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Two new cyclic diarylheptanoids from the stems of Ostryopsis nobilis

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Chinese Chemical Letters 2013年第6期24卷 512-514页

作者： yan-xia zhang Bing xia yan Zhou Li-Sheng Ding Shu-Lin Peng Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization Chengdu Institute of BiologyChinese Academy of Sciences

Two new cyclic diarylheptanoids named ostryopsitrienol（1） and ostryopsitriol（2）,together with six known compounds,were isolated from the stems of endemic medicinal plant of Ostryopsis nobilis （Betulaceae）.The st... 详细信息

关键词： Ostryopsis nobilis Cyclic diarylheptanoid Ostryopsitrienol Ostryopsitriol

Random forest algorithm for classification of multiwavelength data

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Chinese Journal of Astronomy and Astrophysics 2009年第2期9卷 220-226页

作者： Dan Gao yan-xia zhang Yong-Heng Zhao National Astronomical Observatories Chinese Academy of Sciences Beijing 100012 China Graduate University of Chinese Academy of Sciences Beijing 100049 China

We introduced a decision tree method called Random Forests for multiwavelength data classification. The data were adopted from different databases, including the Sloan Digital Sky Survey （SDSS） Data Release five, USNO, FIRST and ROSAT. We then studied the discrimination of quasars from stars and the classification of quasars, stars and galaxies with the sample from optical and radio bands and with that from optical and X-ray bands. Moreover, feature selection and feature weighting based on Random Forests were investigated. The performances based on different input patterns were compared. The experimental results show that the random forest method is an effective method for astronomical object classification and can be applied to other classification problems faced in astronomy. In addition, Random Forests will show its superiorities due to its own merits, e.g. classification, feature selection, feature weighting as well as outlier detection.

关键词： classification-- astronomical databases miscellaneous -- catalogs -- meth- ods data analysis -- methods statistical

A new strategy for estimating photometric redshifts of quasars

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Research in Astronomy and Astrophysics 2019年第12期19卷 223-234页

作者： yan-xia zhang Jing-Yi zhang Xin Jin Yong-Heng Zhao Key Laboratory of Optical Astronomy National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China

Based on the SDSS and SDSS-WISE quasar datasets, we put forward two schemes to estimate the photometric redshifts of quasars. Our schemes are based on the idea that the samples are firstly classified into subsamples by a classifier and then a photometric redshift estimation of different subsamples is performed by a regressor. Random Forest is adopted as the core algorithm of the classifiers, while Random Forest and k NN are applied as the key algorithms of regressors. The samples are divided into two subsamples and four subsamples, depending on the redshift distribution. The performances based on different samples, different algorithms and different schemes are compared. The experimental results indicate that the accuracy of photometric redshift estimation for the two schemes generally improves to some extent compared to the original scheme in terms of the percents in |△z|1+zi< 0.1 and |△z|1+zi<0.2 and mean absolute error. Only given the running speed, k NN shows its superiority to Random Forest. The performance of Random Forest is a little better than or comparable to that of k NN with the two datasets. The accuracy based on the SDSS-WISE sample outperforms that based on the SDSS sample no matter by k NN or by Random Rorest. More information from more bands is considered and helpful to improve the accuracy of photometric redshift estimation. Evidently, it can be found that our strategy to estimate photometric redshift is applicable and may be applied to other datasets or other kinds of objects. Only talking about the percent in |△z|1+zi<0.3, there is still large room for further improvement in the photometric redshift estimation.

关键词： astronomical databases:catalogs (galaxies:)quasars:general methods:statistical techniques:miscellaneous

Two Novel Approaches for Photometric Redshift Estimation based on SDSS and 2MASS

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Chinese Journal of Astronomy and Astrophysics 2008年第1期8卷 119-126页

作者： Dan Wang yan-xia zhang Chao Liu Yong-Heng Zhao National Astronomical Observatories Chinese Academy of Sciences Beijing 100012 Graduate School of Chinese Academy of Sciences Beijing 100049

We investigate two training-set methods; support vector machines （SVMs） and Kernel Regression （KR） for photometric redshift estimation with the data from the databases of Sloan Digital Sky Survey Data Release 5 and Two Micron All Sky Survey. We probe the performances of SVMs and KR for different input patterns. Our experiments show that with more parameters considered, the accuracy does not always increase, and only when appropriate parameters are chosen, the accuracy can improve. For different approaches, the best input pattern is different. With different parameters as input, the optimal bandwidth is dissimilar for KR. The rms errors of photometric redshifts based on SVM and KR methods are less than 0.03 and 0.02, respectively. Strengths and weaknesses of the two approaches are summarized. Compared to other methods of estimating photometric redshifts, they show their superiorities, especially KR, in terms of accuracy.

关键词： galaxies distances and redshifts - galaxies general - methods data analysis - techniques photometric

Photometric redshift estimation for quasars by integration of KNN and SVM

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Research in Astronomy and Astrophysics 2016年第5期16卷 31-40页

作者： Bo Han Hong-Peng Ding yan-xia zhang Yong-Heng Zhao International School of Software Wuhan University Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of Sciences

The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is an unsolved problem with a long history and it still exists in the current photometric redshift estimation approaches （such as the k-nearest neighbor （KNN） algorithm）. In this paper, we propose a novel two-stage approach by integration of KNN and support vector machine （SVM） methods together. In the first stage, we apply the KNN algorithm to photometric data and estimate their corresponding Zphot. Our analysis has found two dense regions with catastrophic failure, one in the range of Zphot E [0.3, 1.2] and the other in the range of Zphot E [1.2, 2.1]. In the second stage, we map the photometric input pattern of points falling into the two ranges from their original attribute space into a high dimensional feature space by using a Gaussian kernel function from an SVM. In the high dimensional feature space, many outliers resulting from catastrophic failure by simple Euclidean distance computation in KNN can be identified by a classification hyperplane of SVM and can be further corrected. Experimental results based on the Sloan Digital Sky Survey （SDSS） quasar data show that the two-stage fusion approach can significantly mitigate catastrophic failure and improve the estimation accuracy of photometric redshifts of quasars. The percents in different /△z/ ranges and root mean square （rms） error by the integrated method are 83.47%, 89.83%, 90.90% and 0.192, respectively, compared to the results by KNN （71.96%, 83.78%, 89.73% and 0.204）.

关键词： catalogs -- galaxies: distances and redshifts -- methods: statistical -- quasars: general --surveys -- techniques: photometric

Astronomical data fusion tool based on PostgreSQL

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Research in Astronomy and Astrophysics 2016年第11期16卷 105-112页

作者： Bo Han yan-xia zhang Shou-Bo Zhong Yong-Heng Zhao International School of Software Wuhan University Key Laboratory of Optical Astronomy National Astronomical ObservatoriesChinese Academy of Sciences

With the application of advanced astronomical technologies, equipments and methods all over the world, astronomical observations cover the range from radio, infrared, visible light, ultraviolet, X-ray and gamma-ray bands, and enter into the era of full wavelength astronomy. How to effectively integrate data from different ground- and space-based observation equipments, different observers, different bands and different observation times, requires data fusion technology. In this paper we introduce a cross-match tool that is developed in the Python language, is based on the PostgreSQL database and uses Q3C as the core index, facilitating the cross-match work of massive astronomical data. It provides four different cross- match functions, namely：（I） cross-match of the custom error range; （II） cross-match of catalog errors; （III） cross-match based on the elliptic error range; （IV） cross-match of the nearest neighbor algorithm. The resulting cross-matched set provides a good foundation for subsequent data mining and statistics based on multiwavelength data. The most advantageous aspect of this tool is a user-oriented tool applied locally by users. By means of this tool, users can easily create their own databases, manage their own data and cross- match databases according to their requirements. In addition, this tool is also able to transfer data from one database into another database. More importantly, it is easy to get started with the tool and it can be used by astronomers without writing any code.

关键词： astronomical databases: miscellaneous - catalogs - surveys

A novel stellar spectrum denoising method based on deep Bayesian modeling

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Research in Astronomy and Astrophysics 2021年第7期21卷 127-142页

作者： Xin Kang Shi-Yuan He yan-xia zhang Institute of Statistics and Big Data Renmin University of ChinaBeijing 100872China CAS Key Laboratory of Optical Astronomy National Astronomical ObservatoriesBeijing 100101China

Spectrum denoising is an important procedure for large-scale spectroscopical surveys. This work proposes a novel stellar spectrum denoising method based on deep Bayesian modeling. The construction of our model includes a prior distribution for each stellar subclass, a spectrum generator and a flow-based noise model. Our method takes into account the noise correlation structure, and it is not susceptible to strong sky emission lines and cosmic rays. Moreover, it is able to naturally handle spectra with missing flux values without ad-hoc imputation. The proposed method is evaluated on real stellar spectra from the Sloan Digital Sky Survey(SDSS) with a comprehensive list of common stellar subclasses and compared to the standard denoising auto-encoder. Our denoising method demonstrates a superior performance to the standard denoising auto-encoder, in respect of denoising quality and missing flux imputation. It may be potentially helpful in improving the accuracy of the classification and physical parameter measurement of stars when applying our method during data preprocessing.

关键词： methods:data analysis methods:numerical methods:statistical techniques:spectroscopic

Estimating Photometric Redshifts with Artificial Neural Networks and Multi-Parameters

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Chinese Journal of Astronomy and Astrophysics 2007年第3期7卷 448-456页

作者： Li-Li Li yan-xia zhang Yong-Heng Zhao Da-Wei yang National Astronomical Observatories Chinese Academy of Sciences Beijing 100012 College of Physics Science and Information Engineering Hebei Normal University Shijiazhuang 050016

We calculate photometric redshifts from the Sloan Digital Sky Survey Data Release 2 （SDSS DR2） Galaxy Sample using artificial neural networks （ANNs）. Different input sets based on various parameters （e.g. magnitude, color index, flux information） are explored. Mainly, parameters from broadband photometry are utilized and their performances in redshift prediction are compared. While any parameter may be easily incorporated in the input, our results indicate that using the dereddened magnitudes often produces more accurate photometric redshifts than using the Petrosian magnitudes or model magnitudes as input, but the model magnitudes are superior to the Petrosian magnitudes. Also, better performance resuits when more effective parameters are used in the training set. The method is tested on a sample of 79 346 galaxies from the SDSS DR2. When using 19 parameters based on the dereddened magnitudes, the rms error in redshift estimation is σz = 0.020184. The ANN is highly competitive tool compared to the traditional template-fitting methods when a large and representative training set is available.

关键词： galaxies fundamental parameters - techniques photometric - method dataanalysis

A novel graph oversampling framework for node classification in class-imbalanced graphs

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Science China(Information Sciences) 2024年第6期67卷 214-229页

作者： Riting xia Chunxu zhang yan zhang Xueyan LIU Bo yanG Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University College of Artificial Intelligence Jilin University College of Computer Science and Technology Jilin University College of Communication Engineering Jilin University

Graph neural network(GNN) is a promising method to analyze graphs. Most existing GNNs adopt the class-balanced assumption, which cannot deal with class-imbalanced graphs well. The oversampling technique is effective in alleviating class-imbalanced problems. However, most graph oversampling methods generate synthetic minority nodes and their edges after applying GNNs. They ignore the problem that the representations of the original and synthetic minority nodes are dominated by majority nodes caused by aggregating neighbor information through GNN before oversampling. In this paper, we propose a novel graph oversampling framework, termed distribution alignment-based oversampling for node classification in classimbalanced graphs(named Graph-DAO). Our framework generates synthetic minority nodes before GNN to avoid the dominance of majority nodes caused by message passing in GNNs. Additionally, we introduce a distribution alignment method based on the sum-product network to learn more information about minority nodes. To our best knowledge, it is the first to use the sum-product network to solve the class-imbalanced problem in node classification. A large number of experiments on four real datasets show that our method achieves the optimal results on the node classification task for class-imbalanced graphs.

关键词： graph neural network class-imbalanced graphs sum-product network oversampling node classification