Publications

Interprofessional collaboration and health policy: results from a Quebec mixed method legal research
Marie-Andree Girard
Catherine Régis
Jean-Louis Denis
2021-05-05
Journal of Interprofessional Care (publié)
doi.org
Pharmacists' perceptions of a machine learning model for the identification of atypical medication orders
Sophie-Camille Hogue
Flora Chen
Geneviève Brassard
Denis Lebel
Jean-François Bussières
Audrey Durand
Maxime Thibault
2021-05-05
J. Am. Medical Informatics Assoc. (publié)
doi.org
Impact of individual rater style on deep learning uncertainty in medical imaging segmentation
Olivier Vincent
Charley Gros
Julien Cohen-Adad
While multiple studies have explored the relation between inter-rater variability and deep learning model uncertainty in medical segmentatio… (voir plus)n tasks, little is known about the impact of individual rater style. This study quantifies rater style in the form of bias and consistency and explores their impacts when used to train deep learning models. Two multi-rater public datasets were used, consisting of brain multiple sclerosis lesion and spinal cord grey matter segmentation. On both datasets, results show a correlation (
2021-05-04
ArXiv (prépublication)
arxiv.org
arxiv.org
PNS-GAN: Conditional Generation of Peripheral Nerve Signals in the Wavelet Domain via Adversarial Networks
Olivier Tessier-Larivière
Luke Y. Prince
Pascal Fortier-Poisson
Lorenz Wernisch
Oliver Armitage
Emil Hewage
Guillaume Lajoie
Blake Aaron Richards
Simulated datasets of neural recordings are a crucial tool in neural engineering for testing the ability of decoding algorithms to recover k… (voir plus)nown ground-truth. In this work, we introduce PNS-GAN, a generative adversarial network capable of producing realistic nerve recordings conditioned on physiological biomarkers. PNS-GAN operates in the wavelet domain to preserve both the timing and frequency of neural events with high resolution. PNS-GAN generates sequences of scaleograms from noise using a recurrent neural network and 2D transposed convolution layers. PNS-GAN discriminates over stacks of scaleograms with a network of 3D convolution layers. We find that our generated signal reproduces a number of characteristics of the real signal, including similarity in a canonical time-series feature-space, and contains physiologically related neural events including respiration modulation and similar distributions of afferent and efferent signalling.
2021-05-03
International IEEE/EMBS Conference on Neural Engineering (publié)
doi.org
Reinforcement Learning with Random Delays
Yann Bouteiller
Simon Ramstedt
Giovanni Beltrame
Christopher Pal
Jonathan Binas
Action and observation delays commonly occur in many Reinforcement Learning applications, such as remote control scenarios. We study the ana… (voir plus)tomy of randomly delayed environments, and show that partially resampling trajectory fragments in hindsight allows for off-policy multi-step value estimation. We apply this principle to derive Delay-Correcting Actor-Critic (DCAC), an algorithm based on Soft Actor-Critic with significantly better performance in environments with delays. This is shown theoretically and also demonstrated practically on a delay-augmented version of the MuJoCo continuous control benchmark.
2021-05-02
International Conference on Learning Representations (Poster)
doi.org
openreview.net
Associations Between Relative Morning Blood Pressure, Cerebral Blood Flow, and Memory in Older Adults Treated and Controlled for Hypertension
Adrián Noriega de la Colina
Atef Badji
Marie-Christine Robitaille-Grou
Christine Gagnon
Tommy Boshkovski
Maxime Lamarre-Cliche
Sven Joubert
Claudine J. Gauthier
Louis Bherer
Julien Cohen-Adad
Hélène Girouard
Supplemental Digital Content is available in the text. Hypertension, elevated morning blood pressure (BP) surges, and circadian BP variabili… (voir plus)ty constitute risk factors for cerebrovascular events. Nevertheless, while evidence indicates that hypertension is associated with cognitive dysfunctions, the link between BP variability and cognitive performance during aging is not clear. The purpose of this study is to determine the interaction between relative morning BP, cerebral blood flow (CBF) levels, and cognitive performance in hypertensive older adults with controlled BP under antihypertensive treatment. Eighty-four participants aged between 60 and 75 years old were separated into normotensive (n=51) and hypertensive (n=33) groups and underwent 24-hour ambulatory BP monitoring. They were also examined for CBF in the gray matter (CBF-GM) by magnetic resonance imaging and 5 cognitive domains: global cognition, working memory, episodic memory, processing speed, and executive functions. There was no difference in cognitive performance and CBF between normotensive and controlled hypertensive participants. Through a sensitivity analysis, we identified that, among relative morning BP variables, the best fit for CBF values in this cohort was the morning-evening difference in BP. The relative morning BP was negatively associated with CBF-GM in these hypertensive older adults only. In turn, CBF-GM levels were negatively associated with working and episodic memory scores in hypertensive older adults. This is the first extended study demonstrating an association between high relative morning BP and lower levels of CBF-GM, including the further impact of CBF-GM levels on the cognitive performance of specific domains in a community-based cohort of older adults with hypertension.
2021-04-30
Hypertension (publié)
doi.org
Robotic Object Manipulation with Full-Trajectory GAN-Based Imitation Learning
Haoxu Wang
David Meger
This paper develops a novel generative imitation learning system capable of capturing the distribution of expert demonstrations in trajector… (voir plus)y space, which allows longer temporal context within complex motion sequences to be captured. While auto-regressive models that model time-steps sequentially can in principle be recursively applied to capture long sequences, there are known issues with learning such models reliably. In contrast, our model represents full trajectories a first-class entities, which has required us to adapt the typical generative adversarial learning architecture. We pair a full-trajectory discriminator with an imitation-inspired generative trajectory model and train these two in adversarial fashion. Our results show that our method matches the performance of existing approaches for simple tasks, in simulation and on real robot deployments. We produce state-of-the-art accuracy in replicating motions that contain long-term dependencies such as pouring.
2021-04-30
Canadian Conference on Computer and Robot Vision (publié)
doi.org
A Strategic Markovian Traffic Equilibrium Model for Capacitated Networks
Maëlle Zimmermann
Emma Frejinger
Patrice Marcotte
In the realm of traffic assignment over a network involving rigid arc capacities, the aim of the present work is to generalize the model of … (voir plus)Marcotte, Nguyen, and Schoeb [Marcotte P, Nguyen S, Schoeb A (2004) A strategic flow model of traffic assignment in static capacitated networks. Oper. Res. 52(2):191–212.] by casting it within a stochastic user equilibrium framework. The strength of the proposed model is to incorporate two sources of stochasticity stemming, respectively, from the users’ imperfect knowledge regarding arc costs (represented by a discrete choice model) and the probability of not accessing saturated arcs. Moreover, the arc-based formulation extends the Markovian traffic equilibrium model of Baillon and Cominetti [Baillon JB, Cominetti R ( 2008 ) Markovian traffic equilibrium. Math. Programming 111(1-2):33–56.] through the explicit consideration of capacities. This paper is restricted to the case of acyclic networks, for which we present solution algorithms and numerical experiments.
2021-04-30
Transportation Science (publié)
doi.org
Deep Generative Models for Galaxy Image Simulations
François Lanusse
Rachel Mandelbaum
Siamak Ravanbakhsh
Chun-Liang Li
Peter Freeman
Barnabás Póczos
Image simulations are essential tools for preparing and validating the analysis of current and future wide-field optical surveys. However, t… (voir plus)he galaxy models used as the basis for these simulations are typically limited to simple parametric light profiles, or use a fairly limited amount of available space-based data. In this work, we propose a methodology based on Deep Generative Models to create complex models of galaxy morphologies that may meet the image simulation needs of upcoming surveys. We address the technical challenges associated with learning this morphology model from noisy and PSF-convolved images by building a hybrid Deep Learning/physical Bayesian hierarchical model for observed images, explicitly accounting for the Point Spread Function and noise properties. The generative model is further made conditional on physical galaxy parameters, to allow for sampling new light profiles from specific galaxy populations. We demonstrate our ability to train and sample from such a model on galaxy postage stamps from the HST/ACS COSMOS survey, and validate the quality of the model using a range of second- and higher-order morphology statistics. Using this set of statistics, we demonstrate significantly more realistic morphologies using these deep generative models compared to conventional parametric models. To help make these generative models practical tools for the community, we introduce GalSim-Hub, a community-driven repository of generative models, and a framework for incorporating generative models within the GalSim image simulation software.
2021-04-29
Monthly Notices of the Royal Astronomical Society (publié)
doi.org
arxiv.org
Editorial: Social Interaction in Neuropsychiatry
Victoria Leong
Danilo Bzdok
Frieder M. Paulus
Kevin Pelphrey
Elizabeth Redcay
Leonhard Schilbach
2021-04-28
Frontiers in Psychiatry (publié)
doi.org
What is Going on Inside Recurrent Meta Reinforcement Learning Agents?
Safa Alver
Doina Precup
Recurrent meta reinforcement learning (meta-RL) agents are agents that employ a recurrent neural network (RNN) for the purpose of"learning a… (voir plus) learning algorithm". After being trained on a pre-specified task distribution, the learned weights of the agent's RNN are said to implement an efficient learning algorithm through their activity dynamics, which allows the agent to quickly solve new tasks sampled from the same distribution. However, due to the black-box nature of these agents, the way in which they work is not yet fully understood. In this study, we shed light on the internal working mechanisms of these agents by reformulating the meta-RL problem using the Partially Observable Markov Decision Process (POMDP) framework. We hypothesize that the learned activity dynamics is acting as belief states for such agents. Several illustrative experiments suggest that this hypothesis is true, and that recurrent meta-RL agents can be viewed as agents that learn to act optimally in partially observable environments consisting of multiple related tasks. This view helps in understanding their failure cases and some interesting model-based results reported in the literature.
2021-04-28
ArXiv (prépublication)
arxiv.org
arxiv.org
Recovering the Wedge Modes Lost to 21-cm Foregrounds
Samuel Gagnon-Hartman
Yue Cui
Adrian Liu
Siamak Ravanbakhsh
One of the critical challenges facing imaging studies of the 21-cm signal at the Epoch of Reionization (EoR) is the separation of astrophysi… (voir plus)cal foreground contamination. These foregrounds are known to lie in a wedge-shaped region of
2021-04-21
Monthly Notices of the Royal Astronomical Society (inconnu)
doi.org
arxiv.org