Computational Modeling of Hierarchically Polarized Groups by Structured Matrix Factorization

The paper extends earlier work on modeling hierarchically polarized groups on social media. An algorithm is described that 1) detects points of agreement and disagreement between groups, and 2) divides them hierarchically to represent nested patterns of agreement and disagreement given a structural guide. For example, two opposing parties might disagree on core issues. Moreover, within a party, despite agreement on fundamentals, disagreement might occur on further details. We call such scenarios hierarchically polarized groups. An (enhanced) unsupervised Non-negative Matrix Factorization (NMF) algorithm is described for computational modeling of hierarchically polarized groups. It is enhanced with a language model, and with a proof of orthogonality of factorized components. We evaluate it on both synthetic and real-world datasets, demonstrating ability to hierarchically decompose overlapping beliefs. In the case where polarization is flat, we compare it to prior art and show that it outperforms state of the art approaches for polarization detection and stance separation. An ablation study further illustrates the value of individual components, including new enhancements.

An Infrared DoLP Model Considering the Radiation Coupling Effect

The polarization degree of objects in the marine background are affected by infrared radiation from sea surface. Taking into account the radiation coupling effect (RCE), a degree of linear polarization (DoLP) model is deduced. The DoLP of painted aluminum plates at different observation angles are simulated. The simulation results show the trend of the DoLP of the object decreases first and then increases as the observation angle θO, with the minimum value at θO=53∘. Nevertheless, we get a monotonically increasing trend and the minimum value is at θO=0∘ without considering RCE. The experimental results accord closely with those of the simulation with RCE. This conclusion is useful for the polarization detection and identification of infrared objects in the marine background.

Cross-nanofin-based waveplate pixels for broadband hybrid polarization coding in near-field

As an inherent characteristic of light, polarization plays important roles in information storage, display and even encryption. Metasurfaces, composed of specifically designed subwavelength units in a two-dimensional plane, offer a great convenience for polarization manipulation, yet improving their integrability and broadband fidelity remain significant challenges. Here, based on the combination of various subwavelength cross-nanofins (CNs), a new type of metasurface for multichannel hybrid polarization distribution in near-field is proposed. Sub-wavelength CN units with various waveplate (WP) functionalities, such as frequency-division multiplexing WP, half-WP and quarter-WP are implemented with high efficiency in broadband. High-resolution grayscale image encryption, multi-image storage and rapid polarization detection are demonstrated by encoding the WP pixels into single, double and four channels, respectively. All these applications possess good fidelity in an ultrabroad wavelength band from 1.2 to 1.9 µm, and the high degree of integrability, easy fabrication and multifunction make the CN-shaped WP pixels a promising candidate in optical device miniaturization, quantum applications and imaging technologies.