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
Reduced Axon Calibre in the Associative Striatum of the Sapap3 Knockout Mouse
Eliana Lopes Rio da Silva Lousada
Mathieu Boudreau
Julien Cohen‐Adad
Brahim Nait‐Oumesmar
Eric Burguière
Christiane Schreiweis
Pathological repetitive behaviors are a common feature of different neuropsychiatric disorders such as obsessive-compulsive disorder or Gill… (voir plus)es de la Tourette syndrome. The Sapap3 knockout mouse (Sapap3-KO) is the current reference model used in translational psychiatry to study co-morbid repetitive behaviors, presenting both compulsive-like as well as tic-like behaviors. Consistent with clinical and fundamental research literature relating compulsive-like symptoms to associative cortico-striatal dysfunctions and tic-like symptoms to sensorimotor cortico-striatal dysfunctions, abnormalities comprising both circuits have been described in this mouse model. Findings reported on these mice point towards not only macro-, but also micro-circuitry deficits, both of which can be affected by neuronal structural changes. As such, in the present study, we aimed to investigate structural changes in associative and sensorimotor striatal areas that could affect information conduction. We used AxonDeepSeg, an open-source software to automatically segment and measure myelin thickness and axon caliber, and found that axon caliber, the main contributor for changes in conduction speed, is specifically reduced in the associative but not the sensorimotor striatum of the Sapap3-KO mouse. This smaller axon caliber in Sapap3-KO mice is not a general neuronal feature of this region, but specific to a subpopulation of axons with large caliber. These results point to a primary structural deficit in the associative striatum, affecting signal conduction and consequent connectivity.
2021-10-13
Brain Sciences (publié)
doi.org
Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers
Gabriele Prato
Simon Guiroy
Ethan Caballero
Irina Rish
Sarath 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)
doi.org
openreview.net
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
ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models
Salva Rühling Cachay
Venkatesh Ramesh
Jason N. S. Cole
Howard Barker
David Rolnick
Numerical simulations of Earth's weather and climate require substantial amounts of computation. This has led to a growing interest in repla… (voir plus)cing subroutines that explicitly compute physical processes with approximate machine learning (ML) methods that are fast at inference time. Within weather and climate models, atmospheric radiative transfer (RT) calculations are especially expensive. This has made them a popular target for neural network-based emulators. However, prior work is hard to compare due to the lack of a comprehensive dataset and standardized best practices for ML benchmarking. To fill this gap, we build a large dataset, ClimART, with more than 10 million samples from present, pre-industrial, and future climate conditions, based on the Canadian Earth System Model. ClimART poses several methodological challenges for the ML community, such as multiple out-of-distribution test sets, underlying domain physics, and a trade-off between accuracy and inference speed. We also present several novel baselines that indicate shortcomings of datasets and network architectures used in prior work.
2021-10-10
NeurIPS.cc/2021/Track/Datasets_and_Benchmarks/Round2 (accepté)
openreview.net
openreview.net
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
Nan Rosemary Ke
Aniket Didolkar
Sarthak Mittal
Anirudh Goyal
Guillaume Lajoie
Stefan Bauer
Danilo Rezende
Yoshua Bengio
Michael 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
Datasets and Benchmarks Track @ Neural Information Processing Systems (accepté)
doi.org
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
FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters
Yuwei Cheng
Jiannan Zhu
Mengxin Jiang
Jie Fu
Changsong Pang
Peidong Wang
Kris Sankaran
Olawale Moses Onabola
Yimin Liu
Dianbo Liu
Yoshua Bengio
Marine debris is severely threatening the marine lives and causing sustained pollution to the whole ecosystem. To prevent the wastes from ge… (voir plus)tting into the ocean, it is helpful to clean up the floating wastes in inland waters using the autonomous cleaning devices like unmanned surface vehicles. The cleaning efficiency relies on a high-accurate and robust object detection system. However, the small size of the target, the strong light reflection over water surface, and the reflection of other objects on bank-side all bring challenges to the vision-based object detection system. To promote the practical application for autonomous floating wastes cleaning, we present FloW†, the first dataset for floating waste detection in inland water areas. The dataset consists of an image sub-dataset FloW-Img and a multimodal sub-dataset FloW-RI which contains synchronized millimeter wave radar data and images. Accurate annotations for images and radar data are provided, supporting floating waste detection strategies based on image, radar data, and the fusion of two sensors. We perform several baseline experiments on our dataset, including vision-based and radar-based detection methods. The results show that, the detection accuracy is relatively low and floating waste detection still remains a challenging task.
2021-10-09
IEEE/CVF International Conference on Computer Vision (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
Circulating proteins to predict adverse COVID-19 outcomes
Chen-Yang Su
Sirui Zhou
Edgar Gonzalez-Kozlova
Guillaume Butler-Laporte
Elsa Brunet-Ratnasingham
Tomoko Nakanishi
Wonseok Jeon
David Morrison
Laetitia Laurent
Jonathan Afilalo
Marc Afilalo
Danielle Henry
Yiheng Chen
Julia Carrasco-Zanini
Yossi Farjoun
Maik Pietzner
Nofar Kimchi
Zaman Afrasiabi
Nardin Rezk
Meriem Bouab … (voir 47 de plus)
Louis Petitjean
Charlotte Guzman
Xiaoqing Xue
Chris Tselios
Branka Vulesevic
Olumide Adeleye
Tala Abdullah
Noor Almamlouk
Yara Moussa
Chantal DeLuca
Naomi Duggan
Erwin Schurr
Nathalie Brassard
Madeleine Durand
Diane Marie Del Valle
Ryan Thompson
Mario A. Cedillo
Eric Schadt
Kai Nie
Nicole W Simons
Konstantinos Mouskas
Nicolas Zaki
Manishkumar Patel
Hui Xie
Jocelyn Harris
Robert Marvin
Esther Cheng
Kevin Tuballes
Kimberly Argueta
Ieisha Scott
Celia M T Greenwood
Clare Paterson
Michael A. Hinterberg
Claudia Langenberg
Vincenzo Forgetta
Joelle Pineau
Vincent Mooser
Thomas Marron
Noam Beckmann
Ephraim Kenigsberg
Seunghee Kim-schulze
Alexander W. Charney
Sacha Gnjatic
Daniel E. Kaufmann
Miriam Merad
J Brent Richards
J Brent Richards
Predicting COVID-19 severity is difficult, and the biological pathways involved are not fully understood. To approach this problem, we measu… (voir plus)red 4,701 circulating human protein abundances in two independent cohorts totaling 986 individuals. We then trained prediction models including protein abundances and clinical risk factors to predict adverse COVID-19 outcomes in 417 subjects and tested these models in a separate cohort of 569 individuals. For severe COVID-19, a baseline model including age and sex provided an area under the receiver operator curve (AUC) of 65% in the test cohort. Selecting 92 proteins from the 4,701 unique protein abundances improved the AUC to 88% in the training cohort, which remained relatively stable in the testing cohort at 86%, suggesting good generalizability. Proteins selected from different adverse COVID-19 outcomes were enriched for cytokine and cytokine receptors, but more than half of the enriched pathways were not immune-related. Taken together, these findings suggest that circulating proteins measured at early stages of disease progression are reasonably accurate predictors of adverse COVID-19 outcomes. Further research is needed to understand how to incorporate protein measurement into clinical care.
2021-10-04
medRxiv (prépublication)
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