No abstract

We investigate the effect of chemical doping on the electric and magnetic domain pattern in multiferroic hexagonal ErMnO 3 . Hole-and electron doping are achieved through the growth of Er 1−x Ca x MnO 3 and Er 1−x Zr x MnO 3 single crystals, which allows for a controlled introduction of divalent and tetravalent ions, respectively. Using conductance measurements, piezoresponse force microscopy and nonlinear optics we study doping-related variations in the electronic transport and image the corrsponding ferroelectric and antiferromagnetic domains. We find that moderate doping levels allow for adjusting the electronic conduction properties of ErMnO 3 without destroying its characteristic domain patterns. Our findings demonstrate the feasibility of chemical doping for nonperturbative property-engineering of intrinsic domain states in this important class of multiferroics.

No abstract

White matter tractography, based on diffusion-weighted magnetic resonance images, is currently the only available in vivo method to gather information on the structural brain connectivity. The low resolution of diffusion MRI data suggests to employ probabilistic methods for streamline reconstruction, i.e., for fiber crossings. We propose a general probabilistic model for spherical regression based on the Fisher-von-Mises distribution, which efficiently estimates maximum entropy posteriors of local streamline directions with machine learning methods. The optimal precision of posteriors for streamlines is determined by an information-theoretic technique, the expected log-posterior agreement concept. It relies on the requirement that the posterior distributions of streamlines, inferred on retest measurements of the same subject, should yield stable results within the precision determined by the noise level of the data source.

No abstract