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融合信息通信和空中计算的认知无线网络鲁棒资源分配优化

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计算机应用 2024年第4期44卷 1195-1201页

作者：罗华亮李全忠张旗中山大学电子与信息工程学院广州510006 中山大学计算机学院广州510006

针对空中计算网络中无线传感器的功率资源限制及其与现有无线通信网络的频谱竞争,研究一个包含信息通信和空中计算功能的认知无线网络,其中,主网络实现无线信息通信功能,次网络实现空中计算功能,且次网络中的传感器利用主网络基站发送... 详细信息

针对空中计算网络中无线传感器的功率资源限制及其与现有无线通信网络的频谱竞争,研究一个包含信息通信和空中计算功能的认知无线网络,其中,主网络实现无线信息通信功能,次网络实现空中计算功能,且次网络中的传感器利用主网络基站发送的信号收集无线能量。考虑空中计算的均方误差(MSE)约束和网络中各节点的功率约束,基于随机信道不确定性构建目标函数为无线通信用户的和速率最大化的鲁棒资源分配优化问题,提出一个交替优化(AO)-改进的约束随机连续凸逼近(ICSSCA)算法,即AO-ICSSCA算法,将原鲁棒优化问题转换为确定性优化的子问题,并以交替的方式优化主网络基站的下行波束成形向量、次网络中传感器的功率因子和融合中心的融合波束成形向量。仿真实验结果表明,相较于改进前的约束随机连续凸逼近(CSSCA)算法,AO-ICSSCA算法能以更短的计算用时实现更好的优化性能。

关键词：信息通信空中计算认知无线网络鲁棒资源分配随机优化

Functional characterization of piggyBac-like elements from Nilaparvata lugens(St?l)(Hemiptera:Delphacidae)

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Journal of Zhejiang university-science B(Biomedicine & Biotechnology) 2022年第6期23卷 515-527页

作者： Jun LYU Qin SU Jinhui LIU Lin CHEN Jiawei sun Wenqing zhang State Key Laboratory of Biocontrol School of Life SciencesSun Yat-sen UniversityGuangzhou 510275China

Piggy Bac is a transposable DNA element originally discovered in the cabbage looper moth(Trichoplusia ni).The *** piggyBac transposon can introduce exogenous fragments into a genome,constructing a transgenic ***,the comprehensive analysis of endogenous piggyBac-like elements(PLEs)is important before using piggyBac,because they may influence the genetic stability of transgenic ***,we conducted a genome-wide analysis of PLEs in the brown planthopper(BPH)Nilaparvata lugens(St?l)(Hemiptera:Delphacidae),and identified a total of 28 PLE *** *** piggy Bac-like elements(NlPLEs)were present as multiple copies in the genome of *** the identified NlPLEs,NlPLE25 had the highest copy number and it was distributed on five *** full length of NlPLE25 consisted of terminal inverted repeats and sub-terminal inverted repeats at both terminals,as well as a single open reading frame transposase encoding546 amino ***,NlPLE25 transposase caused precise excision and transposition in cultured insect cells and also restored the original TTAA target sequence after excision.A cross-recognition between the NlPLE25 transposon and the piggy Bac transposon was also revealed in this *** findings provide useful information for the construction of transgenic insect lines.

关键词： Nilaparvata lugens piggyBac-like elements Transposon NlPLE25

Evidence for particle acceleration approaching PeV energies in the W51 complex

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science Bulletin 2024年第18期69卷 2833-2841页

作者： LHAASO Collaboration Zhen Cao F.Aharonian Axikegu Y.X.Bai Y.W.Bao D.Bastieri X.J.Bi Y.J.Bi W.Bian A.V.Bukevich Q.Cao W.Y.Cao Zhe Cao J.Chang J.F.Chang A.M.Chen E.S.Chen H.X.Chen Liang Chen Lin Chen Long Chen M.J.Chen M.L.Chen Q.H.Chen S.Chen S.H.Chen S.Z.Chen T.L.Chen Y.Chen N.Cheng Y.D.Cheng M.Y.Cui S.W.Cui X.H.Cui Y.D.Cui B.Z.Dai H.L.Dai Z.G.Dai Danzengluobu X.Q.Dong K.K.Duan J.H.Fan Y.Z.Fan J.Fang J.H.Fang K.Fang C.F.Feng H.Feng L.Feng S.H.Feng X.T.Feng Y.Feng Y.L.Feng S.Gabici B.Gao C.D.Gao Q.Gao W.Gao W.K.Gao M.M.Ge L.S.Geng G.Giacinti G.H.Gong Q.B.Gou M.H.Gu F.L.Guo X.L.Guo Y.Q.Guo Y.Y.Guo Y.A.Han M.Hasan H.H.He H.N.He J.Y.He Y.He Y.K.Hor B.W.Hou C.Hou X.Hou H.B.Hu Q.Hu S.C.Hu D.H.Huang T.Q.Huang W.J.Huang X.T.Huang X.Y.Huang Y.Huang X.L.Ji H.Y.Jia K.Jia K.Jiang X.W.Jiang Z.J.Jiang M.Jin M.M.Kang I.Karpikov D.Kuleshov K.Kurinov B.B.Li C.M.Li Cheng Li Cong Li D.Li F.Li H.B.Li H.C.Li Jian Li Jie Li K.Li S.D.Li W.L.Li W.L.Li X.R.Li Xin Li Y.Z.Li Zhe Li Zhuo Li E.W.Liang Y.F.Liang S.J.Lin B.Liu C.Liu D.Liu D.B.Liu H.Liu H.D.Liu J.Liu J.L.Liu M.Y.Liu R.Y.Liu S.M.Liu W.Liu Y.Liu Y.N.Liu Q.Luo Y.Luo H.K.Lv B.Q.Ma L.L.Ma X.H.Ma J.R.Mao Z.Min W.Mitthumsiri H.J.Mu Y.C.Nan A.Neronov L.J.Ou P.Pattarakijwanich Z.Y.Pei J.C.Qi M.Y.Qi B.Q.Qiao J.J.Qin A.Raza D.Ruffolo A.Sáiz M.Saeed D.Semikoz L.Shao O.Shchegolev X.D.Sheng F.W.Shu H.C.Song Yu.V.Stenkin V.Stepanov Y.Su D.X.sun Q.N.sun X.N.sun Z.B.sun J.Takata P.H.T.Tam Q.W.Tang R.Tang Z.B.Tang W.W.Tian C.Wang C.B.Wang G.W.Wang H.G.Wang H.H.Wang J.C.Wang Kai Wang Kai Wang L.P.Wang L.Y.Wang P.H.Wang R.Wang W.Wang X.G.Wang X.Y.Wang Y.Wang Y.D.Wang Y.J.Wang Z.H.Wang Z.X.Wang Zhen Wang Zheng Wang D.M.Wei J.J.Wei Y.J.Wei T.Wen C.Y.Wu H.R.Wu Q.W.Wu S.Wu X.F.Wu Y.S.Wu S.Q.Xi J.Xia G.M.Xiang D.X.Xiao G.Xiao Y.L.Xin Y.Xing D.R.Xiong Z.Xiong D.L.Xu R.F.Xu R.X.Xu W.L.Xu L.Xue D.H.Yan J.Z.Yan T.Yan C.W.Yang C.Y.Yang F.Yang F.F.Yang L.L.Yang M.J.Yang R.Z.Yang W.X.Yang Y.H.Yao Z.G.Yao L.Q.Yin N.Yin X.H.You Z.Y.You Y.H.Yu Q.Yuan H.Yue H.D.Zeng T.X.Zeng W.Zeng M.Zha B.B.zhang F.zhang H.zhang 不详 Key Laboratory of Particle Astrophysics&Experimental Physics Division&Computing Center Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China university of Chinese Academy of sciences Beijing 100049China TIANFU Cosmic Ray Research Center ChengduChina Dublin Institute for Advanced Studies 31 Fitzwilliam Place2 DublinIreland Max-Planck-Institut for Nuclear Physics P.O.Box 103980Heidelberg 69029Germany school of Physical science and technology&school of information science and technology Southwest Jiaotong UniversityChengdu 610031China school of Astronomy and Space science Nanjing UniversityNanjing 210023China Center for Astrophysics Guangzhou UniversityGuangzhou 510006China Tsung-Dao Lee Institute&school of Physics and Astronomy Shanghai Jiao Tong UniversityShanghai 200240China Institute for Nuclear Research of Russian Academy of sciences Moscow 117312Russia Hebei Normal university Shijiazhuang 050024China university of science and technology of china Hefei 230026China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy&Key Laboratory of Radio Astronomy Purple Mountain ObservatoryChinese Academy of SciencesNanjing 210023China Research Center for Astronomical Computing Zhejiang LaboratoryHangzhou 311121China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical ObservatoryChinese Academy of SciencesShanghai 200030China school of Physics and Astronomy Yunnan UniversityKunming 650091China Key Laboratory of Cosmic Rays(Tibet university) Ministry of EducationLhasa 850000China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China school of Physics and Astronomy(Zhuhai)&school of Physics(guangzhou)&Sino-French Institute of Nuclear Engineering and technology(Zhuhai) Sun Yat-sen UniversityZhuhai 519000&Guangzhou 510275GuangdongChina Institute of Frontier and Interdisciplinary science Shandong UniversityQingdao 266237China APC UniversitéParis Cité

Theγ-ray emission from the W51 complex is widely acknowledged to be attributed to the interaction between the cosmic rays(CRs)accelerated by the shock of supernova remnant(SNR)W51C and the dense molecular clouds in the adjacent star-forming region,***,the maximum acceleration capability of W51C for CRs remains *** on observations conducted with the Large High Altitude Air Shower Observatory(LHAASO),we report a significant detection ofγrays emanating from the W51 complex,with energies from 2 to 200 *** LHAASO measurements,for the first time,extend theγ-ray emission from the W51 complex beyond 100 TeV and reveal a significant spectrum bending at tens of *** combining the"π^(0)-decay bump"featured data from Fermi-LAT,the broadbandγ-ray spectrum of the W51 region can be well-characterized by a simple pp-collision *** observed spectral bending feature suggests an exponential cutoff at~400 TeV or a power-law break at~200 TeV in the CR proton spectrum,most likely providing the first evidence of SNRs serving as CR accelerators approaching the PeV ***,two young star clusters within W51B could also be theoretically viable to produce the most energeticγrays observed by *** findings strongly support the presence of extreme CR accelerators within the W51 complex and provide new insights into the origin of Galactic CRs.

关键词： UHE c-ray Cosmic rays SNR W51C Star clusters

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面向机器人系统的虚实迁移强化学习综述

软件学报 2024年第2期35卷 711-738页

作者：林谦余超伍夏威董银昭徐昕张强郭宪中山大学计算机学院广东广州510006 香港大学机械工程系中国香港999077 国防科技大学智能科学学院湖南长沙410073 大连理工大学计算机科学与技术学院辽宁大连116081 南开大学人工智能学院天津300350

近年来,基于环境交互的强化学习方法在机器人相关应用领域取得巨大成功,为机器人行为控制策略优化提供一个现实可行的解决方案.但在真实世界中收集交互样本存在高成本以及低效率等问题,因此仿真环境被广泛应用于机器人强化学习训练过程... 详细信息

近年来,基于环境交互的强化学习方法在机器人相关应用领域取得巨大成功,为机器人行为控制策略优化提供一个现实可行的解决方案.但在真实世界中收集交互样本存在高成本以及低效率等问题,因此仿真环境被广泛应用于机器人强化学习训练过程中.通过在虚拟仿真环境中以较低成本获取大量训练样本进行策略训练,并将学习策略迁移至真实环境,能有效缓解真实机器人训练中存在的安全性、可靠性以及实时性等问题.然而,由于仿真环境与真实环境存在差异,仿真环境中训练得到的策略直接迁移到真实机器人往往难以获得理想的性能表现.针对这一问题,虚实迁移强化学习方法被提出用以缩小环境差异,进而实现有效的策略迁移.按照迁移强化学习过程中信息的流动方向和智能化方法作用的不同对象,提出一个虚实迁移强化学习系统的流程框架,并基于此框架将现有相关工作分为3大类:基于真实环境的模型优化方法、基于仿真环境的知识迁移方法、基于虚实环境的策略迭代提升方法,并对每一分类中的代表技术与关联工作进行阐述.最后,讨论虚实迁移强化学习研究领域面临的机遇和挑战.

关键词：强化学习迁移学习虚实迁移现实差距机器人控制

Harnessing AI-human synergy for deep learning research analysis in ophthalmology with large language models assisting humans

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Eye science 2024年第1期1卷 7-25页

作者：罗明杰张玮星张哲铭庞健宇林桢哲赵兰琴林铎儒林浩添 State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular DiseasesGuangzhou 510060China Center for Precision Medicine and Department of Genetics and Biomedical Informatics Zhongshan School of MedicineSun Yat-sen UniversityGuangzhou 510060China Hainan Eye Hospital and Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityHaikou 570311China

Background:Research innovations inocular disease screening,diagnosis,and management have been boosted by deep learning(DL)in the last *** assess historical research trends and current advances,we conducted an artificial intelligence(AI)-human hybrid analysis of publications on DL in ***:All DL-related articles in ophthalmology,which were published between 2012 and 2022 from Web of science,were included.500 high-impact articles annotated with key research information were used to fine-tune a large language models(LLM)for reviewing medical literature and extracting *** verifying the LLM's accuracy in extracting diseases and imaging modalities,we analyzed trend of DL in ophthalmology with 2535 ***:Researchers using LLM for literature analysis were 70%(P=0.0001)faster than those who did not,while achieving comparable accuracy(97%versus 98%,P=0.7681).The field of DL in ophthalmology has grown 116%annually,paralleling trends of the broader DL *** publications focused mainly on diabetic retinopathy(P=0.0003),glaucoma(P=0.0011),and age-related macular diseases(P=0.0001)using retinal fundus photographs(FP,P=0.0015)and optical coherence tomography(OCT,P=0.0001).DL studies utilizing multimodal images have been growing,with FP and OCT combined being the most *** the 500 high-impact articles,laboratory studies constituted the majority at 65.3%.Notably,a discernible decline in model accuracy was observed when categorizing by study design,notwithstanding its statistical ***,43 publicly available ocular image datasets were ***:This study has characterized the landscape of publications on DL in ophthalmology,by identifying the trends and breakthroughs among research topics and the fast-growing *** study provides an efficient framework for combined AI-human analysis to comprehensively assess the current status and future trends in the field.

关键词： large language model AI-human collaboration research trends ophthalmology model performance

非完美信息博弈综述:对抗求解方法与对比分析

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计算机学报 2024年第9期47卷 2211-2246页

作者：余超刘宗凯胡超豪黄凯奇张俊格中山大学计算机学院广州510006 中国科学院自动化研究所智能系统与工程研究中心北京100190

当前,人工智能成为经济发展的新引擎,是新一轮产业变革的核心驱动力.结合人工智能与博弈论形成的新兴研究领域“博弈智能”吸引了越来越多学者的研究兴趣,并在现实生活中得到了广泛应用.作为一类典型的博弈智能,非完美信息博弈通过建模... 详细信息

当前,人工智能成为经济发展的新引擎,是新一轮产业变革的核心驱动力.结合人工智能与博弈论形成的新兴研究领域“博弈智能”吸引了越来越多学者的研究兴趣,并在现实生活中得到了广泛应用.作为一类典型的博弈智能,非完美信息博弈通过建模多智能体在私有信息下的博弈行为,能够刻画相较完美信息博弈更广泛的决策过程,在现实世界中具有广泛应用,例如金融贸易、商业谈判、军事对抗等.近年来,非完美信息博弈求解研究取得了突破性进展,涌现出以遗憾最小化(Regret Minimization)和最佳响应(Best Response)为核心技术的两大类离线求解方法.前者通过反省智能体过往决策以使自身策略向均衡点改进,成功解决了以德州扑克为代表的经典非完美信息博弈.后者通过特定应对方式针对对手决策以使自身策略向均衡点改进,在例如星际争霸、DOTA等大型实时战略游戏AI训练中发挥着关键作用.此外,一系列在线求解方法能够进一步实时优化离线算法求解所得的蓝图策略,使其在实时对局中得到进一步改进,成为求解非完美信息博弈的关键技术.本文将从非完美信息博弈的概念和特点切入,全面介绍这三类方法的基本原理、发展脉络和改进技巧,深入对比不同方法间的优缺点并展望未来研究方向.希望通过对非完美信息博弈求解这一研究领域的全方位细致梳理,能够进一步推动博弈智能技术向前发展,为迈向通用人工智能赋能.

关键词：非完美信息博弈遗憾最小化最佳响应在线求解强化学习

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基于深度学习的半监督信号调制样式识别算法

电子科技大学学报 2024年第4期53卷 511-518页

作者：张柏林姬港朱宇轩许向楠唐万斌中山大学系统科学与工程学院广州510275 中国人民解放军军事科学院系统工程研究院北京100191 电子科技大学通信抗干扰全国重点实验室成都611731

得益于深度学习的发展,使用神经网络提升信号识别性能取得了很大进步。使用半监督方法充分利用未标记数据来辅助深度模型的训练,但是现有的半监督信号识别方法未考虑噪声的影响,因此提出了一种基于深度残差网络(Resnet)的半监督信号识... 详细信息

得益于深度学习的发展,使用神经网络提升信号识别性能取得了很大进步。使用半监督方法充分利用未标记数据来辅助深度模型的训练,但是现有的半监督信号识别方法未考虑噪声的影响,因此提出了一种基于深度残差网络(Resnet)的半监督信号识别方法,并利用梯度逆转层改善了噪声对性能的影响。在开源数据集RML2016.10A、RML2016.10B和RML2016.10C上的实验结果表明,该半监督方法可借助少量标签数据信息和未标记数据来有效地训练深度模型,并且能缓解噪声对性能的影响。

关键词：调制样式半监督学习卷积神经网络信号识别

一种抵御计时攻击的指数Bernoulli精确采样算法

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信息网络安全 2024年第6期24卷 855-862页

作者：杜育松江思维沈静张家豪中山大学计算机学院广州510006 广东工贸职业技术学院广州510510

整数上的离散高斯采样是格密码的基础构建之一。拒绝采样是实现整数上离散高斯采样的一种主要的方法,而使用拒绝采样的关键是实现一个以指数函数为参数的Bernoulli分布的采样过程。这一采样过程也是决定整个采样算法能否抵御计时攻击的... 详细信息

整数上的离散高斯采样是格密码的基础构建之一。拒绝采样是实现整数上离散高斯采样的一种主要的方法,而使用拒绝采样的关键是实现一个以指数函数为参数的Bernoulli分布的采样过程。这一采样过程也是决定整个采样算法能否抵御计时攻击的关键。对于实数x>0,借鉴sun等人提出的一种针对指数函数Bernoulli分布β_(2)-x的等时采样算法,文章给出了一种可供选择的针对Bernoulli分布β_(2)-x的抵御计时攻击的精确采样算法,该算法能防止x的取值因计时攻击而遭到泄露,并且不需要(在线的)浮点运算,同时还能避免sun等人的采样算法在实际实现中产生的统计误差,从而确保采样结果的精确性。实验结果验证了这一精确采样算法的有效性。

关键词：格密码离散高斯采样 Bernoulli分布计时攻击

Integrated top-down and bottom-up proteomics mass spectrometry for the characterization of endogenous ribosomal protein heterogeneity

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Journal of Pharmaceutical Analysis 2023年第1期13卷 63-72页

作者： Ying zhang Qinghua Cai Yuxiang Luo Yu zhang Huilin Li school of Pharmaceutical sciences Sun Yat-sen UniversityGuangzhou510006China Henan Engineering Laboratory for Mammary Bioreactor School of Life SciencesHenan UniversityKaifengHenan475004China The Shennong Laboratory Zhengzhou450002China Guangdong Key Laboratory of Chiral Molecule and Drug Discovery School of Pharmaceutical SciencesSun Yat-sen UniversityGuangzhou510006China

Ribosomes are abundant,large RNA-protein complexes that are the sites of all protein synthesis in *** in ribosomal proteins(RPs),including proteoforms arising from genetic variations,alternative splicing of RNA transcripts,post-translational modifications and alterations of protein expression level,have been linked to a diverse range of diseases,including cancer and *** characterization of ribosomal proteoforms is challenging but important for the discovery of potential disease biomarkers or protein *** the present work,using *** 70S RPs as an example,we first developed a top-down proteomics approach on a Waters Synapt G2 Si mass spectrometry(MS)system,and then applied it to the HeLa 80S *** results were complemented by a bottom-up *** total,50 out of 55 RPs were identified using the top-down *** these,more than 30 RPs were found to have their N-terminal methionine *** modifications such as methylation,acetylation,and hydroxylation were also observed,and the modification sites were identified by bottomup *** a HeLa 80S ribosomal sample,we identified 98 ribosomal proteoforms,among which multiple truncated 80S ribosomal proteoforms were observed,the type of information which is often overlooked by bottom-up *** their relevance to diseases is not yet known,the integration of topdown and bottom-up proteomics approaches paves the way for the discovery of proteoform-specific disease biomarkers or targets.

关键词： Ribosomal proteins Top-down MS Bottom-up MS Proteoforms