Unwrapping microcomputed tomographic images for measuring cortical osteolytic lesions in the 5T2 murine model of myeloma treated by bisphosphonate

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Porosity imaged by a vector projection algorithm correlates with fractal dimension measured on 3D models obtained by microCT

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An Information Theoretic Approach to Automatic Query Expansion

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SEQUENTIAL DECISIONS AND PREDICTIONS IN NATURAL LANGUAGE PROCESSING

Natural language processing has achieved great success in a wide range of ap- plications, producing both commercial language services and open-source language tools. However, most methods take a static or batch approach, assuming that the model has all information it needs and makes a one-time prediction. In this disser- tation, we study dynamic problems where the input comes in a sequence instead of all at once, and the output must be produced while the input is arriving. In these problems, predictions are often made based only on partial information. We see this dynamic setting in many real-time, interactive applications. These problems usually involve a trade-off between the amount of input received (cost) and the quality of the output prediction (accuracy). Therefore, the evaluation considers both objectives (e.g., plotting a Pareto curve). Our goal is to develop a formal understanding of sequential prediction and decision-making problems in natural language processing and to propose efficient solutions. Toward this end, we present meta-algorithms that take an existent batch model and produce a dynamic model to handle sequential inputs and outputs. Webuild our framework upon theories of Markov Decision Process (MDP), which allows learning to trade off competing objectives in a principled way. The main machine learning techniques we use are from imitation learning and reinforcement learning, and we advance current techniques to tackle problems arising in our settings. We evaluate our algorithm on a variety of applications, including dependency parsing, machine translation, and question answering. We show that our approach achieves a better cost-accuracy trade-off than the batch approach and heuristic-based decision- making approaches. We first propose a general framework for cost-sensitive prediction, where dif- ferent parts of the input come at different costs. We formulate a decision-making process that selects pieces of the input sequentially, and the selection is adaptive to each instance. Our approach is evaluated on both standard classification tasks and a structured prediction task (dependency parsing). We show that it achieves similar prediction quality to methods that use all input, while inducing a much smaller cost. Next, we extend the framework to problems where the input is revealed incremen- tally in a fixed order. We study two applications: simultaneous machine translation and quiz bowl (incremental text classification). We discuss challenges in this set- ting and show that adding domain knowledge eases the decision-making problem. A central theme throughout the chapters is an MDP formulation of a challenging problem with sequential input/output and trade-off decisions, accompanied by a learning algorithm that solves the MDP.

Elevated hepatocyte growth factor levels in osteoarthritis osteoblasts contribute to their altered response to bone morphogenetic protein-2 and reduced mineralization capacity

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Bone Mass and Bone Quality Are Altered by Hypoactivity in the Chicken

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Glucose-dependent insulinotropic polypeptide (GIP) directly affects collagen fibril diameter and collagen cross-linking in osteoblast cultures.

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Use of GLP-1 mimetic in type 2 diabetes mellitus: is it the end of fragility fractures?

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Double incretin receptor knock-out (DIRKO) mice present with alterations of trabecular and cortical micromorphology and bone strength.

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Neurophysiologie du langage : apports de la neuro-imagerie et état des connaissances

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Glucose-dependent insulinotropic polypeptide (GIP) dose-dependently reduces osteoclast differentiation and resorption

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High fat-fed diabetic mice present with profound alterations of the osteocyte network

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A comparative study of navigation meshes

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Effects of aluminum on cells and tissues

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La Politique au fil de l’âge

Révolutionnaire à 20 ans, conservateur à 60 ans : quelle part de vérité détient cet adage ? Le vieillissement modifie-t-il les comportements politiques ? Que pèsent la génération ou la classe d'âge auxquelles on appartient ? Cet ouvrage veut éclairer les liens entre âge, génération, cycle de vie et politique. Il décortique, au travers de la philosophie, de l'histoire, de la science politique, de la sociologie ou encore de la psychologie, les multiples paramètres historiques, culturels et sociaux qui sous-tendent les effets de l'âge. La politisation des individus au fil des ans est analysée sous ses différents aspects : construction des choix idéologiques et politiques, rapport au vote et à l'abstention, engagement partisan, participation à des mobilisations collectives... Il apparaît que les incidences politiques de l'âge biologique sont ténues. L'âge social et biographique ou encore l'appartenance générationnelle sont au contraire décisifs. Une réflexion sur les usages sociaux et les recompositions de l'âge dans les démocraties contemporaines, avec un regard sur d'autres pays d'Europe et sur le monde arabe. (Résumé éditeur)