To understand the rare decay Bs→π+π−ℓ+ℓ−B_s\to\pi^+\pi^-\ell^+\ell^-

Motivated by the LHCb measurement, we analyze the $B_s\to \pi^+\pi^-\ell^+\ell^-$ decay in the kinematics region where the pion pairs have invariant mass in the range $0.5$-$1.3$ GeV and muon pairs do not originate from a resonance. The scalar $\pi^+\pi^-$ form factor induced by the strange $\bar ss$ current is predicted by the unitarized approach rooted in the chiral perturbation theory. Using the two-hadron light-cone distribution amplitude, we then can derive the $B_s\to \pi^+\pi^-$ transition form factor in the light-cone sum rules approach. Merging these quantities, we present our results for differential decay width which can generally agree with the experimental data. More accurate measurements at the LHC and KEKB in future are helpful to validate our formalism and determine the inputs in this approach.Comment: 8 pages, 4 figures; v2: references added, match the published versio

Radiative Leptonic Bc→γℓνˉB_c\to \gamma \ell\bar\nu Decay in Effective Field Theory beyond Leading Order

We study the radiative leptonic $B_c\to \gamma\ell\bar\nu$ decays in the nonrelativistic QCD effective field theory, and consider a fast-moving photon. As a result the interactions with the heavy quarks can be integrated out, and thus we arrive at a factorization formula for the decay amplitude. We calculate not only the relevant short-distance coefficients at leading order and next-to-leading order in $\alpha_s$, but also the nonrelativistic corrections at the order $|\bold{v}|^2$ in our analysis. We find out that the QCD corrections can sizably decrease the branching ratio and thus is of great importance in extracting the long-distance operator matrix elements of $B_c$. For the phenomenological application, we present our results for the photon energy, lepton energy and lepton-neutrino invariant mass distribution.Comment: 24 pages, 5 figures, and 2 tables;new references and a new table added and typos correcte

An Expressive Deep Model for Human Action Parsing from A Single Image

This paper aims at one newly raising task in vision and multimedia research: recognizing human actions from still images. Its main challenges lie in the large variations in human poses and appearances, as well as the lack of temporal motion information. Addressing these problems, we propose to develop an expressive deep model to naturally integrate human layout and surrounding contexts for higher level action understanding from still images. In particular, a Deep Belief Net is trained to fuse information from different noisy sources such as body part detection and object detection. To bridge the semantic gap, we used manually labeled data to greatly improve the effectiveness and efficiency of the pre-training and fine-tuning stages of the DBN training. The resulting framework is shown to be robust to sometimes unreliable inputs (e.g., imprecise detections of human parts and objects), and outperforms the state-of-the-art approaches.Comment: 6 pages, 8 figures, ICME 201