Voir le monde à travers les lunettes du métacontraste : un modèle mathématique inspiré de la psychologie sociale

Résumé La théorie de l'autocatégorisation est une théorie de psychologie sociale qui porte sur la relation entre l'individu et le groupe. Elle explique le comportement de groupe par la conception de soi et des autres en tant que membres de catégories sociales, et par l'attribution aux individus des caractéristiques prototypiques de ces catégories. Il s'agit donc d'une théorie de l'individu qui est censée expliquer des phénomènes collectifs. Les situations dans lesquelles un grand nombre d'individus interagissent de manière non triviale génèrent typiquement des comportements collectifs complexes qui sont difficiles à prévoir sur la base des comportements individuels. La simulation informatique de tels systèmes est un moyen fiable d'explorer de manière systématique la dynamique du comportement collectif en fonction des spécifications individuelles. Dans cette thèse, nous présentons un modèle formel d'une partie de la théorie de l'autocatégorisation appelée principe du métacontraste. À partir de la distribution d'un ensemble d'individus sur une ou plusieurs dimensions comparatives, le modèle génère les catégories et les prototypes associés. Nous montrons que le modèle se comporte de manière cohérente par rapport à la théorie et est capable de répliquer des données expérimentales concernant divers phénomènes de groupe, dont par exemple la polarisation. De plus, il permet de décrire systématiquement les prédictions de la théorie dont il dérive, notamment dans des situations nouvelles. Au niveau collectif, plusieurs dynamiques peuvent être observées, dont la convergence vers le consensus, vers une fragmentation ou vers l'émergence d'attitudes extrêmes. Nous étudions également l'effet du réseau social sur la dynamique et montrons qu'à l'exception de la vitesse de convergence, qui augmente lorsque les distances moyennes du réseau diminuent, les types de convergences dépendent peu du réseau choisi. Nous constatons d'autre part que les individus qui se situent à la frontière des groupes (dans le réseau social ou spatialement) ont une influence déterminante sur l'issue de la dynamique. Le modèle peut par ailleurs être utilisé comme un algorithme de classification automatique. Il identifie des prototypes autour desquels sont construits des groupes. Les prototypes sont positionnés de sorte à accentuer les caractéristiques typiques des groupes, et ne sont pas forcément centraux. Enfin, si l'on considère l'ensemble des pixels d'une image comme des individus dans un espace de couleur tridimensionnel, le modèle fournit un filtre qui permet d'atténuer du bruit, d'aider à la détection d'objets et de simuler des biais de perception comme l'induction chromatique. Abstract Self-categorization theory is a social psychology theory dealing with the relation between the individual and the group. It explains group behaviour through self- and others' conception as members of social categories, and through the attribution of the proto-typical categories' characteristics to the individuals. Hence, it is a theory of the individual that intends to explain collective phenomena. Situations involving a large number of non-trivially interacting individuals typically generate complex collective behaviours, which are difficult to anticipate on the basis of individual behaviour. Computer simulation of such systems is a reliable way of systematically exploring the dynamics of the collective behaviour depending on individual specifications. In this thesis, we present a formal model of a part of self-categorization theory named metacontrast principle. Given the distribution of a set of individuals on one or several comparison dimensions, the model generates categories and their associated prototypes. We show that the model behaves coherently with respect to the theory and is able to replicate experimental data concerning various group phenomena, for example polarization. Moreover, it allows to systematically describe the predictions of the theory from which it is derived, specially in unencountered situations. At the collective level, several dynamics can be observed, among which convergence towards consensus, towards frag-mentation or towards the emergence of extreme attitudes. We also study the effect of the social network on the dynamics and show that, except for the convergence speed which raises as the mean distances on the network decrease, the observed convergence types do not depend much on the chosen network. We further note that individuals located at the border of the groups (whether in the social network or spatially) have a decisive influence on the dynamics' issue. In addition, the model can be used as an automatic classification algorithm. It identifies prototypes around which groups are built. Prototypes are positioned such as to accentuate groups' typical characteristics and are not necessarily central. Finally, if we consider the set of pixels of an image as individuals in a three-dimensional color space, the model provides a filter that allows to lessen noise, to help detecting objects and to simulate perception biases such as chromatic induction.

Voir le monde comme une image : le schème de l'image mimétique dans la philosophie de Platon (Cratyle, Sophiste, Timée)

Résumé de la thèse Le travail de thèse «VOIR LE MONDE COMME UNE IMAGE. Le schème de l'image mimétique dans la philosophie de Platon (Cratyle, Sophiste, Timée) » d'Alexandre NEVSKY étudie la conception philosophique de l'image chez Platon. En posant la question : qu'est-ce que l'image pour Platon? l'étude se propose, dans un premier temps, d'analyser la manière précise dont l'idée de l'image fonctionne dans l'articulation logique de l'enquête platonicienne, en se basant avant tout sur trois dialogues majeurs où cette idée est explicitement thématisée par Platon lui-même, à savoir le Cratyle, le Sophiste et le Timée. Par une analyse détaillée de ces textes, Alexandre Nevsky essaie de démontrer que l'idée de l'image fonctionne comme un schème euristique dont la logique interne détermine les moments clés dans le déroulement de chaque dialogue examiné, et constitue ainsi une véritable méthode d'investigation philosophique pour Platon. En suivant cette stratégie platonicienne, l'auteur nous montre quel rôle le schème de l'image joue selon Platon d'abord dans la constitution du langage (le Cratyle), puis, dans celle du discours (le Sophiste) et, enfin, dans celle du monde (le Timée). Une telle approche lui permet de revoir l'interprétation traditionnelle de certains passages clés, célèbres pour leurs difficultés, en mettant en évidence la façon dont la nouvelle perspective platonicienne, introduite grâce au schème de l'image, permet de formuler une solution philosophique originale du problème initial. On y trouve ainsi rediscutés, pour ne citer que quelques exemples, la théorie curieuse de l'imitation phonétique et le problème de la justesse propre du nom-image, la définition philosophique de la notion d'image et la distinction platonicienne entre limage-ressemblance et l'image-apparence, la logique paradoxale de l'introduction d'un troisième genre dans la structure ontologique de l'être et la question du sens exact à donner au «discours vraisemblable » de Platon sur la naissance de l'univers. Dans un deuxième temps, cette étude tente de dégager, derrière la méthode heuristique basée sur le schème de l'image, une véritable conception de l'image mimétique chez Platon. L'une des idées principales de la thèse est ici de montrer que cette conception présente une solution philosophique de Platon au problème de l'apparence archaïque. Car, face à la question sophistique : comment une chose - que ce soit le discours ou le monde - peut-elle être une apparence, quelque chose qui n'existe pas? Platon apporte une réponse tout à fait originale elle le peut en tant qu'image. Or, l'image n'est pas une simple apparence illusoire, elle est le reflet d'une autre réalité, indépendante et véritable, que l'on doit supposer, selon Platon, même quand sa nature exacte nous échappe encore. La conception platonicienne de l'image apparaît ainsi comme un pendant indispensable de la théorie des Formes intelligibles et aussi comme son étape préalable au niveau de laquelle l'âme du philosophe, dans son ascension vers la vérité, se retrouve dans un espace intermédiaire, déjà au-delà des illusions du monde phénoménal, mais encore en-deçà des engagements métaphysiques de la théorie des Formes.

Stimulated-echo acquisition mode (STEAM) MRI for black-blood delayed hyperenhanced myocardial imaging.

PURPOSE: To develop a breathhold method for black-blood viability imaging of the heart that may facilitate identifying the endocardial border. MATERIALS AND METHODS: Three stimulated-echo acquisition mode (STEAM) images were obtained almost simultaneously during the same acquisition using three different demodulation values. Two of the three images were used to construct a black-blood image of the heart. The third image was a T(1)-weighted viability image that enabled detection of hyperintense infarcted myocardium after contrast agent administration. The three STEAM images were combined into one composite black-blood viability image of the heart. The composite STEAM images were compared to conventional inversion-recovery (IR) delayed hyperenhanced (DHE) images in nine human subjects studied on a 3T MRI scanner. RESULTS: STEAM images showed black-blood characteristics and a significant improvement in the blood-infarct signal-difference to noise ratio (SDNR) when compared to the IR-DHE images (34 +/- 4.1 vs. 10 +/- 2.9, mean +/- standard deviation (SD), P < 0.002). There was sufficient myocardium-infarct SDNR in the STEAM images to accurately delineate infarcted regions. The extracted infarcts demonstrated good agreement with the IR-DHE images. CONCLUSION: The STEAM black-blood property allows for better delineation of the blood-infarct border, which would enhance the fast and accurate measurement of infarct size.

Ultra-High Field (7 Tesla) MRI for Gamma Knife Surgery Targeting of the Ventro-Intermediate Nucleus: A Pilot in Vivo Study

Aim: Gamma Knife surgery (GKS) is a non-invasive neurosurgical stereotactic procedure, increasingly used as an alternative to open functional procedures. This includes the targeting of the ventro-intermediate (Vim) nucleus of the thalamus for tremor. We currently perform an indirect targeting, using the "quadrilatere of Guyot," as the Vim nucleus is not visible on current 3 Tesla (T) MRI acquisitions. The primary objective of the current study was to enhance anatomic imaging for Vim GKS using high-field (7 T) MRI, with the aim of refining the visualization and precision of anatomical targeting. Method: Five young healthy subjects (mean age 23 years) were scanned both on 3 and 7 T MRI in Lausanne University Hospital (CHUV) and Center for Biomedical Imaging (CIBM). Classical T1-weighted MPRAGE, T2 CISS sequences (replacing former ventriculography) and diffusion tensor imaging were acquired at 3T. We obtained high-resolution susceptibility weighted images (SWI) at 7T for the visualization of thalamic subparts. SWI was further integrated for the first time into Leksell Gamma Plan® (LGP) software and co-registered with the 3T images. A simulation of targeting of the Vim was done using the "quadrilatere of Guyot" methodology on the 3T images. Furthermore, a correlation with the position of the found target on SWI was performed. The atlas of Morel et al. was used to confirm the findings on a detailed computer analysis outside LGP. Also, 3T and 7T MRI of one patient undergoing GKS Vim thalamotomy, were obtained before and 2 years after the procedure, and studied similarly. Results: The use of SWI provided a superior resolution and improved image contrast within the central gray matter. This allowed visualization and direct delineation of groups of thalamic nuclei in vivo, including the Vim. The position of the target, as assessed with the "quadrilatere of Guyot" method on 3 T, perfectly matched with the supposed one of the Vim on the SWI. Furthermore, a 3-dimensional model of the Vim target area was created on the basis of 3T and 7T images. Conclusion: This is the first report of the integration of SWI high-field MRI into the LGP in healthy subjects and in one patient treated GKS Vim thalamotomy. This approach aims at the improvement of targeting validation and further direct targeting of the Vim in tremor. The anatomical correlation between the direct visualization on 7T and the current targeting methods on 3T seems to show a very good anatomical matching.

Correction of through-plane deformation artifacts in stimulated echo acquisition mode cardiac imaging.

Attempts to use a stimulated echo acquisition mode (STEAM) in cardiac imaging are impeded by imaging artifacts that result in signal attenuation and nulling of the cardiac tissue. In this work, we present a method to reduce this artifact by acquiring two sets of stimulated echo images with two different demodulations. The resulting two images are combined to recover the signal loss and weighted to compensate for possible deformation-dependent intensity variation. Numerical simulations were used to validate the theory. Also, the proposed correction method was applied to in vivo imaging of normal volunteers (n = 6) and animal models with induced infarction (n = 3). The results show the ability of the method to recover the lost myocardial signal and generate artifact-free black-blood cardiac images.

Sex differences in urinary levels of several biological indicators of exposure: a simulation study using a compartmental based toxicokinetic model

Toxicokinetic modeling is a useful tool to describe or predict the behavior of a chemical agent in the human or animal organism. A general model based on four compartments was developed in a previous study in order to quantify the effect of human variability on a wide range of biological exposure indicators. The aim of this study was to adapt this existing general toxicokinetic model to three organic solvents, which were methyl ethyl ketone, 1-methoxy-2-propanol and 1,1,1,-trichloroethane, and to take into account sex differences. We assessed in a previous human volunteer study the impact of sex on different biomarkers of exposure corresponding to the three organic solvents mentioned above. Results from that study suggested that not only physiological differences between men and women but also differences due to sex hormones levels could influence the toxicokinetics of the solvents. In fact the use of hormonal contraceptive had an effect on the urinary levels of several biomarkers, suggesting that exogenous sex hormones could influence CYP2E1 enzyme activity. These experimental data were used to calibrate the toxicokinetic models developed in this study. Our results showed that it was possible to use an existing general toxicokinetic model for other compounds. In fact, most of the simulation results showed good agreement with the experimental data obtained for the studied solvents, with a percentage of model predictions that lies within the 95% confidence interval varying from 44.4 to 90%. Results pointed out that for same exposure conditions, men and women can show important differences in urinary levels of biological indicators of exposure. Moreover, when running the models by simulating industrial working conditions, these differences could even be more pronounced. In conclusion, a general and simple toxicokinetic model, adapted for three well known organic solvents, allowed us to show that metabolic parameters can have an important impact on the urinary levels of the corresponding biomarkers. These observations give evidence of an interindividual variablity, an aspect that should have its place in the approaches for setting limits of occupational exposure.

Numerical simulation of gel electrophoresis of DNA knots in weak and strong electric fields.

Gel electrophoresis allows one to separate knotted DNA (nicked circular) of equal length according to the knot type. At low electric fields, complex knots, being more compact, drift faster than simpler knots. Recent experiments have shown that the drift velocity dependence on the knot type is inverted when changing from low to high electric fields. We present a computer simulation on a lattice of a closed, knotted, charged DNA chain drifting in an external electric field in a topologically restricted medium. Using a Monte Carlo algorithm, the dependence of the electrophoretic migration of the DNA molecules on the knot type and on the electric field intensity is investigated. The results are in qualitative and quantitative agreement with electrophoretic experiments done under conditions of low and high electric fields.

Prediction of Infant Drug Exposure Through Breastfeeding: Population PK Modeling and Simulation of Fluoxetine Exposure.

The likelihood of significant exposure to drugs in infants through breast milk is poorly defined, given the difficulties of conducting pharmacokinetics (PK) studies. Using fluoxetine (FX) as an example, we conducted a proof-of-principle study applying population PK (popPK) modeling and simulation to estimate drug exposure in infants through breast milk. We simulated data for 1,000 mother-infant pairs, assuming conservatively that the FX clearance in an infant is 20% of the allometrically adjusted value in adults. The model-generated estimate of the milk-to-plasma ratio for FX (mean: 0.59) was consistent with those reported in other studies. The median infant-to-mother ratio of FX steady-state plasma concentrations predicted by the simulation was 8.5%. Although the disposition of the active metabolite, norfluoxetine, could not be modeled, popPK-informed simulation may be valid for other drugs, particularly those without active metabolites, thereby providing a practical alternative to conventional PK studies for exposure risk assessment in this population.

Accurate and efficient simulation of multiphase flow in a heterogeneous reservoir by using error estimate and control in the multiscale finite-volume framework

The multiscale finite-volume (MSFV) method is designed to reduce the computational cost of elliptic and parabolic problems with highly heterogeneous anisotropic coefficients. The reduction is achieved by splitting the original global problem into a set of local problems (with approximate local boundary conditions) coupled by a coarse global problem. It has been shown recently that the numerical errors in MSFV results can be reduced systematically with an iterative procedure that provides a conservative velocity field after any iteration step. The iterative MSFV (i-MSFV) method can be obtained with an improved (smoothed) multiscale solution to enhance the localization conditions, with a Krylov subspace method [e.g., the generalized-minimal-residual (GMRES) algorithm] preconditioned by the MSFV system, or with a combination of both. In a multiphase-flow system, a balance between accuracy and computational efficiency should be achieved by finding a minimum number of i-MSFV iterations (on pressure), which is necessary to achieve the desired accuracy in the saturation solution. In this work, we extend the i-MSFV method to sequential implicit simulation of time-dependent problems. To control the error of the coupled saturation/pressure system, we analyze the transport error caused by an approximate velocity field. We then propose an error-control strategy on the basis of the residual of the pressure equation. At the beginning of simulation, the pressure solution is iterated until a specified accuracy is achieved. To minimize the number of iterations in a multiphase-flow problem, the solution at the previous timestep is used to improve the localization assumption at the current timestep. Additional iterations are used only when the residual becomes larger than a specified threshold value. Numerical results show that only a few iterations on average are necessary to improve the MSFV results significantly, even for very challenging problems. Therefore, the proposed adaptive strategy yields efficient and accurate simulation of multiphase flow in heterogeneous porous media.

Advanced Kernel Methods For Remote Sensing Image Classification

Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.