Publications

Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 2 - Ex vivo imaging: added value and acquisition
Kurt G Schilling
Francesco Grussu
Andrada Ianus
Brian Hansen
Manisha Aggarwal
Amy FD Howard
Rachel L C Barrett
Stijn Michielse
Fatima Nasrallah
Warda Syeda
Nian Wang
Jelle Veraart
Alard Roebroeck
Andrew F Bagdasarian
Cornelius Eichner
Farshid Sepehrband
Jan Zimmermann
Ben Jeurissen
Lucio Frydman
Yohan van de Looij … (voir 38 de plus)
Lucas Soustelle
Christien Bowman
David Hike
Benjamin C Tendler
Jeff F Dunn
Andrada Ianus
Karla Miller
Bennett A Landman
Noam Shemesh
Marleen Verhoye
Adam Anderson
Emilie McKinnon
Shawna Farquharson
Flavio Dell' Acqua
Carlo Pierpaoli
Ivana Drobnjak
Alexander Leemans
Kevin D Harkins
Maxime Descoteaux
Duan Xu
Mathieu D Santin
Samuel C. Grant
Andre Obenaus
Gene S Kim
Dan Wu
Denis Le Bihan
Stephen J Blackband
Nian Wang
Luisa Ciobanu
Els Fieremans
Ruiliang Bai
Trygve B Leergaard
Jiangyang Zhang
Tim B Dyrby
G Allan Johnson
Julien Cohen-Adad
Matthew D Budde
Ileana O Jelescu
The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dM… (voir plus)RI is increasingly used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher signal-to-noise ratio and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts. Another major advantage of ex vivo dMRI is the direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a 3-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We give general considerations and foundational knowledge that must be considered when designing experiments. We describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.
2025-03-03
Magnetic Resonance in Medicine (publié)
doi.org
arxiv.org
EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision
Diego Velazquez
Pau Rodríguez
Sergio Alonso
Josep M. Gonfaus
Jordi Gonzalez
Gerardo Richarte
Javier Marin
Yoshua Bengio
Alexandre Lacoste
This paper presents EarthView, a comprehensive dataset specifically designed for self-supervision on remote sensing data, intended to enhanc… (voir plus)e deep learning applications on Earth monitoring tasks. The dataset spans 15 tera pixels of global remote-sensing data, combining imagery from a diverse range of sources, including NEON, Sentinel, and a novel release of 1m spatial resolution data from Satellogic. Our dataset provides a wide spectrum of image data with varying resolutions, harnessed from different sensors and organized coherently into an accessible HuggingFace dataset in parquet format. This data spans five years, from 2017 to 2022. Accompanying the dataset, we introduce EarthMAE, a tailored Masked Autoencoder, developed to tackle the distinct challenges of remote sensing data. Trained in a self-supervised fashion, EarthMAE effectively processes different data modalities such as hyperspectral, multispectral, topographical data, segmentation maps, and temporal structure. This model helps us show that pre-training on Satellogic data improves performance on downstream tasks. While there is still a gap to fill in MAE for heterogeneous data, we regard this innovative combination of an expansive, diverse dataset and a versatile model adapted for self-supervised learning as a stride forward in deep learning for Earth monitoring.
2025-03-03
2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) (publié)
doi.org
arxiv.org
Hardware Synthesizable Exceptions using Continuations
Paul Teng
Christophe Dubach
2025-03-03
Proceedings of the 30th Asia and South Pacific Design Automation Conference (publié)
doi.org
CrystalGym: A New Benchmark for Materials Discovery Using Reinforcement Learning
Prashant Govindarajan
Mathieu Reymond
Antoine Clavaud
Mariano Phielipp
Santiago Miret
A. Chandar
*In silico* design and optimization of new materials primarily relies on high-accuracy atomic simulators that perform density functional the… (voir plus)ory (DFT) calculations. While recent works showcase the strong potential of machine learning to accelerate the material design process, they mostly consist of generative approaches that do not use direct DFT signals as feedback to improve training and generation mainly due to DFT's high computational cost. To aid the adoption of direct DFT signals in the materials design loop through online reinforcement learning (RL), we propose **CrystalGym**, an open-source RL environment for crystalline material discovery. Using CrystalGym, we benchmark value- and policy-based reinforcement learning algorithms for designing various crystals conditioned on target properties. Concretely, we optimize for challenging properties like the band gap, bulk modulus, and density, which are directly calculated from DFT in the environment. While none of the algorithms we benchmark solve all CrystalGym tasks, our extensive experiments and ablations show different sample efficiencies and ease of convergence to optimality for different algorithms and environment settings. Our goal is for CrystalGym to serve as a test bed for reinforcement learning researchers and material scientists to address these real-world design problems with practical applications. Furthermore, we introduce a novel class of challenges for reinforcement learning methods dealing with time-consuming reward signals, paving the way for future interdisciplinary research for machine learning motivated by real-world applications.
2025-03-02
ICLR.cc/2025/Workshop/AI4MAT (spotlight)
doi.org
openreview.net
Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study
Shayan Nejadshamsi
Vania Karami
Negar Ghourchian
Narges Armanfard
Howard Bergman
Roland Grad
Machelle Wilchesky
Vladimir Khanassov
Isabelle Vedel
Vania Karami
Depression, characterized by persistent sadness and loss of interest in daily activities, greatly reduces quality of life. Early detection i… (voir plus)s vital for effective treatment and intervention. While many studies use wearable devices to classify depression based on physical activity, these often rely on intrusive methods. Additionally, most depression classification studies involve large participant groups and use single-stage classifiers without explainability. This study aims to assess the feasibility of classifying depression using nonintrusive Wi-Fi–based motion sensor data using a novel machine learning model on a limited number of participants. We also conduct an explainability analysis to interpret the model’s predictions and identify key features associated with depression classification. In this study, we recruited adults aged 65 years and older through web-based and in-person methods, supported by a McGill University health care facility directory. Participants provided consent, and we collected 6 months of activity and sleep data via nonintrusive Wi-Fi–based sensors, along with Edmonton Frailty Scale and Geriatric Depression Scale data. For depression classification, we proposed a HOPE (Home-Based Older Adults’ Depression Prediction) machine learning model with feature selection, dimensionality reduction, and classification stages, evaluating various model combinations using accuracy, sensitivity, precision, and F1-score. Shapely addictive explanations and local interpretable model-agnostic explanations were used to explain the model’s predictions. A total of 6 participants were enrolled in this study; however, 2 participants withdrew later due to internet connectivity issues. Among the 4 remaining participants, 3 participants were classified as not having depression, while 1 participant was identified as having depression. The most accurate classification model, which combined sequential forward selection for feature selection, principal component analysis for dimensionality reduction, and a decision tree for classification, achieved an accuracy of 87.5%, sensitivity of 90%, and precision of 88.3%, effectively distinguishing individuals with and those without depression. The explainability analysis revealed that the most influential features in depression classification, in order of importance, were “average sleep duration,” “total number of sleep interruptions,” “percentage of nights with sleep interruptions,” “average duration of sleep interruptions,” and “Edmonton Frailty Scale.” The findings from this preliminary study demonstrate the feasibility of using Wi-Fi–based motion sensors for depression classification and highlight the effectiveness of our proposed HOPE machine learning model, even with a small sample size. These results suggest the potential for further research with a larger cohort for more comprehensive validation. Additionally, the nonintrusive data collection method and model architecture proposed in this study offer promising applications in remote health monitoring, particularly for older adults who may face challenges in using wearable devices. Furthermore, the importance of sleep patterns identified in our explainability analysis aligns with findings from previous research, emphasizing the need for more in-depth studies on the role of sleep in mental health, as suggested in the explainable machine learning study.
2025-03-02
JMIR Aging (publié)
doi.org
A physics-based data-driven model for CO$_2$ gas diffusion electrodes to drive automated laboratories
Ivan Grega
Félix Therrien
Abhishek Soni
Karry Ocean
Kevan Dettelbach
Ribwar Ahmadi
Mehrdad Mokhtari
Curtis P. Berlinguette
Yoshua Bengio
The electrochemical reduction of atmospheric CO…
2025-03-02
ICLR.cc/2025/Workshop/AI4MAT (poster)
doi.org
openreview.net
Platform-based Adaptive Experimental Research in Education: Lessons Learned from The Digital Learning Challenge
Ilya Musabirov
Mohi Reza
Haochen Song
Steven Moore
Pan Chen
Harsh Kumar
Tong Li
John Stamper
Norman Bier
Anna Rafferty
Thomas Price
Nina Deliu
Audrey Durand
Michael Liut
Joseph Jay Williams
: We report on our experience with a real-world, multi-experimental evaluation of an adaptive experimentation platform within the XPRIZE Dig… (voir plus)ital Learning Challenge framework. We showcase how EASI (Experiment as a Service) cross-platform software supports quick integration and deployment of adaptive experiments as well as five systematic replications within a 30-day timeframe. The outline the key scenarios of the applicability of platform-supported experiments and reflect on lessons learned from this two-year project that can help researchers and practitioners to integrate adaptive experiments in real-world courses
2025-03-02
Proceedings of the 15th International Learning Analytics and Knowledge Conference (publié)
doi.org
AI Automatons: AI Systems Intended to Imitate Humans
A.R. Olteanu
Solon Barocas
Su Lin Blodgett
Lisa Egede
Alicia DeVrio
Myra Cheng
There is a growing proliferation of AI systems designed to mimic people's behavior, work, abilities, likenesses, or humanness -- systems we … (voir plus)dub AI automatons. Individuals, groups, or generic humans are being simulated to produce creative work in their styles, to respond to surveys in their places, to probe how they would use a new system before deployment, to provide users with assistance and companionship, and to anticipate their possible future behavior and interactions with others, just to name a few applications. The research, design, deployment, and availability of such AI systems have, however, also prompted growing concerns about a wide range of possible legal, ethical, and other social impacts. To both 1) facilitate productive discussions about whether, when, and how to design and deploy such systems, and 2) chart the current landscape of existing and prospective AI automatons, we need to tease apart determinant design axes and considerations that can aid our understanding of whether and how various design choices along these axes could mitigate -- or instead exacerbate -- potential adverse impacts that the development and use of AI automatons could give rise to. In this paper, through a synthesis of related literature and extensive examples of existing AI systems intended to mimic humans, we develop a conceptual framework to help foreground key axes of design variations and provide analytical scaffolding to foster greater recognition of the design choices available to developers, as well as the possible ethical implications these choices might have.
2025-02-28
arXiv (publié)
doi.org
arxiv.org
DialEgg: Dialect-Agnostic MLIR Optimizer using Equality Saturation with Egglog.
Abd-El-Aziz Zayed
Christophe Dubach
MLIR’s ability to optimize programs at multiple levels of abstraction is key to enabling domain-specific optimizing compilers. However, ex… (voir plus)pressing optimizations remains tedious. Optimizations can interact in unexpected ways, making it hard to unleash full performance. Equality saturation promises to solve these challenges. First, it simplifies the expression of optimizations using rewrite rules. Secondly, it considers all possible optimization interactions, through saturation, selecting the best program variant. Despite these advantages, equality saturation remains absent from production compilers such as MLIR. This paper proposes to integrate Egglog, a recent equality saturation engine, with MLIR, in a dialect-agnostic manner. This paper shows how the main MLIR constructs such as operations, types or attributes can be modeled in Egglog. It also presents DialEgg, a tool that pre-defines a large set of common MLIR constructs in Egglog and automatically translates between the MLIR and Egglog program representations. Using a few use-cases, this paper demonstrates the potential for combining equality saturation and MLIR.
2025-02-28
IEEE/ACM International Symposium on Code Generation and Optimization (publié)
doi.org
Divergent responses to SARS-CoV-2 infection in bronchial epithelium with pre-existing respiratory diseases
Justine Oliva
Manon Ruffin
Claire Calmel
Aurélien Gibeaud
Andrés Pizzorno
Clémence Gaudin
Solenne Chardonnet
Viviane de Almeida Bastos
Manuel Rosa-Calatrava
Antoine Soulé
Amin Emad
Simon Rousseau
Harriet Corvol
Olivier Terrier
Loïc Guillot
2025-02-28
iScience (publié)
doi.org
Ensemble machine learning to accelerate industrial decarbonization: Prediction of Hansen solubility parameters for streamlined chemical solvent selection
Eslam G. Al-Sakkari
Ahmed Ragab
Mostafa Amer
Olumoye Ajao
Marzouk Benali
Daria C. Boffito
Hanane Dagdougui
Mouloud Amazouz
2025-02-28
Digital Chemical Engineering (publié)
doi.org
Implicit Generative Modeling by Kernel Similarity Matching
Shubham Choudhary
Paul Masset
Demba Ba
2025-02-28
ArXiv (prépublication)
arxiv.org
arxiv.org