Publications

Season-Based Occupancy Prediction in Residential Buildings Using Machine Learning Models
Bowen Yang
Fariborz Haghighat
Benjamin Fung
Karthik Panchabikesan
2021-10-01
e-Prime (publié)
doi.org
Small, correlated changes in synaptic connectivity may facilitate rapid motor learning
Barbara Feulner
Matt Perich
Raeed H. Chowdhury
Lee Miller
Juan A. Gallego
Claudia Clopath
2021-10-01
Nature Communications (published)
doi.org
THE EFFECT SIZE OF GENES ON COGNITIVE ABILITIES IS LINKED TO THEIR EXPRESSION ALONG THE MAJOR HIERARCHICAL GRADIENT IN THE HUMAN BRAIN
Sébastien Jacquemont
Guillaume Huguet
Elise Douard
Zohra Saci
Guillaume Dumas
Laura Almasy
David C. Glahn
2021-10-01
European Neuropsychopharmacology (publié)
doi.org
Trade-off Between Accuracy and Fairness of Data-driven Building and Indoor Environment Models: A Comparative Study of Pre-processing Methods
Ying Sun
Fariborz Haghighat
Benjamin Fung
2021-10-01
Energy (publié)
doi.org
Trade-off Between Accuracy and Fairness of Data-driven Building and Indoor Environment Models: A Comparative Study of Pre-processing Methods
Ying Sun
Fariborz Haghighat
Benjamin Fung
2021-10-01
Energy (published)
doi.org
Trajectory-Constrained Deep Latent Visual Attention for Improved Local Planning in Presence of Heterogeneous Terrain
Stefan Wapnick
Travis Manderson
David Meger
Gregory Dudek
We present a reward-predictive, model-based learning method featuring trajectory-constrained visual attention for use in mapless, local visu… (voir plus)al navigation tasks. Our method learns to place visual attention at locations in latent image space which follow trajectories caused by vehicle control actions to later enhance predictive accuracy during planning. Our attention model is jointly optimized by the task-specific loss and additional trajectory-constraint loss, allowing adaptability yet encouraging a regularized structure for improved generalization and reliability. Importantly, visual attention is applied in latent feature map space instead of raw image space to promote efficient planning. We validated our model in visual navigation tasks of planning low turbulence, collision-free trajectories in off-road settings and hill climbing with locking differentials in the presence of slippery terrain. Experiments involved randomized procedural generated simulation and real-world environments. We found our method improved generalization and learning efficiency when compared to no-attention and self-attention alternatives.
2021-10-01
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publié)
doi.org
arxiv.org
Transfer functions: learning about a lagged exposure-outcome association in time-series data
Hiroshi Mamiya
Alexandra M. Schmidt
Erica E. M. Moodie
David Buckeridge
Many population exposures in time-series analysis, including food marketing, exhibit a time-lagged association with population health outcom… (voir plus)es such as food purchasing. A common approach to measuring patterns of associations over different time lags relies on a finite-lag model, which requires correct specification of the maximum duration over which the lagged association extends. However, the maximum lag is frequently unknown due to the lack of substantive knowledge or the geographic variation of lag length. We describe a time-series analytical approach based on an infinite lag specification under a transfer function model that avoids the specification of an arbitrary maximum lag length. We demonstrate its application to estimate the lagged exposure-outcome association in food environmental research: display promotion of sugary beverages with lagged sales.
2021-10-01
medRxiv (prépublication)
doi.org
Graph Neural Networks in Natural Language Processing
Bang Liu
Lingfei Wu
Natural language processing (NLP) and understanding aim to read from unformatted text to accomplish different tasks. While word embeddings l… (voir plus)earned by deep neural networks are widely used, the underlying linguistic and semantic structures of text pieces cannot be fully exploited in these representations. Graph is a natural way to capture the connections between different text pieces, such as entities, sentences, and documents. To overcome the limits in vector space models, researchers combine deep learning models with graph-structured representations for various tasks in NLP and text mining. Such combinations help to make full use of both the structural information in text and the representation learning ability of deep neural networks. In this chapter, we introduce the various graph representations that are extensively used in NLP, and show how different NLP tasks can be tackled from a graph perspective. We summarize recent research works on graph-based NLP, and discuss two case studies related to graph-based text clustering, matching, and multihop machine reading comprehension in detail. Finally, we provide a synthesis about the important open problems of this subfield.
2021-09-30
Deep Learning on Graphs (publié)
doi.org
Data-driven approaches for genetic characterization of SARS-CoV-2 lineages
Fatima Mostefai
Isabel Gamache
Jessie Huang
Arnaud N’Guessan
Justin Pelletier
Ahmad Pesaranghader
David J. Hamelin
Carmen Lia Murall
Raphael Poujol
Jean-Christophe Grenier
Martin Smith
Etienne Caron
Morgan Craig
Jesse Shapiro
Guy Wolf
Smita Krishnaswamy
Julie Hussin
The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19)… (voir plus), has been sequenced at an unprecedented scale, leading to a tremendous amount of viral genome sequencing data. To understand the evolution of this virus in humans, and to assist in tracing infection pathways and designing preventive strategies, we present a set of computational tools that span phylogenomics, population genetics and machine learning approaches. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic, using 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets, enabling real-time analyses. Furthermore, time series change of Tajima’s D provides a powerful metric of population expansion. Unsupervised learning techniques further highlight key steps in variant detection and facilitate the study of the role of this genomic variation in the context of SARS-CoV-2 infection, with Multiscale PHATE methodology identifying fine-scale structure in the SARS-CoV-2 genetic data that underlies the emergence of key lineages. The computational framework presented here is useful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of worldwide populations of humans and other organisms.
2021-09-29
bioRxiv (prépublication)
doi.org
Estimating the lagged effect of price discounting: a time-series study using transaction data of sugar sweetened beverages.
Hiroshi Mamiya
Alexandra M. Schmidt
Erica E. M. Moodie
David Buckeridge
2021-09-28
medRxiv (prépublication)
doi.org
Guidelines for the Computational Testing of Machine Learning approaches to Vehicle Routing Problems
Luca Accorsi
Andrea Lodi
Daniele Vigo
2021-09-28
ArXiv (preprint)
doi.org
arxiv.org
Latent Attention Augmentation for Robust Autonomous Driving Policies
Ran Cheng
Christopher Agia
Florian Shkurti
David Meger
Gregory Dudek
Model-free reinforcement learning has become a viable approach for vision-based robot control. However, sample complexity and adaptability t… (voir plus)o domain shifts remain persistent challenges when operating in high-dimensional observation spaces (images, LiDAR), such as those that are involved in autonomous driving. In this paper, we propose a flexible framework by which a policy’s observations are augmented with robust attention representations in the latent space to guide the agent’s attention during training. Our method encodes local and global descriptors of the augmented state representations into a compact latent vector, and scene dynamics are approximated by a recurrent network that processes the latent vectors in sequence. We outline two approaches for constructing attention maps; a supervised pipeline leveraging semantic segmentation networks, and an unsupervised pipeline relying only on classical image processing techniques. We conduct our experiments in simulation and test the learned policy against varying seasonal effects and weather conditions. Our design decisions are supported in a series of ablation studies. The results demonstrate that our state augmentation method both improves learning efficiency and encourages robust domain adaptation when compared to common end-to-end frameworks and methods that learn directly from intermediate representations.
2021-09-27
IEEE/RJS International Conference on Intelligent RObots and Systems (publié)
doi.org