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从二里头文化的人群构成看夏文化概念

中原文化研究 2023年第4期11卷 21-30页

作者：梁云李盼盼西北大学文化遗产学院陕西西安710127

根据墓向葬俗可将含墓葬遗存的二里头文化遗址分为甲、乙、丙三类,甲类以北向墓为主,乙类以南向墓为主,丙类北向、南向墓比例大体相当,三类遗址在空间分布上有一定规律。经分析可知该文化的北向墓和南向墓分别代表了不同人群,该文化的... 详细信息

根据墓向葬俗可将含墓葬遗存的二里头文化遗址分为甲、乙、丙三类,甲类以北向墓为主,乙类以南向墓为主,丙类北向、南向墓比例大体相当,三类遗址在空间分布上有一定规律。经分析可知该文化的北向墓和南向墓分别代表了不同人群,该文化的人群构成是以北向墓人群为主,南向墓人群为辅。参考夏、商、周三代皆流行北首葬的文献记载,以及夏遗民墓葬的考古发现,可知二里头文化的北向墓人群为姒姓夏族。文献又记载了彭姓的韦和己姓的顾、昆吾等夏之与国,考古发现周代彭姓、己姓贵族墓为南向墓,可推知二里头文化的南向墓人群为彭姓、己姓部族;这些部族擅长制陶、铸铜、绿松石工艺。夏王朝的兴起开始于姒姓夏人与部分“祝融”后裔的联盟。“夏文化”概念有广义和狭义两个层次,分别是在考古学文化和族属两个层面的定义。

关键词：二里头文化北向墓南向墓人群构成夏文化

Sex-dependent alterations in extracellular vesicles linking chronic spinal cord injury to brain neuroinflammation and neurodegeneration

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Neural Regeneration Research 2025年第2期20卷 483-484页

作者： Yun Li Junfang Wu Department of Anesthesiology&Shock Trauma and Anesthesiology Research(STAR)CenterUniversity of Maryland School of MedicineBaltimoreMDUSA

Traumatic spinal cord injury(SCI)is a devastating exogenous injury with long-lasting consequences and a leading cause of death and disability worldwide.Advances in assistive technology,rehabilitative interventions,and the ability to identify and intervene in secondary conditions have significantly increased the long-term survival rate of SCI patients,with some people even living well into their seventh or eighth decade.These survival changes have led neurotrauma researchers to examine how SCI interacts with brain aging.Public health and epidemiological data showed that patients with long-term SCI can have a lower life expectancy and quality of life,along with a higher risk of comorbidities and complications.

关键词： alterations inflammation injury

Characterizing prediction errors of a new tree height model for cut-to-length Pinus radiata stems through the Burr TypeⅫdistribution

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林业研究:英文版 2023年第6期34卷 1899-1914页

作者： Xinyu Cao Huiquan Bi Duncan Watt Yun Li School of Information Science and Technology Beijing Forestry UniversityBeijing 100083People’s Republic of China Forest Science NSW Department of Primary IndustriesLevel 304PSQParramattaNSW 2150Australia School of Ecosystem and Forest Sciences Faculty of ScienceThe University of MelbourneWater StreetCreswickVIC 3363Australia Snowy Region Forestry Corporation of NSW76 Capper StreetTumutNSW 2720Australia

Unlike height-diameter equations for standing trees commonly used in forest resources modelling,tree height models for cut-to-length(CTL)stems tend to produce prediction errors whose distributions are not conditionally normal but are rather leptokurtic and heavy-tailed.This feature was merely noticed in previous studies but never thoroughly investigated.This study characterized the prediction error distribution of a newly developed such tree height model for Pin us radiata(D.Don)through the three-parameter Burr TypeⅫ(BⅫ)distribution.The model’s prediction errors(ε)exhibited heteroskedasticity conditional mainly on the small end relative diameter of the top log and also on DBH to a minor extent.Structured serial correlations were also present in the data.A total of 14 candidate weighting functions were compared to select the best two for weightingεin order to reduce its conditional heteroskedasticity.The weighted prediction errors(εw)were shifted by a constant to the positive range supported by the BXII distribution.Then the distribution of weighted and shifted prediction errors(εw+)was characterized by the BⅫdistribution using maximum likelihood estimation through 1000 times of repeated random sampling,fitting and goodness-of-fit testing,each time by randomly taking only one observation from each tree to circumvent the potential adverse impact of serial correlation in the data on parameter estimation and inferences.The nonparametric two sample Kolmogorov-Smirnov(KS)goodness-of-fit test and its closely related Kuiper’s(KU)test showed the fitted BⅫdistributions provided a good fit to the highly leptokurtic and heavy-tailed distribution ofε.Random samples generated from the fitted BⅫdistributions ofεw+derived from using the best two weighting functions,when back-shifted and unweighted,exhibited distributions that were,in about97 and 95%of the 1000 cases respectively,not statistically different from the distribution ofε.Our results for cut-tolength P.radiata stems represented the first case of any tree species where

关键词： Conditional heteroskedasticity Leptokurtic error distribution Skedactic function Nonlinear quantile regression Weighted prediction errors Serial correlation Random sampling and fitting Nonparametric goodnessof-fit tests

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重嵌:乡贤治理形成的社会网络机制分析

华中科技大学学报（社会科学版） 2023年第2期37卷 130-140页

作者：李云新程颖中南财经政法大学公共管理学院华中科技大学公共管理学院

作为传统自治主体,乡贤在全面推进乡村振兴、构建基层治理新格局中展现出新的时代价值。乡贤治理对提升乡村治理效能至关重要。本研究通过对鄂北L湾乡贤治理个案的追踪调研,基于社会网络理论构建“脱嵌—再建—重嵌”时序性分析框架,对... 详细信息

作为传统自治主体,乡贤在全面推进乡村振兴、构建基层治理新格局中展现出新的时代价值。乡贤治理对提升乡村治理效能至关重要。本研究通过对鄂北L湾乡贤治理个案的追踪调研,基于社会网络理论构建“脱嵌—再建—重嵌”时序性分析框架,对乡贤治理形成机制进行分析。研究表明,L湾乡贤治理经历了外出务工经商群体脱离乡土社会网络、乡土网络在城市中再建、乡贤返场重嵌乡土社会网络的过程,最终形成集中、公认、有力的兼具内生性和嵌入性的治理权威。依托乡土关系在城市中再建的外出务工经商群体网络,以情感符号重嵌乡村文化网络,以资源反哺重嵌并激活乡村经济网络,以乡贤权威重嵌乡村治理网络,推动乡贤治理的形成。在城乡融合发展背景下,城乡间流动的乡贤群体在城市找到合适位置的同时,如何进一步在推进乡村振兴中发挥作用,是实践中值得探索的重要方向和理论上亟待深入研究的重要课题。

关键词：乡村振兴乡村治理社会网络乡贤

Lexical simplification via single-word generation

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Frontiers of Computer Science 2023年第6期17卷 163-165页

作者： Jipeng QIANG Yang li Yun Li yunhao YUAN Yi ZHU Department of Computer Science Yangzhou UniversityYangzhou 225127China

1 Introduction Lexical simplification(LS)aims to simplify a sentence by replacing complex words with simpler words without changing the meaning of the sentence,which can facilitate comprehension of the text for people with non-native speakers and children.Traditional LS methods utilize linguistic databases(e.g.,WordNet)[1]or word embedding models[2]to extract synonyms or high-similar words for the complex word,and then sort them based on their appropriateness in context.Recently,BERT-based LS methods[3,4]entirely or partially mask the complex word of the original sentence,and then feed the sentence into pretrained modeling BERT[5]to obtain the top probability tokens corresponding to the masked word as the substitute candidates.They have made remarkable progress in generating substitutes by making full use of the context information of complex words,that can effectively alleviate the shortcomings of traditional methods.

关键词： token utilize speakers

Hypotension prediction index together with cerebral oxygenation in guiding intraoperative hemodynamic management: a case report

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The Journal of Biomedical Research 2022年第1期36卷 73-77页

作者： Yun Li Janet Phan Azaam Mamoor Hong liu Department of Anesthesiology the Second Hospital of Anhui Medical UniversityHefeiAnhui 230601China Department of Anesthesiology and Pain Medicine University of California Davis HealthSacramentoCA 95817USA California Northstate University College of Medicine SacramentoCA 95757USA

Intraoperative hypotension happens in everyday clinical practice. It was suggested to have a strong association with adverse postoperative outcomes. Hypotension prediction index(HPI) was developed to predict intraoperative hypotension(mean arterial pressure linicians with the values of organ oxygenation. We reported a case that the cerebral oxygenation monitor was used together with HPI to guide intraoperative blood pressure management. We found that cerebral oxygenation was maintained in the event of hypotension during surgery. The patient had no intraoperative or postoperative adverse outcomes despite the hypotension. We believe this can provide an individualized intraoperative blood pressure management to avoid over-or under-treating hypotension.

关键词： hypotension prediction index cerebral oxygenation hemodynamic intraoperative

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李祥云补肾祛瘀法改善子宫内膜容受性经验

中国中医基础医学杂志 2018年第9期24卷 1322-1324页

作者：李雪莲李祥云复旦大学附属妇产科医院妇科上海200011 上海市女性生殖内分泌相关疾病重点实验室上海200011 上海中医药大学附属龙华医院妇科上海200032

子宫内膜容受性指母体子宫内膜对胚胎的接受能力,是决定胚胎顺利着床的关键因素。全国名老中医李祥云教授认为"肾虚瘀阻"是子宫内膜容受性下降之根本,肾气不足则气血运行不畅、瘀滞内生,日久则脏腑功能失司又加重肾虚;治疗以"补肾祛瘀"... 详细信息

子宫内膜容受性指母体子宫内膜对胚胎的接受能力,是决定胚胎顺利着床的关键因素。全国名老中医李祥云教授认为"肾虚瘀阻"是子宫内膜容受性下降之根本,肾气不足则气血运行不畅、瘀滞内生,日久则脏腑功能失司又加重肾虚;治疗以"补肾祛瘀"为基本原则,用活血祛瘀药改善微循环、降低血液黏稠度、改善血供,提高子宫内膜容受性,同时根据"肾虚"和"瘀阻"的侧重并兼顾月经周期微调用药,进一步提高妊娠率,改善妊娠结局。

关键词：子宫内膜容受性肾虚瘀阻补肾祛瘀

A Cascade Model-Aware Generative Adversarial Example Detection Method

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Tsinghua Science and Technology 2021年第6期26卷 800-812页

作者： Keji Han Yun Li Bin Xia School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210046China

Deep Neural Networks(DNNs) are demonstrated to be vulnerable to adversarial examples, which are elaborately crafted to fool learning models. Since the accuracy and robustness of DNNs are at odds for the adversarial training method, the adversarial example detection algorithms check whether the specific example is adversarial, which is promising to solve the issue of the adversarial example. However, among the existing methods,model-aware detection methods do not generalize well, while the detection accuracies of the generative-based methods are lower compared to the model-aware methods. In this paper, we propose a cascade model-aware generative adversarial example detection method, namely CMAG. CMAG consists of two first-order reconstructors and a second-order reconstructor, which can illustrate what the model sees to the human by reconstructing the logit and feature maps of the last convolution layer. Experimental results demonstrate that our method is effective and is more interpretable compared to some state-of-the-art methods.

关键词： information security Deep Neural Network(DNN) adversarial example detection

Representation learning via an integrated autoencoder for unsupervised domain adaptation

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Frontiers of Computer Science 2023年第5期17卷 75-87页

作者： Yi ZHU Xindong WU Jipeng QIANG yunhao YUAN Yun Li School of Information Engineering Yangzhou UniversityYangzhou 225127China Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education of China) Hefei University of TechnologyHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230601China

The purpose of unsupervised domain adaptation is to use the knowledge of the source domain whose data distribution is different from that of the target domain for promoting the learning task in the target domain.The key bottleneck in unsupervised domain adaptation is how to obtain higher-level and more abstract feature representations between source and target domains which can bridge the chasm of domain discrepancy.Recently,deep learning methods based on autoencoder have achieved sound performance in representation learning,and many dual or serial autoencoderbased methods take different characteristics of data into consideration for improving the effectiveness of unsupervised domain adaptation.However,most existing methods of autoencoders just serially connect the features generated by different autoencoders,which pose challenges for the discriminative representation learning and fail to find the real cross-domain features.To address this problem,we propose a novel representation learning method based on an integrated autoencoders for unsupervised domain adaptation,called IAUDA.To capture the inter-and inner-domain features of the raw data,two different autoencoders,which are the marginalized autoencoder with maximum mean discrepancy(mAE)and convolutional autoencoder(CAE)respectively,are proposed to learn different feature representations.After higher-level features are obtained by these two different autoencoders,a sparse autoencoder is introduced to compact these inter-and inner-domain representations.In addition,a whitening layer is embedded for features processed before the mAE to reduce redundant features inside a local area.Experimental results demonstrate the effectiveness of our proposed method compared with several state-of-the-art baseline methods.

关键词： unsupervised domain adaptation representation learning marginalized autoencoder convolutional autoen-coder sparse autoencoder