arXiv subjects

Chi Ho Yeung

Publications and source records attributed to Chi Ho Yeung.

At least 19 recordsLinked to original sources

arXiv2026

Guiding Self-Organizing Dynamics of Residential Choice in Cities to Reduce Traffic Congestion and Carbon Emissions

Rapid urbanization and growing vehicle ownership exacerbate traffic congestion and prolong commute times. We examine the self-organizing dynamics of residential choice via a hypothetical home-swapping process to mitigate peak-hour traffic congestion and carbon emissions. Specifically, we analyze over 400,000 trajectories from 9 days in a major Chinese city, revealing that actual average commuting distance is approximately three times shorter than under random residential distribution, indicating significant self-organization. Notably, city-wide home swapping reduces commuting distance by 50.4%, substantially easing traffic congestion, thereby reducing carbon emissions by 77.3%. Even with the consideration of socio-demographic factors and individual needs, the reductions remain significant: 8.1%-10.3% in commuting distance and 27.4%-34.4% in carbon emissions. Considering the potential induction of additional non-commuting trips, the reduction in carbon emissions remains substantial. Given the primacy of distance to the city center, polycentric city layouts can enhance these benefits. For validation, we use another dataset covering China's 28 major cities to confirm these findings. Finally, we introduce a data-driven model to elucidate self-organizing dynamics of residential choice and analyze the feasibility of government coordination. These insights demonstrate that a synergistic alignment of residential choices can leverage individual and city-level benefits, effectively alleviating commuting congestion and associated emissions.

physics.soc-ph↗

arXiv2026

The Crutch or the Ceiling? How Different Generations of LLMs Shape EFL Student Writings

The rapid evolution of Large Language Models (LLMs) has made them powerful tools for enhancing student writing. This study explores the extent and limitations of LLMs in assisting secondary-level English as a Foreign Language (EFL) students with their writing tasks. While existing studies focus on output quality, our research examines the developmental shift in LLMs and their impact on EFL students, assessing whether smarter models act as true scaffolds or mere compensatory crutches. To achieve this, we analyse student compositions assisted by LLMs before and after ChatGPT's release, using both expert qualitative scoring and quantitative metrics (readability tests, Pearson's correlation coefficient, MTLD, and others). Our results indicate that advanced LLMs boost assessment scores and lexical diversity for lower-proficiency learners, potentially masking their true ability. Crucially, increased LLM assistance correlated negatively with human expert ratings, suggesting surface fluency without deep coherence. To transform AI-assisted practice into genuine learning, pedagogy must shift from focusing on output quality to verifying the learning process. Educators should align AI functions, specifically differentiating ideational scaffolding from textual production, within the learner's Zone of Proximal Development.

cs.HC↗

arXiv2026

Exploring how EFL students talk to and through AI to develop texts

Generative Artificial Intelligence (AI) introduces new considerations for English as a foreign language (EFL) writing pedagogy. This study explores how students talk to and through AI by prompt engineering and negotiating authorship, respectively, and whether any patterns in the latter relate to students' writing performance. Using an exploratory mixed methods design, we analyzed screen recordings of 44 Hong Kong secondary students completing a Curricular Writing Task with AI Chatbots. Content analysis identified ten types of prompting strategies students employed, including questions, searches, and detailed instructions. From clustering these strategies, three distinct profiles of human-AI rhetorical load responsibility emerged: AI-dominant (52% of students), Human-dominant (25%) and Collaborative human-AI (14%). A MANOVA analysis indicated no significant multivariate effect of rhetorical load responsibility on three dimensions of students' writing performance: content, language, and organization. Students' prompting strategies and rhetorical load responsibility patterns have implications for their engagement and autonomy in EFL writing pedagogy.

cs.CL↗

arXiv2025

Approaching the Limits to EFL Writing Enhancement with AI-generated Text and Diverse Learners

Generative artificial intelligence (AI) chatbots, such as ChatGPT, are reshaping how English as a foreign language (EFL) students write since students can compose texts by integrating their own words with AI-generated text. This study investigated how 59 Hong Kong secondary school students with varying levels of academic achievement interacted with AI-generated text to compose a feature article, exploring whether any interaction patterns benefited the overall quality of the article. Through content analysis, multiple linear regression and cluster analysis, we found the overall number of words -- whether AI- or human-generated -- is the main predictor of writing quality. However, the impact varies by students' competence to write independently, for instance, by using their own words accurately and coherently to compose a text, and to follow specific interaction patterns with AI-generated text. Therefore, although composing texts with human words and AI-generated text may become prevalent in EFL writing classrooms, without educators' careful attention to EFL writing pedagogy and AI literacy, high-achieving students stand to benefit more from using AI-generated text than low-achieving students.

cs.CL↗

arXiv2024

Exploring Loss Landscapes through the Lens of Spin Glass Theory

In the past decade, significant strides in deep learning have led to numerous groundbreaking applications. Despite these advancements, the understanding of the high generalizability of deep learning, especially in such an over-parametrized space, remains limited. For instance, in deep neural networks (DNNs), their internal representations, decision-making mechanism, absence of overfitting in an over-parametrized space, superior generalizability, etc., remain less understood. Successful applications are often considered as empirical rather than scientific achievement. This paper delves into the loss landscape of DNNs through the lens of spin glass in statistical physics, a system characterized by a complex energy landscape with numerous metastable states, as a novel perspective in understanding how DNNs work. We investigated the loss landscape of single hidden layer neural networks activated by Rectified Linear Unit (ReLU) function, and introduced several protocols to examine the analogy between DNNs and spin glass. Specifically, we used (1) random walk in the parameter space of DNNs to unravel the structures in their loss landscape; (2) a permutation-interpolation protocol to study the connection between copies of identical regions in the loss landscape due to the permutation symmetry in the hidden layers; (3) hierarchical clustering to reveal the hierarchy among trained solutions of DNNs, reminiscent of the so-called Replica Symmetry Breaking (RSB) phenomenon (i.e. the Parisi solution) in spin glass; (4) finally, we examine the relationship between the ruggedness of DNN's loss landscape and its generalizability, showing an improvement of flattened minima.

cond-mat.dis-nn↗

arXiv2024

Re-routing game: The inadequacy of mean-field approach in modeling the herd behavior in path switching

Coordination of vehicle routes is a feasible way to ease traffic congestions amid a fixed road infrastructure. Nevertheless, even the optimal route configurations are provided to individual drivers, it is hard to achieve as greedy drivers may switch to other routes for a lower individual cost. Recent research uses mean-field cavity approach from the studies of spin glasses to analyze the impact of path switching in optimized transportation networks. However, this method only provides a mean-field approximation, which does not take into account the collective herd behavior in path switching due to un-coordinated individual decisions. In this study, we propose an exhaustive cavity approach to investigate the impact of un-coordinated path switching in a re-routing game and reveal that greedy drivers' decision can be highly correlated which leads to the failure of mean-fielded approaches. Our theoretical results fits well with simulations, and our developed framework can be generalized to analyze other games with multiple players and rounds. Our results shed light on the impact of herd behavior of un-coordinated human drivers in suppressing congestions through path coordination.

physics.soc-ph↗

arXiv2024

Complete Realization of Energy Landscape and Non-equilibrium Trapping Dynamics in Spin Glass and Optimization Problem

Energy landscapes are high-dimensional surfaces representing the dependence of system energy on variable configurations, which determine crucially the system's emergent behavior but are difficult to be analyzed due to their high-dimensional nature. In this article, we introduce an approach to reveal the complete energy landscapes of small spin glasses and Boolean satisfiability problems, which also unravels their non-equilibrium dynamics at an arbitrary temperature for an arbitrarily long time. In contrary to our common belief, our results show that it can be less likely to identify the ground states when temperature decreases, due to trapping in individual local minima, which ceases at different time, leading to multiple abrupt jumps with time in the ground-state probability. Simulations agree well with theoretical predictions on these remarkable phenomena. Finally, for large systems, we introduce a variant approach to extract partially the energy landscapes and observe both analytically and in simulations similar phenomena. This work introduces new methodology to unravel the non-equilibrium dynamics of glassy systems, and provides us with a clear, complete and new physical picture on their long-time behaviors inaccessible by modern numerics.

cond-mat.dis-nn↗

arXiv2024

Exploring AI-Generated Text in Student Writing: How Does AI Help?

English as foreign language_EFL_students' use of text generated from artificial intelligence_AI_natural language generation_NLG_tools may improve their writing quality. However, it remains unclear to what extent AI-generated text in these students' writing might lead to higher-quality writing. We explored 23 Hong Kong secondary school students' attempts to write stories comprising their own words and AI-generated text. Human experts scored the stories for dimensions of content, language and organization. We analyzed the basic organization and structure and syntactic complexity of the stories' AI-generated text and performed multiple linear regression and cluster analyses. The results show the number of human words and the number of AI-generated words contribute significantly to scores. Besides, students can be grouped into competent and less competent writers who use more AI-generated text or less AI-generated text compared to their peers. Comparisons of clusters reveal some benefit of AI-generated text in improving the quality of both high-scoring students' and low-scoring students' writing. The findings can inform pedagogical strategies to use AI-generated text for EFL students' writing and to address digital divides. This study contributes designs of NLG tools and writing activities to implement AI-generated text in schools.

cs.CL↗

arXiv2023

Understanding the stochastic dynamics of sequential decision-making processes: A path-integral analysis of multi-armed bandits

The multi-armed bandit (MAB) model is one of the most classical models to study decision-making in an uncertain environment. In this model, a player chooses one of $K$ possible arms of a bandit machine to play at each time step, where the corresponding arm returns a random reward to the player, potentially from a specific unknown distribution. The target of the player is to collect as many rewards as possible during the process. Despite its simplicity, the MAB model offers an excellent playground for studying the trade-off between exploration versus exploitation and designing effective algorithms for sequential decision-making under uncertainty. Although many asymptotically optimal algorithms have been established, the finite-time behaviors of the stochastic dynamics of the MAB model appear much more challenging to analyze, due to the intertwine between the decision-making and the rewards being collected. In this paper, we employ techniques in statistical physics to analyze the MAB model, which facilitates the characterization of the distribution of cumulative regrets at a finite short time, the central quantity of interest in an MAB algorithm, as well as the intricate dynamical behaviors of the model. Our analytical results, in good agreement with simulations, point to the emergence of an interesting multimodal regret distribution, with large regrets resulting from excess exploitation of sub-optimal arms due to an initial unlucky output from the optimal one.

cs.LG↗

arXiv2022

A high-frequency mobility big-data reveals how COVID-19 spread across professions, locations and age groups

As infected and vaccinated population increases, some countries decided not to impose non-pharmaceutical intervention measures anymore and to coexist with COVID-19. However, we do not have a comprehensive understanding of its consequence , especially for China where most population has not been infected and most Omicron transmissions are silent. This paper serves as the first study to reveal the complete silent transmission dynamics of COVID-19 overlaying a big data of more than 0.7 million real individual mobility tracks without any intervention measures throughout a week in a Chinese city, with an extent of completeness and realism not attained in existing studies. Together with the empirically inferred transmission rate of COVID-19, we find surprisingly that with only 70 citizens to be infected initially, 0.33 million becomes infected silently at last. We also reveal a characteristic daily periodic pattern of the transmission dynamics, with peaks in mornings and afternoons. In addition, retailing, catering and hotel staff are more likely to get infected than other professions. Unlike all other age groups and professions, elderly and retirees are more likely to get infected at home than outside home.

physics.soc-ph↗

arXiv2022

Bilevel optimization in flow networks: A message-passing approach

Optimizing embedded systems, where the optimization of one depends on the state of another, is a formidable computational and algorithmic challenge, that is ubiquitous in real world systems. We study flow networks, where bilevel optimization is relevant to traffic planning, network control and design, and where flows are governed by an optimization requirement subject to the network parameters. We employ message-passing algorithms in flow networks with sparsely coupled structures to adapt network parameters that govern the network flows, in order to optimize a global objective. We demonstrate the effectiveness and efficiency of the approach on randomly generated graphs.

math.OC↗

arXiv2021

Scalable Node-Disjoint and Edge-Disjoint Multi-wavelength Routing

Probabilistic message-passing algorithms are developed for routing transmissions in multi-wavelength optical communication networks, under node and edge-disjoint routing constraints and for various objective functions. Global routing optimization is a hard computational task on its own but is made much more difficult under the node/edge-disjoint constraints and in the presence of multiple wavelengths, a problem which dominates routing efficiency in real optical communication networks that carry most of the world's Internet traffic. The scalable principled method we have developed is exact on trees but provides good approximate solutions on locally tree-like graphs. It accommodates a variety of objective functions that correspond to low latency, load balancing and consolidation of routes, and can be easily extended to include heterogeneous signal-to-noise values on edges and a restriction on the available wavelengths per edge. It can be used for routing and managing transmissions on existing topologies as well as for designing and modifying optical communication networks. Additionally, it provides the tool for settling an open and much debated question on the merit of wavelength-switching nodes and the added capabilities they provide. The methods have been tested on generated networks such as random-regular, Erdős Rényi and power-law graphs, as well as on the UK and US optical communication networks. They show excellent performance with respect to existing methodology on small networks and have been scaled up to network sizes that are beyond the reach of most existing algorithms.

physics.soc-ph↗

arXiv2021

Optimally coordinated traffic diversion by statistical physics

Road accidents or maintenance often lead to the blockage of roads, causing severe traffic congestion. Diverted routes after road blockage are often decided individually and have no coordination. Here, we employ the cavity approach in statistical physics to obtain both analytical results and optimization algorithms to optimally divert and coordinate individual vehicle routes after road blockage. Depending on the number and the location of the blocked roads, we found that there can be a significant change in traveling path of individual vehicles, and a large increase in the average traveling distance and cost. Interestingly, traveling distance decreases but traveling cost increases for some instances of diverted traffic. By comparing networks with different topology and connectivity, we observe that the number of alternative routes play a crucial role in suppressing the increase in traveling cost after road blockage. We tested our algorithm using the England highway network and found that coordinated diversion can suppress the increase in traveling cost by as much as 66$\%$ in the scenarios studied. These results reveal the advantages brought by the optimally coordinated traffic diversion after road blockage.

physics.soc-ph↗

arXiv2020

The Futility of Being Selfish -- The Impact of Selfish Routing on Uncoordinated and Optimized Transportation Networks

Optimizing traffic flow is essential for easing congestion. However, even when globally-optimal, coordinated and individualized routes are provided, users may choose alternative routes which offer lower individual costs. By analyzing the impact of selfish route-choices on performance using the cavity method, we find that a small ratio of selfish route-choices improves the global performance of uncoordinated transportation networks, but degrades the efficiency of optimized systems. Remarkably, compliant users always gain in the former and selfish users may gain in the latter, under some parameter conditions. The theoretical results are in good agreement with large-scale simulations. Iterative route-switching by a small fraction of selfish users leads to Nash equilibria close to the globally optimal routing solution. Our theoretical framework also generalizes the use of the cavity method, originally developed for the study of equilibrium states, to analyze iterative game-theoretical problems. These results shed light on the feasibility of easing congestion by route coordination when not all vehicles follow the coordinated routes.

physics.soc-ph↗

arXiv2020

Accumulative time-based ranking method to reputation evaluation in information networks

With the rapid development of modern technology, the Web has become an important platform for users to make friends and acquire information. However, since information on the Web is over-abundant, information filtering becomes a key task for online users to obtain relevant suggestions. As most Websites can be ranked according to users' rating and preferences, relevance to queries, and recency, how to extract the most relevant item from the over-abundant information is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed accumulative time-based ranking (ATR) algorithm, we incorporate two behavioral weighting factors which are updated when users select or rate items, to reflect the evolution of user reputation and item quality over time. We showed that our algorithm outperforms state-of-the-art ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications.

cs.SI↗

arXiv2019

Characteristics of human mobility patterns revealed by high-frequency cell-phone position data

Human mobility is an important characteristic of human behavior, but since tracking personalized position to high temporal and spatial resolution is difficult, most studies on human mobility patterns rely largely on mathematical models. Seminal models which assume frequently visited locations tend to be re-visited, reproduce a wide range of statistical features including collective mobility fluxes and numerous scaling laws. However, these models cannot be verified at a time-scale relevant to our daily travel patterns as most available data do not provide the necessary temporal resolution. In this work, we re-examined human mobility mechanisms via comprehensive cell-phone position data recorded at a high frequency up to every second. We found that the next location visited by users is not their most frequently visited ones in many cases. Instead, individuals exhibit origin-dependent, path-preferential patterns in their short time-scale mobility. These behaviors are prominent when the temporal resolution of the data is high, and are thus overlooked in most previous studies. Incorporating measured quantities from our high frequency data into conventional human mobility models shows contradictory statistical results. We finally revealed that the individual preferential transition mechanism characterized by the first-order Markov process can quantitatively reproduce the observed travel patterns at both individual and population levels at all relevant time-scales.

physics.soc-ph↗

arXiv2019

Learning the optimally coordinated routes from the statistical mechanics of polymers

Many major cities suffer from severe traffic congestion. Road expansion in the cites is usually infeasible, and an alternative way to alleviate traffic congestion is to coordinate the route of vehicles. Various path selection and planning algorithms are thus proposed, but most existing methods only plan paths separately and provide un-coordinated solutions. Recently, an analogy between the coordination of vehicular routes and the interaction of polymers is drawn; the spin glass theory in statistical physics is employed to optimally coordinate transportation routes. To further examine the advantages brought by path coordination, we incorporate the link congestion function developed by the Bureau of Public Roads (BPR) into the polymer routing algorithm. We then estimate in simulations the traveling time of all users saved by the polymer-BPR algorithm in randomly generated networks and real transportation networks in major cities including London, New York and Beijing. We found that a large amount of traveling time is saved in all studied networks, suggesting that the approach inspired by polymer physics is effective in minimizing the traveling time via path coordination, which is a promising tool for alleviating traffic congestions.

physics.soc-ph↗

arXiv2019

Coordinating Dynamical Routes with Statistical Physics on Space-time Networks

Coordination of dynamical routes can alleviate traffic congestion and is essential for the coming era of autonomous self-driving cars. However, dynamical route coordination is difficult and many existing routing protocols are either static or without inter-vehicle coordination. In this paper, we first apply the cavity approach in statistical physics to derive the theoretical behavior and an optimization algorithm for dynamical route coordination, but they become computational intractable as the number of time segments increases. We therefore map static spatial networks to space-time networks to derive a computational feasible message-passing algorithm compatible with arbitrary system parameters; it agrees well with the analytical and algorithmic results of conventional cavity approach and outperforms multi-start greedy search in saving total travel time by as much as 15% in simulations. The study sheds light on the design of dynamical route coordination protocols, and the solution to other dynamical problems via static analytical approaches on space-time networks.

cond-mat.stat-mech↗