Spatial mode (de)multiplexing of orbital angular momentum (OAM) beams is a promising solution to address future bandwidth issues, but the rapidly increasing divergence with the mode order severely limits the practically addressable number of OAM modes. Here we present a set of multi-vortex geometric beams (MVGBs) as high-dimensional information carriers for free-space optical communication, by virtue of three independent degrees of freedom (DoFs) including central OAM, sub-beam OAM, and coherent-state phase. The novel modal basis set has high divergence degeneracy, and highly consistent propagation behaviors among all spatial modes, capable of increasing the addressable spatial channels by two orders of magnitude than OAM basis as predicted. We experimentally realize the tri-DoF MVGB mode (de)multiplexing and data transmission by the conjugated modulation method, demonstrating lower error rates caused by center offset and coherent background noise, compared with OAM basis. Our work provides a potentially useful basis for the next generation of large-scale dense data communication.

Structured light with customized topological patterns inspires diverse classical and quantum investigations underpinned by accurate detection techniques. However, the current detection schemes are limited to vortex beams with a simple phase singularity. The precise recognition of general structured light with multiple singularities remains elusive. Here, we report deep learning (DL) framework that can unveil multi-singularity phase structures in an end-to-end manner, after feeding only two intensity patterns upon beam propagation. By outputting the phase directly, rich and intuitive information of twisted photons is unleashed. The DL toolbox can also acquire phases of Laguerre–Gaussian (LG) modes with a single singularity and other general phase objects likewise. Enabled by this DL platform, a phase-based optical secret sharing (OSS) protocol is proposed, which is based on a more general class of multi-singularity modes than conventional LG beams. The OSS protocol features strong security, wealthy state space, and convenient intensity-based measurements. This study opens new avenues for large-capacity communications, laser mode analysis, microscopy, Bose–Einstein condensates characterization, etc.

A novel consensus model for multi-attribute large-scale group decision making based on comprehensive behavior Classification and adaptive weight updating, Knowledge-Based Systems (2018),

No abstract

The majority of trading in financial markets is executed through a limit order book (LOB). The LOB is an event-based continuouslyupdating system that records contemporaneous demand ('bids' to buy) and supply ('asks' to sell) for a financial asset. Following recent successes in the literature that combine stochastic point processes with neural networks to model event stream patterns, we propose a novel state-dependent parallel neural Hawkes process to predict LOB events and simulate realistic LOB data. The model is characterized by: (1) separate intensity rate modelling for each event type through a parallel structure of continuous time LSTM units; and(2) an event-state interaction mechanism that improves prediction accuracy and enables efficient sampling of the event-state stream. We first demonstrate the superiority of the proposed model over traditional stochastic or deep learning models for predicting event type and time of a real world LOB dataset. Using stochastic point sampling from a well trained model, we then develop a realistic deep learning-based LOB simulator that exhibits multiple stylized facts found in real LOB data. CCS CONCEPTS• Mathematics of computing → Time series analysis; • Computing methodologies → Neural networks; • Applied computing → Economics.