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Publications

Cette page présente les publications du Centre Borelli déposées sur le serveur HAL.

Affichage des publications 551 à 560 sur 1523 au total

Biomechanics

4D flow MRI aortic cross-sectional pressure changes and their associations with flow patterns in health and aneurysm

Background:Ascending thoracic aortic aneurysm (ATAA) is a potentially life-threatening dilation of the ascending aorta (AscAo) that often develops without symptoms. The current clinical practice relies on measuring the maximal diameter of the aorta…

Kevin Bouaou, Thomas Dietenbeck, Gilles Soulat, Ioannis Bargiotas, Sophia Houriez--Gombaud- Saintonge, Alain de Cesare, Umit Gencer, Alain Giron, Elena Jiménez, Emmanuel Messas, Didier Lucor, Emilie Bollache, Elie Mousseaux, Nadjia Kachenoura

Journal of Cardiovascular Magnetic Resonance -

Artificial Intelligence

Diffusion models meet image counter-forensics

From its acquisition in the camera sensors to its storage, different operations are performed to generate the final image. This pipeline imprints specific traces into the image to form a natural watermark. Tampering with an image disturbs these…

Matías Tailanian, Marina Gardella, Álvaro Pardo, Pablo Musé

IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) -

Computer Science

CORPEX : Analyse exploratoire d'un corpus biomédical à l'aide de la classification croisée

Nous proposons une interface d'aide à l'analyse de corpus via la visualisation interactive de coclusters afin d'accompagner l'exploration des thématiques pour un ensemble de textes. Les saisies de l'utilisateur permettent la création ou le…

Amine Ferdjaoui, Amira Tlati, Séverine Affeldt, Mohamed Nadif

23ème conférence francophone sur l'extraction et la gestion des connaissances -

Computer Science

WordGraph: A Python Package for Reconstructing Interactive Causal Graphical Models from Text Data

We present WordGraph, a Python package for exploring the topics of documents corpora. WordGraph provides causal graphical models from text data vocabulary and proposes interactive visualizations of terms networks. Our ease-to-use package is provided…

Amine Ferdjaoui, Séverine Affeldt, Mohamed Nadif

WSDM '24: The 17th ACM International Conference on Web Search and Data Mining -

Life Sciences

Unravelling Robust Brain-Behavior Links of Depressive Symptoms Through Granular Network Models: Understanding Heterogeneity and Clinical Implications

Abstract Background Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological symptoms and biological factors is critical…

René Freichel, Agatha Lenartowicz, Linda Douw, Johann Kruschwitz, Tobias Banaschewski, Gareth Barker, Arun Bokde, Sylvane Desrivières, Herta Flor, Antoine Grigis, Hugh Garavan, Andreas Heinz, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Frauke Nees, Dimitri Papadopoulos Orfanos, Tomáš Paus, Luise Poustka, Nathalie Holz, Christian Baeuchl, Michael Smolka, Nilakshi Vaidya, Robert Whelan, Vincent Frouin, Gunter Schumann, Henrik Walter, Tessa Blanken, Arun L.W. Bokde

Life Sciences

Human cortex development is shaped by molecular and cellular brain systems

Abstract Human brain morphology undergoes complex changes over the lifespan. Despite recent progress in tracking brain development via normative models, current knowledge of underlying biological mechanisms is highly limited. We demonstrate that…

Leon Lotter, Amin Saberi, Justine Hansen, Bratislav Misic, Casey Paquola, Gareth Barker, Arun Bokde, Sylvane Desrivières, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère, Eric Artiges, Dimitri Papadopoulos Orfanos, Tomáš Paus, Luise Poustka, Sarah Hohmann, Juliane Fröhner, Michael Smolka, Nilakshi Vaidya, Henrik Walter, Robert Whelan, Gunter Schumann, Frauke Nees, Tobias Banaschewski, Simon Eickhoff, Juergen Dukart

Machine Learning

Boosting Subspace Co-Clustering via Bilateral Graph Convolution

Subspace clustering seeks to cluster high-dimensional data lying in a union of low-dimensional subspaces. It has achieved state-of-the-art results in image clustering, but text clustering of document-term matrices, has proved more impervious to…

Chakib Fettal, Lazhar Labiod, Mohamed Nadif

IEEE Transactions on Knowledge and Data Engineering -

Machine Learning

More Discriminative Sentence Embeddings via Semantic Graph Smoothing

This paper explores an empirical approach to learn more discriminantive sentence representations in an unsupervised fashion. Leveraging semantic graph smoothing, we enhance sentence embeddings obtained from pretrained models to improve results for…

Chakib Fettal, Lazhar Labiod, Mohamed Nadif

18th Conference of the European Chapter of the Association for Computational Linguistics -