VictorJuiz/thesis huggingface dataset:
Space environment effects on the gravity probe B satellite control system [electronic resource]
Keywords: Aeronautics and Astronautics.

Gravity Probe B (GP-B) was a satellite-based physics experiment designed to test Einstein's General Theory of Relativity. Circling the Earth in a polar orbit, the spacecraft housed four spinning spherical gyroscopes and measured the inertial orientation of their spin axes. Based on the theory, two relativistic effects, the geodetic effect and the frame-dragging effect, were predicted to cause a drift of the gyroscope spin axes in perpendicular directions. GP-B made unprecedented measurements of both of these effects. The primary focus of this dissertation is an improvement to the GP-B spacecraft attitude estimation using an updated model based on the response of the control system to the space environment. Early data analysis revealed to 1% the influence of the geodetic effect in the motion of all four gyroscopes, but also illustrated the need to model and remove several systematic noise sources, in order to measure successfully the much smaller frame-dragging effect. Some disagreement in gyroscope drift rate estimates has been identified as a Newtonian torque, called the misalignment torque, and resulted from the interaction between electrical patches located on the gyroscope and housing surfaces. This torque caused a motion of the gyroscope spin axes that blended in with the science results, but that was modeled and removed from the data. This modeling required an improvement in satellite attitude estimation. The main contribution of the work described in this dissertation is to improve the spacecraft pointing knowledge, in order to model more effectively the misalignment torque disturbance. By modeling the response of the vehicle to the space environment, it was possible, in many cases, to reduce the pointing estimation uncertainty by a factor of two. Much of the improvement in satellite pointing estimation came from an improved understanding of the thermal space environment, specifically the thermal cycles that caused much of the vehicle motion that required additional terms in the attitude estimation model. The improvements made using the new model were used to generate a new time history of vehicle pointing for use in the GP-B data analysis.
Sparsity and Low-Rank Constraints in Functional MRI
Keywords: MRI
fMRI
functional MRI
resting-state functional MRI
rsfMRI
medical imaging
compressed sensing
sparsity
matrix completion
low-rank
k-t FOCUSS
k-t FASTER
NMR
Thesis

Functional MRI (fMRI) has been slow to benefit from data acceleration techniques based on nonlinear image reconstruction for a number of reasons. This is in part due to the uncertain impact of nonlinear filtering effects on image and noise statistics, as well as the difficulty in implementing robust trajectories that satisfy the sampling requirements of the acceleration scheme (such as incoherence) while providing optimal blood oxygen level-dependent (BOLD) contrast and whole-brain coverage. More importantly, no consensus has been reached on the validity of different constraints used to regularise undersampled fMRI data reconstruction. Although early work in sparsity-constrained fMRI acceleration used the k-t FOCUSS framework, recently arguments have emerged for rank-constrained reconstruction. In this thesis, we compare the reconstruction performance of two techniques using temporal sparsity (k-t FOCUSS) and low-rank (k-t FASTER) on synthetic fMRI data based on realistic fMRI signal characteristics. We demonstrate that the strict rank-constraint method outperforms spectral sparsity and Karhunen-Lo`eve transform sparsity across different metrics. These results demonstrate the limitations of sparsity regularization on fMRI signals that, aside from simple block design task fMRI, are not spectrally sparse.
Speaking Truth to Power: Examining the Relationship Between the CIA Director and the President
Keywords: CIA Director
nuclear non-proliferation
DCIA
DCI
Center for International Security and Cooperation
Thesis

Since its earliest days, the Central Intelligence Agency (CIA) has grappled with how to position itself relative to the policy process. Though a close relationship between intelligence officers and policymakers can increase understanding and influence on both sides, it can also compromise the objective perspective that the CIA was created to offer.
This thesis seeks to apply this question to the highest levels of government by studying the relationship between the CIA Director and the President. By examining three case studies of nuclear nonproliferation policy since the end of the Cold War, one can determine the ways in which this relationship affects the interactions between intelligence and policy.
It finds that even if the CIA Director and the President have a distant relationship, the bureaucratic nature of the CIA allows the intelligence community to successfully continue to monitor existing threats. Emerging, unknown and particularly complex threats, however, are more effectively handled when the CIA Director and the President have a more intimate relationship. Additionally, because a close relationship between the CIA Director and the President leads to greater influence of intelligence on policy, this relationship needs to be properly structured in order to ensure that the CIA Director does not become a proponent of any specific policy, and instead remains an objective analyst. This thesis concludes with suggestions as to how the CIA Director and President can structure their relationship in the future so that intelligence can best support policymakers in tackling the toughest challenges of our time.
Species Identification in the American Shark Fin Market
Keywords:  Department of Biology
Biology
Molecular Ecology
DNA Barcoding
Thesis

With a lucrative trade in shark fins driving the unsustainable exploitation of the global shark fishery, tighter regulation of both the fishery and the fin market is needed. Given the important, apex predator role that many sharks play in marine ecosystems, as well as the precarious conservation status of certain species, there is a pressing need for more information regarding the species composition of shark fin imports. Because the removal and processing of shark fins can hinder identification based on morphological characteristics, a molecular approach to species identification has gained popularity. Here, genetic surveys of the San Francisco shark fin market and the Las Vegas shark fin soup market are described, including the discovery of CITES listed endangered species in both. Additionally, a novel, partial-COI DNA barcoding assay is developed to improve identification potential from the highly degraded DNA found in shark fin soup.
Stable isotope measurements from a cold polar desert and marine oxygen deficient zone reveal clues about the global nitrogen cycle [electronic resource]
Keywords: Environmental Earth System Science.

The global nitrogen (N) cycle is closely linked to the carbon cycle. Accordingly, it has been proposed that changes in the N cycle could have played a role in climatic shifts on glacial-interglacial timescales, although these feedbacks are still not well understood. Modern natural and anthropogenic forcings are currently pushing the N cycle out of balance, it is therefore increasingly important to obtain a better understanding of the processes that comprise the modern N cycle. One pressing question surrounding the global N cycle is the source(s) of N2O, a known greenhouse gas. While most N2O is thought to be produced biologically, some studies have presented evidence for abiotic sources, which could alter our current understanding of N2O fluxes to the atmosphere. I present stable isotope measurements of NO2-, NO3-, NH4+, and N2O from the McMurdo Dry Valleys, Antarctica, in an attempt to constrain possible sources of N2O in cold, arid environments. Another problem in the global N cycle concerns the imbalance between sources and sinks of N in the marine environment. Specifically, previous studies have suggested that bioavailable forms of N are being removed faster than they are being supplied, which could thus reduce the ability of marine phytoplankton to take up CO2 from the atmosphere. Much of this N removal occurs in marine oxygen deficient zones (ODZs), where anaerobic bacteria and archaea convert bioavailable NO3- and NO2- to N2, a form of N that cannot be utilized by most organisms. However, recent studies have provided strong evidence for internal recycling between NO3- and NO2- in ODZs, which results in no bioavailable N removal. In order to explore these and other questions surrounding the marine N cycle, I present stable isotope measurements of NO2-, NO3-, and N2 from the ODZ in the eastern tropical south Pacific, which are used to examine the fate of bioavailable N in the marine environment. I also the consider the importance water masses in driving stable isotope distributions. Together these findings suggest that 1) a substantial fraction of bioavailable N is indeed recycled rather removed, which may render current estimates of fixed N removal too high, and 2) physical processes can efficiently transport locally generated isotopic signals across the greater Pacific basin, and thus need to be taken in to account. My hope is that these results enhance our understanding of the global N cycle, and, in turn, yield some additional clues into potential feedbacks with the larger earth system.
Statistical learning in quantum chemistry [electronic resource]
Keywords: Materials Science and Engineering.

This dissertation presents generalized paradigms for combining the most powerful aspects of quantum chemistry and machine learning to provide relevant, accurate and rapid predictions of objective quantities in chemical and materials systems. The focus of the approaches herein is on eliminating the primary areas of deficiency in quantum chemistry, which limit its ability to independently direct applied, experimental research. Using statistical techniques such as Gaussian Process regression, nonlinear optimization, ensemble learning and Bayesian global optimization, quantum chemical modeling errors arising from model inadequacy or inefficiency may be quantified and mitigated in a computational and data efficient manner. Further, incorporating such statistical techniques directly into traditional quantum chemistry models allows for transformative modeling capabilities in the form of predictive uncertainty estimates and systematic, incremental model improvement. The applications addressed here broadly fall into the category of materials design, with specific attention given to high throughput screening of OPV materials, protein folding and photo-excitation dynamics in light-harvesting systems. The formative and ultimate goal of this discourse is to shift the paradigm in quantum chemistry from a focus on the ontological (i.e. descriptive) capabilities of modeling, to the pragmatic (i.e. predictive). In practice, a combination of both approaches allows for computation to interpret and direct experimental processes in real time. As experimentation continues to progress toward rapid, automated chemical and materials prototyping, such computational methods will provide an invaluable counterpart in the form of efficient, automated directives toward materials optimization and discovery.
Statistical mechanics of high dimensional inference [electronic resource]
Keywords: Applied Physics.

Remarkable advances in measurement technologies have thrust us squarely into the modern age of big-data, which has the potential to revolutionize a variety of fields spanning the sciences, engineering, and the humanities. However, the advent of large scale data sets presents severe statistical challenges which must be solved if we are to gain conceptual insights from such data. A fundamental origin of the difficulty in analyzing many large scale data sets lies in their high dimensionality: often we simultaneously measure many variables, or dimensions, but only under a limited number of experimental conditions, or repetitions. For example, in neuroscience, we can now measure thousands of neurons simultaneously, but only under a limited number of behavioral trials. Classical statistics provides well defined prescriptions for finding patterns in low dimensional data sets in which a small number of variables are measured under many conditions or repetitions, but it does not provide analogous optimal guidance for high dimensional data. We use techniques from the statistical mechanics of quenched disorder, particularly replica theory, to derive optimal algorithms for high dimensional inference. We focus on the case of regression and derive fundamental limits on the performance of high dimensional regression with prior information as well as the exact form of optimal algorithms that achieve these limits. Our optimal algorithms are applicable beyond Gaussian distributions of signal and noise, and they out-perform widely cherished algorithms like maximum likelihood (ML) inference and maximum a-posteriori (MAP). Moreover, our results show how central mathematical objects in convex optimization theory and random matrix theory emerge naturally from statistical physics.
Statistical models of protein conformational dynamics [electronic resource]
Keywords: Chemistry.

Understanding the conformational dynamics of biological macromolecules at atomic resolution remains a grand challenge at the intersection of biology, chemistry, and physics. Molecular dynamics (MD) --- which refers to computational simulations of the atomic-level interactions and equations of motions that give rise to these dynamics --- is a powerful approach that now produces immense quantities of time series data on the dynamics of these systems. Here, I describe a variety of new methodologies for analyzing the rare events in these MD data sets in an automatic, statically-sound manner, and constructing the appropriate simplified models of these processes. These techniques are rooted in the theory of reversible Markov chains. They include new classes of Markov state models, hidden Markov models, and reaction coordinate finding algorithms, with applications to protein folding and conformational change. A particular focus herein is on methods for model selection and model comparison, and computationally efficient algorithms.
Statistics of random integral matrices [electronic resource]
Keywords: Mathematics.

This thesis consists of two separate projects. The first part investigates the asymptotic behavior of the number of integral m-by-n matrices, with entries bounded by T, whose cokernel is isomorphic to a fixed abelian group G. We answer this question by building on work of Katznelson, who obtained asymptotics on the number of such matrices of given rank. In particular, we show that if G has torsion B for a finite abelian group B, then a positive proportion of matrices of rank r have cokernel isomorphic to G, and we compute this proportion explicitly (as an infinite product over primes). The corresponding problem for symmetric matrices is also discussed, with a different answer. Part two of this thesis deals with the infinitesimal frequency of a monic polynomial appearing as the characteristic polynomial of an n-by-n matrix with coefficients in the p-adic integers. Relying on the concept of rational singularities, we prove that this frequency is described by a continuous density on the space of monic polynomials, and show that the normalized density function is multiplicative, thus reducing its computation to the case of a monic irreducible polynomial. In the monic irreducible case, we express the density function as a finite sum over modules in the ring of integers of a finite extension of Q_p, and compute it in the case of degree < =3. For the general case, we conjecture bounds on the size of this function, as well as conjectures on the underlying geometric structures. In the end, we study a modification of this question as n goes to infinity. As an aside, we also use Cohen-Lenstra measures to compute the distribution of the Jordan blocks of matrices with coefficients in a fixed finite field as the size goes to infinity.
Stereotype threat perseverance [electronic resource]
Keywords: Psychology.

Across a series of studies, the present research documents instances of stereotype threat perseverance, where negative effects of stereotype threat persist beyond an initial threatening experience. In Studies 1-3, math identified women under stereotype threat showed immediate and long-term (one week to one year) negative effects on math efficacy, performance, and gender identification. In Study 4, Black premed students who completed a stressful interview task under stereotype threat were more likely, a week later, to perceive racial discrimination in their day-to-day lives. Together, these results suggest that even single instantiations of threat can have lasting consequences, leading to stereotype threat perseverance over time, beyond the initial context in which they were first experienced.
Stimulating an exclusionary market [electronic resource] : the institutional logic of Chile's enabling markets housing policy regime
Keywords: Law JSD.

Urban segregation represents a central challenge for housing law and policy around the world. A large body of research has demonstrated the negative impact on life opportunities of the concentration of disadvantaged groups in isolated urban areas. Many governments are therefore making great effort to implement inclusionary housing policies. This dissertation centers the analysis on Chile, a country that has successfully provided massive access to formal housing in recent decades under an institutional logic known as an enabling markets housing policy approach. Although a quantitative success, the approach also has an important downside: the agglomeration of low-income families on the periphery of Chile's urban areas, often in isolated and segregated conditions. Addressing this problematic outcome of Chile's low-income housing policy has become central priority in the governmental agenda, but the Ministry of Housing and Urbanism (MHU) has not been able to make significant headway. Through in-depth case study research on the Chilean regime comprising more than 50 interviews conducted with key stakeholders, as well as the analysis of documentary information and administrative data, the dissertation asks why the MHU has not been able to implement an effective inclusionary housing policy. The main argument I offer is that, since the adoption of the enabling markets approach, Chile's governments have been successively trapped in a singular institutional logic for the provision of affordable housing that confines the role of the government to the stimulation of the housing market through the allocation of targeted subsidies aimed at promoting homeownership. This strategy has shown very limited success in enabling low-income households to compete with location preferences of higher-income groups. In the dissertation I also argue that this institutional logic has been maintained over the years, by administrations with different political ideologies, because of certain obstacles to effectuate radical policy changes. Prominent among the constraints has been the increasing alignment of interests between the real estate industry and the government bureaucracy, which are the two most powerful stakeholders in this social policy arena. The implementation of the subsidy-based policy regime has been very convenient for these two stakeholders. The analysis of Chile's low-income housing policy offers interesting lessons for many countries around the world that are struggling to implement laws and policies that might stimulate more inclusionary responses from housing markets. The analysis is also relevant for countries that may have urgent short-term housing needs, but that remain nonetheless interested in housing laws and policies that may avoid creating or reinforcing the kinds of segregation patterns that are very costly to change in the long run.
Strain engineering the band structure of graphene [electronic resource]
Keywords: Electrical Engineering.

The purpose of this work is to explore whether strain can modify the band structure of graphene and produce a bandgap. This thinnest material possible, has generated interest in the semiconductor industry for many reasons, including its potential for ballistic conduction, very high carrier mobility and saturation velocity, large current density and natural ambipolar (both n- and p-type) carrier transport. However, its lack of a bandgap suggests a limited future as a silicon replacement material in highly scaled electronics. One route towards the use of graphene transistors for logic devices relies on creating a controllable bandgap. A solution to this problem is sought that uses strain as a way to modulate the band structure and potentially open a bandgap. We have characterized changes in the electronic properties of biaxially strained graphene in the elastic range with Synchrotron Radiation Photoelectron Spectroscopy (SRPES) and Near Edge X-ray Absorption Fine Structure (NEXAFS). Biaxial tensile strain was induced in polycrystalline graphene that was attached to a nanostructured substrate. Strain of approximately 1% was determined by shifts in the Raman spectra. Band structure characterization was performed using photoemission from valence bands, shifts in the secondary electron emission, and NEXAFS absorption from the C1s to the unoccupied graphene conduction bands. We also have used 3-terminal FET structures to study changes in the transconductance of transistors with strained graphene as channel material as compared to FETs with flat graphene channel. The results shows that biaxial strain increased the graphene work function, which is due to the shift of the Fermi energy with strain, and induced band broadening in the valence bands and conduction bands. Furthermore, the ambipolar behavior in strained graphene is suppressed at positive gate voltages. This suggests that there is potentially an increase in the effective bandgap in strained graphene. Strained graphene can thus create a possible route toward a broad class of future graphene electronics. In chapter I, an overview of the current state of the nanoelectronic industry is given, followed by an overview of the electronic properties of graphene relevant to the experimental results of this work. Chapter II is a survey of theoretical and experimental work done on strained graphene. In chapter III, fabrication process, SEM images and Raman characterization of graphene is detailed. In chapter IV, band structure characterization of strained graphene is presented. In chapter V, fabrication of field effect transistors is given, and the effect of strain on electrical properties of graphene is examined. Finally, in chapter VI, possible future work about this project is suggested.
Strategic sourcing under supplier responsibility and relationship risks [electronic resource]
Keywords: Management Science and Engineering.

Traditional literature in supply disruption examines strategies such as multi-sourcing, backup production options, and quick response. As supply chains have entered and grown on the global stage, new challenges emerge in light of strategic sourcing. And supplier risks caused by certain characteristics are no longer restricted to supply uncertainty or immune to other market conditions. In this dissertation, we focus on supplier risks caused by two aspects: supplier responsibility (Chapters 2 and 3) and supplier relationship (Chapter 4). Our study in supplier risk caused by responsibility is motivated by the series of incidents at Foxconn and subsequent media scrutiny centering on Apple, one of Foxconn's primary customers. And our study in supplier risk caused by relationship is motivated by the joint research project with Intel Corp, who is concerned with the consequences of uncertainty in supplier relationship, specifically for smartphone and tablet industries; whereas it has no such concern for traditional PC industry thanks to its strong leading position.