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Actor-Critic For Continuous Action Chunks: A Reinforcement Learning Framework For Long-Horizon Robotic Manipulation With Sparse Reward, Jiarui Yang, Bin Zhu, Jingjing Chen, Yu-Gang Jiang Jan 2026

Actor-Critic For Continuous Action Chunks: A Reinforcement Learning Framework For Long-Horizon Robotic Manipulation With Sparse Reward, Jiarui Yang, Bin Zhu, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Existing reinforcement learning (RL) methods struggle with long-horizon robotic manipulation tasks, particularly those involving sparse rewards. While action chunking is a promising paradigm for robotic manipulation, using RL to directly learn continuous action chunks in a stable and data-efficient manner remains a critical challenge. This paper introduces AC3 (Actor-Critic for Continuous Chunks), a novel RL framework that learns to generate high-dimensional, continuous action sequences. To make this learning process stable and dataefficient, AC3 incorporates targeted stabilization mechanisms for both the actor and the critic. First, to ensure reliable policy improvement, the actor is trained with an asymmetric update rule, learning …

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Interpretable Machine Learning For In-Home Mild Cognitive Impairment Detection, Budhitama Subagdja, Shanthoshigaa D, Ah-Hwee Tan, Iris Rawtaer Jan 2026

Interpretable Machine Learning For In-Home Mild Cognitive Impairment Detection, Budhitama Subagdja, Shanthoshigaa D, Ah-Hwee Tan, Iris Rawtaer

Research Collection School Of Computing and Information Systems

This paper introduces a novel system for in-home cognitive health assessment using ambient sensors and a machine learning technology that can robustly detect mild cognitive impairment (MCI) despite limited available data. The learned model can explain the aspects of individuals’ daily lives led to the prediction, while reliably predicting MCI, providing more insights to healthcare workers for further clinical interventions. We developed the robust transparent machine learning model, based on fusion adaptive resonance theory (Fusion ART) neural network to learn individuals’ daily patterns of activity from continuous sensor data in terms of a suite of digital biomarkers reflecting four key …

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Ai Systems For Physicians: A Review From Socio-Technical And Human-Computer Interaction Perspectives, Wu Jiaqi Young, Fiona Fui-Hoon Nah Jan 2026

Ai Systems For Physicians: A Review From Socio-Technical And Human-Computer Interaction Perspectives, Wu Jiaqi Young, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

The adoption of artificial intelligence (AI) in healthcare is accelerating, yet successful implementations of physician-facing AI systems remain limited and uneven. This paper presents a literature review of 40 peer-reviewed studies published between November 2022 and November 2024, spanning clinical, technical, and human-computer interaction (HCI) domains. Anchored in a socio-technical perspective, the review examines our existing understanding of how technical design, user expertise, and organizational factors shape the effectiveness of AI systems in real-world clinical settings. Our analysis identifies two meta-themes: (1) context as a dynamic, multi-level influence that actively reshapes AI system behavior, and (2) trust as an emergent …

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Leveraging Large Language Models For Career Mobility Analysis: A Study Of Gender, Race, And Job Change Using Us Online Resume Profiles, Palakorn Achananuparp, Ye Xu, Yao Lu, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim Jan 2026

Leveraging Large Language Models For Career Mobility Analysis: A Study Of Gender, Race, And Job Change Using Us Online Resume Profiles, Palakorn Achananuparp, Ye Xu, Yao Lu, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

We present a large-scale analysis of career mobility of college-educated U.S. workers using online resume profiles to investigate how gender, race, and job change options are associated with upward mobility. This study addresses key research questions of how the job changes affect their upward career mobility, and how the outcomes of upward career mobility differ by gender and race. We address data challenges – such as missing demographic attributes, missing wage data, and noisy occupation labels – through various data processing and Artificial Intelligence (AI) methods. In particular, we develop a large language models (LLMs) based occupation classification method known …

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Thinkmatter: Panoramic-Aware Instructional Semantics For Monocular Vision-And-Language Navigation, Guangzhao Dai, Shuo Wang, Hao Zhao, Bin Zhu, Qianru Sun, Xiangbo Shu Jan 2026

Thinkmatter: Panoramic-Aware Instructional Semantics For Monocular Vision-And-Language Navigation, Guangzhao Dai, Shuo Wang, Hao Zhao, Bin Zhu, Qianru Sun, Xiangbo Shu

Research Collection School Of Computing and Information Systems

Vision-and-Language Navigation in continuous environments (VLN-CE) requires an embodied robot to navigate the target destination following the natural language instruction. Most existing methods use panoramic RGB-D cameras for 360° observation of environments. However, these methods struggle in real-world applications because of the higher cost of panoramic RGB-D cameras. This paper studies a low-cost and practical VLN-CE setting, e.g., using monocular cameras of limited field of view, which means “Look Less” for visual observations and environment semantics. In this paper, we propose a ThinkMatter framework for monocular VLN-CE, where we motivate monocular robots to “Think More” by 1) generating novel views …

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Generalized Visual Relation Detection With Diffusion Models, Kaifeng Gao, Siqi Chen, Hanwang Zhang, Jun Xiao, Yueting Zhuang, Qianru Sun Jan 2026

Generalized Visual Relation Detection With Diffusion Models, Kaifeng Gao, Siqi Chen, Hanwang Zhang, Jun Xiao, Yueting Zhuang, Qianru Sun

Research Collection School Of Computing and Information Systems

Visual relation detection (VRD) aims to identify relationships (or interactions) between object pairs in an image. Although recent VRD models have achieved impressive performance, they are all restricted to pre-defined relation categories, while failing to consider the semantic ambiguity characteristic of visual relations. Unlike objects, the appearance of visual relations is always subtle and can be described by multiple predicate words from different perspectives, e.g., “ride” can be depicted as “race” and “sit on”, from the sports and spatial position views, respectively. To this end, we propose to model visual relations as continuous embeddings, and design diffusion models to achieve …

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Memoryart: Enhancing Llms Via Multi-Memory Models With Adaptive Resonance Theory For Healthcare Agents, Renke Dai, Hebin Hu, Jiahui Zhang, Yilin Kang, Ah-Hwee Tan Jan 2026

Memoryart: Enhancing Llms Via Multi-Memory Models With Adaptive Resonance Theory For Healthcare Agents, Renke Dai, Hebin Hu, Jiahui Zhang, Yilin Kang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Though promising in healthcare consultation applications, large language models (LLMs) face critical limitations in retaining and utilizing long-term memory across multiturn interactions. In particular, existing memory enhancing paradigms are constrained by limited context windows and embedding-based retrieval, often failing to maintain task relevance and still suffering from memory prototype collapse in multi-turn healthcare consultation. To address these challenges, we propose a cognitively-inspired memory framework named MemoryART, which is grounded in Adaptive Resonance Theory (ART)—a cognitive and learning theory of how humans and animals adapt to dynamic environments. MemoryART employs three memory modules—working memory, episodic memory, and semantic memory to support …

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Gui Test Migration Via Abstraction And Concretization, Yakun Zhang, Chen Liu, Xiaofei Xie, Yun Lin, Jin Song Dong, Dan Hao, Lu Zhang Jan 2026

Gui Test Migration Via Abstraction And Concretization, Yakun Zhang, Chen Liu, Xiaofei Xie, Yun Lin, Jin Song Dong, Dan Hao, Lu Zhang

Research Collection School Of Computing and Information Systems

GUI test migration aims to produce test cases with events and assertions to test specific functionalities of a target app. Existing migration approaches typically focus on the widget-mapping paradigm that maps widgets from source apps to target apps. However, since different apps may implement the same functionality in different ways, direct mapping may result in incomplete or buggy test cases, thus significantly impacting the effectiveness of testing the target functionality and the practical applicability of migration approaches.In this article, we propose a new migration paradigm (i.e., the abstraction-concretization paradigm) that first abstracts the test logic for the target functionality and …

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Paid Search Marketing Vs. Search Engine Optimization: Analytical Models Of Search Marketing Based On Search Engine Quality, Kai Li, Chunyang Shen, Mei Lin, Zhangxi Lin Jan 2026

Paid Search Marketing Vs. Search Engine Optimization: Analytical Models Of Search Marketing Based On Search Engine Quality, Kai Li, Chunyang Shen, Mei Lin, Zhangxi Lin

Research Collection School Of Computing and Information Systems

As search engines are leading revenue growth in online marketing, search marketing has become a popular area of academic research. Although search engine advertising has interested researchers for decades and much has been learned, one thing that puzzles scholars is why search engine optimization companies are tolerated rather than excluded from the market, even though they capture a significant share of the advertising market. In this paper, we shed light on this phenomenon and establish an analytical model based on organic search quality. Through analysis of the model, we were able to draw several intriguing conclusions. First, there is no …

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Security-Enhanced Decentralized Conditional Privacy-Preserving Authentication In Vanets, Suqin Luo, Xinghua Li, Yinbin Miao, Xuelin Cao, Zhan Zhang, Yunwei Wang, Deng R.H. Jan 2026

Security-Enhanced Decentralized Conditional Privacy-Preserving Authentication In Vanets, Suqin Luo, Xinghua Li, Yinbin Miao, Xuelin Cao, Zhan Zhang, Yunwei Wang, Deng R.H.

Research Collection School Of Computing and Information Systems

To ensure the legitimacy of communicators while ad dressing the privacy concerns of vehicles in vehicular ad-hoc networks (VANETs), conditional privacy-preserving authentication (CPPA) schemes have been proposed. Given that existing schemes suffer from single point of failure due to centralized authorities, several distributed CPPA schemes have been proposed. However, these schemes all ignore the tight cementation between system secret keys and the authority, which could be a serious threat to system security, that the compromised authority may leak the system secret key. To address these issues, we propose a security enhanced decentralized conditional privacy-preserving authentication (DCPPA) scheme. DCPPA first introduces …

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Tempo: Training-Time Equilibration Of Modalities For Per-Sample Optimization In Multimodal Sentiment, Yi Zhao, Erik Cambria, Xiaosong E, Xianxun Zhu Jan 2026

Tempo: Training-Time Equilibration Of Modalities For Per-Sample Optimization In Multimodal Sentiment, Yi Zhao, Erik Cambria, Xiaosong E, Xianxun Zhu

Research Collection School Of Computing and Information Systems

Multimodal sentiment models often become over-reliant on the “easiest” modality (typically text), leading to three coupled sub-problems: (i) representation-level dominance, where weaker modalities contribute little to the fused representation; (ii) optimization-level dominance, where the strongest modality drives most gradient updates and suppresses learning in others; and (iii) robustness degradation, where audio or vision fail under noise or missing inputs at test time. We present TEMPO, a plug-and-play training framework that mitigates these issues by rebalancing learning pressure across modalities while leaving inference unchanged. For each mini-batch, TEMPO estimates relative modality strength and applies two synchronized, training-only controls: selective forward attenuation …

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Airaclex: Automated Detection Of Price Oracle Manipulations Via Llm-Driven Knowledge Mining And Prompt Generation, Bo Gao, Yuan Wang, Qingsong Wei, Yong Liu, Rick Siow Mong Goh, David Lo Jan 2026

Airaclex: Automated Detection Of Price Oracle Manipulations Via Llm-Driven Knowledge Mining And Prompt Generation, Bo Gao, Yuan Wang, Qingsong Wei, Yong Liu, Rick Siow Mong Goh, David Lo

Research Collection School Of Computing and Information Systems

Decentralized finance (DeFi) applications depend on accurate price oracles to ensure secure and fair transactions. However, poorly integrated oracles remain susceptible to manipulation, enabling attackers to exploit smart contract logic for unfair asset valuation and financial gain. While many such vulnerabilities are only detected after deployment, smart contracts are typically immutable once deployed, making post-hoc fixes costly or infeasible. This highlights the critical need for detecting oracle manipulation risks before deployment. In this paper, we propose AiRacleX, a novel LLM-driven framework that enables pre-deployment detection of price oracle manipulation vulnerabilities by leveraging the complementary strengths of multiple large language models …

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Look, Compare And Draw: Differential Query Transformer For Automatic Oil Painting, Lingyu Liu, Yaxiong Wang, Li Zhu, Lizi Liao, Zhedong Zheng Jan 2026

Look, Compare And Draw: Differential Query Transformer For Automatic Oil Painting, Lingyu Liu, Yaxiong Wang, Li Zhu, Lizi Liao, Zhedong Zheng

Research Collection School Of Computing and Information Systems

This work introduces a new approach to automatic oil painting that emphasizes the creation of dynamic and expressive brushstrokes. A pivotal challenge lies in mitigating the duplicate and common-place strokes, which often lead to less aesthetic outcomes. Inspired by the human painting process, i.e., observing, comparing, and drawing, we incorporate differential image analysis into a neural oil painting model, allowing the model to effectively concentrate on the incremental impact of successive brushstrokes. To operationalize this concept, we propose the Differential Query Transformer (DQ-Transformer), a new architecture that leverages differentially derived image representations enriched with positional encoding to guide the stroke …

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Portfoliopilot: An Agentic Platform For Financial Portfolio Management Algorithm Development And Evaluation, Jared Chan Xu Yang, Haokai Ma, Yunshan Ma Jan 2026

Portfoliopilot: An Agentic Platform For Financial Portfolio Management Algorithm Development And Evaluation, Jared Chan Xu Yang, Haokai Ma, Yunshan Ma

Research Collection School Of Computing and Information Systems

Developing new portfolio-management algorithms typically demands substantial programming effort, limiting rapid experimentation and excluding finance professionals without coding skills. Current robo-advisory tools offer pre-built but rigid strategies, restricting customization and experimentation. We introduce PortfolioPilot, an open-source, agentic platform that enables users to generate bespoke portfolio through natural-language descriptions. Leveraging the Anthropic Claude API, PortfolioPilot dynamically synthesizes executable TypeScript algorithms that run in the frontend with security validation. The system integrates real-time backtesting with historical market data, classical optimization algorithms (Markowitz, LSTM, ARIMA), and interactive performance visualizations.

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Potent But Stealthy: Rethink Profile Pollution Against Sequential Recommendation Via Bi-Level Constrained Reinforcement Paradigm, Jiajie Su, Zihan Nan, Yunshan Ma, Xiaobo Xia, Xiaohua Feng, Weiming Liu, Xiang Chen, Xiaolin Zheng, Chaochao Chen Jan 2026

Potent But Stealthy: Rethink Profile Pollution Against Sequential Recommendation Via Bi-Level Constrained Reinforcement Paradigm, Jiajie Su, Zihan Nan, Yunshan Ma, Xiaobo Xia, Xiaohua Feng, Weiming Liu, Xiang Chen, Xiaolin Zheng, Chaochao Chen

Research Collection School Of Computing and Information Systems

Sequential Recommenders, which exploit dynamic user intents through interaction sequences, are vulnerable to adversarial attacks. While existing attacks primarily rely on data poisoning, they require large-scale user access or fake profiles, thus lacking practicality. In this paper, we focus on the Profile Pollution Attack that subtly contaminates partial user interactions to induce targeted mispredictions. Previous PPA methods suffer from two limitations, i.e., i) overreliance on sequence horizon impact restricts fine-grained perturbations on item transitions, and ii) holistic modifications cause detectable distribution shifts. To address these challenges, we propose a constrained reinforcement driven attack CREAT that synergizes a bi-level optimization framework …

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Nondeterministic Polynomial-Time Problem Challenge: An Ever-Scaling Reasoning Benchmark For Llms, Chang Yang, Ruiyu Wang, Junzhe Jiang, Qi Jiang, Qinggang Zhang, Yanchen Deng, Shuxin Li, Shuyue Hu, Bo Li, Florian T. Pokorny, Xiao Huang, Xinrun Wang Jan 2026

Nondeterministic Polynomial-Time Problem Challenge: An Ever-Scaling Reasoning Benchmark For Llms, Chang Yang, Ruiyu Wang, Junzhe Jiang, Qi Jiang, Qinggang Zhang, Yanchen Deng, Shuxin Li, Shuyue Hu, Bo Li, Florian T. Pokorny, Xiao Huang, Xinrun Wang

Research Collection School Of Computing and Information Systems

Reasoning is the fundamental capability of large language models (LLMs). Due to the rapid progress of LLMs, there are two main issues of current benchmarks: i) these benchmarks can be crushed in a short time (less than 1 year), and ii) these benchmarks may be easily hacked. To handle these issues, we propose the ever-scalingness for building the benchmarks which are scaling over complexity against crushing, instance against hacking and exploitation, oversight for easy verification, and coverage for real-world relevance. This paper presents Nondeterministic Polynomial-time Problem Challenge (NPPC), an ever-scaling reasoning benchmark for LLMs. Specifically, the NPPC has three main …

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Evaluating Large Language Models For Line-Level Vulnerability Localization, Jian Zhang, Chong Wang, Anran Li, Weisong Sun, Cen Zhang, Wei Ma, Yang Liu Jan 2026

Evaluating Large Language Models For Line-Level Vulnerability Localization, Jian Zhang, Chong Wang, Anran Li, Weisong Sun, Cen Zhang, Wei Ma, Yang Liu

Research Collection School Of Computing and Information Systems

Recently, Automated Vulnerability Localization (AVL) has attracted growing attention, aiming to facilitate diagnosis by pinpointing the specific lines of code responsible for vulnerabilities. Large Language Models (LLMs) have shown potential in various domains, yet their effectiveness in line-level vulnerability localization remains underexplored. In this work, we present the first comprehensive empirical evaluation of LLMs for AVL. Our study examines 19 leading LLMs suitable for code analysis, including ChatGPT and multiple open-source models, spanning encoder-only, encoder-decoder, and decoder-only architectures, with model sizes from 60M to 70B parameters. We evaluate three paradigms including few-shot prompting, discriminative fine-tuning, and generative fine-tuning with and …

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Interpretable Multimodal Zero Shot Ecg Diagnosis Via Structured Clinical Knowledge Alignment, Jialu Tang, Hung Manh Pham, Ignace De Lathauwer, Henk S. Schipper, Yuan Lu, Dong Ma, Aaqib Saeed Jan 2026

Interpretable Multimodal Zero Shot Ecg Diagnosis Via Structured Clinical Knowledge Alignment, Jialu Tang, Hung Manh Pham, Ignace De Lathauwer, Henk S. Schipper, Yuan Lu, Dong Ma, Aaqib Saeed

Research Collection School Of Computing and Information Systems

Electrocardiogram (ECG) interpretation is essential for cardiovascular disease diagnosis, but current automated systems often struggle with transparency and generalization to unseen conditions. To address this, we introduce ZETA, a zero-shot multimodal framework designed for interpretable ECG diagnosis aligned with clinical workflows. ZETA uniquely compares ECG signals against structured positive and negative clinical observations, which are curated through an LLM-assisted, expertvalidated process, thereby mimicking differential diagnosis. Our approach leverages a pre-trained multimodal model to align ECG and text embeddings without disease-specific fine-tuning. Empirical evaluations demonstrate ZETA’s competitive zero-shot classification performance and, importantly, provide qualitative and quantitative evidence of enhanced interpretability, grounding …

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Multihoming And Single-Homing Complementors' Responses To Intensified Between-Platform Competition: Evidence From The Youtube–Twitch Rivalry, Niloofar Abolfathi Jan 2026

Multihoming And Single-Homing Complementors' Responses To Intensified Between-Platform Competition: Evidence From The Youtube–Twitch Rivalry, Niloofar Abolfathi

Research Collection Lee Kong Chian School Of Business

Research Summary: This paper examines how heterogeneous YouTube content creators-specifically, single-homing and multihoming channels-respond to a sudden increase in the exposure of the rival platform, Twitch. Contrary to expectations, I find that multihoming channels reduce their content creation efforts and underperform relative to single-homers. To make sense of these surprising results, I develop a formal model that characterizes the strategic behavior of different types of complementors operating across two platforms with varying levels of user exposure. The model offers a plausible explanation for the observed empirical patterns, suggesting that intensified competition between platforms, as a consequence of their changing exposure, …

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Over-Reliance On Aesthetics? The Appearance-Reveals-Character Lay Theory Increases Consumers’ Devaluation Of Unattractive Produce, Shilpa Madan, Krishna Savani, Gita Venkataramani Johar Dec 2025

Over-Reliance On Aesthetics? The Appearance-Reveals-Character Lay Theory Increases Consumers’ Devaluation Of Unattractive Produce, Shilpa Madan, Krishna Savani, Gita Venkataramani Johar

Research Collection Lee Kong Chian School Of Business

Approximately 40% of all produce is rejected because it appears unattractive, contributing significantly to food waste. Whereas previous research attributes this devaluation to the ugly-is-bad effect, this research identifies an important moderator of consumers’ reliance on this heuristic: the lay theory that a person’s outer appearance reveals their inner character. Specifically, consumers who believe that people’s appearances reveal their character are less willing to accept unattractive (vs. attractive) produce because they generalize their lay theory to produce and are, thus, more likely to infer that unattractive produce is of lower quality. Consumers who do not hold the lay theory do …

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The Token-Effort Effect: Trivial Redemption Effort Increases Price Promotions Effectiveness, Kuangjie Zhang, Thomas Allard, Nidhi Agrawal, Rajesh Bagchi Dec 2025

The Token-Effort Effect: Trivial Redemption Effort Increases Price Promotions Effectiveness, Kuangjie Zhang, Thomas Allard, Nidhi Agrawal, Rajesh Bagchi

Research Collection Lee Kong Chian School Of Business

This research documents how introducing redemption tasks requiring a token (i.e., trivial) amount of effort—for instance, by asking the consumer to enter a promo code or solve a CAPTCHA to receive a discount—increases price promotion effectiveness compared with equivalent straight discounts (i.e., applied automatically). Eight studies (including two field experiments) and three additional Web Appendix studies provide robust evidence for the beneficial effect of token-effort requirements on redemption rates. This effect occurs because the easy-to-attain redemption task induces a high perceived return on effort. Thus, this effect only occurs when the redemption task requires token-type effort but not when it …

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The Persistence Of Homophobia In Men’S Friendship Norms, Sherrie Y. Xue, Stephanie C. Lin, Christilene Du Plessis Dec 2025

The Persistence Of Homophobia In Men’S Friendship Norms, Sherrie Y. Xue, Stephanie C. Lin, Christilene Du Plessis

Research Collection Lee Kong Chian School Of Business

Across five studies and one supplementary study (five preregistered; N = 3,215 adults), we found that men, more than women, avoided shared experiences (e.g., going to the movies, sharing food) with individuals of the same gender. Furthermore, persistent societal expectations that men should be unambiguously heterosexual underpinned this pattern: Men felt more apprehensive about signaling same-gender romance in platonic relationships than women did. In turn, romantic prototypicality drove the pattern of men (more than women) avoiding shared activities, above and beyond differences in how hedonic, enjoyable, and feminine the activities were; our findings further suggested that men’s reluctance to share …

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Growing Up Under Mao And Deng: On Politician Ideology And Corporate Policies, Hao Liang, Rong Wang, Haikun Zhu Dec 2025

Growing Up Under Mao And Deng: On Politician Ideology And Corporate Policies, Hao Liang, Rong Wang, Haikun Zhu

Research Collection Lee Kong Chian School Of Business

We provide firm-level evidence on how politicians’ ideologies shape economic outcomes, using a unique setting of ideological discontinuity in China, transitioning from Maoism to Dengism around 1978. We find that ideological exposure during a politician’s early adulthood has a lasting impact on contemporary firm policies in their city. Firms governed by “Maoist mayors” show greater stakeholder spending, lower pay inequality, and less internationalization compared with those governed by “Dengist mayors.” Further evidence suggests that politicians’ ideologies influence corporate behaviors through nonpolicy channels, and these results cannot be fully explained by selection bias, endogenous matching, or the age of the mayor.

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Exploratory Innovation: A New Perspective On Family Firms' Under-Diversification Puzzle, Po-Hsuan Hsu, Sterling Huang, Massimo Massa, Yaru Qian, Hong Zhang Dec 2025

Exploratory Innovation: A New Perspective On Family Firms' Under-Diversification Puzzle, Po-Hsuan Hsu, Sterling Huang, Massimo Massa, Yaru Qian, Hong Zhang

Research Collection Lee Kong Chian School Of Business

We propose a new perspective on family firms’ puzzling under-diversification in product spaces: these firms first need to succeed in exploratory innovation so they may diversify into new product markets. We construct a large database of family ownership and patent records of U.S. public firms, and show that family firms produce more exploratory patents than others, a relation that is stronger among under-diversified family firms. In addition, we find that such innovation indeed helps family firms diversify business risks. A causal interpretation of our result is supported by (i) using the property division standard in state-level divorce laws as an …

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National Halo And Culture Mixing: U.S. Consumer Acceptance Of Korean Cultivated Meat Made With Seaweed Components, Mark Chong, Angela K. Y. Leung, Tricia Marjorie Fernandez Dec 2025

National Halo And Culture Mixing: U.S. Consumer Acceptance Of Korean Cultivated Meat Made With Seaweed Components, Mark Chong, Angela K. Y. Leung, Tricia Marjorie Fernandez

Research Collection Lee Kong Chian School Of Business

The purpose of this qualitative study was to investigate U.S. consumers’ perception and acceptance of a “culturally mixed” product – i.e., cultivated meat (beef) made with Korean seaweed components – a product that involves the blending of a foreign cultural element (Korean seaweed) with a local cultural element (cultivated beef) to create a mixed cultural representation. Subsequently, it aimed to assess if the product’s country of origin (i.e., South Korea) would influence consumer perception and acceptance. Online in-depth interviews were conducted with 20 U.S. consumers recruited through two market research companies. The interview transcripts were analyzed by two trained coders …

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Incident-Driven Esg Engagements, Hao Liang, Yongheng Sun, T. Mandy Tham Dec 2025

Incident-Driven Esg Engagements, Hao Liang, Yongheng Sun, T. Mandy Tham

Research Collection Lee Kong Chian School Of Business

We examine when and why institutional investors engage portfolio firms on ESG issues using proprietary engagement records from a European asset manager. Salient negative incidents emerge as a powerful trigger—second only to firm size—because they heighten reputational accountability for investors and reveal new information about hidden ESG weaknesses, particularly at firms perceived as ESG leaders. Monitoring also extends along the supply chain: incidents at key suppliers prompt investor engagement with focal firms. Finally, engagement is associated with higher firm value, stronger ESG performance, and increased cash flows. Together, the findings illuminate the drivers and mechanisms of ESG engagement and underscore …

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Designing Payment Models For The Poor, Bhavani Shanker Uppari, Sasa Zorc Dec 2025

Designing Payment Models For The Poor, Bhavani Shanker Uppari, Sasa Zorc

Research Collection Lee Kong Chian School Of Business

Millions of people in poverty lack access to basic services such as energy, clean water, and cooking gas. Private firms are increasingly delivering these services—for example, by offering solar home systems or clean cooking packages with remote lockout capabilities—through pay-as-you-go (PAYGo) contracts that give consumers flexibility over payment timing and amount to match their erratic cash flows. Because firms cannot observe consumer liquidity, however, consumers may misuse this flexibility and prioritize other needs over repayment. We study how to design contracts that preserve flexibility while creating incentives for timely repayment using an optimal contracting approach. The optimal contract summarizes each …

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A Learning‑Augmented Dynamic Programming Approach For Orienteering Problem With Time Windows, Guansheng Peng, Lining Xing, Fuyan Song Ma, Aldy Gunawan, Aldy Gunawan Dec 2025

A Learning‑Augmented Dynamic Programming Approach For Orienteering Problem With Time Windows, Guansheng Peng, Lining Xing, Fuyan Song Ma, Aldy Gunawan, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Recent years have witnessed a surge of interest in solving combinatorial optimization problems (COPs) using machine learning techniques. Motivated by this trend, we propose a learning-augmented exact approach for tackling an NP-hard COP, the Orienteering Problem with Time Windows, which aims to maximize the total score collected by visiting a subset of vertices in a graph within their time windows. Traditional exact algorithms rely heavily on domain expertise and meticulous design, making it hard to achieve further improvements. By leveraging deep learning models to learn effective relaxations of problem restrictions from data, our approach enables significant performance gains in an …

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Riding Attention Spikes: How Analysts Respond To Advertising, Minjae Koo, Annika Yu Wang, Yin Wang, Liandong Zhang Dec 2025

Riding Attention Spikes: How Analysts Respond To Advertising, Minjae Koo, Annika Yu Wang, Yin Wang, Liandong Zhang

Research Collection School Of Accountancy

Product market advertising, while containing little new information, triggers spikes in investor attention. Using weekly advertising data, we find that sell-side analysts issue optimistic earnings forecasts in response to heavier advertising in the prior week. This effect is not driven by confounding earnings or product news. It is more pronounced for experienced analysts and analysts affiliated with brokerages relying solely on trading revenues. The optimistic forecast bias intensifies the impact of advertising on investor trades, especially on retail buying, of the underlying stock during the following week. Overall, analysts appear to issue optimistic forecasts to exploit retail investor attention spikes …

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Registration Is A Powerful Rotation-Invariance Learner For 3d Anomaly Detection, Yuyang Yu, Zhengwei Chen, Xuemiao Xu, Lei Zhang, Haoxin Yang, Yongwei Nie, Shengfeng He Dec 2025

Registration Is A Powerful Rotation-Invariance Learner For 3d Anomaly Detection, Yuyang Yu, Zhengwei Chen, Xuemiao Xu, Lei Zhang, Haoxin Yang, Yongwei Nie, Shengfeng He

Research Collection School Of Computing and Information Systems

3D anomaly detection in point-cloud data is critical for industrial quality control, aiming to identify structural defects with high reliability. However, current memory bank-based methods often suffer from inconsistent feature transformations and limited discriminative capacity, particularly in capturing local geometric details and achieving rotation invariance. These limitations become more pronounced when registration fails, leading to unreliable detection results. We argue that point-cloud registration plays an essential role not only in aligning geometric structures but also in guiding feature extraction toward rotation-invariant and locally discriminative representations. To this end, we propose a registration-induced, rotation-invariant feature extraction framework that integrates the objectives …

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