In this dissertation, we introduce a novel text representation method mainly used for text classification purpose. The presented representation method is initially based on a variety of closeness relationships between pairs of words in text passages within the entire corpus. This representation is then used as the basis for our multi-level lightweight ontological representation method (TOR-FUSE), in which documents are represented based on their contexts and the goal of the learning task. The method is unlike the traditional representation methods, in which all the documents are represented solely based on the constituent words of the documents, and are totally isolated from the goal that they are represented for. We believe choosing the correct granularity of representation features is an important aspect of text classification. Interpreting data in a more general dimensional space, with fewer dimensions, can convey more discriminative knowledge and decrease the level of learning perplexity. The multi-level model allows data interpretation in a more conceptual space, rather than only containing scattered words occurring in texts. It aims to perform the extraction of the knowledge tailored for the classification task by automatic creation of a lightweight ontological hierarchy of representations. In the last step, we will train a tailored ensemble learner over a stack of representations at different conceptual granularities. The final result is a mapping and a weighting of the targeted concept of the original learning task, over a stack of representations and granular conceptual elements of its different levels (hierarchical mapping instead of linear mapping over a vector). Finally the entire algorithm is applied to a variety of general text classification tasks, and the performance is evaluated in comparison with well-known algorithms.

In this dissertation, we introduce a novel text representation method mainly used for text classification purpose. The presented representation method is initially based on a variety of closeness relationships between pairs of words in text passages within the entire corpus. This representation is then used as the basis for our multi-level lightweight ontological representation method (TOR-FUSE), in which documents are represented based on their contexts and the goal of the learning task. The method is unlike the traditional representation methods, in which all the documents are represented solely based on the constituent words of the documents, and are totally isolated from the goal that they are represented for. We believe choosing the correct granularity of representation features is an important aspect of text classification. Interpreting data in a more general dimensional space, with fewer dimensions, can convey more discriminative knowledge and decrease the level of learning perplexity. The multi-level model allows data interpretation in a more conceptual space, rather than only containing scattered words occurring in texts. It aims to perform the extraction of the knowledge tailored for the classification task by automatic creation of a lightweight ontological hierarchy of representations. In the last step, we will train a tailored ensemble learner over a stack of representations at different conceptual granularities. The final result is a mapping and a weighting of the targeted concept of the original learning task, over a stack of representations and granular conceptual elements of its different levels (hierarchical mapping instead of linear mapping over a vector). Finally the entire algorithm is applied to a variety of general text classification tasks, and the performance is evaluated in comparison with well-known algorithms.

In this dissertation, we introduce a novel text representation method mainly used for text classification purpose. The presented representation method is initially based on a variety of closeness relationships between pairs of words in text passages within the entire corpus. This representation is then used as the basis for our multi-level lightweight ontological representation method (TOR-FUSE), in which documents are represented based on their contexts and the goal of the learning task. The method is unlike the traditional representation methods, in which all the documents are represented solely based on the constituent words of the documents, and are totally isolated from the goal that they are represented for. We believe choosing the correct granularity of representation features is an important aspect of text classification. Interpreting data in a more general dimensional space, with fewer dimensions, can convey more discriminative knowledge and decrease the level of learning perplexity. The multi-level model allows data interpretation in a more conceptual space, rather than only containing scattered words occurring in texts. It aims to perform the extraction of the knowledge tailored for the classification task by automatic creation of a lightweight ontological hierarchy of representations. In the last step, we will train a tailored ensemble learner over a stack of representations at different conceptual granularities. The final result is a mapping and a weighting of the targeted concept of the original learning task, over a stack of representations and granular conceptual elements of its different levels (hierarchical mapping instead of linear mapping over a vector). Finally the entire algorithm is applied to a variety of general text classification tasks, and the performance is evaluated in comparison with well-known algorithms.

By a representation of a C*-algebra A on a Hilbert space H we mean a morphism : A → L(H). After summing up neccessary knowledge from the theory of Banach and Hilbert spaces and C*-al- gebras we show that for every C*-algebra a representation exists. We describe its structure detiledly and we focus on examining cyclic representations. We find out that cyclic representations relate to the state space. Because every state can be expressed as an integral with respect to an appropriate measure on the states, in is possible to assign a measure on the state space to each cyclic represen- tation. Therefore, we investigate connexion of a representation with this measure as same as with the corresponding state. This leads us to the definition of an orthogonal measure. We find out that its properties relate with certain subalgebras of L(H). At the end we show that for a separable C*-algebra it is possible to express a representation fulfilling suitable assumptions in the form of a direct integral. 1

La robotique autonome est employée avec succès dans des environnements industriels contrôlés, où les instructions suivent des plans d’action prédéterminés.La robotique domestique est le challenge des années à venir et comporte un certain nombre de nouvelles difficultés : il faut passer de l'hypothèse d'un monde fermé borné à un monde ouvert. Un robot ne peut plus compter seulement sur ses données capteurs brutes qui ne font qu'indiquer la présence ou l'absence d'objets. Il lui faut aussi comprendre les relations implicites entre les objets de son environnement ainsi que le sens des tâches qu'on lui assigne. Il devra également pouvoir interagir avec des humains et donc partager leur conceptualisation à travers le langage. En effet, chaque langue est une représentation abstraite et compacte du monde qui relie entre eux une multitude de concepts concrets et purement abstraits. Malheureusement, les observations réelles sont plus complexes que nos représentations sémantiques simplifiées. Elles peuvent donc rentrer en contradiction, prix à payer d'une représentation finie d'un monde "infini". Pour répondre à ces difficultés, nous proposons dans cette thèse une architecture globale combinant différentes modalités de représentation d'environnement. Elle permet d'interpréter une représentation physique en la rattachant aux concepts abstraits exprimés en langage naturel. Le système est à double entrée : les données capteurs vont alimenter la modalité de perception tandis que les données textuelles et les interactions avec l'humain seront reliées à la modalité sémantique. La nouveauté de notre approche se situe dans l'introduction d'une modalité intermédiaire basée sur la notion d'instance (réalisation physique de concepts sémantiques). Cela permet notamment de connecter indirectement et sans contradiction les données perceptuelles aux connaissances en langage naturel.Nous présentons dans ce cadre une méthode originale de création d'ontologie orientée vers la description d'objets physiques. Du côté de la perception, nous analysons certaines propriétés des descripteurs image génériques extraits de couches intermédiaires de réseaux de neurones convolués. En particulier, nous montrons leur adéquation à la représentation d'instances ainsi que leur usage dans l'estimation de transformation de similarité. Nous proposons aussi une méthode de rattachement d'instance à une ontologie, alternative aux méthodes de classification classique dans l'hypothèse d'un monde ouvert. Enfin nous illustrons le fonctionnement global de notre modèle par la description de nos processus de gestion de requête utilisateur. / Autonomous robotics is successfully used in controled industrial environments where instructions follow predetermined implementation plans.Domestic robotics is the challenge of years to come and involve several new problematics : we have to move from a closed bounded world to an open one. A robot can no longer only rely on its raw sensor data as they merely show the absence or presence of things. It should also understand why objects are in its environment as well as the meaning of its tasks. Besides, it has to interact with human beings and therefore has to share their conceptualization through natural language. Indeed, each language is in its own an abstract and compact representation of the world which links up variety of concrete and abstract concepts. However, real observations are more complex than our simplified semantical representation. Thus they can come into conflict : this is the price for a finite representation of an "infinite" world.To address those challenges, we propose in this thesis a global architecture bringing together different modalities of environment representation. It allows to relate a physical representation to abstract concepts expressed in natural language. The inputs of our system are two-fold : sensor data feed the perception modality whereas textual information and human interaction are linked to the semantic modality. The novelty of our approach is in the introduction of an intermediate modality based on instances (physical realization of semantic concepts). Among other things, it allows to connect indirectly and without contradiction perceptual data to knowledge in natural langage.We propose in this context an original method to automatically generate an ontology for the description of physical objects. On the perception side, we investigate some properties of image descriptor extracted from intermediate layers of convolutional neural networks. In particular, we show their relevance for instance representation as well as their use for estimation of similarity transformation. We also propose a method to relate instances to our object-oriented ontology which, in the assumption of an open world, can be seen as an alternative to classical classification methods. Finally, the global flow of our system is illustrated through the description of user request management processes.

Résumé : La bande marine côtière, au-delà d’être un espace physique attaché aux territoires terrestres communaux, est aujourd’hui un lieu à la fois d’enjeux socio-économiques importants et de risques majeurs tels que la submersion marine. Les élus locaux, en première ligne face à ces enjeux, semblent considérer pour la plupart que la mer est en dehors de leur territoire. Nous postulons que sans une évolution de cette vision de la situation, aucune décision durable ne pourra être envisagée. Pour accompagner ce changement de perception, nous préconisons l’étude des représentations, véritables viviers d’information pour identifier les freins et les leviers à la prise de décision. Cerner la représentation qu’ont les élus locaux de leur territoire littoral et marin et comprendre leur cheminement vers la prise décision constitue ainsi l’objectif de cette étude. La combinaison entre une population d’un certain statut et un objet social physique nous a conduit à élaborer une méthodologie multidimensionnelle dont la singularité réside dans la mobilisation du processus d’ancrage à la fois pour l’étude de la représentation sociale, pour celle de la représentation iconospatiale et enfin dans la relation d’emboîtement. Un questionnaire, une carte graphique à main levée et un scénario de submersion marine ont constitué les trois outils clés du travail avec lesquels nous avons établi diverses corrélations s’appuyant sur des techniques d’analyses propres à la psychologie sociale et à la théorie des représentations sociales. Trois recherches se sont succédées. La première a mis à l’épreuve la méthodologie pour cerner la représentation sociale et la représentation iconospatiale. La deuxième s’est attachée à révéler le lien entre les contenus des représentations sociales et ceux des représentations iconospatiales en établissant une corrélation entre une typologie de dessins et une typologie d’élus.Enfin la troisième recherche a tenté de démontrer que certains points d’ancrage d’une représentation stabilisée (la politique communale), au travers d’une relation d’emboitement avec une représentation émergente portant sur un objet social environnemental (la submersion marine), constituaient de potentiels indicateurs d’acceptabilité sociale d’une problématique.Globalement, cette étude suggère l’intérêt certain de l’usage de carte graphique pour l’étude de ce type d’objet social physique sous couvert d’un processus d’ancrage particulièrement efficient pour structurer encore davantage les méthodes de compréhension de la relation homme-territoire. / Abstract : The coastal land strip, beyond being physical space attached to communal land territories, is today a focal point of heightening socio-economic issues, with significant risks of marine flooding and submersion. The majority of elected representatives on the front line facing these various issues seem to consider that the sea is beyond their jurisdiction. We postulate that without overhaul of this viewpoint, no long term decision can be envisaged. To accompany this new viewpoint, we recommend the study of representations, which are true breeding grounds for information that can help in identifying what holds back and what facilitates decision-making. The objective of the present study is to understand the representation that the locally elected have of the coastal and marine territory and further, the pathways in decision making. Linking a population of a certain social standing with a physical social object led us to develop a multi-dimensional methodology whose uniqueness resides in the mobilisation of the anchoring process, this for the study of social and icono-spatial representations, and how they interlink. We have established various correlations founded on analysis techniques used in social psychology and the theory of social representations, this by way of three key tools : a questionnaire, a hand-drawn map and a scenario of marine submersion. Three areas of research ensued. The first deployed the methodology to discern the social and icono-spatial representations. The second was to reveal the link between the contents of social and icono-spatial representations by establishing a correlation between a type of drawing and type of elected representative. The third area of research aimed to demonstrate that certain anchoring points of a stabilised representation (municipal politics) by their interlinking with an emerging representation focussed on an environmental social object (marine submersion) constituted potential indicators of social acceptability of the problem.Overall, this study suggests a strong interest for the map method for the study of this type of social physical object under the guise of the mobilisation of an anchoring process. This is particularly efficient in further structuring the methods to understand man-territory relationships.

Thesis (Ph. D.)--University of Texas at Austin, 1999. / Vita. Includes bibliographical references (leaves 490-544). Available also in a digital version from Dissertation Abstracts.

The purpose of this thesis is to examine in what ways female representation in municipal councils in Sweden has an influence on the quality of eldercare. The theory of the politics of presence constitutes the theoretical framework. The theory suggests that eldercare is one of several issues that are in women’s interest and hence is best represented by female politicians. According to Statistics Sweden (SCB), the elder part of the population will increase with 76 percent over the coming 20 years. It is a remarkable demographic change and, therefore, it is of interest to map out what influence female politicians have on eldercare as a political matter. To do this, statistical analysis, primarily multiple regression, is used as the methodological tool. The results indicate that female representation does not have a significant effect on the quality of eldercare. After controlling for institutional and structural variables, it appears that the portion of elderly amongst the population is what has the most evident effect on the quality of eldercare. Some final words are given on how to interpret the results, and when placing this study in relation to earlier research, it is however argued that there is not sufficient support to question the relevance of the theory of the politics of presence.

La présente thèse étudie la modélisation conjointe des contenus visuels et textuels extraits à partir des documents multimédias pour résoudre les problèmes intermodaux. Ces tâches exigent la capacité de ``traduire'' l'information d'une modalité vers une autre. Un espace de représentation commun, par exemple obtenu par l'Analyse Canonique des Corrélation ou son extension kernelisée est une solution généralement adoptée. Sur cet espace, images et texte peuvent être représentés par des vecteurs de même type sur lesquels la comparaison intermodale peut se faire directement.Néanmoins, un tel espace commun souffre de plusieurs déficiences qui peuvent diminuer la performance des ces tâches. Le premier défaut concerne des informations qui sont mal représentées sur cet espace pourtant très importantes dans le contexte de la recherche intermodale. Le deuxième défaut porte sur la séparation entre les modalités sur l'espace commun, ce qui conduit à une limite de qualité de traduction entre modalités. Pour faire face au premier défaut concernant les données mal représentées, nous avons proposé un modèle qui identifie tout d'abord ces informations et puis les combine avec des données relativement bien représentées sur l'espace commun. Les évaluations sur la tâche d'illustration de texte montrent que la prise en compte de ces information fortement améliore les résultats de la recherche intermodale. La contribution majeure de la thèse se concentre sur la séparation entre les modalités sur l'espace commun pour améliorer la performance des tâches intermodales. Nous proposons deux méthodes de représentation pour les documents bi-modaux ou uni-modaux qui regroupent à la fois des informations visuelles et textuelles projetées sur l'espace commun. Pour les documents uni-modaux, nous suggérons un processus de complétion basé sur un ensemble de données auxiliaires pour trouver les informations correspondantes dans la modalité absente. Ces informations complémentaires sont ensuite utilisées pour construire une représentation bi-modale finale pour un document uni-modal. Nos approches permettent d'obtenir des résultats de l'état de l'art pour la recherche intermodale ou la classification bi-modale et intermodale. / This thesis investigates the joint modeling of visual and textual content of multimedia documents to address cross-modal problems. Such tasks require the ability to match information across modalities. A common representation space, obtained by eg Kernel Canonical Correlation Analysis, on which images and text can be both represented and directly compared is a generally adopted solution.Nevertheless, such a joint space still suffers from several deficiencies that may hinder the performance of cross-modal tasks. An important contribution of this thesis is therefore to identify two major limitations of such a space. The first limitation concerns information that is poorly represented on the common space yet very significant for a retrieval task. The second limitation consists in a separation between modalities on the common space, which leads to coarse cross-modal matching. To deal with the first limitation concerning poorly-represented data, we put forward a model which first identifies such information and then finds ways to combine it with data that is relatively well-represented on the joint space. Evaluations on emph{text illustration} tasks show that by appropriately identifying and taking such information into account, the results of cross-modal retrieval can be strongly improved. The major work in this thesis aims to cope with the separation between modalities on the joint space to enhance the performance of cross-modal tasks.We propose two representation methods for bi-modal or uni-modal documents that aggregate information from both the visual and textual modalities projected on the joint space. Specifically, for uni-modal documents we suggest a completion process relying on an auxiliary dataset to find the corresponding information in the absent modality and then use such information to build a final bi-modal representation for a uni-modal document. Evaluations show that our approaches achieve state-of-the-art results on several standard and challenging datasets for cross-modal retrieval or bi-modal and cross-modal classification.

In this paper we examine the Swedish Social Democratic Party within the context of representation. Party membership, voter engagement and citizen influence over politics are all trending downwards in Sweden. At the same time, the interest in politics among citizens is peaking and voter turnout remains strong. This sparks a question regarding how the Social Democratic Party may have changed itself because of this development. The one specific question that we’ve decided to focus on in this paper is if the party manages to sustain sufficient inter-party democracy towards its own members. By applying opinion-based representation as understood in Hanna Pitkin’s book “The Concept of Representation” we will examine this by the usage of three critical case studies that have brought this idea into question. The first case deals with the financing of the party and focuses on the role of lotteries as a means of party finance. The second case deals with the party's process and subsequent decision to join NATO. The third case deals with a party election in a Stockholm suburb (Botkyrka) and the exclusion of party members. The cases were chosen because of their differences and their ability to encase different aspects of opinion-based representation. The analysis was done from a question-based instrument taken from the ideas of Pitkin and then applied to the actions of the party and its representatives. What we found was that the party systematically fails to fulfill Pitkin’s idea of representation in all three cases. We find this to be significant due to its implications for the development of democracy. A representational democracy without representation is not a fully functioning democracy.