Zobrazeno 1 - 10
of 838
pro vyhledávání: '"WANG Yiqi"'
Publikováno v:
罕见病研究, Vol 3, Iss 2, Pp 232-236 (2024)
Myotonic dystrophy type 1 (DM1) is a multisystem trinucleotide repeat expansion disorder usually referred to the department of neurology with complaints of progressive muscle weakness and myotonia. This article reported a 33-year-old female patient w
Externí odkaz:
https://doaj.org/article/57e8269bd14f4122868fc9a84927552d
Publikováno v:
Yuanzineng kexue jishu, Vol 56, Iss 11, Pp 2004-2014 (2022)
For selective separation of 90Sr from HLLW, dodecyl benzenesulfonic acid (DBS) was applied to modified the 4′,4′(5″)-di(tert-butylcyclohexano)-18-crown-6 (DtBuCH18C6) and a novel silica-based crown ether was synthesized. The successful synthesi
Externí odkaz:
https://doaj.org/article/3dd50347c08645778b1b6566e4238e61
Publikováno v:
Meikuang Anquan, Vol 53, Iss 10, Pp 212-221 (2022)
In order to study the effect of different parameters on drainage effect of bedding drilling in high gas coal seam, based on Fick diffusion, Darcy’s law, ideal state gas equation and Langmuir equation, the flow-solid coupled mathematical model of ga
Externí odkaz:
https://doaj.org/article/c594f3c295c84dfcbcbd75be5cc3a8f1
Top-k Nearest Neighbors (kNN) problem on road network has numerous applications on location-based services. As direct search using the Dijkstra's algorithm results in a large search space, a plethora of complex-index-based approaches have been propos
Externí odkaz:
http://arxiv.org/abs/2408.05432
Autor:
Yang, Xihong, Wang, Yiqi, Chen, Jin, Fan, Wenqi, Zhao, Xiangyu, Zhu, En, Liu, Xinwang, Lian, Defu
Deep learning has been widely applied in recommender systems, which has achieved revolutionary progress recently. However, most existing learning-based methods assume that the user and item distributions remain unchanged between the training phase an
Externí odkaz:
http://arxiv.org/abs/2407.15620
Knowledge of the domain of applicability of a machine learning model is essential to ensuring accurate and reliable model predictions. In this work, we develop a new approach of assessing model domain and demonstrate that our approach provides accura
Externí odkaz:
http://arxiv.org/abs/2406.05143
Autor:
Zhang, Jiaxin, Wang, Yiqi, Yang, Xihong, Wang, Siwei, Feng, Yu, Shi, Yu, Ren, Ruicaho, Zhu, En, Liu, Xinwang
Graph Neural Networks have demonstrated great success in various fields of multimedia. However, the distribution shift between the training and test data challenges the effectiveness of GNNs. To mitigate this challenge, Test-Time Training (TTT) has b
Externí odkaz:
http://arxiv.org/abs/2404.13571
Autor:
Liu, Haogeng, You, Quanzeng, Han, Xiaotian, Wang, Yiqi, Zhai, Bohan, Liu, Yongfei, Tao, Yunzhe, Huang, Huaibo, He, Ran, Yang, Hongxia
Multimodal Large Language Models (MLLMs) have experienced significant advancements recently. Nevertheless, challenges persist in the accurate recognition and comprehension of intricate details within high-resolution images. Despite being indispensabl
Externí odkaz:
http://arxiv.org/abs/2403.01487
Autor:
Hu, Jiaxi, Gao, Jingtong, Zhao, Xiangyu, Hu, Yuehong, Liang, Yuxuan, Wang, Yiqi, He, Ming, Liu, Zitao, Yin, Hongzhi
The integration of multimodal information into sequential recommender systems has attracted significant attention in recent research. In the initial stages of multimodal sequential recommendation models, the mainstream paradigm was ID-dominant recomm
Externí odkaz:
http://arxiv.org/abs/2402.17334
Autor:
Wang, Maolin, Pan, Yu, Xu, Zenglin, Guo, Ruocheng, Zhao, Xiangyu, Wang, Wanyu, Wang, Yiqi, Liu, Zitao, Liu, Langming
Temporal Point Processes (TPPs) hold a pivotal role in modeling event sequences across diverse domains, including social networking and e-commerce, and have significantly contributed to the advancement of recommendation systems and information retrie
Externí odkaz:
http://arxiv.org/abs/2402.00388