Zobrazeno 1 - 10
of 321 461
pro vyhledávání: '"Bauer A."'
In this work, we propose an efficient algorithm for the calculation of the Betti matching, which can be used as a loss function to train topology aware segmentation networks. Betti matching loss builds on techniques from topological data analysis, sp
Externí odkaz:
http://arxiv.org/abs/2407.04683
Autor:
Bauer, Peter
This paper provides an outlook on the future of operational weather prediction given the recent evolution in science, computing and machine learning. In many parts, this evolution strongly deviates from the strategy operational centres have formulate
Externí odkaz:
http://arxiv.org/abs/2407.03787
Telegraph noise caused by frequent switching of the magnetization in small magnetic devices has become a useful resource for probabilistic computing. Conventional theories have been based on a linearization of the fluctuations at the extrema of the m
Externí odkaz:
http://arxiv.org/abs/2407.02799
Autor:
Bhat, Aakash, Bauer, Evan B., Pakmor, Rüdiger, Shen, Ken J., Caiazzo, Ilaria, Rajamuthukumar, Abinaya Swaruba, El-Badry, Kareem, Kerzendorf, Wolfgang E.
Recent observations have found a growing number of hypervelocity stars with speeds of $\approx 1500-2500\,$km\,s$^{-1}$ which could have only been produced through thermonuclear supernovae in white dwarf binaries. Most of the observed hypervelocity r
Externí odkaz:
http://arxiv.org/abs/2407.03424
Autor:
Pauloski, J. Gregory, Hayot-Sasson, Valerie, Ward, Logan, Brace, Alexander, Bauer, André, Chard, Kyle, Foster, Ian
Workflow and serverless frameworks have empowered new approaches to distributed application design by abstracting compute resources. However, their typically limited or one-size-fits-all support for advanced data flow patterns leaves optimization to
Externí odkaz:
http://arxiv.org/abs/2407.01764
Autor:
Stottler, Z. S., Pedlar, T. K., Fulsom, B. G., Adachi, I., Adamczyk, K., Aihara, H., Said, S. Al, Asner, D. M., Atmacan, H., Aushev, T., Ayad, R., Babu, V., Banerjee, Sw., Bauer, M., Behera, P., Belous, K., Bennett, J., Bernlochner, F., Bessner, M., Bilka, T., Biswas, D., Bobrov, A., Bodrov, D., Bonvicini, G., Borah, J., Bozek, A., Bračko, M., Branchini, P., Browder, T. E., Budano, A., Campajola, M., Cao, L., Červenkov, D., Chang, M. -C., Cheon, B. G., Chilikin, K., Cho, H. E., Cho, K., Choi, S. -K., Choi, Y., Choudhury, S., Cinabro, D., Das, S., De Nardo, G., De Pietro, G., Dhamija, R., Di Capua, F., Doležal, Z., Dong, T. V., Dubey, S., Ecker, P., Epifanov, D., Ferber, T., Ferlewicz, D., Gaur, V., Garmash, A., Giri, A., Goldenzweig, P., Graziani, E., Gu, T., Guan, Y., Gudkova, K., Hadjivasiliou, C., Hara, T., Hayasaka, K., Hazra, S., Hedges, M. T., Herrmann, D., Hou, W. -S., Hsu, C. -L., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Iwasaki, Y., Jacobs, W. W., Jia, S., Jin, Y., Kaliyar, A. B., Kawasaki, T., Kiesling, C., Kim, C. H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kodyš, P., Korobov, A., Korpar, S., Kovalenko, E., Križan, P., Krokovny, P., Kuhr, T., Kumar, M., Kumar, R., Kumara, K., Kuzmin, A., Kwon, Y. -J., Lai, Y. -T., Lam, T., Laurenza, M., Lee, S. C., Levit, D., Lewis, P., Li, L. K., Libby, J., Lieret, K., Liventsev, D., Luo, T., Ma, Y., Masuda, M., Maurya, S. K., Meier, F., Merola, M., Miyabayashi, K., Mohanty, G. B., Mussa, R., Nakamura, I., Nakao, M., Natochii, A., Nayak, L., Nisar, N. K., Nishida, S., Ogawa, K., Ogawa, S., Ono, H., Oskin, P., Pakhlov, P., Pakhlova, G., Pang, T., Pardi, S., Park, J., Park, S. -H., Patra, S., Paul, S., Pestotnik, R., Piilonen, L. E., Podobnik, T., Prencipe, E., Prim, M. T., Rout, N., Russo, G., Sandilya, S., Sangal, A., Santelj, L., Savinov, V., Schnell, G., Schwanda, C., Seino, Y., Senyo, K., Shan, W., Shapkin, M., Sharma, C., Shiu, J. -G., Sokolov, A., Solovieva, E., Starič, M., Sumihama, M., Sutcliffe, W., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Tiwary, R., Uchida, M., Unno, Y., Uno, S., Vahsen, S. E., Varner, G., Wang, D., Wang, E., Wang, M. -Z., Watanuki, S., Werbycka, O., Won, E., Yabsley, B. D., Yan, W., Yin, J. H., Yuan, C. Z., Yuan, L., Yusa, Y., Zhang, Z. P., Zhilich, V., Zhukova, V.
We report a study of the hadronic transitions $\chi_{bJ}(2P)\to\omega\Upsilon(1S)$, with $\omega\to\pi^{+}\pi^{-}\pi^{0}$, using $28.2\times10^6~\Upsilon(3S)$ mesons recorded by the Belle detector. We present the first evidence for the near--threshol
Externí odkaz:
http://arxiv.org/abs/2407.00879
Autor:
Chazelas, Bruno, Ivanisenko, Yevgeniy, Lanotte, Audrey, Diaz, Pablo Santos, Genolet, Ludovic, Sordet, Michael, Hughes, Ian, Lovis, Christophe, Schmidt, Tobias M., Amate, Manuel, Castro, José Peñate, Moreno, Afrodisio Vega, Tenegi, Fabio, Simoes, Roberto, Hernández, Jonay I. González, Osorio, María Rosa Zapatero, Piqueras, Javier, Dávila, Tomás Belenguer, Ortega, Rocío Calvo, González, Roberto Varas, Fernández, Luis Miguel González, Amado, Pedro J., Kern, Jonathan, Dionies, Frank, Bauer, Svend-Marian, Önel, Hakan, Järvinen, Arto, Brynnel, Joar, Füßlein, Christine, Bellido, Olga, Weingrill, Jörg, Giannone, Domenico, Gaessler, Wolfgang, lehmitz, Michael, Kaminski, Adrian, Stilz, Ingo, Sigwarth, Michael, Marconi, Alessandro, Di Marcantonio, Paolo, Oliva, Ernesto, Coretti, Igor, Aliverti, Matteo, Pariani, Giorgio, Cabona, Lorenzo, Radaelli, Edouardo Maria Alberto, Scalera, Marcello, Balestera, Andrea
We present here the preliminary design of the RIZ module, one of the visible spectrographs of the ANDES instrument 1. It is a fiber-fed high-resolution, high-stability spectrograph. Its design follows the guidelines of successful predecessors such as
Externí odkaz:
http://arxiv.org/abs/2406.18317
Autor:
Yadav, Rohan, Bauer, Michael, Broman, David, Garland, Michael, Aiken, Alex, Kjolstad, Fredrik
Implicitly parallel task-based runtime systems often perform dynamic analysis to discover dependencies in and extract parallelism from sequential programs. Dependence analysis becomes expensive as task granularity drops below a threshold. Tracing tec
Externí odkaz:
http://arxiv.org/abs/2406.18111
Autor:
Yadav, Rohan, Sundram, Shiv, Lee, Wonchan, Garland, Michael, Bauer, Michael, Aiken, Alex, Kjolstad, Fredrik
We introduce Diffuse, a system that dynamically performs task and kernel fusion in distributed, task-based runtime systems. The key component of Diffuse is an intermediate representation of distributed computation that enables the necessary analyses
Externí odkaz:
http://arxiv.org/abs/2406.18109
Deep neural networks learn increasingly complex functions over the course of training. Here, we show both empirically and theoretically that learning of the target function is preceded by an early phase in which networks learn the optimal constant so
Externí odkaz:
http://arxiv.org/abs/2406.17467