scholarly journals Three simple scenarios for high-dimensional sphere packings

2021 ◽  
Vol 104 (6) ◽  
Author(s):  
Patrick Charbonneau ◽  
Peter K. Morse ◽  
Will Perkins ◽  
Francesco Zamponi
1987 ◽  
Vol 54 (1) ◽  
pp. 147-161 ◽  
Author(s):  
S. N. Litsyn ◽  
M. A. Tsfasman

Author(s):  
Peter K. Morse ◽  
Francesco Zamponi ◽  
Patrick Charbonneau ◽  
Will Perkins

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Hansol Park

<p style='text-indent:20px;'>We present a high-dimensional Winfree model in this paper. The Winfree model is a mathematical model for synchronization on the unit circle. We generalize this model compare to the high-dimensional sphere and we call it the Winfree sphere model. We restricted the support of the influence function in the neighborhood of the attraction point to a small diameter to mimic the influence function as the Dirac delta distribution. We can obtain several new conditions of the complete phase-locking states for the identical Winfree sphere model from restricting the support of the influence function. We also prove the complete oscillator death(COD) state from the exponential <inline-formula><tex-math id="M1">\begin{document}$ \ell^1 $\end{document}</tex-math></inline-formula>-stability and the existence of the equilibrium solution.</p>


2019 ◽  
Vol 30 (10) ◽  
pp. 2309-2325 ◽  
Author(s):  
Konstantinos Nikitopoulos ◽  
Georgios Georgis ◽  
Chathura Jayawardena ◽  
Daniil Chatzipanagiotis ◽  
Rahim Tafazolli

Fractals ◽  
2017 ◽  
Vol 25 (04) ◽  
pp. 1740004 ◽  
Author(s):  
SHUAI LIU ◽  
ZHENG PAN ◽  
XIAOCHUN CHENG

Fractal encoding method becomes an effective image compression method because of its high compression ratio and short decompressing time. But one problem of known fractal compression method is its high computational complexity and consequent long compressing time. To address this issue, in this paper, distance clustering in high dimensional sphere surface is applied to speed up the fractal compression method. Firstly, as a preprocessing strategy, an image is divided into blocks, which are mapped on high dimensional sphere surface. Secondly, a novel image matching method is presented based on distance clustering on high dimensional sphere surface. Then, the correctness and effectiveness properties of the mentioned method are analyzed. Finally, experimental results validate the positive performance gain of the method.


2020 ◽  
Vol 12 ◽  
Author(s):  
Irfan Alam

The coordinates along any fixed direction(s), of points on the sphere $S^{n-1}(\sqrt{n})$, roughly follow a standard Gaussian distribution as $n$ approaches infinity. We revisit this classical result from a nonstandard analysis perspective, providing a new proof by working with hyperfinite dimensional spheres. We also set up a nonstandard theory for the asymptotic behavior of integrals over varying domains in general. We obtain a new proof of the Riemann--Lebesgue lemma as a by-product of this theory. We finally show that for any function $f \co \mathbb{R}^k \to \mathbb{R}$ with finite Gaussian moment of an order larger than one, its expectation is given by a Loeb integral integral over a hyperfinite dimensional sphere. Some useful inequalities between high-dimensional spherical means of $f$ and its Gaussian mean are obtained in order to complete the above proof.


2020 ◽  
Vol 32 (8) ◽  
pp. 1431-1447 ◽  
Author(s):  
Shun-ichi Amari

It is known that any target function is realized in a sufficiently small neighborhood of any randomly connected deep network, provided the width (the number of neurons in a layer) is sufficiently large. There are sophisticated analytical theories and discussions concerning this striking fact, but rigorous theories are very complicated. We give an elementary geometrical proof by using a simple model for the purpose of elucidating its structure. We show that high-dimensional geometry plays a magical role. When we project a high-dimensional sphere of radius 1 to a low-dimensional subspace, the uniform distribution over the sphere shrinks to a gaussian distribution with negligibly small variances and covariances.


2013 ◽  
Vol 41 (6) ◽  
pp. 4214-4247 ◽  
Author(s):  
Antonio Auffinger ◽  
Gerard Ben Arous

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