Uniqueness theorems for meromorphic mappings sharing hyperplanes in general position

The purpose of this article is to study the uniqueness problem for meromorphic mappings from $\mathbb{C}^{n}$ into the complex projective space $\mathbb{P}^{N}(\mathbb{C}).$ By making using of the method of dealing with multiple values due to L. Yang and the technique of Dethloff-Quang-Tan respectively, we obtain two general uniqueness theorems which improve and extend some known results of meromorphic mappings sharing hyperplanes in general position.Comment: 10 page

Solar Modulation Utilizing VO2-Based Thermochromic Coatings for Energy-Saving Applications

Energy consumption has become an urgent issue not only for the global environment, but also for people’s lives. Among total energy consumption, buildings take nearly 40%. For buildings, energy exchange through windows accounts for over 50% by means of conduction, convection, and radiation. To reduce energy consumption, new structures should be developed for glass surfaces to enhance their thermal insulation properties. Vanadium dioxide (VO2) is the most well-known thermochromic material, which exhibits a notable optical change from transparent to reflecting in the infrared upon a semiconductor-to-metal phase-transition. In this chapter, we provide a comprehensive summary of advances on the VO2-based thermochromic coatings. Although the research on VO2 smart window has been carried on for several decades, the real commercial use of it has not yet been achieved. The hindrance factors against commercial use are conventionally known as the unsatisfactory intrinsic properties of VO2 material and have recently emerged as new challenges

Non-local Attention Optimized Deep Image Compression

This paper proposes a novel Non-Local Attention Optimized Deep Image Compression (NLAIC) framework, which is built on top of the popular variational auto-encoder (VAE) structure. Our NLAIC framework embeds non-local operations in the encoders and decoders for both image and latent feature probability information (known as hyperprior) to capture both local and global correlations, and apply attention mechanism to generate masks that are used to weigh the features for the image and hyperprior, which implicitly adapt bit allocation for different features based on their importance. Furthermore, both hyperpriors and spatial-channel neighbors of the latent features are used to improve entropy coding. The proposed model outperforms the existing methods on Kodak dataset, including learned (e.g., Balle2019, Balle2018) and conventional (e.g., BPG, JPEG2000, JPEG) image compression methods, for both PSNR and MS-SSIM distortion metrics