IA et IRM pour mieux comprendre le cerveau après un AVC - Retour d’expérience

National audienc

Integrating ethical, societal and environmental issues into algorithm design courses

This document, intended for computer science teachers, describes a case study that puts into practice a questioning of ethical, societal and environmental issues when designing or implementing a decision support system. This study is based on a very popular application, namely road navigation software that informs users of real-time traffic conditions and suggests routes between a starting point and a destination, taking these conditions into account (such as Waze). The approach proposes to intertwine technical considerations (optimal path algorithms, data needed for location, etc.) with a broader view of the ethical, environmental and societal issues raised by the tools studied. Based on the authors' experience conducting sessions with students over several years, this document discusses the context of such a study, suggests teaching resources for implementing it, describes ways to structure discussions, and shares scenarios in different teaching contexts

Complete Abstractions for Verification of Polymorphic Functions with Equality -- extended version

This paper is concerned with automatically proving properties on polymorphic programs over algebraic data types by reducing the verification of such properties to the verification of properties on monomorphised, abstract programs. For programs without polymorphic equality, the reduction exploits Wadler's "Theorem for Free". For programs using polymorphic equality, we provide a sufficient condition for the reduction to hold. The condition relies on the existence of a locally complete abstraction function whose image is a finite set of arbitrary constants chosen for abstracting primitive values. When such a condition exists, the number of arbitrary constants depends on the functions under concern and the properties to prove. We present an implementation that automatically computes the number of constants and, thus, ensures that proving the polymorphic case with equality can be reduced to the proof carried out on a monomorphic instance of the program. Experimental results show that this reduction is indeed possible with small abstract domains. Target programs support user-defined recursive ADTs and recursive first-order functions.</div

Tubes in sub-Riemannian geometry and a Weyl's invariance result for curves in the Heisenberg groups

The purpose of the paper is threefold: first, we prove optimal regularity results forthe distance from Ck submanifolds of general rank-varying sub-Riemannian structures. Then,we study the asymptotics of the volume of tubular neighbourhoods around such submanifolds.Finally, for the case of curves in the Heisenberg groups, we prove a Weyl’s invariance result:the volume of small tubes around curves does not depend on the way the curve is isometricallyembedded, but only on its Reeb angle. The proof does not need the computation of the actualvolume of the tube, and it is new even for the three-dimensional Heisenberg group

Renal Cell Carcinoma subtyping: Learning from multi-resolution localization

International audienceBackground and Objective Renal Cell Carcinoma (RCC) is often diagnosed at advanced stages, limitingtreatment options. Since prognosis depends on tumour subtype, accurate and efficient classification is essential.Artificial intelligence tools can assist diagnosis, yet their dependence on large annotated datasets hindersbroader adoption. This study investigates a Self-Supervised Learning (SSL) framework that exploits the multiresolution structure of Whole histological Slide Images (WSIs) to reduce annotation requirements whilemaintaining reliable diagnostic performance.Methods: We developed a SSL model inspired by the pathologist’s multi-scale reasoning, integrating information across magnification levels. Robustness and generalization were evaluated through an external validationon a public RCC benchmark and one internal validation using cohorts from the same institution but collectedin different periods, with distinct scanners and laboratory workflows.Results and Conclusions The proposed SSL approach demonstrated stable classification performance acrossall validation settings, reducing dependence on manual labels and improving robustness under heterogeneousacquisition conditions. These findings support its potential as a generalizable and annotation-efficient strategyfor RCC subtype classification

Group-robust Machine Unlearning

International audienceMachine unlearning is an emerging paradigm to remove the influence of specific training data (i.e., the forget set) from a model while preserving its knowledge of the rest of the data (i.e., the retain set). Previous approaches assume the forget data to be uniformly distributed from all training datapoints. However, if the data to unlearn is dominant in one group, we empirically show that performance for this group degrades, leading to fairness issues. This work tackles the overlooked problem of non-uniformly distributed forget sets, which we call group-robust machine unlearning, by presenting a simple, effective strategy that mitigates the performance loss in dominant groups via sample distribution reweighting. Moreover, we present MIU (Mutual Information-aware Machine Unlearning), the first approach for group robustness in approximate machine unlearning. MIU minimizes the mutual information between model features and group information, achieving unlearning while reducing performance degradation in the dominant group of the forget set. Additionally, MIU exploits sample distribution reweighting and mutual information calibration with the original model to preserve group robustness. We conduct experiments on three datasets and show that MIU outperforms standard methods, achieving unlearning without compromising model robustness. Source code available at https://github.com/tdemin16/group-robust_machine_unlearning

Rapid cell turnover to model adipocyte size distribution

International audienceWhite adipose tissue, composed of adipocyte cells, primarily stores energy as lipid droplets. The size of adipocytes varies significantly within the tissue according to the amount of stored lipids. A striking observation is that the adipocyte size distribution is bimodal, and thus, this tissue is lacking a characteristic size.We propose a novel dynamical model, based on a partial differential equation, to represent the adipocyte size distribution. The model assumes continuous adipocyte growth, with a velocity dependent on cell radius and extracellular lipid availability, together with constant rates of cell recruitment and death.We prove the existence and local stability of a unique stationary solution for a broad range of growth velocity functions. Choosing a parsimonious formulation, we show that only three parameters are enough to describe adipocyte size distributions measurements in rats. These parameters are robustly estimated through approximate Bayesian computation, and the model demonstrates excellent agreement with experimental data. This mechanistic, three-parameter framework offers a new and interpretable approach to characterizing adipocyte size distributions

An Improved Bound for Equitable Proper Labellings

International audienceFor every graph $G$ with size $m$ and no connected component isomorphic to $K_2$, we prove that, for $L=(1,1,2,2,\dots,\lfloor m/2 \rfloor+2,\lfloor m/2 \rfloor+2)$, we can assign labels of $L$ to the edges of $G$ in an injective way so that no two adjacent vertices of $G$ are incident to the same sum of labels. This implies that every such graph with size $m$ can be labelled in an equitable and proper way with labels from $\{1,\dots,\lfloor m/2 \rfloor+2\}$, which improves on a result proved by Haslegrave, and Szabo Lyngsie and Zhong, implying this can be achieved with labels from $\{1,\dots,m\}$

ProvSQL: A General System for Keeping Track of the Provenance and Probability of Data

International audienceWe present the data model, design choices, and performance of ProvSQL, a general and easy-to-deploy provenance tracking and probabilistic database system implemented as a PostgreSQL extension. ProvSQL’s data and query models closely reflect that of a large core of SQL, including multiset semantics, the full relational algebra, and aggregation. A key part of its implementation relies on generic provenance circuits stored in memory-mapped files. We propose benchmarks to measure the overhead of provenance and probabilistic evaluation and demonstrate its scalability and competitiveness with respect to other state-of-the-art systems

Coupled data recovery and shape identification : Nash games for the nonlinear Cauchy-Stokes case

International audienceIn this work, we investigate nonlinear Cauchy-type problems arising in quasi-Newtonian Stokes flows, where the viscosity exhibits a nonlinear dependence on the deformation tensor, modeled by the Carreau law. To tackle the inherent ill-posedness of the Cauchy-Stokes problem, we propose three iterative methods, each reformulating the original problem into a sequence of well-posed mixed boundary value problems (BVPs). A classical control framework is employed to construct a control-type algorithm for the nonlinear inverse problem. Then, we introduce two novel algorithms based on a Nash game formulation; the second algorithm enables each player to linearize the adverse state equations, enhancing computational efficiency and convergence. We further extend this linearized Nash approach to simultaneously recover missing boundary data and identify the location and shape of unknown inclusions. Finite element simulations validate the robustness and effectiveness of the proposed methods