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We present an analytic solution to computation of power and sample size calculations for genetic association with CNP quantitative measures.
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It is worth noting that these approaches based dimensionality reduction are well known in open literature as a means to facilitate practical solutions to computation problems.
This paper provides an area-efficient hardware solution to the computation of EDT on a binary image.
In fact, any deterministic solution to the computation of foremost betweenness is inevitably very costly and, when faced with large networks, it is feasible to apply it only on small components.
Even though pooling of populations violates certain assumptions of Migrate, it can be a reasonable solution to keep computation feasible [ 10].
This design is quite innovative as it provides a viable solution to the fitness computation problem, which depends heavily on the problem-specific knowledge.
In quantum computing, information may be represented not only as bits of either 1 or 0, but also as superpositioned quantum bits (qubits) of both 1 AND 0. Qubits interact with other qubits by nonlocal entanglement, and then collapse/reduce to specific output states as the solution to the quantum computation.
This basic approach is not guaranteed to achieve an optimum solution to the problem of computation placement and can result in sub optimal decision making.
It is hence necessary to develop a good solution to efficiently apply incremental iterative computations on a large-scale datasets.
It does not require computation of a solution to the ODE (Eq. 1), but only evaluating the dynamics function f along the estimated trajectory ŷ(t).
Due to solution complexity, manual computation to obtain solutions may produce erroneous results.
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