Publications

Evaluating In-Context Learning of Libraries for Code Generation
Arkil Patel
Siva Reddy
Dzmitry Bahdanau
Pradeep Dasigi
2024-05-31
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (published)
doi.org
arxiv.org
Immunotherapeutic targeting of surfaceome heterogeneity in AML
Marie-Eve Bordeleau
Éric Audemard
Arnaud Metois
Louis Theret
Véronique Lisi
Azer Farah
Jean-François Spinella
Jalila Chagraoui
Ossama Moujaber
Léo Aubert
Banafsheh Khakipoor
Laure Mallinger
Isabel Boivin
Nadine Mayotte
Azadeh Hajmirza
Éric Bonneil
Francois Béliveau
Sybille Pfammatter
Albert Feghaly
Geneviève Boucher … (see 9 more)
Patrick Gendron
Pierre Thibault
Frederic Barabe
Sébastien Lemieux
Guillaume Richard-Carpentier
Josée Hébert
Vincent-Philippe Lavallee
Philippe P. Roux
Guy Sauvageau
2024-05-31
Cell Reports (published)
doi.org
Implementation of a Global Pediatric Trauma Course in an Upper Middle–Income Country: A Pilot Study
Abbie Naus
Madeleine Carroll
Ayla Gerk
David P. Mooney
Natalie L. Yanchar
Julia Ferreira
Dan Poenaru
Karen E. Gripp
Caroline Ouellet
Fabio Botelho
2024-05-31
Journal of Surgical Research (published)
doi.org
A machine learning pipeline for automated insect monitoring
Aditya Jain
F. Cunha
M. J. Bunsen
L. Pasi
Anna Viklund
Maxim Larrivée
David Rolnick
Climate change and other anthropogenic factors have led to a catastrophic decline in insects, endangering both biodiversity and the ecosyste… (see more)m services on which human society depends. Data on insect abundance, however, remains woefully inadequate. Camera traps, conventionally used for monitoring terrestrial vertebrates, are now being modified for insects, especially moths. We describe a complete, open-source machine learning-based software pipeline for automated monitoring of moths via camera traps, including object detection, moth/non-moth classification, fine-grained identification of moth species, and tracking individuals. We believe that our tools, which are already in use across three continents, represent the future of massively scalable data collection in entomology.
2024-05-31
arXiv (published)
doi.org
arxiv.org
Methods, Applications, and Directions of Learning-to-Rank in NLP Research
Justin Lee
Gabriel Bernier-Colborne
Tegan Maharaj
Sowmya Vajjala
Learning-to-rank (LTR) algorithms aim to order a set of items according to some criteria. They are at the core of applications such as web s… (see more)earch and social media recommendations, and are an area of rapidly increasing interest, with the rise of large language models (LLMs) and the widespread impact of these technologies on society. In this paper, we survey the diverse use cases of LTR methods in natural language processing (NLP) research, looking at previously under-studied aspects such as multilingualism in LTR applications and statistical significance testing for LTR problems. We also consider how large language models are changing the LTR landscape. This survey is aimed at NLP researchers and practitioners interested in understanding the formalisms and best practices regarding the application of LTR approaches in their research.
2024-05-31
Findings of the Association for Computational Linguistics: NAACL 2024 (published)
doi.org
"One-Size-Fits-All"? Examining Expectations around What Constitute"Fair"or"Good"NLG System Behaviors
Li Lucy
Su Lin Blodgett
Milad Shokouhi
Hanna Wallach
A.R. Olteanu
Fairness-related assumptions about what constitute appropriate NLG system behaviors range from invariance, where systems are expected to beh… (see more)ave identically for social groups, to adaptation, where behaviors should instead vary across them. To illuminate tensions around invariance and adaptation, we conduct five case studies, in which we perturb different types of identity-related language features (names, roles, locations, dialect, and style) in NLG system inputs. Through these cases studies, we examine people's expectations of system behaviors, and surface potential caveats of these contrasting yet commonly held assumptions. We find that motivations for adaptation include social norms, cultural differences, feature-specific information, and accommodation; in contrast, motivations for invariance include perspectives that favor prescriptivism, view adaptation as unnecessary or too difficult for NLG systems to do appropriately, and are wary of false assumptions. Our findings highlight open challenges around what constitute"fair"or"good"NLG system behaviors.
2024-05-31
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (published)
doi.org
arxiv.org
PCR191 Patient-Centric Assessment of Treatment Experience in Breast Cancer: Development and Validation of a Patient Questionnaire
K. Gurjar
B. Rattanavong
L. Bennetts
J. Sahota
M. Ouerghi
C. Ammendolea
J. Asselah
S. Bartlett
C. Brezden-Masley
J. Croke
T. Hijal
J. Kildea
J. Papadakos
L. Watson
D. Soliman
2024-05-31
Value in Health (published)
doi.org
Pioneering women in nuclear and radiation sciences
Mirta Dumancic
S. Enger
2024-05-31
Radiotherapy and Oncology (published)
doi.org
Protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique
Manqi Zhou
Hao Zhang
Zilong Bai
Dylan Mann-Krzisnik
Fei Wang
Yue Li
2024-05-31
STAR Protocols (published)
doi.org
Reducing Two-Way Ranging Variance by Signal-Timing Optimization
Mohammed Ayman Shalaby
Charles Champagne Cossette
James Richard Forbes
Jerome Le Ny
Time-of-flight-based ranging among transceivers with different clocks requires protocols that accommodate varying rates of the clocks. Doubl… (see more)e-sided two-way ranging (DS-TWR) is widely adopted as a standard protocol due to its accuracy; however, the precision of DS-TWR has not been clearly addressed. In this paper, an analytical model of the variance of DS-TWR is derived as a function of the user-programmed response delays, which is then compared to the Cramer-Rao Lower Bound (CRLB). This is then used to formulate an optimization problem over the response delays in order to maximize the information gained from range measurements. The derived analytical variance model and optimized protocol are validated experimentally with 2 ranging UWB transceivers, where 29 million range measurements are collected.
2024-05-31
IEEE Transactions on Aerospace and Electronic Systems (published)
doi.org
arxiv.org
A responsible framework for applying artificial intelligence on medical images and signals at the point-of-care: the PACS-AI platform.
Pascal Thériault-Lauzier
Denis Cobin
Olivier Tastet
Élodie Labrecque Langlais
B. Taji
Guson Kang
A. Chong
Derek So
An Tang
Judy Wawira Gichoya
A. Chandar
Pierre-Luc Deziel
Julie G Hussin
Samuel Kadoury
Robert Avram
2024-05-31
Canadian Journal of Cardiology (published)
doi.org
Revisiting the 2023 wildfire season in Canada
Flavie Pelletier
Jeffrey A. Cardille
Michael A. Wulder
Joanne C. White
Txomin Hermosilla
2024-05-31
Science of Remote Sensing (published)
doi.org