Graph neural networksTemporal networksNetwork analysisBlockchain-dataIn Web3 social platforms, i.e. social web applications that rely on blockchain technology to support their functionalities, interactions among users are usually multimodal, from common social interactions such as following, liking, or ...
Discrete-time canonical forms cation graph topology. The ADHDP structure for sin- for the Hamiltonian functions are found in [7–9]. The co- gle agent [2] is extended for the case of the dynamic operative control problems involve the consensus con- graphical games and it is used to solve...
Machine learning is not exactly a new concept in physics. In particular, the connection between artificial neural networks and dynamical systems has been noticed for decades1,2,3,4,5,6,7,8. What is the new contribution brought by the present study? Most current applications of machine learning...
Bursting is a diverse and common phenomenon in neuronal activation patterns and it indicates that fast action voltage spiking periods are followed by resting periods. The interspike interval (ISI) is the time between successive action voltage spikes of n
AI-enhanced power quality management in distribution systems: implementing a dual-phase UPQC control with adaptive neural networks and optimized PI controllers Arvind R. Singh Masoud Dashtdar Stanislav Misak Artificial Intelligence Review (2024) Deep learning and feature fusion-based lung sound recogni...
0 征稿 Aims and scope The research on discrete event dynamic systems (DEDSs) is multi-disciplinary in nature and its development has been dynamic. Examples of DEDSs include manufacturing plants, communication networks, computer systems, management information databases, logistics systems, command-control...
Codes for CIKM'24 paper: DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning - chenxi1228/DTFormer
Pierreval (2010) adopted a trained neural networks (NN) to dynamically determine the dispatching rules in real-time flexible manufacturing systems. Some other techniques or approaches, such as shifting bottleneck (Wenqi and Aihua2004), branch and bound (Della Croce et al.2002; Artigues and Feil...
Trend motif: a graph mining approach for analysis of dynamic complex networks. In Seventh IEEE International Conference on Data Mining (ICDM 2007) 541–546 (IEEE, 2007). Bezsudnov, I. & Snarskii, A. From the time series to the complex networks: the parametric natural visibility graph. ...
Neural superstatistics for Bayesian estimation of dynamic cognitive models Article Open access 23 August 2023 Introducing a new approach for modeling a given time series based on attributing any random variation to a jump event: jump-jump modeling Article Open access 12 January 2024 Introduction...