This paper proposes a new distributed coordinated control scheme based on heterogeneous roles for Unmanned Aerial Vehicle (UAV) swarm to achieve formation control. First, the framework of the distributed coordinated control scheme is designed on the basis of Distributed Model Predictive Control (DMPC). Then, the effect of heterogeneous roles including leader, coordinator and follower is discussed, and the role-based cost functions are developed to improve the performance of coordinated control for UAV swarm. Furthermore, a group of coordination strategies are proposed for UAVs with different roles to achieve swarm conflict resolution. Numerical simulations demonstrate that the presented distributed coordinated control scheme is effective to formulate and maintain the desired formation for the UAV swarm.

To achieve the perception-based autonomous control of UAVs, schemes with onboard sensing and computing are popular in state-of-the-art work, which often consist of several separated modules with respective complicated algorithms. Most methods depend on handcrafted designs and prior models with little capacity for adaptation and generalization. Inspired by the research on deep reinforcement learning, this paper proposes a new end-to-end autonomous control method to simplify the separate modules in the traditional control pipeline into a single neural network. An image-based reinforcement learning framework is established, depending on the design of the network architecture and the reward function. Training is performed with model-free algorithms developed according to the specific mission, and the control policy network can map the input image directly to the continuous actuator control command. A simulation environment for the scenario of UAV landing was built. In addition, the results under different typical cases, including both the small and large initial lateral or heading angle offsets, show that the proposed end-to-end method is feasible for perception-based autonomous control.

Text summarization plays an important role in various NLP applications. Using templates with generation methods is an effective way to address abstractive summarization. However, existing template-enhanced generation approaches use templates in a naive way and mainly adopt RNN-based Seq2Seq models, so they cannot make full use of valid information in the templates and suffer from templates’ noise. To mitigate these problems, we propose a new abstractive summarization model called Summarization Transformer with Template-aware Representation (STTR), which uses a template-aware document encoding module and a document representation shifting loss to preserve the useful information and filter the noise of the template. The experiments on the Gigaword and LCSTS datasets show that our method outperforms baseline models and achieves a new state-of-the-art.

In this paper, a face liveness detection benchmark is established and maintained, wherein 400 images pairs captured with binocular camera are made openly available for research purposes. This image dataset contains numbers of people with varied expressions, illumination, and background environment conditions, etc., among which 200 image pairs characterize lively human faces, and the other half are planar face pictures. The benchmark provides a platform for researchers to test stereo matching algorithms for liveness detection, where the detection performance is evaluated via a binary classification on the detection response for being a lively human or not. The feasibility of SIFT features are verified based on a comparative analysis of the classification result, and a set of optimal parameters for the classification is given which provides a reference for further research. * denotes the equal contributions.