Discovery potential for supernova relic neutrinos with slow liquid scintillator detectors

Detection of supernova relic neutrinos could provide key support for our current understanding of stellar and cosmological evolution, and precise measurements of these neutrinos could yield novel insights into the universe. In this paper, we studied the detection potential of supernova relic neutrinos using linear alkyl benzene (LAB) as a slow liquid scintillator. The linear alkyl benzene features good separation of Cherenkov and scintillation lights, thereby providing a new route for particle identification. We further addressed key issues in current experiments, including (1) the charged current background of atmospheric neutrinos in water Cherenkov detectors and (2) the neutral current background of atmospheric neutrinos in typical liquid scintillator detectors. A kiloton-scale LAB detector at Jinping with $\mathcal{O}$(10) years of data could discover supernova relic neutrinos with a sensitivity comparable to that of large-volume water Cherenkov detectors, typical liquid scintillator detectors, and liquid argon detectors.Comment: 9 pages, 6 figure

Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations

Collaborative filtering algorithms haven been widely used in recommender systems. However, they often suffer from the data sparsity and cold start problems. With the increasing popularity of social media, these problems may be solved by using social-based recommendation. Social-based recommendation, as an emerging research area, uses social information to help mitigate the data sparsity and cold start problems, and it has been demonstrated that the social-based recommendation algorithms can efficiently improve the recommendation performance. However, few of the existing algorithms have considered using multiple types of relations within one social network. In this paper, we investigate the social-based recommendation algorithms on heterogeneous social networks and proposed Hete-CF, a Social Collaborative Filtering algorithm using heterogeneous relations. Distinct from the exiting methods, Hete-CF can effectively utilize multiple types of relations in a heterogeneous social network. In addition, Hete-CF is a general approach and can be used in arbitrary social networks, including event based social networks, location based social networks, and any other types of heterogeneous information networks associated with social information. The experimental results on two real-world data sets, DBLP (a typical heterogeneous information network) and Meetup (a typical event based social network) show the effectiveness and efficiency of our algorithm

Hete-CF : Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations

The work described here was funded by the National Natural Science Foundation of China (NSFC) under Grant No. 61373051; the National Science and Technology Pillar Program (Grant No.2013BAH07F05), the Key Laboratory for Symbolic Computation and Knowledge Engineering, Ministry of Education, China, and the UK Economic & Social Research Council (ESRC); award reference: ES/M001628/1.Preprin