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Metalenses, artificially engineered subwavelength nanostructures to focus light within ultrathin thickness, promise potential for a paradigm shift of conventional optical devices. However, the aperture sizes of metalenses are usually bound within hundreds of micrometers by the commonly-used scanning-based fabrication methods, limiting their usage on practical optical devices like telescopes. Here, for the first time, we demonstrate a high-efficiency, single-lens, refractive metalens telescope. We developed a mass production-friendly workflow for fabricating wafer-scale (80-mm aperture) metalenses using deep-ultraviolet (DUV) photolithography and a multi-exposure process involving reticle rotation and pattern stitching to leverage the radial symmetry of metalenses. Our metalens works in the near-infrared region (1200 - 1600 nm) with diffraction-limited performance and a high peak focusing efficiency of 80.84% at 1450 nm experimentally. Based on the metalens, we built a single-lens telescope and acquired images of the lunar surface, revealing its geographical structures. We believe our demonstration of the metalens telescope proves the exciting potential lying in the metasurfaces and could bring new possibilities for areas involving large optical systems, including geosciences, planetary observation, and astrophysical science.

We show that a metasurface-coated two-dimensional (2D) slab waveguide enables the generation of arbitrary complex light fields by combining the extreme versatility and freedom on wavefront control of optical metasurfaces with the compactness of photonic integrated circuits. We demonstrated off-chip 2D focusing and holographic projection with our metasurface-dressed photonic integrated devices. This technology holds the potential for many other optical applications requiring 2D light field manipulation with full on-chip integration, such as solid-state LiDAR and near-eye AR/VR displays.

Optical levitation of dielectric particles in vacuum is a powerful technique for precision measurements, testing fundamental physics, and quantum information science. Conventional optical tweezers require bulky optical components for trapping and detection. Here we design and fabricate an ultrathin dielectric metalens with a high numerical aperture of 0.88 at 1064 nm in vacuum. It consists of 500 nm-thick silicon nano-antennas, which are compatible with ultrahigh vacuum. We demonstrate optical levitation of nanoparticles in vacuum with a single metalens. The trapping frequency can be tuned by changing the laser power and polarization. We also transfer a levitated nanoparticle between two separated optical tweezers. Optical levitation with an ultrathin metalens in vacuum provides opportunities for a wide range of applications including on-chip sensing. Such metalenses will also be useful for trapping ultacold atoms and molecules., Comment: 5 pages

Rotation is a common motional form in nature, existing from atoms and molecules, industrial turbines to astronomical objects. However, it still lacks an efficient and reliable method for real-time image processing of a fast-rotating object. Since the Fourier spectrum of a rotating object changes rapidly, the traditional Fourier transform (FT) techniques become extremely complicated and time consuming. Here, we propose a Laguerre-Gaussian (LG) transform to analyze the rotating object with LG-mode basis. The rotation operation provides a feasible way to acquire LG spectrum, which is similar to the function of lens in FT. Particularly, the obtained LG spectrum does not change even the object working at a high rotating speed. By analyzing the LG spectrum, one can perform image processing such as reconstruction, edge enhancement, and pattern replication. Such LG transform provides an efficient and convenient way to real-time monitor and analyze a fast-rotating object in scientific research and industry.

The nonlocal-based blocks are designed for capturing long-range spatial-temporal dependencies in computer vision tasks. Although having shown excellent performances, they lack the mechanism to encode the rich, structured information among elements in an image. In this paper, to theoretically analyze the property of these nonlocal-based blocks, we provide a unified approach to interpreting them, where we view them as a graph filter generated on a fully-connected graph. When the graph filter is approximated by Chebyshev polynomials, a generalized formulation can be derived for explaining the existing nonlocal-based blocks ($\mathit{e.g.,}$ nonlocal block, nonlocal stage, double attention block). Furthermore, we propose an efficient and robust spectral nonlocal block, which can be flexibly inserted into deep neural networks to catch the long-range dependencies between spatial pixels or temporal frames. Experimental results demonstrate the clear-cut improvements and practical applicabilities of the spectral nonlocal block on image classification (Cifar-10/100, ImageNet), fine-grained image classification (CUB-200), action recognition (UCF-101), and person re-identification (ILID-SVID, Mars, Prid-2011) tasks.

Pedestrian trajectory prediction is a challenging task because of the complexity of real-world human social behaviors and uncertainty of the future motion. For the first issue, existing methods adopt fully connected topology for modeling the social behaviors, while ignoring non-symmetric pairwise relationships. To effectively capture social behaviors of relevant pedestrians, we utilize a directed social graph which is dynamically constructed on timely location and speed direction. Based on the social graph, we further propose a network to collect social effects and accumulate with individual representation, in order to generate destination-oriented and social-aware representations. For the second issue, instead of modeling the uncertainty of the entire future as a whole, we utilize a temporal stochastic method for sequentially learning a prior model of uncertainty during social interactions. The prediction on the next step is then generated by sampling on the prior model and progressively decoding with a hierarchical LSTMs. Experimental results on two public datasets show the effectiveness of our method, especially when predicting trajectories in very crowded scenes., Comment: 10 pages, 5 figures