The present study proposes a new symbolization algorithm for multidimensional time series. We view temporal sequences as observed data generated by a dynamical system, and therefore the goal of symbolization is to estimate symbolic sequences that minimize loss of information, which is called generating partition in nonlinear physics. In order to utilize the theoretical property of symbol dynamics in data mining, our algorithm estimates symbols on multivariate time series by integrating both spatial and temporal information and selecting those dimensions in multidimensional time series containing useful information. Probabilistic symbolic sequences derived from our symbolization method can be used in various supervised and unsupervised data-mining tasks. To demonstrate this, the algorithm is evaluated by applying it to both simulated data and a real-world dataset. In both cases, the new algorithm outperforms its alternative approaches.
{"title":"Spatio-Temporal Symbolization of Multidimensional Time Series","authors":"S. Hidaka, Chen Yu","doi":"10.1109/ICDMW.2010.86","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.86","url":null,"abstract":"The present study proposes a new symbolization algorithm for multidimensional time series. We view temporal sequences as observed data generated by a dynamical system, and therefore the goal of symbolization is to estimate symbolic sequences that minimize loss of information, which is called generating partition in nonlinear physics. In order to utilize the theoretical property of symbol dynamics in data mining, our algorithm estimates symbols on multivariate time series by integrating both spatial and temporal information and selecting those dimensions in multidimensional time series containing useful information. Probabilistic symbolic sequences derived from our symbolization method can be used in various supervised and unsupervised data-mining tasks. To demonstrate this, the algorithm is evaluated by applying it to both simulated data and a real-world dataset. In both cases, the new algorithm outperforms its alternative approaches.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"04 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127449134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multivariate temporal sequence classification is an important and challenging task. Several attempts to address this problem exist, but none provide a full solution. In this paper we present CUBS: Classification Using Bounded Z-Score with Sampling. CUBS uses item set mining to produce frequent subsequences, and then selects among them the statistically significant subsequences to compose a classification model. We introduce an improved item set mining algorithm that solves the short sequence bias present in many item set mining algorithms. Unfortunately, the z-score normalization hinders pruning. We provide a bound on the z-score to address this issue. Calculation of the z-score normalization requires knowledge of some statistical values of the data gathered using a small sample of the database. The sampling causes a distortion in the values. We analyze this distortion and correct it. We evaluate CUBS for accuracy and scalability on a synthetic dataset and on two real world dataset. The results demonstrate how short subsequence bias is solved in the mining, and show how our bound and sampling technique enable speedup.
{"title":"CUBS: Multivariate Sequence Classification Using Bounded Z-score with Sampling","authors":"A. Richardson, G. Kaminka, Sarit Kraus","doi":"10.1109/ICDMW.2010.38","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.38","url":null,"abstract":"Multivariate temporal sequence classification is an important and challenging task. Several attempts to address this problem exist, but none provide a full solution. In this paper we present CUBS: Classification Using Bounded Z-Score with Sampling. CUBS uses item set mining to produce frequent subsequences, and then selects among them the statistically significant subsequences to compose a classification model. We introduce an improved item set mining algorithm that solves the short sequence bias present in many item set mining algorithms. Unfortunately, the z-score normalization hinders pruning. We provide a bound on the z-score to address this issue. Calculation of the z-score normalization requires knowledge of some statistical values of the data gathered using a small sample of the database. The sampling causes a distortion in the values. We analyze this distortion and correct it. We evaluate CUBS for accuracy and scalability on a synthetic dataset and on two real world dataset. The results demonstrate how short subsequence bias is solved in the mining, and show how our bound and sampling technique enable speedup.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130929951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A new image classification method with multiple feature-based classifier (MFC) is proposed in this paper. MFC does not use the entire feature vectors extracted from the original data in a concatenated form to classify each datum, but rather uses groups of features related to each feature vector separately. In the training stage, a confusion table calculated from each local classifier that uses a specific feature vector group is drawn throughout the accuracy of each local classifier and then, in the testing stage, the final classification result is obtained by applying weights corresponding to the confidence level of each local classifier. The proposed MFC algorithm is applied to the problem of image classification on a set of image data. The results demonstrate that the proposed MFC scheme can optimally enhance the classification accuracy of individual classifiers that use specific feature vector group.
{"title":"Multiple Feature-Based Classifier and Its Application to Image Classification","authors":"Dong-Chul Park","doi":"10.1109/ICDMW.2010.82","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.82","url":null,"abstract":"A new image classification method with multiple feature-based classifier (MFC) is proposed in this paper. MFC does not use the entire feature vectors extracted from the original data in a concatenated form to classify each datum, but rather uses groups of features related to each feature vector separately. In the training stage, a confusion table calculated from each local classifier that uses a specific feature vector group is drawn throughout the accuracy of each local classifier and then, in the testing stage, the final classification result is obtained by applying weights corresponding to the confidence level of each local classifier. The proposed MFC algorithm is applied to the problem of image classification on a set of image data. The results demonstrate that the proposed MFC scheme can optimally enhance the classification accuracy of individual classifiers that use specific feature vector group.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130464587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problems of data classification can be studied in the framework of regularization theory as ill-posed problems. In this framework, loss functions play an important role in the application of regularization theory to classification. In this paper, we review some important convex loss functions, including hinge loss, square loss, modified square loss, exponential loss, logistic regression loss, as well as some non-convex loss functions, such as sigmoid loss, $phi$-loss, ramp loss, normalized sigmoid loss, and the loss function of 2 layer neural network. Based on the analysis of these loss functions, we propose a new differentiable non-convex loss function, called smoothed 0-1 loss function, which is a natural approximation of the 0-1 loss function. To compare the performance of different loss functions, we propose two binary classification algorithms for binary classification, one for convex loss functions, the other for non-convex loss functions. A set of experiments are launched on several binary data sets from the UCI repository. The results show that the proposed smoothed 0-1 loss function is robust, especially for those noisy data sets with many outliers.
{"title":"From Convex to Nonconvex: A Loss Function Analysis for Binary Classification","authors":"Lei Zhao, M. Mammadov, J. Yearwood","doi":"10.1109/ICDMW.2010.57","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.57","url":null,"abstract":"Problems of data classification can be studied in the framework of regularization theory as ill-posed problems. In this framework, loss functions play an important role in the application of regularization theory to classification. In this paper, we review some important convex loss functions, including hinge loss, square loss, modified square loss, exponential loss, logistic regression loss, as well as some non-convex loss functions, such as sigmoid loss, $phi$-loss, ramp loss, normalized sigmoid loss, and the loss function of 2 layer neural network. Based on the analysis of these loss functions, we propose a new differentiable non-convex loss function, called smoothed 0-1 loss function, which is a natural approximation of the 0-1 loss function. To compare the performance of different loss functions, we propose two binary classification algorithms for binary classification, one for convex loss functions, the other for non-convex loss functions. A set of experiments are launched on several binary data sets from the UCI repository. The results show that the proposed smoothed 0-1 loss function is robust, especially for those noisy data sets with many outliers.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131325756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we analyse the behaviour of osteosarcoma cancer cells which are either exposed to the anticancer agent Topotecan or not exposed to any external agent. For the analyses of cell lineage data encoded from time lapse microscopy, we choose data mining tools that generate interpretable models of the data, and we address their statistical significance. We consider the mortality of unexposed cancer cells, the static and dynamic cytotoxic effects of the anticancer agent, the prediction of the clonal potential of resistant populations, and the differences between exposed and unexposed populations. We find that the anticancer agent affects the cells dynamics and events ratios i.e. (death/division, etc.) proportionately to its concentration, but it is ineffective at stopping the proliferation of the cancer at all dosages considered. In addition, we observe that cells exposed to the anticancer agent have greater displacements over time, indicating a putative relationship between cytotoxic effect and cell motility.
{"title":"Is Topotecan Effective at Killing Cancer Cells?","authors":"R. Santiago-Mozos, I. Khan, M. G. Madden","doi":"10.1109/ICDMW.2010.129","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.129","url":null,"abstract":"In this paper, we analyse the behaviour of osteosarcoma cancer cells which are either exposed to the anticancer agent Topotecan or not exposed to any external agent. For the analyses of cell lineage data encoded from time lapse microscopy, we choose data mining tools that generate interpretable models of the data, and we address their statistical significance. We consider the mortality of unexposed cancer cells, the static and dynamic cytotoxic effects of the anticancer agent, the prediction of the clonal potential of resistant populations, and the differences between exposed and unexposed populations. We find that the anticancer agent affects the cells dynamics and events ratios i.e. (death/division, etc.) proportionately to its concentration, but it is ineffective at stopping the proliferation of the cancer at all dosages considered. In addition, we observe that cells exposed to the anticancer agent have greater displacements over time, indicating a putative relationship between cytotoxic effect and cell motility.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128878914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qiong Cheng, Jinpeng Wei, A. Zelikovsky, M. Ogihara
The accumulation of high-throughput genomic and proteomic data allows for the reconstruction of the increasingly large and complex metabolic networks. In order to analyze accumulated data and reconstructed networks, it is critical to identify network patterns and evolutionary relations between metabolic networks. But even finding similar networks is computationally challenging. Based on the property of gene duplication and function sharing in biological network, we have formulated the network alignment problem which asks the optimal vertex-to-vertex mapping allowing path contraction, vertex deletion, and vertex insertions. In this paper we present fixed parameter tractable combinatorial algorithms, which take into account the enzymes' functions and the similarity of arbitrary network topologies such as trees and arbitrary graphs wit hallowing the different types of vertex deletions. The proposed algorithms are fixed parameter tractable in the liner or square of the size of feedback vertex set respectively for the case of disallowing or allowing the deletions. We have developed the web service tool MetNetAligner which aligns metabolic networks. We evaluated our results by the randomizedP-Value computation. In the computation, we followed two standard randomization procedures and further developed two other random graph generators which keep the more stringent and consistent topology constraints. By comparing their distribution of the significant alignment pairs, we observed that the more stringent constraints in the topology the random graph generator has, the more pairs of significant alignments there exist. We also performed pair wise mapping of all pathways for four organisms and found a set of statistically significant pathway similarities. We have applied the network alignment to identifying pathway holes which are resulted by inconsistency and missing enzymes. MetNetAligner is available athttp://alla.cs.gsu.edu:8080/MinePW/pages/gmapping/GMMain.html Two random graph generations and the list of identified pathway holes are available online.
{"title":"Fixed-Parameter Tractable Combinatorial Algorithms for Metabolic Networks Alignments","authors":"Qiong Cheng, Jinpeng Wei, A. Zelikovsky, M. Ogihara","doi":"10.1109/ICDMW.2010.179","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.179","url":null,"abstract":"The accumulation of high-throughput genomic and proteomic data allows for the reconstruction of the increasingly large and complex metabolic networks. In order to analyze accumulated data and reconstructed networks, it is critical to identify network patterns and evolutionary relations between metabolic networks. But even finding similar networks is computationally challenging. Based on the property of gene duplication and function sharing in biological network, we have formulated the network alignment problem which asks the optimal vertex-to-vertex mapping allowing path contraction, vertex deletion, and vertex insertions. In this paper we present fixed parameter tractable combinatorial algorithms, which take into account the enzymes' functions and the similarity of arbitrary network topologies such as trees and arbitrary graphs wit hallowing the different types of vertex deletions. The proposed algorithms are fixed parameter tractable in the liner or square of the size of feedback vertex set respectively for the case of disallowing or allowing the deletions. We have developed the web service tool MetNetAligner which aligns metabolic networks. We evaluated our results by the randomizedP-Value computation. In the computation, we followed two standard randomization procedures and further developed two other random graph generators which keep the more stringent and consistent topology constraints. By comparing their distribution of the significant alignment pairs, we observed that the more stringent constraints in the topology the random graph generator has, the more pairs of significant alignments there exist. We also performed pair wise mapping of all pathways for four organisms and found a set of statistically significant pathway similarities. We have applied the network alignment to identifying pathway holes which are resulted by inconsistency and missing enzymes. MetNetAligner is available athttp://alla.cs.gsu.edu:8080/MinePW/pages/gmapping/GMMain.html Two random graph generations and the list of identified pathway holes are available online.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125402440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recommender systems have become essential navigational tools for users to surf through vast on-line catalogs. However, recommender algorithms are often tuned to improve accuracy, without paying any attention to the consistency of the recommendations when small changes happen to the user profile or to the model. Consistency of recommendations is closely related with user satisfaction and trust. In this work we analyze how small changes in either the user profile or the recommender model may affect the consistency of Top-N recommendation systems. We also design two mechanisms able to promote consistency without degrading accuracy and novelty of recommendations. Finally, we investigate the consistency of Top-N recommendation algorithms over time by analyzing real data from a production IPTV recommender system.
{"title":"Controlling Consistency in Top-N Recommender Systems","authors":"P. Cremonesi, R. Turrin","doi":"10.1109/ICDMW.2010.65","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.65","url":null,"abstract":"Recommender systems have become essential navigational tools for users to surf through vast on-line catalogs. However, recommender algorithms are often tuned to improve accuracy, without paying any attention to the consistency of the recommendations when small changes happen to the user profile or to the model. Consistency of recommendations is closely related with user satisfaction and trust. In this work we analyze how small changes in either the user profile or the recommender model may affect the consistency of Top-N recommendation systems. We also design two mechanisms able to promote consistency without degrading accuracy and novelty of recommendations. Finally, we investigate the consistency of Top-N recommendation algorithms over time by analyzing real data from a production IPTV recommender system.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125547917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Wojnarski, P. Góra, Marcin S. Szczuka, H. Nguyen, Joanna Swietlicka, Demetrios Zeinalipour-Yazti
In this foreword, we summarize the IEEE ICDM 2010 Contest: “TomTom Traffic Prediction for Intelligent GPS Navigation”. The challenge was held between Jun 22, 2010 and Sep 7, 2010 as an interactive on-line competition, using the TunedIT platform (http://tunedit.org). We present the scope of the ICDM contest series in general, the scope of this year’s contest, description of its tasks, statistics about participation, details about the TunedIT platform and the Traffic Simulation Framework. A detailed description of winning solutions is part of this proceeding series.
{"title":"IEEE ICDM 2010 Contest: TomTom Traffic Prediction for Intelligent GPS Navigation","authors":"M. Wojnarski, P. Góra, Marcin S. Szczuka, H. Nguyen, Joanna Swietlicka, Demetrios Zeinalipour-Yazti","doi":"10.1109/ICDMW.2010.51","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.51","url":null,"abstract":"In this foreword, we summarize the IEEE ICDM 2010 Contest: “TomTom Traffic Prediction for Intelligent GPS Navigation”. The challenge was held between Jun 22, 2010 and Sep 7, 2010 as an interactive on-line competition, using the TunedIT platform (http://tunedit.org). We present the scope of the ICDM contest series in general, the scope of this year’s contest, description of its tasks, statistics about participation, details about the TunedIT platform and the Traffic Simulation Framework. A detailed description of winning solutions is part of this proceeding series.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123134824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to deal with sudden unexpected changes of circumstances, we propose a new forecast method based on paired evaluators, the stable evaluator and the reactive evaluator. These two evaluators are good at detecting consecutive concept drifts. We conduct a back-testing using financial data in order to demonstrate the performance of our proposing forecast method. The results of the back-testing show that our method is effective and robust even against the late-2000s recessions.
{"title":"Paired Evaluators Method to Track Concept Drift: An Application for Hedge Funds Operations","authors":"Masabumi Furuhata, T. Mizuta, J. So","doi":"10.1109/ICDMW.2010.131","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.131","url":null,"abstract":"In order to deal with sudden unexpected changes of circumstances, we propose a new forecast method based on paired evaluators, the stable evaluator and the reactive evaluator. These two evaluators are good at detecting consecutive concept drifts. We conduct a back-testing using financial data in order to demonstrate the performance of our proposing forecast method. The results of the back-testing show that our method is effective and robust even against the late-2000s recessions.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116638481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many recent applications deal with continues flows of data (data streams). One important area of applications that is based on data streams is the area of Wireless Sensor Networks (WSNs) applications. Since sensors have limited lifetime, the need for developing algorithms for aggregating sensors' data forms an important concern in the area of WSNs. We present W-LEACH, a data-stream aggregation algorithm for WSNs that extends LEACH algorithm by Heinzelman et al. W-LEACH is able to handle non-uniform networks as well as uniform networks, while not affecting the network lifetime. It, instead, increases the average lifetime for sensors. We simulate our algorithm to evaluate its performance. Results show that W-LEACH increases the network lifetime and the average lifetime for sensors for uniform and non-uniform WSNs.
{"title":"W-LEACH: Weighted Low Energy Adaptive Clustering Hierarchy Aggregation Algorithm for Data Streams in Wireless Sensor Networks","authors":"Hanady M. Abdulsalam, Layla K. Kamel","doi":"10.1109/ICDMW.2010.28","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.28","url":null,"abstract":"Many recent applications deal with continues flows of data (data streams). One important area of applications that is based on data streams is the area of Wireless Sensor Networks (WSNs) applications. Since sensors have limited lifetime, the need for developing algorithms for aggregating sensors' data forms an important concern in the area of WSNs. We present W-LEACH, a data-stream aggregation algorithm for WSNs that extends LEACH algorithm by Heinzelman et al. W-LEACH is able to handle non-uniform networks as well as uniform networks, while not affecting the network lifetime. It, instead, increases the average lifetime for sensors. We simulate our algorithm to evaluate its performance. Results show that W-LEACH increases the network lifetime and the average lifetime for sensors for uniform and non-uniform WSNs.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122666658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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