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 limit...
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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.
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 inf...
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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-...
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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.
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...
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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 be...
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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...
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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 GP...
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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.
Differential equations play an important role in the fields of artificial intelligence, imageprocessing, and aerospace, and the solution of differential equations is the mathematical basis for research in each field....
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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 G...
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 parallelprocessing 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 parallelprocessing 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.
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-si...
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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.
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