Publications

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
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
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
Human attachments shape interbrain synchrony toward efficient performance of social goals
Amir Djalovski
Guillaume Dumas
Sivan Kinreich
Ruth Pinkenson Feldman
2020-11-26
NeuroImage (publié)
doi.org
Interactive Psychometrics for Autism With the Human Dynamic Clamp: Interpersonal Synchrony From Sensorimotor to Sociocognitive Domains
Florence Baillin
Aline Lefebvre
Amandine Pedoux
Yann Beauxis
Denis-Alexander Engemann
Anna Maruani
Frederique Amsellem
J. A. Scott Kelso
Thomas Bourgeron
Richard Delorme
Guillaume Dumas
The human dynamic clamp (HDC) is a human–machine interface designed on the basis of coordination dynamics for studying realistic social in… (voir plus)teraction under controlled and reproducible conditions. Here, we propose to probe the validity of the HDC as a psychometric instrument for quantifying social abilities in children with autism spectrum disorder (ASD) and neurotypical development. To study interpersonal synchrony with the HDC, we derived five standardized scores following a gradient from sensorimotor and motor to higher sociocognitive skills in a sample of 155 individuals (113 participants with ASD, 42 typically developing participants; aged 5 to 25 years; IQ > 70). Regression analyses were performed using normative modeling on global scores according to four subconditions (HDC behavior “cooperative/competitive,” human task “in-phase/anti-phase,” diagnosis, and age at inclusion). Children with ASD had lower scores than controls for motor skills. HDC motor coordination scores were the best candidates for stratification and diagnostic biomarkers according to exploratory analyses of hierarchical clustering and multivariate classification. Independently of phenotype, sociocognitive skills increased with developmental age while being affected by the ongoing task and HDC behavior. Weaker performance in ASD for motor skills suggests the convergent validity of the HDC for evaluating social interaction. Results provided additional evidence of a relationship between sensorimotor and sociocognitive skills. HDC may also be used as a marker of maturation of sociocognitive skills during real-time social interaction. Through its standardized and objective evaluation, the HDC not only represents a valid paradigm for the study of interpersonal synchrony but also offers a promising, clinically relevant psychometric instrument for the evaluation and stratification of sociomotor dysfunctions.
2020-11-26
Frontiers in Psychiatry (publié)
doi.org
Linear Lower Bounds and Conditioning of Differentiable Games
Adam Ibrahim
Waiss Azizian
Gauthier Gidel
Ioannis Mitliagkas
Recent successes of game-theoretic formulations in ML have caused a resurgence of research interest in differentiable games. Overwhelmingly,… (voir plus) that research focuses on methods and upper bounds on their speed of convergence. In this work, we approach the question of fundamental iteration complexity by providing lower bounds to complement the linear (i.e. geometric) upper bounds observed in the literature on a wide class of problems. We cast saddle-point and min-max problems as 2-player games. We leverage tools from single-objective convex optimisation to propose new linear lower bounds for convex-concave games. Notably, we give a linear lower bound for
2020-11-21
Proceedings of the 37th International Conference on Machine Learning (publié)
proceedings.mlr.press
Revisiting Fundamentals of Experience Replay
William Fedus
Prajit Ramachandran
Rishabh Agarwal
Yoshua Bengio
Hugo Larochelle
Mark Rowland
Will Dabney
Experience replay is central to off-policy algorithms in deep reinforcement learning (RL), but there remain significant gaps in our understa… (voir plus)nding. We therefore present a systematic and extensive analysis of experience replay in Q-learning methods, focusing on two fundamental properties: the replay capacity and the ratio of learning updates to experience collected (replay ratio). Our additive and ablative studies upend conventional wisdom around experience replay -- greater capacity is found to substantially increase the performance of certain algorithms, while leaving others unaffected. Counterintuitively we show that theoretically ungrounded, uncorrected n-step returns are uniquely beneficial while other techniques confer limited benefit for sifting through larger memory. Separately, by directly controlling the replay ratio we contextualize previous observations in the literature and empirically measure its importance across a variety of deep RL algorithms. Finally, we conclude by testing a set of hypotheses on the nature of these performance benefits.
2020-11-21
Proceedings of the 37th International Conference on Machine Learning (publié)
proceedings.mlr.press
arxiv.org