Branching Fractions and CP Asymmetries of the Quasi-Two-Body Decays in Bs→K0(K‾0)K±π∓B_{s} \to K^0(\overline K^0)K^\pm \pi^\mp within PQCD Approach

Motivated by the first untagged decay-time-integrated amplitude analysis of $B_s \to K_SK^{\mp}\pi^{\pm}$ decays performed by LHCb collaboration, where the decay amplitudes are modeled to contain the resonant contributions from intermediate resonances $K^*(892)$, $K_0^*(1430)$ and $K_2^*(1430)$, we comprehensively investigate the quasi-two-body $B_{s} \to K^0/\overline{\kern -0.2em K}^0 K^{\pm}\pi^{\mp}$ decays, and calculate the branching fractions and the time-dependent $CP$ asymmetries within the perturbative QCD approach based on the $k_T$ factorization. In the quasi-two-body space region the calculated branching fractions with the considered intermediate resonances are in good agreement with the experimental results of LHCb by adopting proper $K\pi$ pair wave function, describing the interaction between the kaon and pion in the $K\pi$ pair. Furthermore,within the obtained branching fractions of the quasi-two-body decays, we also calculate the branching fractions of corresponding two-body decays, and the results consist with the LHCb measurements and the earlier studies with errors. For these considered decays, since the final states are not flavour-specific, the time-dependent $CP$ could be measured. We calculate six $CP$-violation observables, which can be tested in the ongoing LHCb experiment.Comment: 20 page

Cabibbo-Kobayashi-Maskawa-favored BB decays to a scalar meson and a DD meson

Within the perturbative QCD approach, we investigated the Cabibbo-Kobayashi-Maskawa-favored $B \to \overline{D} S$ ("$S$" denoting the scalar meson) decays on the basis of the two-quark picture. Supposing the scalar mesons are the ground states or the first excited states, we calculated the the branching ratios of 72 decay modes. Most of the branching ratios are in the range $10^{-4}$ to $10^{-7}$, which can be tested in the ongoing LHCb experiment and the forthcoming Belle-II experiment. Some decays, such as $B^+ \to \overline{D}^{(*)0} a_0^+(980/1450)$ and $B^+ \to D^{(*)-} a_0^+(980/1450)$, could be used to probe the inner structure and the character of the scalar mesons, if the experiments are available. In addition, the ratios between the $Br(B^0\to \overline{D}^{(*)0}\sigma)$ and $Br(B^0\to \overline{D}^{(*)0}f_0(980))$ provide a potential way to determine the mixing angle between $\sigma$ and $f_0(980)$. Moreover, since in the standard model these decays occur only through tree operators and have no $CP$ asymmetries, any deviation will be signal of the new physics beyond the standard model.Comment: 2 figures, 6 table

Background Subtraction via Generalized Fused Lasso Foreground Modeling

Background Subtraction (BS) is one of the key steps in video analysis. Many background models have been proposed and achieved promising performance on public data sets. However, due to challenges such as illumination change, dynamic background etc. the resulted foreground segmentation often consists of holes as well as background noise. In this regard, we consider generalized fused lasso regularization to quest for intact structured foregrounds. Together with certain assumptions about the background, such as the low-rank assumption or the sparse-composition assumption (depending on whether pure background frames are provided), we formulate BS as a matrix decomposition problem using regularization terms for both the foreground and background matrices. Moreover, under the proposed formulation, the two generally distinctive background assumptions can be solved in a unified manner. The optimization was carried out via applying the augmented Lagrange multiplier (ALM) method in such a way that a fast parametric-flow algorithm is used for updating the foreground matrix. Experimental results on several popular BS data sets demonstrate the advantage of the proposed model compared to state-of-the-arts

Beyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation

Despite the tremendous achievements of deep convolutional neural networks (CNNs) in many computer vision tasks, understanding how they actually work remains a significant challenge. In this paper, we propose a novel two-step understanding method, namely Salient Relevance (SR) map, which aims to shed light on how deep CNNs recognize images and learn features from areas, referred to as attention areas, therein. Our proposed method starts out with a layer-wise relevance propagation (LRP) step which estimates a pixel-wise relevance map over the input image. Following, we construct a context-aware saliency map, SR map, from the LRP-generated map which predicts areas close to the foci of attention instead of isolated pixels that LRP reveals. In human visual system, information of regions is more important than of pixels in recognition. Consequently, our proposed approach closely simulates human recognition. Experimental results using the ILSVRC2012 validation dataset in conjunction with two well-established deep CNN models, AlexNet and VGG-16, clearly demonstrate that our proposed approach concisely identifies not only key pixels but also attention areas that contribute to the underlying neural network's comprehension of the given images. As such, our proposed SR map constitutes a convenient visual interface which unveils the visual attention of the network and reveals which type of objects the model has learned to recognize after training. The source code is available at https://github.com/Hey1Li/Salient-Relevance-Propagation.Comment: 35 pages, 15 figure