VictorJuiz/thesis huggingface dataset:
Impediments to English speaking language learner acquisition of the Chinese perfective aspect : a quantitative analysis of four features of the post-verbal perfective "le"
Keywords: Humanities and Sciences.
East Asian Languages and Cultures.

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.
Improving Approach Noise Predictions for Aircraft Environmental Impact Models Using Deep Neural Networks and Sound-level Monitor Data
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

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.
Improving cosmological predictions : baryonic effects and analytical techniques in the effective field theory of large scale structure
Keywords: Humanities and Sciences.
Physics.

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
Improving energy efficiency for CGRA architectures
Keywords: Engineering.
Electrical Engineering.

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
Improving the oil production greenhouse gas emissions estimator (OPGEE) : validation, modeling, and system design
Keywords: Sustainability.
Energy Resources Engineering.

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.
Improving the privacy, scalability, and ecological impact of blockchains
Keywords: Engineering.

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
In touch with causation : the role of haptics in multisensory phenomenal causality
Keywords: Engineering.
Mechanical Engineering.

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.
The inequalities of well-being and income
Keywords: Humanities and Sciences.
Sociology.

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
Inference and learning in nonlinear latent variable models
Keywords: Engineering.

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.
Inferring social structure from digital trace data : applications to organizations and markets
Keywords: Business.

This thesis explores the use of large, unstructured, digital-trace data toward inferring aspects of latent social structure in one market context and one organizational context. It is structured as three separate papers (chapters), where, although starkly di erent in setting, topic, and methods, each exhibits this overarching theme in some way. In Chapter 1, the context is cultural product markets, where quality assessments are largely subjective; consumers are subject to rapid satiation upon repeated exposure to the same stimuli; and the forms and attributes of the products o ered are determined principally by cultural norms. Although organizational sociologists often test theories in these contexts as empirical settings, as is argued in that chapter, those theories are often impoverished by the coarseness of the data they typically work with. In Chapter 1's analysis, digital-trace data (in the form of crowd-sourced identifications of product attributes) is applied toward identifying implicit category spanning and characterizing the products to a richer extent than is typical in the literature. This ultimately allows more nuanced hypotheses of strategic positioning to be tested. In Chapter 2, a natural language processing model is developed for quantifying the extent to which an input text is "elaborate" vs "compact", i.e., for quantifying the text's linguistic code. The notion derives from a concept in sociolinguistics which asserts that the linguistic codes used between two speakers reveals the nature of the relationship between them, with regard to the subject matter at hand. Elaborated code is accessible to outsiders, and its use implies an assumption of a lack of shared contextual background, whereas compact code implies an assumption of insider status. The model combines multiple methods in an ensemble approach, and post training is validated using two distinct assessments. In Chapter 3 this model is applied on digital trace data of communication in an organizational setting, toward an investigation of gender-based returns to communication styles. The analysis revisits an empirical inconsistency in the literature, leveraging the trace data to explore consequences of speech patterns at scale. I  nd signals of distinct patterns of communication between men and women, which correlate with disparate outcomes in performance evaluations and hiring
Influence of Land Cover on Woody Plant Encroachment in the Northern Rocky Mountains: A Geospatial Analysis
Keywords: Woody plant encroachment
Conifer expansion
forest densification
High Divide
Northern Rocky Mountains
Text
Thesis

Over the last 100 to 160 years, shrubs and trees have been proliferating across grasslands in the American West as part of a global phenomenon called woody plant encroachment (WPE). WPE has resulted from changing climatic conditions and the removal of natural processes that have historically inhibited the spread of woody plants and leads to decreased forage availability for livestock, uncertain or negative changes to ecosystem services, and worsening wildfires. In this study, LANDFIRE Existing Vegetation Type data and tree-rangeland classifications from the 1950’s and 2019 were used to calculate the amount of conifer cover change that occurred in a variety of vegetation types and elevation zones over approximately 60 years across a portion of the High Divide region in Idaho. This was done in an effort to determine if and to what degree WPE is influenced by biophysical conditions and existing vegetation type. This data was also used to investigate whether conifer cover growth tends to densify existing forest stands or spread to previously unforested areas and how strong this preference is over relatively long time scales. Existing vegetation type, and specifically the amount of pre-existing forest cover, was found to be strongly related to conifer cover change. Varying amounts of pre-existing forest cover across elevation zone classes created a confounding effect that prevented determination of the impact of elevation on WPE without additional analysis. Finally, the analysis indicated that conifer cover increase was more likely to densify existing forest stands than to result in colonization of unforested areas which has substantial implications for increased wildfire severity risk across U.S. rangelands.
Initial Transit Effects of New York City's Ride-Hailing Congestion Tax
Keywords: Public Policy Program
Congestion pricing
Subways
Taxicabs
Ride-Hailing
Stanford University
Humanities and Sciences
Public Policy Program
Text
Thesis

In February 2019, New York City implemented a $2.75 tax on non-pooled ride-hailing trips, a $0.75 tax on pooled trips, and a $2.50 tax on taxi trips that pass through a designated congestion zone in Manhattan. This paper offers an initial empirical analysis of how this tax may have impacted ride-hailing, taxi, and subway usage. Using a log-linear difference-in-difference approach between trip counts within the congestion zone versus outside the congestion zone, I find that the congestion tax was associated with a statistically significant decline in relative ride-hailing and taxi usage. Declines were generally larger during the day than at night, and shared ride-hailing trips actually saw a relative increase within the congestion zone, indicating potential substitution between non-shared and shared trips. Meanwhile, the tax was not significantly associated with a relative rise in subway ridership in the congestion zone, suggesting that, on average, there was likely not a large substitution effect between these modes after the tax. Additional analysis of concurrent policy changes, such as New York City’s August 2018 ride-hailing license cap, will be required to more confidently separate the tax’s impact from other ongoing changes in the city.