In this paper, we study estimation and hypothesis testing for a failure time distribution function at a point in the current status model with observation times supported on a grid of potentially unknown sparsity and with multiple subjects sharing the same observation time. This is of interest since observation time ties occur frequently with current status data. The grid resolution is specifie...

The analysis of surface wave dispersion curves is a way to infer the vertical distribution shear-wave velocity. range applicability extremely wide going, for example, from seismological studies geotechnical characterizations and exploration geophysics. However, inversion severely ill-posed only limited efforts have been put into development effective regularization strategies. In particular, re...

Background First-pass perfusion CMR utilizes accelerated imaging to achieve high spatial resolution and coverage within a small acquisition window. Several compressed sensing (CS) methods have been proposed to accelerate perfusion imaging. However, patient motion due to imperfect breathholding and other factors leads to degraded quality of CS-reconstructed images. We recently demonstrated a CS ...

Compressed Sensing (CS) has been applied in dynamic Magnetic Resonance Imaging (MRI) to accelerate the data acquisition without noticeably degrading the spatial-temporal resolution. A suitable sparsity basis is one of the key components to successful CS applications. Conventionally, a multidimensional dataset in dynamic MRI is treated as a series of two-dimensional matrices, and then various ma...

Principal components analysis (PCA) is the optimal linear auto-encoder of data, and it is often used to construct features. Enforcing sparsity on the principal components can promote better generalization, while improving the interpretability of the features. We study the problem of constructing optimal sparse linear auto-encoders. Two natural questions in such a setting are: (i) Given a level ...

Hyperspectral imagery (HSI) is an important imaging modality for remote sensing applications in many fields, including oceanic and atmospheric sciences [1], agriculture [2], defense, and space exploration [3]. Despite the richer potential of HSI sensors for scientific studies and applications, engineering tradeoffs, such as memory and communication bandwidth constraints, typically favor multisp...

Abstract A basic framework for exploiting sparsity via positive semidefinite matrix completion is presented for an optimization problem with linear and nonlinear matrix inequalities. The sparsity, characterized with a chordal graph structure, can be detected in the variable matrix or in a linear or nonlinear matrix-inequality constraint of the problem. We classify the sparsity in two types, the...

This works addresses the problem of reconstructing multi-echo T2 weighted MR images from partially sampled K-space data. Previous studies in reconstructing MR images from partial samples of the K-space used Compressed Sensing (CS) techniques to exploit the spatial correlation of the images (leading to sparsity in transform domain). Such techniques can be employed to reconstruct the individual T...