Publications

An exploratory cross-sectional study of the effects of ongoing relationships with accompanying patients on cancer care experience, self-efficacy, and psychological distress
Marie-Pascale Pomey
Monica Iliescu Nelea
Louise Normandin
Cécile Vialaron
Karine Bouchard
Marie-Andrée Côté
Maria Alejandra Rodriguez Duarte
Djahanchah Philip Ghadiri
Israël Fortin
Danielle Charpentier
Mélanie Lavoie-Tremblay
Nicolas Fernandez
Antoine Boivin
Michel Dorval
Mado Desforges
Catherine Régis
Isabelle Ganache
Lynda Bélanger
Zeev Rosberger
Michel Alain Danino … (see 3 more)
Jean-François Pelletier
Thi Trinh Thuc Vu
Michèle de Guise
Centre hospitalier de l’Université de Montréal in Canada introduced accompanying patients (APs) into the breast cancer care trajectory. … (see more)APs are patients who have been treated for breast cancer and have been integrated into the clinical team to expand the services offered to people affected by cancer. This study describes the profiles of the people who received the support and explores whether one-offs vs ongoing encounters with APs influence their experience of care, on self-efficacy in coping with cancer, and on their level of psychological distress. An exploratory cross-sectional study was carried out among patients to compare patients who had one encounter with an AP (G1) with those who had had several encounters (G2). Five questionnaires were administered on socio-demographic characteristics, care pathway, evaluation of the support experience, self-efficacy in coping with cancer, and level of psychological distress. Logbooks, completed by the APs, determined the number of encounters. Linear regression models were used to evaluate the associations between the number of encounters, patient characteristics, care pathway, number of topics discussed, self-efficacy measures in coping with cancer, and level of psychological distress. Between April 2020 and December 2021, 60% of 535 patients who were offered support from an AP accepted. Of these, one hundred and twenty-four patients participated in the study. The study aimed to recruit a minimum of 70 patients with the expectation of obtaining at least 50 participants, assuming a response rate of 70%. There were no differences between G1 and G2 in terms of sociodemographic data and care pathways. Statistical differences were found between G1 and G2 for impacts on and the return to daily life (p = 0.000), the return to the work and impacts on professional life (p = 0.044), announcement of a diagnosis to family and friends (p = 0.033), and strategies for living with treatment under the best conditions (p = 0.000). Significant differences were found on the topics of cancer (p = 0.000), genetic testing (p = 0.023), therapeutic options (p = 0.000), fatigue following treatment (p = 0.005), pain and discomfort after treatment or surgery (p = 0.000), potential emotions and their management (p = 0.000) and the decision-making processes (p = 0.011). A significant relationship was found between the two groups for patients’ ability to cope with cancer (p = 0.038), and their level of psychological distress at different stages of the care pathway (p = 0.024). This study shows differences between one-time and ongoing support for cancer patients. It highlights the potential for APs to help patients develop self-efficacy and cope with the challenges of cancer treatment.
2023-04-21
BMC Cancer (published)
doi.org
SSS3D: Fast Neural Architecture Search For Efficient Three-Dimensional Semantic Segmentation
Olivier Therrien
Marihan Amein
Zhuoran Xiong
Warren J. Gross
Brett Meyer
We present SSS3D, a fast multi-objective NAS framework designed to find computationally efficient 3D semantic scene segmentation networks. I… (see more)t uses RandLA-Net, an off-the-shelf point-based network, as a super-network to enable weight sharing and reduce search time by 99.67% for single-stage searches. SSS3D has a complex search space composed of sampling and architectural parameters that can form 2.88 * 10^17 possible networks. To further reduce search time, SSS3D splits the complete search space and introduces a two-stage search that finds optimal subnetworks in 54% of the time required by single-stage searches.
2023-04-20
ArXiv (preprint)
doi.org
arxiv.org
The Flag and the Cross: White Christian Nationalism and the Threat to American Democracy by Philip S. Gorski and Samuel L. Perry (review)
David M. Krueger
2023-04-20
(published)
www.semanticscholar.org
Aspirations and Practice of ML Model Documentation: Moving the Needle with Nudging and Traceability
Avinash Bhat
Austin Coursey
Grace Hu
Sixian Li
Nadia Nahar
Shurui Zhou
Christian Kästner
Jin L.C. Guo
The documentation practice for machine-learned (ML) models often falls short of established practices for traditional software, which impede… (see more)s model accountability and inadvertently abets inappropriate or misuse of models. Recently, model cards, a proposal for model documentation, have attracted notable attention, but their impact on the actual practice is unclear. In this work, we systematically study the model documentation in the field and investigate how to encourage more responsible and accountable documentation practice. Our analysis of publicly available model cards reveals a substantial gap between the proposal and the practice. We then design a tool named DocML aiming to (1) nudge the data scientists to comply with the model cards proposal during the model development, especially the sections related to ethics, and (2) assess and manage the documentation quality. A lab study reveals the benefit of our tool towards long-term documentation quality and accountability.
2023-04-18
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (published)
doi.org
arxiv.org
Co-Writing Screenplays and Theatre Scripts with Language Models: Evaluation by Industry Professionals
Piotr Mirowski
Kory Mathewson
Jaylen Pittman
Richard Evans
2023-04-18
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (published)
doi.org
arxiv.org
From Plane Crashes to Algorithmic Harm: Applicability of Safety Engineering Frameworks for Responsible ML
Shalaleh Rismani
Renee Shelby
Andrew J Smart
Edgar Jatho
Joshua A. Kroll
AJung Moon
Negar Rostamzadeh
2023-04-18
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (published)
doi.org
openreview.net
Investigating the Nature of 3D Generalization in Deep Neural Networks
Shoaib Ahmed Siddiqui
David M. Krueger
Thomas M. Breuel
2023-04-18
ArXiv (preprint)
doi.org
arxiv.org
Playing the System: Can Puzzle Players Teach us How to Solve Hard Problems?
Renata Mutalova
Roman Sarrazin-Gendron
Eddie Cai
Gabriel Richard
Parham Ghasemloo Gheidari
Sébastien Caisse
Rob Knight
Mathieu Blanchette
Attila Szantner
Jérôme Waldispühl
With nearly three billion players, video games are more popular than ever. Casual puzzle games are among the most played categories. These g… (see more)ames capitalize on the players’ analytical and problem-solving skills. Can we leverage these abilities to teach ourselves how to solve complex combinatorial problems? In this study, we harness the collective wisdom of millions of players to tackle the classical NP-hard problem of multiple sequence alignment, relevant to many areas of biology and medicine. We show that Borderlands Science players propose solutions to multiple sequence alignment tasks that perform as well or better than standard approaches, while exploring a much larger area of the Pareto-optimal solution space. We also show the strategies of the players, although highly heterogeneous, follow a collective logic that can be mimicked with Behavioral Cloning with minimal performance loss, allowing the players’ collective wisdom to be leveraged for alignment of any sequences.
2023-04-18
International Conference on Human Factors in Computing Systems (published)
doi.org
Using rare genetic mutations to revisit structural brain asymmetry
Jakub Kopal
Kuldeep Kumar
Kimia Shafighi
Karin Saltoun
Claudia Modenato
Clara A. Moreau
Guillaume Huguet
Martineau Jean-Louis
Charles-Olivier Martin
Charles-Olivier Martin
Zohra Saci
Nadine Younis
Elise Douard
Khadije Jizi
Alexis Beauchamp-Chatel
Leila Kushan
Ana I. Silva
Marianne B. M. van den Bree
David E. J. Linden
Michael J. Owen … (see 11 more)
Jeremy Hall
Sarah Lippé
Bogdan Draganski
Ida E. Sønderby
Ole A. Andreassen
David C. Glahn
Paul M. Thompson
Carrie E. Bearden
Robert Zatorre
Sébastien Jacquemont
Danilo Bzdok
Asymmetry between the left and right brain is a key feature of brain organization. Hemispheric functional specialization underlies some of t… (see more)he most advanced human-defining cognitive operations, such as articulated language, perspective taking, or rapid detection of facial cues. Yet, genetic investigations into brain asymmetry have mostly relied on common variant studies, which typically exert small effects on brain phenotypes. Here, we leverage rare genomic deletions and duplications to study how genetic alterations reverberate in human brain and behavior. We quantitatively dissected the impact of eight high-effect-size copy number variations (CNVs) on brain asymmetry in a multi-site cohort of 552 CNV carriers and 290 non-carriers. Isolated multivariate brain asymmetry patterns spotlighted regions typically thought to subserve lateralized functions, including language, hearing, as well as visual, face and word recognition. Planum temporale asymmetry emerged as especially susceptible to deletions and duplications of specific gene sets. Targeted analysis of common variants through genome-wide association study (GWAS) consolidated partly diverging genetic influences on the right versus left planum temporale structure. In conclusion, our gene-brain-behavior mapping highlights the consequences of genetically controlled brain lateralization on human-defining cognitive traits.
2023-04-17
bioRxiv (preprint)
doi.org
Fast D
<sub>M,M</sub> calculation in LDR brachytherapy using deep learning methods
Francisco Berumen
S. Enger
Luc Beaulieu
2023-04-13
Physics in Medicine and Biology (published)
doi.org
Meta Pseudo Labels for Anomaly Detection via Partially Observed Anomalies
Sinong Zhao
Zhaoyang Yu
Xiaofei Wang
T. Marbach
Gang Wang
Xiaoguang Liu
2023-04-13
Database Systems for Advanced Applications (published)
doi.org
VulANalyzeR: Explainable Binary Vulnerability Detection with Multi-task Learning and Attentional Graph Convolution
Litao Li
Steven H. H. Ding
Yuan Tian
Benjamin C. M. Fung
Philippe Charland
Weihan Ou
Leo Song
Congwei Chen
2023-04-13
ACM Transactions on Privacy and Security (published)
doi.org