Physical Sciences and Mathematics Commons^™

Assessing Gtfs Accuracy, Gregory L. Newmark

Mineta Transportation Institute

The promised benefits of the General Transit Feed Specification (GTFS) Schedule and Realtime standards are dependent on the underlying quality of the data. Despite this fundamental reliance, there has been relatively little research on techniques and strategies to assess GTFS accuracy. The need for such assessment is growing as federal and state governments increasingly require transit agencies to make these data available to the public. This research fills this gap by presenting a suite of methods and metrics to assess the temporal accuracy of GTFS Realtime and the spatial accuracy of GTFS Schedule feeds. The temporal assessment demonstrates an approach …

Materials Data Science Ontology (Mds-Onto): Unifying Domain Knowledge In Materials And Applied Data Science, Van D. Tran, Jonathan E. Gordon, Alexander Harding Bradley, Balashanmuga Priyan Rajamohan, Quynh D. Tran, Gabriel Ponón, Yinghui Wu, Laura S. Bruckman, Erika I. Barcelos, Roger H. French

Student Scholarship

Ontologies have gained popularity in the scientific community as a means of standardizing concepts and terminology used in metadata across different institutions to facilitate data comprehension, sharing, and reuse. Despite the existence of frameworks and guidelines for building ontologies, the processes and standards used to develop ontologies still differ significantly, particularly in Materials Science. Our goal with the MDS-Onto Framework is to provide a unified and automated system for ontology development in the Materials and Data Sciences. This framework offers recommendations on where to publish ontologies online, how to best integrate them within the semantic web, and which formats to …

An Llm-Assisted Easy-To-Trigger Poisoning Attack On Code Completion Models: Injecting Disguised Vulnerabilities Against Strong Detection, Shenao Yan, Shen Wang, Yue Duan, Hanbin Hong, Kiho Lee, Doowon Kim, Yuan Hong

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have transformed code completion tasks, providing context-based suggestions to boost developer productivity in software engineering. As users often fine-tune these models for specific applications, poisoning and backdoor attacks can covertly alter the model outputs. To address this critical security challenge, we introduce CODEBREAKER, a pioneering LLM-assisted backdoor attack framework on code completion models. Unlike recent attacks that embed malicious payloads in detectable or irrelevant sections of the code (e.g., comments), CODEBREAKER leverages LLMs (e.g., GPT-4) for sophisticated payload transformation (without affecting functionalities), ensuring that both the poisoned data for fine-tuning and generated code can evade strong …

Style: Improving Domain Transferability Of Asking Clarification Questions In Large Language Model Powered Conversational Agents, Yue Chen, Chen Huang, Yang Deng, Wenqiang Lei, Dingnan Jin, Jia Liu, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Equipping a conversational search engine with strategies regarding when to ask clarification questions is becoming increasingly important across various domains. Attributing to the context understanding capability of LLMs and their access to domain-specific sources of knowledge, LLM-based clarification strategies feature rapid transfer to various domains in a posthoc manner. However, they still struggle to deliver promising performance on unseen domains, struggling to achieve effective domain transferability. We take the first step to investigate this issue and existing methods tend to produce one-size-fits-all strategies across diverse domains, limiting their search effectiveness. In response, we introduce a novel method, called STYLE, to …

Self-Chats From Large Language Models Make Small Emotional Support Chatbot Better, Zhonghua Zheng, Lizi Liao, Yang Deng, Libo Qin, Liqiang Nie

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have shown strong generalization abilities to excel in various tasks, including emotion support conversations. However, deploying such LLMs like GPT-3 (175B parameters) is resource-intensive and challenging at scale. In this study, we utilize LLMs as “Counseling Teacher” to enhance smaller models’ emotion support response abilities, significantly reducing the necessity of scaling up model size. To this end, we first introduce an iterative expansion framework, aiming to prompt the large teacher model to curate an expansive emotion support dialogue dataset. This curated dataset, termed ExTES, encompasses a broad spectrum of scenarios and is crafted with meticulous strategies …

Watme: Towards Lossless Watermarking Through Lexical Redundancy, Liang Chen, Yatao Bian, Yang Deng, Deng Cai, Shuaiyi Li, Peilin Zhao, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Text watermarking has emerged as a pivotal technique for identifying machine-generated text. However, existing methods often rely on arbitrary vocabulary partitioning during decoding to embed watermarks, which compromises the availability of suitable tokens and significantly degrades the quality of responses. This study assesses the impact of watermarking on different capabilities of large language models (LLMs) from a cognitive science lens. Our finding highlights a significant disparity; knowledge recall and logical reasoning are more adversely affected than language generation. These results suggest a more profound effect of watermarking on LLMs than previously understood. To address these challenges, we introduce Watermarking with …

A New Hope: Contextual Privacy Policies For Mobile Applications And An Approach Toward Automated Generation, Shidong Pan, Zhen Tao, Thong Hoang, Dawen Zhang, Tianshi Li, Zhenchang Xing, Xiwei Xu, Mark Staples, Thierry Rakotoarivelo, David Lo

Research Collection School Of Computing and Information Systems

Privacy policies have emerged as the predominant approach to conveying privacy notices to mobile application users. In an effort to enhance both readability and user engagement, the concept of contextual privacy policies (CPPs) has been proposed by researchers. The aim of CPPs is to fragment privacy policies into concise snippets, displaying them only within the corresponding contexts within the application’s graphical user interfaces (GUIs). In this paper, we first formulate CPP in mobile application scenario, and then present a novel multimodal framework, named SEEPRIVACY, specifically designed to automatically generate CPPs for mobile applications. This method uniquely integrates vision-based GUI understanding …

Mesenchymal Stem Cells In Autoimmune Disease: A Systematic Review And Meta-Analysis Of Pre-Clinical Studies, Hailey N. Swain, Parker D. Boyce, Bradley A. Bromet, Kaiden Barozinksy, Lacy Hance, Dakota Shields, Gayla R. Olbricht, Julie A. Semon

Mathematics and Statistics Faculty Research & Creative Works

Mesenchymal Stem Cells (MSCs) Are of Interest in the Clinic Because of their Immunomodulation Capabilities, Capacity to Act Upstream of Inflammation, and Ability to Sense Metabolic Environments. in Standard Physiologic Conditions, They Play a Role in Maintaining the Homeostasis of Tissues and Organs; However, there is Evidence that They Can Contribute to Some Autoimmune Diseases. Gaining a Deeper Understanding of the Factors that Transition MSCs from their Physiological Function to a Pathological Role in their Native Environment, and Elucidating Mechanisms that Reduce their Therapeutic Relevance in Regenerative Medicine, is Essential. We Conducted a Systematic Review and Meta-Analysis of Human MSCs …

Incorporating Ai-Assisted Sensing Into The Metaverse: Opportunities For Interactions, Esports, And Security Enhancement, Yi Wu

Doctoral Dissertations

With the rapid growth and development of Virtual Reality (VR) and Augmented Reality (AR), extensive research has been carried out in the domain of the Metaverse, including immersive gaming, human-computer interaction, eSports, and the associated security & privacy concerns.

My research explores the potential of incorporating Artificial Intelligence (AI)-assisted sensing technologies to facilitate a more immersive, convenient, authentic, and secure virtual experience. This dissertation mainly focus on the following topics: (1) how to perform facial expression tracking to improve the users' awareness in the Metaverse; (2) fitness tracking for immersive eCycling; (3) running gait analysis for immersive indoor running, and …

Multiscale Modeling Of Morphology And Proton/Ion Transport In Electrolytes, Zhenghao Zhu

Doctoral Dissertations

Understanding structure-function relationships in electrolytes is essential for advancing energy conversion and storage. This dissertation employs multiscale modeling and simulations to study the morphology and proton/ion transport in various electrolytes for electrochemical systems, including anion exchange membranes (AEMs), protic ionic liquids (PILs), pure phosphoric acid (PA) and aqueous acid solutions, ionic liquids (ILs), and polymerized ionic liquids (polyILs).

Mesoscale dissipative particle dynamics (DPD) simulations were employed to study the hydrated morphology of polystyrene-b-poly(ethylene-co-butylene)-b-polystyrene (SEBS)-based AEMs. The results indicate that the choice of the functional group moderately affects the water distribution and has little influence on the …

Investigation Of Molecular Transition Metal Complexes: Structures, Magnetic Properties, And Reactivities, Adam T. Hand

Doctoral Dissertations

The dissertation describes the work on transition metal complexes to determine their structures, magnetic properties, and reactivities. Molecular magnetic complexes containing one cobalt(II) or rhenium(IV) ion have been studied to obtain their characteristic zero-field splittings and spin-phonon couplings by magnetometry and advanced spectroscopies. The investigation of spin relaxation and phonon features gives insight into potential magnetic relaxation mechanisms. Studies by ligand field theory, including ab initio ligand-field analysis, show how coordination environments of the metal centers affect the magnetic properties. Through the analyses, the impact of the coordination geometry/symmetry on the zero-field splittings can be explained. Such understanding of magneto-structural …

Probabilistic Frames And Concepts From Optimal Transport, Dongwei Chen

All Dissertations

As the generalization of frames in the Euclidean space $\mathbb{R}^n$, a probabilistic frame is a probability measure on $\mathbb{R}^n$ that has a finite second moment and whose support spans $\mathbb{R}^n$. The p-Wasserstein distance with $p \geq 1$ from optimal transport is often used to compare probabilistic frames. It is particularly useful to compare frames of various cardinalities in the context of probabilistic frames. We show that the 2-Wasserstein distance appears naturally in the fundamental objects of frame theory and draws consequences leading to a geometric viewpoint of probabilistic frames.

We convert the classic lower bound estimates of 2-Wasserstein distance \cite{Gelbrich90, …

Single Charge Carrier Dynamics In A Single Polymer Chain, Ming Lei

All Dissertations

Understanding how charge carrier move through conjugated polymers is crucial for optimizing the functionality and efficiency of various applications, from organic solar cells and light-emitting diodes (LEDs) to field-effect transistors. Previous studies have demonstrated that tracking the fluorescence centroid displacement provides a method to determine the position of single charge carriers in a conjugated polymer nanoparticle. This method allows the observation of charge carrier trajectories over time and allow researchers to obtain insights into the dynamic processes. In addition, a detailed map of the energy landscape can be constructed by analyzing the fluorescence spectrum. These techniques were previously applied to …

Assessment Of Tce And Chiral Pcb Dechlorination Rate, Congener Diversity, And Enantioselectivity In Town Creek, Sc, Usa Sediment Microcosms, Catherine P. Sumner

All Dissertations

Polychlorinated biphenyls (PCBs) and trichloroethene (TCE) are ubiquitous contaminants and are recognized as persistent organic pollutants due to their extreme chemical stability. PCBs were manufactured by chlorinating biphenyls that created 209 congeners with various structures, of which 19 are chiral and can exist as a pair of stable atropisomers. PCBs have been known to cause developmental and neurological toxicity in humans and wildlife; they can act as endocrine disrupters, carcinogens, and teratogens. Sangamo Weston Inc. was an industrial plant located near Town Creek in Pickens Country, South Carolina, that manufactured capacitors and used Aroclors 1016 and 1254 as dialectic fluids …

Optimization Strategies For Political Redistricting, Blake Splitter

All Dissertations

Political redistricting has remained a hot-button issue in the United States for several decades. Every ten years, most states need to redraw their districts to account for changing populations. Sometimes, these district plans can be drawn with the malevolent intention of aiding one political party over another. This dissertation summarizes four distinct methods of drawing these districts using computer algorithms while keeping several objectives in mind. We test these approaches on the case study state of South Carolina, since it provides a sufficiently challenging problem for us to test various algorithms. We find that many of these approaches improve upon …

We Train Ai, Why Not Humans, Too? An Exploration Of Human-Ai Team Training For Future Workplace Viability, Caitlin M. Lancaster

All Dissertations

The integration of Artificial Intelligence (AI) in the workforce is transforming team dynamics, leading to the emergence of Human-AI Teams (HATs). These teams offer opportunities to capitalize on human strengths with AI's prowess, offering significant opportunities for innovation and efficiency. Effective HAT functioning requires aligning human expectations with AI capabilities and bridging knowledge gaps between teammates. Despite this potential, key integration challenges remain, such as developing shared mental models, addressing skill limitations, and overcoming negative AI perceptions. Existing training efforts often apply human-human teaming principles directly to HATs, overlooking AI's role as a teammate and limiting the development of HAT-specific …

Codont5: A Multi-Task Codon Language Model For Species-To-Species Translation, Ashley N. Babjac

Doctoral Dissertations

DNA (DeoxyriboNucleic Acid) carries the genetic information for the biological processes and function of all organisms. It is composed of nucleotides, which can be grouped into 3-mer triplets called codons. It is well known that codons encoding the same amino acid, referred to as "synonymous" codons, are selected with differing frequencies between organisms. Prior research has revealed there are codons used with much higher frequency than others, causing to them being "preferred" in highly expressed genes. This has led to the development of multiple computational models that do a good job predicting gene expression in some protein-coding genes; however, their …

Understanding Traits To Support Crowdworkers' Flexibility, Senjuti Dutta

Doctoral Dissertations

Crowdworkers are drawn to the profession in part due to the flexibility it affords. However, the current design of crowdsourcing platforms limits this flexibility. Therefore, it is important to support the overall flexibility of crowdworkers. Incorporating a variety of device types in the workflow plays an important role in supporting the flexibility of crowdworkers, however each device type requires a tailored workflow. The standard workflow of crowdworkers consists of stages of work such as managing and completing tasks. I hypothesize that different devices will have unique traits for task completion and task management. Therefore in this dissertation, I explore what …

Enabling Reproducibility, Scalability, And Orchestration Of Scientific Workflows In Hpc And Cloud-Converged Infrastructure, Paula Fernanda Olaya

Doctoral Dissertations

Scientific communities across different domains increasingly run complex workflows for their scientific discovery. Scientists require that these workflows ensure robustness; where workflows must be reproducible, scale in performance; and exhibit trustworthiness in terms of the computational techniques, infrastructures, and people. However, as scientists leverage advanced techniques (big data analytics, AI, and ML) and infrastructure (HPC and cloud), their workflows grow in complexity, leading to new challenges in scientific computing; hindering robustness.

In this dissertation, we address the needs of diverse scientific communities across different fields to identify three main challenges that hinder the robustness of workflows: (i) lack of traceability, …

General Relativistic Gravity In Core-Collapse Supernova Simulations, James Nicholas Roberts Ii

Doctoral Dissertations

Core-collapse supernovae (CCSNe) are some of the most extreme and complex phenomena in the universe. The toolkit for high-order neutrino-radiation hydrodynamics (thornado) is being developed to simulate CCSNe which will provide insight into the mechanisms underlying these events. The thornado framework is a collection of modules used to calculate the effects of gravity, hydrodynamics, neutrino transport, and nuclear physics through the Weaklib equation of state table. This dissertation will present the development of the Poseidon code, which provides the general relativistic gravity solver for the thornado framework.

The Poseidon code solves for the general relativistic metric using the xCFC formulation …

Evaluating The Effects Of Forage Availability And Landscape Composiiton On Whte-Tailed Deer Morphometrics Across The Eastern U.S., Mark Turner

Doctoral Dissertations

White-tailed deer (Odocoileus virginianus) management often focuses on improving nutrition to increase deer morphometrics, and many landowners use harvest data to track management progress. Better understanding the relationship among deer morphology, nutrition, landscape characteristics, and climate should inform deer management throughout much of the eastern US. I collected deer forage data in 2021–2023 from 43 sites in 25 states across the eastern US and worked with cooperating landowners and managers to collect harvest data from 35 of those sites. Adult female body mass explained 64% of the variation in mature male antler size on sites across the eastern …

Exploring Overbank Sediment Deposition Variation In Heavily Modified Floodplains Of The Lower Mississippi River: A Sedimentological And Geophysical Analysis, Seth Fradella

Master's Theses

Since the 1930s, the Lower Mississippi River (LMR) has experienced large-scale modifications to the channel profile and surrounding floodplains through dams, dikes, revetments, dredging, and channel cutoffs. Although these changes have improved navigation and reduced flood risk, unanticipated changes to the major flood return period, individual flood severity and duration, and sediment regime have become increasingly apparent and sometimes problematic, such as the 2011 and 2018-2020 floods. Flood control levees along the LMR have reduced the natural floodplain area by 70-90%, resulting in heavily restricted overbank storage capacity of water and sediment. For the same flood events in recent history, …

Optimization Strategies To Enhance Performance In Matrix/Tensor Factorization And Multi-Source Data Integration, Mengyuan Zhang

All Dissertations

Optimization in the realm of machine learning constitutes a fundamental process aimed at refining the parameters of models to enhance their performance. It serves as the backbone of various machine learning techniques, encompassing diverse algorithms and methodologies tailored to address specific tasks and objectives.

In machine learning, datasets are commonly structured as matrices or tensors, making techniques like matrix factorization and tensor factorization indispensable for extracting meaningful representations from intricate data. Furthermore, datasets commonly comprise multiple sets of features, which has inspired our exploration of effective strategies for leveraging information from diverse sources during optimization. Additionally, the interconnected nature of …

Accretion Of Warm Chondrules In Weakly Metamorphosed Ordinary Chondrites And Their Subsequent Reprocessing, Alex M. Ruzicka, Richard C. Hugo, Jon M. Friedrich, Michael T. Ream

Geology Faculty Publications and Presentations

To better understand chondrite accretion and subsequent processes, the textures, crystallography, deformation, and compositions of some chondrite constituents in ten lithologies of different cluster texture strength were studied in seven weakly metamorphosed (Type 3) and variably shocked ordinary chondrites (Ragland—LL3 S1, Tieschitz—H/L3 S1, NWA 5421—LL3 S2, NWA 5205—LL3 S2, NWA 11905—LL3-5 S3, NWA 5781—LL3 S3, NWA 11351—LL3-6 S4) using optical and electron microscopy and microtomography techniques.

Results support a four-stage model for chondrite formation. This includes 1) limited annealing following collisions during chondrule crystallization and rapid cooling in space prior to accretion, as evidenced by olivine microstructures consistent with dislocation …

Querymate: A Custom Llm Powered By Llamacpp, Pegah Khosravi

Open Educational Resources

No abstract provided.

Developing Educational Tools For Sustainable Stormwater Management, Lauren Houskeeper

All Graduate Reports and Creative Projects, Fall 2023 to Present

Rapid population growth and development in Western states are exerting strain on the region’s limited water resources. Urbanization exacerbates this issue by increasing impervious surfaces, limiting infiltration of precipitation during storm events and snowmelt, which results in changes to hydrologic conditions with higher runoff volumes and higher peak flows. Stormwater transports pollutants as it flows across impervious surfaces, discharging high volumes of runoff and elevated loads of urban contaminants into receiving waters. The amount of pollution entering waterways continually increases as urban areas expand. Utah is currently experiencing a rapid transition from undeveloped to developed landscapes, necessitating the implementation of …

2024 August - Tennessee Monthly Climate Report, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Monthly Report

No abstract provided.

Data Collector Selection Ranking-Based Method For Collaborative Multi-Tasks In Ubiquitous Environments, Belal Z. Hassan, Ahmed. A. A. Gad-Elrab, Mohamed S. Farag, S. E. Abu-Youssef

Al-Azhar Bulletin of Science

In Ubiquitous Computing and the Internet of Things, the sensing and control of objects involve numerous devices collecting and transmitting data. However, connecting these devices without fostering collaboration leads to suboptimal system performance. As the number of connected sensing devices in Internet of Things increases, efficient task accomplishment through collaboration becomes imperative. This paper proposes a Data Collector Selection Method for Collaborative Multi-Tasks to address this challenge, considering task preferences and uncertainty in data collectors' contributions. The proposed method incorporates three key aspects: (1) Using Fuzzy Analytical Hierarchy Process to determine optimal weights for task preferences; (2) Ranking data collectors …

Geomechanical Study Of Rock Properties In The Kafr El-Sheikh Formation At Sapphire Field, West Delta Deep Marine, Egypt, Moustafa Mohamed Ahmed Attia, Ali El-Sayed Farag, Mahmoud Y. Zein El-Din

Al-Azhar Bulletin of Science

Numerous challenges were encountered during the drilling operations conducted at the Sapphire oilfield. Instances of stuck pipe, wellbore instability, breakouts, and washouts have been documented in many wells within this field, resulting in unproductive time and additional expenditures. To mitigate these challenges, it is important to conduct a one-dimensional geomechanical model to get a viable resolution. This entails the creation of three primary in situ stress profiles and the assessment of mechanical characteristics of the geological formations. The primary focus of this investigation was to ascertain the mechanical characteristics of the rock. Therefore, this work offers great input while building …

Data Driven Acceleration Of Coupled-Cluster Calculations Using Machine Learning, Multitask Learning And Physics Imposed Learning, Perera Don Varuna Sanjaya Pathirage

Doctoral Dissertations

Data-driven coupled-cluster singles and doubles (DDCCSD) method developed by Townsend and Vogiatzis aims at predicting the coupled-cluster t₂ amplitudes using MP2-level electronic structure data with machine learning. In this work we address limitations of the DDCCSD method to expand the applicability and increase the accuracy. First, we implement localized molecular orbitals (LMO) to the DDCCSD method. There is a ten-fold increase in accuracy when the LMO implementation is used compared to the canonical molecular orbital implementation. Next, we introduced five data selection techniques to select data for testing and training. Here we were able to achieve accuracies less than …