Zobrazeno 1 - 10
of 40
pro vyhledávání: '"SUI TANG"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Abstract The Coronavirus Disease 2019 (COVID-19) has had a profound impact on global health and economy, making it crucial to build accurate and interpretable data-driven predictive models for COVID-19 cases to improve public policy making. The extre
Externí odkaz:
https://doaj.org/article/40706f44d4d24dc7b7eca564819e4a00
Publikováno v:
In International Journal of Applied Earth Observation and Geoinformation August 2024 132
Publikováno v:
SIAM Journal on Applied Mathematics; 2024, Vol. 84 Issue 5, p2067-2086, 20p
Publikováno v:
In Food Hydrocolloids October 2020 107
Akademický článek
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Autor:
Ya-Ling Li, Ya-Li Gao, Xue-Li Niu, Yu-Tong Wu, Yi-Mei Du, Ming-Sui Tang, Jing-Yi Li, Xiu-Hao Guan, Bing Song
Publikováno v:
Frontiers in Oncology, Vol 10 (2020)
Background: Sarcomas are heterogeneous rare malignancies constituting approximately 1% of all solid cancers in adults and including more than 70 histological and molecular subtypes with different pathological and clinical development characteristics.
Externí odkaz:
https://doaj.org/article/c35a85f695bc416a886df4e5a0a75b87
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Publikováno v:
Applied and Computational Harmonic Analysis. 48:570-598
This paper studies sensor calibration in spectral estimation where the true frequencies are located on a continuous domain. We consider a uniform array of sensors that collects measurements whose spectrum is composed of a finite number of frequencies
Publikováno v:
Applied and Computational Harmonic Analysis. 48:395-414
Phaseless reconstruction from space–time samples is a nonlinear problem of recovering a function x in a Hilbert space H from the modulus of linear measurements { | 〈 x , ϕ i 〉 | , …, | 〈 A L i x , ϕ i 〉 | : i ∈ I } , where { ϕ i ; i
We consider stochastic systems of interacting particles or agents, with dynamics determined by an interaction kernel which only depends on pairwise distances. We study the problem of inferring this interaction kernel from observations of the position
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::385354b5b60e32e7079323dd8b7638c8
http://arxiv.org/abs/2007.15174
http://arxiv.org/abs/2007.15174