Sentence examples for furthermore arbitrary from inspiring English sources

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Furthermore, arbitrary definitions of established statistical concepts, as is the case with the new definition of confidence limits by InMed, should be firmly rejected.

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Furthermore, the arbitrary definition of coagulopathy (i.e. SLTs are prolonged by more than 1.5-fold) has been questioned.

Furthermore, the arbitrary interconnection of rattan rods into long continuous axis lines revealed knotting challenges in node areas where all three axis directions happened to have overlaps.

Furthermore, for arbitrary functions ϕ t ∈ L 2 ( B ), which are used in the formulation of the rate function, we use the notation ϕ t ⋅, N to denote the projection onto the first N Galerkin coefficients.

Analogous notation is used for functions on the set D T. Furthermore, for arbitrary ( 0, x ″ ) ∈ M ∩ U and t ∈ [ 0, T ], we denote by A ( x ″, t ; λ ) the operator pencil (5) and by λ j + ( x ″, t ) its eigenvalues: ⋯ ≤ λ 2 − < λ 1 − < 2 − m ≤ 0 < λ 1 + ≤ λ 2 + < ⋯  .

Furthermore, the arbitrary odorant vanilla was poorly reactogenic as compared to the AG odor, ruling out that possibility that responses to AG secretion were caused by any odorant or by the effect of stimulus novelty.

Furthermore, for achieving arbitrary ratio enlargement, the EDI process generally becomes more complicated since edge direction estimation and interpolation are performed within an asymmetric interpolation lattice.

Extensive empirical evaluations of Meta-AQUA show that it performs significantly better in a deliberative, planful mode than in a reflexive mode in which learning goals are ablated and, furthermore, that the arbitrary ordering of learning algorithms can lead to worse performance than no learning at all.

Furthermore, theories of transforming arbitrary information granule into necessary, sufficient, sufficient and necessary information granules are addressed carefully.

Furthermore, efficient modelling of arbitrary crack growth by use of XFEM (extended finite element method) requires accurate knowledge of the asymptotic crack tip fields.

Furthermore, GRNN approximates any arbitrary function between input and target vectors; fast training and convergence to the optimal regression surface as the training data becomes very large (Specht 1991).

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