Publications

Pandemic policy assessment by artificial intelligence
Sirui Song
Xue Liu
Ying Li
Yang Yu
2022-08-15
Scientific Reports (publié)
doi.org
VisPaD: Visualization and Pattern Discovery for Fighting Human Trafficking
Pratheeksha Nair
Yifei Li
Catalina Vajiac
Andreas Olligschlaeger
Meng-Chieh Lee
Namyong Park
Duen Horng Chau
Christos Faloutsos
Reihaneh Rabbany
Chieh Lee
2022-08-15
Companion Proceedings of the Web Conference 2022 (publié)
doi.org
Good Scientific Practice in MEEG Research: Progress and Perspectives
Guiomar Niso
Laurens R. Krol
Etienne Combrisson
A.-Sophie Dubarry
Madison A. Elliott
Clément François
Yseult Héjja-Brichard
Sophie K. Herbst
Karim Jerbi
Vanja Kovic
Katia Lehongre
Steven J. Luck
Manuel Mercier
John C. Mosher
Yuri G. Pavlov
Aina Puce
Antonio Schettino
Daniele Schön
Walter Sinnott-Armstrong
Bertille Somon … (voir 6 de plus)
Anđela Šoškić
Suzy J. Styles
Roni Tibon
Martina G. Vilas
Marijn van Vliet
Maximilien Chaumon
Good scientific practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce w… (voir plus)ork that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization. For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated and adapted to new findings. However, GSP also needs to be regularly revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasized intangible GSP: a general awareness of personal, organizational, and societal realities and how they can influence MEEG research. This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, it covers GSP with respect to data acquisition, analysis, reporting, and sharing, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges. Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today’s prioritization of fairness, privacy, and the environment, we find that current GSP tends to favor collective and cooperative work, for both scientific and for societal reasons.
2022-08-14
NeuroImage (publié)
doi.org
High Fidelity Visualization of What Your Self-Supervised Representation Knows About
Florian Bordes
Randall Balestriero
P Vincent
Discovering what is learned by neural networks remains a challenge. In self-supervised learning, classification is the most common task used… (voir plus) to evaluate how good a representation is. However, relying only on such downstream task can limit our understanding of what information is retained in the representation of a given input. In this work, we showcase the use of a Representation Conditional Diffusion Model (RCDM) to visualize in data space the representations learned by self-supervised models. The use of RCDM is motivated by its ability to generate high-quality samples -- on par with state-of-the-art generative models -- while ensuring that the representations of those samples are faithful i.e. close to the one used for conditioning. By using RCDM to analyze self-supervised models, we are able to clearly show visually that i) SSL (backbone) representation are not invariant to the data augmentations they were trained with -- thus debunking an often restated but mistaken belief; ii) SSL post-projector embeddings appear indeed invariant to these data augmentation, along with many other data symmetries; iii) SSL representations appear more robust to small adversarial perturbation of their inputs than representations trained in a supervised manner; and iv) that SSL-trained representations exhibit an inherent structure that can be explored thanks to RCDM visualization and enables image manipulation.
2022-08-14
TMLR (accepté)
openreview.net
openreview.net
Automatic Phenotyping by a Seed-guided Topic Model.
Ziyang Song
Yuanyi Hu
Aman Verma
David L. Buckeridge
Yue Li
Electronic health records (EHRs) provide rich clinical information and the opportunities to extract epidemiological patterns to understand a… (voir plus)nd predict patient disease risks with suitable machine learning methods such as topic models. However, existing topic models do not generate identifiable topics each predicting a unique phenotype. One promising direction is to use known phenotype concepts to guide topic inference. We present a seed-guided Bayesian topic model called MixEHR-Seed with 3 contributions: (1) for each phenotype, we infer a dual-form of topic distribution: a seed-topic distribution over a small set of key EHR codes and a regular topic distribution over the entire EHR vocabulary; (2) we model age-dependent disease progression as Markovian dynamic topic priors; (3) we infer seed-guided multi-modal topics over distinct EHR data types. For inference, we developed a variational inference algorithm. Using MixEHR-Seed, we inferred 1569 PheCode-guided phenotype topics from an EHR database in Quebec, Canada covering 1.3 million patients for up to 20-year follow-up with 122 million records for 8539 and 1126 unique diagnostic and drug codes, respectively. We observed (1) accurate phenotype prediction by the guided topics, (2) clinically relevant PheCode-guided disease topics, (3) meaningful age-dependent disease prevalence. Source code is available at GitHub: https://github.com/li-lab-mcgill/MixEHR-Seed.
2022-08-13
Knowledge Discovery and Data Mining (publié)
doi.org
TITRATED: Learned Human Driving Behavior without Infractions via Amortized Inference
Vasileios Lioutas
Adam Ścibior
Frank N. Wood
2022-08-11
TMLR (accepté)
openreview.net
openreview.net
Heatmap Regression for Lesion Detection using Pointwise Annotations
Chelsea Myers-colet
Julien Schroeter
Douglas Arnold
Tal Arbel
In many clinical contexts, detecting all lesions is imperative for evaluating disease activity. Standard approaches pose lesion detection as… (voir plus) a segmentation problem despite the time-consuming nature of acquiring segmentation labels. In this paper, we present a lesion detection method which relies only on point labels. Our model, which is trained via heatmap regression, can detect a variable number of lesions in a probabilistic manner. In fact, our proposed post-processing method offers a reliable way of directly estimating the lesion existence uncertainty. Experimental results on Gad lesion detection show our point-based method performs competitively compared to training on expensive segmentation labels. Finally, our detection model provides a suitable pre-training for segmentation. When fine-tuning on only 17 segmentation samples, we achieve comparable performance to training with the full dataset.
2022-08-10
ArXiv (prépublication)
doi.org
arxiv.org
RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data
Thibaud Godon
Pier-Luc Plante
Baptiste Bauvin
Élina Francovic-Fontaine
Alexandre Drouin
J. Corbeil
Background: Understanding the relationship between the Omics and the phenotype is a central problem in precision medicine. The high dimensio… (voir plus)nality of metabolomics data challenges learning algorithms in terms of scalability and generalization. Most learning algorithms do not produce interpretable models -- Method: We propose an ensemble learning algorithm based on conjunctions or disjunctions of decision rules. -- Results : Applications on metabolomics data shows that it produces models that achieves high predictive performances. The interpretability of the models makes them useful for biomarker discovery and patterns discovery in high dimensional data.
2022-08-10
ArXiv (prépublication)
doi.org
arxiv.org
Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning
Siba Moussa
Michael Kilgour
Clara Jans
Alex Hernández-García
Miroslava Cuperlovic‐culf
Yoshua Bengio
Lena Simine
2022-08-09
ArXiv (prépublication)
doi.org
arxiv.org
Learning to Improve Code Efficiency
Binghong Chen
Daniel Tarlow
Kevin Swersky
Martin Maas
Pablo Heiber
Ashish V Naik
Milad Hashemi
Parthasarathy Ranganathan
2022-08-08
ArXiv (prépublication)
doi.org
openreview.net
Endorsing Complexity Through Diversity: Computational Psychiatry Meets Big Data Analytics
Jakub Kopal
Danilo Bzdok
2022-08-04
Biological Psychiatry (inconnu)
doi.org
Estimating the lagged effect of price discounting: a time-series study using transaction data of sugar sweetened beverages.
Hiroshi Mamiya
Alexandra M. Schmidt
Erica E. M. Moodie
David L. Buckeridge
Price discount is an unregulated obesogenic environmental risk factor for the purchasing of unhealthy food, including Sugar Sweetened Bevera… (voir plus)ges (SSB). Sales of price discounted food items are known to increase during the period of discounting. However, the presence and extent of the lagged effect of discounting, a sustained level of sales after discounting ends, is previously unaccounted for. We investigated the presence of the lagged effect of discounting on the sales of five SSB categories, which are soda, fruits juice, sport and energy drink, sweetened coffee and tea, and sweetened drinkable yogurt. We fitted a distributed lag model to weekly volume-standardized sales and percent discounting generated by a supermarket in Montreal, Canada between 2008 and 2013. While the sales of SSB increased during the period of discounting, there was no evidence of a prominent lagged effect of discounting in four of the five SSB; the exception was sports and energy drinks, where a posterior mean of 28,459 servings (95% credible interval: 2,661 to 67,253) of excess sales can be attributed to the lagged effect in the target store during the study period. Our results indicate that previous studies may have underestimated the effect of price discounting for some food categories. Temporary price discounting is an important component of obesogenic food environment, as it has been shown to increase the sales of discretionary food items during the period of discounting. Even after a period of price discounting has ended, the sales of sports and energy drinks remain at a higher level relative to the sales before discounting. Previous research focusing on the immediate effect (i.e., same time period) of price discounting may have systematically underestimated the impact of price discounting for some food categories. The findings and analytical method in this study promote improved validity of future food environment research targeting the impact of discounting and other types of food promotions on the sales of energy-dense and nutrition-poor food items.
2022-08-04
BMC Public Health (publié)
doi.org