J. Comput. Sci.最新文献_第3页

NIE-GAT: node importance evaluation method for inter-domain routing network based on graph attention network 基于图关注网络的域间路由网络节点重要性评价方法

J. Comput. Sci. Pub Date : 2022-10-01 DOI: 10.1016/j.jocs.2022.101885

Zimian Liu, Han Qiu, Wei Guo, Junhu Zhu, Qingxian Wang

引用次数: 1

Parkinson's disease gene prioritising using an efficient and biologically appropriate network-based consensus strategy 帕金森氏病基因优先使用有效和生物学上适当的基于网络的共识策略

J. Comput. Sci. Pub Date : 2022-10-01 DOI: 10.1016/j.jocs.2022.101879

B. Kumari, P. S. Dholaniya

引用次数: 0

Padasip: An open-source Python toolbox for adaptive filtering Padasip:用于自适应过滤的开源Python工具箱

J. Comput. Sci. Pub Date : 2022-10-01 DOI: 10.1016/j.jocs.2022.101887

Matous Cejnek, J. Vrba

引用次数: 0

Chaos follow the leader algorithm: Application to data classification 混沌跟随先导算法:在数据分类中的应用

J. Comput. Sci. Pub Date : 2022-10-01 DOI: 10.1016/j.jocs.2022.101886

Priyanka Singh, Rahul Kottath

引用次数: 2

Estimation of Correlation Matrices from Limited time series Data using Machine Learning 用机器学习估计有限时间序列数据的相关矩阵

J. Comput. Sci. Pub Date : 2022-09-02 DOI: 10.48550/arXiv.2209.01198

Nikhil Easaw, Woo Soek, Prashant Singh Lohiya, S. Jalan, Priodyuti Pradhan

{"title":"Estimation of Correlation Matrices from Limited time series Data using Machine Learning","authors":"Nikhil Easaw, Woo Soek, Prashant Singh Lohiya, S. Jalan, Priodyuti Pradhan","doi":"10.48550/arXiv.2209.01198","DOIUrl":"https://doi.org/10.48550/arXiv.2209.01198","url":null,"abstract":"Correlation matrices contain a wide variety of spatio-temporal information about a dynamical system. Predicting correlation matrices from partial time series information of a few nodes characterizes the spatio-temporal dynamics of the entire underlying system. This information can help to predict the underlying network structure, e.g., inferring neuronal connections from spiking data, deducing causal dependencies between genes from expression data, and discovering long spatial range influences in climate variations. Traditional methods of predicting correlation matrices utilize time series data of all the nodes of the underlying networks. Here, we use a supervised machine learning technique to predict the correlation matrix of entire systems from finite time series information of a few randomly selected nodes. The accuracy of the prediction validates that only a limited time series of a subset of the entire system is enough to make good correlation matrix predictions. Furthermore, using an unsupervised learning algorithm, we furnish insights into the success of the predictions from our model. Finally, we employ the machine learning model developed here to real-world data sets.","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"41 1","pages":"102053"},"PeriodicalIF":0.0,"publicationDate":"2022-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77158547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Verification of a real-time ensemble-based method for updating earth model based on GAN 基于GAN的实时集成地球模型更新方法的验证

J. Comput. Sci. Pub Date : 2022-07-07 DOI: 10.1016/j.jocs.2022.101876

K. Fossum, S. Alyaev, J. Tveranger, A. Elsheikh

引用次数: 2

AVIDA: Alternating method for Visualizing and Integrating Data AVIDA:数据可视化和集成的交替方法

J. Comput. Sci. Pub Date : 2022-05-31 DOI: 10.48550/arXiv.2206.00135

Kathryn Dover, Zixuan Cang, A. Ma, Qing Nie, R. Vershynin

{"title":"AVIDA: Alternating method for Visualizing and Integrating Data","authors":"Kathryn Dover, Zixuan Cang, A. Ma, Qing Nie, R. Vershynin","doi":"10.48550/arXiv.2206.00135","DOIUrl":"https://doi.org/10.48550/arXiv.2206.00135","url":null,"abstract":"High-dimensional multimodal data arises in many scientific fields. The integration of multimodal data becomes challenging when there is no known correspondence between the samples and the features of different datasets. To tackle this challenge, we introduce AVIDA, a framework for simultaneously performing data alignment and dimension reduction. In the numerical experiments, Gromov-Wasserstein optimal transport and t-distributed stochastic neighbor embedding are used as the alignment and dimension reduction modules respectively. We show that AVIDA correctly aligns high-dimensional datasets without common features with four synthesized datasets and two real multimodal single-cell datasets. Compared to several existing methods, we demonstrate that AVIDA better preserves structures of individual datasets, especially distinct local structures in the joint low-dimensional visualization, while achieving comparable alignment performance. Such a property is important in multimodal single-cell data analysis as some biological processes are uniquely captured by one of the datasets. In general applications, other methods can be used for the alignment and dimension reduction modules.","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"16 1","pages":"101998"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82291790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Deep Reinforcement Learning for Computational Fluid Dynamics on HPC Systems HPC系统计算流体力学的深度强化学习

J. Comput. Sci. Pub Date : 2022-05-13 DOI: 10.1016/j.jocs.2022.101884

Marius Kurz, Philipp Offenhauser, Dominic Viola, Oleksandr Shcherbakov, Michael M. Resch, A. Beck

引用次数: 10

An improved numerical method for hyperbolic Lagrangian Coherent Structures using Differential Algebra 用微分代数改进双曲拉格朗日相干结构的数值计算方法

J. Comput. Sci. Pub Date : 2022-04-13 DOI: 10.1016/j.jocs.2022.101883

J. Tyler, A. Wittig

引用次数: 3

Technical solution to counter potential crime: Text analysis to detect fake news and disinformation 打击潜在犯罪的技术解决方案:文本分析，以检测假新闻和虚假信息

J. Comput. Sci. Pub Date : 2022-04-01 DOI: 10.1016/j.jocs.2022.101576

R. Kozik, Sebastian Kula, M. Choraś, Michael Wozniak

引用次数: 3