1. N. Sharon and N. Dyn.
Bivariate interpolation based on univariate subdivision
schemes. Journal of Approximation Theory, 164(5):709–730,
2012
2. U. Itai and N. Sharon. Subdivision schemes
for positive definite matrices. Foundations of Computational
Mathematics, 13(3):347–369, 2013
3. N. Sharon and U. Itai. Approximation schemes
for functions of positive-definite matrix values. IMA
Journal of Numerical Analysis, 33(4):1436–1468, 2013
4. N. Sharon and Y. Shkolnisky. A class of
laplacian multiwavelets bases for high-dimensional data.
Applied and Computational Harmonic Analysis, 38(3):420–451,
2015
5. N. Dyn, A. Heard, K. Hormann, and N. Sharon.
Univariate subdivision schemes for noisy data with geometric
applications. Computer Aided Geometric Design,
37:85–104, 2015
6. V. May, Y. Keller, N. Sharon, and Y.
Shkolnisky. An algorithm for improving non-local means
operators via low-rank approximation. IEEE Transactions on
Image Processing, 25(3):1340–1353, 2016
7. N. Dyn and N. Sharon. A global approach to
the refinement of manifold data. Mathematics of Computation,
86(303):375–395, 2017
8. N. Dyn and N. Sharon. Manifold-valued
subdivision schemes based on geodesic inductive averaging.
Journal of Computational and Applied Mathematics, 311:54–67,
2017
9. O. Ozyesil, N. Sharon, and A. Singer.
Synchronization over Cartan motion groups via contraction.
SIAM Journal on Applied Algebra and Geometry (SIAGA), 2(2):
207-241, 2018.
10. N. Sharon and Y. Shkolnisky. Evaluating
non-analytic functions of matrices. Journal of Mathematical
Analysis and Applications, 462(1):613–636, 2018.
11. E. Abbe, T. Bendory, W. Leeb, J. M. Pereira,
N. Sharon, and A. Singer. Multireference alignment is easier
with an aperiodic translation distribution.IEEE Transactions
on Information Theory, 65(6), 3565-3584, 2018.
12. R. Mitz, N. Sharon, and Y. Shkolnisky.
Out-of-sample extension based on rank-one updating with
partial spectrum. SIAM Journal on Matrix Analysis and
Applications, 40(3): 973-997, 2019.
13. N. Sharon, J. Kileel, Y. Khoo, B. Landa and A.
Singer. Method of moments for 3-D single particle ab initio
modeling with non-uniform distribution of viewing angles.
Inverse Problems, 36(4), 044003, 2020.
14. V. Peiris, N. Sharon, N. Sukhorukova J. Ugon.
Rational approximation and its application to improving deep
learning classifiers. Applied Mathematics and Computation,
389:125560, 2021.
15. T. Bendory, A. Jaffe, W. Leeb, N.
Sharon, A. Singer. Super-resolution multi-reference
alignment. Information and Inference: a Journal of the IMA,
iaab003, https://doi.org/10.1093/imaiai/iaab003,
2021.
16. A. Heimowitz, N. Sharon, and A. Singer. Centering
noisy images with application to cryo-EM. SIAM Journal on
Imaging Sciences, 14(2), 689-716
, 2021.
17. W. Mattar and N. Sharon. Pyramid transform of
manifold data via subdivision operators. IMA Journal on
Numerical Analysis. 2021.
18. T. Bendory, D. Edidin, W. Leeb, N. Sharon.
Dihedral multi-reference alignment. IEEE Transactions on
Information Theory 68(5), 3489-3499. 2022.
19. A. Abas, T. Bendory, N. Sharon. The generalized
method of moments for multi-reference alignment. IEEE
Transactions on Signal Processing. 2022.
20. T. Bendory, I. Hadi, N. Sharon.
Compactification of the Rigid Motions Group in Image
Processing. SIAM Journal on Imaging Sciences. 2022.
21. H. Ben Zion Vardi, N. Dyn, N. Sharon. Geometric
Hermit Interpolation in $R^n$ by refinements. Advances in
Computational Mathematics. To appear 2023.
Preprint
1. N. Sharon, V. Peiris, N. Sukhorukova, J. Ugon.
Flexible rational approximation and its application for
matrix functions. 2021. URL
https://arxiv.org/abs/2108.09357.
submitted.
2. N. Sharon, R. Sherbu Cohen, H. Wendland . On multiscale
quasi-interpolation of scattered scalar- and manifold-valued
functions. 2022. URL
https://arxiv.org/abs/2210.14333.
submitted
.
3. Y. Khoo, S. Paul, N. Sharon. Deep Neural-network
Prior for Orbit Recovery from Method of Moments . 2023. URL
http://arxiv.org/abs/2304.14604.
submitted.
4. W. Mattar, N. Sharon. Pseudo-reversing and its
application for multiscaling of manifold-valued data . 2023.
URL
https://arxiv.org/abs/2305.06261.
submitted.
Unpublished
1. N.
Sharon and J. Zandberg.
Strategic alliances under risk
adjusted payoffs. SSRN, 2017. URL
https://ssrn.com/abstract=3020968.
2 N. Dyn, U. Itai, and N. Sharon. Approximation
operators for matrix-valued functions based on matrix
decomposition. 2014. URL https://arxiv.org/abs/1611.03893.
U.S.-Israel Binational Science Foundation (BSF)
NSF-BSF joint program
TAU-Swinburne
Research Collaboration
German Research
Foundation (DFG)
