arXiv subjects

M. Haase

Publications and source records attributed to M. Haase.

4 recordsLinked to original sources

arXiv2016

AARC: First draft of the Blueprint Architecture for Authentication and Authorisation Infrastructures

AARC (Authentication and Authorisation for Research Communities) is a two-year EC-funded project to develop and pilot an integrated cross-discipline authentication and authorisation framework, building on existing authentication and authorisation infrastructures (AAIs) and production federated infrastructure. AARC also champions federated access and offers tailored training to complement the actions needed to test AARC results and to promote AARC outcomes. This article describes a high-level blueprint architectures for interoperable AAIs.

cs.CR↗

arXiv2013

Principal axes for stochastic dynamics

We introduce a general procedure for directly ascertaining how many independent stochastic sources exist in a complex system modeled through a set of coupled Langevin equations of arbitrary dimension. The procedure is based on the computation of the eigenvalues and the corresponding eigenvectors of local diffusion matrices. We demonstrate our algorithm by applying it to two examples of systems showing Hopf-bifurcation. We argue that computing the eigenvectors associated to the eigenvalues of the diffusion matrix at local mesh points in the phase space enables one to define vector fields of stochastic eigendirections. In particular, the eigenvector associated to the lowest eigenvalue defines the path of minimum stochastic forcing in phase space, and a transform to a new coordinate system aligned with the eigenvectors can increase the predictability of the system.

physics.data-an↗

arXiv2012

Evaluating strong measurement noise in data series with simulated annealing method

Many stochastic time series can be described by a Langevin equation composed of a deterministic and a stochastic dynamical part. Such a stochastic process can be reconstructed by means of a recently introduced nonparametric method, thus increasing the predictability, i.e. knowledge of the macroscopic drift and the microscopic diffusion functions. If the measurement of a stochastic process is affected by additional strong measurement noise, the reconstruction process cannot be applied. Here, we present a method for the reconstruction of stochastic processes in the presence of strong measurement noise, based on a suitably parametrized ansatz. At the core of the process is the minimization of the functional distance between terms containing the conditional moments taken from measurement data, and the corresponding ansatz functions. It is shown that a minimization of the distance by means of a simulated annealing procedure yields better results than a previously used Levenberg-Marquardt algorithm, which permits a rapid and reliable reconstruction of the stochastic process.

physics.data-an↗

arXiv2003

Superparamagnetic behaviour of antiferromagnetic DyPO4 nanoparticles

We report on the low-temperature magnetic ac-susceptibility of antiferromagnetic DyPO4 nanoparticles with a very high surface to volume ratio. The results are interpreted in terms of superparamagnetic relaxation of the Neel vector arising from a relatively large number approx 0.2 Na of uncompensated spins probably existing on the surface of the nanoparticles. The activation energy of the relaxation process is found to be Ea / kB = (2.6+-0.1) K within a model taking into account the magnetic interaction between nanoparticles.

cond-mat.mtrl-sci↗