Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Bevan L. Cheeseman"'
Autor:
Suryanarayana Maddu, Dominik Sturm, Bevan L. Cheeseman, Christian L. Müller, Ivo F. Sbalzarini
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-15 (2023)
Abstract We present an artificial neural network architecture, termed STENCIL-NET, for equation-free forecasting of spatiotemporal dynamics from data. STENCIL-NET works by learning a discrete propagator that is able to reproduce the spatiotemporal dy
Externí odkaz:
https://doaj.org/article/edb7ceb939634ad6a8c1cca2b85f2908
Publikováno v:
Nature Communications, Vol 9, Iss 1, Pp 1-13 (2018)
Modern microscopes can generate high volumes of 3D images, driving difficulties in data handling and processing. Here, the authors present a content-adaptive image representation as an alternative to standard pixels that goes beyond data compression
Externí odkaz:
https://doaj.org/article/76b3cb7eac1b4e3ca9f1de3e9da886cb
Autor:
Jules Scholler, Joel Jonsson, Tomás Jordá-Siquier, Ivana Gantar, Laura Batti, Bevan L. Cheeseman, Stéphane Pagès, Ivo F. Sbalzarini, Christophe M. Lamy
The large size of imaging datasets generated by next-generation histology methods limits the adoption of those approaches in research and the clinic. We propose pAPRica (pipelines for Adaptive Particle Representation image compositing and analysis),
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::47674468bfa035a1accc686f07c01478
https://doi.org/10.1101/2023.01.27.525687
https://doi.org/10.1101/2023.01.27.525687
Publikováno v:
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
We present data structures and algorithms for native implementations of discrete convolution operators over Adaptive Particle Representations (APR) of images on parallel computer architectures. The APR is a content-adaptive image representation that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a09e0b40753c5d59573c54447102683
https://hdl.handle.net/21.11116/0000-000B-036F-6
https://hdl.handle.net/21.11116/0000-000B-036F-6
Publikováno v:
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
We present a statistical learning framework for robust identification of differential equations from noisy spatio-temporal data. We address two issues that have so far limited the application of such methods, namely their robustness against noise and
Autor:
Boaz Jurkowicz, Dongcheng Zhang, James M. Osborne, Kwaku Dad Abu-Bonsrah, Donald F. Newgreen, Bevan L. Cheeseman, Kerry A. Landman
Publikováno v:
Developmental Biology. 444:S287-S296
We quantified cell population increase in the quail embryo enteric nervous system (ENS) from E2.5 (about 1500 cells) to E12 (about 8 million cells). We then probed ENS proliferative capacity by grafting to the chorio-allantoic membrane large (600 cel
Publikováno v:
Nature Communications, Vol 9, Iss 1, Pp 1-13 (2018)
Nature communications
Nature Communications
Nature communications
Nature Communications
Modern microscopes create a data deluge with gigabytes of data generated each second, and terabytes per day. Storing and processing this data is a severe bottleneck, not fully alleviated by data compression. We argue that this is because images are p
Autor:
Christian L. Müller, Suryanarayana Maddu, Ivo F. Sbalzarini, Bevan L. Cheeseman, Dominik Sturm
Publikováno v:
14th World Congress in Computational Mechanics (WCCM), ECCOMAS Congress 2020, 11-15 January 2021, Virtual Congress
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa52b539197dd7f04047f89620c9e75a
https://hdl.handle.net/21.11116/0000-0008-DA5A-E
https://hdl.handle.net/21.11116/0000-0008-DA5A-E
Autor:
Dominik Sturm, Bevan L. Cheeseman, Christian L. Müller, Suryanarayana Maddu, Ivo F. Sbalzarini
Publikováno v:
14th WCCM-ECCOMAS Congress.
Publikováno v:
Phys. Rev. E 103:042310 (2021)
Physical Review E
Physical Review E
We propose a statistical learning framework based on group-sparse regression that can be used to (i) enforce conservation laws, (ii) ensure model equivalence, and (iii) guarantee symmetries when learning or inferring differential-equation models from