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The Persistence of an Idiot: A Film Student\u27s Journey Through the Feature Film Format
No full textThe Sweet Idiot is a microbudget comedy feature written, produced and directed by Austin LeMaster. The film aims to make a case for reinstating novelty and singularity to the cinema-going experience. The film uses basic filmmaking tools, putting special emphasis on human performances in order to capture the unique resulting theatricality and ritualistic spirit that warrants attending a feature film. This thesis document serves as a record of the processes, hindrances, and ultimately the prevalence that resulted in the final product
Perezhivanie In Practice: Exploring Affective Memory and Imagination Through The Stanislavski System
No full textThis thesis explores the enduring significance of The Stanislavski System in modern American actor training, addressing why it remains essential over a century after its creation by Konstantin Stanislavski. While studying the history of how The Stanislavski System was introduced and adapted in America, I identified a fundamental divide that shaped actor training: Lee Strasberg emphasized affective memory to generate emotion, while Stella Adler advocated for imagination to connect with character. Their public dispute has had a lasting impact on American theatre pedagogy, often leaving actors caught between methodologies without a clear understanding of the System’s original intent. At the core of Stanislavski’s work is the idea of consciously arousing the subconscious, a principle that connects to his concept of perezhivanie, often translated as “experiencing.” Rather than a full immersion where the actor becomes the character, perezhivanie is a state of heightened awareness that allows for spontaneity while maintaining artistic control. In contemporary training, actors are exposed to various acting techniques, yet without a strong foundation in Stanislavski’s principles, these fragmented approaches can lead to confusion. This thesis documents my research into The Stanislavski System alongside my practical exploration of affective memory and imagination while performing Lyubov Andreyevna in The Cherry Orchard (Spring 2024). Throughout this process, I encountered moments of perezhivanie and examined how this elusive state manifests in performance. By bridging the gap between affective memory and imagination, this project aims to provide actors with a unified methodology that honors Stanislavski’s foundational work
Assessing Multi-Hazard Risks And Impacts Of Compound Climate And Weather Extreme Events For Socio-Economic Risk Management
No full textIn recent decades, compound climate and weather extremes, events driven by multiple drivers such as precipitation, storm surge, river discharge, and waves, have increasingly affected coastal regions, causing severe impacts on human life, infrastructure, ecosystems, and economies. Traditional risk assessments often underestimate these compound events by focusing on individual drivers independently. This dissertation provides a systematic assessment of multi-hazard risks and socio-economic consequences associated with compound events in the coastal counties along the U.S. East and Gulf Coasts. It introduces and applies a novel bottom-up impact-based methodology to assess the role of compound events in causing socio-economic losses in the U.S. East and Gulf coastal counties. By combining historical socio-economic loss data from the Spatial Hazard Events and Losses Database (SHELDUS) with reanalysis and observational data sets of hydrological, meteorological and oceanographic drivers, this dissertation identifies which natural hazards were predominantly compound events. We first applied the framework in Miami-Dade County and found that approximately 99% of flood-related property damage and 98% of crop damage during 1979 to 2019 were linked to compound hazards. The impact-based approach was further improved and applied in 203 coastal counties to study the impacts and drivers of compound flooding. Results show that about 80% of recorded flooding events between 1980 to 2018 were compound, with compound flooding causing over 80% of total property and crop losses in most counties, with median losses being significantly higher (over 76 times for crop losses and over 26 times for property losses) compared to univariate events. A multivariate clustering analysis further highlights distinct regional patterns of compound flood drivers, emphasizing the varying relevance of meteorological and oceanographic processes. The research iv also identified substantial spatial variability in the relationship between peak magnitudes and intensities of extreme sea level events across the U.S. Gulf and East coasts, highlighting that conventional, univariate risk assessments significantly underestimate actual coastal flooding risk. These findings emphasize the importance of adopting an integrated, multi-driver, impact-based frameworks in hazard assessment, helping communities better plan for climate adaptation planning, improve resilience measures, and protect critical infrastructure in vulnerable coastal regions
Utilizing Programmable Spatial Light Modulators for Beam Shaping and Spectral Synthesis
No full textProgrammable spatial light modulators (SLMs) built on Liquid Crystal on Silicon (LCoS) technology enable precise control of multiple degrees of freedom of light, such as amplitude, phase, frequency, polarization, and time. We demonstrate applications of SLM technology that go beyond the conventional 4-f ultrafast pulse shaping approach. By developing a dual-oscillator power amplifier (DOPA), we show that nonlinear wave interactions inside optical fibers can be managed through the spectral phase adjustment of optical frequency combs produced by non-degenerate, cascaded four-wave mixing (CFWM). Our approach utilizes a programmable spectral phase profile, which allows precise regulation of an optical comb’s spectral content. We achieve high power-spectral-density (PSD) values of around 60 dBm/nm and pump power conversion efficiencies greater than 70%, which surpasses the performance of microcomb systems. High PSD at specific wavelengths better meets the demands for multiwavelength LiDAR and remote sensing applications than traditional supercontinuum generation technology. In the final step of this work, we reveal a technique for accurate control of bandwidth through the optimization of dispersion profile parameters from β2 to β5 using a Genetic Algorithm (GA) based on self-supervised machine learning techniques. Utilizing our C-band DOPA in conjunction with the optimized SLM phase control, we achieve a figure of merit (FOM = maximum bandwidth / minimum bandwidth) of 9. Our results show that the GA-optimized phase profile can extend the CFWM bandwidth from the C-band to the O-band (1260-1360 nm) on the short wavelength side all the way to 1850 nm on the long wavelength side. This 530 nm broad comb spectrum contains 167 comb lines with 518 GHz spacing at an average power of 120 mW.
In addition, this work extends the SLM’s functionality beyond standard spatial beam shaping to produce highpower Hermite-Gaussian (HG) modes and Orbital Angular Momentum (OAM) modes, which use mode division multiplexing (MUX)/de-multiplexing(de-MUX) to improve the optical system’s channel capacity. This dissertation illustrates how wavefront synthesis techniques using SLM combine multiple laser beams into a single high-power, high-quality beam while emphasizing that ML advances digitized phase mask creation to surpass the conventional wavefront matching method
Authorship Attribution of AI-generated Codes
Get PDFThe field of code authorship attribution focuses on identifying the author(s) of source code written in a specific programming language. Various methods, including manual crafting, automated crafting, and deep learning-generated features, have been developed for this task. These techniques leverage the unique stylistic patterns inherent in code, such as structure, comments, variable names, and function names, to attribute authorship accurately. This field has practical applications in software forensics, cybersecurity, and code plagiarism detection, enabling investigations into issues like piracy, intellectual property violations, and malware attribution. In educational settings, it helps detect plagiarism in programming assignments. However, traditional methods face challenges from code transformation techniques that alter stylistic patterns, making authorship attribution difficult. Additionally, the rise of AI programming tools like ChatGPT introduces complexities, as these tools generate code in various styles and may evade detection. Furthermore, AI-generated code often exhibits lower security standards, raising concerns about copyright infringement, cheating, and vulnerabilities. To address these challenges, our research investigates whether existing attribution techniques can identify AI-generated code. Initial findings suggest they cannot, underscoring the need for novel approaches. By leveraging a feature-based method using pretrained models, we accurately classify ChatGPT and non-ChatGPT code, creating a jointly trained model for reliable attribution. We also explore ChatGPT\u27s ability to generate diverse code styles, akin to code transformation, and evaluate the resilience of attribution techniques against evasion attempts. Additionally, we propose SCAE, a machine learning-based Seq2Seq code transformation technique that mitigates the limitations of Monte Carlo Tree Search (MCTS). SCAE achieves efficient processing while maintaining transformation quality, offering a robust solution for code authorship obfuscation. Our work advances the field by addressing the unique challenges posed by AI-generated and transformed code, ensuring accurate and secure authorship attribution
Queering the Queer Bildungsroman: Literature of AIDS, The Rhizome, and Rat Bohemia
No full textThe advent of the bildungsroman with Johann Wolfgang Von Goethe’s Wilhelm Meister’s Apprenticeship was a seminal moment in literature. From that point onward, the genre, which would come to be known as the “coming-of-age” novel, evolved and as such developed a variety of subgenres. This thesis focuses on the queer bildungsroman, a subgenre which has been dubbed the “coming-out” novel. Through Judith Butler’s work, I conceptualize genre as gender. Additionally, I utilize the Deleuzian concepts of the rhizome, becoming, and minor literature along with literature of AIDS to argue that the queer bildungsroman is not solely the coming out novel. In doing so I argue that there is room for a multiplicity of understanding, one that does not center cisgender heteronormative society in the queer bildungsroman. Sarah Schulman’s Rat Bohemia is a rhizomatic, queer bildungsroman, that largely deals with AIDS and by performing a close reading of her novel I am able to provide working examples of my theoretical undertakings to broaden the queer bildungsroman beyond solely the “coming-out” novel
Cigarettes, Flesh, & Other Cravings
No full textCigarettes, Flesh, & Other Cravings is a collection of short stories that explores themes of motherhood, death, and desire. Each character struggles with the friction between society\u27s expectations and their desires for happiness, all through the lens of the surreal and the absurd. A destructive raccoon’s nightly visits unravel a woman’s sense of control. A teenager begins an internship with a conspiracy-obsessed clown. A minuscule old woman tumbles out of a chicken egg. A fissure in a kitchen floor births an imp. A Choose Your Own Adventure examines the illusion of choice. In Hell, torture is personalized. Cigarettes, Flesh, & Other Cravings aims to question the meaning of how desire can alter a person’s life, and whether there is any choice at all
Developing Real-Time Crash Prediction System using AI-Based Methods on Interstate Incorporating Express Lanes
No full textThis research develops a real-time crash prediction system that integrates machine learning techniques and real-time data to forecast crash likelihood across various road segments. The system particularly addresses newly constructed Interstate 4 Express managed lanes, modeling them separately due to their unique challenges, such as limited crash data and new road designs. By treating these segments individually, the system ensures more accurate predictions that account for their distinct traffic behaviors. By leveraging anomaly detection learning (ADL), the system identifies rare crash events by detecting deviations from normal traffic behavior, even with imbalanced data. ADL is applied specifically to the influenced segments of the I-4 Express Lanes, effectively addressing the challenges of limited crash data by identifying traffic flow anomalies and providing reliable predictions. Furthermore, the system’s real-time crash prediction capabilities are expanded by integrating multiple models for segment types with a multi-layered approach to primary, secondary, and severity crashes, offering a comprehensive view of traffic safety. Additionally, the system is designed to specialized modules. This modular approach ensures adaptability across different segment types, optimizing the system for large-scale, real-world traffic data. The inclusion of specialized modules, such as the user interface, further enhances the system\u27s ability to deliver crash risk summary reports and visual updates for traffic operators. Additionally, fine-tuned large language models (LLMs) are introduced to improve the prediction\u27s interpretability, generating textual explanations for predicted crashes and offering actionable insights for informed decision-making. The system is evaluated, demonstrating its ability to accurately forecast crashes and contribute to proactive traffic management. By combining machine learning, anomaly detection, and LLMs, this integrated approach offers a significant advancement in traffic safety, providing timely, accurate, and explainable predictions that improve overall transportation safety and management
The Spray and Atomization Characteristics of an LRDRE Injector
No full textThe Ongoing effort to advance the Technology Readiness Level (TRL) of Rotating Detonation Engines (RDEs) has faced significant challenges with respect to the development of accurate analytical models for the performance of liquid fueled detonations. A key issue lies in the lack of comprehensive experimental data to validate and refine these models. To address this gap, the present study seeks to provide detailed characterization data on a specific Liquid Rotating Detonation Rocket Engine (LRDRE) injector, which can be leveraged to improve simulation accuracy and predictive capabilities. This investigation is divided into two primary phases. The first phase focuses on the macroscopic analysis of liquid spray formation, specifically examining the influence of varying liquid fuel temperatures on injector behavior. By analyzing parameters such as beta angle and average frame intensity, this phase aims to determine whether fuel temperature plays a significant role in spray development and liquid atomization. The second phase delves into the microscopic characteristics of the liquid spray, employing a Phase Doppler Interferometer (PDI) to generate parameter maps detailing mean droplet diameter (D10), Sauter Mean Diameter (D32 or SMD), and mean velocity at multiple distinct locations within the spray. These measurements provide crucial insights into the droplet dynamics and their dependence on operating conditions, ultimately contributing to a more refined understanding of injector performance and aiding in the development of more reliable liquid-fueled RDE models
