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During the past few years, metasurface polarization optics has experienced remarkable advances, resulting in revolutionary applications in imaging, sensing, computing, etc. The realization of complex optical operations requires the consideration of both the individual meta‐atoms as well as their intricate couplings. However, conventional design methods face challenges as design degrees of freedom and functionality complexity. Additionally, previous studies are restricted to the local design of single meta‐atoms based on explicit mapping relationships while ignoring interactions, resulting in an inability to meet the on‐demand requirements of complex light‐field operations. Here, a global design strategy based on field‐driven polygon evolution to achieve the inverse design of large‐scale coupled meta‐atoms is proposed. Through two global simulations, it can effectively reshape any given target optical field into an optimal structural distribution of devices without knowing mapping relationship. Near‐perfect spin‐decoupled beam‐splitting and high‐performance focusing, as well as the generation of arbitrary vector optical fields on the Poincaré sphere with a maximal diffraction efficiency closely approaching 100%, are experimentally demonstrated. This strategy opens up a new avenue for a rapid inverse design of large‐scale, high‐performance multifunctional meta‐devices, which can hold significant implications for both classical and quantum information processing domains. [ABSTRACT FROM AUTHOR]

With the development of the Internet, cyberattacks are becoming increasingly complex, sustained, and organized. Cyber threat intelligence sharing is one of the effective ways to alleviate the pressure on organizational or individual cyber security defense. However, the current cyber threat intelligence sharing lacks effective incentive mechanisms, resulting in mutual distrust and a lack of motivation to share among sharing members, making the security of sharing questionable. In this paper, we propose a blockchain-based cyber threat intelligence sharing mechanism (B-CTISM) to address the problems of free riding and lack of trust among sharing members faced in cyber threat intelligence sharing. We use evolutionary game theory to analyze the incentive strategy; the resulting evolutionarily stable strategy achieves the effect of promoting sharing and effectively curbing free-riding behavior. Then, the incentive strategy is deployed to smart contracts running in the trusted environment of blockchain, whose decentralization and tamper-evident properties can provide a trusted environment for participating members and establish trust without a third-party central institution to achieve secure and efficient cyber threat intelligence sharing. Finally, the effectiveness of the B-CTISM in facilitating and regulating threat intelligence sharing is verified through experimental simulation and comparative analysis. [ABSTRACT FROM AUTHOR]