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Metasurfaces have been rapidly advancing our command over the many degrees of freedom of light within compact, lightweight devices. However, so far, they have mostly been limited to manipulating light in free space. Grating couplers provide the opportunity of bridging far-field optical radiation and in-plane guided waves, and thus have become fundamental building blocks in photonic integrated circuits. However, their operation and degree of light control is much more limited than metasurfaces. Metasurfaces integrated on top of guided wave photonic systems have been explored to control the scattering of light off-chip with enhanced functionalities – namely, point-by-point manipulation of amplitude, phase or polarization. However, these efforts have so far been limited to controlling one or two optical degrees of freedom at best, and to device configurations much more complex compared to conventional grating couplers. Here, we introduce leaky-wave metasurfaces, which are based on symmetry-broken photonic crystal slabs that support quasi-bound states in the continuum. This platform has a compact form factor equivalent to the one of conventional grating couplers, but it provides full command over amplitude, phase and polarization (four optical degrees of freedom) across large apertures. We present experimental demonstrations of various functionalities for operation at λ= 1.55 μm based on leaky-wave metasurfaces, including devices for phase and amplitude control at a fixed polarization state, and devices controlling all four optical degrees of freedom. Our results merge the fields of guided and free-space optics under the umbrella of metasurfaces, exploiting the hybrid nature of quasi-bound states in the continuum, for opportunities to advance in disruptive ways imaging, communications, augmented reality, quantum optics, LIDAR, and integrated photonic systems. 

Containerized microservices have been widely deployed in industry. Meanwhile, security issues also arise. Many security enhancement mechanisms for containerized microservices require predefined rules and policies. However, it is challenging when it comes to thousands of microservices and a massive amount of real-time unstructured data. Hence, automatic policy generation becomes indispensable. In this paper, we focus on the automatic solution for the security problem: irregular traffic detection for RPCs. We propose Informer, which is a two-phase machine learning framework to track the traffic of each RPC and report anomalous points automatically. Firstly, we identify RPC chain patterns by density-based clustering techniques and build a graph for each critical pattern. Next, we solve the irregular RPC traffic detection problem as a prediction problem for time-series of attributed graphs by leveraging spatial-temporal graph convolution networks. Since the framework builds multiple models and makes individual predictions for each RPC chain pattern, it can be efficiently updated upon legitimate changes in any of the graphs. In evaluations, we applied Informer to a dataset containing more than 7 billion lines of raw RPC logs sampled from an large Kubernetes system for two weeks. We provide two case studies of detected real-world threats. As a result, our framework found fine-grained RPC chain patterns and accurately captured the anomalies in a dynamic and complicated microservice production scenario, which demonstrates the effectiveness of Informer.