VictorJuiz/thesis huggingface dataset:
Learning disjunction

To understand language, we rely on mental representations of words and their meanings. What constitutes these representations? How are they learned? To address these questions, I investigate how children learn and interpret the disjunction word "or". The highly abstract and context-dependent interpretation of or challenges word learning theories and provides an exceptional opportunity to better understand how words are associated with their meanings. "Or" has several interpretations, including exclusive and inclusive disjunction. Inclusive disjunction holds when A is true, B is true, or both. For example, a waiter may ask if you would like something to eat or drink, not excluding the possibility that you choose both. Exclusive disjunction is true when only A is true, or only B is true, but not both. If the waiter later asks whether you would like to see the dessert menu or have the check, his "or" is most likely interpreted as exclusive. He is suggesting that you should choose one or the other. Given these complexities in the interpretation of "or", how do children learn it? A previous study has shown that when parents talk to their children, the majority of "or"-examples they use are exclusive. I present an annotation study on parents' speech to children that replicated this finding. Nevertheless, comprehension studies have found that preschool children understand the inclusive interpretation of disjunction around four years of age. In an experimental study with a novel paradigm, I replicated this finding in simple existential sentences. These two findings lead to a puzzle in the literature: How can children learn the inclusive interpretation of "or" if they rarely hear it? I argue that this puzzle arises in models of word learning that directly map words to their meanings, thereby ignoring accompanying linguistic and conceptual cues. I present an in-depth annotation study demonstrating that exclusive interpretations correlate with contextual cues (such as intonation and the semantic relation of the alternatives "or" combines with) in children's input. Applying supervised learning techniques to the annotated data, I found that a learner who makes use of these contextual cues can learn the inclusive as well as exclusive interpretation of disjunction from the language heard. These findings indicate that the representation of a word like "or" cannot be isolated from the linguistic and conceptual environment in which it appears. The linguistic and conceptual aspects of "or"'s environment can act as cues that aid its acquisition and interpretation. Together, these studies show that learning a function word like "or" requires richer lexical representations than are currently assumed by our theories of word learning.

Keywords: Linguistics.
Advisor: Clark Eve V. 
Potts Christopher
Frank Michael C. (Professor of human biology) 
Markman Ellen M.
Learning from professional race car drivers to make automated vehicles safer

Autonomous vehicles have the potential to eliminate the vast number of motor-vehicle accidents that occur each year. However, as the burgeoning technology becomes more publicly available, self-driving cars will continue to encounter emergency situations. To maximize the vehicle's ability to navigate these situations safely, autonomous driving technology needs to be able to use all of the vehicle's performance capability. Race car drivers can inspire autonomous systems with full authority over a vehicle's capabilities because they routinely drive at or near the limits of handling without losing control. This dissertation aims to advance the understanding of how highly skilled human drivers operate vehicles at the limits by analyzing their trajectories, steering behavior, and stability characteristics observed while maximizing vehicle performance. First, a sensor suite and related methods are used to acquire research-quality, multimedia data from vintage race cars during real-world race events. The resulting publicly available database not only further documents and characterizes select, historically significant automobiles, but also provides open access to vehicle dynamics data recorded while expert drivers operated at the friction limits. To investigate the extent to which highly skilled drivers follow a theoretically ideal trajectory when maximizing vehicle performance, a statistical analysis quantifies the dispersion of paths driven by professional race car drivers during live races. The method reveals that two drivers operating the same vehicle followed measurably dissimilar trajectories, yet achieved similar overall results as measured by lap time. A state-of-the-art autonomous race car, which applies racing theory in the real world, is used to explore whether the variance observed in the human driver data is a result of error, which would imply following an optimal trajectory leads to greater performance, or whether the observed variance is a consequence of purposeful behavior essential to fully utilizing a vehicle's capabilities. The autonomous race car is as fast as a skilled, proficient driver, but currently not as fast as an expert driver. Using a quasi-steady-state model as a baseline, an analysis of each driver's steering behavior shows that the human drivers purposely operate around the handling limits, including beyond the limits, while the autonomous race car's design leads to operation up to the handling limits. Furthermore, a phase portrait analysis illustrates that the human drivers consistently operate the vehicle closer to, and sometimes beyond, the boundaries separating inherently stable and unstable vehicle states, whereas the autonomous race car's trajectories always remain within the stable region. This information can help design algorithms that operate over the full range of vehicle performance, to maximize the vehicle's ability to operate not only at speed during racing maneuvers but also safely during emergency maneuvers.

Keywords: Mechanical Engineering.
Advisor: Gerdes J. Christian 
Okamura Allison 
Rock Stephen M.
Learning in the rational speech acts model

When a person says something that has multiple possible interpretations, which interpretation stands out as the most likely intended meaning often depends on context outside the utterance itself: salient objects in the environment, utterances the speaker could have chosen but didn't, common-sense knowledge, etc. Systematically predicting these contextual effects is a major unsolved problem in computational natural language understanding. A recently-developed framework, known in cognitive science as the rational speech acts (RSA) model, proposes that speaker and listener reason probabilistically about each other's goals and private knowledge to produce interpretations that differ from literal meanings. The framework has shown promising experimental results in predicting a wide variety of previously hard-to-model contextual effects. This dissertation describes a variety of methods combining RSA approaches to context modeling with machine learning methods of language understanding and production. Learning meanings of utterances from examples avoids the need to build an impractically large, brittle lexicon, and having models of both speaker and listener also provides a way to reduce the search space by sampling likely subsets of possible utterances and meanings. Using recently-collected corpora of human utterances in simple language games, I show that a combination of RSA and machine learning yields more human-like models of utterances and interpretations than straightforward machine learning classifiers. Furthermore, the RSA insight relating the listener and speaker roles enables the use of a generation model to improve understanding, as well as suggesting a new way to evaluate natural language generation systems in terms of an understanding task.

Advisor: Jurafsky Dan
Potts Christopher
Goodman Noah (Noah D.)
Leaving lessons : learning from the exit of experienced teachers

Teacher attrition has increased significantly over the last thirty years. Most public school teachers in the United States now leave the career early, before reaching retirement. Research on teacher attrition has tended to focus on novice teachers, using human capital or organizational theories to understand trends in teacher exit. This qualitative study takes another path, considering the accounts of certified, experienced teachers who left teaching after making significant investments in the career. The accounts of these invested leavers are examined through a lens of resistance, illuminating issues of power as well as the idea(l)s of teaching that may be expressed through the decision to leave.

Keywords: Education.
Advisor: Labaree David F.
Langer-Osuna Jennifer 
Pope Denise Clark
Leveraging Kindergarten and First-Grade Literacy Behaviors to Predict Third-Grade Reading Failure

Early identification of students at-risk is an essential component of effective reading interventions. While Informal Reading Inventories (IRI) are used in districts across the country to identify students who need additional support, they have been shown to have poor psychometric properties and low diagnostic accuracy. The current longitudinal study serves as a “proof of concept” to show that utilizing more data-driven classification measures can significantly increase the accuracy of IRIs. Classification accuracies from three statistical learning models trained on Reading Inventory data were compared to the accuracy of classifications based on predefined cut-off scores for 1,928 students attending public schools in a large metropolitan district. Using binary classifiers significantly improved the overall accuracy of predicting third grade Reading achievement based on first grade Reading Levels, with an average increase of 27%. Results indicated that incorporating kindergarten Reading Skills (i.e. phonemic awareness, letter knowledge, etc.) to the training data significantly increased classification accuracy for the Logistic Regression Model and Support Vector Machine, but not for the Random Forest Classifier. The findings also showed that letter knowledge variables were more important than phonemic awareness skills in predicting third grade reading outcomes. Implications for practice and future research are discussed.

Keywords: Informal Reading Inventories
Fountas and Pinnell Benchmark Assessment System
Statistical Learning Models
Diagnostic Accuracy
Phonemic Awareness
Letter Knowledge
Graduate School of Education
Stanford
Thesis
Advisor: McCandliss Bruce
Leveraging mixed-valency for the development of electrically conductive and porous hybrid materials

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.

Keywords: Chemistry.
Advisor: Karunadasa Hemamala
Solomon Edward I. 
Waymouth Robert M.
Lexicon Integrated Deep Neural Networks for Fine-grained Hate Speech Detection on Twitter

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.

Keywords:  Department of Computer Science
computer science
engineering
Stanford
natural language processing
hate speech
computational social science
deep learning
neural networks
Thesis
Advisor: Potts Christopher
Listening for Noise, Sounding Out Silence: Latinidad as Aural Dissonance

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.

Keywords:  CSRE
Latinidad
sound
panethnicity
art
Stanford University Comparative Studies in Race and Ethnicity
Thesis
Advisor: Rosa Jonathan
Hernandez-Clarke Gina
Load monitoring in composite structures with built-in piezoelectric sensors

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.

Keywords: Aeronautics and Astronautics.
Advisor: Chang Fu-Kuo 
Senesky Debbie 
Springer George S.
Load-dependent kinetics and mechanics of single cardiac myosin molecules

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.

Keywords: Biochemistry.
Advisor: Spudich James A.
Harbury Pehr 
Krasnow Mark
Locating conviction within organizations

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.

Keywords: Business.
Advisor: Laurin Kristin
Pfeffer Jeffrey 
Kosinski Michal
Logic and Qualitative Probability

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.

Keywords: logic
qualitative probability
Thesis
Advisor: Icard Thomas
van Benthem Johan