Thermal Timescale Mass Transfer Rates in Intermediate-Mass X-ray Binaries

Thermal timescale mass transfer generally occurs in close binaries where the donor star is more massive than the accreting star. The mass transfer rates are usually estimated in terms of the Kelvin-Helmholtz timescale of the donor star. But recent investigations indicate that this method may overestimate the real mass transfer rates in accreting white dwarf or neutron star binary systems. We have systematically investigated the thermal-timescale mass transfer processes in intermediate-mass X-ray binaries, by calculating binary evolution sequences with various initial donor masses and orbital periods. From the calculated results we find that on average the mass transfer rates are lower than traditional estimates by a factor of $\sim 4$.Comment: 13 pages, 4 figures, and 2 tables, accepted for publication in A&

Dark Energy

We review the problem of dark energy, including a survey of theoretical models and some aspects of numerical studies.Comment: 185 pages, 29 figures, 8 tables, more references added. We will continue to update this article, comments and suggestions are welcom

Modeling Surface Appearance from a Single Photograph using Self-augmented Convolutional Neural Networks

We present a convolutional neural network (CNN) based solution for modeling physically plausible spatially varying surface reflectance functions (SVBRDF) from a single photograph of a planar material sample under unknown natural illumination. Gathering a sufficiently large set of labeled training pairs consisting of photographs of SVBRDF samples and corresponding reflectance parameters, is a difficult and arduous process. To reduce the amount of required labeled training data, we propose to leverage the appearance information embedded in unlabeled images of spatially varying materials to self-augment the training process. Starting from an initial approximative network obtained from a small set of labeled training pairs, we estimate provisional model parameters for each unlabeled training exemplar. Given this provisional reflectance estimate, we then synthesize a novel temporary labeled training pair by rendering the exact corresponding image under a new lighting condition. After refining the network using these additional training samples, we re-estimate the provisional model parameters for the unlabeled data and repeat the self-augmentation process until convergence. We demonstrate the efficacy of the proposed network structure on spatially varying wood, metals, and plastics, as well as thoroughly validate the effectiveness of the self-augmentation training process.Comment: Accepted to SIGGRAPH 201