Traffic has been a major problem in recent times. Traffic management is a must for safer and faster transportation. Automatic smart signal controlling systems respond to day-to-day world traffic densities to provide p...
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Deep learning has been widely applied for jobs involving face inpainting, however, there are usually some problems, such as incoherent inpainting edges, lack of diversity of generated images and other problems. In ord...
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The Internet of Things (IoT) is a rapidly expanding network characterized by a very significant number of heterogeneous devices with limited resources. Clustering is a very powerful technique used to reduce the commun...
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In this paper, we study the utility-cost ratio (UCR) maximization problem in quantum networks, with a particular focus on secure quantum key distribution (QKD). We propose the Quantum Utility-Cost Optimization (QUCO) ...
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ISBN:
(数字)9798331531591
ISBN:
(纸本)9798331531607
In this paper, we study the utility-cost ratio (UCR) maximization problem in quantum networks, with a particular focus on secure quantum key distribution (QKD). We propose the Quantum Utility-Cost Optimization (QUCO) algorithm to address the challenge of balancing network utility with operational and infrastructure costs. The utility of the network is measured based on the secret key fraction, a key performance metric in QKD, while the costs considered include operational and maintenance expenses, transceiver deployment, auxiliary equipment, and wavelength channel usage. The QUCO algorithm transforms the original non-convex problem into a convex optimization through a series of transformations, enabling efficient resource allocation across quantum network routes. Our simulation results, conducted on a realistic quantum network topology, demonstrate the algorithm’s ability to improve the utility-cost ratio while maintaining high network performance. The QUCO algorithm strikes a balance between minimizing costs and maintaining high end-to-end entanglement fidelity, which is effective for optimizing UCR in quantum networks.
The core functions of centralized payment systems continued to function substantially even after financial transactions were converted from paper to digital records. Nonetheless, the Indian banking sector is currently...
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Graph classification is a challenging problem owing to the difficulty in quantifying the similarity between graphs or representing graphs as vectors, though there have been a few methods using graph kernels or graph n...
ISBN:
(纸本)9798331314385
Graph classification is a challenging problem owing to the difficulty in quantifying the similarity between graphs or representing graphs as vectors, though there have been a few methods using graph kernels or graph neural networks (GNNs). Graph kernels often suffer from computational costs and manual feature engineering, while GNNs commonly utilize global pooling operations, risking the loss of structural or semantic information. This work introduces Graph Reference Distribution Learning (GRDL), an efficient and accurate graph classification method. GRDL treats each graph's latent node embeddings given by GNN layers as a discrete distribution, enabling direct classification without global pooling, based on maximum mean discrepancy to adaptively learned reference distributions. To fully understand this new model (the existing theories do not apply) and guide its configuration (e.g., network architecture, references' sizes, number, and regularization) for practical use, we derive generalization error bounds for GRDL and verify them numerically. More importantly, our theoretical and numerical results both show that GRDL has a stronger generalization ability than GNNs with global pooling operations. Experiments on moderate-scale and large-scale graph datasets show the superiority of GRDL over the state-of-the-art, emphasizing its remarkable efficiency, being at least 10 times faster than leading competitors in both training and inference stages. The source code of GRDL is available at https://***/jicongfan/GRDL-Graph-Classification.
With the continuous development of artificial intelligence technology, the application of traditional manipulator becomes more and more intelligent. One of the key intelligent improvements is to enable the manipulator...
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Non-Factoid (NF) Question Answering (QA) is challenging to evaluate due to diverse potential answers and no objective criterion. The commonly used automatic evaluation metrics like ROUGE or BERTScore cannot accurately...
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We study the problem of variance estimation in general graph-structured problems. First, we develop a linear time estimator for the homoscedastic case that can consistently estimate the variance in general graphs. We ...
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We study the problem of variance estimation in general graph-structured problems. First, we develop a linear time estimator for the homoscedastic case that can consistently estimate the variance in general graphs. We show that our estimator attains minimax rates for the chain and 2D grid graphs when the mean signal has total variation with canonical scaling. Furthermore, we provide general upper bounds on the mean squared error performance of the fused lasso estimator in general graphs under a moment condition and a bound on the tail behavior of the errors. These upper bounds allow us to generalize for broader classes of distributions, such as sub-Exponential, many existing results on the fused lasso that are only known to hold with the assumption that errors are sub-Gaussian random variables. Exploiting our upper bounds, we then study a simple total variation regularization estimator for estimating the signal of variances in the heteroscedastic case. We also provide lower bounds showing that our heteroscedastic variance estimator attains minimax rates for estimating signals of bounded variation in grid graphs, and K-nearest neighbor graphs, and the estimator is consistent for estimating the variances in any connected graph.
Social media became a primary means for individuals to share information and participate in social discourse in modern society. Analyzing tweets on Twitter provided a deep understanding of public opinion and reflected...
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ISBN:
(数字)9798331521219
ISBN:
(纸本)9798331521226
Social media became a primary means for individuals to share information and participate in social discourse in modern society. Analyzing tweets on Twitter provided a deep understanding of public opinion and reflected these insights in various policies. However, research on Japanese tweets related to the Ukraine-Russia war did not progress due to the lack of appropriate datasets and high API costs. In Japan, where pacifism is strong, exploring users' perceptions of this war provided crucial insights for future security policies. This study constructed a Japanese tweet dataset through meticulous preprocessing of a large multilingual tweet dataset from Kaggle. Multiple NLP techniques, including time-series analysis, co-occurrence analysis, LDA-based topic modeling, and BERT-based sentiment analysis, were applied to comprehensively analyze the war-related tweets. The analysis revealed that Japanese users showed a strong interest in the war, particularly concerning attacks on nuclear power plants and China's movements. Concerns about territorial issues and support for Ukraine indicated a recognition of national defense importance and potential involvement in an anti-Russia coalition. These insights provided critical perspectives for Japan's security policy planning. The need for stronger disinformation measures, attention to geopolitical risks, and emergency preparedness was highlighted. This study was one of the few data-driven studies that elucidated Japanese users' war perceptions. Future research should use broader data sources and advanced analytical methods to accurately capture national security trends from social media user interests.
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