Scattering Theory of Current-Induced Spin Polarization

We construct a novel scattering theory to investigate magnetoelectrically induced spin polarizations. Local spin polarizations generated by electric currents passing through a spin-orbit coupled mesoscopic system are measured by an external probe. The electrochemical and spin-dependent chemical potentials on the probe are controllable and tuned to values ensuring that neither charge nor spin current flow between the system and the probe, on time-average. For the relevant case of a single-channel probe, we find that the resulting potentials are exactly independent of the transparency of the contact between the probe and the system. Assuming that spin relaxation processes are absent in the probe, we therefore identify the local spin-dependent potentials in the sample at the probe position, and hence the local current-induced spin polarization, with the spin-dependent potentials in the probe itself. The statistics of these local chemical potentials is calculated within random matrix theory. While they vanish on spatial and mesoscopic average, they exhibit large fluctuations, and we show that single systems typically have spin polarizations exceeding all known current-induced spin polarizations by a parametrically large factor. Our theory allows to calculate quantum correlations between spin polarizations inside the sample and spin currents flowing out of it. We show that these large polarizations correlate only weakly with spin currents in external leads, and that only a fraction of them can be converted into a spin current in the linear regime of transport, which is consistent with the mesoscopic universality of spin conductance fluctuations. We numerically confirm the theory.Comment: Final version; a tunnel barrier between the probe and the dot is considered. To appear in 'Nanotechnology' in the special issue on "Quantum Science and Technology at the Nanoscale

Correlation of internal representations in feed-forward neural networks

Feed-forward multilayer neural networks implementing random input-output mappings develop characteristic correlations between the activity of their hidden nodes which are important for the understanding of the storage and generalization performance of the network. It is shown how these correlations can be calculated from the joint probability distribution of the aligning fields at the hidden units for arbitrary decoder function between hidden layer and output. Explicit results are given for the parity-, and-, and committee-machines with arbitrary number of hidden nodes near saturation.Comment: 6 pages, latex, 1 figur

Weak and Electromagnetic Nuclear Decay Signatures for Neutrino Reactions in SuperKamiokande

We suggest the study of events in the SuperKamiokande neutrino data due to charged- and neutral-current neutrino reactions followed by weak and/or electromagnetic decays of struck nuclei and fragments thereof. This study could improve the prospects of obtaining evidence for $\tau$ production from $\nu_\mu \to \nu_\tau$ oscillations and could augment the data sample used to disfavor $\nu_\mu \to \nu_{sterile}$ oscillations.Comment: 7 pages, latex, to appear in Phys. Rev. Let

Analysis of ensemble learning using simple perceptrons based on online learning theory

Ensemble learning of $K$ nonlinear perceptrons, which determine their outputs by sign functions, is discussed within the framework of online learning and statistical mechanics. One purpose of statistical learning theory is to theoretically obtain the generalization error. This paper shows that ensemble generalization error can be calculated by using two order parameters, that is, the similarity between a teacher and a student, and the similarity among students. The differential equations that describe the dynamical behaviors of these order parameters are derived in the case of general learning rules. The concrete forms of these differential equations are derived analytically in the cases of three well-known rules: Hebbian learning, perceptron learning and AdaTron learning. Ensemble generalization errors of these three rules are calculated by using the results determined by solving their differential equations. As a result, these three rules show different characteristics in their affinity for ensemble learning, that is ``maintaining variety among students." Results show that AdaTron learning is superior to the other two rules with respect to that affinity.Comment: 30 pages, 17 figure

Edge spin accumulation in a ballistic regime

We consider a mesoscopic {\it ballistic} structure with Rashba spin-orbit splitting of the electron spectrum. The ballistic region is attached to the leads with a voltage applied between them. We calculate the edge spin density which appears in the presence of a charge current through the structure due to the difference in populations of electrons coming from different leads. Combined effect of the boundary scattering and spin precession leads to oscillations of the edge polarization with the envelope function decaying as a power law of the distance from the boundary. The problem is solved with the use of scattering states. The simplicity of the method allows to gain an insight into the underlaying physics. We clarify the role of the unitarity of scattering for the problem of edge spin accumulation. In case of a straight boundary it leads to exact cancellation of all long-wave oscillations of the spin density. As a result, only the Friedel-like spin density oscillations with the momentum 2k_F survive. However, this appears to be rather exceptional case. In general, the smooth spin oscillations with the spin precession length recover, as it happens, e.g., for the wiggly boundary. We demonstrate also, that there is no relation between the spin current in the bulk, which is zero in the considered case, and the edge spin accumulation.Comment: Latex, 6 pages, 2 fig