To address the limitations of the FDNA approach, in this paper, we analyze the dependency of the receiver node on the feeder nodes as well as the impact of the node's operability level on the system effectiveness,...
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The continuous miniaturization of 2D electronic circuits results in increased power density during device operation, leading to heat localization and placing higher demands on their performance thresholds. The risk to...
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Dear editor,Software developers tend to reuse existing libraries to facilitate their development process and implement certain functionalities by invoking application programming interfaces(APIs) [1]. However, it rema...
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Dear editor,Software developers tend to reuse existing libraries to facilitate their development process and implement certain functionalities by invoking application programming interfaces(APIs) [1]. However, it remains a challenging task for developers to correctly use APIs [2], so they often consult API learning resources [3, 4]. As one of the most important API learning resources,
Extracting business process models from textual documents remains a significant challenge in enterprises. Traditional rule-based methods suffer from poor applicability due to customized rule sets while most machine-le...
Extracting business process models from textual documents remains a significant challenge in enterprises. Traditional rule-based methods suffer from poor applicability due to customized rule sets while most machine-learning based methods focus on simple process documents. This paper presents Text-based Process Modeling (TeProM), a novel method for extracting business process components and their relations from textual descriptions. By adopting a rule-free design, TeProM departs from traditional rule-based systems and leverages a neural network model to address complex coreference phenomena in text, thereby ensuring the accurate mapping of process components within the model. This approach applies to various types of business process documents, particularly excelling in processing complex textual structures with long-range dependencies. Compared to previous approaches, TeProM is able to effectively address the complex logical structures and coreference issues concealed in business process documents. TeProM achieved the best performance over 10 baselines in multidimensional evaluation. Additionally, evaluations on the PET and SAP-OPC datasets for relation extraction further demonstrated the effectiveness of the proposed method. An annotated dataset consisting of 91 real business process documents is also provided, which serves as a valuable resource for future research.
We investigate the dynamical relaxation behavior of the two-point correlation in extended XY models with a gapless phase after quenches from various initial states. Specifically, we study the XY chain with gapless pha...
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With the development of urbanization,the number of residents' motor vehicles has increased sharply,and traffic congestion problem has become increasingly *** construction of Intelligent Traffic System(ITS) has bec...
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With the development of urbanization,the number of residents' motor vehicles has increased sharply,and traffic congestion problem has become increasingly *** construction of Intelligent Traffic System(ITS) has become the main means to alleviate traffic ***-term traffic flow prediction has guiding significance for residents' travel planning and intelligent management of transportation,and has become one of the research hotspots in intelligent transportation ***,A short-term traffic flow prediction method based on the spatio-temporal characteristics of complex road networks is proposed to further improve the prediction accuracy and reduce the prediction ***,a graph convolutional network(GCN) capable of processing non-Euclidean data structures is used to extract the spatial characteristics of traffic flow ***,the long and short-term memory(LSTM) neural network is used to process the time ***,the two are combined to realize the effective processing of the spatio-temporal characteristics of traffic flow *** results on the real traffic flow dataset prove the feasibility and effectiveness of the proposed method,and can provide a basis for intelligent traffic control and smart city construction.
The polynomial-chaos-kriging (PC-Kriging) method has been derived as a new uncertainty propagation approach and widely used for robust design optimization in a straightforward manner, of which the statistical moments ...
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We investigate the relaxation dynamics of the fermion two-point correlation function Cmn(t) = 〈ψ(t)|c†mcn|ψ(t)〉 in the XY chain with alternating nearest-neighbor hopping interaction after a quench. We find that the ...
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As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or...
As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network’s dataflow graph and device cluster’s topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method.
With the global climate change, the frequency and destructiveness of emergencies in the coastal areas worldwide have increased in recent years. The rapid response of the existing equipment and means is insufficient. I...
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