VictorJuiz/thesis huggingface dataset:
Estimation and Utilization of Reconstruction Uncertainty for Atom Probe Feature Extraction

Atom Probe Tomography (APT) is a powerful analytical technique for 3D characterization of materials at the atomic scale which has been widely used to study irradiation induced features. However, the accuracy and reliability of the atom probe reconstruction and post-processing methods such as cluster analysis is often neglected. In an effort to account for both of these limitations we introduce a two-step method for quantifying the quality of an atom probe reconstruction and the resulting secondary analysis./nFirst, we developed a pointwise measure of uncertainty for APT data based on linear error propagation. This approach provide a systematic way of estimating the uncertainty in the atom positions and the most influential reconstruction parameters. The pointwise uncertainty measure can be used to assess the local quality of APT data and govern alternate reconstruction directions which minimize uncertainty. Furthermore, focusing the error analysis not on resolution but on parameter and coordinate uncertainty enables error to be propagated through complex processes such as the measurement of isotopically enriched thin films./nSecond, we developed a method which extends monte-carlo consensus clustering from K-based clustering algorithms to density-based clustering algorithms. In doing so a measure of relative stability is introduced to describe the ambiguity of clustering observed in an APT sample and automate the selection of the distance parameter for DBSCAN (Density-based spatial clustering of applications with noise). Our approach uses Monte-Carlo perturbation statistics, and thus could be linked to use the pointwise uncertainty established in the first part of this work, to generate alternate atom probe datasets and then apply DBSCAN to each of these datasets. In doing so the sample size for which to calculate grows in magnitude enabling more thorough post-clustering filtration methods with which to extract clusters from high-noise scenarios. We use statistical methods to analyze the results and determine the optimal DBSCAN parameters that maximize the clustering performance and minimize the uncertainty./nThe efficacy and utility of pointwise error propagation is demonstrated through a case study on the measurement of an isotopically enriched iron thin film while our novel clustering algorithm, Density-based Monte-Carlo Consensus Clustering (DMC3), is benchmarked against a round robin study on binary Fe-Cu systems with an emphasis on irradiation induced hardening. Our approaches provides a quantitative and objective way of assessing the quality of APT data and improving the reliability of APT data analysis.

Advisor: Hosemann, Peter
Epidemiology and Pedagogy: Approaches to examining disease dynamics and developing anti-racist teaching practices.

My dissertation integrates theories and methods from pathogen ecology, infectious disease epidemiology and anti-racist pedagogy. For the first part of my dissertation, I focus on two environmentally- mediated, infectious diseases impacting the respiratory system, COVID-19 and coccidioidomycosis. My first chapter reviews key environmental features that characterize the soil niche of the two known fungal species in the Coccidioides genus, the etiologic agents for coccidioidomycosis. In this chapter, I aim to understand the environmental biology of Coccidioides species, C. immitis and C. posadasii, and factors that influence the pathogens’ life history. My second chapter explores the most important health, social, and environmental factors impacting transmission and mortality rates in US counties to determine the sectors of society most vulnerable to infection and mortality during the COVID-19 pandemic. For the last part of my dissertation, I outline a pedagogical framework which integrates social theories in methods for anti-racist praxis. In my third chapter I present a pedagogical framework with anti-racist principles for developing a course centered on uplifting racially minoritized groups in higher education.

Advisor: Elias, Damian
Aspects of Low Dimensional Quantum Gravity

This thesis will be an exploration of various topics within low dimensional quantum gravity,with an emphasis on the gravitational path integral. We begin by studying two-dimensional Jackiw-Teitelboim (JT) gravity deformed by a gas of conical defects and we solve the model non-perturbatively. We then consider the problem of defining the Lorentzian gravity path integral through a contour rotation of the Euclidean path integral within the context of JT gravity. We demonstrate the agreement of integration domains and calculate the measure for the Lorentzian path integral. We then analyze ensemble averaging a family of two dimensional Conformal Field Theories and find a relation with an exotic three-dimensional bulk Chern-Simons theory.

Advisor: Bousso, Raphael
Phase Transitions in Inference

What makes an algorithmic problem easy or hard? Many general algorithmic techniques arising from decades of research, along with the theory of NP-completeness based on reductions between hard problems, offers a good answer for problems where the input is ``worst-case''./nHowever, this theory has very little to say when the input is random, and comprises of independent samples, as is frequently the case for problems in statistics. Statistical problems seemingly go through abrupt phase transitions in complexity, from hard to easy once the number of samples crosses a threshold. Understanding this boundary between ``hard'' and ``easy'' for statistical problems is still in nascent stages./nThis thesis comprises recent progress in understanding these phase transitions from the lens of semidefinite programming.

Advisor: Raghavendra, Prasad
Bridging the Gap between Humans and Machines in 3D Object Perception

Humans possess a remarkable ability to extract general object representations from a single image, capturing not only shape and texture, but also 3D form. In contrast, 3D reasoning in many computer vision systems is often limited. This thesis present three efforts aimed towards bridging this gap in 3D object perception. First we introduce a new dataset that focuses on real-world, object-centered 3D understanding. The dataset provides a diverse set of objects corresponding to real household objects, with varying geometries and physically-based rendering materials. It also includes additional annotations describing each object, making it a valuable resource for training and evaluating computer vision models. Next, we design a method for automatically inferring the articulation of 3D objects. The method enables the interaction of 3D objects and can be used to generate more realistic and dynamic scenes. By understanding how different parts of an object move and interact, computer vision systems can better model and reason about complex 3D scenes in simulation. Finally, we investigate the effectiveness of contrastive learning with 3D data augmentation to generate multiple views of objects, a departure from the typical method of training single view images. We show that generating multiple views of objects can help computer vision systems learn better representations and improve their overall object understanding in terms of classification and shape perception.These contributions represent efforts towards bridging the gap between human and machine 3D object perception, ultimately enabling them to understand 3D objects from single images in ways that are more aligned with human perception.

Advisor: Malik, Jitendra
Improved Interior Point Methods for Some Structured Combinatorial Problems

Over the last few decades, the design of algorithms for combinatorial problems has benefited greatly from a continous optimization perspective. In this dissertation, we will focus on some specific ideas from continous optimization, particularly so-called interior point methods (IPMs), for the design of efficient algorithms for some combinatorial problems in theoretical computer science. We will present algorithms for three different applications using these ideas:/n1) We first present an improved interior point method algorithm for exact $s$-$t$ maximum flow on directed graphs that runs in time $O(m^{4/3+o(1)}U^{1/3})$ where $U$ is the ratio of the maximum to minimum capacities on the edges.2) We then design a faster interior point method for the class of semi-definite programs (SDPs), a class of convex programs that have led to polynomial time algorithms for a variety of problems in combinatorial optimization, statistics and machine learning. Our algorithm runs in $O(\sqrt{n} (mn^2 + m^\omega + n^\omega)\log(1/\varepsilon))$ time for an SDP with $m$ constraints and $n$ sized variable matrix, which improves over the previous results in a wide variety of regimes. 3) We present a constructive proof of the Spencer problem by designing a Stieltjes transform based barrier function and running a random walk analyzed using this barrier function which gives a signing with discrepancy of $O(\sqrt{n})$ with high probability. We then show that unlike previous such approaches, our approach may generalize to resolve matrix discrepancy problems.

Advisor: Raghavendra, Prasad
Effects of Tin Dispersed on Dealuminated BEA Zeolite on the Activity of Platinum for the Dehydrogenation of Propane

The increasing demand for propene exceeds the capacity for its production by petroleum cracking and steam reforming of naphtha, creating what is called a “propene gap.” This gap can be filled by the direct dehydrogenation of propane derived from the condensable fraction of natural gas produced by the fracking of shale. Prior research has demonstrated that supported Pt nanoparticles are quite effective for propane dehydrogenation (PDH) but readily poison due to coking. The rate of this process can be reduced by reducing the size of the Pt nanoparticles and alloying Pt with a non-noble metal such as Sn, Zn, Fe, etc. A further loss of activity with time on stream occurs due to particle agglomeration. This process can be impeded by anchoring Pt nanoparticles using a non-noble metal that is incorporated in the support and interacts strongly with Pt. This thesis explores the possibility of introducing Sn into the silanol nests of dealuminated BEA zeolites to produce Sn/DeAlBEA and then dispersing Pt onto this support to produce PtSn/DeAlBEA. Characterization of these materials by XRD, UV-vis spectroscopy, XANES, EXAFS, and IR spectroscopy of adsorbed pyridine, deuterated acetonitrile, and CO was used to develop a detailed understanding of the structures of Sn/DeAlBEA and PtSn/DeAlBEA. This information aided the interpretation of the effects of catalyst composition and structure on the performance of PtSn/DeAlBEA catalysts for PDH.

Advisor: Bell, Alexis T
Towards a Distributed OS for Data-Intensive Cloud Applications

Commodity hardware is reaching fundamental limits, while the demands of data-intensive applications continue to grow. Thus, we now rely on horizontal scale-out and hardware accelerators to improve application performance and scale, while developing a myriad of distributed execution frameworks that are specialized to specific application domains, from data analytics to machine learning. While this reduces burden for certain applications, it also creates three problems: (1) duplicated system implementation effort, (2) reduced framework evolvability, and (3) difficulty interoperating efficiently between applications, especially when large data is involved./nThis thesis describes the first steps towards a distributed “operating system” that can provide essential services to such data-intensive applications. This would allow currently monolithic frameworks to be built as libraries instead, making them easier to build, evolve, and compose. Towards this vision, we propose an intermediate and interoperable execution layer that handles common problems in distributed execution and memory management./nWe first propose distributed futures, a general-purpose programming interface that extends the RPC abstraction with pass-by-reference semantics and a shared address space. Distributed futures act as a virtual memory-like abstraction but for the distributed setting, enabling distributed memory management to be factored out into a common system. Next, we present a design for this system that provides flexible fault tolerance with low overheads. We first present a fault-tolerant architecture for distributed futures that provides automatic memory management. We show how this system factors out system complexity from data-intensive applications without sacrificing performance, using MapReduce workloads as an example. Finally, we show how stronger recovery guarantees can be layered on top of this core architecture to provide greater recovery flexibility to end applications. Thus, we show how an end-to-end approach to fault tolerance can expand system generality.

Advisor: Stoica, Ion
Estimation Challenges in Batteries: From Prevailing Li-ion Batteries to Emerging Alternative Lithium Sulfur Batteries

This dissertation delves into the challenges associated with modeling, state estimation, andparameter identification for currently prevailing lithium-ion (Li-ion) battery technologies and the emerging frontiers of lithium-sulfur (Li-S) batteries. With rapid development in electrification, Lithium-ion (Li-ion) batteries have reshaped the realms of portable electronics, electric mobility, grid storage, and renewable energy systems. The increasing scale and demand for battery systems warrant research efforts to improve battery management systems and explore alternative high-energy materials. This dissertation explores two crucial areas of focus: the development of scalable algorithms for large battery packs with heterogeneity and the design of algorithms for advanced battery chemistry, specifically Lithium-Sulfur technology. Chapters 2 to 4 focus on the challenges of Li-ion battery packs, addressing real-time power estimation in the presence of heterogeneity among individual cells. A computationally efficient and scalable algorithm for estimating the state of power (SOP) for a heterogeneous battery pack using interval prediction is first introduced in Chapter 2. In Chapter 3, the focus shifts from theoretical exploration to empirical validation, as we experimentally verify the performance of the SOP algorithm proposed in Chapter 2. In Chapter 4, heterogeneity in the cells is further explored, where uncertainty in thermal parameters is also addressed. The addition of thermal heterogeneity needed a more accurate and robust interval algorithm than originally proposed in Chapter 2. Chapter 4 enhances the performance of interval prediction using reachability analysis for mixed monotonic systems. Chapters 2 through 4 unravel the complexities of the SOP framework, elucidating its theoretical foundations, practical limitations, and the prospective transformative influence it could have on the optimization of the performance of a heterogeneous battery pack. Transitioning to emerging technologies, Chapters 5 and 6 shift the focus to Li-S batteries. Chapter 5 investigates global parameter sensitivity for a zero-dimensional Li-S battery model, addressing challenges in parameter identification crucial for battery management systems. Global sensitivity analysis is employed to understand parameter relevance and interdependence, highlighting the impact of different parameter distributions on model performance. Chapter 6 focuses on efficient modeling and state estimation algorithms for Li-S batteries, proposing a physics-informed neural network model with a moving horizon state estimator. The state estimation accuracy is compared with the extended Kalman filter. The developed model and a moving horizon estimator framework demonstrate robust state estimation accuracy under varying current profiles. In conclusion, this dissertation contributes to advancing the understanding and optimization of established and emerging battery technologies, providing valuable insights for applications ranging from automotive to grid storage.

Advisor: MOURA, SCOTT
Modeling System-level Iron Homeostasis in the Human Brain over the Lifespan

Iron is vital for proper functioning of the brain. Deficiency of iron can impair the formation of myelin sheaths, disrupting critical communication between neurons and causing disorder in movement, mood, attention, energy levels, and more. Although iron is critical for many neural processes, it is a delicate balance to maintain a healthy amount of iron. When unbound in the cell, ferrous iron can serve as a catalyst in the formation of reactive oxygen species which can cause DNA damage and cellular death in excess. Even in normal aging, iron accumulates in deep grey matter areas that are known to exhibit damage in neurodegenerative disorders like Alzheimer’s and Parkinson’s disease. We propose a mathematical model to describe the network transmission of iron throughout the brain over the lifespan, resulting in the accumulation seen in the deep grey nuclei./nThe model is a linear Markov model with an input term, and the parameters are estimated using iron concentration estimates from Quantitative Susceptibility Mapping (QSM) Magnetic Resonance Imaging (MRI). We discuss the motivation behind using QSM for model training and how parameters were estimated. We also present an evaluation of the model performance and validation performed using a population collected separately from the training dataset. Finally, we share results for an application case of the model by comparing the model prediction when trained on healthy controls and subjects diagnosed with either Alzheimer’s or mild cognitive impairment. These results show that our model is consistent in predicting iron transport dynamics for separate healthy populations and also predicts altered dynamics when trained on pathological QSM data./nOur model is able to predict iron transport dynamics that accurately reproduce the spatial and temporal distribution of iron seen over the lifespan. This work serves to help unravel the mystery of iron accumulation in the brain, a process which has implications for the treatment and early diagnosis of neurodegenerative disorders.

Advisor: Liu, Chunlei
Advancements in Microwave Optics for Measuring Polarization Anisotropies in the Cosmic Microwave Background

The temperature and polarization anisotropies in the Cosmic Microwave Background (CMB) are direct probes into the physics of the early universe. Increasingly sensitive experiments aim to determine the tensor-to-scalar ratio r through measurement of an impossibly faint B-mode polarized signal shrouded by galactic foregrounds. A direct measurement of primordial B-mode polarization will be a measurement of the energy scale of inflation, unlocking an essential piece of the cosmological puzzle. Next-generation CMB experiments employ a large number of highly sensitive detectors in an attempt to find r and further constrain the cosmological parameters. Such a measurement requires not just high sensitivity to the CMB polarized signal, but large experimental bandwidth to characterize the polarized galactic dust and synchrotron radiation foreground signals. For experiments using lenslet-coupled planar antenna detector array designs, reflection off the surface of the lenslet must be minimized over a given bandwidth to maximize the measured CMB signal. To this end, antireflection (AR) coatings for lenslets were developed for 30/40 GHz Simons Observatory low-frequency detectors, along with next-generation prototype coatings for 90/150 and 220/270 GHz arrays. The JAXA-led space-based mission LiteBIRD will utilize lenslet-coupled sinuous antenna arrays and TES bolometers for frequencies ranging from 40-195 GHz, necessitating broadband lenslet AR coatings that are robust to launch vibrations and differential thermal contraction. To meet these requirements, a metamaterial AR surface has been proposed. A metamaterial coating designed for the LiteBIRD LF-3 band has been laser etched onto a flat surface, achieving 98% in-band transmission. A six-axis positioning system is used to etch the metamaterial pattern onto a sphere, and a completed prototype LF-3 lenslet is expected to be etched in late 2023. Details of the metamaterial design and the etch process are discussed. Cosmic rays at the Lagrange point L2 pose a threat to LiteBIRD’s sensitivity, as they produce a white noise component that cannot be fully deconstructed in analysis. To mitigate this cosmic ray white noise component, on-chip mitigations have been developed for the purpose of minimizing thermal diffusion from the silicon detector wafer to the TES bolometer detectors. Lastly, the mechanical design and fabrication of a continuously rotating warm half-wave plate for the POLARBEAR-2a experiment, used to minimize noise in large-angular-scale measurements from atmospheric fluctuations, are discussed.

Advisor: Lee, Adrian
Impacts of Adaptive Cruise Control on Urban Congestion: A Queuing Perspective

The morning rush in two idealized settings are forecast into the future, assuming that most drivers will engage Adaptive Cruise Control (ACC) while driving their cars. To this end, careful measurements of ACC-equipped cars traveling on roads and a highway confirm an earlier finding reported by others: for a given traffic speed in ACC-rich congestion, the density tends to be smaller than in present-day congestion where all vehicles are manually operated. All else equal, the lower congested densities (i.e., larger car spacings), mean that queues will be less-densely-packed and will expand over longer physical distances in the future, as ACC-equipped vehicles become more prevalent. These uncompacted queues pose problems for urban areas, where queue storage is often already a problem during rush hours.Simulations calibrated using the field-measured data confirm this concern and contradict optimistic predictions of how ACC may lead to a congestion-free future. In a setting with already moderately high congestion and long-street links inspired by Downtown Los Angeles, it is predicted that networkwide vehicle hours traveled (VHT) will increase by up to 12% compared to present-day levels, despite assumptions that are largely favorable to ACC. In a setting inspired by Midtown Manhattan, with short-street links and already high congestion levels, networkwide VHT is predicted to increase by as much as 87%. The higher bottleneck capacities often promised by advocates of ACC are shown to be irrelevant when spillover queues restrict flows from reaching those capacities. Interventions tested through simulations include adjusting onboard ACC controllers to produce lower jam spacings, to prevent a problematic future.

Advisor: Cassidy, Michael
Simplicial Reaction Networks and Dynamics on Graphs

Reaction networks are a powerful tool for modeling the behavior of a wide variety of real-world systems, including population dynamics and chemical processes, as well as algorithms for sampling combinatorial objects. While many such systems have well-understood equilibrium states, the long-standing conjecture that these states will always be achieved remains open. This thesis presents the class of simplicial reaction networks, which includes a wide variety of natural combinatorial examples of use in theoretical computer science. It shows how simplicial structures can be used to understand and control the equilibrium behavior of the network as a whole, and discusses related progress towards the Global Attractor Conjecture. Finally, this thesis presents additional work exploring combinatorial approaches to the Inverse Eigenvalue Problem on graphs, including the randomized Zero Forcing algorithm and a lower bound for the Minimum Rank problem.

Advisor: Sinclair, Alistair