Typologies textuelles et partitions musicales : dissimilarités, classification et autocorrélation

Axée dans un premier temps sur le formalisme et les méthodes, cette thèse est construite sur trois concepts formalisés: une table de contingence, une matrice de dissimilarités euclidiennes et une matrice d'échange. À partir de ces derniers, plusieurs méthodes d'Analyse des données ou d'apprentissage automatique sont exprimées et développées: l'analyse factorielle des correspondances (AFC), vue comme un cas particulier du multidimensional scaling; la classification supervisée, ou non, combinée aux transformations de Schoenberg; et les indices d'autocorrélation et d'autocorrélation croisée, adaptés à des analyses multivariées et permettant de considérer diverses familles de voisinages. Ces méthodes débouchent dans un second temps sur une pratique de l'analyse exploratoire de différentes données textuelles et musicales. Pour les données textuelles, on s'intéresse à la classification automatique en types de discours de propositions énoncées, en se basant sur les catégories morphosyntaxiques (CMS) qu'elles contiennent. Bien que le lien statistique entre les CMS et les types de discours soit confirmé, les résultats de la classification obtenus avec la méthode K- means, combinée à une transformation de Schoenberg, ainsi qu'avec une variante floue de l'algorithme K-means, sont plus difficiles à interpréter. On traite aussi de la classification supervisée multi-étiquette en actes de dialogue de tours de parole, en se basant à nouveau sur les CMS qu'ils contiennent, mais aussi sur les lemmes et le sens des verbes. Les résultats obtenus par l'intermédiaire de l'analyse discriminante combinée à une transformation de Schoenberg sont prometteurs. Finalement, on examine l'autocorrélation textuelle, sous l'angle des similarités entre diverses positions d'un texte, pensé comme une séquence d'unités. En particulier, le phénomène d'alternance de la longueur des mots dans un texte est observé pour des voisinages d'empan variable. On étudie aussi les similarités en fonction de l'apparition, ou non, de certaines parties du discours, ainsi que les similarités sémantiques des diverses positions d'un texte. Concernant les données musicales, on propose une représentation d'une partition musicale sous forme d'une table de contingence. On commence par utiliser l'AFC et l'indice d'autocorrélation pour découvrir les structures existant dans chaque partition. Ensuite, on opère le même type d'approche sur les différentes voix d'une partition, grâce à l'analyse des correspondances multiples, dans une variante floue, et à l'indice d'autocorrélation croisée. Qu'il s'agisse de la partition complète ou des différentes voix qu'elle contient, des structures répétées sont effectivement détectées, à condition qu'elles ne soient pas transposées. Finalement, on propose de classer automatiquement vingt partitions de quatre compositeurs différents, chacune représentée par une table de contingence, par l'intermédiaire d'un indice mesurant la similarité de deux configurations. Les résultats ainsi obtenus permettent de regrouper avec succès la plupart des oeuvres selon leur compositeur.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

CD28-negative cytolytic effector T cells frequently express NK receptors and are present at variable proportions in circulating lymphocytes from healthy donors and melanoma patients.

In humans, NK receptors are expressed by natural killer cells and some T cells, the latter of which are preferentially alphabetaTCR+ CD8+ cytolytic T lymphocytes (CTL). In this study we analyzed the expression of nine NK receptors (p58.1, p58.2, p70, p140, ILT2, NKRP1A, ZIN176, CD94 and CD94/NKG2A) in PBL from both healthy donors and melanoma patients. The percentages of NK receptor-positive T cells (NKT cells) varied strongly, and this variation was more important between individual patients than between individual healthy donors. In all the individuals, the NKT cells were preferentially CD28-, and a significant correlation was found between the percentage of CD28- T cells and the percentage of NK receptor+ T cells. Based on these data and the known activated phenotype of CD28- T cells, we propose that the CD28- CD8+ T cell pool represents or contains the currently active CTL population, and that the frequent expression of NK receptors reflects regulatory mechanisms modulating the extent of CTL effector function. Preliminary results indicate that some tumor antigen-specific T cells may indeed be CD28- and express NK receptors in vivo.

Aplicació en línia d'un sistema variable de control multivariable en un Reactor ..

L’objecte del present treball és la realització d’una aplicació que permeti portar a terme el control estadístic multivariable en línia d’una planta SBR.Aquesta eina ha de permetre realitzar un anàlisi estadístic multivariable complet del lot en procés, de l’últim lot finalitzat i de la resta de lots processats a la planta.L’aplicació s’ha de realitzar en l’entorn LabVIEW. L’elecció d’aquest programa vecondicionada per l’actualització del mòdul de monitorització de la planta que s’estàdesenvolupant en aquest mateix entorn

Efficacy of in-hospital multidimensional interventions of secondary prevention after acute coronary syndrome: a systematic review and meta-analysis

BACKGROUND: Secondary prevention programs for patients experiencing an acute coronary syndrome have been shown to be effective in the outpatient setting. The efficacy of in-hospital prevention interventions administered soon after acute cardiac events is unclear. We performed a systematic review and meta-analysis to determine whether in-hospital, patient-level interventions targeting multiple cardiovascular risk factors reduce all-cause mortality after an acute coronary syndrome. METHODS AND RESULTS: Using a prespecified search strategy, we included controlled clinical trials and before-after studies of secondary prevention interventions with at least a patient-level component (ie, education, counseling, or patient-specific order sets) initiated in hospital with outcomes of mortality, readmission, or reinfarction rates in acute coronary syndrome patients. We classified the interventions as patient-level interventions with or without associated healthcare provider-level interventions and/or system-level interventions. Twenty-six studies met our inclusion criteria. The summary estimate of 14 studies revealed a relative risk of all-cause mortality of 0.79 (95% CI, 0.69 to 0.92; n=37,585) at 1 year. However, the apparent benefit depended on study design and level of intervention. The before-after studies suggested reduced mortality (relative risk [RR], 0.77; 95% CI, 0.66 to 0.90; n=3680 deaths), whereas the RR was 0.96 (95% CI, 0.64 to 1.44; n=99 deaths) among the controlled clinical trials. Only interventions including a provider- or system-level intervention suggested reduced mortality compared with patient-level-only interventions. CONCLUSIONS: The evidence for in-hospital, patient-level interventions for secondary prevention is promising but not definitive because only before-after studies suggest a significant reduction in mortality. Future research should formally test which components of interventions provide the greatest benefit.

Multidimensional Adaptations for Open Learning Management Systems

Our work is focused on alleviating the workload for designers of adaptive courses on the complexity task of authoring adaptive learning designs adjusted to specific user characteristics and the user context. We propose an adaptation platform that consists in a set of intelligent agents where each agent carries out an independent adaptation task. The agents apply machine learning techniques to support the user modelling for the adaptation process

The handaxe and the microscope: individual and social learning in a multidimensional model of adaptation

When individuals learn by trial-and-error, they perform randomly chosen actions and then reinforce those actions that led to a high payoff. However, individuals do not always have to physically perform an action in order to evaluate its consequences. Rather, they may be able to mentally simulate actions and their consequences without actually performing them. Such fictitious learners can select actions with high payoffs without making long chains of trial-and-error learning. Here, we analyze the evolution of an n-dimensional cultural trait (or artifact) by learning, in a payoff landscape with a single optimum. We derive the stochastic learning dynamics of the distance to the optimum in trait space when choice between alternative artifacts follows the standard logit choice rule. We show that for both trial-and-error and fictitious learners, the learning dynamics stabilize at an approximate distance of root n/(2 lambda(e)) away from the optimum, where lambda(e) is an effective learning performance parameter depending on the learning rule under scrutiny. Individual learners are thus unlikely to reach the optimum when traits are complex (n large), and so face a barrier to further improvement of the artifact. We show, however, that this barrier can be significantly reduced in a large population of learners performing payoff-biased social learning, in which case lambda(e) becomes proportional to population size. Overall, our results illustrate the effects of errors in learning, levels of cognition, and population size for the evolution of complex cultural traits. (C) 2013 Elsevier Inc. All rights reserved.

Effect of multidimensional lifestyle intervention on fitness and adiposity in predominantly migrant preschool children (Ballabeina): cluster randomised controlled trial.

OBJECTIVE: To test the effect of a multidimensional lifestyle intervention on aerobic fitness and adiposity in predominantly migrant preschool children. DESIGN: Cluster randomised controlled single blinded trial (Ballabeina study) over one school year; randomisation was performed after stratification for linguistic region. SETTING: 40 preschool classes in areas with a high migrant population in the German and French speaking regions of Switzerland. PARTICIPANTS: 652 of the 727 preschool children had informed consent and were present for baseline measures (mean age 5.1 years (SD 0.7), 72% migrants of multicultural origins). No children withdrew, but 26 moved away. INTERVENTION: The multidimensional culturally tailored lifestyle intervention included a physical activity programme, lessons on nutrition, media use (use of television and computers), and sleep and adaptation of the built environment of the preschool class. It lasted from August 2008 to June 2009. MAIN OUTCOME MEASURES: Primary outcomes were aerobic fitness (20 m shuttle run test) and body mass index (BMI). Secondary outcomes included motor agility, balance, percentage body fat, waist circumference, physical activity, eating habits, media use, sleep, psychological health, and cognitive abilities. RESULTS: Compared with controls, children in the intervention group had an increase in aerobic fitness at the end of the intervention (adjusted mean difference: 0.32 stages (95% confidence interval 0.07 to 0.57; P=0.01) but no difference in BMI (-0.07 kg/m(2), -0.19 to 0.06; P=0.31). Relative to controls, children in the intervention group had beneficial effects in motor agility (-0.54 s, -0.90 to -0.17; P=0.004), percentage body fat (-1.1%, -2.0 to -0.2; P=0.02), and waist circumference (-1.0 cm, -1.6 to -0.4; P=0.001). There were also significant benefits in the intervention group in reported physical activity, media use, and eating habits, but not in the remaining secondary outcomes. CONCLUSIONS: A multidimensional intervention increased aerobic fitness and reduced body fat but not BMI in predominantly migrant preschool children.

Análise multidimensional de consórcios cenoura-alface sob diferentes combinações de densidades populacionais

Este trabalho teve como objetivo avaliar o desempenho biológico de sistemas consorciados de cenoura e alface, sob diferentes combinações de densidades populacionais, com uso das análises bivariada de variância e envoltória de dados (DEA). O delineamento experimental usado foi o de blocos ao acaso completos, com cinco repetições, com os tratamentos arranjados em esquema fatorial 4x4. Os tratamentos resultaram da combinação de quatro populações de plantas de cenoura (40, 60, 80 e 100% da população recomendada no cultivo solteiro - PRCS) com quatro populações de plantas de alface (40, 60, 80 e 100% da PRCS). As populações recomendadas para os cultivos solteiros da cenoura e alface foram 500 mil e 250 mil plantas por hectare, respectivamente. Tanto o método bivariado como o método de análise de envoltória de dados são bastante eficazes na discriminação dos melhores sistemas de cultivo consorciados, por meio dos rendimentos das culturas. Os resultados da eficiência produtiva, medidos por modelos DEA, permitem uma análise estatística simples do ensaio consorciado. A robustez do método de análise bivariada de variância assegura a validade dos resultados.