11,532 research outputs found
Searching for sub-millisecond pulsars from highly polarized radio sources
Pulsars are among the most highly polarized sources in the universe. The NVSS
has catalogued 2 million radio sources with linear polarization measurements,
from which we have selected 253 sources, with polarization percentage greater
than 25%, as targets for pulsar searches. We believe that such a sample is not
biased by selection effects against ultra-short spin or orbit periods. Using
the Parkes 64m telescope, we conducted searches with sample intervals of 0.05
ms and 0.08 ms, sensitive to submillisecond pulsars. Unfortunately we did not
find any new pulsars.Comment: 2 pages 1 figure. To appear in "Young Neutron Stars and Their
Environments" (IAU Symposium 218, ASP Conference Proceedings), eds F. Camilo
and B. M. Gaensle
DDSL: Efficient Subgraph Listing on Distributed and Dynamic Graphs
Subgraph listing is a fundamental problem in graph theory and has wide
applications in areas like sociology, chemistry, and social networks. Modern
graphs can usually be large-scale as well as highly dynamic, which challenges
the efficiency of existing subgraph listing algorithms. Recent works have shown
the benefits of partitioning and processing big graphs in a distributed system,
however, there is only few work targets subgraph listing on dynamic graphs in a
distributed environment. In this paper, we propose an efficient approach,
called Distributed and Dynamic Subgraph Listing (DDSL), which can incrementally
update the results instead of running from scratch. DDSL follows a general
distributed join framework. In this framework, we use a Neighbor-Preserved
storage for data graphs, which takes bounded extra space and supports dynamic
updating. After that, we propose a comprehensive cost model to estimate the I/O
cost of listing subgraphs. Then based on this cost model, we develop an
algorithm to find the optimal join tree for a given pattern. To handle dynamic
graphs, we propose an efficient left-deep join algorithm to incrementally
update the join results. Extensive experiments are conducted on real-world
datasets. The results show that DDSL outperforms existing methods in dealing
with both static dynamic graphs in terms of the responding time
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