1,226 research outputs found
Scheme for preparation of mulipartite entanglement of atomic ensembles
We describe an experimental scheme of preparing multipartite W class of
maximally entangled states between many atomic ensembles. The scheme is based
on laser manipulation of atomic ensembles and single-photon detection, and well
fits the status of the current experimental technology. In addition, we show
one of the applications of the kind of W class states, teleporting an entangled
state of atomic ensembles with unknown coefficients to more than one distant
parties, either one of which equally likely receives the transmitted state.Comment: 4 pages, 3 figure
Automatic Detection of Public Development Projects in Large Open Source Ecosystems: An Exploratory Study on GitHub
Hosting over 10 million of software projects, GitHub is one of the most
important data sources to study behavior of developers and software projects.
However, with the increase of the size of open source datasets, the potential
threats to mining these datasets have also grown. As the dataset grows, it
becomes gradually unrealistic for human to confirm quality of all samples. Some
studies have investigated this problem and provided solutions to avoid threats
in sample selection, but some of these solutions (e.g., finding development
projects) require human intervention. When the amount of data to be processed
increases, these semi-automatic solutions become less useful since the effort
in need for human intervention is far beyond affordable. To solve this problem,
we investigated the GHTorrent dataset and proposed a method to detect public
development projects. The results show that our method can effectively improve
the sample selection process in two ways: (1) We provide a simple model to
automatically select samples (with 0.827 precision and 0.947 recall); (2) We
also offer a complex model to help researchers carefully screen samples (with
63.2% less effort than manually confirming all samples, and can achieve 0.926
precision and 0.959 recall).Comment: Accepted by the SEKE2018 Conferenc
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