Publications

A case–control study on predicting population risk of suicide using health administrative data: a research protocol
JianLi Wang
Fatemeh Gholi Zadeh Kharrat
Jean-François Pelletier
Louis Rochette
Eric Pelletier
Pascale Lévesque
Victoria Massamba
Camille Brousseau-Paradis
Mada Mohammed
Geneviève Gariépy
Christian Gagné
Alain Lesage
2023-02-27
BMJ Open (publié)
doi.org
DisKeyword: Tweet Corpora Exploration for Keyword Selection
Sacha Lévy
Reihaneh Rabbany
2023-02-27
Web Search and Data Mining (publié)
doi.org
Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity
Xiaoxuan Yan
Ru Kong
Aihuiping Xue
Qing Yang
Csaba Orban
Lijun An
Avram J. Holmes
Xing Qian
Jianzhong Chen
Xi-Nian Zuo
Juan Helen Zhou
Marielle V Fortier
Ai Peng Tan
Peter Gluckman
Yap Seng Chong
Michael J Meaney
Danilo Bzdok
Simon B. Eickhoff
B.T. Thomas Yeo
2023-02-25
NeuroImage (publié)
doi.org
Commonality in Recommender Systems: Evaluating Recommender Systems to Enhance Cultural Citizenship
Andres Ferraro
Gustavo Ferreira
Fernando Diaz
Georgina Born
2023-02-22
ArXiv (prépublication)
doi.org
arxiv.org
Recall, Robustness, and Lexicographic Evaluation
Fernando Diaz
Bhaskar Mitra
2023-02-22
ArXiv (prépublication)
arxiv.org
arxiv.org
Unsupervised Layer-wise Score Aggregation for Textual OOD Detection
Maxime DARRIN
Guillaume Staerman
Eduardo Dadalto Câmara Gomes
Jackie Ck Cheung
Pablo Piantanida
Pierre Colombo
2023-02-20
ArXiv (prépublication)
doi.org
arxiv.org
Interpret Your Care: Predicting the Evolution of Symptoms for Cancer Patients
Rupali Bhati
Jennifer Jones
Audrey Durand
Cancer treatment is an arduous process for patients and causes many side-effects during and post-treatment. The treatment can affect almost … (voir plus)all body systems and result in pain, fatigue, sleep disturbances, cognitive impairments, etc. These conditions are often under-diagnosed or under-treated. In this paper, we use patient data to predict the evolution of their symptoms such that treatment-related impairments can be prevented or effects meaningfully ameliorated. The focus of this study is on predicting the pain and tiredness level of a patient post their diagnosis. We implement an interpretable decision tree based model called LightGBM on real-world patient data consisting of 20163 patients. There exists a class imbalance problem in the dataset which we resolve using the oversampling technique of SMOTE. Our empirical results show that the value of the previous level of a symptom is a key indicator for prediction and the weighted average deviation in prediction of pain level is 3.52 and of tiredness level is 2.27.
2023-02-19
ArXiv (prépublication)
doi.org
arxiv.org
LAGrad: Statically Optimized Differentiable Programming in MLIR
Mai Jacob Peng
Christophe Dubach
2023-02-17
International Conference on Compiler Construction (publié)
doi.org
Effects of incoming particle energy and cluster size on the G-value of hydrated electrons.
Alaina Bui
H. Bekerat
Lilian Childress
Jack C Sankey
Jan Seuntjens
Shirin A. Enger
2023-02-16
Physica medica (Testo stampato) (publié)
doi.org
MOT: A Multi-Omics Transformer for Multiclass Classification Tumour Types Predictions
Mazid Osseni
Prudencio Tossou
François Laviolette
Jacques Corbeil
2023-02-16
Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (publié)
doi.org
Refactoring practices in the context of data-intensive systems
Biruk Asmare Muse
Foutse Khomh
Giuliano Antoniol
2023-02-16
Empirical Software Engineering (publié)
doi.org
Learning to Substitute Ingredients in Recipes
Bahare Fatemi
Quentin Duval
Rohit Girdhar
Michal Drozdzal
Adriana Romero Soriano
Recipe personalization through ingredient substitution has the potential to help people meet their dietary needs and preferences, avoid pote… (voir plus)ntial allergens, and ease culinary exploration in everyone's kitchen. To address ingredient substitution, we build a benchmark, composed of a dataset of substitution pairs with standardized splits, evaluation metrics, and baselines. We further introduce Graph-based Ingredient Substitution Module (GISMo), a novel model that leverages the context of a recipe as well as generic ingredient relational information encoded within a graph to rank plausible substitutions. We show through comprehensive experimental validation that GISMo surpasses the best performing baseline by a large margin in terms of mean reciprocal rank. Finally, we highlight the benefits of GISMo by integrating it in an improved image-to-recipe generation pipeline, enabling recipe personalization through user intervention. Quantitative and qualitative results show the efficacy of our proposed system, paving the road towards truly personalized cooking and tasting experiences.
2023-02-15
ArXiv (prépublication)
doi.org
arxiv.org