Publications

Informing the development of an outcome set and banks of items to measure mobility among individuals with acquired brain injury using natural language processing
Rehab Alhasani
Mathieu Godbout
Audrey Durand
Claudine Auger
Anouk Lamontagne
Sara Ahmed
2022-12-09
BMC Neurology (published)
doi.org
PyNM: a Lightweight Python implementation of Normative Modeling
Annabelle Harvey
Guillaume Dumas
The majority of studies in neuroimaging and psychiatry are focussed on case-control analysis (Marquand et al., 2019). However, case-control … (see more)relies on well-defined groups which is more the exception than the rule in biology. Psychiatric conditions are diagnosed based on symptoms alone, which makes for heterogeneity at the biological level (Marquand et al., 2016). Relying on mean differences obscures this heterogeneity and the resulting loss of information can produce unreliable results or misleading conclusions (Loth et al., 2021).
2022-12-08
Journal of Open Source Software (published)
doi.org
Consistency and Rate of Convergence of Switched Least Squares System Identification for Autonomous Markov Jump Linear Systems
Borna Sayedana
Mohammad Afshari
Peter E. Caines
Aditya Mahajan
In this paper, we investigate the problem of system identification for autonomous Markov jump linear systems (MJS) with complete state obser… (see more)vations. We propose switched least squares method for identification of MJS, show that this method is strongly consistent, and derive data-dependent and data-independent rates of convergence. In particular, our data-dependent rate of convergence shows that, almost surely, the system identification error is
2022-12-06
IEEE Conference on Decision and Control (published)
doi.org
A modified Thompson sampling-based learning algorithm for unknown linear systems
Mukul Gagrani
Sagar Sudhakara
Aditya Mahajan
Rahul Jain
Ashutosh Nayyar
Yi Ouyang
We revisit the Thompson sampling-based learning algorithm for controlling an unknown linear system with quadratic cost proposed in [1]. This… (see more) algorithm operates in episodes of dynamic length and it is shown to have a regret bound of
2022-12-06
2022 IEEE 61st Conference on Decision and Control (CDC) (published)
doi.org
arxiv.org
Partially observable restless bandits with restarts: indexability and computation of Whittle index
Nima Akbarzadeh
Aditya Mahajan
We consider restless bandits with restarts, where the state of the active arms resets according to a known probability distribution while th… (see more)e state of the passive arms evolves in a Markovian manner. We assume that the state of the arm is observed after it is reset but not observed otherwise. We show that the model is indexable and propose an efficient algorithm to compute the Whittle index by exploiting the qualitative properties of the optimal policy. A detailed numerical study of machine repair models shows that Whittle index policy outperforms myopic policy and is close to optimal policy.
2022-12-06
IEEE Conference on Decision and Control (published)
doi.org
Thompson-Sampling Based Reinforcement Learning for Networked Control of Unknown Linear Systems
Borna Sayedana
Mohammad Afshari
Peter E. Caines
Aditya Mahajan
In recent years, there has been considerable interest in reinforcement learning for linear quadratic Gaussian (LQG) systems. In this paper, … (see more)we consider a generalization of such systems where the controller and the plant are connected over an unreliable packet drop channel. Packet drops cause the system dynamics to switch between controlled and uncontrolled modes. This switching phenomena introduces new challenges in designing learning algorithms. We identify a sufficient condition under which the regret of Thompson sampling-based reinforcement learning algorithm with dynamic episodes (TSDE) at horizon T is bounded by
2022-12-06
2022 IEEE 61st Conference on Decision and Control (CDC) (published)
doi.org
Stimulus information guides the emergence of behavior related signals in primary somatosensory cortex during learning
Mariangela Panniello
Colleen J Gillon
Roberto Maffulli
Marco Celotto
Stefano Panzeri
Blake Richards
Michael M Kohl
2022-12-05
bioRxiv (preprint)
doi.org
GRAND for Rayleigh Fading Channels
Syed Mohsin Abbas
Marwan Jalaleddine
Warren Gross
Guessing Random Additive Noise Decoding (GRAND) is a code-agnostic decoding technique for short-length and high-rate channel codes. GRAND at… (see more)tempts to guess the channel-induced noise by generating Test Error Patterns (TEPs), and the sequence of TEP generation is the primary distinction between GRAND variants. In this work, we extend the application of GRAND to multipath frequency non-selective Rayleigh fading communication channels, and we refer to this GRAND variant as Fading-GRAND. The proposed Fading-GRAND adapts its TEP generation to the fading conditions of the underlying communication channel, outperforming traditional channel code decoders in scenarios with L spatial diversity branches as well as scenarios with no diversity. Numerical simulation results show that the Fading-GRAND outperforms the traditional Berlekamp-Massey (B-M) decoder for decoding BCH code (127, 106) and BCH code (127, 113) by
2022-12-04
2022 IEEE Globecom Workshops (GC Wkshps) (published)
doi.org
arxiv.org
Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI
Joshua D. Durso-Finley
Jean-Pierre R. Falet
Brennan Nichyporuk
Douglas Arnold
Tal Arbel
Precision medicine for chronic diseases such as multiple sclerosis (MS) involves choosing a treatment which best balances efficacy and side … (see more)effects/preferences for individual patients. Making this choice as early as possible is important, as delays in finding an effective therapy can lead to irreversible disability accrual. To this end, we present the first deep neural network model for individualized treatment decisions from baseline magnetic resonance imaging (MRI) (with clinical information if available) for MS patients which (a) predicts future new and enlarging T2 weighted (NE-T2) lesion counts on follow-up MRI on multiple treatments and (b) estimates the conditional average treatment effect (CATE), as defined by the predicted future suppression of NE-T2 lesions, between different treatment options relative to placebo. Our model is validated on a proprietary federated dataset of 1817 multi-sequence MRIs acquired from MS patients during four multi-centre randomized clinical trials. Our framework achieves high average precision in the binarized regression of future NE-T2 lesions on five different treatments, identifies heterogeneous treatment effects, and provides a personalized treatment recommendation that accounts for treatment-associated risk (side effects, patient preference, administration difficulties,...).
2022-12-04
Proceedings of The 5th International Conference on Medical Imaging with Deep Learning (published)
doi.org
openreview.net
Segmentation-Consistent Probabilistic Lesion Counting
Julien Schroeter
Chelsea Myers-Colet
Douglas Arnold
Tal Arbel
Lesion counts are important indicators of disease severity, patient prognosis, and treatment efficacy, yet counting as a task in medical ima… (see more)ging is often overlooked in favor of segmentation. This work introduces a novel continuously differentiable function that maps lesion segmentation predictions to lesion count probability distributions in a consistent manner. The proposed end-to-end approach—which consists of voxel clustering, lesion-level voxel probability aggregation, and Poisson-binomial counting—is non-parametric and thus offers a robust and consistent way to augment lesion segmentation models with post hoc counting capabilities. Experiments on Gadolinium-enhancing lesion counting demonstrate that our method outputs accurate and well-calibrated count distributions that capture meaningful uncertainty information. They also reveal that our model is suitable for multi-task learning of lesion segmentation, is efficient in low data regimes, and is robust to adversarial attacks.
2022-12-04
Proceedings of The 5th International Conference on Medical Imaging with Deep Learning (published)
doi.org
openreview.net
Tackling hypo and hyper sensory processing heterogeneity in autism: From clinical stratification to genetic pathways
Aline Lefebvre
Julian Tillmann
Freddy Cliquet
Frederique Amsellem
Anna Maruani
Claire Leblond
Anita Beggiato
David Germanaud
Anouck Amestoy
Myriam Ly‐Le Moal
Daniel Umbricht
Christopher H. Chatham
Lorraine Murtagh
Manuel Bouvard
Marion Leboyer
Tony Charman
Thomas Bourgeron
Richard Delorme
Guillaume Dumas
2022-12-04
Autism Research (published)
doi.org
Performative Prediction in Time Series: A Case Study
Rupali Bhati
Jennifer Jones
David Langelier
Anthony Reiman
Jonathan Greenland
Kristin Campbell
Audrey Durand
2022-12-02
NeurIPS.cc/2022/Workshop/TS4H (poster)
openreview.net
openreview.net