Topology-aware MLP for Skeleton-based Action Recognition

Graph convolution networks (GCNs) have achieved remarkable performance in skeleton-based action recognition. However, existing previous GCN-based methods have relied excessively on elaborate human body priors and constructed complex feature aggregation mechanisms, which limits the generalizability of networks. To solve these problems, we propose a novel Spatial Topology Gating Unit (STGU), which is an MLP-based variant without extra priors, to capture the co-occurrence topology features that encode the spatial dependency across all joints. In STGU, to model the sample-specific and completely independent point-wise topology attention, a new gate-based feature interaction mechanism is introduced to activate the features point-to-point by the attention map generated from the input. Based on the STGU, in this work, we propose the first topology-aware MLP-based model, Ta-MLP, for skeleton-based action recognition. In comparison with existing previous methods on three large-scale datasets, Ta-MLP achieves competitive performance. In addition, Ta-MLP reduces the parameters by up to 62.5% with favorable results. Compared with previous state-of-the-art (SOAT) approaches, Ta-MLP pushes the frontier of real-time action recognition. The code will be available at https://github.com/BUPTSJZhang/Ta-MLP.Comment: 10 pages, 9 figure

Crosstalk Impacts on Homogeneous Weakly-Coupled Multicore Fiber Based IM/DD System

We numerically discussed crosstalk impacts on homogeneous weakly-coupled multicore fiber based intensity modulation/direct-detection (IM/DD) systems taking into account mean crosstalk power fluctuation, walk-off between cores, laser frequency offset, and laser linewidth.Comment: 3 pages, 11 figures

EPCFormer: Expression Prompt Collaboration Transformer for Universal Referring Video Object Segmentation

Audio-guided Video Object Segmentation (A-VOS) and Referring Video Object Segmentation (R-VOS) are two highly-related tasks, which both aim to segment specific objects from video sequences according to user-provided expression prompts. However, due to the challenges in modeling representations for different modalities, contemporary methods struggle to strike a balance between interaction flexibility and high-precision localization and segmentation. In this paper, we address this problem from two perspectives: the alignment representation of audio and text and the deep interaction among audio, text, and visual features. First, we propose a universal architecture, the Expression Prompt Collaboration Transformer, herein EPCFormer. Next, we propose an Expression Alignment (EA) mechanism for audio and text expressions. By introducing contrastive learning for audio and text expressions, the proposed EPCFormer realizes comprehension of the semantic equivalence between audio and text expressions denoting the same objects. Then, to facilitate deep interactions among audio, text, and video features, we introduce an Expression-Visual Attention (EVA) mechanism. The knowledge of video object segmentation in terms of the expression prompts can seamlessly transfer between the two tasks by deeply exploring complementary cues between text and audio. Experiments on well-recognized benchmarks demonstrate that our universal EPCFormer attains state-of-the-art results on both tasks. The source code of EPCFormer will be made publicly available at https://github.com/lab206/EPCFormer.Comment: The source code will be made publicly available at https://github.com/lab206/EPCForme

Galaxy-galaxy weak-lensing measurement from SDSS: II. host halo properties of galaxy groups

As the second paper of a series on studying galaxy-galaxy lensing signals using the Sloan Digital Sky Survey Data Release 7 (SDSS DR7), we present our measurement and modelling of the lensing signals around groups of galaxies. We divide the groups into four halo mass bins, and measure the signals around four different halo-center tracers: brightest central galaxy (BCG), luminosity-weighted center, number-weighted center and X-ray peak position. For X-ray and SDSS DR7 cross identified groups, we further split the groups into low and high X-ray emission subsamples, both of which are assigned with two halo-center tracers, BCGs and X-ray peak positions. The galaxy-galaxy lensing signals show that BCGs, among the four candidates, are the best halo-center tracers. We model the lensing signals using a combination of four contributions: off-centered NFW host halo profile, sub-halo contribution, stellar contribution, and projected 2-halo term. We sample the posterior of 5 parameters i.e., halo mass, concentration, off-centering distance, sub halo mass, and fraction of subhalos via a MCMC package using the galaxy-galaxy lensing signals. After taking into account the sampling effects (e.g. Eddington bias), we found the best fit halo masses obtained from lensing signals are quite consistent with those obtained in the group catalog based on an abundance matching method, except in the lowest mass bin. Subject headings: (cosmology:) gravitational lensing, galaxies: clusters: generalComment: 12 pages, 7 figures, submitted to Ap

Design Principle and Development Trends of Silicon-Based Anode Binders for Lithium-ion Batteries: A Mini Review

Abstract: Silicon (Si), recognized as a promising alternative material for the anodes of lithium-ion batteries, boasts a high theoretical specific capacity and abundant natural availability. During the preparation of silicon-based anodes, binders play a pivotal role in ensuring the cohesion of silicon particles, conductive agents, and current collectors. The structure and performance of these binders are critical for the mechanical stability, electrical conductivity, and stress dissipation capacity of the anodes. This review initially outlines the structural characteristics of various binders, including linear, branched, and three-dimensional cross-linked types. It then delves into the relationship between the structure and properties of these binders in the context of their application in high-performance lithium-ion batteries, focusing on their mechanical properties, electrical conductivity, and self-healing capabilities. Particular attention is given to the design strategies for binders that facilitate stress dissipation, with an emphasis on integrating multifunctional polymer binders renowned for their superior conductive and self-healing features. Such binders contribute to the formation of a robust three-dimensional network structure via multiple bonding mechanisms, including chemical, non-covalent, and coordination interactions. This configuration significantly enhances the adhesion between silicon particles, thereby facilitating the efficient dissipation of stress, which is a key aspect for ensuring the long-term cycling stability of lithium-ion batteries. Lastly, the paper explores future development directions for silicon anode binders, advocating for a thorough investigation into the synergy of diverse structural and functional combinations, with the aim of advancing the performance and practical application of silicon-based lithium-ion batteries