32837558
On average P vs. average NP
Structures of polynomial-time computable distributions and polynomial-time many-one reductions on randomized decision problems are investigated. The scope is widened from the most studied class DNP (distributional-NP) to the class ANP (average-NP), which consists of randomized decision problems accepted by nondeterministic Turing machines in average polynomial time. Results indicate that the structures of average-case complexity classes are very different from their counterparts in worst-case complexity due to the complex structures of probability distributions and distribution functions.<<ETX>>
17604851
Efficient point coverage in wireless sensor networks
We study minimum-cost sensor placement on a bounded 3D sensing field, R, which comprises a number of discrete points that may or may not be grid points. Suppose we have ℓ types of sensors available with different sensing ranges and different costs. We want to find, given an integer σ ≥ 1, a selection of sensors and a subset of points to place these sensors such that every point in R is covered by at least σ sensors and the total cost of the sensors is minimum. This problem is known to be NP-hard. Let ki denote the maximum number of points that can be covered by a sensor of the ith type. We present in this paper a polynomial-time approximation algorithm for this problem with a proven approximation ratio $${\gamma = \sum_{i=1}^{\ell} k_{i} -\sigma+1}$$. In applications where the distance of any two points has a fixed positive lower bound, each ki is a constant, and so we have a polynomial-time approximation algorithms with a constant guarantee. While γ may be large, we note that it is only a worst-case upper bound. In practice the actual approximation ratio is small, even on randomly generated points that do not have a fixed positive minimum distance between them. We provide a number of numerical results for comparing approximation solutions and optimal solutions, and show that the actual approximation ratios in these examples are all less than 3, even though γ is substantially larger.
206565969
Immunity of Complete Problems
Abstract Two necessary and sufficient conditions for all E -complete sets to contain dense P subsets are shown. We then prove that every ≤ p m -hard for E set and its complement contain dense E ∩UP subsets. As a corollary, every NE -complete set and its complement contain dense E ∩UP subsets.
37775039
An approximate search engine for structural databases
When a person interested in a topic enters a keyword into a Web search engine, the response is nearly instantaneous (and sometimes overwhelming). The impressive speed is due to clever inverted index structures, caching, and a domain-independent knowledge of strings. Our project seeks to construct algorithms, data structures, and software that approach the speed of keyword-based search engines for queries on structural databases.
A structural database is one whose data objects include trees, graphs, or a set of interrelated labeled points in two, three, or higher dimensional space. Examples include databases holding (i) protein secondary and tertiary structure, (ii) phylogenetic trees, (iii) neuroanatomical networks, (iv) parse trees, (v) molecular diagrams, and (vi) XML documents. Comparison queries on such databases require solving variants of the graph isomorphism or subisomorphism problems (for which all known algorithms are exponential), so we have explored a large heuristic space.
26197775
Using Neural Networks to Determine Sugeno Measures by Statistics
Abstract To replace the traditional weighted average method, Choquet integrals or Sugeno integrals with respect to fuzzy measures are used to obtain a synthetic evaluation of a given object (or its quality, function, etc. respectively) with multi-attribute. Generally, it is not easy to determine fuzzy measures in real problems due to the subjectivity of human thinking. It is even much more difficult than determining weights in the weighted average method, because of the nonadditivity of fuzzy measures. This paper uses a neural network algorithm to optimize the inverse problem of synthetic evaluation, and thus to determine Sugeno measures by the Choquet integral and statistics of given data. Since the Choquet integral is a generalization of the weighted average method, this technology has a broad applicability in areas of multivariate analysis, decision making, pattern recognition, image and speech processing and expert systems. Copyright © 1996 Elsevier Science Ltd.
45865876
Graph theoretic characterization and reliability of the generalized Boolean n-cube network
In this paper, a general class of Boolean n-cube structures is investigated. The interconnection is based on a mixed radix number system which results in a variety of generalized Boolean n-cube structures for a given number of processors N = x·2y, where x and y are positive integers. A number of interesting properties of the network are revealed. By a constructive method, the node connectivity of this network is found. Finally, we show that the graph is super-λ and is an optimal reliable structure for interconnection networks.
2328843
Data-rich section extraction from HTML pages
We propose a novel algorithm, DSE (data-rich subtree extraction) to recognize and extract the data-rich section of an HTML page. We apply the DSE algorithm as a pre-processing "clean-up" step for two typical Web information retrieval problems: topic distillation and Web information extraction. Our experiments show that, for the test data sets used, the DSE algorithm can correctly identify data-rich sections of HTML pages with 100% accuracy. Therefore, it can effectively reduce the root set size for the topic distillation problem thereby improving the precision and accuracy of the IETS algorithm. Furthermore, when applied to the Web information extraction problem using the IEPAD algorithm, it can decrease the number of patterns discovered by this algorithm, thus shortening its time cost to generalize a wrapper for HTML pages.
5498425
Representing moving images with layers
We describe a system for representing moving images with sets of overlapping layers. Each layer contains an intensity map that defines the additive values of each pixel, along with an alpha map that serves as a mask indicating the transparency. The layers are ordered in depth and they occlude each other in accord with the rules of compositing. Velocity maps define how the layers are to be warped over time. The layered representation is more flexible than standard image transforms and can capture many important properties of natural image sequences. We describe some methods for decomposing image sequences into layers using motion analysis, and we discuss how the representation may be used for image coding and other applications.