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A Spatial Baseline of China's Blue Carbon Stocks for Improved Monitoring, Management, and Protection

Earth's Future 2025年第5期13卷

作者： Sun, Shaobo Song, Zhaoliang Guo, Laodong Duarte, Carlos M. Macreadie, Peter I. Zhang, Zhen Fang, Yunying Chen, Baozhang Wang, Zhengang Wang, Weiqi Wang, Yidong Fu, Dongjie Li, Yuan Wang, Yafei Wang, Hailong Liu, Cong-Qiang Institute of Surface-Earth System Science School of Earth System Science Tianjin University Tianjin China School of Freshwater Sciences University of Wisconsin-Milwaukee Milwaukee WI United States Biological and Environmental Sciences & Engineering Division Red Sea Research Center and Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia Centre for Integrative Ecology School of Life and Environmental Sciences Deakin University Burwood VIC Australia State Key Laboratory of Tibetan Plateau Earth System National Tibetan Plateau Data Center (TPDC) Environment and Resource (TPESER) Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing China Earth System Science Interdisciplinary Center University of Maryland College Park MD United States Australian Rivers Institute School of Environment and Science Griffith University Brisbane QLD Australia State Key Laboratory of Resources and Environment Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China School of Geography and Planning Sun Yat-Sen University Guangzhou China Key Laboratory of Humid Subtropical Eco-Geographical Process Ministry of Education Fujian Normal University Fuzhou China Tianjin Key Laboratory of Water Resources and Environment Tianjin Normal University Tianjin China CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation Yantai Institute of Coastal Zone Research Chinese Academy of Sciences Yantai China Key Laboratory of Regional Sustainable Development Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China School of Environmental and Chemical Engineering Foshan University Foshan China

Blue carbon ecosystems (BCEs) exhibit remarkable capacities for organic carbon (C) storage, making them a promising nature-based climate solution. However, the lack of robust, spatially explicit estimates of C stocks in BCEs presents a major challenge to incorporating them into national and global C accounting and climate mitigation strategies. For the first time, we develop spatially explicit estimates of both soil and living biomass C stocks in BCEs across China by integrating open-access remote sensing, climate, and soil property data with 2,899 site measurements using a data-driven method. Our C stock estimates reveal that mean C stocks in BCEs significantly decrease with increasing latitude. We estimate the total C storage within China's BCEs at 121.1 ± 48.4 Tg C, with soil accounting for 90% of the total C storage. Tidal marshes and flats contribute a total of 64.7 ± 8.7 Tg C, while mangroves store a much smaller amount (4.4 ± 1.4 Tg C) due to their limited spatial extent. Seagrasses store a total of 52.0 ± 38.3 Tg C, which is significantly larger than previous estimates. These findings underscore the significant potential of BCEs protection and restoration for China's climate mitigation efforts. Our spatially explicit C stock estimates for China's BCEs provide essential data for national and global C accounting and can serve as baselines for quantifying the impacts of climate change and anthropogenic activities on BCEs C stocks. Additionally, this study offers a practical framework for comprehensively assessing C stocks in BCEs at the national scale. © 2025. The Author(s).

关键词： blue carbon coastal wetlands mangroves salt marshes seagrasses tidal flats

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DBKEM-AACS: a distributed key escrow model in blockchain with anonymous authentication and committee selection

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science China(Information sciences) 2023年第3期66卷 289-290页

作者： Axin XIANG Hongfeng GAO Youliang TIAN Liang WAN College of Computer Science and Technology Guizhou University Institute of Cryptography and Data Security Guizhou University Network and Information Management Center Guizhou University State Key Laboratory of Public Big Data Guizhou University Institute of Computer Software and Theory Guizhou University

Dear editor, key security is of great practical significance and demand to guarantee the security of digital assets in the blockchain system. At present, users prefer to escrow their assets on centralized institutions, but this phenomenon has gone against the unique characteristics of decentralization and anonymity in the blockchain. Among them, the suspense incidents of assets lock or lost are enough to prove that the security of escrowed keys on the exchanges is questionable.

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An extensible Hadoop framework for monitoring performance metrics and events of OpenStack cloud 3

An extensible Hadoop framework for monitoring performance me...

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3rd IEEE International Conference on Big data Analysis, ICBDA 2018

作者： Yang, Jianxi Shen, Chaoxiao Chi, Yaping Xu, Ping Sun, Wei Department of Communication Engineering Beijing Electronic Science and Technology Institute Key Laboratory of Network Assessment Technology Institute of Information Engineering CAS Beijing China

ISBN: (纸本)9781538647936

In recent years, many hot topics for research are manipulation of big data, cloud computing and a combination of them. OpenStack is a complex and evolving system that continuously generates vast amounts of metrics and log data. The more complex your system gets, the harder it is to understand its performance and troubleshoot problems, making monitoring a critical piece of the OpenStack control system. In order to effectively create, test and deploy new algorithms or frameworks one need suitable monitoring solutions. However, the conventional monitoring tools lack some performances to keep data freshness from a variety of sources in OpenStack. In the paper we design a critical monitoring solutions based on existing Hadoop architecture integrated with OpenStack clouds. Hundreds of different metrics are gathered form Hadoop metrics subsystem and all data is stored in HBase database to make it ready for processing or displaying it with MapReduce paradigm. With alarms/events generated, we also present a comprehensive view of the status of the infrastructure resources as well as the network services running on OpenStack. © 2018 IEEE.

关键词： Monitoring

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He’s Homotopy Perturbation Method and Fractional Complex Transform for Analysis Time Fractional Fornberg-Whitham Equation

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Sound & Vibration 2021年第4期55卷 295-303页

作者： Yanni Zhang Jing Pang College of Science Inner Mongolia University of TechnologyHohhot010000China Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling Inner Mongolia University of TechnologyHohhot010000China

In this article,time fractional Fornberg-Whitham equation of He’s fractional derivative is *** transform the fractional model into its equivalent differential equation,the fractional complex transform is used and He’s homotopy perturbation method is implemented to get the approximate analytical solutions of the fractional-order *** graphs are plotted to analysis the fractional-order mathematical modeling.

关键词： Time fractional Fornberg-Whitham equation fractional complex transform He’s homotopy perturbation method

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A Simple Method on Generating any Bi-Photon Superposition State with Linear Optics

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Communications in Theoretical Physics 2017年第4期67卷 391-395页

作者：张婷婷魏杰王琴 Institute of Signal Processing and Transmission Nanjing University of Posts and TelecommunicationsNanjing 210003China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and TelecommunicationsMinistry of EducationNanjing 210003China Key Laboratory of Quantum Information CASUniversity Science and Technology of ChinaHefei 230026China

We present a simple method on the generation of any bi-photon superposition state using only linear *** this scheme, the input states, a two-mode squeezed state and a bi-photon state, meet on a beam-splitter and the output states are post-selected with two threshold single-photon detectors. We carry out corresponding numerical simulations by accounting for practical experimental conditions, calculating both the Wigner function and the state fidelity of those generated bi-photon superposition states. Our simulation results demonstrate that not only distinct nonclassical characteristics but also very high state fidelities can be achieved even under imperfect experimental conditions.

关键词： bi-photon superposition state Wigner function state fidelity

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Identity-Preserving Adversarial Training for Robust network Embedding

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Journal of Computer science & technology 2024年第1期39卷 177-191页

作者：岑科廷沈华伟曹婍徐冰冰程学旗 Data Intelligence System Research Center Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 101480China Beijing Academy of Artificial Intelligence Beijing 100000China Chinese Academy of Sciences Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link ***,existing network embed-ding models are vulnerable to random or adversarial perturbations,which may degrade the performance of network em-bedding when being applied to downstream *** achieve robust network embedding,researchers introduce adversari-al training to regularize the embedding learning process by training on a mixture of adversarial examples and original ***,existing methods generate adversarial examples heuristically,failing to guarantee the imperceptibility of generated adversarial examples,and thus limit the power of adversarial *** this paper,we propose a novel method Identity-Preserving Adversarial Training(IPAT)for network embedding,which generates imperceptible adversarial exam-ples with explicit identity-preserving *** formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class,and we encourage each adversarial example to be discriminated as the class of its original *** experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.

关键词： network embedding identity-preserving adversarial training adversarial the example

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On the optimality of tape merge of two lists with similar size 27

On the optimality of tape merge of two lists with similar si...

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27th International Symposium on Algorithms and Computation, ISAAC 2016

作者： Li, Qian Sun, Xiaoming Zhang, Jialin CAS Key Lab. of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783959770262

The problem of merging sorted lists in the least number of pairwise comparisons has been solved completely only for a few special cases. Graham and Karp [18] independently discovered that the tape merge algorithm is optimal in the worst case when the two lists have the same size. Stockmeyer and Yao[28], Murphy and Paull[24], and Christen[6] independently showed when the lists to be merged are of size m and n satisfying m ≤ n ≤ ⌊3/2m⌋ + 1, the tape merge algorithm is optimal in the worst case. This paper extends this result by showing that the tape merge algorithm is optimal in the worst case whenever the size of one list is no larger than 1.52 times the size of the other. The main tool we used to prove lower bounds is Knuth's adversary methods [18]. In addition, we show that the lower bound cannot be improved to 1.8 via Knuth's adversary methods. We also develop a new inequality about Knuth's adversary methods, which might be interesting in its own right. Moreover, we design a simple procedure to achieve constant improvement of the upper bounds for 2m - 2 ≤ n ≤ 3m. © Qian Li, Xiaoming Sun, and Jialin Zhang.

关键词： Mergers and acquisitions

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Landscape and Dynamics of the Transcriptional Regulatory network During Natural Killer Cell Differentiation

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Genomics, Proteomics & Bioinformatics 2020年第5期18卷 501-515页

作者： Kun Li Yang Wu Young Li Qiaoni Yu Zhigang Tian Haiming Wei Kun Qu Department of Oncology The First Affiliated Hospital of USTCDivision of Molecular MedicineHefei National Laboratory for Physical Sciences at MicroscaleSchool of Basic Medical SciencesDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefei 230021China CAS Center for Excellence in Molecular Cell Sciences The CAS Key Laboratory of Innate Immunity and Chronic DiseaseUniversity of Science and Technology of ChinaHefei 230027China School of Data Science University of Science and Technology of ChinaHefei 230026China

Natural killer(NK)cells are essential in controlling cancer and ***,little is known about the dynamics of the transcriptional regulatory machinery during NK cell *** this study,we applied the assay of transposase accessible chromatin with sequencing(ATAC-seq)technique in a home-developed in vitro NK cell differentiation *** of ATAC-seq data illustrated two distinct transcription factor(TF)clusters that dynamically regulate NK cell ***,two TFs from the second cluster,FOS-like 2(FOSL2)and early growth response 2(EGR2),were identified as novel essential TFs that control NK cell maturation and *** down either of these two TFs significantly impacted NK cell ***,we constructed a genome-wide transcriptional regulatory network that provides a better understanding of the regulatory dynamics during NK cell differentiation.

关键词： NK cell ATAC-seq Programmed differentiation FOSL2 EGR2 Dynamic regulatory network

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A cross-lingual joint aspect/sentiment model for sentiment analysis 14

A cross-lingual joint aspect/sentiment model for sentiment a...

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23rd ACM International Conference on Information and Knowledge Management, CIKM 2014

作者： Lin, Zheng Jin, Xiaolong Xu, Xueke Wang, Yuanzhuo Wang, Weiping Cheng, Xueqi Minzhuang Road Beijing China Key Lab. of Network Data Science and Technology ICT CAS No. 6 Kexueyuan South Road Beijing China

ISBN: (纸本)9781450325981

Sentiment analysis in various languages has been a research hotspot with many applications. However, sentiment resources (e.g., labeled corpora, sentiment lexicons) of different languages are unbalanced in terms of quality and quantity, which arouses interests in cross-lingual sentiment analysis aiming at using the resources in a source language to improve sentiment analysis in a target language. Nevertheless, many existing cross-lingual related works rely on a certain machine translation system to directly adapt the labeled data from the source language to the target language, which usually suffers from inaccurate results generated by the machine translation system. On the other hand, most sentiment analysis studies focus on document-level sentiment classification that cannot solve the aspect dependency problem of sentiment words. For instance, in the reviews on a cell phone, long is positive for the lifespan of its battery, but negative for the response time of its operating system. To solve these problems, this paper develops a novel Cross-Lingual Joint Aspect/Sentiment (CLJAS) model to carry out aspect-specific sentiment analysis in a target language using the knowledge learned from a source language. Specifically, the CLJAS model jointly detects aspects and sentiments of two languages simultaneously by incorporating sentiments into a cross-lingual topic model framework. Extensive experiments on different domains and different languages demonstrate that the proposed model can significantly improve the accuracy of sentiment classification in the target language. Copyright 2014 ACM.

关键词： Sentiment analysis

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Few-shot Link Prediction on Hyper-relational Facts 30

Few-shot Link Prediction on Hyper-relational Facts

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Wei, Jiyao Guan, Saiping Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9782493814104

Hyper-relational facts, which consist of a primary triple (head entity, relation, tail entity) and auxiliary attribute-value pairs, are widely present in real-world Knowledge Graphs (KGs). Link Prediction on Hyper-relational Facts (LPHFs) is to predict a missing element in a hyper-relational fact, which helps populate and enrich KGs. However, existing LPHFs studies usually require an amount of high-quality data. They overlook few-shot relations, which have limited instances, yet are common in real-world scenarios. Thus, we introduce a new task, Few-Shot Link Prediction on Hyper-relational Facts (FSLPHFs). It aims to predict a missing entity in a hyper-relational fact with limited support instances. To tackle FSLPHFs, we propose MetaRH, a model that learns Meta Relational information in Hyper-relational facts. MetaRH comprises three modules: relation learning, support-specific adjustment, and query inference. By capturing meta relational information from limited support instances, MetaRH can accurately predict the missing entity in a query. As there is no existing dataset available for this new task, we construct three datasets to validate the effectiveness of MetaRH. Experimental results on these datasets demonstrate that MetaRH significantly outperforms existing representative models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Knowledge graph

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