Soil erosion is a global issue that threatens food security and causes environmental degradation. Management of water erosion requires accurate estimates of the spatial and temporal variations in the erosive power of rainfall (erosivity). Rainfall erosivity can be estimated from rain gauge stations and satellites. However, the time series rainfall data that has a high temporal resolution are of...

In the problem of one-bit compressed sensing, the goal is to find an ǫ-close estimation of a ksparse vector x ∈ R given the signs of the entries of y = Φx, where Φ is called the measurement matrix. Similarly, in the problem of compressed sensing from phaseless measurements, the goal is to estimate a k-sparse vector x ∈ C given only the magnitudes of the measurements y = |Φx|. For the one-bit co...

We introduce a framework for sparsity structures defined via graphs. Our approach is flexible and generalizes several previously studied sparsity models. Moreover, we provide efficient projection algorithms for our sparsity model that run in nearly-linear time. In the context of sparse recovery, our framework achieves an informationtheoretically optimal sample complexity for a wide range of par...

The central idea of compressed sensing is to exploit the fact that most signals of interest are sparse in some domain and use this to reduce the number of measurements to encode. However, if the sparsity of the input signal is not precisely known, but known to lie within a specified range, compressed sensing as such cannot exploit this fact and would need to use the same number of measurements ...

Modality translation, which aims to translate images from a source modality target one, has attracted growing interest in the field of remote sensing recently. Compared translation problems multimedia applications, often suffers inherent ambiguities, i.e., single input image could correspond multiple possible outputs, and results may not be valid following interpretation tasks, such as classifi...