Physical Sciences and Mathematics Commons^™

A Simple Background Elimination Method For Miniaturized Fiber-Optic Raman Probe, Bohong Zhang

Masters Theses

"Raman scattering is called a photonic - molecular interaction based on the kinetic model of the analytic. Due to the uniqueness of the Raman scattering technique, it can provide a unique fingerprint signal for molecular recognition. However, a serious challenge often encountered in Raman measurement comes from the requirements of fast, real-time remote sensing, background fluorescence suppression, and micro-environmental detection.

A new Miniaturized Fiber-Optic Raman Probe (MFORP) for Raman spectroscopy, used especially for eliminating background fluorescence and enhancing sampling, is presented. Its main purpose is to provide an overview of excellent research on the detection of very small substances and …

Interactive Visual Self-Service Data Classification Approach To Democratize Machine Learning, Sridevi Narayana Wagle

All Master's Theses

Machine learning algorithms often produce models considered as complex black-box models by both end users and developers. Such algorithms fail to explain the model in terms of the domain they are designed for. The proposed Iterative Visual Logical Classifier (IVLC) is an interpretable machine learning algorithm that allows end users to design a model and classify data with more confidence and without having to compromise on the accuracy. Such technique is especially helpful when dealing with sensitive and crucial data like cancer data in the medical domain with high cost of errors. With the help of the proposed interactive and …

Visualization For Solving Non-Image Problems And Saliency Mapping, Divya Chandrika Kalla

All Master's Theses

High-dimensional data play an important role in knowledge discovery and data science. Integration of visualization, visual analytics, machine learning (ML), and data mining (DM) are the key aspects of data science research for high-dimensional data. This thesis is to explore the efficiency of a new algorithm to convert non-images data into raster images by visualizing data using heatmap in the collocated paired coordinates (CPC). These images are called the CPC-R images and the algorithm that produces them is called the CPC-R algorithm. Powerful deep learning methods open an opportunity to solve non-image ML/DM problems by transforming non-image ML problems into …

A Compact Wavelength Meter Using A Multimode Fiber, Ogbole Collins Inalegwu

Masters Theses

“Wavelength meters are very important for precision measurements of both pulses and continuous-wave optical sources. Conventional wavelength meters employ gratings, prisms, interferometers, and other wavelength-sensitive materials in their design. Here, we report a simple and compact wavelength meter based on a section of multimode fiber and a camera. The concept is to correlate the multimodal interference pattern (i.e., speckle pattern) at the end-face of a multimode fiber with the wavelength of the input lightsource. Through a series of experiments, specklegrams from the end face of a multimode fiber as captured by a charge-coupled device (CCD) camera were recorded; the images …

Biochemical Assay Invariant Attestation For The Security Of Cyber-Physical Digital Microfluidic Biochips, Fredrick Eugene Love Ii

Masters Theses

“Due to the devastating global impact that infectious diseases have had, especially in developing countries, the demand for access to adequate resources to combat sickness continues to be a heavy burden. Reliable and affordable diagnostics is a vital first line of defense in fighting outbreaks and providing accurate treatment. Digital microfluidics biochips capable of running multiple diagnostic tests on a single platform are an emerging technology that are increasingly being evaluated as a viable platform for rapid diagnosis and point-of-care field deployment. Although these systems offer many benefits, processing errors are inherent. Therefore, cyber-physical digital biochips are being investigated that …

Neural Network Supervised And Reinforcement Learning For Neurological, Diagnostic, And Modeling Problems, Donald Wunsch Iii

Masters Theses

“As the medical world becomes increasingly intertwined with the tech sphere, machine learning on medical datasets and mathematical models becomes an attractive application. This research looks at the predictive capabilities of neural networks and other machine learning algorithms, and assesses the validity of several feature selection strategies to reduce the negative effects of high dataset dimensionality. Our results indicate that several feature selection methods can maintain high validation and test accuracy on classification tasks, with neural networks performing best, for both single class and multi-class classification applications. This research also evaluates a proof-of-concept application of a deep-Q-learning network (DQN) to …

Values Of Trust In Ai In Autonomous Driving Vehicles, Ru Lian

Masters Theses

“Automation with artificial intelligence technology is an emerging field and is widely used in various industries. With the increasing autonomy, learning, and adaptability of intelligent machines such as self-driving cars, it is difficult to regard them as simple tools in human hands. At the same time, a series of problems and challenges such as predictability, interpretability, and causality arise. Trust in self-driving technology will impact the adoption and utilization of autonomous driving technology. A qualitative research methodology, Value-Focused Thinking, is used to identify the values of trust in autonomous driving vehicles and analyze the relationship between these values”--Abstract, page iii.

Dr. Anastasia Chavez - Testimonios: Stories Of Latinx And Hispanic Mathematicians, Anastasia Chavez

School of Science Faculty Works

No abstract provided.

De Novo Prediction Of Drug–Target Interactions Using Laplacian Regularized Schatten P-Norm Minimization, Gaoyan Wu, Mengyun Yang, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

In pharmaceutical sciences, a crucial step of the drug discovery is the identification of drug–target interactions (DTIs). However, only a small portion of the DTIs have been experimentally validated. Moreover, it is an extremely laborious, expensive, and time-consuming procedure to capture new interactions between drugs and targets through traditional biochemical experiments. Therefore, designing computational methods for predicting potential interactions to guide the experimental verification is of practical significance, especially for de novo situation. In this article, we propose a new algorithm, namely Laplacian regularized Schatten p-norm minimization (LRSpNM), to predict potential target proteins for novel drugs and potential drugs for …

Parallel Anisotropic Unstructured Grid Adaptation, Christos Tsolakis, Nikos Chrisochoides, Michael A. Park, Adrien Loseille, Todd Michal

Computer Science Faculty Publications

Computational fluid dynamics (CFD) has become critical to the design and analysis of aerospace vehicles. Parallel grid adaptation that resolves multiple scales with anisotropy is identified as one of the challenges in the CFD Vision 2030 Study to increase the capacity and capability of CFD simulation. The study also cautions that computer architectures are undergoing a radical change, and dramatic increases in algorithm concurrency will be required to exploit full performance. This paper reviews four different methods to parallel anisotropic grid adaptation. They cover both ends of the spectrum: 1) using existing state-of-the-art software optimized for a single core and …

Detecting Incentivized Review Groups With Co-Review Graph, Yubao Zhang, Shuai Hao, Haining Wang

Computer Science Faculty Publications

Online reviews play a crucial role in the ecosystem of nowadays business (especially e-commerce platforms), and have become the primary source of consumer opinions. To manipulate consumers’ opinions, some sellers of e-commerce platforms outsource opinion spamming with incentives (e.g., free products) in exchange for incentivized reviews. As incentives, by nature, are likely to drive more biased reviews or even fake reviews. Despite e-commerce platforms such as Amazon have taken initiatives to squash the incentivized review practice, sellers turn to various social networking platforms (e.g., Facebook) to outsource the incentivized reviews. The aggregation of sellers who …

Understanding And Predicting Retractions Of Published Work, Sai Ajay Modukuri, Sarah Rajtmajer, Anna Cinzia Squicciarini, Jian Wu, C. Lee Giles

Computer Science Faculty Publications

Recent increases in the number of retractions of published papers reflect heightened attention and increased scrutiny in the scientific process motivated, in part, by the replication crisis. These trends motivate computational tools for understanding and assessment of the scholarly record. Here, we sketch the landscape of retracted papers in the Retraction Watch database, a collection of 19k records of published scholarly articles that have been retracted for various reasons (e.g., plagiarism, data error). Using metadata as well as features derived from full-text for a subset of retracted papers in the social and behavioral sciences, we develop a random forest classifier …

Tunable Optical Filter Using Phase Change Materials For Smart Ir Night Vision Applications, Remona Heenkenda, Keigo Hirakawa, Andrew Sarangan

Electro-Optics and Photonics Faculty Publications

In this paper we present a tunable filter using Ge2Sb2Se4Te1 (GSST) phase change material. The design principle of the filter is based on a metal-insulator-metal (MIM) cavity operating in the reflection mode. This is intended for night vision applications that utilize 850nm as the illumination source. The filter allows us to selectively reject the 850nm band in one state. This is illustrated through several daytime and nighttime imaging applications.

Dales Objects: A Large Scale Benchmark Dataset For Instance Segmentation In Aerial Lidar, Nina M. Singer, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

We present DALES Objects, a large-scale instance segmentation benchmark dataset for aerial lidar. DALES Objects contains close to half a billion hand-labeled points, including semantic and instance segmentation labels. DALES Objects is an extension of the DALES (Varney et al., 2020) dataset, adding additional intensity and instance segmentation annotation. This paper provides an overview of the data collection, preprocessing, hand-labeling strategy, and final data format. We propose relevant evaluation metrics and provide insights into potential challenges when evaluating this benchmark dataset. Finally, we provide information about how researchers can access the dataset for their use at go.udayton.edu/dales3d.

A Data-Driven Method For Online Monitoring Tube Wall Thinning Process In Dynamic Noisy Environment, Chen Zhang, Jun Long Lim, Ouyang Liu, Aayush Madan, Yongwei Zhu, Shili Xiang, Kai Wu, Rebecca Yen-Ni Wong, Jiliang Eugene Phua, Karan M. Sabnani, Keng Boon Siah, Wenyu Jiang, Yixin Wang, Emily Jianzhong Hao, Hoi, Steven C. H.

Research Collection School Of Computing and Information Systems

Tube internal erosion, which corresponds to its wall thinning process, is one of the major safety concerns for tubes. Many sensing technologies have been developed to detect a tube wall thinning process. Among them, fiber Bragg grating (FBG) sensors are the most popular ones due to their precise measurement properties. Most of the current works focus on how to design different types of FBG sensors according to certain physical laws and only test their sensors in controlled laboratory conditions. However, in practice, an industrial system usually suffers from harsh and dynamic environmental conditions, and FBG signals are affected by many …

Attribute-Aware Pedestrian Detection In A Crowd, Jialiang Zhang, Lixiang Lin, Jianke Zhu, Yang Li, Yun-Chen Chen, Yao Hu, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Pedestrian detection is an initial step to perform outdoor scene analysis, which plays an essential role in many real-world applications. Although having enjoyed the merits of deep learning frameworks from the generic object detectors, pedestrian detection is still a very challenging task due to heavy occlusions, and highly crowded group. Generally, the conventional detectors are unable to differentiate individuals from each other effectively under such a dense environment. To tackle this critical problem, we propose an attribute-aware pedestrian detector to explicitly model people's semantic attributes in a high-level feature detection fashion. Besides the typical semantic features, center position, target's scale, …

Discovering Hidden Topical Hubs And Authorities Across Multiple Online Social Networks, Ka Wei, Roy Lee, Tuan-Anh Hoang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Finding influential users in online social networks (OSNs) is an important problem with many possible useful applications. Many methods have been proposed to identify influential users in OSNs. PageRank and HITs are two well known examples that determine influential users through link analysis. In recent years, new models that consider both content and social network links have been developed. The Hub and Authority Topic (HAT) model is one that extends HITS to identify topic-specific hubs and authorities by jointly learning hubs, authorities, and topical interests from users’ relationship and textual content. However, many of the previous works are confined to …

Zone Path Construction (Zac) Based Approaches For Effective Real-Time Ridesharing, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet

Research Collection School Of Computing and Information Systems

Real-time ridesharing systems such as UberPool, Lyft Line and GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the “right” requests to travel together in the “right” available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. This challenge has been addressed in existing work by: (i) generating as many relevant feasible combinations of requests (with respect to the available delay for customers) as …

Why My Code Summarization Model Does Not Work: Code Comment Improvement With Category Prediction, Qiuyuan Chen, Xin Xia, Han Hu, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Code summarization aims at generating a code comment given a block of source code and it is normally performed by training machine learning algorithms on existing code block-comment pairs. Code comments in practice have different intentions. For example, some code comments might explain how the methods work, while others explain why some methods are written. Previous works have shown that a relationship exists between a code block and the category of a comment associated with it. In this article, we aim to investigate to which extent we can exploit this relationship to improve code summarization performance. We first classify comments …

Creators And Backers In Rewards-Based Crowdfunding: Will Incentive Misalignment Affect Kickstarter's Sustainability?, Michael Wessel, Rob Gleasure, Robert John Kauffman

Research Collection School Of Computing and Information Systems

Incentive misalignment in rewards-based crowd-funding occurs because creators may benefit disproportionately from fundraising, while backers may benefit disproportionately from the quality of project deliverables. The resulting principal-agent relationship means backers rely on campaign information to identify signs of moral hazard, adverse selection, and risk attitude asymmetry. We analyze campaign information related to fundraising, and compare how different information affects eventual backer satisfaction, based on an extensive dataset from Kickstarter. The data analysis uses a multi-model comparison to reveal similarities and contrasts in the estimated drivers of dependent variables that capture different outcomes in Kickstarter’s funding campaigns, using a linear probability …

Generation Of Excited Species In A Streamer Discharge, Shirshak K. Dhali

Electrical & Computer Engineering Faculty Publications

At or near atmospheric pressure, most transient discharges, particularly in molecular gases or gas mixture containing molecular gases, result in a space charge dominated transport called a streamer discharge. The excited species generation in such discharges forms the basis for plasma chemistry in most technological applications. In this paper, we simulate the propagation of streamers in atmospheric pressure N₂ to understand the energy partitioning in the formation of various excited species and compare the results to a uniform Townsend discharge. The model is fully two-dimensional with azimuthal symmetry. The results show a significantly larger fraction of the energy goes …

Joint Modeling Of Rnaseq And Radiomics Data For Glioma Molecular Characterization And Prediction, Zeina A. Shboul, Norou Diawara, Arastoo Vossough, James Y. Chen, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

RNA sequencing (RNAseq) is a recent technology that profiles gene expression by measuring the relative frequency of the RNAseq reads. RNAseq read counts data is increasingly used in oncologic care and while radiology features (radiomics) have also been gaining utility in radiology practice such as disease diagnosis, monitoring, and treatment planning. However, contemporary literature lacks appropriate RNA-radiomics (henceforth, radiogenomics) joint modeling where RNAseq distribution is adaptive and also preserves the nature of RNAseq read counts data for glioma grading and prediction. The Negative Binomial (NB) distribution may be useful to model RNAseq read counts data that addresses potential shortcomings. …

The Resistive Barrier Discharge: A Brief Review Of The Device And Its Biomedical Applications, Mounir Laroussi

Electrical & Computer Engineering Faculty Publications

This paper reviews the principles behind the design and operation of the resistive barrier discharge, a low temperature plasma source that operates at atmospheric pressure. One of the advantages of this plasma source is that it can be operated using either DC or AC high voltages. Plasma generated by the resistive barrier discharge has been used to efficiently inactivate pathogenic microorganisms and to destroy cancer cells. These biomedical applications of low temperature plasma are of great interest because in recent times bacteria developed increased resistance to antibiotics and because present cancer therapies often are accompanied by serious side effects. Low …

Adenosine Triphosphate (Atp) As A Metric Of Microbial Biomass In Aquatic Systems: New Simplified Protocols, Laboratory Validation, And A Reflection On Data From The Literature, Alexander B. Bochdansky, Alison N. Stouffer, Nyjaee N. Washington

OES Faculty Publications

The use of adenosine triphosphate (ATP) as a universal biomass indicator is built on the premise that ATP concentration tracks biomass rather than the physiological condition of cells. However, reportedly high variability in ATP in response to environmental conditions is the main reason the method has not found widespread application. To test possible sources of this variability, we used the diatom Thalassiosira weissflogii as a model and manipulated its growth rate through nutrient limitation and through exposure to three different temperatures (15°C, 20°C, and 25°C). We simplified the ATP protocol with hot‐water or chemical extraction methods, modified a commercially available …

Salt Marsh Hydrogeology: A Review, Julia Guimond, Joseph Tamborski

OES Faculty Publications

Groundwater–surface water exchange in salt marsh ecosystems mediates nearshore salt, nutrient, and carbon budgets with implications for biological productivity and global climate. Despite their importance, a synthesis of salt marsh groundwater studies is lacking. In this review, we summarize drivers mediating salt marsh hydrogeology, review field and modeling techniques, and discuss patterns of exchange. New data from a Delaware seepage meter study are reported which highlight small-scale spatial variability in exchange rates. A synthesis of the salt marsh hydrogeology literature reveals a positive relationship between tidal range and submarine groundwater discharge but not porewater exchange, highlighting the multidimensional drivers of …

A Coastal N₂ Fixation Hotspot At The Cape Hatteras Front: Elucidating Spatial Heterogeneity In Diazotroph Activity Via Supervised Machine Learning, Corday R. Selden, P. Dreux Chappell, Sophie Clayton, Alfonso Macías-Tapia, Peter W. Bernhardt, Margaret R. Mulholland

OES Faculty Publications

In the North Atlantic Ocean, dinitrogen (N₂) fixation on the western continental shelf represents a significant fraction of basin‐wide nitrogen (N) inputs. However, the factors regulating coastal N₂ fixation remain poorly understood, in part due to sharp physico‐chemical gradients and dynamic water mass interactions that are difficult to constrain via traditional oceanographic approaches. This study sought to characterize the spatial heterogeneity of N₂ fixation on the western North Atlantic shelf, at the confluence of Mid‐ and South Atlantic Bight shelf waters and the Gulf Stream, in August 2016. Rates were quantified using the ¹⁵N_{2 …}

Using Heat To Trace Vertical Water Fluxes In Sediment Experiencing Concurrent Tidal Pumping And Groundwater Discharge, N. K. Leroux, B. L. Kurylyk, M. A. Briggs, D. J. Irvine, J. J. Tamborski, V. F. Bense

OES Faculty Publications

Heat has been widely applied to trace groundwater-surface water exchanges in inland environments, but it is infrequently applied in coastal sediment where head oscillations induce periodicity in water flux magnitude/direction and heat advection. This complicates interpretation of temperatures to estimate water fluxes. We investigate the convolution of thermal and hydraulic signals to assess the viability of using heat as a tracer in environments with tidal head oscillations superimposed on submarine groundwater discharge. We first generate sediment temperature and head time series for conditions ranging from no tide to mega-tidal using a numerical model (SUTRA) forced with periodic temperature and tidal …

Toward Resolving Disparate Accounts Of The Extent And Magnitude Of Nitrogen Fixation In The Eastern Tropical South Pacific Oxygen Deficient Zone, Corday R. Selden, Margaret R. Mulholland, Brittany Widner, Peter Bernhardt, Amal Jayakumar

OES Faculty Publications

Examination of dinitrogen (N₂) fixation in the Eastern Tropical South Pacific oxygen deficient zone has raised questions about the range of diazotrophs in the deep sea and their quantitative importance as a source of new nitrogen globally. However, technical considerations in the deployment of stable isotopes in quantifying N₂ fixation rates have complicated interpretation of this research. Here, we report the findings of a comprehensive survey of N₂ fixation within, above and below the Eastern Tropical South Pacific oxygen deficient zone. N₂ fixation rates were measured using a robust ¹⁵N tracer method (bubble removal) …

Microbially Induced Sedimentary Structures In Clastic Deposits: Implication For The Prospection For Fossil Life On Mars, Nora Noffke

OES Faculty Publications

Abundant and well-preserved fossil microbenthos occurs in siliciclastic deposits of all Earth ages, from the early Archean to today. Studies in modern settings show how microbenthos responds to sediment dynamics by baffling and trapping, binding, biostabilization, and growth. Results of this microbial-sediment interaction are microbially induced sedimentary structures (MISS). Successful prospection for rich MISS occurrences in the terrestrial lithological record requires unraveling genesis and taphonomy of MISS, both of which are defined only by a narrow range of specific conditions. These conditions have to coincide with high detectability which is a function of outcrop quality, bedding character, and rock type. …

The Renaissance Of Odum's Outwelling Hypothesis In 'Blue Carbon' Science, Isaac R. Santos, David J. Burdige, Tim C. Jennerjahn, Steven Bouillon, Alex Cabral, Oscar Serrano, Thomas Wernberg, Karen Filbee-Dexter, Julia A. Guimond, Joseph J. Tamborski

OES Faculty Publications

The term ‘Blue Carbon’ was coined about a decade ago to highlight the important carbon sequestration capacity of coastal vegetated ecosystems. The term has paved the way for the development of programs and policies that preserve and restore these threatened coastal ecosystems for climate change mitigation. Blue carbon research has focused on quantifying carbon stocks and burial rates in sediments or accumulating as biomass. This focus on habitat-bound carbon led us to losing sight of the mobile blue carbon fraction. Oceans, the largest active reservoir of carbon, have become somewhat of a blind spot. Multiple recent investigations have revealed high …