Digital Commons Network^™

Assessment Of Frailty In Patients With Heart Failure: A New Heart Failure Frailty Score Developed By Delphi Consensus, Cristiana Vitale, Emmanuelle Berthelot, Andrew J S Coats, Hill Loreena, Nancy M Albert, Michal Tkaczyszyn, Stamatis Adamopoulos, Lisa Anderson, Markus S Anker, Stefan D Anker, Derek Bell, Tuvia Ben-Gal, Vasiliki Bistola, Biykem Bozkurt, Poppy Brooks, Miguel Camafort, Juan Jesus Carrero, Ovidiu Chioncel, Dong-Ju Choi, Wook-Jin Chung, Wolfram Doehner, Daniel Fernández-Bergés, Roberto Ferrari, Mona Fiuzat, Juan Esteban Gomez-Mesa, Finn Gustafsson, Ewa Jankowska, Seok-Min Kang, Koichiro Kinugawa, Kamlesh Khunti, F D Richard Hobbs, Christopher Lee, Yuri Lopatin, Matthew Maddocks, Giuseppe Maltese, Elena Marques-Sule, Yuya Matsue, Òscar Miró, Brenda Moura, Massimo Piepoli, Piotr Ponikowski, Giovanni Pulignano, Amina Rakisheva, Robin Ray, Angela Sciacqua, Petar Seferovic, Trinidad Sentandreu-Mañó, Shirley Sze, Alan Sinclair, Anna Strömberg, Olga Theou, Hiroyuki Tsutsui, Izabella Uchmanowicz, Maria Teresa Vidan, Maurizio Volterrani, Stephan Von Haehling, Byungsu Yoo, Jian Zhang, Yuhui Zhang, Marco Metra, Giuseppe Massimo Claudio Rosano

Faculty, Staff and Students Publications

Aims: The Heart Failure Frailty Score (HFFS) is a novel, multidimensional tool to assess frailty in patients with heart failure (HF). It has been developed to overcome limitations of existing frailty assessment tools while being practical for clinical use. The HFFS reflects the concept of frailty as a multidimensional, dynamic and potentially reversible state, which increases vulnerability to stressors and risk of poor outcomes in patients with HF.

Methods and results: The HFFS was developed through a Delphi consensus process involving 54 international experts. This approach involved iterative rounds of questionnaires and interviews, where a panel of experts provided their …

Cardiovascular And Renal Treatment In Heart Failure Patients With Hyperkalemia Or High Risk Of Hyperkalemia: Rationale And Design Of The Care-Hk In Hf Registry, Stephen J Greene, Michael Böhm, Biykem Bozkurt, Javed Butler, John G F Cleland, Andrew J S Coats, Nihar R Desai, Diederick E Grobbee, Ellie Kelepouris, Fausto Pinto, Giuseppe Rosano, Isabelle Morin, Peter Szecsödy, Solenn Fabien, Sandra Waechter, Maria G Crespo-Leiro, Martin Hülsmann, Tibor Kempf, Otmar Pfister, Anne-Catherine Pouleur, Andrew J Sauer, Manish Saxena, Martin Schulz, Maurizio Volterrani, Stefan D Anker, Mikhail N Kosiborod

Faculty, Staff and Students Publications

Background: Despite guideline recommendations, many patients with heart failure (HF) do not receive target dosages of renin-angiotensin-aldosterone system inhibitors (RAASis) in clinical practice due, in part, to concerns about hyperkalemia (HK).

Methods and results: This noninterventional, multinational, multicenter registry (NCT04864795; 111 sites in Europe and the USA) enrolled 2558 eligible adults with chronic HF (mostly with reduced ejection fraction [HFrEF]). Eligibility criteria included use of angiotensin-converting-enzyme inhibitor/angiotensin-II receptor blocker/angiotensin-receptor-neprilysin inhibitor, being a candidate for or treatment with a mineralocorticoid receptor antagonist, and increased risk of HK (eg, current serum potassium > 5.0 mmol/L), history of HK in the previous …

Anti-Mullerian Hormone (Amh) Protects Ovarian Follicle Loss By Downregulating Granulosa Cell Function In In Vitro And In Vivo Models, Laura Detti, Michael C Mari, Michael P Diamond, Ghassan M Saed

Faculty, Staff and Students Publications

Purpose: AMH inhibits hormone production in luteinized granulosa cells (GCs) and stalls ovarian follicle development in vitro and in vivo. We sought to confirm AMH's mechanism of action through SMAD activation and investigate AMH inhibition of follicle development and function, in vitro and in vivo.

Methods: A primary culture of GCs isolated from follicular fluid was used, and cells were treated with recombinant AMH (rAMH) or placebo for 24 h. For the mouse model, 18-weeks old C57BL female mice were either euthanized at the beginning or treated with rAMH or normal saline for 3 weeks. Primordial (PDF), primary follicle (PRF), …

Recreational Outdoor Injury And Mortality In Texas State Parks Between 2012 And 2021, Mohammad I Hirzallah, Ebubechi K Adindu, Julliet C Ogu, Tania Allison, May Kamleh

Faculty, Staff and Students Publications

Introduction

Methods

Efficient Bilateral Trade With Interdependent Values: The Use Of Two-Stage Mechanisms, Takashi Kunimoto, Cuiling Zhang

Research Collection School Of Economics

Efficient, voluntary bilateral trades are generally not implementable in an interdependent values environment with two-sided asymmetric information. To obtain more positive results, we consider two-stage mechanisms in which (i) the outcome (e.g., allocation of the goods) is determined first; (ii) the agents partially learn the state via their own outcome-decision payoffs; and (iii) transfers are finally made. We propose the approximate shoot-the-liar (AS) mechanism and identify Assumption 1, under which “approximately” efficient, voluntary trades are implementable. We further illustrate the permissiveness of the assumption, and characterize it in terms of the nature of interdependence in valuation functions—delineating the boundary between …

Do Government Benefit Programs Reduce Child Labor?: Causal Evidence From The Midday Meal Scheme In India, Vardaan Chawla, Tomoki Fujii, Arpita Khanna, Ray Rohan

Research Collection School Of Economics

This study evaluates the impact of the midday meal program in India on the prevalence of child labor for upper primary school going children. The staggered implementation of the largest school feeding program in the world across states provides exogenous variation in exposure. Using the nationally representative National Sample Survey Office (NSSO) data, we find that exposure to the program reduced prevalence of child labor by 47 percent relative to children in never-treated states, with no significant difference in impact between boys and girls. The effects were primarily driven by a decline in household labor supply, especially performance of household …

Insolvency Law As A Catalyst For Growth, Aurelio Gurrea-Martinez

Research Collection Yong Pung How School Of Law

Insolvency law plays an essential role in the real economy. From an ex ante perspective, that is, before a situation of insolvency arises, the design of insolvency law affects how debtors and creditors make decisions. For instance, if creditors believe that an insolvency system does not protect their rights or it does not help them maximize their recoveries if their debtors become insolvent, they will rationally become reluctant to extend credit. Therefore, an unattractive insolvency regime for creditors will harm firms’ access to finance and the promotion of economic growth. Similarly, an insolvency system that severely punishes honest but unfortunate …

Techno-Geopolitics And Semi-Conductor Chokepoints: Beyond The Us-China Wto Dispute, Han-Wei Liu, Ching-Fu Lin

Research Collection Yong Pung How School Of Law

Much ink has been spilled on the US-China tech and trade war, particularly the role of US export controls in containing China and preserving America’s tech dominance. Less attention, however, has been paid to other critical nodes in the global semiconductor supply chain – Taiwan, South Korea, Japan, and the Netherlands – which we coin as ‘chokepoint economies’. Taiwan alone produces over 90% of the world’s most advanced chips, vital to the digital age and the future of AI in the Indo-Pacific and beyond. Using Taiwan as a case study, this paper shows how chokepoint economies navigate competing pressures amid …

Building Narratives And Probing Concepts: Preparing Materials For Co-Design With Autistic Livestreamers, Terrance Mok, Tyson Hartley, Anthony Tang, Adam Mccrimmon, Lora Oehlberg

Research Collection School Of Computing and Information Systems

Based on ten semi-structured interviews with autistic Twitch streamers, we introduce a series of scenario-based design narratives coupled with technology design concepts as a starting point for co-design discussion about autistic streaming. This work builds on prior thematic analysis of the unique intersection between autism and livestreaming. Our user-centered scenarios highlight the needs, goals, and challenges of autistic individuals in livestreaming contexts. By using evocative narratives, the scenarios serve to facilitate empathy and deeper engagement with the needs of autistic users, and help facilitate and support co-creative dialogues and discussions about new technology designs. We contribute this starting point for …

Meta-Learning Hyperparameters For Foundation Model Adaptation In Remote-Sensing Imagery, Zichen Tian, Yaoyao Liu, Qianru Sun

Research Collection School Of Computing and Information Systems

Training large foundation models of remote-sensing (RS) images is almost impossible due to the limited and long-tailed data problems. Fine-tuning natural image pre-trained models on RS images is a straightforward solution. To reduce computational costs and improve performance on tail classes, existing methods apply parameter-efficient fine-tuning (PEFT) techniques, such as LoRA and AdaptFormer. However, we observe that fixed hyperparameters -- such as intra-layer positions, layer depth, and scaling factors, can considerably hinder PEFT performance, as fine-tuning on RS images proves highly sensitive to these settings. To address this, we propose MetaPEFT, a method incorporating adaptive scalers that dynamically adjust module …

Chatgpt’S Performance Evaluation In Spreadsheets Modeling To Inform Assessments Redesign, Michelle L. F. Cheong

Research Collection School Of Computing and Information Systems

Background: Increasingly, students are using ChatGPT to assist them in learning and even completing their assessments, raising concerns of academic integrity and loss of critical thinking skills. Many articles suggested educators to redesign assessments which are more “Generative-AI-resistant” and to focus on assessing students on higher order thinking skills. However, there is a lack of articles that attempt to quantify assessments at different cognitive levels to provide empirical study insights on ChatGPT’s performance at different levels, which will affect how educators redesign their assessments.Objectives: Educators need new information on how well ChatGPT performs to redesign future assessments to assess their …

Driving Financial Inclusion Through Central Bank Digital Currencies: A Methodology For Cbdc Implementation, Heng Wang

Research Collection Yong Pung How School Of Law

This publication outlines a methodology for the design, testing, and implementation of central bank digital currencies (CBDCs) to advance financial inclusion. As an increasing number of central banks around the world explore this novel form of financial infrastructure, this paper focuses on the potential of CBDCs to address key access and price barriers in developing and emerging economies. CBDCs, especially retail ones, can offer unique opportunities for enhancing financial services accessibility, affordability, and usability for unserved and underserved populations, if implemented responsibly through a structured, inclusive and user-centric approach.

Ai Governance And Algorithmic Auditing In Financial Institutions: Lessons From Singapore, Nydia Remolina Leon

Research Collection Yong Pung How School Of Law

This paper examines the role of algorithmic auditing as a mechanism for responsible AI development and deployment in the financial sector, with a particular focus on Singapore’s regulatory and institutional initiatives. Against the backdrop of fragmented global artificial intelligence (AI) governance frameworks, the study analyses how Singapore has developed operational tools — such as the Veritas Toolkit, AI Verify, Project Moonshot and Project Mindforge — that go beyond abstract ethical principles to provide measurable, use-case-specific standards for auditing AI systems. These initiatives contribute to standardising audit practices, enhancing transparency and bridging trust gaps between financial institutions, regulators and stakeholders. The …

Treaties Establishing Dispute Settlement Frameworks, N. Jansen Calamita, Stefanie Schacherer, Facundo Perez-Aznar, Aikaterini Florou

Research Collection Yong Pung How School Of Law

The purpose of the present report is to provide an overview and analysis of the measures that States adopt domestically to implement treaties that establish mechanisms for the resolution of international disputes. The report’s scope subsumes both treaties that include dispute settlement mechanisms to resolve disputes concerning the substantive obligations contained therein as well as treaties that establish frameworks for dispute resolution simpliciter which may be used to resolve disputes arising under a variety of substantive treaties (collectively “international dispute settlement mechanism” or “IDSM” treaties). The approach of the research contained within this report has been to gather information about …

Outperforming The Best With Minimal Effort: Algorithm Selection For Constrained Multi-Objective Optimization, Mustafa Misir, Aldy Gunawan

Research Collection School Of Computing and Information Systems

The present study performs algorithm selection on a suite of optimization algorithms targeting the constrained multi-objective optimization problems. The idea is to utilize the existing, relevant algorithmic experience in the literature to deliver an improved solver with limited effort. The reason being that algorithm development, in general, is a challenging and time-consuming process, especially with the goal of outperforming the existing methods from varying perspectives such as performance, speed, and robustness. Concerning the multi-objective optimization problems, the required development efforts happen to be even harder than addressing the single-objective ones. Furthermore, referring to the fact that the number of candidate …

Alayadb: The Data Foundation For Efficient And Effective Long-Context Llm Inference, Yangshen Deng, Zhengxin You, Long Xiang, Qilong Li, Peiqi Yuan, Zhaoyang Hong, Yitao Zheng, Wanting Li, Runzhong Li, Haotian Liu, Kyriakos Mouratidis, Man Lung Yiu, Huan Li, Qiaomu Shen, Rui Mao, Bo Tang

Research Collection School Of Computing and Information Systems

AlayaDB is a cutting-edge vector database system natively architected for efficient and effective long-context inference for Large Language Models (LLMs) at AlayaDB AI. Specifically, it decouples the KV cache and attention computation from the LLM inference systems, and encapsulates them into a novel vector database system. For the Model as a Service providers (MaaS), AlayaDB consumes fewer hardware resources and offers higher generation quality for various workloads with different kinds of Service Level Objectives (SLOs), when compared with the existing alternative solutions (e.g., KV cache disaggregation, retrieval-based sparse attention). The crux of AlayaDB is that it abstracts the attention computation …

Reaccept: Automated Co-Evolution Of Production And Test Code Based On Dynamic Validation And Large Language Models, Jianlei Chi, Xiaotian Wang, Yuhan Huang, Lechen Yu, Di Cui, Jianguo Sun, Jun Sun

Research Collection School Of Computing and Information Systems

Synchronizing production and test code, known as PT co-evolution, is critical for software quality. Given the significant manual effort involved, researchers have tried automating PT co-evolution using predefined heuristics and machine learning models. However, existing solutions are still incomplete. Most approaches only detect and flag obsolete test cases, leaving developers to manually update them. Meanwhile, existing solutions may suffer from low accuracy, especially when applied to real-world software projects. In this paper, we propose ReAccept, a novel approach leveraging large language models (LLMs), retrievalaugmented generation (RAG), and dynamic validation to fully automate PT co-evolution with high accuracy. ReAccept employs an …

De-Duplicating Silent Compiler Bugs Via Deep Semantic Representation, Junjie Chen, Xingyu Fan, Chen Yang, Shuang Liu, Jun Sun

Research Collection School Of Computing and Information Systems

The compiler bug duplication problem (where many test failures are caused by the same compiler bug) can lead to huge waste of time and resource in diagnosing test failures produced by compiler testing. It is particularly challenging with regard to the silent compiler bugs that do not produce any error messages. To address this problem, multiple white-box techniques were proposed, but they are inapplicable in many practical scenarios. Black-box techniques are more practical, but the existing ones are less effective as they often rely on irrelevant syntactic information. To bridge this gap, we propose a novel black-box technique (BLADE), which …

A Comprehensive Study Of Oop-Related Bugs In C++ Compilers, Bo Wang, Chong Chen, Junjie Chen, Bowen Xu, Chen Ye, Youfang Lin, Guoliang Dong, Jun Sun

Research Collection School Of Computing and Information Systems

Modern C++, a programming language characterized by its extensive use of object-oriented programming (OOP) features, is widely used for system programming. However, C++ compilers often struggle to correctly handle these sophisticated OOP features, resulting in numerous high-profile compiler bugs that can lead to crashes or miscompilation. Despite the significance of OOP-related bugs, existing studies largely overlook OOP features, hindering their ability to discover such bugs. To assist both compiler fuzzer designers and compiler developers, we conduct a comprehensive study of the compiler bugs caused by incorrectly handling C++ OOP-related features. First, we systematically extract 788 OOP-related C++ compiler bugs from …

Hd-Epic: A Highly-Detailed Egocentric Video Dataset, Toby Perrett, Ahmad Darkhalil, Saptarshi Sinha, Omar Emara, Sam Pollard, Kranti Kumar Parida, Kaiting Liu, Prajwal Gatti, Siddhant Bansal, Kevin Flanagan, Jacob Chalk, Zhifan Zhu, Rhodri Guerrier, Fahd Abdelazim, Bin Zhu, Davide Moltisanti, Michael Wray, Hazel Doughty, Dima Damen

Research Collection School Of Computing and Information Systems

We present a validation dataset of newly-collected kitchenbased egocentric videos, manually annotated with highly detailed and interconnected ground-truth labels covering: recipe steps, fine-grained actions, ingredients with nutritional values, moving objects, and audio annotations. Importantly, all annotations are grounded in 3D through digital twinning of the scene, fixtures, object locations, and primed with gaze. Footage is collected from unscripted recordings in diverse home environments, making HDEPIC the first dataset collected in-the-wild but with detailed annotations matching those in controlled lab environments. We show the potential of our highly-detailed annotations through a challenging VQA benchmark of 26K questions assessing the capability to …

Dupin: A Parallel Framework For Densest Subgraph Discovery In Fraud Detection On Massive Graphs, Jiaxin Jiang, Siyuan Yao, Yuchen Li, Qiange Wang, Bingsheng He, Min Chen

Research Collection School Of Computing and Information Systems

Detecting fraudulent activities in financial and e-commerce transaction networks is crucial. One effective method for this is Densest Subgraph Discovery (DSD). However, deploying DSD methods in production systems faces substantial scalability challenges due to the predominantly sequential nature of existing methods, which impedes their ability to handle large-scale transaction networks and results in significant detection delays. To address these challenges, we introduce Dupin, a novel parallel processing framework designed for efficient DSD processing in billion-scale graphs. Dupin is powered by a processing engine that exploits the unique properties of the peeling process, with theoretical guarantees on detection quality and efficiency. …

Community Detection In Heterogeneous Information Networks Without Materialization, Jiaxin Jiang, Siyuan Yao, Yuhang Chen, Bingsheng He, Yudong Niu, Yuchen Li, Shixuan Sun, Yongchao Liu

Research Collection School Of Computing and Information Systems

Community detection in heterogeneous information networks (HINs) poses significant challenges due to the diversity of entity types and the complexity of their interrelations. While traditional algorithms may perform adequately in some scenarios, many struggle with the high memory usage and computational demands of large-scale HINs. To address these challenges, we introduce a novel framework, SCAR, which efficiently uncovers community structures in HINs without requiring network materialization. SCAR leverages insights from meta-paths to interpret multi-relational data through compact vertex-based sketches, significantly reducing computational overhead and materialization overhead. We propose a sketch-based technique for estimating changes in modularity, improving both the precision …

Large Language Model For Vulnerability Detection And Repair: Literature Review And The Road Ahead, Xin Zhou, Sicong Cao, Xiaobing Sun, David Lo

Research Collection School Of Computing and Information Systems

The significant advancements in Large Language Models (LLMs) have resulted in their widespread adoption across various tasks within Software Engineering (SE), including vulnerability detection and repair. Numerous studies have investigated the application of LLMs to enhance vulnerability detection and repair tasks. Despite the increasing research interest, there is currently no existing survey that focuses on the utilization of LLMs for vulnerability detection and repair. In this paper, we aim to bridge this gap by offering a systematic literature review of approaches aimed at improving vulnerability detection and repair through the utilization of LLMs. The review encompasses research work from leading …

Keep The Balance: A Parameter-Efficient Symmetrical Framework For Rgb+X Semantic Segmentation, Jiaxin Cai, Jingze Su, Qi Li, Wenjie Yang, Shu Wang, Tiesong Zhao, Shengfeng He, Wenxi Liu

Research Collection School Of Computing and Information Systems

Multimodal semantic segmentation is a critical challenge in computer vision, with early methods suffering from high computational costs and limited transferability due to full fine-tuning of RGB-based pre-trained parameters. Recent studies, while leveraging additional modalities as supplementary prompts to RGB, still predominantly rely on RGB, which restricts the full potential of other modalities. To address these issues, we propose a novel symmetric parameter-efficient fine-tuning framework for multimodal segmentation, featuring with a modality-aware prompting and adaptation scheme, to simultaneously adapt the capabilities of a powerful pre-trained model to both RGB and X modalities. Furthermore, prevalent approaches use the global cross-modality correlations …

Learning Spatio-Temporal Dynamics For Trajectory Recovery Via Time-Aware Transformer, Tian Sun, Yuqi Chen, Baihua Zheng, Weiwei Sun

Research Collection School Of Computing and Information Systems

In real-world applications, GPS trajectories often suffer from low sampling rates, with large and irregular intervals between consecutive GPS points. This sparse characteristic presents challenges for their direct use in GPS-based systems. This paper addresses the task of map-constrained trajectory recovery, aiming to enhance trajectory sampling rates of GPS trajectories. Previous studies commonly adopt a sequence-to-sequence framework, where an encoder captures the trajectory patterns and a decoder reconstructs the target trajectory. Within this framework, effectively representing the road network and extracting relevant trajectory features are crucial for overall performance. Despite advancements in these models, they fail to fully leverage the …

Towards Uncertainty Aware Task Delegation And Human-Ai Collaborative Decision-Making, Min Hun Lee, Martyn Zhe Yu Tok

Research Collection School Of Computing and Information Systems

Despite the growing promise of artificial intelligence (AI) in supporting decision-making across domains, fostering appropriate human reliance on AI remains a critical challenge. In this paper, we investigate the utility of exploring distance-based uncertainty scores for task delegation to AI and describe how these scores can be visualized through embedding representations for human-AI decision-making. After developing an AI-based system for physical stroke rehabilitation assessment, we conducted a study with 19 health professionals and 10 students in medicine/health to understand the effect of exploring distance-based uncertainty scores on users’ reliance on AI. Our findings showed that distance-based uncertainty scores outperformed traditional …

Event-Based Eye Tracking: Event-Based Vision Workshop 2025, Qinyu Chen, Et. Al.

Research Collection School Of Computing and Information Systems

No abstract provided.

On Lexicographic Proof Rules For Probabilistic Termination, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Jiří Zárevucký, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider the almost-sure (a.s.) termination problem for probabilistic programs, which are a stochastic extension of classical imperative programs. Lexicographic ranking functions provide a sound and practical approach for termination of non-probabilistic programs, and their extension to probabilistic programs is achieved via lexicographic ranking supermartingales (LexRSMs). However, LexRSMs introduced in the previous work have a limitation that impedes their automation: all of their components have to be non-negative in all reachable states. This might result in a LexRSM not existing even for simple terminating programs. Our contributions are twofold. First, we introduce a generalization of LexRSMs that allows for some …

Human-Computer Interaction And Artificial Intelligence For Ageing Population, Keng Siau, Hailiang Wang, Fiona Fui-Hoon Nah, Runyu Wang, Ruitong Che, Can Liu

Research Collection School Of Computing and Information Systems

As the global population ages rapidly, the field of human-computer interaction (HCI) is in urgent need of innovation, redesign, and reengineering to meet the evolving needs of older adults. The older demographic faces a range of challenges—including physical limitations, cognitive decline, reduced social in-tegration, and varying levels of technological literacy—that can hinder effective engagement with digital technologies. In response to these challenges, research-ers and designers are using inclusive and adaptive approaches to enhance acces-sibility, usability, and emotional well-being. This paper reviews key design prin-ciples in HCI for the ageing population and discusses how artificial intelligence (AI) tools, such as voice …

Unlocking The Power Of Socio-Knowledge Association For Enterprise Risk Identification In Stock Market, Zhenghao Liu, Keng Siau, Shaochen Yang, Feicheng Ma

Research Collection School Of Computing and Information Systems

Potential risk signals reflected in supply chain and equity connections between enterprises and social connections between investors are becoming crucial to identifying enterprise risks in addition to basic financial indicators. Traditional risk management systems face challenges in adapting to these complexities, highlighting the need for a proactive paradigm shift in risk management. Leveraging graph models such as social networks and knowledge graphs offers a promising approach to identifying and managing potential associated risks effectively. To bridge existing research gaps, a novel risk identification framework driven by social-knowledge graphs has been proposed, integrating graph deep learning and reinforcement learning techniques guided …