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Mapping the density distribution pattern of microvasculature in whole-mouse brain 17

Mapping the density distribution pattern of microvasculature...

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17th International conference on Photonics and Imaging in Biology and Medicine, PIBM 2024

作者： Li, Yuxin He, Weijie Cao, Jia Li, Anan Li, Xiangning Jiang, Tao School of Computer Science and Engineering Xi'an University of Technology Xi'an710048 China HUST-Suzhou Institute for Brainsmatics Suzhou215123 China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics MoE Key Laboratory for Biomedical Photonics Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9781510688070

ISBN: (纸本)9781510688063

Analyzing the vascular network structure of the brain is crucial for understanding brain function and preventing and treating cerebrovascular diseases. By studying indicators such as the length density and branching point density of the brain's microvascular network, we can better understand the brain's vascular network and provide a scientific basis for cerebrovascular disease research. Traditional methods rely on statistical analysis of isolated brain regions, which fail to capture the spatial distribution characteristics of micro-vessels across the entire brain and overlook spatial information from different brain regions. To address this, we propose a computational pipeline for the spatial distribution of vascular density in the brain. First, we use a deep learning model to segment brain vascular images from HD-fMOST imaging of mouse brains. Then, we perform skeletonization on the 3D vascular segmentation images and obtain vessel diameter information through distance transformation. Finally, we filter capillaries based on their diameter and calculate the feature parameters of these capillaries to acquire vascular parameter distributions across the entire brain. The entire process is enhanced by MPI parallel processing and multithreading techniques to improve image processing efficiency. Results indicate that our proposed method can capture the spatial distribution characteristics of microvascular density across the whole brain by integrating multiple datasets. This method provides a powerful tool for comprehensive research on brain vasculature. © 2025 SPIE.

关键词： image segmentation

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Fast Katz Centrality on Dynamic Graphs

Fast Katz Centrality on Dynamic Graphs

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Prajjwal Nijhara Dishit Sharma Dip Sankar Banerjee Department of Computer Science and Engineering Indian Institute of Technology Jodhpur NH62 Karwar Jodhpur Rajasthan India

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

In network analysis, Katz centrality is widely used to measure node influence by considering both direct and indirect connections weighted by path length. While numerous studies have examined Katz centrality for static graphs, relatively few address the challenges posed by dynamic graphs. Katz centrality can utilize perturbation theory by exploiting iterative solvers to obtain updated Katz scores in dynamic graphs. However, these methods are limited to handling only small changes, as they generally accept only minor structural updates limited to only edge insertions or removals. Additionally, due to an iterative approach, the solutions can be sequential and provide approximate answers, which may reduce accuracy when the graph undergoes frequent updates over time. This paper introduces a novel algorithm that significantly improves the efficiency of Katz centrality calculations in dynamic graphs. After each update, we identify affected nodes using a Breadth First Search (BFS) frontier and then apply dynamic programming, which allows for both edge/node insertions and deletions. Our parallel implementation on a shared memory platform achieves a 5.29 x speedup on an average over the static version. Our algorithm can update a batch of 1 million edges on an existing graph of 1 billion edges in 24.73 seconds.

关键词： Weight measurement Runtime Heuristic algorithms Perturbation methods Directed graphs Network analyzers parallel processing Length measurement Dynamic programming Iterative methods

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High Performance Stingray: fast spectral timing for all

High Performance Stingray: fast spectral timing for all

引用

Euromicro conference on parallel, distributed and Network-Based processing

作者： Matteo Bachetti Eleonora Veronica Lai Daniela Huppenkothen Guglielmo Mastroserio Matteo Lucchini Osservatorio Astronomico di Cagliari Istituto Nazionale di Astrofisica Cagliari Italy Anton Pannekoek Institute for Astronomy University of Amsterdam Amsterdam the Netherlands Dipartimento di Fisica Università degli Studi di Milano Milano Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Celestial objects are known to be change in brightness over time, driven by a diverse combination of physical processes, whose time scales range from sub-milliseconds to billions of years. Stingray is an open-source Python package that brings advanced time series analysis techniques to the astronomical community, with a focus on high-energy astrophysics, but built on top of general-purpose classes and methods that are designed to be easily adapted and extended to other use cases. We describe the work being done to adapt Stingray to the analysis of large data archives. In particular, we measure the performance and scalability of Stingray and use parallel computing to speed up selected parts of the code.

关键词： Codes Atmospheric measurements Astrophysics Scalability Time series analysis Brightness parallel processing Particle measurements Extraterrestrial measurements Timing

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parallel Computing Optimization of Tourism Big Data based on Crawler Programs 1

Parallel Computing Optimization of Tourism Big Data based on...

引用

1st International conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Zhou, Xiaoyu Wuhan Donghu University School of Management Hubei Wuhan China

ISBN: (纸本)9798331529246

With the rapid development of the tourism industry, traditional tourism methods are undergoing significant transformation, and online tourism is gradually becoming a new highlight in the market. However, faced with the challenges of big data, online tourism data experiences tremendous pressure in terms of storage, computation, and management, and the issue of information overload seriously affects the user experience. Therefore, there is an urgent need for innovative platforms and recommendation algorithms to optimize the screening and recommendation of tourism information. This paper proposes an optimization scheme based on the Spark cloud computing platform to improve the efficiency of tourism data processing. First, utilizing Spark's distributed computing and memory management capabilities, tourism data can be stored and processed in a distributed manner, thus accelerating the computation speed of recommendation algorithms. Secondly, a Python-written crawler program is used to obtain real tourism data, including user ratings and comments on attractions, to evaluate the effectiveness of the improved algorithm. Finally, a weighted algorithm combining the LDA topic model is proposed. This algorithm analyzes the topic distribution of comment texts and weights the comments based on user ratings, calculating the predicted score for unrated attractions for the target user, thereby achieving accurate attraction recommendations. The experimental results show that the algorithm proposed in this study demonstrates significant advantages in prediction accuracy. © 2025 IEEE.

关键词： Cloud platforms

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AFMA-Track: Adaptive Fusion of Motion and Appearance for Robust Multi-object Tracking 27th

AFMA-Track: Adaptive Fusion of Motion and Appearance for ...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Liao, Wei Luo, Lei Zhang, Chunyuan College of Computer Science and Technology National University of Defence Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9783031784439

Motion and appearance cues play a crucial role in Multi-object Tracking (MOT) algorithms for associating objects across consecutive frames. While most MOT methods prioritize accurate motion modeling and distinctive appearance representations, the use of appearance and motion cues is often confined to simplistic association techniques. For instance, fixed weights are commonly employed to combine the intersection-over-union (IoU) matrix and appearance similarity matrix, yielding an association cost matrix. To harness the full potential of motion and appearance cues across diverse scenarios, we propose an innovative approach that dynamically balances motion and appearance cues based on scene and object information during the association process. Furthermore, we introduce a new mechanism for updating appearance representations, effectively mitigating noise introduced by occlusion. Our method demonstrates state-of-the-art performance on the MOT17 and MOT20 test sets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Object detection

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Research of Latent Video Stream Compression methods for FPV Control of UAVs 27th

Research of Latent Video Stream Compression Methods for FP...

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27th International conference on distributed Computer and Communication Networks, DCCN 2024

作者： Chenskiy, A. Berezkin, Aleksandr Vivchar, R. Kirichek, Ruslan Kukunin, D. The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Bolshevikov Avenue 22 St. Petersburg Russia

ISBN: (纸本)9783031808524

The efficiency of video stream transmission links between an unmanned aerial vehicle and its operator in mobile and hybrid orbital-terrestrial communication networks directly depends on solving the problem of compressing video stream frames, while ensuring the quality of the restored image. One of the methods of frame compression is the use of variational autoencoders to transfer the latent space obtained during the processing of individual frames. The present paper is devoted to the research of how effectively different algorithms can compress quantized latent space of variational autoencoder VQ-f16 from Stable Diffusion repository. The system of quantized latent space compression algorithms efficiency indicators and their description are presented. A comparative analysis of the efficiency of quantized latent space compression algorithms is conducted. The results of analyzing the efficiency of quantized latent space compression algorithms are presented and recommendations for improving their efficiency are given. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Unmanned aerial vehicles (UAV)

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Machine Learning Approach for Detecting Potential Anomalous Cosmic Rays Particles Tracks in Earth-Scale Cosmic Ray Extremely distributed Observatory 15th

Machine Learning Approach for Detecting Potential Anomalous...

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15th International conference on parallel processing and Applied Mathematics, PPAM 2024

作者： Tyc, Jan Zub, Marcin Hachaj, Tomasz Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Krakow Al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031857027

In this paper, we present a novel contribution to the problem of the detection of potential anomalous cosmic rays particle tracks acquired by Earth-scale complex distributed observatory infrastructure. Detection of such signals provides an opportunity to analyze the performance of the entire research infrastructure of a complex system (for example, potential failures) and a chance to record unknown physical phenomena that can be recorded simultaneously with space radiation observations. The observatory comprises a worldwide network of CMOS detectors cooperating in the citizen science paradigm. We propose, evaluate, and compare several data processing, feature extractions, and potential anomaly discovery pipelines based on machine learning approaches. Although, to date, there have already been published works performing detection and analysis of anomalies in the Cosmic Ray Extremely distributed Observatory Project (CREDO) dataset, they applied single methods without comparing the results with other possible approaches to analyze potential anomalies. In this work, for the first time, we have used and discussed many anomalous discovery approaches not previously applied to this issue to perform analysis of this extensive dataset (nearly 600,000 images). For this reason, the results published here are of significant cognitive importance to the problem under consideration. We have published both our research’s source codes and dataset so our results can be reproduced. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cosmic rays

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Application of Various Novel Computer image processing (CIP) Algorithms in Digital Media Art images

Application of Various Novel Computer Image Processing (CIP)...

引用

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Xian Du Shanghai Institute of Visual Arts Shanghai China

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the continuous development of digital image processing algorithms, its application scenarios have been integrated from the simple research of a single image and a single algorithm to a multi-algorithm fusion analysis paradigm. Therefore, this study proposes a digital media art image analysis framework that combines multiple computer image processing (CIP) algorithms. This breakthrough covers three core architectures: image edge extraction, image style transfer, and image compression. In the image edge extraction module, this study designed an improved edge detection algorithm based on de-noising auto-encoder. This algorithm improves the accuracy of edge detection through multi-directional feature extraction and (I, O)-fuzzy rough set optimization, while maintaining global stability. In the image style transfer module, this study proposed a dual-module network. The network includes a texture translation network and a perceptual loss network, and achieves cross-domain style transfer through a fusion model. At the same time, the algorithm optimizes the perceptual loss function to enhance semantic matching capabilities. In the image compression module, this study uses chaotic systems to construct an optimized measurement matrix. Specifically, through a distributed data parallel training framework, a compression method based on the LPAC gradient sparse algorithm is constructed. By testing the three modules separately, the experimental results confirm the effectiveness of the proposed algorithm.

关键词： Training image coding Art image analysis Translation image edge detection Digital images Semantics Media Feature extraction

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Undecimated discrete wavelets transform-based image fusion and de-noising in FPGA

Undecimated discrete wavelets transform-based image fusion a...

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International conference on Energy, Power, Environment, Control and Computing (ICEPECC 2025)

作者： Fahad Ahmad Muhammad H. Ahmed Yumna Adnan Department of Electrical Engineering University of Gujrat Gujrat Pakistan

ISBN: (数字)9781837243150

image processing is a vast field of study and has gained significant importance in recent years. image fusion and denoising a re most important methods in the field of image processing. The project develops an image processing system on a Field-Programmable Gate Array (FPGA) platform that uses the Undecimated Discrete Wavelet Transform (UDWT) for image fusion and denoising. By merging several images into a single better image and using sophisticated wavelet techniques to reduce noise the aim is to improve image quality. UDWT preserves image details by maintaining high-frequency components without down sampling. Reconstructing denoised images thresholding to remove noise and dividing images into wavelet coefficients are the steps in the process that culminate in the creation of a superior composite image. Due to its ability to process data in parallel the FPGA platform is preferred over conventional CPU-based techniques for processing large amounts of data quickly. Suitable for real-time applications, this method takes advantage of the intrinsic parallelism of FPGA to process data quickly and efficiently. According to the results the FPGA-based system performs faster and produces better images than traditional techniques making it a reliable option for high-performance image processing application.

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parallel Maximal Common Subgraphs with Labels for Molecular Biology 15th

Parallel Maximal Common Subgraphs with Labels for Molecula...

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15th International conference on parallel processing and Applied Mathematics, PPAM 2024

作者： Agbeto, Wilfried Coti, Camille Reinharz, Vladimir Université du Québec à Montréal Montreal Canada École de Technologie Supérieure Montreal Canada

ISBN: (纸本)9783031856969

Advances in graph algorithmics have allowed in-depth study of many natural objects from molecular biology or chemistry to social networks. Particularly in molecular biology and cheminformatics, understanding complex structures by identifying conserved sub-structures is a key milestone towards the artificial design of novel components with specific functions. Given a dataset of structures, we are interested in identifying all maximum common connected partial subgraphs between each pair of graphs, a task notoriously NP-Hard. In this work, we present parallel algorithms over shared and distributed memory to enumerate all maximal connected common sub-graphs between pairs of arbitrary multi-directed graphs with labels on their edges. We offer an implementation of these methods and evaluate their performance on the non-redundant dataset of all known RNA 3D structures. We show that we can compute the exact results in a reasonable time for each pairwise comparison while taking into account a much more diverse set of interactions—resulting in much denser graphs—resulting in an order of magnitude more conserved modules. All code is available at https://***/cbe/pasigraph and results in the branch results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Directed graphs

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