Reactor Neutrino Experiments: Present and Future

Reactor neutrinos have been an important tool for both discovery and precision measurement in the history of neutrino studies. Since the first generation of reactor neutrino experiments in the 1950s, the detector technology has been greatly advanced. New ideas, new knowledge, and modern software also enhanced the power of the experiments. The current reactor neutrino experiments, Daya Bay, Double Chooz, and RENO have led neutrino physics into the precision era. In this article, we will review these developments and accumulations, address the key issues in designing a state-of-art reactor neutrino experiment, and explain how the challenging requirements of determining the neutrino mass hierarchy with the next generation experiment JUNO could be realized in the near future.Comment: 37 pages, 7 figures. This is the original version, and the final version was published in Annual Review of Nuclear and Particle Science, Vol.67:183-211. According to the copyright agreement, the e-print URL of the final article is posted: http://www.annualreviews.org/eprint/NAcP3pbUGgA3Utpmuhuz/full/10.1146/annurev-nucl-101916-12331

Ultra accurate collaborative information filtering via directed user similarity

A key challenge of the collaborative filtering (CF) information filtering is how to obtain the reliable and accurate results with the help of peers' recommendation. Since the similarities from small-degree users to large-degree users would be larger than the ones opposite direction, the large-degree users' selections are recommended extensively by the traditional second-order CF algorithms. By considering the users' similarity direction and the second-order correlations to depress the influence of mainstream preferences, we present the directed second-order CF (HDCF) algorithm specifically to address the challenge of accuracy and diversity of the CF algorithm. The numerical results for two benchmark data sets, MovieLens and Netflix, show that the accuracy of the new algorithm outperforms the state-of-the-art CF algorithms. Comparing with the CF algorithm based on random-walks proposed in the Ref.7, the average ranking score could reach 0.0767 and 0.0402, which is enhanced by 27.3\% and 19.1\% for MovieLens and Netflix respectively. In addition, the diversity, precision and recall are also enhanced greatly. Without relying on any context-specific information, tuning the similarity direction of CF algorithms could obtain accurate and diverse recommendations. This work suggests that the user similarity direction is an important factor to improve the personalized recommendation performance.Comment: 6 pages, 4 figure