(2016), have been verified with the calculation detailed in Section 2 of the Additional file 1. Fig. 11 a Det and b ssq impedances from the 3D example (as shown in Fig. 9a, b, respectively) distorted with a set of distortion parameters with an SD of 0.3 Fig. 12 Average a det and b ssq impedances from the 3D datasets distorted with different galvanic distortion strengths.
More explicitly, we assumed that each set of distortion parameters has a mean of zero and is bounded by ((-1,+1)).
For the cache replacement strategy, we exploit that access patterns remain deterministic for a set of distortion parameters and a given homography.
Fig. 22 a Comparison of synthetic site gains (crosses) with the mean apparent det (diamonds) and ssq (squares) gains from the 1D example, where a set of distortion parameters with an SD of 0.3 was applied.
Fig. 20 a Comparison of synthetic LDIs (black crosses) with the mean LDIs (red circles) from the 3D example, where a set of distortion parameters with an SD of 0.3 was applied.
Fig. 18 LDIs from the distorted a 1D and b 3D data (in '1D example'3D'3D example' sections, respectively), where a set of distortion parameters with an SD of 0.3 was applied.
Fig. 4 Example of det (diamonds) and ssq (squares) impedances from the synthetic 1D impedance distorted with ((g,t,e,s)=(1.20,0.11,-0.37,0.49)) Fig. 5 Distorted a det and b ssq impedances from the 1D example, where a set of distortion parameters with an SD of 0.3 was applied.
After decomposing the set of economic distortions, we conclude that distortions related to the capacity of the economy to provide credit are the main driver of our results.
Also, it was observed that the most ambitious and active workers tend to display a set of cognitive distortions related to perfectionism, omnipotence and the belief that other people should respond to their needs, data that confirm the relation between type A behavior pattern and some type of cognitive distortion.
