VictorJuiz/thesis huggingface dataset:
Low power audio feature extraction for machine learning applications
Keywords: Electrical Engineering.

Always-on sound classification is a desirable but power-intensive function for a variety of emerging applications such as wearables and IoT devices. The hardware energy consumption of sound classifiers is typically driven by signal digitization, feature extraction, classification model storage, and execution. Yet, the complexity of the signal of interest is often much lower than that of the raw signal acquired from the microphone and processed by a machine learning engine. For instance, semi-stationary sounds (e.g., engine noise, baby cry, running water, human chatter, etc.) are signals with lower information content than more complex sounds such as music or speech. In this dissertation, I will present the benefits of leveraging an engineered feature set to efficiently classify semi-stationary sounds. This approach requires one to three orders of magnitude fewer parameters and can be therefore trained over ten times faster than competitive deep learning models. I will also describe a circuit topology and system architecture that can be used to extract both engineered features as well as more general purpose ones. Our work resulted in a 32-channel analog filterbank IC for audio front-end signal processing. It employs a passive N-path switched capacitor topology to achieve high power efficiency and reconfigurability. The circuit's unwanted harmonic mixing products are absorbed by the machine learning model during training. To enable a systematic pre-silicon study of this effect, we develop a computationally efficient circuit model that can process large machine learning datasets in practical run-times. Measured results using a 130 nm CMOS prototype IC indicate competitive classification accuracy on datasets for baby cry detection (93.7% AUC) and voice commands (92.4% average precision), while lowering the feature extraction energy compared to digital realizations by approximately 2x and 10x, respectively. The 1.44 mm2 chip consumes 800 nW, which corresponds to the lowest normalized power per simultaneously sampled channel in recent literature
The LZ dark matter experiment : from detector development to early data
Keywords: Physics.

Due to a compelling body of astrophysical and cosmological evidence, dark matter has come to be accepted as a crucial ingredient of modern cosmology, yet its physical nature remains one of the most pressing questions in the field of physics. One historically favored model of dark matter is weakly interacting massive particles, or WIMPs. LUX-ZEPLIN (LZ) is a next-generation dark matter detector designed to achieve field-leading sensitivity to much of the remaining accessible parameter space within the WIMP dark matter paradigm. To help realize the full-scale LZ detector, the System Test R&D platform was constructed at SLAC National Accelerator Laboratory to validate the performance of critical LZ subsystems at scales approaching or comparable to the LZ design. In this dissertation, I present results showing that the passivation of the high voltage electrodes in citric acid leads to a significant reduction in spontaneous emission of single electrons, potentially limiting a major instrumental background by up to several orders of magnitude and enabling a more sensitive dark matter search. The LZ detector has now been assembled at the Sanford Underground Research Facility (SURF) in Lead, South Dakota and is taking early data. I also give a first look at commissioning data that captured the first light from electrons in the LZ detector, and share methods to validate the in situ performance of the high voltage electrodes
Machine learning for FEL optimization
Keywords: Applied Physics.

With improvements in linear accelerator (Linac) technology, high-energy electron beams accelerated and compressed by Linac systems can radiate in a high-gain X-ray Free Electron Laser (XFEL). After more than a decade of successful operation at the LINAC Coherent Light Source (LCLS) and other facilities around the world, the scientific community from various fields has a strong demand for further increase in the peak and average power of the next generation of XFELs. Reaching higher peak power is critical for coherent diffraction imaging of complex molecules, a key scientific motivation for the LCLS, which could lead to medical discoveries and drug development, for example, by imaging the structure of a complex virus in situ. In addition, higher power X-rays can facilitate fundamental research in strong field physics and other fields. The first part of the work to be discussed in this dissertation is the theoretical study of achieving higher XFEL power by implementing undulator tapering and related optimization algorithms. Second, simulation and experimental results of taper optimization are presented, showing that with the new devices in the LCLS-II including phase shifters and the variable gap undulator, and with the advent of different kinds of Machine Learning (ML) algorithms, FEL power can be successfully optimized. The next part of this work investigates methods of retrieving transverse and longitudinal information of the X-ray pulses including both power and phase and illustrates the impact of the optimized FEL power. Finally, the longitudinal field reconstruction research is carried out for explanation of the longitudinal pulses, including both power and phase. This research establishes novel techniques for FEL optimization and demonstrates that the improved devices and ML algorithms are promising experimental techniques to maximize FEL power
Machine learning methods for prediction of cardiovascular diseases
Keywords: Medicine. Department of Biomedical Data Science

Artificial intelligence and machine learning methods in cardiology are increasingly used in a wide array of remote monitoring applications and within clinical workflows. The acceptance of such methods is due to the demonstrated benefits of deep learning algorithms to achieve state-of-the-art results and surpass human accuracy in challenging clinical tasks. Concurrently, the advancements in wearable technology have allowed for an unprecedented look into human health and activity. This snapshot into the objective measurements of human health can enable clinicians, researchers, and patients to measure and track a new dimension of human health at a granularity not previously possible. Herein, I present work that focuses on methods to advance remote monitoring and prediction models for cardiovascular diseases. First, I introduce a multi-task deep learning method to jointly assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation. Second, I describe a multimodal neural network model to predict the etiology of left ventricular hypertrophy from electrocardiograms and echocardiograms. Together, these results demonstrate new applications of machine learning tools that may assist in the study of cardiovascular disease progression inside and outside a clinical setting
Making connections and investments : an ethnography of China-to-US venture capital business
Keywords: Anthropology.

From the second half of 2014 to the first half of 2018, with Chinese government's financial and policy support and also a relatively relaxed US-China trade relation, unprecedented amounts of money from Mainland China flooded Silicon Valley and became an important new source of capital for tech startups in the US. This wave of China-to-US venture investment, which is a reversal of more than two decades ago when the US money poured into China, was channeled by a class of Chinese transnational venture capitalists who shuttled between the two countries, navigated and mediated flows of information, capital and people. Given the changing US-China relation, Chinese venture capitalists have to develop different strategies to craft their Chinese identities in order to secure, legitimize and even obtain an advantage when competing with their US counterparts. With their Chinese characteristics, they are not "just a member" of the local investor community in Silicon Valley. Rather, their status and business activities have always been embedded in and understood through the transnational context. My dissertation studies this wave of China-to-US venture capital business from 2016 to 2018. Starting with a historical review of its emergence and decline, my dissertation is mainly concerned about exploring the central ideas and practices through which Chinese venture capitalists manage uncertainty and maximize capital gains from transnational investment. As they describe venture capital being a "people-centered" business, networking, or in their words, "jianli guanxi" (building guanxi/建立关系), has been the key. Through actively making and managing their web of guanxi, Chinese venture capitalists recruit talents, sort startup deals, evaluate investment cases and exit at the right time and at the right price. Contrary to the stereotype of venture capital being a male-dominated industry, my fieldwork reveals that Chinese women have taken a leading position in cultivating and maintaining these human relationships which support the transnational venture capital business. My dissertation thus has a special focus on women venture capitalists and explores how they, as active and full participants who are emotional and rational beings, initiate and engage in cultivating guanxi. In addition to accomplishing business goals, I argue that the process of making connections also produces values and sentiments which structure the business and also shape their life as venture capitalists
Mapping the structure-activity landscape of ARHGAP36
Keywords: Chemical and Systems Biology.

ARHGAP36 is an atypical Rho GTPase-activating protein (GAP) family member that drives both spinal cord development and tumorigenesis, acting in part through an N-terminal motif that suppresses protein kinase A and activates Gli transcription factors. ARHGAP36 also contains isoform-specific N-terminal sequences, a central GAP-like module, and a unique C-terminal domain, and the functions of these regions remain unknown. In this dissertation, I describe our three-part approach to map the ARHGAP36 structure-activity landscape. Using splice variants and truncation mutant analyses, we identified an isoform-specific N-terminal sequence that inhibits ARHGAP36-mediated Gli activation and suppresses ARHGAP36 recruitment to the plasma membrane. The GAP-like and C-terminal domains can counter these inhibitory effects, in part by promoting trafficking to the plasma membrane and primary cilium, respectively. Through a high-throughput mutagenesis screen, we further discovered several residues in the GAP homology domain that are essential for Gli activation. Finally, we applied these variants in a comparative proteomic analysis that identified several GAP-like domain-dependent binding partners and demonstrated one interactor, prolyl oligopeptidase-like protein, to be a direct ARHGAP36 antagonist. In combination, these systems-level analyses have revealed an ensemble of mechanisms that could enable context-specific regulation of ARHGAP36 activity, establishing an experimental framework that can be applied towards other signaling proteins
Market design for platforms, large games, and comparative statics
Keywords: Business.

In recent years the amount of data collected by online platforms has increased massively. These data, together with the unique ability of online platforms to design their marketplaces, provide platforms with an unprecedented opportunity to make better market design choices to enhance the welfare of platforms' participants and increase platforms' revenues. The first chapter of my dissertation (co-authored with my advisors Ramesh Johari and Gabriel Weintraub) studies one such market design problem that relates to quality selection. Online markets typically consist of many small buyers and sellers, and thus, in order to analyze market design decisions in online platforms it is crucial to model and to have a better understanding of large games, i.e., settings with many interacting agents. The second chapter of my dissertation (co-authored with Gabriel Weintraub) studies some properties of mean field models which are used to model settings with a large number of interacting agents. The third and fourth chapters of my dissertation provide tools that enable deriving comparative statics results in complex uncertain environments
Materials design and electrochemical engineering for pollution control
Keywords: Materials Science and Engineering.

The ultimate goal of sustainable development is to preserve the Earth for our future generations. Clean water, fertile soil and fresh air are basic necessities for human wellbeing; however, they are among the natural resources most vulnerable to pollution. Using my expertise in materials science, catalysis, and electrochemistry I developed during my doctoral studies, I synthesized unique materials and designed novel reactors to find solutions for various environmental problems. In Chapter 1, I will motivate the need for developing electrochemical technologies for solving environmental problems and give a brief introduction to the thermodynamics and kinetics in electrochemical systems. Chapter 2 will describe my efforts in reducing the energy and chemical demands for organic wastewater treatment. Specifically, I discovered that copper single atoms incorporated in graphitic carbon nitride can catalytically activate hydrogen peroxide to generate hydroxyl radicals, which then non-selectively oxidizes organic pollutants into harmless small molecules. To further eliminate the dependence of the treatment system on H2O2, I developed a H2O2 electrolyser for on-site generation of H2O2 from air, water and renewable energy. These two innovations work in tandem to deliver a wastewater treatment system with substantially reduced energy and chemical inputs compared with conventional advanced oxidation processes. Chapter 3 will demonstrate a method for removal of heavy metals from contaminated soil. This method is composed of a recirculating soil washing system using EDTA solution and an electrochemical filter that removes heavy metals from the EDTA solution by electrodeposition. I discovered that applying an alternating-current bias with an optimal waveform mitigates side reactions and enhances the electrodeposition efficiency of the electrochemical filter compared with applying a direct-current bias. In addition, the regeneration ability of the electrochemical filter makes this platform suitable for recovery of heavy metals from a diverse range of waste streams. Chapter 4 will present a way to promote the catalytic activity of platinum for the degradation of formaldehyde in air, namely by covering the platinum surface with a nanoscale-thin layer of aqueous electrolyte. This discovery was made possible by a platinum-coated nanoporous polyethylene membrane, which is able to construct and retain an evaporation-stable aqueous electrolyte layer over platinum with an effective thickness of just a few tens of nanometers. In a broader context, my work demonstrates that the knowledge obtained through the development of gas diffusion electrodes in fuel cells can be applied to gas-phase heterogeneous catalysis beyond electrochemical systems
Mean field methods for stochastic control and optimization problems
Keywords: Mathematics.

In probability theory and physics, mean field methods study the dynamics of high dimensional stochastic models by applying the law of large numbers in a sophisticated form, in order to obtain simpler models that approximate the original system. In the setting of a multi agent system, the basic idea is to replace the individual interaction effects induced by other agents upon a single agent with an averaged effect. We can reduce a complex, multi agent system to a single agent problem. In this thesis, we study the use of mean field methods in some stochastic control and stochastic optimization problems. In particular, we study mean field analysis of distributed optimization algorithms, and mean field analysis of a class of stochastic control problems with many entities
Measurement of hall conductivity and gated magnetization by torque magnetometry
Keywords: Physics.

This thesis details the development of a new method to measure Hall conductivity for highly resistive samples utilizing a torque magnetometry method. Traditional methods to measure the Hall effect are inaccurate for highly resistive samples due to pollution from longitudinal resistance and sample heating. We have developed a new technique to measure the Hall effect using dissipationless currents and without point contacts to address such problems. A Corbino disk exhibits a magnetic dipole moment proportional to Hall conductivity when voltage is applied across a test material. This magnetic dipole moment can be measured through torque magnetometry. The symmetry of this contactless technique allows for the measurement of Hall conductivity in previously inaccessible materials. I first describe the method for fabricating cantilevers with integrated Corbino disks and the procedure used to measure Hall conductivity with such devices. The primary measurements of Hall conductivity through the new Corbino disk torque technique were performed on indium tin oxide (ITO). After performing tests for systematic errors with applied voltage and current, we measured the Hall conductivity of conductive ITO as a reference material to establish the accuracy of the Corbino disk torque technique. We next fabricated Corbino disks with resistive ITO and annealed across the metal-insulator transition to measure Hall conductivity of highly resistive samples. After annealing, we found magnetism in the ITO through both bulk torque magnetometry and the anomalous Hall effect. Such findings demonstrated both the accuracy of the new technique and the presence of debated inherent magnetism in annealed ITO. Having integrated a Corbino disk on a cantilever to measure the Hall effect, we are now expanding our ability to fabricate and measure cantilevers with integrated circuit elements. We have begun to measure torque magnetometry of miniaturized cantilevers with integrated gates. The initial miniaturized cantilevers were constructed to hold exfoliated graphene-hBN stacks. Smaller designs improved the torque sensitivity by a factor of 1000. Finally, such miniaturized cantilevers with metal layers could be extended in the future to measure Hall conductivity of smaller exfoliated flake samples
MEASURING PM2.5 VARIATION AND ITS EFFECTS ON LIVED COMMUNITY EXPERIENCE AND HEALTH IN SAN FRANCISCO
Keywords: Air Pollution
PM
San Francisco
Health Disparities
Community Health and Prevention Research
Thesis

Given the clear importance of air quality on health and the sparse amount of air quality monitoring stations around California, Brightline Defense Project (“Brightline Defense” or “Brightline”), a Bay Area based environmental justice non-profit, deployed an air quality monitoring network to better understand air quality and low-income urban resident experience of air quality in San Francisco. This project arose as part of the California Air Resources Board’s (CARB’s) response to AB617. CARB established the Community Air Protection Program
(CAPP) with the goal to reduce exposure in communities most impacted by air pollution. Based on community identified need for accurate and timely data of specific air pollutant levels at more locations than the single BAAQMD monitor was providing in San Francisco, Brightline began their localized air monitoring project. Brightline targeted disadvantaged communities who historically may not have had the resources to monitor air quality with personal low cost sensors. They deployed low cost sensors in five San Francisco neighborhoods, two categorized as disadvantaged communities, two requested by community partners, and one for co-location. They also conducted community surveys with the aim to better understand how air quality is affected by wildfires, the differences in air quality between neighborhoods and across time, and how residents of different neighborhoods feel air quality impacts their health.
Measuring the quality of teaching and teacher training in low-income settings
Keywords: Education.

Despite increases in access to education in recent decades, student learning remains a huge challenge, with 250 million children around the world still unable to read or write. Controlling for socioeconomic factors, teaching is well established as the most important school-based determinant of student learning. High quality teaching is able to offset the learning shortfalls of marginalized students and improve long-term outcomes, such as university graduation rates and wages. However, many teachers in developing countries are under-prepared, lacking the knowledge and skills to support student learning. Beyond the simple knowledge that good teachers are important, it is imperative that policymakers understand which specific teaching practices most contribute to student learning and how to develop these through teacher training. This dissertation measures the quality of teaching practices and of professional development (PD) programs intended to improve teaching and thereby student learning in several low-income settings. Each paper applies a different method to solving this measurement problem and informing the design of future PD programs. The first is a large-scale, randomized evaluation of a national PD program in China. The second develops a survey instrument to standardize reporting on teacher PD programs and applies it PD programs in low- and middle-income countries in a meta-analysis. The third leverages the random assignment of teachers to students in six U.S. school districts to estimate the impact of teaching practices on student learning and how these vary with student characteristics
Media manipulation at home and abroad : evidence from the Arab world
Keywords: Political Science.

This dissertation examines how Arab autocrats use media to influence domestic and foreign publics. While much of the literature on authoritarian media focuses on indoctrination, signaling, and censorship, this dissertation investigates how autocrats manipulate narratives and discourse to maintain their power at home and further their influence abroad. The first paper claims that autocrats advance narratives about threats and conspiracies to reduce dissent. These narratives can shape public beliefs about the regime's competence and are difficult to fact-check by independent media. Using topic modeling on hundreds of thousands of articles from Egypt and Syria, the paper shows that protests were often associated with an increase in discussions of threats and conspiracies. The second paper then turns to the effects of these narratives on public attitudes and online behavior. To do so, the paper uses data from the Arab barometer, twitter followers of major Egyptian newspapers, and an original survey experiment in Egypt. The results show that discussions of conspiracies and threats increase the likelihood that the public prioritizes foreign threats over domestic problems and improve public trust in the government. The third paper examines how regimes employ transnational media to manipulate foreign publics. Using the cases of Al-Arabiya and Al-Jazeera, the paper claims that Saudi Arabia and Qatar utilize the agenda-setting and framing powers of transnational media to influence regional publics. Specifically, the two countries deploy transnational media to highlight topics that advance their influence, undermine competitors, and support the interests of their sponsor countries