296 research outputs found

    Coded Caching Schemes for Two-dimensional Caching-aided Ultra-Dense Networks

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    Coded caching technique is an efficient approach to reduce the transmission load in networks and has been studied in heterogeneous network settings in recent years. In this paper, we consider a new widespread caching system called (K1,K2,U,r,M,N)(K_1,K_2,U,r,M,N) two-dimensional (2D) caching-aided ultra-dense network (UDN) with a server containing NN files, K1K2K_1K_2 cache nodes arranged neatly on a grid with K1K_1 rows and K2K_2 columns, and UU cache-less users randomly distributed around cache nodes. Each cache node can cache at most M≤NM\leq N files and has a certain service region by Euclidean distance. The server connects to users through an error-free shared link and the users in the service region of a cache node can freely retrieve all cached contents of this cache node. We aim to design a coded caching scheme for 2D caching-aided UDN systems to reduce the transmission load in the worst case while meeting all possible users' demands. First, we divide all possible users into four classes according to their geographical locations. Then our first order optimal scheme is proposed based on the Maddah-Ali and Niesen scheme. Furthermore, by compressing the transmitted signals of our first scheme based on Maximum Distance Separable (MDS) code, we obtain an improved order optimal scheme with a smaller transmission load.Comment: 44 page

    Word of Mouth Marketing through Online Social Networks

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    This paper proposes a research agenda for studying factors that may affect marketing effectiveness in the context of online communities. Findings of prior studies are synthesized into a more comprehensive review of the state of the art of research on word of mouth marketing through online social networks. Based on the review, we propose a research model that incorporates both network and individual factors, present the research plan, and discuss the potential implications of the research

    Transcriptome profiling of the floating-leaved aquatic plant Nymphoides peltata in response to flooding stress

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    This table provides all differentially expressed genes meeting the threshold (FDR ≤ 0.01) and the GO terms that the differentially expressed genes were enriched. (XLS 207 kb

    HDRFlow: Real-Time HDR Video Reconstruction with Large Motions

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    Reconstructing High Dynamic Range (HDR) video from image sequences captured with alternating exposures is challenging, especially in the presence of large camera or object motion. Existing methods typically align low dynamic range sequences using optical flow or attention mechanism for deghosting. However, they often struggle to handle large complex motions and are computationally expensive. To address these challenges, we propose a robust and efficient flow estimator tailored for real-time HDR video reconstruction, named HDRFlow. HDRFlow has three novel designs: an HDR-domain alignment loss (HALoss), an efficient flow network with a multi-size large kernel (MLK), and a new HDR flow training scheme. The HALoss supervises our flow network to learn an HDR-oriented flow for accurate alignment in saturated and dark regions. The MLK can effectively model large motions at a negligible cost. In addition, we incorporate synthetic data, Sintel, into our training dataset, utilizing both its provided forward flow and backward flow generated by us to supervise our flow network, enhancing our performance in large motion regions. Extensive experiments demonstrate that our HDRFlow outperforms previous methods on standard benchmarks. To the best of our knowledge, HDRFlow is the first real-time HDR video reconstruction method for video sequences captured with alternating exposures, capable of processing 720p resolution inputs at 25ms.Comment: CVPR 2024; Project website: https://openimaginglab.github.io/HDRFlow

    Fabrication and evolution of multilayer silver nanofilms for surface-enhanced Raman scattering sensing of arsenate

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    Surface-enhanced Raman scattering (SERS) has recently been investigated extensively for chemical and biomolecular sensing. Multilayer silver (Ag) nanofilms deposited on glass slides by a simple electroless deposition process have been fabricated as active substrates (Ag/GL substrates) for arsenate SERS sensing. The nanostructures and layer characteristics of the multilayer Ag films could be tuned by varying the concentrations of reactants (AgNO3/BuNH2) and reaction time. A Ag nanoparticles (AgNPs) double-layer was formed by directly reducing Ag+ ions on the glass surfaces, while a top layer (3rd-layer) of Ag dendrites was deposited on the double-layer by self-assembling AgNPs or AgNPs aggregates which had already formed in the suspension. The SERS spectra of arsenate showed that characteristic SERS bands of arsenate appear at approximately 780 and 420 cm-1, and the former possesses higher SERS intensity. By comparing the peak heights of the approximately 780 cm-1 band of the SERS spectra, the optimal Ag/GL substrate has been obtained for the most sensitive SERS sensing of arsenate. Using this optimal substrate, the limit of detection (LOD) of arsenate was determined to be approximately 5 μg·l-1
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