Pub Date : 2008-03-05DOI: 10.1109/PACIFICVIS.2008.4475460
Kuangyu Shi, H. Theisel, T. Weinkauf, H. Hege, H. Seidel
Modern experimental and computational fluid mechanics are increasingly concerned with the structure nature of fluid motion. Recent research has highlighted the analysis of one transport structure which is called Lagrangian coherent structure. However, the quantity nature of the flow transport is still unclear. In this paper, we focus on the transport characteristics of physical quantities and propose an approach to visualize the finite-time transport structure of quantity advection. This is similar to an integral convolution over a scalar field along path-lines of a flow field. Applied to a well-chosen set of physical quantity fields this yields structures giving insights into the dynamical processes of the underlying flow. We demonstrate our approach on a number of test data sets.
{"title":"Finite-Time Transport Structures of Flow Fields","authors":"Kuangyu Shi, H. Theisel, T. Weinkauf, H. Hege, H. Seidel","doi":"10.1109/PACIFICVIS.2008.4475460","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2008.4475460","url":null,"abstract":"Modern experimental and computational fluid mechanics are increasingly concerned with the structure nature of fluid motion. Recent research has highlighted the analysis of one transport structure which is called Lagrangian coherent structure. However, the quantity nature of the flow transport is still unclear. In this paper, we focus on the transport characteristics of physical quantities and propose an approach to visualize the finite-time transport structure of quantity advection. This is similar to an integral convolution over a scalar field along path-lines of a flow field. Applied to a well-chosen set of physical quantity fields this yields structures giving insights into the dynamical processes of the underlying flow. We demonstrate our approach on a number of test data sets.","PeriodicalId":364669,"journal":{"name":"2008 IEEE Pacific Visualization Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129045884","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}
Pub Date : 2008-03-05DOI: 10.1109/PACIFICVIS.2008.4475458
U. Brandes, J. Lerner, M. Lubbers, C. McCarty, J. Molina
We propose a method to visually summarize collections of networks on which a clustering of the vertices is given. Our method allows for efficient comparison of individual networks, as well as for visualizing the average composition and structure of a set of networks. As a concrete application we analyze a set of several hundred personal networks of migrants. On the individual level the network images provide visual hints for assessing the mode of acculturation of the respondent. On the population level they show how cultural integration varies with specific characteristics of the migrants such as country of origin, years of residence, or skin color.
{"title":"Visual Statistics for Collections of Clustered Graphs","authors":"U. Brandes, J. Lerner, M. Lubbers, C. McCarty, J. Molina","doi":"10.1109/PACIFICVIS.2008.4475458","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2008.4475458","url":null,"abstract":"We propose a method to visually summarize collections of networks on which a clustering of the vertices is given. Our method allows for efficient comparison of individual networks, as well as for visualizing the average composition and structure of a set of networks. As a concrete application we analyze a set of several hundred personal networks of migrants. On the individual level the network images provide visual hints for assessing the mode of acculturation of the respondent. On the population level they show how cultural integration varies with specific characteristics of the migrants such as country of origin, years of residence, or skin color.","PeriodicalId":364669,"journal":{"name":"2008 IEEE Pacific Visualization Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114689186","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}
Pub Date : 2008-03-05DOI: 10.1109/PACIFICVIS.2008.4475476
K. Ma
With the success of open source software projects, such as Apache and Mozilla, comes the opportunity to study the development process. In this paper, we present StarGate: a novel system for visualizing software projects. Whereas previous software project visualizations concentrated mainly on the source code changes, we literally place the developers in the center of our design. Developers are grouped visually into clusters corresponding to the areas of the file repository they work on the most. Connections are drawn between people who communicate via email. The changes to the repository are also displayed. With StarGate, it is easy to look beyond the source code and see trends in developer activity. The system can be used by anyone interested in the project, but it especially benefits project managers, project novices and software engineering researchers.
{"title":"StarGate: A Unified, Interactive Visualization of Software Projects","authors":"K. Ma","doi":"10.1109/PACIFICVIS.2008.4475476","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2008.4475476","url":null,"abstract":"With the success of open source software projects, such as Apache and Mozilla, comes the opportunity to study the development process. In this paper, we present StarGate: a novel system for visualizing software projects. Whereas previous software project visualizations concentrated mainly on the source code changes, we literally place the developers in the center of our design. Developers are grouped visually into clusters corresponding to the areas of the file repository they work on the most. Connections are drawn between people who communicate via email. The changes to the repository are also displayed. With StarGate, it is easy to look beyond the source code and see trends in developer activity. The system can be used by anyone interested in the project, but it especially benefits project managers, project novices and software engineering researchers.","PeriodicalId":364669,"journal":{"name":"2008 IEEE Pacific Visualization Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125325822","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}
Pub Date : 2008-03-05DOI: 10.1109/PACIFICVIS.2008.4475471
Xinghua Lou, Shixia Liu, Tianshu Wang
Radial, space-filling visualization is very useful for representing the distribution of attributes in hierarchical data; however it also suffers from its drawbacks in terms of view transition, context preservation, thin slices, flexibility and large sized data support. To address these problems, we propose FanLens, an enhancement upon existing approaches with new features like incremental layout and fisheye distortion based selecting. This visual toolkit also features dynamic hierarchy specification, dynamic visual property mapping, smooth animation, etc. We illustrate the effectiveness of our technique with two examples of case study and results from informal user experiments.
{"title":"FanLens: A Visual Toolkit for Dynamically Exploring the Distribution of Hierarchical Attributes","authors":"Xinghua Lou, Shixia Liu, Tianshu Wang","doi":"10.1109/PACIFICVIS.2008.4475471","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2008.4475471","url":null,"abstract":"Radial, space-filling visualization is very useful for representing the distribution of attributes in hierarchical data; however it also suffers from its drawbacks in terms of view transition, context preservation, thin slices, flexibility and large sized data support. To address these problems, we propose FanLens, an enhancement upon existing approaches with new features like incremental layout and fisheye distortion based selecting. This visual toolkit also features dynamic hierarchy specification, dynamic visual property mapping, smooth animation, etc. We illustrate the effectiveness of our technique with two examples of case study and results from informal user experiments.","PeriodicalId":364669,"journal":{"name":"2008 IEEE Pacific Visualization Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122255079","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}
Pub Date : 2008-03-05DOI: 10.1109/PACIFICVIS.2008.4475480
Y. Wu, M. Takatsuka
Multivariate networks are data sets that describe not only the relationships between a set of entities but also their attributes. In this paper, we present a new technique to determine the layout of a multivariate network using geodesic self-organizing map (GeoSOM). During the training process of a GeoSOM, graph distances are non-linearly combined with attribute similarities based on the network's graph distance distribution. The resulted layout has less edge crossings than those generated by the previous methods. We conducted a user study to evaluate the effectiveness of this hybrid approach. The results were compared against the most commonly used glyph-based technique. The user study shows that the hybrid approach helps users draw conclusions from both the relationship and vertex attributes of a multivariate network more quickly and accurately. In addition, users found it easier to compare different relationships of the same set of entities. Finally, the capability of the hybrid approach is demonstrated using the world military expenditures and weapon transfer networks.
{"title":"Visualizing Multivariate Networks: A Hybrid Approach","authors":"Y. Wu, M. Takatsuka","doi":"10.1109/PACIFICVIS.2008.4475480","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2008.4475480","url":null,"abstract":"Multivariate networks are data sets that describe not only the relationships between a set of entities but also their attributes. In this paper, we present a new technique to determine the layout of a multivariate network using geodesic self-organizing map (GeoSOM). During the training process of a GeoSOM, graph distances are non-linearly combined with attribute similarities based on the network's graph distance distribution. The resulted layout has less edge crossings than those generated by the previous methods. We conducted a user study to evaluate the effectiveness of this hybrid approach. The results were compared against the most commonly used glyph-based technique. The user study shows that the hybrid approach helps users draw conclusions from both the relationship and vertex attributes of a multivariate network more quickly and accurately. In addition, users found it easier to compare different relationships of the same set of entities. Finally, the capability of the hybrid approach is demonstrated using the world military expenditures and weapon transfer networks.","PeriodicalId":364669,"journal":{"name":"2008 IEEE Pacific Visualization Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122388150","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}
Pub Date : 2008-03-05DOI: 10.1109/PACIFICVIS.2008.4475466
Martin Suntinger, Hannes Obweger, Josef Schiefer, E. Gröller
Event-based systems are gaining increasing popularity for building loosely coupled and distributed systems. Since business processes are becoming more interconnected and event-driven, event-based systems fit well for supporting and monitoring business processes. In this paper, we present an event-based business intelligence tool, the Event Tunnel framework. It provides an interactive visualization of event streams to support business analysts in exploring business incidents. The visualization is based on the metaphor of considering the event stream as a cylindrical tunnel, which is presented to the user from multiple perspectives. The information of single events laid out in the Event Tunnel is encoded in event glyphs that allow for a selective mapping of event attributes to colors, size and position. Different policies for the placement of the events in the tunnel as well as a clustering mechanism generate various views on historical event data. The Event Tunnel is able to display the relationships between events. This facilitates users to discover root causes and causal dependencies of event patterns. Our framework couples the event-tunnel visualization with query tools that allow users to search for relevant events within a data repository. Using query, filler and highlighting operations the analyst can navigate through the Event Tunnel until the required information or event patterns become visible. We demonstrate our approach with use cases from the fraud management and logistics domain.
{"title":"The Event Tunnel: Interactive Visualization of Complex Event Streams for Business Process Pattern Analysis","authors":"Martin Suntinger, Hannes Obweger, Josef Schiefer, E. Gröller","doi":"10.1109/PACIFICVIS.2008.4475466","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2008.4475466","url":null,"abstract":"Event-based systems are gaining increasing popularity for building loosely coupled and distributed systems. Since business processes are becoming more interconnected and event-driven, event-based systems fit well for supporting and monitoring business processes. In this paper, we present an event-based business intelligence tool, the Event Tunnel framework. It provides an interactive visualization of event streams to support business analysts in exploring business incidents. The visualization is based on the metaphor of considering the event stream as a cylindrical tunnel, which is presented to the user from multiple perspectives. The information of single events laid out in the Event Tunnel is encoded in event glyphs that allow for a selective mapping of event attributes to colors, size and position. Different policies for the placement of the events in the tunnel as well as a clustering mechanism generate various views on historical event data. The Event Tunnel is able to display the relationships between events. This facilitates users to discover root causes and causal dependencies of event patterns. Our framework couples the event-tunnel visualization with query tools that allow users to search for relevant events within a data repository. Using query, filler and highlighting operations the analyst can navigate through the Event Tunnel until the required information or event patterns become visible. We demonstrate our approach with use cases from the fraud management and logistics domain.","PeriodicalId":364669,"journal":{"name":"2008 IEEE Pacific Visualization Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115959968","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}
Pub Date : 2008-03-05DOI: 10.1109/PACIFICVIS.2008.4475470
Aidong Lu, Han-Wei Shen
Large amounts of time-varying datasets create great challenges for users to understand and explore them. This paper proposes an efficient visualization method for observing overall data contents and changes throughout an entire time-varying dataset. We develop an interactive storyboard approach by composing sample volume renderings and descriptive geometric primitives that are generated through data analysis processes. Our storyboard system integrates automatic visualization generation methods and interactive adjustment procedures to provide new tools for visualizing and exploring time-varying datasets. We also provide a flexible framework to quantify data differences and automatically select representative datasets through exploring scientific data distribution features. Since this approach reduces the visualized data amount into a more understandable size and format for users, it can be used to effectively visualize, represent, and explore a large time-varying dataset. Initial user study results show that our approach shortens the exploration time and reduces the number of datasets that users visualized individually. This visualization method is especially useful for situations that require close observance or are not capable of interactive rendering, such as documentation and demonstration.
{"title":"Interactive Storyboard for Overall Time-Varying Data Visualization","authors":"Aidong Lu, Han-Wei Shen","doi":"10.1109/PACIFICVIS.2008.4475470","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2008.4475470","url":null,"abstract":"Large amounts of time-varying datasets create great challenges for users to understand and explore them. This paper proposes an efficient visualization method for observing overall data contents and changes throughout an entire time-varying dataset. We develop an interactive storyboard approach by composing sample volume renderings and descriptive geometric primitives that are generated through data analysis processes. Our storyboard system integrates automatic visualization generation methods and interactive adjustment procedures to provide new tools for visualizing and exploring time-varying datasets. We also provide a flexible framework to quantify data differences and automatically select representative datasets through exploring scientific data distribution features. Since this approach reduces the visualized data amount into a more understandable size and format for users, it can be used to effectively visualize, represent, and explore a large time-varying dataset. Initial user study results show that our approach shortens the exploration time and reduces the number of datasets that users visualized individually. This visualization method is especially useful for situations that require close observance or are not capable of interactive rendering, such as documentation and demonstration.","PeriodicalId":364669,"journal":{"name":"2008 IEEE Pacific Visualization Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130568047","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}
Pub Date : 2008-03-05DOI: 10.1109/PACIFICVIS.2008.4475468
S. Ueng, Hai-Peng Cheng, Ruey-Yuan Lu
An adaptive filtering method for volume data is presented in this paper. In this filtering method, the input data set is re-sampled to create a hierarchy of multiple-level data sets. A data classification task is performed at each level of the data pyramid to decide the local structure types. Data voxels are classified as linear, planar, or blob structures, based on the gradients and the eigenvalues of Hessian matrices. The classification results are used to adjust the shapes and orientations of filters such that noises are suppressed while key features are preserved.
{"title":"An Adaptive Gauss Filtering Method","authors":"S. Ueng, Hai-Peng Cheng, Ruey-Yuan Lu","doi":"10.1109/PACIFICVIS.2008.4475468","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2008.4475468","url":null,"abstract":"An adaptive filtering method for volume data is presented in this paper. In this filtering method, the input data set is re-sampled to create a hierarchy of multiple-level data sets. A data classification task is performed at each level of the data pyramid to decide the local structure types. Data voxels are classified as linear, planar, or blob structures, based on the gradients and the eigenvalues of Hessian matrices. The classification results are used to adjust the shapes and orientations of filters such that noises are suppressed while key features are preserved.","PeriodicalId":364669,"journal":{"name":"2008 IEEE Pacific Visualization Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124695446","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}
Pub Date : 2008-03-05DOI: 10.1109/PACIFICVIS.2008.4475475
Shixia Liu, Nan Cao, Hao Lv
This paper presents an interactive visualization toolkit for navigating and analyzing the National Science Foundation (NSF) funding information. Our design builds upon the treemap layout and the stacked graph to contribute customized techniques for visually navigating and interacting with the hierarchical data of NSF programs and proposals, supporting visual search and analysis, and allowing the user to make informed decision. In this visualization toolkit, we propose two visualization techniques to simplify the navigation of the hierarchical data: 2.5 Dimensional treemaps to make the hierarchical structure more easily to be recognized, and labeled treemap to help the user to get a clear overview of the content of the structure and make the internal area of rectangles correspond to the weights of the data set. Furthermore, an incremental layout method is adopted to handle information on a large scale. The improved treemap visualization will help to visually analyze the static funding data and the stacked graph is utilized to analyze the time-series data. Through these visual analysis techniques, research trends of NSF, popular NSF programs are quickly identified. The primary contribution is a demonstration of novel ways to effectively present and analyze NSF funding data.
{"title":"Interactive Visual Analysis of the NSF Funding Information","authors":"Shixia Liu, Nan Cao, Hao Lv","doi":"10.1109/PACIFICVIS.2008.4475475","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2008.4475475","url":null,"abstract":"This paper presents an interactive visualization toolkit for navigating and analyzing the National Science Foundation (NSF) funding information. Our design builds upon the treemap layout and the stacked graph to contribute customized techniques for visually navigating and interacting with the hierarchical data of NSF programs and proposals, supporting visual search and analysis, and allowing the user to make informed decision. In this visualization toolkit, we propose two visualization techniques to simplify the navigation of the hierarchical data: 2.5 Dimensional treemaps to make the hierarchical structure more easily to be recognized, and labeled treemap to help the user to get a clear overview of the content of the structure and make the internal area of rectangles correspond to the weights of the data set. Furthermore, an incremental layout method is adopted to handle information on a large scale. The improved treemap visualization will help to visually analyze the static funding data and the stacked graph is utilized to analyze the time-series data. Through these visual analysis techniques, research trends of NSF, popular NSF programs are quickly identified. The primary contribution is a demonstration of novel ways to effectively present and analyze NSF funding data.","PeriodicalId":364669,"journal":{"name":"2008 IEEE Pacific Visualization Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126253532","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}
Pub Date : 2008-03-05DOI: 10.1109/PACIFICVIS.2008.4475463
Li Chen, I. Fujishiro
Parallel performance has been a challenging topic in streamline visualization for large unstructured flow datasets on parallel distributed-memory computers. It depends strongly on domain partitions. Unsuitable partitions often lead to severe load imbalance and high frequent communications among the domain partitions. To address the problem, we present an approach to flow data partitioning taking account of flow directions and features. Multilevel spectral graph bisection method is employed to reduce communication and synchronization overhead among distributed domains. Edge weights in the corresponding adjacent matrix is defined based on an anisotropic local diffusion operator which assigns strong coupling along flow direction and weak coupling orthogonal to flow. Meanwhile, the distributions of seed points and flow features such as vortex structure are also considered in partitioning so as to obtain good load balance. The experimental results are given to show the feasibility and effectiveness of our method.
{"title":"Optimizing Parallel Performance of Streamline Visualization for Large Distributed Flow Datasets","authors":"Li Chen, I. Fujishiro","doi":"10.1109/PACIFICVIS.2008.4475463","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2008.4475463","url":null,"abstract":"Parallel performance has been a challenging topic in streamline visualization for large unstructured flow datasets on parallel distributed-memory computers. It depends strongly on domain partitions. Unsuitable partitions often lead to severe load imbalance and high frequent communications among the domain partitions. To address the problem, we present an approach to flow data partitioning taking account of flow directions and features. Multilevel spectral graph bisection method is employed to reduce communication and synchronization overhead among distributed domains. Edge weights in the corresponding adjacent matrix is defined based on an anisotropic local diffusion operator which assigns strong coupling along flow direction and weak coupling orthogonal to flow. Meanwhile, the distributions of seed points and flow features such as vortex structure are also considered in partitioning so as to obtain good load balance. The experimental results are given to show the feasibility and effectiveness of our method.","PeriodicalId":364669,"journal":{"name":"2008 IEEE Pacific Visualization Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125672480","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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