Publications

From the Lab to the Theater: An Unconventional Field Robotics Journey
Ali Imran
Vivek Shankar Vardharajan
Rafael Gomes Braga
Yann Bouteiller
Abdalwhab Abdalwhab
Matthis Di-Giacomo
Alexandra Mercader
Giovanni Beltrame
David St-Onge
2024-04-10
ArXiv (preprint)
doi.org
arxiv.org
Maximum flow-based formulation for the optimal location of electric vehicle charging stations
Pierre‐Luc Parent
Margarida Carvalho
Miguel F. Anjos
Ribal Atallah
With the increasing effects of climate change, the urgency to step away from fossil fuels is greater than ever before. Electric vehicles (EV… (see more)s) are one way to diminish these effects, but their widespread adoption is often limited by the insufficient availability of charging stations. In this work, our goal is to expand the infrastructure of EV charging stations, in order to provide a better quality of service in terms of user satisfaction (and availability of charging stations). Specifically, our focus is directed towards urban areas. We first propose a model for the assignment of EV charging demand to stations, framing it as a maximum flow problem. This model is the basis for the evaluation of user satisfaction with a given charging infrastructure. Secondly, we incorporate the maximum flow model into a mixed‐integer linear program, where decisions on the opening of new stations and on the expansion of their capacity through additional outlets is accounted for. We showcase our methodology for the city of Montreal, demonstrating the scalability of our approach to handle real‐world scenarios. We conclude that considering both spacial and temporal variations in charging demand is meaningful when solving realistic instances.
2024-04-10
Networks (published)
doi.org
arxiv.org
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
Aleksandar Botev
Soham De
Samuel L. Smith
Anushan Fernando
George-Cristian Muraru
Ruba Haroun
Leonard Berrada
Razvan Pascanu
Pier Giuseppe Sessa
Robert Dadashi
L'eonard Hussenot
Johan Ferret
Sertan Girgin
Olivier Bachem
Alek Andreev
Kathleen Kenealy
Thomas Mesnard
Cassidy Hardin
Surya Bhupatiraju
Shreya Pathak … (see 43 more)
Laurent Sifre
Morgane Rivière
Mihir Kale
J Christopher Love
Juliette Love
Pouya Dehghani Tafti
Armand Joulin
Noah Fiedel
Evan Senter
Yutian Chen 0001
Srivatsan Srinivasan
Guillaume Desjardins
David Mark Budden
Arnaud Doucet
Sharad Mandyam Vikram
Adam Paszke
Trevor Gale
Sebastian Borgeaud
Charlie Chen
Andy Brock
Antonia Paterson
Jenny Brennan
Meg Risdal
Raj Gundluru
N. Devanathan
Paul Mooney
Nilay Chauhan
Phil Culliton
Luiz GUStavo Martins
Elisa Bandy
David W. Huntsperger
Glenn Cameron
Arthur Zucker
Tris Brian Warkentin
Ludovic Peran
Minh Giang
Zoubin Ghahramani
Clément Farabet
Koray Kavukcuoglu
Demis Hassabis
Raia Hadsell
Yee Whye Teh
Nando de Frietas
We introduce RecurrentGemma, a family of open language models which uses Google's novel Griffin architecture. Griffin combines linear recurr… (see more)ences with local attention to achieve excellent performance on language. It has a fixed-sized state, which reduces memory use and enables efficient inference on long sequences. We provide two sizes of models, containing 2B and 9B parameters, and provide pre-trained and instruction tuned variants for both. Our models achieve comparable performance to similarly-sized Gemma baselines despite being trained on fewer tokens.
2024-04-10
ArXiv (preprint)
doi.org
arxiv.org
Scalable Hierarchical Self-Attention with Learnable Hierarchy for Long-Range Interactions
Thuan Nguyen Anh Trang
Khang Nhat Ngo
Hugo Sonnery
Thieu Vo
Siamak Ravanbakhsh
Truong Son Hy
Self-attention models have made great strides toward accurately modeling a wide array of data modalities, including, more recently, graph-st… (see more)ructured data. This paper demonstrates that adaptive hierarchical attention can go a long way toward successfully applying transformers to graphs. Our proposed model Sequoia provides a powerful inductive bias towards long-range interaction modeling, leading to better generalization. We propose an end-to-end mechanism for a data-dependent construction of a hierarchy which in turn guides the self-attention mechanism. Using adaptive hierarchy provides a natural pathway toward sparse attention by constraining node-to-node interactions with the immediate family of each node in the hierarchy (e.g., parent, children, and siblings). This in turn dramatically reduces the computational complexity of a self-attention layer from quadratic to log-linear in terms of the input size while maintaining or sometimes even surpassing the standard transformer's ability to model long-range dependencies across the entire input. Experimentally, we report state-of-the-art performance on long-range graph benchmarks while remaining computationally efficient. Moving beyond graphs, we also display competitive performance on long-range sequence modeling, point-clouds classification, and segmentation when using a fixed hierarchy. Our source code is publicly available at https://github.com/HySonLab/HierAttention
2024-04-10
TMLR (accepted)
openreview.net
openreview.net
Temporal trends in disparities in COVID-19 seropositivity among Canadian blood donors
Yuan Yu
Matthew J Knight
Diana Gibson
Sheila F O'Brien
David L Buckeridge
W. Alton Russell
In Canada’s largest COVID-19 serological study, SARS-CoV-2 antibodies in blood donors have been monitored since 2020. No study has analyse… (see more)d changes in the association between anti-N seropositivity (a marker of recent infection) and geographic and sociodemographic characteristics over the pandemic. Using Bayesian multi-level models with spatial effects at the census division level, we analysed changes in correlates of SARS-CoV-2 anti-N seropositivity across three periods in which different variants predominated (pre-Delta, Delta and Omicron). We analysed disparities by geographic area, individual traits (age, sex, race) and neighbourhood factors (urbanicity, material deprivation and social deprivation). Data were from 420 319 blood donations across four regions (Ontario, British Columbia [BC], the Prairies and the Atlantic region) from December 2020 to November 2022. Seropositivity was higher for racialized minorities, males and individuals in more materially deprived neighbourhoods in the pre-Delta and Delta waves. These subgroup differences dissipated in the Omicron wave as large swaths of the population became infected. Across all waves, seropositivity was higher in younger individuals and those with lower neighbourhood social deprivation. Rural residents had high seropositivity in the Prairies, but not other regions. Compared to generalized linear models, multi-level models with spatial effects had better fit and lower error when predicting SARS-CoV-2 anti-N seropositivity by geographic region. Correlates of recent COVID-19 infection have evolved over the pandemic. Many disparities lessened during the Omicron wave, but public health intervention may be warranted to address persistently higher burden among young people and those with less social deprivation.
2024-04-10
International Journal of Epidemiology (published)
doi.org
Association between arterial oxygen and mortality across critically ill patients with hematologic malignancies: results from an international collaborative network
Guillaume Dumas
Idunn S. Morris
Tamishta Hensman
Alexandre Demoule
Achille Kouatchet
Virginie Lemiale
Djamel Mokart
Frédéric Pène
Elie Azoulay
Laveena Munshi
Laurent François Dominique Naike Fabrice Emmanuel Yves Mic Argaud Barbier Benoit Bigé Bruneel Canet Cohen Dar
Laurent Argaud
François Barbier
Dominique Benoit
Naike Bigé
Fabrice Bruneel
Emmanuel Canet
Yves Cohen
Michael Darmon
Didier Gruson … (see 31 more)
Kada Klouche
Loay Kontar
Alexandre Lautrette
Christine Lebert
Guillaume Louis
Julien Mayaux
Anne-Pascale Meert
Anne-Sophie Moreau
Martine Nyunga
Vincent Peigne
Pierre Perez
Jean Herlé Raphalen
Carole Schwebel
Jean-Marie Tonnelier
Florent Wallet
Lara Zafrani
Bram Rochwerg
Farah Shoukat
Dean Fergusson
Bruno Ferreyro
Paul Heffernan
Margaret Herridge
Sheldon Magder
Mark Minden
Rakesh Patel
Salman Qureshi
Aaron Schimmer
Santhosh Thyagu
Han Ting Wang
Sangeeta Mehta
Sean M. Bagshaw
2024-04-09
Intensive Care Medicine (published)
doi.org
Deep Generative Sampling in the Dual Divergence Space: A Data-efficient&Interpretative Approach for Generative AI
Sahil Garg
Anderson Schneider
Anant Raj
Kashif Rasul
Yuriy Nevmyvaka
S. Gopal
Amit Dhurandhar
Guillermo A. Cecchi
Irina Rish
Building on the remarkable achievements in generative sampling of natural images, we propose an innovative challenge, potentially overly amb… (see more)itious, which involves generating samples of entire multivariate time series that resemble images. However, the statistical challenge lies in the small sample size, sometimes consisting of a few hundred subjects. This issue is especially problematic for deep generative models that follow the conventional approach of generating samples from a canonical distribution and then decoding or denoising them to match the true data distribution. In contrast, our method is grounded in information theory and aims to implicitly characterize the distribution of images, particularly the (global and local) dependency structure between pixels. We achieve this by empirically estimating its KL-divergence in the dual form with respect to the respective marginal distribution. This enables us to perform generative sampling directly in the optimized 1-D dual divergence space. Specifically, in the dual space, training samples representing the data distribution are embedded in the form of various clusters between two end points. In theory, any sample embedded between those two end points is in-distribution w.r.t. the data distribution. Our key idea for generating novel samples of images is to interpolate between the clusters via a walk as per gradients of the dual function w.r.t. the data dimensions. In addition to the data efficiency gained from direct sampling, we propose an algorithm that offers a significant reduction in sample complexity for estimating the divergence of the data distribution with respect to the marginal distribution. We provide strong theoretical guarantees along with an extensive empirical evaluation using many real-world datasets from diverse domains, establishing the superiority of our approach w.r.t. state-of-the-art deep learning methods.
2024-04-09
ArXiv (preprint)
doi.org
arxiv.org
On the Neurobiological Basis of Chronotype: Insights from a Multimodal Population Neuroscience Study
Le Zhou
Karin Saltoun
Julie Carrier
Kai-Florian Storch
Robin Dunbar
Danilo Bzdok
Abstract

The rapid shifts of society have brought about changes in human behavioral patterns, with increased eveni… (see more)ng activities, increased screen time, and postponed sleep schedules. As an explicit manifestation of circadian rhythms, chronotype is closely intertwined with both physical and mental health. Night owls often exhibit more unhealthy lifestyle habits, are more susceptible to mood disorders, and have poorer physical fitness. Although individual differences in chronotype yield varying consequences, their neurobiological underpinnings remain elusive. Here we carry out a pattern-learning analysis, and capitalize on a vast array of ~ 1,000 phenome-wide phenotypes with three brain-imaging modalities (region volume of gray matter, whiter-matter fiber tracts, and functional connectivity) in 27,030 UK Biobank participants. The resulting multi-level depicts of brain images converge on the basal ganglia, limbic system, hippocampus, as well as cerebellum vermis, thus implicating key nodes in habit formation, emotional regulation and reward processing. Complementary by comprehensive investigations of in-deep phenotypic collections, our population study offers evidence of behavioral pattern disparities linked to distinct chronotype-related behavioral tendencies in our societies.

2024-04-09
Research Square (preprint)
doi.org
AI healthcare research: Pioneering iSMART Lab
Narges Armanfard
Dr Narges Armanfard, Professor, talks us through the AI healthcare research at McGill University which is spearheading a groundbreaking init… (see more)iative – the iSMART Lab. Access to high-quality healthcare is not just a fundamental human right; it is the bedrock of our societal wellbeing, with the crucial roles played by doctors, nurses, and hospitals. Yet, healthcare systems globally face mounting challenges, particularly from aging populations. Dr Narges Armanfard, affiliated with McGill University and Mila Quebec AI Institute in Montreal, Canada, has spearheaded a groundbreaking initiative – the iSMART Lab. This laboratory represents a revolutionary leap into the future of healthcare, with its pioneering research in AI for health applications garnering significant attention. Renowned for its innovative integration of AI across diverse domains, iSMART Lab stands at the forefront of harnessing Artificial Intelligence to elevate and streamline health services.
2024-04-08
Open Access Government (published)
doi.org
Interpretable machine learning for finding intermediate-mass black holes
Mario Pasquato
PIERO TREVISAN
ABBAS ASKAR
Pablo Lemos
GAIA CARENINI
MICHELA MAPELLI
Yashar Hezaveh
Definitive evidence that globular clusters (GCs) host intermediate-mass black holes (IMBHs) is elusive. Machine learning (ML) models trained… (see more) on GC simulations can in principle predict IMBH host candidates based on observable features. This approach has two limitations: first, an accurate ML model is expected to be a black box due to complexity; second, despite our efforts to realistically simulate GCs, the simulation physics or initial conditions may fail to fully reflect reality. Therefore our training data may be biased, leading to a failure in generalization on observational data. Both the first issue -- explainability/interpretability -- and the second -- out of distribution generalization and fairness -- are active areas of research in ML. Here we employ techniques from these fields to address them: we use the anchors method to explain an XGBoost classifier; we also independently train a natively interpretable model using Certifiably Optimal RulE ListS (CORELS). The resulting model has a clear physical meaning, but loses some performance with respect to XGBoost. We evaluate potential candidates in real data based not only on classifier predictions but also on their similarity to the training data, measured by the likelihood of a kernel density estimation model. This measures the realism of our simulated data and mitigates the risk that our models may produce biased predictions by working in extrapolation. We apply our classifiers to real GCs, obtaining a predicted classification, a measure of the confidence of the prediction, an out-of-distribution flag, a local rule explaining the prediction of XGBoost and a global rule from CORELS.
2024-04-08
The Astrophysical Journal (published)
doi.org
arxiv.org
Learning Minimal NAP Specifications for Neural Network Verification
Chuqin Geng
Zhaoyue Wang
Haolin Ye
Saifei Liao
Xujie Si
Specifications play a crucial role in neural network verification. They define the precise input regions we aim to verify, typically represe… (see more)nted as L-infinity norm balls. While recent research suggests using neural activation patterns (NAPs) as specifications for verifying unseen test set data, it focuses on computing the most refined NAPs, often limited to very small regions in the input space. In this paper, we study the following problem: Given a neural network, find a minimal (coarsest) NAP that is sufficient for formal verification of the network's robustness. Finding the minimal NAP specification not only expands verifiable bounds but also provides insights into which neurons contribute to the model's robustness. To address this problem, we propose several exact and approximate approaches. Our exact approaches leverage the verification tool to find minimal NAP specifications in either a deterministic or statistical manner. Whereas the approximate methods efficiently estimate minimal NAPs using adversarial examples and local gradients, without making calls to the verification tool. This allows us to inspect potential causal links between neurons and the robustness of state-of-the-art neural networks, a task for which existing verification frameworks fail to scale. Our experimental results suggest that minimal NAP specifications require much smaller fractions of neurons compared to the most refined NAP specifications, yet they can significantly expand the verifiable boundaries to several orders of magnitude larger.
2024-04-05
ArXiv (preprint)
doi.org
arxiv.org
SAT-DIFF: A Tree Diffing Framework Using SAT Solving
Chuqin Geng
Haolin Ye
Yihan Zhang
Brigitte Pientka
Xujie Si
Computing differences between tree-structured data is a critical but challenging problem in software analysis. In this paper, we propose a n… (see more)ovel tree diffing approach called SatDiff, which reformulates the structural diffing problem into a MaxSAT problem. By encoding the necessary transformations from the source tree to the target tree, SatDiff generates correct, minimal, and type safe low-level edit scripts with formal guarantees. We then synthesize concise high-level edit scripts by effectively merging low-level edits in the appropriate topological order. Our empirical results demonstrate that SatDiff outperforms existing heuristic-based approaches by a significant margin in terms of conciseness while maintaining a reasonable runtime.
2024-04-05
ArXiv (preprint)
doi.org
arxiv.org