Calculation of the Branching Ratio of B−→hc+K−B^{-}\to h_{c}+K^{-} in PQCD

The branching ratio of $B^-\to h_c+K^-$ is re-evaluated in the PQCD approach. In this theoretical framework all the phenomenological parameters in the wavefunctions and Sudakov factor are priori fixed by fitting other experimental data, and in the whole numerical computations we do not introduce any new parameter. Our results are consistent with the upper bounds set by the Babar and Belle measurements.Comment: 12 pages, 1 figure, version to appear in Phys. Rev.

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

Sketch-a-Net that Beats Humans

We propose a multi-scale multi-channel deep neural network framework that, for the first time, yields sketch recognition performance surpassing that of humans. Our superior performance is a result of explicitly embedding the unique characteristics of sketches in our model: (i) a network architecture designed for sketch rather than natural photo statistics, (ii) a multi-channel generalisation that encodes sequential ordering in the sketching process, and (iii) a multi-scale network ensemble with joint Bayesian fusion that accounts for the different levels of abstraction exhibited in free-hand sketches. We show that state-of-the-art deep networks specifically engineered for photos of natural objects fail to perform well on sketch recognition, regardless whether they are trained using photo or sketch. Our network on the other hand not only delivers the best performance on the largest human sketch dataset to date, but also is small in size making efficient training possible using just CPUs.Comment: Accepted to BMVC 2015 (oral

Phase structures of strong coupling lattice QCD with overlap fermions at finite temperature and chemical potential

We perform the first study of lattice QCD with overlap fermions at finite temperature $T$ and chemical potential $\mu$. We start from the Taylor expanded overlap fermion action, and derive in the strong coupling limit the effective free energy by mean field approximation. On the ($\mu,T$) plane and in the chiral limit, there is a tricritical point, separating the second order chiral phase transition line at small $\mu$ and large $T$, and first order chiral phase transition line at large $\mu$ and small $T$