VictorJuiz/thesis huggingface dataset:
Compact high frequency power conversion for high voltage applications

Performance of high-voltage power converters has lagged their low-voltage counterparts on many metrics including power density, efficiency, transient response, and cost. This research presents newly developed circuit techniques and design strategies that allow high-voltage power supplies to be many times smaller and more efficient than the traditional high-voltage supplies on the market today. By implementing unique resonant circuit topologies capable of operating efficiently at 10s of MHz, passive component size is reduced to a fraction of the volume and mass when compared with conventional lower frequency designs. Novel strategies for improving the voltage gain of resonant dc-dc converters without loss in efficiency and stability are presented. This work provides several examples of high-gain resonant converters and demonstrates their benefits when used in various scientific applications. Specifically, several multi-kV prototypes are tested that achieve power densities exceeding 100 watts per cubic inch for conversion ratios of up to 130 and dc-dc efficiencies reaching 90 %. Various power levels are proven from 10s of watts to 2~kW with output voltages reaching 15 kV.

Keywords: Electrical Engineering.
Advisor: Rivas-Davila Juan 
Boswell Rod
Lee Thomas H.
Comparative Case Studies on U.S.-Japan and U.S.-China Semiconductor Trade Disputes

In the past few decades, the rapid development of technologies has significantly reshaped people’s life. The new coming era of artificial intelligence has also signaled the increasing importance of the technology industry. Due to this trend, the competition in technology-related sectors has gradually become one of the top positions when forming governmental economic and trading policies. Meanwhile, certain technology industries are closely related to national security, and therefore are considered having significant strategic importance. This strategic concern might also directly impact government’s mindset. Thus, while defining trade and economic policies and resolving potential trade conflicts, the government might not only take economic and commercial factors solely into account, but also will rely on other political considerations. In some circumstances, strategic and political factors might play bigger roles.
The U.S. used to be a leading power in the semiconductor industry, but this role was later challenged by Japan in the late 1970s to 1980s due to the rise of the Japanese semiconductor industry, as well as their “unfair” practices perceived by the U.S. government. Similarly, starting in the late 1990s, the boom in the use of computers and technology in China has led to a rapid development in the Chinese semiconductor industry. As a result, trade disputes over semiconductors also emerged between the U.S. and China.
This paper examines the similarities and differences between the U.S.-Japan semiconductor trade dispute and the U.S.-China semiconductor trade dispute, and effects of the two trade arrangements on domestic semiconductor industries in Japan and China. This paper argues that despite Japan and China were both huge economic entities in East Asia with high growth during the two periods, they represented two different types of threats against the U.S.
Although strategic and national security factors, and economic interests both played important roles in the two dispute cases, this paper argues that the specific political and policy contexts were the key factors pushing forward the progress of negotiating the two trade disputes. While mechanisms used during the two dispute settlement process were different, this paper finds out with regard to post-effects on domestic semiconductor industries in the two countries, the orientation of the mechanisms, rather than the mechanisms themselves matter.

Keywords:  Stanford Global Studies
U.S.- Japan Semiconductor Trade Dispute
U.S.-China Semiconductor Trade Dispute
East Asian Studies
China
Japan
Semiconductor
Thesis
Advisor: Armacost Michael
COMPARISON OF SYNCHROTRON AND ROVER-BASED CHARACTERIZATIONS OF MARTIAN ANALOG CLAY SAMPLES

Robotic, teleoperated spacecraft and rovers have been sent in mankind’s quest to explore the solar system. With many unresolved questions about its past and present habitability, Mars has been a focus for many exploratory missions. Scientists and researchers have developed orbiters, landers, and rovers to analyze the geological and geochemical processes on Mars in an attempt to answer these questions. Though their instruments continue to improve, it is important to recognize the limit of their capabilities so accurate conclusions can be drawn. Here, we compare the performances of synchrotron and rover-based instruments on characterizing Martian analog clays. The samples from Griffith Park, California were studied using x-ray fluorescence (XRF), x-ray absorption near-edge structure (XANES) spectroscopy, and x-ray diffraction (XRD) instruments to provide detailed elemental maps, determine the iron oxidation and coordination states, and determine the mineral phases present, respectively. At the Stanford Synchrotron Radiation Lightsource facility, elemental maps were produced of our Griffith Park sample. These maps were used to identify specific points at which to gather iron K-edge XANES spectra. By comparison, the Mapping Alpha-particle X-ray Fluorescence spectrometer, an arm-based XRF device, produced elemental maps at significantly lower resolution from which only broad conclusions can be made about a certain area. By isolating the XANES pre-edge and measuring its intensity and energy centroid, an abundance of octahedral Fe3+ and Fe2+ was shown to exist, agreeing well with previous results. However, our analysis also finds the first detection of tetrahedral Fe3+ in these rocks. To determine the mineral phases present in Griffith Park samples, x-ray diffraction experiments were conducted. The results reveal that labradorite and saponite are present within the Griffith Park samples, matching similar plagioclase and tioctahedral smectite discoveries by Treiman et al.. However, our results show a 0.4 degree shift in the 02l diffraction peak locations. This may be caused by variations in clay hydration which will affect interlayer lattice spacings, and/or differences in composition.

Keywords:  Department of Geological Sciences
martian analog clays
XRD
XRF
XANES
School of Earth Energy &amp; Environmental Sciences
Stanford Department of Geological Sciences
Thesis
Advisor: Brown Jr.
Mao Wendy
Composite electrode design for next-generation lithium-sulfur batteries [electronic resource]

Secondary lithium (Li) ion batteries are essential in driving the rapid development of electronic devices. While the rising demand for high performance portable electronics continues to sustain interest in developing more advanced lithium ion (Li-ion) batteries, the emerging applications of grid scale energy storage and electric vehicles are pushing lithium battery research to the next level. To meet the targets for grid storage as well as electric vehicles batteries set by the US Department of Energy (DOE), battery chemistries beyond the current lithium ion systems are highly required. Among all the recently-emerging technologies, lithium sulfur (Li-S) battery is one of the leading candidates that could have high specific energy and low cost. In my thesis, I will very briefly introduce the fundamentals of conventional Li-ion batteries and the next-generation Li-S batteries, compare the different chemistries of conventional lithium-ion batteries with Li-S batteries, and then examine the main challenges and present my study on designing the nanoscale "composite electrode" to address problems from both the lithium metal side and sulfur side. Lithium sulfur battery has a practical energy density of around 600 Wh/kg, about 3 times that of the current lithium ion batteries. Lithium metal anode has long been regarded as the "holy grail" of battery technologies, due to its low potential and high specific capacity. However, the safety hazards and capacity fading problem have prevented lithium metal anode from practical realization. In the first part of this thesis, I will present my research on using various strategies to build a composite Li metal anode. By encapsulating Li inside a three-dimensional porous matrix via melt-infusion, electrochemical deposition and mechanical deformation, we demonstrated the fabrication of such a composite anode. The resulting lithium--matrix composite anode was then subjected to battery cycling and exhibited superior performance compared with bare lithium metal anodes. In the last part of this thesis, I will describe my work on using hydrogen reduced titanium dioxide nanostructures as matrix to improve the sulfur cathode performance. On the cathode side, sulfur and its discharge product are highly insulating. In addition, the intermediate discharge products (lithium polysulfides) can easily dissolve into the electrolyte and shuttle between electrodes, which leads to a fast capacity decay and limited cycle performance. By embedding sulfur in hydrogen reduced titania inverse opal structure, sulfur utilization in the electrode is significantly improved and the polysulfide species are strongly trapped both physically and chemically, resulting in higher specific capacity and longer cycle life. We believe our work will contribute significantly to the energy-related field and also inspire research in other areas.

Keywords: Materials Science and Engineering.
Advisor: Cui Yi
Cui Bianxiao advisor. Thesis advisor
McGehee Michael
Compositional visual intelligence

The field of computer vision has made enormous progress in the last few years, largely due to convolutional neural networks. Despite success on traditional computer vision tasks, our systems are still a long way from the general visual intelligence of people. An important facet of visual intelligence is composition - understanding of the whole derives from an understanding of the parts. To achieve the goal of compositional visual intelligence, we must explore new computer vision tasks, create new datasets, and develop new models that exploit compositionality. In this dissertation I will discuss my work on three different computer vision tasks involving language, where embracing compositionality helps us build systems with richer visual intelligence. I will first discuss image captioning: traditional systems generate short sentences describing images, but by decomposing images into regions and descriptions into phrases we can that generate two types of richer descriptions: dense captions and paragraphs. Second, I will discuss visual question answering: existing datasets consist primarily of short, simple questions; to study more complex questions requiring com- positional reasoning, we introduce a new benchark dataset where existing methods fall short. We then propose an explicitly compositional model for visual question an- swering that internally converts questions to functional programs, and executes these programs by composing neural modules. Third, I will discuss text-to-image: existing systems can retrieve or generate simple images of a single object conditioned on text descriptions, but struggle with more complex descriptions. By replacing freeform natural language with compositional scene graphs of objects and relationships, we can retrieve and generate complex images containing multiple objects.

Advisor: Li Fei Fei
Goodman Noah (Noah D.) 
Ré Christopher
Computational analysis of fluid flow in 2D&3D pore geometry

The motivation of this dissertation is to improve understanding of the geometrical parameters controlling flow properties and to develop toolkits to assist in this task in order to further advance Digital Rock Physics. With digital 2-D or 3-D images we can now investigate rock geometry thoroughly. Geometrical parameters such as cross-section geometry, tortuosity, pore size distribution, and grain size distribution will be discussed in detail in each chapter. Then, combining the knowledge of cross-section geometry and tortuosity, I show that the pore size distribution is the missing parameter crucial for accurately predicting permeability in porous media and I derive the revised Kozeny-Carman equation. Furthermore, the growing number of digital microstructures makes data analysis via machine learning possible. I will also discuss how to employ Machine Learning in Digital Rock Physics on permeability prediction from digital microstructures. These various approaches are designed to advance our fundamental understanding of rock geometry, and to determine the topological factors that are most relevant to the geophysical properties that we wish to simulate.

Keywords: Geophysics.
Advisor: Mavko Gary
Dvorkin Jack
Mukerji Tapan
Schroeder Dustin
Computational and optical tools for enabling high-resolution in vivo functional imaging with optical coherence tomography [electronic resource]

Optical Coherence Tomography (OCT) enables real-time imaging of living tissues at cell-scale resolution over millimeters in three dimensions. Despite these advantages, functional biological studies and clinical applications of OCT have been limited. The first limitation that we address is the lack of exogenous contrast agents for OCT. Such contrast agents can be beneficial for functional and molecular imaging, by labeling specific proteins or cells and providing a better understanding of the underlying biological processes in the tissue in addition to its structure, which is provided by conventional OCT. We tackle this limitation by developing uniquely spectral large gold nanorods (LGNRs) and custom algorithms to spectrally distinguish the LGNRs from the surrounding tissue. To verify our ability to identify the LGNRs in OCT volumes and to localize them with higher resolution, we use a method that combines dark-field microscopy with image-processing and machine-learning algorithms to detect them in tissue samples ex vivo. A second limitation of OCT is the speckle noise caused by coherent interference of multiply scattered light, which hides fine tissue structures and also hinders the detection of our contrast agent. We solve this limitation by developing a method for removing speckle noise in OCT by modulating the phase of the light illuminating the sample. By removing the speckle noise, speckle-modulating OCT (SM-OCT) reveals tissue structures in living mice and humans. Notably, the demonstrated improved image-quality of brain tissue can be beneficial for intraoperative tumor margin detection and for neurological studies of small animals. The combination of SM-OCT with our contrast agent can be used for labeling and tracking immune cells in brain tumors in vivo with high-resolution and over a wide field of view.

Keywords: Electrical Engineering.
Advisor: De la Zerda Adam
Bowden Audrey
Chu Steven
Wetzstein Gordon advisor. Thesis advisor
Computational approach toward rational device engineering of organic photovoltaics

Organic photovoltaics (OPVs) have emerged as a promising alternative to conventional PV technology due to their low cost and industry-level scalability with high-volume production through solution-based processing. OPVs combine the unique flexibility and versatility of plastics with electronic properties, making them amenable to applications in the "Internet of Things" and distributed generation applications. The current key challenge for wide adaptation of OPVs is the lack of high power conversion efficiency (PCE) in large scale roll-to-roll processed devices. A key factor is the morphology: there exists disorder between the electron donor and electron acceptor materials in the active layer, and the mechanisms by which the morphology can be tuned are not well understood. Simulation is a promising inexpensive technique for exploring OPVs in the large parameter space of both processing methods and chemical components. In this work, we leverage and improve upon these computational approaches to reduce the need for iterative design for OPVs. First, we develop a multiscale molecular dynamics (MD) model to provide understanding of morphology evolution during solution processing. In addition, we train and utilize a predictive deep learning model to study the correlation of performance with the chemical and engineering design considerations. These parallel approaches allow for an accelerated sampling of the parameter space of OPV conditions, which in turn leads to targeted experiments.

Keywords: Chemical Engineering.
Advisor: Bao Zhenan 
Pande Vijay 
Qin Jian (Professor of Chemical Engineering)
Computational simulations of whole cells : strategies for framework design and model parameterization

The complexity of living systems, and the mounting wealth of biological data, poses a challenge: can we unify our comprehension of a living system into one coherent representation - and if so, what would we learn? Herein I present strategies for the design of a framework that can integrate diverse mathematical submodels of cellular physiology into one hybrid computational simulation of a whole cell, extending and revising prior work in this space. This framework is realized in the form of a whole-cell model of E. coli that can dynamically reproduce several molecular, cellular, and population-level behaviors across multiple environments and cell cycles. I also present strategies for integrative parameter estimation in the context of whole-cell-like submodels, an emergent need consequent of limited or inconsistent biological data. This work identifies model formulation and parameterization techniques that facilitate exploration of the space of biological dynamics; it also identifies specific inconsistencies in the underlying data as well as systems-level hypotheses about metabolic control and dynamics. Altogether, this research comprises several conceptual and technical advancements towards the end of functionally complete single-cell simulations.

Keywords: Bioengineering.
Advisor: Covert Markus 
Altman Russ 
Huang Kerwyn Casey
Computational social science for communication research

Scholars have long been aware of the potential of computation for social science research, particularly for the study of human communication. Increasingly, more of our social interactions are occurring within contexts enabled by modern advances in computation, specifically the Internet and the ubiquity of computing devices and interfaces aimed at typical consumers. As more of our interactions have moved online, researchers have rushed to use the socio-computational era to their advantage, building research agendas that attempt to answer questions from conventional social science theory through inspection of these new computational experiences in the field. However, it is unclear what delineates \textit{conventional} social science from \textit{computational} social science. I consider this problem in this dissertation and provide some effort to refine the definition of computational social science through illustration via three studies employing the use of a Web browser.

Keywords: Communication.
Advisor: Iyengar Shanto 
Bailenson Jeremy 
Goel Sharad
Hamilton James
Computational strategies for investigating cancer drug resistance using single-cell data

Treatment of metastatic cancer utilizes chemotherapies or newer targeted therapies. However, treatment is thwarted by drug resistance where some cancer cells can survive and lead to relapse. Recent studies suggest that cancer cells can evade cell death after treatment through non-genetic means such as distinct signaling or differentiation states. To better understand these resistance mechanisms, we must study the dynamics of the anti-cancer drug response across time. We use high-dimensional single-cell platforms like mass cytometry and novel algorithms to visualize and trace the trajectories of subpopulations with different fates upon drug exposure. We present the development of a free, open-source R package known as FLOWMAPR that can represent complex timecourse datasets as a single, interpretable 2D graph. We apply this analytical approach to two novel studies of drug resistance: treatment of multiple myeloma using bortezomib and dexamethasone and response to BRAF inhibition in melanoma. Through visualization and computational modeling, we identify specific signaling or metabolic features that distinguish drug-resistant cells in models of both cancer subtypes. Moreover, through combination treatments with inhibitors directed against these features, we demonstrate the ability to reduce the fraction of drug-resistant cells present in both cell lines and in patients. Taken together, these studies demonstrate the potential for computational analysis of high-dimensional single-cell data to reveal mechanistic insights into cancer drug resistance, paving the way for the design of new treatments or more effective combination regimens.

Advisor: Nolan Garry P. 
Artandi Steven E. 
Khavari Paul A. 
Plevritis Sylvia 
Wernig Marius
Computational tools for understanding biological and artificial neural networks

Neural networks are a core component of both biological and artificial intelligence. Despite advances in training artificial networks to solve complex tasks, they are often brittle in ways that biological networks are not, and our understanding of how they work remains incomplete. In this thesis, I take steps towards applying machine learning to better understand biological neural networks, and leverage ideas from biology to improve artificial neural networks. First, I present a new set of computational tools for processing, analyzing and understanding large-scale datasets in neuroscience. Applying these tools to calcium imaging from direction-selective neurons in the fruit fly visual system, I identify the algorithm the fly uses for computing motion, resolving a nearly 60 year old debate in fly visual neuroscience. In the second part of this thesis, I draw inspiration from neuroscience to advance the capabilities of artificial neural networks. By augmenting neural networks with higher-dimensional synapses, I drastically improve the ability of neural networks to acquire new information without forgetting old information when learning sequences of tasks. I also demonstrate how Poisson-like noise often found in biological systems can be incorporated into artificial neural networks to improve their sparsity, robustness, and ability to generalize. Finally, I demonstrate the reason neural networks are so expressive by analyzing signal propagation in random neural networks using tools from Riemannian geometry and mean field theory. Increasing the depth of even random neural networks exponentially increases their expressivity allowing them to disentangle highly complex manifolds in input space. The results presented in this thesis highlight the increasingly strong relationship between machine learning and neuroscience, both in terms of tools for understanding neural systems as well as inspiration for the capabilities and mechanisms needed for building artificial intelligence.

Advisor: Ganguli Surya
Li Fei Fei
Ermon Stefano 
Yamins Daniel