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Creators/Authors contains: "Yue, Sheng"

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  1. Abstract

    Metasurfaces, the ultra-thin media with extraordinary wavefront modulation ability, have shown great promise for many potential applications. However, most of the existing metasurfaces are limited by narrow-band and strong dispersive modulation, which complicates their real-world applications and, therefore require strict customized dispersion. To address this issue, we report a general methodology for generating ultra-broadband achromatic metasurfaces with prescribed ultra-broadband achromatic properties in a bottom-up inverse-design paradigm. We demonstrate three ultra-broadband functionalities, including acoustic beam deflection, focusing and levitation, with relative bandwidths of 93.3%, 120% and 118.9%, respectively. In addition, we reveal a relationship between broadband achromatic functionality and element dispersion. All metasurface elements have anisotropic and asymmetric geometries with multiple scatterers and local cavities that synthetically support internal resonances, bi-anisotropy and multiple scattering for ultra-broadband customized dispersion. Our study opens new horizons for ultra-broadband highly efficient achromatic functional devices, with promising extension to optical and elastic metamaterials.

  2. In order to meet the requirements for safety and latency in many IoT applications, intelligent decisions must be made right here right now at the network edge, calling for edge intelligence. To facilitate fast edge learning, this work advocates a platform-aided federated meta-learning architecture, where a set of edge nodes joint force to learn a meta-model (i.e., model initialization for adaptation in a new learning task) by exploiting the similarity among edge nodes as well as the cloud knowledge transfer. The federated meta-learning problem is cast as a regularized stochastic optimization problem, using Bregman Divergence between the edge model and the cloud pre-trained model as the regularization. We then devise an alternating direction method of multiplier (ADMM) based Hessian-free federated meta-learning algorithm, called ADMM-FedMeta, with inexact Hessian estimation. Further, we analyze the convergence properties and the rapid adaptation performance of ADMM-FedMeta for the general non-convex case. The theoretical results show that under mild conditions, ADMM-FedMeta converges to an $\epsilon$-approximate first-order stationary point after at most $\mathcal{O}(1/\epsilon^2)$ communication rounds. Extensive experimental studies on benchmark datasets demonstrate the effectiveness and efficiency of ADMM-FedMeta, and showcase that ADMM-FedMeta outperforms the existing baselines.