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

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Towards Efficient Satellite data Representation Learning with Consistency Loss

Towards Efficient Satellite Data Representation Learning wit...

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ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Naomi Simumba Daiki Kimura Michiaki Tatsubori IBM Research Tokyo

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Foundation models are often pretrained on large datasets and have been valuable in improving efficiency of model training for language and visual processing tasks. An increasing amount of research has focused on foundation models for satellite data which would be beneficial for a wide range of applications such as climate impact modelling. As large quantities of unlabeled satellite data are collected daily, self-supervised learning methods such as image inpainting are key to pretraining these models. Reducing pretraining data required by improving learning efficiency could improve feasibility of implementation. This research proposes an augmentation based consistency loss to improve pretraining efficiency while enhancing downstream performance. Two variations of the proposed approach are evaluated by finetuning pretrained models on flood segmentation and multilabel land cover downstream tasks. Findings show that incorporating consistency loss can enhance downstream performance, although the degree of improvement depends on the downstream task. It is further demonstrated that the downstream improvements can be achieved even with reduced pretraining data.

关键词： Training Representation learning visualization Satellites Self-supervised learning Transformers data models

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Multi-Modal Image Registration Pipeline Combining Near-Infrared Fluorescence Optical Imaging and MRI

Multi-Modal Image Registration Pipeline Combining Near-Infra...

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ieee International symposium on Biomedical Imaging

作者： Lukas Zerweck Tim Alexander Bergman Stefan Wesarg Jörn Kohlhammer Michaela Köhm Fraunhofer Institute for Translational Medicine and Pharmacology ITMP Frankfurt am Main Germany Fraunhofer Cluster of Excellence Immune-Mediated Diseases CIMD Frankfurt am Main Germany Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany GRIS TU Darmstadt German Goethe-University Frankfurt Division of Rheumatology Germany

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Using near-infrared fluorescence optical imaging (NIR-FOI) in the field of rheumatology for the visualization of the hands’ micro-circulation is still a relatively new method for investigating clinically relevant musculoskeletal inflammation. Previous work has shown that it might enable the detection of sub-clinical arthritis. In this work, we describe a pipeline to register and visualize volumetric MRI data with spatio-temporal NIR-FOI image stacks, as well as the results of a previously developed objective evaluation method of NIR-FOI data. This combination of both imaging modalities connects the high anatomical resolution of MRI and functional information of the NIR-FOI data, simplifies and increases the quality of the diagnosis-making process, and should facilitate the transition of a research project into a real-life application. Additionally, this fusion of imaging modalities should support medical researchers to understand the underlying pathophysiological mechanisms of psoriatic arthritis, which are not fully understood, *** suggested visualization addresses a specific medical condition. However, the visualization pipeline can be reused for any type of spatio-temporal and volumetric data registration as well as the additional inclusion of objective analysis results.

关键词： Image registration Magnetic resonance imaging Pipelines data visualization Rheumatology Fluorescence Optical variables measurement

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EuroVis 2022 - 24th Eurographics Conference on visualization 2022, Posters

EuroVis 2022 - 24th Eurographics Conference on Visualization...

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24th Eurographics Conference on visualization, EuroVis 2022

ISBN: (纸本)9783038681854

The proceedings contain 34 papers. The topics discussed include: scientific convergence and divergence in visualization and visual analytics;digital twins of smart farms;automatic segmentation of tooth images: optimization of multi-parameter image processing workflow;explorative visual analysis of spatio-temporal regions to detect hemodynamic biomarker candidates;visually explaining publication ranks in citation-based literature search with PURE suggest;visualizing the evolution of multi-agent game-playing behaviors;visual exploration of genetic sequence variants in pangenomes;interactive visualization of machine learning model results predicting infection risk;a design space for explainable ranking and ranking models;visual queries on bipartite multivariate dynamic social networks;validating perception of hyperspectral textures in virtual reality systems;situated visualization in motion for video games;and using data comics to enhance visualization literacy.

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Development of a Visual Analytic System for Baum Test Using Psychological Traits dataset

Development of a Visual Analytic System for Baum Test Using ...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Mikihiro Komoto Kaho Takenouchi Naohisa Sakamoto Chieko Kato Kobe University Toyo University

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

We present a visual analytic system that can quantitatively can evaluate the tree images from the Baum test, which assesses psychological traits of subjects. The system quantifies the tree features in the images and creates an overview plot of the set of the tree images by using dimensionality reduction techniques. The system then identifies psychological traits by analyzing the formed clusters using numerical data obtained from additional psychological evaluations and statistical measures, and integrating these traits with the tree features. Experimental results demonstrate the effectiveness of the system in several cases. However, the system has some limitations, such as challenges in analyzing scenarios where statistically significant differences are not observed, and more detailed feature analysis of the tree images is required for complex psychological state analysis.

关键词： Dimensionality reduction Measurement Visual analytics Mental disorders Psychology data visualization Feature extraction

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Research on vulnerable line identification based on PMU data and graph theory

Research on vulnerable line identification based on PMU data...

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2022 Power System and Green Energy Conference, PSGEC 2022

作者： Zhang, Shouzhi Yu, Miao Hu, Jingxuan Sun, Jianqun Wei, Jingjing Wu, Yixiao School of Mechatronics and Vehicle Engineering Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9781665499934

ISBN: (纸本)9781665499934

Cascaded faults in complex power grids are prone to large-scale blackouts, and vulnerable transmission lines in cascading failures play an important role. Finding out these lines in power grid is very important to prevent accidents. The determination indicators of previous research methods are single. In this paper, a comprehensive analysis is carried out from two aspects of system operation structure and line power flow carrying capacity, and a vulnerable line identification method based on PMU data and graph theory is proposed. Firstly, a topology map of the power system is constructed in combination with the characteristics of PMU data visualization in actual power grid. Secondly, combined with power exchange efficiency and energy fluctuation probability, vulnerable lines of the grid are screened, and the effectiveness of the index is tested. Finally, the simulation experiment is carried out in the ieee 30-bus system. Simulation results show that the proposed method can accurately identify weak transmission lines of the power grid. © 2022 ieee.

关键词： data visualization

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Ingest and Visualize CSV Files using AWS Platform for Transition from Unstructured to Structured data 11

Ingest and Visualize CSV Files using AWS Platform for Transi...

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11th International Conference on Emerging Trends in Engineering and Technology - Signal and Information Processing, ICETET - SIP 2023

作者： Gupta, Ayush Dhanda, Namrata Gupta, Kapil Kumar Amity University Computer Science & Engineering Department Uttar Prades Lucknow India

ISBN: (纸本)9798350348422

In addition to computing, storage, databases, analytics, networking, cellular, developer equipment, management equipment, IoT, security, and agency packages, AWS provides a comprehensive range of pay-as-you-go, on-demand, global cloud-based services. Accessible AWS services and solutions number over 200 and include information warehousing, directories, and content distribution. New offers can be immediately provisioned without an upfront capital cost. This makes it possible for businesses, start-ups, small and medium-sized organizations, and clients in the public sector to obtain access to the fundamentals needed to quickly adapt to changing business requirements. The technology at the core of any cloud operation is virtualization. It asserts that virtualization breaks up a physical server's hardware resources into smaller bits. data processing and visualization on Amazon Web Services is the process of gathering, analyzing, and visualizing large amounts of data using a variety of AWS services (AWS). data storage, integration, analysis, and visualization are just a few of the many services that Amazon offers that may be used to manage and display data. With the help of AWS services like Amazon S3, Amazon Kinesis, and Amazon Redshift, huge amounts of data can be acquired, analyzed, and stored. Amazon S3 is a simple storage service that may be used to store and retrieve data, Amazon Kinesis is a real-time data streaming service, and Amazon Redshift is a data warehousing service. Using AWS Glue, Redshift, and QuickSight, we demonstrate a method for processing and displaying CSV files in this paper In author's suggested approach, we extract, convert, and load data from CSV files to Redshift, a cloud-based data warehouse, using AWS Glue. The data saved in Redshift is then visualized using QuickSight, enabling quick and simple data exploration. We also go over how using AWS Glue, Redshift, and QuickSight can scale effectively and fast, automate data processing activities,

关键词： Digital storage

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Research on Improved Semi-Supervised Learning Fault Diagnosis Based on SE Module 7

Research on Improved Semi-Supervised Learning Fault Diagnosi...

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2023 7th International symposium on Computer Science and Intelligent Control, ISCSIC 2023

作者： Ma, Xinna Liang, Xiu Li, Yiyang Tang, Yu Liu, Qinqing Zheng, Xuepeng Shijiazhuang Tiedao University School of Information Science and Technology Shijiazhuang China

ISBN: (纸本)9798350342987

The diagnostic models based on deep learning often rely on a large amount of data. In practical cases, the fault data is usually difficult to obtain, leading to small sample problems. Moreover, the data without labels is easier to obtain than the labeled data, so the semi-supervised small sample learning method is more applicable. To solve this problem, an improved semi-supervised meta-learning fault diagnosis model based on the SE module is proposed. The addition of an SE module to encoders can improve the ability to extract effective features from limited data. Unlabeled samples are used to correct the original prototype to eliminate positional bias caused by limited data supported by labels. The experiments were divided into 4-way and 10-way tasks, and the experimental data were used in the data set provided by the Bearing Center of Case Western Reserve University (CWUR). The comparative experimental results show that the proposed method achieves good results in the fault diagnosis of small sample bearings. © 2023 ieee.

关键词： Failure analysis

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Study on the comfort of the nursing mattress based on analysis of body pressure data

Study on the comfort of the nursing mattress based on analys...

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2022 International Conference on Machine Learning, Control, and Robotics, MLCR 2022

作者： Zeng, Weiliang Wang, Xinzui Cao, Fucheng Li, Zhaobo Fu, Hui Guangdong University of Technology Ji Hua Laboratory Foshan China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Ji Hua Laboratory Foshan China Ji Hua Laboratory Foshan China Guangdong University of Technology Guangdong China

ISBN: (纸本)9781665454599

The mattress is an indispensable part of the nursing bed, the appropriate mattress is conducive to relieving the human body by the pressure is too large, to provide more comprehensive support and protection for the human body. In this paper, through the collection of human body pressure data set, data set cleaning, visualization, partition cutting calculation, and other processing, through comparative analysis to study the impact of different mattresses on human comfort and prevention of bedsores. The analysis results show that the mattress made of polyolefin can provide more support for the human body and reduce the pressure of the human body, alleviate the problem of stress concentration in the main force parts of the human body, reduce the risk of bedsores in the human body, improve the function of the nursing bed, and make the human body get more comfortable experience on the mattress. © 2022 ieee.

关键词： data visualization

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VTrain: A Simulation Framework for Evaluating Cost-Effective and Compute-Optimal large Language Model Training 57

VTrain: A Simulation Framework for Evaluating Cost-Effective...

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57th Annual ieee/ACM International symposium on Microarchitecture, MICRO 2024

作者： Bang, Jehyeon Choi, Yujeong Kim, Myeongwoo Kim, Yongdeok Rhu, Minsoo KAIST School of Electrical Engineering Korea Republic of Samsung Advanced Institute of Technology Korea Republic of

ISBN: (纸本)9798350350579

As large language models (LLMs) become widespread in various application domains, a critical challenge the AI community is facing is how to train these large AI models in a cost-effective manner. Existing LLM training plans typically employ a heuristic based parallel training strategy which is based on empirical observations rather than grounded upon a thorough examination of the search space of LLM parallelization. Such limitation renders existing systems to leave significant performance left on the table, wasting millions of dollars worth of training cost. This paper presents our profiling-driven simulator called vTrain, providing AI practitioners a fast yet accurate software framework to determine an efficient and cost-effective LLM training system configuration. We demonstrate vTrain's practicality through several case studies, e.g., effectively evaluating optimal training parallelization strategies that balances training time and its associated training cost, efficient multi-Tenant GPU cluster schedulers targeting multiple LLM training jobs, and determining a compute-optimal LLM model architecture given a fixed compute budget. © 2024 ieee.

关键词： Costs

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Automating Big data analysis Using Artificial Intelligence 3

Automating Big Data Analysis Using Artificial Intelligence

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Bhimanpallewar, Ratnmala N. Jangid, Satish Kumar Yadav, Rakesh Kumar Arunkumar, D.T. Dehankar, Pooja Geetha, B. Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India School of Engineering Ajeenkya D. Y. Patil University Pune India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai128 India

ISBN: (纸本)9798350319125

Huge information evaluation, the use of synthetic Intelligence, is a manner of using AI structures to automate the system of extracting meaningful insights from massive sets of statistics. A combination of AI-driven algorithms and hardware may be used to quickly perceive styles in unlabeled information that could otherwise take vast quantities of time to extract. This paper presents a method and framework for Automating Big data analysis using Artificial Intelligence (AI). With the increasing volume and complexity of big data, traditional manual analysis methods are becoming inefficient and often result in inaccurate or incomplete insights. To address this challenge, we propose a systematic approach that leverages AI techniques such as machine learning and natural language processing to automate the process of data analysis. Our framework includes measures for data preprocessing, feature selection, model building, and result interpretation, and adapts to different types and sources of big data. We demonstrate the effectiveness of our approach through experiments on real-world datasets and compare the results with manual analysis methods. The proposed automated approach not only improves the speed and accuracy of big data analysis, but also reduces the human effort and expertise required. This has the potential to greatly enhance decision-making processes in various industries and domains that deal with large amounts of data. These talents can be used to permit higher choice-making and create extra efficient, automatic approaches to conducting huge-scale data analysis. © 2023 ieee.

关键词： Permit Processing Quantities Similarly visualization

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