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The Perdiz Problem
No full textIt has been over 70 years since J. Charles Kelley (1947a and b) first defined the Toyah Focus. Dating from approximately AD 1200 to the mid-1600’s. The Toyah Focus begins around the time that people transitioned from spear and atlatl hunting to hunting with bow and arrow technology and continued until the establishment of Spanish mission complexes in Texas in the 1800s (Lohse 2009). The peoples who lived at Toyah sites are believed to have been part of widely spaced small bands of hunter-gatherers who relied primarily on bison (Arnn 2012). Archaeologically, diagnostic artifacts of a Toyah site are Perdiz arrowheads, an expedient stone-toolkit, bison remains, and bone-tempered pottery (Kenmotsu and Boyd 2012). This dissertation takes one aspect of the definition for the Toya Focus - the Perdiz point - and applies new methods of analysis, in the hopes of better understanding variation in lithic technology over the southern Plains landscape as it might relate to the movements of ancient hunter-gatherers and their interactions with other groups (i.e., farmers)
Noise-Embedded Image Processing Based on Quantum Data Encodings
No full textAdvancements in quantum information have significantly impacted the field of image processing, although challenges remain. Especially in the edge detection and image encoding area, distorted feature and noises would affect the further classification or super resolution tasks. In our work, we conduct researches on two stages to both evaluate the potential of Quantum-based Convolutional Structure in extracting distorted feature and further explore the effects of quantum noise channels on quantum image encodings.
In the first stage, we propose a method to extract distorted edge features by applying shallow layers in quantum convolutional neural networks (QCNN). By combining the advantages of quantum computing and the layered structure of convolutional neural networks (CNN), this approach addresses the problem and compares the extracted distorted pattern with the reference pattern, achieving a best matching ratio of 99.71% in images interfered with impulse noise. Furthermore, we also compare the performance with the classical method and other quantum algorithms, our method gains a 97.19% ratio.
In the second stage, we focus on quantum noises interfere with the circuits-based quantum image encoding that store classical image to quantum machine. We explore the potential effects of quantum noise models on varied encodings, combining both quantum evaluation methodology such as circuit depth, qubit number, fidelity comparison and depth growth, and classical image evaluation method such as mean squared error (MSE), structural similarity index measure (SSIM) and peak signal-noise ratio (PSNR) to find the pattern of noise behaviors affecting encodings. We also evaluate the behavior in applying noise to specific gate sets and measurement to distinguish how noise would affect the circuit. In addition, two datasets are evaluated to compare the effects on images with different levels of complexity
Transportation Security Administration Use of Facial Recognition Technology: Turbulence Surrounding Traveler Rights
No full textFacial recognition technology has been employed by law enforcement agencies and private companies for decades. This technology promises safer and more efficient processes to identify individuals in the name of safety. However, not everyone approves of their biometric data being collected. This Article provides an overview of the current use of facial recognition technology by the Transportation Security Administration (TSA) in airport security checkpoints. It begins by explaining the underlying technology behind facial recognition and explores the safety and security concerns behind it. Building on this foundation, this Article then evaluates how the TSA’s use of facial recognition influences science and technology law and policy, considering past, current, and future implications
Levels of Generality, the Limits of Originalism, and the Supreme Court’s Second Amendment Jurisprudence
No full textJust how originalist is the Court’s Second Amendment jurisprudence after United States v. Rahimi? This is perhaps one of the biggest questions left in the decision’s wake. As it turns out, the answer is not altogether clear post-Rahimi. This is because the case produced some seven separate opinions, many of which—even though they agree as to the bottom line—get there by very different paths. This Article suggests that Rahimi, perhaps more than any other recent decision by the Court, underscores the crucial role that levels of generality in constitutional interpretation play, while illustrating the problems with originalism and how, when faced with a choice between strict adherence to originalism’s core ideals and avoiding deeply undesirable results, many justices—even those who purport to be originalists—will forsake originalism for a far more pragmatic approach to constitutional interpretation. In the end, when the various opinions in Rahimi are dissected, one finds considerable support for the notion that the Justices are “look[ing] for the central purposes of the relevant constitutional provision and tr[ying] to apply it in a vastly different world.” Whatever it is, such an approach is decidedly not originalism
Predicting Simulation Times for Multiphase Thermal-Hydraulic Models
No full textAddressing the challenge of computationally intensive OLGA
simulations in the oil and gas industry, a machine learning framework is
developed for accurate runtime prediction. A specialized feature extraction
pipeline identifies key parameters—such as simulation time, time step,
number of branches, and section count—from OLGA input files that serve as
high-impact predictors. Multiple predictive models, including regression,
tree-based ensembles, and neural networks, are implemented to validate
accuracy and robustness. Results reveal that prioritizing simulations based on
predicted runtimes optimizes licensing resources and reduces operational
costs, making real-time scheduling more efficient. This research demonstrates
the effectiveness of data-driven runtime prediction in enhancing both
decision-making and resource allocation for complex engineering
simulations
The Impact of Working Conditions on Productivity: Evidence from the US Public Defense System
No full textPublic defenders provide essential legal representation to people who cannot afford an attorney. Yet, they do so under notoriously heavy caseloads, raising important questions about how workload affects the delivery of justice. As part of a larger study on public defense, Dr. Amy Mahler analyzed case records from two U.S. states and examined how strict case assignment rules created natural variation in attorney workloads. Her research also incorporated detailed time-use data to better understand how public defenders allocated their limited resources across different tasks. Dr. Mahler’s research sheds light on the connection between workloads and case outcomes in public defense and explores what these connections mean for ensuring fair and high-quality, effective representation
A Real-Time Animation Toolkit with Blending, Root Motion Control, Interruption, Events, and Layering
No full textThis thesis presents a real-time animation toolkit designed for interactive 3D applications, with a focus on games. The toolkit integrates advanced features including animation blending, root motion control, interruption handling, event systems, and layering to achieve fluid and responsive character movement. Built around a quaternion-based math library, the system ensures smooth transitions between animations, eliminates joint snapping, and avoids gimbal lock. Root motion extraction and application enable physics-accurate movement driven by animations. The artifact—a third-person action game—demonstrates the toolkit\u27s efficacy, showcasing seamless animation transitions, efficient GPU-based vertex updates, and good performance (0.56–1.25ms per frame). Results confirm the system\u27s ability to synchronize visual and physical motion, support complex interactions (e.g., combat), and maintain realism through layered animations (e.g., item use during locomotion). The toolkit\u27s modular design, leveraging XML for data-driven collision and animation sequencing, ensures scalability and adaptability for diverse projects
Machine Learning, Diversification, and M&A Performance
No full textIn this paper, we study whether acquisitions can improve the financial performance of firms possessing machine learning/artificial intelligence (ML/AI). First, we argue and find that this effect is significant. Then, drawing on studies proposing a weaker effect of ML/AI effectiveness when the data used are complex and contain irrelevant information, we hypothesize and find that the benefit from within-industry deals is higher than that from deals between industries. Furthermore, the effect is more significant among deals between industries when closely related. Finally, we extend these arguments to deals where both the acquirer and target have ML/AI and find the same pattern of effects. We use a number of causal inference methods on U.S. acquisition data to test our theory. Finally, we discuss the contributions of our study to research on ML/AI and acquisitions in general
Advancing the Characterization of Geophysical Signals through Array Processing and Artificial Intelligence
No full textIn geophysics, seismic and infrasound observations are routinely employed to constrain the nature and origin of events. Seismoacoustics, as a discipline, is built upon the simultaneous detection and integrated analysis of these data types. This joint approach is critical not only for advancing scientific understanding but also for supporting global monitoring efforts in hazard mitigation and nuclear explosion treaty verification. The data analyzed in this dissertation were recorded by array deployments, which consist of multiple sensors arranged in predetermined configurations to enhance signal detection, resolve directionality, and quantify waveform coherence. Leveraging these array recordings, I introduce novel approaches which combine traditional signal processing techniques with advanced deep learning algorithms to augment signal detection rates, improve event discrimination, and enhance the characterization of infrasound phases. The dissertation begins with the development of Cardinal, an open-source multifrequency array processing software for seismic and infrasound data. Cardinal integrates a custom convolutional transformer model to predict optimal array configurations across sequential frequency bands, providing a more comprehensive framework for array analysis. The following chapter presents the development of a multimodal deep neural network that leverages adaptive gating to fuse seismic spectrograms with Cardinal infrasound array processing results. This fusion improves earthquake-explosion discrimination within the Korean Peninsula, surpassing unimodal approaches. The final chapter presents a novel framework for automatic infrasound phase identification, leveraging the latent representations of a pre-trained autoencoder to cluster distinct phases without requiring labeled data. Collectively, these contributions illustrate the potential of combining deep learning with seismoacoustic array data to advance geophysical research and improve the effectiveness of global monitoring networks
Slave Servants and Saved Souls: Jesuit Evangelization and the Development of Afro-Catholic Mission, 1605-1654
No full textHistories of Christianity in Latin America root its development in the encounters of Iberian Catholic missionaries with indigenous populations. Beginning with 16th century evangelization and jumping to 20th century liberation movements, the study of Christianity in Latin America remains enigmatic for those who seek to understand the early Atlantic encounter with enslaved Africans. “Slave Servants and Saved Souls: Jesuit Evangelization and the Development of Afro-Catholic Mission, 1605-1654” illuminates this gap through an exploration of the early 17th century Jesuit mission for enslaved Africans in the cosmopolitan port city Cartagena de las Indias, shedding light on the initial processes of Christianization of enslaved Africans—often ignored yet vital to the development of Latin American religion and society.
In 1605, Alonso de Sandoval, S.J. established the first American mission dedicated to the material well-being and spiritual salvation of enslaved Africans. Recording its purpose in a monumental 1627 treatise, De Instauranda Aethiopum Salute, Sandoval elucidates the first comprehensive argument justifying the incorporation of enslaved Africans into the Church. He reveals a network of communication with slaves, traders, Jesuits, travelers, and merchants throughout the Atlantic, through which he and his pupil Pedro Claver, S.J. appraised the conditions of enslavement and errors of evangelization. Proposing serious implications for the Church’s failure to minister to Blacks, they together developed a system of accompanying the enslaved in their “miserable state.” While unprecedented for its time, their singular focus on salvation raises questions about the function of race, slavery, class, and religion in the early colonial processes of evangelization. Utilizing Sandoval’s treatise, Jesuit reports, and the documented testimony of Claver’s life collected by the Catholic Church, this project refreshes our understanding of the Catholic inculturation of enslaved Africans, contending that these innovative missionary methods, while exacerbating particular inequalities, nonetheless created the first sustained space of African engagement with Catholicism in the Americas, marking a pivotal development in the history of Latin American missions