Zobrazeno 1 - 10
of 422
pro vyhledávání: '"JIAO Pengfei"'
Autor:
JIAO Pengfei, WANG Zeyu, WU Heming, YAO Siyue, WANG Huilin, YAO Enhui, ZHANG Yuyao, YUAN Yi, ZHONG Yi
Publikováno v:
口腔疾病防治, Vol 32, Iss 1, Pp 12-21 (2024)
Objective To investigate the impact of exosomal miRNAs derived from endoplasmic reticulum-stressed (ERS) head and neck squamous cell carcinoma (HNSCC) cells on macrophages. Methods This study was reviewed and approved by the Ethics Committee. The exp
Externí odkaz:
https://doaj.org/article/3a6a6ee9dded4a74b75597d9984db526
Publikováno v:
Frontiers in Earth Science, Vol 10 (2022)
The Carboniferous-Permian coal measures in China contain abundant natural gas resources. Shale, coal and tight sandstone reservoirs are developed in coal measures, and the quantitative characterization of the pore structures of different types of res
Externí odkaz:
https://doaj.org/article/2282eef3d84e483c90b004f75e6450ff
Autor:
Gao, Qiang, Chan, Yang-hao, Jiao, Pengfei, Chen, Haiyang, Yin, Shuaishuai, Tangprapha, Kanjanaporn, Yang, Yichen, Li, Xiaolong, Liu, Zhengtai, Shen, Dawei, Jiang, Shengwei, Chen, Peng
Charge density wave (CDW) is a collective quantum phenomenon with a charge modulation in solids1-2. Condensation of electron and hole pairs with finite momentum will lead to such an ordered state3-7. However, lattice symmetry breaking manifested as t
Externí odkaz:
http://arxiv.org/abs/2401.13886
Autor:
Wang, Shuai, Weng, Tengjin, Wang, Jingyi, Shen, Yang, Zhao, Zhidong, Liu, Yixiu, Jiao, Pengfei, Cheng, Zhiming, Wang, Yaqi
Medical image segmentation annotations exhibit variations among experts due to the ambiguous boundaries of segmented objects and backgrounds in medical images. Although using multiple annotations for each image in the fully-supervised has been extens
Externí odkaz:
http://arxiv.org/abs/2311.10380
Publikováno v:
Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 683-699. 2023
Temporal graph representation learning aims to generate low-dimensional dynamic node embeddings to capture temporal information as well as structural and property information. Current representation learning methods for temporal networks often focus
Externí odkaz:
http://arxiv.org/abs/2311.03897
Autor:
Wang, Shengping1 (AUTHOR), Jiao, Pengfei1 (AUTHOR) jiaopf@nynu.edu.cn, Zhang, Zhengtian1 (AUTHOR), Niu, Qiuhong1 (AUTHOR) jiaopf@nynu.edu.cn
Publikováno v:
Molecules. Aug2024, Vol. 29 Issue 16, p3745. 12p.
Publikováno v:
In Information Sciences September 2024 679
Publikováno v:
In Materials Today Communications August 2024 40
Capturing structural similarity has been a hot topic in the field of network embedding recently due to its great help in understanding the node functions and behaviors. However, existing works have paid very much attention to learning structures on h
Externí odkaz:
http://arxiv.org/abs/2201.06972
Graph Convolutional Network (GCN) has shown remarkable potential of exploring graph representation. However, the GCN aggregating mechanism fails to generalize to networks with heterophily where most nodes have neighbors from different classes, which
Externí odkaz:
http://arxiv.org/abs/2112.13507