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Blind Super-Resolution of Point Sources via Projected Gradient Descent

Autor: Sihan Mao, Jinchi Chen

Publikováno v: IEEE Transactions on Signal Processing. 70:4649-4664

Blind super-resolution can be cast as a low rank matrix recovery problem by exploiting the inherent simplicity of the signal and the low dimensional structure of point spread functions. In this paper, we develop a simple yet efficient non-convex proj

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::17901a8eedf4ac041e047efe4015d4ad
https://doi.org/10.1109/tsp.2022.3209006

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Identify the Virus-like Models for COVID-19 as Bio-Threats: Combining Phage Display, Spectral Detection and Algorithms Analysis

Autor: Yuting Wu, Zhiwei Liu, Sihan Mao, Bing Liu, Zhaoyang Tong

Publikováno v: International Journal of Molecular Sciences
Volume 24
Issue 4
Pages: 3209

The rapid identification and recognition of COVID-19 have been challenging since its outbreak. Multiple methods were developed to realize fast monitoring early to prevent and control the pandemic. In addition, it is difficult and unrealistic to apply

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::11d4538d80cd336be3c02df8a797e47e
https://doi.org/10.3390/ijms24043209

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Optimal subsequent treatments for patients with hepatocellular carcinoma resistant to anti-PD-1 treatment

Autor: Yaojun Zhang, Xuqi Sun, Ziliang Yang, Sihan Mao, Minshan Chen, Li Xu, Yuhao Tang, Jinbin Chen, Juncheng Wang, Peiyao Xiong

Publikováno v: Immunotherapy.

Aim: The subsequent treatments for patients with hepatocellular carcinoma (HCC) resistant to immunotherapy remain unclear. This study aimed to identify optimal treatments for HCC patients with progression after anti-PD-1 therapy. Methods: The authors

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::523e8d8dd405b0622bec4d0e86bd0330
https://pubmed.ncbi.nlm.nih.gov/34758630

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Vectorized Hankel Lift: A Convex Approach for Blind Super-Resolution of Point Sources

Autor: Jinchi Chen, Weiguo Gao, Sihan Mao, Ke Wei

We consider the problem of resolving $ r$ point sources from $n$ samples at the low end of the spectrum when point spread functions (PSFs) are not known. Assuming that the spectrum samples of the PSFs lie in low dimensional subspace (let $s$ denote t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5cb881c689e7fd52b4351f665b1057e6

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