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24th Eurographics/ieee VGTC Conference on visualization (EuroVis)

作者： Piccolotto, N. Boegl, M. Muehlmann, C. Nordhausen, K. Filzmoser, P. Miksch, S. TU Wien Inst Visual Comp & Human Ctr Technol Vienna Austria TU Wien Inst Stat & Math Methods Econ Vienna Austria Univ Jyvaskyla Jyvaskyla Finland

analysis of spatial multivariate data, i.e., measurements at irregularly-spaced locations, is a challenging topic in visualization and statistics alike. Such data are inteGral to many domains, e.g., indicators of valuable minerals are measured for mine prospecting. Popular analysis methods, like PCA, often by design do not account for the spatial nature of the data. Thus they, together with their spatial variants, must be employed very carefully. Clearly, it is preferable to use methods that were specifically designed for such data, like spatial blind source separation (SBSS). However, SBSS requires two tuning parameters, which are themselves complex spatial objects. Setting these parameters involves navigating two large and interdependent parameter spaces, while also taking into account prior knowledge of the physical reality represented by the data. To support analysts in this process, we developed a visual analytics prototype. We evaluated it with experts in visualization, SBSS, and geochemistry. Our evaluations show that our interactive prototype allows to define complex and realistic parameter settings efficiently, which was so far impractical. Settings identified by a non-expert led to remarkable and surprising insights for a domain expert. Therefore, this paper presents important first steps to enable the use of a promising analysis method for spatial multivariate data.

关键词： visualization

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Improving Age Prediction: Utilizing LSTM-Based Dynamic Forecasting For data Augmentation in Multivariate Time Series analysis

Improving Age Prediction: Utilizing LSTM-Based Dynamic Forec...

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ieee Southwest symposium on Image analysis and Interpretation

作者： Yutong Gao Charles A. Ellis Vince D. Calhoun Robyn L. Miller Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University Georgia Institute of Technology Emory University Atlanta Georgia

The high dimensionality and complexity of neuroimaging data necessitate large datasets to develop robust and high- performing deep learning models. However, the neuroimaging field is notably hampered by the scarcity of such datasets. In this work, we proposed a data augmentation and validation framework that utilizes dynamic forecasting with Long Short-Term Memory (LSTM) networks to enrich datasets. We extended multivariate time series data by predicting the time courses of independent component networks (ICNs) in both one-step and recursive configurations. The effectiveness of these augmented datasets was then compared with the original data using various deep learning models designed for chronological age prediction tasks. The results suggest that our approach improves model performance, providing a robust solution to overcome the challenges presented by the limited size of neuroimaging datasets.

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Heterogeneous Face Interpretable Disentangled Representation for Joint Face Recognition and Synthesis

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ieee TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第10期33卷 5611-5625页

作者： Liu, Decheng Gao, Xinbo Peng, Chunlei Wang, Nannan Li, Jie Xidian Univ Sch Elect Engn State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing 400065 Peoples R China Xidian Univ Sch Cyber Engn State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

Heterogeneous faces are acquired with different sensors, which are closer to real-world scenarios and play an important role in the biometric security field. However, heterogeneous face analysis is still a challenging problem due to the large discrepancy between different modalities. Recent works either focus on designing a novel loss function or network architecture to directly extract modality-invariant features or synthesizing the same modality faces initially to decrease the modality gap. Yet, the former always lacks explicit interpretability, and the latter strategy inherently brings in synthesis bias. In this article, we explore to learn the plain interpretable representation for complex heterogeneous faces and simultaneously perform face recognition and synthesis tasks. We propose the heterogeneous face interpretable disentangled representation (HFIDR) that could explicitly interpret dimensions of face representation rather than simple mapping. Benefited from the interpretable structure, we further could extract latent identity information for cross-modality recognition and convert the modality factor to synthesize cross-modality faces. Moreover, we propose a multimodality heterogeneous face interpretable disentangled representation (M-HFIDR) to extend the basic approach suitable for the multimodality face recognition and synthesis. To evaluate the ability of generalization, we construct a novel large-scale face sketch data set. Experimental results on multiple heterogeneous face databases demonstrate the effectiveness of the proposed method.

关键词： Face recognition Task analysis Feature extraction Image recognition Sensors Semantics visualization Disentanglement heterogeneous face interpretable representation joint recognition and synthesis

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Cytosplore EvoViewer: Visual Analytics of Conserved Evolutionary Patterns in multi-species single-cell sequencing data

Cytosplore EvoViewer: Visual Analytics of Conserved Evolutio...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Soumyadeep Basu Morgan Wirthlin Jeroen Eggermont Thomas Kroes Boudewijn Lelieveldt Ed Lein Trygve Bakken Thomas Höllt Leiden University Medical Center Delft University of Technology Allen Institute for Brain Science

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

Single-cell transcriptomics has enhanced our understanding of the brain’s cellular composition. Biologists now analyze complex datasets to explore how marker genes influence biological processes, genetic variations, and phenotypic traits. A challenge is comparing these datasets across species to detect subtle differences or similarities to evolutionary development. Here, we present Cytosplore EvoViewer to facilitate examining relationships between transcriptomic datasets across species, simplifying the analysis of marker gene regulation and its impact on biological functions and integrating these findings with prior evolutionary knowledge or species-specific traits. We conducted a design study, including domain analysis, implementation of the results into Cytosplore EvoViewer, and an expert evaluation. Cytosplore EvoViewer offers valuable insights into genetic variations and evolutionary dynamics, helping to understand the diversity and the unity within diversity across species and their evolutionary *** Cytosplore EvoViewer installer application can be downloaded from the Cytosplore Viewer website 1 , and its source code is available on the ManiVault Studio GitHub 2 . 1 https://*** 2 https://***/ManiVaultStudio/CytosploreEvoViewer

关键词： Sequential analysis High dimensional data Visual analytics Source coding Transcriptomics data visualization Collaboration Genetics Regulation Biological information theory

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analysis of the Surface Water Quality in the State of Karnataka using Distributed Platform 9

Analysis of the Surface Water Quality in the State of Karnat...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Dasu, Shravya Shobha, G. Shetty, Jyothi RV College of Engineering Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350397376

Water, one of the essential substances for life on earth is crucial for humans, plants and animals. Regular testing and monitoring of water quality is essential to ensure its safety for consumption. This process includes measuring physical and chemical parameters such as PH, total dissolved solids (TDS) etc. Herein, surface water quality in Karnataka, India was analyzed over 40 years. In order to analyze the large amount of data collected, high performance computing cluster (HPCC) systems was used. Biochemical oxygen demand (BOD) predictive model was built using linear regression. Additionally, a potability classification model was built using the random forest classifier algorithm. These algorithms are present in the LinearRegression and LearningTrees bundle provided by HPCC Systems. Finally, a graphical visualization of water quality in four basins over time was performed with the Visualizer bundle. This was done to understand the improvement or degradation of water quality. The analysis was performed on a single node and an 8-node cluster respectively. In comparison with the former, the latter had a shorter execution time. © 2023 ieee.

关键词： Water quality

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Exploratory data analysis in Electronic Health Records Graphs: Intuitive Features and visualization Tools

Exploratory Data Analysis in Electronic Health Records Graph...

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Annual ieee symposium on Computer-Based Medical Systems

作者： Mirela T. Cazzolato Marco Antonio Gutierrez Caetano Traina Christos Faloutsos Agma J.M. Traina Institute of Mathematics and Computer Science - University of São Paulo (ICMC-USP) São Carlos Brazil The Heart Institute (InCor) - Clinical Hospital of Faculty of Medicine University of São Paulo (HC-FMUSP) São Paulo Brazil Carnegie Mellon University (CMU) Pittsburgh USA

Given a large, unlabeled set of Electronic Health Records (EHRs) acquired from multiple hospitals, how can we analyze the available entities and identify relationships in the data? Also, how can we perform Exploratory data analysis (EDA) over such EHR data? Many medical institutions generate EHRs as tabular data with entities and attributes in common. However, due to a large number of records, attributes, and high cardinality, exploring the different datasets and finding patterns and insights become laborious and prone to errors. In this work, we propose GraF- Eda for EDA over EHR data from different institutions. GraF-EDA models EHRs as time-evolving graphs, allowing the interoperability of such data into a single representation. We extract meaningful features from the graph nodes and provide intuitive visualizations to improve data explainability. We evaluate GraF-EDA with four COVID-19 datasets from hospitals of the São Paulo state, Brazil, resulting in million-scale graphs. Our method identified correlations, similarities and dissimilarities among medical treatments, exams, clinics, and outcomes. With the visual tools provided by GraF-EDA, we were able to spot cases of interest and check more details about them. Our results indicate that GraF-EDA is a fast, effective, open-sourced tool for EDA of EHRs from multiple institutions.

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A Discriminative Cross-Aligned Variational Autoencoder for Zero-Shot Learning

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ieee TRANSACTIONS ON CYBERNETICS 2023年第6期53卷 3794-3805页

作者： Liu, Yang Gao, Xinbo Han, Jungong Shao, Ling Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Shanghai Jiao Tong Univ Key Lab Artificial Intelligence Minist Educ Shanghai 200240 Peoples R China Shandong Univ Key Lab Cryptol Technol & Informat Secur Minist Educ Jinan 250100 Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Minist Educ Nanjing 211189 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing 400065 Peoples R China Aberystwyth Univ Dept Comp Sci Aberystwyth SY23 3DB Dyfed Wales Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

Zero-shot learning (ZSL) aims to classify unseen samples based on the relationship between the learned visual features and semantic features. Traditional ZSL methods typically capture the underlying multimodal data structures by learning an embedding function between the visual space and the semantic space with the Euclidean metric. However, these models suffer from the hubness problem and domain bias problem, which leads to unsatisfactory performance, especially in the generalized ZSL (GZSL) task. To tackle such a problem, we formulate a discriminative cross-aligned variational autoencoder (DCA-VAE) for ZSL. The proposed model effectively utilizes a modified cross-modal-alignment variational autoencoder (VAE) to transform both visual features and semantic features obtained by the discriminative cosine metric into latent features. The key to our method is that we collect principal discriminative information from visual and semantic features to construct latent features which contain the discriminative multimodal information associated with unseen samples. Finally, the proposed model DCA-VAE is validated on six benchmarks including the large dataset ImageNet, and several experimental results demonstrate the superiority of DCA-VAE over most existing embedding or generative ZSL models on the standard ZSL and the more realistic GZSL tasks.

关键词： visualization Semantics Task analysis Training Solid modeling Prototypes Generative adversarial networks Autoencoder cosine discriminative latent zero-shot learning (ZSL)

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Explorative Visual analysis of Spatio-temporal Regions to Detect Hemodynamic Biomarker Candidates

Explorative Visual Analysis of Spatio-temporal Regions to De...

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24th Eurographics Conference on visualization, EuroVis 2022

作者： Derstroff, Adrian Leistikow, Simon Nahardani, Ali Ebrahimi, Mahyasadat Hoerr, Verena Linsen, Lars University of Münster Germany Medical Physics Group Institute of Diagnostic and Interventional Radiology Jena University Hospital Friedrich Schiller University Jena Jena Germany Heart Center Bonn Department of Internal Medicine II University Hospital Bonn Bonn Germany Clinic for Radiology University Hospital Münster Münster Germany Leipniz-Institute of Photonic Technology Jena Germany Friedrich Schiller University Jena Germany Institute of Physical Chemistry Abbe Center of Photonics Jena Germany

ISBN: (纸本)9783038681854

Biomarkers are measurable biological properties that allow for distinguishing subjects of different cohorts such as healthy vs. diseased. In the context of diagnosing diseases of the cardiovascular system, researchers aim - among others - at detecting biomarkers in the form of spatio-temporal regions of blood flow obtained by medical imaging or of derived hemodynamical parameters. As the search space for such biomarkers in time-varying volumetric multi-field data is extremely large, we present an interactive visual exploration system to support the analysis of the potential of spatio-temporal regions to discriminate cohorts. © 2022 The Author(s) Eurographics Proceedings © 2022 The Eurographics Association.

关键词： Diagnosis

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Reimagining TaxiVis through an Immersive Space-Time Cube metaphor and reflecting on potential benefits of Immersive Analytics for urban data exploration

Reimagining TaxiVis through an Immersive Space-Time Cube met...

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ieee Annual International symposium Virtual Reality

作者： Jorge Wagner Claudio T. Silva Wolfgang Stuerzlinger Luciana Nedel Federal University of Rio Grande do Sul New York University Simon Fraser University

Current visualization research has identified the potential of more immersive settings for data exploration, leveraging VR and AR technologies. To explore how a traditional visualization system could be adapted into an immersive framework, and how it could benefit from this, we decided to revisit a landmark paper presented ten years ago at ieee VIS. TaxiVis, by Ferreira et al., enabled interactive spatio-temporal querying of a large dataset of taxi trips in New York City. Here, we reimagine how TaxiVis’ functionalities could be implemented and extended in a 3D immersive environment. Among the unique features we identify as being enabled by the Immersive TaxiVis prototype are alternative uses of the additional visual dimension, a fully visual 3D spatio-temporal query framework, and the opportunity to explore the data at different scales and frames of reference. By revisiting the case studies from the original paper, we demonstrate workflows that can benefit from this immersive perspective. Through reporting on our experience, and on the vision and reasoning behind our design decisions, we hope to contribute to the debate on how conventional and immersive visualization paradigms can complement each other and on how the exploration of urban datasets can be facilitated in the coming years.

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The Unreasonable Effectiveness of CLIP Features for Image Captioning: An Experimental analysis

The Unreasonable Effectiveness of CLIP Features for Image Ca...

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ieee/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Barraco, Manuele Cornia, Marcella Cascianelli, Silvia Baraldi, Lorenzo Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Generating textual descriptions from visual inputs is a fundamental step towards machine intelligence, as it entails modeling the connections between the visual and textual modalities. For years, image captioning models have relied on pre-trained visual encoders and object detectors, trained on relatively small sets of data. Recently, it has been observed that large-scale multi-modal approaches like CLIP (Contrastive Language-Image Pre-training), trained on a massive amount of image-caption pairs, provide a strong zero-shot capability on various vision tasks. In this paper, we study the advantage brought by CLIP in image captioning, employing it as a visual encoder. Through extensive experiments, we show how CLIP can significantly out-perform widely-used visual encoders and quantify its role under different architectures, variants, and evaluation protocols, ranging from classical captioning performance to zero-shot transfer.

关键词： visualization Computer vision Protocols Conferences Detectors Distance measurement data models

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