Results 11 to 20 of about 1,409,223 (260)
Frontiers in Human Neuroscience, 2014 Entrained behavior coordinates, predicts, and modulates multi-scale rhythmic gestures with high spatio-temporal precision even as it shows flexible adaptation in response to perturbation (Clayton et al., 2005; Altenmuller et al., 2006; Phillips-Silver et al., 2010).
Eric eBarnhill, Eric eBarnhill
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Sparse Recovery Using Sparse Matrices [PDF]
Proceedings of the IEEE, 2010 In this paper, we survey algorithms for sparse recovery problems that are based on sparse random matrices. Such matrices has several attractive properties: they support algorithms with low computational complexity, and make it easy to perform incremental updates to signals.
Gilbert, Anna, Indyk, Piotr
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Deterministic sparse FFT for M-sparse vectors [PDF]
Numerical Algorithms, 2017 In this interesting paper, the authors present a new deterministic sparse (inverse) fast Fourier transform (FFT), where the resulting vector \(\mathbf x \in {\mathbb C}^N\) with \(N = 2^J\) is \(M\)-sparse, i.e., \(\mathbf x\) contains only \(M\) nonzero components. This new algorithm which generalizes the sparse FFT of \textit{G. Plonka} and \textit{K.
Plonka, Gerlind +3 more
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Sparse Sparse Bundle Adjustment [PDF]
Procedings of the British Machine Vision Conference 2010, 2010 Sparse Bundle Adjustment (SBA) is a method for simultaneously optimizing a set of camera poses and visible points. It exploits the sparse primary structure of the problem, where connections exist just between points and cameras. In this paper, we implement an efficient version of SBA for systems where the secondary structure (relations among cameras ...
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SSRN Electronic Journal, 2014 Cointegration analysis is used to estimate the long-run equilibrium relations between several time series. The coefficients of these long-run equilibrium relations are the cointegrating vectors. In this paper, we provide a sparse estimator of the cointegrating vectors.
Wilms, Ines, Croux, Christophe
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Estimation of a sparse group of sparse vectors [PDF]
, 2012 We consider a problem of estimating a sparse group of sparse normal mean vectors. The proposed approach is based on penalized likelihood estimation with complexity penalties on the number of nonzero mean vectors and the numbers of their "significant ...
Abramovich, Felix, Grinshtein, Vadim
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Sparse Temporal Disaggregation
Journal of the Royal Statistical Society Series A: Statistics in Society, 2022 AbstractTemporal disaggregation is a method commonly used in official statistics to enable high-frequency estimates of key economic indicators, such as gross domestic product (GDP). Traditionally, such methods have relied on only a couple of high-frequency indicator series to produce estimates.
Mosley, Luke +2 more
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A Novel Texture Extraction-Based Compressive Sensing for Lung Cancer Classification
Journal of Medical Signals and Sensors, 2022 Background: Lung cancer images require large memory storage and transmission bandwidth for sending the data. Compressive sensing (CS), as a method with a statistical approach in signal sampling, provides different output patterns based on information ...
Indrarini Dyah Irawati +4 more
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Non-parametric statistical thresholding for sparse magnetoencephalography source reconstructions. [PDF]
, 2012 Uncovering brain activity from magnetoencephalography (MEG) data requires solving an ill-posed inverse problem, greatly confounded by noise, interference, and correlated sources.
Nagarajan, Srikantan S +2 more
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Frontiers in Genetics, 2023 Background: With the rapid development of high-throughput sequencing technology and the explosive growth of genomic data, storing, transmitting and processing massive amounts of data has become a new challenge.
Youde Ding +10 more
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