Strong impact of light induced conical intersections on the spectrum of diatomic molecules

We show that dressing of diatomic molecules by running laser waves gives rise to conical intersections (CIs). Due to presence of such CIs, the rovibronic molecular motions are strongly coupled. A pronounced impact of the CI on the spectrum of $Na_2$ molecule is demonstrated via numerical calculation for weak and moderate laser intensity, and an experiment is suggested on this basis. The position of the light induced CI and the strength of its non-adiabatic couplings can be chosen by changing the frequency and intensity of the used running laser wave. This offers new possibilities to control the photo-induced rovibronic molecular dynamics.Comment: 4 pages, 7 figure

Depressive Deficits in Recognition: Dissociation of Recollection and Familiarity

Dysphoric and nondysphoric students (48 women and 24 men) participated in an experiment that was designed to separate automatic and controlled uses of memory in a modified recognition paradigm. First, they judged the relation of target words to paired words. Later they made recognition decisions on target items alone or in the context of the original paired item. The use of L.L. Jacoby\u27s (1991) process dissociation procedure revealed depressive deficits in estimates of recollection but not in estimates of familiarity. The paired test improved recollection for all subjects and showed a trend in the direction of increased familiarity. These outcomes support approaches to depressive cognition that emphasize impaired cognitive control

Spatial data modeling by means of Gibbs Markov random fields based on a generalized planar rotator model

We introduce a Gibbs Markov random field for spatial data on Cartesian grids which is based on the generalized planar rotator (GPR) model. The GPR model generalizes the recently proposed modified planar rotator (MPR) model by including in the Hamiltonian additional terms that better capture realistic features of spatial data, such as smoothness, non-Gaussianity, and geometric anisotropy. In particular, the GPR model includes up to infinite number of higher-order harmonics with exponentially vanishing interaction strength, directional dependence of the bilinear interaction term between nearest grid neighbors, longer-distance neighbor interactions, and two types of an external bias field. Hence, in contrast with the single-parameter MPR model, the GPR model features five additional parameters: the number $n$ of higher-order terms and the parameter $\alpha$ controlling their decay rate, the exchange anisotropy parameter $J^{nn}$, the further-neighbor interaction coupling $J^{fn}$, and the external field (bias) parameters $K$ (or $K'$). We present numerical tests on various synthetic data which demonstrate the effects of the respective terms on the model's prediction performance and we discuss these results in connection with the data properties.Comment: 29 pages, 9 figure

A parsimonious, computationally efficient machine learning method for spatial regression

We introduce the modified planar rotator method (MPRS), a physically inspired machine learning method for spatial/temporal regression. MPRS is a non-parametric model which incorporates spatial or temporal correlations via short-range, distance-dependent ``interactions'' without assuming a specific form for the underlying probability distribution. Predictions are obtained by means of a fully autonomous learning algorithm which employs equilibrium conditional Monte Carlo simulations. MPRS is able to handle scattered data and arbitrary spatial dimensions. We report tests on various synthetic and real-word data in one, two and three dimensions which demonstrate that the MPRS prediction performance (without parameter tuning) is competitive with standard interpolation methods such as ordinary kriging and inverse distance weighting. In particular, MPRS is a particularly effective gap-filling method for rough and non-Gaussian data (e.g., daily precipitation time series). MPRS shows superior computational efficiency and scalability for large samples. Massive data sets involving millions of nodes can be processed in a few seconds on a standard personal computer.Comment: 42 pages, 15 figure

Are steady magnetospheric convection events prolonged substorms?

Magnetospheric modes, including substorms, sawtooth events, and steady magnetospheric convection events, have in the past been described as different responses of the magnetosphere to coupling with the solar wind. Using previously determined event lists for sawtooth events, steady magnetospheric convection events, and substorms, we produce a statistical study of these event types to examine their similarities and behavior in terms of solar wind parameters, auroral brightness, open magnetospheric flux, and geomagnetic indices. A superposed epoch analysis shows that individual sawteeth show the same signatures as substorms but occur during more extreme cases of solar wind driving as well as geomagnetic activity. We also explore the limitations of current methods of identifying steady magnetospheric convection events and explain why some of those events are flagged inappropriately. We show that 58% of the steady magnetospheric convection events considered, as identified by criteria defined in previous studies are part of a prolonged version of substorms due to continued dayside driving during expansion phase. The remaining 42% are episodes of enhanced magnetospheric convection, occurring after extended periods of dayside driving