VictorJuiz/thesis huggingface dataset:
Algorithms and execution traces
Keywords: Humanities and Sciences.
Philosophy.

Part I surveys the current literature around defining algorithms. I consider two prominent approaches: the traditional approach (arising from computability theory) and the abstract approach (generalising the traditional approach). I argue that both approaches have shortcomings stemming from attempting reconcile algorithms understood as "effective procedures" with algorithms understood as "distinct methods" for solving problems. I conclude that current attempts to analyse algorithms pay too much heed to the first of these perspectives and propose giving an account of algorithms which fully embraces the second. Part II develops such an account, introducing the framework within which the remainder of the dissertation is developed. I analyse algorithms in terms of their execution trace setsm avoiding any commitment to a particular model of computation. Part II applies the framework to issues around algorithmic identity, level relativity, finiteness and determinism. Part III takes a formal turn, developing the framework from Part II with mathematical rigour. I define algorithmic trace sets, consider abstract implementation, and reconcile the trace set account with the effective procedures perspective by reconstructing computability theory
Algorithms for high-performance brain-computer interfaces
Keywords: Medicine.

Brain-computer interfaces (BCIs) are a set of technologies that enable people to interact with devices by directly translating neural activity into command signals. Noninvasive methods such as EEG and fMRI allow minimal-risk access to brain signals but are limited by low spatial and temporal resolution, respectively. By contrast, intracortical BCIs (iBCIs) benefit from unparalleled spatial and temporal resolution recordings of local neuronal ensembles, resulting in some of the highest-performance communication systems to date. While promising, several key challenges remain for wider adoption of these technologies. First, iBCIs in particular suffer from signal instability that cause performance degradation across time. A key challenge for clinical translation of these iBCI cursor systems is ensuring robust, long-term control for end users without manual retraining. Second, while these technologies have enabled point-and-click based typing interfaces approaching 8-10 words per minute, an ideal interface would match the bandwidth of conversational speech (roughly 150 words per minute). Third, intracortical systems require brain surgery, which has inherent risks due to the invasive nature of the procedure. Developing noninvasive approaches for silent speech decoding may therefore be more suitable for a subset of patients. In this work, we discuss three advances that tackle these key areas by leveraging modern machine learning methods alongside high signal-to-noise (SNR) recording systems. First, we present an unsupervised recalibration procedure for improving cursor BCI robustness. Using data from our clinical trial participant, we highlight the extent and timescales of neural feature drift. We demonstrate how existing state-of-the-art procedures are seemingly ill-equipped to deal with long-term drift using both offline data as well as simulation models. We then introduce a novel procedure that leverages task structure to recalibrate a system automatically, and demonstrate superior performance in-silico and in closed-loop in our participant. Second, we lay the groundwork for intracortical speech BCI efforts by prototyping a system in an offline manner using microelectrode arrays in dorsal motor cortex. We demonstrate that, despite being located in a nontraditional brain area for speech decoding, our arrays provide high SNR compared to existing approaches. Third, we leverage skin-like flexible electronics and deep learning for silent speech decoding from electromyography (EMG) signals
Algorithms, statistical models, and their applications to real-world networks and choice systems
Keywords: Engineering.

With the rise in popularity of social media and e-commerce platforms, discrete math is at the heart of our online experiences, playing a front-end role in the recommendation of what to click, watch, or buy, or who to follow or friend, as well as a back-end role in data storage, access, and transfer. This thesis focuses on two such discrete math problems -- the modeling of discrete choices, and the efficient storage of massive graphs. On the latter, we study the problem of balanced graph partitioning, a problem of paramount importance in domains such as graph clustering, community detection, distributed graph computation, and also efficient data storage. Chapter 2 introduces a taxonomy of existing algorithms for finding a balanced partitioning of an input graph, and pioneers a novel class of prioritized restreaming algorithms, showcasing their superior performance in minimizing the edge-cut objective over industry state-of-the-art methods. We investigate the former, the statistical modeling of choices and rankings, in the context of social choice, eg. school choice and ranked-choice-voting. Such models are used for explanation of user behaviors, forecasting demand, or counterfactual analysis. Chapter 3 unveils the significance of within-agent taste-heterogeneity in the assembly of ranked preference lists, giving rise to the concept of rank-heterogeneous preference models. Applied to real-world school-choice data, these models demonstrate enhanced predictive accuracy and a better fit to observed ranking patterns than commonly-used rank-homogeneous strategies. Chapter 4 further extends the frontier by drawing attention to an under-studied task in the ranking modeling literature -- the modeling of partial orders. Specifically, this task is concerned with the development of models whose sample space is the space of orderings of any length of n items, a space that subsumes S_n. The work develops two classes of models, composite and augmented, and demonstrates their potential for simultaneously capturing preferences and length distributions within real-world partial ranking data from school choice and ranked-choice voting contexts.
Aligning law, policy, and machine learning for responsible real-world deployments
Keywords: Engineering.

Recent advances in machine learning (ML) have seen systems achieve human-like performance in specific tasks, especially in the realms of sequential decision-making and foundation models. Foundation models, powered by Transformer architectures, offer unparalleled abilities to generalize from vast text data, while innovations in reinforcement learning have allowed for mastering of tasks from playing Go to controlling nuclear fusion reactors. Consequently, ML is swiftly integrating into critical sectors like law, healthcare, and government, promising potential public benefits but simultaneously introducing intricate ethical, legal, and policy challenges. As ML deployments grow in real-world scenarios, exploring their interplay with laws, policies, and societal norms becomes paramount. This thesis seeks to address this nexus of ML and legal-policy environments. It endeavors to align AI's potential for public good while ensuring its safe and equitable deployment by bringing together the fields of AI, law, and policy. The first part of this thesis explores machine learning for public benefit. First, it investigates the application of foundation models in legal contexts, examining their strengths and potential pitfalls. Second, it delves into the challenges and opportunities of employing sequential decision-making algorithms in law and public policy, highlighting the unique methodological challenges posed by these domains---as well as potential opportunities for positive impact. Finally, it presents an in-depth case study from a collaboration with the Internal Revenue Service (IRS), introducing the optimize-and-estimate structured bandits problem and offering novel solutions with profound implications for tax compliance and revenue generation. However, realizing ML's societal potential necessitates stringent safeguards, which the second part of the thesis examines. First, it provides an in-depth study of the alignment of foundation models data filtering rules with human-crafted norms in legal settings, leveraging the vast body of legal decision-making as a source of rule-based alignment of machine learning filtering rules. Second, it discusses the dual-use nature of foundation models and introduces a novel approach of task blocking, producing models that are inherently resistant to repurposing for potentially harmful tasks. This work pioneers an interdisciplinary roadmap, blending ML research with legal and policy insights with the aim of fostering responsible real-world machine learning deployments for public good.
Allosteric communication, assembly, and nucleotide hydrolysis in group II chaperonins
Keywords: Medicine.

Protein function is dependent upon adoption of a native 3-D configuration whilst avoiding toxic conformations. Some proteins can fold spontaneously; however, the complexity of the proteome requires cellular cofactors called chaperones to ensure problematic proteins are natively folded. Chaperonins are a class of molecular chaperones that are universally conserved in all domains of life and contribute to the cellular proteostasis toolkit. They are 1-MDa complexes composed of two rings that undergo a dramatic conformational change with ATP hydrolysis to form an inner folding cavity. Chaperonins have significantly diverged in architecture, topology, and client repertoire so they are divided into two classes: group I and group II chaperonins. Group I chaperonins exist in prokarya whilst group II chaperonins are found in eukarya and archaea. All chaperonins help to orchestrate ATP-dependent client protein sequestration and refolding through encapsulation in the central folding cavity. This thesis is centered on enumerating the mechanism of a group II chaperonin found in M. Maripaludis (MmCpn), an archaeal methanogen. Biochemical and biophysical interrogation of structural moieties, chiefly the c-termini, were found to contribute to ATP hydrolysis, substrate re-folding, and complex integrity. New structural elements such as interfacial methionine fingers and electrostatic contacts were observed to undergo novel conformations, yielding new candidates for studying group II chaperonins in general. Electrostatic disruption of the c-termini allowed for dissection of multimeric states, including the long elusive single-ring species which could serve as a useful tool for understanding chaperonin biogenesis and function
An Ecology of Quotation and Quotability: Discourse in Annie Dillard's Pilgrim at Tinker Creek
Keywords: English
Dillard, Annie
Pilgrim at Tinker Creek (Dillard, Annie)
Transcendentalism
Transcendentalism in literature
Citationality
Quotability
Quotation
Thoreau, Henry David, 1817-1862
Emerson, Ralph Waldo, 1803-1882
Emerson, Mary Moody, 1774-1863
Bakhtin, M. M. (Mikhail Mikhaĭlovich), 1895-1975
Foucault, Michel, 1926-1984
Digital humanities
Commonplace books
Fuller, Margaret, 1810-1850
Ecocriticism in literature
Ecocriticism
Semiotics and literature
Dialogism (Literary analysis)
Nonfiction
Feminist literary criticism
Creative nonfiction
Nature writers
Ecoliterature
Mysticism
Mysticism in literature
Lorde, 1996-
Text
Thesis

Annie Dillard’s Pilgrim at Tinker Creek uses quotation and quotability to create a system of discourse founded in the messy ecological processes of consumption, digestion, and decomposition. Dillard quotes heavily from a myriad of sources, creating her narrating self as the processor and digester of a dialogic tangle of citationality. This overwhelming surge of discourse into the self leads to Dillard’s rich transcendence. At the same time as she quotes heavily, she unexpectedly denies easy quotability to her own text. Her prose is captivating but disrupts itself, preventing the reader from excising text out of context. This forces readers into more immersive and digestive meaning making and rejects the traditional Transcendentalist ethic of individualism, domination, and quotability. This process of discourse—of consumption through quotation, and decomposition and reconstruction through the intentional process of meaning-making beyond quotability—is an iterative, brutal, and ecological semiotic system that rejects Emerson’s clean microcosmic semiosis. Dillard's own observations of nature demonstrate this very ecological semiotic system of a tangled food web, with flesh and energy always being torn, eaten, destroyed, and reused. Dillard models a productive and discursive way to achieve transcendence and create meaning.
Anaesthetics : popular music and the flight from feeling
Keywords: Humanities and Sciences.
Music.

This dissertation argues that themes of self-dissolution, emotional emptiness, and empathetic absence are becoming increasingly central to contemporary English-language popular music. It historicizes this phenomenon by relating popular music's "flight from feeling" to late-twentieth and early-twenty-first century transitions in popular music production, drug culture, media, and labor. And it theorizes the aesthetic paradoxes that emerge when themes of nonfeeling are taken up by popular song, often understood as an art form preoccupied with describing, evoking, and producing emotion. This dissertation's three central chapters explore three registers of nonfeeling: dissociation (deprivation of sense of self), numbness (deprivation of sensation or emotion), and coldness (deprivation of empathy or affect). Its first chapter takes up three recent pop, hip-hop, and hyperpop songs that not only describe dissociation in their lyrics but also enact it texturally and formally, theorizing these songs' performances of self-dissolution by relating them to changing music production norms and to the alleged "death of the subject" in postmodern cultural production. Its second chapter traces a history of popular music's engagements with the word "numb," beginning with Pink Floyd's 1979 "Comfortably Numb" and concluding with Em Beihold's 2022 "Numb Little Bug." Its third chapter explores coldness as a metaphor, a sound, and an affect in popular music from the 1970s to the 2020s, arguing that although coldness once represented a term of condemnation directed at unfeeling lovers, it has increasingly become a value that popular songs attribute to their own lyric subjects. Its conclusion reflects on the uses and pleasures that nonfeeling offers these songs and that these songs offer their listeners, asking under what circumstances nonfeeling might come to feel good.
Analyses of market structures
Keywords: Business.

This paper contains three chapters which each develops a new economic model to investigate a question related to market structure. The first chapter develops a search-theoretic model of competing platforms that profit from targeted advertising to understand the impacts of policies, such as those on consumer data and platform interoperability, on this market's function. The second chapter, based on joint work with Darrell Duffie, investigates the welfare implications of the growing fragmentation of trade across lit exchanges. The third chapter investigates the optimal design of a market for trade of a financial asset
An analysis of projection-based reduced order models and their application to supersonic flows
Keywords: Engineering.
Aeronautics and Astronautics.

Computationally expensive physics simulations remain necessary and challenging in the engineering community. Applications that require many queries of the high dimensional model are particularly expensive, including design optimization and uncertainty quantification. Time-critical applications, such as model predictive control, cannot currently rely on high-fidelity simulations due to the time and storage requirements. Projection-based reduced order models (PROMs) offer a promising solution by approximating the full-order simulations while significantly reducing the computational complexity. This dissertation explores projection-based reduced order models (PROM) in fluid dynamics simulations. The objective is to develop a tool for analyzing and applying PROMs in computational fluid dynamics (CFD) problems. The PROM framework is implemented in the SU2 solver using the Least-Squares Petrov-Galerkin equations. Using this tool, a novel hyper-reduction method is investigated. Additionally, a comparative analysis between PROMs and a nonlinear autoencoder model using latent space systems identification is presented. The comparative analysis guides the selection of appropriate modeling approaches for different flow scenarios that contain shocks
Anomaly detection in X-ray physics
Keywords: Engineering.

Anomaly detection is an important task for complex systems (e.g., industrial facilities, manufacturing, large-scale science experiments), where failures in a sub-system can lead to low yield, faulty products, or even damage to components. These systems produce too many signals for the system operators to monitor in real time. In addition, many of these signals are only interpretable by subject matter experts with years of experience. As a result, changes in system performance can require time-intensive consultations with experts to identify the underlying problem. For this reason, despite the wealth of data that these complex systems produce, labeled anomalies are typically rare (or even nonexistent) and expensive to acquire. Since there are no labels, the training data will inevitably be contaminated by anomalies. In Chapter 2, we introduce a fully automated, unsupervised beam-based diagnostic method for RF station fault detection at SLAC's Linac Coherent Light Source (LCLS). Our key contribution is the combination of high fidelity beam position monitoring data with bandwidth limited (and noisy) radio-frequency (RF) station diagnostic data to achieve highly accurate, nearly real time fault identification. In Chapter 3, we introduce coincident learning for anomaly detection (CoAD) and an unsupervised metric \hat{F}_\beta, that assumes and exploits that anomalies are coincident in two different slices of the input data. We present theoretical properties of our metric and demonstrate its performance on four data sets. In Chapter 4, we focus on a broader task of detecting any anomalous beam behavior at LCLS and develop the unsupervised Resilient Variational Autoencoder (ResVAE) model. We illustrate its resilience to contaminated training data and apply it to identifying anomalies in accelerator status at LCLS . In Chapter 5, we switch gears and address a problem in neural network pruning. We introduce a novel channel pruning approach for convolutional neural networks, called Soft Masking for cost-constrained Channel Pruning (SMCP), that is particularly suitable when pruning a large fraction of the network's channels. The core of our approach relies on regularly rewiring the network sparsity, through soft masking of the network weights, to minimize the accuracy drop for large pruning fractions. Our method outperforms prior works on both the ImageNet classification and PASCAL VOC detection datasets.
Applications of absorption spectroscopy to the study of hypersonic flow conditions in ground test facilities
Keywords: Engineering.
Mechanical Engineering.

The hypersonic flight regime is a hostile one, characterized by significant thermal loading on vehicle structures. In order to fortify vehicle platforms and mission architectures employing these high speeds, experimental testing is crucial to better characterize the thermochemical processes prevalent behind the strong shock waves emblematic of hypersonic flight. As part of this effort, ground test facilities fulfill an important niche. These facilities simulate realistic hypersonic flight environments by generating high-speed, high-enthalpy flows in controlled environments. In this dissertation, laser absorption spectroscopy is leveraged to study several different hypersonic flows or relevant high-temperature conditions in three different types of ground test facilities. Spectroscopic targets include nitric oxide (NO), atomic nitrogen (N), atomic oxygen (O), molecular nitrogen (N2), and carbon monoxide (CO). The monitoring of these species permits the assessment of hypersonic-relevant phenomena, including chemical non-equilibrium, vibrational freezing, and electronic excitation. Three major experimental efforts underlie this dissertation. The first is the characterization of the hypersonic flows in the Caltech T5 Reflected Shock Tunnel. Spectroscopic measurements allowed for the determination of freestream temperature, composition, and flow velocity. These measurements were then extended into near-body flows behind the shock waves generated by cylinder and wedge test articles. The second study is the characterization of the NASA Ames Interaction Heating Facility (IHF), a 60 MW Arcjet used to study thermal protection systems (TPS). Spectroscopic investigation targets the determination of stagnation enthalpy, a crucial operational parameter. Finally, the third study utilizes an in-house Stanford shock tube to investigate nitrogen-chemistry electronic excitation and ionization, high-temperature processes relevant to lunar return trajectories.
Applications of cooperative game theory to interpretable machine learning
Keywords: Humanities and Sciences.
Statistics.

Model-agnostic feature importance measures are central to the task of demystifying opaque or "black-box" machine learning models. The proliferation of such models within high-stakes decision making settings such as healthcare or banking necessitates the development of flexible and trustworthy approaches to the problem. With no ground truth feature importance to compare to, competing methods provide contrasting approaches and/or philosophies often with a claim of superiority. Some of the most popular recent approaches are adaptations of tools from cooperative game theory used in reward or cost sharing problems. In this document, we report on recent advances among such feature importance methods. In particular, we discuss a "data-centric" cohort-based framework for model-agnostic local feature importance using Shapley values. We propose a primary importance measure and explore several adaptations of that method better suited for specific use cases or data regimes. We analyze the properties and behaviors of these methods and apply them to a broad range of synthetic and real-world problem settings including voter registration and recidivism data. We then propose and discuss new methods for local importance aggregation and feature importance evaluation
Applications of microlocal analysis in general relativity and inverse problems
Keywords: Engineering.

This thesis consists of three parts: the first part serves as a brief introduction to microlocal analysis; the second part is the application of microlocal analysis in general relativity; the third part is the application of microlocal analysis in inverse problems. In the first part, we introduce various pseudodifferential operator algebras we will use as tools in our applications. These algebras include well-known ones such as classical, scattering and b￾algebras, and also relatively new ones such as 1-cusp (in fact, its semiclassical foliation version) and the one we construct for our wave propagation on Kerr(-de Sitter) spacetimes. In the second part, we prove a propagation estimate with arbitrarily small extra loss compared with the classical non-trapping propagation estimates using the algebra we constructed in Chapter 3. One of the major applications of estimates of this type is to linearized Einstein equations on the Kerr(-de Sitter) spacetimes. In the third part, we consider the injectivity of the X-ray transform on one forms and 2-tensors on asymptotically conic manifolds. This uses the algebra developed by Andras Vasy and Evangelie Zachos. This question is motivated by the boundary rigidity problem of asymptotically conic manifolds, we expect this injectivity result of the X-ray transform to be a linearization of it and serve as a key ingredient in the proof of this rigidity problem