BcB_c Exclusive Decays to Charmonium and a Light Meson at Next-to-Leading Order Accuracy

In this paper the next-to-leading order (NLO) corrections to $B_c$ meson exclusive decays to S-wave charmonia and light pseudoscalar or vector mesons, i.e. $\pi$, $K$, $\rho$, and $K^*$, are performed within non-relativistic (NR) QCD approach. The non-factorizable contribution is included, which is absent in traditional naive factorization (NF). And the theoretical uncertainties for their branching ratios are reduced compared with that of direct tree level calculation. Numerical results show that NLO QCD corrections markedly enhance the branching ratio with a K factor of 1.75 for $B_{c}^{\pm}\to \eta_{c} \pi^{\pm}$ and 1.31 for $B_{c}^{\pm}\to J/\psi \pi^{\pm}$. In order to investigate the asymptotic behavior, the analytic form is obtained in the heavy quark limit, i.e. $m_b \to \infty$. We note that annihilation topologies contribute trivia in this limit, and the corrections at leading order in $z= m_c/m_b$ expansion come from form factors and hard spectator interactions. At last, some related phenomenologies are also discussed.Comment: 20 pages, 7 figures and 5 table

Localization and Mobility Gap in Topological Anderson Insulator

It has been proposed that disorder may lead to a new type of topological insulator, called topological Anderson insulator (TAI). Here we examine the physical origin of this phenomenon. We calculate the topological invariants and density of states of disordered model in a super-cell of 2-dimensional HgTe/CdTe quantum well. The topologically non-trivial phase is triggered by a band touching as the disorder strength increases. The TAI is protected by a mobility gap, in contrast to the band gap in conventional quantum spin Hall systems. The mobility gap in the TAI consists of a cluster of non-trivial subgaps separated by almost flat and localized bands.Comment: 8 pages, 7 figure

BoxSnake: Polygonal Instance Segmentation with Box Supervision

Box-supervised instance segmentation has gained much attention as it requires only simple box annotations instead of costly mask or polygon annotations. However, existing box-supervised instance segmentation models mainly focus on mask-based frameworks. We propose a new end-to-end training technique, termed BoxSnake, to achieve effective polygonal instance segmentation using only box annotations for the first time. Our method consists of two loss functions: (1) a point-based unary loss that constrains the bounding box of predicted polygons to achieve coarse-grained segmentation; and (2) a distance-aware pairwise loss that encourages the predicted polygons to fit the object boundaries. Compared with the mask-based weakly-supervised methods, BoxSnake further reduces the performance gap between the predicted segmentation and the bounding box, and shows significant superiority on the Cityscapes dataset. The code has been available publicly.Comment: ICCV 202