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Program in International Relations Honors Theses
Since 2003, the Sudanese government and its Arab militia proxies have conducted a genocide against Africans in Darfur, killing roughly 500,000 people and displacing 2.7 million. Public outcry compelled President George W. Bush to take diplomatic measures to end the atrocities, though they achieved little success. In 2007, then-Senator Barack Obama included Darfur on his presidential platform and denounced the genocide as a “stain on our souls.” Nevertheless, during his presidency, Obama not only failed to address the genocide, but also lifted sanctions that had been imposed on Khartoum for its criminal activities in Darfur. This thesis examines why the Obama Administration’s foreign policies in relation to Darfur were so weak. In particular, I focus on four main factors that constrained U.S. action in the region: lack of political salience, South Sudan's creation and subsequent implosion, humanitarian crises elsewhere, and China's and Russia's economic interests in Sudan. By understanding these policy constraints, we might learn how to end this long and violent genocide, as well as other humanitarian conflicts in areas with little national strategic import.
Book
1 online resource.
The promise of optical antennas is the ability to tame the light to behave in ways not achievable using traditional optical components. For example, our results here demonstrate that a careful engineering of optical antennas allow the strong, even perfect, absorption of light in ultra-thin geometries, i.e., geometries much thinner than the wavelength of light. Enabled by geometry-sensitive antenna resonances, this absorption behavior can also be realized for a broad selection of colors. A detailed theoretical analysis of the observed perfect absorption phenomenon reveals the role of incoherently interacting degenerate electric and magnetic resonances in overcoming the well-known absorption limit for infinitesimally thin films. With another set of experiments, we show that strongly absorbed optical energy in aluminum nanoantennas can be used to heat them efficiently above their melting temperature and stimulate an explosive exothermic oxidation reaction called melt-dispersion mechanism. Importantly, we see that engineering the specific geometry of the constituent particles allows an unprecedented control of aluminum ignition, both spectrally and spatially, through the fine tuning of the optical antenna resonances.
Book
453 pages : illustrations (partly color) ; 23 cm.
Green Library
Book
1 online resource.
The technological revolution that started with digital electronics more than 50 years ago has pushed the limits of scalability in device fabrication. Device features are currently at the nanometer scale, which requires structures to be built with atomic level accuracy. Advances in nanotechnology have opened up an exciting area of research that allows for molecular level control of device surfaces. Organic molecules can provide the tailorability required to continue the progress of semiconductor technologies. Organic functionalization provides a pathway to control the surface at the molecular level. Fundamental understanding of the adsorption phenomena between organic molecules and the surface is critical to achieve a stable inorganic/organic interface for the creation of hybrid nanostructures. This thesis aims to expand our current toolkit on functionalization to molecules that have multiple functionalities that can react with the surface. The reaction mechanism of these molecules is complex as there are several driving forces that can play a role during adsorption and influence the final reaction products. This thesis covers the adsorption of multifunctional molecules on the Ge(100)-2×1 surface using a combination of experimental and theoretical techniques: Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and density functional theory calculations. We explored the adsorption of four different molecules: 1,2,3-benzenetriol (C6H6O3); 1,3,5-benzenetriol (C6H6O3); 2-hydroxymethyl-1,3-propanediol (C4H10O3); pyrazine (C4H6N2). These are the first reported studies of these molecules on the Ge(100) surface. In order to understand the extent to which molecular geometry affects surface coverage, a detailed comparison of the adsorption of two triol molecules was carried out: 1,3,5-benzenetriol which has a rigid phenyl backbone and 2-hydroxymethyl-1,3-propanediol with a flexible alkyl backbone. DFT results showed that the rigid backbone exhibits a higher degree of strain, which translates to a loss of exothermicity in the reaction coordinate. Experiments showed that the flexibility of the alkyl backbone provides higher rotational degrees of freedom and an enhancement in surface coverage. In order to understand the effect of intermolecular interactions, the adsorption of 1,2,3-benzenetriol was explored. Interestingly, we found that at high coverage, intramolecular hydrogen bonding in singly and dually adsorbate products breaks to form intermolecular hydrogen bonding with a nearby adsorbate, which provides enhanced stabilization of the surface adduct. This additional stabilization may lower the reactivity of unreacted functional groups even if an empty nearby Ge site is available for reaction. The distribution of products resulted in primarily bidentate adsorbates, leaving an unreacted moiety at the surface. We also found evidence of coverage and temperature effects on adsorbed pyrazine molecules on the Ge surface. It was observed that this molecule adsorbs on Ge through both carbon and nitrogen moieties and the product distribution changes as a function of coverage and temperature. At low coverage, incoming molecules react primarily through C-cycloaddition reactions and N dative bonds. However, as the density of adsorbates increases, new incoming molecules adsorbed primarily through the nitrogen moiety. Furthermore, as the temperature increases, the product distribution changed from primarily non-activated dative bond to activated cycloaddition products. Overall, the studies in this thesis provide new insight into competition and selectivity in adsorption of multifunctional molecules on the Ge(100)-2×1 surface.
Book
350 pages : illustrations ; 25 cm
SAL3 (off-campus storage)
Book
1 online resource.
Animal Empires: The Perfection of Nature Between Europe and the Americas, 1492-1615 demonstrates how Renaissance patrons, naturalists, and husbandmen developed useful but dangerous ideas to make sense of natural diversity -- nobility, race, and species -- during the consolidation of the sixteenth-century Spanish Empire. Using the three major techniques at their disposal-relocation, cultivation and training, and selective breeding-elites began a colossal experiment. They sought to create an improved version of Christian nature both in European courts and then, on a larger scale than ever before imagined, in the Americas. Case studies focus on breeding theories and practices in Mantua, Naples, Madrid, Peru, and the Valley of Mexico. Starting in the heart of Renaissance Italy and ending high in the Andes, this project integrates disparate fields of investigation -- ranging from Renaissance aesthetics and animal studies to the histories of the Spanish Empire and of biology -- to reveal an ideal of nature grandly envisioned and prosaically enacted through imperial conquest.
Book
1 online resource.
Mechanistic insight into nuclear reprogramming from one cell state to another is of fundamental and clinical importance. Here we use the heterokaryon cell-fusion model of nuclear reprogramming to capture the dynamic architecture of chromatin accessibility and gene expression. At the onset of reprogramming, we detect a transient, genome-wide increase in accessible sites enriched for the AP-1 transcription factor motif assayed by ATAC-seq. Inhibition of AP-1 by expression of a dominant-negative results in an increase in OCT4 expression in heterokaryons. Moreover, in human iPSC reprogramming, dominant negative AP-1 can replace exogenous OCT4 in the reprogramming cocktail. We identify c-Jun as the inhibitory AP-1 family member and show that its repressive activity is mediated through interaction with NURD complex component MBD3 in a phosphorylation dependent manner. Our findings reveal that AP-1, which is induced at the onset of reprogramming and traditionally thought to be an activator, creates a JUN-MBD3 repressor complex that inhibits nuclear reprogramming to pluripotency through direct targeting of an OCT4 distal regulatory element. These findings reveal an unexpected role for Jun as a repressive epigenetic gatekeeper of reprogramming to pluripotency.
Book
1 online resource.
This thesis covers several applications of Operations Research in the domains of finance and healthcare. There are three chapters, each covering a different application. Chapter 1 applies techniques from deep learning to estimate mortgage risk. The near-elimination of feature engineering is substituted by models with several thousand parameters that require large amounts of training data. The predictive performance of these models strongly exceeds that of baseline models, especially for predicting prepayments. This increased accuracy, however, comes at the cost of a more opaque model which is harder for a human to interpret than simpler models like logistic regression, which are currently the industry standard. The work in Chapter 2 explores the design of policies for biometric authentication. It first develops a model for the joint distribution of similarity scores associated with different fingers and irises. In the second step, this model is harnessed to design near-optimal multi-stage policies that would be used for authentication, and are robust to gaming, can be computed in real-time and are personalized for optimal performance. The work shows that a reduction of several orders of magnitude in the error rates is achievable by solely changing the authentication policies -- and leaving the hardware unchanged. Chapter 3 is motivated by a humanitarian cause geared towards helping developing countries -- to reduce mortality in children by identifying effective interventions at the planning stage. It takes a descriptive health model (called LiST), which estimates mortality of children given coverage of interventions, and embeds that into an optimization engine in order to minimize mortality under a fixed budget. In doing so, it allows LiST to be used in a prescriptive framework, where policymakers can identify the optimal intervention set at a fixed budget as well as recognize the trade-off of mortality reduction and budget allocation. We find that a greedy strategy offers near-optimal performance with ease of implementation. The findings also highlight the critical role that optimization plays in mortality reduction.
Book
1 online resource.
Due to a convergence of large open-source datasets, significant improvements in the parallelization capabilities of hardware (notably, multi-thousand core graphics processing units), and renewed academic interest in decades-old neural network algorithms, primary subfields of AI have flourished in the past 5 years. Natural Language Processing, Computer Vision, and robotics have attained impressive performance across many key AI tasks. In this thesis we focus not on the development of new AI algorithms, but on their application to a series of important problems in healthcare and medicine. Machine learning and AI will impact industries that generate significant amounts of data, by extracting insights that humans cannot. Healthcare and medicine are examples of such industries and thus are excellent use cases for AI deployment. Our story is divided into 4 sections - neuroscience, psychiatry, drug screening, and dermatology - all linked by the common thread of using AI to \textit{enhance the expert}, either in-clinic or in the analysis of data. This underlying motif is the connection to a paradigm in AI development popularized as the \textit{virtual cycle of AI}: build a product that has front-facing user value, generate data with it, use that data to train AI algorithms that improve your product, acquire more data, etc.
Collection
Center for International Security and Cooperation (CISAC) Interschool Honors Program in International Security Studies
Despite a $500 million investment, most Syrian militants receiving U.S. sponsorship did not attack ISIL. This failure is alarming; as civil wars begin to include more militant groups, states are increasingly interested in influencing their behavior in order to advance national interests. It is also surprising the degree to which militants are able to shirk given the power disparity between states and militants. This thesis asks why strong states struggle to control the weak militant groups they fund in Syria. Most answers rely on principal-agent theory alone, but I incorporate more recent literature on competition and alliances within insurgencies. I also employ a mixed-method analysis of 350 attacks conducted by Sunni militant groups with U.S. or Saudi sponsorship from 2014 to 2016. Militant group adherence to state goals is highly mediated by participation in alliances. The extent to which Syrian militant groups in alliances obey their sponsors is influenced by the allocation of power within alliances and alliance members’ preferences. Curiously, militant groups are more inclined to follow state sponsor objectives when they ally with other militants who have different aims. Unlike alliances composed entirely of extremists, militant groups can credibly threaten to defect from alliances with groups that have dissimilar goals. In response, these alliances choose a narrow range of targets that conform to the preferences of all members and their state sponsors. This suggests that to avoid further failures, state sponsorship programs must place more consideration on alliances within insurgencies.
Book
272 pages ; 24 cm.
Green Library
Book
1 online 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.
Book
455 pages ; 24 cm.
Green Library
Book
ix, 277 pages : 2 illustrations ; 25 cm.
Green Library
Book
1 online resource.
Insects, hummingbirds, and nectar bats evolved the ability to hover in front of flowers to get access to energy-rich nectar. It has been established that insects generate up to half of the lift needed to support their body weight during the upstroke. Estimates show that hummingbirds generate at least a quarter of their weight support during the upstroke by inverting their feathered wings more than generalist birds. In contrast, bats evolved membrane wings that they partially fold during the upstroke. While it has been hypothesized that hovering nectar bats generate vertical lift force during the upstroke, the complex structure of their wakes makes it hard to quantify this through flow measurement. To understand how hummingbirds and bats manipulate aerodynamic forces with their wings to perform these feats, we developed a new instrument that accurately measures these aerodynamic forces in vivo. This Aerodynamic Force Platform and an array of 3D-calibrated high-speed cameras simultaneously recorded the vertical aerodynamic forces and wing kinematics. The pressure field generated by the animal travels to the boundaries of the flight volume at the speed of sound. The top and bottom plate mechanically integrate the pressure distribution, which is measured by three force sensors on each plate. By using the Aerodynamic Force Platform to measure these wingbeat-resolved aerodynamic lift forces in vivo, we highlight similarities and differences across species and taxa. While it is known that insects improve efficiency by using elastic recoil for stroke reversal, it is unclear if hummingbirds converged on a similar solution, due to asymmetries in their lift generation and specialized flight muscle apparatus. We measured the aerodynamic force and kinematics of Anna's hummingbirds to resolve wing torque and power within the wingbeat. Comparing wingbeat resolved aerodynamic weight support measurements across species, we find that hummingbirds have low induced power losses similar to flies, much lower than typical for a generalist bird in slow hovering flight. We also show how hummingbirds' early muscle activation furnishes elastic recoil through stroke reversal to stay within the physiological limits of the pectoralis and supracoracoideus flight muscles. Expanding our species of interest, we traveled to Costa Rica with our new measurement device. We resolved the aerodynamic force and wing kinematics of 104 slow hovering hummingbirds and bats across 20 total species in vivo. While all hummingbirds we studied converged on an efficient horizontal wingbeat with an active upstroke to generate lift, the bats did not---they relied on drag to fully support their body weight. Remarkably, the nectar bats generate a significantly elevated vertical force during the upstroke compared to fruit bats---by inverting their wing more like hummingbirds---suggesting convergent evolution.
Collection
Masters Theses in Journalism, Department of Communication, Stanford University
Call Collect is an open source web application for gathering audio. It helps journalists quickly create call-in numbers and simple web widgets where people can record themselves. It's designed so that any newsroom can set up its own copy of the software easily and affordably.
Book
1 online resource.
In this thesis I use physically accurate, end-to-end camera simulation environment to explore different imaging system architectures and to co-optimize the acquisition hardware with computational imaging and computer vision algorithms. The simulation tools use 3D modeling to create different scene models. Scene images that are projected onto the sensor are computed with ray tracing tools. Finally, realistic sensor and camera pipeline simulators compute pixel values that can be used by subsequent computational photography or vision algorithms. I use the simulation tools to optimize the spectral characteristics of cameras and lights used to collect data for inverse estimation methods that characterize surface spectral properties. I show that appropriately selected narrowband lights improve the performance of pixel based surface classification. I also propose a new algorithm that extends beyond narrowband light selection by computing the spectral power distribution of light that is optimal for such classification tasks. I further use simulations to guide and optimize the design of a system that characterizes fluorescent materials which interact with incident light in more complex ways than simple reflection. Spectral illuminant optimization is also useful in consumer photography. I describe a spectrally tunable flash and computational algorithms that can be used to efficiently estimate the ambient illuminant spectrum and to improve the color reproduction of captured images. Such flash is particularly useful when capturing images under extreme ambient lights, for example in underwater photography. The end-to-end nature of the simulation tools makes it possible to explore how viewing conditions (e.g. light level, spectral content, depth), hardware components of an imaging system (e.g. optics, sensor QE, filters, pixel and noise properties) and image processing pipelines affect both the perceived image quality of camera images and the ability of machine learning algorithms to detect and classify objects. I use simulation to render large collections of images in order to evaluate the object detection performance of convolutional neural networks (CNNs) . I demonstrate that the performance of CNNs trained using images that are generated by simplified image processing pipelines (ISP) is similar to the performance of CNNs trained using images generated by more complex and time-consuming ISPs optimized for consumer photography. I also explore and quantify the robustness and performance bounds of detection methods against fundamental algorithms controlling camera exposure and focus.
Book
1 online resource.
Enantioselective dihalogenation is an important method for the total synthesis of stereocomplex polyhalogenated natural products, but only a few methods with significant limitations currently exist for this transformation. In 2015, we developed the chemo-, regio- and enantioselective bromochlorination of allylic alcohols featuring a titanium half-salen catalyst to help address this problem. The utility of this method has been demonstrated by the enantioselective total syntheses of 11 halogenated natural products to date by our lab, each of which was directly enabled by this method. Following development, we applied the enantioselective bromochlorination to various new substrates including homoallylic alcohols, which resulted in the total synthesis of (--)-anverene, a polyhalogenated marine natural product with modest but selective antibiotic activity. Computational and experimental mechanistic work was undertaken to better understand the catalytic enantioselective bromochlorination, as well as the enantiospecific solvolysis of resulting enantioenriched bromochlorides. The enantioselective dihalogenation catalyst system has also been extended to new reactions using other electrophile-nucleophile pairs, including haloazidation, which uses TMSN3 as the nucleophilic azide source.
Book
540 pages : illustrations ; 23 cm.
Music Library
Book
1 online resource.
The anthropogenic and the natural biosphere components of the carbon cycle play critically important roles in determining the future status of earth's climate. While our knowledge of CO2 fluxes, at large (global) and small (~1 km2) scales are fairly well known, much uncertainty exists at intermediate/regional-scales (i.e., sub-continental, biome-level). Knowledge at these regional scales is vital for informing relevant climate change mitigation strategies (e.g., land management, fossil fuel emissions reduction policies) and for improving the characterization of key carbon-climate feedback mechanisms. This dissertation leverages the information content of a recent 4-fold increase in atmospheric CO2 observation locations over North America to investigate carbon fluxes, both anthropogenic and natural, at the regional scale (e.g., biome/sub-continental). This work specifically emphasizes the exploration and characterization of spatial and temporal patterns of surface fluxes rather than focusing solely on carbon budgets. With the growing interest in independently verifying fossil fuel CO2 (FFCO2) emissions using atmospheric CO2 emissions, there exists a crucial need to understand the capabilities of the atmospheric observation network to isolate the FF signal. The first study explores and identifies when and where the space-time patterns of monthly sub-continental FFCO2 emissions are detectable using the atmospheric CO2 observation network. Winter months and regions with a relatively high density of observations (e.g., Midwest) offer the best opportunity to detect FFCO2 emissions patterns. The combined impact of the natural biospheric signal and atmospheric transport model related issues are identified as severely hampering the detection of the FFCO2 signal in spring, summer, and autumn. This first study provides key guidance for future efforts to independently estimate FFCO2 emissions using atmospheric CO2 observations through a systematic examination of the various factors hampering detectability. The second study quantifies the ability of a promising new remote sensing data set, solar-induced fluorescence (SIF), to inform spatiotemporal patterns at previously unexplored regional scales. The potential of SIF lies in its ability to measure an emission that, unlike previous reflectance based vegetation indices, is directly related to photosynthesis rather than a measure of the "greenness" of the land surface. While SIF has been explored at local and global scales, here, SIF is shown to explain CO2 flux patterns within continental and biome regions better than existing vegetation and climate indicators as well as most process-based terrestrial biosphere models (TBMs). By incorporating SIF into an inverse model, SIF is found to inform a significant increase in the net uptake of CO2 over croplands as well as a significant decrease over needleleaf forests. The final study explores the regional drivers of interannual variability (IAV) for the net North American CO2 land sink using inverse estimates of net CO2 flux derived from six years of observations. Understanding the current drivers of IAV is crucial for improving predictions of the future response of the terrestrial biosphere to climate change. This work identifies the deciduous broadleaf and mixed forest biomes as the primary regional drivers of IAV over North America, which differs from the dominant drivers identified for the globe and for the northern hemisphere. When comparing the inverse-modelling-derived estimates to a suite of ten TBMs, the large spread in TBM based biome-level contributions to North American IAV make identifying a dominant biome-level driver not possible. The wide spread among TBM based biome-level IAV contributions is attributed to a trade-off between the contribution of IAV in forested vs non-forested regions in a given TBM. This trade-off corresponds with emergent regional sensitivities to environmental drivers (temperature, precipitation, and radiation) where TBMs with IAV dominated by forested regions exhibit stronger sensitivity to environmental drivers in forested regions relative to non-forested regions and vice-versa. This trade-off helps to explain the inability of TBMs to agree on a dominant regional driver of IAV and calls into question the ability of TBMs to inform regional-scale carbon flux IAV dynamics.