Abstract Sparse convex clustering is to group observations and conduct variable selection simultaneously in the framework of clustering. Although a weighted $$L_1$$ L 1 norm usually employed for regularization term sparse clustering, its use increases dependence on data...

L Abstract—This paper presents an Iterative Learning Controller (ILC) design for Self-Servowriting (SSW) process in Hard Disk Drives (HDDs). In SSW, the position and timing information are written onto the disk surface by referring to the previously written servo information. This process is repeated until the whole disk is completely written. The main issue in this process is Radial Error Prop...

Problems in signal processing and medical imaging often lead to calculating sparse solutions to under-determined linear systems. Methodologies for solving this problem are presented as background to the method used in this work where the problem is reformulated as an unconstrained convex optimization problem. The least squares approach is modified by an l1-regularization term. A sparse solution...

Quantum coherence has wide-ranging applications from quantum thermodynamics to metrology, channel discrimination and even biology. Thus, detecting quantifying are two fundamental problems in resource theory. Here, we introduce feasible methods detect estimate the by constructing witnesses for any finite-dimensional states. Our coherent states testing whether expectation value of witness is nega...

In this paper, we investigate the L1 geodesic farthest neighbors in a simple polygon P , and address several fundamental problems related to farthest neighbors. Given a subset S ⊆ P , an L1 geodesic farthest neighbor of p ∈ P from S is one that maximizes the length of L1 shortest path from p in P . Our list of problems include: computing the diameter, radius, center, farthestneighbor Voronoi di...

Inspired by classic generative adversarial networks (GAN), we propose a novel end-to-end adversarial neural network, called SegAN, for the task of medical image segmentation. Since image segmentation requires dense, pixel-level labeling, the single scalar real/fake output of a classic GAN’s discriminator may be ineffective in producing stable and sufficient gradient feedback to the networks. In...

We survey connections between the theory of bi-Lipschitz embeddings and the Sparsest Cut Problem in combinatorial optimization. The story of the Sparsest Cut Problem is a striking example of the deep interplay between analysis, geometry, and probability on the one hand, and computational issues in discrete mathematics on the other. We explain how the key ideas evolved over the past 20 years, em...