Institution profile

University of Rennes

Academic institutioneurope · fr
Official website
Research library270linked papers
Opportunities0open roles
Selected work

Representative Papers

Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate

May 01, 2023Comput. Biol. Medicine

Clinical monitoring of ascending aortic aneurysms (AAoA) suffers from low accuracy in predicting aneurysm growth rate using conventional radial measurements. Method: We propose a quantitative, 3D morphology–driven prediction framework integrating local and global geometric features. Specifically, we construct a robust shape representation by jointly encoding multi-scale surface curvature and topological invariants, and design a growth-rate–sensitive dynamic feature-weighting regression scheme. Implicit surface reconstruction and differential-geometric feature extraction are performed directly from clinical CT volumes, followed by LASSO-regularized gradient-boosted decision tree (GBDT) regression. Contribution/Results: Validated on a multicenter cohort, our method achieves a mean absolute error of 0.18 mm/yr in growth-rate prediction—37% lower than radial metrics—and an AUC of 0.89 for binary classification of fast versus slow growth. This work is the first to incorporate topological invariants into AAoA growth modeling, substantially improving the reliability of noninvasive, patient-specific risk assessment.

13 citationsRead paper

Promises, Perils, and (Timely) Heuristics for Mining Coding Agent Activity

Jan 26, 2026

This study addresses the lack of systematic empirical evidence regarding the real-world impact of coding agents in software development. Leveraging Mining Software Repositories (MSR) methods, it presents the first large-scale analysis of activity traces from large language model–based coding agents on GitHub, systematically identifying their behavioral patterns, potential risks, and effective usage strategies in authentic development environments. The research yields a set of empirically grounded insights concerning optimal timing for agent adoption, reliability concerns, and practical heuristics for deployment. These findings fill a critical gap in the literature, offering actionable guidance for developers and establishing a foundation for future investigations into AI-assisted programming.

2 citationsRead paper

Distances Between Top-Truncated Elections of Different Sizes

Apr 11, 2025AAAI Conference on Artificial Intelligence

Existing approaches to visualizing election data are constrained by assumptions of fixed candidate and voter sets, as well as complete preference rankings, rendering them ill-suited for real-world scenarios involving heterogeneous scales and top-truncated preferences. This work extends the election map framework to such generalized settings for the first time, introducing a distance-based modeling approach, an algorithm tailored to handle truncated ballots, and a corresponding topological visualization technique. Extensive experiments on large-scale real-world election datasets from the Preflib repository demonstrate that the proposed method effectively overcomes the limitations of prior frameworks, substantially enhancing both the applicability and scalability of visual election analysis.

2 citationsRead paper

Sashimi-Bot: Autonomous Tri-manual Advanced Manipulation and Cutting of Deformable Objects

Nov 14, 2025

This work addresses the challenge of autonomous manipulation and high-precision cutting of natural, deformable 3D objects—exemplified by salmon fillets. Key difficulties include substantial inter-object geometric and dimensional variability, unknown viscoelastic material properties, and slippery, compliant surfaces prone to slippage. To overcome these, we propose a coordinated three-arm robotic framework integrating vision–tactile perception with deep reinforcement learning for real-time, adaptive in-hand tool manipulation and dynamic in-hand cutting. To our knowledge, this is the first system achieving stable multi-point grasping, deformation compensation, pose adjustment, and thin-slice cutting of soft-bodied targets via tri-arm coordination. Experiments demonstrate robust handling of highly heterogeneous salmon fillets, achieving sub-millimeter slicing accuracy; pick-up success rate and slice quality approach human-level performance. The framework establishes a scalable, generalizable paradigm for automated processing of deformable food products.

1 citations1 influentialRead paper
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