VictorJuiz/thesis huggingface dataset:
Clause structure and ergativity in Nukuoro

This dissertation provides a description and analysis of the syntax of Nukuoro, an understudied Polynesian Outlier language spoken in the Federated States of Micronesia. A primary goal of this work is to provide documentation of Nukuoro in its cultural and historical context; as such, the first part of this thesis includes an overview of Nukuoro documentation and revitalization efforts, a grammatical sketch of the language, and an analysis of Nukuoro clause structure. In particular, I offer an analysis of SVO word order in Nukuoro as involving predicate fronting and object shift (e.g. Massam 2001), followed by movement of the subject to the high left periphery. This analysis then bears on the treatment of Nukuoro genitive relative clauses (GRCs): I claim that left-peripheral subjects are high enough to escape the CP phase, resulting in genitive case assignment to pre-verbal subjects./nThe second half of this dissertation centers around ergativity in Nukuoro, which involves exponents of the well-known Polynesian *-Cia suffix. Nukuoro is typologically unusual in that it shows an ergative extraction restriction, but lacks morphological ergative and absolutive case marking; furthermore, I argue that Nukuoro clause structure does not involve movement of the transitive object to a position higher than the subject (i.e., object inversion), which runs counter to standard analyses of syntactic ergativity (e.g., Coon et al. 2014). Building on case discrimination analyses of syntactic ergativity (Otsuka 2006, 2010a; Deal 2017b), I analyze the Nukuoro ergative extraction restriction as a composite Ā probe (e.g., Coon & Bale 2014; Colley & Privoznov 2020; Scott 2021), which seeks an Ā feature and an absolutive Case feature on the same goal. Additionally, using evidence from licensing in non-finite clauses, I develop a theory of Case in Nukuoro where both ergative and absolutive are structural Cases, with ergative assigned by Infl (e.g., Levin & Massam 1985; Bobaljik 1993; Laka 1993; Otsuka 2000; Rezac et al. 2014). The Nukuoro pattern thus carries implications for theories of ergativity, as well as for theories of movement and Case more broadly.

Advisor: Deal, Amy Rose
Climate Change and Atmospheric Aerosols: Assessing the Limits of Human Intervention in Curbing Temperature-Dependent Particulate Matter in the United States

Decades of environmental legislation and emission reduction efforts have resulted in remarkable improvements in air quality in the United States, but we continue to find additional benefits to further abating atmospheric concentrations of toxic air pollutants such as particulate matter. This task, however, is becoming increasingly arduous on account of the current climatic transition. The atmospheric warming associated with climate change is dramatically altering the landscape of particulate matter pollution: influencing precursor emission rates, the chemical processes regulating its production, and the meteorology controlling its accumulation and dispersion. This effect is even more pronounced in urban areas, where warming is occurring fastest and where public exposure potential is greatest. Thus, continued air quality improvements in the face of climate change will necessitate a more precise understanding of the drivers of aerosol pollution that do or do not fall under our control. Focusing on summertime in the Eastern U.S., I begin by showing how the current enhancement of particulate matter with temperature is dominated by the behavior of organic aerosols, arguing that this represents a significant regulatory challenge on account of the myriad emission sources responsible for generating this subset of particulate matter. I continue by assessing the role of chemical transport models for investigating the enhancement of organic aerosols with temperature, showing how current model predictions are lacking in the representation of aerosol speciation at high temperatures, and making suggestions for future model evaluation strategies. Finally, I explore the role of meteorology in exacerbating the accumulation of pollutants, illustrating that the connection between ambient temperature and atmospheric stagnation can be exceptionally consistent, representing an amplifying factor to pollution that falls outside of our direct control but within our prognostic capabilities. Through this work, I inform strategies for probing the nature of air pollution in a warmer future, outlining the circumstances in which human intervention will be more or less effective in lessening exposure.

Advisor: Cohen, Ronald C
Harley, Robert A
Behind the Veil: Moral Reasoning about Gender Equality and Women’s Rights amongst Iranians

The study examined moral reasoning amongst Iranian men and women regarding gender inequality that is sanctioned by religious and legal laws in Iran. Through interviews with 32 recent Iranian immigrants to Australia aged 22 to 44 years, half of whom were male and half female, the study explored moral judgments on gender inequality and restrictions on women’s rights in familial contexts in Iran. The study investigated both the evaluations and justifications provided by participants regarding: (i) specific practices of gender inequality (by men) sanctioned by religious and legal laws, and (ii) transgressions (by women) of religious and legal laws that sanction gender inequality. The practices addressed involved restrictions on females of the rights to travel, work, maintain ownership of financial assets after divorce, and choose whether to wear a hijab. The study also investigated participant evaluations and justifications regarding the mutability of existing laws that sanction gender inequality, by rule abolition, authority dictates from religious and governmental leaders and God, and common practice in countries outside of Iran./nIt was found that Iranian men and women engage in critical evaluation of existing social arrangements pertaining to gender inequality, specifically practices that are religiously and legally sanctioned in Iran. The majority of participants of both sexes evaluated practices that enact gender inequality unacceptable. Most participants also approved of the dissolution of such laws, the prospect of support from religious and legal authority figures for the abolition of such practices, and the practices of other countries which denote gender equality in these areas. Reasoning provided by participants for their evaluations were predominantly moral justifications pertaining to human rights, equality and the well-being of persons. Concern with relationship harmony and honesty were also common concerns. Participants demonstrated a plurality of moral, conventional and personal concerns and engaged in coordination of concerns within and amongst these domains./nWhile most participants evaluated practices that sanction gender inequality as unacceptable, and also condoned the cessation of such laws and practices, there were variegated evaluations regarding the acceptability of transgressions of existing gender restrictive laws. Justifications amongst male participants largely involved concern for honesty and disapproval of the deception involved in violating existing laws. Female participants also demonstrated concern with honesty in their justifications, but this was accompanied by concerns with the wellbeing of females who transgress existing laws and the imperative of challenging unjust social practices.

Advisor: Turiel, Elliot
Effects of microclimate, dispersal, species interactions, and environmental stochasticity on demography

The spatial distribution and species composition of plant communities depend on a multitude of potentially interacting drivers. These drivers include abiotic factors, such as climate conditions, and biotic factors, such as species interactions. Long-term data on vital rates (i.e., survival, growth, reproduction) for individuals in a community can reveal the influence of factors such as climate and neighboring individuals on performance. In this dissertation, I use a demography dataset that tracks the location and vital rates of over 3,300 individuals of 18 species across nine years in an alpine plant community. Here, I test whether vital rates respond to microenvironment modification of ground temperatures and soil moisture by neighboring plants. In the next chapter, I assess how abiotic dispersal may promote the co-location of seeds and host plants and, thus, influence species interactions. Finally, I explore how species respond to increased vital rate variance from environmental stochasticity by estimating the extent that population stochastic growth rates are buffered to perturbations. Collectively, this dissertation demonstrates the sensitivity of demographic processes to macroclimate, seed dispersal, and environmental stochasticity within and across co-occurring species using a highly resolved model system.

Advisor: Blonder, Benjamin W
Applications of Holography in Quantum Information Dynamics: Chaos, Teleportation, and Metrology

The eternal quest to reconcile quantum mechanics and general relativity has recently led to new insights at the interface between gravitational physics, condensed matter, and quantum information science. Much of this progress owes to the development of the holographic principle, a conjectured duality between gravitational systems and ordinary quantum mechanical systems. This opens up the possibility of translating gravitational phenomena into the language of microscopic quantum physics, and vice versa. This thesis explores several surprising connections that have emerged from a sharper understanding of this duality, with a particular emphasis on questions regarding the dynamics of quantum information. What general lessons can we draw from the physics of black holes about the propagation of quantum information in quantum many-body systems? How can we characterize this propagation and leverage it as a resource for performing tasks on large-scale quantum technologies? From a complementary perspective, how can we utilize quantum simulators to probe features of quantum gravity? By investigating these questions, this thesis sheds light on a rich variety of long-standing topics—including quantum chaos, teleportation, and metrology. More broadly, it demonstrates holography as a powerful theoretical framework for guiding our exploration of quantum many-body physics.

Advisor: Yao, Norman Y
Statistical Methods for Characterizing Occurrences and Impacts of Climate

This thesis explores statistical methods for characterizing the occurrences and impacts of climate. Data on climate and the environment more broadly show unique characteristics, posing methodological challenges./nPart I presents an overview of an emerging, accessible earth observation data source, satellite images, and explores its potential applications in statistical and causal inference. We introduce an approach for incorporating image data, represented by image features, into a regression framework as a potential proxy for confounding variables. The study examines the impact of image features on regression estimates, focusing on the characterization of bias with and without the inclusion of images, as well as the conditions under which the inclusion of image data reduces or amplifies bias./nPart II introduces an empirical and methodological problem related to estimating the economic values of the environment. This question is pertinent for understanding how various environmental qualities, such as flood risk and air pollution, are currently reflected in residential property values and how society manages climate and environmental risks. However, a methodological challenge in estimating capitalization lies in the difficulty of accounting for confounders, which invites the application of the method studied in Part I. The results indicate that while some risk-related factors are associated with lower housing values, others, such as PM 2.5 and flood risk score, are associated with higher housing prices. This provides a lens through which to discuss how risks could be reflected in property values./nPart III focuses on statistical methods for characterizing occurrences of extreme climate events. These data are spatio-temporal in nature, and effectively visualizing and summarizing such data is challenging, though crucial for monitoring and identifying hazardous events. A primary challenge stems from the context-dependent nature of extreme events, where definitions vary over time and space. While many adopted approaches involve pre-defining criteria for anomalous events, typically by setting an exceedance threshold, determining appropriate values for the exceedance threshold, time window, and spatial boundary is a non-trivial task. The study applies functional principal component analysis to characterize spatio-temporal trends of extreme precipitation. The method shows potential as a flexible way to identify both the temporal window and geographic location of anomalous events.

Advisor: Pimentel, Samuel SP
Hsiang, Solomon SH
Phonon-protected superconducting qubits

The overhead to construct a logical qubit from physical qubits rapidly increases with the decoherencerate. Current superconducting qubits reduce dissipation due to two-level systems (TLSs) by using large device footprints. However, this approach provides partial protection, and results in a trade-off between qubit footprint and dissipation. This work introduces a new platform using phononics to engineer superconducting qubit-TLS interactions. We realize a superconducting qubit on a phononic bandgap metamaterial that suppresses TLSmediated phonon emission. We use the qubit to probe its thermalization dynamics with the phonon-engineered TLS bath. Inside the phononic bandgap, we observe the emergence of non-Markovian qubit dynamics due to the Purcell-engineered TLS lifetime of 34 μs. We discuss the implications of these observations for extending qubit relaxation times through simultaneous phonon protection and miniaturization.

Advisor: Sipahigil, Alp
Integrated Low-Power Wireless Systems for the Next Generation of IoT, Sensors and Microrobots

The relentless pursuit of smaller, cheaper, and lower-power wireless electronics has driven the design of novel radio designs such as crystal-free radios, that offer a fully functional wireless node with minimal external components. At Berkeley, the Single-Chip Micro Mote (\scum{}), a 3x\SI{2}{\milli\meter}, \SI{4.2}{\milli\gram} crystal-free 802.15.4 and BLE wireless SoC, was developed to make swarms of mm-scale microrobots a reality. This dissertation will begin by discussing \scum{} in the context of system integration, including the challenge of accurate channel frequency tuning in the face of varying temperature and voltage conditions. By characterizing the RF frequency’s dependence on voltage droop during transmission, we were able to compensate for the RF frequency shift, increasing \scum{}’s 802.15.4 packet payload from 10B to 125B while powered from a solar cell./nSeveral integrated systems with \scum{} at their core will also be discussed, including a wirelessly-actuated, solar-powered, quarter-sized, \SI{286}{\milli\gram} microrobot MEMS gripper for microrobotics; and a \SI{244}{\milli\gram}, 5x\SI{8}{\milli\meter} BLE \scum{} tag, which was used to track an Asian hornet—feats not possible with commercial off-the-shelf components. The dissertation will conclude with a look at how future crystal-free radios could be designed to address the inherent instability of power sources in low-power systems, potentially pushing the envelope for even smaller, cheaper, lower-power and more reliable wireless electronics.

Advisor: Pister, Kristofer S.J.
Mapping Complex Protein Energy Landscapes with Covalent Labeling Methods

Proteins are large, complex macromolecules that play a wide variety of essential roles in livingorganisms. It has long been appreciated that the amino acid sequence of a protein encodes its three-dimensional structure, which is essential for biological function. It is becoming increasingly appreciated that protein structure is not static; proteins are dynamic molecules, occupying many conformations with varying populations on a broad range of timescales. This conformational ensemble can be thought of as an energy landscape, and be described using the language of kinetics and thermodynamics. To truly understand how proteins execute their broad set of functions we need to understand how these energy landscapes are encoded by protein sequence, how they determine protein function, and how they are influenced by biological environments. Covalent labeling methods are ideal tools for answering these questions, as the chemical details of different covalent labeling reactions make them sensitive to protein structure, stability, and dynamics, and the temporal separation between labeling and detection facilitates the use of these methods on complex mixtures of proteins and other macromolecules. In this work, I use multiple covalent labeling methods to map the details of protein energy landscapes. First, I provide a background on protein conformational ensembles, their timescales, and on the covalent labeling methods used in this work. Second, I discuss my developments using a combination of hydroxyl radical footprinting mass spectrometry (HRF-MS) and chemical denaturation to extend our ability to measure protein global thermodynamic stability to a broader range of proteins and solution conditions. Third, I report on our use of hydrogen-deuterium exchange mass spectrometry (HDX-MS) to describe previously unknown conformational heterogeneity of the SARS-CoV-2 spike protein and detail how this heterogeneity is modulated by temperature, sequence, receptor binding, and interaction with antibodies. Finally, I describe work using a combination of labeling methods including HDX/MS, thiol labeling, and active site labeling to determine how the folding trajectory of the protein HaloTag is altered by translation. Lastly, I explain early efforts to extend these approaches to obtain more detailed structural information on the folding of proteins during translation.

Advisor: Marqusee, Susan
Learning to Design and Engineer Proteins from Evolution, 3D Structures, and Experiments

Proteins serve many crucial functions in maintaining life, but have also been co-opted for human endeavors such as gene editing, immunotherapy, and plastic degradation. To better serve such needs, we re-engineer naturally existing proteins or design de novo proteins. In recent years, data at an unprecedented scale from evolution, 3D structures, and experiments became available for learning to design and engineer proteins. These distinct data types provide different yet complementary information about proteins. This thesis presents new machine learning methods that learn from multiple types of data for the problems of sequence-based protein fitness prediction and structure-based fixed backbone protein design./nFor sequence-based protein fitness prediction, machine learning-based models typically learn from either unlabelled, evolutionarily-related sequences, or variant sequences with experimentally measured labels. For regimes where only limited experimental data are available, combining both sources of information could improve protein fitness prediction. Toward that goal, we propose a simple combination approach that is competitive with, and on average outperforms more sophisticated methods. The comparative analysis also highlights the importance of systematic evaluations and suﬃcient baselines./nFor structure-based fixed backbone protein design, prior machine learning approaches to this problem have been limited by the number of available experimentally determined protein structures. We present a strategy to augment the training data by nearly three orders of magnitude by predicting millions of structures using AlphaFold2. Graph neural network and Transformer models trained with this additional data achieves an overall improvement of almost 10 percentage points over existing methods in native sequence recovery rate. We also study the generalization to a variety of more complex tasks including design of protein complexes, partially masked structures, binding interfaces, and multiple states.

Advisor: Listgarten, Jennifer
Hardt, Moritz
Second-order perturbative correction to state-specific, excited-state mean field theory

Obtaining predictions for the energies of electronic excited states, especially charge transferstates, is still a challenge in the field of electronic structure theory. Charge transfer states often play an important role in the function of solar cells and organic semiconductors, and having an accurate computational model for their energetics that is not computationally expensive is crucial for the development of new technologies that rely on the existence of these states. In this dissertation, two contributions towards this goal are discussed. First, the formulation of a state-specific perturbative method with a relatively low computational complexity of O(N^5) is described. The approximations made to reduce the scaling from its original complexity of O(N^7) had very little impact on the results of the method and it was shown that it provided accurate energetic predictions for valence and charge transfer excitations of small molecules. Next, this perturbative method was analyzed for its accuracy using a benchmarking set of 105 singlet valence excited states. Through this study it was found that, with regularization, the method can perform even better than many higher-scaling theories and can provide a warning for when a state being studied cannot be mainly described by single excitations. The demonstrated accuracy of the method combined with its relatively low computational cost makes it a promising theory that will be useful in its own right and which can act as a springboard for the development of even more sophisticated excited-state-specific correlation methods.

Advisor: Neuscamman, Eric