Publications

Continuous cutting plane algorithms in integer programming
Didier Chételat
Andrea Lodi
2023-06-30
Operations Research Letters (publié)
doi.org
arxiv.org
Curriculum frameworks and educational programs in artificial intelligence for medical students, residents, and practicing physicians: a scoping review protocol.
Raymond Tolentino
Ashkan Baradaran
Genevieve Gore
Pierre Pluye
S. A. Rahimi
OBJECTIVE The aim of this scoping review is to synthesize knowledge from the literature on curriculum frameworks and current educational pro… (voir plus)grams that focus on the teaching and learning of artificial intelligence (AI) for medical students, residents, and practicing physicians. INTRODUCTION To advance the implementation of AI in clinical practice, physicians need to have a better understanding of AI and how to use it within clinical practice. Consequently, medical education must introduce AI topics and concepts into the curriculum. Curriculum frameworks are educational road maps to teaching and learning. Therefore, any existing AI curriculum frameworks must be reviewed and, if none exist, such a framework must be developed. INCLUSION CRITERIA This review will include articles that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of articles and study designs will be included, except conference abstracts and protocols. METHODS This review will follow the JBI methodology for scoping reviews. Keywords will first be identified from relevant articles. Another search will then be conducted using the identified keywords and index terms. The following databases will be searched: MEDLINE (Ovid), Embase (Ovid), Cochrane Central Register of Controlled Trials (CENTRAL), CINAHL (EBSCOhost), and Scopus. Gray literature will also be searched. Articles will be limited to the English and French languages, commencing from the year 2000. The reference lists of all included articles will be screened for additional articles. Data will then be extracted from included articles and the results will be presented in a table.
2023-06-30
JBI Evidence Synthesis (publié)
doi.org
Dev2vec: Representing Domain Expertise of Developers in an Embedding Space
Arghavan Moradi Dakhel
Michel C. Desmarais
Foutse Khomh
2023-06-30
Information and Software Technology (publié)
doi.org
arxiv.org
A double-oracle, logic-based Benders decomposition approach to solve the K-adaptability problem
A. Ghahtarani
A. Saif
A. Ghasemi
Erick Delage
2023-06-30
Computers & Operations Research (publié)
doi.org
arxiv.org
FairPrism: Evaluating Fairness-Related Harms in Text Generation
Eve Fleisig
Aubrie Amstutz
Chad Atalla
Su Lin Blodgett
Hal Daumé III
A.R. Olteanu
Emily Sheng
Dan Vann
Hanna Wallach
It is critical to measure and mitigate fairness-related harms caused by AI text generation systems, including stereotyping and demeaning har… (voir plus)ms. To that end, we introduce FairPrism, a dataset of 5,000 examples of AI-generated English text with detailed human annotations covering a diverse set of harms relating to gender and sexuality. FairPrism aims to address several limitations of existing datasets for measuring and mitigating fairness-related harms, including improved transparency, clearer specification of dataset coverage, and accounting for annotator disagreement and harms that are context-dependent. FairPrism’s annotations include the extent of stereotyping and demeaning harms, the demographic groups targeted, and appropriateness for different applications. The annotations also include specific harms that occur in interactive contexts and harms that raise normative concerns when the “speaker” is an AI system. Due to its precision and granularity, FairPrism can be used to diagnose (1) the types of fairness-related harms that AI text generation systems cause, and (2) the potential limitations of mitigation methods, both of which we illustrate through case studies. Finally, the process we followed to develop FairPrism offers a recipe for building improved datasets for measuring and mitigating harms caused by AI systems.
2023-06-30
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (publié)
doi.org
Reinforcement Learning-Based Adaptive Feature Boosting for Smart Grid Intrusion Detection
Chengming Hu
Jun Yan
Xue Liu
Intrusion detection systems (IDSs) are crucial in the security monitoring for the smart grid with increasing machine-to-machine communicatio… (voir plus)ns and cyber threats thereafter. However, the multi-sourced, correlated, and heterogeneous smart grid data pose significant challenges to the accurate attack detection by IDSs. To improve the attack detection, this paper proposes Reinforcement Learning-based Adaptive Feature Boosting, which aims to leverage a series of AutoEncoders (AEs) to capture critical features from the multi-sourced smart grid data for the classification of normal, fault, and attack events. Multiple AEs are utilized to extract representative features from different feature sets that are automatically generated through a weighted feature sampling process; each AE-extracted feature set is then applied to build a Random Forest (RF) base classifier. In the feature sampling process, Deep Deterministic Policy Gradient (DDPG) is introduced to dynamically determine the feature sampling probability based on the classification accuracy. The critical features that improve the classification accuracy are assigned larger sampling probabilities and increasingly participate in the training of next AE. The presence of critical features is increased in the event classification over the multi-sourced smart grid data. Considering potential different alarms among base classifiers, an ensemble classifier is further built to distinguish normal, fault, and attack events. Our proposed approach is evaluated on the two realistic datasets collected from Hardware-In-the-Loop (HIL) and WUSTIL-IIOT-2021 security testbeds, respectively. The evaluation on the HIL security dataset shows that our proposed approach achieves the classification accuracy with 97.28%, an effective 5.5% increase over the vanilla Adaptive Feature Boosting. Moreover, the proposed approach not only accurately and stably selects critical features on the WUSTIL-IIOT-2021 dataset based on the significant difference of feature sampling probabilities between critical and uncritical features, i.e., the probabilities greater than 0.08 and less than 0.01, but also outperforms the other best-performing approaches with the increasing Matthew Correlation Coefficient (MCC) of 8.03%.
2023-06-30
IEEE Transactions on Smart Grid (publié)
doi.org
SkillQG: Learning to Generate Question for Reading Comprehension Assessment
Xiaoqiang Wang
Bang Liu
Siliang Tang
Lingfei Wu
2023-06-30
Findings of the Association for Computational Linguistics: ACL 2023 (publié)
doi.org
arxiv.org
EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models
Xinlin Li
Mariana Parazeres
Adam Oberman
Alireza Ghaffari
Masoud Asgharian
Vahid Nia
2023-06-29
SN Computer Science (publié)
doi.org
arxiv.org
Reference panel-guided super-resolution inference of Hi-C data
Yanlin Zhang
Mathieu Blanchette
Abstract Motivation Accurately assessing contacts between DNA fragments inside the nucleus with Hi-C experiment is crucial for understanding… (voir plus) the role of 3D genome organization in gene regulation. This challenging task is due in part to the high sequencing depth of Hi-C libraries required to support high-resolution analyses. Most existing Hi-C data are collected with limited sequencing coverage, leading to poor chromatin interaction frequency estimation. Current computational approaches to enhance Hi-C signals focus on the analysis of individual Hi-C datasets of interest, without taking advantage of the facts that (i) several hundred Hi-C contact maps are publicly available and (ii) the vast majority of local spatial organizations are conserved across multiple cell types. Results Here, we present RefHiC-SR, an attention-based deep learning framework that uses a reference panel of Hi-C datasets to facilitate the enhancement of Hi-C data resolution of a given study sample. We compare RefHiC-SR against tools that do not use reference samples and find that RefHiC-SR outperforms other programs across different cell types, and sequencing depths. It also enables high-accuracy mapping of structures such as loops and topologically associating domains. Availability and implementation https://github.com/BlanchetteLab/RefHiC.
2023-06-29
Bioinformatics (publié)
doi.org
Should We Feed the Trolls? Using Marketer-Generated Content to Explain Average Toxicity and Product Usage
Marcelo Vinhal Nepomuceno
Hooman Rahemi
Tolga Cenesizoglu
Laurent Charlin
2023-06-28
Journal of Interactive Marketing (publié)
doi.org
Cortico-Cerebellar neurodynamics during social interaction in Autism Spectrum Disorders
Fleur Gaudfernau
Aline Lefebvre
Denis-Alexander Engemann
Amandine Pedoux
Anna Bánki
Florence Baillin
Benjamin Landman
Frederique Amsellem
Anna Maruani
Thomas Bourgeron
Richard Delorme
Guillaume Dumas
2023-06-27
NeuroImage : Clinical (publié)
doi.org
Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference Machines
Alexandre Adam
Laurence Perreault-Levasseur
Yashar Hezaveh
MAX WELLING
Modeling strong gravitational lenses in order to quantify the distortions in the images of background sources and to reconstruct the mass de… (voir plus)nsity in the foreground lenses has been a difficult computational challenge. As the quality of gravitational lens images increases, the task of fully exploiting the information they contain becomes computationally and algorithmically more difficult. In this work, we use a neural network based on the Recurrent Inference Machine (RIM) to simultaneously reconstruct an undistorted image of the background source and the lens mass density distribution as pixelated maps. The method iteratively reconstructs the model parameters (the image of the source and a pixelated density map) by learning the process of optimizing the likelihood given the data using the physical model (a ray-tracing simulation), regularized by a prior implicitly learned by the neural network through its training data. When compared to more traditional parametric models, the proposed method is significantly more expressive and can reconstruct complex mass distributions, which we demonstrate by using realistic lensing galaxies taken from the IllustrisTNG cosmological hydrodynamic simulation.
2023-06-26
The Astrophysical Journal (publié)
doi.org
arxiv.org