The problem of how to assess cross-modality medical image synthesis has been largely unexplored. The most used measures like PSNR and SSIM focus on analyzing the structural features but neglect the crucial lesion loca...
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Time series forecasting is a hot spot in recent years. Visibility Graph (VG) algorithm is used for time series forecasting in previous research, but the forecasting effect is not as good as deep learning prediction me...
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In this paper we consider an abstract class of time-dependent quasi variational-hemivariational inequalities which involves history-dependent operators and a set of unilateral constraints. First, we establish the exis...
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Existing online cross-modal hashing methods often treat the semantic label categories independently to correlate the semantically similar data instances, which intrinsically ignore the potential dependency between the...
Existing online cross-modal hashing methods often treat the semantic label categories independently to correlate the semantically similar data instances, which intrinsically ignore the potential dependency between the label categories and thus fail to capture the discriminative information in the hash code learning process. To alleviate this concern, we explore the inter-dependency between the label categories through their co-occurrence correlation from the label set, and present an efficient Label-Semantic-Enhanced Online Hashing (LSE-OH) method for various cross-modal retrieval task. To be specific, the proposed framework integrates the instance-wise similarity and label-category affinity to incrementally learn the discriminative hash codes for the current arriving data, while updating the hash functions at a streaming manner. Further, an iterative discrete optimization algorithm is derived to mine the inter-dependency between the label categories and discriminatively learn the hash codes without relaxation. Accordingly, the hash codes are adaptively learned online with the high discriminative capability and inter-dependency, while avoiding high computation complexity to process the streaming data. Experimental results show its outstanding performance in comparison with the-state-of-arts.
Abstract. In this paper, the weak laws of large numbers for weighted sums and random sums of dependent random vectors taking values in real separable Hilbert spaces are established, which include and generalize some k...
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Abstract. In this paper, the weak laws of large numbers for weighted sums and random sums of dependent random vectors taking values in real separable Hilbert spaces are established, which include and generalize some k...
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Multi-band based terahertz metamaterial absorber is of great importance in current research and applications because of its ability to achieve multi-band absorption of electromagnetic waves and multi-point matching of...
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Staphylococcus aureus is considered to be an increasingly food safety concern. Low temperature is usually used to limit bacterial reproduction on meat. The aim of this study was to investigate the contamination and ch...
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Here we introduce two open-source projects for image aesthetic quality assessment. The first one is ILGnet, an open-source project for the aesthetic evaluation of images based on the convolution neural network. The se...
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Here we introduce two open-source projects for image aesthetic quality assessment. The first one is ILGnet, an open-source project for the aesthetic evaluation of images based on the convolution neural network. The second is CJS-CNN, an opensource project for predicting the aesthetic score distribution of human ratings.
Electricity consumption forecasting has vital importance for the energy planning of a country. Of the enabling machine learning models, support vector regression (SVR) has been widely used to set up forecasting models...
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