Research and Prediction on the Sharing of WeChat Official Accounts’ Articles

With the development of mobile Internet, We Media was born. WeChat Official Account Platform is the largest we media platform in China. In WeChat social network, information can only be rapidly spread through the sharing operation of users. This paper takes WeChat official accounts as the object and uses logistic regression model to explore the influencing factors on sharing. After that, a prediction model is constructed based on logistic regression and support vector machine. The significance of this study is to propose the factors that influence WeChat official accounts’ articles sharing, and to construct a sharing prediction model

BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation

We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered robotics. BEHAVIOR-1K includes two components, guided and motivated by the results of an extensive survey on "what do you want robots to do for you?". The first is the definition of 1,000 everyday activities, grounded in 50 scenes (houses, gardens, restaurants, offices, etc.) with more than 9,000 objects annotated with rich physical and semantic properties. The second is OMNIGIBSON, a novel simulation environment that supports these activities via realistic physics simulation and rendering of rigid bodies, deformable bodies, and liquids. Our experiments indicate that the activities in BEHAVIOR-1K are long-horizon and dependent on complex manipulation skills, both of which remain a challenge for even state-of-the-art robot learning solutions. To calibrate the simulation-to-reality gap of BEHAVIOR-1K, we provide an initial study on transferring solutions learned with a mobile manipulator in a simulated apartment to its real-world counterpart. We hope that BEHAVIOR-1K's human-grounded nature, diversity, and realism make it valuable for embodied AI and robot learning research. Project website: https://behavior.stanford.edu.Comment: A preliminary version was published at 6th Conference on Robot Learning (CoRL 2022

Meta-analysis Followed by Replication Identifies Loci in or near CDKN1B, TET3, CD80, DRAM1, and ARID5B as Associated with Systemic Lupus Erythematosus in Asians

Systemic lupus erythematosus (SLE) is a prototype autoimmune disease with a strong genetic involvement and ethnic differences. Susceptibility genes identified so far only explain a small portion of the genetic heritability of SLE, suggesting that many more loci are yet to be uncovered for this disease. In this study, we performed a meta-analysis of genome-wide association studies on SLE in Chinese Han populations and followed up the findings by replication in four additional Asian cohorts with a total of 5,365 cases and 10,054 corresponding controls. We identified genetic variants in or near CDKN1B, TET3, CD80, DRAM1, and ARID5B as associated with the disease. These findings point to potential roles of cell-cycle regulation, autophagy, and DNA demethylation in SLE pathogenesis. For the region involving TET3 and that involving CDKN1B, multiple independent SNPs were identified, highlighting a phenomenon that might partially explain the missing heritability of complex diseases

Low Compaction Level Detection of Newly Constructed Asphalt Pavement Based on Regional Index

In order to improve the prediction accuracy regarding low compaction level of asphalt pavement, this paper carries out indoor tests to detect the voids and dielectric constants of AC-13, AC-16 and AC-25 asphalt mixtures, obtaining their relationship equations via linear fitting and determining the dielectric constant judgment threshold of low compaction level segregation risk points ε1. Based on the common mid-point method, three-dimensional ground-penetrating radar is used to obtain the dielectric constant of the physical engineering test section. The researcher can draw the distribution map of the low compaction level segregation risk area according to the judgment threshold ε1 of the rough segregation risk points; divide the connected risk areas; determine the regional convex hull; and calculate the regional indicators such as the regional area, the ratio of the convex risk points and the mean value of the regional dielectric constant. The response surface analysis method is used to acquire the model of risk area index and core void ratio. The model is employed to predict and verify the core void ratio in the risk area of the road section and verify the accuracy of the model. The results show that the error range between the predicted voids and the measured voids is −0.4%~+0.4%, and the mean absolute value of the error is 0.25%. Compared with the mean measured voids of 6.63%, the relative error is 3.77%, indicating that the model can accurately predict the regional low compaction level segregation degree

Landslide Identification and Gradation Method Based on Statistical Analysis and Spatial Cluster Analysis

As a type of earth observation technology, interferometric synthetic aperture radar (InSAR) is increasingly widely used in the field of geological disaster detection. However, the application of InSAR in low-coherence areas, such as alpine canyon areas and vegetation coverage areas, is subject to considerable limitations. How to accurately identify landslides from InSAR measurement data in these areas remains the subject of several challenges and shortcomings. Based on statistical analysis and spatial cluster analysis, in this paper, we propose an automatic landslide identification and gradation method suitable for low-coherence areas. The proposed method combines the small baseline subset InSAR (SBAS-InSAR) method and the interferogram stacking (stacking-InSAR) method to obtain a deformation map in the study area, using statistical analysis and spatial cluster analysis to extract deformation regions and landslide polygons to propose a landslide screening model (LSM) based on multivariate features to screen landslides and reduce the interference of noise in landslide identification, in addition to proposing a landslide gradation model (LGM) based on signum function to grade the identified landslides and provide support to distinguish landslides with different deformation degrees. The method was applied to landslide identification in the upper section of the Jinsha River basin, and 47 potential landslides were identified, including 15 high-risk landslides and 13 landslides endangering villages. The experimental results show that the proposed method can identify landslides accurately and hierarchically in low-coherence areas, providing support for geological hazard investigation agencies and local departments

Transcriptional regulation and functional analysis of Nicotiana tabacum under salt and ABA stress

Soil salinization is an important factor that restricts crop quality and yield and causes an enormous toll to human beings. Salt stress and abscisic acid (ABA) stress will occur in the process of soil salinization. In this study, transcriptome sequencing of tobacco leaves under salt and ABA stress in order to further study the resistance mechanism of tobacco. Compared with controlled groups, 1654 and 3306 DEGs were obtained in salt and ABA stress, respectively. The genes function enrichment analysis showed that the up-regulated genes in salt stress were mainly concentrated in transcription factor WRKY family and PAR1 resistance gene family, while the up-regulated genes were mainly concentrated on bHLH transcription factor, Kunitz-type protease inhibitor, dehydrin (Xero1) gene and CAT (Catalase) family protein genes in ABA stress. Tobacco MAPK cascade triggered stress response through up-regulation of gene expression in signal transduction. The expression products of these up-regulated genes can improve the abiotic stress resistance of plants. These results have an important implication for further understanding the mech-anism of salinity tolerance in plants. (c) 2021 Elsevier Inc. All rights reserved

Landslide Identification and Gradation Method Based on Statistical Analysis and Spatial Cluster Analysis

As a type of earth observation technology, interferometric synthetic aperture radar (InSAR) is increasingly widely used in the field of geological disaster detection. However, the application of InSAR in low-coherence areas, such as alpine canyon areas and vegetation coverage areas, is subject to considerable limitations. How to accurately identify landslides from InSAR measurement data in these areas remains the subject of several challenges and shortcomings. Based on statistical analysis and spatial cluster analysis, in this paper, we propose an automatic landslide identification and gradation method suitable for low-coherence areas. The proposed method combines the small baseline subset InSAR (SBAS-InSAR) method and the interferogram stacking (stacking-InSAR) method to obtain a deformation map in the study area, using statistical analysis and spatial cluster analysis to extract deformation regions and landslide polygons to propose a landslide screening model (LSM) based on multivariate features to screen landslides and reduce the interference of noise in landslide identification, in addition to proposing a landslide gradation model (LGM) based on signum function to grade the identified landslides and provide support to distinguish landslides with different deformation degrees. The method was applied to landslide identification in the upper section of the Jinsha River basin, and 47 potential landslides were identified, including 15 high-risk landslides and 13 landslides endangering villages. The experimental results show that the proposed method can identify landslides accurately and hierarchically in low-coherence areas, providing support for geological hazard investigation agencies and local departments

Make parallel programs reliable with stable multithreading

Our accelerating computational demand and the rise of multicore hardware have made parallel programs increasingly pervasive and critical. Yet, these programs remain extremely difficult to write, test, analyze, debug, and verify. In this article, we provide our view on why parallel programs, specifically multithreaded programs, are difficult to get right. We present a promising approach we call stable multithreading to dramatically improve reliability, and summarize our last four years ’ research on building and applying stable multithreading systems.

Mechanism for self-compensation in heavily carbon doped GaN

Heavy carbon (C) doping is of great significance for semi-insulating GaN in power electronics. However, the doping behaviors, especially the atomic configurations and related self-compensation mechanisms, are still under debate. Here, with the formation energy as the input parameter, the concentrations of C defects with different atomic configurations are calculated by taking the configurational entropy into account. The result shows that the concentrations of tri-carbon complexes (CNCiCN, where Ci refers to interstitial carbon) and dicarbon complexes (CNCGa) cannot be neglected under heavy doping conditions. The concentration of CNCiCN can even exceed that of CN at sufficiently high doping levels. Especially, we suggest that it is the tri-carbon complex CNCiCN, instead of the commonly expected CGa, that acts as the self-compensation centers in semi-insulating GaN under heavy C doping conditions. The results provide a fresh look on the long-standing problem about the self-compensation mechanisms in C doped GaN