VictorJuiz/thesis huggingface dataset:
Phenomena of interacting quantum many-body systems
Keywords: Physics.

Strongly correlated electron systems are one of the central topics of condensed matter physics. The myriad of combinations of diverse Fermiologies, phonon spectra and electron-electron, electron-phonon interactions, together with spin-orbit couplings, Kondo couplings, and effects of disorder and external magnetic fields, leads to a truly dazzling range of quantum many-body phenomena. Superconductivity (conventional and unconventional) and magnetism are among the most prominent examples of quantum phases of matter that occur in such systems. We know that powerful emergent principles such as symmetry and topology are required to explain these emergent phenomena. However, due to the inherent difficulty of studying systems with macroscopically large number of strongly interacting particles, there remains the challenge of connecting these somewhat abstract mathematical principles with the underlying microscopic interactions. In this thesis, we illustrate, through two examples of systems with electron-electron and electron-phonon interactions, how one can simplify intractable quantum chemistry problems by reducing them to effective model Hamiltonians that capture the essence of microscopic interactions important to low-energy excitations, which we can then study using a variety of tools, such as determinantal quantum Monte Carlo (DQMC), exact diagonalization, weak and strong coupling considerations and mean-field theory. In the first example we encounter a novel deconfined quantum critical point (DQCP) with emergent O(4) symmetry. In the second example we offer a phenomenological explanation of superconducting and insulating phases of twisted bilayer graphene. Lastly, we also visit the more field-theoretic problem of boson-fermion duality in two spatial dimensions, for which we provide an exact lattice construction. This duality is closely related to the half-filled Landau level problem in quantum Hall physics
Phenomenology of novel models of dark matter : searching for portal interactions at the large Hadron Collider and beyond
Keywords: Physics.

This dissertation explores several novel models of dark matter, and the phenomenology thereof. Chapter 2 focuses on how models can produce the correct dark matter abundance using only Kaluza-Klein modes of the graviton in several realizations of Randall-Sundrum models of extra dimensions. Chapter 3 studies the phenomenology of an extension of the Standard Model in which additional TeV-scale "portal" fermions produce kinetic mixing between the Standard Model photon and a dark photon arising from an extended gauge group, under which dark matter is charged. Chapter 4 investigates a minimal model of kinetic mixing between the Standard Model photon and a dark photon, where the mixing is mediated solely by an extended Higgs sector. Finally Chapter 5 studies the implications of a generalized thermodynamics, known as Tsallis statistics, on the thermal freeze-out of dark matter in the early universe
Photogrammetry as a Tool for Community Based Science in British Virgin Islands, Puerto Rico, and U.S. Virgin Islands
Keywords:  Department of Earth Systems
Earth Systems
School of Earth Energy and Environmental Sciences
Coral Reefs
Photogrammetry
Community-Based Science
Text
Thesis

The Caribbean Sea has seen a degradation of 80%-90% of its coral reefs since the 1970s. This is catastrophic for the 40 million residents of the region who depend on coral reefs for their livelihoods, recreation, storm protection, and culture. There is a need for finer scale, streamlined monitoring of reefs to address the challenges they are facing. Structure from Motion (SFM) Photogrammetry facilitates and enables monitoring using community-based science. Collaboration with community partners across the British Virgin Islands, Puerto Rico, and the United States Virgin Islands led to the co-design of effective methods to implement photogrammetry in their local reefs. Measurements derived from photogrammetric models provided surface rugosity data for 28 sites across the region, providing quantitative data on the state of the reefs after the devastating hurricanes that occurred in September of 2017. The average value for the region was 1.505, and only four sites had regions with a rugosity greater than 2, the common threshold for a structurally complex reef, which is linked to increased biodiversity and high functioning ecosystem services. Regional averages varied between 1.409 in the northeastern region of Puerto Rico, to 1.813 in Culebra, Puerto Rico. This project was an initial run of what will be a longer term project that facilitates community-based monitoring of Caribbean reefs. The most significant contribution of this research is the development of one of the first frameworks for applying photogrammetry as an effective community-based science tool. This framework includes the evaluation of the main objectives of the community partner, access to resources, skill in the water, previous experience with research, capacity for work intensity, and number of volunteers. These frameworks can be applied when using photogrammetry in other coastal environments, and when working with community based scientists in an underwater setting.
Photonic design of dielectric laser accelerators and quantum features of free electrons
Keywords: Electrical Engineering.

Particle accelerators are important in areas ranging from fundamental science to medical care. To advance particle accelerators, it is critical to achieve high acceleration gradient, compact size and low cost. Dielectric laser accelerators (DLAs) are promising candidates which have more than 10 times higher acceleration gradient than conventional radio-frequency accelerators and can be integrated on-chip. In this thesis, we present a few DLA designs to solve critical challenges for DLAs, such as providing continuous acceleration as the electron velocity increases using a tapered slot waveguide and increasing the electron current throughput with a photonic crystal multi-channel DLA. We also discuss the design principles of apodized grating couplers, which are crucial elements in the on-chip power delivery system for DLAs. We demonstrate how basic electromagnetic theory and physical intuitions guide the design of such devices. On the other hand, sub-femtosecond manipulation of the electron in DLAs makes it a versatile electron probe to study fundamental science. Along this direction, we also discuss the quantum features of the free electron by studying the interaction between a free electron and a two-level atom. We show that resonantly modulating the free electron with optical field can enhance the electron-atom interaction. It is possible to probe the atomic coherence with the modulated electron. Also, entanglement between two-level atoms can be created by interacting them with the same free electron. Those provide new perspectives for ultrafast physics studies using the free-electron probe
Photon-mediated atom-atom interaction in a confocal cavity
Keywords: Physics.

Engineering complex and exotic many body states is a long standing goal for ultracold atom experiments, for which strong and tunable interaction is a crucial ingredient. In this thesis, I will describe our experimental efforts using photons in a multimode confocal cavity to mediate effective interaction among atoms confined within the cavity. In contrast with traditional optical cavities where only a single fundamental Gaussian mode is coupled to the atoms, multimode cavities can support near-degenerate resonances that are well approximated by a continuum of photon modes. Superposition of cavity modes with incommensurate spatial profiles then gives rise to supermodes that adapt to atom configurations and allow for further shaping of the interatomic potential landscape. In a single mode cavity, Raman coupling through atom internal states with the cavity mode is demonstrated, leading to a novel spinor organization and Ising type cavity mediated interaction. As a first experiment in a multimode cavity, I will show that the interference of near-degenerate cavity modes leads to a strong and tunable-range local interaction between Bose-Einstein condensates (BECs) trapped within the cavity. Exploiting the symmetry of a confocal cavity, we measure the interaction between the BEC and its mirror image thus avoiding the complications of overlapping atom densities. I will then discuss how a confocal cavity also hosts a non-local sign-changing interaction due to the differing longitudinal Gouy phases of cavity modes. Using holographic imaging techniques, we reconstruct the complex intra-cavity electric field and confirm the spatial character of this non-local interaction. Finally, I will discuss additional control of the relative strength of the local and the non-local interaction by pumping on two degenerate resonances of the cavity. By canceling the non-local interaction, we restore the translational symmetry of the system. The strong local interaction can then induce a crystallization transition of the BEC where the resulting optical lattice responds dynamically to local changes in atomic density and accommodates a phonon excitation. Bragg spectroscopy measurements then reveal the dispersion of the phonon mode dependent on the BEC-photon coupling strength. I will conclude by briefly describing future experiments enabled by both the local and the non-local interactions
Phytoplankton photoacclimation in the Southern Ocean and Arctic Ocean
Keywords: Earth System Science.

Polar regions are undergoing rapid and dramatic changes. Substantial shifts in the extent and thickness of sea ice have cascading effects on marine primary production and polar ecosystems. Primary production is closely tied to environmental variables such as light and nutrient availability, which are sensitive to these climate-induced changes. Climate change-induced shifts in phytoplankton communities can have far-reaching effects on nutrient cycling, carbon drawdown and export, higher trophic levels, and benthic-pelagic coupling. In the spring, light increases from wintertime lows and sea ice begins to retreat. During this time, phytoplankton can experience both very high light (when mixed to the surface) and very low light (when under sea ice or at the base of the mixed layer). To acclimate to this variable light field, phytoplankton must optimize carbon fixation while minimizing photodamage due to periods of excessive light. Determining how phytoplankton photoacclimate will help us understand how phytoplankton might adapt to future changing environmental conditions. My dissertation aims to understand how phytoplankton photoacclimate in spring in both the Southern Ocean and the Arctic Ocean. I employ field experiments investigating how phytoplankton respond to changes in light and, in one chapter, the interactive effects of both light and iron (in much of the Southern Ocean, iron can limit phytoplankton growth). Results from my Southern Ocean (western Antarctic Peninsula) field experiments showed that at moderate light exposure, phytoplankton experienced little photodamage or changes in net primary production, and high light exposure stimulated Phaeocystis antarctica growth (but not diatom growth). My combined light and iron experiments demonstrated that light limits phytoplankton growth, whereas iron does not, despite overall low iron concentrations and evidence of iron stress. In the Arctic Ocean (Baffin Bay), both my in situ measurements and experiments demonstrate that phytoplankton under the ice, in the marginal ice zone, and in open water are photoacclimated similarly. I observed high photoprotective pigments in phytoplankton in all three irradiance regimes, as well as high rates of photosynthesis. These findings lend support to the hypothesis that Arctic phytoplankton under sea ice are primed to bloom once they have access to sufficient light. Both the Southern Ocean and Arctic Ocean results provide evidence that early season polar phytoplankton are acclimated to higher light than they are experiencing and are thus prepared to manage rapid changes in light. As sea ice thins and retreats earlier in the season, phytoplankton will be exposed to light earlier, enabling them to bloom earlier. These findings further our ability to understand the impacts of climate-driven changes on phytoplankton communities
Platforms and algorithms for decision making at scale
Keywords: Management Science and Engineering.

The Internet is strengthening democracy by empowering individuals. Social media enables real-time dissemination of information. Online social networks enable people to come together in support of common causes. But despite the prevalence of Internet technologies, there has yet to be an online platform where large-scale democratic decision making occurs regularly as a society. Multiple obstacles remain in the way of building such platforms. For example, places where users can exercise free-form speech often degenerate into vitriol. The unstructured, large-scale nature of the Internet means that it is not obvious what the optimal way to aggregate opinions is and how to act on them. In this dissertation, we discuss our work on building platforms that tackle these issues from theoretical and practical perspectives. Specifically, - Participatory Budgeting (PB): PB is a democratic process where citizens vote on public budgets. We propose Knapsack Voting, a vote aggregation method in PB, and show that it is welfare-maximizing and strategy-proof under certain utility models. We also discuss the Participatory Budgeting Platform (pbstanford.org), our open-source platform for running online PB elections. It has been used throughout the United States, including Chicago, Boston, Seattle, and New York City, and has distributed over 60 million of public budgets. - Sequential Deliberation: Sequential deliberation is a vote aggregation method that uses rounds of small group interaction and can be used in decision spaces that are too complex for ordinal voting. We show that this method converges to the optimal point quickly under certain utility models. We also show how sequential deliberation can be used in PB to handle project interdependencies. - Online Deliberation Platform: We discuss our platform for conducting large-scale Deliberative Polls with an automated moderator that can support over 1,000 simultaneous participants (stanforddeliberate.org). The platform has been successfully deployed in the US, Canada, Chile, Hong Kong, and Japan, where people deliberated on complex issues such as health care, renewable energy, and foreign policy. We discuss the technical challenges and our study on the efficiency of the platform
Political representation in democratic and autocratic regimes
Keywords: Political Science.

This dissertation studies political representation in democratic and autocratic regimes. It asks two questions: first, how can citizens influence politics in some of the least democratic regimes? Second, how do citizens form their expectations of political representation following authoritarian breakdown? The dissertation answers these questions in the context of the former German Democratic Republic (GDR) and consists of three papers. The first paper challenges a key assumption in models of electoral politics, according to which electoral contestation is a precondition for political decision-makers to respond to the demands of their constituents. Using a unique dataset of petitions to the central government of the former GDR, I show that despite uncontested elections, the GDR engaged in electoral cycles of responsiveness: petitions were answered more quickly and were more likely to be successful before elections. The second paper explores the relationship between emigration and authoritarian stability. Extant work on this topic usually focuses on emigration from open regimes---that is, autocracies that impose few restrictions on citizens' ability to leave---and focus on remittances and norm diffusion. By contrast, I study emigration from a closed autocracy and emphasize alternative mechanisms. Exploiting an involuntary emigration reform in the former GDR in 1983, I argue that emigration can have countervailing effects on the stability of closed autocracies. On the one hand, the exit visa system in such a setting generates information about troublemakers, which improves autocratic stability. But on the other hand, letting some people leave an otherwise closed regime can create more demand for emigration among left-behind citizens, which undermines stability. The third paper asks where public support for democracy comes from after autocratic breakdown. The existing literature usually focuses on the role of autocratic legacies or contemporary economic or political performance. I emphasize instead the importance of the early democratic years: the mode by which countries transition from autocracy to democracy can lastingly shape citizens' expectations of and beliefs about democratic governance. Evidence for this argument comes from an original survey experiment, household panel data, and observational data on historical unemployment and present-day voting for the far-right populist \textit{AfD}. Taken together, the dissertation identifies two new research agendas. I call for a departure from the traditional focus on co-optation and repression in scholarship on authoritarian regimes and for more research on the ways in which ordinary citizens can gain representation and influence politics in non-democratic regimes. Moreover, while research on the legacies of autocratic rule abounds, I emphasize the important consequences that autocratic breakdown can have for political attitudes, behavior, and long-term democratic stability
Polymer sorting of single-walled carbon nanotubes : additives and applications
Keywords: Materials Science and Engineering.

Single-walled carbon nanotubes (CNTs) are a promising candidate for flexible and wearable electronics, offering robust mechanical properties, solution-processability as well as superlative charge carrier mobilities. However, bulk synthesis of CNTs produces a mixture of metallic and semiconducting species, greatly limiting their technological utility. To address this issue, various post-synthesis purification schemes have been investigated. Polymer sorting in particular has emerged as a scalable and accessible solution to this problem, offering fast processing times of 1-2 hours, yields over 20% and semiconducting purities as high as 99.99%. Despite these encouraging developments, there is still much to be desired from this strategy, including greater mechanistic insight, higher yields and purities, chiral selectivity, and reducing the polymer's effect on CNT device performance, among others. This thesis discusses the role of additives on polymer sorting of CNTs. First, I will describe the importance of polymer recyclability to the scalability of the sorting process, and introduce a recyclable, 2-ureido-4-pyrimidinone-based supramolecular polymer. I will then show that the molecular weight and conformation of this polymer can be tuned by the addition of monofunctional chain stoppers, enabling improvements in sorting yields without compromising the purity or properties of sorted CNTs. Next, I will recount the mechanistic principles underlying polymer sorting, and examine the effects of polar cosolvents in the context of the polarizability hypothesis of CNT sorting. Finally, I will present a cosolvent-based flocculation technique for reducing the amount of excess sorting polymer in polymer-sorted CNT dispersions. This method can be used to improve the mobility and on-off ratio of CNT thin-film transistors regardless of the choice of sorting polymer, and also leads to enhanced process compatibility and versatility, facilitating the fabrication of stretchable, all-inkjet-printed CNT transistor arrays. Taken together, these forays demonstrate the importance of additives to the polymer sorting process, both in terms of sorting efficacy as well as the performance of resulting CNT devices
Posterior sampling for efficient reinforcement learning
Keywords: Electrical Engineering.

Reinforcement learning has shown tremendous success over the past few years. Much of this recent success can be attributed to agents learning from an inordinate amount of data in simulated environments. In order to achieve similar success in real environments, it is crucial to address data efficiency. Uncertainty quantification plays a prominent role in designing an intelligent agent which exhibits data efficiency. An agent which has a notion of uncertainty can trade-off between exploration and exploitation and explore in an intelligent manner. Such an agent should not only consider immediate information gain from an action but also its consequences on future learning prospects. An agent which has this capability is said to exhibit deep exploration. Algorithms that tackle deep exploration, so far, have relied on epistemic uncertainty representation through ensembles or other hypermodels, exploration bonuses, or visitation count distributions. An open question is whether deep exploration can be achieved by an incremental reinforcement learning algorithm that tracks a single point estimate, without additional complexity required to account for epistemic uncertainty. We answer this question in the affirmative. In this dissertation, we develop Langevin DQN, a variation of DQN that differs only in perturbing parameter updates with Gaussian noise, and demonstrate through a computational study that Langevin DQN achieves deep exploration. This is the first algorithm that demonstratively achieves deep exploration using a single-point estimate. We also present index sampling, a novel method for efficiently generating approximate samples from a posterior over complex models such as neural networks, induced by a prior distribution over the model family and a set of input-output data pairs. In addition, we develop posterior sampling networks, a new approach to model this distribution over models. We are particularly motivated by the application of our method to tackle reinforcement learning problems, but it could be of independent interest to the Bayesian deep learning community. Our method is especially useful in RL when we use complex exploration schemes, which make use of more than a single sample from the posterior, such as information directed sampling. Finally, we present some preliminary results demonstrating that the Langevin DQN update rule could be used to train posterior sampling networks, as an alternative to index sampling, and further improve data efficiency