Chinese Academy of Sciences

China Institute of Atomic Energy

Microsoft Technology Licensing, LLC

Yangang Wang

Wen Wen Yang

Jue Wang

Ningming Nie

Jian Zhang

Xin Tong

Fang Wen

Yue Gao

Xiaoguang Wang

Hsiang-Tao Wu

Yankun Dou

Zhongxiao Cao

Yangde Feng

Tiancai Liu

Lixia Jia

Fuhai Gao

Xinfu He

Zhikuang Xin

The disclosure relates to a method and a system for predicting the operation time of sparse matrix vector multiplication. The method comprises constructing a convolutional neural network comprising an input layer, a feature processing layer, a data splicing layer and an output layer for outputting prediction results. The method further comprises acquiring a plurality of groups of sparse matrices with known sparse matrix vector multiplication operation time as sample data, inputting the sample data into the convolutional neural network to train the convolutional neural network, and inputting the sparse matrix to be classified into the trained convolutional neural network to realize the prediction of the operation time of sparse matrix vector multiplication.

Method and system for predicting operation time of sparse matrix vector multiplication

A method and device for simulating microstructure evolution of a material based on solution in an exponential time-difference format. The method includes: establishing a reaction rate theory model for substance defects, wherein the model is expressed with equations that comprise linear terms having coefficients characterized with matrixes; and iteratively solving the equations by using an exponential time-difference format, wherein during the iterative solving, the linear terms with exponential powers of the matrixes as the coefficients are integrated. Since a rate theory is not limited by spatial-temporal scales, the advantages of the rate theory can be significantly reflected when the microstructure evolution is simulated under a high damage dose condition; and then, the equations are solved by using the exponential time-difference format, with a solved result better in accuracy and higher in precision.

Simulation of microstructure evolution of material as solved based on exponential time-difference format

The present disclosure provides method, apparatus and system for 3-dimension (3D) face tracking. The method for 3D face tracking may comprise: obtaining a 2-dimension (2D) face image; performing a local feature regression on the 2D face image to determine 3D face representation parameters corresponding to the 2D face image; and generating a 3D facial mesh and corresponding 2D facial landmarks based on the determined 3D face representation parameters. The present disclosure may improve tracking accuracy and reduce memory cost, and accordingly may be effectively applied in broader application scenarios.

Method, apparatus and system for 3D face tracking

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Beijing, China

Yangang Wang