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Results produced by our algorithm are demonstrated using a variety of examples.

The results produced by our algorithm are almost the same as Rules in most of the cases.

Extensive tests verify that the states generated by our algorithm are stable states and show the exponential storage capacity of a HNN.

We show that the approximate solutions obtained by our algorithm are better than those produced by other spline and domain decomposition methods.

The results obtained by our algorithm are compared with five other state-of the-art algorithmstate-of the-artratestate-of the-artithm outperforms those stalgorithms-art algorithms in terms of accuracy and robustness.

The top feature combinations selected by our algorithm are shown in the last column, in ranked order from more important to less as ''Region:feature type" pairs, where the region and feature types are listed using the code letters defined above.

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The experimental results show that the predictive accuracy obtained by our algorithm is statistically higher than that of the compared targets.

We finally demonstrate that the set of potential hexes H built by our algorithm is significantly larger than those built using predefined patterns of subdivision of a hexahedron in tetrahedral elements.

We finally demonstrate that the set of potential hexes built by our algorithm is significantly larger than those built using predefined patterns of subdivision of a hexahedron in tetrahedral elements.

Similarly, the improvement of MSSIM obtained by our algorithm is the largest among all the algorithms.

These results mean that the time per output by our algorithm is close to that by 2-Phase algorithm.

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