Strongly Regular Graphs as Laplacian Extremal Graphs

The Laplacian spread of a graph is the difference between the largest eigenvalue and the second-smallest eigenvalue of the Laplacian matrix of the graph. We find that the class of strongly regular graphs attains the maximum of largest eigenvalues, the minimum of second-smallest eigenvalues of Laplacian matrices and hence the maximum of Laplacian spreads among all simple connected graphs of fixed order, minimum degree, maximum degree, minimum size of common neighbors of two adjacent vertices and minimum size of common neighbors of two nonadjacent vertices. Some other extremal graphs are also provided.Comment: 11 pages, 4 figures, 1 tabl

Wideband mmWave Massive MIMO Channel Estimation and Localization

Spatial wideband effects are known to affect channel estimation and localization performance in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Based on perturbation analysis, we show that the spatial wideband effect is in fact more pronounced than previously thought and significantly degrades performance, even at moderate bandwidths, if it is not properly considered in the algorithm design. We propose a novel channel estimation method based on multidimensional ESPRIT per subcarrier, combined with unsupervised learning for pairing across subcarriers, which shows significant performance gain over existing schemes under wideband conditions

Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey

Point cloud has a wide range of applications in areas such as autonomous driving, mapping, navigation, scene reconstruction, and medical imaging. Due to its great potentials in these applications, point cloud processing has gained great attention in the field of computer vision. Among various point cloud processing techniques, deep learning (DL) has become one of the mainstream and effective methods for tasks such as detection, segmentation and classification. To reduce overfitting during training DL models and improve model performance especially when the amount and/or diversity of training data are limited, augmentation is often crucial. Although various point cloud data augmentation methods have been widely used in different point cloud processing tasks, there are currently no published systematic surveys or reviews of these methods. Therefore, this article surveys and discusses these methods and categorizes them into a taxonomy framework. Through the comprehensive evaluation and comparison of the augmentation methods, this article identifies their potentials and limitations and suggests possible future research directions. This work helps researchers gain a holistic understanding of the current status of point cloud data augmentation and promotes its wider application and development

Why a local moment induces an antiferromagnetic ordering: An RVB picture

Based on a Gutzwiller projected BCS wavefunction, it is shown that a local S=1/2 moment is present around a vacancy site (zinc impurity) in a form of staggered magnetic moments, which is a direct consequence of the short-ranged resonating-valence-bond (RVB) pairing in the spin background.Comment: 4 pages, 3 figure

Bis[(diamino­methyl­idene)aza­nium] 5-(1-oxido-1H-1,2,3,4-tetra­zol-5-yl)-1H-1,2,3,4-tetra­zol-1-olate

The anion of the title salt, 2[C(NH2)3]+·C2N8O2 2−, lies on a center of inversion and its two five-membered rings are coplanar. The guanidinium cation forms N—H⋯O and N—H⋯N hydrogen bonds to the anion, generating an eight-membered ring. Other hydrogen bonds lead to the formation of a three-dimensional network

Simultaneously improving the mechanical and electrical properties of poly(vinyl alcohol) composites by high-quality graphitic nanoribbons

Although carbon nanotubes (CNTs) have shown great potential for enhancing the performance of polymer matrices, their reinforcement role still needs to be further improved. Here we implement a structural modification of multi-walled CNTs (MWCNTs) to fully utilize their fascinating mechanical and electrical properties via longitudinal splitting of MWCNTs into graphitic nanoribbons (GNRs). This nanofiller design strategy is advantageous for surface functionalization, strong interface adhesion as well as boosting the interfacial contact area without losing the intrinsic graphitic structure. The obtained GNRs have planar geometry, quasi-1D structure and high-quality crystallinity, which outperforms their tubular counterparts, delivering a superior load-bearing efficiency and conductive network for realizing a synchronous improvement of the mechanical and electrical properties of a PVA-based composite. Compared to PVA/CNTs, the tensile strength, Young’s modulus and electrical conductivity of the PVA/GNR composite at a filling concentration of 3.6 vol.% approach 119.1 MPa, 5.3 GPa and 2.4 × 10−4 S m−1, with increases of 17%, 32.5% and 5.9 folds, respectively. The correlated mechanics is further rationalized by finite element analysis, the generalized shear-lag theory and the fracture mechanisms