Physical Sciences and Mathematics Commons^™

Pacific Black Duck Ecology And Habitat Assessment In Aunu'u, American Samoa, And Their Relationship To Other Mallard-Like Ducks Of Oceania, Greater Indonesia, And The Philippines, Marissa Kaminski

Open Access Theses & Dissertations

Determining a species’ ecological needs, assessing the quality of their habitat, and determining genetic differentiation and connectivity among populations is essential to their conservation. My dissertation focuses on obtaining such a holistic view for a population of Pacific Black Ducks recently established on the Island of Aunu’u, American Samoa. Specifically, I present the first evaluation of the ecology and habitat of a recently established population of Pacific Black Ducks on the Island of Aunu’u, American Samoa, in Chapter 1, while I assess the genetic connectivity and relationship of this population to other Mallard-like ducks found in Greater Indonesia, Oceania, and …

Sawtooth-Based Chromate Multiferroic - Insight Into Structure And Magnetism, Hector Cein Mandujano

Open Access Theses & Dissertations

The coexistence of two order parameters is a particular occurrence in bulk single-phase materials. Such materials possessing (anti)ferromagnetism, ferroelectricity, and ferroelasticity are known as multiferroics. In this work we revisit BeCr2O4, which is one of the oldest material to be studied in this context. Cr3+ occupies octahedral 4a site and Be2+ occupies tetrahedral 4c site in this compound, forming a close packing structure with a 90° and 138° Cr-O-Cr bonds allowing magnetic superexchange interactions. In the present work, BeCr2O4 powder was prepared using solid-state reaction method and the crystal structure was studied in detail using laboratory and synchrotron X-ray diffraction. …

An Approach To Predicting Performance Of Sparse Computations On Nvidia Gpus, Rogelio Long

Open Access Theses & Dissertations

Sparse problems arise from a variety of applications, from scientific simulations to graph analytics. Traditional HPC systems have failed to effectively provide high bandwidth for sparse problems. This limitation is primarily because of the nature of sparse computations and their irregular memory access patterns.We predict the performance of sparse computations given an input matrix and GPU hardware characteristics. This prediction is done by identifying hardware bottlenecks in modern NVIDIA GPUs using roofline trajectory models. Roofline trajectory models give us insight into the performance by simultaneously showing us the effects of strong and weak scaling. We then create regression models for …

Hardware For Quantized Mixed-Precision Deep Neural Networks, Andres Rios

Open Access Theses & Dissertations

Recently, there has been a push to perform deep learning (DL) computations on the edge rather than the cloud due to latency, network connectivity, energy consumption, and privacy issues. However, state-of-the-art deep neural networks (DNNs) require vast amounts of computational power, data, and energyâ??resources that are limited on edge devices. This limitation has brought the need to design domain-specific architectures (DSAs) that implement DL-specific hardware optimizations. Traditionally DNNs have run on 32-bit floating-point numbers; however, a body of research has shown that DNNs are surprisingly robust and do not require all 32 bits. Instead, using quantization, networks can run on …

Study Of Weakly Bound Cluster Anions Using Self Interaction Corrected Density Functional Scheme, Peter Obinna Ufondu

Open Access Theses & Dissertations

The Kohn–Sham formulation of density functional theory (DFT) is a widely used quantum mechanical theory to study chemical and materials properties. The practical application of DFT requires an approximation to the exchange–correlation (XC) functional. These approximations suffer from self-interaction errors due to the incomplete cancellation of the self-Coulomb energy with the approximate self-exchange and correlation energy for one-electron densities. Systems with weakly-bound electrons impose great challenges to semi-local density functional approximations. We use recently developed local scaled self-interaction correction (LSIC) by Zope et al and the Perdew-Zunger SIC method using the Fermi-Löwdin orbitals to calculate the vertical detachment energies (VDEs) …

A Study On Approximations Of Totally Acyclic Complexes, Tyler Dean Anway

Mathematics Dissertations

Let $R$ be a commutative local ring to which we associate the subcategory $\Ktac(R)$ of the homotopy category of $R$-complexes, consisting of totally acyclic complexes. Further suppose there exists a surjection of Gorenstein local rings $Q \xrightarrowdbl{\varphi} R$ such that $R$ can be viewed as a $Q$-module with finite projective dimension. Under these assumptions, Bergh, Jorgensen, and Moore define the notion of approximations of totally acyclic complexes. In this dissertation we make extensive use of these approximations and define several novel applications. In particular, we extend Auslander-Reiten theory from the category of $R$-modules over a Henselian Gorenstein ring and show …

Manipulation Of Spin Crossover Phenomenon In An Fe (Ii) Molecular Complex And Application To Molecular Spintronics, Guanhua Hao

Department of Physics and Astronomy: Dissertations, Theses, and Student Research

Molecules with a large local magnetic moment have attracted considerable attention for application in spintronic devices. One candidate of a suitable device goes to the spin crossover molecule, where these 3d transition metal compounds are able to exhibit a robust spin state transition between distinct states. By proper design, the spintronic devices fabricated via spin crossover molecular thin films could achieve novel functionality while retaining flexibility and other traits based on its “organic” nature.

Controlling the spin state transition is a key factor of these possibilities. This thesis work investigates the manipulation of the spin state transition in [Fe{H …

The Design, Creation, And Cognitive Evaluation Of Ranking Tasks In Introductory Astronomy, Emily A. Welch

Department of Physics and Astronomy: Dissertations, Theses, and Student Research

Ranking tasks are a type of interactive formative assessment. They allow students to explore a concept by ranking similar situations for a specified variable, preferably without computation of that variable. I created two sets of introductory astronomy ranking tasks: the first connects the Hertzsprung–Russell (HR) diagram and the Stephan-Boltzmann luminosity equation; the second uses the transit method (TM) to rank exoplanets by comparing the depth, duration, and frequency of transits.

These tasks are designed within the constructivist pedagogical framework. They require students to call upon their own relevant schema to establish an assessment rule by which to rank the tasks. …

Free Complexes Over The Exterior Algebra With Small Homology, Erica Hopkins

Department of Mathematics: Dissertations, Theses, and Student Research

Let M be a graded module over a standard graded polynomial ring S. The Total Rank Conjecture by Avramov-Buchweitz predicts the total Betti number of M should be at least the total Betti number of the residue field. Walker proved this is indeed true in a large number of cases. One could then try to push this result further by generalizing this conjecture to finite free complexes which is known as the Generalized Total Rank Conjecture. However, Iyengar and Walker constructed examples to show this generalized conjecture is not always true.

In this thesis, we investigate other counterexamples of …

2021 August - Tennessee Monthly Climate Report, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Monthly Report

No abstract provided.

A Real-World, Hybrid Event Sequence Generation Framework For Android Apps, Jun Sun

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

Generating meaningful inputs for Android apps is still a challenging issue that needs more research. Past research efforts have shown that random test generation is still an effective means to exercise User-Interface (UI) events to achieve high code coverage. At the same time, heuristic search approaches can effectively reach specified code targets. Our investigation shows that these approaches alone are insufficient to generate inputs that can exercise specific code locations in complex Android applications.

This thesis introduces a hybrid approach that combines two different input generation techniques--heuristic search based on genetic algorithm and random instigation of UI events, to reach …

Using Contextual Bandits To Improve Traffic Performance In Edge Network, Aziza Al Zadjali

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

Edge computing network is a great candidate to reduce latency and enhance performance of the Internet. The flexibility afforded by Edge computing to handle data creates exciting range of possibilities. However, Edge servers have some limitations since Edge computing process and analyze partial sets of information. It is challenging to allocate computing and network resources rationally to satisfy the requirement of mobile devices under uncertain wireless network, and meet the constraints of datacenter servers too. To combat these issues, this dissertation proposes smart multi armed bandit algorithms that decide the appropriate connection setup for multiple network access technologies on the …

Prediction Intervals: The Effects And Identification Of Sparse Regions For Nonparametric Regression Methods, Jackson Faires

Electronic Theses and Dissertations

In this work, we provide an overview of different nonparametric methods for prediction interval estimation and investigate how well they perform when making predictions in sparse regions of the predictor space. This sparsity is an extension to the more common concept of extrapolation in linear regression settings. Using simulation studies, we show that coverage probabilities using prediction intervals from quantile k-nearest neighbors and quantile random forest can be biased to low or too high from the nominal level under various situations of sparsity. We also introduce a test that can be used to see if a new data point lies …

Enhancing Microbiome Host Disease Prediction With Variational Autoencoders, Celeste Manughian-Peter

Computational and Data Sciences (MS) Theses

Advancements in genetic sequencing methods for microbiomes in recent decades have permitted the collection of taxonomic and functional profiles of microbial communities, accelerating the discovery of the functional aspects of the microbiome and generating an increased interest among clinicians in applying these techniques with patients. This advancement has coincided with software and hardware improvements in the field of machine learning and deep learning. Combined, these advancements implicate further potential for progress in disease diagnosis and treatment in humans. The ability to classify a human microbiome profile into a disease category, and additionally identify the differentiating factors within the profile between …

Covalent Immobilization Of Molecular Complexes On Metal-Organic Frameworks Towards Robust And Highly Efficient Heterogeneous Water Oxidation Catalysts, Xiangming Liang, Sizhou Yang, Junyi Yang, Wanjun Sun, Xiangyang Li, Baochun Ma, Jier Huang, Jiangwei Zhang, Lele Duan, Yong Ding

Chemistry Faculty Research and Publications

The major challenges to practical implementation of efficient noble metal based molecular water oxidation catalysts are their stability and recycle ability. Herein, noble metal Ru based molecular water oxidation catalysts were covalently anchored in MOFs through “amide bond” as bridges, which leads to the formation of high-efficiency and robust heterogeneous catalysts for water oxidation reaction. We show that the efficiency for Ce^IV-driven water oxidation was significantly enhanced by 120 times when the Ru molecules were immobilized on the frameworks of MIL-101(Cr). The relationship between recycle stability and the structure of the Ru complexes covalently anchored in MOFs was …

Automation Of Radiation Treatment Planning For Cervical Cancer, Dong Joo Rhee

Dissertations & Theses (Open Access)

Cervical cancer is one of the most common cancer in low- and middle-income countries (LMICs). The mortality rate can be reduced if radiation treatment becomes widely available. However, due to the lack of radiation treatment facilities and human resources, many cervical cancer patients in Africa are not able to receive timely treatments or advanced therapies. To increase the availability of radiation treatment in low-and middle-income countries (LMICs) including African countries, many attempts have been made to reduce the cost of medical linear accelerators. However, increasing the number of treatment machines would not instantly resolve the issues, as there would be …

Invertible Grayscale With Sparsity Enforcing Priors, Yong Du, Yangyang Xu, Taizhong Ye, Qiang Wen, Chufeng Xiao, Junyu Dong, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

Color dimensionality reduction is believed as a non-invertible process, as re-colorization results in perceptually noticeable and unrecoverable distortion. In this article, we propose to convert a color image into a grayscale image that can fully recover its original colors, and more importantly, the encoded information is discriminative and sparse, which saves storage capacity. Particularly, we design an invertible deep neural network for color encoding and decoding purposes. This network learns to generate a residual image that encodes color information, and it is then combined with a base grayscale image for color recovering. In this way, the non-differentiable compression process (e.g., …

Gp3: Gaussian Process Path Planning For Reliable Shortest Path In Transportation Networks, Hongliang Guo, Xuejie Hou, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

This paper investigates the reliable shortest path (RSP) problem in Gaussian process (GP) regulated transportation networks. Specifically, the RSP problem that we are targeting at is to minimize the (weighted) linear combination of mean and standard deviation of the path's travel time. With the reasonable assumption that the travel times of the underlying transportation network follow a multi-variate Gaussian distribution, we propose a Gaussian process path planning (GP3) algorithm to calculate the a priori optimal path as the RSP solution. With a series of equivalent RSP problem transformations, we are able to reach a polynomial time complexity algorithm with guaranteed …

Pruning-Aware Merging For Efficient Multitask Inference, Dawei Gao, Xiaoxi He, Zimu Zhou, Yongxin Tong, Lothar Thiele

Research Collection School Of Computing and Information Systems

Many mobile applications demand selective execution of multiple correlated deep learning inference tasks on resource-constrained platforms. Given a set of deep neural networks, each pre-trained for a single task, it is desired that executing arbitrary combinations of tasks yields minimal computation cost. Pruning each network separately yields suboptimal computation cost due to task relatedness. A promising remedy is to merge the networks into a multitask network to eliminate redundancy across tasks before network pruning. However, pruning a multitask network combined by existing network merging schemes cannot minimise the computation cost of every task combination because they do not consider such …

Characterizing Search Activities On Stack Overflow, Jiakun Liu, Sebastian Baltes, Christoph Treude, David Lo, Yun Zhang, Xin Xia

Research Collection School Of Computing and Information Systems

To solve programming issues, developers commonly search on Stack Overflow to seek potential solutions. However, there is a gap between the knowledge developers are interested in and the knowledge they are able to retrieve using search engines. To help developers efficiently retrieve relevant knowledge on Stack Overflow, prior studies proposed several techniques to reformulate queries and generate summarized answers. However, few studies performed a large-scale analysis using real-world search logs. In this paper, we characterize how developers search on Stack Overflow using such logs. By doing so, we identify the challenges developers face when searching on Stack Overflow and seek …

Data Pricing And Data Asset Governance In The Ai Era, Jian Pei, Feida Zhu, Zicun Cong, Luo Xuan, Liu Huiwen, Xin Mu

Research Collection School Of Computing and Information Systems

Data is one of the most critical resources in the AI Era. While substantial research has been dedicated to training machine learning models using various types of data, much less efforts have been invested in the exploration of assessing and governing data assets in end-to-end processes of machine learning and data science, that is, the pipeline where data is collected and processed, and then machine learning models are produced, requested, deployed, shared and evolved. To provide a state-of-the-art overall picture of this important and novel area and advocate the related research and development, we present a tutorial addressing two essential …

Independent Reinforcement Learning For Weakly Cooperative Multiagent Traffic Control Problem, Chengwei Zhang, Shan Jin, Wanli Xue, Xiaofei Xie, Shengyong Chen, Rong Chen

Research Collection School Of Computing and Information Systems

The adaptive traffic signal control (ATSC) problem can be modeled as a multiagent cooperative game among urban intersections, where intersections cooperate to counter the city's traffic conditions. Recently, reinforcement learning (RL) has achieved marked successes in managing sequential decision making problems, which motivates us to apply RL in the ATSC problem. One of the largest challenges of this problem is that the observation of intersection is typically partially observable, which limits the learning performance of RL algorithms. Considering the large scale of intersections in an urban traffic environment, we use independent RL to solve ATSC problem in this study. We …

An Empirical Study Of Gui Widget Detection For Industrial Mobile Games, Jiaming Ye, Ke Chen, Xiaofei Xie, Lei Ma, Ruochen Huang, Yingfeng Chen, Yinxing Xue, Jianjun Zhao

Research Collection School Of Computing and Information Systems

With the widespread adoption of smartphones in our daily life, mobile games experienced increasing demand over the past years. Meanwhile, the quality of mobile games has been continuously drawing more and more attention, which can greatly affect the player experience. For better quality assurance, general-purpose testing has been extensively studied for mobile apps. However, due to the unique characteristic of mobile games, existing mobile testing techniques may not be directly suitable and applicable. To better understand the challenges in mobile game testing, in this paper, we first initiate an early step to conduct an empirical study towards understanding the challenges …

Learning Interpretable Concept Groups In Cnns, Saurabh Varshneya, Antoine Ledent, Rob Vandermeulen, Yunwen Lei, Matthias Enders, Damian Borth, Marius Kloft

Research Collection School Of Computing and Information Systems

We propose a novel training methodology---Concept Group Learning (CGL)---that encourages training of interpretable CNN filters by partitioning filters in each layer into concept groups, each of which is trained to learn a single visual concept. We achieve this through a novel regularization strategy that forces filters in the same group to be active in similar image regions for a given layer. We additionally use a regularizer to encourage a sparse weighting of the concept groups in each layer so that a few concept groups can have greater importance than others. We quantitatively evaluate CGL's model interpretability using standard interpretability evaluation …

Fine-Grained Analysis Of Structured Output Prediction, Waleed Mustafa, Yunwen Lei, Antoine Ledent, Marius And Kloft

Research Collection School Of Computing and Information Systems

In machine learning we often encounter structured output prediction problems (SOPPs), i.e. problems where the output space admits a rich internal structure. Application domains where SOPPs naturally occur include natural language processing, speech recognition, and computer vision. Typical SOPPs have an extremely large label set, which grows exponentially as a function of the size of the output. Existing generalization analysis implies generalization bounds with at least a square-root dependency on the cardinality d of the label set, which can be vacuous in practice. In this paper, we significantly improve the state of the art by developing novel high-probability bounds with …

Receiver-Anonymity In Rerandomizable Rcca-Secure Cryptosystems Resolved, Yi Wang, Rongmao Chen, Guomin Yang, Xinyi Huang, Baosheng Wang, Moti Yung

Research Collection School Of Computing and Information Systems

In this work we resolve the open problem raised by Prabhakaran and Rosulek at CRYPTO 2007, and present the first anonymous, rerandomizable, Replayable-CCA (RCCA) secure public-key encryption scheme. This solution opens the door to numerous privacy-oriented applications with a highly desired RCCA security level. At the core of our construction is a non-trivial extension of smooth projective hash functions (Cramer and Shoup, EUROCRYPT 2002), and a modular generic framework developed for constructing rerandomizable RCCA-secure encryption schemes with receiver-anonymity. The framework gives an enhanced abstraction of the original Prabhakaran and Rosulek’s scheme (which was the first construction of rerandomizable RCCA-secure encryption …

Dynamic Lane Traffic Signal Control With Group Attention And Multi-Timescale Reinforcement Learning, Qize Jiang, Jingze Li, Weiwei Sun, Baihua Zheng

Research Collection School Of Computing and Information Systems

Traffic signal control has achieved significant success with the development of reinforcement learning. However, existing works mainly focus on intersections with normal lanes with fixed outgoing directions. It is noticed that some intersections actually implement dynamic lanes, in addition to normal lanes, to adjust the outgoing directions dynamically. Existing methods fail to coordinate the control of traffic signal and that of dynamic lanes effectively. In addition, they lack proper structures and learning algorithms to make full use of traffic flow prediction, which is essential to set the proper directions for dynamic lanes. Motivated by the ineffectiveness of existing approaches when …

Thunderrw: An In-Memory Graph Random Walk Engine, Shixuan Sun, Yuhang Chen, Shengliang Lu, Bingsheng He, Yuchen Li

Research Collection School Of Computing and Information Systems

As random walk is a powerful tool in many graph processing, mining and learning applications, this paper proposes an efficient inmemory random walk engine named ThunderRW. Compared with existing parallel systems on improving the performance of a single graph operation, ThunderRW supports massive parallel random walks. The core design of ThunderRW is motivated by our profiling results: common RW algorithms have as high as 73.1% CPU pipeline slots stalled due to irregular memory access, which suffers significantly more memory stalls than the conventional graph workloads such as BFS and SSSP. To improve the memory efficiency, we first design a generic …

Learning-Based Extraction Of First-Order Logic Representations Of Api Directives, Mingwei Liu, Xin Peng, Andrian Marcus, Christoph Treude, Xuefang Bai, Gang Lyu, Jiazhen Xie, Xiaoxin Zhang

Research Collection School Of Computing and Information Systems

Developers often rely on API documentation to learn API directives, i.e., constraints and guidelines related to API usage. Failing to follow API directives may cause defects or improper implementations. Since there are no industry-wide standards on how to document API directives, they take many forms and are often hard to understand by developers or challenging to parse with tools. In this paper, we propose a learning based approach for extracting first-order logic representations of API directives (FOL directives for short). The approach, called LeadFOL, uses a joint learning method to extract atomic formulas by identifying the predicates and arguments involved …

Solving Large-Scale Extensive-Form Network Security Games Via Neural Fictitious Self-Play, Wanqi Xue, Youzhi Zhang, Shuxin Li, Xinrun Wang, Bo An, Chai Kiat Yeo

Research Collection School Of Computing and Information Systems

Securing networked infrastructures is important in the real world. The problem of deploying security resources to protect against an attacker in networked domains can be modeled as Network Security Games (NSGs). Unfortunately, existing approaches, including the deep learning-based approaches, are inefficient to solve large-scale extensive-form NSGs. In this paper, we propose a novel learning paradigm, NSG-NFSP, to solve large-scale extensive-form NSGs based on Neural Fictitious Self-Play (NFSP). Our main contributions include: i) reforming the best response (BR) policy network in NFSP to be a mapping from action-state pair to action-value, to make the calculation of BR possible in NSGs; ii) …