Digital Commons Network^™

Understanding The Experiences And Perceptions Of Faculty Supporting Undergraduate Students With Mental Health Challenges, Jenna Ranee Parbati Jamieson

Dissertations

College is a big adjustment for students, and it doesn't come without its challenges, including being in a new place with new responsibilities. This is a stressful time that presents new obstacles and frustrations that can lead to increased stress, anxiety, depression, and mental health challenges that may cause faculty to engage and support undergraduate students with mental health challenges. As students adjust and transition to college, it is vital for university faculty to feel confident and comfortable supporting students from diverse backgrounds with varying levels of distress. By interviewing faculty, a better understanding of their lived experiences and perceptions …

Too Big To Fail? Using Microsatellites To Examine The Population Genetics Of Flathead Catfish (Pylodictis Olivaris) In Missouri Impoundments, Aaron Krolow

Dissertations

Flathead Catfish (Pylodictis olivaris) is a popular sportfish managed for recreational and commercial uses. Currently, managers in Missouri have implemented various reservoir-specific bag and length limits for P. olivaris to create sustainable populations of quality-sized individuals available for anglers to capture. In conjunction with field studies to estimate growth rates and size/age distributions, I developed and scored 15 microsatellite markers to estimate the effective population (Ne) and population structure within 10 Missouri reservoirs. I assessed the power of the markers to get acceptably precise estimates of Ne using NEoGen assuming life-history parameters (e.g., age at maturity, longevity and fecundity) estimated …

Spatial Distributions And Occupancy Dynamics Of Carnivores In A Central Hardwood Forest, Maximillian Larreur

Dissertations

Extirpation of apex predators, caused primarily by anthropogenic alterations to natural environments and persecution, has led to mesocarnivore release in these areas. Mesocarnivore release is an ecological phenomenon in which mesocarnivores thrive in the absence of apex predator dominance within the carnivore hierarchy. Mesopredators fill this dominant role on the landscape as de facto apex predators, however, this can lead to changes in inter-guild species space use, predator-prey dynamics, activity patterns, and interspecific competition. Understanding patterns in wildlife spatial distributions, which indicate variability in habitat space use across spatiotemporal scales, is critical for developing effective management and conservation plans and …

Difficulty In American Dramatic Literature, David Turkel

Dissertations

Speaking broadly, this dissertation explores the marginalization of American drama within the field of literary studies. In attempting to account for this marginalization, theater scholars have tended to invoke the genre’s supposed lack of “difficulty,” meaning its failure to pose meaningful interpretive challenges to its readers. However, this dissertation takes an alternative view of the matter, contending that if American drama has indeed been marginalized, it is not because literary studies departments have regarded it as intrinsically “simpler” than other literary genres; in fact, it is theater scholars who have been more likely to read the genre as simple, or …

Focus: Evaluating Pre-Trained Vision-Language Models On Underspecification Reasoning, Kankan Zhou, Yibin Lai, Kyriakos Mouratidis, Jing Jiang

Research Collection School Of Computing and Information Systems

Humans possess a remarkable ability to interpret underspecified ambiguous statements by inferring their meanings from contexts such as visual inputs. This ability, however, may not be as developed in recent pre-trained visionlanguage models (VLMs). In this paper, we introduce a novel probing dataset called FOCUS to evaluate whether state-of-the-art VLMs have this ability. FOCUS consists of underspecified sentences paired with image contexts and carefully designed probing questions. Our experiments reveal that VLMs still fall short in handling underspecification even when visual inputs that can help resolve the ambiguities are available. To further support research in underspecification, FOCUS will be released …

Non-Homophilic Graph Pre-Training And Prompt Learning, Xingtong Yu, Jie Zhang, Yuan Fang, Renhe Jiang

Research Collection School Of Computing and Information Systems

Graphs are ubiquitous for modeling complex relationships between objects across various fields. Graph neural networks (GNNs) have become a mainstream technique for graph-based applications, but their performance heavily relies on abundant labeled data. To reduce labeling requirement, pre-training and prompt learning has become a popular alternative. However, most existing prompt methods do not distinguish between homophilic and heterophilic characteristics in graphs. In particular, many real-world graphs are non-homophilic-neither strictly nor uniformly homophilic-as they exhibit varying homophilic and heterophilic patterns across graphs and nodes. In this paper, we propose ProNoG, a novel pre-training and prompt learning framework for such non-homophilic graphs. …

Coleclip: Open-Domain Continual Learning Via Joint Task Prompt And Vocabulary Learning, Yukun Li, Guansong Pang, Wei Suo, Chenchen Chen, Yuling Xi, Lingqiao Liu, Hao Chen, Guoqiang Liang, Peng Wang

Research Collection School Of Computing and Information Systems

This article investigates the problem of continual learning (CL) of vision-language models (VLMs) in open domains, where models are required to perform continual updating and inference on a stream of datasets from diverse seen and unseen domains with novel classes. Such a capability is crucial for various applications in open environments, e.g., AI assistants, autonomous driving systems, and robotics. Current CL studies mostly focus on closed-set scenarios in a single domain with known classes. Large pretrained VLMs such as CLIP have showcased exceptional zero-shot recognition capabilities, and several recent studies have leveraged the unique characteristics of VLMs to mitigate catastrophic …

Optimizing Group Utility In Itinerary Planning: A Strategic And Crowd-Aware Approach, Junhua Liu, Aldy Gunawan, Kristin L. Wood, Kwan Hui Lim

Research Collection School Of Computing and Information Systems

Itinerary recommendation is a complex sequence prediction problem with numerous practical applications. The task becomes significantly more challenging when optimizing multiple factors simultaneously, such as user queuing times, crowd levels, attraction popularity, walking durations, and operating hours. These factors, combined with the dynamic and unpredictable nature of visitor flow, introduce substantial complexities, particularly when accounting for collective user behavior. Existing solutions often adopt a single-user perspective, overlooking critical challenges arising from natural crowd dynamics. For example, the Selfish Routing problem illustrates how individual decision-making can lead to suboptimal outcomes for the group as a whole. To address these challenges, we …

Taclr: A Scalable And Efficient Retrieval-Based Method For Industrial Product Attribute Value Identification, Yindu Su, Huike Zou, Lin Sun, Ting Zhang, Haiyang Yang, Chen Li Yu, David Lo, Qingheng Zhang, Shuguang Han, Jufeng Chen

Research Collection School Of Computing and Information Systems

Product Attribute Value Identification (PAVI) involves identifying attribute values from product profiles, a key task for improving product search, recommendation, and business analytics on e-commerce platforms. However, existing PAVI methods face critical challenges, such as inferring implicit values, handling outof-distribution (OOD) values, and producing normalized outputs. To address these limitations, we introduce Taxonomy-Aware Contrastive Learning Retrieval (TACLR), the first retrieval-based method for PAVI. TACLR formulates PAVI as an information retrieval task by encoding product profiles and candidate values into embeddings and retrieving values based on their similarity. It leverages contrastive training with taxonomy-aware hard negative sampling and employs adaptive inference …

Ai-Assisted Risk Assessment In Generative Ai Governance, Wu Jiaqi Young, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Effective risk assessment is paramount for responsible generative AI (GenAI) deployment. Traditional governance approaches that rely on manual reviews are inadequate given the scale and velocity of GenAI outputs. A risk-based approach incorporating real-time monitoring and governance is paramount. In this research, we examine how the efficacy of suggestive versus supportive explanations for AI’s risk assessment of GenAI outputs is moderated by user domain expertise and AI’s risk assessment in determining user acceptance. We hypothesize that cognitive involvement increases with AI’s risk assessment, with higher risks triggering more critical evaluation. By drawing on the elaboration likelihood model, we hypothesize that …

Freqllm: Frequency-Aware Large Language Models For Time Series Forecasting, Shunan Wang, Min Gao, Zongwei Wang, Yibing Bai, Feng Jiang, Guansong Pang

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have recently shown promise in Time Series Forecasting (TSF) by effectively capturing intricate time-domain dependencies. However, our preliminary experiments reveal that standard LLM-based approaches often fail to capture global correlations, limiting predictive performance. We found that embedding frequency-domain signals smooths weight distributions and enhances structured correlations by clearly separating global trends (low-frequency components) from local variations (high-frequency components). Building on these insights, we propose FreqLLM, a novel framework that integrates frequency-domain semantic alignment into LLMs to refine prompts for improved time series analysis. By bridging the gap between frequency signals and textual embeddings, FreqLLM effectively captures …

Fine‑Tuning Multimodal Large Language Models For Product Bundling, Xiaohao Liu, Jie Wu, Zhulin Tao, Yunshan Ma, Yinwei Wei, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Recent advances in product bundling have leveraged multimodal information through sophisticated encoders, but remain constrained by limited semantic understanding and a narrow scope of knowledge. Therefore, some attempts employ In-context Learning (ICL) to explore the potential of large language models (LLMs) for their extensive knowledge and complex reasoning abilities. However, these efforts are inadequate in understanding mulitmodal data and exploiting LLMs' knowledge for product bundling. To bridge the gap, we introduce Bundle-MLLM, a novel framework that fine-tunes LLMs through a hybrid item tokenization approach within a well-designed optimization strategy. Specifically, we integrate textual, media, and relational data into a unified …

Anomalygfm: Graph Foundation Model For Zero/Few-Shot Anomaly Detection, Hezhe Qiao, Chaoxi Niu, Ling Chen, Guansong Pang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD) aims to identify abnormal nodes that differ from the majority of the nodes in a graph, which has been attracting significant attention in recent years. Existing generalist graph models have achieved remarkable success in different graph tasks but struggle to generalize to the GAD task. This limitation arises from their difficulty in learning generalized knowledge for capturing the inherently infrequent, irregular and heterogeneous abnormality patterns in graphs from different domains. To address this challenge, we propose AnomalyGFM, a GAD-oriented graph foundation model that supports zero-shot inference and few-shot prompt tuning for GAD in diverse graph datasets. …

Llm2rec: Large Language Models Are Powerful Embedding Models For Sequential Recommendation, Yingzhi He, Xiaohao Liu, An Zhang, Yunshan Ma, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Sequential recommendation aims to predict users' future interactions by modeling collaborative filtering (CF) signals from historical behaviors of similar users or items. Traditional sequential recommenders predominantly rely on ID-based embeddings, which capture CF signals through high-order co-occurrence patterns. However, these embeddings depend solely on past interactions, lacking transferable knowledge to generalize to unseen domains. Recent advances in large language models (LLMs) have motivated text-based recommendation approaches that derive item representations from textual descriptions. While these methods enhance generalization, they fail to encode CF signals-i.e., latent item correlations and preference patterns-crucial for effective recommendation. We argue that an ideal embedding model …

Achilles: A Formal Framework Of Leaking Secrets From Signature Schemes Via Rowhammer, Junkai Liang, Zhi Zhang, Xin Zhang, Qingni Sheng, Yansong Gao, Xinliang Yuan, Haiyang Xue, Pengfei Wu, Zhonghai. Wu

Research Collection School Of Computing and Information Systems

Signature schemes are a fundamental component of cyber-security infrastructure. While they are designed to be mathematically secure against cryptographic attacks, they are vulnerable to Rowhammer fault-injection attacks. Since all existing attacks are ad-hoc in that they target individual parameters of specific signature schemes, it remains unclear about the impact of Rowhammer on signature schemes as a whole.In this paper, we present Achilles, a formal framework that aids in leaking secrets in various real-world signature schemes via Rowhammer. Particularly, Achilles can be used to find potentially more vulnerable parameters in schemes that have been studied before and also new schemes that …

Improved Secure Two-Party Computation From A Geometric Perspective, Hao Guo, Liqiang Peng, Haiyang Xue, Li Peng, Weiran Liu, Zhe Liu, Lei. Hu

Research Collection School Of Computing and Information Systems

Multiplication and other non-linear operations are widely recognized as the most costly components of secure two-party computation (2PC) based on linear secret sharing. Moreover, the comparison protocol (or Wrap protocol) is essential for various operations such as truncation, signed extension, and signed non-uniform multiplication. This paper aims to optimize these protocols by avoiding invoking the costly comparison protocol, thereby improving their efficiency.We propose a novel approach to study 2PC from a geometric perspective. Specifically, we interpret the two shares of a secret as the horizontal and vertical coordinates of a point in a Cartesian coordinate system, with the secret itself …

Equivalence And Similarity Refutation For Probabilistic Programs, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider the problems of statically refuting equivalence and similarity of output distributions defined by a pair of probabilistic programs. Equivalence and similarity are two fundamental relational properties of probabilistic programs that are essential for their correctness both in implementation and in compilation. In this work, we present a new method for static equivalence and similarity refutation. Our method refutes equivalence and similarity by computing a function over program outputs whose expected value with respect to the output distributions of two programs is different. The function is computed simultaneously with an upper expectation supermartingale and a lower expectation submartingale for …

On The Other Hand: The Unity Of Contraries In A World Of Conflict, Michael Kearney

Faculty Work Comprehensive List

"How difficult will it be to keep our eyes set on God, not idols, in a world of generative AI, political polarization, rampant individualism, and anxiety about the future?"

Posting about ­­­­­­­­skilled communication from In All Things, an online hub that offers insight into maintaining and faithful and orthodox Reformed Christian worldview while fearlessly engaging in every aspect of contemporary life – until all is made new.

On the Other Hand

08-19-25 Hsc Committee Meeting Notice, Patricia Serna

HSC Committee (A subcommittee of the UNM Board of Regents)

08-19-25 HSC Committee Meeting Notice

Advancing Molecular Graph-Text Pre-Training Via Fine-Grained Alignment, Yibo Li, Yuan Fang, Mengmei Zhang, Chuan Shi

Research Collection School Of Computing and Information Systems

Understanding molecular structure and related knowledge is crucialfor scientific research. Recent studies integrate molecular graphswith their textual descriptions to enhance molecular representationlearning. However, they focus on the whole molecular graph andneglect frequently occurring subgraphs, known as motifs, whichare essential for determining molecular properties. Without suchfine-grained knowledge, these models struggle to generalize to un-seen molecules and tasks that require motif-level insights. To bridgethis gap, we propose FineMolTex, a novel Fine-grained Moleculargraph-Text pre-training framework to jointly learn coarse-grainedmolecule-level knowledge and fine-grained motif-level knowledge.Specifically, FineMolTex consists of two pre-training tasks: a con-trastive alignment task for coarse-grained matching and a maskedmulti-modal modeling task for …

Inference-Time Gaze Refinement For Micro-Expression Recognition: Enhancing Event-Based Eye Tracking With Motion-Aware Post-Processing, Panahetipola Mudiyanselage Nuwan Bandara, Thivya Kandappu, Archan Misra

Research Collection School Of Computing and Information Systems

Event-based eye tracking holds significant promise for fine-grained cognitive state inference, offering high temporal resolution and robustness to motion artifacts, critical features for decoding subtle mental states such as attention, confusion, or fatigue. In this work, we introduce a model-agnostic, inference-time refinement framework designed to enhance the output of existing event-based gaze estimation models without modifying their architecture or requiring retraining. Our method comprises two key post-processing modules: (i) Motion-Aware Median Filtering, which suppresses blink-induced spikes while preserving natural gaze dynamics, and (ii) Optical Flow-Based Local Refinement, which aligns gaze predictions with cumulative event motion to reduce spatial jitter and …

A Systematic Review Of School-Based Behavioral Interventions And The Symbolic Labor Of Inclusion For Children With Chronic Illness, Efthymia Efthymiou, Dimitra V. Katsarou, Maria Sofologi, Kalliopi Megari, Soultana Papadopoulou, Evangelos Mantsos, Salma Daiban

All Works

Background: Chronic illness affects children’s health and disrupts the spatial and temporal aspects of schooling by complicating attendance, interrupting learning routines, and exposing institutional rigidity. While many educational systems treat chronicity as an exception to be managed, this review reconceptualizes it as a pedagogical and symbolic challenge to normative assumptions about inclusion, care, and participation. Objective: To systematically examine how school-based behavioral and psychosocial interventions support children and adolescents with chronic health conditions (CHCs) in inclusive educational settings and to analyze what these interventions reveal about institutional practices of care and recognition. Methods: Following PRISMA 2020 guidelines, we conducted a …

Cultivating Identity, Workforce Readiness, And Heutagogical Lifelong Learning: The Case For Student-Trained Ai Agents In Postsecondary Education, James Hutson

Faculty Scholarship

The accelerated integration of human-centered artificial intelligence and human-computer intelligent interaction (HCII) into postsecondary education has inaugurated a paradigm shift in how learners develop cognitive, socio emotional, and professional competencies. This article argues that requiring students to iteratively build and refine custom AI agents—digital “clones” or GPTs trained on their evolving beliefs, knowledge, values, and interests addresses three pressing challenges facing contemporary higher education. First, the process of self-modeling via agent training compels deep self-reflection, fostering socio-economic development and identity formation during the critical neurodevelopmental period that extends into the mid-twenties. Second, as industry trends increasingly prioritize the creation of …

Toward Explainable Machine Learning Methods For Stroke Patient Outcomes In Tennessee, Monireh Rahmati

Masters Theses and Doctoral Dissertations

Stroke is one of the leading causes of long-term disability and death in the United States. Stroke patients often face severe health consequences, significantly impacting their lives and placing a substantial financial burden on their families and the wider healthcare system. Therefore, reliable predictions of various patient outcomes, such as early hospital readmission, length of stay (LOS) in the hospital, and risk of mortality, can help patients and healthcare providers in various aspects. Furthermore, successful modeling of such phenomena can help identify the influential factors affecting the patient outcomes, and, by this, improve the quality of care for patients. In …

Advancing Structural Applications Of Nickel Electrodeposition Onto Additively Manufactured Polymers, George W. Thompson

Masters Theses and Doctoral Dissertations

Metal electrodeposition onto polymeric substrates yields a favorable method to produce high-quality components for injection molding and other manufacturing and engineering applications. This work examines the fundamental electrochemical mechanisms involved in the deposition of nickel from a Watts-type electrolyte. It outlines the complex interplay of electrolyte composition and process parameters to optimize deposition quality. Furthermore, this thesis contrasts traditional primary metallization methods with advanced techniques such as silver conductive paint revealing distinct advantages. Evaluations of additive-free and saccharin-enhanced Watts electrolytes demonstrate that saccharin significantly improves surface smoothness, reduces porosity, enhances hardness to over 550 HV, and increases tensile strength up …

Modeling Reactive Rarefied Flows In Chemical Vapor Infiltration Using Direct Simulation Monte Carlo, Ege C. Ek

Masters Theses and Doctoral Dissertations

Chemical Vapor Infiltration (CVI) is a key method for fabricating silicon carbide (SiC) matrix composites. Gas-phase precursors flow into a porous fiber, react, and deposit to form the ceramic matrix. Deposition quality and rate are strongly influenced by surface reactions, depending on temperature, gas flow, and reactor pressure. This study applies a computational model to better understand rarefied gas behavior and surface chemistry during CVI. We use the Direct Simulation Monte Carlo method to simulate gas flow and chemical reactions around the fibers. Simulations span Knudsen numbers 0.001–20 and temperatures 1000–1600 K (near‑continuum to free‑molecular). As rarefaction increases, deposition rate …

Dean's Research Newsletter, August 2025, Neil W. Schluger

SOM Dean's Research Newsletter

No abstract provided.

Badrobots Activity Guide, Claes Thelemarck

UNH Cooperative Extension

No abstract provided.

World Transit Research August 2025 Newsletter, Institute Of Transport Studies Monash University

World Transit Research Newsletter

No abstract provided.

Technoscientific Imaging And The Territorialization Of Ocean Depth, João Afonso Baptista, Pedro Figueiredo Neto, Irus Braverman, Philip Steinberg, Gabriella Palermo, Stefan Helmreich, Melody Jue, John Childs

Journal Articles

Once the last unclaimed solid expanse on Earth, the ocean floor has become one of the most contested spaces in contemporary geopolitics. The data-imagery produced by technoscience serves as the ultimate tool for nations asserting sovereignty in this territorial race. This symposium gathers diverse perspectives on the ongoing expansionist drive on the seabed, drawing inspiration from Abissal——a film-article featuring the Portuguese modern odyssey on the ocean floor that serves as the symposium’s centerpiece. Aligned with modern ocean law, technoscience strives to render ocean depth visible to politics and territorializable for coastal states. However, the submerged prolongations and divisions it proffers …