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Conservatoire National des Arts et Métiers

Academic institutioneurope · fr
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Research library52linked papers
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Selected work

Representative Papers

Comparative Analysis of Ray Tracing and Rayleigh Fading Models for Distributed MIMO Systems in Industrial Environments

Mar 03, 2025

This work addresses channel modeling and performance evaluation of distributed MIMO (D-MIMO) in industrial environments. We systematically compare, for the first time, deterministic ray-tracing models against stochastic Rayleigh fading models in predicting downlink/uplink single-user capacity. Leveraging a real-world 3D factory map, we construct multiple deployment scenarios to quantify how network densification affects user equipment (UE) multi-access point (AP) connectivity and coverage gain. Results show that densification significantly enhances D-MIMO capacity. Ray tracing more accurately captures spatial correlation and realistic propagation characteristics, whereas the Rayleigh model offers superior computational efficiency and maintains acceptable prediction error (<15%) in typical factory settings. The study establishes fundamental trade-offs among modeling accuracy, spatial correlation fidelity, and computational overhead, providing both theoretical guidance and empirical evidence for selecting appropriate D-MIMO channel models in industrial wireless systems.

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LEED: Local Embedding Evolution Distance for over-smoothing estimation and virtual node selection in GNN

Aug 10, 2026

Graph neural networks often suffer from oversmoothing and overcompression, yet existing global metrics struggle to characterize representation degradation at the node level. To address this, this work proposes LEED—a node-level local metric that fine-grainedly quantifies oversmoothing by tracking the embedding evolution distance of each node across layers. This metric further serves as a unified guide for constructing local virtual nodes to mitigate overcompression. Departing from multiple heuristic centrality strategies, LEED uniquely adopts embedding evolution distance as a unified criterion for both diagnosis and optimization. It preserves global assessment capability while enabling precise, node-wise diagnostics, leading to significant performance improvements across multiple benchmark datasets.

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Recent publications

Latest Papers

LEED: Local Embedding Evolution Distance for over-smoothing estimation and virtual node selection in GNN

Aug 10, 2026

Graph neural networks often suffer from oversmoothing and overcompression, yet existing global metrics struggle to characterize representation degradation at the node level. To address this, this work proposes LEED—a node-level local metric that fine-grainedly quantifies oversmoothing by tracking the embedding evolution distance of each node across layers. This metric further serves as a unified guide for constructing local virtual nodes to mitigate overcompression. Departing from multiple heuristic centrality strategies, LEED uniquely adopts embedding evolution distance as a unified criterion for both diagnosis and optimization. It preserves global assessment capability while enabling precise, node-wise diagnostics, leading to significant performance improvements across multiple benchmark datasets.

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Sinkhorn Hamiltonian Monte Carlo for Entropic Optimal Transport Generalized Bayes

Jul 30, 2026

This work addresses the limitations of traditional Bayesian inference, which relies on exact likelihoods and suffers when the likelihood is misspecified, intractable, or misaligned with the target discrepancy. The authors propose the first integration of Sinkhorn divergence as a generalized Bayesian loss within Hamiltonian Monte Carlo (HMC) and its adaptive variant NUTS, accommodating both balanced and unbalanced optimal transport settings. They further incorporate common random numbers to handle stochastic simulators and introduce a heuristic for hyperparameter selection that preserves gradient consistency with Hamiltonian dynamics. Empirical evaluations on Gaussian models, noisy spiral manifolds, pulse misalignment, and CIFAR-10 image patch alignment demonstrate the method’s effectiveness, robustness, and the nuanced differences between transport mechanisms.

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