Publications

Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey

Aritra Ghosh

C. Urry

Aayush Mishra

Laurence Perreault-Levasseur

P. Natarajan

D. Sanders

Daisuke Nagai

Chuan Tian

Nico Cappelluti

J. Kartaltepe

M. Powell

Amrit Rau

Ezequiel Treister

We use the Galaxy Morphology Posterior Estimation Network (GaMPEN) to estimate morphological parameters and associated uncertainties for ∼… (voir plus)8 million galaxies in the Hyper Suprime-Cam Wide survey with z ≤ 0.75 and m ≤ 23. GaMPEN is a machine-learning framework that estimates Bayesian posteriors for a galaxy’s bulge-to-total light ratio (L B /L T ), effective radius (R e ), and flux (F). By first training on simulations of galaxies and then applying transfer learning using real data, we trained GaMPEN with 1% of our data set. This two-step process will be critical for applying machine-learning algorithms to future large imaging surveys, such a

2023-08-14

The Astrophysical Journal (publié)

doi.org

arxiv.org

The evolution of SARS-CoV-2 seroprevalence in Canada: a time-series study, 2020–2023

Tanya J. Murphy

Hanna Swail

Jaspreet Jain

Maureen Anderson

Philip Awadalla

Lesley Behl

P. Brown

C. Charlton

Karen Colwill

S. Drews

A. Gingras

Deena Hinshaw

P. Jha

J. Kanji

Victoria A. Kirsh

Amanda Lang

Marc-andré Langlois

Stephen Lee

Antoine Lewin

Sheila F O’Brien … (voir 10 de plus)

Chantale Pambrun

Kimberly Skead

David A. Stephens

Derek R. Stein

G. Tipples

Paul G. Van Caeseele

Timothy Grant Evans

Olivia Oxlade

Bruce D. Mazer

David Buckeridge

Background: During the first year of the COVID-19 pandemic, the proportion of reported cases of COVID-19 among Canadians was under 6%. Altho… (voir plus)ugh high vaccine coverage was achieved in Canada by fall 2021, the Omicron variant caused unprecedented numbers of infections, overwhelming testing capacity and making it difficult to quantify the trajectory of population immunity. Methods: Using a time-series approach and data from more than 900 000 samples collected by 7 research studies collaborating with the COVID-19 Immunity Task Force (CITF), we estimated trends in SARS-CoV-2 seroprevalence owing to infection and vaccination for the Canadian population over 3 intervals: prevaccination (March to November 2020), vaccine roll-out (December 2020 to November 2021), and the arrival of the Omicron variant (December 2021 to March 2023). We also estimated seroprevalence by geographical region and age. Results: By November 2021, 9.0% (95% credible interval [CrI] 7.3%–11%) of people in Canada had humoral immunity to SARS-CoV-2 from an infection. Seroprevalence increased rapidly after the arrival of the Omicron variant — by Mar. 15, 2023, 76% (95% CrI 74%–79%) of the population had detectable antibodies from infections. The rapid rise in infection-induced antibodies occurred across Canada and was most pronounced in younger age groups and in the Western provinces: Manitoba, Saskatchewan, Alberta and British Columbia. Interpretation: Data up to March 2023 indicate that most people in Canada had acquired antibodies against SARS-CoV-2 through natural infection and vaccination. However, given variations in population seropositivity by age and geography, the potential for waning antibody levels, and new variants that may escape immunity, public health policy and clinical decisions should be tailored to local patterns of population immunity.

2023-08-13

Canadian Medical Association Journal (publié)

doi.org

A Two-Stage Optimization Framework for Electric Vehicle Fleet Day-ahead Charging Management

Arian Shah Kamrani

Hanane Dagdougui

Nowadays electric vehicles (EVs) have become one of the important means of transportation all over the world. The importance of EV owners’… (voir plus) privacy as well as smart EV fleet charging has always been one of the challenges in smart charging planning and management. Furthermore, in smart charging, the distribution system operator must also coordinate with EV aggregators to insure that the power system is operated within security limits while reducing charging costs and satisfying EV users’ energy needs. In this paper, a semi-private framework for EV owners has been introduced which solves a two-stage optimization problem for the smart control of EV charging. This framework considers charging cost reduction and peak load shaving as well as satisfying power grid constraints. At the higher stage, based on optimal power flow calculations, the proposed control signals are transferred to the lower stage in order to facilitate optimal scheduling in accordance with the mentioned goals. The obtained results based on the proposed optimal method implemented on the IEEE 33-bus network show that compared to uncontrolled charging, the cost of charging and the peak of the network are reduced by 5.31% and 4.90%, respectively. Moreover, all the constraints of the power grid are satisfied.

2023-08-13

2023 IEEE 11th International Conference on Smart Energy Grid Engineering (SEGE) (publié)

doi.org

A Systematic Literature Review of Fashion, Sustainability, and Consumption Using a Mixed Methods Approach

Osmud Rahman

Dingtao Hu

Benjamin Fung

With the growing global awareness of the environmental impact of clothing consumption, there has been a notable surge in the publication of … (voir plus)journal articles dedicated to “fashion sustainability” in the past decade, specifically from 2010 to 2020. However, despite this wealth of research, many studies remain disconnected and fragmented due to varying research objectives, focuses, and approaches. Conducting a systematic literature review with a mixed methods research approach can help identify key research themes, trends, and developmental patterns, while also shedding light on the complexity of fashion, sustainability, and consumption. To enhance the literature review and analytical process, the current systematic literature review employed text mining techniques and bibliometric visualization tools, including RAKE, VOSviewer, and CitNetExplorer. The findings revealed an increase in the number of publications focusing on “fashion and sustainability” between 2010 and 2021. Most studies were predominantly conducted in the United States, with a specific focus on female consumers. Moreover, a greater emphasis was placed on non-sustainable cues rather than the sustainable cues. Additionally, a higher number of case studies was undertaken to investigate three fast-fashion companies. To enhance our knowledge and understanding of this subject, this article highlights several valuable contributions and provides recommendations for future research.

2023-08-10

Sustainability (publié)

doi.org

AI4GCC - Track 3: Consumption and the Challenges of Multi-Agent RL

Marco Jiralerspong

Gauthier Gidel

2023-08-09

ArXiv (prépublication)

doi.org

arxiv.org

Teacher-Student Architecture for Knowledge Distillation: A Survey

Chengming Hu

Xuan Li

Danyang Liu

Haolun Wu

Xi Chen

Ju Wang

Xue (Steve) Liu

Although Deep neural networks (DNNs) have shown a strong capacity to solve large-scale problems in many areas, such DNNs are hard to be depl… (voir plus)oyed in real-world systems due to their voluminous parameters. To tackle this issue, Teacher-Student architectures were proposed, where simple student networks with a few parameters can achieve comparable performance to deep teacher networks with many parameters. Recently, Teacher-Student architectures have been effectively and widely embraced on various knowledge distillation (KD) objectives, including knowledge compression, knowledge expansion, knowledge adaptation, and knowledge enhancement. With the help of Teacher-Student architectures, current studies are able to achieve multiple distillation objectives through lightweight and generalized student networks. Different from existing KD surveys that primarily focus on knowledge compression, this survey first explores Teacher-Student architectures across multiple distillation objectives. This survey presents an introduction to various knowledge representations and their corresponding optimization objectives. Additionally, we provide a systematic overview of Teacher-Student architectures with representative learning algorithms and effective distillation schemes. This survey also summarizes recent applications of Teacher-Student architectures across multiple purposes, including classification, recognition, generation, ranking, and regression. Lastly, potential research directions in KD are investigated, focusing on architecture design, knowledge quality, and theoretical studies of regression-based learning, respectively. Through this comprehensive survey, industry practitioners and the academic community can gain valuable insights and guidelines for effectively designing, learning, and applying Teacher-Student architectures on various distillation objectives.

2023-08-08

ArXiv (prépublication)

doi.org

arxiv.org

Bayesian modeling disentangles language versus executive control disruption in stroke

Gesa Hartwigsen

Jae‐Sung Lim

Hee-Joon Bae

Kyung‐Ho Yu

Hugo J. Kuijf

Nick A. Weaver

J. Matthijs Biesbroek

Jakub Kopal

Danilo Bzdok

2023-08-07

bioRxiv (prépublication)

doi.org

Exploring Security Practices in Infrastructure as Code: An Empirical Study

Alexandre Verdet

Mohammad Hamdaqa

Léuson M. P. Da Silva

Foutse Khomh

Cloud computing has become popular thanks to the widespread use of Infrastructure as Code (IaC) tools, allowing the community to convenientl… (voir plus)y manage and configure cloud infrastructure using scripts. However, the scripting process itself does not automatically prevent practitioners from introducing misconfigurations, vulnerabilities, or privacy risks. As a result, ensuring security relies on practitioners understanding and the adoption of explicit policies, guidelines, or best practices. In order to understand how practitioners deal with this problem, in this work, we perform an empirical study analyzing the adoption of IaC scripted security best practices. First, we select and categorize widely recognized Terraform security practices promulgated in the industry for popular cloud providers such as AWS, Azure, and Google Cloud. Next, we assess the adoption of these practices by each cloud provider, analyzing a sample of 812 open-source projects hosted on GitHub. For that, we scan each project configuration files, looking for policy implementation through static analysis (checkov). Additionally, we investigate GitHub measures that might be correlated with adopting these best practices. The category Access policy emerges as the most widely adopted in all providers, while Encryption in rest are the most neglected policies. Regarding GitHub measures correlated with best practice adoption, we observe a positive, strong correlation between a repository number of stars and adopting practices in its cloud infrastructure. Based on our findings, we provide guidelines for cloud practitioners to limit infrastructure vulnerability and discuss further aspects associated with policies that have yet to be extensively embraced within the industry.

2023-08-07

ArXiv (prépublication)

doi.org

arxiv.org

Toward computing attributions for dimensionality reduction techniques

Matthew Scicluna

Jean-Christophe Grenier

Raphael Poujol

Sébastien Lemieux

Julie Hussin

2023-08-03

Bioinformatics Advances (publié)