Publications

Repeat it without me: Crowdsourcing the T<sub>1</sub> mapping common ground via the ISMRM reproducibility challenge
Mathieu Boudreau
Agah Karakuzu
Julien Cohen‐Adad
Ecem Bozkurt
Madeline Carr
Marco Castellaro
Luis Concha
Mariya Doneva
Seraina A. Dual
Alex Ensworth
Alexandru Foias
Véronique Fortier
Refaat E. Gabr
Guillaume Gilbert
Carri K. Glide‐Hurst
Matthew Grech‐Sollars
Siyuan Hu
Oscar Jalnefjord
Jorge Jovicich
Kübra Keskin … (voir 22 de plus)
Peter Koken
Anastasia Kolokotronis
Simran Kukran
Nam G. Lee
Ives R. Levesque
Bochao Li
Dan Ma
Burkhard Mädler
Nyasha G. Maforo
Jamie Near
Erick Pasaye
Alonso Ramirez‐Manzanares
Ben Statton
Christian Stehning
Stefano Tambalo
Ye Tian
Chenyang Wang
Kilian Weiss
Niloufar Zakariaei
Shuo Zhang
Ziwei Zhao
Nikola Stikov
Eighteen submissions (39 phantom and 56 human datasets) on scanners by three MRI vendors were collected at 3 T (except one, at 0.35 T). … (voir plus)The mean coefficient of variation was 6.1% for intersubmission phantom measurements, and 2.9% for intrasubmission measurements. For humans, the intersubmission/intrasubmission coefficient of variation was 5.9/3.2% in the genu and 16/6.9% in the cortex. An interactive dashboard for data visualization was also developed: https://rrsg2020.dashboards.neurolibre.org.The T1 intersubmission variability was twice as high as the intrasubmission variability in both phantoms and human brains, indicating that the acquisition details in the original paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T1 measures across independent research groups, bringing us one step closer to a practical clinical baseline of T1 variations in vivo.
2024-05-09
Magnetic Resonance in Medicine (publié)
doi.org
Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems
David Dalrymple
David
Joar Max Viktor Skalse
Yoshua Bengio
Stuart Russell
Max Tegmark
Sanjit A. Seshia
Steve Omohundro
Christian Szegedy
Ben Goldhaber
Nora Ammann
Alessandro Abate
Joe Halpern
Clark Barrett
Ding Zhao
Zhi-Xuan Tan
Jeannette Wing
Joshua B. Tenenbaum
Ensuring that AI systems reliably and robustly avoid harmful or dangerous behaviours is a crucial challenge, especially for AI systems with … (voir plus)a high degree of autonomy and general intelligence, or systems used in safety-critical contexts. In this paper, we will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. This is achieved by the interplay of three core components: a world model (which provides a mathematical description of how the AI system affects the outside world), a safety specification (which is a mathematical description of what effects are acceptable), and a verifier (which provides an auditable proof certificate that the AI satisfies the safety specification relative to the world model). We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them. We also argue for the necessity of this approach to AI safety, and for the inadequacy of the main alternative approaches.
2024-05-09
ArXiv (prépublication)
doi.org
arxiv.org
Interpretability Needs a New Paradigm
Andreas Madsen
Himabindu Lakkaraju
Siva Reddy
A. Chandar
2024-05-07
ArXiv (prépublication)
doi.org
arxiv.org
The Canadian VirusSeq Data Portal &amp; Duotang: open resources for SARS-CoV-2 viral sequences and genomic epidemiology
Erin E. Gill
Baofeng Jia
Carmen Lia Murall
Raphaël Poujol
Muhammad Zohaib Anwar
Nithu Sara John
Justin Richardsson
Ashley Hobb
Abayomi S. Olabode
Alexandru Lepsa
Ana T. Duggan
Andrea D. Tyler
Arnaud N'Guessan
Atul Kachru
Brandon Chan
Catherine Yoshida
Christina K. Yung
David Bujold
Dusan Andric
Edmund Su … (voir 50 de plus)
Emma J. Griffiths
Gary Van Domselaar
Gordon W. Jolly
Heather K. E. Ward
Henrich Feher
Jared Baker
Jared T. Simpson
Jaser Uddin
Jiannis Ragoussis
Jon Eubank
Jörg H. Fritz
José Héctor Gálvez
Karen Fang
Kim Cullion
Leonardo Rivera
Linda Xiang
Matthew A. Croxen
Mitchell Shiell
Natalie Prystajecky
Pierre-Olivier Quirion
Rosita Bajari
Samantha Rich
Samira Mubareka
Sandrine Moreira
Scott Cain
Steven G. Sutcliffe
Susanne A. Kraemer
Yann Joly
Yelizar Alturmessov
CPHLN consortium
CanCOGeN consortium
VirusSeq Data Portal Academic
Heather K. E. Ward
Marc Fiume
Terrance P. Snutch
Cindy Bell
Catalina Lopez-Correa
Julie G. Hussin
Jeffrey B. Joy
Caroline Colijn
Paul M. K. Gordon
William W. L. Hsiao
Art F. Y. Poon
Natalie C. Knox
Mélanie Courtot
Lincoln Stein
Sarah P. Otto
Guillaume Bourque
B. Jesse Shapiro
Fiona S. L. Brinkman
The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform p… (voir plus)ublic health response. Millions of SARS-CoV-2 genome sequences have been deposited in global public repositories. The Canadian COVID-19 Genomics Network (CanCOGeN - VirusSeq), a consortium tasked with coordinating expanded sequencing of SARS-CoV-2 genomes across Canada early in the pandemic, created the Canadian VirusSeq Data Portal, with associated data pipelines and procedures, to support these efforts. The goal of VirusSeq was to allow open access to Canadian SARS-CoV-2 genomic sequences and enhanced, standardized contextual data that were unavailable in other repositories and that meet FAIR standards (Findable, Accessible, Interoperable and Reusable). The Portal data submission pipeline contains data quality checking procedures and appropriate acknowledgement of data generators that encourages collaboration. Here we also highlight Duotang, a web platform that presents genomic epidemiology and modeling analyses on circulating and emerging SARS-CoV-2 variants in Canada. Duotang presents dynamic changes in variant composition of SARS-CoV-2 in Canada and by province, estimates variant growth, and displays complementary interactive visualizations, with a text overview of the current situation. The VirusSeq Data Portal and Duotang resources, alongside additional analyses and resources computed from the Portal (COVID-MVP, CoVizu), are all open-source and freely available. Together, they provide an updated picture of SARS-CoV-2 evolution to spur scientific discussions, inform public discourse, and support communication with and within public health authorities. They also serve as a framework for other jurisdictions interested in open, collaborative sequence data sharing and analyses.
2024-05-07
ArXiv (prépublication)
doi.org
arxiv.org
Piecewise Linear Parametrization of Policies: Towards Interpretable Deep Reinforcement Learning
Maxime Wabartha
Joelle Pineau
2024-05-06
International Conference on Learning Representations (Accept (poster))
openreview.net
openreview.net
Quantifying neurodegeneration of the cervical cord and brain in degenerative cervical myelopathy: A multicentre study using quantitative magnetic resonance imaging
Patrick Freund
Viveka Boller
Tim M. Emmenegger
Muhammad Akbar
Markus Hupp
Nikolai Pfender
Claudia A. M. Gandini Wheeler-Kingshott
Julien Cohen-Adad
Michael G. Fehlings
Armin Curt
Maryam Seif
Simultaneous assessment of neurodegeneration in both the cervical cord and brain across multiple centres can enhance the effectiveness of cl… (voir plus)inical trials. Thus, this study aims to simultaneously assess microstructural changes in the cervical cord and brain above the stenosis in degenerative cervical myelopathy (DCM) using quantitative magnetic resonance imaging (MRI) in a multicentre study.
2024-05-06
European Journal of Neurology (publié)
doi.org
Quantifying neurodegeneration of the cervical cord and brain in degenerative cervical myelopathy: A multicentre study using quantitative <scp>magnetic resonance imaging</scp>
Patrick Freund
Viveka Boller
Tim M. Emmenegger
Muhammad Akbar
Markus Hupp
Nikolai Pfender
Claudia A. M. Gandini Wheeler-Kingshott
Julien Cohen-Adad
Michael G. Fehlings
Armin Curt
Maryam Seif
2024-05-06
European Journal of Neurology (publié)
doi.org
Sufficient Conditions for Offline Reactivation in Recurrent Neural Networks
Nanda H Krishna
Colin Bredenberg
Daniel Levenstein
Blake Aaron Richards
Guillaume Lajoie
During periods of quiescence, such as sleep, neural activity in many brain circuits resembles that observed during periods of task engagemen… (voir plus)t. However, the precise conditions under which task-optimized networks can autonomously reactivate the same network states responsible for online behavior is poorly understood. In this study, we develop a mathematical framework that outlines sufficient conditions for the emergence of neural reactivation in circuits that encode features of smoothly varying stimuli. We demonstrate mathematically that noisy recurrent networks optimized to track environmental state variables using change-based sensory information naturally develop denoising dynamics, which, in the absence of input, cause the network to revisit state configurations observed during periods of online activity. We validate our findings using numerical experiments on two canonical neuroscience tasks: spatial position estimation based on self-motion cues, and head direction estimation based on angular velocity cues. Overall, our work provides theoretical support for modeling offline reactivation as an emergent consequence of task optimization in noisy neural circuits.
2024-05-06
International Conference on Learning Representations (Accept (poster))
doi.org
openreview.net
TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characters
Jonathan Wilder Lavington
Ke Zhang
Vasileios Lioutas
Matthew Niedoba
Yunpeng Liu
Dylan Green
Saeid Naderiparizi
Xiaoxuan Liang
Setareh Dabiri
Adam Ścibior
Berend Zwartsenberg
Frank N. Wood
2024-05-06
ArXiv (prépublication)
doi.org
arxiv.org
Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects
D. B. Roy
D. B. Roy
J. Alison
T. A. August
M. Bélisle
K. Bjerge
J. J. Bowden
M. J. Bunsen
F. Cunha
Q. Geissmann
K. Goldmann
A. Gomez-Segura
A. Jain
C. Huijbers
M. Larrivée
J. L. Lawson
H. M. Mann
M. J. Mazerolle
K. P. McFarland
L. Pasi … (voir 8 de plus)
S. Peters
N. Pinoy
D. Rolnick
G. L. Skinner
O. T. Strickson
A. Svenning
S. Teagle
T. T. Høye
Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and faste… (voir plus)st developing sensor technologies, we describe a framework for automated, image-based monitoring of nocturnal insects—from sensor development and field deployment to workflows for data processing and publishing. Sensors comprise a light to attract insects, a camera for collecting images and a computer for scheduling, data storage and processing. Metadata is important to describe sampling schedules that balance the capture of relevant ecological information against power and data storage limitations. Large data volumes of images from automated systems necessitate scalable and effective data processing. We describe computer vision approaches for the detection, tracking and classification of insects, including models built from existing aggregations of labelled insect images. Data from automated camera systems necessitate approaches that account for inherent biases. We advocate models that explicitly correct for bias in species occurrence or abundance estimates resulting from the imperfect detection of species or individuals present during sampling occasions. We propose ten priorities towards a step-change in automated monitoring of nocturnal insects, a vital task in the face of rapid biodiversity loss from global threats. This article is part of the theme issue ‘Towards a toolkit for global insect biodiversity monitoring’.
2024-05-04
Philosophical Transactions of the Royal Society B: Biological Sciences (publié)
doi.org
Characterizing the voxel-based approaches in radioembolization dosimetry with reDoseMC.
Taehyung Peter Kim
S. Enger
BACKGROUND Yttrium-90 ( 90 Y …
2024-05-03
Medical Physics (publié)
doi.org
A Comprehensive Dataset of Four Provincial Legislative Assembly Members
Alex B. Rivard
Marc André Bodet
Jean-François Godbout
Éric Montigny
This research note reports on a new dataset about legislators in four Canadian provinces since the establishment of their colonial assemblie… (voir plus)s in the eighteenth century. Over 7,000 legislators from Ontario, Quebec, New Brunswick, and Nova Scotia are included, with consolidated information drawn from multiple sources about parliamentarians’ years of birth and death, religion, electoral performance, kinship, and several other biographical indicators. We also illustrate the utility of such data with the help of a few descriptive examples drawn from the four provinces. We believe this consolidated dataset offers several opportunities for future research on representation, legislative activities and party politics.
2024-05-02
Canadian Journal of Political Science/Revue canadienne de science politique (publié)
doi.org