From Precision Medicine to Precision Convergence for Multilevel Resilience—The Aging Brain and Its Social Isolation
Laurette Dubé
Patricia P. Silveira
Daiva E. Nielsen
Spencer Moore
Catherine Paquet
J. Miguel Cisneros-Franco
Gina Kemp
Bärbel Knauper
Yu Ma
Mehmood Khan
Gillian Bartlett-Esquilant
Alan C. Evans
Lesley K. Fellows
Jorge L. Armony
R. Nathan Spreng
Jian-Yun Nie
Shawn T. Brown
Georg Northoff
Danilo Bzdok
Citation: Dubé L, Silveira PP, Nielsen DE, Moore S, Paquet C, Cisneros-Franco JM, Kemp G, Knauper B, Ma Y, Khan M, Bartlett-Esquilant G, Ev… (see more)ans AC, Fellows LK, Armony JL, Spreng RN, Nie J-Y, Brown ST, Northoff G and Bzdok D (2022) From Precision Medicine to Precision Convergence for Multilevel Resilience—The Aging Brain and Its Social Isolation. Front. Public Health 10:720117. doi: 10.3389/fpubh.2022.720117 From Precision Medicine to Precision Convergence for Multilevel Resilience—The Aging Brain and Its Social Isolation
2022-07-05
Frontiers in Public Health (published)
doi.org
Incentivized Security-Aware Computation Offloading for Large-Scale Internet of Things Applications
Talal Halabi
Adel Abusitta
Glaucio H.S. Carvalho
Benjamin Fung
2022-07-05
2022 7th International Conference on Smart and Sustainable Technologies (SpliTech) (published)
doi.org
Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge
Ian Porada
Alessandro Sordoni
Jackie Cheung
2022-07-01
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (published)
doi.org
arxiv.org
DyAdvDefender: An instance-based online machine learning model for perturbation-trial-based black-box adversarial defense
Miles Q. Li
Benjamin Fung
Philippe Charland
2022-07-01
Information Sciences (published)
doi.org
Exploring the roles of artificial intelligence in surgical education: A scoping review
Elif Bilgic
Andrew Gorgy
Alison Yang
Michelle Cwintal
Hamed Ranjbar
Kalin Kahla
Dheeksha Reddy
Kexin Li
Helin Ozturk
Eric Zimmermann
Andrea Quaiattini
Samira Abbasgholizadeh-Rahimi
Dan Poenaru
Jason M. Harley
2022-07-01
The American Journal of Surgery (published)
doi.org
IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control
François-Xavier Devailly
Denis Larocque
Laurent Charlin
Scaling adaptive traffic signal control involves dealing with combinatorial state and action spaces. Multi-agent reinforcement learning atte… (see more)mpts to address this challenge by distributing control to specialized agents. However, specialization hinders generalization and transferability, and the computational graphs underlying neural-network architectures—dominating in the multi-agent setting—do not offer the flexibility to handle an arbitrary number of entities which changes both between road networks, and over time as vehicles traverse the network. We introduce Inductive Graph Reinforcement Learning (IG-RL) based on graph-convolutional networks which adapts to the structure of any road network, to learn detailed representations of traffic signal controllers and their surroundings. Our decentralized approach enables learning of a transferable-adaptive-traffic-signal-control policy. After being trained on an arbitrary set of road networks, our model can generalize to new road networks and traffic distributions, with no additional training and a constant number of parameters, enabling greater scalability compared to prior methods. Furthermore, our approach can exploit the granularity of available data by capturing the (dynamic) demand at both the lane level and the vehicle level. The proposed method is tested on both road networks and traffic settings never experienced during training. We compare IG-RL to multi-agent reinforcement learning and domain-specific baselines. In both synthetic road networks and in a larger experiment involving the control of the 3,971 traffic signals of Manhattan, we show that different instantiations of IG-RL outperform baselines.
2022-07-01
IEEE Transactions on Intelligent Transportation Systems (published)
doi.org
arxiv.org
Leishmania parasites exchange drug-resistance genes through extracellular vesicles
Noélie Douanne
George Dong
Atia Amin
Lorena Bernardo
Mathieu Blanchette
David Langlais
Martin Olivier
Christopher Fernandez-Prada
2022-07-01
Cell Reports (published)
doi.org
Naming Autism in the Right Context
Andres Roman-Urrestarazu
Guillaume Dumas
Varun Warrier
2022-07-01
JAMA Pediatrics (published)
doi.org
R-MelNet: Reduced Mel-Spectral Modeling for Neural TTS
Kyle Kastner
Aaron Courville
2022-06-30
ArXiv (preprint)
doi.org
arxiv.org
A guided multiverse study of neuroimaging analyses
Jessica Dafflon
Pedro F. Da Costa
František Váša
Ricardo Pio Monti
Danilo Bzdok
Peter J. Hellyer
Federico Turkheimer
Jonathan Smallwood
Emily J. H. Jones
Robert Leech
2022-06-29
Nature Communications (published)
doi.org
Integrating Equity, Diversity, and Inclusion throughout the lifecycle of Artificial Intelligence in health
Milka Nyariro
Elham Emami
Samira Abbasgholizadeh-Rahimi
Health care systems are the infrastructures that are put together to deliver health and social services to the population at large. These or… (see more)ganizations are increasingly applying Artificial Intelligence (AI) to improve the efficiency and effectiveness of health and social care. Unfortunately, both health care systems and AI are confronted with a lack of Equity, Diversity, and Inclusion (EDI). This short paper focuses on the importance of integrating EDI concepts throughout the life cycle of AI in health. We discuss the risks that the lack of EDI in the design, development and implementation of AI-based tools might have on the already marginalized communities and populations in the healthcare setting. Moreover, we argue that integrating EDI principles and practice throughout the lifecycle of AI in health has an important role in achieving health equity for all populations. Further research needs to be conducted to explore how studies in AI-health have integrated.
2022-06-29
13th Augmented Human International Conference (published)
doi.org
Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract)
Renaud Bernatchez
Audrey Durand
Flavie Lavoie-Cardinal
2022-06-28
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org