Xidian University

About: Xidian University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Antenna (radio) & Synthetic aperture radar. The organization has 32099 authors who have published 38961 publications receiving 431820 citations. The organization is also known as: University of Electronic Science and Technology at Xi'an & Xīān Diànzǐ Kējì Dàxué.

Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis

Abstract: Synthesizing realistic profile faces is promising for more efficiently training deep pose-invariant models for large-scale unconstrained face recognition, by populating samples with extreme poses and avoiding tedious annotations. However, learning from synthetic faces may not achieve the desired performance due to the discrepancy between distributions of the synthetic and real face images. To narrow this gap, we propose a Dual-Agent Generative Adversarial Network (DA-GAN) model, which can improve the realism of a face simulator's output using unlabeled real faces, while preserving the identity information during the realism refinement. The dual agents are specifically designed for distinguishing real v.s. fake and identities simultaneously. In particular, we employ an off-the-shelf 3D face model as a simulator to generate profile face images with varying poses. DA-GAN leverages a fully convolutional network as the generator to generate high-resolution images and an auto-encoder as the discriminator with the dual agents. Besides the novel architecture, we make several key modifications to the standard GAN to preserve pose and texture, preserve identity and stabilize training process: (i) a pose perception loss; (ii) an identity perception loss; (iii) an adversarial loss with a boundary equilibrium regularization term. Experimental results show that DA-GAN not only presents compelling perceptual results but also significantly outperforms state-of-the-arts on the large-scale and challenging NIST IJB-A unconstrained face recognition benchmark. In addition, the proposed DA-GAN is also promising as a new approach for solving generic transfer learning problems more effectively.

NiO/Perovskite Heterojunction Contact Engineering for Highly Efficient and Stable Perovskite Solar Cells

Abstract: Recent research shows that the interface state in perovskite solar cells is the main factor which affects the stability and performance of the device, and interface engineering including strain engineering is an effective method to solve this issue. In this work, a CsBr buffer layer is inserted between NiO x hole transport layer and perovskite layer to relieve the lattice mismatch induced interface stress and induce more ordered crystal growth. The experimental and theoretical results show that the addition of the CsBr buffer layer optimizes the interface between the perovskite absorber layer and the NiO x hole transport layer, reduces interface defects and traps, and enhances the hole extraction/transfer. The experimental results show that the power conversion efficiency of optimal device reaches up to 19.7% which is significantly higher than the efficiency of the device without the CsBr buffer layer. Meanwhile, the device stability is also improved. This work provides a deep understanding of the NiO x /perovskite interface and provides a new strategy for interface optimization.

Cvap: validation for cluster analyses

Abstract: Evaluation of clustering results (or cluster validation) is an important and necessary step in cluster analysis, but it is often time-consuming and complicated work. We present a visual cluster validation tool, the Cluster Validity Analysis Platform (CVAP), to facilitate cluster validation. The CVAP provides necessary methods (e.g., many validity indices, several clustering algorithms and procedures) and an analysis environment for clustering, evaluation of clustering results, estimation of the number of clusters, and performance comparison among different clustering algorithms. It can help users accomplish their clustering tasks faster and easier and help achieve good clustering quality when there is little prior knowledge about the cluster structure of a data set.

Consensus of Switched Multiagent Systems

Abstract: In this brief, we consider the consensus problem of a switched multiagent system composed of continuous-time (CT) and discrete-time (DT) subsystems. By combining the classical consensus protocols of CT and DT multiagent systems, we propose a linear consensus protocol for switched multiagent system. Based on the graph theory and the Lyapunov theory, we prove that the consensus of switched multiagent system is solvable under arbitrary switching with undirected connected graph, directed graph, and switching topologies, respectively. Simulation examples are also provided to demonstrate the effectiveness of the theoretical results.