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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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Government intervention model based on behavioral heterogeneity for China’s stock market

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Financial Innovation 2022年第1期8卷 2569-2587页

作者： Zhong-Qiang Zhou Jie Li Wei Zhang Xiong Xiong School of Big Data Applications and Economics Guizhou University of Finance and EconomicsGuiyang 550025China Guizhou Key Laboratory of Big Data Statistical Analysis Guizhou University of Finance and EconomicsGuiyang 550025China School of Finance Nanjing Audit UniversityNanjing 211815JiangsuChina College of Management and Economics Tianjin UniversityTianjin 300072China China Center for Social Computing and Analytics Tianjin UniversityTianjin 300072China

Active government intervention is a striking characteristic of the Chinese stock *** study develops a behavioral heterogeneous agent model(HAM)comprising fundamentalists,chartists,and stabilizers to investigate investors’dynamic switching mechanisms under government *** model introduces a new player,the stabilizer,into the HAM as a proxy for the *** use the model to examine government programs during the 2015 China stock market crash and find that it can replicate the dynamics of investor sentiment and asset *** addition,our analysis of two simulations,specifically the data-generating processes and shock response analysis,further corroborates the key conclusion that our intervention model not only maintains market stability but also promotes the return of risk asset prices to their fun-damental *** study concludes that government interventions guided by the new HAM can alleviate the dilemma between reducing price volatility and improving price efficiency in future intervention programs.

关键词： Government intervention Excess volatility Behavioral heterogeneity Heterogeneous agent model

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Existence of periodic and S-asymptotically periodic solutions to fractional diffusion equations with analytic semigroups

Existence of periodic and S-asymptotically periodic solution...

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作者： Mu, Jia Nan, Jiecuo Zhou, Yong Key Laboratory of China's Ethnic Languages and Information Technology of Ministry Northwest Minzu University Lanzhou730000 China School of Mathematics and Computer Science Northwest Minzu University Lanzhou730000 China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou730000 China School of Mathematics and Computer Science Xiangtan University Hunan411105 China Nonlinear Analysis and Applied Mathematics Research Group Faculty of Science King Abdulaziz University Jeddah21589 Saudi Arabia

In this paper, we study periodic and S-asymptotically periodic solutions for fractional diffusion equations (FDE). As we all know, there is no exact periodic solution to differential equations with Caputo or Riemann-Liouville fractional derivatives. Even so, in this paper, periodic (S-asymptotically periodic) mild or classical solutions for FDE with Weyl-Liouville fractional derivatives could be obtained in various fractional power spaces. In addition, a numerical simulation example and a specific example of fractional diffusion equation are given to verify the main theoretical results. © 2019 John Wiley & Sons, Ltd.

关键词： Diffusion

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Mobile laser scanning systems for GPS/GNSS-denied environment mapping

Mobile laser scanning systems for GPS/GNSS-denied environmen...

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2018 ISPRS Technical Commission I Midterm Symposium on Innovative Sensing - From Sensors to Methods and applications

作者： Wen, Chenglu Xia, Yan Lian, Yuhan Dai, Yudi Tan, Jinbin Wang, Cheng Li, Jonathan Fujian Key Laboratory of Sensing and Computing for Smart City School of Information Science and Engineering Xiamen University Xiamen361005 China Fujian Collaborative Innovation Center for Big Data Applications in Governments Fuzhou350003 China Faculty of Environment University of Waterloo WaterlooONN2L 3G1 Canada

Indoor 3D mapping provides a useful three-dimensional structure via an indoor map for many applications. To acquire highly efficient and relatively accurate mapping for large-scale GPS/GNSS-denied scene, we present an upgraded backpacked laser scanning system and a car-mounted indoor mobile laser scanning system. The systems provide both 3D laser scanning point cloud and camera images. In this paper, a simultaneous extrinsic calibration approach for multiple multi-beam LIDAR and multiple cameras is also proposed using the Simultaneous Localization and Mapping (SLAM)-based algorithm. The proposed approach uses the SLAM-based algorithm to achieve a large calibration scene using mobile platforms, registers an acquired multi-beam LIDAR point cloud to the terrestrial LIDAR point cloud to acquire denser points for corner feature extraction, and finally achieves simultaneous calibration. With the proposed mapping and calibration algorithms, we can provide centimetre-lever coloured point cloud with relatively high efficiency and accuracy. © Authors 2018. CC BY 4.0 License.

关键词： Mapping

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Genetic and Evolutionary computing 1

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丛书名： Advances in Intelligent Systems and computing

1000年

作者： Jeng-Shyang Pan Jerry Chun-Wei Lin Bixia Sui Shih-Pang Tseng

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Segmentation of bone metastases based on attention mechanism

Segmentation of bone metastases based on attention mechanism

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Power Electronics, Computer applications (ICPECA), IEEE International Conference on

作者： Guoyi Che Yongchun Cao Ao Zhu Qiang Lin Zhengxing Man Haijun Wang Key Laborator of China’s Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Lanzhou China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou China Gansu Provincial Hospital Lanzhou China

ISBN: (数字)9781728190044

ISBN: (纸本)9781728190051

SPECT bone imaging is an important means to assist doctors in diagnosing diseases. The traditional processing method is that radiologists diagnose images. Manual diagnosis is not only cumbersome and time-consuming, but also different diagnosis results will be caused by the different diagnosis experience of doctors. In view of the above problems, this paper uses U-Net network as the basic model, and at the same time conducts model performance optimization research. Based on the U-Net network, the attention mechanism is integrated to segment the bone metastases in the pelvic area. Introducing the attention mechanism into the U-Net network can help improve the correlation of the pelvic region and reduce the interference caused by problems such as uneven brightness and low contrast to the model. Through multiple sets of experimental demonstrations, the U-Net network integrated with the attention mechanism can better segment bone metastases in the pelvic region based on SPECT images, and the model indicators have been significantly improved.

关键词： Image segmentation Medical services Interference Bones Single photon emission computed tomography Optimization Diseases

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Using a high throughput computational grid for the retrieval of aerosol properties over China land 21

Using a high throughput computational grid for the retrieval...

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21st Congress of the International Society for Photogrammetry and Remote Sensing, ISPRS 2008

作者： Wan, Wei Xue, Yong Bai, Linyan Guang, Jie Li, Yinjie Ai, Jianwen Wang, Ying State Key Laboratory of Remote Sensing Science Jointly Sponsored by the Institute of Remote Sensing Applications Chinese Academy of Sciences and Beijing Normal University Institute of Remote Sensing Applications Chinese Academy of Sciences P.O. Box 9718 Beijing100101 China Graduate School of the Chinese Academy of Sciences Beijing China Department of Computing London Metropolitan University 166-220 Holloway Road LondonN78 DB United Kingdom China Center for Resource Satellite Data and Applications Beijing100073 China

This paper describes the need for and the proposed designing of the high performance quantitative retrieval model to be used on Computational Grid for study of aerosol properties, with particular emphasis on Aerosol Optical Thickness (AOT) determination. A methodology using multi-resource remotely sensed data and adapting available aerosol retrieval model in a Grid environment is demonstrated. The algorithm comprises two complementary parts, collectively used in a distributed application. This paper focused on parallelization method based on a resource management and task partition strategy. A module, called DPPA (Dynamic Partition Points Algorithm for workload estimation), is designed as a portable technology for developing and deploying Grid execution in a generic data parallel paradigm. Experimental results are presented in a realistic application, using data collected by MODIS over China land. Derived result and computing performance of the proposed algorithm is given using the Grid test-bed at the Institute of Remote Sensing applications of Chinese Academy of Sciences (IRSA, CAS). Combined, the experimental results show that Grid-enabled model allowed on-demand large volume of ground-based data assimilation with parameters, and achieved substantial reductions in computational times. The research gives a thoughtful perspective on the potential of applying high performance computing practices to remote sensing quantitative retrieving problems.

关键词： Optical remote sensing

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Strong Asymptotic Properties of Kernel Smoothing Estimation for NA Random Variables with Right Censoring

arXiv

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arXiv 2019年

作者： Shi, Jian-Hua Xu, Jian-Sen Xu, Jin-Feng School of Mathematics and Statistics Minnan Normal University Zhangzhou363000 China Fujian Key Laboratory of Granular Computing and Applications Zhangzhou363000 China The Institute of Meteorological Big Data-Digital Fujian Zhangzhou363000 China Fujian Key Laboratory of Data Science and Statistics Zhangzhou363000 China

Most studies for negatively associated (NA) random variables consider the complete-data situation, which is actually a relatively ideal condition in practice. The paper relaxes this condition to the incomplete-data setting and considers kernel smoothing density and hazard function estimation in the presence of right censoring based on the Kaplan-Meier estimator. We establish the strong asymptotic properties for these two estimators to assess their asymptotic behavior and justify their practical use. Copyright © 2019, The Authors. All rights reserved.

关键词： Random variables

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On function spaces related to H-sober spaces

arXiv

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arXiv 2022年

作者： Bao, Meng Zhang, Xiaoyuan Xu, Xiaoquan College of Mathematics Sichuan University Chengdu610064 China School of Big Data Science Hebei Finance University Baoding071051 China Fujian Key Laboratory of Granular Computing and Applications Minnan Normal University Zhangzhou363000 China

In this paper, we mainly study the function spaces related to H-sober spaces. For an irreducible subset system H and T0 spaces X and Y, it is proved that Y is H-sober iff the function space C(X, Y ) of all continuous functions f : X −→ Y equipped with the topology of pointwise convergence is H-sober iff the function space C(X, Y ) equipped with the Isbell topology is H-sober. One immediate corollary is that for a T0 space X, Y is a sober space (resp., d-space, well-filtered space) iff the function space C(X, Y ) equipped with the topology of pointwise convergence is a sober space (resp., d-space, well-filtered space) iff the function space C(X, Y ) equipped with the the Isbell topology is a sober space (resp., d-space, well-filtered space). It is shown that T0 spaces X and Y, if the function space C(X, Y ) equipped with the compact-open topology is H-sober, then Y is H-sober. The function space C(X, Y ) equipped with the Scott topology is also *** Codes 54C35, 54D99, 06B30, 06B35 © 2022, CC BY.

关键词： Topology

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A parallel evolutionary extreme learning machine scheme for electrical load prediction

A parallel evolutionary extreme learning machine scheme for ...

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Science and Information Conference (SAI)

作者： Peikun Chen Wanhua Li Yuzhong Chen Kun Guo Yuzhen Niu College of Mathematics and Computer Science Fuzhou University Fuzhou Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou Fujian Collaborative Innovation Center for Big Data Applications in Governments Fuzhou Fuzhou China

ISBN: (纸本)9781509054442

Application of cloud computing technologies in power system has made a great contribution to the establishment of smart grid. Among applications of smart grid, electrical load prediction plays an important role in efficient use of power resource. However, the exponential growth of data has posed a great challenge to the existing algorithms. In this paper, we firstly propose a novel parallel hybrid algorithm, combining the Improved Particle Swarm Optimization (PSO) with ELM, named PIPSO-ELM. Here a modified particle swarm optimization is presented to find the optimal number of hidden neurons as well as the corresponding input weights and hidden biases. Furthermore, in the iterative search process of PSO, an update strategy employs the mutation operator of evolutionary algorithms is introduced for further improving the global search capability and convergence speed of PSO. After that, to handle the large-scale dataset efficiently, the parallel implementation of PIPSO-ELM is achieved using Spark. Finally, extensive experiments on real-life electrical load data and comprehensive evaluation are conducted to verify the performance of PIPSO-ELM in electrical load prediction. Extensive experimental results demonstrate that PIPSO-ELM outperforms the compared algorithms in terms of stability, efficiency and scalability simultaneously.

关键词： Neurons Particle swarm optimization Algorithm design and analysis Prediction algorithms Smart grids Encoding Convergence

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