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arXiv 2021年

作者： Quadeer, Maria Korzekwa, Kamil Tomamichel, Marco Centre for Quantum Software and Information School of Computer Science University of Technology Sydney NSW2007 Australia Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Kraków30-348 Poland Department of Electrical and Computer Engineering Centre for Quantum Technologies National University of Singapore Singapore119077 Singapore

We study work extraction processes mediated by finite-time interactions with an ambient bath—partial thermalizations—as continuous time Markov processes for two-level systems. Such a stochastic process results in fluctuations in the amount of work that can be extracted and is characterized by the rate at which the system parameters are driven in addition to the rate of thermalization with the bath. We analyze the distribution of work for the case where the energy gap of a two-level system is driven at a constant rate. We derive analytic expressions for average work and lower bound for the variance of work showing that such processes cannot be fluctuation-free in general. We also observe that an upper bound for the Monte Carlo estimate of the variance of work can be obtained using Jarzynski’s fluctuation-dissipation relation for systems initially in equilibrium. Finally, we analyse work extraction cycles by modifying the Carnot cycle, incorporating processes involving partial thermalizations and obtain efficiency at maximum power for such finite-time work extraction cycles under different sets of constraints. Copyright © 2021, The Authors. All rights reserved.

关键词： Carnot cycle

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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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Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks

arXiv

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arXiv 2022年

作者： Grabowski, Bartosz Glomb, Przemyslaw Masarczyk, Wojciech Plawiak, Pawel Yıldırım, Özal Acharya, U. Rajendra Tan, Ru-San Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland Warsaw University of Technology Pl. Politechniki 1 Warsaw00-661 Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland Department of Software Engineering Firat University Elazig Turkey School of Science and Technology Singapore University of Social Sciences Singapore Faculty of Business Education Law and Arts University of Southern Queensland Australia School of Engineering Ngee Ann Polytechnic 535 Clementi Road 599489 Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan Kumamoto University Kumamoto Japan Department of Cardiology National Heart Centre Singapore169609 Singapore Duke-NUS Medical School 169857 Singapore

Interpretation of electrocardiography (ECG) signals is required for diagnosing cardiac arrhythmia. Recently, machine learning techniques have been applied for automated computer-aided diagnosis. Machine learning tasks can be divided into regression and classification. Regression can be used for noise and artifacts removal as well as resolve issues of missing data from low sampling frequency. Classification task concerns the prediction of output diagnostic classes according to expert-labeled input classes. In this work, we propose a deep neural network model capable of solving regression and classification tasks. Moreover, we combined the two approaches, using unlabeled and labeled data, to train the model. We tested the model on the MIT-BIH Arrhythmia database. Our method showed high effectiveness in detecting cardiac arrhythmia based on modified Lead II ECG records, as well as achieved high quality of ECG signal approximation. For the former, our method attained overall accuracy of 87.33% and balanced accuracy of 80.54%, on par with reference approaches. For the latter, application of self-supervised learning allowed for training without the need for expert labels. The regression model yielded satisfactory performance with fairly accurate prediction of QRS complexes. Transferring knowledge from regression to the classification task, our method attained higher overall accuracy of 87.78%. © 2022, CC BY.

关键词： Electrocardiography

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Intrusion Detection Mechanism Based On Modular Neural Network

Intrusion Detection Mechanism Based On Modular Neural Networ...

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International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

作者： Hongyu Luo Kai Shi Fuqiang Qiao Yinghao Li School of Computer Science and Engineering Tianjin University of Technology Tianjin China College of Software and Communications Tianjin Sino-German University of Applied Sciences Tianjin China

ISBN: (数字)9781728196381

ISBN: (纸本)9781728196398

Nowadays, the complexity and diversity of network security issues have caused intrusion detection to become an important research topic. Aiming at the problems of traditional intrusion detection mechanisms, the modular neural network intrusion detection mechanism based on K-means similarity judgment is proposed. The method uses the Meanshift-Kmeans clustering algorithm to calculate the clustering center and data classification, then uses the similarity to determine the dynamic allocation of sub-modules, and finally uses the classifiers trained according to different types of intrusion to achieve intrusion detection. Through experiments on two different intrusion data sets, the experimental results prove the good detection efficiency and model stability of the model.

关键词： Adaptation models Heuristic algorithms Neural networks Intrusion detection Data models Stability analysis Security

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Type and Security Analysis of Computer Network Operating System Based on Improved Data Structure

Type and Security Analysis of Computer Network Operating Sys...

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2019 7th International Conference on Machinery,Materials and Computing technology(ICMMCT 2019)

作者： Zhang Lijun School of Software and Applied Science and Technology Zhengzhou University

With the large-scale application of computer network,the current computer network and information security has become a major issue facing the state and enterprises and *** such a huge network system,it is imperative to standardize and organize the application of computer network operating system,as well as further adjustments in *** improve the quality of contemporary netizen and the great progress and development of computers in an allround *** a general computer operating system,basically,some tool software is provided according to the specific needs of the user to facilitate their operation,monitor,limit or change the data flow across the firewall,and improve the data structure as much as possible to the *** network shields the information and structure of the protected network to achieve security protection for the *** security is the basic guarantee for making full use of network ***,analysing various security risks in the network is very important to ensure network smoothness and information security.

关键词： Improving data structure Computer Network Operating system Security

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The realization of background management system based on SSM

The realization of background management system based on SSM

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作者： Zhe Wang School of Software and Applied Science and Technology Zhengzhou University

In the process of Web application development, the choice of development framework is very important. A good development framework can speed up the development of Web applications, reduce the development cost, reduce the workload of developers, and make Web applications with good extensibility and portability. Based on SpringMVC+Spring+Mybatis(SSM) framework with good performance and faster development efficiency, gradually become the mainstream of Web application development framework combination. SpringMVC is an mvc-based framework that is primarily responsible for presentation layer functions, such as responding to *** framework mainly ACTS as a container, integrates SpringMVC and Mybatis, realizes decoupling between layers, and makes the business logic clearer. Mybatis framework is mainly responsible for the data persistence layer, completing relevant operations with the database. With the authority management module as an example, the application of SSM framework in developing background management system is illustrated, which provides theoretical guidance for developing background management system.

关键词： Web application framework management system

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Study on the Impact of High Speed Rail Construction on Expressways Based on AHP,Logit and Multivariate Statistical Regression Method

Study on the Impact of High Speed Rail Construction on Expre...

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作者： Renyuan He School of Software and Applied Science and Technology Zhengzhou University

In view of whether or not the vehicle traffic pressure has been slowed down after the completion of the high-speed railway,a complete modeling work is carried out based on the theory of AHP analytic hierarchy process and Logit ***,on the basis of consulting a lot of actual data and data,and considering various factors,we consider the alleviation of the high speed rail's pressure on expressway *** the basis of this,combined with the factors that affect the location and quantity of high-speed railway stations,we use the Logit model to analyze and explain the difference of the impact of the high speed rail on the highway traffic *** with the results obtained from the Logit model,taking into account the characteristics of the high speed rail,such as the number of people traveling,the comfort and the speediness,and the traffic congestion determination model of the city,we can find out whether the high speed rail has a slow solution to the traffic pressure of the *** collecting and analyzing the data,we get the factors that influence people's choice of travel *** year,the number of trips used by various means of transportation,combined with the main factors,alleviate the pressure of highspeed rail to the traffic pressure of Expressway vehicles.

关键词： Logit model AHP analytic hierarchy process Logit method Multivariate statistical regression model Factor analysis Variable dimension reduction Urban traffic congestion judgment model MatLab

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Research on Motion Body Detection and Abnormal Behavior Recognition Based on WiFi technology

Research on Motion Body Detection and Abnormal Behavior Reco...

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2019 7th International Conference on Machinery,Materials and Computing technology(ICMMCT 2019)

作者： Wu Fei School of Software and Applied Science and Technology Zhengzhou University

In recent years,sports human behavior analysis has attracted more and more attention of computer vision ***,based on WiFi technology,the author carries out research on human motion detection and abnormal behavior recognition *** shows that it is one of the most attractive research fields in computer *** core of its research is to detect,track and identify moving human body from video sequences and to understand and describe the behavior of moving human *** effectively reduces the cost of manpower and material resources,while reducing the amount of computation,realizing the real-time active early warning function,and solving the after-effects of the traditional video surveillance system.

关键词： WiFi technology sports human detection abnormal behavior recognition technology

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Taxi Problem at Airport

Taxi Problem at Airport

引用

作者： Renyuan He School of Software and Applied Science and Technology Zhengzhou University

This paper mainly analyses whether airport taxi drivers enter the "car storage pool" to wait for passengers or return empty to make decisions when they deliver passengers to the airport. A model is established to determine the factors affecting the waiting time of taxi t and the weight of the influence on t. Model 1 uses the model of entropy weighting method. Firstly, the data are processed in dimensionless way and eliminated twice, respectively, based on the feasibility of the decision-making indicators and based on the correlation of the evaluation indicators. Finally, the main factors affecting the waiting time of airport drivers are: the number of airport "car pool" cars, weather conditions, holidays, time factors, arrival flights, which correspond to each other. The weights of the indicators are 0.0906, 0.0579, 0.0455, 0.0559, 0.0652. Combining the formula and the actual data, the waiting time of the driver can be calculated. Then the final decision-making scheme is given by comparing the driver's income. Given the formula Qi(waiting for passengers to return), Qj(no-load return), taxi fare standard and driver's average monthly salary and average daily working time, we can get the Qx difference between the two decision-making modes. Through the positive and negative of Qx difference, we can judge the advantages and disadvantages of the two decision-making models and make reasonable decisions. Because the problem of taxi queuing for passengers and passenger queuing for boarding is relatively common, and the airport opens two-way channels for related allocation. Therefore, in order to solve this problem, we use genetic algorithm-based scheduling model to construct the genomic relationship between different passengers and different taxis, and bring data into the calculation to simulate how to arrange boarding points, passengers and taxis, so as to maximize the efficiency of riding.

关键词： Entropy Weight Method Genetic Algorithms Queuing Theory Gravity Coefficient Prediction Model

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Description, Analysis, Forecasting, and Evaluation of Opioids' Spread

Description, Analysis, Forecasting, and Evaluation of Opioid...

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作者： Wu Fei School of Software and Applied Science and Technology Zhengzhou University

We build a Drug Spread Model to describe the spread path of top-ten popular drugs. Firstly, we operate on the data to select the top-ten drugs with the total cases over 5000 between 2010 and 2017. We also collect data about each drugs' use cases in each county. Next we find each county's adjacent regions to construct the adjacency matrix. After that we make the drugs' spread principles which help to determine the each drug's birthplace and which counties are more easily to be affected. Finally we use the Depth-first algorithm to find the spread path and obtain possible locations where the ten drugs use might have started in the five states.

关键词： Drug Spread Model Depth-first algorithm Gray prediction algorithm

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