Independent control of circularly polarized light with exceptional topological phase coding metasurfaces:erratum

The funding in this article[1]needs to be supplemented.Coupled-mode theory analysis has been supported by the Priority 2030 Federal Academic Leadership Program.CST simulations have been supported by Russian Science Foundation(project 23-72-10059).

Independent control of circularly polarized light with exceptional topological phase coding metasurfaces

Exceptional points,as degenerate points of non-Hermitian parity-time symmetric systems,have many unique physical properties.Due to its flexible control of electromagnetic waves,a metasurface is frequently used in the field of nanophotonics.In this work,we developed a parity-time symmetric metasurface and implemented the 2πtopological phase surrounding an exceptional point.Compared with Pancharatnam-Berry phase,the topological phase around an exceptional point can achieve independent regulation of several circular polarization beams.We combined the Pancharatnam-Berry phase with the exceptional topological phase and proposed a composite coding metasurface to achieve reflection decoupling of different circular polarizations.This work provides a design idea for polarimetric coding metasurfaces in the future.

A Quarterly High RFM Mining Algorithm for Big Data Management

In today’s highly competitive retail industry,offline stores face increasing pressure on profitability.They hope to improve their ability in shelf management with the help of big data technology.For this,on-shelf availability is an essential indicator of shelf data management and closely relates to customer purchase behavior.RFM(recency,frequency,andmonetary)patternmining is a powerful tool to evaluate the value of customer behavior.However,the existing RFM patternmining algorithms do not consider the quarterly nature of goods,resulting in unreasonable shelf availability and difficulty in profit-making.To solve this problem,we propose a quarterly RFM mining algorithmfor On-shelf products named OS-RFM.Our algorithmmines the high recency,high frequency,and high monetary patterns and considers the period of the on-shelf goods in quarterly units.We conducted experiments using two real datasets for numerical and graphical analysis to prove the algorithm’s effectiveness.Compared with the state-of-the-art RFM mining algorithm,our algorithm can identify more patterns and performs well in terms of precision,recall,and F1-score,with the recall rate nearing 100%.Also,the novel algorithm operates with significantly shorter running times and more stable memory usage than existing mining algorithms.Additionally,we analyze the sales trends of products in different quarters and seasonal variations.The analysis assists businesses in maintaining reasonable on-shelf availability and achieving greater profitability.