Publications

Using Simulated Data to Generate Images of Climate Change
Gautier Cosne
Adrien Juraver
Mélisande Teng
Victor Schmidt
Vahe Vardanyan
Alexandra Luccioni
Yoshua Bengio
Generative adversarial networks (GANs) used in domain adaptation tasks have the ability to generate images that are both realistic and perso… (voir plus)nalized, transforming an input image while maintaining its identifiable characteristics. However, they often require a large quantity of training data to produce high-quality images in a robust way, which limits their usability in cases when access to data is limited. In our paper, we explore the potential of using images from a simulated 3D environment to improve a domain adaptation task carried out by the MUNIT architecture, aiming to use the resulting images to raise awareness of the potential future impacts of climate change.
2020-01-25
ArXiv (prépublication)
arxiv.org
arxiv.org
Navigation in the Service of Enhanced Pose Estimation
Travis Manderson
Ran Cheng
David Meger
Gregory Dudek
2020-01-22
Springer Proceedings in Advanced Robotics (publié)
doi.org
Autism spectrum disorder
Catherine Lord
Traolach S. Brugha
Tony Charman
James Cusack
Guillaume Dumas
Thomas Frazier
Emily J. H. Jones
Rebecca M. Jones
Andrew Pickles
Matthew W. State
Julie Lounds Taylor
Jeremy Veenstra-VanderWeele
2020-01-15
Nature Reviews Disease Primers (publié)
doi.org
Optogenetic activation of parvalbumin and somatostatin interneurons selectively restores theta-nested gamma oscillations and oscillation-induced spike timing-dependent long-term potentiation impaired by amyloid β oligomers
Kyerl Park
Jaedong Lee
Hyun Jae Jang
Blake Aaron Richards
Michael M Kohl
Jeehyun Kwag
2020-01-14
BMC Biology (publié)
doi.org
Conducting gender-based analysis of existing databases when self-reported gender data are unavailable: the GENDER Index in a working population
M. Gabrielle Pagé
Bilkis Vissandjée
Hermine Lore Nguena Nguefack
Joel Katz
Oumar Mallé Samb
Alain Gillian Lucie David Manon Catherine Anaïs Benoit Alexandre Amélie Pasquale Valérie Marie-Pascale Mike Anne-Marie Marc Josiane Mireille Stéphanie Pierre Annie Isabelle Danielle Denis Jaime André Geneviève Jean-François Roxanne Marc-Antoine Pier Sonia Vanasse
Alain Gillian Lucie David Manon Catherine Anaïs Benoit A Vanasse Bartlett Blais Buckeridge Choinière Hudon
Alain Vanasse
Gillian Bartlett
Lucie Blais
David L Buckeridge
Manon Choinière
Catherine Hudon
Anaïs Lacasse
Benoit Lamarche
Alexandre Lebel
Amélie Quesnel-Vallée
Pasquale Roberge
Valérie Émond
Marie-Pascale Pomey … (voir 19 de plus)
Mike Benigeri
Anne-Marie Cloutier
Marc Dorais
Josiane Courteau
Mireille Courteau
Stéphanie Plante
Pierre Cambon
Annie Giguère
Isabelle Leroux
Danielle St-Laurent
Denis Roy
Jaime Borja
André Néron
Geneviève Landry
Jean-François Ethier
Roxanne Dault
Marc-Antoine Côté-Marcil
Pier Tremblay
Sonia Quirion
Objectives Growing attention has been given to considering sex and gender in health research. However, this remains a challenge in the conte… (voir plus)xt of retrospective studies where self-reported gender measures are often unavailable. This study aimed to create and validate a composite gender index using data from the Canadian Community Health Survey (CCHS). Methods According to scientific literature and expert opinion, the GENDER Index was built using several variables available in the CCHS and deemed to be gender-related (e.g., occupation, receiving child support, number of working hours). Among workers aged 18–50 years who had no missing data for our variables of interest ( n  = 29,470 participants), propensity scores were derived from a logistic regression model that included gender-related variables as covariates and where biological sex served as the dependent variable. Construct validity of propensity scores (GENDER Index scores) were then examined. Results When looking at the distribution of the GENDER Index scores in males and females, they appeared related but partly independent. Differences in the proportion of females appeared between groups categorized according to the GENDER Index scores tertiles ( p   0.0001). Construct validity was also examined through associations between the GENDER Index scores and gender-related variables identifi
2020-01-12
Canadian journal of public health (publié)
doi.org
Block planning for intermodal rail: Methodology and case study
Gianluca Morganti
T. Crainic
Emma Frejinger
Nicolettta Ricciardi
2019-12-31
Transportation Research Procedia (publié)
doi.org
On Bonus-Based Exploration Methods in the Arcade Learning Environment
Adrien Ali Taiga
William Fedus
Marlos C. Machado
Aaron Courville
Bellemare Marc-Emmanuel
Research on exploration in reinforcement learning, as applied to Atari 2600 game-playing, has emphasized tackling difficult exploration prob… (voir plus)lems such as Montezuma's Revenge (Bellemare et al., 2016). Recently, bonus-based exploration methods, which explore by augmenting the environment reward, have reached above-human average performance on such domains. In this paper we reassess popular bonus-based exploration methods within a common evaluation framework. We combine Rainbow (Hessel et al., 2018) with different exploration bonuses and evaluate its performance on Montezuma's Revenge, Bellemare et al.'s set of hard of exploration games with sparse rewards, and the whole Atari 2600 suite. We find that while exploration bonuses lead to higher score on Montezuma's Revenge they do not provide meaningful gains over the simpler
2019-12-31
ICLR.cc/2020/Conference (poster)
doi.org
openreview.net
Call for Papers: Novel Informatics Approaches to COVID-19 Research
Huanan Xu
David L Buckeridge
Yi Wang
2019-12-31
(publié)
www.semanticscholar.org
COVI White Paper - Version 1.1
Hannah Alsdurf
Edmond Belliveau
Yoshua Bengio
Tristan Deleu
Prateek Gupta
Daphne Ippolito
Richard Janda
Max Jarvie
Tyler Kolody
Sekoul Krastev
Tegan Maharaj
Robert Obryk
Dan Pilat
Valérie Pisano
Benjamin Prud’homme
Meng Qu
Nasim Rahaman
Irina Rish
Jean-François Rousseau
Victor Schmidt … (voir 5 de plus)
Abhinav Sharma
Brooke Struck
Jian Tang
Martin Weiss
Yun William Yu
The SARS-CoV-2 (Covid-19) pandemic has caused significant strain on public health institutions around the world. Contact tracing is an essen… (voir plus)tial tool to change the course of the Covid-19 pandemic. Manual contact tracing of Covid-19 cases has significant challenges that limit the ability of public health authorities to minimize community infections. Personalized peer-to-peer contact tracing through the use of mobile apps has the potential to shift the paradigm. Some countries have deployed centralized tracking systems, but more privacy-protecting decentralized systems offer much of the same benefit without concentrating data in the hands of a state authority or for-profit corporations. Machine learning methods can circumvent some of the limitations of standard digital tracing by incorporating many clues and their uncertainty into a more graded and precise estimation of infection risk. The estimated risk can provide early risk awareness, personalized recommendations and relevant information to the user. Finally, non-identifying risk data can inform epidemiological models trained jointly with the machine learning predictor. These models can provide statistical evidence for the importance of factors involved in disease transmission. They can also be used to monitor, evaluate and optimize health policy and (de)confinement scenarios according to medical and economic productivity indicators. However, such a strategy based on mobile apps and machine learning should proactively mitigate potential ethical and privacy risks, which could have substantial impacts on society (not only impacts on health but also impacts such as stigmatization and abuse of personal data). Here, we present an overview of the rationale, design, ethical considerations and privacy strategy of `COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
2019-12-31
International Conference on Machine Learning (publié)
doi.org
arxiv.org
Divergent protein-coding genes and brain size in primates
Malesys Bourgeron Dumas
Guillaume Dumas
Simon Malesys
Thomas Bourgeron
17 The human brain differs from that of other primates, but the genetic basis of these differences 18 remains unclear. We investigated the e… (voir plus)volutionary pressures acting on almost all human 19 protein-coding genes ( N =11,667; 1:1 orthologs in primates) on the basis of their divergence 20 from those of early hominins, such as Neanderthals, and non-human primates. We confirm 21 that genes encoding brain-related proteins are among the most strongly conserved protein- 22 coding genes in the human genome. Combining our evolutionary pressure metrics for the 23 protein-coding genome with recent datasets, we found that this conservation applied to genes 24 functionally associated with the synapse and expressed in brain structures such as the 25 prefrontal cortex and the cerebellum. Conversely, several of the protein-coding genes that 26 diverge most in hominins relative to other primates are associated with brain-associated 27 diseases, such as micro/macrocephaly, dyslexia, and autism. We also showed that cerebellum 28 granule neurons express a set of divergent protein-coding genes that may have contributed to 29 the emergence of fine motor skills and social cognition in humans. This resource is available 30 from http://neanderthal.pasteur.fr and can be used to estimate evolutionary constraints acting 31 on a set of genes and to explore their relative contributions to human traits. 32
2019-12-31
(publié)
www.semanticscholar.org
On Efficiency in Hierarchical Reinforcement Learning
Zheng Wen
Doina Precup
Morteza Ibrahimi
Andre Barreto
Benjamin Van Roy
Satinder Singh
Hierarchical Reinforcement Learning (HRL) approaches promise to provide more efficient solutions to sequential decision making problems, bo… (voir plus)th in terms of statistical as well as computational efficiency. While this has been demonstrated empirically over time in a variety of tasks, theoretical results quantifying the ben-efits of such methods are still few and far between. In this paper, we discuss the kind of structure in a Markov decision process which gives rise to efficient HRL methods. Specifically, we formalize the intuition that HRL can exploit well repeating "subMDPs", with similar reward and transition structure. We show that, under reasonable assumptions, a model-based Thompson sampling-style HRL algorithm that exploits this structure is statistically efficient, as established through a finite-time regret bound. We also establish conditions under which planning with structure-induced options is near-optimal and computationally efficient.
2019-12-31
Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (publié)
dblp.uni-trier.de
Electric Vehicles Equilibrium Model that Considers Queue Delay and Mixed Traffic
Nurit Oliker
Miguel F. Anjos
Margarida Carvalho
Emma Frejinger
Bernard Gendron
This study develops an equilibrium model for electric vehicles (EVs) that considers both queue delays in charging stations and flow dependen… (voir plus)t travel times. This is a user equilibrium model that accounts for travel, charging and queuing time in the path choice modelling of EVs and the complementary traffic. Waiting and service times in charging stations are represented by an m/m/k queuing system. The model considers multiple vehicle and driver classes, expressing different battery capacity, initial charge state and range anxiety level. Feasible paths are found for multiple classes given their limited travel range. A numerical application exemplifies the limitations of EVs assignment and their impact on flow distribution.
2019-12-31
(publié)
www.semanticscholar.org