Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Jun-qing Xi"'
Autor:
Hui-ping Zhang, Qi Yang, Jie Yu, Fangyi Liu, Yao Zhang, Jianping Dou, Li-na Tang, Xiaohan Hao, Jie Tian, Yang Yang, Zhiyu Han, Wen Cheng, Jin-tang Liao, Pei Zhou, Xiang Jing, Jun-qing Xi, Ping Liang, Yu Song, Rongqin Zheng, Tian'an Jiang, Jingwei Wei, Yan-Chun Luo, Dexing Kong, Wenjia Cai, Zhigang Cheng, Jin-yu Wu, Xiaoling Yu
Publikováno v:
SSRN Electronic Journal.
Background: The diagnosis performance of B-mode ultrasound (US) for focal liver lesions (FLLs) is relatively limited. The aim of this study was to develop a deep convolutional neural network of US (DCNN-US) for aiding radiologists in classification o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c360f36a8971d61a56ff6c4d7e8c8117
https://doi.org/10.2139/ssrn.3556621
https://doi.org/10.2139/ssrn.3556621
Autor:
Wenjia Cai, Li-na Tang, Jin-tang Liao, Hui-ping Zhang, Yao Zhang, Jie Yu, Jingwei Wei, Xiaohan Hao, Rongqin Zheng, Wen Cheng, Yan-Chun Luo, Jun-qing Xi, Yu Song, Zhiyu Han, Yilin Yang, Jin-yu Wu, Qi Yang, Ping Liang, Tian'an Jiang, Pei Zhou, Jie Tian, Xiang Jing, Fangyi Liu, Jianping Dou, Xiaoling Yu, Dexing Kong, Zhigang Cheng
Publikováno v:
EBioMedicine, Vol 56, Iss, Pp 102777-(2020)
Background The diagnosis performance of B-mode ultrasound (US) for focal liver lesions (FLLs) is relatively limited. We aimed to develop a deep convolutional neural network of US (DCNN-US) for aiding radiologists in classification of malignant from b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0fe8d909c6e9cc02b5ca41e4cb22608b
https://doi.org/10.1016/j.ebiom.2020.102777
https://doi.org/10.1016/j.ebiom.2020.102777
Publikováno v:
The Astrophysical Journal, Vol 972, Iss 1, p 117 (2024)
Primordial magnetic fields (PMFs) play a pivotal role in influencing small-scale fluctuations within the primordial density field, thereby enhancing the matter power spectrum within the context of the ΛCDM model at small scales. These amplified fluc
Externí odkaz:
https://doaj.org/article/02c6ccee5c66426f986735c87eacffb1
Publikováno v:
The Astrophysical Journal Supplement Series, Vol 274, Iss 1, p 4 (2024)
In our previous study, we introduced a machine learning technique, namely Cosmic Microwave Background Foreground Subtraction with Convolutional Neural Networks ( CMBFSCNN ), for the removal of foreground contamination in cosmic microwave background (
Externí odkaz:
https://doaj.org/article/3e14864e6a9b4d1c916e7bf3dd53afb5
Autor:
Jiachen Bai, Jun-Qing Xia
Publikováno v:
The Astrophysical Journal, Vol 971, Iss 1, p 11 (2024)
To investigate gravity in the nonlinear regime of cosmic structure using measurements from stage IV surveys, it is imperative to accurately compute large-scale structure observables, such as nonlinear matter power spectra, for gravity models that ext
Externí odkaz:
https://doaj.org/article/9b0addb9169942ad90fd941c80408072
Autor:
Dong Zhao, Jun-Qing Xia
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 81, Iss 10, Pp 1-11 (2021)
Abstract We test the cosmic anisotropy in the dipole-modulated $$\Lambda $$ Λ CDM model and Finslerian cosmological model with the recently-released quasar sample. Based on the redshift tomographic method, the quasar sample is divided into two subse
Externí odkaz:
https://doaj.org/article/2a23259123f34b5cb8c359d897498aa8
Autor:
Dong Zhao, Jun-Qing Xia
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 81, Iss 8, Pp 1-8 (2021)
Abstract We test the anisotropy in the Finslerian cosmological model with the X-ray and ultraviolet (UV) fluxes of quasars. The 2015 and 2020 compilations of quasars are used in the cosmological constraints. We find that the dipole direction given by
Externí odkaz:
https://doaj.org/article/9ef8679bdd954ff2b6309e493c1ddfa5
Publikováno v:
The Astrophysical Journal Supplement Series, Vol 268, Iss 1, p 7 (2023)
In previous works, we proposed to estimate cosmological parameters with an artificial neural network (ANN) and a mixture density network (MDN). In this work, we propose an improved method called a mixture neural network (MNN) to achieve parameter est
Externí odkaz:
https://doaj.org/article/6ca1263fc2464b28b00f7455e43f5295
Publikováno v:
The Astrophysical Journal, Vol 952, Iss 1, p 15 (2023)
The lensing effect of the cosmic microwave background (CMB) is a powerful tool for our study of the distribution of matter in the universe. The quadratic estimator (QE) method, which is widely used to reconstruct lensing potential, has been known to
Externí odkaz:
https://doaj.org/article/39d2903518d249c39e31e7013aa7defd
Publikováno v:
The Astrophysical Journal Supplement Series, Vol 267, Iss 1, p 2 (2023)
Primordial B -mode detection is one of the main goals of next-generation cosmic microwave background (CMB) experiments. Primordial B -modes are a unique signature of primordial gravitational waves (PGWs). However, the gravitational interaction of CMB
Externí odkaz:
https://doaj.org/article/c628dd7c47c149b88ad9ae208633974e