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 E. 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 Griffiths
Gary Van Domselaar
Gordon Jolly
Heather Ward
Henrich Feher
Jared Baker
Jared T. Simpson
Jaser Uddin
Jiannis Ragoussis
Jon Eubank
Fritz, Jörg H.
José Héctor Gálvez
Karen Fang
Kim Cullion
Leonardo Landa 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
Academic, VirusSeq Data Portal
network, Health
Marc Fiume
Terrance P Snutch
Cindy Bell
Catalina López-Correa
Julie Hussin
Jeffrey B. Joy
Caroline Colijn
Paul M. K. Gordon
William Hsiao
Art F. Y. Poon
Natalie 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
On Diffusion Modeling for Anomaly Detection
Victor Livernoche
Vineet Jain
Yashar Hezaveh
Siamak Ravanbakhsh
Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detectio… (voir plus)n. This paper investigates different variations of diffusion modeling for unsupervised and semi-supervised anomaly detection. In particular, we find that Denoising Diffusion Probability Models (DDPM) are performant on anomaly detection benchmarks yet computationally expensive. By simplifying DDPM in application to anomaly detection, we are naturally led to an alternative approach called Diffusion Time Estimation (DTE). DTE estimates the distribution over diffusion time for a given input and uses the mode or mean of this distribution as the anomaly score. We derive an analytical form for this density and leverage a deep neural network to improve inference efficiency. Through empirical evaluations on the ADBench benchmark, we demonstrate that all diffusion-based anomaly detection methods perform competitively for both semi-supervised and unsupervised settings. Notably, DTE achieves orders of magnitude faster inference time than DDPM, while outperforming it on this benchmark. These results establish diffusion-based anomaly detection as a scalable alternative to traditional methods and recent deep-learning techniques for standard unsupervised and semi-supervised anomaly detection settings.
2024-05-06
International Conference on Learning Representations (Accept (spotlight))
doi.org
openreview.net
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 <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
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
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 Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Dominique Beaini
Shenyang Huang
Joao Alex Cunha
Zhiyi Li
Gabriela Moisescu-Pareja
Oleksandr Dymov
Samuel Maddrell-Mander
Callum McLean
Frederik Wenkel
Luis Müller
Jama Hussein Mohamud
Ali Parviz
Michael Craig
Michał Koziarski
Jiarui Lu
Zhaocheng Zhu
Cristian Gabellini
Kerstin Klaser
Josef Dean
Cas Wognum … (voir 14 de plus)
Maciej Sypetkowski
Guillaume Rabusseau
Reihaneh Rabbany
Jian Tang
Christopher Morris
Mirco Ravanelli
Guy Wolf
Prudencio Tossou
Hadrien Mary
Therence Bois
Andrew Fitzgibbon
Błażej Banaszewski
Chad Martin
Dominic Masters
Recently, pre-trained foundation models have shown significant advancements in multiple fields. However, the lack of datasets with labeled f… (voir plus)eatures and codebases has hindered the development of a supervised foundation model for molecular tasks. Here, we have carefully curated seven datasets specifically tailored for node- and graph-level prediction tasks to facilitate supervised learning on molecules. Moreover, to support the development of multi-task learning on our proposed datasets, we created the Graphium graph machine learning library. Our dataset collection encompasses two distinct categories. Firstly, the TOYMIX category modifies three small existing datasets with additional data for multi-task learning. Secondly, the LARGEMIX category includes four large-scale datasets with 344M graph-level data points and 409M node-level data points from ∼5M unique molecules. Finally, the ultra-large dataset contains 2,210M graph-level data points and 2,031M node-level data points coming from 86M molecules. Hence our datasets represent an order of magnitude increase in data volume compared to other 2D-GNN datasets. In addition, recognizing that molecule-related tasks often span multiple levels, we have designed our library to explicitly support multi-tasking, offering a diverse range of multi-level representations, i.e., representations at the graph, node, edge, and node-pair level. We equipped the library with an extensive collection of models and features to cover different levels of molecule analysis. By combining our curated datasets with this versatile library, we aim to accelerate the development of molecule foundation models. Datasets and code are available at https://github.com/datamol-io/graphium.
2024-05-06
International Conference on Learning Representations (Accept (poster))
doi.org
openreview.net
Linking aerial hyperspectral data to canopy tree biodiversity: An examination of the spectral variation hypothesis
Anna L. Crofts
Christine I. B. Wallis
Sabine St‐Jean
Sabrina Demers‐Thibeault
Deep Inamdar
J. Pablo Arroyo‐Mora
Margaret Kalacska
Étienne Laliberté
Mark Vellend
Imaging spectroscopy is emerging as a leading remote sensing method for quantifying plant biodiversity. The spectral variation hypothesis pr… (voir plus)edicts that variation in plant hyperspectral reflectance is related to variation in taxonomic and functional identity. While most studies report some correlation between spectral and field‐based (i.e., taxonomic and functional) expressions of biodiversity, the observed strength of association is highly variable, and the utility in applying spectral community properties to examine environmental drivers of communities remains unknown. We linked hyperspectral data acquired by airborne imaging spectrometers with precisely geolocated field plots to examine the spectral variation hypothesis along a temperate‐to‐boreal forest gradient in southern Québec, Canada. First, we examine the degree of association between spectral and field‐based dimensions of canopy tree composition and diversity. Second, we ask whether the relationships between field‐based community properties and the environment are reproduced when using spectral community properties. We found support for the spectral variation hypothesis with the strength of association generally greater for the functional than taxonomic dimension, but the strength of relationships was highly variable and dependent on the choice of method or metric used to quantify spectral and field‐based community properties. Using a multivariate approach (comparisons of separate ordinations), spectral composition was moderately well correlated with field‐based composition; however, the degree of association increased when univariately relating the main axes of compositional variation. Spectral diversity was most tightly associated with functional diversity metrics that quantify functional richness and divergence. For predicting canopy tree composition and diversity using environmental variables, the same qualitative conclusions emerge when hyperspectral or field‐based data are used. Spatial patterns of canopy tree community properties were strongly related to the turnover from temperate‐to‐boreal communities, with most variation explained by elevation. Spectral composition and diversity provide a straightforward way to quantify plant biodiversity across large spatial extents without the need for a priori field observations. While commonly framed as a potential tool for biodiversity monitoring, we show that spectral community properties can be applied more widely to assess the environmental drivers of biodiversity, thereby helping to advance our understanding of the drivers of biogeographical patterns of plant communities.
2024-05-05
Ecological Monographs (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