Application of Computational Fluid Dynamics in Modeling Blood Flow in Human Thoracic Aorta

The aim of this study was to simulate blood flow in thoracic human aorta and understand the role of flow dynamics in the initialization and localization of atherosclerotic plaque in human thoracic aorta. The blood flow dynamics in idealized and realistic models of human thoracic aorta were numerically simulated in three idealized and two realistic thoracic aorta models. The idealized models of thoracic aorta were reconstructed with measurements available from literature, and the realistic models of thoracic aorta were constructed by image processing Computed Tomographic (CT) images. The CT images were made available by South Karelia Central Hospital in Lappeenranta. The reconstruction of thoracic aorta consisted of operations, such as contrast adjustment, image segmentations, and 3D surface rendering. Additional design operations were performed to make the aorta model compatible for the numerical method based computer code. The image processing and design operations were performed with specialized medical image processing software. Pulsatile pressure and velocity boundary conditions were deployed as inlet boundary conditions. The blood flow was assumed homogeneous and incompressible. The blood was assumed to be a Newtonian fluid. The simulations with idealized models of thoracic aorta were carried out with Finite Element Method based computer code, while the simulations with realistic models of thoracic aorta were carried out with Finite Volume Method based computer code. Simulations were carried out for four cardiac cycles. The distribution of flow, pressure and Wall Shear Stress (WSS) observed during the fourth cardiac cycle were extensively analyzed. The aim of carrying out the simulations with idealized model was to get an estimate of flow dynamics in a realistic aorta model. The motive behind the choice of three aorta models with distinct features was to understand the dependence of flow dynamics on aorta anatomy. Highly disturbed and nonuniform distribution of velocity and WSS was observed in aortic arch, near brachiocephalic, left common artery, and left subclavian artery. On the other hand, the WSS profiles at the roots of branches show significant differences with geometry variation of aorta and branches. The comparison of instantaneous WSS profiles revealed that the model with straight branching arteries had relatively lower WSS compared to that in the aorta model with curved branches. In addition to this, significant differences were observed in the spatial and temporal profiles of WSS, flow, and pressure. The study with idealized model was extended to study blood flow in thoracic aorta under the effects of hypertension and hypotension. One of the idealized aorta models was modified along with the boundary conditions to mimic the thoracic aorta under the effects of hypertension and hypotension. The results of simulations with realistic models extracted from CT scans demonstrated more realistic flow dynamics than that in the idealized models. During systole, the velocity in ascending aorta was skewed towards the outer wall of aortic arch. The flow develops secondary flow patterns as it moves downstream towards aortic arch. Unlike idealized models, the distribution of flow was nonplanar and heavily guided by the artery anatomy. Flow cavitation was observed in the aorta model which was imaged giving longer branches. This could not be properly observed in the model with imaging containing a shorter length for aortic branches. The flow circulation was also observed in the inner wall of the aortic arch. However, during the diastole, the flow profiles were almost flat and regular due the acceleration of flow at the inlet. The flow profiles were weakly turbulent during the flow reversal. The complex flow patterns caused a non-uniform distribution of WSS. High WSS was distributed at the junction of branches and aortic arch. Low WSS was distributed at the proximal part of the junction, while intermedium WSS was distributed in the distal part of the junction. The pulsatile nature of the inflow caused oscillating WSS at the branch entry region and inner curvature of aortic arch. Based on the WSS distribution in the realistic model, one of the aorta models was altered to induce artificial atherosclerotic plaque at the branch entry region and inner curvature of aortic arch. Atherosclerotic plaque causing 50% blockage of lumen was introduced in brachiocephalic artery, common carotid artery, left subclavian artery, and aortic arch. The aim of this part of the study was first to study the effect of stenosis on flow and WSS distribution, understand the effect of shape of atherosclerotic plaque on flow and WSS distribution, and finally to investigate the effect of lumen blockage severity on flow and WSS distributions. The results revealed that the distribution of WSS is significantly affected by plaque with mere 50% stenosis. The asymmetric shape of stenosis causes higher WSS in branching arteries than in the cases with symmetric plaque. The flow dynamics within thoracic aorta models has been extensively studied and reported here. The effects of pressure and arterial anatomy on the flow dynamic were investigated. The distribution of complex flow and WSS is correlated with the localization of atherosclerosis. With the available results we can conclude that the thoracic aorta, with complex anatomy is the most vulnerable artery for the localization and development of atherosclerosis. The flow dynamics and arterial anatomy play a role in the localization of atherosclerosis. The patient specific image based models can be used to diagnose the locations in the aorta vulnerable to the development of arterial diseases such as atherosclerosis.

Reflexive Space. A Constructionist Model of the Russian Reflexive Marker

This study examines the structure of the Russian Reflexive Marker ( ся/-сь) and offers a usage-based model building on Construction Grammar and a probabilistic view of linguistic structure. Traditionally, reflexive verbs are accounted for relative to non-reflexive verbs. These accounts assume that linguistic structures emerge as pairs. Furthermore, these accounts assume directionality where the semantics and structure of a reflexive verb can be derived from the non-reflexive verb. However, this directionality does not necessarily hold diachronically. Additionally, the semantics and the patterns associated with a particular reflexive verb are not always shared with the non-reflexive verb. Thus, a model is proposed that can accommodate the traditional pairs as well as for the possible deviations without postulating different systems. A random sample of 2000 instances marked with the Reflexive Marker was extracted from the Russian National Corpus and the sample used in this study contains 819 unique reflexive verbs. This study moves away from the traditional pair account and introduces the concept of Neighbor Verb. A neighbor verb exists for a reflexive verb if they share the same phonological form excluding the Reflexive Marker. It is claimed here that the Reflexive Marker constitutes a system in Russian and the relation between the reflexive and neighbor verbs constitutes a cross-paradigmatic relation. Furthermore, the relation between the reflexive and the neighbor verb is argued to be of symbolic connectivity rather than directionality. Effectively, the relation holding between particular instantiations can vary. The theoretical basis of the present study builds on this assumption. Several new variables are examined in order to systematically model variability of this symbolic connectivity, specifically the degree and strength of connectivity between items. In usage-based models, the lexicon does not constitute an unstructured list of items. Instead, items are assumed to be interconnected in a network. This interconnectedness is defined as Neighborhood in this study. Additionally, each verb carves its own niche within the Neighborhood and this interconnectedness is modeled through rhyme verbs constituting the degree of connectivity of a particular verb in the lexicon. The second component of the degree of connectivity concerns the status of a particular verb relative to its rhyme verbs. The connectivity within the neighborhood of a particular verb varies and this variability is quantified by using the Levenshtein distance. The second property of the lexical network is the strength of connectivity between items. Frequency of use has been one of the primary variables in functional linguistics used to probe this. In addition, a new variable called Constructional Entropy is introduced in this study building on information theory. It is a quantification of the amount of information carried by a particular reflexive verb in one or more argument constructions. The results of the lexical connectivity indicate that the reflexive verbs have statistically greater neighborhood distances than the neighbor verbs. This distributional property can be used to motivate the traditional observation that the reflexive verbs tend to have idiosyncratic properties. A set of argument constructions, generalizations over usage patterns, are proposed for the reflexive verbs in this study. In addition to the variables associated with the lexical connectivity, a number of variables proposed in the literature are explored and used as predictors in the model. The second part of this study introduces the use of a machine learning algorithm called Random Forests. The performance of the model indicates that it is capable, up to a degree, of disambiguating the proposed argument construction types of the Russian Reflexive Marker. Additionally, a global ranking of the predictors used in the model is offered. Finally, most construction grammars assume that argument construction form a network structure. A new method is proposed that establishes generalization over the argument constructions referred to as Linking Construction. In sum, this study explores the structural properties of the Russian Reflexive Marker and a new model is set forth that can accommodate both the traditional pairs and potential deviations from it in a principled manner.

Kassavirtojen ennustaminen projektilähtöisessä liiketoiminnassa

Työn tavoitteena oli vastata ensisijaisesti kysymykseen, voidaanko projektiliiketoiminnan kassavirtoja ennustaa 3-15 kuukauden aikavälillä ja jos voidaan, niin miten ja millä tarkkuudella. Tutkimus toteutettiin teoriatutkimuksena aihepiiristä ja tutkimuksen pohjalta luotiin malli kassavirtojen ennustamiseen kohdeyritykselle 3-15 kuukauden aikavälille. Mallin laatimiseksi oli hyödynnettävissä viiden vuoden aineistot kohdeyrityksen kassavirroista, budjetista ja liiketoiminnan toteumatiedoista. Työn teoriaosiossa tutkittiin kirjallisuuden pohjalta projektiliiketoimintaa, budjetointia sekä kassavirtoja ja niiden ennustamista. Tämän jälkeen teorian pohjalta rakennettiin kohdeyritykselle historiatietoihin perustuva malli kassavirtojen ennustamiseksi. Mallia rakennettaessa määritettiin ensimmäiseksi merkittävimmät kassavirran komponentit, minkä jälkeen niille laadittiin ennustemenetelmät. Samalla arvioitiin millä tarkkuudella projektilähtöisen liiketoiminnan kassavirtoja pystytään ennustamaan. Tutkimuksen tuloksena oli historiatietoihin pohjautuva ennustemalli kohdeyritykselle. Mallilla tehtyjen testien pohjalta voitiin todeta, että projektilähtöisen liiketoiminnan kassavirtoja pystytään ennustamaan melko hyvällä tarkkuudella, ennustaminen ei kuitenkaan ole niin luotettavaa, kuin jos ennustettaisiin tasaisemmin kehittyvän liiketoiminnan kassavirtoja. Historiaan pohjautuvaa mallia käytettäessä pitää myös muistaa, että mikään ei takaa historian toistumista tulevaisuudessa.

Service offering classification for logistics services

This thesis studies the development of service offering model that creates added-value for customers in the field of logistics services. The study focusses on offering classification and structures of model. The purpose of model is to provide value-added solutions for customers and enable superior service experience. The aim of thesis is to define what customers expect from logistics solution provider and what value customers appreciate so greatly that they could invest in value-added services. Value propositions, costs structures of offerings and appropriate pricing methods are studied. First, literature review of creating solution business model and customer value is conducted. Customer value is found out with customer interviews and qualitative empiric data is used. To exploit expertise knowledge of logistics, innovation workshop tool is utilized. Customers and experts are involved in the design process of model. As a result of thesis, three-level value-added service offering model is created based on empiric and theoretical data. Offerings with value propositions are proposed and the level of model reflects the deepness of customer-provider relationship and the amount of added value. Performance efficiency improvements and cost savings create the most added value for customers. Value-based pricing methods, such as performance-based models are suggested to apply. Results indicate the interest of benefitting networks and partnership in field of logistics services. Networks development is proposed to be investigated further.

Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

Fuzzy real option analysis in patent related decision making and patent valuation

The shift towards a knowledge-based economy has inevitably prompted the evolution of patent exploitation. Nowadays, patent is more than just a prevention tool for a company to block its competitors from developing rival technologies, but lies at the very heart of its strategy for value creation and is therefore strategically exploited for economic pro t and competitive advantage. Along with the evolution of patent exploitation, the demand for reliable and systematic patent valuation has also reached an unprecedented level. However, most of the quantitative approaches in use to assess patent could arguably fall into four categories and they are based solely on the conventional discounted cash flow analysis, whose usability and reliability in the context of patent valuation are greatly limited by five practical issues: the market illiquidity, the poor data availability, discriminatory cash-flow estimations, and its incapability to account for changing risk and managerial flexibility. This dissertation attempts to overcome these impeding barriers by rationalizing the use of two techniques, namely fuzzy set theory (aiming at the first three issues) and real option analysis (aiming at the last two). It commences with an investigation into the nature of the uncertainties inherent in patent cash flow estimation and claims that two levels of uncertainties must be properly accounted for. Further investigation reveals that both levels of uncertainties fall under the categorization of subjective uncertainty, which differs from objective uncertainty originating from inherent randomness in that uncertainties labelled as subjective are highly related to the behavioural aspects of decision making and are usually witnessed whenever human judgement, evaluation or reasoning is crucial to the system under consideration and there exists a lack of complete knowledge on its variables. Having clarified their nature, the application of fuzzy set theory in modelling patent-related uncertain quantities is effortlessly justified. The application of real option analysis to patent valuation is prompted by the fact that both patent application process and the subsequent patent exploitation (or commercialization) are subject to a wide range of decisions at multiple successive stages. In other words, both patent applicants and patentees are faced with a large variety of courses of action as to how their patent applications and granted patents can be managed. Since they have the right to run their projects actively, this flexibility has value and thus must be properly accounted for. Accordingly, an explicit identification of the types of managerial flexibility inherent in patent-related decision making problems and in patent valuation, and a discussion on how they could be interpreted in terms of real options are provided in this dissertation. Additionally, the use of the proposed techniques in practical applications is demonstrated by three fuzzy real option analysis based models. In particular, the pay-of method and the extended fuzzy Black-Scholes model are employed to investigate the profitability of a patent application project for a new process for the preparation of a gypsum-fibre composite and to justify the subsequent patent commercialization decision, respectively; a fuzzy binomial model is designed to reveal the economic potential of a patent licensing opportunity.

Generating Relation Algebras for Qualitative Spatial Reasoning

Basic relationships between certain regions of space are formulated in natural language in everyday situations. For example, a customer specifies the outline of his future home to the architect by indicating which rooms should be close to each other. Qualitative spatial reasoning as an area of artificial intelligence tries to develop a theory of space based on similar notions. In formal ontology and in ontological computer science, mereotopology is a first-order theory, embodying mereological and topological concepts, of the relations among wholes, parts, parts of parts, and the boundaries between parts. We shall introduce abstract relation algebras and present their structural properties as well as their connection to algebras of binary relations. This will be followed by details of the expressiveness of algebras of relations for region based models. Mereotopology has been the main basis for most region based theories of space. Since its earliest inception many theories have been proposed for mereotopology in artificial intelligence among which Region Connection Calculus is most prominent. The expressiveness of the region connection calculus in relational logic is far greater than its original eight base relations might suggest. In the thesis we formulate ways to automatically generate representable relation algebras using spatial data based on region connection calculus. The generation of new algebras is a two pronged approach involving splitting of existing relations to form new algebras and refinement of such newly generated algebras. We present an implementation of a system for automating aforementioned steps and provide an effective and convenient interface to define new spatial relations and generate representable relational algebras.

The time-course of lexical activation in Japanese morphographic word recognition: Evidence for a character-driven processing model

This lexical decision study with eye tracking of Japanese two-kanji-character words investigated the order in which a whole two-character word and its morphographic constituents are activated in the course of lexical access, the relative contributions of the left and the right characters in lexical decision, the depth to which semantic radicals are processed, and how nonlinguistic factors affect lexical processes. Mixed-effects regression analyses of response times and subgaze durations (i.e., first-pass fixation time spent on each of the two characters) revealed joint contributions of morphographic units at all levels of the linguistic structure with the magnitude and the direction of the lexical effects modulated by readers’ locus of attention in a left-to-right preferred processing path. During the early time frame, character effects were larger in magnitude and more robust than radical and whole-word effects, regardless of the font size and the type of nonwords. Extending previous radical-based and character-based models, we propose a task/decision-sensitive character-driven processing model with a level-skipping assumption: Connections from the feature level bypass the lower radical level and link up directly to the higher character level.

Traduction statistique vers une langue à morphologie riche : combinaison d’algorithmes de segmentation morphologique et de modèles statistiques de traduction automatique

Les systèmes statistiques de traduction automatique ont pour tâche la traduction d’une langue source vers une langue cible. Dans la plupart des systèmes de traduction de référence, l'unité de base considérée dans l'analyse textuelle est la forme telle qu’observée dans un texte. Une telle conception permet d’obtenir une bonne performance quand il s'agit de traduire entre deux langues morphologiquement pauvres. Toutefois, ceci n'est plus vrai lorsqu’il s’agit de traduire vers une langue morphologiquement riche (ou complexe). Le but de notre travail est de développer un système statistique de traduction automatique comme solution pour relever les défis soulevés par la complexité morphologique. Dans ce mémoire, nous examinons, dans un premier temps, un certain nombre de méthodes considérées comme des extensions aux systèmes de traduction traditionnels et nous évaluons leurs performances. Cette évaluation est faite par rapport aux systèmes à l’état de l’art (système de référence) et ceci dans des tâches de traduction anglais-inuktitut et anglais-finnois. Nous développons ensuite un nouvel algorithme de segmentation qui prend en compte les informations provenant de la paire de langues objet de la traduction. Cet algorithme de segmentation est ensuite intégré dans le modèle de traduction à base d’unités lexicales « Phrase-Based Models » pour former notre système de traduction à base de séquences de segments. Enfin, nous combinons le système obtenu avec des algorithmes de post-traitement pour obtenir un système de traduction complet. Les résultats des expériences réalisées dans ce mémoire montrent que le système de traduction à base de séquences de segments proposé permet d’obtenir des améliorations significatives au niveau de la qualité de la traduction en terme de le métrique d’évaluation BLEU (Papineni et al., 2002) et qui sert à évaluer. Plus particulièrement, notre approche de segmentation réussie à améliorer légèrement la qualité de la traduction par rapport au système de référence et une amélioration significative de la qualité de la traduction est observée par rapport aux techniques de prétraitement de base (baseline).

Algorithmes d'apprentissage pour la recommandation

L'ère numérique dans laquelle nous sommes entrés apporte une quantité importante de nouveaux défis à relever dans une multitude de domaines. Le traitement automatique de l'abondante information à notre disposition est l'un de ces défis, et nous allons ici nous pencher sur des méthodes et techniques adaptées au filtrage et à la recommandation à l'utilisateur d'articles adaptés à ses goûts, dans le contexte particulier et sans précédent notable du jeu vidéo multi-joueurs en ligne. Notre objectif est de prédire l'appréciation des niveaux par les joueurs. Au moyen d'algorithmes d'apprentissage machine modernes tels que les réseaux de neurones profonds avec pré-entrainement non-supervisé, que nous décrivons après une introduction aux concepts nécessaires à leur bonne compréhension, nous proposons deux architectures aux caractéristiques différentes bien que basées sur ce même concept d'apprentissage profond. La première est un réseau de neurones multi-couches pour lequel nous tentons d'expliquer les performances variables que nous rapportons sur les expériences menées pour diverses variations de profondeur, d'heuristique d'entraînement, et des méthodes de pré-entraînement non-supervisé simple, débruitant et contractant. Pour la seconde architecture, nous nous inspirons des modèles à énergie et proposons de même une explication des résultats obtenus, variables eux aussi. Enfin, nous décrivons une première tentative fructueuse d'amélioration de cette seconde architecture au moyen d'un fine-tuning supervisé succédant le pré-entrainement, puis une seconde tentative où ce fine-tuning est fait au moyen d'un critère d'entraînement semi-supervisé multi-tâches. Nos expériences montrent des performances prometteuses, notament avec l'architecture inspirée des modèles à énergie, justifiant du moins l'utilisation d'algorithmes d'apprentissage profonds pour résoudre le problème de la recommandation.

Développement d’un modèle centré sur l’individu des déplacements du caribou, du loup et de l’orignal, et de leurs interactions, en forêt boréale aménagée

Le caribou forestier est une espèce menacée au Canada, la principale hypothèse au déclin des populations étant l’intensification de la prédation provoquée par les perturbations anthropiques du paysage. Afin de faire face à cette situation, il est nécessaire d’étudier et comprendre l’impact de l’environnement sur les interactions prédateur-proies entre le caribou et le loup, ainsi qu’avec l’orignal, qui est sa principale proie alternative. Pour cela, cette thèse présente la conception d’un modèle centré sur l’individu des déplacements de ces trois espèces en fonction de leur environnement, dont résulteront les interactions prédateur-proies. Afin de permettre l’application de ce modèle sur de longues périodes, et donc pour un environnement changeant, une méthodologie a été développée, qui s’articule atour de deux aspects principaux. Tout d’abord, la notion de niveaux d’émergence est introduite, permettant d’ordonner les comportements observables du système selon leurs interdépendances, afin de choisir comme trait du modèle un com- portement correspondant au domaine d’applicabilité visé. Ordonner les comportements selon leurs niveaux d’émergence permet également d’identifier la redondance entre les patrons, qui peut être à l’origine d’un phénomène de sur-apprentissage lorsqu’ils sont utilisés lors de la calibration. Dans un second temps, un nouveau protocole pour la calibration et la validation du ou des traits choisis à l’aide des niveaux d’émergence, nommé réplication de système basé sur l’individu (Individual Based System Replication - IBSRtion) est également présenté. Ce protocole met l’emphase sur la modélisation directe, contrairement au principal protocole existant, la modélisation orientée patrons (Pattern Oriented Modelling - POM), et permet une approche empirique en générant artificiellement des données non disponibles ou ne pouvant être récoltées par des études de terrains. IBSRtion a également l’avantage de pouvoir être intégrée dans POM, afin de contribuer à la création d’une méthodologie universelle pour la conception de modèles centrés sur l’individu. Le processus de conception de ce modèle aura entre autre permis de faire une synthèse des connaissances et d’identifier certaines lacunes. Une étude visant à palier le manque de connaissances satisfaisantes sur les réponses comportementales à court-terme des proies face au risque de prédation a notamment permis d’observer que celles-ci sont une combinaison de comportements chroniques et éphémères, et que les mécanismes qui en sont à l’origine sont complexes et non-linéaires. Le résultat de ce travail est un modèle complexe utilisant de nombreux sous-modèles, et calibré de façon empirique, applicable à une grande variété d’environnements. Ce modèle a permis de tester l’impact de l’enfeuillement sur les relations prédateur-proies. Des simulations ont été effectuées pour différentes quantités d’enfeuillement, suivant deux configurations spatiales différentes. Les résultats de simulation suggèrent que des plans d’aménagement considérant également l’habitat de l’orignal pourraient être bénéfiques pour le caribou forestier, car ils permettraient d’améliorer la ségrégation spatiale entre les deux espèces, et donc entre le caribou et le loup. En le couplant avec un module de naissances et de morts naturelles ainsi qu’un modèle d’évolution du paysage, ce modèle permettra par la suite d’évaluer l’impact de plans d’aménagement forestier sur la viabilité des populations de caribou forestier.

Deep learning of representations and its application to computer vision

L’objectif de cette thèse par articles est de présenter modestement quelques étapes du parcours qui mènera (on espère) à une solution générale du problème de l’intelligence artificielle. Cette thèse contient quatre articles qui présentent chacun une différente nouvelle méthode d’inférence perceptive en utilisant l’apprentissage machine et, plus particulièrement, les réseaux neuronaux profonds. Chacun de ces documents met en évidence l’utilité de sa méthode proposée dans le cadre d’une tâche de vision par ordinateur. Ces méthodes sont applicables dans un contexte plus général, et dans certains cas elles on tété appliquées ailleurs, mais ceci ne sera pas abordé dans le contexte de cette de thèse. Dans le premier article, nous présentons deux nouveaux algorithmes d’inférence variationelle pour le modèle génératif d’images appelé codage parcimonieux “spike- and-slab” (CPSS). Ces méthodes d’inférence plus rapides nous permettent d’utiliser des modèles CPSS de tailles beaucoup plus grandes qu’auparavant. Nous démontrons qu’elles sont meilleures pour extraire des détecteur de caractéristiques quand très peu d’exemples étiquetés sont disponibles pour l’entraînement. Partant d’un modèle CPSS, nous construisons ensuite une architecture profonde, la machine de Boltzmann profonde partiellement dirigée (MBP-PD). Ce modèle a été conçu de manière à simplifier d’entraînement des machines de Boltzmann profondes qui nécessitent normalement une phase de pré-entraînement glouton pour chaque couche. Ce problème est réglé dans une certaine mesure, mais le coût d’inférence dans le nouveau modèle est relativement trop élevé pour permettre de l’utiliser de manière pratique. Dans le deuxième article, nous revenons au problème d’entraînement joint de machines de Boltzmann profondes. Cette fois, au lieu de changer de famille de modèles, nous introduisons un nouveau critère d’entraînement qui donne naissance aux machines de Boltzmann profondes à multiples prédictions (MBP-MP). Les MBP-MP sont entraînables en une seule étape et ont un meilleur taux de succès en classification que les MBP classiques. Elles s’entraînent aussi avec des méthodes variationelles standard au lieu de nécessiter un classificateur discriminant pour obtenir un bon taux de succès en classification. Par contre, un des inconvénients de tels modèles est leur incapacité de générer deséchantillons, mais ceci n’est pas trop grave puisque la performance de classification des machines de Boltzmann profondes n’est plus une priorité étant donné les dernières avancées en apprentissage supervisé. Malgré cela, les MBP-MP demeurent intéressantes parce qu’elles sont capable d’accomplir certaines tâches que des modèles purement supervisés ne peuvent pas faire, telles que celle de classifier des données incomplètes ou encore celle de combler intelligemment l’information manquante dans ces données incomplètes. Le travail présenté dans cette thèse s’est déroulé au milieu d’une période de transformations importantes du domaine de l’apprentissage à réseaux neuronaux profonds qui a été déclenchée par la découverte de l’algorithme de “dropout” par Geoffrey Hinton. Dropout rend possible un entraînement purement supervisé d’architectures de propagation unidirectionnel sans être exposé au danger de sur- entraînement. Le troisième article présenté dans cette thèse introduit une nouvelle fonction d’activation spécialement con ̧cue pour aller avec l’algorithme de Dropout. Cette fonction d’activation, appelée maxout, permet l’utilisation de aggrégation multi-canal dans un contexte d’apprentissage purement supervisé. Nous démontrons comment plusieurs tâches de reconnaissance d’objets sont mieux accomplies par l’utilisation de maxout. Pour terminer, sont présentons un vrai cas d’utilisation dans l’industrie pour la transcription d’adresses de maisons à plusieurs chiffres. En combinant maxout avec une nouvelle sorte de couche de sortie pour des réseaux neuronaux de convolution, nous démontrons qu’il est possible d’atteindre un taux de succès comparable à celui des humains sur un ensemble de données coriace constitué de photos prises par les voitures de Google. Ce système a été déployé avec succès chez Google pour lire environ cent million d’adresses de maisons.

A study on the performance of flexible pavements on mature soil subgrades

The country has witnessed tremendous increase in the vehicle population and increased axle loading pattern during the last decade, leaving its road network overstressed and leading to premature failure. The type of deterioration present in the pavement should be considered for determining whether it has a functional or structural deficiency, so that appropriate overlay type and design can be developed. Structural failure arises from the conditions that adversely affect the load carrying capability of the pavement structure. Inadequate thickness, cracking, distortion and disintegration cause structural deficiency. Functional deficiency arises when the pavement does not provide a smooth riding surface and comfort to the user. This can be due to poor surface friction and texture, hydro planning and splash from wheel path, rutting and excess surface distortion such as potholes, corrugation, faulting, blow up, settlement, heaves etc. Functional condition determines the level of service provided by the facility to its users at a particular time and also the Vehicle Operating Costs (VOC), thus influencing the national economy. Prediction of the pavement deterioration is helpful to assess the remaining effective service life (RSL) of the pavement structure on the basis of reduction in performance levels, and apply various alternative designs and rehabilitation strategies with a long range funding requirement for pavement preservation. In addition, they can predict the impact of treatment on the condition of the sections. The infrastructure prediction models can thus be classified into four groups, namely primary response models, structural performance models, functional performance models and damage models. The factors affecting the deterioration of the roads are very complex in nature and vary from place to place. Hence there is need to have a thorough study of the deterioration mechanism under varied climatic zones and soil conditions before arriving at a definite strategy of road improvement. Realizing the need for a detailed study involving all types of roads in the state with varying traffic and soil conditions, the present study has been attempted. This study attempts to identify the parameters that affect the performance of roads and to develop performance models suitable to Kerala conditions. A critical review of the various factors that contribute to the pavement performance has been presented based on the data collected from selected road stretches and also from five corporations of Kerala. These roads represent the urban conditions as well as National Highways, State Highways and Major District Roads in the sub urban and rural conditions. This research work is a pursuit towards a study of the road condition of Kerala with respect to varying soil, traffic and climatic conditions, periodic performance evaluation of selected roads of representative types and development of distress prediction models for roads of Kerala. In order to achieve this aim, the study is focused into 2 parts. The first part deals with the study of the pavement condition and subgrade soil properties of urban roads distributed in 5 Corporations of Kerala; namely Thiruvananthapuram, Kollam, Kochi, Thrissur and Kozhikode. From selected 44 roads, 68 homogeneous sections were studied. The data collected on the functional and structural condition of the surface include pavement distress in terms of cracks, potholes, rutting, raveling and pothole patching. The structural strength of the pavement was measured as rebound deflection using Benkelman Beam deflection studies. In order to collect the details of the pavement layers and find out the subgrade soil properties, trial pits were dug and the in-situ field density was found using the Sand Replacement Method. Laboratory investigations were carried out to find out the subgrade soil properties, soil classification, Atterberg limits, Optimum Moisture Content, Field Moisture Content and 4 days soaked CBR. The relative compaction in the field was also determined. The traffic details were also collected by conducting traffic volume count survey and axle load survey. From the data thus collected, the strength of the pavement was calculated which is a function of the layer coefficient and thickness and is represented as Structural Number (SN). This was further related to the CBR value of the soil and the Modified Structural Number (MSN) was found out. The condition of the pavement was represented in terms of the Pavement Condition Index (PCI) which is a function of the distress of the surface at the time of the investigation and calculated in the present study using deduct value method developed by U S Army Corps of Engineers. The influence of subgrade soil type and pavement condition on the relationship between MSN and rebound deflection was studied using appropriate plots for predominant types of soil and for classified value of Pavement Condition Index. The relationship will be helpful for practicing engineers to design the overlay thickness required for the pavement, without conducting the BBD test. Regression analysis using SPSS was done with various trials to find out the best fit relationship between the rebound deflection and CBR, and other soil properties for Gravel, Sand, Silt & Clay fractions. The second part of the study deals with periodic performance evaluation of selected road stretches representing National Highway (NH), State Highway (SH) and Major District Road (MDR), located in different geographical conditions and with varying traffic. 8 road sections divided into 15 homogeneous sections were selected for the study and 6 sets of continuous periodic data were collected. The periodic data collected include the functional and structural condition in terms of distress (pothole, pothole patch, cracks, rutting and raveling), skid resistance using a portable skid resistance pendulum, surface unevenness using Bump Integrator, texture depth using sand patch method and rebound deflection using Benkelman Beam. Baseline data of the study stretches were collected as one time data. Pavement history was obtained as secondary data. Pavement drainage characteristics were collected in terms of camber or cross slope using camber board (slope meter) for the carriage way and shoulders, availability of longitudinal side drain, presence of valley, terrain condition, soil moisture content, water table data, High Flood Level, rainfall data, land use and cross slope of the adjoining land. These data were used for finding out the drainage condition of the study stretches. Traffic studies were conducted, including classified volume count and axle load studies. From the field data thus collected, the progression of each parameter was plotted for all the study roads; and validated for their accuracy. Structural Number (SN) and Modified Structural Number (MSN) were calculated for the study stretches. Progression of the deflection, distress, unevenness, skid resistance and macro texture of the study roads were evaluated. Since the deterioration of the pavement is a complex phenomena contributed by all the above factors, pavement deterioration models were developed as non linear regression models, using SPSS with the periodic data collected for all the above road stretches. General models were developed for cracking progression, raveling progression, pothole progression and roughness progression using SPSS. A model for construction quality was also developed. Calibration of HDM–4 pavement deterioration models for local conditions was done using the data for Cracking, Raveling, Pothole and Roughness. Validation was done using the data collected in 2013. The application of HDM-4 to compare different maintenance and rehabilitation options were studied considering the deterioration parameters like cracking, pothole and raveling. The alternatives considered for analysis were base alternative with crack sealing and patching, overlay with 40 mm BC using ordinary bitumen, overlay with 40 mm BC using Natural Rubber Modified Bitumen and an overlay of Ultra Thin White Topping. Economic analysis of these options was done considering the Life Cycle Cost (LCC). The average speed that can be obtained by applying these options were also compared. The results were in favour of Ultra Thin White Topping over flexible pavements. Hence, Design Charts were also plotted for estimation of maximum wheel load stresses for different slab thickness under different soil conditions. The design charts showed the maximum stress for a particular slab thickness and different soil conditions incorporating different k values. These charts can be handy for a design engineer. Fuzzy rule based models developed for site specific conditions were compared with regression models developed using SPSS. The Riding Comfort Index (RCI) was calculated and correlated with unevenness to develop a relationship. Relationships were developed between Skid Number and Macro Texture of the pavement. The effort made through this research work will be helpful to highway engineers in understanding the behaviour of flexible pavements in Kerala conditions and for arriving at suitable maintenance and rehabilitation strategies. Key Words: Flexible Pavements – Performance Evaluation – Urban Roads – NH – SH and other roads – Performance Models – Deflection – Riding Comfort Index – Skid Resistance – Texture Depth – Unevenness – Ultra Thin White Topping

Methodische Grundlagen zur Bewahrung und Wiederherstellung großräumig funktionsfähiger ökologischer Beziehungen in der räumlichen Umweltplanung

Mit der Verwirklichung ,Ökologischer Netzwerke‘ werden Hoffnungen zum Stopp des Verlustes der biologischen Vielfalt verknüpft. Sowohl auf gesamteuropäischer Ebene (Pan-European Ecological Network - PEEN) als auch in den einzelnen Staaten entstehen Pläne zum Aufbau von Verbundsystemen. Im föderalen Deutschland werden kleinmaßstäbliche Biotopverbundplanungen auf Landesebene aufgestellt; zum nationalen Biotopverbund bestehen erste Konzepte. Die vorliegende Arbeit ist auf diese überörtlichen, strategisch vorbereitenden Planungsebenen ausgerichtet. Ziele des Verbunds sind der Erhalt von Populationen insbesondere der gefährdeten Arten sowie die Ermöglichung von Ausbreitung und Wanderung. Aufgrund fehlender Datengrundlagen zu den Arten und Populationen ist es nicht ohne weiteres möglich, die Konzepte und Modelle der Populationsökologie in die überörtlichen Planungsebenen zu übertragen. Gemäß der o.g. Zielstellungen sollte sich aber die Planung von Verbundsystemen an den Ansprüchen der auf Verbund angewiesenen Arten orientieren. Ziel der Arbeit war die Entwicklung einer praktikablen GIS-gestützten Planungshilfe zur größtmöglichen Integration ökologischen Wissens unter der Bedingung eingeschränkter Informationsverfügbarkeit. Als Grundlagen dazu werden in Übersichtsform zunächst die globalen, europäisch-internationalen und nationalen Rahmenbedingungen und Anforderungen bezüglich des Aufbaus von Verbundsystemen zusammengestellt. Hier sind die Strategien zum PEEN hervorzuheben, die eine Integration ökologischer Inhalte insbesondere durch die Berücksichtigung räumlich-funktionaler Beziehungen fordern. Eine umfassende Analyse der landesweiten Biotopverbundplanungen der BRD zeigte die teilweise erheblichen Unterschiede zwischen den Länderplanungen auf, die es aktuell nicht ermöglichen, ein schlüssiges nationales Konzept zusammenzufügen. Nicht alle Länder haben landesweite Biotopverbundplanungen und Landeskonzepte, bei denen dem geplanten Verbund die Ansprüche von Arten zugrunde gelegt werden, gibt es nur ansatzweise. Weiterhin wurde eine zielgerichtete Eignungsprüfung bestehender GIS-basierter Modelle und Konzepte zum Verbund unter Berücksichtigung der regelmäßig in Deutschland verfügbaren Datengrundlagen durchgeführt. Da keine integrativen regelorientierten Ansätze vorhanden waren, wurde der vektorbasierte Algorithmus HABITAT-NET entwickelt. Er arbeitet mit ,Anspruchstypen‘ hinsichtlich des Habitatverbunds, die stellvertretend für unterschiedliche ökologische Gruppen von (Ziel-) Arten mit terrestrischer Ausbreitung stehen. Kombiniert wird die Fähigkeit zur Ausbreitung mit einer Grobtypisierung der Biotopbindung. Die wichtigsten Grundlagendaten bilden die jeweiligen (potenziellen) Habitate von Arten eines Anspruchstyps sowie die umgebende Landnutzung. Bei der Bildung von ,Lebensraumnetzwerken‘ (Teil I) werden gestufte ,Funktions- und Verbindungsräume‘ generiert, die zu einem räumlichen System verknüpft sind. Anschließend kann die aktuelle Zerschneidung der Netzwerke durch Verkehrstrassen aufgezeigt werden, um darauf aufbauend prioritäre Abschnitte zur Wiedervernetzung zu ermitteln (Teil II). Begleitend wird das Konzept der unzerschnittenen Funktionsräume (UFR) entworfen, mit dem die Indikation von Habitatzerschneidung auf Landschaftsebene möglich ist. Diskutiert werden schließlich die Eignung der Ergebnisse als kleinmaßstäblicher Zielrahmen, Tests zur Validierung, Vergleiche mit Verbundplanungen und verschiedene Setzungen im GIS-Algorithmus. Erläuterungen zu den Einsatzmöglichkeiten erfolgen beispielsweise für die Bereiche Biotopverbund- und Landschaftsplanung, Raumordnung, Strategische Umweltprüfung, Verkehrswegeplanung, Unterstützung des Konzeptes der Lebensraumkorridore, Kohärenz im Schutzgebietssystem NATURA 2000 und Aufbau von Umweltinformationssystemen. Schließlich wird ein Rück- und Ausblick mit der Formulierung des weiteren Forschungsbedarfs verknüpft.

How are Three-Deminsional Objects Represented in the Brain?

We discuss a variety of object recognition experiments in which human subjects were presented with realistically rendered images of computer-generated three-dimensional objects, with tight control over stimulus shape, surface properties, illumination, and viewpoint, as well as subjects' prior exposure to the stimulus objects. In all experiments recognition performance was: (1) consistently viewpoint dependent; (2) only partially aided by binocular stereo and other depth information, (3) specific to viewpoints that were familiar; (4) systematically disrupted by rotation in depth more than by deforming the two-dimensional images of the stimuli. These results are consistent with recently advanced computational theories of recognition based on view interpolation.