Research on the communication of virtual anchors in E-commerce platforms from the perspective of Embodied Theory -- taking the live streaming sales of the virtual idol Luo Tianyi as an example

In recent years, with the rise of virtual anchors in live streaming, virtual characters represented by "virtual idols" have gradually entered people's life. This paper mainly analyzes the interactive ways and characteristics of virtual idol Luo Tianyi's practice of selling goods in live streaming. In this communication practice, Luo Tianyi has embodied phenomena on two levels "physical and emotional." In addition, this paper also analyzes the communication mode of virtual anchors in e-commerce platforms from the physical-emotional level. Our research believes that in e-commerce live streaming, virtual anchors can integrate technology with their bodies and combine audiences with shopping scenes, thus improving the audience's perception and experience and contributing to the development of the live streaming selling industry. However, the technical construction and practical application of virtual anchors still have some problems to be solved. With the progress and perfection of technology, virtual anchors will have more excellent application space in the future live-streaming industry. Keywords: Embodied theory, virtual anchor, virtual idol, e-commerce live streaming DOI: 10.7176/NMMC/104-05 Publication date: March 31st 202

Phase-change metasurfaces for dynamic image display and information encryption

Optical metasurfaces enable to engineer the electromagnetic space and control light propagation at an unprecedented level, offering a powerful tool to achieve modulation of light over multiple physical dimensions. Here, we demonstrate a Sb$_{2}$S$_{3}$ phase-change metasurface platform that allows active manipulation of both amplitude and phase. In particular, we implement dynamic nanoprinting and holographic image display through tuning crystallization levels of this phase-change material. The Sb$_{2}$S$_{3}$ nanobricks tailored to function the amplitude, geometric and propagation phase modulation constitute the dynamic meta-atoms in the multiplexed metasurfaces. Using the incident polarizations as decoding keys, the encoded information can be reproduced into a naonprinting grayscale image in the near field and two holographic images in the far field. These images can be switched on and off by taking advantages of the reversible tunability of Sb$_{2}$S$_{3}$ nanostructure between amorphous and crystalline states. The proposed phase-change metasurfaces featuring manifold information and multifold encryption promise ultracompact data storage with high capacity and high security, which suggests an exciting direction for modern cryptography and security applications

GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse state

Extracting summaries from long documents can be regarded as sentence classification using the structural information of the documents. How to use such structural information to summarize a document is challenging. In this paper, we propose GoSum, a novel graph and reinforcement learning based extractive model for long-paper summarization. In particular, GoSum encodes sentence states in reinforcement learning by building a heterogeneous graph for each input document at different discourse levels. An edge in the graph reflects the discourse hierarchy of a document for restraining the semantic drifts across section boundaries. We evaluate GoSum on two datasets of scientific articles summarization: PubMed and arXiv. The experimental results have demonstrated that GoSum achieve state-of-the-art results compared with strong baselines of both extractive and abstractive models. The ablation studies further validate that the performance of our GoSum benefits from the use of discourse information

LCPR: A Multi-Scale Attention-Based LiDAR-Camera Fusion Network for Place Recognition

Place recognition is one of the most crucial modules for autonomous vehicles to identify places that were previously visited in GPS-invalid environments. Sensor fusion is considered an effective method to overcome the weaknesses of individual sensors. In recent years, multimodal place recognition fusing information from multiple sensors has gathered increasing attention. However, most existing multimodal place recognition methods only use limited field-of-view camera images, which leads to an imbalance between features from different modalities and limits the effectiveness of sensor fusion. In this paper, we present a novel neural network named LCPR for robust multimodal place recognition, which fuses LiDAR point clouds with multi-view RGB images to generate discriminative and yaw-rotation invariant representations of the environment. A multi-scale attention-based fusion module is proposed to fully exploit the panoramic views from different modalities of the environment and their correlations. We evaluate our method on the nuScenes dataset, and the experimental results show that our method can effectively utilize multi-view camera and LiDAR data to improve the place recognition performance while maintaining strong robustness to viewpoint changes. Our open-source code and pre-trained models are available at https://github.com/ZhouZijie77/LCPR .Comment: Accepted by IEEE Robotics and Automation Letters (RAL) 202