Portail HAL Nantes Université
Not a member yet
120169 research outputs found
Sort by
Concentration of the empirical measure in Wasserstein distance: bounds involving the covering dimension
No full textWe give concentration inequalities in Wasserstein distance for the empirical measure of a sequence of independent and identically distributed random variables with values in a Polish space E. These inequalities involve the covering dimension of the support of the distribution of the variables. More precisely, we obtain a complete extension of the concentration inequalities of Fournier and Guillin [2015] in the case where E = R^d , in which the covering dimension replaces the dimension of the ambient space E
Proches et lointains, dans l’espace et dans le temps : l’imagerie et l’idéologie des céramistes égéens installés en Italie méridionale au VII siècle avant J.-Chr.,
No full textInternational audienc
OMP multidimensionnel déplié sous contraintes physiques pour les systèmes MIMO à grande échelle
No full textSparse recovery methods are essential for channel estimation and localization in modern communication systems, but their reliability relies on accurate physical models, which are rarely perfectly known. Their computational complexity also grows rapidly with the dictionary dimensions in large MIMO systems. In this paper, we propose MOMPnet, a novel unfolded sparse recovery framework that addresses both the reliability and complexity challenges of traditional methods. By integrating deep unfolding with data-driven dictionary learning, MOMPnet mitigates hardware impairments while preserving interpretability. Instead of a single large dictionary, multiple smaller, independent dictionaries are employed, enabling a low-complexity multidimensional Orthogonal Matching Pursuit algorithm. The proposed unfolded network is evaluated on realistic channel data against multiple baselines, demonstrating its strong performance and potential
What are the ethical considerations when supporting single women undergoing medically assisted reproduction (MAR) ?
No full textInternational audienceBackgroundSince the revision of French bioethics laws in 2021, requests for medically assisted reproduction (MAR) from single women have increased significantly. Specific challenges are faced by CECOS (Centre d'Étude et de Conservation des Ovocytes et du Sperme humain).PurposeThis article questions the ethical aspects of current support for single women undergoing MAR and motherhood.MethodsHere, we analyze single motherhood through the lens of the founding principles of medical ethics: respect for beneficence, non-maleficence, autonomy and justice.FindingsThe decision of a ‘single’ woman to start a family through MAR, without a partner (which does not mean that she is isolated or lonely) as being on the margins of dominant social family norms. Concerns may be expressed about the absence of a second parent and may sometimes mask value judgements on the part of relatives and healthcare professionals.ConclusionsIt is important to support all types of motherhood by consolidating services dedicated to maternal and child health
Tailoring the composition and deposition kinetics of RF-sputtered BaTiO3 thin films through working pressure and target-substrate distance adjustments
No full textInternational audienc
Evaluating the strength of molecular interactions in a deep eutectic solvent (DES) by means of ionization mechanisms involved in cold-spray ionization mass spectrometry and by DFT calculations
No full textInternational audienceStrength of molecular interactions in octanoic acid and cholinium chloride based DESs determined using CSI-MS and DFT calculations
A macro-element for circular shallow foundations on rigid inclusion-reinforced soil
No full textInternational audienc
Les biotechnologies : un laboratoire pour construire une réflexivité prudente
No full textInternational audienc
Modular and adaptive implementation of Semantic Segmentation Models for Satellite Images and Open Source tools suitable for complex geographical contexts
No full textInternational audienceSemantic segmentation, the process of assigning a semantic label to each pixel in an image, is a critical computer vision task for extracting detailed information from remote sensing data. However, its application to complex geographical contexts, such as coastal wetlands, is often constrained by the need for highly specialized implementations, class imbalance, and limited accessibility for non-specialists. This paper introduces a novel, modular, and adaptive open-source framework for semantic segmentation tailored to satellite imagery. Designed for maximum flexibility, the framework supports both binary and multi-class segmentation tasks and incorporates specific training strategies to handle severe class imbalances inherent in ecological detection, such as salt marsh mapping. The implementation provides a fully configurable pipeline that bridges the gap between Geographic Information Systems (GIS) and Deep Learning (DL). It integrates QGIS for intuitive spatial preprocessing and grid generation with a Python-based training and prediction workflow, thereby democratizing access to advanced segmentation techniques. The framework is architecture-agnostic, allowing the seamless deployment and benchmarking of various state-of-the-art encoder-decoder models, which are effective at combining multi-scale contextual information with high spatial resolution. A key contribution is the integration of a multifaceted training methodology that includes hybrid loss functions with dynamic class weighting and spectral-consistent data augmentation to ensure robust model generalization from limited and imbalanced datasets. We demonstrate the framework's efficacy and scalability through two distinct case studies: a multi-class land cover classification on the Crozon Peninsula using Pléiades and a binary salt marsh detection in the Mont-Saint-Michel Bay Sentinel-2 imagery. The results show that accurate segmentation can be achieved with modest computational resources, promoting more sustainable and ethical AI applications in environmental monitoring. This work provides a critical tool for researchers and practitioners aiming to apply advanced DL segmentation to domain specific remote sensing challenges beyond conventional benchmarks