Digital Commons Network^™

Using Clinical And Thrombus Characteristics To Predict The Etiology Of Ischemic Stroke, Graham A. Branscom, Carina Seah, Tannishtha Som, James Vicari, Alan R. Dabney, Selva Baltan, Farida Sohrabji, Keith Pennypacker, Keith B. Woodward, Benjamin I. Rapoport, Sol Schulman, Justin F. Fraser, Christopher P. Kellner

Neurology Faculty Publications

Background and purpose: Ischemic stroke comprises about 87% of all stroke cases in the US. 20% of these have a cardioembolic (CE) etiology, and 25% are still considered embolic strokes of undetermined sources (ESUS). ESUS strokes are associated with worse functional outcomes and survival rates, underscoring the need for understanding the clinical differences in stroke etiology. Methods: We analyzed the Investigation of Clot in Ischemic Stroke and Hematoma Evacuation (INSIGHT) registry (NCT04693767, https://clinicaltrials.gov/study/NCT04693767), which includes n=388 patients total (n=208 CE, n=142 AFIB, n=180 non-CE). We analyzed 123 data features, spanning across domains including demographics, past medical history, baseline blood lab …

Adolescent Brain Activity During Associative Memory Encoding Is Related To Dietary Nutrient Intake, K S. Mohankumar, C T. Eickstead, A Bolaram, D K. Grey, C A. Orihuela, R Evans, S Mrug, D C. Knight

Expo Student Presentations

2026 Summer Expo Poster Presentation

• Social & Behavioral Sciences

Practice Brief—Reading Strategies For Parents And Teachers, Kelsey Davison, Rebecca Marks

Science of Reading

Shared reading is an important context for supporting young children's language and emergent literacy development. Research has identified evidence-based strategies that parents and educators can use to make shared reading more interactive, language-rich, and supportive of children's learning. This brief reviews these strategies and how they can strengthen the early language skills that provide the foundation for learning to read.

Addressing Homelessness As A Key Risk Factor For Overdose Deaths In King County, Washington, Catherine Mutti-Driscoll

Selected Social Change Portfolios in Prevention, Intervention, and Consultation

Goal Statement:

In this portfolio, I identify prevention strategies that address the nested problems of overdose deaths and homelessness in King County, Washington. By examining how housing instability, substance abuse, structural racism, and gaps in coordinated care intersect, this paper advocates for a responsive prevention approach that combines evidence-based substance abuse strategies, phased housing approaches, and culturally responsive care.

Significant Findings:

While recent harm reduction measures have improved overdose death rates in King County, unhoused individuals continue to account for 50% of the county’s still concerning fatal overdose numbers. Data shows that historical racism in King County also shapes who …

Reducing Chronic Loneliness Among Lgbtq+ Older Adults In Greater Seattle, Douglas J. Gallup

Selected Social Change Portfolios in Prevention, Intervention, and Consultation

Goal Statement

The goal of this project is to reduce chronic loneliness and social isolation among LGBTQ+ adults age 65 and older in the Greater Seattle area by increasing access to affirming, accessible, and evidence-informed opportunities for meaningful connection.

Significant Findings

LGBTQ+ older adults in Seattle and King County face elevated risks of living alone, social isolation, and limited access to affirming services, while also demonstrating substantial resilience (Fredriksen-Goldsen et al., 2015). A promising approach would combine meaningful activity, social support, practical access assistance, and PEARLS-informed strategies. GenPride is a logical community partner because its programs foster well-being and belonging …

Preventing Mental Health Issues In Children And Adolescents With Learning Disabilities, Laura Barrett

Selected Social Change Portfolios in Prevention, Intervention, and Consultation

Goal Statement: The goal of this social change portfolio is to prevent mental health issues in children and adolescents with learning disabilities in the state of Utah.

Significant Findings: Children and adolescents with learning disabilities (LDs) in Utah face elevated risks of anxiety, depression, low self-esteem, and related difficulties stemming from chronic academic frustration and social challenges, yet substantial service gaps for this vulnerable population exist. Approximately 58% of children needing mental health care in Utah do not receive any, and only about half of the estimated 58,000 students diagnosed with LDs have specialized educational supports such as individualized education …

Adolescent Social Disconnection And Social Media Overuse In San Jose, California, Gilmour Arufo

Selected Social Change Portfolios in Prevention, Intervention, and Consultation

The goal of this Social Change Portfolio is to support the implementation of a school-based prevention program for high school students in San Jose, California, that promotes healthy technology use, strengthens peer and adult connections, and increases participation in face-to-face social activities. The proposed program seeks to increase adolescents' awareness of their social media use patterns while strengthening protective factors associated with positive youth development, including school connectedness, supportive peer relationships, trusted adult relationships, and meaningful social engagement.

Believing And Belonging: Americans’ Religious Practices And Their Trust In Government, Hwayong Shin

Weidenbaum Center Survey (WCS) Reports

Data from the Weidenbaum Center Survey (WCS) offer new insights into Americans’ religious and political lives by addressing two broad questions. First, how religious are Americans today, and how does religiosity vary across age and partisan groups? Waves 1–8 of the WCS, conducted between 2023 and 2026, are used to answer this question. Second, how are different forms of religiosity associated with trust in government, and do these relationships differ across age and partisan groups? To answer this question, this article leverages Waves 6–8 of the WCS, which included measures of trust in government, conducted between 2025 and 2026. The …

Full Marc Examples To Accompany Olac Best Practices For Cataloging Dvd-Video And Blu-Ray Discs, Objects, Streaming Media, Video Games, And Digital Media Storage Devices Using The Original Rda Toolkit And Marc 21, Kristi Bergland, Mary Huismann, John Dickson, Autumn Faulkner, Robert B. Freeborn, Jeannette Ho, Sarah Hovde, Kyla Jemison, Yoko Kudo, Junghae Lee, Jack Nighan, Alex Whelan

OLAC Publications and Training Materials

Examples of MARC 21 catalog records to accompany the OLAC Best Practices for Cataloging DVD-Video and Blu-ray Discs, Objects, Streaming Media, and Video Games Using the Original RDA Toolkit and MARC 21.

Hsv-1 Us3 Hijacks Conserved Actin Regulatory Complexes To Drive F-Actin Remodeling, Md Imran Hossain, Md Arifuzzaman, Md Mehedi Hasan, Seung Jong Park, Leila Rahimian, Ojasvi Dutta, Vladimir Chouljenko, Harikrishnan Mohan, Reza Ghavimi, Konstantin G. Kousoulas

Computer Science Faculty Research & Creative Works

The herpes simplex virus 1 (HSV-1) US3 is a multifunctional serine/threonine kinase that promotes HSV-1 replication and spread. But its role and the mechanisms by which US3 regulates actin cytoskeletal remodeling remain poorly defined. We combined flow cytometry, confocal microscopy, immunoprecipitation-mass spectrometry (IP-MS), protein complex mapping, and machine learning to characterize US3-mediated F-actin dynamics. Flow cytometry and confocal microscopy showed that wild-type HSV-1 induces significant F-actin remodeling, while the ΔUS3 mutant displays F-actin levels comparable to uninfected cells, identifying US3 as a key regulator. IP-MS identified 47 high-confidence US3 interactors enriched in conserved actin regulatory complexes, including Arp2/3 nucleation machinery, …

Multicbr: Multi‑View Contrastive Learning For Bundle Recommendation, Yunshan Ma, Yingzhi He, Xiang Wang, Yinwei Wei, Xiaoyu Du, Yuyangzi Fu, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Bundle recommendation seeks to recommend a bundle of related items to users to improve both userexperience and the profits of platform. Existing bundle recommendation models have progressed from capturing only user-bundle interactions to the modeling of multiple relations among users, bundles, and items.CrossCBR, in particular, incorporates cross-view contrastive learning into a two-view preference learningframework, significantly improving SOTA performance. It does, however, have two limitations: (1) the twoview formulation does not fully exploit all the heterogeneous relations among users, bundles, and items; and(2) the “early contrast and late fusion” framework is less effective in capturing user preference and difficultto generalize to …

Avadclip: Audio-Visual Collaboration For Robust Video Anomaly Detection, Peng Wu, Wanshun Su, Guansong Pang, Yujia Sun, Qingsen Yan, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

With the increasing adoption of video anomaly detection in intelligent surveillance domains, conventional visual-only detection approaches often struggle with information insufficiency and high false-positive rates in complex environments. To address these limitations, we present a novel weakly supervised framework that leverages audio-visual collaboration for robust video anomaly detection. Capitalizing on the exceptional cross-modal representation learning capabilities of Contrastive Language-Image Pretraining (CLIP) across visual, audio, and textual domains, our framework introduces two major innovations: an efficient audio-visual fusion that enables adaptive cross-modal integration through lightweight parametric adaptation while maintaining the frozen CLIP backbone, and a novel audio-visual prompt that dynamically enhances …

Robust Graph Learning On The Web: Challenges, Methods, And Applications, Ao Xiang, Yang Liu, Guansong Pang, Yuanhao Ding, Hezhe Qiao, Dawei Cheng, Qing He

Research Collection School Of Computing and Information Systems

Graph learning is transforming web intelligence, powering applications from recommender systems to anomaly detection. However, most existing approaches implicitly assume ideal conditions where training and testing data are accurate, complete, and free from manipulation. In reality, web environments rarely exhibit such stability. Dynamic user behavior, incomplete or outdated content, adversarial interference, and sudden distribution shifts can all erode the reliability of even state-of-the-art models, leading to biased or unsafe outcomes. This tutorial provides a comprehensive survey of emerging strategies for robust graph learning on the web. We first present a structured taxonomy of the principal robustness threats specific to web …

Generation-Augmented Video Corpus Moment Retrieval, Mingjin Kuai, Qianyin Xiao, Juncheng Li, Jin Peng, Lizi Liao, Wei Ji

Research Collection School Of Computing and Information Systems

Video Corpus Moment Retrieval (VCMR) requires models to efficiently retrieve and precisely locate specific moments relevant to natural language queries within a massive, untrimmed video corpus. However, existing discriminative approaches typically rely on shallow visual-textual feature matching mechanisms, which often struggle to capture fine-grained semantic differences. To address this limitation, we propose Video-GAR, a novel framework that reframes the conventional retrieval task from superficial matching to generative understanding, positing that the capability for query reconstruction evidences deep semantic comprehension. Specifically, Video-GAR orchestrates three synergistic components: To overcome the computational efficiency bottleneck, we construct a Bi-Mamba backbone that leverages the linear …

A Refined Generalization Analysis For Extreme Multi-Class Supervised Contrastive Representation Learning, Minh Hieu Nong, Antoine Ledent

Research Collection School Of Computing and Information Systems

Contrastive Representation Learning (CRL) has achieved strong empirical success in multiple machine learning disciplines, yet its theoretical sample complexity remains poorly understood. Existing analyses usually assume that input tuples are identically and independently distributed, an assumption violated in most practical settings where contrastive tuples are constructed from a finite pool of labeled data, inducing dependencies among tuples. While one recent work analyzed this learning setting using U-Statistics to estimate the population risk, the techniques used therein require the risk of each class to concentrate uniformly, making excess risk bounds scale in the order of where denotes the probability of the …

Universal Rules Of The Russian Sound Verbs’ Synthesis: Representation In A Matrix Of Forms, Irina V. Ivliyeva

Arts, Languages and Philosophy Faculty Research & Creative Works

English Abstract:

The article is devoted to the construction of a system of universal rules for the synthesis of modified verbs of the Russian language and ways of presenting them in a matrix table of forms. The aim of the study is to identify parameters of the sound process relevant for predicting the composition of modified sound verbs’ nests, and in developing a system of rules of word-formation synthesis. The research material consisted of 152 base verbs of sound in the Russian language and their 880 modified derivatives.

Methods of lexical-semantic, word-formation (WF) and component analysis are used in the …

The Anatomy Of Earnings Conference Calls: An Integrative Framework For Management Research, Matthew P. Mount, Gokhan Ertug, Wei Shi, Tengjian Zou

Research Collection Lee Kong Chian School Of Business

Over the last decade, there has been an explosion in the use of diverse data sources by management scholars to observe and capture managerial and organizational constructs that have historically been difficult to access. This surge has been driven by the growing availability of rich, multi-modal data—textual, image, and audio (Luo, Jia, Ouyang, & Fang, 2024)—together with advances in analytical techniques to process and analyze data, such as computer-aided text analysis (Harrison, Thurgood, Boivie, & Pfarrer, 2019), machine learning (Choudhury, Wang, Carlson, & Khanna, 2019; Harrison, Josefy, Kalm, & Krause, 2023), and deep learning (Gouvard, Goldberg, & Srivastava, 2023). These …

Keeping Up With The Times? Rethinking Social Evaluations Research Under Contemporary Technological And Sociopolitical Forces, Marco Clemente, Michael Etter, Yuliya Snihur, Gokhan Ertug, Graffin Scott, Anastasiya Zavyalova

Research Collection Lee Kong Chian School Of Business

The context within which social evaluations form has been fundamentally altered by contemporary forces, such as digital technologies, polarization, activism, politicization of business, and geopolitical tensions. While research on social evaluations has generated rich insights into the formation and development of constructs, such as legitimacy, status, reputation, stigma, trust, and celebrity, much of this work has been developed under the assumptions of relative stability, coherent audiences, and well-defined intermediary roles. These assumptions are increasingly challenged in this changing context, requiring us to rethink the formation and management of social evaluations. The articles in this special issue focus on these changes. …

Multi-Scale Modeling Method For Intelligent Unmanned Aerial Vehicle Swarm Combat, Yelei Zhu, Shoulin Shen, Jiang Zhu, Chuanhua Wen

Journal of System Simulation

Abstract: Intelligent unmanned aerial vehicle swarm combat exhibits multi-scale characteristics of microlevel tactical autonomy, meso-level resource constraints, and macro-level network collaboration. In view of the problem that a single-scale modeling method is difficult to simultaneously characterize individual decision-making details, resource flow process, and system collaboration mechanism, a multi-scale modeling method integrating multi-agent modeling, system dynamics, and complex network theory was proposed. By designing six formal coupling operators to achieve cross-layer information mapping, a time progression mechanism based on a master-slave clock and hybrid synchronization was established, and a proof of error boundedness was provided. A prototype system was implemented on …

Research On Safety Monitoring System For Digital Twin Fully Mechanized Mining Face In Thin Coal Seams, Ya Liu, Zhaoyun Zhang, Cheng An, Canguang Zheng

Journal of System Simulation

Abstract: To solve the problems of opaque production processes, difficult expression of production information, and high difficulty in real-time model driving and safety monitoring in fully mechanized mining faces of thin coal seams, studying the use of digital twin technology and risk early warning models to solve beyond-visual-range visual production operations and safety monitoring control of the working face has important theoretical and practical value. This paper studied the construction methods of the digital twin system for fully mechanized mining faces in thin coal seams and discussed the construction of high-precision three-dimensional models, the real-time collection and processing conversion of …

Operational Agency: A Permeable Legal Fiction For Tracing Culpability In Ai Systems, Anirban Mukherjee, Hannah H. Chang

Research Collection Lee Kong Chian School Of Business

Modern artificial intelligence (AI) systems act with a high degree of independence yet lack legal personhood—a paradox that fractures doctrines grounded in human-centric notions of mens rea and actus reus. This Article introduces Operational Agency (OA)—a permeable legal fiction structured as an ex post evidentiary framework—and Operational Agency Graph (OAG)—a tool for mapping causal interactions among human actors, organizations, and AI systems. OA evaluates an AI’s observable operational characteristics: its goal-directedness (as a proxy for intent), predictive processing (as a proxy for foresight), and safety architecture (as a proxy for standard of care). OAG operationalizes that analysis by embedding these …

Research On Multi-Objective Hybrid Flow-Shop Scheduling With Order Splitting, Junjie Yu, Weixi Ji, Chen Chen, Jingyu Lu, Chaoyang Zhang

Journal of System Simulation

Abstract: To address the order splitting multi-objective hybrid flow-shop scheduling problem (OSMOHFSP), a dual-objective optimization model was formulated with the objectives of minimizing makespan and total order tardiness. Fixed sub-batch specification constraints were incorporated to reflect common splitting limitations in actual production. A sub-batch generation strategy based on a candidate set of sub-batch specifications was designed to efficiently filter feasible splitting combinations, effectively reducing the search space dimensionality and computational complexity. A hybrid multi-objective metaheuristic algorithm integrating NSGA-II and SA was proposed. The global search capability was enhanced by constructing two crossover and four mutation operators. An improved SA embedding …

Ultra-Short-Term Photovoltaic Power Prediction Method Based On Spatio-Temporal Feature Enhancement Of Ground-Based Cloud Images, Zhiwei Kou, Fengyue Xin, Xiaoming Cui, Yu Yin, Feifan Li, Yongsheng Qi

Journal of System Simulation

Abstract: In view of the time lag in ground-based cloud image acquisition and the insufficient accuracy of existing photovoltaic power prediction models, an ultra-short-term photovoltaic power prediction method named spatial-temporal feature enhancement-ground-based cloud image-improved LSTM (STFE-GCI-ILSTM), which is based on spatio-temporal feature enhancement-ground-based cloud images-improved long short-term memory (LSTM) network was proposed. A ground-based cloud image prediction model with multi-scale spatio-temporal feature enhancement (STFE-GCI) was constructed. By spatial feature enhancement, temporal feature enhancement, and multi-scale feature fusion technologies, the deep spatio-temporal features of historical cloud image sequences were extracted to generate future predicted cloud image sequences, thereby eliminating the time …

Prediction Of Industrial Concentration Parameters Based On Caudformer Model, Kaipeng Xu, Yan Wang, Xin Zhang, Yang Liu, Zhenzhong Wang, Xiang Liu, Zhicheng Ji

Journal of System Simulation

Abstract: To solve the problems in the industrial concentration process of traditional Chinese medicine that traditional prediction methods are difficult to deal with complex characteristics such as nonlinearity, high-dimensional coupling, and highly skewed distribution and that existing deep learning models insufficiently consider causal relationships among variables, ignore frequency-domain periodic characteristics, and lack long-range dependency modeling capabilities, an improved multivariate time series prediction model CauDformer was proposed in this paper. Based on the iTransformer architecture, a causal multi-head self-attention mechanism (C-MHSA) was introduced to compulsorily constrain the dependency direction among variables by utilizing a lower triangular causal mask, guiding the model …

Securing A Calibrated Marketing Budget, Junqiu Jiang, Kapil R. Tuli, Nirmalya Kumar

Research Collection Lee Kong Chian School Of Business

This study investigates the sociopolitical processes underlying the development and approval of marketing budgets in large multinational corporations. While prior research has focused extensively on optimizing the level and allocation of marketing budgets, little attention has been paid to the internal organizational dynamics that shape the marketing budgeting process. Drawing on the theories-in-use of both CMOs and CEOs, this study examines how these executives codevelop a calibrated marketing budget (CMKB)—a budget that aligns expected performance with allocated resources through an iterative, participative process. Field data show that CMOs deploy a set of signals to assuage CEO concerns related to goal …

Survey On Learning-Based Dynamic Fault Localization: From Traditional Machine Learning To Large Language Models, Chunyan Liu, Yan Lei, Huan Xie, Jinping Wang, Yue Yu, David Lo

Research Collection School Of Computing and Information Systems

Learning-based dynamic fault localization techniques play a crucial role in the field of software engineering. These techniques dynamically execute test cases to meticulously extract useful knowledge from the execution information in the program, with the aim of identifying fault locations by leveraging machine learning, deep learning, and large language models. Currently, there is already a flourishing body of research that is intensely focused on learning-based dynamic fault localization. Research literature can be categorized into two main aspects for learning-based dynamic fault localization: data-based enhancements (i.e., the datasets) and model-based enhancements (i.e., the suspiciousness algorithms). Thus, we conduct an extensive literature …

A Framework For Top-K Queries With Constrained Preferences, Kyriakos Mouratidis, Nikolaos Chaloulakos, Bo Tang

Research Collection School Of Computing and Information Systems

Traditional rank-aware processing assumes a dataset that contains available options to cover a specific need (e.g., restaurants, hotels, etc) and users who browse that dataset via top-k queries with linear scoring functions, i.e., by ranking the options according to the weighted sum of their attributes, for a set of given weights. In practice, however, user preferences (weights) may only be estimated with bounded accuracy, or may be inherently imprecise due to the inability of a human user to specify exact weight values with absolute accuracy. Motivated by this, we define the constrained-preference top-k (CT) query. Given an approximate description of …

Towards Uniformity And Alignment For Multimodal Representation Learning, Wenzhe Yin, Pan Zhou, Zehao Xiao, Jie Liu, Shujian Yu, Jan-Jakob Sonke, Efstratios Gavves

Research Collection School Of Computing and Information Systems

Multimodal representation learning aims to construct a shared embedding space in which heterogeneous modalities are semantically aligned. Despite strong empirical results, InfoNCE-based objectives introduce inherent conflicts that yield distribution gaps across modalities. In this work, we identify two conflicts in the multimodal regime, both exacerbated as the number of modalities increases: (i) an alignment–uniformity conflict, whereby the repulsion of uniformity undermines pairwise alignment, and (ii) an intra-alignment conflict, where aligning multiple modalities induces competing alignment directions. To address these issues, we propose a principled decoupling of alignment and uniformity for multimodal representations, providing a conflict-free recipe for multimodal learning that …

Rendering Data Unlearnable By Exploiting Llm Alignment Mechanisms, Ruihan Zhang, Jun Sun

Research Collection School Of Computing and Information Systems

Large language models (LLMs) are increasingly trained on massive, heterogeneous text corpora, raising serious concerns about the unauthorised use of proprietary or personal data during model training. In this work, we address the problem of data protection against unwanted model learning in a realistic blackbox setting. We propose Disclaimer Injection, a novel data-level defence that renders text unlearnable to LLMs. Rather than relying on model-side controls or explicit data removal, our approach exploits the models’ own alignment mechanisms: injecting carefully designed alignment-triggers to prevent effective learning. Through layer-wise analysis, we find that finetuning on such protected data induces persistent activation …

Knowledge-State Generative Agents For Pre-Assessment Question Evaluation, Ping Fan Ke, Yi Meng Lau, Siaw Ling Lo

Research Collection School Of Computing and Information Systems

This paper introduces a Knowledge‑State Generative Agent framework for evaluating the quality of pre‑assessment questions. The framework employs large language model (LLM)–based agents prompted to adopt a teacher persona to simulate the responses of students with and without mastery of targeted knowledge components. A preliminary empirical study using archival data from 424 students enrolled in an Information Systems Management course indicates that the proposed approach yields interpretable metrics under Classical Test Theory. Results further show that agents instantiated with the relevant mastered knowledge components exhibit systematically higher performance than agents lacking such mastery. In addition, the study suggests that teacher-persona …