Quasi-two-body decays B→DK∗(892)→DKπB \to D K^*(892) \to D K \pi in the perturbative QCD approach

We study the quasi-two-body decays $B\to D K^*(892) \to D K\pi$ by employing the perturbative QCD approach. The two-meson distribution amplitudes \Phi_{K\pi}^{\text{P-wave}} are adopted to describe the final state interactions of the kaon-pion pair in the resonance region. The resonance line shape for the $P$-wave $K\pi$ component $K^*(892)$ in the time-like form factor $F_{K\pi}(s)$ is parameterized by the relativistic Breit-Wigner function. For most considered decay modes, the theoretical predictions for their branching ratios are consistent with currently available experimental measurements within errors. We also disscuss some ratios of the branching fractions of the concerned decay processes. More precise data from LHCb and Belle-II are expected to test our predictions.Comment: 10 pages, 3 figures and 2 tables.To be published in EPJ

The Implication of Climate Signal for Precipitation in the Heihe River Basin, Northwest China

This paper studies the stochastic dynamic variability of precipitation, for the upper, middle, and lower reaches of the Heihe River basin in Northwest China, by employing Mann-Kendall statistic, Pettitt test, and wavelet transform methods. The possible associations with three prominent climatic patterns, El Niño-Southern Oscillation (ENSO), Artic Oscillation (AO), and Indian Ocean Dipole (IOD), are examined by using multiscale wavelet coherence method. No significant trend is identified for the interannual precipitation variability. However, about 2-year significant variability is detected for the lower reach of the Heihe River basin, and this dominating precipitation variability is essentially depicted by AO. The possible influences of ENSO are exerted on long-term timescale, 8–16 years. The obtained knowledge is helpful for the predications of extreme hydroclimatological events and better reservoir operations for regional water resources

HQDec: Self-Supervised Monocular Depth Estimation Based on a High-Quality Decoder

Decoders play significant roles in recovering scene depths. However, the decoders used in previous works ignore the propagation of multilevel lossless fine-grained information, cannot adaptively capture local and global information in parallel, and cannot perform sufficient global statistical analyses on the final output disparities. In addition, the process of mapping from a low-resolution feature space to a high-resolution feature space is a one-to-many problem that may have multiple solutions. Therefore, the quality of the recovered depth map is low. To this end, we propose a high-quality decoder (HQDec), with which multilevel near-lossless fine-grained information, obtained by the proposed adaptive axial-normalized position-embedded channel attention sampling module (AdaAxialNPCAS), can be adaptively incorporated into a low-resolution feature map with high-level semantics utilizing the proposed adaptive information exchange scheme. In the HQDec, we leverage the proposed adaptive refinement module (AdaRM) to model the local and global dependencies between pixels in parallel and utilize the proposed disparity attention module to model the distribution characteristics of disparity values from a global perspective. To recover fine-grained high-resolution features with maximal accuracy, we adaptively fuse the high-frequency information obtained by constraining the upsampled solution space utilizing the local and global dependencies between pixels into the high-resolution feature map generated from the nonlearning method. Extensive experiments demonstrate that each proposed component improves the quality of the depth estimation results over the baseline results, and the developed approach achieves state-of-the-art results on the KITTI and DDAD datasets. The code and models will be publicly available at \href{https://github.com/fwucas/HQDec}{HQDec}