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As described in Methods, the activity profile of the neuron (spike count in sampling window; Fig. 5 A) was plotted in all 11 canonical representations and then fitted using different Gaussian kernels ranging from 2° to 15° bandwidths, and the quality of fit in each representation (at each kernel bandwidth) was quantified using PRESS residuals (Fig. 5 B).

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If you want it eat the popcorn slow, then put the butter in a little bowl or a small cup and dip each kernel at a time.

The time complexity is at most if we suppose each kernel evaluation is [46].

The concentrations across each kernel were predicted at 5 m intervals and within 225 m of each source, using a constant road emissions rate of 1 g km−1 s−1.

Kernel density estimators do not bin data and have no end points but centre a kernel function at each data point and smooth kernel functions will generate smooth density estimates [1].

The main idea of bolstering is to put a kernel at each of the sample point, called "bolstering kernel" to smooth the variance of counting-based estimation methods (in this paper, we adopt Gaussian bolstering kernels).

Once the fits were made to activity profile distributed in different models, the quality of the fit in each model (at all kernel bandwidths) was quantified using PRESS statistics, which is a form of cross-validation (Keith et al. 2009; DeSouza et al. 2011).

Minimizing electrostatic energy through charge equilibration requires the solution of a large sparse linear system with a shielded electrostatic kernel at each sub-femtosecond long time-step.

As the samples in high-dimensional space are relatively sparse, if the width is too small, it may lead to establish the corresponding Gaussian kernel at each sample, and the proposed incremental learning algorithm is invalid.

The central axis divides the boxplot (at left) and kernel density estimator (at right).

The tome has attracted the mirth and curiosity of the French media, who wonder if it's simply the literary imaginings of an old man with a glint in his eye or hints at a kernel of truth.

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