Semantic communication(SemCom)has emerged as a transformative paradigm for future wireless networks,aiming to improve communication efficiency by transmitting only the semantic meaning(or its encoded version)of the so...Semantic communication(SemCom)has emerged as a transformative paradigm for future wireless networks,aiming to improve communication efficiency by transmitting only the semantic meaning(or its encoded version)of the source data rather than the complete set of bits(symbols).However,traditional deep-learning-based SemCom systems present challenges such as limited generalization,low robustness,and inadequate reasoning capabilities,primarily due to the inherently discriminative nature of deep neural networks.To address these limitations,generative artificial intelligence(GAI)is seen as a promising solution,offering notable advantages in learning complex data distributions,transforming data between high-and low-dimensional spaces,and generating high-quality content.This paper explores the applications of GAI in SemCom and presents a comprehensive study.It begins by introducing three widely used SemCom systems enabled by classical GAI models:variational autoencoders,generative adversarial networks,and diffusion models.For each system,the fundamental concept of the GAI model,the corresponding SemCom architecture,and a literature review of recent developments are provided.Subsequently,a novel generative SemCom system is proposed,incorporating cutting-edge GAI technology—large language models(LLMs).This system features LLM-based artificial intelligence(AI)agents at both the transmitter and receiver,which act as“brains”to enable advanced information understanding and content regeneration capabilities,respectively.Unlike traditional systems that focus on bitstream recovery,this design allows the receiver to directly generate the desired content from the coded semantic information sent by the transmitter.As a result,the communication paradigm shifts from“information recovery”to“information regeneration,”marking a new era in generative SemCom.A case study on point-to-point video retrieval is presented to demonstrate the effectiveness of the proposed system,showing a 99.98%reduction in communication overhead and a 53%improvement in average retrieval accuracy compared to traditional communication systems.Furthermore,four typical application scenarios for generative SemCom are described,followed by a discussion of three open issues for future research.In summary,this paper provides a comprehensive set of guidelines for applying GAI in SemCom,laying the groundwork for the efficient deployment of generative SemCom in future wireless networks.展开更多
Research on inflammatory response,liver injury,and immune regulation has demonstrated that the intricate interactions among immune cells constitute a critical regulatory network.Alcohol consumption alters the liver mi...Research on inflammatory response,liver injury,and immune regulation has demonstrated that the intricate interactions among immune cells constitute a critical regulatory network.Alcohol consumption alters the liver microenvironment,triggering inflammation and immune responses.Elucidating the inhibitory,cooperative,and synergistic effects among lymphocytes and myeloid cells may reveal the core mechanisms of alcohol-associated liver disease(ALD)pathogenesis and identify promising therapeutic targets.This review seeks to elucidate the intricate and multifaceted interactions among immune cells,encompassing both direct cellular interactions and the secretion of various effector molecules.It is essential to underscore that these interactions have broader and more complex roles in ALD than the activities of individual immune cell types.These interactions play a crucial role in mutually regulating one another,thereby preserving the homeostasis of the inflammatory and immune response in the liver environment.Targeting these immune cell interactions is anticipated to offer a novel approach to the prevention and treatment of ALD.展开更多
基金supported in part by the Basic Research Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone(HZQB-KCZYZ-2021067)the National Natural Science Foundation of China(62293482,62301471,and 62471423)+4 种基金the Shenzhen Outstanding Talents Training Fund(202002)the Guangdong Research Projects(2017ZT07X 152 and 2019CX01X104)the Guangdong Provincial Key Laboratory of Future Networks of Intelligence(2022B1212010001)the Shenzhen Key Laboratory of Big Data and Artificial Intelligence(ZDSYS201707251409055)the National Science and Technology Major Project—Mobile Information Networks(2024ZD1300700)。
文摘Semantic communication(SemCom)has emerged as a transformative paradigm for future wireless networks,aiming to improve communication efficiency by transmitting only the semantic meaning(or its encoded version)of the source data rather than the complete set of bits(symbols).However,traditional deep-learning-based SemCom systems present challenges such as limited generalization,low robustness,and inadequate reasoning capabilities,primarily due to the inherently discriminative nature of deep neural networks.To address these limitations,generative artificial intelligence(GAI)is seen as a promising solution,offering notable advantages in learning complex data distributions,transforming data between high-and low-dimensional spaces,and generating high-quality content.This paper explores the applications of GAI in SemCom and presents a comprehensive study.It begins by introducing three widely used SemCom systems enabled by classical GAI models:variational autoencoders,generative adversarial networks,and diffusion models.For each system,the fundamental concept of the GAI model,the corresponding SemCom architecture,and a literature review of recent developments are provided.Subsequently,a novel generative SemCom system is proposed,incorporating cutting-edge GAI technology—large language models(LLMs).This system features LLM-based artificial intelligence(AI)agents at both the transmitter and receiver,which act as“brains”to enable advanced information understanding and content regeneration capabilities,respectively.Unlike traditional systems that focus on bitstream recovery,this design allows the receiver to directly generate the desired content from the coded semantic information sent by the transmitter.As a result,the communication paradigm shifts from“information recovery”to“information regeneration,”marking a new era in generative SemCom.A case study on point-to-point video retrieval is presented to demonstrate the effectiveness of the proposed system,showing a 99.98%reduction in communication overhead and a 53%improvement in average retrieval accuracy compared to traditional communication systems.Furthermore,four typical application scenarios for generative SemCom are described,followed by a discussion of three open issues for future research.In summary,this paper provides a comprehensive set of guidelines for applying GAI in SemCom,laying the groundwork for the efficient deployment of generative SemCom in future wireless networks.
基金supported by the National Natural Science Foundation of China(No.822250008,82200644,2070608,82270589,82070548,81800464)the Natural Science Foundation of Anhui Province(No.2308085J28,2023AH020034)Postgraduate Innovation Research and Practice Program of Anhui Medical Uni-versity(No.YJS20230111).
文摘Research on inflammatory response,liver injury,and immune regulation has demonstrated that the intricate interactions among immune cells constitute a critical regulatory network.Alcohol consumption alters the liver microenvironment,triggering inflammation and immune responses.Elucidating the inhibitory,cooperative,and synergistic effects among lymphocytes and myeloid cells may reveal the core mechanisms of alcohol-associated liver disease(ALD)pathogenesis and identify promising therapeutic targets.This review seeks to elucidate the intricate and multifaceted interactions among immune cells,encompassing both direct cellular interactions and the secretion of various effector molecules.It is essential to underscore that these interactions have broader and more complex roles in ALD than the activities of individual immune cell types.These interactions play a crucial role in mutually regulating one another,thereby preserving the homeostasis of the inflammatory and immune response in the liver environment.Targeting these immune cell interactions is anticipated to offer a novel approach to the prevention and treatment of ALD.
