VictorJuiz/thesis huggingface dataset:
Febrile Swing

I have always considered the explorative aspect of music making to be an essential part of my creative practice. I am not only a composer of acoustic and electroacoustic music, but I am also a drummer, a percussionist, a field recordist, an improvisor, and a sound designer. This piece combines all these creative practices into a long-form composition. Febrile Swing serves as my dissertation — the final requirement of my Ph.D. from the University of California, Berkeley — completed in May of 2023. The first movement, 11-tone mashing, is a solo for the keyboardist. It is a somber ambient composition utilizing an 11-note scale (the octave is divided into 11 unequal tones) and features a fixed media backing track of my field recordings. The mood of this composition is both relaxing and unsettling due to its “mashing” of relaxing synth sounds, unusual harmonies, and earthly sounds recorded from various places and times. Movement II, Fields, opens with pointillistic unison strikes involving the entire ensemble, interspersed with glitchy electromagnetic field recordings captured around the city of Kraków. It gradually crescendos in volume and density over the course of minutes as it climbs towards its apex— deep, loud drones are combined with chaotic, energetic improvisation from the performers. I composed the third movement, Brushstrokes, with a jazz combo in mind; only the drummer, keyboardist, trumpeter, saxophonist, and contrabassist perform in this section. The synthesizer is tuned to a highly unusual scale in which every perfect fifth interval on the keyboard sounds a perfect octave. The keys in between the perfect fifth essentially divide the sounding octave into seven [almost] equal tones. The tones of the synthesizer are transcribed and provided to the acoustic performers in the form of chord changes, and they are asked to improvise using these chord tones. The final movement, Head above water, once again incorporates the full ensemble and utilizes a 21-tone EDO (equally divided octave) scale in the synthesizer. The clustery nature of the synth inspired the orchestration of the piece; dense tone clusters gradually climb and fall by way of alternating half and quarter tone intervals at a brisk tempo.

Advisor: Ueno, Ken
Campion, Edmund
Epistemic modality across syntactic categories in Kipsigis

This dissertation concerns the expression of epistemic modality across syntactic categories in Kipsigis, an understudied Kalenjin language of Western Kenya. In addition to describing a range of morphosyntactic and semantic properties of Kipsigis, this dissertation takes as its empirical focus two case studies of epistemic modality in the nominal and verbal domains. Through these case studies, this dissertation contributes to the integration of data from understudied languages into semantic theory and suggests that the derivation of epistemic modal content—both across languages and across syntactic domains—must be achieved in a range of different ways./nIn the nominal domain, I describe and analyze Kipsigis epistemic indefinites, highlighting the ways in which these forms pose challenges for existing analyses of epistemic indefinites across languages. In particular, I show that Kipsigis epistemic indefinites can signal both first order and higher order ignorance but do not take obligatory wide scope, which challenges the correlation between ignorance type and scope in Dawson (2020). To capture the Kipsigis pattern, I propose that the ignorance effects triggered by use of an epistemic indefinite in Kipsigis arise pragmatically due to competition with another type of Kipsigis nominal, which signals speaker knowledge about the noun’s referent (following work on Russian to and koe indefinites by Geist & Onea 2007)./nIn the verbal domain, I consider biased belief reports with the Kipsigis verb pɑr ‘think’, which can be used in two very different contexts: 1) to suggest that the reported belief is false or unlikely, and 2) to remind the addressee that the reported belief is true. While these negative bias and reminding functions are independently attested in other languages, the Kipsigis pattern is unique in that it combines these two functions in a single lexical item. To account for this behavior, I propose that, in addition to its basic belief semantics, pɑr contributes an instruction for Common Ground management (Krifka 2008): the reported belief must not be added to the Common Ground.

Advisor: Deal, Amy Rose
Evaluating Remote Operations for Advanced Nuclear Reactor Control: Feasibility, Benefits, and Implementation Criteria

This dissertation provides a comprehensive exploration of the feasibility and potential benefits of remote operations as a control strategy for advanced nuclear reactors. While remote monitoring and control have been implemented in other industries, the operational feasibility and guidelines for implementing remote-operated control rooms for nuclear facilities remain largely unproven and scarce. Therefore, this research offers valuable insights for the industry as it navigates the future of nuclear power plant operations./nThe dissertation is organized into six chapters, with each chapter building on the previous one. It begins with a review of state-of-the-art technology for modern control rooms in various industries, including nuclear. The subsequent chapters discuss remote operations in other industries and ways the nuclear industry can implement remote operations. The research then provides a demonstration of remote operations at an advanced nuclear reactor company, highlighting the technical feasibility of remote operations./nMoreover, this dissertation offers two more original contributions. Firstly, it presents a human factors-based study that sheds light on the challenges of remote operations and emphasizes the significance of appropriate training, communication, and workload management to ensure successful remote operations. Secondly, it provides a comprehensive set of criteria that advanced reactor companies need to consider before implementing a remote control room. These criteria encompass safety, cybersecurity, human factors, training, and licensing, serving as a guideline for advanced reactor companies planning to adopt a remote control operational strategy./nOverall, this dissertation provides valuable contributions to the field of nuclear energy. By identifying the key criteria that must be fulfilled for successful implementation of remote operations, this research offers practical insights that can help industry professionals make informed decisions about the future of nuclear power plant operations.

Advisor: Peterson, Per F
Femtosecond Dynamics in Liquid Microjets Studied by Extreme Ultraviolet Photoelectron Spectroscopy

The combination of the liquid microjet technique and femtosecond time-resolved photoelectron spectroscopy offers a valuable means to study the electronic photoinduced dynamics of liquids and solvated species. Here, a liquid microjet formed by forcing fluid through a few micron-diameter capillary is crossed with pump and probe pulses to first excite the sample some known time before the sample is photoionized. By collecting and energy analyzing the resultant photoelectrons, the transient electronic states populated by the pump pulse can be observed and excited state population tracked as it relaxes on femtosecond to picosecond timescales.A major limitation for the study of solvated species using femtosecond photoelectron spectroscopy has been the ability to reach probe energies greater than 10 eV in a tabletop experiment. A readily accessible solution to this limitation exists in the high harmonic generation (HHG) technique, by which femtosecond pulses well in excess of 10 eV can be generated in a tabletop setup. This dissertation details the implementation of an HHG-based beamline used to generate probe pulses of 21.7 eV integrated into the liquid jet time-resolved photoelectron spectroscopy (LJ-TRPES) experiment. Previously, this project had employed UV pump/UV probe LJ-TRPES to study the excited state dynamics of solvated electrons and nucleic acid constituents (NACs). A summary of this work is given and the respective mechanisms for relaxation discussed. Additionally, the advances in tabletop light sources extending into the extreme ultraviolet (XUV) and soft x-ray regimes are discussed in the context of observation of full valence level dynamics and the potential for tracking dynamics with element specificity. Finally, the characterization and application of these new XUV probe capabilities is discussed. The XUV probe has been used to take static, pump-probe, and LAPE spectra of a variety of gas and solution phase systems, thereby demonstrating the capabilities of the technique. In particular, the observation of the ultrafast relaxation pathways in a variety of NACs is presented. The first time-resolved photoelectron spectroscopy studies using a liquid flat jet source and observation of adenine excited state relaxation using an XUV probe are notable results. These studies reveal dynamics that the experiment was previously blind to due to the probe energy limitations.

Advisor: Neumark, Daniel M.
Mineral Modernism: The Mexican Subsoil and the Remapping of American Form in the 1930s

This dissertation theorizes an aesthetics of extraction in the modernist dialogue between U.S. and Mexican artists and critics in the 1930s. I analyze the ways in which perceptions of underground resources featured prominently in artistic theories about what was shared by U.S. and Mexican modernisms in the 1930s, as the United States renegotiated its access to the Mexican subsoil. As Mexico pushed to nationalize its underground resources, U.S. diplomats responded by stressing the transnational properties of minerals, mobilizing cultural diplomacy and the modernist capacity to transcend national borders. The artists and institutions I study reflect such a vision of the borderless underground: I argue that for each of them, minerals and the subsoil were conceptual mechanisms with which to produce expanded boundaries of American culture, challenging borders and the governing logics of flat, cartographic surfaces. To emphasize the apolitical, borderless quality of the subterrain, for instance, agents of the mineral frontier recruited authorities such as Diego Rivera and the Museum of Modern Art, who deployed aesthetic ideas about a formal “substratum” shared by Mexican and U.S. modern art. Primitivist dialogues by two lesser-known artists linked with Rivera, Jean Charlot and William Spratling, positioned Mesoamerican motifs as undeveloped “mines” for abstraction, at the same time that they conceptualized the materiality of minerals in their artwork as racialized reserves of dormant, primordial potential. And while some Mexican artists challenged the U.S. mineral frontier in canvases, murals, and political cartoons that explicitly championed Mexican control, others, like Rivera, did so by troubling ownership altogether, using muralism and cubist techniques to envision the subsoil as a collectivist, decentralized, and environmentally interconnected ecosystem.

Advisor: Kroiz, Lauren
Object and Scene Reconstruction using Neural Radiance Fields

This dissertation explores the synthesis of novel views of complex scenes through the optimization of a volumetric scene function using a sparse set of input views. Our approach represents the scene as a neural radiance field (NeRF), a field of densities and emitted radiance based on 5D coordinates encompassing spatial location (x, y, z) and viewing direction (θ, φ). NeRF enables the rendering of photorealistic novel views that surpass previous techniques, leading to numerous follow-ups and extensions in the computer vision and graphics communities. To enhance the representation of high-frequency details in NeRFs, we introduce a Fourier feature mapping technique that effectively learns high-frequency functions within low-dimensional problem domains, including NeRF. We demonstrate the benefits of leveraging learned initial weight parameters through standard meta-learning algorithms, resulting in accelerated convergence, stronger priors, and improved generalization for coordinate-based networks. In addition, we improve the scalability of NeRFs with a proposed method capable of representing arbitrarily large scenes. This method enables city-scale reconstructions using data captured under diverse environmental conditions. Finally, we present the Nerfstudio framework, a comprehensive suite of modular components and tools designed for the development and deployment of NeRF-based methods. This framework empowers researchers and practitioners with real-time visualization, streamlined data pipelines, and export capabilities, facilitating the democratization of NeRFs and extending their impact beyond research settings. With their potential to transform computer graphics, virtual reality, augmented reality, and other domains, NeRFs hold promise for revolutionizing the way we perceive and interact with digital worlds.

Advisor: Kanazawa, Angjoo
Ng, Ren
Reinforcement Learning from Static Datasets: Algorithms, Analysis, and Applications

Reinforcement learning (RL) provides a formalism for learning-based control. By attempting to learn behavioral policies that can optimize a user-specified reward function, RL methods have been able to acquire novel decision-making strategies that can outperform the best humans even with highly complex dynamics and even when the space of all possible outcomes is huge (e.g., robotic manipulation, chip floorplanning). Yet RL has had a limited applicability compared to standard machine learning (ML) in real-world scenarios. Why? The central issue with RL is that it relies crucially on running large amounts of trial-and-error active data collection for learning policies. Unfortunately though, in the real world, active data collection is generally very expensive (e.g., running wet lab experiments for drug design), and/or dangerous (e.g., robots operating around humans), and accurate simulators are hard to build. Overall, this means that while RL carries the potential to broadly unlock ML in real-world decision-making problems, we are unable to realize this potential via current RL techniques./nTo realize this potential of RL, in this dissertation, we develop an alternate paradigm that aims to utilizes static datasets of experience for learning policies. Such a ``dataset-driven'' paradigm broadens the applicability of RL to a variety of decision-making problems where historical datasets already exist or can be collected via domain-specific strategies. It also brings the scalability and reliability benefits that modern supervised and unsupervised ML methods enjoy into RL. That said, instantiating this paradigm is challenging as it requires reconciling the {static} nature of learning from a dataset with the traditionally active nature of RL, which results in challenges of distributional shift, generalization, and optimization. After theoretically and empirically understanding these challenges, we develop algorithmic ideas for addressing thee challenges and discuss several extensions to convert these ideas into practical methods that can train modern high-capacity neural network function approximators on large and diverse datasets. Finally, we show how the techniques can enable us to pre-train generalist policies for real robots and video games and enable fast and efficient hardware accelerator design.

Advisor: Levine, Sergey
Residential heating electrification in Northern California: locating transitions in infrastructures and institutions

Climate change mitigation policy in California increasingly centers residential heating electrification: the transition from using gas or other combustible fuels to using electricity to heat homes, heat water, and cook. How does the residential heating transition in Northern California interact with physical and institutional networks of electricity and gas provision, home upgrade provision, and planning imperatives and ideals? The first part of this dissertation uses geospatial and statistical analyses of utility, census, and climate data to explore two spatial distributions that will shape the impacts and meanings of residential electrification in Northern California. Chapter 1 assesses the ability of the electric distribution grid – designed for a population that largely uses non-electric fuels, including gas, for heating – to accommodate increasing electric loads due to residential electrification. Chapter 2 investigates the socio-economic distribution of heating and cooling use in the study area to understand the extent to which the services that residential electrification is meant to deliver – space heating and cooling – is, itself, uneven. The second part of this dissertation uses qualitative data, including semi-structured interviews, participant and event observation, and document analysis, to explore the turn to “equitable electrification” as a local climate planning imperative in the Bay Area in Northern California. Chapter 3 studies how concepts of equity are constructed in cities, focusing particularly on how planning relationships – between cities, community-based organizations, and consultants – shape equitable electrification imperatives. Chapter 4 looks at how local actors, particularly those that adopt framings or pledges of equitable electrification, interact with wider structures on which they have seemingly limited authority, focusing on structures of corporate utility energy provision and home retrofit funding. The dissertation concludes with a discussion of how these four chapters, taken together, point to the importance of a broadened understanding of participation and democratization in (equitable) transition, and to the importance of furthering an understanding of energy as a relational system, interdependent with people and places.

Advisor: Callaway, Duncan
Acey, Charisma
Disposing of Single-Use: Sustainability Transitions Towards Waste-Free Systems

Waste is being generated and discarded at unprecedented and alarming rates. Waste of all materials and products is creating air, water, and soil pollution, causing biodiversity loss and harming human health. Such destruction came fast and such destructive practices can be altered. That alteration, I propose, can come through a policy-based sustainability transition towards waste-free systems. I introduce the term ‘towards waste-free systems’ as, I hope, a unifying and galvanizing term to align an otherwise disparate preexisting assemblage of actors fostering or inhibiting action towards less waste. In support of wastelessness, many actors have made substantial and remarkable inroads against the locked-in make-take-discard consumption culture. They have passed policies, created alternative use systems, and conducted cross-disciplinary education and awareness. They have removed persistent organic pollutants from our packaging, increased producer responsibility in the disposal process, and curbed the use of some of the most damaging materials. Yet, waste continues to be generated, ecosystem harm continues, and human health continues to be compromised. In this dissertation, I propose we can make substantially more progress by framing and driving a sustainability transition towards waste-free systems through policy strategies. Co-authors and I conducted numerous studies, at varying geographical scales, through multiple methodologies, to examine policies as they relate to the sustainability transition towards waste-free systems. These studies include a review of all anti-single use disposable (SUD) foodware policies in the US; an assessment of the effectiveness of one historic anti-SUD foodware policy in Berkeley, California; an impact-based approach of China’s policy to no longer accept 70% of the US’s discarded plastics; and a deep dive into the policies paused, by whom, and for what reasons, that spurred the creation of exorbitant SUD foodware and healthcare waste (‘hygiene waste’) during COVID-19. These studies, grounded in sustainability transition theory and discard studies, demonstrate that achieving waste-free systems is both obtainable and necessary, but that such efforts face many obstacles along the way.

Advisor: O'Neill, Kate
Vision and Language Understanding Through Generative Modeling

Language is such a powerful representation for capturing the knowledge and information about our world. It excels at expressing discrete concepts such as objects and their attributes, the relationships between them in a very compact manner all due to its extremely high level of abstraction. Language is the primary means by which we communicate, comprehend, and express our thoughts and ideas, and it lies at the very core of human intelligence. With the advent of powerful generative models, machines also have begun to comprehend and generate natural language with notable fluency and creativity. However, they lack “grounding”—a direct tie to the visual world. Vision plays a pivotal role in our comprehension and production of language. When we describe a scene, understand instructions, or engage in a dialogue, visual contextsignificantly aids our interpretation and generation of language. This highlights the need for integrating vision for generative modeling. Chapter 1 and 2 delve into image-to-text domain, spotlighting the importance of a multimodal approach for text generation. In Chapter 1, we explore how generating textual rationales with attention visualizations can enhance model transparency for visual question answering. In Chapter 2, we build generative models that abandon traditional left-to-right sequencing in favor of an unsupervised technique to determine optimal generation orders. Chapter 3 and 4 shift the focus to text-to-image generation. In Chapter 3, we introduce a training-free framework that combines linguistic cues with reference images, allowing for controllable image synthesis using denoising diffusion probabilistic models. Lastly, Chapter 4 emphasizes the importance of preserving object shapes in text-based image editing, proposing a unique mechanism that augments text-to-image models to be more faithful to input masks and text prompts.

Advisor: Darrell, Trevor
High-Throughput Single-Molecule Diffusivity Analysis: New Approaches and Applications

Following the popularization of super-resolution microscopy for biological research, the advent of functional super-resolution microscopy enabled decoding high-throughput single-molecule signals in single-molecule localization microscopy (SMLM) and extracting multiple physical parameters beyond the structure of the target system. Among those parameters, the diffusivity of single molecules has provided deeper insight into biological systems as diffusion is one of the key mechanisms that carry out life processes and as the diffusivity reflects multiple physicochemical parameters and thus properties of the microenvironment. This dissertation describes applications of the recently developed wide-field ultrahigh-throughput diffusion coefficient analysis and a new method that enables the equivalent measurement without sophisticated hardware setup. By extensively tracking diffusion behaviors of proteins in nanometer-sized matrix structured hydrogel, I drew a comprehensive picture of how different sizes of meshwork affect the protein diffusion coefficients, as well as how such an effect is differentiated by the relative size of proteins to the meshwork. Then, I introduce a new approach to extract diffusion coefficients out of conventional SMLM by incorporating single-molecule image processing through a convolutional neural network. In the last chapter, I summarized efforts on taking the SMLM diffusion analysis to a greater extent.

Advisor: Xu, Ke