Publications

A time-space formulation for the locomotive routing problem at the Canadian National Railways
Pedro L. Miranda
Jean-François Cordeau
Combining Modular Skills in Multitask Learning
Edoardo M. Ponti
A modular design encourages neural models to disentangle and recombine different facets of knowledge to generalise more systematically to ne… (voir plus)w tasks. In this work, we assume that each task is associated with a subset of latent discrete skills from a (potentially small) inventory. In turn, skills correspond to parameter-efficient (sparse / low-rank) model parameterisations. By jointly learning these and a task-skill allocation matrix, the network for each task is instantiated as the average of the parameters of active skills. To favour non-trivial soft partitions of skills across tasks, we experiment with a series of inductive biases, such as an Indian Buffet Process prior and a two-speed learning rate. We evaluate our latent-skill model on two main settings: 1) multitask reinforcement learning for grounded instruction following on 8 levels of the BabyAI platform; and 2) few-shot adaptation of pre-trained text-to-text generative models on CrossFit, a benchmark comprising 160 NLP tasks. We find that the modular design of a network significantly increases sample efficiency in reinforcement learning and few-shot generalisation in supervised learning, compared to baselines with fully shared, task-specific, or conditionally generated parameters where knowledge is entangled across tasks. In addition, we show how discrete skills help interpretability, as they yield an explicit hierarchy of tasks.
More Than Meets the Eye: Art Engages the Social Brain
Janneke E. P. van Leeuwen
Jeroen Boomgaard
Sebastian J. Crutch
Jason D. Warren
Quantitative electrophysiological assessments as predictive markers of lower limb motor recovery after spinal cord injury: a pilot study with an adaptive trial design
Yin Nan Huang
El-Mehdi Meftah
Charlotte H. Pion
Jean-Marc Mac-Thiong
Dorothy Barthélemy
Observational, cohort study. (1) Determine the feasibility and relevance of assessing corticospinal, sensory, and spinal pathways early aft… (voir plus)er traumatic spinal cord injury (SCI) in a rehabilitation setting. (2) Validate whether electrophysiological and magnetic resonance imaging (MRI) measures taken early after SCI could identify preserved neural pathways, which could then guide therapy. Intensive functional rehabilitation hospital (IFR). Five individuals with traumatic SCI and eight controls were recruited. The lower extremity motor score (LEMS), electrical perceptual threshold (EPT) at the S2 dermatome, soleus (SOL) H-reflex, and motor evoked potentials (MEPs) in the tibialis anterior (TA) muscle were assessed during the stay in IFR and in the chronic stage (>6 months post-SCI). Control participants were only assessed once. Feasibility criteria included the absence of adverse events, adequate experimental session duration, and complete dataset gathering. The relationship between electrophysiological data collected in IFR and LEMS in the chronic phase was studied. The admission MRI was used to calculate the maximal spinal cord compression (MSCC). No adverse events occurred, but a complete dataset could not be collected for all subjects due to set-up configuration limitations and time constraints. EPT measured at IFR correlated with LEMS in the chronic phases (r = −0.67), whereas SOL H/M ratio, H latency, MEPs and MSCC did not. Adjustments are necessary to implement electrophysiological assessments in an IFR setting. Combining MRI and electrophysiological measures may lead to better assessment of neuronal deficits early after SCI.
Stringency of containment and closures on the growth of SARS-CoV-2 in Canada prior to accelerated vaccine roll-out
David Vickers
Stefan Baral
Sharmistha Mishra
Jeffrey C. Kwong
Maria Sundaram
Alan Katz
Andrew Calzavara
Mathieu Maheu-Giroux
David L Buckeridge
Tyler Williamson
Population Genomics Approaches for Genetic Characterization of SARS-CoV-2 Lineages
Isabel Gamache
Arnaud N'Guessan
Justin Pelletier
Carmen Lia Murall
Vanda Gaonac’h-Lovejoy
David J. Hamelin
Raphaël Poujol
Jean-Christophe Grenier
Martin Smith
Etienne Caron
Morgan Craig
B. Jesse Shapiro
Julie G. Hussin
The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19)… (voir plus), has been sequenced at an unprecedented scale leading to a tremendous amount of viral genome sequencing data. To assist in tracing infection pathways and design preventive strategies, a deep understanding of the viral genetic diversity landscape is needed. We present here a set of genomic surveillance tools from population genetics which can be used to better understand the evolution of this virus in humans. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic. We analyzed 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets. This approach enables real-time lineage identification, a clear description of the relationship between variants of concern, and efficient detection of recurrent mutations. Furthermore, time series change of Tajima's D by haplotype provides a powerful metric of lineage expansion. Finally, principal component analysis (PCA) highlights key steps in variant emergence and facilitates the visualization of genomic variation in the context of SARS-CoV-2 diversity. The computational framework presented here is simple to implement and insightful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of populations of humans and other organisms.
Selective Credit Assignment
Diana Borsa
Hado Philip van Hasselt
Transformation Coding: Simple Objectives for Equivariant Representations
On the Performance Implications of Deploying IoT Apps as FaaS
Mohab Aly
Soumaya Yacout
Gradients without Backpropagation
Atilim Güneş Baydin
Barak A. Pearlmutter
Don Syme
Frank N. Wood
Philip Torr
Novel informatics approaches to COVID-19 Research: From methods to applications
Huanan Xu
David L Buckeridge
Yi Wang
P. Tarczy-Hornoch
Advanced Diffusion MR Imaging for Multiple Sclerosis in the Brain and Spinal Cord
Masaaki Hori
Tomoko Maekawa
Kouhei Kamiya
Akifumi Hagiwara
Masami Goto
Mariko Yoshida Takemura
Shohei Fujita
Christina Andica
Koji Kamagata
Shigeki Aoki
Diffusion tensor imaging (DTI) has been established its usefulness in evaluating normal-appearing white matter (NAWM) and other lesions that… (voir plus) are difficult to evaluate with routine clinical MRI in the evaluation of the brain and spinal cord lesions in multiple sclerosis (MS), a demyelinating disease. With the recent advances in the software and hardware of MRI systems, increasingly complex and sophisticated MRI and analysis methods, such as q-space imaging, diffusional kurtosis imaging, neurite orientation dispersion and density imaging, white matter tract integrity, and multiple diffusion encoding, referred to as advanced diffusion MRI, have been proposed. These are capable of capturing in vivo microstructural changes in the brain and spinal cord in normal and pathological states in greater detail than DTI. This paper reviews the current status of recent advanced diffusion MRI for assessing MS in vivo as part of an issue celebrating two decades of magnetic resonance in medical sciences (MRMS), an official journal of the Japanese Society of Magnetic Resonance in Medicine.