VictorJuiz/thesis huggingface dataset:
Turned Inside Out: The Rise of Private, Networked, Data-Centric Governance

The pursuit of more sustainable consumption and production of consumer goods has taken an inside-out turn. Instead of external stakeholders devising carrot-and-stick methods of influence, industry players like brands, retailers, and platforms are increasingly finding value in sustainability governance. Able to collect data streams inaccessible to stakeholders, with the market power to implement rapid changes transnationally, and guided by a vision of Industry 4.0, there has been renewed hope in private solutions to public issues. Firms are arguing that they can leverage their considerable power to achieve what NGOs and governments have struggled to achieve in thirty or so years: comparable and actionable data, market transparency, supply chain and product innovation, and enforcement of labor and environmental standards. Using interdisciplinary methods and novel empirical research, I describe and assess the emerging phenomenon of private, networked, data-centric governance (PNDG). First, I present a literature review of sustainable consumption. I demonstrate that the potential impact of so-called citizen consumption is extraordinarily limited, and argue that structural policies and politics are more likely to yield the necessary transformative change. My empirical work, in turn, focuses on production networks. In chapter three, through data analysis, surveys, and case studies, I examine the Higg Index, an apparel-industry wide initiative to collect and utilize environmental and social data. I find that after ten years, the Higg Index is a scale without a diet—it solves a number of measurement issues, but fails to address root issues of incentives and transparency. In chapter four, I perform a primarily econometric analysis of an intervention in a Thai clothing factory. My analysis demonstrates factory worker compensation formulas can incent higher productivity through higher wages, thereby showing a financially viable path towards living wages. And yet, the project highlights limitations on potential gains absent further external pressure by organized labor, NGOs, governments, or consumers. By offering novel empirical insights, as well as describing the phenomenon of PNDG, my findings inform ongoing policy debates, industry investments, and stakeholder projects. While PNDG holds promise, it needs to be complemented with more external accountability mechanisms that promote stronger incentives.

Advisor: O'Rourke, Dara
An Associated Particle Imaging System for the Determination of 3D Isotopic Distributions

Associated Particle Imaging (API) is a nuclear technique that allows for the non-destructive determination of 3D isotopic distributions. The technique is based on the detection of the alpha particle associated with the neutron emitted in the deuterium-tritium (DT) fusion reaction, which provides information regarding the direction and time of emission of the 14 MeV neutron. Inelastic neutron scattering leads to characteristic gamma-ray emission from certain isotopes, which can be correlated with the neutron interaction location. An API system consisting of a sealed-type neutron generator, gamma detectors, and a position-sensitive alpha detector was designed, constructed, and tested at Lawrence Berkeley National Laboratory (LBNL) for the non-destructive quantification of 12C distribution in soils. Additionally, the system is also sensitive to other elements present in the soil such as O, Si, Al, Fe, etc. It is capable of quantifying 12C at the percent level with a resolution of 2 cm x 2 cm x 7 cm for an hour of measurement. The first half of the dissertation describes the design of the system (using the simulation packages MCNP6, SPICE, and COMSOL Multiphysics) and the characterization of its components including the neutron generator, the position-sensitive alpha detector (YAP), the lanthanum bromide (LaBr) and sodium iodide (NaI) gamma detectors, and the systems used to observe the alpha and gamma signal. The second half focuses on data analysis techniques and presents initial experimental data benchmarked against simulations.

Advisor: van Bibber, Karl
Design, Analysis, and Optimization of Breed-and-Burn Reactor Cores

Breed and Burn (B&B) reactors are a special class of fast reactor that promises enormous sustainability and proliferation-resistance benefits compared to current commercial nuclear power systems. Utilizing a very tight neutron economy in the fast spectrum, B&B reactors are able to sustain criticality in the equilibrium state without either fuel enrichment or reprocessing. As with many advanced reactor systems, however, B&B reactors currently suffer from a number of uncertainties and infeasibilities hampering their practical deployment. This dissertation aims to better understand and propose solutions to some of these primary uncertainties. Utilizing state-of-the-art computational tools and techniques, the properties of a prototypical B&B core are investigated and quantified in high fidelity, enabling deeper understanding of the physical phenomenon driving their unique characteristics. Subsequently, three of the most pressing current uncertainties in B&B cores are investigated in detail: (1) reactor orificing, (2) transient performance and inherent safety, and (3) the tradeoff between material integrity and resource utilization./nTo understand the feasibility of adequately cooling fuel assemblies over their in-core lifetimes, new mathematical optimization techniques are developed and applied to devise optimal orificing strategies subject to a variety of important operational constraints, the results of which demonstrate how certain methods of orificing are better suited to B&B operation than others. To understand B&B transient performance, the first open-literature transient models are developed and analyzed for a wide variety of hypothetical accident scenarios, finding challenges associated with the unfavorable reactivity feedback mechanisms inherent to B&B core designs. In order to improve transient performance, a novel passive safety system is incorporated into the core and its behavior simulated using a newly-coupled code system. Finally, the topic of material damage is explored by first using newly-proposed methods for uncertainty quantification on the material damage levels. Once the uncertainties have been understood, the study moves to exploring the tradeoff between material damage and resource utilization when low-enriched uranium is used as a feed fuel. In order to improve this tradeoff, new two-tiered reactor systems are designed and analyzed which enable significantly higher resource utilization than current light water reactor systems without requiring fuel reprocessing or physical/chemical separations. Each of these explorations allows for some of the uncertainties around B&B reactors to be clarified, and this thesis therefore enables improved viability of implementing the breed-and-burn mode of operation as part of future nuclear power systems.

Advisor: Fratoni, Massimiliano
Greenspan, Ehud
Inequality Perceptions and Distributional Preferences

This dissertation studies how inequality perceptions impact Americans’ distributional and other-regarding preferences. The first chapter analyzes original data collected from an online networked experiment and demonstrates that the process through which inequality emerges in the network has a significant impact on individuals’ fairness perceptions and willingness to invest in their communities. The second chapter analyzes original data from an online survey experiment and shows that the addition of pessimistic information about opportunity does not lead to any more support for redistribution when pessimistic information about inequality is already present./nThe third and fourth chapters focus specifically on inequality perceptions during the coronavirus pandemic. The third chapter analyzes original data from another survey experiment and finds that receiving information about class inequalities specifically in relation to the outbreak tends to be much more effective in moving people’s opinions compared to receiving that information in a way that does not directly relate it to coronavirus. The fourth and final chapter analyzes data from the same experiment to understand how different framings of the pandemic are influencing public’s threat perceptions regarding the outbreak and finds that emphasis on the unequal aspect of the pandemic leads the public to become less concerned about the outbreak and its human toll.

Advisor: Harding, David J
Neural Scene Representations for View Synthesis

View synthesis is the problem of using a given set of input images to render a scene from new points of view. Recent approaches have combined deep learning and volume rendering to achieve photorealistic image quality. However, these methods rely on a dense 3D grid representation that only allows for a small amount of local camera movement and scales poorly to higher resolutions./nIn this dissertation, we present a new approach to view synthesis based on neural radiance fields, an efficient way to represent a scene as a continuous function parameterized by the weights of a neural network. In contrast to using a feed-forward neural network to predict scene properties from a small number of inputs, a neural radiance field can be directly optimized to globally reconstruct a scene from tens or hundreds of input images and thus achieve high quality novel view synthesis over a large camera baseline./nThe key to enabling high fidelity reconstruction of a low-dimensional signal using a neural network is a high frequency mapping of the input coordinates into a higher-dimensional space. We explain the connection between this mapping and the neural tangent kernel, and show how manipulating the frequency spectrum of the mapping provides control over the network's interpolation behavior between supervision points.

Advisor: Ng, Ren
Nothing Left but Smoke and Mirrors: Deindustrialisation and the Remaking of British Communities, 1957-1992

In the late 1950s, the United Kingdom remained a heavily industrialised country, with nearly 40 per cent of its workforce employed in manufacturing. Today, that figure is well below 10 per cent. Industries that were at the centre of Britain’s rise to world power in the 19th century, including textiles, shipbuilding, and steelmaking, have disappeared entirely or been reduced to a tiny fraction of their former size. Although deindustrialisation is a global phenomenon, and one which is ongoing, the deindustrialisation of Britain was the most rapid and sustained of any country in the global north. It has had profound and lasting effects on the lives of individuals, families, and communities. Using material from archives across the UK, including government documents, the files of local organisations, and community oral history projects, this dissertation is a comprehensive study of deindustrialisation and its impacts on mid-to-late 20th century Britain./nThis dissertation broadens the temporal frame of study away from a narrow focus on the 1980s, arguing that deindustrialisation’s effects were felt much earlier than the election of Margaret Thatcher and demonstrating the complex bi-directional relationship between industrial decline and the rise of Thatcherite policies. Critically, I argue that deindustrialisation was fundamentally connected to the end of empire, and that it both intersected with pre-existed inequalities in postwar British society and demonstrated the hollowness of the promises of the postwar welfare state. Deindustrialisation impacted women and Commonwealth citizens as much or more as it did white male Britons. I also explore the significant yet understudied role of local organisations, including neighbourhood associations, clubs and support groups, in responding to the effects of deindustrialisation and also to the subsequent abandonment of government. I argue that these organisations often represented a transformation in local political assumptions and helped spur a painful process of adaptation to the post-industrial reality./nIn order to capture the diversity of impacts and responses to deindustrialisation, this dissertation is structured around four interconnected case studies drawn from across the UK: Oldham, Belfast, Coventry, and Motherwell. This approach allows for the comparison of deindustrialisation’s impact in the different nations of the UK, in fragmented and centralised industries, in male and female dominated sectors, and in both multi-racial and primarily white communities. However, the case studies also demonstrate the commonalities and connections across the entire UK, as deindustrialisation remade communities and left behind complex physical and emotional legacies.

Advisor: Vernon, James
Estimation and Evaluation of the Optimal Dynamic Treatment Rule: Practical Considerations, Performance Illustrations, and Application to Criminal Justice Interventions

The optimal dynamic treatment rule (ODTR) framework oers an approach for understand-ing which kinds of patients respond best to specic treatments { in other words, treatment ef- fect heterogeneity. Further, given an (optimal) dynamic treatment rule, it may be of interest to evaluate that rule { that is, to ask the causal question: what is the expected outcome had every subject received treatment according to that rule? Following the \causal roadmap," in this dissertation, we causally and statistically dene the ODTR and its value. Building on work by Luedtke and van der Laan, we provide an introduction to and show nite-sample performance for (1) estimating the ODTR using the ODTR SuperLearner (Chapter 1); and (2) estimating the value of an (optimal) dynamic treatment rule using dierent estimators, such as cross-validated targeted maximum likelihood (CV-TMLE; Chapter 2). We addition- ally augment the ODTR SuperLearner by considering stochastic treatment rules and risk criteria that consider the variability of the value of the rule (Chapter 3). We apply these estimators of the ODTR and its value to the \Interventions" Study, an ongoing random- ized controlled trial, to identify whether assigning cognitive behavioral therapy (CBT) to criminal justice-involved adults with mental illness using an ODTR signicantly reduces the probability of recidivism, compared to assigning CBT in a non-individualized way. We hope this work contributes to understanding the toolbox of methods that can be used to advance the elds of precision medicine, public policy, and health.

Advisor: Petersen, Maya
Singular stochastic differential equations with elliptic and hypoelliptic diffusions

In this thesis, the well-posedness of stochastic differential equations (SDEs) with singular coefficients is discussed. First, it is proved that SDEs with elliptic diffusion possess a unique solution when drift vector fields belong to the Orlicz-critical space. Then, it is shown that SDEs with degenerate and hypoelliptic diffusion are well-posed for a large class of singular drifts. A basic theory on Lorentz spaces and the analysis on the homogeneous Carnot group will also be introduced.

Advisor: Rezakhanlou, Fraydoun
Excited States of Molecules with Density Functional Theory and Many Body Perturbation Theory

The accurate prediction of electronic excitation energies in molecules is an area of intense research of significant fundamental interest and is critical for many applications. Today, most excited state calculations use time-dependent density functional theory (TDDFT) in conjunction with an approximate exchange-correlation functional. In this dissertation, I have examined and critically assessed an alternative method for predicting charged and low-lying neutral excitations with similar computational cost: the \textit{ab initio} Bethe-Salpeter equation (BSE) approach. Rigorously based on many-body Green's function theory but incorporating information from density functional theory, the predictive power of the BSE approach remained at the beginning of this work unexplored for the neutral and charged electronic excitations of organic molecules. Here, the results and implications of several systematic benchmarks are laid out in detail.

Advisor: Neaton, Jeffrey B.
SHAPE (Sound and Habitat Audio Prototyping Environment)

Sound and Habitat Audio Prototyping Environment (SHAPE) is a collection of nature-inspired electroacoustic devices created for sound art in public spaces. It is part of an audio feedback research project at the Center for New Music and Audio Technologies (CNMAT). By repurposing discarded electronics and manufactured objects, low-cost materials are used to make interactive sound sculptures and novel music instruments. Subtle gestures and actions by participants change the sound in real time. The project attempts to question the dichotomy between sound art and common environmental sounds through a zero-waste, collective action framework. With SHAPE, natural and artificial materials converge; construction and deconstruction hold equal weight; raw materials reclaim another existence; and sounds from unusual sources expand into fully resonating bodies. Audio transfer is based on two input types for each device: a piezoelectric contact mic and an electret air mic. These elements combine to sense both vibration in material and pressure waves in the air. Sound energy is then converted into an analog and a digital signal. Both analog and digital electronic environments are highly programmable, allowing for quick on-site prototyping. Six devices from this project will be highlighted and described in detail. Aside from the PCB fabrication, smartphone, and case construction, all of the e-components for these devices can be easily found in old discarded speaker systems and reused. Proprietary devices such as the iRig are currently being used, but these will be reverse engineered for future open-access integration. Open-source software such as Pure Data and MobMuPlat make any Android or iOS device compatible with this system, thus facilitating second-hand use of virtually all smartphone models. Considering the portability and cost effectiveness of this project, SHAPE is particularly adept at facilitating outdoor applications such as sound installations or musical performances.

Advisor: Campion, Edmund
The Literary Abhidhamma Text: Anuruddha's Manual of Defining Mind and Matter (Nāmarūpapariccheda) and the Production of Pali Commentarial Literature in South India

This dissertation is a study of the ancillary works of the well-known but little understood Pali commentator and Abhidhamma scholar, Anuruddha. Anuruddha's Manual of Defining Mind & Matter (nāmarūpapariccheda) and Decisive Treatment of the Abhidhamma Ultimates (paramatthavinicchaya) are counted among the “sleeping texts” of the Pali commentarial tradition (texts not included in standard curricula, and therefore neglected). Anuruddha's well-studied synopsis of the subject matter of the commentarial-era Abhidhamma tradition, the Compendium of Topics of the Abhidhamma (abhidhammatthasaṅgaha), widely known under its English title, A Comprehensive Manual of Abhidhamma, came to be regarded as the classic handbook of the orthodox Theravādin Abhidhamma system. His lesser-known works, however, curiously fell into obscurity and came to be all but forgotten. In addition to shedding new and interesting light on the Compendium of Topics of the Abhidhamma (abhidhammatthasaṅgaha), these overlooked works reveal another side of the famous author – one as invested in the poetry of his doctrinal expositions as in the terse, unembellished clarity for which he is better remembered. Anuruddha's poetic abhidhamma treatises provide us a glimpse of a little-known early strand of Mahāvihāra-affiliated commentarial literature infusing the exegetical project with creative and poetic vision. In this dissertation I examine this genre, the literary abhidhamma text, and reconsider the historical context of the production of Pali exegetical literature in South India. This dissertation presents translation and analysis of major sections of Anuruddha's two minor works, the Manual of Discerning Mind & Matter (nāmarūpapariccheda) and the Decisive Treatment of the Abhidhamma Ultimates (paramatthavinicchaya) and queries the significance and extent of influence of the South Indian Pali exegetes' peripheral localization (with respect to the Mahāvihāra-based interpretive tradition) on the formation of the Theravāda commentarial tradition as we know it.

Advisor: Rospatt, Alexander
Development of novel data applications for improving precipitation-runoff modeling in headwater catchments

Hydrologic modeling is heavily used in water resources for advancing scientific process understanding and supporting operational decision making, but the capabilities of these models are challenged by climate change, land-use changes, and population growth. Novel ground-based and remotely sensed data sources can provide high-quality and reliable information that can enhance hydrologic models to meet current demands. Here, I examine how these data sources can be leveraged to facilitate and improve use of three major model types: data-driven, physically based, and conceptual, which each fall in a different range of complexity (the extent to which a model represents or simplifies physical processes) and scale (the number of simulation or data points). I focus on the state of California, a region that faces significant water management challenges due to its arid Mediterranean climate, highly seasonal precipitation, and high variability in annual precipitation totals. Chapter II investigates snowpack response to rain-on-snow events by leveraging expanded ground-based wireless-sensor networks to drive a machine-learning algorithm. Supported by the high temporal and spatial resolution of these data, I identified a nonlinear and temporally variable relationship between Leaf Area Index and snow-depth change during rain-on-snow events. Chapter III demonstrates how remotely sensed spatial maps can be used to identify the optimal spatial distribution of input data in a physically based hydrologic model through use of a Gaussian-Mixture Model. This novel method addresses issues of overparameterization and improves the efficiency of model design for physically based models while still being grounded in physical principles. Finally, Chapter IV applies new spatially distributed data to a conceptual hydrologic model to quantify the impact of predictable versus unpredictable shifts in the precipitation-runoff relationship during droughts. Collectively, this work demonstrates that 1) data that are gathered strategically by location and water-balance component is most helpful in improving or developing new models; 2) remotely sensed data are most effective when calibrated or coupled with ground-based sensors; 3) scale continues to be a challenge both on the modeling and data sides, whether scaling up physically based models or scaling down conceptual models; and 4) model improvement relies not only on data collection but on making the data usable and easily accessible, particularly for operational applications.

Advisor: Glaser, Steven D.