Статті в журналах: "Gaussian correlation"

1

Vitale, Richard A. "Majorization and Gaussian correlation." Statistics & Probability Letters 45, no. 3 (November 1999): 247–51. http://dx.doi.org/10.1016/s0167-7152(99)00064-4.

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2

Liu, Liang, Jinchuan Hou, and Xiaofei Qi. "Quantum Correlation Based on Uhlmann Fidelity for Gaussian States." Entropy 21, no. 1 (December 22, 2018): 6. http://dx.doi.org/10.3390/e21010006.

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A quantum correlation N F G , A for ( n + m ) -mode continuous-variable systems is introduced in terms of local Gaussian unitary operations performed on Subsystem A based on Uhlmann fidelity F. This quantity is a remedy for the local ancilla problem associated with the geometric measurement-induced correlations; is local Gaussian unitary invariant; is non-increasing under any Gaussian quantum channel performed on Subsystem B;and is an entanglement monotone when restricted to pure Gaussian states in the ( 1 + m ) -mode case. A concrete formula for ( 1 + 1 ) -mode symmetric squeezed thermal states (SSTSs) is presented. We also compare N F G , A with other quantum correlations in scale, such as Gaussian quantum discord and Gaussian geometric discord, for two-mode SSTSs, which reveals that N F G , A has some advantage in detecting quantum correlations of Gaussian states.

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3

Nourdin, Ivan, and Fei Pu. "Gaussian fluctuation for Gaussian Wishart matrices of overall correlation." Statistics & Probability Letters 181 (February 2022): 109269. http://dx.doi.org/10.1016/j.spl.2021.109269.

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4

Liu, Liang, Jinchuan Hou, and Xiaofei Qi. "A Computable Gaussian Quantum Correlation for Continuous-Variable Systems." Entropy 23, no. 9 (September 9, 2021): 1190. http://dx.doi.org/10.3390/e23091190.

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Generally speaking, it is difficult to compute the values of the Gaussian quantum discord and Gaussian geometric discord for Gaussian states, which limits their application. In the present paper, for any (n+m)-mode continuous-variable system, a computable Gaussian quantum correlation M is proposed. For any state ρAB of the system, M(ρAB) depends only on the covariant matrix of ρAB without any measurements performed on a subsystem or any optimization procedures, and thus is easily computed. Furthermore, M has the following attractive properties: (1) M is independent of the mean of states, is symmetric about the subsystems and has no ancilla problem; (2) M is locally Gaussian unitary invariant; (3) for a Gaussian state ρAB, M(ρAB)=0 if and only if ρAB is a product state; and (4) 0≤M((ΦA⊗ΦB)ρAB)≤M(ρAB) holds for any Gaussian state ρAB and any Gaussian channels ΦA and ΦB performed on the subsystem A and B, respectively. Therefore, M is a nice Gaussian correlation which describes the same Gaussian correlation as Gaussian quantum discord and Gaussian geometric discord when restricted on Gaussian states. As an application of M, a noninvasive quantum method for detecting intracellular temperature is proposed.

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5

Liu, Liang, Xiaofei Qi, and Jinchuan Hou. "Fidelity-based unitary operation-induced quantum correlation for continuous-variable systems." International Journal of Quantum Information 17, no. 04 (June 2019): 1950035. http://dx.doi.org/10.1142/s0219749919500357.

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We propose a measure of nonclassical correlation [Formula: see text] in terms of local Gaussian unitary operations based on square of the fidelity [Formula: see text] for bipartite continuous-variable systems. This quantity is easier to be calculated or estimated and is a remedy for the local ancilla problem associated with the geometric measurement-induced nonlocality. A simple computation formula of [Formula: see text] for any [Formula: see text]-mode Gaussian states is presented and an estimation of [Formula: see text] for any [Formula: see text]-mode Gaussian states is given. For any [Formula: see text]-mode Gaussian states, [Formula: see text] does not increase after performing a local Gaussian channel on the unmeasured subsystem. Comparing [Formula: see text] in scale with other quantum correlations such as Gaussian geometric discord for two-mode symmetric squeezed thermal states reveals that [Formula: see text] is much better in detecting quantum correlations of Gaussian states.

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6

Xiao, M. Y., and H. P. Hong. "Unconditional and conditional simulation of nonstationary and non-Gaussian vector and field with prescribed marginal and correlation by using iteratively matched correlation." Disaster Prevention and Resilience 1, no. 1 (2022): 5. http://dx.doi.org/10.20517/dpr.2022.01.

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In many probabilistic analysis problems, the homogeneous/nonhomogeneous non-Gaussian field is represented as a mapped Gaussian field based on the Nataf translation system. We propose a new sample-based iterative procedure to estimate the underlying Gaussian correlation for homogeneous/nonhomogeneous non-Gaussian vector or field. The numerical procedure takes advantage that the range of feasible correlation coefficients for non-Gaussian random variables is bounded if the translation system is adopted. The estimated underlying Gaussian correlation is then employed for unconditional as well as conditional simulation of the non-Gaussian vector or field according to the theory of the translation process. We then present the steps for augmenting the simulated non-Gaussian field through the Karhunen-Loeve expansion for a refined discretized grid of the field. In addition, the steps to extend the procedure described in the previous section to the multi-dimensional field are highlighted. The application of the proposed algorithms is presented through numerical examples.

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7

Elliott, Peter, and Neepa T. Maitra. "Electron correlation via frozen Gaussian dynamics." Journal of Chemical Physics 135, no. 10 (September 14, 2011): 104110. http://dx.doi.org/10.1063/1.3630134.

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8

Fyfe, Colin, Gayle Leen, and Pei Ling Lai. "Gaussian processes for canonical correlation analysis." Neurocomputing 71, no. 16-18 (October 2008): 3077–88. http://dx.doi.org/10.1016/j.neucom.2008.04.037.

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9

Casasent, David, Gopalan Ravichandran, and Srinivas Bollapragada. "Gaussian–minimum average correlation energy filters." Applied Optics 30, no. 35 (December 10, 1991): 5176. http://dx.doi.org/10.1364/ao.30.005176.

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10

Liu, Youming, and Chunguang Ren. "Estimation of canonical correlation directions: From Gaussian to sub-Gaussian population." Journal of Multivariate Analysis 186 (November 2021): 104795. http://dx.doi.org/10.1016/j.jmva.2021.104795.

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11

Bernardeau, Francis. "The gravity-induced quasi-Gaussian correlation hierarchy." Astrophysical Journal 392 (June 1992): 1. http://dx.doi.org/10.1086/171398.

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12

Bhandari, Subir K., and Ayanendranath Basu. "On Gaussian correlation inequalities for “periodic” sets." Statistics & Probability Letters 73, no. 3 (July 2005): 315–20. http://dx.doi.org/10.1016/j.spl.2005.04.007.

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13

Boudt, Kris, Jonathan Cornelissen, and Christophe Croux. "The Gaussian rank correlation estimator: robustness properties." Statistics and Computing 22, no. 2 (April 5, 2011): 471–83. http://dx.doi.org/10.1007/s11222-011-9237-0.

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14

Lingen, Lu. "Adaptive nonparametric detector on correlation gaussian noise." Journal of Electronics (China) 5, no. 3 (July 1988): 206–12. http://dx.doi.org/10.1007/bf02778786.

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15

Mossel, Elchanan. "Gaussian Bounds for Noise Correlation of Functions." Geometric and Functional Analysis 19, no. 6 (January 22, 2010): 1713–56. http://dx.doi.org/10.1007/s00039-010-0047-x.

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16

Draisma, Jan. "Partial correlation hypersurfaces in Gaussian graphical models." Algebraic Combinatorics 2, no. 3 (2019): 439–46. http://dx.doi.org/10.5802/alco.44.

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17

Deng, Xinmin, Pingzhi Fan, and N. Suehiro. "Sequences with zero correlation over Gaussian integers." Electronics Letters 36, no. 6 (2000): 552. http://dx.doi.org/10.1049/el:20000439.

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18

Karpukhin, O. N., and M. A. Aliev. "Correlation properties of arrays of Gaussian coils." Doklady Physical Chemistry 459, no. 1 (November 2014): 161–65. http://dx.doi.org/10.1134/s0012501614110013.

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19

Szarek, Stanislaw J., and Elisabeth Werner. "A Nonsymmetric Correlation Inequality for Gaussian Measure." Journal of Multivariate Analysis 68, no. 2 (February 1999): 193–211. http://dx.doi.org/10.1006/jmva.1998.1784.

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20

Zhang, Yongtao, Haixia Wang, Chaoliang Ding, and Liuzhan Pan. "Correlation singularities of partially coherent beams with multi-Gaussian correlation function." Physics Letters A 381, no. 31 (August 2017): 2550–53. http://dx.doi.org/10.1016/j.physleta.2017.05.059.

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21

PAULESCU, MARIUS, EUGENIA TULCAN-PAULESCU, and PAUL GRAVILA. "PSEUDO–GAUSSIAN SUPERLATTICE." International Journal of Modern Physics C 21, no. 09 (September 2010): 1095–105. http://dx.doi.org/10.1142/s0129183110015695.

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A new family of superlattice structures modulated by the pseudo-Gaussian potential is proposed. The correlation between the configuration of minibands and the superlattice geometry is revealed using the transfer matrix approach.

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22

Owen, Lucy L. W., Tudor A. Muntianu, Andrew C. Heusser, Patrick M. Daly, Katherine W. Scangos, and Jeremy R. Manning. "A Gaussian Process Model of Human Electrocorticographic Data." Cerebral Cortex 30, no. 10 (June 4, 2020): 5333–45. http://dx.doi.org/10.1093/cercor/bhaa115.

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Abstract We present a model-based method for inferring full-brain neural activity at millimeter-scale spatial resolutions and millisecond-scale temporal resolutions using standard human intracranial recordings. Our approach makes the simplifying assumptions that different people’s brains exhibit similar correlational structure, and that activity and correlation patterns vary smoothly over space. One can then ask, for an arbitrary individual’s brain: given recordings from a limited set of locations in that individual’s brain, along with the observed spatial correlations learned from other people’s recordings, how much can be inferred about ongoing activity at other locations throughout that individual’s brain? We show that our approach generalizes across people and tasks, thereby providing a person- and task-general means of inferring high spatiotemporal resolution full-brain neural dynamics from standard low-density intracranial recordings.

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23

Ruikar, Jayesh, Ashoke Sinha, and Saurabh Chaudhury. "Image Quality Assessment Using Edge Correlation." International Journal of Electronics and Telecommunications 63, no. 1 (March 1, 2017): 99–107. http://dx.doi.org/10.1515/eletel-2017-0014.

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Abstract In literature, oriented filters are used for low-level vision tasks. In this paper, we propose use of steerable Gaussian filter in image quality assessment. Human visual system is more sensitive to multidirectional edges present in natural images. The most degradation in image quality is caused due to its edges. In this work, an edge based metric termed as steerable Gaussian filtering (SGF) quality index is proposed as objective measure for image quality assessment. The performance of the proposed technique is evaluated over multiple databases. The experimental result shows that proposed method is more reliable and outperform the conventional image quality assessment method.

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24

Gao, Xin, Binlin Wu, and Tobias Schäfer. "Introducing an analytical solution and an improved one-factor gaussian copula model for the pricing of heterogeneous CDOs." International Journal of Financial Engineering 04, no. 02n03 (June 2017): 1750038. http://dx.doi.org/10.1142/s2424786317500384.

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This paper introduced an analytical solution and improved one-factor Gaussian copula models to the pricing of tranches of a Collateralized debt obligations (CDO) portfolio. Prices of CDO tranches are calculated and compared using the analytical model and different one-factor Gaussian copula models including a two-category heterogeneous model and a completely heterogeneous model that uses individual rate parameter and correlation coefficient for each reference entity in a CDO portfolio. When correlation among reference entities is low, the price calculated from the analytical model matches very well with the one-factor Gaussian copula models. However, as the correlation among reference entities increases, prices calculated using both the analytical solution and the homogeneous or two-category one-factor Gaussian copula models significantly deviate from the completely heterogeneous one-factor Gaussian copula model. This result verifies our belief that uniform parameters cannot completely capture all the heterogeneities in a CDO portfolio. Completely heterogeneous one-factor Gaussian copula model using individual rate parameters and correlation coefficients for each reference entities provides more reliable and accurate prices for structured securities.

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25

Polat, Can. "Gaussian formulation based correlation for Dietz shape factor." Upstream Oil and Gas Technology 7 (September 2021): 100059. http://dx.doi.org/10.1016/j.upstre.2021.100059.

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26

Burtschell, X., J. Gregory, and J. Laurent. "Beyond the Gaussian copula: stochastic and local correlation." Journal of Credit Risk 3, no. 1 (March 2007): 31–62. http://dx.doi.org/10.21314/jcr.2007.059.

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27

Bose, Soumyakanti. "Role of EPR correlation in Gaussian quantum teleportation." Physica Scripta 95, no. 10 (September 14, 2020): 105105. http://dx.doi.org/10.1088/1402-4896/abb635.

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28

Maksimov, D. N., and A. F. Sadreev. "Phase correlation function of complex random Gaussian fields." Europhysics Letters (EPL) 80, no. 5 (October 26, 2007): 50003. http://dx.doi.org/10.1209/0295-5075/80/50003.

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29

Schreiber, Thomas. "Influence of Gaussian noise on the correlation exponent." Physical Review E 56, no. 1 (July 1, 1997): 274–77. http://dx.doi.org/10.1103/physreve.56.274.

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30

Chen, Xiaohong, and Fuchang Gao. "A reverse Gaussian correlation inequality by adding cones." Statistics & Probability Letters 123 (April 2017): 84–87. http://dx.doi.org/10.1016/j.spl.2016.11.031.

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31

Otneim, Håkon, and Dag Tjøstheim. "Conditional density estimation using the local Gaussian correlation." Statistics and Computing 28, no. 2 (February 15, 2017): 303–21. http://dx.doi.org/10.1007/s11222-017-9732-z.

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32

Mossel, Elchanan. "Gaussian bounds for noise correlation of resilient functions." Israel Journal of Mathematics 235, no. 1 (December 18, 2019): 111–37. http://dx.doi.org/10.1007/s11856-019-1951-x.

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33

Phoon, Kok-Kwang, Ser-Tong Quek, and Hongwei Huang. "Simulation of non-Gaussian processes using fractile correlation." Probabilistic Engineering Mechanics 19, no. 4 (October 2004): 287–92. http://dx.doi.org/10.1016/j.probengmech.2003.09.001.

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34

Xiao, Qing, and Shaowu Zhou. "Matching a correlation coefficient by a Gaussian copula." Communications in Statistics - Theory and Methods 48, no. 7 (March 2, 2018): 1728–47. http://dx.doi.org/10.1080/03610926.2018.1439962.

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35

Huang, J., X. Pan, X. Peng, Y. Yuan, C. Xiong, J. Fang, and F. Yuan. "Digital Image Correlation with Self-Adaptive Gaussian Windows." Experimental Mechanics 53, no. 3 (July 13, 2012): 505–12. http://dx.doi.org/10.1007/s11340-012-9639-8.

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36

Tjøstheim, Dag, and Karl Ove Hufthammer. "Local Gaussian correlation: A new measure of dependence." Journal of Econometrics 172, no. 1 (January 2013): 33–48. http://dx.doi.org/10.1016/j.jeconom.2012.08.001.

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37

Tsirel'son, B. S. "Stationary Gaussian processes with a finite correlation function." Journal of Mathematical Sciences 68, no. 4 (February 1994): 597–603. http://dx.doi.org/10.1007/bf01254288.

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38

CAO, LIANG, and YAO-QUAN CHU. "OBSERVABILITY OF THE SZ EFFECT MAP'S NON-GAUSSIAN FEATURES." Modern Physics Letters A 21, no. 19 (June 21, 2006): 1541–46. http://dx.doi.org/10.1142/s0217732306019608.

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We study the high order cross-correlation between the WMAP map and 2MASS galaxy distribution in order to detect the non-Gaussian behaviors of the SZ effect on the CMB fluctuations induced by 2MASS. The 2MASS distribution is significantly non-Gaussian, which is characterized by the fourth order correlations in DWT representation. With an unbiased mock sample we show, if the CMB data contains the information of 2MASS hot gas caused SZ effect, the non-Gaussianity of the cross-correlations between the CMB and 2MASS is observable with the fourth order statistics on scales of clusters. We compared this result with the cross-correlation between the observed WMAP data and 2MASS, finding similar non-Gaussianity to the mock SZ samples. It strongly evidences the existence and observability of the SZ signal in the WMAP data caused by the 2MASS clusters.

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39

Strahov, Eugene. "Dynamical correlation functions for products of random matrices." Random Matrices: Theory and Applications 04, no. 04 (October 2015): 1550020. http://dx.doi.org/10.1142/s2010326315500203.

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We introduce and study a family of random processes with a discrete time related to products of random matrices. Such processes are formed by singular values of random matrix products, and the number of factors in a random matrix product plays a role of a discrete time. We consider in detail the case when the (squared) singular values of the initial random matrix form a polynomial ensemble, and the initial random matrix is multiplied by standard complex Gaussian matrices. In this case, we show that the random process is a discrete-time determinantal point process. For three special cases (the case when the initial random matrix is a standard complex Gaussian matrix, the case when it is a truncated unitary matrix, or the case when it is a standard complex Gaussian matrix with a source) we compute the dynamical correlation functions explicitly, and find the hard edge scaling limits of the correlation kernels. The proofs rely on the Eynard–Mehta theorem, and on contour integral representations for the correlation kernels suitable for an asymptotic analysis.

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40

Tory, E. M., and D. K. Pickard. "Unilateral Gaussian fields." Advances in Applied Probability 24, no. 1 (March 1992): 95–112. http://dx.doi.org/10.2307/1427731.

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The necessary and sufficient condition for unilateral characterization of Gaussian Markov fields and the Besag-Moran positivity condition for second-order autonormal bilateral models define the same tetrahedral domain of achievable regression parameters. A bijective function maps this domain to a different tetrahedral domain of parameters in the Pickard model. These two domains are identical to the corresponding ones in the Welberry-Carroll model. We obtain series solutions for correlation coefficients and study their limits near the boundaries of the first domain.

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41

Tory, E. M., and D. K. Pickard. "Unilateral Gaussian fields." Advances in Applied Probability 24, no. 01 (March 1992): 95–112. http://dx.doi.org/10.1017/s0001867800024186.

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The necessary and sufficient condition for unilateral characterization of Gaussian Markov fields and the Besag-Moran positivity condition for second-order autonormal bilateral models define the same tetrahedral domain of achievable regression parameters. A bijective function maps this domain to a different tetrahedral domain of parameters in the Pickard model. These two domains are identical to the corresponding ones in the Welberry-Carroll model. We obtain series solutions for correlation coefficients and study their limits near the boundaries of the first domain.

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42

Yu, Hua Gang, Gao Ming Huang, and Jun Gao. "Multiset Canonical Correlation Analysis Using for Blind Source Separation." Applied Mechanics and Materials 195-196 (August 2012): 104–8. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.104.

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To solve the problem of blind source separation, a novel algorithm based on multiset canonical correlation analysis is presented by exploiting the different temporal structure of uncorrelated source signals. In contrast to higher order cumulant techniques, this algorithm is based on second order statistical characteristic of observation signals, can blind separate super-Gaussian and sub-Gaussian signals successfully at the same time with relatively light computation burden. Simulation results confirm that the algorithm is efficient and feasible.

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43

Sha, Zhi Gang, and Rulin Xiu. "String Theory Explanation of Large-Scale Anisotropy and Anomalous Alignment." Reports in Advances of Physical Sciences 02, no. 01 (March 2018): 1750012. http://dx.doi.org/10.1142/s2424942417500128.

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The discovery of anomalies in the cosmic microwave background (CMB) indicates large-scale anisotropies, non-Gaussian distributions, and anomalous alignments of the quadrupole and octupole modes of the anisotropy with each other and with both the ecliptic and equinoxes. Further analysis indicates that the statistical anisotropy and non-Gaussian temperature fluctuations are mainly due to long-range correlations. However, the source of the large-scale correlation and the cause of the anomalous alignment in CMB remains unknown. In this work, we show a new development in string theory, the universal wave function interpretation of string theory (UWFIST) indicates the existence of large-scale quantum vibrations. These large-scale quantum vibrations can cause the large-scale correlation and anomalous alignment observed in the background field. They can explain the observed large-scale anisotropies, non-Gaussian distributions, and anomalous alignments of the quadrupole and octupole modes in the microwave background.

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44

Zatula, D. "Estimates for the distribution of Hölder semi-norms of real stationary Gaussian processes with a stable correlation function." Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, no. 1-2 (2020): 25–30. http://dx.doi.org/10.17721/1812-5409.2020/1-2.3.

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Complex random variables and processes with a vanishing pseudo-correlation are called proper. There is a class of stationary proper complex random processes that have a stable correlation function. In the present article we consider real stationary Gaussian processes with a stable correlation function. It is shown that the trajectories of stationary Gaussian proper complex random processes with zero mean belong to the Orlich space generated by the function $U(x) = e^{x^2/2}-1$. Estimates are obtained for the distribution of semi-norms of sample functions of Gaussian proper complex random processes with a stable correlation function, defined on the compact $\mathbb{T} = [0,T]$, in Hölder spaces.

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45

Muirhead, S. "Boundedness of the nodal domains of additive Gaussian fields." Theory of Probability and Mathematical Statistics 106 (May 16, 2022): 143–55. http://dx.doi.org/10.1090/tpms/1169.

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We study the connectivity of the excursion sets of additive Gaussian fields, i.e. stationary centred Gaussian fields whose covariance function decomposes into a sum of terms that depend separately on the coordinates. Our main result is that, under mild smoothness and correlation decay assumptions, the excursion sets { f ≤ ℓ } \{f \le \ell \} of additive planar Gaussian fields are bounded almost surely at the critical level ℓ c = 0 \ell _c = 0 . Since we do not assume positive correlations, this provides the first examples of continuous non-positively-correlated stationary planar Gaussian fields for which the boundedness of the nodal domains has been confirmed. By contrast, in dimension d ≥ 3 d \ge 3 the excursion sets have unbounded components at all levels.

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46

Sanz, J. L., and E. Martinez-González. "Cluster Correlations for Scale-Free Spectra." Symposium - International Astronomical Union 130 (1988): 549. http://dx.doi.org/10.1017/s0074180900136782.

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The cluster autocorrelation function ξc and the galaxy-cluster cross correlation ξgc are used to test the biased structure formation for scale-free spectra P(k)∝kn. Following Kaiser (1984), we assume that rich clusters form only at higfr density regions with the matter distribution represented by a Gaussian random field. Then, the correlation ξ of two regions with characteristic scales R1 and R2 lying above the thresholds v1 and v2 (δ ≡ νσ), is given by the expression for the bivariate Gaussian where z(r) is the coviance function, i.e. and is the correlation of the matter distribution Gaussian filtered on the comoving scales R1 and R2. From the previous equations, one can obtain for P(k)∝ kn, τ≪1 and large r a powers-law form either for ξc or ξgc. Moreover, the amplification factor Ac - gc ≡ ξc/ξgc is

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47

Zhang, Ning Yu, Qing Song Huo, Li Xin Han, Gang Fu, Jun Qing Zhao, and Feng Xiang Wang. "Morphological Character and Statistical Property of Random Surface of ZnO Thin Film Prepared by RF Magnetron Sputtering." Materials Science Forum 663-665 (November 2010): 1159–62. http://dx.doi.org/10.4028/www.scientific.net/msf.663-665.1159.

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A method for characterizing the morphology property of ZnO film surface with Gaussian correlation is investigated. The parameters of root-mean-square roughness w and lateral correlation lengthξare introduced in Gaussian model to describe the correlation properties of the random film surfaces. In the experimental performance, ZnO thin films are grown on quartz glass and silicon substrates by the reactive radio-frequency magnetron sputtering method under different deposition pressure. The surface morphologies of the film surface are scanned by an atomic force microscopy. The height auto-correlation functions and root-mean-square roughness are obtained by using the numerical calculus method. Carried on the fitting with the Gaussian function to the height auto-correlation function data, the lateral correlation lengths are extracted to describe the statistical properties of ZnO thin film in mathematics with other parameters.

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48

Li, Dongxi, Bing Hu, Jia Wang, Yingchuan Jing, and Fangmei Hou. "Coherence resonance in the two-dimensional neural map driven by non-Gaussian colored noise." International Journal of Modern Physics B 30, no. 05 (February 20, 2016): 1650012. http://dx.doi.org/10.1142/s0217979216500120.

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Based on the two-dimensional (2D) neural map, we investigate the impacts of non-Gaussian colored noise on the firing activity of discrete system. Taking the coherence parameter R to measure the regularity of firing behavior, it is demonstrated that coherence parameter R has a pronounced minimum value with the noise intensity and the correlation time of non-Gaussian colored noise, which is the so-called phenomenon of coherence resonance (CR). Besides, the firing activity is not sensitive to the non-Gaussian parameter which determines the departure from the Gaussian distribution when the correlation time is large enough.

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Molteni, Matteo, Udo M. Weigel, Francisco Remiro, Turgut Durduran, and Fabio Ferri. "Hardware simulator for optical correlation spectroscopy with Gaussian statistics and arbitrary correlation functions." Optics Express 22, no. 23 (November 4, 2014): 28002. http://dx.doi.org/10.1364/oe.22.028002.

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50

Zhan, Meng, Suhong Li, and Fan Li. "Wavelet transformed Gaussian network model." Journal of Theoretical and Computational Chemistry 13, no. 06 (September 2014): 1450053. http://dx.doi.org/10.1142/s0219633614500539.

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Accurate prediction of the Debye–Waller temperature factor of proteins is of significant importance in the study of protein dynamics and function. This work explores the utility of wavelets for improving the performance of Gaussian network model (GNM). We propose two wavelet transformed Gaussian network models (wtGNM), namely a scale-one wtGNM and a scale-two wtGNM. Based on a set of 113 protein structures, it shows that the mean correlation with experimental results for the scale-one wtGNM is 0.714 and that for the scale-two wtGNM is 0.738. In contrast, the mean correlation for the original GNM is 0.594. Therefore, the wtGNM is a potential algorithm for improving the GNM prediction of protein B-factors.

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