Open Access. Powered by Scholars. Published by Universities.®

Digital Commons Network™

Open Access. Powered by Scholars. Published by Universities.®

Discipline
Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 79801 - 79830 of 1739250

Full-Text Articles in Entire DC Network

The Association Between Sexually Transmitted Infections And Pregnancy Outcomes In The Northern Territory, Australia: A Population-Based Cohort Study, Jennifer Dunne, Gizachew A. Tessema, Matthew Legge, Sylvester Dodzi Nyadanu, Aditi Roy, Amanuel T. Gebremedhin, Jacqueline Hendriks, Tricia Reid-Moore, Gavin Pereira Jul 2025

The Association Between Sexually Transmitted Infections And Pregnancy Outcomes In The Northern Territory, Australia: A Population-Based Cohort Study, Jennifer Dunne, Gizachew A. Tessema, Matthew Legge, Sylvester Dodzi Nyadanu, Aditi Roy, Amanuel T. Gebremedhin, Jacqueline Hendriks, Tricia Reid-Moore, Gavin Pereira

Research outputs 2022 to 2026

Background: Often asymptomatic in nature, sexually transmitted infections (STIs) are highly prevalent in women of reproductive ages, leading to adverse perinatal outcomes. This study investigated the association between STIs during pregnancy and the risk of adverse perinatal outcomes using comprehensive population-linked data from the Northern Territory (NT), Australia. Methods: This population-based retrospective cohort study examined births (live births and stillbirths) from the NT Perinatal Data Collection and the NT Notifiable Diseases register from 2005 to 2020. All singleton births (N = 59,465) were included, along with infectious disease notifications of four STIs that occurred during pregnancy: chlamydia, gonorrhoea, trichomoniasis, and …

Go to article

The Response And Recovery Of Carbon And Water Fluxes In Australian Ecosystems Exposed To Severe Drought, C. Stephens, B. Medlyn, L. Williams, J. Knauer, A. Inbar, E. Pendall, S. K. Arndt, J. Beringer, C. M. Ewenz, N. Hinko-Najera, L. B. Hutley, P. Isaac, M. Liddell, W. Meyer, C. E. Moore, J. Cranko Page, R. Silberstein, W. Woodgate Jul 2025

The Response And Recovery Of Carbon And Water Fluxes In Australian Ecosystems Exposed To Severe Drought, C. Stephens, B. Medlyn, L. Williams, J. Knauer, A. Inbar, E. Pendall, S. K. Arndt, J. Beringer, C. M. Ewenz, N. Hinko-Najera, L. B. Hutley, P. Isaac, M. Liddell, W. Meyer, C. E. Moore, J. Cranko Page, R. Silberstein, W. Woodgate

Research outputs 2022 to 2026

Climate change-driven increases in drought risk pose a critical threat to global carbon and water cycles. However, ecosystem-scale responses remain poorly quantified, particularly for severe, multiyear drought events. We addressed this gap by examining ecosystem-scale carbon and water flux sensitivity to the extreme 2018–19 drought in Australia using data from 14 eddy covariance flux sites. The ecosystems span grasslands and semi-arid woodlands to tropical and temperate forests. The driest sites (classed as “grass” and “very dry”) experienced drastic productivity impacts, with a 65% decrease in Gross Primary Productivity (GPP) over 2 years relative to the pre-drought average. However, fluxes in …

Go to article

The Therapeutic Potential Of Butyrate And Lauric Acid In Modulating Glial And Neuronal Activity In Alzheimer’S Disease, Rathnayaka Mudiyanselage Uththara Sachinthanie Senarath, Lotta E. Oikari, Prashant Bharadwaj, Vijay Jayasena, Ralph N. Martins, Wanakulasuriya Mary Ann Dipika Binosha Fernando Jul 2025

The Therapeutic Potential Of Butyrate And Lauric Acid In Modulating Glial And Neuronal Activity In Alzheimer’S Disease, Rathnayaka Mudiyanselage Uththara Sachinthanie Senarath, Lotta E. Oikari, Prashant Bharadwaj, Vijay Jayasena, Ralph N. Martins, Wanakulasuriya Mary Ann Dipika Binosha Fernando

Research outputs 2022 to 2026

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder marked by amyloid-β plaque accumulation, tau tangles, and extensive neuroinflammation. Neuroinflammation, driven by glial cells like microglia and astrocytes, plays a critical role in AD progression. Initially, these cells provide protective functions, such as debris clearance and neurotrophic support. However, as AD progresses, chronic activation of these cells exacerbates inflammation, contributing to synaptic dysfunction, neuronal loss, and cognitive decline. Microglia release pro-inflammatory cytokines and reactive oxygen species (ROS), while astrocytes undergo reactive astrogliosis, further impairing neuronal health. This maladaptive response from glial cells significantly accelerates disease pathology. Current AD treatments primarily aim …

Go to article

Winds Of Change: Charting A Pathway To Ecosystem Monitoring Using Airborne Environmental Dna, Rachel L. Tulloch, Clare I.M. Adams, Matthew A. Barnes, Elizabeth L. Clare, Henrik C. Van De Ven, Andrew Cridge, Francisco Encinas-Viso, Kristen Fernandes, Dianne M. Gleeson, Erin Hill, Anna J.M. Hopkins, Anna M. Kearns, Gracie C. Kroos, Anna J. Macdonald, Francesco Martoni, Angela Mcgaughran, Todd G.B. Mclay, Linda E. Neaves, Paul Nevill, Andrew Pugh, Kye J. Robinson, Fabian Roger, Tracey V. Steinrucken, Mieke Van Der Heyde, Cecilia Villacorta-Rath, Jenny Vivian Jul 2025

Winds Of Change: Charting A Pathway To Ecosystem Monitoring Using Airborne Environmental Dna, Rachel L. Tulloch, Clare I.M. Adams, Matthew A. Barnes, Elizabeth L. Clare, Henrik C. Van De Ven, Andrew Cridge, Francisco Encinas-Viso, Kristen Fernandes, Dianne M. Gleeson, Erin Hill, Anna J.M. Hopkins, Anna M. Kearns, Gracie C. Kroos, Anna J. Macdonald, Francesco Martoni, Angela Mcgaughran, Todd G.B. Mclay, Linda E. Neaves, Paul Nevill, Andrew Pugh, Kye J. Robinson, Fabian Roger, Tracey V. Steinrucken, Mieke Van Der Heyde, Cecilia Villacorta-Rath, Jenny Vivian

Research outputs 2022 to 2026

Airborne environmental DNA (airborne eDNA) analysis leverages the globally ubiquitous medium of air to deliver broad species distribution data and support ecosystem monitoring across diverse environments. As this emerging technology matures, addressing critical challenges and seizing key opportunities will be essential to fully realize its potentially transformative impact. In June 2024, the Southern eDNA Society convened over 100 researchers, industry leaders, and biodiversity management stakeholders in a landmark workshop to evaluate the current state of airborne eDNA research and chart a course for future development. Participants explored opportunities for integrating airborne eDNA into existing monitoring systems, but they unanimously agreed …

Go to article

Amyloid Accumulation, Brain Atrophy, And Cognitive Decline In Emergent Alzheimer's Disease, Ying Xia, Pierrick Bourgeat, Vincent Doré, Jurgen Fripp, Yen Ying Lim, Simon M. Laws, Christopher Fowler, Christopher C. Rowe, Colin L. Masters, Elizabeth J. Coulson, Paul Maruff Jul 2025

Amyloid Accumulation, Brain Atrophy, And Cognitive Decline In Emergent Alzheimer's Disease, Ying Xia, Pierrick Bourgeat, Vincent Doré, Jurgen Fripp, Yen Ying Lim, Simon M. Laws, Christopher Fowler, Christopher C. Rowe, Colin L. Masters, Elizabeth J. Coulson, Paul Maruff

Research outputs 2022 to 2026

Introduction : Emergent Alzheimer's disease (AD) represents a transitional stage where cognitively unimpaired (CU) individuals exhibit subthreshold but increasing amyloid-β (Aβ) levels. The impact of Aβ accumulation on brain volume loss and cognition during this early stage remains unclear. Methods: This retrospective cohort study analyzed data from 408 CU participants who were initially Aβ− (< 15 Centiloids) and followed for up to 15 years. Changes in basal forebrain and hippocampal volume, along with domain-specific cognitive performance, were compared between those who progressed to Aβ+ (≥20 Centiloids) and those who remained Aβ−. Results: Sixty-five CU participants progressed to Aβ+, indicating emergent AD, and showed faster Aβ accumulation and subtle memory decline. However, no significant differences in rate of BF and hippocampal atrophy were observed between groups. Discussion: The results suggest that during this emergent phase of AD, Aβ accumulation is associated with episodic memory loss, in the absence of detectable accelerated brain atrophy. Highlights: Identified cognitively unimpaired individuals in the emergent stage of Alzheimer's disease (AD). Emergent AD exhibits a greater rate of amyloid-β (Aβ) accumulation. No accelerated volume loss detected in the basal forebrain or hippocampus. Emergent AD is also associated with a subtle decline in memory. Early Aβ accumulation may impair cognitive function before structural atrophy.

Go to article

Psychological Distress Among Suicide Exposed And Subsequently Bereaved Carers, Myfanwy Maple, Navjot Bhullar, Marie Reid, Sarah Wayland Jul 2025

Psychological Distress Among Suicide Exposed And Subsequently Bereaved Carers, Myfanwy Maple, Navjot Bhullar, Marie Reid, Sarah Wayland

Research outputs 2022 to 2026

People bereaved by suicide are at risk of developing significant mental health issues, including the risk of self-harm. To understand suicide exposure and psychological factors contributing to these adverse outcomes, survey data from 92 participants (Meanage = 53.50 years, SD = 14.5, female = 83.7%) who had provided care to someone who subsequently died by suicide were used. Participants responded to questions relating to suicide exposure, bereavement, suicidal ideation and psychological distress. A hierarchical regression analysis found that the perceived impact of suicide death and one's own suicidal ideation explained 46% variance in psychological distress. Suicide bereaved carers are highly …

Go to article

Climate Fiction Of The Anthropocene, Joseph Paul Ferguson, Cassandra Tytler, Peta J. White, Susan Oliver Jul 2025

Climate Fiction Of The Anthropocene, Joseph Paul Ferguson, Cassandra Tytler, Peta J. White, Susan Oliver

Research outputs 2022 to 2026

No abstract provided.

Go to article

Post-Anthropocene Provocations: Human-Sized Scholars’ Rocks, Cronenbergian Flesh Caves And Zen Garden Design Cues For A Future Proof Re(Image)Ining, Brenton Rossow, Darren Tynan Jul 2025

Post-Anthropocene Provocations: Human-Sized Scholars’ Rocks, Cronenbergian Flesh Caves And Zen Garden Design Cues For A Future Proof Re(Image)Ining, Brenton Rossow, Darren Tynan

Research outputs 2022 to 2026

This research explores concertinaing past, present and future interventional creative and pedagogical practices to address the challenges of the Post-Anthropocene era. We argue that the Post-Anthropocene is marked by biotechnological entanglements, environmental violence and digital overstimulation. The discussions herein critique a hyperattentive achievement society characterised by a scattering of attention, a near-constant screen-mediated stream of digital material and tasks and the commodification of leisure time. Enlisting Byung-Chul Han’s concept of hyperattention and themes and motifs from David Cronenberg’s films, the authors propose “FUTURE PROOF re(image)ining” as a collaborative Cli-Fi narrative concept. The project reimagines objects from an initial art installation …

Go to article

The B2biers System: A Content-Based Perspective On Maximizing Influence And Subscription In Social Networks, Konstantinos Theocharidis, Hady Wirawan Lauw Jul 2025

The B2biers System: A Content-Based Perspective On Maximizing Influence And Subscription In Social Networks, Konstantinos Theocharidis, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

The popular problem of Influence Maximization (IM) asks for the k users who can maximize the influence of a fixed post in a social network. In contrast, the problem of Content- Aware Influence Maximization (CAIM) asks for the k features to form a viral tunable post in a social network starting its diffusion from a fixed set of initial adopters. CAIM paves the way for a number of novel problems to be studied that altogether can lead to the development of a system that would be valuable for advertisers who manage social network pages. This holds since features (brands) in …

Go to article

An On-The-Fly Synthesis Framework For Ltl Over Finite Traces, Shengping Xiao, Yongkang Li, Shufang Zhu, Jun Sun, Jianwen Li, Geguang Pu, Moshe Vardi Jul 2025

An On-The-Fly Synthesis Framework For Ltl Over Finite Traces, Shengping Xiao, Yongkang Li, Shufang Zhu, Jun Sun, Jianwen Li, Geguang Pu, Moshe Vardi

Research Collection School Of Computing and Information Systems

We present an on-the-fly synthesis framework for Linear Temporal Logic over finite traces (LTLf) based on top-down deterministic automata construction. Existing approaches rely on constructing a complete Deterministic Finite Automaton (DFA) corresponding to the LTLf specification, a process with doubly exponential complexity relative to formula size in the worst case. In this case, the synthesis cannot be conducted until the entire DFA is constructed. This inefficiency is the main bottleneck of existing approaches. To address this challenge, we first present a method for converting LTLf into Transition-based DFA (TDFA) by directly leveraging LTLf semantics, incorporating intermediate results as direct components …

Go to article

Efficient Prompt Tuning For Hierarchical Ingredient Recognition, Yinxuan Gui, Bin Zhu, Jingjing Chen, Chong-Wah Ngo Jul 2025

Efficient Prompt Tuning For Hierarchical Ingredient Recognition, Yinxuan Gui, Bin Zhu, Jingjing Chen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Fine-grained ingredient recognition presents a significant challenge due to the diverse appearances of ingredients, resulting from different cutting and cooking methods. While existing approaches have shown promising results, they still require extensive training costs and focus solely on fine-grained ingredient recognition. In this paper, we address these limitations by introducing an efficient prompt-tuning framework that adapts pretrained visual-language models (VLMs), such as CLIP, to the ingredient recognition task without requiring full model finetuning. Additionally, we introduce three-level ingredient hierarchies to enhance both training performance and evaluation robustness. Specifically, we propose a hierarchical ingredient recognition task, designed to evaluate model performance …

Go to article

Dual-Target Disjointed Cross-Domain Recommendation Mediated Via Latent User Preferences, Dinh Hieu Do, Hady Wirawan Lauw Jul 2025

Dual-Target Disjointed Cross-Domain Recommendation Mediated Via Latent User Preferences, Dinh Hieu Do, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Users often navigate multiple platforms online, each characterized by its own set of scarce data. Recommender systems face a significant challenge in such fragmented environments. This paper proposes a novel approach to enhance recommendation systems by leveraging connections across distinct yet conceptually similar datasets from multiple platforms. We introduce a unique scenario of dual-target overlapping-free cross-platform recommendation, presenting a bridging mechanism to mutually improve across platforms and learn latent user preferences. Our approach addresses the data sparsity prevalent in each platform and enhances recommendation quality by harnessing redundant, rich, and similar domain data. Experiments validate the effectiveness of our method, …

Go to article

Advancing Food Nutrition Estimation Via Visual-Ingredient Feature Fusion, Huiyan Qi, Bin Zhu, Chong-Wah Ngo, Jingjing Chen, Ee-Peng Lim Jul 2025

Advancing Food Nutrition Estimation Via Visual-Ingredient Feature Fusion, Huiyan Qi, Bin Zhu, Chong-Wah Ngo, Jingjing Chen, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Nutrition estimation is an important component of promoting healthy eating and mitigating diet-related health risks. Despite advances in tasks such as food classification and ingredient recognition, progress in nutrition estimation is limited due to the lack of datasets with nutritional annotations. To address this issue, we introduce FastFood, a dataset with 84,446 images across 908 fast food categories, featuring ingredient and nutritional annotations. In addition, we propose a new model-agnostic Visual-Ingredient Feature Fusion (VIF2 ) method to enhance nutrition estimation by integrating visual and ingredient features. Ingredient robustness is improved through synonym replacement and resampling strategies during training. The ingredient-aware …

Go to article

Hps: Hard Preference Sampling For Human Preference Alignment, Xiandong Zou, Wanyu Lin, Yuchen Li, Pan Zhou Jul 2025

Hps: Hard Preference Sampling For Human Preference Alignment, Xiandong Zou, Wanyu Lin, Yuchen Li, Pan Zhou

Research Collection School Of Computing and Information Systems

Aligning Large Language Model (LLM) responses with human preferences is vital for building safe and controllable AI systems. While preference optimization methods based on PlackettLuce (PL) and Bradley-Terry (BT) models have shown promise, they face challenges such as poor handling of harmful content, inefficient use of dispreferred responses, and, specifically for PL, high computational costs. To address these issues, we propose Hard Preference Sampling (HPS), a novel framework for robust and efficient human preference alignment. HPS introduces a training loss that prioritizes the most preferred response while rejecting all dispreferred and harmful ones. It emphasizes “hard” dispreferred responses — those …

Go to article

An Incentive Mechanism For Privacy Preserved Data Trading With Verifiable Data Disturbance, Man Zhang, Xinghua Li, Bin Luo, Yanbing Ren, Yinbin Miao, Ximeng Liu, Robert H. Deng Jul 2025

An Incentive Mechanism For Privacy Preserved Data Trading With Verifiable Data Disturbance, Man Zhang, Xinghua Li, Bin Luo, Yanbing Ren, Yinbin Miao, Ximeng Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

To motivate data owners’ (DOs’) trading willingness, the existing incentive mechanisms allow DOs to independently disturb data following data consumer's (DC’s) availability requirement. However, they cannot motivate DOs’ honest disturbance, which is attributed to DOs’ independent disturbance without any supervision. Thus, we implement an incentive mechanism for privacy preserved data trading with verifiable data disturbance where an honest-but-curious disturbance generator (DG) is additionally introduced to supervise DOs’ local disturbance and assist disturbance verification between DOs and DC. Specifically, DG generates the disturbance strategies and secretly distributes to DOs following private information retrieval, guaranteeing DOs's local disturbance's privacy and verifiability with …

Go to article

Sanitizable Cross-Domain Access Control With Policy-Driven Dynamic Authorization, Jianfei Sun, Guowen Xu, Hongwei Li, Tianwei Zhang, Cong Wu, Xuehuan Yang, Robert H. Deng Jul 2025

Sanitizable Cross-Domain Access Control With Policy-Driven Dynamic Authorization, Jianfei Sun, Guowen Xu, Hongwei Li, Tianwei Zhang, Cong Wu, Xuehuan Yang, Robert H. Deng

Research Collection School Of Computing and Information Systems

The increasing demand for secure and efficient data sharing has underscored the importance of developing robust cryptographic schemes. However, many existing endeavors have overlooked the following critical issues: (1) unauthorized access resulting from malicious information leakage by senders; (2) absence of constraints on write and read permissions for participants; (3) and inflexibility of strategies to dynamically designate ciphertexts to multiple recipients. In this paper, we present SCPA, a cross-domain access control scheme imbued with sanitization features and propelled by policy-driven dynamic authorization, tailored for cloud-based data sharing. This scheme not only facilitates access controls, including regulations for no-read and no-write …

Go to article

Understanding The Bad Development Practices Of Android Custom Permissions In The Wild, Xiaohan Zhang, Zhiyuan Yu, Xinghua Li, Cen Zhang, Cong Sun, Ning Zhang, Robert H. Deng Jul 2025

Understanding The Bad Development Practices Of Android Custom Permissions In The Wild, Xiaohan Zhang, Zhiyuan Yu, Xinghua Li, Cen Zhang, Cong Sun, Ning Zhang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Android system provides application developers with the ability to define custom permissions, which serve to moderate the sharing of resources and interactions with other applications. However, poor development practices of developers can render the permission mechanism ineffective, weakening the system protection. This paper presents a comprehensive examination of the problematic practices surrounding custom permissions employed by developers, referred to as Bad Practices of Custom Permissions (BPCP issues). To accomplish this, we conducted an empirical study and identified nine common BPCP issue patterns that can lead to various adverse consequences, such as installation failures, crashes, or even component hijacking. To automatically …

Go to article

Unambiguous Granularity Distillation For Asymmetric Image Retrieval, Hongrui Zhang, Yi Xie, Haoquan Zhang, Cheng Xu, Xuandi Luo, Donglei Chen, Xuemiao Xu, Huaidong Zhang, Pheng Ann Heng, Shengfeng He Jul 2025

Unambiguous Granularity Distillation For Asymmetric Image Retrieval, Hongrui Zhang, Yi Xie, Haoquan Zhang, Cheng Xu, Xuandi Luo, Donglei Chen, Xuemiao Xu, Huaidong Zhang, Pheng Ann Heng, Shengfeng He

Research Collection School Of Computing and Information Systems

Previous asymmetric image retrieval methods based on knowledge distillation have primarily focused on aligning the global features of two networks to transfer global semantic information from the gallery network to the query network. However, these methods often fail to effectively transfer local semantic information, limiting the fine-grained alignment of feature representation spaces between the two networks. To overcome this limitation, we propose a novel approach called Layered-Granularity Localized Distillation (GranDist). GranDist constructs layered feature representations that balance the richness of contextual information with the granularity of local features. As we progress through the layers, the contextual information becomes more detailed, …

Go to article

An Efficient Diffusion-Based Non-Autoregressive Solver For Traveling Salesman Problem, Mingzhao Wang, You Zhou, Zhiguang Cao, Yubin Xiao, Xuan Wu, Wei Pang, Yuan Jiang, Hui Yang, Peng Zhao, Yuanshu Li Jul 2025

An Efficient Diffusion-Based Non-Autoregressive Solver For Traveling Salesman Problem, Mingzhao Wang, You Zhou, Zhiguang Cao, Yubin Xiao, Xuan Wu, Wei Pang, Yuan Jiang, Hui Yang, Peng Zhao, Yuanshu Li

Research Collection School Of Computing and Information Systems

Recent advances in neural models have shown considerable promise in solving Traveling Salesman Problems (TSPs) without relying on much hand-crafted engineering. However, while non-autoregressive (NAR) approaches benefit from faster inference through parallelism, they typically deliver solutions of inferior quality compared to autoregressive ones. To enhance the solution quality while maintaining fast inference, we propose DEITSP, a diffusion model with efficient iterations tailored for TSP that operates in a NAR manner. Firstly, we introduce a one-step diffusion model that integrates the controlled discrete noise addition process with self-consistency enhancement, enabling optimal solution prediction through simultaneous denoising of multiple solutions. Secondly, we …

Go to article

Milpbench: A Large-Scale Benchmark Test Suite For Mixed Integer Linear Programming Problems, Huigen Ye, Yaoyang Cheng, Hua Xu, Zhiguang Cao, Hanzhang Qin Jul 2025

Milpbench: A Large-Scale Benchmark Test Suite For Mixed Integer Linear Programming Problems, Huigen Ye, Yaoyang Cheng, Hua Xu, Zhiguang Cao, Hanzhang Qin

Research Collection School Of Computing and Information Systems

Mixed-integer linear programming (MILP) is a cornerstone of optimization with applications across numerous domains. However, the development and evaluation of MILP-solving algorithms are hindered by existing benchmark datasets, which are often limited in scale, lack diversity, and are poorly structured, making them inadequate for systematic testing across different solving approaches, especially for machine learning (ML)-based methods. To address these issues, we introduce MILPBench, a large-scale benchmark suite comprising 100,000 MILP instances organized into 60 well-categorized classes. Using structural properties and embedding similarity metrics, we developed a novel classification framework to ensure both intra-class homogeneity and inter-class diversity. In addition to …

Go to article

Collaboration With Dynamic Open Ad Hoc Team Via Team State Modelling, Jing Sun, Cong Zhang, Zhiguang Cao Jul 2025

Collaboration With Dynamic Open Ad Hoc Team Via Team State Modelling, Jing Sun, Cong Zhang, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Open ad hoc teamwork presents the challenging problem of designing an autonomous agent that can rapidly adapt to collaborate with teammates without prior coordination in an open environment. Existing methods primarily rely on fixed, predefined teammate types, overlooking the fact that teammates may change dynamically. To address this limitation, we propose a novel reinforcement learning approach, the Open Online Teammate Adaptation Framework (Open-OTAF), which enables a controlled agent to collaborate with dynamic teammates in open ad hoc environments. To achieve this, the controlled agent employs a dual teamwork situation inference model to capture the current teamwork state, facilitating decision-making under …

Go to article

A Mixed-Curvature Based Pre-Training Paradigm For Multi-Task Vehicle Routing Solver, Suyu Liu, Zhiguang Cao, Shanshan Feng, Yew-Soon Ong Jul 2025

A Mixed-Curvature Based Pre-Training Paradigm For Multi-Task Vehicle Routing Solver, Suyu Liu, Zhiguang Cao, Shanshan Feng, Yew-Soon Ong

Research Collection School Of Computing and Information Systems

Solving various types of vehicle routing problems (VRPs) using a unified neural solver has garnered significant attentions in recent years. Despite their effectiveness, existing neural multi-task solvers often fail to account for the geometric structures inherent in different tasks, which may result in suboptimal performance. To address this limitation, we propose a curvature-aware pre-training framework. Specifically, we leverage mixed-curvature spaces during the feature fusion stage, encouraging the model to capture the underlying geometric properties of each instance. Through extensive experiments, we evaluate the proposed pre-training strategy on existing neural multi-task solvers across a variety of testing scenarios. The results demonstrate …

Go to article

Empowering Weight Loss: A Pragmatic Randomized Controlled Trial Of A Theory-Driven Self-Regulation Mobile App For Young Adults With Excess Body Weight, H. S. J. Chew, J. W. Ngooi, R. C. Du, P. Z. Chan, M. Jansson, B. Zhu, Y. Cao, Chong-Wah Ngo, R. Foo, A. Shabbir, D. Ho, N. Sevdalis, K. Y. Ngiam Jul 2025

Empowering Weight Loss: A Pragmatic Randomized Controlled Trial Of A Theory-Driven Self-Regulation Mobile App For Young Adults With Excess Body Weight, H. S. J. Chew, J. W. Ngooi, R. C. Du, P. Z. Chan, M. Jansson, B. Zhu, Y. Cao, Chong-Wah Ngo, R. Foo, A. Shabbir, D. Ho, N. Sevdalis, K. Y. Ngiam

Research Collection School Of Computing and Information Systems

Background/Introduction: Obesity is projected to affect more than half of the global population by 2035, posing significant health and economic challenges. While lifestyle modification is considered a cornerstone of weight management, its effectiveness often relies on substantial support systems. Purpose: This study aimed to evaluate the effectiveness of a 12-week, standalone Temporal Self-Regulation Theory (TST)-based weight loss mobile application, which integrates self-regulation techniques, food logging, and dietary nudging, in promoting weight loss among young adults with excess body weight. Methods: A two-arm, parallel-group, 1:1 randomized controlled trial was conducted, adhering to the CONSORT-Outcomes 2022 Extension guidelines. Participants completed a face-to-face …

Go to article

O-Mapl: Offline Multi-Agent Preference Learning, The Viet Bui, Tien Mai, Hong Thanh Nguyen Jul 2025

O-Mapl: Offline Multi-Agent Preference Learning, The Viet Bui, Tien Mai, Hong Thanh Nguyen

Research Collection School Of Computing and Information Systems

Inferring reward functions from demonstrations is a key challenge in reinforcement learning (RL), particularly in multi-agent RL (MARL). The large joint state-action spaces and intricate inter-agent interactions in MARL make inferring the joint reward function especially challenging. While prior studies in single-agent settings have explored ways to recover reward functions and expert policies from human preference feedback, such studies in MARL remain limited. Existing methods typically combine two separate stages, supervised reward learning, and standard MARL algorithms, leading to unstable training processes. In this work, we exploit the inherent connection between reward functions and Q functions in cooperative MARL to …

Go to article

Rustmap: Towards Project-Scale C-To-Rust Migration Via Program Analysis And Llm, Xuemeng Cai, Jiakun Liu, Xiping Huang, Yijun Yu, Haitao Wu, Chunmiao Li, Bo Wang, Imam Nur Bani Yusuf, Lingxiao Jiang Jul 2025

Rustmap: Towards Project-Scale C-To-Rust Migration Via Program Analysis And Llm, Xuemeng Cai, Jiakun Liu, Xiping Huang, Yijun Yu, Haitao Wu, Chunmiao Li, Bo Wang, Imam Nur Bani Yusuf, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Migrating existing C programs into Rust is increasingly desired, as Rust offers superior memory safety while maintaining C’s high performance. Existing automated translation tools, such as C2Rust, may rely too much on syntactic, template-based translation and generate unsafe Rust code that is hard for human developers to read, maintain, or even compile. More semantic-aware translation that produces safer, idiomatic, and runnable Rust code is much needed. This paper introduces a novel dependency-guided and large language model (LLM)-based C-to-Rust translation approach, RustMap, based on three key ideas: (1) Utilize LLM’s capabilities to produce idiomatic Rust code from given small pieces of …

Go to article

Retrieval Augmented Generation For Dynamic Graph Modeling, Yuxia Wu, Lizi Liao, Yuan Fang Jul 2025

Retrieval Augmented Generation For Dynamic Graph Modeling, Yuxia Wu, Lizi Liao, Yuan Fang

Research Collection School Of Computing and Information Systems

Modeling dynamic graphs, such as those found in social networks, recommendation systems, and e-commerce platforms, is crucial for capturing evolving relationships and delivering relevant insights over time. Traditional approaches primarily rely on graph neural networks with temporal components or sequence generation models, which often focus narrowly on the historical context of target nodes. This limitation restricts the ability to adapt to new and emerging patterns in dynamic graphs. To address this challenge, we propose a novel framework, Retrieval-Augmented Generation for Dy namic Graph modeling (RAG4DyG ), which enhances dynamic graph predictions by incorporating contextually and temporally relevant examples from broader …

Go to article

Cradle: Empowering Foundation Agents Towards General Computer Control, Weihao Tan, Et. Al. Jul 2025

Cradle: Empowering Foundation Agents Towards General Computer Control, Weihao Tan, Et. Al.

Research Collection School Of Computing and Information Systems

Despite their success in specific scenarios, existing foundation agents still struggle to generalize across various virtual scenarios, mainly due to the dramatically different encapsulations of environments with manually designed observation and action spaces. To handle this issue, we propose the General Computer Control (GCC) setting to restrict foundation agents to interact with software through the most unified and standardized interface, i.e., using screenshots as input and keyboard and mouse actions as output. We introduce Cradle, a modular and flexible LMM-powered framework, as a preliminary attempt towards GCC. Enhanced by six key modules, Information Gathering, Self-Reflection, Task Inference, Skill Curation, Action …

Go to article

Quantum Technologies In Decentralisation, Paul Robert Griffin, Rudy Raymond, Tsuyoshi Idé Jul 2025

Quantum Technologies In Decentralisation, Paul Robert Griffin, Rudy Raymond, Tsuyoshi Idé

Research Collection School Of Computing and Information Systems

Quantum technologies, rooted in the manipulation of quantum information, are revolutionizing computing and networking domains. Their impact on blockchains and decentralized systems is twofold. While much attention has been given to the potential of quantum computing to attack blockchains, these advanced technologies also offer avenues for strengthening and optimizing them. This chapter delves into the intricacies of quantum technologies, from the foundational concepts of qubits, quantum gates, and quantum networks to their implications for blockchains. We explore both the vulnerabilities of blockchains in a quantum-dominant era and the promising solutions quantum technologies provide, culminating in a use case examining their …

Go to article

Generalization Analysis For Supervised Contrastive Representation Learning Under Non‑Iid Settings, Minh Hieu Nong, Antoine Ledent Jul 2025

Generalization Analysis For Supervised Contrastive Representation Learning Under Non‑Iid Settings, Minh Hieu Nong, Antoine Ledent

Research Collection School Of Computing and Information Systems

Contrastive Representation Learning (CRL) has achieved impressive success in various domains in recent years. Nevertheless, the theoretical understanding of the generalization behavior of CRL has remained limited. Moreover, to the best of our knowledge, the current literature only analyzes generalization bounds under the assumption that the data tuples used for contrastive learning are independently and identically distributed. However, in practice, we are often limited to a fixed pool of reusable labeled data points, making it inevitable to recycle data across tuples to create sufficiently large datasets. Therefore, the tuple-wise independence condition imposed by previous works is invalidated. In this paper, …

Go to article

Finir: The 2nd Workshop On Financial Information Retrieval In The Era Of Generative Ai, Fengbin Zhu, Yunshan Ma, Fuli Feng, Chao Wang, Huanbo Luan, Guangnan Ye, Shuo Zhang, Dhagash Mehta, Pingping Chen, Bing Xiang, Tat‑Seng Chua Jul 2025

Finir: The 2nd Workshop On Financial Information Retrieval In The Era Of Generative Ai, Fengbin Zhu, Yunshan Ma, Fuli Feng, Chao Wang, Huanbo Luan, Guangnan Ye, Shuo Zhang, Dhagash Mehta, Pingping Chen, Bing Xiang, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Recent advancements in Generative AI, such as Large Language Models (LLMs), have demonstrated remarkable success across various general tasks. Extensive studies have explored leveraging generative models in finance, but significant challenges persist. This half-day workshop explores potential approaches and research directions to address these challenges by equipping generative models with advanced Information Retrieval (IR) models. Specifically, this workshop seeks to provide a platform for discussing innovative ideas that facilitate the advancement of IR technology to enrich generative models in finance from four key perspectives: (i) financial IR techniques (ii) financial IR benchmarking and evaluation (iii) financial systems and agents/assistants (iv) …

Go to article

First Prev Next Last