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Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Near-surface soil moisture is a key state variable of the hydrologic cycle and plays a significant role in the global water and energy balance by affecting several hydrological, ecological, meteorological, geomorphologic, and other natural processes in the land-atmosphere continuum. Presence of soil moisture in the root zone is vital for the crop and plant life cycle. Soil moisture distribution is highly non-linear across time and space. Various geophysical factors (e.g., soil properties, topography, vegetation, and weather/climate) and their interactions control the spatio-temporal evolution of soil moisture at various scales. Understanding these interactions is crucial for the characterization of soil moisture dynamics occurring in the vadose zone. This dissertation focuses on understanding the spatio-temporal variability of near-surface soil moisture and the associated physical control(s) across varying measurement support (point-scale and passive microwave airborne/satellite remote sensing footprint-scale), spatial extents (field-, watershed-, and regional-scale), and changing hydro-climates. Various analysis techniques (e.g., time stability, geostatistics, Empirical Orthogonal Function, and Singular Value Decomposition) have been employed to characterize near-surface soil moisture variability and the role of contributing physical control(s) across space and time. Findings of this study can be helpful in several hydrological research/applications, such as, validation/calibration and downscaling of remote sensing data products, planning and designing effective soil moisture monitoring networks and field campaigns, improving performance of soil moisture retrieval algorithm, flood/drought prediction, climate forecast modeling, and agricultural management practices.

本篇論文的目的在介紹階層貝氏之時間與空間統計模式(spatio-temporal model)，將此模式應用在疾病地圖的分析，以了解疾病在空間上的分佈狀態與時間趨勢。模型中除了納入時間、空間和年齡的效應外，也包括時間與空間、時間與年齡的交互作用，並考慮到空間相關性(spatial correlation)，然後以DIC值(Deviance information criterion)作為模式選取的準則。 本文並以民國88-90年全身紅斑性狼瘡的女性患病人數做為實證分析的資料。配適時間與空間統計模式後，以馬可夫鏈蒙地卡羅法(MCMC)來模擬參數值，估計出各時間、地區、年齡層的對數疾病發生率。由疾病地圖可看出，台灣地區全身紅斑性狼瘡的女性疾病發生率，以20-59歲的年齡層發生率較高，0-19歲的發生率較低。不管在哪一個年齡層，北部和中部地區的發生率都是最高的。時間趨勢方面，88-90年整體疾病發生率有遞減的趨勢，60歲以上的發生率也是遞減的趨勢。但在部分地區，則有發生率遞增的趨勢。 / In this study, we introduce the spatio-temporal model in a hierarchical Bayesian framework and use disease maps to display the spatial patterns and the temporal trends of disease. A special feature of the model is the inclusion of spatial correlations used to examine spatial effects relative to both regional and regional changes over time by group. Then, we use deviance information criterion (DIC) to compare complex hierarchical models. The methodology is illustrated by an analysis of female Systemic Lupls Erythematosus (SLE) morbidity data in Taiwan during the period 1999-2001.The model inference is implemented using Markov chain Monte Carlo method. The outcomes of the practical analysis appear that the higher morbidity rate occurs in 20-year and 40-year period. No matter what age group, the morbidity rate is highest in the north and the middle of Taiwan. Furthermore, the morbidity rate decreases with respect to year as well as over the 60-year period but it increases in some places.

This research determines the ground for a Christian theological anthropology that makes provision for a doctrine that supports human self-realization. It is evident from the study that anthropological self-realization is an involved process of becoming truly human, not an isolated course founded solely on the biblical knowledge of being created in the image and likeness of God. All sciences, and in particular anthropological sciences, enjoy the prerogative of unraveling and analyzing the human person. Whether these sciences are neuro-biological, psychological, theological, philosophical, biblical, spiritual or mystical in character, each with its specific method legitimately attempts to explain the complexities of the human person. In the light of this neither philosophy nor theology possess the exclusive claim to authentic human wisdom. In truth most scientific insights have a combined impact on the self-realizing growth and development of humanity. The process of self-realization links theology to real life questions such as evil, suffering, hope, love, justice and freedom, as well as with the immanent, the transcendent, the human and the divine. This thesis holds to the opinion that a theology of self-realization would contribute to the 'humanization' of theology since it brings praxis and theory into close alignment. This study equally expresses the conviction that the doctrine of consecrated vowed life, an ecclesiastical structure in the Roman Catholic Church, is particularly in need of humanization, as the notion of 'self' as a strength has been notably absent from traditional treatises on the practices of religious life. Conventional forms of consecrated vowed life called the woman to suppress and spiritualize at least some of her femininity. This was due to the distorted theological anthropology that sustained consecrated life in which God was presented as an idea to be grasped intellectually, and not an experience to be lived. / Philosophy, Practical and Systematic Theology / Th. D. (Systematic Theology)

L'objectif de cette thèse est la résolution d’un problème d’optimisation multi-objectif sous incertitudes en présence de simulations numériques coûteuses. Une validation est menée sur un cas test de thermique transitoire. Dans un premier temps, nous développons un algorithme d'optimisation multi-objectif basé sur le krigeage nécessitant peu d’appels aux fonctions objectif. L'approche est adaptée au calcul distribué et favorise la restitution d'une approximation régulière du front de Pareto complet. Le problème d’optimisation sous incertitudes est ensuite étudié en considérant des mesures de robustesse pires cas et probabilistes. Le superquantile intègre tous les évènements pour lesquels la valeur de la sortie se trouve entre le quantile et le pire cas mais cette mesure de risque nécessite un grand nombre d’appels à la fonction objectif incertaine pour atteindre une précision suffisante. Peu de méthodes permettent de calculer le superquantile de la distribution de la sortie de fonctions coûteuses. Nous développons donc un estimateur du superquantile basé sur une méthode d'échantillonnage préférentiel et le krigeage. Il permet d’approcher les superquantiles avec une faible erreur et une taille d’échantillon limitée. De plus, un couplage avec l’algorithme multi-objectif permet la réutilisation des évaluations. Dans une dernière partie, nous construisons des modèles de substitution spatio-temporels capables de prédire des phénomènes dynamiques non linéaires sur des temps longs et avec peu de trajectoires d’apprentissage. Les réseaux de neurones récurrents sont utilisés et une méthodologie de construction facilitant l’apprentissage est mise en place. / This work aims at solving multi-objective optimization problems in the presence of uncertainties and costly numerical simulations. A validation is carried out on a transient thermal test case. First of all, we develop a multi-objective optimization algorithm based on kriging and requiring few calls to the objective functions. This approach is adapted to the distribution of the computations and favors the restitution of a regular approximation of the complete Pareto front. The optimization problem under uncertainties is then studied by considering the worst-case and probabilistic robustness measures. The superquantile integrates every event on which the output value is between the quantile and the worst case. However, it requires an important number of calls to the uncertain objective function to be accurately evaluated. Few methods give the possibility to approach the superquantile of the output distribution of costly functions. To this end, we have developed an estimator based on importance sampling and kriging. It enables to approach superquantiles with little error and using a limited number of samples. Moreover, the setting up of a coupling with the multi-objective algorithm allows to reuse some of those evaluations. In the last part, we build spatio-temporal surrogate models capable of predicting non-linear, dynamic and long-term in time phenomena by using few learning trajectories. The construction is based on recurrent neural networks and a construction facilitating the learning is proposed.

Cette thèse est une collaboration entre le LAboratoire des Sciences et Matériaux pour l’Électronique et d’Automatique (LASMEA) de Clermont-Ferrand et le Laboratoire Vision et Ingénierie des Contenus (LVIC) du CEA LIST à Saclay. La première moitié de la thèse a été accomplie au sein de l’équipe ComSee (1) du LASMEA et la deuxième au LVIC. L’objectif de ces travaux est de concevoir un système de vidéo-assistance temps réel pour la détection d’évènements dans des scènes possiblement denses.La vidéosurveillance intelligente de scènes denses telles que des foules est particulièrement difficile, principalement à cause de leur complexité et de la grande quantité de données à traiter simultanément. Le but de cette thèse consiste à élaborer une méthode de détection d’évènements rares dans de telles scènes, observées depuis une caméra fixe. La méthode en question s’appuie sur l’analyse automatique de mouvement et ne nécessite aucune information à priori. Les mouvements nominaux sont déterminés grâce à un apprentissage statistique non supervisé. Les plus fréquemment observés sont considérés comme des évènements normaux. Une phase de classification permet ensuite de détecter les mouvements déviant trop du modèle statistique, pour les considérer comme anormaux. Cette approche est particulièrement adaptée aux lieux de déplacements structurés, tels que des scènes de couloirs ou de carrefours routiers. Aucune étape de calibration, de segmentation de l’image, de détection d’objets ou de suivi n’est nécessaire. Contrairement aux analyses de trajectoires d’objets suivis, le coût calculatoire de notre méthode est invariante au nombre de cibles présentes en même temps et fonctionne en temps réel. Notre système s’appuie sur une classification locale du mouvement de la scène, sans calibration préalable. Dans un premier temps, une caractérisation du mouvement est réalisée, soit par des méthodes classiques de flot optique, soit par des descripteurs spatio-temporels. Ainsi, nous proposons un nouveau descripteur spatio-temporel fondé sur la recherche d’une relation linéaire entre les gradients spatiaux et les gradients temporels en des zones où le mouvement est supposé uniforme. Tout comme les algorithmes de flot optique, ce descripteur s’appuie sur la contrainte d’illumination constante.Cependant en prenant en compte un voisinage temporel plus important, il permet une caractérisation du mouvement plus lisse et plus robuste au bruit. De plus, sa faible complexité calculatoire est bien adaptée aux applications temps réel. Nous proposons ensuite d’étudier différentes méthodes de classification : La première, statique, dans un traitement image par image, s’appuie sur une estimation bayésienne de la caractérisation du mouvement au travers d’une approche basée sur les fenêtres de Parzen. Cette nouvelle méthode est une variante parcimonieuse des fenêtres de Parzen. Nous montrons que cette approche est algorithmiquement efficace pour approximer de manière compacte et précise les densités de probabilité. La seconde méthode, basée sur les réseaux bayésiens, permet de modéliser la dynamique du mouvement. Au lieu de considérer ce dernier image par image, des séquences de mouvements sont analysées au travers de chaînes de Markov Cachées. Ajouté à cela, une autre contribution de ce manuscrit est de prendre en compte la modélisation du voisinage d’un bloc afin d’ajouter une cohérence spatiale à la propagation du mouvement. Ceci est réalisé par le biais de couplages de chaînes de Markov cachées.Ces différentes approches statistiques ont été évaluées sur des données synthétiques ainsi qu’en situations réelles, aussi bien pour la surveillance du trafic routier que pour la surveillance de foule.Cette phase d’évaluation permet de donner des premières conclusions encourageantes quant à la faisabilité de la vidéosurveillance intelligente d’espaces possiblement denses. / The automatic analysis of crowded areas in video sequences is particularly difficult because ofthe large amount of information to be processed simultaneously and the complexity of the scenes. We propose in this thesis a method for detecting abnormal events in possibly dense scenes observed from a static camera. The approach is based on the automatic classification of motion requiring no prior information. Motion patterns are encoded in an unsupervised learning framework in order to generate a statistical model of frequently observed (aka. normal) events. Then at the detection stage, motion patterns that deviate from the model are classified as unexpected events. The method is particularly adapted to scenes with structured movement with directional flow of objects or people such as corridors, roads, intersections. No camera calibration is needed, nor image segmentation, object detection and tracking. In contrast to approaches that rely on trajectory analysis of tracked objects, our method is independent of the number of targets and runs in real-time. Our system relies on a local classification of global scene movement. The local analysis is done on each blocks of a regular grid. We first introduce a new spatio-temporal local descriptor to characterize the movement efficiently. Assuming a locally uniform motion of space-time blocks of the image, our approach consists in determining whether there is a linear relationship between spatial gradients and temporal gradients. This spatio-temporal descriptor holds the Illumination constancy constraint like optical flow techniques, but it allows taking into account the spatial neighborhood and a temporal window by giving a smooth characterization of the motion, which makes it more robust to noise. In addition, its low computational complexity is suitable for real-time applications. Secondly, we present two different classification frameworks : The first approach is a static (frame by frame) classification approach based on a Bayesian characterization of the motion by using an approximation of the Parzen windowing method or Kernel Density Estimation (KDE) to model the probability density function of motion patterns.This new method is the sparse variant of the KDE (SKDE). We show that the SKDE is a very efficient algorithm giving compact representations and good approximations of the density functions. The second approach, based on Bayesian Networks, models the dynamics of the movement. Instead of considering motion patterns in each block independently, temporal sequences of motion patterns are learned by using Hidden Markov Models (HMM). The second proposed improvement consists in modeling the movement in one block by taking into account the observed motion in adjacent blocks. This is performed by the coupled HMM method. Evaluations were conducted to highlight the classification performance of the proposed methods,on both synthetic data and very challenging real video sequences captured by video surveillance cameras.These evaluations allow us to give first conclusions concerning automatic analyses of possibly crowded area.

CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel Superior / Dunas costeiras exercem um importante papel na manutenÃÃo do fluxo de sedimentos da zona costeira. O Parque Nacional de Jericoacoara, localizado no estado do CearÃ, regiÃo Nordeste do Brasil, possui uma morfologia pouco frequente, trata-se de um promontÃrio associado com um campo de dunas mÃveis denominadas barcanas, dunas individuais, de grande porte com formato de ferraduras que se deslocam em direÃÃo L â O. Elas realizam o by-pass, o transporte de sedimentos, essencial para a manutenÃÃo da linha de costa. Neste trabalho, foi priorizada a definiÃÃo da evoluÃÃo morfodinÃmica de dunas mÃveis isoladas (dunas Papai Noel, PÃr-do-Sol e Arraia), tendo como referencial teÃrico a anÃlise das paisagens e como procdimento tÃcnico principal a anÃlise espaÃo-temporal do recobrimento de imagens multitemporais dos satÃlites Landsat e Quickbird entre os anos de 1973 a 2014. AtravÃs da comparaÃÃo da distribuiÃÃo espaÃo temporal das morfologias dunares, nesse perÃodo de 41 anos, evidenciaram-se mudanÃas significativas na Ãrea, perÃmetro e deslocamento das dunas. Foi possÃvel constatar a aÃÃo dos fluxos de matÃria e energia vinculados com migraÃÃo continuada direcionada para a faixa de praia (setor de bypassing de sedimentos). A dinÃmica de migraÃÃo das dunas, quando analisadas apÃs as imagens de 2000, evidenciou possibilidades de alteraÃÃes dos aspectos morfolÃgicos influenciados pelo incremento do fluxo turÃstico, quando instituÃdo o PARNA de Jericoacoara. As mudanÃas foram mais significativas, sobretudo, entre os anos de 2001 a 2005, o que pode estar relacionado a uma maior intervenÃÃo humana (fluxo de turistas). A utilizaÃÃo das tÃcnicas de geoprocessamento para o mapeamento da evoluÃÃo morfodinÃmica do campo de dunas do Parque Nacional de Jericoacoara constituiu- se uma ferramenta essencial para a produÃÃo de informaÃÃes que certamente subsidiarÃo a continuidade do planejamento ambiental da referida, que se constitui como uma Unidade de ConservaÃÃo de ProteÃÃo Integral. / Coastal dunes play an important role in the sediment flow of the coastal zone. The unique morphology of the Jericoacoara National Park in the northeastern Brazilian state of Cearà consists of a promontory covered by a mobile dune field consisting of large, horseshoe-shaped dunes known locally as barcanas that migrate from east to west. These dunes are responsible for the by-pass, the transport of sediments essential for the maintenance of the coastline. The present study focused on the morphodynamic evolution of these isolated mobile dunes through the recovery of multitemporal Landsat and Quickbird satellite images from the years between 1975 and 2014. The comparison of the spatio-temporal distribution of the morphology of these dunes over this 41-year period revealed significant shifts in their area, perimeter, and movement. It was possible to confirm that the flow of material and energy were linked to a process of continuous migration in the direction of the beach (sediment bypassing sector). The dynamics of the dune migration in the years following 2000, when the national park was established, indicate possible impacts of the increase in tourism within the area on the morphology of the dunes. The changes were most significant between 2001 and 2005, possibly reflecting a greater influx of tourists and thus more intense anthropogenic impacts. The different geoprocessing techniques applied to the mapping of the morphodynamic evolution of the dune field of the Jericoacoara National Park proved to be an essential tool for the production of information that will guarantee the long-term environmental planning of this integral conservation unit.

Le raisonnement spatial et temporel qualitatif est un domaine principal d’études de l’intelligence artificielle et, en particulier, du domaine de la représentation des connaissances, qui traite des concepts cognitifs fondamentaux de l’espace et du temps de manière abstraite. Dans notre thèse, nous nous focalisons sur les formalismes du domaine du raisonnement spatial et temporel qualitatif représentant les informations par des contraintes et apportons des contributions sur plusieurs aspects. En particulier, étant donnée des bases de connaissances d’informations qualitatives sur l’espace ou le temps, nous définissons des nouvelles conditions de consistance locale et des techniques associées afin de résoudre efficacement les problèmes fondamentaux se posant. Nous traitons notamment du problème de la satisfiabilité qui est le problème de décider s’il existe une interprétation quantitative de toutes les entités satisfaisant l’ensemble des contraintes qualitatives. Nous considérons également le problème de l’étiquetage minimal qui consiste à déterminer pour toutes les contraintes qualitatives les relations de base participant à au moins une solution ainsi que le problème de redondance consistant à déterminer les contraintes qualitatives non redondantes. En outre, nous enrichissons le domaine des formalismes spatio-temporels par des contributions concernant une logique spatio-temporelle combinant la logique temporelle propositionnelle (PTL) avec un langage de contraintes qualitatives spatiales et une étude de la problématique consistant à gérer une séquence temporelle de configurations spatiales qualitatives devant satisfaire des contraintes de transition. / Qualitative Spatial and Temporal Reasoning is a major field of study in Artificial Intelligence and, particularly, in Knowledge Representation, which deals with the fundamental cognitive concepts of space and time in an abstract manner. In our thesis, we focus on qualitative constraint-based spatial and temporal formalisms and make contributions to several aspects. In particular, given a knowledge base of qualitative spatial or temporal information, we define novel local consistency conditions and related techniques to efficiently solve the fundamental reasoning problems that are associated with such knowledge bases. These reasoning problems consist of the satisfiability problem, which is the problem of deciding whether there exists a quantitative interpretation of all the entities of a knowledge base such that all of its qualitative relations are satisfied by that interpretation, the minimal labeling problem, which is the problem of determining all the atoms for each of the qualitative relations of a knowledge base that participate in at least one of its solutions, and the redundancy problem, which is the problem of obtaining all the non-redundant qualitative relations of a knowledge base. Further, we enrich the field of spatio-temporal formalisms that combine space and time in an interrelated manner by making contributions with respect to a qualitative spatio-temporal logic that results by combining the propositional temporal logic (PTL) with a qualitative spatial constraint language, and by investigating the task of ordering a temporal sequence of qualitative spatial configurations to meet certain transition constraints.