48 citations · 152 across the 18 of their papers we have counts for
5 papers · 1 filter
Non-parametric Message Important Measure: Storage Code Design and Transmission Planning for Big Data
Shanyun Liu, Rui She, Pingyi Fan +1
Storage and transmission in big data are discussed in this paper, where message importance is taken into account. Similar to Shannon Entropy and Renyi Entropy, we define non-parame…
Amplifying Inter-message Distance: On Information Divergence Measures in Big Data
Rui She, Shanyun Liu, Pingyi Fan
Message identification (M-I) divergence is an important measure of the information distance between probability distributions, similar to Kullback-Leibler (K-L) and Renyi divergenc…
Chandra Survey of Nearby Galaxies: A Significant Population of Candidate Central Black Holes in Late-type Galaxies
Rui She, Luis C. Ho, Hua Feng
Based on the Chandra data archive as of March 2016, we have identified 314 candidate active galactic nuclei in 719 galaxies located closer than 50 Mpc, among them late-type (Hubble…
Shannon Shakes Hands with Chernoff: Big Data Viewpoint On Channel Information Measures
Shanyun Liu, Rui She, Jiaxun Lu +1
Shannon entropy is the most crucial foundation of Information Theory, which has been proven to be effective in many fields such as communications. Renyi entropy and Chernoff inform…
Focusing on a Probability Element: Parameter Selection of Message Importance Measure in Big Data
Rui She, Shanyun Liu, Yunquan Dong +1
Message importance measure (MIM) is applicable to characterize the importance of information in the scenario of big data, similar to entropy in information theory. In fact, MIM wit…