检索结果-内蒙古大学图书馆

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

学校读者我要写书评

暂无评论

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

Anomaly detection in multi-class time series

学校读者我要写书评

暂无评论

Journal of Physics: Conference Series 2021年第1期2113卷

作者： Weihong Wang Zhuolin Wu Xuan Liu Lei Jia Xiaoguang Wang College of Computer Science and Technology Zhejiang University of Technology Hangzhou Zhejiang 310000 China Faculty of Computer Science Dalhousie University Halifax Nova Scotia B3H 4R2 Canada Research and Development Center of Agricultural Bank of China Hangzhou Zhejiang 310000 China Beijing Broada Software Ltd Hangzhou Zhejiang 310000 China

For modern operation and maintenance systems, they are usually required to monitor multiple types and large quantities of machine's key performance indicators (KPIs) at the same time with limited resources. In this paper, to tackle these problems, we propose a highly compatible time series anomaly detection model based on K-means clustering algorithm with a new Wavelet Feature Distance (WFD). Our work is inspired by some ideas from image processing and signal processing domain. Our model detects abnormalities in the time series datasets which are first clustered by K-means to boost the accuracy. Our experiments show significant accuracy improvements compared with traditional algorithms, and excellent compatibilities and operating efficiencies compared with algorithms based on deep learning.

关键词：

Learning-based computer-aided prescription model for Parkinson’s disease: A data-driven perspective

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Shi, Yinghuan Yang, Wanqi Thung, Kim-Han Wang, Hao Gao, Yang Pan, Yang Zhang, Li Shen, Dinggang State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University China School of Computer Science Nanjing Normal University China Department of Radiology BRIC UNC Chapel Hill United States Inception Institute of Artificial Intelligence Korea Republic of Nanjing Medical University Nanjing Brain Hospital China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai China Department of Brain and Cognitive Engineering Korea University United Arab Emirates

In this paper, we study a novel problem: "automatic prescription recommendation for PD patients." To realize this goal, we first build a dataset by collecting 1) symptoms of PD patients, and 2) their prescription drug provided by neurologists. Then, we build a novel computer-aided prescription model by learning the relation between observed symptoms and prescription drug. Finally, for the new coming patients, we could recommend (predict) suitable prescription drug on their observed symptoms by our prescription model. From the methodology part, our proposed model, namely Prescription viA Learning lAtent Symptoms (PALAS), could recommend prescription using the multi-modality representation of the data. In PALAS, a latent symptom space is learned to better model the relationship between symptoms and prescription drug, as there is a large semantic gap between them. Moreover, we present an efficient alternating optimization method for PALAS. We evaluated our method using the data collected from 136 PD patients at Nanjing Brain Hospital, which can be regarded as a large dataset in PD research community. The experimental results demonstrate the effectiveness and clinical potential of our method in this recommendation task, if compared with other competing methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Large dataset

Based on the analysis of mobile terminal application software performance test 18

学校读者我要写书评

暂无评论

Based on the analysis of mobile terminal application softwar...

18th IEEE/ACIS International Conference on software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2017

作者： Chunye, Du Wei, Song Jianhua, Wu Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Shanghai Ruanzhong Information Technology Co. Ltd. Shanghai China

ISBN: (纸本)9781509055043

With the rapid development of mobile terminal, mobile applications are gradually penetrated into all aspects of people's life and work. Mobile games, mobile streaming media, location services, mobile Internet news, instant messaging, mobile music and other rich and colorful information era are changing the social life. In view of this, we propose the application software performance test based on the mobile terminal, and analyze the performance test technology and method of the mobile application. Experimental results show that the performance test of the application system can predict the pressure in real environment, the system will be applied in the problems exposed, through the analysis of the data of the test, it will provide help for performance optimization of application system. © 2017 IEEE.

关键词： Application programs

Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Sun, Liang Mo, Zhanhao Yan, Fuhua Xia, Liming Shan, Fei Ding, Zhongxiang Shao, Wei Shi, Feng Yuan, Huan Jiang, Huiting Wu, Dijia Wei, Ying Gao, Yaozong Gao, Wanchun Sui, He Zhang, Daoqiang Shen, Dinggang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing211106 China Department of Radiology Third Hospital of Jilin University Changchun China Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Radiology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology WuhanHubei China Department of Radiology Shanghai Public Health Clinical Center Fudan University Shanghai China Department of Radiology Hangzhou First Peoples Hospital Zhejiang University School of Medicine HangzhouZhejiang China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai China Department of Radiology Qianjiang Central Hospital Jishou University School of Medicine Chongqing China

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE) and AUC achieved by our method are 91.79%, 93.05%, 89.95% and 96.35%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

Private key encryption and recovery in blockchain

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Aydar, Mehmet Çetin, Salih Cemil Ayvaz, Serkan Aygün, Betül Technology Introduction Department Huawei Turkey Research and Development Center Istanbul Turkey Department of Computer Science Kent State University KentOH United States Department of Software Engineering Bahcesehir University Besiktas Istanbul Turkey

The disruptive technology of blockchain can deliver secure solutions without the need for a central authority. In blockchain, assets that belong to a participant are controlled through the private key of an asymmetric key pair that is owned by the participant. Although, this lets blockchain network participants to have sovereignty on their assets, it comes with the responsibility of managing their own keys. Currently, there exists two major bottlenecks in managing keys;a) users don't have an efficient and secure way to store their keys, b) no efficient recovery mechanism exists in case the keys are lost. In this study, we propose secure methods to efficiently store and recover keys. For the first, we introduce an efficient encryption mechanism to securely encrypt and decrypt the private key using the owner's biometric signature. For the later, we introduce an efficient recovery mechanism using biometrics and secret sharing scheme. By applying the proposed key encryption and recovery mechanism, asset owners are able to securely store their keys on their devices and recover the keys in case they are lost. Copyright © 2019, The Authors. All rights reserved.

关键词： Blockchain

Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of covid-19 in ct images

学校读者我要写书评

暂无评论

arXiv 2020年

作者： He, Kelei Zhao, Wei Xie, Xingzhi Ji, Wen Liu, Mingxia Tang, Zhenyu Shi, Feng Gao, Yang Liu, Jun Zhang, Junfeng Shen, Dinggang Medical School of Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University China Department of Radiology Second Xiangya Hospital Central South University Changsha Hunan China Department of Radiology Quality Control Center Changsha Hunan China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Biomedical Research Imaging Center Department of Radiology University of North Carolina Chapel HillNC United States Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai200232 China

Understanding chest CT imaging of the coronavirus disease 2019 (COVID-19) will help detect infections early and assess the disease progression. Especially, automated severity assessment of COVID-19 in CT images plays an essential role in identifying cases that are in great need of intensive clinical care. However, it is often challenging to accurately assess the severity of this disease in CT images, due to variable infection regions in the lungs, similar imaging biomarkers, and large inter-case variations. To this end, we propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images, by jointly performing lung lobe segmentation and multi-instance classification. Considering that only a few infection regions in a CT image are related to the severity assessment, we first represent each input image by a bag that contains a set of 2D image patches (with each cropped from a specific slice). A multi-task multi-instance deep network (called M2UNet) is then developed to assess the severity of COVID-19 patients and also segment the lung lobe simultaneously. Our M2UNet consists of a patch-level encoder, a segmentation subnetwork for lung lobe segmentation, and a classification subnetwork for severity assessment (with a unique hierarchical multi-instance learning strategy). Here, the context information provided by segmentation can be implicitly employed to improve the performance of severity assessment. Extensive experiments were performed on a real COVID-19 CT image dataset consisting of 666 chest CT images, with results suggesting the effectiveness of our proposed method compared to several state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

Faster and safer training by embedding high-level knowledge into deep reinforcement learning

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Zhang, Haodi Gao, Zihang Zhou, Yi Zhang, Hao Wu, Kaishun Lin, Fangzhen College of Computer Science and Software Engineering Shenzhen University Shanghai Research Center for Brain Science and Brain-Inspired Intelligence Zhangjiang Laboratory Dorabot Inc Department of Comupter Science and Engineering Hong Kong University of Science and Technology Hong Kong

Deep reinforcement learning has been successfully used in many dynamic decision making domains, especially those with very large state spaces. However, it is also well-known that deep reinforcement learning can be very slow and resource intensive. The resulting system is often brittle and difficult to explain. In this paper, we attempt to address some of these problems by proposing a framework of Rule-interposing Learning (RIL) that embeds high level rules into the deep reinforcement learning. With some good rules, this framework not only can accelerate the learning process, but also keep it away from catastrophic explorations, thus making the system relatively stable even during the very early stage of training. Moreover, given the rules are high level and easy to interpret, they can be easily maintained, updated and shared with other similar tasks. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

Building a Winning Business Case for Standards Adoption

学校读者我要写书评

暂无评论

IEEE Engineering Management Review 2020年第1期48卷 14-17页

作者： Bishop, David David Bishop (Senior Member IEEE) received the Bachelor of Computer Engineering degree from the Georgia Institute of Technology Atlanta GA USA and the M.B.A. degree with a concentration in IT management and a doctorate degree in business administration from Georgia State University Atlanta GA USA. He is a Technologist Consultant Researcher Entrepreneur and Instructor with more than 25 years of experience in telecommunications transportation government and utility industries. He is the author of Metagility: Managing Agile Development for Competitive Advantage Elizabeth's Song The Big Brother's Guides and numerous other books and articles. He is a frequent contributor to Ieee's Engineering Management Review and an inventor of several U.S. patents. He is the CEO and Founder of Agile Worx LLC a firm that provides program and project management software tools training and consulting services. He is a member and Committee Chair for the International Electrotechnical Commission (IEC) based in Geneva Switzerland a member of ANSI and the Association for Computing Machinery. David is also a Research Associate with the Center for Engaged Business Research Georgia State University and the Founding Chair for the Atlanta chapter of the IEEE Technology and Engineering Management Society.

In a recent issue of Ieee Engineering Management Review, we explored the problem of building a winning business case and the elements that can differentiate an 'average' business case from a 'winning' one. For this second installment, we will show how these winning elements were applied in practice. To make it even more interesting, we will illustrate how this can be accomplished with arguably one of the most difficult cases to make: adoption of technical standards. Our stakeholder is a large power utility. © 1973-2011 IEEE.

关键词： Task analysis Acceleration