233 research outputs found

    Nonreciprocal Metasurface with Space-Time Phase Modulation

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    Creating materials with time-variant properties is critical for breaking reciprocity that imposes fundamental limitations to wave propagation. However, it is challenging to realize efficient and ultrafast temporal modulation in a photonic system. Here, leveraging both spatial and temporal phase manipulation offered by an ultrathin nonlinear metasurface, we experimentally demonstrated nonreciprocal light reflection at wavelengths around 860 nm. The metasurface, with traveling-wave modulation upon nonlinear Kerr building blocks, creates spatial phase gradient and multi-terahertz temporal phase wobbling, which leads to unidirectional photonic transitions in both momentum and energy spaces. We observed completely asymmetric reflections in forward and backward light propagations within a sub-wavelength interaction length of 150 nm. Our approach pointed out a potential means for creating miniaturized and integratable nonreciprocal optical components.Comment: 25 pages, 5 figure

    Molding Free-Space Light with Guided-Wave-Driven Metasurfaces

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    Metasurfaces with unparalleled controllability of light have shown great potential to revolutionize conventional optics. However, they mainly work with free-space light input, which makes it difficult for full on-chip integration. On the other hand, integrated photonics enables densely packed devices but has limited free-space light controllability. Here, we show that judiciously designed guided-wave-driven metasurfaces can mold guided waves into arbitrary free-space modes to achieve complex free-space functions, such as beam steering and focusing, with ultrasmall footprints and potentially no diffraction loss. Based on the same concept together with broken inversion symmetry induced by metasurfaces, we also realized direct orbital angular momentum (OAM) lasing from a micro-ring resonator. Our study works towards complete control of light across integrated photonics and free-space platforms, and paves new exciting ways for creating multifunctional photonic integrated devices with agile access to free space which could enable a plethora of applications in communications, remote sensing, displays, and etc.Comment: 37 pages, 5 figure

    Investigation of nanoelectrodes by Transmission Electron Microscopy

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    International audienceElectrodes for making connections to single molecules and clusters must have separations smaller than 10 nm. They are therefore difficult or impossible to image with atomic force microscopes (AFM) or Scanning Electron Microscopes (SEM). We have fabricated nanoelelectrodes by different methods to contacts nanoclusters and conjugated molecules and investigated their properties in transmission electron microscope (TEM) and their electrical characteristics at room temperature and at 4.2K. The electrodes are made on SiN4 membranes, which is transparent to high energy electrons and which make it possible to image features of a few nanometers in TEM

    EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party

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    Federated learning allows multiple participants to conduct joint modeling without disclosing their local data. Vertical federated learning (VFL) handles the situation where participants share the same ID space and different feature spaces. In most VFL frameworks, to protect the security and privacy of the participants' local data, a third party is needed to generate homomorphic encryption key pairs and perform decryption operations. In this way, the third party is granted the right to decrypt information related to model parameters. However, it isn't easy to find such a credible entity in the real world. Existing methods for solving this problem are either communication-intensive or unsuitable for multi-party scenarios. By combining secret sharing and homomorphic encryption, we propose a novel VFL framework without a third party called EFMVFL, which supports flexible expansion to multiple participants with low communication overhead and is applicable to generalized linear models. We give instantiations of our framework under logistic regression and Poisson regression. Theoretical analysis and experiments show that our framework is secure, more efficient, and easy to be extended to multiple participants.Comment: 9pages,2 figure
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