Alternating segment explicit-implicit scheme for nonlinear third-order KdV equation

A group of asymmetric difference schemes to approach the Korteweg-de Vries （KdV） equation is given here. According to such schemes, the full explicit difference scheme and the full implicit one, an alternating segment explicit-implicit difference scheme for solving the KdV equation is constructed. The scheme is linear unconditionally stable by the analysis of linearization procedure, and is used directly on the parallel computer. The numerical experiments show that the method has high accuracy.

Discussion of the Horizontal Intensity of Late Quaternary Fault Activity Along the Tianjingshan Fault Zone in Different Time Intervals and Fault Segments

By computing and classifying the data of gully offset obtained from field surveys along the Tianjingshan fault zone and estimating the ages of three types of gullies,the strike-slip rates along the fault zone are discussed in different time intervals and fault segments.The results suggest that the intensity of activity along the fault zone is not strong,but the differences between different time intervals and fault segments since the late Pleistocene have been obvious.The average rates range from 0.23 mm/a to 1.62 mm/a.The largest average rate is 1.40 mm/a,which occurred in the early and middle of late Pleistocene along the western segment of the fault zone.Since the late stage of the late Pleistocene,the center of faulting activity of the fault zone has shifted to the middle segment,and the average slip rates range have changed from 1.30 mm/a to 1.63 mm/a.

Segment differential aggregation representation and supervised compensation learning of ConvNets for human action recognition

With more multi-modal data available for visual classification tasks,human action recognition has become an increasingly attractive topic.However,one of the main challenges is to effectively extract complementary features from different modalities for action recognition.In this work,a novel multimodal supervised learning framework based on convolution neural networks(Conv Nets)is proposed to facilitate extracting the compensation features from different modalities for human action recognition.Built on information aggregation mechanism and deep Conv Nets,our recognition framework represents spatial-temporal information from the base modalities by a designed frame difference aggregation spatial-temporal module(FDA-STM),that the networks bridges information from skeleton data through a multimodal supervised compensation block(SCB)to supervise the extraction of compensation features.We evaluate the proposed recognition framework on three human action datasets,including NTU RGB+D 60,NTU RGB+D 120,and PKU-MMD.The results demonstrate that our model with FDA-STM and SCB achieves the state-of-the-art recognition performance on three benchmark datasets.