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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
Hydrodynamic limit for an open facilitated exclusion process with slow and fast boundaries
No full text44 pagesInternational audienceWe study the symmetric facilitated exclusion process (FEP) on the finite one-dimensional lattice {1, . . . , N − 1} when put in contact with boundary reservoirs, whose action is subject to an additional kinetic constraint in order to enforce ergodicity, and whose speed is of order N^{−θ} for some parameter θ. We derive its hydrodynamic limit as N goes to infinity, in the diffusive space-time scaling, when the initial density profile is supercritical. More precisely, the macroscopic density of particles evolves in the bulk according to a fast diffusion equation as in the periodic case, which is now subject to boundary conditions that can be of Dirichlet, Robin or Neumann type depending on the parameter θ. In the Dirichlet case, the FEP exhibits a very peculiar behaviour: unlike for the classical SSEP, and due to the two-phased nature of FEP, the reservoirs impose boundary densities which do not coincide with their equilibrium densities. The proof is based on the classical entropy method, but requires significant adaptations to account for the FEP’s non-product stationary states and to deal with the non-equilibrium settin
Bounds on chi-square statistics for localized bases with application to optimal estimation
No full textWe develop new deviation bounds for chi-square statistics that are sharp enough to construct penalties in least-squares density estimation allowing optimal estimation under mild smoothness constraints without imposing assumptions on the density's support or supremum norm. In particular, we establish new minimax rates under conditions that provide a natural transition between compactly supported and infinitely supported densities, as well as between bounded and unbounded ones
High quality-factor terahertz phonon-polaritons in layered lead iodide
No full textInternational audienceWhile hyperbolic phonon-polaritons in van der Waals materials such as h-BN and α-MoO3 have driven major advances in mid-infrared (IR) nanophotonics, further progress at longer THz wavelengths has been hampered due to material limitations and experimental challenges. Here, we report the discovery of long-lived hyperbolic phonon-polaritons in the deep THz range in layered PbI2. Using room-temperature scattering-type scanning near-field optical microscopy, we achieved real-space imaging and broadband spectral analysis of PbI2 2D crystals transferred onto different substrates with high near-field amplitude contrast and good agreement with theoretical models. Our measurements revealed an experimental figure-of-merit related to the propagating efficiency of the polaritons above 15—on par with state-of-the-art mid-IR benchmarks—and extreme field confinement of 264 for a 144 nm-thick flake, which can exceed 300 in slightly thinner samples. These findings demonstrate that PbI2 combines strong anisotropy, low losses, and extreme mode confinement, making it a compelling candidate for deep-THz nanophotonic applications
Beyond Additive Design: An Empirical Taxonomy of Multimodal STEM Accessibility Systems
No full textInternational audienceMultimodal systems combining audio, haptic, and tactile channels are prevalent in STEM accessibility for blind and visually impaired users, yet real-world feedback reports high cognitive load despite technological advances. Through systematic analysis of 66 systems (2015--2025), we identify three architectural regimes: additive (channel stacking), augmentative (partial coordination), and integrative (orchestrated fusion). A five-dimensional scoring framework reveals dramatic architectural separation, yet conventional performance metrics show no regime variation---a decoupling we term the Differential Cognitive Yield (DCY) phenomenon: architecturally distinct systems impose dramatically different cognitive costs while yielding similar task performance. We contribute empirical design thresholds for perceptual integration and call for a shift from interface engineering to perceptual integration engineering
Review of Prognosis Approaches Applied to Power SiC MOSFETs for Health State and Remaining Useful Life Prediction
No full textInternational audienceThe use of Silicon Carbide (SiC) MOSFETs significantly improves converter performance by increasing efficiency and reducing costs, to the detriment of electro-magnetic emission and reliability. Implementing a predictive maintenance strategy based on a prognosis tool can mitigate this limitation. This literature review offers a methodological synthesis of prognosis design tools for SiC MOSFETs, while also encompassing studies on IGBTs and silicon-based power MOSFETs where these approaches are transferable. The analysis focuses on wear-out prognosis under nominal operating conditions of standard package device, excluding environmental constraints. Articles published up to 2025 were identified in the OpenAlex database using a keyword-based search and manually filtered according to the study scope. Most reviewed works rely on Data-Based prognosis methods, mostly based on neural networks, though out-of-sample validation remains uncommon. Our study also highlights the dependence of Data-Based prognosis performance on the shape of degradation indicator trends. Moreover, the estimation of prediction uncertainty is rarely addressed in the reviewed literature. Despite notable methodological advances, ensuring the reliability of prognosis tools for SiC MOSFETs remains an ongoing research challenge
EFFECT OF NON-UNIFORM CASING TREATMENT ON THE PERFORMANCE AND OPERABILITY OF THE ECL5 FAN STAGE
No full textThis study investigates unsteady aeroelastic phenomena occurring at off-design conditions in the open test case ECL5 fan stage, a configuration representative of modern lightweight ultrahigh bypass ratio (UHBR) architectures. Both simulations and experiments reveal the onset of small-scale aerodynamic disturbances along subsonic speed lines, known to trigger convective Non-Synchronous Vibration (NSV). This NSV, observed experimentally across 55-90% design speed and involving multiple blade eigenmodes, poses significant risks to fan operability and structural integrity. A promising mitigation strategy is the use of axial-slot casing treatments, which reduce blockage by extracting low-momentum flow in the rotor tip region and reinjecting it upstream with a more favourable momentum. This mechanism disrupts the development of coherent aerodynamic disturbance patterns, thereby delaying or suppressing NSV inception.The present work focuses on designing and evaluating nonuniform casing treatments for the ECL5 fan stage, with emphasis on maintaining performance outside NSV-critical regions. Unlike previous studies that primarily optimize fully circumferential configurations, this investigation explores the potential of partial treatments. Such circumferentially non-uniform designs reduce performance penalties while still preventing disturbances growth.A comprehensive numerical campaign was conducted, including four treatment configurations assessed over multiple speeds and mass flow rates. Results demonstrate that partial treatments can reproduce much of the stabilizing effect of fullcoverage treatments while limiting efficiency losses. The findings deliver design criteria for maximizing NSV suppression with minimal performance impact.</div
Compact Mamba Multi-View for Object Detection
No full textMulti-view image analysis is a key enabler for robust perception when single viewpoints provide incomplete or ambiguous observations. This challenge is particularly pronounced in industrial inspection of transparent materials, where view-dependent optical effects, subtle surface degradations, and annotation noise significantly hinder reliable detection and severity assessment. In this work, we introduce a compact and efficient multi-view fusion architecture tailored to such constraints. Our approach combines shared-weight hierarchical encoders with selective state-space modeling to explicitly exploit cross-view and multi-scale correlations. Multi-View Mamba Blocks (MVMB) perform adaptive fusion at each feature level by coupling Mamba-based selective state-space layers with FiLM-driven cross-view conditioning, while a Global State-Space Fusion Block enforces long-range coherence across all views and resolutions. Task-specific decoding heads query the resulting global representation via cross-attention to jointly predict object localization and ordinal wear severity. The model is trained using a unified multi-task objective that integrates geometric regression, ordinal classification, cross-view consistency, feature alignment, and sequential smoothness. Extensive experiments on a challenging multi-view glass container inspection dataset demonstrate improved robustness, consistency, and scalability compared to strong baselines. To promote reproducibility and future research, we publicly release the proposed dataset at: https://datasets.liris.cnrs.fr/mvep-version1
Segmentation et reconstruction 3D de lignées cellulaires par apprentissage profond à partir des images confocales
No full textNational audienceCe travail présente un pipeline complet combinant imagerie confocale, segmentation par deep learning et reconstruction 3D réaliste de cellules afin d’améliorer la modélisation radiobiologique en thérapies anticancéreuses utilisant des radioisotopes. En s’appuyant sur nnU-Net et sur une base de données soigneusement prétraitée, la segmentation du noyau et du cytoplasme atteint des performances élevées (Dice > 0,91). Les reconstructions 3D révèlent une forte hétérogénéité morphologique entre lignées cellulaires, incompatible avec des modèles géométriques simplifiés. La base de données obtenue constitue un support clé pour des simulations Monte Carlo avancées (Geant4-DNA), permettant d’affiner l’estimation du RBE et d’améliorer les modèles biophysiques tels que NanOx
Friction on Demand: A Generative Framework for the Inverse Design of Metainterfaces
No full textInternational audienceDesigning frictional interfaces to exhibit prescribed macroscopic behavior is a challenging inverse problem, made difficult by the non-uniqueness of solutions and the computational cost of contact simulations. Traditional approaches rely on heuristic search over low-dimensional parameterizations, which limits their applicability to more complex or nonlinear friction laws. We introduce a generative modeling framework using Variational Autoencoders (VAEs) to infer surface topographies from target friction laws. Trained on a synthetic dataset composed of 200 million samples constructed from a parameterized contact mechanics model, the proposed method enables efficient, simulation-free generation of candidate topographies. We examine the potential and limitations of generative modeling for this inverse design task, focusing on balancing accuracy, throughput, and diversity in the generated solutions. Our results highlight trade-offs and outline practical considerations when balancing these objectives. This approach paves the way for near-real-time control of frictional behavior through tailored surface topographies