MARS
MARS is a repository service of Mason Publishing and the Data and Digital Scholarship Services (DDSS) at the George Mason University Libraries. MARS provides enduring, stable, well-indexed access to a wide range of scholarship from the Mason community, such as Electronic Theses and Dissertations (ETDs), articles, presentations, reports, and creative work. Learn more about publishing, sharing, and preserving research data with the George Mason University Institutional Dataverse, and our other repository services.
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Recent Submissions
From Spikes to Swarms: Evolving Spiking Neural Networks to Create Emergent Swarm Behaviors
(2025) Zhu, Kevin; Nowzari, Cameron; Parsa, Maryam
Drones which can swarm and loiter in a certain area cost hundreds of dollars, but mosquitos can do the same and are essentially worthless. To control swarms of low-cost robots, researchers may end up spending countless hours brainstorming robot configurations and policies to ``organically" create behaviors which do not need expensive sensors and perception.Existing research explores the possible emergent behaviors in swarms of robots with only a binary sensor and a simple but hand-picked controller structure. Even agents in this highly limited sensing, actuation, and computational capability class can exhibit relatively complex global behaviors such as aggregation, milling, and dispersal, but finding the local interaction rules that enable more collective behaviors remains a significant challenge. This is because the collective behavior of a swarm forms from the emergent properties of local agent interactions. Even simple if-then interaction rules can result in complex emergent behaviors, and such rules are deceptively difficult to craft by hand. Efforts to reduce the intuition necessary to craft swarm behaviors largely centers on automating the tuning of controller parameters, or automating search of combinations of human-programmed behaviors. We seek to utilize Spiking Neural Networks (SNNs) as an agent controller, allowing for a wide variety of possible control policies. Provided with a fitness function, the EONS evolutionary algorithm discovers SNNs capable of driving dispersion, aggregation, milling, and diffusion behaviors in a swarm of binary sensing ground vehicles with no modification to the process between these, other than changing the fitness function used. This thesis constitutes three steps towards a generalizable methodology for using SNNs to discover and enact agent-local control policies on binary sensing robots: First, we develop a methodology for training an SNN to replicate the milling behavior in simulation. Then, we apply and refine this methodology for a specific robot model, the TurboPi ground vehicle, and show that the simulated swarm behavior transfers, resulting in milling in a real robot swarm. Finally, we apply this methodology to two more swarm behaviors and metrics from existing literature, aggregation and dispersion, and one behavior with no prior usable metric: diffusion. This represents not only a major step towards lowering the amount of human intuition required to achieve emergent behaviors in swarms, but also presents the ability to reinforce or adjust behaviors more directly than with prior model-based approaches to agent control policies.
Software Based Fault Injection Framework for Radiation Tolerant MSP430 CubeSat Subsystems
(2025) Elsharkawy, Kareem Sameh; Pachowicz, Peter W
This thesis presents a software fault injection framework developed for MSP430-basedCubeSat subsystems to evaluate dependability under radiation-induced fault conditions. We implemented a three phase fault injection approach utilizing the Code Composer Studio API and on-chip JTAG interfaces, the framework automates bit-level fault injection and data collection to assist developers in early stage software hardening. Experimental results demonstrate the feasibility of software-implemented fault injection for low-cost, radiationtolerant verification of CubeSat microcontrollers.
THE PURSUIT OF HIGHER THINGS: BOOKER T. WASHINGTON CONTRASTED WITH W.E.B. DU BOIS
(2025) Wilson, Nicholas J.; Klein, Daniel B
Booker Taliaferro Washington (1856-1915) and William Edward Burghardt Du Bois (1868–1963) were two influential leaders who were both seeking higher things for the black community. This dissertation is primarily an appreciation of Washington’s spirit and vision, using Du Bois as a foil, to develop that appreciation.Their contrasting views of what constitutes “higher” values, education, and progress led to differences in strategies and a shift within black communities whose legacy still impacts communities today. Washington emphasized vocational education, economic self-reliance, and gradual integration, while only insisting on equal treatment under the law. Du Bois advocated for immediate integration, civil rights, and higher education for a "Talented Tenth" of black leaders. Their approaches reflected their personal backgrounds, views, and the challenges of post-Reconstruction America. Washington believed black people could gain respect through hard work and entrepreneurial success as seen in his founding of the Tuskegee Institute and the National Negro Business League (NNBL); one important expression of his views is his 1895 Atlanta Exposition Address. Du Bois criticized Washington’s gradualism, arguing for political activism and leadership by an educated elite and co-founding the National Association for the Advancement of Colored People (NAACP) to push for civil rights. This dissertation suggests that Washington’s bottom-up approach promoted broad-based job-focused education, property ownership, entrepreneurship, and sustainable black advancement, whereas Du Bois’ influence led to greater reliance on government and the politicization of race relations. After Washington’s death and with the rising influence of Du Bois, the focus of black advancement shifted toward political advocacy and collegiate education. The two men set forth rival views of black advancement, views that persist today.
Swollen Shorelines
(2025) Johnson, Elizabeth; Vasquez La Roche, Luis
Swollen Shorelines examines the intersection of womanhood, language, and photographic practice through a collaborative project created in the island nation of Kiribati with linguist Tereua Miracle Oliphant Kaintoka. Drawing on lived experience, the work reflects on the twelve years between an initial eighteen-month stay in Kiribati and a return in 2025, tracing the shifting terrains of personal identity, cross-cultural responsibility, and the complexities of ethical image-making. The project engages the Kiribati concepts of land, rituals, and language as guiding structures, while foregrounding the nuance of consent, collaboration, and representation within photographic practice. Through images, personal narrative, and reflections on translation, Swollen Shorelines explores the emotional geography of return and considers how the expansion and contraction of self mirrors the land’s own shifting edges.
Interpretable Model Learning and Adaptive EM Algorithm for Poisson Mixture Models
(2025) Ye, Shenghao; Sun, Jiayang Sun JS
This dissertation consists of two parts: (a) interpretable model learning, and (b) partialExpectation-Maximization (PEM) algorithm for fitting Poisson mixtures, aimed to address challenges in modeling complex data, and analyzing large or online data in batches, respectively. Statistical modeling is a key step for supporting evidence-based decision-making. In the first part of this dissertation, we developed a comprehensive learning strategy for constructing interpretable statistical models for data with multiple covariates x e and outcome y. Linear models are easy to interpret but limited in the flexibility of modeling nonlinear relations in data, whereas deep neural networks and nonparametric models offer some flexibility at the cost of interpretability. Our strategy is to develop a semiparametric model learning approach —incorporating linear relation of some covariates (denoted by x1) with y, nonlinear relation of some other covariates (denoted by x2) with y, and nonparametric relation of the remaining covariates (denoted by x3) with y. The nonlinear relation here is meant to depend on a few unknown parameters and its shape to be determined by data, and hence is also termed as seeking a nonlinear transformation of x2. This model will be called “S-Interpretable” model, bridging the gap between the fully linear and fully nonparametric models. The challenges to build our “S-Interpretable” models are: (1) properly partitioning covariates x into x1, x2, and x3, (2) determining suitable transformations for x2 when nonlinear effects can be approximated by unknown parametric functions, and (3) jointly estimating linear, nonlinear, and nonparametric components. To address these challenges, we developed a data-driven pipeline that (1) determines x1,x2,x3 based on the data (with an option for human input), (2) suggests transformation forms for x2, (3) enables simultaneous estimation of all components, (4) validates transformation sufficiency, and (5) evaluates overall model performance. Our contributions are not only in developing this interpretable model learning strategy, but also developing (i) A support for transformation necessity and sufficiency using a semiparametric bootstrap method based on effective degrees of freedom (EDF), (ii) A simultaneous estimation procedure allowing transformation terms to follow change-point parametric functions, (iii) Comprehensive model evaluation strategies, including repeated cross-validation and crossconformal prediction sets, and (iv) An application to a real-world study of Tibetan women reproductive success. These developments are not only of interest for each of the four components (i)-(iv) but also contribute to the system science that requires an ensemble for data science, such as our “S-Interpretable” model learning. In the second part of this dissertation, we proposed an adaptive partial EM algorithm for estimating Poisson mixture models to analyze heterogeneous count data. The full EM algorithms require processing the full dataset in each iteration, posing efficiency and scalability issues when having large data. Sun et al. (2025+) developed a partial EM (PEM) estimation method for a Gaussian mixture model, which is competitive with other methods in theory and computational efficiency. In this dissertation, we follow their idea and extend it to the Poisson mixture case. We derive the partial maximum likelihood estimators (MLE) for Poisson mixture parameters for data composed of the previous estimates and a new batch of data. We handle cases with and without an extra component in the new data batch to those from the previous estimates. Our method demonstrates significant improvements in computational efficiency and scalability compared to existing benchmarks. Theoretical guarantees are provided for the convergence of the algorithm and the consistency of the MLE.