L’incertitude inhérente à notre appréhension des systèmes de peuplement passés, engendrée par les modes de collecte ou d’enregistrement des données, mais aussi par nos modes de catégorisation et de réflexion spatio-temporelles sur cette information, conditionnent fortement les hypothèses et les résultats qui en découlent. Cette thèse, menée dans le cadre du projet PaléoSyr/PaléoLib, présente une chaîne de raisonnement méthodologique exploratoire destinée à révéler et formaliser par des méthodes d’analyse spatiale divers niveaux de connaissance et d’incertitude inhérents aux bases de données archéologiques, tout en proposant des alternatives destinées à s’écarter du conditionnement imposé par les catégories spatio-temporelles rigides telles que les périodes ou les limites de zones de prospection. Celle-ci est structurée en deux axes majeurs : le premier a pour objectif de clarifier et de synthétiser les niveaux d’information inhérents à une vaste base de données couvrant la Syrie occidentale et le Liban de -9600 av. J.-C. à nos jours, en adaptant des méthodes d’aide à la décision et d’analyse spatio-morphologique, et par des procédés de géovisualisation dans une optique exploratoire. Le second axe explore et évalue localement les impacts des divers niveaux spatio-temporels que révèlent les données par des méthodes d’analyse géostatistique et de statistique spatiale, et des niveaux d’incertitude sur notre perception du changement spatio-temporel, sur un espace situé au Nord-Ouest de la Syrie. Divers modèles des « passés possibles » du peuplement élaborés à l’aide de la logique floue seront proposés, en fonction des divers niveaux spatio-temporels et d’incertitude envisagés. / The uncertainty inherent to our comprehension of past settlement systems, generated by our ways of collecting or recording data but also by our spatio-temporal categorisation and reflexion on this information, greatly condition hypotheses and results. This thesis was carried out in the framework of PaleoSyr/PaleoLib project, and develops a methodological and exploratory reasoning chain whose aim is to reveal and formalise with spatial analysis several levels of knowledge and uncertainty which are inherent to archaeological databases. It proposes alternatives to try and avoid the conditioning imposed by rigid spatio-temporal categories such as periods or survey zones. It is organised in two major axes : the first one intends to clarify and synthetize several levels of information which are inherent to a big database covering Occidental Syria and Lebanon from -9600 B.C to the present, by adapting decision making and spatio-morphological methods, and through geovisualisation processes in an exploratory dimension. The second axis explores and evaluates locally the impacts of the various spatio-temporal and uncertainty levels revealed by data with geostatistics and spatial statistics on our perception of spatio-temporal change, on a test zone situated at the North-West of Syria. Several models of the “possible pasts” of settlement with the help of fuzzy logic are then proposed, depending on the various spatio-temporal and uncertainty levels taken into account.

Le cycliste de haut niveau a comme objectif principal : gagner et optimiser sa performance. Une question souvent soulevée par le sportif et son encadrement est le lien entre l’asymétrie, la performance et la santé. Dans ce contexte nous analysons dans un premier temps l’adaptation cinématique et musculaire de la jambe dominante chez le cycliste professionnel au cours du test incrémental jusqu’à épuisement proposé par la FFC. Les résultats ont montrés que l’incrément depuissance modifie la coordination musculaire avant d’impacter la cinématique articulaire. Ensuite nous proposons une nouvelle méthodologie afin d’étudier l’évolution cinématique de l’asymétrie spatio-temporelle au cours de ce test. Les résultats ont permis de démontrer que le niveau d’asymétrie est plus important hors du plan sagittal, ce qui peut entraîner un risque de blessures, principalement au genou. Enfin, nous examinons l’influence de la fatigue sur le niveau d’asymétriearticulaire et musculaire par l’intermédiaire d’une modélisation numérique musculo-squelettique sur une population de cyclistes de haut niveau. Les résultats ont permis de démontrer que l’asymétrie musculaire est différente entre l’analyse électromyographique et musculosquelettique. De plus, le côté dominant présente un niveau plus élevé concernant les forces musculaires et de réactions intersegmentaires. D’un point de vue générale, les méthodologies employées dans cemanuscrit comportent une application directe pour le cyclisme. Cependant, elles peuvent être réutilisées dans d’autres activités, sportive ou non, d’un point de vue performance et prévention des blessures. / The high-level cyclist main goal is to win and optimize his performance. A question often raised by the athlete and his coaching is the link between asymmetry, performance and health. In this context, we analyze the kinematic and muscular adaptation of the dominant leg of professional cyclist during the incremental test to exhaustion of the FFC. The results showed that the increase of power output modifies the muscular coordination before impacting joint kinematics. Then, wepropose a new methodology to study the kinematic evolution of the spatio-temporel asymmetry during this test. The results showed that asymmetry level is greater outside the sagittal plane, which can creat a risk of injuries mainly for the knee. Finally, we examine the influence of fatigue on the level of muscular asymmetry though musculosketal modeling on a population of high level cyclists. Results showed that muscle asymmetry is different between electromyographic and musculoskeletal analysis. Moreover, the dominant side has a higher level of muscle strength and intersegmental reactions. From a general point of view, the methodologies used in this manuscipt have a direct application for cycling. However, they can be reused in other activities, from a performance and injury prevention viewpoint.

Responsable d’un quart des accidents vasculaires cérébraux, la fibrillation auriculaire (FA) est l’arythmie cardiaque la plus répandue. La thérapie d’ablation par cathéter (CA) est de plus en plus utilisée pour traiter la FA, mais ses effets sur le substrat cardiaque ne sont pas suffisamment compris, d’où un taux de réussite très variable. L’électrocardiogramme (ECG) à 12 voies représente un outil non invasif peu coûteux pour caractériser la FA à partir de l’activité électrique du cœur. Cependant, les prédicteurs classiques de l’issue de la CA présentent plusieurs inconvénients, notamment leur calcul manuel sur une seule voie de l’ECG. Cette thèse exploite explicitement le caractère multi-capteur de l’ECG au moyen de techniques de décomposition multivariées, démontrant qu’elles peuvent améliorer la puissance prédictive de certaines propriétés de l’ECG dans le cadre de la CA. L’amplitude des ondes fibrillatoires est corrélée avec le résultat de la CA, et traitée par une méthode multi-capteur basée sur l’analyse en composantes principales (PCA). Des variantes comme la PCA pondérée (WPCA) et la factorisation en matrices non négatives (NMF) peuvent aussi quantifier la variabilité spatio-temporelle de la FA sur l’ECG. La théorie de l’information permet également d’estimer le niveau de corrélation entre les voies de l’ECG, mis en relation avec le résultat de la CA grâce à des approches multi-capteurs. Enfin, une dernière ligne de recherche concerne la réponse ventriculaire manifestée sur la variabilité cardiaque. L’approche paramétrique de processus ponctuel est capable de souligner certaines propriétés de cette variabilité, améliorant ainsi la caractérisation de la FA. / Atrial fibrillation (AF) is the most common sustained arrhythmia encountered in clinical practice, and one of the main causes of stroke. Yet its thorough characterization and treatment remain an open issue. Despite the increasing popularity of the radiofrequency catheter ablation (CA) therapy, very little is known about its impact on heart substrate, leading to rather uncertain success rates. This calls for advanced signal processing tools for quantitatively assessing CA outcome. The surface 12-lead electrocardiogram (ECG), a noninvasive and cost-effective cardiac activity recording modality, provides valuable information about AF. However, some issues affect most of the standard CA outcome predictors, e.g., manual computation and limited single-lead perspective. This thesis aims at explicitly exploiting the ECG’s multilead character through multivariate decomposition tools, so as to enhance the role of some ECG features as CA outcome predictors. Fibrillatory wave amplitude is correlated with CA success in a multilead framework through principal component analysis (PCA). Multivariate approaches also enhance AF spatiotemporal variability measured on the ECG (e.g., weighted PCA, nonnegative matrix factorization), evidencing that the less repetitive the AF pattern, the less likely CA success. Information theory also quantifies interlead similarity between AF patterns, and is linked with CA outcome in a multilead framework. Another perspective focuses on the ventricular response as reflected on heart rate variability (HRV). Point process modeling can highlight certain HRV properties typical of AF in a parametric probabilistic context, helping AF pattern recognition.

Cette thèse est consacrée à une analyse de l'activité d'éclairs des systèmes orageux en Afrique équatoriale (10°E - 35°E ; 15°S - 10°N) sur la période de temps 2005-2013. Tout d'abord, les données fournies par le réseau global de détection d'éclairs WWLLN (World Wide Lightning Location Network) ont été comparées à celles obtenues par le capteur optique spatial LIS (Lightning Imaging Sensor) afin d'estimer l'efficacité de détection relative du WWLLN. Ensuite, elles ont permis d'établir une climatologie régionale à haute résolution de l'activité d'éclairs. Enfin, elles ont été associées à des données sur les caractéristiques nuageuses et météorologiques pour des études de cas d'orages dans différentes situations, afin d'examiner les corrélations entre activité d'éclairs, activité orageuse, caractéristiques nuageuses et conditions météorologiques. La méthode adaptée pour estimer l'efficacité de détection du WWLLN dans la zone d'étude a permis d'obtenir des valeurs compatibles avec celles trouvées dans d'autres régions du monde, et de mettre en évidence une variabilité spatio-temporelle qui aide à l'interprétation des changements affectant plusieurs paramètres de l'activité d'éclairs. La climatologie réalisée dévoile des caractéristiques originales de l'évolution temporelle et de la distribution spatiale de l'activité d'éclairs, notamment celles d'un maximum très aigu dans l'Est de la République Démocratique du Congo. Ainsi, la localisation, les dimensions, la forme, la persistance saisonnière et l'environnement de ce maximum ont été précisés. La distribution zonale des éclairs montre une forte proportion dans la bande tropicale sud, liée au maximum principal mais aussi à une forte activité étalée longitudinalement et constituant un large maximum secondaire où l'activité orageuse est plus variable spatialement d'une année à l'autre, temporellement d'une saison à l'autre, et où le cycle diurne est moins marqué.[...] / This thesis is devoted to an analysis of the lightning activity of storm systems in Equatorial Africa (10°E-35°E; 15°S-10°N) over the period 2005-2013. Firstly, data from the World Wide Lightning Location Network (WWLLN) were compared with those from the Lightning Imaging Sensor (LIS) to estimate the relative detection efficiency of the WWLLN. Then, they established a high-resolution regional climatology of lightning activity. Finally, they were combined with data on cloud and meteorological characteristics to carry out thunderstorm case studies in different situations in order to examine the correlations between lightning activity, storm activity, cloud characteristics and meteorological conditions. The appropriate method introduced for estimating the WWLLN detection efficiency in the study area provides values ??consistent with those found in other regions of the world. Its spatial and temporal variability helps to interpret changes affecting several parameters of lightning activity. The climatology realized reveals original characteristics of the temporal evolution and the spatial distribution of the lightning activity, in particular those of a very sharp maximum in the Eastern Democratic Republic of Congo. Thus, the location, the dimensions, the shape, the seasonal persistence and the environment of this maximum have been specified. The zonal distribution of lightning shows a high proportion in the southern tropical band, linked to the principal maximum but also to a high activity spread out longitudinally and constituting a large secondary maximum where the storm activity is more spatially variable from one year to another, temporally from one season to another, and where the diurnal cycle is less marked. [...]

Les cellules détectent et intègrent une multitude de signaux d'instruction provenant de leur microenvironnement via un ensemble de récepteurs transmembranaires. Ces informations sont ensuite collectées au niveau des nœuds de signalisation intracellulaires pour être ensuite dispersées en cascades de signalisation afin de déterminer la destinée cellulaire. La manière dont un nœud de signalisation peut interpréter plusieurs stimuli et transmettre de manière spatio-temporelle les informations appropriées restent incomprises. Le proto-oncogène c-Src est une tyrosine kinase pléiotrope, un nœud signalisation essentiel au pilotage de nombreux processus cellulaires, tels que la migration, l'invasion, la dégradation et la division cellulaire. Nous avons développé une approche synthétique pour explorer la relation entre la structure de la SRC et la multiplicité des processus cellulaires qu’elle régule. Notre approche a abouti au découplage des différents modules composant la protéine SRC afin de comprendre l’impact de chacun d’eux sur son activité dans l’espace et dans le temps. Notre approche pour contrôler plusieurs états de la conformation SRC était la conception d’un OptoSrc capable à la fois de former des oligomères et d’être recruté à la membrane plasmique. Pour ce faire, nous avons modifié la structure de la SRC afin qu'elle soit potentiellement active dans le noir et nous l'avons fusionnée avec le CRY2 sensible à la lumière. La stimulation lumineuse induit l'hétérodimérisation CRY2 avec un CIBN ancré à la membrane plasmique et son homo-oligomérisation et déclenche une relocalisation de l’OptoSrc à la membrane plasmique ou son oligomérisation. Ce double système a permis de générer deux types de mobilitéz différentes au sein des adhérences focales à deux destins différents, la formation de lamellipodes dans un cas et la formation d’invadosomes dans l’autre. / Cells sense and integrate a multitude of instructional signals from their microenvironment through a diverse set of transmembrane receptors. This information is then collected at intracellular signaling nodes to later disperse down signaling cascades to drive cell fate. How one signaling node can interpret multiple stimuli and spatio-temporally transmit the appropriate information remains poorly understood. The proto-oncogene c-Src is a pleiotropic tyrosine kinase, is one such node essential for driving many cellular processes, such as migration, invasion, degradation, and cell division. We developed a synthetic approach to explore the relationship between SRC structure and the multiplicity of cellular processes it regulates. Our approach resulted in the decoupling of the different modules composing SRC protein to understand how each of them impacts its activity in space and time. Our approach to control multiple state of SRC conformation was the design of an OptoSrc both capable of forming oligomers and to be recruited at the plasma membrane. To do so, we modified SRC structure to be potentially active in the dark and fused it with light sensitive CRY2. Light stimulation induces CRY2 hetero-dimerization with a CIBN anchored at the plasma membrane and its homo-oligomerisation triggering relocalization of OptoSrc at the plasma and/or its oligomerization. This system generated two different type of mobility of OptoSrc inside focal adhesion inducing two different adhesion fates, the formation of invadosome in one case and the formation of lamellipodia on the other.

The analysis of emotions in humans is a field that has been studied for centuries. Through the last decade, multiple approaches towards automatic emotion recognition have been developed to tackle the task of making this analysis autonomous. More specifically, facial expressions in the form of Action Units have been considered until now the most efficient way to recognize emotions. In recent years, applying machine learning for this task has shown outstanding improvements in the accuracy of the solutions. Through this technique, the features can now be automatically learned from the training data, instead of relying on expert domain knowledge and hand-crafted rules. In this thesis, I present Syna and DeepSyna, two models capable of classifying emotional expressions by using both spatial and temporal features. The experimental results demonstrate the effectiveness of Syna in constrained environments, while there is still room for improvement in both constrained and in-the-wild settings. DeepSyna, while addressing this problem, on the other hand suffers from data scarcity and irrelevant transfer learning, which can be solved by future work. / Mänsklig känsloigenkänning har studerats i århundraden. Det senaste årtiondet har mängder av tillvägagångssätt för automatiska processer studerats, för att möjliggöra autonomi; mer specifikt så har ansiktsuttryck i form av Action Units ansetts vara mest effektiva. Maskininlärning har dock nyligen visat att enorma framsteg är möjliga vad gäller bra lösningar på problemen. Så kallade features kan nu automatiskt läras in från träningsdata, även utan expertkunskap och heuristik. Jag presenterar här Syna och DeepSyna, två modeller för ändamålet som använder både spatiala och temporala features. Experiment demonstrerar Synas effektivitet i vissa begränsade omgivningar, medan mycket lämnas att önska vad gäller generella sådana. DeepSyna löser detta men lider samtidigt av databristproblem och onödig så kallad transfer learning, vilket här lämnas till framtida arbete.

The transmission of many infectious diseases is affected by climate variations, particularly for diseases spread by arthropod vectors such as malaria and dengue. Previous epidemiological studies have demonstrated statistically significant associations between infectious disease incidence and climate variations. Such research has highlighted the potential for developing climate-based epidemic early warning systems. To establish how much variation in disease risk can be attributed to climatic conditions, non-climatic confounding factors should also be considered in the model parameterisation to avoid reporting misleading climate-disease associations. This issue is sometimes overlooked in climate related disease studies. Due to the lack of spatial resolution and/or the capability to predict future disease risk (e.g. several months ahead), some previous models are of limited value for public health decision making. This thesis proposes a framework to model spatio-temporal variation in disease risk using both climate and non-climate information. The framework is developed in the context of dengue fever in Brazil. Dengue is currently one of the most important emerging tropical diseases and dengue epidemics impact heavily on Brazilian public health services. A negative binomial generalised linear mixed model (GLMM) is adopted which makes allowances for unobserved confounding factors by including spatially structured and unstructured random effects. The model successfully accounts for the large amount of overdispersion found in disease counts. The parameters in this spatio-temporal Bayesian hierarchical model are estimated using Markov Chain Monte Carlo (MCMC). This allows posterior predictive distributions for disease risk to be derived for each spatial location and time period (month/season). Given decision and epidemic thresholds, probabilistic forecasts can be issued, which are useful for developing epidemic early warning systems. The potential to provide useful early warnings of future increased and geographically specific dengue risk is investigated. The predictive validity of the model is evaluated by fitting the GLMM to data from 2001-2007 and comparing probabilistic predictions to the most recent out-of-sample data in 2008-2009. For a probability decision threshold of 30% and the pre-defined epidemic threshold of 300 cases per 100,000 inhabitants, successful epidemic alerts would have been issued for 94% of the 54 microregions that experienced high dengue incidence rates in South East Brazil, during February - April 2008.

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

In a world of ever increasing amounts of video data, we are forced to abandon traditional methods of scene interpretation by fully manual means. Under such circumstances, some form of automation is highly desirable but this can be a very open ended issue with high complexity. Dealing with such large amounts of data is a non-trivial task that requires efficient selective extraction of parts of a scene which have the potential to develop a higher semantic meaning, alone, or in combination with others. In particular, the types of video data that are in need of automated analysis tend to be outdoor scenes with high levels of activity generated from either foreground or background. Such dynamic scenes add considerable complexity to the problem since we cannot rely on motion energy alone to detect regions of interest. Furthermore, the behaviour of these regions of motion can differ greatly, while still being highly dependent, both spatially and temporally on the movement of other objects within the scene. Modelling these dependencies, whilst eliminating as much redundancy from the feature extraction process as possible are the challenges addressed by this thesis. In the first half, finding the right mechanism to extract and represent meaningful features from dynamic scenes with no prior knowledge is investigated. Meaningful or salient information is treated as the parts of a scene that stand out or seem unusual or interesting to us. The novelty of the work is that it is able to select salient scales in both space and time in which a particular spatio-temporal volume is considered interesting relative to the rest of the scene. By quantifying the temporal saliency values of regions of motion, it is possible to consider their importance in terms of both the long and short-term. Variations in entropy over spatio-temporal scales are used to select a context dependent measure of the local scene dynamics. A method of quantifying temporal saliency is devised based on the variation of the entropy of the intensity distribution in a spatio-temporal volume over incraeasing scales. Entropy is used over traditional filter methods since the stability or predictability of the intensity distribution over scales of a local spatio-temporal region can be defined more robustly relative to the context of its neighbourhood, even for regions exhibiting high intensity variation due to being extremely textured. Results show that it is possible to extract both locally salient features as well as globally salient temporal features from contrasting scenerios. In the second part of the thesis, focus will shift towards binding these spatio-temporally salient features together so that some semantic meaning can be inferred from their interaction. Interaction in this sense, refers to any form of temporally correlated behaviour between any salient regions of motion in a scene. Feature binding as a mechanism for interactive behaviour understanding is particularly important if we consider that regions of interest may not be treated as particularly significant individually, but represent much more semantically when considered in combination. Temporally correlated behaviour is identified and classified using accumulated co-occurrences of salient features at two levels. Firstly, co-occurrences are accumulated for spatio-temporally proximate salient features to form a local representation. Then, at the next level, the co-occurrence of these locally spatio-temporally bound features are accumulated again in order to discover unusual behaviour in the scene. The novelty of this work is that there are no assumptions made about whether interacting regions should be spatially proximate. Furthermore, no prior knowledge of the scene topology is used. Results show that it is possible to detect unusual interactions between regions of motion, which can visually infer higher levels of semantics. In the final part of the thesis, a more specific investigation of human behaviour is addressed through classification and detection of interactions between 2 human subjects. Here, further modifications are made to the feature extraction process in order to quantify the spatiotemporal saliency of a region of motion. These features are then grouped to find the people in the scene. Then, a loose pose distribution model is extracted for each person for finding salient correlations between poses of two interacting people using canonical correlation analysis. These canonical factors can be formed into trajectories and used for classification. Levenshtein distance is then used to categorise the features. The novelty of the work is that the interactions do not have to be spatially connected or proximate for them to be recognised. Furthermore, the data used is outdoors and cluttered with non-stationary background. Results show that co-occurrence techniques have the potential to provide a more generalised, compact, and meaningful representation of dynamic interactive scene behaviour.

Mining spatio-temporal reachable regions aims to find a set of road segments from massive trajectory data, that are reachable from a user-specified location and within a given temporal period. Accurately extracting such spatio-temporal reachable area is vital in many urban applications, e.g., (i) location-based recommendation, (ii) location-based advertising, and (iii) business coverage analysis. The traditional approach of answering such queries essentially performs a distance-based range query over the given road network, which have two main drawbacks: (i) it only works with the physical travel distances, where the users usually care more about dynamic traveling time, and (ii) it gives the same result regardless of the querying time, where the reachable area could vary significantly with different traffic conditions. Motivated by these observations, in this thesis, we propose a data- driven approach to formulate the problem as mining actual reachable region based on real historical trajectory dataset. The main challenge in our approach is the system efficiency, as verifying the reachability over the massive trajectories involves huge amount of disk I/Os. In this thesis, we develop two indexing structures: 1) spatio-temporal index (ST-Index) and 2) connection index (Con-Index) to reduce redundant trajectory data access operations. We also propose a novel query processing algorithm with: 1) maximum bounding region search, which directly extracts a small searching region from the index structure and 2) trace back search, which refines the search results from the previous step to find the final query result. Moreover, our system can also efficiently answer the spatio-temporal reachability query with multiple query locations by skipping the overlapped area search. We evaluate our system extensively using a large-scale real taxi trajectory data in Shenzhen, China, where results demonstrate that the proposed algorithms can reduce 50%-90% running time over baseline algorithms.