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A new hybrid strongly coupled multi-time step approach with enhanced robustness for fluid structure interaction problems
No full textInternational audienceThis paper presents a strongly coupled approach within the Arbitrary Lagrangian-Eulerian (ALE) framework for solving Fluid-Structure Interaction (FSI) problems, such as those involving a deformable structure in a supersonic flow or subjected to a blast loading. The vertex-centered Finite Volume Method (FVM) for the fluid subdomain with two different explicit time integrators (first-order and third-order accurate Runge-Kutta schemes) is coupled with the Finite Element Method (FEM) for the structural subdomain with an implicit time integrator (Newmark Constant Average Acceleration scheme). This coupling is performed using mono-and multi-time step strategies. The proposed FSI algorithms adopts a monolithic and simultaneous FSI coupling, by introducing Lagrange Multipliers (LM) to ensure the continuity of the normal velocity at the Fluid-Structure (FS) interface. This adopted dual Schur approach allows decoupling the FSI problem into two solid and fluid discrete systems, along with an interface discrete system involving the time-dependent Steklov-Poincaré operator and the unknown Lagrange Multipliers. The proposed approach is hybrid (explicit-implicit), strongly coupled, with fluid subcycling, and is non-iterative in the sense that it does not require any subiteration. It provides a compromise between the flexibility of loosely coupled staggered schemes and the robustness of strongly coupled monolithic formulations. The proposed method has been validated for several academic cases and FSI benchmarks, including the classical half shock tube, the one-dimensional piston problem with a rod, the two-dimensional deformable panel subjected to a shock-wave, as well as a two-dimensional panel flutter problem in the supersonic flow regime
The role of vision and proprioception in implicit and explicit self-movement recognition
No full textInternational audienceThe recognition of one’s own body is a fundamental component of body self-representation. While several studies have reported a self-advantage (enhanced performance when processing one’s own body parts), this phenomenon appears complex and inconsistently observed across tasks. In particular, a self-advantage often emerges in implicit tasks, where self-recognition is incidental, whereas explicit self-recognition tasks sometimes reveal no advantage or even a self-disadvantage. Although previous research has examined various aspects of movement self-recognition, systematic investigations directly comparing self-advantage effects in implicit versus explicit recognition of one’s own movements, and disentangling the respective contributions of vision and proprioception within this framework, remain scarce. Here, we tested the hypothesis that the self-advantage effect previously reported for static body parts extends to the recognition of one’s own movements, in visual and proprioceptive conditions. In the implicit task, participants judged the perceived lateral direction (left or right) of their own or others’ arm reaching movements, which were pre-recorded and replayed using an upper-limb exoskeleton. In the explicit task, participants judged whether reaching movements were their own or not. In the visual condition, they observed the exoskeleton executing the reaching movements, while in the proprioceptive condition their arm was passively moved by the exoskeleton. Results showed self-advantage in the implicit recognition task, with participants demonstrating higher accuracy in discriminating their own actions in both visual and proprioceptive modalities. Notably, this self-advantage for movement ownership was also observed in the explicit recognition within the visual modality, but was absent in the proprioceptive modality. Thus, individuals can implicitly differentiate distinct proprioceptive and visual kinematic patterns associated with their own movements, this advantage extending to explicit recognition in the visual modality. These findings reveal the role of proprioceptive experience in implicitly favoring action discrimination and highlight the differential influence of visual and proprioceptive cues in motion self-recognition
揭开“熔炉”——移民史的视野与路径
No full textIn this interview, Philippe Rygiel reflects on the foundations, developments, and contemporary challenges of migration history. He emphasizes the fundamentally interdisciplinary nature of the field, which emerged from social history but has been shaped by law, demography, political science, and, more recently, digital humanities. Migration history has traditionally focused on transnational movements in the nineteenth and twentieth centuries, yet its boundaries remain flexible, encompassing international migration, internal mobility, refugee history, and diaspora studies.The interview highlights the central role of the state and legal frameworks in defining migrants’ statuses, rights, and life trajectories. Legal categories profoundly shape individual experiences, influence family strategies, and often generate unintended consequences, such as the long-term settlement of populations initially regarded as temporary. Rygiel also stresses the importance of combining different scales of analysis, from individual life stories to global dynamics, and underlines the growing significance of quantitative methods and large-scale databases made possible by recent advances in computing and artificial intelligence.Finally, the discussion addresses the connections between migration history, gender studies, microhistory, and global history, as well as the political stakes raised by the tightening of migration policies in contemporary Europe. Migration history thus emerges as a key field for understanding social change, power relations, and current debates on otherness, citizenship, and mobility.Dans cet entretien, Philippe Rygiel revient sur les fondements, les évolutions et les enjeux contemporains de l’histoire des migrations. Il souligne d’abord le caractère intrinsèquement interdisciplinaire de ce champ, né de l’histoire sociale mais nourri par le droit, la démographie, la science politique et, plus récemment, les humanités numériques. L’histoire des migrations s’est structurée autour de l’étude des mobilités transnationales des XIXe et XXe siècles, mais ses frontières demeurent mouvantes, englobant aussi bien les migrations internationales que certaines mobilités internes, l’histoire des réfugiés ou des diasporas.L’entretien met en évidence le rôle central de l’État et du droit dans la définition des statuts, des droits et des trajectoires migratoires. Les cadres juridiques façonnent profondément les expériences individuelles, influencent les choix familiaux et produisent des effets souvent inattendus, comme l’installation durable de populations initialement perçues comme temporaires. Rygiel insiste également sur l’importance d’articuler les échelles d’analyse, du parcours individuel aux dynamiques globales, et sur l’apport décisif des approches quantitatives et des bases de données massives, rendues possibles par les progrès récents de l’informatique et de l’intelligence artificielle.Enfin, l’entretien aborde les liens entre histoire des migrations, genre, micro-histoire et histoire globale, ainsi que les enjeux politiques actuels liés au durcissement des politiques migratoires en Europe. L’histoire des migrations apparaît ainsi comme un champ essentiel pour comprendre les transformations sociales, les rapports de pouvoir et les débats contemporains sur l’altérité, la citoyenneté et la mobilité
Pratiques pédagogiques : quels défis à l’enseignement des enjeux socio-écologiques dans une école d’ingénieurs ?
No full textInternational audienceThis chapter examines the professional practices used by teachers in engineering schools to address socio-écological issues. It first reviews the specific aspects of these scientifically and socially sensitive issues, which require certain epistemological, didactic and pedagogical shifts. Drawing on the available literature on teaching sustainability competency frameworks, this text suggests that teachers should acknowledge how sustainability question the aims and methods of teaching itself. Based on the reflective feedback of a group of teachers from INSA Lyon on these research results, this text explores four themes: the expectations of engineering students, the teacher's role, the formalization of the pedagogical framework, and the dynamics within the teaching team.Ce texte s’interroge sur les gestes et les pratiques professionnelles utiles aux enseignants en écoles d’ingénieurs pour traiter des enjeux socio-écologiques. Il revient d’abord sur les spécificités de ces questions scientifiquement et socialement vives qui réclament certains déplacements épistémologiques, didactiques et pédagogiques. A la faveur d’un détour par la littérature disponible sur les référentiels de compétences enseignantes en matière de durabilité, ce chapitre suggère de prendre la mesure de la controverse sur les finalités et les modalités de la tâche même d’enseigner. A partir des retours réflexifs d’un groupe d’enseignants de l’INSA Lyon sur ces résultats de recherche, ce texte se propose d’explorer quatre thèmes : les attentes des élèves-ingénieurs, la posture de l’enseignant, la formalisation du cadre pédagogique et les dynamiques au sein de l’équipe enseignante
AntFlie: Frugal Visual Teach and Repeat on Narrow FoV Micro-Drones
No full textInternational audienceWe present an insect-inspired visual teach-and-repeat framework demonstrated on Antflie, a 33-gram MAV equipped with an ultra-low-resolution camera (24×24 px) and a narrow 87° field of view (FoV). During a one-shot teach flight along an outbound route, the MAV performs periodic physical scans and uses a local compass based on inertial and optic flow cues to categorize views as left or right relative to the path, storing compact, lateralized visual memories in a Mushroom Body (MB) neural network with a footprint under 4 kB. In the repeat phase, the MAV flies the inbound route by retracing the outbound path, and autonomously lands at its home location using only visual familiarity through direct sensorimotor coupling, rather than map-based reasoning. Offline simulations show that the Route Lateralized (R-Lat) algorithm in Antflie matches the accuracy of a state-of-the-art insect visual compass (V-Comp) while running up to 20× faster and supporting narrow FoVs. Real-world indoor experiments further demonstrate 24 autonomous inbound repeats totaling 110 meters of flight, with a 13-cm median lateral error and a mean landing error of 34 cm. These results highlight the feasibility of frugal, bio-inspired, vision-only navigation for MAVs operating under strict size, weight, power, and cost constraints, inspired by the navigation of Cataglyphis and Melophorus ants
Bilateral parking procedures
No full textWe introduce the class of bilateral parking procedures on the integer line. While cars try to park in the nearest available spot to their right in the classical case, we consider more general parking rules that allow cars to use the nearest available spot to their left. We show that for a natural subclass of local procedures, the number of corresponding parking functions of length r is always equal to . The setting can be extended to probabilistic procedures, in which the decision to park left or right is random. We finally describe how bilateral procedures can naturally be encoded by certain labeled binary forests, whose combinatorics shed light on several results from the literature
De la co-construction à la co-évolution entre acteurs humains et IA au sein du cycle de personnalisation des EIAH
No full textThis manuscript presents my main contributions in the field of Technology-Enhanced Learning (TEL), and more specifically in the use of Artificial Intelligence (AI) for personalized learning, closely aligned with learners’ needs and teachers’ constraints. Within this context, I address two major themes.First, I propose a Knowledge Engineering-based approach to support teachers in the co-construction of personalized learning environments. This includes the development of meta-models for modeling pedagogical knowledge and designing authoring tools, as well as the integration of these models into adapted environ-ments. The aim is not to create automated systems that replace teachers, but rather adaptable tools aligned with their practices, enabling the generation and recommendation of learning activities tailored to learner profiles and specific pedagogical constraints. In recent years, these models and tools have been furtherdeveloped to support the implementation of Competency-Based Education and to fully leverage its potential within the personalization cycle of TEL.Second, I explore approaches based on the collection and analysis of learning traces, drawing on Knowledge Engineering, as well as Data Mining and Machine Learning techniques. This has led to the development of platforms that, on the one hand, allow learning data analysis without requiring technical expertise, and, on the other, facilitate the capitalization and sharing of such analyses.This work has also resulted in the design of recommender systems leveraging competency models, integrating both top-down approaches (expert knowledge) and bottom-up approaches (data-driven discovery). Furthermore, analyzing the operational traces of AI engines has enabled the proposal of initial mechanismsfor explainable AI in these systems.Building on these contributions, developed within national and international projects, a research agenda is also presented. It focuses on the following directions : the design of rich and adapted explainable AI mechanisms within the personalization cycle, the development of hybrid AI to detect and leverage learners’ sense of competence, the co-evolution of the Human-AI relationship within TEL, and the enrichment of our models through the paradigm of active learning in AI. These perspectives aim to better integrate AI capabilities into TEL while ensuring their alignment with the pedagogical and human realities of educational stakeholders.Ce manuscrit présente nos principales contributions dans le domaine des Environnements Informatiques pour l’Apprentissage Humain (EIAH), et plus particulièrement dans l’exploitation de l'Intelligence Artificielle (IA) pour la personnalisation de l’apprentissage, en lien étroit avec les besoins des apprenants et les contraintes des enseignants. Dans ce cadre, nous avons abordé deux grandes thématiques.Premièrement, nous avons proposé une approche fondée sur l’Ingénierie des Connaissances pour accompagner les enseignants dans la co-construction d'environnements personnalisés d’apprentissage. Cela comprend l’élaboration de méta-modèles exploités dans la modélisation des connaissances pédagogiques et dans la conception d’outils auteurs, ainsi que l’intégration de ces modèles dans des environnements adaptés. L’objectif est de proposer non pas des systèmes automatiques remplaçant l’enseignant, mais des outils adaptables à leurs pratiques, permettant de générer et recommander des activités pédagogiques en fonction des profils apprenants et des contraintes pédagogiques spécifiques. Ces modèles et outils ont été enrichis ces dernières années pour favoriser la mise en œuvre de l'Approche par Compétences dans l'enseignement et en exploiter toute la richesse lors du cycle de personnalisation des EIAH.Deuxièmement, nous avons exploré des approches fondées sur la collecte et l’analyse des traces d’apprentissage, en nous appuyant de même sur l'ingénierie des connaissances, mais également sur des techniques de fouille de données et d'apprentissage machine. Cela a permis de proposer des plateformes permettant, d'une part, l'analyse des données d'apprentissage sans expertise technique, et d'autre part, la capitalisation et le partage de ces analyses. Ces travaux ont également mené à la construction de systèmes de recommandations exploitant des modèles de compétences, en intégrant à la fois des approches top-down (connaissances expertes) et bottom-up (découverte à partir des données). Enfin, l'exploitation des traces de fonctionnement de nos moteurs d'IA nous a permis de proposer des premiers mécanismes d'IA explicables pour ces moteurs.À partir de ces contributions développées dans le cadre de projets nationaux et internationaux, un plan de recherche est également présenté. Il s’articule autour des axes suivants : la proposition de mécanismes d'IA explicables riches et adaptés au sein du cycle de personnalisation, la construction d'une IA hybride pour détecter et exploiter le sentiment de compétences chez les apprenants, la co-évolution du couple Humain-IA au sein des EIAH, et enfin l'enrichissement de nos modèles via le paradigme d'IA d'apprentissage actif. Ces perspectives visent à mieux intégrer les capacités de l’IA dans les EIAH tout en garantissant leur adéquation aux réalités pédagogiques et humaines des acteurs de l’éducation
Inégalités de Sobolev Logarithmique généralisées par le schéma JKO
No full textUsing a discrete Bakry-Émery method based on the JKO scheme, relying on the dissipation of entropy and Fisher information along a discrete flow, we establish new generalized logarithmic Sobolev inequality for log-concave measures of the form under strict convexity assumptions on . We then show how this method recovers some well-known inequalities. This approach can be viewed as interpolating between the Bakry-Émery method and optimal transport techniques based on geodesic convexity.En utilisant une version discrète de la méthode de Bakry–Émery basée sur le schéma JKO, reposant sur la dissipation de l’entropie et de l’information de Fisher le long d’un flot discret, nous établissons une nouvelle inégalité de Sobolev logarithmique généralisée pour des mesures log-concaves de la forme , sous des hypothèses de stricte convexité sur . Nous montrons ensuite comment cette méthode permet de retrouver certaines inégalités bien connues. Cette approche peut être vue comme une interpolation entre la méthode de Bakry–Émery et les techniques de transport optimal fondées sur la convexité géodésique
Towards absolute measurements of magnetic losses by the rotational single sheet tester (RSST): an interlaboratory comparison
No full textInternational audienceA comparison of the magnetic energy loss measurement in non-oriented Fe-Si sheets under alternating and rotational polarization has been accomplished by four European laboratories using different Rotational Single-Sheet Tester (RSST) setups and different sample shapes. The measurements, performed in the frequency and polarization intervals 5 Hz ≤ f ≤ 200 Hz, 1.0 T ≤ J p ≤ 1.5 T, aimed at providing a benchmark test for these special measurements, looking for a connection between the RSST outcomes and absolute loss values, obtained by a combination of IEC 60404-2 Epstein data and precise local measurements. The laboratory-averaged RSST alternating loss values are found to range in a ±5% interval around the reference values, with the lab-to-lab discrepancies chiefly descending from the heterogeneous variety of the employed magnetic circuits. Numerical analysis highlights the critical role of the effective field and its uniformity across the RSST sensing area. The statistical assessment of the laboratories' best estimates provides the empirical standard deviations s = 4.5% and s = 3.6% for the alternating and rotational loss figures, respectively, thereby showing a significantly reduced dispersion of the results compared with a previous international comparison launched in the '90s. It additionally points to the circular geometry for both sample and magnetizer as best suited for the prospective standardization of 2D measurements.</div
Sound-structure interaction: acoustic radiation and transmission of infinite panels
No full textMasterSound transmission through an elastic structureStudied case in the following (to highlight the main physical phenomena):-Elastic structure ➔ Flat thin panel of infinite extent -Acoustic domains ➔ Semi-infinite acoustic domain (i.e. no reflecting boundary condition excepted the flat panel)</div