Physical Sciences and Mathematics Commons^™

Constraining Decadal-Scale Erosion And Delivery Of Post-Wildfire Debris Flow Deposits, Casey Langstroth

All Graduate Theses and Dissertations, Fall 2023 to Present

Debris flows generated from wildfire pose significant risk to increased sedimentation to instream river networks, degradation of water quality, and accumulated sediment behind downstream reservoirs. Despite an abundance of research investigating the probability of debris flow generation, the constraints on deposit initial volume, and most recently the initial grain size distribution, there remains a significant knowledge gap in understanding the temporal scale at which debris flows supply sediment to river networks after deposition. To provide reliable estimates of sediment delivery from debris flow deposits over time, two important metrics must be constrained: 1) how does sediment delivery to river networks …

Interpretable And Robust Deep Anomaly Detection, He Cheng

All Graduate Theses and Dissertations, Fall 2023 to Present

Anomaly detection is crucial in fields like cybersecurity, healthcare, and finance, as it helps identify unusual or potentially harmful events in data. With the rise of deep learning, advanced models have been developed for anomaly detection, but they often operate as "black boxes" that lack transparency and can be susceptible to malicious attacks. My research addresses these issues by creating methods that make deep learning-based anomaly detection more understandable and by investigating how such models can be compromised by backdoor attacks.

To improve transparency, I propose three methods that explain how these models detect anomalies. The first method, called Anomalous …

Understanding And Influencing Public Concerns About Prescribed Fire Use In Utah, Brooke Richards

All Graduate Theses and Dissertations, Fall 2023 to Present

Prescribed fire is beneficial for improving forest health and reducing wildfire risk, which is especially important for residents who live near forested areas. To expand the use of prescribed fire, Utah land managers must first ensure there is public support. Therefore, better understanding public concerns and what shapes their beliefs about prescribed fire can help managers improve outreach and education methods. Through interviews with south-central Utah land managers, private landowners, and Health departments employees, a focus group discussion with Park City natural resource managers, and observation of Summit County public commentary, two outreach messages were suggested to address the five …

Supervised Generative Adversarial Networks For Time Series Generation In Embedding Space, Mohammadreza Eskandarinasab

All Graduate Theses and Dissertations, Fall 2023 to Present

Time series data, such as weather forecasts, stock market trends, or heart rate monitors, plays a vital role in many areas of our lives. However, creating realistic synthetic time series data for research and testing purposes has been a significant challenge due to limitations in existing methods, which often struggle with accuracy and consistency. In this study, we developed two new approaches to generate high-quality time series data more effectively. The first method introduces a dual-feedback system that helps the model learn and replicate real data patterns more accurately by providing guidance at different stages of the learning process. The …

The Model Of Norm-Regulated Responsibility For Proenvironmental Behavior In The Context Of Littering Prevention, Pengya Ai, Sonny Rosenthal

Research Collection College of Integrative Studies

Previous research suggests that descriptive norms positively influence proenvironmental behavior, including littering prevention. However, in some behavioral contexts, a weak descriptive norm may increase individuals’ feelings of responsibility by signaling a need for action. We examined this effect in the context of litter prevention by conducting structural equation modeling of survey data from 1400 Singapore residents. The results showed that descriptive norms negatively predicted ascription of responsibility and were negatively related to littering prevention behavior via ascription of responsibility and personal norms. It also showed that strong injunctive norms can reduce the inhibitory effect of descriptive norms on ascription of …

Learning De-Biased Representations For Remote-Sensing Imagery, Zichen Tian, Zhaozheng Chen, Qianru Sun

Research Collection School Of Computing and Information Systems

Remote sensing (RS) imagery, requiring specialized satellites to collect and being difficult to annotate, suffers from data scarcity and class imbalance in certain spectrums. Due to data scarcity, training any large-scale RS models from scratch is unrealistic, and the alternative is to transfer pre-trained models by fine-tuning or a more data-efficient method LoRA. Due to class imbalance, transferred models exhibit strong bias, where features of the major class dominate over those of the minor class. In this paper, we propose debLoRA---a generic training approach that works with any LoRA variants to yield debiased features. It is an unsupervised learning approach …

From A Timeline Contact Graph To Close Contact Tracing And Infection Diffusion Intervention, Yipeng Zhang, Zhifeng Bao, Yuchen Li, Baihua Zheng, Xiaoli Wang

Research Collection School Of Computing and Information Systems

This paper proposes a novel graph structure to address the problems of information spreading in a real-world, frequently updating graph, with two main contributions at hand: accurately tracing infection diffusion according to fine-grained user movements and finding vulnerable vertices under the virus immunization scenario to mitigate infection diffusion. Unlike previous work that primarily predicts the long-term epidemic trend at the census level, this study aims to intervene in the short-term at the individual level. Therefore, two downstream tasks are formulated to illustrate practicalities: Epidemic Mitigating in Public Area problem (EMA) and Epidemic Maximized Spread in Public Area problem (ESA), where …

Converting Vocal Performances Into Sheet Music Leveraging Large Language Models, Jinjing Jiang, Nicole Teo, Haibo Pen, Seng-Beng Ho, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Advanced natural language processing (NLP) models are increasingly applied in music composition and performance, particularly for generating vocal melodies and simulating singing voices. While NLP techniques have been effective in analyzing vocal performance data to assess quality and style, the automatic transcription of vocal performances into sheet music remains a significant challenge. Manual transcription tools often fall short due to the intricate dynamics of vocal expression. This study tackles the automation of vocal performance transcription into sheet music using innovative techniques, including large language models (LLMs). We propose a method to translate vocal audio input into display-ready sheet music effectively. …

Unsupervised Modality Adaptation With Text-To-Image Diffusion Models For Semantic Segmentation, Ruihao Xia, Yu Liang, Peng-Tao Jiang, Hao Zhang, Bo Li, Yang Tang, Pan Zhou

Research Collection School Of Computing and Information Systems

Despite their success, unsupervised domain adaptation methods for semantic segmentation primarily focus on adaptation between image domains and do not utilize other abundant visual modalities like depth, infrared and event. This limitation hinders their performance and restricts their application in real-world multimodal scenarios. To address this issue, we propose Modality Adaptation with text-toimage Diffusion Models (MADM) for semantic segmentation task which utilizes text-to-image diffusion models pre-trained on extensive image-text pairs to enhance the model’s cross-modality capabilities. Specifically, MADM comprises two key complementary components to tackle major challenges. First, due to the large modality gap, using one modal data to generate …

Lova3 : Learning To Visual Question Answering, Asking And Assessment, Henry Hengyuan Zhao, Pan Zhou, Difei Gao, Bai Shou, Mike Zheng Shou

Research Collection School Of Computing and Information Systems

Question answering, asking, and assessment are three innate human traits crucial for understanding the world and acquiring knowledge. By enhancing these capabilities, humans can more effectively utilize data, leading to better comprehension and learning outcomes. Current Multimodal Large Language Models (MLLMs) primarily focus on question answering, often neglecting the full potential of questioning and assessment skills. Inspired by the human learning mechanism, we introduce LOVA3 , an innovative framework named “Learning tO Visual question Answering, Asking and Assessment,” designed to equip MLLMs with these additional capabilities. Our approach involves the creation of two supplementary training tasks GenQA and EvalQA, aiming …

3d Snapshot: Invertible Embedding Of 3d Neural Representations In A Single Image, Yuqin Lu, Bailin Deng, Zhixuan Zhong, Tianle Zhang, Yuhui Quan, Hongmin Cai, Shengfeng He

Research Collection School Of Computing and Information Systems

3D neural rendering enables photo-realistic reconstruction of a specific scene by encoding discontinuous inputs into a neural representation. Despite the remarkable rendering results, the storage of network parameters is not transmission-friendly and not extendable to metaverse applications. In this paper, we propose an invertible neural rendering approach that enables generating an interactive 3D model from a single image (i.e., 3D Snapshot). Our idea is to distill a pre-trained neural rendering model (e.g., NeRF) into a visualizable image form that can then be easily inverted back to a neural network. To this end, we first present a neural image distillation method …

Mimicking To Dominate: Imitation Learning Strategies For Success In Multiagent Competitive Games, The Viet Bui, Tien Mai, Hong Thanh Nguyen

Research Collection School Of Computing and Information Systems

Training agents in multi-agent games presents significant challenges due to their intricate nature. These challenges are exacerbated by dynamics influenced not only by the environment but also by strategies of opponents. Existing methods often struggle with slow convergence and instability. To address these challenges, we harness the potential of imitation learning (IL) to comprehend and anticipate actions of the opponents, aiming to mitigate uncertainties with respect to the game dynamics. Our key contributions include: (i) a new multi-agent IL model for predicting next moves of the opponents --- our model works with hidden actions of opponents and local observations; (ii) …

Collaboration! Towards Robust Neural Methods For Routing Problems, Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhiqi Shen

Research Collection School Of Computing and Information Systems

Despite enjoying desirable efficiency and reduced reliance on domain expertise, existing neural methods for vehicle routing problems (VRPs) suffer from severe robustness issues – their performance significantly deteriorates on clean instances with crafted perturbations. To enhance robustness, we propose an ensemble-based Collaborative Neural Framework (CNF) w.r.t. the defense of neural VRP methods, which is crucial yet underexplored in the literature. Given a neural VRP method, we adversarially train multiple models in a collaborative manner to synergistically promote robustness against attacks, while boosting standard generalization on clean instances. A neural router is designed to adeptly distribute training instances among models, enhancing …

Learning To Handle Complex Constraints For Vehicle Routing Problems, Jieyi Bi, Yining Ma, Jianan Zhou, Wen Song, Zhiguang Cao, Yaoxin Wu, Jie Zhang

Research Collection School Of Computing and Information Systems

Vehicle Routing Problems (VRPs) can model many real-world scenarios and often involve complex constraints. While recent neural methods excel in constructing solutions based on feasibility masking, they struggle with handling complex constraints, especially when obtaining the masking itself is NP-hard. In this paper, we propose a novel Proactive Infeasibility Prevention (PIP) framework to advance the capabilities of neural methods towards more complex VRPs. Our PIP integrates the Lagrangian multiplier as a basis to enhance constraint awareness and introduces preventative infeasibility masking to proactively steer the solution construction process. Moreover, we present PIP-D, which employs an auxiliary decoder and two adaptive …

Trustworthy Web3 Domains: A Framework For Digital Identity Verification, Yi Meng Lau, Ping Fan Ke

Research Collection School Of Computing and Information Systems

As decentralized applications evolve, digital identities represented through Web3 domain names gained prominence. This study addresses the challenges of establishing trust in Web3 domain names. The decentralized nature of Web3 introduces complexities in verifying domain name authenticity, making them targets for malicious activities such as cybersquatting and phishing. We propose a comprehensive framework that enhances traditional identification, authentication, and authorization processes by incorporating technological and social trust elements. This framework enables organizations and users to systematically assess the trustworthiness of Web3 domain names, offering a structured approach to managing digital identities in decentralized environments.

Harnessing The Power Of Ai-Instructor Collaborative Grading Approach: Topic-Based Effective Grading For Semi Open-Ended Multipart Questions, Phyo Yi Win Myint, Siaw Ling Lo, Yuhao Zhang

Research Collection School Of Computing and Information Systems

Semi open-ended multipart questions consist of multiple sub questions within a single question, requiring students to provide certain factual information while allowing them to express their opinion within a defined context. Human grading of such questions can be tedious, constrained by the marking scheme and susceptible to the subjective judgement of instructors. The emergence of large language models (LLMs) such as ChatGPT has significantly advanced the prospect of automatic grading in educational settings. This paper introduces a topic-based grading approach that harnesses LLM capabilities alongside a refined marking scheme to ensure fair and explainable assessment processes. The proposed approach involves …

Chain Of Preference Optimization: Improving Chain-Of-Thought Reasoning In Llms, Xuan Zhang, Chao Du, Tianyu Pang, Qian Liu, Wei Gao, Min Lin

Research Collection School Of Computing and Information Systems

The recent development of chain-of-thought (CoT) decoding has enabled large language models (LLMs) to generate explicit logical reasoning paths for complex problem-solving. However, research indicates that these paths are not always deliberate and optimal. The tree-of-thought (ToT) method employs tree-searching to extensively explore the reasoning space and find better reasoning paths that CoT decoding might overlook. This deliberation, however, comes at the cost of significantly increased inference complexity. In this work, we demonstrate that fine-tuning LLMs leveraging the search tree constructed by ToT allows CoT to achieve similar or better performance, thereby avoiding the substantial inference burden. This is achieved …

Gotcha ! This Model Uses My Code ! Evaluating Membership Leakage Risks In Code Models, Zhou Yang, Zhipeng Zhao, Chenyu Wang, Jieke Shi, Dongsum Kim, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

Leveraging large-scale datasets from open-source projects and advances in large language models, recent progress has led to sophisticated code models for key software engineering tasks, such as program repair and code completion. These models are trained on data from various sources, including public open-source projects like GitHub and private, confidential code from companies, raising significant privacy concerns. This paper investigates a crucial but unexplored question: What is the risk of membership information leakage in code models? Membership leakage refers to the vulnerability where an attacker can infer whether a specific data point was part of the training dataset. We present …

Editorial For The Special Issue Of The Metaverse, Fiona Fui-Hoon Nah, Gert-Jan De Vreede, Lakshmi Goel, Eric Lim, Shu Schiller, Chee-Wee Tan

Research Collection School Of Computing and Information Systems

The metaverse is laying the groundwork for more accessible and immersive experiences by blending the physical and virtual worlds into a unified space where people can interact, create, and connect in entirely new ways. It holds the potential to revolutionize how we work, socialize, and learn, which in turn gives rise to unprecedented opportunities for innovation. In this special issue, we present four articles that depict the current state of research in metaverse, the key themes and theoretical underpinnings within this space, as well as emerging directions for future work. This special issue delivers valuable insights for both researchers and …

Characterization Of Water-Soluble Inorganic Ions And Carbonaceous Aerosols In The Urban Atmosphere In Amman, Jordan, Afnan Al-Hunaiti, Zaid Bakri, Xinyang Li, Lian Duan, Asal Al-Abdallat, Andres Alastuey, Mar Viana, Sharif Arar, Tuukka Petäjä, Tareq Hussein

Michigan Tech Publications

The urban particulate matter (PM) carbonaceous and water-soluble ions were investigated in Amman, Jordan during May 2018–March 2019. The PM2.5 total carbon (TC) annual mean was 7.6 ± 3.6 μg/m3 (organic carbon (OC) 5.9 ± 2.8 μg/m3 and elemental carbon (EC) 1.7 ± 1.1 μg/m3), which was about 16.3% of the PM2.5. The PM10 TC annual mean was 8.4 ± 3.9 μg/m3 (OC 6.5 ± 3.1 μg/m3 and elemental carbon (EC) 1.9 ± 1.1 μg/m3), about 13.3% of the PM10. The PM2.5 total water-soluble ions annual mean was 7.9 ± 1.9 μg/m3 (about 16.9%), and that of the PM10 was …

Delidar: Decoupling Lidars For Pervasive Spatial Computing, Kanatta Gamage Ramesh Darshana Rathnayake, Razat Sutradhar, Abbaas A. M. Nishar, Weerakoon Dulaj S., Ashwin Ashok, Archan Misra

Research Collection School Of Computing and Information Systems

Unbounded proliferation of LiDAR-equipped pervasive devices generates two challenges: (a) mutual interference among emitters and (b) significantly higher sensing energy overhead. We propose a fundamentally different approach for LiDAR sensing, in indoor spaces, that decouples the sensor’s emitter and receiver components. Our proposed approach, called DeLiDAR, centralizes the emitter functionality in one or more stationary nodes that continually emit pulses; this decoupling allows each mobile LiDAR sensor to be an ultra-low power, pure receiver unit consisting solely of passive multiple photodiodes. We explain how the emitter can utilize VLC-based encoding of its pulses to convey parameter settings that allow a …

A Full-History Network Dataset For Btc Asset Decentralization Profiling, Ling Cheng, Qian Shao, Fengzhu Zeng, Feida Zhu

Research Collection School Of Computing and Information Systems

Since its advent in 2009, Bitcoin (BTC) has garnered increasing attention from both academia and industry. However, due to the massive transaction volume, no systematic study has quantitatively measured the asset decentralization degree specifically from a network perspective.In this paper, by conducting a thorough analysis of the BTC transaction network, we first address the significant gap in the availability of full-history BTC graph and network property dataset, which spans over 15 years from the genesis block (1st March, 2009) to the 845651-th block (29, May 2024). We then present the first systematic investigation to profile BTC's asset decentralization and design …

Time Series Decomposition Of Land Surface Temperature For Long-Term Trend Forecasting And Impact On Nesting Sea Turtle Habitats In The Arabian Gulf, Sachi Perera, Rommel H. Maneja, Mohamed Allali, Cyril Rakovski, Erik Linstead, Daniele Struppa, Ali Qasem, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Improving land surface temperature (LST) modeling is vital for mitigating climate change effects on various ecosystems and marine habitats such as important sea turtle habitats. Over the past decade, extreme temperatures have likely significantly affected nesting sea turtle habitats in the Arabian Gulf, with predominantly female hatchlings creating an imbalance in the sex ratio. Such shifts have profound implications for these habitats’ long-term survival and conservation management. This study leverages statistical machine learning models to measure ongoing temporal variations in LST. We break down the LST time series into trend, seasonal, and noise components using classical decomposition methods like X11, …

Machine Learning Optimized Depolymerization Of Pet (Polyethylene Terephthalate) Via Glycolysis, Mehwash Aamir

Theses and Dissertations

Over 90% of the approximately 1 million PET bottles sold every minute end up in landfills or oceans, where they can persist for centuries. Plastic pollution urgently needs sustainable management and recycling solutions to mitigate the environmental impact of PET waste. From all techniques to recycle plastic waste, catalytic glycolysis stands out for its rapid reaction time, high monomer yields, lower costs, and enhanced durability.

In this work glycolysis of PET was evaluated under microwave conditions using 1,5,7-Triazabicyclo [4.4.0] dec5-ene (TBD) and 1,8-Diazabicyclo [5.4.0] undec-7-ene (DBU) as catalysts, being DBU as the best catalyst for further analysis. The effect of …

Classifying Supersonic Frequencies For Active Acoustic Side-Channel Exploitation, Destin Hinkel

Graduate Theses and Dissertations (2019 - present)

Computing side-channel research explores the manner in which physical emanations from systems can be used to reconstruct data. Acoustic side-channels are those physical emanations that produce a sonic frequency that is subsonic, supersonic, or considered in the range of human hearing [1]. Acoustic side-channel attacks (SCAs) are typically performed passively: a listening device captures aural frequencies from a machine via a microphone that are transmitted to the attacker for analysis [1]–[3]. Machine learning models have been presented to classify individual keystrokes according to variations in acoustic frequency [4]. Furthermore, the SonarSnoop framework presents a novel active approach that involves both …

Hybrid Deep Learning-Based Model For Eclipse Attack Detection On Ethereum Network, Dhanasak Bhumichai

Graduate Theses and Dissertations (2019 - present)

An eclipse attack is a significant cyber threat targeting the network layer of blockchain platforms. Detecting eclipse attacks is challenging for several reasons. First, there are no available datasets for training and testing models. Second, comprehensive studies identifying features to detect eclipse attacks are lacking. Additionally, the amount of eclipse network traffic is much smaller than that of normal network traffic, which leads to imbalanced samples. Moreover, the characteristics of eclipse network traffic closely resemble those of normal traffic, causing overlapping samples, which makes it challenging for traditional classifiers to learn how to identify eclipse attacks. To address these challenges, …

Recovery Resiliency Of Interdependent Power Systems Infrastructure Subject To Extreme Events, Partha P. Sarker

Graduate Theses and Dissertations (2019 - present)

When Hurricane Maria struck the island of Puerto Rico on September 20, 2017, it devastated the island’s aging power systems infrastructure and inflicted an island-wide power outage that left Puerto Rico in total darkness for an entire week before the system slowly started to recover. This unprecedented failure of the critical power systems infrastructure exacerbated the failure of other critical infrastructures or lifeline systems of the island. This research explores and quantifies the relationships or interdependencies that exist between the power systems and other critical infrastructure systems by investigating the post-hurricane recovery data of these lifeline systems. Subsequently, the research …

The Temperature Trend In The Upper Mesosphere Between ∼84 Km And 98 Km Based On Diurnal Cycle Observations By A Na Lidar At Middle Latitudes Between 2002 And 2017, Melania Carolina Pena

All Graduate Theses and Dissertations, Fall 2023 to Present

Carbon dioxide is one of the main greenhouse gasses that contributes to maintaining Earth’s warm global temperature in its atmosphere. Recent studies have indicated that this cooling trend has been determined to vary between less than 1 K/decade or 2 K/decade. However, these results have been determined using data that have been obtained using nighttime lidar observations and potentially do not give the complete picture on how the cooling trend is behaving. The main goal of this research is to understand the behavior of the linear temperature trend between the daytime and nighttime profiles using a 16-year dataset (2002-2017), when …

Cellulose–Starch Composite Aerogels As Thermal Superinsulating Materials, Safoura Ahmadzadeh, Angelina Sagardui, David Huitink, Jingyi Chen, Ali Ubeyitogullari

Chemistry & Biochemistry Faculty Publications and Presentations

The demand for sustainable packaging materials is rapidly increasing due to growing environmental concerns over the impact of plastic waste. In this study, biodegradable, porous, lightweight, and high-surface-area microcrystalline cellulose–starch (MCC-S) hybrid aerogels were synthesized via supercritical carbon dioxide (SC–CO₂) drying. The samples were generated using five different MCC-S weight ratios and characterized for their morphology, crystallinity, and structural and thermal properties. When MCC and S were used together, aerogels with superior properties were obtained compared to those made from each component individually. Specifically, the 1:2 MCC-S aerogel exhibited the highest porosity (97%), the lowest density (0.058 g/cm …

The Application Of High-Resolution Mass Spectrometry For The Analysis Of Biopolymers, Metabolites, And Biologically Relevant Small Molecules, Christ Duc Tran

Graduate Doctoral Dissertations

High-resolution mass spectrometry (HRMS), as its name suggests, possesses high resolving power that enables the separation of ions with very close mass values. This is particularly beneficial for analyzing complex samples where numerous compounds may have similar masses, as it helps prevent signal overlaps and ensures an accurate spectral analysis. The current work explores the diverse applications of high-resolution mass spectrometry in oligonucleotide research, organic synthesis and medicinal chemistry. Oligonucleotide research: Chapters 2-5 present the research results from studies that employed ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS) to understand the pharmacokinetics of RNA interference (RNAi) therapeutics. The …