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By the same computations, one gets (2.4) and (2.5).
To provide a reference to the found results, the same computations are repeated with historical population and road network changes.
Moreover, the same computations were repeated on sliding windows of variable length (1-hour, 4-hour and 1 day), producing second-order features that capture temporal relationships.
Using the same computations than in the proof of Proposition 3.1 and using the bound (33) on R, we obtain Theorem 2.3.
It then follows that p ∈ T p. Setting x ∗ = p and following the same computations as in the proof of Theorem 3.1, we have from inequality (3.2) that lim ∥ x n − p ∥ exists.
Then, the same computations as in the Euclidean case allow to prove an (H^1) bound on the curves (x^tau ) (i.e. an (L^2) bound on the metric derivatives (|(x^tau )'|)) and prove equicontinuity.
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Each of these calculations on haplotype data have the same computational complexity as the same computation on genotype data.
This suggests that the biophysical oscillatory behavior of neurons and synapses leads to selecting the corresponding frequency of the inputs and performing the same computation as for the Hebbian linear case of the previous section: computing the correlation of the (filtered) inputs.
The same computation carried out for 3 × 1019 atoms would show that one would have to wait interminably (i.e., enormously longer than the age of the universe) for the number on one side to fluctuate even by as little as one part per million.
We repeat the same computation as in Figure 5.
They claimed that the proposed protocols had the same computation complexity as Yeh's protocol.
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