Publications

Pushing the frontiers in climate modelling and analysis with machine learning
Veronika Eyring
William D. Collins
Pierre Gentine
Elizabeth A. Barnes
Marcelo Barreiro
Tom Beucler
Marc Bocquet
Christopher S. Bretherton
Hannah M. Christensen
Katherine Dagon
David John Gagne
David Hall
Dorit Hammerling
Stephan Hoyer
Fernando Iglesias-Suarez
Ignacio Lopez-Gomez
Marie C. McGraw
Gerald A. Meehl
Maria J. Molina
Claire Monteleoni … (voir 9 de plus)
Juliane Mueller
Michael S. Pritchard
David Rolnick
Jakob Runge
Philip Stier
Oliver Watt-Meyer
Katja Weigel
Rose Yu
Laure Zanna
2024-08-22
Nature Climate Change (publié)
doi.org
100 years of EEG for brain and behaviour research
Faisal Mushtaq
Dominik Welke
Anne Gallagher
Yuri G. Pavlov
Layla Kouara
Jorge Bosch-Bayard
Jasper J. F. van den Bosch
Mahnaz Arvaneh
Amy R. Bland
Maximilien Chaumon
Cornelius Borck
Xun He
Steven J. Luck
Maro G. Machizawa
Cyril Pernet
Aina Puce
Sidney Segalowitz
Christine Rogers
Muhammad Awais
Claudio Babiloni … (voir 75 de plus)
Neil W. Bailey
Sylvain Baillet
Robert C. A. Bendall
Daniel Brady
Maria L. Bringas-Vega
Niko Busch
Ana Calzada-Reyes
Armand Chatard
Peter E. Clayson
Michael X. Cohen
Jonathan Cole
Martin Constant
Alexandra Corneyllie
Damien Coyle
Damian Cruse
Ioannis Delis
Arnaud Delorme
Damien Fair
Tiago H. Falk
Matthias Gamer
Giorgio Ganis
Kilian Gloy
Samantha Gregory
Cameron Hassall
Katherine Hiley
Richard B. Ivry
Karim Jerbi
Michael Jenkins
Jakob Kaiser
Andreas Keil
Robert T. Knight
Silvia Kochen
Boris Kotchoubey
Olave Krigolson
Nicolas Langer
Heinrich R. Liesefeld
Sarah Lippé
Raquel E. London
Annmarie MacNamara
Scott Makeig
Welber Marinovic
Eduardo Martínez-Montes
Aleya A. Marzuki
Ryan K. Mathew
Christoph Michel
José d. R. Millán
Mark Mon-Williams
Lilia Morales-Chacón
Richard Naar
Gustav Nilsonne
Guiomar Niso
Erika Nyhus
Robert Oostenveld
Katharina Paul
Walter Paulus
Daniela M. Pfabigan
Gilles Pourtois
Stefan Rampp
Manuel Rausch
Kay Robbins
Paolo M. Rossini
Manuela Ruzzoli
Barbara Schmidt
Magdalena Senderecka
Narayanan Srinivasan
Yannik Stegmann
Paul M. Thompson
Mitchell Valdes-Sosa
Melle J. W. van der Molen
Domenica Veniero
Edelyn Verona
Bradley Voytek
Dezhong Yao
Alan C. Evans
Pedro Valdes-Sosa
2024-08-21
Nature Human Behaviour (inconnu)
doi.org
Development of a Framework for Establishing 'Gold Standard' Outbreak Data from Submitted SARS-CoV-2 Genome Samples
Yannan Shen
Russell Steele
Philip Abdelmalik
David L Buckeridge
Submitted genomic data for respiratory viruses reflect the emergence and spread of new variants. Although delays in submission limit the uti… (voir plus)lity of these data for prospective surveillance, they may be useful for evaluating other surveillance sources. However, few studies have investigated the use of these data for evaluating aberration detection in surveillance systems. Our study used a Bayesian online change point detection algorithm (BOCP) to detect increases in the number of submitted genome samples as a means of establishing 'gold standard' dates of outbreak onset in multiple countries. We compared models using different data transformations and parameter values. BOCP detected change points that were not sensitive to different parameter settings. We also found data transformations were essential prior to change point detection. Our study presents a framework for using global genomic submission data to develop 'gold standard' dates about the onset of outbreaks due to new viral variants.
2024-08-21
Medical Informatics Europe (publié)
doi.org
Learning Valid Dual Bounds in Constraint Programming: Boosted Lagrangian Decomposition with Self-Supervised Learning
Swann Bessa
Darius Dabert
Max Bourgeat
Louis-Martin Rousseau
Quentin Cappart
2024-08-21
ArXiv (prépublication)
doi.org
arxiv.org
Non-invasive electroencephalography in awake cats: Feasibility and application to sensory processing in chronic pain
Aliénor Delsart
Aude Castel
Guillaume Dumas
Colombe Otis
Mathieu Lachance
Maude Barbeau-Grégoire
Bertrand Lussier
Franck Péron
Marc Hébert
Nicolas Lapointe
Maxim Moreau
Johanne Martel-Pelletier
Jean-Pierre Pelletier
Eric Troncy
2024-08-21
Journal of Neuroscience Methods (publié)
doi.org
Concurrent product layout design optimization and dependency management using a modified NSGA-III approach
Yann-Seing Law-Kam Cio
Giovanni Beltrame
Aurelian Vadean
Abolfazl Mohebbi
Sofiane Achiche
The complexity of mechatronic systems has increased with the significant advancements of technology in their components which makes their de… (voir plus)sign more challenging. This is due to the need for incorporating expertise from different domains as well as the increased number and complexity of components integrated into the product. To alleviate the burden of designing such products, many industries and researchers are attracted to the concept of modularization which is to identify a subset of system components that can form a module. To achieve this, a novel product-related dependency management approach is proposed in this paper with the support of an augmented design structure matrix. This approach makes it possible to model positive and negative dependencies and to compute the combination potency between components to form modules. This approach is then integrated into a modified non-dominated sorting genetic algorithm III to concurrently optimize the design and identify the modules. The methodology is exemplified through the case study of a layout design of an automatic greenhouse. By applying the proposed methodology to the case study, it was possible to generate concepts that decreased the number of modules from 9 down to 4 while ensuring the optimization of the design performance.
2024-08-20
Frontiers in Mechanical Engineering (publié)
doi.org
BlueCelluLab: Biologically Detailed Neural NetworkExperimentation API
Anıl Tuncel
Werner Van Geit
Mike Gevaert
Benjamin Torben-Nielsen
Darshan Mandge
İlkan Kılıç
Aurélien T. Jaquier
Eilif Muller
Lida Kanari
Henry Markram
The NEURON simulator, established in 1984 and continuously developed since, stands as\nthe preeminent tool for neuron simulation within comp… (voir plus)utational neuroscience. Its widespread\nadoption and compatibility with computational clusters and supercomputers underscore its\npivotal role in large-scale neuronal research. However, its integration with the Python pro-\ngramming language has introduced complexities, particularly concerning memory management\nand object lifecycle. To conceal these challenges from the user and seamlessly interface\nwith community standards for neural network representation data formats such as SONATA,\nwe introduce BlueCelluLab. The high-level Python API simplifies the execution of neural\nsimulations, ranging from single neurons to intricate networks, by managing complexities\nrelated to memory management and object lifecycle, thus providing a streamlined experience\nfor users. Today, BlueCelluLab is powering various Python packages, command line interfaces,\nweb applications, and data analysis workflows.
2024-08-16
Journal of Open Source Software (publié)
doi.org
Zero-Shot Object-Centric Representation Learning
Aniket Rajiv Didolkar
Andrii Zadaianchuk
Anirudh Goyal
Michael Curtis Mozer
Yoshua Bengio
Georg Martius
Maximilian Seitzer
The goal of object-centric representation learning is to decompose visual scenes into a structured representation that isolates the entities… (voir plus). Recent successes have shown that object-centric representation learning can be scaled to real-world scenes by utilizing pre-trained self-supervised features. However, so far, object-centric methods have mostly been applied in-distribution, with models trained and evaluated on the same dataset. This is in contrast to the wider trend in machine learning towards general-purpose models directly applicable to unseen data and tasks. Thus, in this work, we study current object-centric methods through the lens of zero-shot generalization by introducing a benchmark comprising eight different synthetic and real-world datasets. We analyze the factors influencing zero-shot performance and find that training on diverse real-world images improves transferability to unseen scenarios. Furthermore, inspired by the success of task-specific fine-tuning in foundation models, we introduce a novel fine-tuning strategy to adapt pre-trained vision encoders for the task of object discovery. We find that the proposed approach results in state-of-the-art performance for unsupervised object discovery, exhibiting strong zero-shot transfer to unseen datasets.
2024-08-16
ArXiv (prépublication)
doi.org
arxiv.org
What Secrets Do Your Manifolds Hold? Understanding the Local Geometry of Generative Models
Ahmed Imtiaz Humayun
Ibtihel Amara
Candice Schumann
Golnoosh Farnadi
Negar Rostamzadeh
Mohammad Havaei
2024-08-14
ArXiv (prépublication)
doi.org
openreview.net
<scp>RF</scp> shimming in the cervical spinal cord at <scp>7 T</scp>
Daniel Papp
Kyle M. Gilbert
Gaspard Cereza
Alexandre D'Astous
Nibardo Lopez‐Rios
Mathieu Boudreau
Marcus J. Couch
Pedram Yazdanbakhsh
Robert L. Barry
Eva Alonso‐Ortiz
Julien Cohen‐Adad
The study's findings highlight the potential of RF shimming to advance 7 T MRI's clinical utility for central nervous system imaging by enab… (voir plus)ling more homogenous and efficient spinal cord imaging. Additionally, the research incorporates a reproducible Jupyter Notebook, enhancing the study's transparency and facilitating peer verification.
2024-08-12
Magnetic Resonance in Medicine (publié)
doi.org
Unveiling the Flaws: A Critical Analysis of Initialization Effect on Time Series Anomaly Detection
Alex Koran
Hadi Hojjati
Narges Armanfard
Deep learning for time-series anomaly detection (TSAD) has gained significant attention over the past decade. Despite the reported improveme… (voir plus)nts in several papers, the practical application of these models remains limited. Recent studies have cast doubt on these models, attributing their results to flawed evaluation techniques. However, the impact of initialization has largely been overlooked. This paper provides a critical analysis of the initialization effects on TSAD model performance. Our extensive experiments reveal that TSAD models are highly sensitive to hyperparameters such as window size, seed number, and normalization. This sensitivity often leads to significant variability in performance, which can be exploited to artificially inflate the reported efficacy of these models. We demonstrate that even minor changes in initialization parameters can result in performance variations that overshadow the claimed improvements from novel model architectures. Our findings highlight the need for rigorous evaluation protocols and transparent reporting of preprocessing steps to ensure the reliability and fairness of anomaly detection methods. This paper calls for a more cautious interpretation of TSAD advancements and encourages the development of more robust and transparent evaluation practices to advance the field and its practical applications.
2024-08-12
ArXiv (prépublication)
doi.org
arxiv.org
Oxygen thresholds in critically ill patients: need for personalized targets. Author's reply.
Guillaume Dumas
Laveena Munshi
2024-08-11
Intensive Care Medicine (publié)
doi.org