VictorJuiz/thesis huggingface dataset:
Endogenous Spatial Production Networks: Theory, Estimation, and Evidence from Indian Firm-to-Firm Linkages

In modern economies, production is organized in large-scale complex networks of firms trading intermediate inputs with each other. Larger Indian firms selling inputs to other firms tend to have more customers, tend to be used more intensively by their customers, and tend to have larger customers. Motivated by these regularities, I propose a novel empirical model of trade featuring endogenous formation of input-output linkages between spatially distant firms. The empirical model consists of (a) a theoretical framework that accommodates first order features of firm-to-firm network data, (b) a maximum likelihood framework for structural estimation that is uninhibited by the scale of data, and (c) a procedure for counterfactual analysis that speaks to the effects of micro- and macro- shocks to the spatial network economy. In the model, differences in production costs across firms arise not just from differences in productivity but also from finding the most cost-effective suppliers of intermediate inputs. Firms with low production costs end up larger because they find more customers, are used more intensively by their customers and in turn their customers lower production costs and end up larger themselves. The model is estimated using novel micro-data on firm-to-firm sales between Indian firms. The model's fit is good. The estimated model implies that a 10% decline in inter-state border frictions in India leads to welfare gains ranging between 1% and 8% across districts. Moreover, over half of the variation in changes in firms' sales to other firms can be explained by endogenous changes in the network structure.

Advisor: Rodriguez-Clare, Andres
Resonance and Resistance: Feminist Worldmaking and Musical Practice in Chile

This dissertation engages the concept of resonance in order to explore how Chilean feminist activists, musicians, industry leaders, and audiences have used popular musical practices to create space for themselves and their communities. Centrally, this study asks, how are feminist organizers in Chile engaging music to mobilize artists, audiences, and industries to end patterns of patriarchal oppression? To what extent do feminist musical practices allow participants to navigate, re-sound, and re-envision the physical, social, industrial, and virtual spaces of which they are a part? By examining a diversity of feminist musical practices from the mid-twentieth century to the present, I explain how musical and interpersonal resonances shape feminist coalition-building while also reconfiguring the gender politics of social and geophysical space./nEach chapter in this dissertation makes audible distinct feminist understandings of Chilean music history, spatial politics, and the patriarchal systems that shape these. In Chapters Two and Three, I examine the role of cantautoras (women singer-songwriters) across generations of political movements, specifically addressing the feminist legacies, activism, and travels of folklorist Violeta Parra (b. 1917) and singer Pascuala Ilabaca (b. 1985). The latter two chapters examine two highly distinct community music practices. Chapter Four draws on my participant observation in women’s community cueca classes in Santiago to analyze the process of “sacando la voz” (raising, drawing out, or finding one’s voice) within the urban folk music tradition, cueca brava. Chapter Five examines the history of countercultural arts collective Coordinadora Femfest and their collaboration with trans performer Hija de Perra. I explain how members have used peripheral venues and DIY performance practices to develop a transfeminist coalition building based on “sounding from under”–outside mainstream institutions and in solidarity with marginalized communities./nEach case, I argue, represents a different enactment of feminist worldmaking in which sound is made into a reclamation of space, an amplification of voices, or a mobilization of power. I define feminist worldmaking as the performative means through which musicians and musical collectives practice modes of existence and envision human futurity outside the strictures of sexism, classism, and xenophobia. Based on this perspective, I locate feminist worldmaking possibilities in these practices not only in the way their artistic imaginings envision new futures, but also in the very real ways that their organizing, production, and performance actively transform gender and sexual politics within their social context.

Advisor: Guilbault, Jocelyne
High-throughput volumetric imaging of neural dynamics in vivo

Brain is composed of complex neural networks that work in concert to underlie the animal’s cognition and behavior. Optical microscopy has become an indispensable tool to study brain due to its non-invasiveness and high spatial resolution. Two-photon fluorescence microscopy is the method of choice to image the optically opaque mammalian brain. However, conventional two-photon fluorescence microscopy has to perform serial 3D point scanning for volumetric imaging, which renders it extremely difficult to study neural circuits in 3D at subcellular resolution with sufficient imaging speed.Taking advantage of the fact that neurons remain largely stationary during in vivo imaging, when their temporal activities are the subject of interest, one can acquire an axially projected view of the objects without constantly tracking their 3D location. Incorporating a Bessel-like beam into a conventional two-photon fluorescence microscope extends the system’s depth-of-focus, which turns the 2D frame rate into an axially projected volume rate. As a result, Bessel focus scanning technology enables high-speed volumetric imaging without sacrificing the lateral resolution and reduces data size by an order of magnitude when compared with conventional serial 3D scanning. This thesis first explores the application of Bessel focus scanning technology in two-photon fluorescence microendoscopy to achieve high-throughput neural circuit imaging in deeply-buried nuclei of the mouse brain. The thesis then presents efficient data analysis approaches for high-throughput calcium neural imaging data. Finally, the thesis depicts a custom-designed and easy-to-operate two-photon fluorescence microscope combining several state-of-the-art technologies together, i.e., adaptive optics, Bessel focus scanning technology, and remote focusing, which will open up possibilities for neurobiology questions that could not be well addressed before.

Advisor: Ji, Na
Modified scattering for a scalar quasilinear wave equation satisfying the weak null condition

The objective of this dissertation is to study the long time dynamics of a scalar quasilinear wave equation
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This equation satisfies the weak null condition introduced by Lindblad and Rodnianski \cite{lindrodn,lindrodn2}. Lindblad \cite{lind} proved that, for small and localized initial data, this equation has a global solution. In the present work, we establish a modified scattering theory for the above equation. Such a modified scattering theory provides an accurate description of asymptotic behavior of the global solutions./nTo study modified scattering, we first identify a notion of asymptotic profile and an associated notion of scattering data. One candidate for the asymptotic profile is given by the asymptotic PDE
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which was derived by H\"{o}rmander \cite{horm2,horm,horm3}. In Chapter 2, we derive a new reduced system, called the \emph{geometric reduced system}, by modifying H\"{o}rmander's method. In our derivation, we make use of the optical function, i.e.\ a solution to the eikonal equation. In this setting, the scattering data is the initial data for our geometric reduced system, and it is chosen in a way such that the global solution to the quasilinear wave equation and the exact solution to the reduced system match at infinite time. One may infer, from this dissertation, that this new system is more accurate, in that it both describes the long time evolution and contains full information about it./nIn Chapter 3, we prove the existence of the modified wave operators for the scalar quasilinear wave equation. Fixing a scattering data which is the initial data for the geometric reduced system, we can first construct an approximate solution to the model equation. Then, by studying a backward Cauchy problem, we show that there exists a global solution to the scalar quasilinear wave equation which matches the approximate solution at infinite time./nIn Chapter 4, we prove the asymptotic completeness for the same equation. Given a global solution to the scalar quasilinear wave equation, we rigorously derive the geometric reduced system with error terms. These allow us to recover the scattering data, as well as to construct a matching exact solution to the reduced system.

Advisor: Tataru, Daniel
Efficient Second-Order Methods for Non-Convex Optimization and Machine Learning

Hessian-based analysis/computation is widely used in scientific computing. However, due to the (incorrect, but in our experience widespread) belief that Hessian-based computations are infeasible for large machine learning (ML) problems, the majority of work in ML (except for quite small problems) only performs the first-order methods. However, using sub-sampling and randomized numerical linear algebra algorithms, the computation of second-order methods can be efficiently extracted for large-scale machine learning problems. In this thesis, we consider three use cases of second-order methods as follows: (i) For non-convex optimization and/or ML problems, we propose inexact variants of three classic Newton-type methods---Trust Region method, Cubic Regularization method, and Newton-CG method, as well as a novel adaptive second-order method---AdaHessian. For classic methods, under the relaxed gradient and Hessian approximation, we theoretically prove that our methods achieve the same complexity as their full gradient and Hessian counterparts, and empirically show the efficiency and robustness of those Newton-type algorithms. For the novel AdaHessian algorithm, we incorporate moving average for the Hessian information, and illustrate its superb performance on large-scale deep learning problems compared to first-order optimizers; (ii) For machine learning and data science analysis, we develop a popular Hessian-based computation framework. The framework enables fast computations of the top Hessian eigenvalues, the Hessian trace, and the full Hessian eigenvalue/spectral density, and it can be used to analyze neural networks (NNs), including the topology of the loss landscape (i.e., curvature information) to gain insight into the behavior of different models/optimizers; (iii) For application, first, we show that Hessian-based metric can be used as the sensitivity metric to determine the robustness of every single layer of the NNs, and this can help perform mixed-precision quantization. Second, we demonstrate that second-order based adversarial attacks can achieve smaller perturbation as compared to first-order methods, which can be used as a powerful attack method to verify the robustness of a NN to adversarial attacks.

Advisor: Gu, Ming MG
Mahoney, Michael MM
Extending Temporal-Vector Microarchitectures for Two-Dimensional Computations

Modern computing is shaped by technology trends, like a slowing Moore’s law and lack of Dennard scaling, as well as application trends, like mass application of machine learning. Technology has constrained modern computer architectures to focus on energy-efficiency in order to improve, battery life, total cost of ownership, and even performance. Emerging deep-learning applications require computation volumes that increase exponentially and yet change in structure substantially every few years. One solution for both of these problems is specialized programmable architectures, that can adapt to new applications while specializing for the commonalities, and thus improving energy-efficiency./nThis thesis presents a set of two-dimensional architecture extensions for Hwacha an exist- ing vector-fetch architecture designed to improve energy-efficiency on two-dimensional computation while remaining fully programmable. This thesis discusses the constraints modern CMOS process technologies place on such an architecture, and describes several silicon implementations of similar architectures. Finally, this thesis presents the physical implementation of such extensions and their realized energy-efficiency gains on select applications.

Advisor: Asanović, Krste
How to Train Your Robot: Techniques for Enabling Robotic Learning in the Real World

Reinforcement learning has been a powerful tool for building continuously improving systems in domains like video games and animated character control, but has proven relatively more challenging to apply to problems in real world robotics. In this talk, I will argue that this challenge can be attributed to a mismatch in assumptions between typical RL algorithms and what the real world actually provides, making data collection and utilization difficult. In this talk, I will discuss how to build algorithms and systems to bridge these assumptions and allow robotic learning systems to operate under the assumptions of the real world - under realistic and practical assumptions on non-determinism, uncertainty and human supervision. In particular, I will describe how we can develop algorithms to ensure easily scalable supervision from humans, perform safe, directed exploration in practical time scales and enable uninterrupted autonomous data collection at scale. I will show how these techniques can be applied to real world robotic systems. Lastly, I will provide some perspectives on how this opens the door towards future deployment of robots into unstructured human-centric environments such as our homes, hospitals, and shopping malls.

Advisor: Levine, Sergey
Abbeel, Pieter
A Mathematical Algorithm for Estimating the Rotational Diffusion Coefficients from X-ray Photon Correlation Spectroscopy Data

The Brownian motion of particles can be decomposed into translational and rotational components, characterized by translational and rotational diffusion coefficients respectively. Measuring these two coefficients plays an important role in determining the structure and understanding the dynamic properties of materials, with benefit to research in such areas as molecular biology, material sciences. One of the promising tools for investigation of Brownian motion is the emerging X-ray Photon Correlation Spectroscopy (XPCS), which can capture dynamics of samples comprising large groups of particles in a broad range of time scales and length scales. Methods for estimating translational diffusion coefficients on the basis of the temporal auto-correlation analysis of XPCS images are used widely. However, to the best of our knowledge, there is no XPCS-based algorithm for assessing the rotational diffusion coefficients from such temporal auto-correlation data. In this thesis, we take a different route, and propose exploiting an angular-temporal cross-correlation function whose values are approximated by an estimator based on the collected experimental images. We prove the consistency of this estimator by deriving a tail bound. A numerical algorithm, MTECS, for estimating the rotational diffusion coefficients from the cross-correlation is designed and implemented. We demonstrate the capability of this algorithm by testing it on a range of simulated data.

Advisor: Sethian, James
Practical Solutions to Accelerating ASIC Design Development Using Machine Learning

This thesis presents a method for analyzing the efficacy of automated ASIC design tools, specifically by assessing the accuracy, practicability, automation, interpretability, generalizability and run-time efficiency of the algorithm. This is established by presenting one in-depth case study and two projects where machine learning can be used to address in-efficiencies in ASIC design, and include: 1) an analog circuit design framework that uses reinforcement learning (RL) to size parameters for a given circuit topology to meet a target specification, 2) an analog sub-clustering tool that uses graphical convolutional neural networks (GCNNs), and 3) using convolutional neural networks (CNNs) to detect defects in circuit yield. The goal is demonstrate that machine learning techniques can not only be successfully used for these three applications, but can be comprehensively analyzed to obtain practical and feasible solutions in circuit design./nSpecifically, the results of the RL analog circuit framework show that this solution achieves state-of-the-art run-time efficiency across six unique circuit topologies of varying complexity while considering layout parasitics. Additional analyses is also conducted to explain the decision-making of the algorithm, establishing that the obtained performances are explainable and analyzable in the context of circuit design. Furthermore, for several non-linear circuits, the algorithm obtains initial designs that are better than that of an expert, providing potential for better intuition into the boundaries of performance for these circuits./nThe GCNN framework for analog sub-clustering project demonstrates that high run-time efficiency with over 91% accuracy can be achieved while being fully automated, requiring no input from the designer for classification. In addition, the algorithm successfully scales to a variety of analog circuits, which is crucial in establishing practicality. The yield defect detection framework using CNNs shows that ML can be applied to a post-silicon application, successfully resulting in identification of yield defects in real and noisy scan diagnosis tests while reducing the layout search space significantly.

Advisor: Nikolic, Borivoje
Asanovic, Krste
Bioengineered Materials for the Investigation of Adipose Tissue and Adipocytes

Obesity and the metabolic diseases that result from it continue to increase across the developing and developed world little slow down in sight. As such, novel mechanisms for the study and treatment of these metabolic outcomes are needed to combat the ever-increasing epidemic. Our lab has focused on the development of two mechanisms geared toward the understanding of adipose tissue’s role in these abnormal metabolic processes through the development of an in situ forming implant whose goal is to burn excess circulating glucose and lipids, and through the characterization of a microphysiological device (MPS) to model insulin resistance formation in adipocytes. We have found that our hyaluronic acid (HA)-based implants are ideal candidates to perform the three main goals of the therapeutic implant showing robust capabilities to perform the three main goals: recruit stem cells, differentiate those stem cells to a beige adipocyte fate, and then maintain activation of these cells over a long period of time. Using two well characterized chemistries, thiol-acrylate Michael addition and Strain Promoted Azide Alkyne click (SPAAC), we can tailor our hydrogels to the targeted niche using short adhesion peptides, growth factors, degradable crosslinkers, and compounds that bind receptors on a cell’s surface. To this end we have shown that molecular weight plays no role in adipocyte function in vitro, but altering the hydrogel in increase the loss, or viscous, modulus of a hydrogel can. In addition, peptides and growth factors seem to enhance the differentiation of the cells plated on these substrates in ways that may provide further tailoring opportunities down the road for this therapeutic. I also created a bifunctional HA version of our implant with the goal of pushing forward aim three of the hydrogel project. Modifying the HA backbone with a DBCO group would allow us to conjugate in a tri-azole modified compound like Mirabegron, a β3-adrenergic agonist responsible for the differentiation of beige adipocytes. I was able to illustrate the efficacy of this bi-chemical modification and determine the final ability of the gels to perform in both capacities, which will be crucial for the beige adipocyte therapeutic in the future. Finally, we created a MPS that reproduces obese metabolic disease states in adipose tissue using induced pluripotent stem cells (iPSC) to differentiate them toward adipocytes and macrophages, modelling the interactions and development of these two cell types to validate this method as a testable platform for insulin resistance.

Advisor: Stahl, Andreas
Applications of Time Synchronized Measurements in the Electric Grid

Increased real-time monitoring of the electric grid is vital to meet the burgeoning challenges posed by load growth and diversification, renewable generation integration, extreme weather events, and cyber attacks. Grid operators must have situational awareness---an understanding of the system's evolving state---if they are to respond appropriately to challenging and changing system conditions. The proliferation of measurement devices in the electric grid is critical for situational awareness, but is not sufficient: measurements need to be converted to actionable insight to be useful. Here, computational tools that ingest measurements to infer system parameters and state are critical. While many such tools have been proposed in the research literature, their real-world use is limited, resulting in a circumstance where ballooning volumes of measurements are perceived as overwhelming rather than insightful, diminishing the incentive for further sensor deployments./nThis thesis argues for the creation of usable tools to bridge the chasm between research and deployment. Usable tools have practically realizable data input requirements and---in their forms and outputs---work in effective collaboration with human users. Such tools are well-suited to the demands of real grids, where data and prior knowledge remain scarce, and where safety critical decisions involve human participants./nThe thesis goes on to describe several usable tool algorithms for the use cases of topology estimation and monitoring, event detection, and event classification. Finally, it presents broad principles for the further development of usable tools.Throughout, the thesis emphasizes how high resolution, time synchronized measurements are particularly enabling for the creation of usable tools.

Advisor: Ramchandran, Kannan
von Meier, Alexandra