Hong Kong University of Science and Technology
Hong Kong University of Science and Technology Institutional RepositoryNot a member yet
162821 research outputs found
Sort by
The effect of mask use on cross-race face perception: a simultaneous EEG and eye-tracking study
No full textAbstract: While people are often experts in perceiving and categorizing faces into meaningful social categories (i.e., race), there are suboptimal scenarios such as mask use that may impair face processing. Here we examined how mask use may differentially impact own- and other-race face processing in social categorization, and the underlying neurocognitive mechanisms using simultaneous eye movement and EEG recording. We found that mask use made participants’ face scanning patterns more eyes-focused and consistent, and reduced the differences in both eye movement pattern and early attention-related ERP component P1 between viewing own- and other-race faces. Moreover, mask use did not change how people categorize biracial morphed faces, or the advantage in categorization speed of other-race faces. These results suggest that when perceiving masked faces, information from the eye region may be sufficient for social categorization, and that race-based social categorizations can be impervious to mask use. Interestingly, we found that when viewing other-race faces, where people have less perceptual expertise, those who show more consistent face scanning patterns have more efficient processing of masked faces. These findings have important implications for cross-race face perception, especially when face perception condition becomes suboptimal. Significance statement: As mask use has become a common practice in response to respiratory virus outbreaks, it has inadvertently altered both health practices and the complex dynamics of social interaction. In a world that values diversity and cross-racial interactions, understanding how masks influence our cognitive processes during cross-race face perception is not just timely but vital. Given this context, we examined the effect of mask use on race categorization, by systematically investigating eye movement behavior, and neural representations of own versus other-race faces, and how these mask-induced changes are associated with each other. By utilizing simultaneous eye movement and EEG recording, our study reveals that the eye region can significantly influence social categorization, suggesting that race-based categorizations persist even in the presence of masks. Interestingly, we found that for other-race faces with which people have less perceptual expertise, those who adjust to a more consistent face scanning pattern for masked faces have more efficient processing of masked faces. This highlights the importance of individuals’ visual routine adaptability when the viewing condition is not optimal. Though the current research is called by the demand for COVID-19, our findings can be generalized to a broader context and enhance our understanding of human visual and social cognition.</p
Unconventional hysteresis due to time-reversal symmetry breaking superconductivity in RbV<sub>3</sub>Sb<sub>5</sub>
No full textThe study of kagome materials has recently attracted much attention due to the presence of many electron-electron interaction-driven phases in a single material. In this work, we report the time-reversal symmetry-breaking superconductivity in the thin-flake kagome material RbV3Sb5. Firstly, when an in-plane magnetic field is swept in opposite directions, we observe an unconventional form of hysteresis in magnetoresistance, which is different from the hysteresis induced by extrinsic mechanisms. In contrast, no such hysteresis is observed in CsV3Sb5 samples below their superconducting transition temperature. Strikingly, at a fixed magnetic field, the finite-resistance state in RbV3Sb5 can be transitioned into the superconducting state by applying and subsequently removing a large current. Secondly, at temperatures around 400 mK, the re-entrance of superconductivity occurs during an in-plane field-sweeping process with a fixed sweeping direction. The observations of the unconventional hysteresis and re-entrance suggest the existence of time-reversal symmetry-breaking superconducting states in RbV3Sb5.</p
A blockchain-empowered decentralized and secure spectrum allocation for wireless vehicular communications
No full textSpectrum allocation plays an essential role for maximizing the spectrum efficiency in wireless vehicular networks. There exist many security challenges in existing spectrum allocation systems for wireless vehicular communications, such as how to decentralize the functionalities of the centralized entity, how to customize the security protocol, and how to prove the security formally and rigorously, etc. To overcome these challenges, this paper proposes a blockchain-empowered decentralized and secure spectrum allocation (BDSSA) for wireless vehicular communications, by customizing a blockchain-based security protocol for a general utility maximization problem. Specifically, BDSSA distributes the functionalities of centralized entities to the blockchain network. Then, an optimization algorithm is customized to solve the problem by reformulating it on a weighted complete bipartite graph. Finally, this paper provides a formal and rigorous security proof for attacks in terms of public key cryptosystem and blockchain network, which shows that the proposed BDSSA is able to resist malicious users and attacks in the blockchain-empowered wireless vehicular network. Experimental results show that BDSSA outperforms some state-of-the-art baseline methods in terms of security and overall utility.</p
AR Tunnel: An augmented-reality digital twin for immersive learning of wind tunnel laboratories
No full textEngineering labs face constraints on time, safety, and access, making it difficult for students to develop robust procedural schemas and visualize invisible flow phenomena. We present AR Tunnel, an augmented-reality (AR) digital twin of a university wind tunnel. Its theoretical design draws on cognitive load and multimedia learning principles, along with situated learning via contextualized rehearsal. Pilot testing with students indicated strong user acceptance, identified usability issues, and guided iterative refinements to navigation, performance modes, and instructional content. In controlled tests with undergraduate and postgraduate cohorts, AR pre-lab preparation was compared with equivalent slide-based preparation. Although both groups reported similar increases in self-assessed familiarity after the hands-on lab, students who prepared with AR Tunnel achieved significantly higher scores on objective knowledge quizzes across both cohorts (p'0.05). Within the AR groups, higher engagement – measured across behavioral, emotional, and cognitive dimensions – correlated positively with quiz performance (Spearman’s ρ≈0.42–0.48, p'0.05), suggesting that active exploration mediated learning gains. AR Tunnel therefore functions as a scalable preparatory resource that complements, rather than replaces, in-person lab instruction. Future work should measure retention and transfer, disentangle gamification and scaffolding effects, and evaluate mixed-reality overlays during live-lab operation.</p
Assessing the probabilistic evolution of cascading hazards of landslide-river blockage-dam breaching-flood by integrating physics-based and data-driven methods
No full textLandslide-river blockage-dam breaching-flood (LRDF) events originate from a single landslide and subsequently evolve into a sequence of cascading hazards, often resulting in severe environmental and socio-economic consequences. This study develops an integrated Bayesian network framework that combines physics-based and data-driven approaches to assess the probability of LRDF. The framework consists of four interlinked sub-networks, each representing a distinct stage of the cascade and explicitly identifying the dominant control parameters and their causal dependencies. Parameters uncertainties are systematically incorporated to quantify prior probabilities, and network-based inference is employed to evaluate sensitivity and probabilistic propagation across stages. The proposed method is applied to assess the cascading hazards of LRDF sourced from 305 loose deposits from during the M.S. 8.0 Wenchuan earthquake in the PR303 region. The results demonstrate that the demonstrate framework effectively addresses challenges in quantifying multi-hazard interactions and multi-source parameters uncertainties in probabilistic assessment. The cascading hazards are governed by a quantitative compound control mechanism, in which rainfall intensity, slope gradient, geotechnical strength, and landslide volume jointly influence the transition probabilities between successive stages. Slope angle and soil strength parameters primarily govern slope instability, while interactions among soil and hydraulic parameters indirectly modulate intermediate nodes, such as landslide volume, sliding velocity, and sliding distance through chain-like propagation, further influencing river blockage and landslide-dam morphology, ultimately affecting dam-breaching flood distribution and downstream risk. Overall, the proposed framework provides a unified and interpretable approach for quantifying systemic risk and safety associated with cascading geo-hazards, offering a probabilistic basis for early warning and risk-informed decision-making.</p
Marginal gains in sprint cycling: quantifying the time-varying impact of aerodynamic drag reduction
No full textAerodynamic optimization has gained increasing attention in competitive cycling. It is widely believed that aerodynamics plays a more critical role at higher riding speeds, as the power required to overcome aerodynamic drag is proportional to the cube of the riding speed, making it increasingly dominant at higher riding speeds. This study challenges the applicability of this paradigm for sprint cycling. We demonstrate that neither riding speed nor the percentage of power output used to overcome aerodynamic drag can adequately reflect the importance of aerodynamics in sprint cycling, due to the significant role of transient dynamics in sprint cycling. To address this gap, we present a theoretical framework based on perturbation analysis to examine marginal gains in sprint time resulting from time-dependent parameter variations. Our analysis also explores the trade-off between power output and aerodynamic efficiency. Through several case studies of standing-start sprints, we establish a quantitative relationship between aerodynamic improvements and savings in sprint time. Furthermore, we propose a metric to explicitly quantify the time-dependent importance of aerodynamic drag on sprint performance. Overall, this study advances the understanding of performance optimization in sprint cycling and provides a practical tool for marginal gain analysis.</p
Improving the fate of a Bike: A Usage-Continuity-Driven predictive framework for Bike-Sharing rebalancing under sparse demand
No full textThe sustainable operation of shared micro-mobility service (like bike/e-bike sharing) generally requires demand prediction and bike rebalance to avoid the mismatch between bike inflow and outflow. Current rebalancing strategies typically rely on forecasting one-time site demand, and try to fulfill predicted values. However, related studies overlook the lagged effects of rebalance (e.g., where rebalanced bikes will go), resulting in operational inefficiency. Rebalanced bikes may be used once only to be stranded in a cold-demand zone, necessitating another costly rebalance. Therefore, this study introduces a usage-continuity-driven predictive framework for rebalance, where bike usage continuity is defined as the average of future usage frequency of bikes at one specific site. For sparse-demand systems, it is challenging to directly predict usage continuity due to sparsity and uncertainty. Here, we model the bike usage continuity at the site with a zero-inflated negative binomial (ZINB) distribution, estimating its parameters via graph neural networks. Large Language Models (LLMs) are integrated to enhance urban context understanding. Next, a simple rebalance model is established based on the predicted usage continuity The proposed approach is examined via real-world bike/e-bike sharing datasets in representative cities of China (Taizhou) and the USA (New York City), respectively. The results demonstrate the model’s effectiveness in terms of prediction accuracy, uncertainty quantification, and rebalancing performance, especially in scenarios with high demand dispersion. By discussing the trade-off between capturing demand stochasticity and farsighted decision-making, practical insights are offered into the operation of shared micro-mobility systems.</p
Research Progress on Machine Vision Detection Technology for Foreign Fibers in Cotton
No full textForeign fiber (FF, plural: FFs) contamination has been demonstrated to have a substantial impact on the quality and profitability of cotton textiles. Machine vision technology, characterized by its non-contact approach and high efficiency, has emerged as the primary solution for detecting FFs in cotton. This paper commences with a precise definition and classification of FF and a concomitant analysis of the mechanisms of contamination. Subsequently, a systematic review of global research advancements in imaging technologies and the evolution of algorithms is conducted. This paper emphasizes the use of X-ray, ultraviolet fluorescence, line laser, polarized light, infrared imaging, and hyperspectral imaging techniques for FF detection. Through a comparative analysis, it reveals the applicable scope and effectiveness of various imaging schemes. Regarding the evolution of algorithms, this paper expounds on the technical development process from traditional image processing to machine learning (ML) and deep learning (DL). The study meticulously examines the strengths and weaknesses of each algorithmic stage. In conclusion, this paper synthesizes the prevailing technical challenges confronting machine vision detection of FFs in cotton and proffers recommendations for future research directions in this domain, emphasizing multi-technology integration, algorithm optimization, and hardware innovations.</p
