<div>When a user types in a search query in an Information Retrieval system, a list of top ‘n’ ranked documents relevant to the query are returned by the system. Relevant means not just returning documents that belong to the same category as that of the search query, but also returning documents that provide a concise answer to the search query. Determining the relevance of the documents is a significant challenge as the classic indexing techniques that use term/word frequencies do not consider the term (word) dependencies or the impact of previous terms on the current words or the meaning of the words in the document. There is a need to model the dependencies of the terms in the text data and learn the underlying statistical patterns to find the similarity between the user query and the documents to determine the relevancy.</div><div><br></div><div>This research proposes a solution based on Markov Random Fields (MRF) and Restricted Boltzmann Machines (RBM) to solve the problem of term dependencies and learn the underlying patterns to return documents that are very similar to the user query.</div>

Up-to-date methods of numerical modelling of random fields were applied to investigate some features of solute transport in saturated porous media with stochastic hydraulic conductivity. The paper describes numerical experiments which were performed and presents the first results.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18561号 / 工博第3922号 / 新制||工||1603(附属図書館) / 31461 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 田村 正行, 准教授 須﨑 純一, 准教授 田中 賢治 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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Discrete exponential families have drawn a lot of attention in probability, statistics, and machine learning, both classically and in the recent literature. This thesis studies in depth two discrete exponential families of concrete interest, (i) Exponential Random Graph Models (ERGMs) and (ii) Ising Models. In the ERGM setting, this thesis consider a “degree corrected” version of standard ERGMs, and in the Ising model setting, this thesis focus on Ising models on dense regular graphs, both from the point of view of statistical inference. The first part of the thesis studies the problem of testing for sparse signals present on the vertices of ERGMs. It proposes computably efficient tests for a wide class of ERGMs. Focusing on the two star ERGM, it shows that the tests studied are “asymptotically efficient” in all parameter regimes except one, which is referred to as “critical point”. In the critical regime, it is shown that improved detection is possible. This shows that compared to the standard belief, in this setting dependence is actually beneficial to the inference problem. The main proof idea for analyzing the two star ERGM is a correlations estimate between degrees under local alternatives, which is possibly of independent interest. In the second part of the thesis, we derive the limit of experiments for a class of one parameter Ising models on dense regular graphs. In particular, we show that the limiting experiment is Gaussian in the “low temperature” regime, non Gaussian in the “critical” regime, and an infinite collection of Gaussians in the “high temperature” regime. We also derive the limiting distributions of commonlt studied estimators, and study limiting power for tests of hypothesis against contiguous alternatives (whose scaling changes across the regimes). To the best of our knowledge, this is the first attempt at establishing the classical limits of experiments for Ising models (and more generally, Markov random fields).

Microstructure reconstruction is a necessary tool for use in multi-scale modeling, as it allows for the analysis of the microstructure of a material without the cost of measuring all of the required data for the analysis. For microstructure reconstruction to be effective, the synthetic microstructure needs to predict what a small sample of measured data would look like on a larger domain. The Markov Random Field (MRF) algorithm is a method of generating statistically similar microstructures for this process. In this work, two key factors of the MRF algorithm are analyzed. The first factor explored is how the base features of the microstructure related to orientation and grain/phase topology information influence the selection of the MRF parameters to perform the reconstruction. The second focus is on the analysis of the numerical uncertainty (epistemic uncertainty) that arises from the use of the MRF algorithm. This is done by first removing the material uncertainty (aleatoric uncertainty), which is the noise that is inherent in the original image representing the experimental data. The epistemic uncertainty that arises from the MRF algorithm is analyzed through the study of the percentage of isolated pixels and the difference in average grain sizes between the initial image and the reconstructed image. This research mainly focuses on two different microstructures, B4C-TiB2 and Ti-7Al, which are a ceramic composite and a metallic alloy, respectively. Both of them are candidate materials for many aerospace systems owing to their desirable mechanical performance under large thermo-mechanical stresses. / Master of Science / Microstructure reconstruction is a necessary tool for use in multi-scale modeling, as it allows for the analysis of the microstructure of a material without the cost of measuring all of the required data for the analysis. For microstructure reconstruction to be effective, the synthetic microstructure needs to predict what a small sample of measured data would look like on a larger domain. The Markov Random Field (MRF) algorithm is a method of generating statistically similar microstructures for this process. In this work, two key factors of the MRF algorithm are analyzed. The first factor explored is how the base features of the microstructures related to orientation and grain/phase topology information influence the selection of the MRF parameters to perform the reconstruction. The second focus is on the analysis of the numerical uncertainty that arises from the use of the MRF algorithm. This is done by first removing the material uncertainty, which is the noise that is inherent in the original image representing the experimental data. This research mainly focuses on two different microstructures, B4C-TiB2 and Ti-7Al, which are a ceramic composite and a metallic alloy, respectively. Both of them are candidate materials for many aerospace systems owing to their desirable mechanical performance under large thermo-mechanical stresses.

Ce travail présente une étude linguistique des expressions d'opinions issues de différentes sources dans des textes en espagnol. Le travail comprend la définition d'un modèle pour les prédicats d'opinion et leurs arguments (la source, le sujet et le message), la création d'un lexique de prédicats d'opinions auxquels sont associées des informations provenant du modèle et la réalisation de trois systèmes informatiques.Le premier système, basé sur des règles contextuelles, obtient de bons résultats pour le score de F-mesure partielle: prédicat, 92%; source, 81%; sujet, 75%; message, 89%, opinion, 85%. En outre, l'identification de la source donne une valeur de 79% de F-mesure exacte. Le deuxième système, basé sur le modèle Conditional Random Fields (CRF), a été développé uniquement pour l'identification des sources, donnant une valeur de 76% de F-mesure exacte. Le troisième système, qui combine les deux techniques (règles et CRF), donne une valeur de 83% de F-mesure exacte, montrant ainsi que la combinaison permet d'obtenir des résultats intéressants.En ce qui concerne l'identification des sources, notre système, comparé à des travaux réalisés sur des corpus d'autres langues que l'espagnol, donne des résultats très satisfaisants. En effet ces différents travaux obtiennent des scores qui se situent entre 63% et 89,5%.Par ailleurs, en sus des systèmes réalisés pour l'identification de l'opinion, notre travail a débouché sur la construction de plusieurs ressources pour l'espagnol : un lexique de prédicats d'opinions, un corpus de 13000 mots avec des annotations sur les opinions et un corpus de 40000 mots avec des annotations sur les prédicats d'opinion et les sources. / This work presents a study of linguistic expressions of opinion from different sources in Spanish texts. The work includes the definition of a model for opinion predicates and their arguments (source, topic and message), the creation of a lexicon of opinion predicates which have information from the model associated, and the implementation of three systems.The first system, based on contextual rules, gets good results for the F-measure score (partial match): predicate, 92%; source, 81%; topic, 75%; message, 89%; full opinion, 85%. In addition, for source identification the F-measure for exact match is 79%. The second system, based on Conditional Random Fields (CRF), was developed only for the identification of sources, giving 76% of F-measure (exact match). The third system, which combines the two techniques (rules and CRF), gives a value of 83% of F-measure (exact match), showing that the combination yields interesting results.As regards the identification of sources, our system compared to other work developed for languages ​other than Spanish, gives very satisfactory results. Indeed these works had scores that fall between 63% and 89.5%.Moreover, in addition to the systems made for the identification of opinions, our work has led to the construction of several resources for Spanish: a lexicon of opinion predicates, a 13,000 words corpus with opinions annotated and a 40,000 words corpus with opinion predicates end sources annotated.