We observed the H2CO(110-111) absorption lines and H110{alpha} radio recombination lines (RRL) toward 180 NH3 sources using the Nanshan 25-m radio telescope. In our observation, 138 sources were found to have H2CO lines and 36 have H110{alpha} RRLs.
Among the 138 detected H2CO sources, 38 sources were first detected. The detection rates of H2CO have a better correlation with extinction than with background continuum radiation. Line center velocities of H2CO and NH3 agree well. The line width ratios of H2CO and NH3 are generally larger than 1 and are similar to that of 13CO. The correlation between column densities of H2CO and extinction is better than that between NH3 and extinction. These line width relation and column density relation indicate H2CO is distributed on a larger scale than that of NH3, being similar to the regions of 13CO. The abundance ratios between NH3 and H2CO were found to be different in local clouds and other clouds.
A pattern matching based tracking algorithm, named MdcPatRec, is used for the reconstruction of charged tracks in the drift chamber of the BESIII detector. This paper addresses the shortage of segment finding in MdcPatRec algorithm. An extended segme
nt construction scheme and the corresponding pattern dictionary are presented. Evaluation with Monte-Carlo and experimental data show that the new method can achieve higher efficiency for low transverse momentum tracks.
Many studies have been conducted on seeking the efficient solution for subgraph similarity search over certain (deterministic) graphs due to its wide application in many fields, including bioinformatics, social network analysis, and Resource Descript
ion Framework (RDF) data management. All these works assume that the underlying data are certain. However, in reality, graphs are often noisy and uncertain due to various factors, such as errors in data extraction, inconsistencies in data integration, and privacy preserving purposes. Therefore, in this paper, we study subgraph similarity search on large probabilistic graph databases. Different from previous works assuming that edges in an uncertain graph are independent of each other, we study the uncertain graphs where edges occurrences are correlated. We formally prove that subgraph similarity search over probabilistic graphs is #P-complete, thus, we employ a filter-and-verify framework to speed up the search. In the filtering phase,we develop tight lower and upper bounds of subgraph similarity probability based on a probabilistic matrix index, PMI. PMI is composed of discriminative subgraph features associated with tight lower and upper bounds of subgraph isomorphism probability. Based on PMI, we can sort out a large number of probabilistic graphs and maximize the pruning capability. During the verification phase, we develop an efficient sampling algorithm to validate the remaining candidates. The efficiency of our proposed solutions has been verified through extensive experiments.
