检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Fu, Xiang Yu, Jintao Su, Xing Jiang, Hanru Wu, Hua Cheng, Fucheng Deng, Xi Zhang, Jinrong Jin, Lei Yang, Yihang Xu, Le Hu, Chunchao Huang, Anqi Huang, Guangyao Qiang, Xiaogang Deng, Mingtang Xu, Ping Xu, Weixia Liu, Wanwei Zhang, Yu Deng, Yuxin Wu, Junjie Feng, Yuan Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China College of Computer National University of Defense Technology Changsha410073 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen518055 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen518055 China School of Information Engineering Zhengzhou University Zhengzhou450001 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Department of Computing Science College of Computer National University of Defense Technology Changsha410073 China School of Computer Science and Technology University of Science and Technology of China Hefei230027 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Centre for Quantum Software and Information University of Technology Sydney Sydney2007 Australia

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Τ-FPL: Tolerance-constrained learning in linear time 32

Τ-FPL: Tolerance-constrained learning in linear time

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32nd AAAI Conference on Artificial Intelligence, AAAI 2018

作者： Zhang, Ao Li, Nan Pu, Jian Wang, Jun Yan, Junchi Zha, Hongyuan Shanghai Key Laboratory of Trustworthy Computing MOE International Joint Lab of Trustworthy Software School of Computer Science and Software Engineering East China Normal University Shanghai China Institute of Data Science and Technologies Alibaba Group Hangzhou China IBM Research China Georgia Institute of Technology Atlante United States

ISBN: (纸本)9781577358008

In many real-world applications, learning a classifier with false-positive rate under a specified tolerance is appealing. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which are of limitation in methodology since they do not directly incorporate the false-positive rate tolerance. In this paper, we propose a novel scoring-thresholding approach, τ-False Positive Learning (τ-FPL) to address this problem. We show that the scoring problem which takes the false-positive rate tolerance into accounts can be efficiently solved in linear time, also an out-of-bootstrap thresholding method can transform the learned ranking function into a low false-positive classifier. Both theoretical analysis and experimental results show superior performance of the proposed τ-FPL over the existing approaches. Copyright © 2018, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Mathematical transformations

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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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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Robustness Verification of Classification Deep Neural Networks via Linear Programming

Robustness Verification of Classification Deep Neural Networ...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Wang Lin Zhengfeng Yang Xin Chen Qingye Zhao Xiangkun Li Zhiming Liu Jifeng He School of Information Science and Technology Zhejiang Sci-Tech University Shanghai Key Lab of Trustworthy Computing East China Normal University State Key Laboratory for Novel Software Technology Nanjing University Center for Research and Innovation in Software Engineering Southwest University

ISBN: (纸本)9781728132945

There is a pressing need to verify robustness of classification deep neural networks (CDNNs) as they are embedded in many safety-critical applications. Existing robustness verification approaches rely on computing the over-approximation of the output set, and can hardly scale up to practical CDNNs, as the result of error accumulation accompanied with approximation. In this paper, we develop a novel method for robustness verification of CDNNs with sigmoid activation functions. It converts the robustness verification problem into an equivalent problem of inspecting the most suspected point in the input region which constitutes a nonlinear optimization problem. To make it amenable, by relaxing the nonlinear constraints into the linear inclusions, it is further refined as a linear programming problem. We conduct comparison experiments on a few CDNNs trained for classifying images in some state-of-the-art benchmark-s, showing our advantages of precision and scalability that enable effective verification of practical CDNNs.

关键词： linear programming Verification Robustness Neural network Scalability

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CNN-based Super-resolution Reconstruction for Traffic Sign Detection

CNN-based Super-resolution Reconstruction for Traffic Sign D...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Fan Wang Jianqi Shi Xuan Tang Jielong Guo Peidong Liang Yuanzhi Feng College of Electrical Engineering and Automation Fuzhou university Fujian China Shanghai Key Lab for Trustworthy Computing School of Software Engineering East China Normal University Shanghai Putuo District Quanzhou Institute of Equipment Manufacture Haixi Institutes Chinese Academy of Sciences Quanzhou China Quanzhou HIT Research Institute of Engineering and Technology Quanzhou Fengze District China Henan University North Section of Jinming Avenue Kaifeng Longting District China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Automatic identification for traffic signs is an important part of intelligent driving and traffic safety. Deep learning has already made a great achievement in traffic sign detection. However, the camera on a car may capture a low resolution and blurry image in certain environments, which make it inaccurate for traffic sign detection. Therefore, we propose a method based on image super-resolution reconstruction for improving the detection rate of traffic signs. Firstly, a low-resolution image is transformed by CNN-based super-resolution network into a high-resolution one. Then, to meet the requirements of on-line processing, we use the generated super-resolution image as input for the detection network with 16 filters in this layer. At last, we separately trained two CNNs for super-resolution reconstruction and traffic sign detection, which reduce the processing time. Experimental results demonstrate that our model can achieve better performance than the existing methods for traffic sign detection.

关键词： Image resolution Convolution Feature extraction Signal resolution Image reconstruction Object detection Machine learning

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Research on the pixel-based and object-oriented methods of urban feature extraction with GF-2 remote-sensing images

arXiv

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arXiv 2019年

作者： Zhang, Dong-dong Zhang, Lei Zaborovsky, Vladimir Xie, Feng Wu, Yan-wen Lu, Ting-ting Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China MOE International Joint Lab of Trustworthy Software East China Normal University Shanghai China Department of Computer Systems and Software Engineering Peter the Great St. Petersburg Polytechnic University St. Petersburg Russia Institute of Technical Physics Chinese Academy of Sciences Shanghai China

During the rapid urbanization construction of China, acquisition of urban geographic information and timely data updating are important and fundamental tasks for the refined management of cities. With the development of domestic remote sensing technology, the application of Gaofen-2 (GF-2) high-resolution remote sensing images can greatly improve the accuracy of information extraction. This paper introduces an approach using object-oriented classification methods for urban feature extraction based on GF-2 satellite data. A combination of spectral, spatial attributes and membership functions was employed for mapping the urban features of Qinhuai District, Nanjing. The data preprocessing is carried out by ENVI software, and the subsequent data is exported into the eCognition software for object-oriented classification and extraction of urban feature information. Finally, the obtained raster image classification results are vectorized using the ARCGIS software, and the vector graphics are stored in the library, which can be used for further analysis and modeling. Accuracy assessment was performed using ground truth data acquired by visual interpretation and from other reliable secondary data sources. Compared with the result of pixel-based supervised (neural net) classification, the developed object-oriented method can significantly improve extraction accuracy, and after manual interpretation, an overall accuracy of 95.44% can be achieved, with a Kappa coefficient of 0.9405, which objectively confirmed the superiority of the object-oriented method and the feasibility of the utilization of GF-2 satellite data. Copyright © 2019, The Authors. All rights reserved.

关键词： Image classification

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Joint learning of discriminative low-dimensional image representations based on dictionary learning and two-layer orthogonal projections

arXiv

引用

arXiv 2019年

作者： Wei, Xian Shen, Hao Li, Yuanxiang Tang, Xuan Jin, Bo Zhao, Lijun Murphey, Yi Lu Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences China Technical University of Munich Germany and fortiss GmbH Munich Germany Shanghai Key Lab for Trustworthy Computing School of Computer Science and Software Engineering East China Normal University China School of Aeronautics & Astronautics Shanghai Jiao Tong University Shanghai200240 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150006 China Department of Electrical and Computer Engineering University of Michigan-Dearborn DearbornMI48128 United States

This work investigates the problem of efficiently learning discriminative low-dimensional representations of multi-class large-scale image objects. We propose a generic deep learning approach by taking advantages of Convolutional Neural Networks (CNN), sparse dictionary learning, and orthogonal projections. CNN is not only powerful on feature extraction, but also robust to spatial variance and changes. Sparse dictionary learning is well known for disentangling nonlinear underlying discriminative factors in data. The orthogonal projection is a notable efficient tool to project multi-class data onto low dimensional discriminative subspace. The proposed procedure can be summarized as follows. At first, a CNN is employed to extract high-dimensional (HD) preliminary convolutional features. Secondly, to avoid the high computational cost of direct sparse coding on HD CNN features, we present to learn sparse representation (SR) in an orthogonal projected space over a task-driven sparsifying dictionary. We then exploit the discriminative projection on SR. The whole learning process is treated as a joint optimization problem of trace quotient maximization, which involves the CNN parameters, the orthogonal projection on CNN features, the dictionary and the discriminative projection on sparse codes. The related cost function is well defined on product manifold of the Stiefel manifold, the Oblique manifold, and the Grassmann manifold. It is optimized via a geometrical stochastic gradient descent algorithm. Finally, the quality of dictionary and projections learned by the proposed approach is further investigated in terms of image classification. The experimental results show that the approach can achieve a strongly competitive performance with state-of-the-art image classification methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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1D-Convolutional capsule network for hyperspectral image classification

arXiv

引用

arXiv 2019年

作者： Zhang, Haitao Meng, Lingguo Wei, Xian Tang, Xiaoliang Tang, Xuan Wang, Xingping Jin, Bo Yao, Wei School of Software Liaoning Technical University Huludao125105 China Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fuzhou350002 China Shanghai Key Lab for Trustworthy Computing School of Computer Science and Software Engineering East China Normal University China Department of Land Surveying and Geo-Informatics Hong Kong Polytechnic University 181 Chatham Road South Hung Hom Kowloon Hong Kong

Recently, convolutional neural networks (CNNs) have achieved excellent performances in many computer vision tasks. Specifically, for hyperspectral images (HSIs) classification, CNNs often require very complex structure due to the high dimension of HSIs. The complex structure of CNNs results in prohibitive training efforts. Moreover, the common situation in HSIs classification task is the lack of labeled samples, which results in accuracy deterioration of CNNs. In this work, we develop an easy-to-implement capsule network to alleviate the aforementioned problems, i.e., 1D-convolution capsule network (1D-ConvCapsNet). Firstly, 1D-ConvCapsNet separately extracts spatial and spectral information on spatial and spectral domains, which is more lightweight than 3D-convolution due to fewer parameters. Secondly, 1D-ConvCapsNet utilizes the capsule-wise constraint window method to reduce parameter amount and computational complexity of conventional capsule network. Finally, 1D-ConvCapsNet obtains accurate predictions with respect to input samples via dynamic routing. The effectiveness of the 1D-ConvCapsNet is verified by three representative HSI datasets. Experimental results demonstrate that 1D-ConvCapsNet is superior to state-of-the-art methods in both the accuracy and training effort Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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A New Energy Efficient VM Scheduling Algorithm for Cloud computing Based on Dynamic Programming 4

A New Energy Efficient VM Scheduling Algorithm for Cloud Com...

引用

4th IEEE International Conference on Cyber Security and Cloud computing, CSCloud 2017 and 3rd IEEE International Conference of Scalable and Smart Cloud, SSC 2017

作者： Zhang, Kepi Wu, Tong Chen, Siyuan Cai, Linsen Peng, Chao Shanghai Key Lab of Trustworthy Computing School of Computer Science and Software Engineering East China Normal University Shanghai China

ISBN: (纸本)9781509066438

As a new computing paradigm, cloud computing has significantly contributed to the rapid development of massive data centers. However, the corresponding energy issue becomes increasingly challenging. In this paper, we focus on the energy saving issue for virtual machine (VM) selections on an overloaded host in a cloud computing environment. We analyze the energy influencing factors during a VM migration, then design energy efficient VM selection algorithms based on greedy algorithm and dynamic programming method. We conduct experiments with CloudSim and results show that the proposed algorithm in this paper can effectively reduce energy consumption while satisfying the SLA constraints. © 2017 IEEE.

关键词： Dynamic programming

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Test scenario generation using model checking

引用

International Journal of Performability engineering 2018年第6期14卷 1241-1250页

作者： Yin, Zhixiong Zhang, Min Li, Guoqiang Fang, Ling School of Engineering Sciences University of Chinese Academy of Sciences Beijing100049 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China School of Software Shanghai Jiao Tong University Shanghai200240 China Institute of Technology Innovation Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China

Testing, including designation, execution and bug analysis has been broadly adopted in various industries. Test cases must be designed to confirm the entire behavior of the object system. In practice, a test case normally includes a series of operations that cold conduct the system status eventually to fulfill the precondition of the targeted test case. However, the applications to trigger the function calls of the software system would often be manually composed in traditional test activities, which is difficult especially for complex concurrent systems. Insufficient testing might result in hidden system defects, which increases the likelihood of economic loss or human injury. Model checking is a formal technique that can check the properties of a system automatically with strictness, completeness, and traceability. In this paper, a novel formal approach is proposed to systematically generate test scenarios automatically with traceability by the model checking technique. Meanwhile, the scenarios are reliable and precise, and debugging also becomes easier. Moreover, despite the reduction in demand of an experienced test engineer to design the target test cases, the coverage could be improved as the tedious and complicated processes of test scenario generation are accomplished by the model checker. This method has been applied in two practical case studies, and the results show the effectiveness of the proposed approach in terms of high coverage, automation, traceability and reusability. © 2018 Totem Publisher, Inc. All rights reserved.

关键词： Model checking

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