VictorJuiz/thesis huggingface dataset:
Towards a next-generation measurement of the fine-structure constant

Atom interferometry is a powerful metrological tool that has been developed over the last few decades. Large momentum transfer (LMT) methodsmanipulate atomic trajectories with tens or hundreds of photon momenta in order to increase sensitivity. This thesis furthers progress towards using LMT methods in next-generation atom interferometers. One main result establishes symmetric Bloch oscillations as a new, viable technique for LMT. Theory and numerics are used to show how the process is coherent and adiabatic, and experimentally we demonstrate coherence in an interferometer with up to 240¯hk, where ¯hk is the momentum of a single photon of 852nm light. This was the second largest coherent momentum splitting demonstrated at at the time of publication. The rest of the thesis focuses on design and construction of a new atomic fountain to measure the fine structure constant α. Discrepancies in recent measurements of α [67, 55] are currently limiting theory predictions for the electron gyromagnetic ratio [25] - an improved measurement of α is therefore highly motivated and would enable an improved test of the consistency of the Standard Model. Previously, our group published a measurement of α at the 0.2 ppb level in 2018 [67]. We built a new experiment with a goal of improving the measurement by a factor of 3-10. Much of the thesis focuses on systematic effects related to spatial intensity inhomogeneities on the laser beam, which are some of the hardest to characterize systematic effects looking forward. A large clear aperture vacuum chamber accommodates larger waist laser beams without clipping on chamber walls. In addition, a high-speed, user-friendly Monte Carlo simulation package was made to predict experimental systematic shifts in the measured value of α due to laser beam inhomogeneities.

Advisor: Mueller, Holger
Efficient Zero-Knowledge Proofs: Theory and Practice

Zero-knowledge proof is a cryptographic protocol enabling provers to convince verifiers of a statement's validity without disclosing any additional information beyond its truthfulness. It can be used to address security and privacy challenges in diverse fields, such as artificial intelligence, data analytics, and blockchain./nIn this dissertation, we delve into the zero-knowledge proof, examining both its theoretical foundations and real-world applications. In theory, we introduce Libra, Virgo and Virgo++, a series of pioneering zero-knowledge protocols that boast optimal prover time, rapid verifier time, and succinct proof size while maintaining exceptional efficiency in practical settings. In practice, we explore the groundbreaking implementation of zero-knowledge proof in creating secure, trustless, and permissionless cross-chain bridges for blockchain networks. Furthermore, we also investigate the application of zero-knowledge proof in the realm of machine learning, showcasing its potential to ensure the integrity of machine learning models, as demonstrated with the decision tree model. The applied zero-knowledge proof protocols presented in this dissertation offer robust security assurances coupled with pragmatic efficiency.

Advisor: Song, Dawn DS
High Voltage Considerations for Dark Matter Searches

Cold dark matter is a well motivated astrophysical phenomenon which is currently unexplained by the standard model of particle physics. Presently, time projection chambers such as LUX-ZEPLIN are setting world leading limits in certain mass ranges, and possible improvements for next generation experiments are being researched. Scaling up the size of experiments is one way to improve sensitivity, but poses certain challenges from a high voltage perspective. In this dissertation I detail my contributions to the LZ dark matter direct detection experiment’s understanding of its electric fields and the impacts of high voltage on the future of TPCs in general. In Chapter 3 the testing and installation of LZ’s novel cathode high voltage design are detailed, and in Chapters 4,6 the electric fields simulations for the LZ TPC are documented. The sources of accidental coincidence backgrounds are examined in Chapter 7, and results of a dielectric breakdown study of liquid xenon are reported in Chapter 8. Finally the results of an extension to the LZ dark matter search to be sensitive to Planck-scale masses are reported in Chapter 5. Throughout these work the electric field and grid voltages feature prominently, demonstrating their criticality to future generations of dark matter searches.

Advisor: McKinsey, Daniel
Human-Centric Reward Design

How can we elicit the behaviors we want from artificial agents? One way of guiding behaviors of intelligent systems is through reward design. By specifying reward functions to optimize, we can use reinforcement learning (RL) to enable agents to learn from their own experience and interactions. Thus, RL has seen great success in settings where it is feasible to hand-specify reward functions that are well-aligned with the intended behaviors (e.g., using scores as rewards for games). However, as we progress to developing intelligent systems that have to learn more complex behaviors in the rich, diverse real world, reward design becomes increasingly difficult---and crucial. To address this challenge, we posit that improving reward signals will require new ways of incorporating human input./nThis thesis comprises two main parts: reward design directly using human input or indirectly using general knowledge we have about people. In the first part, we propose a framework for building robust reward models from direct human feedback. We present a reward modeling formulation that is amenable to large-scale pretrained vision-language models, leading to more generalizable multimodal reward functions under visual and language distribution shifts. In the second part, we use broad knowledge about humans as novel forms of input for reward design. In the human assistance setting, we propose using human empowerment as a task-agnostic reward input. This enables us to train assistive agents that circumvent limitations of existing goal inference based methods, while also aiming to preserve human autonomy. Finally, we study the case of eliciting exploratory behaviors in artificial agents. Unlike prior work that indiscriminately optimizes for diversity in order to encourage exploration, we propose leveraging human priors and general world knowledge to design intrinsic reward functions that lead to more human-like exploration. To better understand how intrinsic objectives guiding human behavior can inform agent design, we also compare how well human and agent behaviors in an open-ended exploration setting align with commonly-proposed information theoretic objectives used as intrinsic rewards. We conclude with some reflections on reward design challenges and directions for future work.

Advisor: Abbeel, Pieter
Biases in Improvement Decisions

People often must decide whether to invest scarce resources—such as time, money, or energy—to improve the chances of a positive outcome. For example, a doctor might decide whether to utilize scarce medicine to improve a patient’s chances of recovery, or a student might decide whether to study a few additional hours to increase their chances of passing an important exam. Eleven studies demonstrate that people behave as if they focus on the relative reduction in bad outcomes caused by such improvements. As a consequence, the same improvements (e.g., ten percentage point improvements) are valued very differently depending on whether one’s initial chances of realizing a good outcome are high or low. This focus on the relative reduction of bad outcomes drives risk preferences that violate normative standards (Studies 1a-2), is amplified when decisions become more consequential (Study 2b), and leads even experienced professionals to make suboptimal decisions (Study 3).

Advisor: Evers, Ellen
Engineering microbial hosts to produce fatty-acid derived designer polyketides

Polyketide synthases (PKSs) are versatile multimodular enzymes that biosynthesize molecules in an assembly line fashion. The discrete and sequential processing of the polyketide intermediates by domains in PKSs give rise to our ability to retrobiosynthetically predict the structure of the polyketide produced by a PKS simply by analyzing the amino acid sequence of said PKS. Likewise, much work has been done to rationally engineer novel PKSs to make designer polyketides by using retrobiosynthetic logic to stitch together PKS domains in the order required to produce a desired molecule. In this work, we sought to understand the engineering principles required to construct functional chimeras of PKSs with various reductive loop swaps to produce polyketides with varying degrees of reduction in Streptomyces albus. Furthermore, we sought to make the advantageous heterologous host, Pseudomonas putida, well adapted to produce polyketides, particularly free fatty acid-derived polyketides, by eliminating free fatty acid catabolic pathways and demonstrating the production of medium-chain free fatty acids. Finally, we present ongoing efforts to produce free fatty acid-derived polyketides that can give rise to valuable flavorant delta-lactones.

Advisor: Keasling, Jay D
Constrained machine learning: algorithms and models

This thesis is concerned with designing efficient methods to incorporate known structure in machine learning models. Structure arises either from problem formulation (e.g. physical constraints, aggregation constraints), or desirable model properties (energy efficiency, sparsity, robustness). In many cases, the modeler has a certain knowledge about the system that they are modeling, which must be enforced in an exact manner. This can be necessary for providing adequate safety guarantees, or for improving the system's efficiency: training a system with less data, or less computation costs. This thesis provides methods to do so in a variety of settings, by building on the two foundational fields of continuous, constrained optimization and of differentiable statistical modeling (also known as deep learning)./nThe first part of the thesis is centered on designing and analyzing efficient algorithms for optimization problems with convex constraints. In particular, it focuses on two variants of the Frank-Wolfe algorithm: the first variant proposes a fast backtracking-line search algorithm to adaptively set the step size in the full-gradient setting; the second variant proposes a fast stochastic Frank-Wolfe algorithm for constrained finite-sum problems. I also describe contributions to open-source constrained optimization software. The second part of this thesis is concerned with designing deep learning models which enforce certain constraints exactly: constraints based on physics, and aggregation constraints for probabilistic forecasting models. This part leverages bi-level optimization models, and differentiable optimization to constrain the output of a complex neural network. We demonstrate that complex non-linear constraints can be enforced on complex non-convex models, including probabilistic models./nThese examples showcase the power of hybrid models which couple data-driven learning and leverage complex nonlinear models such as deep neural networks, and well-studied optimization problems allowing for efficient algorithms. These hybrid models help highly flexible models to pick up structural patterns, achieving strong performance with sometimes no data access at all.

Advisor: El Ghaoui, Laurent
Mahoney, Michael W
Neutron-based Measurements in Large Neutrino Detectors and Characterization of Water-based Liquid Scintillator

This dissertation presents neutron-based measurements made using the water-Cherenkov SNO detector filled with heavy water, and the scintillator-based SNO+ detector filled with liquid scintillator, as well as lab-scale R&D measurements of water-based liquid scintillator (WbLS), a candidate material for achieving hybrid Cherenkov/scintillation technology. The results are of interest for the design of future detectors, from the perspectives of both background prediction and reconstruction capabilities, as well as improving the understanding of the nucleus and its interactions and the nature of dark matter, and the cosmos at large./nNeutrons produced by cosmic-ray muons in the SNO detector are characterized, and their production rate is measured to be, in units of 10^-4 cm / (g · μ), 7.28 +/- 0.09 (stat.) +1.59/-1.12 (syst.) and 7.30 +/- 0.07 (stat.) +1.40/-1.02 (syst.) in pure deuterium and deuterium loaded with NaCl at 0.02% by weight, respectively. A comparison of high-level observables in the accumulated data and a set of GEANT4-based simulations reveals generally accurate modeling of the production and transport physics in heavy water, but may indicate shortcomings in high-energy interactions with sodium and chlorine nuclei, which warrants further investigation./nA preliminary search for extraterrestrial antineutrinos with SNO+, which would manifest as an excess of tens-of-MeV inverse beta-decay (IBD) events and be indicative of new astrophysical phenomena, potentially shedding light on the nature of dark matter, has yielded no significant signal. Statistical analysis in a Bayesian framework has produced a 90% credible limit on the flux of astrophysical antineutrinos of approximately 10^3 cm^-2 s^-1 MeV^-1, further decreasing as a function energy. Sensitivity projections for an updated analysis on 5 years worth of data indicate that SNO+ would achieve limits comparable to the current world-leading limits, which would be further improved by extensions to reconstruction techniques to adapt to a higher-energy regime./nAlso presented is a characterization of the response of both WbLS and LAB+PPO, a conventional scintillator cocktail, to MeV-scale protons via a broad-spectrum neutron beam, and electrons and alpha particles via radioactive sources. The results of this characterization are relevant for measurements made in a low-energy regime, including antineutrinos from nuclear reactors and solar neutrinos, and demonstrate timing-based Cherenkov/scintillation discrimination in electron interactions at the benchtop scale.

Advisor: Orebi Gann, Gabriel D
Machine Learning for Semiconducting Solids: Benchmarking, Thermoelectric Discovery, and Dopant Engineering

Adapting machine learning (ML) models to the science of solids holds great promise for designing functional materials with unprecedented scale and speed. However, the increasing popularity of materials-ML models raises concerns about how they should be leveraged. Validation is not standardized, making models difficult to compare and reproduce. Heterogeneous materials datasets generated with dissimilar methods are difficult to fuse into new discoveries and insights. Most importantly, the vast majority of materials data cannot be easily used to train models because it is dispersed in scientific text./nThis thesis examines these concerns through ML approaches to semiconductor material design. This composite approach utilizes high-throughput density functional theory (HT-DFT) calculations, supervised ML models, natural language processing (NLP), and the classical and quantum theories of solids. Towards fusing disparate sources of materials data, the HT-DFT/ML screening presented identifies rare earth phosphide bulk thermoelectrics (REZnCuP2, zTexpt. ≈ 0.5) alongside classes of metallic (but near-band gap) thermoelectric Chevrel phases, La3Te4-type phases, clathrates, and others. By fitting simple linear ML models to HT-DFT data, we discern new chemical rules for engineering valence bands relevant to thermopower in Half-Heusler phases. Expanding the ML approach beyond thermopower, this thesis also describes the first widely-accepted benchmark for evaluating ML models, including state-of-the-art graph neural networks, on structure-property/composition-property prediction tasks. Finally, the NLP approaches detail a framework for materials data in text to be leveraged on a massive scale. Moving from word2vec to large language models such as GPT-3, this thesis presents NLP models of various complexities as tools to extract and analyze materials knowledge in the aggregate (e.g., using downstream supervised models). These tools are used to predict high-performance thermoelectric materials as well as gain a deeper understanding of dopant selection.

Advisor: Ceder, Gerbrand
Jain, Anubhav
The Differential Effects of Occupying Higher Hierarchical Positioning on Men’s and Women’s Perceptions of Inequity

Existing literature suggests that individuals who occupy higher (versus lower) hierarchical rank perceive less inequity. Further, research finds that men tend to perceive less inequity than women, possibly due to the fact that their higher positioning on the social hierarchy protects them from experiencing certain inequities firsthand. In this dissertation, using a sample of U.S. employees in a Fortune 500 company (NStudy1a = 4814; NStudy1b = 5327), I find that occupying higher organizational rank is more strongly associated with lower perceptions of organizational inequity for men than for women (Studies 1a and 1b). Additionally, in a sample of employees (N= 652) from several hundred organizations (Study 2), I find that men and women’s differing perceptions of inequity were associated with their greater feelings of power (i.e., control over valuable resources) and status (i.e., being respected in the eyes of others). This suggests that occupying higher hierarchical rank may be linked to lower perceived inequity for men (to a greater extent than for women) only when higher rank boosts feelings of power and status. However, upon experimentally manipulating participants’ feelings of power and status (Studies 3-4), as well as participants’ relative power and status in a virtual organization (Study 5), I did not find causal evidence of the correlational pattern documented in Studies 1-2. This suggests felt-power and felt-status may not be driving men’s (versus women’s) stronger association between higher hierarchical rank and lower perceived inequity. Study 5 detected correlational evidence suggesting that men (to a greater extent than women) perceive less inequity as a function of their greater felt-advantage, and are also less likely to support redistributive policies as a function of their greater felt-advantage. Thus, it is possible that higher hierarchical rank, felt-power, and felt-status may all be operationalizations of greater felt-advantage. Further, given prior research has found high-ranking men and women to differ on several dimensions (e.g., spans of control, task-segregation, personal experiences with inequity), it could be that one of these differences is driving men’s stronger association between felt-advantage and lower perceived inequity. Considering organizational leadership tends to be male-dominated, and those who perceive less inequity are less willing to support efforts that alleviate inequity, the consequences of advantaged ¬¬men’s relatively low perceptions of inequity may create a cycle in which organizational inequity perpetuates.

Advisor: Kray, Laura J.
Probing Protein Energy Landscapes Under Physiologically Relevant Conditions

It is imperative to investigate protein energy landscapes within the framework of physiological or near-physiological conditions, particularly when emphasizing the significance of the protein folding/unfolding process or of specific unfolded states in relation to a protein's biological function.Ubiquitination, a post-translational modification instrumental in proteasomal degradation of protein substrates, can influence protein stability. Proteins experience force-mediated denaturation when unfolded by the proteasome; therefore, I employed single-molecule force spectroscopy to study rates of unfolding of ubiquitinated proteins. I demonstrate that ubiquitination of the model protein barstar at lysine 60 not only increases its force-induced unfolding rate, but also influences the responsiveness of unfolding rate to force, implicating a change in unfolding pathways. Human γD-crystallin is a monomeric protein abundant in the eye lens nucleus that must remain stably folded for an individual’s entire lifetime to avoid aggregation and cataract formation. It is not clear whether intermediates of γD-crystallin populated under denaturing conditions are also populated under native conditions, or what relevance they hold to the mechanism of aggregation. Therefore, I employed hydrogen-deuterium exchange coupled with mass spectrometry (HDX-MS) to characterize several variants of γD-crystallin under both native and denaturing conditions. By investigating several types of mutations in each of γD-crystallin’s two domains, I show that, for two cataract- prone variants of γD-crystallin, the lowest-energy equilibrium intermediate populated under native conditions is structurally and energetically distinct to the intermediate populated under chemically denaturing conditions. The interface between the two domains is crucial to the formation of this new intermediate, and disruption of the interface either by mutation or by mild denaturation permits direct observation of both intermediates at the same time. The natively populated partially folded conformation of γD-crystallin exposes a surface which is normally buried both in the full-length structure and in isolated folded domains. Together with the noted effects of mutations, this suggests it may be significant to aggregation and to cataract formation.

Advisor: Marqusee, Susan
Implicit Modeling for 3D Applications

Implicit model has emerged as a promising representation for numerous applications, including signal processing, image representation as well as 3D modeling. In thisthesis, we will showcase its applications in three domains. First, we show that implicit representation could aid single view surface reconstruction of scenes by learning from a large collections of indoor scene data, and generalize well to unseen indoor scenes. Second, we show that implicit modeling can facilitate grounding of the language query in the 3D space. Lastly, we show that neural relighting, a type of algorithm that incorporates the neural field for modeling the light field, could achieve promising results. On one hand, by incorporating the pre-computed radiance transfer into the neural radiance modeling, we can enable handling of the type of materials with subsurface scattering effects. A hypernet-based representation could further facilitate fast image-based relighting. On the other hand, by carefully modeling the local microgeometry with the surface normal modeling, as well as incorporating various hints for lighting reflection and self reflection, we could faithfully recover reflection highlights with varying material roughnesses.

Advisor: Darrell, Trevor