VictorJuiz/thesis huggingface dataset:
Commodity crop expansion pathways and impacts in tropical forest regions of Africa and Asia
Keywords: Environmental Earth System Science

Tropical forests are critically important ecosystems that store large amounts of carbon, maintain over half of the planet's biodiversity, and play key roles in the regulation of global as well as regional climate systems. In recent decades, the changing structure of demand for food and energy commodities has had a remarkable impact on land use in tropical forest regions, resulting in rapid deforestation and degradation. My dissertation seeks to understand the impacts of recent commodity crop expansion on deforestation in sub-Saharan Africa and the effects of large-scale forest conversion on characteristics of remaining forest ecosystems in Southeast Asia. Using a variety of methods including farmer surveys, remote sensing and spatial analysis, and econometrics, I demonstrate that commodity crop expansion in sub-Saharan Africa is increasing pressure on tropical forests, albeit through expansion pathways linked to informal economies quite unlike more comprehensively studied regions in Southeast Asia and South America. Using high-resolution airborne imaging spectroscopy and LiDAR data, I identify long-lasting effects of tropical forest conversion to oil palm plantations on neighboring forest structure and function. Understanding the role of informal economies in commodity crop expansion and the ecological impacts of forest conversion for commodity crop production more broadly is important for the identification of regionally relevant, sustainable land use strategies.
Communicating and computing with spikes in neuromorphic systems
Keywords: Electrical Engineering.

We provide an overview of neuromorphic engineering and describe two contributions to Braindrop, a state-of-the-art neuromorphic system. First, we describe a method for performing summing and weighting of spike trains by accumulative thinning, a deterministic procedure for merging and dropping spikes. Previous methods relied on probabilistic thinning, which results in Poissonian statistics. As a result, when the thinned spike-train is filtered with a first-order low-pass synapse, the signal-to-noise ratio (SNR) scales as the square-root of its rate. For our accumulative thinning method, the SNR depends on the weight w; it scales linearly in the best-case scenario (w-> 0) and as the square-root in the worst-case (w-> 1). We find that a three-quarter power scaling minimizes energy consumption. Second, we present a serial H-tree router for two-dimensional (2D) arrays. Existing routing mechanisms for 2D arrays either use low-overhead grids with one or two shared wires per row or column (e.g., RAM) or high-overhead meshes with many wires connecting neighboring clients (e.g., supercomputers). Neither is suitable for intermediate-complexity clients (e.g., small clusters of silicon neurons). We present a router tailored to 2D arrays of such clients. It uses a tree laid out in a fractal pattern (H-tree), which requires less wiring per signal than a grid, and adopts serial-signaling, which keeps link-width constant, regardless of payload size. To route from the tree's leaves to its root (or vise versa), each node prepends (consumes) a delay-insensitive 1-of-4 code that signals the route's previous (next) branch; additional codes carry payload. We employ this serial H-tree router to service a 16x16 array of silicon-neuron clusters, each with 16 spike-generating analog somas, 4 spike-consuming analog synapses, and one 128-bit SRAM. Fabricated in a 28-nm CMOS process, the router communicates 26.8M soma-generated and 18.3M synapse-targeted spikes per second while occupying 43% of the client's 35.1x36.1 sq.um.
Communicating generalizations : probability, vagueness, and context
Keywords: Psychology.

Knowledge that extends beyond the present context is crucial to thrive in our open-ended, dynamic world, and generic language (or generics, e.g., "Birds fly", "Mosquitos carry Zika", "Bill smokes") is a simple way to communicate such generalizations to and from one another. Though found in every language and emerging early in development, generic language is philosphically puzzling and has resisted precise formalization. In this thesis, I propose the first formal account of generic language that makes quantitative predictions about human understanding. This formalism is both general enough to explain behavior in multiple dependent measures across diverse domains and precise enough to capture fine-grained, context-sensitive differences in understanding. Having such a unified, computational framework sheds light on the mechanisms by which conceptual knowledge can influence judgments about generics. It also allows us to quantitatively compare the context-sensitivity of interpretation vs. production, clarifying the role of generics in stereotype propagation.
Community Supported Fisheries and Their Strategies for Social Media Marketing
Keywords:  Department of Communication
Community Supported Fishery
social media
social media marketing
storytelling
consumer engagement
mindful consumption
seafood
Thesis

Today, there is a compelling movement taking shape in the seafood industry, and Community Supported Fisheries (CSFs) are at the heart of it. This movement is working to create a shift in consumer mindsets from careless consumption to mindful consumption. Trending hashtags like #knowyourfisherman and #sustainableseafood have worked to encourage consumers to understand the story behind their seafood products. The movement has been especially prevalent with fish species like salmon and tuna. In supporting such a movement, CSFs are not only working to cultivate a relationship between consumer and product, but they are also working to promote the idea of sourcing products from fishermen who practice sustainable fishing methods. This allows CSFs to share the stories behind their products — stories that make consumers feel more connected to their food and their well-being. The purpose of my research is to gain some perspective on the “how”. How are CSFs connecting with the community, and how are they building their consumer base? How are they working to convince people that it’s worth understanding the story behind a product? I will approach these questions, and questions alike, through the lens of social media; perhaps the CSF method-of-choice when it comes to consumer engagement and connectivity. My research question asks: What are the most successful social media marketing (SMM) strategies that CSFs are using today to promote their business and their products?
Compact high frequency power conversion for high voltage applications
Keywords: Electrical Engineering.

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.
Comparative Case Studies on U.S.-Japan and U.S.-China Semiconductor Trade Disputes
Keywords:  Stanford Global Studies
U.S.- Japan Semiconductor Trade Dispute
U.S.-China Semiconductor Trade Dispute
East Asian Studies
China
Japan
Semiconductor
Thesis

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.
COMPARISON OF SYNCHROTRON AND ROVER-BASED CHARACTERIZATIONS OF MARTIAN ANALOG CLAY SAMPLES
Keywords:  Department of Geological Sciences
martian analog clays
XRD
XRF
XANES
School of Earth Energy &amp; Environmental Sciences
Stanford Department of Geological Sciences
Thesis

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.
Composite electrode design for next-generation lithium-sulfur batteries [electronic resource]
Keywords: Materials Science and Engineering.

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.
Computational analysis of fluid flow in 2D&3D pore geometry
Keywords: Geophysics.

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.
Computational and optical tools for enabling high-resolution in vivo functional imaging with optical coherence tomography [electronic resource]
Keywords: Electrical Engineering.

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.
Computational approach toward rational device engineering of organic photovoltaics
Keywords: Chemical Engineering.

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.
Computational simulations of whole cells : strategies for framework design and model parameterization
Keywords: Bioengineering.

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.
Computational social science for communication research
Keywords: Communication.

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.