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ieee International symposium on Biomedical Imaging

作者： Jiaxin Yue Jianwei Zhang Lujia Zhong Yonggang Shi For the Alzheimer's Disease Neuroimaging Initiative, and the Health and Aging Brain Study (HABS-HD) Study Team Stevens Neuroimaging and Informatics Institute University of Southern California Los Angeles CA USA Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The heterogeneity inherent in tau positron emission tomog-raphy (PET) imaging data across different tracers challenges the integration of multi-site tau PET data, thereby necessitating the trustful harmonization technique for a better utilization of the emerging large-scale datasets. Unlike other imaging modalities, the harmonization among multi-site tau PET data involves more than intensity mapping but contains in-tricate pattern alterations attributed to tracer binding proper-ties, which makes the existing statistical methods inadequate. Meanwhile, the effective data preprocessing is required to eliminate the artifacts caused by off-target binding and partial volume effect for meaningful comparison and harmonization. In this paper, we propose a systematic tau PET harmonization framework that involves the surface-based data preprocessing and diffusion model for generating the vertex-wise mapping between multi-site tau standardized uptake value ra-tio (SUVR) on the cortical surface. In the experiments, using large-scale Alzheimer's Disease Neuroimaging Initiative (ADNI) and Health and Aging Brain Study-Health Dispari-ties (HABS-HD) data with different tracers, we demonstrate our method can successfully achieve harmonization by gen-erating the SUVR maps with consistent pattern distributions and persevering the individual variability.

关键词： Neuroimaging visualization Systematics Statistical analysis data preprocessing Positrons Diffusion models Positron emission tomography Alzheimer's disease Biomedical imaging

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Meta-INR: Efficient Encoding of Volumetric data via Meta-Learning Implicit Neural Representation

Meta-INR: Efficient Encoding of Volumetric Data via Meta-Lea...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Maizhe Yang Kaiyuan Tang Chaoli Wang University of Notre Dame

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

Implicit neural representation (INR) has emerged as a promising solution for encoding volumetric data, offering continuous representations and seamless compatibility with the volume rendering pipeline. However, optimizing an INR network from randomly initialized parameters for each new volume is computationally inefficient, especially for large-scale time-varying or ensemble volumetric datasets where volumes share similar structural patterns but require independent training. To close this gap, we propose Meta-INR, a pretraining strategy adapted from meta-learning algorithms to learn initial INR parameters from partial observation of a volumetric dataset. Compared to training an INR from scratch, the learned initial parameters provide a strong prior that enhances INR generalizability, allowing significantly faster convergence with just a few gradient updates when adapting to a new volume and better interpretability when analyzing the parameters of the adapted INRs. We demonstrate that Meta-INR can effectively extract high-quality generalizable features that help encode unseen similar volume data across diverse datasets. Furthermore, we highlight its utility in tasks such as simulation parameter analysis and representative timestep selection. The code is available at https://***/spacefarers/MetaINR.

关键词： Training Metalearning Codes Pipelines data visualization Feature extraction Rendering (computer graphics) Encoding data mining Convergence

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ImaCytE: Visual Exploration of Cellular Micro-Environments for Imaging Mass Cytometry data

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2021年第1期27卷 98-110页

作者： Somarakis, Antonios Van Unen, Vincent Koning, Frits Lelieveldt, Boudewijn Hollt, Thomas Leiden Univ Dept Radiol Div Image Proc Med Ctr NL-2333 ZA Leiden Netherlands Leiden Univ Dept Immunol Med Ctr NL-2333 ZA Leiden Netherlands Leiden Univ Leiden Computat Biol Ctr Med Ctr NL-2333 ZA Leiden Netherlands Delft Univ Technol Comp Graph & Visualizat Dept NL-2628 CD Delft Netherlands

Tissue functionality is determined by the characteristics of tissue-resident cells and their interactions within their microenvironment. Imaging Mass Cytometry offers the opportunity to distinguish cell types with high precision and link them to their spatial location in intact tissues at sub-cellular resolution. This technology produces large amounts of spatially-resolved high-dimensional data, which constitutes a serious challenge for the data analysis. We present an interactive visual analysis workflow for the end-to-end analysis of Imaging Mass Cytometry data that was developed in close collaboration with domain expert partners. We implemented the presented workflow in an interactive visual analysis tool;ImaCytE. Our workflow is designed to allow the user to discriminate cell types according to their protein expression profiles and analyze their cellular microenvironments, aiding in the formulation or verification of hypotheses on tissue architecture and function. Finally, we show the effectiveness of our workflow and ImaCytE through a case study performed by a collaborating specialist.

关键词： Proteins Imaging Spatial resolution Tools Task analysis visualization Visual analytics imaging mass cytometry spatial omics data high-dimensional images

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QuEST: Quantum Circuit Output Estimation using Gaussian Distribution analysis 25

QuEST: Quantum Circuit Output Estimation using Gaussian Dist...

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25th International symposium on Quality Electronic Design (ISQED)

作者： Kundu, Shamik Choudhury, Navnil Basu, Kanad Univ Texas Dallas Dept Elect & Comp Engn Richardson TX 75083 USA

ISBN: (纸本)9798350309270;9798350309287

Quantum computing has holds considerable potential for accelerating computational tasks beyond the capabilities of classical computation. However, a major obstacle arises from the delicate nature of quantum hardware, where quantum gates and qubits, the fundamental components of a quantum circuit (QC), are vulnerable to external interference. Consequently, even a simple QC can produce significantly noisy output. This noise introduces uncertainty, making it difficult to ascertain whether the output represents meaningful computation or merely random noise. This uncertainty raises questions regarding the fidelity of a QC and the extent to which we can rely on its output. Existing classification-based approaches for output estimation have limitations, including the potential for incorrect results due to misclassification or the requirement for a large number of measurements, which can be expensive. To circumvent this, in this paper, we propose QuEST, which introduces an efficient technique for estimating the output of a QC by analyzing probability distributions of post-measurement data. Specifically, the QuEST framework employs Gaussian distribution functions to compare the measured distribution of a circuit with a pretrained distribution obtained from a training circuit dataset. Moreover, we reformulate this problem by leveraging the properties of sequential time series, thereby deriving a straightforward and intuitive metric to measure the confidence of the QC output. By utilizing this metric, the QuEST framework monitors fidelity evolution over time as the QC interacts with its external environment, enabling the system to preemptively halt QC execution upon reaching a specified confidence threshold. When evaluated against state-of-the-art benchmark quantum circuits, our proposed QuEST framework accurately estimates the output of 100% of the benchmark circuits, while significantly achieving speedup up to 58.3x compared to a standard QC execution. These results highligh

关键词： Quantum Computing Output Estimation Statistical distribution Time Series

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Big data analysis and visualization System for large-Scale Pumped Storage Power Station Equipment

Big Data Analysis and Visualization System for Large-Scale P...

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ieee International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Yu Gong Xuan Liu Mingxuan Yang Hao Wu Branch Company of Maintenance & Test China Southern Power Grid Energy Storage Co. Ltd. Guangzhou Qiming Suzhi Energy Technology Co.LTD. Guangzhou China

ISBN: (数字)9798350377460

ISBN: (纸本)9798350377477

The traditional operation and maintenance management of pumped storage power station group exists problems such as data analysis and mining is not systematic, and the level of intelligence is low. This paper researches the large-scale pumped storage power station big data analysis technology system of multi-source data fusion, and based on this, develops the large-scale pumped storage power station equipment visualisation and big data decision analysis system, constructs the data transmission platform, and realizes the standardized storage of multi-source data. Meanwhile, intelligent algorithms are developed based on the data and platform foundation, while applications are deployed to verify their reliability. This is helpful in promoting the intelligent and digital transformation of pumped storage power station groups.

关键词： Renewable energy sources data analysis Systematics Digital transformation data visualization Big data Prediction algorithms data communication Power generation Maintenance management

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data-Driven City Traffic Planning Simulation 21

Data-Driven City Traffic Planning Simulation

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21st ieee International symposium on Mixed and Augmented Reality (ISMAR)

作者： Nguyen, Tam, V Thanh Ngoc-Dat Tran Viet-Tham Huynh Bao Truong Minh-Quan Le Kumavat, Mohit Patel, Vatsa S. Mai-Khiem Tran Minh-Triet Tran Univ Dayton Dept Comp Sci Dayton OH 45469 USA Vietnam Natl Univ Univ Sci Ho Chi Minh City Vietnam Vietnam Natl Univ John von Neumann Inst Univ Sci Ho Chi Minh City Vietnam

ISBN: (数字)9781665453653

ISBN: (纸本)9781665453653

Big cities are well-known for their traffic congestion and high density of vehicles such as cars, buses, trucks, and even a swarm of motorbikes that overwhelm city streets. large-scale development projects have exacerbated urban conditions, making traffic congestion more severe. In this paper, we proposed a data-driven city traffic planning simulator. In particular, we make use of the city camera system for traffic analysis. It seeks to recognize the traffic vehicles and traffic flows, with reduced intervention from monitoring staff. Then, we develop a city traffic planning simulator upon the analyzed traffic data. The simulator is used to support metropolitan transportation planning. Our experimental findings address traffic planning challenges and the innovative technical solutions needed to solve them in big cities.

关键词： data-driven computer vision simulation user experience evaluation

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analysis of Calibration Procedure for Open-Ended Coaxial Probe

Analysis of Calibration Procedure for Open-Ended Coaxial Pro...

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ieee USNC-URSI Radio Science Meeting / Joint ieee Antennas-and-Propagation-Society (AP-S) International symposium

作者： Joof, Sulayman Aydinalp, Cemanur Dilman, Ismail Akinci, Mehmet Nuri Yilmaz, Tuba Istanbul Tech Univ TR-34467 Istanbul Turkey

ISBN: (数字)9781946815163

ISBN: (纸本)9781946815163

The implementation of machine and deep learning algorithms to solve microwave related problems have been of interest to researchers. These algorithms require large amount of data to design a robust model. In this work, a cost-effective and fast method to retrieve the reflection coefficients of a material under test using the admittance model of the open-ended coaxial probe is presented. The reflection coefficients of three pure liquids and four mixture solutions generated, measured and analyzed. The results from the admittance model is compared with CST Microwave studio simulations and experimental measurements. The reflection coefficients retrieved via the admittance model indicate a good agreement with the simulated and measured results.

关键词： Microwave measurement Deep learning Liquids Conferences Admittance measurement Microwave theory and techniques data models

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large Language Models for Transforming Categorical data to Interpretable Feature Vectors

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ieee Transactions on visualization and Computer Graphics 2024年 PP卷 PP-PP页

作者： Huesmann, Karim Linsen, Lars University of Münster Germany

When analyzing heterogeneous data comprising numerical and categorical attributes, it is common to treat the different data types separately or transform the categorical attributes to numerical ones. The transformation has the advantage of facilitating an integrated multi-variate analysis of all attributes. We propose a novel technique for transforming categorical data into interpretable numerical feature vectors using large Language Models (LLMs). The LLMs are used to identify the categorical attributes' main characteristics and assign numerical values to these characteristics, thus generating a multi-dimensional feature vector. The transformation can be computed fully automatically, but due to the interpretability of the characteristics, it can also be adjusted intuitively by an end user. We provide a respective interactive tool that aims to validate and possibly improve the AI-generated outputs. Having transformed a categorical attribute, we propose novel methods for ordering and color-coding the categories based on the similarities of the feature vectors. © 1995-2012 ieee.

关键词： data assimilation

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Classification of Cortical Bone Thicknesses Based on RF Signal Spectral analysis

Classification of Cortical Bone Thicknesses Based on RF Sign...

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ieee International Ultrasonics symposium (IUS)

作者： Sultan, Hossam H. Grisan, Enrico Peralta, Laura Harput, Sevan London South Bank Univ South Bank Appl Bioengn Res London England Kings Coll London Sch Biomed Engn & Imaging Sci London England

ISBN: (数字)9781665466578

ISBN: (纸本)9781665466578

Cortical bone thickness is an important biomarker of bone fragility that reveals the risk of fractures. However, ultrasound bone assessment is challenging due to the complex nature of bone, such as varying porosity and microarchitecture, and the large difference between bone and soft tissue acoustic impedances. The research objective of this study is to develop a method to estimate cortical bone thickness by using spectral analysis, while avoiding traditional speed of sound measurements' figures due to the porous structure of bone tissue. In this study, multi-frequency ultrasound acquisitions have been used to cover a wide range of bone thickness and porosity values. Frequency modulated chirp waveforms are used as a transmit signal to increase the measurement SNR and the continuous wavelet transformation (CWT) is employed for the spectral analysis. The feasibility of the proposed methodology is demonstrated on simulated datasets and via experiments using ex vivo bone tissue. The preliminary experimental results showed a potential for cortical thickness classification using the received RF data.

关键词： Bone characterization Continuous wavelet transformation Deep learning Chirp signal

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Accelerating Spatial Autocorrelation Computation with Parallelization, Vectorization and Memory Access Optimization 22

Accelerating Spatial Autocorrelation Computation with Parall...

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22nd ieee/ACM International symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Paudel, Anmol Puri, Satish Marquette Univ Dept Comp Sci Milwaukee WI 53233 USA

ISBN: (纸本)9781665499569

Geographic information systems deal with spatial data and its analysis. Spatial data contains many attributes with location information. Spatial autocorrelation is a fundamental concept in spatial analysis. It suggests that similar objects tend to cluster in geographic space. Hotspots, an example of autocorrelation, are statistically significant clusters of spatial data. Other autocorrelation measures like Moran's I are used to quantify spatial dependence. large scale spatial autocorrelation methods are compute-intensive. Fast methods for hotspots detection and analysis are crucial in recent times of COVID-19 pandemic. Therefore, we have developed parallelization methods on heterogeneous CPU and GPU environments. To the best of our knowledge, this is the first GPU and SIMD-based design and implementation of autocorrelation kernels. Earlier methods in literature introduced cluster-based and MapReduce-based parallelization. We have used Intrinsics to exploit SIMD parallelism on x86 CPU architecture. We have used MPI Graph Topology to minimize inter-process communication. Our benchmarks for CPU/GPU optimizations gain upto 750X relative speedup with a 8 GPU setup when compared to baseline sequential implementation. Compared to the best implementation using OpenMP + R-tree data structure on a single compute node, our accelerated hotspots benchmark gains a 25X speedup. For real world US counties and COVID data evolution calculated over 500 days, we gain upto 110X speedup reducing time from 33 minutes to 0.3 minutes.

关键词： Spatial Statistic Getis-Ord Moran's I Geary's C Hotspots Vectorization Cache Intrinsics Parallelization OpenMP OpenACC CUDA MPI MPI Topology

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