Learn to choose coordinate systems, visualize multivariable functions, and parameterize curves. You can use these live scripts as demonstrations in lectures, class activities, or interactive ...

3 Interpolation on the tensor product grid In this section, we study an interpolation operator for multivariable functions on tensor product grids. Our approach continues the investigations in Prestin ...

Abstract: Multivariate time series anomaly detection (MTSAD) plays a crucial role in the Internet of Things (IoT) to identify device malfunction or system attacks. Graph neural networks (GNN) are ...

Abstract: Multivariate time series forecasting has extensive applications in urban computing, such as financial analysis, weather prediction, and traffic forecasting. Using graph structures to model ...

mvsp is a Python implementation of the protocols presented in Quantum state preparation for multivariate functions. The protocols are based on function approximations with finite Fourier or Chebyshev ...

Recent advances in estimation techniques have underscored the growing importance of shrinkage estimation and balanced loss functions in the analysis of multivariate normal distributions. These ...

For the preparation of high-dimensional functions on quantum computers, we introduce tensor network algorithms that are efficient with regard to dimensionality, optimize circuits composed of ...

While graphs for cosine and sine functions are similar, those for tangent functions differ significantly from them, sharing only in that they show periodicity and symmetry. Recall \(\tan(x)= ...