From 9fe5684f0eeb2adbdb8d5bfef05b363b5a465358 Mon Sep 17 00:00:00 2001 From: Blanca Date: Tue, 24 May 2022 07:22:22 +0200 Subject: [PATCH 01/38] =?UTF-8?q?Elimina=20notas=20sucio=20#93=20Esta=20no?= =?UTF-8?q?=20reflejaban=20m=C3=A1s=20que=20ideas=20sueltas=20a=20explorar?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../Objetivos.tex | 2 +- .../feedforward-network-una-capa.tex | 28 ------------------- 2 files changed, 1 insertion(+), 29 deletions(-) diff --git a/Memoria/capitulos/1-Introduccion_redes_neuronales/Objetivos.tex b/Memoria/capitulos/1-Introduccion_redes_neuronales/Objetivos.tex index 5f2defd..2acaf8e 100644 --- a/Memoria/capitulos/1-Introduccion_redes_neuronales/Objetivos.tex +++ b/Memoria/capitulos/1-Introduccion_redes_neuronales/Objetivos.tex @@ -23,7 +23,7 @@ \chapter{Introducción a las redes neuronales} \cite{a-universal-law-of-Robustness} \cite{CHAI2021100134} el sustento de esto no deja de ser experimental o basado en cotas de carácter \textit{en el peor de los casos y por el tamaño del espacio de búsqueda}. Pero estos motivos no constituyen una demostración formal ni rigurosa de porqué decantarnos verdaderamente por -ello y es más otros artículos experimentales demuestran que aumentar el número de capas no mejora los resultado +ello y es más, otros artículos experimentales demuestran que aumentar el número de capas no mejora los resultado \cite{DBLP:conf/iwann/Linan-Villafranca21}. Así pues, sustentados con la demostración de convergencia universal \cite{HORNIK1989359} diff --git a/Memoria/capitulos/1-Introduccion_redes_neuronales/feedforward-network-una-capa.tex b/Memoria/capitulos/1-Introduccion_redes_neuronales/feedforward-network-una-capa.tex index 79d97c4..a02b057 100644 --- a/Memoria/capitulos/1-Introduccion_redes_neuronales/feedforward-network-una-capa.tex +++ b/Memoria/capitulos/1-Introduccion_redes_neuronales/feedforward-network-una-capa.tex @@ -164,31 +164,3 @@ \subsection*{Las funciones de activación $\Gamma$ son la clave del aprendizaje} \caption{Cómo afecta la forma de la función de activación} \label{img:como afecta la forma de la función de aproximación} \end{figure} - -% IDEA -\iconoAclaraciones \textcolor{dark_green}{ Nota idea Blanca: Multiplicar por un coeficiente complejo sería aplicar un giro, - lo que a priori mejoraría la convergencia - ¿Qué pasaría se cambiáramos el cuerpo?} - -Ante esta idea habría que plantearse si: - -\begin{enumerate} - \item Formalizar si esto mejoraría. - \item A nivel de implementar Julia tiene números complejos ¿cuánto supondría de coste computacional? ¿merecería la pena? - \item ¿Cómo habría que actualizar los pesos? -\end{enumerate} - -% IDEA -\iconoAclaraciones \textcolor{dark_green}{ Nota idea Blanca: Vista esta idea sería muy interesante plantearse a partir de los datos cómo deberían de ser las funciones de activación. Es decir, que no se fijen a priori, sino que sean de los datos donde provenga su forma.} - -Para poder utilizarse la idea que acaba de plantearse debería de plantearse: -\begin{enumerate} - \item Formalizar beneficio teórico. - \item Balanza costo y mejora. - \item Una forma de mejorar que acepte esas funciones (no necesariamente \textit{backpropagation}). -\end{enumerate} - -% IDEA -\iconoAclaraciones \textcolor{dark_green}{ Nota idea Blanca: Ante esto mi intuición me dice que que la función de activación sea constante por algún extremo y que no fuera constante en algún intervalo serían las únicas hipótesis para que tal función de activación sirviera para construir redes neuronales que funcionara como aproximador universal} - - From 092a10a2130d777e43d4a1b9f3f8c3591cfc224b Mon Sep 17 00:00:00 2001 From: Blanca Date: Tue, 24 May 2022 10:55:57 +0200 Subject: [PATCH 02/38] =?UTF-8?q?Revisa=20cap=C3=ADtulo=20teor=C3=ADa=20de?= =?UTF-8?q?=20la=20aproximaci=C3=B3n=20#103=20#18=20A=C3=B1ade=20adem?= =?UTF-8?q?=C3=A1s=20un=20ejemplo=20usando=20los=20polinomios=20de=20Lagra?= =?UTF-8?q?nge=20Este=20cap=C3=ADtulo=20no=20ha=20sido=20corregido=20nunca?= =?UTF-8?q?=20(lo=20siento=20Javier=20por=20no=20pasarlo=20antes),=20en=20?= =?UTF-8?q?=C3=A9l=20no=20solo=20se=20demuestran=20los=20teoremas=20m?= =?UTF-8?q?=C3=A1s=20importantes=20que=20se=20utilizan=20en=20la=20demostr?= =?UTF-8?q?aci=C3=B3n=20de=20la=20convergencia=20de=20las=20redes=20neuron?= =?UTF-8?q?ales;=20sino=20que=20me=20he=20tomado=20la=20libertad=20de=20ut?= =?UTF-8?q?ilizarlo=20para=20introducir=20de=20manera=20intuitiva=20porqu?= =?UTF-8?q?=C3=A9=20se=20necesitan=20las=20funciones=20de=20activaci=C3=B3?= =?UTF-8?q?n.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Por lo general cuando se introducen las redes neuronales se hace de manera histórica, pero por mi afán de que todo esté hilado y proceda de una intuición me parece que esta es la forma más natural de casarlo. Si no os gusta lo quito. --- .github/workflows/personal-dictionary.txt | 3 + .../0-Metodologia/Comentarios_previos.tex | 2 +- .../feedforward-network-una-capa.tex | 27 ---- .../3-Teoria_aproximacion/0_objetivos.tex | 19 ++- .../1_polinomios_de_Bernstein.tex | 35 ++++- .../2_Weierstrass_approximation_theorem.tex | 12 ++ .../3-Teoria_aproximacion/4_Conclusiones.tex | 102 +++++++++++++++ .../3-Teoria_aproximacion/Lagrange.ipynb | 123 ++++++++++++++++++ .../img/metodo-lagrange/lagrange-13-datos.png | Bin 0 -> 16676 bytes .../img/metodo-lagrange/lagrange-18-datos.png | Bin 0 -> 12921 bytes .../img/metodo-lagrange/lagrange-3-datos.png | Bin 0 -> 16868 bytes .../img/metodo-lagrange/lagrange-8-datos.png | Bin 0 -> 16612 bytes Memoria/paquetes/comandos-entornos.tex | 2 +- Memoria/tfg.tex | 15 ++- 14 files changed, 290 insertions(+), 50 deletions(-) create mode 100644 Memoria/capitulos/3-Teoria_aproximacion/4_Conclusiones.tex create mode 100644 Memoria/capitulos/3-Teoria_aproximacion/Lagrange.ipynb create mode 100644 Memoria/img/metodo-lagrange/lagrange-13-datos.png create mode 100644 Memoria/img/metodo-lagrange/lagrange-18-datos.png create mode 100644 Memoria/img/metodo-lagrange/lagrange-3-datos.png create mode 100644 Memoria/img/metodo-lagrange/lagrange-8-datos.png diff --git a/.github/workflows/personal-dictionary.txt b/.github/workflows/personal-dictionary.txt index 68cd61a..dfdfcfd 100644 --- a/.github/workflows/personal-dictionary.txt +++ b/.github/workflows/personal-dictionary.txt @@ -66,6 +66,7 @@ Wilcoxon Wortman Zhou approximators +aproximable aproximador aproximadores autres @@ -103,6 +104,7 @@ inasumible inecuación insesgado insesgados +ipynb jejejeje jk lcc @@ -123,6 +125,7 @@ perceptrón png posteriori precompilados +preimágenes primeraCapa qB reenfocar diff --git a/Memoria/capitulos/0-Metodologia/Comentarios_previos.tex b/Memoria/capitulos/0-Metodologia/Comentarios_previos.tex index af33000..c11cdce 100644 --- a/Memoria/capitulos/0-Metodologia/Comentarios_previos.tex +++ b/Memoria/capitulos/0-Metodologia/Comentarios_previos.tex @@ -16,7 +16,7 @@ \section*{Comentario previo} \begin{itemize} \item \iconoAclaraciones \textcolor{dark_green}{ Color 1}: Comentarios para - aclarar conceptos matemáticos o informáticos ofertar la idea intuitiva que + aclarar conceptos matemáticos o informáticos y ofertar la idea intuitiva que se esconde, donde no se presuponen conocimientos avanzados en la materia. \item \iconoProfundizar \textcolor{blue}{ Color 2}: Comentarios para una reflexión más profunda o que indique nuevas áreas que explorar. diff --git a/Memoria/capitulos/1-Introduccion_redes_neuronales/feedforward-network-una-capa.tex b/Memoria/capitulos/1-Introduccion_redes_neuronales/feedforward-network-una-capa.tex index a02b057..ebdbcde 100644 --- a/Memoria/capitulos/1-Introduccion_redes_neuronales/feedforward-network-una-capa.tex +++ b/Memoria/capitulos/1-Introduccion_redes_neuronales/feedforward-network-una-capa.tex @@ -137,30 +137,3 @@ \subsection*{Diferencia con otras definiciones} \label{subsection:diferencia-ot \item \textbf{Se elimina un parámetro} de la transformación afín de la última capa; puesto que no es necesario para la convergencia de nuevo por \ref{teo:MFNAUA} lo hemos eliminado. \item Nuestras funciones de activación son funciones medibles en vez de diferenciables ya que a priori no existe ninguna hipótesis teórica que fuerce a tal restricción, como hemos visto en \ref{teo:MFNAUA}. \end{itemize} - -\subsection*{Las funciones de activación $\Gamma$ son la clave del aprendizaje} - -Notemos que de no ser por las funciones de activación se estarían haciendo transformaciones lineales de los datos, por el contrario estamos realizando \textit{cambios más fuerte} siendo capaz con esto de \textit{de diferenciar puntos claves}. - -\textcolor{red}{Dicho de esta manera queda muy poco claro y habría que profundizar más.} -Vamos a mostrar un ejemplo de la importancia de la función de activación - -\textcolor{red}{TODO : Añadir gráficos cuando esté implementada una red neuronal} - - -% Ejemplo de cómo se aproxima gracias a la forma de la función de activación -\begin{figure}[h!] - \includegraphics[width=\textwidth]{1-Introduccion_redes_neuronales/idea-como-aproxima-redes-neuronales.jpeg} - \caption{Cómo actúa en la aproximación una función de activación} - \label{img:idea-como-aproxima-redes-neuronales} - \end{figure} - -La idea intuitiva es que para una capa oculta con una neurona, -lo que se hace es \textit{colocar} por escalado y simetrías la imagen de la función de activación. - -% Ejemplo trivial de como la forma de la función de activación influye en aproximar mejor -\begin{figure}[h!] - \includegraphics[width=0.8\textwidth]{1-Introduccion_redes_neuronales/Idea-forma-función-Activación.jpg} - \caption{Cómo afecta la forma de la función de activación} - \label{img:como afecta la forma de la función de aproximación} - \end{figure} diff --git a/Memoria/capitulos/3-Teoria_aproximacion/0_objetivos.tex b/Memoria/capitulos/3-Teoria_aproximacion/0_objetivos.tex index 0cbac4a..d1055e7 100644 --- a/Memoria/capitulos/3-Teoria_aproximacion/0_objetivos.tex +++ b/Memoria/capitulos/3-Teoria_aproximacion/0_objetivos.tex @@ -6,17 +6,14 @@ \chapter{Teoría de la aproximación} \label{chapter:teoria-aproximar} -Teniendo siempre presente la busca de una comprensión última de las redes neuronales -con el fin de poder encontrar alguna clave con las que optimizarlas, debemos de -notar que el teorema clave que nos asegura la convergencia se trata del Teorema de Stone Weierstrass -usado en el teorema \ref{teo:TeoremaConvergenciaRealEnCompactosDefinicionesEsenciales}. -El desarrollo de los capítulos comprendidos entre Polinomios de Bernstein \refeq{ch:Bernstein}, -a la demostración del teorema de Stone Weierstrass \refeq{ch:TeoremaStoneWeiertrass} es múltiple. -Se pretende primeramente construir las herramientas esenciales para la demostración del -Teorema Universal de redes neuronales por propagación hacia delante y hacia detrás; -mas comprendiendo la naturaleza del fundamento es posible entender la bondad, alcance e imposición -de las estructuras elementales que conforman las redes neuronales, luego se hará simultáneamente -un análisis y estudio de las implicaciones de la teoría demostrada. + + Puesto que queremos fundamentar desde el origen las redes neuronales, + supongamos que desconociéramos la existencia de éstas + y nos halláramos frente a un problema de aprendizaje \ref{sec:Aprendizaje}; + el enfoque más natural consistiría en abordar el problema + haciendo uso de teoría de la aproximación. + ¿Somos capaces de aproximar una función a partir de sus puntos? + diff --git a/Memoria/capitulos/3-Teoria_aproximacion/1_polinomios_de_Bernstein.tex b/Memoria/capitulos/3-Teoria_aproximacion/1_polinomios_de_Bernstein.tex index b7adb96..0a54c4f 100644 --- a/Memoria/capitulos/3-Teoria_aproximacion/1_polinomios_de_Bernstein.tex +++ b/Memoria/capitulos/3-Teoria_aproximacion/1_polinomios_de_Bernstein.tex @@ -13,16 +13,45 @@ \section{Polinomios de Bernstein}\label{ch:Bernstein} función continua en un compacto y serán esenciales para nuestra prueba del teorema de Stone-Weierstrass, las pruebas se basan en el manual \cite{the-elements-of-real-analysis}. +% Nota margen sobre Idea intuitiva convergencia uniforme +\marginpar{\maginLetterSize +\iconoAclaraciones \textcolor{dark_green}{ +\textbf{¿Qué es la convergencia uniforme?}}. + Cuando se hable de \textbf{convergencia} nos referiremos + a encontrar un elemento que \textbf{aproxime todo lo bien que queramos}; + es decir, fijado cualquier error podemos encontrar un elemento + que lo aproxime cometiendo un error menor. + + Tengamos presente que en el contexto de funciones + el error se puede medir evaluando la imagen de cada punto del dominio; entonces + con \textbf{uniforme} lo que se dice es que independientemente del error que se busque, + se puede encontrar una función que en \textbf{para todos los puntos del dominio + aproxima al objetivo + por debajo de ese error}. +} + Comenzaremos recordando el Teorema del Binomio de Newton. %% Teorema Binomio de Newton - \begin{teorema}[Binomio de Newton] Cualquier potencia de un binomio $x+y$ con $x,y \in \R$, puede ser expandido en una suma de la forma \[ (x+y)^n = \sum_{k=0}^n \binom{n}{k} x^{k}y^{n-k}. \] -\end{teorema} +\end{teorema} + +% Nota margen sobre Idea intuitiva sobre porqué se está procediendo de esta manera +\marginpar{\maginLetterSize +\iconoAclaraciones \textcolor{dark_green}{ +\textbf{¿Con qué idea se está avanzando?}}. + + Nuestro objetivo es ser capaces de aproximar una función + a partir de una muestra de sus preimágenes e imágenes. + Comenzaremos probando primero que dada una función continua, podemos + aproximarla a partir de una muestra concreta. + Por eso se está tratando de reescribir la función en + términos de cierto conjunto de sus imágenes. +} %%% Idea intuitiva y desigualdad Tomando ahora para esta igualdad $x \in \R, y= 1-x$ se tiene que @@ -30,7 +59,7 @@ \section{Polinomios de Bernstein}\label{ch:Bernstein} 1 = (x+ (1-x))^n = \sum_{k=0}^n \binom{n}{k} x^{k} (1-x)^{n-k}. \end{equation} -Dada cualquier función $f$ definida en $x$ podríamos multiplicar la ecuación +Dada cualquier función continua $f: I \longrightarrow \R$ podríamos multiplicar la ecuación \eqref{eq:uno_igual_binomio} por $f(x)$ resultando \begin{equation}\label{eq:f_igual_binomio} diff --git a/Memoria/capitulos/3-Teoria_aproximacion/2_Weierstrass_approximation_theorem.tex b/Memoria/capitulos/3-Teoria_aproximacion/2_Weierstrass_approximation_theorem.tex index 7178950..d2accf0 100644 --- a/Memoria/capitulos/3-Teoria_aproximacion/2_Weierstrass_approximation_theorem.tex +++ b/Memoria/capitulos/3-Teoria_aproximacion/2_Weierstrass_approximation_theorem.tex @@ -14,6 +14,18 @@ Sin embargo, tal dificultad es fácilmente salvable con un homeomorfismo. +% Nota margen sobre Idea intuitiva homeomorfismo +\marginpar{\maginLetterSize +\iconoAclaraciones \textcolor{dark_green}{ +\textbf{¿Qué es un homeomorfismo?}}. + + A nivel intuitivo un homeomorfismo sería + que una transformación de un objeto que lo desforma + \textit{sin romperlo}. Es decir, si este fuera plastilina + sería aquellas figuras que podríamos llegar sin separar la masilla + o crear y eliminar agujeros. +} + Como resultado de relajar el dominio donde se define $f$, pidiéndole tan solo compacidad nace el siguiente corolario. diff --git a/Memoria/capitulos/3-Teoria_aproximacion/4_Conclusiones.tex b/Memoria/capitulos/3-Teoria_aproximacion/4_Conclusiones.tex new file mode 100644 index 0000000..d05167d --- /dev/null +++ b/Memoria/capitulos/3-Teoria_aproximacion/4_Conclusiones.tex @@ -0,0 +1,102 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Conclusiones teoría de la aproximación +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\section{Conclusiones teoría de la aproximación} + +Acabamos de probar en \ref{ch:TeoremaStoneWeiertrass} que cualquier función +continua es aproximable uniformemente con polinomios en un compacto. +Sin embargo este enfoque tiene los siguientes problemas: + +\begin{enumerate} + \item La prueba obtenida no nos permite + una forma constructiva sencilla de obtener el polinomio. + \item Aproximando con polinomios se corre el riesgo de que fuera de la muestra + el error sea demasiado grande. Como muestra de ello damos un ejemplo patológico + reflejado en la figura \ref{fig:aproximacion-lagrage}; se pretende aproximar + la función $f:[-3,3] \longrightarrow \R$ definida como + \begin{equation*} + f(x)= \left\{ \begin{array}{lcc} + e^{-x} + 4 & si & x \leq 1 \\ + \\ \log{x} & si & x > 1 + \end{array} + \right. + \end{equation*} + usando el método de interpolación de Lagrange; en este caso el error tiende a + infinito conforme aumenta el número de nodos. \footnote{Puede encontrar la implementación + del código que genera las gráficas en nuestro repositorio + \url{https://github.com/BlancaCC/TFG-Estudio-de-las-redes-neuronales}, + en el fichero \texttt{Lagrange.ipynb} que se encuentra en el directorio de teoría de la aproximación de la memoria. } + + \item Y otra cuestión, de corte físico o filosófico ¿son todos los fenómenos observables continuos? + Sería extraño que así fueran, lo que evidencia la necesidad de formular una teoría más general. +\end{enumerate} + +\begin{figure}[H] + \centering + \begin{subfigure}[b]{0.475\textwidth} + \centering + \includegraphics[width=\textwidth]{metodo-lagrange/lagrange-3-datos.png} + \caption[Network2]% + {{\small Polinomio de Lagrange utilizando 3 datos}} + \end{subfigure} + \hfill + \begin{subfigure}[b]{0.475\textwidth} + \centering + \includegraphics[width=\textwidth]{metodo-lagrange/lagrange-8-datos.png} + \caption[]% + {{\small Polinomio de Lagrange utilizando 8 datos}} + \end{subfigure} + \vskip\baselineskip + \begin{subfigure}[b]{0.475\textwidth} + \centering + \includegraphics[width=\textwidth]{metodo-lagrange/lagrange-13-datos.png} + \caption[]% + {{\small Polinomio de Lagrange utilizando 13 datos}} + \end{subfigure} + \hfill + \begin{subfigure}[b]{0.475\textwidth} + \centering + \includegraphics[width=\textwidth]{metodo-lagrange/lagrange-18-datos.png} + \caption[]% + {{\small Polinomio de Lagrange utilizando 18 datos}} + \end{subfigure} + \caption{Ejemplo de aproximación de la función $f(x)$ a partir de los polinomios de Lagrange.} + \label{fig:aproximacion-lagrage} +\end{figure} + +El problema que evidencia este caso patológico es el tratar de abarcar todo el dominio +con un mismo polinomio ¿y si en lugar de eso se hicieran aproximaciones +en una partición concreta del dominio? El resultado sería una función definida a trozos. +La cuestión es que esta aproximación sería difícil de implementar de manera eficiente; +sin embargo, es el germen y el enfoque de las \textit{funciones de activación}. + +De todas formas no abandonemos del todo esta teoría, porque como ya veremos el +teorema de Stone Weierstrass \ref{ch:TeoremaStoneWeiertrass} jugará +un papel fundamental es la demostración +de que las redes neuronales son aproximadores universales. + +\subsection*{Las funciones de activación $\Gamma$ son la clave del aprendizaje} + +Notemos que de no ser por las funciones de activación se estarían haciendo transformaciones lineales de los datos, por el contrario estamos realizando \textit{cambios más fuerte} siendo capaz con esto de \textit{de diferenciar puntos claves}. + +Vamos a mostrar un ejemplo de la importancia de la función de activación + +\textcolor{red}{TODO : Añadir gráficos cuando esté implementada una red neuronal} + + +% Ejemplo de cómo se aproxima gracias a la forma de la función de activación +\begin{figure}[h!] + \includegraphics[width=\textwidth]{1-Introduccion_redes_neuronales/idea-como-aproxima-redes-neuronales.jpeg} + \caption{Cómo actúa en la aproximación una función de activación} + \label{img:idea-como-aproxima-redes-neuronales} + \end{figure} + +La idea intuitiva es que para una capa oculta con una neurona, +lo que se hace es \textit{colocar} por escalado y simetrías la imagen de la función de activación. + +% Ejemplo trivial de como la forma de la función de activación influye en aproximar mejor +\begin{figure}[h!] + \includegraphics[width=0.8\textwidth]{1-Introduccion_redes_neuronales/Idea-forma-función-Activación.jpg} + \caption{Cómo afecta la forma de la función de activación} + \label{img:como afecta la forma de la función de aproximación} +\end{figure} diff --git a/Memoria/capitulos/3-Teoria_aproximacion/Lagrange.ipynb b/Memoria/capitulos/3-Teoria_aproximacion/Lagrange.ipynb new file mode 100644 index 0000000..b4e344b --- /dev/null +++ b/Memoria/capitulos/3-Teoria_aproximacion/Lagrange.ipynb @@ -0,0 +1,123 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ejemplo para ver cómo el error tiende a infinito al aproximar con polinomios\n", + "## Utiliza método de los polinomios de Lagrange " + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.interpolate import lagrange\n", + "import matplotlib.pyplot as plt\n", + "from numpy.polynomial.polynomial import Polynomial\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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f/QJ0NMZEAVuBRy9wfn9jTHRJM8CqXNhg2LcSMo+dt8vdxZmHB4eTcCidb+P3OaBxSlVPO3fu5Pfff2fz5s1ER0czcuRInJ2dSzzezc2NAQMG8NVXX1VhK1VJyrPi1VIRCTnnu5+LbK4ErrFRu+yv3eWwdDJs+wU6jT5v9xWRjfmoxS5e+nkrV0Y1wdu9bs1RU9Xcx1eUfky7QXDRP/4+Pvp6iBkLJ1Pg65vOPvbWkhOCnZaYmMill15KXl4eMTExAMycOfPM/v79+/PYY48xcOBA/vOf/3DixAneeOMNhg8fzqOPPsrYsWPLfHnKPmzZh38bML+EfQb4WUTiRWTchQoRkXEiEiciccnJduxDbxIDPg0hsfiVF0WEx6+IIDn9FO/pZCylCAsL4+abb+bpp5/mzz//5ODBg4SEhJzZP2nSJJ555hlmzJjBmjVrePXVVwHo2LEjq1atclCrVVE2uW0VkceBPGBGCYdcZIw5ICINgF9EJMEYs7S4A40x7wPvg5U8zRbtK5aTE4RfAeu+hJxMcDt/bdsuLQO5Iqox7y/dwfXdWtDI38NuzVGqXMpwR17i8d5B5T+/0IYNGxg2bBhHjx4lICDgrH19+vTBGMMrr7zCkiVLznT1ODs74+bmRnp6Or6+vhWqV9lGpe/wReRm4EpgrCkh9WbhouYYY44AM4Fula3XJiKGQ24mbP+lxEMeGRxOQQFMXqDr3yq1adMmOnTogKenJ9nZZ89I37BhAwcPHsTd3f28wH7q1Ck8PPSGydEqFfBFZDDwMDDUGJNZwjHeIuJ7+jNwGbCxuGOrXMuLwKs+bJpV4iHN63lx60UhfLc6ifVJqVXWNKWqm/T0dFxdXfHy8iIwMJD8/PwzQf/gwYOMHTuWH374AW9vbxYsWHDmvJSUFIKDg3F11aSEjlaeYZlfAH8AYSKSJCK3A28BvljdNGtFZErhsU1E5HTneENguYisA/4C5hpjih8PWdWcXSBiKCStOiuZ2rnuuaQNQd5uPDVns66MpeqsjRs30rFjxzPbl112GcuXLyczM5ORI0fy8ssv0759e/773/+etbTf4sWLGTJkiANarM5jjKm2ry5duhi7yzxmTF5OqYd9/uce0/LhH80Pa/fbv01KnWPz5s2ObsJ5Vq9ebW644YZSjxsxYoRJSEioghbVPcX9XABxpoSYWjdn2hblGQjOpf+peW1sc9o39uP5eVvIyin5rwGl6oqYmBj69+9f6sSr4cOHV2h9WmV7GvABtsyBd3pZo3VK4OwkPHlVBAfSsnlfh2kqBcBtt91W6sSrm266qcT9qmppwAfwCADfRpB59IKH9WgVxOUdGzHltx0cTMuqmrYppZSNaMAHCO0NN34PAS1KPfSxIe3JN4YX5idUQcOU+pvRAQOqiIr8PGjALyr9EOReeLWr5vW8uKN3KLPWHmD13uNV1DBV13l4eJCSkqJBXwFWsE9JSSn33AZNEHPa/tUw9RK4dhpEDLvgoXf3a8M3cUlMmrOZmXf1wslJqqiRqq5q1qwZSUlJ2DXdiKpRPDw8aNasWbnO0YB/WqMo8KpnTcIqJeB7u7vw8OBwHvxmHd+uTuLa2OZV00ZVZ7m6uhIaGuroZqgaTrt0TnN2gfZXwdYFkFv6A9kRMU3p3CKAF39KIC0rtwoaqJRSlaMBv6gOIyD3pJUyuRROTsJTwzqScjKH1xZurYLGKaVU5WjAL6rlxeAVBJtmln4s0LGpP9d1a8H0P/aQeEgXPVdKVW8a8ItydrEyaCbOh+wTZTplwmVh+Li7MHH2Jh1BoZSq1jTgn6vTdZCXBVtml+nwQG83/j0ojD92pjBvwyE7N04ppSpOA/65msVCvdbWwihldH23FkQ09uN/czeTmaOLNSulqicN+OcSse7yk1YVu8B5cZydhKeGdeBgWjbvLN5h5wYqpVTFaMAvTrf/gwcTrHH5ZRQbUo8RMU15f+lOdh09acfGKaVUxZRnAZSPROSIiGws8l09EflFRLYVvgeWcO5gEUkUke0i8ogtGm5XnoHWq5weHRKOu4sTT/ywUR/gKqWqnfLc4X8CDD7nu0eAX40xbYFfC7fPIiLOwNvA5UAEcJ2IRFSotVUpeStMHQD7VpX5lAa+Hjx4WTuWbTuqD3CVUtVOmQO+MWYpcG6n9jBgWuHnacDwYk7tBmw3xuw0xuQAXxaeV735NgIM5GSU67QberSkQxM/nvpxExmn9AGuUqr6qGwffkNjzEGAwvcGxRzTFNhXZDup8LvqzcMP7lgErfuX6zQXZyf+N7wjR9JP8dovOgNXKVV9VMVD2+JSSZbYwS0i40QkTkTiqkVmwJyTkLqv9OOKiGkRyJiuLfh4xW62HCzbBC6llLK3ygb8wyLSGKDw/UgxxyQBRdNJNgMOlFSgMeZ9Y0ysMSY2ODi4ks2rJGPgvT4w/6Fyn/rQoDD8PV35z6yNFBToA1yllONVNuDPBm4u/Hwz8EMxx6wC2opIqIi4AWMKz6v+RCBsCGz7GU5eePnDcwV6u/HI5eHE7znOt6uT7NRApZQqu/IMy/wC+AMIE5EkEbkdeB4YKCLbgIGF24hIExGZB2CMyQPuBRYAW4CvjTGbbHsZdtTpOijIg43flfvUazo3I7ZlIM/N28Lxkzl2aJxSSpWdVOfx4rGxsSYuLs7RzYApva338cvKfWrCoRNc+cZyRsQ0ZfKoTjZumFJKnU1E4o0xscXt05m2ZdH5Jji03loGsZzCG/nxf71b8U18En/sSLFD45RSqmw04JdF1LXg6gVxH1Xo9PsHtKV5PU8en7mB7Nx8GzdOKaXKRgN+WXj4Q+Q1Vj9+Vmq5T/d0c+aZ4ZHsPHqSd5ZocjWllGNowC+r2NsgNxPWf12h0/u0C2ZYdBPeXbKd7UfKN3tXKaVsQQN+WTWJsV6HN5Z+bAn+c0UEnq7OPDZzg47NV0pVOQ345XHLPBj6RoVPD/Z157Eh7flr1zG+iS/f7F2llKosDfjl4eZlvZ+qeJfMtbHN6RZSj2fnJZCcfspGDVNKqdJpwC+v1Z/Cy2FwsmJDLJ2chGdHRpKVk8/EOTVn/plSqubTgF9ezbpC55vBVHx4ZZsGPvxjQBvmrj/Igk2aN18pVTU04JdXg3AY/Cz4FJcJuuzu7Nua8Ea+/HfWRtKycm3UOKWUKpkG/IooKICdS+DgugoX4ersxORrOnE04xTPzdtiu7YppVQJNOBXRH4OfHMrLJ1cqWIim/lzR59WfLlqH79vL182TqWUKi8N+BXh6gGdb4SEeZC6t1JF/evSdoQEefHo9xvIytG0C0op+9GAX1Hd7rTy5a+cUqliPFydef7qKPYey+SVXxJt1DillDqfBvyK8m8KHUbA6umQnVaponq0CuL67i34cPkuVu89bqMGKqXU2TTgV0bPeyEnHeKnVbqoRy8Pp5GfBxO+WacZNZVSdlHpgC8iYSKytsjrhIj885xj+olIWpFjnqhsvdVCk2gI6Q1/ToH8yg2t9PVw5YVrotiRfJJXf9lqm/YppVQRlQ74xphEY0y0MSYa6AJkAjOLOXTZ6eOMMU9Vtt5qo+e9cGI/bJpV6aJ6tw3mum4teH/ZTuL3aNeOUsq2bN2lMwDYYYzZY+Nyq6+2l0FQW/jjTbDBcpGPX9GeJv6e2rWjlLI5Wwf8McAXJezrKSLrRGS+iHQoqQARGScicSISl5ycbOPm2YGTE/R7BCKGW4udV5KPuwsvXhPFzqMneWmBjtpRStmOzRYxFxE34ADQwRhz+Jx9fkCBMSZDRIYArxtj2pZWZrVZxNwB/jNrAzP+3Ms3d/YkNqSeo5ujlKohqmoR88uB1ecGewBjzAljTEbh53mAq4jUt2HdjpefC+u/gRTbLGH46OXtaRrgyb+/WacTspRSNmHLgH8dJXTniEgjEZHCz90K661YfuHqKisVZt8Laz+3SXHehV07u1MyeX6+5tpRSlWeiy0KEREvYCBwZ5HvxgMYY6YA1wB3iUgekAWMMbbqS6oufILhjsUQHG6zInu1rs9tF4Xy0e+7uDSiIb3bBtusbKVU3WOzPnx7qLF9+Pm54Oxqk6Kyc/O58s3lZGTnseCfffD3sk25Sqnaqar68BVAwlx4tSOkn/coo0I8XJ15bXQ0RzNO8d8fKr6AulJKacC3teBwOJkMv79usyI7NvXn/gFtmb3uALPXHbBZuUqpukUDvq0FtYaoayHuI8g4YrNi7+rXmpgWAfxn5gYOpWXbrFylVN2hAd8e+kyA/FM2vct3cXbilWujyc03TPh2HQUF1ffZi1KqetKAbw9BrSFyFKz6EDJsN1s4tL43j1/RnmXbjjLtj902K1cpVTdowLeX03f5K96wabFju7dgQHgDnpufwJaDJ2xatlKqdtOAby/120LHq2HVB3DSduvViggvXBOFn4cr//hijSZYU0qVmQZ8e+ozAXKzYMWbNi22vo87L1/biW1HMnh2ns7CVUqVjQZ8ewoOg44jIWW7TVInF9W3XTC3XxzK9D/2sHCzbcb8K6VqNw349jbsbRgzw1rw3MYeGhxG+8Z+PPTdeo6c0KGaSqkL04Bvb66e1nvqXutlQ+4uzrx5XTSZOXk8+I0O1VRKXZgG/KqQmw3v94dfbL+Ub5sGvvz3ygiWbTvK1GU7bV6+Uqr2sEm2TFUKVw8Y9hY07GiX4q/v1oJlW48yeUEiXUPr0blFoF3qUUrVbHqHX1XCLoeA5tbDWxs/wD09VLORvwf3fb6GtMxcm5avlKodNOBXpZNHYfow2DLb5kX7e7ry1vWdOXwimwnfrqM6p71WSjmGTQK+iOwWkQ0islZEzktgL5Y3RGS7iKwXkc62qLfG8Qy0EqotnAh5OTYvPrp5AI9cHs7Pmw8zbcVum5evlKrZbHmH398YE11C4v3LgbaFr3HAuzast+ZwcoaBT8GxnRD/iV2quP3iUAaEN+DZeQlsSEqzSx1KqZqpqrp0hgHTjWUlECAijauo7uql7UAI6Q2/PW+tg2tjIsJLozoR5OPGPZ+v5kS29ucrpSy2CvgG+FlE4kVkXDH7mwL7imwnFX53HhEZJyJxIhKXnGy7TJPVhggMegayjsPiZ+1SRaC3G29eF8P+1Cwe/na99ucrpQDbBfyLjDGdsbpu7hGRPufsL26aabFRyBjzvjEm1hgTGxxcSxftbtwJYm+HVVPh4Dq7VBEbUo+HB4cxf+MhPly+yy51KKVqFpsEfGPMgcL3I8BMoNs5hyQBzYtsNwPq9lp9l/wHvILgxwegoMAuVdzRuxWDOjTkufkJrNp9zC51KKVqjkoHfBHxFhHf05+By4BzV9ueDdxUOFqnB5BmjDlY2bprNM8AuOx/sD8O1nxqlypEhMmjOtE80JN7ZqwmOf2UXepRStUMtrjDbwgsF5F1wF/AXGPMTyIyXkTGFx4zD9gJbAemAnfboN6aL2o0tB0Exn457f08XHn3hi6cyM7lvi9Wk5dvn78mlFLVn1TnB3qxsbEmLu68Yf21izF2yaR5ru/ik3jwm3WM79uaRy4Pt3t9SinHEJH4EobH60xbhxOxgv76byAp3m7VXN2lGdd3b8GU33awYNMhu9WjlKq+NOBXB7mZVibNuA/tWs0TV0YQ1cyfB79ex/YjGXatSylV/WjArw7cvOHWuTDUtkshnsvD1ZkpN3TBw9WJcdPjdFKWUnWMBvzqol4rK/VCRjIcs9+4+SYBnrx9fWf2HsvkX1+u1UVTlKpDNOBXJwUF8MkV8O1tkJ9nt2q6twriiasi+DXhCK8t3Gq3epRS1YsG/OrEyQn6PQIHVsOKN+xa1Y09WjKqSzPeWLSdnzbqQ1yl6gIN+NVNx5EQMQyWPAeHN9utGhHh6eEd6dQ8gAe/XsvWw+l2q0spVT1owK+OrngF3P1g1l2Qb78Hqx6uzrx3Qxc83Vz4v2lxHD9p+xz9SqnqQwN+deRdH654GQ6uhd9fs2tVjfw9eO/GLhw6kc34z+LJydOZuErVVhrwq6sOw6HDSFjyAhxcb9equrQM5MWro/hz1zGenL1R0ykrVUtpwK/Ohrxk3e1/eyucsu9EqeExTbm3fxu++GsfH/2+2651KaUcQwN+deYdBCOnwvE9sHOJ3at7YGA7BndoxDNzN7M44Yjd61NKVS0N+NVdaG+4fy20v9LuVTk5Ca+M7kT7xn7c98UaHbmjVC2jAb8m8G9mve9YBMmJdq3Ky82FqTfF4unmzK0fr+JIerZd61NKVR0N+DVFTiZ8f6c1Pt/OmgR48uHNsRw7mcPtn8SRmWO/Wb9KqapjixWvmovIYhHZIiKbROT+Yo7pJyJpIrK28PVEZeutc9y84MbvYdg7VVJdVLMA3ro+hk0H0vjHF2vI15w7StV4trjDzwMeNMa0B3pgLWIeUcxxy4wx0YWvp2xQb93TKNIK/KfSYddSu1c3oH1DJg3twMItR5g4e5MO11Sqhqt0wDfGHDTGrC78nA5sAZpWtlx1AT//Bz67Bvbbb8GU027sGcK4Pq34dOUepi7baff6lFL2Y9M+fBEJAWKAP4vZ3VNE1onIfBHpYMt665xL/gu+DeHLsZBu/8RnjwwO54rIxjw7L4G56+v22vNK1WQ2C/gi4gN8B/zTGHPinN2rgZbGmE7Am8CsC5QzTkTiRCQuOTnZVs2rXbzrw5gvIPuEFfRz7TuSxslJePnaTsS2DORfX61lxY6jdq1PKWUfNgn4IuKKFexnGGO+P3e/MeaEMSaj8PM8wFVE6hdXljHmfWNMrDEmNjg42BbNq50adYQRU2B/HPz4T2tdXDvycHXmg5tjaRnkxbjp8Wzcn2bX+pRStmeLUToCfAhsMca8UsIxjQqPQ0S6FdabUtm667yIodDvUVj3Bfzxtt2rC/ByY/rt3fDzcOGWj1exJ+Wk3etUStmOLe7wLwJuBC4pMuxyiIiMF5HxhcdcA2wUkXXAG8AYo0M+bKPPQ9B+KPzyX9i6wO7VNfb3ZPrt3ckvKODGD//SiVlK1SBSneNubGysiYuLc3Qzqr+ck/Dx5ZC8Ff5vodXdY2dr9h7n+ql/ElLfm6/u7IGfh6vd61RKlU5E4o0xscXt05m2tYGbN4z9FrreDvXbVUmVMS0CmXJjF7YdTuf/psWRlZNfJfUqpSpOA35t4dMABj0DLm5wMgVO2H/4ZN92wbx8bSdW7T7G+M/iOZWnQV+p6kwDfm1jDHw+Cr683u4jdwCGRTfluRGR/LY1mfu/WEtevq6YpVR15eLoBigbE4EBT4Kzq/W5Cozp1oLMnHye+nEzE75dz8ujOuHkVDV1K6XKTgN+bdSq79+fE+dDq/7g6mHXKm+7OJSs3HwmL0jE082ZZ4Z3RKroF45Sqmw04NdmyVvhi+ugzQAYPcPuQf+e/m04eSqPd5bswNPVmf9c0V6DvlLlMGvNfiYvSORAahZNAjyZMCiM4TG2S02mffi1WXA7uOp12L4QvrJ/CgaACYPCuKVXCB8u38Vz8xM0w6ZSZTRrzX4e/X4D+1OzMMD+1Cwe/X4Ds9bst1kdeodf23W52Xqf8w8r6Nv5Tl9EePKqCAqM4f2lVnbNRy8P1zt9pYrKzYKs45CVCtmpkJXKhnl/cEk+zKXHmcNOd5Pa6i5fA35d4ICgP2molRBVg76qtYyxJj1mHbOCd+YxyM2E8Cus/aunQ+o+uORxa/ubW2HP71aQzz91XnH/Bba5NGVuTo+zvj+QmmWzJmvAryuKBv3Pr4XRn4GHn92q06Cvapy8U5CZAj4NwckZ9q+GA2usCY1g5atKnG8F9qxj1vu5gdvZHf5z2Bohd2ANHNr4d8BvEAHuPuAZCB4B4Blw1vuwDzexK9vtvGY1CfC02SVqwK9LutwMzm7wwz0w7Uprdq5PA7tVd27QN8bw2BB9kKuqiDFWd4m7nxXAD22APSvgZHLh6+jZn08VZnV/MBF8G8G2n601pDvfZA1zzjkJ+bkQGAJNY8CzHngFgVc967NnoPX5tCtfPbs9fSeU2NTFCUfYlH2IfM5+5uXp6syEQWG2+e+BBvy6J/o664f0h7vhxH67Bnz4O+gLMHXZLrJzC5g0tIOO01cVl58LGUesxX8yDhW+Hy58PwID/gsNO1hZZGfdBfethqDWsGOxlWRQnMCrPngHW2tLNOlsvXvVB+8gcPWy6uk+HrreAU6FYbLvQ9bLxhZuPszdM1YT1siX67u14J0lO+w2SkcDfl3U7jK4f721Pi5Y/6P4NrJbdSLCxKEd8HB15r2lOzmZk8eLV0fh4qyDxNQ5ck5ad+buPpB+2OoHjxhmjTjbsQhmjreCOueO/hIraPs0tNZ8BmjWFQY9a3WbgPUXbvT11t24Uxl+9jwDbHddJfhp40Hu/XwNHZr4Mf227vh7uTK2R0u71acBv646HexXT4efHoM7foVg2/3peC4R4ZHLw/Fxd+HlX7aSeSqf16+Lxt3F2W51qmrGGCtYp+6F1D2Qtg/SkiBtf+H7PqsLZvDz0OMuK3Av/h/UC7UCvm9jaHuZ9e7bqPC9ofXuHWx1uxRVv631Os3Dv0ovtzRfx+3jke/WE908gE9u61YlGWc14Nd1bS6FzgkQ1MbuVYkI9w1oi5e7C0//uJlx0+OZckMXPN006Nca2SesIYe+DaEgH+ZNgNDe0GGEFdRfOyd1t4c/+DcH/2bQojv4NYEWhaNU6rWCxw//PaKsQXsY9lbVXo+dfLBsJ/+bu4Xebevz3o1d8HKrmlCsAb+u82sCg5+1Pp84CCvfsRZJdzl/tICt3H5xKD7uzjzy/QZu/vgvPrg5VvPp1yQ5mZCyHVK2QcpOOLYTju2w3k8mQ8RwuHaa9aB012/g19g6z68JXD4ZAltaQT6gObj7llyPkxM42Xd2eFUzxvDKL1t5c9F2hkQ24tXRVftXrk0WQBGRwcDrgDPwgTHm+XP2S+H+IUAmcIsxZnVp5eoCKFUs/hOYcz+06AnXfgo+9l1TeM66Azzw9VpaB/sw7bZuNPSrXf9z1woH1lhjzFtfYm1PHWCto1yUbxPrbjyoFdRrDU1izs7npAAoKDA8OXsTn67cw+jY5jw7MhJnOwxeuNACKJUO+CLiDGwFBgJJwCrgOmPM5iLHDAHuwwr43YHXjTHdSytbA74DbPwOZt1jjeQZMwOaRNu1umXbkhn/afyZ9XJbB/vYtT5VjLwcOJoIhzdZQxczDsPVH1j7vrjOunO/509re+lkqy8+qI3VP16vlbUAj7qgU3n5/Pub9cxZd4A7+7TiETvOSbF3wO8JTDTGDCrcfhTAGPNckWPeA5YYY74o3E4E+hljLrhKhwZ8Bzmw1sqnf/KotahK1/+za6rlDUlp3PLxXxQYw0e3dCWmRaDd6lJF5OfBZyNgzx9QkGt95+wODSPg9l+sh6BHt4GLOwS0cGxba7C0rFzu/DSOlTuP8fDgcO7q19qu9dl7icOmwL4i20mF35X3GABEZJyIxIlIXHJysg2ap8qtSTTcuRRC+8C8f8PXN1nTwe0kspk/393VCz9PV66f+ieLE47YrS5VROoe2LUUwofA1R/C3X/CYwdg3JK/R7zUb6vBvhL2p2ZxzbsriN9znNdGR9s92JfGFgG/uFu/YgbJlnqM9aUx7xtjYo0xscHB9u1DVhfgXR+u/xoGPg2J8+C93pBkv7+2Qup78+34XrRu4M3/TY9jxp977FaXKpSyw3rvcTdEXgMNwsFZx3HYyqYDaYx4+3cOpWUz7dZuNp1AVVG2CPhJQPMi282AAxU4RlU3Tk5w0T/g1p+sX887l9i1umBfd74c15Pebevz+MyN/O/HzeQXaHpluzlWGPDrOfauszZaujWZa6f8gbOT8O1dvejVpr6jmwTYJuCvAtqKSKiIuAFjgNnnHDMbuEksPYC00vrvVTXSvCvctRwufsDa3rXU6tu1Ax93Fz64KZZbeoXwwfJd3PlpPCdP5dmlrjovZQe4+1t/zSmbmf7Hbm79ZBXN63kx8+6LCGt0gaGnVcxWwzKHAK9hDcv8yBjzjIiMBzDGTCkclvkWMBhrWOatxphS+wf0oW01VFAA73S3pqff9pNdH+ZOW7GbSXM2Ed7Ijw9viaWxv+2yBirgVAakHzx7NqqqsNz8AibN2cRnK/dyafsGvDYmBh/3qu8is+soHXvSgF9NpR+ygkX9Nn/nAQ+yT7fA4oQj3Pv5anw8XHjvxliimwfYpR6lKiM1M4e7Z6xmxY4U7uzbiocGhdtljH1Z2HuUjqprfBtZwR5g4SR4pycsed4uSyj2D2/Ad3f3wtXZiWvf+4Ov4/aVfpIqXd4p+OUJa2KVqpTtRzIY/vbvxO0+zkujOvHo5e0dFuxLowFfVU7fh6H9lVbe8Hd6wLaFNq8ivJEfs++9mK4hgTz07Xqe+GEjufkFNq+nTjmxH/54x1roXlXYz5sOMeLt38k4lccX47pzTZdmjm7SBWmXjrKNnUtg7r+t/Crtr7IyHvrb9oc/L7+AF35KYOqyXXQLqcfbYzsT7Otu0zrqlIJ862XHvEm1VX6B4eWfE3lnyQ6imvnz7g1daGrDlakqQ/vwVdXIOwV/vAW/Tba2e94DF91v86UUf1i7n4e/W0+glxtvj+1MZ52Zq6pQSsYp7v9yLcu3H+W6bi148qoIPFyrT8ZX7cNXVcPFHXo/CPf+ZS3kvOwleCvWesBrQ8Oim/Lt+F44OwnXTvmDqYXLJ6pyWPmu1YevymXdvlSuenM5f+0+xotXR/HcyMhqFexLowFf2V5AC7jmQ7hjMVz0T2v1IoB9q6xhnTbQsak/c//RmwHtG/DMvC3cMT2etMxcm5RdJyTOg92/O7oVNYYxhg+W7eSaKSsQEb4b34truzYv/cRqRgO+sp+mnaHn3dbnA2vgw0sh7kObFe/v6cqUG7rwxJUR/Lb1CEPeWMbafak2K79WS9lpt6G0tU1KxilunxbH/+ZuoV9YA36872Iim1Wv1bPKSgO+qhoNI2HE+xB1rbW9/VfYNLPSd/wiwm0Xh/LN+F4AjJqygim/7dCUDBeSmwUnkqpklbOabsWOo1z++jKWbzvKU8M68P6NXQj0rrkPuTXgq6rh7AKdRv+9rmjcR/DNLfBuT1jzmfXAtxKimwcw7x+9ubR9Q56fn8D1U1eSdDyz8u2ujY7ttN7rtXJsO6qx3PwCXv45kbEf/ImPhwsz7+nFTT1D7JbDvqpowFeOce10KyWvOMMP98BrkbD0JWvWbgX5e7nyztjOTL4mio3707j8tWXMWrPfho2uJU5nydQunWJtPZzOiHd+581F27m6czN+vO9iOjSpmV0459KArxzDydlKyXvX73DjTGjYERY9Da92gLkPwpEtFSpWRBgV25z59/ehXSNf/vnVWu77Yg2pmTk2voAaTLNkFiu/wPD+0h1c+eZyDqRmM+WGzrw0qlOVLTBeFWrPlaiaScRaL7X1JXB4M/zxNqyeDqs+gLHfQdtLK1RsiyAvvhrXgym/7eC1hdtYuTOFp4d1YHDHxja+gBooZQd4B9t8fkRNtiflJP/+Zh2rdh/nsoiGPDsykvo+tW9Sn068UtXPyRRY/6W1tKKLuxX8U/fCgIlWjv5y2rg/jYe+Xc/mgycYEtmISUM71u0Zuh8PsWbY3r7A0S1xuLz8Aj5ZsZuXf96Ki7MwaWgHRsQ0rdF99TrxStUs3kHWLF2XwqB8dLu1zu7pYJ8wz1pvt4w6NvXnh3svYsKgMBZuPsLAV39j5pqkujtZy9kVGnV0dCscbuP+NEa8s4L/zd1Cz9ZBLPhnH0Z2blajg31p9A5f1QwF+Va/f+YxeKkwf3vby6Dj1dBuELiXbZGJ7UfSeejb9azem0rvtvWZNLQDrYJ97NhwVd1k5uTx+sJtfLB8F4FebkwcGsEVkY1rTaC3Wy4dEZkMXAXkADuwFjZJLea43UA6kA/kldSYc2nAV8U6tNHq8tnwrbWAh4sHtLkUIoZD2OBSg39+geGzlXt46edETuUWMK5PK+7p3wZPt5ozRV6VnzGGXzYf5um5m9l3LIvrujXnkcHt8fdydXTTbMqeAf8yYJExJk9EXgAwxjxczHG7gVhjTNn/DkcDvipFQQHsWwmbZsHmHyDjEDi7Q6t+MOjZv3P2lyA5/RTPzdvC92v20zTAkyevimBgRMNac6dXrK0LYPlrcPUH4O/4RbWryvYj6Uyas5ll247SpoEP/xvekR6tghzdLLu4UMCv1CgdY8zPRTZXAtdUpjylysXJCVr2sl6Dn7eC/+YfYNvPf0/w2jTLGuLZ9yGrS6iIYF93XhkdzeiuzXnih02M+zSe3m3r89iQ9rRvXEtHsBgDGPAMcHRLqsSJ7FxeX7iNaSt24+nmzBNXRnBjz5a4OtfNx5c268MXkTnAV8aYz4rZtws4DhjgPWPM+xcoZxwwDqBFixZd9uzZY5P2qTrqp8dg+y9w7ypr+4+3wc0bWvWHwJZnDsvNL2D6H3t449dtnMjOZVSXZjwwMIxG/h4OariqjJy8Ar5ctZc3ft1GyskcxnRtwb8va0dQLRxqea5KdemIyEKgUTG7HjfG/FB4zONALDDSFFOgiDQxxhwQkQbAL8B9xpilpTVcu3SUTeSdskb8GANTLobDG63vA1pCyMXQ8iLrPbAlaZm5vLV4G9NW7MHJCcb1bsW4vq0dshi1XRhj14XnHa2gwDBn/QFe/nkre49l0j20Hv+9MoKOTWvHTNmysOsCKCJyMzAeGGCMKTV5iYhMBDKMMS+VdqwGfGVzxsDRrbBjEexeDnt+txZiB/BvDi16QOQo9gb15sUFCfy4/iCBXq6M69Oam3q25JfNh5m8IJEDqVk0CfBkwqAwhsfUoL7w1yKh0/XQ/1FHt8SmjDH8tjWZF39KZPPBE7Rv7MdDg8Po1y64dj+TKYbd+vBFZDDwMNC3pGAvIt6AkzEmvfDzZcBTlalXqQoTgeAw69XjLuvBb/IWKzf8nuXWL4EG7WnRbhBvDQ/h+ROP8EbBKF74KZd3F28lM7eAvMIEn/tTs3j0+w0ANSPoZ6dZE9hcq8dSfLZgjGFx4hHeWrSd1XtTaVHPi9fHRHNVVBOcqulC4o5U2b9T3wLcgV8Kf4uuNMaMF5EmwAfGmCFAQ2Bm4X4X4HNjzE+VrFcp23BygoYdrFf3cdZfAAX51r6Tyfg45fLY5R0YRHumfvA2z7u+w4aCUDaZUDYXtCAhrwWv/ORUMwJ+LUqaVlBg+HnzId5ctJ1NB07QNMCT/w3vyLWxzXFzqZsPZMuisqN0ih33Zow5AAwp/LwT6FSZepSqMiJWKmew/goYtxiALsCkPH/mOXcnymkntzrNx90lD4BT2S4wJcL6pREcBp1vBq96DrqACziTFrnmBvzs3HxmrzvA1KU72XYkg9D63ky+JorhMU3r7Mib8qglT6KUsr8U/w48lmrlkHchj9ZygHDZS3unvfRLP0zrE4twXfcFRI+1TljxFmz4Gv7vVyudwcF1UJBnBVxHDIs8fYdfL7Tq666kIyey+WzlHmb8uZeUkzmEN/Ll9THRXBnVBGftuikzDfhKldGEQWE8+v0GsnLzycOFRNOC3c4hpLYK4o3dx8jMyeeipi4M35LNlZ3y8fRpAPXDrGAPsOQFSJxrffYMhMBQCAyxAnBgiPXQOLCl/RYmObYD/JrVmD58Ywyr96YyY+Ue5qw/QF6BYUB4A267KJSerYPq3MNYW9BcOkqVw6w1+4sdpZOWmcv3a5KY8edeth/JwM/DhZGdm3F99xa0a1iY6iFlByQnWO/Hd8GxXdZ76j4whc8NAkPg/nXW558eA1cPGPCEtb1jEbj5gG8j8Gn4d3K5spo6wJqDcPNsm/y3sJfk9FPMXJPE13FJbD+SgbebM6Nim3NLrxBC6ns7unnVnt1G6ShV1wyPaVrsA1p/L1duvSiUW3qF8NeuY8z4cy8z/tzDJyt2076xH8OjmzA0ugmNw4vpP8/PhRP7rcBfdKnH7DSrC+i078fByeS/t72CwKdR4S+ABtaraReIGGbtT0608t6ffp6Qsh06jrTBfwXby87NZ0liMt+vTmJRwhHyCgxdWgby4tVRXBHVGO/aMg/CwfQOXyk7Sck4xex1B5i19gDr9qUiAt1D6zG0U1MujWhAA99yzuI9tNH6xZB+CDIOW4nj0gvfTyZDxhEr2F891Tr+mSbQ5RYY/Ky1xsDkVnDZM9DrXptfa0Vk5+azdGsyczccZOHmw5zMyae+jxsjOzfj2thmtGlQtgyo6mx2nXhlTxrwVW2x6+hJfli7n1lr9rM7JRMRa+H1gRENGdi+IW0a+FS+T9oYyM/5e1bx5h+sLqIm0ZCVCgset3IKFUkpUdWS00/x29ZkliQeYUliMhmn8gj0cmVwx0ZcEdmEHq3q4aKjbSpFA75S1YQxhoRD6fyy+TALtxxmfVIaAC3qeXFRmyB6tq5Pz1ZBtWZFruzcfNbtS2X59qMsSUxmw37reoN93ekfFswVUU3o1TpIh1TakAZ8paqpQ2nZLNxymCWJyfy5K4X0bKvPvm0DH3q0CiK6eQCdmvvTqr5PjZg5mpqZw5q9qfy1+xirdh1jfVIaOfkFOAl0bhFIv7Bg+oU1IKKxX424nppIA75SNUB+gWHTgTRW7EhhxY4U4ncf42SONXrHx92Fjk39iGoWQNsGPrRu4EObBj74eThm8Y7c/AL2H88i8XA6mw6cYPOBE2w5eIL9qVkAuDgJHZv60y20Hl1D6tE1JJAALzeHtLWu0YCvVA2UX2DYkZzBun2prE9KY11SKgkH08nJLzhzTANfd1oFe9MkwJPG/h408vOgkb8njfw88Pd0xdfDBV8PlzL3ixtjOJVXwPHMHFIyckjOOMXR9FMczcgh6Xgme49lsjvlJAdSs8kvsGKHk0CrYB8iGvsR0cSPqKb+RLcIwMtNR9Y4ggZ8pWqJvPwC9h3PYseRDLYnZ7DjSAY7j57kUFo2h09kk1dQ/P/PXm7O+Li74OrshJMTOIvgJIII5OQXkJVTQFZOHlm5+ZRQBP6eroQEedEiyNt6r+dFmwY+hDfy0+UhqxEdh69ULeHi7ERofW9C63tzKQ3P2pdfYEjJOMXBtGwOncjmRFYu6dl5hS/rc16BocBYr/wCgzHg5uKEp5sznq6FLzdnArxcCfJ2J9jXjfo+7tT3cdex8LWA/gsqVUs4OwkN/Dxo4Oeh2QpVsXQslFJK1REa8JVSqo6oVMAXkYkisl9E1ha+hpRw3GARSRSR7SLySGXqVEopVTG26MN/9ULr04qIM/A2MBBIAlaJyGxjzGYb1K2UUqqMqqJLpxuw3Riz0xiTA3wJDKuCepVSShVhi4B/r4isF5GPRCSwmP1NgX1FtpMKv1NKKVWFSg34IrJQRDYW8xoGvAu0BqKBg8DLxRVRzHclzvYSkXEiEiciccnJySUdppRSqpxK7cM3xlxaloJEZCrwYzG7koDmRbabAQcuUN/7wPtgzbQtS91KKaVKV6mHtiLS2BhzsHBzBLCxmMNWAW1FJBTYD4wBri9L+fHx8UdFZE8Fm1cfOFrBc6ub2nItteU6QK+lOqot1wGVu5YSFzyo7CidF0UkGquLZjdwJ4CINAE+MMYMMcbkici9wALAGfjIGLOpLIUbY4Ir2jARiSspn0RNU1uupbZcB+i1VEe15TrAftdSqYBvjLmxhO8PAEOKbM8D5lWmLqWUUpWjM22VUqqOqM0B/31HN8CGasu11JbrAL2W6qi2XAfY6VqqdT58pZRStlOb7/CVUkoVoQFfKaXqiFod8EXk6cK0D2tF5OfC4aI1johMFpGEwmuZKSIBjm5TRYnIKBHZJCIFIlLjhtDVpsyvhelQjohIcfNnagwRaS4ii0VkS+HP1v2OblNFiYiHiPwlIusKr2WSTcuvzX34IuJnjDlR+PkfQIQxZryDm1VuInIZsKhwTsMLAMaYhx3crAoRkfZAAfAe8G9jTI1ZtLgw8+tWimR+Ba6rqZlfRaQPkAFMN8Z0dHR7KkpEGgONjTGrRcQXiAeG18R/FxERwNsYkyEirsBy4H5jzEpblF+r7/BPB/tC3lwgh091Zoz52RiTV7i5Eis9RY1kjNlijEl0dDsqqFZlfjXGLAWOObodlWWMOWiMWV34OR3YQg1N0GgsGYWbroUvm8WtWh3wAUTkGRHZB4wFnnB0e2zgNmC+oxtRR2nm12pOREKAGOBPBzelwkTEWUTWAkeAX4wxNruWGh/wS8nmiTHmcWNMc2AGcK9jW1uy0q6j8JjHgTysa6m2ynItNVS5Mr+qqiUiPsB3wD/P+eu+RjHG5BtjorH+ku8mIjbrbrPFilcOVdZsnsDnwFzgSTs2p8JKuw4RuRm4EhhgqvmDl3L8m9Q05cr8qqpOYX/3d8AMY8z3jm6PLRhjUkVkCTCY4hNTlluNv8O/EBFpW2RzKJDgqLZUhogMBh4GhhpjMh3dnjrsTOZXEXHDyvw628FtqvMKH3R+CGwxxrzi6PZUhogEnx6FJyKewKXYMG7V9lE63wFhWKNC9gDjjTH7Hduq8hOR7YA7kFL41cqaONoIQERGAG8CwUAqsNYYM8ihjSoHERkCvMbfmV+fcWyLKk5EvgD6YaXiPQw8aYz50KGNqgARuRhYBmzA+n8d4LHCpI01iohEAdOwfr6cgK+NMU/ZrPzaHPCVUkr9rVZ36SillPqbBnyllKojNOArpVQdoQFfKaXqCA34SilVR2jAV0qpOkIDvlJK1RH/D8uQJPhmV308AAAAAElFTkSuQmCC", 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", 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", 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