5,131 research outputs found
Interface between astrophysical datasets and distributed database management systems (DAVID)
This is a status report on the progress of the DAVID (Distributed Access View Integrated Database Management System) project being carried out at Louisiana State University, Baton Rouge, Louisiana. The objective is to implement an interface between Astrophysical datasets and DAVID. Discussed are design details and implementation specifics between DAVID and astrophysical datasets
Gorenstein algebras and Hochschild cohomology
For homomorphism K-->S of commutative rings, where K is Gorenstein and S is
essentially of finite type and flat as a K-module, the property that all
non-trivial fiber rings of K-->S are Gorenstein is characterized in terms of
properties of the cohomology modules Ext_n^{S\otimes_KS}S{S\otimes_KS}.Comment: This is the published version, except for updates to references and
bibliography. Sections 3, 4 and 8 have been removed from the preceding
version, arXiv:0704.3761v2. Substantial generalizations of results in those
sections are proved in our paper with Joseph Lipman and Suresh Nayak,
arXiv:0904.400
Degree Ranking Using Local Information
Most real world dynamic networks are evolved very fast with time. It is not
feasible to collect the entire network at any given time to study its
characteristics. This creates the need to propose local algorithms to study
various properties of the network. In the present work, we estimate degree rank
of a node without having the entire network. The proposed methods are based on
the power law degree distribution characteristic or sampling techniques. The
proposed methods are simulated on synthetic networks, as well as on real world
social networks. The efficiency of the proposed methods is evaluated using
absolute and weighted error functions. Results show that the degree rank of a
node can be estimated with high accuracy using only samples of the
network size. The accuracy of the estimation decreases from high ranked to low
ranked nodes. We further extend the proposed methods for random networks and
validate their efficiency on synthetic random networks, that are generated
using Erd\H{o}s-R\'{e}nyi model. Results show that the proposed methods can be
efficiently used for random networks as well
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