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Large Scale image Classification Using GPU-based Genetic Programming 22

Large Scale Image Classification Using GPU-based Genetic Pro...

引用

Genetic and Evolutionary Computation conference (GECCO)

作者： Zeng, Peng Lensen, Andrew Sun, Yanan Sichuan Univ Coll Comp Sci Chengdu Peoples R China Victoria Univ Wellington Sch Engn & Comp Sci Wellington New Zealand

ISBN: (纸本)9781450392686

Genetic programming (GP) has been applied to image classification and achieved promising results. However, most GP-based image classification methods are only applied to small-scale image datasets because of the limits of high computation cost. Efficient acceleration technology is needed when extending GP-based image classification methods to large-scale datasets. Considering that fitness evaluation is the most time-consuming phase of the GP evolution process and is a highly parallelized process, this paper proposes a CPU multi-processing and GPU parallel approach to perform the process, and thus effectively accelerate GP for image classification. Through various experiments, the results show that the highly parallelized approach can significantly accelerate GP-based image classification without performance degradation. The training time of GP-based image classification method is reduced from several weeks to tens of hours, enabling it to be run on large-scale image datasets.

关键词： genetic programming image classification parallel algorithms

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An unsupervised deep learning framework for gene regulatory network inference from single-cell expression data

An unsupervised deep learning framework for gene regulatory ...

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2023 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Mao, Guo Liu, Jie National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory Changsha410073 China National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Laboratory of Software Engineering for Complex System Changsha410073 China

ISBN: (纸本)9798350337488

Recent advances in single-cell RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for reconstruction gene regulation networks (GRNs). At present, many different models have been proposed to infer GRN from a large number of RNA-seq data, but most deep learning models use a priori gene regulatory network to infer potential GRNs. It is a challenge to reconstruct GRNs from scRNA-seq data due to the noise and sparsity introduced by the dropout effect. Here, we propose GAALink, a novel unsupervised deep learning method. It first constructs the gene similarity matrix and then refines it by threshold value. It then learns feature representations of genes through a graphical attention autoencoder that propagates information across genes with different weights. Finally, we use gene feature expression for matrix completion such that the GRNs are reconstructed. Compared with seven existing GRNs reconstruction methods, GAALink achieves more accurate performance on seven scRNA-seq dataset with four ground truth networks. GAALink can provide a useful tool for inferring GRNs for scRNA-seq expression data. © 2023 IEEE.

关键词： Feature representations Gene similarity matrix Graphical attention autoencoder Matrix completion Unsupervised deep learning method

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Field-VIO: Stereo Visual-Inertial Odometry Based on Quantitative Windows in Agricultural Open Fields

Field-VIO: Stereo Visual-Inertial Odometry Based on Quantita...

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IEEE International conference on Robotics and Automation (ICRA)

作者： Sun, Jianjing Wu, Shuang Dong, Jun He, Junming Anhui Univ Inst Phys Sci Hefei 230601 Peoples R China Chinese Acad Sci Hefei Inst Phys Sci Hefei 230031 Peoples R China

ISBN: (纸本)9798350384581;9798350384574

In agricultural open fields, accurate autonomous localization of robots requires long-term data correlation to reduce cumulative error. Our article presents a Stereo Visual-Inertial Odometry (VIO) system based on ORB-SLAM3 to address the malfunction of the Loop Closure Detection (LCD) methods in this environment. In this method, we first propose a concept of quantitative windows to describe the robot's trajectory along the crop rows. We design a driving state quantification algorithm and accurately separate the quantitative windows between the crop rows. Our system constructs spatial constraints according to the parallelism between the quantitative windows. We apply an anomaly correction method to maintain the constructed parallel matching relationship and implement holistic pose correction for keyframes within abnormal quantitative windows. Our system demonstrated excellent performance over long distances in experiments on the Rosario dataset, verifying its effectiveness in reducing cumulative positioning error in agricultural open fields.

关键词： Stereo image processing

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An Adaptive Partitioning Method for distributed Stream processing Systems 11

An Adaptive Partitioning Method for Distributed Stream Proce...

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11th IEEE Global conference on Consumer Electronics, GCCE 2022

作者： Tomatsu, Kazuma Tanaka, Haruka Kasamatsu, Daisuke Soka University Faculty of Science and Engineering Tokyo Japan

ISBN: (纸本)9781665492324

In recent years, stream processing systems have attracted attention for their ability to process big data, that is constantly generated, with low latency. However, the performance of stream processing deteriorates due to the bias in the amount of input data for parallel processing. This paper proposes an adaptive partitioning method based on the latency of each parallel process. Experimental results using a prototype system show that the latency bias can be reduced to half. © 2022 IEEE.

关键词： Data handling

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Graph Structure Learning via Transfer Entropy for Multivariate Time Series Anomaly Detection

Graph Structure Learning via Transfer Entropy for Multivaria...

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2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Liu, Mingyu Wang, Yijie Zhou, Xiaohui Wang, Yongjun National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

Multivariate time series anomaly detection (MTAD) poses a challenge due to temporal and feature dependencies. The critical aspects of enhancing the detection performance lie in accurately capturing the dependencies between variables within the sliding window and effectively leveraging them. Existing studies rely on domain knowledge to pre-set the window size, and overlook the strength of dependencies while calculating direction based on variable similarity. This paper proposes GSLTE, a graph structure learning method for MTAD. GSLTE employs Fast Fourier Transform to conduct iterative segmentation of the whole series, selecting the dominant Fourier frequency as the window size for each subsequence within the minimum interval. GSLTE quantifies the direction and strength of the dependencies based on variable-lag transfer entropy which is achieved through Dynamic Time Warping method to learn asymmetric links between variables. Extensive experiments show that GNN-based MTAD methods applying GSLTE can further improve anomaly detection performance while outperforming state-of-the-art competitors. © 2025 IEEE.

关键词： Anomaly detection Graph structure learning Multivariate time series Window size selection

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Rock image Contrast Enhancement Method Based on Improved De-sharpening Mask 3

Rock Image Contrast Enhancement Method Based on Improved De-...

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3rd International conference on Information Science, parallel and distributed Systems, ISPDS 2022

作者： Ge, Meizheng Liu, Qiong Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9781665487474

ISBN: (纸本)9781665487474

In engineering applications, traditional methods are usually used to acquire microscopic images, and due to objective factors such as uneven acquisition equipment and uneven illumination, there may be problems such as unclear acquisition images and insufficient local exposure. Traditional de-sharpening mask image enhancement algorithms are suitable for enhancing the edges and details of microscopic images, but are extremely sensitive to noise and do not enhance contrast and detail at the same time. In this paper, an improved sharpening masking algorithm is proposed, which sharpens the edges of rock images in the brightness channel of HSV color space, uses the difference between the non-local mean filter image and the original image to achieve high-frequency components, adaptively enhances the high-frequency components, improves the problem of image light unevenness through the contrast enhancement algorithm, and superimposes it with high-frequency components to effectively enhance the image. Experimental results show that the image processed by the algorithm has outstanding details and clear textures, which better suppresses the amplification of noise. © 2022 IEEE.

关键词： image acquisition

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A synchronization-free incomplete LU factorization for GPUs with level-set analysis

A synchronization-free incomplete LU factorization for GPUs ...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Manuel Freire Ernesto Dufrechou Pablo Ezzatti Instituto de Computación Facultad de Ingeniería - UDELAR Montevideo Uruguay

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Incomplete factorization methods are powerful algebraic preconditioners widely used to accelerate the convergence of linear solvers. The parallelization of ILU methods has been extensively studied, particularly for GPUs, which are ubiquitous parallel computing devices. In recent years, synchronization-free methods have become the mainstream approach for solving sparse triangular linear *** the sparse triangular solver and ILU factorization are closely related, the application of synchronization-free strategies to ILU factorization has not been explored in the literature to the same extent as the triangular solver. In this work, we present synchronization-free implementations of the ILU-0 preconditioner on GPUs. Specifically, we propose three implementations that vary in how row updates are handled after each coefficient elimination, as well as an additional approach that leverages a prior level-set analysis to optimize the execution schedule.

关键词： Linear systems Schedules parallel processing Synchronization Convergence

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parallel Solution Method for Hamiltonian Equations Based on FPGA and Matlab Programming Verification 4

Parallel Solution Method for Hamiltonian Equations Based on ...

引用

4th International conference on Computers, Information processing and Advanced Education, CIPAE 2023

作者： Zhou, Songcheng Zhou, Zijian Hohai University School of Information Science and Engineering Changzhou China Liaoning University Shenyang China

ISBN: (纸本)9798350342710

Differential equations play an important role in the fields of artificial intelligence, image processing, and aerospace, and the solution of differential equations is the mathematical basis for research in each field. Traditional numerical methods can approximate the solution of complex differential equations, but there is a problem of accuracy loss in solving systems that do not have asymptotic stability. As a numerical algorithm that preserves the structural properties of Sin, the Sin algorithm has shown advantages in tracking Hamiltonian systems over long periods. However, few studies on the acceleration of the Sim algorithm for Hamiltonian systems do not possess asymptotic stability. To this end, an improved refined Sim algorithm is proposed in this paper and its effectiveness is verified by MATLAB. In addition, an improved fine Sim algorithm IP based on FPGA devices is constructed and its accuracy and parallel acceleration are verified on a hardware platform. This study provides ideas for the research of accelerated computation of the Xin algorithm for Hamiltonian systems without asymptotic stability, which is important for improving the efficiency of numerical solutions. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Efficient GPU processing Method to Analyze Large GWAS Data Sets

Efficient GPU Processing Method to Analyze Large GWAS Data S...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Giuseppe Agapito Gaetano Guardasole Mario Cannataro dept. of Law Economics and Social Sciences Magna Græcia University Catanzaro Italy ex Expert lCT University of Calabria Rende Italy

Genome-wide association studies (GWAS) have ex-panded rapidly, generating large genetic data sets that require advanced computational strategies for efficient analysis. This study introduces a novel method that uses Graphics processing Units (GPUs) to analyze large scale GWAS data sets, enhancing computational efficiency and reducing processing time. Our approach leverages GPUs' inherent parallel processing capabilities to significantly accelerate computation-intensive tasks commonly encountered in GWAS, such a Genetic Risk Score (GRS). We explain how our GPU-GRS computational approach out-performs traditional CPU (Central processing Unit) methods, outlining the architectural optimizations that enable parallel processing to handle large genomic data sets more effectively. We also demonstrate the scalability of our approach by ana-lyzing increasingly large synthetic G WAS data sets, showcasing its ability to manage the growing size and complexity of genetic data efficiently. Our findings suggest that adopting GPU-based methods in G WAS analysis can play a pivotal role in the era of big genomic data, offering a path towards more time-efficient and scalable solutions.

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Dual Dreamer: Extending Single-View Dreamer with Few Shot of Complementary Views 33rd

Dual Dreamer: Extending Single-View Dreamer with Few Shot of...

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33rd International conference on Artificial Neural Networks and Machine Learning (ICANN)

作者： Zhang, Ziteng Qiao, Peng Yong, Dou Liu, Sidun Li, Wenyu Cao, Li Chen, Luo Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Sch Comp Changsha Peoples R China Minist Nat Resources Key Lab Nat Resources Monitoring & Supervis South Changsha Peoples R China

ISBN: (纸本)9783031723377;9783031723384

Recently, diffusion models demonstrate the potential of generating consistent novel views from a single view. Due to the limited object information contained in a single input view, controlling generation of out-of-sight views becomes challenging. In this work, we propose Dual Dreamer, a method of generating novel views with complementary views. Specifically, we focus on fine-tuning the single-view input diffusion model to extract complementary information from additional known views and combine it with the original information for novel view synthesis (NVS). To achieve this, Dual Dreamer introduces a 3D attention mechanism to enable the model to learn common features of the complementary views and enhance information exchange. By utilizing view difference between predefined and target views as weights, predefined views are fused as priors to enhance the consistency of multi-view synthesis. Finally, in order to avoid consuming resources on large-scale datasets, the proposed Dual Dreamer model is trained in a similar way like ControlNet, enabling efficient training on small datasets. Extensive evaluation on various datasets demonstrates that our approach achieves consistent, high-quality generation results compared to methods that rely on a single input view.

关键词： Novel Views Synthesis Few Shot Diffusion Model

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