Sentence examples for biased solutions from inspiring English sources

On the other hand, it is shown that other approaches based on the "constitutive equation gap" minimization or the "equilibrium gap" minimization provide biased solutions.

Plotting the difference between solutions GMF-NL and GMF-SL against the pressure differences of GPT2 and station log pressure for each site did not reveal any indication that erroneous pressure readings could have biased solutions and shifted the UT1-UTC estimates in the period mentioned before.

Perpetuating an exclusively economic and political research agenda would carry the risk to see ideologically biased solutions prescribed prematurely, while ignoring other important and neglected dimensions of the resource curse phenomenon.

The consensus over several biased solutions might strengthen the bias even more, thus leading to lower solution quality. 1 To verify if this effect might be the reason for the observed deterioration of solution quality of dynamic S-VNS in cases with a higher return transport probability R, we tested a variant of the stochastic algorithms that also samples outbound requests.

Given that SA and optimization are often conducted in parallel, desiring for a combined method, the paper also investigates SA using both randomly generated samples and the biased solutions obtained from an optimization run.

Rejecting all magnetic field models incorporating recent advances in modeling capability, including predictive SV or for the internal induction parts, could lead to biased solutions estimated from candidate models merely relying on similar approaches.

The circuit shows rail-to-rail input and output characteristics and good performance as well as a high efficiency factor when compared with the other adaptive biasing solutions presented in the literature.

This result may be due to the fact that the original problem was seeded with an initial population of individuals containing between two and six removals, which may bias solutions with fewer knockouts (which is certainly experimentally amenable).

PKM algorithm modifies K-Means algorithm to integrate domain knowledge based constraints that the search strategy is biased towards the solutions which respect these constraints as many as possible.

Moreover, a regularization term allows biasing the solution towards preferable modifications and can assure good numerical conditioning.

Consequently, experiments were conducted on low vapour pressure taste-biased solutions and high vapour pressure, smell-biased solutions.