Zobrazeno 1 - 10
of 11 004
pro vyhledávání: '"Sarabia A"'
Autor:
Sarabia, Rafael Pablos, Nyborg, Joachim, Birk, Morten, Sjørup, Jeppe Liborius, Vesterholt, Anders Lillevang, Assent, Ira
Precipitation nowcasting is crucial across various industries and plays a significant role in mitigating and adapting to climate change. We introduce an efficient deep learning model for precipitation nowcasting, capable of predicting rainfall up to
Externí odkaz:
http://arxiv.org/abs/2410.08641
Autor:
Delgado, Martina, Llopart, Marta, Sarabia, Eva, Taboada, Sandra, Vierge, Pol, Vilariño, Fernando, Kohler, Joan Moya, Golijov, Julieta Grimberg, Bilkis, Matías
The advent of increasingly-growing virtual realities poses unprecedented opportunities and challenges to different societies. Artistic collectives are not an exception, and we here aim to put special attention into musicians. Compositions, lyrics and
Externí odkaz:
http://arxiv.org/abs/2410.04921
Autor:
Devnani, Bhavika, Seto, Skyler, Aldeneh, Zakaria, Toso, Alessandro, Menyaylenko, Elena, Theobald, Barry-John, Sheaffer, Jonathan, Sarabia, Miguel
Humans can picture a sound scene given an imprecise natural language description. For example, it is easy to imagine an acoustic environment given a phrase like "the lion roar came from right behind me!". For a machine to have the same degree of comp
Externí odkaz:
http://arxiv.org/abs/2409.11369
Autor:
Cepeda, Santiago, Romero, Roberto, Garcia-Perez, Daniel, Blasco, Guillermo, Luppino, Luigi Tommaso, Kuttner, Samuel, Arrese, Ignacio, Solheim, Ole, Eikenes, Live, Karlberg, Anna, Perez-Nunez, Angel, Escudero, Trinidad, Hornero, Roberto, Sarabia, Rosario
Accurately assessing tumor removal is paramount in the management of glioblastoma. We developed a pipeline using MRI scans and neural networks to segment tumor subregions and the surgical cavity in postoperative images. Our model excels in accurately
Externí odkaz:
http://arxiv.org/abs/2404.11725
Publikováno v:
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain, 2023, pp. 5611-5618
This paper presents the validation of shared control strategies for critical maneuvers in automated driving systems. Shared control involves collaboration between the driver and automation, allowing both parties to actively engage and cooperate at di
Externí odkaz:
http://arxiv.org/abs/2404.04011
Preference-based reinforcement learning (PbRL) aligns a robot behavior with human preferences via a reward function learned from binary feedback over agent behaviors. We show that dynamics-aware reward functions improve the sample efficiency of PbRL
Externí odkaz:
http://arxiv.org/abs/2402.17975
Background: Accurate volumetric assessment of spontaneous subarachnoid hemorrhage (SAH) is a labor-intensive task performed with current manual and semiautomatic methods that might be relevant for its clinical and prognostic implications. In the pres
Externí odkaz:
http://arxiv.org/abs/2312.17553
Autor:
Ahn, Byeongjoo, Yang, Karren, Hamilton, Brian, Sheaffer, Jonathan, Ranjan, Anurag, Sarabia, Miguel, Tuzel, Oncel, Chang, Jen-Hao Rick
We investigate the benefit of combining blind audio recordings with 3D scene information for novel-view acoustic synthesis. Given audio recordings from 2-4 microphones and the 3D geometry and material of a scene containing multiple unknown sound sour
Externí odkaz:
http://arxiv.org/abs/2310.15130
Parallelization techniques have become ubiquitous for accelerating inference and training of deep neural networks. Despite this, several operations are still performed in a sequential manner. For instance, the forward and backward passes are executed
Externí odkaz:
http://arxiv.org/abs/2309.16318
Autor:
Rebekka Wegmann, Ximena Bonilla, Ruben Casanova, Stéphane Chevrier, Ricardo Coelho, Cinzia Esposito, Joanna Ficek-Pascual, Sandra Goetze, Gabriele Gut, Francis Jacob, Andrea Jacobs, Jack Kuipers, Ulrike Lischetti, Julien Mena, Emanuela S. Milani, Michael Prummer, Jacobo Sarabia Del Castillo, Franziska Singer, Sujana Sivapatham, Nora C. Toussaint, Oliver Vilinovszki, Mattheus H. E. Wildschut, Tharshika Thavayogarajah, Disha Malani, The TumorProfiler Consortium, Rudolf Aebersold, Marina Bacac, Niko Beerenwinkel, Christian Beisel, Bernd Bodenmiller, Viola Heinzelmann-Schwarz, Viktor H. Koelzer, Mitchell P. Levesque, Holger Moch, Lucas Pelkmans, Gunnar Rätsch, Markus Tolnay, Andreas Wicki, Bernd Wollscheid, Markus G. Manz, Berend Snijder, Alexandre P. A. Theocharides
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-18 (2024)
Abstract Deep single-cell multi-omic profiling offers a promising approach to understand and overcome drug resistance in relapsed or refractory (rr) acute myeloid leukemia (AML). Here, we combine single-cell ex vivo drug profiling (pharmacoscopy) wit
Externí odkaz:
https://doaj.org/article/d909919fc46044dd87b6ffb2a1e8a194
