Zobrazeno 1 - 10
of 109 078
pro vyhledávání: '"Hicks, A A"'
Autor:
Poitevineau, R., Combes, F., Garcia-Burillo, S., Cornu, D., Herrero, A. Alonso, Almeida, C. Ramos, Audibert, A., Bellocchi, E., Boorman, P. G., Bunker, A. J., Davies, R., Díaz-Santos, T., García-Bernete, I., García-Lorenzo, B., González-Martín, O., Hicks, E. K. S., Hönig, S. F., Hunt, L. K., Imanishi, M., Pereira-Santaella, M., Ricci, C., Rigopoulou, D., Rosario, D. J., Rouan, D., Martin, M. Villar, Ward, M.
The detailed feeding and feedback mechanisms of Active Galactic Nuclei (AGN) are not yet well known. For low-luminosity and obscured AGN, as well as late-type galaxies, determining the central black hole (BH) masses is challenging. Our goal with the
Externí odkaz:
http://arxiv.org/abs/2411.18200
Autor:
Fuller, Lindsay, Lopez-Rodriguez, Enrique, Garcia-Bernete, Ismael, Almeida, Cristina Ramos, Alonso-Herrero, Almudena, Packham, Chris, Zhang, Lulu, Leist, Mason, Levenson, Nancy, Imanishi, Masa, Hoenig, Sebastian, Stalevski, Marko, Ricci, Claudio, Hicks, Erin, Bellocchi, Enrica, Combes, Francoise, Davies, Ric, Burillo, Santiago Garcia, GonzalezMartin, Omaira, TakumaIzumi, Labiano, Alvaro, Santaella, Miguel Pereira, Rigopoulou, Dimitra, Rosario, David, Rouan, Daniel, Shimizu, Taro, Ward, Martin
We present a 19.7 - 214 $\mu$m imaging atlas of local (4 - 181 Mpc; median 43 Mpc) active galactic nuclei (AGN) observed with FORCAST and HAWC+ on board the SOFIA telescope with angular resolutions ~ 3"- 20". This atlas comprises 22 Seyferts (17 Type
Externí odkaz:
http://arxiv.org/abs/2411.18738
Autor:
Esparza-Arredondo, D., Almeida, C. Ramos, Audibert, A., Pereira-Santaella, M., García-Bernete, I., García-Burillo, S., Shimizu, T., Davies, R., Muñoz, L. Hermosa, Alonso-Herrero, A., Combes, F., Speranza, G., Zhang, L., Campbell, S., Bellocchi, E., Bunker, A. J., Díaz-Santos, T., García-Lorenzo, B., González-Martín, O., Hicks, E. K. S., Labiano, A., Levenson, N. A., Ricci, C., Rosario, D., Hoenig, S., Packham, C., Stalevski, M., Fuller, L., Izumi, T., López-Rodríguez, E., Rigopoulou, D., Rouan, D., Ward, M.
Understanding the processes that drive the morphology and kinematics of molecular gas in galaxies is crucial for comprehending star formation and, ultimately, galaxy evolution. Using data obtained with the James Webb Space Telescope (JWST) and the At
Externí odkaz:
http://arxiv.org/abs/2411.12398
Autor:
Hicks, R. Andrew
The eigenmirror problem asks: ``When does the reflection of a surface in a curved mirror appear undistorted to an observer?'' We call such a surface an {\em eigensurface} and the corresponding mirror an {\em eigenmirror}. The data for an eigenmirror
Externí odkaz:
http://arxiv.org/abs/2411.10884
Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problems
In this paper, we address the critical need for interpretable and uncertainty-aware machine learning models in the context of online learning for high-risk industries, particularly cyber-security. While deep learning and other complex models have dem
Externí odkaz:
http://arxiv.org/abs/2411.09393
Autor:
Claveau, Charles-Antoine, Bottom, Michael, Jacobson, Shane, Hodapp, Klaus, Huber, Guillaume, Newland, Matthew, Walk, Aidan, Loose, Markus, Baker, Ian, Zemaityte, Egle, Hicks, Matthew, Barnes, Keith, Powell, Richard, Bradley, Ryan, Moore, Eric
Spectroscopy of Earth-like exoplanets and ultra-faint galaxies are priority science cases for the coming decades. Here, broadband source flux rates are measured in photons per square meter per hour, imposing extreme demands on detector performance, i
Externí odkaz:
http://arxiv.org/abs/2411.09185
Autor:
Lopez-Ramos, Luis M., Leiser, Florian, Rastogi, Aditya, Hicks, Steven, Strümke, Inga, Madai, Vince I., Budig, Tobias, Sunyaev, Ali, Hilbert, Adam
The joint implementation of Federated learning (FL) and Explainable artificial intelligence (XAI) will allow training models from distributed data and explaining their inner workings while preserving important aspects of privacy. Towards establishing
Externí odkaz:
http://arxiv.org/abs/2411.05874
In this paper we consider the contextual bandit problem with a finite (or infinite and clustered) context set. We consider the fully adversarial problem in which, apart from having bounded losses, there are no assumptions whatsoever on the generation
Externí odkaz:
http://arxiv.org/abs/2411.04295
We evaluate OpenAI's o1-preview and o1-mini models, benchmarking their performance against the earlier GPT-4o model. Our evaluation focuses on their ability to detect vulnerabilities in real-world software by generating structured inputs that trigger
Externí odkaz:
http://arxiv.org/abs/2410.21939
A significant challenge for autonomous cyber defence is ensuring a defensive agent's ability to generalise across diverse network topologies and configurations. This capability is necessary for agents to remain effective when deployed in dynamically
Externí odkaz:
http://arxiv.org/abs/2410.17647