Publications

Public Perspectives on Exposure Notification Apps: A Patient and Citizen Co-Designed Study
Esli Osmanlliu
Jesseca Paquette
Maria Alejandra Rodriguez Duarte
Sylvain Bédard
Nathalie de Marcellis-Warin
Majlinda Zhegu
Catherine Régis
Marie-Eve Bouthillier
Annie-Danielle Grenier
Paul Lewis
Marie-Pascale Pomey
Canada deployed a digital exposure notification app (COVID Alert) as a strategy to support manual contact tracing. Our aims are to (1) asses… (voir plus)s the use, knowledge, and concerns of the COVID Alert app, (2) identify predictors of app downloads, and (3) develop strategies to promote social acceptability. A 36-item questionnaire was co-designed by 12 citizens and patients partnered with 16 academic researchers and was distributed in the province of Québec, Canada, from May 27 to 28 June 2021. Of 959 respondents, 43% had downloaded the app. Messaging from government sources constituted the largest influence on app download. Infrequent social contacts and perceived app inefficacy were the main reasons not to download the app. Cybersecurity, data confidentiality, loss of privacy, and geolocation were the most frequent concerns. Nearly half of the respondents inaccurately believed that the app used geolocation. Most respondents supported citizen involvement in app development. The identified predictors for app uptake included nine characteristics. In conclusion, this project highlights four key themes on how to promote the social acceptability of such tools: (1) improved communication and explanation of key app characteristics, (2) design features that incentivize adoption, (3) inclusive socio-technical features, and (4) upstream public partnership in development and deployment.
2022-04-29
Journal of Personalized Medicine (publié)
doi.org
GANSpiration: Balancing Targeted and Serendipitous Inspiration in User Interface Design with Style-Based Generative Adversarial Network
Mohammad Amin Mozaffari
Xinyuan Zhang
Jinghui Cheng
Jin L.C. Guo
Inspiration from design examples plays a crucial role in the creative process of user interface design. However, current tools and technique… (voir plus)s that support inspiration usually only focus on example browsing with limited user control or similarity-based example retrieval, leading to undesirable design outcomes such as focus drift and design fixation. To address these issues, we propose the GANSpiration approach that suggests design examples for both targeted and serendipitous inspiration, leveraging a style-based Generative Adversarial Network. A quantitative evaluation revealed that the outputs of GANSpiration-based example suggestion approaches are relevant to the input design, and at the same time include diverse instances. A user study with professional UI/UX practitioners showed that the examples suggested by our approach serve as viable sources of inspiration for overall design concepts and specific design elements. Overall, our work paves the road of using advanced generative machine learning techniques in supporting the creative design practice.
2022-04-28
CHI Conference on Human Factors in Computing Systems (publié)
doi.org
arxiv.org
Determinants of technology adoption and continued use among cognitively impaired older adults: a qualitative study
Samantha Dequanter
Maaike Fobelets
Iris Steenhout
Marie-Pierre Gagnon
Anne Bourbonnais
S. A. Rahimi
Ronald Buyl
Ellen Gorus
2022-04-27
BMC Geriatrics (publié)
doi.org
Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma
Elizabeth George
Elizabeth Flagg
Kuan-chun Chang
Hai-Yang Bai
H. Aerts
Martin Vallières
David A. Reardon
R.Y. Huang
BACKGROUND AND PURPOSE: Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of t… (voir plus)reatment-related changes with tumor progression. Our purpose was to determine whether MR imaging radiomics-based machine learning can predict progression-free survival and overall survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy. MATERIALS AND METHODS: Post hoc analysis was performed of a multicenter trial on the efficacy of durvalumab in glioblastoma (n = 113). Radiomics tumor features on pretreatment and first on-treatment time point MR imaging were extracted. The random survival forest algorithm was applied to clinical and radiomics features from pretreatment and first on-treatment MR imaging from a subset of trial sites (n = 60–74) to train a model to predict long overall survival and progression-free survival and was tested externally on data from the remaining sites (n = 29–43). Model performance was assessed using the concordance index and dynamic area under the curve from different time points. RESULTS: The mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index  = 0.472–0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692–0.750) and progression-free survival (concordance index = 0.680–0.715). CONCLUSIONS: A radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.
2022-04-27
American Journal of Neuroradiology (publié)
doi.org
Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece
Luke Y. Prince
Roy Henha Eyono
Ellen Boven
Arna Ghosh
Joe Pemberton
Franz Scherr
Claudia Clopath
Rui Ponte Costa
Wolfgang Maass
Blake A. Richards
Cristina Savin
Katharina Anna Wilmes
We provide a brief review of the common assumptions about biological learning with findings from experimental neuroscience and contrast them… (voir plus) with the efficiency of gradient-based learning in recurrent neural networks. The key issues discussed in this review include: synaptic plasticity, neural circuits, theory-experiment divide, and objective functions. We conclude with recommendations for both theoretical and experimental neuroscientists when designing new studies that could help bring clarity to these issues.
2022-04-26
Neurons, Behavior, Data Analysis, and Theory (publié)
doi.org
arxiv.org
Don't Freeze Your Embedding: Lessons from Policy Finetuning in Environment Transfer
Victoria Dean
Daniel Toyama
Doina Precup
A common occurrence in reinforcement learning (RL) research is making use of a pretrained vision stack that converts image observations to l… (voir plus)atent vectors. Using a visual embedding in this way leaves open questions, though: should the vision stack be updated with the policy? In this work, we evaluate the effectiveness of such decisions in RL transfer settings. We introduce policy update formulations for use after pretraining in a different environment and analyze the performance of such formulations. Through this evaluation, we also detail emergent metrics of benchmark suites and present results on Atari and AndroidEnv.
2022-04-26
ICLR.cc/2022/Workshop/GPL (poster)
openreview.net
openreview.net
Embedding Signals on Graphs with Unbalanced Diffusion Earth Mover's Distance
Alexander Tong
Guillaume Huguet
Dennis Shung
Amine Natik
Manik Kuchroo
Guillaume Lajoie
Guy Wolf
Smita Krishnaswamy
In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observation… (voir plus)s in many domains. Further, in many cases the target entities for analysis are actually signals on such graphs. We propose to compare and organize such datasets of graph signals by using an earth mover's distance (EMD) with a geodesic cost over the underlying graph. Typically, EMD is computed by optimizing over the cost of transporting one probability distribution to another over an underlying metric space. However, this is inefficient when computing the EMD between many signals. Here, we propose an unbalanced graph EMD that efficiently embeds the unbalanced EMD on an underlying graph into an
2022-04-26
IEEE International Conference on Acoustics, Speech and Signal Processing (publié)
doi.org
arxiv.org
Embedding Signals on Graphs with Unbalanced Diffusion Earth Mover's Distance
Alexander Tong
Guillaume Huguet
Dennis Shung
Amine Natik
Manik Kuchroo
Guillaume Lajoie
Guy Wolf
Smita Krishnaswamy
In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observation… (voir plus)s in many domains. Further, in many cases the target entities for analysis are actually signals on such graphs. We propose to compare and organize such datasets of graph signals by using an earth mover's distance (EMD) with a geodesic cost over the underlying graph. Typically, EMD is computed by optimizing over the cost of transporting one probability distribution to another over an underlying metric space. However, this is inefficient when computing the EMD between many signals. Here, we propose an unbalanced graph EMD that efficiently embeds the unbalanced EMD on an underlying graph into an
2022-04-26
IEEE International Conference on Acoustics, Speech, and Signal Processing (publié)
doi.org
arxiv.org
Multi-tract multi-symptom relationships in pediatric concussion
Guido I. Guberman
Sonja Stojanovski
Eman Nishat
Alain Ptito
Danilo Bzdok
Anne Wheeler
Maxime Descoteaux
The heterogeneity of white matter damage and symptoms in concussion has been identified as a major obstacle to therapeutic innovation. In co… (voir plus)ntrast, most diffusion MRI (dMRI) studies on concussion have traditionally relied on group-comparison approaches that average out heterogeneity. To leverage, rather than average out, concussion heterogeneity, we combined dMRI and multivariate statistics to characterize multi-tract multi-symptom relationships. Using cross-sectional data from 306 previously concussed children aged 9–10 from the Adolescent Brain Cognitive Development Study, we built connectomes weighted by classical and emerging diffusion measures. These measures were combined into two informative indices, the first representing microstructural complexity, the second representing axonal density. We deployed pattern-learning algorithms to jointly decompose these connectivity features and 19 symptom measures. Early multi-tract multi-symptom pairs explained the most covariance and represented broad symptom categories, such as a general problems pair, or a pair representing all cognitive symptoms, and implicated more distributed networks of white matter tracts. Further pairs represented more specific symptom combinations, such as a pair representing attention problems exclusively, and were associated with more localized white matter abnormalities. Symptom representation was not systematically related to tract representation across pairs. Sleep problems were implicated across most pairs, but were related to different connections across these pairs. Expression of multi-tract features was not driven by sociodemographic and injury-related variables, as well as by clinical subgroups defined by the presence of ADHD. Analyses performed on a replication dataset showed consistent results. Using a double-multivariate approach, we identified clinically-informative, cross-demographic multi-tract multi-symptom relationships. These results suggest that rather than clear one-to-one symptom-connectivity disturbances, concussions may be characterized by subtypes of symptom/connectivity relationships. The symptom/connectivity relationships identified in multi-tract multi-symptom pairs were not apparent in single-tract/single-symptom analyses. Future studies aiming to better understand connectivity/symptom relationships should take into account multi-tract multi-symptom heterogeneity. Financial support for this work came from a Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research (G.I.G.), an Ontario Graduate Scholarship (S.S.), a Restracomp Research Fellowship provided by the Hospital for Sick Children (S.S.), an Institutional Research Chair in Neuroinformatics (M.D.), as well as a Natural Sciences and Engineering Research Council CREATE grant (M.D.).
2022-04-26
eLife (publié)
doi.org
A Probabilistic Perspective on Reinforcement Learning via Supervised Learning
Alexandre Piché
Rafael Pardinas
David Vázquez
Christopher Pal
2022-04-26
ICLR.cc/2022/Workshop/GPL (poster)
openreview.net
openreview.net
Chunked Autoregressive GAN for Conditional Waveform Synthesis
Max Morrison
Rithesh Kumar
Kundan Kumar
Prem Seetharaman
Aaron Courville
Yoshua Bengio
Conditional waveform synthesis models learn a distribution of audio waveforms given conditioning such as text, mel-spectrograms, or MIDI. Th… (voir plus)ese systems employ deep generative models that model the waveform via either sequential (autoregressive) or parallel (non-autoregressive) sampling. Generative adversarial networks (GANs) have become a common choice for non-autoregressive waveform synthesis. However, state-of-the-art GAN-based models produce artifacts when performing mel-spectrogram inversion. In this paper, we demonstrate that these artifacts correspond with an inability for the generator to learn accurate pitch and periodicity. We show that simple pitch and periodicity conditioning is insufficient for reducing this error relative to using autoregression. We discuss the inductive bias that autoregression provides for learning the relationship between instantaneous frequency and phase, and show that this inductive bias holds even when autoregressively sampling large chunks of the waveform during each forward pass. Relative to prior state-of-the-art GAN-based models, our proposed model, Chunked Autoregressive GAN (CARGAN) reduces pitch error by 40-60%, reduces training time by 58%, maintains a fast generation speed suitable for real-time or interactive applications, and maintains or improves subjective quality.
2022-04-24
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net
ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods
Victor Schmidt
Alexandra Luccioni
Mélisande Teng
Tianyu Zhang
Alexia Reynaud
Sunand Raghupathi
Gautier Cosne
Adrien Juraver
Vahe Vardanyan
Alex Hernández-García
Yoshua Bengio
Climate change is a major threat to humanity, and the actions required to prevent its catastrophic consequences include changes in both poli… (voir plus)cy-making and individual behaviour. However, taking action requires understanding the effects of climate change, even though they may seem abstract and distant. Projecting the potential consequences of extreme climate events such as flooding in familiar places can help make the abstract impacts of climate change more concrete and encourage action. As part of a larger initiative to build a website that projects extreme climate events onto user-chosen photos, we present our solution to simulate photo-realistic floods on authentic images. To address this complex task in the absence of suitable training data, we propose ClimateGAN, a model that leverages both simulated and real data for unsupervised domain adaptation and conditional image generation. In this paper, we describe the details of our framework, thoroughly evaluate components of our architecture and demonstrate that our model is capable of robustly generating photo-realistic flooding.
2022-04-24
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net