Stochastic Model Predictive Control (SMPC)
Model predictive control (MPC) has demonstrated exceptional success for the high-performance control of complex systems. The conceptual simplicity of MPC as well as its ability to effectively cope with the complex dynamics of systems with multiple inputs and outputs, input and state/output constraints, and conflicting control objectives have made it an attractive multivariable constrained control approach. This article gives an overview of the main developments in the area of stochastic model predictive control (SMPC) in the past decade and provides the reader with an impression of the different SMPC algorithms and the key theoretical challenges in stochastic predictive control without undue mathematical complexity. The general formulation of a stochastic OCP is first presented, followed by an overview of SMPC approaches for linear and nonlinear systems. Suggestions of some avenues for future research in this rapidly evolving field concludes the article. …

Jazz
Jazz is a lightweight modular data processing framework, including a web server. It provides data persistence and computation capabilities accessible from R and Python and also through a REST API. …

Video Ladder Network (VLN)
We present the Video Ladder Network (VLN) for video prediction. VLN is a neural encoder-decoder model augmented by both recurrent and feedforward lateral connections at all layers. The model achieves competitive results on the Moving MNIST dataset while having very simple structure and providing fast inference. …

Low-Rank Principal Eigenmatrix Analysis
Sparse PCA is a widely used technique for high-dimensional data analysis. In this paper, we propose a new method called low-rank principal eigenmatrix analysis. Different from sparse PCA, the dominant eigenvectors are allowed to be dense but are assumed to have a low-rank structure when matricized appropriately. Such a structure arises naturally in several practical cases: Indeed the top eigenvector of a circulant matrix, when matricized appropriately is a rank-1 matrix. We propose a matricized rank-truncated power method that could be efficiently implemented and establish its computational and statistical properties. Extensive experiments on several synthetic data sets demonstrate the competitive empirical performance of our method. …