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We can intuitively see that if only one neuron, neuron n, has a high characteristic variance, then it acts like the single-neuron integrator as (g_{[n]}) approaches N, and neuron n dominates the global feedback signal.
If the difference in model parameters is much smaller than β, the functional becomes significantly smaller than N, and it approaches N when the difference is much greater than β.
When the gradient of a model parameter is much smaller than β, the functional takes a value much smaller than N, whereas it approaches N when the gradient is much larger than β.
From Figures 4b and 5b, we draw the same conclusion that the closer the number of measurements approaches N (M≤N), the better performance of signal recovery is achieved.
The variation of slope occurs in a top-down manner; for example, for carbonaceous dust, the grain size distribution is nearly n(a) ∝ a−3.5 between a = 10−6 and 10−5 cm at 40 Myr, while it approaches n(a) ∝ a−3.5 at smaller sizes later at 80 Myr.
m represents the number of case samples that exhibit a control-like distribution; as m approaches n the proportion of simulated case samples that are generated from the case distribution decreases.
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Design: Semi-structured interviews using a critical incident approach (n =49).
As peer m's reliability decreases, β increases and N m - 1 * and N m + 1 * approach N m.
In the former case, the slope of the grain size distribution seems to approach n(a) ∝ a−3.5.
In this approach, n total transmissions of the same data take place between a single transmitter and a single receiver.
The study used a two-group, pretest/posttest design of pregnant women who elected either the traditional approach to prenatal care (n = 48) or the Centering approach (n = 50).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com