Zobrazeno 1 - 10
of 1 804
pro vyhledávání: '"P. Stadelmann"'
Autor:
Sager, Pascal J., Deriu, Jan M., Grewe, Benjamin F., Stadelmann, Thilo, von der Malsburg, Christoph
Nets, cooperative networks of neurons, have been proposed as format for the representation of sensory signals, as physical implementation of the Gestalt phenomenon and as solution to the neural binding problem, while the direct interaction between ne
Externí odkaz:
http://arxiv.org/abs/2407.05650
Autor:
Kechris, Christodoulos, Thevenot, Jerome, Teijeiro, Tomas, Stadelmann, Vincent A., Maffiuletti, Nicola A., Atienza, David
Acoustical knee health assessment has long promised an alternative to clinically available medical imaging tools, but this modality has yet to be adopted in medical practice. The field is currently led by machine learning models processing acoustical
Externí odkaz:
http://arxiv.org/abs/2405.15085
Autor:
Schmitt-Koopmann, Felix M., Huang, Elaine M., Hutter, Hans-Peter, Stadelmann, Thilo, Darvishy, Alireza
Publikováno v:
IEEE Access 12 (2024) 76963-76974
Printed mathematical expression recognition (MER) models are usually trained and tested using LaTeX-generated mathematical expressions (MEs) as input and the LaTeX source code as ground truth. As the same ME can be generated by various different LaTe
Externí odkaz:
http://arxiv.org/abs/2404.13667
Humans and animals recognize objects irrespective of the beholder's point of view, which may drastically change their appearances. Artificial pattern recognizers also strive to achieve this, e.g., through translational invariance in convolutional neu
Externí odkaz:
http://arxiv.org/abs/2311.08525
Publikováno v:
Pattern Recognition Letters 181, 2024
While deep neural networks have shown impressive results in automatic speaker recognition and related tasks, it is dissatisfactory how little is understood about what exactly is responsible for these results. Part of the success has been attributed i
Externí odkaz:
http://arxiv.org/abs/2311.00489
Autor:
Yan, Peng, Abdulkadir, Ahmed, Luley, Paul-Philipp, Rosenthal, Matthias, Schatte, Gerrit A., Grewe, Benjamin F., Stadelmann, Thilo
Publikováno v:
IEEE Acess 12 (2024) 3768-3789
Automating the monitoring of industrial processes has the potential to enhance efficiency and optimize quality by promptly detecting abnormal events and thus facilitating timely interventions. Deep learning, with its capacity to discern non-trivial p
Externí odkaz:
http://arxiv.org/abs/2307.05638
Autor:
Emberger, Raphael, Boss, Jens Michael, Baumann, Daniel, Seric, Marko, Huo, Shufan, Tuggener, Lukas, Keller, Emanuela, Stadelmann, Thilo
Patient monitoring in intensive care units, although assisted by biosensors, needs continuous supervision of staff. To reduce the burden on staff members, IT infrastructures are built to record monitoring data and develop clinical decision support sy
Externí odkaz:
http://arxiv.org/abs/2306.14620
Autor:
Nathalie A. Lengacher, Julianna J. Tomlinson, Ann‑Kristin Jochum, Jonas Franz, Omar Hasan Ali, Lukas Flatz, Wolfram Jochum, Josef Penninger, aSCENT-PD Investigators, Christine Stadelmann, John M. Woulfe, Michael G. Schlossmacher
Publikováno v:
Acta Neuropathologica Communications, Vol 12, Iss 1, Pp 1-4 (2024)
Externí odkaz:
https://doaj.org/article/c118a156131a4fa38a92b2f413f8b91a
Autor:
A. Wallimann, Y. Achermann, C. Ferris, M. Morgenstern, M. Clauss, V. Stadelmann, H. A. Rüdiger, L. O'Mahony, T. F. Moriarty
Publikováno v:
Journal of Bone and Joint Infection, Vol 9, Pp 191-196 (2024)
Rifampicin is a key antibiotic in the treatment of staphylococcal biofilm infections. In this pilot study, we found that patients who received rifampicin for treatment of an orthopaedic-device-related infection (ODRI) were colonized with rifampicin-r
Externí odkaz:
https://doaj.org/article/297343e0cd5f45cf8a1922778b8e4400
Autor:
Peter R. Jermain, Martin Oswald, Tenzin Langdun, Santana Wright, Ashraf Khan, Thilo Stadelmann, Ahmed Abdulkadir, Anna N. Yaroslavsky
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Fluorescence polarization (Fpol) imaging of methylene blue (MB) is a promising quantitative approach to thyroid cancer detection. Clinical translation of MB Fpol technology requires reduction of the data analysis time that can be achieved vi
Externí odkaz:
https://doaj.org/article/3f7147153a764906bcc19b7551539fc7