Zobrazeno 1 - 10
of 537 681
pro vyhledávání: '"Kobayashi IS"'
Autonomous manipulation in everyday tasks requires flexible action generation to handle complex, diverse real-world environments, such as objects with varying hardness and softness. Imitation Learning (IL) enables robots to learn complex tasks from e
Externí odkaz:
http://arxiv.org/abs/2411.09942
Autor:
Abe, S., Alekseev, I., Arai, T., Arihara, T., Arimoto, S., Babu, N., Baranov, V., Bartoszek, L., Berns, L., Bhattacharjee, S., Blondel, A., Boikov, A. V., Buizza-Avanzini, M., Capó, J., Cayo, J., Chakrani, J., Chong, P. S., Chvirova, A., Danilov, M., Davis, C., Davydov, Yu. I., Dergacheva, A., Dokania, N., Douqa, D., Doyle, T. A., Drapier, O., Eguchi, A., Elias, J., Fedorova, D., Fedotov, S., Ferlewicz, D., Fuji, Y., Furui, Y., Gendotti, A., Germer, A., Giannessi, L., Giganti, C., Glagolev, V., Hu, J., Iwamoto, K., Jakkapu, M., Jesús-Valls, C., Ji, J. Y., Jung, C. K., Kakuno, H., Kasetti, S. P., Kawaue, M., Khabibullin, M., Khotjantsev, A., Kikawa, T., Kikutani, H., Kobayashi, H., Kobayashi, T., Kodama, S., Kolupanova, M., Korzenev, A., Kose, U., Kudenko, Y., Kuribayashi, S., Kutter, T., Lachat, M., Lachner, K., Last, D., Latham, N., Silverio, D. Leon, Li, B., Li, W., Li, Z., Lin, C., Lin, L. S., Lin, S., Lux, T., Mahtani, K., Maret, L., Caicedo, D. A. Martinez, Martynenko, S., Matsubara, T., Mauger, C., McGrew, C., McKean, J., Mefodiev, A., Miller, E., Mineev, O., Moreno, A. L., Muñoz, A., Nakadaira, T., Nakagiri, K., Nguyen, V., Nicola, L., Noah, E., Nosek, T., Okinaga, W., Osu, L., Paolone, V., Parsa, S., Pellegrino, R., Ramirez, M. A., Reh, M., Ricco, C., Rubbia, A., Sakashita, K., Sallin, N., Sanchez, F., Schefke, T., Schloesser, C. M., Sgalaberna, D., Shvartsman, A., Skrobova, N., Speers, A. J., Suslov, I. A., Suvorov, S., Svirida, D., Tairafune, S., Tanigawa, H., Teklu, A., Tereshchenko, V. V., Tzanov, M., Vasilyev, I. I., Wallace, H. T., Whitney, N., Wood, K., Xu, Y. -h., Yang, G., Yershov, N., Yokoyama, M., Yoshimoto, Y., Zhao, X., Zheng, H., Zhu, T., Zilberman, P., Zimmerman, E. D.
The magnetised near detector (ND280) of the T2K long-baseline neutrino oscillation experiment has been recently upgraded aiming to satisfy the requirement of reducing the systematic uncertainty from measuring the neutrinonucleus interaction cross sec
Externí odkaz:
http://arxiv.org/abs/2410.24099
Elucidating the rationale behind neural models' outputs has been challenging in the machine learning field, which is indeed applicable in this age of large language models (LLMs) and in-context learning (ICL). When it comes to estimating input attrib
Externí odkaz:
http://arxiv.org/abs/2412.15628
Autor:
Bosomworth, Chloe, Forbrich, Jan, Lada, Charles J., Caldwell, Nelson, Kobayashi, Chiaki, Viaene, Sébastien
From a spectroscopic survey of candidate H II regions in the Andromeda galaxy (M31) with MMT/Hectospec, we have identified 294 H II regions using emission line ratios and calculated elemental abundances from strong-line diagnostics (values ranging fr
Externí odkaz:
http://arxiv.org/abs/2412.16069
Autor:
MAGIC Collaboration, Abe, S., Abhir, J., Abhishek, A., Acciari, V. A., Aguasca-Cabot, A., Agudo, I., Aniello, T., Ansoldi, S., Antonelli, L. A., Engels, A. Arbet, Arcaro, C., Artero, M., Asano, K., Baack, D., Babić, A., de Almeida, U. Barres, Barrio, J. A., Batković, I., Bautista, A., Baxter, J., González, J. Becerra, Bednarek, W., Bernardini, E., Bernete, J., Berti, A., Besenrieder, J., Bigongiari, C., Biland, A., Blanch, O., Bonnoli, G., Bošnjak, Ž., Bronzini, E., Burelli, I., Campoy-Ordaz, A., Carosi, A., Carosi, R., Carretero-Castrillo, M., Castro-Tirado, A. J., Cerasole, D., Ceribella, G., Chai, Y., Cifuentes, A., Colombo, E., Contreras, J. L., Cortina, J., Covino, S., D'Amico, G., D'Elia, V., Da Vela, P., Dazzi, F., De Angelis, A., De Lotto, B., de Menezes, R., Delfino, M., Delgado, J., Mendez, C. Delgado, Di Pierro, F., Di Tria, R., Di Venere, L., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., Eisenberger, L., Elsaesser, D., Escudero, J., Fariña, L., Fattorini, A., Foffano, L., Font, L., Fröse, S., Fukami, S., Fukazawa, Y., López, R. J. García, Garczarczyk, M., Gasparyan, S., Gaug, M., Paiva, J. G. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Gradetzke, T., Grau, R., Green, D., Green, J. G., Günther, P., Hadasch, D., Hahn, A., Hassan, T., Heckmann, L., Llorente, J. Herrera, Hrupec, D., Imazawa, R., Ishio, K., Martínez, I. Jiménez, Jormanainen, J., Kankkunen, S., Kayanoki, T., Kerszberg, D., Kluge, G. W., Kobayashi, Y., Kouch, P. M., Kubo, H., Kushida, J., Láinez, M., Lamastra, A., Leone, F., Lindfors, E., Lombardi, S., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Lorini, A., Lyard, E., Fraga, B. Machado de Oliveira, Majumdar, P., Makariev, M., Maneva, G., Manganaro, M., Mangano, S., Mannheim, K., Mariotti, M., Martínez, M., Martínez-Chicharro, M., Mas-Aguilar, A., Mazin, D., Menchiari, S., Mender, S., Miceli, D., Miener, T., Miranda, J. M., Mirzoyan, R., González, M. Molero, Molina, E., Mondal, H. A., Moralejo, A., Morcuende, D., Nakamori, T., Nanci, C., Neustroev, V., Nickel, L., Rosillo, M. Nievas, Nigro, C., Nikolić, L., Nilsson, K., Nishijima, K., Ekoume, T. Njoh, Noda, K., Nozaki, S., Ohtani, Y., Okumura, A., Otero-Santos, J., Paiano, S., Paneque, D., Paoletti, R., Paredes, J. M., Peresano, M., Persic, M., Pihet, M., Pirola, G., Podobnik, F., Moroni, P. G. Prada, Prandini, E., Principe, G., Rhode, W., Ribó, M., Rico, J., Righi, C., Sahakyan, N., Saito, T., Saturni, F. G., Schmidt, K., Schmuckermaier, F., Schubert, J. L., Schweizer, T., Sciaccaluga, A., Silvestri, G., Sitarek, J., Sliusar, V., Sobczynska, D., Spolon, A., Stamerra, A., Strišković, J., Strom, D., Strzys, M., Suda, Y., Tajima, H., Takahashi, M., Takeishi, R., Temnikov, P., Terauchi, K., Terzić, T., Teshima, M., Truzzi, S., Tutone, A., Ubach, S., van Scherpenberg, J., Acosta, M. Vazquez, Ventura, S., Verna, G., Viale, I., Vigorito, C. F., Vitale, V., Vovk, I., Walter, R., Wersig, F., Will, M., Wunderlich, C., Yamamoto, T., collaborators, MWL, Bachev, R., Ramazani, V. Fallah, Filippenko, A. V., Hovatta, T., Jorstad, S. G., Kiehlmann, S., Lähteenmäki, A., Liodakis, I., Marscher, A. P., Max-Moerbeck, W., Omeliukh, A., Pursimo, T., Readhead, A. C. S., Rodrigues, X., Tornikoski, M., Wierda, F., Zheng, W.
The BL Lacertae object VER J0521+211 underwent a notable flaring episode in February 2020. A short-term monitoring campaign, led by the MAGIC (Major Atmospheric Gamma Imaging Cherenkov) collaboration, covering a wide energy range from radio to very-h
Externí odkaz:
http://arxiv.org/abs/2412.15836
Processing large amounts of data fast, in constant and small space is the point of stream processing and the reason for its increasing use. Alas, the most performant, imperative processing code tends to be almost impossible to read, let alone modify,
Externí odkaz:
http://arxiv.org/abs/2412.15768
We study the electrical distribution network reconfiguration problem, defined as follows. We are given an undirected graph with a root vertex, demand at each non-root vertex, and resistance on each edge. Then, we want to find a spanning tree of the g
Externí odkaz:
http://arxiv.org/abs/2412.14583
We derive an analytical form of the joint prior of effective spin parameters, $\chi_\mathrm{eff}$ and $\chi_\mathrm{p}$, assuming an isotropic and uniform-in-magnitude spin distribution. This is a vital factor in performing hierarchical Bayesian infe
Externí odkaz:
http://arxiv.org/abs/2412.14551
Autor:
Kobayashi, Fumiyoshi, Manabe, Hidetaka, White, Gregory A. L., Farrelly, Terry, Modi, Kavan, Stace, Thomas M.
Quantum error correction (QEC) is essential for fault-tolerant quantum computation. Often in QEC errors are assumed to be independent and identically distributed and can be discretised to a random Pauli error during the execution of a quantum circuit
Externí odkaz:
http://arxiv.org/abs/2412.13739
Autor:
Kobayashi, Taisuke, Aotani, Takumi
Publikováno v:
Advanced Robotics, 2023
This paper proposes a new design method for a stochastic control policy using a normalizing flow (NF). In reinforcement learning (RL), the policy is usually modeled as a distribution model with trainable parameters. When this parameterization has les
Externí odkaz:
http://arxiv.org/abs/2412.12894