Deep Realistic Facial Editing via Label-restricted Mask Disentanglement.

With the rapid development of GAN (generative adversarial network), recent years have witnessed an increasing number of tasks on reference-guided facial attributes transfer. Most state-of-the-art methods consist of facial information extraction, latent space disentanglement, and target attribute manipulation. However, they either adopt reference-guided translation methods for manipulation or monolithic modules for diverse attribute exchange, which cannot accurately disentangle the exact facial attributes with specific styles from the reference image.

Construction of Structured Random Measurement Matrices in Semi-Tensor Product Compressed Sensing Based on Combinatorial Designs.

A random matrix needs large storage space and is difficult to be implemented in hardware, and a deterministic matrix has large reconstruction error. Aiming at these shortcomings, the objective of this paper is to find an effective method to balance these performances. Combining the advantages of the incidence matrix of combinatorial designs and a random matrix, this paper constructs a structured random matrix by the embedding operation of two seed matrices in which one is the incidence matrix of combinatorial designs, and the other is obtained by Gram-Schmidt orthonormalization of the random matrix.

A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs.

Body to body networks (BBNs) are a kind of large-scaled sensor network that are composed of several wireless body area networks (WBANs) in the distributed structure, and in recent decades, BBNs have played a key role in medical, aerospace, and military applications. Compared with the traditional WBANs, BBNs have larger scales and longer transmission distances. The sensors within BBNs not only transmit the data they collect, but also forward the data sent by other nodes as relay nodes.