Thesis (M.A.)--Université de Sherbrooke, 1999. / Includes bibliographical references.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Com cada vez mais intenso desenvolvimento urbano e industrial, atualmente um desafio fundamental é eliminar ou reduzir o impacto causado pelas emissões de poluentes para a atmosfera. No ano de 2012, o Rio de Janeiro sediou a Rio +20, a Conferência das Nações Unidas sobre Desenvolvimento Sustentável, onde representantes de todo o mundo participaram. Na época, entre outros assuntos foram discutidos a economia verde e o desenvolvimento sustentável. O O3 troposférico apresenta-se como uma variável extremamente importante devido ao seu forte impacto ambiental, e conhecer o comportamento dos parâmetros que afetam a qualidade do ar de uma região, é útil para prever cenários. A química das ciências atmosféricas e meteorologia são altamente não lineares e, assim, as previsões de parâmetros de qualidade do ar são difíceis de serem determinadas. A qualidade do ar depende de emissões, de meteorologia e topografia. Os dados observados foram o dióxido de nitrogênio (NO2), monóxido de nitrogênio (NO), óxidos de nitrogênio (NOx), monóxido de carbono (CO), ozônio (O3), velocidade escalar vento (VEV), radiação solar global (RSG), temperatura (TEM), umidade relativa (UR) e foram coletados através da estação móvel de monitoramento da Secretaria do Meio Ambiente (SMAC) do Rio de Janeiro em dois locais na área metropolitana, na Pontifícia Universidade Católica (PUC-Rio) e na Universidade do Estado do Rio de Janeiro (UERJ) no ano de 2011 e 2012. Este estudo teve três objetivos: (1) analisar o comportamento das variáveis, utilizando o método de análise de componentes principais (PCA) de análise exploratória, (2) propor previsões de níveis de O3 a partir de poluentes primários e de fatores meteorológicos, comparando a eficácia dos métodos não lineares, como as redes neurais artificiais (ANN) e regressão por máquina de vetor de suporte (SVM-R), a partir de poluentes primários e de fatores meteorológicos e, finalmente, (3) realizar método de classificação de dados usando a classificação por máquina de vetor suporte (SVM-C). A técnica PCA mostrou que, para conjunto de dados da PUC as variáveis NO, NOx e VEV obtiveram um impacto maior sobre a concentração de O3 e o conjunto de dados da UERJ teve a TEM e a RSG como as variáveis mais importantes. Os resultados das técnicas de regressão não linear ANN e SVM obtidos foram muito próximos e aceitáveis para o conjunto de dados da UERJ apresentando coeficiente de determinação (R2) para a validação, 0,9122 e 0,9152 e Raiz Quadrada do Erro Médio Quadrático (RMECV) 7,66 e 7,85, respectivamente. Quanto aos conjuntos de dados PUC e PUC+UERJ, ambas as técnicas, obtiveram resultados menos satisfatórios. Para estes conjuntos de dados, a SVM mostrou resultados ligeiramente superiores, e PCA, SVM e ANN demonstraram sua robustez apresentando-se como ferramentas úteis para a compreensão, classificação e previsão de cenários da qualidade do ar / With increasingly intense urban and industrial development, currently a key challenge is to eliminate or reduce the impact caused by emissions of pollutants into the atmosphere. In the year 2012, the Rio de Janeiro hosted the Rio +20, the United Nations Conference about Sustainable Development, where representatives from around the world participated. At the time, among other issues discussed were the green economy and sustainable development. The tropospheric O3 presents itself as an extremely crucial variable due to its strong environmental impact, and knowing the behavior of the parameters that affect the air quality of a region, is useful for predicting scenarios. The chemistry of atmospheric sciences and meteorology are highly nonlinear and thus the forecasts of air quality parameters are hard to be determined. Air quality depends on emissions, meteorology and topography. The observed data were Nitrogen Dioxide (NO2), Nitrogen Monoxide (NO), Nitrogen Oxides (NOx), Carbon Monoxide (CO), Ozone (O3), Scalar Wind Speed (VEV), Global Solar Radiation (RSG), Temperature (TEM), Relative Humidity (UR) and collected through the mobile station monitoring the Secretaria do Meio Ambiente (SMAC) of Rio de Janeiro City in two places in the metropolitan area at Pontíficia Universidade Católica (PUC-Rio) and the Universidade do Estado do Rio de Janeiro (UERJ) at years 2011 and 2012. This study had three objectives: (1) to analyze the behavior of the variables, using the method of principal components analysis (PCA) of exploratory analysis; (2) propose forecasts of O3 levels from primary pollutants and meteorological factors, comparing the effectiveness of nonlinear methods like as artificial neural networks (ANN) and support vector machine regression (SVM-R), from primary pollutants and meteorological factors and finally, (3) perform data classification method using support vector machine classification (SVM-C). The PCA technique showed that for PUC dataset, variables NO, NOx and VSV have a greater impact on the concentration of O3 and the UERJ data set had the temperature (TEM) and Global Solar Radiation (RSG) as the most important variables. The results from the nonlinear regression techniques ANN and SVM obtained were very closely and acceptable to UERJ dataset presenting coefficient of determination (R2) for validation, 0.9122 and 0.9152 and Root Mean Square Error (RMECV) 7.66 and 7.85, respectively. As for the PUC and PUC + UERJ datasets, both techniques, obtained less satisfactory results. For these datasets, the SVM proved results slightly higher, and PCA, SVM and ANN had demonstrated their robustness presenting themselves as useful tools for understanding, classification and prediction scenarios for air quality

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Com cada vez mais intenso desenvolvimento urbano e industrial, atualmente um desafio fundamental é eliminar ou reduzir o impacto causado pelas emissões de poluentes para a atmosfera. No ano de 2012, o Rio de Janeiro sediou a Rio +20, a Conferência das Nações Unidas sobre Desenvolvimento Sustentável, onde representantes de todo o mundo participaram. Na época, entre outros assuntos foram discutidos a economia verde e o desenvolvimento sustentável. O O3 troposférico apresenta-se como uma variável extremamente importante devido ao seu forte impacto ambiental, e conhecer o comportamento dos parâmetros que afetam a qualidade do ar de uma região, é útil para prever cenários. A química das ciências atmosféricas e meteorologia são altamente não lineares e, assim, as previsões de parâmetros de qualidade do ar são difíceis de serem determinadas. A qualidade do ar depende de emissões, de meteorologia e topografia. Os dados observados foram o dióxido de nitrogênio (NO2), monóxido de nitrogênio (NO), óxidos de nitrogênio (NOx), monóxido de carbono (CO), ozônio (O3), velocidade escalar vento (VEV), radiação solar global (RSG), temperatura (TEM), umidade relativa (UR) e foram coletados através da estação móvel de monitoramento da Secretaria do Meio Ambiente (SMAC) do Rio de Janeiro em dois locais na área metropolitana, na Pontifícia Universidade Católica (PUC-Rio) e na Universidade do Estado do Rio de Janeiro (UERJ) no ano de 2011 e 2012. Este estudo teve três objetivos: (1) analisar o comportamento das variáveis, utilizando o método de análise de componentes principais (PCA) de análise exploratória, (2) propor previsões de níveis de O3 a partir de poluentes primários e de fatores meteorológicos, comparando a eficácia dos métodos não lineares, como as redes neurais artificiais (ANN) e regressão por máquina de vetor de suporte (SVM-R), a partir de poluentes primários e de fatores meteorológicos e, finalmente, (3) realizar método de classificação de dados usando a classificação por máquina de vetor suporte (SVM-C). A técnica PCA mostrou que, para conjunto de dados da PUC as variáveis NO, NOx e VEV obtiveram um impacto maior sobre a concentração de O3 e o conjunto de dados da UERJ teve a TEM e a RSG como as variáveis mais importantes. Os resultados das técnicas de regressão não linear ANN e SVM obtidos foram muito próximos e aceitáveis para o conjunto de dados da UERJ apresentando coeficiente de determinação (R2) para a validação, 0,9122 e 0,9152 e Raiz Quadrada do Erro Médio Quadrático (RMECV) 7,66 e 7,85, respectivamente. Quanto aos conjuntos de dados PUC e PUC+UERJ, ambas as técnicas, obtiveram resultados menos satisfatórios. Para estes conjuntos de dados, a SVM mostrou resultados ligeiramente superiores, e PCA, SVM e ANN demonstraram sua robustez apresentando-se como ferramentas úteis para a compreensão, classificação e previsão de cenários da qualidade do ar / With increasingly intense urban and industrial development, currently a key challenge is to eliminate or reduce the impact caused by emissions of pollutants into the atmosphere. In the year 2012, the Rio de Janeiro hosted the Rio +20, the United Nations Conference about Sustainable Development, where representatives from around the world participated. At the time, among other issues discussed were the green economy and sustainable development. The tropospheric O3 presents itself as an extremely crucial variable due to its strong environmental impact, and knowing the behavior of the parameters that affect the air quality of a region, is useful for predicting scenarios. The chemistry of atmospheric sciences and meteorology are highly nonlinear and thus the forecasts of air quality parameters are hard to be determined. Air quality depends on emissions, meteorology and topography. The observed data were Nitrogen Dioxide (NO2), Nitrogen Monoxide (NO), Nitrogen Oxides (NOx), Carbon Monoxide (CO), Ozone (O3), Scalar Wind Speed (VEV), Global Solar Radiation (RSG), Temperature (TEM), Relative Humidity (UR) and collected through the mobile station monitoring the Secretaria do Meio Ambiente (SMAC) of Rio de Janeiro City in two places in the metropolitan area at Pontíficia Universidade Católica (PUC-Rio) and the Universidade do Estado do Rio de Janeiro (UERJ) at years 2011 and 2012. This study had three objectives: (1) to analyze the behavior of the variables, using the method of principal components analysis (PCA) of exploratory analysis; (2) propose forecasts of O3 levels from primary pollutants and meteorological factors, comparing the effectiveness of nonlinear methods like as artificial neural networks (ANN) and support vector machine regression (SVM-R), from primary pollutants and meteorological factors and finally, (3) perform data classification method using support vector machine classification (SVM-C). The PCA technique showed that for PUC dataset, variables NO, NOx and VSV have a greater impact on the concentration of O3 and the UERJ data set had the temperature (TEM) and Global Solar Radiation (RSG) as the most important variables. The results from the nonlinear regression techniques ANN and SVM obtained were very closely and acceptable to UERJ dataset presenting coefficient of determination (R2) for validation, 0.9122 and 0.9152 and Root Mean Square Error (RMECV) 7.66 and 7.85, respectively. As for the PUC and PUC + UERJ datasets, both techniques, obtained less satisfactory results. For these datasets, the SVM proved results slightly higher, and PCA, SVM and ANN had demonstrated their robustness presenting themselves as useful tools for understanding, classification and prediction scenarios for air quality

A combretastatina A4 é um promissor agente anticâncer. Na célula, inibe a polimerização dos microtúbulos, os quais são fundamentais nos processos de motilidade, manutenção estrutural e mitose. Essa inibição se dá a partir do sítio de interação da αβ-tubulina bloqueando o fluxo do sangue que alimenta os tumores, o que resulta na morte dos mesmos. Com estrutura semelhante às combretastatinas, as chalconas constituem uma classe de compostos que atuam no mesmo sítio de interação na tubulina. Baseando-se nos trabalhos experimentais de Ducki e colaboradores, estudou-se a estrutura molecular de 87 chalconas análogas à combretastatina A4 por meio do método quântico DFT com o propósito de desenvolver modelos de Relações Quantitativas Estrutura-Atividade (QSAR) aplicados a tais antagonistas. A partir dos métodos dos Mínimos Quadrados Parciais (PLS) e de Redes Neurais Artificiais (ANN), foram gerados modelos que conduzem à elucidação da relação dos compostos estudados com suas respectivas atividades biológicas. Os descritores eletrônicos e moleculares selecionados apresentam alta concordância com as características das moléculas, havendo predominância de comportamento linear com a atividade biológica, podendo, eventualmente, apresentar comportamento não-linear, o que torna o modelo gerado altamente consistente. / Combretastatin A4 is a promising anticancer agent. It inhibits the polymerization of microtubules in the cell, which are essential in the process of motility, structural maintenance and mitosis. This inhibition is given from the interaction site of αβ-tubulin blocking the blood flow that feeds the tumor, what results in its death. The chalcones, sharing a similar structure of the combretastatin, are also a class of compounds that act in the same site of interaction in the tubulin. Based on the experimental work of Ducki and co-workers, we proposed a molecular structure study of 87 chalcones similar to combretastatin A4 using the DFT method in order to develop Quantitative Structure-Activity Relationships (QSAR) applied to the given antagonists. Through Partial Least Squares (PLS) and Artificial Neural Network (ANN) methods, some models has been generated to lead the understanding on the relationship between the compounds studied and their respective biological activities. The electronic and molecular descriptors selected have high correlation with the molecule features, being linear most of the time, although with eventual non-linear behavior, which makes the generated model highly consistent.

A depressão, também conhecida como distúrbio depressivo maior, é uma doença extremamente séria caracterizada por distúrbios afetivos que apresenta sintomas como tristeza, pessimismo, baixa autoestima, alterações nas funções vegetativas como sono e apetite e cognitivas como concentração, memória e atenção. A depressão afeta hoje cerca de 300 milhões de pessoas sendo essa a doença mais incapacitante do mundo, segundo dados da Organização Mundial da Saúde. O receptor humano 5-HT2a tem sido associado a inúmeras condições neurológicas e moléculas ligantes seletivas a esse receptor podem apresentar potencial terapêutico no tratamento de distúrbios de comportamento, tais como a depressão. Com o propósito de contribuir para o planejamento de novos fármacos mais eficazes no tratamento da depressão realizou-se um estudo de QSAR (do inglês Quantitative structure-activity Relationship) com um conjunto de 106 compostos arilpiperazínicos utilizando os métodos de Mínimos Quadrados Parciais (PLS) e Redes Neurais Artificias (ANN). O modelo de PLS foi obtido com 76 compostos no conjunto de treinamento e 11 compostos no conjunto teste (r2 = 0,749 e q2 = 0,696). Os testes de validação leave-N-out, randomização e detecção de outliers confirmaram a robustez e estabilidade dos modelos e demonstraram que os mesmos não foram gerados através de correlações ao acaso. O modelos de Redes Neurais Artificiais de Perceptron de Multicamadas (MLP-ANN) foi gerado utilizando funções de transferência tansig-tansig. O treinamento das redes demonstrou que o melhor modelo apresentava arquitetura 7-10-1. Tal modelo apresentou valores de erro quadrático médio (EQM) igual a 0,00964, desvio médio absoluto (DMA) igual a 0,0775 e r2treinamento, r2validação e r2teste iguais a 0,794, 0,795 e 0,788, respectivamente. Os descritores dos modelos matemáticos gerados mostraram concordância com as propriedades moleculares dos compostos e os valores de atividade biológica preditos pelos modelos de PLS e ANN demonstraram que a interação dos descritores é satisfatoriamente relacionada tanto por aproximações lineares quanto não-lineares. / Depression, major depressive disorder or clinical depression, is a serious mood disorder characterized by severe symptoms like emotional suffering and feeling miserable that affects vegetative functions as sleeping and eating and cognitive functions like concentration, memory and attention. The world mental health estimates that depression affects more than 300 million people worldwide and is the leading cause of world disability. The human receptor 5-HT2a has been associated to many neurological dysfunctions. Molecules that selectively bind to this receptor present the potential to be used as an effective treatment of mental health disorders, such as depression. Our primary aim in this work is to contribute to the development of new drugs for the treatment of depression. Quantitative Structure-Activity Relationships (QSAR) studies with a series of 106 arylpiperazines that interact with the neuroreceptor 5-HT2a were conducted employing the methods Partial Least Squares (PLS) and Artificial Neural Networks (ANN). The PLS model was obtained with a training set of 76 compounds and a 19 compounds test set (r² = 0. 749, q² = 0.696). Leave-N-out analysis, biological activity randomization and outliers detection confirmed the robustness and stability of the models and showed that they were not obtained by chance correlations. Predictive models were also generated by Multilayer Perceptron Artificial Neural Network (MLP-ANN) trained with tansig-tansig transfer functions showed that the best results were obtained for a 7-10-1 architecture (r²training = 0. 794, r²validation = 0.795 and r²test = 0.788). The descriptors used to construct the predictive models showed good agreement with the arylpiperazines molecular properties, and the biological activity predicted by the PLS and ANN methods suggested that the descriptor interactions can be described by a linear or a nonlinear approach as well.

Artificiell intelligens är ett område inom datavetenskap som försöker skapa intelligenta system eller system som simulerar intelligens. Sådana system är intressanta för konsumenter eftersom de kan utföra uppgifter som annars krävt mänsklig inblandning.Spelindustrin hjälper till att driva utvecklingen av AI framåt när spelare fortsätter att förvänta sig mer engagerande och verklighetstrogna upplevelser.Akademiskt är spel användbara för studier av artificiell intelligens på grund av att de är relativt simpla. Men även om spel är enklare än verkligheten är det fortfarande en svår uppgift att skapa en artificiell intelligens som kan matcha en mänsklig motståndare.En populär genre av spel är strategispel, exempel på dessa är Age of Empires och Starcraft. I denna rapport undersöks en annorlunda ansats för att lösa problemet med resurshantering för denna typ av spel med hjälp av ett artificiellt neuralt nätverk som klassificerar spelets tillstånd. Detta har inte utforskats tidigare och målet är att ta reda på hurvida det är möjligt. För att träna nätverket används backpropagation i samband med Q- learning vilket gör inlärningen unsupervised. Så författarnas frågeställning är följande: Kan ett Q- nätverk användas för att hantera resursallokeringen för en bot i Starcraft Broodwar?För att kunna se hur väl Q- nätverket löser problemet utförs ett experiment med två olika botar där den ena spelar starcraft med samma möjligheter som en spelare och den andra en förenklad version. Experimentet går ut på att samla data från botarnas träning för att se om de förbättras eller inte. Som kontroll används två extra botar som slumpmässigt väljer handlingar.Resultatet av experimentet var flera grafer som visade botarnas prestanda på olika sätt och hur många spel de vunnit och sannolikheten för vinst. Med stöd av resultatet är det inte möjligt att se någon verklig förbättring i botarnas spelande med 0.7% respektive 0.4% chans för vinst mot standard AI:n. Resultatet visar dessutom att en av botarna är mycket sämre än en som slumpat fram handlingar.Dessutom visade det sig att träningen tog alldeles för lång tid. Om experimentet får mer tid kanske det skulle visat att tekniken är möjlig men först efter orimligt lång tid vilket skulle göra den oanvändbar i praktiken. Om detta hade lyckas hade det inneburit att man skulle kunna skapa bättre AI för strategispel som anpassar sig efter spelaren och kan generalisera när den ställs inför en situation som inte var planerad av utvecklarna.Men i denna studie förblev botarnas beteende mer eller mindre stokastiskt så svaret på frågeställningen är att det inte är möjligt. / Artificial intelligence is a field of computer science that tries to create intelligent systems or systems that simulate intelligence. Such systems are attractive to consumers because they can perform tasks that would otherwise have required human intervention.The gaming industry is helping to drive the development of AI forward as players continue to expect more immersive and lifelike experiences.Academically games are useful for the study of artificial intelligence because they are relatively simple. But even if the game is simpler than the reality, it is still a difficult task to create an artificial intelligence that can match a human opponent.A popular genre of games is strategy, examples of which are Age of Empire and StarCraft. This report examines a different approach to solve the problem of resource management for this type of game with the help of an artificial neural network to classify the game state. This has not been explored previously, and the goal is to find out whether this approach is feasible or not. To train the networks back-propagation is used in conjunction with Q-learning which makes learning unsupervised. So the authors’ research-question is: Can a Q network be used to manage the resource allocation for a cure in StarCraft Broodwar?To see how well the Q networks solve the problem an experiment was conducted with two different bots where one play StarCraft with the same opportunities a player would have and the other a simplified version. The experiment consists of collecting data from the bots training to see if they improve or not. As a control, two additional bots are used with a completely random policy.The results of the experiment were several graphs showing the bots performance in different ways but most importantly, the number of games won and the probability of winning. With the support of the result, it is not possible to see any real improvement in bot gameplay with 0.7% and 0.4% chance to win against the default AI. The results also show that one of the neural net bots performed much worse than the one with random actions.Moreover, the training turned out to be far too long. If the experiment had more time maybe it would have shown that the technology is possible, but still, only after an unreasonably long time, which would make it useless in practice. If this had been successful it would have meant that we might create better AI for the strategy games that adapts to the player and can generalize when faced with a situation that was not planned by the developers.But in this study the bots behavior remained more or less stochastic so the answer to the research- question is that it is not possible.

Forskningsfrågan i denna avhandling var att utvärdera och jämföra två ML-algoritmer som var Support Vector Machine (SVM) och Artificial Neural Network (ANN) i termer av noggrannhet, precision, återkallelse, f1-poäng och förutsägelse när de tränades för att klassificera binära datamängder. Datauppsättningen hämtades från Ladok och bestod av anonyma högskolestudenter från en mängd kurser. Algoritmerna kördes på TensorFlow med Keras som API och byggdes, tränades och kördes för utvärdering, allt på Google Colab. Källkoden skrevs i Python. Det icke-tekniska målet med studien var att försöka hitta ett förutsägelsemönster för studentprestationer och tillhandahålla ett tekniskt ramverktyg för att ge feedback till studenter och universitetsfakulteten. Forskningsfrågan delades upp i tre separata delfrågor. Den första var om ML-algoritmerna var ett lämpligt sätt att hitta dessa elevmönster och den kunskap man fick var att ja eftersom dessa algoritmer var lämpliga för den lilla datauppsättningsstorleken. Den andra handlade om hur man implementerar SVM och ANN och det löstes med TensorFlow med Keras API. Den tredje handlade om mängden som behövdes för att dra slutsatserna och förutsäga dessa algoritmer, och det fastställdes att storleken var tillräcklig på grund av att den tränade noggrannheten var högre än baslinjenoggrannheten i båda algoritmerna. Den huvudsakliga forskningsfrågan resulterade i att SVM-modellen överträffade ANN-modellen vad gäller alla nämnda parametrar. Detta teoretiserades på grund av att SVM har linjärt ökande multiparameter som matchade de ökade ingångarna. Detta var inte fallet med strukturen för ANN. / The research question of this thesis was to evaluate and compare two ML algorithms which were Support Vector Machine (SVM) and Artificial Neural Network (ANN) in terms of accuracy, precision, recall, f1 score, and prediction when trained for classifying binary datasets. The dataset was fetched from Ladok and consisted of anonymous higher education student credit from a multitude of courses. The algorithms were run on TensorFlow with Keras as an API and were built, trained, and run for evaluation all on Google Colab. The source-code was written in Python. The non-technical goal of the study was to try to find a prediction pattern for student performance and provide a technical framework tool to provide feedback for student and university faculty. The research question was broken down into three separate sub questions. The first one was if the ML algorithms were an appropriate way to find these student patterns and the knowledge gained was that yes because theses algorithms were appropriate for the small dataset size. The second one was about how to implement SVM and ANN and that was solved using TensorFlow with Keras API. The third one was about the amount needed to draw the conclusions and prediction these algorithms would make, and it was determined that the size was sufficient due to the trained accuracy being higher that the baseline accuracy in both algorithms. The main research question resulted in the SVM model outperforming the ANN model in terms of all the parameters mentioned. This was theorized due to the nature of SVM having linearly increasing multiparameter that that matched the increased inputs. This was not the case with the structure of the ANN.

Visually extracting textual context from lips consists of pattern matching which results in a frequent use of machine learning approaches for the task of classification. Previous research has consisted of mostly audiovisual (multi modal) approaches and conventional cameras. This study isolates the visual medium and uses event-based cameras instead of conventional cameras. Classifying visual features is computationally expensive and the minimisation of excessive data can be of importance for performance which motivates the usage of event cameras. Event cameras are inspired by the biological vision and only capture changes in the scene while offering high temporal resolution (corresponding to frame rate for conventional cameras). This study investigates the importance of temporal resolution for the task of lip reading by modifying the ∆time used for collecting events. No correlation could be observed within the collected data set. The paper is not able to come to any conclusions regarding suitability of the chosen approach for the particular application. There are multiple other variables that could effect the results which makes it hard to dismiss the technology’s potential within the domain. / Visuell bedömning av vilka ord läppar talar består av mönstermatchning vilket resulterar i att maskininlärning ofta används för att klassificera data som text. Tidigare studier har i hög grad varit audiovisuella(multimodala) och konventionella kameror. Visuell analys är beräkningsmässigt dyrt vilket motiverar en minimering av överflödig data för att öka prestandan, vilket motiverar användningen av eventkameror. Eventkameror är inspirerade av biologisk syn och registrerar endast skillnaden i omgivningen, samtidigt som de har en hög tidsupplösning (motsvarande frame rate för konventionella kameror). Studien undersöker relevansen av tidsupplösning för maskinell läppläsning genom att modifiera ∆time som används för att samla events. Ingen korrelation mellan ∆time och träffsäkerheten kunde observeras med det dataset som användes. Studien kan inte avfärda potentialen för tekniken eftersom det finns många fler parametrar som kan påverka träffsäkerheten.

Les progrès thérapeutiques dans le domaine de l'infection par le Virus de l'Immunodéficience Humaine (VIH) ont transformé le pronostic de cette pathologie. Les personnes séropositives pour le VIH se heurtent aux mêmes difficultés rencontrées dans les autres types de pathologie chroniques : difficultés à suivre un traitement régulièrement, effets secondaires des traitements et perte d'efficacité des molécules. Afin d'améliorer la prise en charge de ces personnes, de nouvelles molécules comme, par exemple, les analogues non-nucléosidiques (ANN), ont été développées en substitution aux antiprotéases. Notre travail de recherche concerne l'évaluation prospective des facteurs prédictifs de l'échec virologique des ANN, hors essai thérapeutique. Concernant les complications à long terme, nous avons comparé les modifications lipidiques des deux principales molécules appartenant à la classe des ANN. Considérant qu'il n'allait pas de soi qu'une simplification de posologie de deux à une prise par jour pouvait significativement améliorer l'observance, nous avons réalisé un essai randomisé afin de comparer les deux posologies. Enfin, nous avons réalisé une revue de la littérature des essais de non infériorité dans le domaine des traitements du VIH. Nous avons ainsi mis en évidence l'impact des interruptions de traitement pendant plus de 48 heures sur le risque de résistance génotypique aux ANN et confirmé le rôle indépendant de la dépression dans le risque d'échappement virologique. Le remplacement de l'efavirenz par la névirapine réduisait le risque cardiovasculaire à 10 ans de 20%. Le passage à la névirapine en une prise par jour était bien toléré mais l'augmentation moyenne du taux d'observance mesuré par pilulier électronique était marginale. Enfin, les standards méthodologiques relatifs aux essais de non infériorité étaient rarement respectés, rendant l'interprétation de ces études difficile. Notre travail a ainsi contribué à améliorer les connaissances de la prise en charge à long terme des personnes infectées par le VIH en explorant les relations complexes entre les données virologiques, les propriétés pharmacologiques des molécules et les comportements humains

This essay analyzes Enclave, discusses the different outcomes of using an intrinsic or an extrinsic approach and argues that there is a lack of aesthetic objectives in the English syllabus in Swedish upper secondary school. Initially it introduces the novel Enclave and states what kind of syllabus Sweden utilizes and what the syllabus' goals are for the students. Secondly, it acknowledges the relationship between the two approaches recognizing a debate and the fact that an intrinsic approach has been ignored by schools and scholars in England. In this context it includes the relationship the English subject in Sweden has with literature. Furthermore, the essay provides thorough definitions of the intrinsic and extrinsic approaches which are used to analyze Enclave. Finally, it concludes that it is more difficult to relate the intrinsic approach to the syllabus because of its lack of aesthetic values in the content of communication, reception, production and interaction objectives. The results yielded were similar in that both required intensive reading but an aesthetic experience only occurred with the intrinsic approach done to Enclave.