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D-LIM: a Neural Network for Interpretable Gene-Gene Interactions
No full textRecent advances in gene editing can produce large genotype-fitness maps for targeted genes, yet predicting the effects of mutations between genes remains challenging. Indeed, biochemical models require knowledge of underlying parameters and interactions, whereas machine learning methods typically lack interpretability, as they do not link model parameters to biological quantities. We introduce D-LIM, a neural network that infers low-dimensional fitness landscapes directly from mutation-fitness data. The distinctive feature of D-LIM is that it assumes genes act through independent gene-specific molecular phenotypes whose nonlinear interactions determine fitness. When this assumption holds, the model yields accurate predictions and interpretable effective phenotypes. Conversely, failure reveals that a low-dimensional model is insufficient. Applied to deep mutational scanning of metabolic pathways, protein-protein interactions, and yeast environmental adaptation, D-LIM achieves state-ofthe-art predictive accuracy. The inferred phenotype-fitness landscapes reveal whether epistatic interactions can be captured by a low-dimensional continuous model and identify potential trade-offs. Moreover, D-LIM estimates mutational effects on the effective phenotypes, enabling weak extrapolation beyond the training domain. D-LIM demonstrates how simple structure constraints in a neural network can help inference and hypothesis generation in biology.</div
Le mirage de la régularisation de la nullité des décisions sociales
No full textInternational audienc
Classement 2024 des villes de plus de 50 000 habitants selon la qualité de leur budget: Classement 2024 des villes
No full textDans cette étude, on propose de classer les villes d'une même strate en fonction de certaines catégories (ou lignes) figurant dans les comptes administratifs (CA) disponibles en ligne sur le site impots.gouv. mais, aussi, en fonction de certains indicateurs traditionnels utilisés pour mesurer la qualité de la gestion des finances publiques. Les résultats présentés sont des moyennes sur les 5 dernières années disponibles jusqu'en 2024 inclus. Les CA des communes et de leur EPCI (Communauté Urbaine, métropole ou autre) ont été agrégés (sommés) car cela reflète mieux la gestion globale de l'équipe municipale en place qui gère à la fois le budget de la commune et de l'EPCI d'autant plus que cela correspond également à la réalité du fonctionnement et des investissements dont bénéficient les habitants des villes concernées. Cela est également recommandé par des universitaires, des spécialistes de la Cour des Comptes, la DGCL.</div
Explainable AI for Marine Ecological Quality Prediction: Integrating Microbiome Data, Metadata, and Diversity
No full textAssessing ecological quality (EQ) is crucial for marine biodiversity monitoring. With the advent of High-Throughput Sequencing technologies, metabarcoding has enabled large-scale microbial community analysis through Operational Taxonomic Unit (OTU) tables, providing an alternative for EQ assessment. Machine learning (ML) models have been successfully applied for this task, but they often treat microbial abundance as the sole predictor, overlooking environmental meta-data (e.g., pH, salinity, temperature) and diversity indices (alpha and beta diversity). This study integrates metadata and diversity indices into an explainable ML framework for EQ prediction. Using SHapley Additive Explanations (SHAP), we assess the contribution of these features to model predictions across five genetic markers (V1V2, V3V4, V4, 37F, and V9). Our results highlight marker-dependent feature importance, demonstrating that while OTU-based models remain dominant, incorporating metadata improves accuracy for certain markers. This work enhances interpretability in AI-driven biomonitoring, fostering more reliable marine ecosystem assessments.</div
Geometric Time-Dependent Density Functional Theory
No full textWe provide a new formulation of Time-Dependent Density Functional Theory (TDDFT) based on the geometric structure of the set of states constrained to have a fixed density. Orbital-free TDDFT is formulated using a hydrodynamics equation involving a new universal density-to-current functional map. In the corresponding Kohn--Sham equation, the density is reproduced using a non-local operator. Numerical simulations for one-dimensional soft-Coulomb systems are provided
Matriarchy, Gender and Power: Interdisciplinary perspectives
No full textInternational audienceThis book explores the conceptualizations of female power through the notion of matriarchy in a variety of historical, cultural and epistemological contexts.Matriarchy has been both marginalized, and even derided, as an object of study albeit consistently referred to as a symbol of female power. The lack of serious engagement with matriarchy has stifled critical inquiry into alternative ways of organizing gendered power, and this gap is that this book seeks to address. Re-examining matriarchy from a scientific and interdisciplinary perspective, this book aims to move beyond the simplistic binaries of male vs. female power through diverse enquiries into the concept of matriarchy that conceptualizes power not as domination, but as interconnection, nurturing, and community-oriented leadership. Through this approach, the contributions examine the emancipatory possibilities of matriarchy, while also acknowledging the limitations and challenges that come with it.An interdisciplinary approach having international scope, this work will appeal to postgraduate students and academic researchers of Sociology, Anthropology, History, Art History, Asian Studies, American Studies, African American and Africana Studies, Women’s Studies, Gender Studies, and Law
Ergodicity of some stochastic Fokker-Planck equations with additive common noise
No full textInternational audienceIn this paper we consider stochastic Fokker-Planck Partial Differential Equations (PDEs), obtained as the mean-field limit of weakly interacting particle systems subjected to both independent (or idiosyncratic) and common Brownian noises. We provide sufficient conditions under which the deterministic counterpart of the Fokker-Planck equation, which corresponds to particle systems that are just subjected to independent noises, has several invariant measures, but for which the stochastic version admits a unique invariant measure under the presence of the additive common noise. The very difficulty comes from the fact that the common noise is just of finite dimension while the state variable, which should be seen as the conditional marginal law of the system given the common noise, lives in a space of infinite dimension. In this context, our result holds true if, in addition to standard confining properties, the mean field interaction term forces the system to be attracted by its conditional mean given the common noise and the intensity of the idiosyncratic noise is small
Élections municipales : quelle place pour les femmes, les minorités et les classes populaires ?: Enquête en Seine-Saint-Denis
No full textNational audienc
Space oddity: How context matters in waste-sorting behavior
No full textInternational audienceThe circular economy gives a prominent role to recycling, entailing the general implementation of waste sorting in private, semi-public, and public locations. Previous literature has extensively explored the psychological and contextual antecedents of waste-sorting behavior, but mainly with a focus on one specific setting, without considering how the sorting location might moderate the influence of these antecedents. To investigate this research question, we develop a dual-route model of waste-sorting behavior based on an integrated TPB-NAM framework. To test this model, we used a survey based on self-reported data considering three successive locations (i.e., home, university and on the way to university) from the same 296 French college students and analyzed it using structural equation modeling with partial least squares (PLS-SEM). Although both psychological and contextual routes influence waste sorting, we show that their relative importance differs across locations. Furthermore, at university and on the way to university, the contextual route influences the psychological one. These results highlight why individuals' self-reported waste-sorting behavior may vary across locations and call on academics to replicate pro-environmental behavior models across all relevant contexts before recommending public policies to promote them
Tail-Aware Density Forecasting of Locally Explosive Time Series: A Neural Network Approach: CRED WORKING PAPER 2026-02
No full textThis paper proposes a Mixture Density Network specifically designed for forecasting time series that exhibit locally explosive behavior. By incorporating skewed t-distributions as mixture components, our approach offers enhanced flexibility in capturing the skewed, heavy-tailed, and potentially multimodal nature of predictive densities associated with bubble dynamics modeled by mixed causal-noncausal ARMA processes. In addition, we implement an adaptive weighting scheme that emphasizes tail observations during training and hence leads to accurate density estimation in the extreme regions most relevant for financial applications. Equally important, once trained, the MDN produces near-instantaneous density forecasts. Through extensive Monte Carlo simulations and two empirical applications, on the natural gas price and inflation, we show that the proposed MDN-based framework delivers superior forecasting performance relative to existing approaches