Zobrazeno 1 - 10
of 427 751
pro vyhledávání: '"Saito A"'
Publikováno v:
Discussiones Mathematicae Graph Theory, Vol 42, Iss 4, Pp 1263-1280 (2022)
There is a long line of research in the literature dedicated to word-representable graphs, which generalize several important classes of graphs. However, not much is known about word-representability of split graphs, another important class of graphs
Externí odkaz:
https://doaj.org/article/3534d0aaee6541bfb49716dc262c5bbe
Autor:
Takaoka H, Terai H, Emoto K, Shigematsu L, Ito F, Saito A, Okada M, Ohgino K, Ikemura S, Yasuda H, Nakachi I, Kawada I, Fukunaga K, Soejima K
Publikováno v:
OncoTargets and Therapy, Vol Volume 15, Pp 981-989 (2022)
Hatsuyo Takaoka,1 Hideki Terai,1 Katsura Emoto,2 Lisa Shigematsu,1 Fumimaro Ito,1 Ayaka Saito,1 Masahiko Okada,1 Keiko Ohgino,1 Shinnosuke Ikemura,3 Hiroyuki Yasuda,1 Ichiro Nakachi,4 Ichiro Kawada,1 Koichi Fukunaga,1 Kenzo Soejima3 1Division of Pulm
Externí odkaz:
https://doaj.org/article/c8a0456b5a224294bb5b77523b1f6b2c
Autor:
He, Yan, Drozd, Vasyl, Ekawa, Hiroyuki, Escrig, Samuel, Gao, Yiming, Kasagi, Ayumi, Liu, Enqiang, Muneem, Abdul, Nakagawa, Manami, Nakazawa, Kazuma, Rappold, Christophe, Saito, Nami, Saito, Takehiko R., Sugimoto, Shohei, Taki, Masato, Tanaka, Yoshiki K., Wang, He, Yanai, Ayari, Yoshida, Junya, Zhang, Hongfei
A novel method was developed to detect double-$\Lambda$ hypernuclear events in nuclear emulsions using machine learning techniques. The object detection model, the Mask R-CNN, was trained using images generated by Monte Carlo simulations, image proce
Externí odkaz:
http://arxiv.org/abs/2409.01657
Autor:
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., 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., Busetto, G., 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., Di Pierro, F., Di Tria, R., Di Venere, L., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., 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., Hütten, M., Imazawa, R., Ishio, K., Martínez, I. Jiménez, Jormanainen, J., 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., 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., Priyadarshi, C., 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., Suutarinen, S., 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., Viale, I., Vigorito, C. F., Vitale, V., Vovk, I., Walter, R., Will, M., Wunderlich, C., Yamamoto, T., Jouvin, L., Linhoff, L., Linhoff, M.
Instruments for gamma-ray astronomy at Very High Energies ($E>100\,{\rm GeV}$) have traditionally derived their scientific results through proprietary data and software. Data standardisation has become a prominent issue in this field both as a requir
Externí odkaz:
http://arxiv.org/abs/2409.18823
Autor:
Saito, Daiki, Yoshida, Daisuke
We investigate static, axially symmetric spacetimes without naked singularities that are constructed by patching Weyl class spacetimes with the flat spacetimes. Once the exterior geometry is specified, the junction conditions determine the shape of a
Externí odkaz:
http://arxiv.org/abs/2409.18520
Autor:
Takeuchi, Makoto, Saito, Haruo
The phase noise of low-noise oscillators is conventionally measured by the cross-spectrum method (CSM), which has a complicated setup. We developed an alternative method called zero-crossing analysis with a double recorder setup (ZCA-DRS) that has mu
Externí odkaz:
http://arxiv.org/abs/2409.15807
The precise determination of the Cabibbo-Kobayashi-Maskawa (CKM) matrix elements is very important, because it could be a clue to new physics beyond Standard Theory. This is particular true of $V_{ud}$, because it is the main contribution to the unit
Externí odkaz:
http://arxiv.org/abs/2409.14764
Autor:
Kelly, Shane, Saito, Shuji
We introduce a pro-cdh topology on formal schemes and prove that the $\infty$-topos of pro-cdh sheaves of spaces has an optimal bound of homotopy dimension. This remedies a defect for a pro-cdh topology on schemes introduced in [KS23]. As an applicat
Externí odkaz:
http://arxiv.org/abs/2409.14295
This paper presents a parameter scan technique for BSM signal models based on normalizing flow. Normalizing flow is a type of deep learning model that transforms a simple probability distribution into a complex probability distribution as an invertib
Externí odkaz:
http://arxiv.org/abs/2409.13201
Autor:
Chen, Zhaoxi, Tang, Jiaxiang, Dong, Yuhao, Cao, Ziang, Hong, Fangzhou, Lan, Yushi, Wang, Tengfei, Xie, Haozhe, Wu, Tong, Saito, Shunsuke, Pan, Liang, Lin, Dahua, Liu, Ziwei
The increasing demand for high-quality 3D assets across various industries necessitates efficient and automated 3D content creation. Despite recent advancements in 3D generative models, existing methods still face challenges with optimization speed,
Externí odkaz:
http://arxiv.org/abs/2409.12957