Atomic Parameters for the 2p53p 2[3/2]2−2p53s 2[3/2]2o2p^53p~^2[3/2]_2 - 2p^53s~^2[3/2]^o_2 Transition of Ne I relevant in nuclear physics

We calculated the magnetic dipole hyperfine interaction constants and the electric field gradients of $2p^53p~^2[3/2]_2$ and $2p^53s~^2[3/2]^o_2$ levels of Ne I by using the multiconfiguration Dirac-Hartree-Fock method. The electronic factors contributing to the isotope shifts were also estimated for the $\lambda = 614.5$ nm transition connecting these two states. Electron correlation and relativistic effects including the Breit interaction were investigated in details. Combining with recent measurements, we extracted the nuclear quadrupole moment values for $^{20}$Ne and $^{23}$Ne with a smaller uncertainty than the current available data. Isotope shifts in the $2p^53p~^2[3/2]_2 - 2p^53s~^2[3/2]^o_2$ transition based on the present calculated field- and mass-shift parameters are in good agreement with the experimental values. However, the field shifts in this transition are two or three orders of magnitude smaller than the mass shifts, making rather difficult to deduce changes in nuclear charge mean square radii. According to our theoretical predictions, we suggest to use instead transitions connecting levels arising from the $2p^53s$ configuration to the ground state, for which the normal mass shift and specific mass shift contributions counteract each other, producing relatively small mass shifts that are only one order of magnitude larger than relatively large field shifts, especially for the $2p^53s~^2[1/2]^o_1 - 2p^6~^1S_0$ transition

A Method for Avoiding Bias from Feature Selection with Application to Naive Bayes Classification Models

For many classification and regression problems, a large number of features are available for possible use - this is typical of DNA microarray data on gene expression, for example. Often, for computational or other reasons, only a small subset of these features are selected for use in a model, based on some simple measure such as correlation with the response variable. This procedure may introduce an optimistic bias, however, in which the response variable appears to be more predictable than it actually is, because the high correlation of the selected features with the response may be partly or wholely due to chance. We show how this bias can be avoided when using a Bayesian model for the joint distribution of features and response. The crucial insight is that even if we forget the exact values of the unselected features, we should retain, and condition on, the knowledge that their correlation with the response was too small for them to be selected. In this paper we describe how this idea can be implemented for ``naive Bayes'' models of binary data. Experiments with simulated data confirm that this method avoids bias due to feature selection. We also apply the naive Bayes model to subsets of data relating gene expression to colon cancer, and find that correcting for bias from feature selection does improve predictive performance