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PCMD:A multilevel comparison database of intraand cross-species metabolic profiling in 530 plant species

Plant Communications 2024年第10期5卷 33-44页

作者： Yue Hu Yao Ruan Xin-Le Zhao Feng Jiang Dongxu Liu Qiang Zhu Qing-Ye Zhang Qing-Yong Yang National Key Laboratory of Crop Genetic Improvement Hubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhan 430070HubeiChina Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data College of InformaticsHuazhong Agricultural UniversityWuhan 430070China Yazhouwan National Laboratory Sanya 572024China

Comparative metabolomics plays a crucial role in investigating gene function,exploring metabolite evolution,and accelerating crop genetic ***,a systematic platform for intra-and crossspecies comparison of metabolites is currently ***,we report the Plant Comparative Metabolome database(PCMD;http://***/PCMD),a multilevel comparison database based on predicted metabolic profiles of 530 plant *** PCMD serves as a platform for comparing metabolite characteristics at various levels,including species,metabolites,pathways,and biological *** database also provides a number of user-friendly online tools,such as species comparison,metabolite enrichment,and ID conversion,enabling users to perform comparisons and enrichment analyses of metabolites across different *** addition,the PCMD establishes a unified system based on existing metabolite-related databases by standardizing metabolite *** PCMD is the most speciesrich comparative plant metabolomics database currently available,and a case study demonstrates its ability to provide new insights into plant metabolic diversity.

关键词： comparative metabolomics plant database multilevel comparison metabolite characteristics

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Identification of FIR systems under difference-driven scheduled quantized observations

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Control Theory and Technology 2024年第2期22卷 163-172页

作者： Dong Liang Ruizhe Jia Fengwei Jing Yong Song Jin Guo School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationBeijing100083China National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing University of Science and Technology BeijingBeijing100083China

In networked system identification,how to effectively use communication resources and improve convergence speed is the focus of ***,there is an inherent contradiction between the two *** this paper,the event-driven communication is used to save communication resources for the identification of finite impulse response systems,and the input design is carried out to meet the requirements of convergence ***,a difference-driven communication is ***,the performance of the communication mechanism is analyzed,and the calculation method of its communication rate is *** that,according to the communication rate and the convergence rate of the identification algorithm,the input design problem is transformed into a constrained optimization problem,and the algorithm for finding the optimal solution is *** addition,considering the case that the output is quantized by multiple thresholds,the way to calculate its communication rate is given and the influence of threshold number on communication rate is ***,the effectiveness of the algorithm is verified by simulation.

关键词： System identification FIR system Difference-driven Quantized observation

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IDIDNG: A Domain Generalization Remaining Useful Life Prediction Method of Unknown Bearings 3

IDIDNG: A Domain Generalization Remaining Useful Life Predic...

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3rd International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Xu, Juan Xu, Zhen Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei China

ISBN: (纸本)9781665492812

Domain adaptation (DA) -based RUL prediction methods have achieved great success for the adaptation ability of the distribution discrepancy between the source and target domains. However, DA methods are powerless when the target domain data are not available for training. To solve this problem, we propose an inter-domain intra-domain normalized generalization (IDIDNG) network, which consists of three modules, respectively, the pre-processing module, the feature transformation module, and the RUL prediction module. First, we design the pre-processing module to process the bearing vibration data with peak-to-peak and Z-score. Finally, it is connected into a four-dimensional array. In the feature transformation module, via intra-domain and inter-domain normalization as well as mean-variance cross-swap, we transform the data distribution expressions of invariant features of bearings from the perspective of different bearings and different degradation stage of one bearing, such that the model enables to learn the intra-domain and inter-domain discrepancy. Further we design four adaptable weighting parameters into the intra-domain normalization to learn the appropriate normalized mean and variance via the model training. Finally, we design the GRU-based RUL prediction module to predict the unknown bearings. We conducted experiments under the PHM2012 dataset, experimental results show that our method achieves satisfactory prediction accuracy in the unknown bearings. © 2022 IEEE.

关键词： adaptive weights cross-swap generalization inter-domain intra-domain normalization RUL

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Detecting the One-Shot Dummy Attack on the Power Industrial Control Processes With an Unsupervised data-Driven Approach

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 550-553页

作者： Zhenyong Zhang Yan Qin Jingpei Wang Hui Li Ruilong Deng the State Key Laboratory of Public Big Data and the College of Computer Science and Technology Guizhou UniversityGuiyang 550025China the School of Chemical and Biomedical Engineering Nanyang Technological UniversitySingapore 639798Singapore the State Key Laboratory of Industrial Control Technology and the College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China IEEE

Dear Editor,Dummy attack(DA), a deep stealthy but impactful data integrity attack on power industrial control processes, is recently recognized as hiding the corrupted measurements in normal measurements. In this letter, targeting a more practical case, we aim to detect the oneshot DA, with the purpose of revealing the DA once it is ***, we first formulate an optimization problem to generate one-shot DAs. Then, an unsupervised data-driven approach based on a modified local outlier factor(MLOF) is proposed to detect them.

关键词： Dummy Power letter

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Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training

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ACM Transactions on knowledge Discovery from data 2024年第9期18卷 1-30页

作者： Liu, Yonghao Li, Mengyu Li, Ximing Huang, Lan Giunchiglia, Fausto Liang, Yanchun Feng, Xiaoyue Guan, Renchu Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China University of Trento Trento Italy Zhuhai Lab. of the Key Lab. of Symbolic Computation and Knowledge Eng. of the Ministry of Education Zhuhai College of Science and Technology Zhuhai China

Node classification is an essential problem in graph learning. However, many models typically obtain unsatisfactory performance when applied to few-shot scenarios. Some studies have attempted to combine meta-learning with graph neural networks to solve few-shot node classification on graphs. Despite their promising performance, some limitations remain. First, they employ the node encoding mechanism of homophilic graphs to learn node embeddings, even in heterophilic graphs. Second, existing models based on meta-learning ignore the interference of randomness in the learning process. Third, they are trained using only limited labeled nodes within the specific task, without explicitly utilizing numerous unlabeled nodes. Finally, they treat almost all sampled tasks equally without customizing them for their uniqueness. To address these issues, we propose a novel framework for few-shot node classification called Meta-GPS. Specifically, we first adopt an efficient method to learn discriminative node representations on homophilic and heterophilic graphs. Then, we leverage a prototype-based approach to initialize parameters and contrastive learning for regularizing the distribution of node embeddings. Moreover, we apply self-Training to extract valuable information from unlabeled nodes. Additionally, we adopt S (scaling and shifting) transformation to learn transferable knowledge from diverse tasks. The results on real-world datasets show the superiority of Meta-GPS. Our code is available here. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Research on performance optimization of virtual data space across WAN

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Frontiers of Computer Science 2024年第6期18卷 167-187页

作者： Jiantong HUO Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China

For the high-performance computing in a WAN environment,the geographical locations of national supercomputing centers are scattered and the network topology is complex,so it is difficult to form a unified view of *** aggregate the widely dispersed storage resources of national supercomputing centers in China,we have previously proposed a global virtual data space named GVDS in the project of“High Performance Computing Virtual data Space”,a part of the National key Research and Development Program of *** GVDS enables large-scale applications of the high-performance computing to run efficiently across ***,the applications running on the GVDS are often data-intensive,requiring large amounts of data from multiple supercomputing centers across *** this regard,the GVDS suffers from performance bottlenecks in data migration and access across *** solve the above-mentioned problem,this paper proposes a performance optimization framework of GVDS including the multitask-oriented data migration method and the request access-aware IO proxy resource allocation *** a WAN environment,the framework proposed in this paper can make an efficient migration decision based on the amount of migrated data and the number of multiple data sources,guaranteeing lower average migration latency when multiple data migration tasks are running in *** addition,it can ensure that the thread resource of the IO proxy node is fairly allocated among different types of requests(the IO proxy is a module of GVDS),so as to improve the application’s performance across *** experimental results show that the framework can effectively reduce the average data access delay of GVDS while improving the performance of the application greatly.

关键词： storage aggregation across WANs large-scale applications GVDS data migration allocation of IO proxy resource

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SeP2CNN: A Simple and Efficient Privacy-Preserving CNN for AIoT Applications

SeP2CNN: A Simple and Efficient Privacy-Preserving CNN for A...

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2024 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2024

作者： Xiao, Tao Zhang, Zhenyong Shao, Kuan Li, Hao School of Computer Science and Engineering Guizhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (纸本)9798331507886

As convolutional neural networks (CNNs) have shown excellent performance in various inference tasks, it has become increasingly critical to enable Artificial Intelligence of Things (A IoT) systems to run CNN-based applications. However, using CNN-based applications in many AIoT scenarios raises privacy concerns. Protecting the inference phase can preserve the user's privacy, which is an urgent issue according to worldwide rules and policies. In this paper, we propose SeP2CNN, a simple and efficient fully homomorphic encryption (FHE) method to realize the privacy-preserving CNN inference in pervasive AIoT applications. By carefully combining cryptography and deep learning, we compute ReLU with a composite polynomial as the activation function over ciphertexts. To improve the efficiency, we transform the three-dimensional convolution into a two-dimensional convolution to simplify the calculation. Finally, through extensive experiments on multiple datasets, we validate and evaluate the performance of SeP2CNN. © 2024 IEEE.

关键词： Differential privacy

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Insights into Reservoir-Triggered Landslides Development and Its Influence Factors in the Three Gorges Reservoir Area,China

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Journal of Earth Science 2024年第6期35卷 1979-1997页

作者： Shilin Luo Da Huang Jianbing Peng Zhouzhou Xie Zhiyu Yang Roberto Tomás Hunan Provincial Key Laboratory for Big Data Smart Application of Natural Disaster Risks Survey of Highway Engineering Changsha UniversityChangsha410022China College of Civil Engineering and Geomatics Chang’an UniversityXi’an710064China Hunan Provincial Key Laboratory for Big Data Smart Application of Natural Disaster Risks Survey of Highway Engineering Changsha410022China School of Civil Engineering Central South UniversityChangsha410075China Dpto.de Ingenieria Civil.Escuela Politécnica Superior de Alicante Universidad de AlicanteAlicanteE-03080Spain

More than 5000 landslides or potential landslides have been triggered in the Three Gorges Reservoir(TGR)area since the impoundment in *** study aims at investigating the reservoirinduced landslides spatiotemporal and size distribution and its influence factors in the TGR by taking 790 landslides as statistical *** landslides exhibit significant regional and sub-regional spatial differences,and numerous landslides occurred at the initial three impoundment stages and the corresponding 2-3 cycles of reservoir operations followed,but the landslide frequency decreased dramatically after 2010 from temporal *** relationship between landslide development and topographical,geological as well as hydrological factors were analyzed *** reservoir-induced landslides in TGR area exhibit self-organized criticality and the rollover is nearly 2.5×10^(4) m^(2),which could not be attributed to the missing data but the coupled influences imposed by affecting *** the double Pareto and inverse gamma functions are more suitable than the power-law function to present the landslide size *** term of the fitting precious,the adaptability of the inverse gamma function is better if the landslide inventories are *** research results provide foundation for the landslide susceptibility maps and hazard risk assessment.

关键词： landslides slope stability affecting factors Three Gorges Reservoir self-organized criticality

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Source Characteristics and Induced Hazards of the 2025 M6.8 Dingri Earthquake,Xizang,China,Revealed by Imaging Geodesy

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Journal of Earth Science 2025年第2期36卷 847-851页

作者： Chen Yu Zhenhong Li Xiaoning Hu Chuang Song Suju Li Haihui Liu Jie Li Bingquan Han Zhenjiang Liu Ming Liu Shuang Zhu Xiaoye Hao Zhiyuan Li Jianbing Peng College of Geological Engineering and Geomatics Chang'an UniversityXi'an 710054China Key Laboratory of Loess Xi'an 710054China Key Laboratory of Western China's Mineral Resources and Geological Engineering Ministry of EducationXi'an 710054China Big Data Center for Geosciences and Satellites Chang'an UniversityXi'an 710054China National Disaster Reduction Center of China Beijing 100124China

0 *** to the China Earthquake Networks Center,an M6.8 earthquake struck Dingri County,Xizang Autonomous Region,China,on 7 January 2025 at 9:05 *** *** epicenter is located at 28.5°N,87.45°E,with a depth of~1... 详细信息

关键词： source characteristics M earthquake Xizang imaging geodesy induced hazards Dingri China

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Task Offloading Optimization Based on Position Prediction in Pedestrian- Robot Mixed Traffic Flows 8

Task Offloading Optimization Based on Position Prediction in...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zheng, Dawen Wang, Lusheng Kai, Caihong Peng, Min Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781665450515

Service robots play an increasingly important role in people's daily life. The density of pedestrians is large and the movement is irregular in pedestrian-robot mixed traffic flows. Robots are prone to collision with pedestrians, and the tasks to be offloaded are closely related to pedestrians. How to analyze the tasks of robots and select the appropriate roadside unit is an important issue. In this paper, the social force model is used to predict the positions of pedestrians and robots, taking into account the influence of various forces to avoid collisions. A task offloading resource optimization algorithm with position prediction is proposed. According to the predicted information, the size and position distribution of all tasks in the scenario are obtained, and then the neural network trained beforehand based on deep Q-Iearning is used to generate a task offloading strategy. The simulation results show that the running time of the proposed algorithm is very short, and the resource allocation required for task offloading is completed in advance based on the predicted information before robots arriving the corresponding positions. Besides, the algorithm significantly reduces the task offloading delay. © 2022 IEEE.

关键词： Edge computing

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