79,152 research outputs found
Inner Disk Oscillations and QPOs in Relativistic Jet Sources
Recent results on the inner disk oscillations found in GRS 1915+105 are
reviewed. QPOs during the low state are used as a marker for such oscillations
and the physical picture emerging from a combined X-ray spectral and timing
analysis is examined. The relationship between inner disk oscillations and
synchrotron radio emission is critically evaluated.Comment: 8 pages, LaTeX, kluwer.cls, 2 figures, to be published in
`Proceedings of the Third Microquasar Workshop: Granada Workshop on galactic
relativistic jet sources', Eds A.J. Castro-Tirado, J. Greiner and J.M.
Paredes, Astrophysics and Space Science, in pres
Extendability of quadratic modules over a polynomial extension of an equicharacteristic regular local ring
We prove that a quadratic -module with Witt index (), where is the dimension of the equicharacteristic regular local ring
, is extended from . This improves a theorem of the second named author
who showed it when is the local ring at a smooth point of an affine variety
over an infinite field. To establish our result, we need to establish a
Local-Global Principle (of Quillen) for the Dickson--Siegel--Eichler--Roy
(DSER) elementary orthogonal transformations.Comment: 19 page
Predicting Item Popularity: Analysing Local Clustering Behaviour of Users
Predicting the popularity of items in rating networks is an interesting but
challenging problem. This is especially so when an item has first appeared and
has received very few ratings. In this paper, we propose a novel approach to
predicting the future popularity of new items in rating networks, defining a
new bipartite clustering coefficient to predict the popularity of movies and
stories in the MovieLens and Digg networks respectively. We show that the
clustering behaviour of the first user who rates a new item gives insight into
the future popularity of that item. Our method predicts, with a success rate of
over 65% for the MovieLens network and over 50% for the Digg network, the
future popularity of an item. This is a major improvement on current results.Comment: 25 pages, 11 figure
Identifying Influential Nodes in Bipartite Networks Using the Clustering Coefficient
The identification of influential nodes in complex network can be very
challenging. If the network has a community structure, centrality measures may
fail to identify the complete set of influential nodes, as the hubs and other
central nodes of the network may lie inside only one community. Here we define
a bipartite clustering coefficient that, by taking differently structured
clusters into account, can find important nodes across communities
- …