Zobrazeno 1 - 10
of 30 761
pro vyhledávání: '"Kemper, A"'
Autor:
Kuchera, A. N., Ryan, G., Selby, G., Snider, D., Anderson, S., Almaraz-Calderon, S., Baby, L. T., Brown, B. A., Hanselman, K., Lopez-Saavedra, E., Macon, K. T., McCann, G. W., Kemper, K. W., Spieker, M., Wiedenhöver, I.
The resonance region of $^{11}$B covering excitation energies from 8.4 MeV to 13.6 MeV was investigated with the $(d,p)$ reaction performed on an enriched $^{10}$B target at the Florida State University Super-Enge Split-Pole Spectrograph of the John
Externí odkaz:
http://arxiv.org/abs/2411.09831
Autor:
Spieker, M., Bazin, D., Biswas, S., Cottle, P. D., Farris, P. J., Gade, A., Ginter, T., Giraud, S., Kemper, K. W., Li, J., Noji, S., Pereira, J., Riley, L. A., Smith, M. K., Weisshaar, D., Zegers, R. G. T.
Publikováno v:
Phys. Rev. C 109, 014307 (2024)
We report new experimental data for excited states of $^{72,74}$Se obtained from proton removal from $^{73,75}$Br secondary beams on a proton target. The experiments were performed with the Ursinus-NSCL Liquid Hydrogen Target and the combined GRETINA
Externí odkaz:
http://arxiv.org/abs/2411.09835
Autor:
Choi, Youngwoo, Kwon, Woojin, Pattle, Kate, Arzoumanian, Doris, Bourke, Tyler L., Hoang, Thiem, Hwang, Jihye, Koch, Patrick M., Sadavoy, Sarah, Bastien, Pierre, Furuya, Ray, Lai, Shih-Ping, Qiu, Keping, Ward-Thompson, Derek, Berry, David, Byun, Do-Young, Chen, Huei-Ru Vivien, Chen, Wen Ping, Chen, Mike, Chen, Zhiwei, Ching, Tao-Chung, Cho, Jungyeon, Choi, Minho, Choi, Yunhee, Coudé, Simon, Chrysostomou, Antonio, Chung, Eun Jung, Dai, Sophia, Debattista, Victor, Di Francesco, James, Diep, Pham Ngoc, Doi, Yasuo, Duan, Hao-Yuan, Duan, Yan, Eswaraiah, Chakali, Fanciullo, Lapo, Fiege, Jason, Fissel, Laura M., Franzmann, Erica, Friberg, Per, Friesen, Rachel, Fuller, Gary, Gledhill, Tim, Graves, Sarah, Greaves, Jane, Griffin, Matt, Gu, Qilao, Han, Ilseung, Hasegawa, Tetsuo, Houde, Martin, Hull, Charles L. H., Inoue, Tsuyoshi, Inutsuka, Shu-ichiro, Iwasaki, Kazunari, Jeong, Il-Gyo, Johnstone, Doug, Karoly, Janik, Könyves, Vera, Kang, Ji-hyun, Lacaille, Kevin, Law, Chi-Yan, Lee, Chang Won, Lee, Hyeseung, Lee, Chin-Fei, Lee, Jeong-Eun, Lee, Sang-Sung, Li, Dalei, Li, Di, Li, Guangxing, Li, Hua-bai, Lin, Sheng-Jun, Liu, Hong-Li, Liu, Tie, Liu, Sheng-Yuan, Liu, Junhao, Longmore, Steven, Lu, Xing, Lyo, A-Ran, Mairs, Steve, Matsumura, Masafumi, Matthews, Brenda, Moriarty-Schieven, Gerald, Nagata, Tetsuya, Nakamura, Fumitaka, Nakanishi, Hiroyuki, Ngoc, Nguyen Bich, Ohashi, Nagayoshi, Onaka, Takashi, Park, Geumsook, Parsons, Harriet, Peretto, Nicolas, Priestley, Felix, Pyo, Tae-Soo, Qian, Lei, Rao, Ramprasad, Rawlings, Jonathan, Rawlings, Mark, Retter, Brendan, Richer, John, Rigby, Andrew, Saito, Hiro, Savini, Giorgio, Seta, Masumichi, Sharma, Ekta, Shimajiri, Yoshito, Shinnaga, Hiroko, Soam, Archana, Kang, Miju, Kataoka, Akimasa, Kawabata, Koji, Kemper, Francisca, Kim, Jongsoo, Kim, Shinyoung, Kim, Gwanjeong, Kim, Kyoung Hee, Kim, Mi-Ryang, Kim, Kee-Tae, Kim, Hyosung, Kirchschlager, Florian, Kirk, Jason, Kobayashi, Masato I. N., Kusune, Takayoshi, Kwon, Jungmi, Tamura, Motohide, Tang, Ya-Wen, Tang, Xindi, Tomisaka, Kohji, Tsukamoto, Yusuke, Viti, Serena, Wang, Hongchi, Wang, Jia-Wei, Wu, Jintai, Xie, Jinjin, Yang, Meng-Zhe, Yen, Hsi-Wei, Yoo, Hyunju, Yuan, Jinghua, Yun, Hyeong-Sik, Zenko, Tetsuya, Zhang, Guoyin, Zhang, Yapeng, Zhang, Chuan-Peng, Zhou, Jianjun, Zhu, Lei, de Looze, Ilse, André, Philippe, Dowell, C. Darren, Eden, David, Eyres, Stewart, Falle, Sam, Gouellec, Valentin J. M. Le, Poidevin, Frédérick, van Loo, Sven
We present 850 $\mu$m polarization observations of the IC 348 star-forming region in the Perseus molecular cloud as part of the B-fields In STar-forming Region Observation (BISTRO) survey. We study the magnetic properties of two cores (HH 211 MMS and
Externí odkaz:
http://arxiv.org/abs/2411.01960
We provide a classification of all dynamical Lie algebras generated by 2-local spin interactions on undirected graphs. Building on our previous work where we provided such a classification for spin chains, here we consider the more general case of un
Externí odkaz:
http://arxiv.org/abs/2409.19797
Adiabatic state preparation aims to prepare the ground state of a target Hamiltonian starting from the easily prepared ground state of an initial Hamiltonian. While effective for time-dependent Hamiltonians with a significant energy gap to the first
Externí odkaz:
http://arxiv.org/abs/2408.03251
Subspace methods are powerful, noise-resilient methods that can effectively prepare ground states on quantum computers. The challenge is to get a subspace with a small condition number that spans the states of interest using minimal quantum resources
Externí odkaz:
http://arxiv.org/abs/2406.17037
Publikováno v:
International Conference on Machine Learning. 2024. Oral
Although recent advances in higher-order Graph Neural Networks (GNNs) improve the theoretical expressiveness and molecular property predictive performance, they often fall short of the empirical performance of models that explicitly use fragment info
Externí odkaz:
http://arxiv.org/abs/2406.08210
Autor:
Agrawal, Anjali A., Job, Joshua, Wilson, Tyler L., Saadatmand, S. N., Hodson, Mark J., Mutus, Josh Y., Caesura, Athena, Johnson, Peter D., Elenewski, Justin E., Morrell, Kaitlyn J., Kemper, Alexander F.
Understanding the physics of strongly correlated materials is one of the grand challenge problems for physics today. A large class of scientifically interesting materials, from high-$T_c$ superconductors to spin liquids, involve medium to strong corr
Externí odkaz:
http://arxiv.org/abs/2406.06511
In the current framework of Geometric Quantum Machine Learning, the canonical method for constructing a variational ansatz that respects the symmetry of some group action is by forcing the circuit to be equivariant, i.e., to commute with the action o
Externí odkaz:
http://arxiv.org/abs/2406.04418
Autor:
Korosteleva, Maria, Kesdogan, Timur Levent, Kemper, Fabian, Wenninger, Stephan, Koller, Jasmin, Zhang, Yuhan, Botsch, Mario, Sorkine-Hornung, Olga
Recent research interest in the learning-based processing of garments, from virtual fitting to generation and reconstruction, stumbles on a scarcity of high-quality public data in the domain. We contribute to resolving this need by presenting the fir
Externí odkaz:
http://arxiv.org/abs/2405.17609