Hi-fi phenomenological description of eclipsing binary light variations as the basis for their period analysis

In-depth analysis of eclipsing binary (EB) observational data collected for several decades can inform us about a lot of astrophysically interesting processes taking place in the systems. We have developed a wide-ranging method for the phenomenological modelling of eclipsing binary phase curves that enables us to combine even very disparate sources of phase information. This approach is appropriate for the processing of both standard photometric series of eclipses and data from photometric surveys of all kind. We conclude that mid-eclipse times, determined using the latest version of our 'hi-fi' phenomenological light curve models, as well as their accuracy, are nearly the same as the values obtained using much more complex standard physical EB models.Comment: 4 pages, 3 figures, EAS - Proceedings of the conference: Setting a new standard in the analysis of binary stars, 16 to 19 September 2013, Leuven, Belgiu

CCD photometric study of the W UMa-type binary II CMa in the field of Berkeley 33

The CCD photometric data of the EW-type binary, II CMa, which is a contact star in the field of the middle-aged open cluster Berkeley 33, are presented. The complete R light curve was obtained. In the present paper, using the five CCD epochs of light minimum (three of them are calculated from Mazur et al. (1993)'s data and two from our new data), the orbital period P was revised to 0.22919704 days. The complete R light curve was analyzed by using the 2003 version of W-D (Wilson-Devinney) program. It is found that this is a contact system with a mass ratio $q=0.9$ and a contact factor $f=4.1$. The high mass ratio ($q=0.9$) and the low contact factor ($f=4.1$) indicate that the system just evolved into the marginal contact stage

Long-Range Grouping Transformer for Multi-View 3D Reconstruction

Nowadays, transformer networks have demonstrated superior performance in many computer vision tasks. In a multi-view 3D reconstruction algorithm following this paradigm, self-attention processing has to deal with intricate image tokens including massive information when facing heavy amounts of view input. The curse of information content leads to the extreme difficulty of model learning. To alleviate this problem, recent methods compress the token number representing each view or discard the attention operations between the tokens from different views. Obviously, they give a negative impact on performance. Therefore, we propose long-range grouping attention (LGA) based on the divide-and-conquer principle. Tokens from all views are grouped for separate attention operations. The tokens in each group are sampled from all views and can provide macro representation for the resided view. The richness of feature learning is guaranteed by the diversity among different groups. An effective and efficient encoder can be established which connects inter-view features using LGA and extract intra-view features using the standard self-attention layer. Moreover, a novel progressive upsampling decoder is also designed for voxel generation with relatively high resolution. Hinging on the above, we construct a powerful transformer-based network, called LRGT. Experimental results on ShapeNet verify our method achieves SOTA accuracy in multi-view reconstruction. Code will be available at https://github.com/LiyingCV/Long-Range-Grouping-Transformer.Comment: Accepted to ICCV 202