Integrating Equity, Diversity, and Inclusion Throughout the Lifecycle of Artificial Intelligence for Better Health and Oral Health Care: A Workshop Summary.
Elham Emami
Milka Nyariro
Professors Elham Emami and Samira Rahimi organized and co-led an international interdisciplinary workshop in June 2023 at McGill University,… (see more) built upon an intersectoral approach addressing equity, diversity and inclusion within the field of AI.
Integrating Equity, Diversity, and Inclusion Throughout the Lifecycle of Artificial Intelligence for Better Health and Oral Health Care: A Workshop Summary.
Elham Emami
Milka Nyariro
Professors Elham Emami and Samira Rahimi organized and co-led an international interdisciplinary workshop in June 2023 at McGill University,… (see more) built upon an intersectoral approach addressing equity, diversity and inclusion within the field of AI.
Interoceptive technologies for psychiatric interventions: From diagnosis to clinical applications
Felix Schoeller
Adam Haar Horowitz
Abhinandan Jain
Pattie Maes
Nicco Reggente
Leonardo Christov-Moore
Giovanni Pezzulo
Laura Barca
Micah Allen
Roy Salomon
Mark Miller
Daniele Di Lernia
Giuseppe Riva
Manos Tsakiris
Moussa A. Chalah
Arno Klein
Ben Zhang
Teresa Garcia
Ursula Pollack
Marion Trousselard … (see 4 more)
Charles Verdonk
Vladimir Adrien
Karl J. Friston
Interoceptive technologies for psychiatric interventions: From diagnosis to clinical applications
Felix Schoeller
Adam Haar Horowitz
Abhinandan Jain
Pattie Maes
Nicco Reggente
Leonardo Christov-Moore
Giovanni Pezzulo
Laura Barca
Micah Allen
Roy Salomon
Mark Miller
Daniele Di Lernia
Giuseppe Riva
Manos Tsakiris
Moussa A. Chalah
Arno Klein
Ben Zhang
Teresa Garcia
Ursula Pollack
Marion Trousselard … (see 4 more)
Charles Verdonk
Vladimir Adrien
Karl J. Friston
Interoceptive technologies for psychiatric interventions: From diagnosis to clinical applications
Felix Schoeller
Adam Haar Horowitz
Abhinandan Jain
Pattie Maes
Nicco Reggente
Leonardo Christov-Moore
Giovanni Pezzulo
Laura Barca
Micah Allen
Roy Salomon
Mark Miller
Daniele Di Lernia
Giuseppe Riva
Manos Tsakiris
Moussa A. Chalah
Arno Klein
Ben Zhang
Teresa Garcia
Ursula Pollack
Marion Trousselard … (see 4 more)
Charles Verdonk
Vladimir Adrien
Karl J. Friston
Invasive Brain Computer Interface for Motor Restoration in Spinal Cord Injury: A Systematic Review.
Jordan J. Levett
Lior M. Elkaim
Farbod Niazi
Michael H. Weber
Christian Iorio-Morin
Alexander G. Weil
Investigation of the Dosimetry Characteristics of the GAFCHROMIC® EBT3 Film Response to Alpha Particle Irradiation
Mélodie Cyr
Victor D. Martinez
S. Devic
Nada Tomic
David F. Lewis
Lag-Llama: Towards Foundation Models for Time Series Forecasting
Kashif Rasul
Arjun Ashok
Andrew Robert Williams
Arian Khorasani
George Adamopoulos
Rishika Bhagwatkar
Marin Biloš
Hena Ghonia
Nadhir Hassen
Anderson Schneider
Sahil Garg
Yuriy Nevmyvaka
Aiming to build foundation models for time-series forecasting and study their scaling behavior, we present here our work-in-progress on Lag-… (see more)Llama, a general-purpose univariate probabilistic time-series forecasting model trained on a large collection of time-series data. The model shows good zero-shot prediction capabilities on unseen "out-of-distribution" time-series datasets, outperforming supervised baselines. We use smoothly broken power-laws to fit and predict model scaling behavior. The open source code is made available at https://github.com/kashif/pytorch-transformer-ts.
Mining Mass Spectra for Peptide Facts
Jeremie Zumer
The current mainstream software for peptide-centric tandem mass spectrometry data analysis can be categorized as either database-driven, whi… (see more)ch rely on a library of mass spectra to identify the peptide associated with novel query spectra, or de novo sequencing-based, which aim to find the entire peptide sequence by relying only on the query mass spectrum. While the first paradigm currently produces state-of-the-art results in peptide identification tasks, it does not inherently make use of information present in the query mass spectrum itself to refine identifications. Meanwhile, de novo approaches attempt to solve a complex problem in one go, without any search space constraints in the general case, leading to comparatively poor results. In this paper, we decompose the de novo problem into putatively easier subproblems, and we show that peptide identification rates of database-driven methods may be improved in terms of peptide identification rate by solving one such subsproblem without requiring a solution for the complete de novo task. We demonstrate this using a de novo peptide length prediction task as the chosen subproblem. As a first prototype, we show that a deep learning-based length prediction model increases peptide identification rates in the ProteomeTools dataset as part of an Pepid-based identification pipeline. Using the predicted information to better rank the candidates, we show that combining ideas from the two paradigms produces clear benefits in this setting. We propose that the next generation of peptide-centric tandem mass spectrometry identification methods should combine elements of these paradigms by mining facts “de novo; about the peptide represented in a spectrum, while simultaneously limiting the search space with a peptide candidates database.
Open design of a reproducible videogame controller for MRI and MEG
Yann Harel
André Cyr
Julie Boyle
Basile Pinsard
Jeremy Bernard
Marie-France Fourcade
Himanshu Aggarwal
Ana Fernanda Ponce
Bertrand Thirion
OpenForest: A data catalogue for machine learning in forest monitoring
Arthur Ouaknine
Teja Kattenborn
Etienne Lalibert'e
SAGE: Smart home Agent with Grounded Execution
Dmitriy Rivkin
Francois Hogan
Amal Feriani
Abhisek Konar
Adam Sigal
Steve Liu