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Articles 781 - 810 of 15937
Full-Text Articles in Entire DC Network
Would Order-By-Order Auctions Be Competitive?, Thomas Ernst, Chester Spatt, Jian Sun
Would Order-By-Order Auctions Be Competitive?, Thomas Ernst, Chester Spatt, Jian Sun
Research Collection Lee Kong Chian School Of Business
We model two methods of executing segregated retail orders: brokers’ routing,whereby brokers allocate orders using the market maker’s overall performance, andorder-by-order auctions, where market makers bid on individual orders, a recent U.S.Securities and Exchange Commission proposal. Order-by-order auctions improve al-locative efficiency, but face a winner’s curse reducing retail investor welfare, partic-ularly when liquidity is limited. Additional market participants competing for retailorders fail to improve total efficiency and investor welfare when entrants possess in-formation superior to incumbent wholesalers. Our results hold when new entrants are less informed or the information structure differs. We also examine the cross-subsidization of brokers’ routing.
Not All Political Ties Are The Same: Firms' Ties To The Government And Pollution, Wen Helena Li, Ilya R. P. Cuypers, Gokhan Ertug, Hari Bapuji, Wei Liu
Not All Political Ties Are The Same: Firms' Ties To The Government And Pollution, Wen Helena Li, Ilya R. P. Cuypers, Gokhan Ertug, Hari Bapuji, Wei Liu
Research Collection Lee Kong Chian School Of Business
Complementing research on the implications of political ties for firms’ strategic decisions and financial performance, recent studies are increasingly focusing on the environmental consequences of firms’ political connections. We contribute to this stream of work by integrating insights from the literature on political ties and the multifaceted nature of governments to explore how the level of government influences the pollution implications of firms’ political ties. Specifically, we theorize and demonstrate that, in China, firms' achieved political ties with high levels of the government reduce pollution, while such ties with low levels of the government increase it. Furthermore, we examine how …
The 6th International Workshop On Talent And Management Computing (Tmc 2025), Hengshu Zhu, Yong Ge, Hui Xiong, Ee-Peng Lim
The 6th International Workshop On Talent And Management Computing (Tmc 2025), Hengshu Zhu, Yong Ge, Hui Xiong, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to deal with talent and management-related tasks in a quantitative manner. Indeed, thanks to the era of big data, the availability of large-scale talent data provides unparalleled opportunities for leaders to deliver intelligence for effective management for organizations. In the past few years, talent and management computing have increasingly attracted attention from KDD communities, and a number of research/applied data science efforts have been devoted. To this end, the purpose of this workshop, i.e., the 6th International Workshop on Talent and Management Computing …
A Review: The Beauty Of Serendipity Between Integrated Circuit Security And Artificial Intelligence, Chen Dong, Decheng Qiu, Bolun Li, Yang Yang, Chenxi Lyu, Dong Cheng, Hao Zhang, Zhenyi. Chen
A Review: The Beauty Of Serendipity Between Integrated Circuit Security And Artificial Intelligence, Chen Dong, Decheng Qiu, Bolun Li, Yang Yang, Chenxi Lyu, Dong Cheng, Hao Zhang, Zhenyi. Chen
Research Collection School Of Computing and Information Systems
Integrated circuits are the core of a cyber-physical system, where tens of billions of components are integrated into a tiny silicon chip to conduct complex functions. To maximize utilities, the design and manufacturing life cycle of integrated circuits rely on numerous untrustworthy third parties, forming a global supply chain model. At the same time, this model produces unpredictable and catastrophic issues, threatening the security of individuals and countries. As for guaranteeing the security of ultra-highly integrated chips, detecting slight abnormalities caused by malicious behavior in the current and voltage is challenging, as is achieving computability within a reasonable time and …
Solving Two-Stage Stochastic Integer Programs Via Representation Learning, Yaoxin Wu, Zhiguang Cao, Wen Song, Yingqian Zhang
Solving Two-Stage Stochastic Integer Programs Via Representation Learning, Yaoxin Wu, Zhiguang Cao, Wen Song, Yingqian Zhang
Research Collection School Of Computing and Information Systems
Solving stochastic integer programs (SIPs) is extremely intractable due to the high computational complexity. To solve two-stage SIPs efficiently, we propose a conditional variational autoencoder (CVAE) for scenario representation learning. A graph convolutional network (GCN) based VAE embeds scenarios into a low-dimensional latent space, conditioned on the deterministic context of each instance. With the latent representations of stochastic scenarios, we perform two auxiliary tasks: objective prediction and scenario contrast, which predict scenario objective values and the similarities between them, respectively. These tasks further integrate objective information into the representations through gradient backpropagation. Experiments show that the learned scenario representations can …
Bhvit: Binarized Hybrid Vision Transformer, Tian Gao, Yu Zhang, Zhiyuan Zhang, Huajun Liu, Kaijie Yin, Chengzhong Xu, Hui Kong
Bhvit: Binarized Hybrid Vision Transformer, Tian Gao, Yu Zhang, Zhiyuan Zhang, Huajun Liu, Kaijie Yin, Chengzhong Xu, Hui Kong
Research Collection School Of Computing and Information Systems
Model binarization has made significant progress in enabling real-time and energy-efficient computation for con-volutional neural networks (CNN), offering a potential solution to the deployment challenges faced by Vision Transformers (ViTs) on edge devices. However, due to the structural differences between CNN and Transformer architectures, simply applying binary CNN strategies to the ViT models will lead to a significant performance drop. To tackle this challenge, we propose BHViT, a binarization-friendly hybrid ViT architecture and its full binarization model with the guidance of three important observations. Initially, BHViT utilizes the local information interaction and hierarchical feature aggregation technique from coarse to fine …
Han Feizi On Reputation-Driven Disobedience: A Comparative Study, Antong Liu
Han Feizi On Reputation-Driven Disobedience: A Comparative Study, Antong Liu
Research Collection School of Social Sciences
Must absolutist states resort to intimidation and coercion to tackle subjects' disobedience driven by their pursuit of reputation? Since canonical early modern Western thinkers broached but did not solve this question, I turn to the most renowned ancient Chinese Legalist Han Feizi's understudied account of reputation for answers. Whether as a means or an end, individuals' pursuit of reputation always challenges the authority of the absolute monarchy that endeavors to centralize state power. Forcefully confronting this pursuit is the barely but only acceptable way for the state to tackle this challenge, as non-confrontational strategies favored by many Western thinkers inevitably …
Gcot: Chain-Of-Thought Prompt Learning For Graphs, Xingtong Yu, Chang Zhou, Zhongwei Kuai, Xinming Zhang, Yuan Fang
Gcot: Chain-Of-Thought Prompt Learning For Graphs, Xingtong Yu, Chang Zhou, Zhongwei Kuai, Xinming Zhang, Yuan Fang
Research Collection School Of Computing and Information Systems
Chain-of-thought (CoT) prompting has achieved remarkable success in natural language processing (NLP). However, its vast potential remains largely unexplored for graphs. This raises an interesting question: How can we design CoT prompting for graphs to guide graph models to learn step by step? On one hand, unlike natural languages, graphs are non-linear and characterized by complex topological structures. On the other hand, many graphs lack textual data, making it difficult to formulate language-based CoT prompting. %Therefore we cannot directly adopt the CoT prompting methods used in the language domain. In this work, we propose the first CoT prompt learning framework …
Ai-Assisted Triage And Decision Support Of Head And Neck Cancer Screening And Diagnosis In Low-Resourced Settings, Min Hun Lee, Sean Shao Wei Lam, Shaun Xin Hong Liew, Michael Dorosan, Nicholas Graves, Jonas Karlström, Hiang Khoon Tan, Walter Tsong Lee
Ai-Assisted Triage And Decision Support Of Head And Neck Cancer Screening And Diagnosis In Low-Resourced Settings, Min Hun Lee, Sean Shao Wei Lam, Shaun Xin Hong Liew, Michael Dorosan, Nicholas Graves, Jonas Karlström, Hiang Khoon Tan, Walter Tsong Lee
Research Collection School Of Computing and Information Systems
The mortality burden of head and neck cancer (HNC) is increasing globally and disproportionately affects people in low-and middle-income countries with limited medical workforce. To address this issue, artificial intelligence (AI) algorithms are increasingly being explored to process medical imaging data, demonstrating competitive performance. However, the clinical adoption of AI remains challenging as clinicians struggle to understand how complex AI works and trust it to use in practice. In addition, AI may not perform well on varying data qualities of endoscopy videos for HNC screening and diagnosis from multiple sites.In this project, our international and interdisciplinary team will collaborate with …
R2dqg: A Quality Meets Diversity Framework For Question Generation Over Knowledge Bases, Yimeng Ren, Yanhua Yu, Lizi Liao, Yuhu Shang, Kangkang Lu, Mingliang Yan
R2dqg: A Quality Meets Diversity Framework For Question Generation Over Knowledge Bases, Yimeng Ren, Yanhua Yu, Lizi Liao, Yuhu Shang, Kangkang Lu, Mingliang Yan
Research Collection School Of Computing and Information Systems
The task of Knowledge-Based Question Generation (KBQG) involves generating natural language questions from structured knowledge sources, posing unique challenges in balancing linguistic diversity and semantic relevance. Existing models often focus on maximizing surface-level similarity to ground-truth questions, neglecting the need for diverse syntactic forms and leading to semantic drift during generation. To overcome these challenges, we propose Refine-Reinforced Diverse Question Generation (R2DQG), a two-phase framework leveraging a generation-then-refinement paradigm. The Generator first constructs a diverse set of expressive templates using dependency parse tree similarity, capturing a wide range of syntactic patterns and styles. These templates guide the creation of question …
L3net: Localized And Layered Reparameterization For Incremental Learning, Xuandi Luo, Huaidong Zhang, Yi Xie, Hongrui Zhang, Xuemiao Xu, Shengfeng He
L3net: Localized And Layered Reparameterization For Incremental Learning, Xuandi Luo, Huaidong Zhang, Yi Xie, Hongrui Zhang, Xuemiao Xu, Shengfeng He
Research Collection School Of Computing and Information Systems
Model-based class incremental learning (CIL) methods aim to address the challenge of catastrophic forgetting by retaining certain parameters and expanding the model architecture. However, retaining too many parameters can lead to an overly complex model, increasing inference overhead. Additionally, compressing these parameters to reduce the model size can result in performance degradation. To tackle these challenges, we propose a novel three-stage CIL framework called Localized and Layered Reparameterization for Incremental Learning (L3Net). The rationale behind our approach is to balance model complexity and performance by selectively expanding and optimizing critical components. Specifically, the framework introduces a Localized Dual-path Expansion structure, …
Towards Context-Aware Traffic Classification Via Time-Wavelet Fusion Network, Ziming Zhao, Zhuoxue Song, Xiaofei Xie, Zhaoxuan Li, Jiongchi Yu, Fan Terry Zhang, Tingting Li
Towards Context-Aware Traffic Classification Via Time-Wavelet Fusion Network, Ziming Zhao, Zhuoxue Song, Xiaofei Xie, Zhaoxuan Li, Jiongchi Yu, Fan Terry Zhang, Tingting Li
Research Collection School Of Computing and Information Systems
Encrypted traffic classification occupies a significant role in cybersecurity and network management. The existing encrypted traffic classification technology mostly relies on intra-flow semantics for extracting features. However, considering that some attack behaviors inherently have similar patterns to legitimate behaviors, and powerful adversaries could simulate benign users to conceal their attack intentions, intra-flow features may be similar between different categories. In this paper, we propose TrafficScope, a time-wavelet fusion network based on Transformer to enhance the performance of encrypted traffic classification. Specifically, in addition to using intra-flow semantics, TrafficScope also extracts contextual information to construct more comprehensive representations. Moreover, to cope …
Cami: A Counselor Agent Supporting Motivational Interviewing Through State Inference And Topic Exploration, Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Phey Ling Kit, Nicholas Gabriel Lim, Cameron Shi Ern Tan, Ee-Peng Lim
Cami: A Counselor Agent Supporting Motivational Interviewing Through State Inference And Topic Exploration, Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Phey Ling Kit, Nicholas Gabriel Lim, Cameron Shi Ern Tan, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Conversational counselor agents have become essential tools for addressing the rising demand for scalable and accessible mental health support. This paper introduces CAMI, a novel automated counselor agent grounded in Motivational Interviewing (MI) – a client-centered counseling approach designed to address ambivalence and facilitate behavior change. CAMI employs a novel STAR framework, consisting of client’s state inference, motivation topic exploration, and response generation modules, leveraging large language models (LLMs). These components work together to evoke change talk, aligning with MI principles and improving counseling outcomes for diverse clients. We evaluate CAMI’s performance through both automated and expert evaluations, utilizing simulated …
Assessing The Robustness Of Test Selection Methods For Deep Neural Networks, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Wei Ma, Mike Papadakis, Lei Ma, Yves Le Traon
Assessing The Robustness Of Test Selection Methods For Deep Neural Networks, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Wei Ma, Mike Papadakis, Lei Ma, Yves Le Traon
Research Collection School Of Computing and Information Systems
Regularly testing deep learning-powered systems on newly collected data is critical to ensure their reliability, robustness, and efficacy in real-world applications. This process is demanding due to the significant time and human effort required for labeling new data. While test selection methods alleviate manual labor by labeling and evaluating only a subset of data while meeting testing criteria, we observe that such methods with reported promising results are simply evaluated, e.g., testing on original test data. The question arises: are they always reliable? In this article, we explore when and to what extent test selection methods fail. First, we identify …
Knowledge Boundary Of Large Language Models: A Survey, Moxin Li, Yong Zhao, Wenxuan Zhang, Shuaiyi Li, Wenya Xie, See-Kiong Ng, Tat-Seng Chua, Yang Deng
Knowledge Boundary Of Large Language Models: A Survey, Moxin Li, Yong Zhao, Wenxuan Zhang, Shuaiyi Li, Wenya Xie, See-Kiong Ng, Tat-Seng Chua, Yang Deng
Research Collection School Of Computing and Information Systems
Although large language models (LLMs) store vast amount of knowledge in their parameters, they still have limitations in the memorization and utilization of certain knowledge, leading to undesired behaviors such as generating untruthful and inaccurate responses. This highlights the critical need to understand the knowledge boundary of LLMs, a concept that remains inadequately defined in existing research. In this survey, we propose a comprehensive definition of the LLM knowledge boundary and introduce a formalized taxonomy categorizing knowledge into four distinct types. Using this foundation, we systematically review the field through three key lenses: the motivation for studying LLM knowledge boundaries, …
Beware Of Your Po! Measuring And Mitigating Ai Safety Risks In Role-Play Fine-Tuning Of Llms, Weixiang Zhao, Yulin Hu, Yang Deng, Jiahe Guo, Xingyu Sui, Xinyang Han, An Zhang, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu
Beware Of Your Po! Measuring And Mitigating Ai Safety Risks In Role-Play Fine-Tuning Of Llms, Weixiang Zhao, Yulin Hu, Yang Deng, Jiahe Guo, Xingyu Sui, Xinyang Han, An Zhang, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu
Research Collection School Of Computing and Information Systems
Although large language models (LLMs) store vast amount of knowledge in their parameters, they still have limitations in the memorization and utilization of certain knowledge, leading to undesired behaviors such as generating untruthful and inaccurate responses. This highlights the critical need to understand the knowledge boundary of LLMs, a concept that remains inadequately defined in existing research. In this survey, we propose a comprehensive definition of the LLM knowledge boundary and introduce a formalized taxonomy categorizing knowledge into four distinct types. Using this foundation, we systematically review the field through three key lenses: the motivation for studying LLM knowledge boundaries, …
Mpo: Multilingual Safety Alignment Via Reward Gap Optimization, Weixiang Zhao, Yulin Hu, Yang Deng, Tongtong Wu, Wenxuan Zhang, Jiahe Guo, An Zhang, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu
Mpo: Multilingual Safety Alignment Via Reward Gap Optimization, Weixiang Zhao, Yulin Hu, Yang Deng, Tongtong Wu, Wenxuan Zhang, Jiahe Guo, An Zhang, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu
Research Collection School Of Computing and Information Systems
Large language models (LLMs) have become increasingly central to AI applications worldwide, necessitating robust multilingual safety alignment to ensure secure deployment across diverse linguistic contexts. Existing preference learning methods for safety alignment, such as RLHF and DPO, are primarily monolingual and struggle with noisy multilingual data. To address these limitations, we introduce Multilingual reward gaP Optimization (MPO), a novel approach that leverages the well-aligned safety capabilities of the dominant language (e.g., English) to improve safety alignment across multiple languages. MPO directly minimizes the reward gap difference between the dominant language and target languages, effectively transferring safety capabilities while preserving the …
Exploring Rehabilitation Therapists' Knowledge And Perspectives On The Use Of Artificial Intelligence And Machine Learning For Persons Poststroke, Hannah Clark, Mia Delvecchio, Min Hun Lee, Elena D. Brown, Kaia Mikula, Robert Halyama, Kasey Stepansky
Exploring Rehabilitation Therapists' Knowledge And Perspectives On The Use Of Artificial Intelligence And Machine Learning For Persons Poststroke, Hannah Clark, Mia Delvecchio, Min Hun Lee, Elena D. Brown, Kaia Mikula, Robert Halyama, Kasey Stepansky
Research Collection School Of Computing and Information Systems
Research Objectives: The use of technology such as robotics, gaming systems, self-monitoring apps, or other sensor-based devices in standard practice is infrequent. Due to the rapid development of artificial intelligence (AI) and machine learning (ML) applications, it is important to look at how therapists perceive AI/ML, and design applications with potential barriers in mind. to support future integration into practice. The purpose of this research project is to gain rehabilitation therapists’ perspectives on AI/ML in post-stroke assessment and intervention.Design: This ongoing study uses a mixed methods design with surveys and focus groups. Participants engaged in a 30-minute webinar to learn …
Dreamanime: Learning Style-Identity Textual Disentanglement For Anime And Beyond, Chenshu Xu, Yangyang Xu, Huaidong Zhang, Xuemiao Xu, Shengfeng He
Dreamanime: Learning Style-Identity Textual Disentanglement For Anime And Beyond, Chenshu Xu, Yangyang Xu, Huaidong Zhang, Xuemiao Xu, Shengfeng He
Research Collection School Of Computing and Information Systems
Text-to-image generation models have significantly broadened the horizons of creative expression through the power of natural language. However, navigating these models to generate unique concepts, alter their appearance, or reimagine them in unfamiliar roles presents an intricate challenge. For instance, how can we exploit language-guided models to transpose an anime character into a different art style, or envision a beloved character in a radically different setting or role? This paper unveils a novel approach named DreamAnime, designed to provide this level of creative freedom. Using a minimal set of 2-3 images of a user-specified concept such as an anime character …
Computational Fact-Checking With Limited Resources, Fengzhu Zeng
Computational Fact-Checking With Limited Resources, Fengzhu Zeng
Dissertations and Theses Collection (Open Access)
The rapid dissemination of information through online platforms has sparked widespread concern about the propagation of misinformation. Manual fact-checking by pro- fessional fact-checkers is time-consuming and lacks scalability to address the vast volume of daily information. Consequently, computational fact-checking, driven by automated techniques in natural language processing (NLP), has garnered interest as
a potential solution. However, computational fact-checking faces critical challenges limited resources, particularly due to the issues of data scarcity and computing resource constraints. One key challenge is data scarcity, which arises from the constant generation of new information and emerging events on social media. This scarcity manifests in …
Language Brokering Conditions The Indirect Association Between Mexican‐Origin Adolescents’ Academic Discrimination And Educational Expectations, Su Yeong Kim, Yayu Du, Chantal Alvarado, Wei Xiang Sim, Wen Wen, Tianlu Zhang, Jingyi Shen
Language Brokering Conditions The Indirect Association Between Mexican‐Origin Adolescents’ Academic Discrimination And Educational Expectations, Su Yeong Kim, Yayu Du, Chantal Alvarado, Wei Xiang Sim, Wen Wen, Tianlu Zhang, Jingyi Shen
Research Collection School of Social Sciences
Mexican‐origin adolescents, a significant portion of the US Latino population, often experience a decline in educational expectations from early to late adolescence. Contextual factors such as academic discrimination and language brokering for parents may contribute to this decline. This study investigates the indirect effect of academic discrimination experienced in middle school on educational expectations in young adulthood through high school grades and engagement, and the moderating role of language brokering experiences in these relations. Data were collected from 604 Mexican‐origin adolescents across four waves from 2012 to 2023. Academic discrimination experiences in middle school were negatively associated with school grades …
Efficacy Of Digital Mental Health Interventions For Attention-Deficit Hyperactivity Disorder: A Meta-Analytic Review Of Randomised Controlled Trials, K. T. A. Kasturiratna, Andree Hartanto, Angel Khaterina Lee, Charmaine Jiali Koh, Nadyanna M. Majeed
Efficacy Of Digital Mental Health Interventions For Attention-Deficit Hyperactivity Disorder: A Meta-Analytic Review Of Randomised Controlled Trials, K. T. A. Kasturiratna, Andree Hartanto, Angel Khaterina Lee, Charmaine Jiali Koh, Nadyanna M. Majeed
Research Collection School of Social Sciences
Attention-Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that significantly impacts various facets of life. While traditional treatments such as medication and behavioural therapy are effective, they often fall short due to limited access and undesirable side effects. In response to these limitations, digital mental health interventions have emerged as a transformative approach in alleviating ADHD symptoms. The current meta-analysis investigates the effectiveness of digital mental health interventions for ADHD synthesising data across 23 randomised controlled trials, with 99 effect sizes across 1,472 participants. Results showed small, significant effects of digital mental health interventions in reducing overall ADHD symptoms …
Being Seen And Being With Others: Shame And Interpersonal Relationships, Brandon Yip
Being Seen And Being With Others: Shame And Interpersonal Relationships, Brandon Yip
Research Collection School of Social Sciences
I seek to vindicate heteronomous shame: shame that one experiences in response to a judgment from another that one does not accept. I suggest that such experiences are instances of interpersonal shame. This is shame that involves a sensitivity to interpersonal ideals, whose instantiation depends partly on the attitudes of others. I defend the importance of such shame by showing how vulnerability to others is a constitutive part of rich interpersonal relationships. The account both casts light on and vindicates the heteronomous shame that is pervasive among marginalised and oppressed groups. Such shame is not irrational but involves an accurate …
Socioeconomic Status Shapes Dyadic Interactions: Examining Behavioral And Physiologic Responses, Jacinth J. X. Tan, Tessa V. West, Wendy B. Mendes
Socioeconomic Status Shapes Dyadic Interactions: Examining Behavioral And Physiologic Responses, Jacinth J. X. Tan, Tessa V. West, Wendy B. Mendes
Research Collection School of Social Sciences
With more opportunities for diverse interactions, little is known about how social interactions involving people of different socioeconomic status (SES) may unfold. We investigated social attunement patterns in dyadic interactions involving SES. Unacquainted individuals recruited from the community interacted with similar-or-different-SES partners in the lab (Ndyads = 130). Attunement was assessed throughout the interaction by examining physiological linkage—how much a person’s physiological change is predicted by another’s physiological change, over time. Overall, low-SES participants showed stronger physiological linkage—indicating greater attunement—to partners across SES. Participants also appeared more comfortable when interacting with low-SES partners. There were no SES differences in dominance …
Constraints Without Tears For Consequentialists, Matthew Hammerton
Constraints Without Tears For Consequentialists, Matthew Hammerton
Research Collection School of Social Sciences
Agent-relative consequentialism uses the theoretical framework of consequentialism to capture the constraints favored by deontologists. Christopher Howard has recently argued that agent-relative consequentialism is implausible because it can only capture such constraints at the expense of getting other normative facts incorrect. In this reply, I argue that Howard’s objection to agent-relative consequentialism fails because it rests on faulty intuitions that arise from a misunderstanding of basic structural features of agent-relative consequentialism.
Longitudinal And Bidirectional Relations Between Problematic Social Media Use And Cognitive Failures, K. T. A. Sandeeshwara Kasturiratna, Andree Hartanto
Longitudinal And Bidirectional Relations Between Problematic Social Media Use And Cognitive Failures, K. T. A. Sandeeshwara Kasturiratna, Andree Hartanto
Research Collection School of Social Sciences
With social media becoming increasingly pervasive in our daily lives, there have been heightened concerns that the distractions caused by compulsive and problematic social media use may lead to cognitive failures. While some preliminary work have explored the link between problematic social media use and cognitive functioning, most are cross-sectional, leaving it uncertain whether cognitive failures are a precursor or consequence of problematic social media use. To address this gap, the current study examines the bidirectional relationship between problematic social media use and cognitive failures. A sample of 252 young adults completed the Problematic Social Media Use Scale and the …
Subjective Socioeconomic Status Moderates Self-Esteem Reactivity To Daily Stressor Exposure: Evidence From A Daily Diary Approach, Yi Jing Chua, Nadyanna M. Majeed, Verity Yu Qing Lua, Chi-Ying Cheng, Andree Hartanto
Subjective Socioeconomic Status Moderates Self-Esteem Reactivity To Daily Stressor Exposure: Evidence From A Daily Diary Approach, Yi Jing Chua, Nadyanna M. Majeed, Verity Yu Qing Lua, Chi-Ying Cheng, Andree Hartanto
Research Collection School of Social Sciences
Research on self-esteem reactivity has demonstrated that self-esteem fluctuates in response to daily stressor exposure, and the strength of this relationship varies between individuals. Drawing upon the positive link between objective socioeconomic status (SES) and self-esteem, how subjective SES influences self-esteem reactivity to daily stressor exposure was explored. Using a 7-day daily diary study, the current study (N-participants = 243, N-days = 1651) adopted a multilevel analysis to demonstrate that subjective SES attenuated the within-person association between daily stressor exposure and daily self-esteem, even after controlling for demographics and objective indicators of SES. The interactions were also consistent across social …
A Lifespan Perspective On Positive Affect Fluctuations On Purpose In Life: Findings From A 9-Year Longitudinal Study, Jonathan L. Chia, Verity Y. Q. Lua, Andree Hartanto
A Lifespan Perspective On Positive Affect Fluctuations On Purpose In Life: Findings From A 9-Year Longitudinal Study, Jonathan L. Chia, Verity Y. Q. Lua, Andree Hartanto
Research Collection School of Social Sciences
Existing research has examined how static or average levels of positive affect (PA) influence purpose in life (PIL), but theoretical work suggests PA fluctuations may also play a role, potentially undermining PIL, especially in older adulthood. To this end, the present study examined the longitudinal association of PA fluctuations on PIL and the moderating role of age among 1,294 midlife and older adults in the United States, over a 9-year period (between 2004 and 2014). Results revealed a conditional effect of PA fluctuations on PIL – particularly among older individuals, and greater PA fluctuations were associated with a poorer sense …
Understanding The Physical Health Associations Of Intra-Individual Variability In Life Satisfaction Among Older Adults, Frosch Y. X. Quek, Kenneth Tan, David Chan
Understanding The Physical Health Associations Of Intra-Individual Variability In Life Satisfaction Among Older Adults, Frosch Y. X. Quek, Kenneth Tan, David Chan
Research Collection School of Social Sciences
It is well established that inter-individual differences in life satisfaction positively predict health, but it is less clear how intra-individual variability in life satisfaction across time could influence health, particularly across individuals with different satisfaction levels. To address this gap, we analysed a large panel dataset from a national sample of 8,674 older adults, aged 50–70 (M = 59, SD = 5.42) in Singapore who completed surveys over 53 consecutive months. Results from multi-level modelling showed an interaction between variability in life satisfaction and life satisfaction mean on physical health. Specifically, there was no association between variability and health among …
3r-Stadte Furs Leben Gestalten [Designing 3r Cities For Life], Lily Kong
3r-Stadte Furs Leben Gestalten [Designing 3r Cities For Life], Lily Kong
Research Collection School of Social Sciences
What does it mean for a city to be resilient in today’s world? In July this year, over 100 city leaders, scholars, and industry partners gathered at City Dialogues in Vienna, co-convened by Singapore Management University and Urban Innovation Vienna, to reflect on the critical question of urban resilience. Held alongside the Mayors Forum of the World Cities Summit 2025, the dialogue provided a timely opportunity to share how cities can navigate complexity—not only in response to disruption, but also in pursuit of sustainable and inclusive futures. Inspired by SMU City Dialogues, I explore here how cities can bounce back, …