VictorJuiz/thesis huggingface dataset:
Random-cluster Dynamics

The random-cluster model has been widely studied as a unifying framework for spin systems, random graphs and electrical networks. The random-cluster model on a graph~$G=(V,E)$ with parameters $p\in(0,1)$ and $q>0$ assigns to each subgraph $(V,A \subseteq E)$ a probability proportional to $p^{|A|}(1-p)^{|E|-|A|} q^{c(A)}$, where $c(A)$ is the number of connected components in~$(V,A)$. When $q=1$ this model corresponds to the standard \textit{bond percolation model}. For integer $q \ge 2$ the random-cluster model is closely related to the classical ferromagnetic \textit{$q$-state Ising/Potts model}. When $q\rightarrow 0$, the set of weak limits that arise contains the uniform measures over the spanning trees, spanning forests and connected subgraphs of $G$./nIn this thesis we investigate the dynamics of the random-cluster model. While dynamics for the Ising/Potts model have been widely studied, random-cluster dynamics have so far largely resisted analysis. We focus on two canonical cases: the case when $G$ is the complete graph on $n$ vertices, known as the \textit{mean-field model}, and the case when $G$ is the infinite 2-dimensional lattice graph $Z^2$. Mean-field models have historically proven to be a useful starting point in understanding dynamics on more general graphs. In statistical mechanics, however, probabilistic models are most frequently studied in the setting of infinite lattice graphs; understanding random-cluster dynamics in $Z^2$ is thus of foremost importance./nIn the first part of this thesis we establish the mixing time of Chayes-Machta dynamics in the the mean-field case. For $q \in (1,2]$ we prove that the mixing time is $\Theta(\log n)$ for all $p \neq p_c(q)$, where $p = p_c(q)$ is the critical value corresponding to the emergence of a ``giant'' component. For $q > 2$, we identify a critical window $(p_s,p_S)$ of the parameter $p$ around $p_c(q)$ in which the dynamics undergoes an exponential slowdown. Namely, we prove that the mixing time is $\Theta(\log n)$ when $p \not\in [p_s,p_S]$ and $\exp(\Omega(\sqrt{n}))$ when $p \in (p_s,p_S)$. We also show that the mixing time is $\Theta(n^{1/3})$ for $p=p_s$ and $\Theta(\log n)$ for $p=p_S$. In addition, we prove that the Glauber dynamics undergoes a similar exponential slowdown in $(p_s,p_S)$./nThe second part of this thesis focuses on the analysis of the Glauber dynamics of the random-cluster model in the case where the underlying graph is an $n \times n$ box in the Cartesian lattice~$Z^2$. Our main result is a tight $\Theta(n^2\log n)$ bound for the mixing time at all values of the model parameter~$p$ except the critical point $p=p_c(q)$, and for all values of~$q\ge 1$.

Advisor: Sinclair, Alistair
Interface and Topology in Magnetism

This dissertation explores interface interactions, topology of spin structures, and the use of interface interactions in manipulating the topology of spin structures. First, I will present our work on interface interactions in magnetic thin film heterostructures. A majority of interesting phenomena in magnetic heterostructures are caused primarily by the interface interactions. In exchange bias system CoO/MgO/Fe/Ag(001), the role of frozen and rotatable antiferromagnetic spins in exchange bias system was investigated using X-ray Magnetic Linear Dichroism (XMLD). In the Py/FeMn/Ni/Cu(001) system, the indirect coupling through an antiferromagnetic spacer layer was studied through anisotropy measurements using Rotating-Field Magneto-Optical Kerr Effect (ROTMOKE)./nSecond, I will present our work on artificial topological spin structures. Traditionally, research on nanostructured magnetic materials has focused on the study of uniformly magnetized thin films. With the increasing capability to prepare and manipulate non-uniform spin configurations in recent years, various topological spin structure have been realized. In magnetic thin films, magnetic vortex states can be stabilized by reducing the lateral dimension of the magnetic thin film to the nanoscale due to the increased shape anisotropy. By combining this shape anisotropy and interlayer exchange coupling at the interface, we showed that more complex topological spin structures such as spin skyrmions and hedgehog spin merons can be stabilized and manipulated. By utilizing uniaxial strain in a ferroelectric-ferromagnetic system Co/Cu/PMN-PT(011), we demonstrated a method to manipulate the circulation of a magnetic vortex state using strain.

Advisor: Qiu, Zi Qiang
Stirling Engine for Solar Thermal Electric Generation

Addressing the challenge of climate change requires the large-scale development of significant renewable energy generation, but also requires these intermittent energy sources to be balanced by energy storage or demand management to maintain a reliable electric grid. In addition, a centralized generation paradigm fails to capture and utilize thermal energy for combined heat and power, abandoning a large portion of the available value from the primary energy source. A solar thermal electric system utilizing Stirling engines for energy conversion solves both of these shortcomings and has the potential to be a key technology for renewable energy generation. The ability to store thermal energy cheaply and easily allows the reliable generation of output power even during absences of solar input, and operating as distributed generation allows the output thermal stream to be captured for local heating applications. Such a system also can achieve relatively high conversion efficiencies, is fabricated using common and benign materials, and can utilize alternate sources of primary energy in an extended absence of solar input./nThis dissertation discusses the design, fabrication, and testing of a Stirling engine as the key component in a solar thermal electric system. In particular, the design addresses the low temperature differential that is attainable with distributed solar with low concentration ratios and is designed for low cost to be competitive in the energy space. The dissertation covers design, fabrication, and testing of a 2.5 kW Stirling Engine with a predicted thermal-to-mechanical efficiency of 20%, representing 60% of Carnot efficiency, operating between 180°C and 30°C. The design process and choices of the core components of the engine are discussed in detail, including heat exchangers, regenerator, pistons, and motor/alternator, and the process for modeling, simulation, and optimization in designing the engine. Finally, the dissertation covers the assembly and experimental testing that validates the design in terms of heat exchanger performance, losses, kinematics, and cycle work.

Advisor: Sanders, Seth R
How to couple trapped ions to superconducting resonators: towards hybrid quantum devices

In this dissertation, we present work on a quantum hybrid device comprising of a trapped calcium atom and a superconducting LC resonator. This hybrid device is a first step towards building a hybrid quantum computer that can take advantage of the different properties of atomic and superconducting quantum systems and combine the best characteristics of both. We model a trapped ion as an LC circuit and calculate its coupling to a mode of an LC resonator. Further, we optimize the trap geometry and placement of coupling electrodes to increase the coupling. We also outline the manufacturing process and simulation of the trapping potentials for the surface traps used in this work. Finally, we report on initial experiments coupling a trapped ion to a resonant mode of the LC resonator, increasing the heating rate to 220 phonons/ms on resonance versus 0.5 phonons/ms without additional heating from the resonator. The ratio of the heating rates for the two secular frequencies of the ion confrm our simulated coupling rates.

Advisor: Haeffner, Hartmut
Aspatial models of zone pricing and parking

It is often practical to collapse spatial information about a transportation system into aggregate variables related by an aspatial model---that is, a model in which individual interactions and directions of travel are not explicitly accounted for. This dissertation brings aspatial modelling to bear on two topics: downtown congestion pricing, referred to as ``zone pricing,'' and parking policy./nRegarding zone pricing, a survey of the history of zone pricing shows that all existing systems fail to toll vehicles according to their use of the downtown network. To explore whether it would be advantageous to charge higher tolls to travelers who travel farther within the network, a static traffic model with probabalistic choice and variable trip lengths is proposed. Distance-based tolling turns out to be more socially efficient than charging all travelers the same price, but it leaves drivers themselves worse off since most of the welfare gains are converted to toll revenues for the government./nRegarding parking, the thesis considers the idea of a ``feedback loop'' among landowners' uncoordinated decisions about how much off-street parking to provide on their parcels, when parking competes with floorspace as a use of land. One landowners' decision affects others' via two opposing channels. First, crowding: when there is too little off-street parking, on-street parking becomes crowded, making off-street parking relatively more valuable. Second, accessibility: when floorspace takes the place of off-street parking, the neighborhood becomes walkable through its higher density, making floorspace more valuable. A stylized model of development decision-making shows that each force leads to positive or negative feedback, resulting in multiple equilibria and counterintuitive results from policy.

Advisor: Daganzo, Carlos F
Essays on Microeconomic Theory with Applications in Political and Resource Economics

This dissertation is an exhibition of applied microeconomic theory in political and resource economics. As three examples, I investigate three different questions, respectively: Does polarization of beliefs always intensify political gridlock in collective decision making? Will input-efficiency improvement in water use, e.g., adoption of more-efficient irrigation technologies and investment in water-conveyance systems, definitely decrease the demand for water-storage capacities, e.g., dams and reservoirs? Is collectivism, rather than individualism, generally helping society overcome the collective action problem? Using the game-theory or stochastic-control approach, I theoretically challenge conventional wisdoms about these questions, and illustrate the empirical relevance of my challenges, qualitatively or quantitatively, in different contexts, e.g., the Chinese transition from the planned economy, World War II and Operation Market--Garden, the irrigation water-inventory management of the California State Water Project, and the histories of collective action in China and Europe.

Advisor: Wright, Brian D.
Essays on Labor Economics and Health Care

This dissertation studies marginal returns to healthcare in a large but under-explored segment of the healthcare sector -- the emergency department (ED). My empirical strategy exploits quasi-random assignment of physicians to coworker teams to generate instruments for case-level inputs based on workplace peer effects. I use time-stamped case-level data on millions of ED visits across New York State from 2005-2013 to infer time-varying coworker groups. In Chapter 1, I find that a physician’s peers are influential in determining her pace of work and the intensity with which she treats each patient. I find robust evidence that physicians in fast-paced team environments ration care on other dimensions (tests and spending). I argue that these peer influences largely represent differential levels of peer pressure that a physician faces when working in different team configurations. Peer effects are estimated to be large in this setting, as they have a variance one quarter to one third as large as physician effects within a hospital./nIn the second chapter, I use peer-induced variation in a physician’s intensity of care to estimate impacts on patient outcomes, namely 30-day mortality. Reducing the amount of time and testing that a physician spends on cases leads to increases in mortality among at-risk patients and cases with particularly vague symptoms. Among fast, low-spending physicians, marginal returns to time are high, whereas among slower physicians marginal returns are 0. At first glance, this is strong evidence of diminishing returns to treatment. However, the cross-physician relationship between intensity of care and patient outcomes is flat, suggesting that physicians operate on very different production functions, even within hospitals, and even within a single department of the hospital. Reallocation of time and testing away from slow physicians to fast physicians could produce efficiency gains. I discuss implications for increasingly popular physician-targeted incentives to cut back on wasteful care.

Advisor: Card, David E
Classical Approaches to Understanding Quantum Systems

While the exponential complexity of quantum systems is the basis of counterintuitive phenomena such as quantum computing, it also represents a fundamental challenge: how can we, classical beings, study, understand, and control quantum systems that are exponentially more powerful than ourselves? In this dissertation, we present classical methods to approach two such issues./nFirstly, we discuss the testing of quantum devices, particularly special-purpose quantum computers. We propose a simple classical model for quantum annealers, arguably the most intensely explored class of special-purpose quantum computers. The model provides a benchmark against which to compare the quantum annealer, in what may be called a "quantum Turing test," to determine whether the quantum annealer exhibits algorithmically significant quantum behavior. An application of the test reveals that the input-output behavior of the benchmark agrees with published data from the D-Wave One quantum annealer on random instances of its native problem on 108 qubits, and closely matches the reported performance of D-Wave 2X on special instances devised to exercise quantum tunneling. In other words, the machine does not pass the quantum Turing test with respect to these inputs. A more detailed analysis of the new classical model yields further algorithmic insights into the nature of quantum annealing./nSecondly, we show that commuting stoquastic quantum k-SAT, an interesting variant of the local Hamiltonian problem, is in NP for any k=O(log n). The result follows from a study of the computational complexity of tensor network nonzero testing, a fundamental problem in quantum Hamiltonian complexity. We show that the problem in its most general form is computationally very hard, i.e., not contained in the polynomial hierarchy unless the hierarchy collapses. On the other hand, we are able to identify two "easy" special cases of tensor network nonzero testing, namely nonnegative tensors and injective tensors, which may be useful in certain contexts. Indeed, our main result follows by exhibiting a direct connection between the special case of nonnegative tensor networks and commuting stoquastic quantum k-SAT.

Advisor: Vazirani, Umesh V
Topics in large-scale statistical inference

Statistical inference may be large-scale in terms of the size of the dataset, the dimension of the data, or the amount of data needed for provably accurate inference. This dissertation presents three applications of large-scale statistical inference. Part I considers finding and characterizing stars and galaxies in images from telescopes. Part II considers figuring out who wrote what in large collection of articles, where authors often do not have unique names. Part III considers approximating a high-dimensional function based on a small number of observations, a common problem when interpreting computer experiments.

Advisor: McAuliffe, Jon D
Statistical models of learning and using semantic representations

How does cognition organize sparse and ambiguous input from the environment into useful representations and concepts for understanding the world? The work in this dissertation explores how people learn and reason with abstract knowledge, focusing on the kinds of processes and representations used in semantic memory. In particular, I present three case studies, each investigating different assumptions for the semantic representations and algorithms used to model cognition. The first chapter introduces this work situated in the framework of probabilistic models of cognition and outlines the goals of each case study. The second chapter focuses on the distinction be- tween process and representation in semantic memory search. The simulations and analyses in this chapter show that behavioral results on a semantic fluency task previously explained as a strategic search process can also be produced by a non-strategic search process, depending on the structure of representation used for semantic memory. The third chapter investigates semantic representations as a means to explore universals and variation in cognition across cultures. In this chapter, I present a simple computational model operating over an irregularly shaped perceptual color space which accounts both for universal tendencies and for variation in focal colors, or best examples of color terms, across the world’s languages. The fourth chapter explores the challenges of develop- ing models that can learn to appropriately apply new labels to concepts from only a few example observations, like people. Building upon a successful Bayesian word learning model, I propose adapting large-scale knowledge representations, typically used in machine learning and computer vision, to automatically construct hypothesis spaces for generalization models that account for these challenges. Finally, in the fifth chapter, I discuss the theoretical and practical implications for this body of work as a whole, and suggest a variety of future directions. Taken together, this research suggests general principles of computation over structured knowledge representations illuminates how people make sense of the world around them, and may lead to developing machines that think more like people do.

Advisor: Griffiths, Thomas L
Maximum Entropy for Resource Allocation: a quantitative theory of species coexistence and population demographics

In the past few decades, ecology has gone through exciting breakthroughs in developing and applying quantitative methods to more accurately describe and predict patterns and dynamics. However, a theoretical framework under which models could be built and results interpreted in one language is still missing, impeding the communication and collaboration among subfields of ecology. To serve the ultimate goal of developing such a framework, in this dissertation, a theory is developed to make unified predictions of several facets of ecology that have so far been addressed mostly in independent ways: starting from a simple scenario of resource allocation, the theory simultaneously predicts species coexistence, community-level energy distribution, population demographic growth function, evolutionary tradeoffs and life history strategies. The approach of maximizing information entropy (MaxEnt) is used to make sure that the theory makes the most objective predictions from the fewest ad hoc assumptions. Previous applications of MaxEnt in ecology are reviewed in Chapter 1. In Chapter 2, the fundamental framework and a first model of the theory is introduced. Based on the same framework, several different approaches to expand model predictions are explored in Chapters 3 and 4. As is discussed in Chapters 2-4, the assumptions and parameters of the theory can be related to many important concepts in ecology (e.g. fitness equality, stabilizing effect, niche and neutrality), and the predictions reveal many previously unidentified links between patterns and processes at the population and community level to metabolism and functional traits. Combining all above, the work in this dissertation is potentially a first step towards a unified theoretical framework of ecology.

Advisor: Harte, John
Between Regimes: Institutional Design in Transitional Groups

This dissertation proposes a new framework from which to study political regime transitions, arguing from a basis in counterfactual reasoning that it is necessary to shift the focus from ex-post coding based on transition outcomes to ex-ante coding of cases with the potential for regime transition. Straddling the literatures on transitions, democratization, authoritarianism, founding elections, and power-sharing, I investigate the inner workings of a rarely studied transitional period, assessing the types of institutions that countries form during the foundational, yet often chaotic, moments of a regime transition./nI systematically characterize a number of paths that countries embark on in the period between regimes and, in so doing, identify a new unit of analysis that I term “transitional groups”: unelected, interim groups formed during potential regime transitions with the stated intent of holding elections and transferring power to a popularly legitimate government. I assemble a novel dataset that describes the existence of transitional groups over the period 1989 to 2010./nI then conduct statistical analysis on this data to analyze how transitional groups affect the extent and direction of regime change. I argue that, while the introduction of transitional groups can often accelerate the pace of democratization, in some circumstances—particularly, when power is either too consolidated in a few actors or too dispersed among many—the introduction of transitional groups can actually make democratization harder. My dissertation provides insights into transitional or foundational events and highlights how formalized transitional groups vary tremendously across space and time.

Advisor: Fish, M. Steven
Wittenberg, Jason
Inorganic-Biological Hybrids for Solar-to-Chemical Production

The interplay of chemistry and biology holds many opportunities to address the woes of the modern world. As synthetic chemistry and cell biology have married together to make great strides in health and medicine, an amalgamation of inorganic materials chemistry and microbiology holds similar promise to revolutionize our approach to transducing solar energy into chemical bonds. By combining the superior light harvesting of inorganic semiconductors with enzymatic biocatalysts, high efficiency solar-to-chemical production of complex molecules and materials may outcompete natural photosynthesis. In this work, I describe a series of investigations into the physical and chemical interactions of inorganic-biological hybrid organisms. In the Sporomusa ovata-silicon nanowire system, changes in salt concentrations predictably controlled cell-nanowire alignment, producing a predictive model of inorganic-biological colloidal interaction. The hybrid organism Moorella thermoacetica-cadmium sulfide self-photosensitized the synthesis of acetic acid from CO2 via bioprecipitated CdS nanoparticles. A titanium dioxide-manganese(II) phthalocyanine photocatalyst system extended oxygenic photosynthesis in M. thermoacetica-CdS through a photoregenerative cysteine-cystine redox couple. Finally, transient absorption spectroscopy, time-resolved infrared spectroscopy and biochemical assays suggested a two-pathway mechanism of charge and energy transport between M. thermoacetica and CdS. These initial works at the nexus of materials chemistry and biology lay the foundation for deeper understanding of this complex interface as well as for the development of advanced solar-to-chemical production systems.

Advisor: Yang, Peidong