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Programmes linéaires en nombre entiers pour la recherche de puissances de circuits Hamiltoniens
No full textInternational audienc
Problème de dimensionnement de lots à un produit et une machine reconfigurable sans capacité
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Désordre muséal à Saadiyat ? Ou comment ce laboratoire à taille urbaine reconfigure le paysage muséal mondial ?
No full textMusées, patrimoines et pouvoir symbolique. Enjeux (géo)politiques et territoriaux du Patrimoine, https://mppsgeo.hypotheses.org/2936Le projet de l’île Saadiyat à Abu Dhabi – soit la construction ambitieuse d’une île aux musées – a commencé il y a une vingtaine d’années. Diversement discuté en aménagement du territoire, en géopolitique et en études muséales, ce projet interroge sur ce qu’il apporte aux musées et à leur relation au territoire. En effet, par son ampleur, ses échecs et ses transformations, de même que par sa conception unifiée comme un laboratoire-musée des musées (et non pas comme un regroupement progressif d’institutions mitoyennes), il apparaît clairement que Saadiyat provoque un chamboulement dans le paysage muséal mondial. En y instaurant son nouvel ordre, le projet a pour effet de provoquer d’autres désordres - qui se répercutent à divers niveaux sur le(s) musée(s), tant ceux de l’île que ceux du reste du monde. Ainsi, comment le projet de Saadiyat transforme cette ville en véritable laboratoire et comment ce laboratoire muséal urbain chamboule le réseau mondial des musées en permettant le déploiement de voix périphériques dans la manière d’appréhender le musée ? Deux chamboulements se juxtaposent. Le premier est interne au territoire : de nombreux musées n’ont finalement jamais ouverts pour des raisons obscures, remodelant le paysage muséal de l’île. Le second a une effectivité qui lui est externe : l’implantation de ces musées bouscule le paysage muséal mondial en changeant les récits muséaux et offrant de nouvelles routes grâce à ce décentrement culturel
La soignantarisation de la question scolaire. Dispositifs extrahospitaliers et poursuite de scolarité des élèves atteints de cancer
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SPOT: An Annotated French Corpus and Benchmark for Detecting Critical Interventions in Online Conversations
No full textInternational audienceWe introduce SPOT (Stopping Points in Online Threads), the first annotated corpus translating the sociological concept of stopping point into a reproducible NLP task. Stopping points are ordinary critical interventions that pause or redirect online discussions through a range of forms (irony, subtle doubt or fragmentary arguments) that frameworks like counterspeech or social correction often overlook. We operationalize this concept as a binary classification task and provide reliable annotation guidelines. The corpus contains 43,305 manually annotated French Facebook comments linked to URLs flagged as false information by social media users, enriched with contextual metadata (article, post, parent comment, page or group, and source). We benchmark fine-tuned encoder models (CamemBERT) and instruction-tuned LLMs under various prompting strategies. Results show that fine-tuned encoders outperform prompted LLMs in F1 score by more than 10 percentage points, confirming the importance of supervised learning for emerging non-English social media tasks. Incorporating contextual metadata further improves encoder models F1 scores from 0.75 to 0.78. We release the anonymized dataset, along with the annotation guidelines and code in our code repository, to foster transparency and reproducible research
A comparative analysis of infection and mortality in reassessing africa’s COVID-19 dynamic using time-varying tests
No full textInternational audienceBackground It is commonly believed that Africa largely evaded the worst of the COVID-19 pandemic, with fewer cases than other continents. However, regional comparisons that ignore differences in testing intensity may misrepresent dynamics. Studying the spread and case-fatality relationship during COVID-19 across WHO regions requires explicitly adjusting for time-varying test volumes. Methods We build a weekly panel dataset spanning May 2020 to December 2021 for the WHO regions: Africa, Eastern Mediterranean, South-East Asia, the Americas, Western Pacific, and Europe. Data on tests, confirmed cases, and COVID-19-attributed deaths were sourced from Our World in Data. We apply a novel metric that corrects for fluctuating test volumes to quantify week-to-week acceleration in infections and in mortality. We then compare the frequency, magnitude, and timing of these acceleration episodes across regions.Results Accounting for testing dynamics, we show that Africa exhibits multiple infectionacceleration episodes whose magnitude and frequency match those in other regions. Mortality accelerations in Africa closely follow infection surges, with an average lag of ten weeks. A positive correlation between infection acceleration in Africa and the Americas further indicates synchrony. These findings hold when using a larger secondary dataset of 140 countries. Conclusions Contrary to prevailing assumptions, Africa was not spared from the pandemic's severe dynamics. Infection surges were on par with those elsewhere and were followed by mortality accelerations. These results underscore that accounting for testing variability is essential to accurately assess pandemic progression, and they highlight the urgent need to strengthen surveillance and healthcare capacity across all regions.The relatively low number of reported COVID-19 cases and deaths in Africa has prompted debates about whether the continent was spared the worst of the pandemic, a phenomenon described by some as the African "puzzle" 1,2 or "paradox" 3 . Early media reports and research articles speculated that Africa's younger population, lower population density in rural areas, and prior experience with infectious diseases and their pharmaceutical treatments might have mitigated the severe impacts observed in other regions.However, emerging evidence from seroprevalence studies indicates that far more individuals in Africa were exposed to SARS-CoV-2 than is reflected in official surveillance data, especially during the pandemic's first 2 years. For example, the ratio of seroprevalence to confirmed cases has been estimated to be as high as 100:1 4 . This gap between seroprevalence estimates and reported cases grew as the pandemic continued 5 , suggesting substantial under-reporting in surveillance data. For example, while 6 mention low testing rates as a likely source of under-reporting in their discussion of the</div
On the fusion category
No full textInternational audienceThere might exist non-rational Virasoro CFTs in two dimensions with a categorical symmetry. We calculate the necessary ingredients for a modular conformal bootstrap analysis of these theories. After reviewing the basics of fusion categories, we present the irreducible representations, the lasso maps that intertwine between different Hilbert spaces, and finally the 22-by-22 modular S matrix. We highlight the peculiarities introduced by the non-invertible nature of the symmetry. This paper is written in a pedagogical manner and can therefore serve as an accessible entry point into the literature
Resource Allocation in Full-Duplex Multi-User RIS-Assisted Wireless-Powered IIoT Networks
No full textInternational audienceThis paper examines radio resource allocation in a full-duplex (FD) reconfigurable intelligent surface (RIS)-assisted wireless-powered (WP) industrial internet-of-Things (IIoT) communication network. The system model includes an FD access point (FD-AP) that communicates with FD energy harvesting (EH) multi-users (MUs), referred to as an FD-FD RIS-assisted WP-IIoT network. The FD-AP is equipped with two sets of multiple antennas: one set for downlink (DL) energy beamforming and another for uplink (UL) information signal reception. The FD-MUs have one antenna for DL energy signal reception and another for UL information transmission. This paper addresses the UL sum-rate maximization problem for the FD-FD RISassisted WP-IIoT system by jointly optimizing the RIS phase shift and power resources. Additionally, this paper presents two benchmarks as specific cases of the FD-FD RIS-assisted WP-IIoT system model, namely, (i) FD-HD RIS-assisted WP-IIoT network: FD AP and half-duplex (HD) two MUs groups, and (ii) HD-HD RIS-assisted WP-IIoT network: HD AP and HD MUs. The numerical results demonstrate that the FD-FD system outperforms both benchmarks in the low transmit power regime. Also, compared to the baseline non-RIS-assisted WP-IIoT network, the RIS-assisted WP-IIoT network achieved a significant UL sum-rate gain
A Bayesian System with Neuron Clocks for Biosignal Classification
No full textA compact Bayesian system for end-to-end inference that uses time-encoded probabilistic computing is presented. The architecture integrates (1) a neuron clocking scheme for timing sequential phases of the system, (2) a feature-extraction module based on rate-coding information with neuron circuits, (3) a circuit able to extract likelihoods from a probability distribution and then calculate Bayes' rule and (4) a race-to-threshold winner-take-all circuit that can drive downstream actuation circuits. All circuits were implemented and simulated at the transistor level in TSMC 130 nm CMOS using a 1.0 V supply. The system was benchmarked using a two-category sleep-stage classification task, and achieved an accuracy of 80.8%, which closely matches the 81.5% result of an equivalent inference in software. The complete architecture uses less than 150 transistors, making it suitable for ultra-lowpower edge biosignal processing
Reconnaissance du comportement humain avec détection sans fil
No full textChronic diseases such as cardiovascular disorders, chronic respiratory illnesses, and diabetes are leading causes of mortality and long-term disability worldwide, posing significant challenges to healthcare systems, especially for aging populations. Traditional diagnostic and monitoring approaches rely heavily on professional personnel and centralized medical infrastructure, limiting their suitability for continuous health management. Wireless signal-based health monitoring has therefore gained increasing attention due to its non-contact, low-cost, and easily deployable nature, enabling long-term physiological sensing in daily environments. Among applications, cardiac motion, respiratory, and sleep monitoring are core research focuses due to their direct relevance to vital physiological activities.Despite progress, existing wireless health monitoring systems face three main challenges: (1) extracting fine-grained cardiac micromotions is difficult due to limited signal sensitivity or interference from respiration; (2) reliable vital sign coverage during sleep is often lost due to involuntary movements; and (3) thoraco-abdominal signal superposition impedes comprehensive respiratory assessment.This thesis addresses these gaps by proposing a model-driven framework that integrates physical signal propagation principles with the intrinsic physiological structure of the human body, improving interpretability and robustness across environments. Three systems were developed, each addressing one key challenge:Accurate Cardiac Micromotion Monitoring: A Mixed-Medium Wi-Fi Fresnel Zone Model is introduced to sense internal cardiac motion in the thoracic cavity. By exploiting Wi-Fi signal penetration and wavelength shortening in biological tissue, this physics-based approach enhances the SNR of heartbeat reflections while suppressing respiratory interference. It accurately extracts heart rate (HR), Q-T intervals, and inter-beat intervals (IBI), advancing Wi-Fi sensing from coarse rate estimation to fine-grained cardiac kinematics.Robust Sleep Vital Sign Detection: A Human Range Mapping (HRM) Model tailored for UWB radar addresses motion-induced signal dropouts during sleep. Although limb or torso movements distort the Line-of-Sight path, vital signs remain embedded in multipath components. Leveraging high range-resolution UWB and time-frequency processing, the system maintains high temporal coverage of HR and respiration rate (RR), improving reliability and longitudinal accuracy of nocturnal monitoring.Thoraco-Abdominal Signal Separation: Using mmWave FMCW radar, an Adjacent-Object Signal Superposition Model quantifies the distance-dependent contribution of thoracic and abdominal reflections. A 3D signal separation and reconstruction algorithm employing beamforming effectively decouples superimposed signals, enabling single-device monitoring of independent chest and abdominal motions, crucial for applications such as sleep apnea diagnosis and respiratory muscle assessment.The systems were validated through extensive real-world experiments with over 30 participants and hundreds of hours of data. Results demonstrate a robust, physics-grounded paradigm for comprehensive non-contact physiological monitoring, addressing the key limitations of existing wireless health sensing methods.Les maladies chroniques telles que les troubles cardiovasculaires, les affections respiratoires chroniques et le diabète sont parmi les principales causes de mortalité et de handicap à long terme dans le monde, représentant un défi majeur pour les systèmes de santé, notamment pour les populations vieillissantes. Les approches traditionnelles de diagnostic et de suivi reposent sur le personnel médical et les infrastructures centralisées, limitant leur capacité à assurer une surveillance continue. La surveillance de la santé par signaux sans fil suscite donc un intérêt croissant grâce à son caractère non invasif, peu coûteux et facilement déployable, permettant une détection physiologique continue dans la vie quotidienne. Les applications principales concernent la surveillance du mouvement cardiaque, de la respiration et du sommeil, directement liées aux fonctions vitales. Cependant, les systèmes existants rencontrent trois défis principaux : (1) l'extraction des micro-mouvements cardiaques reste difficile en raison de la sensibilité limitée des signaux et des interférences respiratoires ; (2) la couverture fiable des signes vitaux pendant le sommeil est perturbée par les mouvements involontaires ; et (3) la superposition des signaux thoraco-abdominaux complique l'évaluation respiratoire complète. Cette thèse propose un cadre basé sur un modèle intégrant les principes physiques de propagation des signaux et la structure physiologique humaine, améliorant interprétabilité et robustesse. Trois systèmes ont été développés pour répondre à ces défis :1. Surveillance précise des micro-mouvements cardiaques : Un modèle de zone de Fresnel Wi-Fi à milieu mixte détecte le mouvement cardiaque interne dans la cavité thoracique. Exploitant la pénétration des signaux Wi-Fi et le raccourcissement de longueur d'onde dans les tissus biologiques, cette approche améliore le rapport signal/bruit et supprime les interférences respiratoires. Elle permet l'extraction précise de la fréquence cardiaque, des intervalles Q-T et inter-battements, faisant progresser la détection Wi-Fi vers la cinématique cardiaque fine.2. Détection robuste des signes vitaux pendant le sommeil : Un modèle de cartographie humaine (HRM) adapté au radar UWB traite les pertes de signal causées par les mouvements nocturnes. Les signes vitaux restent présents dans les composantes multi-trajets malgré la déformation du chemin direct. Grâce à la haute résolution en distance et au traitement temps-fréquence, le système maintient une couverture temporelle élevée de la fréquence cardiaque et respiratoire, améliorant la fiabilité et la précision longitudinale.3. Séparation des signaux thoraco-abdominaux : Un radar FMCW mmWave et un modèle de superposition des signaux d'objets adjacents permettent de quantifier les réflexions thoraciques et abdominales selon la distance. Un algorithme 3D de séparation et reconstruction par beamforming découple efficacement les signaux superposés, permettant la surveillance par un seul dispositif des mouvements thoraciques et abdominaux, utile pour le diagnostic de l'apnée du sommeil et l'évaluation des muscles respiratoires. Les systèmes ont été validés par des expérimentations réelles impliquant plus de 30 participants et des centaines d'heures de données. Les résultats démontrent un paradigme robuste et fondé sur la physique pour une surveillance physiologique complète sans contact, répondant aux principales limites des méthodes actuelles de détection sans fil