Zobrazeno 1 - 10
of 24 607
pro vyhledávání: '"A. A. Konstantinov"'
Autor:
Kutepov, M. E., Kaydashev, V. E., Stryukov, D. V., Konstantinov, A. S., Nikolskiy, A. V., Kozakov, A. T., Morozov, A. D., Kaidashev, E. M.
Vanadium dioxide with metal-to-insulator transition (MIT) that is triggered by heat, current or light is a promising material for modern active THz/mid-IR metasurfaces and all-optical big data processing systems. Multilayer VO$_2$-based active metasu
Externí odkaz:
http://arxiv.org/abs/2411.14920
Autor:
Guldimann, Philipp, Spiridonov, Alexander, Staab, Robin, Jovanović, Nikola, Vero, Mark, Vechev, Velko, Gueorguieva, Anna, Balunović, Mislav, Konstantinov, Nikola, Bielik, Pavol, Tsankov, Petar, Vechev, Martin
The EU's Artificial Intelligence Act (AI Act) is a significant step towards responsible AI development, but lacks clear technical interpretation, making it difficult to assess models' compliance. This work presents COMPL-AI, a comprehensive framework
Externí odkaz:
http://arxiv.org/abs/2410.07959
A method for solving concept-based learning (CBL) problem is proposed. The main idea behind the method is to divide each concept-annotated image into patches, to transform the patches into embeddings by using an autoencoder, and to cluster the embedd
Externí odkaz:
http://arxiv.org/abs/2406.19897
Nonpolar atoms or molecules with light particle mass and weak particle-particle interaction can form quantum liquids and solids (QLS) at low temperatures. Excess electrons can be naturally bound to the surface of a QLS in a vacuum and exhibit unique
Externí odkaz:
http://arxiv.org/abs/2406.15870
Autor:
Tsoy, Nikita, Konstantinov, Nikola
Simplicity bias, the propensity of deep models to over-rely on simple features, has been identified as a potential reason for limited out-of-distribution generalization of neural networks (Shah et al., 2020). Despite the important implications, this
Externí odkaz:
http://arxiv.org/abs/2405.17299
The properties of a two-dimensional (2D) electron system can be drastically altered by a magnetic field applied perpendicular to the 2D plane. In particular, the frequency of its bulk collective excitations becomes gaped at the cyclotron frequency, w
Externí odkaz:
http://arxiv.org/abs/2404.07582
Cross-silo federated learning (FL) allows data owners to train accurate machine learning models by benefiting from each others private datasets. Unfortunately, the model accuracy benefits of collaboration are often undermined by privacy defenses. The
Externí odkaz:
http://arxiv.org/abs/2403.06672
Autor:
Alexeev, G. D., Alexeev, M. G., Alice, C., Amoroso, A., Andrieux, V., Anosov, V., Asatryan, S., Augsten, K., Augustyniak, W., Azevedo, C. D. R., Badelek, B., Barth, J., Beck, R., Beckers, J., Bedfer, Y., Bernhard, J., Bodlak, M., Bradamante, F., Bressan, A., Chang, W. -C., Chatterjee, C., Chiosso, M., Chumakov, A. G., Chung, S. -U., Cicuttin, A., Correia, P. M. M., Crespo, M. L., D'Ago, D., Torre, S. Dalla, Dasgupta, S. S., Dasgupta, S., Delcarro, F., Denisenko, I., Denisov, O. Yu., Donskov, S. V., Doshita, N., Dreisbach, Ch., Dunnweber, W., Dusaev, R. R., Ecker, D., Eremeev, D., Faccioli, P., Faessler, M., Finger, M., Finger jr., M., Fischer, H., Flothner, K. J., Florian, W., Friedrich, J. M., Frolov, V., Ordonez, L. G. Garcia, Gautheron, F., Gavrichtchouk, O. P., Gerassimov, S., Giarra, J., Giordano, D., Grasso, A., Gridin, A., Perdekamp, M. Grosse, Grube, B., Gruner, M., Guskov, A., Haas, P., von Harrach, D., Hoffmann, M., Hoghmrtsyan, A., d'Hose, N., Hsieh, C. -Y., Huber, S., Ishimoto, S., Ivanov, A., Iwata, T., Jary, V., Joosten, R., Kabuss, E., Kaspar, F., Kerbizi, A., Ketzer, B., Khatun, A., Khaustov, G. V., Klasek, T., Klein, F., Koivuniemi, J. H., Kolosov, V. N., Horikawa, K. Kondo, Konorov, I., Konstantinov, V. F., Korzenev, A. Yu., Kotzinian, A. M., Kouznetsov, O. M., Koval, A., Kral, Z., Krinner, F., Kunne, F., Kurek, K., Kurjata, R. P., Kveton, A., Lavickova, K., Levorato, S., Lian, Y. -S., Lichtenstadt, J., Lin, P. -J., Longo, R., Lyubovitskij, V. E., Maggiora, A., Magnon, A., Makke, N., Mallot, G. K., Maltsev, A., Martin, A., Marzec, J., Matousek, J., Matsuda, T., Mattson, G., Pires, C. Menezes, Metzger, F., Meyer, M., Meyer, W., Mikhailov, Yu. V., Mikhasenko, M., Mitrofanov, E., Miura, D., Miyachi, Y., Molina, R., Moretti, A., Movsisyan, A., Nagaytsev, A., Neyret, D., Niemiec, M., Novy, J., Nowak, W. -D., Nukazuka, G., Olshevsky, A. G., Ostrick, M., Panzieri, D., Parsamyan, B., Paul, S., Pekeler, H., Peng, J. -C., Pesek, M., Peshekhonov, D. V., Peskova, M., Platchkov, S., Pochodzalla, J., Polyakov, V. A., Quaresma, M., Quintans, C., Reicherz, G., Riedl, C., Ryabchikov, D. I., Rychter, A., Rymbekova, A., Samoylenko, V. D., Sandacz, A., Sarkar, S., Savin, I. A., Sbrizzai, G., Schmieden, H., Selyunin, A., Sharko, K., Sinha, L., Spulbeck, D., Srnka, A., Stolarski, M., Sulc, M., Suzuki, H., Takanashi, Y., Tessaro, S., Tessarotto, F., Thiel, A., Tosello, F., Townsend, A., Triloki, T., Tskhay, V., Valinoti, B., Veit, B. M., Veloso, J. F. C. A., Ventura, B., Vijayakumar, A., Virius, M., Wagner, M., Wallner, S., Zaremba, K., Zavertyaev, M., Zemko, M., Zemlyanichkina, E., Ziembicki, M.
New results are presented on a high-statistics measurement of Collins and Sivers asymmetries of charged hadrons produced in deep inelastic scattering of muons on a transversely polarised $^6$LiD target. The data were taken in 2022 with the COMPASS sp
Externí odkaz:
http://arxiv.org/abs/2401.00309
Autor:
Alexeev, G. D., Alexeev, M. G., Alice, C., Amoroso, A., Andrieux, V., Anosov, V., Augsten, K., Augustyniak, W., Azevedo, C. D. R., Badelek, B., Barth, J., Beck, R., Beckers, J., Bedfer, Y., Bernhard, J., Bodlak, M., Bradamante, F., Bressan, A., Chang, W. -C., Chatterjee, C., Chiosso, M., Chumakov, A. G., Chung, S. -U., Cicuttin, A., Correia, P. M. M., Crespo, M. L., D'Ago, D., Torre, S. Dalla, Dasgupta, S. S., Dasgupta, S., Delcarro, F., Denisenko, I., Denisov, O. Yu., Donskov, S. V., Doshita, N., Dreisbach, Ch., Dunnweber, W., Dusaev, R. R., Ecker, D., Eremeev, D., Faccioli, P., Faessler, M., Finger, M., Finger jr., M., Fischer, H., Flothner, K. J., Florian, W., Friedrich, J. M., Frolov, V., Ordonez, L. G. Garcia, Gautheron, F., Gavrichtchouk, O. P., Gerassimov, S., Giarra, J., Giordano, D., Grasso, A., Gridin, A., Perdekamp, M. Grosse, Grube, B., Gruner, M., Guskov, A., Haas, P., von Harrach, D., Heitz, R., Hoffmann, M., d'Hose, N., Hsieh, C. -Y., Huber, S., Ishimoto, S., Ivanov, A., Iwata, T., Jary, V., Joosten, R., Kabuss, E., Kaspar, F., Kerbizi, A., Ketzer, B., Khatun, A., Khaustov, G. V., Klein, F., Koivuniemi, J. H., Kolosov, V. N., Horikawa, K. Kondo, Konorov, I., Konstantinov, V. F., Korzenev, A. Yu., Kotzinian, A. M., Kouznetsov, O. M., Koval, A., Kral, Z., Krinner, F., Kunne, F., Kurek, K., Kurjata, R. P., Kveton, A., Lavickova, K., Levorato, S., Lian, Y. -S., Lichtenstadt, J., Lin, P. -J., Longo, R., Lyubovitskij, V. E., Maggiora, A., Magnon, A., Makke, N., Mallot, G. K., Maltsev, A., Martin, A., Marzec, J., Matousek, J., Matsuda, T., Mattson, G., Pires, C. Menezes, Metzger, F., Meyer, M., Meyer, W., Mikhailov, Yu. V., Mikhasenko, M., Mitrofanov, E., Miura, D., Miyachi, Y., Molina, R., Moretti, A., Nagaytsev, A., Neyret, D., Niemiec, M., Novy, J., Nowak, W. -D., Nukazuka, G., Olshevsky, A. G., Ostrick, M., Panzieri, D., Parsamyan, B., Paul, S., Pekeler, H., Peng, J. -C., Pesek, M., Peshekhonov, D. V., Peskova, M., Platchkov, S., Pochodzalla, J., Polyakov, V. A., Quaresma, M., Quintans, C., Reicherz, G., Riedl, C., Ryabchikov, D. I., Rychter, A., Rymbekova, A., Samoylenko, V. D., Sandacz, A., Sarkar, S., Savada, T., Savin, I. A., Sbrizzai, G., Schmieden, H., Selyunin, A., Sharko, K., Sinha, L., Spulbeck, D., Srnka, A., Stolarski, M., Sulc, M., Suzuki, H., Tessaro, S., Tessarotto, F., Thiel, A., Tosello, F., Townsend, A., Triloki, T., Tskhay, V., Valinoti, B., Veit, B. M., Veloso, J. F. C. A., Ventura, B., Vijayakumar, A., Virius, M., Wagner, M., Wallner, S., Zaremba, K., Zavertyaev, M., Zemko, M., Zemlyanichkina, E., Ziembicki, M.
The COMPASS Collaboration performed measurements of the Drell-Yan process in 2015 and 2018 using a 190 GeV/c $\pi^{-}$ beam impinging on a transversely polarised ammonia target. Combining the data of both years, we present final results on the amplit
Externí odkaz:
http://arxiv.org/abs/2312.17379
A new method called the Survival Beran-based Neural Importance Model (SurvBeNIM) is proposed. It aims to explain predictions of machine learning survival models, which are in the form of survival or cumulative hazard functions. The main idea behind S
Externí odkaz:
http://arxiv.org/abs/2312.06638