4,281 research outputs found
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 (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
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