Publications

Foreword to the special issue on responsible artificial intelligence: methodologies, implications, and practices
Ebrahim Bagheri
Robin Cohen
Faezeh Ensan
Benjamin C.M. Fung
Sébastien Gambs
Reihaneh Rabbany
2026-08-13
Information Processing & Management (published)
doi.org
Analyzing Flexible Search Distributions in Black-Box Optimization with Normalizing Flow-based Estimation of Distribution Algorithms
Sara Karami
Christian Gagné
Black-box optimization often requires search distributions that can adapt to complex geometric structures under limited evaluation budgets. … (see more)We propose NF-EDA, a Normalizing Flow-based Estimation of Distribution Algorithm that replaces fixed Gaussian models with a learned, flexible search distribution. Beyond optimization performance, our goal is to better understand how increased distributional expressiveness affects search behavior. In contrast to classical Gaussian-based methods, NF-EDA can adapt to curved, asymmetric, and non-elliptical regions of the search space, enabling broader yet structured exploration during early stages of optimization. By tracking the evolution of the learned distribution over iterations, we analyze how NF-EDA reshapes its sampling behavior compared to predefined parametric approaches such as CMA-ES and Gaussian EDAs. Experimental results on selected COCO BBOB functions, including Rastrigin, Schwefel, Lunacek bi-Rastrigin, and Rosenbrock, show that NF-EDA achieves faster early progress and reduced variability across runs, particularly in higher-dimensional settings. An ablation against a Gaussian EDA with matching update rules further demonstrates that these effects arise from the learned flow transformation rather than from the surrounding EDA procedure alone. These findings highlight the importance of flexible search distributions for understanding and improving model-based black-box optimization.
2026-08-12
Genetic and Evolutionary Computation Conference (published)
doi.org
Conductivity Preservation of Aryl Diazonium-Functionalized Bilayer Graphene Probed by Operando Hall
Bahar Molavi
Claudia M. Bazán
Tony Vuu
Jade Cimmino
Sébastien Côté
Michel Côté
Delphine Bouilly
Thomas Szkopek
Abstract Covalent functionalization of graphene is a means to achieve robust immobilization of functional groups, but it results in a signif… (see more)icant loss of electrical conductivity in monolayer graphene (MonoG) due to the introduction of strong scattering by point defects. This work presents gate-activated covalent functionalization of bilayer graphene (BiG) integrated with an operando Hall characterization to measure the charge carrier density and mobility in real-time. Using an integrated Ag/AgCl gate electrode to modulate the BiG Fermi level, we achieve precise control over the covalent grafting of aryl diazonium groups on BiG. We demonstrate that BiG preserves the majority of its conductivity after functionalization by losing only 20% of its conductivity, whereas MonoG experiences 80% reduction in conductivity under similar conditions. BiG enables a significantly wider tuning range for surface coverage while preserving the conductivity. Operando Hall measurements of BiG functionalization reveal that the observed conductivity decrease is primarily driven by a reduction in charge mobility due to short-range scattering, while the charge carrier density changes only modestly. Furthermore, we characterize the impact of covalent attachments on the graphene density of states (DOS) and interfacial charge storage through Hall-derived quantum capacitance and electrochemical impedance spectroscopy (EIS). Finally, we demonstrate the application of carboxyphenyl functionalized BiG for pH sensing and present a site-binding model that describes the electrostatic coupling between site-binding coverage and the charge density within the conducting channel. This architecture provides a robust and tunable platform for graphene FET sensors.
2026-08-12
ACS Applied Electronic Materials (published)
doi.org
Customers’ Multihoming Behavior in Ride-Hailing: Empirical Evidence from Uber and Lyft
Sandeep Chitla
Maxime C. Cohen
Srikanth Jagabathula
Dmitry Mitrofanov
Problem definition: Are customers loyal to a ride-hailing platform or they see this service as a commodity and multihome (i.e., check severa… (see more)l platforms before booking a ride)? Using a large panel dataset on ride-hailing transactions, we investigate to what extent customers multihome. Our dataset offers a unique opportunity to study this question as we observe the repeated choices of riders for both Uber and Lyft. Our dataset comprises more than 1.4 million rides completed by 162 thousand riders in NYC in 2018. Methodology/results: We develop a comprehensive structural model that incorporates both operational (price and waiting time) and behavioral factors (e.g., platform stickiness) to explain riders’ choices. Our model also accounts for the dynamic interactions between customers and platforms by assuming that riders update their beliefs on price and waiting time in a Bayesian fashion. Finally, the riders’ propensity to multihome is modeled by incorporating the consideration set formation of customers into our framework. We find that riders’ choices are not fully explained by operational factors, hence indicating that customers view the platforms as differentiated service providers. While 83.4% of riders took rides with a single platform, our model shows that even the remaining 16.6%, who used both Uber and Lyft at least once, considered both platforms only 43.4% of the time. Managerial implications: It is crucial for ride-hailing platforms to capture this single (or multi)-homing behavior while designing price discounts. Specifically, personalized discounts may be ineffective if the platform is not part of the customer’s consideration set. Our results show that targeting customers earlier in their lifecycle can enhance the platform’s market share by 77.56% more than their current discounting strategy. We also find that targeting customers with low search friction results in a 24.78% increase in market share relative to targeting customers with high search friction.
2026-08-12
Manufacturing & Service Operations Management (published)
doi.org
IP Protection in the Era of Visual Generative AI: A Survey
Zhuan Shi
Shunchang Liu
Alireza Dehghanpour Farashah
Qian Yang
Han Yu
Cao Yang
Chaochao Chen
Yuping Yan
Yaochu Jin
Golnoosh Farnadi
Lingjuan Lyu
The rapid evolution of visual generative AI has introduced a wide range of intellectual property risks, spanning the unauthorized learning, … (see more)reproduction, extraction, misuse, and redistribution of protected data and model assets. To address these risks, a growing body of technical defenses has been proposed. However, existing surveys typically organize this literature by lifecycle stage or technical mechanism, which can obscure the protective intent of different methods. This survey presents a two-dimensional taxonomy for IP protection in visual generative models. The primary axis is a Control Logic View, which classifies methods into Information Exposure Control, Generative Behavior Constraint, and Attribution&Accountability according to the risk variable they regulate. The secondary axis distinguishes Data IP from Model IP as cross-cutting asset dimensions. Under this framework, we systematically review protection methods, align evaluation protocols with protection objectives, and discuss open challenges including proactive model-level safeguards, standardized evaluation, robustness against adaptive attacks, and explainable evidence. This survey aims to offer a principled, systematic, and easy-to-follow overview for both new and experienced researchers in visual generative AI IP protection.
2026-08-12
arXiv (preprint)
doi.org
arxiv.org
Divergent specializations for motion-driven representations in higher lateral and dorsal visual areas
Nastaran Darjani
Sophia Robert
Maryam Vaziri-Pashkam
Shahab Bakhtiari
The human visual system integrates both static and dynamic information to support form and shape perception, yet the computational principle… (see more)s underlying the integration of motion for object recognition remain unclear. Artificial neural networks (ANNs) offer a computational framework for developing and testing hypotheses about these principles: if ANNs trained on motion-related tasks develop representations that align with brain activity and support object categorization, this would suggest that the training objectives and architectural constraints of these networks may capture key aspects of motion processing in biological visual systems in general, and motion processing for object recognition, in particular. Here, we investigated this question using “object kinematograms”, stimuli in which object form is conveyed solely through motion cues. We measured neural responses of two higher regions of the lateral and the dorsal visual pathways, respectively, with strong sensitivity to dynamic cues from objects: lateral occipitotemporal cortex (LOT bio ), and left supramarginal gyrus (SMG lh ), as well as primary visual cortex (V1). We compared brain responses to representations extracted from two neural networks: SlowFast, a dual-pathway architecture trained on action recognition that processes slow- and fast-varying visual information with cross-pathway integration, and DorsalNet, a model of the primate dorsal visual pathway trained on embodied self-motion estimation. Representational similarity analysis revealed distinct representational profiles across brain areas, demonstrating functional specialization in motion-based form processing. LOT bio was best characterized by the slow pathway of the SlowFast model, whereas SMG lh showed strong similarity to both models. Critically, we found that representations aligned with brain activity also better supported behavioral function: the full SlowFast model, incorporating both slow and fast pathways, outperformed other models in few-shot categorization of object kinematograms and showed the highest similarity to human perceptual judgments. These findings demonstrate that with appropriate inductive biases, specifically, dual-pathway architectures for multi-scale motion processing and training objectives focused on dynamic visual tasks, ANNs can develop functionally useful representations of motion-defined forms that exhibit better alignment with the visual regions involved in processing dynamic visual signals.
2026-08-11
bioRxiv (preprint)
doi.org
Do LLMs Beat Nash? Testing Decentralized Coordination in Self-Play Multi-Agent Games
Deborah Sinishaw
Qile Zhu
Edwin Meriaux
Gregory Dudek
Large language model agents deployed without a central controller are often assumed to require communication to coordinate their actions. We… (see more) ask what remains possible without it: when independent instances of the same model cannot communicate, can they still reason about their counterparts well enough to exceed the standard game-theoretic baseline for uncoordinated play? We introduce a benchmark of one-shot, no-communication games in which each of thirteen language models is told only that its counterparts are running the same model and is evaluated against the Nash equilibrium of the underlying game. In two-player matrix games spanning seven archetypes and two to ten actions per player, two frontier-hosted models consistently exceed their Nash benchmark, approaching the optimal joint outcome in several archetypes, while most open-weight models achieve only partial gains that vary sharply by game structure. Performance degrades substantially in team-based games with four or more interchangeable agents, particularly as the action space grows, suggesting that whatever capability drives self-play gains in dyadic games does not transfer to larger multi-agent teams.
2026-08-11
arXiv (preprint)
doi.org
arxiv.org
Backdoor Decontamination Dynamics in LLM Agents
Gabriel Huang
Abhay Puri
Léo Boisvert
Alexandre Drouin
Perouz Taslakian
Spandana Gella
Christopher Pal
Open-weight LLM agents are vulnerable to backdoors installed during fine-tuning, which may be undetectable if the trigger conditions are nev… (see more)er met during testing. Assuming defenders do not know the existing trigger, they cannot unlearn it directly. One decontamination strategy is to install a known backdoor (defensive poisoning) then to unlearn it, hoping that the original unknown backdoor is removed as a side effect. However, this procedure has uncertain outcomes: the original backdoor may persist or be erased or rerouted, among other possibilities. We introduce a framework for studying these dynamics in tool-calling agents, decoupling trigger, response, teacher, and fine-tuning method across systematic experiments on AgentDyn. Across 115 experiments, defensive poisoning alone erases around 56% of original backdoors; subsequent decontamination then drives almost all survivors to erasure, confirming that trigger recognition and malicious execution are behaviorally dissociable. Interestingly, our experiments find that malicious backdoors never persist when using different triggers of the same general type as the defensive backdoor when followed by decontamination via unlearning. Co-installing up to four backdoors increases resistance (around 36% erased), yet decontaminating a single known co-resident backdoor collaterally clears 52/60 co-residents (87%). Upon visualizing postdecontamination model internals using J-lens, we confirm that although the decontamination restores benign LLM responses, traces of original trigger awareness persist at intermediate layers.
2026-08-10
arXiv (preprint)
doi.org
arxiv.org
Generative Learning for Quantum Measurement Design
Jun Dai
Olivier Nahman-Lévesque
Guillaume Rabusseau
Hong-Ye Hu
Cunlu Zhou
Extracting quantum information from a quantum state is a fundamental task of quantum computation, often requiring the estimation of many non… (see more)-commuting observables under a finite measurement budget. For both near-term and early fault-tolerant settings, the measurement protocol must balance statistical efficiency against implementation resources such as circuit depth, connectivity, and entangling-gate count. Many existing strategies focus on two extremes: hardware-friendly product measurements with high sampling cost, and fully commuting measurements with deep circuits. Here we recast resource-constrained measurement design as a generative learning problem. We introduce FlowMeas, which uses a generative flow network to directly sample finite ensembles of shallow Clifford measurement circuits subject to a prescribed shot budget and hardware constraints. At zero entangling depth, FlowMeas learns qubit-wise commuting measurement schedules and already matches or improves leading product-measurement methods on nearly all molecular benchmarks. Allowing one or two entangling gate layers yields further reductions in energy estimation error of up to
2026-08-10
arXiv (preprint)
doi.org
arxiv.org
Koopman Representation of Nonlinear Virtual Environments in Kinesthetic Haptic Systems
Yanting Zhou
József Kövecses
James Richard Forbes
Rendering haptic feedback with nonlinear virtual environments (VEs) is important in many applications that require highly accurate force fee… (see more)dback. This paper considers the use of the Koopman operator to represent a nonlinear VE interacting with a haptic system. Simulation and experimental results demonstrated that the proposed method provides an effective representation of the nonlinear dynamics of a Duffing-oscillator VE. A multi-user study further confirmed this conclusion. In addition, a closed-loop (CL) stability analysis is performed leveraging the Koopman representation of the nonlinear VE to access stability of the overall haptic system. This alternative way of representing nonlinear VEs enables a convenient CL stability analysis that is less conservative than traditional passivity-based methods. Since a linear combination of all lifted states is used to represent the nonlinearity, such representation is also more robust to uncertainties in the modeling of the haptic device than a traditional nonlinear model.
2026-08-10
arXiv (preprint)
doi.org
arxiv.org
VoxSumm: A Multilingual Corpus of Long-Form Spoken News for Joint Summarization and Translation
Yejin Jeon
Marie Maltais
Virginia Ceccatelli
Min Ma
David Ifeoluwa Adelani
As information increasingly traverses linguistic boundaries, users require concise cross-lingual representations of long-form content. Never… (see more)theless, long-document summarization research remains text-centric, whereas multilingual speech research has largely prioritized translation, preserving source content rather than compressing it. We address this methodological gap by formalizing joint speech summarization and translation (JSumT): the generation of a succinct, faithful target-language summary directly from a long spoken document in a source language. We additionally introduce VoxSumm, the first multilingual and cross-lingual benchmark for this task, comprising 10,045 BBC article-summary pairs across 24 languages and encompassing approximately 703 hours of speech data. Our evaluation of representative speech-language models reveals pronounced variation across models and generation settings: Gemini3.1-Pro demonstrates the greatest consistency, summarization into English generally surpasses generation into non-English target languages, and translating an entire document before summarization compounds instruction-following failures. Through the release of VoxSumm, we establish a foundation for developing and evaluating multilingual systems capable of jointly interpreting, compressing, and translating long-form speech.
2026-08-10
arXiv (preprint)
doi.org
arxiv.org
Assessing the effect of race on machine learning models predicting hospital admissions from emergency department
Aidan Licoppe
Lucy An
David L. Buckeridge
Antony Robert
James Man Git Tsui
Abstract Objectives Machine learning (ML) models are increasingly being developed to support healthcare delivery. However, concerns remain a… (see more)bout their potential to perpetuate existing biases rooted in the data used to develop them. We aim to assess the impact of using race in predicting hospital admission probabilities for patients visiting the emergency department (ED). Materials and Methods Data from the MIMIC-IV ED dataset were used to train2 ML models predicting hospital admission: one included race; the other did not. Differences in predicted admission probabilities were evaluated across racial groups under multiple validation conditions. Results Including race as a model input was associated with meaningful differences in predicted admission probabilities for White (3.2%), Black (−1.5%), and Hispanic (−3.0%) patients, while minimal differences were observed for Asian (0.2%) and Other (0.5%) patients. These differences were associated with large Cohen’s d effect sizes in the baseline model for White (d = 1.00), Black (d = −1.23), and Hispanic (d = −1.35) patients. After balancing racial group prevalence, the effects persisted for White (1.22%; d = 1.22) and Hispanic (−1.00%; d = −1.00) patients. Discussion These findings suggest that race is associated with differences in ML-based hospital admission predictions for ED patients, underscoring the need for caution when incorporating race into clinical prediction models and the importance of rigorous bias assessment. Conclusion As race was associated with predictions, there is a crucial need to address underlying social factors and the use of broader, more equitable clinical data for ML model training.
2026-08-09
Journal of the American Medical Informatics Association Open (published)
doi.org