Publications

Discourse-Aware Unsupervised Summarization for Long Scientific Documents
Yue Dong
Andrei Mircea
Jackie CK Cheung
2021-03-31
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (published)
doi.org
Generative lesion pattern decomposition of cognitive impairment after stroke
Anna K. Bonkhoff
Jae-Sung Lim
Hee-Joon Bae
Nick A. Weaver
Hugo J. Kuijf
J. Matthijs Biesbroek
Natalia S. Rost
Danilo Bzdok
Cognitive impairment is a frequent and disabling sequela of stroke. There is however incomplete understanding of how lesion topographies in … (see more)the left and right cerebral hemisphere brain interact to cause distinct cognitive deficits. We integrated machine learning and Bayesian hierarchical modelling to enable a hemisphere-aware analysis of 1080 acute ischaemic stroke patients with deep profiling ∼3 months after stroke. We show the relevance of the left hemisphere in the prediction of language and memory assessments and relevance of the right hemisphere in the prediction of visuospatial functioning. Global cognitive impairments were equally well predicted by lesion topographies from both sides. Damage to the hippocampal and occipital regions on the left was particularly informative about lost naming and memory functions, while damage to these regions on the right was linked to lost visuospatial functioning. Global cognitive impairment was predominantly linked to lesioned tissue in the supramarginal and angular gyrus, the post-central gyrus as well as the lateral occipital and opercular cortices of the left hemisphere. Hence, our analysis strategy uncovered that lesion patterns with unique hemispheric distributions are characteristic of how cognitive capacity is lost due to ischaemic brain tissue damage.
2021-03-31
Brain Communications (published)
doi.org
Smart about medications (SAM): a digital solution to enhance medication management following hospital discharge
Santiago Márquez Fosser
Nadar Mahmoud
Bettina Habib
Daniala L. Weir
Fiona Chan
Rola El Halabieh
Jeanne Vachon
Manish Thakur
Thai Tran
Melissa Bustillo
Caroline Beauchamp
André Bonnici
David L. Buckeridge
Robyn Tamblyn
To outline the development of a software solution to improve medication management after hospital discharge, including its design, data sour… (see more)ces, intrinsic features, and to evaluate the usability and the perception of use by end-users. Patients were directly involved in the development using a User Center Design (UCD) approach. We conducted usability interviews prior to hospital discharge, before a user started using the application. A technology acceptance questionnaire was administered to evaluate user self-perception after 2 weeks of use. The following features were developed; pill identification, patient-friendly drug information leaflet, side effect checker, and interaction checker, adherence monitoring and alerts, weekly medication schedule, daily pill reminders, messaging service, and patient medication reviews. The usability interviews show a 98.3% total success rate for all features, severity (on a scale of 1–4) 1.4 (SD 0.79). Regarding the self-perception of use (1–7 agreement scale) the 3 highest-rated domains were: (1) perceived ease of use 5.65 (SD 2.02), (2) output quality 5.44 (SD 1.65), and (3) perceived usefulness 5.29 (SD 2.11). Many medication management apps solutions have been created and most of them have not been properly evaluated. SAM (Smart About Medications) includes the user perspective, integration between a province drug database and the pharmacist workflow in real time. Its features are not limited to maintaining a medication list through manual entry. We can conclude after evaluation that the application is usable and has been self-perceived as easy to use by end-users. Future studies are required to assess the health benefits associated with its use.
2021-03-31
JAMIA Open (published)
doi.org
Touch-based Curiosity for Sparse-Reward Tasks
Sai Rajeswar
Cyril Ibrahim
Nitin Surya
Florian Golemo
David Vázquez
Aaron Courville
Pedro O. Pinheiro
2021-03-31
ArXiv (preprint)
arxiv.org
arxiv.org
All in This Together? A Preregistered Report on Deservingness of Government Aid During the COVID-19 Pandemic
Aengus Bridgman
Eric Merkley
Peter John Loewen
Taylor Owen
Derek Ruths
2021-03-30
Journal of Experimental Political Science (published)
doi.org
Loneliness and Neurocognitive Aging
R. Nathan Spreng
Danilo Bzdok
2021-03-28
Advances in Geriatric Medicine and Research (published)
doi.org
Mapping gene transcription and neurocognition across human neocortex
Justine Y. Hansen
Ross D. Markello
Jacob W. Vogel
Jakob Seidlitz
Danilo Bzdok
Bratislav Misic
2021-03-24
Nature Human Behaviour (published)
doi.org
Evaluating the Integration of One Health in Surveillance Systems for Antimicrobial Use and Resistance: A Conceptual Framework
Cécile Aenishaenslin
Barbara Häsler
André Ravel
E. Jane Parmley
Sarah Mediouni
Houda Bennani
Katharina D. C. Stärk
David L. Buckeridge
It is now widely acknowledged that surveillance of antimicrobial resistance (AMR) must adopt a “One Health” (OH) approach to successfull… (see more)y address the significant threats this global public health issue poses to humans, animals, and the environment. While many protocols exist for the evaluation of surveillance, the specific aspect of the integration of a OH approach into surveillance systems for AMR and antimicrobial Use (AMU), suffers from a lack of common and accepted guidelines and metrics for its monitoring and evaluation functions. This article presents a conceptual framework to evaluate the integration of OH in surveillance systems for AMR and AMU, named the Integrated Surveillance System Evaluation framework (ISSE framework). The ISSE framework aims to assist stakeholders and researchers who design an overall evaluation plan to select the relevant evaluation questions and tools. The framework was developed in partnership with the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS). It consists of five evaluation components, which consider the capacity of the system to: [1] integrate a OH approach, [2] produce OH information and expertise, [3] generate actionable knowledge, [4] influence decision-making, and [5] positively impact outcomes. For each component, a set of evaluation questions is defined, and links to other available evaluation tools are shown. The ISSE framework helps evaluators to systematically assess the different OH aspects of a surveillance system, to gain comprehensive information on the performance and value of these integrated efforts, and to use the evaluation results to refine and improve the surveillance of AMR and AMU globally.
2021-03-23
Frontiers in Veterinary Science (published)
doi.org
Beyond Correlation versus Causation: Multi-brain Neuroscience Needs Explanation
Quentin Moreau
Guillaume Dumas
2021-03-18
Trends in Cognitive Sciences (published)
doi.org
Factors influencing consumer choice: a study of apparel and sustainable cues from Canadian and Indian consumers’ perspectives
Osmud Rahman
Benjamin C. M. Fung
Devender Kharb
2021-03-18
International Journal of Fashion Design, Technology and Education (published)
doi.org
Implicit Regularization via Neural Feature Alignment
Aristide Baratin
Thomas George
César Laurent
R Devon Hjelm
Guillaume Lajoie
Pascal Vincent
Simon Lacoste-Julien
We approach the problem of implicit regularization in deep learning from a geometrical viewpoint. We highlight a regularization effect induc… (see more)ed by a dynamical alignment of the neural tangent features introduced by Jacot et al, along a small number of task-relevant directions. This can be interpreted as a combined mechanism of feature selection and compression. By extrapolating a new analysis of Rademacher complexity bounds for linear models, we motivate and study a heuristic complexity measure that captures this phenomenon, in terms of sequences of tangent kernel classes along optimization paths.
2021-03-17
International Conference on Artificial Intelligence and Statistics (unknown)
doi.org
proceedings.mlr.press
Intention estimation and controllable behaviour models for traffic merges
Aditya Mahajan
Takayasu Kumano
Yuji Yasui
This work focuses on decision making for automated driving vehicles in interaction rich scenarios like traffic merges in a flexibly assertiv… (see more)e yet safe manner. We propose a Q-learning based approach, that takes in active intention inferences as additional inputs besides the directly observed state inputs. The outputs of Q-function are processed to select a decision by a modulation function, which can control how assertively or defensively the agent behaves.
2021-03-17
SICE Journal of Control Measurement and System Integration (published)
doi.org