Reality-monitoring characteristics in confirmed and doubtful allegations of abuse

According to reality-monitoring theory, memories of experienced and imagined events are qualitatively different, and can be distinguished by children from the age of 3. Across three studies, a total of 119 allegations of sexual abuse by younger (aged 3-8) and older (aged 9-16) children were analyzed for developmental differences in the presence of reality-monitoring criteria, which should characterise descriptions of experienced events. Statements were deemed likely or unlikely to be descriptions of actual incidents using independent case information (e.g., medical evidence). Accounts by older children consistently contained more reality-monitoring criteria than those provided by younger children, and age differences were particularly strong when the cases were deemed doubtful (Studies 1 and 2)

Field-Induced Breakup of Emulsion Droplets Stabilized by Colloidal Particles

We simulate the response of a particle-stabilized emulsion droplet in an external force field, such as gravity, acting equally on all $N$ particles. We show that the field strength required for breakup (at fixed initial area fraction) decreases markedly with droplet size, because the forces act cumulatively, not individually, to detach the interfacial particles. The breakup mode involves the collective destabilization of a solidified particle raft occupying the lower part of the droplet, leading to a critical force per particle that scales approximately as $N^{-1/2}$.Comment: 4 pages, plus 3 pages of supplementary materia

Fine Grid Asteroseismology Of G117-B15A And R548

We now have a good measurement of the cooling rate of G117-B15A. In the near future, we will have equally well determined cooling rates for other pulsating white dwarfs, including R548. The ability to measure their cooling rates offers us a unique way to study weakly interacting particles that would contribute to their cooling. Working toward that goal, we perform a careful asteroseismological analysis of G117-B15A and R548. We study them side by side because they have similar observed properties. We carry out a systematic, fine grid search for best-fit models to the observed period spectra of those stars. We freely vary four parameters: the effective temperature, the stellar mass, the helium layer mass, and the hydrogen layer mass. We identify and quantify a number of uncertainties associated with our models. Based on the results of that analysis and fits to the periods observed in R548 and G117-B15A, we clearly define the regions of the four-dimensional parameter space occupied by the best-fit models.NSF AST 05-07639Astronom

The Effects Of Rapport-Building Style on Children’s Reports of a Staged Event

Three- to 9-year-old children (N = 144) interacted with a photographer and were interviewed about the event either a week or a month later. The informativeness and accuracy of information provided following either open-ended or direct rapport building were compared. Children in the open-ended rapport-building condition provided more accurate reports than children in the direct rapport-building condition after both short and long delays. Open-ended rapport-building led the 3- to 4-year-olds to report more errors in response to the first recall question about the event, but they went on to provide more accurate reports in the rest of the interview than counterparts in the direct rapport-building condition. These results suggest that forensic interviewers should attempt to establish rapport with children using an open-ended style

Calibration of Distributionally Robust Empirical Optimization Models

We study the out-of-sample properties of robust empirical optimization problems with smooth $\phi$-divergence penalties and smooth concave objective functions, and develop a theory for data-driven calibration of the non-negative "robustness parameter" $\delta$ that controls the size of the deviations from the nominal model. Building on the intuition that robust optimization reduces the sensitivity of the expected reward to errors in the model by controlling the spread of the reward distribution, we show that the first-order benefit of ``little bit of robustness" (i.e., $\delta$ small, positive) is a significant reduction in the variance of the out-of-sample reward while the corresponding impact on the mean is almost an order of magnitude smaller. One implication is that substantial variance (sensitivity) reduction is possible at little cost if the robustness parameter is properly calibrated. To this end, we introduce the notion of a robust mean-variance frontier to select the robustness parameter and show that it can be approximated using resampling methods like the bootstrap. Our examples show that robust solutions resulting from "open loop" calibration methods (e.g., selecting a $90\%$ confidence level regardless of the data and objective function) can be very conservative out-of-sample, while those corresponding to the robustness parameter that optimizes an estimate of the out-of-sample expected reward (e.g., via the bootstrap) with no regard for the variance are often insufficiently robust.Comment: 51 page