Ternary Stochastic Geometry Theory for Performance Analysis of RIS-Assisted UDN

With the fast development of reconfigurable intelligent surface (RIS), the network topology becomes more complex and varied, which makes the network design and analysis extremely challenging. Most of the current works adopt the binary system stochastic geometric, missing the coupling relationships between the direct and reflected paths caused by RISs. In this paper, we first define the typical triangle which consists of a base station (BS), a RIS and a user equipment (UE) as the basic ternary network unit in a RIS-assisted ultra-dense network (UDN). In addition, we extend the Campbell's theorem to the ternary system and present the ternary probability generating functional (PGFL) of the stochastic geometry. Based on the ternary stochastic geometry theory, we derive and analyze the coverage probability, area spectral efficiency (ASE), area energy efficiency (AEE) and energy coverage efficiency (ECE) of the RIS-assisted UDN system. Simulation results show that the RISs can improve the system performances, especially for the UE who has a high signal to interference plus noise ratio (SINR), as if the introduced RIS brings in Matthew effect. This phenomenon of RIS is appealing for guiding the design of complex networks.Comment: 29 pages, 11 figure

CatNorth: An Improved Gaia DR3 Quasar Candidate Catalog with Pan-STARRS1 and CatWISE

A complete and pure sample of quasars with accurate redshifts is crucial for quasar studies and cosmology. In this paper, we present CatNorth, an improved Gaia DR3 quasar candidate catalog with more than 1.5 million sources in the 3$\pi$ sky built with data from Gaia, Pan-STARRS1, and CatWISE2020. The XGBoost algorithm is used to reclassify the original Gaia DR3 quasar candidates as stars, galaxies, and quasars. To construct training/validation datasets for the classification, we carefully built two different master stellar samples in addition to the spectroscopic galaxy and quasar samples. An ensemble classification model is obtained by averaging two XGBoost classifiers trained with different master stellar samples. Using a probability threshold of $p_{\mathrm{QSO\_mean}}>0.95$ in our ensemble classification model and an additional cut on the logarithmic probability density of zero proper motion, we retrieved 1,545,514 reliable quasar candidates from the parent Gaia DR3 quasar candidate catalog. We provide photometric redshifts for all candidates with an ensemble regression model. For a subset of 89,100 candidates, accurate spectroscopic redshifts are estimated with the Convolutional Neural Network from the Gaia BP/RP spectra. The CatNorth catalog has a high purity of > 90% while maintaining high completeness, which is an ideal sample to understand the quasar population and its statistical properties. The CatNorth catalog is used as the main source of input catalog for the LAMOST phase III quasar survey, which is expected to build a highly complete sample of bright quasars with $i < 19.5$.Comment: 24 pages, 13 figures, submitted to AAS journals. Table 4 (The CatNorth quasar candidate catalog) is available at https://nadc.china-vo.org/res/r101313

Radiometric Cross-Calibration of GF-1 PMS Sensor with a New BRDF Model

On-orbit radiometric calibration of a space-borne sensor is of great importance for quantitative remote sensing applications. Cross-calibration is a common method with high calibration accuracy, and the core and emphasis of this method is to select the appropriate reference satellite sensor. As for the cross-calibration of high-spatial resolution and narrow-swath sensor, however, there are some scientific issues, such as large observation angles of reference image, and non-synchronization (or quasi-synchronization) between the imaging date of reference image and the date of sensor to be calibrated, which affects the accuracy of cross-calibration to a certain degree. Therefore, taking the GaoFen-1 (GF-1) Panchromatic and Multi-Spectral (PMS) sensor as an example in this research, an innovative radiometric cross-calibration method is proposed to overcome this bottleneck. Firstly, according a set of criteria, valid MODIS (Moderate Resolution Imagine Spectroradiometer) images of sunny day in one year over the Dunhuang radiometric calibration site in China are extracted, and a new and distinctive bidirectional reflectance distribution function (BRDF) model based on top-of-atmosphere (TOA) reflectance and imaging angles of the sunny day MODIS images is constructed. Subsequently, the cross-calibration of PMS sensor at Dunhuang and Golmud radiation calibration test sites is carried out by using the method presented in this paper, taking the MODIS image with large solar and observation angles and Landsat 8 Operational Land Imager (OLI) with different dates from PMS as reference. The validation results of the calibration coefficients indicate that our proposed method can acquire high calibration accuracy, and the total calibration uncertainties of PMS using MODIS as reference sensor are less than 6%

Source reconstruction for bioluminescence tomography via L1∕2 regularization

Bioluminescence tomography (BLT) is an important noninvasive optical molecular imaging modality in preclinical research. To improve the image quality, reconstruction algorithms have to deal with the inherent ill-posedness of BLT inverse problem. The sparse characteristic of bioluminescent sources in spatial distribution has been widely explored in BLT and many L1-regularized methods have been investigated due to the sparsity-inducing properties of L1 norm. In this paper, we present a reconstruction method based on L1∕2 regularization to enhance sparsity of BLT solution and solve the nonconvex L1∕2 norm problem by converting it to a series of weighted L1 homotopy minimization problems with iteratively updated weights. To assess the performance of the proposed reconstruction algorithm, simulations on a heterogeneous mouse model are designed to compare it with three representative sparse reconstruction algorithms, including the weighted interior-point, L1 homotopy, and the Stagewise Orthogonal Matching Pursuit algorithm. Simulation results show that the proposed method yield stable reconstruction results under different noise levels. Quantitative comparison results demonstrate that the proposed algorithm outperforms the competitor algorithms in location accuracy, multiple-source resolving and image quality

Opening a Window on Attention: Adjuvant Therapies for Inflammatory Bowel Disease

Inflammatory bowel disease (IBD), most commonly known as Crohn’s disease (CD) and ulcerative disease (UC), is a chronic and relapsing intestinal disease which cannot be cured completely. The prevalence of IBD in Europe and in North America has increased over the past 20 years. As most IBD patients are young at onset, their quality of life (QOL) can be influenced to varying degrees. Thus, current treatment goals are typically focused on preventing complications, including maintaining clinical remission and improving the QOL. Adjuvant therapies have been widely concerned as an effective treatment in alleviating IBD symptoms, including dietary intervention, traditional Chinese medicine, smoking, alcohol, and physical activities. This review focuses on different ancillary therapies for IBD treatments, in particular the mechanism of reducing inflammation based on the actual data from research studies. Moreover, comparing the latest data, this review also presented potential future prospect for adjuvant therapies