On the α-spectra of Uniform Hypergraphs and Its Associated Graphs

For 0 ≤α 1 and a k-uniform hypergraph H, the tensor A_α(H) associated with H is defined as A_α(H) = αD(H) +(1-α)A(H), where D(H) and A(H) are the diagonal tensor of degrees and the adjacency tensor of H, respectively. The α-spectra of H is the set of all eigenvalues of A_α(H) and the α-spectral radius ρ_α(H) is the largest modulus of the elements in the spectrum of A_α(H). In this paper we define the line graph L(H) of a uniform hypergraph H and prove that ρ_α(H) ≤■ρ_α(L(H)) + 1 + α(Δ-1-δ~*/k), where Δ and δ~* are the maximum degree of H and the minimum degree of L(H), respectively. We also generalize some results on α-spectra of G~(k,s), which is obtained from G by blowing up each vertex into an s-set and each edge into a k-set where 1 ≤ s ≤ k/2.     

局部共形对称黎曼流形上的保圆曲率张量长度的整体刚性定理

本文我们研究了局部共形对称闭黎曼流形, 建立了一个关于保圆曲率矢量长度的整体刚度定理.     

Graph operations and upper bounds on graph homomorphism counts

We construct a family of countexamples to a conjecture of Galvin [5], which stated that for any n‐vertex, d‐regular graph G and any graph H (possibly with loops), where is the number of homomorphisms from G to H. By exploiting properties of the graph tensor product and graph exponentiation, we also find new infinite families of H for which the bound stated above on holds for all n‐vertex, d‐regular G. In particular, we show that if H_WR is the complete looped path on three vertices, also known as the Widom–Rowlinson graph, then for all n‐vertex, d‐regular G. This verifies a conjecture of Galvin.     

单位球面中具有3个不同 Blaschke 特征值的 Blaschke 平行子流形

Blaschke张量A是单位球面S~n中子流形的M?bius微分几何的一个基本不变量,而A的特征值称为Blaschke特征值.作者研究了S~n中具有平行Blaschke张量的子流形(简称为Blaschke平行子流形).主要结果是对S~n中具有3个不同Blaschke特征值的Blaschke平行子流形进行了完全的分类.     

A randomized tensor singular value decomposition based on the t‐product

The tensor SVD (t‐SVD) for third‐order tensors, previously proposed in the literature, has been applied successfully in many fields, such as computed tomography, facial recognition, and video completion. In this paper, we propose a method that extends a well‐known randomized matrix method to the t‐SVD. This method can produce a factorization with similar properties to the t‐SVD, but it is more computationally efficient on very large data sets. We present details of the algorithms and theoretical results and provide numerical results that show the promise of our approach for compressing and analyzing image‐based data sets. We also present an improved analysis of the randomized and simultaneous iteration for matrices, which may be of independent interest to the scientific community. We also use these new results to address the convergence properties of the new and randomized tensor method as well.