Zobrazeno 1 - 10
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pro vyhledávání: '"Liu AT"'
Autor:
Chen, Zhe, Wang, Weiyun, Cao, Yue, Liu, Yangzhou, Gao, Zhangwei, Cui, Erfei, Zhu, Jinguo, Ye, Shenglong, Tian, Hao, Liu, Zhaoyang, Gu, Lixin, Wang, Xuehui, Li, Qingyun, Ren, Yimin, Chen, Zixuan, Luo, Jiapeng, Wang, Jiahao, Jiang, Tan, Wang, Bo, He, Conghui, Shi, Botian, Zhang, Xingcheng, Lv, Han, Wang, Yi, Shao, Wenqi, Chu, Pei, Tu, Zhongying, He, Tong, Wu, Zhiyong, Deng, Huipeng, Ge, Jiaye, Chen, Kai, Dou, Min, Lu, Lewei, Zhu, Xizhou, Lu, Tong, Lin, Dahua, Qiao, Yu, Dai, Jifeng, Wang, Wenhai
We introduce InternVL 2.5, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data
Externí odkaz:
http://arxiv.org/abs/2412.05271
Autor:
XENON Collaboration, Aprile, E., Aalbers, J., Abe, K., Maouloud, S. Ahmed, Althueser, L., Andrieu, B., Angelino, E., Martin, D. Antón, Arneodo, F., Baudis, L., Bazyk, M., Bellagamba, L., Biondi, R., Bismark, A., Boese, K., Brown, A., Bruno, G., Budnik, R., Cai, C., Capelli, C., Cardoso, J. M. R., Chávez, A. P. Cimental, Colijn, A. P., Conrad, J., Cuenca-García, J. J., D'Andrea, V., Garcia, L. C. Daniel, Decowski, M. P., Deisting, A., Di Donato, C., Di Gangi, P., Diglio, S., Eitel, K., Morabit, S. el, Elykov, A., Ferella, A. D., Ferrari, C., Fischer, H., Flehmke, T., Flierman, M., Fulgione, W., Fuselli, C., Gaemers, P., Gaior, R., Galloway, M., Gao, F., Ghosh, S., Giacomobono, R., Glade-Beucke, R., Grandi, L., Grigat, J., Guan, H., Guida, M., Gyorgy, P., Hammann, R., Higuera, A., Hils, C., Hoetzsch, L., Hood, N. F., Iacovacci, M., Itow, Y., Jakob, J., Joerg, F., Kaminaga, Y., Kara, M., Kavrigin, P., Kazama, S., Kobayashi, M., Koke, D., Kopec, A., Landsman, H., Lang, R. F., Levinson, L., Li, I., Li, S., Liang, S., Lin, Y. -T., Lindemann, S., Lindner, M., Liu, K., Liu, M., Loizeau, J., Lombardi, F., Long, J., Lopes, J. A. M., Luce, T., Ma, Y., Macolino, C., Mahlstedt, J., Mancuso, A., Manenti, L., Marignetti, F., Undagoitia, T. Marrodán, Martens, K., Masbou, J., Masson, E., Mastroianni, S., Melchiorre, A., Merz, J., Messina, M., Michael, A., Miuchi, K., Molinario, A., Moriyama, S., Morá, K., Mosbacher, Y., Murra, M., Müller, J., Ni, K., Oberlack, U., Paetsch, B., Pan, Y., Pellegrini, Q., Peres, R., Peters, C., Pienaar, J., Pierre, M., Plante, G., Pollmann, T. R., Principe, L., Qi, J., Qin, J., García, D. Ramírez, Rajado, M., Singh, R., Sanchez, L., Santos, J. M. F. dos, Sarnoff, I., Sartorelli, G., Schreiner, J., Schulte, P., Eißing, H. Schulze, Schumann, M., Lavina, L. Scotto, Selvi, M., Semeria, F., Shagin, P., Shi, S., Shi, J., Silva, M., Simgen, H., Szyszka, C., Takeda, A., Takeuchi, Y., Tan, P. -L., Thers, D., Toschi, F., Trinchero, G., Tunnell, C. D., Tönnies, F., Valerius, K., Vecchi, S., Vetter, S., Solar, F. I. Villazon, Volta, G., Weinheimer, C., Weiss, M., Wenz, D., Wittweg, C., Wu, V. H. S., Xing, Y., Xu, D., Xu, Z., Yamashita, M., Yang, L., Ye, J., Yuan, L., Zavattini, G., Zhong, M.
Radiogenic neutrons emitted by detector materials are one of the most challenging backgrounds for the direct search of dark matter in the form of weakly interacting massive particles (WIMPs). To mitigate this background, the XENONnT experiment is equ
Externí odkaz:
http://arxiv.org/abs/2412.05264
Autor:
Chen, Junhao, Shu, Peng, Li, Yiwei, Zhao, Huaqin, Jiang, Hanqi, Pan, Yi, Zhou, Yifan, Liu, Zhengliang, Howe, Lewis C, Liu, Tianming
Recent studies show that large language models (LLMs) are powerful tools for working with natural language, bringing advances in many areas of computational linguistics. However, these models face challenges when applied to low-resource languages due
Externí odkaz:
http://arxiv.org/abs/2412.05184
Cognitive Diagnosis (CD) aims to evaluate students' cognitive states based on their interaction data, enabling downstream applications such as exercise recommendation and personalized learning guidance. However, existing methods often struggle with a
Externí odkaz:
http://arxiv.org/abs/2412.05004
Robots can acquire complex manipulation skills by learning policies from expert demonstrations, which is often known as vision-based imitation learning. Generating policies based on diffusion and flow matching models has been shown to be effective, p
Externí odkaz:
http://arxiv.org/abs/2412.04987
Autor:
Yi, Yazhuo, Huang, Haoliang, Shao, Ruiwen, Liu, Yukuai, Chen, Guangzheng, Ou, Jiahui, Zhang, Xi, Hua, Ze, Chen, Lang, Leung, Chi Wah, Zeng, Xie-Rong, Rao, Feng, Liu, Nan, Wang, Heng, Si, Liang, An, Hongyu, Chen, Zhuoyu, Huang, Chuanwei
Ferromagnetism and electrical insulation are often at odds, signifying an inherent trade off. The simultaneous optimization of both in one material, essential for advancing spintronics and topological electronics, necessitates the individual manipula
Externí odkaz:
http://arxiv.org/abs/2412.04957
Autor:
TEXONO Collaboration, Li, H. B., Pandey, M. K., Leung, C. H., Singh, L., Wong, H. T., Chi, H. -C., Deniz, M., C., Greeshma, Chen, J. -W., Hsu, H. C., Karadag, S., Karmakar, S., Kumar, V., Li, J., Lin, F. K., Lin, S. T., Liu, C. -P., Liu, S. K., Ma, H., Mishra, D. K., Saraswat, K., Sharma, V., Singh, M. K., Singh, V., Tanabe, D., Wang, J. S., Wu, C. -P., Yang, L. T., Yeh, C. H., Yue, Q.
After decades of experimental efforts, the DAMA/LIBRA(DL) annual modulation (AM) analysis on the ${\chi}$N (WIMP Dark Matter interactions on nucleus) channel remains the only one which can be interpreted as positive signatures. This has been refuted
Externí odkaz:
http://arxiv.org/abs/2412.04916
Recent advances in GPT-4o like multi-modality models have demonstrated remarkable progress for direct speech-to-speech conversation, with real-time speech interaction experience and strong speech understanding ability. However, current research focus
Externí odkaz:
http://arxiv.org/abs/2412.04917
Autor:
Ding, Zhaojun, Liu, Zhengliang, Jiang, Hanqi, Gao, Yizhu, Zhai, Xiaoming, Liu, Tianming, Liu, Ninghao
Recent studies show that large language models (LLMs) are powerful tools for working with natural language, bringing advances in many areas of computational linguistics. However, these models face challenges when applied to low-resource languages due
Externí odkaz:
http://arxiv.org/abs/2412.04774
Latent confounders are a fundamental challenge for inferring causal effects from observational data. The instrumental variable (IV) approach is a practical way to address this challenge. Existing IV based estimators need a known IV or other strong as
Externí odkaz:
http://arxiv.org/abs/2412.04641