In recent years, Graph Neural Network (GNN) has bloomly progressed for its power in processing graph-based data. Most GNNs follow a message passing scheme, and their expressive power is mathematically limited by the discriminative ability of the Weis
feiler-Lehman (WL) test. Following Tinhofers research on compact graphs, we propose a variation of the message passing scheme, called the Weisfeiler-Lehman-Tinhofer GNN (WLT-GNN), that theoretically breaks through the limitation of the WL test. In addition, we conduct comparative experiments and ablation studies on several well-known datasets. The results show that the proposed methods have comparable performances and better expressive power on these datasets.
Our study aims to recognize M-type stars which are classified as UNKNOWN due to bad quality in Large sky Area Multi-Object fibre Spectroscopic Telescope (LAMOST) DR5 V1. A binary nonlinear hashing algorithm based on Multi-Layer Pseudo Inverse Learnin
g (ML-PIL) is proposed to effectively learn spectral features for the M-type star detection, which can overcome the bad fitting problem of template matching, particularly for low S/N spectra. The key steps and the performance of the search scheme are presented. A positive dataset is obtained by clustering the existing M-type spectra to train the ML-PIL networks. By employing this new method, we find 11,410 M-type spectra out of 642,178 UNKNOWN spectra, and provide a supplemental catalogue. Both the supplemental objects and released M-type stars in DR5 V1 are composed a whole M type sample, which will be released in the official DR5 to the public in June 2019, All the M-type stars in the dataset are classified to giants and dwarfs by two suggested separators: 1) color diagram of H versus J~K from 2MASS; 2) line indices CaOH versus CaH1, and the separation is validated with HRD derived from Gaia DR2. The magnetic activities and kinematics of M dwarfs are also provided with the EW of H_alpha emission line and the astrometric data from Gaia DR2 respectively.
High-index Bi2Se3(221) film has been grown on In2Se3-buffered GaAs(001), in which a much retarded strain relaxation dynamics is recorded. The slow strain-relaxation process of in epitaxial Bi2Se3(221) can be attributed to the layered structure of Bi2
Se3 crystal, where the epifilm grown along [221] is like a pile of weakly-coupled quintuple layer slabs stacked side-by-side on substrate. Finally, we have revealed the strong chemical bonding at the interface of Bi2Se3 and In2Se3 by plotting differential charge contour calculated by first-principle method. This study points to the feasibility of achieving strained TIs for manipulating the properties of topological systems.
In terms of lattice dynamics theory, we study the vibrational properties of the oxygen-functionalized single wall carbon nanotubes (O-SWCNs). Due to the C-O and O-O interactions, many degenerate phonon modes are split and even some new phonon modes a
re obtained, different from the bare SWCNs. A distinct Raman shift is found in both the radial breathing mode and G modes, depending not only on the tube diameter and chirality but also on oxygen coverage and adsorption configurations. With the oxygen coverage increasing, interesting, a nonmonotonic up- and down-shift is observed in G modes, which is contributed to the competition between the bond expansion and contraction, there coexisting in the functionalized carbon nanotube.
