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The negative accuracy scores for adversarial users mainly stems from the penalization scheme where incorrect answers are penalized (i.e., gets negative score)—[recall that score calculations are explained in "User score calculation" section (Table 4)].
In Section 2 we present the penalization scheme and the variational framework.
First, a penalization scheme is proposed for designing the permanent magnets to have discrete magnetization direction angles.
The topology optimization problem is solved using a new penalization scheme as an alternative to the SIMP (power law) approach.
Please note that the lowest possible score is set to 0 to prevent negative scores which may happen due to the penalization scheme.
Please note that due to the penalization scheme, a user may receive a negative score, which may happen if he/she selects more incorrect options than correct options.
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This is achieved by carefully defining different penalization schemes for different components of the merit function.
In the discrete topology optimization, four material penalization schemes were attempted in this study.
Since models selected this way are generally suboptimal in terms of prediction, likelihood penalization schemes are common practice.
Bunea [ 23] showed the asymptotic consistency of variable selection under certain conditions for ℓ1 -type penalization schemes.
To diversify the search, we employ a tabu scheme and a penalization strategy, both compatible with the SMD design.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com