From a38e228f197bec42e1feee96db51f08f6d00f205 Mon Sep 17 00:00:00 2001 From: jsboige Date: Tue, 22 Sep 2026 02:45:15 +0200 Subject: [PATCH] =?UTF-8?q?fix(argument-analysis,#17331):=20Ontology=5FVir?= =?UTF-8?q?tues=20re-anchored=20on=20HEAD=20owl=20=E2=80=94=204=20stale-cl?= =?UTF-8?q?aims=20(resource-drift)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Root cause: committed outputs were executed against the pre-resync blob (848,820 bytes); #13554 updated argumentum_virtues.owl to 1,199,984 bytes with 666 new AnnotationAssertion — every re-execution diverged from the published lectures. Fix: full re-exec on HEAD data (0 error, ec 1-9) + 8 markdown cells re-anchored on the NEW outputs: - F1 arithmetic: 1,199,984 bytes / 1,178,524 chars / 21,460 gap (was 848,820/846,857/1,963) - F2 inventory: graph 3,083 triplets (was 2,639), table closes exactly; narrative of the 444 new literal triplets (aifAttackType 222 + aifAttackTypeProvenance 222; aifAttackedNode objects are IRIs -> 0 triplet) - F3 broader chain: ...correctDeductions -> inferentialMastery -> validArgument (validReasoning no longer exists) - F4 badTenorOf myth removed from 5 cells (intro, S6 intro, lecture 18, Exercice 3 bonus, S8 links): verified firsthand badTenorOf = 0 occurrence in argumentum_fallacies.owl AND none of the 14 virtue scheme names appears there; fallacies classify via inScheme -> fallacyScheme. Bonus rewritten executable (explore the asymmetry itself) Code sources byte-identical to origin/main; outputs = fresh re-exec. Co-Authored-By: Claude Sonnet 5 --- .../Argument_Analysis_Ontology_Virtues.ipynb | 457 +++++++++++++----- 1 file changed, 347 insertions(+), 110 deletions(-) diff --git a/MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argument_Analysis_Ontology_Virtues.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argument_Analysis_Ontology_Virtues.ipynb index e3caea2a48..c3c9633e1d 100644 --- a/MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argument_Analysis_Ontology_Virtues.ipynb +++ b/MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argument_Analysis_Ontology_Virtues.ipynb @@ -3,7 +3,16 @@ { "cell_type": "markdown", "id": "e33a32b5", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.003005, + "end_time": "2026-09-22T00:40:33.282623", + "exception": false, + "start_time": "2026-09-22T00:40:33.279618", + "status": "completed" + }, + "tags": [] + }, "source": [ "# Ontologie des vertus argumentatives — le pôle miroir des sophismes (SKOS + AIF)\n", "\n", @@ -22,8 +31,9 @@ "multilingue. Un même projet, deux choix de modelisation opposes : c'est la lecon centrale\n", "de ce notebook.\n", "\n", - "Le lien entre les deux poles est la propriete AIF `aif:goodTenorOf` (le *bon tenor* d'un\n", - "schema d'argument de Walton), miroir exact du `badTenorOf` cote sophismes.\n", + "Le lien le plus structurant du pôle vertus est la propriété AIF `aif:goodTenorOf` (le *bon\n", + "ténor* d'un schéma d'argument de Walton) -- sans propriété duale côté sophismes, qui\n", + "classent leurs dérives par `inScheme` (voir §6).\n", "\n", "**Objectifs pedagogiques**\n", "1. Charger une ontologie OWL/XML que `rdflib` ne parse pas nativement, via un pont vers un graphe RDF.\n", @@ -34,7 +44,16 @@ { "cell_type": "markdown", "id": "822970f5", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.002998, + "end_time": "2026-09-22T00:40:33.287789", + "exception": false, + "start_time": "2026-09-22T00:40:33.284791", + "status": "completed" + }, + "tags": [] + }, "source": [ "## 1. SKOS, AIF et le format OWL/XML\n", "\n", @@ -62,7 +81,16 @@ { "cell_type": "markdown", "id": "16840649", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.002004, + "end_time": "2026-09-22T00:40:33.291799", + "exception": false, + "start_time": "2026-09-22T00:40:33.289795", + "status": "completed" + }, + "tags": [] + }, "source": [ "## 2. Charger l'ontologie : essai des outils SOTA, puis pont OWL/XML -> RDF\n", "\n", @@ -76,11 +104,19 @@ "id": "b5dd1e9b", "metadata": { "execution": { - "iopub.execute_input": "2026-07-09T22:05:21.421335Z", - "iopub.status.busy": "2026-07-09T22:05:21.419856Z", - "iopub.status.idle": "2026-07-09T22:05:21.573259Z", - "shell.execute_reply": "2026-07-09T22:05:21.572801Z" - } + "iopub.execute_input": "2026-09-22T00:40:33.297794Z", + "iopub.status.busy": "2026-09-22T00:40:33.297794Z", + "iopub.status.idle": "2026-09-22T00:40:33.550624Z", + "shell.execute_reply": "2026-09-22T00:40:33.550089Z" + }, + "papermill": { + "duration": 0.257354, + "end_time": "2026-09-22T00:40:33.551147", + "exception": false, + "start_time": "2026-09-22T00:40:33.293793", + "status": "completed" + }, + "tags": [] }, "outputs": [ { @@ -88,8 +124,8 @@ "output_type": "stream", "text": [ "Fichier : argumentum_virtues.owl\n", - "Existe : True (taille = 848,820 octets)\n", - "Longueur du texte OWL/XML : 846,857 caracteres\n", + "Existe : True (taille = 1,199,984 octets)\n", + "Longueur du texte OWL/XML : 1,178,524 caracteres\n", "\n", "[rdflib] parse direct impossible (TypeError) : le fichier est en OWL/XML\n", " fonctionnel (), pas en RDF/XML -> rdflib ne lit pas cette serialisation.\n", @@ -148,9 +184,18 @@ { "cell_type": "markdown", "id": "590f118e", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.002656, + "end_time": "2026-09-22T00:40:33.556626", + "exception": false, + "start_time": "2026-09-22T00:40:33.553970", + "status": "completed" + }, + "tags": [] + }, "source": [ - "**Lecture chiffree — deux echecs differents, chiffres par le fichier lui-meme.** Le fichier pese `848,820 octets` pour `846,857 caracteres` de texte : 1,963 octets d'ecart, soit la somme des octets supplementaires des caracteres non ASCII (libelles accentues) une fois encodes en UTF-8. Face a lui, les deux outils SOTA echouent chacun de son cote : rdflib leve `parse direct impossible (TypeError)` — son parseur attend du RDF/XML — et owlready2 lit sans erreur mais `recompose 0 classe(s)` : le contenu SKOS n'est pas reconstruit. Un echec de format, un echec de reconstruction. Le pont de la cellule suivante ne contourne aucun moteur : il alimente le vrai, les requetes SPARQL sur un graphe rdflib.\n" + "**Lecture chiffrée — deux échecs différents, chiffrés par le fichier lui-même.** Le fichier pèse `1,199,984 octets` pour `1,178,524 caractères` de texte : 21,460 octets d'écart, soit la somme des octets supplémentaires des caractères non ASCII (libellés accentués) une fois encodés en UTF-8. Face à lui, les deux outils SOTA échouent chacun de son côté : rdflib lève `parse direct impossible (TypeError)` — son parseur attend du RDF/XML — et owlready2 lit sans erreur mais `recompose 0 classe(s)` : le contenu SKOS n'est pas reconstruit. Un échec de format, un échec de reconstruction. Le pont de la cellule suivante ne contourne aucun moteur : il alimente le vrai, les requêtes SPARQL sur un graphe rdflib." ] }, { @@ -159,18 +204,26 @@ "id": "171b256b", "metadata": { "execution": { - "iopub.execute_input": "2026-07-09T22:05:21.594247Z", - "iopub.status.busy": "2026-07-09T22:05:21.593705Z", - "iopub.status.idle": "2026-07-09T22:05:21.660140Z", - "shell.execute_reply": "2026-07-09T22:05:21.659547Z" - } + "iopub.execute_input": "2026-09-22T00:40:33.563168Z", + "iopub.status.busy": "2026-09-22T00:40:33.563168Z", + "iopub.status.idle": "2026-09-22T00:40:33.620365Z", + "shell.execute_reply": "2026-09-22T00:40:33.620365Z" + }, + "papermill": { + "duration": 0.062183, + "end_time": "2026-09-22T00:40:33.621425", + "exception": false, + "start_time": "2026-09-22T00:40:33.559242", + "status": "completed" + }, + "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Graphe SKOS construit : 2,639 triplets\n", + "Graphe SKOS construit : 3,083 triplets\n", "Concepts distincts (sujets) : 224\n" ] } @@ -219,7 +272,16 @@ { "cell_type": "markdown", "id": "658ca1d2", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.002225, + "end_time": "2026-09-22T00:40:33.626703", + "exception": false, + "start_time": "2026-09-22T00:40:33.624478", + "status": "completed" + }, + "tags": [] + }, "source": [ "**Lecture** : ni `rdflib` (parseur RDF/XML) ni `owlready2` (parseur OWL/XML) ne restituent\n", "le contenu de ce fichier -- le premier parce que la serialisation n'est pas du RDF/XML, le second\n", @@ -233,7 +295,16 @@ { "cell_type": "markdown", "id": "ad1c1e05", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.002819, + "end_time": "2026-09-22T00:40:33.631178", + "exception": false, + "start_time": "2026-09-22T00:40:33.628359", + "status": "completed" + }, + "tags": [] + }, "source": [ "## 3. Structure du thesaurus : inventaire des predicats SKOS\n", "\n", @@ -246,11 +317,19 @@ "id": "7b8ad971", "metadata": { "execution": { - "iopub.execute_input": "2026-07-09T22:05:21.672240Z", - "iopub.status.busy": "2026-07-09T22:05:21.671916Z", - "iopub.status.idle": "2026-07-09T22:05:21.691273Z", - "shell.execute_reply": "2026-07-09T22:05:21.690735Z" - } + "iopub.execute_input": "2026-09-22T00:40:33.637195Z", + "iopub.status.busy": "2026-09-22T00:40:33.637195Z", + "iopub.status.idle": "2026-09-22T00:40:33.659979Z", + "shell.execute_reply": "2026-09-22T00:40:33.659979Z" + }, + "papermill": { + "duration": 0.027917, + "end_time": "2026-09-22T00:40:33.661091", + "exception": false, + "start_time": "2026-09-22T00:40:33.633174", + "status": "completed" + }, + "tags": [] }, "outputs": [ { @@ -258,15 +337,17 @@ "output_type": "stream", "text": [ "Predicats du graphe (frequence) :\n", - " 446 skos:prefLabel\n", " 446 skos:definition\n", + " 446 skos:prefLabel\n", " 410 rdfs:seeAlso\n", " 224 rdf:type\n", " 223 skos:inScheme\n", - " 222 aif:goodTenorOf\n", " 222 rdfs:comment\n", + " 222 virt:aifAttackTypeProvenance\n", + " 222 aif:goodTenorOf\n", " 222 skos:narrower\n", " 222 skos:broader\n", + " 222 virt:aifAttackType\n", " 1 skos:hasTopConcept\n", " 1 skos:topConceptOf\n", "\n", @@ -315,15 +396,33 @@ { "cell_type": "markdown", "id": "76ec68e5", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.003102, + "end_time": "2026-09-22T00:40:33.666891", + "exception": false, + "start_time": "2026-09-22T00:40:33.663789", + "status": "completed" + }, + "tags": [] + }, "source": [ - "**Lecture** : le thesaurus compte **223 concepts** (chacun avec un `prefLabel`), **446 définitions** (223 x 2 langues), **222 relations `broader`** et autant de `narrower` -- la double declaration est la convention SKOS (chaque lien hiérarchique est posé dans les deux sens). Le recensement se referme sur le compte de construction : la construction annonçait `Graphe SKOS construit : 2,639 triplets` ; la table des prédicats, additionnée, rend exactement ce total : 446 + 446 + 410 + 224 + 223 + 222 + 222 + 222 + 222 + 1 + 1 = 2,639 -- aucun triplet hors inventaire. Deux cohérences de plus se lisent dans la même table : `Concepts distincts (sujets) : 224` contre 223 avec prefLabel -- le 224e sujet est le scheme lui-même, qui n'est pas un concept mais porte l'unique `hasTopConcept` ; et le compte `rdf:type` monte aussi à 224 : chaque concept typé, plus le scheme. Contrairement au pôle sophismes, il n'y a **aucun** `NamedIndividual` ni `ObjectPropertyAssertion` : la connaissance est portée par la **hiérarchie de concepts** et les **annotations multilingues**, pas par un graphe de relations entre individus. Deux ontologies sœurs, deux paradigmes : ABox relationnel pour les sophismes, thesaurus SKOS pour les vertus." + "**Lecture** : le thesaurus compte **223 concepts** (chacun avec un `prefLabel`), **446 définitions** (223 x 2 langues), **222 relations `broader`** et autant de `narrower` -- la double declaration est la convention SKOS (chaque lien hiérarchique est posé dans les deux sens). Le recensement se referme sur le compte de construction : la construction annonçait `Graphe SKOS construit : 3,083 triplets` ; la table des prédicats, additionnée, rend exactement ce total : 446 + 446 + 410 + 224 + 223 + 222 + 222 + 222 + 222 + 222 + 222 + 1 + 1 = 3,083 -- aucun triplet hors inventaire. Deux entrées de la table racontent l'évolution du pôle vertus : `virt:aifAttackType` et `virt:aifAttackTypeProvenance` (222 chacune) sont arrivées avec le resync #13554 de l'ontologie source, qui a porté le fichier de 848,820 à 1,199,984 octets en ajoutant 666 `AnnotationAssertion` (222 `aifAttackType`, 222 `aifAttackedNode`, 222 `aifAttackTypeProvenance`). Le pont n'en charge que 444 : les objets de `aifAttackedNode` sont des IRI, et pour ces prédicats le pont ne retient que les littéraux -- d'où l'ancien 2,639 + 444 = 3,083, mesuré comme annoncé. Deux cohérences de plus se lisent dans la même table : `Concepts distincts (sujets) : 224` contre 223 avec prefLabel -- le 224e sujet est le scheme lui-même, qui n'est pas un concept mais porte l'unique `hasTopConcept` ; et le compte `rdf:type` monte aussi à 224 : chaque concept typé, plus le scheme. Contrairement au pôle sophismes, il n'y a **aucun** `NamedIndividual` ni `ObjectPropertyAssertion` : la connaissance est portée par la **hiérarchie de concepts** et les **annotations multilingues**, pas par un graphe de relations entre individus. Deux ontologies sœurs, deux paradigmes : ABox relationnel pour les sophismes, thesaurus SKOS pour les vertus." ] }, { "cell_type": "markdown", "id": "4833edd2", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.002014, + "end_time": "2026-09-22T00:40:33.670899", + "exception": false, + "start_time": "2026-09-22T00:40:33.668885", + "status": "completed" + }, + "tags": [] + }, "source": [ "## 4. La hiérarchie SKOS : du concept racine aux familles\n", "\n", @@ -337,24 +436,26 @@ "id": "c01f48b7", "metadata": { "execution": { - "iopub.execute_input": "2026-07-09T22:05:21.703250Z", - "iopub.status.busy": "2026-07-09T22:05:21.703002Z", - "iopub.status.idle": "2026-07-09T22:05:21.846647Z", - "shell.execute_reply": "2026-07-09T22:05:21.846052Z" - } + "iopub.execute_input": "2026-09-22T00:40:33.677514Z", + "iopub.status.busy": "2026-09-22T00:40:33.676893Z", + "iopub.status.idle": "2026-09-22T00:40:33.843671Z", + "shell.execute_reply": "2026-09-22T00:40:33.843671Z" + }, + "papermill": { + "duration": 0.170942, + "end_time": "2026-09-22T00:40:33.844833", + "exception": false, + "start_time": "2026-09-22T00:40:33.673891", + "status": "completed" + }, + "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Concept racine (topConcept) :\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Concept racine (topConcept) :\n", " validArgument = Argument valable\n", "\n", "Concepts les plus ramifies (tetes de familles) :\n", @@ -365,12 +466,18 @@ " 5 narrower | acceptableInformalLogic Logique informelle solide\n", " 4 narrower | tangibleEvidence Preuves tangibles\n", " 4 narrower | credibleSources Sources crédibles\n", - " 4 narrower | wellregardedNewspaper Source bien évaluée\n", + " 4 narrower | wellevaluatedSource Source bien évaluée\n", " 4 narrower | validSyllogism Syllogisme valide\n", - " 4 narrower | perfectmodeSyllogism Syllogisme de mode parfait\n", + " 4 narrower | perfectmodeSyllogism Syllogisme de mode parfait\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "Chaine broader depuis une feuille (absenceOfInternalContradictions) :\n", - " absenceOfInternalContradictions -> coherentDemonstration -> correctDeductions -> validReasoning -> validArgument\n" + " absenceOfInternalContradictions -> coherentDemonstration -> correctDeductions -> inferentialMastery -> validArgument\n" ] } ], @@ -422,15 +529,33 @@ { "cell_type": "markdown", "id": "e786ec67", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.003132, + "end_time": "2026-09-22T00:40:33.850944", + "exception": false, + "start_time": "2026-09-22T00:40:33.847812", + "status": "completed" + }, + "tags": [] + }, "source": [ - "**Lecture** : le concept racine est **`validArgument` (\"Argument valable\")** -- toute vertu est une facette de l'argument valable. L'arithmétique confirme un arbre : `Concepts (avec prefLabel) : 223`, racine unique, 222 relations `broader` -- 223 - 1 = 222, la signature d'un arbre où chaque concept non racine a exactement un parent, pas de DAG à héritage multiple. La racine affiche 7 `narrower` : exactement les 7 familles que l'ontologie s'attribue (`223 nodes, 7 families`), tandis que la tête la plus ramifiée, `simpleInference`, en compte 8 -- une famille qui se subdivise. La lignée la plus profonde visible fait 4 sauts : de la feuille `absenceOfInternalContradictions` par `coherentDemonstration`, `correctDeductions` et `validReasoning` jusqu'à `validArgument`. Les têtes de familles (`simpleInference`, `thirdfigureSyllogism`, `acceptableInformalLogic`, `tangibleEvidence`, `credibleSources`...) recouvrent les **7 familles** annoncées : logique formelle, logique informelle, qualité des sources, des preuves, etc. Et 222 broader = 222 narrower : chaque lien hiérarchique déclaré dans les deux sens, la convention SKOS sans exception. La chaîne `broader` remonte de chaque feuille jusqu'à la racine : c'est la profondeur du thesaurus, exploitable pour situer une vertu dans sa lignée." + "**Lecture** : le concept racine est **`validArgument` (\"Argument valable\")** -- toute vertu est une facette de l'argument valable. L'arithmétique confirme un arbre : `Concepts (avec prefLabel) : 223`, racine unique, 222 relations `broader` -- 223 - 1 = 222, la signature d'un arbre où chaque concept non racine a exactement un parent, pas de DAG à héritage multiple. La racine affiche 7 `narrower` : exactement les 7 familles que l'ontologie s'attribue (`223 nodes, 7 families`), tandis que la tête la plus ramifiée, `simpleInference`, en compte 8 -- une famille qui se subdivise. La lignée la plus profonde visible fait 4 sauts : de la feuille `absenceOfInternalContradictions` par `coherentDemonstration`, `correctDeductions` et `inferentialMastery` jusqu'à `validArgument`. Les têtes de familles (`simpleInference`, `thirdfigureSyllogism`, `acceptableInformalLogic`, `tangibleEvidence`, `credibleSources`...) recouvrent les **7 familles** annoncées : logique formelle, logique informelle, qualité des sources, des preuves, etc. Et 222 broader = 222 narrower : chaque lien hiérarchique déclaré dans les deux sens, la convention SKOS sans exception. La chaîne `broader` remonte de chaque feuille jusqu'à la racine : c'est la profondeur du thesaurus, exploitable pour situer une vertu dans sa lignée." ] }, { "cell_type": "markdown", "id": "7102e28d", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.003002, + "end_time": "2026-09-22T00:40:33.855947", + "exception": false, + "start_time": "2026-09-22T00:40:33.852945", + "status": "completed" + }, + "tags": [] + }, "source": [ "## 5. Un thesaurus bilingue : `prefLabel` et `definition` par langue\n", "\n", @@ -444,39 +569,35 @@ "id": "bd6dafc0", "metadata": { "execution": { - "iopub.execute_input": "2026-07-09T22:05:21.850431Z", - "iopub.status.busy": "2026-07-09T22:05:21.850224Z", - "iopub.status.idle": "2026-07-09T22:05:21.969548Z", - "shell.execute_reply": "2026-07-09T22:05:21.968896Z" - } + "iopub.execute_input": "2026-09-22T00:40:33.860956Z", + "iopub.status.busy": "2026-09-22T00:40:33.860956Z", + "iopub.status.idle": "2026-09-22T00:40:34.024206Z", + "shell.execute_reply": "2026-09-22T00:40:34.023595Z" + }, + "papermill": { + "duration": 0.166268, + "end_time": "2026-09-22T00:40:34.024206", + "exception": false, + "start_time": "2026-09-22T00:40:33.857938", + "status": "completed" + }, + "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Couverture prefLabel par langue : [('fr', 223), ('en', 223)]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Couverture prefLabel par langue : [('fr', 223), ('en', 223)]\n", "Couverture definition par langue : [('fr', 223), ('en', 223)]\n", "\n", - "Echantillon bilingue (concept : FR / EN) :\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Echantillon bilingue (concept : FR / EN) :\n", " absenceOfInternalContradictions Cohérence interne | Absence of internal contradictions\n", - " acceptableDefinitions Définitions claires | Acceptable definitions\n", " acceptableInformalLogic Logique informelle solide | Acceptable informal logic\n", " acceptableRhetoric Rhétorique acceptable | Acceptable rhetoric\n", - " acceptanceOfUncertainty Acceptation de l'incertitude | Acceptance of uncertainty\n", + " acceptanceOfUncertainty Acceptation de l’incertitude | Acceptance of uncertainty\n", " activeListening Écoute active | Active listening\n", + " adequateComparison Comparaison adéquate | Adequate comparison\n", "\n", "Definition FR de 'absenceOfInternalContradictions' :\n", " Un raisonnement est valide si ses prémisses et sa conclusion ne se contredisent pas.\n" @@ -517,7 +638,16 @@ { "cell_type": "markdown", "id": "0c5aa193", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.003468, + "end_time": "2026-09-22T00:40:34.030490", + "exception": false, + "start_time": "2026-09-22T00:40:34.027022", + "status": "completed" + }, + "tags": [] + }, "source": [ "**Lecture** : la couverture est **symetrique -- 223 concepts en FR et 223 en EN** pour les\n", "`prefLabel` comme pour les `definition`. C'est un thesaurus reellement bilingue, ou chaque vertu\n", @@ -529,14 +659,20 @@ { "cell_type": "markdown", "id": "35e59e7b", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.002991, + "end_time": "2026-09-22T00:40:34.035581", + "exception": false, + "start_time": "2026-09-22T00:40:34.032590", + "status": "completed" + }, + "tags": [] + }, "source": [ - "## 6. Le pont AIF `goodTenorOf` : vertus <-> schemas de Walton\n", + "## 6. Le pont AIF `goodTenorOf` : vertus <-> schémas de Walton\n", "\n", - "Chaque vertu est le **\"bon tenor\"** d'un schema d'argument de Walton (`aif:goodTenorOf`) --\n", - "la maniere correcte d'instancier ce schema. Ce sont **les mêmes 14 schemes** que ceux relies aux\n", - "sophismes cote `Ontology_AIF` : `goodTenorOf` et `badTenorOf` sont les deux faces d'un même\n", - "schema. Visualisons leur distribution.\n" + "Chaque vertu est le **« bon ténor »** d'un schéma d'argument de Walton (`aif:goodTenorOf`) -- la manière correcte d'instancier ce schéma. Ce lien n'a pas de propriété duale côté sophismes : le pôle `Ontology_AIF` classe ses dérives par `inScheme` vers le scheme `fallacyScheme`, et aucun des 14 noms de schémas ci-dessous n'apparaît dans `argumentum_fallacies.owl` (vérifié : 0 occurrence) -- le miroir vertu/sophisme se lit par le vocabulaire commun de Walton, il n'est pas matérialisé par une propriété du dépôt. Visualisons la distribution." ] }, { @@ -545,11 +681,19 @@ "id": "ada48f7b", "metadata": { "execution": { - "iopub.execute_input": "2026-07-09T22:05:21.972772Z", - "iopub.status.busy": "2026-07-09T22:05:21.972609Z", - "iopub.status.idle": "2026-07-09T22:05:22.443652Z", - "shell.execute_reply": "2026-07-09T22:05:22.443169Z" - } + "iopub.execute_input": "2026-09-22T00:40:34.041409Z", + "iopub.status.busy": "2026-09-22T00:40:34.040311Z", + "iopub.status.idle": "2026-09-22T00:40:34.529051Z", + "shell.execute_reply": "2026-09-22T00:40:34.528448Z" + }, + "papermill": { + "duration": 0.491466, + "end_time": "2026-09-22T00:40:34.529051", + "exception": false, + "start_time": "2026-09-22T00:40:34.037585", + "status": "completed" + }, + "tags": [] }, "outputs": [ { @@ -575,7 +719,7 @@ }, { "data": { - "image/png": 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+PvTdzZkzZ+jfbHteG96klOdpaszfWGqPHZr1MSIvTehoTuc1b978mO3K3wPT8Lvg+tBskqZu4X8P4WhqSb9R39eO5pisL804eT3b0DfZ9P8P9sdLznGRFBxzHHs0hWW5vLvuusv2ZXgTRZYx2EeMv4VatWol+vfN56EfRTRccJ0YTZTtSfNIpGS9aH7JewWbs9NlgGOFpoZBwb6RHp8VrFNwu7PenTp1su8w37Se6WhaymeGx77s1q2bfdbQtNFPx6A699xzT2g6PlcivXdC0vqzmO8cRiflOyfY943m54zmyjG6d+/eePPkuAj2i/QjZ9O0OiH5/+/34fOLxp8HxAqFPBGJWX5gFd+vjRC1cOFCC3/+C2Pt2rXWX4DgE3z49vnhffqCI9ClpT59+tgXOl/yZ599tvVNCe8nEw19MfgipW8GfWZYfk6SIp1ghi8/68eokIS6cJGeS64yZcrE+zcnqiwnfSbCnw+GF/YLfdLC90vFihVDy53Q+/gv7KQGIrY1+5z+aoQS3uuhhx6y36Uk5OH666+3beuH5eYEjBMv3x/Eryc4YQpfV06E/HrSt4V+ovTd4yIFo3nS14R+eknBiXT4YAJ+WwYvghDEONFlH9EviOWgf1lSjqVoCDxcXKDPG/udfoT0aQr252EQBS4y8J6JSWxfM8gGwdn31Qk+6FOXlOPHhy76HoU/z8WU4PZI6bHDKH/g7z0cFz2COE6YF32SwteJ4ypS3+Mglou+UcEgx0ibPugQAoO/8yEqJcdFUvh1D19P3pfj1P/eox9WeNhPzt93eN9THwAZoIO/J7YD6+SP6ZSuVzi/HuGfpWzD8ODJtOHbAwSs4Lz4P8dMMBxHm47gxPdKUKT3SEhafxbz90lfumjryt8XF1IS+qzx4S2h2yQlJwwGj5Pw4ywz0+iaIhKzuMrN1ccZM2bYSRf/50M8OKomXyhcHacTeST+iyzS1d+kiPaFwdXM4JVJvtx++uknO5kidFKJYFACBlXh5DiaV155xTqUc1WUgUU4CWS+w4cPtxPncMld/qSuSzSRhiyPNox58ESM/cIAHKNHj444bfjJd1LmGQ3bicouxwrvx7w52SSQMYx4SkdQ5aSYq/sM6sEVam7rQegj/Hl+3gxjztX5cFyJ96h6UJ1iUIMPP/zQBrFhPzOce3Bo/JTiAgiDEREgOfY4QaRCQCCLNABQUo8ljhsG6WBQBbYBy06FjMF2eC78JDQxie1rv02p/FAli4SLIkmZZ2LvdbyOnXDMJ6GbeSeldQEVoueff94CMEGOYOfxM4PSUGmlksJnpx8pNbnHxfGQ0r9vwhTHX6QwyGiK3FaFz01ufcNxyHZmsJ/MPGpyWjuRn8XRhH/WcLGF45DBcxKyatUqazlBJTEpOE7CL0BmZgp5IhLTCHScDPNhzwkJV0D9aJigWQlXwn3lLiUSuvLHFcxIo5lxlTW8skIlgADAg6ZMjBA6dOhQ169fv6hD03PyzHy4X1BwOSI1e4uEE0fm/csvvxzzu/Dn/JXn8PUJv+qeFtgv3JyWE+i0urIabT4ED6pKVNyCV6EjNYFL7rJwIsmIkTQXoqkmoc83CYNv1sR+SMoxyPQPPPCAPbjCzskpYYmwn9iJGs2lghctGPkU/v5lXFjgWCCEBe8Jxsl8WmC9eXBM87fI3ybDtdOkl/XifamuJKWalxACD1fuufiQmr/rpEjOsROOmzAHq7lBXPAJYvvQxJGRMFN6oYaQR/WN+TCyoR9t1oc8LkDQAoDjhMqrl5rjItrfi1931jP4OcjnHjdmT6v9xkUStl34zd45macpJRfQuJDmRdoXqeHXk8/SYDWKETzDgyfThu93MGJncF78n+8z/qaD1bxI07GOfL8FL6REeo8T+VnM3ycjh0ZbV9YpPDhG0rp1a7vfHRclIt3qY+HChdZKgRFok4rjhJFKY4Waa4pITPNVO77I6f8Sfm88TsLpt8cJTDjCTLDvTTSEMz99pC9IqhWcvHhU68Kbo4QP2001gH45XP2M1o8peNU0eJV0yZIltk5Jwes5oaI6xE3dPU5KaBoYxNVQrnKG91Pk6n5aY78wBD5f4uE4GWVo7OSKtp8ibUOaa0U6iWUeyRmCnMBOCKBJLRXa8Ht10UeM7Tps2LCI+5mmTaB5E/2Gwo8twkz4MObRPPPMM6GfWVf+TUXG909lO3ASF6zMcpLEsZEanMyGX8UnnMIvO6GCaSJVrZNbAWA9mB/h5Lvvvou6TdNCco6dcFQi2A4cG8HmgfTT9P2vPI4b9sujjz56zHz4jErKMelPhKnIcKwFK3kEfZbHDzcfPGlOzXER7W+Ozxw+45566ql4246bWLMtuIVIWuE2EeG34Yi038AtKlIq0i0U+NsiaIbfUiP4t+hdfvnlbunSpfE+u/mco9kx+8f302Q6mmlz0Sh4DHALHsKcv00A0/F88L3Zh0yXnp/FbPtLL73UblUQbCq+bds2u/jDsZeUyhsXKbjgQYgL//7kYlHnzp0tTAYvZiSE447KfPDvIrNTJU9EYhpXT/nQ9ve+CQ95fAFwFZ6rgjR7pJkSX1qrV6+2KhlfQok13+Bkmz4vNIXipJsTGwZo4L2pUjAfmgDxZcmXCFWX8Hs28aVHcz2u1NNHZM2aNXYiwMlOQv0JWG6qeFdffbVNy5VIloMTguCAHwnhHk70/+K96aTPiQDvzX2OCMZBrM+IESPs/wyiQuDzFaG0xE1saebIFzVVEZaN5eIkiucJ5bx/crBv0b9/f+uXScCh+SPbnhNOfuaEge3GCQ3VtfB7XjEPTpoee+wx62fDNH4AgkguvPBCm473JNAEm2qCkxnmx/oyLcvFle6NGzdaVYX1Zl+wjTlh5Bhi33LiyP3UODHiNYmhEkPIpPkixyYBnvnTjNk39eP4IQBwrNK8lL42zz77rC1/Ys2iEkKI4UIAxyjHPf1k2L6sOyei4N5hbANO+qmm+CZzXI3nd8FBY5KCY5TjhnVl0Aa2GSd+DKhBJYuf00Jyjp1IaG7LdufEliasLJe/X2bw75cTd+bP9PxN8r4cv2wrBsagWsy9/RJCpZEKCSGC0BAcaAl8TvoBcDjuvNQcF4RYTurpT8pJNJVAf09BWigQ6pkvzUGp7Pj7jqbljbjpA0pzaP6GfCWbY8/3ayWc0aSPz8Dwil9yEIRods/fmB98hc9y+v1RbWcdWVeqYvz98b0SrIz17dvXuhQw4BWDqFDR5m+HZWK/+KodA7FwDzi+r1asWGH7ku8YmuASUv33Bcck+5H58j3G3wDfFcntb3g8Pov5/ORiBsc9g9LwecY68RmZ1Psf0iqH7cN3Os1JGcyL71zWlYsFf/75p23P8O/aaPhcIPRzvMSM9B7eU0TkeOMeV3zccf+jSBimmvtIcY8chnXm/kXck4z7KPlh1f3w2NGGsJ8zZ47dy8ffdiA4PPWoUaNsOH3uAcQ94ZYvX37MLRS49xBDR3O/PqY766yz4nr16hW3Z8+eBNeN4eW51xVDXvO6Cy64wO5dxdDcwWGwE1v+Tz75xF7L+vPe3IeNYce5v1r4sOMMT88w9Ax3fd1119lw6NFuoRAcnj/a0ONgW4TfQoJtzz3jeJ51495dNWrUsPuUBbcL7xO871xCt6/glhnsC4YEDw6L/s4779j9o1hfhivnfRnGPXzodIZEZ4hx1p3f+X0YfguFIO55xe84vqLhdQy7z3ZlGdgH3HuNYwXcCoB15B59bD+mq1279jH34YvEb3OGK/f3u+IWG+yj4PD1mDJlit1Pj+3Ne3Ec+30ZFG2bR8J9GLklQZkyZWy+xYsXt/v0+XXzGCKd45P35TjkHnzcwmDFihUp2tfcn49pub8a9xRjyP2mTZvaPdG8aEOq+6Hbw2/7EOm4TuqxE82bb75pw7azbfgM4Z5o4X+/HsvO3wC3HOEYPO+88+w+bAw/nxT+XpHc/y8c92Xz9z8Ml9TjItJ+4L6I3E6C2zSE/41wywTmx/7hmOTemf5+fQl9NiDaNgrH7Rb4TOdvP2jz5s1xV199td3ChL+na6+91rZj+GdZam6h4I9r7kfJ8cd+47YTa9assc/6zp07x5uWv1HulcoycTzxnRV+L0J/bHfo0MHWi78VjoNIt0TgvrDc35Fb57CO/Mz9LJNzC4Xj9VnM5wKfedz2gc8k7vnJveyS8ncYtGrVKjuuuRWJ/zvn36tXrz5m2oRuoXD99dfbrRpiSTb+k95BU0REMh4Gc2FUtbTupyInFlf8udKf1MquSKyhmStNaPksizYwyIlE81X6OFPRosov6euPP/6wKiB9hGOpkqc+eSIiYn0rgjgZYoTARo0aaeuISKZ2//3320UOTuLT+7M12PdPn68Zw9ixY63JZywFPKhPnoiI2Ah3VHz8ParoJ0Zfo2i3lhARySwYkCSxewkeLwyQQh89+p+yHIwGSV8x+lUG+z5K+hkxYkRMbn6FPBERsQEBOPGg2QqDIzAiHSM+RrpRs4iIJA33ZGRgEQYU4VYqfjAWmmqKHE/qkyciIiIiIhJD1CdPREREREQkhijkiYiIiIiIxBD1yRORZOEGxVu2bLEbrgZv5CoiIiIixxd3v9u3b58rVaqUy549er1OIU9EkoWAV7p0aW01ERERkXSyadMmd8YZZ0T9vUKeiCQLFTz/4VKgQAFtPREREZEThFFaudjuz8eiUcgTkWTxTTQJeAp5IiIiIideYl1mNPCKiIiIiIhIDFHIExERERERiSEKeSIiIiIiIjFEIU9ERERERCSGKOSJiIiIiIjEEIU8ERERERGRGKKQJyIiIiIiEkMU8kRERERERGKIQp6IiIiIiEgMUcgTERERERGJIQp5IiIiIiIiMUQhT0REREREJIYo5ImIiIiIiMQQhTwREREREZEYopAnIiIiIiISQxTyREREREREYohCnoiIiIiISAxRyBMREREREYkhOdJ7AUQkc2o+tI3LkUsfISIiIpJ1LR4y32VEquSJiIiIiIjEEIU8ERERERGRGKKQJyIiIiIiEkMU8kRERERERGKIQp6IiIiIiEgMUcgT+T8TJ050pUuXdtmzZ3djx46Nme1Srly5mFofERERkYzq9ttvdyeffLLLly9f6PHll1+Gfn/o0CHXpUsXV6hQIVe4cGHXtWtXd/jw4TRfDoW8LIQD7KSTTnKtWrVyWcULL7zgTj311ESn27t3r/3B9enTx/3++++uU6dOLr0NHjzYZcuWzR7sNwIoy/XXX3+l96KJiIiISBT33nuv279/f+hRp06d0O8ee+wxt2jRIvfDDz+477//3i1cuNANGzbMpTWFvCxkypQpdrXg888/d1u2bElw2ri4uONyVSGj2rhxo11ZIQCXLFnSnXLKKcdM899//53w5apatarbunWrLd+0adPcBx984O65554TvhwiIiIiknpTp051Dz/8sJ1v8ujfv7+do6c1hbwsgqsIM2fOtIBAkKHCFfTZZ59Zxej99993NWrUcLly5bKrDPv27XM33XSTy5s3rx2IY8aMcY0aNXI9evQIvZbXzZ49O978qJ7599iwYYNNM2vWLFe/fn2XJ08eV7NmTffzzz+7ZcuWuYsuushK2ZdddpnbsWNHvPlMnjzZVa5c2eXOndtVqlTJjR8/PvQ7P9+33nrLNW7c2IJZ9erVQyVx1qlDhw5uz549oYoY1bFwLOd5551nP5cvX96mY95Me/7559synHnmmbYMIHC1adPGlrlAgQLuuuuuc9u2bQvNz7+OP+IyZcrYdFzROXLkiBs5cqQrUaKEK168uBs6dGii+y1Hjhw2/emnn+6aNWvmrr32Wjd//v9/083wfYGrrrrKmgpEs3v3btexY0dXrFgxW/4mTZq4b7/9NtFlEREREZHEvfTSS9YUk4v1o0aNckePHrXnd+3a5TZv3mzniR4/c27J+WpaUsjLIghYhKRzzjnH3XzzzRZAqNaF69u3rxsxYoRbs2aNq1atmuvZs6dbvHixe+eddyxcUFJeuXJlipZh0KBBduWC1xNe2rdv73r37u3GjRtn8/3ll1/cwIEDQ9NPnz7d/k0YYnkoZQ8YMMC9+OKL8ebLFZAHH3zQffPNN65ixYruxhtvtCpk3bp1rS8aQYZqGA+mC3f99de7jz/+2H5eunSpTUfTSLBMb775pgVJ5s8fKQGPJpMLFiywbfLrr7/aPILWrVtngZnK24wZM+wKDeGaP2xe9/jjj9u2WLJkSZK3H8Hzww8/tHbeqUFQ3L59uy3fihUr3IUXXuiaNm2qZqAiIiIiqdStWzf3008/WeGC8z/Oc3n4oguCXYn8zxRW0lKONJ2bZFgcZIQ7tGzZ0q4WEDaoBAUNGTLENW/ePHSwEaheffVVCwGgyWCpUqVStAwErBYtWtjP3bt3tzD2ySefuHr16tlzd955Z7wKI6GQqx9t27a1f1NNo/3yhAkT3G233RZvvr6f4SOPPGJXTQhnhNqCBQtaZY5qWDRUFosUKWI/U90KTksTTa7G8DwIdatXr3br168PBUF+z3tSlaRCCcIgQTp//vyuSpUqVmnkD37evHk2sAthm6D36aefutq1a0ddNt6LSiBVwH/++ceeGz16tEspqrMEWUIe1Vo8+eSTVol94403IvZF/Pfff+0R7L8oIiIiIsfi4rl38cUXWwGFc8X777/fzunAeXjRokVDP4NzxrSkkJcFEC44sX/77bft31TRqDwR/MJDHk0nPSpU9FOrVatW6DlCEwElJagMeqeddpr93zeT9M8RPnDgwAGrhhH87rrrrtA0VOhYhmjzpUkpmA8hL7XKli0bCnigoki48wEPhDiuwvA7H/IY0TL4x8q6MXgKAS/S+kbDtqaKSsB75ZVXrJpIv8qUolkmV5F8qPX+/vtv296RDB8+3MKziIiIiCRP8NyPETXPOOMMO58766yz7Dl+5rwy/Pw2tRTysgDCHOEoWIGjqSaVnGeeeSbeQUXfu+SiUhbe9JNwGC5nzpzxXhPpOd9m2ZezJ02adEyli7CU2Hz9fFIrJdsjfJn8ckV6LrHlpGlmhQoV7Gea0VKxJHA9+uijoQ+OpGx7j+1KEKa/Yrhoo5D269fPmu0GK3nBkCsiIiIi/38XKVrNcbGfbjGcv913333/91tn40XQFcm3ZKM7EmMlpDWFvBhHuKNETLPHSy+99JgBOugv1rlz54ivZRASggnNEBlAxJeUGTClQYMGoemodNGPzVu7dq07ePBgqpabKhehlGoiA7+kFCGJpo5phUFgNm3aZA8fdGhCymAmVPSON/rxMVAKA+iwfcK3Pev63XffWfPQaE0I/vjjD6vmUm1MCi4G+KadIiIiIhIdBRS6v3AOzsB5DL73wAMPhH7P+BI7d+60c0rQneqhhx5yaU0hL8bNnTvXRvKh2WN4Gbhdu3ZW5YsW8rgCQd+3Xr162QhBjAhJPzmqR75iBkIHBzT3ACFkcK+58KpVSlCxovMqy80VEfqFLV++3NYnWFlKCEGG6hV9/xh5kxE4I90eIakY4ZImpgRPBnXhD5g/3oYNG8Zr6nq8sI1pnspVH7Y5255t8d5771nZn/56BM6Elp95EPAZ6ZOBaridBq+/+uqrT8g6iIiIiMSqzz//PMHfc4787LPP2uN40uiaMY4Qx4l9pHa+hDxC06pVq6K+ntBAKGjdurXNh9Kyv6WBR5WQqha3R2DETAZCSU2Q8ihdc/sCBnshWBGkGJiFAViSihE2CbH0QaTqRbBJDcLtnDlzrE011Uy2CRVPbk9xotBxl+1CNfGOO+6wIH7rrbfa9mFZolXx/PIz+AvLTnMBQt4NN9zgfvvtt1A/SRERERHJ3LLFRRpHXyQKBkSh9EywozooWQ998rhoUKt3I5cjlxoDiIiISNa1eMj/f//iE3keRhcqbhMWjc7QJEFff/21+/HHH22ETQ4mbrEA7hUnIiIiIiIZj0KeJIr7qHEbBgYxqVGjht243N/bQ0REREREMhaFPEnQBRdcYMO/ioiIiIhI5qCBV0RERERERGKIQp6IiIiIiEgMUcgTERERERGJIeqTJyIpMr//nASH7hURERGR9KFKnoiIiIiISAxRyBMREREREYkhCnkiIiIiIiIxRCFPREREREQkhijkiYiIiIiIxBCNrikiKdJ8aBuXI5c+QkRERJJj8ZD52mBy3KmSJyIiIiIiEkMU8kRERERERGKIQp6IiIiIiEgMUcgTERERERGJIQp5IiIiIiIiMUQhT6KaOHGiK126tMuePbsbO3astlQKvPDCC+7UU0/VthMREZFE/f33365ChQrxzh327t3r2rdv7woUKOBOO+009+ijj2pLSqKydMj78ssv3UknneRatWrlsoqkhg4+ULp06eL69Onjfv/9d9epUyeXEbBc/fv3d5UqVXK5c+d2JUqUcM2aNXNvvfWWi4uLcxnN9ddf737++efQvwcPHuzOP/98l1F89tlnLlu2bG737t3pvSgiIiJZ3sCBA13ZsmXjbYeuXbu6v/76y23cuNEtXLjQTZo0yb300ktZfltJwrJ0yJsyZYr94Xz++eduy5YtCU5LgDh8+LDLKvggOXTokAXgkiVLulNOOeWYaf77778TukwEkbp169oHW79+/dzKlStt3xGkevfu7fbs2eMymjx58rjixYun92KIiIhIBrdixQr3wQcf2AV27+DBg+61115zjz32mF2kr1ixop27cg4rkpAsG/L279/vZs6c6e655x4LMlS4IlU43n//fVejRg2XK1cut2jRIrdv3z530003ubx581r4GTNmjGvUqJHr0aNH6LW8bvbs2fHmxx+mf48NGzbYNLNmzXL169e3IFCzZk2r+CxbtsxddNFFLl++fO6yyy5zO3bsiDefyZMnu8qVK1sVi2rW+PHjQ7/z86Wq1bhxYwtm1atXt4qlX6cOHTpYGGI6HlSWwrGc5513nv1cvnx5m455+yoUy3DmmWfaMvhA2KZNG1tmmhJcd911btu2baH5+ddNnTrVlSlTxqa799573ZEjR9zIkSOtGkcQGjp0aIL77KGHHrLlWLJkibvttttclSpV7MPurrvuct98843NF7t27XK33nqrK1SokG0DtuPatWvjrR/7Y+7cue6cc86xaa655hr7IH3xxRdduXLl7LXdunWzZfR4ng9Z5s17caXtnXfesX3k179atWpu+fLlx7yX//mRRx5x3377bWj7+2OCnydMmOBat25ty8M+Zr/98ssvdnxxvBFw161bF2+bzJkzx1144YW2L9hXzD94MYL5sr+uvvpqm+/ZZ59ty+yPF44TsL5Me/vttye4D0RERCTt8d3N+cyzzz7rTj755NDzP/30k11UD7YC4udVq1ZpN0iCsmzII2ARkjjJv/nmmy2ARGru17dvXzdixAi3Zs0aO4Hv2bOnW7x4sZ0oz58/38rmVJRSYtCgQe7hhx+21+fIkcPaW1ORGjdunM2XE3zK9t706dPt34QhlmfYsGFuwIABFkyCaM744IMPWvAhBN1444324UFIoG8dQWzr1q32YLpwVMY+/vhj+3np0qU2HX3zwDK9+eabFiSZ/9GjRy3g0IxgwYIFtk1+/fVXm0cQ4YTAzBWqGTNm2BUowvXmzZvtdY8//rhtCwJcJLwPV7II2KVKlTrm9wQstiEIKgQt9hFBif16+eWXW2XSI9A99dRTNk+WiQBMEJo3b549Xn75ZQtdb7zxRrz3IdTXq1fPff3117b8t9xyi4U+jiH241lnnWX/jnQssU0eeOABV7Vq1dD2D24n2tjzWrYrxybHw913321VS9aHedKE1uMYYfru3bu7H374wZaX0Bgelgl+BG++ENgObEP2F/uUfem/RFgejj0RERE5sZ544gl3wQUXuAYNGhxTlOBCrz/HARePKTqIJOT/P2KyGEIGJ+Zo2bKlVbcIG1RNgoYMGeKaN29uP/MHRaB69dVXXdOmTe25adOmRQwdSUHAatGihf3MiTph7JNPPrEQgTvvvDNehZFQOGrUKNe2bVv7N9U0f3JPZSs4X9/PkBN8QgXhjOBQsGBBq9hQPYuGymKRIkXs52LFisWblqtJNJfkeRDqVq9e7davXx8Kgvye96QqSYXShzSCdP78+a0CRwWJYEGgYmAXwjZB79NPP3W1a9c+Zpn+/PNPq9CxDgmhYke4I4gTan04Ztmorl577bX2HIHvueees1AGKnkEOyqQBEa/jCxPMIgRkgheIHAzD9bRz5cmFnXq1LH5hG9jtqsPo5G2P1VWwlhwPoT44DHCNB77losQft9TySMocqGAY8Uj9HJsgQsDhFvCO8d94cKF7XkqqdH6av7777/2CPaLFBERkbTBOdrzzz9vF5DDcd7AhWku1vugxzkr51MiCcmSlTzCBSe5/sSXPxpO5CO1b6bppEeFinBQq1at0HOEJgJKSlAZ9BgtCb6ZpH9u+/bt9vOBAwesGkbw4w/eP2g+GN6ELzhfmpTCzye1aKLoAx6oKBKgfMADAYnAwO+CTR2DH0isG9MR8CKtb7ikDqrCe7I/g0GRwMo+Ci4PTRd9wPPvzTL6Jp/Rlicp+yyl2zsp8/7nn39CIYtmn1yECB4PNPWgIscXQqT5cjWQSm5ylm/48OF2nPtHcF+LiIhI6tAdiIvDtL4qWrSotZDiu56f+X/OnDntO9+jxU/w/EAkkixZySPMcUUkWIEjRNDv7plnnrET2eBJcXJRKQsPJcGmgh5/tMHXRHqOCpgv14MRlcIrXYwQmth8/XxSKyXbI3yZ/HJFei7achIsCY4//vhjit4/rZYnKfsspds7ufPmmKCa5yu7Qb6/ZPg8oq1XQmguSjNljy8cBT0REZG0QSseRgr36GrSsWNHC3O0tKEQQcseurtwkfbpp5/WbRQkUVmukke4ozkhzR754/EPrpAQ+vgDiobmcJww0wzRo2QeHCLfBxKqKcEmhMHKSkpQxWH5qCZy/5Tgg2abSUVn3uBgIqnFACGbNm2yh0cTUkbCpFKXVqj43XDDDdb0MtJIqAQe9i3Lw/+Dfft27txp1du0XJ6USsvtz4ArrFf48cAjWCFNbHmQ0DJx8YPqX/AhIiIiaYPWRWeccUbowXkkF2T5me9pX4Dg33TpoVUXffJFEpLlKnmMqEjfLv5AghU7tGvXzqp8nTt3jvhamhvS/6lXr17Wl4mrK/R94oTaV1nQpEkT+4OkTxUnz/SvCq+mpARVG0Z8ZLnpT0U/KQbkYH2ClZaE0CSRQETfP0be5IMl0u0RkoorTzQZYDAPBnUhYDFyZsOGDeM1dU0LDCjCAClUMvmZ+bNdGYCEJoWEb0aPpJkDzRbpq8g+o9/a6aefbs+nN7Y//Re5sMCHNctHiEoJ+gQyGicjltKnkOOQixXfffedNeNNavNbjl3+Luhv6PsNioiISPpgfIjg/Wu5uJpQEUIkkixXySPEEUzCA54PeYSmhIalHT16tIU3Tq6ZD1dU/C0NPKqENGfj9giMkMhAKKkJUh6le4bDZ7AXghVBioFZklPJYzASQiylf64UcQuD1CAgMIw/Q/AzIhTbhIont6dIawTrr776ygbMIcQwChXbmA8+RqXy+5Ttw20v2EfsK5rOMsBLWgTt1OIYI6AzqAvbPzUf2gzIQjj76KOPbPCXiy++2Eb/DL+JakIIv34AF6rFwdE7RURERCRzyhaX1BEtJCIGROFEmWBHdVAk1tEnj0Bdq3cjlyNXlmsMICIikiqLh8zXFpRUn4fRZSyhLjQ6Q0smhrdl8A9G2GTjMrohMkJTQBEREREREYW8FHjyySdtwAs6w9IskD5hDHMrIiIiIiKS3hTykol+YCtWrDg+e0NERERERCSVstzAKyIiIiIiIrFMIU9ERERERCSGKOSJiIiIiIjEEPXJE5EUmd9/ToJD94qIiIhI+lAlT0REREREJIYo5ImIiIiIiMQQhTwREREREZEYopAnIiIiIiISQxTyREREREREYohG1xSRFGk+tI3LkUsfISISmxYPmZ/eiyAikmKq5ImIiIiIiMQQhTwREREREZEYopAnIiIiIiISQxTyREREREREYohCnoiIiIiISAxRyBMJmDhxoitdurTLnj27Gzt2bKbZNtmyZXOzZ89O78UQEYlZ//77r7vrrrvcmWee6fLnz+8qVarkpk6dar/buHGjy5cvX7xHjhw53JVXXpneiy0iWZRCXhbz5ZdfupNOOsm1atXKZRUvvPCCO/XUUxOdbu/eva5Lly6uT58+7vfff3edOnVy6W3w4MEW4PyjYMGCrn79+m7BggXxptu6dau77LLL0m05RURi3eHDh13JkiXdxx9/bN8XfLc88MAD7qOPPnJlypRx+/fvDz3++usv+9654YYb0nuxRSSLUsjLYqZMmeK6du3qPv/8c7dly5YEp42Li7MvtayCK7GHDh2yAMwX+SmnnHLMNP/9998JX66qVataiONBSD/77LNd69at3Z49e0LTlChRwuXKleuEL5uISFaRN29eN2TIEHfWWWfZRbeLL77YNW7c2C1atOiYaWlZcfToUde2bdt0WVYREYW8LISrizNnznT33HOPBRmuQgZ99tln9sX1/vvvuxo1alho4Mtr37597qabbrIvOMLPmDFjXKNGjVyPHj0SbC7IVUz/Hhs2bLBpZs2aZZWoPHnyuJo1a7qff/7ZLVu2zF100UXWvIVq1I4dO+LNZ/Lkya5y5coud+7c1jxm/Pjxod/5+b711lv2ZUswq169uoUhv04dOnSwQOSrYVTHwrGc5513nv1cvnx5m455M+35559vy0ATHZbBB8I2bdrYMhcoUMBdd911btu2baH5+dfRlIcrvEx37733uiNHjriRI0daKCtevLgbOnRoovuNJj9Mz6NKlSp2ksG+ZNtF2/5UIytWrGjbg/UZMGCABVjv22+/te1FkyOWn/29fPnyRJdFRET+n3/++cctXbrUVatWLeIFVb43/XeGiMiJppCXhRCwCEnnnHOOu/nmmy2AUK0L17dvXzdixAi3Zs0a+/Lq2bOnW7x4sXvnnXfc/Pnz3cKFC93KlStTtAyDBg1yDz/8sL2e8NK+fXvXu3dvN27cOJvvL7/84gYOHBiafvr06fZvwhDLM2zYMAssL774Yrz59u/f3z344IPum2++sXBz4403WhWybt261reOIOOrYUwX7vrrr7cmOOBLm+nomweW6c0337Qgyfy5OkvAozkOzSbZJr/++qvNI2jdunUWmD/44AM3Y8YM+9InXG/evNle9/jjj9u2WLJkSbL6hEybNs0CNPsxGsIbwfWHH36wbTtp0iQL5x4nH2eccYYF7BUrVtg+z5kzZ5KXQ0QkK+O7s2PHjtayIrxa99tvv9n3Cb8XEUkvOdLtneWEI2QQ7tCyZUurbhE2qMoFUSlq3ry5/UwVj0D16quvuqZNm9pzhIxSpUqlaBkIWC1atLCfu3fvbmHsk08+cfXq1bPn7rzzzngVRkLhqFGjQl+iVNMILhMmTHC33XZbvPn6foaPPPKINXEknBFq6cdGpYtKWDRUFosUKWI/FytWLN60NNF86aWX7HkQ6lavXu3Wr18fCoL8nvckNFGhBGGQIE3gogJH5eynn35y8+bNs4FdCGkEvU8//dTVrl076rLxXlQCcfDgQZsfFVmCazSER69cuXK2fV577TUL1L4S2atXL9s+4EQloWDJw6MviohIVg54tMzg85wwx+d5EN+RF1xwgbUqERFJL6rkZRF8GVGhIlSBKhqVJ4JfOJpOelSoaOZXq1at0HOEpoSqSAkJNms57bTT7P++maR/bvv27fbzgQMHrBpG8AuOWPbYY4/Z89HmS5NS+PmkVtmyZUMBD1QUCXc+4IEQR3WN3wXDFYEsuG5MFzwhCK5vNGxrKog8qLrR3Pbaa69NsHklIZDgTFhlmxH6CHYe1VmuMjdr1syqtuHbM2j48OG2z/0juN4iIlkt4N13333WAoMBV/hMDOLiHiFPVTwRSW8KeVkEYY7mi1TgCHg8nnvuOWuGGBzAA/S9Sy4qZeFNP4N9wLxgk0BeE+k5viRBvzPQ1NCHHB7fffed++qrrxKdr59PaqVke4Qvk1+uSM8ltpwnn3yyq1Chgj24OkwoO/3006Pe4oH+iDTHvPzyy93cuXPd119/bc1Zg4PG0Gfw+++/t+rn//73Pwufb7/9dsT59evXz44R/9i0aVMytoKISOxgBGa6L9Cio1ChQsf8nuf//PPP0AVVEZH0opCXBRDuaE5Is8dgWGLwDUIf/cWiYdAOggnNED1O9IODfoBKF/3YvLVr11rTwtSgysXyUU30Icc/aLaZVIQkBjxJKwwCQ9AJhh2akO7evdvC0onAbTD+/vvviL/74osvrPpIsKMqS1NM+oiEo+/i/fffb1ejaQ7L1edIGICHpqHBh4hIVsPnKAN/0TKGz1jfuqRz587xLqhec801x1T4RERONPXJywKo5uzatcuaPYZ/8bRr186+lIJfUkE0N6TvG/23ChcubCNC0k+OJoe+YoYmTZq4Z555xtWpU8cCFaM7psVAHvSv69atmy03/QjpG0YzRdaHJodJQbNJqoL0/aOPBCNORro9QlLRxJEmplTLqKYRoumf0bBhw3hNXdMK8//jjz9CfSRpikmoZBtHQqijaSZ98Ogf+N5778Wr0hEO2Z+ciBCWGQiGEM+xICIikRHsIg1WFj7AmYhIRqBKXhZAiCOYRLqyyIk9oWnVqlVRXz969GgLb9ybjfnQ18vf0sCjSkhfLW6PwIiZDPSRmiDl0a+B2xdQZSJYEaQYmCU5lTxG2CTE0geRiiO3MEgNwu2cOXOsqU6DBg1sm1DxJHwdDzSrpJ8hD27LwEkETW1vvfXWiNNfeeWVVqGjWRHTU9ljRNJgFXDnzp32eqp53P6BW1cQqEVEREQk88sWl9hlKZEwDIhCnzCCHdVByVoYXZMLBrV6N3I5cqkxgIjEpsVD5qf3IoiIRD0Po/tUQl1odIYmiWLgjh9//NFG2OSA4hYL4F5xIiIiIiKSsSjkSZI8+eST1tmcQUxq1KhhNy4vWrSotp6IiIiISAajkCeJYth+7s8mIiIiIiIZnwZeERERERERiSEKeSIiIiIiIjFEIU9ERERERCSGqE+eiKTI/P5zEhy6V0RERETShyp5IiIiIiIiMUQhT0REREREJIYo5ImIiIiIiMQQhTwREREREZEYopAnIiIiIiISQzS6poikSPOhbVyOXPoIEZHMYfGQ+em9CCIiJ4wqeSIiIiIiIjFEIU9ERERERCSGKOSJiIiIiIjEEIU8ERERERGRGKKQJyIiIiIiEkMU8kT+z8SJE13p0qVd9uzZ3dixYzPFdilXrlymWVYRkYzm33//dXfddZc788wzXf78+V2lSpXc1KlT400zefJkd84557i8efPaZ+6cOXPSbXlFRJJKIS8L+fLLL91JJ53kWrVq5bKKF154wZ166qmJTrd3717XpUsX16dPH/f777+7Tp06ufR28OBB169fP3fWWWe53Llzu2LFirmGDRvGO8FYtmxZhlhWEZHM6PDhw65kyZLu448/tu8BvjMeeOAB99FHH4Uu/o0aNcq99tprbv/+/W7JkiXuvPPOS+/FFhFJlG5ylYVMmTLFde3a1f6/ZcsWV6pUqajTxsXFuSNHjrgcObLGIbJx40Z36NAhC8B84Ufy33//uZNPPvmELVPnzp3thOLpp592VapUcTt37nRffPGF/d8j+ImISMpQnRsyZEjo3xdffLFr3LixW7RokWvatKkbOHCge+mll9wFF1xgvz/ttNO0qUUkU1AlL4vgCuTMmTPdPffcY0GGq5VBn332mcuWLZt7//33XY0aNVyuXLnsS27fvn3upptusi9Cws+YMWNco0aNXI8ePUKv5XWzZ8+ONz+qZ/49NmzYYNPMmjXL1a9f3+XJk8fVrFnT/fzzz1aJuuiii1y+fPncZZdd5nbs2HFMM5nKlStbJYtmNOPHjw/9zs/3rbfesi/lU045xVWvXt0qln6dOnTo4Pbs2WPT8Rg8ePAx24bl9Fdmy5cvb9Mxb6Y9//zzbRloysMy+EDYpk0bW+YCBQq46667zm3bti00P/86mvyUKVPGprv33nstNI8cOdKVKFHCFS9e3A0dOjTBffbOO++4hx56yF1++eXWRIj9Qki/4447ojbX/PHHH90ll1xiy0ow5Op0cP8kts1ERLKyf/75xy1dutRVq1bN/fTTT/bZvnLlSvusPeOMM6xpJxU/EZGMTiEviyBgEZLoV3DzzTdbAKFaF65v375uxIgRbs2aNfYl17NnT7d48WILHPPnz3cLFy60L7yUGDRokHv44Yft9VQI27dv73r37u3GjRtn8/3ll1/sqqk3ffp0+zdhiOUZNmyYGzBggHvxxRfjzbd///7uwQcfdN98842rWLGiu/HGG60JTt26dS0AEcS2bt1qD6YLd/3111sYAl/uTEffPLBMb775poUi5n/06FELeH/99ZdbsGCBbZNff/3V5hG0bt06C8wffPCBmzFjhlVPCdebN2+21z3++OO2LajURUMYnDdvngXtpCBEXnXVVRbcmC/NjNg2kUTbZiIiWRXfiR07dnRnn322a9u2rX3Og++H5cuX2+fl+vXr3f3335/eiyoikqis0RZPLGQQ7tCyZUurbhE2qMoF0WylefPm9jPhgkD16quvWrMVTJs2LcFmngkhVLRo0cJ+7t69uwWLTz75xNWrV8+eu/POO+NVGAmF9IXgyxZU03744Qc3YcIEd9ttt8Wbr+9n+Mgjj7iqVataOCPUFixY0CpXBKZoqCwWKVIk1PwxOC1NNGmq45tFEupWr15tX/Q+CPJ73pOqJBVKEAYJ0nTkp6JG1YyrwoQ2BnYhbBP0Pv30U1e7du2Iy0VIo4rKslFto0J3zTXXhLZXOJaNcEkF068DAdnvz/B9EW2bRRqYgIenq9giEosBjxYXfE4T6vicphUG6BtdtGjR0M98d4mIZHSq5GUBfGlRofJfTFTRqDwR/MLRdNKjQkU/tVq1aoWeIzQRUFKCyqDn+zUEO7Dz3Pbt2+3nAwcOWGAh+PFF6x+PPfaYPR9tvr4/nZ9PapUtWzZevzcqioQ7H/BAiKN5Kr/zaNpDwAuuG9Nx4hBpfSNp0KCB7QOCMOHu+++/t+aujz76aNT9zHIFQ2pw36V0mw0fPtz2u38E111EJBYC3n333WctIBhwhc858F3nm+mLiGQ2quRlAYQ5muIFK3B8qdHv7plnngl9oYG+d8lFpSy86SfhMFzOnDnjvSbSc1TAfB9CTJo06ZhKFyOEJjZfP5/USsn2CF8mv1yRnktsOXkNwY4HI38Scqm28nNqBoFJzjbjyjXNdoOVPAU9EYkVjKxMt4T//e9/rlChQvFaedAChlYXF154oX1W8jNN9kVEMjpV8mIc4Y7mhDR7pD+Bf3z77bcW+ugvFg2DkBAGaIbo0cyTAVOCqHTRj81bu3atDf+fGlS5WD4qWRUqVIj3oNlmUhGE6KuWVhgEZtOmTfbwaEK6e/duq9Qdb7wH+5TBAcJx1ZnlCg4CE9x3KcXFAPo1Bh8iIrHgt99+swG9aAlByw3faoTRjUG/br6L+N7hM5ZpRo8end6LLSKSKFXyYtzcuXPdrl27rNljsGKHdu3aWZXPf5mFo7khfd969erlChcubCNC0k+OJoe++oMmTZpYRbBOnToWqKgyhVetUoK+Yt26dbPlph8h/cLo/M76BCtLCaHZJFVBmjzSr41BSXikVLNmzayJKX3l+PIncNGPg/vXBZu6pgX6S9LElvnSL48wyWib9O+LFLToe8c99dhnjOJJn0oGd0Fwf4mIyP9DaIs0CFmwNUf4aNQiIpmBKnkxjhBHMAkPeD7kEZpWrVoV9fVcsSS8tW7d2ubDoB/+lgYeVUKa79GkkBEzGdQjNUHKY5Qzbl/AYC8EK4IUX7bJqeQxwiYhlj6IVBwJP6lBWOJm5DTpoc8c24SKJ7enSGsMUsPAN5deeqltc26fwHOMlBoJzVi5VQKhlgFg2H5+dE31KxERERHJOrLFJXQJSyQMA6KcfvrpFuyoDkrGRj8TRuVk5EyqfGmBPnlcNKjVu5HLkUuNAUQkc1g8ZH56L4KISJqdh9GFKqEuNDpDkwR9/fXXdoNtRmnkYGLQD6jjecb09ttvW38S7vNEsONWFVRf0yrgiYiIiEjGp5AniXryySetUzqDmNSoUcNuXO7vGSQZC/3w6BO5ceNG20c0J6XqKiIiIiJZh5prikiyqLmmiGRGaq4pIlmpuaYGXhEREREREYkhCnkiIiIiIiIxRCFPREREREQkhmjgFRFJkfn95yTYFlxERERE0ocqeSIiIiIiIjFEIU9ERERERCSGKOSJiIiIiIjEEIU8ERERERGRGKKQJyIiIiIiEkM0uqaIpEjzoW1cjlz6CBHJ7BYPmZ/eiyAiImlMlTwREREREZEYopAnIiIiIiISQxTyREREREREYohCnoiIiIiISAxRyBMREREREYkhCnnHwcSJE13p0qVd9uzZ3dixY10sy5Ytm5s9e3aq5jF48GB3/vnnp3pZ0mo+SdGoUSPXo0eP0L8PHjzo2rVr5woUKGDbZPfu3a5cuXLHff9/9tlnofcTEUkr//77r7vrrrvcmWee6fLnz+8qVarkpk6dGvr9gAED3Hnnnedy5MgR77NQREQyccj78ssv3UknneRatWrlsooXXnjBnXrqqYlOt3fvXtelSxfXp08f9/vvv7tOnTq59NS1a1dXuXLliL/buHGj7cd33nnHZQZvvvmmhauCBQu6fPnyuWrVqrkhQ4a4v/7664Qvy1tvveUeffTR0L9ffPFFt3DhQvfFF1+4rVu32jIuW7YsTfd/eLBE3bp1Q+8nIpJWDh8+7EqWLOk+/vhj+17jO/CBBx5wH330kf2+QoUKbuTIke7KK6/URhcRiZWQN2XKFAsPn3/+uduyZUuC08bFxdmXRVZBcDp06JAFYL4gTznllGOm+e+//07Y8tx5553uxx9/tPARji/t4sWLu8svvzzZ8z2R64D+/fu766+/3tWsWdO9//777rvvvnOjRo1y3377rXv55ZfdiVa4cGG7uu2tW7fOwvS5557rSpQoYdW1YsWKRdz/aenkk08OvZ+ISFrJmzevXUQ766yz7PPl4osvdo0bN3aLFi2y3992223usssus9YLIiISAyFv//79bubMme6ee+6xIENQiNR8jBPxGjVquFy5ctmXwr59+9xNN91kXxyEnzFjxhxTmYjU9I/qmX+PDRs22DSzZs1y9evXd3ny5LGT/p9//tmqJhdddJFVePji2bFjR7z5TJ482U7Cc+fObc1Oxo8fH/qdny/VGb7EODGvXr26VSz9OnXo0MHt2bPHpuNB08BwLCfNV1C+fHmbjnn7ZoQsA01fWAYfCNu0aWPLzBfldddd57Zt2xaan38dTWTKlClj0917773uyJEjdgWVk3tC2tChQ6PuL15/4YUXxmtm48M3y8sXNc1tCE1sN97jtNNOc7fccov7888/Q9Ozr6hQsr+KFi3qWrRoEfodlSRey/5gvd94441470VVs2LFirZd+T3NfAjCSbV06VI3bNgwC3VPPPGEVa9oCtm8eXOr7rEOkXBMMA3LS6WrYcOGbuXKlfG2AduYbctxWqpUKdetW7fQ7zlGzj77bNtfbJNrrrkm3vbwxy4/s2xc9GCf82+EN9ekSeXdd99t82KeBMK5c+fa73bu3OluvPFGd/rpp9t24jiaMWNG6LW33367W7BggRs3blzoGOTYitRck21StWpVWyeWgWUL4jm25x133GFBlfWnibGISDT//POPfRbTgkJERGIw5BGwCEnnnHOOu/nmmy08cLIcrm/fvm7EiBFuzZo19qXQs2dPt3jxYmsaOH/+fGvaFjzhTo5Bgwa5hx9+2F5PQGnfvr3r3bu3nQAz319++cUNHDgwNP306dPt34QhlocTXIIGTezCq0UPPvig++abbyyUcNJNFZJQwck6QYxAw4PpwlFpomkL+DJkOvrmgWXi5JsgyfyPHj1qAY+mhpy8s01+/fVXm0cQFSIC8wcffGAn/VRRCdebN2+21z3++OO2LZYsWZJgNY/9duDAgdBzhIP169fbiT4BoUmTJu6CCy5wy5cvt/cibBI6g9heVI7Yj88//3zoebYl/dGoqhHkb7jhBtvOHkGCQPnDDz/YPpo0aZKF/KRi//mAG0m0ZrRcWCAAcpHhq6++ssBG1ZLnwf5gOSZMmODWrl1rFxh8SGc7EPi4kv3TTz/ZNmnQoEHE92Gf0nelTp06ts/5dzj2N0GYbffKK6/YtuDvg+ay/gSKiyLvvfeeBW6aeRK0OY7AdmP+vI8/Bv2xFbRixQrbb+yD1atXW4hl/4RfjCH4cVHk66+/tu3KRRvWU0QkHN/xHTt2tM/Qtm3bagOJiGQCOZL7AkIG4Q4tW7a06hZhw1cvPE6OqaKAk2oCwquvvuqaNm1qz02bNs0qJylBwPKVpO7du1sY++STT1y9evVCoSZ4Ukso5KTWfzlRTeMkm5P7YBWI+fp+ho888ohVQwhnhFoqQVRMqJ5FQyWrSJEi9jNN9YLT0rzxpZdesudBqOMknKDlT9b5Pe9JBYoKpQ8HBGmCUpUqVazSyMn4vHnzbGAXwjZB79NPP3W1a9eOuFyEYPpSvP7661YR8tv/kksusTD72GOPWcAj/Hq8J8tFlZRpwBc8FcRw1157rZ0AgH5qrNvTTz8dqpYSQoNVJLbza6+9ZsE8KQhgVABz5szpkoPgGkS1ikDI8dq6dWurpLKPmjVrZvOmolWrVi2blt9RdWY6tn3ZsmVtG0Vrukn1zTedjITwT2Aj/PrtyTp5VPCCFw5oDv3hhx9aOGeZOP6YP++T0DE4evRo+xsj2IH34linAur3PQi7PjRTaSXscgxxPEUagIGHR/8cEck6AY/PCr53+Bzje0dERDK+ZH1a8yHPiSqhClTRqDwR/MJRJfCoUNE8z59Ag5PWSCeUSRFsLkLTN/gKjH9u+/bt9jPVK6phBD+qQf5BsOH5aPOlSSn8fFKLkOADHjjZJ0QFqzGEOEJIsApGKAr2/WLdmC74RRtc30iYJwHXN9nkJJ0qFtsEVOA4wQ9uH4ItgtuISlMkVJjC/x1cB5r3EsAJJ8yb0EeISqpIleKkoBpJ5YtwyvFGJZbmxv69Cad///23hS2me/vtt0P9R7lAwT7jd1TUqCYygmZKUb0944wzQgEvHE1wCcgcx4RGthMhLznbCWx3f7HD498EZd4j0rHuL15EO4aGDx9u288/IlUQRST28Nl73333WUsRBlzRAE8iIjFaySPMcRIcrMDxJUDfn2eeeSbeFwBVkOTiZDP8hD5S361gRccPOBH+HBUwcFIPmgiGV7p8U7mE5uvnk1op2R7hy+SXK9JziS0ngY4KD5VJAh3rTsjx2+iKK66wimA4H3ZTug70a6QJJ5VRqq8cI1TxwvuJJYRgRJNLjoXkVPOo0tLXjaaOBDaOUwKoHzSGsOKvTlN95Go1FS8qfQRrmgPTrJWTG5r70vSRKmtSRlmNVOVNCO/LctIsmKDHtqbP3/Ea4CY5x1C/fv2subXHRQIFPZHYRz9smpj/73//c4UKFYr3Oz6PuXDkHzQ553sluS0uREQknSt5hDuaE3JyTlXCP6gCEfqCg0SE803tOEH2aOZJU8AgKl30NfKoPqSmeuKrXCwf1USGfA4+aLaZVDSVC1ZCUotBYDZt2mQPj2Z19I+jUpfWaObJ+tJMkwd9tnxoY2CW77//3qqG4dsoKcGO/m7h//a3bWBUTwIW/R2p7lJV++2335K17DQ3JYgGB8sJinaPOE5O6FdH00Q/EElwMBkfvgi4Tz31lAU6QinNaH2lmqacNFFdtWqVDXTCyU5KUDmjH2X4MR9cVvpo0hSaQX/4mwmfNinHINudeYXPm6AcflEjqdhuVEGDDxGJbXxO85nLhTA+w30rj86dO9vvaf3A5yd9jLnIy888JyIimaySxyiAu3btsopQeJMNBt2gyuc//MNRFaGq0qtXL2uKxoiQ9JOjyWFw6Hf6UPFlQbWFk1n6CqXFVUGqSJzss9z0I6R/EQNrsD7BCkVCCEAEDfr+cRJO36jUDI9PeKBiQ5WL6g0hmkoSI0AGm7qmFbYzg6zQZ4v1Dg58QnMcKp00w6WfHPuIih8VN0YETSwc0NePZaaPH80aadLrm/AS6mhyyLzoZ8jAIjSLTA4qsCwX/Qq59+DVV19twZ1lZAAY3pe+meF4b26vwLJRfeL4C1bU6LfJccb82ZecrPB7Tmg43rkwwGArXMGmDySVrpQ2MWa/Mi/+VtgHBGhubcF+4ZhkWRmVlFDM+zENzU2DgZ9jkGZThE1OtthP4dhGbGeaftKUmtDK31S0gCwiEgmfgwk1lefzM3xAJxERyYSVPE7aCSaR2uRz4kpootoRDSethDcGsmA+9BPytzTwqBLSDIzbI1C9YSCKtLjPGIOCEFaoYBGsOOHmyyk5lTxG2CTEcuJMxTHSACTJwcn9nDlz7ISek3+2CdUb+q8dLwy8QQWVqlaw6SqBiWoPgefSSy+1bURTQZolJqWTPSGaEEe1imovVV0fTrhR7v3332/NfridAyHGDwqSHDQlZeAeQg7NPlkHAjrvGe0WChyzBFoqlfSrI+hzgcFj/Qi3HIvMh2ab7777rg2ew+8YJZMLDxynhEnWi/dNKfpBEsAI02wfgquvzNFPkeVk3RjEiD5yV111VbzX8/dA4Oa1HIOR+usxDwZrYX9wiwaamTIIUnDQFRERERGJbdniUjqqRSoxIAojChLs/AAgIpLxURXlYk+t3o1cjlzJHqBXRDKYxUPmp/ciiIhIMs/DKNwk1IXmhJ2hcT8umqcxwiYLRXUB9EMSERERERGRtHFCL8M/+eST1ombASQYjp8blxctWvRELoKIiIiIiEhMO2EhjxtJr1ix4kS9nYiIiIiISJaUrJuhi4iIiIiISMamkCciIiIiIhJDFPJERERERERiiMY/F5EUmd9/ToJD94qIiIhI+lAlT0REREREJIYo5ImIiIiIiMQQhTwREREREZEYopAnIiIiIiISQxTyREREREREYohG1xSRFGk+tI3LkUsfIVnR4iHz03sRREREJAGq5ImIiIiIiMQQhTwREREREZEYopAnIiIiIiISQxTyREREREREYohCnoiIiIiISAxRyItxEydOdKVLl3bZs2d3Y8eOTe/FkWSYPXu2q1ChgjvppJNcjx49oj4nkhE888wz7qKLLnK5cuVyV111VbzfDRgwwJ133nkuR44cOm5FREROgEwf8r788ks74W3VqpXLKl544QV36qmnJjrd3r17XZcuXVyfPn3c77//7jp16uQyAparf//+rlKlSi537tyuRIkSrlmzZu6tt95ycXFxLqMaPHiwO//889NkXtmyZYv4eO2110LT3H333e6aa65xmzZtco8++mjU51Ljs88+s/fdvXt3quclWVupUqXcww8/7O66665jfseFiZEjR7orr7wyXZZNREQkq8n0N7maMmWK69q1q/1/y5YtdqIRDQHiyJEjdjU5K9i4caM7dOiQBeCSJUtGnOa///5zJ5988glbJsLEJZdc4vbs2eMee+wxV7NmTdsfCxYscL1793ZNmjRJUoCNBdOmTXMtW7aM95xf9/3797vt27e7Fi1ahI7pSM+JZBRt27a1/3/zzTdu8+bN8X5322232f9nzpyZLssmIiKS1WTqSh4nvZw03HPPPRZkqHBFqlK8//77rkaNGtaMaNGiRW7fvn3upptucnnz5rXwM2bMGNeoUaN4zYh4HU3jwk/A/Xts2LDBppk1a5arX7++y5MnjwWWn3/+2S1btsyaLeXLl89ddtllbseOHfHmM3nyZFe5cmWrYlHNGj9+fOh3fr5UtRo3buxOOeUUV716datY+nXq0KGDhSRf/aHCFI7lpHkUypcvb9Mxb1+NYhnOPPNMWwYfCNu0aWPLXKBAAXfddde5bdu2hebnXzd16lRXpkwZm+7ee++10MwVeqpxxYsXd0OHDk1wnz300EO2HEuWLLETvypVqriKFSva1X9ODpkvXn75ZduG+fPnt3m3b9/eAk5w/cLDIPuL9fS+/fZb24bMg3XiGFi+fHno9xwLft/RpLVbt27uwIEDEZeb93vkkUdsnn67+2MhsW0XDcvPugUf7A/2McsMQi/vFe25pKzHv//+a9VcfsffAFUVLoqwH9g+KFSokM3z9ttvT3S5RURERCRjy9Qhj4BFSDrnnHPczTffbAEkUnO/vn37uhEjRrg1a9a4atWquZ49e7rFixe7d955x82fP98tXLjQrVy5MkXLMGjQIGuixOupSBFGqEiNGzfO5vvLL7+4gQMHhqafPn26/ZswxPIMGzbM+qu8+OKL8eZLc8YHH3zQgg8h6MYbb3SHDx92devWtb51hImtW7fag+nCXX/99e7jjz+2n5cuXWrTcZIPlunNN9+0IMn8jx49aiHlr7/+sooa2+TXX3+1eQStW7fOAvMHH3zgZsyYYUGBcM1Ve173+OOP27YgwEXC+9AckYAdqRJFSPJVViqQNEckVBHeCCTJDSC8zxlnnGGhe8WKFXYc5MyZM7QuVNHatWvnVq1aZRcLCEs0b42EbfHAAw+4qlWrhrY7zyV12yUH+/inn36yn9lPvFe055KyHrfeeqvtr6eeesqOuQkTJti25nhgXmDezJPjVkREREQyt0zdbpGQQbgDJ7pUtzjRpioXNGTIENe8eXP7mSoegerVV191TZs2DTWbS2nzNwIWzefQvXt3C2OffPKJq1evnj135513xqswEgpHjRoVatpENe2HH36wE2/fpMnP1/czpIJEuCCcEWoLFixoVRcqP9FQ1SlSpIj9XKxYsXjT0kTzpZdesudBMFm9erVbv359KAjye96TgESFEgQagjQVJSpwVIEIB/PmzbOBXQjbBL1PP/3U1a5d+5hl+vPPP92uXbtsHRJzxx13hH6mEklAYTmo3vpqX2KosPXq1Sv0fmeffXbod8OHD7cQ6Ku3/I73aNiwoXvuuedCFc7g9vQhNLgtk7rtIuFYoT9pEMcClVKqoihcuHDo/SI9l9h6sA24GMJy0u/Rb0+Pefl5R2smSyWQR7BPpYiIiIhkXJm2kke4oELFiTI4+aZ6QvALR7M/jyoLVaJatWqFniM0EVBSgsqgd9ppp9n/fTNJ/5xvZkgTOiovBD8Cg3/QN43no83X96cLNldMjbJly4YCHqjuEFB8SAEhjpN+fueVK1cu1GTQrxvTEfAirW+45AyqQuXtiiuusMDDexJaQGhJKiq2HTt2tHBDJTe4jakQEr6D+4GwTpAlsCVVUrddJDQTppIafCT3YkNi68E8CZJ++6UEQZK/Ef8IrquIiIiIZDyZtpJHmKP5YvCkmBBBnyOG8uZk1KPvXXJRKQsPJYTDcL75n39NpOc44QZVKEyaNOmYSld4RSfSfP18Uisl2yN8mfxyRXou2nISLAk/P/74Y4LvQxgmqPCgeSuvI9zxb6qQIFgmtn/oR0jz2ffee8+amVJFpbno1VdfbfuCkSrpvxaOYHkiUI2jf1xqJLYeVH9Tq1+/fhaYg5U8BT0Jx+exf/AZ8M8//9jfKQM78bdJ/13/4Hd85oV/foiIiEgWruRxEkGTOJo9BqsgVDUIffQ/ioamapxY0JTOo5knA6YEESzoo+StXbvWHTx4MFXLTZWL5aOayMl98EGzzaTipIkTpbTCIDAMyc8j2GyQkTCpSqUVTvhuuOEGC26MhBopsLBvCYE7d+606hsDitDcMrw6yP6h6W1wgBGOgXD0Z7z//vvdRx99ZE1kaZqLCy+80NYxfD/wiDbaaKTtfqK2XTSJrQdVZU64acYcbZ2Q0PHEhRP6gAYfIuFokUCzZvobv/vuu/bzpZdear9jYCX+/corr9hFOH6OdKsFERERycIhb+7cuda3i2aP5557brwHA1BEarLp0fSPvm/01aLv2Pfff2/zIYAER2ZkBENORr7++msbkbFz585pctWZ/nU0f6PfFMGS/lwEj9GjRyd5HjSbJBDR949+bqkNnzRnJAzQt4sBZGgGy2AdNPELNnVNC5wAUgWikklQJ6AQoOnrd8EFF9h6UYEifDz99NMWiBkgJ/yecLyekUcZrZNmmPSxDPZ9/Pvvv23wEUag/O2332ygHYI9oQyMNvnFF1/YNIRDlmHOnDlRB17x2903gWS7008tNduOIPjHH3/Ee0Qb3TOaxNaDZeZ4p48jA9iw/GwT+un5prsc9/xNMQqsrzaLJBeVc6rrwYcfAZa/zfDfhY+GLCIiIlk85BHiOLkONsn0CHmEMkYajIZAVadOHde6dWubD4Ok+FsaeFQJCSNUkmjyx0AohIrUoo8Yty8g2BEOCAOc7CSnkseoioRO+iBS0eIWBqnBST7BgGH0GzRoYNuEiufxuKcVA3189dVXNmAOV/4Jdmxjqq9PPPGE7VPWiW3y+uuvWzWMit6TTz55zHyoCjDoC9uR1wdvJUFTMKqBBC6qedzWgNtZELJ9n0eqWwRt3p/lYNTThPrEcWwxwA8DzrCMvGdqth23wqC/ZfBBsE2OpKwHA7BwA3VueUFVlAqKD5Onn366bRNGHqXSnFDIFREREZHMIVtcckbDiFGc8HKyS7Cjqici0dEnjzBeq3cjlyNXpu3WK6mweMh8bT8REZF0PA+ju1lCXWiy5BkaTTDp98UIm2wgbrEA7ncmIiIiIiKSmWXJkAea/3EbBvp+1ahRw25cXrRo0fReLBERERERkVTJkiGPfkvch01ERERERCTWZMqBV0RERERERCQyhTwREREREZEYopAnIiIiIiISQ7JknzwRSb35/eckOHSviIiIiKQPVfJERERERERiiEKeiIiIiIhIDFHIExERERERiSEKeSIiIiIiIjFEIU9ERERERCSGaHRNEUmR5kPbuBy59BESCxYPmZ/eiyAiIiJpSJU8ERERERGRGKKQJyIiIiIiEkMU8kRERERERGKIQp6IiIiIiEgMUcgTERERERGJIQp5GcDEiRNd6dKlXfbs2d3YsWNdZrRhwwaXLVs2980336T3omRYt99+u7vqqqvSezFEkuSZZ55xF110kcuVK9cxx+3evXtd+/btXYECBdxpp53mHn30UW1VERGRrBbyvvzyS3fSSSe5Vq1auazihRdecKeeemqi03Gy1KVLF9enTx/3+++/u06dOrn09MEHH1hY++OPP+I9X7JkSVeuXLmIwe6TTz6xkLp161Z37rnn2u8+++wz+93u3btdRuSXPaEH+zAtA++4ceNSPE+RE61UqVLu4Ycfdnfdddcxv+vatav766+/3MaNG93ChQvdpEmT3EsvvaSdJCIikpVC3pQpU+yk4PPPP3dbtmxJcNq4uDh3+PBhl1VwknTo0CELwASpU0455Zhp/vvvvxO2PJdcconLkSOHhTRvzZo17u+//3a7du2yAON9+umndpW/Xr16FuJLlChhr80MfCj1jwceeMBVrVo13nPXX399mr5nwYIFkxT8RTKCtm3bWgWvaNGi8Z4/ePCge+2119xjjz1mx3PFihXt853PeREREckiIW///v1u5syZ7p577rEgE17J8BWf999/39WoUcNCw6JFi9y+ffvcTTfd5PLmzWvhZ8yYMa5Ro0auR48eodfyutmzZ8ebHycd/j18RWXWrFmufv36Lk+ePK5mzZru559/dsuWLbOmSPny5XOXXXaZ27FjR7z5TJ482VWuXNnlzp3bVapUyY0fPz70Oz/ft956yzVu3NiCWfXq1a1i6depQ4cObs+ePaGq0ODBg4/ZNizneeedZz+XL1/epmPeTHv++efbMpx55pm2DD4QtmnTxpaZZlLXXXed27ZtW2h+/nVTp051ZcqUsenuvfded+TIETdy5EgLYcWLF3dDhw6Nur94DdsoGPL4mfBHmAt//uKLL7blC1av+JntgkKFCtnzNFUE+7Bbt26ud+/ernDhwrZM4duG6l/Hjh1dsWLFbD2bNGnivv3229Dv+Zn558+f337PcbN8+XL73W+//eauuOIKe1+OHYLbvHnzjllPH0r9g/UmoPp/s51oOsv257hh/77xxhuh1xN4OT5ZRn5/9tlnu2nTptnveA0uuOACW3fWOVJzTZ7n5JhjmuWl2RsVkQMHDtjxw/pVqFDB/jaCFixY4GrVqmV/K/xt9O3bN96FkcS28R133OFat24db55caGCddaIuifnpp5/swhOfNR4/r1q1ShtPREQkq4Q8AhYh6ZxzznE333yzBRCqdeE4UR0xYoRVjapVq+Z69uzpFi9e7N555x03f/58axK0cuXKFC3DoEGDrNkRr+dEnr4knADTfI75/vLLL27gwIGh6adPn27/JgyxPMOGDXMDBgxwL774Yrz59u/f3z344IMWbLiafeONN9rJdt26dS0gEEB8VYjpwlEp+vjjj+3npUuX2nRUmMAyvfnmmxYkmf/Ro0ct4NFEipN8tsmvv/56TLVp3bp1Fgpodjljxgw7aSdcb9682V73+OOP27ZYsmRJ1O1FgKJK5/EzwaFhw4bxnifk+TAXxDqw7P6EkPViW3tsRwIYy0D4HDJkiK2Pd+2117rt27fbeqxYscJdeOGFrmnTprbuIFydccYZFtT5PcdOzpw57Xf33Xef+/fff61qvHr1altfAlxyDR8+3JqfPf/88+777793999/vx2/bENwPPzwww+2jBwjzz33XKjiwb4E+5Z1Zx9Gw7bgdbyGwMfFENafY4jj9dJLL3W33HKLVU9Ak97LL7/cgjhhl/dlH1NVCZ9vtG1MgOb4YNm8uXPn2nukdfVSYg8X7ji2glV7Lq5xYU5EREQyhuPeto4TUE6O0bJlS6tucaLsqxseJ6HNmze3nzlZ4CT11VdftZN7UCWhj0hKELBatGhhP3fv3t3CGP3IqEzhzjvvjFdhJBSOGjXKmiv5ygwn9BMmTHC33XZbvPn6foaPPPKIVY0IZ4RamuZRxaGKEg0VoCJFitjPVISC03KlnJDB8+AEndCyfv36UBDk97wnYYeTfhAGCdJUgapUqWIhjKBFNYuBXQjbBB/CWu3atSMuF68h2BICqBSxv3r16mUBllABAiaVxUghjyoZFSRQHQpvokiIZxuDChgDPLA/2P9UcQk8hDwqVXjyySetYksljT6LvC/Lw3b28/D4Xbt27eJVSJOLkMj6E9Lq1KkTmg/LxjFA2OV9qNRRDUawv6LfZ+zbhPY/qBASutGvXz+70EHo8/2guNjANqdKQtWUijL7n23G8cU2oAk0fTqZln2c2DYmQHIcvPzyy3axw/99ES4jBWK2B49gP1LJujhGuCDA54EPenyu85kjIiIiWaCSR7jghJ1QBU4IqBREahLmT5Z9gKD5GE3SPEITJ6YpwQmvR5M4+BDgnyNUgKZyVMMIfpzM+AeVEp6PNl/CEPx8Uqts2bKhsACqRZzc+4AHQhwBit95hI3gyRbrxnT+5D98fSMhBJx88slWqSPc0h+Pahr7iGatBE1+R0gleCRXcLv5beeXh+oUlQICUnD7855++1PlpRrVrFkzC0XB/UIzRfYVAZ6Qk5ImZAR1TmIJRMFlIFT796LiRr8kmqkRlL744otkv0/4tiAcs97hxyb89mFfEzwJeB7ryjajWhtpvuHbGGw/37yUJr9UJGnGGa2qyd+ffwSPQcl6+Bymch5sQk1rg+BxKyIiIjFcySPMcbU3WIGjqSYVGioLnDB6NP9JLk50w5t+Eg7D+aZ8/jWRnqMCBk6WQd+o8EoXJ+GJzdfPJ7VSsj3Cl8kvV6TnElpO+hgSsKn20USS/nisOw8CIM/zIFwQBtNiGYPbn0AS7Pvn+Yog/ctocvvee+9ZOCHMEbiuvvpqCy9UbfndRx99ZAGFqixNIZPKHwPM4/TTT4/3O19dpB8n/f+okFJlpeJMU1GqjqndFmlxXCW2z2+99VZr5ko/UgIq1Wr6rUZChZFgHazkKejFPj67/YNj559//rGLRXw+cLGOJss0CefiwdNPP63bKIiIiGSFSh4nBlQ+OMHmKq9/cPWX0MfJQTQ0jeMklWaIHs2BGDAliEpXsF/R2rVrQ32XUorKCctHNZFBL4IPP6BGUhB+GPAkrTAIzKZNm+zhUWVjkBIqdWmNZpgELR7BprUNGjSw52jCGamppufDX3K3ARVDbt9A1Td8+wdH+aMPJP3kCHI0q/VVKRBAOnfubH3hGDWTwJ4cbE/CHE0yw5chGG44/mi++8orr1gfTO53mJp1T+pxQDALXtyg7yrVW/opJhUVQwaBYbvRVJmBXqJhW9C/NPiQ2EdFnGo9fZPfffdd+5k+ovAX6TjmuNhDywcuHIiIiEiMV/IYyIERCPnyD1bsQJ8pqnyciEfCCSsnz/S7om8X/bqo1nAVOdhMjVEXOdmg+Ron1PRLCq9gpAT962j2x3LTj5D+SIzeyPoEKxoJodkkFSH6QdHviqvfkW6PkFQ0TaQ5FIOOECgI0YycSf+wYFPXtEKA4wbHBK7goDG83xNPPGH9JhMKeTQ3ZV9xHDBQCCeISRkAhfVkfxJAGDCEMEefM6pqVOrog8hxcc0111jopokiFwM4psBIlVTZeB37i4ojwSg5OP5YZ0IkFQwqmVxkIEwRcDg26f/GqJ4sD8cH6+nfh+OV9WVwE06CGX00/G8gpdjn7H8qk9xfkSbR/G1wXAab5CYFVU9G2eRvJ9jXVMRXzCONCgz+DhK6UCciIiIxWskjxHHCHunklhNyQlNC/aVGjx5tJ/uchDIfrhb7Wxp4VAmprNDMjOZ7nJinJkgFT365fQFVDoIVwYZqR3IqeTRrJMTSrImKD4ElNQhMc+bMsaH2qaaxTah4cnuK44FtTwWHihFhxqMJK01i/a0WoqGZI2GZJoFURwkkSV1PmkCyjlSXCGs33HCDNY1kPjQZ3blzp1UN+B23kSDU8V4gsNBskmOFgM40wdtfJBUBl+ZoNPf08yJo+mOAah3NGOn7xrKyXDQZBVXIp556ygZpoSrMqKhphe3K9qGvKxcPOMa4kOIHb0kOjiGaxtK8NaWDGomIiIhIxpMtLtL9DDIgBkThBJdgx0mtiKQOlWb+priY4UeSTQr65HHxplbvRi5HruM+QK+cAIuH/P+3MBEREZGMy5+H0cosoS40GfYM7euvv3Y//vijDQDCSnCLBaRlVUQkK6IJ6p9//mkXTBjM5sorr0zvRRIRERGRNJRhQx4YqZA+RzSNo8kgNy4PDr4hIsnHgDI0O6W/IM2Qgze1FhEREZHML8Oe3XGj6RUrVqT3YojEHAYFyiSttEVEREQko90MXURERERERE4shTwREREREZEYopAnIiIiIiISQzJsnzwRydjm95+T4NC9IiIiIpI+VMkTERERERGJIQp5IiIiIiIiMUQhT0REREREJIYo5ImIiIiIiMQQhTwREREREZEYotE1RSRFmg9t43Lk0kfIibJ4yPwT9l4iIiKSuamSJyIiIiIiEkMU8kRERERERGKIQp6IiIiIiEgMUcgTERERERGJIQp5IiIiIiIiMUQhLxObOHGiK126tMuePbsbO3asi2UbNmxw2bJlc998802C0zVq1Mj16NHjhC2XSHr7/fff3VVXXeWKFCniihYt6q677jq3Y8eO9F4sERERSUcZOuR9+eWX7qSTTnKtWrVyWcULL7zgTj311ESn27t3r+vSpYvr06ePneR16tTJpbfBgwdbEOORI0cOV65cOXf//fe7/fv3p3rehNmtW7e6c8891/792Wef2fvs3r073nRvvfWWe/TRR93xFO29UyJSKB03bpzLlSuXe+2111I9f4l99913n/3/t99+c+vXr3f//POP69atW3ovloiIiKSjDB3ypkyZ4rp27eo+//xzt2XLlgSnjYuLc4cPH3ZZxcaNG92hQ4csAJcsWdKdcsopx0zz33//nfDlqlq1qoUxKm+PP/64VRsfeOCBVM+XsF+iRAkLjwkpXLiwy58/v8usBg0a5B566CE3Z84cd8MNN6T34kgm8Ouvv1r1Ll++fHbsX3/99W716tXpvVgiIiKSjjJsyKP6M3PmTHfPPfdYkKHCFama8v7777saNWpY5WPRokVu37597qabbnJ58+a18DNmzJhjqiW8bvbs2fHmR/XMv4dvGjhr1ixXv359lydPHlezZk33888/u2XLlrmLLrrITqguu+yyY5pFTZ482VWuXNnlzp3bVapUyY0fPz70Oz9fqk2NGze2YFa9enWrWPp16tChg9uzZ0+oIkZ1LBzLed5559nP5cuXt+mYN9Oef/75tgxnnnmmLYMPhG3atLFlLlCggJ0Qbtu2LTQ//7qpU6e6MmXK2HT33nuvO3LkiBs5cqSFq+LFi7uhQ4cmut8IYUx/xhln2Mkm++Kdd96x3/37779WYWBeLNsll1xi29PbtWuXTV+sWDHb5meffbabNm3aMc01+Znth0KFCtnzt99+u/07fF8zz1tvvdWmY3uzz9auXRtvW7LvP/zwQ9tvrHvLli0tqEaS0Hsntn6JXaTggsZTTz3l5s+fb8vgUREdNmyYu+OOO+wknn1EeA7ipL5Jkya23Wi2R2XXV1C/++47a9Lrj9W//vrL/h0MkY899pgtr2Q+PXv2dK+//rp9blBdnjFjhrviiivSe7FEREQkHWXYkEfAIiSdc8457uabb7YAwolwuL59+7oRI0a4NWvWuGrVqtkJz+LFiy1YcLK8cOFCt3LlyhRXVR5++GF7PeGlffv2rnfv3tacjvn+8ssvbuDAgaHpp0+fbv8mDLE8nJgPGDDAvfjii/Hm279/f/fggw9aYKlYsaK78cYbrQpZt25d61tHECNk8GC6cISnjz/+2H5eunSpTUdzRrBMb775pgVJ5n/06FELeJzYL1iwwLYJV/6ZR9C6dessMH/wwQd2kkgVlXC9efNmex1VObbFkiVLkrUNCR2+osi2Y9nYHmzTChUquBYtWtiygW31ww8/2HKw/Z577jnrYxSOdWU++Omnn2z92SeREMCWL19uxwNhmmPo8ssvtyqod/DgQffkk0+6l19+2arGhOJI2z2x905s/aJh33OMv/HGG7atOQ7CjRo1yi4ufP311xbAufjB++PAgQP2PoROQiUn/BwfNOf11VWCH/MGx27w3+BnAnIkhFeaBwcfknHUq1fPbd++3fY/lWwubPTr1y+9F0tERETSUYYNeYQMTnxBVYOr1MGTUm/IkCGuefPm7qyzznI5c+a0E2xO2Js2bWr9t6gEUZFKCU70OXmmwtO9e3e3YsUKCyKcVF1wwQXuzjvvdJ9++mm8UMjJeNu2ba2Sxv/pkzZhwoRj5kuAIuA98sgj1peGcHbyySe7ggULWnWIahgPKkvhfLUGVL2YjuaMIFC99NJLtnyE3k8++cSqPK+++qpVPGvXrm2/Z1sGq0yEQYJ0lSpVrApAtYoQQegkaFNh5P/B9U0M24v3pcJEECG0PfHEE1ZN430mTZpk68K+BuGK5SbMUL1q1qxZxIoE68rJLKiasf5st3BU7Ah3VDapyFI1JYjThzFYySXwPf/88/a+F154oYUjtlsk0d47KesXDdMR8Ni27LNICKaEO4Ij/TAJv35fsI3ph8V+5Zhnez/zzDMWWqnYcjw1aNDAKsXBijHh7ccff7T1/+KLL1zDhg0jvvfw4cNtHf3DX1CQ9MffLZ9/fCZRueXBz5deeml6L5qIiIikowwZ8ggXVKiocIEqGpWnSCfLnJh7VKg4Ya1Vq1boOU5KCScpETzhPu200+z/vpmkf44r6OAkn2oYwY9g5h80g+P5aPOlSSn8fFKrbNmyFvw8KmKclAdPzAkgNFHkdx6hKtiXjXVjOpr1RVrfaAiUrDfhhv1Qp04dCxxsA/YNJ6AeoZxp/HJQnWKwEZqOUhUjeKQG8+XYIdh6hGOOh+C604yTiwTBfZLc/ZGU9YuGZpJsMy4gROtXGjxm/EUAv4zMnwBLE2WP5SAA+GofAc6HPAI+QdAHP8J++LIHURXiIot/bNq0KVnbRo4fqsRcJKKZMMcxD5r9UnH/888/telFRESyqIRHsUgnhDlOdkuVKhV6jmZ29LsjMASrNsET26TiJDm86Wew+V7wJD34mkjPcSIN3/+JqkwwVMBX2RKar59PaqVke4Qvk1+uSM8ltpwEKKpnhCv2H9VJBPsARkMFjBPWefPmWbNSqrGMHEhl9niKtJ6RmgYfL1w4oAJM5ZKLGfRFDR9gJiX7Isj3VaS6SZNYgiVVPEIezfu4WBJp8B7wd8dDMh4qulR3n332WWtJAH6mT2ykps4iIiKSNWS4Sh7hjmZnnPTSp8w/vv32WwsN9BeLhkFIOBkONkOk8sCAKUFUuoIDa3DiS7+s1KDKxfJRTeSkK/ig6WZSEYpS2rw0EpqaUnkJVl84yWeABip1aY3lZ52pDPqABypl/Jv+ksFgzb4KLgf75rbbbnOvvPKKNRUNH2Ak+D5IaFux7hxPwX6EO3futOpWatY90nsndf2ioXpJE1H6BDIwTqSLDgmtJ38fVJM9loMqrK9iEyTps0Vlmfeickjwo6pH0IvWH08yPkZipQ/o6aefblVoWkH4wY5EREQka8pwlby5c+daZYFmj+H9rNq1a2dVvs6dO0d8Lc0NCQi9evWyflP0meLqNie7vmIG32eJpoScqNPHKbxSkhL0r6PZFMtNP0L6PDHoB+vDgDBJQTiiKsgJP03wfBOslKI6xAk+o1YSmgg99O2i+V6wqevxRoWR5ph+3zBCJCN3Eq7Z12DQGvoNMlAI245jgQATrVkq+5Rp6K9G89Dw/ouMzsmgM3fddZf1i+T4YKAeToZ5PqWivXdi65cY9vf//vc/q2AS9Bh8KCnHJfuW45xjn5FSGUWTJnu33HJLqJmx75dHn0Q/qAxNQNnOHGtJPT4l4+EiAqPDioiIiGTYSh4hjmASaSANQh6hadWqVVFfP3r0aAtvrVu3tvnQz8jf0sCjSkgfNQbjYMRMTnpTE6S8jh072iAfDPZCsCJIMUR/cip5jKxIiKXZHlUtgkJqcHLPlX6qOJzks02oeNIk8ERjFFT2IeGDAU4YbIaTU5YNVMLo/0X4YFlp5hrthuAENUI1oY0g40eSDMe+IDhyPHBc0AyT5qCpCfXR3jux9UsKjhuCHv0Rr7322iTd65Bjl/ehfxa3+rjmmmssKHIhI4jjkYsavmrHxQ+2M8dItP54IiIiIpL5ZIs7kZ2P0gFN2DgpJ9gltaIiItFxCwUuwtTq3cjlyJXhGgPErMVD5qf3IoiIiEgGOQ+jSxq3XYsm5s7QuI8YA0owqiErzy0WkJrmeSIiIiIiIplFzIU8MBojg2vQ/I+metz8WSPNiYiIiIhIVhBzIY+baXMTbhERERERkawoww28IiIiIiIiIimnkCciIiIiIhJDYq65poicGPP7z0lwVCcRERERSR+q5ImIiIiIiMQQhTwREREREZEYopAnIiIiIiISQxTyREREREREYohCnoiIiIiISAzR6JoikiLNh7ZxOXLpI+REWTxk/gl7LxEREcncVMkTERERERGJIQp5IiIiIiIiMUQhT0REREREJIYo5ImIiIiIiMQQhTwREREREZEYopAnIiIiIiISQxTyJKZMnDjRlS5d2mXPnt2NHTvWxYLbb7/dXXXVVem9GJJB/f7773Z8FClSxBUtWtRdd911bseOHem9WCIiIpKOFPIymS+//NKddNJJrlWrVi6reOGFF9ypp56a6HR79+51Xbp0cX369LET306dOrn01LVrV1e5cuWIv9u4caPtx3feeeeEL5fElvvuu8/+/9tvv7n169e7f/75x3Xr1i29F0tERETSkUJeJjNlyhQLD59//rnbsmVLgtPGxcW5w4cPu6yC4HTo0CELwCVLlnSnnHLKMdP8999/J2x57rzzTvfjjz+6L774ImJwLV68uLv88stP2PJIbPr111+tepcvXz6XP39+d/3117vVq1en92KJiIhIOlLIy0T279/vZs6c6e655x4LMgSFoM8++8xly5bNvf/++65GjRouV65cbtGiRW7fvn3upptucnnz5rXwM2bMGNeoUSPXo0eP0Gt53ezZs+PNj+qZf48NGzbYNLNmzXL169d3efLkcTVr1nQ///yzW7ZsmbvooovsJPOyyy47pqnY5MmTraKVO3duV6lSJTd+/PjQ7/x833rrLde4cWMLZtWrV7eKpV+nDh06uD179th0PAYPHnzMtmE5zzvvPPu5fPnyNh3zZtrzzz/fluHMM8+0ZfCBsE2bNrbMBQoUsJPkbdu2hebnXzd16lRXpkwZm+7ee+91R44ccSNHjnQlSpSwkDZ06NCo+4vXX3jhhTaP8PDN8t522222nIRBlo1tes4557hx48YleByUK1fumKaovFdwu+zevdt17NjRFStWzNavSZMm7ttvvw39np/Z3oQCfs/xsnz58gTfVzKmnj17utdff93+RtjvM2bMcFdccUV6L5aIiIikI4W8TISARUgiCNx8880WHggM4fr27etGjBjh1qxZ46pVq2YngYsXL7amgfPnz3cLFy50K1euTNEyDBo0yD388MP2+hw5crj27du73r17WzBhvr/88osbOHBgaPrp06fbvwlDLM+wYcPcgAED3Isvvhhvvv3793cPPvig++abb1zFihXdjTfeaFXIunXrWqAhiGzdutUeTBeO6sXHH39sPy9dutSmo28eWKY333zTgiTzP3r0qAW8v/76yy1YsMC2CdUQ5hG0bt06C8wffPCBnThTRSVcb9682V73+OOP27ZYsmRJ1O1FgGO/HThwIPQcwZVmdXfccYctyxlnnGEn6T/88INtq4ceeshekxrXXnut2759uy3/ihUrLGw2bdrU1hmEft6XgM7vOWZy5swZcV7//vuvNYUNPiTjqFevnu3rQoUKucKFC7tdu3a5fv36pfdiiYiISDrKoa2feRAyCHdo2bKlXbknbFCVCxoyZIhr3ry5/UwVj0D16quv2kk+pk2b5kqVKpWiZSBgtWjRwn7u3r27hbFPPvnETjR9qAlWGAmFo0aNcm3btrV/U7EizEyYMMEqWcH5+n6GjzzyiKtataqFM0JtwYIFreJF9SwaqmAMPAGqV8FpaaL50ksv2fMg1NGcjaDlgyC/5z0JPVQoQQAjSFPtqlKlilW+fvrpJzdv3jwb2IWwTdD79NNPXe3atSMuFyH4gQcesBDHACp++19yySUWZv36emwfqpiEPKqLKUH1lqDLiT/VXDz55JNWqX3jjTesryKVzF69etn2xdlnnx11fsOHD4+3jJJxcIzyt86xwnENKrqXXnqp++qrr9J78URERCSdqJKXSRAuOHEnVIEqGpUngl84mk56VKjop1arVq3Qc4QmAkpKUBn0TjvtNPu/bybpnyNcgOoV1TCCH80d/eOxxx6z56PNlyal8PNJrbJly4YCHqgoEu58wAMhjuap/C7YLJKAF1w3piPgRVrfSJgnAdc32aQKRlWRbeI9++yz1lySZWT7MEIoISylaIpJ015Cb3C7E2r9dqe6S3POZs2aWdU3fH8EURXigoJ/bNq0KcXLJmmLyiwDrjDQCk2dedBnl+ryn3/+qc0tIiKSRamSl0kQ5mi+GKzA0VSTSs0zzzxjwc2j711yUSkLb/pJOAwXbNLHayI9R3UBBA1MmjTpmEoXI0smNl8/n9RKyfYIXya/XJGeS2w5CXRUUalMUvVj3WlOiddee82qmFQ769SpY6HyiSeeSLAJKCEzoX3Fdico0yw0nB+llGoPVcb33nvPmnRScWVZrr766mNewzHmK4KSsXDLhAoVKtiFAvYh+JmmuPxOREREsiaFvEyAcEdzQoIAzbCCuD8W/cU6d+4c8bUMQkIwoRkiA4iAagwDpjRo0CA0HVUk+rF5a9eudQcPHkzVclPlIpRSTaQPWEqdfPLJNuBJWmEQGKpRPHw1jyakDFpBpS6t0cyTZpg00yTk3XDDDaHgSV9J+h0yqIuXUFUt0r6iOkiVzqP/3R9//GHVXqqR0dBclMf9999vFWKWL1LIk4xtzpw5tg9PP/10u+BwwQUX6NYcIiIiWZxCXiYwd+5cG0yBilCwYod27dpZlS9ayKMyRN83+l8xKAMjQnLFn2qQr5iB0RepCFJNIlBxr7loA3EkB325aErGctOPkEE8GMWR9aHJYFIQVKhO0fePkTd9s7SUookiTUwJngzqQogmZDVs2DBeU9e0wnZmkJXRo0fbejO6qUdfOAL8hx9+aEHw5ZdftkDOz9Gwr+j3yAiKVOYYrCVYGWX92I9cAGAkUIIct9ugakeIo+8hx8M111xj78NAMrwnx5JkPlyY4PgRERER8dQnLxMgxHHiHh7wwIk5oWnVqlVRX0+44KS/devWNh8GSfG3NPCoElLV4vYINOOjCWFqgpRHvy9uX0CViGBFkCKgJBRiwlHpIsTSB5EqFsEltaGL6gejEVLNZJtQ8eT2FMcLg65QQSVgBZuu3n333dZnj3Xj+Z07d8ar6kXrI8d2ZH8yWA1h7qyzzoq3fgwOw7px+wlCHtVD+m5RXSUQ8j633nqr/Y5BO7j1hQZXEREREYkN2eIijcEvMY0BUWjaRbALDgAikhQ0D+WCQ63ejVyOXGoMcKIsHvL/Rs8UERGRrGvv/52HUTzgFmPR6AwtC/j666/djz/+aCNsckBwiwVwrzgREREREYktCnlZBPdJ4zYMDGLCcP3cuFyj74mIiIiIxB6FvCyA0fZWrFiR3oshIiIiIiIngAZeERERERERiSEKeSIiIiIiIjFEzTVFJEXm95+T4KhOIiIiIpI+VMkTERERERGJIQp5IiIiIiIiMUQhT0REREREJIYo5ImIiIiIiMQQhTwREREREZEYotE1RSRFmg9t43Lk0kdIaiweMl9Hn4iIiKQ5VfJERERERERiiEKeiIiIiIhIDFHIExERERERiSEKeSIiIiIiIjFEIU9ERERERCSGKOSJiIiIiIjEEIU8yfAmTpzoSpcu7bJnz+7Gjh3rspJGjRq5Hj16pPdiyAmSL1++eI+cOXO6atWqafuLiIhIsijkZQBffvmlO+mkk1yrVq1cVvHCCy+4U089NdHp9u7d67p06eL69Onjfv/9d9epUyeXUWzevNmdfPLJ7txzz03vRZEYsX///niPypUruxtuuCG9F0tEREQyGYW8DGDKlCmua9eu7vPPP3dbtmxJcNq4uDh3+PBhl1Vs3LjRHTp0yAJwyZIl3SmnnHLMNP/991+6BdXrrrvOguiSJUvSZRkkdi1dutT98MMP7vbbb0/vRREREZFMRiEvnXG1fubMme6ee+6xIENwCPrss89ctmzZ3Pvvv+9q1KjhcuXK5RYtWuT27dvnbrrpJpc3b14LP2PGjDmmaR+vmz17drz5UT3z77FhwwabZtasWa5+/fouT548rmbNmu7nn392y5YtcxdddJE1Gbvsssvcjh074s1n8uTJVmXInTu3q1Spkhs/fnzod36+b731lmvcuLEFs+rVq1vF0q9Thw4d3J49e2w6HoMHDz5m27Cc5513nv1cvnx5m455M+35559vy3DmmWfaMvhA2KZNG1vmAgUKWADbtm1baH7+dVOnTnVlypSx6e6991535MgRN3LkSFeiRAlXvHhxN3To0ET3G2F72rRp7pZbbnHt27e3oB6U2DbAzp073Y033uhOP/10+z3rOmPGjATfd9euXe7WW291hQoVstewb9auXRtvmkmTJlnzVn5/9dVXu9GjR4eqpiwXzV6XL18e7zU0gy1btqw7evRoousuJwbHFPu3VKlS2uQiIiKSLAp56YyARUg655xz3M0332wBhAARrm/fvm7EiBFuzZo11kenZ8+ebvHixe6dd95x8+fPdwsXLnQrV65M0TIMGjTIPfzww/b6HDlyWGjp3bu3GzdunM33l19+cQMHDgxNP336dPs3YYjlGTZsmBswYIB78cUX4823f//+7sEHH3TffPONq1ixogUaqpB169a1UEEQ27p1qz2YLtz111/vPv7441BVg+kIL2CZ3nzzTQtRzJ9wQsD766+/3IIFC2yb/PrrrzaPoHXr1llg/uCDDyxQcSJNuKbpJa97/PHHbVskVpn79NNP3cGDB12zZs1sv7322mvuwIEDx0wXbRvgn3/+seD+3nvvue+++86aohIaWddoqOoQ0NjvBEaOlcsvv9yqneCY6Ny5s+vevbu9Z/PmzeOF1nLlytkyE1CD+DfzJgCG+/fff61aGXzI8cWxxDHVsWNHbWoRERFJthzJf4mkJUIGIQEtW7a06hZhg6pc0JAhQ+yEHVTxCFSvvvqqa9q0aegkPaVX/AkhLVq0sJ8JBwSRTz75xNWrV8+eu/POO+NVGAmFo0aNcm3btrV/U02jWdmECRPcbbfdFm++vp/hI4884qpWrWrhjFBbsGBBq3RRPYuGymKRIkXs52LFisWbliaaL730kj0PQt3q1avd+vXrQ0GQ3/OeVCWpUIIwSJDOnz+/q1KlilXZfvrpJzdv3jwLOIRtgh4hrnbt2gnuN/pK0ZeSPnlUGl9//fVjmtYltA2o4AXDLU12P/zwQwv+tWrVOuY9qdgR7ghyBGUfuFlfKrbXXnute/rpp6364+dLsPziiy/c3LlzQ/MhOBAEqfBRGSbcs+3mzJkTcV2HDx9uyy4nDscSldis1E9XRERE0o4qeemIcEHVhlAFqmhUnsKb/oGmkx4VKio3wSBAaCKgpERw9L7TTjvN/u+bSfrntm/fHqowUA0j+AVHAXzsscfs+WjzpUkp/HxSi6aFPuCBiiJhxwc8EOJopsjvgpUsAl5w3ZguWMEKrm8ku3fvtgqiD+fg50j7LaFtQDPRRx991LZ14cKFbTsS8mh2GgnrwTESDJ+EYPa7X0eOqfCAGP7vq666ysLp22+/bf8mwBN22TaR9OvXzy4++MemTZuibhtJGzRF5oIJ+1tEREQkuXQGkY4IBTTdC1bgaH5HdeWZZ56x4ObR9y65qJSFN/30zfqCGKY9+JpIz/m+WvQh9P2+witdBIfE5ptWfb5Ssj3Cl8kvV6TnElpOKqg0tQyuP9uZ19CfkepZUrbBE088YU1iabpK0GOd6FN5vAeSYURQ+vVR/aUay/qwHNFwPPKQE4OgTvU1vEmtiIiISFKpkpdOCHc0J6TZI32n/OPbb7+10JfQABw0DSQ80AzRo8JCwAii0kU/tmBzP/qRpQZVLpaPamKFChXiPWi2mZygQSUrrTAIDBWmYJWJJqRU3ajUpXU4f+CBB47ZbwxeQ1PQpKLZJf0IqQIyKAv7NXwfhq8jx02wvyCDtxAK/DpS1QseFwj/t2+ySX9HBsxhnr7praQ/ji+OpbPPPju9F0VEREQyKVXy0gl9pBgpkWaPwYod2rVrZyd69JuKhOaGNOXq1auXNfNjREj6ydHk0FeL0KRJE6sI1qlTxwIV95oLr1qlBP2zunXrZstNP0IG5mAwENaHAWGSgqaBVAXp+0fAof9RpNsjJBWDiVANY8RRKmMEF0bObNiwYbymrqlFoKMPG33h6FcXRLNb+k7SdDUpOIl/4403rGrDaJn0kWM00GihlOkJhXfddZf1f+Q4YEAe+vbxvO/X16BBA5vXFVdc4f73v//ZQDPB48IHxosvvtiOiTvuuMP6P0rGwEivIiIiIqmhSl46IcQRTMIDng95hKZVq1ZFfT0n8YS31q1b23wYJMXf0sCjSkgfNaoCjJjJYBypCVLBKhB9hmhORrAiSNGvKzmVPAYOIcTSB5GKY2pPbAkxDBxCWCLksE2ojHF7irTeb4Sw8IAHbldAfzsGcUkKRvG88MILbdAbBtphYBn6yyWEbc6InOx39j/NRHk/H945Dp5//nk7PgjPjCJ6//33xzsuPC4w0DSUkCciIiIisSNbXKTx+iXTYUAUKjoEO07eRTwqfz/++KPdDiOIQV8YxTGhiwmRcAsFLk7U6t3I5cilxgCpsXjIfB2oIiIikuzzMLpqcTuyaHSGlkl9/fXXduLOyInsZJoJwjfbk6zrySeftNttMJALTTW53UbwZvU0k+Wm6DTlTWrTUhERERHJPBTyMvnJPINuMIgJTfio1BQtWjS9F0vSGbfloPkr91OkyepTTz0V76baXbp0sYF9aBqqppoiIiIisUfNNUUkWdRcM+2ouaaIiIgcj+aaGnhFREREREQkhijkiYiIiIiIxBD1yRORFJnff06CzQREREREJH2okiciIiIiIhJDFPJERERERERiiEKeiIiIiIhIDFHIExERERERiSEKeSIiIiIiIjFEo2uKSIo0H9rG5cilj5Ag3dxcREREMgJV8kRERERERGKIQp6IiIiIiEgMUcgTERERERGJIQp5IiIiIiIiMUQhT0REREREJIYo5ImIiIiIiMQQhbwsYuLEia506dIue/bsbuzYsem9OBJFuXLlkrV/PvvsM5ctWza3e/dubdMM7J133nHnn3++y5s3rytVqpR7/vnn03uRREREJIbFTMj78ssv3UknneRatWrlsooXXnjBnXrqqYlOt3fvXtelSxfXp08f9/vvv7tOnTq59DZ48GALJ+GPSpUquYyA5eOkPCn++usv16NHD1e2bFl38skn20n8HXfc4TZu3Jjs9122bFmy9k/dunXd1q1bXcGCBZP9XnJifPDBB+7ee++18M7f4vfff+8aNWqkzS8iIiLHTczcyXjKlCmua9eu9v8tW7bYiXY0cXFx7siRIy5HjphZ/QQRNg4dOmQBuGTJkhGn+e+//yygnEhVq1Z1H3/8cbzn0nuf+GMjqQh4F198sW07qjOs04YNG9zDDz/satasaRcfypcvn+T5FStWLFnLy/uWKFEiWa+RE2vAgAFu4MCBoWBXqFAhe4iIiIgcLzFRydu/f7+bOXOmu+eeeyzIUOGK1KTt/fffdzVq1HC5cuVyixYtcvv27XM33XSTNaEi/IwZM8ZOxKjKeLxu9uzZ8eZH9cy/Byf0TDNr1ixXv359lydPHju5//nnn60qc9FFF7l8+fK5yy67zO3YsSPefCZPnuwqV67scufObRWs8ePHh37n5/vWW2+5xo0bu1NOOcVVr17dQoNfpw4dOrg9e/aEqmBUn8KxnOedd579TNhgOubtK1Usw5lnnmnL4ANhmzZtbJkLFCjgrrvuOrdt27bQ/Pzrpk6d6sqUKWPTUaUgGI0cOdICR/Hixd3QoUMT3W8EOqYPPooWLWq/+/HHH22dX3311dD0bGO27w8//GD/vv32291VV13lHnnkEQtHLG/nzp0tsHpHjx51w4cPt3XktWzDN954I8Fj45VXXrF5fvvtt6FtG35Mef3797eLCoRV9jHbpEGDBu7DDz90OXPmdPfdd19oWo4tKqo8qLyxrgQAgmW05pq8N/vo6quvtu1x9tlnW9O/hJprvvnmmxY2WRfmN2rUqHjLzHPDhg2zamP+/PltmWnOK2nvwIEDbsWKFVZBr1ixoh3j1157rVVfRURERI6XmAh5nPwTks455xx38803WwAJnjh7ffv2dSNGjHBr1qxx1apVcz179nSLFy+2k+b58+e7hQsXupUrV6ZoGQYNGmTVG15PeGnfvr3r3bu3GzdunM33l19+sav53vTp0+3fhCGWh5NuTvhffPHFY0LEgw8+6L755hs7Sbzxxhvd4cOHrZkeYYBgwwkjD6YLd/3114eqZUuXLrXp6JsHlolAQJBk/gQiAh7VqQULFtg2+fXXX20eQevWrbNQRDO0GTNmWPWUcL1582Z73eOPP27bYsmSJS6l2J9PPvmkBUiCJ/MmwDHvKlWqhKb75JNPbPsRdlgW1oWA5hHwXnrpJauy0Uzu/vvvt2OE5Yx2bDRv3tw98MADFpT8tg3fBmB7vfbaa3ahILyaRqBk2Ql7bE+P/cvxwb7g2Bg9erSFuISwPoTtVatWucsvv9zeLzjPIAIF095www1u9erVFso5rsJDKsGPCxBff/21LScXSH766aeI8/z333+tmWHwIUmza9cu+yziQhF/T/zNEb45BkVERESOl5hor0jI8CdNLVu2tOoWJ/Hh/V6GDBliJ/CgiscJN5Wipk2b2nPTpk1LsJlnQghYLVq0sJ+7d+9uYYwAUq9ePXvuzjvvjHeiTSjkRLtt27b2bypNVKgmTJjgbrvttnjz9f0MOdkneHCiSAiiGkQVJ6HmeoSNIkWK2M9Uu4LTUvEiAPkmgpyEEgzWr18fCoL8nvekKkmF0ocbgjRVIAIXlUYCwrx582xgF8I2YezTTz91tWvXjrpsvBeVwCD2ox+UgvDBPHmOZom8P01yg3ieZaHKxXKyj3v16uUeffRRa6JKeCbk1qlTJ1TNpIrLdm7YsGHEYwMsl680RkNllgoa1dhIeJ4TfPZXrVq17Dm2KxVj9hvbiW3Av++6666o70PFkuMJrM9TTz1lIZFjPRyhkeOZYAcuDHBcPfHEEzYfj7DI9gV9NVkG9hfLFI6gHAzOknT++O7WrZv12QTbkoosVT5aEYiIiIiktUwf8ggXnPC+/fbb9m9OzKm6EPzCQx6VC48KFSHAn3yD0BTpJDcpqAx6p512mv3fN5P0z23fvt1+5uSOahjBL3hyT4UufACN4Hx9fzrmkxYDlHDSGewDRhWLEOIDHghxNE/ldz7k0dyPgBdcNwa9IeBFWt9o2NbBpoegMhlEgCOoMG8qcYSjIJpfEvA8whzNdzdt2mT/P3jwYLzw5sPtBRdcEPXYSK5IVeNo6L8XXAeWl7BPc1e2YSTBY4BQwDaKtm3ZT1Rjg7jQQNU3+B7BefoLBdHm2a9fP6t6e1TygseIRMffDs1hU3vciIiIiGSpkEeYIxwFK3CcPNEk6plnnokXmlJy1ZwT4PCTMcJhOPpfBV8T6TkqYCB8YNKkScdUusJP9CPN188ntVJaRQguk1+uSM8ltpxU4SpUqJDgNPSLIxQT8mg2GW3gmEj8dn7vvffc6aefHu93HB+p3RYEZB+AI+F5tkNi65iYlGzbtJwn2yp8e0nSMVrq008/bZXXwoULW9WYamt4FVtEREQkrWTqPnmEO5oTUgmhT5l/EAwIffTRioZme5zo0gzRo5knA6aEn8gHB0lYu3atVYdSgyoXy0c1kQAQfNBsM6kISckZCTIxNC+kAsbDo6kfTRKD/eBOFPqd0cSQfon8n75of//9d7xp2NfB57766is7eabSxDITTujTF76dE6tEJWXbEjzp/0aT3z/++CPe71gmBtKhCS8n9l54P0WWl6Z70ap4KdmH9DMN4t9UQ9PqPSR56O9JqKPqzHHH58fLL7+szSgiIiLHTaau5M2dO9cGNqDZY3gzx3bt2lmVj8E6IqG5IX3f6L/FSTgjQtJPjhP3YHO6Jk2aWEWQZnWc9NN/KbwKkhL0y6GfDsvNFX4Gt1i+fLmtT7BpXEJoNkm1ir5/vtlisOlicjVr1syamBKmaN5HiKbfFn3XUtOcMRrmHx6O2Pa+uSv7jpNiBnFh+9DEkj6Kzz77bLyml+x/pmHUUPYho1eyH9nHTM9gK1SpLrnkEgvyhB6aPAb7PkbatvRN5KLBGWecYfOKVM2ijxzbnyahjC567rnn2utYHiq+wWUFgZP9e/fdd9sgPVR4wke/TA0GjKFZLX0SabbMaKwcv8GRW+XEIlyzj9NyP4uIiIjEbCWPEEcwiXQjaEIeoYkRCaNhkArCW+vWrW0+9F3ytzTwODEjaHB7BEbMJDSkJkh5HTt2tFEVGeyFYEWQYmCW5FTyGGGTIMTJPBVHQkZqELDmzJlj9/DiNgBsEyqe3J7ieKCPHc0vgw8/OAUVWgZdoeJBP0uaU3JrA5q4MrKnR4WEShjLy3a48sor491KgrDDICQMHsK+JVDTfDOx7czxw7QMKsO2jVYVZlAbqnFMR3A766yzrLrH/6kSh98j79Zbb7UqH31Bub0Cg/Sk5c3pL7zwQhttllE/CZyM4ErzwOCgKyIiIiIS27LFqfd/CH2/6LtFsKM6JBkbwYWmpOH3McyoGAiIewwG74OXGTHwChdWavVu5HLkytSNAdLc4iHz03sRREREJIb58zBap4UPWBiUpc/QuEcYN92mqsKGouKB8NEJRUREREREMossHfLADbe5DQMDbdSoUcNuXF60aNH0XiwREREREZEUUXNNEUkWNdeMTs01RUREJCM018zUA6+IiIiIiIhIfAp5IiIiIiIiMSTL98kTkZSZ339Ogs0ERERERCR9qJInIiIiIiISQxTyREREREREYohCnoiIiIiISAxRyBMREREREYkhCnkiIiIiIiIxRKNrikiKNB/axuXIlfk+QnTDchEREYl1quSJiIiIiIjEEIU8ERERERGRGKKQJyIiIiIiEkMU8kRERERERGKIQp6IiIiIiEgMUcgTERERERGJIQp5kuFMnDjRlS5d2mXPnt2NHTs2vRcn07v99tvdVVddld6LkSn8/fffrkKFCu7UU09N70URERERSTGFvHTw5ZdfupNOOsm1atXKZRUvvPBCkk6c9+7d67p06eL69Onjfv/9d9epUyeX3gYPHuyyZct2zKNSpUrpvWiSxgYOHOjKli2r7SoiIiKZmkJeOpgyZYrr2rWr+/zzz92WLVsSnDYuLs4dPnzYZRUbN250hw4dsgBcsmRJd8oppxwzzX///XfCl6tq1apu69at8R6LFi064cshx8+KFSvcBx98YBcYRERERDIzhbwTbP/+/W7mzJnunnvusSBDhSvos88+syrR+++/72rUqOFy5cplYWLfvn3upptucnnz5rXwM2bMGNeoUSPXo0eP0Gt53ezZs+PNj+qZf48NGzbYNLNmzXL169d3efLkcTVr1nQ///yzW7Zsmbvoootcvnz53GWXXeZ27NgRbz6TJ092lStXdrlz57YK1vjx40O/8/N96623XOPGjS2YVa9e3SqWfp06dOjg9uzZE6qCUR0Lx3Ked9559nP58uVtOubNtOeff74tw5lnnmnL4ANhmzZtbJkLFCjgrrvuOrdt27bQ/Pzrpk6d6sqUKWPT3Xvvve7IkSNu5MiRrkSJEq548eJu6NChie63HDly2PTBR9GiRe13P/74o63zq6++Gpqebcz2/eGHH+zfbN/mzZvbawoWLOgaNmzoVq5cGe89WN8JEya41q1b2/zY3mzDX375xfY1+75u3bpu3bp1x6wjr6OJK69jO7Ctozl69KgbPny4bUuWkX31xhtvuKyMCyl33XWXe/bZZ93JJ5+c3osjIiIikioKeScYJ/+EpHPOOcfdfPPNFkCo1oXr27evGzFihFuzZo2rVq2a69mzp1u8eLF755133Pz5893ChQuPCQlJNWjQIPfwww/b6wkv7du3d71793bjxo2z+RIqaLbmTZ8+3f5NGGJ5hg0b5gYMGOBefPHFePPt37+/e/DBB90333zjKlas6G688UY7eSaY0LeOIOarYEwX7vrrr3cff/yx/bx06VKbjuAClunNN9+0IMn8CSoEvL/++sstWLDAtsmvv/5q8wgiEBGYqdDMmDHDqqiE682bN9vrHn/8cdsWS5YscSnF/nzyySctQBI8mXfnzp1t3lWqVLFpCOm33XabBfavvvrKnX322e7yyy+354MeffRRd+utt9o6Ml/2zd133+369evnli9fbscKzVmD2DYcV++++66t59dff23LEg0B76WXXnLPP/+8+/777939999vxyLbI5J///3XmtEGH7HmiSeecBdccIFr0KBBei+KiIiISKrlSP0sJDkIGZxQo2XLllZx4eSaSk3QkCFDrPIDggCBikpR06ZN7blp06a5UqVKpWjjE7BatGhhP3fv3t3C2CeffOLq1atnz915553xKoyEwlGjRrm2bdvav6kAUaGiekRwCc7X9zN85JFHrIkjAYSwQvWKShUVsGioKhUpUsR+LlasWLxpaaJJMOF5EOpWr17t1q9fHwqC/J73pGpGhRKEQYJ0/vz5LXBRafzpp5/cvHnzbGAXwjZh7NNPP3W1a9eOumy8F5XAIPYjQQmEKubJc1SCeH+a5HpNmjQ5ZnAZqqzseyp3HhVPKnGg2WCdOnUsUAf3F9ME/fPPP7bup59+uv376aeftv3APgvf3gQ2Qjphmnn7qinhk/1JhTFSKGR/xiqOUfYj4VhEREQkFijknUCECypUb7/99v/b+DlyWOWJ4Bce8mg66VGhop9arVq1Qs8RmggoKUFl0DvttNPs/76ZpH9u+/bt9vOBAwesGkbwozmbR4WOZYg2X5qUgvmkxQAlDIbhAx6oKBLufMADIY7gxO98yCtXrpwFvOC6MegNAS/S+kbDtqaKGkRlMogwSQWTeVMhI9R6NCOlYkjTVd6LJqMHDx60yl9y9w2hjmqaf3+aovqAB8Ib4ZbjLTzkEWh4X38BIRiiqWRFQhWRSrLHewe3e2ZHwGX/sO/A3xoXVmha+9577yUY/kVEREQyIoW8E4gwRzgKVuBofke/u2eeeSZeaKL/VXIRKsKbfnLCGi5nzpzxXhPpOUKC70OISZMmHXOyS1hKbL5+PqmVku0Rvkx+uSI9l9hyUp1jaP2EfPvttxaKCXk0NfVBF1Q8d+7caU1iCazsc8JY+CAySdk3qdmufn8SXoLBECxTJDwf7XexgMpps2bNQv+mH2THjh2tySx9NkVEREQyG4W8E4RwR5M6mtBdeuml8X7HPczoL0Y/rkhoTseJPs0QqdqAZp4MmBLsQ0Sli3DhrV271qo2qUHliFBKNZGBX1KKkET1Kq0wKMmmTZvs4atKNCHdvXt3qB/ciUTfQO5HR79E9gHbij6PNEEF/SkZrIZ+eGC5//zzzzR5b6qBjNLqLx7Q5883RQ3HtiGw8ZpITTOzIgarCY7iyt8RYfqMM85I1+USERERSSmFvBNk7ty5bteuXdbsMbyZY7t27azKFy3k0dyQSlCvXr1c4cKFrbpAPzlO5INNAun3RUWQChGBij5d4VWrlKA/Vrdu3Wy56UdIvy4GAWF9gs34EkKzSapI9P1jNMfwE+vkovJCM0bCFIO6EKLpF0dwCTZ1TSvM/48//oj3HNveN6lk3xE2aZLJ9qHpI30UGa0RDLTy8ssv27LR3JF96QNgajHaKMcHg78wb/YV1alI/R85llguBluhGnjJJZfYBQNCKM0/g30ssyqaTnOxQERERCSz0uiaJwghjmASHvB8yCM0rVq1KurrR48ebeGNQTqYD4Ok+FsaeFQJCRrcHoFRGTmZT02Q8mi6xu0LGOyFYEWQYmAWBmBJKkbYJAjRB5FKCbcwSA0C1pw5c1yhQoWsmsk2oeLJ7SmOB/rY0fwy+PA3zaZCy6ArhDj6WdK09JVXXrEmrozs6fc/ofjCCy90t9xyiwWxtGoKSDNSBsWhSkiVmH59wVtchGMETwZzYUAVjiGCO803k7M/RURERCTjyhYXafx+yfDo+0WfKoId1UHJmrhPHvdGpP/YiUK1kIsVtXo3cjlyZb7GAIuHzE/vRRARERFJ1XkYLbHCBwEMynxnaFkUw7tz021G2GSncosFcK84ERERERERTyEvE6HPFcPiM4hJjRo17MblDPMuIiIiIiLiqbmmiCSLmmuKiIiIZOzmmhp4RUREREREJIYo5ImIiIiIiMQQ9ckTkRSZ339Ogs0ERERERCR9qJInIiIiIiISQxTyREREREREYohCnoiIiIiISAxRyBMREREREYkhCnkiIiIiIiIxRKNrikiKNB/axuXIlf4fIYuHzE/vRRARERHJUFTJExERERERiSEKeSIiIiIiIjFEIU9ERERERCSGKOSJiIiIiIjEEIU8ERERERGRGKKQJyIiIiIiEkMU8rKgiRMnutKlS7vs2bO7sWPHpvfiiBwXXbt2teO8QIEC7vTTT3c9evRw//33n7a2iIiIxLyYDHlffvmlO+mkk1yrVq1cVvHCCy+4U089NdHp9u7d67p06eL69Onjfv/9d9epUyeXEbBc/fv3d5UqVXK5c+d2JUqUcM2aNXNvvfWWi4uLS+/Fk0zo3nvvdT/++KMdW99++609Ro4cmd6LJSIiInLcpf+djI+DKVOm2FV8/r9lyxZXqlSpqNMSII4cOeJy5IjJTXGMjRs3ukOHDlkALlmyZMRpqHacfPLJJ2yZdu/e7S655BK3Z88e99hjj7maNWva/liwYIHr3bu3a9KkSZICrEhQ5cqV4/2dU7leu3atNpKIiIjEvJir5O3fv9/NnDnT3XPPPRZkqHAFffbZZy5btmzu/fffdzVq1HC5cuVyixYtcvv27XM33XSTy5s3r4WfMWPGuEaNGlkTL4/XzZ49O978CB/+PTZs2GDTzJo1y9WvX9/lyZPHAsvPP//sli1b5i666CKXL18+d9lll7kdO3bEm8/kyZPtpJQqFtWs8ePHh37n50tVq3Hjxu6UU05x1atXt4qlX6cOHTpYSGI6HoMHDz5m27Cc5513nv1cvnx5m455M+35559vy3DmmWfaMvhA2KZNG1tmmrxdd911btu2baH5+ddNnTrVlSlTxqajekJopmJCNa548eJu6NChCe6zhx56yJZjyZIl7rbbbnNVqlRxFStWdHfddZf75ptvbL7YtWuXu/XWW12hQoVsG7Adgyftvpr54Ycf2rbkdS1btnRbt26Nt/9r1apl+5lp69Wr53777bfQ7+fMmeMuvPBC2wZso0ceecQdPnw49Hver0GDBvZ7lnP+/Pnxjgt/fBFcPdbBb2uPY84fIzQp7Natmztw4EDo9+XKlXPDhg1zd9xxh8ufP79tX5rZBm3evNndeOONrnDhwrY+HF9sw6SsC6GH/cd8+RvgQgjLEGtGjBhhxwHHIZU8Lv6IiIiIxLqYC3kELELSOeec426++WYLIJGa+/Xt29dOANesWeOqVavmevbs6RYvXuzeeecdO3FfuHChW7lyZYqWYdCgQe7hhx+211ORat++vVWkxo0bZ/P95Zdf3MCBA0PTT58+3f5NGGJ5OLkfMGCAe/HFF+PNl+aMDz74oIUGQhAn+Jy0161b1/rWEcQINDyYLtz111/vPv74Y/t56dKlNh0BAyzTm2++aUGS+R89etQC3l9//WUVNbbJr7/+avMIWrdunQXmDz74wM2YMcOqp4RrAgive/zxx21bBMNHEO/z2muvWcCOVHHlBN1XWW+//Xa3fPly20cEXPbr5ZdfbpVJ7+DBg+7JJ590L7/8svv8888tqPptwba66qqrXMOGDd2qVatsHjRXJYCBfUOI7N69u/vhhx/chAkTLDj6kMqytm3b1qqcrM/zzz9vzV6Ti21G+GzXrp0tBxclCH00ow0aNWqUBbevv/7awjMXLn766afQxQzWgya3bA8CDMcYy5iUdWFfcyGD5wmuhFR/ASDcv//+a00eg4/Mgr9zthXboHPnznbhQURERCTWxVwbRUIG4Q6cSFPdImxQlQsaMmSIa968uf1MFY9A9eqrr7qmTZvac9OmTUuwmWdCCBUtWrSwnznJJox98sknVjXCnXfeGa/CSCjkhJ4AAapp/sScylZwvr6fIVWZqlWrWjgj1BYsWNDCSkInsVSNihQpYj8XK1Ys3rQ00XzppZfseRDqVq9e7davXx8Kgvye96QqSYUShAqCNNUmKltUGgki8+bNs+ZxhG2C3qeffupq1659zDL9+eefVqFjHRJCECHMEMQJtT4cs2wElGuvvdaeI/ARvs466yz7N8GJfQ3CCcdD69atQ78PNuljmxIK/Dan+vXoo49aeGIfEZDp40Wl0B8bBHIqiskxfPhwC7W+Snz22We7p556ykLbc889F6qkEmAJdyBMEsrYjmxTjlWqwewLKnmoUKFCkteF8Ov7PebMmdMqelQ4oy0v88vM2M9Uv7lQ4C90iIiIiMSqmKrkES6oUBGqQAWIyhPBLxwVEo8KFeEgeJJLaOJkOiWoDHqnnXaa/T9YJeG57du328800aOyQ/CjauUf9E3j+Wjz9f3p/HxSq2zZsqGAByqKBCgf8ECIo4kjvws2KyTgBdeN6Qh4kdY3XFIHVeE92Z/BoEhgZR8Fl4dmnD7A+e3k35swxEk+AfyKK66wymqwKSfVMAJhcD/QZJRpqBD6bRIM/3Xq1HHJxfsQ8oPvwzIRmAnVkfa3D/B+Xai2XnDBBaGAF+k9EloXQvHff/9t4Y/n33777XjNUoP69etn4dg/Nm3a5DIj/sbVJ09ERESygpiq5BHmOFENnoQTIuhz9Mwzz1hw8+jDlFycaIeHkmBTQY/KSPA1kZ7zzepoSoZJkyYdU+lihNDE5uvnk1op2R7hy+SXK9Jz0ZaTYElwpEKWFiK9d3CfUaGl7xnNS2kmSVNSqpYXX3yx7QsqVr6iGuSra4nx4Tb4nuHHCO9z9913R+wDR0UtoXXx25GqbEISWxfCKhdFqGqx/lQMn3jiCat6h78vfz88MhPW//XXX3dXX321/d1/9913duHEV9hFREREYlnMVPIIdzQnpNkjVQ7/oKJB6KO/WDRUMzixpembR8WCAVPCA0mw8kNVgKpIalDlYvmoJtLcLvig2WZS0U+MAU/SsnkbFZtg1YYmpAwoQqUurRCKbrjhBmt6yUiokU7W2bcsD/8P9u3buXOnBZXkLg8VMKpTX3zxhTv33HOt6SMYpIT5he8HHiyn3ybBY+Crr76KN29fDQ1Ow3EYxPuwLSO9T1JHNaXKx3zpMxlJYuvigyIVTZqKMmAMfRRpohsLCMTsV6q6VJrpX0pTZ90XUkRERLKCmKnkzZ071/p20ewxWLEDA1xQ5WPghUg4CaTvUq9evaz5GyPx0W+Jk2FfMQND+VMRpIkegYp+UuFVj5Sg4kJVh+WmHyEDXTDACOvDgDBJQbNJAhF9/+h7RLNFHilFXy2amNJ3jBNjAhbVHvqNBZu6pgUGAyFkUMnkZ+bPdmXwEPqDEb7pt8aJOk0L6avIPqPPGTe55vmkoCkkI1ReeeWVFqwJQQR1BigBg9/QX49q2jXXXGP7n4sEvgrENmHAG44Vql7+3n5BhCiqZIxcybpwoYALD0EcN1QO6S/YsWNHq6IS+qiocXwlBU2S6Q/IQDJsI5qlMkAL68Xxmdi60FyUY5htznHyyiuvWOij2W4sYJuyPUVERESyopip5BHiOAkPD3g+5BGaGMkwmtGjR9vJMSfGzIdBUvwtDTxO1jmBZ+h7RsxkIJTUBCmPE31uX0BTQoIVQYqT8ORU8hiMhBBLH0SqSam96TPhliH4uV0Btwxgm1DxpIljWiNYUxFjwBwCCJU2tjHVV8KU36dsH257wT5iX9EkkgFekhq02Vc0C+V4IKwxsuZ9991nTSdBUz4uFnz00Uc2sAxBjMFOfPAhKNF3jb5s9N9kv4XfHoJlYbl5H6ptDDrDOgXxPM0iCYCsJ+tLKEvOQD9U/FhOLkgwQAvHDaPF+ia+ia0LTWRpIsxxzvLQbPPdd98NDcwjIiIiIplXtrikjnyRxTAgClUigh3VQZGEAjHhj6paVkAFk+Bdq3cjlyNX+jcGWDxEFTsRERFxWeo8bM+ePXb7tGjS/wwtg6CpG9UXKjRsND/sflKbAoqIiIiIiGQECnkB3ESbflo0haNZIH3CihYtmn57R0REREREJJkU8v4P/aJWrFiR3O0nkuR7/YmIiIiInAgxM/CKiIiIiIiIKOSJiIiIiIjEFDXXFJEUmd9/ToKjOomIiIhI+lBzTRERERERkRiikCciIiIiIhJDFPJERERERERiiEKeiIiIiIhIDFHIExERERERiSEaXVNEUqT50DYuR66kfYQsHjJfW1lERETkBFElT0REREREJIYo5ImIiIiIiMQQhTwREREREZEYopAnIiIiIiISQxTyREREREREYohCnoiIiIiISAxRyJNMaeLEia506dIue/bsbuzYsem9OJICzzzzjLvoootcrly53FVXXaVtKCIiIpJGFPIyqC+//NKddNJJrlWrVi6reOGFF9ypp56a6HR79+51Xbp0cX369HG///6769Spk0tvgwcPdtmyZbNHjhw5XNGiRV2DBg0sgP7777/pvXgZUqlSpdzDDz/s7rrrrvReFBEREZGYopCXQU2ZMsV17drVff75527Lli0JThsXF+cOHz7ssoqNGze6Q4cOWQAuWbKkO+WUU46Z5r///jvhy1W1alW3detWW75PP/3UXXvttW748OGubt26bt++fS4zYLueKG3btrUKHoFYRERERNKOQl4GtH//fjdz5kx3zz33WJChwhX02WefWcXo/fffdzVq1LDmbosWLbIgcdNNN7m8efNa+BkzZoxr1KiR69GjR+i1vG727Nnx5kf1zL/Hhg0bbJpZs2a5+vXruzx58riaNWu6n3/+2S1btsya1+XLl89ddtllbseOHfHmM3nyZFe5cmWXO3duV6lSJTd+/PjQ7/x833rrLde4cWMLZtWrV7eKpV+nDh06uD179oQqYlTHwrGc5513nv1cvnx5m455M+35559vy3DmmWfaMoDA1aZNG1vmAgUKuOuuu85t27YtND//uqlTp7oyZcrYdPfee687cuSIGzlypCtRooQrXry4Gzp0aKL7jQoe01OhYhkJ6QsWLHDfffede/zxx0PTvfzyy7Yd8+fPb9O3b9/ebd++/Zj9+8knn9h0bCuC4k8//RTv/R577DFbNubTsWNH17dvX1uX5O4TjrWGDRvaNNOnT090PUVEREQkY1PIy4AIWJyQn3POOe7mm2+2AEK1Lhwn9SNGjHBr1qxx1apVcz179nSLFy9277zzjps/f75buHChW7lyZYqWYdCgQdaUjtcTXggivXv3duPGjbP5/vLLL27gwIGh6QkH/JswxPIMGzbMDRgwwL344ovx5tu/f3/34IMPum+++cZVrFjR3XjjjVaFJMTQtJEgRjWMB9OFu/76693HH39sPy9dutSmo28eWKY333zTgiTzP3r0qAW8v/76y8IW2+TXX3+1eQStW7fOAvMHH3zgZsyYYVVUwvXmzZvtdQQ0tsWSJUuSvR3ZjwRililYLXv00Ufdt99+a4GbsHX77bcf81q21ahRo9zy5cttH9xxxx3xtjfbmmVbsWKFBdTnnnsu3uuTuk84jrp3727TtGjR4pjloLkpTWSDDxERERHJuHKk9wLIsQgZhDu0bNnSqluEDapyQUOGDHHNmze3n6nicfL+6quvuqZNm9pz06ZNs6pSShCw/Ak/AYAwRmWpXr169tydd94Zr8JIKCSQ0AQPVNN++OEHN2HCBHfbbbfFm6/vZ/jII49YE0fCGWGoYMGCVlmiuhUNlcUiRYrYz8WKFYs3LU00X3rpJXsehLrVq1e79evXh4Igv+c9qUpSoQRhkCBNRaxKlSpWaaRqNm/ePBvYhbBNmKIJZu3atZO9LVm3jz76KPTvYFijGvnUU0/ZslDBpZLoEc6osPkgxnb7559/rOL29NNP2z6g+gnCHO/BPJK7T6j0+mkiockp+0pEREREMgdV8jIYwgUVKkIVqOBQeSL4haMpn0eFigpRrVq1Qs8RmggoKUFl0DvttNPs/76ZpH/ONzE8cOCAVcMIHYQU/6A5Ic9Hmy9NShFsqpgaZcuWDQU8UJki3PmAB0IczVP5nVeuXDkLeMF1YzoCXqT1TS6qsIRXj8rbFVdcYdU33tcHOZqWJnVbcZwE9zWC/07OPgkeR5H069fPLjT4x6ZNm1KwFURERETkRFElL4MhzNF8MViBIyTQ744h5wluHn3vkouwEd70M9JgGzlz5oz3mkjPUQGDrx5NmjTpmEoXI4QmNl8/n9RKyfYIXya/XJGeS+lyEiipovnwRYWUB80pCaWEO/4dPlhMarZVcvZJYtuNY49HWuM49w/Wiyolwfrkk09O8/cSERERyUpUyctAONmlOSFN7OhT5h/03SL00V8sGpr9EQpohuhRdWHAlCBCBf3YvLVr17qDBw+marmpcrF8VBMrVKgQ7+HDTVJwcs+AJ2mFAUeoOgUrTzRX3L17t1XqToQff/zR+vq1a9cu9O+dO3daX0oGtqEpZ0oqhFRog/sawX+n1T45nqgq0vyWZqnvvvuu/XzppZem92KJiIiIZHqq5GUgc+fOdbt27bImdsGKHQgJVPk6d+4c8bU0+6OfVa9evVzhwoVt1EX6ZFEZCTYVbNKkiVUE69SpY4GKe82FV61Sgj5b3bp1s+WmHyGDdTBgCOvDgDBJQbNJKlD0/WPkTUaVjHR7hKRq1qyZNTFlxFEGdSFEM3ImzSMTa6KYEsz/jz/+sKoUQY5RMgkyjHjJfgFNNAmz9KljXzLyJoOwJBcjd3J/OdaDQWsYIXPVqlUW9tNynxxPjGwaaQRVEREREUkdVfIyEEIcwSQ84PmQxwk6J/LRjB492sJb69atbT4MkuKHz/eoEtJHjSoSI2YyEEpqgpTHEP4M189gLwQrghQDsySnakRYIfjQB5GKI7cwSA3C7Zw5c1yhQoXsxuRsE0IQgeh4+P77763vHEGOQXIYJZX+bIxG6gdUYb3YLq+//rpVE6noPfnkk8l+L4Ir82b/XXjhhTa4DCN0Bvd1WuwTEREREcl8ssVFGptfYgL9v04//XQLdlQHJbYx0iqjjXIfvuOJWyhwIaJW70YuR66kNQZYPGT+cV0mERERkaxg7/+dh9Eti1uPRaPmmjHk66+/tj5fjLLIjucWC+BecRJb6Ef5/PPP24AtDKRCf03uH8htI0REREQka1PIizE0/WN4ffp91ahRw5oKFi1aNL0XS9IYTVG5jx+DljAqJQOxcCN4mqSKiIiISNamkBdDLrjgArsHm8Q+RqKkciciIiIiEk4Dr4iIiIiIiMQQhTwREREREZEYouaaIpIi8/vPSXBUJxERERFJH6rkiYiIiIiIxBCFPBERERERkRiikCciIiIiIhJDFPJERERERERiiEKeiIiIiIhIDFHIExERERERiSEKeSIiIiIiIjFEIU9ERERERCSGKOSJiIiIiIjEEIU8ERERERGRGKKQJyIiIiIiEkMU8kRERERERGKIQp6IiIiIiEgMUcgTERERERGJIQp5IiIiIiIiMUQhT0REREREJIYo5ImIiIiIiMQQhTwREREREZEYopAnIiIiIiISQ3Kk9wKISOYSFxdn/9+7d296L4qIiIhIlrL3/86//PlYNAp5IpIsO3futP+XLl1aW05EREQkHezbt88VLFgw6u8V8kQkWQoXLmz/37hxY4IfLpJ5rggS2Ddt2uQKFCiQ3osjaUD7NLZof8YW7c/YsjcdvkOp4BHwSpUqleB0CnkikizZs/+/rrwEPIWC2MG+1P6MLdqnsUX7M7Zof8aWAif4OzQpF9k18IqIiIiIiEgMUcgTERERERGJIQp5IpIsuXLlcoMGDbL/S+an/Rl7tE9ji/ZnbNH+jC25MvA5Uba4xMbfFBERERERkUxDlTwREREREZEYopAnIiIiIiISQxTyREREREREYohCnogky7PPPuvKlSvncufO7WrXru2WLl2qLZgJfP755+6KK66wm6dmy5bNzZ49O97v6Z49cOBAV7JkSZcnTx7XrFkzt3bt2nRbXknY8OHDXc2aNV3+/Pld8eLF3VVXXeV++umneNP8888/7r777nNFihRx+fLlc+3atXPbtm3Tps2AnnvuOVetWrXQvbbq1Knj3n///dDvtS8ztxEjRtjnbo8ePULPaZ9mHoMHD7b9F3xUqlQpw+9LhTwRSbKZM2e6nj172khSK1eudNWrV3ctWrRw27dv11bM4A4cOGD7i5AeyciRI91TTz3lnn/+ebdkyRKXN29e27d8eUnGs2DBAjup+Oqrr9z8+fPdoUOH3KWXXmr72bv//vvdu+++615//XWbfsuWLa5t27bputwS2RlnnGFBYMWKFW758uWuSZMmrk2bNu7777+332tfZl7Lli1zEyZMsBAfpH2auVStWtVt3bo19Fi0aFHG35eMrikikhS1atWKu++++0L/PnLkSFypUqXihg8frg2YifDR//bbb4f+ffTo0bgSJUrEPfHEE6Hndu/eHZcrV664GTNmpNNSSnJs377d9uuCBQtC+y9nzpxxr7/+emiaNWvW2DRffvmlNm4mUKhQobjJkydrX2Zi+/btizv77LPj5s+fH9ewYcO47t272/P6+8xcBg36/9q7F6AqyjYO4K8XUNGEFAI0IW9gZKLihSSzCe9WypQpYpKQOpqElZesSEvtopWYqTnlYDqaORhpmmEpqCVoFy01RXFIqCCzFMQQFPab/zOz59s9nIOofXHO+f6/GYLds7xnd989uQ/v8z47RwsNDbX5miP3JUfyiKhWKioq5K/MSOPT1a9fX5azsrJ4Fp1YXl6eKioqMvWtp6enpOOyb51DcXGxfG/RooV8x2cVo3vGPkV6UUBAAPvUwVVWVqoNGzbIqCzSNtmXzguj7cOGDTN9DoF96nxOnjwp0x3atWunYmJiVH5+vsP3ZcM6fXcichpnz56Vmw9fX1/TeiwfP368zvaLbhwCPLDVt/pr5Liqqqpkrk9ERITq3LmzrEO/ubu7Ky8vL9O27FPHdfjwYQnqkCKNeT1paWkqJCREHTp0iH3phBCoY1oD0jWt8fPpXHr37q1Wr16tgoODJVXzpZdeUn379lVHjhxx6L5kkEdEROTkowW42TDOESHngxtIBHQYlU1NTVWxsbEyv4ecT0FBgUpMTJT5sihSRs5tyJAhlp8xtxJBX2BgoNq4caMUKnNUTNckolrx9vZWDRo0qFYxCst+fn48i05M7z/2rfOZOnWq2rp1q8rIyJDiHcY+RYr1+fPnTdvz8+q4MBrQoUMHFRYWJtVTUShpyZIl7EsnhBQ+FCTr3r27atiwoXwhYEdxK/yMUR5+Pp2Xl5eXCgoKUrm5uQ79+WSQR0S1vgHBzcfOnTtNaWJYRooROa+2bdvKP0bGvi0pKZEqm+xbx4T6OQjwkNK3a9cu6UMjfFbd3NxMfYpHLGAeCfvUOeD/r+Xl5exLJxQZGSnptxiZ1b969Oghc7n0n/n5dF6lpaXq1KlT8sghR/5/LdM1iajW8PgEpBDhH6hevXqp5ORkKQ4wfvx4nkUn+EcJf3U0FlvBzQYKdWCCOOZ0zZ8/X3Xs2FEChqSkJJlkjuevkWOmaK5fv15t3rxZnpWnz/1AwRykD+F7fHy8fGbRx3j2WkJCgtx0hIeH1/Xuk5XZs2dLShg+ixcuXJC+zczMVOnp6exLJ4TPpD4/VofH0uA5avp6fj6dx/Tp0+U5s0jRxOMR8BgpZDZFR0c79uezTmt7EpHTWbp0qRYQEKC5u7vLIxWys7PrepeoFjIyMqSks/VXbGys5TEKSUlJmq+vrzw6ITIyUsvJyeG5dVC2+hJfKSkplm3Kysq0KVOmSCl+Dw8PLSoqSissLKzT/Sbb4uLitMDAQPn/qo+Pj3z+duzYYXmdfen8jI9QAPap8xg1apTm7+8vn8/WrVvLcm5ursP3ZT38p27DTCIiIiIiIvqncE4eERERERGRC2GQR0RERERE5EIY5BEREREREbkQBnlEREREREQuhEEeERERERGRC2GQR0RERERE5EIY5BEREREREbkQBnlEREREREQuhEEeERGRE1m9erXy8vKqs/f/+eefVb169dShQ4fU/7tVq1apgQMHKkdw7733qmnTpilX8sknn6gOHTqoBg0ayLF9/vnnqmvXrqqqqqqud43I4THIIyIiugaPPfaYBDmvvfZatRtSrKe6ddttt6nk5OT/+ftcunRJJSUlqTlz5ihHogfhNX3hDwV16YMPPlA9e/ZUHh4e6qabblL9+vVTW7durbbdpEmT1MMPP6wKCgrUvHnz1ODBg5Wbm5tat25dnew3kTNhkEdERHSNGjdurF5//XV17tw5pzh3FRUVytX928eYmpqqmjdvriIiIpQjadOmjSosLLR8PfPMM+qOO+4wrRs1atS/vl+XL1+W79OnT5fgDfvw448/qgMHDqi7775bDR8+XL3zzjuW7UtLS9WZM2fUoEGDVKtWrSQY1P/I8vbbb//r+0/kbBjkERERXaP+/fsrPz8/9eqrr9a43aZNm+QGu1GjRjLC9Oabb5pex7r58+ercePGqWbNmqnAwEC1ZcsW9ccff8hNL9Z16dJFffvtt9Xaxshhx44dJeDEjTBGO3Rz586VtLb3339ftW3bVraB8+fPq8cff1z5+PhIgHLfffepH374ocZjwE14t27dpI0ePXqogwcPVtvmyJEjasiQIbK/vr6+6tFHH1Vnz5612V5JSYlq0qSJ2r59u2l9Wlqa3Mj//fffsozjeeSRRyQ1tUWLFnI+MEqlw83+iBEj1IIFCyQICA4OlpTF06dPq6eeesoyamU8H0YY7cP512VmZqpevXqppk2bynsieENb9mzYsEE98MADpnVXrlxRTz75pPx+y5Yt1axZs1RsbKzsp668vFy2ueWWW+ScIsD55ptvTO3s3r1b9gXXjb+/v3r22Welbd3Fixct1wxeN15XSG3Etal/YZuGDRtalvG+OHZcF+iH0NBQCViN5wHnbefOndLfGG3r06ePysnJMe3jihUrVPv27ZW7u7uc+7Vr15peRxvY5sEHH5Rzin7Kzs6WfV20aJEEe0jFvP322+U1pGM+/fTT0u/YBz2owzWKtrAOcM7xeTh16pTdviEiBnlERETXDDfSr7zyilq6dKn65ZdfbG7z3XffSZAyevRodfjwYQk0kN5nnSq3ePFiCSgQPA0bNkwCJNzAjx07Vn3//fdyI41lTdMsv4NACDfGa9asUV9//bUEb3gfo9zcXAkyP/74Y8v8uZEjR8roCAIs7F/37t1VZGSk+uuvv2weA0ZT7r//fhUSEiLb4xhwc26E98aNOAJB3Hxj3tTvv/8ux24Lgku0uX79etN6pOAhGEJQgVEfBK640d+7d68cI4IVpOsZR+wQiCD4+OKLLyTdD8d66623qpdfftkyalUbCKDw3kgbxOhSVlaWmjhxYo3pt1999ZUEQUYY3cVxpKSkyD4joEUwbjRz5kzpF6Qson8R6OBY9T749ddf1dChQyWdEQE4AiXM/cMfA3QzZsyQQHDz5s1qx44dEgChrdrAHyZw3bz77rvq6NGjEhDjWkN7Rs8//7wEZOhTBIlxcXGmgDwxMVFGCRHgY2Ru/PjxKiMjw9QGrpeoqCi5/vH7H374ofQjtreGttDvODfGoBLL6Eesg4CAAPlDAq4LIqqBRkRERLUWGxurDR8+XH4ODw/X4uLi5Oe0tDREYZbtxowZow0YMMD0uzNmzNBCQkIsy4GBgdrYsWMty4WFhdJGUlKSZV1WVpasw2uQkpIiy9nZ2ZZtjh07Juv2798vy3PmzNHc3Ny0M2fOWLbZu3ev1rx5c+3SpUumfWrfvr22cuVKm8eK9S1bttTKysos61asWCHvdfDgQVmeN2+eNnDgQNPvFRQUyDY5OTk228W5atasmXbx4kVZLi4u1ho3bqxt375dlteuXasFBwdrVVVVlt8pLy/XmjRpoqWnp1v6wdfXV9Yb4ZwuXrzYtA7nIzQ01LQO22Bb+PPPP2V/MzMztdo4d+6cbL9nzx7TeuzPokWLLMtXrlzRAgICLNdLaWmp9Mu6dess21RUVGitWrXSFi5cKMvPPfdctWNftmyZnK/KykrtwoULmru7u7Zx40bL69h/nJvExMRq+2o8dvS9h4eHtm/fPtM28fHxWnR0tPyckZEhx/bll19aXt+2bZus06+DPn36aBMmTDC1MXLkSG3o0KGWZWw/bdo00zaDBw+u1g9GuD4nT55sOsfYH2vdunXT5s6da7cdItI0pmsSERFdJ4zcYETm2LFj1V7DOuv5Wlg+efKkqqystKxDOqYOIxRw5513VluHETgdRlYw0qPr1KmTpAga9wOpn0jL1GFUCCNzSCPEaIr+lZeXZzf1De1h//R0T7jrrrtM26BdjOAY28T+gL12MVKFAhpITdVHazDChzRYvU2MRGIkT28TKZsodmJsE+cJ6YI3Cm0j/RMjakgHXLJkSY2jgGVlZfLdeF6Ki4tlBBNplsYR37CwMMsy9h2jVcbrAucBv6P3Hb7jHBtHEbE9+g6jxmgDo5m9e/c27T9SJq8G5xSjwAMGDDD1F0b2rPvKeF0iJdR4Ddq7tq0/B9YjnWAckb5eSDPV03qJyLaGdtYTERHRVdxzzz0SGMyePVuChOuBm3ydfmNva921lo3HPCgjBAm4WdfnNhndyCMZ0C4CIwS81vTgwBoCM1RNRMom0kzxHYU4ELzqbSI4slVF0Ri4Wh+jPfXr168WXOiFQHRIscRcOaSbfvTRR+qFF16QNNDw8PBq7SFQRr84S+EdHc4rbNu2TbVu3dr0Gub/Gf0vrsGgoCBJc0WQah2c//bbb5Leim2uBqmtxuuAiKrjSB4REdENwKMUPv30U5nHZYSCEpiXZYRl3MRihOdGYA6ZsRgL5i9hbhze0x7MvysqKpJACvPAjF/e3t42fwftYY4aRtB0KJ5h3S7mdqGIiXW7NQVhMTExElDhd3ft2iXLxjYx4okiIdZtenp61nhuEDwYR0oBAQGO3Rjo2XrOH+YVImDft2+f6ty5c7V5g8b3wDzFn376ybIO+4VRV2MRFeyHca6cXqjEeF0g2MTvoD39nONaMu4rtseoJuYbog0EYPv377e8jmDzxIkT6mrwHgjm8vPzq51XVOWsLXvXtn4M9iCgR6C5cuXKaq+98cYbclwPPfRQjW3oo7noKyKyj0EeERHRDUDKIAIU67LuKCSBwiB4vhduwJHWiRLx1oVLrgduhhMSEuRGHwVRMIqIESdjqqA1pEIiDRAFRlCsA5UqEcygwIat6p0wZswYGcWZMGGCBDSfffaZ3IwbPfHEEzKyEh0dLcEKbsDT09OlEId1sGU9Copqjzh3qPRoTD/EOgSeqKiJAhtIKcUIJEba7BW60SHY3LNnjxQw0St8ouomKpYuXLhQ9m/ZsmWm6p5oH8EdgitU1MT5QZBZU9CMEVyMShmhT1DYBAVREHijOAkCMH0kDEHv5MmTpXAKAlycU5xbpB7Gx8fLNlOmTJEKk2jr+PHj0haexYfKkxiRRHoltkUbCI5R+AT9j9euBoEirj8UW8H1iHOBIBQFhLBcW3hvFBBCURicp7feekuK3lzt2sb1h3OC30dRF7w/jhGjpkiRxbqrBZv4IwMCVeu0YSKywomJRERE11d4RZeXlyfFMKz/WU1NTZVCKyi2gQIcxqIc9oqEoA0UJjG2bSx0gsIrnp6e2qZNm7R27dppjRo10vr376+dPn26xkIjUFJSoiUkJEihD+xTmzZttJiYGC0/P9/u8aLwC9rC8XXt2lXe17g/cOLECS0qKkrz8vKSAiCdOnWSohvG4iG2zJw5U9p68cUXq72GQjPjxo3TvL295RhxrCj2gSIt9vpB398uXbrI7xj7AwVjcLxNmzaVdhcsWGApvFJUVKSNGDFC8/f3l+PEeuwTCp3Yc/ToUTnW8+fPW9ZdvnxZmzp1qhQQufnmm7VZs2ZJQZLRo0dbtkHxEvSBflwRERHagQMHTG2jAEzPnj1lX/z8/KQdtK1D8RUU7EERFRR7QdGWfv36XbXwCqBPkpOTpbgLrgEfHx9t0KBB2u7du02FV1D4RIe+xjpci7rly5dLn6CNoKAgbc2aNTVex0arVq3SwsLCpNgO+qNv377ali1bTNvYK7wyceJEbdKkSTbbJaL/qof/WAd+RERERFQzPJICqaUYBbQFc9gwGojHSWBEl24MRmZRYAYjzxj9JSL7mK5JREREdB3wUG+kT+qQ6vnee+9Jei6eDYfUTKSCIu2VbhxSjJcvX84Aj6gWOJJHRERE9A/AXDoUF8E8OSRKoXgLCvNg/iER0b+JQR4REREREZELYbomERERERGRC2GQR0RERERE5EIY5BEREREREbkQBnlEREREREQuhEEeERERERGRC2GQR0RERERE5EIY5BEREREREbkQBnlEREREREQuhEEeERERERGRch3/AWhl0xYe2GLqAAAAAElFTkSuQmCC", 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", 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" ] @@ -614,15 +758,33 @@ { "cell_type": "markdown", "id": "6e503d32", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.002991, + "end_time": "2026-09-22T00:40:34.536269", + "exception": false, + "start_time": "2026-09-22T00:40:34.533278", + "status": "completed" + }, + "tags": [] + }, "source": [ - "**Lecture** : les **14 schemes** de Walton reliés aux vertus sont exactement ceux qui apparaissent côté sophismes. `Schemes de Walton distincts portés par les vertus : 14`, et les 14 barres additionnées rendent le compte du recensement : 50 + 40 + 27 + 26 + 21 + 11 + 10 + 8 + 8 + 7 + 6 + 4 + 3 + 1 = 222, le total des liens `goodTenorOf`. La répartition est très inégale : les quatre schemes de tête -- *Argument from Rule* (50 vertus), *from Commitment* (40), *from Bias* (27), *from Sign* (26) -- portent 143 vertus sur 222 (environ 64 %), là où l'argumentation correcte se décline en nombreuses bonnes pratiques ; à l'autre extrémité, les sept moins productifs (8, 8, 7, 6, 4, 3, 1) totalisent 37 -- moins que Rule seul -- et le scheme Danger ne porte qu'une seule vertu. La propriété `goodTenorOf` est donc le **pivot** qui permettra, dans les exercices, de relier une vertu à son sophisme miroir via le schéma partagé." + "**Lecture** : les **14 schemes** de Walton portés par les vertus dessinent le vocabulaire de l'argumentation correcte. `Schemes de Walton distincts portés par les vertus : 14`, et les 14 barres additionnées rendent le compte du recensement : 50 + 40 + 27 + 26 + 21 + 11 + 10 + 8 + 8 + 7 + 6 + 4 + 3 + 1 = 222, le total des liens `goodTenorOf`. La répartition est très inégale : les quatre schemes de tête -- *Argument from Rule* (50 vertus), *from Commitment* (40), *from Bias* (27), *from Sign* (26) -- portent 143 vertus sur 222 (environ 64 %), là où l'argumentation correcte se décline en nombreuses bonnes pratiques ; à l'autre extrémité, les sept moins productifs (8, 8, 7, 6, 4, 3, 1) totalisent 37 -- moins que Rule seul -- et le scheme Danger ne porte qu'une seule vertu. La propriété `goodTenorOf` est donc le **pivot** qui permettra, dans les exercices, de situer une vertu dans le vocabulaire de Walton -- le rapprochement avec le pôle sophismes est une lecture croisée, pas une requête." ] }, { "cell_type": "markdown", "id": "8798c7a3", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.003007, + "end_time": "2026-09-22T00:40:34.542392", + "exception": false, + "start_time": "2026-09-22T00:40:34.539385", + "status": "completed" + }, + "tags": [] + }, "source": [ "## 7. Exercices\n", "\n", @@ -633,7 +795,16 @@ { "cell_type": "markdown", "id": "363f473d", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.003024, + "end_time": "2026-09-22T00:40:34.548534", + "exception": false, + "start_time": "2026-09-22T00:40:34.545510", + "status": "completed" + }, + "tags": [] + }, "source": [ "### Exercice 1 -- Concepts sans definition dans une langue\n", "\n", @@ -647,11 +818,19 @@ "id": "1c28e1e8", "metadata": { "execution": { - "iopub.execute_input": "2026-07-09T22:05:22.462092Z", - "iopub.status.busy": "2026-07-09T22:05:22.461877Z", - "iopub.status.idle": "2026-07-09T22:05:22.464796Z", - "shell.execute_reply": "2026-07-09T22:05:22.464342Z" - } + "iopub.execute_input": "2026-09-22T00:40:34.556384Z", + "iopub.status.busy": "2026-09-22T00:40:34.555664Z", + "iopub.status.idle": "2026-09-22T00:40:34.559394Z", + "shell.execute_reply": "2026-09-22T00:40:34.558797Z" + }, + "papermill": { + "duration": 0.007936, + "end_time": "2026-09-22T00:40:34.559950", + "exception": false, + "start_time": "2026-09-22T00:40:34.552014", + "status": "completed" + }, + "tags": [] }, "outputs": [ { @@ -682,7 +861,16 @@ { "cell_type": "markdown", "id": "0a979be7", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.002927, + "end_time": "2026-09-22T00:40:34.565806", + "exception": false, + "start_time": "2026-09-22T00:40:34.562879", + "status": "completed" + }, + "tags": [] + }, "source": [ "### Exercice 2 -- Visualiser une famille avec networkx\n", "\n", @@ -697,11 +885,19 @@ "id": "c33e84aa", "metadata": { "execution": { - "iopub.execute_input": "2026-07-09T22:05:22.484178Z", - "iopub.status.busy": "2026-07-09T22:05:22.483863Z", - "iopub.status.idle": "2026-07-09T22:05:22.489354Z", - "shell.execute_reply": "2026-07-09T22:05:22.488521Z" - } + "iopub.execute_input": "2026-09-22T00:40:34.573092Z", + "iopub.status.busy": "2026-09-22T00:40:34.573092Z", + "iopub.status.idle": "2026-09-22T00:40:34.575889Z", + "shell.execute_reply": "2026-09-22T00:40:34.575889Z" + }, + "papermill": { + "duration": 0.007603, + "end_time": "2026-09-22T00:40:34.576941", + "exception": false, + "start_time": "2026-09-22T00:40:34.569338", + "status": "completed" + }, + "tags": [] }, "outputs": [ { @@ -729,13 +925,25 @@ { "cell_type": "markdown", "id": "0d3f18a2", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.002861, + "end_time": "2026-09-22T00:40:34.583183", + "exception": false, + "start_time": "2026-09-22T00:40:34.580322", + "status": "completed" + }, + "tags": [] + }, "source": [ "### Exercice 3 -- Le pont vertus <-> sophismes\n", "\n", "Pour un schema de Walton donne (p. ex. `\"Argument from Sign\"`), lister toutes les vertus qui en\n", - "sont le `goodTenorOf`. Bonus : ouvrir `Argument_Analysis_Ontology_AIF.ipynb` et comparer avec les\n", - "**sophismes** relies au **même** schema -- vous materialisez ainsi l'axe good/bad tenor.\n" + "sont le `goodTenorOf`. Bonus : ouvrir `Argument_Analysis_Ontology_AIF.ipynb` et regarder comment\n", + "les sophismes sont classés côté dérives (`inScheme` vers `fallacyScheme`) -- puis vérifier qu'aucun\n", + "des 14 noms de schémas des vertus n'y figure : les deux pôles ne sont pas reliés par propriété,\n", + "et cette asymétrie (thesaurus SKOS annoté vs ABox relationnelle) est elle-même une leçon de\n", + "modélisation." ] }, { @@ -744,11 +952,19 @@ "id": "6312ff34", "metadata": { "execution": { - "iopub.execute_input": "2026-07-09T22:05:22.497363Z", - "iopub.status.busy": "2026-07-09T22:05:22.496971Z", - "iopub.status.idle": "2026-07-09T22:05:22.504522Z", - "shell.execute_reply": "2026-07-09T22:05:22.503199Z" - } + "iopub.execute_input": "2026-09-22T00:40:34.590533Z", + "iopub.status.busy": "2026-09-22T00:40:34.590533Z", + "iopub.status.idle": "2026-09-22T00:40:34.593422Z", + "shell.execute_reply": "2026-09-22T00:40:34.593422Z" + }, + "papermill": { + "duration": 0.00823, + "end_time": "2026-09-22T00:40:34.594475", + "exception": false, + "start_time": "2026-09-22T00:40:34.586245", + "status": "completed" + }, + "tags": [] }, "outputs": [ { @@ -774,12 +990,21 @@ { "cell_type": "markdown", "id": "4907f827", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.002801, + "end_time": "2026-09-22T00:40:34.599827", + "exception": false, + "start_time": "2026-09-22T00:40:34.597026", + "status": "completed" + }, + "tags": [] + }, "source": [ "## 8. Ponts avec la serie Argument_Analysis\n", "\n", "- [`Argument_Analysis_Ontology_AIF`](Argument_Analysis_Ontology_AIF.ipynb) -- le **pole sophismes**\n", - " (OWL2 ABox, schemes de Walton via `badTenorOf`). A lire en parallele : même projet, paradigme oppose.\n", + " (OWL2 ABox, classement des dérives par `inScheme` vers `fallacyScheme`). A lire en parallele : même projet, paradigme oppose.\n", "- [`Argument_Analysis_Ontology_CrossLinks`](Argument_Analysis_Ontology_CrossLinks.ipynb) -- les liens\n", " inter-noeuds systematises (CSV canonique multilingue).\n", "- [`Argument_Analysis_Restitution_3_Actes`](Argument_Analysis_Restitution_3_Actes.ipynb) -- la\n", @@ -809,7 +1034,19 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.14" + "version": "3.11.9" + }, + "papermill": { + "default_parameters": {}, + "duration": 2.906139, + "end_time": "2026-09-22T00:40:34.851209", + "environment_variables": {}, + "exception": null, + "input_path": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argument_Analysis_Ontology_Virtues.ipynb", + "output_path": "Argument_Analysis_Ontology_Virtues.ipynb", + "parameters": {}, + "start_time": "2026-09-22T00:40:31.945070", + "version": "2.6.0" } }, "nbformat": 4,