Publications

Evaluating Montréal’s harm reduction interventions for people who inject drugs: protocol for observational study and cost-effectiveness analysis
Dimitra Panagiotoglou
Michal Abrahamowicz
David L Buckeridge
J Jaime Caro
Eric Latimer
Mathieu Maheu-Giroux
Erin C Strumpf
The main harm reduction interventions for people who inject drugs (PWID) are supervised injection facilities, needle and syringe programmes … (voir plus)and opioid agonist treatment. Current evidence supporting their implementation and operation underestimates their usefulness by excluding skin, soft tissue and vascular infections (SSTVIs) and anoxic/toxicity-related brain injury from cost-effectiveness analyses (CEA). Our goal is to conduct a comprehensive CEA of harm reduction interventions in a setting with a large, dispersed, heterogeneous population of PWID, and include prevention of SSTVIs and anoxic/toxicity-related brain injury as measures of benefit in addition to HIV, hepatitis C and overdose morbidity and mortalities averted. This protocol describes how we will develop an open, retrospective cohort of adult PWID living in Québec between 1 January 2009 and 31 December 2020 using administrative health record data. By complementing this data with non-linkable paramedic dispatch records, regional monthly needle and syringe dispensation counts and repeated cross-sectional biobehavioural surveys, we will estimate the hazards of occurrence and the impact of Montréal’s harm reduction interventions on the incidence of drug-use-related injuries, infections and deaths. We will synthesise results from our empirical analyses with published evidence to simulate infections and injuries in a hypothetical population of PWID in Montréal under different intervention scenarios including current levels of use and scale-up, and assess the cost-effectiveness of each intervention from the public healthcare payer’s perspective. This study was approved by McGill University’s Institutional Review Board (Study Number: A08-E53-19B). We will work with community partners to disseminate results to the public and scientific community via scientific conferences, a publicly accessible report, op-ed articles and open access peer-reviewed journals.
2021-09-30
BMJ Open (publié)
doi.org
FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters
Yuwei Cheng
Jiannan Zhu
Mengxin Jiang
Jie Fu
Changsong Pang
Peidong Wang
Kris Sankaran
Olawale Moses Onabola
Yimin Liu
Dianbo Liu
Yoshua Bengio
Marine debris is severely threatening the marine lives and causing sustained pollution to the whole ecosystem. To prevent the wastes from ge… (voir plus)tting into the ocean, it is helpful to clean up the floating wastes in inland waters using the autonomous cleaning devices like unmanned surface vehicles. The cleaning efficiency relies on a high-accurate and robust object detection system. However, the small size of the target, the strong light reflection over water surface, and the reflection of other objects on bank-side all bring challenges to the vision-based object detection system. To promote the practical application for autonomous floating wastes cleaning, we present FloW†, the first dataset for floating waste detection in inland water areas. The dataset consists of an image sub-dataset FloW-Img and a multimodal sub-dataset FloW-RI which contains synchronized millimeter wave radar data and images. Accurate annotations for images and radar data are provided, supporting floating waste detection strategies based on image, radar data, and the fusion of two sensors. We perform several baseline experiments on our dataset, including vision-based and radar-based detection methods. The results show that, the detection accuracy is relatively low and floating waste detection still remains a challenging task.
2021-09-30
IEEE International Conference on Computer Vision (publié)
doi.org
Generative Compositional Augmentations for Scene Graph Prediction
Boris Knyazev
Harm de Vries
Cătălina Cangea
Graham W. Taylor
Aaron Courville
Eugene Belilovsky
Inferring objects and their relationships from an image in the form of a scene graph is useful in many applications at the intersection of v… (voir plus)ision and language. We consider a challenging problem of compositional generalization that emerges in this task due to a long tail data distribution. Current scene graph generation models are trained on a tiny fraction of the distribution corresponding to the most frequent compositions, e.g. . However, test images might contain zero- and few-shot compositions of objects and relationships, e.g. . Despite each of the object categories and the predicate (e.g. 'on') being frequent in the training data, the models often fail to properly understand such unseen or rare compositions. To improve generalization, it is natural to attempt increasing the diversity of the training distribution. However, in the graph domain this is non-trivial. To that end, we propose a method to synthesize rare yet plausible scene graphs by perturbing real ones. We then propose and empirically study a model based on conditional generative adversarial networks (GANs) that allows us to generate visual features of perturbed scene graphs and learn from them in a joint fashion. When evaluated on the Visual Genome dataset, our approach yields marginal, but consistent improvements in zero- and few-shot metrics. We analyze the limitations of our approach indicating promising directions for future research.
2021-09-30
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (publié)
doi.org
arxiv.org
Inter-Brain Synchronization: From Neurobehavioral Correlation to Causal Explanation
Guillaume Dumas
2021-09-30
International Journal of Psychophysiology (publié)
doi.org
Normalizing automatic spinal cord cross-sectional area measures
S. Bédard
J. Cohen-Adad
Spinal cord cross-sectional area (CSA) is a relevant biomarker to assess spinal cord atrophy in various neurodegenerative diseases. However,… (voir plus) the considerable inter-subject variability among healthy participants currently limits its usage. Previous studies explored factors contributing to the variability, yet the normalization models were based on a relatively limited number of participants (typically < 300 participants), required manual intervention, and were not implemented in an open-access comprehensive analysis pipeline. Another limitation is related to the imprecise prediction of the spinal levels when using vertebral levels as a reference; a question never addressed before in the search for a normalization method. In this study we implemented a method to measure CSA automatically from a spatial reference based on the central nervous system (the pontomedullary junction, PMJ), we investigated various factors to explain variability, and we developed normalization strategies on a large cohort (N=804). Cervical spinal cord CSA was computed on T1w MRI scans for 804 participants from the UK Biobank database. In addition to computing cross-sectional at the C2-C3 vertebral disc, it was also measured at 64 mm caudal from the PMJ. The effect of various biological, demographic and anatomical factors was explored by computing Pearson’s correlation coefficients. A stepwise linear regression found significant predictors; the coefficients of the best fit model were used to normalize CSA. The correlation between CSA measured at C2-C3 and using the PMJ was y = 0.98 x + 1.78 ( R 2 = 0.97). The best normalization model included thalamus volume, brain volume, sex and interaction between brain volume and sex. With this model, the coefficient of variation went down from 10.09% (without normalization) to 8.59%, a reduction of 14.85%. In this study we identified factors explaining inter-subject variability of spinal cord CSA over a large cohort of participants, and developed a normalization model to reduce the variability. We implemented an approach, based on the PMJ, to measure CSA to overcome limitations associated with the vertebral reference. This approach warrants further validation, especially in longitudinal cohorts. The PMJ-based method and normalization models are readily available in the Spinal Cord Toolbox.
2021-09-30
bioRxiv (prépublication)
doi.org
Reward is enough
David Silver
Satinder Singh
Doina Precup
Richard S. Sutton
2021-09-30
Artificial Intelligence (publié)
doi.org
Season-Based Occupancy Prediction in Residential Buildings Using Machine Learning Models
Bowen Yang
Fariborz Haghighat
Benjamin C. M. Fung
Karthik Panchabikesan
2021-09-30
e-Prime (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 C. M. Fung
2021-09-30
Energy (publié)
doi.org
Weighted automata are compact and actively learnable
Artem Kaznatcheev
Prakash Panangaden
2021-09-30
Information Processing Letters (publié)
doi.org
arxiv.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.
2021-09-29
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 Hamelin
Carmen Lia Murall
Raphaël Poujol
Jean-Christophe Grenier
Martin Smith
Etienne Caron
Morgan Craig
Jesse Shapiro
Guy Wolf
Smita Krishnaswamy
Julie G. 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.
2021-09-28
BioRxiv (prépublication)
doi.org
Promoting and Optimizing the Use of 3D-Printed Objects in Spontaneous Recognition Memory Tasks in Rodents: A Method for Improving Rigor and Reproducibility
Mehreen Inayat
Arely Cruz-Sanchez
Hayley H. A. Thorpe
Jude A. Frie
Blake Aaron Richards
Jibran Y. Khokhar
Maithe Arruda-Carvalho
2021-09-28
eNeuro (publié)
doi.org