Zobrazeno 1 - 10
of 5 796
pro vyhledávání: '"Li,Jianhua"'
Autor:
Guo, Yijia, Huang, Wenkai, Li, Yang, Li, Gaolei, Zhang, Hang, Hu, Liwen, Li, Jianhua, Huang, Tiejun, Ma, Lei
3D Gaussian splatting (3DGS) has demonstrated impressive 3D reconstruction performance with explicit scene representations. Given the widespread application of 3DGS in 3D reconstruction and generation tasks, there is an urgent need to protect the cop
Externí odkaz:
http://arxiv.org/abs/2412.03121
Graph Neural Networks (GNNs) have demonstrated commendable performance for graph-structured data. Yet, GNNs are often vulnerable to adversarial structural attacks as embedding generation relies on graph topology. Existing efforts are dedicated to pur
Externí odkaz:
http://arxiv.org/abs/2408.16537
Autor:
Wang, Chen, Xu, Zhiyong, Wang, Jingyuan, Li, Jianhua, Mou, Weifeng, Zhu, Huatao, Zhao, Jiyong, Su, Yang, Wang, Yimin, Qi, Ailin
The non-perfect factors of practical photon-counting receiver are recognized as a significant challenge for long-distance photon-limited free-space optical (FSO) communication systems. This paper presents a comprehensive analytical framework for mode
Externí odkaz:
http://arxiv.org/abs/2408.13470
A Novel Signal Detection Method for Photon-Counting Communications with Nonlinear Distortion Effects
Autor:
Wang, Chen, Xu, Zhiyong, Wang, Jingyuan, Li, Jianhua, Mou, Weifeng, Zhu, Huatao, Zhao, Jiyong, Su, Yang, Wang, Yimin, Qi, Ailin
This paper proposes a method for estimating and detecting optical signals in practical photon-counting receivers. There are two important aspects of non-perfect photon-counting receivers, namely, (i) dead time which results in blocking loss, and (ii)
Externí odkaz:
http://arxiv.org/abs/2408.10800
Autor:
Li, Weichen, Huang, Xiaotong, Zheng, Jianwu, Wang, Zheng, Wang, Chaokun, Pan, Li, Li, Jianhua
We introduce rLLM (relationLLM), a PyTorch library designed for Relational Table Learning (RTL) with Large Language Models (LLMs). The core idea is to decompose state-of-the-art Graph Neural Networks, LLMs, and Table Neural Networks into standardized
Externí odkaz:
http://arxiv.org/abs/2407.20157
Deep Reinforcement Learning (DRL) is regarded as a promising tool for optical network optimization. However, the flexibility and efficiency of current DRL-based solutions for optical network optimization require further improvement. Currently, genera
Externí odkaz:
http://arxiv.org/abs/2406.15906
Blockchain technology has seen tremendous development over the past few years. Despite the emergence of numerous blockchain systems, they all suffer from various limitations, which can all be attributed to the fundamental issue posed by the DCS trile
Externí odkaz:
http://arxiv.org/abs/2406.12376
Graph Neural Networks (GNNs) have significantly advanced various downstream graph-relevant tasks, encompassing recommender systems, molecular structure prediction, social media analysis, etc. Despite the boosts of GNN, recent research has empirically
Externí odkaz:
http://arxiv.org/abs/2406.10573
As an emerging paradigm of content creation, AI-Generated Content (AIGC) has been widely adopted by a large number of edge end users. However, the requests for generated content from AIGC users have obvious diversity, and there remains a notable lack
Externí odkaz:
http://arxiv.org/abs/2405.08328
AI-generated content (AIGC) models, represented by large language models (LLM), have brought revolutionary changes to the content generation fields. The high-speed and extensive 6G technology is an ideal platform for providing powerful AIGC mobile se
Externí odkaz:
http://arxiv.org/abs/2405.05930