VictorJuiz/thesis huggingface dataset:
Assessing the impact of residential buildings heating electrification on future electric loads and grid capacity requirements in California

We study the impacts of residential heating electrification on 2030 and 2045 California electricity loads and grid capacity requirements, as California complies with increasing Renewable Portfolio Standards and emissions reductions targets. We first develop electricity demand scenarios that account for residential heating electrification through the adoption of a mix of electric heat pump and electric resistance heating technologies. We find that residential heating electrification greatly increases electricity demand (5.9-13.0% demand increase in 2030 and 9.8-21.5% in 2045), especially during the winter. Heating electrification and electric vehicle charging needs bring changes in diurnal load shapes, resulting in bimodal demand profiles with strong morning and evening demand peaks. We also find that California’s yearly electricity loads remain summer peaking driven by cooling needs, even under aggressive heating electrification assumptions of 90% of residential heating electrified by 2045. To assess electric grid impacts, we input our electricity demand scenarios into a California-wide capacity expansion and dispatch model to solve for a least-cost energy system in 2030 and 2045. Residential buildings heating electrification results in overall higher grid capacity requirements, annual system costs, storage needs as well as an increase in imports and associated emissions from out-of-state generation. Added electricity loads from heating electrification increase California’s reliance on electricity imports, which increases modelled CO2 emissions from electricity generation. Nevertheless, heating electrification could achieve net emissions savings of more than 15 MMt CO2 per year in 2030 and 26 MMt CO2 in 2045 through the elimination of on-site natural gas consumption. Our results highlight the challenges associated with the integration of winter heating loads in California’s future solar-dominated grid and the value of flexible low-carbon generation resources such as carbon capture and storage (CCS) in integrating additional loads from residential heating electrification.

Keywords: Energy Resources Engineering Department
School of Earth Energy &amp; Environmental Sciences
Thesis
Advisor: Benson Sally M.
Assessing the Range of Cognitive Processes in the Hong Kong Diploma of Secondary Education Examination (HKDSE)’s English Language Reading Literacy Test

This study examines the range of cognitive processes assessed in the English language reading literacy test as part of the Hong Kong Diploma of Secondary Education Examination (HKDSE), the secondary school exit test in Hong Kong. Prior studies have suggested that higher order cognitive processes are often undermined in high-stakes tests and classrooms, due to what is called the “washback effect.” This prompts the following hypothesis: despite its top-performing education system, Hong Kong has failed to provide a versatile collection of test items that involve complex reasoning. To investigate the cognitive demands of the test, as well as their relationship with student performance, this study maps the test items with reference to the cognitive levels assessed using in-depth document analysis. ANOVA is used to statistically determine differences in accuracy rates to supplement the analysis throughout. Results indicate that the test has placed an overwhelming emphasis on lower order cognitive processes over the past decade (2012–2019), and that items assessing higher order cognitive skills are, as expected, met with statistically significantly poorer performance in the test. Implications for future revision of the test and curriculum policy are discussed.

Keywords: Cognitive processes
high-stakes testing
HKDSE
large-scale assessment
reading literacy
washback effect
Stanford Graduate School of Education International Education Policy Analysis
Thesis
Asymptotic properties of some random evolutions and geometries

This thesis comprises two parts. The first part concerns asymptotic properties of the stochastic heat, Kardar--Parisi--Zhang, and Burgers equations, as well as the fractional parabolic Anderson model. We show several results about long-time behavior and scaling limits of these equations. The second part concerns the Liouville quantum gravity metric. We show the existence of subsequential scaling limits of two natural regularizations of this metric, in the first case at very high temperature and in the second case for all subcritical temperatures

Keywords: Mathematics.
Advisor: Ryzhik Leonid
Chatterjee Sourav
Papanicolaou George
Asymptotic theory for large random matrices and its applications

Random matrix theory has a long history. It was first introduced in mathematical statistics by John Wishart in 1928, and it gained attention during the 1950s due to work by Eugene Wigner studying the distribution of nuclear energy levels. A large number of physicists and mathematicians have been fascinated by random matrix theory, and after decades of study, it has matured into a field with applications in many branches of physics and mathematics. Nowadays, the subject is still very much alive with new and exciting research. Much of my PhD work has revolved around the study of random matrix theory. This dissertation gives a tour of my work on asymptotic theory of large random matrices and its applications in statistics, probability, and the theory of orthogonal polynomials, respectively

Advisor: Dembo Amir 
Chatterjee Sourav
Montanari Andrea
Atomic layer deposition of multi-component catalyst systems for the oxygen evolution reaction

Development of catalysts for the OER continues to be a research priority with the aim of improving the efficiency of electrochemical water splitting. One of the best classes of catalysts for the OER in alkaline electrolytes is based on doped nickel oxyhydroxides. In this dissertation, atomic layer deposition (ALD) was utilized to synthesize model catalyst systems for research. First, the role of iron in nickel oxyhydroxide catalysts will be discussed. ALD was used to perform surface-directed modification of nickel oxyhydroxide catalysts. ALD of iron oxide on these nickel oxyhydroxide catalysts was found to deposit iron on the surface without modifying the bulk nickel properties. Through this selective deposition, an iron-doped nickel oxyhydroxide surface was found to be the active site motif required for high OER activity. Second, the role of aluminum as a dopant will be discussed. ALD was utilized to synthesize well-defined, ultra-thin nickel-aluminum oxide films. A study was performed to understand the growth behavior of ALD nickel-aluminum oxide. Nucleation effects of ALD aluminum oxide on surfaces prepared by ALD nickel oxide was found to be the dominant effect resulting in non-constant growth per cycle. With the knowledge obtained in the growth behavior studies, well-defined nickel-aluminum oxide films were deposited for catalysis. To form the final catalyst, an electrochemical conditioning procedure was employed to convert the as-deposited oxide films to the active oxyhydroxide catalyst. Turnover frequencies (TOFs) were determined for each catalyst, and it was found that the highest performing electrocatalysts were the films containing nickel, aluminum and iron, confirming that aluminum exerts a promotion effect on nickel oxyhydroxide catalysts. For the thinnest films, aluminum doping improved the TOF of nickel-iron oxyhydroxide catalysts by over 300%

Keywords: Chemical Engineering.
Advisor: Bent Stacey 
Cargnello Matteo
Jaramillo Thomas Francisco
Atom-interferometric test of the equivalence principle and observation of a quantum system in curved spacetime

The theory of general relativity is based on the equivalence principle, which states that all gravitational effects vanish in any local measurement. This principle is the best-understood aspect of gravity and has been tested with high precision in classical experiments. However, there is no widely-accepted quantum theory of gravity, and the nature of the relationship between gravity and quantum mechanics is one of the most significant open problems in physics. We study the interaction of gravity with quantum systems in a light-pulse atom interferometer, which uses lasers to measure atomic positions and accelerations. The sensitivity of the interferometer is increased by utilizing large-momentum-transfer pulse sequences and a free-fall time of up to 2 s, made possible by the 10 m scale of the apparatus. The frequencies and angles of each interferometer pulse are optimized to suppress kinematic phase gradients by several orders of magnitude. We use this system to test the equivalence principle between 85Rb and 87Rb, finding no violation at the level of 10^-12 g. With a resolution of up to 1.4 x 10^-11 per shot, we demonstrate the highest relative acceleration sensitivity yet obtained in a laboratory setting. In addition, by operating the interferometer in a gradiometer configuration, we perform the first observation of a single quantum system in curved spacetime. Finally, we propose an experiment to observe gravitational time dilation with an atom interferometer

Keywords: Physics.
Advisor: Kasevich Mark A.
Graham Peter (Peter Wickelgren) 
Gratta Giorgio
Attending to all we elicit : student skepticism of dialogic practices and stances

The way teachers organize classroom talk matters for the way students make sense of texts. Dialogic teaching (Alexander, 2004), a pedagogical approach that centers on student talk and sensemaking, represents a promising, albeit rare (Nystrand, 1997), alternative to instruction that is dominated by the teacher's voice. Beyond a few exceptions (Christoph & Nystrand, 2001; Aukerman et al., 2008), studies rarely offer detailed accounts of how teachers negotiate dialogic relationships with students. This dissertation offers one such account, documenting how I, as a practitioner- researcher (Cochran-Smith & Lytle, 1993), enacted dialogic teaching with a group of seventh graders in a series of small-group text discussions. In my first findings chapter, I explore how students' contestation of my revoicings (O'Connor & Michaels, 1993) challenged pre-conceived notions of how dialogic talk moves function with students. My second findings chapter focused on how students responded to one another's claims to be textual authorities (Aukerman, 2007). My findings suggest that dialogic teaching not only involves responding to the highly contingent nature of students' textual interpretations, but also to their interpretations of the relational aspects of this teaching, particularly the power imbued in these relationships. It also suggests that student criticality is not always text-centric, but can be directed at the pedagogical context - and is sometimes voiced in-the-moment of instruction. Thirdly, my findings suggest that student criticality was not the sole possession of individual students, but was something that emerged in dialogue, a collective critical awareness of how my dialogic pedagogy was sought to position students

Keywords: Education.
Advisor: Aukerman Maren (Maren Songmy)
Martínez Ramón
Williamson Peter
Automated segmentation and uncertainty quantification for image-based cardiovascular modeling with convolutional neural networks

In this thesis we accelerate and extend a path-planning based patient-specific modeling method commonly used for anatomic model creation for cardiovascular fluid dynamics simulations. We further address a longstanding open question of how realistic patient-specific model geometry variability influences simulation output uncertainty. Model building is accelerated by using recently developed deep learning methods and convolutional neural networks to automatically generate vessel surfaces from image data. We enable the quantification of simulation output uncertainty due to geometry variation by modeling the probability distribution of vessel surfaces using convolutional Bayesian dropout networks. In the first part of this thesis we use fully-convolutional neural networks (FCNN) to generate 2D cardiovascular segmentations. Given vessel pathlines, the neural network generates 2D vessel enhancement images along the pathlines. Thereafter, vessel segmentations are extracted using the marching-squares algorithm, which are then used to construct 3D cardiovascular models. The neural network is trained using a novel loss function, tailored for partially labeled segmentation data. An automated quality control method is also developed, allowing promising segmentations to be selected. Thereafter we develop machine learning models to directly predict vessel lumen surface points using a regression formulation. In contrast to the previous method, which identifies the vessel lumen through binary pixel classification, formulating vessel lumen detection as a regression task allows predictions to be made with human expert level accuracy. The regression formulation enables machine learning models to be trained end-to-end for vessel lumen detection without post-processing steps that reduce accuracy. In the third part of this thesis we propose a novel approach to sample from a distribution of patient-specific models for a given image volume. The method uses the previously developed vessel lumen regression networks, combined with dropout layers, to enable Bayesian sampling of vessel geometries. The networks are then applied in the path-planning patient-specific modeling pipeline to generate families of cardiovascular models. A key innovation is the ability to learn geometric uncertainty directly from training data based on medical images. We then quantify geometric uncertainty for clinically relevant anatomies, and provide detailed analysis of its effects on cardiovascular patient-specific fluid dynamics simulation results. The above methods allow for efficient automation of patient-specific model construction from medical images which greatly accelerate the model construction process and reduce laborious user input. These methods are combined with uncertainty quantification methods that enable assessment of how image-based uncertainty propagates to simulation outputs

Advisor: Marsden Alison
Gerritsen Margot (Margot G.) 
Rubin Daniel (Daniel L.)
An automatic feeding system for the African killifish reveals effects of dietrary restriction on lifespan and allows automatic assessment of reward-based learning behavior

The African turquoise killifish, Nothobranchius furzeri, is an exciting new vertebrate model for aging studies. However, a significant challenge for any new model is control over its diet in space and time. To overcome this issue, I created an automated and networked fish feeding system. This system is designed to be transferable, open-source, and built from widely-available components. Compared to manual feeding, this automated feeding system is considerably more precise and flexible. As a proof-of-concept for the feeding schedule flexibility of these automated feeders, I defined an optimal feeding regimen for growth and fertility for the African killifish, which I call "ad libitum" feeding, and a dietary restriction regimen where both feeding timing and quantity are reduced. I show that this ad libitum feeding regimen significantly increases growth in males and fertility in male/female pairs as compared to dietary restriction. However, I show that an overfeeding regimen which I also define increases growth while decreasing fertility compared to ad libitum feeding. I showed that the defined dietary restriction regimen robustly extends lifespan when compared to ad libitum feeding in males but not in females. I fit a Gompertz distribution to the survival curves of males fed either an ad libitum diet or a dietary restricted regimen and find that lifespan extension by dietary restriction results in a significant reduction in the "Rate of Aging" parameter. Combining this automatic feeding system with a video camera also allowed me to create an associative conditioning assay, which I validated using manual scoring. I then proceeded to implement an automatic scoring pipeline which allows for a high-throughput integrative measure of fitness and agrees with the manual scoring approach. The ability to precisely control feeding in the killifish opens new areas to explore lifespan dynamics, dietary interventions, drug delivery, and behavior in a high-throughput manner previously impossible with traditional vertebrate aging model organisms

Keywords: Biology.
Advisor: Brunet Anne
Frydman Judith
O'Connell Lauren A. 
Wyss-Coray Anton
Automatically generating structured information on the as-is status of facilities from visual data

Despite technological advancements, physical reality and its digital representation are currently two disconnected domains. My vision - the seamless interaction of the two domains - motivates this thesis. We are quickly transitioning into a hybrid world, where we will live with virtual and augmented reality, robots, and automation along physical reality. This requires a seamless interaction between the physical and the digital. A big challenge toward their interaction is an accurate, detailed, consistent, and up-to-date knowledge of the as-is status of facilities in the digital domain, structured in a representation that is interpretable by both humans and machines. State-of-the-art practice attempts to acquire the digital representation with manual, time consuming, non-scalable, and error-prone methods. This has a direct, negative, and quantifiable impact on how architects, engineers, constructors, and facility managers (AEC/FM) design, construct, and use facilities. It is a global and ubiquitous challenge since it applies to the entire built environment, and therefore requires automatic, fast, scalable, accurate, and generalizable solutions. This thesis employs Machine Vision and Learning to contribute a set of methods that receive as input 2D and 3D visual data and automatically produce semantic information on objects (e.g., wall, floor, chair) and rooms within entire buildings, to facilitate four major AEC/FM processes: asset management, building energy modeling, structural stability, and construction progress monitoring. It also contributes a graph-based representation that structures this information in a way that humans can interact with it and machines can directly use and learn from it. Last, it produced and made publicly available a set of semantic datasets that played an important role in this work, as well as in that of other researchers. The results of the thesis bring us a step closer to addressing the aforementioned challenge and provide a foundation to fully understand the interaction of humans (e.g., engineers, managers, and occupants) with the built environment throughout its lifecycle

Advisor: Fischer Martin
Rajagopal Ram 
Savarese Silvio
Automating knowledge distillation and representation from richly formatted data

Much of the information created today is scattered throughout books, magazines, web pages, academic papers, and other documents that cannot be directly queried in a structured way. To address this challenge, people are building structured knowledge bases to make this information more accessible. The process of populating knowledge bases from unstructured inputs is called knowledge base construction (KBC). Through KBC, we can make troves of valuable information more accessible. However, existing KBC systems have limited abilities to handle input data from a wide variety of formats, including unstructured text, tables, and figures, contained within highly variable file structures. This data heterogeneity makes automated, scalable KBC difficult to achieve in real-world scenarios. This dissertation focuses on automating and scaling the complex process of building KBC systems from heterogeneous data. In particular, we study both knowledge distillation and representation from richly formatted data, where information is expressed via combinations of textual, structural, tabular, and visual cues, as well as novel techniques for making these processes feasible in practice. This dissertation consists of two parts. In the first part, we aim to discover the fundamental building blocks of KBC from richly formatted data. We present Fonduer, a KBC system enabling extraction of information from richly formatted data. Fonduer automatically models richly formatted data and allows both users and machines to systematically retrieve all of a document's multimodal information in a programmatic way. This information can formalize multimodal signals for both training data generation via weak supervision and for augmenting deep learning models with multimodal features to perform task learning. In the second part of this dissertation, we study how building knowledge bases can be made feasible in practice. We investigate two crucial parts of Fonduer's pipeline: training data generation and task learning. We propose two systems. The first is Dauphin, a training data generation system that uses data augmentation to lower the cost of acquiring training data. The second is Emmental, a system for building multimodal, multi-task learning models to increase the efficiency of learning multiple tasks by leveraging the similarities among them. In Dauphin, we analyze the generalization effects of linear transformations in data augmentation and propose a method to strategically select the augmented data samples that provide the most information. In Emmental, we analyze and improve the information transfer in multi-task learning to make the task learning process more efficient and achieve higher quality. The three systems described in this dissertation---Fonduer, Dauphin, and Emmental---demonstrate that it is both possible and practical to build knowledge bases from richly formatted data. We believe that these innovations hold great promise for future KBC techniques

Advisor: Ré Christopher 
Levis Philip 
Olukotun Oyekunle Ayinde
Autonomy, intimacy, and the law

Inspired by the "Me Too" movement in the end of the second decade of the 21st century, this dissertation is an attempt to articulate a grounding theory for the value of human sexuality through the lens of personal autonomy. It derives a concept of "sexual autonomy" from the value of sex and sexuality in human life and argues that autonomy grounds the value of intimate expression and provides a framework for its proper regulation. As a foundational value, autonomy underpins and explains the notion of "consent", which defines contemporary understandings of sexual assault. The first two chapters of this dissertation develop the connection between the value of sexuality in human life, with the notion of sexual autonomy as a version of personal autonomy. Subsequent chapters examine the practical implications of this understanding of sexual autonomy for legal and social reform of the regulation of the sexual sphere

Keywords: Political Science.
Advisor: Reich Rob
McQueen Alison (Alison E. J.)
Ober Josiah