VictorJuiz/thesis huggingface dataset:
Local structure-redox relationship in Li-excess layered oxides

In the last decades, lithium-ion batteries (LIB) have significantly contributed to technological progress. Recently, Li-excess layered materials are attracting interest as a promising cathode material for the next generation, since they exhibit high energy densities and capacities significantly higher than commercially available cathode materials. Unlike conventional layered oxides where the only redox center is transition metal cations, an oxygen anion redox in the Li-excess layered material plays an important role to achieve high capacity. However, despite their promising performance, a deeper understanding about the origin and details of anion redox is necessary for commercialization. Understanding the state of the material is crucial as property of material is determined and can be changed by structure. I introduce various X-ray techniques to understand and analyze the structure of Li-excess materials. Rietveld refinement reveals an increase in structural distortion, including antisite defect, in the Li-excess material during anion redox. A strong correlation between structure distortion and anion redox is identified and suggested as a powerful indicator to estimate the existence of anion redox. Not only as an indicator, exact analysis of crystal structure and oxidation state suggest methods to understand the anion redox in Li-excess material. Anion redox can also be tuned by altering composition and crystal structure of Li-excess material. Different amount of Sn substitution in Li1-xIri-ySnyO3 material change the extent of anion redox. Operando X-ray absorption spectroscopy analysis support different electrochemical behaviors. XRD analysis confirmed a distortion in the crystal structure in the existence of oxygen redox. Density functional theory simulation predicts possible local structure as a result of distortion, which suggests multiple ways of oxygen oxidation in different situations. Not only doping for changing oxygen redox properties, changing the synthesis condition affect anion redox strongly. Different annealing temperature and partial oxygen pressure during synthesis do not affect transition metal redox property in Li2RuO3 material. However, difference in synthesis conditions only alters anion redox capacity. I confirm and suggest that crystal structure determine the anion redox property in the Li-excess material, which suggests that we can tune the oxygen redox in various methods, adjust doping or changing synthesis conditions. Over this thesis, systematic analysis of various Li-excess material will be revealed. Study on identifying the structure-property relation is suggested, and methods to control anion redox is verified. This study will suggest powerful and robust direction to understand the origin of anion redox in Li-excess materials. This study will also show a guideline for optimizing properties of cathode materials for next-generation batteries.

Keywords: Materials Science and Engineering.
Advisor: Chueh William
Toney Michael Folsom
Lindenberg Aaron Michael
Localization and free energy asymptotics in disordered statistical mechanics and random growth models

This dissertation develops, for several families of statistical mechanical and random growth models, techniques for analyzing infinite-volume asymptotics. In the statistical mechanical setting, we focus on the low-temperature phases of spin glasses and directed polymers, wherein the ensembles exhibit localization which is physically phenomenological. We quantify this behavior in several ways and establish connections to properties of the limiting free energy. We also consider two popular zero-temperature polymer models, namely first- and last-passage percolation. For these random growth models, we investigate the order of fluctuations in their growth rates, which are analogous to free energy.

Keywords: Mathematics.
Advisor: Chatterjee Sourav 
Dembo Amir 
Diaconis Persi
Long Live the Motherland: Chen Man’s Visual Language and New Imaginings of Chinese Womanhood

Given the rising prominence and profitability of the fashion industry in China, this thesis offers a close analysis of images that illuminate shifting visions of women’s representation. The rise of the fashion industry is not simply due to economic changes in the post-Maoist era. It is instead a reflection of broader social and cultural shifts in how the nation and women’s place in it are defined. In taking these contexts seriously within the realm of women’s fashion, this thesis contributes to post-structuralist critique on the role of women in China.
This project answers the question: how do fashion images in popular Chinese media perpetuate narratives around women’s roles in China’s shifting socio-political climate? This question is examined through close analysis of images by Chinese fashion photographer Chen Man within the framework of existing critical theory, specifically post-structuralist feminist discourse on China.
While the focus of this study is centered around fashion and aesthetics since China’s Opening and Reform (1979), it also places Chen Man’s work in conversation with historical images of women in China. The purpose of this comparison is two-fold: (1) in order to demonstrate the direct mapping of fashion cultures onto theoretical framework, Chen Man’s work must be considered within the historical context of 20th century China’s shifting cultural landscape; (2) although fashion is an inherently global industry, the Communist Revolution in China transformed Chinese fashion into a state-mediated performance of nationalism. China’s fashion today cannot only be considered within the global fashion market framework but must also exist in relation to the specifically Chinese context.

Keywords:  Stanford Global Studies
Stanford Global Studies
East Asian Studies
China
Chen Man
Fashion Photography
Media Studies
Gender Studies
Fashion
China
Thesis
Advisor: Lee Haiyan
Love as We Know It: A Consideration of Romance through the Lens of Trust in the Era of Technology

Trust and love are two peculiarities of the human nature that influence our social and interpersonal relationships. This paper is a literature review and synthesis of philosophies that considers what each phenomenon is, how they develop independently of one another, how they interact, and how trust and love may change over the course of a relationship. Considering love, the particular partnership type of consideration in this paper is romantic relationships. The purpose is to educate the current generation on how trust and love influence social and romantic interaction while also providing an exploration into the concepts themselves. Through this exploration, the paper also considers the implications of the expression in trust and love for members of the current generation, one that has been cultivated in an age of ever-present technology.

Keywords:  Department of Communication
Trust
Love
Violations
Recovery
Thesis
Advisor: Hancock Jeffrey
Low-cost unsteady discrete adjoint techniques for aeroacoustic optimization

With the rapid growth of aircraft traffic and the new modes of transport such as Urban Air Mobility systems crowding the air space, aircraft noise is no longer a mere design constraint but an important factor to design and optimize for. Reducing aircraft noise however requires efficient coupling of simulation tools with design methods to be able to meet the stringent future aircraft noise requirements that allow for sustainable growth. Gradient-based design optimization based on adjoint method for sensitivity analysis offers a feasible design approach. Adjoint methods based on steady state physics have been widely in practice in industrial applications mainly for aerodynamic optimization so far. However, extending this approach to aeroacoustic optimization is not straightforward and not a common practice in industrial settings due the requirement of unsteady adjoint solutions that is prohibitively expensive. This dissertation presents temporal and spatial coarsening techniques for the computation of low-cost unsteady adjoints to obtain sensitivities for aeroacoustic shape optimization. The effects of the coarsening techniques on the accuracy of the gradients are analyzed by using different levels of temporal and spatial coarsening using multiple two dimensional and three dimensional test cases. Computational cost savings as well as reduction of memory storage requirements up to 10% of the base adjoint solutions are presented while maintaining reasonable accuracy in the gradients driving the optimization for these test cases. Finally, an extension to the temporal coarsening technique is proposed with non-uniform time stepping of adjoint solver based on the local flow truncation error estimates of the flow solver. The proposed extension is demonstrated to further improve the accuracy of the low-cost gradients providing motivations for the future directions of the work done in this thesis.

Keywords: Aeronautics and Astronautics.
Advisor: Alonso Juan José
Jameson Antony
Lele Sanjiva K. (Sanjiva Keshava)
"Lurid interiors" : the anti-Gothic impulse in early American war literature

This dissertation builds a theory of anti-genre by attending to the surprising absences, mockeries, and active repudiations of the Gothic tradition in a swathe of literature where one would very much expect to see the Gothic thrive. "Lurid Interiors" asks in what contexts, and for what reasons, the Gothic (or indeed any genre) fails to appear. The dissertation discovers a counterintuitive case study in American Revolutionary and Civil War fiction, wherein—despite the seeming Gothicism of the subject matter, i.e. the profound bodily violence and cultural trauma that these wars produced—puzzling lacks, parodies, hoaxes, and vicious dismissals of the Gothic instead abound. I begin by showing that the starkly divergent ways in which character operates in Gothic fiction and Revolutionary War fiction reveal conflicting definitions of individuality and interiority. James Fenimore Cooper's tremendously unsuccessful attempt to combine the two genres into a single novel, Lionel Lincoln; Or, the Leaguer of Boston, stems from Gothic and military literature's incompatible visions of how much individuals—and particularly individual deaths—can be represented, mourned, and made to matter. Chapter Two focuses on setting, contrasting the places in which Gothic narratives and American Civil War narratives unfold. Whereas the haunted castles, houses, and forests of Gothic narrative imply a flattering (if eerie) perseverance of human history and memory, the material conditions of the Civil War—in which soldiers witnessed nature rapidly re-growing over the traces of battles fought only months prior—instead implied an amnesiac landscape that would rapidly swallow any traces of human event, however seemingly monumental. Ambrose Bierce's short story "A Tough Tussle" and Stephen Crane's novel The Red Badge of Courage demonstrate the extent to which the Civil War made the landscape anti-Gothic for Americans. The third and final chapter compares the respective ethics of witness that underlie Gothic versus war fiction. American authors felt compelled to record the violence of the Revolutionary and Civil Wars, but were wary of the accusations of sadism that routinely greeted the era's other major literary genre that centered on violence—the Gothic. To avoid such backlash, writers of war fiction actively contrasted their presentations of bloodshed and death with those of the Gothic, making use of two character types to parody the Gothic: the perverse doctor (whose fascination with bodily suffering mimics that of the Gothic reader, spellbound by tales of brutality) and the overly superstitious dupe (whose gullibility renders him vulnerable to hoax ghosts). By offering up these ridiculous anti-Gothic characters, Washington Irving's "The Legend of Sleepy Hollow, " James Fenimore Cooper's The Spy, Louisa May Alcott's Hospital Sketches, and John William De Forest's Miss Ravenel's Conversion from Secession to Loyalty posit that Gothic literature's means of portraying and relating to violence are improper, and indeed dangerous, in wartime.

Advisor: Ruttenburg Nancy 
Jones Gavin Roger
Richardson Judith
The LZ dark matter search and development of a new gas phase technique to characterize low level electron emission from electrode grids

Dark Matter is needed to explain many cosmological observations and therefore has been proposed for many decades, but it awaits direct detection. One of the most popular classes of dark matter candidates is Weakly Interacting Massive Particles (WIMPs), which have masses in the order of 100 GeV and couple to ordinary matter at weak scale. In WIMP direct detection experiments, we look for WIMPs being scattered by nuclei, a process which produces low energy (smaller than 100 keV) recoiling nuclei that can be observed. We are building LZ, a detector looking for WIMPs using liquid xenon in a dual-phase time projection chamber (TPC), at 4850 feet underground at the Sanford Underground Research Facility (SURF) in Lead, South Dakota, USA. LZ aims to achieve the world's highest sensitivity to find WIMPs via WIMP-nucleon interactions. After a brief discussion of dark matter and the LZ experiment, this dissertation presents the details of my study to solve the electron emission problem. The LZ TPC will consist of electrode grids and other metallic surfaces that can emit electrons when operated under voltage. Because the charge measurement in the LZ detector is sensitive to single electrons, electrons from the grids can be both a significant nuisance for data collection and a source of background at low-energies, limiting the sensitivity of the experiment for low-mass WIMPs. This has motivated us to develop a test detector to study how to reduce this background. The test detector consists of a pair of grids biased to high voltage and operated in xenon gas. The electric field between the grid causes the electrons to produce electroluminescence scintillation light that is measured by PMTs. This new technique is sensitive to single electrons emitted by the grids, allowing a measurement of emission currents as low as atto-amperes. We used this detector to study the properties of different grids and to determine what treatments can be done to reduce their electron emission. We found that passivation with citric acid reduces electron emission from stainless steel surfaces. This work was supervised by Professor Thomas Shutt and was completed in collaboration with members of the LZ collaboration and the SLAC LZ group.

Keywords: Physics.
Advisor: Shutt Thomas (Thomas Alan) 
Akerib Daniel S.
Graham Peter (Peter Wickelgren) 
Gratta Giorgio
Machine learning and control methodologies with applications to medical computing

Machine Learning (ML) in adult healthcare has demonstrated significant benefits in a wide range of applications, and provides a proof-of-concept for extended work in pediatrics. In contrast, ML in pediatric healthcare is a relatively immature field with huge potential for improving quality of patient care. Tools built using a combination of unique data sources, with novel theoretical and practical approaches, provide significant benefits when delivered to the hospital via the Electronic Medical Record (EMR). Statistical analysis of detailed in-hospital datasets enable standardizing patient care and construct a sound foundation for data-driven and automated decision support systems. Similarly, machine learning methodologies enable predictive models to optimize patient outcomes, and create frameworks for analyzing variable importances to improve efficiency in resource-limited critical care environments. Beyond the EMR, continuous bedside monitors record physiological waveforms to track each patient's state throughout their hospitalization. These dense, real-time data enable continuous provision of results, alerts, and predictions, but require novel deep learning models and adaptations to process and interpret. Convolutional Neural Networks (CNNs) provide a structured framework for processing such data sources, which is powerful yet flexible enough to adapt to a wide range of applications. Both in and outside the hospital environment, wearable devices provide similar physiological data streams to the in-hospital monitors. Such devices and the Wireless Body-Area Networks (WBANs) they comprise support remote patient monitoring in the ``e-Health'' paradigm. These systems are supported and enabled by theoretical and practical developments in fundamental wireless communication and queuing theory, yet require particular considerations for application to patient monitoring and care. In this thesis, I present novel contributions to both theoretical models and practical applications for each of these three lines of research. Each of these either directly addresses or indirectly supports a specific use case in the pediatric hospital environment.

Keywords: Electrical Engineering.
Advisor: Bambos Nicholas 
Ashlagi Itai 
Poon Ada Shuk Yan
Machine learning and social science for fair and private data products

Technological innovations are fundamentally transforming information flow and social behavior. Digital platforms are increasingly embedded in established social systems highlighting the importance of developing fair and private machine learning approaches to managing these platforms. As social networks and social media data are increasingly leveraged for business and policy decisions, this thesis discusses work to develop methods for analyzing data privacy in attribute-rich network data, and to promote fair and equitable digital systems. Chapter 2 discusses work on privacy in digital systems. Privacy attacks in networked systems commonly leverage homophilous interactions, or what's commonly referred to as "birds of a feather flock together". While homophily describes a bias in attribute preferences for similar others, it gives limited attention to variability. We observe that attribute preferences can exhibit variation beyond what can be explained by models of homophily, and observe that this excess variation can induce a similarity among friends-of-friends on a network without requiring any similarity among friends. These findings offer an alternative perspective on network structure and attributes in general and prediction in particular, complicating the already difficult task of protecting privacy on social networks. Then, Chapter 3 examines node attribute prediction tasks more generally and provides a framework for distinguishing different types of prediction tasks as within-network, across-network, or across-layer tasks. This work highlights that methods aimed at across-network tasks are in fact evaluated on across-layer problems and have limited performance on across-network problems. Chapter 4 assesses novel uses of social media data to target regulatory enforcement efforts. This work provides a more cautionary perspective on the leading case that Yelp reviews are useful to target food safety inspections. This work highlights that prior results are sensitive to ``extreme imbalanced sampling'': extreme because the dataset was restricted from roughly 13k inspections to a sample of only 612 inspections with only extremely high or low inspection scores, and imbalanced by not accounting for class imbalance in the population. Finally, Chapter 5 studies the challenge of class imbalance more generally, comparing more standard approaches like Synthetic Minority Oversampling Technique to more modern approaches like Weighted Random Forest and Balanced Random Forest. Taken together, this thesis highlights the importance of understanding how new technologies intersect with existing consumer behavior and demonstrate the unique role of machine learning and social science research in the design and management of data products. As technology continues to promote instantaneous connectivity, there is a pressing need for analytical tools that facilitate secure and equal-opportunity platforms

Keywords: Management Science and Engineering.
Advisor: Ugander Johan 
Ho Daniel
Johari Ramesh
Machine learning assisted biomedical diagnosis and chemical optimizations

This thesis focuses on applying machine learning methods on the process of biomedical diagnosis and chemical optimizations. The first part describes the combinination of ambient ionization mass spectrometry and machine learning for non-invasive biomedical diagnosis. In this design, mass spectrometry is used to collect chemical information from a biological sample; while machine learning methods are employed to give predictions based on the information. Moreover, the important chemicals in the prediction process can be pin-pointed by feature selection algorithms. Those chemical structures are further identified by tandem mass spectrometry. The second part of this thesis concerns solving chemical optimization problems with reinforcement learning. The processes of optimizing the yield of a chemical reaction and the property of a molecule are formulated as Markov decision processes, and solved by different reinforcement learning algorithms.

Keywords: Chemistry.
Advisor: Zare Richard N. 
Cegelski Lynette 
Markland Thomas E.
Machine learning for a sustainable world

In 2015, the United Nations General Assembly set 17 Sustainable Development Goals to serve as the guiding principles for international development over the next 15 years. This thesis explores how machine learning could help to address some of these global sustainability challenges. The first part of the thesis focuses on data gaps in the developing world that make it hard to measure progress and target intervention effectively. Traditional data collection methods like household surveys are slow and expensive. Combining machine learning with passively collected remote sensing data could prove to be a scalable alternative, but a lack of labeled data poses a major challenge for sustainability applications. To combat this data scarcity, we propose a transfer learning approach that uses nighttime lights as a proxy for economic development. By extracting predictive features from daytime satellite imagery, we can generate fine-grained poverty and wealth estimates and create high-resolution maps of poverty. Next, we present semi-supervised deep kernel learning (SSDKL) to leverage the large amounts of unlabeled satellite data. We demonstrate that SSDKL learns more generalizable features and improves performance on a range of semi-supervised regression tasks. Finally, we introduce Tile2Vec, an unsupervised representation learning algorithm. We evaluate Tile2Vec on a wide range of remote sensing datasets, and show that it even works on non-image spatial data. The second part of the thesis explores culture-free diagnostics for bacterial infections, a leading cause of death in developing nations. Current diagnostic methods require sample culturing to identify the bacteria and its antibiotic susceptibility, a slow process that can take days even in state-of-the-art labs. Broad spectrum antibiotics are often prescribed while waiting for culture results, leading to suboptimal therapy and contributing to the increased prevalence of antibiotic resistance. We present a proof-of-concept system that combines Raman spectroscopy and deep learning to achieve accurate bacterial identification and susceptibility testing in a single step. We generate an extensive dataset of bacterial Raman spectra, and show that we can accurately identify 30 common bacterial pathogens. Even on low signal-to-noise spectra, we achieve antibiotic treatment identification accuracies of 97% and distinguish between methicillin-resistant and -susceptible isolates of Staphylococcus aureus (MRSA and MSSA) with 89% accuracy. Finally, we validate our results on clinical samples spanning 50 patients, where we achieve treatment identification accuracies of 99.7%.

Keywords: Electrical Engineering.
Advisor: Ermon Stefano
Johari Ramesh
Sadigh Dorsa