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Tensor-based modeling and computation emerge prominently with urgent demands from practical applications in the big data era. With the intrinsic sparsity in real data sets and the dimensionality reduc...
Conventional inferential methods for (deep) Gaussian Processes models can suffer from high computational complexity as they require large-scale operations with kernel matrices for training and inferen...
The brain’s cerebral cortex produces perception based on the sensory information it’s fed through a region called the thalamus.
A cut of a hypergraph is a partition of its vertex set into two parts, and the size of the cut is the number of edges which have nonempty intersection with each of the two parts. A classical result of...
This talk considers the distributed sparse identification problem over wireless sensor networks such that all sensors cooperatively estimate the unknown sparse parameter vector of stochastic dynamic s...
Quantitative analysis of hydrated minerals from hyperspectral remote sensing data is fundamental for understanding Martian geologic process. Because of the difficulties for selecting endmembers from h...
Graphical models are popular statistical tools which are used to represent dependent or causal complex systems. Statistically equivalent causal or directed graphical models are said to belong to a Mar...
Sparse constraints on signal decompositions are justified bytypical sensor data used in a variety of signal processing fields such as acoustics, medical imaging, or wireless, but moreover can lead to ...
In this paper we present a new source separation method based on dynamic sparse source signal models. Source signals are modeled in frequency domain as a product of a Bernoulli selection variable with...
In this paper we present a new source separation method based on dynamic sparse source signal models. Source signals are modeled in frequency domain as a product of a Bernoulli selection variablewith ...
In this paper I present a Minimum Description Length Estimator for number of sources in an anechoic mixture of sparse signals.The criterion is roughly equal to the sum of negative normalized maximum l...
In this paper we present an Information Theoretic Estimator for the number of sources mutuallydisjoint in a linear mixing model. The approach follows the Minimum Description Length prescription and is...
Consider a large scale sensor array having N sensors that monitors a surveillance area. Using all sensors simultaneously may be unreasonable in terms of power consumption and data processing.For examp...
The purpose of this note is to prove, for real frames, that signal reconstruction from the absolute value ofthe frame coefficients is equivalent to solution of a sparse signal optimization problem, na...
This study discusses the scope of historical earthquake analysis in low-seismicity regions. Examples of non-damaging earthquake reports are given from the Eastern Baltic (Fennoscandian) Shield in nort...

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