Zobrazeno 1 - 10
of 11 567
pro vyhledávání: '"Grosu, A"'
Autor:
Chan, Yute, Grosu, Cristina, Kick, Matthias, Jakes, Peter, Seidlmayer, Stefan, Gigl, Thomas, Egger, Werner, Eichel, Ruediger-A., Granwehr, Josef, Hugenschmidt, Christoph, Scheurer, Christoph
The spinel Li4Ti5O12 (LTO) has emerged as a promising anode material for the next generation of all-solid-state Li-ion batteries (ASSB), primarily due to its characteristic "zero strain" charge/discharge behavior and exceptional cycling stability, wh
Externí odkaz:
http://arxiv.org/abs/2410.02535
Dynamic Vision Sensors (DVS), offer a unique advantage in control applications, due to their high temporal resolution, and asynchronous event-based data. Still, their adoption in machine learning algorithms remains limited. To address this gap, and p
Externí odkaz:
http://arxiv.org/abs/2409.18038
We analyzed the Hartree-Fock approximation for an electron system. The interaction between particles is modeled by a non-Coulombian potential. We analyzed both the three-dimensional and two-dimensional systems. We obtained accurate analytical results
Externí odkaz:
http://arxiv.org/abs/2408.14967
Autor:
Bhandary, Shrajan, Kuhn, Dejan, Babaiee, Zahra, Fechter, Tobias, Spohn, Simon K. B., Zamboglou, Constantinos, Grosu, Anca-Ligia, Grosu, Radu
Accurate segmentation of prostate tumours from PET images presents a formidable challenge in medical image analysis. Despite considerable work and improvement in delineating organs from CT and MR modalities, the existing standards do not transfer wel
Externí odkaz:
http://arxiv.org/abs/2407.10537
Autor:
Shahzadi, Iram, Zwanenburg, Alex, Lattermann, Annika, Linge, Annett, Baldus, Christian, Peeken, Jan C., Combs, Stephanie E., Diefenhardt, Markus, Rödel, Claus, Kirste, Simon, Grosu, Anca-Ligia, Baumann, Michael, Krause, Mechthild, Troost, Esther G. C., Löck, Steffen
Radiomics analyses commonly apply imaging features of different complexity for the prediction of the endpoint of interest. However, the prognostic value of each feature class is generally unclear. Furthermore, many radiomics models lack independent e
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A90623
https://tud.qucosa.de/api/qucosa%3A90623/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A90623/attachment/ATT-0/
Autor:
Lygizou, Elpiniki Maria, Reiter, Michael, Maurer-Granofszky, Margarita, Dworzak, Michael, Grosu, Radu
Acute Leukemia is the most common hematologic malignancy in children and adolescents. A key methodology in the diagnostic evaluation of this malignancy is immunophenotyping based on Multiparameter Flow Cytometry (FCM). However, this approach is manua
Externí odkaz:
http://arxiv.org/abs/2406.18309
Autor:
Hartong, Nanna E., Sachpazidis, Ilias, Blanck, Oliver, Etzel, Lucas, Peeken, Jan C., Combs, Stephanie E., Urbach, Horst, Zaitsev, Maxim, Baltas, Dimos, Popp, Ilinca, Grosu, Anca-Ligia, Fechter, Tobias
Background: The aim of this study was to investigate the role of clinical, dosimetric and pretherapeutic magnetic resonance imaging (MRI) features for lesion-specific outcome prediction of stereotactic radiotherapy (SRT) in patients with brain metast
Externí odkaz:
http://arxiv.org/abs/2405.20825
The automated generation of diverse and complex training scenarios has been an important ingredient in many complex learning tasks. Especially in real-world application domains, such as autonomous driving, auto-curriculum generation is considered vit
Externí odkaz:
http://arxiv.org/abs/2403.17805
Autor:
Sprave, Tanja, Zöller, Daniela, Stoian, Raluca, Rühle, Alexander, Kalckreuth, Tobias, Haehl, Erik, Fahrner, Harald, Binder, Harald, Grosu, Anca-Ligia, Heinemann, Felix, Nicolay, Nils Henrik
Publikováno v:
JMIR Research Protocols, Vol 9, Iss 12, p e21693 (2020)
BackgroundHead and neck cancers (HNCs) are among the most common malignancies, which often require multimodal treatment that includes radiation therapy and chemotherapy. Patients with HNC have a high burden of symptoms due to both the damaging effect
Externí odkaz:
https://doaj.org/article/dee4a94c4e9d4515bece40e957a82a89
We introduce liquid-resistance liquid-capacitance neural networks (LRCs), a neural-ODE model which considerably improve the generalization, accuracy, and biological plausibility of electrical equivalent circuits (EECs), liquid time-constant networks
Externí odkaz:
http://arxiv.org/abs/2403.08791