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

Critical currents for vortex defect motion in superconducting arrays

We study numerically the motion of vortices in two-dimensional arrays of resistively shunted Josephson junctions. An extra vortex is created in the ground states by introducing novel boundary conditions and made mobile by applying external currents. We then measure critical currents and the corresponding pinning energy barriers to vortex motion, which in the unfrustrated case agree well with previous theoretical and experimental findings. In the fully frustrated case our results also give good agreement with experimental ones, in sharp contrast with the existing theoretical prediction. A physical explanation is provided in relation with the vortex motion observed in simulations.Comment: To appear in Physical Review

An optimal superconducting hybrid machine

Optimal engine performances are accomplished by quantum effects. Here we explore two routes towards ideal engines, namely (1) quantum systems that operate as hybrid machines being able to perform more than one useful task and (2) the suppression of fluctuations in doing such tasks. For classical devices, the absence of fluctuations is conditioned by a high entropy production as dictate the thermodynamic uncertainty relations. Here we generalize such relations for multiterminal conductors that operate as hybrid thermal machines. These relations are overcome in quantum conductors as we demonstrate for a double quantum dot contacted to normal metals and a reservoir being a generator of entangled Cooper pairs.Comment: 5 pages, 3 figures + Supplemental materia

Empirical Validation of Objective Functions in Feature Selection Based on Acceleration Motion Segmentation Data

Recent change in evaluation criteria from accuracy alone to trade-off with time delay has inspired multivariate energy-based approaches in motion segmentation using acceleration. The essence of multivariate approaches lies in the construction of highly dimensional energy and requires feature subset selection in machine learning. Due to fast process, filter methods are preferred; however, their poorer estimate is of the main concerns. This paper aims at empirical validation of three objective functions for filter approaches, Fisher discriminant ratio, multiple correlation (MC), and mutual information (MI), through two subsequent experiments. With respect to 63 possible subsets out of 6 variables for acceleration motion segmentation, three functions in addition to a theoretical measure are compared with two wrappers, k-nearest neighbor and Bayes classifiers in general statistics and strongly relevant variable identification by social network analysis. Then four kinds of new proposed multivariate energy are compared with a conventional univariate approach in terms of accuracy and time delay. Finally it appears that MC and MI are acceptable enough to match the estimate of two wrappers, and multivariate approaches are justified with our analytic procedures