Studies on X(4260) and X(4660) particles

Studies on the X(4260) and X(4660) resonant states in an effective lagrangian approach are reviewed. Using a Breit--Wigner propagator to describe their propagation, we find that the X(4260) has a sizable coupling to the $\omega\chi_{c0}$ channel, while other couplings are found to be negligible. Besides, it couples much stronger to $\sigma$ than to $f_0(980)$: $|g_{X\Psi \sigma}^2/g^2_{X\Psi f_0(980)}|\sim O(10) \ .$ As an approximate result for X(4660), we obtain that the ratio of $\frac{Br(X\rightarrow\Lambda_c^+\Lambda_c^-)}{Br(X\rightarrow\Psi(2s)\pi^+\pi^-)}\simeq 20$. Finally, taking X(3872) as an example, we also point out a possible way to extend the previous method to a more general one in the effective lagrangian approach.Comment: Talk given by H. Q. Zheng at "Xth Quark Confinement and the Hadron Spectrum", October 8-12, 2012, TUM Campus Garching, Munich, Germany. 6 pages, 3 figures, 3 table

Weakly-Supervised Neural Text Classification

Deep neural networks are gaining increasing popularity for the classic text classification task, due to their strong expressive power and less requirement for feature engineering. Despite such attractiveness, neural text classification models suffer from the lack of training data in many real-world applications. Although many semi-supervised and weakly-supervised text classification models exist, they cannot be easily applied to deep neural models and meanwhile support limited supervision types. In this paper, we propose a weakly-supervised method that addresses the lack of training data in neural text classification. Our method consists of two modules: (1) a pseudo-document generator that leverages seed information to generate pseudo-labeled documents for model pre-training, and (2) a self-training module that bootstraps on real unlabeled data for model refinement. Our method has the flexibility to handle different types of weak supervision and can be easily integrated into existing deep neural models for text classification. We have performed extensive experiments on three real-world datasets from different domains. The results demonstrate that our proposed method achieves inspiring performance without requiring excessive training data and outperforms baseline methods significantly.Comment: CIKM 2018 Full Pape

The envelope mass of red giant donors in Type Ia supernova progenitors

We compute the remaining amounts of hydrogen in red giant donors to see whether the conflict between theory and observations can be overcome. By considering the mass-stripping effect from an optically thick wind and the effect of thermally unstable disk, we systematically carried out binary evolution calculation for WD + MS and WD + RG systems. Here, we focus on the evolution of WD + RG systems. We found that some donor stars at the time of the supernova explosion contain little hydrogen-rich material on top of the helium core (as low as 0.017 $M_{\odot}$), which is smaller than the upper limit to the amount derived from observations of material stripped-off by explosion ejecta. Thus, no hydrogen line is expected in the nebular spectra of these SN Ia. We also derive the distributions of the envelope mass and the core mass of the companions from WD + RG channel at the moment of a supernova explosion by adopting a binary population synthesis approach. We rarely find a RG companion with a very low-mass envelope. Furthermore, our models imply that the remnant of the WD + RG channel emerging after the supernova explosion is a single low-mass white dwarf (0.15 $M_{\odot}$ - 0.30 $M_{\odot}$). The absence of a hydrogen line in nebular spectra of SNe Ia provides support to the proposal that the WD + RG system is the progenitor of SNe Ia.Comment: 5 pages, 4 figures, accepted for publication in A&A, by language edito