Publications

Evaluation of real-life use of Point-Of-Care Rapid Antigen TEsting for SARS-CoV-2 in schools (EPOCRATES)
Ana C. Blanchard
Marc Desforges
Annie-Claude Labbé
Cat Tuong Nguyen
Yves Petit
Dominic Besner
Kate Zinszer
Olivier Séguin
Zineb Laghdir
Kelsey Adams
Marie-Ève Benoit
Geneviève Leduc
Jean Longtin
Ioannis Ragoussis
David L. Buckeridge
Caroline Quach
We evaluated the use of rapid antigen detection tests (RADT) for the diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-… (voir plus)2) infection in school settings to determine RADT’s performance compared to PCR. In this real-world, prospective observational cohort study, high-school students and staff were recruited from two high-schools in Montreal (Canada) and followed from January 25 th to June 10 th , 2021. Twenty-five percent of asymptomatic participants were tested weekly by RADT (nasal) and PCR (gargle). Class contacts of cases were tested. Symptomatic participants were tested by RADT (nasal) and PCR (nasal and gargle). The number of cases and outbreaks were compared to other high schools in the same area. Overall, 2,099 students and 286 school staff members consented to participate. The overall RADT’s specificity varied from 99.8 to 100%, with a lower sensitivity, varying from 28.6% in asymptomatic to 83.3% in symptomatic participants. Secondary cases were identified in 10 of 35 classes. Returning students to school after a 7-day quarantine, with a negative PCR on D6-7 after exposure, did not lead to subsequent outbreaks. Of cases for whom the source was known, 37 of 57 (72.5%) were secondary to household transmission, 13 (25%) to intra-school transmission and one to community contacts between students in the same school. RADT did not perform well as a screening tool in asymptomatic individuals. Reinforcing policies for symptom screening when entering schools and testing symptomatic individuals with RADT on the spot may avoid subsequent significant exposures in class. Rapid antigen tests were compared to standard PCR to diagnose SARS-CoV-2 infections in high-school students. They performed better in symptomatic individuals. Rapid antigen detection tests (RADT) are often used to diagnose respiratory pathogens at the point-of-care. Their performance characteristics vary, but they usually have high specificity and moderate sensitivity compared with PCR. RADT sensitivity ranged from 28.6% in asymptomatic individuals to 83.3% in symptomatic individuals. Return to school after 7 days of quarantine was safe in exposed students. Secondary cases were identified in 28% of classes with an index case.
2021-10-13
medRxiv (prépublication)
doi.org
Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers
Gabriele Prato
Simon Guiroy
Ethan Caballero
Irina Rish
A. Chandar
Empirical science of neural scaling laws is a rapidly growing area of significant importance to the future of machine learning, particularly… (voir plus) in the light of recent breakthroughs achieved by large-scale pre-trained models such as GPT-3, CLIP and DALL-e. Accurately predicting the neural network performance with increasing resources such as data, compute and model size provides a more comprehensive evaluation of different approaches across multiple scales, as opposed to traditional point-wise comparisons of fixed-size models on fixed-size benchmarks, and, most importantly, allows for focus on the best-scaling, and thus most promising in the future, approaches. In this work, we consider a challenging problem of few-shot learning in image classification, especially when the target data distribution in the few-shot phase is different from the source, training, data distribution, in a sense that it includes new image classes not encountered during training. Our current main goal is to investigate how the amount of pre-training data affects the few-shot generalization performance of standard image classifiers. Our key observations are that (1) such performance improvements are well-approximated by power laws (linear log-log plots) as the training set size increases, (2) this applies to both cases of target data coming from either the same or from a different domain (i.e., new classes) as the training data, and (3) few-shot performance on new classes converges at a faster rate than the standard classification performance on previously seen classes. Our findings shed new light on the relationship between scale and generalization.
2021-10-12
ArXiv (prépublication)
openreview.net
openreview.net
A cognitive fingerprint in human random number generation
Marc-Andre Schulz
Sebastian Baier
Benjamin Timmermann
Benjamin Timmermann
Danilo Bzdok
Karsten Witt

Most work in the neurosciences collapses data from multiple subjects to obtain robust statistical results. This research agenda ignores t… (voir plus)hat even in healthy subjects brain structure and function are known to be highly variable. Recently, Finn and colleagues showed that the brain's functional organisation is unique to each individual and can yield human-specific connectome fingerprints. This raises the question whether unique functional brain architecture may reflect a unique implementation of cognitive processes and problem solving - i.e. "Can we identify single individuals based on how they think?". The present study addresses the general question of interindividual differences in the specific context of human random number generation. We analyzed the deployment of recurrent patterns in the pseudorandom sequences to develop an identification scheme based on subject-specific volatility patterns. We demonstrate that individuals can be reliably identified based on how they how they generate randomness patterns alone. We moreover show that this phenomenon is driven by individual preference and inhibition of patterns, together forming a cognitive fingerprint.

2021-10-11
Scientific Reports (publié)
doi.org
Diagnosing as autistic people increasingly distant from prototypes lead neither to clinical benefit nor to the advancement of knowledge
Laurent Mottron
Danilo Bzdok
2021-10-11
Molecular Psychiatry (publié)
doi.org
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
Nan Rosemary Ke
Aniket Rajiv Didolkar
Sarthak Mittal
Anirudh Goyal
Guillaume Lajoie
Stefan Bauer
Danilo Jimenez Rezende
Yoshua Bengio
Michael Curtis Mozer
Christopher Pal
Inducing causal relationships from observations is a classic problem in machine learning. Most work in causality starts from the premise tha… (voir plus)t the causal variables themselves are observed. However, for AI agents such as robots trying to make sense of their environment, the only observables are low-level variables like pixels in images. To generalize well, an agent must induce high-level variables, particularly those which are causal or are affected by causal variables. A central goal for AI and causality is thus the joint discovery of abstract representations and causal structure. However, we note that existing environments for studying causal induction are poorly suited for this objective because they have complicated task-specific causal graphs which are impossible to manipulate parametrically (e.g., number of nodes, sparsity, causal chain length, etc.). In this work, our goal is to facilitate research in learning representations of high-level variables as well as causal structures among them. In order to systematically probe the ability of methods to identify these variables and structures, we design a suite of benchmarking RL environments. We evaluate various representation learning algorithms from the literature and find that explicitly incorporating structure and modularity in models can help causal induction in model-based reinforcement learning.
2021-10-10
NeurIPS.cc/2021/Track/Datasets_and_Benchmarks/Round2 (publié)
openreview.net
openreview.net
DoMoBOT: An AI-Empowered Bot for Automated and Interactive Domain Modelling
Rijul Saini
Gunter Mussbacher
Jin L.C. Guo
Jörg Kienzle
Domain modelling transforms informal requirements written in natural language in the form of problem descriptions into concise and analyzabl… (voir plus)e domain models. As the manual construction of these domain models is often time-consuming, error-prone, and labor-intensive, several approaches already exist to automate domain modelling. However, the current approaches suffer from lower accuracy of extracted domain models and the lack of support for system-modeller interactions. To better assist modellers, we introduce DoMoBOT, a web-based Domain Modelling BOT. Our proposed bot combines artificial intelligence techniques such as natural language processing and machine learning to extract domain models with higher accuracy. More importantly, our bot incorporates a set of features to bring synergy between automated model extraction and bot-modeller interactions. During these interactions, the bot presents multiple possible solutions to a modeller for modelling scenarios present in a given problem description. The bot further enables modellers to switch to a particular solution and updates the other parts of the domain model proactively. In this tool demo paper, we demonstrate how the implementation and architecture of DoMoBOT support the paradigm of automated and interactive domain modelling for assisting modellers.
2021-10-09
2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) (publié)
doi.org
Impact of Aliasing on Generalization in Deep Convolutional Networks
Cristina Vasconcelos
Hugo Larochelle
Vincent Dumoulin
Rob Romijnders
Nicolas Roux
Ross Goroshin
We investigate the impact of aliasing on generalization in Deep Convolutional Networks and show that data augmentation schemes alone are una… (voir plus)ble to prevent it due to structural limitations in widely used architectures. Drawing insights from frequency analysis theory, we take a closer look at ResNet and EfficientNet architectures and review the trade-off between aliasing and information loss in each of their major components. We show how to mitigate aliasing by inserting non-trainable low-pass filters at key locations, particularly where networks lack the capacity to learn them. These simple architectural changes lead to substantial improvements in generalization on i.i.d. and even more on out-of-distribution conditions, such as image classification under natural corruptions on ImageNet-C [11] and few-shot learning on Meta-Dataset [26]. State-of-the art results are achieved on both datasets without introducing additional trainable parameters and using the default hyper-parameters of open source codebases.
2021-10-09
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (publié)
doi.org
arxiv.org
GPU acceleration of finite state machine input execution: Improving scale and performance
Vanya Yaneva
Ajitha Rajan
Christophe Dubach
Model‐based development is a popular development approach in which software is implemented and verified based on a model of the required s… (voir plus)ystem. Finite state machines (FSMs) are widely used as models for systems in several domains. Validating that a model accurately represents the required behaviour involves the generation and execution of a large number of input sequences, which is often an expensive and time‐consuming process. In this paper, we speed up the execution of input sequences for FSM validation, by leveraging the high degree of parallelism of modern graphics processing units (GPUs) for the automatic execution of FSM input sequences in parallel on the GPU threads. We expand our existing work by providing techniques that improve the performance and scalability of this approach. We conduct extensive empirical evaluation using 15 large FSMs from the networking domain and measure GPU speed‐up over a 16‐core CPU, taking into account total GPU time, which includes both data transfer and kernel execution time. We found that GPUs execute FSM input sequences up to 9.28× faster than a 16‐core CPU, with an average speed‐up of 4.53× across all subjects. Our optimizations achieve an average improvement over existing work of 58.95% for speed‐up and scalability to large FSMs with over 2K states and 500K transitions. We also found that techniques aimed at reducing the number of required input sequences for large FSMs with high density were ineffective when applied to all‐transition pair coverage, thus emphasizing the need for approaches like ours that speed up input execution.
2021-10-07
Software Testing, Verification and Reliability (publié)
doi.org
Action-Based Representation Learning for Autonomous Driving
Yi Xiao
Felipe Codevilla
Christopher Pal
Antonio M. López
Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems. Unfortunately, seeming… (voir plus)ly straightforward approaches for creating end-to-end driving models that map sensor data directly into driving actions are problematic in terms of interpretability, and typically have significant difficulty dealing with spurious correlations. Alternatively, we propose to use this kind of action-based driving data for learning representations. Our experiments show that an affordance-based driving model pre-trained with this approach can leverage a relatively small amount of weakly annotated imagery and outperform pure end-to-end driving models, while being more interpretable. Further, we demonstrate how this strategy outperforms previous methods based on learning inverse dynamics models as well as other methods based on heavy human supervision (ImageNet).
2021-10-03
Proceedings of the 2020 Conference on Robot Learning (publié)
proceedings.mlr.press
proceedings.mlr.press
Minimum detectable spinal cord atrophy with automatic segmentation: Investigations using an open-access dataset of healthy participants
Paul Bautin
Julien Cohen-Adad
•Evaluate the robustness of an automated analysis pipeline for detecting SC atrophy.•Simulate spinal cord atrophy and scan-rescan variab… (voir plus)ility.•Fully automated analysis method available on an open access database.•Evaluation of sample size and inter/intra-subject variability for T1w and T2w images. Evaluate the robustness of an automated analysis pipeline for detecting SC atrophy. Simulate spinal cord atrophy and scan-rescan variability. Fully automated analysis method available on an open access database. Evaluation of sample size and inter/intra-subject variability for T1w and T2w images.
2021-10-03
NeuroImage : Clinical (publié)
doi.org
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
Pau Rodríguez
Massimo Caccia
Alexandre Lacoste
Lee Zamparo
Issam Laradji
Laurent Charlin
David Vázquez
Explainability for machine learning models has gained considerable attention within the research community given the importance of deploying… (voir plus) more reliable machine-learning systems. In computer vision applications, generative counterfactual methods indicate how to perturb a model's input to change its prediction, providing details about the model's decision-making. Current methods tend to generate trivial counterfactuals about a model's decisions, as they often suggest to exaggerate or remove the presence of the attribute being classified. For the machine learning practitioner, these types of counterfactuals offer little value, since they provide no new information about undesired model or data biases. In this work, we identify the problem of trivial counterfactual generation and we propose DiVE to alleviate it. DiVE learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss to uncover multiple valuable explanations about the model's prediction. Further, we introduce a mechanism to prevent the model from producing trivial explanations. Experiments on CelebA and Synbols demonstrate that our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods. Code is available at https://github.com/ElementAI/beyond-trivial-explanations.
2021-09-30
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (publié)
doi.org
arxiv.org
Evaluating Montréal’s harm reduction interventions for people who inject drugs: protocol for observational study and cost-effectiveness analysis
Dimitra Panagiotoglou
Michal Abrahamowicz
David L Buckeridge
J Jaime Caro
Eric Latimer
Mathieu Maheu-Giroux
Erin C Strumpf
The main harm reduction interventions for people who inject drugs (PWID) are supervised injection facilities, needle and syringe programmes … (voir plus)and opioid agonist treatment. Current evidence supporting their implementation and operation underestimates their usefulness by excluding skin, soft tissue and vascular infections (SSTVIs) and anoxic/toxicity-related brain injury from cost-effectiveness analyses (CEA). Our goal is to conduct a comprehensive CEA of harm reduction interventions in a setting with a large, dispersed, heterogeneous population of PWID, and include prevention of SSTVIs and anoxic/toxicity-related brain injury as measures of benefit in addition to HIV, hepatitis C and overdose morbidity and mortalities averted. This protocol describes how we will develop an open, retrospective cohort of adult PWID living in Québec between 1 January 2009 and 31 December 2020 using administrative health record data. By complementing this data with non-linkable paramedic dispatch records, regional monthly needle and syringe dispensation counts and repeated cross-sectional biobehavioural surveys, we will estimate the hazards of occurrence and the impact of Montréal’s harm reduction interventions on the incidence of drug-use-related injuries, infections and deaths. We will synthesise results from our empirical analyses with published evidence to simulate infections and injuries in a hypothetical population of PWID in Montréal under different intervention scenarios including current levels of use and scale-up, and assess the cost-effectiveness of each intervention from the public healthcare payer’s perspective. This study was approved by McGill University’s Institutional Review Board (Study Number: A08-E53-19B). We will work with community partners to disseminate results to the public and scientific community via scientific conferences, a publicly accessible report, op-ed articles and open access peer-reviewed journals.
2021-09-30
BMJ Open (publié)
doi.org