The paper presents a recommender algorithm for visual analysis based on Data field Schema and Aggregation, and developed an automated data analysis solution recommendation system (AutoEDA) in conjunction with the Expl...
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An abundance of data have been generated from various embedded devices, applications, and systems, and require cost-efficient storage services. Data deduplication removes duplicate chunks and becomes an important tech...
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An abundance of data have been generated from various embedded devices, applications, and systems, and require cost-efficient storage services. Data deduplication removes duplicate chunks and becomes an important technique for storage systems to improve space efficiency. However, stored unique chunks are heavily fragmented, decreasing restore performance and incurs high overheads for garbage collection. Existing schemes fail to achieve an efficient trade-off among deduplication, restore and garbage collection performance, due to failing to explore and exploit the physical locality of different chunks. In this paper, we trace the storage patterns of the fragmented chunks in backup systems, and propose a high-performance deduplication system, called HiDeStore. The main insight is to enhance the physical-locality for the new backup versions during the deduplication phase, which identifies and stores hot chunks in the active containers. The chunks not appearing in new backups become cold and are gathered together in the archival containers. Moreover, we remove the expired data with an isolated container deletion scheme, avoiding the high overheads for expired data detection. Compared with state-of-the-art schemes, HiDeStore improves the deduplication and restore performance by up to 1.4x and 1.6x, respectively, without decreasing the deduplication ratios and incurring high garbage collection overheads.
This paper first investigates and compares the uplink spectral efficiency(SE) of distributed cell-free massive multiple-input multiple-output(mMIMO) and cellular mMIMO networks, both with user equipment(UE)hardware im...
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This paper first investigates and compares the uplink spectral efficiency(SE) of distributed cell-free massive multiple-input multiple-output(mMIMO) and cellular mMIMO networks, both with user equipment(UE)hardware impairments. We derive a lower bound on the uplink ergodic channel capacity of the cellular mMIMO with UE hardware impairments, based on which we determine the optimal receive combining that maximizes the instantaneous effective signal-to-interference-and-noise ratio. Then, a lower bound on the uplink capacity of a distributed cellfree mMIMO with UE hardware impairments is derived using the use-and-then-forget technique. On this basis, the optimum large-scale fading decoding vector is found using generalized Rayleigh entropy. By using three combining schemes of minimum mean-square error(MMSE), regularized zero-forcing(RZF), and maximum ratio, the uplink SEs of distributed cell-free mMIMO and cellular mMIMO networks are analyzed and compared. The results show that the two-layer decoding distributed cell-free mMIMO network with MMSE combining outperforms the cellular mMIMO network, and the advantage is more evident as the hardware impairment factor increases. Finally, the uplink energy efficiency(EE) of the distributed cell-free mMIMO networks is analyzed and evaluated through the established realistic power consumption model with hardware impairments. Simulation results show that two-layer decoding provides higher SE and EE than single-layer decoding. In addition, RZF achieves almost the same SE and EE as MMSE in a two-layer decoding architecture.
As urbanization accelerates, data on the diverse aspects of urban life, including the environment, finance, and transportation, are increasing exponentially. Single-domain data analysis falls short for complex tasks, ...
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The visual noise of each light intensity area is different when the image is drawn by Monte Carlo ***,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy...
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The visual noise of each light intensity area is different when the image is drawn by Monte Carlo ***,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed *** we propose a rendered image denoising method with filtering guided by lighting ***,we design an image segmentation algorithm based on lighting information to segment the image into different illumination ***,we establish the parameter prediction model guided by lighting information for filtering(PGLF)to predict the filtering parameters of different illumination *** different illumination areas,we use these filtering parameters to construct area filters,and the filters are guided by the lighting information to perform sub-area ***,the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the *** the physically based rendering tool(PBRT)scene and Tungsten dataset,the experimental results show that compared with other guided filtering denoising methods,our method improves the peak signal-to-noise ratio(PSNR)metrics by 4.2164 dB on average and the structural similarity index(SSIM)metrics by 7.8%on *** shows that our method can better reduce the noise in complex lighting scenesand improvethe imagequality.
Interpretable visual recognition is essential for decision-making in high-stakes situations. Recent advancements have automated the construction of interpretable models by leveraging Visual Language Models (VLMs) and ...
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Radar can enhance target sensing capability after fusion with visible light to achieve all-weather target detection and identification due to lower requirements for weather and light conditions. However, the mainstrea...
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With the surge in computational data, Mobile Edge Computing (MEC) is set to become a crucial technology for reducing communication latency and congestion. However, the widespread adoption of MEC faces several challeng...
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Electroencephalography (EEG) is a highly random and nonlinear time series signal, and it is easily affected by other physiological artifacts. The interference from various physiological artifacts is detrimental to sub...
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Traditional methods for predicting airfoil flow fields primarily rely on computational fluid dynamics (CFD) simulations and wind tunnel experiments. However, solving the N avier-Stokes (NS) equations typically require...
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