Physical Sciences and Mathematics Commons^™

Transition Metal Chalcogenide Hybrid Systems As Catalysts For Energy Conversion And Biosensing, Siddesh Umapathi

Doctoral Dissertations

"Generation of hydrogen and oxygen through catalyst-aided water splitting which has immense applications in metal air batteries, PEM fuel cells and solar to fuel energy production, has been one of the critical topics in recent times. The state of art oxygen evolution reaction (OER), oxygen reduction reaction (ORR), hydrogen evolution reaction (HER) catalysts are mostly comprised of precious metals. The current challenge lies in replacing these precious metal-based catalysts with non-precious earth-abundant materials without compromising catalytic efficiency.

This research explores mixed metal selenides containing Fe-Ni, Fe-Co and RhSe which were hydrothermally synthesized and/or electrodeposited and tested for OER and ORR …

Enhanced Electrochemical Performance Of Li-Ion Battery Cathodes By Atomic Layer Deposition, Yan Gao

Doctoral Dissertations

”Li-ion battery now plays an irreplaceable role in supplying green and convenient energy. In this work, atomic layer deposition (ALD) was used to modify Li-ion battery cathode particles for performance enhancement.

An ultrathin and conductive CeO₂ ALD film was deposited on Li-rich layered cathode particles, of which the specific capacity and cyclic stability were significantly improved. On the same cathode particles, FeO_x ALD and post-annealing resulted in a stable and conductive surface spinel phase to improve the performance.

Synergetic TiN coating and Ti doping were performed on a LiFePO₄ (LFP) cathode and extended its cycle life. The …

Attention Mechanism In Deep Neural Networks For Computer Vision Tasks, Haohan Li

Doctoral Dissertations

“Attention mechanism, which is one of the most important algorithms in the deep Learning community, was initially designed in the natural language processing for enhancing the feature representation of key sentence fragments over the context. In recent years, the attention mechanism has been widely adopted in solving computer vision tasks by guiding deep neural networks (DNNs) to focus on specific image features for better understanding the semantic information of the image. However, the attention mechanism is not only capable of helping DNNs understand semantics, but also useful for the feature fusion, visual cue discovering, and temporal information selection, which are …

Computer Vision Based Deep Learning Models For Cyber Physical Systems, Muhammad Monjurul Karim

Masters Theses

“Cyber-Physical Systems (CPSs) are complex systems that integrate physical systems with their counterpart cyber components to form a close loop solution. Due to the ability of deep learning in providing sensor data-based models for analyzing physical systems, it has received increased interest in the CPS community in recent years. However, developing vision data-based deep learning models for CPSs remains critical since the models heavily rely on intensive, tedious efforts of humans to annotate training data. Besides, most of the models have a high tradeoff between quality and computational cost. This research studies deep learning algorithms to achieve affordable and upgradable …

The Role Of Tectonic Inheritance, Plate-Reorganization, And Magma Flare-Ups In The Evolution Of The Sevier Orogeny, J. Daniel Quick

Masters Theses

"The temporal and spatial distribution of strain associated with the Sevier Orogeny in western North America is significantly different in the southern end of the belt, at the latitude of Las Vegas, than further to the north at the latitude of Salt Lake City. Until this study, reasons for these differences were speculative as a lack of thrust kinematic data in the intervening region hindered along strike correlation across the belt. A crystallization age of 100.18 ± 14 0.04 Ma was determined for zircons extracted from a recently recognized ash fall tuff near the base of the synorogenic Iron Springs …

On Predicting Stopping Time Of Human Sequential Decision-Making Using Discounted Satisficing Heuristic, Mounica Devaguptapu

Masters Theses

“Human sequential decision-making involves two essential questions: (i) "what to choose next?", and (ii) "when to stop?". Assuming that the human agents choose an alternative according to their preference order, our goal is to model and learn how human agents choose their stopping time while making sequential decisions. In contrary to traditional assumptions in the literature regarding how humans exhibit satisficing behavior on instantaneous utilities, we assume that humans employ a discounted satisficing heuristic to compute their stopping time, i.e., the human agent stops working if the total accumulated utility goes beyond a dynamic threshold that gets discounted with time. …

Characterization Of A Plasma Source Simulating Solar Wind Plasma In A Vacuum Chamber, Blake Anthony Folta

Masters Theses

"The United States has set an aggressive time line to not only return to the Moon, but also to establish a sustained human presence. In the Apollo missions dust was a significant factor, but the duration of those missions was short so dust and surface charging were problems, but they did not pose an immediate threat. For a long-term mission, these problems instead become incredibly detrimental. Because of this, research needs to be conducted to investigate these phenomena so that mitigation techniques can be developed and tested. To this end, this thesis serves to demonstrate the Gas and Plasma Dynamics …

The Application Of Machine Learning Models In The Concussion Diagnosis Process, Sujit Subhash

Masters Theses

“Concussions represent a growing health concern and are challenging to diagnose and manage. Roughly four million concussions are diagnosed every year in the United States. Although research into the application of advanced metrics such as neuroimages and blood biomarkers has shown promise, they are yet to be implemented at a clinical level due to cost and reliability concerns. Therefore, concussion diagnosis is still reliant on clinical evaluations of symptoms, balance, and neurocognitive status and function. The lack of a universal threshold on these assessments makes the diagnosis process entirely reliant on a physician’s interpretation of these assessment scores. This study …

Quantifying Effects Of Sleep Deprivation On Cognitive Performance, Quang Nghia Le

Masters Theses

“The most commonly used metric for evaluating alertness and vigilance is the Psychomotor Vigilance Test (PVT), previous studies have indicated that alertness and vigilance can be affected by the lack of sleep as a function of sleep loss. This study explores methods to predict median psychomotor vigilance reaction times. The data used in this study comes from a series of tests and surveys conducted on volunteer students. The data set contains many potential predictors of PVT and one aspect of the study was to identify variables that are useful in prediction. The performances of various prediction methods that allow for …

Values Of Artificial Intelligence In Marketing, Yingrui Xi

Masters Theses

“Artificial Intelligence (AI) is causing radical changes in marketing and emerging as a competent assistant supporting all areas of the marketing field. The influences and impacts AI has created in various marketing segments have aroused much interest among marketing professionals and academic scholars. Comprehensive and systematic studies on the values of AI in marketing, however, are still lacking and the existing literature fragmented. This research provides a comprehensive review of the existing literature in the relevant fields as well as a series of systematic interviews using the Value-Focused Thinking approach to understand the values of AI in marketing. This research …

A Hybrid And Scalable Error Correction Algorithm For Indel And Substitution Errors Of Long Reads, Arghya Kusum Das, Sayan Goswami, Kisung Lee, Seung Jong Park

Computer Science Faculty Research & Creative Works

Background: Long-read sequencing has shown the promises to overcome the short length limitations of second-generation sequencing by providing more complete assembly. However, the computation of the long sequencing reads is challenged by their higher error rates (e.g., 13% vs. 1%) and higher cost ($0.3 vs. $0.03 per Mbp) compared to the short reads. Methods: In this paper, we present a new hybrid error correction tool, called ParLECH (Parallel Long-read Error Correction using Hybrid methodology). The error correction algorithm of ParLECH is distributed in nature and efficiently utilizes the k-mer coverage information of high throughput Illumina short-read sequences to rectify the …

Two Algorithms For The Reorganisation Of The Problem List By Organ System, Daniel B. Hier, Joshua Pearson

Chemistry Faculty Research & Creative Works

Objective Long Problem Lists Can Be Challenging to Use. Reorganization of the Problem List by Organ System is a Strategy for Making Long Problem Lists More Manageable. Methods in a Small-Town Primary Care Setting, We Examined 4950 Unique Problem Lists over 5 Years (24 033 Total Problems and 2170 Unique Problems) from Our Electronic Health Record. All Problems Were Mapped to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) and SNOMED CT Codes. We Developed Two Different Algorithms for Reorganizing the Problem List by Organ System based on Either the ICD-10-CM or the SNOMED CT Code. Results the …

Efficient Smooth Non-Convex Stochastic Compositional Optimization Via Stochastic Recursive Gradient Descent, Wenqing Hu, Chris Junchi Li, Xiangru Lian, Ji Liu, Huizhuo Yuan

Mathematics and Statistics Faculty Research & Creative Works

Stochastic compositional optimization arises in many important machine learning applications. The objective function is the composition of two expectations of stochastic functions, and is more challenging to optimize than vanilla stochastic optimization problems. In this paper, we investigate the stochastic compositional optimization in the general smooth non-convex setting. We employ a recently developed idea of Stochastic Recursive Gradient Descent to design a novel algorithm named SARAH-Compositional, and prove a sharp Incremental First-order Oracle (IFO) complexity upper bound for stochastic compositional optimization: 𝒪((n + m)^1/2ε^-2) in the finite-sum case and 𝒪(ε^-3) in the online case. …

Learning Nearest Neighbor Graphs From Noisy Distance Samples, Blake Mason, Ardhendu S. Tripathy, Robert Nowak

Computer Science Faculty Research & Creative Works

We consider the problem of learning the nearest neighbor graph of a dataset of n items. The metric is unknown, but we can query an oracle to obtain a noisy estimate of the distance between any pair of items. This framework applies to problem domains where one wants to learn people's preferences from responses commonly modeled as noisy distance judgments. In this paper, we propose an active algorithm to find the graph with high probability and analyze its query complexity. In contrast to existing work that forces Euclidean structure, our method is valid for general metrics, assuming only symmetry and …

Maxgap Bandit: Adaptive Algorithms For Approximate Ranking, Sumeet Katariya, Ardhendu S. Tripathy, Robert Nowak

Computer Science Faculty Research & Creative Works

This paper studies the problem of adaptively sampling from K distributions (arms) in order to identify the largest gap between any two adjacent means. We call this the MaxGap-bandit problem. This problem arises naturally in approximate ranking, noisy sorting, outlier detection, and top-arm identification in bandits. The key novelty of the MaxGap bandit problem is that it aims to adaptively determine the natural partitioning of the distributions into a subset with larger means and a subset with smaller means, where the split is determined by the largest gap rather than a pre-specified rank or threshold. Estimating an arm's gap requires …

Search For Gravitational Waves From Scorpius X-1 In The Second Advanced Ligo Observing Run With An Improved Hidden Markov Model, B. P. Abbott, R. Abbott, T. D. Abbott, Marco Cavaglia, For Full List Of Authors, See Publisher's Website.

Physics Faculty Research & Creative Works

We present results from a semicoherent search for continuous gravitational waves from the low-mass x-ray binary Scorpius X-1, using a hidden Markov model (HMM) to track spin wandering. This search improves on previous HMM-based searches of LIGO data by using an improved frequency domain matched filter, the J-statistic, and by analyzing data from Advanced LIGO's second observing run. In the frequency range searched, from 60 to 650 Hz, we find no evidence of gravitational radiation. At 194.6 Hz, the most sensitive search frequency, we report an upper limit on gravitational wave strain (at 95% confidence) of h₀^95% = …

Cuinse₂ Nanotube Arrays For Efficient Solar Energy Conversion, Wipula Priya Liyanage, Manashi Nath

Chemistry Faculty Research & Creative Works

Highly uniform and vertically aligned p-type CuInSe₂ (CISe) nanotube arrays were fabricated through a unique protocol, incorporating confined electrodeposition on lithographically patterned nanoelectrodes. This protocol can be readily adapted to fabricate nanotube arrays of other photoabsorber and functional materials with precisely controllable design parameters. Ternary CISe nanotube arrays were electrodeposited congruently from a single electrolytic bath and the resulting nanotube arrays were studied through powder X-ray diffraction as well as elemental analysis which revealed compositional purity. Detailed photoelectrochemical (PEC) characterizations in a liquid junction cell were also carried out to investigate the photoconversion efficiency. It was observed that the …

Local Orbital Degeneracy Lifting As A Precursor To An Orbital-Selective Peierls Transition, E. S. Bozin, W. G. Yin, R. J. Koch, M. Abeykoon, Yew San Hor, H. Zheng, H. C. Lei, C. Petrovic, J. F. Mitchell, S. J. L. Billinge

Physics Faculty Research & Creative Works

Fundamental electronic principles underlying all transition metal compounds are the symmetry and filling of the d-electron orbitals and the influence of this filling on structural configurations and responses. Here we use a sensitive local structural technique, x-ray atomic pair distribution function analysis, to reveal the presence of fluctuating local-structural distortions at high temperature in one such compound, CuIr₂S₄. We show that this hitherto overlooked fluctuating symmetry-lowering is electronic in origin and will modify the energy-level spectrum and electronic and magnetic properties. The explanation is a local, fluctuating, orbital-degeneracy-lifted state. The natural extension of our result would …

Lattice Thermal Conductivity Of Quartz At High Pressure And Temperature From The Boltzmann Transport Equation, Xue Xiong, Eugene J. Ragasa, Aleksandr V. Chernatynskiy, Dawei Tang, Simon R. Phillpot

Physics Faculty Research & Creative Works

The thermal conductivities along the basal and hexagonal directions of α-quartz silica, the low-temperature form of crystalline SiO₂, are predicted from the solution of the Boltzmann transport equation combined with the van Beest, Kramer, and van Santen potential for the temperature up to 900 K and the pressure as high as 4 GPa. The thermal conductivities at atmospheric pressure, which show a negative and nonlinear dependence on temperature, are in reasonable agreement with the experimental data. The influence of pressure on thermal conductivity is positive and linear. The pressure (P) and temperature (T) dependences of the thermal conductivity …

The Conundrum Of Relaxation Volumes In First-Principles Calculations Of Charged Defects In Uo₂, Anuj Goyal, Kiran Mathew, Richard G. Hennig, Aleksandr V. Chernatynskiy

Physics Faculty Research & Creative Works

The defect relaxation volumes obtained from density-functional theory (DFT) calculations of charged vacancies and interstitials are much larger than their neutral counterparts, seemingly unphysically large. We focus on UO2 as our primary material of interest, but also consider Si and GaAs to reveal the generality of our results. In this work, we investigate the possible reasons for this and revisit the methods that address the calculation of charged defects in periodic DFT. We probe the dependence of the proposed energy corrections to charged defect formation energies on relaxation volumes and find that corrections such as potential alignment remain ambiguous with …

A Granger Causality Analysis Of Groundwater Patterns Over A Half-Century, Nitin K. Singh, David M. Borrok

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Groundwater depletion in many areas of the world has been broadly attributed to irrigation. However, more formal, data-driven, causal mechanisms of long-term groundwater patterns have not been assessed. Here, we conducted the first Granger causality analysis to identify the "causes" of groundwater patterns using the rice-producing parishes of Louisiana, USA, as an example. Trend analysis showed a decline of up to 6 m in groundwater level over 51 years. We found that no single cause explained groundwater patterns for all parishes. Causal linkages were noted between groundwater and area harvested, number of irrigation wells, summer precipitation totals, and drought. Bi-directional …

Facile One-Pot Synthesis Of Nico₂Se₄-Rgo On Ni Foam For High Performance Hybrid Supercapacitors, Bahareh Golrokh Amin, Jahangir Masud, Manashi Nath

Chemistry Faculty Research & Creative Works

A facile, innovative synthesis for the fabrication of NiCo₂Se₄-rGO on a Ni foam nanocomposite via a simple hydrothermal reaction is proposed. The as-prepared NiCo₂Se₄-rGO@Ni foam electrode was tested through pxrd, TEM, SEM, and EDS to characterize the morphology and the purity of the material. The bimetallic electrode exhibited outstanding electrochemical performance with a high specific capacitance of 2038.55 F g^-1 at 1 A g^-1. NiCo₂Se₄-rGO@Ni foam exhibits an extensive cycling stability after 1000 cycles by retaining 90% of its initial capacity. A superior energy density …

Sample Transfer Optimization With Adaptive Deep Neural Network, Hemanta Sapkota, Md Arifuzzaman, Engin Arslan

Computer Science Faculty Research & Creative Works

Application-layer transfer configurations play a crucial role in achieving desirable performance in high-speed networks. However, finding the optimal configuration for a given transfer task is a difficult problem as it depends on various factors including dataset characteristics, network settings, and background traffic. The state-of-the-art transfer tuning solutions rely on real-time sample transfers to evaluate various configurations and estimate the optimal one. However, existing approaches to run sample transfers incur high delay and measurement errors, thus significantly limit the efficiency of the transfer tuning algorithms. In this paper, we introduce adaptive feed forward deep neural network (DNN) to minimize the error …

Introduction Of A Hybrid Monitor For Cyber-Physical Systems, J. Ceasar Aguma, Bruce M. Mcmillin, Amelia Regan

Computer Science Faculty Research & Creative Works

Computing systems and mobile technologies have changed dramatically since the introduction of firewall technology in 1988. The internet has grown from a simple network of networks to a cyber and physical entity that encompasses the entire planet. Cyber-physical systems(CPS) now control most of the day to day operations of human civilization from autonomous cars to nuclear energy plants. While phenomenal, this growth has created new security threats. These are threats that cannot be blocked by a firewall for they are not only cyber but cyber-physical. In light of these cyber-physical threats, this paper proposes a security measure that promises to …

Analytical Treatment Of The Interaction Quench Dynamics Of Two Bosons In A Two-Dimensional Harmonic Trap, G. Bougas, Simeon I. Mistakidis, P. Schmelcher

Physics Faculty Research & Creative Works

We Investigate The Quantum Dynamics Of Two Bosons, Trapped In A Two-Dimensional Harmonic Trap, Upon Quenching Arbitrarily Their Interaction Strength And Thereby Covering The Entire Energy Spectrum. Utilizing The Exact Analytical Solution Of The Stationary System, We Derive A Closed Analytical Form Of The Expansion Coefficients Of The Time-Evolved Two-Body Wave Function, Whose Dynamics Is Determined By An Expansion Over The Postquench Eigenstates. The Emergent Dynamical Response Of The System Is Analyzed In Detail By Inspecting Several Observables Such As The Fidelity, The Reduced One-Body Densities, The Radial Probability Density Of The Relative Wave Function In Both Real And Momentum …

Social And Geographical Disparities In Twitter Use During Hurricane Harvey, Lei Zou, Nina S.N. Lam, Shayan Shams, Heng Cai, Michelle A. Meyer, Seungwon Yang, Kisung Lee, Seung Jong Park, Margaret A. Reams

Computer Science Faculty Research & Creative Works

Social media such as Twitter is increasingly being used as an effective platform to observe human behaviors in disastrous events. However, uneven social media use among different groups of population in different regions could lead to biased consequences and affect disaster resilience. This paper studies the Twitter use during 2017 Hurricane Harvey in 76 counties in Texas and Louisiana. We seek to answer a fundamental question: did social-geographical disparities of Twitter use exist during the three phases of emergency management (preparedness, response, recovery)? We employed a Twitter data mining framework to process the data and calculate two indexes: Ratio and …

Tests Of General Relativity With The Binary Black Hole Signals From The Ligo-Virgo Catalog Gwtc-1, B. P. Abbott, R. Abbott, T. D. Abbott, Marco Cavaglia, For Full List Of Authors, See Publisher's Website.

Physics Faculty Research & Creative Works

The detection of gravitational waves by Advanced LIGO and Advanced Virgo provides an opportunity to test general relativity in a regime that is inaccessible to traditional astronomical observations and laboratory tests. We present four tests of the consistency of the data with binary black hole gravitational waveforms predicted by general relativity. One test subtracts the best-fit waveform from the data and checks the consistency of the residual with detector noise. The second test checks the consistency of the low- and high-frequency parts of the observed signals. The third test checks that phenomenological deviations introduced in the waveform model (including in …

Enhanced Piezoresponse And Nonlinear Optical Properties Of Fluorinated Self-Assembled Peptide Nanotubes, Soma Khanra, Sandra V. Vassiliades, Wendel A. Alves, Kaidi Yang, Rainer Glaser, Kartik Ghosh, Payal Bhattacharya, Ping Yu, Suchismita Guha

Chemistry Faculty Research & Creative Works

Self-assembled L,L-diphenylalanine (FF) nanostructures offer an attractive platform for photonics and nonlinear optics. The nonlinear optical (NLO) coefficients of FF nanotubes depend on the diameter of the tube [S. Khanra et al. Phys. Chem. Chem. Phys. 19(4), 3084-3093 (2017)]. To further enhance the NLO properties of FF, we search for structural modifications. Here, we report on the synthesis of fluorinated FF dipeptides by replacing one ortho-hydrogen atom in each of the phenyl groups of FF by a fluorine atom. Density-functional theoretical calculations yield insights into minimum energy conformers of fluorinated FF (Fl-FF). Fl-FF self-assembles akin to FF into micron-length tubes. …

Collective Representation Learning On Spatiotemporal Heterogeneous Information Networks, Dakshak Keerthi Chandra, Pengyang Wang, Jennifer Leopold, Yanjie Fu

Computer Science Faculty Research & Creative Works

Representation learning is a technique that is used to capture the underlying latent features of complex data. Representation learning on networks has been widely implemented for learning network structure and embedding it in a low dimensional vector space. In recent years, network embedding using representation learning has attracted increasing attention, and many deep architectures have been widely proposed. However, existing network embedding techniques ignore the multi-class spatial and temporal relationships that crucially reflect the complex nature among vertices and links in spatiotemporal heterogeneous information networks(SHINs).

To address this problem, in this paper, we present two types of collective representation learning …

A Deep Learning Approach For Tweet Classification And Rescue Scheduling For Effective Disaster Management, Md. Yasin Kabir, Sanjay Kumar Madria

Computer Science Faculty Research & Creative Works

Every activity in disaster management demands accurate and up-todate information to allow a quick, easy, and cost-efective response to reduce the possible loss of lives and properties. It is a challenging and complex task to acquire information from diferent regions of a disaster-afected area in a timely fashion. The extensive spread and reach of social media and networks such as Twitter allow people to share information in real-time. However, gathering of valuable information requires a series of operations such as (1) processing each tweet for the text classiication, (2) possible location determination of people needing help based on tweets, and …