Reliable predictor of BCI motor imagery performance using median nerve stimulation

International audienceObjective. Predicting performance in Brain-Computer Interfaces (BCI) is crucial for enhancing user experience, optimizing training and identifying the most efficient BCI approach for each individual. Approach. This study explores the use of Median Nerve Stimulation (MNS) as a predictor of Motor Imagery (MI)-BCI performance. MNS induces Event Related (De)Synchronization (ERD/ERS) patterns in the brain that are similar to those generated during MI tasks, providing a non-invasive, user-independent, and easy-to-setup method for performance prediction. Main results. Our proposed predictor, based on the minimum value of the ERD induced by the MNS, not only exhibits a robust correlation with the MI-BCI performance accuracy (rho = -0.71, p < 0.001), but also effectively predicts this performance with a significant correlation (rho = 0.61, mean absolute error = 9.0, p < 0.01). These results demonstrate its validity as a reliable predictor of MI-BCI performance. Significance. By systematically analyzing patterns induced by MNS and correlating them with subsequent MI-BCI task performance, we aim to establish a robust predictive method of motor activity to each individual only based on MNS, making it possible, among other things, to passively predict BCI deficiency or proficiency, and to potentially adapt BCI parameters for an efficient BCI experience or BCI-based recovery

Strategic Input Selection For Deep Neural Networks Reliability Evaluation

International audienceWe present a robust methodology for selecting inputs for DNNs in radiation experiments. Using our approach, we obtained a 12.19× higher DNN misclassification rate on average compared to the commonly used random input selection

Au-delà de la superposition et de l’effondrement : interférences à double fente issues des géodésiques d’un espace-temps frémissant

Das Doppelspalt-Experiment wurde lange Zeit als Beleg für die Welle-Teilchen-Dualität und die quantenmechanische Superposition interpretiert. Hier schlagen wir eine deterministische Alternative vor, die auf der Theorie der zitternden Raumzeitrelativität (TSRT) basiert, in der Teilchen stets lokalen Geodäten der Eigenzeit innerhalb einer kausal strukturierten, aber metrisch fluktuierenden Raumzeit folgen. Die beobachteten Interferenzmuster entstehen nicht durch Superposition von Wellenfunktionen, sondern durch die deterministische Umlenkung von Teilchengeodäten infolge von Krümmungsstörungen, die durch die Geometrie der Spalte bestimmt werden. Die Verteilung der Ankunftspositionen wird durch zwei geometrische Mechanismen gesteuert: (i) den Spaltabstand, der den Winkelbereich der abgelenkten Geodäten festlegt, und (ii) eine minimale Wirkungsschwelle, die eine Auflösungsgrenze auf Basis kausaler Unterscheidbarkeit erzwingt. Das Plancksche Wirkungsquantum ergibt sich auf natürliche Weise aus dieser kausalen Schranke als kleinster auflösbarer Unterschied in der Geodätenwirkung – ganz ohne quantenmechanische Postulate. Die TSRT liefert analytische Vorhersagen für die Interferenzabstände und Intensitätsverteilungen bei einer Vielzahl von Teilchen – Photonen, Elektronen und Neutronen – durch Anwendung geometrischer Ablenkungsgesetze auf kausal zulässige Geodäten. Diese Vorhersagen stimmen eng mit etablierten experimentellen Ergebnissen überein. Darüber hinaus ermöglicht der TSRT-Rahmen die Simulation beliebiger Konfigurationen durch Modellierung von Geodäten-Ensembles unter metrischem Zittern und stellt damit eine vollständig geometrische Alternative zur quantenmechanischen Superposition dar.The double-slit experiment has long been interpreted as evidence of wave-particle duality and quantum superposition. Here we propose a deterministic alternative rooted in Trembling Spacetime Relativity Theory (TSRT), where particles always follow localized proper-time geodesics within a causally structured but metrically fluctuating spacetime. Apparent interference fringes arise not from wavefunction superposition, but from the deterministic redirection of particle geodesics due to curvature perturbations shaped by the slit geometry. The observed distribution of arrival positions is governed by two geometric mechanisms: (i) the slit separation, which sets the angular deflection range of redirected geodesics, and (ii) a minimal action threshold that enforces a resolution limit based on causal distinguishability. Planck’s constant emerges naturally from this causal constraint as the smallest resolvable geodesic action difference, without any quantum postulates. TSRT provides analytical predictions for fringe spacing and intensity distribution across a wide range of particles—photons, electrons, and neutrons—by applying geometric deflection laws to causally permitted geodesics. These predictions align closely with established experimental results. In addition, the TSRT framework enables simulation of arbitrary configurations by modeling ensembles of geodesics subject to metric trembling, providing a fully geometric alternative to quantum superposition.Durante mucho tiempo, el experimento de la doble rendija ha sido interpretado como evidencia de la dualidad onda-partícula y de la superposición cuántica. Aquí proponemos una alternativa determinista basada en la Teoría de la Relatividad del Espaciotiempo Tembloroso (TSRT), en la cual las partículas siguen siempre geodésicas localizadas de tiempo propio dentro de un espaciotiempo estructurado causalmente pero con fluctuaciones métricas. Las franjas de interferencia no surgen de la superposición de funciones de onda, sino de la redirección determinista de las geodésicas de las partículas debido a perturbaciones de la curvatura moldeadas por la geometría de las rendijas. La distribución observada de las posiciones de llegada está gobernada por dos mecanismos geométricos: (i) la separación entre rendijas, que determina el rango de desviación angular de las geodésicas redirigidas, y (ii) un umbral mínimo de acción que impone un límite de resolución basado en la distinguibilidad causal. La constante de Planck surge de manera natural de esta restricción causal como la menor diferencia de acción geodésica resoluble, sin recurrir a postulados cuánticos. TSRT proporciona predicciones analíticas para el espaciamiento de las franjas y la distribución de intensidad en una amplia gama de partículas —fotones, electrones y neutrones— aplicando leyes de desviación geométrica a geodésicas permitidas causalmente. Estas predicciones concuerdan estrechamente con los resultados experimentales establecidos. Además, el marco de TSRT permite simular configuraciones arbitrarias mediante el modelado de conjuntos de geodésicas sujetas a temblores métricos, ofreciendo así una alternativa completamente geométrica a la superposición cuántica.L’expérience des deux fentes est depuis longtemps interprétée comme une preuve de la dualité onde-corpuscule et de la superposition quantique. Nous proposons ici une alternative déterministe fondée sur la théorie de la relativité de l’espace-temps frémissant (TSRT), dans laquelle les particules suivent toujours des géodésiques locales de temps propre au sein d’un espace-temps causalement structuré mais soumis à des fluctuations métriques. Les franges d’interférence observées ne résultent pas d’une superposition de fonctions d’onde, mais d’une redirection déterministe des géodésiques de particules, induite par les perturbations de courbure façonnées par la géométrie des fentes. La distribution spatiale des positions d’arrivée est gouvernée par deux mécanismes géométriques : (i) la séparation entre les fentes, qui fixe la plage angulaire des géodésiques redirigées, et (ii) un seuil d’action minimale imposant une limite de résolution fondée sur la distinguabilité causale. La constante de Planck émerge naturellement de cette contrainte causale comme la plus petite différence d’action géodésique résoluble, sans aucun postulat quantique. La TSRT fournit des prédictions analytiques de l’espacement des franges et de la distribution d’intensité pour une large gamme de particules — photons, électrons et neutrons — en appliquant des lois de déviation géométrique aux géodésiques causalement permises. Ces prédictions concordent étroitement avec les résultats expérimentaux établis. De plus, le cadre TSRT permet de simuler des configurations arbitraires en modélisant des ensembles de géodésiques soumises à des tremblements métriques, offrant ainsi une alternative entièrement géométrique à la superposition quantique

LpL p ASYMPTOTIC STABILITY OF 1D DAMPED WAVE EQUATION WITH NONLINEAR DAMPING

International audienceIn this paper, we study the one-dimensional wave equation with localized nonlinear damping and Dirichlet boundary conditions, in the L p framework, with p ∈ [1, ∞).We begin by addressing the well-posedness problem, establishing the existence and uniqueness of weak and strong solutions for p ∈ [1, ∞), under suitable assumptions on the damping function.Next, we study the asymptotic behaviour of the associated energy when p ∈ (1, ∞), and we provide decay estimates that appear to be almost optimal compared to similar problems with boundary damping. Our work is motivated by earlier studies, particularly, those by Chitour, Marx and Prieur [Journal of Differential Equations, 269 (2020)], and Haraux [C.R.A.S Paris, 287 (1978)]. The proofs combine arguments from Kafnemer, Mebkhout and Chitour [ESAIM: COCV, 28 (2022)] for wave equation in the L p framework with a linear damping, techniques of weighted energy estimates introduced in Martinez [ESAIM: COCV, 4 (1999)], new integral inequalities for p > 2, and convex analysis tools when p ∈ (1, 2)

Optimizing stilbene recovery from cell cultures media: A comprehensive study of the adsorption process

International audienceStilbenes are of significant interest due to their potential health benefits and applications in pharmaceuticals and nutraceuticals. Traditional extraction methods often involve organic solvents, which pose environmental and safety concerns.This study investigates the extraction of stilbenes (E-resveratrol, labruscol, leachianol, ε-viniferin, and δ-viniferin) from grapevine (Vitis vinifera and Vitis labrusca) cell cultures using adsorption technology. Five food-grade resins (XAD-7, XAD-16, XAD-4, XAD-1180, and FPX-66) were tested for stilbene adsorption. XAD-7 was chosen as the optimum adsorbent, displaying the highest adsorbed quantity (86.94 ± 4.90 mgstilbenes/gdry resin) and desorbed quantity (74.28 ± 0.38 mgstilbenes/gdry resin). Adsorption kinetics using XAD-7 followed a pseudo-second-order model, with intraparticle diffusion limiting approximately 10 % of total adsorption. Desorption occurs more rapidly than adsorption, achieving equilibrium in about 60 min. Isotherm curves fitted well to a multicomponent Langmuir model, indicating a maximum adsorption capacity of 0.280–0.360 mmolstilbenes/gdry resin, close to the experimental value of 0.271 mmolstilbenes/gdry resin. Stilbene affinity for XAD-7 decreased in the following order: ε-viniferin > (labruscol, E-resveratrol, leachianol) > δ-viniferin. The optimal desorbed quantity of 59.74 ± 0.14 mgstilbenes/gdry resin was achieved with a 70 % ethanol solution and a 160:1 desorption solution-to-adsorbent ratio (v/w). XAD-7 resin coupled with an optimized washing step increased stilbene purity by 4.6 times (from 5.41 ± 0.05 % to 23.19 ± 0.31 % w/w). XAD-7 can be reused for multiple cycles with consistent adsorption capacity and desorption yield, maintaining the same stilbenes purity after 5 cycles.This study underscores the viability of polymeric resin adsorption as an eco-friendly and efficient method for stilbene extraction from grapevine cell cultures, paving the way for sustainable production processes in the nutraceutical and pharmaceutical industries

Semantic vs. LLM-based approach: A case study of KOnPoTe vs. Claude for ontology population from French advertisements

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Strict Discretization Error Bounds on Quantities of Interest in Transient Dynamics

International audienceABSTRACT This work proposes a guaranteed error estimator for linear transient elastodynamics, accounting for both time and space discretization errors. The key lies in the definition of a novel dynamic constitutive relation error formulation, which is proven to be a strict bound of the discretization error. Moreover, based on the established dynamic constitutive relation error and the goal‐oriented error estimation framework, strict upper and lower bounds on quantities of interest are also obtained. Numerical examples are conducted to verify the proposed strict bounds and to explore the application of these bounds to adaptive time stepping and mesh refinement

Hitting and cover times of the path graph

We consider the moments and the distribution of the hitting times and cover time of the path graph. We obtain recurrence relations for the moments of all order of both the hitting and cover times and we propose two conjectures giving explicit expressions of these moments. We use the recurrence relations on the moments to analyze their asymptotic behavior together with the asymptotic behavior of the distribution of these random variables, for different values of the initial state, when the number of nodes tends to infinity.</div

Improving Digital Twin Using AI and SCADA

International audienceRenewable energy transition is undergoing at a fast pace. To ensure an economic viability, thus ensuring its continuous development, an accurate estimation, and means of improvement regarding the asset production capability and expected operational lifetime is demanded. For a wind turbine, its controller is a key aspect governing its lifetime performance, with a direct impact on the development of fatigue [1]. In this work, we seek to improve wind farm digital twins by identifying the controller through a black box approach leveraging on-site SCADA data and deep learning. Because current controller's implementations mostly rely on virtual controller with hand-tweaked parameters [2], we aim to develop this method to automatically find these laws. To effectively leverage deep learning techniques, SCADA data are preliminary pre-processed (filling missing values, removing abnormal operation, differentiating, etc.). A random forest-based surrogate model is then trained, to predict the pitch and torque time evolution, based on a selected time window. The surrogate model is integrated in close loop within the digital twin, providing accurate prediction in region 2 &amp; 3 (see appendix [a] for regions details), but the model struggles to understand the behaviour when reaching region 2.5 where the wind turbine highly undergoes strong cycling loading and experience fatigue. Our preliminary results indicate that the deep learning model shows promising improvements in predictive accuracy over the previous method. We expect that further fine-tuning and work on SCADA will enhance the model's understanding of region 2.5. Preliminary simulations suggest a better reproduction of the controller behaviour in this critical region, resulting in a more reliable representation of the wind turbine's behaviour. We then propose a comparison with ROSCO, an open-source reference controller for wind turbines

BrowserFM: A Feature Model-based Approach to Browser Fingerprint Analysis

International audienceWeb browsers have become complex tools used by billions of people. The complexity is in large part due to its adaptability and variability as a deployment platform for modern applications, with features continuously being added. This also has the side effect of exposing configuration and hardware properties that are exploited by browser fingerprinting techniques. In this paper, we generate a large dataset of browser fingerprints using multiple browser versions, system and hardware configurations, and describe a tool that allows reasoning over the links between configuration parameters and browser fingerprints. We argue that using generated datasets that exhaustively explore configurations provides developers, and attackers, with important information related to the links between configuration parameters (i.e., browser, system and hardware configurations) and their exhibited browser fingerprints. We also exploit Browser Object Model (BOM) enumeration to obtain exhaustive browser fingerprints composed of up to 16, 000 attributes. We propose to represent browser fingerprints and their configurations with feature models, a tree-based representation commonly used in Software Product Line Engineering (SPLE) to respond to the challenges of variability, to provide a better abstraction to represent browser fingerprints and configurations. With translate 89, 486 browser fingerprints into a feature model with 35, 857 nodes from 1, 748 configurations. We show the advantages of this approach, a more elegant tree-based solution, and propose an API to query the dataset. With these tools and our exhaustive configuration exploration, we provide multiple use cases, including differences between headless and headful browsers or the selection of a minimal set of attributes from browser fingerprints to re-identify a configuration parameter from the browser