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Based on these properties, we will study in a second subsection the optimal remittances decision of risk-neutral migrants.
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In a first subsection, Section 4.1, evaluation measures are defined, which correspond to well-known measures in e.g. binary classification in Machine Learning.
In addition, a third subsection optionally references other electronic contracting provisions that are not part of the core legal and business context.
At first, we present an abstract formulation of the considered optimization problem whereas in the second subsection a more algorithmic approach of the proposed mechanism is given.
We omit the details of the proofs since they can be derived from similar ideas used in Section 3. We state our main results in the first subsection and we give a concrete example in the second subsection.
In the second subsection, we introduce a way of reducing the information leakage of privacy-sensitive user inputs.
In the second subsection we establish a robust inverse-U relationship between informal sector size and financial development.
The first subsection is related to presenting a bound for the error of the proposed method; meanwhile, in the second subsection, the stability of the numerical solution is investigated briefly.
We accomplish this in the second subsection by transforming the network construction task into an optimization problem, and then developing an approach equipped with Simulated Annealing [12] to resolve this fundamental construction.
L. 99 570, § 1864(1), (4), which directed the substitution of "subsection (n)" for "subsection (o)" in "the second subsection (h)", and directed the redesignation of "the second subsection (h)" as subsection (k), were executed to this subsection because the "second subsection (h)" had been editorially redesignated subsec.
It applies the model mentioned in the first subsection and the sensitivity and what-if analysis referred to in the second subsection.
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