Skeleton-based action recognition is a widely used task in action related research because of its clear features and the invariance of human appearances and illumination. Furthermore, it can also effectively improve the robustness of the action recognition. Graph convolutional networks have been implemented on those skeletal data to recognize actions. Recent studies have shown that the graph convolutional neural network works well in the action recognition task using spatial and temporal features of skeleton data. The prevalent methods to extract the spatial and temporal features purely rely on a deep network to learn from primitive 3D position. In this paper, we propose a novel action recognition method applying high-order spatial and temporal features from skeleton data, such as velocity features, acceleration features, and relative distance between 3D joints. Meanwhile, a method of multi-stream feature fusion is adopted to fuse these high-order features we proposed. Extensive experiments on Two large and challenging datasets, NTU-RGBD and NTU-RGBD-120, indicate that our model achieves the state-of-the-art performance.

Background: Scoliosis is a type of spinal deformity, which is harmful to a person's health. In severe cases, it can trigger paralysis or death. The measurement of Cobb angle plays an essential role in assessing the severity of scoliosis. Purpose: The aim of this paper is to propose an automatic system for landmark detection and Cobb angle estimation, which can effectively help clinicians diagnose and treat scoliosis. Methods: A novel hybrid framework was proposed to measure Cobb angle precisely for clinical diagnosis, which was referred as W-Transformer due to its wshaped architecture. First, a convolutional neural network of cascade residual blocks as our backbone was designed. Then a transformer was fused to learn the dependency information between spine and landmarks. In addition, a reinforcement branch was designed to improve the overlap of landmarks, and an improved prediction module was proposed to fine-tune the final coordinates of landmarks in Cobb angles estimation. Besides, the public Accurate Automated Spinal Curvature Estimation (AASCE) MICCAI 2019 challenge was served as data set. It supplies 609 manually labeled spine anterior-posterior (AP) X-ray images, each of which contains a total of 68 landmark labels and three Cobb Angles tags. Results: From the perspective of the AASCE MICCAI 2019 challenge, we achieved a lower symmetric mean absolute percentage error (SMAPE) of 8.26% for all Cobb angles and the lowest averaged detection error of 50.89 in terms of landmark detection, compared with many state-of -the-art methods. We also provided the SMAPEs for the Cobb angles of the proximal-thoracic (PT), the main-thoracic (MT), and the thoracic-lumbar (TL) area, which are 5.27%, 14.59%, and 20.97% respectively, however, these data were not covered in most previous studies. Statistical analysis demonstrates that our model has obtained a high level of Pearson correlation coefficient of 0.9398 (p < 0.001), which shows excellent reliability of our model. Our model can yield 0.9489 (p < 0.001), 0.8817 (p < 0.001), and 0.9149 (p < 0.001) for PT, MT, and TL, respectively. The overall variability of Cobb angle measurement is less than 4 • , implying clinical value. And the mean absolute deviation (standard deviation) for three regions is 3.64 • (4.13 • ), 3.84 • (4.66 • ), and 3.80 • (4.19 • ). The results of Student paired t-test indicate that no statistically significant differences are observed between manual measurement and our automatic approach (p-value is always >0.05). Regarding the diagnosis of scoliosis (Cobb angle >10 • ), the proposed method achieves a high sensitivity of 0.9577 and a specificity of 0.8475 for all spinal regions. Conclusions: This study offers a brand-new automatic approach that is potentially of great benefit of the complex task of landmark detection and Cobb angle 3246

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The complexity of the 3D buildings and road networks gives the simulation of urban noise difficulty and significance. To solve the problem of computing complexity, a systematic methodology for computing urban traffic noise maps under 3D complex building environments is presented on a supercomputer. A parallel algorithm focused on controlling the compute nodes of the supercomputer is designed. Moreover, a rendering method is provided to visualize the noise map. In addition, a strategy for obtaining a real-time dynamic noise map is elaborated. Two efficiency experiments are implemented. One experiment involves comparing the expansibility of the parallel algorithm with various numbers of compute nodes and various computing scales to determine the expansibility. With an increase in the number of compute nodes, the computing time increases linearly, and an increased computing scale leads to computing efficiency increases. The other experiment is a comparison of the computing speed between a supercomputer and a normal computer; the computing node of Tianhe-2 is found to be six times faster than that of a normal computer. Finally, the traffic noise suppression effect of buildings is analyzed. It is found that the building groups have obvious shielding effect on traffic noise.

The traffic noise map is an important tool for environmental noise management, whose update is notably necessary. A method to quickly update the noise map based on a few noise-monitoring data points is proposed to avoid recollecting regional traffic data and a large amount of noise monitoring. The noise-monitoring data of roads with monitoring are compared with the original noise data in the noise map to obtain the deviation, and the roads that share the similar characteristic as sound sources are clustered. The roads without monitoring are corrected by the average noise deviation of the road class whose characteristic as a sound source is close to the characteristic as a sound source. The update accuracy of this method is analyzed, and a formula to calculate the precision is proposed. Finally, experiments to verify the method and its accuracy are organized; the results show that the method accurately and rapidly updates the noise map.