Publications

Study Beekeeping potential data and development of a decision support system involving a web mapping platform
Philippe Doyon
Mickaël Germain
Guy Armel Fotso Kamga
Étienne Laliberté
Yacine Bouroubi
Madeleine Chagnon
The role of a decision support system is to gather, synthesize and present information in order to make informed decisions. In this project,… (voir plus) a mapping platform and a decision support system are proposed to present beekeeping data in Quebec. A complete review of the data and factors influencing honey production must first be carried out. The decision support system will be designed according to the nature of the data and access to available technologies. Continuous and real-time data management must be configured to make data interoperable. Multi-dimensional data loading tools will need to be configured to display data and analyses in a dashboard. Beekeepers will be able to optimize or move their hives according to their interpretation of the results displayed in the decision support system.
2023-07-15
IEEE International Geoscience and Remote Sensing Symposium (publié)
doi.org
The default network dominates neural responses to evolving movie stories
Enning Yang
Filip Milisav
Jakub Kopal
Avram J. Holmes
Georgios D. Mitsis
Bratislav Misic
Emily S. Finn
Danilo Bzdok
Neuroscientific studies exploring real-world dynamic perception often overlook the influence of continuous changes in narrative content. In … (voir plus)our research, we utilize machine learning tools for natural language processing to examine the relationship between movie narratives and neural responses. By analyzing over 50,000 brain images of participants watching Forrest Gump from the studyforrest dataset, we find distinct brain states that capture unique semantic aspects of the unfolding story. The default network, associated with semantic information integration, is the most engaged during movie watching. Furthermore, we identify two mechanisms that underlie how the default network liaises with the amygdala and hippocampus. Our findings demonstrate effective approaches to understanding neural processes in everyday situations and their relation to conscious awareness.
2023-07-13
Nature Communications (publié)
doi.org
An 8-channel Tx dipole and 20-channel Rx loop coil array for MRI of the cervical spinal cord at 7 Tesla
Nibardo Lopez-Rios
Kyle M. Gilbert
Daniel Papp
Gaspard Cereza
Alexandru Foias
Deshpande Rangaprakash
Markus W. May
Bastien Guerin
Lawrence L. Wald
Boris Keil
Jason P. Stockmann
Robert L. Barry
Julien Cohen-Adad
The quality of cervical spinal cord images can be improved by the use of tailored radiofrequency coil solutions for ultra-high field imaging… (voir plus); however, very few commercial and research 7 Tesla radiofrequency coils currently exist for the spinal cord, and in particular those with parallel transmit capabilities. This work presents the design, testing and validation of a pTx/Rx coil for the human neck and cervical/upper-thoracic spinal cord. The pTx portion is composed of 8 dipoles to ensure high homogeneity over this large region of the spinal cord. The Rx portion is made of 20 semi-adaptable overlapping loops to produce high Signal-to-noise ratio (SNR) across the patient population. The coil housing is designed to facilitate patient positioning and comfort, while being tight fitting to ensure high sensitivity. We demonstrate RF shimming capabilities to optimize B1+ uniformity, power efficiency and/or specific absorption rate (SAR) efficiency. B1+ homogeneity, SNR and g-factor was evaluated in adult volunteers and demonstrated excellent performance from the occipital lobe down to the T4-T5 level. We compared the proposed coil with two state-of-the-art head and head/neck coils, confirming its superiority in the cervical and upper-thoracic regions of the spinal cord. This coil solution therefore provides a convincing platform for producing the high image quality necessary for clinical and research scanning of the upper spinal cord.
2023-07-12
NMR in Biomedicine (publié)
doi.org
Subcortical Brain Alterations in Carriers of Genomic Copy Number Variants.
Kuldeep Kumar
Claudia Modenato
Clara A. Moreau
C. Ching
Christopher R. K. Ching
Annabelle Harvey
Sandra Martin-Brevet
Guillaume Huguet
Martineau Jean-Louis
Élise Douard
Charles-Olivier Martin
C.O. Martin
Nadine Younis
Petra Tamer
Anne M. Maillard
Borja Rodriguez-Herreros
Aurélie Pain
Sonia Richetin
Leila Kushan
Dmitry Isaev … (voir 26 de plus)
Kathryn Alpert
Anjani Ragothaman
Jessica A. Turner
Wei Wang
T. Ho
Tiffany C. Ho
Lianne Schmaal
Ana I. Silva
Marianne B.M. van den Bree
V. Marianne
David E.J. Linden
M. J. Owen
Marie Owen
Jeremy Hall
Sarah Lippé
Guillaume Dumas
Bogdan Draganski
Boris A. Gutman
Ida E. Sønderby
Ole A. Andreassen
Laura Schultz
Laura Almasy
David C. Glahn
Carrie E. Bearden
Paul M. Thompson
Sébastien Jacquemont
OBJECTIVE Copy number variants (CNVs) are well-known genetic pleiotropic risk factors for multiple neurodevelopmental and psychiatric disord… (voir plus)ers (NPDs), including autism (ASD) and schizophrenia. Little is known about how different CNVs conferring risk for the same condition may affect subcortical brain structures and how these alterations relate to the level of disease risk conferred by CNVs. To fill this gap, the authors investigated gross volume, vertex-level thickness, and surface maps of subcortical structures in 11 CNVs and six NPDs. METHODS Subcortical structures were characterized using harmonized ENIGMA protocols in 675 CNV carriers (CNVs at 1q21.1, TAR, 13q12.12, 15q11.2, 16p11.2, 16p13.11, and 22q11.2; age range, 6-80 years; 340 males) and 782 control subjects (age range, 6-80 years; 387 males) as well as ENIGMA summary statistics for ASD, schizophrenia, attention deficit hyperactivity disorder, obsessive-compulsive disorder, bipolar disorder, and major depression. RESULTS All CNVs showed alterations in at least one subcortical measure. Each structure was affected by at least two CNVs, and the hippocampus and amygdala were affected by five. Shape analyses detected subregional alterations that were averaged out in volume analyses. A common latent dimension was identified, characterized by opposing effects on the hippocampus/amygdala and putamen/pallidum, across CNVs and across NPDs. Effect sizes of CNVs on subcortical volume, thickness, and local surface area were correlated with their previously reported effect sizes on cognition and risk for ASD and schizophrenia. CONCLUSIONS The findings demonstrate that subcortical alterations associated with CNVs show varying levels of similarities with those associated with neuropsychiatric conditions, as well distinct effects, with some CNVs clustering with adult-onset conditions and others with ASD. These findings provide insight into the long-standing questions of why CNVs at different genomic loci increase the risk for the same NPD and why a single CNV increases the risk for a diverse set of NPDs.
2023-07-11
American Journal of Psychiatry (publié)
doi.org
Transformers in Reinforcement Learning: A Survey
Pranav Agarwal
Aamer Abdul Rahman
Pierre-Luc St-Charles
Simon J. D. Prince
S Ebrahimi Kahou
Transformers have significantly impacted domains like natural language processing, computer vision, and robotics, where they improve perform… (voir plus)ance compared to other neural networks. This survey explores how transformers are used in reinforcement learning (RL), where they are seen as a promising solution for addressing challenges such as unstable training, credit assignment, lack of interpretability, and partial observability. We begin by providing a brief domain overview of RL, followed by a discussion on the challenges of classical RL algorithms. Next, we delve into the properties of the transformer and its variants and discuss the characteristics that make them well-suited to address the challenges inherent in RL. We examine the application of transformers to various aspects of RL, including representation learning, transition and reward function modeling, and policy optimization. We also discuss recent research that aims to enhance the interpretability and efficiency of transformers in RL, using visualization techniques and efficient training strategies. Often, the transformer architecture must be tailored to the specific needs of a given application. We present a broad overview of how transformers have been adapted for several applications, including robotics, medicine, language modeling, cloud computing, and combinatorial optimization. We conclude by discussing the limitations of using transformers in RL and assess their potential for catalyzing future breakthroughs in this field.
2023-07-11
ArXiv (prépublication)
doi.org
arxiv.org
Data Imputation with an Autoencoder and MAGIC
Devin Eddington
Andres Felipe Duque Correa
Guy Wolf
Kevin R. Moon
Missing data is a common problem in many applications. Imputing missing values is a challenging task, as the imputations need to be accurate… (voir plus) and robust to avoid introducing bias in downstream analysis. In this paper, we propose an ensemble method that combines the strengths of a manifold learning-based imputation method called MAGIC and an autoencoder deep learning model. We call our method Deep MAGIC. Deep MAGIC is trained on a linear combination of the mean squared error of the original data and the mean squared error of the MAGIC-imputed data. Experimental results on three benchmark datasets show that Deep MAGIC outperforms several state-of-the-art imputation methods, demonstrating its effectiveness and robustness in handling large amounts of missing data.
2023-07-09
International Conference on Sampling Theory and Applications (publié)
doi.org
openreview.net
AI For Global Climate Cooperation 2023 Competition Proceedings
Yoshua Bengio
Prateek Arun Gupta
Li Li
Soham Rajesh Phade
Sunil Srinivasa
Andrew Robert Williams
Tianyu Zhang
Yang Zhang
Stephan Zheng
The international community must collaborate to mitigate climate change and sustain economic growth. However, collaboration is hard to achie… (voir plus)ve, partly because no global authority can ensure compliance with international climate agreements. Combining AI with climate-economic simulations offers a promising solution to design international frameworks, including negotiation protocols and climate agreements, that promote and incentivize collaboration. In addition, these frameworks should also have policy goals fulfillment, and sustained commitment, taking into account climate-economic dynamics and strategic behaviors. These challenges require an interdisciplinary approach across machine learning, economics, climate science, law, policy, ethics, and other fields. Towards this objective, we organized AI for Global Climate Cooperation, a Mila competition in which teams submitted proposals and analyses of international frameworks, based on (modifications of) RICE-N, an AI-driven integrated assessment model (IAM). In particular, RICE-N supports modeling regional decision-making using AI agents. Furthermore, the IAM then models the climate-economic impact of those decisions into the future. Whereas the first track focused only on performance metrics, the proposals submitted to the second track were evaluated both quantitatively and qualitatively. The quantitative evaluation focused on a combination of (i) the degree of mitigation of global temperature rise and (ii) the increase in economic productivity. On the other hand, an interdisciplinary panel of human experts in law, policy, sociology, economics and environmental science, evaluated the solutions qualitatively. In particular, the panel considered the effectiveness, simplicity, feasibility, ethics, and notions of climate justice of the protocols. In the third track, the participants were asked to critique and improve RICE-N.
2023-07-09
ArXiv (prépublication)
doi.org
arxiv.org
Graph Fourier MMD for Signals on Graphs
Samuel Leone
Aarthi Venkat
Guillaume Huguet
Alexander Tong
Guy Wolf
Smita Krishnaswamy
While numerous methods have been proposed for computing distances between probability distributions in Euclidean space, relatively little at… (voir plus)tention has been given to computing such distances for distributions on graphs. However, there has been a marked increase in data that either lies on graph (such as protein interaction networks) or can be modeled as a graph (single cell data), particularly in the biomedical sciences. Thus, it becomes important to find ways to compare signals defined on such graphs. Here, we propose Graph Fourier MMD (GFMMD), a novel distance between distributions and signals on graphs. GFMMD is defined via an optimal witness function that is both smooth on the graph and maximizes difference in expectation between the pair of distributions on the graph. We find an analytical solution to this optimization problem as well as an embedding of distributions that results from this method. We also prove several properties of this method including scale invariance and applicability to disconnected graphs. We showcase it on graph benchmark datasets as well on single cell RNA-sequencing data analysis. In the latter, we use the GFMMD-based gene embeddings to find meaningful gene clusters. We also propose a novel type of score for gene selection called "gene localization score" which helps select genes for cellular state space characterization.
2023-07-09
2023 International Conference on Sampling Theory and Applications (SampTA) (publié)
doi.org
openreview.net
International Institutions for Advanced AI
Lewis Ho
Joslyn N. Barnhart
Robert Trager
Yoshua Bengio
Miles Brundage
Allison Sovey Carnegie
Rumman Chowdhury
Allan Dafoe
Gillian K. Hadfield
Margaret Levi
D. Snidal
2023-07-09
ArXiv (prépublication)
doi.org
arxiv.org
Manifold Alignment with Label Information
Andres F. Duque Correa
Myriam Lizotte
Guy Wolf
Kevin R. Moon
Multi-domain data is becoming increasingly common and presents both challenges and opportunities in the data science community. The integrat… (voir plus)ion of distinct data-views can be used for exploratory data analysis, and benefit downstream analysis including machine learning related tasks. With this in mind, we present a novel manifold alignment method called MALI (Manifold alignment with label information) that learns a correspondence between two distinct domains. MALI belongs to a middle ground between the more commonly addressed semi-supervised manifold alignment, where some known correspondences between the two domains are assumed to be known beforehand, and the purely unsupervised case, where no information linking both domains is available. To do this, MALI learns the manifold structure in both domains via a diffusion process and then leverages discrete class labels to guide the alignment. MALI recovers a pairing and a common representation that reveals related samples in both domains. We show that MALI outperforms the current state-of-the-art manifold alignment methods across multiple datasets.
2023-07-09
2023 International Conference on Sampling Theory and Applications (SampTA) (publié)
doi.org
openreview.net
Robust and Versatile Bipedal Jumping Control through Reinforcement Learning
Zhongyu Li
Xue Bin Peng
Pieter Abbeel
Sergey Levine
Glen Berseth
Koushil Sreenath
2023-07-09
Robotics: Science and Systems XIX (publié)
doi.org
arxiv.org
Overcoming the Technical Challenges of Coordinating Distributed Load Resources at Scale
Johanna Mathieu
Ian Hiskens
Ioannis Marios Granitsas
Oluwagbemileke Oyefeso
Gregory Ledva
Sebastian Nugroho
Salman Nazir
Scott Hinson
Suzanne Russo
Steve Mock
Rachel Jenkins
Jill Harlow
Grant Fisher
Drew Geller
Duncan Callaway
Phillippe Phanivong
Antoine Lesage-Landry
2023-07-07
(publié)
doi.org