Canadian Spine Society
Antoine Dionne
Majeed Al-Zakri
Hubert Labelle
Julie Joncas
Baron Lonner
Ali Eren
Patrick Cahill
Peter Newton
Liisa Jaakkimainen
Teresa To
Maryse Bouchard
Sarah Hardy
Dilani Thevarajah
Rajendra Sakhrekar
Ayesha Hadi
Andrea Doria
Aya Mitani
Andrew Howard
Samuel Yoon
Karen Mathias … (see 346 more)
Tracey Bastrom
Amer Samdani
Marjolaine Roy-Beaudry
Marie Beausejour
Rachelle Imbeault
Justin Dufresne
Stefan Parent
Jessica Romeo
Holly Livock
Kevin Smit
James Jarvis
Andrew Tice
Vivien K. Chan
Robert Cho
Selina Poon
David L. Skaggs
Geoffrey K. Shumilak
Brett Rocos
Juan P. Sardi
Anastasios Charalampidis
Jeff Gum
Peter S. Tretiakov
Oluwatobi Onafowokan
Jamshaid Mir
Ankita Das
Tyler Williamson
Pooja Dave
Bailey Imbo
Jordan Lebovic
Pawel Jankowski
Peter G. Passias
Yousef Aljamaan
Vishal P. Varshney
Ramesh Sahjpaul
Jill Osborn
Rémi Pelletier-Roy
Michael Asmussen
Manjot Birk
Taryn Ludwig
Fred Nicholls
Ariel Zohar
Janneke Loomans
Ferran Pellise
Justin Smith
So Kato
Zeeshan Sardar
Lawrence G. Lenke
Stephen J. Lewis
Aazad Abbas
Jay Toor
Gurjovan Sahi
Dusan Kovacevic
Johnathan Lex
Firoz Miyanji
Anthony V. Perruccio
Nizar Mahomed
Mayilee Canizares
Yousef Kamel
Galil Osman
Nikolaus Koegl
Brandon Herrington
Renan R. Fernandes
Jennifer C. Urquhart
Ramtin Hakimjavadi
Zachary DeVries
Noah Fine
Laura Stone
Mohit Kapoor
Alexandre Chenevert
Sonia Bédard
Julien Goulet
Jerome Couture
Bernard LaRue
Meaghan Rye
Alexa Roussac
Neda Naghdi
Luciana G. Macedo
James Elliott
Richard DeMont
Véronique Pepin
Zhi Wang
Maroun Rizkallah
Jesse Shen
Michel Alexandre Lebreton
Edisond Florial
Fidaa Alshakfa
Ghassan Boubez
Abdullah A.S.M. AlDuwaisan
Kim Phan
Sarah Nowell
Niels Wedderkopp
Michael Craig
Abdul Al-Shawwa
Kalum Ost
Saswati Tripathy
Bradley W. Jacobs
Nathan Evaniew
Chris Bailey
W. Bradley Jacobs
Andrew Nataraj
David W. Cadotte
Kenneth C. Thomas
Hamilton Hall
Eva Y. Liu
Amit R.L. Persad
Nathan Baron
Daryl Fourney
Jingyi Huang
Thamer Alfawaz
Tinghua Zhang
CSORN Investigators
Karlo M. Pedro
Mohammed Ali Alvi
Jessica C.W. Wang
Nicolas Dea
Tamir Ailon
Scott Paquette
John Street
Charlotte Dandurand
Rohail Mumtaz
Khaled Skaik
Eugene K. Wai
Alexandra Stratton
Ragavan Manoharan
Jenna Smith-Forrester
JoAnne E. Douglas
Evan Nemeth
Jacob Alant
Sean Barry
Andrew Glennie
William Oxner
Lutz M. Weise
Sabahat Saeed
Patrick Toyota
Jack Su
Braeden Newton
Nicole Coote
Maria S. Rachevits
Helen Razmjou
Susan Robarts
Albert Yee
Joel Finkelstein
Alysa Almojuela
Frederick Zeiler
Sarvesh Logsetty
Perry Dhaliwal
Mark Abdelnour
Yuxin Zhang
Stephen P. Kingwell
Philippe Phan
Taylor A. Smith
Michael Bond
Stephan Dombrowski
Gwyneth Price
Jose Manuel García-Moreno
Steven Qiu
Vithushan Surendran
Victoria Shi Emily Cheung
Sophie Ngana
Muhammad A. Qureshi
Sunjay V. Sharma
Markian Pahuta
Daipayan Guha
Ahmed Essa
Husain Shakil
James Byrne
Andrew S. Jack
Francois Mathieu
Eva Yuan
Christopher W. Smith
Erin M. Harrington
Rachel H. Jaffe
Alick P. Wang
Karim Ladha
Avery B. Nathens
Ryan V. Sandarage
Ahmad Galuta
Eve C. Tsai
Naama Rotem-Kohavi
Marcel Dvorak
Jijie Xu
Nader Fallah
Zeina Waheed
Melody Chen
Vanessa K. Noonan
Toluyemi Malomo
Charles G. Fisher
Rachael Jaffe
Peter Coyte
Brian Chan
Armaan Malhotra
Rebecca Hancock-Howard
Jefferson R. Wilson
Christopher D. Witiw
Newton Cho
Jordan Squair
Viviana Aureli
Nicholas James
Lea Bole-Feysot
Inssia Dewany
Nicolas Hankov
Laetitia Baud
Anna Leonhartsberger
Kristina Sveistyte
Michael Skinnider
Matthieu Gautier
Katia Galan
Maged Goubran
Jimmy Ravier
Frederic Merlos
Laura Batti
Stéphane Pagès
Nadia Bérard
Nadine Intering
Camille Varescon
Stefano Carda
Kay Bartholdi
Thomas Hutson
Claudia Kathe
Michael Hodara
Mark Anderson
Bogdan Draganski
Robin Demesmaeker
Leonie Asboth
Quentin Barraud
Jocelyne Bloch
Gregoire Courtine
Sean D. Christie
Ryan Greene
Mustafa Nadi
Bill Oxner
Lisa Julien
Clara Lownie
Cumhur F.C. Öner
Alexander Joeris
Klaus Schnake
Mark Phillips
Alexander R. Vaccaro
Richard Bransford
Eugen Cezar Popescu
Mohammed El-Sharkawi
Shanmuganathan Rajasekaran
Lorin M. Benneker
Greg D. Schroeder
Jin W. Tee
John France
Jérôme Paquet
Richard Allen
William F. Lavelle
Emiliano Vialle
David Magnuson
Andréane Richard-Denis
Yvan Petit
Francis Bernard
Dorothy Barthélemy
Lukas Grassner
Daniel Garcia-Ovejero
Evelyn Beyerer
Orpheus Mach
Iris Leister
Doris Maier
Ludwig Aigner
Angel Arevalo-Martin
Mark Alexander MacLean
Antoinette Charles
Raphaële Charest-Morin
Rory Goodwin
Michael H. Weber
Emile Brouillard
Ismail Laassassy
Paul Khoueir
Étienne Bourassa-Moreau
Gilles Maurais
Jean-Marc Mac-Thiong
Julien Francisco Zaldivar-Jolissaint
Aysha Allard Brown
Kitty So
Neda Manouchehri
Megan Webster
Jay Ethridge
Audrey Warner
Avril Billingsley
Rochelle Newsome
Kirsten Bale
Andrew Yung
Mehara Seneviratne
Jimmy Cheng
Jing Wang
Shenani Basnayake
Femke Streijger
Manraj Heran
Piotr Kozlowski
Brian K. Kwon
Jeff D. Golan
Lior M. Elkaim
Qais Alrashidi
Miltiadis Georgiopoulos
Oliver Lasry
Drew A. Bednar
Alyson Love
Soroush Nedaie
Pranjan Gandhi
Prarthan C. Amin
Christopher J. Neilsen
Amanda Vandewint
Y. Raja Rampersaud
Jeffrey Hebert
Eden Richardson
Jillian Kearney
Raja Rampersaud
Aditya Raj
Nanadan Marathe
Greg McIntosh
Manmeet Dhiman
Taylor J. Bader
David Hart
Ganesh Swamy
Neil Duncan
Dragana Ponjevic
John R. Matyas
Connor P. O’Brien
Erin Bigney
Edward Abraham
Neil Manson
Najmedden Attabib
Chris Small
Luke LaRochelle
Gabriella Rivas
James Lawrence
Robert Ravinsky
Lily S. Switzer
David E. Lebel
Chanelle Montpetit
Nicolas Vaillancourt
Emma Nadler
Jennifer A. Dermott
Dorothy J. Kim
Brent Rosenstein
Daniel Wolfe
Geoffrey Dover
Mathieu Boily
Maryse Fortin
Jetan Badhiwala
Vishu Karthikeyan
Yingshi He
Michael G. Fehlings
2024-11-13
Canadian journal of surgery. Journal canadien de chirurgie (published)
doi.org
Abstract 4142894: Multimorbidity Trajectories Across the Lifespan in Patients with Congenital Heart Disease
Chao Li
Aihua Liu
Solomon Bendayan
Liming Guo
Judith Therrien
Archer Yang
Robyn Tamblyn
Jay Brophy
Yue Li
Ariane Marelli
Background: Befitted from advances in medical care, patients with congenital heart disease (CHD) now survive to adulthood but face elevated… (see more) risks of both cardiac and non-cardiac complications. Understanding the trajectories of comorbidity development over a patient's lifespan is cornerstone to optimize care expected to improve long-term health outcomes. Research Aim: This study aims to investigate the temporal sequences and evolution of comorbidities in CHD patients across their lifespan. We hypothesize that multimorbidity trajectories in CHD patients are linked to CHD lesion severity and age at onset of specific comorbidities. Methods: Using the Quebec CHD database which comprised data in outpatient visits, hospitalization records and vital status from 1983 to 2017, we designed a longitudinal cohort study evaluating the development of 39 comorbidities coded using ICD-9/10. Temporal sequences were mapped using median age of onset. Associations between disease pairs were quantified by hazard ratios from Cox proportional hazard models adjusting for age, sex, genetic syndrome, competing risks of death, and taking into account the time-varying nature of the predictor diseases. Results: The cohort included 9,764 individuals with severe and 127,729 with non-severe CHD lesions. In severe CHD patients, most comorbidities developed between ages 25 and 40. Comorbidity progression began with childhood cardiovascular diseases, followed by systemic diseases such as diabetes, liver and kidney diseases, and advanced to heart failure and dementia in middle adulthood. In addition, mental disorders emerged in early adulthood and were associated with subsequent development of kidney diseases and dementia. Different trajectories were observed in non-severe CHD patients with 2-3 decades later disease onsets and non-differential onsets between cardiovascular and systemic complications (Figure). Conclusions: Distinct multimorbidity trajectories were observed in CHD patients by CHD lesion severity. In patients with severe CHD lesions, early systemic diseases significantly influenced subsequent complications. These findings highlight the need for well-timed surveillance guidelines and interventions to improve health outcomes.
2024-11-12
Circulation (published)
doi.org
Beyond the Safety Bundle: Auditing the Helpful and Harmless Dataset
Khaoula Chehbouni
Jonathan Colaco Carr
Yash More
Jackie Cheung
Golnoosh Farnadi
In an effort to mitigate the harms of large language models (LLMs), learning from human feedback (LHF) has been used to steer LLMs towards o… (see more)utputs that are intended to be both less harmful and more helpful. Despite the widespread adoption of LHF in practice, the quality of this feedback and its effectiveness as a safety mitigation technique remain unclear. This study addresses these issues by auditing the widely-used Helpful and Harmless (HH) dataset by Anthropic. Our work includes: (1) a thorough investigation of the dataset's content through both manual and automated evaluation; (2) experiments demonstrating the dataset's impact on models' safety; and (3) an analysis of the 100 most influential papers citing this dataset. Through our audit, we showcase how conceptualization failures and quality issues identified in the HH dataset can create additional harms by leading to disparate safety behaviors across demographic groups. Our findings highlight the need for more nuanced, context-sensitive approaches to safety mitigation in LLMs.
2024-11-12
ArXiv (preprint)
doi.org
arxiv.org
Beyond the Safety Bundle: Auditing the Helpful and Harmless Dataset
Khaoula Chehbouni
Jonathan Colacco-Carr
Yash More
Jackie Ck Cheung
Golnoosh Farnadi
In an effort to mitigate the harms of large language models (LLMs), learning from human feedback (LHF) has been used to steer LLMs towards o… (see more)utputs that are intended to be both less harmful and more helpful. Despite the widespread adoption of LHF in practice, the quality of this feedback and its effectiveness as a safety mitigation technique remain unclear. This study addresses these issues by auditing the widely-used Helpful and Harmless (HH) dataset by Anthropic. Our work includes: (1) a thorough investigation of the dataset's content through both manual and automated evaluation; (2) experiments demonstrating the dataset's impact on models' safety; and (3) an analysis of the 100 most influential papers citing this dataset. Through our audit, we showcase how conceptualization failures and quality issues identified in the HH dataset can create additional harms by leading to disparate safety behaviors across demographic groups. Our findings highlight the need for more nuanced, context-sensitive approaches to safety mitigation in LLMs.
2024-11-12
ArXiv (preprint)
doi.org
arxiv.org
Fault Localization in Deep Learning-based Software: A System-level Approach
Mohammad Mehdi Morovati
Amin Nikanjam
Foutse Khomh
2024-11-12
ArXiv (preprint)
doi.org
arxiv.org
Fault Localization in Deep Learning-based Software: A System-level Approach
Mohammad Mehdi Morovati
Amin Nikanjam
Foutse Khomh
Over the past decade, Deep Learning (DL) has become an integral part of our daily lives. This surge in DL usage has heightened the need for … (see more)developing reliable DL software systems. Given that fault localization is a critical task in reliability assessment, researchers have proposed several fault localization techniques for DL-based software, primarily focusing on faults within the DL model. While the DL model is central to DL components, there are other elements that significantly impact the performance of DL components. As a result, fault localization methods that concentrate solely on the DL model overlook a large portion of the system. To address this, we introduce FL4Deep, a system-level fault localization approach considering the entire DL development pipeline to effectively localize faults across the DL-based systems. In an evaluation using 100 faulty DL scripts, FL4Deep outperformed four previous approaches in terms of accuracy for three out of six DL-related faults, including issues related to data (84%), mismatched libraries between training and deployment (100%), and loss function (69%). Additionally, FL4Deep demonstrated superior precision and recall in fault localization for five categories of faults including three mentioned fault types in terms of accuracy, plus insufficient training iteration and activation function.
2024-11-12
ArXiv (preprint)
doi.org
arxiv.org
Investigating the Effectiveness of Explainability Methods in Parkinson's Detection from Speech
Eleonora Mancini
Francesco Paissan
Paolo Torroni
Mirco Ravanelli
Cem Subakan
Speech impairments in Parkinson's disease (PD) provide significant early indicators for diagnosis. While models for speech-based PD detectio… (see more)n have shown strong performance, their interpretability remains underexplored. This study systematically evaluates several explainability methods to identify PD-specific speech features, aiming to support the development of accurate, interpretable models for clinical decision-making in PD diagnosis and monitoring. Our methodology involves (i) obtaining attributions and saliency maps using mainstream interpretability techniques, (ii) quantitatively evaluating the faithfulness of these maps and their combinations obtained via union and intersection through a range of established metrics, and (iii) assessing the information conveyed by the saliency maps for PD detection from an auxiliary classifier. Our results reveal that, while explanations are aligned with the classifier, they often fail to provide valuable information for domain experts.
2024-11-12
ArXiv (preprint)
doi.org
arxiv.org
Investigating the Effectiveness of Explainability Methods in Parkinson's Detection from Speech
Eleonora Mancini
Francesco Paissan
Paolo Torroni
Mirco Ravanelli
Cem Subakan
Speech impairments in Parkinson's disease (PD) provide significant early indicators for diagnosis. While models for speech-based PD detectio… (see more)n have shown strong performance, their interpretability remains underexplored. This study systematically evaluates several explainability methods to identify PD-specific speech features, aiming to support the development of accurate, interpretable models for clinical decision-making in PD diagnosis and monitoring. Our methodology involves (i) obtaining attributions and saliency maps using mainstream interpretability techniques, (ii) quantitatively evaluating the faithfulness of these maps and their combinations obtained via union and intersection through a range of established metrics, and (iii) assessing the information conveyed by the saliency maps for PD detection from an auxiliary classifier. Our results reveal that, while explanations are aligned with the classifier, they often fail to provide valuable information for domain experts.
2024-11-12
ArXiv (preprint)
doi.org
arxiv.org
Refining SARS-CoV-2 Intra-host Variation by Leveraging Large-scale Sequencing Data
Fatima Mostefai
Jean-Christophe Grenier
Raphael Poujol
Julie Hussin
2024-11-12
NAR Genomics and Bioinformatics (published)
doi.org
Combining Domain and Alignment Vectors to Achieve Better Knowledge-Safety Trade-offs in LLMs
Megh Thakkar
Yash More
Quentin Fournier
Matthew D Riemer
Pin-Yu Chen
Amal Zouaq
Payel Das
Sarath Chandar
There is a growing interest in training domain-expert LLMs that excel in specific technical fields compared to their general-purpose instruc… (see more)tion-tuned counterparts. However, these expert models often experience a loss in their safety abilities in the process, making them capable of generating harmful content. As a solution, we introduce an efficient and effective merging-based alignment method called \textsc{MergeAlign} that interpolates the domain and alignment vectors, creating safer domain-specific models while preserving their utility. We apply \textsc{MergeAlign} on Llama3 variants that are experts in medicine and finance, obtaining substantial alignment improvements with minimal to no degradation on domain-specific benchmarks. We study the impact of model merging through model similarity metrics and contributions of individual models being merged. We hope our findings open new research avenues and inspire more efficient development of safe expert LLMs.
2024-11-11
ArXiv (preprint)
doi.org
arxiv.org
Combining Domain and Alignment Vectors to Achieve Better Knowledge-Safety Trade-offs in LLMs
Megh Thakkar
Yash More
Quentin Fournier
Matthew D Riemer
Pin-Yu Chen
Amal Zouaq
Payel Das
Sarath Chandar
There is a growing interest in training domain-expert LLMs that excel in specific technical fields compared to their general-purpose instruc… (see more)tion-tuned counterparts. However, these expert models often experience a loss in their safety abilities in the process, making them capable of generating harmful content. As a solution, we introduce an efficient and effective merging-based alignment method called \textsc{MergeAlign} that interpolates the domain and alignment vectors, creating safer domain-specific models while preserving their utility. We apply \textsc{MergeAlign} on Llama3 variants that are experts in medicine and finance, obtaining substantial alignment improvements with minimal to no degradation on domain-specific benchmarks. We study the impact of model merging through model similarity metrics and contributions of individual models being merged. We hope our findings open new research avenues and inspire more efficient development of safe expert LLMs.
2024-11-11
ArXiv (preprint)
doi.org
arxiv.org
Comparing Bottom-Up and Top-Down Steering Approaches on In-Context Learning Tasks
Madeline Brumley
Joe Kwon
David Scott Krueger
Dmitrii Krasheninnikov
Usman Anwar
A key objective of interpretability research on large language models (LLMs) is to develop methods for robustly steering models toward desir… (see more)ed behaviors. To this end, two distinct approaches to interpretability -- ``bottom-up"and ``top-down"-- have been presented, but there has been little quantitative comparison between them. We present a case study comparing the effectiveness of representative vector steering methods from each branch: function vectors (FV; arXiv:2310.15213), as a bottom-up method, and in-context vectors (ICV; arXiv:2311.06668) as a top-down method. While both aim to capture compact representations of broad in-context learning tasks, we find they are effective only on specific types of tasks: ICVs outperform FVs in behavioral shifting, whereas FVs excel in tasks requiring more precision. We discuss the implications for future evaluations of steering methods and for further research into top-down and bottom-up steering given these findings.
2024-11-11
ArXiv (preprint)
doi.org
arxiv.org