Publications

A three-state coupled Markov switching model for COVID-19 outbreaks across Quebec based on hospital admissions (preprint)
Dirk Douwes-Schultz
Alexandra M. Schmidt
Yannan Shen
David L Buckeridge
2025-02-28
The Annals of Applied Statistics (publié)
doi.org
arxiv.org
Tractable Representations for Convergent Approximation of Distributional HJB Equations
Julie Alhosh
Harley Wiltzer
David Meger
2025-02-28
arXiv (publié)
doi.org
arxiv.org
Assessing the adoption of security policies by developers in terraform across different cloud providers
Alexandre Verdet
Mohammad Hamdaqa
Leuson Da Silva
Foutse Khomh
Cloud computing has become popular thanks to the widespread use of Infrastructure as Code (IaC) tools, allowing the community to manage and … (voir plus)configure cloud infrastructure using scripts. However, the scripting process does not automatically prevent practitioners from introducing misconfigurations, vulnerabilities, or privacy risks. As a result, ensuring security relies on practitioners’ understanding and the adoption of explicit policies. To understand how practitioners deal with this problem, we perform an empirical study analyzing the adoption of scripted security best practices present in Terraform files, applied on AWS, Azure, and Google Cloud. We assess the adoption of these practices by analyzing a sample of 812 open-source GitHub projects. We scan each project’s configuration files, looking for policy implementation through static analysis (Checkov and Tfsec). The category Access policy emerges as the most widely adopted in all providers, while Encryption at rest presents the most neglected policies. Regarding the cloud providers, we observe that AWS and Azure present similar behavior regarding attended and neglected policies. Finally, we provide guidelines for cloud practitioners to limit infrastructure vulnerability and discuss further aspects associated with policies that have yet to be extensively embraced within the industry.
2025-02-26
Empirical Software Engineering (publié)
doi.org
Stochastic gradient descent-based inference for dynamic network models with attractors
Hancong Pan
Xiaojing Zhu
Cantay Caliskan
Dino P. Christenson
Konstantinos Spiliopoulos
Dylan Walker
Eric D. Kolaczyk
2025-02-26
Journal of Computational and Graphical Statistics (publié)
doi.org
arxiv.org
Combining Sampling Methods with Attractor Dynamics in Spiking Models of Head-Direction Systems
Vojko Pjanovic
Jacob Zavatone-Veth
Paul Masset
Sander Keemink
Michele Nardin
Uncertainty is a fundamental aspect of the natural environment, requiring the brain to infer and integrate noisy signals to guide behavior e… (voir plus)ffectively. Sampling-based inference has been proposed as a mechanism for dealing with uncertainty, particularly in early sensory processing. However, it is unclear how to reconcile sampling-based methods with operational principles of higher-order brain areas, such as attractor dynamics of persistent neural representations. In this study, we present a spiking neural network model for the head-direction (HD) system that combines sampling-based inference with attractor dynamics. To achieve this, we derive the required spiking neural network dynamics and interactions to perform sampling from a large family of probability distributions—including variables encoded with Poisson noise. We then propose a method that allows the network to update its estimate of the current head direction by integrating angular velocity samples—derived from noisy inputs—with a pull towards a circular manifold, thereby maintaining consistent attractor dynamics. This model makes specific, testable predictions about the HD system that can be examined in future neurophysiological experiments: it predicts correlated subthreshold voltage fluctuations; distinctive short- and long-term firing correlations among neurons; and characteristic statistics of the movement of the neural activity “bump” representing the head direction. Overall, our approach extends previous theories on probabilistic sampling with spiking neurons, offers a novel perspective on the computations responsible for orientation and navigation, and supports the hypothesis that sampling-based methods can be combined with attractor dynamics to provide a viable framework for studying neural dynamics across the brain.
2025-02-25
bioRxiv (prépublication)
doi.org
Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3 -- Ex vivo imaging: data processing, comparisons with microscopy, and tractography
Kurt G Schilling
Amy F. D. Howard
Francesco Grussu
Andrada Ianus
Brian Hansen
Rachel L. C. Barrett
Manisha Aggarwal
Stijn Michielse
Fatima Nasrallah
Warda Syeda
Nian Wang
Jelle Veraart
Alard Roebroeck
Andrew F. Bagdasarian
Cornelius Eichner
Farshid Sepehrband
Jan Zimmermann
Lucas Soustelle
Christien Bowman
Benjamin C. Tendler … (voir 38 de plus)
Andreea Hertanu
Ben Jeurissen
Marleen Verhoye
Lucio Frydman
Yohan van de Looij
David Hike
Jeff F. Dunn
Karla Miller
Bennett Landman
Noam Shemesh
Arthur Anderson
Emilie McKinnon
Shawna Farquharson
Flavio Dell’Acqua
Carlo Pierpaoli
Ivana Drobnjak
Alexander Leemans
Kevin D. Harkins
Maxime Descoteaux
Duan Xu
Hao Huang
Mathieu D. Santin
Samuel C. Grant
Andre Obenaus
Gene S. Kim
Dan Wu
Denis Le Bihan
Stephen J. Blackband
Luisa Ciobanu
Els Fieremans
Ruiliang Bai
Trygve B. Leergaard
Jiangyang Zhang
Tim B. Dyrby
G. Allan Johnson
Julien Cohen-Adad
Matthew D. Budde
Ileana O. Jelescu
2025-02-25
Magnetic Resonance in Medicine (publié)
doi.org
arxiv.org
Considerations and recommendations from the <scp>ISMRM</scp> diffusion study group for preclinical diffusion <scp>MRI</scp>: Part 1: In vivo small‐animal imaging
Ileana O. Jelescu
Francesco Grussu
Andrada Ianus
Brian Hansen
Rachel L. C. Barrett
Manisha Aggarwal
Stijn Michielse
Fatima Nasrallah
Warda Syeda
Nian Wang
Jelle Veraart
Alard Roebroeck
Andrew F. Bagdasarian
Cornelius Eichner
Farshid Sepehrband
Jan Zimmermann
Lucas Soustelle
Christien Bowman
Benjamin C. Tendler
Andreea Hertanu … (voir 37 de plus)
Ben Jeurissen
Marleen Verhoye
Lucio Frydman
Yohan van de Looij
David Hike
Jeff F. Dunn
Karla Miller
Bennett Landman
Noam Shemesh
Arthur Anderson
Emilie McKinnon
Shawna Farquharson
Flavio Dell’Acqua
Carlo Pierpaoli
Ivana Drobnjak
Alexander Leemans
Kevin D. Harkins
Maxime Descoteaux
Duan Xu
Hao Huang
Mathieu D. Santin
Samuel C. Grant
Andre Obenaus
Gene S. Kim
Dan Wu
Denis Le Bihan
Stephen J. Blackband
Luisa Ciobanu
Els Fieremans
Ruiliang Bai
Trygve B. Leergaard
Jiangyang Zhang
Tim B. Dyrby
G. Allan Johnson
Julien Cohen-Adad
Matthew D. Budde
Kurt G Schilling
2025-02-25
Magnetic Resonance in Medicine (publié)
doi.org
Deep multimodal representations and classification of first-episode psychosis via live face processing
Rahul Singh
Yanlei Zhang
Dhananjay Bhaskar
Vinod Srihari
Cenk Tek
Xian Zhang
J. Adam Noah
Smita Krishnaswamy
Joy Hirsch
Schizophrenia is a severe psychiatric disorder associated with a wide range of cognitive and neurophysiological dysfunctions and long-term s… (voir plus)ocial difficulties. In this paper, we test the hypothesis that integration of multiple simultaneous acquisitions of neuroimaging, behavioral, and clinical information will be better for prediction of early psychosis than unimodal recordings. We propose a novel framework to investigate the neural underpinnings of the early psychosis symptoms (that can develop into Schizophrenia with age) using multimodal acquisitions of neural and behavioral recordings including functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), and facial features. Our data acquisition paradigm is based on live face-to-face interaction in order to study the neural correlates of social cognition in first-episode psychosis (FEP). We propose a novel deep representation learning framework, Neural-PRISM, for learning joint multimodal compressed representations combining neural as well as behavioral recordings. These learned representations are subsequently used to describe, classify, and predict the severity of early psychosis in patients, as measured by the Positive and Negative Syndrome Scale (PANSS) and Global Assessment of Functioning (GAF) scores. We found that incorporating joint multimodal representations from fNIRS and EEG along with behavioral recordings enhances classification between typical controls and FEP individuals. Additionally, our results suggest that geometric and topological features such as curvatures and path signatures of the embedded trajectories of brain activity enable detection of discriminatory neural characteristics in early psychosis.
2025-02-25
Frontiers in Psychiatry (publié)
doi.org
Determinants of pleiotropy and monotonic gene dosage responses across human traits
Sayeh Kazem
Kuldeep Kumar
Martineau Jean‐Louis
Guillaume Huguet
Thomas Renne
Zohra Saci
Worrawat Engchuan
Omar Shanta
Bhooma Thiruvahindrapuram
Jeffrey R. MacDonald
Celia M. T. Greenwood
Stephen W. Scherer
Laura Almasy
Jonathan Sebat
David C. Glahn
Guillaume Dumas
Sébastien Jacquemont
Sébastien Jacquemont
Pleiotropic effects of gene dosage are central to understanding comorbidities in developmental pediatric and psychiatric disorders, yet the … (voir plus)underlying biological processes are unknown. We developed Functional Burden analysis (FunBurd) to investigate the association of all protein-coding copy-number-variants (CNVs), genome-wide, with 43 complex traits in ∼500,000 UK-Biobank participants. We tested CNV associations disrupting 172 tissue or cell-type gene-sets, observing associations across all traits. Pleiotropy was correlated with genetic constraint and was higher in the brain compared to non-brain functions, even after normalizing for genetic constraint. Cognition and mental health traits showed specific gene-dosage effects across cortical/sub-cortical and neuronal/glial functional categories. The levels of pleiotropy, measured by burden correlation, were similar in deletions and loss-of-function SNVs, and higher compared to common variants and duplications. Gene sets under high genetic constraint showed less monotonic gene dosage responses across traits. Across most traits, we observed a negative deletion-duplication effect size correlation, indicating that functional gene sets are preferentially sensitive to either deletion or duplication, but rarely both. Our results highlight the key role of genetic constraint and brain-specific mechanisms in shaping CNV-driven pleiotropy, providing a mechanistic basis for the whole-body multimorbidity observed in neurodevelopmental and psychiatric conditions.
2025-02-25
medRxiv (publié)
doi.org
Origin of Nonlinear Circular Photocurrent in 2D Semiconductor MoS_{2}.
Yanchong Zhao
Fengyu Chen
Jing Liang
Mohammad Saeed Bahramy
Mingwei Yang
Yao Guang
Xiaomei Li
Zheng Wei
Jian Tang
Jiaojiao Zhao
Mengzhou Liao
Cheng Shen
Qinqin Wang
Rong Yang
Kenji Watanabe
Takashi Taniguchi
Zhiheng Huang
Dongxia Shi
Kaihui Liu
Zhipei Sun … (voir 3 de plus)
Ji Feng
Luojun Du
Guangyu Zhang
2025-02-25
Physical Review Letters (publié)
doi.org
Origin of Nonlinear Circular Photocurrent in 2D Semiconductor
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi>MoS</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math>
Yanchong Zhao
Fengyu Chen
Jing Liang
Mohammad Saeed Bahramy
Mingwei Yang
Yao Guang
Xiaomei Li
Zheng Wei
Jian Tang
Jiaojiao Zhao
Mengzhou Liao
Cheng Shen
Qinqin Wang
Rong Yang
Kenji Watanabe
Takashi Taniguchi
Zhiheng Huang
Dongxia Shi
Kaihui Liu
Zhipei Sun … (voir 3 de plus)
Ji Feng
Luojun Du
Guangyu Zhang
2025-02-25
Physical Review Letters (publié)
doi.org
Recommendations and guidelines from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 -- In vivo small-animal imaging
Ileana O. Jelescu
Francesco Grussu
Andrada Ianus
Brian Hansen
Rachel L. C. Barrett
Manisha Aggarwal
Stijn Michielse
Fatima Nasrallah
Warda Syeda
Nian Wang
Jelle Veraart
Alard Roebroeck
Andrew F. Bagdasarian
Cornelius Eichner
Farshid Sepehrband
Jan Zimmermann
Ben Jeurissen
Lucio Frydman
Lucas Soustelle
Christien Bowman … (voir 37 de plus)
Yohan van de Looij
Benjamin C. Tendler
David Hike
Jeff F. Dunn
Andrada Ianus
Karla Miller
Bennett Landman
Marleen Verhoye
Noam Shemesh
Arthur Anderson
Emilie McKinnon
Shawna Farquharson
Flavio Dell’Acqua
Carlo Pierpaoli
Ivana Drobnjak
Alexander Leemans
Kevin D. Harkins
Maxime Descoteaux
Duan Xu
Mathieu D. Santin
Samuel C. Grant
Andre Obenaus
Gene S. Kim
Dan Wu
Denis Le Bihan
Stephen J. Blackband
Nian Wang
Luisa Ciobanu
Els Fieremans
Ruiliang Bai
Trygve B. Leergaard
Jiangyang Zhang
Tim B. Dyrby
G. Allan Johnson
Julien Cohen-Adad
Matthew D. Budde
Kurt G Schilling
The value of in vivo preclinical diffusion MRI (dMRI) is substantial. Small-animal dMRI has been used for methodological development and val… (voir plus)idation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. Many of the influential works in this field were first performed in small animals or ex vivo samples. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the data. This work aims to serve as a reference, presenting selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. In each section, we also highlight areas for which no guidelines exist (and why), and where future work should focus. We first describe the value that small animal imaging adds to the field of dMRI, followed by general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss how they are appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, imaging sequences and data processing, including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.
2025-02-25
Magnetic Resonance in Medicine (publié)
doi.org
arxiv.org