s z)}{t z)}.left nabla beta delta right)right} (12 where, ' O: Output check-message, and Db i : Blocks of data sent in check-message.
Therefore, the Monte Carlo approach requires to draw samples from four Gaussian variables ρ n s, 0 ( i ), ρ ̇ n s, 0 ( i ), ρ ̈ n s, 0 ( i ), and ψ dB ( i ), i=1,…,N.
Conventionally, SdB i, k, n, Pe) the spreading function of band i for an energy component at band k is defined as a two-sided exponential S dB ( i, k, n, P e ) = 27 ( i - k ) Δ z ; k < i - 24 - 230 f c + 2 log 10 P e ( k, n ) ( i - k ) Δ z ; k > i, (11).
Smearing the spectral energy over frequency gives the frequency domain spreading function, Es[k, n] which is called as the "unsmeared excitation pattern" [24, 25], E s [ k, n ] = ∑ k = 0 N c - 1 P e [ k, n ] S dB ( i, k, n, P e [ k, n ] ) 0. 4 1 0. 4 B s [ i, k, n ], (12).
We then applied the Jeffreys' rule [ 79] which quantifies the strength of evidence (here to consider a SNP as being under selection) based on BF using the following scale: "strong evidence" when 10< dB i < 15, "very strong evidence" when 15< dB i < 20 and "decisive evidence" when dB i > 20.
Plots of % B vs. dB were derived for each rat, and the indifference delay (dB 50): the value of dB corresponding to % B = 50%) was estimated by linear interpolation between the two delays which fell on either side of % B = 50% (i and j) using the formula: dB 50) = dB(i ) + ([ dB(j )- dB(i )].[% B i − 50]/[% B i − % B j ]) [39].
We assumed a sigmoidal shape of the transduction function, I C = θ (I dB ) = I C, sat / (1 + exp (− I dB − I 50, θ w θ ) ), with transduction saturation current I C, sat, half-maximum sound intensity I 50, θ, and dynamic-range width w θ.
ARR = 1 DB ∑ i = 1 DB R I i, n n ≤ N G (21) ARP = 1 DB ∑ i = 1 DB P I i, n (22 where, |DB| is the total number of images in the database.
Here, LTE fractional power control (FPC) [26] is used, where P dBm m = min Γ dB + I avg, dBm m + α L des, dB + ( 1 - α ) L int, dB, P max, dBm, (29).
A better approach would require us to realize an intersection between the feature sets, in order to effectively curbing the database growth: FP db = ⋂ i = 1 k − 1 FP i (13).
We fit a three-parameter sigmoid to each response curve, r = ρ (I dB ) = r sat / (1 + exp (− I dB − I 50, ρ w ρ ) ), with saturation spike rate r sat, half-maximum sound intensity I 50, ρ, and dynamic-range width w ρ.
