An integrated approach is proposed to investigate the fuzzy multi-attribute decision-making （MADM） problems, where subjective preferences are expressed by a pairwise comparison matrix on the relative weights of attr...An integrated approach is proposed to investigate the fuzzy multi-attribute decision-making （MADM） problems, where subjective preferences are expressed by a pairwise comparison matrix on the relative weights of attributes and objective information is expressed by a decision matrix. An eigenvector method integrated the subjective fuzzy preference matrix and objective information is proposed. Two linear programming models based on subjective and objective information are introduced to assess the relative importance weights of attributes in an MADM problem. The simple additive weighting method is utilized to aggregate the decision information, and then all the alternatives are ranked. Finally, a numerical example is given to show the feasibility and effectiveness of the method. The result shows that it is easier than other methods of integrating subjective and objective information.展开更多
As same as the conventional inverse synthetic aperture radar（ISAR）, the compressed ISAR also requires the echo signal based motion compensation, which consists of the range alignment and the phase autofoeusing. A ph...As same as the conventional inverse synthetic aperture radar（ISAR）, the compressed ISAR also requires the echo signal based motion compensation, which consists of the range alignment and the phase autofoeusing. A phase autofocusing algorithm for compressed ISAR imaging is presented. In the algorithm, phase autofocusing for the sparse ISAR echoes is accomplished using the eigenvector method. Experimental results validate the effectiveness of the algorithm.展开更多
A novel decorrelating DOA estimation algorithm of multipath signals for CDMA frequency selective fading channels based only on the principal eigenvector of its corresponding covariance matrix is proposed. The propose...A novel decorrelating DOA estimation algorithm of multipath signals for CDMA frequency selective fading channels based only on the principal eigenvector of its corresponding covariance matrix is proposed. The proposed algorithm has the advantages that the DOAs of the multipath signals can be estimated independently and all the other resolved multipath signal interference is eliminated. Simulation results show that this algorithm estimates the DOAs of multipath signals efficiently and accurately.展开更多
This paper presents a preliminary result on the retrieval of atmospheric ozone profiles using an im- proved regression technique and utilizing the data from the Atmospheric InfraRed Sounder (AIRS), a hyper-spectral In...This paper presents a preliminary result on the retrieval of atmospheric ozone profiles using an im- proved regression technique and utilizing the data from the Atmospheric InfraRed Sounder (AIRS), a hyper-spectral Instrument expected to be flown on the EOS-AQUA platform in 20D2. Simulated AIRS spectra were used to study the sensitivity of AIRS radiance on the tropospheric and stratospheric ozone changes, and to study the impact of various channel combinations on the ozone profile retrieval. Sensitivity study results indicate that the AIRS high resolution spectral channels between the wavenumber 650- 800 cm-1 provide very useful information to accurately retrieve tropospheric and stratospheric ozone pro- files Eigenvector decomposition of AIRS spectra indicate that no more than 100 eigenvectors are needed to retrieve very accurate ozone profiles. The accuracy of the retrieved atmospheric ozone profile from the pres- ent technique and utilizing the AIRS data was compared with the accuracy obtained from current Advanced TIROS Operational Vertical Sounder (ATOVS) data aboard National Oceanic and Atmospheric Admini- stration (NOAA) satellites As expected, a comparison of retrieval results confirms that the ozone profile re- trieved with the AIRS data is superior to that of ATOVS展开更多
ABSTRACT Satellite-based observations provide great opportunities for improving weather forecasting. Physical retrieval of atmo spheric profiles from satellite observations is sensitive to the uncertainty of the firs...ABSTRACT Satellite-based observations provide great opportunities for improving weather forecasting. Physical retrieval of atmo spheric profiles from satellite observations is sensitive to the uncertainty of the first guess and other factors. In order to improve the accuracy of the physical retrieval, an ensemble methodology was developed with an emphasis on perturbing the first guess. In the methodology, a normal probability density function （PDF） is used to select the optimal profile from the ensemble retrievals. The ensemble retrieval algorithm contains four steps： （1） regression retrieval for original first guess; （2） perturbation of the original first guess to generate new first guesses （ensemble first guesses）; （3） using the ensemble first guesses and nonlinear iterative physical retrieval to generate ensemble physical results; and （4） the final optimal profile is selected from the ensemble physical results by using PDE Temperature eigenvectors （EVs） were used to generate the pertur- bation and generate the ensemble first guess. Compared with the regular temperature profile retrievals from the Atmospheric InfraRed Sounder （AIRS）, the ensemble retrievals RMSE of temperature profiles selected by the PDF was reduced between 150 and 320 hPa and below 400 hPa, with a maximum improvement of 0.3 K at 400 hPa. The bias was also reduced in many layers, with a maximum improvement of 0.69 K at 460 hPa. The combined optimal （CombOpt） profile and a mean optimal （MeanOpt） profile of all ensemble physical results were improved below 150 hPa. The MeanOpt profile was better than the CombOpt profile, and was regarded as the final optimal （FinOpt） profile. This study lays the foundation for improving temperature retrievals from hyper-spectral infrared radiance measurements.展开更多
Certain deterministic nonlinear systems may show chaotic behavior. We consider the motion of qualitative information and the practicalities of extracting a part from chaotic experimental data. Our approach based on a ...Certain deterministic nonlinear systems may show chaotic behavior. We consider the motion of qualitative information and the practicalities of extracting a part from chaotic experimental data. Our approach based on a theorem of Takens draws on the ideas from the generalized theory of information known as singular system analysis. We illustrate this technique by numerical data from the chaotic region of the chaotic experimental data. The method of the singular-value decomposition is used to calculate the eigenvalues of embedding space matrix. The corresponding concrete algorithm to calculate eigenvectors and to obtain the basis of embedding vector space is proposed in this paper. The projection on the orthogonal basis generated by eigenvectors of timeseries data and concrete paradigm are also provided here. Meanwhile the state space reconstruction technology of different kinds of chaotic data obtained from dynamical system has also been discussed in detail.展开更多
An eigenvector method for ranking alternatives whose measurements are given as vague values is provided. Firstly, a positive matrix is constructed which is defined as evaluation information matrix （EIM）. Based on fo...An eigenvector method for ranking alternatives whose measurements are given as vague values is provided. Firstly, a positive matrix is constructed which is defined as evaluation information matrix （EIM）. Based on four assumptions for evaluating alternatives, a ranking eigenvector is defined. And then it is proved, based on positive matrix theory, that the EIM＇s eigenvector corresponding to the maximal eigenvalue is the ranking vector. For alternatives whose characteristics are presented by vague sets, the proposed techniques can evaluate the degree of suitability to which an alternative satisfies the decision-maker＇ s requirement efficiently.展开更多
The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a se...The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool to reduce multidimensional data to lower dimensions while retaining most of the information. It covers standard deviation, covariance, and eigenvectors. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.展开更多
Although China was one of the countries with the fastest-growing aging population in the world,limited scholarly attention has been paid to migration among older adults in China.The full picture of their migration in ...Although China was one of the countries with the fastest-growing aging population in the world,limited scholarly attention has been paid to migration among older adults in China.The full picture of their migration in the entire country over time remains unknown.This study examines the spatial patterns of older interprovincial migration flows and their drivers in China over the period 1995 to 2015,using four waves of census data and intercensal population sample survey data.Results from eigenvector spatial filtering negative binomial regressions indicate that older adults tend to migrate away from low cost-of-living rural areas to high cost-of-living urban and rural areas,moving away from areas with extreme temperature differences.The location of their grandchildren is among the most important attractions.Our findings suggest that family-oriented migration is more common than amenity-led migration among retired Chinese older adults,and the cost-of-living is an indicator of economic opportunities for adult children and the quality of senior care services.展开更多
The sensitivity calculating formulas in structural dynamics was developed by utilizing the mathematical theorem and new definitions of sensitivities. So the singularity problem of sensitivity with repeated eigenvalues...The sensitivity calculating formulas in structural dynamics was developed by utilizing the mathematical theorem and new definitions of sensitivities. So the singularity problem of sensitivity with repeated eigenvalues is solved completely. To improve the computational efficiency, the reduction system is obtained based on Lanczos vectors. After incorporating the mathematical theory with the Lanczos algorithm, the approximate sensitivity solution can be obtained. A numerical example is presented to illustrate the performance of the method.展开更多
Social relationships formed within a network of interacting group members can have a profound impact on an indi- vidual＇s behavior and fitness. However, we have little understanding of how individuals perceive their ...Social relationships formed within a network of interacting group members can have a profound impact on an indi- vidual＇s behavior and fitness. However, we have little understanding of how individuals perceive their relationships and how this perception relates to our external measures of interactions. We investigated the perception of affiliative and agonistic relation- ships at both the dyadic and emergent social levels in two captive groups of monk parakeets （Myiopsitta monachus, n = 21 and 19） using social network analysis and playback experiments. At the dyadic social scale, individuals directed less aggression to- wards their strong affiliative partners and more aggression towards non-partner neighbors.At the emergent social scale, there was no association between relationships in different social contexts and an individual＇s dominance rank did not correlate with its popularity rank. Playback response pattems were mainly driven by relationships in affiliative social contexts at the dyadic scale. In both groups, individual responses to playback experiments were significantly affected by strong affiliative relationships at the dyadic social scale, albeit in different directions in the two groups. Response pattems were also affected by affiliative relation- ships at the emergent social scale, but only in one of the two groups. Within affiliative relationships, those at the dyadic social scale were perceived by individuals in both groups, but those at the emergent social scale only affected responses in one group. These results provide preliminary evidence that relationships in affiliative social contexts may be perceived as more important than agonistic relationships in captive monk parakeet groups. Our approach could be used in a wide range of social species and comparative analyses could provide important insight into how individuals perceive relationships across social contexts and social scales [Current Zoology 61 （1）： 55-69, 2015].展开更多
The increase in China’s skilled labor force has drawn much attention from policymakers,national and international firms and media.Understanding how educated talent locates and re-locates across the country can guide ...The increase in China’s skilled labor force has drawn much attention from policymakers,national and international firms and media.Understanding how educated talent locates and re-locates across the country can guide future policy discussions of equality,firm localization and service allocation.Prior studies have tended to adopt a static cross-national approach providing valuable insights into the relative importance of economic and amenity differentials driving the distribution of talent in China.Yet,few adopt longitudinal analysis to examine the temporal dynamics in the stregnth of existing associations.Recently released official statistical data now enables space-time analysis of the geographic distribution of talent and its determinants in China.Using four-year city-level data from national population censuses and 1%population sample surveys conducted every five years between 2000 and 2015,we examine the spatial patterns of talent across Chinese cities and their underpinning drivers evolve over time.Results reveal that the spatial distribution of talent in China is persistently unequal and spatially concentrated between 2000 and 2015.It also shows gradually strengthened and significantly positive spatial autocorrelation in the distribution of talent.An eigenvector spatial filtering negative binomial panel is employed to model the spatial determinants of talent distribution.Results indicate the influences of both economic opportunities and urban amenities,particularly urban public services and greening rate,on the distribution of talent.These results highlight that urban economic-and amenity-related factors have simultaneously driven China’s talent’s settlement patterns over the first fifteen years of the 21st century.展开更多
COVID-19 outbreaks in China in late December 2019,then in the United States(US)in early 2020.In the initial wave of diffusion,the virus respectively took 14 and 33 days to spread across the provinces/states in the Chi...COVID-19 outbreaks in China in late December 2019,then in the United States(US)in early 2020.In the initial wave of diffusion,the virus respectively took 14 and 33 days to spread across the provinces/states in the Chinese mainland and the coterminous US,during which there are 43%and 70%zero entries in the space-time series for China and US respectively,indicating a zero-inflated count process.A logistic growth curve as a function of the number of days since the first case appeared in each of these countries accurately portrays the national aggregate per capita rates of infection for both.This paper presents two space-time model specifications,one based upon the generalized linear mixed model,and the other upon Moran eigenvector space-time filtering,to describe the spread of COVID-19 in the initial 19 and 58 days across the Chinese mainland and the coterminous US,respectively.Results from these case studies show both models shed new light on the role of spatial structures in COVID-19 diffusion,models that can forecast new cases in subsequent days.A principal finding is that describing the spatiotemporal diffusion of COVID-19 benefits from including a hierarchical structural component to supplement the commonly employed contagion component.展开更多
A family of modal methods for computing eigenvector derivatives with repeated roots are directly derived from the constraint generalized inverse technique which is originally formulated by Wang and Hu. Extensions are ...A family of modal methods for computing eigenvector derivatives with repeated roots are directly derived from the constraint generalized inverse technique which is originally formulated by Wang and Hu. Extensions are made to Akgun's method to allow treatment of eigensensitivity with repeated roots for general nondefective systems, and Bernard and Bronowicki's modal expansion approach is expanded to a family of modal methods.展开更多
An improved face recognition method is proposed based on principal component analysis （PCA） compounded with genetic algorithm （GA）, named as genetic based principal component analysis （GPCA）. Initially the eigen...An improved face recognition method is proposed based on principal component analysis （PCA） compounded with genetic algorithm （GA）, named as genetic based principal component analysis （GPCA）. Initially the eigenspace is created with eigenvalues and eigenvectors. From this space, the eigenfaces are constructed, and the most relevant eigenfaees have been selected using GPCA. With these eigenfaees, the input images are classified based on Euclidian distance. The proposed method was tested on ORL （Olivetti Research Labs） face database. Experimental results on this database demonstrate that the effectiveness of the proposed method for face recognition has less misclassification in comparison with previous methods.展开更多
In this paper the mathematical definition of the minimum energy generalized inverse is given and its application for computing eigen-sensitivity is demonstrated.By comparing it with the others,this paper clarifies the...In this paper the mathematical definition of the minimum energy generalized inverse is given and its application for computing eigen-sensitivity is demonstrated.By comparing it with the others,this paper clarifies the merits of the present algorithm.Furthermore,an erroneous relation which is still widely used is rectified.展开更多
In this paper, we consider the eigenvalue problem of a class of fourth-order operator matrices appearing in mechan- ics, including the geometric multiplicity, algebraic index, and algebraic multiplicity of the eigenva...In this paper, we consider the eigenvalue problem of a class of fourth-order operator matrices appearing in mechan- ics, including the geometric multiplicity, algebraic index, and algebraic multiplicity of the eigenvalue, the symplectic orthogonality, and completeness of eigen and root vector systems. The obtained results are applied to the plate bending problem.展开更多
A procedure is presented for computing the derivatives of repeated eigenvalues and the corresponding eigenvectors of damped systems. The derivatives are calculated in terms of the eigenvalues and eigenvectors of the s...A procedure is presented for computing the derivatives of repeated eigenvalues and the corresponding eigenvectors of damped systems. The derivatives are calculated in terms of the eigenvalues and eigenvectors of the second-order system, and the use of rather undesirable state space representation is avoided. Hence the cost of computation is greatly reduced. The efficiency of the proposed procedure is illustrated by considering a 5-DOF non-proportionally damped system.展开更多
This paper deals with a class of upper triangular infinite-dimensional Hamilto- nian operators appearing in the elasticity theory. The geometric multiplicity and algebraic index of the eigenvalue are investigated. Fur...This paper deals with a class of upper triangular infinite-dimensional Hamilto- nian operators appearing in the elasticity theory. The geometric multiplicity and algebraic index of the eigenvalue are investigated. Furthermore, the algebraic multiplicity of the eigenvalue is obtained. Based on these properties, the concrete completeness formulation of the system of eigenvectors or root vectors of the Hamiltonian operator is proposed. It is shown that the completeness is determined by the system of eigenvectors of the operator entries. Finally, the applications of the results to some problems in the elasticity theory are presented.展开更多
Snow water equivalent(SWE)is an important factor reflecting the variability of snow.It is important to estimate SWE based on remote sensing data while taking spatial autocorrelation into account.Based on the segmentat...Snow water equivalent(SWE)is an important factor reflecting the variability of snow.It is important to estimate SWE based on remote sensing data while taking spatial autocorrelation into account.Based on the segmentation method,the relationship between SWE and environmental factors in the central part of the Tibetan Plateau was explored using the eigenvector spatial filtering(ESF)regression model,and the influence of different factors on the SWE was explored.Three sizes of 16×16,24×24 and 32×32 were selected to segment raster datasets into blocks.The eigenvectors of the spatial adjacency matrix of the segmented size were selected to be added into the model as spatial factors,and the ESF regression model was constructed for each block in parallel.Results show that precipitation has a great influence on SWE,while surface temperature and NDVI have little influence.Air temperature,elevation and surface temperature have completely different effects in different areas.Compared with the ordinary least square(OLS)linear regression model,geographically weighted regression(GWR)model,spatial lag model(SLM)and spatial error model(SEM),ESF model can eliminate spatial autocorrelation with the highest accuracy.As the segmentation size increases,the complexity of ESF model increases,but the accuracy is improved.展开更多
文摘An integrated approach is proposed to investigate the fuzzy multi-attribute decision-making （MADM） problems, where subjective preferences are expressed by a pairwise comparison matrix on the relative weights of attributes and objective information is expressed by a decision matrix. An eigenvector method integrated the subjective fuzzy preference matrix and objective information is proposed. Two linear programming models based on subjective and objective information are introduced to assess the relative importance weights of attributes in an MADM problem. The simple additive weighting method is utilized to aggregate the decision information, and then all the alternatives are ranked. Finally, a numerical example is given to show the feasibility and effectiveness of the method. The result shows that it is easier than other methods of integrating subjective and objective information.
基金Supported by the National Natural Science Foundation of China(61071165)the Program for NewCentury Excellent Talents in University(NCET-09-0069)the Defense Industrial Technology Development Program(B2520110008)~~
文摘As same as the conventional inverse synthetic aperture radar（ISAR）, the compressed ISAR also requires the echo signal based motion compensation, which consists of the range alignment and the phase autofoeusing. A phase autofocusing algorithm for compressed ISAR imaging is presented. In the algorithm, phase autofocusing for the sparse ISAR echoes is accomplished using the eigenvector method. Experimental results validate the effectiveness of the algorithm.
文摘A novel decorrelating DOA estimation algorithm of multipath signals for CDMA frequency selective fading channels based only on the principal eigenvector of its corresponding covariance matrix is proposed. The proposed algorithm has the advantages that the DOAs of the multipath signals can be estimated independently and all the other resolved multipath signal interference is eliminated. Simulation results show that this algorithm estimates the DOAs of multipath signals efficiently and accurately.
文摘This paper presents a preliminary result on the retrieval of atmospheric ozone profiles using an im- proved regression technique and utilizing the data from the Atmospheric InfraRed Sounder (AIRS), a hyper-spectral Instrument expected to be flown on the EOS-AQUA platform in 20D2. Simulated AIRS spectra were used to study the sensitivity of AIRS radiance on the tropospheric and stratospheric ozone changes, and to study the impact of various channel combinations on the ozone profile retrieval. Sensitivity study results indicate that the AIRS high resolution spectral channels between the wavenumber 650- 800 cm-1 provide very useful information to accurately retrieve tropospheric and stratospheric ozone pro- files Eigenvector decomposition of AIRS spectra indicate that no more than 100 eigenvectors are needed to retrieve very accurate ozone profiles. The accuracy of the retrieved atmospheric ozone profile from the pres- ent technique and utilizing the AIRS data was compared with the accuracy obtained from current Advanced TIROS Operational Vertical Sounder (ATOVS) data aboard National Oceanic and Atmospheric Admini- stration (NOAA) satellites As expected, a comparison of retrieval results confirms that the ozone profile re- trieved with the AIRS data is superior to that of ATOVS
基金financially supported by the Meteorological Foundation of China (Grant No.GYHY 201406015)a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)open project of the Key Laboratory of Meteorological Disaster of Ministry of Education (KLME1104)
文摘ABSTRACT Satellite-based observations provide great opportunities for improving weather forecasting. Physical retrieval of atmo spheric profiles from satellite observations is sensitive to the uncertainty of the first guess and other factors. In order to improve the accuracy of the physical retrieval, an ensemble methodology was developed with an emphasis on perturbing the first guess. In the methodology, a normal probability density function （PDF） is used to select the optimal profile from the ensemble retrievals. The ensemble retrieval algorithm contains four steps： （1） regression retrieval for original first guess; （2） perturbation of the original first guess to generate new first guesses （ensemble first guesses）; （3） using the ensemble first guesses and nonlinear iterative physical retrieval to generate ensemble physical results; and （4） the final optimal profile is selected from the ensemble physical results by using PDE Temperature eigenvectors （EVs） were used to generate the pertur- bation and generate the ensemble first guess. Compared with the regular temperature profile retrievals from the Atmospheric InfraRed Sounder （AIRS）, the ensemble retrievals RMSE of temperature profiles selected by the PDF was reduced between 150 and 320 hPa and below 400 hPa, with a maximum improvement of 0.3 K at 400 hPa. The bias was also reduced in many layers, with a maximum improvement of 0.69 K at 460 hPa. The combined optimal （CombOpt） profile and a mean optimal （MeanOpt） profile of all ensemble physical results were improved below 150 hPa. The MeanOpt profile was better than the CombOpt profile, and was regarded as the final optimal （FinOpt） profile. This study lays the foundation for improving temperature retrievals from hyper-spectral infrared radiance measurements.
基金The project supported by the National Natural Science Foundation of China(19672043)
文摘Certain deterministic nonlinear systems may show chaotic behavior. We consider the motion of qualitative information and the practicalities of extracting a part from chaotic experimental data. Our approach based on a theorem of Takens draws on the ideas from the generalized theory of information known as singular system analysis. We illustrate this technique by numerical data from the chaotic region of the chaotic experimental data. The method of the singular-value decomposition is used to calculate the eigenvalues of embedding space matrix. The corresponding concrete algorithm to calculate eigenvectors and to obtain the basis of embedding vector space is proposed in this paper. The projection on the orthogonal basis generated by eigenvectors of timeseries data and concrete paradigm are also provided here. Meanwhile the state space reconstruction technology of different kinds of chaotic data obtained from dynamical system has also been discussed in detail.
基金Sponsored by the Basic Research Foundation of Beijing Institute of Technology(BIT-UBF-20070842009)
文摘An eigenvector method for ranking alternatives whose measurements are given as vague values is provided. Firstly, a positive matrix is constructed which is defined as evaluation information matrix （EIM）. Based on four assumptions for evaluating alternatives, a ranking eigenvector is defined. And then it is proved, based on positive matrix theory, that the EIM＇s eigenvector corresponding to the maximal eigenvalue is the ranking vector. For alternatives whose characteristics are presented by vague sets, the proposed techniques can evaluate the degree of suitability to which an alternative satisfies the decision-maker＇ s requirement efficiently.
文摘The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool to reduce multidimensional data to lower dimensions while retaining most of the information. It covers standard deviation, covariance, and eigenvectors. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.
基金National Natural Science Foundation of China,No.42001153,No.42001161。
文摘Although China was one of the countries with the fastest-growing aging population in the world,limited scholarly attention has been paid to migration among older adults in China.The full picture of their migration in the entire country over time remains unknown.This study examines the spatial patterns of older interprovincial migration flows and their drivers in China over the period 1995 to 2015,using four waves of census data and intercensal population sample survey data.Results from eigenvector spatial filtering negative binomial regressions indicate that older adults tend to migrate away from low cost-of-living rural areas to high cost-of-living urban and rural areas,moving away from areas with extreme temperature differences.The location of their grandchildren is among the most important attractions.Our findings suggest that family-oriented migration is more common than amenity-led migration among retired Chinese older adults,and the cost-of-living is an indicator of economic opportunities for adult children and the quality of senior care services.
文摘The sensitivity calculating formulas in structural dynamics was developed by utilizing the mathematical theorem and new definitions of sensitivities. So the singularity problem of sensitivity with repeated eigenvalues is solved completely. To improve the computational efficiency, the reduction system is obtained based on Lanczos vectors. After incorporating the mathematical theory with the Lanczos algorithm, the approximate sensitivity solution can be obtained. A numerical example is presented to illustrate the performance of the method.
文摘Social relationships formed within a network of interacting group members can have a profound impact on an indi- vidual＇s behavior and fitness. However, we have little understanding of how individuals perceive their relationships and how this perception relates to our external measures of interactions. We investigated the perception of affiliative and agonistic relation- ships at both the dyadic and emergent social levels in two captive groups of monk parakeets （Myiopsitta monachus, n = 21 and 19） using social network analysis and playback experiments. At the dyadic social scale, individuals directed less aggression to- wards their strong affiliative partners and more aggression towards non-partner neighbors.At the emergent social scale, there was no association between relationships in different social contexts and an individual＇s dominance rank did not correlate with its popularity rank. Playback response pattems were mainly driven by relationships in affiliative social contexts at the dyadic scale. In both groups, individual responses to playback experiments were significantly affected by strong affiliative relationships at the dyadic social scale, albeit in different directions in the two groups. Response pattems were also affected by affiliative relation- ships at the emergent social scale, but only in one of the two groups. Within affiliative relationships, those at the dyadic social scale were perceived by individuals in both groups, but those at the emergent social scale only affected responses in one group. These results provide preliminary evidence that relationships in affiliative social contexts may be perceived as more important than agonistic relationships in captive monk parakeet groups. Our approach could be used in a wide range of social species and comparative analyses could provide important insight into how individuals perceive relationships across social contexts and social scales [Current Zoology 61 （1）： 55-69, 2015].
基金Under the auspices of the National Social Science Foundation of China(No.17ZDA055).
文摘The increase in China’s skilled labor force has drawn much attention from policymakers,national and international firms and media.Understanding how educated talent locates and re-locates across the country can guide future policy discussions of equality,firm localization and service allocation.Prior studies have tended to adopt a static cross-national approach providing valuable insights into the relative importance of economic and amenity differentials driving the distribution of talent in China.Yet,few adopt longitudinal analysis to examine the temporal dynamics in the stregnth of existing associations.Recently released official statistical data now enables space-time analysis of the geographic distribution of talent and its determinants in China.Using four-year city-level data from national population censuses and 1%population sample surveys conducted every five years between 2000 and 2015,we examine the spatial patterns of talent across Chinese cities and their underpinning drivers evolve over time.Results reveal that the spatial distribution of talent in China is persistently unequal and spatially concentrated between 2000 and 2015.It also shows gradually strengthened and significantly positive spatial autocorrelation in the distribution of talent.An eigenvector spatial filtering negative binomial panel is employed to model the spatial determinants of talent distribution.Results indicate the influences of both economic opportunities and urban amenities,particularly urban public services and greening rate,on the distribution of talent.These results highlight that urban economic-and amenity-related factors have simultaneously driven China’s talent’s settlement patterns over the first fifteen years of the 21st century.
文摘COVID-19 outbreaks in China in late December 2019,then in the United States(US)in early 2020.In the initial wave of diffusion,the virus respectively took 14 and 33 days to spread across the provinces/states in the Chinese mainland and the coterminous US,during which there are 43%and 70%zero entries in the space-time series for China and US respectively,indicating a zero-inflated count process.A logistic growth curve as a function of the number of days since the first case appeared in each of these countries accurately portrays the national aggregate per capita rates of infection for both.This paper presents two space-time model specifications,one based upon the generalized linear mixed model,and the other upon Moran eigenvector space-time filtering,to describe the spread of COVID-19 in the initial 19 and 58 days across the Chinese mainland and the coterminous US,respectively.Results from these case studies show both models shed new light on the role of spatial structures in COVID-19 diffusion,models that can forecast new cases in subsequent days.A principal finding is that describing the spatiotemporal diffusion of COVID-19 benefits from including a hierarchical structural component to supplement the commonly employed contagion component.
基金The project supported by the National Natural Science Foundation of China
文摘A family of modal methods for computing eigenvector derivatives with repeated roots are directly derived from the constraint generalized inverse technique which is originally formulated by Wang and Hu. Extensions are made to Akgun's method to allow treatment of eigensensitivity with repeated roots for general nondefective systems, and Bernard and Bronowicki's modal expansion approach is expanded to a family of modal methods.
文摘An improved face recognition method is proposed based on principal component analysis （PCA） compounded with genetic algorithm （GA）, named as genetic based principal component analysis （GPCA）. Initially the eigenspace is created with eigenvalues and eigenvectors. From this space, the eigenfaces are constructed, and the most relevant eigenfaees have been selected using GPCA. With these eigenfaees, the input images are classified based on Euclidian distance. The proposed method was tested on ORL （Olivetti Research Labs） face database. Experimental results on this database demonstrate that the effectiveness of the proposed method for face recognition has less misclassification in comparison with previous methods.
基金This work is supported by the National Natural Science Foundation.
文摘In this paper the mathematical definition of the minimum energy generalized inverse is given and its application for computing eigen-sensitivity is demonstrated.By comparing it with the others,this paper clarifies the merits of the present algorithm.Furthermore,an erroneous relation which is still widely used is rectified.
基金supported by the National Natural Science Foundation of China (Grant Nos. 11061019 and 10962004)the Chunhui Program of Ministry of Education of China (Grant No. Z2009-1-01010)+1 种基金the Natural Science Foundation of Inner Mongolia, China(Grant Nos. 2010MS0110 and 2009BS0101)the Cultivation of Innovative Talent of ‘211 Project’ of Inner Mongolia University
文摘In this paper, we consider the eigenvalue problem of a class of fourth-order operator matrices appearing in mechan- ics, including the geometric multiplicity, algebraic index, and algebraic multiplicity of the eigenvalue, the symplectic orthogonality, and completeness of eigen and root vector systems. The obtained results are applied to the plate bending problem.
基金Project supported by the Mathematical Tianyuan Foundation of China (No. 10626019)
文摘A procedure is presented for computing the derivatives of repeated eigenvalues and the corresponding eigenvectors of damped systems. The derivatives are calculated in terms of the eigenvalues and eigenvectors of the second-order system, and the use of rather undesirable state space representation is avoided. Hence the cost of computation is greatly reduced. The efficiency of the proposed procedure is illustrated by considering a 5-DOF non-proportionally damped system.
基金supported by the National Natural Science Foundation of China (Nos. 11061019,10962004,11101200,and 11026175)the Chunhui Program of Ministry of Education of China (No. Z2009-1-01010)+1 种基金the Natural Science Foundation of Inner Mongolia of China (No. 2010MS0110)the Cultivation of Innovative Talent of "211 Project" of Inner Mongolia University
文摘This paper deals with a class of upper triangular infinite-dimensional Hamilto- nian operators appearing in the elasticity theory. The geometric multiplicity and algebraic index of the eigenvalue are investigated. Furthermore, the algebraic multiplicity of the eigenvalue is obtained. Based on these properties, the concrete completeness formulation of the system of eigenvectors or root vectors of the Hamiltonian operator is proposed. It is shown that the completeness is determined by the system of eigenvectors of the operator entries. Finally, the applications of the results to some problems in the elasticity theory are presented.
基金funded by the National Key S&T Special Projects of China(grant number:2018YFB0505302)the National Nature Science Foundation of China(grant number:41671380)。
文摘Snow water equivalent(SWE)is an important factor reflecting the variability of snow.It is important to estimate SWE based on remote sensing data while taking spatial autocorrelation into account.Based on the segmentation method,the relationship between SWE and environmental factors in the central part of the Tibetan Plateau was explored using the eigenvector spatial filtering(ESF)regression model,and the influence of different factors on the SWE was explored.Three sizes of 16×16,24×24 and 32×32 were selected to segment raster datasets into blocks.The eigenvectors of the spatial adjacency matrix of the segmented size were selected to be added into the model as spatial factors,and the ESF regression model was constructed for each block in parallel.Results show that precipitation has a great influence on SWE,while surface temperature and NDVI have little influence.Air temperature,elevation and surface temperature have completely different effects in different areas.Compared with the ordinary least square(OLS)linear regression model,geographically weighted regression(GWR)model,spatial lag model(SLM)and spatial error model(SEM),ESF model can eliminate spatial autocorrelation with the highest accuracy.As the segmentation size increases,the complexity of ESF model increases,but the accuracy is improved.
