On the Common Envelope Efficiency

In this work, we try to use the apparent luminosity versus displacement (i.e., $L_{\rm X}$ vs. $R$) correlation of high mass X-ray binaries (HMXBs) to constrain the common envelope (CE) efficiency $\alpha_{\rm CE}$, which is a key parameter affecting the evolution of the binary orbit during the CE phase. The major updates that crucial for the CE evolution include a variable $\lambda$ parameter and a new CE criterion for Hertzsprung gap donor stars, both of which are recently developed. We find that, within the framework of the standard energy formula for CE and core definition at mass $X=10$\%, a high value of $\alpha_{\rm CE}$, i.e., around 0.8-1.0, is more preferable, while $\alpha_{\rm CE}< \sim 0.4$ likely can not reconstruct the observed $L_{\rm X}$ vs. $R$ distribution. However due to an ambiguous definition for the core boundary in the literature, the used $\lambda$ here still carries almost two order of magnitude uncertainty, which may translate directly to the expected value of $\alpha_{\rm CE}$. We present the detailed components of current HMXBs and their spatial offsets from star clusters, which may be further testified by future observations of HMXB populations in nearby star-forming galaxies.Comment: 14 pages, 10 figures, 7 tables, accepted for publication in MNRA

Character-level Convolutional Networks for Text Classification

This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.Comment: An early version of this work entitled "Text Understanding from Scratch" was posted in Feb 2015 as arXiv:1502.01710. The present paper has considerably more experimental results and a rewritten introduction, Advances in Neural Information Processing Systems 28 (NIPS 2015