Publications

Team Optimal Control of Coupled Major-Minor Subsystems with Mean-Field Sharing
Jalal Arabneydi
Aditya Mahajan
2020-12-04
ArXiv (prépublication)
arxiv.org
arxiv.org
Historical and cross-disciplinary trends in the biological and social sciences reveal an accelerating adoption of advanced analytics
Taylor Bolt
Jason S. Nomi
Danilo Bzdok
Lucina Q. Uddin
2020-12-03
bioRxiv (prépublication)
doi.org
Horizontal gene transfer and recombination analysis of SARS-CoV-2 genes helps discover its close relatives and shed light on its origin
Vladimir Makarenkov
Bogdan Mazoure
Guillaume Rabusseau
Pierre Legendre
The SARS-CoV-2 pandemic is one of the greatest global medical and social challenges that have emerged in recent history. Human coronavirus s… (voir plus)trains discovered during previous SARS outbreaks have been hypothesized to pass from bats to humans using intermediate hosts, e.g. civets for SARS-CoV and camels for MERS-CoV. The discovery of an intermediate host of SARS-CoV-2 and the identification of specific mechanism of its emergence in humans are topics of primary evolutionary importance. In this study we investigate the evolutionary patterns of 11 main genes of SARS-CoV-2. Previous studies suggested that the genome of SARS-CoV-2 is highly similar to the horseshoe bat coronavirus RaTG13 for most of the genes and to some Malayan pangolin coronavirus (CoV) strains for the receptor binding (RB) domain of the spike protein. We provide a detailed list of statistically significant horizontal gene transfer and recombination events (both intergenic and intragenic) inferred for each of 11 main genes of the SARS-CoV-2 genome. Our analysis reveals that two continuous regions of genes S and N of SARS-CoV-2 may result from intragenic recombination between RaTG13 and Guangdong (GD) Pangolin CoVs. Statistically significant gene transfer-recombination events between RaTG13 and GD Pangolin CoV have been identified in region [1215–1425] of gene S and region [534–727] of gene N. Moreover, some statistically significant recombination events between the ancestors of SARS-CoV-2, RaTG13, GD Pangolin CoV and bat CoV ZC45-ZXC21 coronaviruses have been identified in genes ORF1ab, S, ORF3a, ORF7a, ORF8 and N. Furthermore, topology-based clustering of gene trees inferred for 25 CoV organisms revealed a three-way evolution of coronavirus genes, with gene phylogenies of ORF1ab, S and N forming the first cluster, gene phylogenies of ORF3a, E, M, ORF6, ORF7a, ORF7b and ORF8 forming the second cluster, and phylogeny of gene ORF10 forming the third cluster. The results of our horizontal gene transfer and recombination analysis suggest that SARS-CoV-2 could not only be a chimera virus resulting from recombination of the bat RaTG13 and Guangdong pangolin coronaviruses but also a close relative of the bat CoV ZC45 and ZXC21 strains. They also indicate that a GD pangolin may be an intermediate host of this dangerous virus.
2020-12-03
BMC Ecology and Evolution (publié)
doi.org
Intervention Design for Effective Sim2Real Transfer
Melissa Mozian
Amy Zhang
Joelle Pineau
David Meger
The goal of this work is to address the recent success of domain randomization and data augmentation for the sim2real setting. We explain th… (voir plus)is success through the lens of causal inference, positioning domain randomization and data augmentation as interventions on the environment which encourage invariance to irrelevant features. Such interventions include visual perturbations that have no effect on reward and dynamics. This encourages the learning algorithm to be robust to these types of variations and learn to attend to the true causal mechanisms for solving the task. This connection leads to two key findings: (1) perturbations to the environment do not have to be realistic, but merely show variation along dimensions that also vary in the real world, and (2) use of an explicit invariance-inducing objective improves generalization in sim2sim and sim2real transfer settings over just data augmentation or domain randomization alone. We demonstrate the capability of our method by performing zero-shot transfer of a robot arm reach task on a 7DoF Jaco arm learning from pixel observations.
2020-12-03
ArXiv (prépublication)
arxiv.org
arxiv.org
An Analysis of Dataset Overlap on Winograd-Style Tasks
Ali Emami
Adam Trischler
Kaheer Suleman
Jackie Cheung
The Winograd Schema Challenge (WSC) and variants inspired by it have become important benchmarks for common-sense reasoning (CSR). Model per… (voir plus)formance on the WSC has quickly progressed from chance-level to near-human using neural language models trained on massive corpora. In this paper, we analyze the effects of varying degrees of overlaps that occur between these corpora and the test instances in WSC-style tasks. We find that a large number of test instances overlap considerably with the pretraining corpora on which state-of-the-art models are trained, and that a significant drop in classification accuracy occurs when models are evaluated on instances with minimal overlap. Based on these results, we provide the WSC-Web dataset, consisting of over 60k pronoun disambiguation problems scraped from web data, being both the largest corpus to date, and having a significantly lower proportion of overlaps with current pretraining corpora.
2020-12-01
Proceedings of the 28th International Conference on Computational Linguistics (publié)
doi.org
arxiv.org
Autonomous navigation of stratospheric balloons using reinforcement learning
Marc Gendron-Bellemare
S. Candido
Pablo Samuel Castro
Jun Gong
Marlos C. Machado
Subhodeep Moitra
Sameera S. Ponda
Ziyun Wang
2020-12-01
Nature (publié)
doi.org
Learning Efficient Task-Specific Meta-Embeddings with Word Prisms
Jingyi He
Kc Tsiolis
Kian Kenyon-Dean
Jackie Cheung
Word embeddings are trained to predict word cooccurrence statistics, which leads them to possess different lexical properties (syntactic, se… (voir plus)mantic, etc.) depending on the notion of context defined at training time. These properties manifest when querying the embedding space for the most similar vectors, and when used at the input layer of deep neural networks trained to solve downstream NLP problems. Meta-embeddings combine multiple sets of differently trained word embeddings, and have been shown to successfully improve intrinsic and extrinsic performance over equivalent models which use just one set of source embeddings. We introduce word prisms: a simple and efficient meta-embedding method that learns to combine source embeddings according to the task at hand. Word prisms learn orthogonal transformations to linearly combine the input source embeddings, which allows them to be very efficient at inference time. We evaluate word prisms in comparison to other meta-embedding methods on six extrinsic evaluations and observe that word prisms offer improvements in performance on all tasks.
2020-12-01
Proceedings of the 28th International Conference on Computational Linguistics (publié)
doi.org
arxiv.org
Learning Lexical Subspaces in a Distributional Vector Space
Kushal Arora
Aishik Chakraborty
Jackie Cheung
Abstract In this paper, we propose LexSub, a novel approach towards unifying lexical and distributional semantics. We inject knowledge about… (voir plus) lexical-semantic relations into distributional word embeddings by defining subspaces of the distributional vector space in which a lexical relation should hold. Our framework can handle symmetric attract and repel relations (e.g., synonymy and antonymy, respectively), as well as asymmetric relations (e.g., hypernymy and meronomy). In a suite of intrinsic benchmarks, we show that our model outperforms previous approaches on relatedness tasks and on hypernymy classification and detection, while being competitive on word similarity tasks. It also outperforms previous systems on extrinsic classification tasks that benefit from exploiting lexical relational cues. We perform a series of analyses to understand the behaviors of our model.1 Code available at https://github.com/aishikchakraborty/LexSub.
2020-12-01
Transactions of the Association for Computational Linguistics (publié)
doi.org
Recommandations pratiques pour une utilisation responsable de l’intelligence artificielle en santé mentale en contexte de pandémie
Carl-Maria Mörch
Pascale Lehoux
Marc-Antoine Dilhac
Catherine Régis
Xavier Dionne
La pandémie actuelle a provoqué une onde de choc dont les conséquences se font sentir dans tous les aspects de notre vie. Alors que la sa… (voir plus)nté physique a été généralement au cœur de l’attention scientifique et politique, il est devenu clair que la pandémie de COVID-19 a influé significativement sur la santé mentale de nombreux individus. Plus encore, elle aurait accentué les fragilités déjà existantes dans nos systèmes de santé mentale. Souvent moins financé ou soutenu que la santé physique, le domaine de la santé mentale pourrait-il bénéficier d’innovations en intelligence artificielle en période de pandémie ? Et si oui comment ? Que vous soyez développeur.e.s en IA, chercheur.e.s ou entrepreneur.e.s, ce document vise à vous fournir une synthèse des pistes d’actions et des ressources pour prévenir les principaux risques éthiques liés au développement d’applications d’IA dans le champ de la santé mentale. Pour illustrer ces principes, ce document propose de découvrir quatre cas fictif, à visée réaliste, à partir desquels il vous sera proposé de porter attention aux enjeux éthiques potentiels dans cette situation, aux enjeux d’innovation responsable à envisager, aux pistes d’action possibles inspirées de la liste de contrôle (Protocole Canadien conçu pour favoriser une utilisation responsable de l’IA en santé mentale et prévention du suicide, Mörch et al., 2020), aux ressources pratiques et à certains enjeux juridiques pertinents. Ce document a été élaboré par Carl-Maria Mörch, PhD, Algora Lab, Université de Montréal, Observatoire International sur les impacts sociétaux de l’Intelligence Artificielle et du Numérique (OBVIA), Mila – Institut Québécois d’Intelligence Artificielle, avec les contributions de Pascale Lehoux, Marc-Antoine Dilhac, Catherine Régis et Xavier Dionne.
2020-12-01
(publié)
doi.org
The default network of the human brain is associated with perceived social isolation
R. Nathan Spreng
Emile Dimas
Laetitia Mwilambwe-Tshilobo
Alain Dagher
Philipp Koellinger
Gideon Nave
Anthony Ong
Julius M Kernbach
Thomas V. Wiecki
Tian Ge
Yue Li
Avram J. Holmes
B.T. Thomas Yeo
Gary R. Turner
Robin I. M. Dunbar
Danilo Bzdok
2020-12-01
Nature Communications (publié)
doi.org
Inductive biases for deep learning of higher-level cognition
Anirudh Goyal
Yoshua Bengio
A fascinating hypothesis is that human and animal intelligence could be explained by a few principles (rather than an encyclopaedic list of … (voir plus)heuristics). If that hypothesis was correct, we could more easily both understand our own intelligence and build intelligent machines. Just like in physics, the principles themselves would not be sufficient to predict the behaviour of complex systems like brains, and substantial computation might be needed to simulate human-like intelligence. This hypothesis would suggest that studying the kind of inductive biases that humans and animals exploit could help both clarify these principles and provide inspiration for AI research and neuroscience theories. Deep learning already exploits several key inductive biases, and this work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing. The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans’ abilities in terms of flexible out-of-distribution and systematic generalization, which is currently an area where a large gap exists between state-of-the-art machine learning and human intelligence.
2020-11-30
ArXiv (preprint)
doi.org
arxiv.org
#EEGManyLabs: Investigating the replicability of influential EEG experiments
Yuri G Pavlov
N. Adamian
Stefan Appelhoff
Mahnaz Arvaneh
C. Benwell
Christian Beste
A. Bland
Daniel E. Bradford
Florian Bublatzky
Niko A. Busch
Peter E. Clayson
Damian Cruse
Artur Czeszumski
Anna Dreber
Guillaume Dumas
Benedikt V. Ehinger
Giorgio Ganis
Xun He
J. Hinojosa
Christoph Huber-Huber … (voir 39 de plus)
Michael Inzlicht
B. Jack
Magnus Johannesson
Rhiannon Jones
Evgenii Kalenkovich
Laura Kaltwasser
Hamid Karimi-rouzbahani
And Andreas Keil
P. König
Layla Kouara
Louisa V. Kulke
C. Ladouceur
Nicolas Langer
Heinrich R. Liesefeld
David Luque
Annmarie MacNamara
Liad Mudrik
Muthuraman Muthuraman
Lauren Browning Neal
Gustav Nilsonne
Guiomar Niso
Sebastian Ocklenburg
Robert Oostenveld
Cyril R. Pernet
G. Pourtois
Manuela Ruzzoli
S. Sass
Alexandre Schaefer
Magdalena Senderecka
Joel S. Snyder
Christian Krog Tamnes
E Tognoli
M. V. Vugt
Edelyn Verona
Robin Vloeberghs
Dominik Welke
J. Wessel
Ilya V Zakharov
Faisal Mushtaq
2020-11-27
Cortex (publié)
doi.org