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pro vyhledávání: '"Lima, Rafael"'
Rigid (Uniform) rotation is usually assumed when investigating the properties of mature neutron stars (NSs). Although it simplifies their description, it is an assumption because we cannot observe the NS's innermost parts. Here, we analyze the struct
Externí odkaz:
http://arxiv.org/abs/2410.13793
Autor:
Lima, Rafael P.
We study Deaconu-Renault groupoids corresponding to surjective local homeomorphisms on locally compact, Hausdorff, second countable, totally disconnected spaces, and we characterise when the C*-algebras of these groupoids are AF embeddable. Our main
Externí odkaz:
http://arxiv.org/abs/2410.01868
Autor:
Auer, Christoph, Lysak, Maksym, Nassar, Ahmed, Dolfi, Michele, Livathinos, Nikolaos, Vagenas, Panos, Ramis, Cesar Berrospi, Omenetti, Matteo, Lindlbauer, Fabian, Dinkla, Kasper, Mishra, Lokesh, Kim, Yusik, Gupta, Shubham, de Lima, Rafael Teixeira, Weber, Valery, Morin, Lucas, Meijer, Ingmar, Kuropiatnyk, Viktor, Staar, Peter W. J.
This technical report introduces Docling, an easy to use, self-contained, MIT-licensed open-source package for PDF document conversion. It is powered by state-of-the-art specialized AI models for layout analysis (DocLayNet) and table structure recogn
Externí odkaz:
http://arxiv.org/abs/2408.09869
Autor:
Lima, Rafael Pereira
We study Deaconu-Renault groupoids corresponding to surjective local homeomorphisms on locally compact, Hausdorff, second countable, totally disconnected spaces, and we characterise when the C*-algebras of these groupoids are AF embeddable. Our main
Externí odkaz:
http://arxiv.org/abs/2407.16510
Autor:
Bhattacharjee, Bishwaranjan, Trivedi, Aashka, Muraoka, Masayasu, Ramasubramanian, Muthukumaran, Udagawa, Takuma, Gurung, Iksha, Pantha, Nishan, Zhang, Rong, Dandala, Bharath, Ramachandran, Rahul, Maskey, Manil, Bugbee, Kaylin, Little, Mike, Fancher, Elizabeth, Gerasimov, Irina, Mehrabian, Armin, Sanders, Lauren, Costes, Sylvain, Blanco-Cuaresma, Sergi, Lockhart, Kelly, Allen, Thomas, Grezes, Felix, Ansdell, Megan, Accomazzi, Alberto, El-Kurdi, Yousef, Wertheimer, Davis, Pfitzmann, Birgit, Ramis, Cesar Berrospi, Dolfi, Michele, de Lima, Rafael Teixeira, Vagenas, Panagiotis, Mukkavilli, S. Karthik, Staar, Peter, Vahidinia, Sanaz, McGranaghan, Ryan, Lee, Tsendgar
Large language models (LLMs) trained on general domain corpora showed remarkable results on natural language processing (NLP) tasks. However, previous research demonstrated LLMs trained using domain-focused corpora perform better on specialized tasks
Externí odkaz:
http://arxiv.org/abs/2405.10725
Publikováno v:
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2023, pp. 145-148
Up-to-date sea ice charts are crucial for safer navigation in ice-infested waters. Recently, Convolutional Neural Network (CNN) models show the potential to accelerate the generation of ice maps for large regions. However, results from CNN models sti
Externí odkaz:
http://arxiv.org/abs/2310.17135
Publikováno v:
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 2023, pp. 1983-1986
Deploying deep learning on Synthetic Aperture Radar (SAR) data is becoming more common for mapping purposes. One such case is sea ice, which is highly dynamic and rapidly changes as a result of the combined effect of wind, temperature, and ocean curr
Externí odkaz:
http://arxiv.org/abs/2310.17126
Autor:
de Lima, Rafael Pires, Vahedi, Behzad, Hughes, Nick, Barrett, Andrew P., Meier, Walter, Karimzadeh, Morteza
Publikováno v:
International Journal of Remote Sensing 4:17, 5344-5374 (20230
Due to the growing volume of remote sensing data and the low latency required for safe marine navigation, machine learning (ML) algorithms are being developed to accelerate sea ice chart generation, currently a manual interpretation task. However, th
Externí odkaz:
http://arxiv.org/abs/2310.17122