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The problems were resolved with sparse matrices and the linear programming algorithm.
For simplicity (in this appendix only), we will restrict attention to finite-length inputs (to avoid infinite matrices), and the output signal will also be length.
The two methodologies were successfully applied to 71 samples including three different matrices and the quantitative results were compared.
The uncertainties of the systems include the same multiplicative noises in state and measurement matrices, and the uncertain noise variances.
We also provide the optimal compression matrices and the optimal linear unbiased estimator when these conditions are satisfied.
Symmetry in the connection weight matrices and the boundedness of the activation functions are not required in this paper.
The properties of the matrices and the transformation are studied, and the discussion is then extended to input-output systems.
The method is based on the slope concept between two circulant permutation matrices and the concept of slope matrices.
Symmetry in the connection weight matrices and the boundedness of the activation functions are abandoned in this paper.
Here, the coupling matrices and the controller node sets change with time, induced by a continuous-time Markov chain.
We constructed neighbor-joining trees (Figure 5a and Supplementary Figure S20) from distance matrices, and the Native American diverged after the divergence of the Sanganji Jomon from the modern East Eurasians in these trees.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com