Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Jonathan Niles-Weed"'
Autor:
W. Christopher Carleton, Sarah Klassen, Jonathan Niles-Weed, Damian Evans, Patrick Roberts, Huw S. Groucutt
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-15 (2023)
Abstract Lidar (light-detection and ranging) has revolutionized archaeology. We are now able to produce high-resolution maps of archaeological surface features over vast areas, allowing us to see ancient land-use and anthropogenic landscape modificat
Externí odkaz:
https://doaj.org/article/0e9c1a56c5ee454692f45cc89944c168
Publikováno v:
Foundations of Computational Mathematics. 22:1767-1799
This paper develops nonasymptotic growth and concentration bounds for a product of independent random matrices. These results sharpen and generalize recent work of Henriksen-Ward, and they are similar in spirit to the results of Ahlswede-Winter and o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a9565f3b2d74def01fb8b744d10335e
https://doi.org/10.1007/s10208-021-09533-9
https://doi.org/10.1007/s10208-021-09533-9
Publikováno v:
arXiv
This paper studies convergence of empirical measures smoothed by a Gaussian kernel. Specifically, consider approximating $P\ast\mathcal{N}_\sigma$, for $\mathcal{N}_\sigma\triangleq\mathcal{N}(0,\sigma^2 \mathrm{I}_d)$, by $\hat{P}_n\ast\mathcal{N}_\
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::525f384365f145b1ccce582c70a678d3
https://doi.org/10.1109/tit.2020.2975480
https://doi.org/10.1109/tit.2020.2975480
A recent approach to the Beck-Fiala conjecture, a fundamental problem in combinatorics, has been to understand when random integer matrices have constant discrepancy. We give a complete answer to this question for two natural models: matrices with Be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d3e9d1c703bf192139ec285a7393f6c2
Autor:
Jonathan Niles-Weed, Ilias Zadik
We establish a phase transition known as the "all-or-nothing" phenomenon for noiseless discrete channels. This class of models includes the Bernoulli group testing model and the planted Gaussian perceptron model. Previously, the existence of the all-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a7fcc6e6598f38a9e75362e8b6298d2f
Publikováno v:
Journal of Functional Analysis. 281:109236
We prove that if ${(P_x)}_{x\in \mathscr X}$ is a family of probability measures which satisfy the log-Sobolev inequality and whose pairwise chi-squared divergences are uniformly bounded, and $\mu$ is any mixing distribution on $\mathscr X$, then the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::16235a53ad40c5df946bd7b14affdef5
https://doi.org/10.1016/j.jfa.2021.109236
https://doi.org/10.1016/j.jfa.2021.109236
Publikováno v:
arXiv
In this paper, we establish optimal rates of adaptive estimation of a vector in the multi-reference alignment model, a problem with important applications in fields such as signal processing, image processing, and computer vision, among others. We de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b44f931a70be43f973a26fad43cd8ed
https://hdl.handle.net/1721.1/135952
https://hdl.handle.net/1721.1/135952
Autor:
Sarah Klassen, Christophe Pottier, Gary P. Marriner, Jonathan Niles-Weed, Michael Hill, Piphal Heng, Alison Carter, Scott G. Ortman, Martin Polkinghorne, Pelle Wijker, Roland Fletcher, Damian Evans, Miriam T. Stark, Alyssa A. Loyless
Publikováno v:
Science Advances
Sarah Klassen
Sarah Klassen
The Greater Angkor Region was home to approximately 700,000 to 900,000 inhabitants at its apogee in the 13th century CE.
Angkor is one of the world’s largest premodern settlement complexes (9th to 15th centuries CE), but to date, no comprehens
Angkor is one of the world’s largest premodern settlement complexes (9th to 15th centuries CE), but to date, no comprehens
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ba68c739a2aa6abf57d56fc6fac20faf