Due to the lack of powerful model description methods, the identification of Hammerstein systems based on the non-uniform input-output dataset remains a challenging problem. This paper introduces a time-varying backward shift operator to describe periodically non-uniformly sampled-data Hammerstein systems, which can simplify the structure of the lifted models using the traditional lifting techn...

This paper presents a new randomized approach to high-dimensional low rank (LR) plus sparse matrix decomposition. For a data matrix D ∈ RN1×N2 , the complexity of conventional decomposition methods is O(N1N2r), which limits their usefulness in big data settings (r is the rank of the LR component). In addition, the existing randomized approaches rely for the most part on uniform random sampling,...

Abstract In this note, we study the mean length of longest increasing subsequence a uniformly sampled involution that avoids pattern 3412 and another pattern.