We can formulate the rate function of the Poisson distribution (lambda^{k}(t)) as the product of the initial rate (lambda ^{k}_{0}), the daily effect (delta^{k}_{i}), and the hourly effects (eta ^{k}_{j, i}).
We can formulate the rate function of the Poisson distribution (lambda^{k}(t)) as the product of the initial rate (lambda ^{k}_{0}), the daily effect (delta^{k}_{i}), and the hourly effects (eta ^{k}_{j, i}), Equation (10).
Further, it is necessary to formulate the wave function of the electron, injected into the protein nanowire, finding the corresponding vector of the current state and the states themselves.
Finally, in order to discard the redundant and irrelevant feature components from the original high-dimensional feature representation, we formulate the objective function of AMFS as a general trace ratio optimization problem, and design an effective algorithm to solve the corresponding optimization problem.
We first formulate the Lagrange function of S for optimization (12) L (S ) = α tr (W (D S − S ) W T ) + γ | | S − S 0 | | F 2. The partial derivative of the Lagrange function with respect to S is (13) ∇ S L = − α W T W − 2 γ S 0 + 2 γ S + α diag (W T W J J K. Using the KKT complementarity condition for the non-negative constraint on S, we have (14) ∇ S L ○ S = 0.
Many hypotheses that are commonly formulated for the function of scent marking (e.g., Ralls 1971; Kappeler 1998; Brady and Armitage 1999; Lazaro-Perea et al. 1999; Rostain et al. 2004; Heymann 2006a; Lewis 2006) are also applicable to the function of latrine use, as latrine behavior is a special form of olfactory communication.
By interrogating this dataset we targeted two objectives: to assay possible functional redundancy between SRF genes and to formulate hypotheses regarding the function of individual SRF genes.
First, biologists and engineers formulate the intended function or behavior of the robot, the subprocesses that will create the function or behavior, and the specific design plans.
The proportions of the H-containing groups that determine the c-axis value are formulated as the functions of the interlayer space, which can be used to quantitatively clarify the changes of the surface functional groups after the samples experiencing different treatments.
The author extends an improved structure of feedback neural network to formulate the error function in the optimization of QMF banks as a Lyapunov energy function to find the Hopfield-related parameters.
