Zobrazeno 1 - 10
of 7 912
pro vyhledávání: '"A Krumm"'
In the last decade, the free energy principle (FEP) and active inference (AIF) have achieved many successes connecting conceptual models of learning and cognition to mathematical models of perception and action. This effort is driven by a multidiscip
Externí odkaz:
http://arxiv.org/abs/2411.14991
Human mobility modeling from GPS-trajectories and synthetic trajectory generation are crucial for various applications, such as urban planning, disaster management and epidemiology. Both of these tasks often require filling gaps in a partially specif
Externí odkaz:
http://arxiv.org/abs/2411.04381
The abundance of vehicle trajectory data offers a new opportunity to compute driving routes between origins and destinations. Current graph-based routing pipelines, while effective, involve substantial costs in constructing, maintaining, and updating
Externí odkaz:
http://arxiv.org/abs/2411.01325
We present an explicit construction of a relativistic quantum computing architecture using a variational quantum circuit approach that is shown to allow for universal quantum computing. The variational quantum circuit consists of tunable single-qubit
Externí odkaz:
http://arxiv.org/abs/2411.00105
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
Poly2Vec: Polymorphic Encoding of Geospatial Objects for Spatial Reasoning with Deep Neural Networks
Encoding geospatial data is crucial for enabling machine learning (ML) models to perform tasks that require spatial reasoning, such as identifying the topological relationships between two different geospatial objects. However, existing encoding meth
Externí odkaz:
http://arxiv.org/abs/2408.14806
Simulating human mobility data is essential for various application domains, including transportation, urban planning, and epidemic control, since real data are often inaccessible to researchers due to expensive costs and privacy issues. Several exis
Externí odkaz:
http://arxiv.org/abs/2408.13918
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
Publikováno v:
Environmental Research Letters, Vol 18, Iss 9, p 094008 (2023)
Prior research suggests that energy demand-side interventions have a large potential in climate change mitigation, connected to co-benefits in human well-being and several Sustainable Development Goals. However, it is challenging to translate such st
Externí odkaz:
https://doaj.org/article/a67ebd72ced6438ab5a2ab7d6199d65e
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