6473522
Algorithms and Average Time Bounds of Sorting on a Mesh-Connected Computer
We give three new parallel sorting algorithms on a mesh-connected computer with wraparound connections (i.e. a torus). These three algorithms, with the minimum queue size of 1, sort n/sup 2/ random input data items into a blocked snakelike row major order, a row major order, and a snakelike row major order, in 1.5n+o(n), 2n+o(n), and 2n+o(n) average steps, respectively. These results improve the previous results of 2n+o(n), 2.5n+o(n), and 2.5n+o(n), respectively. In addition, we prove that the distance bound n on a torus is an average-time lower bound independent of indexing schemes of sorting random input data items on it. >
33040944
Convergence Properties of Optimization Algorithms for the SAT Problem
The satisfiability (SAT) problem is a basic problem in computing theory. Presently, an active area of research on SAT problem is to design efficient algorithms to find a solution for a satisfiable conjunctive normal form (CNF) formula. A new formulation, the universal SAT problem model, which transforms the SAT problem on Boolean space into an optimization problem on real space has been developed (J. Gu, 1988; 1992; 1994). Many optimization techniques, such as the steepest descent method, Newton's method, and the coordinate descent method, can be used to solve the universal SAT problem. We prove that when the initial solution is sufficiently close to the optimal solution, the steepest descent method has a linear convergence ratio /spl beta/<1, Newton's method has a convergence ratio of order two, and the convergence ratio of the steepest descent method is approximately (1-/spl beta//m) for the universal SAT problem with m variables. An algorithm based on the coordinate descent method for the universal SAT problem is also presented. Experimental results show that this algorithm is more efficient than some previous ones in finding a solution for certain classes of the satisfiable CNF formulas.
1395057
Time-varying surface appearance: acquisition, modeling and rendering
For computer graphics rendering, we generally assume that the appearance of surfaces remains static over time. Yet, there are a number of natural processes that cause surface appearance to vary dramatically, such as burning of wood, wetting and drying of rock and fabric, decay of fruit skins, and corrosion and rusting of steel and copper. In this paper, we take a significant step towards measuring, modeling, and rendering time-varying surface appearance. We describe the acquisition of the first time-varying database of 26 samples, encompassing a variety of natural processes including burning, drying, decay, and corrosion. Our main technical contribution is a Space-Time Appearance Factorization (STAF). This model factors space and time-varying effects. We derive an overall temporal appearance variation characteristic curve of the specific process, as well as space-dependent textures, rates, and offsets. This overall temporal curve controls different spatial locations evolve at the different rates, causing spatial patterns on the surface over time. We show that the model accurately represents a variety of phenomena. Moreover, it enables a number of novel rendering applications, such as transfer of the time-varying effect to a new static surface, control to accelerate time evolution in certain areas, extrapolation beyond the acquired sequence, and texture synthesis of time-varying appearance.
105572
Online feature selection based on generalized feature contrast model
To really bridge the gap between high-level semantics and low-level features in content-based image retrieval (CBIR), a problem that must be solved is which features are suitable for explaining the current query concept. We propose a novel feature selection (FS) criterion based on a psychological similarity measurement, generalized feature contrast model, and implement an online feature selection algorithm in a boosting manner to select the most representative features and do classification during each feedback round. The advantages of the proposed method are: it does not require a Gaussian assumption for "relevant" images as other online FS methods do; it accounts for the intrinsic asymmetry between "relevant" and "irrelevant" image sets in CBIR online learning; it is very fast. Extensive experiments have shown our algorithm's effectiveness.