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检索条件"任意字段=14th Symposium on Large Data Analysis and Visualization"

共 540 条记录，以下是31-40 订阅

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Standardized data-Parallel Rendering Using ANARI

Standardized Data-Parallel Rendering Using ANARI

引用

IEEE symposium on large data analysis and visualization (LDAV)

作者： Ingo Wald Stefan Zellmann Jefferson Amstutz Qi Wu Kevin Griffin Milan Jaros Stefan Wesner NVIDIA University of Cologne Davis University of California IT4Innovations VSB Technical University of Ostrava

ISBN: (数字)9798331516925

ISBN: (纸本)9798331516932

We propose and discuss a paradigm that allows for expressing data-parallel rendering with the classically non-parallel ANARI API. We propose this as a new standard for data-parallel rendering, describe two different implementations of this paradigm, and use multiple sample integrations into existing applications to show how easy it is to adopt, and what can be gained from doing so.

关键词： data analysis Prototypes data visualization Rendering (computer graphics) Standards

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Graphical Representation through a User Interface for In Situ Scientific visualization with Ascent

Graphical Representation Through a User Interface for In Sit...

引用

IEEE symposium on large data analysis and visualization (LDAV)

作者： Colleen Heinemann Jefferson Amstutz Joseph A. Insley Victor A. Mateevitsi Michael E. Papka Silvio Rizzi University of Illinois at Urbana-Champaign NVIDIA Argonne National Laboratory University of Illinois Chicago Northern Illinois University

ISBN: (数字)9798331516925

ISBN: (纸本)9798331516932

this work presents a graphical user interface (GUI) designed to allow users to interactively create scientific visualizations and workflows with the in situ visualization software, Ascent [5]. Traditionally, in situ pipelines are configured through a YAML file, which can be cumbersome. Our GUI offers an alternative by automating the conversion of user inputs into a format that Ascent can process, eliminating the need for manual file editing. this interactive capability increases the potential for a more diverse group of users to leverage Ascent's powerful visualization tools.

关键词： data analysis Pipelines data visualization Organizations Software Graphical user interfaces

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data Shunt: Collaboration of Small and large Models for Lower Costs and Better Performance 38

Data Shunt: Collaboration of Small and Large Models for Lowe...

引用

38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th symposium on Educational Advances in Artificial Intelligence

作者： Chen, Dong Zhuang, Yueting Zhang, Shuo Liu, Jinfeng Dong, Su Tang, Siliang Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Zhejiang Univ Sch Software Technol Hangzhou Peoples R China Ant Grp Hangzhou Peoples R China

ISBN: (纸本)1577358872

Pretrained large models, particularly large language models, have garnered increasing attention, as they have demonstrated remarkable abilities through contextual learning. Pre-trained large models are increasingly recognized as fundamental tools for solving various tasks. However, the substantial computational demands of large models have dissuaded most product teams and individuals from running them. In such scenarios, to leverage the exceptional performance of large models, one must solely depend on costly APIs, further burdening product teams and individuals. On the other hand, despite the overall inferior performance of small models compared to large models, there are certain distributions where small models can achieve comparable or even superior results. For instance, during training, small models may become trapped in a local optimum that is unique to certain distributions, leading to superior performance. Hence, we propose data Shunt (DS), a general paradigm for collaboration of small and large models. DS not only substantially reduces the cost associated with deploying large models but also effectively enhances overall performance. Specifically, DS determines the shunting direction by evaluating the confidence level of small models. When the confidence level falls below a specific threshold, the input data is forwarded to large models. To further leverage the advantages of the small and large models, we introduce Prompt Pruning (PP) and 2-Stage Confidence Distillation (2CD), which facilitate mutual collaboration, leading to better results and less cost. the remarkable performance across diverse modalities and tasks demonstrates the superiority of the proposed DS over large models. For instance, ChatGPT achieves an accuracy of 94.43% on Amazon Product sentiment analysis, and DS achieves an accuracy of 95.64%, while the cost has been reduced to only 31.18%. the code for the proposed method are provided for research purposes https://***/Anfeather/data-

关键词： Sentiment analysis

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A Customized Validator Recommender System for PoS Networks Using Similarity-Based Circular visualization

A Customized Validator Recommender System for PoS Networks U...

引用

IEEE symposium on large data analysis and visualization (LDAV)

作者： Jaeuk Lee Jisu Kim Hyunwoo Han Kyungwon Lee Department of Digital Media Ajou University Department of Business Intelligence Ajou University Stamper Co. Ltd

ISBN: (数字)9798331516925

ISBN: (纸本)9798331516932

this study analyzes the impact of validator behavior on investor rewards in proof-of-stake (PoS) based blockchain networks and proposes a visualization system to assist investors in selecting appropriate validators. this system enables personalized evaluations through five adjustable indicators tailored to the investor's preferences. By utilizing similarity-based circular visualizations and radar charts, it facilitates the selection and comparison of validators. Additionally, it provides time-series data-based line graphs and raw data-based table views to support detailed comparative analysis among validators. the introduction of such a multifaceted evaluation methodology in staking investments is expected to contribute to the formation of user-customized portfolios and the establishment of optimized investment strategies.

关键词： Proof of stake data visualization Radar Market research Real-time systems Portfolios Recommender systems Optimization Investment Sorting

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Distributed Path Compression for Piecewise Linear Morse-Smale Segmentations and Connected Components

Distributed Path Compression for Piecewise Linear Morse-Smal...

引用

IEEE symposium on large data analysis and visualization (LDAV)

作者： Michael Will Jonas Lukasczyk Julien Tierny Christoph Garth RPTU Kaiserslautern-Landau CNRS and Sorbonne Université

ISBN: (数字)9798331516925

ISBN: (纸本)9798331516932

this paper describes the adaptation to a distributed computational setting of a well-scaling parallel algorithm for computing Morse-Smale segmentations based on path compression. Additionally, we extend the algorithm to efficiently compute connected components in distributed structured and unstructured grids, based either on the connectivity of the underlying mesh or a feature mask. Our implementation is seamlessly integrated with the distributed extension of the Topology ToolKit (TTK), ensuring robust performance and scalability. To demonstrate the practicality and efficiency of our algorithms, we conducted a series of scaling experiments on large-scale datasets, with sizes of up to 4096 3 vertices on up to 64 nodes and 768 cores.

关键词： data analysis Instruction sets Scalability data visualization Solids Topology Global communication Parallel algorithms Distributed computing Optimization

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Identifying Locally Turbulent Vortices within Instabilities

Identifying Locally Turbulent Vortices within Instabilities

引用

IEEE symposium on large data analysis and visualization (LDAV)

作者： Fabien Vivodtzev Florent Nauleau Jean-Philippe Braeunig Julien Tierny CEA CESTA CNRS Sorbonne Université LIP6.

ISBN: (数字)9798331516925

ISBN: (纸本)9798331516932

this work presents an approach for the automatic detection of locally turbulent vortices within turbulent 2D flows such as instabilites. First, given a time step of the flow, methods from Topological data analysis (TDA) are leveraged to extract the geometry of the vortices. Specifically, the enstrophy of the flow is simplified by topological persistence, and the vortices are extracted by collecting the basins of the simplified enstrophy 's Morse complex. Next, the local kinetic energy power spectrum is computed for each vortex. We introduce a set of indicators based on the kinetic energy power spectrum to estimate the correlation between the vortex's behavior and that of an idealized turbulent vortex. Our preliminary experiments show the relevance of these indicators for distinguishing vortices which are turbulent from those which have not yet reached a turbulent state and thus known as laminar.

关键词： Geometry data analysis Correlation data visualization Kinetic energy data mining Optimization

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Cross-Domain Contrastive Learning for Time Series Clustering 38

Cross-Domain Contrastive Learning for Time Series Clustering

引用

38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th symposium on Educational Advances in Artificial Intelligence

作者： Peng, Furong Luo, Jiachen Lu, Xuan Wang, Sheng Li, Feijiang Shanxi Univ Inst Big Data Sci & Ind Taiyuan Peoples R China Shanxi Univ Sch Comp & Informat Technol Taiyuan Peoples R China Shanxi Univ Coll Phys & Elect Engn Taiyuan Peoples R China Zhengzhou Univ Aeronaut Sch Automat Zhengzhou Peoples R China

ISBN: (纸本)1577358872

Most deep learning-based time series clustering models concentrate on data representation in a separate process from clustering. this leads to that clustering loss cannot guide feature extraction. Moreover, most methods solely analyze data from the temporal domain, disregarding the potential within the frequency domain. To address these challenges, we introduce a novel end-to-end Cross-Domain Contrastive learning model for time series Clustering (CDCC). Firstly, it integrates the clustering process and feature extraction using contrastive constraints at both cluster-level and instance-level. Secondly, the data is encoded simultaneously in both temporal and frequency domains, leveraging contrastive learning to enhance within-domain representation. thirdly, cross-domain constraints are proposed to align the latent representations and category distribution across domains. With the above strategies, CDCC not only achieves end-to-end output but also effectively integrates frequency domains. Extensive experiments and visualization analysis are conducted on 40 time series datasets from UCR, demonstrating the superior performance of the proposed model.

关键词： Feature extraction

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Visuals on the House: Optimizing HPC Workflows with No-Cost CPU visualization

Visuals on the House: Optimizing HPC Workflows with No-Cost ...

引用

IEEE symposium on large data analysis and visualization (LDAV)

作者： Victor A. Mateevitsi Andres Sewell Mathis Bode Paul Fischer Jens Henrik Göbbert Joseph A. Insley Ioannis Kavroulakis Damaskinos Konioris Yu-Hsiang Lan Misun Min Dimitrios Papageorgiou Michael E. Papka Steve Petruzza Silvio Rizzi Ananias Tomboulides Argonne National Laboratory University of Illinois Chicago Utah State University University of Illinois Urbana-Champaign Forschungszentrum Jülich Northern Illinois University Aristotle University of Thessaloniki

ISBN: (数字)9798331516925

ISBN: (纸本)9798331516932

the rise of heterogeneous resources in modern High Performance Computing (HPC) systems has propelled the scientific community beyond the exascale threshold. To maximize simulation performance on HPCs, applications increasingly rely on device resources, such as GPUs, leading to under-utilization of host resources, partic-ularly CPUs. In situ analysis and visualization techniques minimize data movement by operating on data in-memory, but this still in-volves blocking operations that incur a small penalty on simulation performance. We explore a novel instrumentation approach where GPU-based time step data is copied from device memory to host memory, enabling CPUs to concurrently perform visualization and analysis tasks. this strategy allows simulations to continue uninter-rupted by an in situ library's analysis and visualization processes.

关键词： Performance evaluation visualization Leadership Ion radiation effects High performance computing data visualization Propulsion Rendering (computer graphics) Resource management Magnetosphere

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Exploring large-Scale Scientific data in Virtual Reality

Exploring Large-Scale Scientific Data in Virtual Reality

引用

IEEE symposium on large data analysis and visualization (LDAV)

作者： Idunnuoluwa A. Adeniji Joseph A. Insley David Joiner Victor A. Mateevitsi Michael E. Papka Silvio Rizzi Argonne National Laboratory Kean University Argonne National Laboratory Northern Illinois University Kean University Argonne National Laboratory University of Illinois Chicago

ISBN: (数字)9798331516925

ISBN: (纸本)9798331516932

the conventional method of data exploration primarily relies on 2D and 3D visualization tools. However, with the advent of lower-cost virtual reality (VR) hardware, a transformation is underway. this study presents a novel data pipeline developed from ParaView (Open source Scientific visualization software) to the Unity Game Engine (Cross platform used to develop interactive contents such as games, animations and in this case VR) to investigate this transition. Specifically, we apply VR technology to the exploration of particle-based scientific datasets, focusing on data generated by a Hardware/Hybrid Accelerated Cosmology Code (HACC) simulation. this project applies VR to the identification of patterns and clusters within HACC particle-based datasets. We enable effective user interaction by integrating VR into the broader field of large data exploration, which includes features like data interaction, manipulation, and in-depth analysis. We implement custom interactions to enable interrogation of underlying data streams to provide deeper insight.

关键词： three-dimensional displays Pipelines data visualization Focusing Virtual reality Games Software Hardware Streams Engines

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On the visualization of Hierarchical Multivariate data 14

On the Visualization of Hierarchical Multivariate Data

引用

14th IEEE Pacific visualization symposium (IEEE PacificVis)

作者： Zheng, Boyan Sadlo, Filip Heidelberg Univ Heidelberg Germany

ISBN: (纸本)9781665439312

In this paper, we study the visual design of hierarchical multivariate data analysis. We focus on the extension of four hierarchical univariate concepts-the sunburst chart, the icicle plot, the circular treemap, and the bubble treemap-to the multivariate domain. Our study identifies several advantageous design variants, which we discuss with respect to previous approaches. and whose utility we evaluate with a user study and demonstrate for different analysis purposes and different types of data.

关键词： Human-centered computing visualization visualization techniques Treemaps Human-centered computing visualization visualization application domains Information visualization

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