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Essays On Public Policy And Health Outcomes: Unintended Consequences And The Cost Of Influence, Wenhan Yan

Dissertations and Theses Collection (Open Access)

This dissertation investigates the complex behavioral responses to public policy shifts and institutional cues, quantifying their intended and unintended societal impacts. Across two distinct empirical contexts, this research examines how individuals adapt their risk profiles when signals from political or legal authorities change and how these adaptations generate measurable externalities.

The first chapter, "From Punishment to Treatment: Unintended Social Impacts of Changing Drug Policies," shifts the focus to the intersection of health policy and criminal justice. This chapter evaluates the sudden transition from punitive drug enforcement to health-oriented decriminalization, utilizing the 2021 State v. Blake decision in Washington as a …

Examining The Effects Of Viewing Nature And Animal Smartphone Wallpapers On Affect, Behaviour, And Cognition: A Randomised Cross-Over Trial, Nadyanna M. Majeed, Nicole R. Y. Chen, Adalia Y. H. Goh, Meilan Hu, Kenneth J. J. Koh, Yuolmae H. G. Ang, Andree Hartanto

Research Collection School of Social Sciences

This study aims to investigate the effects of different smartphone lock screen wallpapers on weekly perceived well-being, procrastination, and productivity in young adults. Through a pre-registered within-subject experiment, 60 participants were exposed to three smartphone wallpaper conditions: nature, animal, and neutral (control). Each participant experienced each condition over three weeks, with the order of conditions counterbalanced. Using Frequentist and Bayesian analyses, we did not find any differences between conditions across the pre-registered confirmatory outcomes (i.e., life satisfaction, positive affect, negative affect, stress, productivity, and procrastination). Exploratory outcomes related to lock screen engagement, however, revealed some meaningful effects. Animal-themed smartphone wallpapers …

Beyond Resilience: The Case For Regenerative, Restorative Cities, Lily Kong

Research Collection School of Social Sciences

In a commentary, SMU President Professor Lily Kong mentioned that at the City Dialogues event, co-convened by SMU and Urban Innovation Vienna, participants agreed that cities must equip their residents to withstand shocks and thrive in an evolving urban environment. Prof Kong said resilient cities anticipate, adapt and respond effectively to disruptions, which involves attention not only to physical infrastructure, but also to social cohesion and institutional foresight. She said that embedding sustainability into urban design is fundamental to ensuring cities flourish in a world of climate uncertainty and resource constraints. She added that the value of urban resilience lies …

Counter-Urbanization In China: A Systematic Review Toward A Comprehensive Typology, Chengxue Yang, Qian Forrest Zhang

Research Collection School of Social Sciences

Drawing on a systematic review of both international and Chinese-language scholarship on counter-urbanization in China, this article proposes a comprehensive typology of counter-urbanization migrations—consisting of eight types of new migrants—based on the diverse motivations driving such migration. We build a bilingual corpus of 273 research papers published between the 1970s and 2025. Integrating bibliometric mapping and qualitative synthesis, we conduct a systematic review to trace the temporal evolution and thematic diversification of counter-urbanization research. The review reveals persistent conceptual ambiguity surrounding counter-urbanization in the Chinese context, which this study addresses by conceptually distinguishing counter-urbanization from suburban expansion, population decentralization, and …

Secondary School Students' Engagement In Learning Activities: Validation Of A Short Scale, Feliciano Henriques Veiga, Zi Yang Wong, Johnmarshall Reeve, Shane Jimerson, Antonio Leite, Joan Perales, Sonia Valente, Isabel Martinez

Research Collection School of Social Sciences

Student engagement is a multidimensional construct strongly associated with learning outcomes and academic success. However, its measurement remains challenging, as existing instruments often conflate engagement in learning activities with engagement in the school community. In addition, brief measures are scarce despite their increasing value, and most available instruments do not incorporate agentic engagement. Assessing student engagement in learning activities is, therefore, crucial for monitoring academic progress, identifying students at risk of dropping out, and predicting academic success. This study aimed to validate the 12-item Secondary School Student Engagement in Learning Activities: Short Scale, adapted from a higher education measure, which …

Living Well: The Built, Lived, And Social Determinants Of Well-Being, Yi Wen Tan, Zidane Tiew, Emi Loh, Shania Go, Wensi Lim

ROSA Research Briefs

As Singapore transitions into a super-aged society, it becomes imperative for us to develop sustainable, inclusive, and effective models of care. In recent years, there has been growing emphasis on embedding healthcare, social services, and everyday amenities within the community. During this year’s National Day Rally, the Government introduced Age Well Neighbourhoods, a new scheme that builds on existing efforts from the Age Well SG initiative. Age Well Neighbourhoods seeks to expand active ageing centre networks, bring healthcare closer to homes with the establishment of community health posts, and provide greater home personal care services, further cementing its focus on …

New Asymptotics Applied To Functional Coefficient Regression And Climate Sensitivity Analysis, Qiying Wang, Peter C. B. Phillips, Ying Wang

Research Collection School Of Economics

A general asymptotic theory is established for sample cross moments of nonstationary time series, allowing for long-range dependence and local unit roots. The theory provides a substantial extension of earlier results on nonparametric regression that include near-cointegrated nonparametric regression as well as spurious nonparametric regression. Many new models are covered by the limit theory, among which are functional coefficient regressions in which both regressors and the functional covariate are nonstationary. Simulations show finite sample performance matching well with the asymptotic theory and having broad relevance to applications, while revealing how dual nonstationarity in regressors and covariates raises sensitivity to bandwidth …

How Consistent Friendlike Conversation With Ai Companions Influences Our Attitudes And Perceptions Toward Ai: An Exploratory Experiment, Qi Hui Jerlyn Ho, Meilan Hu, Adalia Yin Hui Goh, Emma Jane Pragasam, Andree Hartanto

Research Collection School of Social Sciences

Despite skepticism and distrust in artificial intelligence (AI), it is increasingly integrated into daily life, with its potential benefits drawing interest. Yet little is known about the attitudinal and psychological effects of human–AI interactions, and whether consistent interactions with AI chatbots can change users’ attitudes and perceptions. Our within-subjects experiment (N = 52) investigated how five days of socially oriented, friendlike interactions with an AI chatbot, versus a journaling control, influenced changes in attitudes and perceptions of AI. Participants’ attitudes towards AI, trust, perceived empathy, anthropomorphism, animacy, likeability, perceived intelligence and safety, dependency, and exploratory well-being indicators were recorded. Results …

Market Culture And Innovation Acceptance In Fire Safety Products: The Moderating Role Of Perceived Market Regulation, Xu Zhang

Dissertations and Theses Collection (Open Access)

Against the backdrop of continuously rising global fire safety standards and ongoing technological iteration in fire protection products, whether innovative fire protection products can be effectively accepted by the market has become a critical issue influencing the returns on firms’ innovation investments and the high-quality development of the industry. Existing studies have predominantly explored innovation diffusion mechanisms from the perspectives of technological attributes or organizational factors, while paying relatively limited attention to market culture as a contextual factor, as well as to the differentiated role that government regulation may play in this process. Based on this, this study takes institutional …

Are Cities Losing Innovation Advantages? Online Versus Face-To-Face Interactions, Ruben Gaetani, Naqun Huang, Jing Li, Yanmin Yang

Research Collection School Of Economics

How did COVID-19 affect the innovation advantages of dense locations? Using data on the universe of U.S. patent applications, we find that the density premium in the production of novel inventions declined by 18.5%-22.9% in 2020-2021 relative to its pre-pandemic level. Smartphone data on local mobility suggest that the drop in the frequency of local interactions can explain a significant portion of this effect. While COVID-19 resulted in a temporary setback in the innovation advantages of dense locations, the role of urban density in facilitating the exchange and recombination of ideas is unlikely to be persistently replaced by online communication.

Removed: When Taxi Drivers Meet Dynamic Pricing: A Lesson From Singapore's Justgrab Program, Shih-Fen Cheng, Wen-Tai Hsu, Jing Li

Research Collection School Of Economics

This paper studies how dynamic pricing influences taxi drivers’ behaviors using a unique event, the inception of the JustGrab program in Singapore in 2017, which introduces dynamic pricing to some, but not all, taxi drivers. This is the first time in history that traditional taxi drivers have access to dynamic pricing. Using data covering the universe of taxi trips before and after the inception of JustGrab, we find that there is spatial reallocation that directs more taxi drivers to the previously less-served areas, that there is also a temporal reallocation that directs more taxi drivers to rush hours, as well …

G-Trac: Graph-Textual Representations Alignment For Cold-Start Recommendations, Li Yang Chang, Yuan Fang, Ming Feng Tsai, Chuan Ju Wang

Research Collection School Of Computing and Information Systems

The cold-start problem remains a significant challenge in recommendation systems, particularly for new users or unseen items with little to no historical data. Existing methods, including graph neural networks, often struggle in such scenarios. Inspired by the success of transformer models in natural language processing, we propose G-TRAC (Graph-Textual Representations Alignment for Cold-start Recommendations), a novel approach that integrates transformer-based textual modeling with graph neural networks. By effectively leveraging both textual and structural information, G-TRAC addresses cold-start challenges more effectively. Extensive experiments demonstrate its ability to enhance recommendation quality and generalize well across diverse scenarios.

Modernising Wills For A Smart Nation, Hang Wu Tang, Edward Ti

Research Collection Yong Pung How School Of Law

In a commentary, SMU Lee Kong Chian Professor of Law Tang Hang Wu and SMU Associate Dean (Undergraduate Curriculum and Teaching) and Associate Professor of Law Edward Ti highlighted that Singapore has digitalised almost everything around the will, but not the will itself. Prof Tang and Assoc Prof Ti noted that the current legislation insists that the will itself remains a paper ritual, which is not just an inconvenience but also risks Singapore’s competitiveness. They argued that Singapore should modernise without compromising on steps to prevent fraud and undue influence. They highlighted that the law should be modernised so that …

Bridging Policy And Grassroots Action With Technology: A Framework For Civic Engagement In Environmental Sustainability, Mikhail Ola Adisa, Sonny Rosenthal, Shola Oyedeji, Ifeoma Adaji, Jari Porras

Research Collection College of Integrative Studies

Grassroots and civic organizations are increasingly recognized as “middle actors” in sustainability, leveraging ICT-driven solutions to bridge top-down policies with bottom-up citizen engagement and behavioral change. This study examines how civic organizations in Finland and Singapore integrate digital tools to support sustainable waste management practices aligned with Sustainable Development Goals 11, 12, and 17. Guided by a Design Science Research (DSR) approach and drawing on Middle-Out and multi-level governance theories, we developed and validated the Integrated Sustainability Engagement Framework (ISEF) based on interviews with 21 civic organizations and iterative feedback. Findings highlight the central roles of policy translation, localized practices, …

The Affective Economy Of Toxic Atmospheres: Sensory Governance, Ecological Intelligences And The Performative Politics Of Urban Mosquito Management, Orlando Woods, Creighton Connolly

Research Collection College of Integrative Studies

This paper considers how atmospheres can play a powerful role in troubling the management of nature in the multispecies city. It brings the cyclical nature of atmospheric effects, the affective values and il/logics that determine human responses to non-human threats, and the misguided and potentially deleterious health outcomes that ensue into one analytical frame. Drawing on the empirical case of urban mosquito management in Singapore, it considers how the creation of toxic atmospheres through fogging – blowing insecticides into the air in a bid to kill mosquitoes – is ineffective and sets in motion a cascading series of socio-environmental consequences. …

Atmospheric Circulation Reconstructions Over The Earth (Acre) 2025: Climate Data Recovery, Fiona Williamson

Research Collection College of Integrative Studies

On 15-16 September 2025, Singapore Management University hosted the annual Atmospheric Circulation Reconstructions over the Earth (ACRE, www.met-acre.net/) workshop, ’ACRE 2025: Climate Data Recovery’, chaired by Fiona Williamson, Rob Allan and Praveen Teleti. The 2-day event took place at the College of Integrative Studies and saw around 30 participants from multi-disciplinary backgrounds all working in meteorological data rescue (DaRe, www.community.wmo.int/en/data-rescue-projects-and-initiatives-dare) sharing their latest work and discoveries. Speakers were a mix of climate scientists and historians, archivists and historical climatologists from across the globe. ACRE has been operating for 18 years under Professor Rob Allan, formerly of the UK Meteorological Office …

Geopolitical Ecologies Of Cloud Capitalism: Territorial Restructuring And The Making Of National Computing Power In The Us And China, Justin Kollar, Sol Andrew Stokols

Research Collection College of Integrative Studies

As computing power becomes central to geopolitical rivalry, cloud infrastructure is increasingly framed as critical to national security, economic resilience and technological sovereignty. Current debates often focus on global competition – especially between the U.S. and China – highlighting strategic investments, export controls and infrastructure diplomacy abroad. Yet far less attention has been paid to the domestic territorial transformations that make such geopolitical projection possible. This paper argues that national strategies for AI and cloud dominance depend on the reorganization of land, energy and regulatory systems to sustain large-scale computation. Using a geopolitical ecology framework, we examine how the U.S. …

Vercation: Precise Vulnerable Open-Source Software Version Identification Based On Static Analysis And Llm, Yiran Cheng, Ting Zhang, Lwin Khin Shar, Shouguo Yang, Chaopeng Dong, David Lo, Shichao Lv, Zhiqiang Shi, Limin Sun

Research Collection School Of Computing and Information Systems

Open-source software (OSS) has experienced a surge in popularity, attributed to its collaborative development model and cost-effective nature. However, the adoption of specific software versions in development projects may introduce security risks when these versions bring along vulnerabilities. Current methods of identifying vulnerable versions typically analyze and extract the code features involved in vulnerability patches using static analysis with pre-defined rules. They then use code clone detection to identify the vulnerable versions. These methods are hindered by imprecision due to (1) the exclusion of vulnerability- irrelevant code in the analysis and (2) the inadequacy of code clone detection. This paper …

Efficient Function Orchestration For Large Language Models, Xiaoxia Liu, Peng Di, Cong Li, Jun Sun, Jingyi Wang

Research Collection School Of Computing and Information Systems

Function calling is a fundamental capability of today's large language models, but sequential function calling posed efficiency problems. Recent studies have proposed to request function calls with parallelism support in order to alleviate this issue. However, they either delegate the concurrent function calls to users for execution which are conversely executed sequentially, or overlook the relations among various function calls, rending limited efficiency. This paper introduces LLMOrch, an advanced framework for automated, parallel function calling in large language models. The key principle behind LLMOrch is to identify an available processor to execute a function call while preventing any single processor …

Exploring Jvm Garbage Collector Testing With Event-Coverage, Kai Zheng, Yingquan Zhao, Junjie Chen, Hanmo You, Haoyu Wang, Haoyu Wang, Tianchang Gao

Research Collection School Of Computing and Information Systems

Garbage Collection (GC) in the Java Virtual Machine (JVM) serves as an automatic memory management mechanism, efficiently reclaiming unused memory space in different production scenarios. To optimize JVM performance, developers typically fine-tune the garbage collector by identifying an optimal set of GC configurations for specific scenarios. Despite the sophisticated design of garbage collectors, they still have the potential for bugs in different settings, and these bugs can result in more severe consequences. Hence, comprehensive testing of these garbage collectors is imperative before their release. Code coverage criteria are typically employed to assess the comprehensiveness of a test suite. However, traditional …

Less Is More: Docstring Compression In Code Generation, Guang Yang, Yu Zhou, Wei Cheng, Xiangyu Zhang, Xiang Chen, Terry Yue Zhuo, Xin Zhou, Ke Liu, David Lo, Taolue Chen

Research Collection School Of Computing and Information Systems

The widespread use of Large Language Models (LLMs) in software engineering has intensified the need for improved model and resource efficiency. In particular, for neural code generation, LLMs are used to translate function/method signature and DocString to executable code. DocStrings, which capture user requirements for the code and are typically used as the prompt for LLMs, often contain redundant information. Recent advancements in prompt compression have shown promising results in Natural Language Processing (NLP), but their applicability to code generation remains uncertain. Our empirical study shows that the state-ofthe-art prompt compression methods achieve only about 10% reduction, as further reductions …

Constrained Pricing In Logit-Based Revenue Management, Qian Shao, Tien Mai, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

We consider a dynamic pricing problem in network revenue management in which customer behavior is predicted by a choice model, that is, the multinomial logit model. The problem, even in the static setting (i.e., customer demand remains unchanged over time), is highly nonconcave in prices. Existing studies mostly rely on the observation that the objective function is concave in terms of purchasing probabilities, implying that the static pricing problem with linear constraints on purchasing probabilities can be efficiently solved. However, this approach is limited in handling constraints on prices, noting that such constraints could be highly relevant in some real …

Fcghunter: Towards Evaluating Robustness Of Graph-Based Android Malware Detection, Shiwen Song, Xiaofei Xie, Ruitao Feng, Qi Guo, Sen Chen

Research Collection School Of Computing and Information Systems

Graph-based detection methods leveraging Function Call Graph (FCG) have shown promise for Android malware detection (AMD) due to their semantic insights. However, the deployment of malware detectors in dynamic and hostile environments raises significant concerns about their robustness. While recent approaches evaluate the robustness of FCG-based detectors using adversarial attacks, their effectiveness is constrained by the vast perturbation space, particularly across diverse models and features. To address these challenges, we introduce FCGHunter, a novel robustness testing framework for FCG-based AMD systems. Specifically, FCGHunter employs innovative techniques to enhance exploration and exploitation within this huge search space. Initially, it identifies critical …

Quantum Chebyshev Transform-Based Graph Neural Networks For Financial Fraud Detection, Minrui Xu, Bingyan Guan, Bethel Hui Ting Loke, Paul R. Griffin

Research Collection School Of Computing and Information Systems

Financial fraud detection is a critical challenge requiring accurate identification of anomalous patterns in complex transaction networks. Graph Neural Networks (GNNs) have emerged as powerful tools for fraud detection by capturing relational structures among entities. Meanwhile, quantum computing offers new possibilities to enhance machine learning through high-dimensional Hilbert spaces and parallelism. In this paper, we propose a hybrid classical-quantum model called QCTGNN (Quantum Chebyshev Transform-based Graph Neural Network) for financial fraud detection. The QCTGNN integrates a classical graph neural network component based on Simplified Graph Convolutions (SGConv) with a quantum component that performs a Chebyshev polynomial-based transform via variational quantum …

Lagrangian Motion Fields For Long-Term Motion Generation, Yifei Yang, Zikai Huang, Chenshu Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

Long-term motion generation is a challenging task that requires producing coherent and realistic sequences over extended durations. Current methods primarily rely on framewise motion representations, which capture only static spatial details and overlook temporal dynamics. This approach leads to significant redundancy across the temporal dimension, complicating the generation of effective long-term motion. To overcome these limitations, we introduce the novel concept of Lagrangian Motion Fields, specifically designed for long-term motion generation. By treating each joint as a Lagrangian particle with uniform velocity over short intervals, our approach condenses motion representations into a series of "supermotions" (analogous to superpixels). This method …

Zero-Shot Video Translation Via Token Warping, Haiming Zhu, Yangyang Xu, Jun Yu, Shengfeng He

Research Collection School Of Computing and Information Systems

With the revolution of generative AI, video-related tasks have been widely studied. However, current state-of-the-art video models still lag behind image models in visual quality and user control over generated content. In this paper, we introduce TokenWarping, a novel framework for temporally coherent video translation. Existing diffusion-based video editing approaches rely solely on key and value patches in self-attention to ensure temporal consistency, often sacrificing the preservation of local and structural regions. Critically, these methods overlook the significance of the query patches in achieving accurate feature aggregation and temporal coherence. In contrast, TokenWarping leverages complementary token priors by constructing temporal …

Light Cone Cancellation For Variational Quantum Eigensolver In Solving Noisy Max-Cut, Xinwei Lee, Xinjian Yan, Ningyi Xie, Yoshiyuki Saito, Leo Kurosawa, Nobuyoshi Asai, Dongsheng Cai, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Variational Quantum Eigensolver (VQE) is a quantum-classical hybrid algorithm used to estimate the ground energy of a given Hamiltonian. It consists of a parameterized quantum circuit, which the parameters are optimized using a classical optimizer. With the increasing need in solving large-scale problems in real-world applications, solving those large problems with fewer qubits and fewer gates becomes essential, so that we reduce the simulation difficulty and mitigate the effect of noise in real quantum hardware. In this study, we applied the Light Cone Cancellation (LCC) method to reduce the number of qubits and gates required in a two-local ansatz. LCC …

Grounding Is All You Need? Dual Temporal Grounding For Video Dialog, You Qin, Wei Ji, Xinze Lan, Hao Fei, Xun Yang, Dan Guo, Roger Zimmermann, Lizi Liao

Research Collection School Of Computing and Information Systems

In the realm of video dialog response generation, capturing both the essence of video content and the temporal nuances of conversation history is crucial. While some approaches rely on large-scale pretrained visual-language models, often neglecting temporal dynamics, others emphasize spatial-temporal relationships within videos but demand intricate object trajectory pre-extractions and overlook dialog temporal dynamics. This paper introduces the Dual Temporal Grounding-enhanced Video Dialog model (DTGVD), designed to bridge the gap between these two approaches. DTGVD uniquely integrates the strengths of both by emphasizing dual temporal relationships. It achieves this by predicting dialog turn-specific temporal regions, selectively filtering video content, and …

Learnable Game-Theoretic Policy Optimization For Data-Centric Self-Explanation Rationalization, Yunxiao Zhao, Zhiqiang Wang, Xingtong Yu, Xiaoli Li, Jiye Liang, Ru Li

Research Collection School Of Computing and Information Systems

Rationalization, a data-centric framework, aims to build self-explanatory models to explain the prediction outcome by generating a subset of human-intelligible pieces of the input data. It involves a cooperative game model where a generator generates the most human-intelligible parts of the input (i.e., rationales), followed by a predictor that makes predictions based on these generated rationales. Conventional rationalization methods typically impose constraints via regularization terms to calibrate or penalize undesired generation. However, these methods are suffering from a problem called mode collapse, in which the predictor produces correct predictions yet the generator consistently outputs rationales with collapsed patterns. Moreover, existing …

The Gains Do Not Make Up For The Losses: A Comprehensive Evaluation For Safety Alignment Of Large Language Models Via Machine Unlearning, Weixiang Zhao, Yulin Hu, Xingyu Sui, Zhuojun Li, Yang Deng, Yanyan Zhao, Bing Qin, Wanxiang Che

Research Collection School Of Computing and Information Systems

Machine Unlearning (MU) has emerged as a promising technique for aligning large language models (LLMs) with safety requirements to steer them forgetting specific harmful contents. Despite the significant progress in previous studies, we argue that the current evaluation criteria, which solely focus on safety evaluation, are actually impractical and biased, leading to concerns about the true effectiveness of MU techniques. To address this, we propose to comprehensively evaluate LLMs after MU from three aspects: safety, over-safety, and general utility. Specifically, a novel benchmark MuBench with 18 related datasets is first constructed, where the safety is measured with both vanilla harmful …