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-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 1. GAN 소개"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[](https://colab.research.google.com/github/Pseudo-Lab/Tutorial-Book/blob/master/book/chapters/GAN/Ch1-Introduction.ipynb)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 개요 (Overview)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "GAN은 Generative Adversarial Networks의 약자로 우리말로는 \"적대적 생성 신경망\"이라고 번역되는 AI기술 중 하나입니다. GAN은 실제에 가까운 이미지나 사람이 쓴 것과 같은 글 등 여러 가짜 데이터들을 생성하는 모델입니다. \"적대적 생성 신경망\"이라는 이름에서 알 수 있듯 GAN은 서로 다른 두 개의 네트워크를 적대적으로(adversarial) 학습시키며 실제 데이터와 비슷한 데이터를 생성(generative)해내는 모델이며 이렇게 생성된 데이터에 정해진 label값이 없기 때문에 비지도 학습 기반 생성모델로 분류됩니다. \n",
- "\n",
- "GAN은 구글 브레인에서 머신러닝을 연구했던 Ian Goodfellow에 의해 2014년 처음으로 신경정보처리시스템학회(NIPS)에서 제안되었고 이후 이미지 생성, 영상 생성, 텍스트 생성 등에 다양하게 응용되고 있습니다.\n",
- "\n",
- "이번 1장에서는 GAN의 개념을 비롯하여 GAN 모델의 구조와 평가지표, 적용 사례 등에 대해 알아봅니다. 1.1절에서는 GAN의 등장과 개념에 대해서 설명하고 1.2절에서는 GAN 모델을 구조와 GAN의 한 종류인 Conditional GAN (cGAN) 모델, 그리고 GAN 모델의 평가지표에 대해서 설명합니다. 이어 1.3절에서는 GAN을 적용한 사례들을 살펴보며 1.4절에서는 GAN이 가진 한계점을 알아 봅니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 1.1. 개념 (Concept)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "GAN은 Generator(G,생성모델/생성기)와 Discriminator(D,판별모델/판별기)라는 서로 다른 2개의 네트워크로 이루어져 있으며 이 두 네트워크를 적대적으로 학습시키며 목적을 달성합니다. 생성모델(G)의 목적은 진짜 분포에 가까운 가짜분포를 생성하는 것이고 판별모델(D)의 목적은 표본이 가짜분포에 속하는지 진짜분포에 속하는지를 결정하는 것입니다. 이 2가지 모델을 포함한 GAN의 궁극적인 목적은 \"실제 데이터의 분포\"에 가까운 데이터를 생성하는 것이며, 따라서 판별기가 진짜인지 가짜인지를 한 쪽으로 판단하지 못하는 경계(가짜와 진짜를 0과 1로 보았을 때 0.5의 값)에서 가짜 샘플과 실제 샘플을 구별할 수 없는 최적 솔루션으로 간주하게 됩니다. 제안자 Ian Goodfellow은 논문에서 다음과 같이 '경찰과 위조지폐범'을 예시로 들어 GAN 모델의 개념을 설명하고 있습니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- " - 그림 1-1 경찰(왼쪽)과 도둑(오른쪽) 이미지"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "생성 모델은 진짜 지폐와 비슷한 가짜 지폐를 만들어 경찰을 속이려 하는 위조지폐범과 같고, 반대로 판별모델은 위조지폐범이 만들어낸 가짜 지폐를 탐지하려는 경찰과 유사합니다. 이러한 경쟁이 계속됨에 따라 위조지폐범은 경찰을 속이지 못한 데이터를, 경찰은 위조지폐범에게 속은 데이터를 각각 입력받아 적대적으로 학습하게 되는 것입니다. 이 게임에서의 경쟁은 위조지폐가 진짜 지폐와 구별되지 않을 때까지 즉, 주어진 표본이 실제 표본이 될 확률이 0.5에 가까운 값을 가질 때까지 계속됩니다. 가짜로 확신하는 경우 판별기의 확률값이 0, 실제로 확신하는 경우 판별기의 확률값이 1을 나타내게 되며, 판별기의 확률값이 0.5라는 것은 가짜인지 진짜인지 판단하기 어려운 것을 의미하게 되는 것입니다.\n",
- "\n",
- "다음 절에서는 GAN 모델의 구조와 그 한 갈래인 cGAN 모델, 그리고 GAN 모델의 평가지표에 대해서 살펴봅니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 1.2. GAN 모델"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.2.1. 모델 구조"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "GAN의 아키텍처를 도식화하면 다음과 같습니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- " - 그림 1-2 GAN의 아키텍처 (출처: Hamed Alqahtani. 2019. An Analysis Of Evaluation Metric Of GANs)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "생성기(G)는 실제 데이터와 비슷한 데이터를 만들어내도록 학습되며, 판별기(D)는 실제 데이터와 G가 생성한 가짜 데이터를 구별하도록 학습됩니다. GAN의 목적함수는 다음과 같은데, 이는 게임이론 타입의 목적함수로 G와 D 2명의 플레이어가 싸우면서 서로 균형점(nash equilibrium)을 찾아가도록 하는 방식입니다. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- " - 그림 1-3 GAN Value Function (출처: Ian J.Goodfellow. 2014. Generative Adversarial Nets) "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "여기서 V(D,G)의 값은 확률값으로 도출되는데, 이 수식을 각각 D와 G의 관점에서 살펴보면 다음과 같습니다.
\n",
- "\n",
- "먼저 D의 관점에서 실제 데이터(x)를 입력하면 D(x)가 커지면서 log값이 커지면서 높은 확률값이 나오도록 하고, 가짜 데이터(G(z))를 입력하면 log값이 작아짐에 따라 낮은 확률값이 나오도록 학습됩니다. 다시 말해 D는 실제 데이터와 G가 만든 가짜 데이터를 잘 구분하도록 조금씩 업데이트되는 것입니다.
\n",
- "\n",
- "G에서는 Zero-Mean Gaussian 분포에서 노이즈 z를 멀티레이어 퍼셉트론에 통과시켜 샘플들을 생성하며 이 생성된 가짜 데이터 G(z)를 D에 input으로 넣었을 때 실제 데이터처럼 확률이 높게 나오도록 학습됩니다. 즉 D(G(z))값을 높도록, 그리고 전체 확률 값이 낮아지도록 하는 것이며 이는 다시 말해 G가 'D가 잘 구분하지 못하는' 데이터를 생성하도록 조금씩 업데이트되는 것입니다.
\n",
- "\n",
- "실제 학습을 진행할 때는 G와 D 두 네트워크를 동시에 학습시키지 않고 하나의 네트워크를 고정한 상태에서 다른 한 네트워크를 업데이트하는 방식으로 따로따로 업데이트합니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.2.2. cGAN"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "cGAN은 Conditional Generative Adversarial Networks의 약자로 생성기와 판별기가 훈련하는 동안 추가 정보를 사용해 조건이 붙는 생성적 적대 신경망입니다. GAN과는 '샘플링을 어디서 해오는지'와 '데이터셋에 라벨이 있어야 하는지', 2가지 측면에서 차이가 있습니다. cGAN을 이용하면 인위적으로 원하는 클래스의 데이터를 생성할 수 있으며 생성기와 판별기를 훈련하는 데에 label을 사용합니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- " - 그림 1-4 GAN과 cGAN 차이점 "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "쉽게 말하면 Generator와 Discriminator에 특정 condition을 나타내는 정보 y를 추가해주는 것인데, 이 때 y는 형태가 정해진 것은 아니기 때문에 다양한 형태를 가질 수 있습니다. 예를 들어, 필기된 숫자를 인식하는 MNIST 데이터에서 원하는 숫자를 생성하고 싶다면 숫자의 class에 해당하는 label을 추가 정보 y로 입력해주는 것입니다. 논문에서는 생성하고 싶은 숫자 class를 one-hot encoding 하였는데, MNIST 데이터셋에서는 이 class를 one-hot encoding하게 되면 10bit가 필요하기 때문에 정보 y는 10bit 크기를 가지게 됩니다. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- " - 그림 1-5 cGAN Value Function (출처: Mehdi Mirza. 2014. Conditional Generative Adversarial Nets)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "cGAN의 value 함수는 위와 같이 정의되는데 위의 GAN의 경우와 비교해 보면 Generator와 Discriminator 각각에 추가된 정보 y에 대해 조건부 확률인 점이 차이점으로 나타납니다. 아래는 cGAN의 간단한 구조로 input에서 y정보가 추가로 들어가는 것과 output으로 나오는 확률값이 y정보에 대한 조건부인 것을 확인할 수 있습니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- " - 그림 1-6 Conditional adversarial net (출처: Mehdi Mirza. 2014. Conditional Generative Adversarial Nets)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "2014년 11월 몬트리올대학교에서 최초로 발표된 cGAN 논문에서는 아래와 같이 MNIST 데이터셋으로부터 원하는 숫자(0~9까지 각각)에 대한 생성 결과를 행별로 총 10행의 결과 이미지로 보여주고 있습니다. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- " - 그림 1-7 Generated MNIST digits, each now conditioned on one label
(출처: Mehdi Mirza. 2014. Conditional Generative Adversarial Nets)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.2.3. 평가 지표 (Evaluation Metrics)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "GAN 창시자 Ian Goodfellow는 2014년 GAN을 소개한 이후 2016년에 학습(training)방식을 향상시키는 기술을 추가로 발표했는데 논문에서 저자들은 \"GAN은 object function이 없으며 이는 서로 다른 모델들의 퍼포먼스를 비교하는 것을 어렵게 만드는 요인이다\"라고 언급했습니다. 즉 주어진 GAN 모델에 대해 일반적으로 합의된 평가 방식이 없다는 것인데, 이는 학습(training)이 실행되고 있는 동안 최종적인 GAN 모델을 선택할 때, GAN 모델의 활용성을 증명하기 위해 생성된 이미지를 선택할 때, GAN 모델 아키텍처 간 비교할 때 등의 경우에 이슈가 되곤 합니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "2018년 GAN의 평가 지표의 장단점을 기술한 논문에서 Ali Borji는 \"GAN 모델의 강점과 한계점을 반영하는 가장 적절한 지표에 대해서는 아직도 의견의 일치가 이루어지지는 않았다\"고 언급했습니다. 이와 같이 GAN 모델은 생성 대상의 도메인 문맥에 따라 생성된 이미지의 퀄리티에 기반하여 평가되고는 했습니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "그 이후인 2019년 Macquarie 대학교의 Hamed Alqahtani가 발표한 논문에서는 GAN 모델의 평가지표에 대한 연구를 통해 10여 개의 GAN 모델의 평가지표를 제시하였습니다. Hamed에 따르면 GAN의 평가지표는 크게 정성적인 지표와 정량적인 지표 2가지로 분류되며 일반적으로 정성적인 지표는 사람이 이미지를 평가하므로 비용이 많이 든다고 합니다.\n",
- "\n",
- "정성적인 지표에는 Nearest Neighbor, Rating and Preference Judgement, Rapid Scene Categorization이 있으며 정량적인 지표에는 FID(Freechet Inception Distance), IS(Inception Score), Mode Score, Maximum Mean Discrepancy 등이 있습니다. 우리는 아래에서 NVIDIA의 논문에 모델 비교시 핵심적으로 사용했던 IS와 자주 쓰이는 FID 2가지에 대해서 알아보겠습니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Inception Score (IS) "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Inception Score는 Salimans et al.에 의해 제안되었는데 GAN의 평가에 널리 쓰이는 지표입니다. 이 지표는 클래스 label과 관련하여 특징적인 속성들을 잡아내기 위해 pre-trained 신경망을 사용합니다. 아래는 IS 값을 도출하는 수식인데 샘플의 조건부 분포 p(y|x)와 모든 샘플에서 얻은 주변분포 p(y) 사이의 평균적인 KL 발산 정도(Average KL Divergence)를 측정하는 것이며 이 값이 높을수록 좋은 성능를 낸다고 해석할 수 있습니다. 하지만 IS에는 실제 샘플 대신 생성된 이미지를 사용해 계산하고 클래스 당 하나의 이미지만 생성하면 다양성이 부족하더라도 p(y)가 균등 분포에 가깝게 나오기 때문에 성능을 왜곡할 수 있다는 단점이 있습니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- " - 그림 1-8 Average KL Divergence (출처: Hamed Alqahtani. 2019. An Analysis Of Evaluation Metric Of GANs) "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Frechet Inception Distance (FID)\n",
- "Frechet Inception Distance는 생성되는 이미지의 퀄리티 일관성을 유지하기 위해 이용되는 지표입니다. 실제 데이터의 분포를 활용하지 않는 단점을 보완하여 실제 데이터와 생성된 데이터에서 얻은 feature의 평균과 공분산을 비교하는 방식이며 FID가 낮을수록 이미지의 퀄리티가 더 좋아지는데 이는 실제 이미지와 생성된 이미지의 유사도가 높아지는 것을 말합니다. 즉 쉽게 말해 FID는 생성된 샘플들의 통계와 실제 샘플들의 통계를 비교하는 것입니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "이어서 다음 절에서는 GAN을 적용한 사례들에 대해서 살펴보겠습니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 1.3. 적용 사례 (Use Case)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "GAN은 이미지 생성을 중심으로 다양하게 응용되어 왔는데 예시를 살펴보면 다음과 같습니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.3.1. 가짜이미지 생성 / NVIDIA \n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- " - 그림 1-9 Images generated using the CELEBA-HQ dataset
(출처: Tero Karras. 2018. Progressive Growing of GANs for Improved Quality, Stability, and Variation)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "2017년 글로벌 GPU 설계 회사 NVIDIA에서 공개한 '실존하지 않는 사람들의 이미지'가 GAN의 대표적인 적용 사례입니다. 당시 NVIDIA는 기존의 GAN 결과들보다 이미지 품질, 안정성, 다양성 등을 향상시킨 새로운 훈련 방법론을 제시하였고 그 결과 생성된 이미지는 위와 같이 사람의 눈으로는 실존 인물인지 가상 인물인지 판별하기 어려운 수준으로 나타났습니다. \n",
- "\n",
- "NVIDIA가 제시한 GAN의 새로운 훈련 방법론의 핵심은 Generator와 Discriminator를 둘 다 낮은 '결과값'에서 시작하여 훈련이 진행되면서 모델이 아주 서서히 '학습'하도록 새로운 레이어를 쌓아가며 점진적으로 성장시키는 것입니다. NVIDIA는 이 방식을 통해 기존보다 높은 IS 값(GAN의 평가지표)를 달성했으며 나아가 이미지 퀄리티와 다양성을 모두 고려한 새로운 평가지표를 제안하고 있습니다.\n",
- "\n",
- "또한, 논문에서 NVIDIA는 사람뿐 아니라 침실, 화분, 소파, 버스 등의 사물도 실제와 같은 이미지로 만들어낼 수 있음을 보여주고 있으며 이는 저해상도의 사진을 고해상도로 만드는 등 손상된 이미지를 복원할 때에도 활용될 수 있습니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.3.2. 가짜 오바마 연설 영상 / University of Washington"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- " - 그림 1-10 Fake Obama Video (출처: Supasorn Suwajanakorn. 2017. Synthesizing Obama: Learning Lip Sync from Audio)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "2017년 워싱턴대학교(University of Washington)에서는 GAN을 이용하여 버락 오바마(Barack Obama) 전 미국 대통령의 가짜 연설 영상을 만들어 발표했습니다. 이 영상은 오바마 전 대통령의 과거 연설 영상들로부터 음성을 따고, 이 음성에 맞는 입모양을 만들어 합성한 것으로 가짜입니다. 논문에서 저자는 먼저 오디오 인풋을 시간에 따라 달라지는 입모양으로 변환한 후 진짜같은 입모양을 생성하고, 이를 대상(타겟) 비디오의 입모양 부분에 삽입하여 생성했다고 밝혔습니다. 이렇게 최종 합성 전 입모양 시퀀스와 대상 비디오를 일치시키고 타이밍을 다시 맞추어 머리 움직임과 인풋 스피치가 자연스럽게 나타날 수 있도록 한 것입니다. 영상은 링크에서 시청할 수 있습니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.3.3. Eye In-Painting / Facebook"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- " - 그림 1-11 Eye In-Painting Examples from Facebook Inc.
(출처: Brian Dolhansky. 2018. Eye In-Painting with Exemplar Generative Adversarial Networks) "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "2017년 12월 Facebook은 ExGAN 기술을 개발하여 사람이 눈을 감은 사진에서 눈을 뜬 사람의 모습의 사진으로 바꾸는 과정을 공개했습니다. Real Eye Opener라는 명칭으로 불리는 이 작업은 GAN을 통해 진짜같은 가짜 눈을 만들어 눈을 감은 사진에 합성시킨 이미지입니다. 특정 장소나 다시 찍을 수 없는 사진에서 눈을 감고 있는 모습을 보정할 때에 활용할 수 있는 것입니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 1.4. 한계점"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "지금까지 GAN이란 무엇인지, GAN 모델의 내부와 성능 평가 방식, 그리고 GAN을 적용한 사례들에 대해서 살펴보았습니다. 이렇듯 유용해 보이는 GAN 모델 역시 초기부터 한계점을 가지고 있어 왔는데 이를 살펴보면 아래와 같습니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "GAN은 기술적으로 고해상도 이미지를 생성할 수 없다는 점과 학습이 불안정하다는 점을 한계점으로 가지고 있습니다. 이러한 한계점들을 극복하고 다양하게 응용되면서 처음 Ian Goodfellow에 의해 제안된 Vanilla GAN을 시작으로 DCGAN, SRGAN, CycleGAN 등의 GAN 모델이 개발되어 왔습니다. \n",
- "\n",
- "사용성 측면에서는 위의 예시들에서처럼 진짜같은 가짜를 생성하는 것이 활용도가 높은 반면 그만큼의 악용 가능성도 존재합니다. 진짜와 가짜를 구별하기 힘들다는 점을 이용한 딥페이크 기술로 만든 포르노 영상이 대표적인 예시인데, 유명인사들의 이미지를 포르노와 합성하여 배포하는 것입니다. 더하여 GAN을 이용하면 이러한 문제가 되는 데이터들을 빠르게 많이 만들어낼 수 있기에 디지털 성범죄 등 윤리적인 이슈도 수반합니다.\n",
- "\n",
- "이 외에도, GAN을 통해 생성된 미디어의 지식재산권 이슈, 가짜 이미지를 이용한 사기 등 여러가지 법적, 윤리적인 범주의 이슈가 존재하는데 이는 GAN의 기술적 발전에 따른 제도적 대안이 수반되어야 함을 시사합니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "이어지는 2장에서는 Victorian400 데이터셋에 저장된 이미지를 분석 환경으로 가져오고 시각화 해보도록 하겠습니다."
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/book/chapters/GAN/.ipynb_checkpoints/Ch4-pix2pix-checkpoint.ipynb b/book/chapters/GAN/.ipynb_checkpoints/Ch4-pix2pix-checkpoint.ipynb
deleted file mode 100644
index bda4ef0..0000000
--- a/book/chapters/GAN/.ipynb_checkpoints/Ch4-pix2pix-checkpoint.ipynb
+++ /dev/null
@@ -1,1473 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "7fU_ydUbK77j"
- },
- "source": [
- "# 4. pix2pix"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "PFF2C1IyZQ-B"
- },
- "source": [
- "[](https://colab.research.google.com/github/Pseudo-Lab/Tutorial-Book/blob/master/book/chapters/GAN/Ch4-pix2pix.ipynb)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "VxcdVj_gZUtQ"
- },
- "source": [
- "이전 장에서는 GAN 모델을 이용하여, 흑백 이미지를 컬러 이미지로 변환해보았습니다. \n",
- "\n",
- "이번 장에서는 cGAN (conditional Generative Adversarial Network) 기반인 [pix2pix](https://phillipi.github.io/pix2pix/) 모델과 19세기 일러스트로 이루어진 [Victorian400](https://www.kaggle.com/elibooklover/victorian400) 데이터셋을 이용하여, 해당 모델을 학습하고 색채를 입히는 테스트 해보도록 하겠습니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "9bUYZBpIGG94"
- },
- "source": [
- "## 4.1 데이터셋 다운로드"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "YqCBWTybuhkV"
- },
- "source": [
- "우선 Victorian400 데이터셋을 내려 받도록 하겠습니다. 가짜연구소에서 제작한 툴을 통해 해당 데이터셋을 내려받고 압축을 풀도록 하겠습니다."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "executionInfo": {
- "elapsed": 8259,
- "status": "ok",
- "timestamp": 1612927705189,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "IINQg8WGueiO",
- "outputId": "97ffe05f-e1df-4d3b-c75e-16633d51b79d"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Cloning into 'Tutorial-Book-Utils'...\n",
- "remote: Enumerating objects: 27, done.\u001b[K\n",
- "remote: Counting objects: 100% (27/27), done.\u001b[K\n",
- "remote: Compressing objects: 100% (23/23), done.\u001b[K\n",
- "remote: Total 27 (delta 7), reused 13 (delta 3), pack-reused 0\u001b[K\n",
- "Unpacking objects: 100% (27/27), done.\n",
- "Victorian400-GAN-colorization-data.zip is done!\n"
- ]
- }
- ],
- "source": [
- "!git clone https://github.com/Pseudo-Lab/Tutorial-Book-Utils\n",
- "!python Tutorial-Book-Utils/PL_data_loader.py --data GAN-Colorization\n",
- "!unzip -q Victorian400-GAN-colorization-data.zip"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "-tisggbpSo62"
- },
- "source": [
- "기본적인 모듈들을 import 해줍니다."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "executionInfo": {
- "elapsed": 12427,
- "status": "ok",
- "timestamp": 1612927709366,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "OzX6Zps2e8qc"
- },
- "outputs": [],
- "source": [
- "import os\n",
- "import glob\n",
- "import matplotlib.pyplot as plt\n",
- "import cv2\n",
- "import random\n",
- "import numpy as np\n",
- "\n",
- "from torch.utils.data import Dataset, DataLoader\n",
- "from PIL import Image\n",
- "import torchvision.transforms as transforms"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "axrUB8zjGWMt"
- },
- "source": [
- "## 4.2 데이터셋 클래스 정의"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "p1FHqcz4YFtK"
- },
- "source": [
- "`VictorianDataset` 클래스는 흑백사진(gray)과 컬러사진(resized)을 함께 파일명 순서대로 불러오는 `__init__` 함수, 각각의 이미지 파일을 픽셀로 저장하는 `__getitem__` 함수, 파일 갯수를 반환하는 `__len__` 함수가 지정되어 있습니다."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "executionInfo": {
- "elapsed": 12419,
- "status": "ok",
- "timestamp": 1612927709367,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "-gb8BzfnC6wA"
- },
- "outputs": [],
- "source": [
- "class VictorianDataset(Dataset):\r\n",
- " def __init__(self, root, transforms_=None):\r\n",
- " self.transform = transforms.Compose(transforms_)\r\n",
- "\r\n",
- " self.gray_files = sorted(glob.glob(os.path.join(root, 'gray') + \"/*.*\"))\r\n",
- " self.color_files = sorted(glob.glob(os.path.join(root, 'resized') + \"/*.*\"))\r\n",
- " \r\n",
- " def __getitem__(self, index):\r\n",
- "\r\n",
- " gray_img = Image.open(self.gray_files[index % len(self.gray_files)]).convert(\"RGB\")\r\n",
- " color_img = Image.open(self.color_files[index % len(self.color_files)]).convert(\"RGB\")\r\n",
- " \r\n",
- " gray_img = self.transform(gray_img)\r\n",
- " color_img = self.transform(color_img)\r\n",
- "\r\n",
- " return {\"A\": gray_img, \"B\": color_img}\r\n",
- "\r\n",
- " def __len__(self):\r\n",
- " return len(self.gray_files)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "kyOFeZt2yEbD"
- },
- "source": [
- "배치 사이즈와 이미지 사이즈를 미리 지정해줍니다. 폴더 위치를 `root`로 지정해줍니다. 이미지 사이즈의 경우 높이와 가로 모두 256으로 맞춰줍니다. pix2pix 모델의 경우 256 x 256 이미지 사이즈를 활용합니다. (추가 할 것)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "executionInfo": {
- "elapsed": 11535,
- "status": "ok",
- "timestamp": 1612927709367,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "3g4VdbWx7MKK"
- },
- "outputs": [],
- "source": [
- "root = ''\n",
- "\n",
- "batch_size = 1\n",
- "img_height = 256\n",
- "img_width = 256"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "2Q0gZFvR_1YQ"
- },
- "source": [
- "`transform.Normalize`에서 `Normalize` 크기를 지정해줍니다."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "executionInfo": {
- "elapsed": 10459,
- "status": "ok",
- "timestamp": 1612927709367,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "zKveFZhX9tBP"
- },
- "outputs": [],
- "source": [
- "transforms_ = [\n",
- " #transforms.Resize((img_height, img_width), Image.BICUBIC),\n",
- " transforms.ToTensor(),\n",
- " transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
- "]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "executionInfo": {
- "elapsed": 9961,
- "status": "ok",
- "timestamp": 1612927709368,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "4sgcMC6U9uCR"
- },
- "outputs": [],
- "source": [
- "dataloader = DataLoader(\n",
- " VictorianDataset(root, transforms_=transforms_),\n",
- " batch_size=batch_size,\n",
- " shuffle=True\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "aeWVvTLQZEwa"
- },
- "source": [
- "이제 불러온 데이터가 픽셀로 잘 저장이 되었는지, 시각화 해보도록 하겠습니다."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 318
- },
- "executionInfo": {
- "elapsed": 10335,
- "status": "ok",
- "timestamp": 1612927710060,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "mVtTMaXA7kWj",
- "outputId": "6e977f20-81c5-4dca-ea05-0caf8b5fde90"
- },
- "outputs": [
- {
- "data": {
- "image/png": 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Iw5vXg1MUBcuySrKSJAmDwYDBYMDc3Bz33HMPURRx5coVhBB4nsfMzAxPPPEEYRhy/vx5Op0O29vbGIbBaDRidXWVkydPYlkWp0+fZmNjAyEEtVqNer2O4zilElYoIcWg7jhOSXCKQbpQ1orjKo6zXq/v2+bu7m55vAWhkFKWxzc3N4dt2yWpklLiui5pmuJ5HpqmkaYp4/GY4XCIpmlYlsV4PC6Xe56H53lEUVT+XxDAJEl44YUX6HQ6PPHEE0RRxBe/+EXG4zG9Xo+FhQU+85nP8GM/9mMsLCxw9epVXNctyczZs2eZn59nb2+PdruNqqrMzMywurqKYRgcPnwYRVH43Oc+xxNPPMHu7i6+79Pr9TBNE8uySoWyaFMYhuzt7d1UzbwVVFXFsix0Xce27VIZNE0TwzDKc16v10vVrFi3ULUMw6DRaOy7znEcl9cviiKyLMO2bZrNJpZl0Ww2UVUV3/dLQt3v9+n1egC0Wi3G4zGu62KaJkEQ0O12EUKwtLSEqqpsbW2V6m1xTw2HQzzPQwiB67plO5aWllhZWSnJebE8CALOnj37Tlbf3rD/gtf2YZI8vfWTGbwb+MlteKgGmgWOkysEipIPIMEQhATdAFogbVBSUCNYnYVGDBsj6ETQakK3mw9e7UySKJB5IUqsEBkJMtWRYUjkZ+jkA8dD5IPcENCEoKVr+A0D2Z7n1UzhbORxcGPIsZbKyv2LKF5GemEPJYGhB405i8N/cQExiume3mKwA9mWj3H6ZfTeHta9jzHz8JOoThO+8XnExZewsgjDMqg5NTSrTmbqaKaKriZoWoxMII186nMNlHob6VgEqUri+yhhRiwDUj9FJh5J7CFUnRo6MzUd226TNVZwt/uMhUSmkHkKcqLgWFZKVnewFx5AbZhYpiRNBamEYLiHCKHn+Qg1RYl94pFH0ndRtQTLchmNfWSqkw4GBN4e2TBCei5hMEQEI7Q0V/eI4OVn82rI75GQ9SW9T32MkR9wYRSwNAf//tPwQz9jYy0cJTpzGro5gRkBW+swq8CLGSxq0NAVBj91mNmjj2EqiyQn26SG4NV/+ykWfuInObdbJxj7RF0wDWhbEAowtAmBEBCFsBfmZKMPuOSkItcD81fMddWoATSBFaAmoGWApYDIoK5BTYWGDXYL6vV8eZyAMBVMR0FTMzKhIC0Hu9VA1QWKKTE0gQgT0jghiiRhGJPJALVpUGs1MW0Lq91GUTU8P0JVLGSS4HdGjLodRJbQaDeJxmPGowGaaZO6PqNOgtSgfqCFpkh2d1yiJCOMYCkBLYHBEHwvf6AJhkCYP8gsLMLSARi5OdnyY/AyGEbwynZ+XgaT83WnSBbcOaL1FeBeIcRR8g7qF8nnt3vT6HQ6dDodhBC8+93vRtd1Go0GlmUxGo1oNBq4rsvOzg6QE4Z+v4+UklqtRhAEpWLQ7XbJsgzXdV8TXrqZKjGNQjEoQkJAqaLt7u6SJAlHjhzh6aefZmNjgzAMybKM8XiM4zicOnWKzc1Nrl69yoULFxiNRgyHQ3Z3d1lZWSFJEhzHQVVVhsMh29vbaJrG7Owss7OzZfjRMIySdBZhp0KVK8iDrutl2M0wDKSUJZGwLKtUVQplJwzDkpQVoVG4rniMx2P6/T6+71Or1YjjmF6vRxzHuK5LrVbbp34VRKtQDZMkn9nCsixmZmaYm5vj1KlT6LrO8ePHeeaZZzh27BgHDx5kfX2dZrPJL/zCL2BZFg899BAbGxvcd999vPDCCwAsLy8zGAw4fvw4ly9fpl6vc/nyZXzfZ3Nzk5mZGWzbplarMTc3x/r6Oo1Gg4WFBeI4xjRNWq3WPkVzfX0dx3FK4tRsNmk0GtRqNXRdL8+b53mkacqRI0dYXV1F07TyvBWKVUEqi/NYKIfFNZ6fn8dxnJLcBEFQKpQFmVYUBcMwAPA8D9/3sW27JMD9fp8oikpVr2gHwMbGBv1+n8XFRVRVxbZtZmZmcF23DL0W5K9Q1sIwxLIs9vb22NnZKZXgQsEtiBnAcDhka2vrnUywCnzH/dfXJ6/WGH5qDD+uwoyAVgMsBwYjqNUgcsHbyp+oY6A3gFQBrZEPAF3gaAYbPkgdfFcS1kIMAegaqlCRaUaGYPIPQf5kr5APHmkm8ML8FTk1BDqBWmO3UeNcNuZoCA+vtjl8/xHCK2eQ3pg0g8CNUBp1zCdO0by8wfjVAZuvJiz1rxL3hoy2tjCOvJskVtFaM6hiTNwdsruzhuhpOAsHceaXiHUL09BQWwLD1sGZI8gWiWUdDIFUApK6SmSPSfw5siglA4RlIBOBn7qYuynqeARpiqYqxL5LEmbIcUQaw1hV8DUFTVNQFJ9Uk2SxJBqPiLb6ROGIWk2QhAK3MyCIwXXBbEweptFIDYgGEZE7CZkKSDLQMkhrUG+DsWTx7nsDbAtmHzjGF794kUcPmDSPLtD/6mUs0+DH/7pgVL+P1Yf/I66eOc/77on44tn8uhzVYJDAU/Mw2oOGlGxe2EbvnKa79rtYiwcQzjwztQht/gT6pXPMtjLMefAzEBbMmKBKMIxc6bm0AUaah4WXDbDmYc6BOQt0XYWaQ5alRF6AEsPRw01WVg9gKRqmAoYS5w8AEggiSDI0zSab2Es0Q4U0xLIdrKUl1FaTNAOv3yMbB6i6jmaZKLpOliWggmYa6JkkGg0IPQ/TrmM5NYRtMeoO0PwhmmqREILmo4gMXVXpXt0l2AuYOeggNQW1buOIhHAYEHQ87LkFmmaGND3MhkYcxISxxLKgvw39a9Cqg9YGy4JxDJe384cPAYQdOBPlqlubvIZRRE6c18kVsIBcKVTJVa4iHApvP8R4R4iWlDIRQvwK8CfkbfstKeW33ua2+NrXvkaj0eDQoUPMzMyg6zq+76NpGkmSlAMSQK1Ww/M8RqNROQgVoZlut0uj0SjDKbquk6ZpqVrdDAWJMQyj9AUValgR7vva177G3Nwcq6urzMzMlKpD4d8qlKKtrS2GwyHj8Zjd3V1effVVGo0GJ0+eZGZmhna7TZIkZYipGHyBcmAcDAalciWlLP8OgoB+v89oNCJJklLBmfaDLS0t4bouvV6PKIrK4/d9vxzsC5UqTVPa7XZJxIqBvjgPlmURBEH5uWVZJEmCYRjU6/kk9FEUMT8/T5IkDIdDsizjypUrfOhDH+LatWt0Oh0ajQZRFPHAAw+wvb2NEIIXXngB3/d56aWX2N3dLa/F9vY2c3Nz1Go1fvRHf5TTp0+Tpimzs7OsrKyUZGdmZoZer8fLL7/Mgw8+SLPZpN/vs7y8TLPZBCgJ6XA4LFWpQnWbDp8W2yyuZZZlpSJW3J++72NZFktLSwC4rovrukRRVBK64XBYhmCL+zTLMtbX18trY5rmPqWpUG6L862qKo1GgziOqdVq+zxwRYi5IL9FmDhNU65du1b+XgzDYGlpad91KtRN27ZLFa0IBXueVy5P07QMYR89erQklru7u2xsbJQPIm81BPv9htvZfw3Ii7r9yQ78ZeARF5bnwNDBn4SQPA8SF9wk79BbFvT9nOFZE6PJOSDNYLcLzqIgUwWZBpZmE6cZvgKGnTd2ZrLvHhPvkQp+U8OvN/BDhSzxidIAVYOeoTPKFF493+W4HfHo4gnmFiQ1kSBGY7IwIUNiLs9jOQbRlV36fbBGfcyrz9N/8UvIRYfFR+7FmTuBNhthphGKIZBak0i1kGkMwsEWbcKhh7/dIfPWSLKILI2JAx8lkKReyKjfwR0NkbGPpjtYjo6R+CSBJHUcFpZniAcj3G6PLExRdUgTGPtgm6CrgAW2YZDECvU5CzXxqYmEuj1LbKoYFgSRwOoPiAJJGmZEaoRmGCQJ2AY4dZAKeAm05k3iLKE3TDH7Ga9swV/9z+qcWxtxbQBzdo+VpMdj954gvHYeRR7ixecuog5CrrwUMRrnpLcOXErysLJVh/uOCjZekdhxj3D1Ce4/fIqx0BlEiyzPPsB496u89MoV3vvoYyz/iM+wP2L24EH02VbuRwoFWiR5YDQga9ZJlBpSNYlSgZYkKFqGNnFlRVGCDDO0yIcsRrUFMk1IiFBEQjT2sEydmcUlBOC6Ht54ROINMQyTTLXpZSnGZh9nF1JFoLYNMlLGl/ZQTIXWwQUsyyaJMpIEgiwjHYOSCYIkQmoOpqVgztWI0gi71kAkdbQU8ANIY4RqYNckQlVIg4TR0CVNUrbWwQtiVHUDy4TWQTCNiEbDJBpHZLHMQ4atnBjV6tAL8/t/GIFi5w8zYZKHMI8Dq/dCkOQEs7ML62Mw89uHhPwhB3JStkM+r9ssuUpskIfn3yzumEdLSvlHwB/dru2NRiO+/e1vA3D8+HHm5uZKUlCEW6bVgJuhCP0VykGhvBShm1t9xzCMUh0rBsKCaBVm593dXXZ2dtB1ndXVVZaXl0sfjmEYrKyssLq6yng8Zmtri/F4XPqMwjCk3++ztrZGFEX7PFXFvgzDKI3kRRuKQXs4HCKlRNd1RqMRlmURRVEZ8qnX66VXbWZmhpmZmTJc1+3mt1MURaWJvSBuvu/jOE45oBfqVpZlpc+o0WiURKXdbrO0tMThw4dLAtdsNkv15plnnkHXdfb29rj//vtJ05Rms8nFixd5/vnn+dCHPkS/3y/VwKeffprf+Z3f2Xc9HnnkER555BFs2+bEiROcPHmSz372szz11FPYts2XvvQlHnnkEba3t+n3+wghaLVaJdnyPI8sy0oTfUE2i9AesC+MbFkW9Xod3/cZDoclgSmIVpIkuK5betw0TSvviUIZK0KHjuOUPsNCTWw2m+U9O60gKopShmGLdhYEb2dnh83NzZKUt1otFhcXCYKgJEa2bbO7u1sSb6D8jXS73fL4ixCxaZpcu3aN8XiM7/ssLS0xHA5JkoTLly/vC6+PRqOS/BmGQavVwjRNOp0Onufd1SSrwO3uv7rAbwLGGJ4Yw8+YcHQmDwE1NQg0CNLckI4BSOgHcIQ8dLNGrmLMJhIv8UmFioxVND0lVlVUy0bR8/NeeJRL346m4ztN/MhnIARxKlBTBUczSDOBFBmJqnBmHLB25hrLWcy7Fmc4utgA30W4HopRp7l6AOPYEtGwT3B5l6gX4khJGvhkY4843aJ35Spx5FO3TazMws0UhoqKNHTqqko9GBH0u2RhhMxAM3OPWtidqAgGiD7YFiDHqJ383BhNaNfnUaSkOXeQ+twK0cil3+0y3huiIHFTMBIghMCOiQNJ5MfYjkmaxridEf1+SDqSqBO/Uz+BRgssAyw9oj4LrdV5Fo8dJpYxsedizZhEezEznuCFPz1PQ4X1rkvrsUd5yGyxagV86+qQ5549z3/1oR9m2N0i8l1Eu8kjHzjMJz51lVfJSfBIwAceEzTe8zTztavMn6wz8xfewzN/8hz3vu9+9MYSF577CrMPPUh47QpZ7wKh/RAsHKYxn7HbH5FuhkTSQG+0kZlEd0yk1DD0GrZtgrCIg5BIQKwoGLqNpiTEnp9HfcYd9O4mdtLBkAGjOCMcBmgiY7jYRugCLVVASWm2TKx6gzBRwFbQGjpJMECkMVmgk8QSbc5Bt1QyNWA8HjPujgjHAULVCMYe8bCPSFOEYaMYBhJBb7NP94pLFmcoFtRmVdorM0SeRzj20Ws6eq3OlUHEYJggFYitSbhdg64LdhOsNCQTkOkwbMNmB4ZBntSxvAhDN79/ru1AMnHDj8m9YnPXwLRAsaC+koftNzsgx7l5NAWOkT8sFa+rb/P3f1dWhr9w4QIXL17kxIkTtFqt0q9UeJWKgUVRlFLpKrLs4jguB5liWUGaboYoijh37hzXrl2jXq+zsLDA7OwslmVhWVYZskzTtPTZXLhwAdd1OX78OJZlsbm5iZSS5eVlDh48yJEjR+h0OgyHwzK8ZJomhw8fLrdRECHbtmm1WiURXFxc3JfRVgysRRipGNCL81B4uAB2dnbK/fm+TxiGJdEswl2Li4vYtl1mDxbkyzCMsm2appXnsPCLKYpSEsRiAB+NRqWqsrOzU57Hp556ik9+8pMEQcDi4iKWZXHkyBHW1tY4ffo0H/zgB/nqV7/KwsICDz/8MC+//HJ5vM8++yxXrlzhxIkTPPnkk4RhyKOPPkq/32dpaYn77iho6GEAACAASURBVLsPwzBoNpscOnQITdNKglTcF7ZtMxgM9vmmfN/fpyZNZ38WIdgoioiiiIWFBVqt1j6vW0E+C7+aaZolMQqCANd1y2vT7/fp9/tlYoWu60RRxN7eHhsbG3Q6HdI0LVXMgtgJIUq1aW1tjZ2dHeI4ZnZ2llOnTrGyskIcx/T7fUzTZG9vj/Pnz6NpGjMzM2XY+vVC5ZATqWvXXmNJ2od+v//6P9IKN0UEfBE4HcKDW/Af23CkBlh5pleY5tmH3iQLbF6C5+cmY1XCgQDORzFZY4Y0Z1FkiiBTExJyjtYjfwofTv5Owogr395kdDGGZVAOmjizi2QNh7rpYMWScXedRKb0bA03AXejw7bvcf+xw7RqEu/KWSQtWocOoJ9Yxr7/3aTXrqF0t5C6j2Zl1OyM2uoBNEWQJAFjN0B0QmxnAWeugaFEGFmN5soCShKCIoiFSjQa49f2iDQDRbFIrS6y7hClGVGcEXkBUjigNvB2uqAlaLrA9xOi2CIxAwwsMltFZint5QWMpoEMJLoQpIqCzCAzTaTdR50P0DSB03CYkwlG3UHTHBRNwbEsMGCY5C7v3jBB70dooWBnq8e5V2ErhHf5go/94fMEUQyWoO3UeXhV4L16jgund/nxX/p5es98npmlhFOH4VtXcyVlT8JzX5X0e3/Oo0fmeM/jD+JHA971F+6n23WZP/QuTtz3EEHdgrklVg4eAm2Wvb5OGAuyTJJlKmatzajnY+oKil1HFTrJ2GccjEmkgZpGpDJFUSRSRJBExLEHSUSWjGmu2My07iNLg0nWIpCF+H5KNAyJkhDdNAnqFpEmiPyAzA9pmDp6o8a4OyDq7mHaOtbcHMK0CIOI3l6H3bVNRtt9MpkidQhiCAZAOkQIMGoQ+rC1DVvDXMVt1lOOR3scWrJxhUJ/L0KEXXoDOHclJz32AowG0PegK/MHiSKMp7A/45C9yet1cN7jDY1Z33xzP+s3xF1JtIAyk3BpaanM7Cqy7HZ3dxFCMDs7u6+Ew3A4pNVq4fs+hmGUZu0bU/qnoes6KysrpGnKxsZG6a968MEHSyIXhmGZjVeUMihCY8ePH2dlZYUwDOl2u6Unp/BKXb58mVarxepq7r2dDg8W6sd0WYlWq1USq6L9RdZZkSEI0Gw2y5BnQTCOHj1ahsWmTe5pmrK9vV0uazQaZfZjkiRsbm4yGAxwXRdVVUtPU5ZltFqt0lhe+MdqtVpJOJrNJq7r0u/3mZubw3Vd2u027Xab48ePs7a2xrFjx3j88cfZ2NjAdV2+8IUv8P73v5/19XUWFq5n/wshWF5eJggC1tfXy/O6vLxMGIa89NJLOI7DN77xDebn50uCWxDZIhzb7XbLc9jv9wmCgCAIaLVapQoJlN65K1eu8OyzzzIajQA4dOgQP//zP8/S0hL9fp9Op1OS10LZK5TQgsDHccyZM2dK5bMgcOPxuAz7djqdt2TKL7C3t8cLL7ywr+zFjXBd9y1ts/DpCSGo1+tYlsVwOCQMw1v+Viq8eXSBzwOv+PBf+3n2lSFAtyHIYGuUE622gCzNB5JYwrZLHiYZjzF0gR/4yFiFJII0H2xG5NVWJ1E0MHWaR9vIZBf3rCRbCwkObqM/rpIpNuNRnywJsTQFFRVXJlwxYW/oceWlMzy62uDo0QPIccRot8OcYkFNENQseruCnbM9mhs+hw4tIrJ8wB31OySeRxhLPLmLohnopoGmC1qtNoZTJ9Mk8ahPFo5JAonUJgp/VkeJLFpNHc1UsI0aaZwSp5KlEyeJFR1dZIQDlzRVGXs+MvbobW3RcwPiDNoDk3rdwGg1SZOYnbUNRj2XsC9JTLDqJjVLQ018WjM6MrVIdY1hs0UqQ/TGCkrvGpFUELNzDD2Xbj9GPQjORRgsPsl8d43FB46Qfusy9sPH+fH3/gzDq+epJx/nPzz3Jf7KU0+yd7HL7PKXOHI1nw/sHgH2EogBbF0csbNwmuBVjeaBY/j+gGsv/BmOM8vO82cxllfpbVzDkQM8YVEzVDRToik28Y6gaVmgZoRZTBqMiQKfxmyNzIuJwoBQzbDrNhYKZ6/s8kcvjDkb5OTk1MEaf+NnTzB7cIHewKe/20cxLKJxgFAk7fYypu3QbDQRqU6gjwnSIcPzffZ2LzHc2CVRVVxVIR6eJvUyegPY6sEgvp4RyeQetMnv5yG5F1GfvMLJfaq58MorIM/4DDIIgutVPkpscD1d8oYuaN+qNvnEcRr5EwrQ0qFp5FmKcQC3Et2tSZtutwv1riVaQFljqVarlQOUlJLRaISqqhw4cADPu05Zpw3kBaZVmpuhGKiPHj3K/Pw86+vrdLtdnnvuOUzTZHFxsRyIplP1Cy/Y1atXieOYe++9tyRgRTsKRW1tba30+RS+pyI0OF2uQVVVPM9jMBiU4RvHcajX67iuS7fbxff9sgTG/Px8qew4joPv+6V/yPM8dF2nVqtRq9V44IEHSqJaKCfFe8MwCIKgJG7FsRbmd6D0IxWZlcPhENM0S+VlOBzy8ssv8/jjj1Or1ZidncV13ZJ4rq2t7VOYilBp4UtyXZcnnniCn/7pn+bLX/4yJ0+epNFoYBgGzz77LE8++STj8bhMnFBVlePHj9NqtZibm9tHgotwaLH/er1OGIaEYYimaWUm33A4pNfrcenSpX33xPr6Or/xG79RkvVb4Ubf153ErbJm3w6K+64oDRKGYamEvt7xVnjrGAGKA2oLhJd38EmSD8otBeYPwW4Aqne9NpFumSSKgCxDoKCqSl6+4Ab3gwEsA9sSMBy0R+6lvbrL8FKPZD2i96nLMH8Z5YCCVbeILAc1SYn8BF8D2TLZUDL6A58TRp3HD5+kFXpsbV5GpBLMOomqEWsOl6969Pw9LEsl9TyyKEVkud+pARCGaGJi4ndDor1NwmFMEoLhwExdEPY9xjuAlwsNcR3qSw1a7S4HDy9jOw3Gfp/EdRmNPNxxijAljZqDU7NZeNcxhK2hBrmVWWk4WKZBGCa0rSZ1L0bXTVLbRLdbiCgkG/VI3C0ENYZJhunMYRgqg8GQyNMxdYPdS326O5u43ZhvXYT3nYK5RsRyu0Fj6zKm2MRWDzO69BUc0WBOj4l1QVRXgE1ULTdeB8CPPCg49sEf5rkvv8qpk8c4WPNIzVU++8XzPP34Cfphh4X3vQf5VRXNHLJychlnZhZj+RCOdEn9AZEq8nBenOK5PprSIFVahIGC70Wo9YSBt01/p0806BD1Mp7d2p82+61rY/7333iJIwpcTPNsxQJFYShJno3oCPDSvP0FZoBlktK/9HqIyAnWNOLJCyBVIcrAC3n9Euq1yf/TFXBulLKMycvmeqEwPS+XEqPh1xRkFOWMKp1qxAQBdwZ3NdGC3FQ8GAxKQjI9oHmex/b2dvm+MGsXg1/h0Sr+vxmKkFhBnubn53FdlyAISnXCdV2azSa1Wn4nFJ6lwoh+4cIFtra2OHLkCCsrK3iex+7uLoZh0G63cRwHXdfLNHxFUQiCoPQHhWHIcDjEdV3W19dL9c4wDGZnZ1laWiJJEprNZqmuFF6xgggW5GlmZqb0lk2bnNM0xXGc0ptVKGWF76ZQMYqkgYJsFedIVVX6/T62beO6bnl+LctieXm59IK1221Onz7NH//xH/OBD3yAp556ikuXLrG6usrW1hb9fp8XX3yR++67j3q9zvLyMu12m/F4zEMPPcRgMOCee+7h8uXLHDhwgEOHDtFut3n++edLNfCVV14pa5FpmsZzzz1HEASlN+5mis9bxXSI+lZ4o4zW71dMk7aiFtyt6ntV+M4ggGYdBv18HEnTvNBnCKxKiEbQ6eVGXA/wpUA1bDRDkMURqmqjCBUhJIr0UMnYm2xXIzdgbygqjm1jaArynoNoq4v4gx7aaIy3MyZez/DqHtFMiNE0kUJS0w1MVHwp8GTM7tYaF3pbvGvxAKdWVjF7A8abFzBNmG1L5iywFB8lNWnMtNDUlCgYY9gWRnOW0IfxYMh45LJ7OUQVedkLxVKxl1dQDi5hhzHx7C5ep0tdB8OKWT68Qm3lHoxGnVSoKGFEXcbYqUpTQMs08nCYlMRpglHTScKIgR8hkowoGuPubBGORpAIApFgWxqhKqjpOlkaY5IhlYym8Al29lBqNuZgTBz7JKaFXTdY0By8zpAZJOoifO30Dh/53Do/9/6D/NhP/CjfvrLDsbpDf32Ps57Fl1/ssPygjzK/TKpopGcvILtw/7thczxm5Z4HuXD1AuPl+7DuOUW4KPjoS32kN+bB0Ve5+rWv02gYDA2LuZrO+if+HdLN6G7BOMy9Qh65ebtwJY+YKjo6+dwGFnhtbRIFGEn42o3itALSICcvUa6uBlPfYfJRj+uZeK8LjddWEp1uxHQFVdhPom7E+CbLTK6nCZK3mYj8ZBQy2qSh3ijJ398GmLzJ45/grida/X6/9C8V5vLi76JoY4Hi8+lyCYXP5lZm+IKIFRmORWZXUVS1MAtfuXKlNL3X63WCINhXMX48Hpeho/n5eRqNBvV6nSiKSkWt0+mwtrZGEASlybgwPBc+oUJRKjxmUkq63S6j0YhOp4Pv+yVJ29vbw3EcWq0WReV9XdfLMghCCAaDQZndNh36KxIA2u02s7OzZeJBoQAVpuciQ7IgFUUdp+naVLVajRMnTvDlL3+ZlZUVXnzxRd773veWbazVanzmM5/h4sWLHD16lNXVVU6fPs2nP/3pfdfiz//8zzl37tzr3g+f+9znXnP9HMcpCUOFCncSGm+tL5fAqJ/7s2KRlxSIskmhRwlGmI8ZRS+WKJBkKQgTzWhgmjrIlCAxyAwFk4yHgPu4XuqhjsRQUzB0ApmhCgWr0YRajfqyghJ4uN/aIfxaStLy0O6zCNo2QZwiZYCmqqjCYT2A/uVNNnf7PFC3mJudx6xbWP6INKqRGfN0d/psbO2SeimWAc3WmIaM0TWVVESkQUJdA7OWF8BUZYotO6Q7A6Keh9tJCfy8IKZiw9bOeRrmGvMtG8vRULQMwxQ0Z2epWQskUmPUGzEOxuhpTMN0SJQMu2mhGBoyiJiZcUgX6qSWgWWBrtWQ7SVc1yNLExTNAMPESlKsRKHrB0TDiP5ujyCMUNAxZzTEQxEbp7/Ok6vLfPHFLR576gRxfYEz3VmE3eCTn/kKF89ErN5/D43j8Nw3G/zxpz5bXuv7gc/+B8lvrX1j6g5YAz7LNP70z/YnuAoBzUZeK+pGFEU6p5Fx3Xrkc2sDt0WutO1TpDJuKevcyJXelLb9ej+G4gb9TsT+WxGzjNeemNtEsiAPd74V3NVEqyAfhflb07SyrtHS0hIzMzNl1XLISyQUoakiK68gCq+nThTlHwpCUqhMRZhtfn6+9ICtr68zGo1YXFzcFx4s0vULpahQmsIwLGsbFcU5i9ICRQgniiJc10VKyezsbKlYFXWRRqMRhmGUmWdFhmSj0SgLdh45cqQkXEWIr3hBboAustx6vR69Xg9VVRkMBly+fBlVVRmNRmRZxoULF952KOwjH/kIAKdPX5+f98Mf/jCXLl3i/PnznD9/nvvvv59nnnnmNd99I5JVXKtDhw6xvr7O8vIyGxsbFcmq8F3DW+3LBXm1eG1SIV5VINZzH++MCmIJ4kvXxyTDEKg1i0Q3iaMxDCIMRaGBYBQneOQP9A75OLMJuEKwoFlYlkMaBkgh0Gs2YRghwgBpaYjDDYTaR+5A8krAuLuFekhBsXWkIpCqBonCrh8RJh5x6PNg00BrNUlijRXLQWQRg94uepzm07VooDkWiqbhJz6un5Do4MyAoQt0XeLMtZAzdaLeEOKMhgOqD0aUp+I3awqeH3DV9zm0skyz3UaxDAKjQWQ5KLaGnyjERp3O2GNd6AxDn/qOz3BvDzSHbjfj8uUtdGHQH7uIBF5dg558raDyZvDy705mEDlzniLJ/1c+/AEuXTrLNy8nfPMyHH9E4fTn9z8orgOfWeMNoShw4LjG5oWEg4c01taTvAjnbcREsHrLhKFCjrdqoLiriRZQ+q2KzMHCI1WYwItQ4fS8hIVxHChJyRulpE/PnVgQqGKbkKs2RYir0+nw6quv0m63WV5eLkOVxbr9fp/BYFBW115aWqLZbGKaJrVabV8Rym63WxbbbDQaZZmGnZ0dzp8/X05tY5oms7Oz5T48z6NWq+H7PmfOnOHIkSMkSVIWvByPx983afgf/ehH97Xl1VdffVvbmZ7H0jAMZmZm2NjYKD8vlt9OP1OFCt8JWuQRFCMF9HzOuEGUEyXVgnEKiQlKUdE0yOe4S40MLQKRRUgJSSpJ4rxDj8hJWcTEDA+ILCONM2SmkCoSyzCQikGYxKSxTqIYMGOhzkvkOCK7KkleyNAOhGSrEZ6hksUZIsrwpcoFxaS/EbH67W9Sj2BnucFiTSBqCsYMOLoATcMVOtvDkDRKMO0adtska80zos56p8/mSxsY0YBkM6+DpR40cFOToa4wSFxmYxNtkHD+TMShKx3GcptuX5KEMBpDL80rgL8hBJMzMsFttkz+5sc+S+hTVry88NJr42Vjbh79moam5N9SMpOWlbDYgqtXr4ephICGBcPvMJKvk0+/o5OHAd/qd4v76zvCD1BOzV1PtAriVCgsvV6vzDgsCpRKKdnb29v3HaAsZloYn2+GadN0MUBPT99TlAQo/FQLCwssLCyU/q1vf/vbJUkqzOVFhfbd3V16vR5f//rXy1Dn21GKhBDMzc2V5SKKsgTF1CpZlpUhukajUZZlcF23NIFPo1hnOoNxuoDl7cD0xNWtVmvf9ZlGkSDwZlBs78qVfAq9b31rvwRfKKB3C4oEjR/AyabvWkyHDjVywnQrMUKQF07UAVPJJ5SOVdiJoKlAaxZ2upAugppBdg3GQlInQ40zlBTQFcZRxLDnQzffVlERXmFSkivNCEYeMkzxohhPTwnSGE0zIc2IwoA0C1FtFatVh0MG0YEx0caI5FJMck5CK4HlnPyFIsWrG2SaoL8doGyD+fURCXli2HUykbA/DjUGMQbZvb5IUBqXzQVIXA2pQJYqoNYgnMxWPAvnNKBRhwUlr4ERZjAKc+lrTF7oSJ8c9IKex1njMJdtmhZEAaQadJKcaPl8R4P9vJlPfQMw18ir99807LYCbN6kBMFNkExWuHohP4vdVxIEufncB3z51klW4debfqyOucmcUuSevgVyc/ytCNitHs8n86mTct2Tbmn5vOHD9M4Zze8G3NVESwiBZVnYtl0SiqICdlFuwXGcci64IoxX+Its2y49WrcyLU9P/FxMPlxUVZ+ejLggMYWpvpjzrigJUQzwiqKwurrK/Px8OQlwp9N5XXWpOM5ibr4iE9C27XL5tOesqANVhDyL7LhiGqOCPE7PI1igqPU07b+anmLmTuBWJAt40yTr9VBUs7/bcLNwdlF2oSqx8P2J4qosk4sb66+zrgKcZJKJXoPYy5WrLXLvjKnnxCuYg9EWICCwFWzVwR8PUUKfmqYhYr90QafkpSN8rpM+mWWkI5cw7RBLQUwKzRqxXkdkMTKOERLMeg271SRSFBQM9KM2aWtAdsbNR+VBniGW2qCfELCs4S7nk5L41/JJhl+DIseoPjkonXzEDcgdxY4Dlg1CENo62E5ez0KmeaVWRUBNgj/I51MxrXx+mdCDcQCD9DojUIptauC0IEtysmWqeTwuiHNSFnJb1JSOTs46Mti4IfWuTj7nIHniIfD6JKtFzhNvBgHoJnTf4Blx2gQ/DcmtydGNcMkvjcb1CgnT7TIm7blZU6Z7q8KT7t7QhbWBqJHPN4jPGzPPdwjuaqJVzMdWhOWmjfBFbaRTp06xtZXH1NM0ZTAYlBWyi6rZ0yn+N6KYRLrwMtm2XXrCbNsua00V2wfKeQNnZ2fLzLfxeFxOWRKGYWmoL+atu3FQLcznxQTThSl/erJmoMwaBMp59YIgKMlXcUxSyrLOVlHd/WaYno4lTdMyi3C6ivp3G4Xp/+3ibiRZt8I76VjeaVgiV7AukQ9YLm/s2WoyGfMjkEk+yA2Be8nT3a9lcPCwgrWl4skY6aX4vS3iJMHMNJLAJ80ANS8TkZATtR4TTxeQxgnD/pCoZpIIBa1moNUNwjTD0izqap1YxqQiw09UpJJiiQSpQjrbJLgPkrqbG8f6wJj/n703+5ErTc/8ft/ZT+yRkRvJIoss1tLV1dVdvUgjyLZGhhsaGxjNXIwxEiwYxtjA/AueS8NX8y9Yd4YFD7wKXtCWZEgad2s0vVW1pnayqsgimclcY1/Ofj5ffOc7EZnMJDO5FJNd+QCJ3CJOnIg4cb7nPO/zPi/ZOKDy2jqy4tGNdogniUpZ1STGQLGHNlAzwbbVYD7LLKb65kptAuWKl0BiqTyBOIJwBoYJlqPyLhIb0gCyMcwSGMrCMV+cF3LmLf1BCsZUDTJME6i4MAkhycp2fzyKWS2oBV8zkWNkJ6e4yeJZqDk5GIuwiDK1rtjWozzfx5EsvYmdEwjxiyb4J0GKenk8Hsz8PO7ssxgJcRwE6vVaGs8Fxa8LXmiiValUyogCnfquy4Z63M6lS5dYWloqQ0wXYxx0VpCOOzgKSZIwm824dOlSqRhpwqPvo0fh6EHNOstKZ2HpJPlms1kSPT1kWI+2WVRudFq7HuGis7/0bbWKpuc0giKd+mdNijRB0gqIHlqtR64cBcuy2N/fL0mWHj3zpKWrSqVyoOz4sNgDXZbVj7m+vs7W1taJHmexJHmOc3xVMFDZVxoniYcVwCpQF2Ck8xzGKap8AxClJq9ceIPXajv8jC5IgY2LlBkmktRxCWO1CudCLY4d5kTLAOJEEk2h9eo6eBUyKUkcgUgSsjQnMy0M2wIysiQjTSLi8RiSlCw3sHwTseaR1ELclsDYkMiZgVm5QHOtQ57dItnaIJ0VRVI9jbcC1GpqkrbrYQoD0ox80kdGM5BCpbC6ppqt4jogUtUdEMfgeKrVLkMlTMY53I+VkpWgiNzh674ERZ5uBSpSv21BI4d+rBjs4dPYYcH8mFNHDFz34IuF2x8mWYtkyrIM8kySF+ew11Yq3Nx7NA2qA6FpkGQPP4cdJm4WitPuHX3zU2PCo/1kGj5qBqCHmkhwFPlcRe1jl/kMwa8TXmiipRdjXdbTRvharUa32y2jGPQgYU2odNlPL+gPS4aHeZaWVr90jpVW1PSXTtKezWbs7+9jGEY5UFqTG505pX1btVrtQICq3k8dlRAEAdVqtfQqdbvdUqWKoujYxG8dNLlIWBZxnFdpUVkLguCp5Sc9ytulZyIC/P7v/z4ffvghn3+uOnr0DEINHdCq/6f30TRN3nnnHd59991jH8cwDOr1OsPhw64hz3GO0+FxqH0NRYSkVKKNLvW5QNWG/S5EnknYvoTZCeHzLgCRIZGeTZpkmLZBHlGmmUrmWUp63qEUkNsGVs0hkBkYFsI0cE0T37aJs5Q4ycniGJHFWHlCNIqJtqdggnXBx6iqGAk8n9xIMGwTy/fwG2s41QmIhQuhDJVJkXuQ2TCYYDVCDN8lDSLknSmM9SsWgBUUWUvdQoIrnkB7BiMJsVRPRsIBenFYbrGLx108tfVS9fUU8MURLgafuTKz3oKtgmX8g//0He78fJ8Pb6lwhXTJOMCCWnUYqEETNH0YFhuZWhYv/fZvcvfHf3vsflgGXKzC3eL+2ov3tEiWRg11LO6jSJT2/mmIhe81FDEbc7S65aDI25lwyKpriqcf//6Ih3xhocsoQRCU5ArmPpbl5WVmsxmVSqW8jyZluqQHjzYbp2lamuwbjUaZjaVnAmpFSm9PdxU6jlOOs9GhoZqMWZZVjrI5TLT0Pi0SJe2R0jPvNHQ5T4/O0QqW67r4vo9lWWUS/FkzVC96pxabEXTUw+Lvi1gsny0SwSzLDpAs27Yf8L7pwdwvCnSjx1l7787xZBDANeaLdATUTRhn0BTg+JDFYFyCzdgkbnaAW4qVJQkZCQmCisznCddSiUlrzCtiKbAhwQgTor1dYtvFbrfxhIM0bNxKnXDaJehtwGSC4VoYeUKyN4UdEC5k7QRqBla9RoaPTCf4ZHhC4FV8XN898hyGlIgggzAlkwl5nhZGLqkCwzRxajDPWWib6pdUQs2FjgDTVAPu9rKHr9RPOQLhJGg5EBSno60FKWfri0/58Naclty/eeiCeDbXpIYL17J5mi6QLIFhV8iTg9pSms9JFhwsGWqb2tO4PFb5+opMam+WfhwTuIg6voYoBXVa7MuFYj/6FPMIa6raWwivTxyd9cR4inlaJ8ULS7R0GVDPptMzDPXCVKvVuHz5cumDWoSUkjiOS8Ki/U9HwXGcMg1+MBiU29RGdp2krrv3xuMxvV6vLP3plHidPr9Y4tSdijptXcOyLF5++WXW1tZKcletVsmyjK2tLXZ2dkiSpBzXo43v2ugehiHtdrscTq07Bs8ajvMbHVbRWq3WgYaGK1euHCBix+GsxFc8Cc7LoL9e0BYggQqw1KpTDNRs2M3gkla4mrBy1cRxasRRd2EraoA0kSSWaVkjNITafrX4clClmrEDjWWT0ThlNBpQNcDwQ7xGFZEJjPFErfZBTNibwU4Gd9T4HK9tEvguUphgGUhhIQ2LNE4RhqDqOdTqNVzfJTaLJ5apHai+foHapYtkhoHXqlOrOYg0YWtrh+H2NjJKIYohnUFugCWgWYM0Uwa1zhKkIczGil3o1rnn/JEoEhwA2Fo4hS12nNqTgwr+hTbcLmpmQsBL110GNx/V6CMfIFka2od/uLNVe8yfBiaot/ObxXY/Zq6UwpzIH57NfL/YP92ByETxY/3anL2V6NnjhSVatVqNWq12IGoBKOfldbtdqtVqObduEYvlsSRJ6Ha7xy5otm3T6XTKGYP9fr9UzAaDAUEQlJ2Je3t7ZUlOlxPr9XqZ7K7nxWmzer1eZ2VlhWazycbGvD9Jm9b7/X4ZXqq3/RD+oAAAIABJREFUU61WWVlZIQgChsMh0+kU0zRLk74mmtq/NZvNDuSGvQh45ZVXuHnzZvn7YtlRCME3v/nNExGtc5zjLGDRX/0m8BFqAbuK8mJpFcJIVZfibwvVfPfRzGRv/Rqt/YT+YK7CmlmxzEoDiU2eZLAD+cKanCx8Nx0Hf22dUQr5cETQHeBctEhTk3AyIZ1GmFlKMkrh3ryLL4ghjx3qtQ44FpnMsIRFYEGUphiWQbNWg84qO40242yeWUcKUZITjkZkNngVA9tp4lo1vFpMtGYQhTH5cKDM8aYJfh0qDfWzzAtJz4JRCkYCfkEqpxwcLKwzLczi+5RnupofR48WhZK3rsPPPpn/3ltgP0IIXn/rbT68+YviD5x6fxPU013m2ZrKM+AOqiToouJIRqjj9Phe8Qe7HKfM3x63+P9zEJYUKsWDn6SvqMrJzWoPwQtLtBzHoVarMRgMkFKWKlGz2SxDOfUg4UUSpYchw8nVAl2uS5KE/f39cuCy4zhMJhN2d3fZ3X1wtGaapkRRVA5jdl0X27aBuSJ3lA9KSkkQBGWMw2Lgqu5a1ARTh5vq21iWRRiGZTlRR118FYONT4vjugkvX7584PfDHaGL75vv+8cqdovGeNd1y23o1+qrhlZQz9r7cI6nj8W1cxVlEA4BHcWrlaemCb1MLUJh4dOqStiewH3XYGZeIMQklQvLeyJVh5/MSKQEYShiEs67uYaoBa1RPFokPUQuITSQmzNCf4rpLGO6LmIgSO5mpF9k5U7r/U9iCEOB55rYno9j+8TOFJMcRxYBzPGM8WHlJYR0NAIxBt8htCXbWUqapJAJsmhGngjVVWg5YGeqIzFPwfYgGhfxDHEx0E+o1X7GglSiXw+UMT4pnvxX+PESQlVzD+PKywfbFyu1gyVCL5lbwitNmA1Z2O/50WOYgjyTCAE1D8aB+q9rQpgp0vM28HOezdOuMe8+jIpn1OFgbMlJOg5BKbi6c3OX51LpVbA5eXvmUxoq8sISLSllmREVx3FJdCzLIsuysltPm9f1ffQInMlkQqfTKbsDH/Y4OmdLz/yzbbs0t6dpeiTJAmU41wntQJnZpY30k8mEnZ2dI43iw+GwLDmOx+MyE6vRaJQl0lqtVprB9XNajH3QaflntfzUaDSO9Es1m80Dvy92ZJqmWfq5DMPgtdde4969e6X6Z9s2URRhmibXrl3j888/x7Isrl27xqeffgrw3NS9c4L19UGDeX5mHUWkNplfxS8VP3s+uBO1WN1HLUS2gJEBsyWQFY/M9EiN4lQtQQbJPPBI5GpDhYSQMI93SFGdaGMJlSTFTKHVXsayU4RwcbKQireEOXPo3QnKlbKCKjsOATELsAZDZGOVTNpkmUOSChzXw/F9xpMxu7s7zKZHrFy9sQoc9QQMpwSmCZ6HUWsjwyngYNVdMq+FDCYQDiEWkGypFV1mYGRq0naSzVf6ozBEMYKv+CO21obtI9ro3KU1dICWAMahiSBDArYpmQzVkSAMuPZqg9v3EmY7AcIQUG0hx30My+DCGytsfrSDZcHV1zw+eD9U5ebiFBYAHzI/3p4mDBRB0BVhiYouWXTjCRTxSjk+6kJjiDpsEx5UvKocLC8+U4wffZOnjReWaOmuuvF4XCpDhmFQrVapVCplYCjMu9Y00dLm98FgcMBYfhwcx2FtbQ3P84iiiF6vx+7uLu+//36Z0XUUdNlPkx2tKmnD+nQ6ZXNzk8HgwUNUZ33p3CzTNEtDvZQSx3EIw7Cch6jLoXrA82Liu1bPztpCf/ny5SOJ1uFMsatXr3Ljxg1s2+bNN9/krbfe4sc//jGGYfDOO+9w584dhBC0222Wl5cfGOFjmiYXL14sidY5zvGsMUSRlRbqvL54lhCoEmIDpRjpCsY+ioDZHrgdMFZNKtUqMrepVJWPU8rCT56jVqU+atUaz7cN80VvBvgyZ1mAdfESRqVBEIdEOz3Cz/fY3vic0W1FknRZp818tnBmQG5kIBPsLCbPM8hTkhyyFJLxjNGdDZLeETniUSHVBYCYKFd1LSKXhmqFdHLSOFRmsLx4MJmBlUEmYBKoBzEoJiujFIaQowmVnj/0FaJzrcJ2b04ytY7lzWaoKFdV3nv1UpUvb4yIXfitb1hc/e4bWD+5gzRMXv/e73Lrix8jRMDKkoG4dJGdf9cv2kXV2mWYNu1Lr8P77x9QS7WCWXsGz81g3gVbRylQuq6gHc3t4vntL/xdcjQfPiqJXmPKPI7kmcsCz0F3eCGJliYgujymFQod9TCdTrl06dKBOYj6/9PptFSDbNum13t4qofuLqzX60RRVGZRffrpp48kLtPplO3tbZrNJvV6/cC8RL1fOiPrKCwqUTpsVZOuxa5FXY4EpaLpEmUcxziOg23bZeksiqIzQ7gWn99iOe+DDz4o/y6E4OLFiwB8//vfL4dpL25jMQi2Xq+X27t69Sqff/45eZ4/4NPT29Zl4bPympzj1wcxqkRyGAJ4ufg+jNXilaCM6z6QJ9ANDMTll8gtiSRVRnGAHIz94oYNlGT2xXzbi1U1B7UQTg2B53t4jSaj2GLW32Nwv8vkF3cJmJcx9SDqKaoSZwLhAOSdlOaqQb1SIU5U8ccUxawb28R0DZWDddQLoKUL7fqfhjDYAdtU0fc1DxJP/S5SlLkrBNdXIac5ShFzPKilUA2hK4/2YT1M8XqKWCQD1WC+E6YpqGSSMXDj52PWUeRiCXhrZYn/ZHPEt/4juFO7iHQu8Ruokt+MKklsIyXMUrhc89gBDEtw6Y06mzcgzyW9boSJek+HKBKk+wOmz6CVr1p8v33o7wZwpXi4Lzno1TqyVuAW/3iIXOWiBEmtpD4WXy4aVpVsWGxMl5o1dBfBV4yjV/gzDp1aHgRBGSIK8+5BnT2lFSy9iGoiNpvNDvilHgZNhvb399ne3uajjz7ik08+OfHCPJvNuH//PhsbG+XjjsfjcuE/quvwYVjsNozjmCRJCIKAMAzxPI+lpSUqlUpJxnQ8hOu6J1LvnjV0wj5QKo5ASaYA/vIv/7L8WUrJX/3VX7G3t0cQBNy8eZO/+Iu/AJTy9Sd/8ielx20ymfDLX/7ygceyLIvr168f6CzV2Wi60eAc5/gqUUGtPRFqkZmhFqwOYNkQTAyi5kUyKQmyKWZWqNMSrBBVg3GZD5groFv9dQfcLmCZFr5jE25tENz7nOFPP2LvF1+WJGsNZXLWv9c5GJaeTgJGdzfZu3uXdDrGdyziYZ9pd5fElNiNGtahrLsSZf4V89j7vRx6iSJUcQBJqMJFg0jVUpeXoFkFx1JDIKVUX56n0uL1VOPD+IoW0Av2fNm84s+NV1deqpRVqX/1k7xUcG4A7/3kS0YTsIdg/eIud/63/4FVIEsz/vyP/xXpVKVgTQYZn/wbFVFjYFDNLkNenMNeewnXVC9nA5V88Q0PfAOu+Ue/JE8CD3UsLEKgjpcIRbIeBkFRdjR45M5FzGMiNEc6NYrB7IA6FjwelJKeUyP6C0m0ZrMZk8mENE0Zj8elUqQ9WbZtl+U03eGnEUURWZbR7/cZj8fHxjpo6BE8mjAdTih/FHkJgoBer1cmretE9tlsRrfbZTKZPJZnSHvHRqNRORxadx/qnK9qtUqz2SyzujzPK7O+njUWX5dFcvXWW2+VPy+qTPfu3Tt2W1mWMR6P2djYoNfrHTsAfBFxHPOjH/0IUO/hp59+WmaYadKpOzYXfXznOMdXgZR5gOM+qvIXokqNqQOTCmCDTHNmsxy30BekhNkURVj2UcpOe75dgVKj3kTNW+wARpyS7A2IxlOmX2yze2+sDNWoLKSXUebmdVQX5D1UerfmR6IP3AshHeP5As+zySVMgoBud8R0HJE/Isn8AYTFk48S1ZLXSyBI1AieWg1MF0wLGlXoLEOtqWYM+Q3wzaMX7qf8ETbFfIG0F85hK99bm99oQc65fWfunD5siPgVsC9hcwOmPVjbSx9aSgOIo5i//r/UOSxJUj756D6zwgDvGOprHMK6ARUDWk/x+QtUAOqCWKoyZFFv3f2j7nQIEnU81xZZ+zHQPrDLxeOsM1fUTgV9GGqS9ViM7enjhSwdajVJRyiAWthbrRb1er0kM3mel1+HkWXZI9PK9WPcvXuXvb09xuMHXXQnUbZ0ppbruqXKpkNENeE7LXSavSZpjUajLKPpWYaO45RGftd1y+HYXwVqtVr5vN566y1+9atfAZShssCpIhqCIOCjjz46EDR7UiRJwocffghQlhhBqZVLS0u4rvtUU/DP8fXFYpbScdDDrwzmIs8UdUFeNyDrQOIDRkaeZ8gsJ1uoiRmo8YDsoySAhcWk7GZEqR594Hacsnl7h529GZO+us0qxTxFVHmrjlK/Ft2immS4EqqWhd1sY/otpuyTxjAdhbiTCWlvQD5+jPasOFfRDlkKMoCOqzxaYaxmE2YZ1JvKUJ+nqtQ4yVRa/FG8Tk9yfsISmu4kvFKD8Vi9zG++/TbvF2HIO735c/1f75xsmxJFtsRn83zZXz78LgeQJik3/07lRSTAVhErdsmAl5pgW7Afq0lDTwrBvCq9v/C3VeAt5ofdSdBFHUeP8l7pit8IpfaaQMeAJD9FGVFHfOSoF2fAGYmif0GJFigp9fDC6HlemZAO8y6/k6gVx7X8B0HArVu3yt8PJ3Vrf5X+23F+H620wZwk6W09Dg77m/Tgah2M6rpuSbxs28ayrNKc/yy77rQXbPG90SQL4O7du4+97ZMoWadFmqbs7Ow8+obnOMcJcBJR4TrzxUSbgFMUOXIFjDIQLYPEMLHzCXac4GdhuX1XFNlJJopcZMU/LBCJ+rPudpwBYZDx5c0ZmboJl1GRAFpReRNl1t9FkTRd0tRnibD4eck2MVwTKSQkYKZgiaKs9zhqig5L1zts2TAKwIhUjdSTkAXKVG9JMBz1DHJxNJl6St1kngdBAMZYCSNASbIAtj47/XlIN0RuADdR2VRPigyYSBAz+HfBgzMEtUp0mktrE3UcGhwkWTXUsfQzTlYGsxYeO0ddXEgeHMyt4aMIlZY++sDruXpOJyZaLeY5auZp7vjs8cISLZ2FpQmSEALHcRiPx3Q6nTJINAzDB+IBgLJcpA3jnU6Hzc2Hi7mWZeG6bvnYruvSbrfL+YZJkmDb9gNDmHXXn23beJ5X+qaiKDqQUH9a6O36vl92VuqxM4ZhlDESOoZieXm5bBZ4VuZv/Vy+KuXsSZBl2QGF7RzneBII5jPhjruQ1uURiVpEBGqR2aBQERzYDwT91TpBkJDJnDyckAXqOJUCIs3SKqgzeKhG5TjrAvGhJGUeJ6UtXBlK+HodpUpsorrFXhHQMSEUqrSVZmqB/bFU65S+rylMLAx80yFyfYjBiEziKCaKg8cjWvVipyRKyqtZICxwXDVE2gDcqioV2rZKj79og5zCl9kz89sYxXP54uE3OzEE8Juo13uPp0OyQL1sfQl/GxxtQndQ43C2Uf/X7+fD9vMS8BIqBV5Dk82H0cvDXnx94aCHpNuo43yxc3ERmmCto4i9XXw/1SqyWDbU0fTHMbuvGC8s0dLQuVl6gPSdO3d44403ygypRSVJj7PRxCPPcxqNBrVa7YCP6CgYhlH6erRitLKywpUrV8qQ0J2dnbKzb5Hc6SgH7ckCSiO7JkInKWMuQvvRPM+j2WxSq9WYzWbla+H7funJ0gGoYRg+867Ds06w9OgjTcJPcvuvS0fiUbMhz3Ey2CjeU0ddjT+sYuFRqBGoxaeJWgx/IMCtwcYAZk0baZpk0sCyDGy/UNAFNJowbhaLqwTaIOoGvvCBKSnKZxWrf5V5SJeBbwC3ise9YMJaA15agcSBtRD2t6CZQx7A58Vz6QN2BLKfseWNieIZWBDHGVacI31Teasmp/BpOcUL4QO+DUstqLeUKT6zwDTAc8GvIkwLiYBpBHGoWvOeYXdh8JQ/7nqR/YCnk38phKDiuRh5zjg6XrYJUKT5N1CesU+Zp9ovlvIKMRS/+L7NwRLyYt1IO5r1ffVLpfny4t4Mmc9JnDAvDz4M4+L2MY/+HD0ALb3FzHMpnkNm1lF44YmW53nEcUyn06HVanHnzh2yLMNxHBzHYTqd0mg0ytvrIE+tAJ20nKbJleu61Go1Xn/9dV5//fUytbzb7eL7Prdv335gsZpMJty5c4d+v18Srel0ymQyKecfnhZawatUKgghGI1GpGlaJtBXKhWq1SqWZTEej5+5kqVx1omW4zh85zvfIQxD/u7v/u6Rt/+6kCygbCD5Oj3npwWBKrvFPHox1apXiFqIbNRitl60dEUTSTXdR7qvYLomeRpRbdaAvlrMcshdMH0l8iBEGS2foxannWK7HRT5uwx8FxWGet2F6zZ8+wK88Sa4TQhC6O7DlgM3N6AVKHVBl4+yQcT0sw1m433YE5DAdDYmn43AysHOTzdKRisNngXNinKej4bFkMc6wvWw/BpmpYJpuwTjCXkewbTIdXiGvSv5U7RqCpSSVUe9H//2CbalyZHj2Pzg7W+RByN+8sHNh95nBPx/h/6m1aWw2D+HomkCRcIfBl2OpPiuiVvG/K3XjX9aWfWKr0eF9pscJFYJSsFF8GhibTH3aOkNnSF2c4Z25fTQ5AfUIpEkCZ1Op4w3ME2zJBz69lrhWcxQWjTVHwfHcbhy5Qpvv/02nU6HlZUVarUarVaLMAyZTqfEccx7771HmqZsb2+X5C1JEnq9Hr1er1zEoihiMBgwHA6PHa78MOiZjosdc3meYxgGrVaLdrtdDpjWnqk4jvF9n+l0embT4o+DVvBOq/zp91mP44miiJ/+9KfPaC/PDjSB12Xqk+AkCt85jsZJLy900vYYRcq0dydjfgHuXYDUd/HMGCwbs+4TtYusQAGpr/xRZmaSkINpI+OMcBqV2Vm6NLmMUicuoUSn73bg7W/C0irULoHbhkoHkhmsT+DlCNp/A/JdGIXzgcVRDMkuJP2wlDiyMCToDoj3wvmqfVKiJQHHVEMdLQ+kqczvGeBI/HqddnOZRKYM45w8ymEWq7TWqqmIXfr8Lwge9ZS1J/uXQNuCqgWzE3zMFhUnq3iQKkWnahTzNz//5YlCSvUQ6MV91MOoLzBvzPiSk1ma9G30xYLeT/04GepYA0X2I9Sh0Ua9Vvr+WtbQyQ+y2Ka+fZleWin+MeD4UrHBvCFEv7aSM2OEh18DoqVJhmVZ9Ho9PM/D87wy4kAHjsI8EkHnaRmGUWZNHafECCGo1+tcu3aNt99+m9dff51Wq1WWEnUY5u7uLmmaYts2ly9fLjsKNQ4vdmEYcv/+ffb39x9LBUrTlDRNy9Kj4zhld6HuotPZWjreQXvSXkTF4pVXXmFlZYWf/OQnp7qf53llF6bG16EcqP15X0W5+BxqsWmjvCYPuwhfZZ6hlRe3i1A+XhPY78LWSyYV/01Sq4krYkwjxqipU7WQYASF3ybOyB3AU8tXWIwqHKCUKD07dw142YbvdeCb34aXvg1LbbAsEEWXVrgD8YbynVcteH0NRndhUyqTfAZkIQcmKsfBFO4FyDuzxyvRpJkaHC188BqKUUwH4PoYbp1xlDCNIuyKg+E55CMBuQlhdPSAwecAq+iMOw563EwK/Jer4K7Df/PeMTdeYG2qCKw4RMtWHYWLkRFSKCP8o3DcrgU8mVcsZB4yGnKQpOnWIv3/MfMuxaXi+5j5+B6bYtwTc7ImLRAtMFzFv7FRL+JRzzlnzh5FsYFDx+rzxguXo+X7fqlu+L7PZDLBNE1s2+bevXvUaorn6/R4rWzBnGjFccx0OiWKovLKXytjh1GpVPjWt77Ft7/9bVZXV8skd8dxqNfrJdmSUjKbzZjNZkgpabfbD/V96Y7Ax+0A1Cn1WomzbZtms1mqPlEUlV2NmsjpuIcXcdHVQbPH4bjyq+5+XPz/SYJqX3SYpslsNjt24PY5ni4E8BEqRdtFkZyjbvMmanHpCJVh5aIWoeuAYcHHu5C0BSOzRWZViVKJMBtgqfmfRg6twlGcTSjVJSEElmOSoJSsNeDvodSFVRt+4zJc/z60LoBvqTgAqwLWkqo8OqmafGOOgQk4ObzShKuW8pAdBZkmyCBWQ64fBxEqCCpLVFehbyGaywjDI5zNCOMIaZmYWY6IE7XICgcG8syoFQ8jWYsQQC5h/LDCyYLsocvPOYpkQUGKKd4v5/l7vCPUhcXhl0ATrzHqOLyEOs5t1LGu/V9LqOP+LeZqneZLZgJOBu09cLd4tNyWMM+W03NAzxBeKEXLMAyWlpYwDKMkStPplE6ng23bBEFQJsbrmINFoqFLhzpZHih/N03zyNBMXaLb2tqiUqkwGAxwHKdcxIbDIUEQkCTJAR/U9evXcRyHGzduHLnQOY6D7/vldk4L/bx0fIX2auny2Hg8Js9zbNtmMBgwHo/Lv72IeFTm1sOel2EYLC8vl8O/H6dU+6JBd8ae46vB4if8YZ/mN1ELjGGpfM4EZTr/IRDXYHcEE0ycYIbvgJACkVoqRZ2iS9EAswqZVAqXsAWe51PJfYZ0WQO+iVJFEiCwwfdheBvWbZAOUIVpAL1tGPbBHoCdQZxCPAQzg+tXYLgPX9w/OjfJcV2MJkT7CfJxFrYEtYBGajqeUfMwcw+ZCbI4IB2NQWZEpiQfTqA3ge0hBPnzZxmnRAL87RbEW4+40TEwTGhddBjdi0klRJEi83rkpU5hNzlYLtRlPR0vRfH3RZ+VvgTNmSut2rh+uPS4uN3DJUkNC7VvunrXKB5/guJCOvIhQo3d7hW3bzA31feAqKdG/VSArfQUTaZnsJ/nhSJauluw1+uVZbPFTkLXdWk0GmUavGmaB0bPaNVJD53WZMW27WOVpSRJyrJgvV6n1WoRxzGfffYZN2/e5N69e5imSafTYX19Hdd1y3iHtbU17t27d+Si57ounU6H6XT62N4Y7cEKw7A0+evyoC4tTiYTut3u18p/o71JGpZlce3atZJofZ3wdSiTvijwUOqQAPYKkqXLiE0DEhdGriBfaRPFI8Qsw8MicwyMInMgR4VSSkuVrcwUklhg1GzSyMBg3sloU8wuzOB+T3UOrjWgswKzBD76BP72Fny4ofxb367Dd1vguhCNwZDw8hJc3IKPDx9CAjzPpLZkM9iNmI3S05OftNjBWQDBjDyBnAqYtpq2HSUQx6TpFHYHsBudst//7EACpzE9uAZEC9eOpmVy7ZVL3L53mx5q4X4ZdezoslsDZbrXXifJvMRXYR7voFUjA3WMuMXPAfMJBTPUS607ZPXLrmMi9NdhTqMHUQ+Ln/X886OiJXRH67GvAepYXkcRr2P5k2aLGXPT2HOaaXgcXiiilSQJk8kEKSW+71Or1QjDsBwO3Wg0MAyDNE3LfKxFHxdQDliO45gwDMtQ0+PM8LPZjBs3blCpVOh0Orz11luMRiN6vR7NphLVu90ut27dKpUT27ZLb1ilUnmAaOnxOC+99BKTyYT9/f2jHvpEiKKIfr9f+sxs2y6HZ39dW/UrlUrZ3QmKkG5sbDzHPfrqocmmZVnnnYRnBK+i1gR9NtBkSGd13h0AywIrazOLTaQN0smxLLNUIHIB0wqYhTkmDUEiyZsZcRKQoxbMLeYdZVsRfLKl8rJWenClAmEP+jvQqsD1C/D5GH60rboSX22BnKgN1G1o2mDEh0pELrhLVZZfvkg23GS2u336gEidcdGPQOxBLCASKkcL1ADqYawUrxf48D3tmi8ENKqCvfH8SaeZ5LN7kzLjNeFg1hWo9/yrhjazLypkiw0CWr3S/198Gx3m/Egu3E9ny72KOpYjiu7D444BnZml5Ti9Y2cIZ4JonfSqWytanudRrVbLsE/P8+j3+6ysrJR+KSllqWpof44eOh2GIZPJhDAM8X2fnZ2dAwOOF6G3pbv7tIm+2WyW8QrtdrscZj2ZTOj3+1SrVSaTyZFjXWzbptVqsbKywsbGxhOpDocDWb/u0AGuh4nWo8Jof52goz90Vts5nj10jtbhGXcaAjXyporyqLioyB9tWjcrsDMCbxXC1CATObkQGJnEyA2EVLGRBtCR0JtBPEKtRLEkkBMcX601u8wH80YUqoIA01E7khWmeWcJLjlQc5U5/ssMBgEMXNgfwqUKTIeQxmpbB84yDvhtn5XVZSa1kw5kOQQtk+xK6MYLGQG/XuezJvOYjJOQLgHUHJu9grkK1MzL0a29sjyo/774pb3iurN1UUnS5UNdCoQ5qVksHS6WBBe7CY8rERaHVKmQWcwVtZR5jIRWyBb97OvFfSYUobjFa2MD11DernvA+0BocjBDYhGazS3ijHj4NM4E0dIkRhOO4zw0QogyTV2rNrpzMAxDms0mUkqyLCt9WYsLjb6PDh3Vv+tk+aNgGAadToff+q3f4tVXX8XzvHIfut0u0+mU8XhMrVZDSsne3h5pmmKaZjns+TB03IIecK3zi87xdPB1J54nDWM9x9PDsoDfkfAzoc77oVTlDn3+N1ELi4nyqeizzUdAW6iy4TZgLxtEuYFIMqwwQpiS1I2p54W1QUI0Ul4qJOrBZmpEYCzni+aXxWMkqIX+Yg1+7y14+ZvgexDOII1gsAXTGYymsOpBM4PdPbWw5Y7yb+mmrwNH1BTEWJIGEUkWq5Xkca2P+nn8mmKRhlqcQN2SqCgL5t4r7cPScSBaSbKL/1vMCUsFReJD1KxBXUnzUQRfH5N6cpPP3E+lRy9pD5XNvKlvEfr4XRzVBPMuxLC4z9XiNsdFk42YT2KCObn7JSqZfwwk+gU4Di+A7fhMEK0sy+h0OuU4nV6vh23bpXdKG801GVkkY9oIDtBsNsnzvDSka8P5UWRnUUHSpcaj4LouV65c4cqVKywvL5dDm6WUDIdDBoMB3W6XarVa+rOazSau62KaJvV6neFw+IAHbDqdsre3x2QyeWEN6mcRUsrHGtJ9jnM8EaRK4P5OsLIqAAAgAElEQVS2pRbWnQTeLQhUmqquPu2ZGjKfe7uBGnliN2E8gGy5jkwF2WSKSCYIN0X6LnFxwZijmu6YMp9gXfhSZHEKs1FRE3cofDsmfKMDq6+BexnSAOJAZYQOJrA/gJ0etOvgmZD1oN4BzwErhesmbGRwIN43g+lwwna3y3QaHz0D5hzAwZfmJFmoOXC7uOEiB9Vv+6MwY66gLe7D41x6HScMHceND6cq7B1zO1AxEDVUtIlWzSLmsVnl7MZfg2PrTBCtPM/Z29srfzdNk6WlJVqtFtPplNlsRhAEB9r7tVKlx+LoocmajO3v75ceqYfFAuhOxIf9X0rJ/fv32d3dPRB8ee/ePZIkod/v0+/3abVaB2YLjkYjoiiiUqk8sPjrsmer1SpDRM9xjnO8mMiBuwCJWiSqwH9mQa2l1KJgrJSGPnPPyhbzhagfwKQisKwOcZqBmbAzGFHxLDrNKUk4X23KS8TFFazwsGSox9em5B7KaJ8BO3fg3p4iWHauIqw278Aoh80QeoGKfegYIAyY5SpaIMqUGueysPAKSNIEBFSXPMYVgyQ6v2D8uuJweCnMex2Og1bQDuPX8TL5TBAt7avR/qo4jrl//z57e3tkWUaj0Sg9T0fdd3NzkyzL2NnZIc9zRqMRg4FK69CeruOghzsf55HSXXwffPABhmFgmmY5P3E6nZYE0DAMxuNxqcANBoNyfxeT6DXCMGQwGGBZ1pGG+XOcHjp8VoezgiLt+m+6yzTLsrIhQZdvgYdmmunStv7S2Wwai95AXQrP85w8zw/8vPj/xeNBH1v6+Dk3r79YyIGeAzJV6QMzIE/A21bEaAlVotH+Ka1mxajZeje2YNSAcGcEWUY0nDAYRnSlIJCwzDznT9tVDj44ZeBVwjyryAQQMIrglx/ApAgpNV3lOd/MYT+Bu8Xh5qWFt2sP7k5gK4SRofa3k8N9/ZgSwlnAcNDFtQz8qkvSf4qza76GaKM6SbNcGeIFivAaJuS5Gv8oJSSpKhNjgFdV+a1mqqI+Yjn3bC36q3Sp0dDbFaBPYfr/ea4iQwxZhNmifs+K6UrIeWSaUxwTWdGnYBmqU1IaKhokTJWqdlaDdBZN+3qs0KKX/mnjTBAt0zS5fPlyWR4cDAZluVBKyWAwKA3th1v3O51OmYXV7/exLIv9/X2yLCsJ2/Ly8kMfX5Ot4/6nB0Kvrq4SxzGj0YjxeHzgPnmeE4ZhOTdRxy3on23bPlCe1I+5WBI9x+PB933W1tZYWlpiOByWTQppmtJsNstSb57n7O7usr+/j+M4vPbaa/R6vdLzNx6PEUJg2zZCiJKQaQKmj0E9zDvLsvI2eZ6XBKler5cjoXRgaBzHzGYz8jynWq1i23ZJwnRDh85V29/fP/Z4fJ7QkxbOj9cHYQBrLahPQCTQT1UDXT9Xi80ERbAMFB+aofwzV4C6B/er4C87jLsjHNNgupdCBnImmcgBK3UVsqt9Og9clmWUtRYbNXrnPorc5VI19nXHUGvAOIWtEcyKQPcdYBNV1pqhlKtGAomAmQX9DLxMlSHvLz5mLsnChCDKlbp1jseCAK448O+tWzQaPrPBGMMR+F6VLIvxWy7RLMaSEUjB1r5ks688dKuvQq8HtQDMHHqhIlG+DW5BvKSpjkkzn3uuLAsqjiJRaVYQq1RNF7AyaFRAWBCkEEQgMjX9qF9UiS9Z8/nNoQWRBb4D9QoEM9joQ5AdJFoWc8P9w2Axv5B44stNLa/JosPXKHxtmTre9expfdmsCVHEQe/Yk+JMEC2db6UT3tfW1nBdl+l0Sq/XK83nmrho6AwtKSVvv/12qUz5vs9oNGI2mx2YBfg4iKKIXq9HtVoty4GTyeTIUTZJkjAajajVamVXZBiGD0RMAKWSNRgMzuSi+qJACMHa2ho//OEPWV9fp9vtYlkWq6urzGYzGo1GmaKvxx7duHGD0WhEEARcuHChJE1JkpSkXR9LlmWVyqXOK9PJ8sPhkCRJqNVqWJZVNjYsZriZplkeK3oAuibh9Xodx3HKYeCe52Hb9rFNFM8DpmmWx7lt22VcxHE4fCH0dUEGiAlUUpVJ1bHB9WA/UgkFqVTGX7u4bYQyv/8GUGlA0BJc+J1VrNzCTiGtbTPrh8RTA8OQWLnqV5M8WiXIUITJouh6TyAbwFIF0gQGoVpQdafaHeZm5y5wA/iBVArLMIedrDD4H3ocwzZxag5BFBAm5+T7pPBRqmEFVT42gd+rw3d/18O/skayPUZYBktrl5iEE5UNmUREgx7JJGB5M8O+kdIdAzO4vgZNVylSL6Vg2Iq8p6F6Xw1Xla7zCaQzNZTccUHkMOmrrlLbU8TLS8G0lNolDKj6Klw3iSAxVNlZpIXSZYNXg7oPWQq2rbpnTUcNJXcWrgYs5hcYx+meOs9Lz9dc7IJ8bDjKd+jHSqnzLKgJcAMVj6EtjhNgBeWXFKgSv06rD3hypetMEK0sy7h3714ZOlqr1ajX6+UwYMMwSrVK+7U08dIdhHEcH0iMbzQaNJvNUjl4kn0TQvD222+TJAkff/xxab4/KrpBSlkmxeuxPrqktIggCNjd3f1a5109LeR5zs2bNxkOh9i2XRKdZrPJdDql1Wph2zYbGxusrKxgmiZffPEFg8GApaWlshS4tLSEZVlEUVSWg3Uem5SyzGCr1+sYhlF69BqNRjlOSR+vmlzphgyteEZRhGma+L7P0tISjuMwHA7L/dXNIDr09nnCdd1S0YvjmCiKHkkAv44kC1Sp7m9mqiW9Alwy4RsOrJuwloJhKMHJMGFDghOpETzLQDiCe00wpCAzXNJZQDgT+I0qlY6BZRgk0zpqmXp015qJWshbKHKUAfjw9huwL2H7Y7WoRLlSsxa3N0Yl1V+QcN1Q+V77oXrkw3G/cRAy2N0nG+WlEf/XEosTnp8CXkKVkq8A/0vxN5nBzocT3NEdqiZIK0dYW9SW2oSTPt7SOrbpsTe6y9JqhdecCe5N6A0TzA7EjlJpmivg2GBMoTtVwbZpCJ4NmQeOBU0PKg2BsEy27qT4EdSbkEVgJEWJ0lAETKBGNtVdRTxaMzVr0zbA8aG+osrQo4EiaPWmUsj6A5ALRKvFvBPyMHzUxUOVueqlY7EeCj3b8KgPRFEXXDXV57GaqFL9fqrU2yXmFscq6sJnr/gC9Rn6DRTJ6qGOfX3me5zD4YmIlhDiS9RnMwNSKeUPhBBLwP+E6uz8EvinUsoTBa3oE7lWG/Qit7S0RKfToVKpkCQJGxsbSClptVrU6/Wy07BareJ5Xrl46jLO1taTRbnp0o4QopxneNxsPaD0AT3MGxYEAdvb24RheE60nhC2bTMcDrl27Rq9Xo/t7W0uXrzIysoKnU6HJEmYTqesr68DakD1tWvXeP/99+l2u4zH4zJHTXul6vV6OYy6UqlQqVRoNpvlz5VKhbW1NQaDAWmaHph3qSNGFn1ZoN5zTc4tyyoJuC4tJklSPo4uaT4vmKZJtVql3+8/EeE76wrX0zyH3QB+hjpJX8/gW/uqzLeCCgn1clhyoZbDl5FK9l6xIG6BUYdwFGE3Pag2aC4LpEiRZkyt0mI8OplFWCsCus3epVAEBCzZSnnIQ3WlnnK08XiEaq3vOBBVYGeoiOThoyCbSaabkWqj/HW2mC66vJ/wUBYoZaeJen98VIk2d2DaEzTfXGWw12VzO2R9PKIzCmmv1DHTKUGwx/KVBhkul5wVrr4ScuPdLTaGCcMJVGsQj9Vsbl+A24YgUCH7fg3sKjRb4FUFvu/i1D0aF0dMezl5CjITiFyST8BIlUKWp6psiITMgNCGxFZlSMNWL4dZGMHiSCmmlSq06uAMKNlMlbkP6jBWUER+xry06XH0HMWyrihRVxM6+uHwwWlCO4OrBmxOlHpVQ302dFmwwTzuolq8HzrNPgN+BbyC+pz6qEyvxARHwOyUAu7TULT+QynlYjfpvwD+Ukr5L4UQ/6L4/b8+7Ub7fXVem06n9Pv9Um3QvpbV1dUybkGX7GazWak6RFGEbdvlAOonQRAE3L17F5ib4x8GPUx6NBod+Ls2aWtvjy4jnePJMJ1OefXVV/nDP/xD3n//ff78z/+cW7dulQQ2yzJqtRqNRqMs1y0tLeH7Pp988gm3bt1iNpvR7/fxfR/XdcvAz0Xjuz6WtKneMIwDymuSJGUpUJe4dTp7pVIhDEP6/T6TyQTLskrFtdVqlQRPb1M//vOALuH3er0Df39UB+9R0Me3vvA5o8Tric9hFZSaVUOdxL9EleQi1BDpd8ZqQWlP56NFvueqsuEdAfZUEu6FROOc3KtQtQ1miYFj2rgupM7JjOY6W0lHASxRpGuHcP+WMsM7KAVhB7V4LMJCjXHpCKVudCfz7CZQqoUUyuhPWjzBmBciy+ixoUOsfJ4Koayjjokx6pjYlLAbwuU3Lf7+P/s9Pn33Nnf/n9t8/OUdXkkhNVJacp9qvYK7tEK12sbybaqrU5yKwP2ox60vx0xDpSRVHQg9NUopCSG3lN/KkGC5AtO0SYWFzEykaeK2cnIpsBHksSS3wYyUyiYLxciywa4KohCGPUk4VpOSam3VqdpsKy+YYSkiUq+D7ajXzi6e94yjxaeseGlN5iOpFkf9HIiMX0yBDzi2rriUKjXr3fH8LgmwbkE1nefLaYKlO3X1iKyk2KdPUJ/XbwLfAO5akOqa+ynwLEqH/xj43eLn/x741zwG0TqMxZO+XqSSJGFnZ6f0wWhlot1ul52MlmU9sGCcFlmW0e12y4Wi0WiQpmlpsj4Mz/OQUpajdUzTPFBCWlRBzvHksG2ber2O53n88Ic/5I033uDP/uzP+Pjjj3n//fe5du0aly5dYm1trewWXV9fx/M82u026+vrbG5ucvfuXZIkOeCp0lEdOm0+y7KyJKyHmgdBUPqrdFkwjmMqlQqGYZSDyfM8x3Vder0eeZ6Xx4TOYNMNIPq4fV7Q0xM06vU6YRhSqVQIggDHcajX62V58yQXH4tNIi8ATn0Oy1F+mzdRi8NtYA0l9sSoAMYx6qS9DrwBXHWhYsG9LsgEZvGQ/gSchmCpVSOzXZKGRWZMyLdPbs3Ni8dNmQeNphn0hoon+AIumGDl8GV+sJxTBa4Dr5pKzfhwqp6Xa0HVBaMNUQrjbdRCZ/JCj8c5MXS0+lOADjKvLW7ehlpdICpNvvcP/wGXvrXB3/zff8Xt9/fYfX+fa9c9XnrJoH3Rw3RsJpOA5YvruH6NRqfL+sXPubsVsfllSJRAfwb5TPmufEtNA7B9mI4hT3KMPMEVgjxICCYwiSQVV+JYal/cKuQRVHwBQmA64FcsyExsL2TYk2S58n9ZPnh1cF1BHoMRSyo+WMXrpUnU4WB3zZ8eObNjMco+OOLvh+ADqwI+y9Rj1oGXq1Cdwhu2alTZNsCsw94Y0qKrslPct4MqdW6hPscpSt36j1GvyWeP2t8j8KRncwn8hRBCAv+dlPKPgTUppa7VbaPONw+FTnd/1Mm60+nQ7XZJ05Tbt28feZvRaFSWCqvVKp1Oh3v3Dl+3nQ5JkpQma210112Hh835i9ECGpoYNpvNMnpClyO1d+ccJ8diNIJWNw3DYDAYlKW+P/qjP+Lzzz/nRz/6EZubm2Xcx/Xr18vsMk1mDMNgZWWFSqXC5uYm/X6/VLMcxymz3CaTSVnG1h2FusRt2zbtdrs0xGsyogd9a/+hNtPrbVerVarVKr7vk2UZs9mM6XT60LLzs8bi51CXMnV23PLyMrVajeXlZTY3Nw+UTPXrqdXoRTwsOuM54+mcw1CK1hClIjVRHhzJ3FDbA1arcH8KfwP82QjckfJqDfaht69OyLM9yYAxiDFGB1aWLJKbJ7uE1nxgyDw13AFmUkU8CGDNUWGk10JVzvwpalEJUYrCRVRafSaU4rKBIoSry8C62s8xgFALLQbkv86lQ5jPZXwgW+P0qDOP+NBRC6urYCOJetuIVFKtWvzjf/aP+PLGZ/zsT/8t9+9tMemmZHLAxdcF9eU2VqVBW3jYaYYrLtNZC6m5XfbvTtkbxcxSpSrVLDWuKZ7AZCRZ6qS0lzNknBIGaqRklILpWNTqLiJJEWaO46R4dQNh2Qhh4LgOvuNCJrHsUEU5eCZu3cJr2Pieh0wk8SSgHoS4t3MYz49BXenTl1qF4PXU80ibwM2imaMHdFz4zRrszeBWDu0mfNdU/rL7Rey9FOozsuEUn4WREmuXUaTrc9QUh28XP58WT0q0/n0p5aYQYhX4f4UQny7+U0opixPYAxBC/HPgn4NSJNbX18uSiu7g0ouOvgpeWlp6aEfW4WwiPW7nSZHnOUEQlGUe3Xl41OPr/V/0cGnfmTZaA6XScd4qf3ro8pN+r7WapAkRwPLyMlevXuUP/uAPePfdd/nVr37FX//1X9Pr9bhw4QJXrlzhwoULZbmv0+nwne98h7W1NW7dukWv1yujHiqVShk+Ox6PqdfrRFFUkiXdqVitVkmSpDwONanyPI9arYbneWU0hG6W0AqsJmm6tOj7/vN8iUv4vs/m5ia1Wo0rV67QbrfLfdT5b1mWldMQLMsqOzRfEDyVc1hbwD9xFQGJLfiOrczm44nq8OugDF/f9+HzKfwcZbA1UCTMQnlGLqK2MUJ5X6b7sL9/8nOEQJElG0W2asX2viwea7kgT+NEKVZvoha8v0RFN1RRC5UFjMXcAD8q6iv1KiSFqiBcMGsgRy9W5XCRK52KNwnmrWiP+YQF6j3Rxu9l4L4A3wXfkiSjCekgAivDWlnlyiuXWfvPcz7+6Xt89vM+v/iLz3i922L5pQYXrg5Zu3ARGWXYrkdzucpb7zQYruyy8UWP/f4MKVRpz/ShP4IghtEEanVJHGdggO2p85VZsXAbFfIowpYJVp6SSYltGjhehUrdx3cqmKaF7fdJkhjDdzFbTaqdFq12mzzNGfb28cL7WJX52usU3/NDr8XTxhKqRClRRKsCfMeFv91V8RP/wQo0WjAcQjBR3ZkDE5IMmiZ0KoAB/3qm/IyvFdv9HFVmv45SpL845X49EdGSUm4W33eFEH8K/CawI4S4IKXcEkJc4MFmFX3fPwb+GKBarcqlpSUuXLiA7/vUajUMw6DX65WZQkmSMB6PWVtb4+7du0cOYn6S4cyPeJ5kWVaWTrrdLp7nlZ4V3ZmolTmtTixiNpsdiHnQKsAZvtI/szg8m3IwGPDhhx/SbDa5evUqq6ur7Ozs4Ps+tm3z27/921y7do333nuPnZ2d0jD/zjvvlH66PM/xPI+1tTU8zyu7ErXvb3l5mdXVVaIownEc8jynXq9j2zbdbreMelgceK7JtibUcRxTr9fLvKw0TalUKqWqpY3xWgl73nBdl263S71eZ319HdM0GY/HxHHM8vIy/X6f4XBIFEVYllV6u3TjyFFduWcNT+scdtUR8kIDXrPBryjvVWZBf1/5b0apUg0mAbzkwJ1YKWAx6srZQnmmLhfb/gRFjOooIpAw74h66PNBLWAtVIt6gLpCj4vtXZRgxPMJPosz7mqoffJR3ZE75ryskwGjGeR9tQABGLkiWdnpbHtfObSaoi+NTeYl1SrKg6ZT9B8KPanb5rGHFuvXe4Z6bz1UCOl0ADc+yun+mw2uv2yzdGGF7P49fL+G4Zq88/d/k6vXt7jx0zvsbU/4cvcuw40R6Q8CqoZPZjrIPMatWrQuLlNzDe5+vkF/kDOegtWE9SW4uAxhAq6jjqlq08CyDPr7KcNeyF4Y4vsGli8II4kwBR45ws6J0xyMFLfdpFnzSaYzkjTDqFTwag3ceks19hgmVnOKsFUwgiaXAeoY1CvegUsxXUd8QiyhLiokihz9EwHvjlR58+/V1XE9mKnPYmcFhnsw7qmLoQQwBspn9pYBnxqwm6v9XmVu1H+zeIzTrNyPTbSEEFXAkFKOi59/D/hvgf8T+C+Af1l8/z9Osr08z/F9H9M08TyPRqPB+vo6aZqSJEmpCunFaTgcPtBNqI3HWZYdiIZ4GqqRLgclSYJt2wcWU93Wr3OGjhrqq306el/0vp6kZPoi4HDG2bOGJtR5nnPv3j3G4zG3bt3ie9/7Hq+99hoXL17E930cx2F9fZ2rV6+yvr7O1tYWN27c4NNPP+W9995jfX2dZrPJ0tJSSbYALl26VJIo3dVq23Z5EaAN81JKlpeX6XQ6ZayHHhKufXjaHF+rKVdGtVqlXq+XkwT0bfV2Pc977kRLN5LEcVzGUOjmDU0EdSTFZDIpSZX2a+nX4yzjaZ7DhARyqFTUibrqQ7UJl9cUMYlDtcANh6pEYY9h1oNfjOaL7xhFpi5RZFgB14BYqFiGkxAtE0WUytE+zIcC14uvCOUV81DkYrP4/mrx5Qilxg0OOZdtR3l3Qk2sMrCFSpo/i33T2j6m1UKYt/JfRC3EPnMFr4V63Y7t7xQ8FeO/Vhr1pqSE7hbciXJu7/8dP/helddeu8IraynLto3lmrQurbPy2nUaly6zvbnPlx/fZuODe2Q//YTVC2tU2hWWOm0iJL5rkbfqrL7UxrRn5P0ImeaEoSr1eg3IzCKY2TaQMqO96tJYsQhnKWmSYtgm4SwjjXKSPCTJUvI8xa9JMmHg16o0Wg0moxlxnpOnKWmSIC0by63hejUMYz5pXL8PJsccK7qj86RkS8e5LyztNkp10kPQXxLqvRwCv+eriIuoCFyt1Yt0ewuWHQgDRagSwE/VXn/fhf89gm+hjpsA5dn6HdQxcxrn95MoWmvAnxbKggX8j1LKPxNC/AL4n4UQ/xWq8eafPmpDSZIwmUxwXbc0FAM0GurjYRhGma3l+z6vvvoqN2/eLPOO/n/23rTJkfw69/vljh0o1Nr7Os3pGS4iRdKiFZJv2KbX6wiH3/hD2B/Jbx3WfSspfB1xry2JutRQEkdDajgLe6q7p7uWrg0o7EDufnHyJFDVVV1LV/fUDOdEdFQXCkgk/pnI8+Q5z3keBTXK1wFy4q0qcr+uhIImW91ut9slTdN8u7Oijlqtmg1tGWnyMU0T13W/FhUtFX09ag1V0FI/z1eRXFVctN/v83d/93d8+OGHvPfeezQaDRYWFnjnnXdyQHT79m0WFhZYWFjg0aNHPH78ONfW0mpMqVTK+VOLi4u0Wi3W1tZot9sHZEX0eBaLxZwwvrOzw2QyyYnuruvma6RALE1TarUajuPkNxGTySS3CzpK4PZtx8LCAtvb27n0RKVSIYqiXI6k3W5TLBZZXFxkcXHxwNTmbHv8kpPfL+wa5sfQDqA4gpIF+66Ar0pNNIlsS4QdF2qQVuBBAKu/h/AxtHz4jwgZ7AfAz0rwUxs6CbRT+DxT7z6Nn0nCdBjLY9qyUSLDNpKjxkhV6zlC7q0gLREbEXgcWbB16P0sB9wS6H2hY4hhtW9Kq/OyceIryGfUAYS72eNtpGU3RABYnD13HrhlwMfpy6bMwNSZ+TU/aMDUJUA3Zfoy+bbZgX//t0P+4cPP+MG7cKfmcKNhcLvXgdTFXrlN9cEd3l15QHPxA7Z+94znq2ssLFUoGgZ44jDhlooUCjaVSovmcMDeXsjac5/WfkTDgNqcQ61axy2ajPwRbtmjXq0SjgP2t3eZjANMz8Arm3iOVKsjDIYTnzBMiNMEGhUMzyUejgnGAZEXkDoGaWxgpRZm1hxUoH8cGT5/0lnWVT2FZmIJ+Q4pqPtvyvB/DeVLHvpQWgQnFsmLxIf2COyKeHvaqcEotdnphNRTKKYib3EN+Y7cA/41ex8Hab+/FaCVpukT5Lpw+PEW8F+dZVuqjaUXb8MwaDabLCws0Gg0cm9BvatW0cfbt2/z5ZdfHgBa8/PzuUipWufoRNfrhPK0NHkcrpJpQmk0GsRx/BKPbFZbCUQqoFKpHNBWuqyhhO6j1tA0TZaXl2m329i2TbVapdVqvVUAqXIeGoPBgH/6p3/C8zyuX7/Ozs4OYRjm4Mt1XX7wgx+wuLjIl19+Sbvd5smTJ9RqNZaWllhcXGRhYSEHTFrFevToEXt7exiGQblczk3E1QFg1j9xVs5A7Xj0vNUKlgqCKmDXc0vFT99mHF7DSqWSm6gXi0Umk0k+IOB5HrVa7QAgNU2Tp0+f5kBsf3+fSqXyksTJZYqLvIb5KTzuQacn03mTFtyswFINygtQtKAYQuSCNYHIF/HI78zBx1vT3qRlwNUSuK5Uwj4G/jUBJ591P+EzITnoDgJ+msi2Y+AJcideQRTgO0jy2AaukAlHGlArClB8duiyZKQyHak9k6ILi2UYxDA2Mv+9SxAW0zx8A/lsLrI268juP84eC5FE/P48/KYtHLb/sgr/2BOEfSBeW6ZcwmXaQusyVY5oA5NUQPWoA3u/grlyyMMV+N7uBj8KTa6ZZaxmCcstcfsnP2Vhqcne6iqjvX22v3hEpX4Fc6VJYWWO2tI8/miA7VoUqzGG0WPyaEB3O8FOUurFmPKch1NqMI5C+n4kciKVItHEIDWkYGRZLpbt4EcxUTDBM0YkkU8Sh9iFApZhQmIShLFMGiYJYRARJ2l+PCLk9D12+c66rkek81sm7CVTf8eFAsRDqeKWiuIJGQTCfbQKwtWyK1Cwy9RLDpbhsv64RTCJGUewvw8/CuCvYtnG1ZnPcdZ+w6VQhte2WxiGeaWo1+uxublJuVymUqnkZOOFhYXcCkcJ87Ph+34uNDkcDplMJhdKOD98hz6bSIFcH+twKEFeX6/aSlqJucx3/q9SA1dSt4qG3rx5k2azyeeff37sa143DldK1BLncPi+z+PHj3nx4kUuEHv37l0WFhaYm5tjcXGRYrHIzs4OL168oNfrsbe3x8rKCrdu3cpBEEiFJ01Tnjx5wsbGRi7zYVlWPrRRKpXyqpgCJvWzBHINtcP7rK1n9b988eIF/f7b9bCv1Wp5mxTIpRu0mtnpdHKNOm0b6oRlFEXYts3c3FzuS1oqlYjj+FIDrYsMCzHW7Ve6bZcAACAASURBVAJPfbmD7g/h6TaUt2GuAhUL6jbML2XJtC1ec0OkUGIhVj39QERFQ2A3hp4jnJLThBKsFxFAZWT/DxGu1gpyRx4ibZAvkarOPAK8lg1JJLvR1JjaNETZ3nGl3aJija4FlaLoLTmdaaXrq4wK8tlqTPWqBsjEWDzznBrSMnWRikW5CO8Z8KsE/pcqLMzB//HsELa9oDRiIFW2kGmb0iqANzn4NjGwNYT+Y1jdSlgP1/n+Hty683vmlq7QrNdpLC1TK1j0t9bYX3tB0NljbXubTvs2K3fniS2HMIogjVlccogNlyerCTvrAZ7TpdqMiK0ik1FId9yjUnJxbROrWCJME2I/ZByGhHGMSYphpIRpRECK7ZpgxKSJRZymJLaFh0E0Cdl50WHQl9XzkPMyOLSEFQRwDi9oaa8UwRlBM5UbjH4fSIUQb6fCxbJcuYkxbUgsiEOXqFBmEth4Tkh9zqHbjQlCsAOo+bKDDjPejpydoncpgJbjONy9e5c4jtnZ2cmrAe12O7feUSNerRgNh8O8laGhE2BqRq3tmjfZhpkFiUmSMBgMXgJ/pmnmvBtNuip6eZjYfRnirEMFjx/LDIbruqyvr/PjH/+Y3d1dWq3WG9u/2f9rFe24GI1GfPDBB6yurnL79m1u3LjBgwcPuHHjRl6xeeedd+j1eqyurvLFF1+wu7ubSzSoxU673SaKolzWQ/Ww4jjOK5N6rhYKhdxeR7lfs5VZBd4qUqpVUAVtb3saVauWMOVZqZbYeDymUqnkshT6ndKq1+xrtLrX7/cvZOL36xKeBdeXJXFGPSgZYlWyHUHSg0IPygZUUmh2RDqhN4BRIjwqM/vXNMBwpHKUJFJFGo6k/XhSGNk2VLeogFS1ygj46iKVrAJT4AUCOhaRqcMkhf0xbBkHt+sUwKvJhsMZx98khDAU77uvOhpMqT7vIAnxE9S4aBqDmec6SJL/h3Xh4twA/qkFP/8efLcH/3IqT5OzRY0p2PIQeYdaDWpHDOqm2ed4PoStf4j57PEzHtx8zv1bz3l4r8nNmwu4BniFItfevcWoO2Dj8112Pv2CyYsNjEIZt25QrhcJfYv99pAwtognQBDT3x3ipyP8BPwgIhyF2LZDuWzjFl0816TfGTCJfUoFA8exMBwbs+CA65CYNqZrYZgQxQEEJuFowngQEGdVp5hpBW+2nFDPfj9xlmJWDf4VUTKmx/QuQCDnetWEaCgTs4YrCvfjkVRvy2WLoNdjlCSYZkQUWoShCPyO+tDKzvUh8Ch7nx8i4O0scSmAluu6LC8v4/t+zmtZXV3FMAxKpRKVSgXLstjb22N/f//YJGQYRv53rSiooOLrhMo2HNU6m61gxXGc62vNxqwZr1YMoig6Uovr6xaqbQbkXpO/+93veP/99/nFL37xRt7zcPVPJwdPes329jY7Ozusrq7y5MkTbt++zf3797l16xa2befEedM02dnZod/vUywWuX79Ouvr6/z6178mTVPm5uZI05Rut5uDJ5VmSJKElZWV/HgrmALyipC2YeM4zocktKKlwO+4Kp2G8rm06qQDIOcNrQJrNJvNXJJE2+YKCpUUr9wyvbHR6qZ6NX4ThjxOG64DV1dgNwG3LKPiXzwRLlTVgoYNXghbKazvCyALkaSvdiAWot697kMQSbVrlIAVibL3UaE8F7XeKSLASqsHmtB1TL2H7NOnTDkmUbYvEcIJ8yOpds1uH0uELMMI0uw0CWLoDSUhxZeADV9CgORVpPrwIVJ5MJFK3hbTRL+e/ZwA9+pAV4jU9xEpi88/h//xAXzy4bkHDI8MAxl0UL0zvXKV3amp8uHehma7IIFHL+DJdsq1J/t88rjD9+5s8eBug3vXFjEdB6NQpnEtwbB8ui/GjLpDqDo07szz7HnMbz4QNLfYACuEcSsmdcHxpKLW6YWEhJhXPEqGSRT7eEaM6RjYgOcYOJ5FahgEYYIRhThugld0cQwDKwlJjYhqMcXLLmE6CXv4XqGJgHsbmMuqwf5RjZ1Tpu94JID/OQKaF6sydFIxxFIo8KGYIbF0IrxKhhPSYcokEzeN4gTPg60OfBnA5swltcG0zeudbpfyuBRAS++GO51ODmhUzV3bLePx+ERDW8MwchHJJEly3ovruifq+ryqiqPSDUeFVqaAvLoxux3XdSmVSvlnVFAVx3GebC9TqITFaWNpaYlOp0McxzmnaWtri5/85CdvbB8dxzl3Ek/TlFarlfOynj59yg9/+EOuXbuWHw/P83Kw9Omnn/L73/+e3d1dxuMxy8vLNBqNvOqkJPjRaJRLPyhp3nXdnB+oZHfXdTEM48BEomVZOa9L226qPn9czN48zIKu84a2CLW6trKywvPnz+n3+zno04lK/Qza3pz9bg0GA7rd7h+cPpxpgjGBuANGAfZt+EUgSaZhyF21lQgQqDOVF9jNfi4jVaXIkrvo3kQu/G2kEmZaMg2loEhDgZCDJK45JBF0EEDlMAVdc9l7fclB/tEIuWOfz/4+YlrtMi0olKBYk8pLGIjKOMjPMAAjkUTyVWKtZaZ+jA8s+CSe7k8dIUpvH/PahwvwSV+A7WNkfP/DPvxvRdE++/1r7tthra6IaRtKr2Kh+TIQgYPuM2n22iiBx7vwbC/ls6dDHjyZ8Kc/Sri3XITJGCtMiYsFSjdsxl7Eo098+o+3WN+DYAi3FqHagMlAqpOFhkzF+iMwR1AqwGQnYBAF2IWUomcQhQaxn4AZYDoGBi5xCGkUkIYGLi7hyCc2UyzDoFpI8DIUaSOAsnBoLerI98EAbhvwRXIMqD3liZW6gA/XDBkkWbkOwe/hWSznfnkglkSWLd9RN4XxIMUEgjEEplS71gJ47MOOJ0bbMCXYK9DaPN0u5XEpgJZyU4C8IqUApN/vn5qvotIQas4L0O128TzvtQQU9e79NDGb7FT9vV6v52P8Cti05XjZuFmO45wJaO3t7dFoNPKqlgKgjY0NCoXCGxGuPAy0zrOGaZrS6XT47W9/y+bmJnfu3MF1XR4+fIjjOPT7fTY3N+n3+7RaLXq9Hq7r5sdNxVF1wEHlHpQ3aNt2bmKtOnBJkuQVK9WdMk0zN1NX8HWaNbtoIKPnpQK42ZsDBVjdbjcXadWbl1lxVr0h+kOMNDMXLCVCot1DqkZAlh3lvxZSXVFT4TGSeK6TeSUaYJkCkKpF4ZS0x3LHbyDEdj/bzjB7rY1c/AsIL2mQPaeLJO828M9I1WyIJInZxK8TiO8jOW1v9u+GyAFUFyTBp9oHQsyLC5Z43F2UDtJ5Yx5ZBxPwDPkMCbLO15Bq1VFXiRLwogPVulQa55ChgRTYXROPu9cFWofDRIDHmKkNzzieto91Pw3AsaSSRZpVLU2p+oQIn+/LNqx3Y1a3t3n/RokVy+eP3/HAten3U55sGWyNYLcNm2MoZ5L0tiN2PPEESqmcL24RLA/SCEaDFMeGhYqJmZhEo4golJ0zzZhS5GM6Bli2rPtoQDzx8aOUNI2Jh2PM7HxQFXyVd9BzS7uBMeDFr+9wlGT3ni5yM+FUhF/4CXIeNIBaBNXMA6kci8adb4CbVY/9FNYtGDjklbQV5Lu0h9zMDPiaAi0g1yhS1enzxGAwyE2A6/V6nvhOU/14XaFTHcmfrVDNJiCtaswSo5Xzclnah2o3M2tvc9K6DIdD5ubmXppae/r0KcvLyzx79tLszmuHSjFoK1eFYk8CH0d9Hm0pqsBpmqa8//77DAYDHj9+TLfbJY7jXJ5DK2L9fp9SqZQDaT3XBoMBYRjm6ujaUlMQpbpuKlKqvoL6OtVVO8v58LptQxAe22x1dn19/cDfZ0HUaDQ60vRapSwuw7n81iOVJGbasNGTqcKjIkZI2LP+bgZyIW4iSa4Yy915uQ5uAa5EsDKWZKFXRr1w95BEpslsiFRJVKDTRzDepxwf6g2tBO1ZdcI4AT8U8n4QwWgC+jUzAMcBCuCNYBJ8dVhrW3aDhwZ8Hk2LIDezn1rtOhwFIOzBQh26FtixtJ5qwOo6XKmB0Xu9z3X426CAqoAAOxBw2EDOAT2GtilVRJ3WK5oyTDEI5NjqdqMYnmwkfLkx4GoBxqnJD7/r0R8aPPtiQmcg2m0lQ1rYRiLAq9eDYgWsEpTnpH048mEwlGNtj6A6NCCFIHMSSEfgEuF4EV4lwa2WsQ2TaDJk3B8RhSmuCUwyUI6cJ0dRrHSA9aIYyt2xbOtp9kar6/BuZjG1ydT5oIq0uuvAi2gq3hvZoqsV68hqRh67iYBvEzGWPg86uRRAa/Zu+HW8/wqFAo1GI9+exmmrUa8TytU5agqy3+/nWkpafdGWpuM4eVtJjaq/qlCrmbOEYRhHmnZ3u903YiOj8gKlUomtra0DPoIrKys4jnMA3J2l2jUej/n1r39NGIbMzc3RaDQAmcDTipQeKx1i0KlYPWfVhkb9MRV8qHjubGVLCegKcMIwJAxDdnd3XzqPXhUXUd0KguBA5VflLtRM/XBUq9UDLULluF3GdvjbiCSFUI0N40z36pSRIuCmgpCxyzUYGsKH8mK4HUrb0UKSRIQAB5spV2SEVEhGSAuyioCFLpJIXhUlpgk/NmDj0OGOJjDJ3jDJ9IVA2qUFGxqJAIXYFcCz/ZapeS6yHgVEcmLTn4KQPlPR1qNCQc2X+/KaFrKODaAdyxTmRRfrLLKE78ggAUDBE7Pvny1CWoDfrUHkwCCdTnkmRlbNNMFLXiaRJ8DGBP7v3wR04xE/KBncqMjx3RtmVk+xiHOmllTEUhOKZaiXTUbjBDMR1XjDlrZwZzfGc6WF7JjgJGCMwffBMiIcc0RkWqRpDL6BMUlJEuhuwzg78QyOVsYYI+eyOhS87hqvI9ysZ8hx/esX8Oce/Hoy5bkNs38mUCpnnLCsJU9BbKWIyX18lphKoTSQ6uirblqOi0sBtIB8RPy85HXDMFhZWSFN07ytYxhGniTfdBzF45olSitXSycllQyv05Eqmrm+vv6Vga1arXagTXuaKslxk2VJkrC1tXVh+zYblmWxsLCQb19bzdevX2dhYYHl5WXCMGRjY4N2u51XXk5TqRuPx3z00UdcvXoVy7JYWlqiVquxt7fHcDjE933m5+cplUq5XVSz2cxtaXQIQyuW2lJMkiTnUUVRlLeNtZqpnMJer8fW1taZgNZFhNoHqfirDqaogv3h9VIi/yzQUn2wP9RIApE8iM95iz4ErjZgyxGbEG8IN2IYBVK5MpEWmY5J7CHE7wECJjpMuT8hAp6M7G+vCp0Ia2evm71aOp60lOIIPE/AQepkSdKH9j40Q1gJ4Moi7Lnwy10RYX1boYkcoF6DtDV94DnHgywQ4PI4fJkGVETWze8eTVB/nUiQxFsvwyDT0PAj6Bjwp02ZXv2jBdix4IsN+KQDvbFw4hJTZAmKGRCKOAhQUqA9SvmHfxlTnxfwebMJyxURQ+0O5Lg15sXFoFSEWtWj3qzh+QnWgoDpcRBhGSYF28TzTFzPhjjGjGKsJCSOYmw7I5mPIwhD3BiCEYw70H0OYVb60axocLB6pVWuw5/hvAv+BPhfkZuWIfDbVFry/7kNv4g4ICFhp0AowLwfyXs6Jli+3OAQyE3MPFIJi5BqVsTZfQ7hkgAtrfR4nnfuC7WKnH7++ee5MORFCJWeNmYnCoFcvLJYLB6Y6NK7fdUMA3J9sEKhQLvd/sqAlk6/XURF4k2pgqvw62ziV1L5wsICt27d4u7du2xsbLC4uJiL4e7u7hIEQT4996p2ssqHlEql3Mev0WhQKpVotVqEYUiz2aRarVKtVmk0GgRBkANr5eY5jpNzusbjcc530qqRqsArhysIAqIoolgs4nneqc+Di1rryWRyoKrV6XSoVqu5efRsqAG8hoLdN+E1+nWINLPg8QqZ3MF5tmGAMw8frIldz1wKNwdyc+0z3aZOGELG5ULuuoPs8RLT9kYdSRbdV7zvCAFtHmL1o2E5UJkDryoJNEnBCARQAgwDGLdlvxYsaCzCfAGe9N4u0FKJhgliEmzOZHOt6r3qrDwqQ0wQsKVcr4sMBcJWNK1KGam0Ce814P5VeLcAa5vw/RL8iQMfb8LzXeEQdcYy5ZrGMvkJ08+vEccwHAIF0YNyTFgsQc2BFz0wJ1BbEFmJesmlWW/gBz6ObWC7NhgmBa9AwbHANjFdj2A4JvEnpMEYf+wTmTFJdlJEk5A0SPAD4To5RdGrYiz7dtT3YZTtdw05Z1+XozUCPkc4VZvIgMR/msB/ZsOfAr9kCrYSxEqqaUl1cRdZwDQEMh2uGKnQ+ogy/FWE73WeftulAFp6968Vn/OALdX10YSgXoLnDcdxjmwFHhdasZh9falUOgBctD10OHRqTI2Gv6pQTtFlj1kuEUi7bmlpiVu3buVSIEtLSzkfqtls4vs+rVYrVy8fDod5G+8wr0hlRa5du5ZXHhVIKUFcjabn5uYolUo5eVx1plSsVEGU6qtpBUuHI5RDOB6PGQ6H+XTtaacqdQJQpUxeJ3RKt1qtMhgMcjFdHSyZDZV0mP39DzkMQ/hKpZKQxs/aPUsRXR+nBp9mL64D/50F7VDAhBLeAyQJzCNVF/XxU4eYMgKaSki7I0Hu9o+LGGmxbXOQ5Ot4UKwDpiRuK4BwBH6G/6PsPQqAZwqHJxiBGUo7L+Mcv/FIkDUYICP+9szX4LzfiDGy/l9y8Z9BedbGZJqAiwV4vwTX7oBdk0rL/Tm44cB7MTwsQesOPO3Dp89gK5HPGWRqn4YFz2fQjGFAuQQr10TrbJwJypqOKKMnEyjZMLfoUpuvUCwVKVaKQIJtpPjDIakf4Y/BD0KCwGLc85mMIoJJRBpHRElCFCWkYUrkp4Rj6E5gEELPh162+AZyTqoKvobyB28i6v15S+41Fvy3wJ8AP3XgWSiV4P8UwY8Qt4QE+c4MgX6SadUhkiVhIMejRMaJQyRBbiKTqJ8j/pjniUsBtGA69aR3+WcNwzAOcIIMw8i9Es8Ttm3nLaK9vSOdr165LypIqbyb40AWCNAaDof0+31GoxELCwuYppkLZL6t2NvbuzB+zZuqaCnnaVb+wDAMFhYWcoBQrVZzeYbd3V3m5+exbZt6vc7KygrD4ZDV1VX29/dzY+fZUCshbacp4NjZ2cnbfePxmMFgkFerVA1+MBgwHA7z81irbZPJhE6nk/P1lEc4+7PT6eQWNqc97loNviiArBW8ZrOZy1Qcd7Pxh8jFemVYUtlK0qNH9WdDeSkmGTkXwJDqkUZkQLUqBPSr2WOLCIjpISBqk6lIqYvciSvR+g6wbIkA6t+/4jbcy7YVM73jdywZ88eEOADDF42iccZd0f1eQACJ6cjf9sfg+vC9OanafNmD3hs+TUYIt20ArPfgVpIJxyLJvcDZqxAj5DNe/My0JHIHacku9afJ+1pRZDQ6Iyi4sHITEgcm61DxICzBzQK8WxdQ9fljaLdgdzDlemnYtnD9tOVrVqA7hHbmRjAJRc7B76aM7QnRcA+34GDECeNeyHDYgyTCNhKSJBJZjzF02yK0a1tgRJDEwk2MU9luZyBWN6rXBlOgVebg90KvWPvIsbuIbDECPgJ+ksL9MnQz4L1xaPtLTHlhWgXuM1Wq30dET99DJD5C4DPOV6mGSwK0dDJLWxbnuYCrgORFhBLVVS7ipOfOWvDAVDvLNE08z6NYLOZSAIdDk1q9Xs9/L5VKTCYTfN9/qyrhw+Ew97V73cQ9q4J/kaFtuVmR0iiKMAyD4XDIL3/5y3zdf/zjH1Ov17FtG9/3WVhYAKZtryRJeP78OZubmwcI/Y7jsLi4mFcjFVT1+/0DXCR1LlDbHa206U+1q1GLHfVNVAFQmA6CaFtR/Q9P24Z7E63x4XB4omDqt3EwkuyOeMxBs+CTQsfbEzIV+EMGB9omVGX3QvZPpR0WkWpOK9vWPFNhxQRpRxbT4wndJlJNeA8xl9YoeFCtQGBJoi54MOyJGe9c9j6qWpEa4C4AS2D34UZJnj8ZgjsSfaTuGy6U6+ZXQ/geIskQHfrb4VhE1vRw9bHJtEp4ER1QBzmGmp0MMhmPEswrJTYQ4NIfwV/9Gro2fL8Af/J9uFaXiuEggltzwju6PYCb1yG4Cp9swmdb8Ggm/RUcWFoGJxXOUc+H1gjWA9FlK0fyeGc3pNfex3L2KXoydBFGsmZJAIWCEOOjWAjxrieAKU0hGGRrm0jVLAhlmq9oy7nsqidm9k91qGbXxUSAZqaOciGxD/xzBD9w4HuprPs+wt1SD1CDaUtdpSe6yGdT26oici48Q/iPr3O/cCmA1uyk4WGJhNOG+iPOxuskem3lndQ6tG37JS6Y8nJUbyhN09x0+PDnc12Xer3OwsIC9XqdKIoYDoeYpsloNMqT8tsCWxdRHTmN1MJ5IwzDA557aZrmfoOffPIJH330ESDHYGtri6WlJb7zne9w+/ZtCoVCbul09+5dwjCk0Whw7949PvvsMx49epTv/8LCQg7e9vb28vaZ53lcuXKFxcXFfJ8qlUpe9VLVdJVLUE6ZVrfUJ1Cn9HQARLljCgqVs/VVRafTyf+v5tGj0egPSu39TJHINJedCtfkpNnd2W+H5sc0heFMskwRDaUmU/mGcvbaK4g2VIgkqwAhwg+RSlMdSQydWBJ0gWmFYTaqCMm3asD2zNfeMMF0hQtUccD0xUqonvFXQqSt0gPmHPDmoHgF7CWo++D3pcJ2cyDJ+/exiEi+qXiBrMNG9tnvIGAr5niwpN/g3ZnHdJLzj5BW6kU48CiY1ugiQxOlwrSN6Ngw9uGDz+H//EyO49+b8Dcv4M8X4Ye34Po9qWx196SC9N2b8uGuleD9W9D4Aj58KlprZQfmFgRoDfsyBdhwITSg7sD1JVhcBBJIU4NSzcJKEoa9RKpjMYyGQtZPsmqVgfwfGyxLpiMtK9NSS6VCZo/ksX4goJt4augdcLCq1GfKV3vG2dvtr4p94J/GAkpVS+17NhQi+R6Mke/pTrZvVYTbVWOqx7aBVLHavH617VIALU1ItVrtWAX2s4bybGZ/1yrLrAbTUTHLl3qVNMRRIEtfrxwxNdetVCq5GfZs1Go1rly5QrPZpN/v0263CcMwF7WMoohKpXIqZfyLiIt4D1U6fxOh/nvqQ9jtdhkOhy95KyZJwpMnT3j27Bm7u7t0Op0czKpsw2AwoFarUavVqFQqXLt2je3tbVqtFqurq0wmE5aXl3Fdl6WlJWDapiwWizmYnEwm7O/vs7e3lwMsrUgeDtX80kqo/j+KohzEKHfsssSs44FWAi/T/l2GSFMBFNWaXKRPe/ZXkYTTQNoeyewtvwFuFSotScg1BGi1kQrXLpLAB/JUIiSB7CDAKkUqSQECzI7iaTUQDsrT9CA4jLMWaMEW42izCFUXikXYGEsFTdtrzTpcuwKlJvQHYstDKOPzoQvNIdyqyrTb4E304piu4QD5HDeQz/2E45OkEp1n4wpSsbsB/A2n8OE7RURMhxPS7H2tinDgbA/SANojeDyBjz6bKtg/SkQL6uNN+LM9+PEQ7s/DfA3q86ImP4lgqSIemVe+D390DT7Zlhbf49/DTgzvNKXKdGNeVNMNA5oLUp2KQjmG4Thlr5WytwejsVSkOiF042lLOczW1LKkNZlaohvnCHeeNJAWZhqKAKveqiVMxXlnj8Vs/+lNjH+NEH/CZeT7cS2VyuKfIZ/JRapa2iJWZQcTOW/2uZjjD5cEaCknSxPP627nuHBd9yUS73GhljKneb/Dz7MsKxekdF2XWq1GFEXs7u6+lKBWVlZ47733qFQqdDodgiCg1WrlZGnlCalf3mUP5aWdJe7fv8/q6ulphs1mk1KplLe3+v3+sfpfcRzz9OlTut0u9XqdcrnM/Pw8d+/epVKpYNs2w+GQer3O8vIyGxsbbG9v0+v1+OKLL/JjoZWoNE3Z29s7oIsWBEFe7dE2pvKuvgkxmUzyc/zwjdBJNy1/SJECqS1tHveUhz4Xncx+Jocuf4khSaCHgDL1yIuRxLCI/F2nzjYQwFFGEomKtjc5GmjptnYOPW5bUKpAuQJFC4pVKJRhcyg+iClT377rVy3uf7dGuWbS2x/z5chncytmHCH/YrEhqpjnE3s8bfjAd5AE/gxZmzmEs3NUfV1vyyymlaUm0npsMfVDPCnKwM+vwQcbx9v8zEZqQHkRzJq049IUuj342xjWDy1QAjxJoL8Ga224Uge3Du9V4Ht3oF4C14a4DytVWFqCBzVY24FuF37xFL5oyYCCYQowShNwMpScppAkKWEYMxqCH8gwh8FBcKRga8YY4NRVHgVoqgN6llBJiNe5km5n/+ZjAVZXkWM2Rm5IfAd2Qjl/dD93j9vYOeNSAC1tV73uBfsoUrPG7KTaadtapVKJ5eVlOp3OkaKccRwfWbVQbpfytAqFQj5VdjgKhQK1Wi3n5ty4cYNOp8OLFy/y1qG+7jRK7a8bOrl5nvcpFAp56+wscZwW11GhQw6lUolqtXoqcc8kSfLpuY8//ph6vc6zZ8+o1+vcu3ePYrFIp9PJW75Xr17Ftm1u3bpFt9s98zDENzkOH9tvCph83UgzQnAaSOI+7YV1hCSSGiIlYIYCoHwyxffMA089Cx2m9jsO09aY2vKk2f8fI3fvd21YqMG+mhjOhJoa7yFtyANhg10WMctiBPNFiMuw5U+rAZn8EMUCVKslanZKpRYSX0+Y7MZsbUBnIiCxOBBlc8fIhF3fQIwRkHXVgW4oYGsJMYpWvaZtBOxVkGSr6vkJ8ENkjD8B/oFpReakiICtyemqmAbSyio1wKuIlhYj8eDbOOY1KZL4P52AVYPffgbbDny6JeDqj67CvAvJtgiQLtgwNw87CQwW4B9H8GHv9dtfel5Hhx7LlE2ODT1/Pc4u4ZAi55qq479OtLJ/aw54jlT20gxZKRn/TV3NLg3QUlXps1a0DMOgXq/T6/Ve/SgOBwAAIABJREFU8hnU1uFsxeysYKVQKFAul48EWseF8qzUHLhYLFIqlV5KSpZlMR6PabVa+WScvkYn02YFMN9GRFGE67pnav2pIOd5qzgvXrw4+UlZqEfg7FSp53kn2japjY5OhLbbbTqdDr1ejytXrtBoNPB9P59mTNOUGzduvBGvxm9SfDt5KBGl0gqKM7Jx5kxzqqm1q8CtIuxNwI6mEg4lAwoVSWzlbFsuU3BmMQVYZD+XkYS2g3CollK4kUDtiAxXQngpEw5xkUx5//5QWoVlEyqWS1wwiZngIcDQR9TMw0FMtzWi0EyxXINKyaLsIlYs2fOIBAS86avYOlJNvIUAyBYCDJ1sP7QaoxXACPg+Usm6iSTcf+Sg6fZJ4QO/ap34tDzqyFSnbcBcxm5xHLFfOi5SBPg5lqjwmyas7YmVTnsP7i/DzUxArVAQwFAO4E/mIeiKNMHpHINfHYev7jq5V83+dtQts4Gckx4nT+MeFSUOTsS+VrgwLsB4ljT2Fi5hlwJoqXK62pechUxdrVYP+AfOxmybQ0nJSkAeDocngq7xePyS59vsttWrbpYgrETt8XjM/v4+tVotF65U8dLZ5yooW15exjRNxuNxDjgPV+TeRkwmk5w3VK1WXwKws6FaUp1O5yXB1jcZOjzh+z6GYVCtVg/ws46K2Yqm6rZpJXFhYYGbN2/mcg1hGPL8+XN2d3fP7bt5VNi2TaVSyfldvV7vrfDuvo03HwEynWcXxaRX5RZOAlomcNedWvakzFSmDAhcAQxlBCwkyLZjpFJ0B/m7zVTd/RYCHNaR7Y560D+U4QwyfpYJu3Z2Z5+FU4BCVcjN/RYUShA4CZUw5YYhopkRkjjLlohfjoc+8bUiGAnBICLxhfDdNAXEtYDxWyp+PokF/NwHlkuwOpI1XWNq4hwgAPcq8OMqtPoCRp5y0OvxTYQNGDEkQ+FIGWSA6xREpTQUgrvKEpSrEBYgvmqT3lwk3h9iWCnOZMJwNaS9I5Y5F0E0t4BFE5YLoigfJPB0ALuZsr7LtPo0GwVk3Y+axtXz9lXZ7QZSob0QoKUl4ONUVN9QXAqglaZpblWjIOM0QMs0Te7fv59Pmh1W0tZWpNr6aMVotlV5Eim+1+sdWU1S/7rZ99Qqy9LSEpPJhNFolBObVcxyNhScqG1Qt9vlxYsXdDodut3uV5aElWd0WATzMDDVoYHztAvPGyqV4Ps+k8kkl3s4KZIkod1u58e0Uqnw4MEDqtUqd+7coVqtsri4SK1WY2tri0ePHtFqtS4MaCnAV4sb9VJ88eLFhYK5b+OriRCZ0ktD8U9zOd3F1TDg4R3429/L75Px9K5/ghg6q/GuxTRpJQghPkWqV9r22Mn2pYIAshawlkDr0NezirTJYkMI0/n+OFBdhKVrmZ3KSIja+15EaSLb1Ck6BwiTzHIojRn2fUb7AZ3NkN4+DPeh78s+6HTX21IF3CCbxIymEhnzCAgdM62s7QB/F8o+7vL6pOxbnFwNGyN8qdAXz0DDgEpB1jZE1sgiI6nPvC5M4FFPcMJaAFcL8IPvQKNucPN+AbdaILzawKvOkz59Tvfz52x1BTi+biaxgWsW3DLFsiaYwJ05WKnDv+zA+mjKdcvE1fNwmd4kHM4SJ2UNZ/Y/L3n1nCNi3irA0riYEb8LiN3d3ZwbdFqulmma/OhHP8qT/+zrZg2c9XcFNlphOm077qiKjtq0zIIhrVotLi7moqO9Xi/3ijvcZlF/R61wqHq4kqu/SpKxSg68KuI4ptvtXmj7aG5u7qXHbty4cWC/9KdW/E4zqZqmaQ5ofN8niiKWl5d5+PAh169fz42p2+02m5ubuVn1RclUzCrAd7tdut1ufi5+G1//SIEXezISnzC9wz8p5k34wU8X8tadEQgI0o3GiVSE2kiy7GTbbiBACwQk6JUuRipcXaQSsIwkycNEeB+peH0cw6OZr69lQbkMS/MymRbb0O5Bq2sSmxakUwK9iYDKwAOnZEAYEvQCJoOUyBcF+bVYWpgBL4/3v+nYBZ4GIur6HGm5biJr2UIA0SPgo4n8/SIm3/rAnx2677sB/A8L098LCNDLwTkQ2geV9I/qaEUpPBtLe3MvhbYFi1cMvvPdKis3lykUwSi6tNsD2ptdTAsmRWmFvm54QCGB3Rh+H8DvhrDWgUowbUv7TB0JZiPOPutRx/4os+nZMMnw1UlPvORxKSpaAEEQ5JNdp03c169f5+bNm/nvh+1UDifJNE0P+M29ThXmqBF3FaVUzzsVKg2CIK+YHG5FtlotNjY2mJ+fp9Vq0e/38xbqZeO/aGXwTbYxJ5NJPgHYarXyNdFYXFzMq4PKpzrPROZkMuHx48e88847eTuv3W7z2Wef0Wq1aLfbJ7YjzxKHj2Wv18vNxb+Nr39YwCAWw2XLkKRYPOE1JvCjJYPl29cJyQYuQgFRGrEv4KqFELt1GkwlJI6bjhojgGuZzByZKQ9pM3vdU6RQMMm2axpS0TILEHtgJFAowtCEUZJizaXUFyHdFdCixPy9Hdh/GmItwHALRl2RDoiL0O1LkrV5s2TjV8Uk+4cFQ2VlRxyvkHlUOemU0QY2IvhTD5aXhUO1PoAnGYozgKsZWSlF1hpEKmH27ZT7dDhCBGgFQBTA509Nbj+sU6g2McoJk1bE2se/x3oxptuB301ONwl5UvjAeiqDDNoO/3QIL3zYzw6qWjLZHFzaJNtfrcqeJVRF/6uoQl1kXAqgpVYptm2fmnysiXZzc+rOpfIQs8bNs6Eq3/r/88asJpeG4zi5wrvyqwqFAoZhsL+/z82bN7l27dpLQGt3d5fHjx9jGAa7u7tsbGzkfn2X0XfwTe+TGnC3Wq38WM2KZyp41fV5lUXMqyIIAn77298ymUz48z//cxqNBuvr6+zt7eX8ulmAd9Exey5+G1//cA3YmMDi3oxR8AmvUQXqnS8nefIyXZnyAyAVde5dBHzpwz4CpJ4xFQ3VMBE+l+pzlZDKlU6GeQjYupdt75OZfXEKUFoBd94m8CzS2McpSlVru5MyXnG5ct3E3Z3kqvAJYgOzuRrjxdDbgP018Opg2dOR/kuRJxOmfbnZ1KAlutnn6UjnOZDhk0iA9n5XjLd3gPEs8nCgFciwg6ahwRlk0XXt/RD++pOE7ajNf/9nAXP1Mv2nbcIXY+IB7LThHy+IfRLxMpm+l4qv4ewSWbwMprSalYOmV8Td7L1eZK8rIuf31/129FIALcdx8rbOaQGQYRj88Ic/zK1rYDpVOPv7bChX6nXBwuG2JMhnqFaruTWMtiqV7N5ut3MD4llgEIYhOzs7ua9ivV6n1WodABd/aKFmzIdDOXBqFfQ61SB1Evjoo4/Y3t6mWq3myvzj8fitTXl+G9+MsG3YCOD7jiRZl5N5GYYJP/hxg+q8g4UkI9OeqYRlFSafKbVEpww7SDvwOdPqgYlws+rINKFOgenoeh8hfOtEpLY49bWOB+4cmJ5JaLjERsgwSRhGsN+Dx/sp71VSqtmM/xgBbpMENneh3BBDZGcednuwOpD9LJEBg/Mt7cWFksQOs68PgymL19YTeB5zUJFzJowmjHviZ2hmB8A6tA+HZRSOijiF54OU9m+HfL4zYqXk8qeOjxcCfRhax+7CueKwjZN282YfO+qcV1cDteE5Lm6STbsyPT9ryM3E112p71IALeXYjEajU1e0TNPk3r17B9TfVY0dXjaZBs7UljwpZoFWsVjk5s2bLC0t5ZYqtm3nXKs0TXn27BmTyYRarfZSBUYn6JaWlhiPx6ytrX07jXZMFItFTNPM25iGYRzwPTxrRFHE8+fPcV2XW7du4ThOTpy/DPE2tNO+jdcP0xKj3lEwFT086eJqGHDj3RKFiolNRoSOpTpGKn/3SpKoekilrIEAljYCtGYLISnS1lG9I+2AzZK/u9ljCVPxU4BiCa7dcXCulXAtgzQ2wSoQ+iNGPfFy/GgjwqnDrRJs9Kb7NEZI74M489cbw6Nd+CyUv2nb8NKELraG6g6oMJjB1KMFzlyOqyEt2xccIdBqSDvWjLPWYYZMCofKQKddrxQhp/9uLWXb8vmjBoSebOBv30AKmQVbRy3PUVQqBVg2R7cOF5B1spDzesCUPA8Xo6H1VcelAFqaSNTj7bSv2dzcPECUVsHJ2d9n47ycrEajwU9/+tNceHR9fT2fEoyiiHfffZfbt2/nulhql6PTlGEYMhqNcpL14RgMBnz22Wesra3lBsZftzBN80ImD0/aTrfbzaUwtre3L0ToFuR88n2ffr+fH8fLEN+CrK9HpEnW0gmnk2Mnwf8isPGizfLNORaR6hSZcS+TDDi5sr0XwMdMk0+bKchShXiQabseYhDdZEqqrjc9/vjPrmKXKkSDIdvP1pgMQwZtGEZw7wdlFr9zjaBgMxkN8UdD0iGUfKhYss3uKOWfA9HOUkCXIByg0QSer0LzBSQj4STp7eSlqEYUmO6IjWTyEVIC1PKjthbDmdcoADvD5eB7SEv2SJyTijk3NXER2Mzu5/xDYO6sV1IbWMiQ9mAAI0damBcZh69ERxUCx7z8uVWyRFueGgvIOZw5NuWVV5geAnVAGB/x/l+nuBRAS0OlFxSsvCqSJOEXv/jFgfbgLIhRyYTZOG/SKhQKvPPOO9RqNcbjMUtLS7mkQRAELC4uUi6XGY/HuUbX5uYmOzs7BzS2jqtShWFIu91mf3//tfbzbcVRYOgocHQe8PWq56uH4Gg0wjTNvPp5nP3OWWLWe3Bn57AhyR9efFtJO3tEQNGFepCpoZ/w/LkYPvsPY/6x8oz97PmFVKodIGrzg4GAmhQBTyFTQVILAVOqbN5A+Fw+Qp5XD0QHqJQcbr6/TKm2yGQSU7wxTxAFEMNkEjF3pYRXrTIZ+zimQ9zzefHlhO4aBDOXrU40VUw3Zt57GMN2B5yuPB5cplPHQIhrQ6YL4jH13VESmZb5lJ+lbH8t/YUc2//0Zv5kI8DA5WWQmSISHpPMqmk8lscGR1zCdLdPc9tdBN5HKqueAR+N3nwV8fAhvpY9dniESGlxhxXkBxwEXx2k5Z0i63eTqdPBeU4nw4NUD8phHt5perPI9+siehuXAmgp18Z1XRzHOZW0gLbjZnk6s16JaZqemufkeR737t1jbW3tSAK02ujo1GIQBPm2m80mrusyGo3o9/s8evSI58+fMxgMDoCs08RlTWyHW66u6+aaZ8eF67oHkvVZ1+JV+6KelQqsq9Xqa4Ej0zTxPI8gCLh69eqxIrVfRZwX8GiL+iQe28OHDxmNRiwtLVGtVun3+/kgB8h3U/0rkyTJTdkVmKp4bKlUwjCMXOBW1fpV/kP/vr29/UaHDL6qiGO5mHpGBpg4PjksIBOBu8Bfb8N4u08PqBmQuGBl8/BpAqPWdDsjpF2oxZd7CHl4Cxg48G+X4F834P9h2kYsZ8/pWTZOsY4dQzL08QY+YXeHhJS5xTkqDvjDPWiP2fvdNs+f+vS7CeErmByqp6X/B+ENXZqYTabuzP9thDimpZIAQUQqVGZmP0tMVTYNBAkcA7Rmeye/ZQoQjgwXShYYfob1DCjUeGmEVI/haT7mdeBqAk4Cxavwm0eneOEFR4+jZaqUo3W4rajnJ0wLiS2mgqc/seCfk0zF/RXx374L8RBuLojw6/4QLEcspIwUgkhEXQNf3jPMxiPNWJT2SWHiS5veNmDUhlIIcxYU92GjLzImpZLwKje3YTs42jv0uLgUQEsTiZoon7Z1NptElJOlF/GzTKNpslhaWjo2Cai4abvd5sMPP6TVauE4Du+88w6lUokoitjf3+fRo0fnmoK7zKHARrlR5XI51yQbj8cvJXPLsiiVSrkZcxiGFwa0lIdnmib1ep3xeHwqHa3jwrIs5ubmcmD4xRdfXMh+XlScF3yPx+NT8RF/8pOfsLi4SKVSwXEcLMvKfTe1YgvkZux6zNM0xTTNfHjA8zx832d/fz8/H7SFPh6P8ynRer3O6uoq3e5F0nS/+kgTSTBBKImiw/HqAQPE9uU5B1XIJ4BdgXQmux6+lOifXCRJrSLk91oMwQCuL4G3I4mriSRgGxgmMeakz8RwGbT6PP1PqzxvTyi5Ke++F1ItgmkE+Hs+Gx/32B9+vVs1AIaV2dpopi8z5WCVmcrbDxDApdMGOi1QQhZBOV0Fji0v+cB7wKfIsT9MHD+wX2Fm02RBswhMJMEfFafp/pWAn9mwEkMYw282L8Zu51WhwGF2/477Rs8WDA/H4TWaPd0/S6Cdvvo8NID/6cdwZwlqZRlKwQG7AKZhQJISBgJmwxACQ6qtfR/SrGqZTGDgg1uAaAjDPbADcGKI5uF6CH52jNIU7pbg6Wo28HDKuBRASysT9Xr9tYQ6Hcc5QIw+SQxS/RA9z+P69es0m022t7dfAnrK3xmNRgwGA2zbptvt4rpuLnCpavZfhS6SVhbeREVMAc1kMiEMw1zWQpX89f1nk3qSJAwGgwvX3VJJD1WH1ziLKfXh0H2rVCpUKpVLVc2C81e0Tvsd2tvbo9Fo8Omnn7K/v5/Lk+jxnDUYj+MYz/MIwzCvPIMc70qlwvPnz+l2u4RhmLfSXdfNpzjVbeCbdiMCgCmtwGYd+vuvnrCbIHYwh4nBiQGuCwWPHKXZztHtC7Vqu4IIYhYtuHUDnCX44v8TU2S16gFI04RxMKY/HDMe9gi9iE4nwfZguDOk83yHpAiT2MNPvgKQNYd85ou5HwNEpR+AMlh1iMNs++r7ouxsrWTlgltMpxNVBOoET7wEAbVPmLZ6jwqDzBMzlqpLmo3tJee8hNlIi20pgXIJesvwH89SajlnHLUUx31mXe4BL7dSJwinbY2D53gD2E1PruilQLIh1ajfPIWtDlTrwn8zghTLEBHhNHtyaEhL0Q/BKmQeimnWkq/C5lOY7IsllVOFaCSTv8FEThPDgHgC28nZZiQuBdBShexOp/NaJr5hGB6YNDxJANV1XR4+fIhhGDx48IC1tTWazeaRQGs4HLK6usr+/n4+kRaGIc+ePaPb7XLz5k08z6NQKFxY9ea08aZAlm57f38/N4zWqpb+nG3hacz6Ch4VKtFwnglQNR8vl8uUy2XSND13K0pBZKVSodlssrt7nPzjVxcKaN+UeG273WZvb4+///u/p9fr5Z6jnufhum5uc6QVLRWJjeM4ryLrzU2n08n3VW9ERqMR4/E4b7lr+/GbFmEiWGF/IBdtJfEeF/sccfFN5SJeniF3GZYkohZSxfqX6VOJgds2/PE1iGy4/y48fQbXqxD1pT2pnC4TCAZj9j7ZJN32mexGGMid++hpwH435No9G0oh7il99y40znr5yipBr3qdl2QTawNIvmSqmKqXK/2pp2MGrJxIqkT+SKofRiJThAMEALjAnAPtUDz4NH4N/BukdXtULCHHseBC4oNTg0pJKpiTMbyLyG+cJQrA90zZn+IS/GY05dC9yTjL4fKYDk4cRag/SvZhiKzzaXhm4Rb0q/AXH8DaAGolKDlQtuWmZTKR70cSyfSsXciqbNnOFEtyg5OY0G1LxTGOBWQVbPHynIwhDKSyFSWiIfa1A1qarFVV/byhrYzZ3xuNBsPh8EgAp+2MpaUlnj9/zvb29pHPUz89z/N4+vQpURRRKBQol8u0Wi2CIKBcLmPbNnNzcxdCzj5LvGlul5L4lZcza5Q9y5MyTTMHmXEc56bPQRDgOA7z8/NEUcR3v/tdDMPgL/7iL860H9oONgyDMAy5desWq6ur55521IlFBRdra2vn2s6bDAU4byp836fX69HtdhmPx4xGo9wsvVgs5q1f/V4qwBoOh/k0bbPZxDAMms0mlmUxGAwYDod51VFbjm/7BuRtRpBdeM2MAe1zsmDpUXDTQi7oIBWuVhGcCtQmkigOP7eagDuB5euw9Rh2d6AVTjnefSSRRRh4boGCV+OTJ+u0I1hy4KcePB6kDIIUs2ZC0aS4yMuM5jNGGVmPU9829zkbe/sIhnSJqWn0CKn0jYBKKNWtAlP++2JR2ky2AaYHXiQ6YJgQB5lnXyiCo6khLcjQgxtz0p77/kMIHJP//S+nO93OPsYdRHX/cNQQ0GF35XwJfFi5Iy/0E6lc3s2e2+FkEraDGGc3E/AsGNXgry7fJYyQzHngmL+/QNZl1pBab1ZOc0okYxjtw1oPNnzo+LBUhbQGiZetdQS2Kxy2OJYbmO5YvCbLQ5hvyiBBsyladv0BDIdSwTIyhJgk0AnkGJ/1VvFSAC2AK1eu5BpG5wm9u55tI2mL6bhtRlGUtzqKxSK+7x+pn5SmKZZlcffuXYbDIZ988gm+7+dVhjiO82rAZRa6dByHBw8e0O12D7QAtcWqPJvZip5hGCRJktsCKf8KJElrC6nRaFCtikubEqJLpRILCwskSYLrugwGAz7//HNWVlbY29s7ch9PmlRUUdrZqcPzVEi0baxVmk6ncymBwJs26w7D8CWO23g8Zn5+Htd1sSwL3/cpFAr591Mrm3puaItYAeve3h62beO6bv5Yr9f7ZrYMs4iBWwWZJAuYGkGfJQxD2j96FTdSKIaw7cD+6CDnS4fnvkzgRkeseqwKRD48yUjWTYRfHQN2muJZDssPbpN2xzz+sEU/gr1ESMx2CP5+wDCNSM/uaPVSlJF1ODXQOmV5YMWGP74Hky44BliBtOCczPqo7kA8FkJ0MZ2q4SeRVDlMR2yS6kUggbEPlivJtDoHtZq8Zn8gh+FaCSpLNXw7IPFqDLsx8T+3eGcRnvdf/m7+BvivORpo+cBDoJyCYQvQi2cuOVeZDjiuMOX57XKQu6ThIbwwJ1OqfTyEvUtYLFbR3AlHA6dNpJrXZ6oVexZR/jiQNWgaIm8ySMAbQW1ReFdFA0YRlF0oZAJdMQKy4xpYoXz3EsAsynO2WoADni3nh1UCaz8DYNn7ngU8XRqgVSqVKBQKFAoFLMuiUCici3szmywtyzpQZTkcqsperVa5du3asWrjagitoML3/QNeh77v0+l0WF5ezlsrlzFc1+XnP/85T58+5ZNPPmEymeC6LuVymVKphOM4OSfIsiwcx8mJ57VajWKxmLd5a7VaXonUqpBt2wwGgzxx1+v1fCrTdV3+8i//kvX1dX7zm98cKzKaJAnlcvnIY28YBrZtHwB6IPIbOhV3mtDPEwQBtVqNarXKRx99dJ4l/UaE8qdABGHDMGRubo4bN26wv7+fgylt/U0mk7xNrsfeNE1arVY+vQlyLH3fp1qtHhAW/iZGClQtuZiXkcpKGWknnSUsY1rRIptgTG3oJgdbQjGSoHzggS9Cp4tVMMuwtScJWyUHdoCrcYwRhpjeHD4eZiK6S/uxVBIqY5i0EtJGgnMBlzBVR3A4pY6Wx/HOw7qhBBZj+J9/fof11S0+/9cxk4kkw0YR3Aq4jiS1uiEG2a4Nlm2CBcV6g0KpSpr4mK5Fvd5kHCaklkEYhVheiu0aDPt95kyb1DSYr3pUFpZIilUcp8J/+Hd/w/YerP4K/ObLuzoEfgn8W+CvZx63gAdkbTIHKl6mBh8JaK4xBeY35GHmkWN+NXv9Wvb7XLa+77lQC6DmQrgA/262j3mJYnbY86ibD61kzSETtGclSajOrIuYtFtlaf8tN+D+FejtSVXSjIX4noYwnMC8AU5JQJjhQGKLMXzFhmgshu4jW1qKlYaArRHn4y9eGqDleV5+Z3wSif24UO7P2tpavp1SqXRslURBkz7nuGSgZtEgLbLZbTmOw5UrV7h9+zaFQuG1OGZvOjTx3b17lytXrtBut6lWq1QqFarVap5swzDM21WzibRQKOTEZyWlKwenVCrhui7NZpM4jqlUKhQKBYrFYs7PieMY13VptVrcv3//2P08ag31GA6HQ5aWlnLeEEyrbmdZh+FwmLd/J5PJpREofduhYGlWNNgwDOI45le/+lUOZrX9pz+1qjir0B8EAaZp5pVePZ/29vYunUH6m4i6LWRbtco7a1gGFOagls25G0ilpuBCbAmY0kiQKa8rTLWsjGyaTpW1U0TbKABs06Rg2/huRLnh0EmnoqMVG25fsbAfOAzrKX5P9eTPH7oGh20Fjw2Xqe/KBMmeuWYEuQiTk0I67rD0zhXsm0UG+3vUKh7NkkupViUyTUzDIAwjkkR6fmnYx/VMArtC2XUwkxFWvYpdrlDxA0b+EH8cUPEaOAWX8kKDMEnwqg2KnkuxVGcYxZjDEXYYYJsQ7MDStZc/xjzCjXsB/BfA32WPN5Eq1b8HfujDnQWILGlPguDM/xepyHyOHPtlhOh+PVuCBnLMJ0jCb4YycVq0YTeGnUtqJuIhNx0uLwOtd5A1a2V/W0HWboKAUj23XxVOSSq5o+wcCcfirmCF8De/gp2xAC1SmTRMszagYUgLMVb5DgP8KOOTJfI808gGT3eF86f7bx3xWV4VlwJozV60Z1tTZ61oqRr7bCgAOCp5l8tl7ty5k4+hH5eslcvjOA4PHz6kUCjwwQcfMBgMuHnzJj/72c/yxP9VeRRq6+ZVlR2dHEyShGazSbPZzEf6tVoURVFuCVQul3OQ6bpuLtmgBOh+v5+/rlgs5s+PoijXNHMch1arRbfbpVar5RXGV1kMHZWU9TOptIBa8cDpzhPl2SngU9/Eq1ev8utfn7Xu8M2J2elBmK5zGIb5EMT/z96bxViSXnd+v1hv3H3JPWtfe2E3WxRFgUOONDvtGcMw/GJDgDFjw4AePIBfPc9+MgwYhp8Mj2Fzxrst2dCMBqOhOBIlDdVNskWWelFXVdaalXvmzbsvcWP1w4kv7s2sXG5mZZPVFA9QqMybsXzxxXfj/OOc//mfV7WLUO5/3S0HYIwjOUWVjT1D5teO5U1bn5hyDSjmQXfA7x/8vAS8acLCPPhdaEbQ6o9b8MwgDqsL9DDoRFnC2ObWO5fJ2zaf/ptH6F0o3ixQ+5tX8CwPnR7dXMhJQEtU1CNNAAAgAElEQVQ5mJPe7IsICJgs5DvRQsSzqjBcwLgKUZHYkYiHG3j4gwGZpWVKczWKRoyth0RajGNbxH6AOxgSeh0y+QKjKINu5yk4GQo5kyiMyeQyWKZFb+iSiQJ0IySXg0w+R0kHPwjQrRjDjDAMjf7OHv3mNjOFDPVImn17R0xRjICFTxG9NJD7cRX4EIlIDYcCPvM5MJLHYISA5jzwMLn0NhLFyiT/3gLeY1xxWo6hnIH8dfjt10uV5oCpr4LByzytALmne4wLN9TTYpFxn8PD7SknLXbAT9LdJtKSKAb0ITxtwOAi3vH8cVut8JhrOcleC6CleCCq6XK/33+pIfRppkROlWOI45her5cSeo+z+fl5NjY2CMPwxNJ+0zTTaqxbt26xt7fH/fv3mZ+fTxXtL6oNzXlMpfoU0DmKRK3SP3Nzc0RRhOM4zM7K40DphKmqsX6/T7PZTHk1qnBgf38/bRPU6XTwPC8FyGqe1XkVgH777be5fft2quyuREfPY6oKDiSCCZKi7Ha76fVpmkalUjkgWjuZ8lVWLBbpdDp/aaNZQFoF+LqK5X5RLIOk/EpVyBdB74772E1jOZJIxUDSHiBv1MMORBbYxsEWfQYS4cCAmTnY6wuhenVHjqVwi/rZBWwzwsxo2IUSS28YDLdaPH24R+6ShVksMNKHxJFOEJ/8snjaSlF6oObEtjZHi1keMANBqTpjzxoc3GkfGHkjStV5mpFO7DgUZh0ycQ/fGzLyB4TDEVE4oNt32Wm3GA484kgHI4MeBezXQ4aezqits98N6YcRMRGascVAN/ASvSSShpNXNZNfvuNx/S0T34xoJ2Rq+4iwpeol6SNznkFA0RXGqUQtBCcANwNGkn4sAL+MAA7VmmbBgK4Nz4dS3dhEOGCKn/dlHb5ZgLU+/MVrGs0Cua0ZJGB52BNvIdfiMuY1qjVTQe73KcWlEArw7cfjvoh28t27SMqa8hwmSTP1M+z7WgAtRbLe29tLxTHPCrSAtFIKSBsDX7t27djtR6MRz58/p1KpHAAVh00phxcKhRSgKLJwuVxOeS1xHP/MHJZS7z5tHKpNkGp+7fs+v/M7v8OzZ8/SfRXYmuShwVi2IYqi9O/TXK/v+8zNzaXCpYPB4NxAy7IsPM9LtcM0TWN2dhZd11leXsbzPJ48eXKkKvrktfi+z7NnR1FW//KZmhclhaIA7C9sejMNIcLv7ElJuSpnn9ZUexLNlnJzgCiG3T5k5hKOyaF9dKQqbu0pzJTheRfWw4PdYrIkWRENsraFmc+hRz5G1mR2Nsfac7BqJma2iB1nCP0O0SuWHCpApSofQaJbQ05pJ6MlA/cYN807NIkjYNQPIKOjWRYREbo74J/99kd8/8UQN4yJwyh5RkWEUUQUieplrLkQxwRJmX8USKm+g2r94lFHAM2krQFmHyqLOp5vMgLcPmSPAFqTYLLFWNJgAznPEMjE0hrGBoaaXHZhBuYb8MtVAQyfNKX/oTYSbleVcUVeC1hFIpg/aYHfOZvT/2mbuo1HpdtcDhaQTjaQfsCRxaUvmRULeDWQvpx6CLOlJJV+wZZl3KT9C5c6jKKIwWDA4uIi/X4/rQA8qyl9J2UKDBxnQRCws7NDPp9nfX392Oq1MAzpdrtp9ZzneSnpW1XjKVDys+KiqPOfFlFTgEw5VdXQ+iwaUnfv3sXzPNbW1iiXy5TLZXK5HN1ulxcvXry0/Wg0wnXdlDzf6/XOBaRhzClScgNKxLTT6TAzM0O5XObtt9/Gtu206ffe3t5LSuQqwvVFsMn1ddGmNOLUC4RKsf+sIrNfVIsi6A5haVkqkwomdM7wOq10NI1QKtKUjZK2KhriqNWKDYB1oBBDdQjVPHzWFSFFEGffRpx5DMRhgNuug6ZRNkKy3R5es0Ffg7ylY5gWRgSRq/OqMmeqlD/D2MEobdATLUaAliLF+xzpZS3A90IiJyYOQsLONt9b6fC87h/YXGmQHjyBpFz/5lUIPFjbgWIOLhUgX4BmFz7clUIDZR4iA+C6EX7yfO9GYJziPZUnKHFQ79TIiHhqVgc94QfpWajHkHFhpgq/bopUSKMt07DWE+DRQu7pFcThPwylz6SDtGTaQ4ofXidTXQyO4y3Gx/w8BK4n+69ygghwCIOekNdzjpDdtVi6NVy02cg6noY7NmmvBdDSNA3DMGi32ymp9qx6WpOVU8pUCu241JCqsFtZWTmRR9LtdvnhD3+Y8sjCMOTZs2fous7Ozg4LCwsUCoW0HP4izLbtM4k7TrudaZq4rnsAqJxVHsEwDIbDIVEU0W63D6Roj0qfKmCkuF4qknYeUxWpqlWTEix1XZcnT56k1XC1Wo1yuYxt2xQKhTRK2uv1GAwG7O7ufiEI2pP8xc/LVCRTpYeVIOxxUiWWZf2l4F2dxTQNIkMED2MdhgEYqinhFKb4uAYTTaURom4cCHfr8GqNEBFMAvjxHmxHknJSPZHbpMV69Dsun/7pCp71lJgY049oPBRid7jaJ1zepVCZZWTbWLrBGYb+kpWTf8sIMbyOcLYUgf8lUz0JO4wB1gmPf93WcYcBcTZgFIGn+fSD+CVMdpxjrgAlHXaTErL9IeyP4HJTAOJCss1nkzt5Uuaf9R1s+gSAN+Xjw0GAm0vSRlEDKyvtmkYtGcOwKVy6Hw5hwUs4bhZUy1LJOmvBuxGMLOj0oOfDg1AihE0EzBWR+/26AS0FsHRO5zVdR8CioiMWGVdkPuPoxgERUlTgxyLNELmi7J4pvhzVMoBrJN8b4EecbZ1HyP3McrZ5fm2AlmpkqxTDX0W4VJkCKidpWwVBcKp+khLKHAwGZLNZWq0Wpmly/fp1wjBMU55Kk+lV287oup5W6032GLwIZ6vmxPO8c0lR1Go1ZmZmME0znbdms4mmaeTz+SMjIUoUU2kvqfYsZzVN09L0ra7rqRZXFEVcunSJcrmM7/u0Wi3q9TrNZhPP81I5gsuXLzM/P4+u61y+fJm9vT2eP3+eRsE++OCDM4/p8zYVpT1PhEmR/0/bd3LNqvOVy+WXvjeVSoVKpcLNmzdxXZd79+79pea3TZqmSdqvYEOvn6QXTkArVcYpKo1x0Z0RHnyrD4AwEL7XJLRVLfs0BMishhLtmEzFdJO/AdjE9Pseo56Hk4O9hvBY/q1F6A0G9La3cGwbIx7iZE1MU0QeD5tK/5z0apZPrq/CWB2/muy7f9S+6oNo4h8cmzPSw4jAt/CHIXrWwYisl9I4Fca6TYef7stlKCxC6IgGU6zBp0PhRhXscY/pm4wbB/dG0O6AGQ3QNelVGB2RBy0wTo+q268SsepynIL03DOVXhOS+rqugVMB14NGHzYD2PNgpEuacB64WoDFWeHs3fHh7Sb80RCuO/DWNfivHx49Zz9LU+nUEadLN2xycH1cRdbbBscDIs+CnC0vOFGc/DOgVD4ItPLANzJy/6/eFpX37D14Pz65ZdakTS7JL1zqEEir0ZSG0jSNghW3S1UbKqK1MiVKOU014Ulv6I7jcPfu3ZQTtLa2xsOHD3ny5An5fD5Vnx+NRgdK5c9r2WyWYrGYprvO4mSr1Sq9Xu/Y6wnDEMdx0mKBcrn80jZ37tzh2rVrjEYjnjx5wtbWVnpN169fZ35+noWFBWzbZnV1lcXFxTQieZQG1mg0IpvN0ul00qbE55HBUPdYRacWFhZYW1ujUCjQ7/exbZv19fW0P59pmty+fZt6vU6/32dlZSWV/Oj1emlEy3Vdtra2Tjn7z8ZU1edpbY2OsqOqcA/bZF9DVdGpPp8EWkp8tlwu4zgOw+Hw3ID559UcTSqcrHJSYn7E7dKAXyNJRyHOPUbe4ncStXAS+mIMdDRYKkAw8fpsIE63B/wQIZ7PMu4lpwjFSiXBALKOw+07S8RhhKVHbDwbsL2yz8oaDKoRueseuD6RO8Qeetjh0dEDRWg+qfdFFgFWJROcONEv4uTKMSBl+7+bgRc+tI/ZOAghk7UYaS5+O8TP68QTbq8K/LVrcPOqFCisrMCHLTmeCbxxGRZnTK7OBKzH8HwVvlaQlF67CTUd9qOxyjxAL4ZMHob1hO8Vw+CIpT/J8lVP7cPgouOJHIFhQHUBaEChDM4ukJGxfiRTQRX4G0sw+0Lu7593QOuLFMhmDG4oVYobPmh7sHPS/P4MTTFyT/NkHhLFGiJr94+QCOMux4N72xQyfBSDN4BhBDOxCMJOAq1f0uF6VaKEBUuqeBccuD6U5uzTBChVkckxWe1j7VSgpWna/4zor+3GcfxO8lkN+L+RSN9z4D+I47ipyZP5vwP+HrLm/uM4jn9y1HEPWz6fJ5PJ4Lpuqs1zxFhYXFxMIxpBEKRRDRXtmJRXmGzZcpzDmSaNVSqV+NrXvobv+1y5coXt7W1+8pOf8N3vfhfDMNjd3U3Vsy+C9zMajWi32+dSKm+32ycCs263m6bUoig6EhhevXqVK1euYNs2c3NzbGxs8Gd/9mepPla1WmUwGKQtjpRq+HA4xDAM+v1+KhUB46iMZVmUy+W0Jc9ZTd1/TdNwXTeN9Pm+z2g04rPPPsP3fTRN4/Lly7x48YLnz5/z1ltvUa1W07ZAH3zwQTqmXC73WlfdqWjWeSJa01xTGIapvIdt24RhSC6Xo1AoHPgOFovFtP/ho0ePWFtbey2V9I+yn9YzrJCDTBaa7hhYOCRq4IjjNoE3deGPxAhoeYZEtyJN0h37CYoJgWcxXEocq8n4zVulGpvIuUzGYooxEk2xkIhSFrhacPj1r95iTy9w91KVvY19tn/0gB99d0Wc2lqXgfsUOwe+G6R94CZNpX5OWok240q7ViAOM0bK9Icn7atKFGN46oF7wtJ1W2BXuxg5izAOcMMhk4nDLHD7Mly/aqDZBWbnOlxbj/mTT8AOYSYLC2XpX2cYUKtALisyAYO+tLPZ6Y9ToO1k3LquY2UiCkWwu2CZY3I0iDzD4de1PAcV/dGgfBmsPoz64DlyzfEQtmL4F3vwKIZ3NbhchT9pwD/dgd+8AV8pg+2JeO1/+0B4Sw7wJQvsSIj9i0jrnpjpozQnmRIZjZNryaq5SD43Ga9rtT5GSCS1xZh7qDPW0jrN+sk5ygjoqnMy2T8MwDJE88rOAIGA4sykCiwwn4O8JRfx6WP4aA8CX15SdIQDd9oTc4Tc49IU207aNAXI/wT4tw999o+AP4jj+A6is/aPks//LqJBdgf4TeC/n2oQus7W1hamaabNmVXq8M6dO1y+fJmlpSU0TUtb3VSrVd577z2+9a1vpVpAg8HgANA5jd9iGAbz8/NH/m3SFGm83W7z+PFj+v0+t27dYnFxkaWlJfb29lhfX2dzc/NC2owocvl5nP9pDnk4HBIEQRrZchzngENVnKbhcMiTJ09otVrMzs7y9ttvUywW6fV6abXlrVu3mJubw7Is5ufnmZmZoVqtAryUlhwMBqnSfD6fp1AocB7LZrNks1niOE6jcXEcs7OzkwK43/iN3+C9995Lz7uysoJhGFy6dIlCoZBGS1Xbpmw2y97eHplMhvfee49f//VfP9fYPg9T3KlpIryHbdqWUGEYHkh7qxeZSavX66ysrPD06VO2trZwXZcoik6s6n2N7J/wOT/DNB3WdyV9OH9rDHLeA/7dEnzThrf1RJwxEsK7vQCzvwK//E2JXHRi6AxFB0iZC8TGGJgBaUNpC1jS4Bt5qYybvNMRAgD6SArRCqDZM1gZmPzxiyGbI4tLNy5Rq2rMLens7PrUn7fZX+/SH7hHOpEIiaqcpFqXKnQ70qhXVZEpLaRjLSBlGPfjk7f1+xAFQ6Fy5YvknBL6hCur6pAvgdsLWX3cpdmKWZiFb96ASzp4PakMrZZsbtyGhVlRBp+dhZkZIaPbyXWop9QIGAwibENI7CXAKSX6aYntIqBaFQLA0a1zcg5kC8K/y8jjktCAe8BGCHdsjX/nPyxz9ysVisDWCP6nbUkhzl+GYkHWkQcUNCjakkrc7MCcDf/Rm/Cf/JWTJntsM8CvIuR6izFQnkPEbm8hKdRLyBpVZPAsAlBmk+1zCCiqJtsuTcxBzFhAd5rXRbWNhrQYemPiWEeZhqSA9RgyGchp0s/w8Br+cR8+3ITVFWhsQ92DRgxvVcdjPM0rqdZAIRfcgieO4z/RNO36oY//PaRROcA/RSJ8/0Xy+f8SC0L4gaZpFU3TluI4PjEvoyQHfN+n3W6nD34lKuk4DtVqleXlZRYXF/nKV77CzMwMjUaDdrvNd77znbSa7rBswEncpiiKUu7QadWJ3W6X4XDIgwcPWF5eplKpEEURd+/e5fbt29y7dy/lcb2qqZTh50HWnmzcrdodTZrneaysrFAqlVhZWaFcLvOlL32JUqnE4uIijuPQ6XR4+vQpURRRrVa5ceMG9XqdSqVCp9PBMIyX5iGbzbK/v89gMKBUKp1bQsD3/RQcTgqWqsjMu+++y+zsLK7rUi6XabfbdLtdPvjgA77xjW+Qy+Uol8u0Wi1yuRzNpjBllOK90hj7aZriKKqCisPaXucBWoZhpNd5UsRWCfqq74/6vhxOOyrJj729Pebm5tK2VUq09nWNCMJP5xnW98Xx9X0IG2OncheYy0PXgvws1AYwexlufP0u1nKB3f2I/v4A609XBECNxHlOmq8JDplclSp60IqhNRDiuVIBNBin6Yzkf833oFEn1OFHjR3uzueZNfP0dZNbb1ynaM7xyY8/QW926brHq7mfdpeVWOpgJKkc9QSTxtbna19y2PQM6LaJr+XIZPNo2oiMNo68NCO4/whmcnD/UUSxCl/+kqTnZpfAcmC/G7O24hHG0kj4Si3Lbn1IrQK9lgCHDcTxOsn/uQy0d6QZsWdDriaAS3GwQiQ6GTNOu6rrnYwEhr6owpuRKJgDtLsClgMdfumORnmugtfdp+xA7ML2EP6vB/AP3wQqcNmA90KpfPz+AOZj4TddMuFmDRrauMbgJDMQcKTGrKoDLQQ4VRCQ6SXbKr2rCjJHCpypF4tiMl+rybx0GUdfp+U0/VUkzBwjnLktH4ITFk4uC7kKVFYhGiVAXYOBf3C9rSdvKMFAuG7fMOHmIkT7ErX0kYjgcRIkOjKfGWQ9dKe8HrXveWxh4sGzjaRRQcDsZP/w9eSzE01VjrVaLcIwTMnapmlSKpXwfZ9cLkelUkn5VEEQUC6XDzjFww5JcXqOMyUNUKsd0bRqwtR4arUapVKJwWDAkydPCMOQRqOBYRi89957qbbWq9pxgqMXYapS7zguDpCmhd544w2+/vWvpxWFSsKh0WiwurrK+vp6CoYNw2BlZYVGo8HMzEzaaFqZYRjk8/nUqY9Go3NFaUajUTo/6loUKAnDkM8++4z333+fP/qjP6LTGTNJXNel2+2mPTUVKV6JmwKpPMfs7Cxzc3NnHpsyBZqmLTZQaczJvpFqHZ1njubn5zFNk263eypYV1IfmqYd4GhN/jxpS0tLaQskBbxeZ5B1gl3oM2wA/ADpq2aG8uBWIo2lkgCofBaqFUlXuSOXIBhQKZUJsFICu2Uk1YrKYtA64AcHPkr5O3eQZrpXS2PNLLW7Q0LwBnqRzyhoc6lqM5+t0O5ZrDxbJ+/5uPUWgWXx1S/fpFYrUDTO3m8OxBGXkHG4sUyq+gYWODkqcRZzu2AFI6xwgKGN0G0La+IZtg1srIpW1dtvwDe/bpLPyrw7DnQ7UN8PWX0Oa+tSaFAq2WQ0ePYImnWoJVHCSxlpgeMBGQ1KWRPNAc8EbTBeNOnYOJi+VTYJkoeB8MyiCPo92bYfyEJrRvCnjyM+/oN1vvcHfT5KEjQ+8LEHjSGULKia8FZBwI6W8PhGSHNsIlich3dnOdWUbEgVuUfXgXeRKNKN5G8tROn+HvBx8u/7wO8D/xJ4H/gxIqq6a8ILQ6JvZnJLDM6mpJ4D7hjQ0+BPdXiindzGaZiEdw1NAL6R3AC3zwGkFSXHfqMCCwuQD0QceLchaUp4WUNt0pQmmrqms3xHXpkMH8dxrGnamZ+0mqb9JhKaV8fh0aNHRFHEwsIC3W43BTi+77O5uYmmaWxubtJoNLhy5QqLi4uEYUg+n2cwGLykzxRF0bEtcRSnq1Ao0OudKKMHwP7+PqurqywvL3Pz5k1arRYvXrxIK+3m5uaOjOScxy6yyvCwqchNp9NJU4iHz1Or1chkMszNzVGtVtNKRdUjcDgccvPmTZaXl1laWkoBcq1WYzQaHZiDbDbLcDjEsizy+TxhGNLpdFKQdFZTUhJK+FQJljYaDUAAwr179166LiVgCwLIMplMqhSvxqtAW6PRmLoJsmo1pHpmLi0tce3aNa5fv06z2eTDDz88seMAwLVr13jnnXcOzM8nn3zC9vZ2ug7OMleu66YvLKfZJF8ul8sxHA7Rdf1Yknu1Wk3nrtlsTvXded3top5h3wMyHfhVHWoOZN2E1OuK8OTOukQyBpuws7+B86bJwpJB7AXkk2N0ehBMPJWjGJqtlzkqLtDU4IYDWkYiaTZjR28jFVvzSJShGAb0d/e4t+uwsLTEV67MYexH/MUDC72YZ6BlmF+6zLKZo+c+psze0VIMJ5iNODI9GUOWMSdHcXouxAIoFGw2vT6WXyLUdfRYSyNaEZCbkz6RszNQqAkNwwu6hIE4YG8Id29LdHH+SoXe7gBCmCtDfwSjoaTNnECiHKuAkdHJFmz8MKA7hGHrZG2wwxHICBlgrw9eHkwXIl/mpVKBqCXO++kIcg9DPgkPAgw3gqcd0B6LmGnBToBWJB0FbGA5grAjzZArzulTaSJ9GEMEoGYNuFSG2TtQe0Njr6nR+nGEt3kwDapAk5rzN6/AV96EchaCQCo0Hz8A6uNtJyOaecbVnYdtC3gcCnjbOkLW5LD1A5i3JfJrFiCbaJxoAzCTEyou3RrwN2YkKjoYwWAP2uH4mqZ5mqnGBWex8wKtHRVO1zRtibGkxAaS7lV2OfnsJYvj+B8D/xhAPeSCIODx48epU+73+2kkYnV1Ve3H48eP+fjjj/nWt77F3NxcCrRGo9FLshDHpU0Up8txnAPOQgl5Tv7u+z67u7usra2xs7OD4zgUi8VUOqJYLOI4Dm+99RZPnz496nRnss8zdTUYDOj3+yc64VKpxOrqKnEcc/PmTfr9ftr0W0lcFIvFVJF9NBqlPRBV5LHVaqUpUBUBUfdiNBoxGo3S4odpTbXYAYkaNRqNNO02aUddWxiG1Ot1ZmdnU56aGufk8dU6moyGnWST+m2GYaSp7Vu3btHv9+l0OicCrWq1ytWrV5mfn6dYLBKGIcvLy+zu7rK9vZ1WaU5bcXjt2jUcx2F1dXWqfRTIsm07Jbur6NpR9uDBA3K5XPpic56I22ShxM/QLvQZZmhavAA8iKHRgreTB7yPVEK1ffh4Xx7kLlBohbzzPMT+q88xr1whk5EIjO+Jw5g0LamAU4LpGklLwFiqzfSEgA+yXU2HzUhIxKq9yYswprzTob53n+f7m+Sz7/IVzcAPfIqax0w+j19Y4PrcXTYet8kwvYCxMgWm+rysmXSRr4yjHrjdIbGWxYpMolinzBjMZIBiCZ6tSYHB0hsGg8EIyzKJ4oDhAKw8lHMwO5slxMYbtYlCiTpmczBTgt114Uy1gTtlMGOdfhASaVKF6PriwA/PwVFPb6V5tg9oPSAPoQ3NHZm00JJoYAe5zx+HL6f9/AhWOnCrKBHOYAjzVZjbG6eKvQi0NnQ9Ec49zTwkBaaqSR0T5pbgjV8pM/vOZbo9j/X+Jh9v9okZE/+V0nsVuFaEG0swMw/Fko0fxpQNg6V2xKd1j1FyjkmV9wHHr4naNeiZMFiXSPBpppkyHjsjlaMZM3lZ0QWI9ifG3Qd+9ByuaCJf8iyQe6Y6OUyOKc+Y6zhpIZAzwTzDI+y8qcN/DvyD5Od/APyzic//vib2daB9GrfhsIVhiOu6FAqFNDVYqVTSdFEYhmlj5Hq9fkADaHNzM61CBIkMnZQWjKLoABfFtm1u3LjB4uJiur/jOARBwL1792g0GnQ6HR49esT9+/dZXV3l4cOHqe5Qu92mWCxy+fLls1zykaYI8RdtqjrSMIwUKB5Od96/fz8Vj43jOCWu7+zsUK/X2d3dZTgc4vs+/X6fXq+XRkaUxEUul0v3VynDTCZDFEVpX8azplmVsK2u6ynHS6WdT9NdU3IWSpRTVbkqAVcYdwBQLX6msUlRWUXQf/PNN7l06RILCwsnpiBN02RmZiaVoPit3/otfu/3fi+NZME4YjYNqR1gfX2dx48fTw1gVeWp+l+dT6VWD1sQBGk0VMmEnNVek1TjhT7DcoijvAV4sZQxZhxJLWmBNIYexbAXQzcGKxRhRXd7SME0sRKye6ML0fgRhgFczolDW5o4n+LGaCH4CXHGAmYs+CuX4W9XBASUgKoGjgv/32cxD1sRjY7PZyu7/OTJJttbsPnRiHLkM58v0Gm6LFhws3a+RJ/i8pSBJR1KybLtcFKb6rNZMIKCkyeLjuaG6LFBDi2NmOnAjx4l1Zs6aFgUcmU0Iuo70NyF/W0RgfX8CLc/pNePcX0hxfdGko6qMOYo5UtAxsbIWgyTR0PGAufQVySaGMNhU7c1sqUNT6kGeiRSEZ0eYI73P8qHe8BnI5EtsDQoZCHrSAPzXrJjHMKgC6NA0punzmVy3AxJ9DGGYs3hxpeXmb+8xOzyLJWFGi7j+zeDREuzCGn+dhlu3i7S2crz+/+bx5/8C5+9jQgzjlLdNcUVVNen+F3LkEZzlb2/Dr//HNpTlk3GsYi/RrFUEWIBJhQXIdRfBnQfhfAvA/mOZmMZX4eXtxtwcM2qAKGFnOtCqw41Tfs/gQ+ANzRNW9c07T8F/ivg72ia9gj428nvICnbp4gsxf8I/GfTDEI5yUmnlMlkyOfzxz6Ue70erVYr1d6CsaOcxpRwpnqDNwwDz/N49uwZu7vycqtSZSoCpCJNjUaDjwIqEsUAACAASURBVD76CF3X0XWdlZUVvvOd71Cv18nlcq9c9q4q8y6C73XY9vf3UxFP1aJIgVVlKu0UBAG+76cAKYoier0e9XqdTqfD8+fP0954vV6P7e3tNEoVhmHKNdrf308BXqlUSsVGzyNKWy6XU6ClOFBnUdC3bTuNwCkulVo/MI5Q7e9P1+8tjuN0XRSLRW7cuJHKXyiNtuPMNM20StM0TbLZLEEQpOlxdfyTIkyHTZHWpzWlN5bJZFJeluM4U609JYh6VjuPUO6r2E/jGZbRJAVzzRxXq+XNxIloQuZtImGz5wgf50UMzXaMG7ap5STi0Y+F4J6OHSj4kv4zJz6zkOqq2azsdwupcFzwhXf0pC1OrQV8GsOeD/shRJGJEZTYa3l8/7NVAgMsLebxX2zxz7/zAXv7dey8SSuOT031Ke7N5O9Zxn2hjaykNWFMmL4Iqzdg6AW47pA48CH2yZlx6swyCODwY/BDcL2YTCZPHBn4EbQ8aOxCqwPPn43QfZdyUaPThbVdkegYeuNj3QD292AYjKBg41Tkelpb0ibnKPtrCCBRppTxAbJlEdc0axAl3jsKpdr0pG9uhJDc47yspRGALdWPZXtcFeojqcO/mOIR5iMROyXuWizB8p0i9uwi7WHIyNNw7GLKr1Okd1V9qAHlMkSM0C2PYlZS5t01j0E9ONAffNIWke+GA7yDANoy8kIRJW2Fpn2K+YBuCvAdJC8djgNZS095i5OmwOUjIGPLdzHg5YrDw2N2Jz63MhesoxXH8W8c86e/dcS2MfAPz3B+YJzqUdGo5eXltMqvWq0e2YcvjmOazSa+71Or1Y5M2anqqXw+nxK5QZzaJGdI13UuXbpEu91mOBwey08JgoCtrS12dnbShsr3799H0zSuXr3K1atXefHiRVrJdl5TkQ5d12m1WqkDvqi2J5N9EV3XpVarsbm5eQCsKCV1lYZaWVlJJRQ8z6PVanHnzh0sy2J/fz8FbUowVclsqOIF5dAty0pBznnSTr1ej5mZmQOK/9M67iAIWF1dxff9NDWoxmIYBqVSib29vTMVIhzmT7muy/3791O+Yb1eT2UxlFSDEtG1LItKpYLv+9TrdQzD4MaNG9i2feBef54RINu2qdVq2Lad3qvTUsvKdnd3zzW289z3V7GfxjPMj8Xx1AN5E77qCBfHB7Iz0Hs0llsIkUiTD7h7Mc6gx1wNSm1xPNvJMTUkYqD78MtIJOOrgezXB4a2ONiCJ82Jr2dg6EIrEqfgICmbTnKuPND3fNpr23TWdQqRTy6Gzect1l98zKVry0RLJT7barPX9k51JIcdoYLcGZKqtZkszjAE16OcfNaY2O685mkQxhFhGBDFEa7bZ74WYTREHNVGHFtJh6uXIJt1WH22ysoDn/qeRCPqAVTbcOtNMOyY/l6MH4omU7kKvZ444Jlk3JELRqiRt3QcPUkfaZL+O0rkKYMA7wxy3yeZwoMRWFVJ/+kJUjUz0J5iYvohfH9HQLrdh+wuFFyILSj68EYGtrrwrMeRWmiHzUU4UTXGQMMbDln/6C948VmDONKIGzFv2aJvZiBk+YIhaVDDBnPWphdDY8/HCmD+NgxKsL0u166ir1nGgEO1s3yOrAkbAXyTTaWnNduRylHbBssXqQevC5EfMZdc31GgzQf+H0+I/yrNPo3FSAHFF1IZHsYO5erVqzx8+DAlBpdKJTY2DtIkqtUquq7z7W9/+9g3+DiOU57QYDA4UGlXKpWIoojt7W00TaNer6eO+yS5B9VrT/G61Ln39/f58Y9/zO7u7oVwrCa1xObn59Nqv1e1ZrOJZVkp52o0Gh3QllIWhiHtdpsHDx6wu7uLYRhUKpWUW+U4Dt1ul729PR4/fpzqoE1GGCcBw6R8gUrfnTU1qtTRVe9EJao5beTP930ePXqUXnexWEwrWFUF6auAml6vx/vvv88HH3yQVsWWSiWuXr2aAnKVwiwWi1y5coUrV66Qz+dTpfu9vb20gTl8vhWo6vgg91v1GYXpeILnHdPhzgE/DzZC3sjXEEf01jKsPJU35f2BONa55P1LqV4vIuKYv/vbLZqhRIH2kAjV30Oc8yLyQI8AN0jkC4CKJnpRXgQPB1Lx9dfdcW/DKiI1sIs4rwj5mwYU0ZgxMuihjw80owg3imjstvnwz56y0Wiei7muevkVSKJdWQsz1LDwmJlNNriA2ol9H6ysgxPrBHqM60ZUcpDRoRoKoAyRhtzbXWh8tkF3UzSwKjn5LKeLY+50YG874PGK6FBZkTSbzmfH4EBJa9i6TiWXo5Sko+IYKsc8el4k/w7LPIDwjnqhpJVrCcnLtKeL4HQD+F9fyLjmNbichz0NMhEMYvjUlTWUiSXK+eSU4wXIbTGR6+114f1/02P0gz7OIGamBJka3LgGg0eyhvaB9RAWsnD5MixfrZEphmTvNrBnY7rbEb2+tJNSIrs9DqZD1fJSUVdFqj+PhSPRIQsjAUC9AHIjSdWftow9pNBB9TKfxnyEJ3iW18XXCmgB3L59m6WlJV68eMHm5iZ//ud/fuQDvdls0mq1TnUIikcyKWSqnKriZymezzSmZAUOW6fTYTQapWXv5zWVjpxMVU5qX72qTcpndDodNE1L5QCUk11eXmY0GnHv3j1M02Rubi6NqsGYi9Tv93nw4AH7+/tks9k03VssFrl//z6O46DrelrQMDs7m/KNXNc9NwdNgWQlVHuW+VZzqmlaKqCqPntVMOO6Ls+fP09bDlmWlYJywzBS+QiQ9bK3t8eLFy8IgiBVut/f38eyrAOpQsWhmsZUS51pr0UR7dW99TyPcrmctmf6POQbXhOO1oXaCMk13gS+UoLaZfDX4Z4H/8OmOBFVdRUjmkefAtd6cJM4TcHlEd5KH+FkqbL7IfLWvUsSDYhhcwg/Go57yP0uL4uWgkRVtORYfaATBfS8HvOIwGYGcYTZbgu936eExs4Jy005mKM2Ud9EjeQlIQpFcsLWyfkxFvErR7QCIPJDwkGIFuzSw2NpPkAzRKMqDyxXwXPh0w/BsCJm56CSEWcMApKKFQhb8PAhPE96HRoO5PNQLEIpATQeCV+n6xEs6mk61HWhekxl3wPga0iLpMMWa1CqSHrTSibTjcfRr9PMjyTV1tUkyuloMkYXSYdFnE23SlkAdEbQ3oA6MZd1IZfHQLMv6c8QWed7QHMIThNWX9TBB7MXko1guAWaI0UcAbK2DkerDss9TD4RFm2RuRhNSTbXenLwKExeSkZKsBbmLdBHJ4PY6cqexjbkaCHak+y1AlrKSUyW158UrTrtga2iHYd5JKfpax1luq4fACNHjUfpcr1K/zfljCer2RT4uihTxH3V7++wY97e3k7HYlkWjUaDb37zm5TL5ZQzBKKptLW1RaPRYDAYpOrwhmFw/fr1tGdlPp9PU4ZKsymO4xMjUaqy7ShTfK/JqM9xZpomd+7cYW9vD9d1U5Cs+mA2m80Lm1vFFbMsi1KpRLPZZH19PU2rHl7L7XY7bcwcx3Ha/UBVZU4edxpwoipmzwpk1IuIUv1X9/2nLdz6RTfVTDnuS2/CqiPaR0e1k1GNdp8iUYLLCKBqIc5JRYYm25ao1jZtRLso5qADUd8CDXHEl5D0l50cf41xm5gI6Yv3r5K/LyME5ZiABV0/EWgd9yf1LeonY8jrDpHmEzEisg3ikdLUfjUbAPlaEbO9TdhuMPBjrCBOG0i7wFpL9JR0JJ209wL+1leh1EtGYIBvQu2STWnbozpMUr5FUYfXDLg9C6MODAKR6zAsHc8XPS6dJIV5jJR4hKTjVAufSTMC0UaLHBiach8H0fEgq6DDf74I63tQ9+FPEP00dGk1U+xC0xifF8aAfhozkHXWTeZWjxMNNhN2mrC5KwUeOxpsx2MJkQ2g3IDohwH1SFKVb9Rk7Q0H0hxbHV/jIFfrqAbT88mcrXtnax/kJoK+nSGs96Fmy0SY5slCp69iW/wcpA6Xl5cpl8s8fvz4lY+poidntcn0oeITnUb8PYnfdRYbjUa8ePEiPd9kGvEiTGlFua5LJpOh3+8fuFbFIVKcJcXlUr0Nh8NhWkX47rvvsrW1xWAwSEn8jUYj7SOoonGqEbRq76JaAB1nx4Esz/NoNpvYtp0WLZxkpmnyzjvv8PjxY6rVKn/4h38ISJRISVicZ30cZapiLwgCTNOkWCymMghH2eH1oio3TdM8cL+nBTzniaSqNK/jOPT7/TStrPh0v7DpTKXlQqBjwhu34cosfPn7wsI/7lUgRKIDTnIMA+FoLSKOL0B4Qp3kZxOJhp30sl8GvkoCBkj4WZrOyLDoBWMXlkUAmIs4zQDIRTHlKBR9JgTcNZnOaavr30vGHm806CdL0sxYeG6AfwFAywe8foN20yPoRhRMSeUshDJeE9GYigKo5mG3Kw6370pqqVaE4VBa6VhZm7ff9dnejYkicCzI5x32Gi62JlV9TiRcLH0UU4xD4uTraAyhdoIo6HeBv4NEdDpIo2gQWYh2C8ol2N0bV+UdZQZw14KvvA2XP4H8PHzyiTj6PR8uvYDAgu4rNDZUsgZKBV8HWhoMC1Aogr4LuwPhUk16txh4GsG+O96vl6jdh1lpKG4mj/HJSOfk/spuI3P0mNPV7A/bTBbsGMoOrA1EI8vKCn+MDCf3jDqn2ZwNDL5WQEultb7zne+k3KpXsUmO1llt0rkFQYCu66lQ40/Dstksmqal6t4XGV0YDof0er2UUD4YDA5Eh/r9Pnfv3iWXy9Hr9dA0jVarlUaj1PiKxSIPHz5kOBySyWRYWFhIgYvruqm+mOqLp/hdSqbhypUr/OhHP5p63EqFPk4aSasiidPmxnVdms3mgf6KSp8qm81eWHWnapytgPna2tqJKWk1l5MRqHw+T6lUYjQapZWPh7e7yJY3qkhhUlNMrT31/89jqu+iLellyxqwGMD//nvwOBbHUWGsj6RMpXZUpdgGosodJsfoMgY5XeRBfSfZVrW5Oc4htRDxVBsBaQNgFEfMBCNuIk66j0RadpJt7iZjWU3GWkuOrzG9UwkRR6xK+YuOVEwGI/CiAC+Kpup1d5oNgVHPxW9GRInelN+DeiTzkgWMEdy9LhII/RXIhNBvino4poCCYkba8jy8H9OKE0mKRYjjEb4Lg6GAoVJGwJGvxfj+AEOVshmwfAXJAR9hAaKeDkkEKrFyVaI/4QiUGlHphHd4I4ZMF7wm6LVxY+fFGLyRaF9lHc7dRToRk8dANNyymvQMzMfwYhN2PAHjuWSbSaZwAXlB2EPmvqfDXAGKZdC2IWgJCFO9ENWr2zzjtOIs8nJxSMh9avMN6TU6U4HiACJdgJYew6wtyvkXKuTG2af6tQJaIDpAhx/sFyFwqHrjqZ5up5lt2xSLxQN8IiXK2el0pgI+Km2WzWaJogjHcRgOh1NJBziOk0YoisXihUTKlPV6vRQMGIbB7OxsKswKAnh3dna4desWb731FoZh0Gg0KJfL+L6P53ksLy+zurrKvXv3iKIovc79/X02NjaoVqsUCgWazWb6s3LqitB+nns6WdAwNzdHt9s9Fszous78/DylUom7d+8eaAkEpNIV00o5HGdqbSmAmcvlaLfbacTwOHA+uc5N00wjSkq7TX2uKkSP2u9VTUWvVP9IBZT7/X4KtH5hp5uJ8LO6QD1x/teBNxk7mQ7isFSkaMhYW6iYHKeFPMTNZLt5kgbGCOC5BXwTcWzrSHRLS7aZR8BCNjlPduK4w2QcITK2brKNxzhVqXozGsl+WSSllGHccsTW4QMN/tWhwFQtuRalj5QBSo7oPVlAsZSn743QzxyveNlGgNsegSmyGqYO1RqUNsEL5e8+0NiA2dvw7puS7ms0BAD4vvB4li47rD/p8ckDGXzGEWmH9l7Mzqq0SyoVYL8NtSoUHQdPC9BsXaoefYmanWRqmh5OfGb40p7GQZTr2YTOMaQfXYelBchX4c5b0FqG6n25X0qbTI9h6xXiEiqilkFSg49jmPdhMQLTEyDuMl4v+WSfHuPahjKyXbYpVbKWBXHiZrOM04cqvTxkTD5vcbQExLQWOWBbUoGbCSAyRQvN7UsfRF0TvayL86Ay9umUFsVeO6B1lBM5j0NWOlmzs7Op0xsOh1OlV1TERfWdU4rZing9Go2OBT6GYaQVfaoZtQICp3GBJiMXSqEcSFvmXJQpYDTZN/DwvLuum0YDczlpYdHpdOj3+1SrVUzTZDAYsLi4SL/fp1gs0m63U56Q4zhpX8EgCNLokZoXRbo+qyltLpV+VGM9ytQ8FovFl5orq79dRMskOBhVO+tx1dpSa2WSo/XTUlBXUcfhcMju7i5LS0v82q/9Gk+fPr1QkP/zbBnGQEZDolEhY45VHnng5pBIQBV5+HuM9acqjFM5qrnvXLJNwvlNq/uU9EBr4rjqCTOXfKaOkWNMig+Tn7uMuWJKuVvxwsqMo21Pk/NaQCWS7VRLk0kzGUctdCQiNPIT4FbO0e7HaNPSiBUL/YjHdQ8IIsgXwehD7Il+0jCWsQfJtQx8AUkakM8JAX7Qgn4fZsoQZXT8HlzNwagPThG6SbujMJTfnRJcteWcfX9EZmgTxSYDPFxPGoZPYweerlnSZogjT348Lu6tRaA1gSrEu7L9LhJ5NBFifK73Mg/sLKb4XOoJNgKeBFLh6DBOGSpl9ywJ9w0BeyNgUZN13vSg15BejIqjFST7T97Kyd/zjL8H57Eolt6KPVfkL+IhRDvQn4O/+9d1nvwfEQ9GqY4pJ9Xu5xgLk84w1hirJtcxqX1woTpaX1RTZfT9fj8lqisx0dOcl+p5dx4HE4bhsU72tCiYcrie56XCno1Gg1ardWGAAEhV31XK8CiydRRFPHr0iHK5zM2bN7FtOxUi7XQ6vP/++1iWxcLCAsVikdu3b6ctk5aXl9ne3qbZbBJFEW+++WYqCKuU4YvF4jGjO9lUT8WFhYVTGy/HcUy/3+eDDz44UFxw0faqEaZJLl4URQcq/RToPw/JfRpzXRfP89Jjq4jWs2fPaLfbXLp0iW63mwLnKIowTTMFzuplQrVZcl2XIAgYjUYYhpHue1q/xy+6echDOIeAJVVWryquioyjSmXkoa1SirOIo1X6T1by+w5jUFRj3KKli6QXW4gTUMKY95NjKlqKcqAlxmmfPIr0LueLkNSiIkIHyd8XGKcS95Jjzif73EjOfS85ryoCCJN9FVC0k1ROoQfx3iZaN56+JP6ER28bGDTAtKA9kCbeWjgmPuuM5/zBJswYcOsG2KZUCroxNAbw0fcGBFkoX4JqAa6/WaDf7RENoHxVZ389Ym8f9ACufdmhaZkMghDdiwXAlse99KaxtFrTh9xlcJbBeHHyPjHgDuCTfy1ro70r1aeqslCB71chtKiUbyqoiqwfFclUIGyI3P8Kcp9dZO36wEYsa0Bx/nZ3x5V5IQlvjnHqcPJJXEiOcV6g1R2JwKwWyzjDQD57+Ay6rYjMFbjeFb2tbE7myjDBH0I2K3Iewy7ML0qa0R9A5EnkMzag0xXx3bV1DqCrs6TBf26BlrJJB3KRYEWZSr9dhJjoZIppUnX7vGk2RT4/7KAHgwGVSiXVs7p58yaZTIZ6vU4YhhSLxbTfXxiGDIdDDMOgVqtx586dFKwWi0UqlQq5XA7LsjAMg0uXLrG4uJgq7P/SL/0Sy8vLPH78GM/zKBaLlEqlVFLirJbP59PUYxRFaJrG4uLisYUTw+GQTz75hHw+z9LS0pHbfJ6mdMOmAe1HpRjVvB4nYzHty8NxpqQ6VEskVTVpmia+76f9K5UpvuAkd6tSqaDrOgsLC7x48SJt/K1aWL2qgO8XwQZIemgHAUWzSNVfxPgNuYCAlhES7bqEaBL9KWMQs5v87CFcKpL9VcpRRbLqjCsT68k5O8k48oiDVE2elUZSHvETOxO/qyhaBdHdIjn3pwhQySEViSoa8BBJWU6Wd/QRYKnOl0mOrecLGFFImSHGCOxAwyY+WwpHaVscWvreALI1mZfAgNvX4e9noSFKA5RywoFqd4QP5ftSSTg7D2/NQDyAOIBSEfKz4OQgY/XIVqB4qUBt6Qqbj5+yvzvina8uk7l1g/uP1wjcHDvl5xRyPrOLYJ8hf1RDGoHncyJFEPgQR3JfF48qT0SA74cRbO3BOwaU5wUUW0iqWmcscHteU1ypGAHQKq3cQu7jHWTNqSIMla5WLwZV4BqybnaR9W0zxsoWAt7UOWAcCQOYN8ENRQfsXOPXhGtnZMApyL02LYgzsKNDtioq/J4u9zwOZd4xZN/QhOIsxCbMzcHaC9jpCAjzHFlfvX2ox3JdOkdq1J5oP/dA6/O2w/yZizBd1+l0Omm12nl0tCzLIpfLHanyPRgMKBQKDIfDNAV37do1lpeX04iTUnNX+8/OzlIsFrEsK+0FmMvlUjJ5oVAgiqJU/PTu3bvcuHGDOI7Z2dlheXmZtbW1NA052UPxLLa/v0+lUmEwGKRk/tPmJoqiE7lcr2qqSvCoFOa0LWqUJpj6p9aU+vy4NabStNOmxQ9bJpMhl8sxMzOTasupXpgq6qiAnFobSn7Etm2uXr2atova3d1Nt3Uch0qlQqPRSAssft5tM/l/L/n34IhtJn3J1sTvmxOfPz+0j3JYKrKwk/weIoANxh2xXQ76a23ifxWdsJJ9NcbaXkrXyEOcSTE5pmrSXGecMgo5mp+i+F41EtHQnR6alzRezlYZDUaE05SAFZJBKDS4wUtAa9ADqyBcnNiElg7zb8DsTRm0YUjj5ytJ7z83gtIsZErgmCZhFGBpwiNzHIgzFfzyJbTYJswXqBsZMl9ZZDkYsGXEjJoDCte/xM6TDYyKychIonal0y9HWRloxqIzFRWEQ+QlQCt7TKgvRlJdTeDTkBR9RwhIHzGObp3XVHo6REC0m5yvdcS2k+t3hAB0HQESeU32y8QHtWkVR0sBMxhz+lxEmsKbRRB7n7OFigAnA3ZR1OFnO9Dx5bPQglwZQk001aKRzHdGA82X6JXtQPUm5Is2OdNma7fHPtDVIVeAhYqA97AIxeZBgPWFlXf4hY1NNU8GWFtbO1M0TvXGO07oUjlKFZFot9spSV2pvivnPjMzkzpeFe1SWmFKqFTXdYbDYZpWchwnlYkwTZNWq5VWHKp0n+KxndVu376dRknU/Jw3DXlRpsjwr2IqIqrmXqXzFKftuLmqVqtpVWy/3z8z2CqXy9i2zfz8PIPBANd103uoQHS9Xsd13QMNrpXcx8bGRppWrNfrKf9PpRnjOGZ+fp5Lly6xs7Pzc59CnLTTVverJoIzSPuQj085h5Jx2Eec6a8wVo5XKc4w2aaL+Loygm8mHYsCfJlDYy8kv/cZO9gComMUh/L5xrMGdX/K0n2F5HQkPGUxJggl5kegW+AZArT2OiJMGYXSh8505NwuUJiT0v/IkhY91XwRJ5PFHfbZbrZZzFbRzHmCXpGBGeI6GWwrh5k3yetLWI5Jq9Oj70eQd8ApEUU9rCQHW2A6wfsnSNWj8+WEI9dMmhprsFSAf78p90SlBsNkPhWY8nhZjXyAAJhFThbeTIotj1Q/V+dSvSprjNfBYdORCKuS0YiRFwMfKCXpw1kDZmLRBlN8Qi85nrr/Kr0MUKyBZkKmKI2k/TPmEAszUsiwNAvXffD70rg7mwHHFjC15Ut60NITFX494ZDp0FqFXs6jbHt09iHjwUIG5m2wfUn1LlbgjTnY2IMXLyQdfxQQPc5+AbTOaJlMhmvXrpHJZPjkk09SvaHhcMjCwgLVapUHD456l53eVDNrFQ1RAqJn2V/1JDzKlFK5aqmjQJCqcBsOh6n6umq6PKl/pX62LIu1tbUUkGUymbRFkWrwrXpMtlotgiCg3W7jeR61Wu3MZHjFsztLn8RsNntq2u5Vq1pPijid1Q5HxU6LmJ51bRw2FS1VvTp1XU+5gsPhMJ3rfr+fctwUX8y27bTgod/vp71KlTTE5BpUwrFKGPV73/veT43o/7rZAuOo1KvYCAFZGpKyU5pZh01JScSIk2zaoF11MEoFCj+pY5iQMyFwoXgpT3k+y9a9+kvpEeUYj7pr6u1+gDhbD9gfwa4n0bp8R5y4SoMeayrPNEJCOZMeecJ6XVg0pY/kyBPSfSkr1XxxCMFAohlDDcyig11wCGMTCxgSY0Q6gWljGFl2njUZ0KQ6N4NplTHCIplZaWg49LoYYZ5ov0ur1SHwbFrN5Govgzkn1aXHKDy8ZFlAL4EzhH5lPG86km5WBQuqMtVAQNQg+Tdk7LRLCCDOJdO1i0RSVd++PAJutpJtSslUbiOyIwpIxci6CZPjGMlx1VNTyUkMGacMQ8ZAQ92eZnLMTjiubIVxAYc/sa26rTPA5pZEYTOcOZgl82BJS6pfXYavLUM+hCgroLo/kgrI4JZEENEEnAcBRBroDuRyAsy9LrSKoEdSXVtw5DvBDKBLGrF1DbS3wG/Df/mD6cf4C6A1hZXLZZaWlnjw4AG6rlMoFJibm0sbMS8sLNDpdLh8+fKF8cBGo9GZeV9KkqLT6Zy4b6PRoF6vs7GxQbfbPZBenCRhX4QdpcMUxzHf/va3WVpaolarTd3DUTUSX1hYSKtKJ8d8lE3DjXpVh3/ROmeTprh604xRyYCc5Xocx6FQKJDJZGi32+m1qOhjuVxOgZNt26nWnQLbSh9tMtI5OztLqVTC8zzW19fZ2NigUChgmia1Wo1er0elUkmB2V82uwiQNWkqmnScmcCVAtxags8ewXMdqlWb4nKVq4/rZHWIZiCug76s0fH0E0nrh1fXZDRngDjcLOANQX0trGS/U1dmFclj7ScHO+arvd2C5U3orEOzB/1oXFGpkQTCNPW7i4mLmcxFyLjQQKU7pT/kPhn2yfEU0Ghpkurb1cCP4zGIjeV67v+/8KvAsgmfHnNhNQ5WuQ0Bbx+CkoyxnId4Tz7/lW2L6AAAIABJREFU17ycNj5sSsrjanIspbOmKlsVD0qBtSxjyY9ExSIVwlVmJP96SJFFm7FW22QES6X6TorkeAjQm7Q8Yy6hekoqId4qUtVaScY1PyPX5MaMVXpPMTsPWhXKRSmS8AKwArDyMJsDexZGJjhZMOwy/bhAGNUxvRhMnSiAaORiFqFqSNPv3BKUyjlcN2Bj3aPxDGZyEjnLzUKnB+YvgNbFWr/fZ3NTWBRRFNFqteh2u+zv76NpWtrKptVqXZj45WRj4WlNVYydtl+z2eQHP/hB2u/vKFB2kYKYx32u5vQs1uv1yOVyKWEfpldO/zzt8xL1PAvgdV2XUqmUSpCoKs/jLJPJ8Md//Mf87u/+bto+SEWgDqcgDcPAtu20mlDxsOB0MKuqfpUpTtkvbDqbjALpiPM+C0T1gP0eXH4kzvKKCzcfdfhxt8t6R0j5w0TWoPpxj5HVO3cVW5axmKo/HOMkn3GK6ljTEXRw+OQWY3Z1spw/C8D7bAwCPMYSFYq4b8djCQsln6EiKSYS6Wkl+2rJ/xnkmB1i6nEylCO+2h7wfizHqJ8ABi7zspxAbyARFWte5BBAQM4zTjdFWD8Mdg5Pmao4VbSnk0zNSZYx4FGSIw5y75R8iMO4iEJVmSqtNQ3Bxio6toWANgXWSoyrDh3GWHrERIViF/QrjEtkVWTzuDmegf/mhzD8fWmJFPuAC1kPmqGMJQAMC+wMOJk29NuMMnIPjBw4HrQ9CIeyzDxUlHAw1vdS6DWxGwWJHk5rvwBaU5hKpwBpNd7du3dptVpkMhkqlQqPHz9maWmJQqHAJ5988srnnJZEPWm+7x8Jmo5LjSku0BfJDMNgZmYmbcT8OtjnqZzu+z6GYaQcudO2VdFBlfo96f5eu3YNy7K4ceMGhiH0ZtUYXaUKFe9LNQgHDkSyFF9L3Ytms3lk9eTkHP08gqwMx5fYH6XifoPpHCuMKw4LJIKoZxybD+xY4Beh3UiqyWyH4kKRhZ094lyW3GIe88/reFcKhNUcgw9Pb2913Ln6jH2kEqpUQpeT1WgvWS45wBZjifmZZCelmJqExZ5zevTnLBZzsCBBne40O+0eHtWhx9fBWYJhBFEyGT9LRzxC5jKPNBm/xbgBtALIanw6YxmwBgI2OhwUx1WpRYVLlAaXybiIQlXvaRO/BwhAKj5J0opFWDGhbSR/LDDOYSp13C/neZyxKM79/+y9aXBd533m+Tvr3Tfci50ECZIgKVIkLZHWGkuK7E5GWStxujJJj5OZdMU9U5maL13pqZ75kKnpSnVNKpnuD7Ml6Ulcnix2Ymc8kdsd27JlKxElypREihRJcQOxAxe4+3ruPct8eO97LgACBECCJKTwqUIBd8E57z3nved9zn95nl4Mw0bBoWG1qSseVq0GXhCn0sTxmrRCbapaFNX10FUP1YZqs0FN9fA6+c2mCxSgOkk3D59hZU4zCDe32Ej9iGhtANM0OXjwIENDQ3znO9/xVcYTiQRPPvkk0WgUwzAYGxtD13XGxzd7+by/kIuz9N2TyvSBQIB6ve6ngHYadu3adceC6XK57Fv+SJmBh10M/yCg6/qWasHK5bKv+J/L5fx5IM+5YRjUajW/SzSZTPoitNLnUtbkSW2sarXqK91Lh4B8Pk+r1VpBvpZDFtnfqWbwk4A7RYDW+pZt9SpxJ1HLjRAx4dBjMRpju7G+dgkjpWMfT6EnMxj/pB8l0YceMFCfLKDrLqXLeYJk78oiTt76LFcBl11xUhx1XciIVZCu+aMMMZiIPJMuWvdtWVld4O4KezbAvTgy/hdJOF0Uw/7Rqtc8D6pVcGvQTIjUqqKIFOKdIA/DZnGn4vfVyCP8OO8XZFeibCaFjijsst/DnffJWrQiUKjA0C5o7YbGAuLDhDsvilwvNGqgh7ANhXBmEE3XcapVcIWOm6eaeG4Dy6pjO0WciotrGLSsNjgueA7egiUOrtbZh9y+hDRibNJt9dziveIjorUBAoEAfX19RCIRent7fXuSmZkZJiYmGBgY4NlnnyUWi+E4zraaP98NpBGw7BBbDinHEA6HURSF+fn5HUe2ZmZm7vh6IpHwa4gsy/KLtj+pkARms4KrUgvLdV2SySSmafrpYZnqq1areJ6HpmnE43FfQkLuT3YeQteip1Qq+REtaS0kxU5d1yUWi5HNZleYsYNwNZDm4hMTEw/MK3QnIsPWo1HbATUIoSENLRgkNgBVy2OxWaVxocr8RxWSu2Mc+M8ew0j0YbQsgnp9S/YiyyFrnmQdkCRXLt1FdU3IFVjmmZb7wizXoYgJOQYn0UkNVkXbPnHEarZ6kXwIqBZF3dFaUBTojQsyEQhArhM62uhrsRmSpSHITIluNGonQF32W17BpHuAjHOPI8bfh4iq1RBSEwMxKKXBCnfOcwQxiaRSqwFqTwjTNHA8D8XVcbUQVatOyNDQPIeoaRAwwpRKQl5ExcatF4XirGt1Q8ZBugWGKt3cYJiO3IgqKuXvosjyEdHaADL9ItMkEnLhSqVSvpaT1J56mGi326RSKX/xVFXVF5A0TZNgMLijF7uNjp9lWZim6XtOwvYV7u9ESM2yeDxOtVrdsNliedRI1kb19fXRarUwDANFUejp6SGXy2EYBqqqouu6LyciO01N0/T3JfXKTNOk1Wr5XaQy+rX8+K+OuMXjcXp6enzpjxs3buzo+Xe3WB69WQsKov7loZT/O+A0HFp2kwAadRQCtgK1OpkwJPo8dEUhoGvYTYeq695RKmAjyLRQqfMji84b3KHjUBYF2Z2/exGrsayNKQJRUE3QAwifweUHe/WA7+S6vQZ6Or8315ZzZ3xvg9fdBhgDEIhAsQ4ot8s2bASFbq2UzGI53JsVz/1CE3Eae+hoqtEttNfpRoNlXdcccAIxX3qLENVgqh/motAOQkPX8fo8rHkHTxFc3NRVQgroqo3tVBiM6IRUnXKjgW5rWI6L4Sm0gyZe1YacC16+S9hkfrvKbaFjNR6kpyeJ3tRZ1GdwLG/L4eVHRGsD1Ot1rl27xujoqH/HnsvlePrpp33xTWm++7AXfNM0iUQifqdYpVLB8zy/BV/TNJrNJtVq1Y96fNwgIy1SbdzzvHuWONjJ8DzPdwbYKom/ceOG//eePXtIp9N+veHw8DCGYRCLxQiHw77CfCQSWSEoK2U8pD6WFKddWlryOyLL5fK66u/5fB7XdX2F+08iyYJuK/56ekoeoqX+YcCtgXXRwvEWqc07aLoQYTz8uZOo0TA2YEZDuJqLp7tEPYcYd5eqVBEBAOn1KG8Lamwy9SVb0RKdjRVY4SOk6dBoio5GTxYQrYVNkizpO+kgskLbgY2OW9gTWavKYieq40FuboN/WgWPzWl37QSodPmzPF2yAH8d9Q6GOu//f+egPQeBi/DiCBQOwI2EghZU6NmlEdACaIkogYBGWA9iOQ0CAYOWpmDhEkwlUIFY20J1SqiJELF0CsIOi0t52iZiApTptnCugltsskSOsBMhYanky1tPLD8iWhvA8zxyuZyfnjIMg1QqRSQSodVq+f5/oVDIVwh/WDBNk3g87qeLbNv2u8WkwGQ4HEZVVVqt1n2xJHoQ0DSNWq32sSSKW4Wsf9qs12EkEvHn5HJMTEzgOA7Hjh2jUqlgGIavzJ/NZn3zdOmBKW2VYrGYHwVTFIVgMOhLOdRqNZ+0A2s2Ykhl+IMHD/Lhhx9uwxHZmdgoeb3VGpt7hYIoTt6DiHxUci3CpxdFZ1gA4oM6TiqKZam0mzXqqooeNdFMBS+g3XWNkoJYPGdhhX308tqtdWdxp7VNjYOhAUGDVtvG0zz/ADoG6AlQqqA1hIUKIF6P0209XIZYUBgOr96vvFJngWOdx7sRmaGprXzoVQgAg6xfrK8ros6suXxRV7cchPvYwIJu914H0i98rRT1j2sQcyHqifMSRpyjH0zCcw488aLOh56NHY2SCPTiqlCazVIxa5ghjVBbwa3VicTCOKEWgUSUmglmuh/dMyAaRo2BEwrTbNSolqs4dltM3LW6IJaAVpvMSJzJ68W7YriPiNYmYNu2n6YCfGPlQCAAiOhKrVbzHz8sSA0k13VxHMf3qAuFQjiO4y+E9Xp9heBnKBSi2WzeddpzowL27YRlWeTzefL5vE9AZF3aZmCaJkePHsWyLP9YSV2xnQpZZL6Zero7Wd1MT08TCARIp9O+f6Wmab6yvUx/Q9c9oFar0W63aTabvvirpmmEQiHfeUBV1XVJu6wTk8X5i4tbaYr+5OBBtwHoCGHUUUTdS9WDersjuO6B2mjRLBZww1FsxcJZyqNWTCKmuIbdbWzeQOxjFyJAIM/2ptamTueXa4NlgGa3MQLQ9kAPCdNotw1aR7fAcJct4BnWLQKrrJOvtBHH6FngSBD+j+b2CcneWu9FD+wGlJegtijSySAidZshWTpwxIDDB0GpiHNZBxZzcHoH3ze3ETcj8jPK87Y6KPkM8IFze3RR+of+wwx85kcNBkbhaqSBkioQ0Eyamo5uqoR0nabTpmWoonTCtnFqTaqeQ7NeJxkIYdcaxBSXaCiB2/Zw3Cao7bXnj9RTUcEuThMZgFqRrhHpJvGIaG0RlmXx0Ucfsbi4yMDAAK1Wy++CGxoaeqg1WtKuRapyt9ttwuEwPT09xGIxqtUquq6TTqepVqt+qvNui8l1Xefll1/2NbEeRITJMAwmJiYAEe1RFGXTtjOaprF//35+7dd+jXg8ztWrV5menub999/f0URL1lJtx9y6ceMGAwMDNBoNyuUyiURiham01MpqtVq+TVK73SYWi+F5HuVy2Te4lqblsot1LchIquu6fhH9JzV9uJNgILJwspbHQxCABLBYh4X3q7RnP6Q5lsSym6jlOjFdRxkZxCZyT/ZAFe4hDechFjsVPAtsC7Q4RNI9tBNtvFINQ3MJ90C7QpcRSqfjNQYuza5La7z888DLJ4Tg6Z6LcLPzhj66/pHbCkWYHc/fgJK5LOq3AcsKIEhIVYXRPRF+7jc/j55KUjz3PuPTRXLnJ+HqTqzQ8nsY/GgndGu0lhP6MeBD1k695oEnEOfw9Wvw8n4w6w3qigY9QYLpGCEtgGaaGBkTvWHhNltoYY2apmNXNDKhCD21BgvNIhXNpunYKEZHSlfWZxmIg738zqAJmMInsSY7YLeIR0RrDcji9kgkwqlTp5idneXmzZu+VpDjOCwuLt52d16v1+nv738YQwbwW+6XmwBLNW9ZSyYjEXDvReQ/+7M/y5EjR6jX60xPT3P58uXt+Bh3RD6fp16v+4bbcLttzZ3gui43btzAdV3a7TbZbHZHpyBl+k56H24FmqZhGIbfnSnRbrep1Wq+3MNyvS1ZFC8Jl5wjS0tLWJbF3Nwc7Xbb72SUZGs9yFquarXqb/MR7j8MRJ3LgQgMHoCbi9CcFWsIgOXA3KwFs90YTpU2drFAatjhboMjUihzNZRlr98RNmJhC4rIFh5EDQPTslBth2Y4ghtK4rZbGF6OlrqMoazauBR6lc2Iq/FfAr/wDAyfgFoVfnMSvlQS7z0JXGBrKcQA8ArwjTu8x/OEqn2gBPl6V6+qskbIUwqGFhFEcanzWZS2za0rN3Dw0JoVbi16GO2doSm4FgIIsi+L4uVzGiJKtYC4CZhjZbPEIeBxBDn7PjBBl5xlShCyoRZoQtsi0DJoGQ46LiEVbNXFNjWaLQtVAVeHbG6KSqnC3GJF1PfJNKFUsgVxd6LTJVouov0xCkumK9Vst4xHRGsNyDt22ZouTZrXQjQapb+/n1arRSKReGgimjKKVa/X0XUdx3H8+p5cLuenE+WiJ1NC60F2mK2HF198kWeeeYZUKuVHW7LZLLncdpWU3g4ZUZmdnfXTooAvW7CZ/6/VahQKBcrlMvV6nVqttqXU44OGPKeyQH0rcByH/fv3UyqVWFpa8qNO+Xye6elpstmsH2Xq7e31O1abzaZPpmRjxfj4uC9tAvjRr0wmQzAY9OsCPc9jYWFlAqbdbn+iJTh2ImShsapDMRViuhKnzQLhZe+R6gmhiMnedBDdKhMNNymWxcJ+N2RrOY1eTq42M3PNMNhtcCusECgtKxplpwG2C16TsFqhWeuIfd4hJxuIgFWH+ho7/03gxcMw/E/ATO8hqAR4InCN3Jc8/sqFb616/2bqp07eeTiAOCalIlTqdHwXxXO96u3vSyDEQ1UEyUgBuNAsu1TzOdxClla1gGLpBIx7M7W/XwghTqPsaZB8Rs6JOQS3kQEliVMIYpkFPjMMQ0vwdaszp4H2AngzYM3YlJsLLBoL9Keh4kC5V6fWtlGbnQGk+7E0jdqNOQp1T+R1PUQhXQQRvtQRYl5Buu2PMkiv0rU1cFm/AeMOeES01oHrur4NzsmTJ5mfn/cXi0AgQCKRwLZtkskkPT09vkbV+Pg4AwMD6ypk3y/IsTUajRWL2vLasq3gTiTr+PHjjI6OkkgkiMfj6LrOyZMnsW2br371q/e1DkdKFKTTaer1OoqiMDAwcJtQrKZpHDp0iIWFBWq1mp8e279/P7t27aJarbK4uEhfX98DicTdCzYiKWNjYywsLPjnWnYD/vRP/zSZTIa//du/XZHak52DgE82JYHaSMdMQkZOl0cWV0NRFIaHh2m32+i6fsf3PsL2QgqGKhaMVh1GjzlcuCkW9jAwpsGAKWQSotEWiViL0ECAUFhn5lqB4TDcqItUzt1iq+nHlmxczQGTy19ZefO6WQJYWqdc8fPA80nYvxdiqTBGsgf0AGOf2UfVnebGX1pMWSvHv5n6qdMbvK4gyJimiyjVrjgUyhBWYHAQwaYQTXCHgYwKp3phahEGXUhqHbPkxwySo4exe3opzM8xTC/NS1e4s2zuw4ECzNDVF5WrSpauSXYQ+MIwvFaA652Tu7fzf7/6WTiWgN/7Phyw4H0E3+kHDgThigrtuohYzeUQJ+qj1Wdrnco7WWd1p3bgPkRoWH6hOtHWrepoPCJa60DXdQzD4MKFC37qJhqNUq1WiUQijI2N+VIKUtRResU1m03f0uSThnQ6TTKZJBqN+j6PwWAQ13V54YUXqNfr/Omf/qlfqL7dkPVZU1PdoP5yIiIX9/3795PNZn0l/L6+PhRFoVwuo2ka8/PzBINBP536cca1a9dWPJZ6bq+++uqa7zcMg2effdZPo7bbbRRFIZFI0Gg0/IhorVYjm836jRRSn61UKvk3EdKaR1VV2u026XSagYEBPM/z04WmaaLrOlevXr3vx+IRBFw6YtdNqL3fInhliRSi+NoAhoJwdC+EYxrhVBRCUTTVpO3aMAxL1QobiJV/bDEC6H2gdvxx7OIiajSK7gR47KdO8vna28x/zSXhicX+Qud/Jtff5KYRAr5ehnkgWhZRnBSQbYkGgqOd9wyF4bFRWMxCShdp1F19UNJhuqIQ9lQuzy8RCylogRDBqLqmIO4duzwfAFTgGnCZlZ23y8e0AHxlZiWBziE6Zv/374n5WkNIq0koJjz/NOwJwbvTMG2KOR+Lh6g1LJygSqBho5agMSOMph0bGOk4PS1Ce4muvocOuid4mtoPyaE4jtem3GxgBsEOGAS9BLVbd1fLqzxsgU0ARVEe/iBWIRQKcfjwYQzD4Pr16xiGQSAQYHJy5dfNMAyCwSC1Wm3D9E4oFOKJJ57gxIkTVKtVzpw5s+2Lj2EY6Lrud9Utx3qeh5tBMBj0C5uffvpp9u3bx969e4nFYvT29vrq4KVSiS9/+cuYpsmVK1eYm9tie8YWoaoqJ06cIJfLMTo66kepDMMgFAoxMzODZVkcP36ceDxOPB6nUqkQCAQoFos0m00ikQjf/OY3MQxjze5JKWtgmqavkL762MrU8nrnPxQK+bIUUvxTGjnD9htjx2IxBgcHqdVqLCwsrHveh4aGSCQSaJpGMBj0Owyl4rtMswK+fMnymq9gMLhC0sQ0TUZGRshkMj4BlnVctVqNYrG4PPr4rud5p7b1gz8k7LRrWAx4yYBj/TDmQXEGUOGyDn/UWe3CCDmDXkSaSmo1ysBSnttv3A/q8EtH4MQJhVJN4e/fd/nuuCAOq5FCdJVVEcoLoc721i2u0Do/sjipQHdlDiBW3FDneXvZ87J4+Q5fIRNIxSFbgX/mwSt7YOAQZA6AljIJD+zGc5uEUgEWSkH++v+6hJOEixfhP25jjfljCNKxYmwK/NshQapG9kFhEXqHhQmyEYHpaXBqcOAUmEkVJZ6kWnUJBSxqiw1qLYVIPMibX29gBGGy0k3DRem6FPWpYOlwpSVIjIUoN4p33ifNo2t0zbYdxOHtAfo0cVrqTkcJQemYNncISg5B9HJ0LZfWi7GNIuaXRrdQXgrcyvTgLwJHM1Cswd83RHR1re09swcm90JbA8OMYDkWNdvGrYPuQnse2lI5VxpuWvhzRk2BG6Sbz0yCvg96MruwGhUMt4WrpDDbKuXmHM28g/um2LfneZsqdH4U0VoHjUaDxcVFXnnlFX+xWSudtp6R82ooisKnPvUpfv3Xf51Dhw6hKAo/93M/x4cffsjrr7/Ou+++uy2pFTkW2YovF3DpDbhVyIhEOBz2i6Ydx+HatWu8+uqr9Pf3c+rUKT7/+c/7XY8vvfQS7XabH/7wh/f8eTY7xsnJSZ8EX7p06bb3XLx48a63L21+JHFY6zhudMOyPOrmed59r+WrVCpUKhtLTkpiDlAqlfz6RCl9IS2PFEXxC+UNw8AwDF8CQgqqtlotX8pB1s2ZpunLQMi51N/ff1sd1yOsRJR7E6T0gMU2vOXoHH4pTPivytQcsFvd8qc6ImuyiCBbTUR0Qc5MqdwtOc1u4POfgl/8jQNkjj2Nis6Pz97kv3r/Q978Tp6vXoCLyy6FyyWJgoj17Y6rkvTrkZ1dgwgGZ3V+4twuoS5fuwOC/RA0wDBhwIbzdXgBmPkQvvJdCKdbHHn+Bq/8ygkcr4ntWpz4mRFqjssbb4kbr10IslFlfXudzWC9WW8F4A/bUPhIPDbz4rgvv9IY3wQPF3uVfr2Oh0NDRIlWLUUm3UhS0IV2S/ydZWPRUxPBSxTEafjIEVxX1o43va5wv9nZXhlxOjZaaeR8qyIaRtuI+WUh5qULfA342iYCSHubokO1pEOhUQMPDAeqGrRUUPsQk9EDAh15Ew28tBCOdaXT+bKB23UoV6uY2LTMCJalUdHrtOMZqGfhpAfvbjw2iUdEax1Eo1Eef/xxvwNxde3TVrF7926efvppRkZG6OvrA/AtfHbv3k0ikeD73//+tpGt1eTvburFTNNkYGDAj+BIkVbDMLhx4wbVapVyucwv//Ivs3fvXmZmZrBtm4mJCUqlEl/84hf5wz/8w/sqeeF53n2PmknI4nDDMHz5g3A4TLPZpNFoUKvVfD0q0zT94nspkSC1y2zb9jXX5HnaCgkOBALYtr0tqVmZiu3t7SWZTPpjrdfrDAwM0Gw26evr80V5K5UKlmVRKBR8SQgpJQJdJfhkMunb7kiZCMdxiMfjaxbMP0IXCmIxuxd4QCgCnzrqYihtbEN0GrqI9NQHCN6yGxFFWEQscMvp/+q6313D8MSne+kdPUx0aA+qYxA1NeI9UXr3ThL96k3+wz80+LBz6VluO5hFLDabnuWLiMXRXfXcFhHqgZ5B0FTQHai4UJqAWgSufiQ+7ztL8NlDA0QOP0vzxjnM+jiVjxZYrMK//FX4538mGs+2QylwLYsfz4OZrDg+g3Sb3pZ7LaiIqGOs87iO4AUaIgqpIdLCUbrORSpdX25pHdkHxExBOuttUAMQD4HThLoFrc4E0BQRUdM9KFtiXzbdUiULEYlzAMfrWulUELXmy1uiFET01KbLiYc6ny9GV7h0hrtrwPiHBVAW4HAGzGFx7BZc8FoQ2Q3JRfB6oKhC2oRmCaplaBYRTFKKmS12BtiZuEGzTguTdkYnEArhEMSjit3bC0p2S75Jj4jWOpAF71evXqVWq9FoNHxxzM1EsFYjHo+TyWRQFMVXbNd1nVQqheM4HDlyhMuXL99Wb/Mw0Wq1/CiR9EkEePNNETf9nd/5HU6fPu0rzauqSjAYpKenh5dffpnFxUUSiQTF4lrN1dsDz/NuS+feL7Tb7XvS21pOpu6lUUKm4jYiWoqibJrkLi4uoigK+/fvx7ZtCoUCgUDAr8mS/obL9bAURVkzRW2aJsVi0S/Kl8/J2rGdZmS+07AdhsAO4JoKe00d+3SDYlOsH03EuhJCrCmy2F3jzouBCQxGIZM28FQdr9XG09uoAZtQJkPa8fjUkSx7rzX4sMNIYnRThyBIgMXGKvorPsQ9opGHGdkP1AsExKL/P14SBef//reDvPv9JjHbxm00UHUdoklifVme/KmjKFM3+HmlwevevanF3wlt4KvVLhGpsTLyJ4l3EHGOGojzIVNusja73HnN7TxfYyVPlYQn3gKjJbapWBArizlX6IzFQIjEak2xbamLFkCcQ0niFE/Mpwobz9cIQsphFjEPZZa4gOisvInY1lrcZaMrmGzf6VmCA23YOwZvJOB6A5wwuL1QKkO9KMilfwcRo9v6KcOH8oCGoVhuoYZb0NRRVQjqSXK4omDOlq2Im8MjorUOyuUyZ86cIR6PMzg4yL59+8jlcszOzt6VhIFUNC+VSsTjcSKRCIqi4Lou4XCYkZERenp6Nt7QQ4LsMlsOVVWJRCJMTEzwve99jyeffNIXSE0mk9RqNX7sx36Ms2fPfqL9CB8kNE1j7969JJNJ6vW6bzodDAZJJsWtWaPRIBaL+Yr/lmWhaZqvn2XbNpZl0Ww2/ZSgjFjF43GazaZvJK1pGoFAgFgsRrvdplKp4DiOH82zLOu2FOXS0hK2bfudhrquk8lkcF2XYrF4152w/1ggDajvNbbdKHhMfqdFOwzWCISSYDSgvAhWceUyIbN26yEJZOrgLizB0nWURAw1lkBRHPDahCMm/XtDxNMKTItDJJ5CAAAgAElEQVSlcXVp0/273doAktd3At8yauIBjhojGWhSv7zE7N/8P6ReegEzGqUvGSLZE8QpmDz+YoPp92DqPk7bO92+SbHZzUTU7kRiO4L6rO8dcWcsJ81bgYeIaEoB2E8Bx3bDT0egUQWvCa4BsRDEkuL9jSaEo6CHodwCywJTBceCRh1sWzzXaojvS0AFpQ1qBAY98f+pFiwUdLLBMK1wR0a/hWCGdbrMsYZgqdLGyegMNgluHagXKesQj0MIhXq1CWV3SzIPj4jWOmi329y6dWtbtqVpGpFIxFfFzuVy1Go1YrGYv8hZlnVXkbKHiT/4gz8gnU7T19fH3r17WVpaIpVKMTg4iKqqDA4OcvjwYfr6+viTP/mThz3cTwQcx1mzBm278OGHH24qjVmv12+z3enp6aFWq/k3FcsxPT3tF86vfu0RVkIuTPcCC7gEzNnQLsN8mXtqm4sBvUFQIhrVFrQXi+g1h3A8gGIoeE6ZdquN3e7GH3auDLDANeAP/pdF9sRhbxriR3XM7HUSid3og70oOkT2DDH6eIVn9h7h+1+6+zrPR+jiHHBujfBgBjH3ZUG8jLLKSF4FkeqOIghlq/N3mG5EzaxAclYQwjZgYVOlvLY8x+pUdBRRiFdjpYdSEqHnlpzvDmaLhXqPiNYDgCwgzmazXL16lVu3blEoFHjuued4/PHHmZ+f59KlSw/ML3C7UC6XKZfLjI+Pk8/n+cVf/EVfRsEwDOr1Ort37+b48eOPiNbHBPfS/XgnArVWRPQR1ofOxuKXKqKzL45Iay1fTGSh871GxWTd0H5ANWBmocHlv7jIxM2LZEtw8sUeDjyZYWl6iblzeaY2J8O2I6AD3wQRzSjDr/55i3/6T+sMDEyQsAuktTjFlkpy31M82fcyPCJa9xWro3ouXY9EiftaWFNdZwfFZa/fJT5RRGsrNSkPEul0mrGxMQYHB6lUKui6juu6vPnmm/zgBz/wlds/zvYkMzMzfO1rX+Oxxx7j6NGjxGIxGo0GIyMj91Ut/h877jTnd+r34RHWhgIoHeGjABsXBruIa38SIfB4J93Fu0UQGAAOJGFsNMTggE69UEHTIeDCzR/kufR6nrm8iETcuA9juB+I0k2VSq2p1hz86D/kOXIcRp+ASGKKbHsI89Dz1HLbW1eYYG3NSxXYx/05l1vBg9TfknVZa93iaQq43sPVAtsOfGyIlqIoK2xw5GO5mITDYV566SXeeOON+1p8vVWk02mGhoZotVqk02lefPFFbt26RTabJRqNksvlOHPmzMe2C0sKszabTcbHxykWi5imcN10XZfPfvaz2y7zkEgkKJVuv0wpisLQ0JCvbr6ebdKDJB8b6WvdK44fP06pVGJmZua21PNLL73EuXPnKBQK6/z3IzxIKIC6rNpXUVQUVUEBhl2PARWe+fHdvP+9Ca453RTKndACxhFplvuBKuBFYCABTqtFpi/BwHCM4ZtZSvM2PRHIL8Lbb4FSgfBOzxd2UEVINtSB5xRYUuGsC0sNuPkOvBSFRbPNombz9C98honvrjbl2RjS0Lu67HEIUfT9aRVeW+NYxYHPxGCu0onkrMN4vHX+3ixCiDRci5VZMhCWOKOICNMEXfJ/r/tcCzrwGwcgW4PXs1BaVSj4r56Cf7gM75Tvv+79/SSXO55o6bqOqqqMjY1x/PhxQNRPPfnkk4yNjaEoCu+99x6nT5+m1WqRTCZ3FNGS9Viu6zI4OMixY8c4ceIE8/PzOI7D4OAgyWSSs2fPcv78+Y9dndbnPvc5jh49yuXLl2m321iWRSgUYmJigmeeeYZcLsd77723rftci2SBIDMzMzOoqsrQ0BA9PT1+l5skOlL3SXbROY7jm24vh6IovuL58u1LmYLNYPfu3ezdu5eFhQU/qre8KH35mDzPQ9f1Fdt3XXeFCO7y8Uitq/Pnz6+7/9dff33FZ5HblY/l3xJyf/KzPsK9QwU0XcPQFF7YrfL4E3swsfAaCseffIbop06i4nH97Xf58Ad/R6upk4iDWuiqG+iI6Iva+W0i0oQpRPRDQ8gG5FjZVr9dmK7BezXQXYddg/OMnBrm4MmnyM3Mglchudshkyxy9l346CE3TUsis1pOYC1MI0ygf+JZ2HUsxeSFIu2WxyUDbqV3MT6jcPz5ZygtTXH1nQtbHstqnSoPQewU1iZZILJUf1oBQ4FXelTMXuEjG2gAroKtQVv1qDvQKHs4VY8skHM8Kt7t3YqKCpqmYqDgeRDFw3U9sq7HelLZch7pwKeGFY6PqCzMu5RzHraq0DYVGo5CvQaKI64T7Q5LCevQ8KDgeNiA53p4Hnidz6tqoPjXMBfbhVevizm8lrLgvz3TsR9UQFcVXEdeM8VnE3+reHgogNvZHwjZjK1cxR5D1DXeD+xoohUKhfj93/99Hn/8cYaGhqjX67z77rvMzMxgGAYDAwNks1k++ugjcrncju3ak8bOH3zwgd8ddunSJS5fvkw4HCYSiZDL5T52JAvg29/+Nq1Wi6eeeopsNkupVCKTyfCVr3yFN954g4MHD96TWOhqSBukOyEYDPLbv/3bHDt2zCcqpmmiqqrfhVcul5mZmaFer/Od73yH9957j1Qq5Su3Dw0N8dxzz/Hiiy/6Egf5fJ6LFy/yZ3/2ZxuOc3h4mC984Qs8/fTTnDt3jrm5Ofbs2cOxY8fo6enBsiyy2Sye5xGLxfzOPdM0fU2uq1evcvHiRS5fvoyu67zyyis8/fTTuK7Ll7/8Zd58881NCZ8+++yzHD9+nFdffZV2u82uXbt46qmnCAQCDA8PU6/XcV2X999/n8XFRSqVCjdv3qTVavnHTxqrr3Y/uBuz6086JDXvBx7TFH7r3/8Ldp8cI7nrAI1qmwtnLlOYmKBlhOgZOEhpfoIbH14nl6vR09tCd7u6SEuIqEgAUa9SRpCtQUT0o4LQRtrV+dvg3qUhVsMCyprwkrv6HkSDs2iZMu9fhkuXKgyasNuES2Xfru+hIIAgoU0231n3bWD2NPzX5QLHPpOisFAga8dpDZ9i5t/9f8x/48s4z36D8+e3p+VQAR4z4dIGBXjBkMFv/etXGHvyCKojyJERMNE1h3qzTcuyaJSWaH90k9m2zjvfOsPUZZceBeyWi6dCf1rhyEsZRn7mc6hqH418iUphkncuLvK7f70xcTw8BP/Nr53i+PNPcvPs98jPjpM6OMruk58mlk6hNZrUpuZBaRBMxqgUW3iKSyCg0m6UaFWKzFyY4ea7ZSYvCI/Hk58f5tBnjuG4Cn/+x2f5xluL5Fq3i7MuRxP4/HPwwmO7eOMbC7RbLXr3wGPPp9DNMNE9h8g5LRzb5vrZ81TnLApVhYnrHpblMueJbUdVQdiaHiy/ZLme+Ll/LUY7kGhJ9WlN0/jJn/xJXnjhBV84MZ/Pk8lkGB4eZmpqiqmpKXRdJxKJ0Gw2mZqa2nEXfVVVicViOI7DO++8w8LCAqqqcuvWLer1ui/guBkV752Kubk5IpEIwWCQixcv+p8zGo3y9ttvb6sK+mYEXRVF8f0n0+k0qqr6YpvSZiaTyZBMJpmYmOCXfumX+MIXvkC5XOZb3/oWkUiEl156iWPHjqHrOtevX2dxcRHP8zYtU2FZFtPT04yOjpJKpUgmk4yNjXHo0CFfPV0KnCaTSZrNJqZp+rIY8ntQr9fp7e0lGAwSjUbRNA3TNH1pkM1gZGQERVE4cOAAhUKBxx57jJ/4iZ9g//79JBIJFEWhVCrxmc98hvn5eT+l/dprrzE+Po7neRw4cIB9+/YxPT3NzMyMn7a/ceMGN27c2HHfu4eBIGIhfYJOEbkCj7+U4vHPvkR8JEO1GaeYKxDN7GX3cJzqjSvkbl0iYJZJRVSmqg5zJQ9VEf8vhScbdMUnE4ioh4MgVFKZO4QgZJvRNNoqAkBPBOoa/PBDuLbo0TQr3JyFaQeGNEH2Tjt3Jzi5XdiESPxtcBHk8PISHIz10qq2GL9UZurtb5Dz4OdsuPwPZS5s0/T22JhkgYjYmKEWht4gNthHyzQJBhNYlTyhRpNQC9L9uykn0zTG5/ncP3ueaEilkW/y3tffIhQ3OfpTh+g79ePoaozipR/B/E3Cnk595tamxtq0ID85jrqnxd64x+74HnoeP8rgocMETJN2fonc0gKqEkGPZ7AqJYKmSjoZom4GqTsaRWeJcAnGEhAMwkiwRkKfR1GDJFwbzdm46QNgaE8aw2tyYtihkoPdY/BjP/UEfYefQE/vQvE0ysUC9WcfpzY5B+kU7bkZrn39LT6abtJWFXqPegwe6iN3c5HqhAj/1QJw5SP41q372yW744iWvHvetWsXzz//PJqmMTk5iaIoxONxVFXl+vXrzM3NcerUKa5fv87CwoKfkkmn077a9U5Af38/J06cYGFhgdnZWa5cubLi9U+CplA6nSYejzM9Pc27777L4cOHAUGKjh49yocffrjBFrYfmqZh2zY3btwgkUhQr9f53ve+x7Fjx3jhhRdoNpsUCgUsy+Lw4cOEw2EWFxc5dOgQ5XKZ999/n1wux8jICKdPn6ZUKhGLxTatQh8MBtmzZw+jo6PMz88TCoXo6+vz69ekaKeiKDiO44vYLjdrjsfj9Pf3U6/XyWQypFIp3+qmVqttOoVp2zaxmNCUHhkZ4dChQ4yMjPgp1Ewm449tbm6O/fv3c+LECeLxON///vfZu3cvAMlkksuXL1OtVonH476S/COSJcjIHkQEyqJjq5aCwz9xjHa4yfStKm1PJxXLYO7SmL0yTWGuyMH9B5i7cpXS7ASGBjXHJZ2J0SpWsBGyCgG6djjSjq2GqLHJIMiXh4jmbEGsetPo6YWjhxVqix7vF+CdBUHmTMTvcWdztis7FYeAgR6IxZM0Zpf45vs1PrcX5rNwFhgd4b6H6qQoact/7BFWbfRmm4WPLhBLxshVXC6/+p8YffIIoz/7ChXLopFXKFttDh47QSwUYym7wMCxK2hLBfJvXMCbmyI5epip197GKghfv/r42vuXqWkJPQSZ/YP07z9GY+oqRkwn1LcL3UzheApOK4/iqLiGgmOD09KxVQ272cZ0QQkGiSV1UkMKbtQj0a+QzNhozQquU8eutnA3K0rr5UglguRUl+F9cOhIlN59o6g1A8erEhrYRX8mRVVN0b41S9/BMZSnXiAaG0b5zl/Tf6APT20RSWXIvrsEeY9AClJBmFjrziTAthaF7RiiJU2LpeK1tL3J5/MUi0WSyaRv/bG8c8/zPEKhkK9aLn/vFPT09PhdeDdv3qRUKvkGxdK89+MOGS3yPI9gMLhC5ykejz/w8XieR7FYZGJiwpfSmJubY3x8nHfffdev+zt37hy6rvvRolu3bvmfw7Is5ufn8TwPTdPQNI3x8fFNRx7D4TCpVIp4PO6bedfrdT+iubCwQLVaZffu3fT09FAqlfy5oSgKrVaL/v5+nnjiCSzLIpPJEAgEKJfLvkjoZolWKpViz549TE1NsW/fPnp6erhy5Yofaezr62PPnj2+yXU4HMYwDF9Y98iRI1y4cIEf/OAHXL4sLHErlQpXr159YKr8Ox0e3U6yG8CTQEAHt9KgtVBiMQ9mj4ljtSjk6tQLJiE0lGaYhpciEHUIB4XgdDTk0kYselJeoYYgNXlE5KwXQWwcuirgJUTK8V59Elcj2htn6MkUjWsFojfLzLSEqvoBhDTXObag9r4DcR1oxmDBylFXohwgz3+6CccQ6Vsnw30lWkFENLKfbvrK9SBbquPeyrJw8zKtxVnqtwq0LsHE6RLlSD+eavLB2XF63DYNvYYRbVG9PolWKBByQWmBNVmm5L5P3BO1U7lLQldtNTxuF6yNhyGdMjBSQ2BE8ewqTrVJfXoaT9GozUxRrTikDuwh1psgX8xTKRaJB10Cqk3bapIcSqE908Kut0j0pwiHWrj5eZymTcVqr61vtQZ6Y1GG9w+ydGuS3QfCmAMZFs5dx6tdwFEMQkND9IwdJGApmPUKZiiOpsQxU8OEAz3sPfYs02dPc+X0Jebe99AVqFQgfxUm59eo5drm7pINiZaiKH8C/AyQ9Tzv8c5z/xPwm3Qlv/4Hz/O+1XntXwP/HHHe/jvP8769mYEsTy85jkM+n2dmZgbXdUmn0wwODnL+/Hmi0Si2bVMqlWg2m2SzWSqVCrZtEwwGd9zddalUwrIs9uzZQyqVolQq+elEaV3zccdrr73Ga6+9xqlTp27zg3zrrbce+HiazSZ//Md/jKIofkquXC5j2zaqqvJ7v/d7pFIp6vW6H2GS6TuZVqzX6zQaDd9aSM7PUCi0qTEUi0XOnTuHbdvkcjkURfFJlKZpNBoNUqmUX581Pz/PxMQE8XicaDTK/Pw8MzMzTE1N0W63fSPnWq2GYRhbsgK6fv06iqJQqVT8CPHly5eJRCKYpslHH33Et7/9bSKRCLZt0263KRaLqKqKaZqcPXuW9957j0qlwtLSEtnsvcppPlg8iGuYirCzUYCRzj9Ol2B+Os9uJ0Aq1UtqpJ8LP5oiGldR2zaNQpFq06Y0U8BeEn4mRgTwLJqIdKDUTkwiiFSTrg3LPKIY3kUQHg2xYK9O3+mdbd1tcUKxXGXJHWTkwD56zp4nkvVEpC0o9B0jzbuyIdwxyAH/8xkYOnODx06luNJZQi4gCubTWzAPvhtI0c3lXK7VsPnGvztNxAG3CSEdKsWOiOdCjbl/9edE+6BUh4oK8wokktBuCONodQC8CtTrLnHLQgdqLWjo0JKinBugkIer79wkXfkrrIUlNLWBGlVxNHC1KLWagta3l3ZmhEIlz8JslvFbBWYTCr1hh9LUFPnJWRbH2ygWGGYFw4BWDXQDJhY2HzTKXqoy2byGU4CAkqL00QQz9RuEIhHMgE7rg4vUv/5N+gJxaBeoOCbzJZOA6mCYAS6/9R4X35nFy3vcLMIHVfGdWYstmAjF+e3EZiJaXwL+N+DLq57/d57n/f7yJxRFOQL85wjf0iHgNUVRDnqetyXXqkgk4hcKF4tFWq0Wuq6jaRqZTIb5+Xn6+vr8VEuj0aBer9PT0+PLDewUTE9Pc+bMGV5++WV2797t12atVtXeLGSX2FZqdB4Uzp49+7CHAIjuuatX1+upEb5+9xvZbJYvf3n1V2Yl7rf0g8TFixeZnZ3169ZSqRSpVIpKpYJhGH6N4Ouvv87Ro0ep1+sYhkEgEGBpaYnJyUneeOONTXsUhsPhu57f9wlf4j5fw3RE11IFke7TgBsW/LirUM2VKNoqVjiMErBID6RpThnoQ2naYYVWs067XsRuQCoGuuagISIdswgSF+v8VBFF8PsQi4SOSB/WO+/Ldcag0k0z2tw9yQK4Ou3yrXcq/MqLcYZH+5jMLjAH3GyK8Wyl09HXCus88FYVJt8r5DG6m03OArNnV0qhFOiaO2/N3W7zWOuWyfLgS+sp/rgIO6E7VTFcvudhcW0J/vu/LCIVO4VKu4MCNCngAmny6LxHjvVJk5S5CCFuEGS0divKZB9cgOIkBAMwmciRSiZIxftYsIqYtoppO1jVKm99d4GxExD2fohphAjpJvlsk6szNf76DMw6G8+NFhDtg2qWbdN72JBoeZ73hqIoeze5vZ8HvuJ5ngWMK4pyHXgK2DCssVxcURY8z83NUa1W/Y5DwzCYmpoiEAhw8+ZNRkZGiEajRCIRqtUqg4ODpNPpTQ71weFHP/oRzWaTnp4eTNO8qyiWruuEw2FCoRDRaBTTNMnn834qaaeRrkfYGA8q+rq4uOiTyw8++OCO792qvc9aWmWe5+0osdQHcQ2THrUlBPG4iFhcSjPjtKtL9O0awK3mCBkwNb5ExIhx48YMj489Rk+sTS4aZKLcYHBIIZxJESbvL+oKgigFERpHCcSFe7jznNPZp9Z5r4uIgEmD4u3Am2dmsRoen46F6ENod02xteL3iAHxKERDGomIghOCxSUoFj3qlrP5ep07QAq9bsOmbsNhBH/ZGbP6wcPm9pT0ZmLb2+FS8EaJrsLrtTaCnq4T1Z+GzdxeRGJQlwWV4LfsKjpYQVHP5m6TW9i91Gj9t4qi/BqiXvBfep5XQHz33172nunOcxsinU6vSIdI25rr168TjUZpt9skEglisRhvvvkm2WyWqakpyuUy1WqVZDJJPB5n9+7dW/oQhmE8EFmFCxe2rsMiEQqFGBoaYs+ePRw6dIhdu3b5Ka+5uTn+5m/+5q58GaWW0iOS9gibgewGlhpesktSpkMBXxIiGAx+HIRSt/UaNqhAxRNpIEmAdE1n9tJN9kf6aLfbJFNJjFSS62cvkZ8bp3H9PE5hArVosTsOiYRKct9ugt8TREsWKLcRKaYYIjIg04HSDy6DiDvoCJJVYXPdXFvB2Q/mGO+MYzNiqhIKkIjB0C6Vg8MRjhwaZdfuXrSQjoPKzPwUf/GNG4xfbWyJIWl0OzNlNEWhqzu23ZhBdFcW2P5juxakjto/VmJ3P6BqoOvgaFCrQiINoT5oa0AANBvMJug2aFG48ZCJ1v8J/BvEHPg3wB8Av7GVDSiK8kXgiyA6xFKpFPl8nlAoRK1WI5PJcOrUKUzTZHJyEtM0qVarfpv6pz/9aeLxuK9Gnsvl/CLmzUJVVb/df6ciHA7T29vLwMAAo6OjnDx5ktHRUV/u4syZM3e1oElRT0VRmJ2d3XRh9SNsDlL+4JNEYoeGhjhy5Ajj4+OMj4/7NW+BQIBms0m9XsdxnJ2WNlwP23oNiwDBHgjmxOIPcMSETz9zADe0hHXtbRK6irEQoRSOMPpYHyee+yJ6zwgL48O0lpaILWaxlBAVS6WFqBXR6HYU9iDIVQpB5MzOazK1mO08V+k8fz+uancjipqJQLpPZ3e/xr79aQ499zi79h3GbjbJ54u8fvYa+XlrS+zIBI4APxYRUbu/rkPVwz9uLbafoCwPqtxvaIhU9CRCO+1OMBCHbitXGqmCrnb21e78rSJiQQqdLlYF2h4roqveGn/LMcv56nXGs5woLv8/+bMZIrltiu0K7DsCB47BlVswdR6qNTANiAaAmnhcc6GYBW/7VInujmh5nudnjxVF+WM63pwI0r88pLSr89xa2/gj4I8AwuGwJxclKRgZDAaZnZ3lqaeeQtd1Tp48SavVolqtomka2WyWRCJBOp3Gtm0sy2JkZGRLhEHubyfD8zzi8ThjY2McO3aMwcFBQCjOv/rqq7z99ttbXtgURaG/v59QKHRb8fonBQ/7syUSCYLB4KZ1t6DbMWtZ1o5Ju0moqkqtVuPcuXP09vYyNjaG4zjEYjF0XWdmZuZjpQW33dewgYDiGZooCpeLSMKE2bkZDj93jLo5xMhzB6nZQexyBE8PkJ2aIpYZINwzim2bNJsu/ftS1GyVcGcbMQRxUuiShzkE2Yohal4KiAt5pPPTQiy+96umaCvQAcWFgZDB46N7OXziJJnhg7iEWcjlefXVv+PNd8apNTY/34PAy8D+IKghVuQvS3QjfdvrTigQohvNup+3phrCvzLHnYlWGPg0IpW72f5fs/N/RcRE3w98H1GQ2A+8i+hq/VwIMga8XRaNF1KCwqIbOWwhjrO6bFs2oju2gEju1TrPyatxElFnGEI0epTpuh2sRej6Otu7lxsHLQRaDGZbMH0JEv2w6xSEbMjExHiWKjB56/5EEO+KaCmKMuh5nizF+wVESQLA3wJ/oSjK/4o4b2PAOxttz3XdFVGlaDRKLBYjHo+TSCR48sknuXXrFkNDQ77Ni2EYFItFZmdnWVpaotlssri4uOUarZ22oK2GNKQeGRkhFovRarW4fv063/zmN/nggw/I5/NbishJqQLZaCANrT9peNgE8m5soLZT2HW7YRiG72KQTqd9RXvHcThz5syOsr3aDLb7Gua4YNlisUsDpxSIxSCQSGFnhjn21I9x81aBgV29BCyXuuVgGCpWIUtpcp5SdoaGZbOYLZLoTdGiWzysILiEjBLk6S6UdcRCFuy8JuuTpO7Ww+5pdoHetMLYwQEG9h4iEOul1XS5deUqf/sfv8U7l6Yp5sHb5CVMAz6lCKPrpgftPPzIXWnfYtFN3d6Pq/uuzjimEZ/vfhC6FoL5dzJaWHQjTss/Vx3YqpNsi+68qAHyjqNN9zi2gXMNqDdEbZUUwpWtZsujVTKKNYe40XAQdybrHRcpvCsjtgDPI+rf5G1pFDH3pdXUvQjxmoBngZICNQrBmEgZ4sFoAAIt+NHfw8zUXe5gE9iMvMNfAi8BGUVRpoHfAV5SFOVTYqjcAv4FgOd5HyqK8lcIORAb+K3NdhyOj3dV1Hp6elAUBcuycByH8+fPs7i4SKvVIh6P02g0SKfTFAoFNE0jHA5TKBT8VMZmoSgK4XB4R0a1FEUhGo36Xoi6rlOtVrl8+TLf/e53mZiYoFqtbroTTGLv3r0oikIkEmF6evqhEM3V3nv/GPCgOgy3E/K75Hkeg4OD/s1PtVolHA6TzWaZmJjY8STrQVzDPOBiXiwcGaAv2BEYtaq0HIN3zy2yOKtSazZIJTW8Wp10uo+Zso2rh1DCw1i5SVQ7SFTXOp1dYpGV1jL9nX3t6TwOIAjXQcSiP0mXaMU6Y3qYREtXYCQEB/uCpJIKht6kUSlx9dJHvP13Z/hgIUe5AN4W7on2AS/EIOSBHoVLi9BedZ8oCefalvL3hgaC4KrAHhUM9/4VyMvUsIwgJRGaapN0o5x32q/GxlG35SXlC3RJl4xc3eo8VhCRpb2d53OI41BZtY+bG+wPxFydXvXcj7idLMvtLpf4NhBfyk0dbwWUThGjYUPfAFgZMJJQrkFvAHKLMH4dFlcPaJuxma7DX1nj6f/7Du//XeB3tzII13XRdd0399U0jWg0SjQaxXVdxsfHiUQijI+Ps2/fPhzH8Qtwr1y5wvT0tK8MHw6H77gvRVEwTRPLslYU8e4kKIpCJpOht7cXwzBotVrMzMxw/vx5Tkq9KIAAACAASURBVJ8+vWl18vW2PTQ0RLPZXGFs/CAh9aOmp6fXJVuy6PrjjOWddzudYC03mgZx/NPptC+hoqoqtm3TaDTwPI98Po+iKFvS83pYeBDXMM+FhAoHXLFAYoARg1A0g+MpTF5pEomEmPyogHkogmfbaIqNqsP1izeZn8hi6iY02gTDYRqI7egI4iCV2KUgaRwRtanT9ferIxY/SbAeRHxUKorLuhy//kaBQ0kYTYNhWNhth4XpORbfu8Fbp2eZnqtRY+vptzCiKyzdD7WGyi1cbrF29EQKt96PWq3nwmDEYWEBNK9b1yT3pQOaBtZdXsIMRJSnCLzf2W6+8wPimCcQxE9Gl1Yfg3u5eso0YRgx92SDwUW60dXl75Udr8trswy6orqrPxurXmuyMs293g3CnaJaitn5vnQGpychNgABRdRbtQ1QmmDXIOhAsyj2uXSN+8OUl2FHKMN7nsfY2BgzMzN+MbuMePT09FCr1fzUIMDRo0fJZrO+Inw8HieXy/nmv3eCoiiEQiFfEuFhWeAYhuErxLuu63dryWjT0NAQ0WiU3t5eVFXlhz/8IVevXr3nNF+xWGR0dJQnnniC69evP5R01eHDhykWi6RSKYrFIq7rrvhc8XicoaEhxsfHt9TcsByqqj7UlKhpmgQCARqNxpajjg8D6XTat/gxDIOenh5UVSWfz9Pb20u5XKZUKrGwsEChUNjxxPFBw3PhsTRcL4HaOTSaCm4betNRFqsq9bpNYaGEqhY4dmKExewigWiKeEAlHU9QyC7SNprUcmWfPFmIxaWNSKEM0ZV5kGnDElLpqFvUfL+61WSUyANCJqQCGn2aStt1KdoOQa/juxiC/iEIxyDTl0RRQrz++iTnrhZo3uXXUu3sv1ICfRT2fCbNrb/K0yquTSnuR8WgJBMHjkApD4UopBtQ6+TzqohF9UgUBoeD/PCWRdPy7rqgW5afLT/usoi8CTyNqHEq0I0+3Q1Wjy8IPK7AUQWudKJ2bdaeVwFEfZasi5PaY6sjT/Iz7Of/Z+9NgiRJzzO9x7fw8NgzItfKyqylqwqFrkZ1o7A0SBDggpHIoUZG6KKTZJJszHiRzCQzHTSmg85z0kGXoY2ZjCYZaKKGmhE5NNJaBAYgQLAbqG50o7q7OrvWzMp9iX3z8F2HP36PyKwtqzq3qorXLCwyPTw8PMLd///173u/9xNkawcRGfMYkERJ3J/p/O2/aewMZCfA90BPwMSYiqeHNKtgGdCsQrcB9hL0ajy9yuAAcSKIlpxoX3vttVhbs7a2Ft8tN5tNNE0jn89TLBZjo8VEIsGpU6cIwxDbtuPy86d9lkx1HNdkLCNW3/nOd7h27Rq2bXP37l08z4vb2bTbbVRVxbZtrl+/ztraI/W4+4KqqnH0bnp6Gl3XuX79+rFVhy0uLsZO7XNzc3FrGhC/zbVr1/i93/s9fvrTn/LjH//4uSb149adua67yy9NRowkmQZOTMQumUwyPz9PKpVC13UKhQKzs7Pcvn2bcrnMjRs3CMPwyKxQXkT4iCjOxdPgdcX/97bgtY0d1NDFqW2hKzNMZgwmi1lazTYpXUexOszNJfk88ujZPprhYelhXGnoMIiWyLOpxUDjMo5Iw9QYVJHBYEI+aMgrManAV3Lwn/1Gka9/bQrHrrByexsnCEgnoNeA9Z5CK6Hjdn3+7v1Fbq8/f/M4UxXk8mIoUkCWCnf+cQfniIewDOL3v30f7CZsqzAzB/kKXG8KAvGaAl/9Jrz+H/8e6R99yF/9eJNE+GxtijzgJ/2/VQYaPKk/s/uPZ9VnPQrSImT46ETAnQhuiN7L5ICSCl0FmoG4idCAXl8fd2fovcn++sPnro8o1AiA2zy+SCPLwxGzpyIF2hiULkKYUTANmClEXC6N8cntKu1uxOcfQRiAngW/z8AfVSxyYBWOe3BiiFa1WqVcLpNMJvF9n3K5TLfbxfM8rly5Qq1WIwxDcrkcP//5z2m1WszNzVGtVvF9P74THx8f39dnqqpKoVCg3W4feRucXC7H9PQ0lUqFpaUlNE1jY2ODUqlEOp2OieTY2Bgff/xxTEKeF+Pj41iWRbvdxjRNarUaN27cODbxtUx9Xrt2jYWFhbiAQdM0EokEa2trZLNZfud3focPP/zwue0rnka2jtJUM4oidF0nkUjEvnAnIe0mK1Cnp6djEug4Drdu3WJhYYH19fX4NxqRrMfDATabwpsnpUHgw44DvbZL1HN568005XILAo1CRuMXP/6MqFLl9DnoVhfRgzoZOyCXN8hOjeMiiAWIiWwbMbE2EZNDBTGZZRHarU5/vfuISIEUwx8GlVeACxb8XhYKyzu0My66HtFbCjCnFTIlaLci9ITFRKnELz9tsLn5xTr0Xi4Jw9C5OhRUcLfh/XWwj/iUlFGyX1RF5O73v6Lyk6WQL6eh1xT9EaMErKzB24Usv/G97/LLj/4Na8/oiaEgSF3AgAjJUdAeWifJINL0vLeWj7KGCPoPma62geUQzurwZgKKloik/qIxIGhyJO31H1lEBDaBOCerDFLMMPAJkxE6EPrDZZ7NRkMxYHoapqahpRkoUYKubfPprQq3P4LKXRFxhgHJ4hHfGQY3NweNE0G0DMPg6tWrcSVhtVoliiKCIODTTz/l8uXLBEGA53msr6+j6zrZbJa33nqLzc3NmJiUSqXYyFPiSRNuEARkMpm4V+JRTLqKojA5ORmnOzOZDHNzc+RyOTqdDq1Wixs3blCv1wmC4EBI4OXLl1EUhUajgaIoMTk9bnz44YdYlhUfM9lI/N69e/zkJz/h6tWrZDKZZyZaiqKQy+WeKNJWFCXWvx0mZIQ1CAIcx4kbQp8E6LpOJpPh9OnTBEFAp9PBcRzW1tbY3Nw89qjgi4SUCm9eVXHMEHcbajUwA+hpPrdvfM6FKxcxA4/ADbBXHjAdruCO+Zz51j+jsjFDq9FjR/uIzGQWL5UigUgVphkIj9MIAlVAlN+XEZNhCpG6aTJoiWNyOKkzEATvK2Mwb4K3Dm6qQf4clDLQbkTcbcM7n8Nqo4sddqn6X7wy78ol+Fa9r+8JYLED7wbH28zaBv6/T0LMDBhW31JDhTUHqnfgVz/8f7n4ld8mleWZzccM4A802A4eH7VKAl9DpAw7DIjYXuwnSrP3dQNxnp1DVHjWEM3S7/qiCbcMr6oMIlXyWEjNXouHz8EkAx2XrCZUGUS/yjwb0dFNyOTgzBSEPrhNF6/nUr0N9VUInzHieRgkC04I0Uomk8zMzHDnzh263S6qqpLL5djY2GBzc5NSqcSlS5fI5XIoisKbb75JtVpF13UMw6DdbtNqtQiCgLNnz8bbVRSFfD7/yIla13V6vR7ZbJZUKkUQBHHrn8OErNySxo6NRoPp6WlM02R1dZX79+9TrR6QHW0fExMTFAoFHjx4wOLiIsvLy0canZANkaXA3fM8NE2j1+th2zae56GqaqzHymaznDt3jp2dHVZWnr3mVlpXPG2do4hkDqcHNU2L04fyRuI4cfr0aXK5XKxXrNfr+9JgaZqGrg+GjiAITgRxP04kk2DNZVi+1UTpitRKKQXtRYf23Oe0ShazV76MVprD1SyKvwVbOx7dhIVqlOk1O3TrPbwoINcTv6XUqZxTIQz7PdgYmEpmEROXFC3riMiXwSAKchhHZTYJZ5PCkqHTgUYDxjzhVbS8BD9Zh1+3D3bSmpsweW3So6KGfLQMP2hC9QhlgrphoCcSqJpGGIDneuhahN3rYbehNgaRCiuhSOvm03Dq/EWaWys8R9MOPOCHgSDUErIKUY7cNvDzx7xfHn8ZEXvWYyHb5txC2Dbk+svTap8sRWKbWr8TwrDcyWLQy1A69/sMImcZBu2hvP625evbPENkToG5L0G21NfINcDfgsYK1DZAfcKGjARo/SEsBBQPXO/wNPEngmj5vs/S0hIrKytMT0/HKcDXX3+d7373u1y9ehXf99na2oonq06nw4ULF/j5z3/O9vY2zWaTqamphzRaj5rMFEXBNM04VeK6bmwWeZjQdZ1SqRSbacpqQukivrW19VQx//OgUCjEhHJra+vIU0BTU1PMzc2Rz+dptVqsrq6Sz+fjvnsrKyvkcrm4MEGS4Dt37jxpsy8cxsbG4kIMz/OOvUVNKpUiiiJ6vR5BEMQ6wadBargSiQS6rtNoNFhZWTmSG5WTitCDpcU2S4tweVIM4jPzCqeuneLif/Q9zn/tGzjeGJubVTzTIxmpeG2H05emeO+dVapbLWodmJszsdQeOmJi9YCxULjCy8qsImICKyIm9TZiojIRUYhTiEnL5GCJlopIi84WIGeA3YZuCO01sE1oJeDjOtzvHKythAKM5UsYapP2mMuf3/PYOeJijImZWUpnz5DPj9FoBWwsVxjPedz69fsA3FuByRys9XVaRQMcO+DzO7Xn8n+KEFFMeUVpiOPq87A1wl7IqOc4giTtV/M9rNUaTvG1ELopE/hGCl5XheD8PvAL+2FyJM2STiGiYTJSVUacF3vDCPKG4pmsGwAs0PPQ1kBzIO3C0kfgOQM7DJmu3AUFpmdh+iwkTLAToK8Jm4dK43BMfk8E0QqCgEpFxFZl+mhubg7XdZmZmaFSqWDbNkEQ0Gw2KRaLhGHI9vY2ruvG+q5UKsVrr70WbzeKosdWFQZBEJMd13UP3eBSURTm5uZ44403cF2XnZ0d6vU6juPE7UwOC7lcjkKhwKVLl3j33XePfEJcXV1ldXUwPGQyGdbX13etM3ycqtUqP/nJT7h9+/aR7ePT8Kx6LlVVY8sSSfYfpck6rubLyWSSSqVCPp+Pb172i0qlQjabZWxsjEuXLsXtnH7xi1+cSE+6o0AQQnk7xNEgsqDehksX07RdldLcOOVKm1ZHxQsMam2NqYJBENjsbNXoegbb5Q7LdQjWPYpfEdpJH1hHDNJFxKSbZ9DrUPpFyUbW5f5zioHdw0GiAHxnWuE7F8DqRJS3YKsH5R78h4+EyLlKvx0RB5fW0wG1NE3DmiXKBSg3F4ST5hFiY3mJjeWl/n8q5N+ksiJulBTEd13rD2GbQFiDv/vh53x8i3idZ73KVYT+rowg3EtDr8nqPJOBpYasUA0RJK02tB2LAZEZNiwdboVT6j8W+v8nFBhXRRVtKhTvfb8N7yKiUEUEmWqz+1hLjdZ6/6GyW4eVGlovRBC8BIKg7ec30hWxnp6BegVSY6Crol/hsL3GY7O1OqysiqbSY0V4/QL45zWyFwJuvgObjYPvM3kiiJamabiuSzabJQxDWq0WmUyGer1OsVjk/v37TE1N0Ww2SaVSTE5O8vHHH1Ov17FtG8dx4klsP4RJVVWy2Wwstj8KWJbF3NwcMzMzVKtVCoUCOzs7R+JgvrCwwLVr15iZmXlIw3YckETvcf0Aoyh6LpKlqiqZTAbDMOh2uwf228oG5/u1mpAWHdLc80lRShmB/SJEe9iD7mkwDIMwDJmcnCSTycQ2KbquP5Mma2lpiaWlJSqVCqVSCc/z+OpXv8p777137CnR44CmgedCKQteYNBqeSRzBarVNumpcW7dUijN5HDqPqlEnuJkmpsfPqBcD2n0XGzXZ6ML4ZLPlZZHC0GgNhDRqQuIlMt0//M8ROpwePLyEZOX9DQ6SLIDYCVh9nSSyVMa4WaXTiFiox3xM2+3ePmgqbYHXL9VIfrqZdLn51GSDzhedVYIjbtACxXIqNAMBz0WAbYj2L4lCEkTQYieVnqkII5jNgO6odPr+hiO0N+tMyDOkgTkgPn+tlWEiHy9/7rUTfX3li8jzE63++tIQYbBQM/nMoig6cAFAy6nRFHHki3IW54ByW8hzskv9z9Pksl7CB2XHC2l4W4XcSwvI87Vu/1lDnBag80QGtHTyc2kJfovZmagkIatNQgzEAyxuSfO6v0XPxcJFYIl4HRAW4UL34XaO5D2vpgb/V6cCKIVRRGrq6u89dZbsW5E+mi5rksmk0HXder1OvPz8wAx6bpy5Qrvv/8+uVyOZDK570ouWW7/LBPoF0E+n2d2dpZTp04xMzNDqVSi3W7HPlKHiXfeeYcbN25QKpUOXP/1RaDrOqZpPhRhk/YHMsW6HwIhq+f+4A/+gDNnzvCrX/2K69evUy6Xv/DEH4bhM50jURTRarX2JXo/iEhmPp+n3W7vax9TqRSWZTEzM8PGxga1Wo1ut0uv13uu83B5eZnlZdFl7a233iKbzZ54p/jDQBTCvTK89brO/OkShfwmmqKgeh18J2AsbZEwfBo7EafPpUDVaNcCAlyuXD3H1s91ZoGiEdLa3qCHoBIeYnLMIPQ63f5yB6GXsUIxOecYTHQywnDQdDdXgJk5neKZIvrpMfSZBsovW3QWo8PJtwzh3//NA25+8IDUVJFq9Xi8D3dDyLhTakgxCc2uiPDIFjSKCkoEGUvYfdg8vfdkBvitosI//f0ik+fP8usPVvngozof7zg4Q0OgPK7Sr2ovAh5OF37wmM8cjm7VGfixLQFLwy8+AU8TeEgrCokP97zuAPkUOD1oe09Pd6tZUXhROgVbD6C5CpkU1GwInoMZXV8ClsTxufo9OFWCxf23qN0XTgTRAuLqskqlwtmzZ2k2m8zMzMR6nlu3btFut7ly5QqNRoPf+I3fAKDRaDA2NoaqqvR6vX05vUdRRLfbPdLojm3bcUpU9ogbGxsjlUodSSpvY2PjCznKHwY8z3tkRFEKrc+dO8ft27f3FXXUNI1vfOMbfP/73+fNN9/kD//wD/npT3/KD37wA+7fv39iKv0OAzLtvh8YhsHs7CyFQoFPPvkkLso4CMiG068iFAVmgUbZ5/ONTS5cgG6txanZabYfrKPnvsQnnzWxGx6vj03SqPX4re/MYusWXmWVyXyae/2oWEoVEQIZqVARd/81RGrHox8FCEXUykVEt0BMlCFCx3PQpR4tG2quRurCJazsNFX1BoW7nzO26bJzyBljF1jYArZOzo0ihLRDaPcvn01ExKcBJBJCKH7pDKwviOMnnfwfh7QG3/imzm/+F/8Jp7/+u7y1ucq3//Yd/u8/XeCdtRrlzstpEuwBv3yG4dlJwfwZGE/Dp5vQ9UFp7j/1+DiEwMJ7cHrqqas+M04E0fI8D0VR8H0fx3HodDoEQRC3ykmn03Q6HTRNo1wu4zgO7XabXC7H+vo6Dx48oFAoUK/X91VJFkURnucRhuEzaVO+COr1Ovfu3YutK6rVKs1m89hMQ08yJLG6efPmvt8ThmHciqlarTI+Ps73vvc9pqen+Yu/+At+9KMfxRGfZ4lwaZoWV0yGYRin6GTK83ERKWkSe9LSaOVymXK5zJUrVw5l305Cavo44Hp9R/QuaF0I66C7HnrCwu4FTM5aOM1tEhrUt3ZwbROnUyeXt1lb3WTj3hZjebBdaPghWQaC3gJCp5NHpJ/GEZ+VQZCxsf7/LiIaIN25DxqVBnx23+br9S6l0KG+06bVCrBHQ1gMmUJ1+nnCv18YvPa0n6kSwvqKgopBs7JJZvwMr/+z/5R/PjfH5A/e4Qc/qeM5ECnQigb6Kp2BZcLe2U9VQDfEQ9VE9WrQV5yrQBDB46ZMVRWPk1JQLHs37iyKKNY334SgK66Bg2q15HShoIko8UGe1ieCaAVBgGmamKZJMplkZ2eHsbEx5ubmUFWVu3fvkk6nYzuEarXKwsJCHA0aGxvDcRyKxeK+0kySaIFw8JZaIVl2f1hYXFzkL//yL5meno6J3sir6GAQhiGfffYZH330ET/84Q95/fXXyeVydLtdrl27xurqKsvLy/i+T6PRiHtqapq265gHQUAYhjHByufz5PP5uJl5s9mMzxXf92MrhDAM4/NItnmSjcAlIRsmNsfdZLrVah3KuReGYZzyfZXgAMkUjBlg5qC+CcmJLoULLrqqsHLrNpNWnl6ng91cZ2dLY+dXdyhkujTaNhMFk8jtUkgouEpSCMARJCtERLikE3yu/zzNQEeSRkwOE4ho1mGMKjbw4aJD6t/e4PzU5wQObCsp2tHLGy0+SgQRfHgv4Ob7n7L1d7/g8tWvkMlqtHtVvvaNc9j3P2JnWRzzD1wgAkuHMU281wG2IuGAHkRgqJA2oTgFqYLozdi1odUEPQIthJYH2zvCriEMBTHTVKE5TFuQMKBaE6nxwAdvqKmjRp/8hIdjjLsXUnMYAo4H7SqowUBXdlBI9OAbBvz0AOXbJ4JoKYrCxYsX4xYf3W43jiJUKhWSySRbW1u0Wi3m5+dptVq7NFzy7rzVajE5Obmvz5STrJxU5eR5mOL4drvNP/7jPx7a9l91BEHABx98wF/91V+RSqX4/ve/z9TUFIVCgUwmE1faST8vy7JIp9OoqoppmoA4RrZtY1lWbInR6XTiwoswDMlkMvR6vVjw7nkejuNgGAamaZJIJHYZlQZBEKfFoyiKo6hhGB6b27rUVR00NjY2uHr1amzd8arAAM69BlkVVAfaDoSWhq4ZOBtbmJbP9uoyQb1B7tx32altYildKkqRiqdSfPurKO/+PbWWwqmJHH1bIJqItGCRQXpQ9kCUlWfSQ0vrr2chiNdhKE+3O/Dv3mtj0kbhaBpXvyqIgGYQ8uH1Bf7V3za5bH7GH/7RFGensuTGUpgF8BtQaQpC3wxE43I1A4YBJQNmI+i2oWlD3oKCBek0bHehFULLAS8EJQ/NHiQLMD8JPR9sH9I6pAwwTEG4lAiKAag+NCqwtQO+IkhaIhTNmTudgy+AeBT2fsZn9w8+PQ6wsQp/fBo+Xj24yPCJIFrSdFRRlDhtqKoq5XKZS5cu4TgOX/va12LtzltvvUUul+PP/uzPuHr1Kr/+9a/jSq/9CIJldVqtVosjGjJCMcKLiyiKYm+qbrfLn//5n3P27FmuXbsWk3ZpzgmCVHW7XYrFIhMTE6TT6dj8NpFIYFlW7B6/s7MTdw+QVaNjY2Mkk0m63S6pVArTNLEsKybwknxZlkUqlSIMQzqdDrqu4/v+sbVAOmwUCoXj3oUjh6lC2OwL0R0xMXkrPu7pZcwrb9Dopbn03S+hk6ajTTL/9nkWxzf5v/70U37z6gQ3ft1mVtPIZiPodqkjJpYWIjWSZpAeNBg0FV4CLiKiWg1ERVnQX/cwcfjlQ0eDpwnUjxIqUAuhVdHoATeciJv/zya/e3qLt69l6SahVhWRJbMfCN+qQrUJ86cgMQUFE+pdIcBPacInKjTAV6FahsCBQIVaE/JZmM1CJgHtHtgpsb6VglAHN4KeI9KOxQRkLfAjaLaF9YPnQbUriNehOX0+AYd1DprAeB6+ugo/PqBtngiipaoqm5ubcWpQ+vrMz8+TTqdj2wfpGq/rOuPj43z3u9/l3r17bG9vo+s61Wp1X5orGXEYpe5eLvi+z89+9rNdqeClpSU8z2N6eppsNovv+w8d82azSSKRoFwux4USmqYxPj5OKpVienqadDpNr9fDdV10XSefz2NZFoqikEwm4/ShrutxxExV1ThV2el0Yh2iJFiy6jUMw/j9LzoURWFz84BLdl4AKArU1mDHF+kNHVGWX7x0lm5pnnx6mk4wQeD7BKiYus7suMbv/u6XWL/1GWubDjo6tUqPWbNFCxGhAkGcCoiIlqw4PIMgXR8jtFsqwkDyMwau3CM8HSfpijOBiSDix7+siSilCk4I/7AS4ThNpuYhmYV6A6qBINMTQDsEuwxVHeoROCq0euBEoJSgMCUIVTYFWgt6HtgWJMdEVaQOaGkRwfJCiAyRprMCMAJB7NyW6BOYjERRRKsrtGKaBoqJOOkikUZ8UYcxlYHdxcaOuGk5KJwIoiUFxtJ6QE4+EhsbG6ytrfGd73wH3/cplUrs7OzEOpjx8XEePHhAs9nkvffee+JnqapKMpl8pV2sX1YYhrErbSerGldWVh7ZykdRlFi8vb29TRRFcaowlUrR6/Uol8txyyTP85iamiKfz8fESX6m1GHJv8MwJJ1Oo+s6uq4zMTHBxMREHFVTFCVuQ+Q4Dr7vx75uUj8mxfeSiJ10yGvrJBnNHhWCEAJDlLpnERoqPYAgMjGCBBsrLnfWNvitf/JlQs8jP2nR3nTJpF3S+g6TMzblWz0arYit94S9QwkR1bqIIF0mIk1oMPBski7xPQQZyyHSHY8a2GWq8WSVZ4wg4QK6LqJJp/s6qZ4LTR/e3QJra+Bh1QZOI86DeQ0iF2oPoKsDWej1QE1A1wOnCukcBAF4AZRmhMlnqytSgGpCeKQlItEvUDf7LZ9CKKQgqYGqQzQJpSaMF8HuaQQqRIkApwuRLd5bs8HuQeQL8X0QiuWhB0q423H+UZB9EoeNR4OnvOegkEdct6dU+Ptt+PwAt31iiNawM7hsCdJoNMjlcty8eZNPP/2UUqnElStX6Ha7sQhZtmsxTRNFURgfH3+iO3UYhjQaDSzrsIPrIxw10uk0pVIpTkVLDd/j7A9kv8NsNks2m6XVasVky7IsXNelXC6zsrJCOp1mfHwcz/OwbRvDMFBVNdb3SdJlmiaGYcTCdxmpMgyDZDJJLpeLyZjUg0liZds23W43jpq5rkun04kjab7vH1nz8+fB2NjYkfSPPImIIqi7IsqwiCBGZwJo1TrMZJN8+tEWCzd8pkoF3rhawLV36FW20UOTjNHAs2soSZVADZjLQrElqgoriLSgrCaUqcN1BPGSvQ+j/v9nEOSsAKzt2Uejv/5JMkgYYQAN8NKQmtCxVA2/5WD4YHlAXUQyh000H4jFnPNhOgmFBCz3xHmYTYJpishWcwf8AHIZmB6Hjg+eDaqBOHn6lRc6gAKmqaCrOr6lo4cRWhAQRgFKQiVhQCYTkjASeKFCu2vjtcV4FCRUWj0Nuxfi+CGqFtF1oNeBoA5GW1TntnyRlnzU9zdU8MNBRDahQG8fJqYHgRRwLgeBC+8dsKrjRBAtRVEe8jlyXZdut8v9+/epVCqxp9Zrr73GysoKQRCg9PyAmQAAIABJREFUaRqZTIZEIkGz2SSbze4rdSjTQyO8XJB6K0lqbNuOI1Se5z3RzkBWGGYyGTY2NtjZ2dn1ehRFZLNZFEWh2+2i6zrJZBLLskgmk5imGYvqU6kUyWQS13VjHZgkSe12exd5AmE4KlPkUlgvGzWn02l834+vh+EImIx+nRSMj49z69at496NY4HKwDm7TX/SdKHX6nH/bpXyTsj46Wk2Hmxw6WKK9eUapuei6TpaYRLVGKfZ2GEyDUkdxlrCvbuAuKOvICafLoNGw7K1SQORUlQQREyW+u9tKq0iIiAjnEy4iIhQRBItaaKGLolORDIBYQdagegtKQXaEYJ4lYFpFTIanMtAWIBflWGlPNAxqYqIZlmWSClGbQhNSKRFStHq67l0BTRDIZHKkUuMEdo2bruJYztojkHoRdhNh0rLxnEG1hCZgkI6bZBJJ/C9gE7Qw1UisoGwTEgWwOiA1xbC/FpPpCQdR0TOQNwIeHsqGLtHNLxpiOs3Mwkf3j34Io8TQbSCICCZTO4SB3e7XVqtFouLi6RSKebn57Ftm3a7Ta/X4/Tp07RarZiE3b9/H8dxWFvbex8nIAXII7y8GBatS8Lt+z75fD4+n/YiDEOazWZMXnq93iNJuOM4LC0txf9rmkY+n2dycjLWEMrIVCaToVAo4Hle7Niu6/oux/hOp0MURZimGUfdZBTXNE2mpqbiKG0URTERLBaL2LZNp9OhVqudKLK1t3/lqwTZK27YVNTvgVNzqN4tY6XmOXshg9+u0Wv2cDoB0+fGqCzUcDfWuDCVwrsp0iz3N8X2ZFNeKYFJM1RSj5iYHASxk6lGpb+ejyBVwyOeiyBk0/39ky7zIxw9HqUfjxDRKHe7g11UyKOhKz5RAJkcGDbsHZoCRLufxQ4UHEGiHAfW9yhjwgiqLfGQ0BJCvzU1I+wfAkAJINRCUpke+WwP3e5Bo0vQC9B1l0CBThNadWh3xHsSFnTcCKoOXuTQqAMpyMyBkQZXh44BZhaUnCCEZhfCBmyvQ+SJiFvvGIexJCLtfmtdtDI6aJwIoqWqKvl8Hsdx4knDdV0sy+IrX/kKxWKRsbExFhYW6HQ6ZDKZOEohS/ajKGJlZYXz589z9+7deNvSr8gwjHgCjqKIRCKB53mxsWQymYyjZMCBO4lLXyZZ3i/343Hu6CM8HySRMU2TXC6Hoii7zqtHIYqimOQ/qS/hMIIgiE1npTBekqxer0ez2SSZTALiHJQpbs/zcF03JnRSNK9pGolEAsMwUBQFz/NIp9Nx5aNMNcqqXBn1UlWVMAwxTTPWOqqq+hABk+sdJl5m9/2nIURos7qI1J2KIEGZVMjrV3MYEyVSEyk++8Sl1WuQyyUJ29sknS0m9B47TpVMALdr8NYELO+IbdYQOqxxhPN8BzFJgyBgHQZNpzMGTAaQ0WFdhbt7bstlA98v99+3jCBiOoPGyE+a60wdkoZYR1VBU4TuxveEBifof+ej0tS8aFAQ50iTx2vlXMBtRtSbLepJGMsKUbziieOtMTAole93EA29c75IXW/sU34cuFBZgfoOZKaFD5ySADMPDa9DpeGSS1gkQwNDC0gkFFwXOl5EO4CaL1zx/UpfSG6ICJmRgEgV54WpCSIW6OCGCu1WRNsW2rDAh14IZkJoukgKbZgegqGA2xNRONm709BF1aN3CCJDBXHDst0dXF8HiRNBtMIwxHVdcrlcPNE5jkM+n8c0TfL5PNVqlUajgeu6mKZJq9Vie3sbVVWxLItCoYCqqly6dGkX0ZKTjW3bsQA5iiIsy4q1M4qiUCgU8H2fTCYDHPykYRgG6XR6lxmmbKC9l2gdt5nlywDHcdjZ2YkrAA8Lvu/HGrBUKoVhGKRSKZrNJtvb2/i+H6cZZVRN9hXcq9/SdT2OXKmqGvfBNAxjl2brUedMKpWK06OyyfTw95ZdFkbn1OFAeltN6uD4ItXXBVIljYQVkimqVKtbtOotFK9GAY1ma4Pa5jpZunhmSHYCrAacuwzGjthGFVFNOIkgRtJLKwIKiohU9BBte8KEiA4ULPASYqLaiyaiUlFOLCZicpaeWE86OywDpjLCWwldiLU1HzotQbRARMpk2yAgbo79qucSkogI42kGlaFPQ6snHsl+DjiFOD5pBkQrQBy3kN29Co3+c98E/okIetBYgoYKiRLkLNDTCq22R3nHgx6kAoVEBupORKsporWhVK17kElCygJFB8WAzJjo99ipqURBhGJGBLqK041wmyFuCxJ1UHyxj6EOZgl8F0wPCoYwJG3aIp2pRjCZFunTeqf/2QcI2ZDd4OAaSQ/jRBAtEBVi2Ww2JlqyFL/dbrO0tMT169fjMnpN0+I+ge+88w69Xo87d+6gKMoTK56GDUll41v5v2zxI58PGr1eb9++SVJcfdLat7yIeNxvKJuKa5p2YALuXq/H+vo6yWQydor3fZ9Wq4Wu66RSqZjoDEeYHMfZ5f8mbSbkvsvqxU6n89j099OahY8I1uFCRQzQCV14aOUQDzuETj2i6e7wy3dXsUwVK1qlqhikJkuE2ZCbf/E33AcqizCmwIeLgmRtIyZVGyF8XkG02+kiBm4jEkRJB9QQ8h0xGRc9qBiPnmB9xLaHsd/666YNri0IQw8RfZGRGb3/fYv9fZSEcBtBMqq8umnKBDCFiMw8T617r3/J9/rbyCHOiyQQKtDURL8/GU2UNgUKgoDtexYJhY1DZRG0ZEQYin3XbKhUxMmm5fvblSRLAyxoB9CuDH1Yub8jYQgeaBmIzICwTHwiSKf3HoAP9rLYBy8C3wLFFR8hG2rbPrj+4URL5cx8WLmlE0O02u32LssF13XZ2tri9OnTpFIpdF0nnU7HUYB2u82ZM2fY3NykWq3GJGw4mvWs2Fv9eFx4GfyUTjpUVSWdTsdWH7Kq74tAer1JvzfZD1ESrr3nlkwh7z3eUpcln59URbtf7E0lJpPJUYTrAKEDnyCiSFInVQdaK02Kszs4uRS+dha/YKIEAa3IRGk2Kb72On9T+Rs+78HlFJgufLI6cLwePvIhD5OkvTCB6QgeHELxZ4gged09y0BMUJVH7J+FSHlOIJouv4o1qRlEUUOdQcWn1NlJ5BDRxqchQPz+soI0l4D5BFRd2HaFj5aCOB7PRRp6EG0KQq7q4GmC3KgahLaIfsUMDsSJrov3xV9IGfqCfaYXbD/8UQ+JNIZ+EFtuZwjbAYNqD0ArQNDiGZjk8eFoOio/B5rNJvfu3WNycpLXX3+d3/7t3+b8+fPxxON5Htvb23GqpVQqkcvlSCaTcepthBEeB6llMk2TycnJ2HbhoDBs/fCoSlhpVipfkya90vBU7uN+IT3B5PYsy4q3J7smyO2apsnp06fJZDKja+WA4AELiLTQrf7ft4GV210S8zOcvzbH7/yTOb78mo6nQNRoofRc6hvbhEk4b4KRAjctHL2//Jz74SCiXycFNnAPQbjGjnlfjgs9BInqMIhoDUc4pHZrv3AQhLalAAaYFswX4EJKeGqNIyKbX/TKVnUhmFc10GSITJa6IlKEqnRJkncXFmiW0O8BA1+S/cAENY8IdWlCPK8miclcYlpoyAD0HEx8GZIT7GYxstP2CcOJJVogTCRlU14pCl5ZWaHdbuN5HtevX8f3fSzLinvYTU9PjyaPEZ4KqY+TGqpcLkcmk4l7Hn5RSEsGaTq6F7JXp+/7sWZPNlbfq696HIaJlTQLVVWVVCpFJpNhbGyMdDodaxNBaAWnp6djd/v92KGM8HREiMkt0f9bRm6qqxGdRkAYKiSzNgkjZPlBE7tdxffa3Hv3pxRsiFJ5NqpCZP6VrJhbXpZRrC/p4uAFGS8GughvtfLQsuHIXsTDnmdPQwBsRLBi91PJBpQyMG9Cse/s/riR7FFXvIrQQg3D7/Ud4T1hVYLCQBhGn/TINgQ5UMZAK4Fe6i9vIZj2E4YyyxImrQAkQZ0U20mMQXJKVC4mpsSX0fR+sCwNpUvihiQ7LQih2CF2EcGThBM9yjabTTY3N1lfX2dlZSUul9d1nVarxc2bN/F9n6mpKQzDoNFoxE7aI4zwNARBwPb2dmy1oGkauVzuUDR6eyF9tSQ8z4ubTD+tClX2TsxkMnH/T9mTMQxDxsfH48bYhUKBVCqFbduxQ30ymWRxcRHf90cR4AOCgbBjsPqPHCKCUe/A1lqD8nKZ9cUdPN+lW/NRNYtG06H80RJGpFCczpDUodUWZK3FiZwvnhtb7C819qrgIBL2IbAVwK0KVJ2+PkuDcUto/VI8TG4lF6H/LF9XFOG19did9XmoX1HYFf09CYWonQYoNvhdCJ5UwqpALgeFPIxNgJkGRYOMAnQgCiA7JZpjm2mYKkAmA24bFAsK58FMwfI9wAUzx8C1V6YrTxhONNGybZtGo8Hy8jILCwt0u11s2yaXyxGGIYVCAdu24zv7er3+yFYrI4zwOARBwNbWVtxpwHXdI4/yyAiW4zj7EuYnEonYf0tGvqR9idQvOo4TtxWSlbUyPbm+vo7neYdW+PEqQkVEFhQGbXJ0oOVBq6aytrjG7RtbOC2HoNMlny2hRi2soo7tRmTNGpYGdz24WRdE6wTOF8+NMvsTgr9M5PIoEAKVAO6UYaMFtgJtTxAneQ4OQ3Im2E26gmhgHLpvSGLTg6gJUUtYPQQVRH7zMURLUUQkK5WCbk84wSsqmAaobaAFzjaEZagtgtKDREJUuGr95427ovdipyJaCJ30e8UTI4Z/FBzHoVAokM1m2dzcxHEccrkcpmnG7VZUVSWTyVCr1WKrh4MQD4/w6kCSE8uyYuPSk4xHeX0N25EMVyAuL++23xs2Y32Vfa8OGtKAMoPQ23QQk6AHjBcDMgUTZaWH0u0xkQ1JJx3MtEphTCUfgpeJuG2LaJiaAMMVhO15+1c8yhDzRcCLut/HiQCoRqDaMJmArita3Mi4uMqjSftQFvCL4RkPWBTC1tbDyytDzTjactjqwfKe4W57SFjffUFaFp/oiFY6nebUqVMkEgkUReHevXvUajV2dnZYWVlhdXWVr3/967GH0NjYGJOTk8e92yO8gAjDMDYDNQzj6W8YYYQhaIjUYRZBtsYRKcC8BafnVVJGiInH2u1PYXsZd32R+t11yg885r59BiNdogHMWVDMCeF4/gvsz4tKVg4qiveqtRpygHIEG44gWFLPZDAw/NwvhlOKIxwMTkRES9M0dF3f5SUEMDs7Szab5cGDB9y/fx/XdWOjz8XFRVRV5dSpU3GZ/Ozs7CgVMsIXwuOqBEcY4UlQgUvAp4iKsCRi8jtdhELeZeNOha3bO4TeBtmEhuY7rN/dIqlqGOfeYsLYQFdWmZwAT8/SLbeeO5o1gvj9PV5cwvk8kNWICYSzuoYgrj6D1k3O498eQ6YUXwDXhANFgsOzHzkRM0oURbzxxhuMje0uADZNk3K5TLvdJp/PMz4+zptvvomu6zSbTSYmJmi1WnzyySeAmCRTqdRxfIURXhKEYfgQ4R9hhKchBL55Fs5bom3ONmKi1y3orm/iNcpMjnU4W4o4+83LkNRwa5CfUqk3E3z+q/u0AT1SsKwcTZ7P3HIEgSG7pVcKAVCLoOENuhVIb9H9TvYBh2fceVKR4XDJ0ImIaIVhyMWLF9nZ2aFWq8XLZX+4a9eukc/nqVQqbG5u8td//dcEQcDbb79NKpXio48+IooiqtUqhULhGL/JCCOM8CrCA167APd3xKDdRjwnDOg6AWe//Sbh5Cbexhb31lR++tcfcKEBhddmsCyLzz4u04ygUYXpko3HqxdROEi8qrdKIUIf6DMoxANxLr2qzvxPQg6R5q9wuDc2J4JoAayvr8c94ySk11A+n2dxcZFer8fCwgLb29skk0mKxWLcwFfqavL5L6JsGGGEEUZ4dkTAyirc6IjJLkSI2TMh2FFIslRk6Z0beG2V5Rv36ZXB0xQy4wWU7gr4wj0dBabyEQYi/bW/pl0j7MWrGM2SCBFE0+Dlqlw9DEwBZ4GlQ/6cE0G0VFVlYWHhoWrBBw8esLi4SLFYZGVlBVVV+fjjj1EUhV6vR6vV4p133kHXdSzLOpbS/BFGGGEEHfh0CX6BIEd9WyHWtmH680W2xk+zdr9NwByffvYBE0CViE5rh/d/eEeQMoAgIolDBmE4OSJaIzwPJNka4cnwERHAwybmJ4JoJRIJdnZ2Hlq+vLzMBx98EPd9y2azmKZJtVoll8sB8NlnnzE5OUm73abRaDwUFRthhBFGOGyYGtzsCf8rECmJB8D9KhR/uoPifYQeGuRyaTIm3HdgHgVI8PldmzNpaHZgx4FGpUuSUerwZcCXEC2ZRjiZcDmaqN+JIFqP6+mmKApbW1v8wz/8A4VCgXK5HC+fnZ1lcXERRVFQFIVut4umabt8gkZ4sWEYxlNd0kcY4SRAYXeLFQPhgWUC9VW48eNFmCjR3fyYSUUI5s+ORaze6ZFBVITV+88dW+hrsv3tjtzOXkyowLd4eYlWpv/8IhdtjCG+x2H7t50IovUk2LbN2toayWQS13XpdrtxCf7777/P+Ph43HIniiJ6vVGwfYQRRjhiRML3KotI2ch2My6CgN1fhblyhVqvQhUoApoGn/1qi7k0uG4/1RNBwxGTlw6U+tuRBqgaogw9xaCSzEeQtJMAi1Haahgvs5DF4MXXwgWIa+eVSB0qisKlS5fodrusrq7Gy6MootPp0Ov1sCzrIVfrXq+Hrutx1COKojjqNcKLj1E0a4QXBYoKv3kKZpvwblM0EVaAVaDpGGzgMR7BMkK7lQbsOug+hAkRtVoBiOBMWUS8ygjisncSUBF34gnEAJ4EdvrrJRDE6/5hf+ERnooQeO+4d+IQ8TJEWmUz78MmWieCcCuKwpUrV5iamnrkaxMTE5w/f37X8lqthu/7VCoVmk1x/xhF0S57iBFGGGGEo0CkwtXz8K2MiGqBGLy7QAuFTK5EYm4GFxGdygA7HmwCN7uw5vWbUAMrPSGCt4EqIroldSSyTL+MKEnf7j+HCJJVAGYRBO044PLqeTA9CZ8f9w4cInwGfRNfNEhb8yZH0+z8RES0wjCkUqk8UhDf7Xa5ePEitr3bBUT2aVNVNU4dAvtqyjvCCCOMcJAIAyiXYbsxuEsG2ALykcd8MUvN71JDRALWgG4oiIkUzkudSJknVxuGPDpVaCIiW0mOr6zf7H/2SMAxwkmBgbj56SEIj464zuTNDDy+H+RB4UQQLc/zKJfLj6wYdF2Xra2tXWnDx4nnRxhhhMNHqVQiDMNR9HgIfgDNbSh3RGpQogK4RAT1Mr1OmwoiKvUxIn0oNVhVBukLaTr5rIO/w/FrowxG1ZIjHD+yCB1jiIgeS08xA3EzMJUCO4Sf947mpuSpqUNFUeYURfmJoiifKYpyU1GU/76/vKgoyg8VRbnTfx7rL1cURfnfFEW5qyjKx4qiXHvaZ4RhSDKZRNcfzfvK5fJjqwmHo1kjjDDCwUNVVSzLwjAMEokEpVKJfD6PouxuVZtIJHY15LYsi3Q6/djr+ihwFOMXiMG6kBJ3rsMNjccQ5Gep3mbDG5AQh4GI/W7//ZJoqYhI17M0Ah5hhBEGkG2E5HUVAjkDdEWQrYkxmM4NrjEFkXa/CJxnEIGaN+CbiOrRNxDk7Xma/O1nBPSB/zGKog8VRckCv1IU5YfAfw38hyiK/qWiKP8C+BfA/wT80/7+XgTeBv5V//mJUFUV0zSf4yuMMMIIhwmZnldVFV3Xqdfr9Hq9XZHlRCJBJpOJTYOLxSKpVArbtul0OjiOc1wVwUcyfoUIQXyK3RqliMc3N7b7j72aJvkrvYi3kLK33ggjHBVUBhEjqRnrIq6tPIObls0I0KEQwlIb2v7gGlOBogLTCei50CRFZnycC3qdsztNIl9clxYiNd8A7jzDPj6VaEVRtAFs9P9uKYqygNBb/hHwO/3V/g/g7xED1R8B/2ckRuFfKIpSUBRlpr+dx6LdbqNp2pNWGWGEEY4BQRDg+z6qqhJFEY7jxAUoErquo6oqmqZhWVbcCqvb7TI7O4vrutRqNUzTJJ/Po6oqN2/ePPR9P6rxywe6TUgq4A0xjQbi7vpRacBHicYjhIZL58UUGssGxiOMcFSQ51wBUa07fJ1JkuUBtg+WKl73bVhxxXUZIK61UDHZ1nWCEDLJLMXMOLrr0qDJ1CSkAzhnQyYJ2Rxsru9/H58ppq8oylngq8AvgamhwWcT0TYIxCC2MvS21f6yJw5UvV4PxzluhcEII4ywFzJyFYYhmUyG8fFxbNveVXiSSCTwPA/DMMhmsxiGQbVaxXVdfN9nbGyMyclJzp07x4ULF2g2m0dCtIZxmOOXAnR6YmAfhrRbyCAmgf3AQdw1yxTii4QEYiI7SfudY+DtNaqIfPkQMajGjfYsl4KjAv2blxDGUzBehNoauNHAFDg0U3QCBcvUSeczZFUPo9bADaAdQrYktF0Xzqe4+Nop/vpPH+x7H/dNtBRFyQD/FvgfoihqDuszoiiKFEV5phsZRVH+GPhj+X+z2aTdfpE9ZkcY4eWH4zjkcrld0ed0Oo1hGLF5sKIocRQsm83i+34c5Zqfn4+J2FHioMev/jbjMSwJ1F3o7tmKDpxi4DlURNg1PImIyE28iKLyk5A61Nj92wWIidZhoN1REBOzz+FXnO0H0oDWfsI6J2E/TzIe1xPGQ2ghPcR54PkwkxYVug8Qmq1TBmSNLl/KgRckaGswZpdJ9WyMPAQhGFlIl+DUmQJWIYVq7N8da19ES1EUAzFI/VkURf+uv3hLhtQVRZlBWLqAqFyeG3r7aXZXPAMQRdG/Bv51f/tRs9kkCF7EoeXlgWVZBEEwssgY4bHodDosLS3tOkc0TaNYLDI5Ocn4+DiWZbG5ucnExASmacbGwlevXuXUqVPYtv2QXcth4jDGL9g9hqUVJWr4DxOoJPA68KP+/+Psz7fH5cUkWscxcpxDkBATMZGGwK8YRK+6wMTQvkmipTNoIxP01zuO31xBhFNVhKHt3qibiiADuf7fMgLTYWAJYiD0Qy1EVPGk5YZk1d9x/L4eg2iyArRdaG1AORK6Kxe4FsJ02uFiEUqTKpbpED5oQAba41BzIanB197MMzlTomtHBM/Aep9KtBRx6/e/AwtRFP2vQy/9e+C/Av5l//mvhpb/d4qi/DlCRNp4mr4BwPdfREXCi4tMJkMul8OyhLVhs9lEkl1N00ak9xVCoVCg2WzuquC1LCsmQ8lkkrNnz6JpGul0GtM00XWdn/3sZwRBQLfbZW5ujlKpxIULF2i325RKJcrlMlEUkUgkSKVSjI+Ps7OzQxRFR+b6f1TjFwwEuMPoIby0pMBdtsx5Gl7Uq6+E+H6Np634HLAQlhh1RGTwSypMZGE2Ca4OnQZUOlCP4DKw0N8XWZAgCW7Y/19jQFRkZdphocDj2yRFiCjnJCJHvfSIdTII8qQhfoMsg/SyJI1S+F3lZBEtHbGPh/X7TgFnE7Dlipy/vHZ+v/8sNZLz0xAYkMgDKVBN+Ok/QjWERABzp2fIjBW4+KUEUxufk/oSbFTB1cCZAC1jcCGTof1gHVVNoD6D48F+IlrfBv5L4BNFUX7dX/Y/Iwaof6Moyj9HROD+8/5rfwv8IaJquQv8N/vZkVED4cOFruuEYRhPpq7r0m63Y11cr9fDdV2iKDrWcvwRjhaqqjI9PU2n09lFtDKZDBcuXMC2bRqNBpZlYZomly9f5o033iCdTjM3N8f169f56le/yre//W1c16Ver9PtdjFNkyiKSKVSJJNJOp0OCwsLdDodxsfHyWQyT9irA8WRjF9SULu3LYkN3GSQTntkaOwlgpzsD5ponQWmEWRrDUFM1iOoO7AYgq9AzxMkywS+xO5mzi0GES2Z8JG6nqPA04hPFRGJKiIidBvsrj7tIMhhtr+ehSCOYf+hI6JGTR6fQjsOqAwKOw4jpVxEnBtvluCTbWgEgoxawJeB86eANqw2YUyFhAGXz8LMN85gjOm8Ob/Nu++2+OY3ilz81m+SaHcZW/s1etMhrcOUDWFOdH4wl0O8nRpBu0tmVsOIDjB1GEXRz3m8pcv3HrF+BPy3+96DPkZ+WIeLMAx3leO7rvvYFOEomvXqIIoiNjc3CYIA0zRRVRXbtikUCszPz9Pr9djc3KTdbqMoCvV6na2tLaanp5mdneXtt99mdnYW27bxPI979+7hui6lUonZ2VnS6TStVgvf98lkMrEtxFGdY0c1fvmIyX8v0QoQk6TE8Ot5Difyc5wwefSEmmSgj3oe1BFEKYOI6DSBpQiCHg/Z0KcRv+twDXuH49W+PY3QKYhzqIcgUtLJXKLXX8dFpMEMBs2QIwbtm7rsL2IqU4303xvseS2BOFZ+/3Of5zeThMd/zvfvBzair2irCYkQLjEgdt0J8Ocz5Jod1FrE3UBcc+N1KDxoUHCSfMtyOfMlnbmMg7pzg8gJUD7YwIkgKEH+NSAPvQpEqYCwpNFzTPQAlGj/WbgTE7oYTe6Hi2chsiPn/VcHURRRr4ukhu/7sQnp1tYW7Xab8fFxXNcllUoxNTWFpmmsrq5SqVTijg5ra2tks9k4FX327Fl6vR6vv/46YRiyurrK+Pg4uq7T6XS4ceMGqdTz2P6dbJQRA3yKQVThSVfSy9imxuXRE73PF0sd1fuPNIIIuE/Yno1w5E/1123xeKYtU4fHCQWRMswgiInG4LsNN6WTYnmbgS2BhM/+onNJYIaBqa6MtA3rBgMGGsHnteuQadmA3Wa8Bw35e5Q74tyQo4oF/LwDv7zn8s10RMKBmSScKsGED8andZw7Km01ZL2hsrzcZda6y6ylYaVVcmcnobOJ/vbXiDwXTbmHdy7ESY5TdlSu31yi090/ZzkxRGuEEb4IdF0f6fy+IIZvdqRez3XdOBWYSCSYn59H13UqlQqO47CwsEAikWBubo5kMkkmk0HZJizaAAAgAElEQVTTNGZmZuL0oKZp3L59m3K5zK1bt3Bdl1OnTh3X1zwURIhIg87+BeFyknuUfifFw+XqLwIeZ6FwUFemze4I4aOg99cbnuDHEFGu4WOTZFB9eJxQEW7k6f6+KIh9KyIISwcRLZXfxeo/hhvWaYgoWKO/vUJ/mY9IS0r4iPM02d/Go5qAy3Tk8LZlxG0/30VuQz4Pn8NJvlgzaqP/XvndJEIEoW4NrRd14RQuH3ag14P5BKgJmJsGRYNaOaQWwMJqiGOAMgNmMiCfT5M1FTh9Ab9wGV9J0VrVuP/5p6zu7LD0QMWpBrjPcOegnIToxfOUVo8wwjD2Nhcf4YvDMAyiKCKbzZJKpcjn82SzWYrFIrZt8+DBA7a3t4miiEwmQzabJQgC5ubmmJubI4oiFEVhZWUFz/PQdZ1SqRSv+yd/8ie/iqLo68f9PQ8CmqJE04gBf7O/bDiytRfTiMliDZFu26vhMfqPk6S32Q9kQ+vjrFuWVXoymlJAkBAPQWTqDIhM2F/W5fj2WUWYuxURUU6Z/pNNwpsIciSrDumvMxwRVRik+1IMIl4Bj46cagwqAWXnggSCkJr9z2yzO2o23CJKpoKd/menGUTczP4+9Pqf4TGIkMl0ohTxt/uvZ/rfX++/b4eHCaDc577Be/w7PI48FIHTumj4XtIhmQMzM8lZPeRCrkrUCfn1Gqx3oKNBPgU5E9qazmulaSZeK9GNTJKKj3v7AapXIQsUZi2SaZ3/5Zc296revjpljSJaI7wUGJGs/SOdTmPb9iN/M+P/b+/NYiRZs/u+3xcRGZkZmVn72tu93TO3587c4fByAU1rRrReRFt8ofVGP9iyIVh+kGALsB8o6cF8tA2Lhg0YAkiIgGTIJgyIggmZAkUKGoztIYfLcDgL507frW9v1V17Ve6ZkRF+ON/JLyq79u5aMjv+QHdVZUZGfLFkfP8453/+p1DA8zwWFhZYW1tjeXmZdrtNHMdsbW0xOzvLZ599RhRFJEky7GW4trbG1tbWUOu1uLjIgwcPWF9fZ25ujq9+9avs7+9TLpeZmZl5qU/iuMNDJsLnmdeyE3cZmcTmkMniGS6KdZhQ+qi2Pdcd1yEdquakiiYiMPc5aBqrpEYLGUDIxmUTrgT4AGdPoYL2ALkOlJTUkPFno4ZZobnuc4eXo1KjUBJaQkiSrk9TcSlyrSqh2sKd22xkLZuuU1f2nl1Gj6lqtHRMGpFT8rFvx76N+KrcsMdhEzkfBbvulh3nNLBSgKTvtHsDYB+flAG37Gfuz0glajhdpFlP6HQH7MY+5bfv8Om6x91Ck5bfpFyARQMf1eHTOjSIqfZiVhYHPHrwAzb2+twr9fjLf/kWwcYTiqUeldkCwRm6keZEK8ehyJb355gsdLvdIcn6/Oc/z+LiIk+ePOHFixfEcTysTr19W+ykNjc3AdFzffDBB0M7h9XVVXzfZ39/n4WFBR48eDC0C9nc3KRQKAyX+cY3vjF0jV9cXOT+/ftXs/MXBH1Sz0agdKJZQiasKgfL79+2y3zIy5PiaW0gchyNt0uw1pFj/TEilNaKvU37cwmXZuzb9wdICkojjcb+u0jLhCYuRTiqZ/MQ4rKJkJ0sAT9MJXTcdVNAvNwq9m9NCcbIQ4KSNq0WXESOQ8VuW/Vh2WhUl4MtozSSlh1fdszaM1Db43h2PX3kuHtIREstL0Kc+/vAvv504Gw7vnoHbi7U+Oxhi8c7sJFCSpmbgw4LiykbSY0Pkz3ebqdU0y2+/bDDZqtId36Jt282mWOdvQa8V4j45ictZj2Yj7dY/qhB2m+yMwu1GP7w2zvUmrBQGbC63MQzr9feIccbiNy0dHKR1bLV63XefvttvvzlL3Pnzh2KxSKPHj0iSRI2NzcpFou89957VCoVjDHU63UqlQr7+/v0+30+97nPUalU+OSTT3j27BmdTofHjx+zvr7O7du3WVxcxPd9jDFsbGzw+PFjlpaWrnDvLwba7mP0WzOHm7DbCOGaRUxMnyNl/IfdrvW1ce15eB2w2Rdfrz5CUn6ITPBKngZIFDLERVU01aYaoBiJJJ2kC3tdOOyuq9fCYY+9J0U9DRJxmsVVADbtuno4kqnL6rWqKUodT83+U6+4uv1do4dKhk4zPjUQVaG9j5wHJW/buKjcczvu0f6FvcSR4qcNWLh7m7d+wnBjb4tSGLL9oxf0vZTPGrDvTfETX5zhbukpAQOet5sQrbC2dYe15Blf+/wGzFT4s087dLbnaO2l/G6rzfdaDd5bLrK43GUmhe5+k50N+Gwfbm8NzkS8c6J1xfA8uaSvW+orrwKdXExPT7OwsDBslbOzs8Pi4iLz8/OsrKywsrLCJ598Qq1W4/79+ywvLzMYDJienmZvb49Op8PXv/513nnnHWZnZ9na2uLZM+mwGkUR6+vrRFHE/Pw8jx8/ptvtDitZy+UyzWaT7e3t44Y4dlBn8mz7F3UprwNvIROVTh4fI0amNWQifM7hOM+3UPU63czfWqn3uuEjEZI+B0XX1wGNgYvQaLPurKZHCdgyMrFv4iIsi8jk30Em/zZO0zROj6ABco3p/nftP22AFeLShRFCTAu446CWEqp56+KIv1ZJBrgHAj3GRz0ceEjqL0CE+wkugqep0uxnNU2qxFgtO37Wh8VlqPs+68lNHu4tsrxYY2p+iZvLEe/M1ah/9AHvFwfMfbHB/O2I/aBIbWaW9vY2zzpz/OD32/z8/ReEyyEf7ob80UZI3atQmTKs7e0zVYXwdp8/fQqFBvixuMMDvOhAfAb2lBOtK8Z1I1g5Jh+tVot6vT4kUCp0N8YQBAFBELC0tES/36ff7/PgwQOWl5fZ3d3l4cOHdDod2u02H3/8Mdvb23S7XeI45u7du/T7fdrtNu+//z7tdpsgCOh0OkM9V5qmB4xzJwWqafkKQqI0QvI5pJy+BPwhbuLPtnw5SunhIf1/XnB82kotCrIu51khsYrDXzdBqCETcwVnQ6Bk8rIiQCdhVDM2QCr8HiHnYgO4g9grtJBIi2qSNE0V4hzZ4fAOANcVWmWYcFC3pVG9EOd91kX2WysSK3aZPs4wVasUVUMW4loC9ZBrbdTGUx9CphFyNoOQciW96q+mEbdRJIf8vpXAzD7cWE4oDzapBAG3K7PEnqEYevRLIcntGVb2tkkG23z40S61mz5b2xs8edih0f4+M+2ER5+02N1J2e01iHrwV261KbdSPqkP+PkvQ9JMiKuwNYCgCzNlj1IjYaYLZynhy6sOc+R4A+H7PsViERABvNo3LC0tDbsGeJ43TPlNTU3RarVotVpDsuR5HrVajVqtxsbGBr7vkyQJvV6P9957j+npaTY3N+n1ejSbTRYXF0mShGazSb1e58WLFxNTdVgxJlVSFAOfR9I1BeAJMqHvIsSkzMFWISAERUvTPUQU3EKiDA2cE3iD8/lRjTZa1u2U7bb9zDKqlTmJTGQnWp2AA7ue/jnGqVqki54MKhyMbP243faPOKix0+PxFq7irYScz6y1wjjD4Hy79Hx5uOtBW+dkW+goOQJXBagPEFmyn4WPaxMUId8FrWrUKsJ5+3rW6f6o6FgIlA0Ui5CkEIQF5ooBUwXD0lKA6QxoNPpUkx6DANZ2YKZm2G6nrLehm0JvALMGVqpQqcHOJjQCCGPwO/DjPwZfLMPvNqvsdG5R3HrOu9USn28/Z3ET/jsDnyXpqRTxOdHKkSMHAMYYwjAcOrcbYzDG0O/3j7TP8DyPIAgOaPqMMZRKJX7qp36K7e1tBoMB1WqVOI5ZW1sjSRIKhQJra2sTQ7QKxqSLSMXUIhId2EPSUTscdP02HIy0aHWXioRX7fLZysQaBz2KAlwq6LTpRZ0RsjdbFfEr2dIqtxhHRjQtpLqZw6Bjq+JawWSjappe6p9hvJeBKpI63EaOtY4R5LjcwFkeKMHqkOvmhtCc2CkPiI+znsheS9qvMc78rcudlhz4QMFAoSDRpmQAxlYy9GPwDfTTg8UGPlD0wA8g7skDQ8UXEnajBP/ZF+HDLZ/pepkbpQ5R26fc6BLF8N8CH6WnI1p56vCU0AhAq3V2Zxvf94miCGMMzWbzjdI/5f5W44M0TYe9L0dx1DnUCNboetrtNru7u9y9e5cf/OAHtNttNjc36XQ6GGO4e/fuax//VSJAqtp6CAFS76M68uSuT/pZ3ZSaVGZTNRpN+ggXvVpCyEuEiy4UEA2RpnG2OXyu02UrCAEMPVhLZHmNTKhJplbW1XCRCiV1i0ClDIkHjTa0E+fllI1eTdmf2lpIo1RKxHTSDHB+SiqoVi3O645qjXpqKbSx9B7ORV5/v2nHuGrHtIsULhyXfi1wMCr4RuCMU1m2IjGL0XT3eZp8D4BBCp2jTtIhF9UAaI0I7xp2w90OPN6Hf+/2gP4fNIib8CCOeZJI0/KzYOyJ1tLSEq1Wi0ajcaHb8X1/KFw/KwaDwZCgZSesVxHC+74/bMR9nYlbTrLeXDx8+JB3332XOI55/vw5aZoOxfNPnjy56uG9VgRIxGMZiYC0kDSiOmNn7/G37b86QoB+CiEsjxBR/BYy4ae4CJH2ptOSeBUF73F4L7kAicKoyHkBmDPgGfmMNjHWVE4Ll6oMcCRKiVACpF0YGCglQqg0naZ9HrX0v4erCCvan5pmrOLIVNWDqCDNoHuJfFajf9ifKtZ+lbtItqGxkt6sg/88sj+f2O2o15m2UdwHHnI4yVJypXq1aZxdQRE51i9wTu0fIJowrWpUWwkPOfbagkcJbNmuZxPRSWmln45/wEHvL3W7n7X73LDrriPHXvVziR17DXduVJjes9uZwZ2vBCeGr9sxzgNhKtvYHDk+GrnS6z+bljxrpOp1QI1TS/bvRWSfN5BjAPJd3EHsIbDv/dkz+NoXYC2Fb/XEkuVtpFH8WfqUjj3RSpLkwvyePM+jXC4PTRx1olAforOQnMOWOysJqVarhGFIo9EYbltTv1EUUS6XieOYVqtFuVweErzrkB7OMdkolUp0Ogelx41GgwcPHgCwsLDA3t4e3W6XKIqGlY2Tgh5i2dAG/gjnMaQIgfeQm/ou0gg3wUWlniOT8B4HGyCDTHpaAaZERv2PBrhIV1aArpGvW8CqgSiEgZ1N73pwswmejUptIROMl9nOlF2vTogx8uSvkYc2Lh2ok34Pp+0pIpObZ5dVC4DZCGoBxC3wYij0JAqRAJUSzBch7UG/A1EJTA/qAxchPKwt0Ukmo9nllcQt4kjJqNt4H1hHSM4LZNuHzTDT9vhWkRSjWie0EGI2jTOqTZHzvmKPSdcuNwu8Y8eiAvMCLiVcxZG9EImCFnBk3Mf5WJWRc1a34wrtZ5s4Upa1bSjZf9qfUsXsZZztRQVHwLS1kVYA1u1y2uQ7ew6yPQ6VjBYy/wKc+apCq3b1QUI/o55eu7jrv8vLKXAluGX7XmTHrcclsJ/rIVHiChKxbNhtv4UUsmhhRw940IZvfgwLPtyPndXECpJyPC3GmmhNTU2xs7NzYRGdNE3p9XoMBgPiOCZN06FAeDAYvNbtKpE7jBTNzc0NIwHtdpswDJmenmYwGLC/v0+SJHQ6HRYXFwHxSVLvIs/zrnXEK8f4o1KpHJlyrNfrLC4uDtvubG1t0Wq1mJ+fnyii5QHfwYl89Vu8DHwBSTs9wOmgasDPIRP0uv2nny3gWpKo3ksjUwGu1Qn2720OEgG1XIiw6cAU+n1oxEJqigZqiUzA28iEmU0TlnGNhQOcfmwfmZDATc6alitwMJqjnkxKglZmYHnWY3MnZb+VUvahVJGS+UIbnifwvAdfWoJqDPEGlAwYT7Q2WjWZNews4dzSFcf1iJzCpTHncX5Rb+Fa8SjJSoD3cUUL2WKCot32st3+LM5dvoq7DnZxxGHaHues3YRGlZQoTSPnRCd6jXT1kWtBI177OF8sjS4VM8diK3Meu3a90xz0xGraY6kNtyO7jEY2Q3u8VKenxqVKBnftOgJ7HO7Y9elx0CpFJbHapifCNQRftGPXFHbZjk8tIxKcP1eAM5dtZbatxE0fQjQKrOPPErImrnl2gHPbn7M/1Vct22i8DjxtweotmClDeQ66LyCqg3eGkNbYEq0gCC5E76SVVNPT09Tr9WGrEjV5HN2eMeYAOSoUCgRBcOYom1ZxAcP1qRi51WpRKBQIw3C4/sFgQJIkFIvFoXj52bNnzM7OEgQBjUZjuMxFIoqic+nWckwGSqUSvu8Tx/Ghery1tTVAyFihUODu3bsUCoXDVjXW6CJ9C3Xvi8AXkYnkz5BJJxvv08l7DYlkbSKTyDu4CSnApZS0KbKmpAZItEUntGwfumkDt0uwOA37dWjb3JumctZxzui+Xbc6pieImLjkw3ZHJk0lJBqZ0UiEjqODTJ46zqwRZojt69iAxCTMAF4JwgKkNoxStcvUE3i4BjdrUAhgtwvNgSMM9czx1WPYw/XWG+0tqWnN7ES7gJCkj+177yMpsofIBFu1Y9+x52UaRxRquDY5mtZ8hqu0/FNcmk6JlEbQVMc2SgC3EWKmlX5ZDZOaeSo02ph9XyOOGgXS/VUyoi10dNs6vjW7TBE5Zyv2+K3jCiGUSCtZ3kOuca0W1NRwlpjp9aIVmmpAqlWp2XS4+nM17Dq69r3YbkvXHyPfCY2uBbhIWsn+/RyX+tYonp5/TZmm9jM+Qmardj0rdtlNXDshvZYS4MVzeDEFNyO5LoP7NWrFKubfHuV+9zImvurQ9/0zkbEwDJmbmyOKIprNJjs7O/T7/ZciTVqhpYQvu70gCOj3+6+V5IRhOOwvZ4whiiJ836fdbrO3tzckgr7vD8vvT4soimi32+dKMeZi9zcHnuexurrKxsbGUACvDwN6XR52DQVBQBiGVKtVZmZm2NiQZ/adnZ2JqTrM3sNuIhNZiYPRp1FM4ywgargokDYGbuCE2eqWrf5D2zgdF/Zzmq674cHSFEQVaDVhZ08iWaqlauHE9FUPyj7s2jxnAvQMFHzoD6CTutRliutjpxOtTp4aRZvGNTd+jkx4Gm1Y8WDZpi8DoGpDF/Ue7Lak0qsF1D0Zbyl10YUmL3uJlZB9WgTmQ+j25O993KTasH8P7N/zOF+yEmLDESGTrBKGILPMLEK67iEEpY9MzM9wvSvngP+H69UAXCOfSuBGvdb6I+9rik7d8LOaqmwF4GgPzuz6s/YcmjKMM58xHD6ebAVi1lIizaxT09qjrYlM5nWNhLWR74oeA9WZLSGpwps16NUdGVPC17Lb/53MGEC+f+97EBQkhVgpeXx34PMHjT6dOLd3oFarUS6XKRaLPHv27FSEyxgzTLslSXLsZ7KTzOjrF3FcNeKlE14URdTrdba2to5M3RyGMAyH0bB2u43v+/T7b0ydTI5XQLlcJgxD9vbOIgUV6Hcr0wJooohWGecgftK3KUJ0NCs43Yimc5aQyWcBmRiWDcQRfKspgu0dXBpIoznqYp5inbc9GNgZqJi4BspqvqlEpIgI5Hupq4rcxRlaYpfXtF1WqK6T1yIuzaUpmQ5CRtQbDPvejIFqCpGBm8tQjqTxb2PXabHWcXohnZBHH+XuevJ+NQDTl+WfpDKWGGeOuYv4Y6k+SaM+b+Emcd1egpAnJWtVexyWcZWXfXvcHiEE6w7wbxBCN4r5zLibOLH1aa6PHGeHXi+jkUOtqA2Q78rbHnwpkbRwB0m1KmF7B/hXI5/3kSpD/c6tAn+BkPP0lPYOE0e0NNoThiG+7w9v7Ftbr24xFwQBxWLxQATrvMhaPvT7/aHuq9frHUvSjJHzqiaTvV7vxM8ctg7Vm+XRqBxnxVHXzdTUFPv7+4d84khMDNHy7D3sNN/CO0jlUlZbpdqVWeRGHiApqgAhTDse/EUsKa4mMikoEdMJvYlLfanuZ8+uV6vXygYCH7Zil4ZRHVa2obJGJfZwqSDVPgWZ7d8s2GjAAPqB6KkGA2dzoS1fVKRcs9uJkRX5oa067Dttj6YIDzNZVZTtmEsG4lRITwen1xkgpEZTUjM4PVKcWS6b3ovs+Lbsax5CYKdxpp6q50kRkvw7HE6ydNLX3oEJLiqU1QDluHwUgJ9Gel+GyPdwAblG/l3gG0hqOYu3kfO4j1wDz7Bp2zeNaM3NzbG9vU0QBKyurtLr9QgCkaCpo/V50nmj/lmnjVYdtZyaOYZhOHyyj+N4SLbOkva7qMhZjhznwTmI+8QQrdPew0rIBL2IkJFNZBJ+F7F8UBf4xQiaXeiVoB5AsyOapTpO7LyIEKsqQoj2kElciZZqXOaAioFmelArlDXnVK1VB2cHkeDsI7KeVwWgUhBrBq8PfgKtFLZSKKbOKmCQGYvaGKiGqJnZdjYKMRqNqOJShBFCnlRkrekebSGTjRIt2+PURoiRFgeU7WeUTA6QNK+KxXu4KroVZF/mRtavmrKv48TVh+GynO5znB3aHUCvPa1avIV8J+sjywe41HgbiWjC6YnW2IrhFb7vMz09TalUGlbYdbtdfN8fptOWl5cxxrC1tcX6+vqp163rO0rYrq7YcRwPJ5hisTiMNI2Wu4MI3TudzoFGu+clSznJynGdkEdHD8cirqy+i5CZF8iEvYCksW4DfgEafUu+EkkXbhmIPbh1S5bd2AKzK+vYQyaEAk6UrJVVUzixbxPYS125vkattJS/asem0RbPB1OQKFNxIK/v4qoIQ+S9RizEKrHvq4ZH045du23VnbVxEaqTrhQtydc+elrJGOOiSypeVyG4utGnCHm6b8egVZQgRGoRSXn+BY5QKeFNELL7Yzibixl7rIPM+n+fkzVZ+bfhZGgk97Kd9jUnpdeSag7LuIbbWai9herKzoqxJ1pTU1MEQTC0OQDY3NwkCAJqtRqFQmHY9Paspqa6viyhyf5+mCt2r9c7VDyfxVE2DpOMPE2Z403BFDKBe0jKYcm+ruJgrY4qI0/IdxEB+NM+fIYQk0+7UOnDdAQFD1pdsWjYqzs7BXWdV+1VFUmDZIXrG7hokKasdhBiobqVcmYs84hWqpOIIF1JmE4wOuEEiFD+KUJmKri2Q9rEWqNmDZyI/ijoZ/o4MqfHSiNJuzjiqL5PES5ilr276PHVKsPvIxGubCWf6rI2OJiifAuX8pvBid5DO4av40jWlF12gxznwVFWHBcN/Y6OEjwP1/R6FClyns8kjrAYW6JljOHmzZvD1h6jFgtRFLGzszMUrKsFwkk4a5Xi6JiyJEr1VONGqorFIrVajf39fYwxZxLaH4YgCKhUKucSUOfIMU64g9yIKwjJUgPLFhI5UY3PUGNVhMcJfNtqlDaRSWDOg1oZNhqQGtjaFS1SnLgU1y6ugnAGIRQVhBBoWqSNa+WjkSG1Y9AIlzb8VdPMNkDqhONF+3l1i9/BCee1V6Luo4riwRGfBOe/lXKwqk2h0TfVR6nBpk6EWsmoFYW6LjW5VFsBFbSvFCxJ3YcoFb8yjfaNCtGzd/t37TH5DDk/+wgBVYPTP+JgQ2kluznOh6uaGbOpwVu4gogfIdHlw1BCItHnYQdjTbTW1tYOaJqCIGB5eRmQyJLnecRxTLlcplarDdOJ29vbL61LSdF5oy5ZvZR6XnU6nbEyC/V9f1im/7qMYMvlMsaYs4qkc+QYK6hOSAkMyIQ8hUzYasaoKa8lZOL/3a7znOoA9wzcmYe+D80eFFJopzLZL0SAD5sJPGsKoVF373lc+kNJifpBaXm8Wi2oIapWHaolg1Y+eojtQ+BJerCN82Sax1UtaiWiGn3O4Iw/sw2oQw6W4Gv6VKFl+Srcz/YKVEH8qPO77mM2BVQDZmy1ZTWGuA4/TB2ZPAl37Dg27X5qNK9q9/dfI+nGLDnINVjumlLPq6yp7HVFdnxZ8p29pkahXmPfO8f2xl4Mb4wZGiCqkedgMKBQKAy1VZ7nDXsVqlt6r9djampqmObr9/uvHLkZHdd1OLZXAdWtZQ1YL/tYlMvlC2vNlOO1YWLE8IEx6W1ksp5CNEJgtVc4olJF0g/fw4nV53H+TQWg48G2B8UCbLSdEWjJZHyMrCVDrQyDGNIU2gPoZ54Tt3GTiIrQVdekol61btD1hnacEa59Th9H4tQbTHspahPsNkJoYpzQHFx1n0adsgaiCtW8aEpSI1PqVH6YFcIULlIXIBErTW1qanYel+JTT6+j7kKfQ1KGqtlSg1iDEOVvIG2TLktLZAzMzMLOYTmsQ6DO6X1cGtPHVaEe16LodaCAs8bY5fL1aSWcC3xgx1HjYJeGUej1O0qW1TttFF+22/mTzGtvRNWhTqbGGIrFInEcMz8/T61WI4oi+v0+vu+zubnJ5ubmsMpPSZBGseBi03tBEAxtHN4kHNb/7jR466236Pf7PH/+PNd1TRBqtRr1+oF6nokhWr4x6RIyYdeQp3ofIQQJEt1KkAl/DZmY7uDc1tXjacfA7ALMVWC2CGlXxOkbm7C25yaxwG5j10A/PdhqxMNFY1SQrmSnjFhC6ISofePApR1LOPNTTSsOfOgbaMUyeWnVoOq+VEgPry+acZwNQsWOT9O0FVyLG60oU1Kk4zsKfwkhKdqPsYpLk84B/4KDEbrDoJWRuv37n4N2DB9oN2id/c8AY4RAn2pZXJQvGXn9oma20fOdrbJUt/zR8Vwm1MQ3S/yzUEKl/neKo47ZVxHy/23kewxvSNWhRizUmLTf7zM7O0u5XGZ5eZl2u8329ja+7w8d3+M4HqYOL4tkZgwa3xh4nncqkhUEAeVy+cAErI78KqAPw/ClooMc44ezFqOME0Jcz7zHSBRE24t8jNMe6USsbW+2sE2AIzARTHfhZgUqEdxagL0urG9DuwBxAMs16MTwaV3WbdLDq7YaOAKSTReqTYGm2h4jkZ+7CB94goviaCVjDMQD5+0V4poXqzAdXs+Erq1bOhxv6tlCdFMqutfo4AyuQXCKiPU37WcO4zpfRo7DOi66t4Icgxngt5ELoyIAACAASURBVDja2R+wPY8gLcPA9q9pIe2LNvYh8qETwHQF9vbORjrOMj3pMVjCNS5Xfd5FYXR42X3Ta/0qoUbARx3GXfvz88j5f2H/Pmr5j5GHlN0j3j8OYx3Rynx+6DpdqVQYDAYsLS3R6/XodrtDy4c0TYnj+JVShJqGnITo1EVVAp41bTeaZtW/FxcXh+1azhsdy3GtMTERrZox6V/FtfEoAh8h/fK0H51W9T1HSM9NXCWTj5iJLgCzHrQMLC5Bsy//uqn87A+EaNUHkhrMTmZaqq5aqjmc7UHXjm2ATMRLyNN8Ayekf4xMTLcRIqji8TpON5X13RpNuWjETF3qz5pC0lSX+mMdV5E2hXhlhUiKT8ea4gxF28jxmcFpb0Zbq6jb+02cqF///r855aRasR/KPAt6nojwl27BzgsoxNJyqNGG/eSgRu11IuuODpenlXrd0czLgFYeLiDflzVO9kW7hfPQgjckdTiyDpaXlymXy6ytrdHv96nVaoRhSLfbZX9//43VTI0b9LwVi0WCIGBzczNvXD0BmJ2dZWfngPphoojWLyDkZR15+lVtU4gQr13kZn0HmZhUyJ7iIjNvV6FSho83oOHBTABJCRoD2GxmHKk5fFKzAZah55PBRZ38zOsRLgqignONZGnT5Bc4jUuA8xsK7M/Rx1U1T1WCk+1zd9Kdt4hrPaTRGNWMqSt9lrRpqx1tcKwkSe0bssanZV4WaN/BRc2+hGssXEfOyfeAD08x7qMwvyyNuSslaJSh/hzSfYj7sl0tPpgEqO/UuIUesv0WNdqpRH3UsBTkmvnJAP5lLMuWgPabkDocRblcptfrUa1WSZKEVqtFo9FgMBhcCsmKoohSqfRSVeNlQJ3mT4pQlctlut3uhUSyVAT/qmi1WkMD2na7TRAEzM/Ps7W1NTSJzVOJ44fd3fME3ccDCfBdXP80tQSo2PfXkSjREkJGyva9HvJk3QU6BtIabHehXAXfQL0NW01YH7xMNg6Dj0tJFpAndexrOrEoEfLtGFJcK5qgDFEJdnckIpY1DPXtv+wjTwVZro8TyGtV4Wlqln17TG7Zde2RsZjAVSFqpWOKmIk+s8smOIf8LMHLOs9nY+tTSJrUIGnSu8iEuYFrtfMtu/5XmTE6LajMQjOCdluc9MsLsLMDtQBqqZzX4wxv1EvsuqtUx41gKVKEYJeQSJZ2ZTiMZIFcL7VYvlOziAXIaXGtI1pHpbYKhQKzs7O88847hGHIw4cPh+SmUqkMU4ZxHF9o5ZlWMmbTiJNcbTiqldKqwosgbaOFCsYYFhYWhqnEHBOBiYloBcakSljmEfKwgESY6rj+ferGvgTUKnD/HSiF8P0n0FyXhbw5EZ03u2IK+v3O2SZ9H5k8Cgj5aeI8r8C25LGvVTzY9WAjdqJ8FWHrU/hJjzRZnZSSuZPGq/0Sq8AXkSjaGi6C9RFCTkfJ2tv2dY14zSNEaxqXNvseL1ebaSTxx+zfanA6Z/9+bMeg6d6z3tG0iEAJawc5jsbYcaRCnL+0CM/WYTs9eJyyES7VqbV4uSVRjtcLJbN6Lqocr2u7jZD1H9rlk0lNHWo14fy8PKttbGwMvbSMMVSrVdI0vXDhrWrC3kShu+KySeVh2ysWi8zOztJoNPA8j2KxiOd5vHjx4oi15LgqlEolVldX+fTTT/WliSFaeg+bRyZK1So9tO8XcW1lFhACsVKV9joPm9IvcCq1EZkytA2st4Q4qI1BBSEZ4EhQFdejENyE38E1RS4gKc2Sfa2F9eVCohHPcTqskxDhUpMeB60hPIS86XiPQxXXEDvCVTIOcNWPL5AoQsFuU13us3cAraZUQ9EGknrUR18PIVMriKarhquq1MKAdbuN7yFk7yIjSJ4lsboPBrhXhsIqDPYgTKFdgW4Azz517YO0B+PklpNcH8xz0JRWEQI/BSzehKdP4QecPnU4dkQr8xngdJWDxhiiKKJYLA5tFi7KQNP3fYIgONDAWv27xsm89DIxMzPD7u7uucT5hUKBWq1Gp9Oh1WqNrRv/m4IRsjwxRMs3Jr2JTNhPEfLQwumY9G6s1YZZg0+N4nRwRKWAIz6aBvxKUarYEl+8s562Xasf7fmn0ZRsuk8jbToO1aXo56ZxztglJJ22jKxbBeYlJIqkdgpdMp5euEiO+m4de6yQyEAFp/tSvVRsx1REokxNhBxVkerBo9Z9mNVAghDNm/anGpeqZ5mHpO4GiEfWY06OHkW8bMa6GEC4CDt7UhnaOOXUEobQ78GdMnjz0O7Afh3aGtpKnYavw8VaNeRwOO44e8C7BjZTeXjpTZpGS72ytPLsrBNpsVgcpvouUlithqlZKHkolUpUKhWSJBlqkLTJ9O7u7rX0jKrVajQajeHxzqYPX5cmS7U759n/fr9/QBOXpumw+jRN01HfphxXjEklwBp56iNRCCUzSlzUXsFDyEoNF2naQTRBDeSGvMrByibdQKkAgZEUVK/t2tlM4yoM9+z6Vfg+KpxIMz8HdpuqqQrsGBMkkqT+QwGu1c9SEWaK0vS6XJbqujSFfhue1w82Z1bSNXrG9RhoOyBwxFMbcD+1P79if3+BIzdqzrli171p92MPRywjHEF7126nZc/NPEIsdxHiu2b39zRX5ujM0QOexcBzZ8cQetA7xa1MVRiP2nYnbb43MlDxoVaCQQ8e2lvYJH5ztLDiOu3bcWN5H/ggdd+302JsiJYSEnWCj6KITqdzqpL/NE3Z3d090ZbhohsfZ8erkZcgCIZap+uEarVKs9mk0WhQKpXo9XqkaUq5XB6SyatKm55k9TAYDKjX6xM7qY8rXhcxv44YIISpyMH0zhISSWkixMVHokUDpJqwmcDTnmtP08GJcrMkKUzheVN+qieXOrgbhDREdvsrCAFQx3YPibzoNybAGanu4rRVoR3fOmIdkU1vFexnul3Y6NrXGlAzMJ26CFzTbkftJXZ4uToRhGjt4NzwVaOkpqMNe5w+5uWWKFMIIZu1n48QE8lsY+lp4AsBBLGsV20javY9JbUfAw94tXRhOvxPcBLJqpagXLQRsOzn7QlvIYSaPjQmvOZn3O4GEU73eJZrZmyIFsDNmzeHPljdbnfYT/A0SJLkRBJ1mRGlbF/EbL/Gy4Dv+/i+T6FQoNVqHUpIsho3JYKlUok0Tbl//z6fffbZK0UGp6amzpW+Pa0Rak6yrh+yROuiH2quAuqN1cKlp2YQ8vICIV33p6RNznYMO30oFmGxJxEd1YVka5YNQp56wHN7SWc1O9mqQHA3fw/Xx7DHQdI2QIiOTnJlRK/VQvRa2W+OptgCu5yHs41IkChOxcgY1lMhWRHOt2r0sXYWF3mr2PFpBCpCyCMIWfx0ZNwzuFTnKq6H4h+NLKd9CuPYWTbM2vem7bg/vwJPXsBHqezLedJy783A4w7sn9GdsxlC45hywxTYS5kc/4cJQoSLGBdPWDaLsSFapVKJ58+fH0jLqR5qHJ+SS6USxpjXmsY8rcXDYDCgWq0eSAkehXK5zNTUFLOzs2xubtLtdvnggw9I0/SVJsvzGr5O2uT8JiH7YDRp59HgnKi1X56Kl3vI5F8DHu679F4NKCRS+ffimMOheqPQbkNL/o9LiqvPleHl9iKeXUcBiTrp5HGYAHgeaSvUQMToWr3oA/NFmDaw15FUZwfpOTiFEMvRx6HAbkcr6RawhAhnc6F2E3/Oy/3mdnH6tcfAXQ8+TVwEsYLYRCzhrClaSOp2FZkY1ZTym89F/F6JYO8ct+C5UNzyB+dwHk0vRh6c4xKQ2C/jgJfTyMfh2ovhoyi6dmaVQRBQqVSI4/jIiJBChfC6zEVNMJ7nMT09TaPRODWJKZfLdDqdU0V/pqamCMOQra2tPFqU49wYeTCaGDG83sO07Y32zqvhGiDX7XtrCJmo427WWrUHh6ckjF2XGo1qr8PDyJH6Y3XsODoI2dIm0n3E1+luDYK2NK5Wobn6banXVxlJ4XVxuqouB1OPWwhRmsmMS13iG7xcKRchYvglu46nuNY3t0N4GMOPjnFPn8f1hpwpwk4PBqmsdw7rrm/XvWnHMWPHHALfB/5fDgahSh50znhr1nTr+fuM5LguCHHavSVcU/hRvINcS39s/x77XodRFA09qq5br7s4jtnbO85qzuEyntyDIBhGqM4SKVKPsSiKaLfbQw1WmqakaTrUZQH0ej1KpdJxq8uR40SMY/T5tAiQ1NQeMvnetq/1cPqnzziof8rCQ8iG6na0Sq6DqwhUUXqbgylG7Ovag1BNPjWiFuGaLfeBbh+eb8u41GcLHEmKESLRwwnFlax5CIEZdVvftZ+dR0jNDs5XTHsmap9EPR4FRG/lA7OBCMsfJUL0tO1PFmoqqu2HWl0hgjettfcUEr1K7b6v2s9plWSAaLJG6787mZxrUIL4FM/2qV2v2mnkGE/cQ4h5CVcVfBTR+hCRB5wV15JoBUHAYDCg3+8PdURXhWxvw8O8szzPI4oiCoXCmYnO60KxWKTdbp9729moVr/fZ2Zmhn6/P3RmD8OQMAzZ2dnJo1k5chyBCNc3cJGDIvMtjo5AgUSrKri+hEVc1KZq11FFIjYdpEpR4dnP1nFO422cWF5b6nSB0Egvxb2BI2RV4CsR+CXY3xfrCG2IrZ9TonQSVBCvKU7VqLVxXmJ9xBR0zv5dQiaveiy6rL3MvpcQ8qT7aJC0YWw/q+L2MHamkw1kwiwjRG8P2Z8+In7f5Bg9VnI6kqWY42Wx/lXCIPutadocJ+MTnD3Iaub3o3AeL7NrnzrMcfkIw3AofNco1+bmZk6ycrxuTFTqsIA8HWvT6C1EH9Swr+ljkDazVWg7Hq1kKiI3/Gmcc7U2dI4Q0pLVeTU4nMCpmaeamKrthPp7DezrajWhAvlC5vMtzqZFwX5+BSdA1zFgx625iSIuOrdl912duZUs1XApyUeITkyjUTeRNI9WH2rT7Bt2X3XC7ABVT9KRf8EpG0XneCOwysHolUGuqyfHfEZ7g8JrbCptjLkN/FMkQpsCv5am6f9sjPkV4D9Hrn2Av5+m6e/Yz/w94G/a8fyXaZr+7gnbeO0z+GmF4eOImZkZQCoDXyUVc5SYfXl5mXq9PjQAzQlWjgvChROty7h/AXi2Bc+ZxoZoPtpIlAaEMCwjBOgWclPvIgTlOS4lqO1ZphHh+XHQli49hEhVcAak2pMwexfwcIL5s0IrAvt2e0WcAH8ZIVI9ICrAYCBi9k9xGq0lewx8+5mBXccPOJhyrSET4lu4akhN33nA2wH0bZRrH0kXfsQxUZ4zdHnW4oTrDo2mGi4/tTmDnBdtED5J0Aclw+lb8JwmdRgD/3Wapt82xtSAPzXG/J59739K0/R/zC5sjPkS8EvAe8jDxe8bY+6naXoptujGGCqVCt1ulyiKGAwGr7Xf4VWXpUdRRBiG7O/vv7Le5bD98DyPnZ0djDFDv6rZ2Vl2d3cPJVzz8/Pcu3ePer3OZ599RqVSOVQwv7Kywvb29rXS2uV4I3Ap96/zPIqkCAHIYoBMjsVIROqzBTA94QAtHFkC5591HNTmIUQIVg1p+/MicZEqbRmkKbCsH9VZUMaJ0A0uEqW6rpYd75yRVjTPEYKpd+e9zGcHOCH+jh2bHmONzmlKsmd/n7Kfi4BO7KIOnwLbK9Coc3ie7wuw+vk5WntN9h53ZeUbHAwTTsHCCsTPYXdMcnKqIbsK7DKekcMQ6acZ4dLYo9DTf5bv/IlEK03TNWx0LU3TujHmhxyvB/tF4DfTNO0CnxpjPgJ+BviDM4wLgKWlJfb390/tlWXHOPSAel16KWMMxphTeXG9Dvi+f6S3VqvVem2aNTVLrVQqVKtVkiRhe3ubwWBAtytf0VKpdMBSI1s1FgQBU1NTNJtNwjDkxo0b9Ho9wjAcfl5Rr9eZmppic3PztYw9x/jiMi1ZLvv+NYVED2qILcKdKjxrSDXTI1zQJMS1rTkwXuQGT0vIyXzPhdxe2jeOnkg9nIGp9vdTndKz5GA6UNOS6mSvbt12GCdCBe1K1nYQAbzB8ZqCXZeP+IF9t+ec9BUJcnzUlqKLpHD0GBUR8raCnMAarnBgHlj0IE1kHdtI4cFHQGMWBh6kKmAq20GmssK5CBrNDoUooXgL4g4MRr0l9mGzCv4S11b8pNWiGrp9TG7FdRIi5Luiuqse8vAziytsOQx/CfjmGbZzJktyY8zbwE8A37Iv/R1jzHeNMb9hjNGU/E1cJBzku3IeoT7r6+tnIlkXhcsyFFUj0cFgcCnbW15e5nOf+xyzs7PcunWLW7duMT8/PzQonZubw/d99vf3hxGq7AS5urqK7/uEYTj01YrjmCiKXtpWs9k89eR6HZ3yc7w+aOr7snGR9y/NH7SRlN9t5Kb9xw0RX9eR8NjbSHosG5k6CglHk6zTQNOMBYR4PETIx1HibU3xwem1WVPIfmmfxUUDt41rN1TDCeC152Kbg62AyPxeQo7PF5HjqJEAbSw9jbNqGABvFeCnpyVVeOsGLL4FoS/M+g+A+i3Yj2BvE9KG/dC229j8bQgKMBu0KPRTSgl4HQgPK7B+JtHFU2HulMu9Rmhl6B6y/znJOhnTOLKv15V2ETguGrgSHvPmITj1jGaMqQL/HPi7aZruA/8IeWh7Hzmv//AsGzbG/C1jzJ8YY/7kLJ+bZBzWJ/F1I0ti0jQdEqN2u02j0WBmZobBYDC0jDgs1RdFEVEUUa/X6XQ6bG1t8ezZs6Guq1Ao4Psvd4LSnoYnYRJ1dTkcriKq+brvX3adw3uYEgZtYKyT3TbOMmEHeXI+7aNjiTM+CVsoOWkjkbQP7U9tyXMUNG03hxAk1Wodtx31DSvYf8UUFlMn5lfhsFYNasrvJkI6F5Goggrwt5Hj9BD4kX19BiFzd5Fig3u4htme3a/GvDDirTZ0V+CBD4MaVKpgGmD6DEVfUx58IYK7IYQbUN2F8DF0PokxGxDsSvNuM/3yPscvNaE8AqPeG5eEALluTiUcysEaQqhmkFTnPvId1TZUR6Hek+v2tDiVvYMxpoDcpP5Zmqa/BZCm6YvM+78O/Ev751PkgU5xy752AGma/hrwa/bzKUgEJY7jc7VmGVd4nke5XD5gG9Hv949Me/q+T5Ik5xaoa1qyVCqRJAlxHNPr9Wg2m1QqFUBSO+12m0ePHlEqlfB9n263izGGarU6XCZbmZgkCd1ulyRJaDabeJ534aQxx/ji3r17fPLJJ5eyrYu4f9l1DO9hgTHpbeDdEhCLRuhTJM31mPNpuI4iZCfptl8lKXuYwSg4UqN3JRW57yKk7I61i+8n4A/ASyVyptqqXYQ81YAvIKTrU5wJqrYsUq8uhW6vgE0P4sTyU0B/INuM9yTl51dgPYKkCF4d2g9gNYJSJG2PmgWYjaDYh1kfSgH0m7BfgJkWdAwUUui1oBSPl5Bb2wg9PmnBHC/hsBDAccKjJlLJelqcSLSMdD/+x8AP0zT91czrq1b/APDXEcNdgN8G/ndjzK8iDyLvIO2ojtsGpVKJarXK9vYVPQpcEcIwHPqGFQqFoVt7t9ulUCjQ6XQORJU0InVWElMoFCiVSlQqFcIwZH5+fuhRtr6+zo0bNwjDkNu3b7O9vc2TJ0/o9XrEcTxMH87OzhLHMcVikampKfr9/jCtGAQBnU5nSLRy5DgKnue9pOG7KFzG/QskUnMfWJiC9Q2pBCzxsjlpxNntErIo4SoIFQUcSTH298MmCU2/dTm+fc9hGHDQ5FMjVEWgGsiGTR9qHlQj6Pelh+PNADYTqCdCjJbsGJoIubqBHCu1ehi9KkK7/OcQkqW9Du94ML0CZV8qF188h5Ub0JyGe1+A0h48fgKlDsQ9SAtg2vCFmhychRKsTEGnD89sCWfJ9qDcNdA7Ivh+huLEU0HP5euI4V+l+P1Nw33gozOctNNEtL4K/MfA94wx37Gv/X3gPzLGvI+c34fAfwGQpukPjDH/J2JZEgN/+8SKnTSl3W7TarUm2jla4XnekFiqrikIArrdLr1eD9/3qdVqNJvNl1J3ZxH4T01NMT09zdzc3DCCNT8/T7fb5caNGwRBQJIkPHr0iJ2dnWGECuDp06fDCVF1WM+ePaPf7zM3N0ej0SCKIjqdDpVKhX6/T5Ik9Hq9oUu9Qqs/1W4jt4t4s1GtVnn69NAg0UXgwu9fIMTmMRDtQ2JTZ7s4Y9EbSEpsxf4s2p+aHpzBRSK0F2CEa96smqTR9J9KRXoIEbltl+8iZEZJmCbydzn+Sf0klJB9W0YEwwsF2Zc4BpNCewCdgYyhXIRuAoPEmbWWkLRqiCuTryLRqrrdb9/u+xQSIfyyB29HUCxDoQ/zC9BvQ+m+HLxaAp/NwWctaM5ZYX4Cz57CdAyDPvQKUAlgdxc2BpDOScTLq8BeH6oVCAOJZPViWI5gpyXj6AHFGTlwpQ70EminZyOrqsHXMMIdhHA3cCRLhew+UjihxQGXgQDXTijPQzjcQc7FKEqczaj2WhmWTk9PUy6X2d/fP1VlXaFQGFa+vXhxkpvM9YKmDCuVyrCa0fM8Zmdn6XQ67O7u0ul0XiJW9+7dA0Tn0mg0jtQzLSwsDAnQ/Pw85XKZ1dVV7t69C8De3h5pmrK+vk6r1WJrawvf9ymXy3zyySesr68PI1Wj5DcMQ9566y3iOMYYw87ODq1W69KiFMdB9yFN00uLrHmeNyTMOU7GvXv3WFlZ4Zvf/OZEGZa+A7xrYLUA+z34DnKTToEfRybNHYQI9OzrBkh9KM/B5oabeDU6BU5Qrn5a2pLHwxGTBIkwVXHtZuYQEqR2DXvI5KAu8meZxNWFXSNMWp1YKkCpbLeZiEZraQaSFtR3ZBt9xMqhgYQHf3pOIkmPGrCVOkPWbTvGwG7ri1V4y4daA5ZrYCoieF++X6VnQnbre8RmwPMXsN6HD1sywFoJfvgx/Nkm3Ell7Ps437AEuBXCv7MMaSzE+MMubHSg0XZeY+DIX4irzOxmztFpMI/o0bQKU4sePx1ZzsNWqiKRuraBrViO3fPMckWEsH92yu2fBtMG5j1YG4xXuvS8eNXI8n+6AL9XhyedMex1qHqh09oX9Pt9pqam8H2fYrGIMYZer3dtxNSaCtTxlMtlut0u+/v7JEkybDPUbreHkSHP86jX67Tb7aGtBIgmqlwuMxgMKBaLzMzMUCwW2dvbeynqNT09TRAEFItFarXa8KfaLmjK79GjR6yurhJFEU+ePOHp06cveY4ddix7vR5ra2tDYnUZbYfK5TLVapVyucyzZ8+OJDWDweBANO0ykKbplerRCoUCcRy/tkihtlwqlUqUSqVhtNn3fWZmZpiZmSEMw2ElaRzH1Go1ut3uoWNI03R4HX/ta1/j3XffJQgCvvnNsxRIX28YbJPkFFo9uYnvIVGZNjKpTiEkS9FHIkxf8SQKtISQI7V96CCTesGuX0Xn2mqmjtzAZ+26Xth/+o1Vp/mCXUb7J0YIkena1wp2XYdly24CX7G/J540o95K4Hlfxhd6UDKw34G0C6sB7G5DsyfE4SZCUu4j5KwC1PagXYJmGdIBPOvb9CLOBuILISx1YakIZgZaFZiZhqQEjX6XTtykvZ/y8SPgLtR9eLELG59Aq+MiRw27XSWb2sNxqwc/XJNIXIJEE5s4z68mr8/kc9tu+34BlqagVoRupoeSbdM4NJH9PlC2hCfhZdf/HnKu1EX/tAiQc69Gt12kItYDsNHIMs6t/7KayfnAfAG6lvSqgW6M/H7cXa2I6yUKco32ASL5PoUlmC7BXhu6LSj6sDgj/7wgoFuP8Rsh9dTjbqlDvR/SSqskXkAv3CP1unhANw3xDQy8Hn/1Z3+Gn723wP/363946n28LhGtDeTaHneTpQXGfx9gMvZjEvYBJmM/jtqHt9I0XTzk9bGDMaaOFMqNOyb5ehs3TMJ+TMI+wOH7cer717UgWgDGmD8Z9zTCJOwDTMZ+TMI+wGTsxyTsw0mYlH2chP2YhH2AydiPSdgHePX9yJ0hc+TIkSNHjhw5Lgg50cqRI0eOHDly5LggXCei9WtXPYDXgEnYB5iM/ZiEfYDJ2I9J2IeTMCn7OAn7MQn7AJOxH5OwD/CK+3FtNFo5cuTIkSNHjhyThusU0cqRI0eOHDly5JgoXDnRMsb8B8aYHxljPjLG/PJVj+csMMY8NMZ8zxjzHW2ObYyZM8b8njHmQ/tz9qrHmYUx5jeMMevGmO9nXjt0zEbwv9hz811jzE9e3cgP4oj9+BVjzFN7Pr5jjPmFzHt/z+7Hj4wx//7VjPogjDG3jTH/1hjzF8aYHxhj/iv7+lidj2P2Y6zOx3kxrvewcbx/wWTcwybh/gWTcQ+7lPuXNgW+in+IV9nHSEP2EPhz4EtXOaYzjv8hsDDy2v8A/LL9/ZeB//6qxzkyvp8DfhL4/kljBn4B+FeIx9/PAt+66vGfsB+/Avw3hyz7JXttFYG79przr8E+rAI/aX+vAQ/sWMfqfByzH2N1Ps6572N7DxvH+5cd19jfwybh/mXHNvb3sMu4f111ROtngI/SNP0kTdMe8JvAL17xmF4Vvwj8E/v7PwH+wyscy0tI0/QbOONkxVFj/kXgn6aCPwRmjDGrlzPS43HEfhyFXwR+M03TbpqmnwIfIdfelSJN07U0Tb9tf68DP0TMtMfqfByzH0fhWp6Pc2LS7mHX+v4Fk3EPm4T7F0zGPewy7l9XTbRu4nqpAjzh+B28bkiBf22M+VNjzN+yry2nabpmf3+O9Am97jhqzON4fv6ODUn/Ribtce33wxjzNvATwLcY4/Mxsh8wpufjDBjnfZmU+xeM8XdmBGP7fZmEe9hF3b+ummiNO76WpulPAn8N+NvGmJ/LvplKnHGsyjrHccwZ/COkL+v7wBrwD692OKeDMaYK/HPg76Zpup99b5zOxyH7MZbn4w3CxN2/YHzHzRh/XybhHnaRjqkYXAAAAgBJREFU96+rJlpPgduZv2/Z18YCaZo+tT/XgX+BhA9faCjU/ly/uhGeGkeNeazOT5qmL9I0HaRpmgC/jgvnXtv9MMYUkC/3P0vT9Lfsy2N3Pg7bj3E8H+fA2O7LBN2/YAy/M6MY1+/LJNzDLvr+ddVE64+Bd4wxd40xIfBLwG9f8ZhOBWNMxRhT09+Bn0car/828DfsYn8D+L+uZoRnwlFj/m3gP7GVIj8L7GXCwdcOI7n+v46cD5D9+CVjTNEYcxd4B/ijyx7fKIwxBvjHwA/TNP3VzFtjdT6O2o9xOx/nxFjewybs/gVj9p05DOP4fZmEe9il3L9eVbH/qv+QKoQHiHL/H1z1eM4w7ntI5cGfAz/QsQPzwL8BPgR+H5i76rGOjPv/QMKgfSS3/DePGjNSGfK/2nPzPeCnr3r8J+zH/2bH+V37ZVjNLP8P7H78CPhrVz1+O6avISH17wLfsf9+YdzOxzH7MVbn4xX2f+zuYeN6/7JjHPt72CTcv+y4xv4edhn3r9wZPkeOHDly5MiR44Jw1anDHDly5MiRI0eOiUVOtHLkyJEjR44cOS4IOdHKkSNHjhw5cuS4IOREK0eOHDly5MiR44KQE60cOXLkyJEjR44LQk60cuTIkSNHjhw5Lgg50cqRI0eOHDly5Lgg5EQrR44cOXLkyJHjgvD/A6t02hu8aBp0AAAAAElFTkSuQmCC\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light",
- "tags": []
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig = plt.figure(figsize=(10, 5))\n",
- "rows = 1 \n",
- "cols = 2\n",
- "\n",
- "for X in dataloader:\n",
- " \n",
- " ax1 = fig.add_subplot(rows, cols, 1)\n",
- " ax1.imshow(np.clip(np.transpose(X[\"A\"][0], (1,2,0)), 0, 1))\n",
- " ax1.set_title('gray img')\n",
- "\n",
- " ax2 = fig.add_subplot(rows, cols, 2)\n",
- " ax2.imshow(np.clip(np.transpose(X[\"B\"][0], (1,2,0)), 0, 1))\n",
- " ax2.set_title('color img') \n",
- "\n",
- " plt.show()\n",
- " break"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "MB6cp3-CGvpO"
- },
- "source": [
- "## 4.3 모델 구축"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "TFlHqFdhxE2z"
- },
- "source": [
- "이제 pix2pix 모델을 설계하도록 하겠습니다. pix2pix의 특징은 일반적인 인코더-디코더(Encoder-Decoder)보다는 U-NET을 사용합니다. U-NET의 특징은 일반적인 인코더-디코더와 달리 스킵 커넥션 (Skip Connections)이 있어, 인코더 레이어와 디코더 레이어 간의 연결을 보다 로컬라이징(localization)을 잘 해주는 특징이 있습니다. 예를 들어, 첫 인코더 레이어 크기가 256 x 256 x 3이라면, 마지막 디코더 레이어 크기도 똑같이 256 x 256 x 3이게 됩니다. 이렇게 같은 크기의 인코더-디코더 레이어가 결합하여, 보다 효과적이고 빠른 성능을 발휘할 수 있게 하는게 U-NET의 특징입니다.\n",
- "\n",
- "이제 스킵 커넥션이 내장된 U-NET 생성자(Generator)를 설계해보도록 하겠습니다. 앞장에서 설명했듯이, GAN 모델에는 U-NET 생성자(Generator)가 있으며, 스킵 커넥션을 통해 인코더-디코더 레이어 간의 로컬라이징을 해줍니다."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "executionInfo": {
- "elapsed": 9867,
- "status": "ok",
- "timestamp": 1612927710060,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "GMA6cgcPPtr5"
- },
- "outputs": [],
- "source": [
- "import torch.nn as nn\n",
- "import torch.nn.functional as F\n",
- "import torch\n",
- "\n",
- "\n",
- "def weights_init_normal(m):\n",
- " classname = m.__class__.__name__\n",
- " if classname.find(\"Conv\") != -1:\n",
- " torch.nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
- " elif classname.find(\"BatchNorm2d\") != -1:\n",
- " torch.nn.init.normal_(m.weight.data, 1.0, 0.02)\n",
- " torch.nn.init.constant_(m.bias.data, 0.0)\n",
- "\n",
- "# U-NET 생성\n",
- "\n",
- "class UNetDown(nn.Module):\n",
- " def __init__(self, in_size, out_size, normalize=True, dropout=0.0):\n",
- " super(UNetDown, self).__init__()\n",
- " layers = [nn.Conv2d(in_size, out_size, 4, 2, 1, bias=False)]\n",
- " if normalize:\n",
- " layers.append(nn.InstanceNorm2d(out_size))\n",
- " layers.append(nn.LeakyReLU(0.2))\n",
- " if dropout:\n",
- " layers.append(nn.Dropout(dropout))\n",
- " self.model = nn.Sequential(*layers)\n",
- "\n",
- " def forward(self, x):\n",
- " return self.model(x)\n",
- "\n",
- "\n",
- "class UNetUp(nn.Module):\n",
- " def __init__(self, in_size, out_size, dropout=0.0):\n",
- " super(UNetUp, self).__init__()\n",
- " layers = [\n",
- " nn.ConvTranspose2d(in_size, out_size, 4, 2, 1, bias=False),\n",
- " nn.InstanceNorm2d(out_size),\n",
- " nn.ReLU(inplace=True),\n",
- " ]\n",
- " if dropout:\n",
- " layers.append(nn.Dropout(dropout))\n",
- "\n",
- " self.model = nn.Sequential(*layers)\n",
- "\n",
- " def forward(self, x, skip_input):\n",
- " x = self.model(x)\n",
- " x = torch.cat((x, skip_input), 1)\n",
- "\n",
- " return x\n",
- "\n",
- "\n",
- "class GeneratorUNet(nn.Module):\n",
- " def __init__(self, in_channels=3, out_channels=3):\n",
- " super(GeneratorUNet, self).__init__()\n",
- " \n",
- " self.down1 = UNetDown(in_channels, 64, normalize=False)\n",
- " self.down2 = UNetDown(64, 128)\n",
- " self.down3 = UNetDown(128, 256)\n",
- " self.down4 = UNetDown(256, 512, dropout=0.5)\n",
- " self.down5 = UNetDown(512, 512, dropout=0.5)\n",
- " self.down6 = UNetDown(512, 512, dropout=0.5)\n",
- " self.down7 = UNetDown(512, 512, dropout=0.5)\n",
- " self.down8 = UNetDown(512, 512, normalize=False, dropout=0.5)\n",
- "\n",
- " self.up1 = UNetUp(512, 512, dropout=0.5)\n",
- " self.up2 = UNetUp(1024, 512, dropout=0.5)\n",
- " self.up3 = UNetUp(1024, 512, dropout=0.5)\n",
- " self.up4 = UNetUp(1024, 512, dropout=0.5)\n",
- " self.up5 = UNetUp(1024, 256)\n",
- " self.up6 = UNetUp(512, 128)\n",
- " self.up7 = UNetUp(256, 64)\n",
- "\n",
- " self.final = nn.Sequential(\n",
- " nn.Upsample(scale_factor=2),\n",
- " nn.ZeroPad2d((1, 0, 1, 0)),\n",
- " nn.Conv2d(128, out_channels, 4, padding=1),\n",
- " nn.Tanh(),\n",
- " )\n",
- "\n",
- " def forward(self, x):\n",
- " # U-Net generator with skip connections from encoder to decoder\n",
- " d1 = self.down1(x)\n",
- " d2 = self.down2(d1)\n",
- " d3 = self.down3(d2)\n",
- " d4 = self.down4(d3)\n",
- " d5 = self.down5(d4)\n",
- " d6 = self.down6(d5)\n",
- " d7 = self.down7(d6)\n",
- " d8 = self.down8(d7)\n",
- " u1 = self.up1(d8, d7)\n",
- " u2 = self.up2(u1, d6)\n",
- " u3 = self.up3(u2, d5)\n",
- " u4 = self.up4(u3, d4)\n",
- " u5 = self.up5(u4, d3)\n",
- " u6 = self.up6(u5, d2)\n",
- " u7 = self.up7(u6, d1)\n",
- "\n",
- " return self.final(u7)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "uLse_pz0_4R9"
- },
- "source": [
- "이제 구분자(Discriminator)를 생성해보겠습니다."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "executionInfo": {
- "elapsed": 9547,
- "status": "ok",
- "timestamp": 1612927710061,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "9RhzX7-J99Cr"
- },
- "outputs": [],
- "source": [
- "class Discriminator(nn.Module):\n",
- " def __init__(self, in_channels=3):\n",
- " super(Discriminator, self).__init__()\n",
- "\n",
- " def discriminator_block(in_filters, out_filters, normalization=True):\n",
- " \"\"\"Returns downsampling layers of each discriminator block\"\"\"\n",
- " layers = [nn.Conv2d(in_filters, out_filters, 4, stride=2, padding=1)]\n",
- " if normalization:\n",
- " layers.append(nn.InstanceNorm2d(out_filters))\n",
- " layers.append(nn.LeakyReLU(0.2, inplace=True))\n",
- " return layers\n",
- "\n",
- " self.model = nn.Sequential(\n",
- " *discriminator_block(in_channels * 2, 64, normalization=False),\n",
- " *discriminator_block(64, 128),\n",
- " *discriminator_block(128, 256),\n",
- " *discriminator_block(256, 512),\n",
- " nn.ZeroPad2d((1, 0, 1, 0)),\n",
- " nn.Conv2d(512, 1, 4, padding=1, bias=False)\n",
- " )\n",
- "\n",
- " def forward(self, img_A, img_B):\n",
- " # Concatenate image and condition image by channels to produce input\n",
- " img_input = torch.cat((img_A, img_B), 1)\n",
- " return self.model(img_input)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "executionInfo": {
- "elapsed": 9368,
- "status": "ok",
- "timestamp": 1612927710061,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "Sif7ZoAQGZwp"
- },
- "outputs": [],
- "source": [
- "gpu = 0\n",
- "\n",
- "def weights_init(m):\n",
- " classname = m.__class__.__name__\n",
- " if classname.find('Conv') != -1: # Conv weight init\n",
- " m.weight.data.normal_(0.0, 0.02)\n",
- " elif classname.find('BatchNorm') != -1: # BatchNorm weight init\n",
- " m.weight.data.normal_(1.0, 0.02)\n",
- " m.bias.data.fill_(0)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "7p03HrL50PsB"
- },
- "source": [
- "이제 생성자(Generator)와 분별자(Discriminator)의 구조를 살펴보도록 하겠습니다."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "executionInfo": {
- "elapsed": 8979,
- "status": "ok",
- "timestamp": 1612927710865,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "iZZ1WG2kzilM",
- "outputId": "f1c6ec07-4b1d-4fee-be7b-6f8cd99c896b"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "GeneratorUNet(\n",
- " (down1): UNetDown(\n",
- " (model): Sequential(\n",
- " (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
- " (1): LeakyReLU(negative_slope=0.2)\n",
- " )\n",
- " )\n",
- " (down2): UNetDown(\n",
- " (model): Sequential(\n",
- " (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
- " (1): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
- " (2): LeakyReLU(negative_slope=0.2)\n",
- " )\n",
- " )\n",
- " (down3): UNetDown(\n",
- " (model): Sequential(\n",
- " (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
- " (1): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
- " (2): LeakyReLU(negative_slope=0.2)\n",
- " )\n",
- " )\n",
- " (down4): UNetDown(\n",
- " (model): Sequential(\n",
- " (0): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
- " (1): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
- " (2): LeakyReLU(negative_slope=0.2)\n",
- " (3): Dropout(p=0.5, inplace=False)\n",
- " )\n",
- " )\n",
- " (down5): UNetDown(\n",
- " (model): Sequential(\n",
- " (0): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
- " (1): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
- " (2): LeakyReLU(negative_slope=0.2)\n",
- " (3): Dropout(p=0.5, inplace=False)\n",
- " )\n",
- " )\n",
- " (down6): UNetDown(\n",
- " (model): Sequential(\n",
- " (0): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
- " (1): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
- " (2): LeakyReLU(negative_slope=0.2)\n",
- " (3): Dropout(p=0.5, inplace=False)\n",
- " )\n",
- " )\n",
- " (down7): UNetDown(\n",
- " (model): Sequential(\n",
- " (0): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
- " (1): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
- " (2): LeakyReLU(negative_slope=0.2)\n",
- " (3): Dropout(p=0.5, inplace=False)\n",
- " )\n",
- " )\n",
- " (down8): UNetDown(\n",
- " (model): Sequential(\n",
- " (0): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
- " (1): LeakyReLU(negative_slope=0.2)\n",
- " (2): Dropout(p=0.5, inplace=False)\n",
- " )\n",
- " )\n",
- " (up1): UNetUp(\n",
- " (model): Sequential(\n",
- " (0): ConvTranspose2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
- " (1): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
- " (2): ReLU(inplace=True)\n",
- " (3): Dropout(p=0.5, inplace=False)\n",
- " )\n",
- " )\n",
- " (up2): UNetUp(\n",
- " (model): Sequential(\n",
- " (0): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
- " (1): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
- " (2): ReLU(inplace=True)\n",
- " (3): Dropout(p=0.5, inplace=False)\n",
- " )\n",
- " )\n",
- " (up3): UNetUp(\n",
- " (model): Sequential(\n",
- " (0): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
- " (1): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
- " (2): ReLU(inplace=True)\n",
- " (3): Dropout(p=0.5, inplace=False)\n",
- " )\n",
- " )\n",
- " (up4): UNetUp(\n",
- " (model): Sequential(\n",
- " (0): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
- " (1): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
- " (2): ReLU(inplace=True)\n",
- " (3): Dropout(p=0.5, inplace=False)\n",
- " )\n",
- " )\n",
- " (up5): UNetUp(\n",
- " (model): Sequential(\n",
- " (0): ConvTranspose2d(1024, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
- " (1): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
- " (2): ReLU(inplace=True)\n",
- " )\n",
- " )\n",
- " (up6): UNetUp(\n",
- " (model): Sequential(\n",
- " (0): ConvTranspose2d(512, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
- " (1): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
- " (2): ReLU(inplace=True)\n",
- " )\n",
- " )\n",
- " (up7): UNetUp(\n",
- " (model): Sequential(\n",
- " (0): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
- " (1): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
- " (2): ReLU(inplace=True)\n",
- " )\n",
- " )\n",
- " (final): Sequential(\n",
- " (0): Upsample(scale_factor=2.0, mode=nearest)\n",
- " (1): ZeroPad2d(padding=(1, 0, 1, 0), value=0.0)\n",
- " (2): Conv2d(128, 3, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))\n",
- " (3): Tanh()\n",
- " )\n",
- ")"
- ]
- },
- "execution_count": 11,
- "metadata": {
- "tags": []
- },
- "output_type": "execute_result"
- }
- ],
- "source": [
- "GeneratorUNet().apply(weights_init)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "executionInfo": {
- "elapsed": 7925,
- "status": "ok",
- "timestamp": 1612927710865,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "UVq1trxl0BE6",
- "outputId": "7a2e61ff-0e60-42d9-ec14-778e52d40380"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Discriminator(\n",
- " (model): Sequential(\n",
- " (0): Conv2d(6, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
- " (1): LeakyReLU(negative_slope=0.2, inplace=True)\n",
- " (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
- " (3): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
- " (4): LeakyReLU(negative_slope=0.2, inplace=True)\n",
- " (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
- " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
- " (7): LeakyReLU(negative_slope=0.2, inplace=True)\n",
- " (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
- " (9): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
- " (10): LeakyReLU(negative_slope=0.2, inplace=True)\n",
- " (11): ZeroPad2d(padding=(1, 0, 1, 0), value=0.0)\n",
- " (12): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " )\n",
- ")"
- ]
- },
- "execution_count": 12,
- "metadata": {
- "tags": []
- },
- "output_type": "execute_result"
- }
- ],
- "source": [
- "Discriminator().apply(weights_init)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "jj7EabeaPybF"
- },
- "source": [
- "이제 파라미터를 지정하고 pix2pix 모델을 학습해보도록 하겠습니다. 학습에 필요한 기본적인 모듈들을 불러옵니다."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "executionInfo": {
- "elapsed": 4384,
- "status": "ok",
- "timestamp": 1612927710866,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "BfG0HFURe6gV"
- },
- "outputs": [],
- "source": [
- "import argparse\n",
- "import numpy as np\n",
- "import math\n",
- "import itertools\n",
- "import time\n",
- "import datetime\n",
- "import sys\n",
- "\n",
- "import torchvision.transforms as transforms\n",
- "from torchvision.utils import save_image\n",
- "\n",
- "from torch.utils.data import DataLoader\n",
- "from torchvision import datasets\n",
- "from torch.autograd import Variable\n",
- "\n",
- "import torch.nn as nn\n",
- "import torch.nn.functional as F\n",
- "import torch"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "FpPZSdeOfQsD"
- },
- "source": [
- "이제 세부적인 파라미터를 지정하도록 하겠습니다. 여기서 `epoch`은 0에서 시작하며 `n_epoch`은 에폭 횟수를 의미합니다. `batch_size`는 메모리 용량에 따라 배수로 조정을 하고, `lr`는 학습 손실값(Learning Loss)을 의미합니다. `sample_interval`은 학습중 샘플 파일을 출력하는 간격값입니다."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "executionInfo": {
- "elapsed": 3138,
- "status": "ok",
- "timestamp": 1612927710982,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "6OXgkwZPe96F"
- },
- "outputs": [],
- "source": [
- "epoch = 0\n",
- "n_epochs = 100\n",
- "dataset_name = \"Victorian400\"\n",
- "batch_size = 12\n",
- "lr = 0.0002\n",
- "b1 = 0.5 # adam: decay of first order momentum of gradient\n",
- "b2 = 0.999 # adam: decay of first order momentum of gradient\n",
- "decay_epoch = 100 # epoch from which to start lr decay\n",
- "#n_cpu = 8 # number of cpu threads to use during batch generation\n",
- "img_height = 256\n",
- "img_width = 256\n",
- "channels = 3 # number of image channels\n",
- "sample_interval = 500 # interval between sampling of images from generators\n",
- "checkpoint_interval = -1 # interval between model checkpoints"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "-6xsosGEfyAp"
- },
- "source": [
- "`sample_images` 함수 정의 부분을 보시면, `gray`, `color`, `output`가 있는데, 저는 `gray`를 흑백사진, `color`를 컬러사진, `output`를 흑백을 컬러화한 사진으로 정의하였습니다. `gray`가 `color`와 비교되면서 학습이 되고, 이를 바탕으로 `output`를 생성하게 됩니다."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "executionInfo": {
- "elapsed": 12056,
- "status": "ok",
- "timestamp": 1612927721800,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "Njce4kdBP0YN"
- },
- "outputs": [],
- "source": [
- "os.makedirs(\"images/%s/val\" % dataset_name, exist_ok=True)\n",
- "os.makedirs(\"images/%s/test\" % dataset_name, exist_ok=True)\n",
- "os.makedirs(\"saved_models/%s\" % dataset_name, exist_ok=True)\n",
- "\n",
- "cuda = True if torch.cuda.is_available() else False\n",
- "\n",
- "# Loss functions\n",
- "criterion_GAN = torch.nn.MSELoss()\n",
- "criterion_pixelwise = torch.nn.L1Loss()\n",
- "\n",
- "# Loss weight of L1 pixel-wise loss between translated image and real image\n",
- "lambda_pixel = 100\n",
- "\n",
- "# Calculate output of image discriminator (PatchGAN)\n",
- "patch = (1, img_height // 2 ** 4, img_width // 2 ** 4)\n",
- "\n",
- "# Initialize generator and discriminator\n",
- "generator = GeneratorUNet()\n",
- "discriminator = Discriminator()\n",
- "\n",
- "if cuda:\n",
- " generator = generator.cuda()\n",
- " discriminator = discriminator.cuda()\n",
- " criterion_GAN.cuda()\n",
- " criterion_pixelwise.cuda()\n",
- "\n",
- "if epoch != 0:\n",
- " # Load pretrained models\n",
- " generator.load_state_dict(torch.load(\"saved_models/%s/generator_%d.pth\" % (dataset_name, epoch)))\n",
- " discriminator.load_state_dict(torch.load(\"saved_models/%s/discriminator_%d.pth\" % (dataset_name, epoch)))\n",
- "else:\n",
- " # Initialize weights\n",
- " generator.apply(weights_init_normal)\n",
- " discriminator.apply(weights_init_normal)\n",
- "\n",
- "# Optimizers\n",
- "optimizer_G = torch.optim.Adam(generator.parameters(), lr=lr, betas=(b1, b2))\n",
- "optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=lr, betas=(b1, b2))\n",
- "\n",
- "# Configure dataloaders\n",
- "transforms_ = [\n",
- " transforms.Resize((img_height, img_width), Image.BICUBIC),\n",
- " transforms.ToTensor(),\n",
- " transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
- "]\n",
- "\n",
- "dataloader = DataLoader(\n",
- " VictorianDataset(root, transforms_=transforms_),\n",
- " batch_size=batch_size,\n",
- " shuffle=True\n",
- "# num_workers=n_cpu,\n",
- ")\n",
- "\n",
- "# Tensor type\n",
- "Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor\n",
- "\n",
- "\n",
- "def sample_images(batches_done, loader, mode):\n",
- " imgs = next(iter(loader))\n",
- " gray = Variable(imgs[\"A\"].type(Tensor))\n",
- " color = Variable(imgs[\"B\"].type(Tensor))\n",
- " output = generator(gray)\n",
- " img_sample = torch.cat((gray.data, output.data, color.data), -2)\n",
- " save_image(img_sample, \"images/%s/%s/%s.png\" % (dataset_name, mode, batches_done), nrow=6, normalize=True)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "UHzJhW9nG6Oc"
- },
- "source": [
- "## 4.4 모델 학습"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Evw-nQyFgrjJ"
- },
- "source": [
- "이제 지정한 `epoch`만큼 학습을 시작해보도록 하겠습니다."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "executionInfo": {
- "elapsed": 1857316,
- "status": "ok",
- "timestamp": 1612929623291,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "3bIP8Nh2f9-A",
- "outputId": "f7f29a23-98b8-40d0-fb68-a688ed02e610"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[Epoch 99/100] [Batch 33/34] [D loss: 0.170797] [G loss: 6.816781, pixel: 0.058293, adv: 0.987505] ETA: 0:00:00.499183"
- ]
- }
- ],
- "source": [
- "# ----------\n",
- "# Training\n",
- "# ----------\n",
- "\n",
- "prev_time = time.time()\n",
- "\n",
- "for epoch in range(epoch, n_epochs):\n",
- " for i, batch in enumerate(dataloader):\n",
- "\n",
- " # Model inputs\n",
- " gray = Variable(batch[\"A\"].type(Tensor))\n",
- " color = Variable(batch[\"B\"].type(Tensor))\n",
- "\n",
- " # Adversarial ground truths\n",
- " valid = Variable(Tensor(np.ones((gray.size(0), *patch))), requires_grad=False)\n",
- " fake = Variable(Tensor(np.zeros((gray.size(0), *patch))), requires_grad=False)\n",
- "\n",
- " # ------------------\n",
- " # Train Generators\n",
- " # ------------------\n",
- "\n",
- " optimizer_G.zero_grad()\n",
- "\n",
- " # GAN loss\n",
- " output = generator(gray)\n",
- " pred_fake = discriminator(output, gray)\n",
- " loss_GAN = criterion_GAN(pred_fake, valid)\n",
- " # Pixel-wise loss\n",
- " loss_pixel = criterion_pixelwise(output, color)\n",
- "\n",
- " # Total loss\n",
- " loss_G = loss_GAN + lambda_pixel * loss_pixel\n",
- "\n",
- " loss_G.backward()\n",
- "\n",
- " optimizer_G.step()\n",
- "\n",
- " # ---------------------\n",
- " # Train Discriminator\n",
- " # ---------------------\n",
- "\n",
- " optimizer_D.zero_grad()\n",
- "\n",
- " # Real loss\n",
- " pred_real = discriminator(color, gray)\n",
- " loss_real = criterion_GAN(pred_real, valid)\n",
- "\n",
- " # Fake loss\n",
- " pred_fake = discriminator(output.detach(), gray)\n",
- " loss_fake = criterion_GAN(pred_fake, fake)\n",
- "\n",
- " # Total loss\n",
- " loss_D = 0.5 * (loss_real + loss_fake)\n",
- "\n",
- " loss_D.backward()\n",
- " optimizer_D.step()\n",
- "\n",
- " # --------------\n",
- " # Log Progress\n",
- " # --------------\n",
- "\n",
- " # Determine approximate time left\n",
- " batches_done = epoch * len(dataloader) + i\n",
- " batches_left = n_epochs * len(dataloader) - batches_done\n",
- " time_left = datetime.timedelta(seconds=batches_left * (time.time() - prev_time))\n",
- " prev_time = time.time()\n",
- "\n",
- " # Print log\n",
- " sys.stdout.write(\n",
- " \"\\r[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f, pixel: %f, adv: %f] ETA: %s\"\n",
- " % (\n",
- " epoch,\n",
- " n_epochs,\n",
- " i,\n",
- " len(dataloader),\n",
- " loss_D.item(),\n",
- " loss_G.item(),\n",
- " loss_pixel.item(),\n",
- " loss_GAN.item(),\n",
- " time_left,\n",
- " )\n",
- " )\n",
- "\n",
- " # If at sample interval save image\n",
- " if batches_done % sample_interval == 0:\n",
- " sample_images(batches_done, dataloader, 'val')\n",
- "\n",
- " if checkpoint_interval != -1 and epoch % checkpoint_interval == 0:\n",
- " # Save model checkpoints\n",
- " torch.save(generator.state_dict(), \"saved_models/%s/generator_%d.pth\" % (dataset_name, epoch))\n",
- " torch.save(discriminator.state_dict(), \"saved_models/%s/discriminator_%d.pth\" % (dataset_name, epoch))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ro2QMV_mh7T1"
- },
- "source": [
- "학습이 잘 되었는지 샘플 파일을 열어보도록 하겠습니다. 배치가 500회 돌 때 마다 샘플 파일을 `images/Victorian400/` 경로에 저장하였습니다. 각각 1회, 1000회, 2000회 샘플 파일을 확인해보도록 하겠습니다."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 1000,
- "output_embedded_package_id": "1VZWKgMt2qW7zzQue_L6A9CmGpE9rKAlA"
- },
- "executionInfo": {
- "elapsed": 5145,
- "status": "ok",
- "timestamp": 1612929802302,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "_s2fkpG88RIt",
- "outputId": "1f5e367a-2353-4361-f130-ac2b8f4372a5"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Output hidden; open in https://colab.research.google.com to view."
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from IPython.display import Image\n",
- "Image('images/Victorian400/val/0.png')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 1000,
- "output_embedded_package_id": "12IiEfukfnC3UEDUJFJKLPd7YA9Hj56Cl"
- },
- "executionInfo": {
- "elapsed": 4330,
- "status": "ok",
- "timestamp": 1612929819423,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "2GIWnxd-8MVG",
- "outputId": "ef89e956-c4e3-41af-e1fb-57da692efa9f"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Output hidden; open in https://colab.research.google.com to view."
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "Image('images/Victorian400/val/1500.png')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 1000,
- "output_embedded_package_id": "1h45r9ctAMXLAfVn4ze34BydSAkkKnHLo"
- },
- "executionInfo": {
- "elapsed": 4596,
- "status": "ok",
- "timestamp": 1612929825597,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "L4lhb8Hs8T5B",
- "outputId": "5cb2fb54-824c-4912-f950-de8f4e08e86e"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Output hidden; open in https://colab.research.google.com to view."
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "Image('images/Victorian400/val/3000.png')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ciVjMHHxjKZw"
- },
- "source": [
- "위의 샘플 사진들을 보시면, 위에서 아래 순서대로 흑백-아웃풋-타켓 이미지입니다. 확실히 에폭수가 늘어남으로써 학습 효과가 나타나고 있는 걸 확인 할 수 있습니다. 이렇게 샘플링 된 이미지를 확인하면서 적절한 배치사이즈와 에폭수를 찾을 수 있습니다."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "3XQKGu0ZHGcf"
- },
- "source": [
- "## 4.5 예측 및 성능 평가"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "9w-gKIWuHLo1"
- },
- "source": [
- "이제 학습된 모델을 이용해 6장의 테스트셋으로 실험해보도록 하겠습니다."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "executionInfo": {
- "elapsed": 201,
- "status": "ok",
- "timestamp": 1612929844211,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "wmsdFlcjiLsy"
- },
- "outputs": [],
- "source": [
- "test_root = root + 'test/'\n",
- "test_batch_size = 6\n",
- "\n",
- "test_loader = DataLoader(\n",
- " VictorianDataset(test_root, transforms_=transforms_),\n",
- " batch_size=test_batch_size,\n",
- " # shuffle=True\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "kKl1w-h6vmC4"
- },
- "source": [
- "테스트셋 이미지 파일이 잘 출력되는지 확인해보도록 하겠습니다."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 318
- },
- "executionInfo": {
- "elapsed": 693,
- "status": "ok",
- "timestamp": 1612929845763,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "FdCeSuc96buT",
- "outputId": "2e0d2762-57fb-419c-9387-b2c19fb6d6b9"
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light",
- "tags": []
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig = plt.figure(figsize=(10, 5))\n",
- "rows = 1 \n",
- "cols = 2\n",
- "from PIL import Image\n",
- "\n",
- "for X in test_loader:\n",
- " \n",
- " ax1 = fig.add_subplot(rows, cols, 1)\n",
- " ax1.imshow(np.clip(np.transpose(X[\"A\"][0], (1,2,0)), 0, 1))\n",
- " ax1.set_title('gray img')\n",
- "\n",
- " ax2 = fig.add_subplot(rows, cols, 2)\n",
- " ax2.imshow(np.clip(np.transpose(X[\"B\"][0], (1,2,0)), 0, 1))\n",
- " ax2.set_title('color img') \n",
- "\n",
- " plt.show()\n",
- " break"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "executionInfo": {
- "elapsed": 687,
- "status": "ok",
- "timestamp": 1612929855566,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "NQxvulJBHbdI"
- },
- "outputs": [],
- "source": [
- "discriminator.eval()\r\n",
- "generator.eval()\r\n",
- "\r\n",
- "sample_images(batches_done, test_loader, 'test')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 789,
- "output_embedded_package_id": "1oAXIQvfdP2RKUzLmS_Ejd85bpNxdpTlP"
- },
- "executionInfo": {
- "elapsed": 1992,
- "status": "ok",
- "timestamp": 1612929866373,
- "user": {
- "displayName": "Hoyeol Kim",
- "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgAJhkAeVuFBMLIi8tcgU6SKWRLeY_jH1KvF2bjVw=s64",
- "userId": "03416073058539940221"
- },
- "user_tz": 360
- },
- "id": "HteJpCj_DHlG",
- "outputId": "3d652cbe-7277-4b78-cee7-378cc3d01dca"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Output hidden; open in https://colab.research.google.com to view."
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from IPython.display import Image\r\n",
- "Image('images/Victorian400/test/3399.png')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "3N0p6CdkvuVk"
- },
- "source": [
- "위에서 아래 순서대로 흑백-아웃풋-타켓 테스트 이미지입니다. 어떤 사진은 원본보다 출력이 더 잘 되는 것을 확인 할 수 있습니다. 생성자(Generator)에 U-NET이 추가된 cGAN 모델이 GAN 모델보다 색채를 예측하는 점에 있어서, 보다 나은 결과물을 생성하는 것을 확인해 볼 수 있었습니다.\n",
- "\n",
- "모델 구조가 구체적인 목표에 맞춰 잘 짜여져 있다면, 이렇게 작은 데이터셋으로도 좋은 결과물을 만들 수 있습니다. 물론 많은 양의 질 좋은 데이터가 추가된다면, 향상 된 결과를 얻을 수 있습니다. 이제 학습된 모델을 바탕으로 여러분의 흑백 그림을 채색해보는게 어떨까요?"
- ]
- }
- ],
- "metadata": {
- "accelerator": "GPU",
- "colab": {
- "collapsed_sections": [],
- "name": "Ch4-pix2pix.ipynb",
- "provenance": []
- },
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/book/chapters/GAN/References.md b/book/chapters/GAN/References.md
new file mode 100644
index 0000000..8ef1d51
--- /dev/null
+++ b/book/chapters/GAN/References.md
@@ -0,0 +1,40 @@
+# 5. 참고 문헌
+
+## Dataset
+
+- [Victorian400 Dataset for Colorizing Victorian Illustrations](https://www.kaggle.com/elibooklover/victorian400)
+
+## Papers
+
+- [Pros and Cons of GAN Evaluation Measures](https://doi.org/10.1016/j.cviu.2018.10.009)
+- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597)
+- [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661)
+- [Image-to-Image Translation with Conditional Adversarial Networks](https://arxiv.org/abs/1611.07004)
+- [Conditional Generative Adversarial Nets](https://arxiv.org/abs/1411.1784)
+- [Generative Adversarial Nets 분석과 적용사례](https://www.koreascience.or.kr/article/CFKO201736257096695.pdf)
+- [Synthesizing Obama: Learning Lip Sync from Audio](https://grail.cs.washington.edu/projects/AudioToObama/siggraph17_obama.pdf)
+- [An_Analysis_of_Evaluation_Metrics_of_GANs](https://www.researchgate.net/publication/337876790_AN_ANALYSIS_OF_EVALUATION_METRICS_OF_GANS)
+- [Progressive_Growing_of_GANs_for_Improved_Quality_Stability_and_Variation](https://arxiv.org/abs/1710.10196)
+- [Eye In-Painting with Exemplar Generative Adversarial Networks](https://arxiv.org/abs/1712.03999)
+- [Deepfakes and Disinformation: Exploring the Impact of Synthetic Political Video on Deception, Uncertainty, and Trust in News](https://journals.sagepub.com/doi/full/10.1177/2056305120903408)
+- [Improved Techniques For Training GANs](https://arxiv.org/abs/1606.03498)
+- [On the Evaluation of Conditional GANs](https://arxiv.org/abs/1907.08175)
+
+## Code
+
+- [PyTorch-GAN](https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/gan/gan.py)
+- [GAN_colorization](https://github.com/hichoe95/GAN_colorization)
+- [clearing-a-subplot-in-matplotlib](https://stackoverflow.com/questions/47282918/clearing-a-subplot-in-matplotlib)
+- [row-and-column-headers-in-matplotlibs-subplots](https://stackoverflow.com/questions/25812255/row-and-column-headers-in-matplotlibs-subplots)
+- [matplotlib-subplots-get-rid-of-tick-labels-altogether](https://stackoverflow.com/questions/25124143/matplotlib-subplots-get-rid-of-tick-labels-altogether)
+- [matplotlib-imshow-stretch-to-fit-width](https://stackoverflow.com/questions/12806481/matplotlib-imshow-stretch-to-fit-width)
+
+
+## Blog
+
+
+- [쉽게 씌어진 GAN](https://dreamgonfly.github.io/blog/gan-explained/)
+- [cCAN(conditionial GAN)](https://blog.naver.com/laonple/221306150417)
+- [CGAN](https://leechamin.tistory.com/229)
+- [Best Resources for Getting Started With GANs](https://machinelearningmastery.com/resources-for-getting-started-with-generative-adversarial-networks/)
+
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