VictorJuiz/thesis huggingface dataset:
Toward a predictive composite Higgs model : a dual approach in 5D

An interesting possibility of explaining the electroweak symmetry breaking is to consider the Higgs boson as a composite particle, i.e. a pseudo-Goldstone boson of a new strong dynamics. Although it is hard to make quantitative predictions under a strong interaction, we can use its dual description in 5D and compute physical observables. In this thesis, I introduce gauge-Higgs unification models in warped 5-dimensional space as a setting for composite Higgs models. Gauge bosons and fermions are in the bulk and the Higgs boson is identified with the 5th component of a gauge field. We formalize the computation of the Coleman-Weinberg potential of the Higgs field and present a simple, general formula. Using this tool, we consider a specific model with SO(5) × U (1) global symmetry and introduce a new fermion multiplet, which competes with the top quark and creates a little hierarchy characterized by a small parameter s = v/f . Using a Green's function method, we compute the properties of the model systematically as a power series in s. We discuss the constraints on this model from the measured value of the Higgs mass, the masses of top quark partners, and precision electroweak observables. Finally, we compute fermion pair creation cross sections in general SO(5) × U (1) models. Our Green's function method provides an analytic understanding of the new physics effects across the parameter space. We find that the predicted effects will be visible in precision measurements and different models of b quark mass generation can be distinguished by these measurements already at 250 GeV in the center of mass.

Keywords: Toro, Natalia degree committee member.
Physics.
Advisor: Peskin Michael Edward
Graham Peter (Peter Wickelgren)
Toward faster and more data-efficient computational biology

Limited computing power and limited sample size are two central challenges in computational biology. While the next-generation sequencing technology offers a highly scalable way for measuring genomic information, the size of the data for a single biological sample can be as large as tens of gigabytes and presents a tremendous challenge for data storage and processing. At the same time, the data is usually ultra-high-dimensional, e.g., tens of thousands of genes or millions of mutations, requiring a large number of samples for effective inference. In this thesis, I address these two challenges by designing fundamentally better algorithms for several key applications. First, we address the limited computing power problem by presenting two works of algorithm acceleration using a strategy that we call adaptive Monte Carlo computation. Such a strategy first converts the deterministic computational problem into a statistical estimation problem and then accelerates the process by adaptive sampling. Then we move on to the limited sample size problem and consider two aspects, i.e., optimizing the experimental design and borrowing information from other datasets. For the former, we present work on the optimal experimental design for single-cell RNA-Seq. For the latter, we consider multiple hypothesis testing using side information and dimensionality reduction guided by additional datasets.

Keywords: Electrical Engineering.
Advisor: Tse David 
Montanari Andrea 
Zou James
Towards Broad-Coverage AMR Parsing with Dynamic Continuized CCG

Dynamic continuized CCG is a method of compositional parsing that allows indefinites to take scope out of scope islands in a predictable and formalized manner. For example, it allows us to achieve the correct reading of the sentence "If one of my relatives dies, I'll inherit a fortune" (wherein the speaker is identifying a specific relative). At the same time, it prevents the universal from taking wide scope in the sentence "If all of my relatives die, I'll inherit a fortune" - if it did, we would get the reading that as long as any of my relatives die, I will inherit the fortune.
Unfortunately, previous work in dynamic continuized CCG (DC-CCG for short) has not provided a way to allow it to learn from datasets. Rather, it can only be used with a strictly specified lexicon, making it not useful for practical purposes. However, if we were able to reverse the rules of DC-CCG, we would be able to implement lexicon generation, and thus be able to learn from large datasets. As such, this thesis will demonstrate how these rules can be reversed, and how this extended system can be implemented in an existing semantic parsing framework.

Keywords:  Symbolic Systems Program
semantic parsing
ccg
amr
dynamic continuized ccg
symbolic systems
Thesis
Advisor: Potts Christopher
Manning Christopher
Towards Photon-Mediated Creation of Twin Fock States in an Optical Resonator

This thesis describes the development of experimental tools for preparing many-particle entangled states of cold atoms in an optical cavity. We focus on the illustrative goal of preparing twin Fock states, which requires two key components: coherent photon-mediated interactions and sensitive state detection. We report the development of a phase-locked laser system that provides high power and tunability for driving interactions. Spin-exchange interactions driven by this new laser system demonstrate enhanced coherence compared with past experiments. We also report the design and characterization of avalanche photodetectors for quantum-limited detection, and we present a theoretical analysis of atomic state detection by Faraday rotation. These developments move us towards being able to generate and detect the highly entangled twin Fock state, which ultimately offers the potential for precision sensing near the fundamental Heisenberg limit.

Keywords:  Program in Engineering Physics
Twin Fock State
Photon-Mediated Interactions
Optical Resonator
Cavity Quantum Electrodynamics
Thesis
Advisor: Schleier-Smith Monika H.
Fan Jonathan A.
Towards seismic resilience : a comprehensive approach to modeling earthquake consequences from regional hazard to household well-being

Understanding disaster risk is an integral aspect of disaster risk management, and it is essential for efficient risk reduction investments. Regional disaster risk assessment frameworks, including seismic risk analysis, are commonly used to quantify disaster risk and inform risk reduction strategies. However, few of the frameworks are able to quantify the full extent of disaster consequences especially as related to household recovery and the disproportionate effect on the socioeconomically vulnerable population. This dissertation proposes a set of new computational methods along various steps of the seismic risk assessment process and lays out the overall methodology for quantifying disaster impacts on the society considering the entire recovery process. This is achieved by integrating and leveraging analytical tools from other fields, namely, geostatistics, real estate investment, macroeconomics, and welfare economics. The dissertation first proposes a framework for simulating spatially distributed ground motions at multiple intensity measures. The framework allows modelers to rapidly simulate multiple realizations of earthquake ground motion intensity, which are required for regional risk assessments. Furthermore, the research investigates post-earthquake decisions on damaged commercial buildings considering the influence of real estate market conditions. An investment decision model for individual buildings that unifies seismic performance estimation and real estate investment analysis is proposed and applied to four commercial buildings in the Los Angeles area. Results suggest that the state of the real estate market has a significant influence on decisions and recovery trajectories of commercial buildings. Finally, this dissertation integrates regional probabilistic seismic risk analysis, which quantifies asset losses, with an economic recovery model to estimate the overall economic impact of a large earthquake on the San Francisco Bay Area. In addition to quantifying changes in economic output, this approach gives insight into changes in income and employment across different economic sectors. The results of this analysis are combined with information on income, employment sector, housing values, savings, mortgage, rent, and insurance of households across the Bay Area, to calculate consumption changes throughout the recovery. Furthermore, well-being losses (i.e., changes in the utility of consumption over the recovery period) are calculated and analyzed for different socioeconomic groups. Well-being losses, unlike asset losses which use absolute monetary values, are able to reflect the differential impact that a loss in consumption of

Advisor: Baker Jack W. 
Deierlein Gregory G. (Gregory Gerard)
Kiremidjian Anne S. (Anne Setian)
Tracing the moche spectacles of death : performance, ancestrality, and political power in ancient Peru; a view from Huaca la Capilla-San José de Moro (AD 650-740)

The Moche culture thrived in the north coast of Peru between the second and ninth centuries AD. While still a point of debate, many consider the Moche one of the first state-level societies in the pre-Columbian Americas. One fascinating aspect of this society is the elaborate burials, with sumptuous displays of wealth. Traditionally, scholars have envisioned Moche death as an inherent and static manifestation of hierarchy, status, and power. My dissertation moves away from such object-centered approaches, studying death and its effects as a historically and culturally situated phenomenon. My dissertation draws on extensive archaeological excavations conducted in Huaca La Capilla, a monumental adobe structure located within the Late Moche elite cemetery of San José de Moro, Jequetepeque Valley. Based on the striking similarities between the architectural enclosures discovered in this huaca and those represented in the Moche "Burial Theme, " I suggest that Huaca La Capilla was one of the loci where Moche funerary performances systematically occurred. These performances constituted large public spectacles orchestrated around the physical transformation of the corpses of elite individuals and their preparation for their symbolic journey into the afterlife: a process of transformation from a human to a divine entity, of becoming ancestors. Integrating multidisciplinary methods of analysis, my dissertation presents a comprehensive study of Huaca La Capilla, its architectural layout, and its many (and still enigmatic) transformations. I examine the role of Huaca La Capilla within the dynamic deathscape of San José de Moro, providing new insights on the relationship of huacas with death, the regeneration of life, and the cosmological order in the Moche world. In the context of political balkanization that characterized the Jequetepeque Valley during Late Moche times, I argue that the participation in these ritual spectacles was a key means for the creation of political and religious subjectivities. Ultimately, this case study offers novel anthropological perspectives on how the dramatic nature of the rituals orchestrated around the burial of elite individuals (the-body-as-spectacle, sensu Foucault 1977) constituted a means through which pre-modern states were held and constantly reproduced.

Keywords: Anthropology.
Advisor: Rick John W. 
Castillo Luis Jaime
Hodder Ian 
Meskell Lynn
Trajectory optimization methods for drone cameras

Drone cameras are now being deployed in a wide range of applications, including Hollywood filmmaking, search and rescue, wildlife monitoring, and large-scale 3D scanning. However, drones remain difficult to control, both for humans and for computers. In this dissertation, we introduce a variety of trajectory optimization methods that make it easier for people to use drone cameras. We focus specifically on two different applications: drone cinematography and drone 3D scanning. Throughout this dissertation, we leverage domain knowledge that is specialized to each application, and we reformulate classical trajectory optimization problems in terms of the drone camera's visual output, i.e., in terms of what the drone is seeing.

Advisor: Hanrahan P. M. (Patrick Matthew) 
Durand Frédo
James Doug L.
Transcribe.it - Audio Transcription Made Simple

Transcribe.it is a tool to help journalists transcribe audio files. This tool is intended to be run locally on a user's machine, in the spirit of tools such as OpenRefine and Tabula. It provides a simple web interface that lets users transcribe audio files using Google Cloud services, and saves the transcription locally. Transcribe.it is one of many solutions journalists can use to transcribe audio files, but this tool aims to simplify the process in a cost-effective way to the user without having to interact directly with Google’s Speech-to-Text API.

Keywords:  Department of Communication
Journalism M.A.
Department of Communication
Stanford University
transcription
audio
newsroom
tool
speech
text
Thesis
Advisor: Tumgoren Serdar
Phillips Cheryl
Transient fault tolerant asynchronous circuits

Asynchronous digital circuits are circuits that don't have clocks. They sometimes have advantages including speed, lower power consumption, and relative insensitivity to environmental conditions such as temperature. Transient faults, which are changes in the circuit that do not permanently damage the circuit, are a growing problem in digital circuits. Transient faults are mainly due to radiation effects such as Single Event Upsets (SEUs). These faults occur on the ground and are exacerbated in space. In this work, I present an overview of asynchronous circuits, in particular speed-independent control circuits, and our model for transient faults. I then present a fault-tolerant transformation of the circuit based on duplication. I discuss how to define fault tolerance formally. Then I show using logic and formal methods why the transformed circuit is fault-tolerant to single transient faults.

Keywords: Electrical Engineering.
Advisor: Dill David L. 
Barrett Clark C.
Mitra Subhasish
Transient reprogramming for multifaceted reversal of aging phenotypes

Though aging is generally associated with tissue and organ dysfunction, these can be considered the emergent consequences of fundamental transitions in the state of cellular physiology. These transitions have multiple manifestations at different levels of cellular architecture and function but the central regulator of these transitions is the epigenome, the most upstream dynamic regulator of gene expression. Reproduction is the only general phenomena in nature where the age of (parental) cells is truly reset - to produce an embryo and ultimately an age 0 offspring - and core to this process is a dramatic reprogramming of the epigenome. Here we present a technology that captures part of this age reset mechanism but using a transient reprogramming - to drive more youthful phenotypes but without the full reset back to an embryo. This reprogramming technology is distinct from previous anti-aging/pro-longevity interventions as instead of just modulating a few identified aging pathways, reprogramming engages a global and balanced state transition, in the case of reproduction, or state perturbation, in our transient approach, which we show leads to a multifaceted age reversal effect at the DNA, metabolic, whole cell and local environmental levels. We further discuss the emergent tissue and organ level benefits when transplanted with cells undergoing this treatment.

Keywords: Applied Physics.
Advisor: Sebastiano Vittorio
Spakowitz Andrew James 
Mahajan Vinit
Translational Suppression is not an Early Response to Synchronous, Inducible, Intracellular Protein Aggregation

Cellular protein homeostasis (proteostasis) is maintained by a complex, adaptive proteostasis network (PN) composed of tightly regulated synthesis, quality control, and clearance systems. Disturbances in the PN that result in the misfolding and subsequent aggregation of proteins are implicated in many neurodegenerative disorders (NDs) such as Huntington’s, Alzheimer’s, and Parkinson’s diseases. While the PN’s late-stage responses to aggregation such as transcriptional changes and deposition of aggregated proteins have been well-documented, there remains a substantial gap in our understanding of the immediate responses of the PN to cytosolic protein aggregation. Elucidating these early responses to aggregation is essential to a comprehensive understanding of ND pathogenesis and can offer insight into previously unknown, potentially therapeutically tractable targets. Previous investigations into the earliest actions of the PN in response to acute shifts in protein solubility were limited by stochastic onset of aggregation in cells overexpressing folding-defective proteins. These models also lacked the synchronicity in aggregation necessary to achieve a temporal resolution capable of characterizing the PN response in the minutes following the onset of protein misfolding. Of particular interest is whether the rapid modulation of protein synthesis, a common fast-acting response to severe cellular stressors that mitigates insults to homeostasis, makes up part of the PN’s earliest response to the aggregation of a single protein in the absence of global stressors. To probe this question, this study used a novel system of inducible, widespread, synchronous aggregation of a single protein in cells, coupled with metabolic protein labeling and click chemistry, to investigate the immediate impact of protein aggregation on protein synthesis rate. The results showed that induced aggregation in our model system produced no significant change in global protein synthesis rate. Furthermore, induced aggregation did not increase phosphorylation of eIF2α, which mediates translational suppression in other models of aggregation.

Keywords:  Department of Biology
Biology
proteostasis
AgDD
metabolic labeling
click chemistry
eIF2α
translational suppression
protein synthesis
aggregation
Thesis
Advisor: Kopito Ron
Dixon Scott
Transport and behavior of non-spherical particles in waves

Plastic pollution in the oceans is a global reality. Small dispersed plastic pieces impact ecosystems, fisheries, and water resources throughout the world. Most of this debris breaks down and persists as small particles, or microplastics. Accurately estimating microplastics accumulation rates and assessing ecosystem risk requires understanding the transport and fate of microplastics in the ocean. Of particular interest is how the characteristics of microplastic particles, such as their shape and size, affect their transport and how these effects interact with wavy flows inherent to the coasts and ocean surface. Regarding particle shape, the orientation of a non-spherical particle controls the lift and drag that it feels while moving through a fluid, which in turn affect transport. This thesis presents an analysis of the orientation of non-spherical particles in surface gravity waves, and the effects these findings have on transport. A theoretical study showed that spheroidal particles tend to a preferred orientation with a superimposed oscillation under waves. This behavior is a consequence of how the particle samples the flow and can be thought of as the angular analog of Stokes drift. The mean preferred orientation is found to be solely a function of the particle's shape; it is independent of the wave parameters. The implications of this theoretical result were explored with both numerical and laboratory experiments. Under both waves and shear flow, the theoretical limit for the preferential orientation was also derived. The effects of the waves and the particle characteristics are shown to be non-trivial for predicting dispersal of the particles. In particular, the vertical distribution of buoyant particles is found to be a strong function of particle settling velocity, a result with significant implications for microplastic transport models and in situ sampling. Results from this work show that accurately predicting the transport, settling velocity, vertical distribution and horizontal dispersion of anisotropic particles in wavy environments requires consideration of the shape and preferred orientation of the particles.

Advisor: Ouellette Nicholas (Nicholas Testroet)
Dabiri John O. (John Oluseun)
Koseff Jeffrey Russell 
Monismith Stephen Gene
Treatment of Geometric Constraints in Well Placement Optimization

In well placement optimization, the goal is generally to determine the well configuration that maximizes a profitability or productivity measure. An important requirement in these optimizations is the satisfaction of geometric constraints that limit the locations where wells can be placed. In this work, we introduce and test new constraint-handling treatments within the context of particle swarm optimization
(PSO), a stochastic global search method. These treatments enable the satisfaction
of three types of constraints, namely maximum well length, minimum inter-well distance, and minimum well-to-boundary distance, during the PSO run. Our treatment 'repairs' infeasible solutions by projecting them into feasible space. The repair procedure entails the application of alternating projection using sequential quadratic programming (SQP), along with a perturbation step. This perturbation step acts to shift infeasible solutions out of poor local minima found by SQP. 'Post-evaluation' treatments are also required to handle solutions, within the optimization, that remain infeasible after repair. Three types of post-evaluation treatments are considered in this work - the filter method, and static and dynamic penalty treatments.
The performance of our treatments is assessed for optimizations involving the
placement of multiple deviated wells in 3D models. Two cases (one with 18,225 active cells and one with 42,886 active cells), with irregularly shaped boundaries, are considered. In both cases, PSO without the repair procedure has difficulty finding
reasonable feasible solutions. The repair procedure is shown to render feasible the majority of infeasible solutions, thus providing substantial improvement in optimizer
performance. For the first case, we assess the impact of the parameters in the repair procedure and in the (post-evaluation) penalty treatments. The effect of each
parameter is evaluated based on the resulting median objective function value over
five PSO runs. We observe that the use of smaller-magnitude perturbation, and the
replacement of all infeasible PSO solutions by their repaired versions, leads to the
best overall performance. In addition, the penalty treatments are found to outperform the filter method if suitable penalty parameters are assigned. For both models, the use of the constraint-handling treatment greatly improves optimizer performance compared to PSO without the repair procedure. This is observed over a wide range of parameter values, and for all post-evaluation treatments considered.

Keywords:  Energy Resources Engineering
School of Earth Energy &amp; Environmental Sciences
Thesis
Advisor: Durlofsky Louis J.
Volkov Oleg