From correspondence analysis to multiple and joint correspondence analysis

The generalization of simple (two-variable) correspondence analysis to more than two categorical variables, commonly referred to as multiple correspondence analysis, is neither obvious nor well-defined. We present two alternative ways of generalizing correspondence analysis, one based on the quantification of the variables and intercorrelation relationships, and the other based on the geometric ideas of simple correspondence analysis. We propose a version of multiple correspondence analysis, with adjusted principal inertias, as the method of choice for the geometric definition, since it contains simple correspondence analysis as an exact special case, which is not the situation of the standard generalizations. We also clarify the issue of supplementary point representation and the properties of joint correspondence analysis, a method that visualizes all two-way relationships between the variables. The methodology is illustrated using data on attitudes to science from the International Social Survey Program on Environment in 1993.

The choice of the form of representation in multinational banking: Evidence from Spain

This paper investigates foreign direct investment in the bankingsector. The attention has been addressed to test the importanceof OLI advantages as the determinants of the bank's decisionto invest in foreign locations. Nevertheless, since banks canexpand their activities abroad through different organizationalforms that imply different levels of foreign involvement, theissue of the form of representation has been tackled. Theresults show the importance of OLI advantages in the form ofrepresentation in multinational banking.

The connectome viewer toolkit: an open source framework to manage, analyze, and visualize connectomes.

Advanced neuroinformatics tools are required for methods of connectome mapping, analysis, and visualization. The inherent multi-modality of connectome datasets poses new challenges for data organization, integration, and sharing. We have designed and implemented the Connectome Viewer Toolkit - a set of free and extensible open source neuroimaging tools written in Python. The key components of the toolkit are as follows: (1) The Connectome File Format is an XML-based container format to standardize multi-modal data integration and structured metadata annotation. (2) The Connectome File Format Library enables management and sharing of connectome files. (3) The Connectome Viewer is an integrated research and development environment for visualization and analysis of multi-modal connectome data. The Connectome Viewer's plugin architecture supports extensions with network analysis packages and an interactive scripting shell, to enable easy development and community contributions. Integration with tools from the scientific Python community allows the leveraging of numerous existing libraries for powerful connectome data mining, exploration, and comparison. We demonstrate the applicability of the Connectome Viewer Toolkit using Diffusion MRI datasets processed by the Connectome Mapper. The Connectome Viewer Toolkit is available from http://www.cmtk.org/

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

UniPathway: a resource for the exploration and annotation of metabolic pathways.

UniPathway (http://www.unipathway.org) is a fully manually curated resource for the representation and annotation of metabolic pathways. UniPathway provides explicit representations of enzyme-catalyzed and spontaneous chemical reactions, as well as a hierarchical representation of metabolic pathways. This hierarchy uses linear subpathways as the basic building block for the assembly of larger and more complex pathways, including species-specific pathway variants. All of the pathway data in UniPathway has been extensively cross-linked to existing pathway resources such as KEGG and MetaCyc, as well as sequence resources such as the UniProt KnowledgeBase (UniProtKB), for which UniPathway provides a controlled vocabulary for pathway annotation. We introduce here the basic concepts underlying the UniPathway resource, with the aim of allowing users to fully exploit the information provided by UniPathway.

The Impact of ICTs on Lecturer and Student Interaction in University Education Processes

Interaction is a basic element in any educational process, and it is something that needs to be reconsidered in the light of technology. In order to examine the methodological changes that ICTs bring to teaching from an interaction perspective, a study was carried out at the University of Lleida to observe interaction processes in various face-to-face, blended learning and e-learning subjects. The methodological design was based on three data collection techniques: documentary analysis of subject curricula, lecturer and student questionnaires, and lecturer interviews. The data showed that, as the online component of subjects increased, the lecturers and students used more technological tools to communicate (e-mail, forums, chats, social networks, etc.). Furthermore, we found that the lecturers and students basically communicated for academic purposes. While they hardly ever communicated for personal reasons (guidance, support, etc.), they claimed that closer contact with a non-academic focus would be preferable. We also observed that the students’ work was more individual in e-learning subjects. Although there is still a considerable way to go in ICT-mediated lecturer-student interaction, both the lecturers and students recognise the potential of such technologies, even though they still do not use them as they feel they should.

Automated monitoring of medical protocols: a secure and distributed architecture

The control of the right application of medical protocols is a key issue in hospital environments. For the automated monitoring of medical protocols, we need a domain-independent language for their representation and a fully, or semi, autonomous system that understands the protocols and supervises their application. In this paper we describe a specification language and a multi-agent system architecture for monitoring medical protocols. We model medical services in hospital environments as specialized domain agents and interpret a medical protocol as a negotiation process between agents. A medical service can be involved in multiple medical protocols, and so specialized domain agents are independent of negotiation processes and autonomous system agents perform monitoring tasks. We present the detailed architecture of the system agents and of an important domain agent, the database broker agent, that is responsible of obtaining relevant information about the clinical history of patients. We also describe how we tackle the problems of privacy, integrity and authentication during the process of exchanging information between agents.

Karyotype and NOR-banding of mitotic chromosomes of some Vitis L. species

Chromosome studies were performed in V. champinii, V. cinerea, V. girdiana, V. labrusca, V. rotundifolia, V. rupestris and V. vinifera with the purpose of species characterization using chromosome morphometric data and NOR banding. A median ideogram was obtained for each species. The karyotype formula obtained varied from 7m + 12sm to 9m + 11sm. The species showed moderate chromosome asymmetry values according to TF% form, Stebbins, Romero Zarco and Paszko indices. V. champinii and V. girdiana were apart from the other species by CVcl and CVci graphic representation and also formed a group apart in the dendrogram based on Euclidian distances. The chromosome pair number 3 harbors the secondary constriction and a satellite segment in all species analyzed with Giemsa staining and it may be the same observed after NOR banding technique. It seems that the process of speciation in the North American Euvitis species studied involved some discrete changes in chromosome morphometry which have been reflected in the asymmetry index.

Standardized evaluation methodology and reference database for evaluating IVUS image segmentation

This paper describes an evaluation framework that allows a standardized and quantitative comparison of IVUS lumen and media segmentation algorithms. This framework has been introduced at the MICCAI 2011 Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop, comparing the results of eight teams that participated. We describe the available data-base comprising of multi-center, multi-vendor and multi-frequency IVUS datasets, their acquisition, the creation of the reference standard and the evaluation measures. The approaches address segmentation of the lumen, the media, or both borders; semi- or fully-automatic operation; and 2-D vs. 3-D methodology. Three performance measures for quantitative analysis have been proposed. The results of the evaluation indicate that segmentation of the vessel lumen and media is possible with an accuracy that is comparable to manual annotation when semi-automatic methods are used, as well as encouraging results can be obtained also in case of fully-automatic segmentation. The analysis performed in this paper also highlights the challenges in IVUS segmentation that remains to be solved.

Fluoroscopic-Guided Radial Endobronchial Ultrasound Without Guide Sheath For Peripheral Pulmonary Lesions: A Safe And Efficient Combination.

BACKGROUND: Several guidelines recommend computed tomography scans for populations with high-risk for lung cancer. The number of individuals evaluated for peripheral pulmonary lesions (PPL) will probably increase, and with it non-surgical biopsies. Associating a guidance method with a target confirmation technique has been shown to achieve the highest diagnostic yield, but the utility of bronchoscopy with radial probe endobronchial ultrasound using fluoroscopy as guidance without a guide sheath has not been reported. METHODS: We conducted a retrospective analysis of bronchoscopy with radial probe endobronchial ultrasound using fluoroscopy procedures for the investigation of PPL performed by experienced bronchoscopists with no specific previous training in this particular technique. Operator learning curves and radiological predictors were assessed for all consecutive patients examined during the first year of application of the technique. RESULTS: Fifty-one PPL were investigated. Diagnostic yield and visualization yield were 72.5 and 82.3% respectively. The diagnostic yield was 64.0% for PPL ≤20mm, and 80.8% for PPL>20mm. No false-positive results were recorded. The learning curve of all diagnostic tools showed a DY of 72.7% for the first sub-group of patients, 81.8% for the second, 72.7% for the third, and 81.8% for the last. CONCLUSION: Bronchoscopy with radial probe endobronchial ultrasound using fluoroscopy as guidance is safe and simple to perform, even without specific prior training, and diagnostic yield is high for PPL>and ≤20mm. Based on these findings, this method could be introduced as a first-line procedure for the investigation of PPL, particularly in centers with limited resources.

Change points detection in crime-related time series: an on-line fuzzy approach based on a shape space representation

The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.

A Comparative Study of Desktop, Fishtank, and Cave Systems for the Exploration of Volume Rendered Confocal Data Sets

We present a participant study that compares biological data exploration tasks using volume renderings of laser confocal microscopy data across three environments that vary in level of immersion: a desktop, fishtank, and cave system. For the tasks, data, and visualization approach used in our study, we found that subjects qualitatively preferred and quantitatively performed better in the cave compared with the fishtank and desktop. Subjects performed real-world biological data analysis tasks that emphasized understanding spatial relationships including characterizing the general features in a volume, identifying colocated features, and reporting geometric relationships such as whether clusters of cells were coplanar. After analyzing data in each environment, subjects were asked to choose which environment they wanted to analyze additional data sets in - subjects uniformly selected the cave environment.

Spontaneous Focusing on Quantitative Relations and the Development of Rational Number Conceptual Knowledge

The aim of the present set of studies was to explore primary school children’s Spontaneous Focusing On quantitative Relations (SFOR) and its role in the development of rational number conceptual knowledge. The specific goals were to determine if it was possible to identify a spontaneous quantitative focusing tendency that indexes children’s tendency to recognize and utilize quantitative relations in non-explicitly mathematical situations and to determine if this tendency has an impact on the development of rational number conceptual knowledge in late primary school. To this end, we report on six original empirical studies that measure SFOR in children ages five to thirteen years and the development of rational number conceptual knowledge in ten- to thirteen-year-olds. SFOR measures were developed to determine if there are substantial differences in SFOR that are not explained by the ability to use quantitative relations. A measure of children’s conceptual knowledge of the magnitude representations of rational numbers and the density of rational numbers is utilized to capture the process of conceptual change with rational numbers in late primary school students. Finally, SFOR tendency was examined in relation to the development of rational number conceptual knowledge in these students. Study I concerned the first attempts to measure individual differences in children’s spontaneous recognition and use of quantitative relations in 86 Finnish children from the ages of five to seven years. Results revealed that there were substantial inter-individual differences in the spontaneous recognition and use of quantitative relations in these tasks. This was particularly true for the oldest group of participants, who were in grade one (roughly seven years old). However, the study did not control for ability to solve the tasks using quantitative relations, so it was not clear if these differences were due to ability or SFOR. Study II more deeply investigated the nature of the two tasks reported in Study I, through the use of a stimulated-recall procedure examining children’s verbalizations of how they interpreted the tasks. Results reveal that participants were able to verbalize reasoning about their quantitative relational responses, but not their responses based on exact number. Furthermore, participants’ non-mathematical responses revealed a variety of other aspects, beyond quantitative relations and exact number, which participants focused on in completing the tasks. These results suggest that exact number may be more easily perceived than quantitative relations. As well, these tasks were revealed to contain both mathematical and non-mathematical aspects which were interpreted by the participants as relevant. Study III investigated individual differences in SFOR 84 children, ages five to nine, from the US and is the first to report on the connection between SFOR and other mathematical abilities. The cross-sectional data revealed that there were individual differences in SFOR. Importantly, these differences were not entirely explained by the ability to solve the tasks using quantitative relations, suggesting that SFOR is partially independent from the ability to use quantitative relations. In other words, the lack of use of quantitative relations on the SFOR tasks was not solely due to participants being unable to solve the tasks using quantitative relations, but due to a lack of the spontaneous attention to the quantitative relations in the tasks. Furthermore, SFOR tendency was found to be related to arithmetic fluency among these participants. This is the first evidence to suggest that SFOR may be a partially distinct aspect of children’s existing mathematical competences. Study IV presented a follow-up study of the first graders who participated in Studies I and II, examining SFOR tendency as a predictor of their conceptual knowledge of fraction magnitudes in fourth grade. Results revealed that first graders’ SFOR tendency was a unique predictor of fraction conceptual knowledge in fourth grade, even after controlling for general mathematical skills. These results are the first to suggest that SFOR tendency may play a role in the development of rational number conceptual knowledge. Study V presents a longitudinal study of the development of 263 Finnish students’ rational number conceptual knowledge over a one year period. During this time participants completed a measure of conceptual knowledge of the magnitude representations and the density of rational numbers at three time points. First, a Latent Profile Analysis indicated that a four-class model, differentiating between those participants with high magnitude comparison and density knowledge, was the most appropriate. A Latent Transition Analysis reveal that few students display sustained conceptual change with density concepts, though conceptual change with magnitude representations is present in this group. Overall, this study indicated that there were severe deficiencies in conceptual knowledge of rational numbers, especially concepts of density. The longitudinal Study VI presented a synthesis of the previous studies in order to specifically detail the role of SFOR tendency in the development of rational number conceptual knowledge. Thus, the same participants from Study V completed a measure of SFOR, along with the rational number test, including a fourth time point. Results reveal that SFOR tendency was a predictor of rational number conceptual knowledge after two school years, even after taking into consideration prior rational number knowledge (through the use of residualized SFOR scores), arithmetic fluency, and non-verbal intelligence. Furthermore, those participants with higher-than-expected SFOR scores improved significantly more on magnitude representation and density concepts over the four time points. These results indicate that SFOR tendency is a strong predictor of rational number conceptual development in late primary school children. The results of the six studies reveal that within children’s existing mathematical competences there can be identified a spontaneous quantitative focusing tendency named spontaneous focusing on quantitative relations. Furthermore, this tendency is found to play a role in the development of rational number conceptual knowledge in primary school children. Results suggest that conceptual change with the magnitude representations and density of rational numbers is rare among this group of students. However, those children who are more likely to notice and use quantitative relations in situations that are not explicitly mathematical seem to have an advantage in the development of rational number conceptual knowledge. It may be that these students gain quantitative more and qualitatively better self-initiated deliberate practice with quantitative relations in everyday situations due to an increased SFOR tendency. This suggests that it may be important to promote this type of mathematical activity in teaching rational numbers. Furthermore, these results suggest that there may be a series of spontaneous quantitative focusing tendencies that have an impact on mathematical development throughout the learning trajectory.

Postcolonial readings of resistance and negotiation in selected contemporary African writing

Cette dissertation traite des (re)configurations postcoloniales de la résistance et de la négociation comme concepts permettant d’aborder les représentations des conflits nationaux dans les littératures Africaines contemporaines. Ensemble, ces concepts ouvrent de nouvelles voix et possibilités de se remémorer, de raconter, et de lire la violence en problématisant non seulement les discours sur la guerre civile en Afrique, mais aussi les conceptions d’histoire nationale, de la mémoire, et de leur représentation. Si cette étude cherche à reconfigurer la négociation et la résistance au-delà des définitions qui tendent à les opposer, elle se consacre surtout à développer la notion de négociation comme stratégie de dépassement, de lecture, et d’écriture, qui, néanmoins, ne vise pas de résolution. La négociation implique ainsi une conception pluraliste d’un pouvoir d’action sociale, politique, et culturelle. Cette dissertation avance que la négociation est un concept d’écriture et de lecture qui intervient dans les événements, discours, et pratiques de remémoration en prenant compte de leurs multiplicités et définitions instables. Cette étude explore les manières selon lesquelles Nuruddin Farah, Chenjerai Hove, Yvonne Vera, Chimamanda Adichie, et Sefi Atta déploient la négociation et la résistance comme outils d’engagement esthétique et sociopolitique dans la narration de la violence en Somalie, au Zimbabwe, et au Nigeria. En outre, la négociation marque mon analyse de l’intervention des textes dans les discours d’historiographie et de représentation. Si ces romans mettent en exergue la généalogie complexe du conflit postcolonial, ils négocient aussi les implications multiples, incluant la leur, dans les questions problématiques de la responsabilité et de la représentation. La vii négociation représente un acte conscient à travers lequel nous reconnaissons l’instabilité de toute bataille politique, morale, ou éthique sans pour autant céder à un cynisme paralysant. De par son approche négociée et interdisciplinaire, cette dissertation ne fait pas qu’entrer en débat avec des discours multiples des études postcoloniales, Africaines, et littéraires. Elle intervient aussi dans les conceptions de la nation, la violence, la mémoire, la responsabilité, et la justice selon les études philosophiques, politiques, et culturelles. Outre les critiques littéraires, les chapitres interrogent les théories de penseurs tels Ngugi wa Thiong’o, David Jefferess, Pheng Cheah, et Wole Soyinka. Cette approche éclectique reflète l’attention des romans à la complexité irréductible des responsabilités individuelles et collectives dans les récits d’histoire et d’appartenance nationales. Cet engagement négocié avec les questions entourant la postcolonialité, malgré la dominance actuelle des discours de la globalisation, permet de reconceptualiser l’approche postcoloniale pour contrer les analyses déhistorisées et décontextualisées des conflits sociopolitiques en Afrique. Le chapitre 1 élabore les concepts clés de la dissertation. Le chapitre 2 explore la résistance et la négociation dans le langage figuré métonymique dans les représentations de la guerre en Somalie. Le chapitre 3 se consacre à l’analyse de la figure de la spectralité dans la narration de l’histoire et de la violence nationales au Zimbabwe. Finalement, le chapitre 4 négocie les concepts de représentation et de responsabilité dans les récits du trauma postcolonial au Nigeria. viii Mots-clés : négociation, résistance, littératures africaines, violence, responsabilité, nation, représentation

Redefining U.S. borders : a reading of Sandra Cisneros’s Caramelo, Cristina Garcia’s The Agüero Sisters, and David Plante’s The Family and The Native

Cette thèse propose une redéfinition de la notion de frontière dans le contexte américain, avec pour point de départ les romans de trois voix littéraires issues de trois minorités ethniques : Sandra Cisneros (Caramelo), Cristina Garcia (The Agüero Sisters) et David Plante (The Family et The Native). Je conceptualise la frontière comme fluctuation entre mouvement et immobilité, entre porosité et imperméabilité. Dans le premier chapitre, je fournis des repères sur la théorie des frontières et j'analyse les avancées de ce champ d'étude, du concept de terre frontalière (“Borderland Theories”) jusqu'aux récits d'immigration. Je propose un cadre conceptuel que j'appelle « Écrire la frontière à partir de la perspective de la frontière », lequel permet une lecture neuve des récits de frontière, et une redéfinition de la notion elle-même. Prise comme perspective, la frontière est une dynamique vivante, ce qui la rend plurielle et impossible à fixer définitivement; aussi les récits de frontière présentent-ils une grande variété d’expériences, toutes liées à des moments et à des points de vue uniques. Dans le second chapitre, j’analyse la porosité des frontières dans le contexte géopolitique contemporain, en mettant en lumière comment la colonisation, la mondialisation économique et l’immigration sont autant de mécanismes de transgression des frontières qui suivent des orientations transnationales, dénationales et postnationales. Dans le troisième chapitre, j’étudie la résurgence des frontières dans la vie des immigrants qui habitent aux États-Unis. J’identifie l’insécurité capitaliste ainsi que la marchandisation de l’espace et de l’ethnicité comme étant à l'origine du renforcement des frontières délimitant les quartiers ethniques; génératrices de stéréotypes négatifs, ces divisions physiques deviennent une technologie d’exclusion et d’injustice sociale. Le dernier chapitre présente une lecture des aspects esthétiques de la frontière, voyant comment ils peuvent contribuer à écrire la frontière à partir de la perspective de la frontière. Dans les textes à l'étude, j'examine de près la problématisation du concept de représentation, la multiplicité des points de vue narratifs, l’inaccessibilité du réel, et la partialité de la médiation. Mots clés : Théories et écrits sur les frontières, minorités ethniques aux États-Unis, multiculturalisme, culture, immigration, mondialisation, espace, place, territoire, état-nation, nationalisme, histoire, langue et langage, représentation, communauté, justice sociale, citoyenneté