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A Knowledge Transfer-Based Membrane Evolutionary Algorithm For Solving Large-Scale Sorted Waste Collection Problem With Timeliness, Wenxue Zhang, Boquan Gao, Aldy Gunawan, Yunyun Niu, Jianhua Xiao Mar 2026

A Knowledge Transfer-Based Membrane Evolutionary Algorithm For Solving Large-Scale Sorted Waste Collection Problem With Timeliness, Wenxue Zhang, Boquan Gao, Aldy Gunawan, Yunyun Niu, Jianhua Xiao

Research Collection School Of Computing and Information Systems

The sorted collection of municipal solid waste has emerged as an effective waste management strategy due to varying timeliness requirements across different waste types, giving rise to the critical research challenge of timeliness-based waste collection. While existing algorithms primarily focus on small-scale versions of this problem, solving large-scale timeliness-based waste collection problems remains particularly challenging. To tackle this issue, this paper proposes a knowledge transfer-based membrane evolutionary algorithm. Specifically, the original problem and simplified problem are constructed in different membranes respectively, and the knowledge transfer learning mechanism is incorporated into the membrane evolutionary algorithm, enabling effective information exchange between the …

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Codeultrafeedback: An Llm-As-A-Judge Dataset For Aligning Large Language Models To Coding Preferences, Martin Weyssow, Aton Kamanda, Xin Zhou, Houari Sahraoui Mar 2026

Codeultrafeedback: An Llm-As-A-Judge Dataset For Aligning Large Language Models To Coding Preferences, Martin Weyssow, Aton Kamanda, Xin Zhou, Houari Sahraoui

Research Collection School Of Computing and Information Systems

Evaluating the alignment of large language models (LLMs) with user-defined coding preferences is a challenging endeavor that requires a deep assessment of LLMs' outputs. Existing methods and benchmarks rely primarily on automated metrics and static analysis tools, which often fail to capture the nuances of user instructions and LLM outputs. To address this gap, we introduce the LLM-as-a-Judge evaluation framework and present CodeUltraFeedback, a comprehensive dataset for assessing and improving LLM alignment with coding preferences. CodeUltraFeedback consists of 10,000 coding instructions, each annotated with four responses generated from a diverse pool of 14 LLMs. These responses are annotated using GPT-3.5 …

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Invert Your Prompt: Editing-Aware Diffusion Inversion, Yangyang Xu, Wenqi Shao, Yong Du, Haiming Zhu, Yang Zhou, Jiayuan Xie, Ping Luo, Shengfeng He Mar 2026

Invert Your Prompt: Editing-Aware Diffusion Inversion, Yangyang Xu, Wenqi Shao, Yong Du, Haiming Zhu, Yang Zhou, Jiayuan Xie, Ping Luo, Shengfeng He

Research Collection School Of Computing and Information Systems

Recent advancements in text-guided diffusion models have enabled powerful image manipulation capabilities. However, balancing reconstruction fidelity and editability for real images remains a significant challenge. In this work, we introduce Editing Inversion (EditInv), a novel framework that inverts and edits real images for specific editing tasks by optimizing specific prompt embeddings within the extended  space. By leveraging distinct embeddings across different U-Net layers and time steps, EditInv seamlessly integrates inversion and editing through reciprocal optimization, ensuring both high fidelity and precise editability. This hierarchical editing mechanism classifies tasks into structure, appearance, and global edits, optimizing only those embeddings that are …

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Learning To Search For Vehicle Routing With Multiple Time Windows, Kuan Xu, Zhiguang Cao, Chenlong Zheng, Lindong Liu Mar 2026

Learning To Search For Vehicle Routing With Multiple Time Windows, Kuan Xu, Zhiguang Cao, Chenlong Zheng, Lindong Liu

Research Collection School Of Computing and Information Systems

In this study, we propose a reinforcement learning-based adaptive variable neighborhood search (RL-AVNS) method designed for effectively solving the Vehicle Routing Problem with Multiple Time Windows (VRPMTW). Unlike traditional adaptive approaches that rely solely on historical operator performance, our method integrates a reinforcement learning framework to dynamically select neighborhood operators based on real-time solution states and learned experience. We introduce a fitness metric that quantifies customers’ temporal flexibility to improve the shaking phase, and employ a transformer-based neural policy network to intelligently guide operator selection during the local search. Extensive computational experiments are conducted on realistic scenarios derived from the …

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Does Ethnicity Similarity Affect Analyst Forecasts?, Zichen Tian Mar 2026

Does Ethnicity Similarity Affect Analyst Forecasts?, Zichen Tian

Dissertations and Theses Collection (Open Access)

I examine whether ethnicity similarity between sell-side analysts and corporate senior officers shapes analysts’ forecasting behavior. The baseline results show that ethnicity similarity is associated with higher forecast frequency and larger initial forecast errors. Importantly, I find that the larger initial forecast error associated with ethnicity similarity comes from overestimation and disappears for analysts’ last forecasts, consistent with bias coming from innate favoritism and dissipating as public information accumulates within the fiscal year. However, the market does not discount coethnic analysts’ first forecast revisions and reacts more strongly to their last forecast revisions within the fiscal year, suggesting that investors …

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Cultural Policy Beyond Economy: Democratising Access To The Arts Through Cultural Policy In Singapore, Su Fern Hoe Mar 2026

Cultural Policy Beyond Economy: Democratising Access To The Arts Through Cultural Policy In Singapore, Su Fern Hoe

Research Collection School of Social Sciences

Across the globe, governments have released cultural policies that aim to justify public expenditure on the arts through democratising access to the arts to the wider population. Singapore is no exception. This article critically examines the role of cultural policy in expanding access to the arts in Singapore. More specifically, this article will demonstrate how cultural policy in Singapore has traditionally and consistently adopted a democratisation of culture approach to broaden access to develop arts audiences. Through an analysis of cultural policy documents and statistical data on arts attendance and attitudes in Singapore, this article will highlight some of the …

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A Closer Look At The Innovator: The Interplay Between Divergent And Convergent Processes Of Self-Regulation, Ronald Bledow, Hye Jung Eun, Lien Vossaert Mar 2026

A Closer Look At The Innovator: The Interplay Between Divergent And Convergent Processes Of Self-Regulation, Ronald Bledow, Hye Jung Eun, Lien Vossaert

Research Collection Lee Kong Chian School Of Business

Creativity and innovation are essential for modern organizations. While abundant research has helped to better understand creativity in everyday work life, considerably less attention has been paid to how individuals achieve creative performance during the implementation of innovation projects. We articulate a dialectical interplay between divergent and convergent processes of self-regulation that supports creative performance in innovation projects. We propose that a divergent exploration orientation contributes to creative performance by inducing a broad search for novelty and by stimulating iteration—repeated cycles of experimentation and refinement. Convergent processes of self-regulation—outcome focus and planning—strengthen the relationship between divergent processes and creative performance. …

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Interest Fit Beyond The Riasec: The Comprehensive Assessment Of Basic Interests-O*Net (Cabin-Net), Chu Chu, Kevin A. Hoff, Zihan Liu, Nicholas F. Heimpel, Anthony Greco, Frederick L. Oswald, James Rounds Mar 2026

Interest Fit Beyond The Riasec: The Comprehensive Assessment Of Basic Interests-O*Net (Cabin-Net), Chu Chu, Kevin A. Hoff, Zihan Liu, Nicholas F. Heimpel, Anthony Greco, Frederick L. Oswald, James Rounds

Research Collection Lee Kong Chian School Of Business

Over the past half-century, Holland’s RIASEC model has dominated vocational interest research. Although the RIASEC categories effectively capture general occupational themes, their breadth obscures meaningful variability across underlying basic interests, which are more refined interest scales. In this research, we adapted the Comprehensive Assessment of Basic Interests (CABIN; Su et ., 2019) to be used alongside O*NET for assessing interest fit, resulting in the 60-item CABIN-NET that measures 20 basic interest scales organized under RIASEC domains. In Study 1, we refined items for the CABIN-NET using a nationally representative adult sample (N = 768). In Studies 2 and 3, we …

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Global Value Chains And Economic Inequality, Vivek Soundararajan, Ari Van Assche, Hari Bapuji, Gokhan Ertug Mar 2026

Global Value Chains And Economic Inequality, Vivek Soundararajan, Ari Van Assche, Hari Bapuji, Gokhan Ertug

Research Collection Lee Kong Chian School Of Business

In their inaugural editorial, Van Assche and De Marchi (2024) argued that meaningful progress in international business policy requires a more fine-grained understanding of how private international business practices intersect with public policy objectives. This editorial builds on that insight by applying it to a critical yet still underexplored issue – one on which we hope to see more research in this journal: the role of multinational enterprises (MNEs) and global value chains (GVCs) in shaping economic inequality within developing economies. Decisions related to sourcing, contracting, and GVC governance are inherently distributional rather than neutral, with important implications for how …

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Collaborative Work Management Technologies And Managerial Intensity In U.S. Corporations: An Examination, Piyush Gulati, Arianna Marchetti, Phanish Puranam Mar 2026

Collaborative Work Management Technologies And Managerial Intensity In U.S. Corporations: An Examination, Piyush Gulati, Arianna Marchetti, Phanish Puranam

Research Collection Lee Kong Chian School Of Business

Do digital technologies reinforce managerial hierarchies or, instead, make them less relevant? We propose that the answer to this question depends on the nature of the technology: specifically, its relative impact on managers' capacity to supervise and on subordinates' need for supervision. Applying this framework to collaborative work management (CWM) technologies that facilitate real-time collaboration, communication, and task coordination, we predict that the adoption of such technologies should reduce managerial intensity and increase decentralization in organizations. To test this prediction, we use a difference-in-differences design on a novel data set built from over 26 million job listings (Lightcast) and over …

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High-Frequency Functional Trajectories Predict Depressive Worsening In Singapore's Community-Dwelling Older Adults, Kaung Htet Tun Salai, Yi Wen Tan, Grace Cheong, Paulin T. Straughan Mar 2026

High-Frequency Functional Trajectories Predict Depressive Worsening In Singapore's Community-Dwelling Older Adults, Kaung Htet Tun Salai, Yi Wen Tan, Grace Cheong, Paulin T. Straughan

ROSA Journal Articles and Publications

Background/Objectives: Functional difficulty and depression often coexist in older adults, yet local Singapore-based research often lacks detailed temporal resolution due to heterogeneity in ageing. This study employs non-parametric, data-driven longitudinal clustering to analyse functional trajectories and their association with depression, using high-frequency data to pinpoint key intervention periods. Methods: Data were drawn from 4273 community-dwelling older adults from Singapore Life Panel® (2020–2024). Participants completed quarterly self-reported assessments of ADL, IADL and depressive symptoms (8-item CES-D). We employed k-means longitudinal clustering (kml) to identify functional trajectory groups and Cox regression to evaluate the hazard of increased depression (≥5-point increase in …

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Can’T Get Work Off My Mind: The Effect Of Nonwork Goal Reflection On After-Work Rumination And Well-Being, Trevor A. Foulk, Min-Hsuan Tu, Michael Schaerer, Amber Johnson Mar 2026

Can’T Get Work Off My Mind: The Effect Of Nonwork Goal Reflection On After-Work Rumination And Well-Being, Trevor A. Foulk, Min-Hsuan Tu, Michael Schaerer, Amber Johnson

Research Collection Lee Kong Chian School Of Business

Many employees lament that their inability to stop ruminating about work during nonwork time is one of their most significant work-related challenges, yet limited research has articulated actionable strategies to address this issue on a day-to-day basis. Integrating goal shielding theory with rumination theory, our model introduces nonwork goal reflection as a deliberate behavioral intervention employees can engage in to curtail work-related rumination, in a way that facilitates well-being during nonwork time. Specifically, we argue that engaging in nonwork goal reflection—where employees intentionally reflect on nonwork goals to be completed during their nonwork time—can curtail work-related rumination and is associated …

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Expanding Public Health Insurance To Parents: Effect On Parents' And Children’S Well-Being, Michelle Marcus, Xuan Zhang Mar 2026

Expanding Public Health Insurance To Parents: Effect On Parents' And Children’S Well-Being, Michelle Marcus, Xuan Zhang

Research Collection School Of Economics

Expanding public health insurance to parents may not only improve parental outcomes but also have spillover effects on their children. This paper leverages the natural experiment of the Affordable Care Act (ACA) to estimate the causal effects of expanding public health insurance to low-income parents on their well-being and that of their children. Using a difference-in-differences model and data from the 2010–2017 National Health Interview Surveys, we observe significant improvements in health care access, increased health care utilization, reduced financial burdens, and slight improvements in health status among low-income parents. For children in Medicaid expansion states, we find reductions in …

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Bespoke Realized Volatility: Tailored Measures Of Risk For Volatility Prediction, Andrew John Patton, Haozhe Zhang Mar 2026

Bespoke Realized Volatility: Tailored Measures Of Risk For Volatility Prediction, Andrew John Patton, Haozhe Zhang

Research Collection School Of Economics

Standard realized volatility (RV) measures estimate the latent volatility of an asset price using high frequency data with no reference to how or where the estimate will subsequently be used. This paper presents methods for “tailoring” the estimate of volatility to the application in which it will be used. For example, if the volatility measure will be used in a specific parametric forecasting model, it may be possible to exploit that knowledge to construct a better measure of volatility. We use methods from machine learning to estimate optimal “bespoke” RVs for heterogeneous autoregressive (HAR) and GARCH-X forecasting applications. We apply …

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The Smu Trade Dashboard: Tracking And Quantifying The Trump Ii Tariffs, Pao-Li Chang, Ruoqing Chen, Lin Ma Mar 2026

The Smu Trade Dashboard: Tracking And Quantifying The Trump Ii Tariffs, Pao-Li Chang, Ruoqing Chen, Lin Ma

Research Collection School Of Economics

The SMU Trade Dashboard (https://economics.smu.edu.sg/soetrade/tr ade-dashboard), developed by the SMU Center for Research on International Trade, consists of Tariff Tracker and Trade Simulation functions. The Tariff Tracker allows users to track the tariff applicable to an HS 6-digit product or an ISIC 2-digit sector bymarkets and country origins. The Trade Simulation presents quantitative simulation results of the effects of Trump II tariffs (and the associated tariff responses by its trading partners) on trade flows, welfare, and wages. This companion paper documents the methodologies underlying the tariff data collection protocol and the structuralsimulation designs.

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Financial Valuation Of Retirement Village Via Stochastic Modelling Of Disability Prevalence Rates, Jackie Li, Mingke Wang, Jia Liu, Jessica Wai Yin Leung Mar 2026

Financial Valuation Of Retirement Village Via Stochastic Modelling Of Disability Prevalence Rates, Jackie Li, Mingke Wang, Jia Liu, Jessica Wai Yin Leung

Research Collection School Of Economics

Global mortality rates continue to decline, and life expectancy continues its upward trend. Besides mortality levels, policymakers and providers of financial and health services would also be interested in disability prevalence and its potential future trajectories. The length of time in good health versus the duration with major disabilities or long-term illnesses has significant financial implications for both individuals and society. In this paper, we develop Bayesian common factor models to analyse Australian age- and sex-specific disability prevalence rates. In particular, there are one or more common factors shared by both sexes, as well as specific factors for each sex. …

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High Dimensional Regression Coefficient Test With High Frequency Data, Dachuan Chen, Long Feng, Per A. Myklang, Lan Zhang Mar 2026

High Dimensional Regression Coefficient Test With High Frequency Data, Dachuan Chen, Long Feng, Per A. Myklang, Lan Zhang

Research Collection School Of Economics

This paper presents the first study on high-dimensional regression coefficient tests with high-frequency financial data. These tests allow the number of regressors to be larger than the number of observations within each estimation block and can grow to infinity in asymptotics. In this paper, the sum-type test and max-type test have been proposed, where the former is suitable for the dense alternative (many small betas) and the latter is suitable for the sparse alternative (a very small number of large betas). By showing the asymptotic independence between the sum-type test and max-type test, the paper proposes a third test – …

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Joint Capacity Allocation And Job Assignment Under Uncertainty, Peng Wang, Yun Fong Lim, Gar Goei Loke Mar 2026

Joint Capacity Allocation And Job Assignment Under Uncertainty, Peng Wang, Yun Fong Lim, Gar Goei Loke

Research Collection Lee Kong Chian School Of Business

We study a multi-period joint capacity allocation and job assignment problem. The goal is to simultaneously allocate resources across J different supply nodes and assign jobs from I different demand origins to these J supply nodes, so as to maximize the reward for matching or minimize the cost of failure to match. We consider three features: (i) supply is replenishable after some random time, (ii) demand is random, and (iii) demand can wait and needs not be fully fulfilled immediately. Such problems emerge in many service management settings such as fleet re-positioning for car-sharing, and patient management in healthcare. We …

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Efficient Active Training For Deep Lidar Odometry, Beibei Zhou, Zhiyuan Zhang, Zhenbo Song, Jianhui Guo, Hui Kong Mar 2026

Efficient Active Training For Deep Lidar Odometry, Beibei Zhou, Zhiyuan Zhang, Zhenbo Song, Jianhui Guo, Hui Kong

Research Collection School Of Computing and Information Systems

Robust and efficient deep LiDAR odometry models are crucial for accurate localization and 3D reconstruction, but typically require extensive and diverse training data to adapt to diverse environments, leading to inefficiencies. To tackle this, we introduce an active training framework designed to selectively extract training data from diverse environments, thereby reducing the training load and enhancing model generalization. Our framework is based on two key strategies: Initial Training Set Selection (ITSS) and Active Incremental Selection (AIS). ITSS begins by breaking down motion sequences from general weather into nodes and edges for detailed trajectory analysis, prioritizing diverse sequences to form a …

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Identifying And Mitigating Api Misuse In Large Language Models, Terry Yue Zhuo, Junda He, Jiamou Sun, Zhenchang Xing, David Lo, John Grundy, Xiaoning Du Mar 2026

Identifying And Mitigating Api Misuse In Large Language Models, Terry Yue Zhuo, Junda He, Jiamou Sun, Zhenchang Xing, David Lo, John Grundy, Xiaoning Du

Research Collection School Of Computing and Information Systems

API misuse in code generated by large language models (LLMs) presents a serious and growing challenge in software development. While LLMs demonstrate impressive code generation capabilities, their interactions with complex library APIs are often error-prone, potentially leading to software failures and vulnerabilities. In this paper, we conduct a large-scale study of API misuse patterns in LLM-generated code, analyzing both method selection and parameter usage across Python and Java, using three representative LLMs (StarCoder-7B, Qwen2.5-Coder-7B, and GitHub Copilot). Based on extensive manual annotation of 3,209 method-level and 3,492 parameter-level misuses, we identify and categorize four recurring misuse types by building on …

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Opencil: Benchmarking Out-Of-Distribution Detection In Class Incremental Learning, Wenjun Miao, Guansong Pang, Trong-Tung Nguyen, Ruohuan Fang, Jin Zheng, Xiao Bai Mar 2026

Opencil: Benchmarking Out-Of-Distribution Detection In Class Incremental Learning, Wenjun Miao, Guansong Pang, Trong-Tung Nguyen, Ruohuan Fang, Jin Zheng, Xiao Bai

Research Collection School Of Computing and Information Systems

Class incremental learning (CIL) aims to learn a model that can not only incrementally accommodate new classes, but also maintain the learned knowledge of old classes. Out-of-distribution (OOD) detection in CIL is to retain this incremental learning ability, while being able to reject unknown samples that are drawn from different distributions of the learned classes. This capability is crucial to the safety of deploying CIL models in open worlds. However, despite remarkable advancements in the respective CIL and OOD detection, there lacks a systematic and large-scale benchmark to assess the capability of advanced CIL models in detecting OOD samples. To …

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Improving Credit Card Transaction Fraud Detection Using Cvqboosting, Bethel Hui Ting Loke, Nirvik Sahoo, Bingyan Guan, Minrui Xu, Dev Verma, Paul R. Griffin Mar 2026

Improving Credit Card Transaction Fraud Detection Using Cvqboosting, Bethel Hui Ting Loke, Nirvik Sahoo, Bingyan Guan, Minrui Xu, Dev Verma, Paul R. Griffin

Research Collection School Of Computing and Information Systems

This paper introduces a novel hybrid quantum-classical approach to credit card fraud detection using CVQBoost, a hybrid quantum-classical boosting algorithm executed on the photonic Dirac-3 processor from Quantum Computing Inc. (QCi). By integrating a diverse set of weak classifiers, which includes K-nearest neighbours (KNN), linear discriminant analysis, logistic regression, and XGBoost, within a hybrid quantum-classical ensemble, the proposed method demonstrates significant improvements over the latest published classical benchmarks. Experiments on a Kaggle credit card fraud dataset show that the quantum-enhanced model achieves a mean AUC-PR score of over 0.8, corresponding to an approximately 9% relative improvement over the best published …

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Exploring Neural Network Structure Code Reuse In The Open-Source Community For Improving Maintenance, Xiaoning Ren, Yuekun Wang, Chongyang Liu, Yueming Wu, Qiang Hu, Lijun Zhang, Yinxing Xue Mar 2026

Exploring Neural Network Structure Code Reuse In The Open-Source Community For Improving Maintenance, Xiaoning Ren, Yuekun Wang, Chongyang Liu, Yueming Wu, Qiang Hu, Lijun Zhang, Yinxing Xue

Research Collection School Of Computing and Information Systems

Neural networks (NNs) have rapidly advanced, demonstrating exceptional performance across various fields, leading to a surge in open-source NN projects. The complexity and rapid growth of these projects pose significant challenges for maintenance within the open-source community. Given that NN architecture code is the core asset of NN projects, understanding its reuse in the open-source community is essential for effective maintenance, such as reducing redundancy and identifying potential intellectual property violations. While prior studies have examined code reuse in open-source projects, they have two key limitations: They do not specifically address NN structure code, and they rely on manually selected …

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Plm-Effector: Unleashing The Potential Of Protein Language Models For Bacterial Secreted Protein Prediction, Dandan Zheng, Lihong Chen, Guansong Pang, Jian Yang Mar 2026

Plm-Effector: Unleashing The Potential Of Protein Language Models For Bacterial Secreted Protein Prediction, Dandan Zheng, Lihong Chen, Guansong Pang, Jian Yang

Research Collection School Of Computing and Information Systems

Bacterial secreted proteins, particularly effectors delivered by specialized secretion systems, are key mediators of virulence and host-pathogen interactions. However, accurate computational identification remains challenging, as many existing methods rely heavily on sequence similarity or handcrafted features, and often focus on a single secretion system. Recent studies have reported that some bacterial effectors may be associated with more than one secretion system, highlighting the complexity of secretion system annotation and motivating the development of system-aware computational prediction approaches. Here, we present PLM-Effector, a hybrid deep learning framework that integrates modern protein language models (PLMs) with multiple neural architectures via a two-layer …

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Navigation Beyond Wayfinding: Robots Collaborating With Visually Impaired Users For Environmental Interactions, Shaojun Cai, Nuwan Janaka, Ashwin Ram, Janidu Shehan, Yingjia Wan, Kotaro Hara, David Hsu Mar 2026

Navigation Beyond Wayfinding: Robots Collaborating With Visually Impaired Users For Environmental Interactions, Shaojun Cai, Nuwan Janaka, Ashwin Ram, Janidu Shehan, Yingjia Wan, Kotaro Hara, David Hsu

Research Collection School Of Computing and Information Systems

Robotic guidance systems have shown promise in supporting blind and visually impaired (BVI) individuals with wayfinding and obstacle avoidance. However, most existing systems assume a clear path and do not support a critical aspect of navigation—environmental interactions that require manipulating objects to enable movement. These interactions are challenging for a human–robot pair because they demand (i) precise localization and manipulation of interaction targets (e.g., pressing elevator buttons) and (ii) dynamic coordination between the user’s and robot’s movements (e.g., pulling out a chair to sit). We present a collaborative human–robot approach that combines our robotic guide dog’s precise sensing and localization …

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Compositions Of Variant Experts For Integrating Short-Term And Long-Term Preferences, Dinh Hieu Do, Hady Wirawan Lauw Mar 2026

Compositions Of Variant Experts For Integrating Short-Term And Long-Term Preferences, Dinh Hieu Do, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

In the online digital realm, recommendation systems are ubiquitous and play a crucial role in enhancing user experience. These systems leverage user preferences to provide personalized recommendations, thereby helping users navigate through the paradox of choice. This work focuses on personalized sequential recommendation, where the system considers not only a user’s immediate, evolving session context, but also their cumulative historical behavior to provide highly relevant and timely recommendations. Through an empirical study conducted on diverse real-world datasets, we have observed and quantified the existence and impact of both short-term (immediate and transient) and long-term (enduring and stable) preferences on users’ …

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Addressing Graph Heterogeneity And Heterophily From A Spectral Perspective, Kangkang Lu, Yanhua Yu, Ruopei Guo, Nan Cheng, Zhiyong Huang, Yunshan Ma, Meiyu Liang, Yuling Wang, Xiting Qin, Yimeng Ren, Tat-Seng Chua Mar 2026

Addressing Graph Heterogeneity And Heterophily From A Spectral Perspective, Kangkang Lu, Yanhua Yu, Ruopei Guo, Nan Cheng, Zhiyong Huang, Yunshan Ma, Meiyu Liang, Yuling Wang, Xiting Qin, Yimeng Ren, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) face two key challenges, heterogeneity and heterophily, which often degrade performance. Existing approaches either focus narrowly on specific meta-paths, limiting their expressiveness, or are expressive but cannot effectively leverage higher-order neighbors. In this paper, we propose the Heterogeneous Heterophilic Spectral Graph Neural Network (H2SGNN), which combines local independent filtering to adaptively handle meta-path subgraphs with varying homophily ratios, and global hybrid filtering to capture high-order neighbor interactions with linear computational complexity. On five heterogeneous graph benchmarks—DBLP, ACM, IMDB, AMiner, and Yelp—H2SGNN consistently outperforms strong baselines, for example, achieving +1.0% Macro-F1 and +1.3% Micro-F1 on IMDB. It …

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Institutional Cross-Ownership Of Peer Firms And Revelatory Price Efficiency, Young Jun Cho, Holly I. Yang, Yue Zhao Mar 2026

Institutional Cross-Ownership Of Peer Firms And Revelatory Price Efficiency, Young Jun Cho, Holly I. Yang, Yue Zhao

Research Collection School Of Accountancy

We argue that cross-ownership increases the amount of private information in stock price, enhancing the ability of stock price to provide feedback to managers. Consistent with this argument, we find greater cross-ownership heightens a firm’s investment-q sensitivity. This effect is stronger for firms with a lower propensity for voluntary disclosure and for firms whose managers hold less private information. Furthermore, we find that cross-ownership is negatively associated with the sensitivity of a firm’s investment to its peers’ stock prices. Additionally, cross-ownership has a stronger impact on the investment-q sensitivity when measured among investors who trade more actively the firm’s shares. …

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Nature Versus Non-Natural Environments: Effects On Stress And Creativity, Amy J. Lim, Stephen M. Colarelli, Norman P. Li, Liqun Wei, Ming-Hong Tsai, Xiao-Xiao Liu, Mark Van Vugt Mar 2026

Nature Versus Non-Natural Environments: Effects On Stress And Creativity, Amy J. Lim, Stephen M. Colarelli, Norman P. Li, Liqun Wei, Ming-Hong Tsai, Xiao-Xiao Liu, Mark Van Vugt

Research Collection School of Social Sciences

We examined the effects of nature-based environmental stimuli versus evolutionarily novel built or abstract art environments on creative production in three experiments. In Study 1 (N = 85), we compared people's creative performance in rooms set up to display either a natural or built landscape. In Study 2 (N = 62), participants completed creativity tasks after taking a walk in an urbanized city district or after a walk in a forested park. In Study 3 (N = 108), using a longitudinal field experiment taking place over three months, we examined the creative performance of factory employees by comparing their normal …

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Using Large Language Models To Analyze Political Texts Through Natural Language Understanding, Kenneth Benoit, Scott De Marchi, Conor Laver, Michael Laver, Jinshuai Ma Mar 2026

Using Large Language Models To Analyze Political Texts Through Natural Language Understanding, Kenneth Benoit, Scott De Marchi, Conor Laver, Michael Laver, Jinshuai Ma

Research Collection School of Social Sciences

Large language models (LLMs) offer scalable alternatives to human experts when analyzing political texts for meaning, using natural language understanding (NLU). Qualitative NLU methods relying on human experts are severely limited by cost and scalability. Statistical text-as-data methods are scalable but rely on strong and often unrealistic assumptions. We propose a systematic, scalable, and replicable method that can extend existing qualitative and quantitative approaches by using LLMs to interpret texts meaningfully rather than as mere data. Our ensemble means of LLM-generated estimates of party positions on six key issue dimensions correlate highly with equivalent mean ratings by country specialists. When …

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