556 research outputs found
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 and a contact factor . The high mass
ratio () and the low contact factor () 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
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