CDEC:a constrained deep embedded clustering

Purpose-The aim of this study is to propose a deep neural network(DNN)method that uses side information to improve clustering results for big datasets;also,the authors show that applying this information improves the performance of clustering and also increase the speed of the network training convergence.Design/methodology/approach-In data mining,semisupervised learning is an interesting approach because good performance can be achieved with a small subset of labeled data;one reason is that the data labeling is expensive,and semisupervised learning does not need all labels.One type of semisupervised learning is constrained clustering;this type of learning does not use class labels for clustering.Instead,it uses information of some pairs of instances(side information),and these instances maybe are in the same cluster(must-link[ML])or in different clusters(cannot-link[CL]).Constrained clustering was studied extensively;however,little works have focused on constrained clustering for big datasets.In this paper,the authors have presented a constrained clustering for big datasets,and the method uses a DNN.The authors inject the constraints(ML and CL)to this DNN to promote the clustering performance and call it constrained deep embedded clustering(CDEC).In this manner,an autoencoder was implemented to elicit informative low dimensional features in the latent space and then retrain the encoder network using a proposed Kullback-Leibler divergence objective function,which captures the constraints in order to cluster the projected samples.The proposed CDEC has been compared with the adversarial autoencoder,constrained 1-spectral clustering and autoencoder t k-means was applied to the known MNIST,Reuters-10k and USPS datasets,and their performance were assessed in terms of clustering accuracy.Empirical results confirmed the statistical superiority of CDEC in terms of clustering accuracy to the counterparts.Findings-First of all,this is the first DNN-constrained clustering that uses side information to improve the performance of clustering without using labels in big datasets with high dimension.Second,the author defined a formula to inject side information to the DNN.Third,the proposed method improves clustering performance and network convergence speed.Originality/value-Little works have focused on constrained clustering for big datasets;also,the studies in DNNs for clustering,with specific loss function that simultaneously extract features and clustering the data,are rare.The method improves the performance of big data clustering without using labels,and it is important because the data labeling is expensive and time-consuming,especially for big datasets.

Identification of High-Risk Scenarios for Cascading Failures in New Energy Power Grids Based on Deep Embedding Clustering Algorithms

At present,the proportion of new energy in the power grid is increasing,and the random fluctuations in power output increase the risk of cascading failures in the power grid.In this paper,we propose a method for identifying high-risk scenarios of interlocking faults in new energy power grids based on a deep embedding clustering(DEC)algorithm and apply it in a risk assessment of cascading failures in different operating scenarios for new energy power grids.First,considering the real-time operation status and system structure of new energy power grids,the scenario cascading failure risk indicator is established.Based on this indicator,the risk of cascading failure is calculated for the scenario set,the scenarios are clustered based on the DEC algorithm,and the scenarios with the highest indicators are selected as the significant risk scenario set.The results of simulations with an example power grid show that our method can effectively identify scenarios with a high risk of cascading failures from a large number of scenarios.

Some Comments on Embeddedness, Knowledge Transfer, Industry Clusters and Global Competitiveness

This paper develops a dynamic theoretical framework for global competitiveness, which describes the relationships among organizations in an industry cluster. The spiral for knowledge transfer, culture variables and embeddedness influence knowledge transfer. Embeddedness and knowledge transfer are the key determinants of industry clusters that lead to global competitiveness. Industry clusters are characterized by external economies, generalized reciprocity and flexible specialization.

Study on Recognition Method of Similar Weather Scenes in Terminal Area

Weather is a key factor affecting the control of air traffic.Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air trafficflow management.Current researches mostly use traditional machine learning methods to extract features of weather scenes,and clustering algorithms to divide similar scenes.Inspired by the excellent performance of deep learning in image recognition,this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering(IDCEC),which uses the com-bination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image,retaining useful information to the greatest extent,and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area.Finally,term-inal area of Guangzhou Airport is selected as the research object,the method pro-posed in this article is used to classify historical weather data in similar scenes,and the performance is compared with other state-of-the-art methods.The experi-mental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather;at the same time,compared with the actualflight volume in the Guangz-hou terminal area,IDCEC's recognition results of similar weather scenes are con-sistent with the recognition of experts in thefield.

An Unsupervised Writer Identification Based on Generating Clusterable Embeddings

The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification systems.Due to its importance,numerous studies have been conducted in various languages.Researchers have established several learning methods for writer identification including supervised and unsupervised learning.However,supervised methods require a large amount of annotation data,which is impossible in most scenarios.On the other hand,unsupervised writer identification methods may be limited and dependent on feature extraction that cannot provide the proper objectives to the architecture and be misinterpreted.This paper introduces an unsupervised writer identification system that analyzes the data and recognizes the writer based on the inter-feature relations of the data to resolve the uncertainty of the features.A pairwise architecturebased Autoembedder was applied to generate clusterable embeddings for handwritten text images.Furthermore,the trained baseline architecture generates the embedding of the data image,and the K-means algorithm is used to distinguish the embedding of individual writers.The proposed model utilized the IAM dataset for the experiment as it is inconsistent with contributions from the authors but is easily accessible for writer identification tasks.In addition,traditional evaluation metrics are used in the proposed model.Finally,the proposed model is compared with a few unsupervised models,and it outperformed the state-of-the-art deep convolutional architectures in recognizing writers based on unlabeled data.

Ab Initio Calculation of 4f→5d Transition Energy and Electronic Structure of Ce^(3+) Doped in LiYF_4 Crystal

The electronic structures of LiYF4：Ce^3＋ and LiYF4 crystal simulated by an embedded （in a microcrystal containing 1938 ions） cluster CeY4Li8F24, and Y5LisF24 respectively, were computed by the ab initio self-consistent relativistic DV-Xa （discrete variational Xa） method. The ground-state calculation showed that only the lowest 5d level Ed of Ce^3＋ ion lies around the BCB （bottom of the conduction band） while the lowest 4f levels is 2.5 eV lower than BCB. The CB states consist of 4d of Y mixed with 5d of Ce, even for the wavefunctions （WFS） with energy Ed under BCB there are still 24% of Y-4d and 9% of F-2p as components. So, they are not pure crystal-field states at all. Furthermore, transition state （TS） calculation was performed to obtain the 4f→5d transition energies Efd, to improve the previous calculation performed by Andriessen et al, in which a small CeF8 cluster embedded in an array of point charge was used and the results of ground-state calculation were roughly used to compare with the observed 4f→5 d transition energies. The ionic radius of Ce^3＋ is larger than that of y^3＋ , so we had also modeled approximately the lattice relaxation. As results, the CeY4Li8F24 cluster with 4.56 % outward relaxation （of the nearest-neighbor and next nearest-neighbor eight fluorines） has the lowest total energy and gave satisfactory 4f→5d energies Efd, but the ground-state calculated Ed is 0.68 eV higher than BCB. For another cluster with 7.36% outward relaxation the Ed is 0.43 eV lower than BCB, which makes the observation of fine structure （including zero-phonon line） of the lowest 5 d band understandable easier, but the splits between the transition energies Efd were not as good as the former. Therefore, we consider the relaxation is some how around 4. 56% -7.36% outward, not as large as 10% proposed by Andriessen et al.

Ab initio calculation of electronic structures and 4f-5d transitions of some rare earth ions doped in crystal YPO_4

The ab initio self-consistent DV-Xa （discrete variational Xa） method was used in its relativistic and spin-polarized model to investigate the ground-state electronic structttres of the crystal YPO4 and YPO4：RE^3＋ （RE=Ce, Pr and Sm） and f-d transition energies of the lattice. The calculation was performed on the clusters Y5P10O32 and REY4P10O32 embedded in a microcrystal containing about 1500 ions, respectively. The ground-state calculation provided the locations of the 4f and 5d crystal-field one-electron levels of RE^3＋ relative to the valence and conduction bands of host, the curve of total and the partial density of states, and the corresponding occupation numbers, etc. Especially, the transition-state calculation was performed to obtain the 4f→5d transition energies of RE^3＋ in comparison to the experimental observations. The lattice relaxation caused by the dopant ion RE3＋ was discussed based on the total energy calculation and the transition-state calculation of the f-d transition energies.

Quantum Mechanical Calculations of High-T_cFe-Superconductors

In introduction we presented a short historical survey of the discovery of superconductivity (SC) up to the Fe-based materials that are not superconducting in a pure state. For this type of material, the transition to SC state occurs in presence of different dopants. Recently in the Fe-based materials at high pressures, the SC was obtained at room critical temperature. In this paper, we present the results of calculations of the isolated cluster representing infinitum crystal with Rh and Pd as dopants. All calculations are performed with the suite of programs Gaussian 16. The obtained results are compared with our previous results obtained for embedded cluster using Gaussian 09. In the case of embedded cluster our methodology of the Embedded Cluster Method at the MP2 electron correlation level was applied. In the NBO population analysis two main features are revealed: the independence of charge density transfer from the spin density transfer and, the presence of orbitals with electron density but without spin density. This is similar to the Anderson’s spinless holon and confirms our conclusions in previous publications that the possible mechanism for superconductivity can be the RVB mechanism proposed by Anderson for high Tc superconductivity in cuprates.

Ab Initio Calculation of 4f→5d Transition Energy and Electronic Structure of Ce^(3+) Doped in LiYF_(4) Crystal

The electronic structures of LiYF4：Ce^3＋ and LiYF4 crystal simulated by an embedded （in a microcrystal containing 1938 ions） cluster CeY4Li8F24, and Y5LisF24 respectively, were computed by the ab initio self-consistent relativistic DV-Xa （discrete variational Xa） method. The ground-state calculation showed that only the lowest 5d level Ed of Ce^3＋ ion lies around the BCB （bottom of the conduction band） while the lowest 4f levels is 2.5 eV lower than BCB. The CB states consist of 4d of Y mixed with 5d of Ce, even for the wavefunctions （WFS） with energy Ed under BCB there are still 24% of Y-4d and 9% of F-2p as components. So, they are not pure crystal-field states at all. Furthermore, transition state （TS） calculation was performed to obtain the 4f→5d transition energies Efd, to improve the previous calculation performed by Andriessen et al, in which a small CeF8 cluster embedded in an array of point charge was used and the results of ground-state calculation were roughly used to compare with the observed 4f→5 d transition energies. The ionic radius of Ce^3＋ is larger than that of y^3＋ , so we had also modeled approximately the lattice relaxation. As results, the CeY4Li8F24 cluster with 4.56 % outward relaxation （of the nearest-neighbor and next nearest-neighbor eight fluorines） has the lowest total energy and gave satisfactory 4f→5d energies Efd, but the ground-state calculated Ed is 0.68 eV higher than BCB. For another cluster with 7.36% outward relaxation the Ed is 0.43 eV lower than BCB, which makes the observation of fine structure （including zero-phonon line） of the lowest 5 d band understandable easier, but the splits between the transition energies Efd were not as good as the former. Therefore, we consider the relaxation is some how around 4. 56% -7.36% outward, not as large as 10% proposed by Andriessen et al.

An optimized cluster density matrix embedding theory

We propose an optimized cluster density matrix embedding theory(CDMET).It reduces the computational cost of CDMET with simpler bath states.And the result is as accurate as the original one.As a demonstration,we study the distant correlations of the Heisenberg J_(1)-J_(2)model on the square lattice.We find that the intermediate phase(0.43≤sssim J_(2)≤sssim 0.62)is divided into two parts.One part is a near-critical region(0.43≤J_(2)≤0.50).The other part is the plaquette valence bond solid(PVB)state(0.51≤J_(2)≤0.62).The spin correlations decay exponentially as a function of distance in the PVB.

The Superconductivity in Fe-Based Family of Superconductors and Its Electronic Structure Analysis in Presence of Dopants Rh and Pd

The discovered in 2008 Fe-based superconductors (SC) are a paramagnetic semimetal at ambient temperature and in some cases they become superconductor upon doping. In spite of so many years since its discovery it is still not known the mechanism that leads to superconductivity. The electronic structure study is used for determining key features of the SC mechanism in these materials. The calculations were performed using the modern suite of programs MOLPRO 2021. We performed quantum calculations of a cluster embedded in a background charge distribution that represents the infinite crystal. The Natural Population Analysis was used for determining the charge and spin distribution in the studied materials. As follows from our results, the possible mechanism for superconductivity corresponds to the RVB theory proposed by Anderson for high Tc superconductivity in cuprates.

SPC cluster modeling of metal oxides: ways of determining the values of point charges in the embedded cluster model

Several criteria for determining self consistently the magnitude of point charges employed in the embedded cluster modeling of metal oxides have been proposed and investigated. Merits and demerits of these criteria have been compared. Ab initio study has been performed to show the influence of the values of point charges chosen on the calculated electronic properties of the embedded MgO cluster. The calculation results demonstrate that the electronic properties of the embedded cluster are of great dependence on the magnitude of the embedding point charges; that the employment of the nominal charges, ±2.0, would cause overestimation of the crystal potential even in the case of the so called purely ionic oxide, MgO; and that certain requirements for the consistence between the embedded cluster and the embedding point charges should be reached. It is further found that errors for the calculated properties of the embedded cluster still exist with respect to those of bulk solid even in the case that self consistence in terms of charge, dipole moment, or electrostatic potential was met between the cut out cluster and the embedding point charges. As far as spherical expansion is performed upon the embedding point charges, which furnishes the embedding point charges with a continuous distribution of charge density, a global agreement is reached between the calculated properties of the embedded cluster model and those of the bulk solid.

Reduction of Nitric Oxide over Rutile-supported Cu Surfaces： A Quantum Chemical Study

The adsorption and decomposition of NO on the stoichiometric rutile-supported Cu surfaces have been studied by means of density functional calculations with an embedded cluster model. The calculation results indicate that NO is favorably adsorbed as O-down and could easily attach another NO molecule to form N2O intermediate or directly be dissociated into N2 and O2. On the basis of the calculated energetics, possible mechanism of NO decomposition reaction has been proposed.