This project is a feminist exploration of the muse as a cultural icon, and of the relationship dynamic between artists and their muses, using specific twentieth century photographers and their models as examples. The pairs discussed are Lee Miller and Man Ray, Georgia O'Keeffe and Alfred Stieglitz, and Charis Wilson and Edward Weston.

This research evaluates a cyber test-bed, DEXTAR (Defense Exercises for Team Awareness Research), and examines the relationship between good and bad team performance in increasingly difficult scenarios. Twenty-one computer science graduate students (seven three-person teams), with experience in cybersecurity, participated in a team-based cyber defense exercise in the context of DEXTAR, a high fidelity cybersecurity testbed. Performance measures were analyzed in addition to team process, team behavior, and workload to examine the relationship between good and bad teams. Lessons learned are reported that will inform the next generation of DEXTAR.

Photovoltaic (PV) modules appear to have three classifications of failure: Infant mortality, normal-life failure, and end-of-life failure. Little is known of the end-of-life failures experienced by PV modules due to their inherent longevity. Accelerated Life Testing (ALT) has been at the crux of this lifespan prediction; however, without naturally failing modules an accurate acceleration factor cannot be determined for use in ALT. By observing modules that have been aged in the field, a comparison can be made with modules undergoing accelerated testing. In this study an investigation on about 1900 aged (10-17 years) grid-tied PV modules installed in the desert climatic condition of Arizona was undertaken. The investigation was comprised of a check sheet that documented any visual defects and their severity, infrared (IR) scanning, and current-voltage (I-V) curve measurements. After data was collected on modules, an analysis was performed to classify the failure modes and to determine the annual performance degradation rates.

This dissertation examines the history of multiracial alliances among internationalist radical activists in the San Francisco Bay Area from the late 1960s through the 1970s. Using the approaches of social movement history and intellectual history, I critically assess the ideological motivations radicals held for building alliances and the difficulties they encountered with their subsequent coalitional work in four areas of coalescence—the antiwar movement, political prisoner solidarity, higher education, and electoral politics. Radical activists sought to dismantle the systemic racism (as well as economic exploitation, patriarchy, and the intersections of these oppressions) that structured U.S. society, through the creation of broad-based movements with likeminded organizations. The activists in this study also held an orientation toward internationalist solidarity, linking the structural oppressions against which they struggled in the United States to the Vietnam War and other U.S. militaristic interventions overseas and viewing these entanglements as interconnected forces that exploited the masses around the world.

Scholarly and popular interpretations of Sixties radical movements have traditionally characterized them as narrowly-focused and divisive. In contrast, my research highlights the persistent desire among Bay Area radicals to form alliances across these decades, which I argue demonstrates the importance of collaborative organizing within these activist networks. Scholarship on coalitional politics also tends to emphasize “unlikely alliances” between “strange bedfellows.” In contrast, this project illuminates how sharing similar ideological principles predisposed these radical organizations to creating alliances with others. Coalitions remain integral to contemporary social and political movements, and excavating the possibilities but also problems within previous broad-based organizing efforts provides a usable history for understanding and confronting societal issues in the present day. At the same time, the multifarious manifestations of racism and other systems of inequality demonstrate the need to first understand how these oppressions affect minority groups uniquely, before we can understand how they affect groups in comparison to each other.

ABSTRACT This document introduces singers and voice teachers to Dr. Alfred A. Tomatis's listening training method with a particular emphasis on its relevance to singers. After presenting an overview of Tomatis's work in the field of audio-psycho-phonology (circa 1947 through the 1990s) and specific ways that aspects of his theory are relevant to singers' performance skills, this project investigates the impact of listening training on singers by examining published research. The studies described in this document have investigated the impact of listening training on elements of the singer's skill set, including but not limited to measures of vocal quality such as intonation, vocal control, intensity, and sonority, as well as language pronunciation and general musicianship. Anecdotal evidence, presented by performers and their observers, is also considered. The evidence generated by research studies and anecdotal reports strongly favors Tomatis-based listening training as a valid way to improve singers' performance abilities.

Modified and artificial water sources can be used as a management tool for game and non-game wildlife species. State, federal, and private agencies allocate significant resources to install and maintain artificial water sources (AWS) annually. Capture mark recapture methods were used to sample small mammal communities in the vicinity of five AWS and five paired control sites (treatments) in the surrounding Sonoran desert from October 2011 to May 2012. I measured plant species richness, density, and percent cover in the spring of 2012. A Multi-response Permutation Procedure was used to identify differences in small mammal community abundance, biomass, and species richness by season and treatment. I used Principle Component Analysis to reduce 11 habitat characteristics to five habitat factors. I related rodent occurrence to habitat characteristics using multiple and logistic regression. A total of 370 individual mammals representing three genera and eight species of rodents were captured across 4800 trap nights. Desert pocket mouse (Chaetodipus penicillatus) was the most common species in both seasons and treatments. Whereas rodent community abundance, biomass, and richness were similar between seasons, community variables of AWS were greater than CS. Rodent diversity was similar between treatments. Desert pocket mouse abundance and biomass were twice as high at AWS when compared to controls. Biomass of white-throated woodrat (Neotoma albigula) was five times greater at AWS. Habitat characteristics were similar between treatments. Neither presence of water nor distance to water explained substantial habitat variation. Occurrence of rodent species was associated with habitat characteristics. Desert rodent communities are adapted for arid environments (i.e. Heteromyids) and are not dependent on "free water". Higher abundances of desert pocket mouse at AWS were most likely related to increased disturbance and debris and not the presence of water. The results of this study and previous studies suggest that more investigation is needed and that short term studies may not be able to detect interactions (if any) between AWS and desert small mammal communities.

Splicing of digital images is a powerful form of tampering which transports regions of an image to create a composite image. When used as an artistic tool, this practice is harmless but when these composite images can be used to create political associations or are submitted as evidence in the judicial system they become more impactful. In these cases, distinction between an authentic image and a tampered image can become important.

Many proposed approaches to image splicing detection follow the model of extracting features from an authentic and tampered dataset and then classifying them using machine learning with the goal of optimizing classification accuracy. This thesis approaches splicing detection from a slightly different perspective by choosing a modern splicing detection framework and examining a variety of preprocessing techniques along with their effect on classification accuracy. Preprocessing techniques explored include Joint Picture Experts Group (JPEG) file type block line blurring, image level blurring, and image level sharpening. Attention is also paid to preprocessing images adaptively based on the amount of higher frequency content they contain.

This thesis also recognizes an identified problem with using a popular tampering evaluation dataset where a mismatch in the number of JPEG processing iterations between the authentic and tampered set creates an unfair statistical bias, leading to higher detection rates. Many modern approaches do not acknowledge this issue but this thesis applies a quality factor equalization technique to reduce this bias. Additionally, this thesis artificially inserts a mismatch in JPEG processing iterations by varying amounts to determine its effect on detection rates.

The indigenous communities of Chiapas, Mexico, have long manifested resistance to oppression and discrimination. This study centers on the analysis of Chiapas: el fin del silencio (1998) by Alberto Turok, connecting the work of the photographer to the problems faced by indigenous people in the region, such as inequality and marginalization. Race, class, gender, and globalization, in addition to the emergence of the Ejército Zapatista de Liberación Nacional (EZLN), are essential factors to the discourse of resistance. EZLN, an armed indigenous group in Chiapas, led by its famed leader, Subcomandante Marcos, clearly opposed the implementation of the North American Free Trade Agreement (NAFTA). In examining resistance, ritual, and performance, the photography of Turok serves as testimony of the struggles of indigenous people and the relevance it has for a diverse Mexican society.

This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex performance assessment within a digital-simulation educational context grounded in theories of cognition and learning. BN models were manipulated along two factors: latent variable dependency structure and number of latent classes. Distributions of posterior predicted p-values (PPP-values) served as the primary outcome measure and were summarized in graphical presentations, by median values across replications, and by proportions of replications in which the PPP-values were extreme. An effect size measure for PPMC was introduced as a supplemental numerical summary to the PPP-value. Consistent with previous PPMC research, all investigated fit functions tended to perform conservatively, but Standardized Generalized Dimensionality Discrepancy Measure (SGDDM), Yen's Q3, and Hierarchy Consistency Index (HCI) only mildly so. Adequate power to detect at least some types of misfit was demonstrated by SGDDM, Q3, HCI, Item Consistency Index (ICI), and to a lesser extent Deviance, while proportion correct (PC), a chi-square-type item-fit measure, Ranked Probability Score (RPS), and Good's Logarithmic Scale (GLS) were powerless across all investigated factors. Bivariate SGDDM and Q3 were found to provide powerful and detailed feedback for all investigated types of misfit.