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Therefore, with the common hidden variables and the trained weight matrices we are able to build an auto-encoder machine to reconstruct the input dynamic networks.
By using effective canonical labeling and adopting weighted adjacency matrices, we are able to perform graph isomorphism test in polynomial running time.
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With these matrices we were finally able to calculate the real position of the objects in the arena from the pixel coordinates derived from the recordings.
We demonstrated in this study that, using the PCA subspace based on typical existing matrices, we were able to obtain a sensitive novel matrix, MIQS, empirically.
Because the same scale for intensity was used in both matrices, we were able to sum the exposures to these agents across the full job history for each individual.
Using these matrices, we were able to find orthologs for rhomboid proteases of P. berghei, P. falciparum & P. vivax and large subunit of U2 snRNP auxiliary factor of Cryptosporidium parvum in Arabidopsis thaliana.
Instead of using separate estimations for each matrix, we are interested in a joint estimation approach.
By the mean of this decomposition of the proposed matrix, we are able to get a fast and efficient algorithm.
By defining a word-pattern matrix, we are able to create a new isomorphism check which is much faster than existing checks for certain situations.
By finding a feature weight matrix, we are allowed to map the original data into a low dimensional space based on the pseudo labels.
Here we leverage both approaches and, due to the large size of our initial data matrix, we are able to minimize the impact of various sources of non-phylogenetic signal while retaining a large number of characters for analysis.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com