VictorJuiz/thesis huggingface dataset:
Computational analysis and modeling of expressive timing in music performance

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

Keywords: Music.
Advisor: Berger Jonathan
Arul Kumaran
Smith Julius O. (Julius Orion)
Computational analysis of animal model memory systems

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

Keywords: Electrical Engineering.
Advisor: Schnitzer Mark Jacob
El Gamal Abbas A.
Shenoy Krishna V. (Krishna Vaughn)
Computational fluorescence microscopy for three dimensional reconstruction

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

Keywords: Electrical Engineering.
Advisor: Wetzstein Gordon
Pauly John (John M.)
Solgaard Olav
Computational methods for identification and characterization of metabolites

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

Keywords: Bioengineering.
Advisor: Fischbach Michael
Altman Russ 
Holmes Susan
Computer methods for collision processing : from sound to topology

Contacts and collisions are ubiquitous in physics simulations and computer animations. In this dissertation, we discuss two ways to more effectively simulate and process collisions. In particular, we model ground impact sounds and verify the topology of loopy structures. Firstly, rigid-body sound-synthesis methods often omit the ground sound in object--ground impacts, and we propose and discuss a physics-based analytical expression to model the transient ground-sound emission. This model can be used either in a Rayleigh integral to directly sum the total ground sound or as an "acoustic shader" for a finite-difference time-domain wave simulation. We find that when the object's modal sound is inaudible due to its shape and size, the ground sound is more audible than the transient object sound in scenarios where a dense object impacts a soft ground and scenarios where the impact point has a low elevation angle to the listening point. Secondly, it is increasingly common to model, simulate, and process complex materials based on loopy structures, such as in yarn-level cloth garments, which possess meaningful topological constraints between inter-looping curves. We explore a family of methods for efficiently computing and verifying linking numbers between closed curves, and apply them to applications in geometry processing, animation, and simulation, so as to verify that topological invariants are preserved during and after processing of the input models. We evaluate these methods on a suite of test problems, including yarn-level cloth and chainmail examples, that involve significant processing: physics-based relaxation and animation, user-modeled deformations, and curve compression and reparameterization. We also verify the topology of a stitch pattern of open curves, arranged as a braid. We show that topology errors can be efficiently identified to enable more robust processing of loopy structures

Advisor: James Doug L.
Hanrahan P. M. (Patrick Matthew)
Smith Julius O. (Julius Orion)
Computer vision for environmental and social sustainability policy

Computer vision (CV) has succeeded in many benchmark datasets but has yet to be widely applied to large-scale spatial and temporal correlated image datasets to address environmental or social issues. This thesis demonstrates the challenge, solutions, and policy implications of using computer vision for satellite, aerial, and street view images at scale with four empirical studies: 1) Mapping Vegetation and Classifying the driver of deforestation in Indonesia; 2) Detecting and characterizing high-emission oil and gas facilities in the U.S.; 3) Estimating the prevalence and placement of surveillance cameras in 16 major cities; 4) Mapping the Trend of Crosswalks Visibility Enhancements in the U.S. Transit-Oriented Development (TOD) areas. We conclude with a systematic review of the opportunities and challenges of using computer vision in facility urban and environmental policy research

Advisor: Jackson Rob
Hwang Jackelyn
Ng Andrew Y.
Rajagopal Ram
Concretely efficient interactive proofs and their applications

In the past decade, systems that use probabilistic proofs in real-world applications have seen explosive growth. These systems build upon some of the crown jewels of theoretical computer science---interactive proofs, probabilistically checkable proofs, and zero-knowledge proofs---to solve problems of trust and privacy in a wide range of settings. This dissertation describes three built systems that answer questions ranging from "how can we build trustworthy hardware that uses untrusted components?" to "how can we improve the concrete efficiency of zero-knowledge proof systems?" Along the way, it discusses the pervasive challenges of efficiency, expressiveness, and scalability in this research area; one approach to addressing these challenges; and future directions that promise to bring this exciting technology to bear on an even wider range of applications

Advisor: Boneh Dan
Winstein Keith
Mazières David (David Folkman)
A conserved YAP/Notch/REST network controls the neuroendocrine cell fate in the lungs

The Notch pathway is a conserved cell-cell communication pathway that controls cell fate decisions. Here we sought to determine how Notch pathway activation inhibits the neuroendocrine cell fate in the lungs, an archetypal process for cell fate decisions orchestrated by Notch signaling that has remained poorly understood at the molecular level. Using intratumoral heterogeneity in small cell lung cancer as a tractable model system, we uncovered a role for the transcriptional regulators REST and YAP as promoters of the neuroendocrine to non-neuroendocrine transition. We further identified the specific neuroendocrine gene programs repressed by REST downstream of Notch in this process. Importantly, we validated the importance of REST and YAP in neuroendocrine to non-neuroendocrine cell fate switches in both developmental and tissue repair processes in the lungs. Altogether, these experiments identify conserved roles for REST and YAP in Notch-driven inhibition of the neuroendocrine cell fate in embryonic lungs, adult lungs, and lung cancer

Advisor: Sage Julien
Oro Anthony
Wernig Marius
Winslow Monte
Consolidation in Broadcast Television Markets and Voter Turnout

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.

Keywords:  Department of Economics
Media and Voting
Media Ownership
Voter Turnout
Thesis
Advisor: Rosston Gregory
Constraining primordial gravitational waves and characterizing B-modes with the Keck array and BICEP3 CMB experiments

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

Keywords: Physics.
Advisor: Kuo Chao-Lin (Physics professor)
Allen Steven W. (Steven Ward)
Roodman Aaron J. (Aaron Jay)
Contracts and incentives in environmental sustainability

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

Keywords: Management Science and Engineering.
Advisor: Ashlagi Itai
Lo Irene (Management science professor)
Weyant John P. (John Peter)
Contrast agent diffusion as a CTa biomarker for articular cartilage health

Osteoarthritis (OA) is a degenerative joint disease that affects 12% of the adult population of the United States and costs the health care system 5-80 billion per year in direct and indirect costs. Currently, there is no cure for OA. One of the challenges of developing effective treatments for OA is that it cannot be detected early enough to intervene before irreversible soft tissue damage has occurred. In this dissertation, apparent diffusivity of a contrast agent measured using computed tomography arthrography (CTa) scans is introduced as a potential biomarker for early-stage cartilage degeneration. While in theory, apparent diffusivity will be elevated in cartilage with early signs of degradation compared to healthy cartilage, it is not a perfectly quantitative measurement. This is because cartilage responds to osmotic changes in its environment, so interstitial fluid flow is also occurring, which can accelerate or slow down solute transport. The first aim of this dissertation (Chapter 3) seeks to answer the question of how quantitative apparent diffusivity is by measuring it in enzymatically degraded cartilage explants and healthy, unaltered ones in the presence of different contrast agent concentrations and boundary conditions. Simulations were used to predict the effects of imaging protocol variation on diffusivity measurements with a large range of resolutions and scan times. Aims 2, 3, and 4 of this dissertation are steps towards translating this method for eventual in vivo use. In Aim 2 (Chapter 4), two different methods for making 3D voxel-wise apparent diffusivity measurements are presented. They are used to analyze a bovine explant dataset consisting of enzymatically degraded and unaltered cartilage samples to confirm that voxel-wise measurements show increases in apparent diffusivity in damaged tissue, like the bulk measurements did in Aim 1. Aim 3 (Chapter 5) details a method for identifying the cartilage surface in areas where it is in contact with cartilage or menisci, a step towards overcoming the challenge of accurate cartilage segmentation in CTa datasets. In Aim 4 (Chapter 6), simulations are used to study the effects of contrast agent solutions on cartilage mechanical properties. Solute size, bath ionic strength, and bath osmolarity are altered individually, which is not possible in the laboratory, and their effect on indentation force is quantified. The work presented in this dissertation could enable CTa-based detection of cartilage degeneration, expand access to early OA detection, and greatly increase treatment options in conjunction with the development of new interventions

Keywords: Mechanical Engineering.
Advisor: Levenston Marc Elliot
Gold Garry E.
Wang Adam (Adam Shar)
Controlling the immunotransmitter cGAMP with chemical biology

cGAMP is a second messenger that mediates innate immune response to cancer, infection, and cellular damage. cGAMP synthesis is triggered by a danger signal common to all these pathologies, the presence of DNA in the cytosol. Downstream, cGAMP activates the Stimulator of Interferon Genes (STING) protein which eventually leads to the production of interferon to combat these threats. Originally, cGAMP was thought to function exclusively intracellularly, stuck inside the cell where it was produced. Then, the realization that cGAMP's only hydrolase ENPP1 was extracellular hinted that cGAMP, too, might travel outside of its originating cell. However, the field lacked tools to test this hypothesis. This dissertation builds chemical biology tools and uses them to define and control cGAMP's role as an extracellular immune signal, or immunotransmitter. Chapter 1 contextualizes the role of cGAMP as an immunotransmitter in terms of the entire cGAMP-STING pathway. Chapter 2 demonstrates that cGAMP is constantly exported from cancer cells and activates host anti-cancer immunity. Chapter 3 chronicles the development of best-in-class ENPP1 inhibitors that block extracellular cGAMP degradation, functioning as both chemical tools and investigational cancer drug candidates. Chapter 4 engineers an ENPP1-based tool that selectively enhances extracellular cGAMP in vivo and shows that cGAMP acts as an immunotransmitter in pathologies beyond cancer, including viral infection and cellular damage. Chapter 5 looks ahead to the trajectory of the cGAMP immunotransmitter field. This work pioneers cGAMP's extracellular immunotransmitter role in innate immunity and takes first steps to harness it therapeutically

Keywords: Chemistry.
Advisor: Li Lingyin
Dassama Laura
Khosla Chaitan
Convex optimization and implicit differentiation methods for control and estimation

This disseration covers five applications of convex optimization and implicit differentiation methods to the fields of control and estimation. In the first chapter, we consider the problem of efficiently computing the derivative of the solution map of a convex cone program, when it exists. In the second chapter, we consider the problem of fitting the parameters in a Kalman smoother to data. In the third chapter, we propose a method for tuning parameters in convex optimization-based control policies. In the fourth chapter, we consider the problem of predicting the covariance of a zero mean Gaussian vector, based on another feature vector. In the fifth and final chapter, we describe a method for fitting a Markov chain, with a state transition matrix that depends on a feature vector, to data that can include missing values

Keywords: Electrical Engineering.
Advisor: Boyd Stephen P. 
Hastie Trevor
Pilanci Mert