VictorJuiz/thesis huggingface dataset:
Athletic Performance and Bone Stress Injuries in Collegiate Athletes

Bone stress injuries are a common athletic injury that negatively impact athletes’ performance and well-being; have a high-rate of recurrence; and may reflect poor bone health in some athletes. While many studies have looked at risk factors that modify the load applied to a bone or that modify the ability of a bone to resist load, few have looked about how performance is associated with BSI risk. This study fills that gap by looking at the association between personal-best mile and cross-country times and BSI diagnoses in a large prospective cohort of competitive runners. Student athletes (N=126) from the track and field and cross-country teams at Stanford and UCLA were followed up to 4 years via yearly physical exams and questionnaires to determine which characteristics were associated with BSI events. Analyses looking at the relationship between bone mineral density (BMD) and performance found that a minute slower best mile time was associated with a statistically significant 0.16 standard deviation higher hip BMD in men, after adjusting for known risk factors for low BMD. These analyses also found a statistically significant 0.21 standard deviation higher total body BMD in females, after adjusting for known risk factors. In BSI models for men, a slower mile time was protective against BSIs though this association did not achieve statistical significance; for females, a slower mile time increased BSI rates only after adjusting for known risk factors for low BMD (RR=2.50, p=.06). Models with cross-country times as an exposure showed no association, most likely due to the high variability of cross-country times. Higher performing runners may have reduced BMD.

Keywords: Epidemiology
Stanford
bone stress injuries
performance
running
collegiate
Thesis
Advisor: Sainani Kristin
Atmospheric plasma deposition of optical and optoelectronic materials for photovoltaic applications

With global energy consumption expected to rise above 500 quadrillion BTUs by 2030, aggressive research and development is underway to provide sustainable energy production through the proliferation of thin film photovoltaics. In order to build significant momentum towards lowering the cost of solar energy production below 0.03 $/kWhr over the next ten years, we must consider new materials and synthesis routes which better emphasize scalability, do no compromise on material quality, and enhance the overall thermomechanical reliability of the solar module. One such method is atmospheric plasma deposition, an open-air plasma enhanced thin film growth technique. In this dissertation, I present novel atmospheric plasma chemistries for the deposition of several thin film materials, enabling an all open-air processing route for the fabrication of critical layers in thin film solar modules. In the first and second chapter, I introduce and discuss in detail critical concepts used throughout the work, including spectroscopic and mechanical models. Next, I discuss atmospheric deposition of anti-reflective coatings, establishing strategies for controlling the refractive index of coatings derived from metal alkoxide precursors. In the following two chapters, I translate these metal alkoxide chemistries to a scalable, blown arc discharge and discuss both the molecular structure of the coating as well as describe the as-deposited superhydrophilicity of the coatings. In the final chapter, I discuss a new methodology for rapidly crystallizing metal halide perovskite thin films, a solar absorber material. Atmospheric plasma deposition enables open-air thin film deposition of high quality optical and optoelectronic materials.

Keywords: Materials Science and Engineering.
Advisor: Dauskardt R. H. (Reinhold H.) 
McIntyre Paul Cameron 
Salleo Alberto
Atomic Clouds on the Horizon? Russian and Chinese Opposition to U.S. Ballistic Missile Defense Systems

On December 13, 2001, President George W. Bush announced that the U.S. would withdraw from the 1972 Treaty on the Limitation of Anti-Ballistic Missile Systems (ABM Treaty). Due to the threat posed by Iran and North Korea, the U.S. has sought to deploy missile defenses in Eastern Europe and Northeast Asia. Russia and China have consistently opposed these deployments, claiming that they threaten strategic stability and seek to counter threats from Russia’s and China’s nuclear arsenals. However, past literature has not thoroughly examined Russia and China together to explain this opposition. This thesis seeks to understand why Russia and China object to the deployment of U.S. missile defense systems and what the U.S. does to address those objections. Through examining government documents, official statements, and expert analysis, this thesis analyzes U.S. missile defense policy since its withdrawal from the ABM Treaty, Russian and Chinese responses, and how allies in Eastern Europe and Northeast Asia view U.S. ballistic missile defense (BMD) policy. This research finds that in the short term, the U.S. is able to strengthen alliances and offer extended deterrence through BMD deployments; precisely for this reason, bolstered U.S. alliances are worrisome for Russia and China. In the longer term, the U.S. sees its evolutionary and adaptable future capabilities as an advantage, but Russia and China view the future of U.S. missile defense as a threat likely to turn into a global missile defense, which includes improved missile defense technology. The alliance finding is particularly consequential, and shows that Russian and Chinese opposition of and U.S. support for BMDs is more political than it is technical. Because missile defense will continue to be a point of contention between the U.S., Russia, and China, it is important to understand why this is an issue.

Keywords:  Center for International Security and Cooperation
Center for International Security and Cooperation
missile defense
foreign policy
United States
Russia
China
strategic stability
nuclear deterrence
Thesis
Advisor: Hecker Siegfried S.
Holloway David
Augmenting drug mechanism prediction with text mining

The biomedical literature is the most comprehensive resource for known information and, more specifically, high quality low-throughput data. While expertly curated databases aim to annotate domain-specific biomedical relationships, large-scale curation efforts become infeasible as the biomedical literature grows exponentially. Due to the availability of high-throughput experimental data, many computational methods for drug discovery and repurposing have focused on incorporating this data into their algorithms; however, data extracted from the literature is equally valuable. To assess the impact of literature-extracted biomedical relationships on drug mechanism prediction, I extracted gene-gene, gene-drug, and gene-disease from full text literature articles using the system DeepDive. In addition, I developed Snorkel applications to extract bacteria-gut and chemical reaction relationships from abstracts. To integrate literature and abstract-derived biomedical relationships for augmenting computational approaches for drug mechanism prediction, I focused on two applications: detecting semantic network motifs for drug target prediction and predicting drug metabolism in the human gut microbiome.

Advisor: Altman Russ 
Khatri Purvesh
Ré Christopher
Automated design of photonic devices

Integrated photonic devices are poised to play a key role in a wide variety of applications, ranging from optical interconnects and sensors to quantum computing. Design methods for photonics, however, lag far behind other areas such as digital electronics and aerospace vehicles. Photonic devices are largely designed by hand using a combination of semi-analytic theory and brute-force parameter sweeps, and as a result only a small library of devices is currently known. In this dissertion, I discuss our recent efforts to automate the design of photonic devices. In particular, we have developed an automated design method that explores the full design space of fabricable devices. This has allowed us to design devices with previously unattainable functionalities, performance, fabrication robustness, and compact footprints. Using this method, we designed, fabricated, and experimentally demonstrated a wide variety of passive silicon photonics devices. These include a wavelength splitting grating coupler, compact waveguide-coupled wavelength splitters, and a 3-way power splitter. The design methods we have introduced have the potential to both revolutionize the integrated photonics industry, and open new avenues of research for photonics.

Keywords: Electrical Engineering.
Advisor: Vuckovic Jelena 
Fan Shanhui
Miller D. A. B.
Automatically distributing and load balancing graphical fluid simulations

Distributing graphical fluid simulations across many machines enables faster and more detailed simulations. However, distributing these simulations efficiently is challenging. First, it requires writing efficient distributed simulation code. Graphical fluid simulations use many diverse, novel and optimized data structures. Existing production libraries contain code developed over decades. It is important to leave low level control over simulation data structures to applications, in order to support these different data structures and existing libraries. Second, even with well written code, it is necessary to distribute work evenly in order to run the simulation efficiently. Fluid simulations exhibit spatial and temporal variation in fluid distribution and computational load. This makes distributing work evenly difficult. Optimal decision making requires knowing future computational load, but computing this state automatically for an arbitrary simulation requires running the simulation itself. As a result, many simulations use a manually specified partitioning, a heuristic, or a reactive approach based on the current load. This dissertation shows that using coarse grained geometric descriptions about data and computations, it is possible for a system to automatically distribute such simulations efficiently by addressing these two challenges. It presents a four layer data model that allows a high level framework to automatically distribute grid-based and hybrid simulations over application owned data structures, and proposes a new approach, speculative load balancing to automatically distribute work evenly. The four layer data model supports complex data structures by providing a simple, geometric abstraction to the simulation author and library. It uses geometric information to translate between geometric views and disjoint system views for efficiently analyzing and enforcing dependencies, and to assemble complex application objects containing data in the format that the simulation library expects from underlying system objects. Speculative load balancing runs the same sequence of computations over a lower resolution grid to estimate future load, and uses these estimates to distribute work evenly. The lower resolution simulation runs orders of magnitude faster and adds negligible execution time overhead. Experimental results show that speculative load balancing outperforms traditional ways of distributing work.

Keywords: Electrical Engineering.
Advisor: Levis Philip 
Aiken Alexander 
Fedkiw Ronald P.
Automotive safety validation in simulation [electronic resource]

Fully automated driving will require intelligent systems capable of understanding, reacting to, and interacting with the intricate complexities of the real world. With the onset of autonomous driving it becomes increasingly necessary to develop advanced tools for establishing trust in intelligent safety systems that act without or despite human input. This thesis presents novel contributions to simulation-based validation, including human driver behavior and sensor models, distributions over driving scenes, and a new technique for the accelerated validation of advanced automotive active safety systems such as autonomous vehicles. Advances to human driver behavior models include the introduction of behavioral cloning models based on Bayesian networks that better capture driver behavior over short horizons. A general input architecture for deep sensor models is introduced and used to develop a stochastic model over an automotive radar's power field. Original contributions are made to the representation of distributions over driving scenes and situations, which must capture a variable number of traffic participants on arbitrary roadways. Finally, this thesis introduces a new method for accelerated validation using importance sampling over clusters of critical situations, prioritizing simulation of critical scenes and avoiding countless simulations of benign driving scenarios while backing out the correct performance statistics.

Keywords: Aeronautics and Astronautics.
Advisor: Kochenderfer Mykel J.
Ermon Stefano advisor. Thesis advisor
Pavone Marco
Autonomous vehicle motion planning with ethical considerations

Human drivers navigate the roadways by balancing values such as safety, legality, and mobility. An autonomous vehicle driving on the same roadways as humans likely needs to navigate based on similar values. For engineers of autonomous vehicle technology, the challenge is then to connect these human values to the algorithm design. To address this challenge, a mapping of philosophical frameworks to mathematical frameworks is used in order to motivate various design choices in a motion planning algorithm. Deontological ethics parallels rule-based mathematical concepts while consequentialism parallels cost-based mathematical concepts. The philosophical theory of virtue ethics is also used to help motivate the relative weightings between the design objectives of path tracking, obstacle avoidance, and adherence to traffic laws. Experimental results of an autonomous vehicle navigating an obstructed two-lane roadway with a double yellow line demonstrate the implications of the various design choices in a model predictive steering controller. In order to determine the success of the human values captured in an algorithm, the iterative methodology of value sensitive design (VSD) is used to formalize the connection of human values to engineering specifications. A modified VSD methodology is used to develop an autonomous vehicle speed control algorithm to safely navigate a pedestrian crosswalk. Two VSD iterations are presented that model the problem as a partially observable Markov decision process and use dynamic programming to compute an optimal policy to control the longitudinal acceleration of the vehicle based on the belief of a pedestrian crossing. The speed control algorithms are also tested in real-time on an experimental vehicle on a closed-road course.

Keywords: Mechanical Engineering.
Advisor: Gerdes J. Christian 
Kochenderfer Mykel J.
Pavone Marco
Back-projection imaging of volcanic and hydrothermal seismicity using the ambient seismic field

Seismic signals generated by volcano-tectonic and hydrothermal processes typically have low signal-to-noise ratios (snrs) that make it difficult to locate their subsurface sources using established seismic imaging methods (e.g., phase-picking, waveform or spectral template-matching). High-amplitude bursts easily identifiable on otherwise "noisy" seismograms from these systems (i.e., eruptions) can be suitable for traditional seismic imaging, but these events tend to occur infrequently relative to the duration of a systems total eruption cycle. Studies that utilize only these types of observations are therefore inherently limited to analyses of individual eruptions or high-magnitude seismic events and offer less insight into subsurface processes that occur as a system builds up to and recovers from an eruption. Improving our understanding of significantly more common inter-eruption periods is critical to (1) answer fundamental geologic questions about volcano-tectonic and geyser system processes, (2) make accurate assessments of volcanic and geothermal hazards, and (3) improve our understanding of how magmatic and geothermal reservoirs evolve. Fortunately, the apparent "noise" recorded by seismic stations in volcanic and hydrothermal systems in reality represents a compilation of many overlapping, low-magnitude pressure perturbations at depth. These "micro-events" have also been shown to be directly linked to fluid (magma or brine) and volatile movement in the subsurface. The low snr signals these events generate therefore likely contain valuable information about subsurface processes that occur between volcanic and geyser eruptions. In this thesis I present a new computationally efficient back-projection imaging technique to comprehensively study temporal and spatial variations in extended, diffuse, low-magnitude volcanic and hydrothermal seismicity using low snr seismic array data. I use observations of coherent seismic energy arriving at pairs of receivers in the array to back-project low snr seismic signals to their most likely subsurface source locations. Applying this technique to active volcanic and geothermal systems can help to provide new insights into the fundamental physical processes that occur before, during and after potentially hazardous eruptions.

Keywords: Geophysics.
Advisor: Beroza Gregory C. (Gregory Christian) 
Dunham Eric 
Ellsworth William L.
Basheer: Computational thinking for novice technology users

As the cost of technology decreases, smartphones and data plans are on the rise,
especially in developing countries. This means that there’s an increasing number of
people who are just starting to get access to these technologies. A large proportion of
these users are from low-income households and don’t have the resources to be
educated about the potential constructive use of computers. This results in passive
users, using these devices for watching videos, listening to music and playing games
etc. It is important for these users to realize and apply the potential of technology to
create and solve problems and one of the most powerful skills to do this through is
programming. While there is no shortage of digital platforms to teach people how to
program, most of them are designed for experienced technology users with access to
laptops and computers. Prior research shows that these platforms cannot be imported
as-is into this context as learners have trouble understanding the abstract presentations
and analogies presented in the content (Unnikrishnan 2). I have also found that learners
have trouble navigating complex interfaces on web applications and games on desktop
computers, face language barriers and are also unaware of the existence of these
platforms. The solution is a smartphone game that runs through Facebook Messenger
(a platform that these learners are already familiar with) and is purposefully designed
for these users to teach them computational thinking by providing an accessible and
culturally relevant experience. The learners solve various puzzles in a programming
language in the users native language: Urdu. I found that this was an effective way to
teach computational concepts and resulted in a game that is easily understood and
usable for these learners.

Keywords: computational htinking
gse
ldt
graduate school of education
leanring design and technology
new technology users
computational thinking
pakistan
metaphors
Thesis
Bayesian approaches to building models for biological systems

Understanding the structure and dynamics of biological macromolecules is a central focus of biological research. To be able to study and gain insights into these systems, it is first necessary to have an accurate and informative model for the system of interest. However, such a model is often difficult to build. For example, during protein folding, many proteins collapse into transient kinetic intermediates on timescales too fast for high-resolution experimental techniques to detect, preventing structural characterization of these species. Alternatively, current algorithms for RNA design (i.e. predicting a sequence that folds into a desired target structure) cannot accurately model structure-sequence relationships and rely primarily on brute force stochastic search, leading to poor performance on complex targets. Here, we show that it is possible to improve the quality of models for biological systems by applying a common Bayesian approach to building them, i.e. incorporating prior information to impose informative constraints on the model parameters. Through this approach, it is possible to build high-resolution models of protein dynamics given limited experimental data, as well as a state-of-the-art computational RNA design agent that outperforms all currently existing algorithms.

Keywords: Chemistry.
Advisor: Martinez Todd J. (Todd Joseph)
Pande Vijay 
Fayer Michael D.
Becoming expert problem solvers : a case study in what develops and how

We want students to become good scientific problem solvers, yet little research has focused squarely on the development of scientific problem solving expertise. Psychologists have studied problem solving—and scientific problem solving—extensively. Frameworks have been developed to describe what people do when they solve scientific problems. But this research, and the expertise research more broadly, has stopped short of examining the full trajectory of expertise development in a scientific problem solving domain. The lack of research in this area is due, in part, to methodological challenges. Chess, violin-playing: these are isolated domains, with established measures of expertise and, consequently, are common targets of longitudinal expertise research. Data about students becoming experts in solving a class of physics or biochemistry problems is harder to come by; measures of expertise have not been established and the domains are not as isolated. The psychological laboratory offers a controlled setting, but at the cost of limited time scales and artificially created problems that can be analyzed efficiently. This dissertation uses an unusual source of data that resolves some of these methodological challenges. Citizen science is a form of scientific research that involves members of the public. In a handful of projects, citizen scientists—volunteers who contribute to this research—become experts in solving a specific kind of problem. I examine one of these projects, Eterna, in depth, drawing on qualitative evidence—records of conversations among volunteers—and quantitative evidence—records captured as volunteers solve problems. This permits access to a long period of expertise development. This approach also lets me examine development in an authentic scientific problem. I use a grounded theory approach to address three important questions: 1) the path of the developmental change—what changed into what? 2) the variation in developmental change—how do people differ in their cognitive change?, and 3) the sources of the developmental change—which variables caused the change to take place and how did they do so? The dissertation as a whole offers a new way of talking about expertise development in scientific problem solving: not as primarily the development of a domain-general construct ("scientific reasoning"), but as the product of exploration of the fundamental features in the domain. It also offers insight into the kinds of technological affordances, individual behaviors, and cognitive resources that drive expertise forward. Finally, it offers a conceptual distinction between citizen science projects, suggesting that expertise-centric projects represent an alternative research paradigm.

Keywords: Education.
Advisor: McFarland Daniel (Daniel A.) 
Thille Candace
Wieman C. E. (Carl Edwin)