An in vivo three-dimensional fluorescence method for the determination of algae community structure was developed by parallel factor analysis （PARAFAC） and CHEMTAX. The PARAFAC model was applied to fluo-rescence exc...An in vivo three-dimensional fluorescence method for the determination of algae community structure was developed by parallel factor analysis （PARAFAC） and CHEMTAX. The PARAFAC model was applied to fluo-rescence excitation-emission matrix （EEM） of 60 algae species belonging to five divisions and 11 fluorescent components were identified according to the residual sum of squares and specificity of the composition profiles of fluorescent. By the 11 fluorescent components, the algae species at different growth stages were classified correctly at the division level using Bayesian discriminant analysis （BDA）. Then the reference fluo-rescent component ratio matrix was constructed for CHEMTAX, and the EEM-PARAFAC-CHEMTAX method was developed to differentiate algae taxonomic groups. The correct discrimination ratios （CDRs） when the fluorometric method was used for single-species samples were 100% at the division level, except for Bacil-lariophyta with a CDR of 95.6%. The CDRs for the mixtures were above 94.0% for the dominant algae species and above 87.0% for the subdominant algae species. However, the CDRs of the subdominant algae species were too low to be unreliable when the relative abundance estimated was less than 15.0%. The fluorometric method was tested using the samples from the Jiaozhou Bay and the mesocosm experiments in the Xiaomai Island Bay in August 2007. The discrimination results of the dominant algae groups agreed with microscopy cell counts, as well as the subdominant algae groups of which the estimated relative abundance was above 15.0%. This technique would be of great aid when low-cost and rapid analysis is needed for samples in a large batch. The fluorometric technique has the ability to correctly identify dominant species with proper abundance both in vivo and in situ.展开更多
The feasibility of using fluorescence excitation-emission matrix(EEM) along with parallel factor analysis(PARAFAC) and nonnegative least squares(NNLS) method for the differentiation of phytoplankton taxonomic groups w...The feasibility of using fluorescence excitation-emission matrix(EEM) along with parallel factor analysis(PARAFAC) and nonnegative least squares(NNLS) method for the differentiation of phytoplankton taxonomic groups was investigated. Forty-one phytoplankton species belonging to 28 genera of five divisions were studied. First, the PARAFAC model was applied to EEMs, and 15 fluorescence components were generated. Second, 15 fluorescence components were found to have a strong discriminating capability based on Bayesian discriminant analysis(BDA). Third, all spectra of the fluorescence component compositions for the 41 phytoplankton species were spectrographically sorted into 61 reference spectra using hierarchical cluster analysis(HCA), and then, the reference spectra were used to establish a database. Finally, the phytoplankton taxonomic groups was differentiated by the reference spectra database using the NNLS method. The five phytoplankton groups were differentiated with the correct discrimination ratios(CDRs) of 100% for single-species samples at the division level. The CDRs for the mixtures were above 91% for the dominant phytoplankton species and above 73% for the subdominant phytoplankton species. Sixteen of the 85 field samples collected from the Changjiang River estuary were analyzed by both HPLC-CHEMTAX and the fluorometric technique developed. The results of both methods reveal that Bacillariophyta was the dominant algal group in these 16 samples and that the subdominant algal groups comprised Dinophyta, Chlorophyta and Cryptophyta. The differentiation results by the fluorometric technique were in good agreement with those from HPLC-CHEMTAX. The results indicate that the fluorometric technique could differentiate algal taxonomic groups accurately at the division level.展开更多
Factor analysis is widely utilized to identify latent factors underlying the observed variables.This paper presents a comprehensive comparative study of two widely used methods for determining the optimal number of fa...Factor analysis is widely utilized to identify latent factors underlying the observed variables.This paper presents a comprehensive comparative study of two widely used methods for determining the optimal number of factors in factor analysis,the K1 rule,and parallel analysis,along with a more recently developed method,the bass-ackward method.We provide an in-depth exploration of these techniques,discussing their historical development,advantages,and limitations.Using a series of Monte Carlo simulations,we assess the efficacy of these methods in accurately determining the appropriate number of factors.Specifically,we examine two cessation criteria within the bass-ackward framework:BA-maxLoading and BA-cutoff.Our findings offer nuanced insights into the performance of these methods under various conditions,illuminating their respective advantages and potential pitfalls.To enhance accessibility,we create an online visualization tool tailored to the factor structures generated by the bass-ackward method.This research enriches the understanding of factor analysis methodology,assists researchers in method selection,and facilitates comprehensive interpretation of latent factor structures.展开更多
This paper links parallel factor（PARAFAC） analysis to the problem of nominal direction-of-arrival（DOA） estimation for coherently distributed（CD） sources and proposes a fast PARAFACbased algorithm by establishing...This paper links parallel factor（PARAFAC） analysis to the problem of nominal direction-of-arrival（DOA） estimation for coherently distributed（CD） sources and proposes a fast PARAFACbased algorithm by establishing the trilinear PARAFAC model.Relying on the uniqueness of the low-rank three-way array decomposition and the trilinear alternating least squares regression, the proposed algorithm achieves nominal DOA estimation and outperforms the conventional estimation of signal parameter via rotational technique CD（ESPRIT-CD） and propagator method CD（PM-CD）methods in terms of estimation accuracy. Furthermore, by means of the initialization via the propagator method, this paper accelerates the convergence procedure of the proposed algorithm with no estimation performance degradation. In addition, the proposed algorithm can be directly applied to the multiple-source scenario,where sources have different angular distribution shapes. Numerical simulation results corroborate the effectiveness and superiority of the proposed fast PARAFAC-based algorithm.展开更多
This paper describes the experiments with Korean-to-Vietnamese statistical machine translation(SMT). The fact that Korean is a morphologically complex language that does not have clear optimal word boundaries causes a...This paper describes the experiments with Korean-to-Vietnamese statistical machine translation(SMT). The fact that Korean is a morphologically complex language that does not have clear optimal word boundaries causes a major problem of translating into or from Korean. To solve this problem, we present a method to conduct a Korean morphological analysis by using a pre-analyzed partial word-phrase dictionary(PWD).Besides, we build a Korean-Vietnamese parallel corpus for training SMT models by collecting text from multilingual magazines. Then, we apply such a morphology analysis to Korean sentences that are included in the collected parallel corpus as a preprocessing step. The experiment results demonstrate a remarkable improvement of Korean-to-Vietnamese translation quality in term of bi-lingual evaluation understudy(BLEU).展开更多
基金The National Natural Science Foundation of China under contract Nos 41376106 and 41276069
文摘An in vivo three-dimensional fluorescence method for the determination of algae community structure was developed by parallel factor analysis （PARAFAC） and CHEMTAX. The PARAFAC model was applied to fluo-rescence excitation-emission matrix （EEM） of 60 algae species belonging to five divisions and 11 fluorescent components were identified according to the residual sum of squares and specificity of the composition profiles of fluorescent. By the 11 fluorescent components, the algae species at different growth stages were classified correctly at the division level using Bayesian discriminant analysis （BDA）. Then the reference fluo-rescent component ratio matrix was constructed for CHEMTAX, and the EEM-PARAFAC-CHEMTAX method was developed to differentiate algae taxonomic groups. The correct discrimination ratios （CDRs） when the fluorometric method was used for single-species samples were 100% at the division level, except for Bacil-lariophyta with a CDR of 95.6%. The CDRs for the mixtures were above 94.0% for the dominant algae species and above 87.0% for the subdominant algae species. However, the CDRs of the subdominant algae species were too low to be unreliable when the relative abundance estimated was less than 15.0%. The fluorometric method was tested using the samples from the Jiaozhou Bay and the mesocosm experiments in the Xiaomai Island Bay in August 2007. The discrimination results of the dominant algae groups agreed with microscopy cell counts, as well as the subdominant algae groups of which the estimated relative abundance was above 15.0%. This technique would be of great aid when low-cost and rapid analysis is needed for samples in a large batch. The fluorometric technique has the ability to correctly identify dominant species with proper abundance both in vivo and in situ.
基金Supported by the National Natural Science Foundation of China(Nos.41376106,41176063)the Shandong Provincial Natural Science Foundation of China(No.ZR2013DM017)
文摘The feasibility of using fluorescence excitation-emission matrix(EEM) along with parallel factor analysis(PARAFAC) and nonnegative least squares(NNLS) method for the differentiation of phytoplankton taxonomic groups was investigated. Forty-one phytoplankton species belonging to 28 genera of five divisions were studied. First, the PARAFAC model was applied to EEMs, and 15 fluorescence components were generated. Second, 15 fluorescence components were found to have a strong discriminating capability based on Bayesian discriminant analysis(BDA). Third, all spectra of the fluorescence component compositions for the 41 phytoplankton species were spectrographically sorted into 61 reference spectra using hierarchical cluster analysis(HCA), and then, the reference spectra were used to establish a database. Finally, the phytoplankton taxonomic groups was differentiated by the reference spectra database using the NNLS method. The five phytoplankton groups were differentiated with the correct discrimination ratios(CDRs) of 100% for single-species samples at the division level. The CDRs for the mixtures were above 91% for the dominant phytoplankton species and above 73% for the subdominant phytoplankton species. Sixteen of the 85 field samples collected from the Changjiang River estuary were analyzed by both HPLC-CHEMTAX and the fluorometric technique developed. The results of both methods reveal that Bacillariophyta was the dominant algal group in these 16 samples and that the subdominant algal groups comprised Dinophyta, Chlorophyta and Cryptophyta. The differentiation results by the fluorometric technique were in good agreement with those from HPLC-CHEMTAX. The results indicate that the fluorometric technique could differentiate algal taxonomic groups accurately at the division level.
基金supported by Grants from the Department of Education(R305D140037,R305D210023).
文摘Factor analysis is widely utilized to identify latent factors underlying the observed variables.This paper presents a comprehensive comparative study of two widely used methods for determining the optimal number of factors in factor analysis,the K1 rule,and parallel analysis,along with a more recently developed method,the bass-ackward method.We provide an in-depth exploration of these techniques,discussing their historical development,advantages,and limitations.Using a series of Monte Carlo simulations,we assess the efficacy of these methods in accurately determining the appropriate number of factors.Specifically,we examine two cessation criteria within the bass-ackward framework:BA-maxLoading and BA-cutoff.Our findings offer nuanced insights into the performance of these methods under various conditions,illuminating their respective advantages and potential pitfalls.To enhance accessibility,we create an online visualization tool tailored to the factor structures generated by the bass-ackward method.This research enriches the understanding of factor analysis methodology,assists researchers in method selection,and facilitates comprehensive interpretation of latent factor structures.
基金supported by the National Natural Science Foundation of China(6137116961601167)+2 种基金the Jiangsu Natural Science Foundation(BK20161489)the open research fund of State Key Laboratory of Millimeter Waves,Southeast University(K201826)the Fundamental Research Funds for the Central Universities(NE2017103)
文摘This paper links parallel factor（PARAFAC） analysis to the problem of nominal direction-of-arrival（DOA） estimation for coherently distributed（CD） sources and proposes a fast PARAFACbased algorithm by establishing the trilinear PARAFAC model.Relying on the uniqueness of the low-rank three-way array decomposition and the trilinear alternating least squares regression, the proposed algorithm achieves nominal DOA estimation and outperforms the conventional estimation of signal parameter via rotational technique CD（ESPRIT-CD） and propagator method CD（PM-CD）methods in terms of estimation accuracy. Furthermore, by means of the initialization via the propagator method, this paper accelerates the convergence procedure of the proposed algorithm with no estimation performance degradation. In addition, the proposed algorithm can be directly applied to the multiple-source scenario,where sources have different angular distribution shapes. Numerical simulation results corroborate the effectiveness and superiority of the proposed fast PARAFAC-based algorithm.
基金supported by the Institute for Information&communications Technology Promotion under Grant No.R0101-16-0176the Project of Core Technology Development for Human-Like Self-Taught Learning Based on Symbolic Approach
文摘This paper describes the experiments with Korean-to-Vietnamese statistical machine translation(SMT). The fact that Korean is a morphologically complex language that does not have clear optimal word boundaries causes a major problem of translating into or from Korean. To solve this problem, we present a method to conduct a Korean morphological analysis by using a pre-analyzed partial word-phrase dictionary(PWD).Besides, we build a Korean-Vietnamese parallel corpus for training SMT models by collecting text from multilingual magazines. Then, we apply such a morphology analysis to Korean sentences that are included in the collected parallel corpus as a preprocessing step. The experiment results demonstrate a remarkable improvement of Korean-to-Vietnamese translation quality in term of bi-lingual evaluation understudy(BLEU).
基金National Science&Technology Pillar Program during the 12th Five-year Plan Period(2012BAC19B03)Ministry of water resources "948" project(201328)+2 种基金National Science Foundation for Distinguished Young Scholars(51325804)National Public Benefit(Environmental)Research Foundation(201109009)China Postdoctoral Science Fund Project(2012M520349)
