{ "cells": [ { "cell_type": "markdown", "id": "9931e3bb-941c-43a5-ac0a-2f5e34a99dcd", "metadata": {}, "source": [ "# Stable Matchings" ] }, { "cell_type": "markdown", "id": "c99e8547-709a-4714-92f2-1d081096db56", "metadata": {}, "source": [ "
\n", "Ziel des Notebooks:\n", "\n", "* Grundlegendes Konzept von stabilen Matchings verstehen\n", "* Stable Matching Probleme lösen\n", "* Lösungsalgorithmus verstehen\n", "* Eigenschaften des Lösungsalgorithmus kennenlernen\n", " \n", "
" ] }, { "cell_type": "markdown", "id": "96020078-aa88-4952-bece-f121e27c9762", "metadata": {}, "source": [ "
\n", "

PHASE 1

\n", "
" ] }, { "cell_type": "markdown", "id": "42bd628e-7637-41e6-b354-2c048d1eb401", "metadata": {}, "source": [ "Die fünf Lehrer Arno (Mathematik, Biologie), Bruno (Deutsch, Geschichte), Chloe (Chemie, Physik), David (Englisch, Chemie) und Emma (Mathematik, Erdkunde) haben ihr Refrendariat abgeschlossen und sind nun auf der Suche nach einer festen Stelle. Alle fünf haben sich bei den gleichen drei Schulen beworben. Das Fermat-Gymnasium hat drei freie Stellen, wohingegen am Gauß-Gymnasium nur zwei frei sind und am Hilbert-Gymnasium nur eine weitere Lehrkraft gebraucht wird.\n", "\n", "Offensichtlich lassen sich mehrere Zuordnungen finden, bei denen jeder der fünf Lehrer einen Stelle findet. Allerdings handelt es sich dabei um eine rein wilkürliche Zuordnung der Lehrer auf die Gymnasien, was natürlich keine zufriedenstellende Lösung für alle Beteiligten sein kann. Die einzelnen Präferenzen der fünf Lehrer und der drei Gymnasien sollten also bei der Suche nach einer passenden Zuordnung berücksichtigt werden. Dafür müssen wir natürlich erste einmal die Präferenzen kennen.\n", "\n", "Arno möchte am liebsten am Hilbert-Gymnasium, an dem er schon sein Refrendariat absolviert hat. Gerne würde er vermeiden, an seine ehemalige Schule, das Gauß-Gymnasium, zurückzukehren. Bruno hat von allen drei Schulen nur Gutes gehört und möchte einen möglichst kurzen Arbeitsweg haben. Das Gauß-Gymnasium ist am nächsten zu seiner Wohnung, danach kommt das Hilbert-Gymnasium und zum Fermat-Gymnasium muss er am weitesten fahren. Chloe hat ihr Praxissemseter am Hilbert-Gymnasium gemacht und würde dort am liebsten als Lehrerin arbeiten. Klappt das nicht, würde sie das Fermat- dem Gauß-Gymnasium vorziehen. David denkt genau umgekehrt wie Bruno und möchte möglichst weit weg von dem Gymnasium wohnen, damit er nicht dauernd Schülern der Schule über den Weg läuft. Entsprechend ist ihm das Fermat-Gymnasium am liebsten, etwas näher liegt das Hilbert-Gymnasium und bei ihm um die Ecke steht das Gauß-Gymnasium. Auch Emmas erste Wahl ist das Hilbert-Gymnasium, da es nach landläufiger Meinung eine der besten Schulen der Gegend ist. Danach kommt auf ihrer Liste das Gauß-Gymnasium, weil es eine MINT-Schule ist. An letzter Stelle steht für sie das Fermat-Gymnasium.\n", "\n", "Die drei Gymnasien orientieren sich bei ihren Präferenzen vor allem daran, für welche Unterrichtsfächer sie neue Lehrkräfte brauchen. Das Fermat-Gymnasium hat dringenden Bedarf an Mathematiklehrer, auch in Chemie und Physik werden in Kürze mehrere Lehrkräfte in Rente gehen. Deutsch- und Englischlehrer hat man dagegen mehr als genug. Somit ist ihnen Arno am liebsten, gefolgt von Emma, Chloe, David und zuletzt Bruno. Auch das Gauß-Gymnasium hat keinen Bedarf an Deutsch- und Englischlehrern. Es will in Zukunft seinen MINT-Bereich stärken, insbesondere Physik und Chemie, und ist an Lehrkräften mit entsprechenden Fächern stark interessiert. An erster Stelle steht somit Chloe, die genau die gefragte Fächerkombination besitzt. Ihr folgen David, Arno, Emma und Bruno. Das Hilbert-Gymnasium ist hingegen schon seit Jahren bei den Gesellschaftwissenschaften unterbesetzt und sucht dort Verstärkung. Auch ein weiterer Deutschlehrer würde nicht stören. Der Bedarf an Chemielehrern ist dagegen gedeckt. Entsprechend hat Bruno höchste Priorität, an zweiter Stelle kommt Emma, danach folgen Arno und David. Chloe hat am Hilbert-Gymnasium die geringste Priorität." ] }, { "cell_type": "markdown", "id": "dc4ba6d9-5fd1-4395-8512-f9cc1e33a86b", "metadata": {}, "source": [ "In dieser Form sind die Präferenzen der einzelnen Schulen und Lehrämtler schlecht zu vergleichen. Es lässt sich schlecht damit arbeiten. Zur übersichtlichen Darstellung der Präferenzen werden deswegen sogenannte Präferenzlisten genutzt. " ] }, { "cell_type": "markdown", "id": "9801d558-a547-453c-9fd8-762dcbbe3d29", "metadata": { "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ "
\n", " Präferenzliste\n", "\n", "Eine Präferenzliste ist eine **strikt geordnete** Auflistung der Präferenzen eines Teilnehmers aus der einen Menge $A$ bzgl. aller Teilnehmer $b_i$ der anderen Menge $B$. Für einen einzelnen Teilnehmer $a_1$ notieren wir das wie folgt:\n", " \n", "$$a_1: b_2 > b_3 > b_1 > b_4$$\n", "\n", "Häufig werden alle Präferenzlisten einer Menge in Tabellenform zusammen dargestellt, in der ersten Spalte steht der jeweilige Teilnehmer und danach seine Präferenzen in absteigender Reihenfolge, wobei 1 die höchste Präferenz ist.\n", "\n", "$$\\begin{array}{|c |c c c c|}\n", "\\hline\n", "\\textbf{A} & 1 & 2 & 3 & 4 \\\\\n", "\\hline\n", "a_1 & b_2 & b_3 & b_1 & b_4 \\\\\n", "a_2 & b_4 & b_2 & b_1 & b_3 \\\\\n", "a_3 & b_1 & b_2 & b_3 & b_4 \\\\\n", "\\hline\n", "\\end{array}$$\n", "\n", "Wir sagen $a_3$ bevorzugt $b_1$ vor $b_2$, genau dann wenn $b_1$ vor $b_2$ auf der Präferenzliste von $a_3$ steht.\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "3ab36d20-67a6-4c0b-b770-7c45bcbf88f7", "metadata": {}, "source": [ "
\n", "\n", "### Aufgabe 1\n", "***\n", "1. Erstellen Sie die Präferenzlisten der Gymnasien und Lehrern aus dem obigen Beispiel.\n", "2. Finden Sie eine Zuordnung zwischen den Gymnasien und Lehrern, sodass es keinen Lehrer $l$ und kein Gymnasium $g$ gibt, die in der aktuellen Zuordnung einander gegenseitige bevorzugen und trotzdem nicht miteinander gematcht sind.\n", "\n", "Anmerkung: Notieren Sie die zugeordneten Paare als Tupel (Lehrer, Gymnasium).\n", "
\n", "\n" ] }, { "cell_type": "markdown", "id": "f5b01eb9-1f38-4fd0-93e6-e8d86886bf2e", "metadata": {}, "source": [ "Das, was Sie gerade in Aufgabe 1 gefunden haben, ist die Lösung eines besonderen Zuordnungsproblems. Es wird Krankenhäuser/Assistenzärzte-Problem genannt, wobei in unserem Beispiel die Gymnasien den Krankenhäusern und die Lehrkräfte den Ärzten entsprechen. Hierbei erstellt jeder Teilnehmer (Krankenhaus oder Arzt) eine Präferenzliste für die Teilnehmer der anderen Seite und es wird versucht eine Zuordnung, ein Matching, zu finden, sodass alle Teilnehmer zufrieden sind. Ein solches Matching wird stabil genannt und wir wollen uns nun genauer anschauen, was ein stabiles Matching auszeichnet." ] }, { "cell_type": "markdown", "id": "3e6cfde2-b362-4909-93be-6112f28a5c18", "metadata": {}, "source": [ "
\n", " Stable Matching (Krankenhaus/Assistenzärzte-Problem)\n", " \n", "Es sei $n$ die Anzahl der Ärzte und $m$ die Anzahl der Krankenhäuser, die mindestens einen Arbeitsplatz anbieten. Zudem besitzt jeder Teilnehmer eine vollständige Präferenzliste. Ein **Matching** ist dann eine Zuordnung der Ärzte auf die Krankenhäuser, sodass die Anzahl der verfügbaren Plätze der einzelnen Krankenhäuser nicht überschritten wird und kein Arzt mehrfach zugeordnet wird. Ein Matching wird als Menge der Tupel (Arzt, Krankenhaus) notiert, die einander zugeordnet werden. Ein solches Tupel bezeichnen wir als **Paar**. Ein Teilnehmer heißt **frei**, wenn er keinen Partner hat.\n", " \n", "Ein Matching heißt **stabil**, wenn es keinen Arzt $a$ und kein Krankenhaus $k$ gibt, die folgende zwei Bedingungen erfüllen:\n", " \n", "1. Entweder ist $a$ nicht zugeordnet oder $a$ bevorzugt $k$ gegenüber seinem aktuell zugeordneten Krankenhaus;\n", "2. Entweder hat $k$ noch Plätze frei oder $k$ bevorzugt $a$ gegenüber mindestens einem aktuellen zugeordneten Arzt.\n", " \n", "Ein nicht stabiles Matching wird **instabil** genannt. Ein Paar, das ein stabiles Matching verhindert, wird als **blocking pair** bezeichnet.\n", " \n", "
" ] }, { "cell_type": "markdown", "id": "b61d07d5-f52f-4ccf-8278-473a3a504b74", "metadata": {}, "source": [ "
\n", "\n", "### Aufgabe 2\n", "***\n", "Prüfen Sie, ob folgendes Matching stabil ist. Begründen Sie Ihre Antwort.\n", "\n", "* Die Uniklink (UK) hat fünf freie Plätze, beim Luisenhospital (LH) sind zwei Plätze frei und beim Alexianderkrankenhaus (AK) einer.\n", "* Das Matching: {(Aaron, AK), (Ben, UK), (Carl, LH), (Doris, LH), (Emil, UK), (Freya, UK), (Greta, UK)}\n", " \n", "$$\n", "\\begin{array}{|l | c c c |}\n", "\\hline\n", "\\textbf{Ärzte} & 1 & 2 & 3 \\\\\n", "\\hline\n", "\\text{Aaron} & \\text{LH} & \\text{AK} & \\text{UK} \\\\\n", "\\text{Ben} & \\text{UK} & \\text{AK} & \\text{LH} \\\\\n", "\\text{Carl} & \\text{LH} & \\text{UK} & \\text{AK} \\\\\n", "\\text{Doris} & \\text{LH} & \\text{AK} & \\text{UK} \\\\\n", "\\text{Emil} & \\text{UK} & \\text{LH} & \\text{AK} \\\\\n", "\\text{Freya} & \\text{AK} & \\text{LH} & \\text{UK} \\\\\n", "\\text{Greta} & \\text{AK} & \\text{UK} & \\text{LH} \\\\\n", "\\hline\n", "\\end{array}\n", "\\hspace{2ex}\n", "\\begin{array}{| l | c c c c c c c|}\n", "\\hline\n", "\\textbf{Krankenhäuser} & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\\\\n", "\\hline\n", "\\text{Alexianerkrankenhaus} & \\text{Carl} & \\text{Freya} & \\text{Ben} & \\text{Greta} & \\text{Doris} & \\text{Aaron} & \\text{Emil} \\\\\n", "\\text{Luisenhospital} & \\text{Emil} & \\text{Greta} & \\text{Doris} & \\text{Freya} & \\text{Ben} & \\text{Carl} & \\text{Aaron}\\\\\n", "\\text{Uniklink} & \\text{Freya} & \\text{Doris} & \\text{Carl} & \\text{Emil} & \\text{Ben} & \\text{Greta} & \\text{Aaron} \\\\\n", "\\hline\n", "\\end{array}\n", "$$\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "eb17cd02-26a8-4800-968a-8c34cc2bf2a0", "metadata": {}, "source": [ "
\n", "\n", "### Aufgabe 3\n", "***\n", "Beweisen oder widerlegen Sie. Wenn ein stabiles Matching für ein Krankenhäuser/Assistenzärzte-Problem existiert, ist es eindeutig." ] }, { "cell_type": "markdown", "id": "85ded8df-9160-4ba0-b703-efc7ce214b1c", "metadata": {}, "source": [ "
\n", "\n", "### Aufgabe 4\n", "***\n", "Wir betrachten noch einmal das obige Beispiel, bei dem es fünf Lehrkräfte und drei Gymnasium mit insgesamt sechs Arbeitsplätzen gibt. Wie viele potentielle stabile Matchings gibt es in diesem Fall? Bestimmen Sie außerdem die Anzahl im allgemeinen Fall für $n$ Lehrkräfte und $m$ Gymnasien mit insgesamt $k$ Arbeitsplätzen\n", " \n", "Als potentiell stabile Matchings gelten alle Matchings, bei denen alle Arbeitsplätze vergeben sind oder alle Lehrkräfte einen Arbeitsplatz haben.\n", "\n", "
\n" ] }, { "cell_type": "markdown", "id": "4c773a2d-15e6-4346-a80c-2ccd93f89aab", "metadata": {}, "source": [ "Wie Sie aus dem Ergebnis dieser Aufgabe entnehmen können, ist es sicherlich keine sinnvolle Option, ein stabiles Matching einfach durch systematisches Ausprobieren zu bestimmen. Zudem müsste dann auch jedes Matching einzeln darauf überprüft werden, ob es die Bedingungen für ein stabiles Matching erfüllt, was bei der großen Anzahl an potentiellen stabilen Matchings einen großen Rechenaufwand bedeuten würde. Selbst wenn wir zu diesen Berechnungen in der Lage wären, wüssten wir trotzdem nicht, ob unsere Berechnungen von Erfolg gekrönt wären, wir also schlussendlich ein stabiles Matching finden würden. Es stellen sich also zwei essentielle Fragen:\n", "\n", "* Existiert für jedes Krankenhäuser/Ärzte-Problem ein stabiles Matching?\n", "* Wie bestimmen wir ein stabiles Matching? Gibt es einen Algorithmus?" ] }, { "cell_type": "markdown", "id": "6e7c439c-fd58-4092-a9c7-9d2e36589354", "metadata": {}, "source": [ "
\n", "

PHASE 2

\n", "
" ] }, { "cell_type": "markdown", "id": "c1fb72e0-8044-4309-8825-4e04b0a27e17", "metadata": {}, "source": [ "Wir schauen uns die Voraussetzungen des Krankenhäuser/Assistenzärzte-Problems nochmal an und stellen fest, dass die Anzahl der Krankenhäuser, Arbeitsplätze und Assistenzärzte alle unterschiedlich sein können. Wir müssten hier also viele verschiedene Fälle betrachten, was die Komplexität erhöht. Es scheint also sinnvoll zu sein, erst einmal ein einfacheres *Stable Matching Problem* zu betrachten. Was wir suchen, ist eine Spezialisierung des Krankenhäuser/Assistenzärzte-Problems, die einfacher zu untersuchen ist. Gerade weil die unterschiedlichen Anzahlen von Krankenhäuser, Arbeitsplätzen und Assistenzärzten unser Problem verkomplizieren, scheint es logisch, diese einfach anzugleichen. Also, jedes Krankenhaus hat genau einen Platz frei und es gibt genauso viele Bewerber wie Krankenhäuser." ] }, { "cell_type": "markdown", "id": "756ab939-afa0-4b75-91b8-59e5d5f5bb33", "metadata": {}, "source": [ "
\n", " \n", "### Denkanstoß\n", "***\n", "Verändern sich dadurch unsere Bedingungen für ein stabiles Matching? Bleiben sie gleich oder werden sie sogar einfacher?\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "be92c8ba-2992-4231-b85e-c5d28b8e154f", "metadata": {}, "source": [ "
\n", " Bitte erst nach dem Denkanstoß öffnen. \n", "\n", "
\n", " Stable Matching (Stable Marriage Problem)\n", " \n", "Es sei $n$ die Anzahl der Ärzte und $n$ die Anzahl der Krankenhäuser mit genau einem freien Arbeitsplatz. Ein Matching heißt stabil, wenn es keinen Arzt $a$ und kein Krankenhaus $k$ gibt, die folgende zwei Bedingungen erfüllen:\n", " \n", "1. $a$ bevorzugt $k$ gegenüber seinem aktuell zugeordneten Krankenhaus;\n", "2. $h$ bevorzugt $a$ gegenüber seinem aktuell zugeordneten Arzt.\n", " \n", "
\n", "\n", "Dieses *Stable Matching Problem* wird als *Stable Marriage Problem* bezeichnet, da es zunächst als Heiratsproblem zwischen jeweils $n$ heterosexuellen Männern und Frauen formuliert wurde. Es ist ein Spezialfall des Krankenhäuser/Ärzte-Problems und als solches natürlich für uns von großem Interesse. Wir versuchen also zunächst für das Stable Marriage Problem die beiden oben gestellten Fragen zu klären und hoffen, dass sich die gewonnenen Erkenntnisse auch auf das Krankenhäuser/Assistenzärzte-Problem übertragen lassen und uns so weiterhelfen.\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "03f74014-522f-4762-9bd2-b0848fed7a66", "metadata": {}, "source": [ "
\n", "\n", "### Aufgabe 5\n", "***\n", "Finden Sie ein stabiles Matching für folgendes Stable Marriage Problem. Beschreiben Sie Ihre Vorgehensweise (stickpunktartig).\n", "\n", "$$\n", "\\begin{array}{|l |c c c c c c c|}\n", "\\hline\n", "\\textbf{Menge I} & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\\\\n", "\\hline\n", "a & X & Z & Y & T & V & U & W \\\\\n", "b & Z & T & W & U & V & X & Y \\\\\n", "c & T & V & W & U & X & Y & Z \\\\\n", "d & X & Y & Z & V & T & U & W \\\\\n", "e & U & V & T & Z & W & Y & X \\\\\n", "f & T & X & U & W & Z & Y & V \\\\\n", "g & Y & Z & V & U & T & X & W \\\\\n", "\\hline\n", "\\end{array}\n", "\\hspace{2ex}\n", "\\begin{array}{|l |c c c c c c c|}\n", "\\hline\n", "\\textbf{Menge II} & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\\\\n", "\\hline\n", "T & e & f & a & d & b & c & g \\\\\n", "U & a & b & c & f & d & g & e \\\\\n", "V & d & e & a & f & g & c & b \\\\\n", "W & g & b & d & c & f & e & a \\\\\n", "X & d & f & a & c & b & g & e \\\\\n", "Y & d & c & a & f & g & b & e \\\\\n", "Z & e & a & c & d & g & b & f \\\\\n", "\\hline\n", "\\end{array}\n", "$$\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "52050489-ef1f-4f2d-b228-039642f73252", "metadata": {}, "source": [ "Die Situation, die Sie in Aufgabe 5 bearbeitet haben, lässt schon viele verschiedene potentielle stabile Matchings zu (genauer gesagt 720). Womöglich haben Sie bei der Bestimmung eines stabilen Matchings schon intuitiv den richtigen Lösungs-Algorithmus angewandt, denn er ist sehr simpel und eigentlich ziemlich naheliegend. Wir schauen ihn uns jetzt genauer an." ] }, { "attachments": { "b3c4697f-75e4-48ac-8360-e2094e069218.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "0934bdd9-60d3-48a7-83bc-b3f7a6dc125c", "metadata": {}, "source": [ "
\n", " Bitte erst nach der obiger Aufgabe öffnen. \n", "\n", "
\n", " Gale-Shapley-Algorithmus\n", "\n", "Der Gale-Shapley-Algorithmus (kurz GSA) arbeitet **iterativ** und in Teilen **nicht-deterministisch**. Letzteres bedeutet, dass bei gleicher Eingabe verschiedene Möglichkeiten für den Übergang in den nächsten Zustand vorliegen und das Programm, also hier der Algorithmus, nicht vorgibt, welche dieser Möglichkeiten gewählt werden muss. Der Gale-Shapley-Algorithmus hat eine Laufzeit von $\\mathcal{O}\\,(n^2)$.\n", " \n", "Als Eingabe erhält der Algorithmus nur die Präferenzlisten für die Elemente der beiden Mengen (also in unserem Fall die Assistenzärzte und Krankenhäuser). Er terminiert immer und die am Ende vorliegenden Paare bilden ein stabiles Matching.\n", "\n", "
\n", "\n", "Falls sich die Ärzte bei den Krankenhäusern bewerben, wird von der **Arzt-orientierten Version** gesprochen. Der andere Fall wird entsprechend **Krankenhaus-orientiert** genannt. Der obige Pseudocode zeigt also die Arzt-orientierte Version.\n", "\n", "
\n", " \n", "Informell lässt sich der Algorithmus als Folge von Bewerbungen der Ärzte bei Krankenhäuser interpretieren. Zu jedem Zeitpunkt in der Durchführung des Algorithmus sind Krankenhäuser und Assistenzärzte entweder frei oder zugeordnet. Wenn ein Krankenhaus aber erst einmal zugeordnet ist, wird es nie wieder frei sein, wobei sich der zugeordnete Arzt noch ändern kann.\n", "\n", "Am Anfang ist jedes Krankenhaus und jeder Arzt frei. Der Algorithmus durchläuft nun immer wieder folgenden Vorgang : Ein beliebiger freier Arzt $a$ bewirbt sich bei dem Krankenhaus $k$, das auf seiner Präferenzliste an höchster Stelle steht und bei dem er sich gleichzeitig noch nicht beworben hat. Falls das Krankenhaus frei ist, wird der Arzt $a$ vorläufig vom Krankenhaus $k$ angenommen. Falls das Krankenhaus $k$ aber schon einen Arzt $a'$ angenommen hat, vergleicht es die beiden Ärzte und weist den Arzt ab, den es weniger bevorzugt. Präferiert das Krankenhaus den schon vorläufig zugeordneten Arzt $a'$, wird Arzt $a$ zurückgewiesen. Ist allerdings Arzt $a$ besser geeignet für das Krankenhaus, wird die vorläufige Annahme von Arzt $a'$ rückgängig gemacht und Arzt $a$ stattdessen vom Krankenhaus vorläufig angenommen. $a'$ ist wieder frei. Dieser Vorgang wird solange wiederholt, bis kein Arzt mehr frei ist." ] }, { "cell_type": "markdown", "id": "ad167a69-2061-419e-9306-80cbb74d313b", "metadata": {}, "source": [ "
\n", "\n", "### Aufgabe 6\n", "***\n", "Führen Sie den Gale-Shapley-Algorithmus für das folgende Beispiel mehrfach durch, wobei Sie die Reihenfolge der einzelnen Assistenzärzte, die sich bei den Krankenhäusern bewerben, variieren. Was fällt Ihnen dabei auf? Stellen Sie eine Behauptung auf.\n", "\n", "$$\n", "\\begin{array}{|l |c c c c c c |}\n", "\\hline\n", "\\textbf{Ärzte} & 1 & 2 & 3 & 4 & 5 & 6 \\\\\n", "\\hline\n", "a & W & X & Y & Z & V & U \\\\\n", "b & Y & U & W & V & Z & X \\\\\n", "c & Z & W & V & X & U & Y \\\\\n", "d & U & Y & Z & X & V & W \\\\\n", "e & W & Y & U & X & Z & V \\\\\n", "f & X & Y & W & Z & V & U \\\\\n", "\\hline\n", "\\end{array}\n", "\\hspace{2ex}\n", "\\begin{array}{|l |c c c c c c c |}\n", "\\hline\n", "\\textbf{Krankenhäuser} & 1 & 2 & 3 & 4 & 5 & 6 \\\\\n", "\\hline\n", "U & a & e & f & b & d & c \\\\\n", "V & c & b & d & a & e & f \\\\\n", "W & f & d & c & a & b & e \\\\\n", "X & b & e & c & d & f & a \\\\\n", "Y & e & b & a & f & d & c \\\\\n", "Z & d & a & e & b & c & f \\\\\n", "\\hline\n", "\\end{array}\n", "$$\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "5b2f0b80-a601-4d7d-bb7c-e698630eb041", "metadata": { "tags": [] }, "source": [ "
\n", "\n", "### Aufgabe 7\n", "***\n", "Beweisen Sie Ihre Behauptung aus Aufgabe 6. Sie können dafür annehmen, dass der Gale-Shapley-Algorithmus immer ein stabiles Matching liefert.\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "74ad0196-fb0a-45bf-8a83-52b9e02ba594", "metadata": {}, "source": [ "
\n", " Bitte erst nach der obiger Aufgabe öffnen. \n", "\n", "Damit haben Sie eine ganz besondere Eigenschaft des Gale-Shapley-Algorithmus bewiesen. Obwohl er ein nicht-deterministischer Algorithmus ist, liefert er trotzdem für jede beliebige Ausführung (die bewerbende Gruppe bleibt dabei immer die gleiche) immer das gleiche (stabile) Matching. Das ist für die Implementierung des Algorithmus sehr wichtig, denn im Allgemeinen lassen sich nicht-deterministische Programme nicht praktisch umsetzen.\n", " \n", "
" ] }, { "cell_type": "markdown", "id": "37839abe-9cf4-4017-a247-8e50d8915056", "metadata": {}, "source": [ "
\n", "\n", "### Aufgabe 8\n", "***\n", "Wir haben die Präferenzliste von Krankenhaus $k_1$ gegeben und betrachten folgende Situation: $a_1$ bewirbt sich bei $k_1$ und $k_1$ nimmt die Bewerbung an.\n", "\n", "$$k_1:\\; \\ldots > a_3 > a_5 > a_1 > a_2 > a_4 > \\ldots$$\n", "\n", "Welche Konsequenzen hat das für die anderen Ärzte und für die weiteren Bewerbungen, die das Krankenhaus $k$ erhält?\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "7a2457d2-e43c-4e7c-b33c-c874a501e6a6", "metadata": {}, "source": [ "Aus diesen Beobachtungen heraus, ergibt sich eine logische Modifizierung des Gale-Shapley-Algorithmus, die sein Ergebnis aber nicht verändert." ] }, { "attachments": { "081c211e-344f-4196-b9b1-836a865e66a5.png": { "image/png": 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AOkfrAa69e/cKzvfx8SEiIn9/f8HlCgsL6dChQzijACBo3rx54utK3759lR67z8nJSe7LVgTbAQAUV1RURKtWreJNHzBgAHXu3BkZVI4ZGfE7k/jvv/+QMQBqJtnloGQFLmkVumSNZQsAAAAAoE1aD3AJdU8oEonEfYAPGDCgTGkBABARGRgY0J49e6h79+40adKkUqXh4uIiOL/kAMwAACDs8OHDUoMaU6dOReaUc1WrVuVNO3bsGDIGQI1u3LjB6R7U0tJS3D1hsX79+pGJiQln2sOHD+nhw4fIQAAAAADQKVodg+v27dsUFRUlc36rVq2oRo0aRETUvHlzcnFxoZcvX8pc/tq1a/T69WuqXbu2TmZ2ZmYmJScnU35+PpmamlKFChXIzs5O4fUZY5Samkrp6elUs2bNUu3D69ev6fnz55ScnEzJycmUlpZGVlZWZGdnR7a2tlS/fv1Sp10WGRkZlJGRQQ4ODp/VF5AxRikpKZSRkUFWVlZkZWVFBgaajzvn5+dTfHw8FRYWivdDWq1qRRQUFFBcXByZmpqStbU1mZqa6ly+29ra0qlTp8qUX0IkXwgAAIBsf/75J29a8+bNqVWrVsiccq5169a8aYGBgTRr1iy5lUkAoHQkW2/17duXKlSowLtX9vHx4Y2DvWPHDlq7di0yEQAAAAB0hlYDXPJaXPXv35/zt7+/Py1btkxwnf3791NAQIBOZO6DBw/owIEDdO3aNXrw4AGlpaXxljE3NycXFxdq164dde/enby9vTkvx1++fEkHDhyg8+fP0+3btyk9PZ2IiLZs2ULjxo1TaD9CQkIoMDCQLl26RG/fvpW7fO3atalt27Y0cuRI6tChg8LHe/ToUfrjjz8oLi6OCgoKyMjIiGrUqEHjxo0jX19f3vKRkZG0Zs0aOnHihLjFy4ULF8jLy4u37MePH+n48ePitIk+9Q/ftm1bcnNzk7o/d+/epfDwcMrKyqLr168L7vvq1aupYsWKnGkGBgZUvXp18vX1JSsrK5WVi9jYWNq2bRudOXOG7t69Kz4eIiJTU1Nq2LAheXt707hx4+SOPVfS3r176X//+x8lJCRQYWEhGRoaUvXq1Wn48OFSuxmJiYmhv/76iw4fPkyPHz/m9MNvZGRErVq1os6dO9OgQYNk5jHRp66lTp06Rdu2baNHjx7Ru3fvOGm5uLiQp6cnjRo1ijp27Kj3F82ioiJ69eqV4DKVK1eWm87NmzcpLCyMN/6bSCQiZ2dnhcb60pTCwkIKDQ2liIgIevPmDWVkZFDVqlWpcePG1LVrV6UC9WXN+7dv31Jubi65u7uXix9hXclblCntl6nPNU9ycnIoPDycN3306NE6W37j4+Pp9u3bFBERQcnJyZSSkkIGBgbk6OhITZo0oS5dupClpSW+bwpo164dWVtb08ePHzllwt/fny5cuKDS+y9V52lMTAy9evWKjIyMyMnJiapVq6bSCi66Vs6ysrIoLi6OEhISKC8vj1xdXalatWokEolQxvVITk4O7d+/nzNt0KBBUpcdNGgQL8C1Z88eWrFiBRkbG+vktfnt27eUlpZGVapUITc3NzIzM1NovaSkJEpNTSUnJydydnZWarvv37+ns2fP0ps3b+jdu3fEGKMaNWqQs7MzdevWjezt7XGfrOPXWyEJCQkUFRVF+fn5VKlSJapbt65SY0VmZGTQ27dvKTY2lmxtbcnR0ZGcnJz0/rx8/PiRwsPDKSIigt69e0dpaWlkYWFBjo6O4o+bmxt5eHjgwgsAAOrHtKSwsJA5OTkxIpL6EYlE7M2bN5x17t+/L3P54k/Dhg1LvU/79+8XTHvIkCEKpXPnzh325Zdfyt1XaZ/atWuzv/76i129epV169atTPsSHBzMWrRoUar9KP54enqyCxcuyN1WdHQ0MzY2lpqGkZERe/v2LefcL1y4kBkZGfGWDQwM5KWdnZ3NateuLTVtY2NjdvXqVd46O3bsYCKRqEzHXvxxc3Nj+fn5Uo97y5YtgutOnDhRvGxOTg6bMWMGMzExUWi7JiYmbO3atQqVuWfPnjEDAwOZ36UnT56Il83NzWWLFy9mFStWVGg/jI2N2eLFi6Vu9/Tp0zLPjbRPjx49WFJSEtNnL1++lHucJ06ckJtO+/btBdPIyMjQ+rFmZGSw2bNnMwcHB5n7aWhoyPr378/5jisiMTGRl9br1695y8XExLCZM2ey+vXrM1NTU0ZEbMSIESwzM5MNGTKE8zl8+LDSx/jDDz9w0lDk3EkKCwvjpPHdd9+xwsJCreRtfHw8L1+kXSOL/fzzz7zlnZ2dOfvRqFEj3jIlP6NGjSoXZQp5IjtP1CkkJETqMUdGRurUtf/9+/ds9erVrFGjRgr9fn///fcsLi5O6e1ERUUpdG+k7bKlSgsWLJC6bx4eHiwiIkJj+yEv74uKitjZs2dZv379pN7LWVhYsMGDB7Nbt27pfDljjLH09HTetSskJISzzH///cfmzZvHvv76a6n37dWrV2dr1qxhmZmZWinnqizjOTk5bMSIEZz8WLFiRZnL1U8//cRJ848//tDqtWzv3r2c/KlatSorKCiQumxWVhaztLTk5enRo0fV+l1TRmZmJlu+fDlr2LAhL00zMzPWvXt3dvbsWfHyeXl57MKFC2z69Omsffv2zMbGhrPOpEmTFN72yZMnmbe3NzM0NJRZ/oyMjFi3bt3YuXPntPr7rWv3h9q83krb9oMHDzjLXL9+nfXv359ZWFjwlrW1tWX+/v7s+vXrMreRmprKNm/ezJo2bSrznU9AQECpnoulncu9e/cqlcbOnTt5aaSmpiq0bkREBOvTp4/C7zRq1qzJJk2axB49esQAAADURWsBrgsXLgj+EH711VdS1/viiy/k/og+fvy4VPukigDXihUrZAYaVPmpW7euzH0oKipi8+bNU1mAx8DAgC1fvlzwuE+fPi2YRlBQEGOMsYKCAjZ48GCZy0l7uAkLCxNMe8qUKbx1hIKDpfncuHFD6nErGuBKS0tjrVq1KtW2FXkQlnxYlZWvKSkprEOHDqXaj6VLl3K2uXr16lKV9fbt28sMGOqDdevWCR5fhQoVWEpKitx0PD09BdNJTEzU6nGePXuW1axZU+Hzam5uzn7//XeVPbgnJCSwESNGSH14Kn5wl3xo9PLyUuoYnz17xku7a9euSueV5DWtS5cuWstbZV8aubq6lvn6aGFhUW7KFPJEep6o0/Tp03nbrlq1qs5c81NSUtisWbMUrhQi+RJM2Zeaqnrxq+6ypUppaWkyK70ZGhqyYcOGsfPnz6v93kEo79++fcu8vLwUyksjIyPePZOulTNZ14dNmzYxxhh79OgRGzBggML3eY0aNZL68l2d5VwdZXzAgAG8IGJpA4iMMXb37l3efpw8eVKr17QuXbrIfY4qafjw4bxj8PX1Vdt3TRnnzp1j1apVU+j8+/n5sQkTJjBbW1vB5Tp06CB3uy9fvmQ9evRQ+rvar18/FhMTo5Xfb127P9Tm9VYowBUfH8+8vb0VPsZffvmFFRUVcdI/c+YMq1KlikLrOzk5CQYaFd3/adOmKZXGxIkTeWnEx8fLXW/RokWCAV15nzVr1uANLAAAlK8A1+jRowV//NatWyd1vTlz5sj94ZwzZ45WAlxbt25VaVDFyMiIjR07lg0fPpx17tyZUzvRyMhIZguBYcOGKZR+pUqVmIeHh9wbfWmtkZTNu/379zPGPtViVCQQU9LBgwcF1xk6dChvncmTJ6vsPBgYGMgMNigS4MrMzGRt2rQp9fYrVKggt3aXvP3YsmULy87OZq1bty71fpiZmbGXL18yxhj79ddfy5Sn27dv19uLZr169QSP7ccff1QoHV0OcO3evbvUDy/yguGKPLjfvXuX1ahRQ+Y2ih/cJWv9GxkZKRRcLLZ8+XKpNeLT0tIUTqOgoIDZ2dlx0hB6gabuvNXVAJe+lCnkieYDXNJeJPXt21dnrvmHDx+We69WuXJlZm5uLnN+cSUfTb341UTZUrVr167JrQ1uaWnJfHx82LJly9jt27dltjpR9Uv3S5cuMWtra6Xz8n//+5/OljNZ14f169ezFStWKFwzXzIwrWhLwLKWc3WV8evXr/OWX7BgQanL1JgxY3gtGeS18Fan6OhoXtDyzp07guucPXtWanlLSEjQaoBrx44dUlsVlvVjb28vuN2bN28ye3v7UqdfrVo1hVqyqPr3W18CXJq43soKcN24cYM5Ojoqve2SLT3nz5+vdCVjOzs79uLFC50PcP32228yj6Fy5cqsRo0azM7OTrBixL59+/AGFgAAyk+AKzc3VzCoYmBgILN2U0REhNybBGdnZ40HuN68eSN4k21tbc1mz57NgoOD2a1bt1hYWBjbsGEDq1+/vuA2JV+UJiYmsnPnzrEDBw6UKtDh4uLCtm3bxnuBm5SUxNavXy+3FpysmxJFAlxnz56Ve8MnrXl9ac5LWloamzRpEmvSpAnz8PCQWyPW1dWVeXh4cD5169Zl3t7e7PTp06UOLE2cOJGNGDFC/HeVKlXY1KlTWWBgILty5QrbvXs369ixY5lrOykS4Cr5kG1vb89++ukntmfPHnb16lW2b98+hWoizp8/n12/fl38UsHAwIANHjyYbd68mYWHh7Pjx4+zGTNmyH3gbNasmV5eMMPDwwWPy9TUVOGamboa4Nq3b5/UB5O+ffuykJAQlpSUxFJSUtiVK1fYihUreNdyAwMDdv78+VI/uAcHB7MKFSrIDPa6u7uLa2pK67ZWmQenr7/+mnPuiv9/6NAhhdO4ePEibx+io6O1lrfKvsDo0qUL79pXMi9KVoaQ9ZH3fdanMoU8kZ4n6iStKzZ5LQo06cqVK7z7ufHjx7ODBw9yvuv5+fnszp07rG/fvlLvAaOiojTy4ldTZUsddu3apVTQwsbGhvXt25ft3r2bpaenqyXANXnyZE4XbfXr12dz5sxh27dvZ6tWrWI//PCDzNZn1tbW7OPHjzpZzmRdHyRfLFeoUIF17NiRjR49mo0cOZK1bdtW8By1bNmS5eTkqLWcq7uMS/a44ODgwHJzc5UuTx8/fuQ9f/z2229avZ4tXryY1w2oPAUFBaxq1aoqbYlR1uvcxYsXec8adnZ2bOnSpSwsLIxFRESw/fv3M39/f6nvGaZOncqWLl3KNm/ezAIDA9mxY8fYoUOH2F9//cVWr14t83yHhYVJfaZs3Lgx27RpE3v27BnLzMxkaWlp7NGjR2zNmjVSK8bZ2NjI7VpP1b/funZ/qM3rrbRt7927l3OtqFu3rnjbq1evZhMnTpTZYtTIyIi9efOG/f7775xy5ufnx5YvX862b9/OfvvtN+bn58fMzMxktu7T5QDXmzdveBUfbG1t2bZt23jPvtnZ2ezRo0ds+/btzNfXl1Mx4+nTp3gDCwAA5SfAdfz4cbldmAnx8PAodZdy6gpwTZ06VbD20vPnz2UG+4S6rlOmpl9UVJTMG14iYs2bN5f70vzdu3eC+WtpaSm1qw55eRcYGMjq1q0rOOZajRo1eP1fl/W8FJPXZWFpgwnyAkuNGzcW/9/f3599+PCBl0ZhYaHc8Zj69OlTpv0YOHCg+P/ffvste//+vdR0evXqJbcLGhcXF/H/ZT2cbdu2TTAdkUik8EOIrigsLJTbzaQyDxe6GOB68eIFb6wFW1tbwX7dP3z4wIYMGcJZp0mTJqV6sRYcHMx7uWZsbMxGjx4ts3uqWrVqcZYfNGiQQsf64cMHzguyktcIRcdbZOzTGAWS11lt5q0qakU3adJEZbXX9bFMIU8024WstLFztP0SuKT//vuPERFzdHRkW7duVWisoXnz5vGOyd/fX+0vfjVZttQlKCioVN30VahQgQ0aNEipAI8ieV/yZeWGDRt4XVEV3x8sWbJEaiWu9evX62Q5k3V9KNmd38qVK1lycrLUZ4UffvhBZqW1ZcuWqa2ca6KM79u3j7dvu3fvVro8bd68mXedjY2N1er1zM3NrVTXWmk9Y5Rl3OuyXOdyc3N5934+Pj4y75ulBYTmz5+v9D7HxMSwypUr83q3+PPPP6VeF4oVFBSwjRs38saqrlmzpmAPHar+/da1+0NtXm+lbbs4CGNgYMDWrl0rddtFRUVs7dq1UoP87dq1E0+vVauWzPEj4+LieN2EFm+3LC1g1R3gmjRpEq9C+bt37xTaVnJyMluwYAFzcnJSectrAACAYloJcJV82S7ts3nzZqUf6KTVAFJWaQMpeXl5UgfgLf4MGDBAcLs7duwQ3K6ig6dOmDBBsGsTRWvM3LhxQ7Cl1dy5c5XOO2kBppo1a7KlS5ey0NBQwUFN9TnAVfwRGmRXkTIgr1WiovsxY8YMwXSOHTumUDrNmzcXfPmSmZkps4Za8UfZ/sa1bePGjYLH06ZNG5aXl6dweroY4CrZoqn4haGiAwJLdo0qr6skaQ/uki8OOnXqxP777z/BdCQfuGxtbRV6eCr5nbO1tWWrVq3i1AJW9CW/5LiQv/76q1bzVtdeYOhjmUKeaE5hYaHUF0VbtmzRmWt/eno6+/HHH5WqlFFYWMgaNGjAq9ghq7KTqr7Dmixb6vTkyYTLxTQAACAASURBVJNSj6VqamrKZs+eXaqXaEIvXBVprRIQEMBbz8fHRyfLmazrAxExNzc3hdIICgritego/k2V111wacu5Jsp4fn4+r0eLFi1alPl3o3///lq9ll29epVXVl69eqXQujdv3pRaVu7du1eqfSnLdU7yfrxmzZpyA8JLly4tdWsfxj4FNtq1a8cre9evX1c4jdDQUF4LmF69emns91ufAlzqvt4KbVuRbnqFuuqztLSU291gVlaW1Iq/inbhr40Al+Rz68aNG5U+54q07gUAACgtjQe4MjIyZPYfXzyYtLw+vZ88eSL3AdfBwUHph9vSBlKEbpIUuVmJjo4WXF+RmpCpqanMwsKi1C2AJAm1KKpUqRLLzs5WKu8kH3xXrFjBS0PV56UkbQa4HBwc5D7oX758WW43PGXdj3r16sltDahIF6CGhoYKvfiQN/C3Nl+eKSs6OppZWVkJ9qevyMC8JXXq1Emw5qIitbdVSdoYC1u3blXqJXDJF0Jt27Yt1Yu14s/48eMVCjKdP3+et+6lS5fkrleye6fu3bvzup+8ePGi3DSeP3/O2/bDhw+1mre69AJDX8sU8kRz8vPzpVao+fPPP/X+Bvvo0aOC43So+jus6bKlCRcuXGCDBw8uVYsuX19flpWVpZIXrg0bNhRsoVEsLi6O13VezZo1dbKcybo+VKlSRTzeqiJOnDghNc9Wr16t8nKuyTK+ZMkS3rauXbum8LZu3brFWz80NFSr3yfJ8cCU/Y5LG5OpNBVKy3qvIvmiXZFWO/n5+axOnTql7tL60KFDKmnVJ62ynKxyoerfb30JcGnieitr23Xr1lWo55ykpCSZ4xQqOo7lunXrSt09szYCXJLB1eDgYLxJBQAAnWJAGnb8+HHKysqSOd/Ly4uqVKkimEa9evWofv36gsvEx8fTxYsXNXJMr1+/FpxfqVIlwfn29vaC89+9eyd3H06dOkUZGRky5/fo0UOpYxJaPjExsdR56+LiQg8ePKDp06eTmZkZfQ6WLFlCNjY2gsvY2toKzk9LSyvzfgQEBJCBgfBX3s7OTm4633zzDbm7u8tdTl5aqjgmTcjOziY/Pz+Z+2tnZ0dBQUFUtWpVpdLdvHkz7dq1i3bs2MH57Ny5k0JDQ8nc3Fyjx7ls2TLO3127dqWxY8cqvL6FhQVNnz5d/Pe1a9dKfY59fX1p8+bNZGRkJHfZ9u3b875fJ0+eFFwnNzeXQkJCxH+3bduWmjdvztneiRMn5G5bcju1a9emRo0a6XTeokwZIU90KE+MjIyk/lYkJSXp/W9/p06deNNCQ0M/i7KlKl5eXhQYGEjv37+nM2fO0MyZM+nLL78kQ0NDhZ43unXrRnl5eWXej2HDhpFIJJK7nIODA7Vq1Yoz7cOHD3pVzvz8/MjZ2Vnh5Xv16iW1nO3bt0+vy/jYsWOpQoUKnGnr169XeFt//vkn5283Nzfy8vLS6r3sgQMHONOGDBmiVBqDBg3iTduzZ49KvmOKSklJoZs3byr9jGtkZETdu3fnTDt79qxC22SM0aJFizjTvvrqK6Xzj4howoQJ1KBBA860BQsW6O09jTpo83o7bNgwuc/Kxc9+HTt25E03NDSkoUOHKrStfv368aap+/eiLNLT0zl/P3jwgAAAAHSJxgNce/fuFZw/YMAAhdJRZDl521KVN2/eCM6XfECSNl/oRi4+Pl7uPly/fl1wvuTNtDzyAojytifrOM+cOUM1a9b8fL5gBgbUu3dvhR68hBQVFZV5PxR9AJRHkePRxDFpAmOMhg8fTnfu3JE639bWls6dO0dNmjRROm03NzcaOnQoDR8+nPMZNmwYdejQQaPHGRsbS2FhYZxpY8aMUTqdIUOGiM97YWEhXb58Wek0KleuTLt27VLo4VbWS4ugoCDBdcLCwjgVAtq2bUvm5ubUsmVL8bTjx4/L3bZkEMzX11en8xZlSoQ80bE8ISKplZoSExP1/vff0tKSd2yKVFjS97KlDubm5tS1a1datmwZ3bx5k1JSUigoKIgmT55MtWrVkrleeHg4TZkypUzbFolECj+bEBHVqVOH83d2drZgxT5dK2eluSYsXryYV2ntzp079PbtW70t4/b29rwX1UeOHKHY2Fi528jMzOQF+MaPH6/V6+2RI0c4wTwTExPq37+/UmlIC3AlJSXJvedSpbdv3xJjjPN8o2hA1s3NjfP3q1evFFrv0qVLFBERwZk2a9asUn+/ZsyYwZl2+fJlevjwoV7+fquaNq+3IpFIahmXpXbt2rxpnTp1IkdHR4XWr1atGhkbG3Om6XKASzKv9+zZQ6mpqQQAAKArNBrgSkpK4tSal2RsbEx9+/ZVKC1/f3+FbuY1UatM3ov8wsJCwfkFBQWcm3VJ1tbWcvdBXsCpevXqSh1TjRo1BOdfu3ZN6XwKCAigL7744rP6gtWoUUNuCz1N7YcirbMU0bhx48/m/M2dO5cOHTok8+XHuXPnqFmzZnp/nOfPn+ddc5Rt9VmcJw0bNizzdcLKykqpdSQDS8+fP6eoqCiZy5dseWVhYUFffvklEREnsPjixQt6+vSpzDSSk5N5xyctwKVLeYsyhTzRtTwhIqmtXxMSEkqV1qhRo8jW1lapjzq5urpy/n7//n25L1uaYGlpST169KD169fT69ev6eLFi9S6dWupy27ZsoXX4kMZ9erVk3tPLLlvkiRrvutrOZOlUqVKUl8M3759W6/L+I8//sj5Oz8/n7Zu3Sp3GwcPHuScczMzMxo+fLhWvzM7duzg/N2jRw+lr3/u7u7UvHlzuWmr+12C5PdNkRY30p6nFa1Icfr0ac7fZmZm1Llz51IfQ48ePXj7LLkNffn9VjVtXm89PDyUqoQr7fvTtWtXpbYp2QOFLvfO0K5dO87fjx8/ptatW1NoaKjgeywAAABN0Wi79n/++Yfy8/Nlzu/cubPCL+Hd3d2pUaNGvBpVJaWmplJwcLDUl46qVLlyZcH58moSyZsvrYaQpMjISMH58lqRSZLXfeC///6rVHoGBgY0atSoz+4LJq9s6ON+6MoxqduGDRto8eLFUufVqVOHTp06pVBXjfpAslZ0jRo1FHqJI03JrqOUrb1tZGREI0eOVHqbPj4+ZGJiwqnQcPLkSZo6darU5UsGuLy8vMQ1KDt37kxLly4Vzztx4gTVq1dP5guPkpUXbG1tqW3btjqbtyhTyBNdzJPi+znJbo9ltZqVJysrS6dqFEt2NSt0D1weypa2tG/fnsLDw2nGjBm0Zs0azjzGGM2YMYMuXbpUqrSrVatW6vwrb+VMSM+ePemvv/7iTLt79y598803elvG69WrR126dOFUzty+fTvNnTtXsHKjZD74+/urrJJZaURHR9OFCxc400rTvR7Rp1Zcd+/e5UwLDg6mhIQEpbvqLg3JoEJ6ejoVFRUpFOT6+PEj529FKyCeOXOG8/dXX32l9HN1SXZ2dtSoUSNOF2/BwcEKtQrTtd9vVdPm9VbZysDStq2KNHTV1KlTadu2bZxnn2fPnlHnzp2pZs2a5OPjQ56entSqVStyd3dXOPAMAACgKhoNcMnrj12ZJunFywsFuIg+dVOo7gBXixYtBOfLCwY9f/5ccL68rhcKCgoEx98iIjI1NVXqmOQFuJR9gVS/fn2lb1rLA10ZZ0yV+/E5jJ22a9cumd0atWvXjo4cOaITLfNURfIFz+PHj8vcrRPRp5cqyvjqq6/kjlcnjaWlJXl5eXHGUzh9+rTUANf9+/c5+9WlSxfx/9u0aUMWFhbi6+mJEycoICBA6jYluyfs0aOH1BdeupK3KFPIE13MEyIib29v3ovqZ8+eUWJiotwxTEEzZSs/P79M3eyZmpqq/d7B0NCQVq9eTYWFhbyxksLDw+nFixe8LpYU4eTkhAKmgDZt2vCmKdoFnC5fP6dMmcIJcMXFxdGJEydk9jgSFRVFV65c4Uz7/vvvectp8ju1c+dOXrfgJ0+epHPnzim9XWldqBUUFFBgYCD9/PPPai9nderUIUNDQ/FL9qKiInrx4oVCPYRIPm8r0rVhUVERrzW/IhVP5XF2duYEuIR6DNDl329V0+b1VhXbLs+/F1988QUtWrSIZs+eLfX6vHXrVvG9nIWFBbVq1Yp69uxJgwcP/mwqxwIAgHZpLMD17t07Cg8PlzlfJBKRg4ODUt2ICPW7X/IGPiMjgywsLNR2bJUrVxZsTRYUFCT1ZqDkPso8QUZGvCbhkiRrpEljYmKi9IOTkLS0NCosLFS45pGyNZoAtOXYsWM0cuRIqd0tjBw5krZs2cLrM13fqas7I2W72mjatGmpt+Xr68sJcIWHh0u99kuOrVWyOxETExPq1KmTeJmbN29KrZWcn5/PG5xcVkUKXclblCnkiS7mCRFRx44dycDAgPMCljFGly9fJj8/P6XScnV1JU9PT5nznz59qvLvTnJyMr169Yrev39P79+/p4SEBPH/NTUIu7rL1pEjR2jgwIGlTmfKlCm0du1ajeTFihUrKCQkhJ49e8aZfvDgwVKNm6PNlje6Vs7kPQuZmppSbm6ueFpKSoreXz+7detG7u7unJ4ytm7dKjPAtXPnTs7fjRo1knpN0uR3SnKfiFTfreCOHTs0EuCysrKiFi1acN4XBAUFyWyxXywvL49OnTrFmebj4yN3e4mJibyhBlTxsl6y8kZycjLl5+fLfb7Qtd9vVdPm9VYVFRfLU+VHaQICAiguLo42btwouFxGRgaFhoZSaGgo/fLLLzR16lSaN29euXt+BgAA3aKxANeBAwcE++dljFG3bt1Uvt3s7Gw6duxYqbtiUOZBQ1aXAdevX6clS5ZQQEAAr7l2SEiI4APKwIED5fZFLa/1FhHxau7JI2/csOLtKjI+mC69IAAQEhoaSgMHDpRa/ufOnUu//vpruTxuySC5o6MjVaxYsczpKttdTVm6t+nduzdNmDBB/DuTl5dHoaGhvMBTyQoFbm5uvFq/PXv2FAe4ioqK6NSpU7xr+6VLlzgvxUxNTWX2u68reYsyhTzR1bJja2tLLVu25FVwOnTokNIBrkWLFtGiRYtkzvf29uaN5aOsrKwsCgoKolOnTtHNmzfldhH9OZUtXWBiYkLTp0/nXbdv3bpVqvS01c2SLpYzRe714+LiZJZLfSzjIpGIJk+eTBMmTBBPO3/+PL1584ZX0ZIxRrt37+ZMk9Z6S5OuXLkiOCapqjx+/Jju3r0rdYwuVZs5cyYnwLh69WoaPXq01PGYii1btoxev37N+d3p3r273G1JC6wq2yuKNJKt7xhj9P79e7m9nejjNVkfrreq2nZ575ZPJBLRhg0bqEWLFjRt2jSpLTolZWZm0m+//UYXLlygf/75hxwdHfHCAQAA1EJjAa69e/dq7SD37dun9gDXkCFDaPXq1fTkyROp8+fMmUObNm0iT09PsrOzo8zMTIqIiBDsksDMzIxmzpwpd9tCN/TFcnNzleoepmQNTFk3OIpst+TyALrs5s2b1KdPH6llf8WKFTR9+vRye+x2dnacbnqWL19OQ4cO1fh+lGXQ7GrVqlHz5s05Y/ecPXuWE+CKj4+n+/fvi/+WNkC95AuP4OBg3otSye4JO3bsKPN6qCt5izKFPNHFPCn27bff8gJc//zzD61Zs4YcHBx0Yh/fvHlDv/76Kx04cIAyMzPxfdNh/fr1o1GjRnEq1unLeGK6XM7kSU9PV9u1RptlfPjw4TRnzhxx9+xFRUW0Y8cOmj9/Pme5y5cv05s3b8R/W1hY0ODBg7V6TiRbatnZ2ckcW1QZERERvNZvO3bs0EiAy8/Pj1q1aiX+zYiJiSE/Pz/as2eP1ADQ0aNHacmSJZxpAQEBCj3HShtrKzk5uczHkJSUxJsmOZaevvx+w+dn2LBh9M0339D+/fvpyJEj4l4zhFy7do169epF165dU7pnIQAAAEVoJMAVGRlJ9+7d09pBhoSEqH0sB2NjYwoKCiJPT09KSEiQukxcXBwdPXpUsRNjZEQHDhygBg0ayF3WxsaGRCKRYAs5eQErSTk5OXK3icFDobx4/Pgx+fj48G7ORSIRbd68mcaPH1+uj1/yhYC2xncqayDc19eXF+CS/C0oeZ2UFuBycnKipk2bigNhISEhvG5jJLu5ERrnUVfyFmUKeaKLeVJs9OjRtGjRIs5Lv/z8fNq6dSvvJbKmMcZo3rx5tHLlSpn3UsbGxuTq6kru7u5Up04dqly5Mtna2tKGDRtkVnzSp7LVrFkz2rRpU6nXb9y4sUbPmaWlJTk5OVFMTIx4WmxsrE5fM/WhnAnJysri3UOpctwVbV4/K1asSGPGjKGVK1eKp+3du5d3bZKszDlkyBCZQRRNfKeysrLo4MGDnGmzZs2iadOmlTlPZs+eTUuXLuUd/6pVq1TSwkmegQMHcipFhIaGkpubG/3000/iyqTPnj2jw4cP8yoltW3bVm6XhsWktTZRpNWKPJJpmJqakq2trV7+fsPnqWLFijRq1CgaNWoUFRYW0tOnT+n27dt048YNunTpktQx6O/evUtLly7V+n0dAACUTxoJcO3bt0+rB1lQUED//POP2l9S165dm1q2bElBQUFlSqdatWq0ZcsW6tmzp0LLGxgYkI2NjWBf9xkZGVSlShWF90FeLRx0OQjlxYsXL6hLly6874+hoSHt2LFD7a0/dYGLiwvnb21WSCgLX19fmjt3rvjvly9fUlRUFLm6uhIRN+BlYWFBbdu2lZpOz549xQGutLQ0un79ungsxH///ZdevnzJednQq1evcp+3n2uZQp5oRsWKFWnSpEm0YMECzvR169bR2LFjtdalTX5+Pg0cOJCOHDnCvXk2MiIfHx/q1asXtW/fnlxcXMjIiH9LfejQIY0EHtRdtr744gted666zsbGhhPgUrRLbZSz0rl9+zZvmiq/t9q+fk6cOJHWrFkj7sL633//pXv37lGzZs2I6FPX7ocPH+asM27cOK1+pw4fPsxpVWdoaKiye9rhw4fzAlzJycl08uRJ6tevn1qPKycnh9avX8+bnpaWxvsNkdSoUSP6559/FB4LyNzcnGxsbMSt94g+vaQvC8YYr/zK65oQQJcZGhpSw4YNqWHDhuJeL27cuEGLFi2i06dPc5bdsGEDTZs2TSVdzAIAAJSkkSY48gJclpaWZGNjU+qPhYVFmfdBFcLDwykoKIjMzMwoMDCQvv76a6Uf3ubPn0/Pnz9XOLhVTF5Lr1evXimVXskXuNLUr18f3x7Qe7GxseTt7c0ZM4Lo0xgehw4d+iyCW0REXbp04fx9/vx5Kigo0LvjaNiwITk7O3OmnTt3jnNcxby9vWV2kSHZsqtkYCw4OJgzr2XLluTk5FTu8/ZzLVPIE82ZOHEir7VDamoq/fDDD1rbp5UrV/KCDv3796fIyEg6ceIEjRkzhtzc3KQGHVC2tP/7XpLQdVrb9KWcCQkJCeFNa926dbkp4zVr1uSM+0TEbbEVFhZGiYmJ4r89PT2pSZMmWj0nkt0T+vj4qKzLV3d3d/L09JS7TXXYtWuXeDwtV1dXqlOnjkLrDRw4kK5cuaJUhU8iIi8vL87fL168KFOXp0+ePOF1UdixY0fcBIBGCfX8owqenp508uRJmjRpEmd6cnIyXbhwAScAAABUTu0Brrt370ptolzM3t6ekpKSKCUlpdSf6OhouX35Xr58We3dWWzbtk38/0GDBtGVK1fozZs3dOjQIZo9ezaNGDGCevXqRd26daPu3bvTwIED6ccff6Rt27bRvXv36MWLF7RgwQKFAnaSilsXyFKy2y5FSKuJWVL79u3L1RehuEYmfD6SkpLI29ubF/w1NzenkydPkp+f32eTF506deK8OPv48SMdOnRIL49FsrvAixcvEhHR8+fPOYOFCw0u3rJlS07XSiUDXJLdHgp1T1he8rY0D8HlqUwhTzTD3t5eaq38Y8eO0Z49ezS+P8UDo5c0evRoOnDgAK81Ca7huuXBgwe8VtnaDjaUh3ImS25uLi+wIRKJ5D6b6FsZnzJlCufv/fv3U1FRERF9GjOwpO+//16r5+TNmzcUFhbGmTZixAiVbkNaemfOnOFVGlO1kud93rx59Pz5c/rzzz+pcePGvC78LC0tacCAAXT16lXat2+fUuNHF5PWSn/Xrl2l3n9pQUBlK7bq870Q6AZVjCUnj4GBAa1Zs4bXQvHZs2c4AQAAoPrfHXVvQF7LKX9/f4W7CZDFxsZG8GVl8Q3YgQMH1HqsxS8+c3Jy6NKlS0T0qcZfv379aPHixfT333/TiRMnKDg4mE6dOkX79u2jdevW0ZgxY6hp06Zl2ra8gJOiY38V59XJkyfLtD1dI6+MSQ6MDeVbeno6+fj40NOnTznTrays6MyZM7yawurw22+/kYeHh7ibmuKPm5sbde3aVfzSRBOsrKyof//+nGlLlizRyxYAkgGn4mtxeHi4eJpIJJI6/lbJBzIfHx/x3/fu3aPk5GQqKCigy5cvC26vPOatvC5ry3uZQp5oznfffUfffPMNb/rIkSN5wWV1i4iIoOzsbM602bNnKzUGSn5+Pq7hcoSEhPBaM5TVn3/+yZumq5VWdK2cKTtmLxHR9u3beS3mvv76a7K3ty9XZbx169bUokUL8d8xMTF09epVIuKOzWlra0v+/v5aLVc7d+7kBB/s7e0Fu1MujQEDBpCZmRlnWmFhIQUGBqr12G7duiX+f+PGjcnIyIhGjRpFDx48oA8fPtDdu3fp8uXLFBUVRYmJibR///4ytSb09fXlVT7dsGEDZWZmKp1WSkoKbd++nTOtcuXK5O3t/dncC4Fu+O+//zSyHSMjI957LnUHwQEA4POk1gAXY4z2798vuMygQYNUsq3BgwfLXUad3RSmpqZyuqYYPXq0RgdA7tixo2AXDTdv3uT1gSzLnj17KDIyUub8Zs2aUfPmzfWqoMtrFffixQuZ8+Lj4yknJwdXi3IiJyeHevfuLbWVop+fH0VERNDvv/8u+Nm4cSOtXbuWVq5cSUuWLKFly5bRypUradu2bQo/MAQHB1NkZCRFRUVxPv/99x+FhIRQWlqaRvNlzpw5nBdqjx8/poULF5YpnzVRO1BS27ZtOWMEJiQkUGRkJCfA1bJlS7ljg5SsTcsYo/DwcLp16xbnYb5OnToKddeqb3kr2S9+yd+2z7FMIU80a9u2bbzavnl5eeTn58drkaBOJVt8En0KjFtZWSmVhrzunnEN/3S+mzZtSleuXFFJeo8ePeL0qEBEVKtWLaW7Df9cy9mtW7eUqmATGRlJAQEBvOmSrZ3KSxmXPK7Tp0/To0eP6N27d+JpI0aM4AV+NIkxRjt37uQ9b8vr7URZNjY2Uiv5qLObwry8PM798cePHznz7e3tqVmzZtSmTRuqU6eOSo7Zzs6Opk6dypn24cMHXtdrihg3bhzv/n7OnDlkbm6uF7/PqroXgrIxNDSU+t1Q1OvXr8s8lpwyJCuxVK1aFScRAABUTq0BrvDwcM4Az5Jq1qypsgfOnj17yu124N69e4KBm7KoUKECGRj8f3a+ePGC6tSpQ/369aOZM2fS3LlzOZ+FCxfSsmXLaM2aNbRp0ybavn077dy5k4KDg+nZs2eUlZWl9I2OtAfMkkaMGCG3q8Lw8HCaOHGi4DJz587Vu4Iu70ZKVvDv6NGj1KBBA/ruu+9wtSgHCgoKyN/fX9xtnaSdO3fSxIkT5X4mT55MP/30E82YMYPmzJlDs2bNohkzZtC4ceOoYcOG4hq9QuS9QNJ0t5n169enYcOGcaYtWbKkVC8qrly5Qk2aNOG1dtLUQ59k66zr169zApqK1GLu0qULpzuksLAwcWuwYvJab+lr3pYMEBJ9GihaiKyH6vJSppAnms/r4OBg3jgp2dnZ5O3tTXPnztVIyyhXV1fO34wx3hh8Qo4cOaLRik76WrYePHhA0dHR1K5dOxo5ciTFx8eXOq13795R7969eeVj8eLFnHt0XaJr5ezJkydynyeKvXnzhnr16sVryeLq6kp9+vQpl2Xc39+fU0HmzJkznAo0RJ+CGNp0+fJlXtBTXc8xw4cP5017+vSp3K7uS8vExIRq1Kgh/luyNZS6/Pzzz7zfpL///puWLVumcBpz587ldavp4uJC48eP16vfZ1XcC0HZSBtHTtGW0AUFBTR06FClu5ccNWoU/fXXX0o/n757947u37/PmVavXj2cRAAAUD2mRmPHjmVEJPMTEBCg0u0NHz5ccHtExObPny9z/f379wuuO2TIEMHtu7i4yN2+oh8DAwPWokULFhAQwB48eKDQ8efm5rLmzZsLpmtmZsZGjRrFLly4wF6/fs0yMzPZq1ev2Llz59jQoUOZiYmJ4PpeXl6sqKhI5XknRBVpb9++XTCNChUqsGPHjrGPHz+yf//9l+3atYu1a9dOPN/a2prl5+fz0t2yZYtgum3atFHoGJ8/fy63TAhR1X7Ex8fL3Y/U1FSF0mrRooVgOoGBgUzTFi5cqLLvqNBn+vTpcvfF09NTMI3ExESN5096ejpzd3fn7IdIJGKzZ89m2dnZctd//PgxGzt2LBOJRIyI2JYtW2Qum5iYyDvmTZs2qeQ4/vnnH066o0aNYgYGBuK/Fb2mdujQQbxOs2bNmK+vLyfdS5cu6VzeRkVFlfm7NmrUKF4a58+fl5nXzs7O7OXLl+W6TCFPNO/Zs2fMyclJ6vWxcePG7NChQyw3N1ehtLKzs9nFixeZh4eHwr+tOTk5zNLSkrOstbU1u3Hjhtzt7dq1i5mbm/O2ZWNjI3fdsnyHNVm2VCEtLU28reKPiYkJGzRoEAsPD1cqre3btzNra2te3n311VdS71sVzftp06YptR8TJ07kpREfH69z5UzW9aH4M27cOFZYWChz3TNnzrBq1arx1hOJROzixYtqK+e6UMZ/++038bYNDQ3ZoEGDxH937NhR69fO7777CwsWZQAAIABJREFUjpM/jRo1Utu2CgoKmKOjI+9c/vDDD2q7znXr1o2zXv/+/dm5c+dYZGQki4qK4n1evXrF4uPjFf69kOXixYvMyMiIt9/Dhw9nCQkJMteLiYlhAwYMkPrsee/ePY39fuva/aE2rreq3PYvv/zCS+P58+dKpeHg4MBZ39PTU+F1raysOOs6OjqynJwcud/XyZMny7zuC+Wds7MzIyLm6urKtm7dypKSkuTuY0pKCmvbti1nG+bm5iwrK4sBAAComtoCXHl5eczOzk7wBW5ERIRKt3n27Fm5L57d3Nxkrl/WQMrQoUPV8rJcJBKxAQMGyLxBLOndu3dSHzRU8XF1dZV5M6PrAa6XL1+W+fivXr3KSxcBLtl0McAlGaBQ12fYsGFy90UXA1yMMfbo0SNmb2/P259atWqxX3/9ld27d4+lpKSwvLw8lpCQwC5cuMBWrFgh9XgWLlyolRfv6enpzNTUVJxuyZfktWrVUjidVatWcV66Vq9eXfy3vb09Kygo0Lm8VcULjMDAQF4aVlZWbO3atezKlSssJCSErVq1irVq1Uo8X+glYXkoU8gT7YiKimKurq4yr5OVKlViP/zwA9u0aRMLCgpit27dYufOnWP79u1jGzduZDNnzmRt27YVrLwj5Oeff+Ytb2xszL799lt25swZ9vr1a5aXl8fS09PZgwcP2KZNm1jTpk1lbkvdAS5Nli1VuHLliuDvYL169VhAQAA7evQoi42NlXqtP3bsGPPy8pK6frVq1aSup86XnqV54aqNcibr+lDy07ZtW3bkyBH27t07lpOTw169esUCAwNZly5dZK7z888/q/1lu7bL+IcPH5iZmRnnfqD4/wcPHtTqNTMjI4NZWFhwjnHt2rVq3eb06dN5+Wprayv4sr0s5//gwYOlvj93dHRkHTp0YHPnzmXXrl1TOPhdbPPmzVLTtbS0ZCNGjGD79u1jV65cYeHh4Wz37t1s8ODBrGLFirzlDQ0N2d69e5X+fmo7wKXKeyEEuMoW4CqZx8Wf8ePHs7S0NN6yRUVF7OjRo6xevXqC3w9ZeSetMoqRkRHr2LEjW7JkCQsMDGRhYWEsMjKSPX/+nB09epRNnTpV6nVa1RXcAQAAiqktwBUUFCT4A9qkSROVb7OwsFBqbULJz+3bt6Wuf/z4ccH1xowZI7j9q1evqvWluaOjo0I3To8ePWJubm4q3XaTJk1YVFSUzG2WNe+EqCrt9u3bl/r4DQ0Npdam3blzp+B6Xbp0UWjfXr16JbeFmRBV7cfHjx/l5oW82mHFJGtsSX4OHz6s8Qte//79NRLgUqRM6mqAi7FPLSdq1qxZ5sC80IO4ul+8d+/eXep+TZ48Wal8kHV8w4cP18m8VcULjMTERGZoaKjUPvXt27dclynkifZkZmayGTNmSK05r4r7KiHv3r2T27Jd6GNsbKzxAJemypYqxMbGssGDB3Na2Ap9bG1tmbu7O6tfvz6rWrWq4HpVq1Zl9+/fL/MLYE28cNVGOZN1fWjXrh2nMocynxEjRgi2+lJlOdd2GR89erTUMpeXl6fV6+WOHTt4ZUOodZEqPH78WGr+HjhwQC3nv6ioiLVs2VIlvwENGzZkhw4dUup49+zZw6lEpezHwsKCnTx5slTfT20HuFR5L4QAV9kCXLt375aa3+bm5uzrr79m33zzDevTpw9r2LAhq1ChAmeZunXrSu1pSVbeXbt2TSXfN3d3d5aeno43sAAAoBZq65Be3oDHyvRZrSgDAwOaNWuW3OWioqKkTnd3dxdcz8PDQ3B+69atydvbW23dScbFxZGXlxelpqYKLtegQQO6e/cujRo1SuogpMowNjamKVOm0I0bN6hOnToylytr3glRVdpr164t1aDPHTt2pJs3b1KrVq148+rWrauSfXN0dBQcVFxeOqraDysrK87YApJcXFzI1NRUobTkbVPeeVWHspRDVW+n5EDpsq5n2uLh4UE3btygfv36lWr9Fi1a0NWrV2nChAlaOwZZ42MpOm5WcT64uLiUOR19y1t7e3uaPHmywsubmZnRF198Ua7LFPJEe8zNzWn58uV0584dat26dZnTMzIyop49e9KxY8fo7du3gstWq1aNfv/9d6W3Ubt2bdqzZw99//33uIbLufcJDAyk+/fvU8+ePeUun5KSQpGRkfTkyRNKSEiQOZZl8+bN6fbt29SkSRO9KOO6VM7q1atHt2/fpq5duyr1/DVjxgz63//+p7F7F22X8R9//JE3bfTo0WRsbKzVsiQ5HlmPHj2kjtejSvXr16cWLVrI3RdViYyMlPssrKhHjx5R//79acSIEQqP7Tho0CC6dOkSNW3aVOntff3113Tt2jWFrne6SB33QlA6AwYMkPqMkpWVRVevXqXDhw/TsWPH6NGjR5SdnS0+H9OmTaN79+5Rs2bNFN5Ws2bNaPPmzeTs7Fzq/W3YsCGFhYWRhYUFTh4AAKiHuiJn0dHRrHXr1pxuEoyMjFiDBg3Y/v371RaxKyoqYmvXrmX16tXj9UtvbW3NOnbsKNhn8IoVK1j16tU5tZMqVarEBgwYILXJd7FHjx6xL7/8UiOtQ5YsWaJwfrx584bNnDmT1ahRQ6ltuLi4sIULF7K4uDiFt1XavNNk2mfPnuXVliIprbWaN2/O5s2bp1BNrPnz5zMnJydOTWJbW1vWvXt3pbrGOXToEPPw8ODUCjQ3N2fNmjVj165d09h+BAcHs3r16nH2w8zMjDVq1IiFhoYqVfa8vb05Y2IYGhqy6tWrs6VLl2olop+YmMh8fX2Zra2tWr6bypTJcePGSa19LhKJmIuLi9Qx37QhNDSUde3aVW7rCUtLS+bv76/wuCnqblkSGxvL607D1tZW6XyV1le9mZkZy8jI0Mm8VUUNXcY+9dPftWtXwZrvTZs2ZatXr2bv37//LMoU8kQ3PHr0iM2cOVOpViZVqlRhAwYMYHv27GHJyclKb/PAgQNyu92uWLEi6969O9uyZYt4rJcpU6ZopQWXJsqWOty+fZtNnDiRVa1atdS/wWvWrFG4pbkutSjQdDmTdX0YP368eP7Ro0eldoNFEt0YXr9+XSutSbRdxjt37swZO/n169davTa+fPmSd99z7NgxjWx706ZNUp+nYmJiVHr+z507xxuzTlUfPz8/pY65sLCQ7dy5U25rMgMDA9amTRulW4rpYgsuVd4LoQVX2VpwMcbYixcvFGrNWLduXbZq1SpO7yDShjeQl3eFhYUsJCSEDRo0iFWuXFmh71X16tXZH3/8oXS37gAAAMoSMcYYwnxlExERQZ6enuLaMerm4OBAb9++VbqWYExMDN28eVNc8y01NZUyMzPJ0tKSrK2tydbWlurXr09ffvml2mv7aVNGRgadOHGCbt26RYmJiWRoaEj29vZUu3Zt8vDwoFatWpG1tTUKNkAJSUlJFBwcTK9evaKYmBjKy8sjBwcHcnBwoJYtW1LLli3JyMgIGVWO8rawsJAOHTpE165do7S0NKpcuTJVqVKF3NzcqG3btmRnZ/fZlSnkiU5V0KL79+/T69evKTo6mqKjo+n9+/dkYWFBNjY2ZG9vTx4eHtSkSROqVq1ambeXnZ1NR44coYcPH1JsbCyZmJiQo6MjOTs7k7u7O7Vq1YpMTExwnVHR9+zChQsUHBxM9+7do/v371NaWprUZStUqECdO3emnj170rfffkuWlpZ6Xa41Wc6SkpKoUqVKnGnjx4+nP/74gzPtxYsXdP78eXr79i3FxsaSra0t1a5dm3x8fHSqdYamy7iPjw+dOXOGiIh69uxJJ0+exIVZjeLj46lhw4aUmJgonmZtbU0jR46kDh06kJOTE+/ZmDFGhYWFlJmZSa9evaKnT5/S/fv3KSwsjAoKCnjbOH36NPn4+Ci9b9HR0RQSEkJv376lmJgYMjQ0pGrVqlGtWrWoW7duVLVq1XJ1LtR9LwTK3QtdvnyZLly4QLGxsZSTk0N2dnZUqVIlatSoEX311VdUuXJltWz72bNndPPmTYqLi6MPHz5QYmIiMcbI3t6eXFxcqH379tS4cWOcJAAA0AgEuFRwg1evXj36999/efO++eYb+vXXX8nZ2VnmwyhjjHJycigtLY3S09Pp9evXdO3aNQoKCqJ79+7J3O5///1Hrq6uOAEAAAAAUG4xxig6OpqSkpIoNTWVsrKyqFKlSuTg4ECOjo46HVjUZYoGuIDv7du35OzsLO4mMygoiHr06IGMUaPhw4fTrl27xH/XqFGDLl26VKpu0xISEmjWrFn0999/c6a3aNGCbt++jcwGAAAA0DMIcJVRRESE1JopNjY29P79+1L3xV5UVERLly6lX375Rer8c+fOUefOnXECAAAAAABAKQhwld78+fPp119/JSKiWrVq0cuXL7U6dmp5l5GRQVWrVqWsrCzxtAMHDpC/v3+Z0p0wYQJt3rxZ/LexsTFlZWWhpTQAAACAnsGdeBndunVL6vRGjRqVaaBhAwMDmjVrFjk4OEid//r1a2Q+AAAAAACAhhQWFtJff/0l/nvs2LEIbqnZo0ePOMEtc3Nz8vX1LXO6AQEBnL/z8/Pp5cuXyHAAAAAAPYO78TLKyMiQOv3jx49lPzkGBjLHgkLNMgAAAAAAAM0JCgqid+/eEdGnFj8jR45EpqhZZmYm528zMzMyNTUtc7pOTk68Z2pVPMMDAAAAgGYhwFVGsgZXfvz4MT18+LBMad+7d0/q2F5EVKr+xgEAAAAAAKB0NmzYIP6/n5+fzN42QHU8PDw4fycnJ9PNmzfLnO65c+eooKBA/LdIJCJ3d3dkOAAAAICeQYCrjLy9vXn91xN96r7Cz8+v1Dffly5doj59+pC0IdJsbW3pq6++QuYDAAAAAABowNWrV+nChQviv6dOnYpM0YDq1auTl5cXZ9qwYcPELelKIzIyksaMGcOZ5ufnR1ZWVshwAAAAAD2DAFcZmZiYyHy4efXqFbVu3Zq6dOlCGzZsoPPnz1NMTAwvaFVYWEgvXryg4OBgWrVqFbVt25Y6dOhA0dHRUtNdvnw5mZiYIPMBAAAAAABUpKioSOr0rKwsmjBhgvjvtm3bkqenJzJMQ1asWMHplvDff/+lJk2a0MaNGyk5OVnhdOLj42nJkiXUvHlzToDMwsKCFi1ahIwGAAAA0EMiJq2JECj9INS7d286deqUYpkuEpGZmRkZGBhQYWEh5eTkKLytjh070vnz50kkEiHjAQAAAABAaUlJSbxeKMaPH09//PHHZ50vvr6+lJ+fT127dqVmzZqRg4MDpaam0qxZsyg0NFS83PXr1xHg0rB//vmHvv32W063gkSfxqbu2LEj9ezZk5ydncnR0ZEcHByIMUZxcXEUFxdHL1++pOPHj1N4eDgviGliYkLHjx+nbt26IZMBAAAA9JARsqDsDAwMaO/evfTtt9/S6dP/x96dh0VV9//jfw4wLAKCgggKioKKuJRKouWKmlqK+4KUSy63lZXWXXrbJ03LUCtttcUlbxfUctfcslLR231LUDbFBAQURZCdYd6/P/p5vhxmYYAZYOD5uK65rjnnvJdz3uc1B655zXmfA2WWF0IgLy+v3P1MnToV33zzDZNbRERERERERubk5ISNGzfi4MGDOsvMnDmTya1qMHr0aDRp0gQhISG4c+eOtF6lUuHIkSM4cuRIudv08fHBtm3b0KVLFw4wERERkZniFIVGUr9+fezfvx+bNm1C27Ztjdr2c889h127dmHNmjWwtbXlYBMRERERERlZ48aN9W5/9tlnsWLFCg5UNXn22WcRFRWFsLAwuLi4VOo8r1ixAteuXWNyi4iIiMjMcYpCE7ly5Qq2bt2KkydP4tq1a8jKyjK4rpOTE7p27YpevXphyJAhePrppzmgRERERERkFNnZ2Xj11Vdl64KCgjBlypQ6PS7r1q3DvHnzcP/+fdl6GxsbvPXWW/jggw/g4ODAAKoB8vPzcfjwYezYsQOnTp1CQkICdH21YWFhAR8fH/Ts2ROjR49Gv379+ExrIiIiolqCCa4qIIRAUlIS0tLSkJ6ejsePH0OlUkGtVsPCwgI2NjaoX78+GjZsCG9vbzRs2JCDRkREREREVA3y8/ORlJSEpKQk2Nraok2bNmjQoAEHpgbLzMxEbGwsMjIykJmZCQsLCzg5OaFhw4Zo06YN7O3tOUhEREREtRATXERERERERERERERERGRW+AwuIiIiIiIiIiIiIiIiMitMcBEREREREREREREREZFZYYKLiIiIiIiIiIiIiIiIzAoTXERERERERERERERERGRWmOAiIiIiIiIiIiIiIiIis8IEFxEREREREREREREREZkVJriIiIiIiIiIiIiIiIjIrDDBRURERERERERERERERGbFypiN3b9/Hz179uSoEhERERERERERERERkVYDBgzA119/Xak2jJrgUqlUiImJ4ZkhIiIiIiIiIiIiIiIirdq2bVvpNjhFIREREREREREREREREZkVJriIiIiIiIiIiIiIiIjIrBh1ikIHBwe8++67HFUiIiIiIiIiIiIiIiLSqn379pVuQyGEEBxKIiIiIiIiIiIiIiIiMhecopCIiIiIiIiIiIiIiIjMChNcREREREREREREREREZFaY4CIiIiIiIiIiIiIiIiKzwgQXERERERERERERERERmRUmuIiIiIiIiIiIiIiIiMisMMFFREREREREREREREREZoUJLiIiIiIiIiIiIiIiIjIrTHARERERERERERERERGRWWGCi4iIiIiIiIiIiIiIiMyKVVV0kpeXh/j4eOTm5gIAlEolvL290bBhQ54BIiM4f/481Gq1tBwYGGjU8jVddnY2oqKiylXHwsICLi4ucHNzg4ODQ52On7Nnz+LXX39FdHQ0Hjx4AGdnZ7i5ucHHxwcvvvgi2rZtyw9ZHbo+EBERERERERERmQOFEEKYsoOkpCR06dIF9+7dk623srLCf//7X0yYMIFngaiSHB0dkZ2dLX22ioqKjFq+pjt79iy6detW4fpeXl7o378/goODERwcDAuLunFz6+XLl/Hmm2/i5MmTOsvMnj0bK1eu5Iesjlwfjh07hiVLlsjWvf322xg8eDAHkoiIiIiIiIiIahSTf4v766+/aiS3AEClUuG///0vzwARVbvExET89NNPGDFiBNq2bYutW7fW+mPesWMHnnvuOb3JLap70tLScPToUdnr7t27HBgiIiIiIiIiIqpxTD5F4aNHj3Ruy8rK4hkgIpPo3LkzmjVrpnWbSqVCRkYGkpOTcfv2bdm22NhYhISEYM+ePfjhhx9Qv379KtvnzZs3Y/369dKyUqnEgQMHNMotWbIEx44dk5bbtm2Lr776yuB+zp49i9DQUBQUFEjrAgMD0bdvX7i6uuLx48e4fv06jh8/zkAiIiIiIiIiIiKiGsmKQ0BEtdGbb76JSZMmlVkuLS0Ne/fuxVdffYXIyEhp/datW5GYmIgjR46gXr16VbLPt2/fxtGjR6VlW1tbreUiIyNl5Z5MP2cItVqN6dOnS8ktBwcH7NmzB0FBQRpli4uLkZaWxmAiIiIiIiIiIiKiGseCQ1D3qFQq5OTkoLCwkINBdV7jxo0xffp0XL16FcuWLYOlpaW07dSpU3j55Zdr1fHu378f165dk5bXr1+vNbkFAJaWlmjSpAmDhIiIiIiIiIiIiGocJrjqkPT0dHTt2hU2NjZwcHCAjY0NWrZsidOnT3NwiBdDCwu89957WLdunWz9zp07sWvXrlpznHv27JHe29jYYMSIETz5JBk3bhyEELLX1KlTq3w/kpKSEBMTg5iYGMTHx/PEEBERERERERGRBk5RWIds3boV58+fl61LSEjAypUr0b17dw4QEYCJEyfi0KFD2LJli7Ru0aJFVZII8vX1xZAhQ6RlpVKptVynTp1k0xK2adPG4D4uX74svXdzc4OFBX/nQDXP9OnTcejQIQCAq6sr7t+/z0EhIiIiIiIiIiIZJrjqkHv37mldn56ezsEhKmHevHmyBNfVq1cRFRWFdu3ambTfcePGYdy4cWWWe++99/Dee+9VqI+Sz9QqOR0jERERERERERERkTnhT/frECFEudYT1VUdO3aEh4eHbF3pux/NFZ+9R0RERERERERERLUBE1xERFp4eXnJlhMTEzkoRERERERERERERDVErUtw5eXlITk5GY8ePYJarTZpX0II3L9/H7m5uYwkolqm9OfawcGBg0JERERERERERERUQ9ToZ3Dt2bMHYWFhuHfvHoQQcHJywoABA7B8+XIoFAqp3PXr17F8+XIcPnwYqamp0np7e3sEBARg3rx5GDRoUJn9paSkYP369UhJSYEQAra2tggICMDYsWOl/iIjI7Fp0ybs378fsbGxKCoqgkKhgIeHB1q1aoUpU6YgNDQUVlZlD+2dO3ewYcMG6fhsbGzQoUMHvPTSS2U+GycxMREbNmxAWlqa3rqpqalYt24dUlJScObMGa1txcXF4Y033pCts7a2Ru/evREcHFwrAv3gwYM4ePCgbDpGhUKBfv36YdiwYVW6L0lJSTh58iQuXLiAtLQ0PHz4EPb29vD09ERQUBD69+8PW1vbGj2eubm52LNnD86dO4c7d+5ArVajadOmCAoKwoABA+Do6GjW8ZKfn49bt27J1jVv3lxn+aioKKhUKgD/JMJ8fHw0yhQXFyM+Ph43b95EgwYN0L17d7OOE2PGgCnGz5j7d/XqVem9nZ0dWrduLduek5OD2NhYxMXFISMjA+7u7vD390erVq3qzD8TxcXFOHHiBE6ePImYmBg8ePAASqUSbm5uaNSoEZo3b45evXrB39+/VsQsY4KIiIiIiIiIqAYQJrZ06VIBQOurW7dueuu6urpqrffnn38KIYTIzs4W7777rlAqlTr7ePJ6/vnnRUZGht7+evToobXu9u3bRWpqqggJCREKhaLMvlq2bCnOnj1b5th06NBBa/1PP/20zLrt2rXTWnfFihWycp06dSpzf/W9fv31V1EbBAcHaz2+oKCgKtuHP/74QwwbNkxYWFjoHXN3d3fxww8/lKttBwcHqb6VlZXRyz+Rm5sr3n//fWFvb69z/52dncVXX30lVCpVlY3tmTNnNPZj/fr1FW5v69atsrasra31Xj/c3d2lsv3795dtS0pKEgsXLhSenp5SmdmzZ1dZnMyZM0d4enpKr5LtWlpayrY9eZW+jpg6Bow5fqbYv5J1AwICpPXx8fHitddeE3Z2dlr76dixozh69GiZ7Xfr1k3reSjv69KlS0b5vLdu3Vpqs3nz5nrLqtVq8cMPPwgPDw+D/qY0b95cfPjhhyI1NVVqIzMzU+NYbGxspDoWFhZ6jzs/P7/Kr1umjgkiIiIiIiIiIipbjU1w5eXl6ay3YcMGkZeXJ3r16lWuZM24ceP07quuL78GDhxo8Jd3T15OTk7izJkzOvsqLCzUmSwLDQ3Vu5+FhYU6+504caKsrK4koaGv77//vlYE+pAhQ7QeX58+fUzed1ZWlmjfvn25x37ChAmiqKjIoD6qIsEVFxcnfH19Dd7/QYMGicLCwio5v8ZMcOXk5AgfHx9ZW6NHj9ZbR1eCZt26dbKx1pegMVWcTJ48udxtfvjhh1UaA8YYP1Pun7Zkxrp163QmMUq/1q5dq7d9b2/vSl2nn7x0/c0x1fVBpVKJiRMnVmhfDx48KLXz6NGjSh23vgSXucYEERERERERERGVrcZOUVhcXKx328SJE3HixAkoFAr06dMHAQEBqF+/Ps6cOYNff/1Va72ff/4ZS5Ys0Tr9FQAUFBRoXX/48GHpfcOGDdGqVSsUFxcjOjoa2dnZWutkZmZizJgxiImJgZ2dncb2wsJC2XR5huxHybqGbvvss8+wcOFCpKWlobCwUOtzySwsLGBtbS1bp1Qq0bt3b4wZM4a3OVaSra0tIiMjpWWFQgF/f3/06NED3t7eaNCgAW7fvo1jx47JppEMDw+Hq6srvvzyy2o/hhs3biAoKEg2BahSqUSnTp0QGBgIOzs7XL9+HYcOHZKmmjt06BAmT56MTZs2yaYUrcny8/MxatQo3Lx5U3b+li5dWt47YzFp0iRs3Lix1sRJVcZARcavKvcvLCwM8+fPBwBYWlrCx8cH9vb2uHv3LtLS0jTKv/rqqxg6dCgaNWpksvPj6+uLtm3bVunn5dtvv8WGDRtkf0uCg4PRrVs3uLq6IisrC/fv30dMTAwiIiJw//59qezTTz9dq65bNTEmiIiIiIiIiIhqPVNn0Cp6B1d2drbOesOGDRMARIsWLcQff/yhUffNN9/UWVfflF5WVlY66wUGBorDhw+L4uJiqXxBQYFYuXKlsLa21lnvo48+KvfxlXW3iL6648eP11lv/Pjx1XYXU3Wrzju4hBDC2dlZ2NraijfeeEPExMToLLd8+XKNO/tOnz5dZvumvIMrJydHtG3bVrZPnTt3FpGRkRplExISNOLsl19+Mfn4GuMOruPHjwt/f3+Ndr766qsy65a+A2nOnDmyNiwtLcXIkSPFmjVrxJkzZ0R6enqVxUlKSoqIjo6WXs7OzrJ6V69elW2Pjo7W2D9Tx0Blx8/U+1eybL169aT3Q4YMEXfu3JGVvXr1qhg8eHC5pp69fPmyOHPmjNbX+fPnRWRkpHRubty4IYKCgmRt29vbi7/++qtKrw9FRUWicePGUjkXFxdx6tQpve2eOHFCjB8/Xri7u8vWFxcXa8Rg06ZNZdMHlt5e8qVWq6v8umXqmCAiIiIiIiIiorKZZYLryRd6CQkJWuteunRJZ7233npLZ5+6Elxdu3aVJbZK27hxo87+PD09tX75xgRX1dKV4Orbt2+V9D9z5kwRFxdnUNmQkBDZPg4dOrTMOqZMcM2ePbvcU3hNmzZNKv/000+bfHy1JbhWrFghkpKStL5u374trly5Ig5NHKLPAAAgAElEQVQcOCAWLFig81l1CxYsMKj/kgma0lOUvfDCCyI+Pr5GxIkQmtOWlvX8oqqIgcqOn6n3T1ts9OrVS+Tl5WktX1xcrJGEat++vVFi/ZtvvtHYl61bt1b59SEuLk62D++//77Bx1Dy+Vu6lJy20dXVtdzjVJdigoiIiIiIiIiorjLbBNfKlSt11k1LS9NZb9KkSTrr6UpwDRo0qMzj7N69u84+z549W67jY4LL+BYvXixatGghvL29pVeLFi3EvHnzaty+RkVFyc6PnZ2dyM3N1VvHVAmulJQUYWtrK0vY3r9/v8z28/LyRLNmzaR6J06cMOmYaUtwVebl6OgoNm3aZHD/JRM0JV9z587VmuCurjgRovwJrqqIgcqMX1XsX+n9srKyEikpKXrb37Nnj6yOjY1Npc/5yZMnhVKplLX7zjvvGO3zXp7yhw4dMundSJVJcNWlmCAiIiIiIiIiqssszHVqxSlTpujcVq9ePZ3bynq+VUWNHj1a57bTp09zLsxq9sEHH+DWrVtISEiQXrdu3UJYWFiN21d/f39ZDOfl5SEmJqZa9uX7779Hfn6+tPzxxx/D1dW1zHq2traYM2eOtPz777+bRZzY2tritddeQ2RkJEJDQyvVVmhoKJYuXWqy549VVZxUVwwYOn7VsX8DBw6Eu7u73jIDBgzQ+NuTmZlZ4fOQkpKCMWPGoKioSFrXt29fLFu2rFo+K/Xr15ct7927V+szHuvKdas6YoKIiIiIiIiIqK4zywRXs2bN4OTkpHO7qb5Q1icwMFDntsjISEYalUvLli1ly6mpqdWyHwcOHJDe169fH2PGjDG47vDhw6X3x48fr/J9VyqVcHBw0PlydHSEt7c3unbtismTJ2Pz5s1ISkrCt99+i2bNmlWqbycnJ3zxxRe1Ik6qIwbKM37VsX/6ftDwhJ2dHWxtbWXrHjx4UKFzUFRUhDFjxiAlJUVa5+XlhW3btsHS0rJarg3+/v6y44uIiMCwYcNqxN+7uhATREREREREREQEWJnjTvv7+9e4fercubPObXfv3mWkUbk4ODjIlk1156E+WVlZuHjxorRsbW2NcePGle8CY2UFlUqFmzdvVvn+z507Fx999FG1nL+JEycadMdITY+T6ooBQ8evuvavdGJRX9slCSEqdB7mzJmDU6dOScs2NjbYsWMHGjVqVG3XKCcnJ0yaNAk//PCDtG7//v3Yv38/WrdujW7duuGZZ57BM888gy5dumiMRW27blV1TBARERERERERkZkmuEp/qVsT2NnZwd7eHjk5ORrbHj16xEgjgxQWFiIrK6taElqlJScny6YcS09Px/79+yvU1t27d6FWq2FhYVEnzuPQoUNrRZxUVwwYOn7VtX9NmjSpsljasGEDvv32W9m6b775Bs8880y1x/nSpUvx22+/4datW7L1sbGxiI2NxYYNGwD8cxfV0KFD8c4776BTp061MmarMiaIiIiIiIiIiOgfVhwC47GxsdGa4Cr5zBQiAIiKisLp06dx7tw53Lx5EwkJCUhOTkZhYWGN2cd79+4Zra3i4mLk5+frfT5ebdKqVataESfVFQOGjl917Z+jo2OVjP/ly5cxc+ZM2boZM2Zg2rRpNSLOnZ2d8eeff2Ls2LE4e/asznJZWVnYvHkztmzZgnnz5uHjjz822VTCtT0miIiIiIiIiIjo/2GCy4hK/mq8pOp6RgrVLMnJyVi1ahXCw8Nx+/btGr+/1tbWsuX33nsPY8eOrXB7NjY2deZcu7m51Yo4qa4YMHT8anOMPnjwACNHjkReXp60LjAwEF9//XWNivVmzZohIiICP/30E77++mu9z+BSq9X45JNPkJGRgVWrVvG6RURERERERERElcIEl5EIIfD48WOt25ydnTlAdTw2Fi9ejGXLlsm+rH7C0dERrVu3RtOmTeHm5gZHR0ds27at2p/d5u7uLlu2tLREly5deEINUJGkdk2Mk+qKAUPHr7bGqFqtRkhIiCzB6ebmhu3bt2skcGoCpVKJGTNmYMaMGUhKSkJERAQuXLiAc+fO4dKlS8jNzZWV/+677xAUFITRo0fzukVERERERERERBXGBJeRxMTEoLi4WOs2Dw8Po/ZVk6axo7JNmDABW7dulZYVCgWef/55jB07Fr1794aPj49GndOnT1d7gqtp06ay58qdPn2aJ7OOxUlNj4HaGqPvv/8+fvvtt//3h9rKCj///DM8PT1r/L57enoiJCQEISEhAIC8vDzs3LkTc+fORXJyslRu0aJFJklw8bpFRERERERERFR3WHAIjOPMmTM6tz311FNG7cvY05bpmlqRKi88PFyWtPDx8cGVK1dw6NAhvPLKK1qTFjWFtbU1goKCpOWTJ0/i77//5kmtQ3FS02OgNsbozp07sXTpUtm6Tz/9FL179zbL47Gzs0NoaCjOnTuHRo0aSesjIyORmJhoUBtCCF63iIiIiIiIiIhIAxNcRnL8+HGd27p3727UvmJjYytUT6FQaF2va2rF2uTChQtYsWIFPv/8c+m1YsUKk/+6/+eff5Ytb9myBR07diyzXmZmZo0Yt5EjR0rvVSoVZs+ezQ97HYuTmh4DtSlGo6OjMXnyZNm6kJCQWvG5a9KkCYYOHSpbV/KOLn1KT3HI6xYREREREREREQFMcBmkrARQYmIitmzZonWbr68vunbtqrG+Xr16sLDQPvwqlUpvf6tXr67Qcdjb22tdf+/ePa3rhRDIz8+vFedwyZIleOedd/Dvf/9ber3zzjtYsGCBSftNTU3ViIeyFBUVISEhoUaMW2hoKFq0aCEt7969G/v37y93OxkZGbyQmGmc1PQYqC0x+vjxY4wYMUL296Zjx45Ys2ZNjY3b8iZYS97BBQAuLi46yzo4OEjv8/LyyvVDDF63iIiIiIiIiIjqBia4DHD58mVcvHhR67bs7GyMGjUKBQUFWrf/61//0rpeoVBofNn3hL4vDY8ePYrff/+9QsfRuHFjreuTk5MRExMjW3f37l0MGzYM/fr1qxXnUFfSsKxkYmW1atVKtrx58+Yy63z44Yc1JrGoVCo1pkubOHGiwXe+3b59G6NGjcIHH3zAC4mZxklNj4HaEKNCCEyaNAnR0dHSOmdnZ+zcuRP16tWrkTGblZWFZs2aYf78+Xjw4EGZ5QsLC7F7927ZefPy8tJZvkGDBrJlXX/3EhISND4HvG4REREREREREdUNTHAZIDc3Fz179sT8+fNx7do1PH78GNHR0Vi7di3at2+P8+fPa63XsWNHvPXWWzrb7dGjh9b1Z8+excOHDzXWh4eHIzg4GG5ubqhfv365j0PflGehoaEIDw/H6tWrMXnyZLRu3Rr79u3D6dOnkZKSwiCooD59+siWZ8+ejeXLlyMrK0u2XgiBiIgIjB49Gp988kmNOoaxY8fitddek5YzMjLQp08f/Oc//9G48wgACgoKcPjwYYSEhMDPzw87d+7UeZcgmUec1PQYMPcYDQsLw65du2Tr3n77bTx69AgXL17ExYsXce7cOZw8eRLHjx/X+arKY7h27RqysrIQFhYGb29vvPvuu4iIiND4sYdarcapU6cQEBAg+yHF8OHDYWtrq7P9tm3bypbnzZsn/S1SqVSIiorCm2++CT8/P5w6dYrXLSIiIiIiIiKiOsjK1B3oeu6TqbZVtq4ueXl5CAsLQ1hYmEHlXVxcsGHDBiiVSp1lpk6dih07dmisz83NRYcOHTBy5Ei4urri1q1bOHv2rPTl4KZNmzBr1iyNL7/LOr7g4GC4uroiPT1dY9vFixcRGhqqsV4IgfPnzyM4OJiflgp4+eWXsWjRIiQmJgIAiouLMXfuXCxevBjt2rVDkyZN8OjRI8TFxRn8PJrqsHLlSjx8+BBbt24F8M/dGEuXLsXy5cvx9NNPw8vLC8XFxUhOTkZkZCSKiopk9dPS0hgMZh4nNT0GzDVGf/vtN613Ci1YsKDcU6hu2rRJ63XcFK5duya9z87OxmeffYbPPvsM1tbW8PDwgKurKywtLRETE6NxV7KtrS3ef/99ve0PGjQIP/74o7QcExMDT09PeHt7IykpCYWFhdK2o0ePar3bmNctIiIiIiIiIqLazeQJLnd39wptq1evHhwdHbU+d8PDw0Nvn/Xq1YO9vT1ycnLKXVeb7t27w8nJCYcOHTKovK+vLw4cOKAx7VhpgwcPxrRp07Q+Y+Xu3bv45ptvZOsUCgU+//xzjBw5EosWLdJ6Z5W+47O1tcXrr7+ORYsWGXQcFhYWmDJlCgYNGsRPSgVZW1vju+++w+jRo2XTaOXk5ODcuXMa5S0tLfHKK68gOTkZBw4cqFHHER4ejtatW2Pp0qXSl8tqtRqXLl3CpUuXdNbt0KED5s+fz2Aw8zip6TFgrjF64MABqNVqs4tZHx8ftG7dGrGxsbL1hYWF+Pvvv/H333/r/Pu8YcMGPPXUU3rbf/755+Hr64v4+HhpnVqtxq1btzTKRkVF8bpFRERERERERFQHmXyKwpEjR2LAgAGy54hYWVmhTZs2ePfdd/XWXbZsGRo3bizdlWRtbY3OnTvLph3SV9fd3V1Wt0uXLnj11VfLfQxOTk7Yv38/Fi5cqPFckJL8/f2xatUqXLlypczk1hM//vgjNm/ejK5du+ot5+fnh4MHD2LOnDkAgMWLF8Pb2xuWlpblOr4PPvgAc+fO1R0QFhYICAjA4sWLcevWLaxZswbW1tZmH+je3t4ad7cpFAq0aNHC5H2/+OKLOHLkCFxcXHSWadasGWbOnIlr167hxx9/hJubW40bQ4VCgUWLFiEyMhIhISGwt7fXWVapVGLo0KH45ZdfcOXKFQwcOJBX21oQJzU9BhijVWfAgAG4ceMG9uzZgwkTJsDZ2VlveRcXF7z++uu4dOkSRo0aVWb79vb22LlzJxwcHLRud3R0xIgRI7B3717Zs70YE0REREREREREdYdCCCE4DP9QKpVQqVQa6wcNGoSDBw8C+OeOiiNHjuDatWsoKCiAp6cnmjdvDm9vb/j7+1eq/7/++gvx8fFITU1FWloaVCoV3N3d0atXL3Ts2LHC0ytqc+PGDVy4cAHR0dGwtLSEu7s72rRpg4CAADg5OTEYTCA3Nxf79u1DbGwscnJy0LBhQ/j4+KBdu3bw8/Mzu+PJy8vDn3/+iYSEBKSkpECpVMLV1RUdO3ZEp06ddH4xTbUnTmp6DDBGq45KpUJ0dDT++usvJCYmIjMzE3Z2dnB3d4e3tzf69Omjd8peXVJTU7F9+3akpaXB3d0dXl5e8PLyQrt27Sr04wvGBBERERERERFR7cEEVwmGJLiIiIiIiIiIiIiIiIioellwCIiIiIiIiIiIiIiIiMicMMFFREREREREREREREREZoUJLiIiIiIiIiIiIiIiIjIrTHARERERERERERERERGRWWGCi4iIiIiIiIiIiIiIiMwKE1wl2NjYaF1va2vLwSEiIiIiIiIiIiIiIqohmOAqoXPnzlrXd+nShYNDRERERERERERERERUQyiEEILD8I+0tDT897//RWpqKoQQUCqVaNu2LV566SUolUoOEBERERERERERERERUQ3ABBcRERERERERERERERGZFU5RSERERERERERERERERGaFCS4iIiIiIiIiIiIiIiIyK0xwERERERERERERERERkVlhgouIiIiIiIiIiIiIiIjMChNcREREREREREREREREZFaY4CIiIiIiIiIiIiIiIiKzwgQXERERERERERERERERmRUmuIiIiIiIiIiIiIiIiMisMMFFREREREREREREREREZoUJLiIiIiIiIiIiIiIiIjIrTHARERERERERERERERGRWWGCi4iIiIiIiIiIiIiIiMwKE1xERERERERERERERERkVqxq2g4VFBQgLS0NaWlpSE9Ph5OTE9zc3NC4cWM4OjryjBEREREREREREREREdVx1Z7gSk5Oxu7du7Fjxw5cvnwZjx490lnW1tYWXbp0QXBwMIKDg+Hn58czSFSDXbp0CUVFRdJy165doVAoqn2/zp8/D7VaLS0HBgbyZJVw7tw5CCE01ptqnK5fv47Hjx9rrG/fvj3s7e15Qgxw9uxZ6b2lpSUCAgI4KEREREREREREVKsphLZvMatAREQE3nvvPZw9exYV3QV/f398/vnnGDRoEM8kUQ3k4eGB1NRUaTk/Px82NjZG7ePYsWNYsmSJbN3bb7+NwYMH66zj6OiI7OxsAICVlZUsCUeAnZ0d8vPztV63e/ToYdS+srOz4eHhIZ2Pki5cuIAuXbrwhBjyx7xE4tjZ2RkZGRlG76MinzUiIiIiIiIiIiJTqfI7uDIzMzF37lz8+OOPqGxu7fr16xg8eDBGjRqFL774Ap6enjyjRHVMWloajh49Kls3fvx4DowJrF692ugJrq1bt2pNbhE/a0RERERERERERPpUaYIrOTkZPXr0wO3bt3WWcXBwgK+vL3x8fNC0aVOkpqbi5s2biI+PR2ZmptY6O3bswP/+9z+cOHECvr6+PKtERCbwyy+/4KuvvoKTk5PR2ly9ejUHloiIiIiIiIiIiMqtyhJcjx49wqBBg3Qmtzw9PTF37lxMmzYNtra2GtuLioqwadMmhIWFIS4uTmN7SkoK+vXrhxMnTqB58+Y8s0RERmJnZ4e8vDzk5eVhy5YtmDlzplHavXbtGs6dOyct16tXD7m5uRxwIiIiIiIiIiIiKpNFVXQihMDw4cMRGRmpdfvAgQMRFxeHWbNmaU1uAYBSqcSUKVNw48YNTJgwQWuZO3fu4MUXX4RKparRg65SqZCTk4PCwkJGIMeKqMbr06eP9H7NmjVGa7dkW/7+/mjWrBkHm4iIiIiIiIiIiAxSJQmuLVu24Pjx41q39e3bF7t379aZ2CrN0tISGzZswOjRo7Vuj4qKwqpVq2rkYKenp6Nr166wsbGBg4MDbGxs0LJlS5w+fZqRyLEql6SkJMTExCAmJgbx8fF1eizGjRsHIYTsNXXqVAaJEQ0fPlx6f/HiRVy+fLnSbebn52PTpk3S8tixY/mZ42eNiIiIiIiIiIjIYCZPcBUWFuL//u//tG5TKpVYs2aNwcmtJywtLfH999/rfA7MwoULkZ6eXuMGe+vWrTh//jzUarW0LiEhAStXrmQkcqzKZfr06fDz84Ofnx+6d+/OASGTGjx4MBwcHKTltWvXVrrNnTt34uHDh9Ly+PHj+ZkjIiIiIiIiIiIig5k8wbVx40YkJCRo3TZjxgy0bNmyQu26uLjgvffe07rt0aNH+Omnn2rcYN+7d0/r+pqYjONYEdET9erVw4gRI6TlzZs3Iy8vr1JtlpyeMDAwEG3atOFAExERERERERERkcFMnuDasWOHzm1TpkypVNuTJk3Sue3nn3+ucYMthCjX+rqMY0VUs4SGhkrvHz16pPfaXpb4+HgcO3ZMWp44cSIHmIiIiIiIiIiIiMrFpAmux48f448//tC6zcPDA507d65U+02bNkWnTp20brtw4QJu3brFM0xEZAT9+/eHu7u7tFzyDqzyWrt2rZSsViqVGDduHAeYiIiIiIiIiIiIysWkCa7ff/8dBQUFWrc9++yzUCgUle7j2Wef1bnt6NGjPMP/PyEE7t+/j9zcXA4GEZWbpaWl7C6u48ePIy4urtztqFQqrF+/XloODg6Gi4sLB5iIiIiIiIiIiIjKxcqUjUdFRencZqznrehrR1//d+7cwYYNG3Dv3j0IIWBjY4MOHTrgpZdegqWlpd4+ExMTsWHDBqSlpZVZNzU1FevWrUNKSgrOnDmjtb24uDi88cYbsnXW1tbo3bs3goODtdZJSUnB+vXrkZKSAiEEbG1tERAQgLFjx0qJw8jISGzatAn79+9HbGwsioqKoFAo4OHhgVatWmHKlCkIDQ2FlZX+MDD3sQKAgwcP4uDBg7IpDhUKBfr164dhw4ZVyYetuLgYJ06cwMmTJxETE4MHDx5AqVTCzc0NjRo1QvPmzdGrVy/4+/tXy8UgKSkJJ0+exIULF5CWloaHDx/C3t4enp6eCAoKQv/+/WFra2v0ftPS0nDlyhUkJSVBrVbD19cXgYGBqFevXo29cObm5mLPnj04d+4c7ty5A7VajaZNmyIoKAgDBgyAo6NjrYqNJyZPnozPP/9cWl67di2WLl1arjb279+P1NRUabmyU9XWlDiuqFu3bmHv3r24desWkpKSUFhYiPr168PNzQ2dOnVC37590axZs0r3k5+fjwsXLuDOnTt4/Pgx3N3d4e/vj1atWpn0+K5evSq9t7OzQ+vWrWXbc3JyEBsbi7i4OGRkZBhtv9RqNc6fP4/4+HhkZ2ejSZMmCAwMhJubm86/VTdu3EBKSgqys7Mxffp0WFtb879EIiIiIiIiIqKaTJjQSy+9JABofa1evdoofezbt09nH/369dNZr0OHDlrrfPrpp2X22a5dO611V6xYoVG2U6dOOvfPkNevv/6qdR969Oihtfz27dtFamqqCAkJEQqFosz2W7ZsKc6ePav3eM19rIQQIjg4WGudoKAgYWpqtVr88MMPwsPDw6DjaN68ufjwww9FamqqEEKIzMxM4enpKXvZ2NhI5S0sLDS2l3zl5+fr3b8//vhDDBs2TFhYWOjdL3d3d/HDDz+U69jd3d1lbTzZl6SkJBEWFib8/f219uXi4iI++ugjoVKpDOqndevW0vE2b968zPIODg5SX1ZWVgYfT25urnj//feFvb29znFydnYWX331lUH7XtnYMBVbW1tZv+np6dK2Ll26SOsbN24sioqKytX2iy++KNX38PCQjZOfn5+s3wsXLhjcrjHj2NSfOSGEOHbsmAgMDCzznFtYWIgBAwaIP/74Q297pWPwiTt37ohp06YJJycnre137NhRHD161OBxLu9nrWRfAQEB0vr4+Hjx2muvCTs7O6Ps1xM5OTni448/Fo0aNdJoU6FQiKCgIHHs2DGRm5srfvnlFzFq1CitZa9evSqIiIiIiIiIiKhmM2mC65lnntH5pd2OHTuM0kdERITOPpo2baq1TmFhoc7kT2hoqN7+CgsLdfY3ceJEjfKurq6VStp8//33WvdD1xfsAwcONPjL8icvJycncebMmVo7VkIIMWTIEK11+vTpY9IPmEqlEhMnTqzQ8Rw8eFAIIcSjR48qNS66vmzPysoS7du3L3d7EyZMMDipUTrBlZmZKZYsWaLzS21tSers7Owy+ylvwqoiCa64uDjh6+tr8DgNGjRIFBYWmjQ2TEVfguubb76Rbdu1a5fB7SYlJQlLS0up7ty5c2XbK5LgMkUcm+ozJ4QQBQUF4pVXXqlQu9OnT9f9x1xLgmv16tXC0dHRoLbXrl1r0Dks72dHW4Jr3bp1Bl8DDN0vIf5JmulKmpd+1atXT+/2jRs38j9EIiIiIiIiIqIazqRTFKanp+vcVt4pvHSpX7++zm0ZGRla1xcWFsqmqitJ1zPDStYtz7bPPvsMCxcuRFpaGgoLC6FWqzXKWFhYaEyFpFQq0bt3b4wZM6Zc+3n48GHpfcOGDdGqVSsUFxcjOjoa2dnZWutkZmZizJgxiImJgZ2dXa0bq+r07bffYsOGDbL9Dw4ORrdu3eDq6oqsrCzcv38fMTExiIiIwP3796WyTz/9tEn3zdbWFpGRkdKyQqGAv78/evToAW9vbzRo0AC3b9/GsWPHZFNGhoeHw9XVFV9++WW5++zSpQvi4+OlZVdXV3h5eeHmzZvIysrSKP/7779j4sSJ2L59u1Ge2VdRN27cQFBQkGxqPaVSiU6dOiEwMBB2dna4fv06Dh06BJVKBQA4dOgQJk+ejE2bNmnd95ocG/qEhobi3//+N/Lz8wEAa9aswfDhww2qu27dOhQXF0vxNnXqVLOM44rKzc3FyJEjZddp4J/nmz311FNo1qwZbG1tkZmZidu3byM6Olp2/dV2TdTls88+w7vvviu17+PjA3t7e9y9exdpaWka5V999VUMHToUjRo1MukYhIWFYf78+Ubfr7S0NPTt2xeJiYnSOm9vb/Ts2RPOzs64fv06jh8/Ln0+c3NzoVAo0L17d7i4uMDa2hpCCOTn5+Phw4eyzxsREREREREREdVQpsyeubm56fx19J9//mmUPqKiovT+ClvbNGHZ2dk6y48ePVpvf/rqjh8/Xm/d8ePHG+0uIisrK537ERgYKA4fPiyKi4ul8gUFBWLlypXC2tpaZ72PPvqoVo6VENVzB1dRUZFo3LixbNq9U6dO6a1z4sQJMX78eOHu7i6tKy4uFtHR0bJX06ZNZXdrlN5e8qVWq3X25+zsLGxtbcUbb7whYmJidJZbvny5xp18p0+fLnMMSt/BBUAolUrx1ltviYSEBKmcWq0WN2/eFPPmzdMaoz/99JPefkx5B1dOTo5o27atbH86d+4sIiMjNcomJCRoxO4vv/xistgwFX13cAkhn37W0tJSJCUlldmmWq0W3t7eUr2+fftqlKnoFIXGjmNTfeZKT9trYWEh3n33XZGcnKy1/MOHD8XXX38t3Tn41Vdf6Tw2lJqK78n7IUOGiDt37sjKXr16VQwePLhC085W5g6ukndNGXu/Sk59CUB88sknGndQXrlyRWPa3IsXL/KnTkREREREREREZsqkCS59UwAZK8F148YNvQmuR48eadSpzQmurl27yhJbpW3cuFHn/nt6emp8MVvbE1zavmQ3lri4OFlf77//vsF1y3rGUslEgaura4X3cebMmSIuLs6gsiEhIbLjGTp0aJl1Sie4GjZsKCIiIvTWOXv2rGjYsKHGdKN5eXk665gywTV79uxyTT0ohBDTpk2Tyj/99NNVGhvGUFaC69ixY7LtH3/8cZltHjlyRFYnPDxco0xFE1ymjmNjfOa2bt2qkeg1dKrJgoICsWTJEp1TyQohtF7fevXqpfNzU1xcLIKCgmTl244ofOEAACAASURBVLdvb9TPTlXt1//+9z9Z+UmTJuksGxUVJUuih4SE8D9BIiIiIiIiIiIzZWHKu8P0TackdEx7V15Ppruq6PbapmHDhrCw0H1aX3rpJXTv3l3rtqSkJJw/f75WjkvXrl3RokULeHt7S68WLVogMDDQZH3evHlTtuzs7Gxw3caNG1fJuHz33Xfw9fU1qOz//d//yZaPHj2KvLy8cvW3bt069OjRo8xztW3bNtm65ORk7N27t8rjJjU1Fd9//7207OnpiY0bN0KpVOqt9/XXX6NZs2YAgCtXriAiIsLsYkOf3r17y+Jm7dq1ZV7T16xZI7tOjRw50mzjuCJ/p55My/fE0qVLMWjQIIPqW1tbY/78+eW6XllZWWHbtm2wtbXVut3CwgJvvfWWbF1cXJzJY8cU+7VlyxbpvUKhwLJly3SW9ff3R0hIiLT8yy+/aJ0elYiIiIiIiIiIaj6TJrgcHBx0btP3fKbyKKsdYz3rqzYZPXq0zm2nT5+ulcf8wQcf4NatW0hISJBet27dQlhYmMn6LP18uL1795brGTo1jb+/P+rVqyct5+XlISYmplxteHt7G1Suf//+GDZsmGzd5s2bq/yYv//+e+lZUwDw8ccfw9XVtcx6tra2mDNnjrT8+++/17rYmDZtmvQ+ISFB4xhLevDgAXbv3i0tv/zyy7CxsTHbOC6v7du349atW9Kyi4sLZs6cadI+Bw4cCHd3d71lBgwYIFsuKChAZmam2e1XyR9mtGrVqswkcMeOHaX3KpWqShJ7RERERERERERkfNWW4DLWL6b1tWNra1vmnRZ1kb67ACIjIzlARuLv7y+7SyEiIgLDhg0z6zFu2bKlbDk1NdVkfb322muy5RMnThjtzk9DHThwQHpfv359jBkzxuC6w4cPl94fP3681sXGpEmTYGVlJS2XvEOrtE2bNsl+jDB16tQ6E8cAsHPnTo3YLplkMwV9P2R4ws7OTuNOqgcPHpjdft2/f1967+LiUmb7rVq1ki2XvqOSiIiIiIiIiIjMg5UpG3dyctK5LSMjwyh96GuHd29p17lzZ53b7t69ywEyYvxPmjQJP/zwg7Ru//792L9/P1q3bo1u3brhmWeewTPPPIMuXbrIkgU1VemkdUFBgcn66tmzJ2xsbKQ+Hj16hJs3bxo8FV1lZWVl4eLFi9KytbU1xo0bV74LrJUVVCqVxhfotSE23N3dMWTIEOnOrN27d+Phw4do2LChRtmffvpJeh8YGIgOHTrUmTgWQmjc3TZixAiTH2PpJJ6+GC29v+a2Xx4eHtJnLCkpqcy2c3NzZcul76gkIiIiIiIiIiLzYNJvTVu1aoWrV69q3Xb79m2j9JGQkKBzW1V9EW5u7OzsYG9vj5ycHI1tjx494gAZ0dKlS/Hbb7/JpicDgNjYWMTGxmLDhg0A/vmCdejQoXjnnXfQqVOnGncchYWFyMrKMmkiQFucenl5IT4+XlqXlJRUZZ/r5ORk2bSB6enp2L9/f4Xaunv3LtRqtez5eLUhNqZNmyYluAoKChAeHo5Zs2bJyly+fFn2d2D69Ol1Ko7T09M17j5q0aKFyftt0qRJjbwmmmK/WrdujZMnTwIAEhMT8fjxY70/cLly5Yps2c/Pj3+siIiIiIiIiIjMkEmnKGzXrp3ObSW/tK4Mfe2UfM4Gyel6/k1RUREHx4icnZ3x559/6p0WEvjnbqHNmzcjICAA77//fpVPxfdEVFQU1qxZgxkzZqBfv35o2bIlbGxsYGNjg0aNGuHy5ctVuj+NGjWSLT98+LDK+r53757R2iouLpY9y8scY0ObQYMGoWnTptLy+vXrNcqUvHvLwcGh3HfBmXscl44jJycnODs7m7zfmnoHsyn2a+jQobLlkndGlvbw4UNZnHbu3NngZwMSEREREREREVHNYtI7uNq3b69zW8mHwlfG2bNndW5jgku3knemlGRpacnBMbJmzZohIiICP/30E77++mu9z1lSq9X45JNPkJGRgVWrVlXJ/iUnJ2PVqlUIDw832p2VxlI6mVPyDihTs7a2li2/9957GDt2bIXb05ZUrumxURZLS0tMmTIFH3/8MQDg4sWLiIyMlK79RUVFCA8Pl8qPHz9e77MZa2Mcl77W6rr2UsUNGTIEfn5+iI6OBgAsWLAA3bt3x3PPPScrl5eXh2nTpknPXLOwsMCXX37JASQiIiIiIiIiMlMmTXD17dtXegZNabdv30ZMTAzatGlT4fbT09Nx6dIlndtLf7lF/xBC4PHjx1q3VcWdBXWRUqnEjBkzMGPGDCQlJSEiIgIXLlzAuXPncOnSJY1nwnz33XcICgrC6NGjTRoHixcvxrJly5CXl6ex3dHREa1bt0bTpk3h5uYGR0dHbNu2rUqf01b67hdXV9cq69vd3V22bGlpiS5dutSJ2CiPV155BUuWLJGSkRs2bMDy5csBAAcPHpRNzzdt2rQ6F8eNGzeWLT9+/BgpKSnw8PDghdFY/8hYWWH+/PmYOHEigH8SWT179sRLL72E3r17w8XFBTExMVi1ahXu3Lkj1ZszZw569OjBASQiIiIiIiIiMlMmTXC5uLigb9+++O2337Ru37VrF+bNm1fh9vfs2aNzuq527drhqaeeMvoxFRYWmv1Jj4mJQXFxsdZtxvzStTaMlSl4enoiJCQEISEhAP75Mnbnzp2YO3cukpOTpXKLFi0yaRJjwoQJ2Lp1q7SsUCjw/PPPY+zYsejduzd8fHw06pw+fbrKEgM5OTlISkqSrfPy8qqy89S0aVPZs+pOnz5dZ2KjPFq0aIH+/ftL1/mtW7di2bJlUCgU2LRpk1Suffv2ZU7HWBvj2NXVFU5OTsjMzJRdg5ngMq6SMQD8k/jcuHEjNm7cqLX8Cy+8IN15SERERERERERE5snk832NGTNG57YvvvhC4+4EQxUXF2PZsmU6t7/00ksmOR5TTH1V1VNWnTlzRuc2YyYFa8NYVQU7OzuEhobi3LlzsmdORUZGIjExscz6FXkmU3h4uOwLYR8fH1y5cgWHDh3CK6+8ojUpUNV+//13WZLUy8sLzZs3r7L+ra2tERQUJC2fPHkSf//9t1nFRlUpeWdWYmIiIiIikJWVhX379knrp0+fbvR+qyuOy/OZs7CwQP/+/WXr/vjjD/73YUTnz5/HgQMHAACvvvoqXnjhBZ1lnZ2d8emnn2Lfvn2wtbXl4BERERERERERmTErU3fw8ssvY8mSJVq/GE5LS8MXX3yB+fPnl7vdn376CXFxcVq3OTo6YtKkSSY5ntjY2ArXVSgUWtfrmi7QVI4fP65zW/fu3WvlWF24cAEnTpyQfTGtUCjQvXt3ox5zZTRp0gRDhw7FunXrpHXJycll3rVUkSTxzz//LFvesmWLQc+sK3kXiql9++23suUBAwZU+TkZOXKklKRRqVSYPXs2du3aZTaxUVWGDx8OFxcXaTrCbdu2ITExEfn5+QD+ef6YKX50UF1xXN7P3MiRI7Fjxw5peeXKlZg1axbc3Nz4X4gRnDp1Sno/a9Ys+Pv7IzIyEsePH0dMTAzu378Pd3d3PPvssxg8eLDJngNHRERERERERERVy+QJLltbW3z66acYO3as1u0LFixA27ZtMWLECIPbPHHiBGbNmqVz++LFi/VO/1SvXj1YWFhovRtI2/PCSlq9enWFx8Le3l7r+tLPGXpCCIGCgoJy/cq8rARQYmIitmzZonWbr68vunbtWivHasmSJdi9e7fG+pJTq5lCZmYmnJycDC5f8i4d4J9pPrUp+QVtXl4eHj9+DEdHR4P7SU1N1Tj3ZSkqKkJCQkKlxuP69esG3SW4Y8cOHDlyRLbu1VdfrfILZGhoKBYvXiwd9+7du7F//34MGTKkXO1kZGSgQYMGVRIb1cHa2hoTJ07EypUrpXFKS0uTto8cORINGzY0er9VGceV+cyNGzcOCxcuRHx8PAAgOzsbH374IVatWlXu4y39bDgCoqOjpfdPkqzt27dH+/btOThERERERERERLWYRVV0MmbMGAwcOFDrtuLiYoSEhGD9+vUGTT+3a9cuDBs2DAUFBVq3P/3003jjjTf0tqFQKDS+LH5C3y/7jx49it9//73C49C4cWOt65OTkxETEyNbd/fuXQwbNgz9+vUrVx+XL1/GxYsXtW7Lzs7GqFGjdI7dv/71r1o7VrqScWUl6SojKysLzZo1w/z586UvXfUpLCyUJeGUSqXOO3RKJ0t0jXVCQoJ0F01JrVq1ki1v3ry5zP378MMPtbZVHq+//jpu3Liht8zRo0cxceJE2bqgoCAEBARU+QVSqVRi6dKlsnUTJ040+Hlct2/fxqhRo/DBBx9UWWxUl6lTp8o+k3v27JGWS05haExVGceV+cxZWlpqTKn73XffSQnBsqSlpeGNN97A888/z/9ayvh7sW7dugpN20pERERERERERObHoqo62r59O7p166Z1W0FBAaZMmYKnnnoK4eHhiI6Olp69o1KpcPPmTezatQvPPvssRo4ciUePHmltx9fXF3v37oWlpWWZ+9OjRw+t68+ePYuHDx9qrA8PD0dwcDDc3NxQv379Co2BvqmzQkNDER4ejtWrV2Py5Mlo3bo19u3bh9OnTyMlJcXgPnJzc9GzZ0/Mnz8f165dw+PHjxEdHY21a9eiffv2OH/+vM59e+utt+rUWJnatWvXkJWVhbCwMHh7e+Pdd99FRESERoJRrVbj1KlTCAgIkCXvhg8frvOOtLZt28qW582bJx27SqVCVFQU3nzzTfj5+cmm73qiT58+suXZs2dj+fLlyMrKkq0XQiAiIgKjR4/GJ598UukxycjIwHPPPac1oX379m288cYbGDhwoGwKOHt7e/z444/Vdh7Hjh2L1157TXYMffr0wX/+8x+NO4ieXM8OHz6MkJAQ+Pn5YefOnRp3HpoyNqpLu3btEBgYKC0/SR63bNkSffv2NUmfVRnHlf3MjRw5UhZHAPD2229j8ODBOHHihMbnISUlBT///DPGjx8PLy8vfPPNN1rjjeTPjly/fj26d++OefPmYdGiRRqvsLAwfPfdd9i3bx9u3rzJwSMiIiIiIiIiMmeiCmVkZIiAgAABoMyXhYWFaNKkiVAqlQaV9/PzE8nJyQbvy4EDB3S21aRJEzFr1izx4YcfiokTJ4o2bdpI23bs2CE8PDy01gsJCdHbZ15ennB1dTXoeEq+9uzZo9GWlZVVudvR9XJxcRFXrlyptWMlhBBDhgzRWr5Pnz4mi/fvvvtOa5/W1taiefPmokuXLqJr167CyclJo4ytra3ec7Jz506tn5mWLVsKa2tr2fp58+Zp1C8oKBBeXl4abdjb24uuXbuK4cOHiz59+oimTZvqHe/du3frHQN3d3dZeUdHR+m9g4OD6NWrl+jXr59GuScvS0tLsW3btjLH2sHBQapjZWVl9PIFBQVi/PjxWse8c+fOYtiwYWLIkCGiU6dOWq9ZvXr1qrLYMAZbW1tZn+np6QbVW716tcb+fvLJJwb36+fnJ6t74cKFMs9LVcSxMT5zQghRWFgoQkJCtO6Dg4ODaNOmjfDx8dF63p/0p1KptLZdumxqaqpBY17yswBAxMfHG/WzUxX7VVRUJHx9fSv098/Ly0u8+eab4u+//xZERERERERERGReUNUdFhYWirCwMFGvXj2jJGeUSqWYPXu2yMjIKPe+TJs2zeB+FAqFWLFihRBCiI4dO2ot8/bbb5fZ58KFCw3u08LCQkydOlUUFBRotKMrwdW9e3cxaNAgg/vw9fUVsbGxtXqshKieBNeRI0dE69atyx3T9erVE9u3b9fbdnZ2tsFf6A4dOlRrG/v379dIZOh6WVpaiunTp4sXXnihUgmuAwcOiL59+xo8Dlu3bjVorE2d4BJCCLVaLRYsWKCRzCjr1aFDB3Ho0KEqiw1jqGiC6/Hjx8LLy0u4uroKV1dX0ahRo3L98KC8Ca6qimNjfeYqE0fe3t7iyy+/FMXFxdr/mNfhBJdKpRKjR4+u1P8S1tbW4tNPP+V/hUREREREREREZgTV1fGdO3fEtGnTKnSXzpNf6I8bN86g5IwuarVabN68WXTt2rXMu8NKfkG9e/du4e3tLSwtLaUvxrp06SLi4uLK7FOlUom5c+fqTdQEBASIxYsXi9u3b+tsR1eCa9CgQUKlUomFCxeKBg0a6OzH399frFq1SmRnZ9f6sRJCiFmzZgmFQqGRiJsyZYpJ47y4uFjs2bNHTJgwQTg7O5d5J93rr78uoqOjDWr7r7/+0vgSuOSdUiNGjBB79+7V+YW4EEKcOHFCuLi46NynZs2aiZkzZ4rr168LIYSYPHlypRJcV65cEWq1WmzYsEF07txZZxJi2LBh4ubNmwaPc1UkuJ6IjY0VISEhwt7eXm/ifejQoeKXX37ROf6mjI3KqmiCq7IqkuCqijg25meuPHGkUChE165dxZYtW3TeuSX9Ma+jCa7c3FwxcOBAo93RvGDBAv5nSERERERERERkJhRCVO/T2NVqNU6fPo29e/fi0qVLuHfvHu7du4f09HSoVCpYWFigYcOGaNSoERo1aoSAgAAMHjwYvXr1grW1tdH246+//kJ8fDxSU1ORlpYGlUoFd3d39OrVCx07doRCoTDqcd+4cQMXLlxAdHQ0LC0t4e7ujjZt2iAgIABOTk5l1lcqldIzbkoaNGgQDh48CADIycnBkSNHcO3aNRQUFMDT0xPNmzeHt7c3/P3968xY1RQqlQrR0dH466+/kJiYiMzMTNjZ2cHd3R3e3t7o06cPlEpludpMTU3F9u3bkZaWBnd3d3h5ecHLywvt2rUz+PORm5uLffv2ITY2Fjk5OWjYsCF8fHzQrl07+Pn5mXRMIiMjERUVhfj4eAgh0Lx5c/Tv3x8eHh41/nzm5eXhzz//REJCAlJSUqBUKuHq6oqOHTuiU6dOcHBwqNbYqGuqKo6N8ZkrHUfHjh3TiCNvb290794dDRo04MnVY9asWfj222+lZR8fH7z66qto27Yt7O3tS0/JjMLCQiQnJyM6OhrHjx/H2bNnNf62RkZGonXr1hxcIiIiIiIiIqIartoTXLoIIfD48WM4ODjAwsKCZ6oUQxJcREREtVVCQgJ8fHzw5N+Yp556CkePHoWrq6vBbURERGDOnDm4ePGitG7OnDlYsWIFB5iIiIiIiIiIqIarsZkjhUKB+vXrM7lFREREGvbu3YuSv9FZsmRJuZJbANCzZ08cOHBAdofk9evXObhERERERERERGaA2SMiIiIyO6mpqbLlDh06VKgdNzc3+Pr6SsuJiYkcXCIiIiIiIiIiM8AEFxEREZkdHx8f2XJFp+e9ffs2bt68KS2XTHYREREREREREVHNxQQXERERmZ3Ro0fD2dlZWn7rrbewYsUKlOfRoidOnECfPn1QWFgorZsxYwYHl4iIiIiIiIjIDDDBRURERGbH2dkZa9askZ7VWVBQgHfeeQedO3dGWFgYoqKioFarNeqlpaVh/fr1CA4ORt++ffH3339L26ZMmYIXX3yRg0tEREREREREZAYUojw/daYaw8HBATk5ORrrhw8fjl27dnGAiIioTti+fTumTZuGzMz/j717j4ui7P/H/4LlIIIIioiCaOL5fEDxUGbcVpqRZCKgadptVHZXmqVm3WXdWZp9urvLTLsFyzxkCUpicntWoBQVPKCBJ1BAQCAE5Mxy/f7o534ZdhcW2NkDvJ6Pxz4eO9deM3PNNTPXHN57zRSq/aZQKODq6goXFxcUFxcjOzsb5eXlGqfz+uuv47PPPoOVlRUrlYiIiIiIiIjIDLAHl5kaMWKExvSRI0eycoiIqNWYMWMGLl68iAULFsDa2lrym1KpRFZWFi5evIi0tDSNwa0JEybg2LFj+OKLLxjcIiIiIiIiIiIyI+zBZaZycnLw/fffIzs7G0IIWFtbo3///nj22WfVbvARERG1Brm5udi9ezeio6Nx7tw5pKWlqb2Ty8XFBUOHDsXDDz+M6dOnY+DAgaw4IiIiIiIiIiIzxAAXERERtUgVFRW4e/cuiouLYWdnh/bt28PBwYEVQ0RERERERETUAjDARURERERERERERERERGaF7+AiIiIiIiIiIiIiIiIis8IAFxEREREREREREREREZkVBriIiIiIiIiIiIiIiIjIrDDARURERERERERERERERGaFAS4iIiIiIiIiIiIiIiIyKwxwERERERERERERERERkVlhgIuIiIiIiIiIiIiIiIjMCgNcREREREREREREREREZFYY4CIiIiIiIiIiIiIiIiKzwgAXERERERERERERERERmRUGuIiIiIiIiIiIiIiIiMisMMBFREREREREREREREREZoUBLiIiIiIiIiIiIiIiIjIrDHARERERERERERERERGRWWGAi4iIiIiIiIiIiIiIiMwKA1xERERERERERERERERkVhjgIiIiIiIiIiIiIiIiIrPCABcRERERERERERERERGZFQa4iIiIiIiIiIiIiIiIyKwwwEVERERERERERERERERmhQEuIiIiIiIiIiIiIiIiMisMcBEREREREREREREREZFZYYCLiIiIiIiIiIiIiIiIzAoDXERERERERERERERERGRWGOAiIiIiIiIiIiIiIiIis8IAFxEREREREREREREREZkVBriIiIiIiIiIiIiIiIjIrDDARURERERERERERERERGaFAS4iIiIiIiIiIiIiIiIyKwxwERERERERERERERERkVlhgIuIiIiIiIiIiIiIiIjMCgNcREREREREREREREREZFYY4CIiIiIiIiIiIiIiIiKzwgAXERERERERERERERERmRUGuIiIiIiIiIiIiIiIiMisWLEKdJOQkICqqirV8KhRo2BpyfigLu7cuYPU1FRJ2sCBA+Hg4MDKIWKbw7ozsLKyMly4cEGS1qdPHzg7Ozd72qmpqbhz544kbcCAAWjXrh0rXgcXL15EaWkpAEChUMDb25uVUsupU6dU31k/Lc+tW7eQlZWlGh40aBDs7e1ZMTzmNqqN2LdvH5KTk5Gfnw8nJye4urrCy8sLU6dORf/+/bnCiIiIiIhaGAshhJBzBr/99hvef/99Sdo//vEPTJs2zawqqkuXLsjOzlYN37t3jxfdOpozZw62bt0qSVuzZg2WLl3KyiEyoTaH7XXrcOnSJQwaNEiStmfPnmav5+PHj2Pq1KkoKSlRpc2fPx+hoaGwsLBgxetg+PDhOHfuHADAyckJBQUFrJTaJ621tiPWT8sTEBCAXbt2qYbv3LmDTp06tahlPHbsGFatWiVJe+ONNzBlyhSerzRDYmIiXnvtNcTGxmrNs2jRIvz73//mNkJERERE1MLI3oMrNzcXhw4dkqT5+/uz5luJwsJChIeHq6WHhYUxwEVkYtheU1MdO3YMU6dOVfU+AhjcIqLGqd1Dr2fPni0uuAUAOTk5asfZoKAgrvxmCA8Px5w5c1BWVsZthIiIiIioFeIjCklWO3bs0HjBmZKSgri4OIwfP56VRERkxo4ePYonn3ySwS0iarLs7Gykp6erhn18fFgp1KBTp05h9uzZqKiokGw7jzzyCFxcXFBcXIzLly/j+PHjrCwiIiIiohaKAS6SVWhoqOp7t27dJDcvQkNDGeAiIjJjDG4RkT7U7r0FAKNHj2alUL1qamrwwgsvqIJbDg4OiIyMhK+vr1pepVKJnJwcVhoRERERUQtkySoguVy4cAFnzpwBALRt2xbr16+X/P7TTz+huLiYFUVEZIaOHDnC4BYR6UV8fLxkmD24qCFRUVG4ePGiavi7777TGNwCAIVCga5du7LSiIiIiIhaINl7cE2bNg1CCNZ0K1S795afnx+eeOIJdOnSBVlZWQCAkpIS7Ny5EwsWLGjR9ZCRkYGSkhLVBXavXr24cZBJYntNurof3Kr9CFoGt4ioqWr34LK2tsbw4cNb5HIGBgYiMDCQK1wPIiMjVd9tbW3x9NNPcxshIiIiImqF2IOLZFFRUYFt27aphoOCgmBpaYmZM2dK8tUOgrVUL7zwAvr164d+/fph7Nix3DiIyKwdPnyYwS0i0hshBE6fPq0aHjJkCNq0acOKoXolJiaqvru6usLSkpe1REREREStEa8ESBaRkZHIz88HADg6OmLy5MkA/gp01Xby5ElcvnyZFUZEZAYOHToEPz8/BreISG+Sk5NRVFSkGub7t0gXtd+ppVAoWCFERERERK0UA1wki9o9s/z9/VX/xB0zZgx69OghyRsWFsYKIyIycQcPHsRTTz3F4BYR6VXtxxMCfP8W6aayspKVQEREREREDHCR/t26dQuHDh1SDdfttVV3eMuWLaiqqmLFERGZKAa3iEgu8fHxkmH24CIiIiIiIiJdMcBFerd582bU1NQAADp06IBJkyZJfq8b4MrNzcXevXtZcUREJujAgQN46qmnUF5erkpjcIuI9KV2Dy5HR0f069ePlUJEREREREQ6sTKHQpaWliIyMhLx8fG4desWampq4O7uDl9fXzz66KNo165dk6edk5ODhIQE3Lx5E3Z2dnjggQcwYsQIODg4mOwyXLp0CdXV1QAABwcHeHl5qeVRKpW4du0arl+/DmdnZ4wdO9Yg60oIgc2bN6uGZ8yYAWtra0meoUOHol+/fkhOTlalhYWFYfr06U2apynXh7kQQiAxMRFnz57F+fPnkZubi+LiYjg6OsLLywtTp07FuHHjWtQynz9/XvXdzs4Offr0kfxeUlKCK1eu4OrVqygoKICbmxsGDBiA3r17N2u+hmpzWmKbbci60+cyKJVKnDhxArGxsUhJSUF+fj6sra3h6uqKTp06oXv37pgwYQIGDBhgcuvywIEDmDZtmmzBLX233xkZGYiNjcWZM2eQk5ODP//8E/b29vDw8ICvry8mTZqkemSuqbcV5rKfmlL9lJeXyzqBLAAAIABJREFU48yZM7h16xaKi4v1Mi85tilDMvX2p6ysDBcuXFANjx492iQD58asR56v6J8c1w5yXq8SEREREVE9hAF8/PHHwsPDQ/XZvHmzTuOVlpaKd955R9jb2wsAGj9OTk7iyy+/FNXV1Y0q0/Xr18UzzzwjrKys1KapUCjElClTREREhFAqlUIIIdzc3CR57t27Z7RlqF2WSZMmSX7LyMgQ77//vvDw8FDlWbRokTCUgwcPSpbt8OHDGvOtXLlSrc4zMjKaNM/m1MeYMWMk22ZTPwkJCUIIIQoLC9V+s7W1Vc3b0tKy3umUl5cLQ7px44ZYvHix6Natm9bt8/5n1qxZIj8/X/Yy1a2TO3fu6DRenz59JOOlpqbWm7/2snl7e6vSr127JhYuXCjs7Ow01sOQIUPEoUOHGr1chmpzjNVey9lmG7Lu9LkMNTU1YuPGjaJLly4N7l8ARPfu3cXKlStFdna2rOs4KSlJbd579uxRyxcdHS3atGkjyTd//nxRU1Ojt7Lo63h25MgRMW3aNGFpaVlvHbu5uYmNGzc2qoyGbCuGDRsm2c50sW3bNsk+2717d3HgwAGD7aeGrJ+6Zbzv1q1bYsGCBaJ9+/Z6m5ec25QhjnOm2v7UFRcXJynHihUrGhynuLhYzJgxQ1IX/v7+orCwUO/lk6Mea6/H7t27m9Q+ZsrnK4sXL5as89r7pkKh0Hhe/fnnn8t+LSXHuU9jt5H09HS1ZU9PTxdERERERK2BQQJcy5cvl5zkr1u3rsFxrl69Knr16qXTxSQAMXnyZFFZWalTeXbt2iUcHBx0mu748eNFSkpKky7e5FoGbRdlYWFhGpfLkAGuoKAg1Xw7d+6s9UIuOTlZrZyrVq1q0jybUx89evTQef3U9zl58qQQQoi7d+82azqGDHAtWbJE482L+j7u7u4iLy9P3kapzjx1vdlWd11fu3at0TeMwsLCtN4oqvsJDQ3VeZkM1eYYq72Ws70zZN3pcxmqq6vF3Llzm9QO7N+/X9Z1rEuAS1Nwy9bWVuTk5Oi1LM09nhUVFYlBgwY1uo5nzZolqqqqGt0myd1WNDbAtX37dqFQKCQ3ebdu3WrQ/dSQ9aMpwPXf//5XtGvXTm/zMvQ2JcdxzpTbn7o+//xzyfwjIyPrzX/jxg0xePBgyThBQUGitLRU72WTqx5rr0crKyuTaoNM+Xxl3rx5jV4PK1eulPVaSq5zn8ZuI2lpaWrzSktL450OIiIiImoVTPIRhX/88Qd8fX2RnZ2tSrO2tsbw4cPh4+MDOzs7XL58GdHR0arHS0RHR2PevHnYunVrvY82+fXXXxEUFKQary5PT084OTnh+vXrKCkpQVxcHIYNGwalUmkyy6ChFx6ee+45/PDDD0ZdbwUFBdi9e7dqeObMmVAoFBrz9u3bF8OHD0diYqIqLSwsDG+//XazH01j6Pro1asX+vfvb3a9N3/++WfJftCjRw88/PDD6NGjB1xdXVFQUIDjx4/j4MGDqjyZmZlYtmwZNm3a1OJ6s37yySdYsWIFAEChUMDLywv29va4ffs2cnJy1PK//PLL8PPzQ6dOneqdrqHanJbYZhuy7vS9DF9//TW2bNmiGra0tMRTTz2FMWPGwMXFBUVFRcjNzUVKSgpiYmKQm5uryjts2DCjrs/o6Gj4+/ujoqJCkl5RUYGXX34Z4eHhcvYqb1T73aZNGyQlJamGLSwsMGDAADz44IPo0aMHnJ2dkZaWhmPHjuHkyZOqfNu3b4eLiwv+85//mExb0Vg//vgj5syZo9reFQoFvv/+e8yePduo5yWGrJ/PPvsMb731ll7nZYxtSt/Mqf2Jj4+XDI8ePVpr3uPHj2PGjBnIy8tTrZsPP/wQ7777bquqR56vGP/YY+hrPSIiIiIiqv+E3qR6BJSUlIj+/ftL8o8YMUIkJSWp5U1NTZX0GAIgfv75Z63Tzs7OFk5OTmr/cHvwwQdFVFSUKCoqkvxrMzw8XGsPn/r+nSjnMmj61+HixYvVHh8yffp0sWnTJnHy5EnZe9zc9+WXX0rKERcXV2/+NWvWqNXr0aNHm90DoDH1kZiYKE6ePKnxc/r0aZGUlCSSk5NFcnKy+OOPP4Svr69k2vb29uLChQuq6SmVSlX++x93d3fJP87r/l77o89Hf9VHqVQKKysroVAoxLPPPisuXbqkNe/HH38sWWYLCwtx5coV2coGI/Tgatu2rer7k08+KW7duiXJe/78eTFlyhS1sq1du7beeRiqzTFWey1ne2fIutP3MlRVVYnOnTurfu/YsWOD7eGJEydEUFCQcHNzk30d19eD69dff5U8VlXTZ8uWLXoriz6OZ05OTqJNmzbi1VdfFSkpKVrn9emnnwoLCwvJ9H///fdGtUlytRX36dqD68cff2xUzy05z0sMWT91j0VyzcuQ25S+j3Om3v7U9cADD6jK6unpqTXfhg0bhLW1teT8KyIiQrZyyVmPzenB1ZrPV7KysiTnzHXLe/78ebXzam3XQM099sh9rcceXEREREREujO5ANeiRYsa/SiHBQsWqPIPGzZMa77Zs2ernfx/+OGH9QYVMjIyxIABAxp18SbnMtS9KKv7eJInnniiwRv7cql9Y65bt24NBmvS0tLUbgzNmTOn0fM1VH2sW7dObTv48ccfGxyv9g0AFxcXk9jxs7OzxfPPP69T3VRXVwtPT89GL3eTGyUjBLjufyZMmCDKyso05lcqlWoBzkGDBtU7D0O1OcZqr+Vs7wxZd/pehqtXr0qm98477zRq35SbtgDXvn371IJb8+bNU7uJ1759e7Wbqk2lj/b7pZdeElevXtVpfsHBwZJ5+Pn5NbpNkqOt0HQc1Rbg2rlzZ6OCW3Kflxiyfgw1L0NvU/o8zpl6+1Nbbm6upKwzZsxQy1NVVSVeeeUVST5PT09x7tw5WcsmZz02J8DF85X/x8XFpcmP+W7usUfua73GbiMlJSXixx9/lHxKSkp4p4OIiIiIWgWTCnBlZWVJ3vnh4eEhcnNzG5x+WVmZ5Ab8iRMn1PKkp6dLbggBEEuWLNGp/JmZmTpfvMm5DJouymp/li1bZrAeQHWdPXtWUpY333xTp/HGjRsnGc/Ozk7cvXu3UfM2RH3ExsZK/jncmO3HFANcjTVz5kzJsr/77rvyNUpGCnBZWVmJrKyseseJjIxUeyeRNoZqc4zVXsvZ3hmy7uRYhujo6Cb1TjEUTQGukJAQteDWCy+8IGpqasTp06fV3tf3t7/9TS/tq6GPZ5cuXVI75jT07h6524raGgpw7dy5U7IudAluyX1eYsj6MeS8DLlN6fM4Z+rtT21RUVH1ljU/P18tUDN+/Hi9vwtQEznrsbkBLp6v/EVfAa7GHnsMca3X2G2EiIiIiKg1szSlxyVu2LAB5eXlquGPPvoILi4uDY7Xpk0bLF68WDV8+PBhtTxhYWGSZ8R7eHjgX//6l07lat++vUksQ31mz56N1atXG+157qGhoZLhoKAgncarm6+srAw7duxodnn0WR9ZWVkICAhAVVWVKu2RRx7BmjVrWs2jTB0dHSXDLfG9AY8//jjc3NzqzfPoo49KhisqKlBYWKgxr6HanJbYZhuy7uRYhrr7yy+//IKamhqTXpfffvut5J1bISEh2LhxIywsLODt7Y1//vOfkvyHDx/GunXrZCmLnMezAQMGoG3btpJjTkpKilHbCl3t2rULs2fPVr3LpaF3bhnrvMSQ9WOsdaHvbUrO47Uptz+nTp2SDNd+/9bly5cxevRoHDlyRJU2f/58HDlyBK6urq26Hnm+Ig9djz3GutYjIiIiIiLNTCrA9euvv0ouLAMCAnQe19/fX/X9+PHjar/XvYhYuHAh7OzszGoZ6ru4/OKLL4y23srLy7F9+3bVcK9evTBy5Eidxg0ICIClpXQzrBssayx91kdVVRUCAgKQlZWlSuvWrRt27twJhULRahqKjIwMybC7u3uLW8YZM2Y0mMfOzg5t2rSRpOXn52vMa6g2pyW22YasOzmWYcCAAZLtJCYmBtOmTUNSUpJZrNeXX34ZGzZskNzkW7FiheTmMwAsW7ZM7zfyDXE869mzp2Q4OzvbqG2FLsLDwxEcHNzo4JYxzksMWT/GWBdybFP6ZE7tT3x8vOq7QqFQnTtGRUVhzJgxuH79uuq3zz//HGFhYbCxsWn19cjzFf1rzLHHGNd6RERERESknZWpFKSoqAhnz55VDdvY2CAwMLBxC2NlherqatUF8X3V1dWSi2gAeOKJJ8xqGeozd+5cnf45KJeIiAjcvXtXNaxr7y0AcHNzw8SJEyX/0D1z5gwuXLiAIUOGNKk8+qyPxYsXIy4uTjVsa2uL8PBwdOrUqVU1FJcuXZIM6xrANCd1b1DWt4/W9tfTg2CUNqclttmGrDu5lqF9+/Z47rnnsHHjRlVaVFQUoqKi0KdPH4wZMwajRo3CqFGjMHLkSLVtypheeeUVjT2zrKyssGXLFgwfPhxlZWUA/uqpMmfOHPz22296WwZDHM8cHBwkw7V7rhm6rdD1GBsUFKQKbgHABx98oFNwyxjnJYasH0OvC7m2KX0yl/ZHCCFp6wcOHAh7e3t8+umnePvttyW9pb777js8++yzrEeer8hG12OPsa71iIiIiIionnNsUylIZmam5GI2Ly8PUVFRTZrW7du3UVNTo+oZdOfOHcmjJCwsLDBw4ECzWob6+Pn5GXXd1e1x9euvv+LMmTM6j5+WlqaWFhYW1uR/8eurPrZs2YKvv/5akrZu3TqMGjWqVTQOVVVVKC4uxuXLl5Genq5Kt7W1xdChQ1vc8nbt2lVv0zJUm9MS22xD1p2cbfbq1atx8OBB3LhxQ5LvypUruHLlCrZs2QLgr39/+/n5YcmSJRg+fLhR1+mrr76KL7/8Uuvvffv2xaeffopXX31VlXb69Gl8/PHHeO+99/RSBjmPZ5WVlSgqKmp28EGfbUVDIiIiEBgYKAluAcD333+PRYsWwd7e3uTOSwxZP4acl5zblL6ZQ/tz7do1FBQUqIaHDh2KZ599Ftu2bVPLe+bMGYMHuEy5Hnm+on+6HnuMda1HRERERETamUyA686dO3qbllKpRHl5ueqdCHl5eZLfO3ToIMs/LeVchvr07t3baOstNTUVR48elaQlJCQ0e7pbt27FmjVrYGtr2+hx9VEfiYmJeOmllyRpISEhWLBgQYtrBP78808cOXIE586dQ2JiIlJTU5GZmYmioiKN+UeMGAFra+sWVw/t2rXT27QM1ea0xDbbkHUnZ5vt5OSEo0ePYubMmWrvmamtqKgI27Ztw44dO7B8+XJ89NFHRnnH3YsvvlhvcOu+V155Bb/88gsOHjyoSvvXv/6FqVOn6qVnpz7a70uXLuH3339HfHw8rl+/rmrTKisrTa6tqE9xcbFaz637rl69itdee63BR/oa47zEUPVjyHnJvU3pmzm0P3XLtXPnTq31uXHjRixdutTgAU1TrUeer+ifrsceY13rERERERGRdiZzBVP3mfpLly7FzJkzmzy92oGRuo/kkOvmvJzLUB9DvGxbm7CwML0/6gf46z0BkZGRTaq/5tZHfn4+pk+frnoMFwD4+Pjgq6++ajE7/t27d/H9998jPDwcv/32m+Tl4g3x8fFhy9kAQ7U5LbHNNmTdyd1me3p6IiYmBps3b8ZXX31V77tbampq8PHHH6OgoADr1683+PqcMmWKTvksLCwQFhaGwYMHqx5NW11djTlz5iAhIUHtnS+Gar8zMzOxfv16bN++XWOvYHOkVCpVbbOVlRU2btyINWvW4MqVK6rj7+TJk+t9/4uxzktaAnPfpky9/akbMKod3HrjjTdw8+ZNhIeHA/jrXa+rV6/WKQjf2uqR5yv6oeuxh20qEREREZHpMZkAl5ubm2S49summ6tDhw6SYX2/XNwQy1AfhUJhlHVWU1OD7777TlLPBw8ebFJ5MjMzMXXqVElaaGhoky4am1MfNTU1CA4OltzMcnV1xa5duwz2YnM5VVRUYPXq1Vi7di1KSkrUfndyckLv3r3RtWtXdOrUCY6Ojti2bRtycnJUeRjgMp02pyW22YasO0O02dbW1ggJCUFISAgyMjIQExODM2fOID4+HgkJCSgtLZXk/+abb+Dr64sZM2aY7Lr38PDA119/LXkH1B9//IEVK1bg888/N+jxTAiBDz/8EGvWrJH8KeG+du3aoU+fPnB3d4erqyvatWuHnTt34vbt2+ZzomZlhe3btyMgIABDhw7F2LFjUVVVBeCvnsU+Pj7w9PQ0qfMSc9aStilTbn/qvvcJ+Ct4sHHjRsybNw+XLl3C7t27VY+D+/bbb7Fs2TK4u7uzHnm+YrRrKbapRERERESmeSEvu+XLlwsAqs+6devU8lRUVAh7e3tVnokTJ+pt/uXl5cLKykpShps3b+o8/r179yTjAhD37t0z6DLU5ubmJilLeXm5MIb9+/dLyvHqq682a3pjx46VTM/S0lKn9aTP+qi7rVpZWYljx441a7l69Oihmp6Li4swlsLCQvHII49Ils/Ozk7MmzdP7NixQ2RkZKiNo1QqRbt27STjXL9+XdZy1t3XsrOzdRrPwcFBMt61a9eMNh9DtTnGaq/lbO8MWXeGarO1KS0tFVu3bhXu7u6S8g4aNEjW+SYlJanV0Z49exo9nYCAAMk0LCwsxNGjRw16PAsKClIrw+OPPy5CQ0O1tgFjxoxp1LIbqk0SQohhw4apHYN+/vlnSZ41a9ZI8jz00ENCqVQabRs3ZP0YYl4tbZsytfbn/nZpY2Mjma+rq6uIi4uT5AsMDJTkWbhwoTA1TanH2uvRysrKqNu9OZ+vuLi4NPn40dRjj6HOGxq7jRARERERtWYm81ZbGxsb+Pr6qoZjY2Nx8+ZNvUzb1tZW7aXPR44cMatlMEV13/3x/PPPN2t6f//73yXDdXuIyS0iIgKrV6+WpK1duxYPP/ywPgPKRltfTz31lOR9aS+++CIyMjKwefNmBAUFafxXdEpKCoqLi1XDLi4u6Nmzp0luj/d7NJgCQ7U5LbHNNmTdGbvNtrOzw+zZsxEfH49OnTqp0pOSkpCenm7y28CGDRvQpUsXSfs2b948re/v07ft27fjxx9/VA17eXnh3LlziI6OxvPPPw8vLy+z3sesra2xc+dOtV4gb775pmS7jYmJwccff8zzklawTenzOGfM9ufcuXOSRxIOGzYMp0+fxrhx4yT53n//fVha/r9LlU2bNplc22ju7XhrOF/htR4RERERUctmaUqFmT59uup7dXU1Fi1apLdpT5w4UTK8adMmncfV9IgaYyyDKcnLy8Mvv/yiGh46dCiGDRvWrGkGBgbCwcFBkrZ582aDBIWSk5Mxb948SVpwcLDe11/dx9gYyr59+3D8+HHVcEhICDZs2KD2aJq6YmJiJMOm+njCvLw8VFRUmFSZDNXmtMQ225B1ZwptdteuXeHn5ydJy8zMNPn136FDB7U/Oty8edNgdfjTTz9Jhnfs2IEhQ4Y0OF5hYaFZ7F9Dhw6VbJ+qEzdLS2zZsgUdO3ZUpX3wwQf4/fffW/V5SUvfpuQ6zhmj/an7/q2tW7dqfMxm//79ERgYqBqurKzUGsw1NnNtx1vL+Yq5XK8SEREREVHjmVSAa/bs2XjggQdUw3v27EFUVFSjp1NQUKCWNn/+fMlwXFwcIiIidJre1q1bTWIZTMkPP/wg+fdt3d5XTeHg4KD2zq20tDQcPnxY1mUpLi7G008/LempNGTIkEZd4De0XLVvBNSej6Hs3btXMjxr1qwGx6moqMAnn3wiSTNEgKv2v7Xv3zxoyL59+0xuHzFUm9MS22xD1p1cy9DYG961//kPQBK8MGVTpkzBSy+9JEnbvHkzIiMjZZ93dna2ZLhXr14NjlNVVYXU1FSzP3lzd3eXHKOqq6sxe/Zsjb3nWst5iTltU3If50y9/akd4GrXrh369++vNe97770nqa+wsDCD9ZhpLe14azhfMZfrVSIiIiIiajyTCnBZW1urPSJu7ty5Wv+VXFdaWhqeeeYZ/POf/1T7rX///pg0aZLaBV3df5HWtX//fixdutQklsGUhIWFqb7b2NjoFDDRhaZAWe156ZsQAs899xySk5NVaU5OToiIiEDbtm31Mg9nZ2fJsLaAXWpqKsrLy2VZzro3g1JSUurNX1lZiUWLFiEtLU2SbogAV+fOnSXDeXl59eZPTU3FW2+9ZXL7iKHanJbYZhuy7uRYhqKiInh6emLFihXIz89vcBqVlZXYs2ePpEzdunUzmxOJzz77TC0QEBISgtzcXFnn27t3b8nwtm3bGhxn5cqVsrWzhubv74+QkBBJW7hw4cJWe15iTtuUnMc5c2h/4uPjVd+9vb3VAn619evXD8HBwZLyrlq1SvZtoTW1463hfMVcrlebqqKiAnFxcZKPqT3ZgIiIiIhINoZ40dfy5cslL/Jdt25dvfkXLlwoyW9jYyOWL18usrKyNL4cOTo6WgQFBQlbW1sBQAQEBGic7qVLl4S1tbVk2m3atBEfffSR+PPPPyV5CwsLxcqVK9VevAwdX6As1zI058XI+nLq1CnJ/GfOnKnX6ffv318yfVtbW7X1o6/6WLVqldq6/fDDD8WZM2dUn1OnTomYmBhx7NgxrZ+cnByt8wgJCZFMv2/fvuL27dtCCCGqqqpEUlKSePXVV4WNjY04dOiQLOssODhYUgZnZ2cRFRUlampq1F6WvmfPHjF48GC1erGwsKh3PeiLj4+PZL6vvPKK1rwxMTGiX79+GvdRTS9TN9RL243R5hirvZarvTN03elzGWJjY1XTcXBwEG+++aY4ceKEWtukVCpFbGys2v5WX/uvD0lJSWp1tGfPnmZN87fffhMKhUIyTX9//wbHa077vWnTJsm4CoVCrFmzRhQWFkry1dTUiBMnTohnnnlG4/bR0LIboq24b9iwYar83t7eDeYvKSlRawO3bt1q0PMSQ9aP3PMy1DYl53HO1Nuf/Px8yfyWLVvW4DgpKSmS9sXa2lqkpqbKWk4567H29mhlZWX07d5cz1dcXFyafPzQx7WUnNd6jd1G0tLS1NZDWloa3zZORERERK2CSQa4KioqRFBQkNqJuqWlpRgxYoSYNm2aePLJJ8Xw4cPVLsYAiAkTJmid9pdffqnxYszCwkL07NlTPPnkk+Khhx4SdnZ2qt9Gjx7d6Is3OZfB2AGuugGb6OhovU5/7dq1avXx1Vdf6b0+Dhw4ICwtLTVuD439aLuhKIQQERERGreDnj17ChsbG0n68uXLZVln2rb7bt26iSlTpohp06YJHx8fyXZf99O3b1+DbF+vvvqq2rzHjRsn1qxZIzZv3izWrVsn3nrrLckNQjs7O9G+fXuTC3AZss0xVnstZ3tnyLrT5zJ88803GsttY2MjunfvLkaOHClGjx6tts3ev6F47tw5WdexHAEuIYRYsWKF2nS///572W4yVlRUiG7duqnN097eXowePVr4+/uLiRMnCnd393rbb3MOcAkhREJCguRY4ujoKK5fv26w/bQlBbgMtU3JeZwz9fYnOjpaMs/w8HCdxpszZ45kvL///e+yllPOejTFAJc5nq8YO8Al57UeA1xERERERLozyQCXEH/9O/a9995TCwA09Bk8eHCDAZcPPvhAWFhYNDgtS0tL8c4774i7d+826eJNrmUwZoCrpKREODo6qubt4eEhlEqlXueRk5OjdiE4bNgwvdfHokWL9BLcaijAde/ePdGrVy+dpuPn5yfLeisqKhIdOnTQeXnGjh2rVj9z5841yDaWkZEh2rRpo3NZH3zwQXHlyhXh7e1tkgEuQ7Y5xmqv5WzvDFl3+lqGAwcOiD59+jS6HWnbtq3YtWuX7OtYrgBXZWWlJEBzP9hy8+ZN2Y5nUVFROrcXCoVCvPDCC+KJJ55oUQEuIYT47LPPJPMaM2aMqKqqMsh+2pICXIbapuQ8zpl6+/PBBx9I5puenq7TeFeuXJH04rKystIYyNUXOevRVANc5na+YuwAl5znPgxwERERERHpzmQDXLUvaIODg4W9vb3WiwRra2vh5+cnfv75Z52DLdHR0Vof+WJtbS2effZZkZCQIIT4K0DRnIs3fS+DMQNc33//vWTe77zzjizzmT59ulodnT17Vq/1YagAlxBCXLhwQe1Gw/1Pu3btxNNPPy1++eUXvQcLa7t48aLw8PDQugxubm7i+eefFwcOHBBCqPfG+Prrrw22nUVFRYmuXbtqLWv79u3F3LlzxYkTJ1TjmHKAy9BtjrHaa7nabEPXnT6WQalUisjISDFr1izh5ORUb/vRsWNH8corr4jk5GSDrGO5Alz325n7j1+6//H19VV7HKo+j2cnTpwQHTt21Fq/np6e4qWXXhKXL18WQggxb968FhfgqqmpEY8++qhkfu+++65B9tOWFuAyxDYl93HOlNufqVOnSs47GuO5556TlH3+/PmyllWuejTlAJc5na+YQoBLrnMfBriIiIiIiHRn8f9fOMnq7bfflryMd926dXjllVcaNY2ysjIcPXoUqampyMrKgrW1NVxcXDBkyBAMHz4cDg4OTSpbTEwMzp07h6ysLDg6OmLIkCEYN24cnJyc9F4Pci0DmYfs7Gzs2rULOTk5cHNzQ7du3dCtWzcMHDgQNjY2BilDUVERfvnlF1y9ehU1NTVwcnKCl5cX+vfvjz59+sDCwsJk6quwsBAnTpzA1atXkZ+fjw4dOqBz584YOXIk+vXrZ1JlNdU2x1jttVztnaHrTl/LUF1djeTkZFy4cAHp6ekoLCyEnZ0d3Nzc0KNHD0ycOBHW1tZsJJuhtLQUe/fuxZUrV1BSUoIOHTrAy8sLAwcORL9+/VhBPC8xyW3KEMc5tj/60Rrr0dTPV9imSgkh0LFjRxQUFMDFxQVZWVmwsrLiSiEiIiKiFs9fbzFFAAAgAElEQVRsAlxERCQ/ttdERERE5uXKlSvo27cvAODll1/G+vXrWSlERERE1CpYsgqIiIiIiIiIzNOpU6dU34OCglghRERERNRqGCTAVVFRIRl2dHRkzRMRmSC210RERETm5X6Aq2vXrnjwwQdZIURERETUahgkwHXjxg3JsLu7O2ueiMgEsb0mIiIiMi/3A1yBgYGwtORDWoiIiIio9dDLO7iOHj2Ktm3bwsfHR+23ffv2wd/fH9XV1QAAW1tb/Pnnn2jbti1rn4jIwNheExEREbUc5eXlcHR0RFVVFU6dOoXRo0ezUoiIiIio1bDSx0R+/PFHbNq0CX5+fpg4cSL69OmDrKws7N+/H+Hh4ZK8r776Km+WEhEZCdtrIiIiopZDCIH9+/cDAINbRERERNTq6KUHl5eXl9pjrTTp3r07Ll26BHt7e9Y8EZERsL0mIiIiIiIiIiKilqDZD+iurKxEr169GszXt29f7N69mzdLiYiMhO01ERERERERERERtRR66cEFABcuXMDOnTvxv//9D0lJSaioqICVlRUGDRqEyZMn47333oOdnR1rnIjIyNheExERERERERERkbnTW4CrNqVSiYKCAjg4OKBNmzasZSIiE8X2moiIiIiIiIiIiMyRLAEuIiIiIiIiIiIiIiIiIrlYsgqIiIiIiIiIiIiIiIjInDDARURERERERERERERERGaFAS4iIiIiIiIiIiIiIiIyKwxwERERERERERERERERkVlhgIuIiIiIiIiIiIiIiIjMCgNcREREREREREREREREZFYY4CIiIiIiIiIiIiIiIiKzwgAXERERERERERERERERmRUGuIiIiIiIiIiIiIiIiMisMMBFREREREREREREREREZoUBLiIiIiIiIiIiIiIiIjIrDHARERERERERERERERGRWWGAi4iIiIiIiIiIiIiIiMyKVWtc6LS0NNy5cwdCCNjb28PLywt2dnZ6n09paSnOnz+P0tJSAIBCocADDzyA7t271zteeno6srKyIISAnZ0dvLy8YG9vz62V6nXnzh2kpqZK0gYOHAgHBwdWDlETnDp1Cvv27UNycjLy8/Ph5OQEV1dXeHl5YerUqejfvz8rSc9u376NzMxM1NTUwM7ODj179mQbRkREREREREREGrW6ANeLL76Ib7/9VpLm4uKCuLg49OnTR2/zKSgowMCBA5GVlaX225o1a7B06VKN4y1duhRr166VpDk7O+PYsWMYMmQIt1gNjh07hlWrVknS3njjDUyZMqVV1cOSJUuwdetWnbc14j7T2vcZbRITE/Haa68hNjZWa57MzEz8+9//5rrWo5UrV+KDDz6QpDk6OuLAgQPw8fHhhklERERERERERBKt6hGFQgh89913aul5eXmIjIzU67zi4+M1BrcAqAUhatu8ebNaWkFBAcLDw7m1apGTk4NDhw5JPrdv325VdVBYWKhxGwkLC+MGQtxnGiE8PBzjx4+vN7jFdS2PTZs2qaUVFRXhp59+4oZJRERERERERERqWlUPrtLSUlRWVmr8raioSK/zunv3rtbfCgoKGj2evstHLcuOHTtQVlamlp6SkoK4uDiMHz+elUTUgFOnTmH27NmoqKhQpfn4+OCRRx6Bi4sLiouLcfnyZRw/fpyVJYPCwkIe/4iIiIiIiIiISGdWrALDq6mpYSWQXoWGhqq+d+vWDenp6ZLfGOAiarhdfuGFF1TBLQcHB0RGRsLX11ctr1KpRE5ODiuNiIiIiIiIiIjIiCxZBfKwsbHR+pu9vT0riPTmwoULOHPmDACgbdu2WL9+veT3n376CcXFxawoonpERUXh4sWLquHvvvtOY3ALABQKBbp27cpKIyIiIiIiIiIiMiL24JLJiBEjYG1tjaqqKrXfHn74YVaQHgUGBiIwMLDVLn/t3lt+fn544okn0KVLF9U74EpKSrBz504sWLCgRddDRkYGSkpKAPwVgOjVqxd3DhPfZ0xpndV+D6OtrS2efvpprmsiIiIiIiIiIiITxgCXTLp3746zZ8/iyJEjuHfvHoC/buD26NED06dPZwWRXlRUVGDbtm2q4aCgIFhaWmLmzJn4z3/+o0oPDQ1t8QGuF154AdHR0QAAFxcX5ObmcgPhOtNZYmKi6rurqyssLdnBmYiIiIiIiIiIyJQxwCWjwYMHY/DgwawIkk1kZCTy8/MBAI6Ojpg8eTKAvwJdtQNcJ0+exOXLlzFgwABWGpEGtd+ppVAoWCFEREREREREREQmjn9RJzJjtR9P6O/vjzZt2gAAxowZgx49ekjyhoWFscKItKisrGQlEBERERERERERmREGuIjM1K1bt3Do0CHVcFBQkOT3usNbtmzR+E44IiIiIiIiIiIiIiJzw0cU6qC0tBQFBQWwt7eHo6OjWbybRalUIikpCVlZWcjPz0dJSQk6duyIjh07YujQoXB2duaKNXObN29GTU0NAKBDhw6YNGmS5PegoCCsXr1aNZybm4u9e/fyHXBEZDR5eXmwsLCAs7OzrMdSIQSSk5ORkZGBP//8E3fv3kW7du3QsWNHdOnSBYMGDeJ71oiIiIiIiIiIzJwsAa6bN29i+/btyMrKgpWVFTw9PTFnzhx07NixwXHj4uJw6NAhlJeXY8iQIQgMDNTpJlRKSgp+/vln5OfnQwiBvn374oUXXoCVVdMW8fLly1izZg0OHDiA7OxsVbq9vT28vb2xfPly1fuOtMnKysJ3332HrKwsCCFgZ2cHb29vBAQEwMLCQpYVum/fPmzcuBEnTpxAYWGhxjyWlpYYMWIEnn/+eSxYsADW1tZG2wBLS0sRGRmJ+Ph43Lp1CzU1NXB3d4evry8effRRtGvXTpb5nj9/XvXdzs4Offr0kfxeUlKCK1eu4OrVqygoKICbmxsGDBiA3r17m8SOK4TA5s2bVcMzZsxQW49Dhw5Fv379kJycrEoLCwtrcoDr0qVLqK6uBgA4ODjAy8tLLY9SqcS1a9dw/fp1ODs7Y+zYsWxlNay7xMREnD17FufPn0dubi6Ki4vh6OgILy8vTJ06FePGjTPZ8iuVSpw4cQKxsbFISUlBfn4+rK2t4erqik6dOqF79+6YMGFCq3rfmxz7hrHaRn07fvw41q9fj0uXLiEtLQ0lJSUA/npn4Lhx4xASEoJp06bpLdh06NAhbNiwAcePH0deXp7WfO3bt8eDDz6IkJAQ+Pn5yXZMJiIiIiIiIiIiGQkZTJ06VQCQfAYNGiSqq6vrHe/XX38VlpaWkvHmzZvX4PzS09OFs7Oz2jy3b98uyXfv3j21PPc/7777rhBCiKKiIvGPf/xDWFlZac17//PYY4+JgoICreV68MEHNY5Xt1y1aZvvokWL6q2DpKQkMXz48AbLXPfTs2dPce7cOWFopaWl4p133hH29vZay+bk5CS+/PLLBrcbIYTo06eP8PDwEB4eHqJ79+4N5q89H29vb1X6tWvXxMKFC4WdnZ3GMg0ZMkQcOnRIGNvBgwcl5Tp8+LDGfCtXrpTkUygUIiMjo0nzdHNzU01n0qRJkt8yMjLE+++/Lzw8PLRus2PGjFGto+Z8EhISRGFhoVq6ra2tat6Wlpb1TqO8vNzg6+zGjRti8eLFolu3bg3ul7NmzRL5+fmylqex+0xNTY3YuHGj6NKli05tS/fu3cXKlStFdna2EEKY3DpbvHixZPq1jz0KhUJjGT7//HNZ9g0528amrGt9uHjxos7HJG9vb3Hjxg3h4OCg8fcFCxY0OL/k5GTh4+PT6GPg/fOTxMREQURERERERERE5kWWANf8+fM13kTav39/veM9/vjjauMoFAqRl5dX73j/+te/NM7vyJEjknwNBbhKSkq0BqW0fQIDA7WWS9sNyqVLl2odpykBrn379ol27do16cYeANGuXTvx22+/GWyju3r1qujVq5fO5Zs8ebKorKysd5q1b4xaWVk1vOFrCHCFhYVpDWzV/YSGhhp1xw0KClKVpXPnzlpvdCcnJ6uVfdWqVU2ap7ab+GFhYRpvTNfdZnv06NHkbbT25+TJk+Lu3bvNmoahA1xLlizRKWhe++Pu7t5g29ccjdlnqqurxdy5c5tU1/fbfVNbZ/PmzWt0GVauXCnLviFn29iU9rG5Tpw4IZycnBpVt05OTkKhUDQpwHXw4EHRvn37Zm1fbdu2Fbt27eJZIRERERERERGRGZHlEYWPPfaY5PFp9+3du1frY/3u3buHo0ePqqUrlUocOHAAwcHBWucXHh6ulubg4IDx48c3picbgoODERsbCwsLC0ycOBHe3t5wdHTEyZMnsW/fPo3j/fTTT1i1apXGR1JVVFRoHKeyslJvdZ2cnIyZM2eqHvtU2wMPPIDXX38dPj4+cHZ2RmZmJsLDw7F+/XpJvuLiYsyaNQvnz5+Ho6OjrD0G//jjD/j6+koe+2htbY3hw4fDx8cHdnZ2uHz5MqKjo1WP/IqOjsa8efOwdetW2R4j9cknn2DFihUAAIVCAS8vL9jb2+P27dvIyclRy//yyy/Dz88PnTp1Mnivy4KCAuzevVs1PHPmTCgUCo15+/bti+HDhyMxMVGVFhYWhrfffrvZdSmEwHPPPYcffvjBYMveq1cv9O/fH0IIs+op+/PPP6u2ZwDo0aMHHn74YfTo0QOurq4oKCjA8ePHcfDgQVWezMxMLFu2DJs2bTJ6+b/++mts2bJFNWxpaYmnnnoKY8aMgYuLC4qKipCbm4uUlBTExMQgNzdXlXfYsGGtsWd0o/cNU20bGysrKwtPPvkkioqKNP7u6uqKp556Ct27d0dVVRXOnz+PI0eO4O7du02a3/Xr1zF9+nQUFxer/ebl5YWFCxfC29sbnTt3Rm5uLhITE/HNN9/gjz/+kOQtLS3F7Nmz4eXl1Sq3WSIiIiIiIiIisyRH1CwvL0/tUYMAxAMPPKB1nN27d2v9Z/WcOXO0jnf79m2N4/j5+anlra8H14ABA1RlrNvzSwghXnvtNa3jbty4UXP0sAm9sRozTk1NjRg6dKjWRy7l5ORonMeGDRuEhYWF2jgffvihrNHUkpIS0b9/f8k8R4wYIZKSktTypqamSnopARA///yz1mk3pwdX27ZtVd+ffPJJcevWLUne8+fPiylTpqjV19q1a40Slf7yyy8l5YiLi6s3/5o1a9TKfvTo0UbPt24vlcWLF6v1tpw+fbrYtGmTOHnypFrvo8TERHHy5EmNn9OnT4ukpCSRnJwskpOTxR9//CF8fX0l07e3txcXLlwQQgihVCpVee9/3N3dJb1B6v5e+1NTU2Ow9aVUKoWVlZVQKBTi2WefFZcuXdKa9+OPP5Yss4WFhbhy5Yos5dJ1n6mqqhKdO3dW5e3YsWOD29yJEydEUFCQcHNzk9SDKa2zrKwsyfTr9jg6f/68Whm09ahr7r4hZ9vYlPaxOQICArQeK5ctWyYqKirUxklPTxd+fn5ax6uvB5e2xxL6+vqKe/fuaRynvLxcTJs2TeN4ffv21fnRj0REREREREREZFyQa8Le3t4abx6lpKRozP/iiy9qvbnl6uqq9eZmaGioxnG+/vprtbz1Bbju30BPTU3VOJ+EhASt473++usax5E7wLV3716NeR0dHUVubm6968ff319tPA8PD1lv7C1atKjRj9dasGCBKv+wYcO05mtOgOv+Z8KECaKsrExjfqVSqRZwGTRokFF22mHDhqnK0K1btwZv/KelpakFNOsLGmtT+yZ+3Uc5PvHEE+LatWt6W8Z169aprZ8ff/yx3nFqPwLRxcXFZBrZ7Oxs8fzzz+tUP9XV1cLT07NRy91Uuu4zV69elZTnnXfeadSym8s6c3FxafIjEZu7b8jZNjalfWyq69evN+mPKve3lcYGuA4fPqwxv52dnUhPT693fn/++afGd3fqEjAkIiIiIiIiIiLTYClXz7DHH39cY/r+/fs1pv/vf//TOq07d+4gISGhUdPTNv/6fPTRR+jRo4fG39zd3bWO19RHKzXX119/rTF91qxZcHFxqXfcwMBAtbSMjAxcunRJlrJmZ2djw4YNqmEPDw/88MMPsLa2rne8r776Cp6engCAc+fOISYmRpbyWVlZYefOnWjTpo3G3y0tLfH6669L0q5evWrwdZ6QkIBz585J1mNDjybr3r07xo4dK0nbtWsXCgsLm1yOsrIy1fdly5YhKipK42M6myIuLg6LFy+WpC1ZskTjNmsOOnfujNDQUJ3qR6FQYMyYMZK0pKQko5b/+vXrkmEnJ6dGLXtr09h9w9TbxsbYtm2b1t/uPwLWEMfAcePGwcPDo95xnZ2dMWnSJI2/rVu3jt37iYiIiIiIiIjMgMEDXNHR0WppV69eRVpammpYU3BG03hKpRKHDh1SS/fy8mrSzfb58+dr/a1t27Zaf9P2ri05VVdXa72hOWfOnAbH9/X11Zh+6tQpWcq7YcMGlJeXq4Y/+uijBoNwANCmTRtJsOPw4cOyba9ubm715nn00UfV1ntzgkRNERoaKhkOCgrSaby6+crKyrBjx45ml2f27NlYvXq13t7/k5WVhYCAAFRVVanSHnnkEaxZs6bVNMp134Nn7Hcr1S3PL7/8gpqaGh499bRvmHrb2BjaytCxY0f069dPr/MSQuDIkSMaf6sbJNambuD/vt9//12yToiIiIiIiIiIyDTJFuAaM2YM2rVrp5Z+7NgxyT/cAfXeW0888YTaeJp6av3+++8ae09Nnjy50eX19PRE+/bttf5u7JvMdZ07dw4lJSUafxs4cGCD43fq1AlWVlZq6bdu3ZKlvL/++qvqu6OjIwICAnQe19/fX/X9+PHjspRvxowZDeaxs7NT6+GVn59vsHVeXl6O7du3q4Z79eqFkSNH6jRuQEAALC2lu3vdYFljtW/fHl988YXelq+qqgoBAQHIyspSpXXr1g07d+6EQqFoNY1yRkaGZLi+3qOGMGDAAMl2HxMTg2nTphm9Z5kpa8y+YeptY2OPS9rOB/QtOTlZa+/pYcOG6TQNbfkqKytx9uxZbshERERERERERCbOSq4JW1tbw9fXF5GRkZL08vJyHD9+XBKEqh3gatOmDQIDA7FlyxbJeCdPnsTdu3clj8fS1KsLaNrjCQcMGGBWKy41NVVjuoODAzIyMtRukmvi7OyM3NxcSVpBQYHey1pUVCS5WWhjY9Pox81ZWVmhurpa7XFp+tKzZ0+dy1GbEMJg6zwiIkJyQ1fX3lsA4ObmhokTJ0p6PJw5cwYXLlzAkCFDmlSeuXPn6tTTRFeLFy9GXFycatjW1hbh4eHo1KlTq2qU6z4mVNcgplzat2+P5557Dhs3blSlRUVFISoqCn369MGYMWMwatQojBo1CiNHjtQYOG9tdN03zKFt1FVBQYHWHq0NPS6wKZKTk7X+5uDgoNM06suXkpKC8ePH8yyRiIiIiIiIiMiEyXon8rHHHlMLcAF/9ca6H+CqqqrCsWPHVL95e3tj7NixsLCwkAQPlEolDh48KPl3u6YAl42NDR555JFGl1XXG2KmQlsg6t69exg0aFCTpytHT5nMzEzJI83y8vIQFRXVpGndvn0bNTU1ar2Rmqtr164mv87r9rj69ddfcebMGZ3Hr/0Y0PvCwsKa3AvLz89Pb8u2ZcsWtffprFu3DqNGjWoVDXFVVRWKi4tx+fJlpKenq9JtbW0xdOhQo5dv9erVOHjwIG7cuCFJv3LlCq5cuaL6Q4KjoyP8/PywZMkSDB8+vNUeWHXdN8yhbdRVfb1Ztb3bsDnqe/elra2tTtOoL58cf/YgIiIiIiIiIiL9kjXApct7uOLi4nDv3j3V8EMPPQRnZ2cMGjQIFy9eVBvvfoArNzcXCQkJatMeP3682QWrmqK4uFiW6TYlONiQO3fu6G1aSqUS5eXl9b4TrSk0PU7TlKSmpuLo0aOSNE3bf2Nt3boVa9as0fmGcG29e/fWy7IlJibipZdekqSFhIRgwYIFLW6//fPPP3HkyBGcO3cOiYmJSE1NRWZmJoqKijTmHzFiBKytrY1ebicnJxw9ehQzZ86s9z19RUVF2LZtG3bs2IHly5fjo48+MrnHuxqCrvuGObSNuqqsrNT6W1Pal4bUF+DStRdhffkY4CIiIiIiIiIiMn2yBri8vLzQs2dPjf/6v3HjBnr27KnWC+vBBx8EAEyYMEFjgOu+AwcOaHw8XFMeT2iOHB0dNaZ36NABK1asaPT0FAoFhgwZAl9fX72X1cbGRjK8dOlSzJw5s8nTk+NmqakLCwuT5XGI+fn5iIyMbNL6cHV11cv8p0+fLnkvn4+PD7766qsWs+7u3r2L77//HuHh4fjtt9+gVCp1HtfHx8dklsPT0xMxMTHYvHkzvvrqq3rfwVVTU4OPP/4YBQUFWL9+favbX3XdN1pS21hfILMx27zOJy/1BKfqC7bVVlFR0aTpExERERERERGRaZD9Ds7jjz+Ob775Ri09OjoaCxcuxP79+1VpCoVC9c6LCRMmqD2y7Pbt26p3Bml7/1btd3u1ZNre71JZWYklS5aYVFnd3NwkwwqFwujvFTInNTU1+O6771TDHTp0wMGDB5v0OMnMzExMnTpVkhYaGtqkm+rNfZxlTU0NgoODJY9OdHV1xa5du9Ru/JujiooKrF69GmvXrkVJSYna705OTujduze6du2KTp06wdHREdu2bUNOTo4qjykFuIC/3q0YEhKCkJAQZGRkICYmBmfOnEF8fDwSEhJQWloqyf/NN9/A19cXM2bMaFX7rK77RktqG7X96eL+cUnfOnToUO++p+s+qk3Hjh158CEiIiIiIiIiMnGyB7gee+wxjQGu/fv3Y9q0abhw4YIqbdSoUWjfvj0A4OGHH9Y4vf3792Pw4ME4ePCg2m9ubm4YMmRIq1hx3bp105h+7949FBcXm9Qj99zd3WFvb6+6yf/7779zz2uEAwcOICMjQzU8e/ZsjBgxoknTGjp0KMaOHStZB4cOHcKtW7fg6elp0OV65513JPuxlZUVfvrpJ3h4eJj9OisqKoK/v7/ksZJ2dnYIDAzE448/joceegju7u6ScWpqavDf//5XkjZ69GiTXUYPDw8EBwcjODgYAFBWVoaIiAgsW7YMmZmZqnwffPBBqwtwtca2sVOnTrC0tJS8U+w+TQHe5qovAJWXl6fTNOrLxwAXEREREREREZHpk/1t9L6+vhof9XPkyBHs2bNHkjZp0iTV986dO2Pw4MFq40VHR+P8+fOSXg73Pf74463mfS/Dhw/X+o/52NhYkyqrjY2N5NGHsbGxuHnzJvc+HYWGhkqGn3/++WZN7+9//7tkuG4PMUOIiIjA6tWrJWlr167VGthuLDke59gYTz31lCS49eKLLyIjIwObN29GUFCQWnALAFJSUiTv1nNxcUHPnj3NZju1s7PD7NmzER8fj06dOqnSk5KSkJ6ebvLrjG1j85dF27vH6nucZVPVF+Q/ffq0TtOoL5+3tzcPPkREREREREREJk72AJejoyPGjBmjll5aWopVq1ZJ0moHuADgb3/7m9p4cXFxCA8P1ziv1vL+LeCvR1lNnDhR42/bt29v9PSqqqpw4sQJ2co7ffp01ffq6mosWrSIe58O8vLy8Msvv6iGhw4dimHDhjVrmoGBgXBwcJCkbd682WABhuTkZMybN0+SFhwcrNdtou6j8gxp3759OH78uGo4JCQEGzZsqPeRagAQExMjGTa1xxPqqmvXrvDz85Ok1e7RZYrrzJhaUts4atQojennz59v8DGFjW1/XF1d0b9/f42/nTp1SqdpxMfHa0zv3Lkz+vTpwwMQEREREREREZGJszTETLQFnrKyslTfHRwcMHbsWMnvdQNewF+BmC+++EIt3cLCAo8++mirWnn/+Mc/NKbv3r1b8ki7hqSkpGD8+PHw9fVFeXm5LGWdPXs2HnjgAdXwnj17EBUV1ejpFBQUtKp1/MMPP0huDNftfdUUDg4Oau/cSktLw+HDh2VfnuLiYjz99NOSnkpDhgzBpk2b9LJc95WVlUnmYUh79+6VDM+aNavBcSoqKvDJJ59I0kwpwFVYWNio/LV7cAHaH/dmKuvMmFpS2xgQEKB1+9b0WOHadOnlV5e2dweePXsWCQkJ9Y577do1HDlypFHTJSIiIiIiIiIi02LUAFdtjzzyCGxsbCRpDz/8MKytrdXy3rt3Ty3N29sbLi4urWrlPfrooxg/frxaeklJCaZPn67xMY61lZaW4osvvsCIESNw+vRpKJVK2R6PZW1trfZIurlz5+r8zpm0tDQ888wz+Oc//9mq1nFYWJjqu42NjU7BEl1oCpTVnpcchBB47rnnkJycrEpzcnJCREQE2rZt2+zpOzs7S4a1BexSU1NlC+QCUNuHUlJS6s1fWVmJRYsWIS0tTZJuKgGuoqIieHp6YsWKFcjPz28wf2VlpeTxs9bW1lrfGWgq68yYWlLbOHnyZHTp0kXjb2+++abWd14VFhZi6dKljZ7fa6+9ptYbFfjrsatBQUFITU3VOF52djZmzJihsVeZjY0N3nrrLZ4dEhERERERERGZAYMEuEaOHNngC9snT56slubg4IBx48bpNI/W9HjC2rZs2aIxsHf69GkMHToUH3zwARISEpCXl4eysjKkp6dj7969WLRoETw8PLB48WLJo8Fu374tW1lnzpyJhQsXqoYLCgowceJEvP3228jOzlbLX1FRgf/9738IDg5Gv379EBERgTt37rSadRsfHy95d42/v3+D+5Guxo0bp/Z4r4iICFl7gXzyySfYvXu3JO2NN97A3bt3cfbsWZw9exbx8fGIjY3F8ePHtX60bQN1l2f58uWqXqLV1dW4dOkSXnvtNfTr1w9xcXGyLWfdoM3y5cuxb98+tUewlZWVITIyEt7e3tiwYYPkNwsLC62PezO0ixcvoqioCJ988gl69OiBt956CzExMaioqJDkq9/oXqEAACAASURBVKmpQVxcHLy9vSVBPX9/f7Rp08ak15mxtZS20cbGBv/3f/+n8bfk5GT4+PggLCwMN27cQElJCa5fv45PPvkEAwcOxLFjxxo9vw4dOmDt2rUaf7t69SoGDBiA+fPn45tvvkFERAS+/fZbvPTSS+jduzfOnz+vcbyVK1dqDcgSEREREREREZGJEQYSFBQkAGj93LhxQ+N4n376ab3j3f/ExsY2WIaSkhKt4wcEBDR53ODgYI3jWFtba8y/ePFirfNpyjixsbHC2dlZp3pq6BMTEyPrdlBRUaFxW7C0tBQjRowQ06ZNE08++aQYPny4xrqYMGGCxuk6ODio8lhZWTVYjrrTzc7O1qn8tecDQFy7dk22ugoJCZHMKzo6Wq/TX7t2rVo9fPXVV1rzu7m5SfKWl5frPK8DBw4IS0tLvWyjW7du1TiPiIgIjdtVz549hY2NjSR9+fLlsq23L7/8UmO5u/1/7N15WFRl/z/wNwzDIoIgiygIKkqKK+6mqVHmlmmlCeLazyzzMbXN0jZ7zLT6tpppj5p7mmBoaH7NHTXFDQtJQAUBBUREQfbl/v3hw/lymBkYYGaYGd6v65rr4py5z/Y599xnOJ859926tRg5cqQYO3as6Nevn7Czs9N4jI888oje22RtPzM//PCD2n20trYWPj4+olevXqJv376iWbNmKmVsbW1FdHS0xnUbyzkTQghXV9c61+/6fDb03TbWpX2srxdeeKHWn2t19afi9dJLL1W7vXnz5umkbZk2bZogIiIiIiIiIiLTYWmoRNrLL7+s8b2BAwfKxiCp7IUXXlDpurCqNm3aaPWkV5MmTeDg4KD2PU3dKlVe1t7evlbLtmjRQu18Dw8PjdupyzIDBw5EdHQ0Bg0aVOfz07lzZ2zdurVe69CGtbU1tm3bhg8++EB2XsvLy3HhwgXs3r0bERERuHjxIkpKSmTLdu3aFYsWLWoUief8/Hxs375dmvby8tL5GHNTp05V6QJ03bp1ejmeffv2oby8XK8xe+qpp9C+fXvZvPLycly/fl2lK7LLly/rbT+mT5+O5s2bq8xPSUnB77//jt27d+PMmTMoKCgAAAwYMADz58+XlTWm8bd8fX3h5+enMr+4uBg3btyQnryrOk5XkyZNsGXLFnTv3t3oz5kxMKe2cdu2bbUaL9DPzw9RUVFquyTW5vr89ddfY926dXXu6tTGxgbffPMNNmzYwF89ERERERERERGZEIMluIYOHYply5bB3d0dFhYWAABHR0cMGTKk2pvqPj4+2LhxIx555BHY2NjI3rOzs0PPnj2xbds2aZ01WbFiBVq0aCGVt7a2Rs+ePWXdQ1W3rIeHh2zZXr16Yfbs2WrLf/LJJ2jZsqVU3tbWFgMHDsTUqVM1bqMuywCAt7c3IiMjcejQIYwdO1arG32urq4IDg7G4cOHERMTo7PxnWpiYWGBJUuWICYmBsHBwRoTh8DD8WnGjBmDnTt3Ijo6utF0RRkaGoqcnBxpetq0abC01O3H1d3dHWPGjJHNi46OxoULF0wyZvb29ti1a5faMXkAwMHBAc8++yz27NkjGyNK1xwcHHDs2DF4eXlpLOPh4YEXX3wRBw4cwKlTp1Q+r8aU4Bo2bBj++ecf7N69G5MmTYKTk1O15V1cXDBnzhxcuHABzz//vEmcM2NhLm2jQqHA2rVr8fvvvyMwMFBjORsbG7z55puIioqCn58fPvvsM1mXu7a2tujfv79WybIXX3wR169fx8cff4w2bdpotZ9eXl54//33ce3aNbz22mv8RkhEREREREREZGIshKgyMAyZhdLSUkRHR+Pvv//G3bt3kZ2djfLycjg4OKBt27bo1KkTunXrpnViUJ8KCgpw5MgRJCYmIi0tDUqlEq6urujWrRsCAgI03vwmUic9PR2hoaHIyMiAh4cHWrdujdatW6Nz5841Pg2qSzk5OdizZw8SEhJQXl4OJycn+Pr6olOnTvDz8zOKz15d25YrV67gr7/+QkpKCu7fvw87Ozt4eHigTZs2GDp0qMYncYz9nBkbc2kbr1+/jvj4eCQlJSE1NRX29vbo2LEjnnjiCTg6Ouplm7du3cKZM2dw8+ZN3Lt3Dw8ePEDTpk3h5OSEVq1aoU+fPhxri4iIiIiIiIjIxDHBRURERERERERERERERCbFkiEgIiIiIiIiIiIiIiIiU8IEFxEREREREREREREREZkUJriIiIiIiIiIiIiIiIjIpDDBRURERERERERERERERCaFCS4iIiIiIiIiIiIiIiIyKUxwERERERERERERERERkUlhgouIiIiIiIiIiIiIiIhMChNcREREREREREREREREZFKY4CIiIiIiIiIiIiIiIiKTwgQXERERERERERERERERmRQmuIiIiIiIiIiIiIiIiMikMMFFREREREREREREREREJoUJLiIiIiIiIiIiIiIiIjIpTHARERERERERERERERGRSWGCi4iIiIiIiIiIiIiIiEwKE1xERERERERERERERERkUqzM4SDy8/Oxdu1aAMDMmTPRpEkTnlkiIiIiIiIiIiIiIiIzZSGEEKZ+EOnp6WjZsiUAIC0tDR4eHjyzREREREREREREREREZopdFBIREREREREREREREZFJYYKLiIiIiIiIiIiIiIiITAoTXERERERERERERERERGRSmOAiIiIiIiIiIiIiIiIik8IEFxEREREREREREREREZkUJriIiIiIiIiIiIiIiIjIpFgxBI1HcnIy0tLSpOkuXbrA3t6egSEyMQUFBfjrr79k8/z8/ODs7FzvdScmJuL27duyef7+/nBwcGDgtfD3338jPz8fAKBQKNC7d28GpZIzZ85IfzM+ZMz1dO/evbhy5QqysrLg5OQEd3d3+Pr6YvTo0ejUqZPJHEtxcTEuXrwomxcQEABra2uTOifZ2dmIj4+XzWvTpg1atGjBCktERERERNSIMcHViLzxxhsIDQ2Vpm/fvs0EF5EJun79Ovr37y+bFx4ejrFjx9ZrvceOHcPo0aORl5cnzZsxYwbWrVvHoGtp6tSpiI6OBgA4OTkhOzubQamkcr1lfMjYXLx4Ea+99hpOnDihsczNmzfx1VdfmcwxpaWlqVwvkpKS4OPjY1Ln5vjx4xg3bpxs3sqVKzFnzhydb+vo0aP45JNPZPNef/11jBw5kh8SIiIiIiIiI8MEVyNS+Zfz7dq1g5ubG4NCRAAe3tAbPXq09PQR8H/JLQsLCwaIiMxaWFgYpkyZgoKCAgajkcvIyMDBgwdl84KCghgYIiIiIiIiI8QEVyORnp6OlJQUabpfv34MChEBAI4cOYKnn36ayS0iapTOnDmDkJAQFBUVyb4nPf7443B1dUVubi5iY2Nx7NgxBouIiIiIiIjIiDDB1UhUfnoLAPr27cugEBGTW0TUqJWXl+Oll16SkltNmzbF7t27ERgYqFK2rKwMGRkZDBoRERERERGRkbBkCBqHqKgo2TSf4CKiw4cPM7lFRI1aREQE/v77b2l6w4YNapNbAKBQKNCqVSuTOj4fHx+UlJTA2dkZAODi4gJPT0+eeCIiIiIiIjILfIKrkaj8BJdSqURAQACDQkYjNTUVeXl5AB7eQGzfvj2DomcVya3K480wuUVEjc3u3bulv21sbPDss8+a3TEmJiYiOzsbADB+/HhYWfHrf3UmTpyIiRMn8nsRERERERGRCeB/uI2AEAJnz56Vprt16wZbW1sGhozGSy+9hP379wMAXF1dkZmZyaDo0aFDhzBmzBgmt4io0bt48aL0t7u7Oywtza9zg8o/cgoKCuJJ5/ciIiIiIiIis8EuChuBK1euICcnR5rm+FtEjdfBgweZ3CIi+q/KY2opFAqzPMaKBFfLli0xePBgnnQiIiIiIiIyG0xwNQKVf7kLcPwtosbqjz/+wDPPPMPkFhHRfxUXFzea74ETJ040yyfUiIiIiIiIqPFiF4WNQFRUlGyaT3ARNT4Vya3CwkJpHpNbRETmraioCJcuXQLA7gmJiIiIiIjI/DDB1QhUfoLL0dERHTt2ZFCIGpEDBw5g7NixTG4RETUyZWVl2LNnDwA+wU9ERERERETmhwkuM1dQUIC//vpLmu7bty9vaJuY/Px87N69G1FRUUhOTkZ5eTk8PT0RGBiIYcOGwcHBQet1Xb58GaWlpQCApk2bwtfXV6VMWVkZrl69imvXrsHZ2RkDBgxQW+b48eM4ceIE4uLikJWVBaVSCXd3d7i5ucHHxweDBw+Gv7+/WcTNVI5ZHX0nt3RVpyqkpqbixIkTOHfuHDIyMnD37l3Y29vDy8sLgYGBePLJJ2Fra1vr/ax4ggEA7Ozs4OfnJ3s/Ly8P8fHxSEhIQHZ2Njw8PODv748OHTo0irbBWOJTWFiIc+fOITk5Gbm5uTrZjr7qlCEZa/tjDrE1VkIIXLx4EefPn8elS5eQmZmJ3NxcODo6wtfXF6NHj8ajjz5a43qaNGmC4cOHm1W94+fUeL4bERERERERGcM/0CYvLS1NABAARFpamqD/c/LkSSk2AMSiRYtqXCY3N1eMHz9eeHl5Sa9x48aJ+/fv63TfUlJSZNvw8vISKSkpPGn/lZ+fLxYvXizs7e1l57Dyy8nJSXz77beitLRUq3V6eHhIyz755JOy91JTU8WHH34ovLy8pDLz58+XlSkvLxdr1qwRLVu21LhPlV8+Pj7io48+Eunp6dI67t+/r3LebWxspGUsLS1V3q/8KiwsNGjcdHHMuhYTE6Oy3fDwcJVy+/fvF7a2trJyM2bMEOXl5Trbl/rWqQqHDx8WY8eOFZaWltXG18PDQ6xZs6bW+1l5Hb1795bmX716Vbz66qvCzs5O7fa6desmDh48qPV2evToIatn2ti6dausjvv4+IgDBw4YtG0wVHyq7mOF5ORkMXPmTNGsWTOdbMcQdapq23T79m2tlvPz85Mtl5iYaFLtj75jq++4LliwQFau8jEoFAq1150vv/zSYNf+69eviwULFojWrVvXeL4nTZoksrKydL4PxljvwsPDVba7cuVKvW2vcn3y8fHRa7z0/b1IH9cNfocnIiIiIiJjwQSXmfvyyy9l/7zu3r27xhsrXbt2lS0TFBQk8vPzdb5vSUlJKv9cJyUl8aQJIRISEkT79u21ulECQIwYMUIUFxfXuF5NyYj169eLpk2bqqy3cjKitLRUTJ06Vet9qvz6/fffpfXcu3evTuuoeFV3I0fXcdPVMeuaNgkudcktGxsbkZGRodN9qU+dEkKInJwc0aVLl1rHd9KkSaKkpET7i52aBM769es1Jm6qvtatW6fVdmqb4Nq2bZtQKBSyG+xbtmwxeNtgqPioS3D95z//EQ4ODjrbTkPUKQBa39yv+rm4evWqybQ/hoitvuM6ffr0Wu//Rx99ZJBr/xtvvCGsrKxqtW+enp7izp07OtsHY73uGTrBVbk+WVlZ6TVe+vxepK/rBr/DExERERGRsWAXhWYuKipKNt23b1+NZY8dO4bx48fjzp07AAALCwt8/PHHeO+99xhIA/rnn38QGBiI9PR0aZ5SqURAQAD69esHOzs7xMbGYv/+/VLXcPv378f06dOxZcuWWnU7J4TAtGnTsHnz5hrLfv/999i0aZM0bWlpiWeeeQb9+/eHq6srcnJykJmZibi4OERGRiIzM1Mq26NHD5OMm7Efsyb79+/HuHHjUFRUJJtfVFSE2bNnIywsTJ9PBWtdpwDA1tYWMTEx0rSFhQX8/f0xaNAgtGnTBs7OzkhKSsLRo0dx+vRpqdy2bdvg6uqKb775pk77+emnn2LRokUAAIVCAV9fX9jb2+PWrVvIyMhQKT979myMGTMGbm5uOovV9u3bMWXKFJSVlUn7sXHjRoSEhDR422Co+HzxxRd46623dLqdhqpTumaM7Y+5xNZY7dy5U/rsAkCbNm0wZMgQtGnTBu7u7sjOzsaxY8fwxx9/SGVu3ryJhQsXYu3atWZb74xZY/xuREREREREZFTMIUvHJ7g0a9u2rRQbb29vjeVWr14tlEqlVNbe3l7s2rVLr/tWl19/rlmzRrz22mvilVdeMfhr9uzZ4qOPPtJrHcvLyxOdOnWSxaRnz54iJiZGpWxiYqIICgqSld25c2e166/6tM2CBQtkyysUCvHcc8+JtWvXitOnT0u/Ci8pKREtWrSQyrm4uIiTJ09Wu63jx4+LoKAg4eHhIZtfVlYmrly5Int5enrKnuqo+n7ll7ru9fQRN10es65V9wTXvn37ZF0bqXtt2rRJZ/tS1zpVmZOTk7C1tRVz584VcXFxGrf12WefCQsLC9n6//zzT632s/IyTZo0kf5++umnRXJysqzspUuXxMiRI1Xi9vnnn9e4HW2f4Nq+fXutntzSd9tgqPhULl/5XOp6O4auU9Dxk0bG3P7oO7bQ8xNcaWlpsmuKk5OTbLlLly6pXHd0+YSUJmVlZcLKykooFAoxefJkcfnyZY1lly1bpvJZio+Pr/c+GHO9M8YnuHQVL318L9L3dYNPcBERERERkbFggsuMZWZmyv7xHD9+vNp/zufMmSMr5+3tLaKjo/W+f3l5eWL79u2yV15ensbyN27cqFcXLrp6LVu2TG8xmT9/fq27iZk5c6ZUvkePHtWWrZyMqNr12KhRozTeEExISJCVXbx4sdbHpM3NyTZt2kjrdnV1NYq46fuY60NTgmvv3r0qya3p06er3OBq1qyZSjKhrupapyp75ZVXREJCglbbCw4Olm1jzJgx2l3s1HyWBw8eLAoKCtSWLysrE4GBgbLyXbp0qXE72iS4duzYUavkliHaBkPFx1DbaYg6pctEjDG3P/qOrb4TXFW5urrWqrs3fUlPTxcvvviiVvtdWloqvL29Zfu9ffv2eu+DMdc7Y0xw6TNe9f1epO/rRm2/wxMREREREekLuyg0Y2fOnJFN9+vXTzZ99+5dTJgwAYcPH5bmDRw4ELt27YK7u7ve969JkyaYOHGi1uVbt26NDRs2ID4+HuXl5Q0SU2dnZ0ydOlUv605PT8fq1aulaS8vL2zevBlKpbLa5b777jscOHAAycnJiI6ORmRkJB577LEat1dQUCD9vXDhQnz66acau6K5du2abNrJyUnr42rRooVez4m+4mbMx6zOvn37sHHjRlm3hC+99BLWrFmD8+fPY8CAAVL3Q/fv38eMGTPwxx9/6LT7odrUqcp++OEHrbfx3nvv4eeff5amDx48iIKCAtjZ2dVqX62srLBjxw7Y2tqqfd/S0hLz5s2TtY8JCQn1jtEvv/yCkJAQrbslbIi2wZDx0dd2GqJO6ZIxtz+mHltj1aJFC6xbt06rsgqFAv3790dycrI0LyYmplbfqUyt3hmjxvjdqK7f4YmIiIiIiPSFCS4zVjXBVXn8rdjYWDzzzDOyf85nzJiB1atXw9ra2iiPx8LCAtOmTTPb87V69WoUFhZK00uXLoWrq2uNy9na2mLBggVYsGABAODQoUNa38QGgJCQECxfvrzaMo6OjrLpPXv24PXXX4elpaXZxs2Yj1mdH3/8UTY9a9YsrF69GhYWFujduzfef/99fPjhh9L7hw4dwsqVKzF37lyd74s2daqu/P390aRJE+Tn5wN4mFSLi4ur9Vgmw4cPh4eHR7Vlhg0bJpsuKirC/fv30axZszrte2hoKEJCQqREozbJrYZqGwwVn4Y4D/qqU7pkau2PKcXWXFStI7r4sYK51Dt+ThvuOyUREREREZGh8T9WMxYVFSX9rVAo0KtXLwBAREQE+vfvLyW3FAoFvvzyS6xfv95ok1uNwb59+6S/HR0dMWHCBK2XHTdunPT3sWPHtF6uWbNm+Prrr2ss5+/vL3vCIjIyEmPHjkVMTIzZxs2Yj7kms2fPlpJbFRYtWiRLcgMPn7KKi4vT6ba1rVP10a5dO9l0enp6rdcxfvz4GsvY2dmpPFmUlZVVp30OCwtDcHBwrZNbDdU2GCo+hj4P+qxTumTK7Y+xx9ZcpKamyqY9PT1Z7/g5bdDrBhERERERUUPgE1xmSgghS3B17twZ9vb2+Oyzz/Duu+/KuvjbsGEDJk+ezKA1oJycHJw/f16atra2rnXXL1ZWVigtLVXpMqc6U6dO1eoXvc2aNcO0adOwZs0aaV5ERAQiIiLg5+eH/v37o0+fPujTpw969eoFKysrk4+bsR5zTebMmYOVK1eqPc5NmzYhICBA6kqwoKAAU6ZMwalTp3S2/9rWqfpo2rSpbLpyt4zaqnrTvbr6UbVtra1du3YhKChISm4BwJIlS7RKbjVU22Co+BjyPOi7TumSqbY/phBbc3H58mXZdMWPmFjv+DltqOsGERERERFRgzCHgcTS0tKkAZHT0tI4spoQIj4+Xjaw9JQpU0RISIjKAN0AxLx58xiwBhYbG6v23NTlpVAoRFlZmdrteHh4yMoeOHBA633Mzs4W7dq1q3H7jo6OIiQkRFy4cEHrddd1MHV9x02fx1wfMTExavdj7ty5NS773XffqSy3ZMmSOu9LfepUbRUVFYnMzEwREBAg22Z4eHiNy1Y95oSEBK222bRpU9lyV69erbZ8jx49pLJOTk4iLCxMWFlZqWy/Q4cO4sGDB0bTNhgqPobaTkPUqfT0dJ0ei7G2P/qOrb7jWpWrq6tsucLCQqP9rlBcXCyysrJEZGSkbJ9tbGxEcXGxTrZhrPUuPDxcZR9Wrlypt+1Vrk9WVlYGj1ddvxcZ8rpBRERERERkDNhFoZmqOv7Wjh07sHXrVrVl16xZg1u3bjFoDej27ds6W1dZWZls3IXqdOjQQev1Ojk54ciRI+jXr1+15XJycrB161b07t0bixcv1vmTFoaMmzEesyYvv/wyvv322xrLzZkzR2VMo3//+9+yX3vXR23qlCaXL1/G2rVrMWvWLDzxxBNo164dbGxsYGNjAzc3N1y8eLHe23BwcND7OcnNzVV5cqtCQkICXnvtNaNtGwwRH0NuxxB1StdMpf0xxdgau7t37yI0NBTvvfceRo8eDX9/fzRr1gzW1tZwcXFRGROpZ8+eUCqVjare8XNqfNcNIiIiIiKihsAuCs1U1QRXcXGx9Pfrr7+OGzduICwsDABQWFiI5cuXa3VznPSj6thnb7/9Nl544YU6r8/Gxkarcu7u7rVar7e3NyIjI/HTTz/hu+++q3acifLycixbtgzZ2dlYtWqVycbN2I5Zk5EjR2pVzsLCAuvXr0fXrl1x7949AEBpaSmmTJmCCxcuqIx1VFu1rVMVbt68iVWrVmHbtm1ISkoyi891WVkZysrKHl5sraywZs0arFixAvHx8QCA9evXY8SIEdWOjdJQbYM5MIc6Zaztjzl+XhvavXv3sHHjRoSFheHUqVNS26GNmpIr5lLv+Dk17u+UREREREREDYEJLjNVefytyv/wrlmzBtOnT8fly5fx66+/SmNx/fjjj1i4cKFOBimn2vPw8JBNKxQKnYynUROFQlHrZZRKJWbNmoVZs2YhNTUVkZGROHfuHKKionDhwgXk5+fLyv/www8IDAzE+PHjTTZuxnTMuuDl5YXvv/9eNgbUP//8g0WLFuHLL780aJ0SQuDjjz/GihUrpLHBKnNwcICfnx88PT3h7u4OBwcH7Nixw6SeOrWyssK2bdswYcIEdO/eHQMGDEBJSQkAYNasWejXrx+8vb2Nqm0wZeZWp4yp/WkMn1dDKyoqwvLly/H5558jLy9P5X0nJyd06NABrVq1gpubGxwdHbF161ZkZGRIZXSd4DLH615j+pzyukFERERERI0JE1xmqLi4GNHR0bJ57u7u+PXXX/Hoo48CADp37owJEyZgx44dAB7eYFm2bBm+//57oz629PR0JCUl1eqXzbrUvHlzdOzYERYWFjpdr6enJ+zt7aWbW3/++adJ1DUvLy8EBwcjODgYAFBQUIBdu3Zh4cKFuHnzplRuyZIlermJ0xBxa+hj1pVJkyYhPDwcO3fulOZ9/fXXeOaZZzB06FCD7sf27dulaQsLCzz11FN44YUXMGTIEPj6+qos8+eff5rMDXMrKyv8/PPPUl3o1asXli5dioULFwJ4+NTG5MmTcfToUVhaWppN29DQddtc61RDtz/m/nk1tJycHIwbNw5HjhyR5tnZ2WHixIkYPnw4HnvsMZUfHpWXl+M///mPbF7fvn153ePnlNcNIiIiIiJqnMxhILG0tDRpMOS0tLRGP7DamTNnZANE9+jRQ9y4cUPtINSWlpZSOWtra5GcnGzU51mpVOps4Oy6vr755hu9HN+YMWNkA5onJSXpfBseHh6yYyksLNTLsdy8eVO4ubnJtlVd3ao8mLqLi4vRxU0fx1wfMTExKvUyPDy81uvJysoSLVu2lK3Hx8dH3L9/3yB1auvWrbJlfX19xaVLl2pcrn///rU+9qrxSk9P12ofmzZtKlvu6tWr1Zbv0aOHVFapVIqwsDCVMmVlZSIwMFC23n//+98NWscNFR99b8cU6pSNjU2tYmYs7Y+hYmvouLq6uhrkuqjOkCFDZNt++eWXRVZWVrXLxMbGypZxdXVtsO9lhrzuhYeHq9SNlStX6u3YKrc5VlZWBo9Xfb4XGdN3IyIiIiIiIn2zZIrP/FQdf2vLli1qu7/q1KkTJk6cKE0XFxdj2bJlRntczs7OGD58ONq2bQsvLy+Dv1q3bo1evXqhd+/eejm+5557Tvq7tLQU8+fPN9k62KpVK4wZM0Y2r/KvlqtTtRsfU4lbfY65oTRv3hzr1q2Tzbtx44bBYvjLL7/Ipn/++Wd069atxuXu379vEp+D7t27y+pnBUtLS2zatAkuLi7SvCVLlmj8lb05tQ2NvU7duXMHRUVFJtn+GHNs9RVXfdq7dy+OHTsmTc+aNQurV69G8+bNq10uMjJSNq2P7gnN+bpnit+Navu9iNcNIiIiIiJqTJjgMkOVE1wOnGU7cwAAIABJREFUDg7o1KmTxrIffPCBrFus9evX48aNG0Z5XDY2Nvjtt99w/fp1pKSkGPyVnJyMc+fOSd086lpISAjatm0rTYeHhyMiIqLW68nOztbL/tX2JqWbm5tsuvLN/KqaNm0q/V1QUIDc3FyjiJs+j9lYjBw5Eq+88ops3k8//YTdu3frfdvp6emy6fbt29e4TElJCRITE02+nfb09MTatWul6dLSUoSEhCAnJ8fk2gZjYsg6VbVLydLS0hqX2bt3r1G0ucYcW33H1Vj89ttvsulJkybVuExRURE+/fRT2TxdJ7gaw3XPFOJVn+9FvG4QEREREVFjwgSXGYqKipL+7t27t9pxXSp07NhRGiMAePgU1yeffGKQ/SwqKsLJkydlL1P7BbYuKZVKLF++XDZv6tSpWo+dkJSUhOeffx7vv/++zvctJycH3t7eWLRoEbKysmosX1xcjPDwcNmxtW7dWmN5Z2dn2fShQ4fUlktMTERhYaFB4qbvYzYmX3zxhcrN6lmzZiEzM1Ov2+3QoYNseuvWrTUu89FHH6nUAVM1btw4zJo1S1a/X331VZNqG4yNIetUixYtZNN37typtnxiYiLeeusto2hzjTm2+oyrMan6Y6K4uLgaz/H8+fORlJQkm6/LBFdjuu4Ze7zq873IENcNfocnIiIiIiKjYQ79LHIMrv+TlZUl69t/4cKFNS4TFxcnFAqFbNyYxMREve9rUlKSyngKHCNAiFdffVUWE2tra/HOO++orduFhYVi//79IigoSBp/ZMKECRrXXdfxkk6cOCEt07RpU/Hmm2+K48ePqyxfVlYmTpw4Ibp27SrbTnX7JIQQs2bNkpV/5JFHxK1bt4QQQpSUlIiYmBgxd+5cYW1tLQ4ePGiQuOn7mOtDV2NwVXbq1ClZOwBAjBs3rsbl6jMG19q1a2XLKhQKsWLFCpUxwMrLy8Xx48fF888/r3ZcPGMdg6t37941rj8vL0907NhRto0tW7YYvG2AmYzBZcg61a9fP9kyc+bM0Vg2MjJS5TxXdyzG2P4YKrb6jKs6DTUGV3BwsGy7zs7OIiIiQpSXl8vK5efni/DwcJVzDEBYWFiIu3fv6myfjPm6p24MrqlTp4otW7aIzZs3i02bNokNGzaI9evXi7Vr14off/xRrF69WqxatUqsXLlSfPvtt+Lrr78WX375pfjiiy/EF198Ic6fP69Vm6NpDC59xksX34v0ed3gd3giIiIiIjIWTHCZmf3798v+2QwLC9NquSlTpsiW+3//7//pfV/5z7F6RUVFIigoSCU2lpaWomfPnmLs2LHi6aefFgEBAUKpVKqUGzx4sMZ11zUZ8cMPP6i9gWhtbS18fHxEr169RN++fUWzZs1Uytja2oro6Ohq179r1y61x9uuXTthbW0tm//OO+8YJG76Pub60EeCSwghFi1apLLejRs3VrtMfRJcRUVFonXr1irbtLe3F3379hXjxo0TQ4cOFZ6enmrPhTkkuIQQ4sKFC7J67ujoKK5du2bQtsFcElyGrFNz585VWe7RRx8VK1asED/99JNYuXKleOutt2QJGzs7O5U2Q92xGGP7Y6jY6jOu6jRUguvbb79VG5/WrVuLkSNHirFjx4p+/foJOzs7jbF85JFHdLpPxnzdU5fgqu/r66+/1qrN0ZTg0me8dPG9SJ/XDX6HJyIiIiIiY8EEl5lZsmSJ7J/NlJQUrZaLj4+XPb1hZWWl9garLvGfY83Ky8vFBx98oHITo6ZX165dxf79+zWut67JiAMHDgg/P79a3zxq0qSJCA0NrXH9Dx48EO3bt9dqnWPGjDFI3PR9zPWhrwRXcXGxLEFTkWy5ceOGzutUhYiICGFra6tVbBUKhXjppZfEqFGjzCrBJYQQX3zxhWw7/fv3FyUlJQZrG8wlwWXIOpWamqr1dgCIQYMGifj4eNG7d+8aj8VY2x9DxFafcVWnoRJcOTk5onnz5lof54ABA8T8+fNVnmDSJWO+7hljgkuf8dLV9yJ9XTf4HZ6IiIiIiIwFx+AyM5XH3/Lw8ICXl5dWy3Xo0AGTJ0+WpktLS7F06VIGtIFYWFhgyZIliImJQXBwMOzt7TWWVSqVGDNmDHbu3Ino6GgMHz5c5/szbNgw/PPPP9i9ezcmTZoEJyenasu7uLhgzpw5uHDhAp5//vka129vb49du3bJBlWvzMHBAc8++yz27NkjG79Cn3HT9zEbI6VSic2bN8PGxkaal5OTgxkzZuBhbkL3Ro8ejQMHDsDFxUVjGW9vb7zyyiv4+++/8eOPP8Ld3d3sPvOvv/46hg0bJk2fPn0aS5YsMfq2wRgZqk55enoiNDQUrVq10limWbNmmDp1Ko4fP47IyEiVcawaqs015tjqM67GxMHBAceOHav2e5qHhwdefPFFHDhwAKdOnUKTJk1k7+ty/K3Get0z1njp6nsRrxtERERERGTuLIS+7loaUHp6Olq2bAkASEtLg4eHB88smZWCggIcOXIEiYmJSEtLg1KphKurK7p164aAgACNN0D0pbS0FFeuXMFff/2FlJQU3L9/H3Z2dvDw8ECbNm0wdOhQKJXKOn2WQ0NDkZGRAQ8PD7Ru3RqtW7dG586dYW1t3aBx09cx00P5+fn47bffEB8fj7y8PDRv3hy+vr7o3LkzOnbsyACZSNvQGOvU/fv3cfz4cSQkJCArKwvNmzdHixYt0KtXL3Ts2BEWFhZG2+Yac2wNEVdjkJOTgz179iAhIQHl5eVwcnKCr68vOnXqBD8/vwY9Tl73Gj5euv5epMvrhhACLi4uyM7OhqurK9LS0mBlZcWKQEREREREBsUEFxEREREREWktPj4ejzzyCABg9uzZWLVqFYNCREREREQGxy4KiYiIiIiISGtnzpyR/g4KCmJAiIiIiIioQTDBRURERERERFqrSHC1atUKgwYNYkCIiIiIiKhBMMFFREREREREWqtIcE2cOBGWlvyXkoiIiIiIGgb/GyEiIiIiIiKtFBYW4tKlSwDYPSERERERETUsK4aAiIiIiIiItCGEwO+//w4A6Nu3LwNCREREREQNhgkuIiIiIiIi0oqdnR2eeOIJBoKIiIiIiBocuygkIiIiIiIiIiIiIiIik8IEFxEREREREREREREREZkUJriIiIiIiIiIiIiIiIjIpDDBRURERERERERERERERCaFCS4iIiIiIiIiIiIiIiIyKUxwERERERERERERERERkUlhgouIiIiIiIiIiIiIiIhMChNcREREREREREREREREZFKY4CIiIiIiIiIiIiIiIiKTwgQXERERERERERERERERmRQmuIiIiIiIiIiIiIiIiMikMMFFREREREREREREREREJoUJLiIA9+7dQ3h4OK5du8ZgEBEREREREREREREZOSuGgBq7srIyDBw4ELGxsVAqlThw4ACGDh3KwBARERERERERERERGSk+wUWN3p49exAbGwsAKCkpwe7duxkUIiIiIiIiIiIiIiIjxgQXNXo7d+6UTT/22GMMChERERERERERERGREWOCixq1oqIiRERESNPt2rXD2LFjGRgiIiIiIiIiIiIiIiPGMbjMSH5+Pi5duoT8/HwAgEKhQNu2beHj41Prdf311184cuQIrl27hszMTGRmZqK8vBxOTk7w9fVFt27d8PTTT8PZ2dmkY3bgwAHk5uZK02+//TYUCgUrk5n5+++/ZZ+L3r17Myh6cvbsWZSXl0vT/fr102l5fUtOTkZaWpo03aVLF9jb2/PEklm7cOECSkpKpOk+ffrA0pK/gTJlSUlJuH37NoQQsLe3h6+vL+zs7BgYIiIiIiIiMisWQghh6geRnp6Oli1bAgDS0tLg4eHR6E5kdnY2OnfuLLsxW2HFihV4++23tVrP/v378f777+PcuXM1lh05ciT27dtn0nGbPn06Nm7cCADw8PBAUlISbGxs9LrNo0eP4pNPPpHNe/311zFy5Ei2SHoSEBCA6OhoAICTkxOys7MZFD1xcHDAgwcPAABWVlaym+a6KK9vEyZMQGhoqDR9+/ZtuLm58cSSWWvZsiXS09Ol6QcPHjCxa8Jefvll/Pjjj7J5rq6uOHnyJPz8/BggIiIiIiIiMhv8ea6ZiIqKUpvcAoAtW7ZotY4PP/wQo0aN0iq5BcDkkwQlJSXYvXu3NP3666/rPbkFABkZGTh48KDsdevWLVZiIiNw5swZ6e927doxuUVEJkUIgQ0bNqjMv3Pnjuw7DxEREREREZE5YILLTNy7d0/je9okorZs2YKPP/4Y6h7oc3R0RPfu3eHp6WlWMTt06JAUNycnJ7z88susSESNWHp6OlJSUqTphu4ukYiotvLz81FcXKz2vZycHAaIiIiIiIiIzAoTXI1A5fFt1CkuLsb8+fPVvvfmm2/izp07iI6ORmpqKrKysjB//nxYWFjAysq0h3ALCwuT/p4zZw4cHR1ZWYgascpPbwFA3759GRQiIiIiIiIiIiIjZcUQmAdra2uN79U0jkZERASysrJU5rdr1w6ff/65bF7z5s3x1VdfYfjw4cjMzDTZeJWVlSE8PBwAYGdnh3nz5rESETVyUVFRsmk+wUVEhlZaWoqioiIolcpqv9sRERERERERERNcZqNnz55QKpUoKSlReW/IkCHVLhsZGal2fo8ePTQuM2LECJOO19GjR3Hnzh0AwMyZMw06zs7EiRMxceLEBo9Bamoq8vLyAAAKhQLt27fnB4nxbNQqP8GlVCoREBDAoBCx3TaIO3fuYNSoUTh//rz05H3btm2xdetWDBgwgCeLiIiIiIiISA0muMyEj48Pzp8/j8OHD+PBgwcAHt6sadOmDZ577rlql71586ba+eb8y+GK7gmtrKzwxhtvNMo689JLL2H//v0AAFdXV5N+Io/xpPoSQuDs2bPSdLdu3WBra8vAELHdNojt27fL2iAASExMxFdffcUEFxEREREREZEGTHCZka5du6Jr1661Xi4jI6NRxam8vBy//vorAGDSpEnw8fFh5SFq5K5cuYKcnBxpmuNvEZEh3b59W+38iqfNiYiIiIiIiEiVJUNAFV3hNBYnT55Eeno6LCwssHDhQlYAIpJ1Twhw/C0iMiwhRK3mExERERERERETXNQIhYaGAgDGjh0Lf39/BoSIEBUVJZvmE1xERERERERERETGjV0UUqMTHh4OAHjnnXcYDCICIH+Cy9HRER07dmRQiKjRyM/PR3Z2Nuzt7eHo6AhLy/r9Bq6srAwxMTFIS0tDVlYW8vLy4OLiAhcXF3Tv3h3Ozs56PZ6CggLcvXtXZ8dDRERERERExokJLjOSlpaGDRs2IC0tDUII2NnZoXfv3pgwYQIsLCxUyu/btw8XL17E9evX1a7v/PnzmDt3LgBAqVRi0KBBeO655xr8OIUQuHjxIs6fP49Lly4hMzMTubm5cHR0hK+vL0aPHo1HH31U4/IffPABEhISTLILsrKyMhw/fhwnTpxAXFwcsrKyoFQq4e7uDjc3N/j4+GDw4MEm+2RaamoqTpw4gXPnziEjI0O6OeXl5YXAwEA8+eSTsLW15YddD/Lz87F7925ERUUhOTkZ5eXl8PT0RGBgIIYNGwYHBwezPfaCggL89ddf0nTfvn3VtpmkW+Xl5Th79iyuXr2KBw8eoFWrVujXrx/c3d3Vlk9JScE///yDtLQ0PHjwAC+99BKsra0btG5HR0dj7969SEpKQlpaGpo3bw5PT08MHjwYTz75JJRKpdbrunz5MkpLSwEATZs2ha+vr9prwNWrV3Ht2jU4OztjwIAB1a4zIyMDFy5cwI0bN2BnZ4e2bduiZ8+eaNq0ab3P36VLl6S/7ezs4OfnJ3s/Ly8P8fHxSEhIQHZ2Njw8PODv748OHTqwrfqv9PR0rF+/HmlpaTh9+rTaMgkJCdJ3sQrW1tYYMmQInnnmmXptPzY2FitWrMCBAweQnp4uzbe3t0fv3r3xzjvvYMSIEbVa5969e7FmzRocP34c9+/fV1vG0tISPXv2xIsvvoiZM2fW+DnZvXs3Pv30U9y+fRtCCDRr1gzDhg3DZ599JmurY2Nj8dlnn+F///d/dXY8REREREREZOSEGUhLSxMABACRlpYmGqtBgwZJcaj82rZtm0rZ8PBwtWVreoWFhTXY8V2/fl0sWLBAtG7dusb9nDRpksjKyjLac+Xn5ye8vLyEl5eX8PHxqbF8eXm5WLNmjWjZsqVW58nHx0d89NFHIj09XQghxP3796XtVbxsbGyk8paWlirvV34VFhbqNR6HDx8WY8eOFZaWltUel4eHh1izZk2t19+jRw9pHU5OTlots3XrVlkMfHx8xIEDB/Qez6plb9++Xes65eXlJRITE7VaLj8/XyxevFjY29trjLuTk5P49ttvRWlpaY3ra9q0qbSclZWVzsvrw8mTJ2XHu2jRohqXyc3NFePHj5fFfNy4ceL+/fs637/+/ftXW5+0fV24cMEo6lxeXp5YunSpcHNzU6lrFhYWIjAwUBw9elTk5+eLnTt3iueff15t2UuXLhm0blcWGhoq/Pz8qm2vnJ2dxYcffijy8/O1WqeHh4e07JNPPil7LzU1VXz44YfCy8tLKjN//nyN67p27Zp4/vnnhZWVlcp+KRQKMXLkSLFr1y5RVlamsm0A4sGDBzXub+XyvXv3luZfvXpVvPrqq8LOzk5tXLp16yYOHjxY689Bfc+nMV4HAwIC6vRdrOK1d+9elXU+ePBAY/n33ntPCCFETk6O+Ne//qW2flR9PfXUUyI7O7vGY4mJianT8bRr105ER0dXu25XV1e1yx45ckQ65rfeeksolUqdHQ8RERERERGZBia4zIimmz5vv/22StnvvvuuTjdTvv766wY5tjfeeEOrGzGVX56enuLOnTtGea5qc1O/tLRUTJ06tU7n6/fffxdCCHHv3r163UTTV4IrJydHdOnSpdb7M2nSJFFSUqL1dmqb4Nq2bZtQKBSyG8JbtmyR3tdnPKuWrUhS1qZOARBXr16tcZmEhATRvn17rfd7xIgRori4WGd1uy7l9eHLL7+UHefu3burLX/9+nXRtWtX2TJBQUFaJzJqq02bNvWqbxWv06dPN3idu3r1qvD399dqf5s0aVLt+5s3bzZo3RZCiOzsbPHUU0/VKu4+Pj7i3LlzNa5bU4Jr/fr1KrGuLsEVGhqqtry618CBA0VcXJzOElzr16/XmNiq+lq3bp3WnwFdnE9jvA5qStxo+1q9erXKOmtKcOXl5Wn8QZSm18SJE6s9jr179woHB4c6H4eDg4M4deqU2nUXFBRoXG7Tpk2ioKBADB48WKfHQ0RERERERKaDXRSakaKiIrXzi4uLVeYFBQXh0KFDuHjxIlJSUlBeXq5SxsLCAjY2NgAedlE4cOBATJo0qUGObefOnVLXTQDQpk0bDBkyBG3atIG7uzuys7Nx7Ngx/PHHH1KZmzdvYuHChVi7dq1Jn9fvv/8emzZtkqYtLS3xzDPPoH///nB1dUVOTg4yMzMRFxeHyMhIZGZmSmV79Ohh1Mdma2uLmJgYWZ3z9/fHoEGD0KZNGzg7OyMpKQlHjx6Vdd+0bds2uLq64ptvvtH5Pm3fvh1TpkxBWVkZAEChUGDjxo0ICQkxq/bin3/+QWBgoKwbJ6VSiYCAAPTr1w92dnaIjY3F/v37pc/e/v37MX36dGzZssWsuvCLioqSTfft21dj2WPHjmH8+PG4c+eOVGc//vhjvPfee0Z9jO3bt0enTp0adB8yMjLw+OOPIyUlRdaWP/bYY3ByckJsbCyOHTsm1bf8/HxYWFhgwIABcHFxgbW1NYQQKCwsxN27d2VtnSHq9s2bN/HUU08hNjZWNr9du3bo0aMHOnXqhJSUFERHRyM2NlZa940bNzBkyBCEhYVh+PDhtXnCHtOmTcPmzZu1Xmbfvn0ICgqSXS8r8/b2hpOTE65du4a8vDycPHkSPXr0kNq7+vj000+xaNEiqd309fWFvb09bt26hYyMDJXys2fPxpgxY+Dm5tZo26ovvvgCH374ITIyMlBcXKz2u5ilpaVKV5xKpRJDhgzBhAkTattrA4KDg3HixAlYWFhg6NCh6N27NxwdHXH69Gns3btX7XK//PILPvnkE7XdZl65cgUvvPAC8vLyVN5r27Yt5s2bh379+sHZ2Rk3b95EWFgYVq1aJSuXm5uLSZMm4dKlS3B0dJS9V13dLCsrw9SpU3H8+HGdHQ8RERERERGZGHPI0vEJrv9mKzU84VRdN0ZCaO7asHKXQw2prKxMWFlZCYVCISZPniwuX76sseyyZctUuruKj483unOl7VMrJSUlokWLFlJZFxcXcfLkyWrXffz4cREUFCQ8PDxkMbxy5Yrs5enpKXuqqer7lV/l5eV6i4WTk5OwtbUVc+fOFXFxcRrLffbZZ8LCwkJ2fv/880+ttqHtE1zbt2+v9sktQ8QTBniaJi8vT3Tq1ElWvmfPniImJkalbGJioggKCpKV3blzZ73rdl3L60Pbtm2lffD29tZYbvXq1bIusOzt7cWuXbv0vn8XL14Up0+fVvs6e/asiImJkerWP//8IwIDA2Xny97eXvz1118NWueEEGL06NGy8suWLVN5yiY6Olp07txZVu78+fNax0pfdbuwsFD06dNHVrZLly7i2LFjasvfuHFDZd1NmzYVsbGxGve96hNcCxYsUOla8LnnnhNr164Vp0+fVnlCOT09XTg5Oamcz0GDBomIiAiRk5MjlS0tLRVhYWEanw6s7RNclZ+2e/rpp0VycrKs7KVLl8TIkSNVtvP5558b7Hwa83VQCKGy7xWvoUOH1mo91T3BVfH0ZNu2bcXhw4dVln3ttdc0Lquue+Dy8nLRvXt3teW7dOkiMjIyNLalVa/nAMTHH39cq+MZO3asTo+HiIiIiIiITA8TXGbEXBNc6enp4sUXX9Sq+6vS0lLh7e0tO47t27cb3bnS9qZ+QkKC7FgWL15cq7hVp/KNTVdX1waLxSuvvCISEhK0KhscHCyLx5gxY7RaTpsE144dO7RKbuk7noZINsyfP7/W3bPNnDlTKt+jR4961+26lte1zMxMWSzGjx+vUqakpETMmTNHVs7b27vGcWMawsqVK1XqUE1toCHq3KlTp2Rlp02bprHs5cuXhbW1tVQ2ODhY6+PXV92eN2+eStIhNze3xv1ZsmSJSoJB0/5UTnBV7eZv1KhRNV4DQ0JC1CYMqkvMpKamqu0ysrYJrorX4MGDRUFBgdryZWVlKsnXLl26NMj5NLbroBCGSXBVJLw1jZd34cIFjcvNmzdPpfxvv/2mtqyjo6PIzMysdj/HjRunspyXl5fK+GmGPB4iIiIiIiIyPZZ8ho2MXYsWLbBu3TqtupJRKBTo37+/bF7lLvBMzbVr12TTTk5OtYqbKfjhhx/Qvn17rcpW7Qbu4MGDKCgoqPc+/PLLLwgJCTH7bgkBID09HatXr5amvby8sHnzZiiVymqX++677+Dt7Q0AiI6ORmRkpFnE48yZM7Lpfv36yabv3r2L4cOH4/vvv5fmDRw4EGfPnkX37t2N6lhOnjyJBQsWyOa98cYbmDhxYoPv288//yz9bWFhgRUrVmgs6+/vj+DgYGl6586dyMnJabC6nZiYKOtSzcPDA6GhoWjatGmN+/TBBx/g2WeflaZjY2OxYcOGGper3K4tXLgQERER1V4DU1NTsX37dpVz//7771fbRZ+np6esa9/6sLKywo4dO2Bra6v2fUtLS8ybN082LyEhgW2VgS1duhRt2rTRWB80uXfvnsq8yu1iZZMmTYKrq2u1+6GuXUpNTcXly5cb7HiIiIiIiIjI9DDBRWan6vgNpjxWUNVj2bNnj9oxOhoLf39/NGnSRJouKChAXFxcvdYZGhqKkJAQaewWc05uAcDq1atRWFgoTS9durTGG5HAw/HSKidPDh06ZBbxqJrgqjz+VmxsLPr27YvDhw9L82bMmIHDhw/D3d3dqI4jLS0NEyZMQElJiTTv8ccfrzaRZEhnz56V/u7QoUONCfhu3bpJf5eWllabCNF33V6+fLksrosWLYKLi4vWx758+XIoFArZfmnbjoeEhGD58uU1XsfWr18vG6vIy8sL//73v7XaRrNmzXRyjocPHw4PD49qywwbNkw2XVRUhPv377OtMqAZM2ZofK/y9bWqquO8lpaWakweTpkypcb9CAwM1KpNNtTxEBERERERkWligovMTmpqqmy6ul/wGjt/f3/Zr+EjIyMxduxYk34qrb7atWsnm05PT6/zusLCwhAcHNxoklsAsG/fPulvR0dHTJgwQetlx40bJ/197Ngxs4hHVFSU9LdCoUCvXr0AABEREejfv7/0FKVCocCXX36J9evXw9ra2qiOoaSkBBMmTEBaWpo0r3Xr1tixY4cssdKQMjMzpb+1SQ516NBBNl31aVZD1W0hBMLDw2V1RJub95X5+flh4MCB0nRycjLOnz9f43LNmjXD119/rdU2qiZxXn31VdjZ2Rn0HI8fP77GMnZ2dipPeGVlZbGtMhBvb+9qE5q1+UFQdHQ08vLy1L7XuXPnGpd3c3ODlZWVyvzk5OQGOR4iIiIiIiIyTVYMAZmbqt3bVNywNkXNmjXDtGnTsGbNGmleREQEIiIi4Ofnh/79+6NPnz7o06cPevXqpfZmkbmp2i1YXX+FvWvXLgQFBUnJLQBYsmSJWSe3cnJyZDfWra2ta919nZWVFUpLS7VKOBg7IYQswdW5c2fY29vjs88+w7vvvit7ymbDhg2YPHmyUR7HggULcPLkSWnaxsYGYWFhcHNzM5p9bNmypVRnqv4IQZ38/HzZdNWnWQ1Vt8+dO4fbt29L0wEBAbXqKrbC448/juPHj0vT+/btQ58+fapdZurUqVo9sVRaWiqrxwAwatQog5/jqj8+qC7OVT+HbKsMw9/fX2eMevoSAAAgAElEQVTrSkxM1HiNTk1N1epz7uzsLEt+A0B2dnaDHA8RERERERGZJia4yCyUlJQgNzcXsbGxSElJkebb2NgY3Tg5tbV8+XL88ccfuH79umx+fHw84uPjsWnTJgAPbwCPGTMGb7zxBgICAszuHBcXFyMnJ0cn3Qrt2rULEydOlCW3AGDjxo2YP38+7O3tzfJzcvPmTVnS5s6dO4iIiKjTum7duoXy8nJYWprug8BXr16V3Uzt3r07Jk+ejK1bt6qUPXfunFEmuDZt2qQyDs7KlStrTJ4Ymp+fH06cOAEASElJQW5uLhwcHDSWj46Olk137NixQep2fHy87D1NY/3UpOpy2nStOmbMGK3Wffv2bVlXfhYWFlo9QaNrrVq1Yltl5LQZN05bmhJRDx48QJcuXeq83to8darL4yEiIiIiIiLTxP/2yaTcvXsXoaGheO+99zB69Gj4+/ujWbNmsLa2houLCx577DFZ+Z49e9Y4IL2xc3JywpEjR9CvX79qy+Xk5GDr1q3o3bs3Fi9erPZX8abg8uXLWLt2LWbNmoUnnngC7dq1g42NDWxsbODm5oaLFy/Wa/25ubkqT25VSEhIwGuvvWa2n5/KT6LUV1lZmeymuimqOtbLjh071Ca3AGDNmjW4deuWUe3/xYsX8corr8jmzZo1CzNnzjS6WFdN1lR+KlVdO79hwwZZO15TYklfdbtyt48A0Lx58zqts2q3jFXXq07Vbho1uXPnjso+NsTTvNUlLNlWmZ/c3Fy9rPfxxx9ncImIiIiIiEhrfIKLjN69e/ewceNGhIWF4dSpUygrK9N62ZqSQqbC29sbkZGR+Omnn/Ddd99VOwZXeXk5li1bhuzsbKxatcokju/mzZtYtWoVtm3bhqSkJL1uq6ysTKpDVlZWWLNmDVasWCE9qbF+/XqMGDGiVuO9mIqqY0e9/fbbeOGFF+q8PhsbG5OOR9UEV3FxsfT366+/jhs3biAsLAwAUFhYiOXLl+Pbb781in3PysrCc889h4KCAll799133xllrJ9++ml07NgRV65cAQB88MEHGDBggGxsKgAoKCjAzJkzpbH1LC0t8c033zRY3a5cJwDVrhO19eDBA9m0Nk+iuru7a7Xuqj9mMPUfdbCtMg2aug1t3rw5Fi1aVOv1KRQKdOvWDYGBgQwuERERERERaY0JLjJaRUVFWL58OT7//HO1A5k7OTmhQ4cOaNWqFdzc3ODo6IitW7ciIyNDKmMuCS7g4U3LWbNmYdasWUhNTUVkZCTOnTuHqKgoXLhwQeXG6w8//IDAwECMHz/eaI9JCIGPP/4YK1askN2or+Dg4AA/Pz94enrC3d0dDg4O2LFjh06epLGyssK2bdswYcIEdO/eHQMGDEBJSQmAh0/B9OvXD97e3mb1mfLw8JBNKxQKkx6jrr6qjlsEPLyxvmbNGkyfPh2XL1/Gr7/+KnWV9uOPP2LhwoXw9PRs0P0uLy9HcHCwLBns7u6O0NBQlcSA0XzZsLLCokWLMHXqVAAPE1mPPfYYJk+ejCFDhsDFxQVxcXFYtWoVkpOTpeUWLFiAQYMGNVjdrrreuj5ZVPm6BGjXnZ+2XbVVfaosKyuLbRXpnabx4YqLi/HGG28wQERERERERGQQTHCRUcrJycG4ceNw5MgRaZ6dnR0mTpyI4cOH47HHHlO5yVxeXo7//Oc/snl9+/Y1y/h4eXkhODgYwcHBAB7eLN61axcWLlyImzdvSuWWLFli1AmuSZMmYfv27dK0hYUFnnrqKbzwwgsYMmQIfH19VZb5888/653gsrKyws8//yzFplevXli6dCkWLlwI4OFTg5MnT8bRo0fNatwWT09P2NvbSwnjP//8s9G2McXFxSrjPLm7u+PXX3/Fo48+CgDo3LkzJkyYgB07dgB4mHRftmyZyphXhrZ48WL88ccfsvr8yy+/wMvLy6hjXvmzDjxMcG/evBmbN29WW37UqFFYunRpg9ZtHx8f2fTZs2frNJ5T1acF6zqWlzru7u6wsrKSul0tKSlBcnKySSfo2VYZv9atW6ud/+DBgxrH2CMiIiIiIiLSFY7BRUbpmWeekSW3Xn75ZaSmpuKnn35CUFCQ2ico4uLiZGNCuLq6ol27do0iXnZ2dggJCUFUVBTc3Nyk+TExMUhJSalx+YYYr2vbtm2yG96+vr6Ijo7G/v378eKLL6pNbumCUqnEjh07VBJ/b775pqxrpMjISCxbtqxO22iIeFY8fVYda2tr2TGeOHECN27caJRtTHR0tKz7uR49euDs2bNScqvChx9+KEtmrF27VqvPlL7s2rULy5cvl837/PPPMWTIEKOscxXOnj2Lffv2AQBmz56NUaNGaSzr5OSEzz//HL/99htsbW21Wr++6vagQYPQtGlTaTo7O7vWyZbi4mIcOnRINm/EiBE6Ow82NjYICAiQzTt8+LBJfz4boq0y1nErK54gNTYBAQEauyk8ceIEv8gSERERERGRQTDBRUZn7969OHbsmDQ9a9YsrF69WqUbpqoiIyNl0+bUPaG2WrVqhTFjxsjmVX6iS5O6jitTH7/88ots+ueff0a3bt1qXO7+/fv12m737t3x3HPPqTaGlpbYtGkTXFxcpHlLliyp05MDho7nnTt3tBrTB4Ds2EtLSzF//vxG2c5UfaJmy5Ytap946dSpEyZOnChNFxcX1znxWV9XrlzB9OnTZfOCg4Mb5BzWps4BwMmTJ6W///Wvf2Hv3r34+++/sXLlSsydOxdBQUGYP38+fvnlF6SkpODNN9+s9VNS+qjbNjY2Ksm4//mf/6nVOjZu3IjMzExpunnz5jpPSA4dOlQ2vXbtWq2XVdc9rDEwdFvVENfByiwsLNTOr/zDHWOiUChU6l2Fbdu21Xp9JSUlOH78OL8EExERERERUa0wwUVG57fffpNNT5o0qcZlioqK8Omnn8rmmUuCq7YJncpPcAGQJWwqq/xUQkFBgcFvoqWnp8um27dvX+MyJSUlSExM1Ns+eXp6ym4Ml5aWIiQkBDk5OTUuq6t4Vr2pX9HtWHX27t2r9fpDQkLQtm1baTo8PBwRERG13s/s7GyT/lxVTnA5ODigU6dOGst+8MEHsvOyfv16gz/5lpubi2effVZWr7p161arREZD1TngYXKuQsUYUV26dMGcOXPw7bff4ueff8ZXX32FCRMmyD5LtaGvur148WJZjH799Vfs2rVLq3Wlpqbi3Xfflc179913oVQqdVo/ZsyYIZs+efKk1vu4ZcsWo/yMGqKtaujrYGX29vZq52sa900IgcLCwgY9R//617/Uzv/111+Rmpqq9Xri4uIwcOBABAYGNvgxERERERERkWlhgouMTtUbx3FxcdWWLy4uxvz585GUlCSbbw4JrpycHHh7e2PRokXSTeGaYhEeHi5NK5VKjeNkODs7y6ardqFVITExUS83nDp06CCb3rp1a43LfPTRR3q/+TVu3DjMmjVLdvyvvvpqjcvpKp4tWrSQTd+5c6fa7SYmJuKtt97S+viUSqVKF3dTp07V+km1pKQkPP/883j//fdN+rMVFRUl/d27d+9qnxbq2LGjNN5dxefsk08+Mdi+CiEwbdo0WZLIyckJu3btQpMmTeq9fn3XuarbWL9+vV66g9NX3e7WrZtKAmnq1KnYv39/tetLTk7GiBEjZG23r6+vxqRAfXTq1AlPPvmkbN6MGTNUnlT8/+zdeVxU9f748TerIIuoqKi4JEXiviDuRqa3zAU1LdHc0rppi9qipl3Tbm7V7Zb3tlhm5TfLXMH9mitorhmmmLsoKKAogoAgy+f3hz/mwWGGfWaYGV7Px4PHg8+Zs36WM5/Pec85p7Bt27bJ9OnTLbKNmuNcVdnfg8W1w3zXrl3T6wddv35dQkJC5IknnqjUMurbt690795db3p6eroMHTpUEhMTi10+IyNDPv30U+nQoYMcPXpUcnNzq+xjcwEAAAAA5UOACxan8AWnmTNnypYtW/QuiN67d0/Cw8MlMDBQvvrqK81ndnZ20qlTJ6vPi5MnT0pqaqosXLhQmjZtKm+//bZERkbqPRosLy9PDhw4IIGBgZoLYYMHDy7yHTaF71iZOXOmxMfHi8iDOziio6Pl9ddfl+bNm2seL2YshR9tNHXqVPnwww/17pZSSklkZKQMGzbMbI+G+/e//y3NmzfXpVeuXFliAM5Y+Vn4MXnffPNNkfPu379fnn76ac3jz0rj2Wef1QTtkpOTJTg4WN555x29O+tEHtwh+b///U9CQ0OlefPmsn79+iLvKrAGt2/flvPnz+vSQUFBJS4zZ84ccXBw0KW///57vaC6qSxcuFA2bNigmfbGG2/InTt35Pfff5fff/9djhw5Ivv375d9+/YV+VdUmZmjzrVt21aTd127dpWZM2fKvHnz9P4WLlwoX375pWzatEkuXrxoEXX7P//5j3Ts2FGXTk9Pl/79+8uECRPkxIkTmnnj4+Nl8eLF0rZtW4mOjtZNd3d3l7CwsFK/V6ysPvvsM82dYampqRIcHCzz58/Xu4spNTVV5s2bJ4MGDSrTu9TMzdTnqsr+HiyouEf0jho1Sn766Sf55ptvZNy4ceLv7y+bNm2SgwcP6va3sqxYsUK8vb31ph89elTatm0r8+bNk+PHj0tSUpLcu3dPYmNjZdOmTTJ16lTx9fWVadOmaR4Pef36dTrCAAAAAIDSUzYgPj5eiYgSERUfH6+qKicnJ10+FPybNm1ascv17NnT4HKdOnWqlONYsmSJwf1p1KiR6tevnwoJCVGdO3dWrq6uBucTEfXoo49adFm5u7vr9tXR0bHI+b788kuDx+fs7KyaNGmiOnbsqIKCglSNGjX05nFxcVFRUVFFrnv9+vV6y9jb26tmzZopZ2dnzfSZM2caPQ+ysrJUo0aN9PbBzc1NBQUFqcGDB6vg4GDVsGHDIstZRFRYWFiJ22rXrp1u/sDAwFLt3/HjxzX54OnpqS5evGjy/Hzttdf01tOtWze1ePFi9d1336n//ve/6u2331adO3fWfe7q6qpXBy5cuFBi/o8YMcLgPnfo0EGFhISoAQMGqPbt2xs8t/Tq1atCdbu88xvD9u3bNceybt26Ui03evRozXITJkww+b7u2LFD2dvbF9sGSvv3448/Vlqdy87OVg8//HC59rtRo0bq9ddfV1euXCn1ucUUdfvatWuac0nBv5o1a6qAgADl6+ur7Ozs9D738vJSO3fuLHa/fXx8NMtkZmYa7fvTzs5ONWvWTA0YMED17NlT8/0ZFBSkN39aWlqJ2yq8TEJCQpm//yrzXGUJ34MF3bt3T3l7e5e5fYSHh2vWk56eXuS8w4cPL3Yfils2NDS0yOX279+vatasaZTzVGRkZKUfDwAAAADAehDgsiG+vr4GB/GLFy8udrnhw4cbXG7gwIGVchypqamqVq1apb4Y0rVrVzV16lTNtDFjxlh0WZX2ov6OHTuUv79/mS8QVa9eXa1du7bYfUhLSyv1BWdT1YXNmzcrFxeXUu2Dg4ODevHFF9XTTz9tlgCXUkp9/PHHmm116dJFZWdnmzQ/4+LiSp0nIqJ69Oihzp07pwIDA8t00VgppfLy8tScOXP0LuSW9Ne6dWu1ffv2CtXt8s5vDPPmzdMcT2xsbKmWO3funHJwcNDsb3FBT2MofG4zRYDLHHUuJydHDRs2rEL77+zsrD766KNS5Zsp6nZ+Ox81alSZ1tmuXTt15syZEvfZGAGu/PptKMhmKJAze/ZsdefOHYsOcJm6PCv7e7Cg9957r9THZm9vryZMmKCysrL01uPh4WFwmddff73EfXBzczO47BtvvFHscleuXFE9evQod/tu2bKlWrlypcF1V8bxAAAAAACsAwEuG/LDDz+o+vXr6y5subi4qO7du5eYJ4cPH1bNmzdXjo6Ouou2/v7+av/+/ZV2LCdPniwyYCciysfHR73wwgtqx44dSimlZs2apfn8888/t+iyKstF/dzcXBUeHq5GjhypvLy8ir1AVLt2bfXKK6+U6mKqUkr9+eefehcb8/88PDzUkCFD1MaNG1Vubq7J8iIiIkLVrl27yGNq3Lixevnll9Xp06eVUkqNGzfObAGuvLw81bdvX8323n33XZPn5+bNm1WDBg2KzJMaNWqoMWPGqIiICN0y5Qlw5Tt37pwKDQ0t8kKgiCgnJyc1cOBAtWbNmmL33xoCXP3799ecS8pi7NixmnwZP368SffVHAEuU9e5jIwM9eSTTxrtOObMmVMpdbuggwcPqn79+hV553R+YOuHH34o9TqNFeBS6sFdis2bNy/yeJ9//nl1/PhxpdSDII+lB7hMWZ6W8D2YLycnR82YMaPYoFZgYKB6//33VUxMTJHr+eKLL1S9evV0/UFnZ2fVoUOHUvUN/vvf/yofHx/Nsh07dlTnz58v1THs2rVLhYSEqOrVq5fYlr29vVVoaKjavXt3seuszOMBAAAAAFg2u/9/kcKqJSQkSP369UXkwbsvfHx8ePakDUhNTZWNGzfK+fPnJS8vT7y8vMTPz08CAgLE399f7Ozsqlye5OTkyJkzZ+TPP/+U2NhYSUlJEVdXV/Hx8ZGmTZtKcHCw5h0spW0/a9eulcTERPHx8ZFGjRpJo0aNpGXLluLs7GyW48rIyJBNmzbJuXPnJD09XWrVqiV+fn7SsmVLzbuwrOV8ZIz8TElJkYiICDl//rzcunVLatWqJfXq1ZOOHTtK8+bNTVL/7927J3v27JHLly9LfHy8ODk5ibe3t7Rp00bat28v7u7unJhsmKnq3Kuvviqff/65Lu3n5yeTJk2SgIAAcXNzK/zYZLl//75cu3ZNzpw5I/v27ZPDhw9r5nFycpJTp06Jv79/pdftlJQU2bNnj1y5ckVu3LghXl5e0qBBA+nRo4c0adKk0ss0MjJSoqKiJD4+Xjw9PaVNmzbSrVs38fLysuq6auzytITvwYL++usvOXbsmJw5c0YcHBzEx8dHHn30UQkMDJQaNWpYTX8lKipKTp48Kbdv35bk5GTJy8sTDw8PeeihhyQgIEDatGlTJftyAAAAAADjIcAFAABM4vLly+Ln5yf5XY22bdvKzp07xdvbu9TriIyMlGnTpsnvv/+umzZt2jT55JNPyGAAAAAAAIAqzJ4sAAAAprBx40Yp+Dua+fPnlym4JSLSs2dP2bp1q+bu1NOnT5O5AAAAAAAAVRwBLgAAYBIJCQmadOvWrcu1nrp168rDDz+sS8fGxpK5AAAAAAAAVRwBLgAAYBJ+fn6a9LZt28q1npiYGLl48aIuXTDYBQAAAAAAgKqJABcAADCJYcOGiZeXly49ZcoU+eSTT6Qsr/+MiIiQ4OBguX//vm7aSy+9ROYCAAAAAABUcQS4AACASXh5ecmyZcvE3v5BdyMrK0vefPNN6dChgyxcuFCio6MlLy9Pb7nExET5/vvvZdCgQfL444/LlStXdJ+NHz9e+vfvT+YCAAAAAABUcXaqLD+jtlAJCQlSv359ERGJj48XHx8fShYAAAuxdu1amThxoqSkpOh95uDgIHXr1hVvb2+5e/euJCQkSGZmpsH1TJkyRT7++GNxdHQkUwEAAAAAAKo47uACAAAmNWzYMDl58qRMnDhRnJycNJ/l5uZKfHy8nDx5UmJiYgwGt3r16iV79+6VTz/9lOAWAAAAAAAARIQ7uAAAgBndvHlTNmzYINu3b5eoqCiJiYnReyeXt7e3tG3bVh577DEZOnSotGzZkowDAAAAAACABgEuAABQabKysuTOnTty9+5dcXV1lRo1aoi7uzsZAwAAAAAAgGLxnB8AAFBpqlWrJvXq1ZN69eqRGQAAAAAAACg13sEFAAAAAAAAAAAAq0KACwAAAAAAAAAAAFaFABcAAAAAAAAAAACsCgEuAAAAAAAAAAAAWBUCXAAAAAAAAAAAALAqBLgAAAAAAAAAAABgVQhwoVwCAwOlUaNGsnfvXjIDAAAAAAAAAACYlSNZgPK4du2aJCQkyL1798gMAAAAAAAAAABgVtzBBQAALM6dO3ckLCxMLl68SGYAtEsAAAAAAPRwBxcAALAoubm50r17dzl9+rQ4OTnJjh07JDg4mIwBaJcAAAAAAOhwBxcAALAoGzdulNOnT4uISHZ2toSHh5MpAO0SAAAAAAANAlwAAFRBycnJ0rVrV3FychInJydZtmyZxezbmjVrNOmePXtWiTLJycmRkJAQXZnMnj2bikq7pF0CAAD6kAAAFIEAFwAAVczNmzeld+/ecujQIcnJyZF//OMfMnHiRIvYt6ysLNm8ebMu3axZMwkJCakS5eLo6CirVq2SwMBAycnJkQULFsjUqVOpsLRL2iUAAKAPCQCAoe9BsgAov6NHj0peXp4u3blzZzLFShw/flyys7N16U6dOom9PTH/fGlpaRIdHV2mZezt7aV27dpSt25dcXd3JxMtVGZmpvTv31+ioqJERCQ0NFTmzJljMfu3Y8cOuXv3ri49ffp0cXBwsMi8vHr1qsTHx+vSrVq1Ejc3twqt09XVVcLCwiQoKEiuXr0qn332mXh7e8u7775L5TWhw4cPy5YtW+TMmTNy69Yt8fLykrp164qfn5/0799fAgICaJdW0i7pt1jv+Q/0f0EZ2tJ3u7nRhwQAVFV2Sill7QeRkJAg9evXFxGR+Ph48fHxoWRNrH79+pKQkCBbt26Vfv36Vdl88PDwkLS0NBF58KupgoMNWEcdzpeWlsaFmUIDwi5dupR7+UaNGkmfPn1k0KBBMmjQIAbeFmTs2LGyYsUKERF5+OGHJSoqyqLq/rhx4+SHH34QEREfHx+JiYmRatWqWWReDh8+XNauXatL37hxQ+rUqWOUdR84cEAee+wxyc3NFTs7O9mwYQN3zJjAH3/8Ia+//rrs37+/yHmmTp0q//73v2mXVtIu6bdY//nPUuzdu1fmz5+vmfbGG29Y9dinKvd/baU8GcNYz3d7ZdVZ+pAAgKqGK44AAKOLjY2V7777ToYMGSIBAQGyatUqMsUCfP3117qL6A4ODvJ///d/FnVRJDs7W8LDwzWDeEu+iH748GHd/82aNTPqxd3u3bvLjBkzREREKSWjR4+Wq1evUomNaN26ddK9e/diL4DRLq2vXcL6z3+WIjExUXbu3Kn5u379OoVPeYLvdouus/QhAQBVDY8oBMxs5cqV8v333+vSTk5OsnXrVr355s+fL3v37tWlAwICZMmSJWQgKk2HDh2kcePGBj/LycmR5ORkuXbtmsTExGg+O3funISGhkp4eLgsXbpUPD09ycxKkJCQINOnT9elX3zxxQrdpWcKu3btkjt37oiIiJeXl/z973+36PyMjY3VpU3xiNp//OMfsnLlSrly5YrcvXtXJk+erHkPEsrv8OHDMmrUKMnKytKU4eOPPy7e3t5y9+5dOX36tOzbt492aUXtErZz/gMAa/1utwT0IQEAVQkBLsDMYmJiZOfOnbq0i4uLwflOnTqlmS//UYhAZXn99ddl7NixJc6XmJgoGzdulCVLlsipU6d001etWiWxsbGyY8cOqV69OhlqZlOmTJGUlBQREXF3d5e5c+da3D6uW7dO9/8rr7xi0cHQgncviIgEBQUZfRsuLi6yYMECGTVqlIiIbNmyRdasWSPDhw+nQldAXl6evPjii7oLYO7u7hIeHi69e/fWmzc3N1cSExNpl1bSLmE75z8AsNbvdktAHxIAUJXwiEIAgFHVq1dPXnzxRTlx4oQsXrxYHBwcdJ8dOHBARo8eTSaZ2dGjR2X16tW69LRp06RevXoWtY+5ubkSFhYmIg9ekj1lyhSLztMjR45o0qa6gyE0NFTatm2rS8+aNUtyc3Op1BWwefNmOXnypC79/fffG7wAJvLgkYENGjSgXVpJu4Rtnf8AwNq+2y0JfUgAQFVBgAsAYJovGHt7mT59uixfvlwzff369bJhwwYyyIwWLlyo+9/R0VEmTZpkcfu4d+9eSUpKEhGRiRMnWvz7XAreweDk5CTt27c3yXbs7Ozk1Vdf1aUvXLggv/zyC5W6Agq+T6patWoyZMgQ2qWNtEvY1vmvsj333HOilNL8TZgwgQpAeYLvdquos/QhAQBVBQEuwMwefvhhGTBggO6vX79+Budr3769Zr7u3buTebBKY8aMkdDQUM20efPmkTFm8tdff+nuwBARGTx4sNSvX9/i9jP/MWiOjo7y5ptvWnSeKqXk6NGjunSbNm2KfNysMYSGhmoeCzd//nwqdgX88ccfuv/r1q0r9vb2tEsbaJewzfMfAFjLd7slog8JAKgK+NYHzOy5556TTZs26f7Wr19vcL7p06dr5vv444/JPFitmTNnatInTpyQ6OhoMsYMvvnmG1FK6dIvv/yyxe1jXl6e7q6+kSNHSpMmTSw6T8+cOSOpqam6tKnfP+Pm5qZ5tOfp06fl0KFDVO5yKvjejYKPUKVdWne7hG2e/wDAWr7bLRF9SABAVUCACwBgcm3atNG7O6HgL8BhGnl5eZpHkXh5eUlwcLDF7eeBAwckISFB7OzsZMaMGRafrwUfzyVinvfPhISEaNIrV66kgpfT/fv3aZc22C5hu+c/ALD073ZLRh8SAGDrCHABAMyiUaNGmnRsbCyZYmIRERFy/fp1XfqJJ56wyF+1rl27VjcAb9GihcXn65EjRzRpc9zB0KtXL6levbou/csvv0hOTg6VnHZJu4TNn/8AAPQhAQAoCgEuAIBZZGRkaNLu7u5kiokVfOG2iMiTTz5pkfuZ/y6iwo+ytFQF72Dw9PSU5s2bm3yb1apVk8cee0yXvnnzphw7doxKTrukXcLmz38AAPqQAAAUxZEsACpXVFSUbNmyRWJiYiQ+Pl5q1aolDRs2lF69ekmfPn3EycmpXOvNzc2ViIgI2b9/v5w9e1Zu3bolTk5OUrduXalTp440adJEevXqVec80BAAACAASURBVOZfZWdkZEh4eLgcOXJErl69Knl5edKwYUPp3bu39O3bVzw8PCo1PxMTE+X48eNy5coVcXV1lYceekg6dOhg1GBKXFyc7N+/X44dOyaJiYly+/ZtcXNzE19fX+ndu7f06dOnXC9cN1WZWYLMzEy5dOmSZlpp3udiqrw25Taio6N1v4p0d3cXPz8/g2V94cIFuXjxotSsWVO6du1qknzfv3+/Jt2nTx+TbEcpJX/88Yf8/vvvcuLECbl586bcvXtXPD09xc/PT/r37y/dunUrcvk5c+bI+fPnreJRV/fu3ZM///xTlw4KChI7OzuzbLtv376ybds2XToiIkK6dOli1uM3R5u0dbRLy3TixAnd/66uruLv76/5PD09Xc6dOyfnz5+X5ORk8fHxkRYtWsgjjzxi8f0WWzj/WUufinpk20w5DjJXGRrrGCypv1sRpjgOSxwvW0IfEgAAk1E2ID4+XomIEhEVHx+vYHo+Pj5KRNTWrVurdD64u7vr6p6jo2OZll27dq3y9/fXLW/or2bNmuq9995TGRkZpV5vXl6eWrp0qapfv36x687/a9KkiZo7d65KSEgodr0ZGRlq9uzZys3Nrch1eXl5qSVLlqicnByzl8XFixfVM888oxwdHfX2y8HBQfXr10+tX79e5ebmaupw/l9aWlqJ29i9e7cKCQlR9vb2xeapj4+PWrp0aaWXWUUcOnRIb7vff/99ude3atUqzbqcnZ1VcnKy2fPaHNsoWLf69Omj+SwuLk699957ytfXVzfP1KlTTVKGaWlpmvbg4eFh9G1cunRJTZs2TTVq1KjEejty5Eh169Ytqz/vHzhwQHNcs2bNKnGZu3fvqmHDhilfX1/d3+DBg1VKSkqZtr1z507Ntvv372+24zZ1myyYN76+vurGjRulWs7f31+z3OXLl/XmmTZtmmaegsfg4OCgt21fX1/1ySef0C4tgCnrRWEF8yUwMFA3/cKFC2ry5MnK1dXVYB62adNG7dy50yL7LbZ0/qusPlXButSkSRPqUSmcOXNGU96DBg0q9bLdu3fXLdeoUaNSn5+Sk5NV48aNdcuGhoYapTzNMQ4yVxka+xgqu79rrO92Yx6HKepJeeussfqQsbGxevkYGxvLBTEAgEUhwIVyIcD1QHkCXMnJyepvf/tbqQbdBQffx44dK3HdOTk5asyYMWVad/7ftm3bilzv+fPn1cMPP1zqdT311FPq/v37ZiuHtWvXasqiuL/u3burs2fPlmlwmJqaqlq1alXmPB05cqTKzs6ulDKrKGMGuNLT05Wfn59mXcOGDTN7XptrG0UNlJcvX26wnpoqwLV7927Ndjp16mTU9b/55psGL8YU99ewYUOVlJRk1ef9Tz75RHNM4eHhxc5/6dIl1bp1a80yI0aMKNMPFwpeaCl8kcTUzNEmlVJ6y5c2gF+4TV24cEFvnnHjxpV5/+fOnUu7tISBignrRXHbyg9MLF++vMiAROG/b7/91mL6LbZ4/isNU/SpytrXpx498NBDD2mCDTdv3ixxmZMnT+rt9w8//FCq7a1YsUKz3GeffWa0sZspx0HmKkNTHENl93eN9d1urOMwVT2pyA9qjdGHjImJ0dv3mJgYLogBACwKjygEzOjatWvyt7/9TU6fPq2Z3qxZM2nXrp0EBARIbGysREVFyenTp3WPS7hy5Yo89thjsm7dumLf1fH555/LihUrdGl7e3sZNGiQdOnSRby9vSU1NVVu3rwpZ8+elcjISLl586Zu3nbt2hlc519//SW9e/eWhIQE3TQnJydp3769dO7cWVxdXeX06dOyfft23f5u375dxo0bJz/++KPJH12zdetWGTFiRJEvym3cuLF4eXnJxYsXJT09XQ4cOCDt2rWT3NzcUm/DxcVFTp06pUvb2dlJixYtpEePHtK0aVOpWbOmxMTEyN69e+XQoUO6+X766Sfx9vaWzz77zKxlZkkyMzPlmWeekYsXL2ryc9GiRWbPa3Nuo9Cd0jJ27Fj5v//7P7Pm/V9//aVJBwQEGHX9a9as0bS7pk2bymOPPSZNmzaVunXrSnJysuzbt09+/fVXzTlwxowZsmzZMqs9jx85ckSTDgoKKnLeffv2ybBhwyQpKUlX195//3159913y7Xthg0bioeHh9y9e1dERO7cuSM3btyQunXrmux4zd1ebB3t0nosXLhQZs2aJSIiDg4O4ufnJ25ubnL9+nVJTEzUm3/SpEkycOBAqVOnTqX3W2zx/Fcaltinqqr1aMiQIfLJJ5+IyIPHu23ZskXGjh1b7DLr16/XfNcopSQ8PFzGjBlT4vY2bNigSQ8ePNho52xTjYPMVYbmGstVVn/X2MpzHJY+Xq6sPiQAAOb8Ard63MFlftzBVfZfVGVmZqpOnTppfv3UqlUrtW/fPoPzX7lyRY0YMUIzv7u7uzp9+rTB+bOzs1W9evV089auXVsdOHCg2H2KiIhQI0aMUD4+PgY/T09PVwEBAZp96NChgzp16pTevJcvX9bb3zVr1pg0/xMSEpSXl5fer8p69OihNm/erFJTUzW/6l23bp1q2rSpwV/RlfTrRy8vL+Xi4qJee+01dfbs2SLn+/DDD5WdnZ1m3QcPHjRbmRmLMe7g2rdvn2rRooXeepYsWWL2vDbnNgr/EnTatGl6j5sZOnSoWrZsmTp06JDJ7px44403NNtdsGCB0dadm5urHB0dlYODg3r++edVdHR0kfMuWLBAsx92dnbq3LlzVnveL/ir9MaNGxc531dffaWcnJx087q5uan169dXePuFv0d+++03kx+zOdqkmPBOnfj4eHXmzBndX+HvjRMnTmg+P3PmDO3SQkgl3cFVvXp13f8DBgxQV69e1cx74sQJ1a9fP739++ijjyym32KL57/imKpPVZE7uKpyPYqMjNRsZ8iQISUu065dOyUiysXFRXXo0EGXhyXd8ZeRkaHJ64KPhqxIeZpyHGSuMjTlMVR2f9dY3+0VPQ5Tj5eNdQdXefuQ3MEFALAGBLhQLgS4yt7hnDJliqZjGBwcrO7evVviNubNm6dZrkWLFgYfZ3D+/HnNfLNnzy71cRR1wWjq1KllfpTCxIkTdfO3a9fOpPk/atQovQ73+++/r/Ly8opcJi4uzmDQpaQB/ssvv6zOnz9fqv0KDQ3VrHvgwIEG5zNFmRmLoQDXJ598ouLi4gz+xcTEqKioKLV161Y1Z84c1b59e4OD8Dlz5pS4bVPktTm3UXCgXPiRRE8//XSpLrQaw+DBgzXb/umnn4y27oSEBPXCCy+U6lhycnJU48aNNfuyatUqqzzn37x5s8RHbWZnZ6tXXnlFM1/jxo1VVFSUUfZh+PDhmnWvWLHC5MdtjjZpzkCGt7e3ZpnMzEyz1SHaZRkHKpUU4Mr/69Wrl7p3757B+XNzc1Xv3r31frhkKf0WWzz/FcdUfaqKBLiqcj3Kzc3VBBzd3NyKzAOlHjzOMn/e1q1bqxdeeEGX3rhxY7HbCgsL0xzT/PnzjVKephwHmasMTXkMltLfreh3e0WPw9TjZWMGuMrTh0xPT1erVq3S/KWnp3NBDABgWeNGWzgIAlzmR4CrbB3OS5cuaX7J6uPjU6ZfsQ0ZMkTTGf3666/15tm+fXuZfv1Zmnbl4uKiW5+vr2+pnp9/7949zUWziIgIk+R9bGyscnBw0Bzzm2++Waplr127ZtIBfnR0tGbdrq6uBn99auwyMyZDAa6K/Hl4eKgff/zR6PtZ2rw25zYKvxsh/2/GjBnFXrgwtvxfQuf/bd68udLq07PPPqvZl3fffdcqz/mbN28uts3eunVL70Jl9+7dVWJiotH2YcKECXoXxCxJedtkVQlw0S7LOFCpxACXo6NjieOK8PBwzTLVqlWzyn6LtZz/imOqPlVFA1xVuR69+OKLpT7fffrpp7r5JkyYoD7//HNNujiF38VU1NMuylKephwHmasMTT2Ws5T+bkW/2ytyHOYYLxszwGXpfUgAAMrLnoc0Aqa3aNEiyc7O1qVnzZoltWvXLtPyDg4OuvQHH3wgeXl5mnk8PT016Y0bN+rNUxZfffWVZGZmarbp7e1d4nIuLi4ybdo0XXrXrl0mydPly5drnkHv6+sr//znP0u1bI0aNUxa3i1atJDq1avr0vfu3ZOzZ8/qzWfsMrNELi4uMnnyZDl16pSMGjWq0vK6srcxatQoWbRokVmesZ8vJSVFk/bw8Ki0elC4rpszH4zp8OHDmnTB98+cPn1agoKCZPfu3bpp48ePl927dxv1/Qbu7u6adP67FCyFOdqkNaNdWo8nn3xSfHx8ip2nb9++mnRWVpZeGVtDv8Vazn9lqc+W0qeqyvVo6NChmnR4eHiR827atEn3f8+ePaVr166lKsvc3FzNso8++qhR3m1oynGQucqwMsZyldHfNYXSHoelj5etrQ8JAEB5EeACTEwpJWFhYbq0g4ODjB49ukzr8Pf3l+7du+vSV69eld9//10zT4sWLcTFxUWXjoyMlJCQEDl16lS59nvr1q2aiwbDhw8v9bIFX+y8b98+k+Rr4YHA5MmTxdXV1WLKvVmzZpp0wZcOm6rMTM3JyUnc3d2L/PPw8JCmTZtKUFCQjBs3TlauXClxcXHy+eefS+PGjSs1rytzGzVq1JBPP/3U7OWVnp5e7KDWnOLi4jTphg0bWuX5/MiRI5pzeceOHUVEZPPmzdKlSxe5ePGi7rNPPvlEli9fLs7Ozkbdh8IBkbS0NIvLJ3O0SWtFu7Qew4YNK3EeV1dXzfe4iMitW7esst9iDee/4lhqn6oq16PevXtrAo+bNm2SBze5aaWkpEhERIQuHRwcLG3bthUvLy8REbl586b89ttvBrdx4MABTV4VDqqVlynHQeYqQ3OP5Sqrv2tsZTkOSx8vW2MfEgCA8nAkCwDTOnbsmNy4cUOXbt++vW7AVhaPP/64ZvC3detW6dSpk6YzPnbsWFm6dKlu2ubNm2Xz5s3i7+8vXbp0kU6dOkmnTp2kY8eO4uhYdPNPTU3VBNCcnZ3lueeeK9vJxdFRcnJydBc8jCknJ0dzoUVE5Omnn7aoci980TIrK8vgAMpYZWYOM2bMKPUvTC0trytzG2PGjCnVrzmNrfCF9Mq8UyQ6OlqTzr8wak2UUprzTsuWLcXNzU0+/PBDeeeddzS/Lv/+++/l+eefN8vFicLlXFXapLWiXVqPwoHa4vo7hc8V1thvsYbzX3EstU9VleuRs7Oz9O/fX37++WcRefBjhyNHjkjnzp01823btk33pIsWLVpIkyZNRORBoCv/R4JhYWHSo0cPvW1s3LhRkx4yZEiF99uU4yBzlWFljOUqq79rbKU9DksfL1trHxIAgPIgwAWY2Llz5zTppk2blms9hZcz9MinRYsWya+//iqXLl3S24dz587JihUrROTBL8wGDhwob775prRv315vPdeuXdNcrEhKSpLNmzeXa7+vX78ueXl5Ym9vvBtGb9y4oXkchJ2dnbRs2dIiyvv+/fuSmppa6gu6xiqzqqiseV1Z2xg4cGCl5M+9e/c06YKPjTOH7OxsuXv3rpw+fVpiY2N106tVqyZt27a1uvp24cIFSU5O1qXbtm0rzz//vKxcuVJv3mPHjpnsAq+bm5vFXpwwR5u0drRL69GgQYMq0W+xpvOfNfapqno9Gjp0qC7AJfLgMYWFA1wFg1QFgz1PPfWUJsD18ccf662/4LK+vr4SGBhY4X025TjIXGVYGWO5yurvGltpj8PSx8vW1ocEAKAiCHABJhYfH69J16pVq1zrKfzOrsLrFRHx8vKSPXv2yLPPPqv3roSCUlNTZeXKlfLzzz/LzJkz5YMPPtA8Y7zgHWcVlZubK5mZmUa9iJeUlKSXp+a+uyk6OloOHjwoR44ckYsXL8rly5fl2rVrcv/+/TKtx1hlZsuMldeVtY1HHnmkUvKtWrVqmovpxsyvgm7fvi27d++WqKgo+eOPP3R5l5qaanD+Dh06iJOTk9XVw8Lt85dffikyT5cuXSrTp0836oXNfAUvjImIWR8BZs42aatol9bDmHfXWUK/xRbOf9bYp6rq9ahfv37i4uKi++7avHmzLFiwQDNO2L59uy5dMMA1YMAAsbOzE6WUXLx4UaKjozXBoHPnzsn58+d16SFDhhilHE05DjJXGVbGWK6y+rvGVtrjsPTxsqX2IQEAMAUCXICJFb4AkJGRUa71FH5GdlG/kG/cuLFERkbKd999J//5z3+KffdAXl6eLFiwQJKTk+WLL74osrM7ffp0efbZZ8udB9WqVTNqnhZ+bIu5Lspdu3ZNvvjiC/npp58kJibGaOs1RpnZGlPltbm3ISJSt27dSslDNzc3zYV0Y75I+s6dO/LDDz/IunXr5LffftO8LL0khX+5bS0KXywteG5/44035MqVK7Ju3TrdBYRFixbJkiVLjL4fhcvRXO9wMld7sXW0y6qpsvottnb+q+p9KmusR25ubtK3b1/ZtGmTiIicPHlSrly5onsM4aFDh3R3B3p4eGgeQ9iwYUPp2LGjHDt2TEQe3K1VMMBV+G4ZYzye0NTjIHOVYWWM5Sqrv1tZ/XZLHy9bUh8SAABTI8AFmJiPj48mXd5feyUmJmrSxf0y1snJSV566SV56aWXJC4uTiIjI+XYsWNy5MgROX78uF6Q7csvv5TevXvrXoRdeJ8LvkzcEhS+C66oF3Eb84LC+++/L4sXL9Z7vFT+gNzf318aNmwodevWFQ8PD/nll1/k+vXrpd5GRcvMVpgjr82xjcLtpzK4ublpfilsjBdJZ2VlyaJFi+Sjjz4y+FgTLy8veeSRR6RBgwZSp04d8fT0lJUrV2rOX9Z6Ib3wOzPyL24sXbpUxo0bJ9HR0bJhwwbd42q+/vprmTFjhjRs2NCo+1G4HAs/bsYa22RVQrusmszdb7HV819V71NZaz0aOnSoLsAl8uA9wpMmTRKRB+/fytenTx+9gE9ISIguwLVlyxZ55513dJ8VDHDVrl1bevXqZZKxmzHHQeYqw8oYy1VWf7eyjsPSx8uW0IcEAMBslA2Ij49XIqJERMXHxyuYno+PjxIRtXXr1iqdD+7u7rq65+joaHCenTt36uYREVWzZk2Vm5tb5m2NGDFCs54333yzXPuckZGhfvzxR9WwYUPN+lq1aqWbJysrS7m5uek+Cw4Otqh8z8zMVI6Ojpr9v3LlSqmXT0tL0ywrIiotLa3UeW9nZ6eefPJJ9e2336oLFy4YXKZLly6aZcLCwsp9vKUpM2M7dOiQXh69++67Ji9bc+S1qbeRf37M/8vMzKyUdtKiRQvNfmzfvr1C60tJSVGPP/64Zp2urq5q3Lhx6ueff1ZxcXF6y+Tm5ioPDw/NMhcvXrS6c31WVpZydnbWHEfdunXVgQMHNPM999xzmnkmT55s9H156aWXNNuYPXu21bdJ9eAn7Zq/hISEMn8Pi0iR+1SQt7d3pbVR2mXZmLNemHJb5u632Or5zxjK2qcqTV+felS8pKQk5eDgoNtmSEiI7rOgoCDd9G+++UZv2T///FP3ub29vUpKSlJKKZWamqqcnJx0n40fP95oYzdTjoPMVYbmGMtZSn+3ot/t5T0Oc42Xy3oOsqQ+JAAA5mJPiA8wrR49emhu/09OTpaDBw+WaR3379+XXbt2aaY99dRT5dofV1dXGTVqlBw5ckTq1Kmjm37q1CndC+ednZ2ld+/eus/2798vV65csZg8rVatmt5LwXfv3m2Sbf3000+yatUqXdrPz0+ioqJk+/bt8sILL4ifn5/Jj7c0ZWYLzJHXllCe5lL4l/MVvVNk0KBBsmfPHl3673//u8TFxcl3330nI0aMMPhL/bNnz2oeh+Lt7S3NmjWzuryMiorSPJKrXbt2cvToUenWrZtmvvfee0/zgvBly5YZvY0WfryMr6+vVbfJisrOzqZdVtF2aU31wpz9Fls+/1X1PpW11qPCd1ft3r1bsrOzJSUlRX7//XcREbGzs5MBAwboLdu6dWt56KGHROTBIyXz39eVv458xno8oanHQeYqQ0sfy9kCa8xjc/YhAQAwJwJcgBkGowVfmCwi8q9//atM6/jhhx/k5s2bunStWrXkscceq9B+NWjQQAYOHKiZdu3aNd3/Q4cO1f2fk5MjU6dOtah8DQ4O1qSXLVtW6mUNPWarKKtXr9akf/75Z2nTpk2Jy6WkpBj9mEsqM2tnjry2pPI0tYcffliTjo+PL/e6tmzZIvv27dOlX3rpJfnqq6/0HrVTWGRkpCZtK+/f+vHHH6Vx48Z68wUEBMhzzz2nS9+/f18WLFhg1H0pXI6mDDJZentJSkoq8n2UtEvbb5fWVi/M1W+x5fMffSrrrUcFxxV3796VY8eOSUREhO5dgV26dNF75Fu+QYMGac57IiL/+9//dNPc3d2lb9++JttfY4+DzFWGlj6WswXWlsfm7EMCAGBOBLgAM5g9e7bmV60bNmyQ9evXl2rZuLg4zfPmRUTeeecdgy8lLutFxYK/XBV58AvLfKNGjdL9YlJEJCwsTO9lzqWR/+JoYxs/frwmfeDAgVLn6Y8//ljq7SQkJGjShS9OGpKdnS2XL18u1fqNWWbWztR5ba5tWIrCg9YzZ86Ue10F350hIjJy5MgSl8nKypKFCxdqppnyQnp2drYcPHhQTpw4ofcS94oqeIHXw8NDAgICipx3zpw5mvP98uXLjfqL3sLlaMo7b8zZXgrmmciDC0Ulyb/QSbu03HZZ4YGKDdULc/VbjM2Szn/0qay3Hg0ePFjs7Ox06YiICPntt9906ZCQkCKXLfhZ/t1OBe9c7devn7i4uBh1f005DjJXGVr6WM4WWFsem7MPCQCAWceNZAFgem3atNEbzIwZM0b3mI2iXL16VZ566inNC4j9/Pzk1Vdf1Zs3NTVVGjduLLNmzSrVC4vv378vYWFhurSTk5M0atRIk160aJHePpf28YoxMTHyzDPPyD/+8Q+T5GlAQID06dNHb8BY+JfGhW3btk2mT59e6u088sgjmvTKlStLXGbu3LmSmZlZ4nzGLjNrZ8q8Nuc2LEXhYERFLqQXvkB59uzZEuvq1KlTJSYmRjPdVBfSU1JSJCgoSLp16ybt2rWT4OBgo/5S/ciRI7r/AwMD9S66F9S8eXMJDQ3V5MX8+fONdpwFg05OTk7SpEkTq26T+erVq6dJJyUlFTv/5cuX5e2336ZdWnC7NAZbqhfm6rcYm6Wc/+hTWXc98vX1lcDAQF36t99+0+xzcY8Y7Nmzp9SsWVNERBITE2XPnj2ac6cxH09ojnGQucrQ0sdytsCa8ri8fcisrCw5cOCA5s/a7p4HAFQBtvAisfj4eN2LMuPj43mzmhnkv4x169atVTofyvLS14yMDNWxY0fNi13t7e3VCy+8oKKiojTzXr9+XS1atEh5eXlp5nd3d1cnT540uP79+/dr5nvrrbdURESE3otyc3Nz1f79+1Xr1q016x4+fLjB9U6ePFkzn7Ozs5o5c6bBtpaZmam2b9+uRowYoapVq1bseo0hOjpa84JpEVEuLi7qgw8+ULdv39bMm5KSoubOnav3Ymcp4SXby5Yt08zn4OCgFi9erFJSUjTz5eXlqYiICPXMM88YXH9YWJjZyswYDh06pHcM7777rknbkynz2pzbsJSXbl+/fl2zHw0aNCj3ukJDQzXrqlmzptq8ebPKy8vTO8+FhYXp1VURUXZ2dnrt0lg++OADve3NmjXLKOu+deuWZr0zZswocZmzZ88qBwcH3TJOTk7q8uXLFd6XgwcPavala9euVt8m83Xu3FmzzCuvvFLkvJGRkap58+YGt3XhwgWTvYiedml+5qwXhZdJSEgoc1+wpG2Zo99iTJZ0/iuJqfpUZenrU4+Kt3DhQt12mzVrpurXr69ERLVo0aLEZUeNGqVb9tlnn9WMSwp/Jxlr7GbKcZA5y9BUx2Ap/d2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" } }, "cell_type": "markdown", "id": "62dfcaee-edf1-4f0e-b84c-e5bddb4c8e27", "metadata": {}, "source": [ "
\n", " Bitte erst nach der obiger Aufgabe öffnen. \n", "\n", "
\n", "\n", " Erweiterter Gale-Shapley-Algorithmus\n", "\n", "Der erweiterte GSA liefert für jedes Stable Marriage Problem das gleiche stabile Matching wie die Originalversion. Der große Unterschied im Vergleich zu ursprünglichen Gale-Shapley-Algorithmus ist, dass die erweiterte Version die Präferenzlisten verändert, indem sie die Paare löscht, die nie für ein stabiles Matching in Frage kommen. Die Löschung eines Paares $(a,k)$ erfolgt dabei dadurch, dass $a$ von $k$'s Liste gelöscht wird und auch $k$ von $a$'s Liste. Nach Beendigung des Algorithmus erhält man also neben dem stabilen Matching auch die fertig gekürzten Präferenzlisten, die wir auch **Kurzlisten** nennen.\n", " \n", "
\n", "\n", "Ein weiterer Unterschied liegt darin, dass eine Bewerbung in der erweiterten Version **immer** angenommen wird. Wir betrachten den Ärzte-orientierten Fall und stellen fest, dass aus der Präferenzliste jedes Krankenhauses schon alle Ärzte entfernt wurden, die schlechter als der aktuell zugeordnete Arzt sind. Folglich muss jeder neue Bewerber besser sein als der aktuell zugeordnete Arzt und wird deswegen in jedem Falll vom Krankenhaus angenommen.\n", "\n", "
\n", "\n", "Auch der erweiterte Gale-Shapley-Algorithmus arbeitet bei der Wahl eines freien Bewerbers nicht-deterministisch, was allerdings nicht relevant ist, da er wie der ursprüngliche Gale-Shapley-Algorithmus immer das gleiche stabile Matching liefert. Der Beweis ist der gleiche wie in Aufgabe 7." ] }, { "cell_type": "markdown", "id": "98d4b676-3683-4342-8862-224a5354b7b5", "metadata": {}, "source": [ "
\n", "\n", "### Aufgabe 9\n", "***\n", "Führen Sie den erweiterten Gale-Shapley-Algorithmus per Hand (Ärzte stellen Anfragen) für nachfolgendes Beispiel aus, das Sie schon aus Aufgabe 6 kennen. Notieren Sie dabei die Kurzlisten. Was für Vorteile oder Nachteile sehen Sie bei der erweiterte Version im Vergleich zur originalen?\n", "\n", "$$\n", "\\begin{array}{|l |c c c c c c |}\n", "\\hline\n", "\\textbf{Ärzte} & 1 & 2 & 3 & 4 & 5 & 6 \\\\\n", "\\hline\n", "a & W & X & Y & Z & V & U \\\\\n", "b & Y & U & W & V & Z & X \\\\\n", "c & Z & W & V & X & U & Y \\\\\n", "d & U & Y & Z & X & V & W \\\\\n", "e & W & Y & U & X & Z & V \\\\\n", "f & X & Y & W & Z & V & U \\\\\n", "\\hline\n", "\\end{array}\n", "\\hspace{2ex}\n", "\\begin{array}{|l |c c c c c c c |}\n", "\\hline\n", "\\textbf{Krankenhäuser} & 1 & 2 & 3 & 4 & 5 & 6 \\\\\n", "\\hline\n", "U & a & e & f & b & d & c \\\\\n", "V & c & b & d & a & e & f \\\\\n", "W & f & d & c & a & b & e \\\\\n", "X & b & e & c & d & f & a \\\\\n", "Y & e & b & a & f & d & c \\\\\n", "Z & d & a & e & b & c & f \\\\\n", "\\hline\n", "\\end{array}\n", "$$\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "e033ab5b-f981-487b-9351-8cff0756fe11", "metadata": {}, "source": [ "
\n", "\n", "### Aufgabe 10\n", "***\n", "Beweisen Sie, dass der (erweiterte) Gale-Shapley-Algorithmus für jeden Fall des Stable Marriage Problems terminiert und die gefundenen Paare bei Beendigung immer ein stabiles Matching bilden. Sie können sich aussuchen, ob Sie it der orginalen oder der erweiterten Version arbeiten möchten, da beide äquivalent sind.\n", "\n", "Tipp: Versuchen Sie zunächst zu zeigen, dass kein Arzt von allen Krankenhäusern abgewiesen werden kann.\n", " \n", "
" ] }, { "cell_type": "markdown", "id": "c23a1f70-42cf-4cca-b2c0-c7abd33380c6", "metadata": {}, "source": [ "Wir rekapitulieren unsere bisherigen Ergebnisse: Für das Stable Marriage Problem haben wir bewiesen, dass in jedem Fall ein stabiles Matching existiert. Zudem haben wir mit dem (erweiterten) Gale-Shapley-Algorithmus eine Möglichkeit gefunden, schnell und zuverlässig ein solches stabiles Matching zu bestimmen. Außerdem wissen wir, dass das Stable Marriage Problem einen Spezialfall des Krankenhaus/Assistenzärzte-Problem darstellt. Ersteres ist ein 1-zu-1-Zuordnungsproblem, während es sich bei letzterem um ein 1-zu-viele-Zuordnungsproblem handelt." ] }, { "cell_type": "markdown", "id": "70fd344a-1293-4451-9437-f3aaa9356048", "metadata": {}, "source": [ "
\n", "\n", "### Denkanstoß\n", "*** \n", "Überlegen Sie , ob und wie sich der erweiterte Gale-Shapley-Algorithmus auf das Krankenhaus/Assistenzärzte-Problem anwenden lässt.\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "5e3080ad-994f-4fc5-a4ca-9e8c5dfbd947", "metadata": {}, "source": [ "
\n", " Bitte erst nach dem Denkanstoß öffnen. \n", "\n", "Wir betrachten die Anzahl der Krankenhäuser, Assistenzärtze und Arbeitsplätze. Bei Krankenhaus/Assistenzärzte-Problem haben wir bisher immer Ärzten Krankenhäuser zugeordnet, was durchaus richtig ist. Es liegt ein 1-zu-viele-Zuordnungsproblem vor. Wir können es aber auch so betrachten, dass wir jedem Arzt einen Arbeitsplatz an einem Krankenhaus zuordnen. Wenn die Zahl der Arbeitsplätze der Zahl der Ärzte entspricht, haben wir plötzlich ein 1-zu-1-Zuordnungsproblem. Von diesem wir wissen, dass sich immer ein Lösung mit dem (erweiterten) Gale-Shapley-Algorithmus finden lässt.\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "954fa792-fcb0-4a75-9e7e-1684df49ce43", "metadata": {}, "source": [ "
\n", "

PHASE 3

\n", "
" ] }, { "cell_type": "markdown", "id": "48e62b23-525e-4822-b0b9-6de6ce9bb078", "metadata": {}, "source": [ "
\n", "\n", "### Denkanstoß\n", "***\n", "Wir haben nun einen Plan, eine Idee, wie wir unsere bisherigen Ergebnisse auch auf das Krankenhaus/Assistenzärzte-Problem anwenden können. Überlegen Sie, wie Sie das konkret machen würden.\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "6f0bb391-2f45-4774-82be-a21457e12034", "metadata": {}, "source": [ "
\n", " Bitte erst nach dem Denkanstoß öffnen. \n", "\n", "Konkret bedeutet das, dass jedes Krankenhaus $k$ mit $q$ Arbeitsplätzen durch die Arbeitsplätze $k_1, k_2, k_3, \\ldots, k_q$ ersetzt wird. Weil diese Arbeitsplätze alle zum selben Krankenhaus gehören, können wir einfach die Präferenzliste des Krankenhauses auch für sie eins zu eins übernehmen. Aber auch die Präferenzlisten der Ärzte muss angepasst werden, die Krankenhäuser müssen durch die jeweiligen Arbeitsplätze des Klinikums ersetzt werden. Wir müssen dabei beachten, dass die Präferenzordnung strikt ist, d.h. es ist nicht möglich, dass die Arbeitsplätze eines Krankenhauses alle die gleiche Präferenz erhalten. Folglich tauschen wir bei allen Ärzten jedes Krankenhaus $k$ gegen die Sequenz $k_1 > k_2 > k_3 > \\ldots > k_q$ aus, wobei die Präferenz absteigend ist. Diese strikte Ordnung hat dabei keine Konsequenzen, da sich folgendes ergibt:\n", "\n", "$$k > k' \\hspace{2ex}\\Longrightarrow\\hspace{2ex} k_1 > k_2 > k_3 > \\ldots > k_q > k'_1 > k'_2 > k'_3 > \\ldots > k'_p$$\n", "\n", "Die Präferenzordnung der Ärzte bzgl. der Krankenhäuser wird also beibehalten, da jeder Platz von Krankenhaus $k$ gegenüber dem von $k'$ bevorzugt wird.\n", " \n", "Bisher betrachten wir allerdings noch den sehr unrealistischen Fall, dass die Zahl der Ärzte und der Arbeitsstellen gleich sind. Das Krankenhäuser/Assistenzärzte-Problem umfasst aber gerade auch die Fälle, bei denen das nicht so ist. Was ändert sich also, wenn wir mehr Ärzte als Arbeitsstellen haben oder umgekehrt? Funktioniert der (erweiterte) Gale-Shapley-Algorithmus dann immer noch? Wir probieren das nun aus.\n", " \n", "
" ] }, { "cell_type": "markdown", "id": "dba37fd2-0a50-439e-8189-cbc4cff162ea", "metadata": {}, "source": [ "
\n", "\n", "### Aufgabe 11\n", "***\n", " \n", "Wir betrachten wieder ein Krankenhäuser/Assistenzärzte-Problem, wobei das Luisenhospital (LH) 3 Plätze, das Alexianer Krankenhaus (AK) 1 Platz und die Uniklinik (UK) 3 Plätze hat.\n", " \n", "$$\n", "\\begin{array}{|l |c c c |}\n", "\\hline\n", "\\textbf{Ärzte} & 1 & 2 & 3 \\\\\n", "\\hline\n", "\\text{Anna} & \\text{LH} & \\text{UK} & \\text{AK} \\\\\n", "\\text{Boris} & \\text{AK} & \\text{UK} & \\text{LH} \\\\\n", "\\text{Clara} & \\text{LH} & \\text{AK} & \\text{UK} \\\\\n", "\\text{Dana} & \\text{AK} & \\text{LH} & \\text{UK} \\\\\n", "\\text{Erik} & \\text{AK} & \\text{LH} & \\text{UK} \\\\\n", "\\text{Fiete} & \\text{UK} & \\text{AK} & \\text{LH} \\\\\n", "\\hline\n", "\\end{array}\n", "\\hspace{2ex}\n", "\\begin{array}{|l |c c c c c c c |}\n", "\\hline\n", "\\textbf{Krankenhäuser} & 1 & 2 & 3 & 4 & 5 & 6 \\\\\n", "\\hline\n", "\\text{Luisenhospital} & \\text{Boris} & \\text{Fiete} & \\text{Clara} & \\text{Erik} & \\text{Dana} & \\text{Anna} \\\\\n", "\\text{Alexianer Krankenhaus} & \\text{Fiete} & \\text{Clara} & \\text{Anna} & \\text{Erik} & \\text{Dana} & \\text{Boris} \\\\\n", "\\text{Uniklinik} & \\text{Clara} & \\text{Boris} & \\text{Dana} & \\text{Anna} & \\text{Erik} & \\text{Fiete} \\\\\n", "\\hline\n", "\\end{array}\n", "$$\n", "\n", "1. Überführen Sie zunächst das Problem in ein Stable Marriage Problem.\n", "2. Führen Sie den erweiterten Gale-Shapley-Algorithmus Arzt-orientiert und Krankenhaus-orientiert aus.\n", "3. Funktioniert der erweiterte GSA?\n", "\n", "***\n", "*Als kleine Erinnerung:* Beim Krankenhäuser/Assistenzärzte-Problem heißt ein Matching stabil, wenn es keinen Assistenzarzt $a$ und kein Krankenhaus $k$ gibt, die folgende zwei Bedingungen erfüllen:\n", " \n", "1. Entweder ist $a$ nicht zugeordnet oder $a$ bevorzugt $k$ gegenüber seinem aktuell zugeordneten Krankenhaus;\n", "2. Entweder hat $k$ noch Plätze frei oder $k$ bevorzugt $a$ gegenüber mindestens einem aktuellen zugeordneten Assistenzarzt.\n", "
" ] }, { "cell_type": "markdown", "id": "6ef8681b-b4db-40ed-9989-5e7875b4d13f", "metadata": {}, "source": [ "Wir halten also fest, dass das Krankenhäuser/Assistenzärzte-Problem äquivalent zu einem Stable Marriage Problem mit gleich großen bzw. verschieden großen Mengen ist." ] }, { "cell_type": "markdown", "id": "f4a91eb4-19db-4f2a-8d19-a82babb27a7d", "metadata": {}, "source": [ "
\n", "\n", "### Aufgabe 12\n", "***\n", "Wie müsste der erweiterte Gale-Shapley-Algorithmus modifiziert werden, damit er auch für alle Fälle des Krankenhäuser/Assitenzärzte-Problems terminiert und ein Matching liefert? Geben Sie den entsprechend veränderten Pseudocode an.\n", "\n", "
\n", " Kleine Hilfe zum Schreiben des Pseudocodes\n", "Die Pseudocodes in diesem Notebook wurden mit dem Package algorithm2e in LaTeX erstellt und als Screenshot per Drag-and-Drop in ein Markdown-Fenster eingefügt. Der verwendete LaTeX-Code:\n", "\n", "\n", "```\\begin{algorithm}[H]\n", " \\SetKwInOut{Input}{Input}\\SetKwInOut{Output}{Output}\n", " \\SetKw{Return}{return}\n", " \\caption{erweiterter Gale-Shapley-Algorithmus}\n", " \\Input{alle Präferenzlisten}\n", " \\Output{stabiles Matching}\n", " \\BlankLine\n", " jeder Arzt $a$ und jedes Krankenhaus $k$ sind frei\\;\n", " \\While{\\textup{ein Arzt $a$ ist frei}}\n", " {\n", " $k:=$ erstes Krankenhaus auf $a$'s Liste\\;\n", " \\If{\\textup{$a'$ ist aktuell $k$ zugeordnet}}\n", " {\n", " löse das Paar $(a',k)$ auf ($a'$ und $k$ sind wieder frei)\\;\n", " }\n", " $a$ und $k$ bilden ein Paar\\;\n", " \\For{\\textup{für jeden Vorgänger $b$ von $a$ auf $k$'s Liste}}\n", " {\n", " lösche $b$ von $k$'s Liste\\;\n", " lösche $k$ von $b$'s Liste\\;\n", " }\n", " }\n", " \\Return{\\textup{stabiles Matching $M:=$ alle Paare $(a,k)$}}\n", "\\end{algorithm}```\n", "\n", " \n", "
\n", "
" ] }, { "cell_type": "markdown", "id": "71271647-8929-4e34-850e-2b2982396437", "metadata": {}, "source": [ "
\n", "\n", "### Aufgabe 13\n", "***\n", "Beweisen Sie, dass der modifizierte und erweiterte Gale-Shapley-Algorithmus für jeden Fall des Krankenhäuser/Assistenzärzte-Problems terminiert und das dabei erhaltene Matching stabil ist. \n", " \n", "Tipp: Sie haben schon mal eine ähnliche Aufgabe gelöst.\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "3ccd096e-3861-4059-b05c-955194131c28", "metadata": {}, "source": [ "
\n", "

PHASE 4

\n", "
" ] }, { "cell_type": "markdown", "id": "91a5b89f-66ca-49f6-8a0b-5cea987b2ad3", "metadata": {}, "source": [ "Damit haben wir jetzt die beiden Fragen bzw. Probleme, die am Anfang des Notebooks gestellt wurden, geklärt: Für jedes Krankenhäuser/Assistenzärzte-Problem existiert (mindestens) ein stabiles Matching und eines dieser stabilen Matchings lässt sich mithilfe des Gale-Shapley-Algorithmus in seiner erweiterten und modifizierten Version bestimmen. Der Algorithmus hat einen sehr großen Teil dieses Notebooks eingenommen. Trotzdem wollen wir uns kurz noch eine weitere Eigenschaft anschauen." ] }, { "cell_type": "markdown", "id": "8315128c-911c-474d-9b7a-0de5fe32561c", "metadata": {}, "source": [ "
\n", "\n", "### Aufgabe 14\n", "***\n", "Wir nehmen uns dazu das Beispiel vom Anfang des Notebooks vor, dass durch die beiden untenstehenden Präferenzlisten gegeben ist. Das Fermat-Gymnasium hat drei freie Stellen, das Gauß-Gymnasium zwei und das Hilbert-Gymnasium eine.\n", " \n", "$$\n", "\\begin{array}{|l |c c c|}\n", "\\hline\n", "\\textbf{Lehrkraft} & 1 & 2 & 3 \\\\\n", "\\hline\n", "\\text{Arno} & \\text{Hilbert} & \\text{Gauß} & \\text{Fermat} \\\\\n", "\\text{Bruno} & \\text{Gauß} & \\text{Hilbert} & \\text{Fermat} \\\\\n", "\\text{Chloe} & \\text{Hilbert} & \\text{Fermat} & \\text{Gauß} \\\\\n", "\\text{David} & \\text{Fermat} & \\text{Hilbert} & \\text{Gauß} \\\\\n", "\\text{Emma} & \\text{Hilbert} & \\text{Gauß} & \\text{Fermat} \\\\\n", "\\hline\n", "\\end{array}\n", "\\hspace{2ex}\n", "\\begin{array}{|l |c c c c c|}\n", "\\hline\n", "\\textbf{Gymnasium} & 1 & 2 & 3 & 4 & 5 \\\\\n", "\\hline\n", "\\text{Fermat} & \\text{Arno} & \\text{Emma} & \\text{Chloe} & \\text{David} & \\text{Bruno} \\\\\n", "\\text{Gauß} & \\text{Chloe} & \\text{David} & \\text{Arno} & \\text{Emma} & \\text{Bruno} \\\\\n", "\\text{Hilbert} & \\text{Bruno} & \\text{Emma} & \\text{Arno} & \\text{David} & \\text{Chloe} \\\\\n", "\\hline\n", "\\end{array}\n", "$$\n", "\n", "1. Überführen Sie das Problem in ein für den erweiterten und modifizierten GSA geeignete Form.\n", "2. Führen Sie den Algorithmus zweimal durch, wobei sich einmal die Schulen und einmal die Lehrer bewerben.\n", "3. Untersuchen Sie die sich ergebenden stabilen Matchings auf ihre Eigenschaften. Welche Vermutung haben Sie?\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "3bbd6838-f879-4bcd-a8c8-b04afe52aa80", "metadata": {}, "source": [ "
\n", " Bitte erst der nach obiger Aufgabe öffnen. \n", "\n", "Sie hatten ja schon bewiesen, dass der Gale-Shapley-Algorithmus für jede beliebige Ausführung eines Matching-Problems immer das gleiche stabile Matching liefert, vorausgesetzt die sich bewerbende Gruppe bleibt gleich. Das stabile Matching, was er dabei berechnet, ist aber zudem auch immer optimal für die sich bewerbende Gruppe und gleichzeitig pessimal für die annehmende Gruppe. Konkret heißt das, wenn wir den Gale-Shapley-Algorithmus so ausführen, dass sich die Ärzte bewerben, erhalten wir ein stabiles Matching, in dem jeder einzelne Arzt dem für ihn bestmöglichen Krankenhaus zugeordnet ist, für das ein stabiles Matching existiert. Gleichzeitig erhält jedes Krankenhaus dem am schlechtesten passenden Arzt, der für es in einem stabilen Matching möglich ist. Aufgrund dieser Tatsache werden die beiden Ausführungsvarianten des Gale-Shapley-Algorithmus auch Krankenhaus- bzw. Arzt-orientiert genannt werden.\n", "\n", "Diese Eigenschaft bleibt auch erhalten, wenn wir ein Stable Marriage Problem betrachten, was auf der Hand liegt, da der erweiterte und modifizierte Gale-Shapley-Algorithmus nur eine Verallgemeinerung der Originalversion ist.\n", "\n", "Wir möchten zumindest die Optimalität des Algorithmus beweisen, begnügen uns aber damit, das nur für die Orignalversion (also für ein Stable Marriage Problem mit $n$ Ärzten und $n$ Krankenhäusern) zu machen.\n", " \n", "
" ] }, { "cell_type": "markdown", "id": "08bccd6b-d0d3-418e-96b2-b113ea3132d2", "metadata": {}, "source": [ "
\n", "\n", "### Aufgabe 15\n", "***\n", "Beweisen Sie, dass der Arzt-orientierte GSA das Arzt-optimale stabile Matching liefert.\n", " \n", "Tipp: Schau Sie sich den Beweis von Aufgabe 7 genau an.\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "6bf1cf4d-790a-4c5d-a076-e669dce082fe", "metadata": { "tags": [] }, "source": [ "
\n", "\n", "### Aufgabe 16\n", "***\n", "Beweisen oder widerlegen Sie. Sind das Krankenhaus-optimale und das Arzt-optimale Matching gleich, gibt es für den vorliegenden Fall nur genau dieses stabile Matching.\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "fd41baca-80c7-4f44-baff-31aceb5a46fa", "metadata": {}, "source": [ "Zum Ende des Notebooks soll noch kurz die bekannteste Anwendung des Gale-Shapley-Algorithmus thematisiert werden, das *National Resident Matching Program* (kurz NRMP). Es handelt sich dabei um eine Organisation, die die Zuordnung von Medizinabsolventen auf Krankenhausstellen vornimmt. Seit 1952 wurde der Gale-Shapley-Algorithmus genau in der in Aufgabe 13 dargestellten Form verwendet. Diese wurde 1998 durch eine veränderte Variante ersetzt, die aber sehr ähnlich zum Gale-Shapley-Algorithmus ist. Der Grund für den Einsatz des Algorithmus war, dass die Zuteilung der Plätze Anfang der 1950er-Jahre immer chaotischer wurde. Nachvollziehbarerweise zögerten die Medizinabsolventen ihre Zusage für eine Stelle stark heraus, in der Hoffnung eine noch bessere Stelle würde ihnen ein Angebot machen, was andersherum dazu führte, dass die Krankenhäuser eine immer geringe Chance darin sahen, ihren Wunsch-Medizinabsolventen zu bekommen. Als Resultat dessen stellten die Krankenhäuser zeitlich befristete Angebote aus, sodass es sich soweit zuspitzte, dass Absolventen sich innerhalb von 12 Stunden zwischen möglicherweise dutzenden Angeboten entscheiden mussten. Der Grund für dieses Chaos war, dass die Zuordnung niemals stabil war und sich so jedes Krankenhaus und auch jeder Medizinabsolvent noch verbessern konnten. Entsprechend wurde beschlossen einen Algorithmus zu entwickeln, der zentral ein stabiles Matching bestimmen sollte. Herauskam der Gale-Shapley-Algorithmus, der aber interessanterweise erst 1962 von David Gale und Lloyd Shapley veröffentlicht wurde. Damit war das NRMP den beiden Forschern etwa 10 Jahr zuvorgekommen, ohne das es jemand mitbekommen hatte." ] }, { "cell_type": "markdown", "id": "06fbce3e-4f18-4b04-acbc-9eedc766b4b9", "metadata": {}, "source": [ "
\n", "\n", "### Ausblick-Aufgabe\n", "***\n", "In diesem Notebook haben wir uns das Konzept von **stabilen Matchings** genauer auseinandergesetzt. Ausgehend vom sogenannten **Krankenhäuser/Assistenzärzte-Problem** haben wir versucht zu klären, ob es für jeden Fall ein stabiles Matching gibt und wie sich ein solches bestimmen lässt. Dazu haben wir einen Spezialfall unseres Problems betrachtet, nämlich das **Stable Marriage Problem**. Für diesen Fall konnten wir unsere beiden Fragen beantworten und haben mit dem **Gale-Shapley-Algorithmus** eine Möglichkeit gefunden, mit dem sich immer ein stabiles Matching für das Stable Marriage Problem berechnen lässt. Darüber hinaus haben wir festgestellt, dass sich der Gale-Shapley-Algorithmus in seiner **erweiterten und modifizierten Version** auch zum Lösen jedes Krankenhäuser/Assistenzärzte-Problems eignet. Außerdem haben wir einige spannende Eigenschaften des Algorithmus entdeckt. Zum einen liefert er trotz seines teilweisen **Nondeterminismus** immer das gleiche stabile Matching, wenn die anfragende Gruppe gleich bleibt. Zum anderen ist das so erhaltene stabile Matching sogar **optimal** für die sich bewerbende Gruppe und gleichzeitig **pessimal** für die annehmende Gruppe.\n", " \n", "Bisher haben wir in diesem Notebook allerdings nur Situationen betrachtet, in der wir ein stabiles Matching zwischen zwei voneinander disjunkten Mengen bestimmen mussten. Was passiert aber, wenn wir nur noch eine einzige Menge betrachten? Eine passende Situation dazu ist, dass $2n$ Schüler\\*innen auf $n$ Zimmer in einer Jugendherberge aufgeteilt werden müssen und jeder von ihnen eine Präferenzliste über all seine Mitschüler\\*innen führt. Hier stellen sich dann quasi die gleichen Fragen, wie beim Krankenhäuser/Assistenzärzte-Problem: Existiert für alle Fälle ein stabiles Matching? Gibt es einen funktionierenden Algorithmus?\n", "
" ] }, { "cell_type": "markdown", "id": "bb01cbb2-77c3-4431-bf47-11c4abe9e9a8", "metadata": {}, "source": [ "### Literaturverzeichnis \n", "***\n", "* *National Resident Matching Program*, in: Wikipedia, die freie Enzyklopädie. Zuletzt abgerufen am: 17.09.2024, https://de.wikipedia.org/wiki/National_Resident_Matching_Program\n", "* *Stable Marriage Problem*, in: Wikipedia, die freie Enzyklopädie. Zuletzt abgerufen am: 11.09.2024, https://de.wikipedia.org/wiki/Stable_Marriage_Problem.\n", "* D. Gusfield, R.W. Irving. *The Stable Marriage Problem: Structure and Algorithms*. The MIT Press, Cambridge, Massachusetts, London, 1989.\n", "* J.M. Harris, J.L. Hirst, M.J. Mossinghoff. *Combinatorics and graph theory*. Springer-Verlag, New York, 2000." ] } ], "metadata": { "kernelspec": { "display_name": "Julia 1.4.2", "language": "julia", "name": "julia-1.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.4.2" } }, "nbformat": 4, "nbformat_minor": 5 }