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eScholarship
Open Access Publications from the University of California

Scholarly Works (42 results)

  • Thesis
  • Peer Reviewed
UC San Diego Electronic Theses and Dissertations (2008)
In today's software world, the increasing number of software products creates an equivalently increasing number of software bugs. Software engineers counter this by writing tests to check the functionality of their programs. However, as programs become increasingly complex, the process of writing tests becomes lengthened, tedious, and arduous. In response, certain products have been developed to automatically generate and run tests for software engineers. This study explores two such programs, Jtest and Agitator. Given a software program, these tools automatically generate and run tests for the programmer. This study attempts to show the effectiveness of these tools by feeding them sample programs, and observing the types of tests produced. At the end, the tradeoffs between these tools are compared
    Cover page: A tale of two testing tools
    • Thesis
    • Peer Reviewed
    UC San Francisco Electronic Theses and Dissertations (2000)

      Cover page: Application of fractal analysis on hand-wrist radiographs to assess growth of caucasian females ages 7-15
      • Thesis
      • Peer Reviewed
      UCLA Electronic Theses and Dissertations (2017)

      With increasing power and application demands, heterogeneous multi-core processors are becoming more prevalent. However, the key to proper utilization of heterogeneous multi-cores is assigning, or mapping, the right application to the right core type. Recent work has shown that fine-grained mapping takes advantage of short program phases with highly variant performance requirements, and can elicit greater benefits from tightly coupled heterogeneous multi-cores. This work explores various methods to improve fine-grained mapping techniques to better utilize heterogeneous multi-cores.

        Cover page: Improving Fine-Grained Resource Mapping on Tightly Coupled Heterogeneous Multi-cores
        • Article
        • Peer Reviewed
        Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 43 (2021)
        Context effects in human spoken language are well-documented and play a central role in the theory of language production. However, the role of context in written language production is far less well understood, even though a considerable proportion of the language produced by many people today is written. Here we analyze the factors predictive of English language typing times in a large, naturalistic corpus from the popular TypeRacer.com website. We find broad consistency with the major documented effects of linguistic context on spoken language production, suggesting potential modality-independence in the cognitive mechanisms underlying language production and/or similar optimization pressures on the production systems in both modalities.
          Cover page: On Factors Influencing Typing Time: Insights from a Viral Online Typing GameCreative Commons 'BY' version 4.0 license
          • Article
          • Peer Reviewed
          UC Irvine Previously Published Works (2001)
          Glypicans are major cell surface heparan sulfate proteoglycans, the structures of which are characterized by the presence of a cysteine-rich globular domain, a short glycosaminoglycan (GAG) attachment region, and a glycosylphosphatidylinositol membrane anchor. Despite strong evolutionary conservation of the globular domains of glypicans, no function has yet been attributed to them. By using a novel quantitative approach for assessing proteoglycan glycosylation, we show here that removal of the globular domain from rat glypican-1 converts the proteoglycan from one that bears approximately 90% heparan sulfate (HS) to one that bears approximately 90% chondroitin sulfate. Mutational analysis shows that sequences at least 70 amino acids away from the glypican-1 GAG attachment site are required for preferential HS assembly, although more nearby sequences also play a role. The effects of the glypican-1 globular domain on HS assembly could also be demonstrated by fusing this domain to sequences representing the GAG attachment sites of other proteoglycans or, surprisingly, simply by expressing the isolated globular domain in cells and analyzing effects either on an exogenously expressed glypican-1 GAG attachment domain or on endogenous proteoglycans. Quantitative analysis of the effect of the globular domain on GAG addition to proteoglycan core proteins suggested that preferential HS assembly is achieved, at least in part, through the inhibition of chondroitin sulfate assembly. These data identify the glypican-1 globular domain as a structural motif that potently influences GAG class determination and suggest that an important role of glypican globular domains is to ensure a high level of HS substitution of these proteoglycans.
            Cover page: Mechanisms Underlying Preferential Assembly of Heparan Sulfate on Glypican-1*Creative Commons 'BY' version 4.0 license
            • Article
            • Peer Reviewed
            UC Santa Barbara Previously Published Works (2000)
            The bubble gas partial pressures, dissolved gas and oil, and fluid motions within the rising bubble streams of three shallow (< 70 m) natural hydrocarbon seeps in the Santa Barbara Channel were measured. Aqueous methane concentrations near the surface in the bubble plumes were substantially greater (> 108 times) than atmospheric equilibrium values: at two of the seeps it was slightly supersaturated with respect to the bubble's partial pressure. This indicates that the rate limiting step for methane. CH4 transfer into the water column was not bubble gas transfer, but rather turbulence transfer of water out of the saturated bubble stream to the bulk ocean. Strong upwelling flows were observed, as well as elevated fluorescence indicative of dissolved oils. At the bottom, bubbles are circa 90% CH4, but at the surface they were circa 60% CH4, 30% air, and 10% higher hydrocarbons.
              Cover page: Modifications of the local environment by natural marine hydrocarbon seeps
              • Article
              • Peer Reviewed
              UC Irvine Previously Published Works (2014)
              We present an integrated approach for structure and parameter estimation in latent tree graphical models. Our overall approach follows a "divide-and-conquer" strategy that learns models over small groups of variables and iteratively merges onto a global solution. The structure learning involves combinatorial operations such as minimum spanning tree construction and local recursive grouping; the parameter learning is based on the method of moments and on tensor decompositions. Our method is guaranteed to correctly recover the unknown tree structure and the model parameters with low sample complexity for the class of linear multivariate latent tree models which includes discrete and Gaussian distributions, and Gaussian mixtures. Our bulk asynchronous parallel algorithm is implemented in parallel and the parallel computation complexity increases only logarithmically with the number of variables and linearly with dimensionality of each variable.
                Cover page: Guaranteed Scalable Learning of Latent Tree Models
                • Article
                • Peer Reviewed
                Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 42 (2020)
                What do people learn when they repeatedly try to solve a set ofrelated problems? In a set of three different exploratory phys-ical problem solving experiments, participants consistentlylearn strategies rather than generically better world models.Participants selectively transferred these strategies when thecrucial context and preconditions of the strategy were met,such as needing to “catapult”, “support”, “launch” or “desta-bilize” an object in the scene to accomplish their goals. Weshow that these strategies are parameterized: people can ad-just their strategies to account for new object weights despiteno direct interaction experience with these objects. Taken to-gether, these results suggest that people can make use of lim-ited experience to learn abstract strategies that go beyond sim-ple model-free policies and are instead object-oriented, adapt-able, and can be parameterized by model-based variables suchas weight.
                  Cover page: Abstract strategy learning underlies flexible transfer in physical problem solvingCreative Commons 'BY' version 4.0 license
                  • Article
                  Research Reports (2002)

                  This report concerns vehicle fault detection and identification. A vehicle health monitoring approach based on analytical redundancy is described. To detect and identify actuator and sensor faults, fault detection filters and parity equations are developed for the longitudinal dynamics of the PATH Buick LeSabre. Fault detection filters and parity equations use the control commands and sensor measurements to generate the residuals which have a unique static pattern in response to each fault. This allows the faults not only to be detected, but also identified. Fault detection filters and parity equations are .rst evaluated using simulated data generated by a high-fidelity vehicle simulation. Then, fault detection filters and parity equations are evaluated using empirical data recorded when driving a PATH Buick LeSabre at Crow's Landing. Finally, a real-time testing environment is developed using Linux operating system and C language. This allows the fault detection filters and parity equations to be evaluated in real-time on a PATH Buick LeSabre. The real-time evaluation at Crow's Landing demonstrates that the fault detection .lters can detect and identify actuator and sensor faults as expected even under various disturbances and uncertainties including sensor noise, road noise, system parameter variations, unmodeled dynamics and nonlinearities.

                    Cover page: Testing and Evaluation of Robust Fault Detection and Identification for a Fault Tolerant Automated Highway System
                    • Article
                    Research Reports (2002)
                    This report is continuation of the work of (Douglas et al., 1996) and (Douglas et al., 1997) which concerns vehicle fault detection and identification.  A vehicle health monitoring approach based on analytical redundancy is described. Fault detection filters and parity equations use the control commands and sensor measurements to generate the residuals which have a unique static pattern in response to each fault. This allows the faults not only to be detected, but also identified. Sensor noise, process disturbances, system parameter variations, unmodeled dynamics and nonlinearities can distort these static patterns.   A Shiryayev sequential probability ratio test that has been extended to multiple hypotheses examines the fault detection filter and parity equation residuals and generates the probability of the presence of a fault.  A point design of fault detection filters and parity equations is developed for the longitudinal dynamics of the PATH Buick LeSabre. Fault detection filters are evaluated using empirical data obtained from U.C. Berkeley. The preliminary evaluation is promising in that the fault detection filters can detect and identify actuator and sensor faults as expected even under various disturbances and uncertainties.
                      Cover page: Integration of Fault Detection and Identification into a Fault Tolerant Automated Highway System: Final Report
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