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The estimator at any point in time is obtained by multiplying a sequence of conditional survival probabilities, with the estimate being unchanged between subsequent event times.
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The original time-series voice data s is windowed by multiplying a windowing sequence w(n) via a hamming method, such that x(n) = s(n) ⊗ w(n) where n is the window size.
Read counts from different libraries were normalized by multiplying with a proportionality constant reflecting different sequencing depths to obtain read count distributions similar to the precompetent library, which was used as the reference sample.
For fixed pseudo-sequences c i, ∀i ∈ {1, …, N T }, we only need to compute S † once, and different CIR estimates can be obtained simply by multiplying the received training sequence at each receiver with S †.
The evaluation stencil of the control points on the initial mesh is obtained, through computation, by multiplying the finite matrices in a sequence corresponding to the expansion sequence and eigendecomposition of the contractive matrix related to the period of rational numbers.
However, the probability of a given value of Δ G is obtained by multiplying the probability of each sequence times the degeneracy, that is, the number of sequences corresponding to this Δ G.
By compressing the sensed data in time, the total frame rate of a camera system is multiplied by integrating a sequence of coded images into a single frame from the camera.
The overall probability of a matreshka 'surviving' is derived by multiplying Pno_stop across the entire matreshka sequence, abbreviated here to ∏(Pno_stop).
Each motif is scored at all possible positions in each sequence by multiplying the values of each matching position in the PWM.
Repeat families were then sorted according to their score values; the score of each family was obtained by multiplying the size of the prototype sequence by the copy number determined using FUZZNUC in EMBOSS.
We determined the score by multiplying all the frequencies corresponding to a given sequence.
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