弄碎
发表于 2025-3-21 17:33:03
书目名称Network and Parallel Computing影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0662872<br><br> <br><br>书目名称Network and Parallel Computing影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0662872<br><br> <br><br>书目名称Network and Parallel Computing网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0662872<br><br> <br><br>书目名称Network and Parallel Computing网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0662872<br><br> <br><br>书目名称Network and Parallel Computing被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0662872<br><br> <br><br>书目名称Network and Parallel Computing被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0662872<br><br> <br><br>书目名称Network and Parallel Computing年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0662872<br><br> <br><br>书目名称Network and Parallel Computing年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0662872<br><br> <br><br>书目名称Network and Parallel Computing读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0662872<br><br> <br><br>书目名称Network and Parallel Computing读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0662872<br><br> <br><br>
Pathogen
发表于 2025-3-22 00:10:25
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妨碍议事
发表于 2025-3-22 02:27:32
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Eviction
发表于 2025-3-22 06:25:21
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尖叫
发表于 2025-3-22 12:15:00
CNLoc: Channel State Information Assisted Indoor WLAN Localization Using Nomadic Access Points,ncertainty of nomadic APs. Our implementation and evaluation show that CNLoc can improve the accuracy with unknown location information of nomadic APs. We also discuss some open issues and new possibilities in future nomadic AP based indoor localization.
颂扬国家
发表于 2025-3-22 14:05:18
ALOR: Adaptive Layout Optimization of Raft Groups for Heterogeneous Distributed Key-Value Stores,. and .. We conducted experiments on a practical heterogeneous cluster, and the results indicate that, on average, ALOR improves throughput by 36.89%, reduces latency and 99th percentile tail latency by 24.54% and 21.32%, respectively.
notion
发表于 2025-3-22 18:47:04
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prosthesis
发表于 2025-3-22 22:27:24
GPU-Accelerated Clique Tree Propagation for Pouch Latent Tree Models,each model structure during PLTM training. Our experiments with real-world data sets show that the GPU-accelerated implementation procedure can achieve up to 52x speedup over the sequential implementation running on CPUs. The experiment results signify promising potential for further improvement on the full training of PLTMs with GPUs.
minaret
发表于 2025-3-23 04:46:29
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Basal-Ganglia
发表于 2025-3-23 06:59:03
Data Fine-Pruning: A Simple Way to Accelerate Neural Network Training,g approach. Extensive experiments with different neural networks are conducted to verify the effectiveness of our method. The experimental results show that applying the data fine-pruning approach can reduce the training time by around 14.29% while maintaining the accuracy of the neural network.