Publications

Online Convex Optimization for On-Board Routing in High-Throughput Satellites
Olivier Bélanger
Jean-Luc Lupien
Olfa Ben Yahia
Stéphane Martel
Antoine Lesage-Landry
Gunes Karabulut Kurt
The rise in low Earth orbit (LEO) satellite Internet services has led to increasing demand, often exceeding available data rates and comprom… (voir plus)ising the quality of service. While deploying more satellites offers a short-term fix, designing higher-performance satellites with enhanced transmission capabilities provides a more sustainable solution. Achieving the necessary high capacity requires interconnecting multiple modem banks within a satellite payload. However, there is a notable gap in research on internal packet routing within extremely high-throughput satellites. To address this, we propose a real-time optimal flow allocation and priority queue scheduling method using online convex optimization-based model predictive control. We model the problem as a multi-commodity flow instance and employ an online interior-point method to solve the routing and scheduling optimization iteratively. This approach minimizes packet loss and supports real-time rerouting with low computational overhead. Our method is tested in simulation on a next-generation extremely high-throughput satellite model, demonstrating its effectiveness compared to a reference batch optimization and to traditional methods.
2024-09-01
ArXiv (prépublication)
doi.org
arxiv.org
THInC: A Theory-Driven Framework for Computational Humor Detection
Victor De Marez
Thomas Winters
Ayla Rigouts Terryn
Humor is a fundamental aspect of human communication and cognition, as it plays a crucial role in social engagement. Although theories about… (voir plus) humor have evolved over centuries, there is still no agreement on a single, comprehensive humor theory. Likewise, computationally recognizing humor remains a significant challenge despite recent advances in large language models. Moreover, most computational approaches to detecting humor are not based on existing humor theories. This paper contributes to bridging this long-standing gap between humor theory research and computational humor detection by creating an interpretable framework for humor classification, grounded in multiple humor theories, called THInC (Theory-driven Humor Interpretation and Classification). THInC ensembles interpretable GA2M classifiers, each representing a different humor theory. We engineered a transparent flow to actively create proxy features that quantitatively reflect different aspects of theories. An implementation of this framework achieves an F1 score of 0.85. The associative interpretability of the framework enables analysis of proxy efficacy, alignment of joke features with theories, and identification of globally contributing features. This paper marks a pioneering effort in creating a humor detection framework that is informed by diverse humor theories and offers a foundation for future advancements in theory-driven humor classification. It also serves as a first step in automatically comparing humor theories in a quantitative manner.
2024-09-01
ArXiv (prépublication)
doi.org
arxiv.org
Audio Editing with Non-Rigid Text Prompts
Francesco Paissan
Luca Della Libera
Zhepei Wang
Paris Smaragdis
Mirco Ravanelli
Cem Subakan
In this paper, we explore audio-editing with non-rigid text edits. We show that the proposed editing pipeline is able to create audio edits … (voir plus)that remain faithful to the input audio. We explore text prompts that perform addition, style transfer, and in-painting. We quantitatively and qualitatively show that the edits are able to obtain results which outperform Audio-LDM, a recently released text-prompted audio generation model. Qualitative inspection of the results points out that the edits given by our approach remain more faithful to the input audio in terms of keeping the original onsets and offsets of the audio events.
2024-08-31
Interspeech (publié)
doi.org
arxiv.org
Clinical Care Trajectory Assessment of Children with Congenital Diaphragmatic Hernia and Neurodevelopmental Impairment
Alexandra Dimmer
Gabriel Altit
Sabrina Beauseigle
Elena Guadagno
Louise Koclas
Katryn Paquette
Ana Sant’Anna
Adam Shapiro
Dan Poenaru
Pramod Puligandla
2024-08-31
Journal of Pediatric Surgery (publié)
doi.org
Data Privacy for Record Linkage and Beyond
Shurong Lin
Eric D. Kolaczyk
In a data-driven world, two prominent research problems are record linkage and data privacy, among others. Record linkage is essential for i… (voir plus)mproving decision-making by integrating information of the same entities from different sources. On the other hand, data privacy research seeks to balance the need to extract accurate insights from data with the imperative to protect the privacy of the entities involved. Inevitably, data privacy issues arise in the context of record linkage. This article identifies two complementary aspects at the intersection of these two fields: (1) how to ensure privacy during record linkage and (2) how to mitigate privacy risks when releasing the analysis results after record linkage. We specifically discuss privacy-preserving record linkage, differentially private regression, and related topics.
2024-08-31
Social Science Research Network (publié)
doi.org
A Joint Temporal Model for Hospitalizations and ICU Admissions Due to COVID‐19 in Quebec
Mariana Carmona‐Baez
Alexandra M. Schmidt
Shirin Golchi
David L. Buckeridge
ABSTRACT Infectious respiratory diseases have been of interest in recent years for the great burden they place on health systems, for instan… (voir plus)ce, the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) that caused the global COVID‐19 pandemic. As many of these diseases might require hospitalization and even intensive care unit (ICU) admission, understanding the joint dynamics of hospitalizations and ICU admissions across time and different groups of the population remains of great importance. We aim to understand the joint evolution of hospital and ICU admissions given COVID‐19 test‐positive cases in the province of Quebec, Canada. We obtain the daily counts, by age group, on the number of confirmed COVID‐19 cases, the number of hospitalizations and the number of ICU admissions due to COVID‐19, from March 2020 through October 2021 in Quebec. We propose a joint Bayesian generalized dynamic linear model for the number of hospitalizations and ICU admissions to study their temporal trends and possible associations with sex and age group. Additionally, we use transfer functions to investigate if there is a memory effect of the number of cases on hospitalizations across the different age groups. The results suggest that there is a clear distinction in the patterns of hospitalizations and ICU admissions across age groups and that the number of cases has a persistent effect on the rate of hospitalization.
2024-08-31
Stat (publié)
doi.org
Do machine learning methods Make Better predictions in pharmacoepidemiology?
Ana Paula Pena-Gralle
Mireille E. Schnitzer
Sofia-Nada Boureguaa
Félix Morin
Marc-André Legault
Caroline Sirois
Alice Dragomir
Lucie Blais
2024-08-31
Annals of Epidemiology (publié)
doi.org
Predicting Five-Year All-Cause Mortality in COPD Patients Using Machine Learning
Ana Paula Pena-Gralle
Amélie Forget
Sofia-Nada Boureguaa
Marc-André Legault
Lucie Blais
2024-08-31
Annals of Epidemiology (publié)
doi.org
Prediction of incident atrial fibrillation using deep learning, clinical models, and polygenic scores
Gilbert Jabbour
Alexis Nolin-Lapalme
Olivier Tastet
Denis Corbin
Paloma Jordà
Achille Sowa
Jacques Delfrate
David Busseuil
Julie G. Hussin
Marie-Pierre Dubé
Jean-Claude Tardif
Léna Rivard
Laurent Macle
Julia Cadrin-Tourigny
Paul Khairy
Robert Avram
Rafik Tadros
Deep learning applied to electrocardiograms (ECG-AI) is an emerging approach for predicting atrial fibrillation or flutter (AF). This study … (voir plus)introduces an ECG-AI model developed and tested at a tertiary cardiac centre, comparing its performance with clinical models and AF polygenic score (PGS). Electrocardiograms in sinus rhythm from the Montreal Heart Institute were analysed, excluding those from patients with pre-existing AF. The primary outcome was incident AF at 5 years. An ECG-AI model was developed by splitting patients into non-overlapping data sets: 70% for training, 10% for validation, and 20% for testing. The performance of ECG-AI, clinical models, and PGS was assessed in the test data set. The ECG-AI model was externally validated in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) hospital data set. A total of 669 782 ECGs from 145 323 patients were included. Mean age was 61 ± 15 years, and 58% were male. The primary outcome was observed in 15% of patients, and the ECG-AI model showed an area under the receiver operating characteristic (AUC-ROC) curve of .78. In time-to-event analysis including the first ECG, ECG-AI inference of high risk identified 26% of the population with a 4.3-fold increased risk of incident AF (95% confidence interval: 4.02–4.57). In a subgroup analysis of 2301 patients, ECG-AI outperformed CHARGE-AF (AUC-ROC = .62) and PGS (AUC-ROC = .59). Adding PGS and CHARGE-AF to ECG-AI improved goodness of fit (likelihood ratio test P .001), with minimal changes to the AUC-ROC (.76–.77). In the external validation cohort (mean age 59 ± 18 years, 47% male, median follow-up 1.1 year), ECG-AI model performance remained consistent (AUC-ROC = .77). ECG-AI provides an accurate tool to predict new-onset AF in a tertiary cardiac centre, surpassing clinical and PGS.
2024-08-31
European Heart Journal (publié)
doi.org
TULIPS decorate the three-dimensional genome of PFA ependymoma
Michael J. Johnston
John J.Y. Lee
Bo Hu
Ana Nikolic
Elham Hasheminasabgorji
Audrey Baguette
Seungil Paik
Haifen Chen
Sachin Kumar
Carol C.L. Chen
Selin Jessa
Polina Balin
Vernon Fong
Melissa Zwaig
Kulandaimanuvel Antony Michealraj
Xun Chen
Yanlin Zhang
Srinidhi Varadharajan
Pierre Billon
Nikoleta Juretic … (voir 30 de plus)
Craig Daniels
Amulya Nageswara Rao
Caterina Giannini
Eric M. Thompson
Miklos Garami
Peter Hauser
Timea Pocza
Young Shin Ra
Byung-Kyu Cho
Seung-Ki Kim
Kyu-Chang Wang
Ji Yeoun Lee
Wieslawa Grajkowska
Marta Perek-Polnik
Sameer Agnihotri
Stephen Mack
Benjamin Ellezam
Alex Weil
Jeremy Rich
Guillaume Bourque
Jennifer A. Chan
V. Wee Yong
Mathieu Lupien
Jiannis Ragoussis
Claudia Kleinman
Jacek Majewski
Mathieu Blanchette
Nada Jabado
Michael D. Taylor
Marco Gallo
2024-08-31
Cell (publié)
doi.org
Virtual Reality for Pediatric Trauma Education - A Preliminary Face and Content Validation Study
Fabio Botelho
Said Ashkar
Shreenik Kundu
TJ Matthews
Elena Guadgano
Dan Poenaru
2024-08-31
Journal of Pediatric Surgery (publié)
doi.org
Herbarium collections remain essential in the age of community science
Isaac Eckert
Anne Bruneau
D. Metsger
Simon Joly
T. Dickinson
Laura J. Pollock
2024-08-30
Nature Communications (publié)
doi.org