Measurement of hadron production in 
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 interactions at 158 and 
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Adhikary, H.; Allison, K. K.; Amin, Najia Moureen Binte; Andronov, E. V.; Antičić, T.; Arsene, I. C.; Balkova, Y.; Baszczyk, M.; Battaglia, D.; Bhosale, S.; Blondel, A.; Bogomilov, M.; Bondar, Y.; Bostan, N.; Brandin, A. V.; Bravar, A.; Bryliński, W.; Brzychczyk, J.; Buryakov, M.; Ćirković, M.; Csanád, M.; Cybowska, J.; Czopowicz, T.; Dalmazzone, C.; Damyanova, A.; Davis, N.; Dembinski, H.-P.; Dmitriev, A.; Dominik, W.; Dorosz, P.; Dumarchez, J.; Engel, R.; Феофилов, Г.; Fields, L.; Fodor, Z.; Friend, M.; Гарибов, А. А.; Gaździcki, M.; Golosov, O.; Golovatyuk, V.; Golubeva, M.; Grebieszkow, K.; Guber, F.; Haesler, A.; Haug, M.; Igolkin, S. N.; Ilieva, S.; Ivashkin, A.; Izvestnyy, A.; Johnson, S.; Kadija, K.; Каргин, Н.; Karpushkin, N.; Kashirin, E.; Kielbowicz, M. M.; Kireyeu, V. A.; Kitagawa, Hiroshi; Kolesnikov, R.; Kolev, D.; Korzenev, A.; Koshio, Y.; Коваленко, В.; Kowalski, S.; Kozłowski, B.; Krasnoperov, A.; Kucewicz, W.; Kuchowicz, M.; Kuich, M.; Kurepin, A.; László, Á.; Lewicki, Maciej Piotr; Lykasov, G. I.; Lyubushkin, V.; Maćkowiak-Pawłowska, M.; Maris, Ioana Codrina; Majka, Z.; Махнев, А. А.; Maksiak, B.; Malakhov, A.; Marcinek, A.; Marino, A. D.; Márton, K.; Mathes, H. J.; Matulewicz, T.; Матвеев, В.А.; Melkumov, G. L.; Merzlaya, A. O.; Messerly, B.; Mik, Ł.; Morawiec, A.; Morozov, S. V.; Nagai, Y.; Nakadaira, T.; Naskręt, M.; Nishimori, S.; Ozvenchuk, V.; Panova, O.; Paolone, V.; Petukhov, O.; Pidhurskyi, I.; Płaneta, R.; Podlaski, P.; Popov, Б.; Pórfy, B.; Posiadała-Zezula, M.; Prado, R. R.; Prokhorova, D. S.; Pszczel, D.; Puławski, S.; Puzović, J.; Ravonel, M.; Renfordt, R.; Röhrich, D.; Rondio, E.; Roth, M.; Rozpłochowski, Ł.; Rumyantsev, M.; Ruprecht, Martin; Rustamov, A.; Rybczyński, M.; Rybicki, A.; Sakashita, K.; Schmidt, K.; Seryakov, A. Yu.; Seyboth, P.; Shiraishi, Y.; Słodkowski, M.; Staszel, P.; Stefanek, G.; Steṕaniak, J.; Strikhanov, M.; Ströbele, H.; Šuša, T.; Szuba, M.; Szukiewicz, R.; Taranenko, A.; Tefelska, A.; Tefelski, D.; Терещенко, В.; Toia, A.; Tsenov, R.; Turko, L.; Tveter, Trine Spedstad; Ulrich, R.; Unger, Michael; Urbaniak, M.; Валиев, Ф. Ф.; Veberič, D.; Vechernin, V.; Volkov, V.; Wickremasinghe, A.; Wójcik, K.; Wyszyński, O.; Zaitsev, A.; Zimmerman, E. D.; Zviagina, A.; Zwaska, R.

doi:10.1103/physrevd.107.062004

Cited by 5 publications

(1 citation statement)

References 65 publications

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“…Muons propagate with minimal energy loss in the atmosphere, nearly undecayed, and eventually reach the Earth's surface. Based on the highest-energy cosmic ray data observed so far, the Pierre Auger Observatory (Aab et al 2015(Aab et al , 2016a and Telescope Array (Aab et al 2017) indicated a significant discrepancy in the muon number within extensive air showers (EAS) compared to the data predicted by the hadronic interaction models using in detectors like LHC (Adriani et al 2016) and SPS (Adhikary et al 2023a). Hadronic interaction models such as Sibyll-2.1 (Ahn et al 2009), QGSJetII-04 (Ostapchenko 2011), and EPOS-LHC (Pierog et al 2015) are proposed after further development and adjustment to the data.…”

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confidence: 99%

Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

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Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks (GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily relies on a large amount of task-specific supervision. To reduce labeling requirement, the "pre-train, fine-tune" and "pre-train, prompt" paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of prompting on graphs is still limited, lacking a universal treatment to appeal to different downstream tasks. In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs. GraphPrompt not only unifies pre-training and downstream tasks into a common task template, but also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-trained model in a task-specific manner. Finally, we conduct extensive experiments on five public datasets to evaluate and analyze GraphPrompt. CCS CONCEPTS• Computing methodologies → Learning latent representations; • Information systems → Data mining.

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