La présente étude est une tentative d'approche multidisciplinaire visant à une meilleure compréhension des structures profondes des Alpes Occidentales. Elle est basée principalement sur des données de sismique-réflexion profonde, mais elle accorde une égale importance à des données provenant d'autres disciplines géophysiques (sismique réfraction, gravimétrie, tomographie, etc.) et géologiques (tectonique, stratigraphie, métamorphisme, géochronologie, etc.). Un des fils conducteur de ce travail est la géodynamique: toute interprétation des structures profondes actuelles des Alpes Occidentales doit pouvoir s'expliquer dans un contexte d'évolution géodynamique de cette chaîne de montagne compatible avec les observations de la géologie de surface et avec des modèles géodynamiques actualistes. A cet effet l'approche utilisée part du connu (la géologie de surface) pour descendre progressivement dans le monde moins connu des structures profondes à l'échelle crustale d'abord et lithosphérique ensuite. L'interprétation détaillée de profils sismiques dans le domaine interne des Alpes a permis de mettre en évidence l'importance des déformations ductiles et en particulier le rôle important des rétro-plissement dans la configuration actuelle du système de nappe. A l'échelle crustale et lithosphérique, l'interprétation de cinq traverses sismiques réparties dans les Alpes Occidentales a révélé de nombreuses similitudes, telles que l'importante subduction de la plaque continentale Européenne atteignant une profondeur de 150 km sous la plaine du PÔ. Ces interprétations ont aussi démontré des différences apparaissant progressivement le long de l'axe de la chaîne alpine, telles que la géométrie et la composition de l' indenteur Adriatique. Ces différences ont pu trouver une explication cohérente dans un scénario d'évolution géodynamique qui met en évidence l'héritage de structures liées à l'ouverture de l'océan Téthysien dans la formation des structures résultant de la collision continentale des plaques Européenne et Adriatique

Problems involving optimal sequential making in uncertain dynamic systems arise in domains such as engineering, science and economics. Such problems can often be cast in the framework of Markov Decision Process (MDP). Solving an MDP requires computing the optimal value function and the optimal policy. The idea of dynamic programming (DP) and the Bellman equation (BE) are at the heart of solution methods. The three important exact DP methods are value iteration, policy iteration and linear programming. The exact DP methods compute the optimal value function and the optimal policy. However, the exact DP methods are inadequate in practice because the state space is often large and in practice, one might have to resort to approximate methods that compute sub-optimal policies. Further, in certain cases, the system observations are known only in the form of noisy samples and we need to design algorithms that learn from these samples. In this thesis we study interesting theoretical questions pertaining to approximate and learning algorithms, and also present an interesting application of MDPs in the domain of crowd sourcing. Approximate Dynamic Programming (ADP) methods handle the issue of large state space by computing an approximate value function and/or a sub-optimal policy. In this thesis, we are concerned with conditions that result in provably good policies. Motivated by the limitations of the PBE in the conventional linear algebra, we study the PBE in the (min, +) linear algebra. It is a well known fact that deterministic optimal control problems with cost/reward criterion are (min, +)/(max, +) linear and ADP methods have been developed for such systems in literature. However, it is straightforward to show that infinite horizon discounted reward/cost MDPs are neither (min, +) nor (max, +) linear. We develop novel ADP schemes namely the Approximate Q Iteration (AQI) and Variational Approximate Q Iteration (VAQI), where the approximate solution is a (min, +) linear combination of a set of basis functions whose span constitutes a subsemimodule. We show that the new ADP methods are convergent and we present a bound on the performance of the sub-optimal policy. The Approximate Linear Program (ALP) makes use of linear function approximation (LFA) and offers theoretical performance guarantees. Nevertheless, the ALP is difficult to solve due to the presence of a large number of constraints and in practice, a reduced linear program (RLP) is solved instead. The RLP has a tractable number of constraints sampled from the original constraints of the ALP. Though the RLP is known to perform well in experiments, theoretical guarantees are available only for a specific RLP obtained under idealized assumptions. In this thesis, we generalize the RLP to define a generalized reduced linear program (GRLP) which has a tractable number of constraints that are obtained as positive linear combinations of the original constraints of the ALP. The main contribution here is the novel theoretical framework developed to obtain error bounds for any given GRLP. Reinforcement Learning (RL) algorithms can be viewed as sample trajectory based solution methods for solving MDPs. Typically, RL algorithms that make use of stochastic approximation (SA) are iterative schemes taking small steps towards the desired value at each iteration. Actor-Critic algorithms form an important sub-class of RL algorithms, wherein, the critic is responsible for policy evaluation and the actor is responsible for policy improvement. The actor and critic iterations have deferent step-size schedules, in particular, the step-sizes used by the actor updates have to be generally much smaller than those used by the critic updates. Such SA schemes that use deferent step-size schedules for deferent sets of iterates are known as multitimescale stochastic approximation schemes. One of the most important conditions required to ensure the convergence of the iterates of a multi-timescale SA scheme is that the iterates need to be stable, i.e., they should be uniformly bounded almost surely. However, the conditions that imply the stability of the iterates in a multi-timescale SA scheme have not been well established. In this thesis, we provide veritable conditions that imply stability of two timescale stochastic approximation schemes. As an example, we also demonstrate that the stability of a widely used actor-critic RL algorithm follows from our analysis. Crowd sourcing (crowd) is a new mode of organizing work in multiple groups of smaller chunks of tasks and outsourcing them to a distributed and large group of people in the form of an open call. Recently, crowd sourcing has become a major pool for human intelligence tasks (HITs) such as image labeling, form digitization, natural language processing, machine translation evaluation and user surveys. Large organizations/requesters are increasingly interested in crowd sourcing the HITs generated out of their internal requirements. Task starvation leads to huge variation in the completion times of the tasks posted on to the crowd. This is an issue for frequent requesters desiring predictability in the completion times of tasks specified in terms of percentage of tasks completed within a stipulated amount of time. An important task attribute that affects the completion time of a task is its price. However, a pricing policy that does not take the dynamics of the crowd into account might fail to achieve the desired predictability in completion times. Here, we make use of the MDP framework to compute a pricing policy that achieves predictable completion times in simulations as well as real world experiments.

Sheep breeding is nowadays a developing branch of agriculture again. There is a lot of different breed and they react distinctly on the same conditions of the enviroment. Sheep of breeds charollais, suffolk, šumavská ovce and valaška bred in similar conditions were observed in spring and autumn of years 2007 and 2008. In these seasons were taking blood samples (from {$\pm$}7{--}24) ewes and lambs and were analyzed in hematology laboratory. There were determined haemoglobin level, haematocrit indicator, erytrocytes and leucocytes levels, glucose, cholesterol, triglycerides levels, urea and plasmatic proteins, activity of ALP and GMT enzymes, phosphor, calcium, magnesium, zinc and copper levels. The main objective of this project was determine seasonal changes in observed parametres. Next objectives were determine different changes in blood parametres in different breeds and different aimes of breeds. There were recognized that all the observed breeds don`t react the same way on similar conditions. There were not provably determined seasonal changes in observed parametres, but average Hb level was higher in autumn than in spring. Urea level was conversely higher in spring season than in autumn. The demostrable fact is, that the similar conditions induce different answers not only in different breeds but also in different aimes of breeds.