arXiv subjects

Shaohua Guan

Publications and source records attributed to Shaohua Guan.

4 recordsLinked to original sources

arXiv2026

EveLoad: Cognitive Workload Recognition from Event-Based Eye Movements

Cognitive workload monitoring is important for adaptive rehabilitation and assistive interfaces, where task difficulty, pacing, and feedback should be adjusted according to the user's cognitive state to avoid overload and under-challenge. Emerging extended reality and robot-assisted rehabilitation environments provide controllable training tasks, but they require unobtrusive sensing methods that can capture rapid ocular dynamics during interaction. Existing eye-movement-based cognitive workload recognition methods mainly rely on frame-based eye trackers, which often suffer from limited temporal resolution and degraded robustness under rapid eye movements. In contrast, event cameras provide microsecond-level temporal resolution, high dynamic range and low latency, making them suitable for capturing fine-grained ocular dynamics. Many previous studies rely on free-viewing or similar paradigms, where gaze locations can vary across tasks. As a result, models may learn associations between gaze-location distributions and cognitive workload, rather than workload-related eye movement characteristics themselves. In this work, we introduce EveLoad, which, to the best of our knowledge, is the first event-based eye-movement dataset with graded cognitive workload annotations, collected from 20 healthy participants under spatially constrained and task-driven conditions using a controlled N-back-guided fixation paradigm. Based on this dataset, we establish a benchmark for cognitive workload recognition with six workload levels and propose a learning framework that encodes spatiotemporal event representations. Experimental results show that our approach achieves an average subject-specific accuracy of 96.36% and 96.13% under mixed random split evaluation. These results suggest that event-based eye movements may provide a useful sensing pathway for future workload-aware rehabilitation.

cs.LG↗

arXiv2025

Matrix approach to generalized ensemble theory for nonequilibrium discrete systems

A universal and rigorous ensemble framework for nonequilibrium system remains lacking. Here, we provide a concise framework for the generalized ensemble theory of nonequilibrium discrete systems using matrix-based approach. By introducing an observation matrix, we show that any discrete probability distribution can be formulated as a generalized Boltzmann distribution, with observables and their conjugate variables serving as basis vectors and coordinates in a vector space. Within this framework, we identify the minimal sufficient statistics required to infer the Boltzmann distribution. The nonequilibrium thermodynamic relations and fluctuation-dissipation relations naturally emerge from this framework. Our findings provide a new approach to developing generalized ensemble theory for nonequilibrium discrete systems.

cond-mat.stat-mech↗

arXiv2024

A large deviation approach to superstatistics: thermodynamic duality symmetry between conjugate variables

Superstatistics generalizes Boltzmann statistics by assuming spatio-temporal fluctuations of the intensive variables. It has many applications in the analysis of experimental and simulated data. The fluctuation of the intensity variable is the key to the validity of superstatistical theory, but the law of its distribution is still unclear. In the framework of large deviation theory, we show that the fluctuation of the intensive variable of superstatistics emerges naturally from measurements in the large data limit. Combining Bayes' theorem, we demonstrate the conditional probability distribution of the intensity variable also follows the Boltzmann statistics and the conjugate variable of the intensive variable is the extensive variable, indicating a thermodynamic duality symmetry between conjugate variables in the superstatistical systems. A new thermodynamic relation between the entropy functions of conjugate variables is obtained. We utilized a simple Ising model with fluctuating temperature to verify the dual relationship between temperature and energy. Our work may contribute to the understanding of statistical physics in complex systems and Bayesian inference.

cond-mat.stat-mech↗

arXiv2024

Universal scaling relation and criticality in metabolism and growth of Escherichia coli

The metabolic network plays a crucial role in regulating bacterial metabolism and growth, but it is subject to inherent molecular stochasticity. Previous studies have utilized flux balance analysis and the maximum entropy method to predict metabolic fluxes and growth rates, while the underlying principles governing bacterial metabolism and growth, especially the criticality hypothesis, remain unclear. In this study, we employ a maximum entropy approach to investigate the universality in various constraint-based metabolic networks of Escherichia coli. Our findings reveal the existence of universal scaling relations across different nutritional environments and metabolic network models, similar to the universality observed in physics. By analyzing single-cell data, we confirm that metabolism of Escherichia coli operates close to the state with maximum Fisher information, which serves as a signature of criticality. This critical state provides functional advantages such as high sensitivity and long-range correlation. Moreover, we demonstrate that a metabolic system operating at criticality takes a compromise solution between growth and adaptation, thereby serving as a survival strategy in fluctuating environments.

q-bio.MN↗