Zobrazeno 1 - 10
of 121
pro vyhledávání: '"I, Diakogiannis"'
Publikováno v:
The Astrophysical Journal Letters, Vol 969, Iss 2, p L26 (2024)
We introduce “Class Symbolic Regression” (Class SR), the first framework for automatically finding a single analytical functional form that accurately fits multiple data sets—each realization being governed by its own (possibly) unique set of f
Externí odkaz:
https://doaj.org/article/d7ba15e7421e4d1b9a52f7f63b70ce7d
Publikováno v:
The Astrophysical Journal, Vol 959, Iss 2, p 99 (2023)
Symbolic regression (SR) is the study of algorithms that automate the search for analytic expressions that fit data. While recent advances in deep learning have generated renewed interest in such approaches, the development of SR methods has not been
Externí odkaz:
https://doaj.org/article/2ab99bac24e6422b931a32c38b43fc34
Publikováno v:
Remote Sensing, Vol 13, Iss 18, p 3707 (2021)
Change detection, i.e., the identification per pixel of changes for some classes of interest from a set of bi-temporal co-registered images, is a fundamental task in the field of remote sensing. It remains challenging due to unrelated forms of change
Externí odkaz:
https://doaj.org/article/c2e5c85e36f6447baf7095cf238bba60
Autor:
François Waldner, Foivos I. Diakogiannis, Kathryn Batchelor, Michael Ciccotosto-Camp, Elizabeth Cooper-Williams, Chris Herrmann, Gonzalo Mata, Andrew Toovey
Publikováno v:
Remote Sensing, Vol 13, Iss 11, p 2197 (2021)
Digital agriculture services can greatly assist growers to monitor their fields and optimize their use throughout the growing season. Thus, knowing the exact location of fields and their boundaries is a prerequisite. Unlike property boundaries, which
Externí odkaz:
https://doaj.org/article/361e719f545a4dfc8f2b24bff2865429
Autor:
Nestor N. Papathanasiou, Lazaros Bismpas, Eleftherios Panteris, Anastasia Konsta, Ioannis I. Diakogiannis, Loukas Athanasiadis
Publikováno v:
Journal of Substance Use. :1-8
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c360cb6cb8cb7b587dfa0d59e29fa15f
https://doi.org/10.1080/14659891.2022.2069611
https://doi.org/10.1080/14659891.2022.2069611
Publikováno v:
ISPRS Journal of Photogrammetry and Remote Sensing. 162:94-114
Scene understanding of high resolution aerial images is of great importance for the task of automated monitoring in various remote sensing applications. Due to the large within-class and small between-class variance in pixel values of objects of inte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::787dc56530816815dd4155d185200cdd
https://doi.org/10.1016/j.isprsjprs.2020.01.013
https://doi.org/10.1016/j.isprsjprs.2020.01.013
Autor:
David Blondeau-Patissier, Thomas Schroeder, Gopika Suresh, Zhibin Li, Foivos I. Diakogiannis, Paul Irving, Christian Witte, Andrew D.L. Steven
Publikováno v:
Marine Pollution Bulletin. 188:114598
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f51e600d9ef4906ef91330e643e482d8
https://doi.org/10.1016/j.marpolbul.2023.114598
https://doi.org/10.1016/j.marpolbul.2023.114598
Accurate extractions of the detected gravitational wave (GW) signal waveforms are essential to validate a detection and to probe the astrophysics behind the sources producing the GWs. This however could be difficult in realistic scenarios where the s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::daf333f1f55fbc559fb70b0b70b3adad
http://arxiv.org/abs/2105.03073
http://arxiv.org/abs/2105.03073
Autor:
Nigel Bajema, Liang Wang, Scott Mills, Greg Bishop-Hurley, Ian Atkinson, Foivos I. Diakogiannis, E. Charmley
Publikováno v:
Animal Biotelemetry, Vol 9, Iss 1, Pp 1-12 (2021)
Agriculture is becoming increasingly reliant upon accurate data from sensor arrays, with localization an emerging application in the livestock industry. Ground-based time difference of arrival (TDoA) radio location methods have the advantage of being
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ef8e219b955789bd1920dc0d6140c877
https://doi.org/10.21203/rs.3.rs-195951/v1
https://doi.org/10.21203/rs.3.rs-195951/v1
Publikováno v:
The Astrophysical Journal
The Astrophysical Journal, American Astronomical Society, 2021, 915 (1), pp.5. ⟨10.3847/1538-4357/abfda9⟩
The Astrophysical Journal, American Astronomical Society, 2021, 915 (1), pp.5. ⟨10.3847/1538-4357/abfda9⟩
We introduce the "ACTIONFINDER", a deep learning algorithm designed to transform a sample of phase-space measurements along orbits in a static potential into action and angle coordinates. The algorithm finds the mapping from positions and velocities
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ae37b8920b735f2fe91ebfc6704cc303
http://arxiv.org/abs/2012.05250
http://arxiv.org/abs/2012.05250