For tracking a moving target, we mathematically model the task as searching a location sequence with the most likelihood, in which we first augment the probabilistic estimation learned in localization to construct the Emission Matrix and propose two human mobility models to approximate the Transmission Matrix in the Hidden Markov Model.
In the proposed approach, the original plant is first augmented into a new descriptor system, where the state vector, disturbance vector and fault vector are assembled into the state vector of the new system.
We do this by exploring and comparing two approaches in parallel: first, augmenting the input with multi-resolution features and therefore dealing with the locality outside the model, and second, exploiting the locality with a model that integrates that concept, all within the context of deep learning.
Roth says that, like Jeong, he explores the "poetics in everyday life" and his artwork in the show features a genuine Persian rug planted with grass seeds which grow through and encroach on the carpet at first augmenting it then, eventually, ruining it.
Second, we augmented the statistical power of the variable event timing by sampling the striatum once per second instead of every two seconds.
Second, we augmented their method to include estimates of a shrinkage factor.
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Second, augmenting a taxon-rich data set (data set C; 57 taxa × 10 genes) with additional sequence data for approximately half the taxa substantially increased deep node support in a resource-efficient manner, apparently without inducing phylogenetic artifacts due to large blocks of missing data.
In the third step, we estimate augmented regression models like (6) by OLS, including as additional regressors reduced form residuals from the first step and bias correction terms from the second step.
