR1eP/e675Ux3FNlzIHQIxFA3nHNwfUSI7aB9lk2nytWxXlcR5KUBwwCb9+Xx0HgPRibKaDFVJH5qpmF2QZ9bitD/cNdNEGh3GSmMIfJa8OwOajVWlxM6AgaNHIK2ajGV870MznZTTH4+4aYWS5g9obWKQZncJABt50z/QebA383rOk6Kx/OYVn1WazSVkgKLWvtnmuPHhJExzfkIqJRG36/H5erLf/wbUeAP3tDW6dcZEng6hvkG8+MU86t7Mv5CqU6+ZkUd/IqY/1OxsYnGBsbIxIRjAScm3zvlqxIzb5BzSUT9qgg2lW/YY+KpNawH+pnQC7SqmawCjvHX/o1ZHsQ1dZ+lS9NXyRsvUPYNbdBE7kWoRTDdvo0kYi3Bw2gMvLit8h/9H3EjShh7yKm/gn8wWcYm7A+MA2RvPoaYaers/bAtQjlRVxPvUAkIq+vc81/QtM5j2G0OuaP+DO4vXFqE4JAcamLpkJSmDz+LJJ1+z4+SjjlWFjP/e+k3wRjoxpCUh7o7h9TQAc81iigOytF3vh0mVK5icOsMZ+uMpVPEB4JE3S1b12WZS7NpWgaxuoL/haZBwH5WoPvXVzkpRGli2JwWlVeOTnEbNHMcrGOLEscHfDx/NE+EkuxRyIffivOFqrr7TA7pCAMWMpWHsjm2v8OBp18+dQQac1JptJEMbn5pfOHcdoVyrm9P98vbq1weSFHLVfEodmZSpQw1dNEIhHWujney465L4I8O7tKxbD6EwHoLaz9hwmceAldh1oUTK7OV/nG8hQYWrdsg6yiFVLIsrcnDaDa/Pie+236hgwGx8AQMuVtShncjYYQzToSnm7JCV1DiCaybF1fpzi8ICtdtQpgoFhc2EaHkG6+jjA2JA5QzFgHD6E6/I8MpXM/rNWKVGaukZ7+CKwOVKd39e8AHWc3OQPIquUxBfRZHWsU0MeLTSpNfUtO/TI//PllXjkVXn/tvzW10EVbbMUyCu+l821NHDophuxKnKcnJzEGLMzOGky4GiSWYvue975dXKo1qGaWN/U22DivUdt5O8xSqURy0/tvdmWRw5OTHPbKzMwUyaYXye7D+S5evs58zdLjGS7ze98zGPQ7MDey97SjK0NEywYCAcJoU0CyGevAUySXrHdtqwhQqkwSK1XXP19vLaiYyKre+7dJnJXXu2g8CLWxmYbIiyeIlSxdvigON7k5K5K8sS7S9zL5Yo5YabhjvuL0UyuMMr8kETn8O1TvvE+sbEK22rGFT7CiP8Pc/c70iGCjWSL/4V8xn+qj6pKBGrFSP5p7DNDbLTglGUmWyQa/uuNzPaaAHqHh8Xjw+AIY6RImE1059RXZjt/vX8/x9gWqFEvFnrnim7Gs6/fNPy+Xy4+Ehv29sN8w8PqDGA4fmrFxP7KscCzs2LHNtWyrgz5fsm666zPUTA6iJZlGQePpp904LOpd7Uw8/y36K3PUU3eoSRJjT7+EefyriIV753Z7njxCZu6NTu13RxrF7sIUCe5rLrrnhXMUvv8WAUca9LYvQWcWz/NfRS1s1dbvx2N/nsrrVwi58ghDULeOMvnqL4FJUCpJDIyHMCK/CvMwMdFeq80/Gjn428GO/LtIyiIFi+h4PkFnCmvkNPUZGBkfwjL5POZk6HEdwGd5FAoFctk0Wq1OS6MrD1ytu8lms+u536Nug/laiWalO1d8DauSgqJUSa8mIz8KWvUPg8e9EK9W0Fo6ol5Cr9k5NORDbpZ7njGZTNKSzNRbOoVUosOmJEkP5ctsdIlPokWy5Rb1fIKnijqHh707tlMsZFBFd61CR+5/Mcebl27z/PHB9bUt3eDa1ALXl6tYzSpqrYT3xAtI4y/Qmpmh4p2gkmtuI7c7TL3vK2TLPwRDkGsM4FFdeI5+lfSShMdzr7UPjztz0UdphH+FsriAVkpRMh9BO/kNimKwtw3lNK2J42j9OYQMhQtLTP3tX4JosaQ9iV06guoK7Znve421VAm94uvS5nfVdNyOczQHh6gPt3PyH9cBfMaH3+/n0OQkBOBqNMvm3G9Flvji+dMUWhqzJROFhOB0JMzTp1ssNCwgZJA69e4d/n6sqpnnIsojE8jdDfyNSIRUocrs3DxPnDqKRZGJRqNd80v1FjMfRwEDhEI5p+Kq23jhSGj9vteCwDv1JV+qM1uew+LzYvLKtAyD+boFe8PG5A7rBsbHkmSwszXffysum73rrUfD4RH+/P15EpoTC05oytSLRU7Z/ERCzk32vYC8jcDeMJHR79IsLuFbcjJx3I9eyVJdeBv77QtIVhcj3q9zt8buD4O7A5E+IpFfQgjwR7djQyUSCZF643fp1xXC7vl2ULQocE7/lODX/hvAf+DB2wfBblORxnKsW6ffncIfMWF27/bdP7pB4AMIO+zvWAsCP3eknzOjflRZQlUUTKrCybCXa3MF3pvKkK+1SBSqvHEtQbHR4j//0jF+/VyEX3lqlOeO9OO2qFhVmWNDAX7z+bF2D9xNUZvPAw66bQz4HNjNprvOee3yIsVGC10XtHSBIQQ34llmk8WO+TvZXwhBtaXz0XwGXRhImwLwui64Fs3R0Iwd2Tw56kFVFDYH8nthm6ysr70Sy1KoNdd9aAf+BT+7trj+l7RzL+6PFRWTexTF7seoZsn87Hepx69jFDJoiQWKV35Cee4XO7O5A/wwNvTCAkY+CUJfDUe0uyCga1QWPtgTf/cD24++gKRu9FZo/ywoKN4+FJt9NTD+8Hs9jI39Gp/rN4CL00n+5e+9xi9+8A5efx/Hh3yc8usMj6hYTQoXLi+S0HK0NDoChel8ghvDXtw2M0vxKJOTJr44bmbGaAd1MytLBx68PQhcabSILcR7Bsnf/jDDmLPRFQS+lz0hBNdiOa5ev4XNN4thSD3F2lRF4catBhZV3ra/yaU4zw2NcG0hw1x+BVUBQ5M77LfyK/SPBYlGo8zMzDBbNlOqdIvvqavie7mVNfE9LzMzxqZ92RYu3fqUetpPrDi6/mtfrDQMb9/AL55hbt6yLTvb2msXxODqjhrl4nDPZuuWKYOU6+5rH2k8eZxa8FvEFlJIqhkMnXjjKPJKjPk/+SNixQilI2acR19iLmrbsf21uxdC0ColmZsRGC0XssnxOAi8X+Pj2TT/8i8+It2UsHoHwOnjdlllwhHixJFDCCH4aDaNWbdj2kINNGRQXUFGV6thHwWK5lHA7sAA9qhOy+huh2m1WZkcVXdEASU1O/FGA5OnH8tqPwWjB10jSQonJswP5PupY4eIRgcJj4zy06tL3Ljd7nfQMmBg0MWrXzizPr+0Ao1cteNnAQR2Xz/jo2P4nWu9DbZLAXVi+60P0F05uts6ZvAFksjKyB5SQDvHYZ+T1GIcQ2tsqQlIYjs6idv2aFA6D4RHz2EZrhL2xtFaLcQPf8GQOgsuEMKgr55EWV5gcvKfPdDd25Usjqk/xChX8BeHsF+NYTv9JSYnv/yYAtqP8Udv3qKxKjkgrf5H1wU3F3OI1Rc/u8W0+krfSQ0YQrCc3YjGHDRFoxmCW4kCH06nWEiV0dd+Yh7AZqZc50eX43z/whx//otZ5lPFba/1Oq0ocjeNIssyk4Oujvn3p33g8nwGXRf3pGhUWeHogBvLQ1JuJlXhl54Y4ptnR/mVp0b47kuHeGo81DHn9JgfkyJ3+eCxmvE41/TgzUAfoOzgtV6glVOgWtksobCGMVooZsc27OwcP4wNkzeMGgwjS6YOfyVFxTl2fk/83SkWWhW9nAKh7XitrNqxhI7QWLwBQut6Jnoyil7L7tg3SRIUr/wYo5hDaA3Q2tIStU/fppGbf0wB7ceIZyoAtKplarlldK3efu3PJZi+M4PZrEAlRa2gdLRsXHvtv3ITlFq6/cO1Og6CfqnWmnzv7Y+w+ftpadAqJLgWy/DlE0MsLUZ3ZPPq9dvMVGbRNKjllmnoOkuLUQJyadt2jvs03rq9goxAX6VRhEXFI8zMzCxsmwKaujNFKaOgi+67l1ZxM79C34CHMaeFmZm5XbnXpfgCNotKvdA9Z2JC0G+qkM6uoKoSCAm9lORkcGSdJoJBDGOSubntUQNaKUnxyo+ZT/Ux4rKBCHf1AzAHwtST/l2lOXarH4Ax/J+SmL8NFQX0JsviKXyHD1Fb8R0ojSP0OqWb7zA91SLtWUSWIel8FRjfsc38HFtoufbzkVQTmVtNlHvUe/TC+eU0cwkfmrNbMmM5m8DiG+u57jEFtItj0G9nermIYrWi2j3reeBCrzMw0AcIRocGOHRigB99EuvKFVdsHmYLEs8M7K9u/Vb82uVFsLqQrW5Mq74ZJiczBZnRHWrqZxoKitUNQkLddN7lQg3/6k/dfesG/H5Eq0FF9VKptZBDJl559glkCVr16rbrAAb6+1kwBJWG3nX3wwODfONsmIWFeSYnJzEMg8o+1FSEQiG+HAgw6JKxBQZxqhKljJ/x8NCm+R4Mw0y5LG9DL7+BevsP8YomPrNGwLa8nnO+nmNvm2D8yy+D/Gho0ndjK6Nnn2Bi4gkMA8zzMBA5+Fx+a+wHULyFzzLU7ptgQCn9c1zUMfmP78imdcRGLpXprNmwp5EkBXXUumPfAuYCBVuqyx4G1Ek9rgPYj/HdLx3jf/nzS+j1Olq1QFORUWWFfmuTbDYDtPPGR+wOjEqhZ654oa6QUoxdzbuv1Jp8eH2aT6IlPE4bpnqaREUwtZijsLJE/3yBJ8eCFFZ16GejS2iVAk2Ldd23etnGnUoRW2hnviVSKVQ3dOXGl/MsLCZw2czbstMo5zg66QcszMw0yeey63O2WweQTqc5Ggjx8UyuwxdRK3EoIJPP50in03hWE+bvZmc5kcBktWM1KbtWF1HOZ+kP+ECjhw9FwNhWbvfydJpWMYjQm10550HPOPaxszSXvGTz97azn7nonwWcTNRoRFMgOnP5szU/0Ys38Z07viObuudFcs0PcNYqIETbZl3DOjhBJu/Fu8PeAwSGydRC2JX2m/Waj5KsULIfvmsvg8d1ALs4npkM8T/+xln+1Z8mWfIN4Av0cW7Sj0tUugKF/piO3iP4GPa5mOwTuxZEdfr6+MmdKGU8WE0eKk2ZWl7BrpjA2YepqdOw+Li4Al8YDDM2NoZtqoZBd9BVkVUmJ6078qF/KE7N7GVrPnxDhmOHJ1FWBXIe5ow7rQMYGy3y2nsKuEMEnTbCtgGeOX3svmv7w2HeuLLMrazCnNTEalI5Hhjch6B4P0Koq/jeAcFqNINfiiG0ZlfO+UhwAPdRL8oeBVLvFQQWRpN+6xS61sAYOUQk4nk0ArPbxM3sCq78wqYg+ua+CSVCozu16UUYT+PPJGimF5BtDo6cO4Tj0Mu4Yg9y9zbU4hD+1PsIrbUaOE8h2ez4j4w+UkHgz+0XAMDzRwf43777Ir/XvM7g4CC6rhONdgucvXKij79ZiXcIoqmKwheOhKjlk13zHxS/fX2lnTvPmuBaW2BOGALYCD7qOtyMZXjqtMxkn5Nr2U6xNlmSOTzoAlo78uFEOMCVDLS0TTr+skR/yInZpO6aGNtO5o6EXLx6dpTx8fHV5xPd1tofXoixUqxjiHbvgXJd5xe3Vjg8OYHXYd6Vc9wds4rvLfBl9g9CStChnb+q728ePrz+c3o/Ow+Ke31WS86Re+d1bO4YIJPN9hPSj+A4+uU99WU3cTMT6xa2W8WyzfdA96o6QgRO/Ta6DvUouMcfzk/76BP4jzsoTb+HKebEcfIk9onz1Jbs931m+zk+118A0BaDm5ubo776btUrOCgBEXudcitPua5hb2Q5PzBCLZ/sDFxOTRPLlACJamZnDdmnp6eZS7XVHXsJzG3F5dIKc3NDRGwtrpSSNFQJoyXRLKygWEwMmy3MzMzvyIdKdoljnhBX5rM0cysoMkT6PLj04o5F3+6Gd1IH8KD46vXbLFRMq03pN+6skVvmrQuf8tRk6KHs356a4uOZFNFMmUpmmSOTSU5FfCzFo8AQoG0rILiwEiJkeYlmYX49l16SFeLN4/jrp/aoqfndg8DCaJJ9+2cs5AbRW+1eD7FSGPHuTSzTGknbl1n7J+GRydnvgecWnNTEyKr/nQF1OyEqe3ivvfD0VI1mKo4hQTw7Qqlkbd/95CEYPES2ChkrZJbube9xEHgPxoO0hOwlgbCUKnE9p+BQFIQhU86qjKgejg15tmUTYEZUaenQlWfeA3s9G/TOb1pUFEeAXLVOPdfH+SdPIITAan4wOYoXnxbcnp7lyOQEskTP8+4XBfQgeDlXYTkj0dK671Kz+NelHR7EviEEr30SRXPaUd0OTJpBCgcXV+CF8fEdUUBt/FXK0YsoH8QJB1qYR08SUM4xNq7uKV3SiwIasE5j8yyha/UtdQiLUIsjmcxEIq/sC42jVdIM2jMoniHAtaO19VNWvItJhNbopIAkmaPnziM79t5/odcYGWxSTd0hFfsYd3kJkCgVBhk++jIu19EHsv2YAtrlsRstIVu6wQ+uLKEZBprWlj8whOCdmwkGPdZt2zke9nM9lutukyi1ZQc22itKHB/2ra+VJYmxfg8jupMo1YemMISuYzermFRlTyiS3ba3FXvtFvRkg8202dpdlpvN9Wf/IPbjK3kqTR2LAWt1AAioNXXimQaHJrdPAa3ZdIycxyfO07dKK+SiG3OEXqcy/xGNxAzl8jh64FhXW0XRKFKeuYLRKtOsn0AaG0UI6b4+bP3M0Nu/9W+lTSTa/wg1Fm8h9HOs/Qu6FzSO0Eqk3/lj8vMqTn8CdJ2y5+tIY89v60y6Dpb+SeSSD6OQ3aipUK3Yh49hcof2lIYSepnshb8mc6OCzRtH6AYYYdDqbf+1FoX3/wqe/m+RZccD0Xb7OT73XwA7aQm5kCpyPZojm1gkNLjA6bEgtVyChVSRai7TJYEgA+9+UsPeyt3V5mYcGRsnqufJ5lYwqzKGDvZGlhGLl+mVIvVsApdF5YnxEPnk8nqu+aMiBbEdvB8U0FJ8gWpW9KTTsggWFqzMzs4+kP3bSwUq6d79EaZSBSKhI2yXArpvjn2zwpWfXiBsvQNGi1ipQC36N7jOfpXF3AQA9ewCN96+yYg72p5TlqjNfYr7ya8zO6fe1X4vCsgYOUQ210esNLL+eQeFUh7De7OK6rq3ZMHD4MLHb9LMSsSKYYTR/pcvFiuiOm9jHTy2LTuzs2YmJv5L6vFrLCbrmORBLEMnSFSO4pzbfZ8349zF19CLZaLFUTZaZnbSUBU9S+1yEsm0s5qEzywFJEnS14H/E1CAPxBC/K9b/vy3gX8FLK5+9K+FEH+wG3vfb3g8HkLbyJe/PrfEXEqjpTqR7S7qqoOPFpsc8zpxekOodh3VXu7IVzc5PFjdPkKyuq0884H+Pn41FORm0IqnfxivVWVqbpaa4mNy1IO5387zZ08AgnlFO3BN/QfBO+0HoOs6gUCw55xbCyu8PVWk0NCRyxojRxwMeCwUi0WcTZ1a0+iqIXB4AwQCAUql0gOdoyZZuTwd7+olIMsKww4bwaAbw5B3Jae9fPMdfFJyPY+93LQRsC4jz/wHgkf+BQG/IHPx/8FnHuiY424u4WiGCIXO76gOoG/IhePZ05TemeqZo15puegbdYK0Ozn7uq7hbF5HuvwzRKuB1fgCbuMThAXKFluHD/bsPN6Tx7avnd9vwwido+Xb6EnQmt8NnwUeMYV87XWMRhm7fh7fmRNIlhC5lRTe5A2EWadksfa8w3LTRp91hbKz/B9HPwBJkhTgd4FXgThwUZKk7wshbmyZ+h+EEL/zsPvtdBQKBVKpFKraPmqv3G8hBFduL2DytPvibs5Lv5pI8kshH61yvqtWQHc4cMsOkskkySrciufIrSzSN53jzHiIavYueebFLP0hH1emC1y6PIXF14chZFr5BOmyvq5P38DE7cU8K/EFJjJNjg16di3Xfa/wdusAlhMJbifqRDMVqtklBuaLPD0ZJLc6Zz5Z4qPrM5hX+wnXc2l+8N5VnjsySDWbYtTp4/ZSsauGYMQhOvo77PQcFsOAehGtYkFffc6a1YZZVVHNLdLp7dcB3A9n76TJ1gM4TGVgU754w0Q2VsBGjWIp2FVD4CzVKHwaJ+M/v/M6ANeL1PtGyBT+fiPnvVpDUlTKnnNkc7aHOtNmXLz6UxanBbLLClhZKVUxu4IdZ13DqXwWI3uP3P9kCzl1iebSTeYzQzAP9sPPkkr5d7XOoDp3geinWYRTAA6WSyVMP/ge3i/8A1YWW+j1foTWXdexGUuySlMfuWu+/+etH8CzwLQQYhZAkqQ/A74NbP0COJCxnSCwpuuYPLM9g7ENGU4ePUzV5OHnF8Hqa39JqLLEqSOHOXdqiJ9+0GC+rIIzhKWpoTkCXE5LPB26e15633CYH07VMHn7sa3uVUVQsXjB4UNxhfg4KWhJHnRHiOWWjdyiwRdGIvuQ6767QeDgQBit2WJcbMx969M4NasDi8+JLgyaNh8XExIvjrbP9+bsHUze/o5nYvH2M1+28txqbwDX7RUuXBa4QoNoBvQPuPjmS2fXo2oPeo5fecVg4f9n772CG0uzO8/fdy+89/QETXpTNsu7ru6u7pI03a3WqKWWdkYr7WxoFTHajdnZh415md3Qk+ZlIzZi3UizE6GZjZFmV2NUktqpuqq6qstXZmWlqXQkSAKgAUB4D9x7v30ADUiQTDKTyWRl1xeRkX+C557PACBw/+ec/ylZiGUrCCE5OnmEF0/2U8gu3kEQeIeA360MWrXQG5BVzPgnJKP9TbJTixh6s8dGDYJv4s7qACDKkOdV8hf+FmbnGR0oYj/+PCHTiX0Llg4HM2Ta79FwR3r314MFk8fCeHbI3y9e/Akh/UOkpYXmrBJuLiJufsT4qX9KNOralzWPDldJnf9bhl3hDescci1hr7+FOPkVnFfjSL297V5QzIyc7qfR72N0qIxWLSHHAkSj9gc2CDwEJLp+TgJPbWH394UQLwI3gf9eSpnYwmbfx26CwKqUWFWlpzG6XMmTX8pXODcexlTrpyj8GFLH3gfPPjKMbhhcS+axeCOwKZf/0lyOpx7det6lXG2DsNrqvLouublY5tpMFqtv3ScS6m2D68kyp48fjobytwsCl2pN3riUpHmrjoKgnV/k284gdquJXLWNzQLdAnC6IZlaLHH8qKTW0nuD5QgK9SZgR1EUnjsWoc9UI9A3gMtqZmkhiboP+3BYTPzyE50vBnNzdiYnxtB1nULWhBDrHwB3G1h0HH0GJXFpY0BWKAhvANURwOz3Iky9DdqFyYJj8nFqcm9B4G5sCU4Q+up/Sy0O/SvB6Vz87ve0ilvFJEKYtg44i/U9oZgwma24T32N1bHZZ7OQop1NgLvFWk2FNKCt0UheRTn21L6sWS9mEGKL85Y6zfQ06olfwzpyhmby8w17UVUrloFjYLbgsT6PY2KQzDuvk/7kTVDMZAsDhJqjOE+8CogHLggstnhMbvr5r4E/l1I2hRB/APwZ8NUtnQnx+8DvA2vpfHczdhsEDogy6aLsyS1v5Bf5i9c/Zjjoxi+LHDmyGphdZHbWQb2lUcksYtV7g4bF4tK2+fUOf5VaLt0TWBZIcpSo5bYORN6qpJiZCO24l8MQBJZS8pOLSdILcWyBxtpZ/uVbH3NmONQR6JO9+0u0siSifrRyasu+Ay6rhenp9tqcibkZLCaFyj3a00wshrLyJWJ6ukZHbMyxL4FFaTzFEnZkufMBl6iMojjceId/hblpAJVm9HdJxK6AYgbZJlGZwBoZp64/QWxme//7JQZ3p7gd7qdYGOjtJSBUrH0TLJUCqKIPq2+ArOdhEhnXtj7riwXi5bH1oHG3uFpe4hjfnzXrQz7y2wTJreZRMtMwMfHrNOoXSC6WETUrlsAQWdezBEc66eDJacjNvMvibIuGe3DFxxDy/QSu/OcsNE5vO/8XNQicBEa6fh4GFroNpJTZrh//FPgX2zmTUv4J8CcA586d2/xBsuex2zqA54GS6uHjGxmym3LLLd4IWcPEWJ+z51rdkLg/TWDyhXvoI6vDtC3dMDIa5UrORFvbKPPgDg3wwsNDLObr2Fb4726fLved6eIfNAXk8IeRriZWf2vD/mzeCMLlx+bv79mfUFSiVj9jY2N8RXHy+vtyjXKDjo7/188MojYK92l/FaJRz8p6OrfsWjlD6fJ57Jc+ArONAdcvE42eXktp3Jl6UBBffYTh0BCN/AyeTB9HH/1Gh2JclYgYHcbs8hNRPkNvV/E3TjP50CBSglDulAI6CNxPJlXB0BMMexNrVMmIf5ngS38fTy5MNHoOKcEZ39lny2uj8nmcYfe6n2HvPAgzdp+2j2v2kU2bkTfnGfZ0rTmQwffcK3hqEI2qyOgT2McgGv0qUoKna/3SaMGHb6G6PRspIvc8SimP/eTpQ0UB7cdNx8fAUSHEuBDCAnwfeK3bQAgx0PXjt4Fr+zDvrkY3BbQ6tsMPjwZ59ngIkyJ69Ok1XZLMNnquVQQcH/L1tB80KYLT0dC2c5lUhW+fG8ZiUjCvtKhUFMFTxyIMhdz0eWwoQtng06wqHB3272ov9xtXmzpC9Or7g6ClaQz6HKhK15kJgVlRmOh3oygKZ0dCnB3xYTWpCAROs8qLpwcZj3ju8/7Wb3iNao7sT/8PmksxZKOGUcpTu/Uu5c9e67LnttjsDuMYehJLILpprpX/TU7ck8/iPvoKJu/grnx2U0B7Wcv+YUHw+X+EJTiMUFSEYsLs8hN48XdQXeE9+AGzN4rJ5UOsvl5W6BcLEAkyAAAgAElEQVShqtiHT+3r+v1P/xaW8EhnzSYrJrMD95O/iiV0bHd+tBZIo6fngwCMcola7H3Q67ddy0GNu74DkFJqQog/BH5MJw30X0sprwoh/gj4REr5GvDfCSG+DWhADvjdu513t2MvdQAAqUyZen4JXRobqAdFKmRFkZmZQM+1pkaWcWeYG8k89dwSLqupU0OQW2JmxrHjvKcCOu6AgSZ1qoaGT1aIxyuETRWMliRbadEqdGQbTg0HaeSyzMw4d7WX+4FXKaDBEY1qZqGHxlGVTre1oKmC22JhJlWiVUgxHHBydjDAQmIOu6XzslQbOb42MYFmSOZmNOzNAvF44T7urw6sSopA+cZlGtkQifIoq6xnojyEPL9AwF5hNunqupYDxbuhgIxWjdisAOzb2twd9pL1fgtP9OtIrU1u3sNCFaju3WfW/y3UuptWIUaiHEV1BXCdeInEQmDPev07YyfLrl/GPfIShl4nN+8no6sQ36WfuBNL7SjL5fXnfkOdQDJHI/M3+J/4bk8dxxeVAkJK+QPgB5se++dd+J8B/2w/5trr2G0dwCr2eH18cC3ekwcuhMrJiG/LayORCBMTEzx92mBmxr+mYT87q99eFz8SYWIi2rE3G2uPD/T389zEBJV6m5sxNw+dOIYiYHZWORT5/rerAxgZ7OdMVeXTy7W1szRaNdy+EE+cjJJMCCYmJnjRMJid7dB0hmFgtBs9Gv2GYVCv3vt+AFvhYiGPsxijnriKpeDDFb2JOTxOOCzwJj/HaV3uyWkPu5fx2Jeoho/cN738nfoBFFI51Gt/iZFfQi2MoOoarkd/deV9cg/W0m/HMOxUW3fuJ9JvZ2Lil9C1BuYZg8mjDgyj83F8T9Y86MQwnNTae1xnRODrO4Px8wvb1gkE5CJOeY1w+OwXvw7gsI/d1AFsxpNuSaJeRIhObrled3B2JESjsszySnLvTjr3t9Ow3ytuV4obNPcPQ77/TnUAS0spWpjptwlGnZKqrNPSJS5rm5fGXZSKhUOz3p2wobeJvfs3tGwtMFpkyjq2n72PbdhOxvQSaOM0m8YWefoaWtW/bc734nQaOf0RRrvGfPshXLZhFItrX7Xzt6sDMLQ6sQ8+pu3UQAbJN8IszcdR0n9FfuAf4PHY9mX+e4dtLOfAu8cc+4PEInKG5kAf+fqPMWrFLes4ylN5MqYHow7gUI87EYMD+M7wCHOZMvOJIOcePoXDrOyraNqDitNNM5dzy7hV0A2JyeTlt1595gt5fpX4JYZsbVTfABXVjVNpMuSeQ5Rfx3HqLNH+k2TefI/uJu8IE5NHVXzHg+uB3K4gX3n2XQK5q0TcMZCg1fM4b2YJfv0PAc++BTS3CwJX4pcYcc8x7IqvByg98wjVjtMzQzR68j4FjR8c3Dn7MBHTaUofvtbbCyKQwTFxBrf5wQgCH+qxlyBwN7aaTUz2eRiLeHDbzHu69hcVpwp1PppKY0gDTTPQdSjWWvzo48Qd+bvfuJqc5hP7OT52Pstl28NccTzMm+5voCkWWoUU5mAU37O/gcnmRCgmhGLCGh7F9+xvdflhfegNap/+HRjttQAhUkM2qlSm3u6x3w+8+TGjlgO93ROglHoLo168vW+p00hdoXzrdRqpmwjZ3tn+FxjbB04hVKUnIIwA5/DDO/o4qPHA3wHsNQj8i4gvXrnOe9cWyVdb6MU0zzxRJ+yx79nPJxevUJJWtFqZ7sBvXIGrN5qkF+b35O9+43cWrGTzeWxieWUvCksEeUcbIJjyY58BOENu4DTekSJCsZKJ24knV/3Q5RNaxRyl4mBvbjygXq6Qj2y0vxu8XRC4XjlCorKeybTWAF01kzWN4pjZ3ndHvO5jRuy3kEaLRFmjOvUBvse+zeyCb9/W/kXH62dvpX30D0jOfw5lMwKDZOsY3snv0kj5eq77wgaBD/O4UwroFwWnixWmK1ZsfjfSpdBoaVxISV7t6wSz9+LT4o/jEA5a1uKG3H9XaJBw/yBu+95aWN4LXG1qCGcAt8XM5OT29iOjUUo3aljbtq69NLEFNErSx7PHw12374Jo1IeUoGxxW7+Kh4NmMlNJDK1X2sHUbyYwut80xBa/H5mgMfUJA+ZFpK6t5OanUDx+Qkf7d/Rd/PhvGLLkGHKttGOUBoOWRczLGpOT/9WhoF8OA9549mHMzpcYDp3C0CGUDxGNim19fEkB7fPopoCy5SapQp22bqz9/rDQDfcLv3s9iyGNDbn6uiH52fX0nn0OBtwoitqT+y8lBFzOPfvbTywRvHl1kR9+Gucnl+b5fz+c5c3LyS1fC830LJkf/S9IafTsRSAQihWEumdqwOwJo3gDK3IDsEbBKCZcx1/YtZ+7oYAQKr5z38Ex+TiKzYXJ6sRx5BzBr/wBCGVHf43kNQTGxrVLg/bSNEjjvlMuhw0rCuiVDJXrb1O88NfUly4iW7Vd+Tio8cDfAWSzWa5dv8FrH9yk0tCo5RZ598YCJ4cCWJq5NbvNFEC9pTGbLpNMzHA2XyPiszNzhxrzhxnPzc5sKblQkwo3m0aPvZSSTy5+zvs3UkhpYKpnkVIihECtZ9CLOq1Cfs1fu5BiYizEwnzivu71RiLP5/MFatlF5EqNRzu/xH98w8zTx/rW7Ju5Ba7+/IeMuAU22zXypWbX2YCCwUggtC0FcP1Gk2y5jcNqYSFh6bHRh36X5Ocfrn3t68gjKGQuZkibTm7pcz8pIIBY3IGY/C74v0t+GnI+yN2mXSHAcmFr+goBxWm5JgpzWKiYnXCrtMTNCylKV1KY+8aYLx5h9fvwfp19K5egdPEHxAtD1N11xK1pElUDw3gK1fYlBXQgIxAIEG/YadvsmK0KZt3A6utjrm7i4VD/lhTAUr7K2/EWumGjIr18loWBtsKZ8Yn7TmHsNw7MtWkbRo8SqkkxcfRoL2Xz9rUlEk0HVocTiUKr2cBfNPO1hzp/GM496eODSzHqihenw0JI7eeJh07e972+PdvE7LNi69prDUlOuBgaHl6zz8y+ybBbMOw14Rc3+YH6GBZ/CA0LJiVLqP8arz51jlzKIBpV1m7fR0YN3rw6x9V8C7epgWZI+pR+RqN90CUL0S5WyDlnN8oaeOahsoj9+Ami0ci9pYDuAntPuzAuxTdIOwz7FjH1jVMdVQ8F/bIbHGy9ReXWG0TaEcK1JCJuoyXOEY3+Mt0yH3d39hLH9X+H25nH0FtrlJ9h6ARKBbzHfnNLH19SQPs80oU6+WoTtpB2uLVQ6KEMpIT3b6bRdYlhsKbQuVCsM5epHBrqZnVkKw1+8tk8P7yQ4CcXkyyXGnvy8+SEH7OibKA5TIrKQ2N+xBZzfZ4soHdRRro0iC2XSRdqANhMJs6MBviNF47w6iNDRLyOQ3FOTd2g+/lfb/co0Ix1e1nJrPwWnEaNJ6rvcqZ+gaPaTR4dtvDbL6i4bZ8AcwhRADoXv38jydRiCUNKNM3A0CVzmRIXZ5e61gP11DWQek8WDoZOKzNz7ymgu8Ceh17F7HAhTBZAoKhWFKsD32O/uu9z3SssmwWql14Hrc2q4IvUmmiFBRqpz3ft53ZYNosYlRIbnuOV+VqLt27r46DGA38HMJ9KU8+maDVXFSnX6Y5mNd2j1rlcqm+rxPlZPMt4n3uD/f3EFy5fY7YeQ9egnl+k2mzy+U2VqL2+az82KfFSoFiUgKCVTxHu89KnWpmentlgr88XqC3nqed7KaOPr3RaYx5ES8g7wS7NRSFX7Vm7x2oiteBYsy+1PSTKmZUrJYtlnRFuYlMtFCzPk0zMAgVisTkM4yyKcpqpKR+Xc3Xamo1GvhPrkFLQyDt4/3yFgOjKwlkKkCitf+1by8JRFNKaf8csnL3ge6MGGiAb+T52dwy9kiWjDBI8PUB92XbfKZ3d4huXSoTKUaTe2tTKcRDzJwt4zpzZl7NPLVrxFkZA6j0tIxWnm3q897ovKaB7ME6MDWBxeVE3Szug0u/vbeUorDVMTm+PFIQhBQ7L7aUdDhIvVVVUhwchBaZmFfPKOher7T35OXdqkujYGLW6xtJigKNHOlIWlcpG+YW2PYwlL2k2N7bGtLn9hMMhHG3Tti0hdV3H4vKhmZwEnOY9yXPsBz4zOMp/+GgOvVXH4vQhUZBanW88c5qA07Jm733qm+SW/pSgw1gp3ReE3Bbsx57DYupf8Qml0g0iEYlhVAmHT6HWyiDNaE0Fi7OJIUFrKhi2OoHAulyA5hgndfVKj0yAECZMRyMHIgVxNzjSZ2F44gSGAfoshPvuj9TFneJwSCNoZDBa7R6JhrDTjcNTJhx278PZ2wkKJ83M7Ma2m5qb0dNjWAO9130pBXEPRr1aIeqBuWoJXaPTytHhwKqa8NiaPdIOhiHRayVaZivd7SFNiorH3Gt/P3E2u4xNKhvWKaVCOZ/hjfM3GI+49uwzl11m2be1lMXIqBOt2tsas1114BUeFjJbt4Q02xy88/kimYUEzngJqUNALR34mb086eC9YguL0sTptGIxmxDNCrlmt72gNfQYxeYieilNQSo0hp4C/zEysViXz+mVMv40y8sL2PTjFOoutJqdlk1HSoFWs+PCQi7XLRcQoBF+mWzpRwgB+dYgroaG69SLZIs+fPskcbBtS8hfcJxvDyJr4S3bOiZjC4jYv2WRZ3C7TiMUy12dvSv6qxRyPyTf8uBu6WBolM1nqHgepprrve5LKYh7MAKBAM+fO8vzVicX54rMxy2cPXWcM8N+UovJLYOG33yuyYW0xDAEqgLOYD9HB7xMutqHJngrpcQfmkX4QmzuGQCSRMPKfNLgtK9vX+f9DV+Y/+8NcIT6kUZH9vrXXn6c0ZALu828ZUvIjxI6LXsQs6+F2dPR918ugOoOMuR3HuiZ2a3mNbwbaYpQl40QYgsbAcxxwuPitfNtEAM4AnUkYFLg770wSLDDGnYF/EYZHfotakvXcM27Ofb4c6C6sccPoA7gFx7bGHzsJXJv/zmYzIy4l9ZbPLpXajMqKv7sFJ7Hv3eXZ+9GHvkNPFeWGAwOYHYPEsqFd/TxZRB4n8dqHUC/380rZwf46tlhnjwSwWrubH2roGHQbeP3XjrOV8/089BogO89PcHLpwa2tb9feHLI25OjvqHFog7nYxl0Q+7bvANBF996coxfemiYX3pogG8/OcZ4n7fHfvX/cr1JvtkCuXGNhjS4PJs/8DPbf2wABYaDS/z6k3lGQ0WCThMnh/y88vAIEZ+7y34NolrcOIaexNZ/EtWytc1hCwI/KNgaPkbkW/8jnlMvIyw2NgdqMTQa8c/AaN7VXKv/mzz9OIcf3lMfhIMaD/wdwN1IQZgBpZ6lnF2inD08Ac3VYW5kqedA3xSs7sbt4hLnr9ygz7t3aYft8GwsxuRk5+0yNxPDpIg1m81B4FvTMep1G6227FnjwryFeNA4FGe5PzhLWMkzOfw1YJTpaYWZO9CqL9drTE8LDGlFEcohCgI/SNhGsnKUci6CbPt6ArUIE9WpJrPz1j37v9Oz/zIIfA/Ggy4FsWzRWK612EwBreK2KoiOjhDxOg5kPZspIE03yMw0MGlsWJeqwMOnTzA6Gjw0Z3n3uEMHjY1dxTCcQJDuWoHb0QeVWp0P4jPkm20aVRuZWJNvnBlmcjJw3yggQ2/SWl6g31bDHJwEbIeAxlnHzfQtnLf+Fr1WIqg9wrD3ERTv6K79eMwqzcVkj2LnaF+R8DE3qu1g6bcvKaB9HneqBnq3uK1LqvUW+uqze4/mevpEX6eF5RYUEAgsQhDxufbkcz/w6v8mVeHFE/2YVGVtXaoisJtVHooGdu0PoIvJOkQUUDfu0EFCTCHEh0AcRWl12bAtlkjevrpArtFE1w00Q9LWdH74WYJyfWf5AL2aoZ68RC15HrT6Bpu7oUpamWtkXvtjihd/ROHt/4f0f/ojyp+/iUBfszFqOZrpz9GqmQOndGpz71G+8gZ6fgnZqNPOJVh+809pF+Z27cfz8CsIsxXBujyHophxPforrJY23w/67aDGA38HcNBqoFev3+QnF+IUGhqN/CKOi3EGA05CSvWezDs5CWdDBj+fTVNdoVda+RRmkwJSEHU0iM/NHsjet6KAOmuc5Fyf5MOFHPaWhX6/C4Umi8nErnzPLJZ4/7OrWLxTWE0mgqJ8z/dx9zjL9HQKWKeD1m3owQuLi+TTKhbNTqeGYEU7SSp8kK3jtju2vLZ8612a8UvES2NUbyaB82QD36U8Gb4rCmjqZp1M4h0wQiTKIyA7BW+JZJXm8p+hCJgrDJOxxxCqQjw/TOVaE89D32Qm4bit/7vFUurk3rpKvDCE1LXO2spRQJB+6zMy3nVKZ+pmk8ZijHZhkXh+EL3Zj2pdkWKYHKF97B+z9MEcStuFyeYlG3iGjBbZfRvILymgwzu2aglpc3n54Gaa68kGt1p5TgwFCIZCd51zruk615Y1zB4HZmcnR97s9JFqCFpGjWfvUa57IBDAbdKZmJigUmtx4eoNxiYmGPTaiMfnDjTffrs6gEDAwEJ7rfXj7Ky6K3+5tpUrWR2sLsxOH5oULOTzFEtVxkaHDryeYPcYSqXLhMMedF1QKgUJhRwYhtKT/x3wlUmc/xGq/RksztZaDcFqPYHZaG6Zc56Px3EtvInTolO2WAlYFzq55qkfEX76N5DSunEefx2jWSEU8hEKmXfMZ/cyQ9C+CPoWLQ3VBBhQVCBoT4EBZasJbzuJdbFOOPzb9zyvP+gugjVNyapuqqnIoOgFCH+bUAj0dgXL/I+x2a9jaWs4y2OonybxPPfbhMPjHZ+BMHUjzMTEuU71/+z9qcH4sg7gHozNLSEXFpf42Y0sjbakVSlQtlj5tFjA0ljGd5etHBPZCvViHqla2Jybn8ynmF9IYbWo9zzv3WrUcMg6hUL9wGsUtqoDuBt8vZCn3tQ79Rs2K0ca12jm56l/dJ5b51XS9pH7Xo+xiqWULC2lcLndKEKs1QpIWSSdfhaPZxTobf1oLsSgJNDqDlpVwWoNQcumY0JgMTtZ3qI+IHkziyYDgNyQ056th1m4sUBeG+/Mb2jEL1yl+fGHCCGJl6KYC304oo9un8+eVaEa7vG9I67UEbfyFEYb7Ka1pJ0ltHyS+XQQl6MfxWTfda692+kgWw9uuR5VDZFfsa/e/IzlvBW71ulXkGsEcZYr5H72Drn+MTwecdu5DqoG48s6gHswNgeBr8bzmBUrQu/wrqtByUZB4gkN4HN0un/dSRAwJzNY/f1bBmNVRRLqH8Tvst6x/y8C3qoO4G7w9RsVHM7OWT5jnWVEybKgKYx4AKkhK3P0WRpY+4/d131LR4C3r6VIZxVmlDpnR4OMT0wQjY4gZQ0p3yUadSClA1A2BP/8+Wl8jhskTMOU/G40LIDEGSjiUQVPTRzZMnBYDC0SacXXApdrbSlNZkaHhwmoHdvipb+mT88wvNKG0tBbBJbfwzWpweQTW/rWKn6c15JgGD0B0h2xMBEY13YOehoGpcuv4771887dYnkEx6UF/C/8Q5ic3GXA1Ia/FEFemN8grDcSyOB5+kXS7Y59+urPqLrdvetULXgH80SjgX0PUH8ZBD5EY3MQuFitYxiSzcFSRQgK1SaZUoNryTwX57KUG+0N194Oh5xWlG2CsRLwOqy78vMg4P3y57V2hMdMQmNEm0NFXxNrA8BoU/n8Z/d1r5linZ98Nk+53uGidV1yaTbHxZnsik0LRbmKonyIEDPA4obgsCk4gjDbONK4yaO184T0FG7KnGze5NtnvajK1r0HrANH1oTZNgiOGQbm4HjnJ9mmMXVxQxtKgQJ6i+qVtzb468YmVwTr8Nle37fBisuNYnbuGOisL1yklZkFvQW6BnobtAaF9/4cpL7rgKnnoW9hHzqBUM0I1YLJbMf52DexDZxdt1fMW65TGAZC2bn/wd3i7X9vIEQJIXIIUVj5l0OIEqviggc1Hvg7gM1B4FohRUPU0QyxMV++sMTHn11jIVejkl3AmczxFgr9lt0Hbw0pkZXMWjB21T9SwS9KJJPxXfn5IuP9FoMb9UbILC3TyCVJWnVUqZEor2ahSBJlHWUpQzUev2/7fv+TKzQdfjbXYGQLS9wYDWAxKWv2Uk4Ti/UDX2U1OCzHHiJfv06y7GeEOSaJkSgb+EYcZGvH6GpDsSFwmCwfocWTtKoLHZ1+oXQoOM83sSacpNNgtBrk8n0kyqNr160J0FXNFM1b+56ehomJ71NvHiGxsMxqZ5+efPlurJjxHPkmyWmxrU+A4vkE8cIgUm9vXI9qJnc5ByK87bUbsZmU+SWcjzzV2ee8i6wwke0K3tYdL5Mor78eOzn+ApMnRG7eh2Lbyf+dBoENUqkmoAMGU1OdL5zACtaQ8haxWBnD6HwpjMWauFxOarUzBAIeDmo88B8AmymgZ5sa7yRbtNuwStEIoaLaTBQUN2afG6thYPV3qJuFwhL9wyNYlFVN953pgO++LJmrWrixVAIkvsggzxwN4zIqh4Ki+aJRQNFolJGRIj/+uWDAmMdigKSj14+USFTGx47hGx3dl7nuBKveWRzuXkmOtiqI9A/hWaMVR5BSIMTsploBKyND38H85gUirSwIFfuYj9MvP45cSfHdmjZQiEZ/hUb6OtZPU0QHR7BFn2Ah39dFQzhxXs9jSL2HBlEjUWpjO1ESAhl9AseYZDiUBVXF8d51goWPAQGKibExAykUbE0HJ5/7NoqrH2t8Z5ojMzNPM9e7HmGy4x2p3wF1YkNKG6p9C5vhR2hlf0SfTCEBVDOjkQKBF7/HfO7OaJrOt/Q6Y2M6UhqAwdjYar2HQTDYxuWaY3S0AFQxjDyjo36AFewDSsAiY2P9K6+nFGNjR3A4TnGQ44H/AOimgHRdx2E18WtPDPKzqylm8qAoKsf6PeRSZTK6BNbz1eUKNRRfrnAk4t7gZztsMil8/ZERXtZ14nEH4+Pj6LpOPF657bX7jWv1FqV6J5Nk9TvZQcy73/6iYQ+vPj5GUHqoXPhrBPWOjruioKoCz8Mvrz3fB3nGpWobhEHAZSW/8nrpfu0gwWVf/4q9fu1qrYABHEFR+pAWC+6TL9I//hV0HepxUMywMhWKsh0WWMMncZ84iXd85fF8Nw2h4Hz4FZTXP+2c2SoNYjLjOfNL1Co7+V7FArM7hK6Dc/IZ+kYepV0pUc+4iRy1o+tQi4PZu5v1gmP8UZSZSxvXAwhFYPYN7GI9e8DShPexv0fId5ZGPokn20/fY1FQQORzCNE5KCGMLqwjRBshdKCJENZO74gVDHUM4yZSVpGyjGEsAYNIaaxgN0KkUJQQhqGhKHFUtfN3oBsLsYii2LpwhC8poH0eW9UBTALnBgTequTIpA0h2kxfTlITHowtpBQWE3EsDfehoVluh2/evMX7N5ZYzNWpFxZ548oCDw8HMWqZPfk5DBTQBjw5SSP6MguZn6O0JWZ3P1l7mGS2CtnqgZ1xrlzn796/iNkbQUiFVmkJszeCpq/TiiZFxU+BZGIn2m+O6enXMYyvIMQk09NDgBcw7QMN0Z2L/iRZ/wiK/i40iizK0/iORGlUBm7rr5Gb4dr7cTLWqyhmCynLC0h5DCEixOZAmPa+Rmk8wYIGVARSb5Moj4Fixv3I15iPme+6taSUBtPTmyiYI34QfhIFiTVeA6pMT9/CWPl7G4s1u+iYElLOYBgZYrFFpBxEStmFdWKxKYToQwjJzMw0ijIJwMzMNNVqZOU9MLmyrt29tjweD7VajcABFgI88B8AW9UBrOK+SIRwOIxhGBwfH+FyXqXdlht6ABjtOg8di6IKDkFu+e5wsiKoWswIhxnRqCCsbq5kdU55XfuS314uC0KhSXRdEol4CAQ69Fok4iEcdgBDGx7bGQvC4X6CwSEMQ9DXd4xg0IthyBXsxjBK6/b+SdrWU4yNOZGyDbOzB3rGLp+fv7lSQDe7sNm9GFKg1yt4PAF8DhtxrU4wGObRiTBKNbMLn3H6+uJo2gLlsplw+Hl0PUi5bCYU8mAYpn3JRR84MsDExK9jGGCahYGJ2/vwqjHk9T/HxwBBe6aT71/4EGdmBtuJV3e8NhAwSBcKSIuCP+BGoHbZmIg+9TQjPj+tpWmM3DDHn3gGLAFasx0bKbWVc1JWfBobcCQChtGiVGoQDluQEkqlFuGwBcOoUyrNEAyWMIwK4XCeUCiIrhtr2DBaVCpzhMNhdF2nUskQDvev4BShkAvDUKhU2oTDKp3+GKtYUKnYCIeDe6qFuR0Oh8PYDrgQYF8+AIQQrwL/K6AC/0pK+cebfm8F/g3wOJAFflNKObsfc99ubK4D2C6XW6uVGHX6mF4sYdTK6A4HQiqMuSXFfG7Haw8TrjfbLC6msQSgU4tQoWWTICWX0xoTw/pKrr4Zt3vvGOyk08dxuSYRwk463cLlsiAEpNMtpFTIZIwNj+2EwcLysg+vN4xhmEmlJG63wDAglZI4nRpCTJFOl9euTaVKOJ0xFCVHJmPC4ykA2oGc8VJF0q7ke3oiNCoFhiNhIkM2Jie9QIvp5WW8t60tSeHxTK/gGdzuOaSMkEqdwuU6gxAeMhk7Hs9KxtEB9gMwpi7Trvo25Nrn6gHmLscJBhtkMrYtr52arfLWjSS6FNSyTi4vpnjiSIRm3r9ms7ws8XpHoG+QclmSqwigRDotV3Lzq2Qyt3C7V/03cbuta9jlMhDiAul0DLe78w09nV5cwTqZzBQeT+cb+vLyNF7v5Mq86ziVmsbl6uB0ehrMI+QrTZYXErhcJ++4puVOr8tkMjQOuBDgrj8AhBAq8L8DrwBJ4GMhxGtSys+7zP4RkJdSHhFCfB/4F8Bv3u3cuxl7FYPLlxp8fPk6A6NRxsMusqmFAw4sjmMYLuoLFRy3PkC2W/SbJxkZOI4w2xFiiNHRzl62wsulOs6wRPW6Oznh4ij2wOMIAqg1J+PjRwCQssn4uHXXeH7MqUIAACAASURBVKlQ5OP5JZarTbSiB+94kMeinTfYquCZEAbBIHi9bHhsZ6wghMroqH0Fw+goKzYwPm5gGCcRQuu6VicaPQfoSFkiGp1GyiXAQzQaREr9nj0/iaYdc8m8obk8SGy+PryhMJ6g6y7nCqzgjxkbKyFlP3BkZV+mFZt7m4u+iu2XL2N4Kj35/gOeNBfmP0NTo4T6fDistrVrm+02sx8msfoqKNQx6xYs3iZXshmeHznO2Jh5xX+d0dEYkN4iSOpfeW6nGRtbfd+mNgRMx8cjGIYVIexEo50iPEUpdOEI0ejors/+k+k0txIaAjO1up1aXONbj48yeQfP4d0kQjgOuBBgP+4AngSmpJQxACHEXwDfAbo/AL4D/M8r+C+B/00IIeTqzu/h2BgEloAH8AGiQyHQqaRcxT635NiQwthI58W2LGWPzUasYhh+pOzwdoYRQEr/HWMhhil9Nkfl+in8bgeGhFp5lOwbaSJf/0MMQ10LtBqG0YP9bgMpnkKh1knUwIGCREqVoKcfIQbQ9c4f107D+9vjpXyJ965Xsfn9GBKaup3zsQbNdpkh6yhCgFbPUYtNY11YoK6cgJEJJOa1P+SGwba48/xsHcgDBSk9W17bWeMAQowgRA0pZ5ByHiGWEUK9J4HfYb+dz+MbEwWEAEUI+rx26oXqPsyloShLKIodXU8g5ccI8XUguPKatiOlB1B2DIDudK63xwaq241eTwFloIIUAigjqTBfr1IvTPEX79f53tNHAIkQVmYyeYRIAwXgMlLOA14MQyWR+RvOnDixFixVFBeG0dohSDqPoli2DJgKYWM1YLqbc5USZtIlLkynSWsOjg9612yuJ3MkMlWsfieGlGiGQanZ5seXFnhyUBxoIsRBj/34ABgCEl0/J4GntrORUmpCiCKdV/PyPsy/48hmi8RiDWq1AkI4mJ4eRMphhLATi40CHVrhTjH4mZkZRFU72iozMxJVFXeMadbIfXqLRPEE6B0x+U6e9ALZD+ZZaD2ykpEAsRhreHrawGiWEKqdMJOkiyk0DRp5J4IKqqpg9/iYmVm1Xz+j2+F3rhWoZB3o0qBbqOyj5QanAxqt/BKlT39AvDBEwJol15qhNvMZvse/xUzcvut57gwrgAspHcRiNqBTaRuLXUXKBKDtaxB4fEKiVjO08inWa0hSuLwOankbsa6k/f2aNxabATJI6WJmxkOnGf0ppqedgBXo7Rlwe0EyA6gxNaWzMUd9tbdDm+HAcUpTF0iW7UARAcyVncyZ3VS0G9TzS+j6Ej98RyGkakg5SHKxSHU5hqa5kbJMszCPIjp3SvO5IrOzNYANgdN7nxQxxd99lqDS0CgvL5DIlHlXKAzbO7TWOxcTlLMLaFKnu5ajmVO5Umrved47TYT4ogaBxRaPbf5mvxubjqEQvw/8PsDo6OhWJnsaweApJib+CX19FlgpfB4ft2IYyi7oidtjwzAhhIPRUWXtm2k3hbGOJaOj4jY2EDHN4PAvYxibyuzd85gxYZ98ZMNt+rA3Tu7yj/DOGjjyCUDwkO0FvE8+xvnZHEvJBieO2nhsoo/Csv2OKIGfzZWw+U2dVodS0sl1r2NSFPqHWnhn/g1uZwlDbxKy53DUqwya09g1D8rkq3dFQ+weKwjhYmxsHCGGgBOMjd3CMG6t2OwfHfQ7IyO8/p6LAm6EEATHAnzt6Yc79wNbto3cDxxASoGiLNKRlKghZYCxsTGktADGCv3ReUuHQgZu9/a56mNjGoZxEcNIbkG/gGHkGBsz0xgcRfnp3zKopmkpFqYsx8j2ncHGNFLOYw+4qJngyGSHivEGLFyKT2HzW5HSDkLHEehDVUw8FOk/UDrVE+zn03iOW2UTwhXAbBXY9PUan1QpzWg0ij2uYau1eiRcVMXEyJDyJQV0m5EERrp+HgYWtrFJCiFMdHLdcls5k1L+CfAnAOfOndsHisgGjK9RBuvzrP4BkUitiZRWhBA7UhXd2GjVqC/FOnlw8sjKrVzHd/ctdXt5mvyFvyITt2ILprEdOQfuV1EU85b2JquLTna0sjFPWiiYXL4N9vXkJZY/+fdI3QA5jJCdZuTNVAzXrQV+9dnfI94H4yv54QXujBIIua2khb6+HtH5RJcSrHoV2ayzocweBfQ2jbnLcOLV/c3r3hF3PgQ61JAPIcIIYcYwTICLzgncPR1kkpJTo/6uGo84JvWg6g9KKEoBXc/TkQ+YxDAkhlFCiK8jhJ9OvrqGYUyxfa56P1BAUWa3pF9WsS3sJfjs84SGR/jTn96kqi3gFHkMqSBECUU4sFosa2v0ua2MRzxkDJW21nm/mFSVaNBJxKsfWJ1GqlDnx1MzaBrUWjqde9aNdRptKclVGhwZdLO4IDZQegKBWRF47JYvKaDbjI+Bo0KIcWAe+D7w25tsXgP+S+B94NeBNw6C/wfIZmH6Zp3iUgHFYmFuIbj2u2sfx8n+/A2MRpVEZZLqwwEcY08Qi+1cyl6fv8q1D+cY9c8DknhpjtbLD2HxbMyrvnklRyb1UzCs6+Xu+XkWuQLi0S39y4lJ8vVJEmX/2uOrZfY+ywvEV+yNVo1rHyUZcQ+u26zZj8D1BQLBArMLvh33shs8YB7gYr4IsEYBqapgIuJjLmHGnx8Bo02iHKXaspNrRgBQNT/5e0L77AYrgB8pn2BmZgIhFilU3+CDTz/FcilByOvE0ljusr+3NMS9wyUAZmZmECKDlCqx2CKlkpt0emHbXPU7oV98FMjn1qmvRn4JVRWc9ISYnu7UmGi6gam2zIk+K/O5CvlmjlP+CEM+jenp/afHtsJSSt766CKKO8xmeY7N8i8LyQQ+k4pRStFUoK1BK59CVQWPHu0nFpvuolm/pIB6xgqn/4fAj+mkgf5rKeVVIcQfAZ9IKV8D/m/g3wohpuh88//+3c6726Ff/QvU62UsngxSN7AYjxB49AVahQT25Qv4PXNghUrTjiP1CXZ/mXD4a9vmN/tt88i5/4zfOkDA0tFfL5tMWK6fx/+N/4FweF2D3V27vLWmevkSAfcYmP1baIIL/K/8EtUfvUvInAVM1GSB8ZefxxIKU9c79o3MLEHH8iYt9HUcdi/jcRephn13rcEeCLh4tqSz0CiTrjZQDDNPnfFyZijC3JzAqzXRS7nOvPYlJBBy53GcfQSTfm/04HeHFaT0U6nYaJoc/HwuRUU3sFKhUmrTKqR5/HEfNotyKOo37haHw9a1fPW+PjOwfa76neBXvT7+Q6VK0+YHVIx2nUeOTfDYuJ+5OUg1THw0laGW03HQYjgQ4GSfg6NHxjvrKpcP5DwaLQ1pca/V8nTX9XRjOy3GhweQUvLKk6cwnJ2iwlpe5eUnHsJhUZlVtT2v4ReuDkBK+QPgB5se++dduAF8bz/m2ssoX3uHmR/9BbnGP0Cho76YKcP0Gz9Fthrk6gGcpk53qXwzgqtcR3w6TWHkK3g8asd+c3714hSNmq9Hh9xd1anfmiNTOrZmv5QBkyW07n81l7rVx1KyjsXr3yYPO0Rr6DsYgy9iaBrtVICKokJu3aaRd5JtRHCo5R7/nb00KV+6xXzVwO3sR6i711rfChsNL89NdjInpqdhyAH5fMfGMfF9iuf/inx7EKGYyLfCBG19YH+KzMz+6azfGVZIpRzcuLlIo3UWrXYGk/U6upylXkrz1oVpnjwWvO/1G/uN97svwyqe8AkiQ24aLZ2s2cyED/L5PDem4yTay2gaNMsFFLOVmXKJheYyft/d9dnYK27rBnq1RMtqY3NfDq1WwnA6AYHH3GIxlcZiUslll5n0eQn2m5muajQqRRoHfPZfyDqAwzyWX/+XuNUlhtxxwo70eh6zSCOtKpo7uLWe+ViDaNS5ZcDRm1qgUUgg5aYgrSeLsy+PPSwJK59QvfEuwzbHBq3yNb12YWLyxNMIc6//jdiPlGDeIp+7YW9Qck4z7O3138FLUJ6jUY3huL5M8OU/APbeXHx3OIxx4ndwXZjB73BTMiaZPNvfiZko9zLwuztcazaZklXsug44cAT7QAYRHKdiusboqLJif//F9A6zKN9mHPfY1/Bbl5NYHT4smwTxGgVJZGgYm3qwZzx5ZJm84lqJDa2IPioqfoeJtiOIIlRyOXhzpsmrDw0xOTl5KM7+oIPABx91OMDRLqY7QUm5moa0ooUuFBSnc2s9c7MVRbXTHY/pxtbhkwiTredaZBt78CiVW+9R/uQ19FyqR4NdAEK1YBs8hsnm29L/brBeXqT483/XpYvfpc2uqB0s9c5v9TayVqF06Ud3NNdusVAsWCPHcYw9h8nTf8/muRNsUlUkErdRYridYKK5gMmwgDKB1fIUQpzCMI4AflbfEvejr8C9wAc1T71lbNkHQxGCRkM78H0/NhlmyOtEVQRmVUFRVCYjLkoNDSRoeiffXzckP/xsgWbbOFRnf1Djgb4DcJ96ieLF90lWRmlonYrWRDmKUM04R18m8fmtNdvVQKvr2DMsxJRtBankxFlKcoZEZT2/PVGZwD7xGNWEmZuXC9Td/es+u/wLixX7yGlSxpN3lI+/isufX6ORj/TqsQsToG2p2S4qFYrVjX7uBb7Thtj3EsdnTUzmlnCXpkmWo/hbeXxk+LD5VfrHTzI7K9YCxVK+e09y+Q8a31NRvi2waCxTa7XQtxBTzGcWKWXFgZ5BYi7GQ5OTjLsVbjTqnI6amV7KUM8uYUhjQ0DYpKhcKFewmpV9WcMvVBD4MI/wK/8NgR++x7A3Sci21KFfFDMnXjyJY+IoqjDjz91EK2VRHW6OvfAIjuGz2OM70QoqcuRbWD+5RaS1CBYLXsspjj4apZG+Ts4/z7Czl5YxeQd45Hv/NVKCc0f/t8fu2A1a7l4aaiSQXUlp7W3VJywOQpP3nnbZDwmCzVhvlihd+RjbhXcAQdjyPCODTyBMnl35aeXieIrvIT0tVAxGPJ2zUXmbc4//Q4RqRQgbY2MROvWJDqLR1Urxw0HpHEYKqBu/0NB4b16jpRmsUi6KYuL4kSAT42O39RMaGMaism/r6cZuu5VoNEqOFJZkviffX1FUwp7WoTj7L2IdwKEdJneIiX/8Z9j/p6uozfMImxuP+xXcR8fQdbAEx4ic+yfoOjTj4I7uXmPc1n8Sb7iPViWNmnOjKKBaPQgpe3TOpVAx2Vz7lvduDg7Sys731ApgtFEj44iStnENiglb9CyNu5x3t1i2KlSuvk1t8SaF8iQDjmOYQ8fvyJ+QGtk3/y+aS27wNJBS0MzdJPvGeUKv/FPAdFs/zeTNTvvB7ucEgapAc3kaa+QM4KTT9MqFEGFgik5WcwkhVKTUVnwebE+Hw9SXYSfstJn4/jNjfDKX4Xo1y6DPzWPjPvRafsdr55YK/ODjWYSvjjQgoJQYHh5do+/2c52TIRcfKUpPvj8SBr2OQ3H2Bz0e6A8AgGIrRMbyEqboSwAkp8F6F/QLgDTaXHnjMzJcQKgQzw9RvSlwnfkayeYppCFB6l2t7kzk7I/hvsN5b16vUL5xnnZ2nmRlgmMPH6dSSXXaAK6MRGUC6+BxnH3nSF79FFExd9ZQGUP19eFzvcrMAdAupVyZ6Xc/ZNieBGkmURa08z/DdazBQuvhPftrpqcpL/pIlAZBdrRfEuURxMIi2fMxFsrHbutnNu2nIUdWru2ix6qjuOJmrI1uexOxWJiOTs0QsVgFSCPlIrHY/aN09ooPmgJaxeN2MLwtJvskei2/o32u3ORnVxcoZxewaxpSKuTzS/z7tsFzpwb2fW1SSux6nnpRrsikLKEqcHIwwOJ8DofNvC9zfUkBHaLh9UI4vL955pVrr+NuZQh6Fzt1AFYT7to8tnSb8adfJpCeQsvOU9PchH0lHI/+CuqShptrmLwjhMOuXc+l1XJY4z/A6Z4GHYrtBtaZBXyPPEPzOoSVIsJsxhgeZPy5M0hUJp5/iWHvAFo1i5qLcuSRIaQUVOr3PvfeXXgfv7rYqX9YrU+wLSISf0vo1DFCIfue/Plac5jNi5Stpg11DgHrAg7TLO3wsdv6GZwMEkwVMNrahnqJatvNwPEXQGy+ViEYdAJmqtVfIRyuoevnqVQGCAQcSJknHD4cuf/7nYt+kDhRU8DhwVSvbMjTz0sLNuf2fTzuBj9zZhJXeJCppTK5JZXnHj+Nz25hdla972f/ha0DOMyjWOzkhK+0A7ht3rjeKLC0YMLtdq7k8/baZK8kyTUGcJq7cvArdcS1FMWoB+/x38NoFGhOgRZtk3z/h8xmQmjX44BOyvw8Hs/Du1pP+dpnZCs+HKKrnqBcR1ycpj78u3D0a0igMg3ZFXGN5WUFr/cEOCG31KmG3s1c+4GLiTq5RhCnubK+3lodYTKTi1fw+uy39eF269RiHzF1uUjTOQ0i1Ft30dRpNkfJ5G+/rrw+htl5jlZmtuOn3kIIaIRfJrcibLf1tRZSqT5cLgMpPaTTD+FydQLF6fTyocj3P+g6gP3Ei1UL7Uq7J09fr7lILi5RvAfzLq/0aDgeUJjOaxj1Crn64Tj7L+sA7sEIBGBiYr1DEqwEFivLlK5ewn71A4TFxoDlK7imf047n8FfGMZJAe8TvwmTYz2BRatrFl1r9NYQSEFoYlUwzodqk9iv/TFOSxHNPcTwSvBRlt7Gs3AF39P/xZrP7YKY6c8/pO529gZ1VTu+gQLRaOSeBnX3is1GEWu7uMXZgKn5DsOhF1Ac4R19+DL/EVvhCsPO0LqfDYFtGIkUCT0xjj25m3UJotFv0MxMYfssTXTYgm3oUeZz4V3uryMr8UUKFB90EPhOsLlu40o8R3fdAEisvj5OHT9CuqvO4H6uc3hklFpLw5APXhD4ga4DgHW55O74itHIkf3p/0lz8TqyWkLPZajefB8tuwi6BlLDKOXJ/+zPkM1iT265GhzcuoYg0NepMVixbxUWkbXaBptV3Jq/ilaa71nbZiysW9crYGgoJsuO1+4X1msZmulb6KXEbe2tR59EUS2960XQLi6RfeNfglbf1odsVWnMfQZaa+t9///svVmQZNd55/c79+a+r7VXZS29LwC60dhBEgRIDkFxkUitIWkmRpJl+cV+cNijsV88b7Ij7AjbLx6NxuGxRzMhKUYLKW4gQQIkCDTRaAC9AL3VmlmVVZX7vt57jx+ytqzMqq69G5g+EYj6I/s73/nuye7Ozv//WxQVS3CI4Ev/JUKx7uI5BJbQcVzHX8Bz5kuorvCu7qD1l5MTIXzAKYT4HeASMAL4aM1FWrXvntutS7nWAvfTVgewF3xhNIRJVdvqBkyKwrmhIDaz+sDjlBLen07xFz+5w1/+fIrvXJnh9mJux34e1QE8BCudbvXNX5kJ38pVv/sRtVSAWGFk7Z95XXPnFZXE+0sI63oLBIBG+NeJXfugVeYqjbUaAu+prxHbID7OTAsC2SGQ3XLzBdkP48T1wbXXu4mYVc+XiZUm2/cKFUugn9SCD8W29d794slJncLN12ksTxEtjlG4EcXk9pH2fQ2wd93b03OKhF1BFujy3CMIYSJzZZp47WzXM+/drREoRpBao+POFJeHwDO/TXrGzHzq8J77/rhdKJ6ZKWIYMyhKoavIlylWeePyNaRrEoGKXU/zimZgMSmfOhF4t/i5QfjZQga9oCKAem6Jd68bfHzbgp/8A43t6kyS27fvYfH30KobWOT7b0myx3qpZpe23PtIBH6IVjcKyDX1AU13nG758pux1btIJHJyEzXQi6o+TbBappFdwNZ0cPrFX0Vx9mDZkONvaB7sH0TB0Dv9+5ZwDp/Hrt6Hwhg5jiznCaTfAaGCYmJ8QsH73MvMLx4upVOZ/ZBA/S2kq4FhaAy5W60ylJKDSOSrXfe26gBOEKxFKd34acdzD7oXsNuD2AfPdj3T0Gw46lGk1ty0VzI27MU3an4o2kuAwsiIEyHMCPEKo6M3MYy7KzYb6IDwID+4N4XmDONcoTkqOcmtjMLXLo102B8kBWR3OFFcQVRDMjomHwo6Zas8fc3m50c34qhNHauvl4ZUiGYbXLD5Ge9xH3lsQ0PDfO9eFbOvt52e8vZwO2vifLj/EQX0SVjdKCDF6gehdKcYNrZtMNsweXu7UgOqI4D/0rcIvfxf4z79MmZPT4eNYnLiOvNid/+KwDFwtjO2Ltgxeoneb/xLfJ/5ZwSe/w1Cn/9jFLN7R3v3g+sLH3Xkz2PoNDMx0Kvb7jX7BxGqueu9Viev0sjOdd2nmJzYxy8gTNa2vYpqxnPuc4f2rHvFhmEBRhDiaYTwrLy+bnQ9msHQZRvNYUjJUqFKpljvsD8oHMtU+PaVOX5wbZ5/vL7Id96bZTlb3pWPo8Tv3FpemeOw4Z6A164vUKlrO/ZzULjakCi0x7OKK02Dn3+8zDv3ktv6eUQBPQSrGwU0HPoChVKFWHGdfmlRKwKkXGuroDqcZEoT2GfW966uHeOJVymHvMQWMiBav4lixVHcj71CbTmwC592YJTZOKj2re2NZpXpmVX7/VEeM6leNEdx/X5WVqw4TGC6iWKxd+wtFmExmiNfn6KRCXel1gBib9zC7BtBCNHhY3z8V6kGBonN50AITJ4Q6dDnmc+FIfegaJ/tsIKUJqRMMLOh2GJqaorFmp1SvtbRk95sUrgzZay8vm5/EPjmrbvcTNRplAroa2MOF/mPr7/Lr1yIEJubPfAz94MnJydZSsJWvfu//3aJAMUDO7ehGRQqdZYWolvaGIakll2imuk+S6CWXeTyBwZufYhEfL5t7yMK6CFa3eoABsb78bmep/z6NULuLBhQcxwjcvIY1fkbVEWFkfOD2E58HlPMcgD99J9HC5QZdN7FAMzlEwwddx5oDn4ukUG9/bcY6ThqbhjV0HFf+MZKLvXefPb32wg2O+cNVNUewv1upOzcG/ZmyMx/F7drujVnwWrfcmZBwLGIsA90+ggrGMFn0IIt+s4wQJ3tHmM+V8OZu0w1eh1LLoKrvxfz4NOEw8qh1zxsxP39UK+7KJXac9dNeMkYmY6e9CaHh4mhfhIm/cBz3euKDbPLgWHQ0Qe/rjgOJL9+PlPidlLnTiNL0GGj1+7Zs8+enh48mkGlrnft3Z+smzk5ZDuQu3n/boxbd7KASimlUbJVeOXxgY476ekJ8+ITZn58ubL1XAGXj0qX+3xUB/AQrS3rANTzNMfPoA/kEIqFxoKbSggIfYnmFFR7oZrvkqPu0qgtfMz0zTza7SXs/cdJ1M/i8Zjb/W/CqYwTr781BSwVA2+q02YnOJGQ2PQFtNwCC8kwLnsvCJWZy1doOhogg2TrYZbmoyQT3ybT97t4PLY9nVV0X8CcmAStsZI/X0coClX/50inRde95dkp0uUADqU1qapjTsEGnFhWMHv2XnsgDY2Zd67QsNwFQyVZVLG9cx1rKEvS9eqh1zy01y6AlKWO3O7BYQdGNY++IdddVosETU1qlQILi0ssFnSqmqSZX8Ltdu87hz+TTlGvm9Aqxbb8+nrRQWLZBJX95bln64L3p1MU00nsUqGUVZjMLeF0uQh77HvyOeYN8eFMvqMmoGGzo5pMJJP6vvP045kytyZja6Juo5RjYWmRfyxnGbHXOuxDFhh3S5KNMuWG3hFbs+ykkjVTKj+qA3ho11Z1AC2sEokEkRJU206EP4kn+v9iS84xYArRzwIkblJnjkjk60gpttl7AFjXyL//Azz5d5GGRrk4iuNGHNvJZxhyxRhyRddFZk+rVsDhmSESOb3Hcz0Mv/wtStPvYPq4wdhYL/aJF4lne7bca773Eaq7eF9xHcXEsfNf2dd9VOIfMWi9xZBret2/ewG0eRzhi0QivUcmDo+NKWhaL4rS2Ve+r2+I7719jaLqxkDQUAWKI0y8YefjrBmHYkXXoVG1Y0sqvHpxaG3vbkXEu/E8JdWPI+imYc1vanzWz5Nnx8llPPsSSN+YqWP29mLTjTX/FSRzRQtPntu7sJ3SppjfVBPgCPTitFjIGTl8oQE8DtOe/X+YmOsQdW2BXqqKSv+QeWtRNzTIv39nci2e9dj6eObCcRKL7fe5KsAHe4awW8SuYnwkAh/w6ioC7xE3sjEaqdn2HHWtgZ5fppG8s2//98OV+Ss0cwstYdaQYDRBq1G780vQO2cPSL2BUemsY9gNNtl8uE++iu/Jb+B78puY3D3b23tCCGHaVlwHBfe5lzcJYLuPsZmY6vrcQkIzv3ikgjBs/L3WLux5XBaeOhZGUUTrL3rDIF2p8f50ptWa2JCAQJcG8UKFe0u5rn7uh+PpEj/9aBHNaPXm3yhgmpVWfr3HZdmT71Ws6ZJKU2dz338hIF1t7MnnKv76pRE8NjMmVUUR6/5LdZ2ZRJG/ujxFslBds9elpN40EKsXfx//5brRVdRVhaDe1Lfc63Vb+MLZvpXZAipmk4JJUXj1seGOegUp4cpUhm9fmeU/vH2Pf/vGJHcXco9E4Ae1uonAq2u3+N6tCj2VvnWheGXFigNYr2dxHTtcwTF3ZZ5obhCpayvntmIQitK9jkE1kzKN4NiPiL1LHPK8SKx4mfXGbSuCutXCgnYMq/0YjmNPsRAPYNtnXJXcILGitftzE96feL9LLKWBlMsdIvDqeu2tq1Ss/i2Fzo34ciXDiLPW1c92eL66QKFQp5Zdolkro1WKtL4aKQzbKgzaKkSj0W19SCkp15rMR2e72szOTNHIqzSasiN2t9VENGq/b7yGlNy9dw/DkChK+5yAY54GnjBcnU52CLASyXfeSDHiqHHlboJYukwls0jwRowLEz2UU/Ftz3XqWeqbhtqDxKSqJKXOjMu65V4LcC6gYfNpKIqgamgYlTTRaLrN/vXLH5BqmmkU8+grcweu5JawWhQiYc8jEfio1/YU0C7z4uc1AsvLrZ77bXSGmdExH46R/fnfDg+HM9gd92i43V3bQgibk0HrIlLXWrMB/MsIt5fQid4jzZMPBHw4Evi7zwAAIABJREFUrU/gS9yhmVkCoXLsMT/uJ75GIO5YszXZ93+mHj5Oefpax9hNYXUQPBc60ufejgKSUlK1TnVQG1vhgN/NRM/u8/YX55o4TE1AYnF51yggk2Li9KDC6Ojotj6WsxXenG1Q1SSVrIl6Aj5/fqDD3qs7eW863Ra7qghefu5xRno928bYsPp46+NlshmVuekqZ4cDjI2Pd9i/tyyx+Zsdd1OTColmjqLFhcXvQpMGhjPIBwnB8wPD295TqG+A+UQR1R9G11tNwT3hfj53uh9bM7+r+45GXR2v64ZB4fIMNp8bxWRvo8cWqg4+M3J/euwRBXTA6yApIHvv+MqksHZqQxHgGHl8x352i6vzN0j+4H/DqJS3qFcwEbj0a9jHL6JYnZjMDuwTFwm99F+BUI88N97k6SP8xf+G3m/9K8Kv/BcEnvktFJPjwM9R7AE8F7+EYnMhzDYU1YLq8RP43B8iVOtDQwFB698J3UYmbsYmVeXMsHdLP9vhfp+zjfpZ9w0uu3lbH/lynV/cWabS0NB1iSElC7kK37s632F/aSLMkxP+tVGLVrPK4yN+Tg74t40xnq3w048WqTQNDAm6Lrk5l+XabKbD3rypRcQaXaMIlou1Ndps9XVDl9xZzG77jA6LiVeeGOKJSJher53hgINvXBzhRP/e7nszbuoSQ3bSb0JAqdHclb+jWp/6bwAHSQFNRx0MT/wJpes/IFbqR6hmFLOJdOhrRBfc+/bfDevVLLfejTLs7gc25+OPIlQL7hNfZn7ejZj4JgS+SXYKsn7ILh59bnz7SEgLU9NsOV7zIPB8ZhTL+f8BrZQiM2cheNJLJddZW3DY+H4UEKUk5S1oH5NJIDVBI7tMIOhCrdmY2jSWUjcMEvka87MzaLqBSW1vIzE5OUmwd5hGYYlaZp0CUlXB2aEgs9MLqCufUN1oiA9mUlTS8Q11A60Y61mVD3L1DvseBc4FdYZHzFhMKjMz88yszDndiua4fPUmujPQcQfp3BLnR/xtz9RnLpPIttM1qgJ9HgeZbOdYRykVGpUUMzO928YQj80xMTFBf79gqlKhVkgRLRxMTYOUkmY+QWMD/bYaY9htvS/99ogCOoR10PMA+sf6qJhPUvpFlKAjiRoYQHgPL8+8PPMRAVu6M5deQt11ghOv/jYoburK4ee67wSvUm1HeWarbqCHqmy91w/iubeqA1jFT5+b4HpaQdNpyyc/P+BgYGSUWqNJI+/hwtmTGIZBqVhc2zsVW+YHt/NIKSgXBYk7OV4528pdDwSCfDiX4Wq8jqNawuEOYqOJbnEj7DZeevoswwE7s7Ni21x04nUUh6cj511VTDg99a57e3t6GBroa8VbKm7r3zAMpM3dNafe0Kp4vAFsFmXN/jN+P9lMnoLZA6gYzSrHhoZ5+kSI/2c50+EHFHr9B9fTf6/4iVPj3Mpo1BBtz/f5S6fwOyyP6gCOeu12HsD9sFh6m/r8TVLFQez4oFghVrqK2/UkJmf4wPPMqxkbmVqgffZApQoCco5hMjn3gZ11IPcjDs+3y1GlvnCT2VsNmC1iHT5LMjP8EDy3gdtdR8rilnneWqXA58aHuR3LMNss0WPVODviIxlfwKvU8NpgaiFHKpVq21utNbl8fQpLoJW7Xi9mUcwWvv9OnpP+JrPZJlNLBerFHIrZSlUqNHMpnjvvQzZUnFTJZKr3zUV3UENWizTKm3LwUWmamh1x7QVbtRLVsrnNv5QKejlPqZClorTnzw+6DS4NeyhUNVKqmXODFmrlAgFLnVKtgKa17rVps2NSVTyWg4lzr1g3DErZJH1WN4taGVOjhMdpwRVQkbUSmdqjOoAjXwcpAhtaBce1HyKdDQy90dafPljK4j39O/vy3w1rgQilyasdQieqBf/J4JEKnTsTgQ9+KHzr7qu4Jv9P7OUSfWqYcG0Bpt+hP/xNIpHHH/BzV4lEppDSuaUIvIrPnZJEo33rYqLdtqW91RPi+/fmMft7O8RQIVQUR43FqhWz196Rkx9vOHlxoqfNn8MfJl9pEugd6jizb0BnMp5FeMIYxrpAemLAx5jjYAamv3CpzrtLOk2NtedQFBPHxgKMjW0vUEe967MBvggUhIdrc2lyqsLJExM8MxEml1k6kDj3gn2hAf723VkKDScmixvcOieOHePz5weYj0V37O+RCHzA6yBFYL2aX3G4Ob8dmtn5HfvZDVZdYVzjTyJUEygqQqgI1Yzj9LOYXOEjF3h3ig/aX33+Y4xyEbRmWw1GZfIyUq8c4FkSrZxELyVhpXv//ffqSJkANOrZecqz79PMJVZs9iYsNjSD77wfQ9/USG5N9DSg1pToErqJyenKeqO5hib5yfV5/u5KlJ98tMRPb8b5h1/OoelyzcZiUnj5sSEeGw7isZnxOSx89lQfnzvVt6/n2Ij9Liu/9lSE4aATs6oQdNj40vk+jvf7duVHAE+MBvm9Fyf4+jOjfPniyL7rG/aLf/BhlEpDp2kYayL6dKrIvXh2T/6Oan3qvwEcpAg8G/fgywyCbHbk3VsqUIkejsgYNy5hPhGhnpxkueHHf8JPzdF75ELnTnC7CHxwvu/erjGgtv4yah/sHsFzLYElOLrvsxr5OKXrP2I21UPWE0O120l5vkJrAtg2yQHTkmZ1geL7/zOziTwFjwUhIK45kV/4dYSi7lpYvHrtI8q6k+bK8PKO3HVFxXBUqZQsGF2E5WatzN27aRqawc8+WmBxPootUGO1kdk0Bv9YTeE11nvurwqkQ6NmpqYa2LUC0Wj3GQd7xRMT8HgYXAWDiWET1HIH4r+p6cxsEs6PCt+8dZd4QUXTRUcNxi9KKcbcjR35eyQCH8I6SAoInHg8Lurx2x11AGde+iKqb7/+t8NhpAzjjD4cVM9RU0D5niw92iYabOXuxyfOoHj2599oFHHc+HPclhqaq9BqKSHBSNeIDP0+cmXyTre9UgqcsddxWSpoLhhyGyAlslQiWI3hOv3ZFfud0wp34jlyZQtmHTbXCjiDfRzv83Hc0yQ5WWmjVFZtLC4vQxEfV+I6NWsQq7/R3tfe30tSM3HxeOiB0SYHgSt1jRsJQTRbp5pRKCwYvHy+n4kjjKFQabAQ19beh401GA6HnYlh9dNJAQkhAkKIHwkh7q389G9hpwshPlz579v7OXP3MbZ+HhQl4X/6W9jHH0NRzAhFRXF58Dz2Cpbg2IH43wuWzTLV2IcUrv415dmfI7XKp44Cso2dQ5jMtNFvQsXkcGLyDe/bf315EtDpoPd0SSV+c3s/uoa2NA2GsULCrCyjSW32ygb7ndMAvT77apVHB73zzPEwr5xrpQWfHAh0tQk7ncynihTrza6/LhBtFNBeYnzQWAJv3FgglishpUACiVKV/3QlSkMzjiwet8OCRVXYeLdCgKqonBxy7cn3Ua39fgP4U+B1KeWfCSH+dOX//0UXu6qU8ol9nrWndZAUUAtbwf/r5EYaBCJNhNnB/LTAOnNQ/neH9WqG3C//lrnsICVXEqFmiRYrGF96CtW+m3kDB4MPiwKaz4yiB3+VyuQVYuVRUMwtisb3VWZnxY78SCmZnhbdbWat9Na6UEzFAewz4DC28y/x5j2ARqyor7wqiRV1RLpKJRrlygc3+dGHMcoNHYpJPvN0jaDbtiUlkF6cJyQ8RPOltZYIJkXFUc8QoEw0WmFqaorhkVE+riRp5JbXbBq5ZUbH/bx3c4qGo3v7CYGk1+dgamr79gkPM45nymSTC1iazQ567EqmiMWkHEk801NTnA0N8IvbSzQyyzRrZWS1BFYT2cUG08vzGFKiCLGtv08iBfQN4KUV/O+AN+j+AfDA1sFSQBuxhUjEgpStbxkPinLxRP8eh2MKvVldo0UMXcOfXMD34h8ceTyHRQG18JPoz57EczPF+NhpFN8I0ai4z15JZe5D7Ne/g1Gt4NfO0HP6CWx9j7XZ109a8MaSSK3WQTEdP39mW3pPSjN2LMicCYlgyGtqUUCYOHb6AstmL9G6E5vfj8mmUNF03ls2+NXhIBMT21MC0USBX3zgwB/q58SQB1HNMTrabjM2FuHt928hXGECbhVLfYS+njCxnEYaB91aTnh6BvjaU2MPNHNmv7ggUtj8fdhW2jtvfEa7bW8jGfeDTx2f4K33PkLYvMwvLmA4gtwrC2oNB5XZBt+4NNqVmtINSSJXoWbyUTdazeWOau33O0evlHIRYOVnzxZ2NiHEe0KIy0KIX93nmbtaB00BPWy4sTwJko6spEZi6r57P0kU0CpWzC4swVHMwQisPbFOPfkx1egHNJK32Zi9U7z3OpWpd5G1SsuyUqTwzt9QT95q828Nj6H6QgjVst5iw2TF7O/FHNie3hMCfBd/DWG2oiim9b0OB65zL/HW7cRai4BVmkDXJe/cSa/sF9xbzPG9q1H+/Me3+dtfzpEuVlEUheGwm+dP9fKVpyKM9XhQRCdtoCoKkR43nzvbz5nBIBaTiqIoHB9wY1JVNlNANpPC733m+APPnNkvDjttra6hm6gts6oQctmOPB6XzcSZET8Br5liVUM3JLoOmmFQbmr86Ga8Y1+2VOf/e3OSX9xZ5rVrUf7k/3qTf/+zuxzVuu83ACHEj4G+Lr/0P+7inBEpZVwIMQ78RAhxQ0o51c1QCPHHwB8DjKw0T9rPOngK6OHCnsIoGJ1ZSShmatGWjdEoY+hVZhf8gHqo8RwWBbQVvnenTPqdv0VWq0Rzg5QnF1Cd10n7v4Y0zKTfnu7aQXXxzRtkgqfXfU6rjI/+ETX1OvPLVVQ9hL3vOKnG+ftSTNPTEmPUiTz9Gyx/8HMsDgtmXy/pisp0LEE+Ee86WnA2r+IuGdyZz/LxQo5iKo690SCfVLiTW8KQEPbcv7tmN1wsFsklEky4wtyYS9PILWNSBXazmSFPc22M4YOmcfaDpZRQTFLDWMvAUQGb1UyNBjMzrm393L03yVyySLbcpJJaoKkbmDe12NgLvrXcpJRNd7TVqKVVnIX1nkCTk5PceX+OSk2nml1EsVjx1mv8zdvTnBkKcHE8xGGv+34ASCm/sNWvCSGWhRD9UspFIUQ/kOhmJ6WMr/ycFkK8AVwAun4ASCn/HPhzgEuXLsn7PsF91kG3gjhKHAwYNDJ3cRXSONIVLENPdIx4DKsBqvFbbeMWy00nQ8dCOLxlrIl3EIm3UaUJc2kE18g5LH1PfGpaQdgz7+BnGsxQtJoIWBcReoJG8SpB33mwJihaTR2tNITMo276fRHutWGEn6YZWB9F2ZzdYhRltkTAr4KwUyxKgkErQgxSbj7L+Pg4hmHQnJ2lry+M2V3AVCt1tC/wOeyEQhpvLRhg82BytNu8PVvGbdMYsbt23ZpgdSzh+Pg4T54a4cOPnBw/Po7bamZubu6Bt0w4KPzSpZNk8XJvsYCi1zh7YownxwLE56Pb7q00mtxebqI2mzSakkZFoXivwNcvjux7ZOZkuYKp3ux4vxWhEgywZmtx+6ChYFIlpnoZi9OLYjJRa+p8573Zh+MD4D7r28A/A/5s5ec/bDZYyQyqSCnrQogQ8ALwv+zz3B2vvbSCSCzVsFZnMTSdpfwQHo93x3sPCicSOnLy+zRzcRZyAyjpecSHU6S8X8PjGVyzdw2/Sm6xSbbhx1XTEAKycpzqwJMkfvpjEosqVncrjz1d8jHz83fxPBkimR76xLWC2IyloZNYrGF1t/6gbBw5uZAo0ZN3ka73dB1LafKGyezhXJs+T+nWT5ld8tO4Oo81NETC/hxOZx1FyXQt9Y+4JTei7W0WdKeT8SEbC4vL1IutHPJu4xBTJYXF3BI+r4fQLsYtbh5LaNSKaJUS2cqDaZNwWDiTTjEx4WXc52FqyspEUKFUyN1375XJBIV8Fqvaak1RL+Yomq384N1y1/GQu8EBs5nUpnGcDZsdh8VMMd8klWolSyaWEzTrdppN2bIp5zG01jfVcq3187DXfj8A/gz4ayHEHwJR4DcAhBCXgD+RUv4RcBr410IIg5bm8GdSyo/3ee6OV3cRWKdZyGCM2IlEXG2iXiM1RXLhdbylOFJCKduPf3AA9/mvrdkchaBaW7qLL38F6axj6HWGPCttJwrfJTL0TzfkpTuRx34b14dRBrwuVFeYUPUEQ6E0yWvvUXf3tM8PcC9gLr6GdeIPPoEi8CasS5LuWYa8813rA0ZHVQIvHEP+fLKtlcZwIIn3xVdYrt7Hv2HQyMToMeWw+IfRK+C89W9wq3U092DrPWnOYzSzRCI+hOje731oeAQhJXHNji4FdlXh5ecf4/SAj5lZB7HpWtdc/o2tHZbqDi6ODHf13w0HAgHc7qMXQj8p+If3alg2tdiwB3opozA6tnV7jp3giz4/mSrgCdHUQFXAFe7jGxeHqOUza7aabpCeqWFaee/t/h5UswWrWeVzZ1tpvoe99vUBIKVMA690ef094I9W8NvA+f2cs5+1UQTWdagu3iJx7W+QmkEm24t32Ybvqd8EnAjZJPeLvwQtCFoNpACpUZ16F0vfceDUmp+NPg8D1+L3QK/TNuIRgQLU0zNYwqc32AssgQjusUjLTxSMWqGVj7xh7yrWy5l9x6nn5qjl49Sz/YjICHIln2CjCHzo9yRNmH2DCOJIKdefVVGxhkZQFHAf+zyuRAglm8KolTG7AvheeBlL+BREt3m+0jLpN/8txUSIgm8BdIOC8RhOVWt7TwxDQ6sso+WsmP2rfhT0FUeKoiClzvnRIF8ZiVBrNEks2hkfDqLrOqqicHYoyM1Ydk3ElBsEzVWcKa23ZN7sfyu8G9v/3LBQOu9YIEAKhBD78m82qXzhwjC6PcBCpkLNVeczT53AYVaI5jNrtiZV4bNnB3jzxuLa+TaTSiTk4ouPD3EU61NfCbxRBK7n57l1eZZhV4sSiRWHkbcXsCz/mJTvGzTS8xSyfcSKA2v7V0XDxC9nSDpPrb1+2OJmNN1Hk+G2GABi5RHcCw4s1e39FKqT1LO9neKwULB5TpPYY2yGXufm6++TVG8gBETzecq338J74avMLviOXAROOj+PnkyDbhArDqzMaDCTcr+IZwZAEK8/hv18K+0zMwVLNSC6tc/JSUn67dcwKl5ihQEMvSXcxYp2NPfACl65V1lnvmrHNTWFraexs7zx6am1f5hMTU0xPj5OzlbhWq5zXKGUCvXsEuaA87795DfiYrFIYsMb8aAF24cNh5QiyU3jIRUkPT4HM9NVlG1mJ9wPr979xIRgzAFTixlSi91F94mJCZ4dhHdTeUJqld//zBi/8cqTWExHkw76qf8A2EgBpd96jSFXpZ0y8CwA87j6P8NAoEx+bhFDq3eMXTT77XiGj44Cqj8RwBtNdIyfFBYLx5/4AqvqeLe9WimB685bSE+jbW9rVGSa0Iu/iTu1t9jyH/+YfnWeIffMSs1BnX7TEuZEjYmJPzpaCkgC+BgZ+h1K0Q+w3TUYPR3EPnCBuZk6Q74owhEG7LvyqRUSuPKTSLWOlO33N+RbAGMD3UQVaaoyfvIUJrex4mf39EEkEuHscIB43c61aIaNFJCqCL703GP4XbYd+3xEAW2P+waGSOcqGJ4Qug5mVRDuG+TrT4+SWY7vy/9u735ESnxOO+fPn+fVp44d2V/+8J/BB0AbBVTOIrB1UCIIE3qjijU0DtLopE1MFuyRxykZR0Nt1FJzVGau4aVBK79ZRZgsgIrnxJdRVNO2fprpBZA6m+kjgYJ99ByKI7jn2OrTH4Lha78fqdNcnkEOVlEU+6HeTTesmp24Rl/EpYBzqEbm3b8m81EFR2ARJFT8ryJGn0FKsSOfhl7v6Pq6/vtFJa86uWG/yAeeJ7GSpc90G5PbB6xSa3ujDwTwwslevHaFn+WWURSVsMvG2GAfIa9z1z73E8uDwlJKcqUGNvPhxm8xSb5wYQSzK0iqXKWUkTz/xHGQkswB+N/PvqNcn/oPgI0UUJFLxIrFtV9b/RovFJVcIoBl3k61/7eIzc2CUEAaxEoTWIJDVLUnmJ5Z93tY1IZWSpJ794dEcwNo7qGVOEcw+wfxPPYlYlE7lpnt/cwmfJSKEdA76wMchR6c29Af98OpVA+x4rpAtdF/9k6dUsV+pBRQB/V182c0lstEC8NI2fqDFYtmUWy3sQ2c3hn9luzBlxtE6o1N9zeGHHmSX6S9FPMu7GoZMBHPZdHeusG5keqB0BMu4GRAZ2LCvvJ6nJkZ+678fBIpoJmlAm9/cBNHqA+pCayNFF86oNz8bnh6qtWd1AekM0tE55wH4n+vd/9JbAXx0K+NdQC+i+fJzH2PoDMFeisnPOTOYj/xNBaTvZXjHThDXe2hH4mh1VGaJxm/GEGiUCwdfn67M/1zFOtiR+66tzGPP/gSpar9vn4GjvXivZPEaGqb6gPcDJ9/Hix7j9M0ZKVYSXSOqDTAYJaenieQ8sHUTuRzVdy3LiMtkqLV2hajPTWJ/9zpHfnp6bMxMPIkxfd/QKnRelYhzFTNNdLeHqiXMNVrWJx1oEGzrjOTb/CCz7fvHPKDwqt1AA9DLDvBTdXJzVQWYXOjWD0YFkGxXuHWksZL5/semjgP8+4fjYQ8hNVeBxCgPvJ1Svov0bIL5MQE9dHnEYFTJKfW873TpRC+iV8BIDcF6dY3+yPJb9cSDfRKqCN33V3TacZKJFPe+/pJZRzYT/w+xWs/INscwN3QQUpqfa+QKQX2FWfVeo5sPd6RVw+QiGs4hh/caMblJZ1GuQ9ks+P+ktllZGYXPicu0jw/SPnqEiVHD+bwKI3KMebiCzRrVrSKnYZNB+poZRXNUSW6UCL1EOTGd6sDeNjxfLRItVBvq4VolHNMxmIcD32ynmWvd/9oJOQhrM46AA+RyBeREgLRoxQrd4Y9DoN6bL5DvB1yJwidsGPx7tTnCPKxf477epzI4Cim4Cixedsu4pFUotex3/oeslInaDzG0IUn0c8GKN17r+uISmcQhiJHLQJvwNKBvbmMUSlvuj/J+LgH38huffZicfeyOnLSHoVm0mAqXaYl0laBOlLoWH1ejh8L4HWujy5s+XkweC8isG5IhkciCI4+3qVoE4epyeZaCGe4n3DfEHar+b5+JGD3hdCaBpFRY1cxaLqBJ9iHw2p6IHe/io96HsCn/gNACDAaJQpXv0dt/jaZ3CD+x/24zv4TwHkkYqVWLVBdilMmg63nFBDY0t5z5rOk4nfac/8VM9bBMyjWwO5ikGYsgQi2vu42RjVDdfkW1QUvsn8IzL41m9zN71KZXCDgyiOloFlYJPXGXxB4+U8wuYMIJYrU9VacQkGoZuwDp1nVsY5SBF7DQsF14asUL//9+v0JFVW14Hn8i2u/J/Zz1sXjPczlKit5261lVhRO9LmxmdUV+8MXTDVNZz5boqhm6XXbu9rs1F+pqvPmxwvcujeNc7LKSMDJuKt5pCJwv99FrpLryM1HCrwuC6Xs9n4SuQrfvzKLyV9FolBJxXnVEeBEr2fbc4UQXL6X4O33Z3GH6ugG9KglCoqLa7M5MksxTmdVnj4e3tVz7fU+jnp96j8AUskm17//FgElAzJIrNiLfnUB9fZ3yIR/E1YKmA5alGwUFrn7foLMlbvo+QSxQoSSew74gEXjIsYrT6CY7J17J4aoj/8h80szkBcI1cSS6QWCvcfJ70O83Yw/vjxFsvw6Qkii+QilO2/jOv0Z4tXTSK1K+vI80fxQexM1IUi8eZ207yuYjCD1+F1ixQEswQjOk88yF3NSqh9tHUDn/V2gPhFi8co8iu7B7Okl7Xqa+YwPMvv3PzHh5Xwwws+nK9QEIDTcTSejTmVXefr7wcVqgzduximk4tgDywgULNUUr8r1nvM7FSLv3pvk3ntzVBsG1ZXag9splRvFJawWFVU5HAF2M+4dHOZ6YYnahqZ5zdwyY6MhFmKxbfcaUvLd9+bIJhawaxqrYy+/9zOD+rkBUktbzzy4G8/xUSxLOb2IIQ2kVEhnl5hN5NFX/FzXdT6+N8kJT3NLP23vzyMR+OFZ5sU3GHTcI2jZQKd4FhCqHZd/jkhkbGsKQG9Snn4P+82fIrUmYfU5hsOXEPZAd/sVHKy+RuneL+hphumX8+CGtnzygoFv8R6+F/5pdz8jI1j9I0RGXsAwIDqnMTpmOTC6pFlIkiz/eEMuf6M1AnExhuXcf8twuIzdt4xhNNtpKM8CijmP58SrRCIvIeVLhDbQaCb7EbeC2AqPDGP1DxOJPIeU4Dlgqm9kxMdgwEvvUAiLUmJ2psLYWB9gWbE5XLrkckxHuHswN3WsvhZdUpOSkurl3FDrW9xOaYhosoRJUbA1wcBYrz1QQTqCjPS4j+SZIpEIQ0MRXnvnGg2zD7vNQp+ll+cvnLnvXtUdxOKrY2vq7WMvfb3kFR8TE1vTcm/M1DF7Ldh0o22vbVOLCCEUapZKh5+GpnN1KslrU1U0A3yGi3N9w58YCujov3Mc8aov3EE2mxt65a9QA0YDvZBuURZSo7Z8l8LVv6Z45/vo1QyKAplf/iXVmWvISgFZr1Ffukfytf8Do7reSmF1rWKjmqX80c9Aa7DaqbxjzCAKjeU7yHp+Sz9Sr5F//+9I/t2/IvWT/5vka/8rjdS9Le13g6uL76/UO7THJqRCIzGL4vCAoXXELADFFdrRWfuN8eHHAofZgklV1mpN1m2UQ8PlaoNcbbU+ZL3HvyElt6LZDvv7+SvVmuia7PCnGQbZcn3bvQeNPQ4zTx/v4fc/f5Jfe3qEwYBzR3trTQMEXcdeVlaaqm21t6oZHc/eDRuGIJOvte0VQvCP78WYSRbRNANDh4VMib9/b4aGJvd1H0e1PvXfAEqWceYrTWqaFdiQ+6+aSYl+bJM1cle/zcyCh6IrCSJJrAiN1CDlu7UWDWLobXvn/+pvyPT8BtD6JrCRJrhzI0eoONK9R/9GrJgpTjWILq/v3ejnxk8+pF9kQfZvHHxTAAAgAElEQVQRK45AdA6x8BMyPWFamct7pzPKcQ+xwjBIo/1OFIVE04F9wU3R8VlisfWvvKsxe068yvw2/o+6FcRWeHJSp7Z4j/riXebSfTSeCGAOTWw9EnLPWGd6uoGiJID4odMlMzMzVEpmNI2OMY85q4loVN0VDVFOLVDXHDSb7f6a+WVquZ610ZOH+Uz7xQPDEcrJxY6ZCyZVYA8YTE1tfQ+WqoV0qdlxl5uxkAqqnmFmZmZtb6pQIxpdpJJeRK7QR7XsEkazyrulGGCgCMHc7MyOnuMRBXQIa+jZlwjY/4qgdUPeujOFanVgPtaDq3QZtHv4Lf3reeN1O/aF97HZBcWapWvOeyXxE0KhX+/IIQ8H6oSMzhz8zTjsTOIf8lI1OnPRtXIST/MOQe9i+15Aq98gFPrMvnLmnbqLxK1bnT3yhYppPEQoBP4XX6LSeJdQPYvUdaqih7HPv4glNERdeXjmAXTDum5gXf4RVvUqlqaGuzGMeWoRm3aOcPgbB3oWCHK5GsGgEykDh55nPjzYx9yCjmjKtT7zhhQYzSqnj48SCPh3lYt+YnyYakKQrWpr/kDBToPHj7f+YfCw5NdvhYcH+rjUNPPO1cr6fWhV+sK9PH5iiOjc1vcwcXaA77wXa7tLrVHG5vKjGaz38ldUzoW97bULdi8mR7VjjoNi97JQLfDDWzmkVPCjcCkQQNznLh/VARzCKtYcNMZ+jXzqJ2j5RbL1HvzWIK4znyEVN6NnltDLvs6e8WUNgd61lzxAquhgOZZGtQfbcshzRgTKYaTRmYu+EfcNPgk5S9dc9FqmRKbRi7NS6ti7VIC+VLv9brDbbZB9+2rX2GxDp0mXAvhSAGZKnhfoGX8WtDqNOTslRcB9culX5wG4HVVqiWnm50zYdCdm3yDJpDiSmoBaeppkAmwu3/rzFauIj+Nk+nJ4PL4DO0uIGul0DI9nEUUpHXqe+Vsf3qViaQ1635gzr9QL9Dt0MpnMrnLRU6kUz46Ocj2a4c5CEVl3MRRw4bYpZDKZQ3uOg8YjTlj2CTSrRkMzMNt1no7YyWWz99372TEbb6VrKFoZt92CPaBiWDVi6QpGpYjZ6+HCeA+VdJxUKrW+N2xGq+Y65jiARKsUUcxWpFSI5pb47i8+4vnT/Y/qAI56BQJw4oyPnp5vYjSrhGKC0XHbmmjpFikaRqyzaZovhXA4MYyZjsZwq/3mI0OXUFrV4xuEQjv9j3+W7Ft/BSYzw54E0miC2cKwO4niduP1n+H4pfEtRUY97KX4/hxDnk259sKE3XN+XyLmgHsKu2MOzV3seC5bwI+7d/NeFSkdKOad+Q8EwC6XsN/419gNSbHUg2d6GTU8yMTYPycSMR+6CJzPXqfgWuh831QLDvcckYjvwM4SQmAYBUZHw0i5mhxwOCJpud6kbA7g7DIzwOOxcmx8bM+56McmxohOhNfF4Wj0SITfw8K7jX9j/cbq3mS+wtUbdxkbH2U07GYp3ikmT+WtGLrRJhpbXF4a1nzbPIec6iGPg76hyEMlAn/qPwA2NoOTJjtCbc/rth17imZ2qaPpl+KwEXjpj4n//XcRLLQ1j5MITDYbJncYw1j3v+az7xQ9X//vyb0fw92fxxY+Tn3ZR/9Yy6YS3T7nXNoD2PomEPV5pLYqxgpQzdhHzm25t7Z0ncLN18kseHBPCFznvgCMtNnozXpLMOsyJ0Crl7vGsxsshKR480e4TFWkbGV0o9fRE/NUuYkyceHQawKEzY4Qpi5N/xSE2XKgZ4FEiBqKYt3w+uHkzM8m8lvODEiXGweWi/4Iwxu3lrk9n6WcyRKt23jz1jIXeyRjm+y/8fQI/1BLkpUKumwNpBdd5jkg4e07aSrZRQrCwzPHQl3PP+r1qf8AuN9QeDn+JCVbllixCYoZoUCsHME/dIHKso908FvIvAWjlG3NCVixyQRfvc+wcDsLpRPYDGB59yLjsu0lhN1FY+EjYqUBLMEhHCeeJxoPoNo77W+9O0OyeBkMS6vWoRlF3Ps+mZ5fB4Jr9sbwKJlsD7Hi8NreVYHX1f8s8X0Ko7mlFDPLITTX8LrvlTW/XMQ+snffO8UaTxMryE6R22Qm5xrFvqLJTd6t0kjOY6gm5jPDrP5x2M1ZUtaYnl5gtRPoYYqeycVC1+HyUioYxeU2gfKT2AyuG5ZSEs9W+PDGx4TuLjPS46GcWjjUc698cJP5poOmBrXsusD72r1lgi4biiLa7MNKiWfGe5ASXr+cJZ0u0VihgjpF5UUufwii2k851V6f8EgEPoTVfSTkRiyQkS9ijyTps3sRNjfB+qkNefc2IpFvUV36GNvNPKPjXmxDTzC/6D7knHYTkcizSPlsW8sKs6tbvYJG6s0fM+ie3kRVCcz1d4hEvrrB3kVQHke+Nb3WzgHFzOhIhcBzx7HP7y9+n12n+VGss1WEBJMROOQ7W8VBtOIEvti7ra+AJjMjgRTe536XpUprNkBx9hd4YzfxlBYBQak0xNDFL6D4Irs6S1Faw+AjEfeBUBiB8CA3FjIsNZz4pYtTA941m0Bvkyszya5jI4+N+Nv9fErmAdzLmZhKq5TNAZBOsklBj8l7qOe+fXsZi8OJedMda6rA6gvT73Nsuff5ps6bU0VKuUzX9wkkFm8PeTyc7FKf8IgCOuC1eSTkVtjkCuMeC6PrIDooGgVb7zlcdXCPPYAWB/fBzUoBNL2D8pCAXlzusHcf/wKeyhzm0uvo+SVUzYrJ7UEvZYH+fcWjOnox2ayd9ItqwdZ74sjuxtZzkt6Lf0otPUM+7qD38UEMTBAFPTdH5eoPwegFrYaUAlmvkf75vyP81X8JmHdBAa3OGNg/9ZApVvnh5CS6DuVcmeJkkg+m0zw/pKAoCi6ridPDAWJVpUWvrdAMQbeNkwP2Tx0FlCxUmU6Bpq/n+Gu6ZCZdJFuq43NaDicGqSNEJ80mAcNY+fTvsjdXrnF9OklTt7T2ifb3aaM/Tde6+jjq9an/ALgfBfRpwIbmIJof6ahXAJhvniAw07l3bsFNMakjdS+xQhC9GkV8/G3SgW8C4T3HUywKku6voKcbIA1ixUGEasbkDpBqnFujX47mbmzAaeYzYI1uiLExQy3XOS5TmCxkP5hlIX98x2fthwLKlKrcunWPeKZEn8/B65evgSvURhsgFX48l8ZmMSGEwNHIcKl3gKmlHOl6hjP+MMMhmJ6eRmwYY/hpoICufXSbAu4OGqWZXeK9G3c4MeA9lHNFNUNdq61QQBtrI5ZoFJJES+mOvQ3N4IcfRMklFlAd7lYWEBJDKmstNjbWJ7i61Cc8ooAOYW2cB7Cav+1qXqNy5y3Mi0Gsiort9EsrfdwfXO76/rCNwYiXYH19zsFqjn/NUeq6155/l4Al3qoDsNoJ2pOt+obszwmFvrnneFpUW5jRz/421fh19EmVsceOYfKfQJlT8DuWqS5+hKvux+8YRNh6jv7O0kmq1lTruTfUQoTsCRyeEg3LLuYjmMyUSl5CIc+Oc9fz+TwfxurE8jXKFZXcUhPzcgnN7MSxIZ98Nbe8WC9zfaHBS+f7CIfDjI8Pc2pskNlZJ+Pj4xiGQblUOpCe9A8T9gdDVBsONJ22+5BalXBPD4GA81DOPTMxjLNkZy5bRquXsblbabcXhpz09vSsvYeK3QUrd5w3rGD3ojqKmF0+QGBaqanQG9W1ugKpVRnp7eeJEwPMbapPeFQHcAirfR4AzH90h3rll2BAqujCPhVFzP4dmd7fwuNp8a0Pqp/9fnDeeholew2kbMvxT5YgPrWIxdvfvjchsa20dWivb7CQXK4hVNue4lmtA/B43OB4gZITigDZlbu/8ibIJguFCMriFVwTF0jqT+36HJsWo754m7nFILa6B3NoYsd1BhbneUrNbGctREkjoI/t6rmlbJJI5HG7Gyuv3z93/cO7s6QNF5ouaJRyVCxWhFRolgs0rOv55Btzy6fjC/isDbTCp3MeQDdsM6o0yw00XbTfRymPkwqZTP1Qzk2lUpwfH6fPbeNaUWPYK4iEnMTjGVKpFKlCldfe+Rh7MA1SUM0sMRoZo1qotNUBNGx2TIpK2NYg5IFGUwdDcmHERrZLfcKjOoBDWBtFYEOrkqy8ydBGsdSzAELBIq8Sibx8BALl4eDK3ByBWhypax298PuEjmvkq2326dAiA9ZOoRZhYnR877n6WzWDaxYSJCtvrDWgk9JoNaBLz2M+PU4kEtzxOaV7v8Az80Ok1qQqB/BEU1i0CSYmfr8l6t/Pz8gxkpXXkTPxFbFagGLm1POjOE/6UO2HKwKXr85h9QSxbBIIDSG3Fw6lm8cmdibsflJFYAlUzV5uzuXIVGycPznOvWQRiypaoyINwYUzfRyfGD/0eCJS0uNZF2rtdgt9Q8P88Kf3MHl6sHh6WtSQZpA3eXCFvWyuAzApJs739PLUY6fZSX3Co2ZwB7w2isBaKYmy1sd9g0BpSPRCnI0azCcNC1UFpX2Q+dpAebO1w94+dAahmtvshWLGNngCRVH3Hc/m16pL1+nWgA7DoLk8s2PfeilJbe4G6I0VTwrodRrxaerZ6M78qCZCn/tjXKdfxDJwEmvkPN6LX8Zz5kt7eNZ1EXj99ftgKbs2HlOEwGezdLy+itfvdudnHUUTt4PE795b5me3llgu1CjUm0wli/jtVi6dCPPqY0P8wcvH6fE5HlicU4v51r+TNr0/ihCoYv29EqK1z+uw0OPdfbxHtT713wA2isB6zcNcbhBDbzU52ygCLsoJ/EcqUB4sni9PUMoOd21CF+AianSz/XEq+ZG2wedmXy8p9Tlm9nEPWzWDqy56uzagQ0BSc+9YHK7G4kQLI6wNfN/wrMulAlb/TuM1E6+ewT5xpuVnCix7eO69iMDmZpZiVkczRJvISGmZ554e5dvvLXRtbOYNSaamlu/r/5MqAheqNSbvTWHx97QJv1VFBXOZIb+LhXKnAHuUuG7xU0zmOoRdBclEv59GLUOpVkavlBgNujnfY92x70ci8CGs9joAH6WPJb3GElJvbmgRYMU7NvDAaZz9YSc9554hf/k/gWpm2JtESh3nmZcYP9VOr9STd0ksvsagd76NAhJqCs/oPyESsR04BaQFRilNvd9llKSZwOnwjs8piRqle/MMuTa3yVBx+EtHevdjYwZgIEQ/kYh3xxTDS5rOlbikUNcAiTvUj0DhqccGGBsb47f9If7ja+AI9qIboCrw2MmTvHC+n2jU9amlgO7Ec9gD/Vj94Q4aTFVLD0WcVk+IaG0euYmic4X7efbxIZ4YDRIIBMhkMmv7bGZ1x+c8ooAOeG2kgAA8j72Cpf8UQjGhqBYUix33pVexBCIPnMbZL3YOPU7PV/8FrjOfxf3Ur9Dz1f8Ox+DZNhuMOrmf/we6zirQm9SXJg8kns2vqa4wrmNPIxRT6+5F66fjsS+gOgI79u3oO4dCJ40nhIp14MSR3reUJXT9KkIUd/UV32xS+c0Xx3nlXB/jvR6eORbm9z4zTsBlQ1EUerwufuXJCJ89289zJ0K8fG6ALzwxtGP/G/GDpnR2gx0mpTv1JQQOm/mhiLM/4GIk5MCkKGtxmhWFkYCLHq+rzX4/5xzV+tR/A9hcBzA9Z2di4ncxwmUyk02Cp1xUpemhoHEOBjuJl09hN4ClTptGMkYh19OaMbCy2miUimlf7Rq2mwcQ1y5gOTtCIzFNQvPgPxuiZg7u+vlS/q8js98BIYmVR0Ax4zr9AvHFICbH3mPfLZZSQ8opZmburlFSu6EVzICfAn5RJrlUbrOJzc0wMaFgB6aWF5hx2Xbl/5NIARmGQS27hG7oGBsoIJOiImSzrdXFQcdw5+5d7i7kWC5UqGaXeLbawGO3dLU/5TXImUrUGiaEVLCaSpz062sjQXd69/cmJ7k5l2Y6UaSYjPPeTIFzl17gmU8KBSSE+A3gfwJOA09LKd/bwu7LwP8OqMBfSCn/bD/n7mZ1qwNoYSelGoR7HpZc/qPBAWsBsz1LsWbrnFUAKAOOffm/3zyA3uH/n733jJHsyu48f/e98N5H+sisLMcyLEOySDZts9mk1Ea93ZIGGmnUmpUwvdoxwAI7i8VKXwY7wO58WiwwayTtSgNsQxpp1FKrrbrJblLdNE1XZBVZLrMqXaQP7+177+6HyMrKSFeZlZGmqLoAwX++vPece28UMiL+55z/CWL0Bmn6Idp/b366h7sYjP02jfQtxJSNI+eeBsVFc3Jv77K7G+p1F6VSeN9z5lfj+7UO4IVHjnMzbyJb1ZBaFbc/RNBh41Z8gvJkhXOHAks1O53zW21o3EjoqAGDZtNKrWHm5+NFPn+6d0Nfp47GlmswJicniYTD2777uaJC0QK6xY2wu0k1Vf7dty7x/w3FCHvse/L3caffAK4AXwP+eKMJQggV+D+BzwMzwPtCiO9KKa/t0PeWxuo6gIOQs7+f2Nl/iExlnf4HlSqq3U1Wj5HaQb+BO3UAu30mGyinyBmQye3PXbrdLRroIOTMf1rqAEqFDE8fHqZUbXD9hmSmkGYqI6jkMzRUM7OLi3SZyh31e2kyTTGfxaqaW2md5QJVq51X3i3ykF/blbuvVBvMzC9g8beE5rRKgXo5T61e5+/em+RfvPgQezF29AYgpbwOLJegbzAuALeklONLc/8S+Aqw628A9cUxqj/+Jp54H566xHHiWRg+dgACtvuJfYTkMeSbYysCspKBvhLBF/45M/OmHdk/EE3h9ywIrKBpURRleFcCjuGePkZmM6QMNxHVw9Euz5bX3o9B4NX443gWC06kVDBWBF1T+QQDK16Infp6I97E4o+uqcFQFRPRnq0HcLdz9zPJAq5gNybfnYC33R9BKmZGZpc+0ezB2IuoQy8wveLnmaVnuzpqcyPc+g9fonjjFYxKgUZijNzP/5zqwsiBCNjuJ3YfeRHf+S9ij53H3H8Cz+kXib78b1HMzo75Oihn3U0MK5MMOhtwzJbr/PkbN3n3Zpp4qsjbI4v85zfGqda1jgSBm7rBh1NJfnJ5hh98MM10srgr59jRHeSr69ZLNKVBuap3zJdFNW3QCF5iUsU929/s926HFU2urQdRhWAo6mavxl2/AQghfgJ0rfOrP5RSfmcLPtb7eiA38fcN4BsAAwMDG02761j4u/+AUa+Srw4yUxpYbgo/Mz+FLXoEIZQDELDdPzyd6cMy/E8AmB0DW3z9+UazTGX8Q26O1kgH5rH1HGG+eR6wrjv/oDSF35sgsIGUi0xMdD4o+frbH6GvEoZTpMo/zKaxW013tXM7EGlIiZSSydvFHcDo6E3+/sMpag2DYnqObKXGzXEVv8xtaG8/cD2/gK5rrBGDyy2wMB/HrCod8RU1F7mVXVy2fzuvP+C0MzdT29J9A0gptxUEttbS1HISTW/Vg1hsdhTZ5GuPH2Kvxl3fAKSUL+7QxwzQv+LnPmBug7lIKf8E+BOARx99dMM3iruNyviHgMRjy9LrjhN2JFpvO0JloDuHsNxu37f/VMJBxdJo4Bz9v7CXClRsEXrMs5C5RdOYJxb7KlKKNWsfUEA7pyQamo7hCq8rC9HMiy3ZcXh9fDJXYeRmBUMK3A0rj4R68DhMjMzkUIQFsw42w1i2nc5Jon19WJc+re43BfTo+RLXswpNjeU7EIpKMOxqa3+5U18DA5J0qU5Cd6KgYFIEPT39fOGxATKLc3e1o+kGU2Ub1+ayFJOCAbPK8a6+u677pWcMZmp2rs3lAMlg/yD//refptu/d7UAe0EBvQ8cEUIMCSEswG8A391tpyZPS+hMAEKuyBsXoJjvXPB2v/ZLvYxsFO5p7f2Ga4tjGJUi6Npyvj1aE72Yopme2HTtfu99LzDsDgWkKHeqHFbTH6q4ux0J/P3FaeYypdYPUpAo1fnWuy3Z4vlcGd2Qa2wrQpDKVre930KlwdvX5/jjn9zgT1+/yeXJDPrtd8l7uAOEIFWo47GbefxIBLNJwawqqIrK0aiHRw+HO043PXIozD97+igvP9zFZ0918RvPHsa19Mn/bmvfGZnn+ly21UtEwGK+yutX5ijVNqepTKrCZ0/18PufO8avP3mIr3/uIY52+9jLsdM00K8C/5GWgPwPhBCXpJQvCyF6aKV7fkFKqQkh/jXwY1ppoH8mpby6453fZYRe+pfM/eUfkq/5lykgoagsqE8yNX07p/rO/LthvZrj6hvXSSqXEBhM10/RfOYoZk/3gaEkOo5HG0QbUYBV8hI9OK7msccOBgUkDY3a/Ag3P8mRC6ew9hxltnyM259vOumrUKlwfTrH1JSgtz/Lsb5p8onO0x/2eoZqBnR5Ry7CJFTcMnfXfPgPPr7GQt6gXsgjV9Aaqip482KTamaeirnalmt/m1pJLsyhl7cuX1Bv6Pzo0jSF5Cy2QAUpFRK5BUqVOk+d6N72uT/85Do/+GCShiGppObxRXs50eMnni1yqquboLvJ2Ng46oreB52++8ziHJMu+5bmF6o1piansPiry/fcrJWRlRKvv3eZh2PBLfkcHx/H6/XeX1IQUspvA99e5/kc8IUVP/8Q+OFOfG13+B//GlpuHteff5uAPUfImcba+xBmz/Ft53sHAwaZ1/4Ud9NP0JcAA4r1MczX38L34u8TDgcORI5/p3EkohDKZ5FNva3HQEV3E+4dxBLYfh1Ap3E+L7GM/QUkZ3DrXXhq04iJqzjUJwg98UJHfU0vlnljcoyGJtHMDtJ6hrcn85zwuTqeD//4mSNcTyvkazpSq2L3BuhyOzjsCdx1rdXhxdyU6Jpc1tDX6mVUmwfd6uLksUE+Tis0m/KOzj4KdhocGexBSrnl/SaaZsSSDv5KX1lpwWJzbytnv9JocjNrYPO5QAqEvdUj4VKiSaOqUoyXGQiaiIVC+16vsFxDkC5hcvnazm52+WgiaCpOAoHAlu096AfQwSGEIPJL/5ou9+9R/18ryD4bDYuT1Bh4l75lbTXf29acopBxr6sfX/n4Jknj8QOR499pXFAPo1SjSK1x5+wCMs1uisoxRGY/6wBaeGE8QTNTQxrettdnoVQnND6PxdPdMV8fXSlTd1iQUqBV7NSLNgxZ4uL8OIf7o0vzO5MPn8+keXr4EKlijZs1g+P9TgJuK2Njs/i83k3XatUCzbK+pqeA4XRiaVpolvKcDIW5NJlG1oroNScui5negEI6nd7WfhPNCrViZY0vreJkbHaeRm7r5x6Zy6OV8jTM1mU7t23WizkUs5WxYoGsWsC76g6klIxOzjCX13HYTdSyiT2pXdCqNbRSjobZsrxnkMhKCatRJpPJbNneg34AuzCCUTvHTtpb/QD0BlouSZ9forh7AbGlYF/ImMftncPQ6yt09lt69nanG0fk8T0LPmqFBXzJN9CqebrNjxHrO4pU7Lvk107fhZdJv/tXMD1Dv3cOxd9FoOcRBofW7xmw10Hg0q0xgs0V/R2WXx9Bt8WEyV6gpC3iz+exx87DcO+9370zi8NfRUqJEBJHMIuue6mLCH19/cvcfac16cOetc3D74Zr4xXmEwvYlpQ1FSHxh3t56pFh5mZmiMViPH1e5+NrNzl06BBeh/muWvXrYZfmIDueZmWgupXT3sWx4UEqua2J18ViMW4W5zD71ubjr8blstK2tq9/gO99ECdetWOzWVBrKs2siQuhbjwOc8dfkzX1CpM5iiYPmg4s9QMQjiLPP34Gh2V7AfUHTeE7PG4H6apzlyi89x3y2W5S49MIuxtt4L9GUcJ3bf5t8/dSQq5otr4kQmayYArHNl3bSVycfJ/c+xdxeeJIQ1Ip6ySrrxD87L8EnLvi1+wfIPz5/4HKzRyRIQVMHirxzdfu1X3oOqgWO0I1ITW9/fVRVWqzo+hX36KaCVMtzFK9+T7V6NdQhs7eW6P5pXJyiUARdQzjMoICqlBQVJXbUteKsv+N1b9wvpfXPqqSNlQMKQiFXHzxyWFMK3LUVSkJeuwE3LZ79nOiL8ClyWxb43NFEYSddkIeG/Hc1uwYEsYXius2UF/TnF2XbWs/mkyxkK+iGwZIaBqSqq7z6qVZfvUzg7t+308cjzJfc3BtNkdNQJ/fzdEjbtx287bt7fX41L8BpNMwcjWDfeI9MEJMF3ta/QAEzNy6hOp4ASHUzYOAwzHy4jzTRfPy8+liDMXhxt88y+jHs5TH4iiOALPFQTbKkd8JlnqN9M8+Ip7vW9H8vRcpBem3rzKnX+iYr/Xw+IwPYb37/L0OAs9Uj1LKDazTB6Ef5AwY4fbn8Vms0UEUi2/bvjwySCKvoWlQz4FmFLEo87iNLPGpyaX5nQ9K3guORCJ4jRznh4NLz2ZJL8yQ3gWfT3Qb/HQyTQWJgopLz3AqPLgsjrYVO/OZMpVMYk0fhNVYkSoumW0LhI/kTZRq+pr50wJGRnXmZuO7cse3x+T4OMPDw/QdtjImIOJrkJhLMGG3bsveg34AuzACAehRLuH0TIJhtLc/VMz0OG5iDh8HNqcAjL5fxvSzi4Rql5DNJordRa/1JvLSvyeQ7ydgxBE5O5VqjP6nfgVh72ydQY97Frtvfl0aSm1WsB+50BmKqZzEO/8qjcwkwcpxuk+d2NL97BcFBE66zz1L9u2/ApOZfk8STCqWYIyItpYaQjUTsd7A0f/Etn09jZe5epFL8TRWs4LFZ+VI91GGHI19z5lfjfdaCsLrtDOwhKfvgUrS7X4caZWmbmxIAXnC3VhUE0/09retnZ9sYNS0NfPtwW56YzHsNvN9c/cP+gF0eAgBeq2EkAar2yUqgNYoL8/dLPdbKBacw08SfunfIFAwamUwNJAsfTEFqdUwahVyH/7dlmxuC5tttFoqrmj3eFsL3+LoiC+tuED+3W9Tn7mCUSrSzM2TfevPqUy/s+Fa2ayAUVvzfC/z8W3R40S+/Id4z36B4HO/Q+TLf4DqcCOEwnqvuVDWtsjcGhY8dbyf3w0lC1oAACAASURBVH3uFM+e7OW3nznMCye6UZV7lwvYbbyXPlXl9j1vf213wIluyDWSDCZF5XDURX/AyRNHI/zTpw5ht5ja1g73+FCVtW00/XbrlnP5dwPvxMZejU/9N4B0GhYaJykUF9ahCXrwlYbXtEvcDFemRqmkI0wXepc/IrbbHICROKFenfEJdUM728VS9pCtH2G6eKdQZLoYA8WMa+izzHWAUilc+oipXM+alpnKa5+Q7X+MVhlHa36zME/xyutMLnhIeWawhvpYtL9AsejoKAWk13NUJi5za8wgF81hjZ1irnBknfmmlryFHyjBXPMk1cLI2te81I+/fhxlG6/5er6yCyYSthKKsnWaYy/x/dgPYNjd4IOxRRQkulRo5hZxeuwc9dqYyJTxGiUW5kpr1sYGhzBXUjRyLTkHk6LSyC3y0MP926KhOoXv9e4fUEC7MLxe6Dneh1U00Iv55Xx2IcwYIT/Rft+2csL96XFslgWKVvNaPf1lnCQQhGJp5/nnWiOPK3cF8eGbBKyColMScucAQbmZZuB0P/ZjRzqihY92Cb81uuZcQphRvXlCoQCGAblUDvO1P8Ona/it/QTti1BJks/rhM/8ClKKzuT4Z3KoV/4IZ72Jj1489WnEzeu4/F8kFDqz6dquWIiuvsOUr79NWXMTdueQhkA9+hCRLvuO91YsSoJBK0JsPc/7QT+AzXEgEMCuNqmaAjSaEmvDyRNnTwCSUqm44dquaIRfDwV577IFk7cLr9OMKPkZjvV0bG+lahOTw4PXH0AV7MrdP6gD2IWRz0MqbaXrod+jNnOFfL5GwRbE2vsQxdKRbWvfm5QY5XpiXT392zhgi6Bk1Z3rzTurZH/xHWYTARR3q91cuhzEE3Bgi52m6e+hGnFRXScf/15wRjtEtu5ccy6hKOSydnxLdzUzskCzGgKptZ09WbLiHFskW+vqSN799JVZGg0fSL3Nz+ziHOEjR1FM9s3tDL+Ace4c1U+y1PrrWIIxstOOHfU7uIMFTmcdRdl6nvdB6QcwNTPPjYUqqVIdWVikgZmQx96ZvHjdYGQsTr4h7slms5Tn2HBLxmVsrEQms/W6BKVZYsgrgQZj6TQpn2/Hdzm/sMDH0yWSxTqV7DwXpwq4rBYyiRnCk3ke6gtQzXamF8ODOoBdGHeawtuRhx7DPngnqGePbz/42N99lETq75DSaA/GLmFhtXLqpa8iHFu3uREOyUvY7eNo7nybr24lif/oY9hzro4GVcPmo+g/er+tcXt/IIm1/zj5iH15fv7SGBHzxLoB1r5whIClqyP7ylqu0GWbXOvHZKY/MI/qO7QFO35Uu3/5uWLpTPBZSsHAgA8hBpae73/gdyuByGShys3CBFa/E+lwU6k1uZiQ/FLUz/DwzkTtrJ4QP744Q7XhJJNX0bNNjnlCB+I+7hW/PbJI0eLC7BOYDQOrP0pDKijuBjWrn8tplcPu0Jbu/kEQeB/G6qbwO8Ummw/r4Jl1g7EoKr7HvobJHeyIr2YijtTqa3whoJmd6ei5FAXs3edwDT+KMFkQZhuKMGHpfgjvua+1zbf4uhCqmdUBViENLN7uju1LWOxr7xiBMAyE2UHx5itk3/4myb//Xyhc+S6yWdmz4LMQd/5ttZ4fnMDvSrz62Xs3k+jSaAuW6rrkH64t7siPZki+9+E0TV3SNAx0TWLocGkiTaZUPzD3sR1c1wzmsuWl8o61QWaBQNclV6bWF7/byHa21OCNkUXeuDbLR5Mp6k1jzZy9Gp/6bwCrm8J3Iv9cd73MdPkdWCo7uh2MtfUcJjEXwGTvjK8uMUi1lGnltC+N6WIMoVpwZbuZze78LKvxnH4e25mTaLUCuVkXhR47hdn2ObONE1TKlzEa1eUAq1BU5oyzWBajzFwfJ/POB2DUmZOPYTQHUMyObe9lUTxBc3XwXsCcdhrzq+/SzM4Tz/ehlQuI+Rmma++B+hmEYunYfWyEx8clhpG7pyDw6OgtJhIF8pUm1fQ8mm5g6pC2/W28USDy5vjEurn2Vaky0mzes88PLl2lojtoNGnLx69n53nj4hXODob2Pci8XVyuNqhnFmj1fVi/LqEl0b3AtZFby03kNwsCv3/5GtP1cQwNKtl5ksUabysqw57GgyDwbow7FFAn8899SPkYgUyCRnICxe7i2IVjOIafwT3duVz3/ugRktkfYRjaCgoIBrpLhM7EsHXQVzu2ImUYs3OjOXb6P/MVCld+BCPzDASy2IYfIWg7j3X6W9RLObrd4yBAL1ZwjRcIfu7fALZt7iVCSJ6m9NEroJrp9yyghHrwunvwTP4Y6WpgGM07dyNMRMwe7L3nO3wfnaOACpUGo+9PYlZMNDUTDc3Jm/EmX3tiaFt27pUCCsWbNLW1ufZCUTkybLtnn5OJAvmCCdMK7f7b9r3BXgYGoh09315gQ0q8n8whvCE2k6aoK3DkyKHlPgob3b0hJd9/fxLrkj0DidUXBRQq5vK+UECf+jeAlRRQJyUITI4gwZNfR9ehFgf3UOflDkyOAL7nfof5V99CKAsgBWZfN8FnnwehdNTXtvfmDuK98FuEu6Br6ezJ60nqc6Ng+JclGZAaRrlIdfYj4Mk2G8gG9eQE9awNMdCDxLzGj/vQkzj6z1O8niV8yIKwB0i/9RGsquuQCNCbaKlJWHoD2M07WI8C2kqp/1uji1Q1HUWXgIJuGFSaGm/fWOS4f+t2tiorsPrZo0MBfjQ30yaxoCoqJ/r8KErtnn1GvQ6u51vfIFbKNpgVheEu17bv6SBgqeucPRziakalqbGuNIWiCHr8Dhxm813vvlCq0tANbKtsGFKwmC4/oIB2Y+wGBbS3eJBMZBBvfw4wk5l2Mp0EKSWjlxIUb41jsliZqR4DnPu655vXSjjyA6Qrd77C3qZurJcLJJ135o58tEDq9e8Bknh+gOLlV3A9/Hlms4Pr2LYST3RhXmqVOpWIUC7EkHpjVQ3GEM5CF44d5fjfHUtpMDHRxDCy26KApJRcvz5JdR0q4Vo6gTlibMnOVvBGNIRNSjwyRz5nIIRCI7tIMOim12plbGzinn3OzUwxaPNzYy5PbYliMikqlloKvZQiXk7vO6VzL7ieXeTRaC8js1kWSklcHhvpcoNGdhFVQI/fQUCU26QpNrr7akOjkl5AX4dSUssJJiYmHlBAnR67QwHtB/YhJa0slgGN1Jv/iUBWEmqOg2qhlOuj+9xzmEPH9m2fjTSYtRnsanvWUp9vEfuhGB5Xa65ey5HMfJded0uqwdDq9DpmYWIWy8P/lljMtblPI4L9agKjWlqTHXTsiacQ5t09K1SQcpbBQTOwPQrIPlpFZy0Foygqw8PbV/28FzmCzwlBX18/uXKDdHKOo8OHkFKiKsqO/Z/Pl3jj4nX8oV6Gu1zIapbBA0Lp7ASfOymJx7uJxWLousH10XGOHj2EWRFrVFQ3uvtrsxnswSg2/+rX3sTRw6F9oYAeZAEdYKyXkhSufZ/S9depLV4BWp8Qy1PvoidnQG8ut2nEaJL7xX8B9E1t7ia2BAZQrGYEanv2jipwDj6+PLc6/wkCY62kBZL64vjdfSpmgs9/AyUQRREmhGJC8QTxPfJlTDbvrp9VUTQgC2grnm8ts2S4y42yKptEEQpHop5t2dkq3uj3ZrOJgNuK1aR21GfY4+TR4Qgvnu1jIOxG7MKZ9hsrisDtMGMzmzadv/LZ++NJ3ryexJCrXntF4USfj+GI5wEFtBvjfqWAbrw/Q7LwQ5BNpgsxavM3MXvHSHq/SDa7iJaLrKI/WtlB+SuLTCd79mXPo59kcFRPkSjqIEAIhZnaMN7YF6mlwstzK1PmVaqmS+cQkKhbsE9sxWcYDv93ZPUS/piGavMRHwPzltbuFOuMjzdQlAQwt/wV3zAk4+Ob0woDdo3LpQR1k0BqgkZuEdVqps9mZWxsctO128F3kyNoBW6LvHv5KhbPGC6bhYBS7Jj/f8x49d2Pjt7iaq7V3H51JlGXx0bM7uCBFMQuDa8XwuH9b6+4HaxrVeyp1wh65luSDFY7Aes8op6iIY8Rcmdo1lJrJChCjgSeYJMae7/nWuIqlpmL+G0zNK1dBO3JloTEqV+l51i4bW5DiZC4Mbqu5IQp5tuW/2i3i2jf3p4VBPm8QiDgAgLEF1O8NV4iUaxTz9Ux/JILw4EN2yG+fOEENn832UqNUtLBhbMnQEoatfKeSUEk6mauJHUMswvV4aUsBZlsgXMWJ2GXdd9lIe5nvPruvb4gFk3CyhacS+0jqybXctvIB1IQuzDy+SUJh6WTHoRWi3fDtcw86XoER6X1iWylDMJ8KYv75Gcozb25RoLC0xTIev+e79ntlmTfeo9MLcxqeYj5j8fwR8Nt66QcomDKkKnVkEazNb+mYQ33k6l1dUiqYS2WUjI3MoMx+gHSaDKvn8XtHECotm3alCSTBl5viWJ1njcujmJZ6rzVKOW4cmucVGKBqKWybtl/KpVi2OulywZjjRKZbbZhvFcpCJfbjSIEiUSCq9k09aZsa7vYKOX42cUbvHC6/8BIWuwUN5o6N8bi5JuCqMexLzIc+VyKelFB08Wa1pl2rbTcNvKBFMQujPslCKxXMhQ+fhfbxV9gRdDnjNLnvSPJcDvQaTHHOPaZC2R4D3ljjj7PHChmMJl5+IsvovoUhLK3++/vLmCzjaG5S4TsGRzV8p3AbM1CLPbEqnUC8eJ5wiaFysRFTAmN4aM92AbO4Mnt3n7zF/+aYCFHdCn4rFWyuCcbBJ77fcC8jSCwiq5bGBiI8OaNNFZ/FPuKoK7VFyWnqJzpDe9b4HJlIPLjqSTXUio3tDJum5V+TwSLNKHqsDq3XXeEGBhYP7BtSEmpphHt61vOeT8oQdr1sGH389Ob89QbTnI5lSuZJqcC0T29+9vPbFUbV2eybfetKvD5J88Q8TmWbTyoA+jw2K06gE5i9Brp1/+IesIHngbI29nG3Gk/CWCyYYudQFFU/E/+c7zOOVzWawi7k1DzDJagY3/OYrIh2oK5d3LzTRbb+uuEgrP/HM3UJProApWxBSqj75E1zjDQ+xKo7o7uUS8tUpv8BIyuO3UDhoaWT1KbvwKc29yO1qC+OEXJSGAPBpf/XaUKdSRr88NNQqFYbW6aZ77bGODDyRQf3MpQM3ScCApVjY/mUziCLcmO1bntPusdCmKlvelkiZ9+Mk82OYN9uslAwMFhj76v59sMV+saP7s5h65D0zDQNIkhJe/fTHDu1HEc5vVz9XerBuPp411YzIJfZBYQQuCxWTgUjdIddK9Zt5fjU/8GcD8EgWuzY5QSIaYLPcsfNVdKLEyXYqCYsfUdZzE7hHUp0Dmd7sEy3Ar4jsdZTn/c+7NYKTqfZ3q6TrlhJ1OPtPaumki4nmZig8BsdfoK5ZtJ4oU+pFz6Q1O0onzvZ3jPf6mje6xOL1Iu9DNdvBMgv33Hto9TJGwbr9WKSfIXv8NUto+CJw7kWRAq4vkaopqinmnp0K/M7VZRyWhNJibsS3b2PhC5uLjIlaxKoylXBR/nsZpa3bdWykI0c4v0Hetao6H/8ZUb3KpY0DSoZefRpcGNpMpoJYnVrN51L/uBL358jYrhRNNFu2xDboG3P7zK0W7vtmzqhsGHl68xOp8n4LSQS8xuOH+jAHyXCU4HdYYO2VEVhbGxKSYmnMtzHgSBd2HcDxSQPztCxTm+tt2jbxFb7CSOei9Hzz2K4gjjjh+MPa/GRt9TSO113KU5nHWNfs8itmMXCLkGN1znGPkxPmcKQ2+0nTtqLBLqfg5wd2yPUSvkk+u01PQtYB8cxL2h7IXEceM/4bRn0ZtV+tyzQBlZKhBUfLzw2GkmE0UsvvAyjeIJdXMo6qXLVGG8YGUxX0Vobs4GIvhdtiX7u09DWOwObso6Jn3pHCuoHos/wmeGg7zxrkD1hXDbrAwei/DEuRNr7L13M4HV7MCyjgSCJ9SNz2HedC99/QPMpYvYA1309Q+gCO7pTNvBoekshaoFwxBte64JSSDSy8BAYMs2A+Fe/ua9CXINJ6mSmfGSwF638fwGZ9msBkPTDJz+CDbVxPA6Ph9QQB0e9wMFpPqiCJO1jTpp5c8r2GJncFRPYHbv/z5vY9koUbp5Eb2cptY4iRg4hFBseB7+Zfyux9Hm60Qe8oDiJBff2J5sNFgt59BSOxXIZg1wd2zvtuhxCoqy7h07By6QS6+/ViunMMqtYPwdRVIJhkH11ruEB36Nz57uYapkYzZbwWZSODsUYjDk4i9+PIrVH8EwFKrZIt96b4ovnuldst+iB5L5Kh++M0mq0kApJXnZGaQ36Gybc6/YoqqYFIGur6V6AnYbZwZC+BmkfyCGQBKPx9e1V642EFZnG8UlESgCyrXG8hvAemvnshVeuTWCRKGSTvBJWuGXTveQLlYZuTRDoaZha+aI9PZhU3dOy+i6ZGwxR6WpI0S7cmdrz4JY+M4f2a3Y/MnlGSp1Hc0wkIZEl5JUqcGHkykeHQqtu3a9Zx9OLPL6B5O4wnUMHSy1NL+x6tx7PT71bwD3AwVkxM6TLV5nuti1/Hy6GENxeamWjzM+cTD2CdAoLHDltUv0u+Mtlc6Sg8r4R/ge/RUm4g4ikQCJBMuyDZvZCysXqBdH1tQzKHYn1USww+e20Bz+fWZmP0EUzaDAdHkA79mXqaXDG64dn1AI5PuR+srWkgVmCzZMiRtU43HS8zOcGR7mTMTO2JhB1FThlbdvUUzNoRl6GzX0vTeSHPW2NHMW8lV++tZHyxlEtew8f/1anceORGlkF1fs594ooEQiQcwe4moqs4bq6T/Rs+V2iUY5Sa1SRzNW0SnZBcqZReIl07prq/Umr//iUtv5NEPnW68ttGgofwRjyU48UeCF0z3MzcTv+dwfXx3hu+9PoBtQSs3hCEQxpLIsvWFSVFxGjnImQTmzNZs3RkeZzKhrzl7LzvP+ZZWIGmubf+vWLabn02TTSaSUCCEYGxtjNlPivZtJSun5ZSmIRnaBv/yR5HC3j9n4FFab8wEF1OlxP9QBRLrsBH75K1Re/YCgSAGChvshjnz+KTArHWkt2SmcvvQX+M0+grb55Rx+PzPYcz8jHP7lZaptK/YGe86Tef09yo10qyYAKDfdxJ66gCW4C+cOhKk8/Vli0SMYmo453UfPsGnzOoN+P956BqNSX65XkOQpGyl6Tx7DHli/JWTOyGJyeNpyvi1OHzUUAkGVUCjEG2NxhN3dNke1exlNSh7rXr+GYLu56ENDQ4SjYX7+ixqKy4/XYae338mJ4f4t2zt7fIgPFqHaMJbPoSgq/T6Vvp6uDdfemMuhOr1r7sCQAlErYV7x3LC5mK+bN6yd2AqOFyQ43SAFqqOEyekDFKxGHZs/hFk14ZUmfP4Ayl3aOt7GoXAEc0NH6KzJ4Tc7vMs5/KFQiEShwscLTYSjRqOkszha4OUzPYTDYUayCtg8mBylNhtFaefygkalJPje1STnRpL8Zl/fnv193NEbgBDi14F/BzwEXJBSfrDBvEmgSEunQJNSProTv9sZ908dQJhG9y8jDr0EUlCbUMgU92c/brekNneNsUuL1N+/hdkTIGF9DqfFTDZtX9sOs1RFuTpLtk9DCNM2fAbQT/8rShfnyVvcqA4XDe8jlEQQMrtzvlRK4PX2L+HWB4TN5yvYB3+DwuUf32mRaTKTM5KUXb1UM+u3hBS1wpqc74bNjkmopFM6fp+XRGJx3TmZkkJCNTqWi+5X4VS3heHhVqOisbF5UkvFFluxl8ukefZQjKszWW7Ol7DrVY52edEKzU3tJBNZtFKehsXadr71cK1oZ3SsglzSxd/uuavVJvlMBgumNfbr+Qx11YyUCnO5BVIlyWfP9G3Jfj6Txta0kK0222walSJhq7acw2+xOfj7j6Yp5TOYmk20SpGc2crf/jzDCZ9GpmSlWdM2vIN6MUe1kONPX/mY588dpSewQjlxF8dOvwFcAb4G/PEW5n5WSpnaob9tj/shCNyOVaRkz3P5V+Jg/aeUFt6g1xJqBT2NcZrJND0XnsHhnaO9P8HtGgWBkv42sce+jNu9Hd1/L6rNSyx2HCnBs09Bbmk0iJivo9WLaOHDxGJdK+YMoh/7NezvjdLrrmLtPkSwAYNDdm5nL60O5j0jHPzkFwY2/53gsDvUzYlePwOOOrFYjNBkA12uFYazqmYOH7Z2PBd9p/jYYUn80J3ahtUiaGsCmr4w12ZybXewnp7+bdzlczEcvbe9FWtNbMFpbEt000a+KkikJ0TT6t20BWZf/wDjiwVShpsjR/oZmc+jKmDzRzApJjSrgifQxbWMxO4OU7P6sQaaNHUDi8tLw5rHEYiiKiYs3gYnYlE+mUxvegd2fwRUC69fneO3njnCXowdRR2klNellCOd2sxujPtZDG4/sJQNyld/DlqjrQ0jhkZ55B0Ub2BNi8rbsvh6KU118v19PYeQGnopiWyUt7xWK8yT/fk3Kbz7N1Q+fIXc+98h+9Y3WSmspziCOA89if/Cr+PoP4NQGqvsKG34dH+IoagHVVExm1QURTAc9vDkscjynAuHg5gUZU2bQQNJtd5cY/Ne8H6KpoU8dgbCTkyqunw+k6riMKtrBPFMqsq5Qf89+3JZTbht5jV3uR7WdclksryhzYYm+dbb47x+dYF4qsj1+WyLTot6ODMQ4nCPh3rTYGQxz3iixKXxNB+OJTBWBNuXX0tDUmvonB8KYjeZUJSN93V7fqOps1djr2IAEnhFCCGBP5ZS/ske+b0vgsAHCd+8XsWf7Wtvw8hScDaewPPoP2P66odtz+/gPqqXMzR692fv19+fIvWzV0HXied6Kd4w4XroOSbi9k3XZt7+KZOZKHqztnSOXuTIHC55jbnG6TXzpawxPj4LZJaerx9A9Bo5Tg36KNUaLOoaR3was9PTy3NsUmIqpyjL9kAxUuGVqTR2q3lT+zsRgwPIFKt8fGOUiUSBXr+T6fjktv3cDftlgb6Ak6lkgXQjw8OBLnwOEz+aSFNTWO5JEOvzI6vZHfnqMleYKmhoBtQyC6gK6LpY039BASrmCmOZwrp23vjgEnnhWVNDkC0scqrfzw8+iLcFc2vZeSQG0mgFnJu1MlqlyO3Ac7VZJznn54k+wVuJAtQtWE0quVX1IxabHRWNJ49F2atx1zcAIcRPgK51fvWHUsrvbNHPU1LKOSFEBHhVCHFDSvnzDfx9A/gGsFySvpNx/1FAncE9rlFyl1/FO2PFRRXnqRdh+KG7UyG6BXt1Bqk12iQoJAaDvSb8x8KI5hlco28gdW3NnJ6QFW14789dT46QLPzsTo8BvUGksYhpYY7h4d/dcG1fMInVOormDrfTWu5Z1FoJ+5HTa9YqihUheonF3FumKuLu9bX+Fc8UDs/aloP1rKSvrx9lKdG8kxSQIWEkqzKREpQ0F/WCidF8k0e7ejtKGa3Ej8s7evpSSuw2M75QD9VGjVImyeHhQx3x9YW+fsYWi4yN+Th18jCvX1lgUbRTLa5wN8+eH6SSS6xrp3kxjtUVWFP30FAEms2PI9ikabRTd1Z/FItqAkWiOjw0rHk84W5iIQ/Hfdqdc1vNy/iHb9qYbdhofeBvUYTPPzzE8d7WN6G9GHelgKSUL0opT63z31b/+COlnFv6fwL4NnBhk7l/IqV8VEr5aDgc3qqLDcc/Rgqonp0k87NvoqdmkI06WnqW/Bv/mdri6F3XCtWOffhcW10CCBTFjOvU8wCYPFEUp4t2CkigqBYsh87uy7mLN34ORrONmsLQ0BYmMGq5Ddcim0tnWEtryeUc7tVrxRLeOV0ipVyXEpDc7v6wM/vrPZtKFJlMl9B0iaRFiTR1yTs3Ftr6XO42TeR3W4n6XEsUWWdsms0mjnR7ODHgJxby8JVH+vFYLZjUFhVnVhU+e6qXkMe2oR1VXUvLtV4Tid1sWpI6X0vf9IecnI0FiLichFxWnj3Rzecf7tnQz6mBAF99LMaZwQDDUQ9fe3yI//blE+zl2HUKSAjhBBQpZXEJvwT8z7vt9/Y4yBSQoVW5daOOoVlRTPaO2b/2dpw+S+tL20qKZmZxBlv06F3tHDr0K1Q8EabjRRAC1Won5X2J+UovxGFsTBDr+13yH3yH6XI/KGYEBvPyMSyNsyRnN7a9WzgzY2e62L38fFlKw2Qmc1NDsa2/VsouspUBpouR9rWKGWf4DPPr+NoqBbQVbNWylDIGmhRtFJBaTjE3M33P9jejgC5/coOm099GO0mp0Mwv8NEnIwTc1o5RQAcBH/bUCXerNHWdjNSx1LLE4xvTTc5mlsVGo02/n6W2jVTSaLnF5baXt+9QVQXBboVCJc3pSISErGNv5JmO5zfd4/Aw9FuhQQGjlKF6+w/VHo2dpoF+FfiPQBj4gRDikpTyZSFED/D/Sim/AESBb7eq8jABfyGl/NEO973lcRDrAPJ5iT3xIyq3PsJa6EMUp7AOPkwo9CVCIXXH9v1ifI3WfgtPEgxoSExr1gYDGvXUCK6GhkdYCVx4Ei0iGRqsI7FjmhJt87uGIoT7vw4fzTAQcmMKDWFJ9hBdoi/3+l6tMQf5bHLdHgPmfueGa8NhBe9zz1D8wS8IudJITVLW3US7dLznHkaLr/VlMpkplbyEQp4d68c/8tAhPkkKiks59nZvAIHK2WFfRzXpV/7e5vEibGtz86VWJRQJ4rNbDoy2fidwJBLhUKwHwzCYpHnX+SePDuCrObiVKCG1Kg5vAIsw8fARP+FwmK8+7eGvflLB4g0ipcBoVnn81BGO9XmZpHnXXgwb4fuuH4CU8tu0KJ3Vz+eALyzhceDMTvzcy5CGQenGG9z6zgjTHz+LYnShWHwHIN8fZq/dpFGZBsNLuhLAoRYR1xdYsN7E6z2+Y/tZYwBnpZXl0Jaz3+whlVYRon3+wlyJ5pW/RdZqzOZ6UOfnUWwfkgl94JaHCwAAIABJREFUFa/XsYkvKzljmKB7GOqt56tt7xXWAs+Tbb6Hq1oFKZd7DDgPnSGVtuP1bWbnCPWhbirWaxi1MjXbEfSHomRz5nXnS9kkkcjjdjeWnt+7fnwuk+aZw0PMpMuMFpoM+lUGQi5m4pPbytXfSj+A29gjaizUCmgabbnoeqWAVimSqW689qBgt9tNQzNYTCQ6bj+VSnFieJg+j50rVw0OdduIeO1MTIwvvyanu8x4IzYamkHRbKbXoS/XBGx295vhB/0AOjQMrcHk//F1qlOXMRa9hMpVHJ+k8T79WzB8bN+DtOk33qJnKVjZFkQtC2IDR5CoO7JfORMikFhsD+SqVhyHH2FwUKyZn//wH4iYR0DVMfQ6va5ZUMyYtS5isRe2tYdAANzu/bjXAEJeIFDI0EhOYnJXOPb0Y9h6zuGIb8WOi1jsAlJyV8G9ewkC3w0PDUpi4TtBW6tZ3ZHNTYPAhuRaWmEmV+V28BEUzp/sYmhwcFeCwJ3EC9kyb001ydc1alkFc7eVp46Ft23n1mKef5ioU2roqCUzhx0B+kOutjkeu+1OwF4R6wf4484t3/3d8IOm8B0Ymbf+ksrkJYx6pRWu03TQGxTe+S8g9X0P0sp6bZ08egF6k8T3/wONZKu0QgiJVkxuGsRcDzv6T+M88TzCbEdRzAiTBceJx3EOPb7OfJ1GJo6Qevt+dI3Gwq17PuN+3KvqCBB48jcJf/EP8D/xT3D2n9u2HdkoUJ14n+xb36Q4+mNkPb+rQeDdxus/E3zpsRhfPtvDif4AnzkW5XeeGSbidez7fu+Gk4Uqb48sUqg3W/SLlNyYy/H61YVt2flkOsXHExkqDQ2koNRo8qPLs8ymih3b805s7NX4VH4DyL37N8hGi/bI1/zMlAaoaVaEaiZzJQuipeC3X0HgmeYpZL6V47E61x5A/OBnpNxO0m/+BNmoE8/1kf+kiufU55ic869rsx0LGH4BefYZMqN1gkct1BTLuvOlIZkuDCx/xG3bT2mA4MT2zlgsworY474H2reD9Wqa3Dt/y1S2l5K7gFDKxIsSvfEIJvcdwbhOBoH3qjH5RnPtjQwew8/iQvHA7H0z/Pb1ecptOfitYOzVtIrhb27JjiElr78/SSk9h76qBuPHb6cYWqL2DENy8fJVRmayuB1WKpk5irUGs+kKC3NTFKoNPHbLPd/9avygH0CHhlDUZeyxZel1xwk7EgjVhi+m7zsF1Hwihnv81XVz7fu8syDMGLnv0eOdAnMrp72LeZSJWwyf+B+JxdbSOOtjM0K5W6tDE/kBjaicAUOu2IOKtevIts+4fxTQzrEn/h0cznF0rbb8mhiGhj81if/k7y/P3w0KqNN4N6Qg9hInC1UyMkElKegP2xmIDRKLxXh7Wsfqr6yRUzApJqLd61M0q3FXXz+Wm1Vs2loZDl0xMTxsI9zTx7ffmSTZcGKtWFCrCs2siZuyiTQsVDUXb89oXDjsWyMp8YAC2ucRePo3EZZW9acAhFz6v9WCyRPZdwrI7B8g9NnfQ/EG15dVMDRut4Rcfi4NZK1GIxvv+H7cJ59FsTgRZlvLl8mC4nDiPnrhvqKAdoobiTGQrHpNBM1UnNtZ+YqiIWUW0A4MLbIZ3m//94InE0X+9r0pJpNFpjMl3hlN8+qlGWq6TtRvW19CQUqcNvOW7FtUFZOqrmvH77QC8Na1BfKVJpphgISmLqnqOtIAQwoMWt8Q3r2ZJluuoRtyja8HFNA+Dd+Fr1L45KcUr75OvhFitjJMHTee419meuxOocu+Ug/DMRq9X2f62sXl58v0i4Dp4gDQTssI1UzqlsDi7+x+JucCHDr531NbHGexBO5uFUtkiIlJK8K6PZv3MwXkLQ4itcZaWk6YqE4JxsdByhJS/oTx8UvAoaW1B4ciuY3vlYbYb3zr1i2uZ1Wqq9pYNrMLvPrWZYYiLhq5dgkFVRUciviYnprDYlK25Ctmr/FBdpUdBfqjPdy6NcuVpGirzVhdM7ESf//mPK99PIvPYSVsKjM08IAC2tchFJXYv/i/qcY/ofjKZfr0oww8/BIIOzX2vw7gjj59lGSPjRBZZFOn1LATcmUQFhvlZmadnHYV04BrV/YT7rJjRE7SdEPfodbzUvkeehtsox/AQcMRW4Tq1CdttRPlpou+I2GcQUGxCN3dBvW6QqkU2vf89nutA1iNA8EgiWyFkrTQNDmJuC070uXfCba5AygNMDflGv39XMNMrLeb584fJ9FwMFeoYTPqPHH2GMeiLqamprbsy+/3U68UWdBclOs6dlnnc0+cps9vY0I0MFc1hLG2B8BG2OT0UpCCVKbA2XOhLd19fDHFjYROTdexKk6CweD9VQdw0Id94DTqw6cp/hgy2dazg1AHsBJXQ89Rswapzt0gRxeVrpPYuo+TXbiIs1oFqS3ntFujg6RLAXypg7P/1Xi/6gBW47nRWbQbb0O9yGz9YRzqICZnaNO17sGXKMzpZBt23HUdkOTEIao9j1DPLM1xt74FHJR8+HupA1iJ5xYWeOdWjrKmU0nOM55t4jSbiTmq+7L3fDZFvaii6WKNdn6zlieTyVAvZTk/HOA8NsbG0kQsTbLZ7LZ9OUWdl463mq+MjeVwyAqZTIVUKkXQ5GAuW920h8G6uJjj4pUxPGp9U/+35gu8+9Hona5ouQWyhpX/6mu/SqiVo7In41P9BgD3gxicSix2BinPEIjfeS5MjxMsF6nNXsWUL3Lkwkkcg08Snz4Iez7YQeDC6E8J5sfodo+DBbRaBdfI9wm++K+A8CZr7cihr+G8skCX14/Z1UWoONg2Z2hIQdOiKMrGWvIHAW81EPnuaALN5cRsCMyagcUTQUMhJ0r7tvfavMF8ocbtwKyUCiZF8NxjDzMQce/JHi5E+/jWL8ZRFIHVH8akmLCpAnOg1W9AiI37Ddj9PQyHlQ1td/f188Obo5h9Uewr1tUsPj6ayjN0h4Hc9fGpDAKvHAdFDK6enSTz5p+RfO1/pzL+DlIvbTpfMTnxnf0K4V/6A/xP/lPch59qdYk5AGfZCt4v/+g1qlfeWCUMJ0BvUBx5bUt2TJ4u3INPYPYPrrXPyn9T+x8w3WkQeDpTWgo1rQyoCmbSpX3b+0vnBgg6bZgVpSUUJwTDXV4Od3n3bA8eh5nfenaY84dCnB0M88KpLr54YZDf+sxRPnMsyHCXD1W50+fgdjDZrChEvNZNbc9nKqhCWadPgdES5NvD8an/BnAQxOCqUx9w/cMk/a4qYGa6mKY6/7f4H/tVJqadW7ZzP+D9DgJrxTy5Qg/TxZ7l57eDuupog2xh47VbwVIaSLnIxMT+B0x3GgSWUlJLLyydqT2w2cguMDExsW/neKJXcClXJ+rX8TuszM1mmZyc3PP96MUEgxE31KuMjY+jCoEP8OhZCvUq2VKT6pIwnCJVGsUFlGqQsenkhrbdoSqVzMLyutt3b7XZMQmdvRyf+jeA/aaABvrKLF78Ln2udr35XlsCu/EWyvBLB4K6uV8oIL2WpzI9RaiRwRI5CsM9bXP6uywkRqYxtPqa+gpTdJBAbGd72G8KqFBpMFawkCzUEIabc6FufA7zmvlbpYCGDi2SU9xIKVimMlDoCzj35XwrsRB38vrtNvO+72c1fr5/gPfGknx4SWAJRhmKeOi19tPXFcHn9Wy4biAW40pKobmqDsHmj3ArUWUhW6HLvzf1AA8ooF3GzcwMQqhr8v3Rm9Rmb+zr3u43Cqi+eJXUD/83Kjd/QenjV8n85I8ojfy8bY7J7sfccwRFmNvrKExmXMef3/EeYP8ooGShyk8/nmVkIUeqVCeeKvLX74yTLFQ2XHs32+eGgtjNCmZVWW7N6DSbODsY2Hf66qBjs0nlicNhvnRhkP/mxRN87mQ3DovprnevKgpfemQAq2lt34FUqcr/9OfvLr9Z7Pb41H8D2G8KSIsEyOW6mC7e6ZN4m5KwWgZJHhDqplN4qxTQzZEKjfQCwuIinojcdf6t0SqpmbeQeqTVslHXAJieLmMJT2EJxJbnG5FfZ/7aVWRBIBSYLg/iPvkCterxHZ9vPymgn16eppieQ1slX/D9n6UYdDXa5m+1DmB+Ns6zg0NMJ0vcSuYZdofpD1qIT03isN17S8p/zHird2+ppyll7ryWFpudZqNBplRnZC63J53BPvVvAPvdD6BrKIxlokaxmmjL6w+5s3jPfx5yByMH/l5xPi/x2+bRm3VCoV4iEQtSbr6uNPJjzGMLWHKzSB0sjRMEzjwLlsCGfvzmeYLOFEajuarHgR13sYrzcGzFfDuxC48yOPAQRr2CecFP37CpI+fu7oZ63UWptPd58gWRxuTwrMlFLyAIh733XAfQFY0QCYcI2CWxwUEUAdVKecv7KlUamBwefP4AiuDA1ELsFc7n8zhcPkwqhMPhLd+9avNisa58Lb0oJhOKIshXGnf/49aB8al/A8jnW/nbpqWT7kdeun7k6+SmP8FV0xAKZBvddPU8gjCOHoic+XvFzUqS8Tev0LDdRCgQzw/QfPgQeXl0w3XW2gjFK5Nkar04C61MqGTRxK3XXyPw2K9t7DNtRVbCSK3R3uOgHmF2fB5r+acs8Agej2/FWifgJJVqfRDoxLn3sw5A1Arr5p+bVZVk0rinOoBkMonT5ebyVJrr10exXUtht5oJq6Ut1RBcnMqTK2tUsvNcjBd4qDeApZ7pyLlHxuK8M5an3NCQhUU+Y5jo8jkOTK2Fx+NhOlXkjQ+uYfOnwFCwNlJ85ixkUpuvk1KSyaZQ3eLOa1nOY2gaTU3noT3qC/ypfwPY7yBwC/tR7c/S5z+MVi8RLPQzOGzf94DtzrBB4gf/D70WD33uGZBgaHWCixfxnf4GsVh43XXu8dfwOKcw9EZ7M3klQTDwHBvl6RuaH8fl6XUF9Hqds1CeoFYao++Zr6A4o7t27v0MAj/ZtPPmxVaw8Hbg0BPq5uxgkKi5ek9BYICxvIkFzYnZF8UWiGJIhXhO53l/hKDbtuHan1+ZpWr1YTYLzIaB1RdlsmridLBrx2cdX8wTrzux+f2Y7QoV3eByEnp7/v/2zj240es67L/z4cOLBPgAwcfyAe6Sy31oV6totSvJkt3IlmUrGseP1J4mmbRSbI+jZtxpp/9EHs+0nsx0PG46mWmaZDRN2plkmthO0lqyHLmRZNmW164eu/K+nySXJLh8gQQfAPHGd/sHQC4f4BJcPEgt72+GgwN89zvnfAcgLr5zz723cd3ia9slu+r9XLyRwKxrwdmQG8xNzClGIk4e2MTHiXAER10bzsbm5ffS3diCze7gkb5W6mocVAM9CFyinFkIEh86w+LQL1CpyB3b2xvacfgPIKZ72wdpS5VToeuoVGqpyp7bG7FnSQQvb3heNh1dbrvqPDHIpmOk5sZITl5eF0vDrMH78GcRmwNDzAIL6CnIZFg4/3rFrlskg1JzSL5Ub7OBQqUUZ4em+V9vDfDyuzd58/w40WSmqHMLySd6m+ny12IYNhymDcMQ9rc1cqK3ecNzN9MdT2W4PhXByq7e6NxSFu8Pzmx47mIyw1Q0uW4OQTaruDE6W/JA66krU1jKWl0nbylOXZ+5a53lln85GCabVWviphieixFP3nmxwFA0iYUqsPk8yxvWV4N7/g6gUoPA/f2KhUtvkpq4wch8J9GBMUQuMtP4WaClrLZ2opz0QGR+fb39YsrNQsyN52butf7+LOmFEIjB8GQzrfIYyei5/GJ3t8/D5mDshz9lKNTCQkMQrAxTNR8DDt6233uczJG9TJyZRC38GJRas3BbALkxgb+jUtcdI5v9f4hcYnBw8wHBV986TdLlI5MVErPjnM1muXzd5EB9atNzN5KbJMqxQBOLyTShbIZeb5LgyMhdD0Revd5PMu4ktWbxtcTsBMM37Yw0ZgueO78YJzlbeA5BLDpZ0hwCSymmxkeJF1h8LT5jYyBp3XX8yikH5+3EEpl1cVOZBFdv2HA7zQ11RMOxdfFzuNzYydDZdHtuUKW55zuASqWAklP91AffQnmSWFaaTm8uJcHCy3QHnkNhL5utnSh3NbcwFVxfb9/sWSDeeZLubkhMXWN67Od450dQQGNiHweeepS51BwKG53eICAowyTgn8RKxMl45nMpHQUq+lPaa8FsOnjbfraO9OwEDbeCq/cvWJEOco7/Nb09v0N3t1HW61YKAgEnIl2IqOXb+tBCjHNTwkwkATEnh+r81JomCWcTroYWHEt13r5WDMOGcifKkoYY8bo3bFMoBeSo8/PujWlGwzYSdQYn+nwcPdTHxM0kZhaW5wHklzro3d9LILCnoP1s1uInFycwG26nMGp8rQgGbXtK34fAN5BihvXLLXhdTnq7d8acAHPRycXR2VVxA0VNXSP3H264o47OLsXZmyGUp2lVCsjldPOxo7crBiuNTgHdpRy/dQ2ySdalM7KK1PRA0Xo+qLLp8eE+cALD5ly+dsOwY3i8uDv6sBJh5k99G5WMQzYBmSRWLMrcL75D00efp7bnVzD93TgCR/EeehyVybB2LX6sNNFr7942qlJM//RFFq+eQpSiUCpJFKTGBomPXalACuj25yn3usH4zCI/uzzBrdkosbTFTDTJD84EuTI+jyGy7hY/m4VwJL5hemAxlSW0ECeVscqSqlh6HJ2N8sqZIKOzceKZLMPhCN97b5SFeIr7O5swbauXJjDF4KF9/g11G4ZwbK8f03Z7OQTDsOEybRxub9yyvyLCZHiR/vF5xmainOhrxDRW18mbNhuP7PeXFI+18uTsIu8MhLg6Oks0nt3Sucf3NWNfs6+A3TB4oCcXzzvpMAQ+erSDvU11uU+uQEedh//0O49Qmy+/rQb3/B1ApVJAI9N+0nQBa7ZRXAzgDTpxxHZOuqZSsvJ+mun6MUxOo7JJpmoewNEVIHTTRTx4kdhcK8FI1/J5wUg3YnMQPj/FuPUINb2PADDyywkW57sKrsVvn8yyMJKzmRi9TnTYzshcOyqbXm6zsv0S46E53B3lve7BQYVlzWEYt1Mub5wNEi2wReGZ+UliM+NkrNU1+wY2FplblyJJZy1eO3WGpHMAMQxi0+McuzXL0YCPwcHBFf5svRZdKcWp9y5g8zavS9e8NTzNZ588Saw2yfuDUyRtQlOti3pPkrmZceZmNrZjRac43tLOjVtzTERDdLQ30NdWx9jo8B3TH2vl69dv8Mb5IJF4htjMOBdGZ3CbJg3MEV2AdBrSC1P07mvGmZ4vSxrHUor/+9YZkm4f6QykZie4fCvMyb5WUrOTRet5rEP42dgsVsTEYRo0ORapz8wzMLDxUhBLjI0Oc6S3l4ONTgYHFCf2OvC5q/ub/J7vACo1D6C9x4c/NIeVzqyqS49lvbT1PQHsjDr9yspCe18HPT0dWBbYhqC1FTDAFxvH5Zgi4nSu3tfAPU6NJ0S69tCyvra9ddRdnUBlrNVr8We8dPT14vTlbHomzuCwTxBx2tftlbBWTshs2a87ElE0NTkR8S3Xcy8a4YK1+WklNPj8WLWNZFasK28YNo61NayrC//ZlSki1OCuqcdSgrijDC8KnVnnXa3Nv7IWPZ3JYjk8uAusZZ9Mx2lubqapqYk2j9DT04NlWQwNDRVlp6eni4N7Oxgaql0+N52Mb8nfqZRJ3KgBF4g7iuGsI4mBy5nhuY+fIJPJEhwZpre3p2z1+8GZKBFx43LVYc/HxOau5+xYmg93Fr/Xg8+yeDydWBW31tZcFdpW/IotRmlubtb7AZSbSs0DmFP7cbgeJBUbzdWlx9NAhojnJNMTSQyHq6q1+VNTCo9rETFdhELmts0PWNoPwFV3mIX0+LqafU8sQ71aPf9hOlyDo+PTLPa/vdxebCazVjfR2uMshnM6iflJJqIFdK6WxWYS9dzHdNn3TRBqa5MYRni5nttMRTasze/1waxKMxaOY8UiuOrreGh/C/OhMabzzuXq8D1c6R8mFZ3D5nAu64nPu3n7fJQD9ekt16ivnAdgKYWKR0gtrl+/nuTCKl+2o6Z+ODiOoyG7zrfR2QlmpqcREaanp6nPT+Yoh91fXhsnGZnDsDtX2TVtNvpvLtLYcPe2tjIHY6UcCoVIJBJUk3u+A6jcPACD7u5fIxm6geP0KC2J8fwg8M+oOR/EffQj9FZpobfoyDvMjF2iZnYADDutdR+nu/tDKGWrqN1C8u3F4PYRWkij1FhusFcBhp3eg24ajrbiGFmr4wGSh2pwvDNIh9eOPXAfTeoh9vbcni/R5u5h9tS76wZ+O+tvgWFHYdHlm8bm34O/q2sDH1Veli1fn1JCINCASCD/ejcfNb388JSFa0U9d13zHk72+vFJjO7ubtKZLDeHhznQ25MbvB1ZPXjb0tGJqz9BRq3fpNy02entdW55gHLtIPCx+2YZS7tIZ1jWbRgmB/f7t31A1dV4c1X8Vg5Ed+/du/xGlNPujQU7ozOFN5fv8mdL0v9B2hT+nu8ARCAbD5Odi2B42wAnhgHZ/HhPabLg8O8nG/4FOJK5z2kmDVaa2IWfEt/ThrHvWJlsFZYTkxdZfO8fsZKt4MqiMhaJ4CUi52bwHPtMxezeSc492vD/6peZsV/GloqDCJ6Ox/A9fpil/bPXnufw91F3fx/+fbnXwyOr27jbDpM4eBLj7VHEdAA2THsNvieeJTk/Sc2QnYZjPhzNB1kcMVada8XDhN9/idDVBK76UWxtPVj+z2MYDUVfX6FB4EPtjYx0NjCSsGFZYDcMjgX8/Eq3n5GREQzDwLQpnKYNwzDI5pWulGvsJrV2k8X8YKJaMWjc2eQGrA3PvZO88rWjAR+NCTcXg2HshoFps3GytxGfxO9Kdznl9gY3syIodfu6FUJLnQtbheweavNw8bKxLt6I0NxQc1c6E6kMAxPzXJ1R2JLztHd2YRqy5fesmtzTHUAmGubsn3yTc//4GM0104Ai5HkK2AeUZ3AwNT/BzVAbGc8isGZQMjhCenYc75GnGLxp3FHP3crh8BWyc61rBkM7UGfGaPImuDnkKkpPueTVi8E5GUs/iHv/gwCMDYB7eGMd2cQc187OEB9ewGzpZGSseb0dz6cId0Tw1I+i7CbhuW7GYw6wH2bSAE8cGFmtv78/zczPf4AVjxNcyP+cn08yevY04vgIYnPe9SAwgD0Z5smeHlLJNEHJsscRY6RAbf6d5L76NKNXJhEUVn49ftM06AjYGBi4VbSeJXntPIDBwQF6e3tp7XVyLZvmcI8Tm8S3tY5+iXoWCC8kSWct4uEJTJtgiEGTJ1mxPQmUUnisWSJzFlgGqdlJTJvw6ME9DOXX/d+Kzngqw4/OjTIfGkPcHlQ8ytVbszxxtJ2psdGidOhN4cvM8ItfwjEToqN2D801U7mUwcLrtHuewPQdAEpPfSSmFphvGKGzNrg6JZGXW1LTeJ3NSO/xiqRc3NHLWHXxdXa7GkL422IYNtc2pYC2dl6z7QwLl16mebGTxlAQFYK2xmfyqay17b10dx9GKXCvSyWtl5NTg9TPD6LMBErdjhOGnRZHA+6OE0X5WSgFtPZW3u123NWtfyCgcNptTKdrCS+mMFs8fOLRB/HW2Bhxu8qahrDbNt6ucLvkT3R2cWNynstXa+nr28+BtjomxkYravcpEeqa2giGI4Qnm3js+BHshqxL0RUjX5uzQV0L9nQWh6eeVHQeo66ZwYhr02UhtjMFVNI9h4j8kYhcFZHzIvI9EWnYoN3TInJNRPpF5IVSbBZLcmqIePAyYmUQxara8sjVny+3K7U+3NEYQCxr/dIEy+v+J4kNnC6LrUKyrW5PYbuGDcNVVzG7m8lbaauSESLvvrScPsNKQzZDfOgc2bnhkn3JLs6ClWJtnMimseanitZTKAVUTrnJ6+LphwL8i8f3cXJ/Cw1ex13pWSnvhCUTipHtdpODexp4qLeFB7qbcJjV8b+pzs39XU3sa6nDZTe3dO5KeSgUhRUprKXH0XAMpbaur1qUegfwOvA1pVRGRL4FfA34g5UNRMQG/BnwFDAKvCci31dKXS7R9h3JREKIzc58opHRaIBEJrdPZzDSjW0sTmRNmuDu5RpC3k+iFl4DlSlYl+4wWplOl8PWernL/+ssDL5MMNJ52260B8/BD5MImlWfH3A3W0JePT+NPxKA7Np5AJ043xvBe6C7JL9GZruIRfah1uqP9lK3uBdnkZ+FjVJAO1EudikILZdHjs8Zy8tppBOLZGIRyG8TOTCQXf7hcE+lgJRSr614+jbw+QLNHgb6lVKDACLyHeAzQEU7AFfHYVQ2ja8mzMGmq/jcIUDw1CQ4cHw/9UdzcwQCgdztfWnyPlqdTzL1+ovUuhZp99wCJCc3ztDw8HFm7eWytVbeQ6Lv4zS8+S7NMoLhrqe9OcChx45UwNbmss8H4fDWzjPnF/AG+1Hp5Or4uWMc6NpLQ4nvVZ2nFfv5SbKRudv6DZP6RjsPPvExxFacHq/XRnd3B5Chvr6eQCCAUmpHyj6fj3A4vCN82Q2yZ1Y4PzrHosuN6aknE52ntqmVA231HGsxitJx33334fF4Kvm1uI5yjgF8Efhugdc7gOCK56PAI2W0WxCby0Prp/49mVf+K5878hIOWwIMOxlHK0de+F1MD8RisJRyK13eT+TJz3HjL1/AZYuhMklS4sN38AECX3yYeKKcttbK+4k9t5+amt+ukP7iZZcLElu81vmPtHPrj99AZZMkMm5cZq6uPymNHPrX/42avaX6ZeKwfp+Jl/4LU+dewWWL473vSeqf/g3qWhxb0FNDTc2HgJPEYrHlfO1OlF0uF4lEYkf4shtkbA6+8Z13GZ4MI4YJKkObr4E//K2HMckUpcPj8SzPC6gWsjQAsWEDkTeAtgKHvq6Uejnf5uvACeA31BqFIvIF4JNKqS/nn/9L4GGl1L/ZwN5XgK8ABAKBh4aHhws1K5qFCz/0EtkmAAAGo0lEQVRi+kd/QSYyjffIR2l+6vcwvf7NT7xL0gsh5k+/QiYaxnPow9T2PYKsTBxrCjJz6m8Z//tvoCwLrCxid+J7/Ldo/8J/3G7XNJqiUEpxKTjLUChCp6+WY3ubMLbhf19EziilThTVdrMOoAhjzwLPA08qpWIFjn8I+IZS6pP5518DUEp9czPdJ06cUKdPny7JP80Hh2RomPkzr2Clk9Q/8Ancgfu32yWN5gPHVjqAklJAIvI0uUHfXy305Z/nPaBPRPYBt4DfBH67FLuaexNnczctT391u93QaHYNpdYd/SngBV4XkbMi8iKAiLSLyKsASqkM8FXgn4ArwN8ppS6VaFej0Wg0JVJqFdD+DV4fA55Z8fxV4NVSbGk0Go2mvFR/5oFGo9FodgS6A9BoNJpdiu4ANBqNZpeiOwCNRqPZpegOQKPRaHYpugPQaDSaXYruADQajWaXojsAjUaj2aXoDkCj0Wh2KboD0Gg0ml2K7gA0Go1ml1LyctCVRERCQGkbAoAfmC6DO+VmJ/qlfSqenejXTvQJdqZfO9EnKI9f3Uqp5mIa7ugOoByIyOli18auJjvRL+1T8exEv3aiT7Az/dqJPkH1/dIpII1Go9ml6A5Ao9Fodim7oQP479vtwAbsRL+0T8WzE/3aiT7BzvRrJ/oEVfbrnh8D0Gg0Gk1hdsMdgEaj0WgKcM91ACLyRyJyVUTOi8j3RKRhg3ZPi8g1EekXkRcq7NMXROSSiFgisuEIv4gMiciF/P7Kpyvp0xb9qmasfCLyuojcyD82btAum4/TWRH5fgX9ueO1i4hTRL6bP/6OiOytlC9b8Ok5EQmtiM+Xq+DT/xSRKRG5uMFxEZE/yft8XkSO7wCfnhCR+RVx+g9V8KlLRH4sIlfy/3v/tkCb6sVKKXVP/QGfAMy8/C3gWwXa2IABoAdwAOeA+yro02HgIPAT4MQd2g0B/irGalO/tiFW/xl4IS+/UOj9yx+LViE+m1478PvAi3n5N4Hv7gCfngP+tFqfo7zNfwYcBy5ucPwZ4IeAAI8C7+wAn54AflDlOO0BjudlL3C9wPtXtVjdc3cASqnXlFKZ/NO3gc4CzR4G+pVSg0qpFPAd4DMV9OmKUupapfTfLUX6VdVY5XX/VV7+K+CzFbS1GcVc+0p//wF4UkRkm32qOkqpt4DwHZp8BvhrleNtoEFE9myzT1VHKTWulHo/L0eAK0DHmmZVi9U91wGs4YvketK1dADBFc9HWf8mbAcKeE1EzojIV7bbmTzVjlWrUmoccv8sQMsG7VwiclpE3haRSnUSxVz7cpv8D495oKlC/hTrE8A/z6cP/kFEuiroT7Hs1P+5D4nIORH5oYgcqabhfLrwQeCdNYeqFiuzEkorjYi8AbQVOPR1pdTL+TZfBzLA3xRSUeC1ksqhivGpCB5XSo2JSAvwuohczf+K2U6/qhqrLagJ5GPVA7wpIheUUgOl+FWAYq697PHZhGLsvQJ8WymVFJHnyd2hfKyCPhVDteNUDO+TWzYhKiLPAC8BfdUwLCIe4H8D/04ptbD2cIFTKhKrD2QHoJT6+J2Oi8izwKeAJ1U+qbaGUWDlr6JOYKySPhWpYyz/OCUi3yN3u19SB1AGv6oaKxGZFJE9Sqnx/G3v1AY6lmI1KCI/IfdLqtwdQDHXvtRmVERMoJ7Kph029UkpNbPi6V+QGwvbbsr+OSqVlV+8SqlXReTPRcSvlKroGkEiYif35f83Sqn/U6BJ1WJ1z6WARORp4A+ATyulYhs0ew/oE5F9IuIgN3hXsUqSYhCRWhHxLsnkBrMLVi9UmWrH6vvAs3n5WWDdXYqINIqIMy/7gceByxXwpZhrX+nv54E3N/jRUTWf1uSLP00uz7zdfB/4V/kKl0eB+aVU33YhIm1L4zUi8jC578OZO59Vsk0B/gdwRSn1xxs0q16sqjkCXo0/oJ9c/uxs/m+pQqMdeHVFu2fIjcAPkEuHVNKnz5Hr1ZPAJPBPa30iV9VxLv93qdI+FevXNsSqCfgRcCP/6Mu/fgL4y7z8GHAhH6sLwJcq6M+6awf+kNwPDAAX8Pf5z927QE8V3rfNfPpm/jN0DvgxcKgKPn0bGAfS+c/Ul4DngefzxwX4s7zPF7hDNVwVffrqiji9DTxWBZ8+TC6dc37Fd9Qz2xUrPRNYo9Fodin3XApIo9FoNMWhOwCNRqPZpegOQKPRaHYpugPQaDSaXYruADQajWaXojsAjUaj2aXoDkCj0Wh2KboD0Gg0ml3K/wcy54hWLWXcKAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ff6ff946320>"
+       "<keras.callbacks.History at 0x7fe8e7465438>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "execution_count": 194,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "#color=\"steelblue\",color=\"chocolate\" marker=marker,\n",
+    "# Building the keras model\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
     "\n",
+    "model = Sequential()\n",
     "\n",
-    "def plot_points(plt=plt, marker='o'):\n",
-    "    colors = [[\"steelblue\", \"chocolate\"][i] for i in labels]\n",
-    "    plt.scatter(features.iloc[:, 0], features.iloc[:, 1], color=colors, marker=marker);\n",
+    "model.add(Dense(64,input_shape=(28*28,), activation=\"relu\"))\n",
     "\n",
-    "_, ax = plt.subplots(figsize=(6, 6))\n",
+    "model.add(Dense(64, activation = \"relu\"))\n",
     "\n",
-    "xor = pd.read_csv(\"xor.csv\")\n",
-    "features = xor.iloc[:, :-1]\n",
-    "# Convert boolean to integer values (True->1 and False->0)\n",
-    "labels = xor.iloc[:, -1]\n",
+    "model.add(Dense(10, activation = \"softmax\"))\n",
     "\n",
-    "train_and_plot_decision_surface(\"Neural Net\", model_scikit, features, labels, plt=ax)\n",
-    "plot_points(plt=ax)"
+    "model.compile(loss=\"categorical_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
+    "\n",
+    "model_history = model.fit(X_train_prep, y_train_cat, epochs=20, batch_size=512);"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 196,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10000/10000 [==============================] - 1s 85us/step\n",
+      "The [loss, accuracy] are:  [0.08737125840586377, 0.974]\n"
+     ]
+    }
+   ],
    "source": [
-    "**Exercise: Create a neural network to classify the 2d points example from chapter 2 and **"
+    "# Evaluating the model on test dataset\n",
+    "print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 144,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "df = pd.read_csv(\"2d_points.csv\")\n",
-    "features = df.iloc[:, :-1]\n",
-    "labels = df.iloc[:, -1]\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
+    "# Work in Progress\n",
     "\n",
-    "\n"
+    "## Network results on dataset used in previous notebooks"
    ]
   },
   {
@@ -3635,20 +3693,237 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "TODO: \n",
     "\n",
-    "- does keras support scikit-learn api ? (.fit and .predict methods) ?\n",
-    "- if yes: we could use cross validation and hyper parameter optimzation for scikit-learn to evaluae / improve keras network.    \n",
     "      \n",
     "      "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/linux-graphics/anaconda3/envs/machine_learning_workshop/lib/python3.6/site-packages/ipykernel_launcher.py:9: UserWarning: get_ipython_dir has moved to the IPython.paths module since IPython 4.0.\n",
+      "  if __name__ == '__main__':\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "    \n",
+       "    @import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
+       "    \n",
+       "    @import url('http://fonts.googleapis.com/css?family=Kameron');\n",
+       "    @import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
+       "    \n",
+       "    @import url('http://fonts.googleapis.com/css?family=Lato');\n",
+       "    @import url('http://fonts.googleapis.com/css?family=Source+Sans+Pro');\n",
+       "    \n",
+       "    @import url('http://fonts.googleapis.com/css?family=Lora'); \n",
+       "\n",
+       "    \n",
+       "    body {\n",
+       "        font-family: 'Lora', Consolas, sans-serif;\n",
+       "       \n",
+       "        -webkit-print-color-adjust: exact important !;\n",
+       "        \n",
+       "      \n",
+       "       \n",
+       "    }\n",
+       "    \n",
+       "    .alert-block {\n",
+       "        width: 95%;\n",
+       "        margin: auto;\n",
+       "    }\n",
+       "    \n",
+       "    .rendered_html code\n",
+       "    {\n",
+       "        color: black;\n",
+       "        background: #eaf0ff;\n",
+       "        background: #f5f5f5; \n",
+       "        padding: 1pt;\n",
+       "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
+       "    }\n",
+       "    \n",
+       "    p {\n",
+       "      line-height: 140%;\n",
+       "    }\n",
+       "    \n",
+       "    strong code {\n",
+       "        background: red;\n",
+       "    }\n",
+       "    \n",
+       "    .rendered_html strong code\n",
+       "    {\n",
+       "        background: #f5f5f5;\n",
+       "    }\n",
+       "    \n",
+       "    .CodeMirror pre {\n",
+       "    font-family: 'Source Code Pro', monocco, Consolas, monocco, monospace;\n",
+       "    }\n",
+       "    \n",
+       "    .cm-s-ipython span.cm-keyword {\n",
+       "        font-weight: normal;\n",
+       "     }\n",
+       "     \n",
+       "     strong {\n",
+       "         background: #f5f5f5;\n",
+       "         margin-top: 4pt;\n",
+       "         margin-bottom: 4pt;\n",
+       "         padding: 2pt;\n",
+       "         border: 0.5px solid #a0a0a0;\n",
+       "         font-weight: bold;\n",
+       "         color: darkred;\n",
+       "     }\n",
+       "     \n",
+       "    \n",
+       "    div #notebook {\n",
+       "        # font-size: 10pt; \n",
+       "        line-height: 145%;\n",
+       "        }\n",
+       "        \n",
+       "    li {\n",
+       "        line-height: 145%;\n",
+       "    }\n",
+       "\n",
+       "    div.output_area pre {\n",
+       "        background: #fff9d8 !important;\n",
+       "        padding: 5pt;\n",
+       "       \n",
+       "       -webkit-print-color-adjust: exact; \n",
+       "        \n",
+       "    }\n",
+       " \n",
+       "    \n",
+       " \n",
+       "    h1, h2, h3, h4 {\n",
+       "        font-family: Kameron, arial;\n",
+       "    }\n",
+       "    \n",
+       "    div#maintoolbar {display: none !important;}\n",
+       "    </style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#REMOVEBEGIN\n",
+    "# THE LINES BELOW ARE JUST FOR STYLING THE CONTENT ABOVE !\n",
+    "\n",
+    "from IPython import utils\n",
+    "from IPython.core.display import HTML\n",
+    "import os\n",
+    "def css_styling():\n",
+    "    \"\"\"Load default custom.css file from ipython profile\"\"\"\n",
+    "    base = utils.path.get_ipython_dir()\n",
+    "    styles = \"\"\"<style>\n",
+    "    \n",
+    "    @import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
+    "    \n",
+    "    @import url('http://fonts.googleapis.com/css?family=Kameron');\n",
+    "    @import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
+    "    \n",
+    "    @import url('http://fonts.googleapis.com/css?family=Lato');\n",
+    "    @import url('http://fonts.googleapis.com/css?family=Source+Sans+Pro');\n",
+    "    \n",
+    "    @import url('http://fonts.googleapis.com/css?family=Lora'); \n",
+    "\n",
+    "    \n",
+    "    body {\n",
+    "        font-family: 'Lora', Consolas, sans-serif;\n",
+    "       \n",
+    "        -webkit-print-color-adjust: exact important !;\n",
+    "        \n",
+    "      \n",
+    "       \n",
+    "    }\n",
+    "    \n",
+    "    .alert-block {\n",
+    "        width: 95%;\n",
+    "        margin: auto;\n",
+    "    }\n",
+    "    \n",
+    "    .rendered_html code\n",
+    "    {\n",
+    "        color: black;\n",
+    "        background: #eaf0ff;\n",
+    "        background: #f5f5f5; \n",
+    "        padding: 1pt;\n",
+    "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
+    "    }\n",
+    "    \n",
+    "    p {\n",
+    "      line-height: 140%;\n",
+    "    }\n",
+    "    \n",
+    "    strong code {\n",
+    "        background: red;\n",
+    "    }\n",
+    "    \n",
+    "    .rendered_html strong code\n",
+    "    {\n",
+    "        background: #f5f5f5;\n",
+    "    }\n",
+    "    \n",
+    "    .CodeMirror pre {\n",
+    "    font-family: 'Source Code Pro', monocco, Consolas, monocco, monospace;\n",
+    "    }\n",
+    "    \n",
+    "    .cm-s-ipython span.cm-keyword {\n",
+    "        font-weight: normal;\n",
+    "     }\n",
+    "     \n",
+    "     strong {\n",
+    "         background: #f5f5f5;\n",
+    "         margin-top: 4pt;\n",
+    "         margin-bottom: 4pt;\n",
+    "         padding: 2pt;\n",
+    "         border: 0.5px solid #a0a0a0;\n",
+    "         font-weight: bold;\n",
+    "         color: darkred;\n",
+    "     }\n",
+    "     \n",
+    "    \n",
+    "    div #notebook {\n",
+    "        # font-size: 10pt; \n",
+    "        line-height: 145%;\n",
+    "        }\n",
+    "        \n",
+    "    li {\n",
+    "        line-height: 145%;\n",
+    "    }\n",
+    "\n",
+    "    div.output_area pre {\n",
+    "        background: #fff9d8 !important;\n",
+    "        padding: 5pt;\n",
+    "       \n",
+    "       -webkit-print-color-adjust: exact; \n",
+    "        \n",
+    "    }\n",
+    " \n",
+    "    \n",
+    " \n",
+    "    h1, h2, h3, h4 {\n",
+    "        font-family: Kameron, arial;\n",
+    "    }\n",
+    "    \n",
+    "    div#maintoolbar {display: none !important;}\n",
+    "    </style>\"\"\"\n",
+    "    return HTML(styles)\n",
+    "css_styling()\n",
+    "#REMOVEEND"
+   ]
   },
   {
    "cell_type": "code",
-- 
GitLab