Publications

Genetic landscape of an<i>in vivo</i>protein interactome

Savandara Besse

Tatsuya Sakaguchi

Louis Gauthier

Zahra Sahaf

Olivier Péloquin

Lidice Gonzalez

Xavier Castellanos-Girouard

Nazli Koçatug

Chloé Matta

Julie G. Hussin

Stephen W. Michnick

Adrian W.R. Serohijos

Protein-interaction quantitative trait locus (“piQTL”) mapping reveals sensitivity ofin vivoPPIs to polymorphisms across the yeast genom… (voir plus)eTrans-piQTLs significantly outnumber and are stronger than cis-piQTLsSNPs in non-coding RNAs and 3’ UTRs have comparable effects to PPI as SNPs in coding regionspiQTL mapping reveals known and novel mechanism of yeast and human drugs

2023-12-14

bioRxiv (prépublication)

doi.org

Temporal encoding in deep reinforcement learning agents

Dongyan Lin

Ann Zixiang Huang

Blake Aaron Richards

Neuroscientists have observed both cells in the brain that fire at specific points in time, known as “time cells”, and cells whose activ… (voir plus)ity steadily increases or decreases over time, known as “ramping cells”. It is speculated that time and ramping cells support temporal computations in the brain and carry mnemonic information. However, due to the limitations in animal experiments, it is difficult to determine how these cells really contribute to behavior. Here, we show that time cells and ramping cells naturally emerge in the recurrent neural networks of deep reinforcement learning models performing simulated interval timing and working memory tasks, which have learned to estimate expected rewards in the future. We show that these cells do indeed carry information about time and items stored in working memory, but they contribute to behavior in large part by providing a dynamic representation on which policy can be computed. Moreover, the information that they do carry depends on both the task demands and the variables provided to the models. Our results suggest that time cells and ramping cells could contribute to temporal and mnemonic calculations, but the way in which they do so may be complex and unintuitive to human observers.

2023-12-14

Scientific Reports (publié)

doi.org

Cone-Traced Supersampling with Subpixel Edge Reconstruction.

Andrei Chubarau

Yangyang Zhao

Ruby Rao

D. Nowrouzezahrai

Paul Kry

While signed distance fields (SDFs) in theory offer infinite level of detail, they are typically rendered using the sphere tracing algorithm… (voir plus) at finite resolutions, which causes the common rasterized image synthesis problem of aliasing. Most existing optimized antialiasing solutions rely on polygon mesh representations; SDF-based geometry can only be directly antialiased with the computationally expensive supersampling or with post-processing filters that may produce undesirable blurriness and ghosting. In this work, we present cone-traced supersampling (CTSS), an efficient and robust spatial antialiasing solution that naturally complements the sphere tracing algorithm, does not require casting additional rays per pixel or offline prefiltering, and can be easily implemented in existing real-time SDF renderers. CTSS performs supersampling along the traced ray near surfaces with partial visibility – object contours – identified by evaluating cone intersections within a pixel's view frustum. We further introduce subpixel edge reconstruction (SER), a technique that extends CTSS to locate and resolve complex pixels with geometric edges in relatively flat regions, which are otherwise undetected by cone intersections. Our combined solution relies on a specialized sampling strategy to minimize the number of shading computations and correlates sample visibility to aggregate the samples. With comparable antialiasing quality at significantly lower computational cost, CTSS is a reliable practical alternative to conventional supersampling.

2023-12-13

IEEE Transactions on Visualization and Computer Graphics (publié)

doi.org

Feasibility of cognitive neuroscience data collection during a speleological expedition

Anita Paas

Hugo R. Jourde

Arnaud Brignol

Marie-Anick Savard

Zseyvfin Eyqvelle

Samuel Bassetto

Giovanni Beltrame

Emily B.J. Coffey

In human cognitive neuroscience and neuropsychology studies, laboratory-based research tasks have been important to establish principles of … (voir plus)brain function and its relationship to behaviour; however, they differ greatly from real-life experiences. Several elements of real-life situations that impact human performance, such as stressors, are difficult or impossible to replicate in the laboratory. Expeditions offer unique possibilities for studying human cognition in complex environments that can transfer to other situations with similar features. For example, as caves share several of the physical and psychological challenges of safety-critical environments such as spaceflight, underground expeditions have been developed as an analogue for astronaut training purposes, suggesting that they might also be suitable for studying aspects of behaviour and cognition that cannot be fully examined under laboratory conditions. While a large range of topics and tools have been proposed for use in such environments, few have been evaluated in the field. We tested the feasibility of collecting human physiological, cognitive, and subjective experience data concerning brain state, sleep, cognitive workload, and fatigue, during a speleological expedition in a remote region. We document our approaches and challenges experienced, and provide recommendations and suggestions to aid future work. The data support the idea that cave expeditions are relevant naturalistic paradigms that offer unique possibilities for cognitive neuroscience to complement laboratory work and help improve human performance and safety in operational environments.

2023-12-13

bioRxiv (prépublication)

doi.org

Asymmetric Actor-Critic with Approximate Information State

Amit Sinha

Aditya Mahajan

Reinforcement learning (RL) for partially observable Markov decision processes (POMDPs) is a challenging problem because decisions need to b… (voir plus)e made based on the entire history of observations and actions. However, in several scenarios, state information is available during the training phase. We are interested in exploiting the availability of this state information during the training phase to efficiently learn a history-based policy using RL. Specifically, we consider actor-critic algorithms, where the actor uses only the history information but the critic uses both history and state. Such algorithms are called asymmetric actor-critic, to highlight the fact that the actor and critic have asymmetric information. Motivated by the recent success of using representation losses in RL for POMDPs [1], we derive similar theoretical results for the asymmetric actor-critic case and evaluate the effectiveness of adding such auxiliary losses in experiments. In particular, we learn a history representation-called an approximate information state (AIS)-and bound the performance loss when acting using AIS.

2023-12-12

IEEE Conference on Decision and Control (publié)

doi.org

Current Practices in Voice Data Collection and Limitations to Voice AI Research: A National Survey.

Emily Evangelista

Rohan Kale

Desiree McCutcheon

Anais Rameau

Alexander Gelbard

Maria Powell

Michael Johns

Anthony Law

Phillip Song

Matthew Naunheim

Stephanie Watts

Paul C. Bryson

Matthew G. Crowson

Jeremy Pinto

Yael Bensoussan

INTRODUCTION Accuracy and validity of voice AI algorithms rely on substantial quality voice data. Although commensurable amounts of voice da… (voir plus)ta are captured daily in voice centers across North America, there is no standardized protocol for acoustic data management, which limits the usability of these datasets for voice artificial intelligence (AI) research. OBJECTIVE The aim was to capture current practices of voice data collection, storage, analysis, and perceived limitations to collaborative voice research. METHODS A 30-question online survey was developed with expert guidance from the voicecollab.ai members, an international collaborative of voice AI researchers. The survey was disseminated via REDCap to an estimated 200 practitioners at North American voice centers. Survey questions assessed respondents' current practices in terms of acoustic data collection, storage, and retrieval as well as limitations to collaborative voice research. RESULTS Seventy-two respondents completed the survey of which 81.7% were laryngologists and 18.3% were speech language pathologists (SLPs). Eighteen percent of respondents reported seeing 40%-60% and 55% reported seeing >60 patients with voice disorders weekly (conservative estimate of over 4000 patients/week). Only 28% of respondents reported utilizing standardized protocols for collection and storage of acoustic data. Although, 87% of respondents conduct voice research, only 38% of respondents report doing so on a multi-institutional level. Perceived limitations to conducting collaborative voice research include lack of standardized methodology for collection (30%) and lack of human resources to prepare and label voice data adequately (55%). CONCLUSION To conduct large-scale multi-institutional voice research with AI, there is a pertinent need for standardization of acoustic data management, as well as an infrastructure for secure and efficient data sharing. LEVEL OF EVIDENCE Level 5 Laryngoscope, 2023.

2023-12-12

The Laryngoscope (publié)

doi.org

Detection and evaluation of bias-inducing features in machine learning

Moses Openja

Gabriel Laberge

Foutse Khomh

2023-12-12

Empirical Software Engineering (publié)

doi.org

arxiv.org

Preserving Privacy in GANs Against Membership Inference Attack

Mohammadhadi Shateri

Francisco Messina

Fabrice Labeau

Pablo Piantanida

Generative Adversarial Networks (GANs) have been widely used for generating synthetic data for cases where there is a limited size real-worl… (voir plus)d dataset or when data holders are unwilling to share their data samples. Recent works showed that GANs, due to overfitting and memorization, might leak information regarding their training data samples. This makes GANs vulnerable to Membership Inference Attacks (MIAs). Several defense strategies have been proposed in the literature to mitigate this privacy issue. Unfortunately, defense strategies based on differential privacy are proven to reduce extensively the quality of the synthetic data points. On the other hand, more recent frameworks such as PrivGAN and PAR-GAN are not suitable for small-size training datasets. In the present work, the overfitting in GANs is studied in terms of the discriminator, and a more general measure of overfitting based on the Bhattacharyya coefficient is defined. Then, inspired by Fano's inequality, our first defense mechanism against MIAs is proposed. This framework, which requires only a simple modification in the loss function of GANs, is referred to as the maximum entropy GAN or MEGAN and significantly improves the robustness of GANs to MIAs. As a second defense strategy, a more heuristic model based on minimizing the information leaked from generated samples about the training data points is presented. This approach is referred to as mutual information minimization GAN (MIMGAN) and uses a variational representation of the mutual information to minimize the information that a synthetic sample might leak about the whole training data set. Applying the proposed frameworks to some commonly used data sets against state-of-the-art MIAs reveals that the proposed methods can reduce the accuracy of the adversaries to the level of random guessing accuracy with a small reduction in the quality of the synthetic data samples.

2023-12-12

IEEE Transactions on Information Forensics and Security (inconnu)

doi.org

arxiv.org

Privacy-preserving analysis of time-to-event data under nested case-control sampling

Lamin Juwara

Archer Yang

Ana M Velly

Paramita Saha-Chaudhuri

2023-12-12

Statistical Methods in Medical Research (publié)

doi.org

Relative Almost Sure Regret Bounds for Certainty Equivalence Control of Markov Jump Systems

Borna Sayedana

Mohammad Afshari

Peter E. Caines

Aditya Mahajan

In this paper, we consider learning and control problem in an unknown Markov jump linear system (MJLS) with perfect state observations. We f… (voir plus)irst establish a generic upper bound on regret for any learning based algorithm. We then propose a certainty equivalence-based learning alagrithm and show that this algorithm achieves a regret of

2023-12-12

2023 62nd IEEE Conference on Decision and Control (CDC) (publié)

doi.org

Weighted-Norm Bounds on Model Approximation in MDPs with Unbounded Per-Step Cost

Berk Bozkurt

Aditya Mahajan

Ashutosh Nayyar

Yi Ouyang

We consider the problem of designing a control policy for an infinite-horizon discounted cost Markov Decision Process (MDP) …

2023-12-12

IEEE Conference on Decision and Control (publié)

doi.org