Physical Sciences and Mathematics Commons^™

Large Language Model Powered Agents In The Web, Yang Deng, An Zhang, Yankai Lin, Xu Chen, Ji-Rong Wen, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Web applications serve as vital interfaces for users to access information, perform various tasks, and engage with content. Traditional web designs have predominantly focused on user interfaces and static experiences. With the advent of large language models (LLMs), there’s a paradigm shift as we integrate LLM-powered agents into these platforms. These agents bring forth crucial human capabilities like memory and planning to make them behave like humans in completing various tasks, effectively enhancing user engagement and offering tailored interactions in web applications. In this tutorial, we delve into the cutting-edge techniques of LLM-powered agents across various web applications, such as …

The Impact Of Ai On Ux: Challenges And Opportunities, Susan Stephanie Wells

Theses

Integrating artificial intelligence (AI) in user experience (UX) design is reshaping the field of UX, offering new opportunities and challenges for designers. This thesis project explores the multifaceted relationship between AI and UX design, focusing on the challenges, opportunities, and skills demanded of UX designers in the age of AI. Through a review of academic research and real-world experiences, this project studies the impact of AI on web design processes, UX testing, and data analysis. Key findings highlight the transformative potential of AI in enhancing user experiences, from suggesting website structures to facilitating UX testing and data analysis.

Comparative analysis …

Learning, Optimizing, And Simulating Fermions With Quantum Computers, Andrew Zhao

Physics & Astronomy ETDs

Fermions are fundamental particles which obey seemingly bizarre quantum-mechanical principles, yet constitute all the ordinary matter that we inhabit. As such, their study is heavily motivated from both fundamental and practical incentives. In this dissertation, we will explore how the tools of quantum information and computation can assist us on both of these fronts. We primarily do so through the task of partial state learning: tomographic protocols for acquiring a reduced, but sufficient, classical description of a quantum system. Developing fast methods for partial tomography addresses a critical bottleneck in quantum simulation algorithms, which is a particularly pressing issue for …

Dlvs4audio2sheet: Deep Learning-Based Vocal Separation For Audio Into Music Sheet Conversion, Nicole Teo, Zhaoxia Wang, Ezekiel Ghe, Yee Sen Tan, Kevan Oktavio, Alexander Vincent Lewi, Allyne Zhang, Seng-Beng Ho

Research Collection School Of Computing and Information Systems

While manual transcription tools exist, music enthusiasts, including amateur singers, still encounter challenges when transcribing performances into sheet music. This paper addresses the complex task of translating music audio into music sheets, particularly challenging in the intricate field of choral arrangements where multiple voices intertwine. We propose DLVS4Audio2Sheet, a novel method leveraging advanced deep learning models, Open-Unmix and Band-Split Recurrent Neural Networks (BSRNN), for vocal separation. DLVS4Audio2Sheet segments choral audio into individual vocal sections and selects the optimal model for further processing, aiming towards audio into music sheet conversion. We evaluate DLVS4Audio2Sheet’s performance using these deep learning algorithms and assess …

Flipped Classroom For Linear Algebra At Undergraduate Level, M. Thulasidas

Research Collection School Of Computing and Information Systems

In this article, we describe our experience in developing an undergraduate Linear Algebra course tailored to highlight its relevance and applicability in Computer Science. Over the course of three years, the course transitioned from a traditional direct-instruction format to a flipped-classroom design, resulting in positive student learning outcomes. This article covers the course design philosophy, its syllabus, learning objectives, and the incorporation of both quantitative and qualitative student feedback in shaping the course. Furthermore, the article shares the insights gleaned from our experience, which can serve as best practices for instructors aiming to deliver a successful Linear Algebra course for …

The Grader: A Grading Assistant For Lab Tests And A Teaching Tool, M. Thulasidas, David Lo

Research Collection School Of Computing and Information Systems

This article presents the design and implementation of the Grader, a grading assistant application deployed for a Web Application Development course at our school. The Grader is equipped to handle various logistical aspects of lab tests, including file management, consistent application of rubrics, and auto-grading of questions with test cases. Additionally, it incorporates heuristic rules to detect cheating attempts. We anticipate that the Grader will find widespread utility in programming courses where lab tests serve as summative assessments. Developed within the same programming environment taught in the class, the Grader also serves as a pedagogical tool, demonstrating to students a …

Exploring Diffusion Time-Steps For Unsupervised Representation Learning, Zhongqi Yue, Jiankun Wang, Qianru Sun, Lei Ji, Eric I-Chao Chang, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Representation learning is all about discovering the hidden modular attributes that generate the data faithfully. We explore the potential of Denoising Diffusion Probabilistic Model (DM) in unsupervised learning of the modular attributes. We build a theoretical framework that connects the diffusion time-steps and the hidden attributes, which serves as an effective inductive bias for unsupervised learning. Specifically, the forward diffusion process incrementally adds Gaussian noise to samples at each time-step, which essentially collapses different samples into similar ones by losing attributes, e.g., fine-grained attributes such as texture are lost with less noise added (i.e., early time-steps), while coarse-grained ones such …

Regret-Based Defense In Adversarial Reinforcement Learning, Roman Belaire, Pradeep Varakantham, Thanh Hong Nguyen, David Lo

Research Collection School Of Computing and Information Systems

Deep Reinforcement Learning (DRL) policies are vulnerable to adversarial noise in observations, which can have disastrous consequences in safety-critical environments. For instance, a self-driving car receiving adversarially perturbed sensory observations about traffic signs (e.g., a stop sign physically altered to be perceived as a speed limit sign) can be fatal. Leading existing approaches for making RL algorithms robust to an observation-perturbing adversary have focused on (a) regularization approaches that make expected value objectives robust by adding adversarial loss terms; or (b) employing "maximin'' (i.e., maximizing the minimum value) notions of robustness. While regularization approaches are adept at reducing the probability …

Robust Auto-Scaling With Probabilistic Workload Forecasting For Cloud Databases, Haitian Hang, Xiu Tang, Jianling Sun, Lingfeng Bao, David Lo, Haoye Wang

Research Collection School Of Computing and Information Systems

Auto-scaling is crucial for achieving elasticity in cloud databases as well as other cloud systems. Predictive auto-scaling, which leverages forecasting techniques to adjust resources based on predicted workload, has been widely adopted. However, the inherent inaccuracy of forecasting presents a significant challenge, potentially causing resource under-provisioning. To address this challenge, we propose robust predictive auto-scaling that considers the uncertainty in forecasts. Unlike previous predictive approaches that rely on single-valued forecasts, we leverage probabilistic forecasting techniques to generate quan-tile forecasts, providing a more comprehensive understanding of the potential future workloads. By formulating the auto-scaling problem as a robust optimization problem, we …

Automatic Grading Of Short Answers Using Large Language Models In Software Engineering Courses, Nguyen Binh Duong Ta, Yi Meng Chai

Research Collection School Of Computing and Information Systems

Short-answer based questions have been used widely due to their effectiveness in assessing whether the desired learning outcomes have been attained by students. However, due to their open-ended nature, many different answers could be considered entirely or partially correct for the same question. In the context of computer science and software engineering courses where the enrolment has been increasing recently, manual grading of short-answer questions is a time-consuming and tedious process for instructors. In software engineering courses, assessments concern not just coding but many other aspects of software development such as system analysis, architecture design, software processes and operation methodologies …

Lecture-Style Tutorial: Towards Graph Foundation Models, Chuan Shi, Cheng Yang, Yuan Fang, Lichao Sun, Philip Yu

Research Collection School Of Computing and Information Systems

Emerging as fundamental building blocks for diverse artificial intelligence applications, foundation models have achieved notable success across natural language processing and many other domains. Concurrently, graph machine learning has gradually evolved from shallow methods to deep models to leverage the abundant graph-structured data that constitute an important pillar in the data ecosystem for artificial intelligence. Naturally, the emergence and homogenization capabilities of foundation models have piqued the interest of graph machine learning researchers. This has sparked discussions about developing a next-generation graph learning paradigm, one that is pre-trained on broad graph data and can be adapted to a wide range …

The Role Of Student Motivation In Integrating Ai Into Web Design Education: A Longitudinal Study, Jason Lively, James Hutson

Faculty Scholarship

Amidst the current wave studies of artificial intelligence (AI) in education, this longitudinal case study, spanning Spring 2023 to Spring 2024, delves into the integration of AI in the UI/UX web design classroom. By introducing both text-based and image-based AI tools to students with varying levels of skill in introductory web design and user experience (UX) courses, the study observed a significant enhancement in student creative capabilities and project outcomes. The utilization of text-based generators markedly improved writing efficiency and coding, while image-based tools facilitated better ideation and color selection. These findings underscore the potential to augment traditional educational methods, …

Analyzing Information Diffusion In Social Media Networks, Amin Riazi

Masters Theses and Doctoral Dissertations

Social media is a dynamic platform where a wide range of information is shared, including both true and false content, and it involves interactions between human users and social bots. This study investigates information diffusion patterns on X (formerly Twitter) by analyzing retweet (repost) network topologies. The results reveal distinct behavioral patterns for humans and bots when spreading true and false information, highlighting the need for further examination of their roles in information dissemination. Moreover, this study tackles the challenge of differentiating between broadcast and viral information diffusion on X, acknowledging the possibility of genuine information also being potentially misleading. …

Biotinylated Protein Capture And Tween-20 Assisted Exosome Isolations Via Capillary Chanelled Polymer Fiber, Md Khalid Bin Islam

All Theses

In the past two decades, Marcus and colleagues have led the advancement of capillary-channeled polymer (C-CP) fiber stationary phases for biomacromolecule separations, including proteins and nanovesicles. These stationary phases offer notable advantages, including low cost ($5/column), straightforward column fabrication, and the capacity for on-column surface modification, enabling high chemical selectivity. This study examines a C-CP fiber modification strategy for capturing target proteins and develops a Tween-20-assisted C-CP-based hydrophobic interaction chromatography (HIC) method for the isolation and quantification of exosomes from human urine.

The quest for higher-throughput and cost-effective protein isolation techniques remains a key focus in a variety of end …

A Post-Quantum Mercurial Signature Scheme, Madison Mabe

All Theses

This paper introduces the first post-quantum mercurial signature scheme. We also discuss how this can be used to construct a credential scheme, as well as some practical applications for the constructions.

Mechanical And Thermal Measurement Techniques For Crystalline-Core/Crystalline-Clad Optical Fibers, Evan Watkins

All Theses

Optical fiber laser systems offer advantages such as high optical gain, efficient cooling, and the production of high-quality optical beams. Fiber lasers are characterized by their unique core-cladding structure, providing optical benefits and mechanical properties that impact their performance. Interests in materials such as yttrium aluminum garnets (YAG) and lutetium oxide (Lu2O3 also lutetia) as laser mediums are due to their high average power capabilities, but thermal management remains a challenge. This thesis discusses the choice of ytterbium (Yb3+) as a dopant in YAG and lutetia, exploring its electronic structure and relevance to thermal properties. The thesis focuses on the …

Photobiomodulation Of Bovine Oocytes During Maturation Increases Atp Content And Enhances Subsequent Embryonic Development, Kendall Richey

All Theses

Bovine in-vitro production(IVP) of embryos is a growing field for producing offspring with desirable genetics in the cattle industries. While IVP is effective, oocyte cytoplasmic maturation is compromised, and bovine oocytes matured in-vitro have reduced metabolic activity than those matured in-vivo. Mitochondria are the central unit of oocyte metabolism, producing ATP through OXPHOS. Photobiomodulation is a light treatment reported to improve metabolic activity. In five experiments, we measured the effects of photobiomodulation treatment with red LED at 16-(L-16) and 20 h(L-20) of bovine oocyte in-vitro maturation on subsequent embryonic development, mitochondrial activity and nuclear progression. Bovine COCs were aspirated …

Remnant Polarization And Structural Arrangement In P(Vdf-Trfe) Electrospun Fiber Meshes Affect Osteogenic Differentiation Of Human Mesenchymal Stromal Cells, Bahareh Azimi, Massimiliano Labardi, Mohammad Sajad Sorayani Bafqi, Teresa Macchi, Claudio Ricci, Veronica Carnicelli, Lorenzo Scarpelli, Istiak Hussain, Francesca Matino, Mohammed Uddin

Physics & Astronomy Faculty Publications

Highlights

• The type of solvent had noticeable effects on morphology and piezoelectric properties of P(VDF-TrFE) electrospun fibers.

• Using MEK as a solvent and specific environmental conditions led to the obtainment of surface nanopores.

• Uniaxially aligned fibers exhibited higher β phase and mechanical properties than random ones.

• Randomly oriented fibers had higher remnant piezoelectric properties (Vout, d31 piezoelectric coefficient) than aligned ones.

• Human mesenchymal stem cells cultured on randomly oriented fibers showed an accelerated osteogenic differentiation.

Abstract

Many tissues and cells are influenced by mechano-electric stimulation, thus the application of piezoelectric materials has recently received considerable attention in tissue engineering. …

Weighted Ehrhart Theory: Extending Stanley's Nonnegativity Theorem, Esme Bajo, Robert Davis, Jesús A. De Loera, Alexey Garber, Sofía Garzón Mora, Katharina Jochemko, Josephine Yu

School of Mathematical & Statistical Sciences Faculty Publications

We generalize R. P. Stanley's celebrated theorem that the h∗-polynomial of the Ehrhart series of a rational polytope has nonnegative coefficients and is monotone under containment of polytopes. We show that these results continue to hold for weighted Ehrhart series where lattice points are counted with polynomial weights, as long as the weights are homogeneous polynomials decomposable as sums of products of linear forms that are nonnegative on the polytope. We also show nonnegativity of the h∗-polynomial as a real-valued function for a larger family of weights.

We generalize R. P. Stanley's celebrated theorem that the h ⁎ -polynomial of …

A Statistical Fetch Model For Water Wave Glint Correction Using Worldview-3 Imagery, Amanda Jade Quintanilla

Theses and Dissertations

Sun glint in satellite imagery of the water surface contaminates the upwelling signal received by a detector. Many models exist that attempt to correct for this wave facet effect and phenomena. In this work a model for sun glint correction is created using the comparison of image transects between two nearly simultaneously collected images of the same area, although with differing sensor geometry. One image utilized in this research is almost entirely glint free while the other is contaminated by water wave facet glint. Although many models for removing sun glint exist based on various techniques, none are completely accurate, …

Modelling Terrestrial And Potential Extraterrestrial Photopigments Via Density Functional Theory, Dorothea Illner

Theses and Dissertations

In the search for extraterrestrial life, biosignatures play a crucial role in identifying its putative traces. A commonly known and robust biosignature is the Vegetation Red Edge (VRE), which can be described as a sharp increase of reflectance observed from a planet and stems from the light absorption of photopigments in specific regions in the electromagnetic spectrum. For Earth, this VRE is known to occur around 700 nm, however, if the photopigments absorb light in different regions and have different structures the VRE could experience a wavelenght shift.

In this work, Chlorophyll a and a potential photopigment precursor called Phot0 …

Denoising Diffusion Probabilistic Models Based Accelerated Mri, Alexander Francis Bugielski

Theses and Dissertations

Magnetic Resonance Imaging (MRI) is a cornerstone in obtaining intricate visualizations of anatomy and physiological processes within the human body. However, its extensive scan duration not only causes patient discomfort but also increases the likelihood of motion-induced artifacts in the images. To address such a challenge, this study investigates deep neural network models for reconstructing high-resolution MRI images from noisy and significantly undersampled data in a supervised learning manner. Specifically, it compares three models: a conventional U-Net, a self-attentive U-Net, and an innovative probabilistic diffusion model that builds upon the self-attentive U-Net architecture. These models are evaluated on their ability …

Representation Learning For Generative Models With Applications To Healthcare, Astronautics, And Aviation, Van Minh Nguyen

Theses and Dissertations

This dissertation explores applications of representation learning and generative models to challenges in healthcare, astronautics, and aviation.

The first part investigates the use of Generative Adversarial Networks (GANs) to synthesize realistic electronic health record (EHR) data. An initial attempt at training a GAN on the MIMIC-IV dataset encountered stability and convergence issues, motivating a deeper study of 1-Lipschitz regularization techniques for Auxiliary Classifier GANs (AC-GANs). An extensive ablation study on the CIFAR-10 dataset found that Spectral Normalization is key for AC-GAN stability and performance, while Weight Clipping fails to converge without Spectral Normalization. Analysis of the training dynamics provided further …

Analyzing The Impact Of Oil And Gas-Contaminated Fluids On Soil In Eddy And Lea Counties Using Sequential Extraction, Mary Adu-Gyamfi

Open Access Theses & Dissertations

The study investigates the impacts of oil and gas-contaminated fluids on soil within the context of Eddy and Lea Counties in New Mexico, the United States. The increase in oil and gas operations has led to enormous wastewater production that is handled, stored, and transported, leading to thousands of spills yearly. The continuous and accidental release of this wastewater poses several risks to the environment due to its high levels of hazardous pollutants. Several studies were conducted on how to reduce the contaminants; however, the impact caused on the environment and the fate of the spilled fluids is unclear due …

Water Sufficiency For Organismal Function In Dryland Critical Zones, Lindsey Dacey

Open Access Theses & Dissertations

Water availability is crucial for organismal survival and growth in dryland environments, affecting both ecological interactions and carbon dynamics. The goal of this thesis is to develop soil water release curves (SWRCs) that link soil water potentials (Ψ) to soil water content (θ). Using the SWRCs, temporal soil water sufficiency curves are developed, which quantify the amount of time that dryland critical zones have enough water to sustain the physiology of organisms. These curves allow for effectively indicating water availability across different species, coverage types, and soil conditions, enhancing our understanding of water dynamics in drylands and contributing important parameters …

Assessing Nordihydroguaiaretic Acid Properties And Its Potential Therapeutic Effect For Glioblastoma, Jose Arturo Guerrero

Open Access Theses & Dissertations

This study employs a combination of theoretical and experimental analysis to spectroscopically investigate the biomechanistic structure relationship and therapeutic effects of the Nordihydroguaiaretic Acid (NDGA) chemical derived from the Larrea Tridentata plant. These relationships are crucial for understanding NDGA's efficacy in disease prevention, treatment, and potential toxicological effects. While the medicinal and antiviral properties of the NDGA have been studied extensively, there remains a gap in optically identifying and reporting its structural changes. The current research successfully reveals evident trends in NDGA's vibrational signatures, particularly highlighting the absence of the Raman feature at 780 〖cm〗^(-1) as indicative of a fully …

Earth Abundant Catalyst For Evironmental Sustainability Applications, Javier Hernandez

Open Access Theses & Dissertations

The development of experimental methodologies for synthesizing a diverse range of metallic and metal oxide nanoparticles tailored for sustainable water treatment applications was investigated. These nanoparticles are prepared using environmentally friendly and scalable synthesis methods, underscoring their potential for large-scale production. Synthesized nanoparticles are harnessed for various processes, including catalysis and electrocatalysis, with a primary objective of degrading and removing organic pollutants from water. Key to this research is the encapsulation of nanoparticles within solid supports. Multiple methodologies are explored to engineer supports that ensure nanoparticle stabilization, monodispersion, and prevent unintended release into the environment. Two alternative approaches were evaluated …

Local Map Of The Interstellar Fuv Field, David Lomeli

Open Access Theses & Dissertations

The strength of the far-ultraviolet field is an important parameter in determining the chemistry that takes place in star-forming regions. FUV radiation is produced by bright stars in the galaxy and absorbed by interstellar dust. By using newly available maps of the dust distribution, in conjunction with catalogs of stellar positions and spectra, we created a local map of the FUV field ranging from 6-13.6 eV. We find a value of 6.57*10^-14 erg/cm^3 at the location of the Sun. This value, alongside its spectra, follow previous measurements and estimates of the FUV field at the Sun. We further study the …

Modeling The Spatiotemporal Variations Of The Magnetic Field In Active Regions On The Sun Using Deep Neural Networks, Godwill Asare Mensah Mensah

Open Access Theses & Dissertations

Solar active regions are areas on the Sun's surface that have especially strong magnetic fields. Active regions are usually linked to a number of phenomena that can have serious detrimental consequences on technology and, in turn, human life. Examples of these phenomena include solar flares and coronal mass ejections, or CMEs. The precise predictionof solar flares and coronal mass ejections is still an open problem since the fundamental processes underpinning the formation and development of active regions are still not well understood. One key area of research at the intersection of solar physics and artificial intelligence is deriving insights from …

Computational Study Of Fluorescent Molecular Probes For The Early Detection Of Alzheimer's Disease, Gabriela Elizabeth Molina Aguirre

Open Access Theses & Dissertations

Alzheimer's disease is a debilitating brain disorder that affects memory, thinking, and behavior. It is the most prevalent form of dementia and the seventh leading cause of death in the United States. As, researchers have identified biomarkers that can indicate the presence of the disease many years before the first symptoms appear, so there is an opportunity for early detection and treatment follow-ups, which could significantly improve the quality of life for those affected by this disease.This dissertation investigates the relationship between molecular structure and optical properties of donor-bridge-acceptor (DBA) fluorescent molecular probes designed for detecting Amyloid-β and p-Tau protein …