Générer des données hétérogènes sur des arbres génétiques
Nous présentons GenPhylo, a Python module that simulates nucleotide sequence data along a phylogeny avoiding the restriction of continuous-time Markov processes. GenPhylo uses directly a general Markov model and therefore naturally incorporates heterogeneity across lineages. We solve the challenge of generating transition...
FailLite: Modèle résilient aux pannes au service des périphéries aux ressources limitées Environnements
Model serving systems have become popular for deploying deep learning models for various latency-sensitive inference tasks. While traditional replication-based methods have been used for failure-resilient model serving in the cloud, such methods are often infeasible in edge environments due to significant resource...
Nanolithographie en tonalité positive du trisulfure d'antimoine avec femtoseconde Gravure humide au laser
Trisulfure d'antimoine ($Sb_{2}S_{3}$), as an emerging material for integrated photonic devices, a attiré une attention considérable en raison de son indice élevé, faible perte, et propriété de changement de phase dans le régime optique. Cependant, les méthodes de fabrication conventionnelles basées sur la lithographie impliquent des, prend du temps, processus en plusieurs étapes, rendering the photonic...
L'aire des sphères dans le plan brownien
We consider the area of spheres centered at the distinguished point in the Brownian plane. En fonction du rayon, the resulting process has continuously differentiable sample paths. En outre, the pair consisting of the process and its derivative is time-homogeneous Markov...
Les effets des charges de pression dans la réduction dimensionnelle de l'élasticité modèles
We study the dimensional reduction from three to two dimensions in hyperelastic materials subject to a live load, modeled as a constant pressure force. Our results demonstrate that this loading has a significant impact in higher-order scaling regimes, namely those associated with...
MedNNS: Supernet-based Medical Task-Adaptive Neural Network Search
Deep learning (DL) has achieved remarkable progress in the field of medical imaging. Cependant, adapting DL models to medical tasks remains a significant challenge, primarily due to two key factors: (1) architecture selection, as different tasks necessitate specialized model designs, et (2)...
Une prise de décision tactique à horizon étendu pour une conduite automatisée basée sur sur Recherche d'arbres de Monte Carlo
This paper introduces COR-MCTS (Conservation of Resources – Monte Carlo Tree Search), a novel tactical decision-making approach for automated driving focusing on maneuver planning over extended horizons. Traditional decision-making algorithms are often constrained by fixed planning horizons, typically up to 6 seconds...
Couverture satellitaire-terrestre active aérienne assistée par STAR-RIS Communications
An integration of satellites and terrestrial networks is crucial for enhancing performance of next generation communication systems. Cependant, the networks are hindered by the long-distance path loss and security risks in dense urban environments. Dans ce travail, we propose a satellite-terrestrial covert...
Actions de jauge améliorées par RG apprises par machine et classiquement parfaites flux dégradés
Extracting continuum properties of quantum field theories from discretized spacetime is challenging due to lattice artifacts. Renormalization-group (RG)-improved lattice actions can preserve continuum properties, but are in general difficult to parameterize. Machine learning (ML) with gauge-equivariant convolutional neural networks provides a way...
Détection multi-échelle de changements pratiquement significatifs dans un environnement progressivement séries chronologiques variables
In many change point problems it is reasonable to assume that compared to a benchmark at a given time point $t_0$ the properties of the observed stochastic process change gradually over time for $t >t_0$. Souvent, these gradual changes are not of...