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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Identification of winter wheat plantation based on sentinel-2 imagery

Identification of winter wheat plantation based on sentinel-...

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2019 ASABE Annual International Meeting

作者： Zhang, Dongyan Fang, Shengmei Duan, Hongrui Zhang, Huihui She, Bao Ding, Yang Yang, Yuying National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Anhui University Hefei China Water Management and Systems Research Unit USDA Agricultural Research Service Fort CollinsCO United States School of Surveying and Mapping Anhui University of Science and Technology Huainan China

As an important food crop in the world, remote sensing identification of winter wheat (Triticum Aestivum L.) and extraction of planting areas are the basis of growth monitoring, disaster assessment and yield prediction. ESA Sentinel-2A/B satellite has significant advantages in crop identification with its high spatial resolution and short revisit period. In this study, northern and central Anhui, the main wheat production area in Anhui Province in China, were selected as the study area to explore the identification algorithm of winter wheat under different planting conditions. Based on the variation curve of the normalized difference vegetation index (NDVI) time series in the key phenological period of winter wheat, the specificity of its greenness was explored, and the screening rules were constructed accordingly. The spatial distribution of winter wheat in the study area was screened out by Decision Tree algorithm, and the extraction effect was improved by using a cultivated land distribution product. The extraction effect was evaluated by means of random samples, and the extraction area was tested with reference to the county-level wheat planting area data recorded in the statistical yearbook. The results showed that the estimation accuracy of winter wheat area in northern Anhui counties was 86.86%, and the Kappa coefficient was 0.80 (the Omission was 8.48%, the Commission was 9.58%). The estimation accuracy of winter wheat area in central Anhui counties was 80.00%, and the Kappa coefficient was 0.62 (the Omission was 17.30%, the Commission was 40.20%). The overall extraction effect is inferior to that of the main producing areas in northern Anhui. This study provides a valuable reference for mapping wheat production under fragmented and complicated land surface conditions, which will further contribute to decision-making by government and relevant departments. © 2019 ASABE Annual International Meeting. All rights reserved.

关键词： Extraction

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PDRM: A Probability Distribution Based Resource Management for Batch Workloads in Heterogeneous Cluster 16th

PDRM: A Probability Distribution Based Resource Management f...

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16th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2019

作者： Zhou, Jun Feng, Dan Wang, Fang School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Wuhan National Laboratory for Optoelectronics Wuhan430074 China Key Laboratory of Information Storage System Engineering Research Center of data storage systems and Technology Ministry of Education of China Wuhan China

ISBN: (纸本)9783030307080

Resource consumption prediction and dynamic resource provision based on historical consumption are common methods to improve cluster resource utilization, however they have to face the challenge of fluctuation in resource consumption for accurate prediction. We propose PDRM, an efficient resource management scheme based on resource consumption probability distribution for batch workloads to deal with this dilemma. Based on the common sense that the same type of tasks have similar resource consumption on the same node, we get the resource consumption probability distribution of each type of task to describe the fluctuations in its resource consumption. Based on the resource consumption distribution function, we can allocate resources precisely for tasks. Experimental results demonstrate that PDRM achieves good performance for various application in the heterogeneous cluster. PDRM can effectively improve resource utilization and reduce job completion time. © 2019, IFIP International Federation for Information Processing.

关键词： big data

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Unbalanced incomplete multi-view clustering via the scheme of view evolution: Weak views are meat;strong views do eat

arXiv

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arXiv 2020年

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver The School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China The Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous methods assume that all views have the same incompleteness, i.e., balanced incompleteness. However, different views often have distinct incompleteness, i.e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views). The unbalanced incompleteness prevents us from directly using previous methods. In this paper, inspired by the effective biological evolution theory, we design the novel scheme of view evolution to cluster strong and weak views. Moreover, we propose an Unbalanced Incomplete Multi-view Clustering method (UIMC), which is the first effective method based on view evolution for unbalanced incomplete multi-view clustering. Compared with previous methods, UIMC has two unique advantages: 1) it proposes weighted multi-view subspace clustering to integrate unbalanced incomplete views, which effectively solves the unbalanced incomplete multi-view clustering problem;2) it designs the low-rank representation to recover the data, which diminishes the impact of the incompleteness and noises. Extensive experimental results demonstrate that UIMC improves the clustering performance by up to 40% on three evaluation metrics over other state-of-the-art methods. We provide codes for all of our experiments in https://***/ZeusDavide/TETCI_UIMC. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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Background estimation in X-ray photoelectron spectroscopy data using an active Shirley method with automated selection of the analytical range

Background estimation in X-ray photoelectron spectroscopy da...

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作者： Murakami, Ryo Kageyama, Hironori Nakamura, Kazuki Tanaka, Hiromi Shinotsuka, Hiroshi Yoshikawa, Hideki Yoshihara, Kazuhiro Department of Electrical and Computer Engineering National Institute of Technology Yonago College Tottori683-8502 Japan Materials Data Platform Center Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 Japan Scienta Omicron Inc. Tokyo140-0013 Japan

In this study, we introduce a novel algorithm that can recognize the concavo-convex shapes of X-ray photoelectron spectroscopy (XPS) data and estimate the optimum background (BG) in XPS spectra with fine structures near the endpoints. In this algorithm, the active Shirley method was improved by incorporating a function for automatically selecting the analytical range. This autoselection function first investigates all the candidates for the initial endpoints. These estimates are then used to decide the BG shape according to the Shirley method. In order to exclude false-positive candidates caused by the recognition of noise peaks as small XPS peaks, the function evaluates the con-cavo-convex shape of the XPS spectrum after the long-period noise is removed using a smoothing process. The proposed algorithm was demonstrated to successfully estimate the optimal spectral BG from an XPS spectrum with a poor signal-to-noise ratio of about 40%. 10.1380/ejssnt.2019.61

关键词： X ray photoelectron spectroscopy

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Design of Command and Dispatch system for Automatic Reading of Meter Images in Substations by UAV Inspection Photos

Computing Open

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Computing Open 2024年第2期computing-open卷

作者： Guanghong Deng Liangpei Lin Baihao Lin Wenlong Jing Xiaodan Zhao Yong Li Rui Yang Shidun Xie Pengfeng Wei GuangDong Engineering Technology Research Center of UAV Remote Sensing Network Guangzhou iMapCloud Intelligent Technology Co. Ltd. Guangzhou 510095 P. R. China Guangdong Province Engineering Laboratory for Geographic Spatiotemporal Big Data Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou 510070 P. R. China Foshan iMapCloud Intelligent Technology Co. Ltd. Foshan 528251 P. R. China

The application of the combination of unmanned aerial vehicles (UAVs) and artificial intelligence is a hot topic in the intelligent inspection of substations, and meter reading is a very challenging task. This paper proposes a method based on the combination of YOLOv6n object detection and Deeplabv3 + image segmentation and performs post-processing on the segmented images to obtain meter readings. First, YOLOv6n is used to detect the meter area of the aerial image and classify the meters. Second, the detected meter images are fed into the image segmentation model. The backbone network of the Deeplabv3 + algorithm is improved by using the MobileNetv3 network, which not only effectively extracts pointers and scales, but also makes the model more lightweight. Third, License Plate Recognition Network (LPRNet) is used to recognize digital meter images. In order to solve the problem of inaccurate pointer meter readings, to begin with, the segmented image is corroded; in addition, the circular dial area is flattened into a rectangular area by concentric circle sampling method. Finally, the meter reading is calculated by the position of the pointer, the scale and the total range of the meter. The post-processing part uses numba to optimize the inference speed. The experimental results show that in two datasets, The mean average precision of 50 (mAP50) accuracy of the YOLOv6n model using this method reached 99.71% and 98.60%, respectively, and the inference speed of a single image was 17.1 ms and 13.2 ms, respectively. The mean intersection over union (mIoU) of the image segmentation model reached 82.00%, 74.73%, 73.50%, 82.26% and 73.20%, respectively, and the single segmentation speed reached 33.7 ms. The LPRNet model has a recognition accuracy of 99.17% and a single image inference speed of 14.7 ms. At the same time, several mainstream object detection and semantic segmentation algorithms are compared. The experimental results show that the method in this paper greatly im

关键词： Object detection image segmentation YOLOv6n Deeplabv3 + meter reading

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Analysis of Anti-interference Ability of Hyperspectral Sensitive Features to Wheat Powdery Mildew

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IOP Conference Series: Earth and Environmental Science 2021年第1期693卷

作者： Jinling Zhao Guomin Chu Hao Yan Lei Hu Yongan Xue National Engineering Research Center for Analysis and Application of Agro-Ecological Big Data Anhui University Hefei 230601 China College of Mining Engineering Taiyuan University of Technology Taiyuan 030024 China

The development of ground-based, airborne and spaceborne remote sensing has greatly facilitated the identification and diagnosis of various objects. Corresponding algorithms and methods of removing interference from remotely sensed imagery have been proposed. Nevertheless, the studies on anti-interference ability of selected features have not been fully considered. In our study, the hyperspectral reflectance of leaf-scale powdery mildew (Erysiphe graminis) on winter wheat were collected as the testing dataset. A total of seven representative spectral features of Landsat-8 Operational Land Imager (OLI) and GaoFen-1 Wide-Field-View (WFV) was selected, namely, original blue, green, red, near-infrared (NIR) bands and normalized difference vegetation index (NDVI), normalized difference greenness index (NDGI), structure insensitive pigment index (SIPI). Four hyperspectral vegetation indices including red edge (MSR) simple ratio index, NDVI, green band and SIPI were also selected. Three primary background noises including soil, cloud and white poplar (Populus alba L.) were added into the spectral signal. The correlation coefficient (R) between disease severities (0, 1, 2, 3 and 4) and spectral features was used to estimate the anti-interference ability. The results show that there is a generally similar spectral performance for the two sensors, but Landsat-8 OLI is superior to GF-1 WVF in terms of spectral response. The green band was greatly affected with the R values decreasing from 0.77 to 0.35. The MSR and NDVI showed a gradual decrease with the increase of three background noises. The study shows that background noises must be removed when acquiring spectral data and stable spectral features should be also selected by evaluating the anti-interference ability.

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LUneXt: Simple and Efficient U-shaped Network Design for Medical Image Segmentation with Nonlinear Activation

Computing Open

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Computing Open 2024年第2期computing-open卷

作者： Guanghong Deng Bing Yu Wenlong Jing Yong Li Xiaodan Zhao GuangDong Engineering Technology Research Center of UAV Remote Sensing Network Guangzhou iMapCloud Intelligent Technology Co. Ltd. Guangzhou 510095 P. R. China Guangdong Province Engineering Laboratory for Geographic Spatiotemporal Big Data Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou 510070 P. R. China Foshan iMapCloud Intelligent Technology Co. Ltd. Foshan 528251 P. R. China

Medical image segmentation has always been a challenging task. This paper proposes a new LUneXt medical image segmentation model based on the characteristics analysis of medical image data sets and testing of different nonlinear activation units. Both normalized activations for the original negative input image activation have good optimization capabilities for the tokenization module parameters proposed in the original UneXt model. Using different activation coefficients for different foreground and background areas has achieved better results. The experimental results of this paper on the Breast Ultrasound Images (BUSI) data set reached an intersection over union (IoU) value of 62.64%, a Dice value of 76.12%, and a single inference speed of 807.57 ms. The experimental IoU value of the International Skin Imaging Collaboration (ISIC 2018) data set reached 82.95%, and the Dice value reached 90.50%. The single inference speed reached 842.58 ms. The LUneXt model is more robust than other models. While improving model performance, it does not introduce higher computational complexity and does not have a major impact on the processing speed of a single image.

关键词： Image segmentation medical images neural networks activation function

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Monthly methane emissions in Chinese mainland provinces from 2013-2022

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Scientific data 2025年第1期12卷 948页

作者： Duo Cui Siyao Yang Xuanren Song Xiaoting Huang Cuncun Duan Mingrui Ji Zhongyan Li Siqi Yu Zhu Deng Piyu Ke Xinyu Dou Taochun Sun Zhu Liu International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China. duocui@hku.hk. Department of Earth Sciences The University of Hong Kong Hong Kong SAR China. duocui@hku.hk. Department of Earth System Science Tsinghua University Beijing 100084 China. Faculty of Information Technology Monash University Melbourne 3168 Australia. College of Water Sciences Beijing Normal University Beijing 100875 China. College of Environmental & Resource Sciences Zhejiang University Hangzhou Zhejiang 310058 China. Department of Civil and Environmental Engineering Stanford University Stanford CA 94305 USA. Department of Geography The University of Hong Kong Hong Kong SAR China. Department of Earth System Science Stanford University Stanford CA 94305 USA. International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China. zhuliu@***. Department of Earth System Science Tsinghua University Beijing 100084 China. zhuliu@***.

As the world's largest source of methane emissions, accurately measuring and tracking China's emissions across various sectors is essential for global climate change efforts. Methane, a potent greenhouse gas, is emitted from diverse anthropogenic and natural sources, many of which exhibit pronounced temporal variability. In particular, emissions from rice cultivation, energy use, and livestock management show strong seasonal patterns, yet high-frequency and spatially detailed methane emission inventories have been lacking. This study introduces the Monthly Methane Emission Inventory for China's Provinces (MMCP), a comprehensive dataset covering the period from January 2013 to December 2022. The dataset includes emissions data from eight key sectors: coal mining, oil and gas systems, energy combustion, rice cultivation, livestock, solid waste, wastewater, and wetlands. By offering sector-specific and temporally resolved emission estimates, MMCP serves as a valuable resource for scientific research, policy evaluation, and emission mitigation planning. This inventory facilitates improved understanding of emission trends and supports more accurate modeling of atmospheric methane concentrations and climate feedbacks.

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Author Correction: Mapping annual 10-m soybean cropland with spatiotemporal sample migration

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Scientific data 2024年第1期11卷 565页

作者： Hongchi Zhang Zihang Lou Dailiang Peng Bing Zhang Wang Luo Jianxi Huang Xiaoyang Zhang Le Yu Fumin Wang Linsheng Huang Guohua Liu Shuang Gao Jinkang Hu Songlin Yang Enhui Cheng Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China. International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China. University of Chinese Academy of Sciences Beijing 100094 China. Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China. pengdl@***. International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China. pengdl@***. Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China. zhangbing@***. University of Chinese Academy of Sciences Beijing 100094 China. zhangbing@***. Jiangxi Nuclearindustry Surveying and Mapping Institute Group Co. Ltd Nanchang 330038 China. College of Land Science and Technology China Agricultural University Beijing 100083 China. Geospatial Sciences Center of Excellence Department of Geography Geospatial Sciences South Dakota State University Brookings SD 57007 USA. Department of Earth System Science Tsinghua University Beijing 100084 China. Institute of Applied Remote Sensing & Information Technology Zhejiang University Hangzhou 310058 China. National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei 230601 China. Innovation Academy for Microsatellites Chinese Academy of Sciences Shanghai 200120 China.

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