Zobrazeno 1 - 10
of 26 001
pro vyhledávání: '"Xu Xin"'
Autor:
Zhang, Yan-Lei, Li, Ming, Xu, Xin-Biao, Dong, Chun-Hua, Guo, Guang-Can, Xiang, Ze-Liang, Zou, Chang-Ling, Zou, and Xu-Bo
Quantum frequency converters that enable the interface between the itinerant photons and qubits are indispensable for realizing long-distance quantum network. However, the cascaded connection between converters and qubits usually brings additional in
Externí odkaz:
http://arxiv.org/abs/2411.13158
Deep reinforcement learning has led to dramatic breakthroughs in the field of artificial intelligence for the past few years. As the amount of rollout experience data and the size of neural networks for deep reinforcement learning have grown continuo
Externí odkaz:
http://arxiv.org/abs/2411.05614
Autor:
Chen, Guang-Jie, Zhao, Dong, Wang, Zhu-Bo, Li, Ziqin, Zhang, Ji-Zhe, Chen, Liang, Zhang, Yan-Lei, Xu, Xin-Biao, Liu, Ai-Ping, Dong, Chun-Hua, Guo, Guang-Can, Huang, Kun, Zou, Chang-Ling
Precise control and manipulation of neutral atoms are essential for quantum technologies but largely dependent on conventional bulky optical setups. Here, we demonstrate a multifunctional metalens that integrates an achromatic lens with large numeric
Externí odkaz:
http://arxiv.org/abs/2411.05501
Autor:
Xu, Xin
The Barzilai-Borwein (BB) method is an effective gradient method for solving unconstrained optimization problems. Based on the observation of two classical BB step sizes, by constructing a variational least squares model, we propose a new class of BB
Externí odkaz:
http://arxiv.org/abs/2411.03899
Conventional approaches for video captioning leverage a variety of offline-extracted features to generate captions. Despite the availability of various offline-feature-extractors that offer diverse information from different perspectives, they have s
Externí odkaz:
http://arxiv.org/abs/2410.16624
Autor:
Xu, Xin, An, Congpei
We develop a Trust Region method with Regularized Barzilai-Borwein step-size obtained in a previous paper for solving large-scale unconstrained optimization problems. Simultaneously, the non-monotone technique is combined to formulate an efficient tr
Externí odkaz:
http://arxiv.org/abs/2409.14383
Autor:
Tan, Yizhou, Wu, Yanru, Hou, Yuanbo, Xu, Xin, Bu, Hui, Li, Shengchen, Botteldooren, Dick, Plumbley, Mark D.
Audio Event Recognition (AER) traditionally focuses on detecting and identifying audio events. Most existing AER models tend to detect all potential events without considering their varying significance across different contexts. This makes the AER r
Externí odkaz:
http://arxiv.org/abs/2409.06580
While Large Language Models (LLMs) exhibit remarkable generative capabilities, they are not without flaws, particularly in the form of hallucinations. This issue is even more pronounced when LLMs are applied to specific languages and domains. For exa
Externí odkaz:
http://arxiv.org/abs/2409.05806
Autor:
Xue, Hongfei, Gong, Rong, Shao, Mingchen, Xu, Xin, Wang, Lezhi, Xie, Lei, Bu, Hui, Zhou, Jiaming, Qin, Yong, Du, Jun, Li, Ming, Zhang, Binbin, Jia, Bin
The StutteringSpeech Challenge focuses on advancing speech technologies for people who stutter, specifically targeting Stuttering Event Detection (SED) and Automatic Speech Recognition (ASR) in Mandarin. The challenge comprises three tracks: (1) SED,
Externí odkaz:
http://arxiv.org/abs/2409.05430
Autor:
Batista, Rafael Alves, Benoit-Lévy, Aurélien, Bister, Teresa, Bohacova, Martina, Bustamante, Mauricio, Carvalho, Washington, Chen, Yiren, Cheng, LingMei, Chiche, Simon, Colley, Jean-Marc, Correa, Pablo, Laurenciu, Nicoleta Cucu, Dai, Zigao, de Almeida, Rogerio M., de Errico, Beatriz, de Jong, Sijbrand, Neto, João R. T. de Mello, de Vries, Krijn D, Decoene, Valentin, Denton, Peter B., Duan, Bohao, Duan, Kaikai, Engel, Ralph, Erba, William, Fan, Yizhong, Ferrière, Arsène, Gou, QuanBu, Gu, Junhua, Guelfand, Marion, Guo, Jianhua, Guo, Yiqing, Guépin, Claire, Gülzow, Lukas, Haungs, Andreas, Havelka, Matej, He, Haoning, Hivon, Eric, Hu, Hongbo, Huang, Xiaoyuan, Huang, Yan, Huege, Tim, Jiang, Wen, Koirala, Ramesh, Kong, ChuiZheng, Kotera, Kumiko, Köhler, Jelena, Lago, Bruno L., Lai, Zhisen, Coz, Sandra Le, Legrand, François, Leisos, Antonios, Li, Rui, Li, Xingyu, Li, YiFei, Liu, Cheng, Liu, Ruoyu, Liu, Wei, Ma, Pengxiong, Macias, Oscar, Magnard, Frédéric, Marcowith, Alexandre, Martineau-Huynh, Olivier, McKinley, Thomas, Minodier, Paul, Mitra, Pragati, Mostafá, Miguel, Murase, Kohta, Niess, Valentin, Nonis, Stavros, Ogio, Shoichi, Oikonomou, Foteini, Pan, Hongwei, Papageorgiou, Konstantinos, Pierog, Tanguy, Piotrowski, Lech Wiktor, Prunet, Simon, Qian, Xiangli, Roth, Markus, Sako, Takashi, Schoorlemmer, Harm, Szálas-Motesiczky, Dániel, Sławiński, Szymon, Tian, Xishui, Timmermans, Anne, Timmermans, Charles, Tobiska, Petr, Tsirigotis, Apostolos, Tueros, Matías, Vittakis, George, Wang, Hanrui, Wang, Jiale, Wang, Shen, Wang, Xiangyu, Wang, Xu, Wei, Daming, Wei, Feng, Wu, Xiangping, Wu, Xuefeng, Xu, Xin, Xu, Xing, Yang, Fufu, Yang, Lili, Yang, Xuan, Yuan, Qiang, Zarka, Philippe, Zeng, Houdun, Zhang, Chao, Zhang, Jianli, Zhang, Kewen, Zhang, Pengfei, Zhang, Qingchi, Zhang, Songbo, Zhang, Yi, Zhou, Hao, Wissel, Stephanie, Zeolla, Andrew, Deaconu, Cosmin, Hughes, Kaeli, Martin, Zachary, Mulrey, Katharine, Cummings, Austin, Krömer, Oliver, Plant, Kathryn, Schroeder, Frank G.
This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 202
Externí odkaz:
http://arxiv.org/abs/2409.03427