VictorJuiz/thesis huggingface dataset:
Communication and signal processing methods for efficient optical link component utilization
Keywords: Electrical Engineering.

The exponential growth trend of network traffic during the past few decades continues to be driven by emerging applications. As global traffic demands rise, the share of traffic carried by optical fiber is increasing simultaneously. Therefore, it is imperative that future optical fiber links take full advantage of the resources available to maximize capacity. This work aims to apply communication theory concepts and signal processing techniques to optical communication links to ultimately increase capacity in systems that are subject to component limitations. First, we explore the advantages of adaptive-rate access networks, implemented through adaptive coding and modulation that adjusts the forward error correction code rate and modulation order in response to the channel conditions of each user. Our adaptive coding and modulation scheme can achieve an equalized bit rate of 40 Gb/s with 25G-class components on a network with fiber lengths of 5 to 25 km. Next, we study the fundamental performance of Kramers-Kronig detection, a technique that uses signal processing to retrieve the phase of a signal from its intensity measurement, making comparisons to standard coherent detection and standard direct detection. Finally, we study the benefit of successive interference cancellation in frequency-selective space-division multiplexed ultra-long-haul submarine links with mode-dependent gain caused by optical amplifier limitations. We show that successive interference cancellation, in conjunction with coding across modes, decreases capacity loss resulting from mode-dependent gain by 39% at 15,000 km and increases system reach by 28% compared to minimum-mean-square error detection in a link with moderate mode-dependent gain
Companionate Marriage: A Continued Confucian Construct
Keywords:  Stanford Global Studies
Stanford Global Studies
East Asian Studies
China
Ming-Qing
Companionate Marriage
Gender
Confucianism
Thesis

The hierarchical relationship between husband and wife served as a cornerstone of Confucian society. The rise of companionate marriages— marital relationships rooted in romance during the Ming and Qing—supposedly signified the breakdown of the traditional family structure by emphasizing the conjugal couple and increasing elite women’s agency. Thus, some scholars posited that the changing ideals of female virtue driven by the concept of companionate marriage demonstrated the early decline of Confucian hegemony and the start of modernity during the late imperial era. This increase of female independence and influence also conflicted with the May Fourth’s dark depiction of Confucianism. This thesis was inspired by the academic debate over the shifts companionate marriages caused in the traditionally Confucian-prescribed family and social structures, as well as the implications these changes had on the conceptions of modernity in China. This thesis aims to add to this scholarly discourse by building upon the foundations laid by Dorothy Ko’s revisionist history, Teachers of the Inner Chambers. Women’s voices historically have been dismissed in discussions of dynastic China despite their importance in the maintenance of the traditional social order. Therefore, this thesis places women’s voices back into the academic discussion by primarily focusing on literary works authored by elite women within companionate marriages. Contrary to challenging traditional society, women’s words prove that companionate marriages perpetuated traditional Confucian constructs. As narrated by women’s works, the emotional bonds and shifting ideals of female virtue resulting from companionate marriage ideals resided within and reinforced classical values. Their pieces demonstrate the active commitment of elite women to upholding traditional proprieties, as evidenced by comparisons to traditional moral works for women. While elite women were inspired by the romantic ideals of companionate marriage depicted in fictional works such as The Peony Pavilion, they sought to balance traditional female virtues with their desire to fulfill their new roles as companionate wives. This thesis seeks to show that companionate marriages did not represent the early decline of traditional China but instead displayed the dynamic nature of Confucianism during the late imperial era.
Comparative and population genomics of large carnivores
Keywords: Biology.

The combined effects of land-use change, habitat loss, and climate change have placed incredible stress on species living through the Anthropocene. For charismatic megafauna, especially predators, these affects are exacerbated by additional losses from poaching and human-wildlife conflict. An open question in conservation biology and genomics, concerns how these declines manifest at the genomic level, and whether we can distinguish between populations that are on the precipice of extinction, compared to those which are simply small. In addition, there are several open questions as to how we can best manage these populations into the future; should low-diversity populations be mixed to maximize adaptive potential, or should the populations be managed separately to maintain unique lineages? Can genomics tell us something about recent and historic declines? In order to answer these questions, genome-level resources for the species in question are preferred, since patterns in the genome (such as heterozygosity and the accuracy of run of homozygosity detection) rely on highly-continguous assemblies and large amounts of whole-genome resequencing data. Here, I focus on the generation of high quality genomic data for members of the order which have experienced substantial declines over the last 100 years. I generate high-quality genome assemblies from several members of the Panthera group, including the lion, the snow leopard, the African leopard, and the tiger, in addition to high-quality genomes for several African wild dogs. I examine how using different reference assemblies can lead to biased estimates of various statistics of interest, such as heterozygosity and runs of homozygosity. I then focus on the tiger, detailing the patterns of variation and demographic history in the wild tiger, with a focus on individuals from India, where a previous lack of sampling has inhibited our evolutionary inference. Together, this scholarship provides an abundance of genomic data for species where little to no data existed, investigates the biases and pitfalls of genomic data, and uses range-wide sampling of a species of conservation concern to generate management recommendations based on signals of recent demographic declines
Complex behavior enforced by protein-based inheritance : investigating the consequences of novel prion elements on sexual reproduction and genome integrity in the budding yeast Saccharomyces cerevisiae
Keywords: Biology.

Prions are proteins that can adopt multiple conformations, at least one of which is self-templating. Ensuing changes in protein activity are heritable over long biological timescales, in a non-Mendelian fashion. In budding yeast, specific prions have been identified that confer distinct phenotypes and fitness advantages in different conditions with induction and loss at rates higher than mutation. These prions can be induced by different stimuli -- in some cases, stimuli to which they provide an adapted response. This bi-stable switch behavior provides a framework for bet-hedging strategies whereby organisms can adapt to fluctuating environments in a reversible manner. Here, I review phase-separation dynamics and their biological relevance as they relate to and inform prion behavior (Chapter 1), followed by reports of two cellular states: [MIX+] (Chapter 2), and Ste+ (Chapter 3). [MIX+] is a prion state associated with the helicase, Mph1. It decreases mutation frequency, increases the incidence of recombination, and provides broad-spectrum resistance to a variety of DNA damaging conditions. Its induction can be stimulated by DNA-damaging conditions to which it provides resistance. Ste+ is a putative prion state associated with the Aurora B Kinase scaffold, Sli15. Ste+ cells demonstrate sterility, distinct histone modification, and a unique transcriptional profile. However, the sterility phenotype is in part explained by a nonsense mutation in STE7. Nevertheless, together, these efforts represent an effort to investigate mechanisms by which S. cerevisiae can modulate genomic integrity and diversity through switch-like control of sexual reproduction, mutation, and recombination
Compression of raw genomic data
Keywords: Electrical Engineering.

With the rapid advances in genomic sequencing, the amount of genomic data being produced is growing exponentially. Several large scale sequencing projects for humans and other species are expected to further increase the volume of this data. While the initial progress was led by second generation high-throughput sequencers such as Illumina, more recently there has been increasing interest in third generation sequencers like Oxford Nanopore that enable real-time and portable sequencing of long reads. In this context, compression techniques play a crucial role in enabling efficient storage and transfer of this data. Unfortunately, the traditional general-purpose compressors like Gzip are unable to fully exploit the inherent redundancy in this data. Furthermore, in many cases the data is noisy, and it is possible to deploy lossy compression algorithms that can reduce the storage space without adverse impacts on the data quality for downstream analysis. This thesis presents two specialized compressors for genomic data, focusing on raw genomic data which consists of sequencing reads (FASTQ format) as well as raw signal data produced by nanopore sequencing (FAST5 format). We first describe SPRING, which is an efficient compressor for unaligned single and paired-end genomic reads, supporting various lossless and lossy compression modes. Next, we discuss lossy compression of nanopore raw signal data using LFZip, which is a general-purpose lossy compressor for time series and sensor data. We also discuss the evaluation of the impact of lossy compression on the performance of downstream applications like basecalling, consensus and methylation calling
Computational analysis and modeling of expressive timing in music performance
Keywords: Music.

This thesis presents a machine learning model of expressive performance of piano music (specifically of Chopin Mazurkas) and a critical analysis of the output based upon statistical analyses of the musical scores and of recorded performances. Given the multidimensionality of the task, generating compelling computer generated interpretations of a musical score represents a formidable challenge, and a significant goal of MIR and computer music research. Here I seek to characterize the problems and suggest solutions. Performers' distortion of notated rhythms in a musical score is a significant factor in the production of convincingly expressive music interpretations. Sometimes exaggerated, and sometimes subtle, these distortions are driven by a variety of factors, including schematic features (both structural such as phrase boundaries and surface events such as recurrent rhythmic patterns), as well as relatively rare veridical events that characterize the individuality and uniqueness of a particular piece. Performers tend to adopt similar pervasive approaches to interpreting schemas, resulting in common performance practices, while often formulating less common approaches to the interpretation of veridical events. Furthermore, some performers choose anomalous interpretations of schemas. This thesis presents statistical analyses of timings of recorded human performances of selected Mazurkas by Frédéric Chopin. These include a dataset of 456 expressive piano performances of historical piano rolls that I automatically translated to MIDI format, as well as timing data of acoustic recordings from an available collection. I compared these analyses to the performances of the same works generated by the neural network trained with recorded human performances of the entire corpus. This thesis demonstrates that while machine learning succeeds, to some degree, in expressive interpretation of schemata, convincingly capturing performance characteristics remains very much a work in progress
Computational analysis of animal model memory systems
Keywords: Electrical Engineering.

The field of neuroscience uses a variety of technologies and animal models to ask fundamental questions about the functionality of the brain. Advancements in biology and recording technologies work together to provide new neurological insight. However, as the recorded information becomes more complex, new analytical methods are needed to extract meaning from the data. This dissertation will specifically look at computational techniques applied to various neural imaging methodologies to advance our understanding of animal model memory systems. In the following chapters, I will describe analysis pipelines developed to study various aspects of both flies and mice. First, I will discuss a method for computing the cross-sectional head and body areas of Drosophila using a dexterous high-throughput robot. This project provides one of the first applications for automatically assessing fly attributes without the need for anesthesia methods that can potentially disrupt the neurological function of the fly. Next, I will present an algorithm to extract and clean GCaMP signals from the mushroom bodies of freely moving flies. This technology will enable the recording of neural function in Drosophila during behaviors that cannot be studied while the fly is head fixed. Lastly, I will present the results of an experiment studying context modulated associations in the mouse hippocampus. Using a miniature microscope, we record ensembles of neurons in mice as they perform a novel task that requires the use of both associative and procedural memory. The analysis presented will show how both memory types are represented in the hippocampus and how procedural learning can transition to accommodate higher order associations
Computational fluorescence microscopy for three dimensional reconstruction
Keywords: Electrical Engineering.

With rapidly increasing computational power, computational fluorescence microscopy is advancing the frontier of biological imaging. Computational algorithms tailored for specific experimental settings are demanded to solve given tasks such as denoising, spectral unmixing, 3D localization and reconstruction, and ptychography. In this thesis, we present the reconstruction of dense and sparse three dimensional fluorescent volumes. In the first half, we present a volumetric reconstruction method designed for 3D fluorescence imaging of biological samples in the low-light regime. Our method deconvolves a captured focal stack through optimization. As deconvolution is an ill-posed problem, the uniqueness of the solution is imposed through regularization. We formulate the objective function as a sum of a data fidelity term and a regularization term, and minimize it using the alternating direction method of multipliers algorithm. The data fidelity is accurately evaluated with a negative log-likelihood function based on a mixed Poisson-Gaussian model of photon shot noise and camera read noise, which are both present in low-light imaging. Among several possible regularization strategies, we show that a Hessian-based regularizer is most effective for describing locally smooth features present in biological specimens. We demonstrate its performance for fixed and live cell imaging, showing its applicability as a tool for biological research. In the second half, we introduce a hybrid optical-electronic computing approach to three dimensional localization microscopy. Driven by artificial intelligence, this approach learns a set of depth-dependent point spread functions (PSFs) and a localization network jointly in an end-to-end fashion, co-designing an optical imaging system and a neural network. We also present a custom grayscale lithography process to fabricate freeform diffractive optical elements that optically implement the designed PSFs and outline several biological experiments with fixed and live cells that demonstrate the efficacy of the proposed computational microscopy approach
Computational methods for identification and characterization of metabolites
Keywords: Bioengineering.

Metabolites are the small organic molecules that serve as the precursors to, actors in, and products of cellular metabolism. They create a unique dynamic fingerprint of a living system's chemical state, yet the metabolite composition of almost any biological sample cannot be completely determined using current methods. Liquid chromatography - mass spectrometers are workhorse instruments for analyzing metabolites and can detect analytes at ultra low concentrations. As such, untargeted metabolomics studies in which the global metabolite fingerprint of a system is measured generate incredibly rich datasets with signals coming from potentially thousands of metabolites. However, only a small fraction of the metabolite signals in an untargeted metabolomics dataset can be identified and mapped to a chemical structure. The work here describes the development of computational analysis tools for metabolite identification and characterization in untargeted metabolomics data to increase the amount of biochemical insight generated from a given study. First, a pipeline for mapping raw data to chemical features of interest is created, and this pipeline is used in the creation of a map of the human colon metabolome. Next, a supervised topic modeling approach is explored for the structural characterization of individual unknown molecules measured via tandem mass spectrometry. Finally, an approach for integrating mass spectrometry metabolite data from heterogeneous experimental sources and protocols is investigated. Together, these approaches represent advances towards more comprehensive and insightful analysis of metabolomics data using computational methods
Consolidation in Broadcast Television Markets and Voter Turnout
Keywords:  Department of Economics
Media and Voting
Media Ownership
Voter Turnout
Thesis

Broadcast television ownership in the United States is regulated in order to promote diverse local news sources and ensure a robust democratic process by limiting common ownership of television stations in the same market. I attempt to establish a causal relationship between a reduction in the number of owners in a market and voter turnout. I also examine the impact of consolidation at a national level by measuring both the impact of entry by large broadcast owners to a market and consolidating mergers by large firms. I find that consolidation at a local level leads to a reduction in turnout, consolidation at a national level leads to a reduction in turnout, and the reduction in turnout is highest in concentrated local markets with multiple large firms that own two broadcast television stations.
Constraining primordial gravitational waves and characterizing B-modes with the Keck array and BICEP3 CMB experiments
Keywords: Physics.

Cosmic Inflation is currently the best theory of what took place in the first instant of the Universe. It postulates a brief exponential expansion right after the Big Bang in which the quantum fluctuations are expanded in size to become the initial conditions that seed the growth of structure in the Universe. Most inflationary models predict a stochastic gravitational wave background which would leave a unique B-mode polarization imprint on the Cosmic Microwave Background. The goal of the BICEP/Keck program is to measure the inflationary B-modes, parametrized by the tensor-to-scalar ratio r. A nonzero measurement of r would provide smoking gun evidence for inflation. The BICEP/Keck program have deployed a series of small-aperture, on-axis refracting telescopes, which have been continuously observing at the South Pole since 2006. The tightest constraint on inflationary gravitational waves to date is r< 0.036 using data through the end of 2018. As the statistical power of the measurement increases, so must the effort on systematics control. In this thesis, we review the BICEP/Keck pipeline with an emphasis on both statistical and systematics error mitigation. On reducing statistical uncertainties, we introduce two promising avenues: improved ground subtraction timestream filter and the optimal quadratic estimator on power spectrum. On the systematics mitigation, we present two case studies in which the calibration data are applied to understand the physics behind systematics that are related to the gain calibration. Finally, we introduce the framework of the CMB distortion analysis, which is used as a powerful diagnostics for characterizing the observed B-modes. The distinct signatures in the EB and TB correlations generated by the distortions are used to study the associated physical processes or systematic effects. It is demonstrated that the BK18 data set has no detectable distortion field signatures after the standard systematics mitigations, and we proceed to set competitive constraints on the relevant processes including weak gravitational lensing, cosmic birefringence, and patchy reionization
Contracts and incentives in environmental sustainability
Keywords: Management Science and Engineering.

As the threats of climate change and environmental degradation worsen, policy makers and non-profits worldwide have developed a plethora of incentive-based solutions that induce environmental efforts from individuals. This dissertation studies these incentive-based solutions using the theoretical modeling tools of contract theory, mechanism design and queueing theory. In the first two sections of this dissertation, I ask how to better design contracts and payment structures to increase the efficiency and effectiveness of environmental payment programs. First, I develop a stylized principal-agent model to study Payment for Ecosystem Services programs, which are used by governments worldwide to pay forest owners to conserve forest. I focus on simple and robust contracts that have performance guarantees even when the government has limited information. Next, I investigate the optimal payment structures in long-term and large-scale afforestation programs with smallholder farmers, finding that paying farmers early can increase the environmental benefits generated per dollar spent. Lastly, motivated by operational challenges in the palm oil value chain, I study the impact of travel delay and real-time announcements on queues at a congested server, finding that travel delay can cause reduction in customer welfare in a queueing system