Multi-node wind speed forecasting is greatly important for offshore power. It a challenging task due to unknown complex spatial dependencies. Recently, graph neural networks (GNN) have been applied because of their capability in modeling However, existing methods usually require pre-defined structure, which not optimal the downstream and limits application scope GNN. In this paper, we propose a...

The relationship between the number of hidden nodes in a neural network, the complexity of a multiclass discrimination problem, and the number of samples needed for effect learning are discussed. Bounds for the number of samples needed for effect learning are given. It is shown that Omega(min (d,n) M) boundary samples are required for successful classification of M clusters of samples using a t...

The Casimir effect is a quantum phenomenon induced by the zero-point energy of relativistic fields confined in finite-size system. This for photon has been studied long time, while realization counterparts fermion Dirac/Weyl semimetals an open question. We theoretically demonstrate typical properties electron and show results from effective Hamiltonian realistic materials such as Cd$_3$As$_2$ N...

We examine methods for clustering in high dimensions. In the first part of the paper, we perform an experimental comparison between three batch clustering algorithms: the Expectation–Maximization (EM) algorithm, a “winner take all” version of the EM algorithm reminiscent of the K-means algorithm, and model-based hierarchical agglomerative clustering. We learn naive-Bayes models with a hidden ro...