Zobrazeno 1 - 10
of 42 233
pro vyhledávání: '"A. Geyer"'
Autor:
D. Trabert, A. Geyer, N. Anders, M. Hofmann, M. S. Schöffler, L. Ph. H. Schmidt, T. Jahnke, M. Kunitski, R. Dörner, S. Eckart
Publikováno v:
Physical Review Research, Vol 5, Iss 4, p 043245 (2023)
We study strong-field ionization of molecular hydrogen in highly intense corotating two-color laser fields. The measured electron momentum distributions show alternating half rings (AHRs) that are characteristic of subcycle interference. We report on
Externí odkaz:
https://doaj.org/article/274a147c1b644ab8871c9f4a1a7ef04b
Developing foundational world models is a key research direction for embodied intelligence, with the ability to adapt to non-stationary environments being a crucial criterion. In this work, we introduce a new formalism, Hidden Parameter-POMDP, design
Externí odkaz:
http://arxiv.org/abs/2411.01342
Autor:
Reardon, Daniel J., Main, Robert, Ocker, Stella Koch, Shannon, Ryan M., Bailes, Matthew, Camilo, Fernando, Geyer, Marisa, Jameson, Andrew, Kramer, Michael, Parthasarathy, Aditya, Spiewak, Renée, van Straten, Willem, Krishnan, Vivek Venkatraman
The interstellar medium of the Milky Way contains turbulent plasma with structures driven by energetic processes that fuel star formation and shape the evolution of our Galaxy. Radio waves from pulsars are scattered off the small (au-scale and below)
Externí odkaz:
http://arxiv.org/abs/2410.21390
We find a direct map that determines moduli-space integrands for one-loop superstring amplitudes in terms of field-theory loop integrands in the BCJ form. The latter can be computed using efficient unitarity methods, so our map provides an alternativ
Externí odkaz:
http://arxiv.org/abs/2410.19663
Autor:
Project, CTA-LST, Abe, K., Abe, S., Abhishek, A., Acero, F., Aguasca-Cabot, A., Agudo, I., Alispach, C., Crespo, N. Alvarez, Ambrosino, D., Antonelli, L. A., Aramo, C., Arbet-Engels, A., Arcaro, C., Asano, K., Aubert, P., Baktash, A., Balbo, M., Bamba, A., Larriva, A. Baquero, de Almeida, U. Barres, Barrio, J. A., Jiménez, L. Barrios, Batkovic, I., Baxter, J., González, J. Becerra, Bernardini, E., Medrano, J. Bernete, Berti, A., Bezshyiko, I., Bhattacharjee, P., Bigongiari, C., Bissaldi, E., Blanch, O., Bonnoli, G., Bordas, P., Borkowski, G., Brunelli, G., Bulgarelli, A., Burelli, I., Burmistrov, L., Buscemi, M., Cardillo, M., Caroff, S., Carosi, A., Carrasco, M. S., Cassol, F., Castrejón, N., Cauz, D., Cerasole, D., Ceribella, G., Chai, Y., Cheng, K., Chiavassa, A., Chikawa, M., Chon, G., Chytka, L., Cicciari, G. M., Cifuentes, A., Contreras, J. L., Cortina, J., Costantini, H., Da Vela, P., Dalchenko, M., Dazzi, F., De Angelis, A., de Lavergne, M. de Bony, De Lotto, B., de Menezes, R., Del Burgo, R., Del Peral, L., Delgado, C., Mengual, J. Delgado, della Volpe, D., Dellaiera, M., Di Piano, A., Di Pierro, F., Di Tria, R., Di Venere, L., Díaz, C., Dominik, R. M., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., Eisenberger, L., Elsässer, D., Emery, G., Escudero, J., Ramazani, V. Fallah, Ferrarotto, F., Fiasson, A., Foffano, L., Coromina, L. Freixas, Fröse, S., Fukazawa, Y., López, R. Garcia, Gasbarra, C., Gasparrini, D., Geyer, D., Paiva, J. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Godinovic, N., Grau, R., Green, D., Green, J., Gunji, S., Günther, P., Hackfeld, J., Hadasch, D., Hahn, A., Hassan, T., Hayashi, K., Heckmann, L., Heller, M., Llorente, J. Herrera, Hirotani, K., Hoffmann, D., Horns, D., Houles, J., Hrabovsky, M., Hrupec, D., Hui, D., Iarlori, M., Imazawa, R., Inada, T., Inome, Y., Inoue, S., Ioka, K., Iori, M., Iuliano, A., Martinez, I. Jimenez, Quiles, J. Jimenez, Jurysek, J., Kagaya, M., Kalashev, O., Karas, V., Katagiri, H., Kataoka, J., Kerszberg, D., Kobayashi, Y., Kohri, K., Kong, A., Kubo, H., Kushida, J., Lainez, M., Lamanna, G., Lamastra, A., Lemoigne, L., Linhoff, M., Longo, F., López-Coto, R., López-Oramas, A., Loporchio, S., Lorini, A., Bahilo, J. Lozano, Luciani, H., Luque-Escamilla, P. L., Majumdar, P., Makariev, M., Mallamaci, M., Mandat, D., Manganaro, M., Manicò, G., Mannheim, K., Marchesi, S., Mariotti, M., Marquez, P., Marsella, G., Martí, J., Martinez, O., Martínez, G., Martínez, M., Mas-Aguilar, A., Maurin, G., Mazin, D., Méndez-Gallego, J., Guillen, E. Mestre, Micanovic, S., Miceli, D., Miener, T., Miranda, J. M., Mirzoyan, R., Mizuno, T., Gonzalez, M. Molero, Molina, E., Montaruli, T., Moralejo, A., Morcuende, D., Morselli, A., Moya, V., Muraishi, H., Nagataki, S., Nakamori, T., Neronov, A., Nickel, L., Rosillo, M. Nievas, Nikolic, L., Nishijima, K., Noda, K., Nosek, D., Novotny, V., Nozaki, S., Ohishi, M., Ohtani, Y., Oka, T., Okumura, A., Orito, R., Otero-Santos, J., Ottanelli, P., Owen, E., Palatiello, M., Paneque, D., Pantaleo, F. R., Paoletti, R., Paredes, J. M., Pech, M., Pecimotika, M., Peresano, M., Pfeifle, F., Pietropaolo, E., Pihet, M., Pirola, G., Plard, C., Podobnik, F., Pons, E., Prandini, E., Priyadarshi, C., Prouza, M., Rainò, S., Rando, R., Rhode, W., Ribó, M., Righi, C., Rizi, V., Fernandez, G. Rodriguez, Frías, M. D. Rodríguez, Ruina, A., Ruiz-Velasco, E., Saito, T., Sakurai, S., Sanchez, D. A., Sano, H., Šarić, T., Sato, Y., Saturni, F. G., Savchenko, V., Schiavone, F., Schleicher, B., Schmuckermaier, F., Schubert, J. L., Schussler, F., Schweizer, T., Arroyo, M. Seglar, Siegert, T., Sitarek, J., Sliusar, V., Strišković, J., Strzys, M., Suda, Y., Tajima, H., Takahashi, H., Takahashi, M., Takata, J., Takeishi, R., Tam, P. H. T., Tanaka, S. J., Tateishi, D., Tavernier, T., Temnikov, P., Terada, Y., Terauchi, K., Terzic, T., Teshima, M., Tluczykont, M., Tokanai, F., Torres, D. F., Travnicek, P., Tutone, A., Vacula, M., Vallania, P., van Scherpenberg, J., Acosta, M. Vázquez, Ventura, S., Verna, G., Viale, I., Vigliano, A., Vigorito, C. F., Visentin, E., Vitale, V., Voitsekhovskyi, V., Voutsinas, G., Vovk, I., Vuillaume, T., Walter, R., Wan, L., Will, M., Wójtowicz, J., Yamamoto, T., Yamazaki, R., Yeung, P. K. H., Yoshida, T., Yoshikoshi, T., Zhang, W., Zywucka, N.
Imaging atmospheric Cherenkov telescopes (IACTs) are used to observe very high-energy photons from the ground. Gamma rays are indirectly detected through the Cherenkov light emitted by the air showers they induce. The new generation of experiments, i
Externí odkaz:
http://arxiv.org/abs/2410.16042
Symbolic neural networks, such as Kolmogorov-Arnold Networks (KAN), offer a promising approach for integrating prior knowledge with data-driven methods, making them valuable for addressing inverse problems in scientific and engineering domains. This
Externí odkaz:
http://arxiv.org/abs/2411.00800
Autor:
Zhou, Yujun, Yang, Jingdong, Guo, Kehan, Chen, Pin-Yu, Gao, Tian, Geyer, Werner, Moniz, Nuno, Chawla, Nitesh V, Zhang, Xiangliang
Laboratory accidents pose significant risks to human life and property, underscoring the importance of robust safety protocols. Despite advancements in safety training, laboratory personnel may still unknowingly engage in unsafe practices. With the i
Externí odkaz:
http://arxiv.org/abs/2410.14182
Autor:
Wagner, Nico, Desmond, Michael, Nair, Rahul, Ashktorab, Zahra, Daly, Elizabeth M., Pan, Qian, Cooper, Martín Santillán, Johnson, James M., Geyer, Werner
LLM-as-a-Judge is a widely used method for evaluating the performance of Large Language Models (LLMs) across various tasks. We address the challenge of quantifying the uncertainty of LLM-as-a-Judge evaluations. While uncertainty quantification has be
Externí odkaz:
http://arxiv.org/abs/2410.11594
Autor:
Ye, Jiayi, Wang, Yanbo, Huang, Yue, Chen, Dongping, Zhang, Qihui, Moniz, Nuno, Gao, Tian, Geyer, Werner, Huang, Chao, Chen, Pin-Yu, Chawla, Nitesh V, Zhang, Xiangliang
LLM-as-a-Judge has been widely utilized as an evaluation method in various benchmarks and served as supervised rewards in model training. However, despite their excellence in many domains, potential issues are under-explored, undermining their reliab
Externí odkaz:
http://arxiv.org/abs/2410.02736
Autor:
Ashktorab, Zahra, Desmond, Michael, Pan, Qian, Johnson, James M., Cooper, Martin Santillan, Daly, Elizabeth M., Nair, Rahul, Pedapati, Tejaswini, Achintalwar, Swapnaja, Geyer, Werner
Evaluation of large language model (LLM) outputs requires users to make critical judgments about the best outputs across various configurations. This process is costly and takes time given the large amounts of data. LLMs are increasingly used as eval
Externí odkaz:
http://arxiv.org/abs/2410.00873