VictorJuiz/thesis huggingface dataset:
Fundamental performance limits of analog-to-digital compression [electronic resource]

Analog-to-digital conversion is a fundamental operation in many electronic and communication systems. The principles describing the information loss as a result of transforming from continuous-time, continuous-amplitude signals to a sequence of bits lie at the intersection of sampling theory and quantization or lossy source coding theory. Classic results in sampling theory established the Nyquist rate of a signal, or, more precisely, its spectral occupancy, as the critical sampling rate above which the signal can be perfectly reconstructed from its samples. However, these results do not incorporate the quantization precision of the samples. Since it is impossible to obtain an exact digital representation of any continuous-amplitude sequence of samples, any digital representation of an analog signal will introduce some error, regardless of the sampling rate. This raises the question as to when sampling at the Nyquist rate is necessary. In other words, is it possible to achieve the same or better performance by sampling at a rate lower than Nyquist without any further assumptions about the input signal other than a limited bitrate to describe the samples? When only quantization or limited bitrate is considered, the minimal distortion in encoding a random process subject to this bitrate limitation is described by Shannon's distortion-rate function (DRF). However, this DRF is given in terms of an optimization over a family of conditional probability distributions subject to a mutual information constraint and does not explicitly incorporate sampling and other inaccuracies arising from signal processing with a limited time-resolution. In fact, the standard achievability scheme in source coding assumes that the encoder can access the realization of the analog process, or expand it with respect to an analog basis function, without any restriction on the time resolution. Since this restriction often occurs in practice, the following question arises: Given a finite number of samples per unit time from the analog process, what is the minimal distortion in recovering it from a digital encoding of these samples subject to a bitrate constraint ? The goal of this thesis is to address the two questions posed above by characterizing the minimal distortion that can be attained in recovering a random process using the most general form of quantization applied to its samples. By explicitly incorporating sampling into the minimum distortion optimization, the characterization of the minimal distortion bridges the missing theoretical gap between sampling theory and lossy data compression theory. By taking into account signal sampling and the associated distortion, the resulting distortion function generalizes and unifies sampling theory and source coding theory.

Keywords: Electrical Engineering.
Advisor: Goldsmith Andrea
El Gamal Abbas A. advisor. Thesis advisor
Weissman Tsachy advisor. Thesis advisor
Gaussian process regression for response spectrum [electronic resource]

Design spectra are an important tool to determine peak structural response to seismic loads. A common approach to obtain a design spectrum is to approximate the mean response spectrum to a set of ground motion records by linear segments. The idea behind such method is to obtain a curve that is smoother than the response spectrum and, therefore, can be a better generalization to new ground motions that might occur. We propose the use of Gaussian Process regression as an alternative method to obtain such curves. Once model parameters have been defined, Gaussian Process regression is simple to be implemented to any ground motion record and can be a more natural approximation to the mean response spectrum. In addition, regression results take into account the uncertainties on the process and allow for prediction confidence regions to be readily obtained. The model proposed was able to generate smooth curves that can be appropriate to be used as design spectra.

Advisor: Law K. H. (Kincho H.) advisor. Thesis advisor
Gender identity shapes preferences for health and environmental behaviors [electronic resource]

Relative to women, men are less likely to engage in many health-promoting and pro-environmental behaviors. In this dissertation, I argue that men avoid healthy and sustainable practices in order to demonstrate their masculinity and distance themselves from feminine-typed domains. Across three papers I draw on survey and experimental methods to examine the role of masculinity maintenance and gender attitudes on health and environmental preferences. Chapter 1 provides causal evidence that men's gender identity maintenance efforts negatively affect health-related preferences through two pathways: first, unhealthy choices that express masculinity, and secondly, elevated blood pressure in response to gender identity threats. In chapter 2, I show that men's lower levels of environmental concern can be explained by differences in gender beliefs and perceptions about the masculinity and femininity of pro-environmental views. Finally, in chapter 3 I test a potential intervention strategy intended to increase men's rates of sustainable practices by drawing on masculine-typed frames that fit more closely with widely held definitions of masculinity. Overall, this research shows how men's gender identity maintenance can shape preferences for health and environmental behaviors.

Keywords: Sociology.
Advisor: Cook Karen S.
Ridgeway Cecilia L. primary advisor. Thesis advisor
Willer Robert Bartley advisor. Thesis advisor
Generalized blind deconvolution with missing data [electronic resource]

A blind deconvolution problem seeks two signals that convolve (approximately) to some known data signal. Such problems appear in application areas that can be described by a convolution model, e.g., a linear time invariant (LTI) system or a convolutional (shift invariant) dictionary. Data signals may have multiple rows and columns, entries drawn from a non-numeric set, missing or corrupt values, or any combination of these. In this dissertation, we present a general-purpose method for formulating blind deconvolution problems when data signals suffer from real-world issues, e.g., have arbitrary data type, are corrupt or incomplete, include additional constraints, and so on. Our approach extends Generalized Low Rank Models (an exploratory analysis tool for heterogeneous data tables) to the signal setting. By extension, missing values can be overlooked with a careful choice of optimization formulation and later imputed. We use alternating proximal gradient descent to leverage the Fast Fourier transform and parallelize updates over rows and columns of the data. We introduce a new problem, histogram unmixing, that is particularly well-suited for our framework. Finally we report results for some numerical experiments, demonstrating the improvement of our method over a naive approach.

Keywords: Electrical Engineering.
Advisor: Boyd Stephen
Lall Sanjay advisor. Thesis advisor
Osgood Brad advisor. Thesis advisor
Generating multi-purpose accelerators using neural networks [electronic resource]

Approximate computing (AC) is a very promising design paradigm for crossing the CPU power wall, primarily driven by the potential to sacrifice application output quality for significant gains in performance, energy, and fault tolerance. AC exploits the tolerance of many application domains (e.g., multimedia processing, data mining, and scientific computing) to errors and/or low-precision in their computations. However, existing solutions on the software side have not thoroughly explored the compilation and runtime stages, which have a critical impact on system performance. This work introduces a software-only, general-purpose acceleration framework that utilizes neural networks (NNs) and denoising autoencoders to restructure an application's data flow into a hybrid of exact and approximate computations. Specifically, this paper proposes advanced compilation and runtime techniques that solve the very difficult challenge of utilizing multiple interacting subtask approximators. Unlike previous work, our system exploits the hierarchical structure of an application to introduce significant flexibility in how the approximations are performed. Additionally, the framework is able to automatically generate an application.s subroutine structure in order to minimize design costs. By restructuring algorithms to have a mixed exact-NN data flow, EMEURO is able to achieve significant speedup across several domains, achieving 7x-109x maximum speedup over the original algorithm, with 0.1%-10% approximation error. Design costs are significantly reduced by allowing the NNs to learn the application.s functionality rather than being explicitly programmed and optimized by a human. NNs have also been shown to be very fault tolerant, which is particularly important in high-performance systems, where sophisticated designs can yield complex and hard to trace bugs. Although this work focuses on CPU-only acceleration, the very data-parallel nature of NNs makes them amenable to running efficiently on many different acceleration platforms.

Keywords: Electrical Engineering.
Advisor: Olukotun Oyekunle Ayinde primary advisor. Thesis advisor
Kozyrakis Christoforos
Ng Andrew Y.
Genetic and epigenetic control of complex traits in C. elegans [electronic resource]

The ability of organisms to adapt behavior to environmental changes as well as pass on information to their offspring is critical for survival. While the inheritance of traits is overwhelmingly governed by Mendelian genetics, there has been a growing body of evidence making the case for certain acquired traits, including complex ones such as behavioral tendencies and lifespan, to be passed on in a 'Lamarkian' fashion. My graduate research has encompassed two primary topics of research using the nematode C. elegans as a model system: first, transgenerational epigenetic inheritance of lifespan, and second, molecular and cellular mechanisms underlying a form of behavioral plasticity called salt aversive learning. This dissertation will first describe my explorations into whether environmentally-induced traits can be inherited in a non-genetic manner. I will then report on my discovery that a calcium/calmodulin-dependent kinase, CMK-1, is essential for salt aversive learning behavior in worms. In brief, we found that CMK-1 acts in the primary salt-sensing ASE neurons to regulate this behavior. By characterizing calcium flux using imaging and microfluidics, we revealed that cmk-1-deficient ASE neurons exhibit significant defects in calcium flux that correlate with behavior. Our study implicates this conserved protein as an essential cell-autonomous regulator for behavioral plasticity to environmental salt. In sum, I have used the roundworm C. elegans to shed light on the complex traits of lifespan and learning.

Advisor: Brunet Anne
Goodman Miriam Beth primary advisor. Thesis advisor
Clandinin Thomas R. (Thomas Robert)
Shen Kang
Wyss-Coray Anton advisor. Thesis advisor
Genetic mapping and biochemical basis of yellow feather pigmentation in budgerigars [electronic resource]

Birds display some of the most diverse coloration mechanisms of all animals, yet relatively little is known about the molecular genetics of feather colors in parrots. This family of birds exhibits a unique class of yellow, orange and red polyene pigments called psittacofulvins, which are not known to exist in other vertebrates. The budgerigar or common parakeet (Melopsittacus undulatus) has dozens of simple Mendelian feather pigmentation traits that have arisen over the last several hundred generations, has been the subject of classical genetic studies dating back nearly 100 years, a sequenced genome, and is widespread as a household pet. It is thus an ideal species for mapping under-studied feather traits, which may reveal new facts about feather development, and about the molecular changes underlying color-related adaptations in wild parrots. Genetic mapping in non-model organisms is commonly performed by identifying single-nucleotide polymorphisms through reduced representation sequencing, which involves the high-throughput sequencing of restriction fragment ends. A common problem with this technique is allelic dropout due to restriction site polymorphism, which occurs because marker alleles linked to the non-cut restriction site allele are unobservable. These linked sites can be detected using a novel statistical method that involves modeling sequencing coverage across individuals as a function of the unobserved restriction site alleles. Thus error-prone sites can be removed from the data set. Genome-wide association mapping, using these techniques and others, shows that psittacofulvin pigmentation in budgerigars is controlled by a single gene encoding a novel polyketide synthase. This enzyme was sufficient to cause psittacofulvin pigment accumulation when heterologously expressed in yeast. Budgerigar polyketide synthase is strongly expressed in developing feathers, but not those of other bird species tested, suggesting that this pigmentation mechanism in parrots evolved through a derived regulatory mutation.

Keywords: Genetics.
Advisor: Bustamante Carlos (Carlos D.) primary advisor. Thesis advisor
Bassik Michael advisor. Thesis advisor
Fire Andrew Zachary advisor. Thesis advisor
Kingsley David M. (David Mark) advisor. Thesis advisor
Genetic regulation and evolution of gene expression [electronic resource]

Since the early 20th century, a core goal of biology has been understanding the connection between genotype and phenotype. A deep understanding of this link could help inform our understanding of disease etiology and potential mechanisms for evolutionary adaptation. Here, I extend our knowledge of the genotype-phenotype connection in three ways. In the first two chapters I leverage the genotype-phenotype intermediary of mRNA expression to identify genetic loci that control mRNA expression in an age dependent manner (Chapter 1), and that control sets of genes rather than individual genes (Chapter 2). These loci's phenotypic consequences are assessed and in both cases we observed enrichment for loci relevant to human disease. In Chapter 3, I again leverage mRNA expression but now using measurement across diverse eukaryotic species in order to identify novel genes that share coordinated evolution and thus likely functional associations.

Keywords: Biology.
Advisor: Fraser Hunter B. primary advisor. Thesis advisor
Montgomery Stephen
Petrov Dmitri Alex
Genetic variation across human populations [electronic resource] : clinical and demographic implications

Precision medicine offers a vision of medicine where each individual's clinical care is informed by their unique pattern of genetic markers. However, in order to understand the importance of any given genetic marker, it is essential to understand the background patterns of genetic variation in different people and identify the genetic variants which contribute to disease. In this thesis, I use population genetic and statistical approaches to provide insight into these patterns of genetic variation. First, I use genome-wide microarray data to analyze the population history of five metropolitan South American populations. I identify distinct differences in ancestry between the different populations and demonstrate that the Native American ancestry, but not European ancestry, of South American individuals is highly correlated with geography. Second, I analyze the patterns of genetic variation throughout Oceania and demonstrate that clinically important genetic variants occur at varying frequencies throughout the islands, highlighting the importance of understanding the genetic background of all of the different Oceanian populations. Third, I demonstrate that regions of the cardiac myosin protein are enriched for genetic variation seen in cardiomyopathy patients when compared with genetic variation seen in the general population. These results demonstrate the importance of understanding the expected patterns of genetic variation in different populations for the implementation of precision medicine.

Keywords: Genetics.
Advisor: Ashley Euan A. primary advisor. Thesis advisor
Bustamante Carlos (Carlos D.) primary advisor. Thesis advisor
Montgomery Stephen
Pritchard Jonathan (Jonathan K.) advisor. Thesis advisor
Genetics and biochemistry of Dengue virus inhibitors [electronic resource]

Drug resistance remains a major obstacle in developing therapeutic treatments for viruses and other diseases. Typical solutions proposed for this problem are the development of host-targeted antivirals and multiple-drug therapy. Work presented in this thesis builds on the new paradigm of dominant drug targets, which prevent the outgrowth of drug resistant variants by virtue of their genetic properties. Ideally, for any emerging or neglected viral disease we would be able to utilize all three strategies mentioned through direct-acting and host-targeted antiviral compounds with good drug resistance profiles. Here, I describe one direct-acting antiviral compound in detail, a viral protease inhibitor. I show that specific dengue NS2B/3 protease precursors, which accumulate upon treatment with the protease inhibitor, have dominant effects on wild-type dengue virus. We were unable to isolate adaptive mutations to this compound, suggesting that selection is blunted as predicted for dominant drug targets. I then show that there are multiple FDA-approved compounds with a high degree of chemical similarity, and importantly, similar biologic activities, to antiviral tool compounds commonly used in the laboratory. Several of these are highly promising as potential dengue or Zika therapies. Through this work, I hope to show that practical treatments for widespread diseases such as dengue and Zika viruses are within our grasp.

Keywords: Biology.
Advisor: Kirkegaard Karla primary advisor. Thesis advisor
Carette Jan
Mudgett Mary Beth
Stearns Tim advisor. Thesis advisor
Genome assembly methods using read clouds [electronic resource]

The field of genomics has been revolutionized by the advent of high throughput DNA sequencing techniques, which allow us to efficiently obtain billions of sequenced DNA fragments from a biological specimen with a single experiment. Computational genome assembly methods have been developed to reconstruct the source genomic sequence from these billions of DNA fragments or reads. Applications of these sequencing and assembly techniques have enabled us to study the genetic underpinnings of human diseases and have also allowed us to learn about the existence of vast prokaryotic life in nature beyond the small minority of culturable microorganisms. However, the completeness and contiguity of genome reconstructions are limited by the short read lengths currently produced by these high throughput methods. Recent "read cloud" techniques mitigate this limitation by partitioning groups of large DNA molecules, then barcoding short fragments derived from them, to produce short fragment sequences tagged with long-range information. However, existing sequence assembly algorithms fail to take advantage of the long-range information provided by these platforms. Consequently, read clouds have had limited adoption in large scale sequencing projects. In this thesis we present applications of these read clouds and novel computational techniques to the following areas: (1) We introduce a novel alignment algorithm, Random Field Aligner (RFA), which captures the relationships among the short-reads governed by the read cloud generative process via a Markov Random Field. Utilization of this probabilistic model allows us to confidently align short reads and discover variants within 155Mb of repeats within the human genome (6% of GRCh37) that were previously dark to short reads. (2) Nearly 131Mb of the human genome (4.35% of GRCh37) lies within large high sequence identity repeats (or segmental duplications), which can vary in sequence structure across individuals, and are not amenable to alignment approaches. We show how to reformulate the problem of assembling a subset of these loci as a read cloud haplotype phasing problem, which we solve approximately with a merge-split proposal Markov Chain Monte Carlo inference procedure. We apply this methodology to assemble a repeat family consisting of ten nearly identical (~99.9%) and very large (~200kbp) repeats within the human genome. (3) We provide the first application of read clouds to sequence a metagenome, and introduce an assembler, Athena, which uses read clouds to produce near complete genome drafts from metagenomic samples.

Advisor: Batzoglou Serafim primary advisor. Thesis advisor
Bhatt Ami (Ami Siddharth) advisor. Thesis advisor
Kundaje Anshul
Genome-wide chromatin accessibility prediction with deep neural networks [electronic resource]

Transcriptional regulation is closely associated with chromatin accessibility. Critical regulatory elements such as enhancers and repressors undergo a very dynamic chromatin structural variation to initiate gene regulation during cell differentiation. Large collaborative consortiums have widely applied the assays of measuring genome-wide chromatin accessibility onto hundreds of human cell types to investigate the mechanism of cell differentiation. Many integrative analyses focus on identifying epigenetic changes of chromatin structure that can lead to differential gene expression. However, it remains a mystery how chromatin accessibility is governed in the first place. To address this problem, we present a standard pipeline of processing sequencing data from the two most commonly adopted chromatin accessibility assays, DNase-seq and ATAC-seq, and then introduce an approach of deep neural networks (DNNs) to learn the underlying rules of chromatin regulation and predict open genomic regions in a wide variety of human cell types, using only the gene expression of transcriptional factors (TFs). We train DNNs on 682,425 enhancer related regulatory regions with 620 TFs that have sequence motifs in 100 cell conditions, and demonstrate high accuracy of prediction. Our model shows great generalizability on new cell conditions that it has not been trained on. The learned coefficients of our input features reveal useful insights into the functional roles of TFs. Therefore, our DNN offers an interpretable model of transcriptional regulation of chromatin accessibility.

Advisor: Wong Wing Hung primary advisor. Thesis advisor
Hastie Trevor advisor. Thesis advisor
Saunders Michael