Zobrazeno 1 - 10
of 18
pro vyhledávání: '"ERIK BOLLT"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)
Abstract An interesting feature of the brain is its ability to respond to disparate sensory signals from the environment in unique ways depending on the environmental context or current brain state. In dynamical systems, this is an example of multi-s
Externí odkaz:
https://doaj.org/article/f201dbe8bcda49018f1e500e2da8802c
Publikováno v:
Applied Network Science, Vol 7, Iss 1, Pp 1-22 (2022)
Abstract In this work, we introduce a new methodology for inferring the interaction structure of discrete valued time series which are Poisson distributed. While most related methods are premised on continuous state stochastic processes, in fact, dis
Externí odkaz:
https://doaj.org/article/ad553e2829bd4528a09a999b49134299
Autor:
Carmela Calabrese, Maria Lombardi, Erik Bollt, Pietro De Lellis, Benoît G. Bardy, Mario di Bernardo
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Abstract Synchronization of human networks is fundamental in many aspects of human endeavour. Recently, much research effort has been spent on analyzing how motor coordination emerges in human groups (from rocking chairs to violin players) and how it
Externí odkaz:
https://doaj.org/article/14f57a0fcd6c4312a7c38574533cc562
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-8 (2021)
Reservoir computers are artificial neural networks that can be trained on small data sets, but require large random matrices and numerous metaparameters. The authors propose an improved reservoir computer that overcomes these limitations and shows ad
Externí odkaz:
https://doaj.org/article/a188e94135aa40e8af0b78655f0dc46a
Autor:
Jakob Runge, Sebastian Bathiany, Erik Bollt, Gustau Camps-Valls, Dim Coumou, Ethan Deyle, Clark Glymour, Marlene Kretschmer, Miguel D. Mahecha, Jordi Muñoz-Marí, Egbert H. van Nes, Jonas Peters, Rick Quax, Markus Reichstein, Marten Scheffer, Bernhard Schölkopf, Peter Spirtes, George Sugihara, Jie Sun, Kun Zhang, Jakob Zscheischler
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-13 (2019)
Questions of causality are ubiquitous in Earth system sciences and beyond, yet correlation techniques still prevail. This Perspective provides an overview of causal inference methods, identifies promising applications and methodological challenges, a
Externí odkaz:
https://doaj.org/article/c9cb78b8c1d74e3fbd7d14afb1c5eee7
Publikováno v:
European Journal of Applied Mathematics. 34:346-366
Flow fields are determined from image sequences obtained in an experiment in which benthic macrofauna, Arenicola marina, causes water flow and the images depict the distribution of a tracer that is carried with the flow. The experimental setup is suc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e6798361c4289ca8553a5ba605ebe283
https://doi.org/10.1017/s095679252200016x
https://doi.org/10.1017/s095679252200016x
Autor:
Austin Jantzi, William Jemison, Prashant Athavale, Mahesh Banavar, Erik Bollt, Luke Rumbaugh, David Illig
Publikováno v:
OCEANS 2022, Hampton Roads.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2c48cd27143e98725c8ff9a4be454e96
https://doi.org/10.1109/oceans47191.2022.9977327
https://doi.org/10.1109/oceans47191.2022.9977327
Publikováno v:
Patterns. 3:100631
Boolean functions, and networks thereof, are useful for analysis of complex data systems, including from biological systems, bioinformatics, decision making, medical fields, and finance. However, automated learning of a Boolean networked function, fr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f8d164d9149e5bc85c745828039b919a
https://doi.org/10.1016/j.patter.2022.100631
https://doi.org/10.1016/j.patter.2022.100631
The stability analysis of synchronization patterns on generalized network structures is of immense importance nowadays. In this article, we scrutinize the stability of intralayer synchronous state in temporal multilayer hypernetworks, where each dyna
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9c9846cf8cef3f37ec7cbe024296f381
Autor:
Abd AlRahman AlMomani, Erik Bollt
In this paper, we introduce a new tool for data-driven discovery of early warning signs of critical transitions in ice shelves, from remote sensing data. Our approach adopts principles of directed spectral clustering methodology considering an asymme
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d2c3bc49504f4292d45dd4ee2dda7d17
https://npg.copernicus.org/preprints/npg-2020-26/
https://npg.copernicus.org/preprints/npg-2020-26/