The Strong Decays of Orbitally Excited BsJ∗B^{*}_{sJ} Mesons by Improved Bethe-Salpeter Method

We calculate the masses and the strong decays of orbitally excited states $B_{s0}$, $B'_{s1}$, $B_{s1}$ and $B_{s2}$ by the improved Bethe-Salpeter method. The predicted masses of $B_{s0}$ and $B'_{s1}$ are $M_{B_{s0}}=5.723\pm0.280 {\rm GeV}$, $M_{B'_{s1}}=5.774\pm0.330 {\rm GeV}$. We calculate the isospin symmetry violating decay processes $B_{s0}\to B_s \pi$ and $B'_{s1}\to B_s^* \pi$ through $\pi^0-\eta$ mixing and get small widths. Considering the uncertainties of the masses, for $B_{s0}$ and $B'_{s1}$, we also calculate the OZI allowed decay channels: $B_{s0}\to B\bar K$ and $B'_{s1}\to B^*\bar K$. For $B_{s1}$ and $B_{s2}$, the OZI allowed decay channels $B_{s1}\to B^{*}\bar K$, $B_{s2}\to B\bar K$ and $B_{s2}\to B^{*}\bar K$ are studied. In all the decay channels, the reduction formula, PCAC relation and low energy theorem are used to estimate the decay widths. We also obtain the strong coupling constants $G_{B_{s0}B_s\pi}$, $G_{B_{s0}B\bar K}$, $G_{B'_{s1}B_s^*\pi}$, $F_{B'_{s1}B_s^*\pi}$, $G_{B'_{s1}B^*\bar K}$, $F_{B'_{s1}B^*\bar K}$, $G_{B_{s1}B^{*}\bar K}$, $F_{B_{s1}B^{*}\bar K}$, $G_{B_{s2}B\bar K}$ and $G_{B_{s2}B^{*}\bar K}$.Comment: 21 pages, 1 figure, 4 table

The rare semi-leptonic BcB_c decays involving orbitally excited final mesons

The rare processes $B_c\to D_{(s)J} ^{(*)}\mu\bar{\mu}$, where $D_{(s)J}^{(*)}$ stands for the final meson $D_{s0}^*(2317)$, $D_{s1}(2460,2536)$,~$D_{s2}^*(2573)$, $D_0^*(2400)$, $D_{1}(2420,2430)$ or~$D_{2}^*(2460)$, are studied within the Standard Model. The hadronic matrix elements are evaluated in the Bethe-Salpeter approach and furthermore a discussion on the gauge-invariant condition of the annihilation hadronic currents is presented. Considering the penguin, box, annihilation, color-favored cascade and color-suppressed cascade contributions, the observables $\text{d}Br/\text{d}Q^2$, $A_{LPL}$, $A_{FB}$ and $P_L$ are calculated

LRMM: Learning to Recommend with Missing Modalities

Multimodal learning has shown promising performance in content-based recommendation due to the auxiliary user and item information of multiple modalities such as text and images. However, the problem of incomplete and missing modality is rarely explored and most existing methods fail in learning a recommendation model with missing or corrupted modalities. In this paper, we propose LRMM, a novel framework that mitigates not only the problem of missing modalities but also more generally the cold-start problem of recommender systems. We propose modality dropout (m-drop) and a multimodal sequential autoencoder (m-auto) to learn multimodal representations for complementing and imputing missing modalities. Extensive experiments on real-world Amazon data show that LRMM achieves state-of-the-art performance on rating prediction tasks. More importantly, LRMM is more robust to previous methods in alleviating data-sparsity and the cold-start problem.Comment: 11 pages, EMNLP 201