A Numerical Analysis to the {π\pi} and {K} Coupled--Channel Scalar Form-factor

A numerical analysis to the scalar form-factor in the $\pi\pi$ and KK coupled--channel system is made by solving the coupled-channel dispersive integral equations, using the iteration method. The solutions are found not unique. Physical application to the $\pi\pi$ central production in the $pp\to pp\pi\pi$ process is discussed based upon the numerical solutions we found.Comment: 8 pages, Latex, 3 figures. Minor changes and one reference adde

Deep Metric Learning with Angular Loss

The modern image search system requires semantic understanding of image, and a key yet under-addressed problem is to learn a good metric for measuring the similarity between images. While deep metric learning has yielded impressive performance gains by extracting high level abstractions from image data, a proper objective loss function becomes the central issue to boost the performance. In this paper, we propose a novel angular loss, which takes angle relationship into account, for learning better similarity metric. Whereas previous metric learning methods focus on optimizing the similarity (contrastive loss) or relative similarity (triplet loss) of image pairs, our proposed method aims at constraining the angle at the negative point of triplet triangles. Several favorable properties are observed when compared with conventional methods. First, scale invariance is introduced, improving the robustness of objective against feature variance. Second, a third-order geometric constraint is inherently imposed, capturing additional local structure of triplet triangles than contrastive loss or triplet loss. Third, better convergence has been demonstrated by experiments on three publicly available datasets.Comment: International Conference on Computer Vision 201