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Fair and efficient multi-agent routing for cooperative and autonomous agricultural fleets with implements
No full textInternational audienceThe growing use of autonomous tractor fleets with detachable implements presents complex logistical challenges in agriculture. Current systems often rely on simple heuristics and avoid implement swapping, limiting efficiency. A central challenge is to dynamically coordinate vehicle routing and implement exchanges to enable efficient, low-intervention task execution. Due to high costs, such fleets are owned mainly by large enterprises or cooperatives, where fair task allocation and profit sharing are critical. Addressing both coordination and fairness, in this paper, we introduce the Agricultural Fleet Vehicle Routing Problem with Implements (AFVRPI). We propose a distributed model derived from a centralized formulation also presented in this paper. This model is embedded within a Distributed Multi-Agent System Architecture (DIMASA), where autonomous vehicle agents manage routing and implement use under limited fuel autonomy, while implement agents ensure compatibility and sufficient capacity to meet task demands. Our solution applies systematic egalitarian social welfare optimization to iteratively maximize the profit of the worst-off vehicle, balancing fairness with system efficiency. To enhance scalability, we use column generation in the distributed model, achieving solution quality comparable to the centralized model while significantly reducing computational time. Simulation results on new benchmark instances demonstrate that our distributed multi-agent AFVRPI approach is scalable, efficient, and fair
Renal Cell Carcinoma subtyping: Learning from multi-resolution localization
No full textInternational 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
Unsupervised Detection of Post-Stroke Brain Abnormalities
No full textInternational audiencePost-stroke MRI not only delineates focal lesions but also reveals secondary structural changes, such as atrophy and ventricular enlargement. These abnormalities, increasingly recognised as imaging biomarkers of recovery and outcome, remain poorly captured by supervised segmentation methods. We evaluate REFLECT, a flow-based generative model, for unsupervised detection of both focal and non-lesional abnormalities in post-stroke patients. Using dual-expert central-slice annotations on ATLAS data, performance was assessed at the object level with Free-Response ROC analysis for anomaly maps. Two models were trained on lesion-free slices from stroke patients (ATLAS) and on healthy controls (IXI) to test the effect of training data. On ATLAS test subjects, the IXI-trained model achieved higher lesion segmentation (Dice = 0.37 vs 0.27) and improved sensitivity to non-lesional abnormalities (FROC = 0.62 vs 0.43). Training on fully healthy anatomy improves the modelling of normal variability, enabling broader and more reliable detection of structural abnormalities
Abstract Lipschitz Continuity: Combining Semantic and Quantitative Approximations
No full textInternational audienceWe introduce Abstract Lipschitz Continuity (ALC), an extensional (i.e., input/output) property that ensures proportionally bounded differences in the semantic approximations of the output of a function (e.g., a program semantics) when the semantic approximations of the input differ slightly. ALC explicitly discerns between two complementary notions of approximation: quantitative differences, expressed via pre-metrics, and qualitative (or semantic) differences, captured through upper closure operators. This explicit separation of approximations has two main advantages. First, it enables ALC to be related to other important extensional program properties, including partial abstract non-interference in language-based security, partial completeness in abstract interpretation, and abstract robustness in machine learning. Second, ALC enables reasoning about its validity for programs through inductive reasoning on their syntax and on the chosen semantic abstractions. To this end, we propose a sound deductive system, parameterized by the quantitative and semantic approximations of interest, for proving ALC of programs. This proof system makes explicit the assumptions required for ALC, thereby ensuring a compositional proof approach
Computations of higher elliptic units
No full text20 pages. Submitted. This paper presents a simplified version of a conjecture presented in the series of papers "Geometric families of multiple elliptic Gamma functions and applications" which supersedes the working paper arXiv:2406.06094 (see arXiv:2510.16515 for the first paper in this series). In this paper, we focus on optimal examples in the conjecture and how we find them algorithmically.In this paper we present a conjecture on the construction of generalised elliptic units above number fields with exactly one complex place. These elliptic units obtained as values of multiple elliptic Gamma functions. These form a collection of multivariate meromorphic functions which were studied in the late 1990s and early 2000s in mathematical physics. Our construction extends the scheme of a recent article by Bergeron, Charollois and Garc\'ia where they constructed conjectural elliptic units above complex cubic fields using the elliptic Gamma function. The elliptic units we construct are expected to generate specific abelian extensions of the base field where they are evaluated, thus giving a conjectural solution to Hilbert's 12th problem for the number fields with exactly one complex place. We provide several examples to support our conjecture in optimal cases for cubic, quartic and quintic fields
Variational autoencoder for inference of nonlinear mixed effect models based on ordinary differential equations
No full textInternational audienceWe propose a variational autoencoder (VAE) approach for parameter estimation in nonlinear mixed-effects models based on ordinary differential equations (NLME-ODEs) using longitudinal data from multiple subjects. In moderate dimensions, likelihood-based inference via the stochastic approximation EM algorithm (SAEM) is widely used,but it relies on Markov Chain Monte-Carlo (MCMC) to approximate subject-specific posteriors. As model complexity increases or observations per subject are sparse and irregular, performance often deteriorates due to a complex, multimodal likelihood surface which may lead to MCMC convergence difficulties. We instead estimate parameters by maximizing the evidence lower bound (ELBO), a regularized surrogate for the marginal likelihood. A VAE with a shared encoder amortizes inference of subject-specific random effects by avoiding per-subject optimization and the use of MCMC. Beyond pointwise estimation, we quantify parameter uncertainty using observed-information–based variance estimator and verify that practical identifiability of the model parameters is not compromised by nuisance parameters introduced in the encoder. We evaluate the method in three simulation case studies (pharmacokinetics,humoral response to vaccination, and TGF-β activation dynamics in asthmatic airways) and on a real-world antibody kinetics dataset, comparing against SAEM baselines
EMG-to-torque models for exoskeleton assistance: a framework for the evaluation of in situ calibration
No full textInternational audienceIn the field of robotic exoskeleton control, it is critical to accurately predict the intention of the user. While surface electromyography (EMG) holds the potential for such precision, current limitations arise from the absence of robust EMG-to-torque model calibration procedures and a universally accepted model. This paper introduces a practical framework for calibrating and evaluating upper-limb EMG-to-torque models, accompanied by a novel nonlinear model. The framework includes an in situ procedure that involves generating calibration trajectories and subsequently evaluating them using standardized criteria. A comprehensive assessment on a dataset with 17 participants, encompassing single-joint and multi-joint conditions, suggests that the novel model outperforms the others in terms of accuracy while conserving computational efficiency. This contribution introduces an efficient model and establishes a versatile framework for EMG-to-torque model calibration and evaluation, complemented by a dataset made available. This further lays the groundwork for future advancements in EMG-based exoskeleton control and human intent detection
Sparse Vector Reconstruction from Distance Spectrum using Soft Information
No full textQC-MDPC based schemes feature secret sparse cyclic binary vectors. When those vectors are sparse enough, they can be reconstructed from their distance spectrum, that is the set of all distances between the coordinates of the non-zero coefficients. In this work, we revisit the reconstruction algorithms and we explore to what extent a secret sparse vector can be recovered from a partial knowledge of its distance spectrum. In particular, we show how to efficiently use reliability (soft information) in the reconstruction process. Another aspect of this work is to investigate which kind of side-channel leaks information about the distance spectrum and to understand the models that enable us to quantify the reliability on leaking data depending on the amount of side-channel observations (or queries). For instance, we show that for BIKE level 1, assuming that a side-channel leaks information about the syndrome weight, using soft information in the reconstruction process reduces the number of queries by a factor 10. Our technique can also be applied to HQC, which also features sparse secret vector, with similar figures, assuming there exists a side-channel leaking relevant information, the error weight in the case of HQC
Convergence of the Cumulant Expansion and Polynomial-Time Algorithm for Weakly Interacting Fermions
No full textWe propose a randomized algorithm to compute the log-partition function of weakly interacting fermions with polynomial runtime in both the system size and precision. Although weakly interacting fermionic systems are considered tractable for many computational methods such as the diagrammatic quantum Monte Carlo, a mathematically rigorous proof of polynomial runtime has been lacking. In this work we first extend the proof techniques developed in previous works for proving the convergence of the cumulant expansion in periodic systems to the non-periodic case. A key equation used to analyze the sum of connected Feynman diagrams, which we call the tree-determinant expansion, reveals an underlying tree structure in the summation. This enables us to design a new randomized algorithm to compute the log-partition function through importance sampling augmented by belief propagation. This approach differs from the traditional method based on Markov chain Monte Carlo, whose efficiency is hard to guarantee, and enables us to obtain a algorithm with provable polynomial runtime
Intégrer des enjeux éthiques, sociétaux et environnementaux dans un cours de conception d'algorithmes
No full textThis 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.Ce document, à destination des enseignants d'informatique, décrit une étude de cas qui met en pratique un questionnement sur les enjeux éthiques, sociétaux et environnementaux lors de la conception ou de la mise en œuvre d’un algorithme d'aide à la décision. Cette étude s'appuie sur une application très populaire, à savoir un logiciel d’aide à la navigation routière informant les utilisateurs des conditions de trafic en temps réel, et leur proposant des itinéraires entre une origine et une destination en tenant compte de ces conditions (type Waze). L'approche propose d'entrelacer des considérations techniques (algorithmes de chemins optimaux, données nécessaires pour se localiser, etc.) et une prise de recul sur les enjeux éthiques, environnementaux et sociétaux des outils étudiés. En s'appuyant sur l'expérience conduite par les auteurs depuis plusieurs années en séance face à des étudiants, ce document discute le contexte d'une telle étude, propose des ressources pédagogiques pour mettre en œuvre cette étude, décrit des pistes pour structurer les échanges et partage des scénarisations dans différents contextes pédagogiques