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In this paper, we present a novel multimodal sparse dictionary learning algorithm based on a hierarchical sparse Bayesian framework. The framework allows for enforcing joint sparsity across dictionaries without restricting the actual entries to be equal. We show that the proposed method is able to learn dictionaries of higher quality than existing approaches. We validate our claims with extensive...
In this paper, we present a novel Bayesian approach to recover simultaneously block sparse signals in the presence of outliers. The key advantage of our proposed method is the ability to handle non-stationary outliers, i.e. outliers which have time varying support. We validate our approach with empirical results showing the superiority of the proposed method over competing approaches in synthetic...
We investigated the electronic structure of crystalline coronene within the framework of the dispersion corrected density‐functional theory (DFT‐D). We use the DFT‐D2 and DFT‐D3 methods. Utilization of the DFT‐D3 leads to a correct account of the dispersion forces, which results in a good agreement of the computed lattice parameters and cohesive energy. In the framework of DFT‐D3 the cohesive energy...
This paper discusses a paradigm shift in power delivery which enables low supply voltages for digital circuits — methods for connecting the voltage domains in series to enhance system efficiency and performance. Multiple, independent voltage levels enabled by this approach can reduce power consumption dramatically in microprocessor applications. Since voltage regulators only process a fraction of...
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