Electric dipole sheets in BaTiO3_{3}/BaZrO3_{3} superlattices

We investigate two-dimensional electric dipole sheets in the superlattice made of BaTiO$_{3}$ and BaZrO$_{3}$ using first-principles-based Monte-Carlo simulations and density functional calculations. Electric dipole domains and complex patterns are observed and the complex dipole structures with various symmetries (e.g. Pma2, Cmcm and Pmc2_{1}) are further confirmed by density functional calculations, which are found to be almost degenerate in energy with the ferroelectric ground state of the Amm2 symmetry, therefore strongly resembling magnetic sheets. More complex dipole patterns, including vortices and anti-vortices, are also observed, which may constitute the intermediate states that overcome the high energy barrier of different polarization orientations previously predicted by Lebedev\onlinecite{Lebedev2013}. We also show that such system possesses large electrostrictive effects that may be technologically important

Enhancing Scene Graph Generation with Hierarchical Relationships and Commonsense Knowledge

This work presents an enhanced approach to generating scene graphs by incorporating a relationship hierarchy and commonsense knowledge. Specifically, we propose a Bayesian classification head that exploits an informative hierarchical structure. It jointly predicts the super-category or type of relationship between the two objects, along with the detailed relationship under each super-category. We design a commonsense validation pipeline that uses a large language model to critique the results from the scene graph prediction system and then use that feedback to enhance the model performance. The system requires no external large language model assistance at test time, making it more convenient for practical applications. Experiments on the Visual Genome and the OpenImage V6 datasets demonstrate that harnessing hierarchical relationships enhances the model performance by a large margin. The proposed Bayesian head can also be incorporated as a portable module in existing scene graph generation algorithms to improve their results. In addition, the commonsense validation enables the model to generate an extensive set of reasonable predictions beyond dataset annotations

A CLT for the LSS of large dimensional sample covariance matrices with diverging spikes

In this paper, we establish the central limit theorem (CLT) for linear spectral statistics (LSS) of large-dimensional sample covariance matrix when the population covariance matrices are not uniformly bounded, which is a nontrivial extension of the Bai-Silverstein theorem (BST) (2004). The latter has strongly influenced the development of high-dimensional statistics, especially in applications of random matrix theory to statistics. However, the assumption of uniform boundedness of the population covariance matrices has seriously limited the applications of the BST. The aim of this paper is to remove the barriers for the applications of the BST. The new CLT, allows spiked eigenvalues to exist, which may be bounded or tend to infinity. An important feature of our result is that the roles of either spiked eigenvalues or the bulk eigenvalues predominate in the CLT, depending on which variance is nonnegligible in the summation of the variances. The CLT for LSS is then applied to compare four linear hypothesis tests: The Wilk's likelihood ratio test, the Lawly-Hotelling trace test, the Bartlett-Nanda-Pillai trace test, and Roy's largest root test. We also derive and analyze their power function under particular alternatives.Comment: Comparing with the old manuscript, we modified the title of the paper. arXiv admin note: text overlap with arXiv:2205.07280. arXiv admin note: text overlap with arXiv:2205.0728

Road network detection based on improved FLICM-MRF method using high resolution SAR images

The automatic detection of road network from satellite and aerial images is highly significant in many actual applications, for instance, urban traffic measurement, military emergency response, and vehicle target tracking. Compared with other high-resolution satellite remote sensing images, high-resolution synthetic aperture radar (SAR) has become a popular research perspective for road detection owing to its insensitivity to the atmosphere and sun-illumination. However, the method of road network detection is still lagging due to the strong multiplicative speckle noise and complex background interference, causing the loss and break in the road segment extraction results. Aiming to solve this problem, a three-step road network detection framework is proposed. In the first step, the road segment candidates are extracted by the Fuzzy Local Information C-Means (FLICM) algorithm based on the gray-level co-occurrence matrix(GLCM) with Markov Random Fields (MRF), and it contains an adaptive parameter selection procedure which is presented for adjusting joint clustering parameters. In order to reduce false segments, we perform the local processing which combines the morphological operation, linearity index, and local Hough transform in the second step. Finally, as for the global road segment connection, we propose an improved region growing algorithm which fully considering the rationality of road elements to gain the road network. Compared with the traditional region growing algorithm, the proposed method can effectively promote the improvement of the integrity of the road network detection. Moreover, the performance of the proposed method is evaluated by comparing the results with the ground truth road map and the evaluation index including the completeness, correctness, and quality factor. In experiments, the algorithm has been verified with the SAR images from the different resolutions of the GF-3 satellite SAR image. The results of the various real images demonstrate that the proposed algorithm has improved considerably the adaptability and efficiency of road detection compared with other methods

Analytical Properties for the Fifth Order Camassa-Holm (FOCH) Model

This paper devotes to present analysiswork on the fifth order Camassa-Holm (FOCH) modelwhich recently proposed by Liu and Qiao. Firstly, we establish the local and global existence of the solution to the FOCH model. Secondly, we study the property of the infinite propagation speed. Finally, we discuss the long time behavior of the support of momentum density with a compactly supported initial data