VictorJuiz/thesis huggingface dataset:
Leveraging mixed-valency for the development of electrically conductive and porous hybrid materials
Keywords: Chemistry.

Hybrid inorganic-organic materials combine the synthetic tunability of organic molecules with the electronic diversity of inorganic coordination compounds. Specifically, I synthesized and characterized novel hybrid materials that are both electrically conductive and porous. Conductive and porous hybrid materials have shown potential for energy applications such as energy storage, gas sensing, and heterogeneous catalysis. Finding conductivity and porosity in a single material is difficult, since these properties are orthogonal and tend to oppose one another. Because of these challenges, much effort has been dedicated to developing more of these multifunctional materials. The work presented below describes efforts to leverage mixed-valency to induce electrical conductivity in porous hybrid materials. I utilized electronic, magnetic, and optical spectroscopies to characterize the properties of these new materials. To understand structure-property relationships, I used Rietveld refinement and pair distribution function analysis of X-ray diffraction data to elucidate these materials' structures. I discovered that oxidation of a new Prussian blue analogue creates a mixed-valence material and induces an insulator to semiconductor transformation. I observed a similar transformation in novel copper-azobispyridine metallopolymers, whose electrical conductivity and porosity can be turned on and off through electrochemistry. These same metallopolymers also display reactivity with nitrogen dioxide that drastically changes the electronic, magnetic, and optical properties of the metallopolymers. Lastly, I synthesized and characterized a class of porous and conductive copper dithiolene materials.
Lexicon Integrated Deep Neural Networks for Fine-grained Hate Speech Detection on Twitter
Keywords:  Department of Computer Science
computer science
engineering
Stanford
natural language processing
hate speech
computational social science
deep learning
neural networks
Thesis

The pernicious problem of hate speech online has drawn attention from companies, governments, and regulatory bodies and necessitates reliable systems for automated hate speech detection. In recent years, an increased amount of research has explored using deep neural network classifiers for this task. We perform comparative evaluation on a public English Twitter corpus of 25k tweets labeled as \textit{hate speech}, \textit{offensive language}, or \textit{neither} and find that a combined convolutional and gated recurrent unit (CNN-GRU) network is the most competitive deep architecture, but still performs poorly on the \textit{hate speech} class compared to linear classifiers with hand-engineered features. We further propose a method to enrich deep neural network models by integrating lexicon-based features. Our proposed method improves the performance of deep models by up to 4\% in F1 score. An analysis of the prediction errors shows that certain keywords are strong indicators of hate speech for all models, while the CNN-GRU is robust in ambiguous contexts. Finally, we present a simple heuristic for discovering new hate speech terms using a trained model and evaluate it against an existing hate speech lexicon.
Listening for Noise, Sounding Out Silence: Latinidad as Aural Dissonance
Keywords:  CSRE
Latinidad
sound
panethnicity
art
Stanford University Comparative Studies in Race and Ethnicity
Thesis

This creative thesis is devoted to exploring the sonic qualities of Latinx identity through an interdisciplinary approach that lies at the intersection of sound art, sound studies, and art history. The creative component of this thesis—a sound art piece composed of vernacular sounds, silences, and noises recorded in the metropolitan areas of Los Angeles, Chicago, New York, and Miami—serves as a rich, varied, and contradictory repertoire, what I call a dissonant soundtrack of Latinx identity. The sounds are united in their vernacularity; they are stories mentioned offhandedly, music played unconsciously, noises often rendered inaudible. These pages complement the soundtrack by providing a kind of guided listening, a meditation on the creation and affect of the work. I structure this critical artist statement as “tracks” that situate the project within the context of larger bodies of knowledge and creative practices. Each track explores key sonic metaphors that imbue the soundtrack with meaning. I argue that these metaphors provide a unique epistemological standpoint from which to investigate Latinx identity; specifically, that understanding Latinx identity through the critical lens of dissonance—as something unfinished, undone, unknown, and in constant motion—allows for the creative reimagination of its terrain. In giving up the prospect of finding sonority in Latinx identity, I clear the way to listen for noises and silences, spaces from which I seek to renegotiate notions of belonging, history, and place. Ultimately, this project urges audiences to fully listen, to understand and search for that which has created and sustained the inaudibility of marginal Latinx narratives.
Load monitoring in composite structures with built-in piezoelectric sensors
Keywords: Aeronautics and Astronautics.

Load monitoring of high-performance engineering structures is a key challenge to meet in order to improve reliability and life estimation especially for critical components. Engineering structures manufactured with composite materials increase the complexity of this problem. Composite materials are desirable for many high-performance applications, but pose monitoring and maintenance challenges due to their heterogeneous composition and complicated damage mechanics. This work will focus on a strategy to monitor applied load on a composite material with minimal data input and few verification experiments. Applied load levels will be monitored utilizing an ultrasonic-based non-destructive inspection (NDI) strategy which propagates guided ultrasonic waves through a composite material using attached piezoelectric actuators and sensors. The goal is to create a load estimation algorithm using experimentally-collected sensor signal data at different magnitudes of applied load. Signal features extracted from the ultrasonic sensor signals, including energy and time of flight, are used to distinguish effects from applied load. Using combinations of these signal features and a statistical analysis approach models are created which perform the inverse problem of estimating applied load from sensor signal data. This research will demonstrate a method utilizing partial least squares regression to estimate applied load on a composite specimen using training data from only one experimental sample and multiple guided wave propagation paths. By minimizing the number of necessary validation experiments this method is widely and easily applicable to many engineering applications.
Load-dependent kinetics and mechanics of single cardiac myosin molecules
Keywords: Biochemistry.

Cardiomyopathy-causing mutations and small molecule drugs can alter contractility of the heart muscle. Here, I investigate the underlying mechanism at the level of individual molecules of human β-cardiac myosin, the isoform responsible for power production in the ventricles of the heart. I measured single myosin molecules' rate of detachment kdet(F) from actin and its load dependence by optical trapping. I find that the rate and its load dependence are modulated to various extents by (i) cardiac myosin-specific small molecule compounds, including omecamtiv mecarbil (OM), a drug in phase III clinical trials for treatment of heart failure, and (ii) mutations in myosin that cause hypertrophic (HCM: D239N, H251N) or dilated (DCM: A223T, R237W, S532P) cardiomyopathies. Furthermore, effects of mutations can be reversed by introducing appropriate compounds. The duty ratio, average force, and average power of single myosin molecules can be determined given kdet(F), and herein lies a striking separation between activating vs. inhibitory perturbations consistent with physiological expectations. Stated simply, hypo- and hyper-contractility are observed at the level of individual myosin molecules. I next investigate the load-dependent detachment kinetics of OM in greater detail to understand its mechanism to activate the heart. The drug dramatically slows myosin's detachment rate so that myosin stays bound to actin ~6x longer, from ~10 ms to ~60 ms. Single-molecule dosage analysis reveals two populations of events -- one fast in which the drug is not bound to myosin, and the other slow in which the drug is bound. The fraction of OM-bound events depends on the concentration of the drug. It is proposed that the prolonged attachment duration between myosin and actin in the presence of OM prolongs the activation of the thick and thin filaments in the sarcomere, thereby prolonging contraction as observed clinically. Finally, in addition to the load-dependent detachment rate kdet(F), another key parameter of cardiac contractility is myosin's stroke size. I present a novel analysis method which extracts stroke size information from data collected originally for determining kdet(F). Preliminary analyses show that stroke size is also modulated by mutations and small molecule compounds. Together, these investigations of myosin's load-dependent kinetics and stroke size provide an opportunity in molecular medicine to understand modulation of cardiac contractility from the level of individual myosin molecules.
Locating conviction within organizations
Keywords: Business.

Organizations often signal values to stakeholders, and can face both benefits and costs when they do so. This dissertation examines how and why organizations—potentially inadvertently—signal values as a function of their communications and behaviors. In Chapter 1, I provide a brief outline of relevant literatures, and summarize the focus of this dissertation. In Chapter 2, I explore how people attribute moral conviction to different agents within organizations. In Chapter 3, I investigate how people attribute moral conviction to superordinate organizations with different levels of access to resources. In Chapter 4, I examine how different rates of transition can influence people's perceptions of how committed organizations are to the values underlying changes or improvements. I discuss implications concerning signaling, reputation and impression management, and ethics.
Logic and Qualitative Probability
Keywords: logic
qualitative probability
Thesis

While theories of probability have long been adequately axiomatised, these
generally take a quantitative approach. Yet humans are also able to reason
about probability in a qualitative sense, such as when comparing the relative
likelihoods of sets of events and making inferences based on those comparisons.
This suggests that probabilistic reasoning ultimately has a strong
qualitative basis, which can be supplemented by various other components
in order to obtain complete theories of probability.
We analyse a recent approach to capturing qualitative probabilistic reasoning
in the form of the logic IP and the corresponding multi-measure semantics.
By restricting our attention to a fragment of the language of comparative
probability that exclusively contains probabilistic comparisons between
propositional formulas in disjunctive normal form, we reduce the logic IP to
its qualitative probabilistic essence. We show that the resulting restricted
logic IP-DNF is nevertheless sound and complete with respect to the multimeasure
semantics, and also show that the computational complexity of the
satisfiability problem for the logic IP-DNF is in polynomial time, whereas
that of the logic IP is NP-complete.
In the course of this analysis we encounter strong similarities between
qualitative probabilistic reasoning and counting and arithmetic. This leads
us to hypothesize that the multi-measure semantics, because it lacks the
facilities for differentiating between cardinalities, cannot be finitely axiomatised,
but instead exclusively possesses infinitary axiom-schemes such as the
logics IP and IP-DNF, which are both based on the infinitary Scott-axioms.
We proceed to prove this hypothesis, and ultimately conclude that a purely
qualitative approach to probabilistic reasoning seems insufficient.
A longitudinal analysis of the human immune repertoire [electronic resource]
Keywords: Genetics.

The human immune system is crucial for maintaining our health throughout our entire lives. In spite of its importance, we still do not fully understand 1) what a healthy immune baseline is, 2) how this baseline changes over time, 3) how the healthy baseline responds to natural infection, and 4) how class switching plays in to creating and maintaining this healthy baseline. To this end, I analyzed the immune repertoire dynamics of a healthy individual. I accomplished this by profiling the B cell receptors of a relatively healthy individual through two periods of infection to better understand how a healthy immune repertoire responds to everyday life versus an infectious challenge. I found that a healthy immune repertoire fluctuates within predetermined patterns during periods of infection and that longitudinal profiling allows the determination of a personal baseline immune repertoire that can be used as both a personal standard for future medical care and a public resource for future studies.
The low-mass limit : dark matter detectors with eV-scale energy resolution
Keywords: Physics.

The SuperCDMS SNOLAB experiment will be a 20-kg scale Si and Ge direct dark matter detection experiment designed to probe down to 300 MeV in dark matter (DM) mass through DM-nucleus scattering and 500 keV in DM electron scattering. In order to reach these low masses with appreciable sensitivity to dark matter, it needs to achieve very low energy resolution (≤ 10 ev) for nuclear recoils in both detector materials, which will be achieved using a new detector design and operating mode, CDMS HV. This detector is designed to operate at a bias of 100V to convert charges liberated in our detector targets to into phonon energy in order to resolve individual electron-hole pairs. This has never before been achieved in a kg-scale detector. In this thesis, I cover three elements of the design of the CDMS HV detectors. I discuss the detector physics controlling how charges and phonons are generated in our detector crystals, com- paring theory to results of recent experiments carried out at Stanford. I move on to describe the operating principles of our phonon-mediated charge readout, as well as the design of the CDMS HV detector. I then describe the performance tests of early CDMS HV prototypes in conjunction with the SuperCDMS SNOLAB electronics, and discuss the path towards achieving single electron-hole pair resolving detectors at the kg-scale given the performance obtained thus far. As a result of these tests, we were able to refine our noise and sensor dynamics models, and develop new metrics for diagnosing non-ideal sources of noise to aid in reducing coupling of the external environment to our detectors. In order to study the microphysics of phonon and charge production in our target crystals, we fabricated a number of gram-scale devices with various sensor designs in order to separate sensor and environmental effects from intrinsic crystal properties. These devices provided the first successful demonstrating of using voltage to amplify charge energy by production of phonons (the Neganov-Trofimov-Luke effect) in order to resolve electron-hole pairs, and opened up a new regime of dark matter and photon science at the gram-scale that we are just beginning to explore. A first dark matter search was carried out with one of these gram-scale devices, producing world-leading limits on electron-recoiling dark matter between 0.5 and 5 MeV in dark matter mass for multiple form factors. This device achieved a phonon resolution of 10 eV, allowing a single gram-day of exposure to rival kg-days of exposure in the competing liquid-noble based electron-recoil search.
Low-power and highly-stable encapsulated MEMS-based clocks
Keywords: Mechanical Engineering.

In today's economy, timing references are everywhere from smart-phones to cars. Timing references or clocks provide a stable signal that serves as a reference for communication between digital systems from processors within a smart-phone to cellphones on a wireless network. In the past decades, quartz clocks have dominated the field of portable timing references, and their size and power consumption have limited the design specifications of electronics and other devices. Recently, encapsulated MEMS-based clocks have entered this market, due to their small size, low cost and low power consumption. The main challenge of MEMS-based clocks is that they are fabricated with silicon, which has elastic properties with a strong linear temperature dependence that contributes to lower frequency stability over temperature. Previous research aimed to solve this problem with temperature compensation techniques including passive methods such as changes in the wafer doping and resonator orientation, and active methods such as ovenization. These methods aimed to reduce the linear relationship between the frequency and temperature characteristic of silicon resonators. These efforts are expanded in this work to demonstrate novel dual-mode MEMS-based clocks with in-chip device layer micro-oven that controls the temperature of the resonators with 10x reduced power consumption and provides more than 30x improved frequency stability when compared to recent results with a micro-oven embedded in the encapsulation layer. This device layer micro-oven enables the correction of ambient temperature fluctuations and achieves long-term frequency stability over temperature of +/- 1.5ppb, which is better than the stability of TCXOs and competes with state-of-the-art quartz OCXOs and miniature atomic clocks, while requiring much less power. This work also explored techniques to improve the yield of devices with micro-ovens embedded in the device layer and the thermal effects of ovenized devices on other sensors located in the same chip.
Machine Learning Applied to Multiphase Production Problems
Keywords: School of Earth Energy &amp; Environmental Sciences
production
multiphase flow
machine learning
Thesis

In reservoir engineering, it is important to understand the behavior of the reservoir, often by seeing the dynamics of bottom hole pressure (pwf) and flow rate (q) in each well. Bottom hole pressure and flow rate are commonly modeled using physical model, for example, reservoir numerical simulator. This approach requires physical parameters, including rock properties and reservoir geometry, which we do not always know. In some cases, collecting those physical parameters and building the physical model might not be practical. Applying this approach in a field that has thousands of wells with a very dynamic environment requires a lot of effort.
Another approach uses historical bottom hole pressure and flow rate, which are commonly easier to obtain. Using machine learning algorithms, we can build a reservoir model by learning the historical pattern of bottom hole pressure and flow rate (or training set) without the physics of the flow being programmed explicitly. Using this model, we can predict flow rate given bottom hole pressure (or vice versa). We can also utilize this model as a diagnostic tool. For example, if liquid loading or condensate banking or wax/asphaltene deposition starts to happen, the actual pressure and flow rate response will be different than that suggested to the model, hence flagging that the reservoir performance has changed in character.
Previous efforts by Liu (2013) and Tian (2014), have been successful in applying machine learning method in single-phase cases. Important features were identified and gave satisfactory results in several different cases. The study found that although pressure and flow rate relationship can be nonlinear, the problem can be formulated as a linear problem and the nonlinearity is expressed in the features.
This research focused on solving multiwell and multiphase problems using machine/deep learning approaches. The study has identified important features in such problems. This study explored ten different machine/deep learning algorithms and their performance evaluation. Python is very helpful for this purpose as it has a lot of machine/deep learning libraries to choose from and is handier in solving machine learning related problems. The study also examined the impact of multiphase flow, reservoir heterogeneity, and data noise.
Machine learning approaches for permanent downhole gauge data interpretation
Keywords: Energy Resources Engineering.

The continuous record of pressure, temperature, and sometimes flow rate by Permanent Downhole Gauges (PDGs) contains rich information about the reservoir. Due to the different conditions where data are collected, PDG data have unique characteristics that require different interpretation approaches than conventional well testing. One of the machine learning approaches developed earlier was the convolution-kernel-based data mining (Liu and Horne, 2013a, 2013b). The method was shown to be promising for pressure-rate deconvolution on noisy data. However, the bottlenecks of computation efficiency and incomplete recovery of reservoir behaviors limit the application of the convolution kernel method to interpret real PDG data. In this work, two classes of methods were applied for PDG data interpretation. One was the machine learning approach with feature handcrafting based on physics, the other was the deep learning approach by using recurrent neural networks to learn on raw PDG measurements. Both approaches were shown effective to extract the patterns containing the reservoir information from PDG data. The extracted patterns were demonstrated to be useful in three ways: one was deconvolution by predicting the pressure corresponding to a constant rate history; the other two were to predict pressure in the future with given rate control, and vice versa. Three feature-based machine learning techniques were explored to interpret PDG data, namely, the linear approach, the kernel method, and kernel ridge regression. First the linear approach was proved to learn with the same features as the previous convolution kernel method, while it required much lower computational cost. The linear approach was used further for pressure-rate deconvolution and it outperformed two conventional deconvolution methods when noise or outliers were contained in the data. Next the kernel method and kernel ridge regression were utilized to adjust the machine learning model capability and to model the pressure behaviors covering early-transient until late-transient periods. Additional machine learning models were developed for the problems of rate reconstruction and multiwell testing, by handcrafting new set of features. Recurrent neural networks were investigated for PDG data interpretation with a focus on nonlinear autoregressive exogenous models (NARX). It was shown that NARX learned the patterns behind PDG time series data with its uniquer recurrent architecture, by feeding the memory of previous computations back into the model. NARX was also applied for pressure-rate deconvolution and it identified different reservoir models successfully. One key advantage of NARX is that it learns on raw data, although thought and effort are needed to tune the neural network. A case study on temperature transient analysis demonstrated NARX's advantage over feature-based machine learning when feature handcrafting was difficult because of limited knowledge of the physics.