The nanostructures shape factor could be accordingly fit to the process parameters, and the resulting response equation was formulated, as given by Eq. 10.
The problem which must be solved can be formulated as follows: given a hybrid automaton, which models the system behavior, restrict its region associated with the controllable transition, in a systematic way, so that all the remaining behaviors satisfy certain properties.
The generalized Riemann problems (GRP) for nonlinear hyperbolic systems of balance laws in one space dimension are now well-known and can be formulated as follows: Given initial data which are smooth on two sides of a discontinuity, determine the time evolution of the solution near the discontinuity.
To this end, considering the frequency characterizations of (d(t)) and (f(t)), the finite frequency FD problem can be formulated as follows: Given three scalars α, γ, and β, under the condition that the filter (4) is stable, solve the following optimization problem: textstylebegin{array}c@ max beta mbox{s.t.
Formally, the person re-identification problem can be formulated as follows: Given n persons crossing camera 1, some of them appear later in camera 2. For each image (or person) recorded from the camera 2 (called probe image), determine a list of images (or persons) recorded from camera 1 (called gallery images).
The segmentation problem can be formulated as follows: given an image I ∈ L2 on a two-dimensional domain Ω (assumed to be bounded, smooth, and open), one seeks out a closed 'edge set' C and all the connected components Ω1,…,Ω k of ΩC, so that by certain suitable visual measure, the image I is discontinuous along C while smooth or homogeneous on each segment Ω i (i = 1, …, k).
Both constraints can be formulated as follows: Given two sequences, they are said to tolerate each other if both overlap at least an absolute amount t, the tolerance, or a relative tolerance c% of the length of the other sequence.
The associated computational problem can be formulated as follows: Given a gene tree T and a corresponding species tree Tsp, reconcile all differences between the two trees by postulating an appropriate DLT scenario.
The planted (l, d) motif discovery is the famous version for motif discovery [ 1], which can be formulated as follows: given a set of n-length DNA sequences S = { s i ∣ i = 1,…, t} over the alphabet Σ = {A, C, G, T}, two nonnegative integers l and d (d < l < n), where l is the length of a motif and d is the maximum number of mutations between the motif and a predicted binding site.
