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1. Non-Hermitian metasurface with non-trivial topology

   https://doi.org/10.1515/nanoph-2021-0731  

   Yang, Frank ; Prasad, Ciril S. ; Li, Weijian ; Lach, Rosemary ; Everitt, Henry O. ; Naik, Gururaj V. ( February 2022 , Nanophotonics)

   Abstract The synergy between topology and non-Hermiticity in photonics holds immense potential for next-generation optical devices that are robust against defects. However, most demonstrations of non-Hermitian and topological photonics have been limited to super-wavelength scales due to increased radiative losses at the deep-subwavelength scale. By carefully designing radiative losses at the nanoscale, we demonstrate a non-Hermitian plasmonic–dielectric metasurface in the visible with non-trivial topology. The metasurface is based on a fourth order passive parity-time symmetric system. The designed device exhibits an exceptional concentric ring in its momentum space and is described by a Hamiltonian with a non-Hermitian Z 3 ${\mathbb{Z}}_{3}$ topological invariant of V = −1. Fabricated devices are characterized using Fourier-space imaging for single-shot k -space measurements. Our results demonstrate a way to combine topology and non-Hermitian nanophotonics for designing robust devices with novel functionalities. 

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2. Non-reciprocal propagation versus non-reciprocal control

   https://doi.org/10.1038/s41566-020-00723-5  

   Khurgin, Jacob B. ( December 2020 , Nature Photonics)

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3. Black-Box Non-interactive Non-malleable Commitments

   Garg, Rachit ; Lu, George ; Waters, Brent ( January 2021 , Eurocrypt)

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4. Non-local and non-Hermitian acoustic metasurfaces

   https://doi.org/10.1088/1361-6633/acfbeb  

   Wang, Xu ; Dong, Ruizhi ; Li, Yong ; Jing, Yun ( October 2023 , Reports on Progress in Physics)

   Acoustic metasurfaces are at the frontier of acoustic functional material research owing to their advanced capabilities of wave manipulation at an acoustically vanishing size. Despite significant progress in the last decade, conventional acoustic metasurfaces are still fundamentally limited by their underlying physics and design principles. First, conventional metasurfaces assume that unit cells are decoupled and therefore treat them individually during the design process. Owing to diffraction, however, the non-locality of the wave field could strongly affect the efficiency and even alter the behavior of acoustic metasurfaces. Additionally, conventional acoustic metasurfaces operate by modulating the phase and are typically treated as lossless systems. Due to the narrow regions in acoustic metasurfaces’ subwavelength unit cells, however, losses are naturally present and could compromise the performance of acoustic metasurfaces. While the conventional wisdom is to minimize these effects, a counter-intuitive way of thinking has emerged, which is to harness the non-locality as well as loss for enhanced acoustic metasurface functionality. This has led to a new generation of acoustic metasurface design paradigm that is empowered by non-locality and non-Hermicity, providing new routes for controlling sound using the acoustic version of 2D materials. This review details the progress of non-local and non-Hermitian acoustic metasurfaces, providing an overview of the recent acoustic metasurface designs and discussing the critical role of non-locality and loss in acoustic metasurfaces. We further outline the synergy between non-locality and non-Hermiticity, and delineate the potential of using non-local and non-Hermitian acoustic metasurfaces as a new platform for investigating exceptional points, the hallmark of non-Hermitian physics. Finally, the current challenges and future outlook for this burgeoning field are discussed.

    

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5. Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion

   Cutkosky, Ashok ; Mehta, Harsh ; Orabona, Francesco ( July 2023 , International Conference on Machine Learning)

   We present new algorithms for optimizing non-smooth, non-convex stochastic objectives based on a novel analysis technique. This improves the current best-known complexity for finding a (δ,ϵ)-stationary point from O(ϵ^(-4),δ^(-1)) stochastic gradient queries to O(ϵ^(-3),δ^(-1)), which we also show to be optimal. Our primary technique is a reduction from non-smooth non-convex optimization to online learning, after which our results follow from standard regret bounds in online learning. For deterministic and second-order smooth objectives, applying more advanced optimistic online learning techniques enables a new complexity of O(ϵ^(-1.5),δ^(-0.5)). Our techniques also recover all optimal or best-known results for finding ϵ stationary points of smooth or second-order smooth objectives in both stochastic and deterministic settings. 

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