ConvMS:Improving Convolutional Knowledge Graph Embeddings via Integrating Information of Multiple Subspaces

Knowledge graphs are involved in more and more applications to further improve intelligence.Owing to the inherent incompleteness of knowledge graphs resulted from data updating and missing,a number of knowledge graph completion models are proposed in succession.To obtain better performance,many methods are of high complexity,making it time-consuming for training and inference.This paper proposes a simple but e®ective model using only shallow neural networks,which combines enhanced feature interaction and multi-subspace information integration.In the enhanced feature interaction module,entity and relation embeddings are almost peer-to-peer interacted via multi-channel 2D convolution.In the multi-subspace information integration module,entity and relation embeddings are projected to multiple subspaces to extract multi-view information to further boost performance.Extensive experiments on widely used datasets show that the proposed model outperforms a series of strong baselines.And ablation studies demonstrate the e®ectiveness of each submodule in the model.

Joint DOA and polarization estimation for unequal power sources based on reconstructed noise subspace

In most literature about joint direction of arrival（DOA） and polarization estimation, the case that sources possess different power levels is seldom discussed. However, this case exists widely in practical applications, especially in passive radar systems. In this paper, we propose a joint DOA and polarization estimation method for unequal power sources based on the reconstructed noise subspace. The invariance property of noise subspace（IPNS） to power of sources has been proved an effective method to estimate DOA of unequal power sources. We develop the IPNS method for joint DOA and polarization estimation based on a dual polarized array. Moreover, we propose an improved IPNS method based on the reconstructed noise subspace, which has higher resolution probability than the IPNS method. It is theoretically proved that the IPNS to power of sources is still valid when the eigenvalues of the noise subspace are changed artificially. Simulation results show that the resolution probability of the proposed method is enhanced compared with the methods based on the IPNS and the polarimetric multiple signal classification（MUSIC） method. Meanwhile, the proposed method has approximately the same estimation accuracy as the IPNS method for the weak source.

Reducing subspaces of tensor products of weighted shifts

A unilateral weighted shift A is said to be simple if its weight sequence {α_n} satisfies ▽~3(α_n^2)≠0for all n≥2.We prove that if A and B are two simple unilateral weighted shifts,then AI+IB is reducible if and only if A and B are unitarily equivalent.We also study the reducing subspaces of A^kI+IB^l and give some examples.As an application,we study the reducing subspaces of multiplication operators Mzk+αωl on function spaces.

Dual Lie bialgebra structures of Poisson types

Let A = F [x, y] be the polynomial algebra on two variables x, y over an algebraically closed field F of characteristic zero. Under the Poisson bracket, A is equipped with a natural Lie algebra structure. It is proven that the maximal good subspace of A* induced from the multiplication of the associative commutative algebra A coincides with the maximal good subspace of A* induced from the Poisson bracket of the Poisson Lie algebra A. Based on this, structures of dual Lie bialgebras of the Poisson type are investigated. As by-products,five classes of new infinite-dimensional Lie algebras are obtained.