Electronic Structure and Linear Optical Properties of Sr2_{2}CuO2_{2}Cl2_{2} Studied from the First Principles Calculation

First-principles calculations with the full-potential linearized augmented plane-wave (FP-LAPW) method have been performed to investigate detailed electronic and linear optical properties of Sr$_{2}$CuO$_{2}$Cl$_{2}$, which is a classical low-dimensional antiferromagnet (AFM) charge transfer ({\it CT}) insulator. Within the local-spin-density approximation (LSDA) plus the on-site Coulomb interaction $U$ (LADA+$U$) added on Cu 3d orbitals, our calculated band gap and spin moments are well consistent with the experimental and other theoretical values. The energy dispersion relation agrees well with the angle resolved photoemission measurements. Its linear optical properties are calculated within the electric-dipole approximation. The absorption spectrum is found to agree well with the experimental result.Comment: 5 pages, 5 figure

Population Density-based Hospital Recommendation with Mobile LBS Big Data

The difficulty of getting medical treatment is one of major livelihood issues in China. Since patients lack prior knowledge about the spatial distribution and the capacity of hospitals, some hospitals have abnormally high or sporadic population densities. This paper presents a new model for estimating the spatiotemporal population density in each hospital based on location-based service (LBS) big data, which would be beneficial to guiding and dispersing outpatients. To improve the estimation accuracy, several approaches are proposed to denoise the LBS data and classify people by detecting their various behaviors. In addition, a long short-term memory (LSTM) based deep learning is presented to predict the trend of population density. By using Baidu large-scale LBS logs database, we apply the proposed model to 113 hospitals in Beijing, P. R. China, and constructed an online hospital recommendation system which can provide users with a hospital rank list basing the real-time population density information and the hospitals' basic information such as hospitals' levels and their distances. We also mine several interesting patterns from these LBS logs by using our proposed system

Super-resolving Compressed Images via Parallel and Series Integration of Artifact Reduction and Resolution Enhancement

In this paper, we propose a novel compressed image super resolution (CISR) framework based on parallel and series integration of artifact removal and resolution enhancement. Based on maximum a posterior inference for estimating a clean low-resolution (LR) input image and a clean high resolution (HR) output image from down-sampled and compressed observations, we have designed a CISR architecture consisting of two deep neural network modules: the artifact reduction module (ARM) and resolution enhancement module (REM). ARM and REM work in parallel with both taking the compressed LR image as their inputs, while they also work in series with REM taking the output of ARM as one of its inputs and ARM taking the output of REM as its other input. A unique property of our CSIR system is that a single trained model is able to super-resolve LR images compressed by different methods to various qualities. This is achieved by exploiting deep neural net-works capacity for handling image degradations, and the parallel and series connections between ARM and REM to reduce the dependency on specific degradations. ARM and REM are trained simultaneously by the deep unfolding technique. Experiments are conducted on a mixture of JPEG and WebP compressed images without a priori knowledge of the compression type and com-pression factor. Visual and quantitative comparisons demonstrate the superiority of our method over state-of-the-art super resolu-tion methods.Code link: https://github.com/luohongming/CISR_PS