Zobrazeno 1 - 10
of 1 205
pro vyhledávání: '"Lingg, A."'
Autor:
Ghoul, Aya, Hammernik, Kerstin, Lingg, Andreas, Krumm, Patrick, Rueckert, Daniel, Gatidis, Sergios, Küstner, Thomas
In Magnetic Resonance Imaging (MRI), high temporal-resolved motion can be useful for image acquisition and reconstruction, MR-guided radiotherapy, dynamic contrast-enhancement, flow and perfusion imaging, and functional assessment of motion patterns
Externí odkaz:
http://arxiv.org/abs/2410.18834
Autor:
Xu, Siying, Hammernik, Kerstin, Lingg, Andreas, Kuebler, Jens, Krumm, Patrick, Rueckert, Daniel, Gatidis, Sergios, Kuestner, Thomas
Cardiac Cine Magnetic Resonance Imaging (MRI) provides an accurate assessment of heart morphology and function in clinical practice. However, MRI requires long acquisition times, with recent deep learning-based methods showing great promise to accele
Externí odkaz:
http://arxiv.org/abs/2407.03034
Autor:
Ghoul, Aya, Pan, Jiazhen, Lingg, Andreas, Kübler, Jens, Krumm, Patrick, Hammernik, Kerstin, Rueckert, Daniel, Gatidis, Sergios, Küstner, Thomas
Accurate motion estimation at high acceleration factors enables rapid motion-compensated reconstruction in Magnetic Resonance Imaging (MRI) without compromising the diagnostic image quality. In this work, we introduce an attention-aware deep learning
Externí odkaz:
http://arxiv.org/abs/2404.17621
Autor:
Yarici, Metin C., Amadori, Pierluigi, Davies, Harry, Nakamura, Takashi, Lingg, Nico, Demiris, Yiannis, Mandic, Danilo P.
Ear EEG based driver fatigue monitoring systems have the potential to provide a seamless, efficient, and feasibly deployable alternative to existing scalp EEG based systems, which are often cumbersome and impractical. However, the feasibility of dete
Externí odkaz:
http://arxiv.org/abs/2301.06406
Autor:
Yarici, Metin, Von Rosenberg, Wilhelm, Hammour, Ghena, Davies, Harry, Amadori, Pierluigi, Lingg, Nico, Demiris, Yiannis, Mandic, Danilo P.
Wearable technologies are envisaged to provide critical support to future healthcare systems. Hearables - devices worn in the ear - are of particular interest due to their ability to provide health monitoring in an efficient, reliable and unobtrusive
Externí odkaz:
http://arxiv.org/abs/2301.02475
Human skeleton point clouds are commonly used to automatically classify and predict the behaviour of others. In this paper, we use a contrastive self-supervised learning method, SimCLR, to learn representations that capture the semantics of skeleton
Externí odkaz:
http://arxiv.org/abs/2211.05304
One of the most fundamental problems in computational learning theory is the the problem of learning a finite automaton $A$ consistent with a finite set $P$ of positive examples and with a finite set $N$ of negative examples. By consistency, we mean
Externí odkaz:
http://arxiv.org/abs/2206.10025
Autor:
Bond, Jacob, Lingg, Andrew
To evaluate the robustness of non-classifier models, we propose probabilistic local equivalence, based on the notion of randomized smoothing, as a way to quantitatively evaluate the robustness of an arbitrary function. In addition, to understand the
Externí odkaz:
http://arxiv.org/abs/2206.02539
Autor:
Herdina, Anna Nele, Bozdogan, Anil, Aspermair, Patrik, Dostalek, Jakub, Klausberger, Miriam, Lingg, Nico, Cserjan-Puschmann, Monika, Aguilar, Patricia Pereira, Auer, Simone, Demirtas, Halil, Andersson, Jakob, Lötsch, Felix, Holzer, Barbara, Steinrigl, Adi, Thalhammer, Florian, Schellnegger, Julia, Breuer, Monika, Knoll, Wolfgang, Strassl, Robert
Publikováno v:
In Biosensors and Bioelectronics 1 January 2025 267
Autor:
Sprague, Dani, Culhane, Connor, Kounkel, Marina, Olney, Richard, Covey, K. R., Hutchinson, Brian, Lingg, Ryan, Stassun, Keivan G., Román-Zúñiga, Carlos G., Roman-Lopes, Alexandre, Nidever, David, Beaton, Rachael L., Borissova, Jura, Stutz, Amelia, Stringfellow, Guy S., Ramírez, Karla Peña, Ramírez-Preciado, Valeria, Hernández, Jesús, Kim, Jinyoung Serena, Lane, Richard R.
We train a convolutional neural network, APOGEE Net, to predict $T_\mathrm{eff}$, $\log g$, and, for some stars, [Fe/H], based on the APOGEE spectra. This is the first pipeline adapted for these data that is capable of estimating these parameters in
Externí odkaz:
http://arxiv.org/abs/2201.03661