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