Zobrazeno 1 - 10
of 20 928
pro vyhledávání: '"Cuellar, A. A."'
Autor:
Gold, Jackson, Cuellar, Maria
The assumption of fingerprint uniqueness is foundational in forensic science and central to criminal identification practices. However, empirical evidence supporting this assumption is limited, and recent findings from artificial intelligence challen
Externí odkaz:
http://arxiv.org/abs/2412.13135
Autor:
Cuellar, Maria
The medical and legal controversy surrounding the diagnosis of Shaken Baby Syndrome/Abusive Head Trauma (SBS/AHT) raises critical questions about its scientific foundation and reliability. This article argues that SBS/AHT can only be understood by st
Externí odkaz:
http://arxiv.org/abs/2412.10648
Autor:
Cuellar, Maria
Despite calls for reform to enhance forensic science, insufficient attention has been paid to the potential errors arising from exclusions. Often based on intuitive judgment rather than empirical evidence, exclusions can lead to significant errors. A
Externí odkaz:
http://arxiv.org/abs/2412.05398
Autor:
Cuéllar, Mariano-Florentino, Dean, Jeff, Doshi-Velez, Finale, Hennessy, John, Konwinski, Andy, Koyejo, Sanmi, Moiloa, Pelonomi, Pierson, Emma, Patterson, David
Artificial Intelligence (AI), like any transformative technology, has the potential to be a double-edged sword, leading either toward significant advancements or detrimental outcomes for society as a whole. As is often the case when it comes to widel
Externí odkaz:
http://arxiv.org/abs/2412.02730
Autor:
León-Domínguez, U., Flores-Flores, E. D., García-Jasso, A. J., Gómez-Cuellar, M. K., Torres-Sánchez, D., Basora-Marimon, A.
Large Language Models based on transformer algorithms have revolutionized Artificial Intelligence by enabling verbal interaction with machines akin to human conversation. These AI agents have surpassed the Turing Test, achieving confusion rates up to
Externí odkaz:
http://arxiv.org/abs/2411.13749
Autor:
Cuéllar, Antonio, Amico, Enrico, Serpieri, Jacopo, Cafiero, Gioacchino, Baars, Woutijn J, Discetti, Stefano, Ianiro, Andrea
We present an experimental setup to perform time-resolved convective heat transfer measurements in a turbulent channel flow with air as the working fluid. We employ a heated thin foil coupled with high-speed infrared thermography. The measurement tec
Externí odkaz:
http://arxiv.org/abs/2410.12778
Autor:
Lopez, S., Afruni, A., Zamora, D., Tejos, N., Ledoux, C., Hernandez, J., Berg, T. A. M., Cortes, H., Urbina, F., Johnston, E. J., Barrientos, L. F., Bayliss, M. B., Cuellar, R., Krogager, J. K., Noterdaeme, P., Solimano, M.
Publikováno v:
A&A 691, A356 (2024)
We present VLT/MUSE integral-field spectroscopy ($R\approx 1\,800$) of four giant gravitational arcs exhibiting strong C IV absorption at 8 intervening redshifts, $z_{abs}\approx 2.0$--$2.5$. We detect C IV absorption in a total of 222 adjacent and s
Externí odkaz:
http://arxiv.org/abs/2410.03029
Common narratives about automation often pit new technologies against workers. The introduction of advanced machine tools, industrial robots, and AI have all been met with concern that technological progress will mean fewer jobs. However, workers the
Externí odkaz:
http://arxiv.org/abs/2409.20387
In this work we assess the impact of the limited availability of wall-embedded sensors on the full 3D estimation of the flow field in a turbulent channel with Re{\tau} = 200. The estimation technique is based on a 3D generative adversarial network (3
Externí odkaz:
http://arxiv.org/abs/2409.07348
Autor:
Cuéllar, Antonio, Güemes, Alejandro, Ianiro, Andrea, Flores, Óscar, Vinuesa, Ricardo, Discetti, Stefano
Publikováno v:
Cu\'ellar, A., G\"uemes, A., Ianiro, A., Flores, \'O., Vinuesa, R., Discetti, S.: Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements. J. Fluid Mech. 991, A1 (2024)
Different types of neural networks have been used to solve the flow sensing problem in turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements. Generative adversarial networks (GANs) are among the most promising me
Externí odkaz:
http://arxiv.org/abs/2409.06548