Physical Sciences and Mathematics Commons^™

Some New Techniques And Their Applications In The Theory Of Distributions, Kevin Kellinsky-Gonzalez

LSU Doctoral Dissertations

This dissertation is a compilation of three articles in the theory of distributions. Each essay focuses on a different technique or concept related to distributions.

The focus of the first essay is the concept of distributional point values. Distribu- tions are sometimes called generalized functions, as they share many similarities with ordi- nary functions, with some key differences. Distributional point values, among other things, demonstrate that distributions are even more akin to ordinary functions than one might think.

The second essay concentrates on two major topics in analysis, namely asymptotic expansions and the concept of moments. There are many variations …

The Existence Of Solutions To A System Of Nonhomogeneous Difference Equations, Stephanie Walker

Rose-Hulman Undergraduate Mathematics Journal

This article will demonstrate a process using Fixed Point Theory to determine the existence of multiple positive solutions for a type of system of nonhomogeneous even ordered boundary value problems on a discrete domain. We first reconstruct the problem by transforming the system so that it satisfies homogeneous boundary conditions. We then create a cone and an operator sufficient to apply the Guo-KrasnoselâA˘Zskii Fixed Point Theorem. The majority of the work involves developing the constraints ´ needed to utilized this fixed point theorem. The theorem is then applied three times, guaranteeing the existence of at least three distinct solutions. Thus, …

How Effective Is The Efficiency Gap?, Thomas Q. Sibley

Mathematics Faculty Publications

Gerrymandering has affected U. S. politics since at least 1812. A political cartoon that year decried this tactic by then Massachusetts Governor Elbridge Gerry. (Gerrymandering is manipulating the boundaries of districts to benefit a group unfairly.)

While we may feel we know a gerrymander when we see one, finding a meaningful metric has proven challenging. This article uses elementary mathematics to investigate the efficiency gap, a recent model proposed to measure gerrymandering.

Deep Learning-Based Diagnosis Of Disease Activity In Patients With Graves’ Orbitopathy Using Orbital Spect/Ct, Ni Yao, Longxi Li, Zhengyuan Gao, Chen Zhao, Yanting Li, Chuang Han, Jiaofen Nan, Zelin Zhu, Yi Xiao, Fubao Zhu, Min Zhao, Weihua Zhou

Michigan Tech Publications, Part 1

Purpose: Orbital [99mTc]TcDTPA orbital single-photon emission computed tomography (SPECT)/CT is an important method for assessing inflammatory activity in patients with Graves’ orbitopathy (GO). However, interpreting the results requires substantial physician workload. We aim to propose an automated method called GO-Net to detect inflammatory activity in patients with GO. Materials and methods: GO-Net had two stages: (1) a semantic V-Net segmentation network (SV-Net) that extracts extraocular muscles (EOMs) in orbital CT images and (2) a convolutional neural network (CNN) that uses SPECT/CT images and the segmentation results to classify inflammatory activity. A total of 956 eyes from 478 patients with GO …

Sociocultural Barriers For Female Participation In Stem: A Case Of Saudi Women In Cybersecurity, Alanoud Aljuaid, Xiang Michelle Liu

Journal of Cybersecurity Education, Research and Practice

The participation of women in Science, Technology, Engineering, and Mathematics (STEM) workforces is overwhelmingly low as compared to their male counterparts. The low uptake of cybersecurity careers has been documented in the previous studies conducted in the contexts of the West and Eastern worlds. However, most of the past studies mainly covered the Western world leaving more knowledge gaps in the context of Middle Eastern countries such as Saudi Arabia. Thus, to fill the existing knowledge gaps, the current study focused on women in Saudi Arabia. The aim of the study was to investigate the factors behind the underrepresentation of …

Face Readers: The Frontier Of Computer Vision And Math Learning, Beverly Woolf, Margrit Betke, Hao Yu, Sarah Adel Bargal, Ivan Arroyo, John J. Magee Iv, Danielle Allessio, William Rebelsky

Computer Science

The future of AI-assisted individualized learning includes computer vision to inform intelligent tutors and teachers about student affect, motivation and performance. Facial expression recognition is essential in recognizing subtle differences when students ask for hints or fail to solve problems. Facial features and classification labels enable intelligent tutors to predict students’ performance and recommend activities. Videos can capture students’ faces and model their effort and progress; machine learning classifiers can support intelligent tutors to provide interventions. One goal of this research is to support deep dives by teachers to identify students’ individual needs through facial expression and to provide immediate …

How Effective Are Seta Programs Anyway: Learning And Forgetting In Security Awareness Training, David Sikolia, David Biros, Tianjian Zhang

Journal of Cybersecurity Education, Research and Practice

Prevalent security threats caused by human errors necessitate security education, training, and awareness (SETA) programs in organizations. Despite strong theoretical foundations in behavioral cybersecurity, field evidence on the effectiveness of SETA programs in mitigating actual threats is scarce. Specifically, with a broad range of cybersecurity knowledge crammed into in a single SETA session, it is unclear how effective different types of knowledge are in mitigating human errors in a longitudinal setting. his study investigates how knowledge gained through SETA programs affects human errors in cybersecurity to fill the longitudinal void. In a baseline experiment, we establish that SETA programs reduce …

Anonymity And Gender Effects On Online Trolling And Cybervictimization, Gang Lee, Annalyssia Soonah

Journal of Cybersecurity Education, Research and Practice

The purpose of this study was to investigate the effects of the anonymity of the internet and gender differences in online trolling and cybervictimization. A sample of 151 college students attending a southeastern university completed a survey to assess their internet activities and online trolling and cybervictimization. Multivariate analyses of logistic regression and ordinary least squares regression were used to analyze online trolling and cybervictimization. The results indicated that the anonymity measure was not a significant predictor of online trolling and cybervictimization. Female students were less likely than male students to engage in online trolling, but there was no gender …

Case Study: The Impact Of Emerging Technologies On Cybersecurity Education And Workforces, Austin Cusak

Journal of Cybersecurity Education, Research and Practice

A qualitative case study focused on understanding what steps are needed to prepare the cybersecurity workforces of 2026-2028 to work with and against emerging technologies such as Artificial Intelligence and Machine Learning. Conducted through a workshop held in two parts at a cybersecurity education conference, findings came both from a semi-structured interview with a panel of experts as well as small workgroups of professionals answering seven scenario-based questions. Data was thematically analyzed, with major findings emerging about the need to refocus cybersecurity STEM at the middle school level with problem-based learning, the disconnects between workforce operations and cybersecurity operators, the …

Cyberbullying: Senior Prospective Teachers’ Coping Knowledge And Strategies, Kürşat Arslan, İnan Aydın

Journal of Cybersecurity Education, Research and Practice

This study aimed to determine senior prospective teachers’ coping knowledge and strategies for cyberbullying in terms of demographic variables. The sample consisted of 471 prospective teachers (324 female and 147 male) studying in the 4th grade in Dokuz Eylül University Buca Education Faculty in Izmir in the 2019-2020 academic year. It was a quantitative study using a causal-comparative research design to find out whether prospective teachers’ coping knowledge differed by independent variables. The "Coping with Cyberbullying Scale" developed by Koç et al. (2016) was employed to discover prospective teachers’ coping strategies for cyberbullying. A "Personal Information" form was also prepared …

Compete To Learn: Toward Cybersecurity As A Sport, Tj Oconnor, Dane Brown, Jasmine Jackson, Bryson Payne, Suzanna Schmeelk

Journal of Cybersecurity Education, Research and Practice

To support the workforce gap of skilled cybersecurity professionals, gamified pedagogical approaches for teaching cybersecurity have exponentially grown over the last two decades. During this same period, e-sports developed into a multi-billion dollar industry and became a staple on college campuses. In this work, we explore the opportunity to integrate e-sports and gamified cybersecurity approaches into the inaugural US Cyber Games Team. During this tenure, we learned many lessons about recruiting, assessing, and training cybersecurity teams. We share our approach, materials, and lessons learned to serve as a model for fielding amateur cybersecurity teams for future competition.

Editorial - 2023 - 1, Hossain Shahriar, Herbert J. Mattord, Michael E. Whitman

Journal of Cybersecurity Education, Research and Practice

No abstract provided.

Examination Of Cybersecurity Technologies, Practices, Challenges, And Wish List In K-12 School Districts, Florence Martin, Julie Bacak, Erik Jon Byker, Weichao Wang, Jonathan Wagner, Lynn Ahlgrim-Delzell

Journal of Cybersecurity Education, Research and Practice

With the growth in digital teaching and learning, there has been a sharp rise in the number of cybersecurity attacks on K-12 school networks. This has demonstrated a need for security technologies and cybersecurity education. This study examined security technologies used, effective security practices, challenges, concerns, and wish list of technology leaders in K-12 settings. Data collected from 23 district websites and from interviews with 12 district technology leaders were analyzed. Top security practices included cloud-based technologies, segregated network/V-LAN, two-factor authentication, limiting access, and use of Clever or Class Link. Top challenges included keeping users informed, lack of buy-in from …

A Systematic Mapping Study On Gamification Applications For Undergraduate Cybersecurity Education, Sherri Weitl-Harms, Adam Spanier, John Hastings, Matthew Rokusek

Journal of Cybersecurity Education, Research and Practice

Gamification in education presents a number of benefits that can theoretically facilitate higher engagement and motivation among students when learning complex, technical concepts. As an innovative, high-potential educational tool, many educators and researchers are attempting to implement more effective gamification into undergraduate coursework. Cyber Security Operations (CSO) education is no exception. CSO education traditionally requires comprehension of complex concepts requiring a high level of technical and abstract thinking. By properly applying gamification to complex CSO concepts, engagement in students should see an increase. While an increase is expected, no comprehensive study of CSO gamification applications (GA) has yet been undertaken …

Effects Of Coastal Freshening On Porewater Salinity And Nitrogen Cycling In Brackish Marsh Soils: Implications For Biogeochemical Resilience In Barataria Basin, La, Robert C. Feder

LSU Master's Theses

The Mid-Barataria Sediment Diversion will divert sediments and fresh water from the Mississippi River into mid-Barataria Basin, LA for the purposes of building and sustaining coastal marshes, potentially reducing the estuarine salinity to 0 during operation. Increased freshwater inputs to the area of project influence may reduce porewater salinity and induce ammonium adsorption in brackish marsh soils of the region, potentially impacting the activity of ammonium-producing and denitrifying bacteria. However, brackish soils occur in zones of intermediate salinity and may, therefore, exhibit biogeochemical resilience to episodic changes in surface water salinity. I hypothesized that inundating brackish marsh soils with fresh …

Pathogen Emergence As Complex Biological Invasion: Lessons From Dynamical Systems Modeling, Sudam Surasinghe, Marisabel Rodriguez, Victor Meszaros, Jane Molofsky, Salvador Almagro-Moreno, Brandon Ogbunugafor

Northeast Journal of Complex Systems (NEJCS)

Infectious disease emergence has become the target of cross-disciplinary efforts
that aim to understand and predict the shape of outbreaks. The many challenges
involved with the prediction of disease emergence events is a characteristic that in-
fectious diseases share with biological invasions in many subfields of ecology (e.g.,
how certain plants are able to successfully invade a new niche). Like infectious
diseases, biological invasions by plants and animals involve interactions between
agents (pathogens and plants in their respective cases) and a recipient niche. In
this study, we examine the problem of pathogen emergence through the lens of a
framework first …

Lagrange’S Study Of Wilson’S Theorem, Carl Lienert

Number Theory

No abstract provided.

Target-Based Offensive Language Identification, Marcos Zampieri, Skye Morgan, Kai North, Tharindu Ranasinghe, Austin Simmons, Paridhi Khandelwal, Sara Rosenthal, Preslav Nakov

Natural Language Processing Faculty Publications

We present TBO, a new dataset for Target-based Offensive language identification. TBO contains post-level annotations regarding the harmfulness of an offensive post and token-level annotations comprising of the target and the offensive argument expression. Popular offensive language identification datasets for social media focus on annotation taxonomies only at the post level and more recently, some datasets have been released that feature only token-level annotations. TBO is an important resource that bridges the gap between post-level and token-level annotation datasets by introducing a single comprehensive unified annotation taxonomy. We use the TBO taxonomy to annotate post-level and token-level offensive language on …

Investigation Of Vitamin D Metabolites Using Different Ion Mobility-Mass Spectrometry (Im-Ms) Methods, Selena Kingsley, Christopher D. Chouinard

Chemistry Summer Research Program

No abstract provided.

Development Of A Rapid Sars-Cov-2 Nucleocapsid Assay Using Buoyant And Magnetic (Bam) Beads, Spasenija Radenovic, Zahra Karimpourkalou, Chaunlei Wang, Jeffrey N. Anker

Chemistry Summer Research Program

No abstract provided.

Isolation Of Bovine Milk-Derived Exosomes Via A Polyester Capillary- Channeled Polymer (C-Cp) Fiber Stationary Phase, Jerisa Pimentel, Carolina Mata, R. Kenneth Marcus

Chemistry Summer Research Program

No abstract provided.

Improved Logical Reasoning Of Language Models Via Differentiable Symbolic Programming, Hanlin Zhang, Jiani Huang, Ziyang Li, Mayur Naik, Eric Xing

Machine Learning Faculty Publications

Pre-trained large language models (LMs) struggle to perform logical reasoning reliably despite advances in scale and compositionality. In this work, we tackle this challenge through the lens of symbolic programming. We propose DSR-LM, a Differentiable Symbolic Reasoning framework where pre-trained LMs govern the perception of factual knowledge, and a symbolic module performs deductive reasoning. In contrast to works that rely on hand-crafted logic rules, our differentiable symbolic reasoning framework efficiently learns weighted rules and applies semantic loss to further improve LMs. DSR-LM is scalable, interpretable, and allows easy integration of prior knowledge, thereby supporting extensive symbolic programming to robustly derive …

Bertnet: Harvesting Knowledge Graphs With Arbitrary Relations From Pretrained Language Models, Shibo Hao, Bowen Tan, Kaiwen Tang, Bin Ni, Xiyan Shao, Hengzhe Zhang, Eric P. Xing, Zhiting Hu

Machine Learning Faculty Publications

It is crucial to automatically construct knowledge graphs (KGs) of diverse new relations to support knowledge discovery and broad applications. Previous KG construction methods, based on either crowdsourcing or text mining, are often limited to a small predefined set of relations due to manual cost or restrictions in text corpus. Recent research proposed to use pretrained language models (LMs) as implicit knowledge bases that accept knowledge queries with prompts. Yet, the implicit knowledge lacks many desirable properties of a full-scale symbolic KG, such as easy access, navigation, editing, and quality assurance. In this paper, we propose a new approach of …

Phase-Aware Adversarial Defense For Improving Adversarial Robustness, Dawei Zhou, Nannan Wang, Heng Yang, Xinbo Gao, Tongliang Liu

Machine Learning Faculty Publications

Deep neural networks have been found to be vulnerable to adversarial noise. Recent works show that exploring the impact of adversarial noise on intrinsic components of data can help improve adversarial robustness. However, the pattern closely related to human perception has not been deeply studied. In this paper, inspired by the cognitive science, we investigate the interference of adversarial noise from the perspective of image phase, and find ordinarily-trained models lack enough robustness against phase-level perturbations. Motivated by this, we propose a joint adversarial defense method: a phase-level adversarial training mechanism to enhance the adversarial robustness on the phase pattern; …

High-Probability Bounds For Stochastic Optimization And Variational Inequalities: The Case Of Unbounded Variance, Abdurakhmon Sadiev, Marina Danilova, Eduard Gorbunov, Samuel Horváth, Gauthier Gidel, Pavel Dvurechensky, Alexander Gasnikov, Peter Richtárik

Machine Learning Faculty Publications

During recent years the interest of optimization and machine learning communities in high-probability convergence of stochastic optimization methods has been growing. One of the main reasons for this is that high-probability complexity bounds are more accurate and less studied than in-expectation ones. However, SOTA high-probability non-asymptotic convergence results are derived under strong assumptions such as the boundedness of the gradient noise variance or of the objective's gradient itself. In this paper, we propose several algorithms with high-probability convergence results under less restrictive assumptions. In particular, we derive new high-probability convergence results under the assumption that the gradient/operator noise has bounded …

Analysis Of Predictive Performance And Reliability Of Classifiers For Quality Assessment Of Medical Evidence Revealed Important Variation By Medical Area, Simon Šuster, Timothy Baldwin, Karin Verspoor

Natural Language Processing Faculty Publications

Objectives: A major obstacle in deployment of models for automated quality assessment is their reliability. To analyze their calibration and selective classification performance. Study Design and Setting: We examine two systems for assessing the quality of medical evidence, EvidenceGRADEr and RobotReviewer, both developed from Cochrane Database of Systematic Reviews (CDSR) to measure strength of bodies of evidence and risk of bias (RoB) of individual studies, respectively. We report their calibration error and Brier scores, present their reliability diagrams, and analyze the risk–coverage trade-off in selective classification. Results: The models are reasonably well calibrated on most quality criteria (expected calibration error …

Bertastic At Semeval-2023 Task 3: Fine-Tuning Pretrained Multilingual Transformers – Does Order Matter?, Tarek Mahmoud, Preslav Nakov

Natural Language Processing Faculty Publications

The naïve approach for fine-tuning pretrained deep learning models on downstream tasks involves feeding them mini-batches of randomly sampled data. In this paper, we propose a more elaborate method for fine-tuning Pretrained Multilingual Transformers (PMTs) on multilingual data. Inspired by the success of curriculum learning approaches, we investigate the significance of fine-tuning PMTs on multilingual data in a sequential fashion language by language. Unlike the curriculum learning paradigm where the model is presented with increasingly complex examples, we do not adopt a notion of “easy” and “hard” samples. Instead, our experiments draw insight from psychological findings on how the human …

Conformal Prediction For Federated Uncertainty Quantification Under Label Shift, Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov

Machine Learning Faculty Publications

Federated Learning (FL) is a machine learning framework where many clients collaboratively train models while keeping the training data decentralized. Despite recent advances in FL, the uncertainty quantification topic (UQ) remains partially addressed. Among UQ methods, conformal prediction (CP) approaches provides distribution-free guarantees under minimal assumptions. We develop a new federated conformal prediction method based on quantile regression and take into account privacy constraints. This method takes advantage of importance weighting to effectively address the label shift between agents and provides theoretical guarantees for both valid coverage of the prediction sets and differential privacy. Extensive experimental studies demonstrate that this …

A Statistical Framework For Automating Resonance Detection: Modelling Pion Proton Collision Activity, Shahnaz Abdul Hameed

2023 REYES Proceedings

In this paper, we analyze π− − p elastic collision data from the Particle Data Group (PDG), by creating a general framework to study resonance activity: automating peak detection, extrapolating, parametrizing thresholds, filtering resonances and further comparing and extracting characteristics, to identify Delta (Δ) baryons. We then analyse experimental Energy vs Phase-Shift (δ) data for the collision π+ +π− → π− +π+, model the T matrix from a curve fitted polynomial representation of the K⁻¹ matrix, simulate its Riemann sheets and analyse it to identify the characteristics of ρ⁰(770) meson, as well as estimate their uncertainties. …

How To Best Retrain A Neural Network If We Added One More Input Variable, Saeid Tizpaz-Niari, Vladik Kreinovich

Departmental Technical Reports (CS)

Often, once we have trained a neural network to estimate the value of a quantity y based on the available values of inputs x1, ..., xn, we learn to measure the values of an additional quantity that have some influence on y. In such situations, it is desirable to re-train the neural network, so that it will be able to take this extra value into account. A straightforward idea is to add a new input to the first layer and to update all the weights based on the patterns that include the values of the new input. The problem with …