Using Constraint Satisfaction Techniques and Variational Methods for Probabilistic Reasoning

RÉSUMÉ Cette thèse présente un certain nombre de contributions à la recherche pour la création de systèmes efficaces de raisonnement probabiliste sur les modèles graphiques de problèmes issus d'une variété d'applications scientifiques et d'ingénierie. Ce thème touche plusieurs sous-disciplines de l'intelligence artificielle. Généralement, la plupart de ces problèmes ont des modèles graphiques expressifs qui se traduisent par de grands réseaux impliquant déterminisme et des cycles, ce qui représente souvent un goulot d'étranglement pour tout système d'inférence probabiliste et affaiblit son exactitude ainsi que son évolutivité. Conceptuellement, notre recherche confirme les hypothèses suivantes. D'abord, les techniques de satisfaction de contraintes et méthodes variationnelles peuvent être exploitées pour obtenir des algorithmes précis et évolutifs pour l'inférence probabiliste en présence de cycles et de déterminisme. Deuxièmement, certaines parties intrinsèques de la structure du modèle graphique peuvent se révéler bénéfiques pour l'inférence probabiliste sur les grands modèles graphiques, au lieu de poser un défi important pour elle. Troisièmement, le re-paramétrage du modèle graphique permet d'ajouter à sa structure des caractéristiques puissantes qu'on peut utiliser pour améliorer l'inférence probabiliste. La première contribution majeure de cette thèse est la formulation d'une nouvelle approche de passage de messages (message-passing) pour inférer dans un graphe de facteurs étendu qui combine des techniques de satisfaction de contraintes et des méthodes variationnelles. Contrairement au message-passing standard, il formule sa structure sous forme d'étapes de maximisation de l'espérance variationnelle. Ainsi, on a de nouvelles règles de mise à jour des marginaux qui augmentent une borne inférieure à chaque mise à jour de manière à éviter le dépassement d'un point fixe. De plus, lors de l'étape d'espérance, nous mettons à profit les structures locales dans le graphe de facteurs en utilisant la cohérence d'arc généralisée pour effectuer une approximation de champ moyen variationnel. La deuxième contribution majeure est la formulation d'une stratégie en deux étapes qui utilise le déterminisme présent dans la structure du modèle graphique pour améliorer l'évolutivité du problème d'inférence probabiliste. Dans cette stratégie, nous prenons en compte le fait que si le modèle sous-jacent implique des contraintes inviolables en plus des préférences, alors c'est potentiellement un gaspillage d'allouer de la mémoire pour toutes les contraintes à l'avance lors de l'exécution de l'inférence. Pour éviter cela, nous commençons par la relaxation des préférences et effectuons l'inférence uniquement avec les contraintes inviolables. Cela permet d'éviter les calculs inutiles impliquant les préférences et de réduire la taille effective du réseau graphique. Enfin, nous développons une nouvelle famille d'algorithmes d'inférence par le passage de messages dans un graphe de facteurs étendus, paramétrées par un facteur de lissage (smoothing parameter). Cette famille permet d'identifier les épines dorsales (backbones) d'une grappe qui contient des solutions potentiellement optimales. Ces épines dorsales ne sont pas seulement des parties des solutions optimales, mais elles peuvent également être exploitées pour intensifier l'inférence MAP en les fixant de manière itérative afin de réduire les parties complexes jusqu'à ce que le réseau se réduise à un seul qui peut être résolu avec précision en utilisant une méthode MAP d'inférence classique. Nous décrivons ensuite des variantes paresseuses de cette famille d'algorithmes. Expérimentalement, une évaluation empirique approfondie utilisant des applications du monde réel démontre la précision, la convergence et l'évolutivité de l'ensemble de nos algorithmes et stratégies par rapport aux algorithmes d'inférence existants de l'état de l'art.----------ABSTRACT This thesis presents a number of research contributions pertaining to the theme of creating efficient probabilistic reasoning systems based on graphical models of real-world problems from relational domains. These models arise in a variety of scientific and engineering applications. Thus, the theme impacts several sub-disciplines of Artificial Intelligence. Commonly, most of these problems have expressive graphical models that translate into large probabilistic networks involving determinism and cycles. Such graphical models frequently represent a bottleneck for any probabilistic inference system and weaken its accuracy and scalability. Conceptually, our research here hypothesizes and confirms that: First, constraint satisfaction techniques and variational methods can be exploited to yield accurate and scalable algorithms for probabilistic inference in the presence of cycles and determinism. Second, some intrinsic parts of the structure of the graphical model can turn out to be beneficial to probabilistic inference on large networks, instead of posing a significant challenge to it. Third, the proper re-parameterization of the graphical model can provide its structure with characteristics that we can use to improve probabilistic inference. The first major contribution of this thesis is the formulation of a novel message-passing approach to inference in an extended factor graph that combines constraint satisfaction techniques with variational methods. In contrast to standard message-passing, it formulates the Message-Passing structure as steps of variational expectation maximization. Thus it has new marginal update rules that increase a lower bound at each marginal update in a way that avoids overshooting a fixed point. Moreover, in its expectation step, we leverage the local structures in the factor graph by using generalized arc consistency to perform a variational mean-field approximation. The second major contribution is the formulation of a novel two-stage strategy that uses the determinism present in the graphical model's structure to improve the scalability of probabilistic inference. In this strategy, we take into account the fact that if the underlying model involves mandatory constraints as well as preferences then it is potentially wasteful to allocate memory for all constraints in advance when performing inference. To avoid this, we start by relaxing preferences and performing inference with hard constraints only. This helps avoid irrelevant computations involving preferences, and reduces the effective size of the graphical network. Finally, we develop a novel family of message-passing algorithms for inference in an extended factor graph, parameterized by a smoothing parameter. This family allows one to find the ”backbones” of a cluster that involves potentially optimal solutions. The cluster's backbones are not only portions of the optimal solutions, but they also can be exploited for scaling MAP inference by iteratively fixing them to reduce the complex parts until the network is simplified into one that can be solved accurately using any conventional MAP inference method. We then describe lazy variants of this family of algorithms. One limiting case of our approach corresponds to lazy survey propagation, which in itself is novel method which can yield state of the art performance. We provide a thorough empirical evaluation using real-world applications. Our experiments demonstrate improvements to the accuracy, convergence and scalability of all our proposed algorithms and strategies over existing state-of-the-art inference algorithms

Factor Graphs for Computer Vision and Image Processing

Factor graphs have been used extensively in the decoding of error correcting codes such as turbo codes, and in signal processing. However, while computer vision and pattern recognition are awash with graphical model usage, it is some-what surprising that factor graphs are still somewhat under-researched in these communities. This is surprising because factor graphs naturally generalise both Markov random fields and Bayesian networks. Moreover, they are useful in modelling relationships between variables that are not necessarily probabilistic and allow for efficient marginalisation via a sum-product of probabilities. In this thesis, we present and illustrate the utility of factor graphs in the vision community through some of the field’s popular problems. The thesis does so with a particular focus on maximum a posteriori (MAP) inference in graphical structures with layers. To this end, we are able to break-down complex problems into factored representations and more computationally realisable constructions. Firstly, we present a sum-product framework that uses the explicit factorisation in local subgraphs from the partitioned factor graph of a layered structure to perform inference. This provides an efficient method to perform inference since exact inference is attainable in the resulting local subtrees. Secondly, we extend this framework to the entire graphical structure without partitioning, and discuss preliminary ways to combine outputs from a multilevel construction. Lastly, we further our endeavour to combine evidence from different methods through a simplicial spanning tree reparameterisation of the factor graph in a way that ensures consistency, to produce an ensembled and improved result. Throughout the thesis, the underlying feature we make use of is to enforce adjacency constraints using Delaunay triangulations computed by adding points dynamically, or using a convex hull algorithm. The adjacency relationships from Delaunay triangulations aid the factor graph approaches in this thesis to be both efficient and competitive for computer vision tasks. This is because of the low treewidth they provide in local subgraphs, as well as the reparameterised interpretation of the graph they form through the spanning tree of simplexes. While exact inference is known to be intractable for junction trees obtained from the loopy graphs in computer vision, in this thesis we are able to effect exact inference on our spanning tree of simplexes. More importantly, the approaches presented here are not restricted to the computer vision and image processing fields, but are extendable to more general applications that involve distributed computations

Oceanographic surveys with autonomous underwater vehicles : performance metrics and survey design

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1998.Includes bibliographical references (p. 127-134).by Jeffrey Scott Willcox.M.S

Recalage déformable à base de graphes : mise en correspondance coupe-vers-volume et méthodes contextuelles

Image registration methods, which aim at aligning two or more images into one coordinate system, are among the oldest and most widely used algorithms in computer vision. Registration methods serve to establish correspondence relationships among images (captured at different times, from different sensors or from different viewpoints) which are not obvious for the human eye. A particular type of registration algorithm, known as graph-based deformable registration methods, has become popular during the last decade given its robustness, scalability, efficiency and theoretical simplicity. The range of problems to which it can be adapted is particularly broad. In this thesis, we propose several extensions to the graph-based deformable registration theory, by exploring new application scenarios and developing novel methodological contributions.Our first contribution is an extension of the graph-based deformable registration framework, dealing with the challenging slice-to-volume registration problem. Slice-to-volume registration aims at registering a 2D image within a 3D volume, i.e. we seek a mapping function which optimally maps a tomographic slice to the 3D coordinate space of a given volume. We introduce a scalable, modular and flexible formulation accommodating low-rank and high order terms, which simultaneously selects the plane and estimates the in-plane deformation through a single shot optimization approach. The proposed framework is instantiated into different variants based on different graph topology, label space definition and energy construction. Simulated and real-data in the context of ultrasound and magnetic resonance registration (where both framework instantiations as well as different optimization strategies are considered) demonstrate the potentials of our method.The other two contributions included in this thesis are related to how semantic information can be encompassed within the registration process (independently of the dimensionality of the images). Currently, most of the methods rely on a single metric function explaining the similarity between the source and target images. We argue that incorporating semantic information to guide the registration process will further improve the accuracy of the results, particularly in the presence of semantic labels making the registration a domain specific problem.We consider a first scenario where we are given a classifier inferring probability maps for different anatomical structures in the input images. Our method seeks to simultaneously register and segment a set of input images, incorporating this information within the energy formulation. The main idea is to use these estimated maps of semantic labels (provided by an arbitrary classifier) as a surrogate for unlabeled data, and combine them with population deformable registration to improve both alignment and segmentation.Our last contribution also aims at incorporating semantic information to the registration process, but in a different scenario. In this case, instead of supposing that we have pre-trained arbitrary classifiers at our disposal, we are given a set of accurate ground truth annotations for a variety of anatomical structures. We present a methodological contribution that aims at learning context specific matching criteria as an aggregation of standard similarity measures from the aforementioned annotated data, using an adapted version of the latent structured support vector machine (LSSVM) framework.Les méthodes de recalage d’images, qui ont pour but l’alignement de deux ou plusieurs images dans un même système de coordonnées, sont parmi les algorithmes les plus anciens et les plus utilisés en vision par ordinateur. Les méthodes de recalage servent à établir des correspondances entre des images (prises à des moments différents, par différents senseurs ou avec différentes perspectives), lesquelles ne sont pas évidentes pour l’œil humain. Un type particulier d’algorithme de recalage, connu comme « les méthodes de recalage déformables à l’aide de modèles graphiques » est devenu de plus en plus populaire ces dernières années, grâce à sa robustesse, sa scalabilité, son efficacité et sa simplicité théorique. La gamme des problèmes auxquels ce type d’algorithme peut être adapté est particulièrement vaste. Dans ce travail de thèse, nous proposons plusieurs extensions à la théorie de recalage déformable à l’aide de modèles graphiques, en explorant de nouvelles applications et en développant des contributions méthodologiques originales.Notre première contribution est une extension du cadre du recalage à l’aide de graphes, en abordant le problème très complexe du recalage d’une tranche avec un volume. Le recalage d’une tranche avec un volume est le recalage 2D dans un volume 3D, comme par exemple le mapping d’une tranche tomographique dans un système de coordonnées 3D d’un volume en particulier. Nos avons proposé une formulation scalable, modulaire et flexible pour accommoder des termes d'ordre élevé et de rang bas, qui peut sélectionner le plan et estimer la déformation dans le plan de manière simultanée par une seule approche d'optimisation. Le cadre proposé est instancié en différentes variantes, basés sur différentes topologies du graph, définitions de l'espace des étiquettes et constructions de l'énergie. Le potentiel de notre méthode a été démontré sur des données réelles ainsi que des données simulées dans le cadre d’une résonance magnétique d’ultrason (où le cadre d’installation et les stratégies d’optimisation ont été considérés).Les deux autres contributions inclues dans ce travail de thèse, sont liées au problème de l’intégration de l’information sémantique dans la procédure de recalage (indépendamment de la dimensionnalité des images). Actuellement, la plupart des méthodes comprennent une seule fonction métrique pour expliquer la similarité entre l’image source et l’image cible. Nous soutenons que l'intégration des informations sémantiques pour guider la procédure de recalage pourra encore améliorer la précision des résultats, en particulier en présence d'étiquettes sémantiques faisant du recalage un problème spécifique adapté à chaque domaine.Nous considérons un premier scénario en proposant un classificateur pour inférer des cartes de probabilité pour les différentes structures anatomiques dans les images d'entrée. Notre méthode vise à recaler et segmenter un ensemble d'images d'entrée simultanément, en intégrant cette information dans la formulation de l'énergie. L'idée principale est d'utiliser ces cartes estimées des étiquettes sémantiques (fournie par un classificateur arbitraire) comme un substitut pour les données non-étiquettées, et les combiner avec le recalage déformable pour améliorer l'alignement ainsi que la segmentation.Notre dernière contribution vise également à intégrer l'information sémantique pour la procédure de recalage, mais dans un scénario différent. Dans ce cas, au lieu de supposer que nous avons des classificateurs arbitraires pré-entraînés à notre disposition, nous considérons un ensemble d’annotations précis (vérité terrain) pour une variété de structures anatomiques. Nous présentons une contribution méthodologique qui vise à l'apprentissage des critères correspondants au contexte spécifique comme une agrégation des mesures de similarité standard à partir des données annotées, en utilisant une adaptation de l’algorithme « Latent Structured Support Vector Machine »

Frozen-State Hierarchical Annealing

There is significant interest in the synthesis of discrete-state random fields, particularly those possessing structure over a wide range of scales. However, given a model on some finest, pixellated scale, it is computationally very difficult to synthesize both large and small-scale structures, motivating research into hierarchical methods. This thesis proposes a frozen-state approach to hierarchical modelling, in which simulated annealing is performed on each scale, constrained by the state estimates at the parent scale. The approach leads significant advantages in both modelling flexibility and computational complexity. In particular, a complex structure can be realized with very simple, local, scale-dependent models, and by constraining the domain to be annealed at finer scales to only the uncertain portions of coarser scales, the approach leads to huge improvements in computational complexity. Results are shown for synthesis problems in porous media

Seismic Performance of High Strength Steel Building Frames

Tese de doutoramento em Engenharia Civil, no ramo de Construção Metálica e Mista, apresentada ao Departamento de Engenharia Civil da Faculdade de Ciências e Tecnologia da Universidade de CoimbraIn steel building frames under seismic action, the members designed to remain elastic during an earthquake are responsible for the robustness of the structure and prevention of collapse, being characterised by high strength demands. On the other hand, seismic resistant building frames designed as dissipative structures should allow the development of plastic deformations in specific members and locations. In the present work, the framing solution studied is the one obtained by combining two different steel grades: mild carbon steel (MCS) used in dissipative members and high strength steel (HSS) used in non-dissipative “elastic” members. The current seismic design rules, at least in Europe, do not cover the specific configuration of such ‘Dual-Steel’ structures. Therefore, a comprehensive parametric study devoted to investigate the seismic design and performance of EN1998-1 compliant dual-steel Moment-Resisting Frames (MRF), Concentrically Braced Frames (CBF) and Dual-Concentrically Braced Frames (D-CBF) is presented and discussed in this dissertation. The overall seismic performance has been analysed through static and dynamic nonlinear analyses against three limit states: damage limitation (DL), severe damage (SD) and near collapse (NC). The investigated parameters cover both, geometric and mechanical variables, as the type columns, span length, number of storeys and spectral shape. The comparison between dual-steel structures with those entirely made of MCS showed that: i) in order to fulfil the codified drift requirements and to limit the stability coefficients, the same shapes for members should be used for both structures for the MRFs, but there is a reduction in both, weight and cost for the CBFs and D-CBFs using HSS, which proves it is efficient in economic terms, ii) a similar performance can be recognized in both, dual steel and single grade steel structures; iii) In all examined structural typology, the behaviour factors obtained from incremental dynamic analyses for SD limit state were smaller than the used in the seismic design. These results suggest the need to calibrate the behaviour factors given by EN1998-1. The analyses have shown that the use of HSS in EN1998-1 compliant MRFs is effective in providing overall ductile mechanisms with limited plastic demand, due to the large design overstrength. For the braced frames, the use of the HSS in the non-dissipative members ensured that plastic hinges occurred in the dissipative structural elements with large brace ductility demand, mainly for the braces in compression. In addition, the beams from the braced bay plays an important role in the seismic performance of these structural systems and is concluded that the use of HSS in beams of braced bays is not advisable.Em edifícios metálicos submetidos à acção sísmica, os elementos dimensionados para permanecerem elásticos durante um sismo são responsáveis pela robustez da estrutura e pela prevenção de um colapso, sendo caracterizados por altas exigências de resistência. Por outro lado, edifícios resistentes à acção sísmica dimensionados como estruturas dissipativas devem permitir deformações plásticas em elementos ou zonas específicas. No presente trabalho, a solução porticada estudada é obtida pela combinação de dois diferentes tipos de aço: Aço Carbono (MCS) usado em elementos dissipativos e Aço de Alta Resistência (HSS) a ser aplicado em elementos não dissipativos. As normas atuais para o dimensionamento sísmiconão abordam as estruturas usando o conceito “dual-steel”. Justifica-se, portanto, um estudo paramétrico dedicado a investigar o dimensionamento e desempenho sísmico daquele tipo de estruturas, compativeis com as regras definidas no eurocodigo EN1998-1. Nesta dissertação são investigados Pórticos Simples (MRF), Pórticos com contraventamento centrado (CBF) e Pórticos “dual-system” com contraventamento centrado (D-CBF). O desempenho global sísmico é avaliado através de análises não lineares estáticas e dinâmicas tendo em conta três tipos de estados limites: Limitação de Danos (DL), Danos Significativos (SD) e Colapso (NC). Os parâmetros investigados levam em conta a variação geométrica e mecânica, o tipo de pilar misto, o vão, o número de pisos e o tipode espectro de resposta. A comparação entre uma solução “dual-steel” soluções correntes utilizando apenas MCS mostrou: (i) um desempenho sísmico semelhante em ambas soluções (ii) que os MRFs apresentaram as mesmas secções transversais para ambas soluções havendo, no entanto, uma redução do peso e do custo para os CBFs e D-CBFs mostrando que o HSS é eficiente em termos económicos, , e ainda, (iii) em todas as tipologias estudas, os fatores de comportamento obtidos para o estado limite SD foram menores do que utilizado no dimensionamento sísmico. Este resultado sugere a necessidade, aliás já reconhecida por outros investigadores, de uma melhor calibração dos fatores de comportamentos fornecidos pelo EN1998-1. As análises mostraram que o uso de HSS é eficiente em proporcionar um mecanismo global dúctil para os pórticos simples compatíveis com o EN1998-1 com limitada exigências plásticas devido a sua grande sobreresistência. Para os pórticos contraventados, o uso do HSS nos elementos não dissipativos permitiu que as rótulas plásticas ocorressem nos elementos dissipativos com grandes exigências de deformações nos contraventamentos. Além disso, as vigas do vão contraventado desempenham um importante papel para os CBFs e D-CBFs pelo que se conclui que a utitlização do HSS nestes elementos não é recomendável

Advanced Image Acquisition, Processing Techniques and Applications

"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution

Recycled Polymer Composites for Structural Applications

This thesis documents the development and testing of recycled, immiscible polymer blends for structural applications. The project was a Knowledge Transfer Partnership co-funded by Innovate UK and a Plastic Lumber manufacturer, who had a development contract with Network Rail. Network Rail contributed towards a permanent fatigue testing facility for full-size sleepers. Recycled plastic lumber converts lower grade, recyclate waste streams into products for decking, fencing, etc.. The aim was to create formulations capable of carrying significant in-service, dynamic loads over a wide spectrum of outdoor temperatures and conditions with 50 years minimum service life for railway sleepers. Mixed polyethylene/polypropylene recyclates were tested in iterative laboratory trials reinforced with polystyrene, mineral fillers and glass fibre. Flexural properties and impact resistance amongst other tests aided formulation design for production trials. A synergistic reinforcing effect was found between glass fibre and mica within an immiscible recycled polymer blend. Polymer blends and fibre reinforced grades were manufactured by intrusion moulding into profiles up to 2800x250x130 mm. Profiles of four trial and two production grades were tested in flexure, compression and thermal expansion. Large statistical sample sizes were required due to waste stream batch-to-batch variability. Strength and modulus were found to change with manufacturing technique, profile size, profile orientation, test type, and test parameters. Strengths were good, though lower than predicted due to premature failure. The fracture process was found to initiate at inclusions, ductile crack growth continued to a critical size followed by brittle facture. Glass fibre significantly improved strength, modulus, maximum operating temperature and thermal expansion. In 2012, two major product approvals were attained after extensive qualification testing that included fatigue testing equivalent to 20 years in service. British Board of Agrément accredited a crib earth retaining wall system. Network Rail approved for track trial sleepers made from the glass fibre reinforced grade

Approximation and Relaxation Approaches for Parallel and Distributed Machine Learning

Large scale machine learning requires tradeoffs. Commonly this tradeoff has led practitioners to choose simpler, less powerful models, e.g. linear models, in order to process more training examples in a limited time. In this work, we introduce parallelism to the training of non-linear models by leveraging a different tradeoff--approximation. We demonstrate various techniques by which non-linear models can be made amenable to larger data sets and significantly more training parallelism by strategically introducing approximation in certain optimization steps. For gradient boosted regression tree ensembles, we replace precise selection of tree splits with a coarse-grained, approximate split selection, yielding both faster sequential training and a significant increase in parallelism, in the distributed setting in particular. For metric learning with nearest neighbor classification, rather than explicitly train a neighborhood structure we leverage the implicit neighborhood structure induced by task-specific random forest classifiers, yielding a highly parallel method for metric learning. For support vector machines, we follow existing work to learn a reduced basis set with extremely high parallelism, particularly on GPUs, via existing linear algebra libraries. We believe these optimization tradeoffs are widely applicable wherever machine learning is put in practice in large scale settings. By carefully introducing approximation, we also introduce significantly higher parallelism and consequently can process more training examples for more iterations than competing exact methods. While seemingly learning the model with less precision, this tradeoff often yields noticeably higher accuracy under a restricted training time budget