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Joint Power Control and User Assignment in RIS-based NOMA: A Multi-kernel Neural Network Approach
No full textInternational audienceAs mobile devices and data usage continue to grow rapidly, wireless communication systems are being pushed to meet more stringent demands for ultra-low latency, high reliability, and massive connectivity. However, conventional resource allocation algorithms are increasingly unable to cope with the growing problem dimensionality and the rising complexity of the objective functions. These approaches struggle to meet the scalability demands and adapt to the dynamic nature of modern communication environments, highlighting the need for more advanced solutions, such as machine learning-based techniques. While traditional Artificial Intelligence (AI)-driven methods show promise, they often face challenges, including reliance on labeled data and difficulties in generalizing across diverse scenarios. To address these limitations, we propose a novel supervised learning framework for wireless networks that utilizes pre-trained models to tackle scalability issues. Our approach leverages a pre-trained model optimized for a relatively simple scenario and extends its patterns to a generalized scenario using a multi-kernel neural network architecture. The proposed system is applied to achieve joint power control and user assignment for a RIS-based non-orthogonal multiple access (NOMA) systems. We formally establish that the estimation error is upper bounded and does not scale with the size or dimensionality of the generalized network architecture. Simulation results demonstrate the effectiveness of our method, achieving scalable and efficient wireless network optimization
Community-Curated Galaxy Interfaces with the Galaxy Labs Engine
No full textThe Galaxy platform is a globally distributed environment for data-intensive research, providing thousands of analysis tools across major public servers. However, this decentralised ecosystem presents usability challenges for both users and administrators, particularly in surfacing relevant tools and workflows for specific communities. To improve discoverability and support global collaboration, the Galaxy project has employed community-driven "Galaxy Flavours"—subdomains with curated content for defined research domains. While conceptually valuable, Flavours suffer from critical limitations: they are statically deployed, difficult to replicate across servers, and often provide inconsistent and unintuitive user interfaces.To address these challenges, we developed the Galaxy Labs Engine (GLE), a service that enables the creation of Galaxy Labs. This new paradigm enables globally synchronised, domain-specific entry points built from structured, reusable web content. GLE separates content from deployment, allowing communities to define a shared canonical representation of their domain, while enabling individual Galaxy servers to locally customise presentation. Labs are designed to guide users through curated tools, workflows, and training resources, and are aimed at researchers who are new to the analytical methods or technologies specific to the domain.The Galaxy Labs Engine provides a consistent, customisable, and community-driven interface layer for the Galaxy ecosystem. By fostering FAIR principles, Labs offer a scalable improvement to Flavours and enhance Galaxy’s ability to support diverse research communities. GLE is open-source and currently deployed at https://labs.usegalaxy.org.au, with multiple Labs already supporting active user groups. This work strengthens Galaxy’s role as a collaborative platform for reproducible, user-centered science
Assessing the eco-efficiency of inter-municipal waste management services in France
No full textInternational audienceWe assess the eco-efficiency of French inter-municipal cooperation entities in charge of waste management. We employ a conditional order-m approach to (i) estimate their eco-efficiency considering variables characterizing their environmental context, (ii) evaluate the effect of these contextual variables on the eco-efficiency. Our results demonstrate that the population size, the type of area (e.g., tourist, rural), and the waste pricing systems significantly influence eco-efficiency. These findings underscore the importance of tailoring local waste management policies to the specific characteristics and available resources of each area. They provide valuable insights for local authorities seeking to enhance eco-efficiency and optimize waste management practices. © 2025 The Author
Un bestiaire tragique : figures et figuralités animales dans La Tragédie du sac de Cabrières
No full textInternational audienceIn La Tragédie du Sac de Cabrières, animality transcends simple rhetorical and stereotypical use to become an essential key to understanding the existential and ideological tensions that run through the text. This article examines how animal figures, between binary axiology and disturbing hybridity, question the limits of humanity and religious disorders. Through a stylistic and pragmatic approach, I show how these analogies serve both a polemical rhetoric and a metaphysical reflection on otherness.Dans La Tragédie du Sac de Cabrières, l’animalité dépasse la simple stéréotypie rhétorique pour devenir une clé de lecture essentielle des tensions existentielles et idéologiques qui traversent le texte. Cet article examine la manière dont les figures animales, entre axiologie binaire et hybridité inquiétante, interrogent les limites de l’humanité et les désordres confessionnels. Par le biais d’une approche stylistique et pragmatique, il montre comment ces analogies servent à la fois une rhétorique polémique et une réflexion métaphysique sur l’altérité
Detecting Changes in the Mean of Maximum and Minimum Temperatures Across European Cities
No full textWe propose a fully automated algorithm for detecting weak changes in the mean of time series, applied to monthly maximum and minimum temperatures across European cities. The method, based on the theoretical power of a likelihood ratio test within CHARN models, overcomes threshold selection issues and preserves all information in the data. Application to six cities (coastal and inland) reveals contrasting dynamics: smoother prolonged changes in coastal regions versus sharper regime shifts inland. Key climatic events, including El Niño, the 2003 heatwave, and recent increases in tropical nights, are accurately identified. The approach provides a robust framework for real-time climate monitoring and historical climate signal extraction
Evolution de la mixité porcs - herbivores dans le Massif central
No full textInternational audienceLe Massif central (MC) est un territoire de prairies et de montagne où l'élevage prédomine. Sur 15% de la SAU française, il concentre 54% des ovins lait, 37% des bovins viande, 33% des agneaux et brebis mais seulement 5% des porcs de France. Entre les recensements de 1988 et 2020, le nombre d'exploitations avec porcs y est passé de plus de 38.000 à 1.700 et le cheptel a diminué de 937.300 à 673.800 porcs (-28%). Malgré cela, la filière porcine joue un rôle essentiel pour le maintien d’outils d’abattage et de transformation, pour les emplois et l’identité culinaire du MC.Le projet de recherche-action APORTHE vise à favoriser la reprise des exploitations et l’installation de nouveaux éleveurs en s’appuyant sur la mixité porcins-bovins caractérisée ici
Multicenter inter‐laboratory quality control of monocyte HLA‐DR expression by flow cytometry
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Typicality of operators on Fréchet algebras admitting a hypercyclic algebra
No full textInternational audienceThis paper is devoted to the study of typical properties (in the Baire Category sense) of certain classes of continuous linear operators acting on Fr\'echet algebras, endowed with the topology of pointwise convergence. Our main results show that within natural Polish spaces of continuous operators acting on the algebra of entire functions on , a typical operator supports a hypercyclic algebra. We also investigate the case of the complex Fr\'echet algebras , 1\le p<+\infty, or endowed with the coordinatewise product, and show that whenever M>1, a typical operator on of norm less than or equal to admits a hypercyclic algebra
ZTF SN Ia DR2: The spectral diversity of Type Ia supernovae in a volume-limited sample
No full textInternational audienceMore than 3000 spectroscopically confirmed Type Ia supernovae (SNe Ia) are presented in the Zwicky Transient Facility SN Ia Data Release 2 (ZTF DR2). In this paper, we detail the spectral properties of 482 SNe Ia near maximum light, up to a redshift limit of 0.06. We measure the velocities and pseudo-equivalent widths (pEW) of key spectral features (Si II 5972 and Si II 6355) and investigate the relation between the properties of the spectral features and the photometric properties from the SALT2 light-curve parameters as a function of spectroscopic sub-class. We discuss the non-negligible impact of host galaxy contamination on SN Ia spectral classifications, as well as investigate the accuracy of spectral template matching of the ZTF DR2 sample. We define a new subclass of underluminous SNe Ia (`04gs-like') that lie spectroscopically between normal SNe Ia and transitional 86G-like SNe Ia (stronger Si II 5972 than normal SNe Ia but significantly weaker Ti II features than `86G-like' SNe). We model these `04gs-like' SN Ia spectra using the radiative-transfer spectral synthesis code tardis and show that cooler temperatures alone are unable to explain their spectra; some changes in elemental abundances are also required. However, the broad continuity in spectral properties seen from bright (`91T-like') to faint normal SN Ia, including the transitional and 91bg-like SNe Ia, suggests that variations within a single explosion model may be able to explain their behaviour
A Joint Kriging Model with Application to Constrained Classification
No full textInternational audienceInterpolating or predicting data is of utmost importance in machine learning, and Gaussian Process Regression is one of the numerous techniques that are often used in practice. In this paper, we consider the case of multi-input and multi-output data. A simple Joint Kriging model is proposed, where common combination weights are applied to all output variables at the same time. This drastically reduces the number of hyperparameters to be optimised while keeping nice interpolating properties. An original constraint on predicted values is also introduced, useful for considering external information or adverse scenarios. Finally, it is shown that, when applied to membership degrees, the model is especially helpful for constrained fuzzy classification problems. In particular, the model allows for prescribed average percentages of each class in predictions. Numerical illustrations are provided for both simulated and real data and show the importance of the constraint on predicted values. The method also competes with the 69 other models of an open real-world benchmark