Purpose:Citations can be used in evaluative bibliometrics to measure the impact of papers.However,citation analysis can be extended by a multi-dimensional perspective on citation impact which is intended to receive mo...Purpose:Citations can be used in evaluative bibliometrics to measure the impact of papers.However,citation analysis can be extended by a multi-dimensional perspective on citation impact which is intended to receive more specific information about the kind of received impact.Design/methodology/approach:Bornmann,Wray,and Haunschild(2019)introduced citation concept analysis(CCA)for capturing the importance and usefulness certain concepts have in subsequent research.The method is based on the analysis of citances-the contexts of citations in citing papers.This study applies the method by investigating the impact of various concepts introduced in the oeuvre of the world-leading French sociologist Pierre Bourdieu.Findings:We found that the most cited concepts are‘social capital’(with about 34%of the citances in the citing papers),‘cultural capital’,and‘habitus’(both with about 24%).On the other hand,the concepts‘doxa’and‘reflexivity’score only about 1%each.Research limitations:The formulation of search terms for identifying the concepts in the data and the citation context coverage are the most important limitations of the study.Practical implications:The results of this explorative study reflect the historical development of Bourdieu’s thought and its interface with different fields of study.Originality/value:The study demonstrates the high explanatory power of the CCA method.展开更多
The impacts of rainfall direction on the degree of hydrological response to rainfall properties were investigated using comparative rainfall-runoff experiments on a small-scale slope(4 m×1 m),as well as canonical...The impacts of rainfall direction on the degree of hydrological response to rainfall properties were investigated using comparative rainfall-runoff experiments on a small-scale slope(4 m×1 m),as well as canonical correlation analysis(CCA).The results of the CCA,based on the observed data showed that,under conditions of both upstream and downstream rainfall movements,the hydrological process can be divided into instantaneous and cumulative responses,for which the driving forces are rainfall intensity and total rainfall,and coupling with splash erosion and wash erosion,respectively.The response of peak runoff(Pr)to intensity-dominated rainfall action appeared to be the most significant,and also runoff(R)to rainfall-dominated action,both for upstream-and downstream-moving conditions.Furthermore,the responses of sediment erosion in downstream-moving condition were more significant than those in upstream-moving condition.This study indicated that a CCA between rainfall and hydrological characteristics is effective for further exploring the rainfall-runoff-erosion mechanism under conditions of moving rainfall,especially for the downstream movement condition.展开更多
In standard canonical correlation analysis (CCA), the data from definite datasets are used to estimate their canonical correlation. In real applications, for example in bilingual text retrieval, it may have a great po...In standard canonical correlation analysis (CCA), the data from definite datasets are used to estimate their canonical correlation. In real applications, for example in bilingual text retrieval, it may have a great portion of data that we do not know which set it belongs to. This part of data is called unlabeled data, while the rest from definite datasets is called labeled data. We propose a novel method called regularized canonical correlation analysis (RCCA), which makes use of both labeled and unlabeled samples. Specifically, we learn to approximate canonical correlation as if all data were labeled. Then, we describe a generalization of RCCA for the multi-set situation. Experiments on four real world datasets, Yeast, Cloud, Iris, and Haberman, demonstrate that, by incorporating the unlabeled data points, the accuracy of correlation coefficients can be improved by over 30%.展开更多
Objective： To identify aberrant gene expression in primary human T-cell acute lymphoblastic leukemia （T-ALL） and to evaluate the differently expressed level of Lyll and LMO2 genes in human LMO2 positive/TALl negati...Objective： To identify aberrant gene expression in primary human T-cell acute lymphoblastic leukemia （T-ALL） and to evaluate the differently expressed level of Lyll and LMO2 genes in human LMO2 positive/TALl negative T-ALL tumors. Methods： Three methods, representational difference analysis （RDA） of cDNA, cDNA microarrays and RT-PCR were used to detect if Lyll and LMO2 genes were differently expressed in human LMO2 positive/tall negative T-ALL tumors. Results： The results of cDNA RDA and cDNA array shown that Lyll and LMO2 genes are differently expressed in human T-cell tumors. The result of RT-PCR also shown Lyll and LMO2 are high expressed in human T-cell tumors and very low level or no expressed in normal group. Conclusion： We have found that cDNA RDA and cDNA microarray can be successfully used to identify aberrant gene expression in T-ALL cells. In the study described in this manuscript we found that Lyll and LMO2 are aberrantly expressed in human T-ALL LMO2 positive/TALl negative T-ALL tumors.展开更多
A novel algorithm for voice conversion is proposed in this paper. The mapping function of spectral vectors of the source and target speakers is calculated by the Canonical Correlation Analysis (CCA) estimation based o...A novel algorithm for voice conversion is proposed in this paper. The mapping function of spectral vectors of the source and target speakers is calculated by the Canonical Correlation Analysis (CCA) estimation based on Gaussian mixture models. Since the spectral envelope feature remains a majority of second order statistical information contained in speech after Linear Prediction Coding (LPC) analysis, the CCA method is more suitable for spectral conversion than Minimum Mean Square Error (MMSE) because CCA explicitly considers the variance of each component of the spectral vectors during conversion procedure. Both objective evaluations and subjective listening tests are conducted. The experimental results demonstrate that the proposed scheme can achieve better per- formance than the previous method which uses MMSE estimation criterion.展开更多
Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based ...Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based DOA estimation methods trained on simulated Gaussian noised array data cannot be directly applied to actual underwater DOA estimation tasks.In order to deal with this problem,environmental data with no target echoes can be employed to analyze the non-Gaussian components.Then,the obtained information about non-Gaussian components can be used to whiten the array data.Based on these considerations,a novel practical sonar array whitening method was proposed.Specifically,based on a weak assumption that the non-Gaussian components in adjacent patches with and without target echoes are almost the same,canonical cor-relation analysis(CCA)and non-negative matrix factorization(NMF)techniques are employed for whitening the array data.With the whitened array data,machine learning based DOA estimation models trained on simulated Gaussian noised datasets can be used to perform underwater DOA estimation tasks.Experimental results illustrated that,using actual underwater datasets for testing with known machine learning based DOA estimation models,accurate and robust DOA estimation performance can be achieved by using the proposed whitening method in different underwater con-ditions.展开更多
文摘Purpose:Citations can be used in evaluative bibliometrics to measure the impact of papers.However,citation analysis can be extended by a multi-dimensional perspective on citation impact which is intended to receive more specific information about the kind of received impact.Design/methodology/approach:Bornmann,Wray,and Haunschild(2019)introduced citation concept analysis(CCA)for capturing the importance and usefulness certain concepts have in subsequent research.The method is based on the analysis of citances-the contexts of citations in citing papers.This study applies the method by investigating the impact of various concepts introduced in the oeuvre of the world-leading French sociologist Pierre Bourdieu.Findings:We found that the most cited concepts are‘social capital’(with about 34%of the citances in the citing papers),‘cultural capital’,and‘habitus’(both with about 24%).On the other hand,the concepts‘doxa’and‘reflexivity’score only about 1%each.Research limitations:The formulation of search terms for identifying the concepts in the data and the citation context coverage are the most important limitations of the study.Practical implications:The results of this explorative study reflect the historical development of Bourdieu’s thought and its interface with different fields of study.Originality/value:The study demonstrates the high explanatory power of the CCA method.
基金Project supported by the National Basic Research Program (973) of China (No. 2011CB409901-01)the National Natural Science Foundation of China (No. 4081011)
文摘The impacts of rainfall direction on the degree of hydrological response to rainfall properties were investigated using comparative rainfall-runoff experiments on a small-scale slope(4 m×1 m),as well as canonical correlation analysis(CCA).The results of the CCA,based on the observed data showed that,under conditions of both upstream and downstream rainfall movements,the hydrological process can be divided into instantaneous and cumulative responses,for which the driving forces are rainfall intensity and total rainfall,and coupling with splash erosion and wash erosion,respectively.The response of peak runoff(Pr)to intensity-dominated rainfall action appeared to be the most significant,and also runoff(R)to rainfall-dominated action,both for upstream-and downstream-moving conditions.Furthermore,the responses of sediment erosion in downstream-moving condition were more significant than those in upstream-moving condition.This study indicated that a CCA between rainfall and hydrological characteristics is effective for further exploring the rainfall-runoff-erosion mechanism under conditions of moving rainfall,especially for the downstream movement condition.
基金Project (No. 5959438) supported by Microsoft (China) Co., Ltd
文摘In standard canonical correlation analysis (CCA), the data from definite datasets are used to estimate their canonical correlation. In real applications, for example in bilingual text retrieval, it may have a great portion of data that we do not know which set it belongs to. This part of data is called unlabeled data, while the rest from definite datasets is called labeled data. We propose a novel method called regularized canonical correlation analysis (RCCA), which makes use of both labeled and unlabeled samples. Specifically, we learn to approximate canonical correlation as if all data were labeled. Then, we describe a generalization of RCCA for the multi-set situation. Experiments on four real world datasets, Yeast, Cloud, Iris, and Haberman, demonstrate that, by incorporating the unlabeled data points, the accuracy of correlation coefficients can be improved by over 30%.
文摘Objective： To identify aberrant gene expression in primary human T-cell acute lymphoblastic leukemia （T-ALL） and to evaluate the differently expressed level of Lyll and LMO2 genes in human LMO2 positive/TALl negative T-ALL tumors. Methods： Three methods, representational difference analysis （RDA） of cDNA, cDNA microarrays and RT-PCR were used to detect if Lyll and LMO2 genes were differently expressed in human LMO2 positive/tall negative T-ALL tumors. Results： The results of cDNA RDA and cDNA array shown that Lyll and LMO2 genes are differently expressed in human T-cell tumors. The result of RT-PCR also shown Lyll and LMO2 are high expressed in human T-cell tumors and very low level or no expressed in normal group. Conclusion： We have found that cDNA RDA and cDNA microarray can be successfully used to identify aberrant gene expression in T-ALL cells. In the study described in this manuscript we found that Lyll and LMO2 are aberrantly expressed in human T-ALL LMO2 positive/TALl negative T-ALL tumors.
文摘A novel algorithm for voice conversion is proposed in this paper. The mapping function of spectral vectors of the source and target speakers is calculated by the Canonical Correlation Analysis (CCA) estimation based on Gaussian mixture models. Since the spectral envelope feature remains a majority of second order statistical information contained in speech after Linear Prediction Coding (LPC) analysis, the CCA method is more suitable for spectral conversion than Minimum Mean Square Error (MMSE) because CCA explicitly considers the variance of each component of the spectral vectors during conversion procedure. Both objective evaluations and subjective listening tests are conducted. The experimental results demonstrate that the proposed scheme can achieve better per- formance than the previous method which uses MMSE estimation criterion.
基金supported by the National Natural Science Foundation of China(No.51279033).
文摘Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based DOA estimation methods trained on simulated Gaussian noised array data cannot be directly applied to actual underwater DOA estimation tasks.In order to deal with this problem,environmental data with no target echoes can be employed to analyze the non-Gaussian components.Then,the obtained information about non-Gaussian components can be used to whiten the array data.Based on these considerations,a novel practical sonar array whitening method was proposed.Specifically,based on a weak assumption that the non-Gaussian components in adjacent patches with and without target echoes are almost the same,canonical cor-relation analysis(CCA)and non-negative matrix factorization(NMF)techniques are employed for whitening the array data.With the whitened array data,machine learning based DOA estimation models trained on simulated Gaussian noised datasets can be used to perform underwater DOA estimation tasks.Experimental results illustrated that,using actual underwater datasets for testing with known machine learning based DOA estimation models,accurate and robust DOA estimation performance can be achieved by using the proposed whitening method in different underwater con-ditions.
