Prior work using a matching task between images that were complementary in spatial frequency and orientation information suggested that the representation of faces, but not objects, retains low-level spatial frequency (SF) information [Biederman, I., & Kalocsai, P. (1997). Neurocomputational bases of object and face recognition. Philosophical Transactions of the Royal Society of London, Series ...

Phylogenetic networks have gained prominence over the years due to their ability to represent complex non-treelike evolutionary events such as recombination or hybridization. Popular combinatorial objects used to construct them are triplet systems and cluster systems, the motivation being that any network N induces a triplet system [Formula: see text] and a softwired cluster system [Formula: se...

In the context of compressed sensing (CS), both Subspace Pursuit (SP) and Compressive Sampling Matching Pursuit (CoSaMP) are very important iterative greedy recovery algorithms which could reduce the recovery complexity greatly comparing with the well-known l1-minimization. Restricted isometry property (RIP) and restricted isometry constant (RIC) of measurement matrices which ensure the converg...

Orthogonal Matching Pursuit (OMP) is a simple, yet empirically competitive algorithm for sparse recovery. Recent developments have shown that OMP guarantees exact recovery of K-sparse signals with K or more than K iterations if the observation matrix satisfies the restricted isometry property (RIP) with some conditions. We develop RIP-based online guarantees for recovery of a K-sparse signal wi...