On acceleration and motion of ions in corona and solar wind

Equations of motion for ions in corona and solar win

Searching for magnetic monopoles trapped in accelerator material at the Large Hadron Collider

If produced in high energy particle collisions at the LHC, magnetic monopoles could stop in material surrounding the interaction points. Obsolete parts of the beam pipe near the CMS interaction region, which were exposed to the products of pp and heavy ion collisions, were analysed using a SQUID-based magnetometer. The purpose of this work is to quantify the performance of the magnetometer in the context of a monopole search using a small set of samples of accelerator material ahead of the 2013 shutdown.Comment: 11 page

Gradient-based adaptive HMC

Hamiltonian Monte Carlo (HMC) is a popular Markov Chain Monte Carlo (MCMC) algorithm to sample from an unnormalized probability distribution. A leapfrog integrator is commonly used to implement HMC in practice, but its performance can be sensitive to the choice of mass matrix used therein. We develop a gradient-based algorithm that allows for the adaptation of the mass matrix by encouraging the leapfrog integrator to have high acceptance rates while also exploring all dimensions jointly. In contrast to previous work that adapt the hyperparameters of HMC using some form of expected squared jumping distance, the adaptation strategy suggested here aims to increase sampling efficiency by maximizing an approximation of the proposal entropy. We illustrate that using multiple gradients in the HMC proposal can be beneficial compared to a single gradientstep in Metropolis-adjusted Langevin proposals. Empirical evidence suggests that the adaptation method can outperform different versions of HMC schemes by adjusting the mass matrix to the geometry of the target distribution and by providing some control on the integration time

Entropy-based adaptive Hamiltonian Monte Carlo

Hamiltonian Monte Carlo (HMC) is a popular Markov Chain Monte Carlo (MCMC) algorithm to sample from an unnormalized probability distribution. A leapfrog integrator is commonly used to implement HMC in practice, but its performance can be sensitive to the choice of mass matrix used therein. We develop a gradient-based algorithm that allows for the adaptation of the mass matrix by encouraging the leapfrog integrator to have high acceptance rates while also exploring all dimensions jointly. In contrast to previous work that adapt the hyperparameters of HMC using some form of expected squared jumping distance, the adaptation strategy suggested here aims to increase sampling efficiency by maximizing an approximation of the proposal entropy. We illustrate that using multiple gradients in the HMC proposal can be beneficial compared to a single gradient-step in Metropolis-adjusted Langevin proposals. Empirical evidence suggests that the adaptation method can outperform different versions of HMC schemes by adjusting the mass matrix to the geometry of the target distribution and by providing some control on the integration time

Entropy-based adaptive Hamiltonian Monte Carlo

Hamiltonian Monte Carlo (HMC) is a popular Markov Chain Monte Carlo (MCMC) algorithm to sample from an unnormalized probability distribution. A leapfrog integrator is commonly used to implement HMC in practice, but its performance can be sensitive to the choice of mass matrix used therein. We develop a gradient-based algorithm that allows for the adaptation of the mass matrix by encouraging the leapfrog integrator to have high acceptance rates while also exploring all dimensions jointly. In contrast to previous work that adapt the hyperparameters of HMC using some form of expected squared jumping distance, the adaptation strategy suggested here aims to increase sampling efficiency by maximizing an approximation of the proposal entropy. We illustrate that using multiple gradients in the HMC proposal can be beneficial compared to a single gradient-step in Metropolis-adjusted Langevin proposals. Empirical evidence suggests that the adaptation method can outperform different versions of HMC schemes by adjusting the mass matrix to the geometry of the target distribution and by providing some control on the integration time

Spatio-temporal overview of neuroinflammation in an experimental mouse stroke model.

After ischemic stroke, in the lesion core as well as in the ischemic penumbra, evolution of tissue damage and repair is strongly affected by neuroinflammatory events that involve activation of local specialized glial cells, release of inflammatory mediators, recruiting of systemic cells and vascular remodelling. To take advantage of this intricate response in the quest to devise new protective therapeutic strategies we need a better understanding of the territorial and temporal interplay between stroke-triggered inflammatory and cell death-inducing processes in both parenchymal and vascular brain cells. Our goal is to describe structural rearrangements and functional modifications occurring in glial and vascular cells early after an acute ischemic stroke. Low and high scale mapping of the glial activation on brain sections of mice subjected to 30 minutes middle cerebral artery occlusion (MCAO) was correlated with that of the neuronal cell death, with markers for microvascular changes and with markers for pro-inflammatory (IL-1β) and reparative (TGFβ1) cytokines. Our results illustrate a time-course of the neuroinflammatory response starting at early time-points (1 h) and up to one week after MCAO injury in mice, with an accurate spatial distribution of the observed phenomena

Mutual Validation of GNSS Height Measurements and High-precision Geometric-astronomical Leveling

The method of geometric-astronomical leveling is presented as a suited technique for the validation of GNSS (Global Navigation Satellite System) heights. In geometric-astronomical leveling, the ellipsoidal height differences are obtained by combining conventional spirit leveling and astronomical leveling. Astronomical leveling with recently developed digital zenith camera systems is capable of providing the geometry of equipotential surfaces of the gravity field accurate to a few 0.1 mm per km. This is comparable to the accuracy of spirit leveling. Consequently, geometric-astronomical leveling yields accurate ellipsoidal height differences that may serve as an independent check on GNSS height measurements at local scales. A test was performed in a local geodetic network near Hanover. GPS observations were simultaneously carried out at five stations over a time span of 48 h and processed considering state-of-the-art techniques and sophisticated new approaches to reduce station-dependent errors. The comparison of GPS height differences with those from geometric-astronomical leveling shows a promising agreement of some millimeters. The experiment indicates the currently achievable accuracy level of GPS height measurements and demonstrates the practical applicability of the proposed approach for the validation of GNSS height measurements as well as the evaluation of GNSS height processing strategies