Digital Commons Network^™

Cooperative Success Under Shared Cognitive States And Valuations, Shannon Marie Klotz

Scripps Senior Theses

A mental model of the another person’s state of mind including their thoughts, feelings, and beliefs, otherwise known as Theory of Mind (ToM), can be created to better predict their behavior and optimize our own decisions. These representations can be explicitly modeled during both the development and presence of stable cooperation via communication outcomes, allowing us to understand the sophistication or depth of mental coordination, involved in an individual’s social perception and reasoning. Almost all current scientific studies of ToM take a spectatorial approach, relying on observation followed by evaluation (e.g., the Sally-Anne Task). However given evidence that social cognition …

Age Grading An. Gambiae And An. Arabiensis Using Near Infrared Spectra And Artificial Neural Networks, Masabho Peter Milali, Maggy T. Sikulu-Lord, Samson S. Kiware, Floyd E. Dowell, George F. Corliss, Richard J. Povinelli

Electrical and Computer Engineering Faculty Research and Publications

Background

Near infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into < or ≥ 7 days old with an average accuracy of 80%, achieved by training a regression model using partial least squares (PLS) and interpreted as a binary classifier.

Methods and findings

We explore whether using an artificial neural network (ANN) analysis instead of PLS regression improves the current accuracy of NIRS models for age-grading malaria transmitting mosquitoes. We also explore if directly training a binary classifier instead of training a regression model and interpreting it as a binary classifier improves the accuracy. A total of 786 and 870 NIR spectra collected from laboratory reared An. gambiae and An. arabiensis, respectively, were used and pre-processed according …

Predicting Cascading Failures In Power Grids Using Machine Learning Algorithms, Rezoan Ahmed Shuvro, Pankaz Das, Majeed M. Hayat, Mitun Talukder

Electrical and Computer Engineering Faculty Research and Publications

Although there has been notable progress in modeling cascading failures in power grids, few works included using machine learning algorithms. In this paper, cascading failures that lead to massive blackouts in power grids are predicted and classified into no, small, and large cascades using machine learning algorithms. Cascading-failure data is generated using a cascading failure simulator framework developed earlier. The data set includes the power grid operating parameters such as loading level, level of load shedding, the capacity of the failed lines, and the topological parameters such as edge betweenness centrality and the average shortest distance for numerous combinations of …

The Near-Peer Mathematical Mentoring Cycle: Studying The Impact Of Outreach On High School Students’ Attitudes Toward Mathematics, Aaron T. Wilson, Sergey Grigorian

School of Mathematical & Statistical Sciences Faculty Publications

College students may be seen as near-peers to high school students and high school students are often able to see themselves in the college students who are but one step ahead. This nearness in maturity and educational level may place college students in a particularly powerful position when it comes to reaching out to high school students to promote higher education in math and science. In this study college students gave dynamic mathematics outreach presentations, MathShows, to minority and low-income high school students in a mid-sized public school district on the U.S. border with Mexico. The study investigated the impacts …

Sediment Patterns From Fluid-Bed Interactions: A Direct Numerical Simulations Study On Fluvial Turbulent Flows, ‪Nadim Zgheib, S. Balachandar

Mechanical Engineering Faculty Publications

We present results on the initial formation of ripples from an initially flattened erodible bed. We use direct numerical simulations (DNS) of turbulent open channel flow over a fixed sinusoidal bed coupled with hydrodynamic stability analysis. We use the direct forcing immersed boundary method to account for the presence of the sediment bed. The resolved flow provides the bed shear stress and consequently the sediment transport rate, which is needed in the stability analysis of the Exner equation. The approach is different from traditional linear stability analysis in the sense that the phase lag between the bed topology, and the …

On The Regularity Radius Of Delone Sets In R3, Nikolay Dolbilin, Alexey Garber, Undine Leopold, Egon Schulte

School of Mathematical & Statistical Sciences Faculty Publications

We complete the proof of the upper bound ρ^3≤10R for the regularity radius of Delone sets in three-dimensional Euclidean space. Namely, summing up the results obtained earlier, and adding the missing cases, we show that if all 10R-clusters of a Delone set X with parameters (r,R) are equivalent, then X is a regular system.

Connections Between Pre-Service Teachers’ Mathematical Dispositions And Self-Efficacy For Teaching Mathematics, Jasmine M. Cruz, Aaron T. Wilson, Xiaohui Wang

School of Mathematical & Statistical Sciences Faculty Publications

Mathematical dispositions (MD) and self-efficacy for teaching mathematics (SEFTM) are important elements of teachers‟ beliefs that significantly influence their behaviors and educational practices in the classroom. This study looked at relationships between pre-service teachers‟ (PSTs) MD and their SEFTM in connection with other descriptors related to their progress through the teacher education program and prior mathematical experiences. Survey data were collected from 238 PSTs at a midsized university in the US. Results include the finding that PSTs‟ intended grade-level and subject approximately mirror their MD and SEFTM: those planning to be elementary school (generalist) teachers were slightly behind both middle …

Blocks In The Category Of Finite-Dimensional Representations Of Principal W-Algebra For Q(2), Elena Poletaeva

School of Mathematical & Statistical Sciences Faculty Publications

We describe the blocks in the category of finite-dimensional representations of the principal finite W-algebra for the Lie superalgebra Q(2).

On A Class Of Parameter Dependent Series Generalizing Euler’S Constant, Paul Bracken

School of Mathematical & Statistical Sciences Faculty Publications

Series which depend on a parameter and generalize the constant discovered by Euler are introduced and studied. Convergence results are established. An infinite series expansion is obtained from these generalized formulas which can be used to evaluate the generalized constant. Euler’s constant can be obtained as a special case. Some asymptotic results are formulated and limits of some closely related sequences are given.

Attitudes Toward Using And Teaching Confidence Intervals: A Latent Profile Analysis On Elementary Statistics Instructors, Hyung Won Kim, Woo Jin Kim, Aaron T. Wilson, Ho Kyoung Ko

School of Mathematical & Statistical Sciences Faculty Publications

The use of confidence intervals (CIs) for making a statistical inference is gaining popularity in research communities. To evaluate college statistics instructors’ readiness to teach CIs, this study explores their attitudes toward teaching CIs in elementary statistics courses, and toward using CIs in inferential statistics. Data were collected with a survey that classifies instructors’ attitudes on the basis of three previously established pedagogical components: affective, cognitive, and behavioral. Based on the survey responses from 270 participants, we created three profiles (subgroups) via latent profile analysis, and identified each profile’s pattern of attitudes toward CIs and common characteristics of the instructors …

Coastal Zone Access And The Pell Elementary School Sail Newport Program: Experiential Education Outcomes In Newport, Rhode Island, Leah Feldman

Open Access Master's Theses

Newport, Rhode Island is a diverse community. Although there is a population of very wealthy households in Newport, roughly 11% of the population lives below the poverty line. Newport is one of the more demographically diverse communities in Rhode Island, with 49.7% of its populous identifying as white, 27.1% identifying as black, and 18.2% identifying as Hispanic. The state of Rhode Island consists of 81% white, 6.5% African American and 5.8% other races and ethnicities. In this thesis I discuss the effects of a place-based experiential environmental education program, the Pell Elementary School Sail Newport Program, on this diverse demographic …

The Role Of Ethnic/Racial Classroom Dynamics In The Learning Environment And Mathematics Achievement, Christina Stephens

Open Access Master's Theses

In the U.S., ethnic and racial disparities in education persist throughout K-12 education. Empirical research has linked classroom ethnic/racial structures and achievement, and the learning environment and achievement. However, few studies have considered the complex manner in which ethnicity/race may interact with the learning environment and its relation to student success across diverse groups. The current study investigated the role that ethnic/racial congruence between the student, teacher, and classroom could have on the learning environment and mathematics achievement. A multilevel regression approach examined if these factors predicted academic achievement. Secondary data were from The National Center for Teacher Effectiveness, collected …

Ugv Direction Control By Human Arm Gesture Recognition Via Deterministic Learning, Xiaotian Chen

Open Access Master's Theses

With the recent development of machine learning and neural networks, different applications have been developed to improve the intelligence of unmanned robotic systems. In this thesis, we present a novel method for controlling an unmanned ground vehicle (UGV) by using a new machine learning technique, called deterministic learning [1], to learn and recognize four specifically designed body language, which represent four corresponding moving directions (i.e., left, right, up, and down) of the controlled UGV. The Microsoft Kinect sensor is employed to collect the human body skeleton data, including (x, y, z) coordinates of the human arm …

Design And Performance Analysis Of A Programmable Near Field Communication Antenna On Soft Materials, Muhammad Enayetur Rahman

Open Access Master's Theses

In the era of ubiquitous computing objects connected to the Internet are increasing day by day. Near Field Communication (NFC) is a perfect example of ubiquitous computing that is secure, short-range, low powered contactless communication. Powerless, single-tapping NFC tag is growing in popularity. Massive demand for NFC requires to study implementing this fast-growing technology onto different materials and places.

In this research study, NFC implementation on flexible material is investigated with different types of materials. NFC antennas were designed with mathematical modeling. Simulation for NFC coil inductance calculation was performed and was verified with mathematical modeling. The NFC antenna was …

Effects Of Incorporating Inertial Measurements On The Localization Accuracy Of The Seaglider Auv, Wendy Snyder

Open Access Master's Theses

The Kongsberg Seaglider M1 is a commercially available autonomous under- water vehicle (AUV) primarily used as a platform for oceanographic measurements of salinity, temperature, and oxygen. The Seaglider currently uses two different dead-reckoned solutions, the glide slope model (GSM) and the hydrodynamic model (HDM), to provide a localization solution for each dive. While the accuracy of these solutions was not previously known explicitly, for the purposes of oceanographic profiling they were generally deemed sufficient. As the platform matures, there has been a growing interest in expanding its application and measurement capabilities. With some of the desired new applications, such as …

All-Solid Lithium-Ion Batteries For High Safety And Stability, Lukas Duwe

Open Access Master's Theses

Operational safety and cycle stability are important attributes for all rechargeable batteries. To meet these stringent demands specifically for biomedical applications, an all-solid lithium-ion battery (ASLIB) consisting of a polyethylene oxide\ (PEO)-based polymer electrolyte with a lithium bis(trifluoromethanesulfonyl) imide (LiTFSI) salt, lithium titanate (LTO) anode and lithium iron phosphate (LFP) cathode is proposed. This work implements fabrication methods, composition optimizations and an assembly procedure, all tailored to the unique cell chemistry and ending in the all-solid LTO-PEO/LiTFSI-LFP cells. Subsequently, these ASLIBs are tested close to body temperature at 40 °C. This assures solid-state, but augments bulk electrolyte and interfacial resistance …

System Modeling Of A Lithium-Ion Battery, Tyler Creighton

Open Access Master's Theses

Lithium Ion Batteries are widely used in today's portable world. A working model is required in order to safely maximize the output of these batteries, and to efficiently choose appropriate sized systems for any given application. A model of this battery would require parameters that are not easy to measure, and need to be estimated. This work further explored on one existing model for the lithium ion battery, the linear dynamics single particle model, and improved upon it. Data was collected then modeled through the use of several matlab functions, and the final model was then used to predict the …

Online Symptom Checker Diagnostic And Triage Accuracy For Hiv And Hepatitis C, A. C. Berry, B. D. Cash, B. Wang, M. S. Mulekar, A. B. Van Haneghan, Kyle Yuquimpo, A. Swaney, M. C. Marshall, W. K. Green

Student Publications

We sought to address the prior limitations of symptom checker accuracy by analysing the diagnostic and triage feasibility of online symptom checkers using a consecutive series of real-life emergency department (ED) patient encounters, and addressing a complex patient population - those with hepatitis C or HIV. We aimed to study the diagnostic and triage accuracy of these symptom checkers in relation to an emergency room physician-determined diagnosis. An ED retrospective analysis was performed on 8363 consecutive adult patients. Eligible patients included: 90 HIV, 67 hepatitis C, 11 both HIV and hepatitis C. Five online symptom checkers were utilised for diagnosis …

How Blockchain Could Increase The Need For And Availability Of Contractual Ordering For Companies And Their Investors, Carol R. Goforth

North Dakota Law Review

No abstract provided.

Solid Spherical Energy (Sse) Cnns For Efficient 3d Medical Image Analysis, Vincent Andrearczyk, Valentin Oreiller, Julien Fageot, Xavier Montet, Adrien Depeursinge

Session 2: Deep Learning for Computer Vision

Invariance to local rotation, to differentiate from the global rotation of images and objects, is required in various texture analysis problems. It has led to several breakthrough methods such as local binary patterns, maximum response and steerable filterbanks. In particular, textures in medical images often exhibit local structures at arbitrary orientations. Locally Rotation Invariant (LRI) Convolutional Neural Networks (CNN) were recently proposed using 3D steerable filters to combine LRI with Directional Sensitivity (DS). The steerability avoids the expensive cost of convolutions with rotated kernels and comes with a parametric representation that results in a drastic reduction of the number of …

Deep Convolutional Neural Networks For Estimating Lens Distortion Parameters, Sebastian Lutz, Mark Davey, Aljosa Smolic

Session 2: Deep Learning for Computer Vision

In this paper we present a convolutional neural network (CNN) to predict multiple lens distortion parameters from a single input image. Unlike other methods, our network is suitable to create high resolution output as it directly estimates the parameters from the image which then can be used to rectify even very high resolution input images. As our method it is fully automatic, it is suitable for both casual creatives and professional artists. Our results show that our network accurately predicts the lens distortion parameters of high resolution images and corrects the distortions satisfactory.

Multi-Person Tracking By Multi-Scale Detection In Basketball Scenarios, Adria Arbués-Sanguesa, Gloria Haro, Coloma Ballester

Session 1: Active Vision, Tracking, Motion Analysis

Tracking data is a powerful tool for basketball teams in order to extract advanced semantic information and statistics that might lead to a performance boost. However, multi-person tracking is a challenging task to solve in single-camera video sequences, given the frequent occlusions and cluttering that occur in a restricted scenario. In this paper, a novel multi-scale detection method is presented, which is later used to extract geometric and content features, resulting in a multi-person video tracking system. Having built a dataset from scratch together with its ground truth (more than 10k bounding boxes), standard metrics are evaluated, obtaining notable results …

A Decade Of Disability Depictions In Newbery Award Books, H. Grow, Sharon Black, K. Egan, Tina Taylor, K. Moss, Rachel L. Wadham, Mary Anne Prater

Faculty Publications

Newbery awards are conferred annually on books recognized as having made the most distinguished contribution to children’s literature; these books reach a wide audience, and their depictions of characters with disabilities can influence children's perceptions and attitudes toward individuals with disabilities. Eight Newbery Medal and Honor books chosen from 2010 to 2019 were identified as portraying 11 main or supporting characters with a disability. Six disabilities were represented: emotional disturbance, deafness, specific learning disability, speech/language impairment, orthopedic impairment, and traumatic brain injury. Applying the Rating Scale for Quality Characterizations of Individuals with Disabilities in Children’s Literature, we found most of …

On-Road Remote Sensing Of Automobile Emissions In The Chicago Area: Fall 2018, Gary A. Bishop

Fuel Efficiency Automobile Test Publications

No abstract provided.

Rethinking Global-Regulation: World’S Law Meets Artificial Intelligence, Nachshon Sean Goltz, Addison Cameron-Huff, Giulia Dondoli

Research outputs 2014 to 2021

This article takes a critical look at Machine Translation of legal text, especially global legislation, through the discussion of Global-Regulation, a state of the art online search engine of the world’s legislation in English. Part 2 explains the rationale for an online platform such as Global-Regulation. Part 3 provides a brief account of the history of the development of machine translation, and it describes some of the limits of the use of statistical machine translation for translating legal texts. Part 4 describes Neural Machine Translation (NMT), which is a new generation of machine translation systems. Finally, Parts 5 and 6 …

Fake News And Propaganda: Trump’S Democratic America And Hitler’S National Socialist (Nazi) Germany, David E. Allen, Michael Mcaleer

Research outputs 2014 to 2021

This paper features an analysis of President Trump’s two State of the Union addresses, which are analysed by means of various data mining techniques, including sentiment analysis. The intention is to explore the contents and sentiments of the messages contained, the degree to which they differ, and their potential implications for the national mood and state of the economy. We also apply Zipf and Mandelbrot’s power law to assess the degree to which they differ from common language patterns. To provide a contrast and some parallel context, analyses are also undertaken of President Obama’s last State of the Union address …

Entropy Analysis Of High-Definition Transcranial Electric Stimulation Effects On Eeg Dynamics, Diego C. Nascimento, Gabriela Depetri, Luiz H. Stefano, Osvaldo Anacleto, Joao P. Leite, Dylan J. Edwards, Taiza E. G. Santos, Francisco Louzada Neto

Research outputs 2014 to 2021

A foundation of medical research is time series analysis—the behavior of variables of interest with respect to time. Time series data are often analyzed using the mean, with statistical tests applied to mean differences, and has the assumption that data are stationary. Although widely practiced, this method has limitations. Here we present an alternative statistical approach with sample analysis that provides a summary statistic accounting for the non-stationary nature of time series data. This work discusses the use of entropy as a measurement of the complexity of time series, in the context of Neuroscience, due to the non-stationary characteristic of …

Optimal Allocation Of Distributed Energy Storage Systems To Improve Performance And Power Quality Of Distribution Networks, Choton K. Das, Octavian Bass, Thair S. Mahmoud, Ganesh Kothapalli, Navid Mousavi, Daryoush Habibi, Mohammad A.S. Masoum

Research outputs 2014 to 2021

The placement of grid-scale energy storage systems (ESSs) can have a significant impact on the level of performance improvements of distribution networks. This paper proposes a strategy for optimal allocation of distributed ESSs in distribution networks to simultaneously minimize voltage deviation, flickers, power losses, and line loading. The optimal ESS allocation is investigated through the PQ injection (considering a variable power factor on the dispatch of ESSs) and the results are compared in terms of performance and power quality improvements. An IEEE-33 bus distribution system (medium voltage), having a high influence of renewable (wind and solar) distributed generation, is used …

A Simheuristic Approach For Evolving Agent Behaviour In The Exploration For Novel Combat Tactics, Chiou-Peng Lam, Martin Masek, Luke Kelly, Michael Papasimeon, Lyndon Benke

Research outputs 2014 to 2021

The automatic generation of behavioural models for intelligent agents in military simulation and experimentation remains a challenge. Genetic Algorithms are a global optimization approach which is suitable for addressing complex problems where locating the global optimum is a difficult task. Unlike traditional optimisation techniques such as hill-climbing or derivatives-based methods, Genetic Algorithms are robust for addressing highly multi-modal and discontinuous search landscapes. In this paper, we outline a simheuristic GA-based approach for automatic generation of finite state machine based behavioural models of intelligent agents, where the aim is the identification of novel combat tactics. Rather than evolving states, the proposed …

Surficial And Deep Earth Material Prediction From Geochemical Compositions, Hassan Talebi, Ute Mueller, Raimon Tolosana-Delgado, Eric C. Grunsky, Jennifer M. Mckinley, Patrice De Caritat

Research outputs 2014 to 2021

Prediction of true classes of surficial and deep earth materials using multivariate spatial data is a common challenge for geoscience modelers. Most geological processes leave a footprint that can be explored by geochemical data analysis. These footprints are normally complex statistical and spatial patterns buried deep in the high-dimensional compositional space. This paper proposes a spatial predictive model for classification of surficial and deep earth materials derived from the geochemical composition of surface regolith. The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest predictive model is trained, and features are ranked based …