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The PetDetSim environment is designed to use on a computer cluster and in a grid computing environment in order to reduce the computation time of the simulation.
Since the mobile agents can overlap with each other their random walk steps could be computed in parallel in order to reduce the computation time (either on a multicore CPU or GPU [ 13, 47]).
A reduced mechanism (36 species and 198 reactions) is developed from GRI 3.0 in order to reduce the computation time.
Subsequently, in order to reduce the computation burden of dual network algorithm, we develop a single network one.
A reduction of the number of state variables was done in order to reduce the computation time.
A 3D Finite Element (FE) model is used for the ASRM and only 2D FE model for the radial one in order to reduce the computation time.
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In order to reduce the computations in transform and quantization, a novel prediction algorithm is designed to early detect all-zero 4×4 blocks prior to transform and quantization.
Second, in order to reduce the total computation cost for the face detection, we organize the neural network ensembles in a pruning cascade.
In order to reduce the memory and computation load of the optimal design, two suboptimal estimators are provided as well.
In [10, 11], a down-sampling approach is applied on the CU in order to reduce the prediction related computation.
Locality sensitive hashing method is combined with the kNN method in order to reduce the number of computations.
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