Query processing of spatial objects: Complexity versus Redundancy

The management of complex spatial objects in applications, such as geography and cartography, imposes stringent new requirements on spatial database systems, in particular on efficient query processing. As shown before, the performance of spatial query processing can be improved by decomposing complex spatial objects into simple components. Up to now, only decomposition techniques generating a linear number of very simple components, e.g. triangles or trapezoids, have been considered. In this paper, we will investigate the natural trade-off between the complexity of the components and the redundancy, i.e. the number of components, with respect to its effect on efficient query processing. In particular, we present two new decomposition methods generating a better balance between the complexity and the number of components than previously known techniques. We compare these new decomposition methods to the traditional undecomposed representation as well as to the well-known decomposition into convex polygons with respect to their performance in spatial query processing. This comparison points out that for a wide range of query selectivity the new decomposition techniques clearly outperform both the undecomposed representation and the convex decomposition method. More important than the absolute gain in performance by a factor of up to an order of magnitude is the robust performance of our new decomposition techniques over the whole range of query selectivity

ft Value of O14 and the Universality of the Fermi Interaction

The conserved-vector-current theory of the strangeness-conserving weak decays predicts that GV, the vector coupling constant in nuclear beta decay, should be equal to Gμ, the coupling constant in the muon decay. To make possible a more precise comparison of GV and Gμ, the ft value of O14 has been remeasured. The endpoint energy of the positron decay has been determined by measuring the Q values of the reactions C12(He3, n)O14 and C12(He3, p)N14* (2.311-MeV state), using the same techniques and equipment where possible in order to minimize the uncertainty in the difference of the Q values. The results of these measurements are Qn=-1148.8±0.6 keV and Qp=2468.4±1.0 keV, which yield Emax(β+)=1812.6±1.4 keV, all energies relative to the Li7(p, n)Be7 threshold assumed as 1880.7±0.4 keV. The half-life of O14 has also been remeasured as 71.00±0.13 sec, which implies a partial half-life of 71.43±0.15 sec for the transition to the 2.311-MeV state of N14. Averaged with the recent half-life measurement of Hendrie and Gerhart, we obtain an ft value of 3075±10 sec for the O14 decay, after correcting for nuclear form factors, electron screening, and K-capture competition. With the radiative corrections of Kinoshita and Sirlin, the value obtained for GV is (1.4025±0.0022)×10^-49 erg-cm^3, where the quoted error is experimental in origin. This is to be compared with the value computed from recent muon decay measurements, Gμ=(1.4312±0.0011)×10^-49 erg-cm^3, which is (2.0±0.2)% larger. As there appear to be several possible theoretical explanations for this small discrepancy, the present results are consistent with the conserved-vector-current hypothesis

Adaptive Path Planning for Depth Constrained Bathymetric Mapping with an Autonomous Surface Vessel

This paper describes the design, implementation and testing of a suite of algorithms to enable depth constrained autonomous bathymetric (underwater topography) mapping by an Autonomous Surface Vessel (ASV). Given a target depth and a bounding polygon, the ASV will find and follow the intersection of the bounding polygon and the depth contour as modeled online with a Gaussian Process (GP). This intersection, once mapped, will then be used as a boundary within which a path will be planned for coverage to build a map of the Bathymetry. Methods for sequential updates to GP's are described allowing online fitting, prediction and hyper-parameter optimisation on a small embedded PC. New algorithms are introduced for the partitioning of convex polygons to allow efficient path planning for coverage. These algorithms are tested both in simulation and in the field with a small twin hull differential thrust vessel built for the task.Comment: 21 pages, 9 Figures, 1 Table. Submitted to The Journal of Field Robotic

Theory of Electron Spin Relaxation in ZnO

Doped ZnO is a promising material for spintronics applications. For such applications, it is important to understand the spin dynamics and particularly the spin coherence of this II-VI semiconductor. The spin lifetime $\tau_{s}$ has been measured by optical orientation experiments, and it shows a surprising non-monotonic behavior with temperature. We explain this behavior by invoking spin exchange between localized and extended states. Interestingly, the effects of spin-orbit coupling are by no means negligible, in spite of the relatively small valence band splitting. This is due to the wurtzite crystal structure of ZnO. Detailed analysis allows us to characterize the impurity binding energies and densities, showing that optical orientation experiments can be used as a characterization tool for semiconductor samples.Comment: 7 pages, 1 figure: minor changes Accepted by Phys. Rev.

LINVIEW: Incremental View Maintenance for Complex Analytical Queries

Many analytics tasks and machine learning problems can be naturally expressed by iterative linear algebra programs. In this paper, we study the incremental view maintenance problem for such complex analytical queries. We develop a framework, called LINVIEW, for capturing deltas of linear algebra programs and understanding their computational cost. Linear algebra operations tend to cause an avalanche effect where even very local changes to the input matrices spread out and infect all of the intermediate results and the final view, causing incremental view maintenance to lose its performance benefit over re-evaluation. We develop techniques based on matrix factorizations to contain such epidemics of change. As a consequence, our techniques make incremental view maintenance of linear algebra practical and usually substantially cheaper than re-evaluation. We show, both analytically and experimentally, the usefulness of these techniques when applied to standard analytics tasks. Our evaluation demonstrates the efficiency of LINVIEW in generating parallel incremental programs that outperform re-evaluation techniques by more than an order of magnitude.Comment: 14 pages, SIGMO

Electron localization by a magnetic vortex

We study the problem of an electron in two dimensions in the presence of a magnetic vortex with a step-like profile. Dependending on the values of the effective mass and gyromagnetic factor of the electron, it may be trapped by the vortex. The bound state spectrum is obtained numerically, and some limiting cases are treated analytically.Comment: 8 pages, latex, 4 figure

Review of the mathematical foundations of data fusion techniques in surface metrology

The recent proliferation of engineered surfaces, including freeform and structured surfaces, is challenging current metrology techniques. Measurement using multiple sensors has been proposed to achieve enhanced benefits, mainly in terms of spatial frequency bandwidth, which a single sensor cannot provide. When using data from different sensors, a process of data fusion is required and there is much active research in this area. In this paper, current data fusion methods and applications are reviewed, with a focus on the mathematical foundations of the subject. Common research questions in the fusion of surface metrology data are raised and potential fusion algorithms are discussed

High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso

The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on linear dependency between input features and output values. In this paper, we consider a feature-wise kernelized Lasso for capturing non-linear input-output dependency. We first show that, with particular choices of kernel functions, non-redundant features with strong statistical dependence on output values can be found in terms of kernel-based independence measures. We then show that the globally optimal solution can be efficiently computed; this makes the approach scalable to high-dimensional problems. The effectiveness of the proposed method is demonstrated through feature selection experiments with thousands of features.Comment: 18 page