Network Visualization

Network science has become increasingly popular over the last several years as people have realized that networks have the ability to represent the relationships or connections between any objects. While some networks are small and easy to gather information from, others can be very large. It can be very difficult and time consuming to map out these large networks if we collect data from all the nodes in the network. Instead of examining all nodes, we seek to collect data incrementally from a portion of the network at a time to discover the whole network. This discovery occurs by successively placing monitors which can see a local portion of the graph. We then tested all of our algorithms on four different networks. Although there was no one algorithm that did best overall, we were able to see some of the strengths and weaknesses of each on various structures of networks

Sentiment Analysis on New York Times Articles Data

Sentiment Analysis on New York Times Coverage Data Departmental Affiliation: Data Science/ Political Science College of Arts and Sciences The extant political science literature examines media coverage of immigration and assesses the effect of that coverage on partisanship in the United States. Immigration is believed to be a unique factor that induces large- scale changes in partisanship based on race and ethnicity. The negative tone of media coverage pushes non-Latino Whites into the Republican Party, while Latinos trend toward the Democratic Party. The aim for this project is to look at New York time data in order to identify how much immigration is covered in newspaper outlets, specifically Latino immigration, and to determine the overall tone of these stories. In this research, we seek to determine individual articles take a positive, neutral or negative stance. We achieve this using a dictionary-based approach, meaning we look at individual words to assess if it has a positive, neutral or negative connotation. We train our data using publicly accessible sentiment dictionaries such as VADER (Valence Aware Dictionary and Sentiment Reasoner). However, this task can be difficult because certain words can be dynamic and may pertain to a positive or negative sentiment in context of the article. In order to resolve this issue, we use reliability measures to ensure that the words of high frequencies are in the correct sphere of negative, neutral, and positive light. Information about the Author(s): Faculty Sponsor(s): Professor Gregg B. Johnson and Professor Karl Schmitt Student Contact: Gabriel Carvajal – [email protected]

Developing A Model Approximation Method and Parameter Estimates for Solid State Reaction Kinetics

The James S. Markiewicz Solar Energy Research Facility was built to research solar chemistry and currently being used to research the change in metal oxides such as iron or magnesium oxide that act as a medium for the production of hydrogen from water. This is significant because hydrogen can be used in vehicles equipped with appropriate fuel cells and due the decreased cost of producing hydrogen with this method. The shrinking core model which governs this process has proved difficult to solve due to the high number of unknown constants and its non-linearity. We detail in this work the implementation of less common heuristics, mainly Particle Swarm Optimization. This technique was used because of its wide unbiased search for the possible constants. The development and method we are using to solve these unknown constants will be shown

"Strongly interacting matter in magnetic fields": an overview

This is an introduction to the volume of Lecture Notes in Physics on "Strongly interacting matter in magnetic fields". The volume combines contributions written by a number of experts on different aspects of the problem. The response of QCD matter to intense magnetic fields has attracted a lot of interest recently. On the theoretical side, this interest stems from the possibility to explore the plethora of novel phenomena arising from the interplay of magnetic field with QCD dynamics. On the experimental side, the interest is motivated by the recent results on the behavior of quark-gluon plasma in a strong magnetic field created in relativistic heavy ion collisions at RHIC and LHC. The purpose of this introduction is to provide a brief overview and a guide to the individual contributions where these topics are covered in detail.Comment: 12 pages, introduction to "Strongly interacting matter in magnetic fields", Lect. Notes Phys. 871, 1 (2013), edited by D. Kharzeev, K. Landsteiner, A. Schmitt, H.-U. Yee; v2: references update

Network Algorithms for Complex Systems with Applications to Non-linear Oscillators and Genome Assembly

Network and complex system models are useful for studying a wide range of phenomena, from disease spread to traffic flow. Because of the broad applicability of the framework it is important to develop effective simulations and algorithms for complex networks. This dissertation presents contributions to two applied problems in this area First, we study an electro-optical, nonlinear, and time-delayed feedback loop commonly used in applications that require a broad range of chaotic behavior. For this system we detail a discrete-time simulation model, exploring the model's synchronization behavior under specific coupling conditions. Expanding upon already published results that investigated changes in feedback strength, we explore how both time-delay and nonlinear sensitivity impact synchronization. We also relax the requirement of strictly identical systems components to study how synchronization regions are affected when coupled systems have non-identical components (parameters). Last, we allow wider variance in coupling strengths, including unique strengths to each system, to identify a rich synchronization region not previously seen. In our second application, we take a complex networks approach to improving genome assembly algorithms. One key part of sequencing a genome is solving the orientation problem. The orientation problem is finding the relative orientations for each data fragment generated during sequencing. By viewing the genomic data as a network we can apply standard analysis techniques for community finding and utilize the significantly modular structure of the data. This structure informs development and application of two new heuristics based on (A) genetic algorithms and (B) hierarchical clustering for solving the orientation problem. Genetic algorithms allow us to preserve some internal structure while quickly exploring a large solution space. We present studies using a multi-scale genetic algorithm to solve the orientation problem. We show that this approach can be used in conjunction with currently used methods to identify a better solution to the orientation problem. Our hierarchical algorithm further utilizes the modular structure of the data. By progressively solving and merging sub-problems together we pick optimal `local' solutions while allowing more global corrections to occur later. Our results show significant improvements over current techniques for both generated data and real assembly data

Sentiment Analysis on New York Times Articles Data

Sentiment Analysis on New York Times Coverage Data Departmental Affiliation: Data Science/ Political Science College of Arts and Sciences The extant political science literature examines media coverage of immigration and assesses the effect of that coverage on partisanship in the United States. Immigration is believed to be a unique factor that induces large- scale changes in partisanship based on race and ethnicity. The negative tone of media coverage pushes non-Latino Whites into the Republican Party, while Latinos trend toward the Democratic Party. The aim for this project is to look at New York time data in order to identify how much immigration is covered in newspaper outlets, specifically Latino immigration, and to determine the overall tone of these stories. In this research, we seek to determine individual articles take a positive, neutral or negative stance. We achieve this using a dictionary-based approach, meaning we look at individual words to assess if it has a positive, neutral or negative connotation. We train our data using publicly accessible sentiment dictionaries such as VADER (Valence Aware Dictionary and Sentiment Reasoner). However, this task can be difficult because certain words can be dynamic and may pertain to a positive or negative sentiment in context of the article. In order to resolve this issue, we use reliability measures to ensure that the words of high frequencies are in the correct sphere of negative, neutral, and positive light. Information about the Author(s): Faculty Sponsor(s): Professor Gregg B. Johnson and Professor Karl Schmitt Student Contact: Gabriel Carvajal – [email protected]

Konfessioneller Konflikt und politisches Verhalten in Deutschland: vom Kaiserreich zur Bundesrepublik

In diesem Beitrag wird die Bedeutung von Religion, Konfession und Kirche für das politische Verhalten der deutschen Bevölkerung von der Reichsgründung bis zur Gegenwart der Bundesrepublik untersucht. Drei Untersuchungsebenen werden unterschieden, nämlich gesellschaftliche Konflikte als erste Ebene, dann als zweite Ebene die aus der Austragung dieser Konflikte erwachsenen gesellschaftlichen Großgruppen ("Milieus") und als dritte Ebene die durch Parteien strukturierten Muster politischen Massenverhaltens. Auf dieser Grundlage wird drei Fragekomplexen nachgegangen. Erstens, wie sind die Frontstellungen konfessioneller Konflikte in Deutschland beschaffen und auf welche historischen Konstellationen sind ihre Entstehung und jeweilige Intensität zurückzuführen? Zweitens, welche konfessionellen Milieus bilden sich heraus und wie entwickeln sich deren innere Struktur und mobilisierende Kraft? Drittens, in welchem Verhältnis stehen konfessionelle Milieus zu politischen Parteien und in welchem Ausmaß prägen sie das Wahlverhalten der deutschen Bevölkerung? Die Analyse macht den historischen Charakter der Kategorie "Katholizismus" deutlich. (ICF