B_{s1}(5830) and B_{s2}^*(5840)

In this paper we investigate the strong decays of the two newly observed bottom-strange mesons $B_{s1}(5830)$ and $B_{s2}^*(5840)$ in the framework of the quark pair creation model. The two-body strong decay widths of $B_{s1}(5830)^0\to B^{*+}K^-$ and $B_{s2}^*(5840)^0\to B^+K^-, B^{*+}K^-$ are calculated by considering $B_{s1}(5830)$ to be a mixture between $|^1P_1>$ and $|^3P_1>$ states, and $B_{s2}^*(5840)$ to be a $|^3P_2>$ state. The double pion decay of $B_{s1}(5830)$ and $B_{s2}^*(5840)$ is supposed to occur via the intermediate state $\sigma$ and $f_0(980)$. Although the double pion decay widths of $B_{s1}(5830)$ and $B_{s2}^*(5840)$ are smaller than the two-body strong decay widths of $B_{s1}(5830)$ and $B_{s2}^*(5840)$, one suggests future experiments to search the double pion decays of $B_{s1}(5830)$ and $B_{s2}^*(5840)$ due to their sizable decay widths.Comment: 9 pages, 8 figures and 6 tables. More references and discussions added, typos corrected, some descriptions changed. Publication version in PR

X(3915) and X(4350) as new members in P-wave charmonium family

The analysis of the mass spectrum and the calculation of the strong decay of P-wave charmonium states strongly support to explain the newly observed X(3915) and X(4350) as new members in P-wave charmonium family, i.e., $\chi_{c0}^\prime$ for X(3915) and $\chi_{c2}^{\prime\prime}$ for X(4350). Under the P-wave charmonium assignment to X(3915) and X(4350), the $J^{PC}$ quantum numbers of X(3915) and X(4350) must be $0^{++}$ and $2^{++}$ respectively, which provide the important criterion to test P-wave charmonium explanation for X(3915) and X(4350) proposed by this letter. The decay behavior of the remaining two P-wave charmonium states with the second radial excitation is predicted, and experimental search for them is suggested.Comment: 4 pages, 2 figures, 2 tables. More references and discussions added, typos corrected. Accepted for publication in Phys. Rev. Lett

Interchange reconnection associated with a confined filament eruption: Implications for the source of transient cold-dense plasma in solar winds

The cold-dense plasma is occasionally detected in the solar wind with in situ data, but the source of the cold-dense plasma remains illusive. Interchange reconnections (IRs) between closed fields and nearby open fields are well known to contribute to the formation of solar winds. We present a confined filament eruption associated with a puff-like coronal mass ejection (CME) on 2014 December 24. The filament underwent successive activations and finally erupted, due to continuous magnetic flux cancellations and emergences. The confined erupting filament showed a clear untwist motion, and most of the filament material fell back. During the eruption, some tiny blobs escaped from the confined filament body, along newly-formed open field lines rooted around the south end of the filament, and some bright plasma flowed from the north end of the filament to remote sites at nearby open fields. The newly-formed open field lines shifted southward with multiple branches. The puff-like CME also showed multiple bright fronts and a clear southward shift. All the results indicate an intermittent IR existed between closed fields of the confined erupting filament and nearby open fields, which released a portion of filament material (blobs) to form the puff-like CME. We suggest that the IR provides a possible source of cold-dense plasma in the solar wind

Multi-scale Deep Learning Architectures for Person Re-identification

Person Re-identification (re-id) aims to match people across non-overlapping camera views in a public space. It is a challenging problem because many people captured in surveillance videos wear similar clothes. Consequently, the differences in their appearance are often subtle and only detectable at the right location and scales. Existing re-id models, particularly the recently proposed deep learning based ones match people at a single scale. In contrast, in this paper, a novel multi-scale deep learning model is proposed. Our model is able to learn deep discriminative feature representations at different scales and automatically determine the most suitable scales for matching. The importance of different spatial locations for extracting discriminative features is also learned explicitly. Experiments are carried out to demonstrate that the proposed model outperforms the state-of-the art on a number of benchmarksComment: 9 pages, 3 figures, accepted by ICCV 201