VictorJuiz/thesis huggingface dataset:
Improving and leveraging the interpretability of deep neural networks for genomics

In recent years, the field of genomics has been characterized by an extraordinary influx of novel high-throughput technologies and techniques. These methods produce enormous datasets which quantify cellular state across several axes of measurement. Given their large size and complexity, machine-learning algorithms (particularly deep neural networks) have increasingly been relied upon to ingest and model these datasets. In fitting to the data, these neural networks demonstrably learn the overarching patterns and subtle nuances in the underpinning biology. In order to be most useful for scientific discovery, however, these models need to be able to convert their learned scientific principles into a form that human scientists can then understand and use. Unfortunately, this transfer of knowledge has been limited due to the overall uninterpretability of deep neural networks, and the difficulty of distilling a model's individual decisions across a dataset into a set of human-understandable rules. In this thesis, I will present my work on addressing these challenges. First, I will discuss my development of the Fourier-transform-based attribution prior, which trains deep neural networks to be more stable and interpretable, thereby allowing them to more consistently and reliably reveal the biological patterns driving various genome-regulatory events. Subsequently, I will present my work on a computational framework which distills and summarizes a neural network's individual decisions into a small set of global protein-binding rules that can then be understood by a human scientist. Through several case studies, I will show that these methods can be applied in a real-world setting for scientific discovery. Although the methods presented in this thesis are primarily applied to the problem of genomics, many ideas discussed here are easily (if not directly) applicable to many other domains of machine learning. Together, these developments represent a major advancement in how humans extract learned information from deep neural networks. This can have resounding improvements and impacts in not only how neural networks can be used for science, but also in our understanding of how they internally learn in general

Advisor: Kundaje Anshul
Fordyce Polly
Horowitz Mark (Mark Alan)
Improving cloud efficiency with online learning

Cloud computing has emerged as the dominant platform for computing due to its ease of use and scalability advantages. To maintain reliable and high-performance services at a cost-effective manner, cloud providers need to efficiently administer a large number of management tasks. Traditional heuristic-based static management policies often fall short of adapting to the varying demands of diverse cloud workloads. In contrast, machine learning (ML) assisted policies have the ability to learn patterns of different workloads and use predictions to guide their management decisions. This dissertation focuses on how to effectively and safely use online learning techniques to improve management of cloud platforms. We first present SmartHarvest, a system that improves server CPU utilization by dynamically assigning the number of CPU cores to Virtual Machines (VMs) of different priorities. It uses an online cost-sensitive multi-class classification algorithm to predict the core demand of primary VMs and safely harvest the predicted unused cores to run a secondary best-effort VM. In order to incorporate online learning more easily to various types of server node management agents, we then introduce SOL, a Safe On-node Learning framework for developing and operating learning-based node agents. SOL simplifies agent implementation and enables developers to focus on the design of agent-specific components (e.g., power management or memory migration). SOL further ensures that agents implemented on top of it are robust to the range of abnormal or failure conditions that can occur in production, so they can be safely deployed alongside customer workloads in the cloud. We demonstrate the use of SOL by implementing three node agents that leverage online learning to improve management of CPU cores, node power, and memory placement

Keywords: Electrical Engineering.
Advisor: Kozyrakis Christoforos
Rosenblum Mendel
Zaharia Matei
Improving contacts and defect characterization toward industry-ready 2D semiconductors

For six decades, silicon technology has driven advances in computing; however, silicon is approaching its fundamental scaling limits. In contrast, two-dimensional (2D) semiconductors offer excellent electrical properties in sub-nanometer thin channels, and they are compatible with 3D heterogeneous integration. This could enable ultra-dense logic and memory stacked vertically on a chip, unlocking vast new possibilities in computing advances. While 2D materials show great promise, their parasitic contact resistance and defect densities are well-known limitations for their electrical performance. In this thesis, I will describe the problem of contact resistance to 2D semiconductors and uncover our advances in understanding the interaction between metal contacts and the 2D semiconductor MoS2. I will also present a statistical analysis on the effect of specific metal properties on contact resistance with MoS2. I will then show how we have utilized these findings to demonstrate record-low contact resistances to monolayer MoS2. Next, I will address MoS2 defects, how they affect transistor performance, and how to quantify their densities quickly and non-destructively using Raman spectroscopy, which enables 100x higher throughput for material optimization. Combined, these advances provide fundamental insights into 2D semiconductor optimization, while bringing them closer to industrial adoption

Keywords: Electrical Engineering.
Advisor: Pop Eric
Salleo Alberto
Saraswat Krishna
Improving TRN performance over incomplete and inaccurate map regions

The primary focus of this thesis is on improving the operation of terrain relative navigation (TRN) in areas with incomplete map information. TRN is a navigation algorithm for vehicles operating in GPS denied environments. It operates by correlating measurements of the terrain to a prior terrain map in order to localize the vehicle. To address self-similarity between different sections of the terrain, TRN uses a non-parametric sample based representation of the belief distribution of vehicle locations such as a particle filter. The basic TRN measurement update requires map information for every particle in order to perform the TRN correlation step. When a subset of the particles has projected measurements that fall in an unmapped area, these off-map particles create an incomplete map problem. This thesis addresses the incomplete map problem through a new measurement update for TRN. This is called the Subcloud measurement update as it performs different updates for the on-map and off-map subsets of the particle cloud. At the core of the Subcloud measurement update is a new measurement probability for the off-map particles which is derived from the distribution of terrain heights in the off-map area. When this distribution is correct and other standard TRN assumptions are met, the Subcloud measurement update provably produces the correct posterior distribution in the on-map area. This means TRN will converge correctly when the vehicle is on-map and will correctly identify when the vehicle is off-map. For more realistic situations where the true distribution of terrain heights is not known, this thesis develops bounds on the safe operation and a modification to the Subcloud measurement update which guarantees the Subcloud measurement update will remain within these safe bounds. Finally, working with the Monterey Bay Aquarium Research Institute (MBARI), the Subcloud measurement update is demonstrated in the field for autonomous return-to-site missions with incomplete maps near the ocean floor. This thesis also presents preliminary work on improving the operation of TRN in areas where the map is inaccurate. This contribution is a pre-filter for TRN measurements that works to prevent inaccurate map information from entering the primary TRN estimator. This pre-filter exploits the presence of a multibeam sonar to run a bank of secondary TRN filters which vote to identify a consensus estimate and reject information from inaccurate map regions. This framework is tested in post processing on data from the site of an underwater volcanic eruption

Keywords: Mechanical Engineering.
Advisor: Gerdes J. Christian
Rock Stephen M.
Schwager Mac
In Mail We Trust: Exploring the Effects of All-Mail Voting on Voter Turnout in the 2020 US Presidential Election

The highly polemicized 2020 US presidential election occurred within the logistically challenging contexts of the Covid-19 pandemic, prompting states to implement widely varying electoral reforms, including increased access to Vote-by-Mail (“VBM”) and adoption of All-Mail Voting (“AMV”). Despite polarized rhetoric around the increased use of mail ballots and varied degrees of VBM expansion, voter turnout universally increased across the US. Historic literature fails to explain the disparate effects of various electoral reforms on voter turnout; this paper thus seeks consensus through consideration of the 2020 election and contemporary literature. This paper begins with a synthesis of the current state of electoral reform legislation and theories on voter participation. It then synthesizes current and historic literature on the effects of various electoral reforms on voter turnout, turning ultimately to the effects of VBM and AMV on voter turnout in US elections. It then addresses historic use of VBM before analyzing the performance of VBM and AMV in the 2020 election. Lastly, it considers the effects of electoral reforms on demographic subgroups and contests commonly cited alternative explanations. Historic findings indicate that AMV increases voter turnout by 4%, while the 2020 election suggests rates closer to 6%. Increased access to VBM was found to have less significant, though nonetheless positive effects on voter turnout. Analysis of 2020 election data also demonstrates that partisan lean correlates with increased availability of VBM in the 2020 election, and that such availability, with the exception of states experiencing already high turnout rates, positively correlates with significant increases in voter turnout. Data from administration of the 2020 election in California also suggests that AMV spurs increased turnout among Black, Latino, Asian, and youth voters and accordingly decreases the turnout gap across demographic subgroups. Further research into the effects of AMV on the turnout of demographic subgroups is nonetheless required; additionally, further study on the intersectional effects of AMV and other electoral reforms is necessary to fully understand the mechanisms by which voter turnout changes.

Keywords:  Fisher Family Honors Program in Democracy
Electoral Reforms
Voter Turnout
Vote-by-Mail
All-Mail Voting
2020 US Presidential Election
US Elections
Covid-19 Legislation
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Thesis
Advisor: Bonica Adam
In search of 'other' venuses : images of vulva-covering Venus at the margins of the ancient world and modern typological discourse

Praxiteles' Knidian Aphrodite was one of the most famous statues in the ancient Roman world. It was so famous that, 1500 years after its destruction, more than 500 images that replicate its distinctive vulva-covering gesture survive. In the past, antiquarians and art historians have collected, categorized, and cataloged metadata about these "replicas." Using this metadata, they have constructed a typology that has allowed them to reconstruct the Knidian Aphrodite's form. However, it has also established a material and visual hierarchy of images. Moreover, it has severed the contextual ties that bind surviving images to the diverse ancient contexts for which they were created. This dissertation investigates the ancient significances of these vulva-covering goddess (VCG) images and their modern discursive formations. It studies three groups of VCGs: alabaster cinerary urns made in Republican Volterra; bronze Provincial Roman coins from Knidos, Philadelphia, and Saitta in the Roman province of Asia; and bronze statuettes from Antarados and Marathus on the coast of the Roman province of Syria. This dissertation demonstrates that scholars have constructed each of these groups of ancient images as being at or beyond the margins of the modern typology used to describe and interpret "replicas" of Praxiteles' Knidian Aphrodite. It deconstructs this discourse. Then, it writes three counternarratives of the contextual meanings of VCG images for each of these three groups of images in the ancient times and places for which they were created. This project demonstrates that the images that comprise these three assemblages cannot be dismissed as poor-quality imitations of Praxiteles' Knidian Aphrodite. Instead, it shows that the patrons and makers of these images selectively appropriated elements of the form and reputation of the Knidian Aphrodite. They gradually created the tradition of simulacra of the Knidian Aphrodite that remains apparent to the present day. However, while these images share aspects of Praxiteles' statue's form, their significances are significantly altered. My investigation contributes three things to ancient studies. First, it offers three chapter-length case studies that articulate how the signification of form was a dynamic process that changed over time. Second, it analyzes the theories and methodologies by which modern historians of ancient art have collected, categorized, and cataloged VCG images. Third, this project concludes that we will only discover what "Other" VCG images exist with stories to be told if we discard the deeply problematic VCG typology

Keywords: Classics.
Advisor: Trimble Jennifer
Ceserani Giovanna
Derbew Sarah F. 
Parker Grant Richard
In the World of Disorder: Trafficking, Vagabonds, and State in Late Qing Jianghuai

In recent years, historians of late imperial and modern China have paid increasing attention to the practices and logic of traffic in women in the contexts of wife-selling and transactional families. Trafficking in women as a large-scale and networked crime, however, has remained insufficiently researched. This paper examines crimes of trafficking in late Qing Jianghuai primarily based on materials including legal cases, governmental documents, periodicals, and literature. I argue that the development of crimes of trafficking in late Qing Jianghuai was a result of integrated factors, including the market for women, the growing group of vagabonds that emerged in social disorder, and the mobile local society characterized by the brokered labor market and transportation networks. The provincial state’s regulation of the crimes thus reveals the growing conflict between the unruly and mobile population who took advantage of the disorder and the local government that aimed to restore the order by controlling its people’s information and mobility. In Part I, I explore how crimes of trafficking developed by increasingly integrating with the flourishing crimes of smuggling and banditry that produced an enlarging population of vagabonds. In Part II, I discuss trafficking as a networked and professional crime from the perspective of family, community, and employment relations and the social groups of professional traffickers. Part III investigates the local state of Jianghuai’s efforts of eliminating the crimes by regulating the gender order and bureaucracy. By illustrating how regional dynamics created a distinctive image of crimes of trafficking in Jianghuai, this study serves as a building block for future studies that explore how the entanglements between trafficking as an organized crime, river travel, and local state-building reflected and influenced the socio-economic and political changes in late Qing China.

Keywords:  Stanford Global Studies
Qing Dynasty (China)
Human trafficking
Organized crime
East Asian Studies
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Thesis
Advisor: Sommer Matthew
In vivo mapping of neural genes in neuroendocrine, neuronal, and glial cells during embryonic lung development

Small-cell lung cancer (SCLC) is the most deadly form of lung cancer. The disease primarily arises from pulmonary neuroendocrine cells (PNECs), a specialized epithelial cell subtype with numerous sensory and regulatory functions. During development, PNEC progenitors undergo slithering, a unique form of directed migration. Reactivation of slithering in adulthood may explain how normal PNECs transform into tumor cells. Currently, the molecular basis of slithering is unclear. Furthermore, expression of the earliest known PNEC marker, Ascl1, is not exclusive to the cell class during embryonic development. The timing and pattern of Ascl1 expression in these non-PNEC lineages have not been well characterized. Furthermore, subtypes of SCLC may arise from or be influenced by these non-PNEC Ascl1+ cells. Using an inducible-Cre mouse model, this study finds two distinct waves of Ascl1 expression in pulmonary development. Neuronal and glial cell precursors exhibit the first, transient wave; PNEC progenitors exhibit the second wave and sustain Ascl1 expression into adulthood. The same model was used to evaluate the slithering candidacy of several proneural genes identified via in silico analysis of single-cell RNA sequencing (scRNA-seq), with results verified by RNA hybridization and/or immunostaining. The proneural transcription factor Myt1 appears more consistent with a post-slithering role, as well as roundabout receptors Robo1,2 canonically known for their involvement in axon migration. scRNA-seq analysis also detected expression of the actin bundler Fscn1 in PNEC progenitors along with cells of the pulmonary stroma. These results provide the beginnings of a temporal framework for neuroendocrine, neuronal, and glial development that may aid in mapping expression timelines for other predicted lineage markers. More broadly, it is a first step towards understanding the signal pathways that underlie slithering, which may provide insight into developing targeted therapies for SCLC and other pulmonary diseases of neuroendocrine origin.

Keywords:  Department of Biology
Biology
Pulmonary
Neuroendocrine
Slithering
Ascl1
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Thesis
Advisor: Kuo Christin
Red-Horse Kristy
InAlN/GaN high electron mobility transistors for Venus surface exploration

Historical and proposed missions to the surface of Venus have been severely limited in both scope and duration by the lack of robust microelectronics that can operate within Venus' extreme environment. Traditional silicon technology cannot survive on Venus without the use of expensive and bulky active cooling measures due to the scalding surface temperature of 465°C. To address this technological gap and enable extended duration robotic surface exploration on Venus, I propose the use of wide-bandgap gallium nitride (GaN) technology for uncooled microelectronics (e.g., sensors, micromechanical resonators, transistors). In this thesis, I present original research on the design and microfabrication of InAlN/GaN high electron mobility transistors (HEMTs), which leverage a two-dimensional electron gas (2DEG) conducting channel and can provide sensing, power, and telecommunications for a Venus lander or rover. I explore the use of several Schottky gate electrode materials that are attractive candidates for reliable high-temperature operation. The experimental results from electrically characterizing HEMTs utilizing these gate materials up to 600°C in air for durations of up to 6 days are presented. I then analyze Schottky barrier properties and make recommendations for engineering superior Schottky contacts. The electrical operation of HEMTs exposed to simulated Venus surface conditions (465°C, > 90 bar, supercritical CO2 ambient) for up to 10 days is presented. Finally, I present the first every demonstration of an uncooled GaN device successfully operating in situ simulated Venus surface conditions. The in situ experiment concluded after 5 days and 21 hours of device operation, which is a 70-fold greater duration than the 2 hour record operating length of silicon-based microelectronics used on the Venera 13 mission. As a whole, these contributing proof-of-concept experiments support the use of the InAlN/GaN HEMT platform and provide a road map for further maturing this technology for uncooled microelectronics

Keywords: Electrical Engineering.
Advisor: Senesky Debbie
Chowdhury Srabanti
Saraswat Krishna
Inflation in Inhomogeneous Universes

The standard analysis of inflation relies on the fine-tuned initial condition of a homogeneous patch. Besides the improbability of such an initial condition, homogeneity should ultimately be a consequence of inflation and not its cause. To resolve this “initial patch problem”, this thesis will discuss how inflationary expansion can arise in 2+1D and 3+1D cosmologies with arbitrarily large inhomogeneities, by leveraging a tool from geometric analysis known as mean-curvature flow. It will also propose future directions for studying the onset of inflation under inhomogeneities and possible applications of mean-curvature flow to holography.

Keywords: Physics
Inflationary universe
Inhomogeneous initial conditions
Initial patch problem
Mean-curvature flow
Inhomogeneous universes
Cosmology
General relativity (Physics)
High-energy theory
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Thesis
Advisor: Senatore Leonardo
Silverstein Eva
Information and fairness in resource allocation problems

This thesis explores the role of information and fairness in resource allocation settings with applications for social good. The thesis has two parts. At a high level, the first part asks whether and how we can use information as a natural lever to maximize social welfare in the presence of strategic incentives. More specifically, we consider a mechanism design model of objects and agents arriving over time. Agents have private types and waiting costs. We show that the welfare-maximizing mechanism can be implemented in two simple ways that both require some pooling of information. Furthermore, we partially extend this result to a setup where agents have heterogeneous outside options. The second part asks whether and how inherent informational differences across different individuals can explain documented disparities in the allocation of social goods, and if so, what alternative policies can mitigate such disparities. Motivated by disparities in college admissions, we introduce a theoretical framework to study how a decision-maker, concerned with both merit and diversity, selects candidates under imperfect information, limited capacity, and possibly legal constraints. We apply this framework to study the tradeoffs of existing policies (dropping standardized testing, using the top percent rule) as well as to find the optimal selection policy for a generalized optimization problem

Keywords: Management Science and Engineering.
Advisor: Ashlagi Itai
Lo Irene (Management science professor)
Weintraub Gabriel