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

Towards Effective Codebookless Model for Image Classification

The bag-of-features (BoF) model for image classification has been thoroughly studied over the last decade. Different from the widely used BoF methods which modeled images with a pre-trained codebook, the alternative codebook free image modeling method, which we call Codebookless Model (CLM), attracted little attention. In this paper, we present an effective CLM that represents an image with a single Gaussian for classification. By embedding Gaussian manifold into a vector space, we show that the simple incorporation of our CLM into a linear classifier achieves very competitive accuracy compared with state-of-the-art BoF methods (e.g., Fisher Vector). Since our CLM lies in a high dimensional Riemannian manifold, we further propose a joint learning method of low-rank transformation with support vector machine (SVM) classifier on the Gaussian manifold, in order to reduce computational and storage cost. To study and alleviate the side effect of background clutter on our CLM, we also present a simple yet effective partial background removal method based on saliency detection. Experiments are extensively conducted on eight widely used databases to demonstrate the effectiveness and efficiency of our CLM method