Zobrazeno 1 - 10
of 180
pro vyhledávání: '"Shi Tianyi"'
This paper presents a multilevel tensor compression algorithm called tensor butterfly algorithm for efficiently representing large-scale and high-dimensional oscillatory integral operators, including Green's functions for wave equations and integral
Externí odkaz:
http://arxiv.org/abs/2411.03029
The tensor-train (TT) format is a data-sparse tensor representation commonly used in high dimensional function approximations arising from computational and data sciences. Various sequential and parallel TT decomposition algorithms have been proposed
Externí odkaz:
http://arxiv.org/abs/2407.11290
Publikováno v:
Open Physics, Vol 18, Iss 1, Pp 58-73 (2020)
A dielectric barrier discharge plasma controlled diffusion flame experimental system was built based on the designed coaxial swirling plasma injector. The air plasma was generated within the annulus gap of the injector by alternating current dielectr
Externí odkaz:
https://doaj.org/article/94bd12cc9c7648469275056f3be3043d
Curvilinear object segmentation is critical for many applications. However, manually annotating curvilinear objects is very time-consuming and error-prone, yielding insufficiently available annotated datasets for existing supervised methods and domai
Externí odkaz:
http://arxiv.org/abs/2307.07245
Autor:
Liu, Delong, Li, Shichao, Shi, Tianyi, Meng, Zhu, Chen, Guanyu, Huang, Yadong, Dong, Jin, Zhao, Zhicheng
Among numerous studies for driver state detection, wearable physiological measurements offer a practical method for real-time monitoring. However, there are few driver physiological datasets in open-road scenarios, and the existing datasets suffer fr
Externí odkaz:
http://arxiv.org/abs/2304.04203
Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. However, achieving high pixel-wise accuracy, complete topology structure and robustness to various co
Externí odkaz:
http://arxiv.org/abs/2211.06578
Publikováno v:
Journal of Machine Learning Research 23 (2022) 1-34
Neural operators are a popular technique in scientific machine learning to learn a mathematical model of the behavior of unknown physical systems from data. Neural operators are especially useful to learn solution operators associated with partial di
Externí odkaz:
http://arxiv.org/abs/2204.12789
Publikováno v:
In Methods November 2024 231:144-153
Autor:
Hou, Chunyu, Yu, Kexin, Shi, Tianyi, Jiang, Benchao, Cao, Liangzi, Wang, Wenyuan, Han, Mei, Tang, Jing, Zhao, Yuting, Pan, Xuming, Li, Jianye, Lee, Duu-Jong, Wang, Li
Publikováno v:
In Journal of Environmental Management November 2024 370
Publikováno v:
In Physical Communication October 2024 66