„68“ als politische Herausforderung.: Der Einfluss der 68er-Bewegung auf den Wandel der politischen Parteien in Frankreich und in der Bundesrepublik

International audienc

Numerical investigations of fault propagation and forced-fold using a non smooth discrete element method

Geophysical problems as forced-fold evolution and fault propagation induce large deformations and many localisation. The continuum mechanics does not seem the more appropriate for their description and it appears more interesting to represent the media as initially discontinuous. To face both phenomena, a non smooth Discrete Element Method is used. Geophysical structures are considered as collection of rigid disks which interact by cohesive frictional contact laws. Numerical geophysical formations are correlated to mechanical properties of structures through observation and mechanical analysis.Les problèmes géophysiques tels que l’évolution des plis et la propagation de failles induisent de grandes déformations et de nombreuses localisations. Il apparaît donc difficile de décrire le problème avec les outils de la mécanique des milieux continus, et il est donc preferable de représenter la structure comme initialement divisée. Ces deux phénomènes sont étudiés via une approche non régulière par éléments discrets. Les structures géologiques sont considérées comme des collections de particules dont les interactions répondent à des lois de contact cohésif frottant. Les observations des structures géophysiques numériques sont corrélées aux propriétés des structures au travers d’une analyse mécanique

Optimal diversification in the presence of parameter uncertainty for a risk averse investor

We consider an investor who faces parameter uncertainty in a continuoustime financial market. We model the investor's preference by a power utility function leading to constant relative risk aversion. We show that the loss in expected utility is large when using a simple plug-in strategy for unknown parameters. We also provide theoretical results that show the trade-off between holding a well-diversified portfolio and a portfolio that is robust against estimation errors. To reduce the effect of estimation, we constrain the weights of the risky assets with an L1-norm leading to a sparse portfolio. We provide analytical results that show how the sparsity of the constrained portfolio depends on the coefficient of relative risk aversion. Based on a simulation study, we demonstrate the existence and the uniqueness of an optimal bound on the L1-norm for each level of relative risk aversion

Optimal diversification in the presence of parameter uncertainty for a risk averse investor

We consider an investor who faces parameter uncertainty in a continuoustime financial market. We model the investor's preference by a power utility function leading to constant relative risk aversion. We show that the loss in expected utility is large when using a simple plug-in strategy for unknown parameters. We also provide theoretical results that show the trade-off between holding a well-diversified portfolio and a portfolio that is robust against estimation errors. To reduce the effect of estimation, we constrain the weights of the risky assets with an L1-norm leading to a sparse portfolio. We provide analytical results that show how the sparsity of the constrained portfolio depends on the coefficient of relative risk aversion. Based on a simulation study, we demonstrate the existence and the uniqueness of an optimal bound on the L1-norm for each level of relative risk aversion

Continuation of Nesterov's Smoothing for Regression with Structured Sparsity in High-Dimensional Neuroimaging

Predictive models can be used on high-dimensional brain images for diagnosis of a clinical condition. Spatial regularization through structured sparsity offers new perspectives in this context and reduces the risk of overfitting the model while providing interpretable neuroimaging signatures by forcing the solution to adhere to domain-specific constraints. Total Variation (TV) enforces spatial smoothness of the solution while segmenting predictive regions from the background. We consider the problem of minimizing the sum of a smooth convex loss, a non-smooth convex penalty (whose proximal operator is known) and a wide range of possible complex, non-smooth convex structured penalties such as TV or overlapping group Lasso. Existing solvers are either limited in the functions they can minimize or in their practical capacity to scale to high-dimensional imaging data. Nesterov's smoothing technique can be used to minimize a large number of non-smooth convex structured penalties but reasonable precision requires a small smoothing parameter, which slows down the convergence speed. To benefit from the versatility of Nesterov's smoothing technique, we propose a first order continuation algorithm, CONESTA, which automatically generates a sequence of decreasing smoothing parameters. The generated sequence maintains the optimal convergence speed towards any globally desired precision. Our main contributions are: To propose an expression of the duality gap to probe the current distance to the global optimum in order to adapt the smoothing parameter and the convergence speed. We provide a convergence rate, which is an improvement over classical proximal gradient smoothing methods. We demonstrate on both simulated and high-dimensional structural neuroimaging data that CONESTA significantly outperforms many state-of-the-art solvers in regard to convergence speed and precision.Comment: 11 pages, 6 figures, accepted in IEEE TMI, IEEE Transactions on Medical Imaging 201

Simulateur compilé d’une description multi-langage des systèmes hétérogènes

La conception de systèmes hétérogènes exige deux étapes importantes, à savoir : la modélisation et la simulation. Habituellement, des simulateurs sont reliés et synchronisés en employant un bus de co-simulation. Les approches courantes ont beaucoup d’inconvénients : elles ne sont pas toujours adaptées aux environnements distribués, le temps d’exécution de simulation peut être très décevant, et chaque simulateur a son propre noyau de simulation. Nous proposons une nouvelle approche qui consiste au développement d’un simulateur compilé multi-langage où chaque modèle peut être décrit en employant différents langages de modélisation tel que SystemC, ESyS.Net ou autres. Chaque modèle contient généralement des modules et des moyens de communications entre eux. Les modules décrivent des fonctionnalités propres à un système souhaité. Leur description est réalisée en utilisant la programmation orientée objet et peut être décrite en utilisant une syntaxe que l’utilisateur aura choisie. Nous proposons ainsi une séparation entre le langage de modélisation et la simulation. Les modèles sont transformés en une même représentation interne qui pourrait être vue comme ensemble d’objets. Notre environnement compile les objets internes en produisant un code unifié au lieu d’utiliser plusieurs langages de modélisation qui ajoutent beaucoup de mécanismes de communications et des informations supplémentaires. Les optimisations peuvent inclure différents mécanismes tels que le regroupement des processus en un seul processus séquentiel tout en respectant la sémantique des modèles. Nous utiliserons deux niveaux d’abstraction soit le « register transfer level » (RTL) et le « transaction level modeling » (TLM). Le RTL permet une modélisation à bas niveau d’abstraction et la communication entre les modules se fait à l’aide de signaux et des signalisations. Le TLM est une modélisation d’une communication transactionnelle à un plus haut niveau d’abstraction. Notre objectif est de supporter ces deux types de simulation, mais en laissant à l’usager le choix du langage de modélisation. De même, nous proposons d’utiliser un seul noyau au lieu de plusieurs et d’enlever le bus de co-simulation pour accélérer le temps de simulation.The design of heterogeneous systems requires two main steps, modeling and simulation. Usually, simulators are connected and synchronized by using a cosimulation bus. These current approaches have many disadvantages: they are not always adapted to the distributed environments, the execution time can be very disappointing, and each simulator has its own core of simulation. We propose a new approach which consists in developing a multi-language compiled simulator where each model can be described by employing various modeling languages such as SystemC, ESyS.Net or others. Each model contains modules and communication links between them. These modules describe functionalities for a desired system. Their description is realized by using the programming object and can be described by using a syntax that a user will have chosen. We thus propose a separation between the language of modeling and simulation. The models are transformed into the same internal representation which could be seen like unique objects. Our environment compiles these internal objects by producing a unified code instead of using several languages of modeling which add many mechanisms of communications and extra informations. Optimizations can include various mechanisms such as merging processes into only one sequential process while respecting the semantics of the models. We will use two abstraction levels, the “register transfer level”(RTL) and the “transaction-level modeling”(TLM). RTL allows a low level abstraction for modeling and the communication between the modules is done with signals. The TLM is a modeling for transactional communication with a higher abstraction level than RTL. Our aim is to support these two types of simulation, but the user can choose the language of modeling. In the same way, we propose to use a single core and to remove the cosimulation bus to accelerate the simulation time

Développement de techniques analytiques pour l'évaluation des protéines thérapeutiques et des biomarqueurs par spectrométrie de masse

Although liquid chromatography coupled to mass spectrometry is a commonly used technique for low molecular weigh compounds quantification in biological matrixes, it is no longer the case for macromolecules. The aim of this work was to evaluate immunoaffinity extraction coupled to mass spectrometry possibilities and difficulties in recombinant proteins and biomarkers quantification. This approach was first applied for absolute quantification of an intact small therapeutic protein, Epi-hNE4, an inhibitor of human neutrophil elastase. Immunoaffinity extraction from plasma led to a lower limit of quantification of 80 pM. The method was then applied to a therapeutic monoclonal antibody used for the treatment of colorectal cancer, Cetuximab. A lower limit of quantification of 130 pM was achieved through an extraction from plasma involving recognition of its biological target, EGFR. This last development offer possibilities for the immunogenicity testing of therapeutic proteins. The golden standard remains ELISA technique, which we implemented in a screening test for immunogenicity testing of Epi-hNE4, but difficulties encountered suggested that mass spectrometry is a potential alternative. Finally, multiplexed quantitative mass spectrometry was applied to apelins, a peptide biomarkers family between 12 and 36 amino acids, involved in cardiovascular functions. A limit of quantification of 25 pM was achieved, and interestingly, this method brought evidence of the absence of presupposed circulating forms.Si la chromatographie liquide couplée à la spectrométrie de masse est un outil de bioanalyse largement utilisé pour la quantification des molécules de faible poids moléculaire dans les fluides biologiques, il n'en est pas de même pour les macromolécules. Au cours de ce travail, nous avons exploré les possibilités et les difficultés d'une méthode d'extraction par immunoaffinité couplée de la spectrométrie de masse pour la quantification des protéines recombinantes et des biomarqueurs. Cette stratégie a tout d'abord été appliquée à une petite protéine thérapeutique, Epi-hNE4, un inhibiteur de l'élastase humaine et pour lequel nous avons obtenu une sensibilité de 80 pM. Cette méthode a également été appliquée à un anticorps thérapeutique utilisé pour le traitement du cancer colorectal, Cetuximab, pour lequel une sensibilité de130 pM a été obtenue grâce à une extraction par l'EGFR, sa cible biologique. Ce dernier développement offre des perspectives pour évaluer l'immunogénecité des protéines thérapeutiques. La méthode de référence est la technique ELISA, que nous avons appliquée à la détection d'anticorps anti-Epi-hNE4, mais les difficultés rencontrées suggèrent que la spectrométrie de masse serait une alternative interessante. Enfin, une dernière application de la spectrométrie de masse pour la quantification multiplexée des biomarqueurs a été menée sur les apelines, une famille de peptides de 12 à 36 acides aminés jouant un rôle crucial dans le système cardiovasculaire. Une limite de détection de l'ordre de 25 pM a été atteinte, et de façon interessante, cette méthode nous a permis de conclure à l'absence des formes supposées circulantes

Die liberale Kraft Europas: Die Soziale Marktwirtschaft in der Europapolitik der Bundesrepublik, 1953-1993

Nach dem Zweiten Weltkrieg entschied sich Europa für den Liberalismus und wählte dafür eine spezifische Wirtschafts- und Sozialordnung: die Soziale Marktwirtschaft. Mathieu Dubois zeichnet den Einfluss der Bundesrepublik Deutschland und des Ordoliberalismus auf dieses Modell nach. Seine These: Im Gegensatz zum Bild eines "politischen Zwergs" erwies sich die westdeutsche Europapolitik als entscheidender Vermittler europäischer Kompromisse. Damit leistete die Bundesrepublik einen wichtigen Beitrag zur Liberalisierung des Kontinents und zur Gründung einer Wirtschafts- und Stabilitätsgemeinschaft - aber auch zu einer Währungsunion, die heute durch Zwang funktioniert

Topics in portfolio optimisation and systemic risk

This thesis is concerned with different sources of risk occurring in financial markets. We follow a bottom-up approach by carrying out an analysis from the perspective of a single investor to the whole banking system. We first consider an investor who faces parameter uncertainty in a continuous-time financial market. We model the investor’s preference by a power utility function leading to constant relative risk aversion. We show that the loss in expected utility is large when using a simple plug-in strategy for unknown parameters. We also provide theoretical results that show the tradeoff between holding a well-diversified portfolio and a portfolio that is robust against estimation errors. To reduce the effect of estimation, we constrain the weights of the risky assets with a norm leading to a sparse portfolio. We provide analytical results that show how the sparsity of the constrained portfolio depends on the coefficient of relative risk aversion. Based on a simulation study, we demonstrate the existence and the uniqueness of an optimal bound on the norm for each level of relative risk aversion. Next, we consider the interbank lending market as a network in which the nodes represent banks and the directed edges represent interbank liabilities. The interbank network is characterised by the matrix of liabilities whose entries are not directly observable, as bank balance sheets provide only total exposures to the interbank market. Using a Bayesian approach, we assume that each entry follows a Gamma distributed prior. We then construct a Gibbs sampler of the conditional joint distribution of interbank liabilities given total interbank liabilities and total interbank assets. We illustrate our methodology with a stress test on two networks of eleven and seventysix banks. We identify under which circumstances the choice of the prior influences the stability and the structure of the network