Recent advances in optical metasurfaces for polarization detection and engineered polarization profiles

Like amplitude, phase and frequency, polarization is one of the fundamental properties of light, which can be used to record, process and store information. Optical metasurfaces are ultrathin inhomogeneous media with planar nanostructures that can manipulate the optical properties of light at the subwavelength scale, which have become a current subject of intense research due to the desirable control of light propagation. The unprecedented capability of optical metasurfaces in the manipulation of the light’s polarization at subwavelength resolution has provided an unusual approach for polarization detection and arbitrary manipulation of polarization profiles. A compact metasurface platform has been demonstrated to detect polarization information of a light beam and to arbitrarily engineer a polarization profile that is very difficult or impossible to realize with conventional optical elements. This review will focus on the recent progress on ultrathin metasurface devices for polarization detection and realization of customized polarization profiles. Optical metasurfaces have provided new opportunities for polarization detection and manipulation, which can facilitate real-world deployment of polarization-related devices and systems in various research fields, including sensing, imaging, encryption, optical communications, quantum science, and fundamental physics

MNN: Mixed Nearest-Neighbors for Self-Supervised Learning

In contrastive self-supervised learning, positive samples are typically drawn from the same image but in different augmented views, resulting in a relatively limited source of positive samples. An effective way to alleviate this problem is to incorporate the relationship between samples, which involves including the top-K nearest neighbors of positive samples. However, the problem of false neighbors (i.e., neighbors that do not belong to the same category as the positive sample) is an objective but often overlooked challenge due to the query of neighbor samples without supervision information. In this paper, we present a simple self-supervised learning framework called Mixed Nearest-Neighbors for Self-Supervised Learning (MNN). MNN optimizes the influence of neighbor samples on the semantics of positive samples through an intuitive weighting approach and image mixture operations. The results demonstrate that MNN exhibits exceptional generalization performance and training efficiency on four benchmark datasets.Comment: 31 pages, 7 figures, source code and pretrained models are available https://github.com/pc-cp/MN

Calculation for Moment Capacity of Beam-to-Upright Connections of Steel Storage Pallet Racks

Steel storage pallet rack structures are three-dimensional framed structures, which are widely used to store different kinds of goods. For the easy accessibility to stored products, pallet racks are not usually braced in the down-aisle direction. The down-aisle stability is mostly provided by the characteristics of beam-to-upright connections, and the characteristics of upright base connections. In this paper, calculation for moment capacity of beam-to-upright connections is carried out. A mechanical model is presented firstly. Based on the model, moment capacity is related to the failure capacity, directly determined by the failure mode, of the topmost tab of the beam-end-connector and the corresponding upright wall. Different methods to predict the failure capacity are derived for two types of failure modes, i.e. crack of tab and crack of upright wall. The new method has shown a satisfactory agreement with experimental results demonstrating the reliability of the model in predicting the moment capacity of beam-to-upright connections

Experimental Analysis of Beam-to-upright Connections in Cold-formed Steel Storage Pallet Racks

A research program is currently in progress at Department of Building Engineering of Tongji University with the aim of investigating the behavior of cold-formed steel storage pallet racks under static and dynamic loading. This paper presents preliminary experimental analysis on the monotonic behavior of beam-to-upright connections. In the experimentation, the set-up was specially designed to accommodate precise requirement of boundary conditions and the measurement method was refined from the general ones used in rack design codes. It is shown that deformation modes of the connections were similar before failure while the failure modes were different depending on the specific constructional details. Moment-rotation characteristic curves are obtained and compared. On the base of these curves, the main parameters controlling the stiffness and moment capacity of connections, such as thickness of upright section, depth of pallet beam section, construction of beam end connector (mainly the number of tabs) and the loading direction are discussed

Helicity Dependent Directional Surface Plasmon Polariton Excitation Using A Metasurface with Interfacial Phase Discontinuity

Surface plasmon polaritons (SPPs) have been widely exploited in various scientific communities, ranging from physics, chemistry to biology, due to the strong confinement of light to the metal surface. For many applications it is important that the free space photon can be coupled to SPPs in a controllable manner. In this Letter, we apply the concept of interfacial phase discontinuity for circularly polarizations on a metasurface to the design of a novel type of polarization dependent SPP unidirectional excitation at normal incidence. Selective unidirectional excitation of SPPs along opposite directions is experimentally demonstrated at optical frequencies by simply switching the helicity of the incident light. This approach, in conjunction with dynamic polarization modulation techniques, opens gateway towards integrated plasmonic circuits with electrically reconfigurable functionalities.Comment: 17 pages, 5 figures. Published on <Light:Science & Applications