Rapid ecotoxicological assessment of heavy metal combined polluted soil using canonical analysis

Quick, simple to perform, and cheap biomarkers were combined in a rapid assessment approach to measure the effects of metal pollutants, Cu, Cd, Pb and Zn in meadow burozem on wheat. Analysis of orthogonal design showed that the significant zinc factor indicated both the inhibition rate of shoot mass and that of root elongation were affected by zinc(P<0.05 and P<0.01, respectively). The first toxicity canonical variable (TOXI), formed from the toxicity data set, explained 49% of the total variance in the toxicity data set; the first biological canonical variable(BIOL) explained 42% of the total variation in the biological data set. The correlation between the first canonical variables TOXI and BIOL (canonical correlation) was 0.94 (P<0.0001). Therefore, it is reliable and feasible to use the achievement to assess toxicity of heavy metal combined polluted soil using canonical analysis. Toxicity of soil combined polluted by heavy metals to plant community was estimated by comparing the IC 50 values describing the concentration needed to cause 50% decrease with grow rate compared to no metal addition. Environmental quality standard for soils prescribe that all these tested concentration of heavy metals in soil should not cause hazard and pollution ultimately, whereas it indicated that the soils in second grade cause more or less than 50% inhibition rates of wheat growth. So environmental quality standard for soils can be modified to include other features.

Cross-Project Software Defect Prediction Based on SMOTE and Deep Canonical Correlation Analysis

Cross-Project Defect Prediction(CPDP)is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project.However,existing CPDP methods only consider linear correlations between features(indicators)of the source and target projects.These models are not capable of evaluating non-linear correlations between features when they exist,for example,when there are differences in data distributions between the source and target projects.As a result,the performance of such CPDP models is compromised.In this paper,this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique(SMOTE)and Deep Canonical Correlation Analysis(DCCA),referred to as S-DCCA.Canonical Correlation Analysis(CCA)is employed to address the issue of non-linear correlations between features of the source and target projects.S-DCCA extends CCA by incorporating the MlpNet model for feature extraction from the dataset.The redundant features are then eliminated by maximizing the correlated feature subset using the CCA loss function.Finally,cross-project defect prediction is achieved through the application of the SMOTE data sampling technique.Area Under Curve(AUC)and F1 scores(F1)are used as evaluation metrics.This paper conducted experiments on 27 projects from four public datasets to validate the proposed method.The results demonstrate that,on average,our method outperforms all baseline approaches by at least 1.2%in AUC and 5.5%in F1 score.This indicates that the proposed method exhibits favorable performance characteristics.

Canonical Correlation Analysis of Agronomic Characters of Brassica juncea in Western China

[Objective] The study aimed at exploring the relationship among the agronomic characters of B. juncea in western China, in order to provide scientific basis for the breeding of B. juncea in western China. [Method] 39 B. juncea materials from western China were used for the canonical correlation analysis, and canonical correlations between each pair of the four ecological character （containing 18 variables） were verified, including yield characters （5 variables）, caulis characters （6 variables）, branch characters （3 variables） and pod characters （3 variables）. [Result] Yield per plant of B. juncea in western China suffered a tremendous influence from effective pod number per plant while was not significantly affected by the total pod number per plant, seed number per pod and 1 000-seed weight; the most important character related with the yield character of B. juncea in western China was caulis character, followed by the branch character and pod character; yield characters, caulis characters, branch characters and pod characters of B. juncea in western China were closely correlated. [Conclusion] In order to improve the yield characters of B. juncea in western China, caulis characters should be focused on, followed by branch characters and pod characters; rapeseed varieties with high performance in total pod number per plant and effective pod number per plant should be chosen through the perspectives of effective branch number, plant height, pod number of main inflorescence, fruit stalk number of main inflorescence and other traits, while rapeseed varieties with high performance in seed number per pod and 1 000-seed weight should be chosen through the perspectives of beak length and other traits.

Studying beta diversity: ecological variation partitioning by multiple regression and canonical analysis

Aims Beta diversity is the variation in species composition among sites in a geographic region.Beta diversity is a key concept for understanding the functioning of ecosystems,for the conservation of biodiversity and for ecosystem management.The present report describes how to analyse beta diversity from community composition and associated environmental and spatial data tables.Methods Beta diversity can be studied by computing diversity indices for each site and testing hypotheses about the factors that may explain the variation among sites.Alternatively,one can carry out a direct analysis of the community composition data table over the study sites,as a function of sets of environmental and spatial variables.These analyses are carried out by the statistical method of partitioning the variation of the diversity indices or the community composition data table with respect to environmental and spatial variables.Variation partitioning is briefly described herein.Important findings Variation partitioning is a method of choice for the interpretation of beta diversity using tables of environmental and spatial variables.Beta diversity is an interesting‘currency’for ecologists to compare either different sampling areas or different ecological communities cooccurring in an area.Partitioning must be based upon unbiased estimates of the variation of the community composition data table that is explained by the various tables of explanatory variables.The adjusted coefficient of determination provides such an unbiased estimate in both multiple regression and canonical redundancy analysis.After partitioning,one can test the significance of the fractions of interest and plot maps of the fitted values corresponding to these fractions.

Feature Fusion Multi-View Hashing Based on Random Kernel Canonical Correlation Analysis

Hashing technology has the advantages of reducing data storage and improving the efficiency of the learning system,making it more and more widely used in image retrieval.Multi-view data describes image information more comprehensively than traditional methods using a single-view.How to use hashing to combine multi-view data for image retrieval is still a challenge.In this paper,a multi-view fusion hashing method based on RKCCA(Random Kernel Canonical Correlation Analysis)is proposed.In order to describe image content more accurately,we use deep learning dense convolutional network feature DenseNet to construct multi-view by combining GIST feature or BoW_SIFT(Bag-of-Words model+SIFT feature)feature.This algorithm uses RKCCA method to fuse multi-view features to construct association features and apply them to image retrieval.The algorithm generates binary hash code with minimal distortion error by designing quantization regularization terms.A large number of experiments on benchmark datasets show that this method is superior to other multi-view hashing methods.

Novel DDoS Feature Representation Model Combining Deep Belief Network and Canonical Correlation Analysis

Distributed denial of service(DDoS)attacks launch more and more frequently and are more destructive.Feature representation as an important part of DDoS defense technology directly affects the efficiency of defense.Most DDoS feature extraction methods cannot fully utilize the information of the original data,resulting in the extracted features losing useful features.In this paper,a DDoS feature representation method based on deep belief network(DBN)is proposed.We quantify the original data by the size of the network flows,the distribution of IP addresses and ports,and the diversity of packet sizes of different protocols and train the DBN in an unsupervised manner by these quantified values.Two feedforward neural networks(FFNN)are initialized by the trained deep belief network,and one of the feedforward neural networks continues to be trained in a supervised manner.The canonical correlation analysis(CCA)method is used to fuse the features extracted by two feedforward neural networks per layer.Experiments show that compared with other methods,the proposed method can extract better features.

Study on soil water characteristics of tobacco fields based on canonical correlation analysis

In order to identify the principal factors influencing soil water characteristics （SWC） and evaluate SWC effectively, the multivariate-statistical canonical correlation analysis （CCA） method was used to study and analyze the correlation between SWC and soil physical and chemical properties. Twenty-two soil samples were taken from 11 main tobacco-growing areas in Guizhou Province in China and the soil water characteristic curves （SWCC） and basic physical and chemical properties of the soil samples were determined. The results show that： （1） The soil bulk density, soil total porosity and soil capillary porosity have significant effects on SWC of tobacco fiels. Bulk density and total porosity are positively correlated with soil water retention characteristics （SWRC）, and soil capillary porosity is positively correlated with soil water supply characteristics （SWSC）. （2） Soil samples from different soil layers at the same soil sampling point show similarity or consistency in SWC. Inadequate soil water supply capability and imbalance between SWRC and SWSC are problems of tobacco soil. （3） The SWC of loamy clay are generally superior to those of silty clay loam.

SPATIAL REGULARIZATION OF CANONICAL CORRELATION ANALYSIS FOR LOW-RESOLUTION FACE RECOGNITION

Canonical correlation analysis ( CCA ) based methods for low-resolution ( LR ) face recognition involve face images with different resolutions ( or multi-resolutions ), i.e.LR and high-resolution ( HR ) .For single-resolution face recognition , researchers have shown that utilizing spatial information is beneficial to improving the recognition accuracy , mainly because the pixels of each face are not independent but spatially correlated.However , for a multi-resolution scenario , there are no related works.Therefore , a method named spatial regularization of canonical correlation analysis ( SRCCA ) is developed for LR face recognition to improve the performance of CCA by the regularization utilizing spatial information of different resolution faces.Furthermore , the impact of LR and HR spatial regularization terms on LR face recognition is analyzed through experiments.

Canonical correlation analysis to land-use structure and its driving forces——Taking Yulin Prefecture as an example

In this paper, one of the most classical statistical methods, Canonical Correlation Analysis (CCA) is applied to identify quantitatively the driving forces of landuse structure in Yulin Prefecture. The main analysis is carried out through the software SPSS with the data on the level of towns and townships in 1992. The results indicate that landuse structure is determined by comprehensive action of different factors. Landuse structure with rural characteristics is mainly determined by geographical factors such as the elevation, temperature and precipitation, while the landuse structure with urban characteristics is mainly determined by demographic and socioeconomic conditions. At the same time, tests were carried out through the canonical correlation coefficient and redundancy analysis.

Canonical Correlation Analysis and climate research

Correlation analysis as used by meteorologists and oceanographers is a tool for the analysisof the spacial or temporal variability of physical fields. In his notes, Dr. Hasselmann pro-posed to combine correlation analysis and linear regression analysis in climate prediction re-search. The main idea is to decompose the physical field into its principal oscillation patterns.

ON USING NON-LINEAR CANONICAL CORRELATION ANALYSIS FOR VOICE CONVERSION BASED ON GAUSSIAN MIXTURE MODEL

Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality.The main object of this paper was to build a nonlinear relationship between the parameters for the acoustical features of source and target speaker using Non-Linear Canonical Correlation Analysis(NLCCA) based on jointed Gaussian mixture model.Speaker indi-viduality transformation was achieved mainly by altering vocal tract characteristics represented by Line Spectral Frequencies(LSF).To obtain the transformed speech which sounded more like the target voices,prosody modification is involved through residual prediction.Both objective and subjective evaluations were conducted.The experimental results demonstrated that our proposed algorithm was effective and outperformed the conventional conversion method utilized by the Minimum Mean Square Error(MMSE) estimation.

A NOVEL ALGORITHM FOR VOICE CONVERSION USING CANONICAL CORRELATION ANALYSIS

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.

Multiple moving sources passive location based on multiset canonical correlation analysis

To solve the problem of multiple moving sources passive location, a novel blind source separa- tion （BSS） algorithm based on the muhiset canonical correlation analysis （MCCA） is presented by exploiting the different temporal structure of uncorrelated source signals first, and then on the basis of this algorithm, a novel multiple moving sources passive location method is proposed using time difference of arrival （TDOA） and frequency difference of arrival （FDOA） measurements. The key technique of this location method is TDOA and FDOA joint estimation, which is based on BSS. By blindly separating mixed signals from multiple moving sources, the multiple sources location problem can be translated to each source location in turn, and the effect of interference and noise can also he removed. The simulation results illustrate that the performance of the MCCA algorithm is very good with relatively light computation burden, and the location algorithm is relatively simple and effective.

Canonical Correspondence Analysis of Phytoplankton Community and Environmental Factors in Genhe River

To understand the status of phytoplankton community of the Genhe River in the summer of 2015,we investigated the phytoplankton in Genhe River. We identified 5 phyla and 36 species,among which Bacillariophyta(23) were the most,followed by Chlorophyta(10),Cyanophyta(1),Chrysophyta(1),Pyrrophyta(1). The phytoplankton abundance was(15. 6-810) × 104 ind·L^(-1); the biomass was(0. 07-2. 876) mg·L-1; Shannon-wiener index was 1. 05-3. 24; Pielou evenness index was 0. 27-0. 96. Using Shannon-wiener index and Pielou index,the water quality of Genhe River was assessed,and the results showed that the water quality was the best at 5# sampling point,the water quality was good in 3#,4#,7#,8#,9# sampling points,and there was a state of pollution at other sampling points. Canonical correspondence analysis and Pearson correlation analysis showed that iron ion,transparency,p H value,water depth and water temperature were important environmental factors that affect the distribution of phytoplankton,and copper ion,nitrite ion and COD also significantly affected the distribution of phytoplankton.

Morphometric analysis of sand fly(Diptera:Psychodidae:Phlebotominae), Sergentomyia anodontis Quate and Fairchild, 1961, populations in caves of southern Thailand

Objective:To determine the morphological characteristics of variations in populations of female adult sand fly,Sergentomyia anodontis Quate and Fairchild,1961 in caves in southern Thailand using morphometric analysis.Methods:A total of 107 female Sergentomyia anodontis were isolated from 651 sand flies captured by CDC light traps overnight in caves in Surat Thani,Nakhon Si Thammarat,Satun and Songkhla provinces from February to December 2017.Measurement of 23 external and internal morphological characteristics was conducted.Data were tested with preliminary statistics(Kolmogorov-Smirnov test,Levene’s test and Box’s test of equality of covariance matrices)and by one-way ANOVA or Kruskal-Wallis test.Measurements were analyzed using canonical discriminant analysis.Results:There were 11 morphological characteristics with high variability while two characteristics exhibited low variation.The sand fly populations from Nakhon Si Thammarat,Satun and Songkhla provinces were very similar but were separate from that in Surat Thani province based on canonical discriminant analysis data.This indicates that the morphological variation founding is a result of the diversity of habitats in each population and the geographic features of caves in each area,such as their altitude above sea level.Conclusions:There is a certain variation in the morphology of Sergentomyia anodontis sand flies at the population level which may be used for future classification of sand flies.

Influences of Institutional Pressures on Corporate Social Performance： Empirical Analysis on the Panel Data of Chinese Power Generation Enterprises

Institutional theory has proved the influence of institutional pressures on organization practices and structures. Meanwhile, with the soaring use of corporate social performance （CSP）, more researchers are focusing on exploring the relationship between institution pressures and CSP which is still not completely understood yet. Against this background, the paper aims to fill the gap through generally hypothesizing that different types of institutional pressures individually and collectively affect CSP via the mediating effect of corporate environmental strategy. First, based on the previous and extensive literature review, the theoretical framework and research hypotheses are constructed. Next, canonical correlation analysis about the panel data of 51 Chinese large-scale power generation enterprises from 2004 to 2009 is made to test the relevant hypotheses. Finally, based on the data analysis results, the study draws some conclusions and policy implications for promoting the CSP of Chinese enterprises, including enhancing the steering function of government policies and industry regulations and emphasizing the intermediary role of media.

A Novel 3D Gait Model for Subject Identification Robust against Carrying and Dressing Variations

Subject identification via the subject’s gait is challenging due to variations in the subject’s carrying and dressing conditions in real-life scenes.This paper proposes a novel targeted 3-dimensional(3D)gait model(3DGait)represented by a set of interpretable 3DGait descriptors based on a 3D parametric body model.The 3DGait descriptors are utilised as invariant gait features in the 3DGait recognition method to address object carrying and dressing.The 3DGait recognitionmethod involves 2-dimensional(2D)to 3DGaitdata learningbasedon3Dvirtual samples,a semantic gait parameter estimation Long Short Time Memory(LSTM)network(3D-SGPE-LSTM),a feature fusion deep model based on a multi-set canonical correlation analysis,and SoftMax recognition network.First,a sensory experiment based on 3D body shape and pose deformation with 3D virtual dressing is used to fit 3DGait onto the given 2D gait images.3Dinterpretable semantic parameters control the 3D morphing and dressing involved.Similarity degree measurement determines the semantic descriptors of 2D gait images of subjects with various shapes,poses and styles.Second,using the 2D gait images as input and the subjects’corresponding 3D semantic descriptors as output,an end-to-end 3D-SGPE-LSTM is constructed and trained.Third,body shape,pose and external gait factors(3D-eFactors)are estimated using the 3D-SGPE-LSTM model to create a set of interpretable gait descriptors to represent the 3DGait Model,i.e.,3D intrinsic semantic shape descriptor(3DShape);3D skeleton-based gait pose descriptor(3D-Pose)and 3D dressing with other 3D-eFators.Finally,the 3D-Shape and 3D-Pose descriptors are coupled to a unified pattern space by learning prior knowledge from the 3D-eFators.Practical research on CASIA B,CMU MoBo,TUM GAID and GPJATK databases shows that 3DGait is robust against object carrying and dressing variations,especially under multi-cross variations.

Trace Lasso Regularization for Adaptive Sparse Canonical Correlation Analysis via Manifold Optimization Approach

Canonical correlation analysis(CCA)describes the relationship between two sets of variables by finding a linear combination that maximizes the correlation coefficient.However,in high-dimensional settings where the number of variables exceeds sample size,or in the case that the variables are highly correlated,the traditional CCA is no longer appropriate.In this paper,a new matrix regularization is introduced,which is an extension of the trace Lasso in the vector case.Then we propose an adaptive sparse version of CCA(ASCCA)to overcome these disadvantages by utilizing the trace Lasso regularization.The adaptability of ASCCA is that the sparsity regularization of canonical vectors depends on the sample data,which is more realistic in practical applications.The ASCCA model is further reformulated to an optimization problem on the Riemannian manifold.Then we adopt a manifold inexact augmented Lagrangian method to solve the resulting optimization problem.The performance of the ASCCA model is compared with some existing sparse CCA techniques in different simulation settings and real datasets.

A Novel CCA-NMF Whitening Method for Practical Machine Learning Based Underwater Direction of Arrival Estimation

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.

Evaluation of Soil Fertility Quality and Environmental Driving Factors in Different Soil Types of Artificial Forests

The aim was to clarify the environmental driving factors of soil fertility indicators in artificial forests of Guangxi and comprehensively evaluate the soil fertility level.By collecting data on the current status of soil in artificial forests,the spatial distribution of major soil fertility indicators was analyzed,and the distribution map of the fertility index of artificial forests in the entire region and the comprehensive fertility index of artificial forests of different soil types were obtained.Canonical correspondence analysis method was used to analyze soil fertility indicators and environmental factors,and the environmental driving factors of soil fertility indicators for artificial forests of the main soil types in Guangxi were obtained.The results showed that over 90%of the soil fertility index of artificial forests in the entire region was between 0.20 and 0.50.The order of soil fertility index of different soil types of artificial forests from high to low was yellow brown soil>yellow red soil>yellow soil>red soil>limestone soil>latosolic red soil>laterite.In artificial forests of latosolic red soil,the correlation between soil alkaline nitrogen and organic matter,annual average temperature was high,while the correlation between soil available phosphorus and organic matter,pH was high,and the correlation between soil available potassium and environmental factors such as slope,altitude,rainfall,accumulated temperature,and slope aspect was high.In artificial forests of red soil,the correlation between soil alkaline nitrogen and slope,altitude was high,while the correlation between soil available phosphorus and accumulated temperature,rainfall was high,and the correlation between soil available potassium and pH was high.In artificial forests of limestone soil,there was a high correlation between soil alkaline nitrogen and slope,organic matter,a high correlation between soil available phosphorus and accumulated temperature,rainfall,and a high correlation between soil available potassium and pH.