Orbital stability of periodic traveling waves to some coupled BBM equations

In this work, we show some results concerning the orbital stability of dnoidal wave solutions to some Benjamin-Bona-Mahony equations (BBM equations henceforth). First, by the standard argument, we prove the existence of a smooth curve of positive traveling wave solutions of dnoidal type. Then, we show that this type of solutions are orbitally stable by perturbations with the same period L. The major tools to obtain these results are the Grillaks, Shatah and Strauss' general theory in the periodic case. The results in the present paper extend some previous stability results for the BBM equations

Uniaxial Tension Simulation Using Real Microstructure-based Representative Volume Elements Model of Dual Phase Steel Plate

AbstractDual-phase steels have become a favored material for car bodies. In this study, the deformation behavior of dual-phase steels under uniaxial tension is investigated by means of 2D Representative Volume Elements (RVE) model. The real metallographic graphs including particle geometry, distribution and morphology are considered in this RVE model. Stress and strain distributions between martensite and ferrite are analyzed. The results show that martensite undertakes most stress without significant strain while ferrite shares the most strain. The tensile failure is the result of the deforming inhomogeneity between martensite phase and ferrite phase, which is the key factor triggering the plastic strain localization on specimen section during the tensile test

Interaction-aware Spatio-temporal Pyramid Attention Networks for Action Classification

Local features at neighboring spatial positions in feature maps have high correlation since their receptive fields are often overlapped. Self-attention usually uses the weighted sum (or other functions) with internal elements of each local feature to obtain its weight score, which ignores interactions among local features. To address this, we propose an effective interaction-aware self-attention model inspired by PCA to learn attention maps. Furthermore, since different layers in a deep network capture feature maps of different scales, we use these feature maps to construct a spatial pyramid and then utilize multi-scale information to obtain more accurate attention scores, which are used to weight the local features in all spatial positions of feature maps to calculate attention maps. Moreover, our spatial pyramid attention is unrestricted to the number of its input feature maps so it is easily extended to a spatio-temporal version. Finally, our model is embedded in general CNNs to form end-to-end attention networks for action classification. Experimental results show that our method achieves the state-of-the-art results on the UCF101, HMDB51 and untrimmed Charades.Comment: Accepted by ECCV201

Resource-efficient quantum key distribution with integrated silicon photonics

Integrated photonics provides a promising platform for quantum key distribution (QKD) system in terms of miniaturization, robustness and scalability. Tremendous QKD works based on integrated photonics have been reported. Nonetheless, most current chip-based QKD implementations require additional off-chip hardware to demodulate quantum states or perform auxiliary tasks such as time synchronization and polarization basis tracking. Here, we report a demonstration of resource-efficient chip-based BB84 QKD with a silicon-based encoder and decoder. In our scheme, the time synchronization and polarization compensation are implemented relying on the preparation and measurement of the quantum states generated by on-chip devices, thus no need additional hardware. The experimental tests show that our scheme is highly stable with a low intrinsic QBER of $0.50\pm 0.02\%$ in a 6-h continuous run. Furthermore, over a commercial fiber channel up to 150 km, the system enables realizing secure key distribution at a rate of 866 bps. Our demonstration paves the way for low-cost, wafer-scale manufactured QKD system.Comment: comments are welcome