A nonlinear transformation of the dispersive long wave equations in (2+1) dimensions and its applications

A nonlinear transformation of the dispersive long wave equations in (2+1) dimensions is derived by using the homogeneous balance method. With the aid of the transformation given here, exact solutions of the equations are obtained

Thermal gradient driven domain wall dynamics

The issue of whether a thermal gradient acts like a magnetic field or an electric current in the domain wall (DW) dynamics is investigated. Broadly speaking, magnetization control knobs can be classified as energy-driving or angular-momentum driving forces. DW propagation driven by a static magnetic field is the best-known example of the former in which the DW speed is proportional to the energy dissipation rate, and the current-driven DW motion is an example of the latter. Here we show that DW propagation speed driven by a thermal gradient can be fully explained as the angular momentum transfer between thermally generated spin current and DW. We found DW-plane rotation speed increases as DW width decreases. Both DW propagation speed along the wire and DW-plane rotation speed around the wire decrease with the Gilbert damping. These facts are consistent with the angular momentum transfer mechanism, but are distinct from the energy dissipation mechanism. We further show that magnonic spin-transfer torque (STT) generated by a thermal gradient has both damping-like and field-like components. By analyzing DW propagation speed and DW-plane rotation speed, the coefficient ( \b{eta}) of the field-like STT arising from the non-adiabatic process, is obtained. It is found that \b{eta} does not depend on the thermal gradient; increases with uniaxial anisotropy K_(||) (thinner DW); and decreases with the damping, in agreement with the physical picture that a larger damping or a thicker DW leads to a better alignment between the spin-current polarization and the local magnetization, or a better adiabaticity

Deep Attributes Driven Multi-Camera Person Re-identification

The visual appearance of a person is easily affected by many factors like pose variations, viewpoint changes and camera parameter differences. This makes person Re-Identification (ReID) among multiple cameras a very challenging task. This work is motivated to learn mid-level human attributes which are robust to such visual appearance variations. And we propose a semi-supervised attribute learning framework which progressively boosts the accuracy of attributes only using a limited number of labeled data. Specifically, this framework involves a three-stage training. A deep Convolutional Neural Network (dCNN) is first trained on an independent dataset labeled with attributes. Then it is fine-tuned on another dataset only labeled with person IDs using our defined triplet loss. Finally, the updated dCNN predicts attribute labels for the target dataset, which is combined with the independent dataset for the final round of fine-tuning. The predicted attributes, namely \emph{deep attributes} exhibit superior generalization ability across different datasets. By directly using the deep attributes with simple Cosine distance, we have obtained surprisingly good accuracy on four person ReID datasets. Experiments also show that a simple metric learning modular further boosts our method, making it significantly outperform many recent works.Comment: Person Re-identification; 17 pages; 5 figures; In IEEE ECCV 201

An Unusual Application of NASTRAN Contour Plotting Capability

A procedure is presented for obtaining contour plots of any physical quantity defined on a number of points of the surface of a structure. Rigid Format 1 of HEAT approach in Cosmic NASTRAN is ALTERED to enable use of contour plotting capability for scalar quantities. The ALTERED DMAP sequence is given. Examples include temperature distribution on the face of a cooled laser mirror and the angle of incidence or a radome surface