Zobrazeno 1 - 10
of 1 219
pro vyhledávání: '"Santos Fernando P."'
Autor:
Szłapczyńska Joanna, Vettor Roberto, Szłapczyński Rafał, Łącki Mirosław, Życzkowski Marcin, Hinostroza Miguel A., Santos Fernando P., Tycholiz Wojciech, Soares C. Guedes
Publikováno v:
Polish Maritime Research, Vol 29, Iss 2, Pp 87-95 (2022)
This paper describes the architecture of a weather routing system consisting of two key elements: onboard monitoring and route optimiser sub-systems. The former is responsible for collecting various onboard measurements, such as current ship position
Externí odkaz:
https://doaj.org/article/16200635c6ed4165be349dc5005b7eb5
Autor:
Iurada, Leonardo, Cavagnero, Niccolò, Santos, Fernando Fernandes Dos, Averta, Giuseppe, Rech, Paolo, Tommasi, Tatiana
Deep learning models are crucial for autonomous vehicle perception, but their reliability is challenged by algorithmic limitations and hardware faults. We address the latter by examining fault-tolerance in semantic segmentation models. Using establis
Externí odkaz:
http://arxiv.org/abs/2408.16952
Autor:
Lopes, Alexandre, Santos, Fernando Pereira dos, de Oliveira, Diulhio, Schiezaro, Mauricio, Pedrini, Helio
Publikováno v:
Computers & Graphics, Volume 123, October 2024, 104015
Deep neural networks have consistently represented the state of the art in most computer vision problems. In these scenarios, larger and more complex models have demonstrated superior performance to smaller architectures, especially when trained with
Externí odkaz:
http://arxiv.org/abs/2408.08250
Autor:
Góis, António, Mofakhami, Mehrnaz, Santos, Fernando P., Lacoste-Julien, Simon, Gidel, Gauthier
Predictions often influence the reality which they aim to predict, an effect known as performativity. Existing work focuses on accuracy maximization under this effect, but model deployment may have important unintended impacts, especially in multiage
Externí odkaz:
http://arxiv.org/abs/2408.05146
Autor:
Smit, Martin, Santos, Fernando P.
Publikováno v:
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence Main Track (2024). Pages 220-228
Altruistic cooperation is costly yet socially desirable. As a result, agents struggle to learn cooperative policies through independent reinforcement learning (RL). Indirect reciprocity, where agents consider their interaction partner's reputation, h
Externí odkaz:
http://arxiv.org/abs/2408.04549
Autor:
Vasconcelos, Vítor V., Marquitti, Flávia M. D., Ong, Theresa, McManus, Lisa C., Aguiar, Marcus, Campos, Amanda B., Dutta, Partha S., Jovanelly, Kristen, Junquera, Victoria, Kong, Jude, Krueger, Elisabeth H., Levin, Simon A., Liao, Wenying, Lu, Mingzhen, Mittal, Dhruv, Pascual, Mercedes, Pinheiro, Flávio L., Rocha, Juan, Santos, Fernando P., Sloot, Peter, Chenyang, Su, Taylor, Benton, Tekwa, Eden, Terpstra, Sjoerd, Tilman, Andrew R., Watson, James R., Yang, Luojun, Yitbarek, Senay, Zhan, Qi
Complex adaptive systems (CASs), from ecosystems to economies, are open systems and inherently dependent on external conditions. While a system can transition from one state to another based on the magnitude of change in external conditions, the rate
Externí odkaz:
http://arxiv.org/abs/2309.07449
Autor:
de Souza, Danillo Barros, da Cunha, Jonatas T. S., Santos, Fernando A. N., Jost, Jürgen, Rodrigues, Serafim
Forman-Ricci curvature (FRC) is a potent and powerful tool for analysing empirical networks, as the distribution of the curvature values can identify structural information that is not readily detected by other geometrical methods. Crucially, FRC cap
Externí odkaz:
http://arxiv.org/abs/2308.11763
Autor:
Santos, Fernando Montenegro-Dos, Pérez-Galarce, Francisco, Monardes-Concha, Carlos, Cruz-Zárate, Sergio, Candia-Véjar, Alfredo
Vaccination campaigns have saved thousands of lives, reaching the farthest places in the world. These campaigns have required substantial investments and accurate coordination between several actors within the vaccine supply chain. Despite these succ
Externí odkaz:
http://arxiv.org/abs/2307.08718
Autor:
Guerrero-Balaguera, Juan-David, Condia, Josie E. Rodriguez, Santos, Fernando F. dos, Sonza, Matteo, Rech, Paolo
Publikováno v:
ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis, ACM; IEEE, Nov 2023, Denver, United States
Graphics Processing Units (GPUs) are over-stressed to accelerate High-Performance Computing applications and are used to accelerate Deep Neural Networks in several domains where they have a life expectancy of many years. These conditions expose the G
Externí odkaz:
http://arxiv.org/abs/2306.10856
Autor:
Santos, Fernando Fernandes dos, Kritikakou, Angeliki, Condia, Josie Esteban Rodriguez, Balaguera, Juan David Guerrero, Reorda, Matteo Sonza, Sentieys, Olivier, Rech, Paolo
The reliability evaluation of Deep Neural Networks (DNNs) executed on Graphic Processing Units (GPUs) is a challenging problem since the hardware architecture is highly complex and the software frameworks are composed of many layers of abstraction. W
Externí odkaz:
http://arxiv.org/abs/2211.13094