Publications

The epidemiological impact of the Canadian COVID Alert app

Shuo Sun

Mairead Shaw

Erica E.M. Moodie

We analyzed the effectiveness of the Canadian COVID Alert app on reducing COVID-19 infections and deaths due to the COVID-19 virus. … (see more) Two separate but complementary approaches were taken. First, we undertook a comparative study to assess how the adoption and usage of the COVID Alert app compared to those of similar apps deployed in other regions. Next, we used data from the COVID Alert server and a range of plausible parameter values to estimate the numbers of infections and deaths averted in Canada using a model that combines information on number of notifications, secondary attack rate, expected fraction of transmissions that could be prevented, quarantine effectiveness, and expected size of the full transmission chain in the absence of exposure notification. The comparative analysis revealed that the COVID Alert app had among the lowest adoption levels among apps that reported usage. Our model indicates that use of the COVID Alert app averted between 6284 and 10,894 infections across the six Canadian provinces where app usage was highest during the March–July 2021 period. This range is equivalent to 1.6–2.9% of the total recorded infections across Canada in that time. Using province-specific case fatality rates, 57–101 deaths were averted during the same period. The number of cases and deaths averted was greatest in Ontario, whereas the proportion of cases and deaths averted was greatest in Newfoundland and Labrador. App impact measures were reported so rarely and so inconsistently by other regions that the relative assessment of impact is inconclusive. While the nationwide rates are low, provinces with widespread adoption of the app showed high ratios of averted cases and deaths (upper bound was greater than 60% of averted cases). This finding suggests that the COVID Alert app, when adopted at sufficient levels, can be an effective public health tool for combatting a pandemic such as COVID-19.

2022-01-04

Canadian Journal of Public Health (published)

doi.org

Analysis of the Human Pineal Proteome by Mass Spectrometry

Mariette Matondo

Guillaume Dumas

Erik Maronde

2021-12-31

Methods in molecular biology (published)

doi.org

Appendix: On the Expressivity of Markov Reward

David Abel

Will Dabney

Anna Harutyunyan

Mark K. Ho

Michael L. Littman

Doina Precup

Satinder Singh

(Q1) What does it mean for Bob to *solve* one of these tasks? That is, if Alice chooses a SOAP, PO, or TO for Bob to learn to solve, when ca… (see more)n Alice determine Bob has solved the task? A: Bob can be said to be doing better on a given task if his behavior improves, as is typical in evaluating behavior under reward. The difference with SOAPs, POs, and TOs is that we measure improvement relative to the task rather than reward. For instance, given a SOAP, we might say that Bob has solved the task once he has found one of the good policies, and we might measure Bob’s progress on a task in terms of the distance of his greedy policy to one of the good policies (as done in our learning experiments). The same reasoning applies to POs and TOs: Bob is doing better on a task in so far as his greedy policy (or trajectories) is (are) higher up the ordering.

2021-12-31

(published)

www.semanticscholar.org

Approximate information state for approximate planning and reinforcement learning in partially observed systems

Jayakumar Subramanian

Amit Sinha

Raihan Seraj

Aditya Mahajan

We propose a theoretical framework for approximate planning and learning in partially observed systems. Our framework is based on the fundam… (see more)ental notion of information state. We provide two equivalent definitions of information state---i) a function of history which is sufficient to compute the expected reward and predict its next value; ii) equivalently, a function of the history which can be recursively updated and is sufficient to compute the expected reward and predict the next observation. An information state always leads to a dynamic programming decomposition. Our key result is to show that if a function of the history (called approximate information state (AIS)) approximately satisfies the properties of the information state, then there is a corresponding approximate dynamic program. We show that the policy computed using this is approximately optimal with bounded loss of optimality. We show that several approximations in state, observation and action spaces in literature can be viewed as instances of AIS. In some of these cases, we obtain tighter bounds. A salient feature of AIS is that it can be learnt from data. We present AIS based multi-time scale policy gradient algorithms. and detailed numerical experiments with low, moderate and high dimensional environments.

2021-12-31

Journal of Machine Learning Research (published)

arxiv.org

Approximate minimization of weighted tree automata

Borja Balle

Guillaume Rabusseau

2021-12-31

Information and Computation (published)

doi.org

α-ReQ: Assessing representation quality by measuring eigenspectrum decay

Kumar Krishna Agrawal

Arnab Kumar Mondal

Arna Ghosh

Blake A. Richards

2021-12-31

Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (published)

openreview.net

Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceability

Avinash Bhat

Austin Coursey

Grace Hu

Sixian Li

Nadia Nahar

Shurui Zhou

Christian KÃ¤stner

Jin L.C. Guo

2021-12-31

(published)

doi.org

Assessing the Quality of Direct-to-Consumer Teleconsultation Services in Canada

Jean Noel Nikiema

Eleah Stringer

Marie-Pierre Moreault

Priscille Pana

Marco Laverdiere

Catherine Régis

Jean-Louis Denis

Béatrice Godard

Mylaine Breton

Guy Paré

Aviv Shachak

Claudia Lai

Elizabeth M. Borycki

Andre W. Kushniruk

Aude Motulsky

The objective of this study was to describe and assess the quality of the direct-to-consumer medical teleconsultation landscape in three Can… (see more)adian provinces. An environmental scan of primary care teleconsultation platforms was conducted in January 2022 to identify medical teleconsultation platforms in Quebec (Qc), Ontario, and British Columbia (BC). The quality of each teleconsultation platform was assessed using a modified version of the HONcode principles. Nineteen different direct-to-consumer medical teleconsultation platforms were identified across the three provinces. The quality of these teleconsultation platforms was very heterogeneous. The landscape of virtual primary care is changing rapidly in the Canadian ecosystem, and the transparency of current teleconsultation platforms could be improved.

2021-12-31

Medical Informatics Europe (published)

doi.org

Attention-based Neural Cellular Automata

Mattie Tesfaldet

Derek Nowrouzezahrai

Christopher Pal

Recent extensions of Cellular Automata (CA) have incorporated key ideas from modern deep learning, dramatically extending their capabilities… (see more) and catalyzing a new family of Neural Cellular Automata (NCA) techniques. Inspired by Transformer-based architectures, our work presents a new class of

2021-12-31

Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (published)

doi.org

openreview.net

Augmenting Human Selves Through Artificial Agents – Lessons From the Brain

Georg Northoff

Maia Fraser

John Griffiths

Dimitris A. Pinotsis

Prakash Panangaden

Rosalyn Moran

Karl Friston

Much of current artificial intelligence (AI) and the drive toward artificial general intelligence (AGI) focuses on developing machines for f… (see more)unctional tasks that humans accomplish. These may be narrowly specified tasks as in AI, or more general tasks as in AGI – but typically these tasks do not target higher-level human cognitive abilities, such as consciousness or morality; these are left to the realm of so-called “strong AI” or “artificial consciousness.” In this paper, we focus on how a machine can augment humans rather than do what they do, and we extend this beyond AGI-style tasks to augmenting peculiarly personal human capacities, such as wellbeing and morality. We base this proposal on associating such capacities with the “self,” which we define as the “environment-agent nexus”; namely, a fine-tuned interaction of brain with environment in all its relevant variables. We consider richly adaptive architectures that have the potential to implement this interaction by taking lessons from the brain. In particular, we suggest conjoining the free energy principle (FEP) with the dynamic temporo-spatial (TSD) view of neuro-mental processes. Our proposed integration of FEP and TSD – in the implementation of artificial agents – offers a novel, expressive, and explainable way for artificial agents to adapt to different environmental contexts. The targeted applications are broad: from adaptive intelligence augmenting agents (IA’s) that assist psychiatric self-regulation to environmental disaster prediction and personal assistants. This reflects the central role of the mind and moral decision-making in most of what we do as humans.

2021-12-31

Frontiers in Computational Neuroscience (published)

doi.org

Author Correction: Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion

Matthew Farrell

Stefano Recanatesi

Timothy Moore

Guillaume Lajoie

Eric Shea-Brown

2021-12-31

Nature Machine Intelligence (published)

doi.org