VictorJuiz/thesis huggingface dataset:
Disappearing acts : Allen Ruppersberg's ephemeral impulse

This dissertation analyzes the work of the American artist Allen Ruppersberg (b. 1944), positioning his use of ephemera as a model for understanding the circulation and afterlives of undervalued objects. Ephemera is a mutable term, but it is typically used to refer to materially slight objects originally designed for passing use. This detritus--things such as pamphlets, postcards, newspaper clippings, pulp paperbacks, and roadside posters--is altered, rearranged, and repurposed within Ruppersberg's complex and often-humorous installations, collages, and drawings. In contrast to his peers in the Conceptual art movement of the 1960s and 1970s, whose work focused on the primacy of the idea over the object, Ruppersberg is concerned with the aesthetic and narrative potential of forgotten or outmoded items, which attain fresh resonance in the face of an increasingly disembodied, digitized media landscape. The aims of the dissertation are two-fold: one, to examine the implications of Ruppersberg's heterogeneous artistic project, in particular his engagement with the discarded materials of vernacular American life, and two, to examine how the unstable category of ephemera bedevils conventional medium hierarchies and metrics of worth, creating space for subversive, idiosyncratic considerations of value

Keywords: Art and Art History.
Advisor: Lee Pamela M. 
Meyer Richard
Phelan Peggy 
Troy Nancy J.
Discomfort with Disinformation: Assessing U.S. Aversion to Sustained Counter Disinformation Efforts

A series of crises during the 2010s brought disinformation and propaganda to the forefront of the foreign policy agenda, ranging from ISIS jihadist propaganda to state-sponsored disinformation campaigns interfering with the 2016 presidential election. While scholars and policymakers began to focus on the effect 21st century social media would have on international affairs, there has been less attention given to the willingness of the United States government to engage in “information wars.” Since World War I, U.S. counter disinformation organizations have undergone a cycle of rapid formation and decay. They are built during times of crisis, and after a short duration, they are dismantled. This pattern is puzzling because scholars of organizations have found government bureaucracies to be notoriously enduring. This thesis examines why counter disinformation organizations (CDO) are an exception and what are the underlying forces contributing to their short life spans. Drawing on presidential speeches and strategic documents, academic and journalistic reporting, internal memos, and interviews of key bureaucratic actors, I test four hypotheses that affect CDO tenure in a series of case studies: the legal mechanisms through which the CDO was created, the sense of urgency that propelled policymakers to create the organization in the first place, executive and congressional policymakers’ attention in the counter disinformation mission, and bureaucratic tension with peer and host organizations. My results indicate that a disinclination for strategic communications and information work exists within the U.S. national security apparatus that requires high level executive support to overcome. This support tends to be tied to a crisis that happens to have informational elements. These findings illuminate institutional habits within the U.S. national security bureaucracy that will help determine what government’s role should be in future information conflicts as the country moves towards a “whole-of-society” approach to combating disinformation and propaganda in a digital age.

Keywords: Disinformation
Propaganda
Organizational behavior
Organizational culture
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Thesis
Advisor: Zegart Amy
Lin Herbert
Discovery of lifestyle-associated microbes, pathogens, and bacteriophages through deep metagenome sequencing of the gut microbiome

The human gut microbiome, or microbiota, consists of trillions of microbes. These bacteria, archaea, eukaryotes, and viruses influence each other and the human host. The gut microbiota responds to environmental pressures including diet and colonization by external microbes, and in turn, has a profound impact on human health and disease. Recent advances in cultivation of human gut microbes, high-throughput metagenome sequencing, and cloud computing, together with more than a decade of accumulated data have culminated in a watershed moment for the field with the publication of several large-scale human-associated prokaryote and viral reference databases. These reports also highlight a wealth of knowledge yet to be acquired from two sources harboring the most unexplored diversity: the gut microbiome of non-industrialized humans, and the global gut virome. This dissertation significantly expands our understanding of these communities through discovery of nearly 100,000 microbial genomes, integration with more than 1 million more, and profiling them in human gut metagenomes around the globe, revealing striking lifestyle-associated patterns. This work also includes two targeted studies which describe the utility of metagenomics to understand microbes associated with host disease and identify advantages of using in vitro cultivation to understanding complex host-bacteriophage relationships

Keywords: Microbiology and Immunology.
Advisor: Sonnenburg Justin
Bhatt Ami (Ami Siddharth)
Fischbach Michael
Huang Kerwyn Casey
Schneider David (David Samuel)
Dissecting gene regulation at the RNA and chromatin levels

Development, homeostasis, and healthy aging depend on precise regulation of gene expression programs. As a result, a central challenge in biology is to understand, predict, and ultimately control gene expression. In the first part of the work, we leverage high-throughput biophysical data to develop quantitative models of protein-nucleic acid interactions. We apply this to Argonaute proteins, which load microRNAs (miRNAs) or small interfering RNAs (siRNAs) to form the RNA-induced silencing complex (RISC) and represses target RNA expression. We measure the association kinetics, equilibrium binding energies, and single-turnover cleavage rates of mouse AGO2 RISC, and ultimately derive quantitative models for RISC binding and target cleavage. We then show that our in vitro measurements and models predict knockdown in an engineered cellular system. We next employ a similar approach to study PUM2 binding affinity and develop a predictive model of PUM2 binding that accounts for the ensemble of possible PUM2 binding modes. We then measure equilibrium binding of PUM2 to structured RNAs and demonstrate that RNA secondary structure can facilitate the formation of cooperative interactions between RNA-binding proteins. Finally, we expand on this work to develop a quantitative description of the DNA guided bacterial proteins TtAgo and Cas9, which have emerged as tools for the control of gene expression. In the second part of this work, we shift to studying gene regulation in normal human colon and during the transformation of healthy colon to precancerous adenomas to colorectal cancer (CRC). We collect single-cell chromatin accessibility and transcriptomic measurements and find that a large fraction of polyp and CRC cells exhibit a stem-like phenotype. We define a continuum of epigenetic and transcriptional changes occurring in these stem-like cells as they progress from normal to CRC. Advanced polyps contain increasing numbers of stem-like cells, regulatory T-cells, and a subtype of pre-cancer associated fibroblasts. In the cancerous state, we observe T-cell exhaustion, RUNX1-regulated cancer associated fibroblasts, and increasing accessibility associated with HNF4A motifs in epithelia. Finally, we show that methylation changes in sporadic CRC are strongly anti-correlated with accessibility changes along this continuum, further identifying regulatory markers for molecular staging of polyps

Advisor: Greenleaf William James
Herschlag Daniel
Porteus Matthew H.
Dissecting pathway-level complex traits in yeast

Understanding the genetic and evolutionary basis of complex traits is an outstanding question in genetics. In contrast to Mendelian traits which are usually affected by one or a few genes, complex traits are affected by variation in multiple genes, often hundreds to thousands. This complexity makes it difficult to determine which biological processes and genetic variants affect these traits. Furthermore, identifying natural selection affecting these traits can be difficult without knowledge of which variants are relevant. One method for identifying the genetic basis of complex traits is quantitative trait locus (QTL) mapping using offspring from genetic crosses of laboratory organisms. In Chapter 2, S. cerevisiae offspring from two parental strains between which polygenic selection on gene expression in the ergosterol pathway was identified are examined to see the effect of this selection on metabolite levels, a more downstream endophenotype. While metabolite QTL and expression QTL overlapped well, the selection on gene expression did not lead to the expected changes in metabolite levels. A new test was developed to identify selection on the metabolite levels, and while there was significant evidence of natural selection affecting metabolite levels, it was clear that the selection on gene expression did not predict the selection on metabolite levels, suggesting the need for studying pathways at multiple levels of endophenotypes to understand selection on complex traits. An additional complexity in understanding complex traits is the effects of environment. Not only can the environment in which an organism lives directly affect a complex trait, but some genetic variants will have different effects on fitness or other traits based on the environment. These effects are known as gene-by-environment interactions (GxE). In chapter 3, we develop an improved method for precisely measuring the effects of natural genetic variants in yeast using precision editing and growth competitions. We then use this technique to identify natural variants in S. cerevisiae which have GxE interactions, first within QTL regions and then within the ergosterol biosynthesis pathway. Together, these two chapters advance our understanding of complex traits at the pathway-level, first by looking between levels of endophenotypes, and then looking at complexity imparted by the environment through GxE interactions

Keywords: Genetics.
Advisor: Fraser Hunter B.
Gitler Aaron D.
Montgomery Stephen
Sherlock Gavin
Dissecting structure-function relationships in molecular motors using protein engineering and single-molecule methods

Biological cells can harness the free energy of ATP hydrolysis to perform mechanical tasks using molecular motor proteins. These nanoscale machines are able to generate directional motion through mechanochemical cycles which rely on allosteric communication and large rearrangements of protein domains. In studies of molecular motors, protein engineering allows us to test our understanding of relationships between structure and function, while single-molecule methods allow us to directly observe motor dynamics. Here we consider two systems which undergo large conformational changes: cytoplasmic dynein and DNA gyrase. We use protein engineering to investigate structural features that contribute to dynein velocity and processivity. Building on our initial findings, we are able to design dynein motors that change speed in response to light. The speed and controllability of future designs may be improved with further engineering, in order to generate light-activatable, dynein-based tools which can be used to study transport functions in vivo. In the second half of this dissertation, we consider a single-molecule technique for multimodal measurements of mechanics and fluorescence in DNA and DNA:protein complexes. Mechanical measurements based on magnetic tweezers are combined with simultaneous fluorescence imaging that can report on macromolecular binding and local conformational changes. We outline how this method can be applied to study the mechanism of DNA gyrase, a motor which introduces negative supercoils by coordinating protein domain motions and ATP hydrolysis with DNA cleavage and religation. We observe binding coincident with mechanics and report on challenges in using FRET-labeled enzymes to correlate domain motions with mechanical substeps. We anticipate that correlative multimodal measurements will be valuable tools for characterizing the dynamics of DNA gyrase and other large nucleoprotein machines

Advisor: Bryant Zev David
Fordyce Polly
Huang Possu
Dissolved methane management and post-treatment for non-potable water reuse of staged anaerobic fluidized-bed membrane bioreactor effluent

Mainstream anaerobic wastewater treatment with the staged anaerobic fluidized-bed membrane bioreactor (SAF-MBR) shows promise to transform secondary wastewater treatment into an energy-positive, decentralizable process. However, prior to deployment in the field, more information is required to address the issues of dissolved methane recovery, compliance with prevailing regulations, and compatibility with polishing unit processes. California water reuse regulations presuppose aerobic treatment, presenting compliance challenges for the SAF-MBR without additional post-treatment compared to conventional activated sludge. Field experiments were conducted with a demonstration-scale SAF-MBR process to evaluate dissolved methane recovery and effluent polishing to reduce trace organic contaminants. Post-treatment consisted of an air stripping column for dissolved methane recovery, aerobic polishing, regenerated activated carbon filtration, and UV disinfection. Post-treated water was applied to planter beds, and the fate and transport of trace organic contaminants as well as water quality surrogates was analyzed through the post-treatment train. Results will inform future designs for non-potable water reuse utilizing the SAF-MBR, advancing this energy saving technology closer to full scale use in the wastewater sector

Advisor: Luthy Richard G.
Criddle Craig
Tarpeh William
Distributed and error resilient convex optimization formulations in machine learning

Neural networks have been very successful across many domains in machine learning. Training neural networks typically requires minimizing a high-dimensional non-convex function. Stochastic gradient descent and variants are often used in practice for training neural networks. In this thesis, we describe convex optimization formulations for optimally training neural networks with polynomial activation functions. More specifically, we present semidefinite programming formulations for training neural networks with second degree polynomial activations and show that its solution provides a globally optimal solution to the original non-convex training problem. We then extend this strategy to train quantized neural networks with integer weights. We show that we can globally optimize the training loss with respect to integer weights in polynomial time via semidefinite relaxations and randomized rounding. In the second part of the thesis, we describe a distributed computing and optimization framework to train models, including our convex neural networks. The proposed second order optimization methods in this part rely on approximating the Hessian matrix via random projections. In particular, we describe how to employ randomized sketches in reducing the problem dimensions as well as preserving privacy and improving straggler resilience in asynchronous distributed systems. We present novel approximation guarantees as well as closed-form expressions for debiasing the update directions of the optimization algorithm. Finally, we establish a novel connection between randomized sketching and coded computation. The proposed approach builds on polar codes for straggler-resilient distributed computing

Keywords: Electrical Engineering.
Advisor: Pilanci Mert
Lall Sanjay
Wootters Mary
Distributed ice penetrating radar sounding using passive synchronization

A passive wireless synchronization approach is presented for distributed radar arrays. The application to bistatic radar tomography and orthogonal wave beamforming is investigated. Rather than using a separate synchronization channel, the passive synchronization approach uses the transmit phase and time information embedded in the direct path signal to synchronize echoes. Coherent summation recovery of echoes is demonstrated from the bottom of a 1 km thick glacier up to antenna separations of 1.45 km. The system achieves the largest bistatic offsets in glaciological literature and opens the door to bistatic radar tomography. The proof-of-concept demonstration could enable observations with antenna separations of 4 km or longer, which would provide sufficient inversion constraints for 2D mapping of a glacier's subsurface structure. Based on a simulated experiment with a 4 km transect, the system is expected to estimate temperature distribution on 50 m x 50 m grids with a predicted performance of 0.84±0.34°C mean temperature error, 3.62±0.89°C maximum temperature error and an error in relative basal permittivity of 0.65±0.46 using a Newton's Method inversion approach. This new measurement approach can provide valuable insight into the processes controlling shear margins and improve modeling of ice streams behavior, evolution, and projected sea level contribution. In-situ, high fidelity observations such as this are most valuable when paired with continent-scale measurements that enable extrapolation and placing the findings in a larger glaciological context. Therefore, the Jet Propulsion Laboratory is developing a distributed orbital radar sounder to obtain high coverage with sufficient clutter suppression for ice sheet sounding from orbit. This dissertation matures orthogonal wave beamforming which is required to passively synchronize the CubeSat radars. Specifically, an analytical orthogonality scheme titled Sinusoidal Frequency Modulated Continuous Wave is developed for radar applications. In total, our passively synchronized radar system is providing new measurement techniques for monitoring glaciers at scales that were not previously feasible

Keywords: Electrical Engineering.
Advisor: Schroeder Dustin
Pauly John (John M.)
Zebker Howard A.
Distributed simulation and optimization of large-area metasurfaces

Emerging technologies such as augmented reality, lidar, and mobile imaging have opened a large market for complex, compact, and mass-producible optical systems. Metasurfaces are a promising building block for such next-generation systems. These flat optical elements can use subwavelength scatterers to control light, and can be mass-produced in the same advanced semiconductor foundries that have enabled successful scaling of consumer electronics. However, although metasurface functionality can be experimentally demonstrated, simulating metasurfaces is a central challenge in metasurface design. This simulation challenge arises because metasurfaces typically span thousands of wavelengths in linear dimension, rendering traditional electromagnetic simulation techniques (e.g. Finite-Difference and Finite-Element methods) intractable. Here, we present a metasurface simulation distribution strategy that can preserve the simulation accuracy while allowing scalability to arbitrarily-large areas. Using this distribution strategy with a GPU-based implementation of the Transition-matrix (T-matrix) method, we show a record-size 3-dimensional metasurface simulation (over 600 lambda by 600 lambda) that accurately accounts for scatterer-scatterer interactions significantly beyond the commonly-used locally periodic approximation. We then demonstrate gradient-based optimization of single and multilayer metasurfaces using our distributed T-matrix method. Finally, we discuss using the distribution strategy with Finite-Difference Time-Domain solvers to handle arbitrary scatterer geometries

Keywords: Electrical Engineering.
Advisor: Vuckovic Jelena
Fan Jonathan Albert
Miller D. A. B.
Do Levees Redistribute Floodwater Risk? A Bayesian Assessment with the National Levee Database

Since the 1800s, levees, embankments that prevent water from entering a floodplain, have played a critical role in U.S. flood management.
While physics-based models are used by engineers prior to construction projects to limit adverse impacts, flood victims, law suits, and academic papers have asserted that in redistributing floodwater, levees redistribute flood risk up or downstream—to communities without this infrastructure. However, there has been little empirical research examining the actual effect of levees on stage (surface water level). We examine this question at a national scale, extending the simple statistical method of a small-scale study (in IL, IA) to levees in the National Levee Database, then generalize this approach to a fully Bayesian treatment using a multi-level model.
While the original small-scale study found that levee construction significantly increased stage upstream and adjacent to levees, we find that this result does not generalize nationally. However, the mutli-level model offers many advantages over the previous methods in this application, and allows us to study effect heterogeneity, where preliminary results suggest that non-accredited levees and those in regions with high silt soil composition may be more likely to exhibit stage increases.

Keywords: Levees
Statistics
Bayesian Models
Flood control
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Thesis
Advisor: Ho Daniel
Does Distance Matter at Scale? Extending the “Distance Matters” Framework from Distributed Teams to Distributed Organizations

Distributed work has been a major area of focus for CSCW for the past two decades, as improved collaboration tools have made it possible to assemble distributed teams, abandon physical offices, and engage with collaborators across the world. Much of the CSCW literature on distributed work, however, focuses on intra-team dynamics. Notably, the "Distance Matters" framework has been influential in outlining five dimensions along which distributed teams must coordinate in order to work effectively: common ground, collaboration readiness, collaboration technology readiness, coupling of work, and organizational managerial aspects.
However, an under-considered aspect of the literature is the question of how distributed collaboration scales from a single team to an entire organization. As remote work takes hold on an increasingly large scale, this question becomes crucial. Indeed, teams do not exist in a vacuum; realistically, they exist in relation to other teams, which in turn exist within a broader organization. An effective distributed organization must therefore engage with three levels of analysis: intra-team, inter-team, and organizational.
In this paper, we expand the canonical "Distance Matters" framework and introduce a three-tiered model for studying distributed work, in which we describe how the canonical five dimensions of remote collaboration, developed at the intra-team level, should also be considered in a separate design process for the inter-team and organizational levels. The inter-team and organizational levels both produce novel collaboration and coordination challenges, as well as novel opportunities for resolving these challenges. Finally, we build this theory by presenting supporting ethnographic data from a field study at a major California National Laboratory, which transitioned the majority of its workforce to remote operations in the wake of the COVID-19 pandemic.

Keywords: remote work
distributed work
collaboration
organizations
CSCW
distance matters
Symbolic Systems Program
Management Science and Engineering
Computer Science
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Thesis
Advisor: Valentine Melissa A
Bernstein Michael S