VictorJuiz/thesis huggingface dataset:
Imagining the self : neuroscience and twentieth-century American poetry

What is the self? How do we know? And how do we imagine it in relation to the world? It is difficult to think of a poet without a fundamental investment in these questions. In a similar way, neuroscientists have long wrestled with the epistemological and metaphorical constructs that govern our understanding of the brain and the mind. Nevertheless, despite decades of arguments about the cultural influence of cognitive research, little scholarly attention has been devoted to the mutuality between the histories of neuroscience and twentieth-century poetry. This dissertation focuses on the cultural context that fostered the birth of modern neuroscience, the United States, to shed light on the extent and the significance of this cross-disciplinary nexus. Over a period that goes from the early decades of the twentieth century to the present day, it examines three interconnected instances of poetic engagement with neuroscientific discourse in the form of encounters between the work of a poet (W. H. Auden, John Ashbery, and Jorie Graham) and that of a scientist (Jakob von Uexküll, Oliver Sacks, and Antonio Damasio). In this way, a representative lineage of writers reframes the evolution of the poetic imagination along the timeline of the history of neuroscience, tracking the synergy between these seemingly distant realms and their explorations of the idea of the self. The result is a methodological framework wherein the questions of literary, scientific, and cultural historians do not simply overlap, but must be addressed in dialogue with one another, modeling a new perspective on our understanding of human nature and its place in the world.

Keywords: Humanities and Sciences.
Comparative Literature.
Advisor: Greene Roland
Jenkins Nicholas (Nicholas Richard)
Nemerov Alexander
Wittman Laura
Impact of Price Cap Regulation on Phone Call Costs for U.S. Inmates

It is well documented that incarcerated individuals in the U.S. prisons and jails pay an exorbitant fee for phone calling services. Regulatory agencies have suggested the monopoly power and contract structure of phone providers in jails and prisons as the reason for these high per-minute phone calling rates. In this thesis, I will analyze the effect of price cap regulation (PCR) imposed by the Federal Communications Commission on per-minute phone calling rates for incarcerated individuals on phone calls that are made between two states. To identify the impact of this rulemaking, I conduct a difference-in-difference approach to self-reported data submitted to the FCC from 2016-2021. I use this provider reporting data to examine if the rulemaking resulted in a decrease in the total cost of a phone call. I demonstrate that regulation did not significantly decrease the total cost of a standard interstate phone call. Rather, cost shifted up to a slight degree or stayed roughly the same. I hypothesize that this effect could be due to ancillary fees increasing along with the mandated per-minute rate decrease.

Keywords: Inmate Calling Service
Federal Communications Commission
Prisons
price cap regulation
Text
Thesis
Advisor: Rosston Gregory
Impact of the Office for Civil Rights’ Enforcement of Federal Law for Students Classified as English Language Learners in California and Texas

In the United States, the Civil Rights Act of 1964 serves a critical role in protecting and ensuring that all students, including students classified as English Language Learners (ELLs), have access to educational opportunities. This study explores how federal enforcement of civil rights laws and regulations protects students and their families from discrimination. In particular, the study addresses how the U.S. Department of Education’s Office for Civil Rights (OCR) enforces federal laws for ELLs in the states of California and Texas, and the impact of that enforcement on ELLs and their school communities. Through an analysis of four OCR complaint cases, the study explores how OCR investigates cases and reaches resolution agreements that influence school districts’ and states’ educational policies and procedures for ELLs. The findings of this study reveal that OCR follows a rigid protocol during the investigation process of complaints limited to the parameters of the legal standards in question. Yet, once OCR finds evidence of deficiencies and/or noncompliance, OCR conducts a thorough investigation and provides specific next steps and accountability procedures that impact ELLs and their school communities. This study provides policy recommendations on OCR’s complaint process and the agency’s role in providing resources and support to school districts as they build infrastructures to provide meaningful educational opportunities to ELLs.

Keywords: Public Policy Program
English learners
education policy
Office for Civil Rights
federal agency
Enforcement
U.S. Department of Education
Stanford University
Humanities and Sciences
Public Policy Program
Text
Thesis
Advisor: Koski William
Impediments to English speaking language learner acquisition of the Chinese perfective aspect : a quantitative analysis of four features of the post-verbal perfective "le"

The Modern Standard Chinese perfective aspect marker "le" is among the most widely used Chinese characters. Despite its ubiquitous presence, however, "le" remains difficult for even advanced language learners to master (c.f. Tong and Shirai 2016; Xu et al. 2019, inter alia). Previous studies have widely noted that English-speaking language learners often conflate "le" with the English past tense marker "-ed" (ibid). The present dissertation employs a novel methodological approach to investigate such errors. Whereas the predominant research approach (c.f. Duff and Li 2002; Yang 2016; Li et al. 2022, inter alia) examines language learner "le" production errors to probe for underlying principles, the current dissertation inverts this methodology by examining principles of "le" usage to probe for patterned language learner errors. In tandem, results suggest that many English-speaking language learners tend to incorrectly assume that the perfective "le" entails that the relevant situation has occurred before Speech Time, tend to incorrectly assume that the endpoint framed by "le" is inherently completive, tend to conflate the perfective "le" and the current relevancy marker "le" as a singular marker of past tense, and tend to be unaware of "le"'s syntactic and pragmatic restrictions. These findings highlight concrete, patterned "le" errors that may potentially be rectified through pedagogical intervention.

Keywords: Humanities and Sciences.
East Asian Languages and Cultures.
Advisor: Sun Chao
Hubbard Phil
Matsumoto Yoshiko
Improving Approach Noise Predictions for Aircraft Environmental Impact Models Using Deep Neural Networks and Sound-level Monitor Data

In order to minimize the impacts of noise on overflown communities by re-designing flight procedures, predictive techniques such as the Federal Aviation Administration’s (FAA) Aviation Environmental Design Tool (AEDT) have been developed and are widely used in the aviation community. Such tools are usually based on the simulation of flight trajectories and noise generation as well as propagation models of various fidelity levels. To more accurately describe the impacts of noise and changes in noise metrics that result from flight procedure redesigns, errors in the predictions must be sufficiently small so that changes to air traffic patterns do not result in unforeseen consequences. At Stanford University, we have developed the Metroplex Overflight Noise Analysis (MONA) system to measure, predict, and analyze the noise generated by aircraft overflights in complex multi-airport settings and to provide accurate and actionable data to inform consensus-building and, possibly, procedure redesigns in the context of FAA’s NextGen system. The capabilities of the MONA system include generating vast amounts of data (approximately 3,000-5,000 flights/day around Bay Area airports alone). This data contains (a) detailed flight trajectories and equipment combinations extracted from ADS-B data feeds, (b) noise levels at several locations measured by a network of sound-level monitoring (SLM) stations, and (c) predictions of the noise produced by the aircraft/engine combinations computed by automated analyses using AEDT. Over an entire year, millions of predictions and measurements are collected and indexed resulting in curated datasets for further analysis. This paper explores an application of these datasets, using machine learning (ML) techniques to create models of the noise generated by aircraft on approach trajectories to San Francisco International airport (SFO). Using deep neural net ML architectures we show that the errors in the mean for Lmax noise metrics are reduced to within the margin of measurement error of the SLMs.

Keywords: Recurrent Neural Network
Airplanes > Noise
Aviation Environmental Design Tool
San Francisco International Airport (Calif.)
Flight Trajectory
Federal Aviation Administration
Machine learning
Aeronautics, Commercial
Statistical Distributions
System Wide Information Management
Text
Thesis
Advisor: Alonso Juan
Improving cosmological predictions : baryonic effects and analytical techniques in the effective field theory of large scale structure

The Effective Field Theory of Large Scale Structure (EFTofLSS) is a powerful framework for making accurate predictions of the distribution of matter in the universe on large scales. In this thesis, we have worked in developing the theory in two aspects. First, we studied how to incorporate complex astrophysical processes such as star formation or AGN into the theory, and showed that it is possible to do so, even if not knowing the complete details of the physical processes. We also showed the influence of these baryonic effects on the lensing power spectrum. Second, we used analytical methods borrowed from Quantum Field Theory to calculate the non-linear corrections in the theory. This allowed to do cosmological inference not only using the one-loop power spectrum, but using the one-loop bispectrum as well

Keywords: Humanities and Sciences.
Physics.
Advisor: Senatore Leonardo
Wechsler Risa H. (Risa Heyrman)
Abel Tom G.
Silverstein Eva
Improving energy efficiency for CGRA architectures

The rise of edge computing has resulted in a growing need for executing computationally intensive tasks within strict energy constraints. ASICs are typically used to achieve high performance and energy efficiency, but at the expense of hardware flexibility. Given the fast-paced evolution of edge applications, there is a pressing requirement for flexible yet energy-efficient architectures that can keep up with the latest trends. Traditionally, reconfigurable computing devices have used processors, where instructions configure the processor in each clock cycle to perform the desired operation. More recently, researchers have explored using Field Programmable Gate Arrays (FPGA), and Coarse-Grained Reconfigurable Architectures (CGRA), which configure the hardware in space (and not time) to provide programmable computing devices. In the space of spatial programmable architectures, CGRAs are a promising alternative to FPGAs due to their higher energy efficiency that comes from operating at a word-level granularity in logic and routing. This thesis introduces two methods to improve the energy efficiency of CGRAs. First, low-access-cost distributed memories are introduced into the processing elements. While similar to conventional register files, these memories are optimized to work with applications with streaming data, so they "push" the data to the computing elements. These memories help improve the energy efficiency of Deep Neural Network (DNN) applications on the CGRAs. The second method aims to improve the energy efficiency of CGRAs by introducing low-overhead fine-grained power domains to better optimize both active and idle power. Both these techniques have been integrated into a taped out SoC with a CGRA optimized for Deep Learning and Computer Vision applications

Keywords: Engineering.
Electrical Engineering.
Advisor: Horowitz Mark (Mark Alan)
Hanrahan P. M. (Patrick Matthew)
Raina Priyanka (Assistant Professor of Electrical Engineering)
Improving the oil production greenhouse gas emissions estimator (OPGEE) : validation, modeling, and system design

The oil and gas industry is responsible for significant upstream greenhouse gas (GHG) emissions, amounting to approximately 5.1 billion tonnes. This research, employing the Oil Production Greenhouse gas Emission Estimator (OPGEE), seeks to comprehend the intricate landscape of these emissions. One key discovery is the vast discrepancy in Carbon Intensity (CI) across different countries and operations. Interestingly, high CI countries can have emissions quadruple those of lower CI countries, emphasizing the importance of this metric. A critical examination of OPGEE's output versus actual emissions shows a disparity. This arises from OPGEE's reliance on default literature values rather than specific field data. In a detailed exploration of this issue, it's found that nearly half of the discrepancies could be attributed to this reliance. Additionally, variations in system boundaries and accounting methods account for another 30% of the differences. This investigation paves the way for refining emission estimations, an effort augmented by the study of potential mitigation paths like the integration of solar photovoltaics into oil and gas operations. Further enriching this research is the introduction of a novel "Pattern-Based Modelling" technique. This model provides a mechanism to history match and project future emissions for vast thermal enhanced oil recovery operations. Two in-depth case studies - the well-documented Kern River oilfield in California and the emerging Mukhaizna oilfield in Oman - showcase this model's utility. A significant advancement in this study is the evolution of OPGEE from an Excel-based platform to a more dynamic Python-based system, termed OPGEE v4. This transformation addresses computational limitations and introduces a suite of new features, increasing efficiency and precision. Comparative studies between OPGEE v3 and v4 highlight the stark improvements made in the latest version, emphasizing its readiness to handle complex global oil field datasets. In essence, this dissertation offers a three-fold contribution: a rigorous validation of OPGEE against actual field data, the pioneering of "Pattern-Based Modelling" for predicting entire field lifespan emissions, and the innovative transformation of OPGEE to a Python-centric platform, enhancing its capabilities.

Keywords: Sustainability.
Energy Resources Engineering.
Advisor: Brandt Adam
Azevedo Ines
Kovscek Anthony
Improving the privacy, scalability, and ecological impact of blockchains

Despite the excitement around them, Blockchains today still suffer from major limitations. Popular blockchains have high transaction fees, limited privacy, and substantial ecological impact. These issues are largely due to the fact that in decentralized blockchain systems, each action performed by one party gets seen and verified by all parties in the network. In this dissertation, we develop solutions to these limitations. They rely on a common proof paradigm, in which a prover can generate a certificate that the actions it performed were in accordance with a set of rules. Importantly, the certificates can be highly efficient and privacy-preserving, even if the actions are not. We develop four such proof systems, each tailored to resolve one of the limitations of blockchains. Bulletproofs, a zero-knowledge proof system that is designed for private blockchain transactions. HyperPlonk, an efficient SNARK that enables outsourcing the processing of entire blocks of transactions. Verifiable Delay Functions, a key component for ecologically friendly blockchains, and ProtoStar, an incrementally verifiable proof system designed for building efficient Verifiable Delay Functions and succinct blockchains. These tools are not merely theoretical but have already found practical applications and are securing systems worth billions of dollars

Keywords: Engineering.
Advisor: Boneh Dan
Mazières David (David Folkman)
Wootters Mary
In touch with causation : the role of haptics in multisensory phenomenal causality

Humans use their senses to explore and learn about interactions and relationships between objects, such as cause and effect. Our sense of touch -- haptics -- is core to how we learn about and interact with our environment. For example, to strike a match, humans balance the force and speed that they apply. However, little research has studied how haptic feedback shapes causal perception in concert with information from other sensory modalities, despite its key role in forming our perception of the world. In this thesis, I address the question of how humans utilize haptic information in causal judgment tasks. I use a combination of haptic system design, psychophysical experiments, and generative modeling. First, I demonstrate that haptic information plays an important role in shaping causal perception through a series of psychophysical human experiments. Second, I introduce a computational model that can explain how people form these perceptions through the integration of spatiotemporal evidence across sensory modalities. Finally, I explore the relationship between causal perception and the realism of sensory information, specifically considering what information is contained within sensory cues, through an additional psychophysical user study. Throughout the work, I highlight the underlying importance of haptic information within causal perception and interactions as well as in terms of providing insights into what processes people are using to make these choices. My findings will inform the design of future devices and systems that utilize human perception and sensory integration.

Keywords: Engineering.
Mechanical Engineering.
Advisor: Follmer Sean
Gerstenberg Tobias
Okamura Allison
The inequalities of well-being and income

The past five decades have witnessed a cultural movement towards greater consideration of well-being and mental health. Hallmarks of this cultural change are reflected in media discourse as well as a number of disparate social trends, including the growth of mental health and coaching industries, management practices, and shifting grounds for resource contention in organizational, legal, and political spaces. Does this change in our cultural logics impede or augment the pursuit of a fair and just society? As I argue in this dissertation, the answer depends on both how well-being and income are related, and how the lay public perceives this relationship. The first study reviews some of the key challenges to estimating the causal effect of income on mental well-being, and uses a novel approaches to estimate income effects in two US datasets. The findings suggest that the income-wellbeing link varies over the life course, with psychological well-being becoming more tethered to material conditions in older adulthood. These findings underscore the possibility that the income-wellbeing link may only roughly map on to disparities in material conditions of living. The second study investigates a potential reason for divergence between well-being indicators and income. Using a longitudinal case study of two types of bereavement events, I demonstrate that mental well-being can become decoupled from material conditions due to income differences in psychological reactivity to negative shocks. In the third paper, evidence from an experimental study indicates that certain arguments about the consequences of income for unhappiness can convince the public to increase support for income redistribution. Taken together, this work suggests that the well-being movement is a double-edged sword, making us more attuned to the sufferings of others, while simultaneously blurring our focus away from abject material suffering

Keywords: Humanities and Sciences.
Sociology.
Advisor: Freese Jeremy
Grusky David B.
Torche Florencia
Inference and learning in nonlinear latent variable models

A core goal of modeling is to help us understand the world around us, but often the phenomena we wish to model are only observed indirectly. For example, we often detect black holes via the gravitational effects they have on surrounding objects. Unobserved phenomena are commonly modeled using latent variables that are statistically related to observed variables, but never directly observed themselves. These latent variable models are a powerful formalism that can enable parsimonious and interpretable representations of data, but are difficult to use, especially when the relationships between variables are complex. This thesis develops techniques for fitting latent variable models where the dependencies between variables are parameterized by nonlinear functions such as deep neural networks or nonlinear differential equations. The nonlinear dependencies make analytic methods intractable, and the main thrust of this thesis is in extending sampling algorithms from the Monte Carlo literature to work with deep generative models. In particular, this thesis focuses on modeling sequential data such as neural voltage traces or speech audio. First, I introduce FIVO, a method for fitting nonlinear sequential latent variable models using filtering sequential Monte Carlo, and use it to improve models of speech audio and piano sheet music. Then, I develop a smoothing-based extension of FIVO called SIXO that successfully fits biophysical models of neural membrane potential. Next, I introduce NAS-X, an extension of SIXO that works with discrete latent variables. Finally, I develop methods for fitting models with embedded sampling algorithms and draw connections to energy-based modeling. These methods establish new standards for inference and learning in nonlinear latent variable models. For example, in the Hodgkin-Huxley model of neural membrane potential, NAS-X and SIXO achieve a 32-fold improvement in inference log-likelihood over previous methods. Improved inference performance results in downstream gains in parameter learning, and enables fitting latent variable models based on nonlinear differential equations with hundreds of parameters. Overall, this thesis extends Monte Carlo algorithms to bring powerful models to bear on tough problems in sequence modeling.

Keywords: Engineering.
Advisor: Liang Percy
Linderman Scott
Ermon Stefano
Fox Emily