Digital Commons Network^™

The Discontinuous Petrov–Galerkin Method, Leszek Demkowicz, Jay Gopalakrishnan

Mathematics and Statistics Faculty Publications and Presentations

The discontinuous Petrov–Galerkin (DPG) method is a Petrov–Galerkin finite element method with test functions designed for obtaining stability. These test functions are computable locally, element by element, and are motivated by optimal test functions which attain the supremum in an inf-sup condition. A profound consequence of the use of nearly optimal test functions is that the DPG method can inherit the stability of the (undiscretized) variational formulation, be it coercive or not. This paper combines a presentation of the fundamentals of the DPG ideas with a review of the ongoing research on theory and applications of the DPG methodology. The …

Research On The Capacity Development Of National Science And Technology Intelligence System, Hui Li, Shu Chen, Xiao Tan

Journal of Scientific Information Research

[Purpose/significance] The unique form and systematic characteristics of great power competition, such as system-establishment and whole of society confrontation, multi-domain and multi-depth high-tech containment and counter-containment, and multi-means and multi-element mixed deterrence, determine that national scientific and technological intelligence must have considerable systemic capabilities to match and follow up.

[Method/process] This article focuses on scientific and technological intelligence institutions within the traditional system, defines the connotation of national scientific and technological intelligence system capabilities, constructs a six-dimensional capability cognition model of ""institutional improvement capability-system coordination capability-resource guarantee capability-technological innovation capability-intelligence shaping capability-safe operation capability"", and expounds its main contents.

[Result/conclusion] …

Thematic Hotspots And Strategy Analysis Of International Ai Regulatory Texts Based On Lda Models, Taitian Mao, Yihe Peng

Journal of Scientific Information Research

[Purpose/significance]This article conducts an in-depth exploration of international artificial intelligence (AI) regulatory policies and gains insights into the regulatory focuses and trends of various countries, with the aim of providing valuable references for global AI governance strategies.

[Method/process] This paper applies the LDA topic clustering analysis method to conduct an in-depth study of twenty-seven international policy documents. The aim is to accurately identify the topics, analyze the key theme words, and further reveal the regulatory hotspots in the field of artificial intelligence.

[Results/conclusion] The study reveals six core regulatory themes: systemic risk assessment, ethical and legal regulation, social impact governance, …

Culture Bears The Way, While Technology Facilitates Action: Research Of The Adoption Willingness Of Aigc In Academic Writing Among Young Scholars, Jiangfeng Liu, Zihui Wang, Zhiwei Hu, Lei Pei

Journal of Scientific Information Research

[Purpose/significance] Clarifying the influencing factors of young scholars' willingness to use AIGC and its path of action,then making effective enhancement strategies,would help to further expand the application of AIGC in academic writing.

[Method/process] This paper combs through the studies on AIGC information behavior, and identifies TOE(Technology-Organization-Envifonment，TOE) theory and research life cycle theory as the base theory. Semi-structured interviews were conducted for procedural grounded theory coding. Then, a questionnaire survey and qualitative comparative analysis using fuzzy sets were conducted to obtain the influencing factors grouping path.

[Result/conclusion] Five factors, induding content quality, system quality, perceived risk, expectation confirmation, and group norms, …

Analysis Of Vision Transformers And Domain Adaptation In Long-Range Facial Recognition, Zachary Michael Swanson

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Atmospheric turbulence presents a significant barrier to long-range facial recognition, introducing severe geometric distortions and blur that degrade image quality. This thesis investigates deep learning approaches for mitigating these effects, with a focus on transformer based architectures and domain adaptation strategies.

An in-depth benchmarking study was performed using convolutional neural networks (CNNs) and vision transformers (ViTs) on the Husker BRIAR Research Collection from up to 500m (HBRC-500) face dataset. The results demonstrated that vision transformers, particularly hierarchical vision transformers like the shifted-window (Swin) transformer, outperform CNN-based models at long distances due to their ability to model global spatial relationships and …

Cazyme Gene Cluster Diversity In Human Gut Microbiome, Yi Xing

Department of Food Science and Technology: Dissertations, Theses, and Student Research

In gut microbiome research, carbohydrate-active enzyme gene clusters (CGCs) have emerged as key functional units for understanding microbial glycan degradation. Unlike taxonomic or broad pathway annotations, CGCs offer gene-cluster-level resolution and capture substrate-specific microbial functions. However, their diversity and distribution in relation to host metabolic phenotypes, such as obesity, remain poorly characterized. This study tests the hypothesis that the composition and abundance of fiber-targeting CGCs vary between obese and healthy human gut microbiomes, reflecting distinct microbial carbohydrate utilization strategies. To examine this, we constructed a high-quality reference CGC dataset comprising 94,019 clusters from the Unified Human Gastrointestinal Genome and profiled …

Unm Pathology, Pathfinder Summer 2025, Unm Department Of Pathology

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Artificial Intelligence And Humanoid Robotics: Bioethical Implications Of Replacing Human Agency In Healthcare And Beyond, Sara Feizyab, Gabriele Bonetti, Maria Chiara Medori, Cecilia Micheletti, Immacolata De Luca, Kevin Donato, Jan Miertuš, Mehmet Sait Dundar, Miroslava Vráblová, Gary Henehan, Richard Brown, Robert Marks, Stanislav Miertus, Lorenzo Lorusso, Gianluca Martino Tartaglia, Munis Dundar, Sandro Michelini, Stephen Thaddeus Connelly, Ariola Bacu, Tommaso Beccari, Ornela Gordani, Xhilda Dhamo, Eglantina Kalluci, Dominika Vešelényiová, Iveta Dirgová Luptáková, Jiri Pospíchal, Matteo Bertelli

Research Outputs: 2025-Present

The integration of advanced artificial intelligence (AI) and humanoid robotics into healthcare represents a critical evolution in biotechnology with profound societal implications. This review explores the bioethical implications of these technologies, and their potential to displace human agency in life-critical decisions. It adopts an interdisciplinary approach encompassing ethics, law, and technology. The review examines how innovations in AI and robotics might shift autonomy from humans to machines, and addresses the accountability challenges inherent in such transitions. We synthesize discussions on the ethical management of AI and robotics, underscoring the importance of maintaining human oversight and integrating ethical standards in technology …

An Exponential Cone Integer Programming And Piece-Wise Linear Approximation Approach For 0-1 Fractional Programming, Hoang Giang Pham, Thuy Anh Ta, Tien Mai

Research Collection School Of Computing and Information Systems

We study a class of binary fractional programs commonly encountered in important application domains such as assortment optimization and facility location. These problems are known to be NP-hard to approximate within any constant factor, and existing solution approaches typically rely on mixed-integer linear programming or second-order cone programming reformulations. These methods often utilize linearization techniques (e.g., big-M or McCormick inequalities), which can result in weak continuous relaxations. In this work, we propose a novel approach based on an exponential cone reformulation combined with piecewise linear approximation. This allows the problem to be solved efficiently using standard cutting-plane or branch-and-cut procedures. …

Position: Trustworthy Ai Agents Require The Integration Of Large Language Models And Formal Methods, Yedi Zhang, Yufan Cai, Xinyue Zuo, Xiaokun Luan, Kailong Wang, Zhe Hou, Yifan Zhang, Zhiyuan Wei, Meng Sun, Jun Sun, Jing Sun, Jin Song Dong

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have emerged as a transformative AI paradigm, profoundly influencing broad aspects of daily life. Despite their remarkable performance, LLMs exhibit a fundamental limitation: hallucination—the tendency to produce misleading outputs that appear plausible. This inherent unreliability poses significant risks, particularly in high-stakes domains where trustworthiness is essential. On the other hand, Formal Methods (FMs), which share foundations with symbolic AI, provide mathematically rigorous techniques for modeling, specifying, reasoning, and verifying the correctness of systems. These methods have been widely employed in mission-critical domains such as aerospace, defense, and cybersecurity. However, the broader adoption of FMs remains constrained …

Llmscan: Causal Scan For Llm Misbehavior Detection, Mengdi Zhang, Kai Kiat Goh, Peixin Zhang, Jun Sun, Lin Xin Rose, Hongyu Zhang

Research Collection School Of Computing and Information Systems

Despite the success of Large Language Models (LLMs) across various fields, their potential to generate untruthful and harmful responses poses significant risks, particularly in critical applications. This highlights the urgent need for systematic methods to detect and prevent such misbehavior. While existing approaches target specific issues such as harmful responses, this work introduces LLMSCAN, an innovative LLM monitoring technique based on causality analysis, offering a comprehensive solution. LLMSCAN systematically monitors the inner workings of an LLM through the lens of causal inference, operating on the premise that the LLM’s ‘brain’ behaves differently when generating harmful or untruthful responses. By analyzing …

Sigfinger: A Subtle And Interactive Gnn Fingerprinting Scheme Via Spatial Structure Inference Perturbation, Ju Jia, Renjie Li, Cong Wu, Siqi Ma, Lina Wang, Robert H. Deng

Research Collection School Of Computing and Information Systems

There have been significant improvements in intellectual property (IP) protection for deep learning models trained on euclidean data. However, the complex and irregular graph-structured data in non-euclidean space poses a huge challenge to the IP protection of graph neural networks (GNNs). To address this issue, we propose a subtle and interactive GNN fingerprinting scheme through spatial structure inference perturbation, which captures the stable coordination patterns of fingerprint to guarantee the reliability of copyright verification. Specifically, the data augmentation based on adaptive graph diffusion is first exploited to generate more samples, which enables the exploration of fingerprint information from coarse to …

Llm-Based Multi-Agent Systems For Software Engineering: Literature Review, Vision And The Road Ahead, Junda He, Christoph Treude, David Lo

Research Collection School Of Computing and Information Systems

Integrating Large Language Models (LLMs) into autonomous agents marks a significant shift in the research landscape by offering cognitive abilities that are competitive with human planning and reasoning. This paper explores the transformative potential of integrating Large Language Models into Multi-Agent (LMA) systems for addressing complex challenges in software engineering (SE). By leveraging the collaborative and specialized abilities of multiple agents, LMA systems enable autonomous problem-solving, improve robustness, and provide scalable solutions for managing the complexity of real-world software projects. In this paper, we conduct a systematic review of recent primary studies to map the current landscape of LMA applications …

Diversity Optimization For Travelling Salesman Problem Via Deep Reinforcement Learning, Qi Li, Zhiguang Cao, Yining Ma, Yaoxin Wu, Yue-Jiao Gong

Research Collection School Of Computing and Information Systems

Existing neural methods for the Travelling Salesman Problem (TSP) mostly aim at finding a single optimal solution. To discover diverse yet high-quality solutions for Multi-Solution TSP (MSTSP), we propose a novel deep reinforcement learning based neural solver, which is primarily featured by an encoder-decoder structured policy. Concretely, on the one hand, a Relativization Filter (RF) is designed to enhance the robustness of the encoder to affine transformations of the instances, so as to potentially improve the quality of the found solutions. On the other hand, a Multi-Attentive Adaptive Active Search (MA3S) is tailored to allow the decoders to strike a …

Surrogate Learning In Meta-Black-Box Optimization: A Preliminary Study, Zeyuan Ma, Zhiyang Huang, Jiacheng Chen, Zhiguang Cao, Yue-Jiao Gong

Research Collection School Of Computing and Information Systems

Recent Meta-Black-Box Optimization (MetaBBO) approaches have shown possibility of enhancing the optimization performance through learning meta-level policies to dynamically configure low-level optimizers. However, existing MetaBBO approaches potentially consume massive function evaluations to train their meta-level policies. Inspired by the recent trend of using surrogate models for cost-friendly evaluation of expensive optimization problems, in this paper, we propose a novel MetaBBO framework which combines surrogate learning process and reinforcement learning-aided Differential Evolution algorithm, namely Surr-RLDE, to address the intensive function evaluation in MetaBBO. Surr-RLDE comprises two learning stages: surrogate learning and policy learning. In surrogate learning, we train a Kolmogorov-Arnold Networks …

Fashiondpo: Fine‑Tune Fashion Outfit Generation Model Using Direct Preference Optimization, Mingzhe Yu, Yunshan Ma, Lei Wu, Changshuo Wang, Xue Li, Lei Meng

Research Collection School Of Computing and Information Systems

Personalized outfit generation aims to construct a set of compatible and personalized fashion items as an outfit. Recently, generative AI models have received widespread attention, as they can generate fashion items for users to complete an incomplete outfit or create a complete outfit. However, they have limitations in terms of lacking diversity and relying on the supervised learning paradigm. Recognizing this gap, we propose a novel framework FashionDPO, which fine-tunes the fashion outfit generation model using direct preference optimization. This framework aims to provide a general fine-tuning approach to fashion generative models, refining a pre-trained fashion outfit generation model using …

Enhancing Project-Specific Code Completion By Inferring Internal Api Information, Le Deng, Xiaoxia Ren, Chao Ni, Ming Liang, David Lo, Zhongxin Liu

Research Collection School Of Computing and Information Systems

Project-specific code completion, which aims to complete code based on the context of the project, is an important and practical software engineering task. The state-of-the-art approaches employ the retrieval-augmented generation (RAG) paradigm and prompt large language models (LLMs) with information retrieved from the target project for project-specific code completion. In practice, developers always define and use custom functionalities, namely internal APIs, to facilitate the implementation of specific project requirements. Thus, it is essential to consider internal API information for accurate project-specific code completion. However, existing approaches either retrieve similar code snippets, which do not necessarily contain related internal API information, …

Learning To Solving Vehicle Routing Problems Via Local-Global Feature Fusion Transformer, Wei Li, Bing Tian Dai, Xueming Yan, Junying Zou, Zhijie Liang, Jingwen Li

Research Collection School Of Computing and Information Systems

Applying Combinatorial optimization problems such as the Vehicle Routing Problems (VRPs) have attracted increasing interest with the emergence of learning-based methods. However, existing neural approaches often struggle to generalize across diverse problem sizes and node distributions, limiting their applicability in real-world scenarios. To overcome these challenges, we propose Local-Global Feature Fusion Transformer (FusionFormer), a novel deep reinforcement learning framework that enhances both solution quality and generalization capability for solving VRPs. Specifically, we introduce a Distance-Assisted Multi-Head Attention (DA-MHA) mechanism that incorporates explicit spatial distance information into the attention computation, thereby preserving spatial consistency and facilitating more robust global representation learning. …

Personal Bankruptcy: A Bibliometric Analysis And Future Research Directions, Aqilah Nadiah Md Sahiq, Saida Farhanah Sarkam, Putri Aliah Mohd Hidzir, Nurwahida Yaakub, Shafinar Ismail, Nurbaity Sabri, Hanifiyah Yuliatul Hijriah, Himmatul Kholidah

The Indonesian Capital Market Review

Personal bankruptcy is a pertinent topic to discuss due to the surge in personal bankruptcy cases around the world and it is an important indicator of household financial problems nationally. However, only few comprehensive reviews have been conducted to date. Hence, this study provides a bibliometric review of 210 studies on personal bankruptcy authored by 496 scholars. Bibliographical data were extracted from the Scopus database and analyzed it using the Bibliometrix-R software. Based on the citation analysis metrics, we revealed the most influential articles, journals, authors, and institutions. Using the network and conceptual structure analysis, we identified three underlying research …

Duration Of Inter-Pregnancy Interval And Its Predictors Among A Sample Of Pregnant Women In Reproductive Age Attending Primary Health Care Centers In Al-Russafa /Al-Shaab Sector, Russul Mohammed, Huda Habib

Iraqi Postgraduate Medical Journal

Background: Inter-pregnancy interval is defined as the time in completed months from the reported date of live birth of the previous child to the self-reported last normal menstrual period.Study objectives: To assess the duration of inter-pregnancy interval, and to assess the possible predictors associated with the duration of inter-pregnancy interval.Participants & Methods: A cross-sectional study was carried out during the period from 1st March to 1st August 2023. A convenient sample of pregnant women in the reproductive age group (15-49 years) who attended PHCCS in Al-Shaab sector, Baghdad-Alrusafa Health Directorate. Data was collected by direct interviews with the target population, …

Level Of Awareness About Diabetic Retinopathy And Eye Checkup Practice Among Type 2 Diabetic Patients Attending Baghdad Teaching Hospital 2023., Ali Olaiwei, Mohammed Ibrahim

Iraqi Postgraduate Medical Journal

Background: Diabetic retinopathy is a common and serious complication of diabetes that can lead to vision loss or even blindness if not detected and treated early. However, some people may not have any symptoms until the condition is advanced. Therefore, regular eye exams with dilated pupils are recommended for people with diabetes to check for signs of diabetic retinopathy and other eye problems regardless of the symptoms.Objectives of the study: To measure patients’ awareness and practices regarding diabetic retinopathy, to detect the barriers for eye screening and receiving eye check-up, and to find if there is any association between level …

Septoplasty Pre And Postoperative Evaluation Regarding: (Nasal Obstruction, Rhinogenic Headache, And Eustachian Tube Function), Omar Ali, Said Al-Jaf

Iraqi Postgraduate Medical Journal

Background: Septoplasty is a surgical correction of the deviated nasal septum it is one of the most common Otorhinolaryngology operationObjective: purpose of this study is to evaluate and compare outcome of Septoplasty regarding nasal obstruction, improvement associated headache and Eustachian Tube functions by tympanometryStudy design: it is a prospective comparative study Methods: study was done from February 2021 to December 2021 at otorhinolaryngology department in Rizgary Teaching Hospital Erbil City/Iraq on Overall (60) patients 23 were female and 37 were male their age ranged between 18 and 50 years patient undergoing septoplasty because of Deviated Nasal Septum were included All …

Genu Valgum Relation With Sociodemographic Factors In A Sample Of Iraqi Children Attending Sadr Al Qanat Center For Prosthetics And Orthotics, Abdulnasser Mohammed, Faiq Gorial

Iraqi Postgraduate Medical Journal

Background: Genu valgum is a deformity resulting from the separation of ankles, when the medial faces of the knees are in contact while the person is in anatomical position, the patella and the hallux turning to the anterior direction.Aim: To evaluate the features of genu valgum and associated sociodemographic factors in children.Patients and methods: A cross-sectional study was conducted at Sader Al-Qanat Center for Prosthetics and Orthotics, a tertiary referral center in Baghdad/Iraq during the period from January to December 2022. A convenient sampling method was adopted to enrol 60 patients aged 2-14 years with genu valgum who attained the …

Effect Of Ramadan Fasting On Lipid Profile And Bmi Among A Group Of Healthy Adult People In Mosul City/ Iraq, Safana Mohammed, Manal Rasheed

Iraqi Postgraduate Medical Journal

Background :- There is increasing interest regarding the health implications of Ramadan fasting in the recent years. Many reports indicate that fasting during the holy month of Ramadan results in reduced body weight and improved metabolic parameters like lipid profile in healthy individuals . Aim of the study :- To study the effect of fasting during the holy month of Ramadan on serum lipid profile and BMI in healthy adult volunteers. Subjects and methods :- prospective cohort study was done including a total of 50 healthy volunteers. Venous blood samples were taken from volunteer, 1week before Ramadan, and , in …

Connecting Children With Artists Through A Children's Art History Book, Katie Meyer

Theses

This project is a children’s art history book that is set in an art museum. The goal of the book is to help children connect with Contemporary artists and their art. A museum setting is utilized to introduce children to museums and as an appropriate environment for viewing art. The theme of the exhibit that will be presented in the book is titled the “Innovators Exhibit.” This exhibit features six Contemporary artists from different art styles/mediums. The artists come from diverse backgrounds to expose children to a variety of cultures. Thought provoking questions will also be asked throughout the book …

Ms4 Swmpp 2025, National Technology & Engineering Solutions Of Sandia, Llc (Ntess) And Department Of Energy (Doe)

Municipal Separate Storm Sewer System (MS4) Permit

Stormwater discharges from Sandia National Laboratories/New Mexico (SNL/NM) are eligible for coverage under the National Pollutant Discharge Elimination System (NPDES) Municipal Separate Storm Sewer System (MS4) Permit. This Stormwater Management Plan (SWMP) was prepared by National Technology and Engineering Solutions of Sandia (NTESS) for the Department of Energy (DOE) National Nuclear Security Administration (NNSA) Sandia Field Office (SFO), owner of SNL/NM, located in Albuquerque, NM.

Evaluation Of Snap-Ed New Mexico’S Pilot “I Choose H2o” Campaign To Increase Water Consumption, Theresa H. Cruz, Mahtab Soleimani, Camille R. Velarde, Belinda E. Vicuna

Reports & Documents

The “I Choose H2O” campaign is a school-based pilot initiative developed by the University of New Mexico Prevention Research Center with SNAP-Ed NM funding from the USDA. Designed as a three-week social marketing effort, the campaign aimed to increase daily water consumption and reduce intake of sugar-sweetened beverages (SSBs) among middle school students. The campaign engaged students and staff through activities such as water challenges, events, tastings, and prize incentives, while also encouraging broader school-level policy, systems, and environmental (PSE) changes.

This evaluation used direct observation before and after implementation to assess changes in water fountain usage and lunchtime beverage …

Vocabulary Learning Strategies Among Saudi Efl Military Cadets: Patterns And Preferences, Fahad Aldosari

Language, Literacy, and Sociocultural Studies ETDs

This study investigates the vocabulary learning strategies (VLSs) of 126 Saudi military EFL cadets, an underexplored group in second language acquisition research. Using an explanatory sequential mixed-methods design, a questionnaire identified the most and least used strategies, followed by interviews with 10 participants for deeper insights. Findings show cadets frequently use bilingual dictionaries, vocabulary lists, and group work, while cognitive strategies like written repetition and note-taking are underutilized. No significant differences in VLS use were found between Level 1 and Level 2 cadets, suggesting stable strategy preferences. Additionally, emerging strategies such as gamified learning and collaborative storytelling indicate a shift …

Baa Nihiłdah)Zh) - Chronicles Of Diné College Graduates In Pursuit Of Educational Attainment, Kerenthia J. Swan

Teacher Education, Educational Leadership & Policy ETDs

This study investigated the educational, cultural, familial, and tribal factors that influenced the academic success of four Diné (Navajo) students who earned a college degree from the University of New Mexico. Utilizing the Diné Educational Philosophy Paradigm and Indigenous methodology grounded in Diné values and cultural perspectives, this study centered the voices and lived experiences of Diné graduates to gain insight into the supports and obstacles encountered throughout their higher educational journey. The findings highlight the crucial role of family encouragement, traditional teachings, cultural values centered on collective responsibility, and the availability of culturally supportive academic resources. A recurring theme …

Breaking The Reasoning Barrier: A Survey On Llm Complex Reasoning Through The Lens Of Self-Evolution, Tao He, Hao Li, Jingchang Chen, Runxuan Liu, Yixin Cao, Lizi Liao, Zihao Zheng, Zheng Chu, Jiafeng Liang, Ming Liu, Bing Qin

Research Collection School Of Computing and Information Systems

The release of OpenAI’s O1 and subsequent projects like DeepSeek R1 has significantly advanced research on complex reasoning in LLMs. This paper systematically analyzes existing reasoning studies from the perspective of self-evolution, structured into three components: data evolution, model evolution, and self-evolution. Data evolution explores methods to generate higher-quality reasoning training data. Model evolution focuses on training strategies to boost reasoning capabilities. Self-evolution research autonomous system evolution via iterating cycles of data and model evolution. We further discuss the scaling law of self-evolution and analyze representative O1-like works through this lens. By summarizing advanced methods and outlining future directions, this …