Publications

Artificial Intelligence for Detection of Dementia Using Motion Data: A Scoping Review
Lily Puterman-Salzman
Jory Katz
Howard Bergman
Roland Grad
Vladimir Khanassov
Genevieve Gore
Isabelle Vedel
Machelle Wilchesky
Narges Armanfard
Negar Ghourchian
Samira Abbasgholizadeh-Rahimi
Background: Dementia is a neurodegenerative disease resulting in the loss of cognitive and psychological functions. Artificial intelligence … (see more)(AI) may help in detection and screening of dementia; however, little is known in this area. Objectives: The objective of this study was to identify and evaluate AI interventions for detection of dementia using motion data. Method: The review followed the framework proposed by O’Malley’s and Joanna Briggs Institute methodological guidance for scoping reviews. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist for reporting the results. An information specialist performed a comprehensive search from the date of inception until November 2020, in five bibliographic databases: MEDLINE, EMBASE, Web of Science Core Collection, CINAHL, and IEEE Xplore. We included studies aimed at the deployment and testing or implementation of AI interventions using motion data for the detection of dementia among a diverse population, encompassing varying age, sex, gender, economic backgrounds, and ethnicity, extending to their health care providers across multiple health care settings. Studies were excluded if they focused on Parkinson’s or Huntington’s disease. Two independent reviewers screened the abstracts, titles, and then read the full-texts. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. The reference lists of included studies were also screened. Results: After removing duplicates, 2,632 articles were obtained. After title and abstract screening and full-text screening, 839 articles were considered for categorization. The authors categorized the papers into six categories, and data extraction and synthesis was performed on 20 included papers from the motion tracking data category. The included studies assessed cognitive performance (n = 5, 25%); screened dementia and cognitive decline (n = 8, 40%); investigated visual behaviours (n = 4, 20%); and analyzed motor behaviors (n = 3, 15%). Conclusions: We presented evidence of AI systems being employed in the detection of dementia, showcasing the promising potential of motion tracking within this domain. Although some progress has been made in this field recently, there remain notable research gaps that require further exploration and investigation. Future endeavors need to compare AI interventions using motion data with traditional screening methods or other tech-enabled dementia detection mechanisms. Besides, future works should aim at understanding how gender and sex, and ethnic and cultural sensitivity can contribute to refining AI interventions, ensuring they are accessible, equitable, and beneficial across all society.
2023-09-13
Dementia and Geriatric Cognitive Disorders EXTRA (published)
doi.org
A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale
Hao-Jun Michael Shi
Tsung-Hsien Lee
Shintaro Iwasaki
Jose Gallego-Posada
Zhijing Li
Kaushik Rangadurai
Dheevatsa Mudigere
Michael Rabbat
2023-09-12
ArXiv (preprint)
doi.org
arxiv.org
Leveraging ChatGPT to Democratize and Decolonize Global Surgery: Large Language Models for Small Healthcare Budgets
Fabio Botelho
Jean Marie Tshimula
Dan Poenaru
2023-09-10
World Journal of Surgery (published)
doi.org
Local field potentials in human motor and non-motor brain areas encode the direction of upcoming movements: An intracerebral EEG classification study
Etienne Combrisson
Franck Di Rienzo
Anne-Lise Saive
Marcela Perrone-Bertolotti
Juan LP Soto
Philippe Kahane
Jean-Philippe Lachaux
Aymeric Guillot
Karim Jerbi
2023-09-10
bioRxiv (preprint)
doi.org
Neural Causal Structure Discovery from Interventions
Nan Rosemary Ke
Olexa Bilaniuk
Anirudh Goyal
Stefan Bauer
Hugo Larochelle
Bernhard Schölkopf
Michael Curtis Mozer
Chris Pal
Yoshua Bengio
Recent promising results have generated a surge of interest in continuous optimization methods for causal discovery from observational data.… (see more) However, there are theoretical limitations on the identifiability of underlying structures obtained solely from observational data. Interventional data, on the other hand, provides richer information about the underlying data-generating process. Nevertheless, extending and applying methods designed for observational data to include interventions is a challenging problem. To address this issue, we propose a general framework based on neural networks to develop models that incorporate both observational and interventional data. Notably, our method can handle the challenging and realistic scenario where the identity of the intervened upon variable is unknown. We evaluate our proposed approach in the context of graph recovery, both de novo and from a partially-known edge set. Our method achieves strong benchmark results on various structure learning tasks, including structure recovery of synthetic graphs as well as standard graphs from the Bayesian Network Repository.
2023-09-10
TMLR (accepted)
openreview.net
openreview.net
Let Coarse-Grained Resources Be Shared: Mapping Entire Neural Networks on FPGAs
Tzung-Han Juang
Christof Schlaak
Christophe Dubach
2023-09-09
ACM Transactions on Embedded Computing Systems (published)
doi.org
Leveraging World Model Disentanglement in Value-Based Multi-Agent Reinforcement Learning
Zhizun Wang
David Meger
In this paper, we propose a novel model-based multi-agent reinforcement learning approach named Value Decomposition Framework with Disentang… (see more)led World Model to address the challenge of achieving a common goal of multiple agents interacting in the same environment with reduced sample complexity. Due to scalability and non-stationarity problems posed by multi-agent systems, model-free methods rely on a considerable number of samples for training. In contrast, we use a modularized world model, composed of action-conditioned, action-free, and static branches, to unravel the environment dynamics and produce imagined outcomes based on past experience, without sampling directly from the real environment. We employ variational auto-encoders and variational graph auto-encoders to learn the latent representations for the world model, which is merged with a value-based framework to predict the joint action-value function and optimize the overall training objective. We present experimental results in Easy, Hard, and Super-Hard StarCraft II micro-management challenges to demonstrate that our method achieves high sample efficiency and exhibits superior performance in defeating the enemy armies compared to other baselines.
2023-09-08
ArXiv (preprint)
doi.org
arxiv.org
Multidomain Object Detection Framework Using Feature Domain Knowledge Distillation.
Da-Wei Jaw
Shih-Chia Huang
Zhihui Lu
Benjamin Fung
Sy-Yen Kuo
Object detection techniques have been widely studied, utilized in various works, and have exhibited robust performance on images with suffic… (see more)ient luminance. However, these approaches typically struggle to extract valuable features from low-luminance images, which often exhibit blurriness and dim appearence, leading to detection failures. To overcome this issue, we introduce an innovative unsupervised feature domain knowledge distillation (KD) framework. The proposed framework enhances the generalization capability of neural networks across both low-and high-luminance domains without incurring additional computational costs during testing. This improvement is made possible through the integration of generative adversarial networks and our proposed unsupervised KD process. Furthermore, we introduce a region-based multiscale discriminator designed to discern feature domain discrepancies at the object level rather than from the global context. This bolsters the joint learning process of object detection and feature domain distillation tasks. Both qualitative and quantitative assessments shown that the proposed method, empowered by the region-based multiscale discriminator and the unsupervised feature domain distillation process, can effectively extract beneficial features from low-luminance images, outperforming other state-of-the-art approaches in both low-and sufficient-luminance domains.
2023-09-07
IEEE Transactions on Cybernetics (published)
doi.org
Bridging the Gap Between Target Networks and Functional Regularization
Alexandre Piché
Valentin Thomas
Joseph Marino
Gian Maria Marconi
Rafael Pardinas
Chris Pal
Mohammad Emtiyaz Khan
2023-09-06
TMLR (accepted)
doi.org
openreview.net
Cardiomyocyte orientation recovery at micrometer scale reveals long‐axis fiber continuum in heart walls
Drisya Dileep
Tabish A Syed
Tyler FW Sloan
Perundurai S Dhandapany
Kaleem Siddiqi
Minhajuddin Sirajuddin
2023-09-06
The EMBO Journal (published)
doi.org
Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks
Daniel Levy
Sékou-Oumar Kaba
Carmelo Gonzales
Santiago Miret
Siamak Ravanbakhsh
2023-09-06
ArXiv (preprint)
doi.org
arxiv.org
Breaking Barriers to Creative Expression: Co-Designing and Implementing an Accessible Text-to-Image Interface
Atieh Taheri
Mohammad Izadi
Gururaj Shriram
Negar Rostamzadeh
Shaun Kane
Text-to-image generation models have grown in popularity due to their ability to produce high-quality images from a text prompt. One use for… (see more) this technology is to enable the creation of more accessible art creation software. In this paper, we document the development of an alternative user interface that reduces the typing effort needed to enter image prompts by providing suggestions from a large language model, developed through iterative design and testing within the project team. The results of this testing demonstrate how generative text models can support the accessibility of text-to-image models, enabling users with a range of abilities to create visual art.
2023-09-05
ArXiv (preprint)
doi.org
arxiv.org