Strong-field non-sequential double ionization: wavelength dependence of ion momentum distributions for neon and argonAbstract Strong-field double ionization of atoms in a non-sequential regime produces longitudinal ion momentum distributions with a characteristic double-peak structure. At 800 nm laser wavelengthinNe 2+ the structure is very pronounced with a well-resolved dip at zero momentum, while for Ar 2+ the dip is very shallow, possibly indicating different mechanisms in the two atoms. We investigated the source of this difference by measuring longitudinal momentum distributions of Ne 2+ and Ar 2+ ions at different laser wavelengths (485, 800, 1313 and 2000 nm) and intensities. The shapes of the experimental momentum distributions for the two atoms exhibit strong dependence on laser wavelength: for both the dip becomes more pronounced at longer wavelengths. At 1300 nm the longitudinal momentum spectrum for Ar

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We present the R package mnlogit for estimating multinomial logistic regression models, particularly those involving a large number of categories and variables. Compared to existing software, mnlogit offers speedups of 10-50 times for modestly sized problems and more than 100 times for larger problems. Running in parallel mode on a multicore machine gives up to 4 times additional speedup on 8 processor cores. mnlogit achieves its computational efficiency by drastically speeding up computation of the log-likelihood function's Hessian matrix through exploiting structure in matrices that arise in intermediate calculations. This efficient exploitation of intermediate data structures allows mnlogit to utilize system memory much more efficiently, such that for most applications mnlogit requires less memory than comparable software by a factor that is proportional to the number of model categories.

The R package sns implements the stochastic Newton sampler (SNS), a MetropolisHastings Markov chain Monte Carlo (MCMC) algorithm where the proposal density function is a multivariate Gaussian based on a local, second-order Taylor-series expansion of log-density. The mean of the proposal function is the full Newton step in the NewtonRaphson optimization algorithm. Taking advantage of the local, multivariate geometry captured in log-density Hessian allows SNS to be more efficient than univariate samplers, approaching independent sampling as the density function increasingly resembles a multivariate Gaussian. SNS requires the log-density Hessian to be negative-definite everywhere in order to construct a valid proposal function. This property holds, or can be easily checked, for many GLM-like models. When the initial point is far from density peak, running SNS in non-stochastic mode by taking the Newton step -augmented with line search -allows the MCMC chain to converge to high-density areas faster. For high-dimensional problems, partitioning the state space into lower-dimensional subsets, and applying SNS to the subsets within a Gibbs sampling framework can significantly improve the mixing of SNS chains. In addition to the above strategies for improving convergence and mixing, sns offers utilities for diagnostics and visualization, sample-based calculation of Bayesian predictive posterior distributions, numerical differentiation, and log-density validation.

In the measurements reported here, we measured the total cross sections for electron-capture from 2 N by slow 2 O + ions, such measurements are not found in the literature. The increasing availability of data on this system may stimulate work in this direction.