Digital Commons Network^™

Tetd: Trusted Execution In Trust Domains, Zhanbo Wang, Jiaxin Zhan, Xuhua Ding, Fengwei Zhang, Ning Hu

Research Collection School Of Computing and Information Systems

Intel TDX empowers cloud service providers to construct confidential virtual machines called trust domains (TDs) on x86 platforms. Similar to its counterparts from AMD and Arm, TDX's hardware based protection over integrity and secrecy of virtual machine memory and vCPU states inevitably hinders legitimate virtual machine management such as introspection. At the presence of compromised high-privileged software (e.g., the guest kernel), neither the cloud service provider nor the TD owner can securely carry out a task within the TD. To tackle this problem, we propose TETD, an in-TD trusted execution technique without trusting any TD system software. Our design does …

Oblivious Digital Tokens, Mihael Liskij, Xuhua Ding, Gene Tsudik, David A. Basin

Research Collection School Of Computing and Information Systems

A computing device typically identifies itself by exhibiting unique measurable behavior or by proving its knowledge of a secret. In both cases, the identifying device must reveal information to a verifier. Considerable research has focused on protecting identifying entities (provers) and reducing the amount of leaked data. However, little has been done to conceal the fact that the verification occurred.We show how this problem naturally arises in the context of digital emblems, which were recently proposed by the International Committee of the Red Cross to protect digital resources during cyber-conflicts. To address this new and important open problem, we define …

Zero-Shot Generalist Graph Anomaly Detection With Unified Neighborhood Prompts, Chaoxi Niu, Hezhe Qiao, Changlu Chen, Ling Chen, Guansong Pang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD), which aims to identify nodes in a graph that significantly deviate from normal patterns, plays a crucial role in broad application domains. However, existing GAD methods are one-model-for-one-dataset approaches, i.e., training a separate model for each graph dataset. This largely limits their applicability in real-world scenarios. To overcome this limitation, we propose a novel zero-shot generalist GAD approach UNPrompt that trains a one-for-all detection model, requiring the training of one GAD model on a single graph dataset and then effectively generalizing to detect anomalies in other graph datasets without any retraining or fine-tuning. The key insight …

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 …

Consistent Client Simulation For Motivational Interviewing-Based Counseling, Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Nicholas Gabriel Lim, Cameron Shi Ern Tan, Phey Ling Kit, Jenny Xiuhui Giam, John Pinto, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Simulating human clients in mental health counseling is crucial for training and evaluating counselors (both human or simulated) in a scalable manner. Nevertheless, past research on client simulation did not focus on complex conversation tasks such as mental health counseling. In these tasks, the challenge is to ensure that the client’s actions (i.e., interactions with the counselor) are consistent with with its stipulated profiles and negative behavior settings. In this paper, we propose a novel framework that supports consistent client simulation for mental health counseling. Our framework tracks the mental state of a simulated client, controls its state transitions, and …

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

Empowering The User Through Inclusive Technical Writing, Robert Enriquez

English Theses & Dissertations

This thesis examines the issues in technical writing due to a disconnect between writer and reader/user. I am basing this study on my real-life job. I am a manager at a well-known sneaker retailer. In August 2024, there was a Workforce Management (WFM) software that the sneaker retailer launched in the retail stores. The WFM itself was glitched and unfinished. Not much disclaimer, training or preparation was given other than these procedural documents the sneaker retailer gave. If all the store managers had to depend on was technical documentation to prepare for this already glitched WFM, then I argue there …

Advanced Analytical Biosensing For Cancer Detection And Neural Diagnostics Using Tapered Optical Fiber (Tof), Protonic, And High Throughput Microplate –Based Technologie, Bayan Hassan Alharbi

Chemistry & Biochemistry Theses & Dissertations

This dissertation investigates the development and application of advanced biosensing technologies to enhance early disease detection, neurological diagnostics, and bioactive compound evaluation. The research spans four key areas. First, it introduces tapered optical fiber (TOF)-based plasmonic biosensors for the non-invasive detection of prostate cancer, demonstrating high sensitivity and specificity compared to conventional diagnostic methods.

Second, it explores the use of fluorescent biosensors to test the Transmembrane Electrostatically Localized Proton (TELP) theory, shedding light on the role of localized protons in neuronal signaling and energy transfer. Third, the work presents a high-throughput, microplate-based biosensing platform for analyzing mitochondrial function under nanosecond …

Synergistic Enhancement Of Tribological Behavior And Colloidal Stability In Cuo Nanolubricants Via Ligand Tuning, Sherif Elsoudy, Sayed Akl Akl Dr, Sayed Akl A. Akl Dr, Esm Lane, Abas Hadawey, Philip D. Howes

Mechanical Engineering

Nanoparticle-based lubricants, or nanolubricants, can exhibit superior tribological properties compared to unmodified base oils. However, these performance gains are highly dependent on the nanoparticle surface chemistry, particularly in maintaining stable colloidal dispersions. This study explores the influence of oleic acid (OA) and oleylamine (OAm) functionalization on the tribological and colloidal properties of CuO nanoparticles dispersed in an SAE 20W50 base oil. We present a hybrid optimization framework combining Response Surface Methodology (RSM) with Bayesian Optimization (BO) to identify the optimal OA to OAm ratio (OA–OAm) for CuO nanolubricants. Unlike prior studies that employed either RSM alone or trial-and-error approaches, this …

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

Mitigating Ai Bias With Prompt Augmentation, Verónica C. Gonzales

Faculty Scholarship

Large-language-model (“LLM”) outputs are inevitably shaped by assumptions, biases, and attitudes rooted in racism and other systemic inequities embedded in the civil-law tradition. This Equity-in-Justice CLE focuses on helping civil legal service providers recognize and work to eliminate biases in generative AI outputs through equity-centered prompt augmentation and engineering. These small, cost-free interventions re-orient LLMs assistance toward fairness, cultural competency, and client-centered outcomes. Participants leave with an action plan for embedding anti-bias safeguards in everyday research, drafting, and client communication tasks.

Debate, Reflect, And Distill: Multi-Agent Feedback With Tree-Structured Preference Optimization For Efficient Language Model Enhancement, Xiaofeng Zhou, Heyan Huang, Lizi Liao

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) continue to set new standards in knowledge-intensive and complex reasoning tasks, yet their high computational demands limit widespread adoption. While distilling large models into smaller ones offers a sustainable solution, current techniques—such as static knowledge distillation, resource-intensive reinforcement learning from human feedback, or limited self-reflection—struggle to yield substantial and lasting performance gains. In this paper, we present a novel Debate and Reflect (D&R) framework that orchestrates multi-turn debates between smaller models and stronger teacher models, eliciting actionable feedback (e.g., error analysis, corrective strategies) to guide student models. Further, we introduce Tree-structured Direct Preference Optimization (T-DPO) to …

Memotune: A Measure And Moment-Driven Fine-Tuning Framework For Quantized Large Language Models, Yun Zhang, Xue Geng, Lizi Liao, Jintong Sun, Minghe Yu, Ge Yu

Research Collection School Of Computing and Information Systems

Quantizing large language models (LLMs) is essential for reducing memory and computational costs in natural language processing. Existing methods combine quantization with parameter-efficient fine-tuning but often fail to meet practical performance requirements. This paper introduces MeMoTune, a novel fine-tuning framework for quantized LLMs. By employing a measure and moment approach within a low-rank approximation framework in probability measure space, MeMoTune optimizes the objective function for superior fine-tuning results. The update process is further refined through scaled gradient, enhancing convergence efficiency and noise robustness. Experiments on tasks like text generation, summarization, and understanding show MeMoTune significantly outperforms state-of-the-art methods, e.g. fine-tuning …

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 …

Mathematical Modeling Of Zoonotic Disease Transmission Under The Impact Of Land Use Change, Collin A. Kilmer

Masters Theses and Doctoral Dissertations

Land conversion is occurring worldwide due to a growing population and expanding economy. This process increases the likelihood of pathogen spillover, posing significant economic and public health risks. The objective of this thesis is to model pathogen spillover under the impact of land use change. We develop a zoonotic disease transmission model that describes spillover of Puumala virus from bank voles to humans during land conversion. Our model introduces a land conversion index to capture the dependence of the carrying capacity and the death rate of bank voles on the proportion of converted land. This index is then used to …

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.

Understanding The Fluid Source During Alteration Of Former Oceanic Lithosphere In The Polygenetic Ingalls Ophiolite Complex, Washington, Usa, Zachary Russell Stevens

Masters Theses

The Ingalls Ophiolite Complex is an ophiolite of Jurassic age in central WA, USA. Much of the complex formed in a supra-subduction zone setting in a back-arc ocean basin during subduction of the Farallon plate beginning 192 Ma. Between formation and obduction, mafic and ultramafic lithologies experienced alteration in the presence of water, which previous studies inferred to be seawater. Previously published δ¹⁸O and δD data from the Ingalls indicates isotopic depletion possibly caused by interaction with meteoric water at current latitude (~47.5 °N). The current study reports new δD, δ¹⁸O, and Δ ′¹⁷O measurements from mafic and ultramafic lithologies …

An Injectable, Dual-Curing Hydrogel For Controlled Bioactive Release In Regenerative Endodontics, Meisam Omidi, Daniela S. Masson-Meyers, Jeffrey M. Toth

Biomedical Engineering Faculty Research and Publications

Regenerative endodontics seeks to restore the vascularized pulp–dentin complex following conventional root canal therapy, yet reliable neovascularization within the constrained root canal remains a key challenge. This study investigates the development of an injectable, dual-curing hydrogel based on methacrylated decellularized amniotic membrane (dAM-MA) and compares its performance to a conventional gelatin methacryloyl (GelMA). The dAM-MA platform was designed for biphasic release, incorporating both free vascular endothelial growth factor (VEGF) for an initial burst and matrix-metalloproteinase-cleavable VEGF conjugates for sustained delivery. The dAM-MA hydrogel achieved shape-fidelity via thermal gelation at 37 °C and possessed tunable stiffness (0.5–7.8 kPa) after visible-light irradiation. …

Predictors Of Permanent And Temporary Motor Deficits In Patients Undergoing Glioma Resection: A Systematic Review And Meta-Analysis, Brian J. Conway, Stephanie A. Armstrong, Nada Botros, Sergey Tarima, Max O. Krucoff

Biomedical Engineering Faculty Research and Publications

Objective

To identify predictors of permanent and temporary motor deficits in patients undergoing glioma resection.

Methods

A literature search accessed the databases Ovid Medline, Scopus, Web of Science, Cumulative Index to Nursing and Allied Health Literature/EBSCO, PsychInfo, Cochrane, and Wiley for studies reporting motor outcomes following surgical resection of glioma. Outcomes were stratified by patient/tumor characteristics, preoperative condition, and intraoperative factors for both permanent and temporary motor deficits. Generalized estimating equations were used to generate odds ratios.

Results

A total of 1332 titles and abstracts were reviewed resulting in the evaluation of 261 full-text articles. Data were extracted from 67 …

Anisotropic Object-Based Shifts Of Attention Arise From Unequal Influences Of Visual Field Meridians On Neural Attention Gradients, David H. Hughes, Adam J. Barnas, Adam S. Greenberg

Biomedical Engineering Faculty Research and Publications

Object-based attention prioritizes the processing of information appearing within a selected object. We previously showed an object-based shift direction anisotropy (SDA) whereby horizontal shifts are more efficient than vertical shifts (Barnas AJ, Greenberg AS. Atten Percept Psychophys 78: 1985–1997, 2016; Barnas AJ, Greenberg AS. Vis Cogn 27: 768–791, 2019), an effect modulated by the visual field meridians (Barnas AJ, Greenberg AS. Q J Exp Psychol (Hove) 77: 2516–2532, 2024). Our present aim was to understand how the functional brain mechanisms of attention give rise to the SDA. We hypothesized that the SDA arises from attentional resources being partitioned differently by …

Letter From The Editors, Raven L. Jones, Tanya Upthegrove Gregory, Alexandra M. Sánchez

Language Arts Journal of Michigan

No abstract provided.

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Language Arts Journal of Michigan

No abstract provided.