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Scalable In-Situ visualization for Extreme-scale SPH Simulations

Scalable In-Situ Visualization for Extreme-Scale SPH Simulat...

2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and analysis, SC Workshops 2024

作者： Zhu, Yiqing Simsek, Osman Seckin Favre, Jean M. Cabezon, Ruben Ciorba, Florina M. University of Basel Basel Switzerland Swiss National Supercomputing Centre Lugano Switzerland

ISBN: (纸本)9798350355543

large-scale scientific simulations present significant challenges in data processing efficiency. This paper addresses the critical issue of I/O and data processing performance bottlenecks within the domain of extreme-scale Smoothed-particle Hydrodynamics (SPH) and gravity simulations. We present a novel I/O software architecture implemented in the scalable SPH-EXA framework [1], incorporating a variety of in-situ and post-hoc data analysis pipelines, facilitating rapid analysis and visualization of extreme-scale physical datasets. The performance of our I/O architecture is evaluated through comprehensive benchmarking across a wide range of data scales, conducted on the Piz Daint supercomputer [2]. © 2024 ieee.

关键词： extreme-scale HPC in-situ visualization SPH

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EmoInt-Trans: A Multimodal Transformer for Identifying Emotions and Intents in Social Conversations

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ieee-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2023年 31卷 290-300页

作者： Singh, Gopendra Vikram Firdaus, Mauajama Ekbal, Asif Bhattacharyya, Pushpak IIT Patna Dept Comp Sci Engn Patna 800013 Bihar India

In the natural language processing community, open-domain conversational agents, also known as chatbots, are gaining popularity. One of the difficulties is getting them to communicate in an emotionally intelligent manner. To generate dialogues, current neural response generation methods depend solely on end-to-end learning from large scale conversation data. Therefore, we introduce a large-scale multi Emotion and Intent guided Multimodal Dialogue (EmoInt-MD) dataset labelled with 32 emotions and 15 empathetic intents having 32 k dialogues taken from different movie genres. We propose a novel multi-task multimodal contextual Transformer framework for simultaneously identifying the emotions and intents in a given utterance utilizing audio and visual features in addition to the textual information. Experimental analysis proves that the proposed framework outperforms several unimodal and multimodal baselines on the EmoInt-MD dataset. This dataset along with our baseline and proposed framework implementations will be made publicly available for research purposes.

关键词： Motion pictures visualization Transformers Emotion recognition Feature extraction Task analysis Speech processing Emotion empathetic intent transformers fusion context

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Research on New Business data analysis and visualization Technology Assisted by Artificial Intelligence 4

Research on New Business Data Analysis and Visualization Tec...

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4th International Signal Processing, Communications and Engineering Management Conference, ISPCEM 2024

作者： Liang, Kun Zhang, Lili Guangxi Vocational Normal University Nanning Guangxi China

ISBN: (纸本)9798331528676

This study introduces a novel approach to business data analysis and visualization, leveraging artificial intelligence techniques, particularly the t-distributed stochastic neighbor embedding (t-SNE) algorithm for dimensionality reduction and the graphical representation of high-dimensional datasets. The proposed system integrates a variety of algorithmic frameworks, including principal component analysis (PCA) and various clustering algorithms, aiming to enhance both the efficiency and precision of business data interpretation. Through extensive simulation tests conducted on large-scale business data sets, the t-SNE algorithm effectively transforms intricate high-dimensional information into a two-dimensional format, providing a clear visualization of data clusters and inherent patterns. The experimental outcomes reveal that the visualization platform utilizing t-SNE significantly enhances the depth of data interpretation, particularly when handling multidimensional data, demonstrating superior accuracy and resilience. Compared to conventional visualization methods, this system exhibits approximately a 15% increase in accuracy during large-scale business data processing, offering substantial support for corporate decision-making processes. This research presents innovative intelligent tools for business data analysis, further advancing the data-driven decision-making framework in the corporate domain. ©2024 ieee.

关键词： data accuracy

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large SCENE MICRO-DOPPLER analysis ON SAR IMAGES

LARGE SCENE MICRO-DOPPLER ANALYSIS ON SAR IMAGES

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Focsa, Adrian Anghel, Andrei Nico, Giovanni Patruno, Jolanda Datcu, Mihai Natl Univ Sci & Technol POLITEHN Bucharest Bucharest Romania Mil Tech Acad Ferdinand I Bucharest Romania Dian SRL Matera Italy Natl Res Council Italy Inst Appl Math Bari Italy RHEA Grp Frascati Italy

ISBN: (纸本)9798350360332;9798350360325

In this work, the perspective of micro-Doppler analysis from synthetic aperture radar (SAR) images is assessed for large-scale areas. We propose a processing chain that implies a coarse vibrometry estimation based on azimuth sub-aperture decomposition and local Doppler centroid computations, followed by an in-depth analysis based on conventional SAR micro-Doppler algorithms. The methdology is assessed quantitatively using simulated data in keeping with Sentinel-1 IW imaging parameters.

关键词： micro-Doppler SAR vibrometry sub-aperture decomposition

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Refined Urban Informal Settlements Mapping at Agglomeration scale With the Guidance of Background Knowledge From Easy-Accessed Crowdsourced Geospatial data

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ieee TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Fan, Runyu Niu, Hongyang Xu, Zijian Chen, Jiajun Feng, Ruyi Wang, Lizhe China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Peoples R China

Urban informal settlements (UISs) denote densely populated locales characterized by inadequate urban infrastructure standards, often exhibiting an amalgamation of rural and urban attributes, primarily situated within the confines of major cities or metropolitan regions. UIS mapping is a typical task that aims to identify pixels corresponding to UISs in remote sensing images. The extremely similar visual characteristics, sample uncertainty, and highly manual costs bring large-scale UIS mapping noteworthy challenges. In this article, we propose a sample uncertainty-aware semisupervised learning method guided by background knowledge from crowdsourced geospatial data (namely, SemiUIS) for UIS mapping and produces a 1-m resolution UIS map in the urban agglomeration in the middle reaches of the Yangtze River (UAMRYR), China. Our proposed SemiUIS method integrates semisupervised learning with the guidance of background knowledge from crowdsourcing data to reduce the uncertainty of the generated pseudosemantic segmentation samples to improve the quality of the constructed dataset by jointly optimizing the background (Not UIS areas) and the foreground (UIS areas). In contrast, traditional semisupervised learning methods only focus on explicitly optimizing the foreground (UIS areas) in the pseudosample generation process. Experiments were conducted in the 31 prefecture-level cities of UAMRYR. Visual interpretation of labeled remote sensing samples, street view images, and two novel knowledge validation indicators, global knowledge precision (GKP) and local knowledge precision (LKP), are proposed to verify the UIS mapping results. The proposed method reached an overall accuracy (OA) of 90.58%, mean intersection over union (mIoU) of 75.88%, accuracy (Acc) of 78.77%, Intersection over Union (IoU) of 62.97%, GKP of 90.33%, and LKP of 85.01%, respectively, with only 36 (5%) training samples. This work will be available at https://***/RunyuFan/UisYangtze.

关键词： Remote sensing Semantic segmentation Accuracy Semisupervised learning Feature extraction visualization Geospatial analysis Costs Uncertainty Crowdsourcing Crowdsourced geospatial data deep learning (DL) remote sensing semisupervised learning urban informal settlement (UIS) mapping

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PMUVis : A large-scale Platform to Assist Power System Operators in a Smart Grid

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ieee COMPUTER GRAPHICS AND APPLICATIONS 2022年第6期42卷 84-95页

作者： Arunkumar, Anjana Gupta, Nitin Pinceti, Andrea Sankar, Lalitha Bryan, Chris Arizona State Univ Tempe AZ 85281 USA Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85281 USA Arizona State Univ Sch Comp & Augmented Intelligence Tempe AZ 85281 USA

Electric transmission power grids are being revamped with the widespread deployment of GPS-enabled phasor measurement units (PMUs) for real-time wide-area monitoring and control via precise, time-synchronized measurements of voltage and current. large, concurrently produced volumes of noisy data hinder PMU usability, particularly for the analysis of power oscillation and load fluctuation events in the grid. We examine visualization challenges for events in the electric power grid and develop PMUVis, a visualization platform that supports scalable analysis of grid network topology and anomalous events in near time. PMUVis incorporates a novel FFT-based approach over raw and temporally aggregated data to examine oscillation event propagation through the grid network. We validate PMUVis with expert reviews and a case study and discuss how visualization can be leveraged to enhance real-time spatiotemporal grid analysis by advancing operator capabilities.

关键词： Phasor measurement units data visualization Real-time systems Monitoring Power systems Oscillators Industries

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Diagnostic analysis Approach to Correlation Maps Through large Language Models 17

Diagnostic Analysis Approach to Correlation Maps Through Lar...

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17th ieee International Scientific Conference on Informatics, INFORMATICS 2024

作者： Dudas, Adam Vagac, Michal Matej Bel University Faculty of Natural Sciences Department of Computer Science Banská Bystrica Slovakia

ISBN: (纸本)9798350387674

Diagnostic data analysis is a methodological process aimed at the identification of underlying causes of events, the effect of which is the presence of trends and patterns in a multidimensional dataset. Since in most cases this cause originates in the area described by the data itself, knowledge from the field of data analysis, computer science or statistics is rarely enough for the analyst to explain the patterns, and it is necessary to examine the field of data expertise itself. In this work, we focus on creating a model for diagnostic data analysis, which automatically describes naturally correlated pairs of attributes using a large language model and provides an output in the form of a Diagnostic Sheet visualization. The proposed concept is then experimentally verified in case studies on two multidimensional datasets focused on environmental variable sensor measurements and cubic graph properties. © 2024 ieee.

关键词： Artificial Intelligence Applications Correlation analysis Diagnostic analysis large Language Models

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A new remote sensing method to map field- scale time-series wheat without training data in Bulgaria

A new remote sensing method to map field- scale time-series ...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Phalke, Aparna R. Ozdogan, Mutlu Filchev, Lachezar H. Univ Alabama Huntsville Earth Syst Sci Ctr Huntsville AL 35899 USA SERVIR Sci Coordinat Off NASA Marshall Space Flight Ctr Huntsville AL 35812 USA Univ Wisconsin Madison Nelson Inst Ctr Sustainabil & Global Environm Madison WI 53706 USA Bulgarian Acad Sci Space Res & Technol Inst Sofia Bulgaria

ISBN: (纸本)9798350360332;9798350360325

Accurate and up-to date wheat area estimate is a critical input for food security and policy related studies. Wheat as a staple crop covers fifty percent of agricultural lands in Bulgaria. In Bulgaria, current wheat area statistics is available at regional scale (equivalent to census regions in the United States) until year 2017 from government departments. Remote sensing provides extraordinary capability to map field-scale temporally consistent wheat area but, traditional large area mapping methods using remote sensing require large volume of training data and large computing resources. This study presents a new approach called "Peak Value Method" (PVM) to automatically map the wheat cultivated areas in Bulgaria without training data. This study mapped wheat areas between 2001 and 2020 using PVM through Landsat derived enhanced vegetation index (EVI) time series data and phenological understanding of crop. To our knowledge, this is the first study to map wall to wall high resolution time-series wheat areas with reliable accuracy assessment in this study area. This study offers three main outputs: 1. a new, simple but effective remote sensing method to map wheat crop without training data in the study area, 2. first field scale (30-m) time series (2001-2020) wheat maps with reliable accuracy covering Northcentral, Northeast, Southcentral and Southeast regions of Bulgaria and, 3. fills the gap in the knowledge about field-scale wheat information in the study area and it's accessibility through remote sensing to study the environment related questions.

关键词： Crop type mapping Remote sensing Food security Phenology Time-series analysis

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USING A DEPLOYABLE analysis ENVIRONMENT TO STUDY CONTINENTAL-scale PHENOLOGY AT HIGH-SPATIAL RESOLUTION

USING A DEPLOYABLE ANALYSIS ENVIRONMENT TO STUDY CONTINENTAL...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Nattino, Francesco Izquierdo-Verdiguier, Emma Chandramouli, Pranav Grootes, Meiert W. Ku, Ou Alidoost, Fakhereh Zurila-Milla, Rant Netherlands eSci Ctr Sci Pk 402 Matrix 3 NL-1098 XH Amsterdam Netherlands Univ Nat Resources & Life Sci BOKU Inst Geomat A-1190 Vienna Austria Univ Twente Fac Geoinformat Sci & Earth Observat ITC POB 217 NL-7500 AE Enschede Netherlands

ISBN: (纸本)9798350360332;9798350360325

The increasing availability of large open datasets and computational resources offers unprecedented opportunities for new insight and discoveries. However, researchers are often faced with the challenge of running analyses at scale. We consider here an example from the domain of plant phenology and show how a Python-based deployable analysis environment leveraging Dask for parallel and distributed computing as well as Jupyter for interactive data exploration could be employed to run regression-based phenological models at high spatial resolution and continental scale.

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Explainable automated data estimation in Logic State Imaging of embedded SRAM

Explainable automated data estimation in Logic State Imaging...

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ieee International symposium on Circuits and Systems (ISCAS)

作者： Chef, Samuel Chua, Chung Tah Tay, Jing Yun Gan, Chee Lip Nanyang Technol Univ Temasek Labs NTU Singapore Singapore Nanyang Technol Univ Natl Integrated Ctr Evaluat NiCE Singapore Singapore Nanyang Technol Univ Sch Mat Sci & Engn Singapore Singapore

ISBN: (纸本)9798350330991;9798350331004

Semi-invasive logic state imaging of SRAM cells via optical analysis has been demonstrated to be a powerful attack vector on advanced ICs. Reported applications tend to focus on the recovery of small size secrets such as encryption keys. However, the emergence of new cryptographic algorithms with encryption keys or other assets of larger size makes manual approach challenging. In this manuscript, a framework for the automated estimation of bitcell data is developed. It focuses on understanding signal generation and detection processes to assess viability of well-established classification and clustering algorithms. A main advantage of such an approach is that it makes optical attacks for large scale recovery more feasible since limited to no training data is required. The approach is evaluated on a dataset generated under Thermal Laser Stimulation on a commercial SoC-FPGA manufactured in 28 nm technology.

关键词： Hardware Security Optical analysis Thermal Laser Stimulation SRAM Clustering Classification data Forensics analysis

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