Let n≥2 be an integer. We give necessary and sufficient conditions for an integral quadratic form over dyadic local fields to be n-universal by using invariants from Beli's theory of bases of norm ***, we provide...
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Let n≥2 be an integer. We give necessary and sufficient conditions for an integral quadratic form over dyadic local fields to be n-universal by using invariants from Beli's theory of bases of norm ***, we provide a minimal set for testing n-universal quadratic forms over dyadic local fields, as an analogue of Bhargava and Hanke's 290-theorem(or Conway and Schneeberger's 15-theorem) on universal quadratic forms with integer coefficients.
Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have sh...
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Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have shown promising performance for representation learning on graphs, which train models by maximizing agreement between original graphs and their augmented views(i.e., positive views). Unfortunately, these methods usually involve pre-defined augmentation strategies based on the knowledge of human experts. Moreover, these strategies may fail to generate challenging positive views to provide sufficient supervision signals. In this paper, we present a novel approach named graph pooling contrast(GPS) to address these *** by the fact that graph pooling can adaptively coarsen the graph with the removal of redundancy, we rethink graph pooling and leverage it to automatically generate multi-scale positive views with varying emphasis on providing challenging positives and preserving semantics, i.e., strongly-augmented view and weakly-augmented view. Then, we incorporate both views into a joint contrastive learning framework with similarity learning and consistency learning, where our pooling module is adversarially trained with respect to the encoder for adversarial robustness. Experiments on twelve datasets on both graph classification and transfer learning tasks verify the superiority of the proposed method over its counterparts.
In this work,we propose a comprehensive theoretical framework for the multilevel NAND(NOT AND logic)flash memory,built upon the modified Student’s t distribution where the distortion of the threshold voltage caused b...
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In this work,we propose a comprehensive theoretical framework for the multilevel NAND(NOT AND logic)flash memory,built upon the modified Student’s t distribution where the distortion of the threshold voltage caused by the random telegraph noise,cell-to-cell interference and data retention noise are jointly *** on the superposition modulation,we build a non-orthogonal multiuser communication model where a linear mapping is conducted between the verify voltages and binary antipodal *** at improving the storage efficiency,we propose an unequal amplitude mapping(UAM)solution by optimizing the weighting coefficients of verify voltages to intelligently adjust the width of each ***,the uniform storage efficiency region and sum storage efficiency of different labelings with various decoding schemes are *** results validate the effectiveness of our proposed UAM solution where an up to 20.9%storage efficiency gain can be achieved compared to the current used benchmark *** addition,analytical and simulation results also demonstrate that the successive cancellation decoding outperforms other decoding schemes for all labelings.
Urban road network form and commercial layout play an important role in urban development, the study of the relationship between the two can provide support for urban commercial functional area layout, road planning a...
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With the recent advances in the field of deep learning, an increasing number of deep neural networks have been applied to business process prediction tasks, remaining time prediction, to obtain more accurate predictiv...
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With the recent advances in the field of deep learning, an increasing number of deep neural networks have been applied to business process prediction tasks, remaining time prediction, to obtain more accurate predictive results. However, existing time prediction methods based on deep learning have poor interpretability, an explainable business process remaining time prediction method is proposed using reachability graph,which consists of prediction model construction and visualization. For prediction models, a Petri net is mined and the reachability graph is constructed to obtain the transition occurrence vector. Then, prefixes and corresponding suffixes are generated to cluster into different transition partitions according to transition occurrence vector. Next,the bidirectional recurrent neural network with attention is applied to each transition partition to encode the prefixes, and the deep transfer learning between different transition partitions is performed. For the visualization of prediction models, the evaluation values are added to the sub-processes of a Petri net to realize the visualization of the prediction models. Finally, the proposed method is validated by publicly available event logs.
In recent years, great success has been achieved in many tasks of natural language processing (NLP), e.g., named entity recognition (NER), especially in the high-resource language, i.e., English, thanks in part to the...
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ExpertRecommendation(ER)aims to identify domain experts with high expertise and willingness to provide answers to questions in Community Question Answering(CQA)web *** to model questions and users in the heterogeneous...
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ExpertRecommendation(ER)aims to identify domain experts with high expertise and willingness to provide answers to questions in Community Question Answering(CQA)web *** to model questions and users in the heterogeneous content network is critical to this *** traditional methods focus on modeling questions and users based on the textual content left in the community while ignoring the structural properties of heterogeneous CQA networks and always suffering from textual data sparsity *** approaches take advantage of structural proximities between nodes and attempt to fuse the textual content of nodes for ***,they often fail to distinguish the nodes’personalized preferences and only consider the textual content of a part of the nodes in network embedding learning,while ignoring the semantic relevance of *** this paper,we propose a novel framework that jointly considers the structural proximity relations and textual semantic relevance to model users and questions more ***,we learn topology-based embeddings through a hierarchical attentive network learning strategy,in which the proximity information and the personalized preference of nodes are encoded and ***,we utilize the node’s textual content and the text correlation between adjacent nodes to build the content-based embedding through a meta-context-aware skip-gram *** addition,the user’s relative answer quality is incorporated to promote the ranking *** results show that our proposed framework consistently and significantly outperforms the state-of-the-art baselines on three real-world datasets by taking the deep semantic understanding and structural feature learning *** performance of the proposed work is analyzed in terms of MRR,P@K,and MAP and is proven to be more advanced than the existing methodologies.
The factors like production accuracy and completion time are the determinants of the optimal scheduling of the complex products work-flow,so the main research direction of modern work-flow technology is how to assure ...
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The factors like production accuracy and completion time are the determinants of the optimal scheduling of the complex products work-flow,so the main research direction of modern work-flow technology is how to assure the dynamic balance between the *** on the work-flow technology,restraining the completion time,and analyzing the deficiency of traditional minimum critical path algorithm,a virtual iterative reduction algorithm(VIRA)was proposed,which can improve production accuracy effectively with time *** VIRA with simplification as the core abstracts a virtual task that can predigest the process by combining the complex structures which are cyclic or parallel,finally,by using the virtual task and the other task in the process which is the iterative reduction strategy,determines a path which can make the production accuracy and completion time more balanced than the minimum critical path *** deadline,the number of tasks,and the number of cyclic structures were used as the factors affecting the performance of the algorithm,changing the influence factors can improve the performance of the algorithm effectively through the analysis of detailed ***,comparison experiments proved the feasibility of the VIRA.
This paper investigates an unmanned aerial vehicle(UAV)-assisted multi-object offloading scheme for blockchain-enabled Vehicle-to-Everything(V2X)*** to the presence of an eavesdropper(Eve),the system’s com-munication...
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This paper investigates an unmanned aerial vehicle(UAV)-assisted multi-object offloading scheme for blockchain-enabled Vehicle-to-Everything(V2X)*** to the presence of an eavesdropper(Eve),the system’s com-munication links may be *** paper proposes deploying an intelligent reflecting surface(IRS)on the UAV to enhance the communication performance of mobile vehicles,improve system flexibility,and alleviate eavesdropping on communication *** links for uploading task data from vehicles to a base station(BS)are protected by IRS-assisted physical layer security(PLS).Upon receiving task data,the computing resources provided by the edge computing servers(MEC)are allocated to vehicles for task *** blockchain-based computation offloading schemes typically focus on improving network performance,such as minimizing energy consumption or latency,while neglecting the Gas fees for computation offloading and the costs required for MEC computation,leading to an imbalance between service fees and resource *** paper uses a utility-oriented computation offloading scheme to balance costs and *** paper proposes alternating phase optimization and power optimization to optimize the energy consumption,latency,and communication secrecy rate,thereby maximizing the weighted total utility of the *** results demonstrate a notable enhancement in the weighted total system utility and resource utilization,thereby corroborating the viability of our approach for practical applications.
This study investigates an automated COVID-19 diagnosis system based on convolution networks, which aims to improve the effectiveness and accuracy of diagnosis. In a systematic analysis of 1709 chest X-rays, the resea...
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