Oscillating chiral currents in nanotubes: a route to nanoscale magnetic test tubes

With a view to optimising the design of carbon-nanotube (CNT) windmills and to maximising the internal magnetic field generated by chiral currents, we present analytical results for the group velocity components of an electron flux through chiral carbon nanotubes. Chiral currents are shown to exhibit a rich behaviour and can even change sign and oscillate as the energy of the electrons is increased. We find that the transverse velocity and associated angular momentum of electrons is a maximum for non-metallic CNTs with a chiral angle of 18$^o$. Such CNTs are therefore the optimal choice for CNT windmills and also generate the largest internal magnetic field for a given longitudinal current. For a longitudinal current of order $10^{-4}$ amps, this field can be of order $10^{-1}$Teslas, which is sufficient to produce interesting spintronic effects and a significant contribution to the self inductance.Comment: 4 pages, 1 figur

Superconductivity-Induced Anderson Localisation

We have studied the effect of a random superconducting order parameter on the localization of quasi-particles, by numerical finite size scaling of the Bogoliubov-de Gennes tight-binding Hamiltonian. Anderson localization is obtained in d=2 and a mobility edge where the states localize is observed in d=3. The critical behavior and localization exponent are universal within error bars both for real and complex random order parameter. Experimentally these results imply a suppression of the electronic contribution to thermal transport from states above the bulk energy gap.Comment: 4 pages, revtex file, 3 postscript figure

General Green's function formalism for transport calculations with spd-Hamiltonians and giant magnetoresistance in Co and Ni based magnetic multilayers

A novel, general Green's function technique for elastic spin-dependent transport calculations is presented, which (i) scales linearly with system size and (ii) allows straightforward application to general tight-binding Hamiltonians (spd in the present work). The method is applied to studies of conductance and giant magnetoresistance (GMR) of magnetic multilayers in CPP (current perpendicular to planes) geometry in the limit of large coherence length. The magnetic materials considered are Co and Ni, with various non-magnetic materials from the 3d, 4d, and 5d transition metal series. Realistic tight-binding models for them have been constructed with the use of density functional calculations. We have identified three qualitatively different cases which depend on whether or not the bands (densities of states) of a non-magnetic metal (i) form an almost perfect match with one of spin sub-bands of the magnetic metal (as in Cu/Co spin valves); (ii) have almost pure sp character at the Fermi level (e.g. Ag); (iii) have almost pure d character at the Fermi energy (e.g. Pd, Pt). The key parameters which give rise to a large GMR ratio turn out to be (i) a strong spin polarization of the magnetic metal, (ii) a large energy offset between the conduction band of the non-magnetic metal and one of spin sub-bands of the magnetic metal, and (iii) strong interband scattering in one of spin sub-bands of a magnetic metal. The present results show that GMR oscillates with variation of the thickness of either non-magnetic or magnetic layers, as observed experimentally.Comment: 22 pages, 9 figure

Forming disk galaxies in wet major mergers. I. Three fiducial examples

Using three fiducial Nbody+SPH simulations, we follow the merging of two disk galaxies with a hot gaseous halo component each, and examine whether the merger remnant can be a spiral galaxy. The stellar progenitor disks are destroyed by violent relaxation during the merging and most of their stars form a classical bulge, while the remaining form a thick disk and its bar. A new stellar disk forms subsequently and gradually in the remnant from the gas accreted mainly from the halo. It is vertically thin and well extended in its equatorial plane. A bar starts forming before the disk is fully in place, contrary to what is assumed in idealised simulations of isolated bar-forming galaxies. It has morphological features such as ansae and boxy/peanut bulges. Stars of different ages populate different parts of the box/peanut. A disky pseudobulge forms also, so that by the end of the simulation, all three types of bulges coexist. The oldest stars are found in the classical bulge, followed by those of the thick disk, then by those in the thin disk. The youngest stars are in the spiral arms and the disky pseudobulge. The disk surface density profiles are of type II (exponential with downbending), and the circular velocity curves are flat and show that the disks are submaximum in these examples: two clearly so and one near-borderline between maximum and submaximum. On average, only roughly between 10 and 20% of the stellar mass is in the classical bulge of the final models, i.e. much less than in previous simulations.Comment: 17 pages, 8 figures, accepted for publication in ApJ. V2: replaced Figure 4 with correct versio

Negative 4-Probe Conductances of Mesoscopic Superconducting Wires

We analyze the longitudinal 4-probe conductance of mesoscopic normal and superconducting wires and predict that in the superconducting case, large negative values can arise for both the weakly disordered and localized regimes. This contrasts sharply with the behaviour of the longitudinal 4-probe conductance of normal wires, which in the localized limit is always exponentially small and positive.Comment: Latex, 3 figures available on request to [email protected] (Simon Robinson

High Dimensional Classification with combined Adaptive Sparse PLS and Logistic Regression

Motivation: The high dimensionality of genomic data calls for the development of specific classification methodologies, especially to prevent over-optimistic predictions. This challenge can be tackled by compression and variable selection, which combined constitute a powerful framework for classification, as well as data visualization and interpretation. However, current proposed combinations lead to instable and non convergent methods due to inappropriate computational frameworks. We hereby propose a stable and convergent approach for classification in high dimensional based on sparse Partial Least Squares (sparse PLS). Results: We start by proposing a new solution for the sparse PLS problem that is based on proximal operators for the case of univariate responses. Then we develop an adaptive version of the sparse PLS for classification, which combines iterative optimization of logistic regression and sparse PLS to ensure convergence and stability. Our results are confirmed on synthetic and experimental data. In particular we show how crucial convergence and stability can be when cross-validation is involved for calibration purposes. Using gene expression data we explore the prediction of breast cancer relapse. We also propose a multicategorial version of our method on the prediction of cell-types based on single-cell expression data. Availability: Our approach is implemented in the plsgenomics R-package.Comment: 9 pages, 3 figures, 4 tables + Supplementary Materials 8 pages, 3 figures, 10 table