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In our method, we built a new fingerprint filtering method.
Based on the proposed method, we built a predictor, called MycoMemSVM, which is freely available at http://lin.uestc.edu.cn/server/MycoMemSVM.uestc.edu.cn/server/MycoMemSVM
In order to test our method we built a specially crafted setup that we used to perform an exhaustive set of experiments.
Based on this method, we built a full parallel architecture that is efficient in memory usage as well as equivalent to the original belief propagation (BP) method in terms of accuracy.
To evaluate for our normalization method, we built a tri-gram model that had a volume of about 81 MB and the number of tri-grams was around 3.75 million.
To evaluate the NER method, we built a training set of more than 40,000 named entities and a testing set of 3186 named entities to evaluate our system.
Similar(51)
To maintain scalability of the proposed method, we build an abstract model of the apps that represent only potential leaks.
For each sampling method, we build 100 different instances of the surrogate contact network, and perform 100 simulations on each surrogate network.
In support of the barrier Lyapunov method, we build an adaptive neural network controller based on state feedback and output feedback methods.
To quantitatively test our method, we build three kinds of formation models with different porosities: a sandstone model, a mixed model of sandstone and limestone, and mixed model of sandstone, limestone, and ferric oxide.
In the case of the CCA method, we build several PCA clusters and then join them in the following way: we set one of the clusters in the origin, and we put another one far away from the first (in such a way that they do not overlap), in a random direction.
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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