322 research outputs found
Electronic Structure and Linear Optical Properties of SrCuOCl 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 SrCuOCl, 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 (LADA+) 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
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