CiteSeerX Scientific Literature Digital Library and Search Engine
CiteSeerX is a scientific literature digital library and search engine that focuses primarily on the literature in computer and information science. CiteSeerx aims to improve the dissemination of scientific literature and to provide improvements in functionality, usability, availability, cost, comprehensiveness, efficiency, and timeliness in the access of scientific and scholarly knowledge
Consideration of Receiver Interest for IP Multicast Delivery - Brian Neil Levine; Jon Crowcroft; Christophe Diot; J.J. Garcia-Luna-Aceves; James F. Kurose
Large-scale applications are characterized by a large number of dynamic and often interactive group members. The nature of these applications is such that participants are not interested in all the content transmitted. We examine three currently available techniques to scope delivery of content to interested receivers in IP multicast: filtering, where data is filtered by middleware before passed to the application; addressing, where data is routed only to those receivers that express their interest; and hybrid approaches. We propose a framework that models large-scale application behavior. We use this framework to evaluate the performance of these applications and related protocols...
Complete geometrical query languages (Extended Abstract) - Marc Gyssens; Jan Van den Bussche; Dirk van Gucht
We introduce query languages for spatial databases that are complete, in the sense that they can express precisely all computable queries that are generic with respect to certain classes of transformation8 of space, corresponding to certain geometric interpretations of spatial data. We thus extend Chandra and Hare & seminal work on computable queries for relational databases to a spatial setting. We use a constraint-based spatial data model which models spatial data as semi-algebraic relations over the real numbers. We also introduce natural point-based geometric query languages that are complete relative to the basic class of queries expressible in the relational...
Complete geometric query languages - Marc Gyssens; Jan Van Den Bussche; Dirk Van Gucht
We extend Chandra and Harel's seminal work on computable queries for relational databases to a setting in which also spatial data may be present, using a constraint-based data model. Concretely, we introduce both coordinate-based and point-based query languages that are complete in the sense that they can express precisely all computable queries that are generic with respect to certain classes of transformations of space, corresponding to certain geometric interpretations of spatial data. The languages we introduce are obtained by augmenting basic languages with a while construct. We also show that the respective basic point-based languages are complete relative to the...
Declarative Specification of Z39.50 Wrappers using Description Logics - Yannis Velegrakis; Vassilis Christophides; Panos Constantopoulos
Z39.50 is a client/server protocol widely used in digital libraries and museums for searching and retrieving information spread over a number of heterogeneous sources. To overcome semantic and schematic discrepancies among the various data sources the protocol relies on a world view of information as a at list of fields, called Access Points (AP). One of the major issues for building Z39.50 wrappers is to map this unstructured list of APs to the underlying source data structure and semantics. For highly structured sources (e.g. Database Management Systems, Knowledge Base Systems) this mapping is quite complex and considerably affects the quality...
APPROXIMATE INVERSION OF A LARGE SEMISEPARABLE POSITIVE MATRIX - Alle-jan Van Der Veen
The inversion of a large (n × n) positive matrix is considered. We assume that the matrix has a semi-separable structure, which implies that all submatrices away from the main diagonal have rank less than q (the matrix itself may be full). In practice, a specified matrix will not exactly have low-rank submatrices. Given a threshold and a positive matrix T, the submatrices of T are rank truncated to this threshold (balanced model reduction) and the inverse of a Cholesky factor of T is computed using time-varying state-space techniques. The proposed algorithm requires O(n 2 q) operations, where q is...
Subspace Tracking Using A Constrained Hyperbolic URV Decomposition - Alle-jan Van Der Veen
The class of Schur subspace estimators provides a parametrization of all minimal-rank matrix approximants that lie within a specified distance of a given matrix, and in particular gives expressions for the column spans of these approximants. In this paper, we derive an updating algorithm for an interesting member of the class, making use of a constrained hyperbolic URV-like decomposition.
A SCHUR METHOD FOR LOW-RANK MATRIX APPROXIMATION - Alle-Jan van der Veen
The usual way to compute a low-rank approximant of a matrix H is to take its singular value decomposition (SVD) and truncate it by setting the small singular values equal to 0. However, the SVD is computationally expensive. This paper describes a much simpler generalized Schur-type algorithm to compute similar low-rank approximants. For a given matrix H which has d singular values larger than ε, we find all rank d approximants ˆH such that H − ˆH has 2-norm less than ε. The set of approximants includes the truncated SVD approximation. The advantages of the Schur algorithm are that it...
Internet Multicasting Based on Group-Relative Addressing - Brian Neil Levine; J. J. Garcia-Luna-Aceves
We introduce the Addressable Internet Multicast (AIM) architecture, which extends the IP-multicast architecture with group-relative addressing information along multicast routing trees. AIM permits more efficient and sophisticated multicast routing options and encourages communication and cooperation between IP and higher-layer protocols. AIM enables sources to restrict the delivery of packets to a subset of the receivers in a multicast group on a per-packet basis. AIM also permits receivers to listen to subsets of sources on a subscription basis, and quick formation of new multicast groups defined within the original routing tree. AIM also provides a versatile, scalable anycast service. This new...
ACCELERATING MACHINES - Robert Fraser; Selim G. Akl
This paper presents an overview of accelerating machines. We begin by exploring the history of the accelerating machine model and the potential power that it provides. We look at some of the problems that could be solved with an accelerating machine, and review some of the possible implementation methods that have been presented. Finally, we expose the limitations of accelerating machines and conclude by posing some problems for further research.
The Large Deviations of Estimating Rate-Functions - Ken Duffy; Anthony P. Metcalfe
Given a sequence of bounded random variables that satisfies a well known mixing condition, it is shown that empirical estimates of the rate-function for the partial sums process satisfies the large deviation principle in the space of convex functions equipped with the Attouch-Wets topology. As an
Computing Differential Properties of 3-D Shapes from Stereoscopic Images without 3-D Models - Frédéric Devernay; Olivier Faugeras
We are considering the problem of recovering the three-dimensional geometry of a scene from binocular stereo disparity. Once a dense disparity map has been computed from a stereo pair of images, one often needs to calculate some local differential properties of the corresponding 3-D surface such as orientation or curvatures. The usual approach is to build a 3-D reconstruction of the surface(s) from which all shape properties will then be derived without ever going back to the original images. In this paper, we depart from this paradigm and propose to use the images directly to compute the shape properties. We...
Weighted ACMA - Alle-Jan van der Veen
The analytical constant modulus algorithm (ACMA) is a deterministic array processing algorithm to separate sources based on their constant modulus. It has been derived without detailed regard to noise processing. In particular, the estimates of the beamformer are known to be asymptotically biased. In the present paper, we investigate this bias, and obtain a straightforward weighting scheme that will whiten the noise and remove the bias. This leads to improved performance for larger data sets.