Physical Sciences and Mathematics Commons^™

Cloud Tracking Winds On Mars Using Emm-Exi Imaging, Shaimaa Ahmed Alblooki

Theses

The Martian atmospheric wind structure is a major unknown in our understanding of Mars’ climate because it is difficult to measure wind remotely. The Emirates eXploration Imager (EXI) instrument on board the Emirates Mars Mission (EMM) takes visible and ultraviolet images of the whole hemisphere of the planet at a time, and can capture complete weather systems at once, along with their evolution over time. This project uses EXI 320 nm observations to measure winds on Mars using Correlation Imaging Velocimetry (CIV), a cloud tracking method based on software developed for laboratory fluid dynamics experiments, and with significant heritage in …

An Efficient Strategy For Deploying Deception Technology, Noora Abdulla Alhosani

Theses

Implementations of deception technology is crucial in discovering attacks by creating a controlled and monitored environment for detecting malicious activity. This technology involves the deployment of decoys, traps, and honeypots that mimic natural systems and network assets to attract and identify attackers. The use of deception technology provides an early warning system for detecting cyber-attacks, allowing organizations to respond quickly and mitigate damage. This article proposed a framework that focuses on maximizing the efficiency of deception technology in detecting sophisticated attacks. The framework employs multi-layered deception techniques at various levels of the network, system, and application to provide comprehensive coverage …

Tree-Based Unidirectional Neural Networks For Low-Power Computer Vision, Abhinav Goel, Caleb Tung, Nick Eliopoulos, Amy Wang, Jamie C. Davis, George K. Thiruvathukal, Yung-Hisang Lu

Computer Science: Faculty Publications and Other Works

This article describes the novel Tree-based Unidirectional Neural Network (TRUNK) architecture. This architecture improves computer vision efficiency by using a hierarchy of multiple shallow Convolutional Neural Networks (CNNs), instead of a single very deep CNN. We demonstrate this architecture’s versatility in performing different computer vision tasks efficiently on embedded devices. Across various computer vision tasks, the TRUNK architecture consumes 65% less energy and requires 50% less memory than representative low-power CNN architectures, e.g., MobileNet v2, when deployed on the NVIDIA Jetson Nano.

Finite Element Analysis Of A Coconut (Cocos Nucifera L.) Climbing Mechanism, Arjay O. Afan, Omar F. Zubia, Adrian A. Borja, Ralph Kristoffer B. Gallegos

The Philippine Agricultural Scientist

A coconut climber mechanism was subjected to Finite Element Analysis (FEA) to improve its overall design and analyze its strength characteristics. The von Mises stress, total deformation, and factor of safety were evaluated using static structural analysis for the seat and pedal assemblies of the mechanism. The design has a minimum yielding factor of safety of 7.03 for the seat assembly and 12.09 for the pedal assembly. If aluminum is used as an alternative to steel, the prototype's weight can be reduced to at least 64.56% without compromising its strength properties. The weight reduction by using aluminum is expected to …

A System Dynamics Approach To Evaluate Advanced Persistent Threat Vectors, Mathew Nicho, Christopher D. Mcdermott, Hussein Fakhry, Shini Girija

All Works

Cyber-attacks targeting high-profile entities are focused, persistent, and employ common vectors with varying levels of sophistication to exploit social-technical vulnerabilities. Advanced persistent threats (APTs) deploy zero-day malware against such targets to gain entry through multiple security layers, exploiting the dynamic interplay of vulnerabilities in the target network. System dynamics (SD) offers an alternative approach to analyze non-linear, complex, and dynamic social-technical systems. This research applied SD to three high-profile APT attacks - Equifax, Carphone, and Zomato - to identify and simulate socio-technical variables leading to breaches. By modeling APTs using SD, managers can evaluate threats, predict attacks, and reduce damage …

Artificial Intelligence Cad Tools In Trauma Imaging: A Scoping Review From The American Society Of Emergency Radiology (Aser) Ai/Ml Expert Panel, David Dreizin, Pedro V Staziaki, Garvit D Khatri, Nicholas M Beckmann, Zhaoyong Feng, Yuanyuan Liang, Zachary S Delproposto, Maximiliano Klug, J Stephen Spann, Nathan Sarkar, Yunting Fu

Student and Faculty Publications

BACKGROUND: AI/ML CAD tools can potentially improve outcomes in the high-stakes, high-volume model of trauma radiology. No prior scoping review has been undertaken to comprehensively assess tools in this subspecialty.

PURPOSE: To map the evolution and current state of trauma radiology CAD tools along key dimensions of technology readiness.

METHODS: Following a search of databases, abstract screening, and full-text document review, CAD tool maturity was charted using elements of data curation, performance validation, outcomes research, explainability, user acceptance, and funding patterns. Descriptive statistics were used to illustrate key trends.

RESULTS: A total of 4052 records were screened, and 233 full-text …

Exploring The Genotypic And Phenotypic Differences Distinguishing Lactobacillus Jensenii And Lactobacillus Mulieris, Adriana Ene, Swarnali Banerjee, Alan J. Wolfe, Catherine Putonti

Mathematics and Statistics: Faculty Publications and Other Works

Lactobacillus crispatus, Lactobacillus gasseri, Lactobacillus iners, and Lactobacillus jensenii are dominant species of the urogenital microbiota. Prior studies suggest that these Lactobacillus species play a significant role in the urobiome of healthy females. In our prior genomic analysis of all publicly available L. jensenii and Lactobacillus mulieris genomes at the time (n = 43), we identified genes unique to these two closely related species. This motivated our further exploration here into their genotypic differences as well as into their phenotypic differences. First, we expanded genome sequence representatives of both species to 61 strains, including publicly available …

Failure Distributions For Parallel Dependent Identical Weibull Components, Gina S. Sigler

Theses and Dissertations

For a parallel system, when one component fails, the failure distribution of the remaining components will have an increased failure rate. This dissertation takes a novel approach to finding the associated failure distribution of the full system using ordinal statistic distributions for correlated Weibull components, allowing for unknown correlations between the dependent components. A Taylor series approximation is presented for the two component; system failure time distributions are also derived for two failures in a two component system, two failures in an n component system, three failures in a three component system, and k failures in an n component system. …

Abstract Algebra: An Inquiry-Based Approach, Second Edition, Supplemental Material, Jonathan K. Hodge, Steven Schlicker, Ted Sundstrom

Open Textbooks

This text is a supplement to Abstract Algebra: An Inquiry-Based Approach, Second Edition. It includes applications of algebra to RSA encryption, check digits, the games of NIM and the 15 Puzzle, and the determination of groups of small order. In addition, reference material that could be useful for some students appears in the appendices, such as background material on functions, methods of proof (including the equivalencies of the Well-Ordering Principle and different versions of mathematical induction), and complex roots of unity. The appendices also contain a complete proof that polynomial rings are rings, a proof of the Fundamental Theorem of …

Is Fully Explainable Ai Even Possible: Fuzzy-Based Analysis, Miroslav Svitek, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

One of the main limitations of many current AI-based decision-making systems is that they do not provide any understandable explanations of how they came up with the produced decision. Taking into account that these systems are not perfect, that their decisions are sometimes far from good, the absence of an explanation makes it difficult to separate good decisions from suspicious ones. Because of this, many researchers are working on making AI explainable. In some applications areas -- e.g., in chess -- practitioners get an impression that there is a limit to understandability, that some decisions remain inhuman -- not explainable. …

Why Softmax? Because It Is The Only Consistent Approach To Probability-Based Classification, Anatole Lokshin, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical problems, the most effective classification techniques are based on deep learning. In this approach, once the neural network generates values corresponding to different classes, these values are transformed into probabilities by using the softmax formula. Researchers tried other transformation, but they did not work as well as softmax. A natural question is: why is softmax so effective? In this paper, we provide a possible explanation for this effectiveness: namely, we prove that softmax is the only consistent approach to probability-based classification. In precise terms, it is the only approach for which two reasonable probability-based ideas -- Least …

Ocapo: Occupancy-Aware, Pdc Control For Open-Plan, Shared Workspaces, Anaradha Ravi, Archan Misra

Research Collection School Of Computing and Information Systems

Passive Displacement Cooling (PDC) has gained popularity as a means of significantly reducing building energy consumption overheads, especially in tropical climates. PDC eliminates the use of mechanical fans, instead using chilled-water heat exchangers to perform convective cooling. In this paper, we evaluate the impact of different parameters affecting occupant comfort in a 1000m2 open-floor area (consisting of multiple zones) of a ZEB (Zero Energy Building) deployed with PDC units and tackle the problem of setting the temperature setpoint of the PDC units to assure occupant thermal comfort. We tackle two key practical challenges: (a) the zone-level (i.e., occupant-experienced) temperature differs …

An Investigation Into Machine Learning Techniques For Designing Dynamic Difficulty Agents In Real-Time Games, Ryan Adare Dunagan

Electronic Theses and Dissertations

Video games are an incredibly popular pastime enjoyed by people of all ages world wide. Many different kinds of games exist, but most games feature some elements of the player overcoming some challenge, usually through gameplay. These challenges are insurmountable for some people and may turn them off to video games as a pastime. Games can be made more accessible to players of little skill and/or experience through the use of Dynamic Difficulty Adjustment (DDA) systems that adjust the difficulty of the game in response to the player’s performance. This research seeks to establish the effectiveness of machine learning techniques …

Search For Low-Mass Dark Matter Via Bremsstrahlung Radiation And The Migdal Effect In Supercdms, M. F. Albakry, I. Alkhatib, D. Alonso-González, D. W.P. Amaral, T. Aralis, T. Aramaki, I. J. Arnquist, I. Ataee Langroudy, E. Azadbakht, S. Banik, C. Bathurst, R. Bhattacharyya, P. L. Brink, R. Bunker, B. Cabrera, R. Calkins, R. A. Cameron, C. Cartaro, D. G. Cerdeño, Y. Y. Chang, M. Chaudhuri, R. Chen, N. Chott, J. Cooley, H. Coombes, J. Corbett, P. Cushman, S. Das

All Works

We present a new analysis of previously published SuperCDMS data using a profile likelihood framework to search for sub-GeV dark matter (DM) particles through two inelastic scattering channels: bremsstrahlung radiation and the Migdal effect. By considering these possible inelastic scattering channels, experimental sensitivity can be extended to DM masses that are undetectable through the DM-nucleon elastic scattering channel, given the energy threshold of current experiments. We exclude DM masses down to 220 MeV/c2 at 2.7×10-30 cm2 via the bremsstrahlung channel. The Migdal channel search provides overall considerably more stringent limits and excludes DM masses down to 30 MeV/c2 at 5.0×10-30 …

Solitary Waves In A Quantum Droplet-Bearing System, Garyfallia Katsimiga, Simeon I. Mistakidis, G. N. Koutsokostas, D. J. Frantzeskakis, R. Carretero-González, P. G. Kevrekidis

Physics Faculty Research & Creative Works

We Unravel The Existence And Stability Properties Of Dark Soliton Solutions As They Extend From The Regime Of Trapped Quantum Droplets Towards The Thomas-Fermi Limit In Homonuclear Symmetric Bose Mixtures. Leveraging A Phase-Plane Analysis, We Identify The Regimes Of Existence Of Different Types Of Quantum Droplets And Subsequently Examine The Possibility Of Black And Gray Solitons And Kink-Type Structures In This System. Moreover, We Employ The Landau Dynamics Approach To Extract An Analytical Estimate Of The Oscillation Frequency Of A Single Dark Soliton In The Relevant Extended Gross-Pitaevskii Model. Within This Framework, We Also Find That The Single Soliton Immersed …

Stability Of Retinol In Liposomes As Measured By Fluorescence Lifetime Spectroscopy And Flim, Louis Sumrall, L. Smith, Elmukhtar Ehmed Alhatmi, Yekaterina G. Chmykh, D. Mitchell, Jay Nadeau

Physics Faculty Publications and Presentations

Retinol shows complex photophysical properties that make it potentially useful as an exogenous or endogenous probe of membrane microenvironment, but it has not been fully explored. In this study, we use bulk fluorescence lifetime measurements and fluorescence lifetime imaging microscopy (FLIM) to examine the stability of retinol in phosphatidylcholine (PC) multilamellar and unilamellar vesicles with and without cholesterol. We find that both light and exposure to ambient temperature and oxygen contribute to retinol degradation, with the addition of an antioxidant such as butylated hydroxytoluene (BHT) essential to provide stability, especially in the absence of cholesterol. With exposure to ultraviolet light …

Evaluating Neural Networks As Cognitive Models For Learning Quasi-Regularities In Language, Xiaomeng Ma

Dissertations, Theses, and Capstone Projects

Many aspects of language can be categorized as quasi-regular: the relationship between the inputs and outputs is systematic but allows many exceptions. Common domains that contain quasi-regularity include morphological inflection and grapheme-phoneme mapping. How humans process quasi-regularity has been debated for decades. This thesis implemented modern neural network models, transformer models, on two tasks: English past tense inflection and Chinese character naming, to investigate how transformer models perform quasi-regularity tasks. This thesis focuses on investigating to what extent the models' performances can represent human behavior. The results show that the transformers' performance is very similar to human behavior in many …

Quantitative Modeling Of Text-Based Intelligence Source Uncertainty, Adam D. Nesmith

Theses and Dissertations

An all-source intelligence analyst’s primary job is delivering timely, well-sourced assessments on relevant targets based on uncertain and incomplete information. Each assessment includes a likelihood that the assessment is true, and a confidence level based on the uncertainty of the sources used. Quantitative all-source intelligence analysis is not widely implemented despite the acknowledged limitations of qualitative intelligence assessments and the existence of proposed quantitative methods. This is due to the challenge of quantitatively representing uncertainty in text-based intelligence reporting (i.e., HUMINT, OSINT, SIGINT), which limits the effectiveness and usability of previously suggested methods. This research creates a novel framework for …

Gis Data: Caroline County, Maryland – Shoreline Inventory Data 2023, Karinna Nunez, Tamia Rudnicky, Sharon Killeen, Jessica Hendricks, Catherine R. Duning, Evan Hill

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shore zone into three regions:

1) the …

Optimal Domain-Partitioning Algorithm For Real-Life Transportation Networks And Finite Element Meshes, Jimesh Bhagatji, Sharanabasaweshwara Asundi, Eric Thompson, Duc T. Nguyen

Civil & Environmental Engineering Faculty Publications

For large-scale engineering problems, it has been generally accepted that domain-partitioning algorithms are highly desirable for general-purpose finite element analysis (FEA). This paper presents a heuristic numerical algorithm that can efficiently partition any transportation network (or any finite element mesh) into a specified number of subdomains (usually depending on the number of parallel processors available on a computer), which will result in “minimising the total number of system BOUNDARY nodes” (as a primary criterion) and achieve “balancing work loads” amongst the subdomains (as a secondary criterion). The proposed seven-step heuristic algorithm (with enhancement features) is based on engineering common sense …

The X-Ray Variation Of M81* Resolved By Chandra And Nustar, Shu Niu, Fu-Guo Xie, Q. Daniel Wang, Li Ji, Feng Yuan, Min Long

Computer Science Faculty Publications and Presentations

Despite advances in our understanding of low-luminosity active galactic nuclei (LLAGNs), the fundamental details about the mechanisms of radiation and flare/outburst in hot accretion flow are still largely missing. We have systematically analysed the archival Chandra and NuSTAR X-ray data of the nearby LLAGN M81*, whose L_bol ∼ 10⁻⁵L_Edd. Through a detailed study of X-ray light curve and spectral properties, we find that the X-ray continuum emission of the power-law shape more likely originates from inverse Compton scattering within the hot accretion flow. In contrast to Sgr A*, flares are rare in M81*. Low-amplitude variation …

Early Permian Zircon Ages From The P. Confluens And P. Pseudoreticulata Spore-Pollen Zones In The Southern Bonaparte And Canning Basins, Northwestern Australia, A. J. Mory, J. Crowley, J. Backhouse, R. S. Nicoll, J. D. Gorter

Geosciences Faculty Publications and Presentations

The Pseudoreticulatispora confluens–P. pseudoreticulata spore-pollen zonal datum typically coincides with the end of widespread Permian glacial deposits in Western Australia. Although previously attributed to the mid-Sakmarian, chemical abrasion isotope dilution thermal ionisation mass spectrometry (TIMS) dating of zircons from volcanic tuffs in the Ditji Formation of the Bonaparte Basin and the Grant Group in the Canning Basin point to an Asselian age of about 295.25 Ma for this datum. All dated zircons from the Ditji Formation came from petroleum well cuttings but the accompanying palynology was mostly from sidewall cores; however, all Grant Group samples were from conventional core. TIMS …

Synthesis And Study Of High-Spin Stable Organic Radicals For Electrical Conductors And Mannosamine Nitroxide For Mri Contrast Agents, Shuyang Zhang

Department of Chemistry: Dissertations, Theses, and Student Research

In the first project, we describe the synthesis of an ambient stable high spin organic diradical 4 based on the Blatter moiety. The high-spin (S = 1) organic diradical 4, which consists of two Blatter radical moieties in a conjugated structure, exhibits a nearly exclusive population (88%) on triplet ground state at room temperature as a consequence of a large single-triplet energy gap (ΔEST = 0.5 kcal/mol). The target diradical molecule is synthesized over five steps with structural confirmation by single-crystal X-ray diffraction. The thermogravimetric analysis (TGA) shows the onset of decomposition at ~264 oC, indicating the diradical molecule has …

2023 June - Tennessee Monthly Climate Report, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Monthly Report

No abstract provided.

Gnnlens: A Visual Analytics Approach For Prediction Error Diagnosis Of Graph Neural Networks., Zhihua Jin, Yong Wang, Qianwen Wang, Yao Ming, Tengfei Ma, Huamin Qu

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) aim to extend deep learning techniques to graph data and have achieved significant progress in graph analysis tasks (e.g., node classification) in recent years. However, similar to other deep neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), GNNs behave like a black box with their details hidden from model developers and users. It is therefore difficult to diagnose possible errors of GNNs. Despite many visual analytics studies being done on CNNs and RNNs, little research has addressed the challenges for GNNs. This paper fills the research gap with an interactive visual analysis …

Tracing The Twenty-Year Evolution Of Developing Ai For Eye Screening In Singapore: A Master Chronology Of Sidrp, Selena+ And Eyris, Steven M. Miller

Research Collection School Of Computing and Information Systems

This working paper is entirely comprised of a timeline table that begins in 2002 and runs through mid-2023. Across these two decades, this timeline traces the evolutionary development of the following:

• The early Singapore R&D efforts to apply software-based image analysis algorithms and methods to analyse eye retina images for diabetic retinopathy and other eye diseases. This was based on a collaboration between the Singapore Eye Research Institute (SERI) and its parent organization, the Singapore National Eye Centre (SNEC), with faculty from the School of Computing at National University of Singapore.
• The establishment and operation of the Singapore Integrated Diabetic …

Ldptrace: Locally Differentially Private Trajectory Synthesis, Yuntao Du, Yujia Hu, Zhikun Zhang, Ziquan Fang, Lu Chen, Baihua Zheng, Yunjun Gao

Research Collection School Of Computing and Information Systems

Trajectory data has the potential to greatly benefit a wide-range of real-world applications, such as tracking the spread of the disease through people's movement patterns and providing personalized location-based services based on travel preference. However, privacy concerns and data protection regulations have limited the extent to which this data is shared and utilized. To overcome this challenge, local differential privacy provides a solution by allowing people to share a perturbed version of their data, ensuring privacy as only the data owners have access to the original information. Despite its potential, existing point-based perturbation mechanisms are not suitable for real-world scenarios …

Non-Binary Evaluation Of Next-Basket Food Recommendation, Yue Liu, Palakorn Achananuparp, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Next-basket recommendation (NBR) is a recommendation task that predicts a basket or a set of items a user is likely to adopt next based on his/her history of basket adoption sequences. It enables a wide range of novel applications and services from predicting next basket of items for grocery shopping to recommending food items a user is likely to consume together in the next meal. Even though much progress has been made in the algorithmic NBR research over the years, little research has been done to broaden knowledge about the evaluation of NBR methods, which is largely based on the …

How Does Credit Risk Affect Cost Management Strategies? Evidence On The Initiation Of Credit Default Swap And Sticky Cost Behavior, Jing Dai, Nan Hu, Rong Huang, Yan Yan

Research Collection School Of Computing and Information Systems

In this paper, we examine the effect of credit defaults swaps (CDS) initiation on reference firms' cost management strategies. CDS contracts provide insurance protection for creditors, inducing a shift in bargaining power from borrowers to creditors and an excessive incidence of bankruptcy. Anticipating more intransigent creditors in debt renegotiations and higher bankruptcy risk, CDS firms are incentivized to mitigate risk through decreasing cost stickiness after CDS initiation, as cost stickiness lowers liquidity and triggers early covenant violations. We find that, on average, CDS initiation is associated with a decline in reference firms' cost stickiness. This association is more pronounced for …

Glocal Energy-Based Learning For Few-Shot Open-Set Recognition, Haoyu Wang, Guansong Pang, Peng Wang, Lei Zhang, Wei Wei, Yanning Zhang

Research Collection School Of Computing and Information Systems

Few-shot open-set recognition (FSOR) is a challenging task of great practical value. It aims to categorize a sample to one of the pre-defined, closed-set classes illustrated by few examples while being able to reject the sample from unknown classes. In this work, we approach the FSOR task by proposing a novel energy-based hybrid model. The model is composed of two branches, where a classification branch learns a metric to classify a sample to one of closedset classes and the energy branch explicitly estimates the open-set probability. To achieve holistic detection of openset samples, our model leverages both class-wise and pixelwise …