Zobrazeno 1 - 10
of 1 902
pro vyhledávání: '"Rodrigues, Joao"'
Autor:
Moura, Filipe, Rodrigues, João
We compute analytically greybody factors for asymptotically flat spherically symmetric black holes with stringy higher derivative corrections in d dimensions in the high frequency limit. Our calculations include both the eikonal limit - where the rea
Externí odkaz:
http://arxiv.org/abs/2410.08773
Sentence encoder encode the semantics of their input, enabling key downstream applications such as classification, clustering, or retrieval. In this paper, we present Serafim PT*, a family of open-source sentence encoders for Portuguese with various
Externí odkaz:
http://arxiv.org/abs/2407.19527
Autor:
Rodrigues, João, Branco, António
Retrieval-augmented generation resorts to content retrieved from external sources in order to leverage the performance of large language models in downstream tasks. The excessive volume of retrieved content, the possible dispersion of its parts, or t
Externí odkaz:
http://arxiv.org/abs/2407.03955
Autor:
Osório, Tomás, Leite, Bernardo, Cardoso, Henrique Lopes, Gomes, Luís, Rodrigues, João, Santos, Rodrigo, Branco, António
Leveraging research on the neural modelling of Portuguese, we contribute a collection of datasets for an array of language processing tasks and a corresponding collection of fine-tuned neural language models on these downstream tasks. To align with m
Externí odkaz:
http://arxiv.org/abs/2404.05333
Autor:
Lima, Guilherme, Rodrigues, João M. B., Machado, Marcelo, Soares, Elton, Fiorini, Sandro R., Thiago, Raphael, Azevedo, Leonardo G., da Silva, Viviane T., Cerqueira, Renato
We present a Wikidata-based framework, called KIF, for virtually integrating heterogeneous knowledge sources. KIF is written in Python and is released as open-source. It leverages Wikidata's data model and vocabulary plus user-defined mappings to con
Externí odkaz:
http://arxiv.org/abs/2403.10304
Autor:
Santos, Rodrigo, Rodrigues, João, Gomes, Luís, Silva, João, Branco, António, Cardoso, Henrique Lopes, Osório, Tomás Freitas, Leite, Bernardo
To foster the neural encoding of Portuguese, this paper contributes foundation encoder models that represent an expansion of the still very scarce ecosystem of large language models specifically developed for this language that are fully open, in the
Externí odkaz:
http://arxiv.org/abs/2403.01897
To advance the neural decoding of Portuguese, in this paper we present a fully open Transformer-based, instruction-tuned decoder model that sets a new state of the art in this respect. To develop this decoder, which we named Gerv\'asio PT*, a strong
Externí odkaz:
http://arxiv.org/abs/2402.18766
Autor:
Moura, Filipe, Rodrigues, João
After a brief introduction to quasinormal modes in dissipative systems, we review the WKB formalism in the context of the analytical calculation of quasinormal frequencies. We apply these results to the calculation of quasinormal frequencies associat
Externí odkaz:
http://arxiv.org/abs/2311.00119
Autor:
Eastman, Peter, Galvelis, Raimondas, Peláez, Raúl P., Abreu, Charlles R. A., Farr, Stephen E., Gallicchio, Emilio, Gorenko, Anton, Henry, Michael M., Hu, Frank, Huang, Jing, Krämer, Andreas, Michel, Julien, Mitchell, Joshua A., Pande, Vijay S., Rodrigues, João PGLM, Rodriguez-Guerra, Jaime, Simmonett, Andrew C., Swails, Jason, Zhang, Ivy, Chodera, John D., De Fabritiis, Gianni, Markland, Thomas E.
Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added
Externí odkaz:
http://arxiv.org/abs/2310.03121
With the availability of extensive databases of inorganic materials, data-driven approaches leveraging machine learning have gained prominence in materials science research. In this study, we propose an innovative adaptation of data-driven concepts t
Externí odkaz:
http://arxiv.org/abs/2306.16496