The HapticSpider: a 7-DoF Wearable Device for Cutaneous Interaction with the Palm

International audienceThis paper introduces a 7-degrees-of-freedom (7-DoF) hand-mounted haptic device, the “HapticSpider”. It is composed of a parallel mechanism characterised by eight legs with an articulated diamond-shaped structure, in turn connected to an origami-like shape-changing end-effector. The device can render surface and edge touch simulationsas well as apply normal, shear, and twist forces to the palm. This paper presents the device’s mechanical structure, a summary of its kinematic model, actuation control, and preliminary device evaluation, characterizing its workspace and force output

Demonstrating the relevance of spatial-functional statistical analysis in marine ecological studies : the case of environmental variations in micronektonic layers

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Multi-UAVs end-to-end Distributed Trajectory Generation over Point Cloud Data

This paper introduces an end-to-end trajectory planning algorithm tailored for multi-UAV systems that gener-ates collision-free trajectories in environments populated withboth static and dynamic obstacles, leveraging point cloud data.Our approach consists of a 2-fork neural network fed withsensing and localization data, able to communicate intermediatelearned features among the agents. One network branch craftsan initial collision-free trajectory estimate, while the otherdevises a neural collision constraint for subsequent optimiza-tion, ensuring trajectory continuity and adherence to physicalactuation limits. Extensive simulations in challenging clutteredenvironments, involving up to 25 robots and 25% obstacledensity, show a collision avoidance success rate in the range of100 − 85%. Finally, we introduce a saliency map computationmethod acting on the point cloud data, offering qualitativeinsights into our methodology

Web, smartphone, et AdTech sous l'angle de la vie privée

National audienceThis tutorial addresses the topic of personal data collection in web and smartphone environments, the management of user consent, and the underlying ecosystem setup by the AdTech industry to build user profiles and use them in real-time bidding (RTB) systems.This tutorial does not enters complex technical details. Its goal is to provide an overview and identify trends, with on the one hand, a highly profitable business that benefits from new data sources and advanced profiling technologies, and on the other hand, a business that is more and more limited by strict regulations and a growing privacy and sustainability awareness

Enseigner divisibilité et binomiaux avec Coq

The goal of this contribution is to provide worksheets in Coq for students to learn about divisibility and binomials. These basic topics are a good case study as they are widely taught in the early academic years (or before in France). We present here our technical and pedagogical choices and the numerous exercises we developed. As expected, it required additional Coq material such as other lemmas and dedicated tactics. The worksheets are freely available and flexible in several ways.Le but de cette contribution est de fournir des feuilles d'exercices en Coq à destination des étudiants pour l'apprentissage de la divisibilité et des coefficients binomiaux. Ces domaines élémentaires sont un bon sujet d'étude car ils sont largement enseignés durant les premières années universitaires (ou avant en France). Nous présentons nos choix techniques et pédagogiques et les nombreux exercices que nous avons développés. Sans surprise, cela a nécessité des développements {\Coq} supplémentaires tels que de nouveaux lemmes et des tactiques dédiées. Les feuilles d'exercices sont en accès libre et d'un usage flexible

Effective pruning for top-k feature search on the basis of SHAP values

With the increasingly pervasive use of advanced machine learning models comes the need to explain their predictions.The SHAP framework, based on Shapley values, provides explanations to highlight which features could be important for a given prediction.However, its use is hampered by its computational cost, especially in its model-agnostic formulation.Model-specific algorithms offer a restricted solution to this problem, whereasalternative approximation strategies can maintain the model-agnostic property.Here we propose TopShap as an agnostic algorithm that searches the k most important features by interleaving pruning of candidates and refinement of the approximate SHAP values.TopShap is built on three insights:(i) it performs an iterative approximation taking advantage of a previously developed sampling strategy,(ii) it uses confidence interval bounds around approximate SHAP values to determine on-the-fly which features can no longer be part of the top-k,(iii) it stops when these interval bounds are stable.Evaluating TopShap on publicly available datasets shows it performs an effective pruning of the feature search space and leads to an important reduction of the execution cost when compared to the other agnostic approaches.We also apply TopShap to a use case in biology and show that top-k search is meaningful in this context

Performance of a Region of Interest–based Algorithm in Diagnosing International Society of Urological Pathology Grade Group ≥2 Prostate Cancer on the MRI-FIRST Database—CAD-FIRST Study

International audienceBackground and objective: Prostate multiparametric magnetic resonance imaging (MRI) shows high sensitivity for International Society of Urological Pathology grade group (GG) ≥2 cancers. Many artificial intelligence algorithms have shown promising results in diagnosing clinically significant prostate cancer on MRI. To assess a region-of-interest-based machine-learning algorithm aimed at characterising GG ≥2 prostate cancer on multiparametric MRI.Methods: The lesions targeted at biopsy in the MRI-FIRST dataset were retrospectively delineated and assessed using a previously developed algorithm. The Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) score assigned prospectively before biopsy and the algorithm score calculated retrospectively in the regions of interest were compared for diagnosing GG ≥2 cancer, using the areas under the curve (AUCs), and sensitivities and specificities calculated with predefined thresholds (PIRADSv2 scores ≥3 and ≥4; algorithm scores yielding 90% sensitivity in the training database). Ten predefined biopsy strategies were assessed retrospectively.Key findings and limitations: After excluding 19 patients, we analysed 232 patients imaged on 16 different scanners; 85 had GG ≥2 cancer at biopsy. At patient level, AUCs of the algorithm and PI-RADSv2 were 77% (95% confidence interval [CI]: 70-82) and 80% (CI: 74-85; p = 0.36), respectively. The algorithm's sensitivity and specificity were 86% (CI: 76-93) and 65% (CI: 54-73), respectively. PI-RADSv2 sensitivities and specificities were 95% (CI: 89-100) and 38% (CI: 26-47), and 89% (CI: 79-96) and 47% (CI: 35-57) for thresholds of ≥3 and ≥4, respectively. Using the PI-RADSv2 score to trigger a biopsy would have avoided 26-34% of biopsies while missing 5-11% of GG ≥2 cancers. Combining prostate-specific antigen density, the PI-RADSv2 and algorithm's scores would have avoided 44-47% of biopsies while missing 6-9% of GG ≥2 cancers. Limitations include the retrospective nature of the study and a lack of PI-RADS version 2.1 assessment.Conclusions and clinical implications: The algorithm provided robust results in the multicentre multiscanner MRI-FIRST database and could help select patients for biopsy.Patient summary: An artificial intelligence-based algorithm aimed at diagnosing aggressive cancers on prostate magnetic resonance imaging showed results similar to expert human assessment in a prospectively acquired multicentre test database

Bevacizumab, olaparib, and durvalumab in patients with relapsed ovarian cancer: a phase II clinical trial from the GINECO group

International audienceAbstract Most patients with advanced ovarian cancer (AOC) ultimately relapse after platinum-based chemotherapy. Combining bevacizumab, olaparib, and durvalumab likely drives synergistic activity. This open-label phase 2 study (NCT04015739) aimed to assess activity and safety of this triple combination in female patients with relapsed high-grade AOC following prior platinum-based therapy. Patients were treated with olaparib (300 mg orally, twice daily), the bevacizumab biosimilar FKB238 (15 mg/kg intravenously, once-every-3-weeks), and durvalumab (1.12 g intravenously, once-every-3-weeks) in nine French centers. The primary endpoint was the non-progression rate at 3 months for platinum-resistant relapse or 6 months for platinum-sensitive relapse per RECIST 1.1 and irRECIST. Secondary endpoints were CA-125 decline with CA-125 ELIMination rate constant K (KELIM-B) per CA-125 longitudinal kinetics over 100 days, progression free survival and overall survival, tumor response, and safety. Non-progression rates were 69.8% (90%CI 55.9%-80.0%) at 3 months for platinum-resistant relapse patients (N = 41), meeting the prespecified endpoint, and 43.8% (90%CI 29.0%-57.4%) at 6 months for platinum-sensitive relapse (N = 33), not meeting the prespecified endpoint. Median progression-free survival was 4.1 months (95%CI 3.5–5.9) and 4.9 months (95%CI 2.9–7.0) respectively. Favorable KELIM-B was associated with better survival. No toxic deaths or major safety signals were observed. Here we show that further investigation of this triple combination may be considered in AOC patients with platinum-resistant relapse

Repurposing Holocaust-Related Digital Scholarly Editions to Develop Multilingual Domain-Specific Named Entity Recognition Tools

International audienceThe European Holocaust Research Infrastructure (EHRI) aims to support Holocaust research by making information about dispersed Holocaust material accessible and interconnected through its services. Creating a tool capable of detecting named entities in texts such as Holocaust testimonies or archival descriptions would make it easier to link more material with relevant identifiers in domain-specific controlled vocabularies, semantically enriching it, and making it more discoverable. With this paper, we release EHRI-NER, a multilingual dataset (Czech, German, English, French, Hungarian, Dutch, Polish, Slovak, Yiddish) for Named Entity Recognition (NER) in Holocaust-related texts. EHRI-NER is built by aggregating all the annotated documents in the EHRI Online Editions and converting them to a format suitable for training NER models. We leverage this dataset to fine-tune the multilingual Transformer-based language model XLM-RoBERTa (XLM-R) to determine whether a single model can be trained to recognize entities across different document types and languages. The results of our experiments show that despite our relatively small dataset, in a multilingual experiment setup, the overall F1 score achieved by XLM-R fine-tuned on multilingual annotations is 81.5\%. We argue that this score is sufficiently high to consider the next steps towards deploying this model

Fluid Dynamics Simulation on a GPU

We solve the Navier-Stokes equations for incompressible fluid flow onmodern GPU (Graphics Processing Unit) computing devices, using theDirectX11 API by Microsoft. We implement the well-known projectionmethod on DirectX compute shaders in the languages HLSL (High LevelShader Language) and C++. We compute the flow inside of a lid-drivencavity and compare results to those from the standard benchmark. Wealso discuss future developments to enable the use of open software standardsto achieve better results