Publications

A Study of Condition Numbers for First-Order Optimization
Charles Guille-Escuret
Baptiste Goujaud
Manuela Girotti
Ioannis Mitliagkas
2021-03-18
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (published)
proceedings.mlr.press
arxiv.org
[Strengthening the culture of public health surveillance and population health monitoring].
Arnaud Chiolero
David Buckeridge
St'ephane Cullati
Public health surveillance is the systematic and ongoing collection, analysis and interpretation of data to produce information useful for d… (see more)ecision-making. With the development of data science, surveillance methods are evolving through access to big data. More data does not automatically mean more information. For example, the massive amounts of data on Covid-19 was not easily transformed in useful information for decision-making. Further, data scientists have often difficulties to make their analyses useful for decision-making. For the implementation of evidence-based and data-driven public health practice, the culture of public health surveillance and population health monitoring needs to be strengthened.
2021-03-17
Revue medicale suisse (published)
pubmed.ncbi.nlm.nih.gov
Price discounting as a hidden risk factor of energy drink consumption
Hiroshi Mamiya
Erica E. M. Moodie
Alexandra M. Schmidt
Yu Ma
David Buckeridge
Global consumption of caffeinated energy drinks (CED) has been increasing dramatically despite increasing evidence of their adverse health e… (see more)ffects. Temporary price discounting is a rarely investigated but potentially powerful food marketing tactic influencing purchasing of CED. Using grocery transaction records generated by food stores in Montreal, we investigated the association between price discounting and purchasing of CED across socio-economic status operationalized by education and income levels in store neighbourhood. The outcome, log-transformed weekly store-level sales of CED, was modelled as a function of store-level percent price discounting, store- and neighbourhood-level confounders, and an interaction term between discounting and each of tertile education and income in store neighbourhood. The model was separately fit to transactions from supermarkets, pharmacies, supercentres, and convenience stores. There were 18,743, 12,437, 3965, and 49,533 weeks of CED sales from supermarkets, pharmacies, supercentres, and convenience stores, respectively. Percent price discounting was positively associated with log sales of CED for all store types, and the interaction between education and discounting was prominent in supercentres: −0.039 [95% confidence interval (CI): −0.051, −0.028] and −0.039 [95% CI: −0.057, −0.021], for middle- and high-education neighbourhoods relative to low-education neighbourhoods, respectively. Relative to low-income areas, the associations of discounting and log CED sales in supercentres for neighbourhoods with middle- and high-income tertile were 0.022 [95% CI: 0.010, 0.033] and 0.015 (95% CI: −0.001, 0.031), respectively. Price discounting is an important driver of CED consumption and has a varying impact across community education and income.
2021-03-16
Canadian Journal of Public Health (published)
doi.org
Local Data Debiasing for Fairness Based on Generative Adversarial Training
Ulrich Aivodji
François Bidet
Sébastien Gambs
Rosin Claude Ngueveu
Alain Tapp
The widespread use of automated decision processes in many areas of our society raises serious ethical issues with respect to the fairness o… (see more)f the process and the possible resulting discrimination. To solve this issue, we propose a novel adversarial training approach called GANSan for learning a sanitizer whose objective is to prevent the possibility of any discrimination (i.e., direct and indirect) based on a sensitive attribute by removing the attribute itself as well as the existing correlations with the remaining attributes. Our method GANSan is partially inspired by the powerful framework of generative adversarial networks (in particular Cycle-GANs), which offers a flexible way to learn a distribution empirically or to translate between two different distributions. In contrast to prior work, one of the strengths of our approach is that the sanitization is performed in the same space as the original data by only modifying the other attributes as little as possible, thus preserving the interpretability of the sanitized data. Consequently, once the sanitizer is trained, it can be applied to new data locally by an individual on their profile before releasing it. Finally, experiments on real datasets demonstrate the effectiveness of the approach as well as the achievable trade-off between fairness and utility.
2021-03-14
Algorithms (published)
doi.org
arxiv.org
Continuing professional education of Iranian healthcare professionals in shared decision-making: lessons learned
Samira Abbasgholizadeh-Rahimi
Charo Rodriguez
Jordie Croteau
Alireza Sadeghpour
Amir-Mohammad Navali
France Légaré
2021-03-12
BMC Health Services Research (published)
doi.org
Staying Ahead of the Epidemiologic Curve: Evaluation of the British Columbia Asthma Prediction System (BCAPS) During the Unprecedented 2018 Wildfire Season
Sarah B. Henderson
Kathryn T. Morrison
Kathleen E. McLean
Yue Ding
Jiayun Yao
Gavin Shaddick
David Buckeridge
2021-03-12
Frontiers in Public Health (published)
doi.org
Parallel inference of hierarchical latent dynamics in two-photon calcium imaging of neuronal populations
Luke Y. Prince
Shahab Bakhtiari
Colleen J Gillon
Blake Richards
Dynamic latent variable modelling has provided a powerful tool for understanding how populations of neurons compute. For spiking data, such … (see more)latent variable modelling can treat the data as a set of point-processes, due to the fact that spiking dynamics occur on a much faster timescale than the computational dynamics being inferred. In contrast, for other experimental techniques, the slow dynamics governing the observed data are similar in timescale to the computational dynamics that researchers want to infer. An example of this is in calcium imaging data, where calcium dynamics can have timescales on the order of hundreds of milliseconds. As such, the successful application of dynamic latent variable modelling to modalities like calcium imaging data will rest on the ability to disentangle the deeper- and shallower-level dynamical systems’ contributions to the data. To-date, no techniques have been developed to directly achieve this. Here we solve this problem by extending recent advances using sequential variational autoencoders for dynamic latent variable modelling of neural data. Our system VaLPACa (Variational Ladders for Parallel Autoencoding of Calcium imaging data) solves the problem of disentangling deeper- and shallower-level dynamics by incorporating a ladder architecture that can infer a hierarchy of dynamical systems. Using some built-in inductive biases for calcium dynamics, we show that we can disentangle calcium flux from the underlying dynamics of neural computation. First, we demonstrate with synthetic calcium data that we can correctly disentangle an underlying Lorenz attractor from calcium dynamics. Next, we show that we can infer appropriate rotational dynamics in spiking data from macaque motor cortex after it has been converted into calcium fluorescence data via a calcium dynamics model. Finally, we show that our method applied to real calcium imaging data from primary visual cortex in mice allows us to infer latent factors that carry salient sensory information about unexpected stimuli. These results demonstrate that variational ladder autoencoders are a promising approach for inferring hierarchical dynamics in experimental settings where the measured variable has its own slow dynamics, such as calcium imaging data. Our new, open-source tool thereby provides the neuroscience community with the ability to apply dynamic latent variable modelling to a wider array of data modalities.
2021-03-08
bioRxiv (preprint)
doi.org
Comment on Starke et al.: “Computing schizophrenia: ethical challenges for machine learning in psychiatry”: From machine learning to student learning: pedagogical challenges for psychiatry – Corrigendum
Christophe Gauld
Jean‐Arthur Micoulaud‐Franchi
Guillaume Dumas
2021-03-04
Psychological Medicine (published)
doi.org
QBSUM: a Large-Scale Query-Based Document Summarization Dataset from Real-world Applications
Mingjun Zhao
Shengli Yan
Bang Liu
Xinwang Zhong
Qian Hao
Haolan Chen
Di Niu
Bowei Long
Wei-dong Guo
2021-03-01
Computer Speech & Language (published)
doi.org
arxiv.org
Towards robust and replicable sex differences in the intrinsic brain function of autism
Dorothea L. Floris
José O. A. Filho
Meng-Chuan Lai
Steve Giavasis
Marianne Oldehinkel
Maarten Mennes
Tony Charman
Julian Tillmann
Guillaume Dumas
Christine Ecker
Flavio Dell’Acqua
Tobias Banaschewski
Carolin Moessnang
Simon Baron-Cohen
Sarah Durston
Eva Loth
Declan Murphy
Jan K. Buitelaar
Christian Beckmann
Michael P. Milham … (see 1 more)
Adriana Di Martino
2021-03-01
Molecular Autism (published)
doi.org
From Generative Models to Generative Passages: A Computational Approach to (Neuro) Phenomenology
Maxwell J. D. Ramstead
Anil K. Seth
Casper Hesp
Lars Sandved-Smith
Jonas Mago
Michael Lifshitz
Giuseppe Pagnoni
Ryan Smith
Guillaume Dumas
Andrew E. Lutz
Antoine Lutz
Karl Friston
Axel Constant
2021-02-23
Review of Philosophy and Psychology (published)
doi.org
Interacting brains revisited: A cross‐brain network neuroscience perspective
Christian Gerloff
Kerstin Konrad
Danilo Bzdok
Christina Büsing
Vanessa Reindl
Elucidating the neural basis of social behavior is a long-standing challenge in neuroscience. Such endeavors are driven by attempts to exten… (see more)d the isolated perspective on the human brain by considering interacting persons’ brain activities, but a theoretical and computational framework for this purpose is still in its infancy. Here, we posit a comprehensive framework based on bipartite graphs for interbrain networks and address whether they provide meaningful insights into the neural underpinnings of social interactions. First, we show that the nodal density of such graphs exhibits nonrandom properties. While the current analyses mostly rely on global metrics, we encode the regions’ roles via matrix decomposition to obtain an interpretable network representation yielding both global and local insights. With Bayesian modeling, we reveal how synchrony patterns seeded in specific brain regions contribute to global effects. Beyond inferential inquiries, we demonstrate that graph representations can be used to predict individual social characteristics, outperforming functional connectivity estimators for this purpose. In the future, this may provide a means of characterizing individual variations in social behavior or identifying biomarkers for social interaction and disorders.
2021-02-20
bioRxiv (preprint)
doi.org