activity
20242026
collaborators

6 papers

cs.NE2026

Identification of fixations and saccades in eye-tracking data using adaptive threshold-based method

Charles Orioma, Josef Krivan, Rujeena Mathema +4

Properties of ocular fixations and saccades are highly stochastic during many experimental tasks, and their statistics are often used as proxies for various aspects of cognition. A…

cs.NE2026

Jump-diffusion models of parametric volume-price distributions

Anup Budhathoki, Leonardo Rydin Gorjão, Pedro G. Lind +1

We present a data-driven framework to model the stochastic evolution of volume-price distribution from the New York Stock Exchange (NYSE) equities. The empirical distributions are…

cs.LG2026

PARWiS: Winner determination under shoestring budgets using active pairwise comparisons

Shailendra Bhandari

Determining a winner among a set of items using active pairwise comparisons under a limited budget is a challenging problem in preference-based learning. The goal of this study is…

quant-ph2025

Optimizing quantum circuits with evolutionary algorithms for stable Boolean gates, elementary cellular automata, and highly entangled quantum states

Shailendra Bhandari, Stefano Nichele, Sergiy Denysov +1

We investigate the potential of bio-inspired evolutionary algorithms for designing quantum circuits with specific goals, focusing on two particular tasks. The first one is motivate…

cs.NE2025

IntLevPy: A Python library to classify and model intermittent and Lévy processes

Shailendra Bhandari, Pedro Lencastre, Sergiy Denysov +2

IntLevPy provides a comprehensive description of the IntLevPy Package, a Python library designed for simulating and analyzing intermittent and Lévy processes. The package includes…

cs.NE2024

Modeling Eye Gaze Velocity Trajectories using GANs with Spectral Loss for Enhanced Fidelity

Shailendra Bhandari, Pedro Lencastre, Rujeena Mathema +3

Accurate modeling of eye gaze dynamics is essential for advancement in human-computer interaction, neurological diagnostics, and cognitive research. Traditional generative models l…