Publications

From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards
Khaoula Chehbouni
Megha Roshan
Emmanuel Ma
Futian Andrew Wei
Afaf Taïk
Jackie CK Cheung
Golnoosh Farnadi
2023-12-31
Association for Computational Linguistics (published)
doi.org
arxiv.org
Game Theoretical Formulation for Residential Community Microgrid via Mean Field Theory: Proof of Concept
Mohamad Aziz
Hanane Dagdougui
Issmail ElHallaoui
Incentive-based demand response aggregators are widely recognized as a powerful strategy to increase the flexibility of residential communit… (see more)y MG (RCM) while allowing consumers’ assets to participate in the operation of the power system in critical peak times. RCM implementing demand response approaches are of high interest as collectively, they have a high impact on shaping the demand curve during peak time while providing a wide range of economic and technical benefits to consumers and utilities. The penetration of distributed energy resources such as battery energy storage and photovoltaic systems introduces additional flexibility to manage the community loads and increase revenue. This letter proposes a game theoretical formulation for an incentive-based residential community microgrid, where an incentive-based pricing mechanism is developed to encourage peak demand reduction and share the incentive demand curve with the residential community through the aggregator. The aggregator’s objective is to maximize the welfare of the residential community by finding the optimal community equilibrium electricity price. Each household communicates with each other and with the distributed system operator (DSO) through the aggregator and aims to minimize the local electricity cost.
2023-12-31
IEEE Control Systems Letters (published)
doi.org
Gaussian-Sum Filter for Range-based 3D Relative Pose Estimation in the Presence of Ambiguities
Syed S. Ahmed
Mohammed A. Shalaby
Charles C. Cossette
Jerome Le Ny
James R. Forbes
Multi-robot systems must have the ability to accurately estimate relative states between robots in order to perform collaborative tasks, pos… (see more)sibly with no external aiding. Three-dimensional relative pose estimation using range measurements oftentimes suffers from a finite number of non-unique solutions, or ambiguities. This paper: 1) identifies and accurately estimates all possible ambiguities in 2D; 2) treats them as components of a Gaussian mixture model; and 3) presents a computationally-efficient estimator, in the form of a Gaussian-sum filter (GSF), to realize range-based relative pose estimation in an infrastructure-free, 3D, setup. This estimator is evaluated in simulation and experiment and is shown to avoid divergence to local minima induced by the ambiguous poses. Furthermore, the proposed GSF outperforms an extended Kalman filter, demonstrates similar performance to the computationally-demanding particle filter, and is shown to be consistent.
2023-12-31
IEEE Conference on Control Technology and Applications (published)
doi.org
arxiv.org
Generative Active Learning for the Search of Small-Molecule Protein Binders
Maksym Korablyov
Cheng-Hao Liu
Moksh Jain
Almer Van Der Sloot
Éric Jolicoeur
Edward Ruediger
Andrei Nica
Emmanuel Bengio
Kostiantyn Lapchevskyi
Daniel St-Cyr
Doris Alexandra Schuetz
Victor Ion Butoi
Jarrid Rector-Brooks
Simon Blackburn
Leo Feng
Hadi Nekoei
Saikrishna Gottipati
Priyesh Vijayan
Prateek Gupta
Ladislav Rampasek … (see 14 more)
Sasikanth Avancha
Pierre-Luc Bacon
William Hamilton
Brooks Paige
Sanchit Misra
Stanislaw Jastrzebski
Bharat Kaul
Doina Precup
José Miguel Hernández-Lobato
Marwin Segler
Michael Bronstein
Anne Marinier
Mike Tyers
Yoshua Bengio
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exh… (see more)ibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. Powered by deep reinforcement learning, LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small molecules that inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing constraints on synthesizability and drug-likeliness. LambdaZero provides an exponential speedup in terms of the number of calls to the expensive molecular docking oracle, and LambdaZero de novo designed molecules reach docking scores that would otherwise require the virtual screening of a hundred billion molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH. In in vitro experimental validation, a series of ligands from a generated quinazoline-based scaffold were synthesized, and the lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide (UM0152893) displayed sub-micromolar enzyme inhibition of sEH.
2023-12-31
arXiv (preprint)
doi.org
arxiv.org
Generative Adversarial Neural Networks for Realistic Stock Market Simulations
Badre Labiad
Abdelaziz Berrado
Loubna Benabbou
—Stock market simulations are widely used to create synthetic environments for testing trading strategies before deploying them to real-ti… (see more)me markets. However, the weak realism often found in these simulations presents a significant challenge. Improving the quality of stock market simulations could be facilitated by the availability of rich and granular real Limit Order Books (LOB) data. Unfortunately, access to LOB data is typically very limited. To address this issue, a framework based on Generative Adversarial Networks (GAN) is proposed to generate synthetic realistic LOB data. This generated data can then be utilized for simulating downstream decision-making tasks, such as testing trading strategies, conducting stress tests, and performing prediction tasks. To effectively tackle challenges related to the temporal and local dependencies inherent in LOB structures and to generate highly realistic data, the framework relies on a specific data representation and preprocessing scheme, transformers, and conditional Wasserstein GAN with gradient penalty. The framework is trained using the FI-2010 benchmark dataset and an ablation study is conducted to demonstrate the importance of each component of the proposed framework. Moreover, qualitative and quantitative metrics are proposed to assess the quality of the generated data. Experimental results indicate that the framework outperforms existing benchmarks in simulating realistic market conditions, thus demonstrating its effectiveness in generating synthetic LOB data for diverse downstream tasks.
2023-12-31
International Journal of Advanced Computer Science and Applications (published)
doi.org
Hierarchical Classification of Narwhal Subpopulations Using Social Distance
Bertrand Charry
Marianne Marcoux
Jeffrey A. Cardille
Xavier Giroux‐Bougard
Murray M. Humphries
Effective wildlife management and conservation require knowledge of distribution, sex composition, and age structure of a population. We exp… (see more)lored the distribution of the Baffin Bay narwhal (Monodon monoceros) population in August 2013 by documenting sex and age distribution across the Canadian Arctic Archipelago covering 2,317,152 km2. For 6,314 narwhals identified in 3,393 aerial images taken across the Eastern Canadian Arctic, we calculated a matrix of swimming distances between all individuals. We then used a quantitative clustering approach to partition our dataset (partitioning around the medoids). The clusters obtained from the analysis supported the delimitation of the 5 narwhal management stocks currently used by the Department of Fisheries and Oceans but did not support the hypothesized division of Jones Sound and Smith Sound stocks. Across the 5 clusters, male:female ratios varied between 0.72 and 1.44 and the proportion of newborns relative to the number of females varied between 0.07 and 0.18. As a highly detailed snapshot of narwhal distribution across a very large region, our study is a step toward better documentation of the basic population information required for stock assessment, sustainable harvest, and habitat protection of narwhals in an era of rapid Arctic change. © 2019 The Wildlife Society.
2023-12-31
International Conference on Machine Learning (published)
doi.org
How Should We Extract Discrete Audio Tokens from Self-Supervised Models?
Pooneh Mousavi
Jarod Duret
Salah Zaiem
Luca Della Libera
Artem Ploujnikov
Yusuf Cem Sübakan
Mirco Ravanaelli
2023-12-31
INTERSPEECH (published)
doi.org
arxiv.org
ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Images
Chen Liu
Ke Xu
Liangbo L. Shen
Guillaume Huguet
Zilong Wang
Alexander Tong
Danilo Bzdok
Jay Stewart
Jay C. Wang
Lucian V. Del Priore
Smita Krishnaswamy
Advances in medical imaging technologies have enabled the collection of longitudinal images, which involve repeated scanning of the same pat… (see more)ients over time, to monitor disease progression. However, predictive modeling of such data remains challenging due to high dimensionality, irregular sampling, and data sparsity. To address these issues, we propose ImageFlowNet, a novel model designed to forecast disease trajectories from initial images while preserving spatial details. ImageFlowNet first learns multiscale joint representation spaces across patients and time points, then optimizes deterministic or stochastic flow fields within these spaces using a position-parameterized neural ODE/SDE framework. The model leverages a UNet architecture to create robust multiscale representations and mitigates data scarcity by combining knowledge from all patients. We provide theoretical insights that support our formulation of ODEs, and motivate our regularizations involving high-level visual features, latent space organization, and trajectory smoothness. We validate ImageFlowNet on three longitudinal medical image datasets depicting progression in geographic atrophy, multiple sclerosis, and glioblastoma, demonstrating its ability to effectively forecast disease progression and outperform existing methods. Our contributions include the development of ImageFlowNet, its theoretical underpinnings, and empirical validation on real-world datasets. The official implementation is available at https://github.com/KrishnaswamyLab/ImageFlowNet.
2023-12-31
arXiv (preprint)
doi.org
Improving the Generalizability and Robustness of Large-Scale Traffic Signal Control
Tianyu Shi
François-Xavier Devailly
Denis Larocque
Laurent Charlin
A number of deep reinforcement-learning (RL) approaches propose to control traffic signals. Compared to traditional approaches, RL approache… (see more)s can learn from higher-dimensionality input road and vehicle sensors and better adapt to varying traffic conditions resulting in reduced travel times (in simulation). However, these RL methods require training from massive traffic sensor data. To offset this relative inefficiency, some recent RL methods have the ability to first learn from small-scale networks and then generalize to unseen city-scale networks without additional retraining (zero-shot transfer). In this work, we study the robustness of such methods along two axes. First, sensor failures and GPS occlusions create missing-data challenges and we show that recent methods remain brittle in the face of these missing data. Second, we provide a more systematic study of the generalization ability of RL methods to new networks with different traffic regimes. Again, we identify the limitations of recent approaches. We then propose using a combination of distributional and vanilla reinforcement learning through a policy ensemble. Building upon the state-of-the-art previous model which uses a decentralized approach for large-scale traffic signal control with graph convolutional networks (GCNs), we first learn models using a distributional reinforcement learning (DisRL) approach. In particular, we use implicit quantile networks (IQN) to model the state-action return distribution with quantile regression. For traffic signal control problems, an ensemble of standard RL and DisRL yields superior performance across different scenarios, including different levels of missing sensor data and traffic flow patterns. Furthermore, the learning scheme of the resulting model can improve zero-shot transferability to different road network structures, including both synthetic networks and real-world networks (e.g., Luxembourg, Manhattan). We conduct extensive experiments to compare our approach to multi-agent reinforcement learning and traditional transportation approaches. Results show that the proposed method improves robustness and generalizability in the face of missing data, varying road networks, and traffic flows.
2023-12-31
IEEE Open Journal of Intelligent Transportation Systems (published)
doi.org
arxiv.org
Inertia-Based Indices to Determine the Number of Clusters in K-Means: An Experimental Evaluation
Andrei Rykov
Renato Cordeiro De Amorim
Vladimir Makarenkov
Boris Mirkin
This paper gives an experimentally supported review and comparison of several indices based on the conventional K-means inertia criterion fo… (see more)r determining the number of clusters,
2023-12-31
IEEE Access (published)
doi.org
Initial Separation of Poles to form a Bipolar Spindle is a Rapid and Irreversible Process
Allen Y. Leary
Elena Nazarova
Shannon Sim
Eileen O'Toole
Paul François
Jackie Vogel
2023-12-31
International Conference on Machine Learning (published)
doi.org
Interoceptive technologies for psychiatric interventions: From diagnosis to clinical applications
Felix Schoeller
Adam Haar Horowitz
Abhinandan Jain
Pattie Maes
Nicco Reggente
Leonardo Christov-Moore
Giovanni Pezzulo
Laura Barca
Micah Allen
Roy Salomon
Mark Miller
Daniele Di Lernia
Giuseppe Riva
Manos Tsakiris
Moussa A. Chalah
Arno Klein
Ben Zhang
Teresa Garcia
Ursula Pollack
Marion Trousselard … (see 4 more)
Charles Verdonk
Guillaume Dumas
Vladimir Adrien
Karl Friston

The perception of body signals play a crucial role in cognition and emotion, which may lead to catastrophic outcomes when it becomes dysf… (see more)unctional. To characterize these mechanisms and intervene on interoception for either diagnostic or treatment purposes, a mounting body of research is concerned with interventions on interoceptive channels such as respiration, cardioception, or thermoception. However, we are still lacking a mechanistic understanding of the underlying psychophysiology. For example, interoceptive signals are often both the cause and consequences of some distress in various mental disorders, and it is still unclear how interoceptive signals bind with exteroceptive cues. In this article, we present existing technologies for manipulating interoception and review their clinical potential in light of the predictive processing framework describing interoception as a process of minimization of prediction errors. We distinguish between three kinds of stimuli: artificial sensations that concern the direct manipulation of interoceptive signals, interoceptive illusions that manipulate contextual cues to induce a predictable drift in body perception, and emotional augmentation technologies that blend artificial sensations with contextual cues of personal significance to generate specific moods or emotions. We discuss how each technology can assess and intervene on the precision-weighting of prediction errors along the cognitive and emotional processing hierarchy and conclude by discussing the clinical relevance of interoceptive technologies in terms of diagnostic stress tests for evaluating interoceptive abilities across clinical conditions and as intervention protocols for conditions such as generalized anxiety disorders, post-traumatic stress disorders, and autism spectrum disorders.

2023-12-31
Neuroscience & Biobehavioral Reviews (published)
doi.org