2022 IEEE 15th Pacific Visualization Symposium (PacificVis)最新文献_第2页

2022 IEEE 15th Pacific Visualization Symposium PacificVis 2022 2022 IEEE第15届太平洋可视化研讨会PacificVis 2022

2022 IEEE 15th Pacific Visualization Symposium (PacificVis) Pub Date : 2022-04-01 DOI: 10.1109/pacificvis53943.2022.00002

引用次数: 0

Detecting Critical Points in 2D Scalar Field Ensembles Using Bayesian Inference 利用贝叶斯推理检测二维标量场系综中的临界点

2022 IEEE 15th Pacific Visualization Symposium (PacificVis) Pub Date : 2022-04-01 DOI: 10.1109/PacificVis53943.2022.00009

Dominik Vietinghoff, M. Böttinger, G. Scheuermann, Christian Heine

{"title":"Detecting Critical Points in 2D Scalar Field Ensembles Using Bayesian Inference","authors":"Dominik Vietinghoff, M. Böttinger, G. Scheuermann, Christian Heine","doi":"10.1109/PacificVis53943.2022.00009","DOIUrl":"https://doi.org/10.1109/PacificVis53943.2022.00009","url":null,"abstract":"In an era of quickly growing data set sizes, information reduction methods such as extracting or highlighting characteristic features become more and more important for data analysis. For single scalar fields, topological methods can fill this role by extracting and relating critical points. While such methods are regularly employed to study single scalar fields, it is less well studied how they can be extended to uncertain data, as produced, e.g., by ensemble simulations. Motivated by our previous work on visualization in climate research, we study new methods to characterize critical points in ensembles of 2D scalar fields. Previous work on this topic either assumed or required specific distributions, did not account for uncertainty introduced by approximating the underlying latent distributions by a finite number of fields, or did not allow to answer all our domain experts' questions. In this work, we use Bayesian inference to estimate the probability of critical points, either of the original ensemble or its bootstrapped mean. This does not make any assumptions on the underlying distribution and allows to estimate the sensitivity of the results to finite-sample approximations of the underlying distribution. We use color mapping to depict these probabilities and the stability of their estimation. The resulting images can, e.g., be used to estimate how precise the critical points of the mean-field are. We apply our method to synthetic data to validate its theoretical properties and compare it with other methods in this regard. We also apply our method to the data from our previous work, where it provides a more accurate answer to the domain experts' research questions.","PeriodicalId":117284,"journal":{"name":"2022 IEEE 15th Pacific Visualization Symposium (PacificVis)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127909240","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}

引用次数: 3

UNICON: A UNIform CONstraint Based Graph Layout Framework UNICON:基于统一约束的图形布局框架

2022 IEEE 15th Pacific Visualization Symposium (PacificVis) Pub Date : 2022-04-01 DOI: 10.1109/PacificVis53943.2022.00015

Jiacheng Yu, Yifan Hu, Xiaoru Yuan

引用次数: 2

Scalar2Vec: Translating Scalar Fields to Vector Fields via Deep Learning Scalar2Vec:通过深度学习将标量场转换为向量场

2022 IEEE 15th Pacific Visualization Symposium (PacificVis) Pub Date : 2022-04-01 DOI: 10.1109/PacificVis53943.2022.00012

Pengfei Gu, J. Han, D. Chen, Chaoli Wang

引用次数: 0

Visualization for neural-network-based person re-identification 基于神经网络的人物再识别可视化

2022 IEEE 15th Pacific Visualization Symposium (PacificVis) Pub Date : 2022-04-01 DOI: 10.1109/PacificVis53943.2022.00027

Teng-Yok Lee

{"title":"Visualization for neural-network-based person re-identification","authors":"Teng-Yok Lee","doi":"10.1109/PacificVis53943.2022.00027","DOIUrl":"https://doi.org/10.1109/PacificVis53943.2022.00027","url":null,"abstract":"Given images of a person, person re- identification (Person ReID) techniques aim to find images of the same person from previously collected images. Because of large data sets of person images and the advance of deep learning, convolutional neural networks (CNNs) successfully boost the accuracy of Person ReID algorithms, but it can be difficult to explain and to troubleshoot issues due to the complexity of CNNs. In this paper, we present a visualization-based approach to understand a CNN-based Person ReID algorithm. As Person ReID algorithms are often designed to map images of the same person into similar feature vectors, given two images, we design an algorithm to estimate how much each element in a CNN layer contributes to the similarity between their feature vectors. Based on the estimation, we build a visualization tool to interactively locate and visualize the activation of highly-contributing elements, other than manually examining all. Our visualization tool also supports various user interaction widgets to explore a Person ReID data set, locate difficult cases, and analyze the reason behind their similarities. We show a use case with our tool to understand and troubleshoot issues in a CNN-based Person ReID algorithm.","PeriodicalId":117284,"journal":{"name":"2022 IEEE 15th Pacific Visualization Symposium (PacificVis)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129273190","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}

引用次数: 1

Boundary-Aware Rectilinear Grid: Accurate Approximation of Unstructured Dataset into Rectilinear Grid with Solid Boundary Handling Capabilities 边界感知的直线网格:将非结构化数据集精确逼近为具有实体边界处理能力的直线网格

2022 IEEE 15th Pacific Visualization Symposium (PacificVis) Pub Date : 2022-04-01 DOI: 10.1109/PacificVis53943.2022.00013

Dana El-Rushaidat, Raine Yeh, X. Tricoche

{"title":"Boundary-Aware Rectilinear Grid: Accurate Approximation of Unstructured Dataset into Rectilinear Grid with Solid Boundary Handling Capabilities","authors":"Dana El-Rushaidat, Raine Yeh, X. Tricoche","doi":"10.1109/PacificVis53943.2022.00013","DOIUrl":"https://doi.org/10.1109/PacificVis53943.2022.00013","url":null,"abstract":"Computational fluid dynamics simulations produce increasingly large datasets that are often defined over unstructured grids with solid boundaries. Though unstructured grids allow for the flexible representation of this geometry and the refinement of the grid resolution, they suffer from high storage cost, non-trivial spatial queries, and low reconstruction smoothness. On the other hand, rectilinear grids do not have these drawbacks, but they cannot represent complex boundaries. We present in this paper a technique for the high-quality approximation of large unstructured datasets with solid boundaries onto modified rectilinear grids that we endow with boundary handling capabilities. The resulting data representation can accommodate challenging boundaries while supporting high-order reconstruction kernels with a much-reduced memory footprint. As such, our data representation enjoys all the benefits of conventional rectilinear grids while addressing their fundamental geometric limitations. We demonstrate the proposed approach on several CFD datasets and show that our method achieves an accurate and high-quality approximation of simulation datasets.","PeriodicalId":117284,"journal":{"name":"2022 IEEE 15th Pacific Visualization Symposium (PacificVis)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127212773","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}

引用次数: 0

A Study of the Locality of Persistence-Based Queries and Its Implications for the Efficiency of Localized Data Structures 基于持久化查询的局部性及其对本地化数据结构效率的影响研究

2022 IEEE 15th Pacific Visualization Symposium (PacificVis) Pub Date : 2022-04-01 DOI: 10.1109/PacificVis53943.2022.00021

Pavol Klacansky, A. Gyulassy, P. Bremer, Valerio Pascucci

{"title":"A Study of the Locality of Persistence-Based Queries and Its Implications for the Efficiency of Localized Data Structures","authors":"Pavol Klacansky, A. Gyulassy, P. Bremer, Valerio Pascucci","doi":"10.1109/PacificVis53943.2022.00021","DOIUrl":"https://doi.org/10.1109/PacificVis53943.2022.00021","url":null,"abstract":"Scientific datasets are often analyzed and visualized using isosurfaces. The connected components at or above the isovalue defining these isosurfaces are called superlevel-set components. The vertex set of these superlevel-set components can be used to compute local statistics, such as mean temperature or histogram per component, or to segment the data. However, in datasets produced by acquisition devices or simulations, noise induces many spurious components that clutter the visualization and analysis results. Many of these spurious components would disappear if the data values were slightly adjusted. The notion of persistence captures the stability of a component with respect to function value changes, and so we are interested in computing persistence quickly. Locality of computation is critical for parallel scalability, minimization of communication in a distributed environment, or an out-of-core processing. The recently introduced merge forest attained high performance by exploiting locality, thereby avoiding communication until needed to resolve a feature query. We extend the merge forest to support persistence-based queries and study the locality of these queries by evaluating the traversals of regions of data during a query. We confirm that the majority of evaluated datasets have the property that the noise is mostly local, and thus can be efficiently eliminated without performing a global analysis. Finally, we compare the query running times with those of a triplet merge tree because a triplet merge tree answers all proposed queries in constant time and can be constructed from a merge tree in linear time.","PeriodicalId":117284,"journal":{"name":"2022 IEEE 15th Pacific Visualization Symposium (PacificVis)","volume":"12 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131922845","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}

引用次数: 0

VANT: A Visual Analytics System for Refining Parallel Corpora in Neural Machine Translation 神经网络机器翻译中并行语料库的可视化分析系统

2022 IEEE 15th Pacific Visualization Symposium (PacificVis) Pub Date : 2022-04-01 DOI: 10.1109/PacificVis53943.2022.00029

Sebeom Park, S. Lee, Youngtaek Kim, Hyeon Jeon, Seokweon Jung, Jinwook Bok, Jinwook Seo

引用次数: 2

Evaluating StackGenVis with a Comparative User Study 通过比较用户研究评估StackGenVis

2022 IEEE 15th Pacific Visualization Symposium (PacificVis) Pub Date : 2022-04-01 DOI: 10.1109/PacificVis53943.2022.00025

Angelos Chatzimparmpas, Vilhelm Park, A. Kerren

引用次数: 0

SET-STAT-MAP: Extending Parallel Sets for Visualizing Mixed Data 为可视化混合数据扩展并行集

2022 IEEE 15th Pacific Visualization Symposium (PacificVis) Pub Date : 2022-04-01 DOI: 10.1109/PacificVis53943.2022.00024

Shisong Wang, Debajyoti Mondal, S. Sadri, C. Roy, J. Famiglietti, Kevin A. Schneider

{"title":"SET-STAT-MAP: Extending Parallel Sets for Visualizing Mixed Data","authors":"Shisong Wang, Debajyoti Mondal, S. Sadri, C. Roy, J. Famiglietti, Kevin A. Schneider","doi":"10.1109/PacificVis53943.2022.00024","DOIUrl":"https://doi.org/10.1109/PacificVis53943.2022.00024","url":null,"abstract":"Multi-attribute dataset visualizations are often designed based on attribute types, i.e., whether the attributes are categorical or numerical. Parallel Sets and Parallel Coordinates are two well-known techniques to visualize categorical and numerical data, respectively. A common strategy to visualize mixed data is to use multiple information linked view, e.g., Parallel Coordinates are often augmented with maps to explore spatial data with numeric attributes. In this paper, we design visualizations for mixed data, where the dataset may include numerical, categorical, and spatial attributes. The proposed solution SET-STAT-MAP is a harmonious combination of three interactive components: Parallel Sets (visualizes sets determined by the combination of categories or numeric ranges), statistics columns (visualizes numerical summaries of the sets), and a geospatial map view (visualizes the spatial information). We augment these components with colors and textures to enhance users' capability of analyzing distributions of pairs of attribute combinations. To improve scalability, we merge the sets to limit the number of possible combinations to be rendered on the display. We demonstrate the use of Set-stat-map using two different types of datasets: a meteorological dataset and an online vacation rental dataset (Airbnb). To examine the potential of the system, we collaborated with the meteorologists, which revealed both challenges and opportunities for Set-stat-map to be used for real-life visual analytics.","PeriodicalId":117284,"journal":{"name":"2022 IEEE 15th Pacific Visualization Symposium (PacificVis)","volume":"54 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129426101","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}

引用次数: 0