Search

The main aim of this note is first to give the definition of the local classes. It also aims at studying the local property of some subclasses of plurisubharmonic functions by using these concepts. [ABSTRACT FROM AUTHOR]

The combination of feature extraction and classification methods is an often used approach for wafer map failure pattern recognition. Recently, the convolutional neural network (CNN) method is used to raw wafer map data without feature extraction. CNN can improve the accuracy, but the drawback is the expensive computation cost. In our study, we have extracted local property analysis based features and used them with the traditional classification methods, ensemble methods, and CNN for wafer map failure pattern recognition. The decision tree showed better performance than the other methods when evaluating single traditional classification methods. Therefore, we used the decision tree as the base classifier of the proposed ensemble learning. We built a constituent model on a single type of feature set first and then made an ensemble of models to produce the final decision. We found that it is better than directly building a model on total features. Also, we have tested and compared CNN on extracted feature sets and raw wafer map image data. The results showed that raw image based CNN outperforms extracted features under the same number of epochs. However, the training cost of the raw image is much expensive. Furthermore, the performance of extracted features becomes closer to the raw image when increasing the number of epochs with a much lower training cost. [ABSTRACT FROM AUTHOR]

This work exploits the Kohn nearsightedness principle in the algorithms arising from the anti‐Hermitian contracted Schrödinger equation. The procedure provides the implementation of approximations yielding a considerable saving of computational costs in descriptions of N‐electron molecular systems by means of that technique. We report energies, at correlated level, of linear hydrogen chains designed at different geometries, so that we can analyze the strength of the interactions between first‐, second‐, third‐, fourth‐, and so forth neighbor atoms. Using localized molecular orbital basis sets, we observe rapid decays in the values of the elements of the reduced‐density‐matrix cumulant matrices, beyond a cut‐off radius, which justify the proposed approximations. The results, in terms of energies and reduced density matrices, have been compared with those obtained from the full configuration interaction method, showing the usefulness of the nearsightedness principle in this methodology. [ABSTRACT FROM AUTHOR]

In the present paper, a theorem concerning local property of /A, pn/k summability of factored Fourier series, which generalizes a result dealing with /N, pn/k summability of factored Fourier series, has been obtained. Also, some results have been given. [ABSTRACT FROM AUTHOR]

In this paper, a general theorem on the local property of $|A,p_{n};\delta|_{k}$ summability of factored Fourier series has been proved. This new theorem also includes several new and known results.

Previous research has revealed the uniqueness-facilitation effect in the multiple object tracking (MOT) task: simple distinct identities and surface features of moving targets could facilitate attentional tracking. By adapting compound stimuli, the present study investigated whether the global or local properties played the main role in the uniqueness-facilitation effect in the MOT task. The uniqueness of local properties, of global properties or of both local and global properties were considered. Observers’ tracking performance in alternative conditions were compared with that in the homogeneous condition wherein all stimuli have identical local and global properties. Results from two experiments suggest that the global properties played the key role in facilitating tracking. The distinctiveness of local properties can also facilitate tracking with global properties being homogeneous. However, when the stimuli’s global properties are distinct from each other—whether the local properties being unique or not—observers’ tracking performance can achieve the same level as that in the unitary-uniqueness condition wherein the moving objects were distinct unitary letters. These results revealed a global superiority effect in the MOT task. Finally, the facilitation effects of the global and local properties might depend on the stimulus sparsity. When the compound stimuli had fewer local elements, the uniqueness facilitation effect on tracking decreased.

Any physical laws are scale-dependent, the same phenomenon might lead to debating theories if observed using different scales. The two-scale thermodynamics observes the same phenomenon using two different scales, one scale is generally used in the conventional continuum mechanics, and the other scale can reveal the hidden truth beyond the continuum assumption, and fractal calculus has to be adopted to establish governing equations. Here basic properties of fractal calculus are elucidated, and the relationship between the fractal calculus and traditional calculus is revealed using the two-scale transform, fractal variational principles are discussed for 1-D fluid mechanics. Additionally planet distribution in the fractal solar system, dark energy in the fractal space, and a fractal ageing model are also discussed. [ABSTRACT FROM AUTHOR]

In this paper, a theorem on local property of φ - ∣A, pn∣k summability of factored Fourier series, which generalizes a known result, has been proved. This new theorem also includes several new and known results. [ABSTRACT FROM AUTHOR]

Abnormalities in the brain connectivity in patients with neurodegenerative diseases, such as early mild cognitive impairment (EMCI), have been widely reported. Current research shows that the combination of multiple features of the threshold connectivity network can improve the classification accuracy of diseases. However, in the construction of the threshold connectivity network, the selection of the threshold is very important, and an unreasonable setting can seriously affect the final classification results. Recent neuroscience research suggests that the minimum spanning tree (MST) brain functional network is helpful, as it avoids the methodological biases while comparing networks. In this paper, by employing the multikernel method, we propose a framework to integrate the multiple properties of the MST brain functional network for improving the classification performance. Initially, the Kruskal algorithm was used to construct an unbiased MST brain functional network. Subsequently, the vector kernel and graph kernel were used to quantify the two different complementary properties of the network, such as the local connectivity property and the topological property. Finally, the multikernel support vector machine (SVM) was adopted to combine the two different kernels for EMCI classification. We tested the performance of our proposed method for Alzheimer's Disease Neuroimaging Initiative (ANDI) datasets. The results showed that our method achieved a significant performance improvement, with the classification accuracy of 85%. The abnormal brain regions included the right hippocampus, left parahippocampal gyrus, left posterior cingulate gyrus, middle temporal gyrus, and other regions that are known to be important in the EMCI. Our results suggested that, combining the multiple features of the MST brain functional connectivity offered a better classification performance in the EMCI.

Abnormalities in the brain connectivity in patients with neurodegenerative diseases, such as early mild cognitive impairment (EMCI), have been widely reported. Current research shows that the combination of multiple features of the threshold connectivity network can improve the classification accuracy of diseases. However, in the construction of the threshold connectivity network, the selection of the threshold is very important, and an unreasonable setting can seriously affect the final classification results. Recent neuroscience research suggests that the minimum spanning tree (MST) brain functional network is helpful, as it avoids the methodological biases while comparing networks. In this paper, by employing the multikernel method, we propose a framework to integrate the multiple properties of the MST brain functional network for improving the classification performance. Initially, the Kruskal algorithm was used to construct an unbiased MST brain functional network. Subsequently, the vector kernel and graph kernel were used to quantify the two different complementary properties of the network, such as the local connectivity property and the topological property. Finally, the multikernel support vector machine (SVM) was adopted to combine the two different kernels for EMCI classification. We tested the performance of our proposed method for Alzheimer's Disease Neuroimaging Initiative (ANDI) datasets. The results showed that our method achieved a significant performance improvement, with the classification accuracy of 85%. The abnormal brain regions included the right hippocampus, left parahippocampal gyrus, left posterior cingulate gyrus, middle temporal gyrus, and other regions that are known to be important in the EMCI. Our results suggested that, combining the multiple features of the MST brain functional connectivity offered a better classification performance in the EMCI. [ABSTRACT FROM AUTHOR]

Based on the rule that numerical algorithms should preserve the intrinsic properties of the original problem as many as possible, we propose two local energy-preserving algorithms for the nonlinear fourth-order Schrödinger equation with a trapped term. The local energy conservation law is preserved on any local time-space region. With appropriate boundary conditions, the first algorithm will be both globally charge- and energy-preserving and the second one will be energy-preserving. Numerical experiments show that the proposed algorithms provide more accurate solution than many existing methods and also exhibit excellent performance in preserving conservation laws. [ABSTRACT FROM AUTHOR]

The aim of this paper is to generalize a main theorem dealing with local property of Fourier series to the ∣A; θn∣k summability. Also some new and known results are obtained dealing with some basic summability methods. [ABSTRACT FROM AUTHOR]

The comparison of Atanassov intuitionistic fuzzy sets (AIF-sets) is a topic that has been widely studied due to its several applications in image segmentation or decision making, among other fields. Divergences for AIF-sets (AIF-divergences) were introduced as an adequate measure of comparison for AIF-sets. This study investigates a family of AIF-divergences that satisfies a local property. Such a property allows us to compute the divergence between AIF-sets pointwise. A characterization of those AIF-divergences satisfying the local property is provided. Several interesting properties of local divergence are also discussed. Some applications of these AIF-divergences in pattern recognition and decision making illustrate their utility. [ABSTRACT FROM PUBLISHER]