Publications

Estimating the population effectiveness of interventions against COVID-19 in France: a modelling study

Iris Ganser

Jane M. Heffernan

M. Prague

Rodolphe Thiébaut

Background Non-pharmaceutical interventions (NPIs) and vaccines have been widely used to manage the COVID-19 pandemic. However, uncertainty … (voir plus)persists regarding the effectiveness of these interventions due to data quality issues, methodological challenges, and differing contextual factors. Accurate estimation of their effects is crucial for future epidemic preparedness. Methods To address this, we developed a population-based mechanistic model that includes the impact of NPIs and vaccines on SARS-CoV-2 transmission and hospitalization rates. Our statistical approach estimated all parameters in one step, accurately propagating uncertainty. We fitted the model to comprehensive epidemiological data in France from March 2020 to October 2021. With the same model, we simulated scenarios of vaccine rollout. Results The first lockdown was the most effective, reducing transmission by 84% (95% confidence interval (CI) 83-85). Subsequent lockdowns had diminished effectiveness (reduction of 74% (69-77) and 11% (9-18), respectively). A 6pm curfew was more effective than one at 8 pm (68% (66-69) vs. 48% (45-49) reduction), while school closures reduced transmission by 15% (12-18). In a scenario without vaccines before November 2021, we predicted 159,000 or 194% (95% prediction interval (PI) 74-424) more deaths and 1,488,000 or 340% (136-689) more hospitalizations. If a vaccine had been available after 100 days, over 71,000 deaths (16,507-204,249) and 384,000 (88,579-1,020,386) hospitalizations could have been averted. Conclusion Our results highlight the substantial impact of NPIs, including lockdowns and curfews, in controlling the COVID-19 pandemic. We also demonstrate the value of the 100 days objective of the CEPI initiative for vaccine availability.

2023-09-14

medRxiv (prépublication)

doi.org

Addressing uncertainty when projecting marine species' distributions under climate change

Sarah C. Davies

Patrick L. Thompson

Catalina Gómez

Jessica Nephin

Anders Knudby

Ashley E. Park

Sarah K. Friesen

Laura J. Pollock

Emily M. Rubidge

Sean C. Anderson

Josephine C. Iacarella

Devin A. Lyons

Andrew MacDonald

Andrew McMillan

Eric J. Ward

Amber M. Holdsworth

Neil Swart

Jeff Price

Karen L. Hunter

2023-09-13

Ecography (publié)

doi.org

Artificial Intelligence for Detection of Dementia Using Motion Data: A Scoping Review

Lily Puterman-Salzman

Jory Katz

Howard Bergman

Roland Grad

Vladimir Khanassov

Genevieve Gore

Isabelle Vedel

Machelle Wilchesky

Narges Armanfard

Negar Ghourchian

Samira Abbasgholizadeh-Rahimi

Background: Dementia is a neurodegenerative disease resulting in the loss of cognitive and psychological functions. Artificial intelligence … (voir plus)(AI) may help in detection and screening of dementia; however, little is known in this area. Objectives: The objective of this study was to identify and evaluate AI interventions for detection of dementia using motion data. Method: The review followed the framework proposed by O’Malley’s and Joanna Briggs Institute methodological guidance for scoping reviews. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist for reporting the results. An information specialist performed a comprehensive search from the date of inception until November 2020, in five bibliographic databases: MEDLINE, EMBASE, Web of Science Core Collection, CINAHL, and IEEE Xplore. We included studies aimed at the deployment and testing or implementation of AI interventions using motion data for the detection of dementia among a diverse population, encompassing varying age, sex, gender, economic backgrounds, and ethnicity, extending to their health care providers across multiple health care settings. Studies were excluded if they focused on Parkinson’s or Huntington’s disease. Two independent reviewers screened the abstracts, titles, and then read the full-texts. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. The reference lists of included studies were also screened. Results: After removing duplicates, 2,632 articles were obtained. After title and abstract screening and full-text screening, 839 articles were considered for categorization. The authors categorized the papers into six categories, and data extraction and synthesis was performed on 20 included papers from the motion tracking data category. The included studies assessed cognitive performance (n = 5, 25%); screened dementia and cognitive decline (n = 8, 40%); investigated visual behaviours (n = 4, 20%); and analyzed motor behaviors (n = 3, 15%). Conclusions: We presented evidence of AI systems being employed in the detection of dementia, showcasing the promising potential of motion tracking within this domain. Although some progress has been made in this field recently, there remain notable research gaps that require further exploration and investigation. Future endeavors need to compare AI interventions using motion data with traditional screening methods or other tech-enabled dementia detection mechanisms. Besides, future works should aim at understanding how gender and sex, and ethnic and cultural sensitivity can contribute to refining AI interventions, ensuring they are accessible, equitable, and beneficial across all society.

2023-09-13

Dementia and Geriatric Cognitive Disorders EXTRA (publié)

doi.org

A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale

Hao-Jun Michael Shi

Tsung-Hsien Lee

Shintaro Iwasaki

Jose Gallego-Posada

Zhijing Li

Kaushik Rangadurai

Dheevatsa Mudigere

Michael Rabbat

2023-09-12

ArXiv (prépublication)

doi.org

arxiv.org

Leveraging ChatGPT to Democratize and Decolonize Global Surgery: Large Language Models for Small Healthcare Budgets

Fabio Botelho

Jean Marie Tshimula

Dan Poenaru

2023-09-10

World Journal of Surgery (publié)

doi.org

Local field potentials in human motor and non-motor brain areas encode the direction of upcoming movements: An intracerebral EEG classification study

Etienne Combrisson

Franck Di Rienzo

Anne-Lise Saive

Marcela Perrone-Bertolotti

Juan LP Soto

Philippe Kahane

Jean-Philippe Lachaux

Aymeric Guillot

Karim Jerbi

2023-09-10

bioRxiv (prépublication)

doi.org

Neural Causal Structure Discovery from Interventions

Nan Rosemary Ke

Olexa Bilaniuk

Anirudh Goyal

Stefan Bauer

Hugo Larochelle

Bernhard Schölkopf

Michael Curtis Mozer

Chris Pal

Yoshua Bengio

Recent promising results have generated a surge of interest in continuous optimization methods for causal discovery from observational data.… (voir plus) However, there are theoretical limitations on the identifiability of underlying structures obtained solely from observational data. Interventional data, on the other hand, provides richer information about the underlying data-generating process. Nevertheless, extending and applying methods designed for observational data to include interventions is a challenging problem. To address this issue, we propose a general framework based on neural networks to develop models that incorporate both observational and interventional data. Notably, our method can handle the challenging and realistic scenario where the identity of the intervened upon variable is unknown. We evaluate our proposed approach in the context of graph recovery, both de novo and from a partially-known edge set. Our method achieves strong benchmark results on various structure learning tasks, including structure recovery of synthetic graphs as well as standard graphs from the Bayesian Network Repository.

2023-09-10

TMLR (accepté)

openreview.net

Let Coarse-Grained Resources Be Shared: Mapping Entire Neural Networks on FPGAs

Tzung-Han Juang

Christof Schlaak

Christophe Dubach

2023-09-09

ACM Transactions on Embedded Computing Systems (publié)

doi.org

Leveraging World Model Disentanglement in Value-Based Multi-Agent Reinforcement Learning

Zhizun Wang

David Meger

In this paper, we propose a novel model-based multi-agent reinforcement learning approach named Value Decomposition Framework with Disentang… (voir plus)led World Model to address the challenge of achieving a common goal of multiple agents interacting in the same environment with reduced sample complexity. Due to scalability and non-stationarity problems posed by multi-agent systems, model-free methods rely on a considerable number of samples for training. In contrast, we use a modularized world model, composed of action-conditioned, action-free, and static branches, to unravel the environment dynamics and produce imagined outcomes based on past experience, without sampling directly from the real environment. We employ variational auto-encoders and variational graph auto-encoders to learn the latent representations for the world model, which is merged with a value-based framework to predict the joint action-value function and optimize the overall training objective. We present experimental results in Easy, Hard, and Super-Hard StarCraft II micro-management challenges to demonstrate that our method achieves high sample efficiency and exhibits superior performance in defeating the enemy armies compared to other baselines.

2023-09-08

ArXiv (prépublication)

doi.org

arxiv.org

Bridging the Gap Between Target Networks and Functional Regularization

Alexandre Piché

Valentin Thomas

Joseph Marino

Gian Maria Marconi

Rafael Pardinas

Chris Pal

Mohammad Emtiyaz Khan