Spin Hot Spots in vertically-coupled Few-electron Quantum Dots

The effects of spin-orbit (SO) coupling arising from the confinement potential in single and two vertically-coupled quantum dots have been investigated. Our work indicates that a dot containing a single electron shows the lifting of the degeneracy of dipole-allowed transitions at B=0 due to the SO coupling which disappears for a dot containing two electrons. For coupled dots with one electron in each dot, the optical spectra is not affected by the coupling and is the same as the dot containing one electron. However, for the case of two coupled dots where one partner dot has two interacting electrons while the other dot has only one electron, a remarkable effect is observed where the oscillator strength of two out of four dipole-allowed transition lines disappears as the distance between the dots is decreased

Tuning of the Gap in a Laughlin-Bychkov-Rashba Incompressible Liquid

We report on our investigation of the influence of Bychkov-Rashba spin-orbit interaction (SOI) on the incompressible Laughlin state. We find that experimentally obtainable values of the spin-orbit coupling strength can induce as much as a 25% increase in the quasiparticle-quasihole gap Eg at low magnetic fields in InAs, thereby increasing the stability of the liquid state. The SOI-modulated enhancement of Eg is also significant for filling factors 1/5 and 1/7, where the FQH state is usually weak. This raises the intriguing possibility of tuning, via the SO coupling strength, the liquid to solid transition to much lower densities.Comment: 4 pages, 3 figure

Framing of Crimean Annexation and Eastern Ukraine Conflict in Newspapers of Kazakhstan and Kyrgyzstan in 2014

Peer reviewe

Errors-in-Variables Modeling of Personalized Treatment-Response Trajectories

Estimating the impact of a treatment on a given response is needed in many biomedical applications. However, methodology is lacking for the case when the response is a continuous temporal curve, treatment covariates suffer extensively from measurement error, and even the exact timing of the treatments is unknown. We introduce a novel method for this challenging scenario. We model personalized treatment-response curves as a combination of parametric response functions, hierarchically sharing information across individuals, and a sparse Gaussian process for the baseline trend. Importantly, our model accounts for errors not only in treatment covariates, but also in treatment timings, a problem arising in practice for example when data on treatments are based on user self-reporting. We validate our model with simulated and real patient data, and show that in a challenging application of estimating the impact of diet on continuous blood glucose measurements, accounting for measurement error significantly improves estimation and prediction accuracy.Peer reviewe