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Methylation Of The D2 Dopamine Receptor Affects Binding With The Human Regulatory Proteins Par-4 And Calmodulin, Alexander Bowitch, Ansuman Sahoo, Andrea M. Clark, Christiana Ntangka, Krishna K. Raut, Paul Gollnick, Michael C. Yu, Steven M. Pascal, Sarah E. Walker, Denise M. Ferkey Feb 2021

Methylation Of The D2 Dopamine Receptor Affects Binding With The Human Regulatory Proteins Par-4 And Calmodulin, Alexander Bowitch, Ansuman Sahoo, Andrea M. Clark, Christiana Ntangka, Krishna K. Raut, Paul Gollnick, Michael C. Yu, Steven M. Pascal, Sarah E. Walker, Denise M. Ferkey

Chemistry & Biochemistry Faculty Publications

No abstract provided.

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Statistical Analysis And Comparison Of Optical Classification Of Atmospheric Aerosol Lidar Data, Mohammed Alqawba, Norou Diawara, Kwasi G. Afrifa, Mohamed I. Elbakary, Mecit Cetin, Khan Iftekharuddin Feb 2021

Statistical Analysis And Comparison Of Optical Classification Of Atmospheric Aerosol Lidar Data, Mohammed Alqawba, Norou Diawara, Kwasi G. Afrifa, Mohamed I. Elbakary, Mecit Cetin, Khan Iftekharuddin

Mathematics & Statistics Faculty Publications

In this article, we present a new study for the analysis and classification of atmospheric aerosols in remote sensing LIDAR data. Information on particle size and associated properties are extracted from these remote sensing atmospheric data which are collected by a ground-based LIDAR system. This study first considers optical LIDAR parameter-based classification methods for clustering and classification of different types of harmful aerosol particles in the atmosphere. Since accurate methods for aerosol prediction behaviors are based upon observed data, computational approaches must overcome design limitations, and consider appropriate calibration and estimation accuracy. Consequently, two statistical methods based on generalized linear …

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Role Of Artificial Intelligence In The Internet Of Things (Iot) Cybersecurity, Murat Kuzlu, Corinne Fair, Ozgur Guler Feb 2021

Role Of Artificial Intelligence In The Internet Of Things (Iot) Cybersecurity, Murat Kuzlu, Corinne Fair, Ozgur Guler

Engineering Technology Faculty Publications

In recent years, the use of the Internet of Things (IoT) has increased exponentially, and cybersecurity concerns have increased along with it. On the cutting edge of cybersecurity is Artificial Intelligence (AI), which is used for the development of complex algorithms to protect networks and systems, including IoT systems. However, cyber-attackers have figured out how to exploit AI and have even begun to use adversarial AI in order to carry out cybersecurity attacks. This review paper compiles information from several other surveys and research papers regarding IoT, AI, and attacks with and against AI and explores the relationship between these …

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Professors Study Evacuation Practices, Marcus Coles Jan 2021

Professors Study Evacuation Practices, Marcus Coles

News Items

No abstract provided.

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Odu To Lead Coastal Virginia Consortium, Sherry Dibarri Jan 2021

Odu To Lead Coastal Virginia Consortium, Sherry Dibarri

News Items

No abstract provided.

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Using Torchattacks To Improve The Robustness Of Models With Adversarial Training, William S. Matos Díaz Jan 2021

Using Torchattacks To Improve The Robustness Of Models With Adversarial Training, William S. Matos Díaz

Cybersecurity: Deep Learning Driven Cybersecurity Research in a Multidisciplinary Environment

Adversarial training has proven to be one of the most successful ways to defend models against adversarial examples. This process consists of training a model with an adversarial example to improve the robustness of the model. In this experiment, Torchattacks, a Pytorch library made for importing adversarial examples more easily, was used to determine which attack was the strongest. Later on, the strongest attack was used to train the model and make it more robust against adversarial examples. The datasets used to perform the experiments were MNIST and CIFAR-10. Both datasets were put to the test using PGD, FGSM, and …

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Solving Relativistic Three-Body Integral Equations In The Presence Of Bound States And Resonances, Taylor R. Powell, Raúl A. Briceño, Andrew W. Jackura Jan 2021

Solving Relativistic Three-Body Integral Equations In The Presence Of Bound States And Resonances, Taylor R. Powell, Raúl A. Briceño, Andrew W. Jackura

Physics: Accelerator and Nuclear Physics at the Thomas Jefferson National Accelerator Facility in Newport News, Virginia

Three-body interactions play an important role throughout modern-day particle, nuclear, and hadronic physics; many experimentally observed reactions of interest for testing the Standard Model result in final states composed of three particles or more. Due to these issues, a full description of three-body interactions from Quantum Chromodynamics is required. The focus of this project was to extend previous results for a two-body subsystem with a bound state to include resonance channels. We first derived a novel single-variable observable, denoted as an intensity distribution, which is proportional to the probability density of the three-body scattering amplitude. We explored this distribution in …

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Hybrid Models As Transdisciplinary Research Enablers, Andreas Tolk, Alison Harper, Navonil Mustafee Jan 2021

Hybrid Models As Transdisciplinary Research Enablers, Andreas Tolk, Alison Harper, Navonil Mustafee

Computational Modeling & Simulation Engineering Faculty Publications

Modelling and simulation (M&S) techniques are frequently used in Operations Research (OR) to aid decision-making. With growing complexity of systems to be modelled, an increasing number of studies now apply multiple M&S techniques or hybrid simulation (HS) to represent the underlying system of interest. A parallel but related theme of research is extending the HS approach to include the development of hybrid models (HM). HM extends the M&S discipline by combining theories, methods and tools from across disciplines and applying multidisciplinary, interdisciplinary and transdisciplinary solutions to practice. In the broader OR literature, there are numerous examples of cross-disciplinary approaches in …

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Electroosmotic Mixing Of Non-Newtonian Fluid In A Microchannel With Obstacles And Zeta Potential Heterogeneity, Lanju Mei, Defu Cui, Jiayue Shen, Diganta Dutta, Willie Brown, Lei Zhang, Ibibia K. Dabipi Jan 2021

Electroosmotic Mixing Of Non-Newtonian Fluid In A Microchannel With Obstacles And Zeta Potential Heterogeneity, Lanju Mei, Defu Cui, Jiayue Shen, Diganta Dutta, Willie Brown, Lei Zhang, Ibibia K. Dabipi

Computational Modeling & Simulation Engineering Faculty Publications

This paper investigates the electroosmotic micromixing of non-Newtonian fluid in a microchannel with wall-mounted obstacles and surface potential heterogeneity on the obstacle surface. In the numerical simulation, the full model consisting of the Navier–Stokes equations and the Poisson–Nernst–Plank equations are solved for the electroosmotic fluid field, ion transport, and electric field, and the power law model is used to characterize the rheological behavior of the aqueous solution. The mixing performance is investigated under different parameters, such as electric double layer thickness, flow behavior index, obstacle surface zeta potential, obstacle dimension. Due to the zeta potential heterogeneity at the obstacle surface, …

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Matlab Modeling Of Ofdm Modulation Technique Across A 24 Khz, 48 Khz, And 3 Mhz Bandwidth In The High-Frequency Radio Band (3-30) Mhz, Josiah Myer, Tyler Collins, Sarah Taylor, Natalia Anglero Jan 2021

Matlab Modeling Of Ofdm Modulation Technique Across A 24 Khz, 48 Khz, And 3 Mhz Bandwidth In The High-Frequency Radio Band (3-30) Mhz, Josiah Myer, Tyler Collins, Sarah Taylor, Natalia Anglero

Faculty-Sponsored Student Research & Capstones

The goal of this project is to use MATLAB to model orthogonal frequency division multiplexing (OFDM) modulation technique across 24 kHz, 48 kHz, and 3 MHz bandwidths in the high frequency (HF) radio band (3-30 MHz). The purpose of our design is to make HF long distance communication faster and more reliable so that every part of the world, including the most remote parts, will have access to high speed, long distance wireless communication. Our MATLAB model will allow us to modify the bandwidth, carrier frequency, modulation type, signal to noise ratio (SNR), and image size to determine which combination …

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Monarch Science Observer, Volume 8, College Of Sciences, Old Dominion University Jan 2021

Monarch Science Observer, Volume 8, College Of Sciences, Old Dominion University

College of Sciences Newsletter

January-Winter 2021 issue of Monarch Science Observer, ODU College of Sciences Newsletter.

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Measurement Study Of Energy Impact On Blockchain Technologies: Cryptocurrency Mining, Qaylin Holliman Jan 2021

Measurement Study Of Energy Impact On Blockchain Technologies: Cryptocurrency Mining, Qaylin Holliman

Cybersecurity: Deep Learning Driven Cybersecurity Research in a Multidisciplinary Environment

Blockchain technology facilitates the flow of information and the speed of information through a faster and more decentralized network. It has its advantages as compared to more centralized networks and legacy networks. With the evolution of mainstream technology, blockchains is predicted to be more effective and sufficient to consumers and commercial companies. In this paper, blockchains will be scaled to cryptocurrency mining, where cryptocurrencies utilize blockchain technology to record transactions and orders. Mining will also be examined through energy consumption, the algorithms behind some cryptocurrencies, their sustainability issue, and resolutions to combat high energy consumption. While the pace of energy …

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Exploring Cybersecurity Education At The K-12 Level, Weiru Chen, Yuming He, Xin Tian, Wu He, E. Langran (Ed.), D. Rutledge (Ed.) Jan 2021

Exploring Cybersecurity Education At The K-12 Level, Weiru Chen, Yuming He, Xin Tian, Wu He, E. Langran (Ed.), D. Rutledge (Ed.)

Information Technology & Decision Sciences Faculty Publications

K-12 cybersecurity education is receiving growing attention with the growing number of cyberattacks and a shortage of cybersecurity professionals. However, there are many barriers for teachers to implement effective cybersecurity education in formal classroom environments. This study conducts a systematic literature review to examine the current state-of-the-art on K-12 cybersecurity education. Through the systematic literature review, we identified 20 closely relevant papers and recognized that a well-designed curriculum in cybersecurity education at the K-12 level is strongly needed to motivate students to pursue cybersecurity pathways and careers. The challenge and suggestions of curriculum design, teaching strategy, and learning assessment are …

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Promoting Diversity In Teaching Cybersecurity Through Gicl, Yuming He, Wu He, Xiaohong Yuan, Li Yang, Theo Bastiaens (Ed.) Jan 2021

Promoting Diversity In Teaching Cybersecurity Through Gicl, Yuming He, Wu He, Xiaohong Yuan, Li Yang, Theo Bastiaens (Ed.)

Information Technology & Decision Sciences Faculty Publications

In summary, it is necessary to develop a diverse group of K-12 students’ interest and skills in cybersecurity as cyber threats continue to grow. Evidence shows that educating the next generation of cyber workers is a crucial job that should begin in elementary school. To ensure the effectiveness of cybersecurity education and equity at the K-12 level, teachers must create thoughtful plans for considering communities’ interests and needs, and to continually reconsider what’s working and how to adjust our strategies, approaches, design, and research plan to meet their specific needs, challenges, and strengths, particularly with students from under-served and underrepresented …

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Are Entrepreneurial Capabilities And Prior Knowledge The Silver Bullet For The Generation Of New Digital Venture Ideas In A Digital Context?, Meifang Yao, Dan Ye, Gao Yang, Hui Shi, Xianrong Zheng Jan 2021

Are Entrepreneurial Capabilities And Prior Knowledge The Silver Bullet For The Generation Of New Digital Venture Ideas In A Digital Context?, Meifang Yao, Dan Ye, Gao Yang, Hui Shi, Xianrong Zheng

Information Technology & Decision Sciences Faculty Publications

Digital technology has had an important impact on entrepreneurship. It has changed the uncertain nature of the process of new venture idea generation, and it has also brought unprecedented opportunities for the generation of new digital venture ideas. In order to explore how startups can deal with major challenges brought by digital technology and create new digital venture ideas, this paper focuses on micro level entrepreneurial actions and constructs a theoretical model of the relationship among networking capabilities, IT capabilities, prior knowledge, and new digital venture ideas. Furthermore, through the hierarchical linear regression analysis of 278 sample data, the paper …

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A Hybrid Gene Selection Strategy Based On Fisher And Ant Colony Optimization Algorithm For Breast Cancer Classification, Mohammed Hamim, Ismail El Moudden, Mohan D. Pant, Hicham Moutachaouik, Mustapha Hain Jan 2021

A Hybrid Gene Selection Strategy Based On Fisher And Ant Colony Optimization Algorithm For Breast Cancer Classification, Mohammed Hamim, Ismail El Moudden, Mohan D. Pant, Hicham Moutachaouik, Mustapha Hain

EVMS School of Health Professions Faculty Publications

Breast cancer poses the greatest threat to human life and especially to women's life. Despite the progress made in data mining technology in recent years, the ability to predict and diagnose such fatal diseases based on gene expression data still reveals a limited prediction performance, which may not be surprising since most of the genes in expression data are believed to be irrelevant or redundant. The dimensionality reduction process may be considered as a crucial step to analyze gene expression data, as it can reduce the high dimensionality of the breast cancer datasets, which may result into a better prediction …

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Novel Statistical Analysis In The Context Of A Comprehensive Needs Assessment For Secondary Stem Recruitment, Norou Diawara, Sarah Ferguson, Melva Grant, Kumer Das Jan 2021

Novel Statistical Analysis In The Context Of A Comprehensive Needs Assessment For Secondary Stem Recruitment, Norou Diawara, Sarah Ferguson, Melva Grant, Kumer Das

Mathematics & Statistics Faculty Publications

There is a myriad of career opportunities stemming from science, technology, engineering, and mathematics (STEM) disciplines. In addition to careers in corporate settings, teaching is a viable career option for individuals pursuing degrees in STEM disciplines. With national shortages of secondary STEM teachers, efforts to recruit, train, and retain quality STEM teachers is greatly important. Prior to exploring ways to attract potential STEM teacher candidates to pursue teacher training programs, it is important to understand the perceived value that potential recruits place on STEM careers, disciplines, and the teaching profession. The purpose of this study was to explore students’ perceptions …

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Best Cybersecurity Practices For Companies, Post Van Buren, Mary Riley Jan 2021

Best Cybersecurity Practices For Companies, Post Van Buren, Mary Riley

Cybersecurity Undergraduate Research Showcase

Imagine this scenario: you are a small business owner, and you’ve just been informed of a network security breach. In your Zoom meeting with the IT Department, you learned the details: network activity logs revealed aberrant behavior after hours. Threat actors accessed systems containing sensitive information and downloaded copies of key files containing company trade secrets, market strategies, and customer data – all within a matter of minutes. Immediately, you review the consequences of the breach in your mind: reputational harm, monetary loss, and potential lawsuits. You ask the IT professional about the root cause of the breach. Was it …

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Tackling Ai Bias With Gans, Noam Stanislawski Jan 2021

Tackling Ai Bias With Gans, Noam Stanislawski

Cybersecurity Undergraduate Research Showcase

Throughout the relatively short history of artificial intelligence (AI), there has been a significant concern surrounding AI’s ability to incorporate and maintain certain characteristics which were not inherently modeled out in its coding. These behaviors stem from the prominent usage of neural network AI, which can inherit human biases from the input data it receives. This paper argues for two possible avenues to combat these biases. The first is to rethink the traditional framework for neural network projects and retool them to be usable by a Generative Adversarial Network (GAN). In a GAN’s zero-sum game, two network techniques can combat …

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Leverage Psychological Factors Associated With Lapses In Cybersecurity In Organizational Management, Chad Holm Jan 2021

Leverage Psychological Factors Associated With Lapses In Cybersecurity In Organizational Management, Chad Holm

Cybersecurity Undergraduate Research Showcase

With computers being a standard part of life now with the evolution of the internet, many aspects of our lives have changed, and new ways of thinking must come. One of the biggest challenges in most cyber security problems is not related to the software or the hardware; it is the people that are using the computers to access the data and communicate with others, where the hackers could simply find a weak entry point that naturally exists and a weak link caused by human hands. The human factor as an “insider threat” will affect unauthorized access, credentials stealing, and …

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On The Usage And Vulnerabilities Of Api Systems, Conner D. Yu Jan 2021

On The Usage And Vulnerabilities Of Api Systems, Conner D. Yu

Cybersecurity Undergraduate Research Showcase

To some, Application Programming Interface (API) is one of many buzzwords that seem to be blanketed in obscurity because not many people are overly familiar with this term. This obscurity is unfortunate, as APIs play a crucial role in today’s modern infrastructure by serving as one of the most fundamental communication methods for web services. Many businesses use APIs in some capacity, but one often overlooked aspect is cybersecurity. This aspect is most evident in the 2018 misuse case by Facebook, which led to the leakage of 50 million users’ records.1 During the 2018 Facebook data breach incident, threat actors …

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Carbon Dioxide And Particulate Matter Concentration On Hampton Roads Air Quality, Gregory Hubbard Jan 2021

Carbon Dioxide And Particulate Matter Concentration On Hampton Roads Air Quality, Gregory Hubbard

OUR Journal: ODU Undergraduate Research Journal

Hampton Roads has been a maritime crossroads for the last 400 years. Industrialization has impacted the coastal region for the last 250 years. The expansion of the Port of Virginia in 2019 has created dense traffic in the region resulting in impacts to air quality. Two waste products that affect humans are particulate matter and carbon dioxide. Both respective emissions can cause adverse effects on humans, such as asthma, some lung cancers, and other respiratory distress. Scientists and health practitioners are studying the effects of particulate matter on human health. Hampton Roads, in particular, because of its unique location on …

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Standardizing America: Why It Should Be A Method Of The Past, Samantha N. Jackson Jan 2021

Standardizing America: Why It Should Be A Method Of The Past, Samantha N. Jackson

OUR Journal: ODU Undergraduate Research Journal

This paper examines, critiques, and suggests improvements on the method of standardized testing in American schools. This paper discusses the history and development of standardized testing and its initial purpose and intentions. Additionally, the effects of standardized testing on students, teachers, and parents are evaluated, with special consideration on how high stakes testing adversely affects disadvantaged student groups such as children in minorities and low-income districts, bilingual students, and children with disabilities. The research suggests that standardized testing is not only damaging to students in these groups, but most likely not the most efficient way of testing student performance in …

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Fixed-Point Proximity Minimization: A Theoretical Review And Numerical Study, Daniel Weddle, Jianfeng Guo Jan 2021

Fixed-Point Proximity Minimization: A Theoretical Review And Numerical Study, Daniel Weddle, Jianfeng Guo

OUR Journal: ODU Undergraduate Research Journal

This study examines the relatively recent development of a “fixed-point proximity” approach to one type of minimization problem, considers its application to image denoising, and explores convergence and divergence of the iterative algorithm beyond a (previously supplied) theoretically guaranteed convergence bound on one of the parameters (𝜆). While reviewing the fixed-point proximity approach and its application to image denoising, we aim to communicate the concepts and details in a way that will facilitate understanding for undergraduates and for scholars from other subfields. In the latter portion of our study, the numerical experiment provides thought-provoking data on the effects that parameters …

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The Continuum And Leading Twist Limits Of Parton Distribution Functions In Lattice Qcd, Joseph Karpie, Kostas Orginos, Anatoly Radyushkin, Savvas Zafeiropoulos, For The Hadstruc Collaboration Jan 2021

The Continuum And Leading Twist Limits Of Parton Distribution Functions In Lattice Qcd, Joseph Karpie, Kostas Orginos, Anatoly Radyushkin, Savvas Zafeiropoulos, For The Hadstruc Collaboration

Physics Faculty Publications

In this study, we present continuum limit results for the unpolarized parton distribution function of the nucleon computed in lattice QCD. This study is the first continuum limit using the pseudo-PDF approach with Short Distance Factorization for factorizing lattice QCD calculable matrix elements. Our findings are also compared with the pertinent phenomenological determinations. Inter alia, we are employing the summation Generalized Eigenvalue Problem (sGEVP) technique in order to optimize our control over the excited state contamination which can be one of the most serious systematic errors in this type of calculations. A crucial novel ingredient of our analysis is the …

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Multivariate Distributions Of Correlated Binary Variables Generated By Pair-Copulas, Huihui Lin, N. Rao Chaganty Jan 2021

Multivariate Distributions Of Correlated Binary Variables Generated By Pair-Copulas, Huihui Lin, N. Rao Chaganty

Mathematics & Statistics Faculty Publications

Correlated binary data are prevalent in a wide range of scientific disciplines, including healthcare and medicine. The generalized estimating equations (GEEs) and the multivariate probit (MP) model are two of the popular methods for analyzing such data. However, both methods have some significant drawbacks. The GEEs may not have an underlying likelihood and the MP model may fail to generate a multivariate binary distribution with specified marginals and bivariate correlations. In this paper, we study multivariate binary distributions that are based on D-vine pair-copula models as a superior alternative to these methods. We elucidate the construction of these binary distributions …

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A Class Of Copula-Based Bivariate Poisson Time Series Models With Applications, Mohammed Alqawba, Dimuthu Fernando, Norou Diawara Jan 2021

A Class Of Copula-Based Bivariate Poisson Time Series Models With Applications, Mohammed Alqawba, Dimuthu Fernando, Norou Diawara

Mathematics & Statistics Faculty Publications

A class of bivariate integer-valued time series models was constructed via copula theory. Each series follows a Markov chain with the serial dependence captured using copula-based transition probabilities from the Poisson and the zero-inflated Poisson (ZIP) margins. The copula theory was also used again to capture the dependence between the two series using either the bivariate Gaussian or “t-copula” functions. Such a method provides a flexible dependence structure that allows for positive and negative correlation, as well. In addition, the use of a copula permits applying different margins with a complicated structure such as the ZIP distribution. Likelihood-based inference was …

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A Semiparametric Modeling Approach For Analyzing Clinical Biomarkers Restricted To Limits Of Detection, Sandipan Dutta, Susan Halabi Jan 2021

A Semiparametric Modeling Approach For Analyzing Clinical Biomarkers Restricted To Limits Of Detection, Sandipan Dutta, Susan Halabi

Mathematics & Statistics Faculty Publications

Before biomarkers can be used in clinical trials or patients' management, the laboratory assays that measure their levels have to go through development and analytical validation. One of the most critical performance metrics for validation of any assay is related to the minimum amount of values that can be detected and any value below this limit is referred to as below the limit of detection (LOD). Most of the existing approaches that model such biomarkers, restricted by LOD, are parametric in nature. These parametric models, however, heavily depend on the distributional assumptions, and can result in loss of precision under …

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Representer Theorems In Banach Spaces: Minimum Norm Interpolation, Regularized Learning And Semi-Discrete Inverse Problems, Rui Wang, Yusheng Xu Jan 2021

Representer Theorems In Banach Spaces: Minimum Norm Interpolation, Regularized Learning And Semi-Discrete Inverse Problems, Rui Wang, Yusheng Xu

Mathematics & Statistics Faculty Publications

Learning a function from a finite number of sampled data points (measurements) is a fundamental problem in science and engineering. This is often formulated as a minimum norm interpolation (MNI) problem, a regularized learning problem or, in general, a semi discrete inverse problem (SDIP), in either Hilbert spaces or Banach spaces. The goal of this paper is to systematically study solutions of these problems in Banach spaces. We aim at obtaining explicit representer theorems for their solutions, on which convenient solution methods can then be developed. For the MNI problem, the explicit representer theorems enable us to express the infimum …

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Cybersecurity: Building A Better Defense With A Great Offense, David M. Cooke Jan 2021

Cybersecurity: Building A Better Defense With A Great Offense, David M. Cooke

Cybersecurity Undergraduate Research Showcase

The current industry standard for cybersecurity is risk mitigation, which is the identification, evaluation, and categorization of threats that are posed to an organization's network. The goal is to prevent attacks and if an organization is attacked popular standard is to react and remedy the attack. This form of cyber defense isn’t very reassuring to an organization and its users, once an attack is executed based on a study conducted by Booz Allen the average time an advanced persistent threat (APT) dwells on a victims’ network before it’s discovered is 200-250 days. That’s plenty of time for a malicious third …

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