Zobrazeno 1 - 10
of 51 373
pro vyhledávání: '"Li, ZHANG"'
Publikováno v:
Comptes Rendus. Chimie, Vol 25, Iss S2, Pp 145-153 (2022)
A series of supported solid base catalysts were prepared by impregnation of chloride salts over CaO. The catalytic activity of the prepared catalysts was tested for the transesterification reaction of biodiesel from rapeseed oil and methanol. The KCl
Externí odkaz:
https://doaj.org/article/9b01fc1e532b407b869141f584a429ab
Autor:
Paischer, Fabian, Yang, Liu, Liu, Linfeng, Shao, Shuai, Hassani, Kaveh, Li, Jiacheng, Chen, Ricky, Li, Zhang Gabriel, Gao, Xialo, Shao, Wei, Feng, Xue, Noorshams, Nima, Park, Sem, Long, Bo, Eghbalzadeh, Hamid
Sequential recommendation systems aim to provide personalized recommendations for users based on their interaction history. To achieve this, they often incorporate auxiliary information, such as textual descriptions of items and auxiliary tasks, like
Externí odkaz:
http://arxiv.org/abs/2412.08604
Autor:
Yang, Liu, Paischer, Fabian, Hassani, Kaveh, Li, Jiacheng, Shao, Shuai, Li, Zhang Gabriel, He, Yun, Feng, Xue, Noorshams, Nima, Park, Sem, Long, Bo, Nowak, Robert D, Gao, Xiaoli, Eghbalzadeh, Hamid
Sequential dense retrieval models utilize advanced sequence learning techniques to compute item and user representations, which are then used to rank relevant items for a user through inner product computation between the user and all item representa
Externí odkaz:
http://arxiv.org/abs/2411.18814
Autor:
Wang, Song, Wang, Xun, Mei, Jie, Xie, Yujia, Muarray, Sean, Li, Zhang, Wu, Lingfeng, Chen, Si-Qing, Xiong, Wayne
Hallucination, a phenomenon where large language models (LLMs) produce output that is factually incorrect or unrelated to the input, is a major challenge for LLM applications that require accuracy and dependability. In this paper, we introduce a reli
Externí odkaz:
http://arxiv.org/abs/2407.15441
While large multi-modal models (LMM) have shown notable progress in multi-modal tasks, their capabilities in tasks involving dense textual content remains to be fully explored. Dense text, which carries important information, is often found in docume
Externí odkaz:
http://arxiv.org/abs/2405.06706
Optical-SAR image matching is a fundamental task for image fusion and visual navigation. However, all large-scale open SAR dataset for methods development are collected from single platform, resulting in limited satellite types and spatial resolution
Externí odkaz:
http://arxiv.org/abs/2404.00838
We present TextMonkey, a large multimodal model (LMM) tailored for text-centric tasks. Our approach introduces enhancement across several dimensions: By adopting Shifted Window Attention with zero-initialization, we achieve cross-window connectivity
Externí odkaz:
http://arxiv.org/abs/2403.04473
Autor:
Li, Zhang, Yang, Biao, Liu, Qiang, Ma, Zhiyin, Zhang, Shuo, Yang, Jingxu, Sun, Yabo, Liu, Yuliang, Bai, Xiang
Large Multimodal Models (LMMs) have shown promise in vision-language tasks but struggle with high-resolution input and detailed scene understanding. Addressing these challenges, we introduce Monkey to enhance LMM capabilities. Firstly, Monkey process
Externí odkaz:
http://arxiv.org/abs/2311.06607
Autor:
Bei Qin, Xiao Chang, Jiayang Pang, Zhishun Yu, Jitao Liu, Wanquan Deng, Li Zhang, Bing Yao, Xiaobing Liu
Publikováno v:
Energy Science & Engineering, Vol 12, Iss 12, Pp 5410-5417 (2024)
Abstract Developing highly accurate sediment erosion testing methods for Pelton turbine overflow components is challenging due to their irregularly curved surfaces. In this study, we designed a method to evaluate the sediment erosion of the Pelton tu
Externí odkaz:
https://doaj.org/article/7d2d6f8b3dbf49319934fad53532cfe0