VictorJuiz/thesis huggingface dataset:
A probabilistic framework for synapse localization and class discovery in the mouse whisker barrel cortex

A thorough understanding of the brain's diverse molecular architecture and complex neural circuitry is critical to uncovering the mechanisms that underlie higher-level information processing. Synapses are an integral component of neural circuits because they propagate signals from one neuron to the next. However, because of the astounding multitude and diversity of synapses in the brain, a set of high-resolution, high-precision tools is necessary for extracting enough information from a tissue sample to recover its synaptic structure. To this end, many state-of- the-art molecular imaging methods, including array tomography, have recently focused their efforts on building large-scale datasets annotated with synaptic tissue markers. Manual processing of these images, however, is too time-intensive to be practical. Here, we present a computational method for synapse detection and classification that aims to locate and characterize all synapses in a mouse barrel cortex. Our technique uses a nonparametric Bayesian network to represent the underlying biological process in order to allow for flexibility in the total numbers of synapses and
synapse types that are found. Using this data-driven approach, we are able to detect subtle but significant patterns with implications for neuroscience research.

Keywords: Computer Science.
Neural computation
Artificial intelligence
Neural circuitry
Hoefer Prize for Writing in the Major
Thesis
Advisor: Koller Daphne
A Righteous Collusion: The Prisoner’s Dilemma and the Civil Rights Act

Various attempts have been made to explain the sources of long-term discriminatory activity – behavior which does not seem to be profit-maximizing, and yet persists in the face of market competition. Research interest has focused on the effect of Title VII of the Civil Rights Act, and whether it has served to decrease employment discrimination or merely exposed firms to costly litigation. Following in the footsteps of Becker (1957), this paper develops a model of employment discrimination that arises as the result of consumer preference as opposed to employer or employee tastes. It predicts that many or all firms in a local industry will conspire to halt discriminatory activity simultaneously during periods of rising costs to discrimination, effectively forming a type of cartel. I perform an empirical test based on employment data collected in the period following the passage of the Civil Rights Act. The results provide suggestive evidence of the model’s validity, and confirm the importance of viewing the question of discrimination through a regional lens.

Keywords:  Department of Economics
Stanford Department of Economics
employment discrimination
American South
Civil Rights Act
prisoner’s dilemma
Thesis
Advisor: Wright Gavin
Achieving clarity on value [electronic resource]

Decision analysis is the pursuit of clarity of action, and lies at the intersection of action and thought. Before moving to clarity of action, we need to achieve clarity of thought on our values so that we can make decisions that are consistent with our deepest values. This dissertation focuses on helping decision-makers achieve such clarity by suggesting frameworks to discover, appreciate and communicate sources of value. Apart from the theoretical contributions, several case studies are presented to illustrate the use of such methods to achieve consistent valuation and aid mutual understanding on value.

Keywords: Management Science and Engineering.
Advisor: Howard Ronald A. (Ronald Arthur)
Barley Stephen R. advisor. Thesis advisor
Spetzler Carl S. advisor. Thesis advisor
Acoustic detection of ultra-high energy neutrinos [electronic resource]

Understanding the origin and evolution of cosmic accelerators by measuring ultra-high energy cosmic rays is one of the main goals of astroparticle physics. UHE neutrinos are thought to be better indicators of cosmic accelerators since they travel from their source undeflected by magnetic fields and unimpeded by interactions with the cosmic microwave background. Both cosmic rays and neutrinos have extremely low fluxes at these energies, which makes measurements difficult. Neutrino measurements have the added challenge of a longer interaction length that makes the atmosphere not suitable as a target. Here, we present the Study of Acoustic Ultra-high energy Neutrino Detection (SAUND) which uses an underwater acoustic sensor array spanning 1500 cubic kilometers to search for UHE neutrinos interacting in the ocean. A description of the data acquisition system, results of the background noise study, and an analysis based on an integrated 130 days of data are presented here. Two events are found to have properties compatible with UHE-neutrino-induced particle showers. Since our understanding of transient backgrounds is limited, a flux upper limit is set providing the most sensitive limit to date on UHE neutrinos using the acoustic technique.

Keywords: Applied Physics
Advisor: Gratta Giorgio primary advisor. Thesis advisor
Petrosian Vahe primary advisor. Thesis advisor
Burchat P. (Patricia) advisor. Thesis advisor
Adaptive distributed source coding [electronic resource]

Distributed source coding, the separate encoding and joint decoding of statistically dependent sources, has many potential applications ranging from lower complexity capsule endoscopy to higher throughput satellite imaging. This dissertation improves distributed source coding algorithms and the analysis of their coding performance to handle uncertainty in the statistical dependence among sources. We construct sequences of rate-adaptive low-density parity-check (LDPC) codes that enable encoders to switch flexibly among coding rates in order to adapt to arbitrary degrees of statistical dependence. These code sequences operate close to the Slepian-Wolf bound at all rates. Rate-adaptive LDPC codes with well-designed source degree distributions outperform commonly used rate-adaptive turbo codes. We then consider distributed source coding in the presence of hidden variables that parameterize the statistical dependence among sources. We derive performance bounds for binary and multilevel models of this problem and devise coding algorithms for both cases. Each encoder sends some portion of its source to the decoder uncoded as doping bits. The decoder uses the sum-product algorithm to simultaneously recover the source and the hidden statistical dependence variables. This system performs close to the derived bounds when an appropriate doping rate is selected. We concurrently develop techniques based on density evolution to analyze our coding algorithms. Experiments show that our models closely approximate empirical coding performance. This property allows us to efficiently optimize parameters of the algorithms, such as source degree distributions and doping rates. We finally demonstrate the application of these adaptive distributed source coding techniques to reduced-reference video quality monitoring, multiview coding and low-complexity video encoding.

Keywords: Electrical Engineering
Advisor: Girod Bernd primary advisor. Thesis advisor
Montanari Andrea primary advisor. Thesis advisor
Gill John T.
Adhesion and degradation of hard transparent coatings [electronic resource]

Hard transparent coatings are an essential component of structures involving polymer surfaces. An important application is airplane passenger windows, which are subject to large variations in pressure, temperature and humidity as well as UV exposure and abrasive conditions. The coatings suitable for such extreme environments must have high hardness, resistance to UV degradation and excellent adhesion to the polymer substrate. Measuring the adhesion of a hard coating on a compliant substrate is not trivial; hence, qualitative cross-cut tape tests have been the standard for automotive and aerospace industries. In this dissertation we address the quantitative assessment of adhesion of hard transparent coatings on polymer substrates. An asymmetric double cantilever beam specimen was employed to sample the interface without contribution from crazing in the substrate. Fracture energies of coatings that had passed standard tests were found to vary from 1.4 J/m2 to 22 J/m2. Significant subcritical crack growth was observed in coatings on windows with interfacial fracture energy of 7 J/m2. Cracks were found to continue to grow at driving forces below 2 J/m2 due to viscoelastic deformation at the crack tip. Simulated outdoor weathering decreased the adhesion energy and led to cracking and delamination. Driving force for cracking and delamination was correlated to UV cross-linking of the coating; FTIR evidence showed that the ratio of network forming Si—O—Si bonds increased with outdoor exposure. The effect of surface pre-treatments on adhesion was assessed. Mechanical polishing and UV-Ozone treatment were found to increase adhesion significantly. The effect of UV absorbers on stress generation in coatings exposed to UV was investigated. A cyanoacrylate absorber was found to perform better than a benzophenone absorber at high concentrations. However, both absorbers seemed to change the molecular structure of the coating and did not increase the lifetime of the coating significantly. Atmospheric plasma deposition was employed as a solvent-free and wasteless alternative to sol-gel processing of hybrid coatings. We focused on molecularly bridged hybrid coatings for improved cohesion and resistance to water assisted crack growth. A high temperature precursor delivery system was designed and constructed for deposition of molecularly bridged hybrid silica coatings. Bridged precursors with very low vapor pressure were deposited on silicon and polycarbonate substrates. The effects of precursor flow rate, temperature and other plasma parameters on deposition rate were investigated. Bridged precursor coatings developed significantly higher growth stresses than curing stresses in sol-gel coatings, but the former were comparable to atmospheric plasma coatings deposited using conventional silanes.

Keywords: Materials Science and Engineering
Advisor: Dauskardt Reinhold
Lindenberg Aaron Michael advisor. Thesis advisor
Salleo Alberto
Advances in breast MRI [electronic resource]

Breast magnetic resonance imaging (MRI) is becoming an increasingly valuable technology for breast cancer detection, especially for women at high risk for the disease. Stanford Hospital alone performed approximately 1,000 breast MRIs last year. This demand and need to increase specificity of breast MRI motivates improving current breast MR protocols, as well as exploring new applications of MR technology to breast imaging. Most clinical breast MR exams are performed at a 1.5T field strength. 3T scanners, however, are becoming more prevalent in the clinical setting and thus the breast MR protocol needs to be tuned accordingly. The first project includes measurements of T1 and T2 of breast fat and glandular tissue at 1.5T and 3T and the corresponding recommendation for altering the TR and TE for 3T breast imaging. The beating heart posterior to the breast often produces artifacts in breast MR images. The second project is a cylindrical saturation method that reduces unwanted signal from the heart while avoiding signal loss from the breast tissue of interest. Detecting tumor oxygenation non-invasively may give insight on tumor differentiation, predict susceptibility to antiangiogenic therapeutics, and monitor chemotherapeutics. The third project introduces the application of BOLD contrast imaging, a technique usually applied in functional brain MR imaging for characterizing tumor oxygenation in the breast.

Keywords: Biophysics.
Advisor: Glover Gary H.
Daniel Bruce (Bruce Lewis) advisor. Thesis advisor
Hargreaves Brian Andrew advisor. Thesis advisor
Advection of Crust through a Restraining Bend at Mass Flux Steady State in the Santa Cruz Mountains, CA, Inferred from Cosmogenic Radionuclide Denudation Rates and Topographic Analysis

The asymmetric topography of the Santa Cruz Mountains, CA has been ascribed to the advection of material southwest of the right-lateral San Andreas Fault through a restraining bend, while material northeast of the fault remains trapped in the zone of contraction resulting from the fault's restraining geometry. This model predicts a rapid decrease in rock uplift rates with distance along the fault from the restraining bend, which is observed in rock uplift recorded by marine terraces along the Santa Cruz and San Mateo County coast. Here, I present eighteen cosmogenic radionuclide (CRN) denudation rates from basins southwest of the fault, which decrease systematically from 0.60 to 0.15 mm/yr with distance north of the bend. These rates capture the expected spatial distribution of rock uplift rates associated with the advection model, and their magnitude falls in the same range as those recorded by coastal marine terraces.

Keywords:  Department of Geological and Environmental Sciences
School of Earth Energy &amp; Environmental Sciences
topography
Santa Cruz Mountains
Thesis
Advisor: Hilley George E.
Graham Stephan A.
Airborne fine particulate matter pollution in casinos [electronic resource] : performance of a real-time particle monitor and personal exposure to secondhand smoke

Exposure to secondhand smoke (SHS) has been associated with a number of health outcomes including cardiovascular diseases, pulmonary malfunction, lung cancer, and mortality. To protect public health, over 100 countries have adopted comprehensive or partial legislations against smoking. In the United States, California was the first state to enact a statewide ban on smoking in indoor workplaces in 1994. However, due to the sovereign nation status of California Indian tribes, smoking is still allowed and exposure to SHS continues in nearly all California Indian casinos. Fine particulate matter (PM2.5) is a major component of SHS pollution, and has been implicated as a risk factor for a range of chronic and acute diseases. The EPA's reference method for quantifying PM2.5 mass concentrations uses filter-based gravimetric samplers, which must be operated for many hours to collect enough sample. Since the gravimetric approach cannot measure short-term variations in PM2.5 levels, many personal exposure and environmental monitoring studies have employed real-time monitors for measuring PM2.5 concentrations. However, real-time monitors require proper calibration to accurately represent particle mass concentrations. This study characterized PM2.5 concentrations with real-time SidePak particle monitors in a large sample of California Indian casinos, spanning all sizes and distributed widely across the state. By comparing the SidePak measurements with the standard gravimetric method, we first calibrated the SidePak monitors for different types of aerosols commonly encountered indoors and outdoors, including SHS. We then modeled the response of SidePak monitors to SHS for a range of relative humidity levels. Finally, we designed a systematic casino monitoring protocol, using the calibrated real-time monitors, to examine PM2.5 concentrations in a field survey of 36 California Indian casinos and 8 Reno casinos.

Advisor: Hildemann Lynn M. (Lynn Mary) primary advisor. Thesis advisor
Jacobson Mark Z. (Mark Zachary) advisor. Thesis advisor
Ott Wayne R. advisor. Thesis advisor
Algorithmic problems in social and geometric influence [electronic resource]

Online social and information networks promise to yield insights into social relationships and also open possibilities for new market paradigms. Moving forward towards these goals requires addressing several new computational challenges, and in this thesis we explore two themes: algorithmic challenges arising due to the massive scale of online social networks, and questions related to social network monetization. In the first part of the thesis, we study the algorithmic problem of finding nearest neighbors that is computationally challenging due to the large scale and dynamic nature of social networks. In particular, we map the relationship discovery problem to a geometric proximity problem and undertake a theoretical study of algorithms needed for solving these proximity problems in the highly dynamic online setting. Our contributions are new data structures for resolving these proximity queries that can be updated dynamically for a changing point set. In the second part of the thesis, we study two problems relating to social network monetization. First, we propose a new monetization paradigm for social networks that works via a referral scheme to market a product on social networks. In particular, we show that computational limitations limit optimal implementations of such referral schemes, and propose an algorithm that leverages the social network topology to achieve revenue that is guaranteed to be within a constant factor of the optimal revenue. The second monetization problem we study is that of inferring the quality of an advertisement (or of generic content) given the knowledge of user clicks. Our contribution for this problem is to introduce a new online learning algorithm (i.e. an algorithm with no prior knowledge of content quality) that can account for the position bias that is inherent in the presentation of the content to users.

Advisor: Roughgarden Tim primary advisor. Thesis advisor
Goel Ashish advisor. Thesis advisor
Kleinberg Jon
Algorithmic reconstruction methods in diffraction microscopy using a priori information [electronic resource]

Recent technological and algorithmic advances have enabled lensless imaging techniques, paving the way for high resolution three dimensional analysis. In these methods, a coherent x-ray source illuminates a sample and the amplitude of the far-field diffraction pattern is recorded. Reconstruction of the sample object requires recovery of the missing phase information by exploiting additional side information in conjunction with a computational reconstruction algorithm. Two different sources of additional information are considered. First, the phase recovery problem is formulated as an optimization problem where knowledge of the form of the object (smoothness, positivity, particular material characteristics) are included as part of the objective or enforced as constraints. This problem is then approximated as a convex problem and solved using existing methods. The second approach assumes that the prior knowledge is in the form of a low resolution image of the sample and uses the wavelet domain to express this information. Experimental limitations include coherence, noise and missing data as well as algorithmic limitations such as data centering, support determination and complex valued reconstructions. Reconstruction results include those of optical equivalent experiments and of both soft and hard x-ray experiments. In all three settings the two proposed sources of information are successfully used to obtain reconstructions for a variety of objects. For the application of non-destructively examining the buried metallization pattern of integrated circuits, we employed a coherent beam of 0.17nm X-rays to image a 100 nm metal pattern. The metal layer was fabricated on a 100 micron silicon substrate and buried beneath one micron of siliocn dioxide. From the diffraction pattern were able to successfully reconstruct the image of the metal pattern.

Keywords: Electrical Engineering
Advisor: Pease R. (R. Fabian W.) primary advisor. Thesis advisor
Pianetta Piero primary advisor. Thesis advisor
Miao Jianwei
Algorithms for the equilibration of matrices and their application to limited-memory quasi-newton methods [electronic resource]

Diagonally scaling a matrix can reduce its condition number. Equilibration scales a matrix so that the row and column norms are equal. We review the existence and uniqueness theory for exact equilibration. Then we introduce a formalization of approximate equilibration and develop its existence and uniqueness theory. Next we develop approximate equilibration algorithms that access a matrix only by matrix-vector products. We address both the nonsymmetric and symmetric cases. Limited-memory quasi-Newton methods may be thought of as changing the metric so that the steepest-descent method works effectively on the problem. Quasi-Newton methods construct a matrix using vectors of two types involving the iterates and gradients. The vectors are related by an approximate matrix-vector product. Using our approximate matrix-free symmetric equilibration method, we develop a limited-memory quasi-Newton method in which one part of the quasi-Newton matrix approximately equilibrates the Hessian. Often a differential equation is solved by discretizing it on a sequence of increasingly fine meshes. This technique can be used when solving differential-equation-constrained optimization problems. We describe a method to interpolate our limited-memory quasi-Newton matrix from a coarse to a fine mesh.

Keywords: Computational and Mathematical Engineering.
Advisor: Murray Walter primary advisor. Thesis advisor
Saunders Michael A. advisor. Thesis advisor
Ye Yinyu advisor. Thesis advisor
Alignment of cryo-electron tomography images using Markov Random Fields [electronic resource]

Cryo-Electron tomography (CET) is the only imaging technology capable of visualizing the 3D organization of intact bacterial whole cells at nanometer resolution in situ. However, quantitative image analysis of CET datasets is extremely challenging due to very low signal to noise ratio (well below 0dB), missing data and heterogeneity of biological structures. In this thesis, we present a probabilistic framework to align CET images in order to improve resolution and create structural models of different biological structures. The alignment problem of 2D and 3D CET images is cast as a Markov Random Field (MRF), where each node in the graph represents a landmark in the image. We connect pairs of nodes based on local spatial correlations and we find the "best'' correspondence between the two graphs. In this correspondence problem, the "best'' solution maximizes the probability score in the MRF. This probability is the product of singleton potentials that measure image similarity between nodes and the pairwise potentials that measure deformations between edges. Well-known approximate inference algorithms such as Loopy Belief Propagation (LBP) are used to obtain the "best'' solution. We present results in two specific applications: automatic alignment of tilt series using fiducial markers and subtomogram alignment. In the first case we present RAPTOR, which is being used in several labs to enable real high-throughput tomography. In the second case our approach is able to reach the contrast transfer function limit in low SNR samples from whole cells as well as revealing atomic resolution details invisible to the naked eye through nanogold labeling.

Keywords: Electrical Engineering
Advisor: Horowitz Mark (Mark Alan) primary advisor. Thesis advisor
Downing Kenneth (Kenneth H.) advisor. Thesis advisor
Koller Daphne advisor. Thesis advisor