Seismic data recovery from data with missing traces on otherwise regular acquisition grids forms a crucial step in the seismic processing flow. For instance, unsuccessful recovery leads to imaging artifacts and to erroneous predictions for the multiples, adversely affecting the performance of multiple elimination. A non-parametric transform-based recovery method is presented that exploits the c...

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 fundamental principle underlying compressed sensing is that a signal, which is sparse under some basis representation, can be recovered from a small number of linear measurements. However, prior knowledge of the sparsity basis is essential for the recovery process. This work introduces the concept of blind compressed sensing, which avoids the need to know the sparsity basis in both the samp...

Sparsity often leads to efficient and interpretable representations for data. In this paper, we introduce an architecture to infer the appropriate sparsity pattern for the word embeddings while learning the sentence composition in a deep network. The proposed approach produces competitive results in sentiment and topic classification tasks with high degree of sparsity. It is computationally che...

Compressed sensing (CS) [1, 2] demonstrates that sparse signals can be recovered from underdetermined linear measurements. The idea of iterative support detection (ISD, for short) method first proposed by Wang et. al [3] has demonstrated its superior performance for the reconstruction of the single channel sparse signals. In this paper, we extend ISD from sparsity to the more general structured...

We analyse the one-loop fermionic contribution for the scalar effective potential in the temperature dependent Yukawa model. In order to regularize the model a mix between dimensional and analytic regularization procedures is used. We find a general expression for the fermionic contribution in arbitrary spacetime dimension. It is found that in D = 3 this contribution is finite.

The Hard Thresholding Pursuit algorithm for sparse recovery is revisited using a new theoretical analysis. The main result states that all sparse vectors can be exactly recovered from compressive linear measurements in a number of iterations at most proportional to the sparsity level as soon as the measurement matrix obeys a certain restricted isometry condition. The recovery is also robust to ...

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...