Results 261 to 270 of about 925,288 (310)
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Applied Mathematics and Computation, 1999
The paper studies (non-Gaussian) diffusions classified as either ``hypo-diffusion'' or ``hyper-diffusion'', where the \(\beta\) order moments are of the type \(t^{\beta/\alpha}\), with \(\beta\) and \(\alpha\) belonging to \(\mathbb{R}^*_+\). The authors introduce signed measures corresponding to non-Gaussian diffusions on \(\mathbb{R}\), inspired by ...
Mastrangelo, Michèle +2 more
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The paper studies (non-Gaussian) diffusions classified as either ``hypo-diffusion'' or ``hyper-diffusion'', where the \(\beta\) order moments are of the type \(t^{\beta/\alpha}\), with \(\beta\) and \(\alpha\) belonging to \(\mathbb{R}^*_+\). The authors introduce signed measures corresponding to non-Gaussian diffusions on \(\mathbb{R}\), inspired by ...
Mastrangelo, Michèle +2 more
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Laguerre-Gaussian supercontinuum
SPIE Proceedings, 2006We show what is believed to be the first coherent white-light optical vortices generated from supercontinuum that have the azimuthally varying phase structure consistent with a monochromatic Laguerre-Gaussian beam. Two methods of Laguerre-Gaussian supercontinuum generation are discussed and contrasted.
H I, Sztul, V, Kartazayev, R R, Alfano
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Gaussian Optics and Gaussian Brackets*†
Journal of the Optical Society of America, 1943Not ...
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MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images
European Conference on Computer VisionWe introduce MVSplat, an efficient model that, given sparse multi-view images as input, predicts clean feed-forward 3D Gaussians. To accurately localize the Gaussian centers, we build a cost volume representation via plane sweeping, where the cross-view ...
Donny Y. Chen +7 more
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From almost Gaussian to Gaussian
AIP Conference Proceedings, 2015We consider lower and upper bounds on the difference of differential entropies of a Gaussian random vector and an approximately Gaussian random vector after they are “smoothed” by an arbitrarily distributed random vector of finite power. These bounds are important to establish the optimality of the corner points in the capacity region of Gaussian ...
Max H. M. Costa, Olivier Rioul
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Model-Based Gaussian and Non-Gaussian Clustering
Biometrics, 1993Summary: The classification maximum likelihood approach is sufficiently general to encompass many current clustering algorithms, including those based on the sum of squares criterion and on the criterion of \textit{H. P. Friedman} and \textit{J. Rubin} [J. Am. Stat. Assoc. 62, 1159-1178 (1967)].
Banfield, Jeffrey D., Raftery, Adrian E.
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A Survey on 3D Gaussian Splatting
arXiv.org3D Gaussian splatting (GS) has emerged as a transformative technique in radiance fields. Unlike mainstream implicit neural models, 3D GS uses millions of learnable 3D Gaussians for an explicit scene representation.
Guikun Chen, Wenguan Wang
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Street Gaussians: Modeling Dynamic Urban Scenes with Gaussian Splatting
European Conference on Computer VisionThis paper aims to tackle the problem of modeling dynamic urban streets for autonomous driving scenes. Recent methods extend NeRF by incorporating tracked vehicle poses to animate vehicles, enabling photo-realistic view synthesis of dynamic urban street ...
Yunzhi Yan +8 more
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Gaussian and non-Gaussian statistics
Proceedings of International Symposium on Electromagnetic Compatibility ELMAGC-97, 1997The article presents a description of Gaussian statistical theory in relation to signal analysis. Special attention is placed on the correlation phenomena and the spectra. The main part of the paper deals with the higher-order statistics. The notions of the 3rd-moment and the cumulant function are introduced and their relation to the spectra and the ...
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Proceedings of the IEEE, 1967
The random variable generated by adding two Gaussian variables may or may not have a Gaussian distribution. Also, the random variable generated by adding two non-Gaussian variables may or may not have a non-Gaussian distribution. Of several examples given, one illustrates how the sum may be Gaussian while the individual variables are not.
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The random variable generated by adding two Gaussian variables may or may not have a Gaussian distribution. Also, the random variable generated by adding two non-Gaussian variables may or may not have a non-Gaussian distribution. Of several examples given, one illustrates how the sum may be Gaussian while the individual variables are not.
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