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 … (see 22 more)

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). … (see more)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 (published)

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 … (see more)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 (preprint)

doi.org

arxiv.org

Interpretability Needs a New Paradigm

Andreas Madsen

Himabindu Lakkaraju

Siva Reddy

A. Chandar

2024-05-07

ArXiv (preprint)

doi.org

arxiv.org

The Canadian VirusSeq Data Portal & 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 … (see 50 more)

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… (see more)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 (preprint)

doi.org

arxiv.org

On Diffusion Modeling for Anomaly Detection

Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detectio… (see more)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

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 (published)

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… (see more)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 (published)

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 (preprint)

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

Jama Hussein Mohamud

Michael Craig

Cristian Gabellini

Kerstin Klaser

Josef Dean

Cas Wognum … (see 14 more)

Maciej Sypetkowski

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… (see more)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… (see more)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 (published)

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 (published)

doi.org

Mila on Udemy

AI Policy Fellowship Publications

Mila Ventures Launchpad

Publications

Mila on Udemy

AI Policy Fellowship Publications

Mila Ventures Launchpad

Popular keywords:

Publications