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

Polyphonic audio tagging with sequentially labelled data using CRNN with learnable gated linear units

Audio tagging aims to detect the types of sound events occurring in an audio recording. To tag the polyphonic audio recordings, we propose to use Connectionist Temporal Classification (CTC) loss function on the top of Convolutional Recurrent Neural Network (CRNN) with learnable Gated Linear Units (GLU-CTC), based on a new type of audio label data: Sequentially Labelled Data (SLD). In GLU-CTC, CTC objective function maps the frame-level probability of labels to clip-level probability of labels. To compare the mapping ability of GLU-CTC for sound events, we train a CRNN with GLU based on Global Max Pooling (GLU-GMP) and a CRNN with GLU based on Global Average Pooling (GLU-GAP). And we also compare the proposed GLU-CTC system with the baseline system, which is a CRNN trained using CTC loss function without GLU. The experiments show that the GLU-CTC achieves an Area Under Curve (AUC) score of 0.882 in audio tagging, outperforming the GLU-GMP of 0.803, GLU-GAP of 0.766 and baseline system of 0.837. That means based on the same CRNN model with GLU, the performance of CTC mapping is better than the GMP and GAP mapping. Given both based on the CTC mapping, the CRNN with GLU outperforms the CRNN without GLU.Comment: DCASE2018 Workshop. arXiv admin note: text overlap with arXiv:1808.0193

Metallic Nanowire Interconnections for Integrated Circuit Fabrication

A method for fabricating an electrical interconnect between two or more electrical components. A conductive layer is provided on a substarte and a thin, patterned catalyst array is deposited on an exposed surface of the conductive layer. A gas or vapor of a metallic precursor of a metal nanowire (MeNW) is provided around the catalyst array, and MeNWs grow between the conductive layer and the catalyst array. The catalyst array and a portion of each of the MeNWs are removed to provide exposed ends of the MeNWs

Bis[2-(3,4-disulfanylphen­yl)acetato]bis­(2-methyl-1H-imidazole-κN 3)zinc(II)

In the title mononuclear zinc(II) complex, [Zn(C8H7O2S2)2(C4H6N2)2], the ZnII atom, lying on a twofold axis, is coordinated by two O atoms from two 2-(3,4-disulfanylphen­yl)acetate anions and by two N atoms from 2-methyl­imidazole ligands in a distorted tetra­hdral coordination. The crystal structure is stabilized by inter­molecular C—H⋯O and N—H⋯O hydrogen bonds and π–π inter­actions with a centroid–centroid distance of 3.6136 (16) Å