Zobrazeno 1 - 10
of 61 323
pro vyhledávání: '"Lind, A"'
Electroencephalography (EEG) data provides a non-invasive method for researchers and clinicians to observe brain activity in real time. The integration of deep learning techniques with EEG data has significantly improved the ability to identify meani
Externí odkaz:
http://arxiv.org/abs/2409.17815
A liquid jet plunging into a quiescent bath of the same liquid is a fundamental fluid mechanical problem underpinning a range of processes in industry and the natural world. Significant attention has been given to the study of plunging laminar Newton
Externí odkaz:
http://arxiv.org/abs/2409.11879
Autor:
Herman, Daniel I., Walsh, Mathieu, Kreider, Molly Kate, Lordi, Noah, Tsao, Eugene J., Lind, Alexander J., Heyrich, Matthew, Combes, Joshua, Genest, Jérôme, Diddams, Scott A.
Laser spectroscopy and interferometry have provided an unparalleled view into the fundamental nature of matter and the universe through ultra-precise measurements of atomic transition frequencies and gravitational waves. Optical frequency combs have
Externí odkaz:
http://arxiv.org/abs/2408.16688
We explore the use of quantum generative adversarial networks QGANs for modeling eye movement velocity data. We assess whether the advanced computational capabilities of QGANs can enhance the modeling of complex stochastic distribution beyond the tra
Externí odkaz:
http://arxiv.org/abs/2408.00673
We investigate the potential of bio-inspired evolutionary algorithms for designing quantum circuits with specific goals, focusing on two particular tasks. The first one is motivated by the ideas of Artificial Life that are used to reproduce stochasti
Externí odkaz:
http://arxiv.org/abs/2408.00448
Autor:
Glover, Tom Eivind, Jahren, Ruben, Martinuzzi, Francesco, Lind, Pedro Gonçalves, Nichele, Stefano
Elementary Cellular Automata (ECA) are a well-studied computational universe that is, despite its simple configurations, capable of impressive computational variety. Harvesting this computation in a useful way has historically shown itself to be diff
Externí odkaz:
http://arxiv.org/abs/2407.18017
Autor:
Tsao, Eugene J., Lind, Alexander J., Fredrick, Connor, Cole, Ryan K., Chang, Peter, Chang, Kristina F., Lee, Dahyeon, Heyrich, Matthew, Hoghooghi, Nazanin, Quinlan, Franklyn, Diddams, Scott A.
The detection of light of thermal origin is the principal means by which humanity has learned about our world and the cosmos. In optical astronomy, in particular, direct detection of thermal photons and the resolution of their spectra have enabled di
Externí odkaz:
http://arxiv.org/abs/2405.14842
Autor:
Díaz, Luisa F. Rodríguez, Lagae, Cis, Amarsi, Anish M., Bigot, Lionel, Zhou, Yixiao, Børsen-Koch, Víctor Aguirre, Lind, Karin, Trampedach, Regner, Collet, Remo
Publikováno v:
A&A 688, A212 (2024)
Context: Traditional one-dimensional (1D) hydrostatic model atmospheres introduce systematic modelling errors into spectroscopic analyses of FGK-type stars. Aims: We present an updated version of the STAGGER-grid of 3D model atmospheres, and explore
Externí odkaz:
http://arxiv.org/abs/2405.07872
Autor:
D'Orazi, Valentina, Storm, Nicholas, Casey, Andrew R., Braga, Vittorio F., Zocchi, Alice, Bono, Giuseppe, Fabrizio, Michele, Sneden, Christopher, Massari, Davide, Giribaldi, Riano E., Bergemann, Maria, Campbell, Simon W., Casagrande, Luca, de Grijs, Richard, De Silva, Gayandhi, Lugaro, Maria, Zucker, Daniel B., Bragaglia, Angela, Feuillet, Diane, Fiorentino, Giuliana, Chaboyer, Brian, Dall'Ora, Massimo, Marengo, Massimo, Martínez-Vázquez, Clara E., Matsunaga, Noriyuki, Monelli, Matteo, Mullen, Joseph P., Nataf, David, Tantalo, Maria, Thevenin, Frederic, Vitello, Fabio R., Kudritzki, Rolf-Peter, Bland-Hawthorn, Joss, Buder, Sven, Freeman, Ken, Kos, Janez, Lewis, Geraint F., Lind, Karin, Martell, Sarah, Sharma, Sanjib, Stello, Dennis, Zwitter, Tomaž
Stellar mergers and accretion events have been crucial in shaping the evolution of the Milky Way (MW). These events have been dynamically identified and chemically characterised using red giants and main-sequence stars. RR Lyrae (RRL) variables can p
Externí odkaz:
http://arxiv.org/abs/2405.04580
When deploying deep neural networks on robots or other physical systems, the learned model should reliably quantify predictive uncertainty. A reliable uncertainty allows downstream modules to reason about the safety of its actions. In this work, we a
Externí odkaz:
http://arxiv.org/abs/2405.04278