{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,3]],"date-time":"2024-08-03T22:48:12Z","timestamp":1722725292451},"reference-count":91,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,9,9]],"date-time":"2019-09-09T00:00:00Z","timestamp":1567987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["2014\/50851-0"],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["2014\/50851-0"],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"Generated and collected data have been rising with the popularization of technologies such as Internet of Things, social media, and smartphone, leading big data term creation. One class of big data hidden information is causality. Among the tools to infer causal relationships, there is Delay Transfer Entropy (DTE); however, it has a high demanding processing power. Many approaches were proposed to overcome DTE performance issues such as GPU and FPGA implementations. Our study compared different parallel strategies to calculate DTE from big data series using a heterogeneous Beowulf cluster. Task Parallelism was significantly faster in comparison to Data Parallelism. With big data trend in sight, these results may enable bigger datasets analysis or better statistical evidence.<\/jats:p>","DOI":"10.3390\/a12090190","type":"journal-article","created":{"date-parts":[[2019,9,9]],"date-time":"2019-09-09T15:26:17Z","timestamp":1568042777000},"page":"190","source":"Crossref","is-referenced-by-count":5,"title":["Parallelism Strategies for Big Data Delayed Transfer Entropy Evaluation"],"prefix":"10.3390","volume":"12","author":[{"given":"Jonas R.","family":"Dourado","sequence":"first","affiliation":[{"name":"Department of Electrical and Computational Engineering, University of S\u00e3o Paulo, 13566-590 S\u00e3o Carlos-SP, Brazil"}]},{"given":"Jord\u00e3o Natal de","family":"Oliveira J\u00fanior","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computational Engineering, University of S\u00e3o Paulo, 13566-590 S\u00e3o Carlos-SP, Brazil"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0137-6678","authenticated-orcid":false,"given":"Carlos D.","family":"Maciel","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computational Engineering, University of S\u00e3o Paulo, 13566-590 S\u00e3o Carlos-SP, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1016\/j.future.2017.09.016","article-title":"Internet-of-Things and big data for smarter healthcare: From device to architecture, applications and analytics","volume":"78","author":"Firouzi","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.is.2014.07.006","article-title":"The rise of \u201cbig data\u201d on cloud computing: Review and open research issues","volume":"47","author":"Hashem","year":"2015","journal-title":"Inf. Syst."},{"key":"ref_3","first-page":"170","article-title":"In Search of a Language of Causality in the Age of Big Data for Management Practices","volume":"Surrey","author":"Cheng","year":"2018","journal-title":"Acad. Manag. Glob. Proc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s10479-016-2158-8","article-title":"Environmental performance evaluation with big data: Theories and methods","volume":"270","author":"Song","year":"2018","journal-title":"Ann. Oper. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.compeleceng.2017.04.006","article-title":"Spatial cumulative sum algorithm with big data analytics for climate change detection","volume":"65","author":"Manogaran","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.nbd.2018.05.026","article-title":"Big data sharing and analysis to advance research in post-traumatic epilepsy","volume":"123","author":"Duncan","year":"2019","journal-title":"Neurobiol. Dis."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.neuroimage.2017.06.077","article-title":"Discovering dynamic brain networks from big data in rest and task","volume":"180","author":"Vidaurre","year":"2018","journal-title":"Neuroimage"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s40471-019-0179-y","article-title":"Sampling and Sampling Frames in Big Data Epidemiology","volume":"6","author":"Mooney","year":"2019","journal-title":"Curr. Epidemiol. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s10654-018-0385-9","article-title":"Epidemiology in wonderland: Big data and precision medicine","volume":"33","author":"Saracci","year":"2018","journal-title":"Eur. J. Epidemiol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bragazzi, N.L., Guglielmi, O., and Garbarino, S. (2019). SleepOMICS: How big data can revolutionize sleep science. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16020291"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yetton, B.D., McDevitt, E.A., Cellini, N., Shelton, C., and Mednick, S.C. (2018). Quantifying sleep architecture dynamics and individual differences using big data and Bayesian networks. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0194604"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"036207","DOI":"10.1103\/PhysRevE.83.036207","article-title":"Reducing the bias of causality measures","volume":"83","author":"Papana","year":"2011","journal-title":"Phys. Rev. E"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1007\/s10827-015-0548-6","article-title":"Delayed mutual information infers patterns of synaptic connectivity in a proprioceptive neural network","volume":"38","author":"Endo","year":"2015","journal-title":"J. Comput. Neurosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1111\/j.1467-9469.2011.00774.x","article-title":"Shannon Entropy and Mutual Information for Multivariate Skew-Elliptical Distributions","volume":"40","author":"Genton","year":"2013","journal-title":"Scand. J. Stat."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1103\/PhysRevLett.85.461","article-title":"Measuring Information Transfer","volume":"85","author":"Schreiber","year":"2000","journal-title":"Phys. Rev. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lindner, B., Auret, L., and Bauer, M. (2019). A systematic workflow for oscillation diagnosis using transfer entropy. IEEE Trans. Control Syst. Technol.","DOI":"10.1109\/TCST.2019.2896223"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, X., and Hui, X. (2018). Cross-Sectoral Information Transfer in the Chinese Stock Market around Its Crash in 2015. Entropy, 20.","DOI":"10.3390\/e20090663"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.chaos.2017.03.039","article-title":"Causal relationship between the global foreign exchange market based on complex networks and entropy theory","volume":"99","author":"Cao","year":"2017","journal-title":"Chaos Solitons Fractals"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"076002","DOI":"10.1088\/1741-4326\/aabf5d","article-title":"Study of radial heat transport in W7-X using the transfer entropy","volume":"58","author":"Hoefel","year":"2018","journal-title":"Nuclear Fusion"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Berger, E., Grehl, S., Vogt, D., Jung, B., and Amor, H.B. (2016, January 16\u201321). Experience-based torque estimation for an industrial robot. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487127"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"022116","DOI":"10.1103\/PhysRevE.93.022116","article-title":"Sensory capacity: An information theoretical measure of the performance of a sensor","volume":"93","author":"Hartich","year":"2016","journal-title":"Phys. Rev. E"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"045101","DOI":"10.1088\/0957-0233\/27\/4\/045101","article-title":"The measurement of gas\u2013liquid two-phase flows in a small diameter pipe using a dual-sensor multi-electrode conductance probe","volume":"27","author":"Zhai","year":"2016","journal-title":"Meas. Sci. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1967","DOI":"10.1038\/s41598-018-20109-6","article-title":"Locating order-disorder phase transition in a cardiac system","volume":"8","author":"Ashikaga","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Marzbanrad, F., Kimura, Y., Palaniswami, M., and Khandoker, A.H. (2015). Quantifying the Interactions between Maternal and Fetal Heart Rates by Transfer Entropy. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0145672"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Murari, A., Lungaroni, M., Peluso, E., Gaudio, P., Lerche, E., Garzotti, L., Gelfusa, M., and Contributors, J. (2018). On the Use of Transfer Entropy to Investigate the Time Horizon of Causal Influences between Signals. Entropy, 20.","DOI":"10.3390\/e20090627"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"77","DOI":"10.3389\/fncir.2016.00077","article-title":"Hodge Decomposition of Information Flow on Small-World Networks","volume":"10","author":"Haruna","year":"2016","journal-title":"Front. Neural Circuits"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.physa.2017.12.019","article-title":"Time series analysis of the Antarctic Circumpolar Wave via symbolic transfer entropy","volume":"499","author":"Oh","year":"2018","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sendrowski, A., Sadid, K., Meselhe, E., Wagner, W., Mohrig, D., and Passalacqua, P. (2018). Transfer Entropy as a Tool for Hydrodynamic Model Validation. Entropy, 20.","DOI":"10.3390\/e20010058"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yao, C.Z., Kuang, P.C., Lin, Q.W., and Sun, B.Y. (2017). A Study of the Transfer Entropy Networks on Industrial Electricity Consumption. Entropy, 19.","DOI":"10.3390\/e19040159"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1080\/19312458.2018.1479843","article-title":"Communicating with algorithms: A transfer entropy analysis of emotions-based escapes from online echo chambers","volume":"12","author":"Hilbert","year":"2018","journal-title":"Commun. Methods Meas."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.3390\/e16031743","article-title":"Transfer entropy expressions for a class of non-Gaussian distributions","volume":"16","author":"Tyrcha","year":"2014","journal-title":"Entropy"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"11061","DOI":"10.1038\/ncomms11061","article-title":"Dynamic information routing in complex networks","volume":"7","author":"Kirst","year":"2016","journal-title":"Nat. Commun."},{"key":"ref_33","first-page":"6531051","article-title":"Nonlinear Dynamic Identification of Beams Resting on Nonlinear Viscoelastic Foundations Based on the Time-Delayed Transfer Entropy and Improved Surrogate Data Algorithm","volume":"2018","author":"Liu","year":"2018","journal-title":"Math. Probl. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Berger, E., M\u00fcller, D., Vogt, D., Jung, B., and Amor, H.B. (2014, January 18\u201320). Transfer entropy for feature extraction in physical human-robot interaction: Detecting perturbations from low-cost sensors. Proceedings of the 2014 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids), Madrid, Spain.","DOI":"10.1109\/HUMANOIDS.2014.7041459"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.chemolab.2016.09.006","article-title":"Data-driven root cause diagnosis of faults in process industries","volume":"159","author":"Li","year":"2016","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Shao, S., Guo, C., Luk, W., and Weston, S. (2014, January 10\u201312). Accelerating transfer entropy computation. Proceedings of the 2014 International Conference on Field-Programmable Technology (FPT), Shanghai, China.","DOI":"10.1109\/FPT.2014.7082754"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wollstadt, P., Mart\u00ednez-Zarzuela, M., Vicente, R., D\u00edaz-Pernas, F.J., and Wibral, M. (2014). Efficient Transfer Entropy Analysis of Non-Stationary Neural Time Series. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0102833"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1439","DOI":"10.1007\/s10586-017-1385-3","article-title":"Performance prediction of parallel computing models to analyze cloud-based big data applications","volume":"21","author":"Shen","year":"2018","journal-title":"Cluster Comput."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Booth, J.D., Kim, K., and Rajamanickam, S. (2016, January 23\u201327). A Comparison of High-Level Programming Choices for Incomplete Sparse Factorization Across Different Architectures. Proceedings of the 2016 IEEE International Parallel and Distributed Processing Symposium Workshops, Chicago, IL, USA.","DOI":"10.1109\/IPDPSW.2016.41"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1145\/1168919.1168877","article-title":"Exploiting Coarse-grained Task, Data, and Pipeline Parallelism in Stream Programs","volume":"34","author":"Gordon","year":"2006","journal-title":"SIGARCH Comput. Archit. News"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Choudhury, O., Rajan, D., Hazekamp, N., Gesing, S., Thain, D., and Emrich, S. (2015, January 8\u201311). Balancing Thread-level and Task-level Parallelism for Data-Intensive Workloads on Clusters and Clouds. Proceedings of the 2015 IEEE International Conference on Cluster Computing, Chicago, IL, USA.","DOI":"10.1109\/CLUSTER.2015.60"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jprocont.2018.04.004","article-title":"Big data quality prediction in the process industry: A distributed parallel modeling framework","volume":"68","author":"Yao","year":"2018","journal-title":"J. Process Control"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1177\/0047287517747753","article-title":"Sentiment analysis in tourism: Capitalizing on big data","volume":"58","author":"Alaei","year":"2019","journal-title":"J. Travel Res."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hassan, M.K., El Desouky, A.I., Elghamrawy, S.M., and Sarhan, A.M. (2019). Big Data Challenges and Opportunities in Healthcare Informatics and Smart Hospitals. Security in Smart Cities: Models, Applications, and Challenges, Springer.","DOI":"10.1007\/978-3-030-01560-2_1"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/ACCESS.2014.2332453","article-title":"Toward scalable systems for big data analytics: A technology tutorial","volume":"2","author":"Hu","year":"2014","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2053951714528481","DOI":"10.1177\/2053951714528481","article-title":"Big Data, new epistemologies and paradigm shifts","volume":"1","author":"Kitchin","year":"2014","journal-title":"Big Data Soc."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1891","DOI":"10.1109\/TII.2017.2650204","article-title":"Next,-generation big data analytics: State of the art, challenges, and future research topics","volume":"13","author":"Song","year":"2017","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.procs.2015.07.286","article-title":"Big Data Analytics in the Cloud: Spark on Hadoop vs. MPI\/OpenMP on Beowulf","volume":"53","author":"Oneto","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.asoc.2017.04.015","article-title":"FPGA based hardware implementation of Bat Algorithm","volume":"58","author":"Ameur","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1016\/j.asoc.2012.08.032","article-title":"Particle swarm optimization of interval type-2 fuzzy systems for FPGA applications","volume":"13","author":"Maldonado","year":"2013","journal-title":"Appl. Soft Comput."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.asoc.2015.10.054","article-title":"Multicores and GPU utilization in parallel swarm algorithm for parameter estimation of photovoltaic cell model","volume":"40","author":"Ting","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.asoc.2017.07.007","article-title":"Implementation of neuro-fuzzy system with modified high performance genetic algorithm on embedded systems","volume":"60","author":"Nasrollahzadeh","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.cpc.2017.01.015","article-title":"Massively parallel simulations of relativistic fluid dynamics on graphics processing units with CUDA","volume":"225","author":"Bazow","year":"2018","journal-title":"Comput. Phys. Commun."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2550","DOI":"10.1016\/j.asoc.2012.04.001","article-title":"A dynamic model selection strategy for support vector machine classifiers","volume":"12","author":"Kapp","year":"2012","journal-title":"Appl. Soft Comput."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1016\/j.asoc.2017.04.025","article-title":"A novel improved particle swarm optimization algorithm based on individual difference evolution","volume":"57","author":"Gou","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.asoc.2017.01.012","article-title":"A novel fuzzy adaptive configuration of particle swarm optimization to solve large-scale optimal reactive power dispatch","volume":"53","author":"Naderi","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.asoc.2016.12.034","article-title":"Improving the performance of embedded systems with variable neighborhood search","volume":"53","author":"Sevaux","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yao, Y., Chang, J., and Xia, K. (2009, January 8\u201311). A case of parallel eeg data processing upon a beowulf cluster. Proceedings of the 2009 15th International Conference on Parallel and Distributed Systems (ICPADS), Shenzhen, China.","DOI":"10.1109\/ICPADS.2009.65"},{"key":"ref_59","unstructured":"Sterling, T., Becker, D.J., Savarese, D., Dorband, J.E., Ranawake, U.A., and Packer, C.V. (1995, January 14\u201318). Beowulf: A Parallel Workstation For Scientific Computation. Proceedings of the International Conference on Parallel Processing, Champain, IL, USA."},{"key":"ref_60","unstructured":"Yamakov, V.I. (2019, September 07). Parallel Grand Canonical Monte Carlo (ParaGrandMC) Simulation Code, Available online: https:\/\/ntrs.nasa.gov\/archive\/nasa\/casi.ntrs.nasa.gov\/20160007416.pdf."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1002\/minf.201600025","article-title":"A Simple and Resource-efficient Setup for the Computer-aided Drug Design Laboratory","volume":"35","author":"Moretti","year":"2016","journal-title":"Mol. Inform."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Schuman, C.D., Disney, A., Singh, S.P., Bruer, G., Mitchell, J.P., Klibisz, A., and Plank, J.S. (2016, January 14). Parallel evolutionary optimization for neuromorphic network training. Proceedings of the 2016 2nd Workshop on Machine Learning in HPC Environments (MLHPC), Salt Lake City, UT, USA.","DOI":"10.1109\/MLHPC.2016.008"},{"key":"ref_63","first-page":"19","article-title":"Comparison of two methods of parallelizing GEANT4 on beowulf computer cluster","volume":"61","author":"Hulsey","year":"2016","journal-title":"Bull. Am. Phys. Soc."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MCSE.2007.53","article-title":"IPython: A System for Interactive Scientific Computing","volume":"9","author":"Granger","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_65","unstructured":"IPython developers (open source) (2019, September 07). IPython 3.2.1 Documentation\u20140.11 Series. Available online: https:\/\/ipython.org\/ipython-doc\/3\/index.html."},{"key":"ref_66","unstructured":"IPython developers (open source) (2019, September 07). Ipyparallel 5.2.0 Documentation\u2013Changes in IPython Parallel. Available online: https:\/\/ipyparallel.readthedocs.io\/en\/5.2.0\/."},{"key":"ref_67","unstructured":"IPython developers (2019, September 07). Ipyparallel 5.2.0 Documentation\u2013IPython Parallel Overview and Getting Started. Available online: https:\/\/ipyparallel.readthedocs.io\/en\/5.2.0\/."},{"key":"ref_68","unstructured":"Kershaw, P., Lawrence, B., Gomez-Dans, J., and Holt, J. (2015, January 12\u201317). Cloud hosting of the IPython Notebook to Provide Collaborative Research Environments for Big Data Analysis. Proceedings of the EGU General Assembly Conference Abstracts, Vienna, Austria."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"44","DOI":"10.3389\/fninf.2013.00044","article-title":"An automated and reproducible workflow for running and analyzing neural simulations using Lancet and IPython Notebook","volume":"7","author":"Stevens","year":"2013","journal-title":"Front. Neuroinform."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.3389\/fphys.2018.01350","article-title":"Surrogate data method requires end-matched segmentation of electroencephalographic signals to estimate nonlinearity","volume":"9","author":"Bachmann","year":"2018","journal-title":"Front. Physiol."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Lindner, M., Vicente, R., Priesemann, V., and Wibral, M. (2011). TRENTOOL: A Matlab open source toolbox to analyse information flow in time series data with transfer entropy. BMC Neurosci., 12.","DOI":"10.1186\/1471-2202-12-119"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Magri, C., Whittingstall, K., Singh, V., Logothetis, N.K., and Panzeri, S. (2009). A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings. BMC Neurosci., 10.","DOI":"10.1186\/1471-2202-10-81"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"056202","DOI":"10.1103\/PhysRevE.85.056202","article-title":"Improvements to surrogate data methods for nonstationary time series","volume":"85","author":"Lucio","year":"2012","journal-title":"Phys. Rev. E"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.sigpro.2012.05.018","article-title":"Detecting information flow direction in multivariate linear and nonlinear models","volume":"93","author":"Yang","year":"2013","journal-title":"Signal Process."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1103\/PhysRevLett.77.635","article-title":"Improved Surrogate Data for Nonlinearity Tests","volume":"77","author":"Schreiber","year":"1996","journal-title":"Phys. Rev. Lett."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"321","DOI":"10.5194\/npg-13-321-2006","article-title":"A stochastic iterative amplitude adjusted Fourier transform algorithm with improved accuracy","volume":"13","author":"Venema","year":"2006","journal-title":"Nonlinear Process. Geophys."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/S0167-2789(00)00043-9","article-title":"Surrogate time series","volume":"142","author":"Schreiber","year":"2000","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"2640","DOI":"10.1049\/iet-gtd.2015.1062","article-title":"Impact of operators\u2019 performance in the reliability of cyber-physical power distribution systems","volume":"10","author":"Bessani","year":"2016","journal-title":"IET Gener. Transm. Distrib."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.epsr.2015.12.003","article-title":"Combining exhaustive search and multi-objective evolutionary algorithm for service restoration in large-scale distribution systems","volume":"134","author":"Camillo","year":"2016","journal-title":"Electr. Power Syst. Res."},{"key":"ref_80","unstructured":"De Lima, D.R., Santos, F.P., and Maciel, C.D. (May, January 28). Network Structural Reconstruction Base on Delayed Transfer Entropy and Synthetic data. Proceedings of the CBA 2016, Manitou\/Colorado Springs, CO, USA."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.cnsns.2016.12.008","article-title":"Transfer entropy between multivariate time series","volume":"47","author":"Mao","year":"2017","journal-title":"Commun. Nonlinear Sci. Numer. Simul."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Ito, S., Hansen, M.E., Heiland, R., Lumsdaine, A., Litke, A.M., and Beggs, J.M. (2011). Extending transfer entropy improves identification of effective connectivity in a spiking cortical network model. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0027431"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s10827-010-0262-3","article-title":"Transfer entropy\u2014A model-free measure of effective connectivity for the neurosciences","volume":"30","author":"Vicente","year":"2011","journal-title":"J. Comput. Neurosci."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1109\/JPROC.2004.840301","article-title":"The Design and Implementation of FFTW3","volume":"93","author":"Frigo","year":"2005","journal-title":"Proc. IEEE"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Kantz, H., and Schreiber, T. (2004). Nonlinear Time Series Analysis, Cambridge University Press.","DOI":"10.1017\/CBO9780511755798"},{"key":"ref_86","unstructured":"Van Rossum, G., and Drake, F.L. (2011). The Python Language Reference Manual, Network Theory Ltd."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MCSE.2011.37","article-title":"The NumPy Array: A Structure for Efficient Numerical Computation","volume":"13","author":"Colbert","year":"2011","journal-title":"Comput. Sci. Eng."},{"key":"ref_88","unstructured":"Muhammad, H. (2019, September 07). Htop-an Interactive Process Viewer for Linux; 2015. Available online: http:\/\/hisham.hm\/htop\/."},{"key":"ref_89","unstructured":"Hennessy, J.L., and Patterson, D.A. (2011). Computer Architecture: A Quantitative Approach, Elsevier Morgan Kaufmann."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1038\/533452a","article-title":"Is there a reproducibility crisis?","volume":"533","author":"Baker","year":"2016","journal-title":"Nature"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"(2016). Reality check on reproducibility. Nature, 533, 437.","DOI":"10.1038\/533437a"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/12\/9\/190\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T12:33:50Z","timestamp":1718886830000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/12\/9\/190"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,9]]},"references-count":91,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["a12090190"],"URL":"https:\/\/doi.org\/10.3390\/a12090190","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,9]]}}}