Difficulty in the Fermi-Liquid-Based Theory for the In-Plane Magnetic Anisotropy in Untwinned High-T_c Superconductor

Recently, Eremin and Manske [1] presented a oneband Fermi-liquid theory for the in-plane magnetic anisotropy in untwinned high-Tc superconductor YBa2Cu3O6:85 (YBCO). They claimed that they found good agreement with inelastic neutron scattering (INS) spectra. In this Comment, we point out that their conclusion on this important problem may be questionable due to an error in logic about the orthorhombicity delta_0 characterizing the lattice structure of YBCO. In Ref. [1], a single band at delta_0>0 is proved to be in accordance with the angle resolved photoemission spectroscopy (ARPES) on untwinned YBCO. But in their Erratum in PRL[3], they admit that delta_0= -0.03 was used to fit the INS data. Hence publications [1,3] contain errors that we believe invalidate their approach.Comment: This is a Comment on the paper of I. Eremin, and D. Manske, Phys. Rev. Lett. 94, 067006(2005

Exact heat kernel on a hypersphere and its applications in kernel SVM

Many contemporary statistical learning methods assume a Euclidean feature space. This paper presents a method for defining similarity based on hyperspherical geometry and shows that it often improves the performance of support vector machine compared to other competing similarity measures. Specifically, the idea of using heat diffusion on a hypersphere to measure similarity has been previously proposed, demonstrating promising results based on a heuristic heat kernel obtained from the zeroth order parametrix expansion; however, how well this heuristic kernel agrees with the exact hyperspherical heat kernel remains unknown. This paper presents a higher order parametrix expansion of the heat kernel on a unit hypersphere and discusses several problems associated with this expansion method. We then compare the heuristic kernel with an exact form of the heat kernel expressed in terms of a uniformly and absolutely convergent series in high-dimensional angular momentum eigenmodes. Being a natural measure of similarity between sample points dwelling on a hypersphere, the exact kernel often shows superior performance in kernel SVM classifications applied to text mining, tumor somatic mutation imputation, and stock market analysis