English

This paper describes applications of the optimized pattern discovery framework to text and Web mining. In particular, we introduce a class of simple combinatorial patterns over phrases, called proximity phrase association patterns, and consider the problem of finding the patterns that optimize a given statistical measure within the whole class of patterns in a large collection of unstructured texts. For this class of patterns, we develop fast and robust text mining algorithms based on techniques in computational geometry and string matching. Finally, we successfully apply the developed text mining algorithms to the experiments on interactive document browsing in a large text database and keyword discovery from Web bases.

COMBINATORIAL AND DISCRETE GEOMETRY Finite Point Configurations, J. Pach Packing and Covering, G. Fejes Toth Tilings, D. Schattschneider and M. Senechal Helly-Type Theorems and Geometric Transversals, R. Wenger Pseudoline Arrangements, J.E. Goodman Oriented Matroids, J. Richter-Gebert and G.M. Ziegler Lattice Points and Lattice Polytopes, A. Barvinok New! Low-Distortion Embeddings of Finite Metric Spaces, P. Indyk and J. Matousek New! Geometry and Topology of Polygonal Linkages, R. Connelly and E.D. Demaine New! Geometric Graph Theory, J. Pach Euclidean Ramsey Theory, R.L. Graham Discrete Aspects of Stochastic Geometry, R. Schneider Geometric Discrepancy Theory and Uniform Distribution, J.R. Alexander, J. Beck, and W.W.L. Chen Topological Methods, R.T. Zivaljevic Polyominoes, S.W. Golomb and D.A. Klarner POLYTOPES AND POLYHEDRA Basic Properties of Convex Polytopes, M. Henk, J. Richter-Gebert, and G.M. Ziegler Subdivisions and Triangulations of Polytopes, C.W. Lee Face Numbers of Polytopes and Complexes, L.J. Billera and A. Bjoerner Symmetry of Polytopes and Polyhedra, E. Schulte Polytope Skeletons and Paths, G. Kalai Polyhedral Maps, U. Brehm and E. Schulte ALGORITHMS AND COMPLEXITY OF FUNDAMENTAL GEOMETRIC OBJECTS Convex Hull Computations, R. Seidel Voronoi Diagrams and Delaunay Triangulations, S. Fortune Arrangements, D. Halperin Triangulations and Mesh Generation, M. Bern Polygons, J. O'Rourke and S. Suri Shortest Paths and Networks, J.S.B. Mitchell Visibility, J. O'Rourke Geometric Reconstruction Problems, S.S. Skiena New! Curve and Surface Reconstruction, T.K. Dey Computational Convexity, P. Gritzmann and V. Klee Computational Topology, G. Vegter Computational Real Algebraic Geometry, B. Mishra GEOMETRIC DATA STRUCTURES AND SEARCHING Point Location, J. Snoeyink New! Collision and Proximity Queries, M.C. Lin and D. Manocha Range Searching, P.K. Agarwal Ray Shooting and Lines in Space, M. Pellegrini Geometric Intersection, D.M. Mount New! Nearest Neighbors in High-Dimensional Spaces, P. Indyk COMPUTATIONAL TECHNIQUES Randomization and Derandomization, O. Cheong, K. Mulmuley, and E. Ramos Robust Geometric Computation, C.K. Yap Parallel Algorithms in Geometry, M.T. Goodrich Parametric Search, J.S. Salowe New! The Discrepancy Method in Computational Geometry, B. Chazelle APPLICATIONS OF DISCRETE AND COMPUTATIONAL GEOMETRY Linear Programming, M. Dyer, N. Megiddo, and E. Welzl Mathematical Programming, M.H. Todd Algorithmic Motion Planning, M. Sharir Robotics, D. Halperin, L.E. Kavraki, and J.-C. Latombe Computer Graphics, D. Dobkin and S. Teller New! Modeling Motion, L.J. Guibas Pattern Recognition, J. O'Rourke and G.T. Toussaint Graph Drawing, R. Tamassia and G. Liotta Splines and Geometric Modeling, C.L. Bajaj New! Surface Simplification and 3D Geometry Compression, J. Rossignac Manufacturing Processes, R. Janardan and T.C. Woo Solid Modeling, C.M. Hoffmann New! Computation of Robust Statistics: Depth, Median, and Related Measures, P.J. Rousseeuw and A. Struyf New! Geographic Information Systems, M. van Kreveld Geometric Application of the Grassmann-Cayley Algebra, N.L. White Rigidity and Scene Analysis, W. Whiteley Sphere Packing and Coding Theory, G.A. Kabatiansky and J.A. Rush Crystals and Quasicrystals, M. Senechal New! Biological Applications of Computational Topology, H. Edelsbrunner New! GEOMETRIC SOFTWARE Software, J. Joswig Two Computation Geometry Libraries: LEDA and CGAL, L. Kettner and S. Naher Index of Defined Terms New! Index of Cited Authors

An AUSM-based discretization method, using an explicit third-order discretization for the convective part and a line-implicit central discretization for the acoustic part and for the diffusive part, has been developed for incompressible and low-speed compressible Navier-Stokes equations. The lines are chosen in the direction of the gridpoints with shortest connection. The semi-implicit line method is used in multistage form because of the explicit third-order discretization of the convective part. Multigrid is used as acceleration technique. Because of the implicit treatment of the acoustic and the diffusive terms, the stiffness otherwise caused by high aspect ratio cells is removed. Low-Mach-number stiffness is treated by a preconditioning technique. To ensure physical correct behavior of the discretization for vanishing Mach number, extreme care has been taken. For vanishing Mach number, stabilization terms are added to the mass flux. Pressure and temperature stabilization terms are necessary. The coefficients of these terms are chosen so that correct scaling with Mach number is obtained. A blend of the low-speed algorithm with the original AUSM algorithm developed for high-speed applications has been constructed so that the resulting algorithm can be used at all speeds

1k x 1-bit static RAM

Random search optimization approach for highly multi-modal nonlinear problems

Achieving high robustness and performance in QoS-aware route planning for IPTV networks

The article firstly introduces two respective theories on research and evolvement about Wolf Pack Intelligent Optimization Algorithm, and compares with the differences and similarities between the two theories. It also illustrates the differences in performance on optimization solution through the experimental result. Then, it concludes the improvement research on wolf pack algorithm, focused on the improvement on parameter setting and hybrid algorithm. Thirdly, the article elaborates the typical applications about wolf pack algorithm on function optimization, combination optimization and engineering optimization. At last, the article summarizes the deficiency in the research and proposes the research goal of next step.

Computational geometry: Algorithms and applications: By Mark de Berg, Marc van Kreveld, Mark Overmars and Otfried Schwarzkopf. Springer, Berlin. (1997). 365 pages. $32.95

Abstract—An evolutionary algorithm with guided mutation (EA/G) has been proposed recently for solving the maximum clique problem. In the framework of estimation-of-distribution algorithms (EDA), guided mutation uses a model distribution to generate the offspring by combining the local information of solutions found so far with global statistical information. Each individual is then subjected to a Marchiori’s repair heuristic, based on randomized extraction and greedy expansion, to ensure that it represents a legal clique. The novel reactive and evolutionary algorithm (R-EVO) proposed in this paper starts from the same evolutionary framework but considers more complex individuals, which modify tentative solutions by local search with memory, in the reactive search framework. In particular, the estimated distribution is used to periodically initialize the state of each individual based on the previous statistical knowledge extracted from the population. We demonstrate that the combination of the estimation-ofdistribution concept with reactive search produces significantly better results than EA/G and is remarkably robust w.r.t. the setting of the algorithm parameters. R-EVO adopts a drastically simplified low-knowledge version of reactive local search (RLS), with a simple internal diversification mechanism based on tabu-search, with a prohibition parameter proportional to the estimated best clique size. R-EVO is competitive with the more complex full-knowledge RLS-EVO which adopts the original RLS algorithm. For most of the benchmark instances, the hybrid scheme version produces significantly better results than EA/G for comparable or smaller CPU times.

Following the breakthrough work of Tardos (Oper. Res. '86) in the bit-complexity model, Vavasis and Ye (Math. Prog. '96) gave the first exact algorithm for linear programming in the real model of computation with running time depending only on the constraint matrix. For solving a linear program (LP) max cx, Ax = b, x ≥ 0, A g m × n, Vavasis and Ye developed a primal-dual interior point method using a g€layered least squares' (LLS) step, and showed that O(n3.5 log(χA+n)) iterations suffice to solve (LP) exactly, where χA is a condition measure controlling the size of solutions to linear systems related to A. Monteiro and Tsuchiya (SIAM J. Optim. '03), noting that the central path is invariant under rescalings of the columns of A and c, asked whether there exists an LP algorithm depending instead on the measure χA∗, defined as the minimum χAD value achievable by a column rescaling AD of A, and gave strong evidence that this should be the case. We resolve this open question affirmatively. Our first main contribution is an O(m2 n2 + n3) time algorithm which works on the linear matroid of A to compute a nearly optimal diagonal rescaling D satisfying χAD ≤ n(χ∗)3. This algorithm also allows us to approximate the value of χA up to a factor n (χ∗)2. This result is in (surprising) contrast to that of Tunçel (Math. Prog. '99), who showed NP-hardness for approximating χA to within 2poly(rank(A)). The key insight for our algorithm is to work with ratios gi/gj of circuits of A - i.e., minimal linear dependencies Ag=0 - which allow us to approximate the value of χA∗ by a maximum geometric mean cycle computation in what we call the g€circuit ratio digraph' of A. While this resolves Monteiro and Tsuchiya's question by appropriate preprocessing, it falls short of providing either a truly scaling invariant algorithm or an improvement upon the base LLS analysis. In this vein, as our second main contribution we develop a scaling invariant LLS algorithm, which uses and dynamically maintains improving estimates of the circuit ratio digraph, together with a refined potential function based analysis for LLS algorithms in general. With this analysis, we derive an improved O(n2.5 lognlog(χA∗+n)) iteration bound for optimally solving (LP) using our algorithm. The same argument also yields a factor n/logn improvement on the iteration complexity bound of the original Vavasis-Ye algorithm.

In this internet world, most of the transactions are done in online especially in ecommerce field which creates enormous size of transactional database that grows dynamically at each second. Really it is tough job to analyses the growing transactional database for the betterment of ecommerce business growth. Determining the Interesting pattern form incremental transactional database is evolving research area in the field of Data mining. Frequent Itemset Mining (FIM) approach is followed in earlier days to find the frequent pattern from transactional database. FIM has significant drawback of omitting the interesting factor about each items, such as quantity, price, profit and etc. This drawback is addressed in High Utility Itemset Mining (HUIM) which considers interesting factors of each item. This paper focuses on reviewing the existing state of art algorithms to create a path for the future research in the area of high utility itemset mining.

This paper describes the further development of the virtual flow meter concept based on the author’s simulator of an unsteady gas–liquid flow in wells. The results of comparison with commercial simulators based on real well data are given as practical applications. The results of the comparison of the simulators demonstrated high correspondence (<10% error) for a number of target parameters. The description of the architecture and results of testing the algorithm for automatic settings of the model parameters are given. Operating speed was the key criterion in the architecture development. According to the test results, it became possible to achieve the adaptation accuracy of 5% specified.

This paper aims to improve the accuracy and the efficiency of high resolution land cover mapping in urban area. To this end, an improved approach for classification of hyperspectral imagery is proposed and evaluated. This approach benefits from both inherent spectral and spatial information of an image. The weighted genetic (WG) algorithm is first used to obtain the subspace of hyperspectral data. The obtained features are then fed into the enhanced marker-based minimum spanning forest (EMSF) classification algorithm. In this algorithm, the markers are extracted from the classification maps obtained by both support vector machine and watershed segmentation algorithm classifiers. For this purpose, the class’s pixels with the largest population in the classification map are kept for each region of the segmentation map. Then, the most reliable classified pixels are chosen from among the exiting pixels as markers. To evaluate the efficiency of the proposed approach, three hyperspectral data sets acquired by ROSIS-03, Hymap and Hyper-Cam LWIR are used. Experimental results showed that the proposed WG–EMSF approach achieves approximately 9, 8 and 6% better overall accuracy than the original MSF-based algorithm for these data sets respectively.

This paper introduced points and directed line segment relation judgment method, the characteristics of generation and Graham method using the original convex hull generation algorithm of convex hull discrete points of the convex hull, an improved algorithm for planar discrete point set is proposed. The main idea is to use quadrilateral to divide planar discrete point set into five blocks, and then by judgment in addition to the four district quadrilateral internally within the point is in a convex edge. The result shows that the method is relatively simple program, high computational efficiency.

Recent progress in quantum physics shows that quantum computers may be a reality in the not too distant future. Post-quantum cryptography (PQC) refers to cryptographic schemes that are based on hard problems which are believed to be resistant to attacks from quantum computers. The supersingular isogeny Diffie-Hellman (SIDH) key exchange protocol shows promising security properties among various post-quantum cryptosystems that have been proposed. In this paper, we propose two efficient modular multiplication algorithms with special primes that can be used in SIDH key exchange protocol. Hardware architectures for the two proposed algorithms are also proposed. The hardware implementations are provided and compared with the original modular multiplication algorithm. The results show that the proposed finite field multiplier is over 6.79 times faster than the original multiplier in hardware. Moreover, the SIDH hardware/software codesign implementation using the proposed FFM2 hardware is over 31 percent faster than the best SIDH software implementation.