Zobrazeno 1 - 10
of 3 854
pro vyhledávání: '"Wang, Ben"'
Accurately estimating the overlap between quantum states is a fundamental task in quantum information processing. While various strategies using distinct quantum measurements have been proposed for overlap estimation, the lack of experimental benchma
Externí odkaz:
http://arxiv.org/abs/2406.06810
Autor:
Li, Mingyu, Zhang, Haibin, Cai, Zheng, Liang, Yongming, Kashikawa, Nobunari, Ma, Ke, Fan, Xiaohui, Prochaska, J. Xavier, Emonts, Bjorn H. C., Wang, Xin, Wu, Yunjing, Zhang, Shiwu, Li, Qiong, Johnson, Sean D., Yue, Minghao, Battaia, Fabrizio Arrigoni, Cantalupo, Sebastiano, Hennawi, Joseph F., Kikuta, Satoshi, Ning, Yuanhang, Ouchi, Masami, Shimakawa, Rhythm, Wang, Ben, Wang, Weichen, Zheng, Zheng, Zheng, Zhen-Ya
Circumgalactic Lyman-alpha (Ly$\alpha$) nebulae are gaseous halos around galaxies exhibiting luminous extended Ly$\alpha$ emission. This work investigates Ly$\alpha$ nebulae from deep imaging of $\sim12~\mathrm{deg}^2$ sky, targeted by the MAMMOTH-Su
Externí odkaz:
http://arxiv.org/abs/2405.13113
Modern large-scale recommender systems are built upon computation-intensive infrastructure and usually suffer from a huge difference in traffic between peak and off-peak periods. In peak periods, it is challenging to perform real-time computation for
Externí odkaz:
http://arxiv.org/abs/2404.14961
Autor:
Wang, Ben, Liu, Jiqun
Publikováno v:
Proceedings of the 2024 ACM SIGIR International Conference on the Theory of Information Retrieval
When interacting with information retrieval (IR) systems, users, affected by confirmation biases, tend to select search results that confirm their existing beliefs on socially significant contentious issues. To understand the judgments and attitude c
Externí odkaz:
http://arxiv.org/abs/2403.17286
Autor:
Wang, Ben
Publikováno v:
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024)
The advent of ChatGPT and similar large language models (LLMs) has revolutionized the human-AI interaction and information-seeking process. Leveraging LLMs as an alternative to search engines, users can now access summarized information tailored to t
Externí odkaz:
http://arxiv.org/abs/2403.17089
Autor:
Gu, Yuxuan, Jin, Yi, Wang, Ben, Wei, Zhixiang, Ma, Xiaoxiao, Ling, Pengyang, Wang, Haoxuan, Chen, Huaian, Chen, Enhong
In this work, we observe that the generators, which are pre-trained on massive natural images, inherently hold the promising potential for superior low-light image enhancement against varying scenarios.Specifically, we embed a pre-trained generator t
Externí odkaz:
http://arxiv.org/abs/2402.09694
Publikováno v:
Proceedings of the 2024 ACM SIGIR Conference on Human Information Interaction and Retrieval
Large language model (LLM) applications, such as ChatGPT, are a powerful tool for online information-seeking (IS) and problem-solving tasks. However, users still face challenges initializing and refining prompts, and their cognitive barriers and bias
Externí odkaz:
http://arxiv.org/abs/2402.06170
Autor:
Zhao, Zhixiang, Wu, Che-Ming, Zhang, Shuping, He, Fanping, Liu, Fangfen, Wang, Ben, Huang, Yingxue, Shi, Wei, Jian, Dan, Xie, Hongfu, Yeh, Chao-Yuan, Li, Ji
Publikováno v:
JMIR Medical Informatics, Vol 9, Iss 3, p e23415 (2021)
BackgroundRosacea is a chronic inflammatory disease with variable clinical presentations, including transient flushing, fixed erythema, papules, pustules, and phymatous changes on the central face. Owing to the diversity in the clinical manifestation
Externí odkaz:
https://doaj.org/article/1a5f788898944f5694c60627a05335b6
Autor:
Ma, Xiaoxiao, Wei, Zhixiang, Jin, Yi, Ling, Pengyang, Liu, Tianle, Wang, Ben, Dai, Junkang, Chen, Huaian, Chen, Enhong
In this work, we observe that the model, which is trained on vast general images using masking strategy, has been naturally embedded with the distribution knowledge regarding natural images, and thus spontaneously attains the underlying potential for
Externí odkaz:
http://arxiv.org/abs/2401.14966
In recent years, there has been a growing interest in utilizing reinforcement learning (RL) to optimize long-term rewards in recommender systems. Since industrial recommender systems are typically designed as multi-stage systems, RL methods with a si
Externí odkaz:
http://arxiv.org/abs/2401.06470