Under dynamic and heterogenous environment, the need for adaptability and rapid response time to information service systems has become increasingly important. To cope with the continuously changing conditions of service provision and utilization, Faded Information Field (FIF) has been proposed, which is an agent-based distributed information service system architecture. In the case of a mono-service request, the system is designed to improve users' access time and preserve load balancing through the information structure. However, with interdependent requests of multi-service increasing, adaptability, reliability and timeliness have to be assured by the system. In this paper, the relationship between the timeliness and the reliability of correlated services allocation and access is clarified. Based on these factors, the autonomous network-based heterogeneous information services integration technology to provide one-stop service for users' multi-service requests is proposed. We proved the effectiveness of the proposed technology through the simulation and the results show that the integrated service can reduce the total users access time compared with the conventional systems.

Machine learning, especially deep learning, is dramatically changing the methods associated with optical thin-film inverse design. The vast majority of this research has focused on the parameter optimization (layer thickness, and structure size) of optical thin-films. A challenging problem that arises is an automated material search. In this work, we propose a new end-to-end algorithm for optical thin-film inverse design. This method combines the ability of unsupervised learning, reinforcement learning and includes a genetic algorithm to design an optical thin-film without any human intervention. Furthermore, with several concrete examples, we have shown how one can use this technique to optimize the spectra of a multi-layer solar absorber device.