Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Yenson Lau"'
Autor:
Sky C. Cheung, John Y. Shin, Yenson Lau, Zhengyu Chen, Ju Sun, Yuqian Zhang, Marvin A. Müller, Ilya M. Eremin, John N. Wright, Abhay N. Pasupathy
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
Aperiodic structure imaging suffers limitations when utilizing Fourier analysis. The authors report an algorithm that quantitatively overcomes these limitations based on nonconvex optimization, demonstrated by studying aperiodic structures via the ph
Externí odkaz:
https://doaj.org/article/4fac58aeeeba4907ad62d0897dc58f8c
Leishmanisis, a neglected tropical disease caused by protozoan parasites of the genusLeishmania, affects millions of individuals living in poverty across the world and is second to malaria in parasitic causes of death. Although current drugs for trea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f9cb21979b6318b36ec778ce73e4dfa3
https://doi.org/10.1101/2023.02.11.528117
https://doi.org/10.1101/2023.02.11.528117
Publikováno v:
SIAM Journal on Mathematics of Data Science. 2:216-245
We study the $\textit{Short-and-Sparse (SaS) deconvolution}$ problem of recovering a short signal $\mathbf a_0$ and a sparse signal $\mathbf x_0$ from their convolution. We propose a method based on nonconvex optimization, which under certain conditi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f4f7e6b420565b13801f6ba1804761c6
https://doi.org/10.1137/19m1237569
https://doi.org/10.1137/19m1237569
Publikováno v:
Jesse Cresswell
In the traditional federated learning setting, a central server coordinates a network of clients to train one global model. However, the global model may serve many clients poorly due to data heterogeneity. Moreover, there may not exist a trusted cen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee3fada334635175100d86e07d3ad89a
Autor:
Ju Sun, Yenson Lau, John Y. Shin, Abhay Pasupathy, Marvin A. Müller, Zhengyu Chen, Sky C. Cheung, John Wright, Ilya Eremin, Yuqian Zhang
Publikováno v:
Nature Communications
Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
Modern high-resolution microscopes are commonly used to study specimens that have dense and aperiodic spatial structure. Extracting meaningful information from images obtained from such microscopes remains a formidable challenge. Fourier analysis is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f41691ac8ba9d1118af4d33c5d4bf83d
http://europepmc.org/articles/PMC7044214
http://europepmc.org/articles/PMC7044214
Autor:
Zsuzsa Márka, Imre Bartos, R. Colgan, K. Rainer Corley, Yenson Lau, John Wright, Szabolcs Marka
The LIGO observatories detect gravitational waves through monitoring changes in the detectors' length down to below ${10}^{\ensuremath{-}19}\text{ }\text{ }\mathrm{m}/\sqrt{\mathrm{Hz}}$ variations---a small fraction of the size of the atoms that mak
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db426fce12ffc47f836ce1bc97397731
http://arxiv.org/abs/1911.11831
http://arxiv.org/abs/1911.11831
Publikováno v:
CVPR
Blind deconvolution is the problem of recovering a convolutional kernel $\boldsymbol{a}_0$ a 0 and an activation signal $\boldsymbol{x}_0$ x 0 from their convolution $\boldsymbol{y} = \boldsymbol{a}_0 \circledast \boldsymbol{x}_0$ y = a 0 ⊛ x 0 . T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd3dee6047e12b895c7bffd97a4ec077
https://doi.org/10.1109/cvpr.2017.466
https://doi.org/10.1109/cvpr.2017.466
Autor:
Gao, Wenbo1 (AUTHOR) goldfarb@columbia.edu, Goldfarb, Donald1 (AUTHOR), Curtis, Frank E.2 (AUTHOR)
Publikováno v:
Optimization Methods & Software. Apr2020, Vol. 35 Issue 2, p257-303. 47p.
Autor:
Cheung, Sky C., Shin, John Y., Lau, Yenson, Chen, Zhengyu, Sun, Ju, Zhang, Yuqian, Müller, Marvin A., Eremin, Ilya M., Wright, John N., Pasupathy, Abhay N.
Publikováno v:
Nature Communications; 2/26/2020, Vol. 11 Issue 1, p1-11, 11p
Efficient gravitational-wave glitch identification from environmental data through machine learning.
Autor:
Colgan, Robert E.1,2, Corley, K. Rainer3,4, Yenson Lau2,5, Bartos, Imre6, Wright, John N.2,5, Márka, Zsuzsa4, Márka, Szabolcs3
Publikováno v:
Physical Review D: Particles, Fields, Gravitation & Cosmology. 5/15/2020, Vol. 101 Issue 10, p1-1. 1p.