Stabilization and Optimal Control of a Multi Input-Delayed SDE System

In this paper, we design a controller for a input-delayed Stochastic Differential Equation (SDE) with distinct input delays and a stochastic drift. Our objective is to steer the system to a desired final state on average while minimizing variance over time, thereby improving robustness to disturbances. We first establish a controllability result, highlighting lower bounds for the variance, demonstrating that the system cannot reduce variance beyond strict structural limits. Under standard controllability conditions, we then design a controller that drives the mean of the states while ensuring bounded variance. Finally, we analyze the optimal control problem for variance minimization over the entire trajectory. Under additional controllability assumptions, we show that the optimal control can achieve any variance level above the fundamental structural limit

Distributed Non-Interactive Zero-Knowledge Proofs

Distributed certification is a set of mechanisms that allows an all-knowing prover to convince the units of a communication network that the network's state has some desired property, such as being 3-colorable or triangle-free. Classical mechanisms, such as proof labeling schemes (PLS), consist of a message from the prover to each unit, followed by one round of communication between each unit and its neighbors. Later works consider extensions, called distributed interactive proofs, where the prover and the units can have multiple rounds of communication before the communication among the units. Recently, Bick, Kol, and Oshman (SODA '22) defined a zero-knowledge version of distributed interactive proofs, where the prover convinces the units of the network's state without revealing any other information about the network's state or structure. In their work, they propose different variants of this model and show that many graph properties of interest can be certified with them. In this work, we define and study distributed non-interactive zero-knowledge proofs (dNIZK); these can be seen as a non-interactive version of the aforementioned model, and also as a zero-knowledge version of PLS. We prove the following: - There exists a dNIZK protocol for 3-coloring with O(log n)-bit messages from the prover and O(log n)-size messages among neighbors. - There exists a family of dNIZK protocols for triangle-freeness, that presents a trade-off between the size of the messages from the prover and the size of the messages among neighbors. - There exists a dNIZK protocol for any graph property in NP in the random oracle models, which is secure against an arbitrary number of malicious parties

Physical Layer Location Privacy in SIMO Communication Using Fake Path Injection

International audienceFake path injection is an emerging paradigm for inducing privacy over wireless networks. In this paper, fake paths are injected by the transmitters into a single-input multiple-output (SIMO) communication channel to obscure their physical location from an eavesdropper. The case where the receiver (Bob) and the eavesdropper (Eve) use a linear uniform array to locate the transmitter's (Alice) position is considered. A novel statistical privacy metric is defined as the ratio between the smallest (resp. largest) eigenvalues of Eve's (resp. Bob's) Cramér-Rao lower bound (CRB) on the SIMO channel parameters to assess the privacy enhancements. Leveraging the spectral properties of generalized Vandermonde matrices, bounds on the privacy margin of the proposed scheme are derived. Specifically, it is shown that the privacy margin increases quadratically in the inverse of the angular separation between the true and the fake paths under Eve's perspective. Numerical simulations validate the theoretical findings on CRBs and showcase the approach's benefit in terms of bit error rates achievable by Bob and Eve

Qualité des articles de recherche et modèles de langue neuronaux : applications au domaine biomédical

The quality of research articles in the biomedical domain is important. For example, it can ensure that clinicians make correct clinical decisions. However, the increasing number of articles published each year makes it difficult for experts to assess this quality. Natural language processing (NLP) methods may therefore prove helpful in assisting them. This quality may also be an issue when training the models used in NLP for biomedical tasks. Indeed, these models are often fine-tuned on large corpora of in-domain research articles to obtain better performance for domain-specific tasks. It is therefore important to verify which type of quality criteria can have an impact when fitting these models. Thus, in this thesis, we are interested firstly in the automatic detection of quality problems in articles using neural models, and secondly in data selection for training these models. For the detection of quality criteria, we are particularly interested in research articles reporting on clinical trials. We identify problems that have not been explored before or try to improve the methods employed. These include consistency between an article and the associated registry, as well as completeness of the article. For article consistency, we fine-tune bidirectional encoders (from the general domain and adapted to the medical domain) on task-specific corpora and produce a system using these models. We then develop a graphical web interface to help domain experts access and visualize our methods. Then, to detect completeness, we use large autoregressive language models (testing models for the general or biomedical domain) by reformulating the quality criteria evaluation task as a question-answering task and taking advantage of in-context learning methods. Finally, we select data from a corpus of biomedical research articles to pre-train a bidirectional encoder for biomedical domain adaptation, using a confidence criterion: journal impact.La qualité des articles de recherche dans le domaine biomédical est importante, elle permet par exemple d'assurer une prise de décision clinique correcte par les médecins. Cependant, l'augmentation du nombre d'articles publiés chaque année rend l'évaluation de cette qualité par des experts difficile. Ainsi, l'utilisation de méthodes de traitement automatique des langues (TAL) peut s'avérer utile pour les assister. Cette qualité peut également être un enjeu pour l'apprentissage des modèles utilisés en TAL pour les tâches du domaine biomédical. En effet, ces modèles sont souvent ajustés sur de larges corpus d'articles de recherche du domaine afin d'obtenir de meilleures performances pour les tâches spécifiques au domaine. Il est donc important de vérifier quel type de critères de qualité peut avoir un impact lors de l'adaptation de ces modèles. Ainsi, dans cette thèse, nous nous intéressons dans un premier temps à la détection automatique de problèmes de qualité dans les articles à l'aide de modèles neuronaux, puis dans un second temps à la sélection de données pour l'entraînement de ces modèles. Pour la détection de critères de qualité, nous nous penchons particulièrement sur les articles de recherche rapportant des essais cliniques. Nous tentons d'identifier des problèmes n'ayant pas été explorés auparavant ou tentons d'améliorer les méthodes employées. Ces problèmes sont la cohérence entre un article et le registre associé, ainsi que la complétude de l'article. Pour la cohérence des articles, nous affinons des encodeurs bidirectionnels (du domaine général et adaptés au domaine médical) sur des corpus spécifiques aux tâches considérées et produisons un système utilisant ces modèles. Nous développons ensuite une interface graphique pour aider les experts du domaine à accéder et visualiser nos méthodes. Ensuite, pour détecter la complétude, nous utilisons de larges modèles de langue autorégressifs (en testant des modèles pour le domaine général ou biomédical) en reformulant la tâche d'évaluation de critères de qualité en tant que tâche de question-réponse et en tirant parti des méthodes d'apprentissage en contexte. Enfin, nous sélectionnons des données dans un corpus d'articles de recherche biomédicale afin de préentraîner un modèle de langue de type encodeur bidirectionnel pour son adaptation au domaine biomédical, en utilisant un critère de confiance : l'impact des journaux

Compression vidéo faciale à faible débit avec des modèles d’animation génératifs

This thesis addresses the challenge of achieving ultra-low bitrate video compression for video conferencing by focusing on the preservation of high visual quality while minimizing transmission bandwidth. Traditional codecs like HEVC and VVC struggle at very low bitrates, particularly with accurately representing dynamic facial expressions, head movements, and occlusions, which are important for realism and accuracy in face-to-face communication. To overcome these limitations, this research develops learning-based compression method using deep generative models and enhances their performance through side information transmission or predictive coding at low bitrates.The Deep Animation Codec (DAC) is introduced as a solution that uses generative models to encode speech-related facial motion through a compact representation of motion keypoints, achieving substantial bitrate reductions. To address DAC's limitations with complex head poses and occlusions, the Multi-Reference DAC (MRDAC) uses multiple reference frames and contrastive learning to enhance reconstruction accuracy under challenging conditions. Building on this, the Hybrid Deep Animation Codec (HDAC) integrates traditional video codecs with generative frameworks to achieve adaptive quality, further improved by variable bitrate learning and a High-Frequency (HF) shuttling mechanism for detailed reconstruction. Finally, we explored an approach to predictive coding at ultra-low bitrates showing the associated challenges and optimization tools that can be used to effectively learn compact residual coding at low bitrates. Specifically, the proposed predictive coding framework (RDAC) exploits temporal dependencies and conditional residual learning to achieve a robust trade-off between information loss and quality scalability within the constraints of low bitrate coding. Collectively, these contributions advance the field by enabling robust, high-quality video compression at ultra-low bitrates, enhancing the feasibility of video conferencing and potential applications in virtual reality and efficient storage of talking-head video content.Cette thèse aborde le défi de réaliser une compression vidéo à ultra-faible débit pour la vidéoconférence, en se concentrant sur la préservation d'une haute qualité visuelle tout en minimisant la bande passante de transmission. Les codecs traditionnels comme HEVC et VVC éprouvent des difficultés à très bas débits, particulièrement pour représenter avec précision les expressions faciales dynamiques, les mouvements de tête et les occlusions, qui sont essentiels pour le réalisme et la précision dans la communication en face à face. Pour surmonter ces limitations, cette recherche développe une méthode de compression basée sur l'apprentissage utilisant des modèles génératifs profonds et améliore leurs performances grâce à la transmission d'informations auxiliaires ou au codage prédictif à bas débit.Le Deep Animation Codec (DAC) est introduit comme une solution qui utilise des modèles génératifs pour encoder les mouvements faciaux liés à la parole via une représentation compacte des points clés de mouvement, réalisant ainsi des réductions substantielles du débit binaire. Pour traiter les limitations du DAC avec des poses de tête complexes et des occlusions, le Multi-Reference DAC (MRDAC) utilise plusieurs images de référence et l'apprentissage contrastif pour améliorer la précision de reconstruction dans des conditions difficiles. En s'appuyant sur cela, le Hybrid Deep Animation Codec (HDAC) intègre des codecs vidéo traditionnels avec des cadres génératifs pour atteindre une qualité adaptative, encore améliorée par l'apprentissage à débit binaire variable et un mécanisme de transfert des hautes fréquences (HF) pour une reconstruction détaillée. Enfin, nous avons exploré une approche de codage prédictif à ultra-faible débit, mettant en évidence les défis associés et les outils d'optimisation qui peuvent être utilisés pour apprendre efficacement un codage résiduel compact à bas débit. Plus précisément, le cadre de codage prédictif proposé (RDAC) exploite les dépendances temporelles et l'apprentissage résiduel conditionnel pour atteindre un compromis robuste entre la perte d'information et l'évolutivité de la qualité dans les contraintes du codage à faible débit.Collectivement, ces contributions font progresser le domaine en permettant une compression vidéo robuste et de haute qualité à ultra-faible débit, améliorant la faisabilité de la vidéoconférence et les applications potentielles en réalité virtuelle ainsi que le stockage efficace de contenu vidéo de type « talking-head »

Network-Realized Model Predictive Control Part II: Distributed Constraint Management

A two-layer control architecture is proposed, which promotes scalable implementations for model predictive controllers. The top layer acts as both reference governor for the bottom layer, and as a feedback controller for the regulated network. By employing set-based methods, global theoretical guarantees are obtained by enforcing local constraints upon the variables of the network and of the first layer's implementation. The proposed technique offers recursive feasibility guarantees as one of its central features, and the expressions of the resulting predictive strategies bear a striking resemblance to classical formulations from model predictive control literature, allowing for flexible and easily customizable implementations

Theoretical and numerical modeling of ultrasonic scattering in polycrystalline materials

International audienceThe strong interaction between the polycrystalline microstructure and elastic waves makes it challenging to fully understand and master the relationship between ultrasonic grain scattering and the crystallographic and morphological characteristics of the microstructure, which is nonetheless of paramount importance for the success of ultrasonic non-destructive testing of polycrystalline materials. In this seminar, I will present the theoretical and numerical models developed to investigate the effects of microstructural properties – such as grain size distribution, grain shape, and preferred crystallographic orientation – on scattering-induced attenuation, wave velocity variation, and the backscattering coefficient. Regarding coherent wavefronts, the theoretical study is conducted within the frameworks of two well-known seminal models: Stanke and Kino’s unified theory and Weaver’s model. Structural noise due to incoherent waves is studied using an approach based on the reciprocity theorem. For the numerical study, grain-scale finite element modeling is presented, along with the development of a space discontinuous Galerkin finite element solver

DepthLight: a Single Image Lighting Pipeline for Seamless Integration of Virtual Objects into Real Scenes

International audienceWe present DepthLight, a method to estimate spatial lighting for photorealistic Visual Effects (VFX) using a single image as input. Previous techniques rely either on estimated or captured light representations that fail to account for localized lighting effects, or use simplified lights that do not fully capture the complexity of the illumination process. DepthLight addresses these limitations by using a single LDR image with a limited field of view (LFOV) as an input to compute an emissive texture mesh around the image (a mesh which generates spatial lighting in the scene), producing a simple and lightweight 3D representation for photorealistic object relighting. First, an LDR panorama is generated around the input image using a photorealistic diffusion-based inpainting technique, conditioned on the input image. An LDR to HDR network then reconstructs the full HDR panorama, while an off-the-shelf depth estimation technique generates a mesh representation to finally build a 3D emissive mesh. This emissive mesh approximates the bidirectional light interactions between the scene and the virtual objects that is used to relight virtual objects placed in the scene. We also exploit this mesh to cast shadows from the virtual objects on the emissive mesh, and add these shadows to the original LDR image. This flexible pipeline can be easily integrated into different VFX production workflows. In our experiments, DepthLight shows that virtual objects are seamlessly integrated into real scenes with a visually plausible estimation of the lighting. We compared our results to the ground truth lighting using Unreal Engine, as well as to state-of-the-art approaches that use pure HDRi lighting techniques (see Figure 1). Finally, we validated our approach conducting a user evaluation over 52 participants as well as a comparison to existing techniques

Easing Optimization Paths: a Circuit Perspective

Accepted at ICASSP 2025International audienceGradient descent is the method of choice for training large artificial intelligence systems. As these systems become larger, a better understanding of the mechanisms behind gradient training would allow us to alleviate compute costs and help steer these systems away from harmful behaviors. To that end, we suggest utilizing the circuit perspective brought forward by mechanistic interpretability. After laying out our intuition, we illustrate how it enables us to design a curriculum for efficient learning in a controlled setting. The code is available at \url{https://github.com/facebookresearch/pal}

Guide des labels climatiques à l'usage des étudiantes et étudiants de CentraleSupélec

Ce guide présente les conclusions du groupe de travail de CentraleSupélec sur les partenariats et le développement durable. Son principal objectif est d’outiller ses étudiantes et étudiants pour qu’ils puissent prendre des décisions plus éclairées sur leurs choix professionnels. Ce guide propose donc des clefs de lecture pour analyser les labels et les scores RSE relatifs aux enjeux climatiques. Ainsi, deux labels (CDP et SBTi) semblent aujourd’hui constituer la source d’information la plus pertinente pour juger du sérieux de la stratégie climat d’une entreprise. Leurs limites ont également été soulignées pour permettre aux lecteurs de garder un regard critique