Stencils are finite-difference algorithms for solving large-scale and high-dimension partial differential equations. Due to the data dependences among the iterative statements in Stencils, traditional Stencil computat...
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Stencils are finite-difference algorithms for solving large-scale and high-dimension partial differential equations. Due to the data dependences among the iterative statements in Stencils, traditional Stencil computations are be executed serially, rather than in parallel. It's challenging to design an effective and scalable Stencil parallelized method. To address the issue of 3D data space computing, we present a serial execution model based on multi-layers symmetric Stencil method and time skewing techniques. Within this model, the iteration space is divided to multiple tiles based on time skewing, where the executive process is ordered by the sequence of tiles, and the nodes in each individual tile can be swept repeatedly to improve the data locality. In addition, we propose a novel 3D iterative space alternate tiling Stencil parallel method, which subdivides the iteration space along high dimension, and changes the execution sequence of tiles to reduce the data dependency and communication cost, where the partial order of tiles is still guaranteed. Experimental results demonstrate our proposed alternative tiling parallel method achieves better parallel efficiency and scalability compared with the domain-decomposition methods.
Discrete-event process simulation, originally the benefactor of the manufacturing sector of the economy, has expanded aggressively into the service sector of the economy, much to the benefit and gratitude of its new c...
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Rational least-squares techniques are commonly used to build compact macromodels of passive microwave components. This paper describes a technique which calculates rational least-squares fitting models by matching S-p...
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Rational least-squares techniques are commonly used to build compact macromodels of passive microwave components. This paper describes a technique which calculates rational least-squares fitting models by matching S-parameter frequency data-samples and higher-order frequency derivatives (or moments) using orthonormal polynomial basis functions to improve the numerical accuracy. Some considerations are given about the optimal choice ofpolynomial basisfunctions.
作者：
Xin ZhangHongzhi FengM. Shamim HossainYinzhuo ChenHongbo WangYuyu YinHangzhou Dianzi University
China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education China Zhoushan Tongbo Marine Electronic Information Research Institute Hangzhou Dianzi University China and Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics China Hangzhou Dianzi University
China Department of Software Engineering
College of Computer and Information Sciences King Saud University Saudi Arabia Hangzhou Dianzi University
China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education China and Zhoushan Tongbo Marine Electronic Information Research Institute Hangzhou Dianzi University China
Action Quality Assessment (AQA) has become crucial in video analysis, finding wide applications in various domains, such as healthcare and sports. A significant challenge faced by AQA is the background bias due to the...
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Action Quality Assessment (AQA) has become crucial in video analysis, finding wide applications in various domains, such as healthcare and sports. A significant challenge faced by AQA is the background bias due to the dominance of the background in videos. Especially, the background bias tends to overshadow subtle foreground differences, which is crucial for precise action evaluation. To address the background bias issue, we propose a novel data augmentation method named Scaled Background Swap. Firstly, the background regions between different video samples are swapped to guide models focus toward the dynamic foreground regions and mitigate its sensitivity to the background during training. Secondly, the video’s foreground region is up-scaled to further enhance models’ attention to the critical foreground action information for AQA tasks. In particular, the proposed Scaled Background Swap method can effectively improve models’ accuracy and generalization by prioritizing foreground motion and swapping backgrounds. It can be flexibly applied with various video analysis models. Extensive experiments on AQA benchmarks demonstrate that Scaled Background Swap method achieves better performance than baselines. Specifically, the Spearman’s rank correlation on datasets AQA-7 and MTL-AQA reaches 0.8870 and 0.9526, respectively. The code is available at: https://***/Emy-cv/Scaled-Background Swap.
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