Spatial Correlations around Magnetic Impurities in Metals

1987 ◽  
pp. 527-530
Author(s):  
Kan Chen ◽  
C. Jayaprakash ◽  
H. R. Krishnamurthy
Keyword(s):  
Magnetic Impurities ◽  
Spatial Correlations

Spatial Correlations in Monte Carlo Criticality Simulations

2014 ◽  
Author(s):  
E. Dumonteil ◽  
F. Malvagi ◽  
A. Zoia ◽  
A. Mazzolo ◽  
D. Artusio ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Spatial Correlations

Magnetic impurities in nonmagnetic metals

1969 ◽  
Vol 97 (3) ◽  
pp. 403-427 ◽  
Author(s):  
A.A. Abrikosov
Keyword(s):  
Magnetic Impurities ◽  
Nonmagnetic Metals

Certain Properties of Metals with Magnetic Impurities (Kondo Effect).

1972 ◽  
Vol 107 (5) ◽  
pp. 160
Author(s):  
A.A. Abrikosov
Keyword(s):  
Kondo Effect ◽  
Magnetic Impurities

Zero-gap semiconductors with magnetic impurities forming resonance donor states

1992 ◽  
Vol 162 (2) ◽  
pp. 63 ◽  
Author(s):  
I.M. Tsidil'kovskii
Keyword(s):  
Magnetic Impurities ◽  
Donor States

Quadratic Discriminant Analysis of Spatially Correlated Data

2001 ◽  
Vol 6 (2) ◽  
pp. 15-28 ◽  
Author(s):  
K. Dučinskas ◽  
J. Šaltytė
Keyword(s):  
Discriminant Function ◽  
Error Rate ◽  
Gaussian Random Field ◽  
Correlated Data ◽  
Covariance Matrices ◽  
Classification Rule ◽  
Spatial Correlations ◽  
Linear Quadratic ◽  
Spatial Correlation Function ◽  
Spatially Correlated

The problem of classification of the realisation of the stationary univariate Gaussian random field into one of two populations with different means and different factorised covariance matrices is considered. In such a case optimal classification rule in the sense of minimum probability of misclassification is associated with non-linear (quadratic) discriminant function. Unknown means and the covariance matrices of the feature vector components are estimated from spatially correlated training samples using the maximum likelihood approach and assuming spatial correlations to be known. Explicit formula of Bayes error rate and the first-order asymptotic expansion of the expected error rate associated with quadratic plug-in discriminant function are presented. A set of numerical calculations for the spherical spatial correlation function is performed and two different spatial sampling designs are compared.

Magnetic impurities in superconductors

Physica ◽  
1971 ◽  
Vol 55 ◽  
pp. 143-154 ◽  
Author(s):  
T. Sugawara
Keyword(s):  
Magnetic Impurities

scholarly journals Knowledge and Geo-Object Based Graph Convolutional Network for Remote Sensing Semantic Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3848
Author(s):  
Wei Cui ◽  
Meng Yao ◽  
Yuanjie Hao ◽  
Ziwei Wang ◽  
Xin He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Prior Knowledge ◽  
Contextual Information ◽  
Information Aggregation ◽  
Semantic Segmentation ◽  
Spatial Correlations ◽  
Convolutional Network ◽  
Object Based ◽  
Graph Neural Networks ◽  
Salt And Pepper

Pixel-based semantic segmentation models fail to effectively express geographic objects and their topological relationships. Therefore, in semantic segmentation of remote sensing images, these models fail to avoid salt-and-pepper effects and cannot achieve high accuracy either. To solve these problems, object-based models such as graph neural networks (GNNs) are considered. However, traditional GNNs directly use similarity or spatial correlations between nodes to aggregate nodes’ information, which rely too much on the contextual information of the sample. The contextual information of the sample is often distorted, which results in a reduction in the node classification accuracy. To solve this problem, a knowledge and geo-object-based graph convolutional network (KGGCN) is proposed. The KGGCN uses superpixel blocks as nodes of the graph network and combines prior knowledge with spatial correlations during information aggregation. By incorporating the prior knowledge obtained from all samples of the study area, the receptive field of the node is extended from its sample context to the study area. Thus, the distortion of the sample context is overcome effectively. Experiments demonstrate that our model is improved by 3.7% compared with the baseline model named Cluster GCN and 4.1% compared with U-Net.

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